MMTK-2.7.9/0000755000076600000240000000000012156626561012723 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/0000755000076600000240000000000012156626561013430 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/CHANGELOG0000644000076600000240000007244012156626006014643 0ustar hinsenstaff000000000000002.7.8 --> 2.7.9 =============== Improvements: - Allow a Collection to be constructed from a generator - Make Amber file parser more tolerant to accept more mod_files - More pdb_alternative atom names: GROMACS-style OC1/OC2 in C-terminal peptide group - Compile C versions of extension modules if Cython is not installed Bug fixes: - Remove long delays when deallocating thread-based energy evaluators 2.7.7 --> 2.7.8 =============== Improvements: - Collections can be used in place of ChemicalObjects as arguments to HarmonicDistanceTerm() and Universe.distance(). - MMForceField and its components can be used directly in user code, in addition to serving as subclasses (e.g. for AmberForceField). Bug fixes: - Building the Spinx documentation now works as part of a build procedure, without requiring installation of MMTK. - No more test case failures on some Linux systems. 2.7.6 --> 2.7.7 =============== New features: - The Amber12SB force field is implemented in addition to Amber94 and Amber99. - New restraint term: HarmonicTrapForceField restrains the center of mass of an object to a fixed point in space using a spring term. - HarmonicDistanceRestraint now accepts non-atom objects, applying the restraint between their centers of mass. A distance restraint can optionally be treated like a chemical bond, suppressing any non-bonded interactions between the same pair of atoms. - The Amber parameter files have been updated to the versions contained in AmberTools 12. Removed features: - The DPMTA library and its MMTK interface code, implementing the multipole method for electrostatic interactions, has been removed. The MMTK interface has been unusable for years due to a typo that made it crash. No one ever complained, so it seems safe to assume that nobody uses it. DPMTA has become an obstacle to the distribution and packaging of MMTK because it doesn't have a clear licence. Improvements: - The visualization interface under MacOSX and Linux has been modified. When no PDBVIEWER is defined, it defaults to the system mechanism for opening PDB or VRML files, which is "open" under MacOSX and "xdg-open" under Linux. 2.7.5 --> 2.7.6 =============== Improvements: - Allow unicode strings in filenames. - New methods precedingResidue() and nextResidue() for peptide and nucleotide chains. - NormalModes: use scipy.linalg if available, don't use symeig which is no longer maintained. - Use PDB chain_id as name for nucleotide chains (just like for peptide chains) if segment_id is missing. - Make read-only trajectories picklable (the pickle contains just the file name). - Added database files for bromine and iodine. Bug fixes: - Strip spaces from chemical element names determined from a PDB file. - Universe attributes of type string could cause MMTK to hang when updated or removed. - Reading the trajectory variable box_size yielded a wrong result for trajectories with block_size > 1. 2.7.4 --> 2.7.5 =============== Improvements: - Adding items to a universe became significantly faster. This is most noticeable when opening a trajectory file for a big system. - New example script NormalModes/conformational_change_analysis.py - Added plotting to the example script NormalModes/calpha_modes.py - New subcommand "test" for setup.py runs all the test cases and works before installation. - The default visulization module was changed from VRML to VRML2. - General cleanup of the code and the installation process. There are many fewer warnings now. - Definition for He added to the database. Bug fixes: - NormalModes.effectiveMassAndForceConstant crashed due to a forgotten name change in a method call. - Writing DCD trajectories could crash on 64-bit machines. - Distance constraints were handled incorrectly in peptide chains connected by disulfide bridges. - Universes using the SPCE forcefield could not be read back from a trajectory file. - Creation of graphics representatios of Collection objects crashed. - Interators and minimizers could hang if an error occured during trajectory output. - Energy calculations on configurations containing NaN values crashed Python with a segment fault. 2.7.3 --> 2.7.4 =============== Improvements: - MMTK.Dynamics.Heater now accepts negative temperature gradients. - More comprehensive test suite. - New example script Visualization/vector_field_chimera.py Bug fixes: - Dipole calculation (method dipole) used to crash. - Methods boundingBox and boundingSphere used to crash. - Indexing a subset of a trajectory crashed with NumPy. (I hope this is the last NumPy-related bug!) - Replacing residues in a peptide chain could crash. - Corrections in the manual. 2.7.2 --> 2.7.3 =============== New features: - Trajectory actions in Cython - Trajectory generators (MD, minimization) run as a background task provide access to their current state in a thread-safe way. - New method atomIterator() complements atomList() and may become more efficient in future versions. New examples: - MDintegrator (a simple Velocity Verlet written in Cython) - Visualization/additional_objects.py Improvements: - Documentation updated, completed, and built with Sphinx. - Universe description string in trajectories made unambiguous. - Cython replaces Pyrex everywhere in MMTK. - Name conflicts in the database cause a warning to be printed. Bug fixes: - Memory leak in trajectory output removed. - The force contribution from bond terms was NaN at zero bond length. - Elastic Network Model force fields now work correctly with subsets. - A universe is marked as updated when the mass of one of its atoms changes. - Subspace.RigidMotionSubspace was wrong for linked rigid bodies. - Internal coordinate objects detected cyclic bond structures in cases where there are none. - Opening a trajectory whose universe contains named objects could fail. 2.7.1 --> 2.7.2 =============== Improvements: - ParticleProperty objects (including ParticleScalar, ParticleVector, and Configuration objects) have a new method selectAtoms. Its argument is a function that is called with the property of one particle and returns a boolean deciding whether or not that particle is part of the selection. - CalphaForceField has a new option (version=2) for the pair force constants. For reasonable input structures, it is equivalent to the default, version=1. It adds a threshold for small nearest-neighbour C-alpha distances: for distances less than 0.37 nm, the force constant does not decrease below its value at 0.37 nm. This ensures that the Hessian remains positive semi-definite for proteins with unrealistically short C-alpha distances. - MMTK.Subspae.Subspace has a new method complement() which returns the orthogonal complement. - Two new example scripts: - Miscellaneous/bad_contacts.py - Miscellaneous/two_models.py - Protein friction constants for Brownian Dynamics (module MMTK.ProteinFriction) are calculated with a threshold that assures a minimal value of 1000/ps. Without this threshold, friction constants could become negative for solvent-exposed loops. Bug fixes: - The VelocityVerletIntegrator could crash, or produce NaN results, for systems with distance constraints and a thermostat but no barostat. - All examples now work with recent releases of NumPy. - Installation could break on systems whose C compiler's sizeof returns another type than "int". - Trajectory actions of type "function" could crash integrators. 2.7.0 --> 2.7.1 =============== Improvements: - The identification of protonation states when creating molecules from a PDB file now takes into account the common situation of crystallographic PDB files that contain no hydrogens at all. In that case, the most frequent protonation is used for each individual residue. - Ewald summation works for non-orthogonal universes. Bug fixes: - Universe.distanceVector raised an exception when called with the optional configuration argument and for an InfiniteUniverse. - MMTK.Universe.contiguousObjectOffset could crash when input arrays do not use contiguous storage (very unlikely in normal MMTK usage) - NumPy compatibility problem: indexing trajectory variables did not work with indices coming from a NumPy array. - Removal of objects from a universe (e.g. by calling replaceResidue() on a biopolymer chain) could cause incorrect atom indices in the universe. - A force field containing more than one restraint term did not permit the retrieval of the evaluator parameters. 2.5.25 --> 2.7.0 ================ Improvements: - Better support of pickle and cPickle. Until now, MMTK's specialized pickler had to be used to pickle MMTK's chemical objects. From this version, the standard pickler and unpickler can be used as well, although MMTK's pickler is more efficient in terms of pickle size and memory use of unpickled objects. - AtomReference objects implement the rich comparison protocol. Bug fixes: - The use of force field terms implemented in Python with an integrator or minimizer crashed Python on some platforms. - Modifying a universe after initializing its velocities could lead to an infinite recursion. - Reading a ParticleTrajectory for atoms outside of the trajectory's universe now raises an exception. - Fixed PDB names of oxygens in phosphate groups of DNA - Added missing database file MMTK/Database/Groups/lysine_neutral_noh 2.5.24 --> 2.5.25 ================= Improvements: - General code revision - Conversion of docstrings to epytext - NumPy 1.2 compatibility fixes - AtomCluster objects have a default name (the empty string) - PDBMoleculeFactory guarantees that ADP tensors are exactly symmetric - New method Trajectory.flush() Bug fixes: - Dihedral restraints lead to a segmentation fault. - Dihedral restraints for angles close to pi/-pi caused instabilities. - Multiple assignments to the same attribute of a universe lead to all the objects being added to the universe, with the last assignment defininge the value of the attribute. For consistency with assignment semantics, an assignment now removes the previous value of the attribute from the universe. - Under certain conditions, nucleic acid chains in PDB files were wrongly identified as peptide chains. - Wrong import in MMTK.InternalCoordinates - Visualization of periodic universes with undefined atom positions didn't work. - Creation of AtomCluster objects from PDB files was sometimes incomplete (mission positions) - Opening a trajectory with undefined atom positions lead to a crash. 2.5.23 --> 2.5.24 ================= License change: MMTK is now distributed under the CeCILL-C license, which is an adaptation of the LGPL to French law. The previously used CeCILL license, similar to the GPL, was considered too restrictive. New features: - PDBConfiguration: new method createUnitCellUniverse() creates an empty universe that has the shape of the unit cell from the PDB file. - PDBConfiguration: new method asuToUnitCell() applies crystallographic symmetry operations to the molecules in the asymmetric unit. - PDBConfiguration: accept file objects as well as file names. Improvements: - The residue name list in Scientific.IO.PDB is now synchronized with the one in MMTK, meaning that all residues defined as such in MMTK will also be recognized when reading PDB files. Bug fixes: - NormalModes objects handle temperature=None correctly when calculating fluctuations. - PDBMoleculeFactory now works with PDB configurations that have no symmetry information. - Opening a trajectory for reading could crash depending on the force field used in the system. - Proteins with disulphide bonds between chains had an inconsistent internal representation that would cause a crash when writing to a PDB file. - More alternative PDB atom names for ribose. - Fixed installation with NumPy under Windows. 2.5.22 --> 2.5.23 ================= Bug fixes: - NumPy compatibility fixes. - Residue name conflicts were not handled correctly in PDBMoleculeFactory. - VMD support in MMTK.Visualization didn't work under Windows. - ParallelepipedicPeriodicUniverse.largestDistance() returned wrong values. - Cell parameter handling in ParallelepipedicPeriodicUniverse was fixed. - LennardJonesForceField would not initialize correctly in rare circumstances. - Fixed setup.py to work with ScientificPython < 2.7.8. Improvements: - Better handling of add-on forcefields in trajectory descriptions. - Support for atom-dependent force fields. - Atom and residue filters were added to PDBMoleculeFactory. 2.5.21 --> 2.5.22 ================= Bug fixes: - The Amber atom type of HD in N-terminal proline was wrong. - Make CYM a known amino acid residue (useable in PDB files). - Universe.randomPoint() returned points shifted by half the box size. - When opening a PDB file in a PDB viewer fails under Windows, the error message given mentioned VRML instead of PDB. Improvements: - Hydrogen placement extended to more situations. - Universe objects have a few new methods introduced for CDTK compatibility. 2.5.20 --> 2.5.21 ================= Incompatible changes: - PDB.PDBConfiguration stores atom positions and temperature factors (isotropic and anisotropic) in internal units (nm, nm**2) rather than in PDB units (Ang, Ang**2). Scripts that access these parameters directly must be updated! New features: - New module PDBMoleculeFactory permits to work with molecule objects that represent exactly the contents of a PDB file. Improvements: - The universe method contiguousObjectOffset behaves more reasonably for Collection arguments. It doesn't try to make the whole Collection contiguous, which for large Collections is impossible anyway. - New method Universe.configurationDifference. - PDB.PDBConfiguration: new attributes basis and reciprocal_basis describe the crystallographic unit cell and the reciprocal lattice cell. - New method ParticleTensor.trace. - Additional pressure units kbar, MPa and GPa in module Units. Bug fixes: - Hierarchical molecule definitions (using groups) in a MoleculeFactory lacked the bonds from the subgroups. - HarmonicForceField now works correctly with periodic boundary conditions. - NumPy compatibility fixes. 2.5.19 --> 2.5.20 ================= New features: - ParallelepipedicPeriodicUniverse makes it possible to work with non-orthogonal universes that are periodic in three dimensions. At the moment, energy evaluation is slow (the nonbonded list iterates over all atom pairs), and the reciprocal part of the Ewald sum is not yet implemented. - When the method view() is called on a universe object that represents a periodic universe, the simulation box is shown as twelve lines representing its edges, if VMD has been defined as PDBVIEWER. - New force field for C-alpha models of proteins: AnisotropicNetworkForceField. This is very similar to DeformationForceField, the only difference being that the pair force constant as a function of the pair distance is a step function rather than an exponential. Modifications: - The method Universe.configuration() no longer folds the atomic coordinates into the central box of a periodic universe. Sometimes this is not desired, and when it it, it can always be achieved with an explicit call to Universe.foldCoordinatesIntoBox(). Improvements: - The methods that perform calculations on normal modes now all take an optional argument first_mode (default: 6) that defines the first mode to be taken into account. - Database entries for C- and N-terminal neutral lysine. - More unt tests. Bug fixes: - When Configuration objects were copied, the cell parameters were not copied. The copy contained the cell parameters of the current configuration of the universe instead. - Nonbonded list generation for periodic universes could in rare cases miss atom pairs inside the cutoff. - Multi-threaded energy evaluation got stuck for more than two threads. - CompoundForceField objects could not be pickled. - Dihedral energy terms were wrong for phase offsets other than 0 and pi. Note: this has no incidence on AMBER and OPLS force fields because they use only offsets of 0 and pi. - MoleculeFactory.retrieveMolecule had a left-over print statement. - TrajectoryViewer crashed when single-precision trajectories had a discontinuous time axis. - The charges for the Amber force field in the database files for nucleotides were lacking the last digit. - NumPy compatibility fixes. 2.5.18 --> 2.5.19 ================= New features: - PDB.PDBConfiguration contains crystallographic information read from the PDB file: the edge lengths of the cell (a, b, c, in units of nm), the unit cell angles (alpha, beta, gamma, in units of radians), the space group (space_group, a string), the crystallographic symmetry transformations corresponding to the space group (cs_transformations, a list of transformation objects), and the non-crystallographic symmetry transformations (ncs_transformations, a list of transformation objects). The transformations are transformed to Cartesian coordinates, so they can be applied directly to the atom positions. This information is available only if ScientificPython >= 2.7.5 is installed. Improvements: - Improved NumPy compatibility. - Use the symeig package for normal modes if it is installed. - universe.configuration() has been accelerated. The speedup can be very important (a factor of ten), but in most situations it is rather modest. - AMBER atom types are handled as case-sensitive, as is done by the AMBER program. - Better identification of protonation states in PDB files that contain hydrogens. 2.5.17 --> 2.5.18 ================= Bug fixes: - The module lapack_mmtk that consisted of CLAPACK2 code and caused compilation problems with recent GCC versions was removed. Instead, CLAPACK3 routines from Numeric/numpy are used. They are also faster, but use more working memory. 2.5.16 --> 2.5.17 ================= Bug fixes: - PartitionedCollection.selectShell() used wrong distance criteria - Fixed iteration over force constant matrices > 5 GB - Improved NumPy compatibility - AtomCluster objects were not correctly written to trajectory files - Compilation/linking issues under Fedora 6 (GCC 4.1) Improvements: - New atom definitions and updates to existing ones to include scattering lengths - Methods setBondAttributes and clearBondAttributes available also on complexes (and thus proteins) New features: - Method anisotropicFluctuations for normal modes. 2.5.15 --> 2.5.16 ================= Bug fixes: - Removed the dependency on Scientific 2.7.2 introduced into Random.py. MMTK 2.5.16 should again work with ScientificPython > 2.5. - Updated README 2.5.14 --> 2.5.15 ================= New features: - New method energyEvaluatorTerms() on universes. It returns a data structure (dictionary of dictionaries) that contains all the force field parameters for the given universe. It can be used to use MMTK as an energy term generator for other simulation programs. - There is a first draft for an interface to the CCPN data model. Since the data model itself is still evolving, changes to the MMTK interface are very likely as well. - New method GroupOfAtoms.normalizingTransformation. Improvements: - MMTK should now work with NumPy as an alternative to Numeric, though this combination still requires a lot of testing. See README for installation instructions. Bug fixes: - Minor bug fixes in MMTK.ProteinFriction and MMTK.NormalModes.BrownianModes. 2.5.13 --> 2.5.14 ================= Bug fixes: - MMTK.NormalModes.Core.reduceToRange() didn't work correctly for VibrationalModes. - MMTK.Subspace.RigidMotionSubspace could fail for some object collections. - Various 64-bit issues. - Crashes at termination of Python when running under Python 2.5. - Molecule creation through MoleculeFactory didn't work. Improvements: - MMTK.PDB.PDBConfiguration accepts optional arguments for model and alternate code. - The C modules are adapted to the new 64-bit features in Python 2.5. - Installation on Linux makes CLAPACK code compile correctly with GCC 4. - Improved handling of PDB residues with non-unique atom names. - Trajectory files contain a netCDF attributes "Conventions" that identifies them as MMTK trajectories. 2.5.12 --> 2.5.13 ================= License change: MMTK is now distributed under the CeCILL license. See LICENSE (English) or LICENCE (French) for the license text, or www.cecill.info for more information. In short, CeCILL is an OpenSource license based on French law. It is compatible with the GPL, so the change from GPL to CeCILL should not make a difference in practice. 2.5.11 --> 2.5.12 ================= New features: - MMTK.Geometry.Box has new method cornerPoints() and implements intersectWith() for box-box-intersections. 2.5.10 --> 2.5.11 ================= Bug fixes: - Some pickle files from pre-2.5 could not be read any more. - MMTK_energy_term.c wouldn't compile with GCC 4, it has been regenerated with a corrected Pyrex 2.5.9 --> 2.5.10 ================ Bug fixes: - MMTK.Proteins.PeptideChain.replaceResidue() did not update the atom list of the universe. - Compiler errors with gcc 4.0 Changes: - Module "Collection" is now named "Collections" to avoid confusion with the class "Collection". Application code should not import that module directly, so there should be no compatibility issues. 2.5.8 --> 2.5.9 =============== Improvements: - Subspace.RigidMotionSubspace avoids the costly SVD calculation by constructing an orthonormal basis immediately. - Src/lapack_subset.c should compile with gcc 4 now. - MMTK.Proteins.PeptideChain.replaceResidue() could only be used prior to the inclusion of the chain into a protein. Now it can be used later as well, permitting mutations. 2.5.7 --> 2.5.8 =============== Bug fix: - Bugs concerning storing certain force field terms in trajectory descriptions. - Universe descriptions didn't contain distance constraints in AtomCluster objects. 2.5.6 --> 2.5.7 =============== New features: - Support for force field development in Python 2.5.5 --> 2.5.6 =============== New features: - Support for force field development with Pyrex. Bug fixes: - LAPACK error -13 with certain normal mode and subspace operations fixed. - MMTK.Proteins.Residue.phiPsi() could fail for proline residues. 2.5.4 --> 2.5.5 =============== New features: - New module MoleculeFactory permits the construction of molecules from Python code, without requiring database entries. - Any object can be written to an XML file that uses CML conventions as much as possible, the method to call is writeXML(). These files can also be read in using XML.XMLMoleculeFactory. Bug fixes: - A few C modules crashed on Opteron systems running Linux in 64 bit mode. 2.5.3 --> 2.5.4 =============== New features: - Reorganization of the normal mode module into a package providing vibrational, energetic, and Brownian normal modes. - New module MMTK.ProteinFriction to go with Brownian modes. 2.4.2 --> 2.5.3 =============== New features: - Amber 99 force field - New module MMTK.InternalCoordinates - New methods phiAngle, psiAngle, chiAngle in MMTK.Proteins.Residue 2.4.1 --> 2.4.2 =============== Bug fixes: - TrajectorySet with a list of tuples as argument would crash. 2.4 --> 2.4.1 ============= Bug fixes: - A bug in Dynamics.RotationRemover could destabilize an MD run. - Visualization of periodic universes could produce strange results (some molecules out of the box) Portability: - MMTK now compiles without errors under Windows using Microsoft's VisualStudio. It should still compile with MinGW, of course. 2.2 --> 2.4 =========== New features: - Interface to PyMOL. Running an MMTK script from within PyMOL automatically makes all visualization use PyMOL. For more explicit control, see the module MMTK.PyMol. Bug fixes: - Rotation removal during simulation had some wrong formulas. - Compound force fields could not be used in trajectory generation. - Wrong forces from Ewald reciprocal when run with more than one thread. 2.1.3 --> 2.2 ============= New module: - MolecularSurface, a reimplementation by Peter McCluskey, which does not have the license restrictions of the NSC code. The NSC-based MolecularSurface (which according to Peter is faster and more accurate) will remain available as an add-on that replaces the standard module. New features: - New method "rotateAroundAxis()" in class GroupOfAtoms. - New protein model "polar_oldopls", which defines polar hydrogens according to the old OPLS conventions. The model "polar" follows the new OPLS conventions (i.e. includes the hydrogens on aromatic rings). - TrajectorySet class permits the treatment of a sequence of trajectory files as a single trajectory. Bug fixes: - Nonbonded list updated could crash for non-periodic universes under certain (rare) circumstances. - Method "normalizeConfiguration" sometimes performed a reflection in addition to rotation and translation. - Multi-threaded Ewald summation tended to hang after a couple of energy evaluations. 2.1.2 --> 2.1.3 =============== New features: - New method "contiguousObjectConfiguration" for universes. - Three independent scale factors (bonded, Lennard-Jones, electrostatic) can be specified for the Amber force field, all are 1. by default. Improvements: - Snapshots can now specify arbitrary energy terms and pressure (pressure should have worked before as well, but didn't). Bug fixes: - The Amber94 charges for the N-terminal versions of THR, ARG, HIP, and LYS in the MMTK database were wrong, due to mistakes in early versions of the Amber parameter files from which the database entries were generated. For more information see http://www.amber.ucsf.edu/amber/bugfixes41.html and then bugfix.76 and bugfix.91. - molecule.view() failed for non-protein molecules (this bug was introduced in 2.1.2). - RigidBodyTrajectory generation crashed due to a typo. - ParticleTrajectory reads from trajectories with block_size > 1 could return wrong data. 2.1.1 --> 2.1.2 =============== New features: - The TrajectoryViewer tool (in Tools/TrajectoryViewer) was extended by normal mode projections for proteins. - The structure of trajectory files can be influenced by specifying a "block size", which can be used to optimize I/O performance on very large files. - Reading of single-atom and rigid-body trajectories has been optimized. Bug fixes: - The functions MMTK.Biopolymers.defineAminoAcidResidue and MMTK.Biopolymers.defineNucleicAcidResidue didn't work. - The method MMTK.Collection.GroupOfAtoms.findTransformationAsQuaternion returned randomly one of the two equivalent quaternions that desribe the rigid-body rotation. This doesn't matter for most purposes, but it creates non-continuous quaternion trajectories when applied to a sequence of configurations. The method has been changed to return the quaternion that has a positive real part. - Rigid-body trajectories (in module Trajectory) were wrong in certain circumstances. 2.1.0 --> 2.1.1 =============== Modifications: - Improved load balance for shared memory parallelization. - The united-atom models for amino acids were changed from Amber 91 conventions to OPLS by removing the fake "lone pair" atoms. Note that there was never proper support for lone pair atoms, so this model wasn't functional, and therefore nothing should be broken by this change. Now the united-atom model is actually fully usable together with the OPLS force field. (Thanks to Krzysztof Murzyn for fixing this!) Additions: - Basic MPI support. Only energy evaluation has been parallelized, using a data-replication approach. All processors execute the same code and cooperate only during energy evaluation. See the Example MPI/md.py for more information. - New force fields: HarmonicForceField, CalphaForceField, SPCEForceField. The first two are designed for proteins, the last one is only for water. 2.0 --> 2.1.0 ============= Bug fixes: - Addition of force fields didn't work when one of the terms was already a compound force field. - Memory allocation bug in DCD output. - Restarting NPT dynamics simulations didn't work due to a reported universe mismatch. This was caused by the different box size. - MMTK.DCD.writeDCDPDB produced a PDB file with non-contiguous molecules for periodic universes. - In the electrostatic options for the Amber force field, the "screened" option was misinterpreted as "ewald". Modifications: - The method objectList() for collections and universes now takes an optional argument specifying a class; only the objects corresponding to this class are returned. This permits a simple identification of proteins etc. - The universe description that is stored in a trajectory now contains the force field and environment objects (thermostats and barostats). - Optional name argument for C evaluator objects for bonded interactions. Additions: - Thread support. - Module MMTK.ForceFields.Restraints. - New function MMTK.DCD.writeVelocityDCDPDB for exporting velocities to velocity DCD files. - New method pairIndices for nonbonded list objects. 2.0b1 --> 2.0 ============= Bug fixes: - Subtraction of a ParticleVector from a Configuration returned a ParticleVector (now a Configuration). - The Amber atom types for two hydrogens in proline were wrong. Additions: - group definitions for neutral versions of aspartic acid, glutamic acid, and lysine (provided by Alan Grossfield) MMTK-2.7.9/Doc/conf.py0000644000076600000240000001625011662461637014735 0ustar hinsenstaff00000000000000# -*- coding: utf-8 -*- # # MMTK User Guide documentation build configuration file, created by # sphinx-quickstart on Thu Oct 21 18:14:14 2010. # # This file is execfile()d with the current directory set to its containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys, os # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. #sys.path.insert(0, os.path.abspath('.')) #sys.path.insert(0, os.path.abspath('../Documents/mmtk_with_pi2_branch/Src')) #sys.path.insert(0, os.path.abspath('../Documents/mmtk_with_pi2_branch')) import MMTK # -- General configuration ----------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = ['sphinx.ext.autodoc', 'sphinx.ext.viewcode'] autodoc_default_flags=['members', 'show-inheritance'] autoclass_content="both" # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'MMTK User Guide' copyright = u'2010, Konrad Hinsen' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = MMTK.__version__ # The full version, including alpha/beta/rc tags. release = MMTK.__version__ # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['_build'] # The reST default role (used for this markup: `text`) to use for all documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # -- Options for HTML output --------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'default' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". #html_static_path = ['_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = 'MMTKUserGuidedoc' # -- Options for LaTeX output -------------------------------------------------- # The paper size ('letter' or 'a4'). #latex_paper_size = 'letter' # The font size ('10pt', '11pt' or '12pt'). #latex_font_size = '10pt' # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ ('index', 'MMTKUserGuide.tex', u'MMTK User Guide Documentation', u'Konrad Hinsen', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Additional stuff for the LaTeX preamble. #latex_preamble = '' # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output -------------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ('index', 'mmtkuserguide', u'MMTK User Guide Documentation', [u'Konrad Hinsen'], 1) ] MMTK-2.7.9/Doc/Examples/0000755000076600000240000000000012156626561015206 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/Examples/DNA/0000755000076600000240000000000012156626561015610 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/Examples/DNA/construction.py.rst0000644000076600000240000000027012041514572021511 0ustar hinsenstaff00000000000000:orphan: Constructing a DNA strand with a ligand ####################################### .. literalinclude:: ../../../Examples/DNA/construction.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/Forcefield/0000755000076600000240000000000012156626561017250 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/Examples/Forcefield/ElectricField/0000755000076600000240000000000012156626561021746 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/Examples/Forcefield/ElectricField/Cython/0000755000076600000240000000000012156626561023212 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/Examples/Forcefield/ElectricField/Cython/ElectricField.py.rst0000644000076600000240000000032512041514572027060 0ustar hinsenstaff00000000000000:orphan: An electric field term (Python part) #################################### .. literalinclude:: ../../../../../Examples/Forcefield/ElectricField/Cython/ElectricField.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/Forcefield/ElectricField/Cython/MMTK_electric_field.pyx.rst0000644000076600000240000000033412041514572030337 0ustar hinsenstaff00000000000000:orphan: An electric field term (Cython part) #################################### .. literalinclude:: ../../../../../Examples/Forcefield/ElectricField/Cython/MMTK_electric_field.pyx :language: cython :linenos: MMTK-2.7.9/Doc/Examples/Forcefield/ElectricField/Python/0000755000076600000240000000000012156626561023227 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/Examples/Forcefield/ElectricField/Python/ElectricField.py.rst0000644000076600000240000000035712041514572027102 0ustar hinsenstaff00000000000000:orphan: An electric field term implemented in pure Python ################################################# .. literalinclude:: ../../../../../Examples/Forcefield/ElectricField/Python/ElectricField.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/Forcefield/HarmonicOscillator/0000755000076600000240000000000012156626561023044 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/Examples/Forcefield/HarmonicOscillator/Cython/0000755000076600000240000000000012156626561024310 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/Examples/Forcefield/HarmonicOscillator/Cython/HarmonicOscillatorFF.py.rst0000644000076600000240000000037712041514572031477 0ustar hinsenstaff00000000000000:orphan: An efficient harmonic oscillator term (Python part) ################################################### .. literalinclude:: ../../../../../Examples/Forcefield/HarmonicOscillator/Cython/HarmonicOscillatorFF.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/Forcefield/HarmonicOscillator/Cython/MMTK_harmonic_oscillator.pyx.rst0000644000076600000240000000040412041514572032531 0ustar hinsenstaff00000000000000:orphan: An efficient harmonic oscillator term (Cython part) ################################################### .. literalinclude:: ../../../../../Examples/Forcefield/HarmonicOscillator/Cython/MMTK_harmonic_oscillator.pyx :language: cython :linenos: MMTK-2.7.9/Doc/Examples/Forcefield/HarmonicOscillator/Python/0000755000076600000240000000000012156626561024325 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/Examples/Forcefield/HarmonicOscillator/Python/HarmonicOscillatorFF.py.rst0000644000076600000240000000035312041514572031506 0ustar hinsenstaff00000000000000:orphan: A harmonic oscillator term in pure Python ######################################### .. literalinclude:: ../../../../../Examples/Forcefield/HarmonicOscillator/Python/HarmonicOscillatorFF.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/LangevinDynamics/0000755000076600000240000000000012156626561020441 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/Examples/LangevinDynamics/LangevinDynamics.py.rst0000644000076600000240000000033512041514572025045 0ustar hinsenstaff00000000000000:orphan: An integrator for Langevin dynamics (Python part) ################################################# .. literalinclude:: ../../../Examples/LangevinDynamics/LangevinDynamics.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/LangevinDynamics/MMTK_langevin.c.rst0000644000076600000240000000031212041514572024032 0ustar hinsenstaff00000000000000:orphan: An integrator for Langevin dynamics (C part) ############################################ .. literalinclude:: ../../../Examples/LangevinDynamics/MMTK_langevin.c :language: c :linenos: MMTK-2.7.9/Doc/Examples/MDIntegrator/0000755000076600000240000000000012156626561017545 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/Examples/MDIntegrator/VelocityVerlet.pyx.rst0000644000076600000240000000027412041514572024070 0ustar hinsenstaff00000000000000:orphan: A simple Velocity Verlet integrator ################################### .. literalinclude:: ../../../Examples/MDIntegrator/VelocityVerlet.pyx :language: cython :linenos: MMTK-2.7.9/Doc/Examples/Miscellaneous/0000755000076600000240000000000012156626561020011 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/Examples/Miscellaneous/charge_fit.py.rst0000644000076600000240000000035012041514572023252 0ustar hinsenstaff00000000000000:orphan: Fitting point charges to an electrostatic potential surface ########################################################### .. literalinclude:: ../../../Examples/Miscellaneous/charge_fit.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/Miscellaneous/construct_from_pdb.py.rst0000644000076600000240000000032212041514572025052 0ustar hinsenstaff00000000000000:orphan: Building a complete universe from a PDB file ############################################ .. literalinclude:: ../../../Examples/Miscellaneous/construct_from_pdb.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/Miscellaneous/lattice.py.rst0000644000076600000240000000024712041514572022611 0ustar hinsenstaff00000000000000:orphan: Place molecules on a lattice ############################ .. literalinclude:: ../../../Examples/Miscellaneous/lattice.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/Miscellaneous/vector_field.py.rst0000644000076600000240000000037012041514572023626 0ustar hinsenstaff00000000000000:orphan: Vector fields for analysis and visualization of collective motions ################################################################## .. literalinclude:: ../../../Examples/Miscellaneous/vector_field.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/MolecularDynamics/0000755000076600000240000000000012156626561020621 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/Examples/MolecularDynamics/argon.py.rst0000644000076600000240000000024412041514572023077 0ustar hinsenstaff00000000000000:orphan: Simulation of liquid argon ########################## .. literalinclude:: ../../../Examples/MolecularDynamics/argon.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/MolecularDynamics/protein.py.rst0000644000076600000240000000024012041514572023445 0ustar hinsenstaff00000000000000:orphan: Simulation of a protein ####################### .. literalinclude:: ../../../Examples/MolecularDynamics/protein.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/MolecularDynamics/restart.py.rst0000644000076600000240000000026412041514572023457 0ustar hinsenstaff00000000000000:orphan: Restarting the protein simulation ################################# .. literalinclude:: ../../../Examples/MolecularDynamics/restart.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/MolecularDynamics/solvation.py.rst0000644000076600000240000000026212041514572024007 0ustar hinsenstaff00000000000000:orphan: Solvation of a protein in water ############################### .. literalinclude:: ../../../Examples/MolecularDynamics/solvation.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/MonteCarlo/0000755000076600000240000000000012156626561017251 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/Examples/MonteCarlo/backbone.py.rst0000644000076600000240000000031212041514572022161 0ustar hinsenstaff00000000000000:orphan: Sampling a backbone-only configuration ensemble ############################################### .. literalinclude:: ../../../Examples/MonteCarlo/backbone.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/MPI/0000755000076600000240000000000012156626561015633 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/Examples/MPI/md.py.rst0000644000076600000240000000023212041514572017400 0ustar hinsenstaff00000000000000:orphan: Protein solvation in parallel ############################# .. literalinclude:: ../../../Examples/MPI/md.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/NormalModes/0000755000076600000240000000000012156626561017426 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/Examples/NormalModes/calpha_modes.py.rst0000644000076600000240000000033212041514572023213 0ustar hinsenstaff00000000000000:orphan: Slow normal modes of a protein using a C-alpha model #################################################### .. literalinclude:: ../../../Examples/NormalModes/calpha_modes.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/NormalModes/constrained_modes.py.rst0000644000076600000240000000034112041514572024274 0ustar hinsenstaff00000000000000:orphan: Normal modes of a protein using a rigid-residue model ##################################################### .. literalinclude:: ../../../Examples/NormalModes/constrained_modes.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/NormalModes/harmonic_force_field.py.rst0000644000076600000240000000035212041514572024717 0ustar hinsenstaff00000000000000:orphan: Normal modes of a protein using a simplified force field ######################################################## .. literalinclude:: ../../../Examples/NormalModes/harmonic_force_field.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/NormalModes/modes.py.rst0000644000076600000240000000023512041514572021705 0ustar hinsenstaff00000000000000:orphan: Normal modes of a protein ######################### .. literalinclude:: ../../../Examples/NormalModes/modes.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/Proteins/0000755000076600000240000000000012156626561017011 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/Examples/Proteins/analysis.py.rst0000644000076600000240000000025312041514572022004 0ustar hinsenstaff00000000000000:orphan: Comparing protein configurations ################################ .. literalinclude:: ../../../Examples/Proteins/analysis.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/Proteins/construction.py.rst0000644000076600000240000000030712041514572022713 0ustar hinsenstaff00000000000000:orphan: Protein construction beyond the simple cases ############################################ .. literalinclude:: ../../../Examples/Proteins/construction.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/Trajectories/0000755000076600000240000000000012156626562017645 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/Examples/Trajectories/calpha_trajectory.py.rst0000644000076600000240000000034712041514572024516 0ustar hinsenstaff00000000000000:orphan: Extract a C-alpha trajectory from an all-atom trajectory ######################################################## .. literalinclude:: ../../../Examples/Trajectories/calpha_trajectory.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/Trajectories/dcd_export.py.rst0000644000076600000240000000026412041514572023151 0ustar hinsenstaff00000000000000:orphan: Convert a trajectory to DCD format ################################## .. literalinclude:: ../../../Examples/Trajectories/dcd_export.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/Trajectories/dcd_import.py.rst0000644000076600000240000000027012041514572023137 0ustar hinsenstaff00000000000000:orphan: Importing a trajectory in DCD format #################################### .. literalinclude:: ../../../Examples/Trajectories/dcd_import.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/Trajectories/fluctuations.py.rst0000644000076600000240000000032412041514572023533 0ustar hinsenstaff00000000000000:orphan: Calculating atomic fluctuations from a trajectory ################################################# .. literalinclude:: ../../../Examples/Trajectories/fluctuations.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/Trajectories/pdb_export.py.rst0000644000076600000240000000032412041514572023161 0ustar hinsenstaff00000000000000:orphan: Converting a trajectory to a sequence of PDB files ################################################## .. literalinclude:: ../../../Examples/Trajectories/pdb_export.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/Trajectories/snapshot.py.rst0000644000076600000240000000026612041514572022657 0ustar hinsenstaff00000000000000:orphan: Assembling a trajectory step by step #################################### .. literalinclude:: ../../../Examples/Trajectories/snapshot.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/Trajectories/trajectory_average.py.rst0000644000076600000240000000032412041514572024673 0ustar hinsenstaff00000000000000:orphan: Compute an average structure from a trajectory ############################################## .. literalinclude:: ../../../Examples/Trajectories/trajectory_average.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/Trajectories/trajectory_extraction.py.rst0000644000076600000240000000027712041514572025450 0ustar hinsenstaff00000000000000:orphan: Extract a subset from a trajectory ################################## .. literalinclude:: ../../../Examples/Trajectories/trajectory_extraction.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/Trajectories/view_trajectory.py.rst0000644000076600000240000000026712041514572024241 0ustar hinsenstaff00000000000000:orphan: Show an animation of a trajectory ################################# .. literalinclude:: ../../../Examples/Trajectories/view_trajectory.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/Visualization/0000755000076600000240000000000012156626562020050 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/Examples/Visualization/additional_objects.py.rst0000644000076600000240000000035612041514572025044 0ustar hinsenstaff00000000000000:orphan: Adding custom graphics to a molecular system visualization ########################################################## .. literalinclude:: ../../../Examples/Visualization/additional_objects.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/Visualization/graphics_data.py.rst0000644000076600000240000000032612041514572024011 0ustar hinsenstaff00000000000000:orphan: Extracting numerical values from graphics objects ################################################# .. literalinclude:: ../../../Examples/Visualization/graphics_data.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/Visualization/vector_field_chimera.py.rst0000644000076600000240000000036112041514572025354 0ustar hinsenstaff00000000000000:orphan: Vector field visualization of a normal mode (using Chimera) ########################################################### .. literalinclude:: ../../../Examples/Visualization/vector_field_chimera.py :language: python :linenos: MMTK-2.7.9/Doc/Examples/Visualization/vector_field_vmd.py.rst0000644000076600000240000000034512041514572024534 0ustar hinsenstaff00000000000000:orphan: Vector field visualization of a normal mode (using VMD) ####################################################### .. literalinclude:: ../../../Examples/Visualization/vector_field_vmd.py :language: python :linenos: MMTK-2.7.9/Doc/examples.rst0000644000076600000240000002044112117632051015765 0ustar hinsenstaff00000000000000.. _Examples: .. |C_alpha| replace:: C\ :sub:`α` Code Examples ############# One of the best ways to learn how to use a new tool is to look at examples. The examples given in this manual were adapted from real-life MMTK applications. They are also contained in the MMTK distribution (directory "Examples") for direct use and modification. The example molecules, system sizes, parameters, etc., were chosen to reduce execution time as much as possible, in order to enable you to run the examples interactively step by step to see how they work. If you plan to modify an example program for your own use, don't forget to check all parameters carefully to make sure that you obtain reasonable results. .. _Example-MolecularDynamics: - Molecular Dynamics examples - The program :doc:`argon.py ` contains a simulation of liquid argon at constant temperature and pressure. - The program :doc:`protein.py ` contains a simulation of a small (very small) protein in vacuum. - The program :doc:`restart.py ` shows how the simulation started in :doc:`protein.py ` can be continued. - The program :doc:`solvation.py ` contains the solvation of a protein by water molecules. .. _Example-MonteCarlo: - Monte-Carlo examples - The program :doc:`backbone.py <../Examples/MonteCarlo/backbone.py>` generates an ensemble of backbone configuration (C-alpha atoms only) for a protein. .. _Example-Trajectories: - Trajectory examples - The program :doc:`snapshot.py <../Examples/Trajectories/snapshot.py>` shows how a trajectory can be built up step by step from arbitrary data. - The program :doc:`dcd_import.py <../Examples/Trajectories/dcd_import.py>` converts a trajectory in DCD format (used by the programs CHARMM, X-Plor, and NAMD) to MMTK's format. - The program :doc:`dcd_export.py <../Examples/Trajectories/dcd_export.py>` converts an MMTK trajectory to DCD format (used by the programs CHARMM, X-Plor, and NAMD). - The program :doc:`pdb_export.py <../Examples/Trajectories/pdb_export.py>` converts an MMTK trajectory to a sequence of PDB files. - The program :doc:`trajectory_average.py <../Examples/Trajectories/trajectory_average.py>` calculates an average structure from a trajectory. - The program :doc:`trajectory_extraction.py <../Examples/Trajectories/trajectory_extraction.py>` reads a trajectory and writes a new one containing only a subset of the original universe. - The program :doc:`view_trajectory.py <../Examples/Trajectories/view_trajectory.py>` shows an animation of a trajectory, provided that an external molecule viewer with animation is available. - The program :doc:`calpha_trajectory.py <../Examples/Trajectories/calpha_trajectory.py>` shows how a much smaller |C_alpha|-only trajectory can be extracted from a trajectory containing one or more proteins. - The program :doc:`fluctuations.py <../Examples/Trajectories/fluctuations.py>` shows how to calculate atomic fluctuations from a trajectory file. .. _Example-NormalModes: - Normal mode examples - The program :doc:`modes.py <../Examples/NormalModes/modes.py>` contains a standard normal mode calculation for a small protein. - The program :doc:`constrained_modes.py <../Examples/NormalModes/constrained_modes.py>` contains a normal mode calculation for a small protein using a model in which each amino acid residue is rigid. - The program :doc:`calpha_modes.py <../Examples/NormalModes/calpha_modes.py>` contains a normal mode calculation for a mid-size protein using a |C_alpha| model and an elastic network model. - The program :doc:`harmonic_force_field.py <../Examples/NormalModes/harmonic_force_field.py>` contains a normal mode calculation for a protein using a detailed but still simple harmonic force field. .. _Example-Proteins: - Protein examples - The program :doc:`construction.py <../Examples/Proteins/construction.py>` shows some more complex examples of protein construction from PDB files. - The program :doc:`analysis.py <../Examples/Proteins/analysis.py>` demonstrates a few analysis techniques for comparing protein conformations. .. _Example-DNA: - DNA examples - The program :doc:`construction.py <../Examples/DNA/construction.py>` contains the construction of a DNA strand with a ligand. .. _Example-Forcefield: - Forcefield examples - Electric field term - A pure Python implementation (rather slow in general, but tolerable for a simple term like this one) is given in :doc:`Python/ElectricField.py <../Examples/Forcefield/ElectricField/Python/ElectricField.py>`. - A more efficient implementation has the evaluation code written in Cython (:doc:`Cython/MMTK_electric_field.pyx <../Examples/Forcefield/ElectricField/Cython/MMTK_electric_field.pyx>`) while the bookkeeping part remains in Python (:doc:`Cython/ElectricField.py <../Examples/Forcefield/ElectricField/Cython/ElectricField.py>`). - Harmonic oscillator term - A pure Python implementation (rather slow in general, but tolerable for a simple term like this one) is given in :doc:`Python/HarmonicOscillatorFF.py <../Examples/Forcefield/HarmonicOscillator/Python/HarmonicOscillatorFF.py>`. - A more efficient implementation has the evaluation code written in Cython (:doc:`Cython/MMTK_harmonic_oscillator.pyx <../Examples/Forcefield/HarmonicOscillator/Cython/MMTK_harmonic_oscillator.pyx>`) while the bookkeeping part remains in Python (:doc:`Cython/HarmonicOscillatorFF.py <../Examples/Forcefield/HarmonicOscillator/Cython/HarmonicOscillatorFF.py>`). .. _Example-MPI: - MPI examples (parallelization) - The program :doc:`md.py <../Examples/MPI/md.py>` contains a parallelized version of :doc:`solvation.py <../Examples/MolecularDynamics/solvation.py>`. .. _Example-MDIntegrator: - Molecular Dynamics integrators - The program :doc:`md.py <../Examples/MDIntegrator/VelocityVerlet.pyx>` illustrates how Molecular Dynamics integrators can be implemented in Cython. .. _Example-LangevinDynamics: - Langevin dynamics integrator - The programs :doc:`LangevinDynamics.py <../Examples/LangevinDynamics/LangevinDynamics.py>` and :doc:`MMTK_langevinmodule.c <../Examples/LangevinDynamics/MMTK_langevin.c>` implement a simple integrator for Langevin dynamics. It is meant as an example of how to write integrators etc. in C, but of course it can also be used directly. .. _Example-Visualization: - Visualization examples - The program :doc:`additional_objects.py <../Examples/Visualization/additional_objects.py>` describes the addition of custom graphics objects to the representation of a molecular system. - The program :doc:`vector_field_chimera.py <../Examples/Visualization/vector_field_chimera.py>` shows how to create a vector-field visualization of a normal mode using the graphics program Chimera for visualization. The program :doc:`vector_field_vmd.py <../Examples/Visualization/vector_field_vmd.py>` does the same but uses the graphics program VMD. - The program :doc:`graphics_data.py <../Examples/Visualization/graphics_data.py>` shows how to use a fake graphics module to extract numerical values from a vector field object. .. _Example-Miscellaneous: - Micellaneous examples - The example :doc:`charge_fit.py <../Examples/Miscellaneous/charge_fit.py>` demonstrates fitting point charges to an electrostatic potential energy surface. - The program :doc:`construct_from_pdb.py <../Examples/Miscellaneous/construct_from_pdb.py>` shows how a universe can be built from a PDB file in such a way that the internal atom ordering is compatible. This is important for exchanging data with other programs. - The program :doc:`lattice.py <../Examples/Miscellaneous/lattice.py>` constructs molecules placed on a lattice. - The program :doc:`vector_field.py <../Examples/Miscellaneous/vector_field.py>` shows how vector fields can be used in the analysis and visualization of collective motions. MMTK-2.7.9/Doc/glossary.rst0000644000076600000240000000330411662461637016027 0ustar hinsenstaff00000000000000Glossary ======== .. glossary:: Abstract base class A :term:`Base Class` that is not directly usable by itself, but which defines the common properties of several subclasses. Example: the class :class:`MMTK.ChemicalObjects.ChemicalObject` is an abstract base class which defines the common properties of its subclasses :class:`MMTK.ChemicalObjects.Atom`, :class:`MMTK.ChemicalObjects.Group`, :class:`MMTK.ChemicalObjects.Molecule`, :class:`MMTK.ChemicaObjects.Complex`, and :class:`MMTK.ChemicalObjects.AtomCluster`. A :term:`Mix-in class` is a special kind of abstract base class. Base class A class from which another class inherits. In most cases, the inheriting class is a specialization of the base class. For example, the class :class:`MMTK.ChemicalObjects.Molecule` is a base class of :class:`MMTK.Proteins.PeptideChain`, because peptide chains are special molecules. Another common application is the :term:`Abstract base class`. Mix-in class A class that is used as a :term:`Base class` in other classes with the sole intention of providing methods that are common to these classes. Mix-in classes cannot be used to create instances. They are a special kind of :term:`Abstract base class`. Example: class :class:`MMTK.Collections.GroupOfAtoms`. Subclass A class that has another class as its :term:`Base class`. The subclass is usually a specialization of the base class, and can use all of the methods defined in the base class. Example: class :class:`MMTK.Proteins.Residue` is a subclass of :class:`MMTK.ChemicalObjects.Group`. MMTK-2.7.9/Doc/HTML/0000755000076600000240000000000012156626562014175 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/.buildinfo0000644000076600000240000000034612013143456016140 0ustar hinsenstaff00000000000000# Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. config: e2db96d3c837982ac06a36b828a4b432 tags: fbb0d17656682115ca4d033fb2f83ba1 MMTK-2.7.9/Doc/HTML/_modules/0000755000076600000240000000000012156626562016004 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/_modules/index.html0000644000076600000240000001344412013143456017773 0ustar hinsenstaff00000000000000 Overview: module code — MMTK User Guide 2.7.7 documentation

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Source code for MMTK.Biopolymers

# This module implements the base classes for proteins and
# nucleic acid chains.
#
# Written by Konrad Hinsen
#

"""
Base classes for proteins and nucleic acids
"""

from MMTK import Bonds, ChemicalObjects, Collections, Database, PDB
import Scientific.IO.PDB


[docs]class Residue(ChemicalObjects.Group):

    """
    Base class for aminoacid and nucleic acid residues
    """

    def __setstate__(self, state):
        self.__dict__.update(state)
        try:
            self.model = self.hydrogens
        except AttributeError:
            pass
        self._init()

    def _init(self):
        # construct PDB map and alternative names
        type = self.type
        if not hasattr(type, 'pdbmap'):
            pdb_dict = {}
            type.pdbmap = [(type.symbol, pdb_dict)]
            offset = 0
            for g in type.groups:
                for name, atom in g.pdbmap[0][1].items():
                    pdb_dict[name] = Database.AtomReference(atom.number + \
                                                            offset)
                offset = offset + len(g.atoms)
        if not hasattr(type, 'pdb_alternative'):
            alt_dict = {}
            for g in type.groups:
                if hasattr(g, 'pdb_alternative'):
                    for key, value in g.pdb_alternative.items():
                        alt_dict[key] = value
            setattr(type, 'pdb_alternative', alt_dict)

    def _residueThroughLinkAtom(self, link_atom):
        if link_atom is None:
            return None
        levels = 0
        obj = link_atom
        while obj is not None and obj != self:
            obj = obj.parent
            levels += 1
        for atom in link_atom.bondedTo():
            if atom.parent is not link_atom.parent:
                obj = atom
                while levels > 0:
                    obj = obj.parent
                    levels -= 1
                return obj
        return None


[docs]    def precedingResidue(self):
        """
        :returns the preceding residue in the chain
        """
        return self._residueThroughLinkAtom(self.chain_links[0])



[docs]    def nextResidue(self):
        """
        :returns the next residue in the chain
        """
        return self._residueThroughLinkAtom(self.chain_links[1])



[docs]class ResidueChain(ChemicalObjects.Molecule):

    """
    Chain of residues

    Base class for peptide chains and nucleotide chains
    """
    
    is_chain = 1

    def _setupChain(self, circular, properties, conf):
        self.atoms = []
        self.bonds = []
        for g in self.groups:
            self.atoms.extend(g.atoms)
            self.bonds.extend(g.bonds)
        for i in range(len(self.groups)-1):
            link1 = self.groups[i].chain_links[1]
            link2 = self.groups[i+1].chain_links[0]
            self.bonds.append(Bonds.Bond((link1, link2)))
        if circular:
            link1 = self.groups[-1].chain_links[1]
            link2 = self.groups[0].chain_links[0]
            self.bonds.append(Bonds.Bond((link1, link2)))
        self.bonds = Bonds.BondList(self.bonds)
        self.parent = None
        self.type = None
        self.configurations = {}
        try:
            self.name = properties['name']
            del properties['name']
        except KeyError:
            self.name = ''
        if conf:
            conf.applyTo(self)
        try:
            self.translateTo(properties['position'])
            del properties['position']
        except KeyError:
            pass
        self.addProperties(properties)

    def __len__(self):
        return len(self.groups)

    def __getitem__(self, item):
        return self.groups[item]

    def __setitem__(self, item, value):
        self.replaceResidue(self.groups[item], value)


[docs]    def residuesOfType(self, *types):
        """
        :param types: residue type codes
        :type types: str
        :returns: a collection that contains all residues whose type
                  (residue code) is contained in types
        :rtype: :class:`~MMTK.Collections.Collection`
        """
        types = [t.lower() for t in types]
        rlist = [r for r in self.groups if r.type.symbol.lower() in types]
        return Collections.Collection(rlist)



[docs]    def residues(self):
        """
        :returns: a collection containing all residues
        :rtype: :class:`~MMTK.Collections.Collection`
        """
        return Collections.Collection(self.groups)



[docs]    def sequence(self):
        """
        :returns: the residue sequence as a list of residue codes
        :rtype: list of str
        """
        return [r.type.symbol.lower() for r in self.groups]

#
# Find the full name of a residue
#

def _fullName(residue):
    residue = residue.lower()
    try:
        return _aa_residue_names[residue]
    except KeyError:
        return _na_residue_names[residue]

_aa_residue_names = {'ala': 'alanine',          'a': 'alanine',
                     'arg': 'arginine',         'r': 'arginine',
                     'asn': 'asparagine',       'n': 'asparagine',
                     'asp': 'aspartic_acid',    'd': 'aspartic_acid',
                     'cys': 'cysteine',         'c': 'cysteine',
                     'gln': 'glutamine',        'q': 'glutamine',
                     'glu': 'glutamic_acid',    'e': 'glutamic_acid',
                     'gly': 'glycine',          'g': 'glycine',
                     'his': 'histidine',        'h': 'histidine',
                     'ile': 'isoleucine',       'i': 'isoleucine',
                     'leu': 'leucine',          'l': 'leucine',
                     'lys': 'lysine',           'k': 'lysine',
                     'met': 'methionine',       'm': 'methionine',
                     'phe': 'phenylalanine',    'f': 'phenylalanine',
                     'pro': 'proline',          'p': 'proline',
                     'ser': 'serine',           's': 'serine',
                     'thr': 'threonine',        't': 'threonine',
                     'trp': 'tryptophan',       'w': 'tryptophan',
                     'tyr': 'tyrosine',         'y': 'tyrosine',
                     'val': 'valine',           'v': 'valine',
                     'cyx': 'cystine_ss',
                     'cym': 'cysteine_with_negative_charge',
                     'app': 'aspartic_acid_neutral',
                     'glp': 'glutamic_acid_neutral',
                     'hsd': 'histidine_deltah', 'hse': 'histidine_epsilonh',
                     'hsp': 'histidine_plus',
                     'hid': 'histidine_deltah', 'hie': 'histidine_epsilonh',
                     'hip': 'histidine_plus',
                     'lyp': 'lysine_neutral',
                     'ace': 'ace_beginning',    'nme': 'nmethyl',
                     'nhe': 'amide',
                     }

_na_residue_names = {'da': 'd-adenosine',
                     'da5': 'd-adenosine_5ter',
                     'da3': 'd-adenosine_3ter',
                     'dan': 'd-adenosine_5ter_3ter',
                     'dc': 'd-cytosine',
                     'dc5': 'd-cytosine_5ter',
                     'dc3': 'd-cytosine_3ter',
                     'dcn': 'd-cytosine_5ter_3ter',
                     'dg': 'd-guanosine',
                     'dg5': 'd-guanosine_5ter',
                     'dg3': 'd-guanosine_3ter',
                     'dgn': 'd-guanosine_5ter_3ter',
                     'dt': 'd-thymine',
                     'dt5': 'd-thymine_5ter',
                     'dt3': 'd-thymine_3ter',
                     'dtn': 'd-thymine_5ter_3ter',
                     'ra': 'r-adenosine',
                     'ra5': 'r-adenosine_5ter',
                     'ra3': 'r-adenosine_3ter',
                     'ran': 'r-adenosine_5ter_3ter',
                     'rc': 'r-cytosine',
                     'rc5': 'r-cytosine_5ter',
                     'rc3': 'r-cytosine_3ter',
                     'rcn': 'r-cytosine_5ter_3ter',
                     'rg': 'r-guanosine',
                     'rg5': 'r-guanosine_5ter',
                     'rg3': 'r-guanosine_3ter',
                     'rgn': 'r-guanosine_5ter_3ter',
                     'ru': 'r-uracil',
                     'ru5': 'r-uracil_5ter',
                     'ru3': 'r-uracil_3ter',
                     'run': 'r-uracil_5ter_3ter',
                     }

for code in _aa_residue_names:
    if len(code) == 3:
        Scientific.IO.PDB.defineAminoAcidResidue(code)
for code in _na_residue_names:
    Scientific.IO.PDB.defineNucleicAcidResidue(code)

#
# Add a residue to the residue list
#

[docs]def defineAminoAcidResidue(full_name, code3, code1 = None):
    """
    Add a non-standard amino acid residue to the internal residue table.
    Once added to the residue table, the new residue can be used
    like any of the standard residues in the creation of peptide chains.

    :param full_name: the name of the group definition in the chemical database
    :type full_name: str
    :param code3: the three-letter residue code
    :type code3: str
    :param code1: an optionel one-letter residue code
    :type code1: str
    """
    code3 = code3.lower()
    if code1 is not None:
        code1 = code1.lower()
    if _aa_residue_names.has_key(code3) or _na_residue_names.has_key(code3):
        raise ValueError("residue name " + code3 + " already used")
    if _aa_residue_names.has_key(code1):
        raise ValueError("residue name " + code1 + " already used")
    _aa_residue_names[code3] = full_name
    if code1 is not None:
        _aa_residue_names[code1] = full_name
    Scientific.IO.PDB.defineAminoAcidResidue(code3)



[docs]def defineNucleicAcidResidue(full_name, code):
    """
    Add a non-standard nucleic acid residue to the internal residue table.
    Once added to the residue table, the new residue can be used
    like any of the standard residues in the creation of nucleotide chains.

    :param full_name: the name of the group definition in the chemical database
    :type full_name: str
    :param code: the residue code
    :type code3: str
    """
    code = code.lower()
    if _aa_residue_names.has_key(code) or _na_residue_names.has_key(code):
        raise ValueError("residue name " + code + " already used")
    _na_residue_names[code] = full_name
    Scientific.IO.PDB.defineNucleicAcidResidue(code)

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MMTK-2.7.9/Doc/HTML/_modules/MMTK/Bonds.html0000644000076600000240000017114412013143454020501 0ustar hinsenstaff00000000000000 MMTK.Bonds — MMTK User Guide 2.7.7 documentation

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Source code for MMTK.Bonds

# This module implements classes that represent lists of bonds,
# bond angles, and dihedral angles.
#
# Written by Konrad Hinsen
#

"""
Bonds, bond lists, bond angle lists, and dihedral angle lists

The classes in this module are normally not used directly from
client code. They are used by the classes in ChemicalObjects and
ForceField.
"""

__docformat__ = 'restructuredtext'

from MMTK import Database, Utility
from copy import copy

#
# Bonds
#
# Bond objects are created from the specifications in the data base.
#

[docs]class Bond(object):

    """
    Chemical bond

    A bond links two atoms (attributes a1 and a2)
    """

    def __init__(self, blueprint, memo = None):
        if type(blueprint) is type(()):
            self.a1 = blueprint[0]
            self.a2 = blueprint[1]
        else:
            self.a1 = Database.instantiate(blueprint.a1, memo)
            self.a2 = Database.instantiate(blueprint.a2, memo)
        if Utility.uniqueID(self.a2) < Utility.uniqueID(self.a1):
            self.a1, self.a2 = self.a2, self.a1
        Utility.uniqueID.registerObject(self)

    blueprintclass = Database.BlueprintBond

    __safe_for_unpickling__ = 1
    __had_initargs__ = 1

    def __repr__(self):
        return 'Bond(' + `self.a1` + ', ' + `self.a2` + ')'
    __str__ = __repr__


[docs]    def hasAtom(self, a):
        """
        :param a: an atom
        :type a: :class:`~MMTK.ChemicalObjects.Atom`
        :returns: True if a participates in the bond
        """
        return a is self.a1 or a is self.a2



[docs]    def otherAtom(self, a):
        """
        :param a: an atom involved in the bond
        :type a: :class:`~MMTK.ChemicalObjects.Atom`
        :returns: the atom at the other end of the bond
        :rtype: :class:`~MMTK.ChemicalObjects.Atom`
        :raises ValueError: if a does not belong to the bond
        """
        if a is self.a1:
            return self.a2
        elif a is self.a2:
            return self.a1
        else:
            raise ValueError('atom not in bond')


    def _graphics(self, conf, distance_fn, model, module, options):
        objects = []
        if model == 'ball_and_stick' or model == 'vdw_and_stick':
            radius = options.get('stick_radius', 0.01)
        elif model == 'wireframe':
            radius = None
        else:
            return []
        p1 = self.a1.position(conf)
        p2 = self.a2.position(conf)
        if p1 is not None and p2 is not None:
            bond_vector = 0.5*distance_fn(self.a1, self.a2, conf)
            cut = bond_vector != 0.5*(p2-p1)
            color1 = self.a1._atomColor(self.a1, options)
            color2 = self.a2._atomColor(self.a2, options)
            material1 = module.EmissiveMaterial(color1)
            material2 = module.EmissiveMaterial(color2)
            if color1 == color2 and not cut:
                if radius is None:
                    objects.append(module.Line(p1, p2, material = material1))
                else:
                    objects.append(module.Cylinder(p1, p2, radius,
                                                   material = material1))
            else:
                if radius is None:
                    objects.append(module.Line(p1, p1+bond_vector,
                                               material = material1))
                    objects.append(module.Line(p2, p2-bond_vector,
                                               material = material2))
                else:
                    objects.append(module.Cylinder(p1, p1+bond_vector, radius,
                                                   material = material1))
                    objects.append(module.Cylinder(p2, p2-bond_vector, radius,
                                                   material = material2))
        return objects


Database.registerInstanceClass(Bond.blueprintclass, Bond)

#
# Bond angles
#

[docs]class BondAngle(object):

    """
    Bond angle
    
    A bond angle is the angle between two bonds that share a common atom.
    It is defined by two bond objects (attributes b1 and b2) and an atom
    object (the common atom, attribute ca).
    """

    def __init__(self, b1, b2, ca):
        self.b1 = b1 # bond 1
        self.b2 = b2 # bond 2
        self.ca = ca # common atom
        if Utility.uniqueID(self.b2) < Utility.uniqueID(self.b1):
            self.b1, self.b2 = self.b2, self.b1
        self.a1 = b1.otherAtom(ca)
        self.a2 = b2.otherAtom(ca)
        Utility.uniqueID.registerObject(self)

    def __repr__(self):
        return 'BondAngle(' + `self.a1` +',' + `self.ca` +','+ `self.a2` +')'


[docs]    def otherBond(self, bond):
        """
        :param bond: a bond involved in the angle
        :type bond: :class:`~MMTK.Bonds.Bond`
        :returns: the other bond involved in the angle
        :rtype: :class:`~MMTK.Bonds.Bond`
        :raises ValueError: if bond does not belong to the angle
        """
        if bond is self.b1:
            return self.b2
        elif bond is self.b2:
            return self.b1
        else:
            raise ValueError('bond not in bond angle')

#
# Dihedral angles
#


[docs]class DihedralAngle(object):

    """
    Dihedral angle
    
    A dihedral angle is the angle between two planes that are defined by
    BondAngle objects (attributes ba1 and ba2) and their common bond
    (attribute cb).

    There are proper dihedrals (four atoms linked by three bonds in
    sequence) and improper dihedrals (a central atom linked to three
    surrounding atoms by three bonds). The boolean attribute improper
    indicates whether a dihedral is an improper one.    
    """

    def __init__(self, ba1, ba2, cb):
        self.ba1 = ba1 # bond angle 1
        self.ba2 = ba2 # bond angle 2
        # cb is the common bond, i.e. the central bond for a proper dihedral
        if Utility.uniqueID(self.ba2) < Utility.uniqueID(self.ba1):
            self.ba1, self.ba2 = self.ba2, self.ba1
        self.improper = (self.ba1.ca is self.ba2.ca)
        if self.improper:
            self.b1 = self.ba1.otherBond(cb)
            self.b2 = cb
            self.b3 = self.ba2.otherBond(cb)
            self.a1 = self.ba1.ca # central atom
            self.a2 = self.b1.otherAtom(self.ba1.ca)
            self.a3 = cb.otherAtom(self.ba1.ca)
            self.a4 = self.b3.otherAtom(self.ba2.ca)
            # each improper dihedral will come in three versions;
            # identify an arbitrary unique one for constructing the list
            self.normalized = Utility.uniqueID(cb) < Utility.uniqueID(self.b1)\
                              and Utility.uniqueID(cb) < \
                                  Utility.uniqueID(self.b3)
        else:
            self.b1 = self.ba1.otherBond(cb)
            self.b2 = cb
            self.b3 = self.ba2.otherBond(cb)
            self.a1 = self.b1.otherAtom(self.ba1.ca)
            self.a2 = self.ba1.ca # these two are
            self.a3 = self.ba2.ca #   on the common bond
            self.a4 = self.b3.otherAtom(self.ba2.ca)
            self.normalized = self.a1 is not self.a4

    def __repr__(self):
        if self.improper:
            return 'ImproperDihedral(' + `self.a1` +','+ `self.a2` +','+ \
                   `self.a3` +','+ `self.a4` +')'
        else:
            return 'Dihedral(' + `self.a1` +','+ `self.a2` +','+ \
                   `self.a3` +','+ `self.a4` +')'

#
# Bond lists
#
# Bond lists can create bond angle and dihedral angle lists
# for themselves. These are cached for efficiency. The cached
# copy is deleted whenever the bond list is modified.
#

class BondList(list):

    def __init__(self, initlist=None):
        list.__init__(self, initlist)
        self._clearCache()

    __safe_for_unpickling__ = 1

    def _clearCache(self):
        self.bond_angles = None
        self.dihedral_angles = None

    def __getinitargs__(self):
        return (None,)

    def __getstate__(self):
        self._clearCache()
        return self.__dict__

    def __setitem__(self, i, item):
        list.__setitem__(self, i, item)
        self._clearCache()

    def __setslice__(self, i, j, data):
        list.__setslice__(self, i, j, data)
        self._clearCache()

    def __delslice__(self, i, j):
        list.__delslice__(self, i, j)
        self._clearCache()

    def append(self, item):
        list.append(self, item)
        self._clearCache()

    def extend(self, data):
        list.extend(self, data)
        self._clearCache()

    def insert(self, i, item):
        list.insert(i, item)
        self._clearCache()

    def remove(self, item):
        list.remove(self, item)
        self._clearCache()

    def bondAngles(self):
        """
        :returns: a list of all bond angles that can be formed from the
                  bonds in the list
        :rtype: :class:`~MMTK.Bonds.BondAngleList`
        """
        if self.bond_angles is None:
            # find all atoms that are involved in more than one bond
            bonds = {}
            atom_list = []
            for bond in self:
                try:
                    bl = bonds[bond.a1]
                except KeyError:
                    bl = []
                    bonds[bond.a1] = bl
                    atom_list.append(bond.a1)
                bl.append(bond)
                try:
                    bl = bonds[bond.a2]
                except KeyError:
                    bl = []
                    bonds[bond.a2] = bl
                    atom_list.append(bond.a2)
                bl.append(bond)
            angles = []
            for atom in atom_list:
                # each pair of bonds at the same atom defines a bond angle
                for p in Utility.pairs(bonds[atom]):
                    angles.append(BondAngle(p[0], p[1], atom))
            self.bond_angles = BondAngleList(angles)
        return self.bond_angles

    def dihedralAngles(self):
        """
        :returns: a list of all dihedral angles that can be formed from the
                  bonds in the list
        :rtype: :class:`~MMTK.Bonds.DihedralAngleList`
        """
        if self.dihedral_angles is None:
            self.dihedral_angles = self.bondAngles().dihedralAngles()
        return self.dihedral_angles

    def bondedTo(self, atom):
        """
        :param atom: an atom
        :type atom: :class:`~MMTK.ChemicalObjects.Atom`
        :returns: a list of all atoms to which the given atom is bound
        :rtype: list
        """
        return [b.otherAtom(atom) for b in self if b.hasAtom(atom)]

    def bondsOf(self, atom):
        """
        :param atom: an atom
        :type atom: :class:`~MMTK.ChemicalObjects.Atom`
        :returns: a list of all bonds in which the given atom is involved
        :rtype: list
        """
        return [b for b in self if b.hasAtom(atom)]

    def setBondAttributes(self):
        """
        Create an attribute in all atoms of all bonds that points to the
        bonded atom.

        :note: Bond attributes are only set temporarily for optimization
               purposes.
        """
        for b in self:
            b.a1.setBondAttribute(b.a2)
            b.a2.setBondAttribute(b.a1)

#
# Bond angle lists
#

[docs]class BondAngleList(object):

    """
    Bond angle list
    """

    def __init__(self, angles):
        self.data = angles

    def __repr__(self):
        return repr(self.data)
    def __len__(self):
        return len(self.data)
    def __getitem__(self, i):
        return self.data[i]


[docs]    def dihedralAngles(self):
        """
        :returns: a list of all dihedral angles that can be formed from the
                  bond angles in the list
        :rtype: :class:`~MMTK.Bonds.DihedralAngleList`
        """
        # find all bonds that are involved in more than one bond angle
        angles = {}
        bond_list = []
        for angle in self.data:
            try:
                al = angles[angle.b1]
            except KeyError:
                al = []
                angles[angle.b1] = al
                bond_list.append(angle.b1)
            al.append(angle)
            try:
                al = angles[angle.b2]
            except KeyError:
                al = []
                angles[angle.b2] = al
                bond_list.append(angle.b2)
            al.append(angle)
        dihedrals = []
        for bond in bond_list:
            # each pair of bond angles with a common bond defines a dihedral
            for p in Utility.pairs(angles[bond]):
                d = DihedralAngle(p[0], p[1], bond)
                if d.normalized:
                    dihedrals.append(d)
        return DihedralAngleList(dihedrals)

#
# Dihedral angle lists
#


[docs]class DihedralAngleList(object):

    """
    Dihedral angle list
    """

    def __init__(self, dihedrals):
        self.data = dihedrals

    def __repr__(self):
        return repr(self.data)
    def __len__(self):
        return len(self.data)
    def __getitem__(self, i):
        return self.data[i]

#
# Dummy bond length database, for constraints without a force field
#

class DummyBondLengthDatabase(object):

    def __init__(self, universe):
        pass

    def bondLength(self, bond):
        return None

    def bondAngle(self, angle):
        return None

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Source code for MMTK.ChargeFit

# This module contains code for charge fitting.
#
# Written by Konrad Hinsen
#

"""
Fit of point chages to an electrostatic potential surface

This module implements a numerically stable method (based on
Singular Value Decomposition) to fit point charges to values of an
electrostatic potential surface. Two types of constraints are
avaiable: a constraint on the total charge of the system or a subset
of the system, and constraints that force the charges of several atoms
to be equal. There is also a utility function that selects suitable
evaluation points for the electrostatic potential surface. For the
potential evaluation itself, some quantum chemistry program is needed.

The charge fitting method is described in:

  | K. Hinsen and B. Roux,
  | An accurate potential for simulating proton transfer in acetylacetone,
  | J. Comp. Chem. 18, 1997: 368

See also Examples/Miscellaneous/charge_fit.py.
"""

__docformat__ = 'restructuredtext'

from MMTK import Random, Units, Utility
from Scientific.Geometry import Vector
from Scientific import N
from Scientific import LA



[docs]class ChargeFit(object):

    """
    Fit of point charges to an electrostatic potential surface

    A ChargeFit object acts like a dictionary that stores the fitted charge
    value for each atom in the system.
    """

    def __init__(self, system, points, constraints = None):
        """
        :param system: any chemical object (usually a molecule)
        :param points: a list of point/potential pairs (a vector for the
                       evaluation point, a number for the potential),
                       or a dictionary whose keys are Configuration objects
                       and whose values are lists of point/potential pairs.
                       The latter case permits combined fits for several
                       conformations of the system.
        :param constraints: an optional list of constraint objects
                            (:class:`~MMTK.ChargeFit.TotalChargeConstraint`
                            and/or
                            :class:`~MMTK.ChargeFit.EqualityConstraint` objects). 
                            If the constraints are inconsistent, a warning is
                            printed and the result will satisfy the
                            constraints only in a least-squares sense.
        """
        self.atoms = system.atomList()
        if type(points) != type({}):
            points = {None: points}
        if constraints is not None:
            constraints = ChargeConstraintSet(self.atoms, constraints)
        npoints = sum([len(v) for v in points.values()])
        natoms = len(self.atoms)
        if npoints < natoms:
            raise ValueError("Not enough data points for fit")

        m = N.zeros((npoints, natoms), N.Float)
        phi = N.zeros((npoints,), N.Float)
        i = 0
        for conf, pointlist in points.items():
            for r, p in pointlist:
                for j in range(natoms):
                    m[i, j] = 1./(r-self.atoms[j].position(conf)).length()
                phi[i] = p
                i = i + 1
        m = m*Units.electrostatic_energy

        m_test = m
        phi_test = phi

        if constraints is not None:
            phi -= N.dot(m, constraints.bi_c)
            m = N.dot(m, constraints.p)
            c_rank = constraints.rank
        else:
            c_rank = 0

        u, s, vt = LA.singular_value_decomposition(m)
        s_test = s[:len(s)-c_rank]
        cutoff = 1.e-10*N.maximum.reduce(s_test)
        nonzero = N.repeat(s_test, N.not_equal(s_test, 0.))
        self.rank = len(nonzero)
        self.condition = N.maximum.reduce(nonzero) / \
                         N.minimum.reduce(nonzero)
        self.effective_rank = N.add.reduce(N.greater(s, cutoff))
        if self.effective_rank < self.rank:
            self.effective_condition = N.maximum.reduce(nonzero) / cutoff
        else:
            self.effective_condition = self.condition
        if self.effective_rank < natoms-c_rank:
            Utility.warning('Not all charges are uniquely determined' +
                            ' by the available data')

        for i in range(natoms):
            if s[i] > cutoff:
                s[i] = 1./s[i]
            else:
                s[i] = 0.
        q = N.dot(N.transpose(vt),
                  s*N.dot(N.transpose(u)[:natoms, :], phi))
        if constraints is not None:
            q = constraints.bi_c + N.dot(constraints.p, q)

        deviation = N.dot(m_test, q)-phi_test
        self.rms_error = N.sqrt(N.dot(deviation, deviation))
        deviation = N.fabs(deviation/phi_test)
        self.relative_rms_error = N.sqrt(N.dot(deviation, deviation))

        self.charges = {}
        for i in range(natoms):
            self.charges[self.atoms[i]] = q[i]

    def __getitem__(self, item):
        return self.charges[item]




[docs]class TotalChargeConstraint(object):

    """
    Constraint on the total system charge

    To be used with :class:`~MMTK.ChargeFit.ChargeFit`
    """

    def __init__(self, system, charge):
        """
        :param system: any chamical object whose total charge
                       is to be constrained
        :param charge: the total charge value
        :type charge: number
        """
        self.atoms = system.atomList()
        self.charge = charge

    def __len__(self):
        return 1

    def setCoefficients(self, atoms, b, c, i):
        for a in self.atoms:
            j = atoms.index(a)
            b[i, j] = 1.
        c[i] = self.charge




[docs]class EqualityConstraint(object):

    """
    Constraint forcing two charges to be equal

    To be used with :class:`~MMTK.ChargeFit.ChargeFit`

    Any atom may occur in more than one EqualityConstraint object,
    in order to keep the charges of more than two atoms equal.
    """

    def __init__(self, atom1, atom2):
        """
        :param atom1: the first atom in the equality relation
        :type atom1: :class:`~MMTK.ChemicalObjects.Atom`
        :param atom2: the second atom in the equality relation
        :type atom2: :class:`~MMTK.ChemicalObjects.Atom`
        """
        self.a1 = atom1
        self.a2 = atom2

    def __len__(self):
        return 1

    def setCoefficients(self, atoms, b, c, i):
        b[i, atoms.index(self.a1)] = 1.
        b[i, atoms.index(self.a2)] = -1.
        c[i] = 0.



class ChargeConstraintSet(object):

    def __init__(self, atoms, constraints):
        self.atoms = atoms
        natoms = len(self.atoms)
        nconst = sum([len(c) for c in constraints])
        b = N.zeros((nconst, natoms), N.Float)
        c = N.zeros((nconst,), N.Float)
        i = 0
        for cons in constraints:
            cons.setCoefficients(self.atoms, b, c, i)
            i = i + len(cons)
        u, s, vt = LA.singular_value_decomposition(b)
        self.rank = 0
        for i in range(min(natoms, nconst)):
            if s[i] > 0.:
                self.rank = self.rank + 1
        self.b = b
        self.bi = LA.generalized_inverse(b)
        self.p = N.identity(natoms)-N.dot(self.bi, self.b)
        self.c = c
        self.bi_c = N.dot(self.bi, c)
        c_test = N.dot(self.b, self.bi_c)
        if N.add.reduce((c_test-c)**2)/nconst > 1.e-12:
            Utility.warning("The charge constraints are inconsistent."
                            " They will be applied as a least-squares"
                            " condition.")



[docs]def evaluationPoints(system, n, smallest = 0.3, largest = 0.5):
    """
    Generate points in space around a molecule that are suitable
    for potential evaluation in view of a subsequent charge fit.
    The points are chosen at random and uniformly in a shell around the system.

    :param system: the chemical object for which the charges
                   will be fitted
    :param n: the number of evaluation points to be generated
    :param smallest: the smallest allowed distance of any evaluation
                     point from any non-hydrogen atom
    :param largest: the largest allowed value for the distance
                    from an evaluation point to the nearest atom
    :returns: a list of evaluation points
    :rtype: list of Scientific.Geometry.Vector
    """
    atoms = system.atomList()
    p1, p2 = system.boundingBox()
    margin = Vector(largest, largest, largest)
    p1 -= margin
    p2 += margin
    a, b, c = tuple(p2-p1)
    offset = 0.5*Vector(a, b, c)
    points = []
    while len(points) < n:
        p = p1 + Random.randomPointInBox(a, b, c) + offset
        m = 2*largest
        ok = 1
        for atom in atoms:
            d = (p-atom.position()).length()
            m = min(m, d)
            if d < smallest and atom.symbol != 'H':
                ok = 0
            if not ok: break
        if ok and m <= largest:
            points.append(p)
    return points



if __name__ == '__main__':

    from MMTK import *

    a1 = Atom('C', position=Vector(-0.05,0.,0.))
    a2 = Atom('C', position=Vector( 0.05,0.,0.))
    system = Collection(a1, a2)

    a1.charge = -0.75
    a2.charge = 0.15

    points = []
    for r in evaluationPoints(system, 50):
        p = 0.
        for atom in system.atomList():
            p = p + atom.charge/(r-atom.position()).length()
        points.append((r, p*Units.electrostatic_energy))

    constraints = [TotalChargeConstraint(system, 0.)]
    constraints = [EqualityConstraint(a1, a2)]
    constraints = None

    f = ChargeFit(system, points, constraints)
    print f[a1], a1.charge
    print f[a2], a2.charge

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Source code for MMTK.ChemicalObjects

# This module implements classes that represent atoms, molecules, and
# complexes. They are made as copies from blueprints in the database.
#
# Written by Konrad Hinsen
#

"""
Atoms, groups, molecules and similar objects
"""

__docformat__ = 'restructuredtext'

from MMTK import Bonds, Collections, Database, \
                 Units, Utility, Visualization
from Scientific.Geometry import Vector
from Scientific.Geometry import Objects3D
from Scientific import N
import copy

#
# The base class for all chemical objects.
#

[docs]class ChemicalObject(Collections.GroupOfAtoms, Visualization.Viewable):

    """
    General chemical object

    This is an abstract base class that implements methods which
    are applicable to any chemical object (atom, molecule, etc.).
    """

    def __init__(self, blueprint, memo):
        if isinstance(blueprint, basestring):
            blueprint = self.blueprintclass(blueprint)
        self.type = blueprint.type
        if hasattr(blueprint, 'name'):
            self.name = blueprint.name
        if memo is None: memo = {}
        memo[id(blueprint)] = self
        for attr in blueprint.instance:
            setattr(self, attr,
                    Database.instantiate(getattr(blueprint, attr), memo))

    is_chemical_object = True
    is_incomplete = False
    is_modified = False

    __safe_for_unpickling__ = True
    __had_initargs__ = True

    def __hash__(self):
        return id(self)

    def __getattr__(self, attr):
        if attr[:1] == '_' or attr[:3] == 'is_':
            raise AttributeError
        else:
            return getattr(self.type, attr)

    def isSubsetModel(self):
        return False

    def addProperties(self, properties):
        if properties:
            for item in properties.items():
                if hasattr(self, item[0]) and item[0] != 'name':
                    raise TypeError('attribute '+item[0]+' already defined')
                setattr(self, item[0], item[1])

    def binaryProperty(self, properties, name, default):
        value = default
        try:
            value = properties[name]
            del properties[name]
        except KeyError:
            pass
        return value


[docs]    def topLevelChemicalObject(self):
        """Returns the highest-level chemical object of which
        the current object is a part."""
        if self.parent is None or not isChemicalObject(self.parent):
            return self
        else:
            return self.parent.topLevelChemicalObject()



[docs]    def universe(self):
        """
        :returns: the universe to which the object belongs,
                  or None if the object does not belong to any universe
        :rtype: :class:`~MMTK.Universe.Universe`
        """
        if self.parent is None:
            return None
        else:
            return self.parent.universe()



[docs]    def bondedUnits(self):
        """
        :returns: the largest subobjects which can contain bonds.
                  There are no bonds between any of the subobjects
                  in the list.
        :rtype: list
        """
        return [self]



[docs]    def atomList(self):
        """
        :returns: a list containing all atoms in the object
        :rtype: list
        """
        pass



[docs]    def atomIterator(self):
        """
        :returns: an iterator over all atoms in the object
        :rtype: iterator
        """
        pass



[docs]    def fullName(self):
        """
        :returns: the full name of the object. The full name consists
                  of the proper name of the object preceded by
                  the full name of its parent separated by a dot.
        :rtype: str
        """
        if self.parent is None or not isChemicalObject(self.parent):
            return self.name
        else:
            return self.parent.fullName() + '.' + self.name



[docs]    def degreesOfFreedom(self):
        """
        :returns: the number of degrees of freedom of the object
        :rtype: int
        """
        return Collections.GroupOfAtoms.degreesOfFreedom(self) \
               - self.numberOfDistanceConstraints()



[docs]    def distanceConstraintList(self):
        """
        :returns: the distance constraints of the object
        :rtype: list
        """
        return []


    def _distanceConstraintList(self):
        return []

    def traverseBondTree(self, function = None):
        return []


[docs]    def numberOfDistanceConstraints(self):
        """
        :returns: the number of distance constraints of the object
        :rtype: int
        """
        return 0



[docs]    def setBondConstraints(self, universe=None):
        """
        Sets distance constraints for all bonds.
        """
        pass



[docs]    def removeDistanceConstraints(self, universe=None):
        """
        Removes all distance constraints.
        """
        pass



[docs]    def setRigidBodyConstraints(self, universe = None):
        """
        Sets distance constraints that make the object fully rigid.
        """
        if universe is None:
            universe = self.universe()
        if universe is None:
            import Universe
            universe = Universe.InfiniteUniverse()
        atoms = self.atomList()
        if len(atoms) > 1:
            self.addDistanceConstraint(atoms[0], atoms[1],
                                       universe.distance(atoms[0], atoms[1]))
        if len(atoms) > 2:
            self.addDistanceConstraint(atoms[0], atoms[2],
                                       universe.distance(atoms[0], atoms[2]))
            self.addDistanceConstraint(atoms[1], atoms[2],
                                       universe.distance(atoms[1], atoms[2]))
        if len(atoms) > 3:
            for a in atoms[3:]:
                self.addDistanceConstraint(atoms[0], a,
                                           universe.distance(atoms[0], a))
                self.addDistanceConstraint(atoms[1], a,
                                           universe.distance(atoms[1], a))
                self.addDistanceConstraint(atoms[2], a,
                                           universe.distance(atoms[2], a))



[docs]    def getAtomProperty(self, atom, property):
        """
        Retrieve atom properties from the chemical database.

        Note: the property is first looked up in the database entry
        for the object on which the method is called. If the lookup
        fails, the complete hierarchy from the atom to the top-level
        object is constructed and traversed starting from the top-level
        object until the property is found. This permits database entries
        for higher-level objects to override property definitions in
        its constituents.

        At the atom level, the property is retrieved from an attribute
        with the same name. This means that properties at the atom
        level can be defined both in the chemical database and for
        each atom individually by assignment to the attribute.
        
        :returns: the value of the specified property for the given
                  atom from the chemical database.
        """


    def writeXML(self, file, memo, toplevel=1):
        if self.type is None:
            name = 'm' + `memo['counter']`
            memo['counter'] = memo['counter'] + 1
            memo[id(self)] = name
            atoms = copy.copy(self.atoms)
            bonds = copy.copy(self.bonds)
            for group in self.groups:
                group.writeXML(file, memo, 0)
                for atom in group.atoms:
                    atoms.remove(atom)
                for bond in group.bonds:
                    bonds.remove(bond)
            file.write('<molecule id="%s">\n' % name)
            for group in self.groups:
                file.write('  <molecule ref="%s" title="%s"/>\n'
                           % (memo[id(group.type)], group.name))
            if atoms:
                file.write('  <atomArray>\n')
                for atom in atoms:
                    file.write('    <atom title="%s" elementType="%s"/>\n'
                               % (atom.name, atom.type.symbol))
                file.write('  </atomArray>\n')
            if bonds:
                file.write('  <bondArray>\n')
                for bond in bonds:
                    a1n = self.relativeName(bond.a1)
                    a2n = self.relativeName(bond.a2)
                    file.write('    <bond atomRefs2="%s %s"/>\n' % (a1n, a2n))
                file.write('  </bondArray>\n')
            file.write('</molecule>\n')
        else:
            name = self.name
            self.type.writeXML(file, memo)
            atom_names = self.type.getXMLAtomOrder()
        if toplevel:
            return ['<molecule ref="%s"/>' % name]
        else:
            return None

    def getXMLAtomOrder(self):
        if self.type is None:
            atoms = []
            for group in self.groups:
                atoms.extend(group.getXMLAtomOrder())
            for atom in self.atoms:
                if atom not in atoms:
                    atoms.append(atom)
        else:
            atom_names = self.type.getXMLAtomOrder()
            atoms = []
            for name in atom_names:
                parts = name.split(':')
                object = self
                for attr in parts:
                    object = getattr(object, attr)
                atoms.append(object)
        return atoms

    def relativeName(self, object):
        name = ''
        while object is not None and object != self:
            name = object.name + ':' + name
            object = object.parent
        return name[:-1]

    def relativeName_unused(self, object):
        name = ''
        while object is not None and object != self:
            for attr, value in object.parent.__dict__.items():
                if value is object:
                    name = attr + ':' + name
                    break
            object = object.parent
        return name[:-1]

    def description(self, index_map = None):
        tag = Utility.uniqueAttribute()
        s = self._description(tag, index_map, 1)
        for a in self.atomList():
            delattr(a, tag)
        return s

    def __repr__(self):
        return self.__class__.__name__ + ' ' + self.fullName()
    __str__ = __repr__

    def __copy__(self):
        return copy.deepcopy(self, {id(self.parent): None})

# Type check



[docs]def isChemicalObject(object):
    """
    :returns: True if object is a chemical object
    """
    return hasattr(object, 'is_chemical_object')

#
# The second base class for all composite chemical objects.
#


[docs]class CompositeChemicalObject(object):

    """
    Chemical object containing subobjects

    This is an abstract base class that implements methods
    which can be used with any composite chemical object,
    i.e. any chemical object that is not an atom.
    """

    def __init__(self, properties):
        if properties.has_key('configuration'):
            conf = properties['configuration']
            self.configurations[conf].applyTo(self)
            del properties['configuration']
        elif hasattr(self, 'configurations') and \
             self.configurations.has_key('default'):
            self.configurations['default'].applyTo(self)
        if properties.has_key('position'):
            self.translateTo(properties['position'])
            del properties['position']
        self.addProperties(properties)

    def atomList(self):
        return self.atoms

    def atomIterator(self):
        return iter(self.atoms)

    def setPosition(self, atom, position):
        if atom.__class__ is Database.AtomReference:
            atom = self.atoms[atom.number]
        atom.setPosition(position)

    def setIndex(self, atom, index):
        if atom.__class__ is Database.AtomReference:
            atom = self.atoms[atom.number]
        atom.setIndex(index)

    def getAtom(self, atom):
        if atom.__class__ is Database.AtomReference:
            atom = self.atoms[atom.number]
        return atom

    def getReference(self, atom):
        if atom.__class__ is Database.AtomReference:
            return atom
        return Database.AtomReference(self.atoms.index(atom))

    def getAtomProperty(self, atom, property, levels = None):
        try:
            return atom.__dict__[property]
        except KeyError:
            try:
                return getattr(self, property)[self.getReference(atom)]
            except (AttributeError, KeyError):
                if levels is None:
                    object = atom
                    levels = []
                    while object != self:
                        levels.append(object)
                        object = object.parent
                if not levels:
                    raise KeyError('Property ' + property +
                                    ' not defined for  ', `atom`)
                return levels[-1].getAtomProperty(atom, property, levels[:-1])

    def deleteUndefinedAtoms(self):
        delete = [a for a in self.atoms if a.position() is None]
        for a in delete:
            a.delete()

    def _deleteAtom(self, atom):
        self.atoms.remove(atom)
        self.is_modified = True
        self.type = None
        if self.parent is not None:
            self.parent._deleteAtom(atom)

    def distanceConstraintList(self):
        dc = self._distanceConstraintList()
        for o in self._subunits():
            dc = dc + o._distanceConstraintList()
        return dc

    def _distanceConstraintList(self):
        try:
            return self.distance_constraints
        except AttributeError:
            return []

    def numberOfDistanceConstraints(self):
        n = len(self._distanceConstraintList()) + \
            sum(len(o._distanceConstraintList()) for o in self._subunits())
        return n

    def setBondConstraints(self, universe=None):
        if universe is None:
            universe = self.universe()
        bond_database = universe.bondLengthDatabase()
        for o in self.bondedUnits():
            o._setBondConstraints(universe, bond_database)

    def _setBondConstraints(self, universe, bond_database):
        self.distance_constraints = []
        for bond in self.bonds:
            d = bond_database.bondLength(bond)
            if d is None:
                d = universe.distance(bond.a1, bond.a2)
            self.addDistanceConstraint(bond.a1, bond.a2, d)

    def addDistanceConstraint(self, atom1, atom2, distance):
        try:
            self.distance_constraints.append((atom1, atom2, distance))
        except AttributeError:
            self.distance_constraints = [(atom1, atom2, distance)]

    def removeDistanceConstraints(self, universe=None):
        try:
            del self.distance_constraints
        except AttributeError:
            pass
        for o in self._subunits():
            o.removeDistanceConstraints()

    def traverseBondTree(self, function = None):
        self.setBondAttributes()
        todo = [self.atoms[0]]
        done = {todo[0]: True}
        bonds = []
        while todo:
            next_todo = []
            for atom in todo:
                bonded = atom.bondedTo()
                for other in bonded:
                    if not done.get(other, False):
                        if function is None:
                            bonds.append((atom, other))
                        else:
                            bonds.append((function(atom), function(other)))
                        next_todo.append(other)
                        done[other] = True
            todo = next_todo
        self.clearBondAttributes()
        return bonds

    def _description(self, tag, index_map, toplevel):
        letter, kwargs = self._descriptionSpec()
        s = [letter, '(', `self.name`, ',[']
        s.extend([o._description(tag, index_map, 0) + ','
                  for o in self._subunits()])
        s.extend([a._description(tag, index_map, 0) + ','
                  for a in self.atoms if not hasattr(a, tag)])
        s.append(']')
        if toplevel:
            s.extend([',', `self._typeName()`])
        if kwargs is not None:
            s.extend([',', kwargs])
        constraints = self._distanceConstraintList()
        if constraints:
            s.append(',dc=[')
            if index_map is None:
                s.extend(['(%d,%d,%f),' % (c[0].index, c[1].index, c[2])
                          for c in constraints])
            else:
                s.extend(['(%d,%d,%f),' % (index_map[c[0].index],
                                           index_map[c[1].index], c[2])
                          for c in constraints])
            s.append(']')
        s.append(')')
        return ''.join(s)

    def _typeName(self):
        return self.type.name

    def _graphics(self, conf, distance_fn, model, module, options):
        glist = []
        for bu in self.bondedUnits():
            for a in bu.atomList():
                glist.extend(a._graphics(conf, distance_fn, model,
                                         module, options))
            if hasattr(bu, 'bonds'):
                for b in bu.bonds:
                    glist.extend(b._graphics(conf, distance_fn, model,
                                             module, options))
        return glist

#
# The classes for atoms, groups, molecules, and complexes.
#


[docs]class Atom(ChemicalObject):

    """
    Atom
    """

    def __init__(self, atom_spec, _memo = None, **properties):
        """
        :param atom_spec: a string (not case sensitive) specifying
                          the chemical element
        :type atom_spec: str
        :keyword position: the position of the atom
        :type position: Scientific.Geometry.Vector
        :keyword name: a name given to the atom
        :type name: str
        """
        Utility.uniqueID.registerObject(self)
        ChemicalObject.__init__(self, atom_spec, _memo)
        self._mass = self.type.average_mass
        self.array = None
        self.index = None
        if properties.has_key('position'):
            self.setPosition(properties['position'])
            del properties['position']
        self.addProperties(properties)

    blueprintclass = Database.BlueprintAtom

    def __getstate__(self):
        state = copy.copy(self.__dict__)
        if self.array is not None:
            state['array'] = None
            state['pos'] = Vector(self.array[self.index,:])
        return state

    def atomList(self):
        return [self]

    def atomIterator(self):
        yield self


[docs]    def setPosition(self, position):
        """
        Changes the position of the atom.

        :param position: the new position
        :type position: Scientific.Geometry.Vector
        """
        if position is None:
            if self.array is None:
                try: del self.pos
                except AttributeError: pass
            else:
                self.array[self.index,0] = Utility.undefined
                self.array[self.index,1] = Utility.undefined
                self.array[self.index,2] = Utility.undefined
        else:
            if self.array is None:
                self.pos = position
            else:
                self.array[self.index,0] = position[0]
                self.array[self.index,1] = position[1]
                self.array[self.index,2] = position[2]


    translateTo = setPosition


[docs]    def position(self, conf = None):
        """
        :returns: the position in configuration conf. If conf is 
                  'None', use the current configuration. If the atom has
                  not been assigned a position, the return value is None.
        :rtype: Scientific.Geometry.Vector
        """
        if conf is None:
            if self.array is None:
                try:
                    return self.pos
                except AttributeError:
                    return None
            else:
                if N.logical_or.reduce(
                    N.greater(self.array[self.index, :],
                              Utility.undefined_limit)):
                    return None
                else:
                    return Vector(self.array[self.index,:])
        else:
            return conf[self]


    centerOfMass = position


[docs]    def setMass(self, mass):
        """
        Changes the mass of the atom.

        :param mass: the mass
        :type mass: float
        """
        self._mass = mass
        universe = self.universe()
        if universe is not None:
            universe._changed(True)


    def getAtom(self, atom):
        return self

    def translateBy(self, vector):
        if self.array is None:
            self.pos = self.pos + vector
        else:
            self.array[self.index,0] = self.array[self.index,0] + vector[0]
            self.array[self.index,1] = self.array[self.index,1] + vector[1]
            self.array[self.index,2] = self.array[self.index,2] + vector[2]

    def numberOfPoints(self):
        return 1

    numberOfCartesianCoordinates = numberOfPoints

    def setIndex(self, index):
        if self.index is not None and self.index != index:
            raise ValueError('Wrong atom index')
        self.index = index

    def setArray(self, index):
        self.index = index
        self.array = None

    def getArray(self):
        return self.array

    def unsetArray(self):
        self.pos = self.position()
        self.array = None

    def setBondAttribute(self, atom):
        try:
            self.bonded_to__.append(atom)
        except AttributeError:
            self.bonded_to__ = [atom]

    def clearBondAttribute(self):
        try:
            del self.bonded_to__
        except AttributeError:
            pass


[docs]    def bondedTo(self):
        """
        :returns: a list of all atoms to which a chemical bond exists.
        :rtype: list
        """
        try:
            return self.bonded_to__
        except AttributeError:
            if self.parent is None or not isChemicalObject(self.parent):
                return []
            else:
                return self.parent.bondedTo(self)


    def delete(self):
        if self.parent is not None:
            self.parent._deleteAtom(self)           

    def getAtomProperty(self, atom, property, levels = None):
        if self != atom:
            raise ValueError("Wrong atom")
        return getattr(self, property)

    def _description(self, tag, index_map, toplevel):
        setattr(self, tag, None)
        if index_map is None:
            index = self.index
        else:
            index = index_map[self.index]
        if toplevel:
            return 'A(' + `self.name` + ',' + `index` + ',' + \
                   `self.symbol` + ')'
        else:
            return 'A(' + `self.name` + ',' + `index` + ')'

    def _graphics(self, conf, distance_fn, model, module, options):
        #PJC change:
        if model == 'ball_and_stick':
            color = self._atomColor(self, options)
            material = module.DiffuseMaterial(color)
            radius = options.get('ball_radius', 0.03)
            return [module.Sphere(self.position(), radius, material=material)]
        elif model == 'vdw' or model == 'vdw_and_stick':
            color = self._atomColor(self, options)
            material = module.DiffuseMaterial(color)
            try:
                radius = self.vdW_radius
            except:
                radius = options.get('ball_radius', 0.03)
            return [module.Sphere(self.position(), radius, material=material)]
        else:
            return []

        if model != 'ball_and_stick':
            return []
        color = self._atomColor(self, options)
        material = module.DiffuseMaterial(color)
        radius = options.get('ball_radius', 0.03)
        return [module.Sphere(self.position(), radius, material=material)]

    def writeXML(self, file, memo, toplevel=1):
        return ['<atom title="%s" elementType="%s"/>'
                % (self.name, self.type.symbol)]

    def getXMLAtomOrder(self):
        return [self]



[docs]class Group(CompositeChemicalObject, ChemicalObject):

    """
    Group of bonded atoms

    Groups can contain atoms and other groups, and link them by chemical
    bonds. They are used to represent functional groups or any other
    part of a molecule that has a well-defined identity.

    Groups cannot be created in application programs, but only in
    database definitions for molecules or through a MoleculeFactory.
    """

    def __init__(self, group_spec, _memo = None, **properties):
        """
        :param group_spec: a string (not case sensitive) that specifies
                           the group name in the chemical database
        :type group_spec: str
        :keyword position: the position of the center of mass of the group
        :type position: Scientific.Geometry.Vector
        :keyword name: a name given to the group
        :type name: str
        """
        if group_spec is not None:
            # group_spec is None when called from MoleculeFactory
            ChemicalObject.__init__(self, group_spec, _memo)
            self.addProperties(properties)

    blueprintclass = Database.BlueprintGroup
    is_incomplete = True

    def bondedTo(self, atom):
        if self.parent is None or not isChemicalObject(self.parent):
            return []
        else:
            return self.parent.bondedTo(atom)

    def setBondAttributes(self):
        pass

    def clearBondAttributes(self):
        pass

    def _subunits(self):
        return self.groups

    def _descriptionSpec(self):
        return "G", None



[docs]class Molecule(CompositeChemicalObject, ChemicalObject):

    """Molecule

    Molecules consist of atoms and groups linked by bonds.
    """

    def __init__(self, molecule_spec, _memo = None, **properties):
        """
        :param molecule_spec: a string (not case sensitive) that specifies
                              the molecule name in the chemical database
        :type molecule_spec: str
        :keyword position: the position of the center of mass of the molecule
        :type position: Scientific.Geometry.Vector
        :keyword name: a name given to the molecule
        :type name: str
        :keyword configuration: the name of a configuration listed in the
                                database definition of the molecule, which
                                is used to initialize the atom positions.
                                If no configuration is specified, the
                                configuration named "default" will be used,
                                if it exists. Otherwise the atom positions
                                are undefined.
        :type configuration: str
        """
        if molecule_spec is not None:
            # molecule_spec is None when called from MoleculeFactory
            ChemicalObject.__init__(self, molecule_spec, _memo)
            properties = copy.copy(properties)
            CompositeChemicalObject.__init__(self, properties)
            self.bonds = Bonds.BondList(self.bonds)

    blueprintclass = Database.BlueprintMolecule

    def bondedTo(self, atom):
        return self.bonds.bondedTo(atom)

    def setBondAttributes(self):
        self.bonds.setBondAttributes()

    def clearBondAttributes(self):
        for a in self.atoms:
            a.clearBondAttribute()

    def _subunits(self):
        return self.groups

    def _descriptionSpec(self):
        return "M", None

    def addGroup(self, group, bond_atom_pairs):
        for a1, a2 in bond_atom_pairs:
            o1 = a1.topLevelChemicalObject()
            o2 = a2.topLevelChemicalObject()
            if set([o1, o2]) != set([self, group]):
                raise ValueError("bond %s-%s outside object" %
                                  (str(a1), str(a2)))
        self.groups.append(group)
        self.atoms = self.atoms + group.atoms
        group.parent = self
        self.clearBondAttributes()
        for a1, a2 in bond_atom_pairs:
            self.bonds.append(Bonds.Bond((a1, a2)))
        for b in group.bonds:
            self.bonds.append(b)

    # construct positions of missing hydrogens

[docs]    def findHydrogenPositions(self):
        """
        Find reasonable positions for hydrogen atoms that have no
        position assigned.

        This method uses a heuristic approach based on standard geometry
        data. It was developed for proteins and DNA and may not give
        good results for other molecules. It raises an exception
        if presented with a topology it cannot handle.
        """
        self.setBondAttributes()
        try:
            unknown = {}
            for a in self.atoms:
                if a.position() is None:
                    if a.symbol != 'H':
                        raise ValueError('position of ' + a.fullName() + \
                                          ' is undefined')
                    bonded = a.bondedTo()[0]
                    unknown.setdefault(bonded, []).append(a)
            for a, list in unknown.items():
                bonded = a.bondedTo()
                n = len(bonded)
                known = [b for b in bonded if b.position() is not None]
                nb = len(list)
                try:
                    method = self._h_methods[a.symbol][n][nb]
                except KeyError:
                    raise ValueError("Can't handle this yet: " +
                                      a.symbol + ' with ' + `n` + ' bonds (' +
                                      a.fullName() + ').')
                method(self, a, known, list)
        finally:
            self.clearBondAttributes()

    # default C-H bond length and X-C-H angle

    _ch_bond = 1.09*Units.Ang
    _hch_angle = N.arccos(-1./3.)*Units.rad
    _nh_bond = 1.03*Units.Ang
    _hnh_angle = 120.*Units.deg
    _oh_bond = 0.95*Units.Ang
    _coh_angle = 114.9*Units.deg
    _sh_bond = 1.007*Units.Ang
    _csh_angle = 96.5*Units.deg

    def _C4oneH(self, atom, known, unknown):
        r = atom.position()
        n0 = (known[0].position()-r).normal()
        n1 = (known[1].position()-r).normal()
        n2 = (known[2].position()-r).normal()
        n3 = (n0 + n1 + n2).normal()
        unknown[0].setPosition(r-self._ch_bond*n3)

    def _C4twoH(self, atom, known, unknown):
        r = atom.position()
        r1 = known[0].position()
        r2 = known[1].position()
        plane = Objects3D.Plane(r, r1, r2)
        axis = -((r1-r)+(r2-r)).normal()
        plane = plane.rotate(Objects3D.Line(r, axis), 90.*Units.deg)
        cone = Objects3D.Cone(r, axis, 0.5*self._hch_angle)
        sphere = Objects3D.Sphere(r, self._ch_bond)
        circle = sphere.intersectWith(cone)
        points = circle.intersectWith(plane)
        unknown[0].setPosition(points[0])
        unknown[1].setPosition(points[1])

    def _C4threeH(self, atom, known, unknown):
        self._tetrahedralH(atom, known, unknown, self._ch_bond)

    def _C3oneH(self, atom, known, unknown):
        r = atom.position()
        n1 = (known[0].position()-r).normal()
        n2 = (known[1].position()-r).normal()
        n3 = -(n1 + n2).normal()
        unknown[0].setPosition(r+self._ch_bond*n3)

    def _C3twoH(self, atom, known, unknown):
        r = atom.position()
        r1 = known[0].position()
        others = filter(lambda a: a.symbol != 'H', known[0].bondedTo())
        r2 = others[0].position()
        try:
            plane = Objects3D.Plane(r, r1, r2)
        except ZeroDivisionError:
            # We get here if all three points are colinear.
            # Add a small random displacement as a fix.
            from MMTK.Random import randomPointInSphere
            plane = Objects3D.Plane(r, r1, r2 + randomPointInSphere(0.001))
        axis = (r-r1).normal()
        cone = Objects3D.Cone(r, axis, 0.5*self._hch_angle)
        sphere = Objects3D.Sphere(r, self._ch_bond)
        circle = sphere.intersectWith(cone)
        points = circle.intersectWith(plane)
        unknown[0].setPosition(points[0])
        unknown[1].setPosition(points[1])

    def _C2oneH(self, atom, known, unknown):
        r = atom.position()
        r1 = known[0].position()
        x = r + self._ch_bond * (r - r1).normal()
        unknown[0].setPosition(x)

    def _N2oneH(self, atom, known, unknown):
        r = atom.position()
        r1 = known[0].position()
        others = filter(lambda a: a.symbol != 'H', known[0].bondedTo())
        r2 = others[0].position()
        try:
            plane = Objects3D.Plane(r, r1, r2)
        except ZeroDivisionError:
            # We get here when all three points are colinear.
            # Add a small random displacement as a fix.
            from MMTK.Random import randomPointInSphere
            plane = Objects3D.Plane(r, r1, r2 + randomPointInSphere(0.001))
        axis = (r-r1).normal()
        cone = Objects3D.Cone(r, axis, 0.5*self._hch_angle)
        sphere = Objects3D.Sphere(r, self._nh_bond)
        circle = sphere.intersectWith(cone)
        points = circle.intersectWith(plane)
        unknown[0].setPosition(points[0])

    def _N3oneH(self, atom, known, unknown):
        r = atom.position()
        n1 = (known[0].position()-r).normal()
        n2 = (known[1].position()-r).normal()
        n3 = -(n1 + n2).normal()
        unknown[0].setPosition(r+self._nh_bond*n3)

    def _N3twoH(self, atom, known, unknown):
        r = atom.position()
        r1 = known[0].position()
        others = filter(lambda a: a.symbol != 'H', known[0].bondedTo())
        r2 = others[0].position()
        plane = Objects3D.Plane(r, r1, r2)
        axis = (r-r1).normal()
        cone = Objects3D.Cone(r, axis, 0.5*self._hnh_angle)
        sphere = Objects3D.Sphere(r, self._nh_bond)
        circle = sphere.intersectWith(cone)
        points = circle.intersectWith(plane)
        unknown[0].setPosition(points[0])
        unknown[1].setPosition(points[1])

    def _N4threeH(self, atom, known, unknown):
        self._tetrahedralH(atom, known, unknown, self._nh_bond)

    def _N4twoH(self, atom, known, unknown):
        r = atom.position()
        r1 = known[0].position()
        r2 = known[1].position()
        plane = Objects3D.Plane(r, r1, r2)
        axis = -((r1-r)+(r2-r)).normal()
        plane = plane.rotate(Objects3D.Line(r, axis), 90.*Units.deg)
        cone = Objects3D.Cone(r, axis, 0.5*self._hnh_angle)
        sphere = Objects3D.Sphere(r, self._nh_bond)
        circle = sphere.intersectWith(cone)
        points = circle.intersectWith(plane)
        unknown[0].setPosition(points[0])
        unknown[1].setPosition(points[1])

    def _N4oneH(self, atom, known, unknown):
        r = atom.position()
        n0 = (known[0].position()-r).normal()
        n1 = (known[1].position()-r).normal()
        n2 = (known[2].position()-r).normal()
        n3 = (n0 + n1 + n2).normal()
        unknown[0].setPosition(r-self._nh_bond*n3)

    def _O2(self, atom, known, unknown):
        others = known[0].bondedTo()
        for a in others:
            r = a.position()
            if a != atom and r is not None: break
        dihedral = 180.*Units.deg
        self._findPosition(unknown[0], atom.position(), known[0].position(), r,
                           self._oh_bond, self._coh_angle, dihedral)

    def _S2(self, atom, known, unknown):
        c2 = filter(lambda a: a.symbol == 'C', known[0].bondedTo())[0]
        self._findPosition(unknown[0], atom.position(), known[0].position(),
                           c2.position(),
                           self._sh_bond, self._csh_angle,
                           180.*Units.deg)

    def _tetrahedralH(self, atom, known, unknown, bond):
        r = atom.position()
        n = (known[0].position()-r).normal()
        cone = Objects3D.Cone(r, n, N.arccos(-1./3.))
        sphere = Objects3D.Sphere(r, bond)
        circle = sphere.intersectWith(cone)
        others = filter(lambda a: a.symbol != 'H', known[0].bondedTo())
        others.remove(atom)
        other = others[0]
        ref = (Objects3D.Plane(circle.center, circle.normal) \
               .projectionOf(other.position())-circle.center).normal()
        p0 = circle.center + ref*circle.radius
        p0 = Objects3D.rotatePoint(p0,
                                  Objects3D.Line(circle.center, circle.normal),
                                  60.*Units.deg)
        p1 = Objects3D.rotatePoint(p0,
                                  Objects3D.Line(circle.center, circle.normal),
                                  120.*Units.deg)
        p2 = Objects3D.rotatePoint(p1,
                                  Objects3D.Line(circle.center, circle.normal),
                                  120.*Units.deg)
        unknown[0].setPosition(p0)
        unknown[1].setPosition(p1)
        unknown[2].setPosition(p2)

    def _findPosition(self, unknown, a1, a2, a3, bond, angle, dihedral):
        sphere = Objects3D.Sphere(a1, bond)
        cone = Objects3D.Cone(a1, a2-a1, angle)
        plane = Objects3D.Plane(a3, a2, a1)
        plane = plane.rotate(Objects3D.Line(a1, a2-a1), dihedral)
        points = sphere.intersectWith(cone).intersectWith(plane)
        for p in points:
            if (a1-a2).cross(p-a1)*(plane.normal) > 0:
                unknown.setPosition(p)
                break

    _h_methods = {'C': {4: {3: _C4threeH,
                            2: _C4twoH,
                            1: _C4oneH},
                        3: {2: _C3twoH,
                            1: _C3oneH},
                        2: {1: _C2oneH}},
                  'N': {4: {3: _N4threeH,
                            2: _N4twoH,
                            1: _N4oneH},
                        3: {2: _N3twoH,
                            1: _N3oneH},
                        2: {1: _N2oneH}},
                  'O': {2: {1: _O2}},
                  'S': {2: {1: _S2}},
                  }




[docs]class ChainMolecule(Molecule):

    """
    ChainMolecule

    ChainMolecules are generated by a MoleculeFactory from
    templates that have a sequence attribute.
    """

    def __len__(self):
        return len(self.sequence)

    def __getitem__(self, item):
        return getattr(self, self.sequence[item])



class Crystal(CompositeChemicalObject, ChemicalObject):

    def __init__(self, blueprint, _memo = None, **properties):
        ChemicalObject.__init__(self, blueprint, _memo)
        properties = copy.copy(properties)
        CompositeChemicalObject.__init__(self, properties)
        self.bonds = Bonds.BondList(self.bonds)

    blueprintclass = Database.BlueprintCrystal

    def _subunits(self):
        return self.groups

    def _descriptionSpec(self):
        return "X", None


[docs]class Complex(CompositeChemicalObject, ChemicalObject):

    """
    Complex

    A complex is an assembly of molecules that are not connected by
    chemical bonds.
    """

    def __init__(self, complex_spec, _memo = None, **properties):
        """
        :param complex_spec: a string (not case sensitive) that specifies
                              the complex name in the chemical database
        :type complex_spec: str
        :keyword position: the position of the center of mass of the complex
        :type position: Scientific.Geometry.Vector
        :keyword name: a name given to the complex
        :type name: str
        :keyword configuration: the name of a configuration listed in the
                                database definition of the complex, which
                                is used to initialize the atom positions.
                                If no configuration is specified, the
                                configuration named "default" will be used,
                                if it exists. Otherwise the atom positions
                                are undefined.
        :type configuration: str
        """
        ChemicalObject.__init__(self, complex_spec, _memo)
        properties = copy.copy(properties)
        CompositeChemicalObject.__init__(self, properties)

    blueprintclass = Database.BlueprintComplex

    def recreateAtomList(self):
        self.atoms = []
        for m in self.molecules:
            self.atoms.extend(m.atoms)

    def bondedUnits(self):
        return self.molecules

    def setBondAttributes(self):
        for m in self.molecules:
            m.setBondAttributes()

    def clearBondAttributes(self):
        for m in self.molecules:
            m.clearBondAttributes()

    def _subunits(self):
        return self.molecules

    def _descriptionSpec(self):
        return "C", None

    def writeXML(self, file, memo, toplevel=1):
        if self.type is None:
            name = 'm' + `memo['counter']`
            memo['counter'] = memo['counter'] + 1
            memo[id(self)] = name
            for molecule in self.molecules:
                molecule.writeXML(file, memo, 0)
            file.write('<molecule id="%s">\n' % name)
            for molecule in self.molecules:
                file.write('  <molecule ref="%s"/>\n' % memo[id(molecule)])
            file.write('</molecule>\n')
        else:
            ChemicalObject.writeXML(self, file, memo, toplevel)
        if toplevel:
            return ['<molecule ref="%s"/>' % name]
        else:
            return None

    def getXMLAtomOrder(self):
        if self.type is None:
            atoms = []
            for molecule in self.molecules:
                atoms.extend(molecule.getXMLAtomOrder())
            return atoms
        else:
            return ChemicalObject.getXMLAtomOrder(self)


Database.registerInstanceClass(Atom.blueprintclass, Atom)
Database.registerInstanceClass(Group.blueprintclass, Group)
Database.registerInstanceClass(Molecule.blueprintclass, Molecule)
Database.registerInstanceClass(Crystal.blueprintclass, Crystal)
Database.registerInstanceClass(Complex.blueprintclass, Complex)



[docs]class AtomCluster(CompositeChemicalObject, ChemicalObject):

    """
    An agglomeration of atoms

    An atom cluster acts like a molecule without any bonds or atom
    properties. It can be used to represent a group of atoms that
    are known to form a chemical unit but whose chemical properties
    are not sufficiently known to define a molecule.
    """

    def __init__(self, atoms, **properties):
        """
        :param atoms: a list of atoms in the cluster
        :type atoms: list
        :keyword position: the position of the center of mass of the cluster
        :type position: Scientific.Geometry.Vector
        :keyword name: a name given to the cluster
        :type name: str
        """
        self.atoms = list(atoms)
        self.parent = None
        self.name = ''
        self.type = None
        for a in self.atoms:
            if a.parent is not None:
                raise ValueError(repr(a)+' is part of ' + repr(a.parent))
            a.parent = self
            if a.name != '':
                setattr(self, a.name, a)
        properties = copy.copy(properties)
        CompositeChemicalObject.__init__(self, properties)
        self.bonds = Bonds.BondList([])

    def bondedTo(self, atom):
        return []

    def setBondAttributes(self):
        pass

    def clearBondAttributes(self):
        pass

    def _subunits(self):
        return []

    def _description(self, tag, index_map, toplevel):
        s = 'AC(' + `self.name` + ',['
        for a in self.atoms:
            s = s + a._description(tag, index_map, 1) + ','
        s = s + ']'
        constraints = self._distanceConstraintList()
        if constraints:
            s = s + ',dc=['
            if index_map is None:
                for c in constraints:
                    s = s + '(%d,%d,%f),' % (c[0].index, c[1].index, c[2])
            else:
                for c in constraints:
                    s = s + '(%d,%d,%f),' % (index_map[c[0].index],
                                             index_map[c[1].index], c[2])
            s = s + ']'
        return s + ')'

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MMTK-2.7.9/Doc/HTML/_modules/MMTK/Collections.html0000644000076600000240000045576112013143456021726 0ustar hinsenstaff00000000000000 MMTK.Collections — MMTK User Guide 2.7.7 documentation

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Source code for MMTK.Collections

# This module defines collections of chemical objects.
#
# Written by Konrad Hinsen
#

"""
Collections of chemical objects
"""

__docformat__ = 'restructuredtext'

from MMTK import Utility, Units, ParticleProperties, Visualization
from MMTK.Geometry import superpositionFit
from Scientific.Geometry import Vector, Tensor, Objects3D
from Scientific.Geometry import Transformation
from Scientific import N
import copy, itertools

#
# This class defines groups of atoms. It is used as a base class
# for anything containing atoms, including chemical objects, collections,
# universes etc., but it can't be used directly. All its subclasses
# must define a method atomList() that returns a list of all their atoms.
#

[docs]class GroupOfAtoms(object):

    """
    Anything that consists of atoms

    A mix-in class that defines a large set of operations which are
    common to all objects that consist of atoms, i.e. any subset of
    a chemical system. Examples are atoms, molecules, collections,
    or universes.
    """
    

[docs]    def numberOfAtoms(self):
        """
        :returns: the number of atoms
        :rtype: int
        """
        return len(self.atomList())



[docs]    def numberOfPoints(self):
        """
        :returns: the number of geometrical points that define the
                  object. It is currently equal to the number of atoms,
                  but could be different e.g. for quantum systems, in which
                  each atom is described by a wave function or a path integral.
        :rtype: int
        """
        return sum([a.numberOfPoints() for a in self.atomIterator()])


    numberOfCartesianCoordinates = numberOfPoints


[docs]    def numberOfFixedAtoms(self):
        """
        :returns: the number of atoms that are fixed, i.e. that cannot move
        :rtype: int
        """
        n = 0
        for a in self.atomIterator():
            try:
                if a.fixed: n = n + 1
            except AttributeError: pass
        return n



[docs]    def degreesOfFreedom(self):
        """
        :returns: the number of mechanical degrees of freedom
        :rtype: int
        """
        return 3*(self.numberOfAtoms()-self.numberOfFixedAtoms())



[docs]    def atomCollection(self):
        """
        :returns: a collection containing all atoms in the object
        """
        return Collection(self.atomList())



[docs]    def atomsWithDefinedPositions(self, conf = None):
        """
        :param conf: a configuration object, or None for the
                     current configuration
        :type conf: :class:`~MMTK.ParticleProperties.Configuration` or NoneType
        :returns: a collection of all atoms that have a position in the
                  given configuration
        """
        return Collection([a for a in self.atomIterator()
                           if Utility.isDefinedPosition(a.position(conf))])



[docs]    def mass(self):
        """
        :returns: the total mass
        :rtype: float
        """
        return sum(a._mass for a in self.atomIterator())



[docs]    def centerOfMass(self, conf = None):
        """
        :param conf: a configuration object, or None for the
                     current configuration
        :type conf: :class:`~MMTK.ParticleProperties.Configuration` or NoneType
        :returns: the center of mass in the given configuration
        :rtype: Scientific.Geometry.Vector
        """
        offset = None
        universe = self.universe()
        if universe is not None:
            offset = universe.contiguousObjectOffset([self], conf)
        m = 0.
        mr = Vector(0.,0.,0.)
        if offset is None:
            for a in self.atomIterator():
                m += a._mass
                mr += a._mass * a.position(conf)
        else:
            for a in self.atomIterator():
                m += a._mass
                mr += a._mass * (a.position(conf)+offset[a])
        return mr/m


    position = centerOfMass


[docs]    def centerAndMomentOfInertia(self, conf = None):
        """
        :param conf: a configuration object, or None for the
                     current configuration
        :type conf: :class:`~MMTK.ParticleProperties.Configuration` or NoneType
        :returns: the center of mass and the moment of inertia tensor
                  in the given configuration
        """
        from Scientific.Geometry import delta
        offset = None
        universe = self.universe()
        if universe is not None:
            offset = universe.contiguousObjectOffset([self], conf)
        m = 0.
        mr = Vector(0.,0.,0.)
        t = Tensor(3*[3*[0.]])
        for a in self.atomIterator():
            ma = a._mass
            if offset is None:
                r = a.position(conf)
            else:
                r = a.position(conf)+offset[a]
            m += ma
            mr += ma*r
            t += ma*r.dyadicProduct(r)
        cm = mr/m
        t -= m*cm.dyadicProduct(cm)
        t = t.trace()*delta - t
        return cm, t



[docs]    def rotationalConstants(self, conf=None):
        """
        :param conf: a configuration object, or None for the
                     current configuration
        :type conf: :class:`~MMTK.ParticleProperties.Configuration` or NoneType
        :returns: a sorted array of rotational constants A, B, C
                  in internal units
        """
        com, i = self.centerAndMomentOfInertia(conf)
        pmi = i.eigenvalues()
        return N.sort(Units.h / (8.*N.pi*N.pi*pmi))[::-1]



[docs]    def boundingBox(self, conf = None):
        """
        :param conf: a configuration object, or None for the
                     current configuration
        :type conf: :class:`~MMTK.ParticleProperties.Configuration` or NoneType
        :returns: two opposite corners of a bounding box around the
                  object. The bounding box is the smallest rectangular
                  bounding box with edges parallel to the coordinate axes.
        :rtype: tuple of two Scientific.Geometry.Vector
        """
        atoms = self.atomList()
        min = atoms[0].position(conf).array
        max = min
        for a in atoms[1:]:
            r = a.position(conf).array
            min = N.minimum(min, r)
            max = N.maximum(max, r)
        return Vector(min), Vector(max)



[docs]    def boundingSphere(self, conf = None):
        """
        :param conf: a configuration object, or None for the
                     current configuration
        :type conf: :class:`~MMTK.ParticleProperties.Configuration` or NoneType
        :returns: a sphere that contains all atoms in the object.
                  This is B{not} the minimal bounding sphere, just B{some}
                  bounding sphere.
        :rtype: Scientific.Geometry.Objects3D.Sphere
        """
        atoms = self.atomList()
        center = sum((a.position(conf) for a in atoms),
                     Vector(0., 0., 0.)) / len(atoms)
        r = 0.
        for a in atoms:
            r = max(r, (a.position(conf)-center).length())
        return Objects3D.Sphere(center, r)



[docs]    def rmsDifference(self, conf1, conf2 = None):
        """
        :param conf1: a configuration object
        :type conf1: :class:`~MMTK.ParticleProperties.Configuration`
        :param conf2: a configuration object, or None for the
                      current configuration
        :type conf2: :class:`~MMTK.ParticleProperties.Configuration` or NoneType
        :returns: the RMS (root-mean-square) difference between the
                  conformations of the object in two universe configurations,
                  conf1 and conf2
        :rtype: float
        """
        universe = conf1.universe
        m = 0.
        rms = 0.
        for a in self.atomIterator():
            ma = a._mass
            dr = universe.distanceVector(a.position(conf1), a.position(conf2))
            m += ma
            rms += ma*dr*dr
        return N.sqrt(rms/m)


    def findTransformationAsQuaternion(self, conf1, conf2 = None):
        universe = self.universe()
        if universe.is_periodic:
            raise ValueError("superposition in periodic universe "
                             "is not defined")
        if conf1.universe != universe:
            raise ValueError("conformation is for a different universe")
        if conf2 is None:
            conf2 = conf1
            conf1 = universe.configuration()
        else:
            if conf2.universe != universe:
                raise ValueError("conformation is for a different universe")
        weights = universe.masses()
        return superpositionFit([(weights[a], conf1[a], conf2[a])
                                 for a in self.atomIterator()])


[docs]    def findTransformation(self, conf1, conf2 = None):
        """
        :param conf1: a configuration object
        :type conf1: :class:`~MMTK.ParticleProperties.Configuration`
        :param conf2: a configuration object, or None for the
                      current configuration
        :type conf2: :class:`~MMTK.ParticleProperties.Configuration` or NoneType
        :returns: the linear transformation that, when applied to
                  the object in configuration conf1, minimizes the
                  RMS distance to the conformation in conf2, and the
                  minimal RMS distance.
                  If conf2 is None, returns the transformation from the
                  current configuration to conf1 and the associated
                  RMS distance.
        """
        q, cm1, cm2, rms = self.findTransformationAsQuaternion(conf1, conf2)
        return Transformation.Translation(cm2) * \
               q.asRotation() * \
               Transformation.Translation(-cm1), \
               rms



[docs]    def translateBy(self, vector):
        """
        Translate the object by a displacement vector
        
        :param vector: the displacement vector
        :type vector: Scientific.Geometry.Vector
        """
        for a in self.atomIterator():
            a.translateBy(vector)



[docs]    def translateTo(self, position):
        """
        Translate the object such that its center of mass is at position
        :param position: the final position
        :type position: Scientific.Geometry.Vector
        """
        self.translateBy(position-self.centerOfMass())



[docs]    def normalizePosition(self):
        """
        Translate the center of mass to the coordinate origin
        """
        self.translateTo(Vector(0., 0., 0.))



[docs]    def normalizeConfiguration(self, repr=None):
        """
        Apply a linear transformation such that the center of mass of
        the object is translated to the coordinate origin and its
        principal axes of inertia become parallel to the three
        coordinate axes.

        :param repr: the specific representation for axis alignment:
          - Ir    : x y z <--> b c a
          - IIr   : x y z <--> c a b
          - IIIr  : x y z <--> a b c
          - Il    : x y z <--> c b a
          - IIl   : x y z <--> a c b
          - IIIl  : x y z <--> b a c        
        """
        transformation = self.normalizingTransformation(repr)
        self.applyTransformation(transformation)



[docs]    def normalizingTransformation(self, repr=None):
        """
        Calculate a linear transformation that shifts the center of mass
        of the object to the coordinate origin and makes its
        principal axes of inertia parallel to the three coordinate
        axes.

        :param repr: the specific representation for axis alignment:
          Ir    : x y z <--> b c a
          IIr   : x y z <--> c a b
          IIIr  : x y z <--> a b c
          Il    : x y z <--> c b a
          IIl   : x y z <--> a c b
          IIIl  : x y z <--> b a c
        :returns: the normalizing transformation
        :rtype: Scientific.Geometry.Transformation.RigidBodyTransformation
        """
        from Scientific.LA import determinant
        cm, inertia = self.centerAndMomentOfInertia()
        ev, diag = inertia.diagonalization()
        if determinant(diag.array) < 0:
            diag.array[0] = -diag.array[0]
        if repr != None:
            seq = N.argsort(ev)
            if repr == 'Ir':
                seq = N.array([seq[1], seq[2], seq[0]])
            elif repr == 'IIr':
                seq = N.array([seq[2], seq[0], seq[1]])
            elif repr == 'Il':
                seq = N.seq[2::-1]
            elif repr == 'IIl':
                seq[1:3] = N.array([seq[2], seq[1]])
            elif repr == 'IIIl':
                seq[0:2] = N.array([seq[1], seq[0]])
            elif repr != 'IIIr':
                print 'unknown representation'
            diag.array = N.take(diag.array, seq)                
        return Transformation.Rotation(diag)*Transformation.Translation(-cm)



[docs]    def applyTransformation(self, t):
        """
        Apply a transformation to the object

        :param t: the transformation to be applied
        :type t: Scientific.Geometry.Transformation
        """
        for a in self.atomIterator():
            a.setPosition(t(a.position()))



[docs]    def displacementUnderTransformation(self, t):
        """
        :param t: the transformation to be applied
        :type t: Scientific.Geometry.Transformation
        :returns: the displacement vectors for the atoms in the object
                  that correspond to the transformation t.
        :rtype: :class:`~MMTK.ParticleProperties.ParticleVector`
        """
        d = ParticleProperties.ParticleVector(self.universe())
        for a in self.atomIterator():
            r = a.position()
            d[a] = t(r)-r
        return d



[docs]    def rotateAroundCenter(self, axis_direction, angle):
        """
        Rotate the object around an axis that passes through its center
        of mass.

        :param axis_direction: the direction of the axis of rotation
        :type axis_direction: Scientific.Geometry.Vector
        :param angle: the rotation angle (in radians)
        :type angle: float
        """
        cm = self.centerOfMass()
        t = Transformation.Translation(cm) * \
            Transformation.Rotation(axis_direction, angle) * \
            Transformation.Translation(-cm)
        self.applyTransformation(t)



[docs]    def rotateAroundOrigin(self, axis_direction, angle):
        """
        Rotate the object around an axis that passes through the
        coordinate origin.

        :param axis_direction: the direction of the axis of rotation
        :type axis_direction: Scientific.Geometry.Vector
        :param angle: the rotation angle (in radians)
        :type angle: float
        """
        self.applyTransformation(Transformation.Rotation(axis_direction, angle))



[docs]    def rotateAroundAxis(self, point1, point2, angle):
        """
        Rotate the object arond an axis specified by two points

        :param point1: the first point
        :type point1: Scientific.Geometry.Vector
        :param point2: the second point
        :type point2: Scientific.Geometry.Vector
        :param angle: the rotation angle (in radians)
        :type angle: float
        """
        tr1 = Transformation.Translation(-point1)
        tr2 = Transformation.Rotation(point2-point1, angle)
        tr3 = tr1.inverse()
        self.applyTransformation(tr3*tr2*tr1)



[docs]    def writeToFile(self, filename, configuration = None, format = None):
        """
        Write a representation of the object in a given
        configuration to a file.

        :param filename: the name of the file
        :type filename: str
        :param configuration: a configuration object, or None for the
                              current configuration
        :type configuration: :class:`~MMTK.ParticleProperties.Configuration` or NoneType
        :param format: 'pdb' or 'vrml' (default: guess from filename)
                       A subformat specification can be added, separated
                       by a dot. Subformats of 'vrml' are 'wireframe'
                       (default), 'ball_and_stick', 'highlight' (like
                       'wireframe', but with a small sphere for
                       all atoms that have an attribute 'highlight' with a
                       non-zero value), and 'charge' (wireframe plus small
                       spheres for the atoms whose color indicates the
                       charge on a red-to-green color scale)
        :type format: str
        """
        from MMTK import ConfigIO
        universe = self.universe()
        if universe is not None:
            configuration = universe.contiguousObjectConfiguration(
                               [self], configuration)
        file = ConfigIO.OutputFile(filename, format)
        file.write(self, configuration)
        file.close()



[docs]    def view(self, configuration = None, format = 'pdb'):
        """
        Start an external viewer for the object in the given
        configuration.

        :param configuration: the configuration to be visualized
        :type configuration: :class:`~MMTK.ParticleProperties.Configuration`
        :param format: 'pdb' (for running $PDBVIEWER) or 'vrml'
                       (for running $VRMLVIEWER). An optional
                       subformat specification can be added, see
                       :class:`~MMTK.Collections.GroupOfAtoms.writeToFile` for the details.
        """
        universe = self.universe()
        if universe is not None:
            configuration = universe.contiguousObjectConfiguration([self],
                                                                configuration)
        Visualization.viewConfiguration(self, configuration, format)



[docs]    def kineticEnergy(self, velocities = None):
        """
        :param velocities: a set of velocities for all atoms, or
                           None for the current velocities
        :type velocities: :class:`~MMTK.ParticleProperties.ParticleVector`
        :returns: the kinetic energy
        :rtype: float
        """
        if velocities is None:
            velocities = self.atomList()[0].universe().velocities()
        energy = 0.
        for a in self.atomIterator():
            v = velocities[a]
            energy = energy + a._mass*(v*v)
        return 0.5*energy



[docs]    def temperature(self, velocities = None):
        """
        :param velocities: a set of velocities for all atoms, or
                           None for the current velocities
        :type velocities: :class:`~MMTK.ParticleProperties.ParticleVector`
        :returns: the temperature
        :rtype: float
        """
        energy = self.kineticEnergy(velocities)
        return 2.*energy/(self.degreesOfFreedom()*Units.k_B)



[docs]    def momentum(self, velocities = None):
        """
        :param velocities: a set of velocities for all atoms, or
                           None for the current velocities
        :type velocities: :class:`~MMTK.ParticleProperties.ParticleVector`
        :returns: the momentum
        :rtype: Scientific.Geometry.Vector
        """
        if velocities is None:
            velocities = self.atomList()[0].universe().velocities()
        return sum((a._mass*velocities[a] for a in self.atomIterator()),
                   Vector(0., 0., 0.))



[docs]    def angularMomentum(self, velocities = None, conf = None):
        """
        :param velocities: a set of velocities for all atoms, or
                           None for the current velocities
        :type velocities: :class:`~MMTK.ParticleProperties.ParticleVector`
        :param conf: a configuration object, or None for the
                     current configuration
        :type conf: :class:`~MMTK.ParticleProperties.Configuration`
        :returns: the angluar momentum
        :rtype: Scientific.Geometry.Vector
        """
        if velocities is None:
            velocities = self.atomList()[0].universe().velocities()
        cm = self.centerOfMass(conf)
        return sum((a._mass*a.position(conf).cross(velocities[a])
                    for a in self.atomIterator()),
                   Vector(0., 0., 0.))



[docs]    def angularVelocity(self, velocities = None, conf = None):
        """
        :param velocities: a set of velocities for all atoms, or
                           None for the current velocities
        :type velocities: :class:`~MMTK.ParticleProperties.ParticleVector`
        :param conf: a configuration object, or None for the
                     current configuration
        :type conf: :class:`~MMTK.ParticleProperties.Configuration`
        :returns: the angluar velocity
        :rtype: Scientific.Geometry.Vector
        """
        if velocities is None:
            velocities = self.atomList()[0].universe().velocities()
        cm, inertia = self.centerAndMomentOfInertia(conf)
        l = sum((a._mass*a.position(conf).cross(velocities[a])
                 for a in self.atomIterator()),
                Vector(0., 0., 0.))
        return inertia.inverse()*l
        


[docs]    def universe(self):
        """
        :returns: the universe of which the object is part. For an
                  object that is not part of a universe, the result is
                  None
        :rtype: :class:`~MMTK.Universe.Universe`
        """
        atoms = self.atomList()
        if not atoms:
            return None
        universe = atoms[0].universe()
        for a in atoms[1:]:
            if a.universe() is not universe:
                return None
        return universe



[docs]    def charge(self):
        """
        :returns: the total charge of the object. This is defined only
                  for objects that are part of a universe with a force
                  field that defines charges.
        :rtype: float
        """
        return self.universe().forcefield().charge(self)



[docs]    def dipole(self, reference = None):
        """
        :returns: the total dipole moment of the object. This is defined only
                  for objects that are part of a universe with a force field
                  that defines charges.
        :rtype: Scientific.Geometry.Vector
        """
        return self.universe().forcefield().dipole(self, reference)



[docs]    def booleanMask(self):
        """
        :returns: a ParticleScalar object that contains a value of 1
                  for each atom that is in the object and a value of 0 for all
                  other atoms in the universe
        :rtype: :class:`~MMTK.ParticleProperties.ParticleScalar`
        """
        universe = self.universe()
        if universe is None:
            raise ValueError("object not in a universe")
        array = N.zeros((universe.numberOfAtoms(),), N.Int)
        mask = ParticleProperties.ParticleScalar(universe, array)
        for a in self.atomIterator():
            mask[a] = 1
        return mask

#
# This class defines a general collection that can contain
# chemical objects and other collections.
#


[docs]class Collection(GroupOfAtoms, Visualization.Viewable):

    """
    Collection of chemical objects

    Collections permit the grouping of arbitrary chemical objects
    (atoms, molecules, etc.) into one object for the purpose of analysis
    or manipulation.

    Collections permit length inquiry, item extraction by indexing,
    and iteration, like any Python sequence object. Two collections
    can be added to yield a collection that contains the combined
    elements.
    """

    def __init__(self, *objects):
        """
        :param objects: a chemical object or a sequence of chemical objects that
                        define the initial content of the collection.
        """
        self.objects = []
        self.addObject(objects)

    is_collection = 1


[docs]    def addObject(self, object):
        """
        Add objects to the collection.

        :param object: the object(s) to be added. If it is another collection
                       or a list, all of its elements are added
        """
        from MMTK.ChemicalObjects import isChemicalObject
        if isChemicalObject(object):
            self.addChemicalObject(object)
        elif isCollection(object):
            self.addChemicalObjectList(object.objectList())
        elif Utility.isSequenceObject(object):
            if object and isChemicalObject(object[0]):
                self.addChemicalObjectList(list(object))
            else:
                for o in object:
                    self.addObject(o)
        else:
            raise TypeError('Wrong object type in collection')


    def addChemicalObject(self, object):
        self.objects.append(object)

    def addChemicalObjectList(self, list):
        self.objects.extend(list)


[docs]    def removeObject(self, object):
        """
        Remove an object or a list or collection of objects from the
        collection. The object(s) to be removed must be elements of the
        collection.

        :param object: the object to be removed, or a list or collection
                       of objects whose elements are to be removed
        :raises ValueError: if the object is not an element of the collection
        """
        from MMTK.ChemicalObjects import isChemicalObject
        if isChemicalObject(object):
            self.removeChemicalObject(object)
        elif isCollection(object) or Utility.isSequenceObject(object):
            for o in object:
                self.removeObject(o)
        else:
            raise ValueError('Object not in this collection')


    def removeChemicalObject(self, object):
        try:
            self.objects.remove(object)
        except ValueError:
            raise ValueError('Object not in this collection')


[docs]    def selectShell(self, point, r1, r2=0.):
        """
        Select objects in a spherical shell around a central point.

        :param point: the center of the spherical shell
        :type point: Scientific.Geometry.Vector
        :param r1: inner or outer radius of the shell
        :type r1: float
        :param r2: inner or outer radius of the shell (default: 0.)
        :type r2: float
        :returns: a collection of all elements whose
                  distance from point is between r1 and r2
        :rtype: :class:`~MMTK.Collections.Collection`
        """
        if r1 > r2:
            r1, r2 = r2, r1
        universe = self.universe()
        in_shell = []
        for o in self.objects:
            for a in o.atomIterator():
                r =  universe.distance(a.position(), point)
                if r >= r1 and r <= r2:
                    in_shell.append(o)
                    break
        return Collection(in_shell)



[docs]    def selectBox(self, p1, p2):
        """
        Select objects in a rectangular volume

        :param p1: one corner of the rectangular volume
        :type p1: Scientific.Geometry.Vector
        :param p2: the other corner of the rectangular volume
        :type p2: Scientific.Geometry.Vector
        :returns: a collection of all elements that lie
                  within the rectangular volume
        :rtype: :class:`~MMTK.Collections.Collection`
        """
        x1 = N.minimum(p1.array, p2.array)
        x2 = N.maximum(p1.array, p2.array)
        in_box = []
        for o in self.objects:
            r = o.position().array
            if N.logical_and.reduce( \
                N.logical_and(N.less_equal(x1, r),
                              N.less(r, x2))):
                in_box.append(o)
        return Collection(in_box)



[docs]    def objectList(self, type = None):
        """
        Make a list of all objects in the collection that are instances
        of a specific type or of one of its subtypes.

        :param type: the type that serves as a filter. If None,
                     all objects are returned
        :returns: the objects that match the given type
        :rtype: list
        """
        if type is None:
            return self.objects
        else:
            return [o for o in self.objects if isinstance(o, type)]



[docs]    def atomList(self):
        """
        :returns: a list containing all atoms of all objects in the collection
        :rtype: list
        """
        atoms = []
        for o in self.objectList():
            atoms.extend(o.atomList())
        return atoms


    def atomIterator(self):
        return itertools.chain(*(o.atomIterator() for o in self.objects))
    

[docs]    def numberOfAtoms(self):
        """
        :returns: the total number of atoms in the objects of the collection
        :rtype: int
        """
        return sum(o.numberOfAtoms() for o in self.objectList())
    


[docs]    def universe(self):
        """
        :returns: the universe of which all objects in the collection
                  are part. If no such universe exists, the return value
                  is None
        :rtype: :class:`~MMTK.Universe.Universe`
        """
        if not self.objects:
            return None
        universe = self.objects[0].universe()
        for o in self.objects[1:]:
            if o.universe() is not universe:
                return None
        return universe


    def __len__(self):
        """
        :returns: the number of objects in the collection
        :rtype: int
        """
        return len(self.objects)

    def __getitem__(self, item):
        """
        :param item: an index into the object list
        :type item: int
        :returns: the object with the given index
        """
        return self.objects[item]

    def __iter__(self):
        return self.objects.__iter__()

    def __add__(self, other):
        return Collection(self.objectList(), other.objectList())

    def __str__(self):
        return "Collection of %d objects" % len(self.objects)


[docs]    def map(self, function):
        """
        Apply a function to all objects in the collection and
        return the list of the results. If the results are chemical
        objects, a Collection object is returned instead of a list.

        :param function: the function to be applied
        :type function: callable
        :returns: the list or collection of the results
        """
        from MMTK.ChemicalObjects import isChemicalObject
        list = [function(o) for o in self.objectList()]
        if list and isChemicalObject(list[0]):
            return Collection(list)
        else:
            return list


    def bondedUnits(self):
        bu = []
        for o in self.objects:
            bu = bu + o.bondedUnits()
        return bu

    def degreesOfFreedom(self):
        return GroupOfAtoms.degreesOfFreedom(self) \
               - self.numberOfDistanceConstraints()


[docs]    def distanceConstraintList(self):
        """
        :returns: the list of distance constraints
        :rtype: list
        """
        dc = []
        for o in self.objects:
            dc.extend(o.distanceConstraintList())
        return dc



[docs]    def numberOfDistanceConstraints(self):
        """
        :returns: the number of distance constraints
        """
        return sum(o.numberOfDistanceConstraints() for o in self.objects)



[docs]    def setBondConstraints(self, universe=None):
        """
        Set distance constraints for all bonds
        """
        if universe is None:
            universe = self.universe()
        for o in self.objects:
            o.setBondConstraints(universe)



[docs]    def removeDistanceConstraints(self, universe=None):
        """
        Remove all distance constraints
        """
        if universe is None:
            universe = self.universe()
        for o in self.objects:
            o.removeDistanceConstraints(universe)


    def _graphics(self, conf, distance_fn, model, module, options):
        gobs = []
        for o in self.objects:
            gobs.extend(o._graphics(conf, distance_fn, model,
                                    module, options))
        return gobs

    def __copy__(self):
        return self.__class__(copy.copy(self.objects))


# type check for collections



[docs]def isCollection(object):
    """
    :param object: any Python object
    :returns: True if the object is a :class:`~MMTK.Collections.Collection`
    """
    return hasattr(object, 'is_collection')

#
# This class defines a partitioned collection. Such collections
# divide their objects into cubic boxes according to their positions.
# It is then possible to find potential neighbours much more efficiently.
#


[docs]class PartitionedCollection(Collection):

    """
    Collection with cubic partitions

    A PartitionedCollection differs from a plain Collection by
    sorting its elements into small cubic cells. This makes adding
    objects slower, but geometrical operations like 
    selectShell become much faster for a large number of
    objects.
    """

    def __init__(self, partition_size, *objects):
        """
        :param partition_size: the edge length of the cubic cells
        :param objects: a chemical object or a sequence of chemical objects that
                        define the initial content of the collection.
        """
        self.partition_size = 1.*partition_size
        self.undefined = []
        self.partition = {}
        self.addObject(objects)

    def addChemicalObject(self, object):
        p = object.position()
        if p is None:
            self.undefined.append(object)
        else:
            index = self.partitionIndex(p)
            try:
                partition = self.partition[index]
            except KeyError:
                partition = []
                self.partition[index] = partition
            partition.append(object)
        self.all = None

    def addChemicalObjectList(self, list):
        for object in list:
            self.addChemicalObject(object)

    def removeChemicalObject(self, object):
        p = object.position()
        if p is None:
            self.undefined.remove(object)
        else:
            index = self.partitionIndex(p)
            try:
                partition = self.partition[index]
            except KeyError:
                raise ValueError('Object not in this collection')
            try:
                partition.remove(object)
            except ValueError:
                raise ValueError('Object not in this collection')
        self.all = None

    def partitionIndex(self, x):
        return (int(N.floor(x[0]/self.partition_size)),
                int(N.floor(x[1]/self.partition_size)),
                int(N.floor(x[2]/self.partition_size)))

    def objectList(self):
        return sum(self.partition.values(), [self.undefined])

    def __len__(self):
        return sum(len(p) for p in self.partition.values()) + \
               len(self.undefined)

    def __getitem__(self, item):
        if self.all is None:
            self.all = self.objectList()
        if item >= len(self.all):
            self.all = None
            raise IndexError
        return self.all[item]

    def __copy__(self):
        return self.__class__(self.partition_size,
                              copy.copy(self.objectList()))


[docs]    def partitions(self):
        """
        :returns: a list of cubic partitions. Each partition is specified
                  by a tuple containing two vectors (describing the diagonally
                  opposite corners) and the list of objects in the partition.
        :rtype: list
        """
        list = []
        for index, objects in self.partition.items():
            min = Vector(index)*self.partition_size
            max = min + Vector(3*[self.partition_size])
            list.append((min, max, objects))
        return list


    def selectCube(self, point, edge):
        x = int(round(point[0]/self.partition_size))
        y = int(round(point[1]/self.partition_size))
        z = int(round(point[2]/self.partition_size))
        d = (Vector(x, y, z)*self.partition_size-point).length()
        n = int(N.ceil((edge + d)/(2.*self.partition_size)))
        objects = []
        for nx in range(-n, n):
            for ny in range(-n, n):
                for nz in range(-n, n):
                    try:
                        objects.append(self.partition[(nx+x, ny+y, nz+z)])
                    except KeyError: pass
        return Collection(objects)

    def selectShell(self, point, min, max=0):
        if min > max:
            min, max = max, min
        objects = Collection()
        minsq = min**2
        maxsq = max**2
        for index in self.partition.keys():
            d1 = self.partition_size*N.array(index) - point.array
            d2 = d1 + self.partition_size
            dmin = (d1 > 0.)*d1 - (d2 < 0.)*d2
            dminsq = N.add.reduce(dmin**2)
            dmaxsq = N.add.reduce(N.maximum(d1**2, d2**2))
            if dminsq >= minsq and dmaxsq <= maxsq:
                objects.addObject(self.partition[index])
            elif dmaxsq >= minsq and dminsq <= maxsq:
                o = Collection(self.partition[index]).selectShell(point,
                                                                  min, max)
                objects.addObject(o)
        return objects


[docs]    def pairsWithinCutoff(self, cutoff):
        """
        :param cutoff: a cutoff for pair distances
        :returns: a list containing all pairs of objects in the
                  collection whose center-of-mass distance is less than
                  the cutoff
        :rtype: list
        """
        pairs = []
        positions = {}
        for index, objects in self.partition.items():
            pos = map(lambda o: o.position(), objects)
            positions[index] = pos
            for o1, o2 in Utility.pairs(zip(objects, pos)):
                if (o2[1]-o1[1]).length() <= cutoff:
                    pairs.append((o1[0], o2[0]))
        partition_cutoff = int(N.floor((cutoff/self.partition_size)**2))
        ones = N.array([1,1,1])
        zeros = N.array([0,0,0])
        keys = self.partition.keys()
        for i in range(len(keys)):
            p1 = keys[i]
            for j in range(i+1, len(keys)):
                p2 = keys[j]
                d = N.maximum(abs(N.array(p2)-N.array(p1)) -
                              ones, zeros)
                if N.add.reduce(d*d) <= partition_cutoff:
                    for o1, pos1 in zip(self.partition[p1],
                                        positions[p1]):
                        for o2, pos2 in zip(self.partition[p2],
                                            positions[p2]):
                            if (pos2-pos1).length() <= cutoff:
                                pairs.append((o1, o2))
        return pairs

#
# A special form of partitioned collection that stores the atoms
# of all objects that are added to it.
#


[docs]class PartitionedAtomCollection(PartitionedCollection):

    """
    Partitioned collection of atoms

    PartitionedAtomCollection objects behave like PartitionedCollection
    atoms, except that they store only atoms. When a composite chemical
    object is added, its atoms are stored instead.
    """

    def __init__(*args):
        apply(PartitionedCollection.__init__, args)

    def addChemicalObject(self, object):
        for atom in object.atomIterator():
            PartitionedCollection.addChemicalObject(self, atom)

    def removeChemicalObject(self, object):
        for atom in object.atomIterator():
            PartitionedCollection.removeChemicalObject(self, atom)

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Source code for MMTK.ConfigIO

# This module deals with input and output of configurations.
#
# Written by Konrad Hinsen
#

"""
I/O of molecular configurations

Input: Z-Matrix and Cartesian
Output: VRML
PDB files are handled in :class:`~MMTK.PDB`.
"""

__docformat__ = 'restructuredtext'

from MMTK import PDB, Units, Utility
from Scientific.Geometry.Objects3D import Sphere, Cone, Plane, Line, \
                                          rotatePoint
from Scientific.Geometry import Vector
from Scientific.Visualization import VRML
from Scientific import N
import os

#
# This class represents a Z-Matrix. Z-Matrix data consists of a list
# with one element for each atom being defined. Each entry is a
# list containing the data defining the atom.
#

[docs]class ZMatrix(object):

    """
    Z-Matrix specification of a molecule conformation

    ZMatrix objects can be used in chemical database entries
    to specify molecule conformations by internal coordinates.
    With the exception of the three first atoms, each atom is
    defined relative to three previously atoms by a distance,
    an angle, and a dihedral angle.
    """

    def __init__(self, data):
        """
        :param data: a list of atom definitions. Each atom definition (except
                     for the first three ones) is a list containing seven
                     elements:

                       - the atom to be defined
                       - a previously defined atom and the distance to it
                       - another previously defined atom and the angle to it
                       - a third previously defined atom and the dihedral
                         angle to it

                     The definition of the first atom contains only the first
                     element, the second atom needs the first three elements,
                     and the third atom is defined by the first five elements.
        """
        self.data = data
        self.coordinates = {}

    _substitute = True

    def findPositions(self):
        # First atom at origin
        self.coordinates[self.data[0][0]] = Vector(0,0,0)
        # Second atom along x-axis
        self.coordinates[self.data[1][0]] = Vector(self.data[1][2],0,0)
        # Third atom in xy-plane
        try:
            pos1 = self.coordinates[self.data[2][1]]
        except KeyError:
            raise ValueError("atom %d has no defined position"
                              % self.data[2][1].number)
        try:
            pos2 = self.coordinates[self.data[2][3]]
        except KeyError:
            raise ValueError("atom %d has no defined position"
                              % self.data[2][3].number)
        sphere = Sphere(pos1, self.data[2][2])
        cone = Cone(pos1, pos2-pos1, self.data[2][4])
        plane = Plane(Vector(0,0,0), Vector(0,0,1))
        points = sphere.intersectWith(cone).intersectWith(plane)
        self.coordinates[self.data[2][0]] = points[0]
        # All following atoms defined by distance + angle + dihedral
        for entry in self.data[3:]:
            try:
                pos1 = self.coordinates[entry[1]]
            except KeyError:
                raise ValueError("atom %d has no defined position"
                                  % entry[1].number)
            try:
                pos2 = self.coordinates[entry[3]]
            except KeyError:
                raise ValueError("atom %d has no defined position"
                                  % entry[3].number)
            try:
                pos3 = self.coordinates[entry[5]]
            except KeyError:
                raise ValueError("atom %d has no defined position"
                                  % entry[5].number)
            distance = entry[2]
            angle = entry[4]
            dihedral = entry[6]
            sphere = Sphere(pos1, distance)
            cone = Cone(pos1, pos2-pos1, angle)
            plane123 = Plane(pos3, pos2, pos1)
            points = sphere.intersectWith(cone).intersectWith(plane123)
            for p in points:
                if Plane(pos2, pos1, p).normal * plane123.normal > 0:
                    break
            p = rotatePoint(p, Line(pos1, pos2-pos1), dihedral)
            self.coordinates[entry[0]] = p


[docs]    def applyTo(self, object):
        """
        Define the positions of the atoms in a chemical object by the
        internal coordinates of the Z-Matrix.

        :param object: the object to which the Z-Matrix is applied
        """
        if not len(self.coordinates):
            self.findPositions()
        for entry in self.coordinates.items():
            object.setPosition(entry[0], entry[1])
        object.normalizePosition()

#
# This class represents a dictionary of Cartesian positions
#


[docs]class Cartesian(object):

    """
    Cartesian specification of a molecule conformation

    Cartesian objects can be used in chemical database entries
    to specify molecule conformations by Cartesian coordinates.
    """
    
    def __init__(self, data):
        """
        :param data: a dictionary mapping atoms to tuples of length three
                     that define its Cartesian coordinates
        """
        self.dict = data

    _substitute = True


[docs]    def applyTo(self, object):
        """
        Define the positions of the atoms in a chemical object by the
        stored coordinates.

        :param object: the object to which the coordinates are applied
        """
        for a, r in self.dict.items():
            object.setPosition(a, Vector(r[0], r[1], r[2]))

#
# VRML output
#

class VRMLWireframeFile(VRML.VRMLFile):

    def __init__(self, filename, color_values = None):
        VRML.VRMLFile.__init__(self, filename, 'w')
        self.warning = 0
        self.color_values = color_values
        if self.color_values is not None:
            lower = N.minimum.reduce(color_values.array)
            upper = N.maximum.reduce(color_values.array)
            self.color_scale = VRML.ColorScale((lower, upper))

    def write(self, object, configuration = None, distance = None):
        from MMTK.ChemicalObjects import isChemicalObject
        from MMTK.Universe import InfiniteUniverse
        if distance is None:
            try:
                distance = object.universe().distanceVector
            except AttributeError:
                distance = InfiniteUniverse().distanceVector
        if not isChemicalObject(object):
            for o in object:
                self.write(o, configuration, distance)
        else:
            for bu in object.bondedUnits():
                for a in bu.atomList():
                    self.writeAtom(a, configuration)
                if hasattr(bu, 'bonds'):
                    for b in bu.bonds:
                        self.writeBond(b, configuration, distance)

    def close(self):
        VRML.VRMLFile.close(self)
        if self.warning:
            Utility.warning('Some atoms are missing in the output file ' + \
                            'because their positions are undefined.')
            self.warning = 0

    def atomColor(self, atom):
        if self.color_values is None:
            return atom.color
        else:
            return self.color_scale(self.color_values[atom])

    def writeAtom(self, atom, configuration):
        pass

    def writeBond(self, bond, configuration, distance):
        p1 = bond.a1.position(configuration)
        p2 = bond.a2.position(configuration)
        if p1 is not None and p2 is not None:
            bond_vector = 0.5*distance(bond.a1, bond.a2, configuration)
            cut = bond_vector != 0.5*(p2-p1)
            color1 = self.atomColor(bond.a1)
            color2 = self.atomColor(bond.a2)
            material1 = VRML.EmissiveMaterial(color1)
            material2 = VRML.EmissiveMaterial(color2)
            if color1 == color2 and not cut:
                c = VRML.Line(p1, p2, material = material1)
                c.writeToFile(self)
            else:
                c = VRML.Line(p1, p1+bond_vector, material = material1)
                c.writeToFile(self)
                c = VRML.Line(p2, p2-bond_vector, material = material2)
                c.writeToFile(self)


class VRMLHighlight(VRMLWireframeFile):

    def writeAtom(self, atom, configuration):
        try:
            highlight = atom.highlight
        except AttributeError:
            highlight = 0
        if highlight:
            p = atom.position(configuration)
            if p is None:
                self.warning = 1
            else:
                s = VRML.Sphere(p, 0.1*Units.Ang,
                                material = VRML.DiffuseMaterial(atom.color),
                                reuse = 1)
                s.writeToFile(self)


class VRMLBallAndStickFile(VRMLWireframeFile):

    def writeAtom(self, atom, configuration):
        p = atom.position(configuration)
        if p is None:
            self.warning = 1
        else:
            color = self.atomColor(atom)
            s = VRML.Sphere(p, 0.1*Units.Ang,
                            material = VRML.DiffuseMaterial(color),
                            reuse = 1)
            s.writeToFile(self)

    def writeBond(self, bond, configuration, distance):
        p1 = bond.a1.position(configuration)
        p2 = bond.a2.position(configuration)
        if p1 is not None and p2 is not None:
            bond_vector = 0.5*distance(bond.a1, bond.a2, configuration)
            cut = bond_vector != 0.5*(p2-p1)
            color1 = self.atomColor(bond.a1)
            color2 = self.atomColor(bond.a2)
            material1 = VRML.EmissiveMaterial(color1)
            material2 = VRML.EmissiveMaterial(color2)
            if color1 == color2 and not cut:
                c = VRML.Cylinder(p1, p2, 0.03*Units.Ang,
                                  material = material1)
                c.writeToFile(self)
            else:
                c = VRML.Cylinder(p1, p1+bond_vector, 0.03*Units.Ang,
                                  material = material1)
                c.writeToFile(self)
                c = VRML.Cylinder(p2, p2-bond_vector, 0.03*Units.Ang,
                                  material = material2)
                c.writeToFile(self)


class VRMLChargeFile(VRMLWireframeFile):

    color_scale = VRML.SymmetricColorScale(1.)

    def writeAtom(self, atom, configuration):
        p = atom.position(configuration)
        c = atom.charge()
        c = max(min(c, 1.), -1.)
        if p is None:
            self.warning = 1
        else:
            s = VRML.Sphere(p, 0.1*Units.Ang,
                            material = VRML.Material(diffuse_color =
                                                     self.color_scale(c)))
            s.writeToFile(self)

    bond_material = VRML.DiffuseMaterial('black')

    def writeBond(self, bond, configuration, distance):
        p1 = bond.a1.position(configuration)
        p2 = bond.a2.position(configuration)
        if p1 is not None and p2 is not None:
            bond_vector = 0.5*distance(bond.a1, bond.a2, configuration)
            cut = bond_vector != 0.5*(p2-p1)
            if not cut:
                c = VRML.Line(p1, p2, material = self.bond_material)
                c.writeToFile(self)
            else:
                c = VRML.Line(p1, p1+bond_vector, material = self.bond_material)
                c.writeToFile(self)
                c = VRML.Line(p2, p2-bond_vector, material = self.bond_material)
                c.writeToFile(self)

VRMLFile = VRMLWireframeFile

#
# Recognize some standard file types by their extensions
#
def fileFormatFromExtension(filename):
    filename, ext = os.path.splitext(filename)
    if ext in _file_compressions:
        filename, ext = os.path.splitext(filename)
    try:
        return _file_formats[ext]
    except KeyError:
        raise IOError('Unknown file format')

_file_formats = {'.pdb': 'pdb', '.wrl': 'vrml'}
_file_compressions = ['.gz', '.Z']

#
# Output file for a specified format
#
def OutputFile(filename, format = None):
    if format is None:
        format = fileFormatFromExtension(filename)
    format = tuple(format.split('.'))
    try:
        return _file_types[format](filename)
    except KeyError:
        if len(format) == 1:
            return _file_types[format[0]](filename)
        else:
            _file_types[format[0]](filename, format[1])

_file_types = {'pdb': PDB.PDBOutputFile,
               ('vrml',): VRMLFile,
               ('vrml', 'wireframe'): VRMLWireframeFile,
               ('vrml', 'highlight'): VRMLHighlight,
               ('vrml', 'ball_and_stick'): VRMLBallAndStickFile,
               ('vrml', 'charge'): VRMLChargeFile}

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Source code for MMTK.DCD

# This module implements a DCD reader/writer
#
# Written by Lutz Ehrlich
# Adapted to MMTK conventions by Konrad Hinsen


"""
Reading and writing of DCD trajectory files

The DCD format for trajectories is used by CHARMM, X-Plor,
and NAMD. It can be read by various visualization programs.

The DCD format is defined as a binary (unformatted) Fortran
format and is therefore platform-dependent.

"""

__docformat__ = 'restructuredtext'

import MMTK_DCD
from MMTK import PDB, Trajectory, Units
from Scientific import N



[docs]class DCDReader(Trajectory.TrajectoryGenerator):

    """
    Reader for DCD trajectories (CHARMM/X-Plor/NAMD)

    A DCDReader reads a DCD trajectory and "plays back" the
    data as if it were generated directly by an integrator.
    The universe for which the DCD file is read must be
    perfectly compatible with the data in the file, including
    an identical internal atom numbering. This can be guaranteed
    only if the universe was created from a PDB file that is
    compatible with the DCD file without leaving out any
    part of the system.

    Reading is started by calling the reader object.
    The following data categories and variables are available for
    output:

    * category "time": time
    * category "configuration": configuration
    """

    default_options = {}

    available_data = ['configuration', 'time']

    restart_data = None

    def __init__(self, universe, **options):
        """
        :param universe: the universe for which the information from the
                         trajectory file is read
        :param options: keyword options
        :keyword dcd_file: the name of the DCD trajecory file to be read
        :keyword actions: a list of actions to be executed periodically
                          (default is none)
        """
        Trajectory.TrajectoryGenerator.__init__(self, universe, options)

    def __call__(self, **options):
        self.setCallOptions(options)
        configuration = self.universe.configuration()
        MMTK_DCD.readDCD(self.universe, configuration.array,
                         self.getActions(), self.getOption('dcd_file'))

        

def writeDCD(vector_list, dcd_file_name, factor, atom_order=None,
             delta_t=0.1, conf_flag=1):
    universe = vector_list[0].universe
    natoms = universe.numberOfPoints()
    if atom_order is None:
        atom_order = N.arrayrange(natoms)
    else:
        atom_order = N.array(atom_order)

    i_start = 0       # always start at frame 0
    n_savc  = 1       # save every frame
    fd = MMTK_DCD.writeOpenDCD(dcd_file_name, natoms, len(vector_list),
                               i_start, n_savc, delta_t)
    for vector in vector_list:
        if conf_flag:
            vector = universe.contiguousObjectConfiguration(None, vector)
        array = factor*vector.array
        x = N.take(array[:, 0], atom_order).astype(N.Float16)
        y = N.take(array[:, 1], atom_order).astype(N.Float16)
        z = N.take(array[:, 2], atom_order).astype(N.Float16)
        MMTK_DCD.writeDCDStep(fd, x, y, z)
    MMTK_DCD.writeCloseDCD(fd)

def writePDB(universe, configuration, pdb_file_name):
    offset = None
    if universe is not None:
        configuration = universe.contiguousObjectConfiguration(None,
                                                               configuration)
    pdb = PDB.PDBOutputFile(pdb_file_name, 'xplor')
    pdb.write(universe, configuration)
    sequence = pdb.atom_sequence
    pdb.close()
    return sequence


[docs]def writeDCDPDB(conf_list, dcd_file_name, pdb_file_name, delta_t=0.1):
    """
    Write a sequence of configurations to a DCD file and generate
    a compatible PDB file.

    :param conf_list: the sequence of configurations
    :type conf_list: sequence of :class:`~MMTK.ParticleProperties.Configuration`
    :param dcd_file_name: the name of the DCD file
    :type dcd_file_name: str
    :param pdb_file_name: the name of the PDB file
    :type pdb_file_name: str
    :param delta_t: the time step between two configurations
    :type delta_t: float
    """
    universe = conf_list[0].universe
    sequence = writePDB(universe, conf_list[0], pdb_file_name)
    indices = map(lambda a: a.index, sequence)
    writeDCD(conf_list, dcd_file_name, 1./Units.Ang, indices, delta_t, 1)




[docs]def writeVelocityDCDPDB(vel_list, dcd_file_name, pdb_file_name, delta_t=0.1):
    """
    Write a sequence of velocity particle vectors to a DCD file and generate
    a compatible PDB file.

    :param vel_list: the sequence of velocity particle vectors
    :type vel_list: sequence of :class:`~MMTK.ParticleProperties.ParticleVector`
    :param dcd_file_name: the name of the DCD file
    :type dcd_file_name: str
    :param pdb_file_name: the name of the PDB file
    :type pdb_file_name: str
    :param delta_t: the time step between two velocity sets
    :type delta_t: float
    """
    universe = vel_list[0].universe
    sequence = writePDB(universe, universe.configuration(), pdb_file_name)
    indices = map(lambda a: a.index, sequence)
    writeDCD(vel_list, dcd_file_name, 1./(Units.Ang/Units.akma_time),
             indices, delta_t, 0)

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Source code for MMTK.Deformation

# Deformation energy module
#
# Written by Konrad Hinsen
#

"""
Deformation energies in proteins

This module implements deformational energies for use in the analysis
of motions and conformational changes in macromolecules. A description
of the techniques can be found in the following articles:

  |  K. Hinsen
  |  Analysis of domain motions by approximate normal mode calculations
  |  Proteins 33 (1998): 417-429

  |  K. Hinsen, A. Thomas, M.J. Field
  |  Analysis of domain motions in large proteins
  |  Proteins 34 (1999): 369-382
"""

__docformat__ = 'restructuredtext'

try:
    from MMTK_forcefield import NonbondedList
    from MMTK_deformation import deformation, reduceDeformation, \
                                 reduceFiniteDeformation
except ImportError:
    pass
from MMTK import ParticleProperties
from Scientific import N

#
# Deformation energy evaluations
#
class DeformationEvaluationFunction(object):

    def __init__(self, universe, fc_length = 0.7, cutoff = 1.2,
                 factor = 46402., form = 'exponential'):
        self.universe = universe
        self.fc_length = fc_length
        self.cutoff = cutoff
        self.factor = factor

        nothing = N.zeros((0,2), N.Int)
        self.pairs = NonbondedList(nothing, nothing, nothing,
                                   universe._spec, cutoff)
        self.pairs.update(universe.configuration().array)
        self.normalize = 0
        try:
            self.version = self.forms.index(form)
        except ValueError:
            raise ValueError("unknown functional form")

    forms = ['exponential', 'calpha']

    def newConfiguration(self):
        self.pairs.update(self.universe.configuration().array)



[docs]class DeformationFunction(DeformationEvaluationFunction):

    """
    Infinite-displacement deformation function

    The default values are appropriate for a |C_alpha| model of a protein
    with the global scaling described in the reference cited above.

    A DeformationFunction object must be called with a single parameter,
    which is a ParticleVector object containing the infinitesimal displacements
    of the atoms for which the deformation is to be evaluated.
    The return value is a ParticleScalar object containing the
    deformation value for each atom.
    """

    def __init__(self, universe, fc_length = 0.7, cutoff = 1.2,
                 factor = 46402., form = 'exponential'):
        """
        :param universe: the universe for which the deformation function
                         is defined
        :type universe: :class:`~MMTK.Universe.Universe`
        :param fc_length: the range parameter r_0 in the pair interaction term
        :type fc_length: float
        :param cutoff: the cutoff used in the deformation calculation
        :type cutoff: float
        :param factor: a global scaling factor
        :type factor: float
        :param form: the functional form ('exponential' or 'calpha')
        :type form: str
        """
        DeformationEvaluationFunction.__init__(self, universe, fc_length,
                                               cutoff, factor, form)

    def __call__(self, vector):
        conf = self.universe.configuration()
        r = ParticleProperties.ParticleScalar(self.universe)
        l = deformation(conf.array, vector.array, self.pairs,
                        None, r.array, self.cutoff, self.fc_length,
                        self.factor, self.normalize, 0, self.version)
        return r




[docs]class NormalizedDeformationFunction(DeformationFunction):

    """
    Normalized infinite-displacement deformation function

    The default values are appropriate for a |C_alpha| model of a protein
    with the global scaling described in the reference cited above.
    The normalization is defined by equation 10 of reference 1 (see above).
    
    A NormalizedDeformationFunction object must be called with a single
    parameter, which is a ParticleVector object containing the infinitesimal
    displacements of the atoms for which the deformation is to be evaluated.
    The return value is a ParticleScalar object containing the
    deformation value for each atom.
    """

    def __init__(self, universe, fc_length = 0.7, cutoff = 1.2,
                 factor = 46402., form = 'exponential'):
        """
        :param universe: the universe for which the deformation function
                         is defined
        :type universe: :class:`~MMTK.Universe.Universe`
        :param fc_length: the range parameter r_0 in the pair interaction term
        :type fc_length: float
        :param cutoff: the cutoff used in the deformation calculation
        :type cutoff: float
        :param factor: a global scaling factor
        :type factor: float
        :param form: the functional form ('exponential' or 'calpha')
        :type form: str
        """
        DeformationFunction.__init__(self, universe, fc_length,
                                     cutoff, factor, form)

    def __init__(self, *args, **kwargs):
        apply(DeformationFunction.__init__, (self, ) + args, kwargs)
        self.normalize = 1




[docs]class FiniteDeformationFunction(DeformationEvaluationFunction):

    """
    Finite-displacement deformation function

    The default values are appropriate for a |C_alpha| model of a protein
    with the global scaling described in the reference cited above.

    A FiniteDeformationFunction object must be called with a single parameter,
    which is a Configuration or a ParticleVector object containing the
    alternate configuration of the universe for which the deformation is to be
    evaluated. The return value is a ParticleScalar object containing the
    deformation value for each atom.
    """

    def __init__(self, universe, fc_length = 0.7, cutoff = 1.2,
                 factor = 46402., form = 'exponential'):
        """
        :param universe: the universe for which the deformation function
                         is defined
        :type universe: :class:`~MMTK.Universe.Universe`
        :param fc_length: the range parameter r_0 in the pair interaction term
        :type fc_length: float
        :param cutoff: the cutoff used in the deformation calculation
        :type cutoff: float
        :param factor: a global scaling factor
        :type factor: float
        :param form: the functional form ('exponential' or 'calpha')
        :type form: str
        """
        DeformationEvaluationFunction.__init__(self, universe, fc_length,
                                               cutoff, factor, form)

    def __call__(self, vector):
        conf = self.universe.configuration()
        vector = vector-conf
        r = ParticleProperties.ParticleScalar(self.universe)
        l = deformation(conf.array, vector.array, self.pairs, None, r.array,
                        self.cutoff, self.fc_length, self.factor, 0, 1,
                        self.version)
        return r




[docs]class DeformationEnergyFunction(DeformationEvaluationFunction):

    """
    Infinite-displacement deformation energy function

    The deformation energy is the sum of the deformation values over
    all atoms of a system.

    The default values are appropriate for a |C_alpha| model of a protein
    with the global scaling described in the reference cited above.

    A DeformationEnergyFunction is called with one or two parameters.
    The first parameter is a ParticleVector object containing the
    infinitesimal displacements of the atoms for which the deformation
    energy is to be evaluated. The optional second argument can be
    set to a non-zero value to request the gradients of the energy
    in addition to the energy itself. In that case there are two
    return values (energy and the gradients in a ParticleVector
    object), otherwise only the energy is returned.
    """

    def __init__(self, universe, fc_length = 0.7, cutoff = 1.2,
                 factor = 46402., form = 'exponential'):
        """
        :param universe: the universe for which the deformation function
                         is defined
        :type universe: :class:`~MMTK.Universe.Universe`
        :param fc_length: the range parameter r_0 in the pair interaction term
        :type fc_length: float
        :param cutoff: the cutoff used in the deformation calculation
        :type cutoff: float
        :param factor: a global scaling factor
        :type factor: float
        :param form: the functional form ('exponential' or 'calpha')
        :type form: str
        """
        DeformationEvaluationFunction.__init__(self, universe, fc_length,
                                               cutoff, factor, form)

    def __call__(self, vector, gradients = None):
        conf = self.universe.configuration()
        g = None
        if gradients is not None:
            if ParticleProperties.isParticleProperty(gradients):
                g = gradients
            elif isinstance(gradients, N.array_type):
                g = ParticleProperties.ParticleVector(self.universe, gradients)
            elif gradients:
                g = ParticleProperties.ParticleVector(self.universe)
        if g is None:
            g_array = None
        else:
            g_array = g.array
        l = deformation(conf.array, vector.array, self.pairs,
                        g_array, None, self.cutoff, self.fc_length,
                        self.factor, self.normalize, 0, self.version)
        if g is None:
            return l
        else:
            return l, g




[docs]class NormalizedDeformationEnergyFunction(DeformationEnergyFunction):

    """
    Normalized infinite-displacement deformation energy function

    The normalized deformation energy is the sum of the normalized
    deformation values over all atoms of a system.

    The default values are appropriate for a |C_alpha| model of a protein
    with the global scaling described in the reference cited above.
    The normalization is defined by equation 10 of reference 1.

    A NormalizedDeformationEnergyFunction is called with one or two parameters.
    The first parameter is a ParticleVector object containing the
    infinitesimal displacements of the atoms for which the deformation
    energy is to be evaluated. The optional second argument can be
    set to a non-zero value to request the gradients of the energy
    in addition to the energy itself. In that case there are two
    return values (energy and the gradients in a ParticleVector
    object), otherwise only the energy is returned.
    """

    def __init__(self, universe, fc_length = 0.7, cutoff = 1.2,
                 factor = 46402., form = 'exponential'):
        """
        :param universe: the universe for which the deformation function
                         is defined
        :type universe: :class:`~MMTK.Universe.Universe`
        :param fc_length: the range parameter r_0 in the pair interaction term
        :type fc_length: float
        :param cutoff: the cutoff used in the deformation calculation
        :type cutoff: float
        :param factor: a global scaling factor
        :type factor: float
        :param form: the functional form ('exponential' or 'calpha')
        :type form: str
        """
        DeformationEnergyFunction.__init__(self, universe, fc_length,
                                           cutoff, factor, form)
        self.normalize = 1




[docs]class FiniteDeformationEnergyFunction(DeformationEvaluationFunction):

    """
    Finite-displacement deformation energy function

    The deformation energy is the sum of the
    deformation values over all atoms of a system.

    The default values are appropriate for a |C_alpha| model of a protein
    with the global scaling described in the reference cited above.

    A FiniteDeformationEnergyFunction is called with one or two parameters.
    The first parameter is a ParticleVector object containing the
    alternate configuration of the universe for which the deformation
    energy is to be evaluated. The optional second argument can be
    set to a non-zero value to request the gradients of the energy
    in addition to the energy itself. In that case there are two
    return values (energy and the gradients in a ParticleVector
    object), otherwise only the energy is returned.
    """

    def __init__(self, universe, fc_length = 0.7, cutoff = 1.2,
                 factor = 46402., form = 'exponential'):
        """
        :param universe: the universe for which the deformation function
                         is defined
        :type universe: :class:`~MMTK.Universe.Universe`
        :param fc_length: the range parameter r_0 in the pair interaction term
        :type fc_length: float
        :param cutoff: the cutoff used in the deformation calculation
        :type cutoff: float
        :param factor: a global scaling factor
        :type factor: float
        :param form: the functional form ('exponential' or 'calpha')
        :type form: str
        """
        DeformationEvaluationFunction.__init__(self, universe, fc_length,
                                               cutoff, factor, form)

    def __call__(self, vector, gradients = None):
        conf = self.universe.configuration()
        g = None
        if gradients is not None:
            if ParticleProperties.isParticleProperty(gradients):
                g = gradients
            elif isinstance(gradients, N.array_type):
                g = ParticleProperties.ParticleVector(self.universe, gradients)
            elif gradients:
                g = ParticleProperties.ParticleVector(self.universe)
        if g is None:
            g_array = None
        else:
            g_array = g.array
        l = deformation(conf.array, vector.array, self.pairs, g_array, None,
                        self.cutoff, self.fc_length, self.factor,
                        0, 1, self.version)
        if g is None:
            return l
        else:
            return l, g

#
# Deformation energy minimization
#


[docs]class DeformationReducer(DeformationEvaluationFunction):

    """
    Iterative reduction of the deformation energy

    The default values are appropriate for a |C_alpha| model of a protein
    with the global scaling described in the reference cited above.

    A DeformationReducer is called with two arguments. The first
    is a ParticleVector containing the initial infinitesimal displacements
    for all atoms. The second is an integer indicating the number of
    iterations. The result is a modification of the displacements
    by steepest-descent minimization of the deformation energy.
    """

    def __init__(self, universe, fc_length = 0.7, cutoff = 1.2,
                 factor = 46402., form = 'exponential'):
        """
        :param universe: the universe for which the deformation function
                         is defined
        :type universe: :class:`~MMTK.Universe.Universe`
        :param fc_length: the range parameter r_0 in the pair interaction term
        :type fc_length: float
        :param cutoff: the cutoff used in the deformation calculation
        :type cutoff: float
        :param factor: a global scaling factor
        :type factor: float
        :param form: the functional form ('exponential' or 'calpha')
        :type form: str
        """
        DeformationEvaluationFunction.__init__(self, universe, fc_length,
                                               cutoff, factor, form)

    def __call__(self, vector, niter):
        conf = self.universe.configuration()
        reduceDeformation(conf.array, vector.array, self.pairs,
                          self.cutoff, self.fc_length, self.factor, niter,
                          self.version)




[docs]class FiniteDeformationReducer(DeformationEvaluationFunction):

    """
    Iterative reduction of the finite-displacement deformation energy

    The default values are appropriate for a |C_alpha| model of a protein
    with the global scaling described in the reference cited above.

    A FiniteDeformationReducer is called with two arguments. The first
    is a ParticleVector or Configuration containing the alternate
    configuration for which the deformation energy is evaluated.
    The second is the RMS distance that defines the termination
    condition. The return value a configuration that differs from
    the input configuration by approximately the specified RMS distance,
    and which is obtained by iterative steepest-descent minimization of
    the finite-displacement deformation energy.
    """

    def __init__(self, universe, fc_length = 0.7, cutoff = 1.2,
                 factor = 46402., form = 'exponential'):
        """
        :param universe: the universe for which the deformation function
                         is defined
        :type universe: :class:`~MMTK.Universe.Universe`
        :param fc_length: the range parameter r_0 in the pair interaction term
        :type fc_length: float
        :param cutoff: the cutoff used in the deformation calculation
        :type cutoff: float
        :param factor: a global scaling factor
        :type factor: float
        :param form: the functional form ('exponential' or 'calpha')
        :type form: str
        """
        DeformationEvaluationFunction.__init__(self, universe, fc_length,
                                               cutoff, factor, form)

    def __call__(self, vector, rms_reduction):
        conf = self.universe.configuration()
        vector = vector-conf
        reduceFiniteDeformation(conf.array, vector.array, self.pairs,
                                self.cutoff, self.fc_length, self.factor,
                                rms_reduction, self.version)
        return conf+vector

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Source code for MMTK.Dynamics

# This module implements MD integrators
#
# Written by Konrad Hinsen
#

"""
Molecular Dynamics integrators
"""

__docformat__ = 'restructuredtext'

from MMTK import Environment, Features, Trajectory, Units
import MMTK_dynamics
from Scientific import N

#
# Integrator base class
#
class Integrator(Trajectory.TrajectoryGenerator):

    def __init__(self, universe, options):
        Trajectory.TrajectoryGenerator.__init__(self, universe, options)

    default_options = {'first_step': 0, 'steps': 100, 'delta_t': 1.*Units.fs,
                       'background': False, 'threads': None,
                       'mpi_communicator': None, 'actions': []}

    available_data = ['configuration', 'velocities', 'gradients',
                      'energy', 'thermodynamic', 'time', 'auxiliary']

    restart_data = ['configuration', 'velocities', 'energy',
                    'thermodynamic', 'auxiliary']

    def __call__(self, options):
        raise AttributeError

#
# Velocity-Verlet integrator
#

[docs]class VelocityVerletIntegrator(Integrator):

    """
    Velocity-Verlet molecular dynamics integrator

    The integrator can handle fixed atoms, distance constraints,
    a thermostat, and a barostat, as well as any combination.
    It is fully thread-safe.

    The integration is started by calling the integrator object.
    All the keyword options (see documnentation of __init__) can be
    specified either when creating the integrator or when calling it.

    The following data categories and variables are available for
    output:

     - category "time": time

     - category "configuration": configuration and box size (for
       periodic universes)

     - category "velocities": atomic velocities

     - category "gradients": energy gradients for each atom

     - category "energy": potential and kinetic energy, plus
       extended-system energy terms if a thermostat and/or barostat
       are used

     - category "thermodynamic": temperature, volume (if a barostat
       is used) and pressure

     - category "auxiliary": extended-system coordinates if a thermostat
       and/or barostat are used

    """

    def __init__(self, universe, **options):
        """
        :param universe: the universe on which the integrator acts
        :type universe: :class:`~MMTK.Universe.Universe`
        :keyword steps: the number of integration steps (default is 100)
        :type steps: int
        :keyword delta_t: the time step (default is 1 fs)
        :type delta_t: float
        :keyword actions: a list of actions to be executed periodically
                          (default is none)
        :type actions: list
        :keyword threads: the number of threads to use in energy evaluation
                          (default set by MMTK_ENERGY_THREADS)
        :type threads: int
        :keyword background: if True, the integration is executed as a
                             separate thread (default: False)
        :type background: bool
        :keyword mpi_communicator: an MPI communicator object, or None,
                                   meaning no parallelization (default: None)
        :type mpi_communicator: Scientific.MPI.MPICommunicator
        """
        Integrator.__init__(self, universe, options)
        self.features = [Features.FixedParticleFeature,
                         Features.NoseThermostatFeature,
                         Features.AndersenBarostatFeature,
                         Features.DistanceConstraintsFeature]

    def __call__(self, **options):
        """
        Run the integrator. The keyword options are the same as described
        under __init__.
        """
        self.setCallOptions(options)
        used_features = Features.checkFeatures(self, self.universe)
        configuration = self.universe.configuration()
        velocities = self.universe.velocities()
        if velocities is None:
            raise ValueError("no velocities")
        masses = self.universe.masses()
        fixed = self.universe.getAtomBooleanArray('fixed')
        nt = self.getOption('threads')
        comm = self.getOption('mpi_communicator')
        evaluator = self.universe.energyEvaluator(threads=nt,
                                                  mpi_communicator=comm)
        evaluator = evaluator.CEvaluator()
        constraints, const_distances_sq, c_blocks = \
                     _constraintArrays(self.universe)
                                                  
        type = 'NVE'
        if Features.NoseThermostatFeature in used_features:
            type = 'NVT'
            thermostat = self.universe.environmentObjectList(
                                          Environment.NoseThermostat)[0]
            t_parameters = thermostat.parameters
            t_coordinates = thermostat.coordinates
        else:
            t_parameters = N.zeros((0,), N.Float)
            t_coordinates = N.zeros((2,), N.Float)
        if Features.AndersenBarostatFeature in used_features:
            if self.universe.cellVolume() is None:
                raise ValueError("Barostat requires finite volume universe")
            if type == 'NVE':
                type = 'NPH'
            else:
                type = 'NPT'
            barostat = self.universe.environmentObjectList(
                                          Environment.AndersenBarostat)[0]
            b_parameters = barostat.parameters
            b_coordinates = barostat.coordinates
        else:
            b_parameters = N.zeros((0,), N.Float)
            b_coordinates = N.zeros((1,), N.Float)

        args = (self.universe,
                configuration.array, velocities.array,
                masses.array, fixed.array, evaluator,
                constraints, const_distances_sq, c_blocks,
                t_parameters, t_coordinates,
                b_parameters, b_coordinates,
                self.getOption('delta_t'), self.getOption('first_step'),
                self.getOption('steps'), self.getActions(),
                type + ' dynamics trajectory with ' +
                self.optionString(['delta_t', 'steps']))
        return self.run(MMTK_dynamics.integrateVV, args)

#
# Velocity scaling, removal of global translation/rotation
#


[docs]class VelocityScaler(Trajectory.TrajectoryAction):

    """
    Periodic velocity scaling action

    A VelocityScaler object is used in the action list of
    a VelocityVerletIntegrator. It rescales all atomic velocities
    by a common factor to make the temperature of the system equal
    to a predefined value.
    """

    def __init__(self, temperature, window=0., first=0, last=None, skip=1):
        """
        :param temperature: the temperature value to which the velocities
                            should be scaled
        :type temperature: float
        :param window: the deviation from the ideal temperature that
                       is tolerated in either direction before rescaling
                       takes place
        :type window: float
        :param first: the number of the first step at which the action
                      is executed
        :type first: int
        :param last: the number of the first step at which the action
                     is no longer executed. A value of None indicates
                     that the action should be executed indefinitely.
        :type last: int
        :param skip: the number of steps to skip between two applications
                     of the action
        :type skip: int
        """
        Trajectory.TrajectoryAction.__init__(self, first, last, skip)
        self.parameters = N.array([temperature, window], N.Float)
        self.Cfunction = MMTK_dynamics.scaleVelocities



[docs]class Heater(Trajectory.TrajectoryAction):

    """
    Periodic heating action

    A Heater object us used in the action list of a VelocityVerletIntegrator.
    It scales the velocities to a temperature that increases over time.
    """

    def __init__(self, temp1, temp2, gradient, first=0, last=None, skip=1):
        """
        :param temp1: the temperature value to which the velocities
                      should be scaled initially
        :type temp1: float
        :param temp2: the final temperature value to which the velocities
                      should be scaled
        :type temp2: float
        :param gradient: the temperature gradient (in K/ps)
        :type gradient: float
        :param first: the number of the first step at which the action
                      is executed
        :type first: int
        :param last: the number of the first step at which the action
                     is no longer executed. A value of None indicates
                     that the action should be executed indefinitely.
        :type last: int
        :param skip: the number of steps to skip between two applications
                     of the action
        :type skip: int
        """
        Trajectory.TrajectoryAction.__init__(self, first, last, skip)
        self.parameters = N.array([temp1, temp2, gradient], N.Float)
        self.Cfunction = MMTK_dynamics.heat



[docs]class BarostatReset(Trajectory.TrajectoryAction):

    """
    Barostat reset action

    A BarostatReset object is used in the action list of a
    VelocityVerletIntegrator. It resets the barostat coordinate
    to zero.
    """

    def __init__(self, first=0, last=None, skip=1):
        """
        :param first: the number of the first step at which the action
                      is executed
        :type first: int
        :param last: the number of the first step at which the action
                     is no longer executed. A value of None indicates
                     that the action should be executed indefinitely.
        :type last: int
        :param skip: the number of steps to skip between two applications
                     of the action
        :type skip: int
        """
        Trajectory.TrajectoryAction.__init__(self, first, last, skip)
        self.parameters = N.zeros((0,), N.Float)
        self.Cfunction = MMTK_dynamics.resetBarostat



[docs]class TranslationRemover(Trajectory.TrajectoryAction):

    """
    Action that eliminates global translation

    A TranslationRemover object is used in the action list of a
    VelocityVerletIntegrator. It subtracts the total velocity
    of the system from each atomic velocity.
    """

    def __init__(self, first=0, last=None, skip=1):
        """
        :param first: the number of the first step at which the action
                      is executed
        :type first: int
        :param last: the number of the first step at which the action
                     is no longer executed. A value of None indicates
                     that the action should be executed indefinitely.
        :type last: int
        :param skip: the number of steps to skip between two applications
                     of the action
        :type skip: int
        """
        Trajectory.TrajectoryAction.__init__(self, first, last, skip)
        self.parameters = None
        self.Cfunction = MMTK_dynamics.removeTranslation



[docs]class RotationRemover(Trajectory.TrajectoryAction):

    """
    Action that eliminates global rotation

    A RotationRemover object is used in the action list of a
    VelocityVerletIntegrator. It adjusts the atomic velocities
    such that the total angular momentum is zero.
    """

    def __init__(self, first=0, last=None, skip=1):
        """
        :param first: the number of the first step at which the action
                      is executed
        :type first: int
        :param last: the number of the first step at which the action
                     is no longer executed. A value of None indicates
                     that the action should be executed indefinitely.
        :type last: int
        :param skip: the number of steps to skip between two applications
                     of the action
        :type skip: int
        """
        Trajectory.TrajectoryAction.__init__(self, first, last, skip)
        self.parameters = None
        self.Cfunction = MMTK_dynamics.removeRotation

#
# Construct constraint arrays
#

def _constraintArrays(universe):
    nc = universe.numberOfDistanceConstraints()
    constraints = N.zeros((nc, 2), N.Int)
    const_distances_sq = N.zeros((nc, ), N.Float)
    if nc > 0:
        i = 0
        c_blocks = [0]
        for o in universe.objectList():
            for c in o.distanceConstraintList():
                constraints[i, 0] = c[0].index
                constraints[i, 1] = c[1].index
                const_distances_sq[i] = c[2]*c[2]
                i = i + 1
            c_blocks.append(i)
        c_blocks = N.array(c_blocks)
    else:
        c_blocks = N.zeros((1,), N.Int)
    return constraints, const_distances_sq, c_blocks

#
# Enforce distance constraints for current configuration
#
def enforceConstraints(universe, configuration=None):
    constraints, const_distances_sq, c_blocks = _constraintArrays(universe)
    if len(constraints) == 0:
        return
    if configuration is None:
        configuration = universe.configuration()
    MMTK_dynamics.enforceConstraints(universe._spec, configuration.array,
                                     universe.masses().array,
                                     constraints, const_distances_sq, c_blocks)

#
# Project velocity vector onto the constraint surface
#
def projectVelocities(universe, velocities):
    constraints, const_distances_sq, c_blocks = _constraintArrays(universe)
    if len(constraints) == 0:
        return
    MMTK_dynamics.projectVelocities(universe._spec,
                                    universe.configuration().array,
                                    velocities.array, universe.masses().array,
                                    constraints, const_distances_sq, c_blocks)

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Source code for MMTK.Environment

# This module defines environment objects for universes.
#
# Written by Konrad Hinsen
#

"""
Environment objects

Environment objects are objects that define a simulation system without
being composed of atoms. Examples are thermostats, barostats, external
fields, etc.
"""

__docformat__ = 'restructuredtext'

from Scientific import N

#
# The environment object base class
#
class EnvironmentObject(object):

    is_environment_object = 1

    def checkCompatibilityWith(self, other):
        pass

    def description(self):
        return "o('Environment." + self.__class__.__name__ + \
               `tuple(self.parameters)` + "')"

# Type check

def isEnvironmentObject(object):
    return hasattr(object, 'is_environment_object')

#
# Nose thermostat class
#

[docs]class NoseThermostat(EnvironmentObject):

    """
    Nose thermostat for Molecular Dynamics

    A thermostat object can be added to a universe and will then
    modify the integration algorithm to a simulation of an NVT
    ensemble.
    """

    def __init__(self, temperature, relaxation_time = 0.2):
        """
        :param temperature: the temperature set by the thermostat
        :type temperature: float
        :param relaxation_time: the relaxation time of the
                                thermostat coordinate
        :type relaxation_time: float
        """
        self.arguments = (temperature, relaxation_time)
        self.parameters = N.array([temperature, relaxation_time])
        self.coordinates = N.array([0., 0.])

    def setTemperature(self, temperature):
        self.parameters[0] = temperature

    def setRelaxationTime(self, t):
        self.parameters[1] = t

    def checkCompatibilityWith(self, other):
        if other.__class__ is NoseThermostat:
            raise ValueError("the universe already has a thermostat")

#
# Andersen barostat class
#


[docs]class AndersenBarostat(EnvironmentObject):

    """
    Andersen barostat for Molecular Dynamics

    A barostat object can be added to a universe and will then
    together with a thermostat object modify the integration algorithm
    to a simulation of an NPT ensemble.
    """

    def __init__(self, pressure, relaxation_time = 1.5):
        """
        :param pressure: the pressure set by the barostat
        :type pressure: float
        :param relaxation_time: the relaxation time of the
                                barostat coordinate
        :type relaxation_time: float
        """
        self.arguments = (pressure, relaxation_time)
        self.parameters = N.array([pressure, relaxation_time])
        self.coordinates = N.array([0.])

    def setPressure(self, pressure):
        self.parameters[0] = pressure

    def setRelaxationTime(self, t):
        self.parameters[1] = t

    def checkCompatibilityWith(self, other):
        if other.__class__ is AndersenBarostat:
            raise ValueError("the universe already has a barostat")

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Source code for MMTK.Field

# This module defines scalar and vector fields in molecular systems
#
# Written by Konrad Hinsen
#

"""
Scalar and vector fields in molecular systems

This module defines field objects that are useful in the analysis
and visualization of collective motions in molecular systems. Atomic
quantities characterizing collective motions vary slowly in space, and
can be considered functions of position instead of values per atom.
Functions of position are called fields, and mathematical techniques
for the analysis of fields have proven useful in many branches of
physics. Fields can be described numerically by values on a
regular grid. In addition to permitting the application of vector
analysis methods to atomic quantities, the introduction of
fields is a valuable visualization aid, because information defined on
a coarse regular grid can be added to a picture of a molecular system
without overloading it.
"""

__docformat__ = 'restructuredtext'

from MMTK import Collections, ParticleProperties, Visualization
from Scientific.Visualization import Color
from Scientific.Geometry import Vector, TensorAnalysis
from Scientific import N



[docs]class AtomicField(object):

    """
    A field whose values are determined by atomic quantities

    This is an abstract base class. To create field objects,
    use one of its subclasses.
    """

    def __init__(self, system, grid_size, values):
        self.system = system
        if isinstance(grid_size, tuple):
            self.box, self.field, self.points, self.colors = grid_size
        else:
            if values.data_rank != 1:
                raise TypeError("data not a particle variable")
            self.box = Collections.PartitionedAtomCollection(grid_size, system)
            self._setField(values)

    def _setField(self, data):
        points = []
        values = []
        colors = []
        default_color = Color.ColorByName('black')
        for min, max, atoms in self.box.partitions():
            center = 0.5*(min+max)
            points.append(center.array)
            v = data.zero()
            total_weight = 0.
            color = default_color
            for a in atoms:
                d = a.position()-center
                weight = N.exp(-d*d/self.box.partition_size**2)
                v = v + weight*data[a]
                total_weight = total_weight + weight
                c = default_color
                try: c = a.color
                except AttributeError: pass
                if isinstance(c, basestring):
                    c = Color.ColorByName(c)
                color = color + c
            values.append(v/total_weight)
            colors.append(color)
        min = N.minimum.reduce(points)
        max = N.maximum.reduce(points)
        axes = (N.arange(min[0], max[0]+1, self.box.partition_size),
                N.arange(min[1], max[1]+1, self.box.partition_size),
                N.arange(min[2], max[2]+1, self.box.partition_size))
        array = N.zeros(tuple(map(len, axes)) + data.value_rank*(3,), N.Float)
        inside = N.zeros(tuple(map(len, axes)), N.Float)
        for p, v in zip(points, values):
            indices = N.floor((p-min)/self.box.partition_size+0.5)
            indices = tuple(indices.astype(N.Int))
            array[indices] = v
            inside[indices] = 1.
        self.field = self.field_class(axes, array, data.zero())
        inside = TensorAnalysis.ScalarField(axes, inside).gradient().length()
        self.points = []
        self.colors = []
        for i in range(len(points)):
            p = points[i]
            test = 0
            try:
                test = apply(inside, tuple(p)) > 1.e-10
            except ValueError: pass
            if test:
                self.points.append(p)
                self.colors.append(colors[i])

    def __call__(self, point):
        return self.field(point)


[docs]    def particleValues(self):
        """
        :returns: the values of the field at the positions of the atoms
        :rtype: :class:`~MMTK.ParticleProperties.ParticleProperty`
        """
        universe = self.system.universe()
        rank = self.field.rank
        if rank == 0:
            v = ParticleProperties.ParticleScalar(universe)
        elif rank == 1:
            v = ParticleProperties.ParticleVector(universe)
        else:
            raise ValueError("no appropriate return type")
        for a in self.system.atomList():
            v[a] = self.field(a.position())
        return v



[docs]    def writeToFile(self, filename, scale = 1., length_scale=1., color = None):
        """
        Writes a graphical representation of the field to a VRML file.

        :param filename: the name of the destination file
        :type filename: str
        :param scale: scale factor applied to all field values
        :type scale: float
        :param color: the color for all graphics objects
        :type color: Scientific.Visualization.Color
        """
        from Scientific.Visualization import VRML2
        objects = self.graphicsObjects(scale=scale,
                                       length_scale=length_scale,
                                       color=color,
                                       graphics_module=VRML2)
        VRML2.Scene(objects).writeToFile(filename)



[docs]    def view(self, scale = 1., length_scale = 1., color = None):
        """
        Shows a graphical representation of the field using a VRML viewer.

        :param scale: scale factor applied to all field values
        :type scale: float
        :param color: the color for all graphics objects
        :type color: Scientific.Visualization.Color
        """
        from Scientific.Visualization import VRML2
        objects = self.graphicsObjects(scale=scale,
                                       length_scale=length_scale,
                                       color=color,
                                       graphics_module=VRML2)
        VRML2.Scene(objects).view()




[docs]class AtomicScalarField(AtomicField, Visualization.Viewable):

    """
    Scalar field defined by atomic quantities

    For visualization, scalar fields are represented by a small cube
    on each grid point whose color indicates the field's value on a symmetric
    red-to-green color scale defined by the range of the field values.

    Additional keyword options exist for graphics object generation:
      - scale=factor, to multiply all field values by a factor
      - range=(min, max), to eliminate graphics objects for values
        that are smaller than min or larger than max

    """

    def __init__(self, system, grid_size, values):
        """
        :param system: any subset of a molecular system
        :param grid_size: the spacing of a cubic grid on which the field
                          values are defined. The value for a point is obtained
                          by averaging the atomic quantities over all
                          atoms in a cube centered on the point.
        :type grid_size: float
        :param values: the atomic values that define the field
        :type values: :class:`~MMTK.ParticleProperties.ParticleScalar`
        """
        if values is not None and values.value_rank != 0:
            raise TypeError("data not a vector field")
        self.field_class = TensorAnalysis.ScalarField
        AtomicField.__init__(self, system, grid_size, values)


[docs]    def gradient(self):
        """
        :returns: the gradient of the field
        :rtype: :class:`~MMTK.Field.AtomicVectorField`
        """
        field = self.field.gradient()
        return AtomicVectorField(self.system, (self.box, field,
                                               self.points, self.colors), None)



[docs]    def laplacian(self):
        """
        :returns: the laplacian of the field
        :rtype: :class:`~MMTK.Field.AtomicScalarField`
        """
        field = self.field.laplacian()
        return AtomicScalarField(self.system, (self.box, field,
                                               self.points, self.colors), None)


    def _graphics(self, conf, distance_fn, model, module, options):
        scale = options.get('scale', 1.)
        range = options.get('range', (None, None))
        length_scale = options.get('length_scale', 1.)

        lower, upper = range
        size = self.box.partition_size/10.
        objects = []
        color_scale = module.SymmetricColorScale(1.)
        for p, c in zip(self.points, self.colors):
            p = tuple(p)
            v = apply(self.field, p)
            if (lower is None or v > lower) and (upper is None or v < upper):
                p = apply(Vector, p)
                v = scale*v
                if v < -1. or v > 1.:
                    m = module.DiffuseMaterial('black')
                else:
                    m = module.Material(diffuse_color = color_scale(scale*v))
                objects.append(module.Cube(length_scale*p,
                                           length_scale*size,
                                           material = m))
        return objects




[docs]class AtomicVectorField(AtomicField, Visualization.Viewable):

    """
    Vector field defined by atomic quantities

    For visualization, scalar fields are represented by a small arrow
    on each grid point. The arrow starts at the grid point and represents
    the vector value at that point.
    
    Additional keyword options exist for graphics object generation:
      - scale=factor, to multiply all field values by a factor
      - diameter=number, to define the diameter of the arrow objects
        (default: 1.)
      - range=(min, max), to eliminate graphics objects for values
        that are smaller than min or larger than max
      - color=string, to define the color of the arrows by a color name

    """

    def __init__(self, system, grid_size, values):
        """
        :param system: any subset of a molecular system
        :param grid_size: the spacing of a cubic grid on which the field
                          values are defined. The value for a point is obtained
                          by averaging the atomic quantities over all
                          atoms in a cube centered on the point.
        :type grid_size: float
        :param values: the atomic values that define the field
        :type values: :class:`~MMTK.ParticleProperties.ParticleVector`
        """
        if values is not None and values.value_rank != 1:
            raise TypeError("data not a vector field")
        self.field_class = TensorAnalysis.VectorField
        AtomicField.__init__(self, system, grid_size, values)


[docs]    def length(self):
        """
        :returns: a field of the length of the field vectors
        :rtype: :class:`~MMTK.Field.AtomicScalarField`
        """
        field = self.field.length()
        return AtomicScalarField(self.system, (self.box, field,
                                               self.points, self.colors), None)



[docs]    def divergence(self):
        """
        :returns: the divergence of the field
        :rtype: :class:`~MMTK.Field.AtomicScalarField`
        """
        field = self.field.divergence()
        return AtomicScalarField(self.system, (self.box, field,
                                               self.points, self.colors), None)



[docs]    def curl(self):
        """
        :returns: the curl of the field
        :rtype: :class:`~MMTK.Field.AtomicVectorField`
        """
        field = self.field.curl()
        return AtomicVectorField(self.system, (self.box, field,
                                               self.points, self.colors), None)



[docs]    def laplacian(self):
        """
        :returns: the laplacian of the field
        :rtype: :class:`~MMTK.Field.AtomicVectorField`
        """
        field = self.field.curl()
        return AtomicVectorField(self.system, (self.box, field,
                                               self.points, self.colors), None)


    def _graphics(self, conf, distance_fn, model, module, options):
        scale = options.get('scale', 1.)
        diameter = options.get('diameter', 1.)
        color = options.get('color', None)
        range = options.get('range', (None, None))
        length_scale = options.get('length_scale', 1.)

        lower, upper = range
        size = diameter*self.box.partition_size/50.
        if color is not None:
            color = module.ColorByName(color)
        objects = []
        materials = {}
        for p, c in zip(self.points, self.colors):
            p = tuple(p)
            v = apply(self.field, p)
            lv = v.length()
            if (lower is None or lv > lower) and (upper is None or lv < upper):
                p = apply(Vector, p)
                if color is not None:
                    c = color
                try: m = materials[c]
                except KeyError: m = module.Material(diffuse_color = c)
                materials[c] = m
                objects.append(module.Arrow(length_scale*p,
                                            length_scale*(p+scale*v),
                                            length_scale*size,
                                            material = m))
        return objects

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Source code for MMTK.ForceFields.Amber

# Amber forcefield initialization
#
# Written by Konrad Hinsen
#

"""
Amber force fields

"""

__docformat__ = 'restructuredtext'

from AmberForceField import Amber12SBForceField, Amber99ForceField, \
                            Amber94ForceField, Amber91ForceField, OPLSForceField

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Source code for MMTK.ForceFields.ANMFF

# Anisotropic network force field
#
# Written by Konrad Hinsen
#

"""
Anisotropic Network Model
"""

__docformat__ = 'restructuredtext'

from MMTK.ForceFields.ForceField import ForceField, ForceFieldData
from MMTK import Utility
from MMTK_forcefield import NonbondedList, NonbondedListTerm
from MMTK_deformation import ANTerm
from Scientific import N

#
# The deformation force field class
#

[docs]class AnisotropicNetworkForceField(ForceField):

    """
    Effective harmonic force field for an ANM protein model
    """

    def __init__(self, cutoff = None, scale_factor = 1.):
        """
        :param cutoff: the cutoff for pair interactions. Pair interactions
                       in periodic systems are calculated using
                       the minimum-image convention; the cutoff should
                       therefore never be larger than half the smallest
                       edge length of the elementary cell.
        :type cutoff: float
        :param scale_factor: a global scaling factor
        :type scale_factor: float
        """
        ForceField.__init__(self, 'anisotropic_network')
        self.arguments = (cutoff,)
        self.cutoff = cutoff
        self.scale_factor = scale_factor

    def ready(self, global_data):
        return True

    def evaluatorTerms(self, universe, subset1, subset2, global_data):
        nothing = N.zeros((0, 2), N.Int)
        if subset1 is not None:
            set1 = set(a.index for a in subset1.atomList())
            set2 = set(a.index for a in subset2.atomList())
            excluded_pairs = set(Utility.orderedPairs(list(set1-set2))) \
                             | set(Utility.orderedPairs(list(set2-set1)))
            excluded_pairs = N.array(list(excluded_pairs))
            atom_subset = list(set1 | set2)
            atom_subset.sort()
            atom_subset = N.array(atom_subset)
        else:
            atom_subset = N.array([], N.Int)
            excluded_pairs = nothing
        nbl = NonbondedList(excluded_pairs, nothing, atom_subset, universe._spec,
                            self.cutoff)
        update = NonbondedListTerm(nbl)
        cutoff = self.cutoff
        if cutoff is None:
            cutoff = 0.
        ev = ANTerm(universe._spec, nbl, cutoff, self.scale_factor)
        return [update, ev]

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Source code for MMTK.ForceFields.BondFF

# Detailed harmonic force field for proteins
#
# Written by Konrad Hinsen
#

"""
Simplified harmonic force field for normal mode calculations
"""

__docformat__ = 'restructuredtext'

from MMTK.ForceFields.NonBondedInteractions import NonBondedForceField
from MMTK.ForceFields.Amber.AmberForceField import AmberBondedForceField
from MMTK.ForceFields.MMForceField import MMAtomParameters
from MMTK.ForceFields.ForceField import ForceField, CompoundForceField
from MMTK import Utility
from MMTK_deformation import DeformationTerm
from Scientific.Geometry import Transformation
from Scientific import N

class BondForceField(AmberBondedForceField):

    def __init__(self, bonds=True, angles=True, dihedrals=True):
        AmberBondedForceField.__init__(self)
        self.arguments = (bonds, angles, dihedrals)
        self.bonded = self

    def addBondTerm(self, data, bond, object, global_data):
        if not self.arguments[0]:
            return
        a1 = bond.a1
        a2 = bond.a2
        i1 = a1.index
        i2 = a2.index
        global_data.add('excluded_pairs', Utility.normalizePair((i1, i2)))
        t1 = global_data.atom_type[a1]
        t2 = global_data.atom_type[a2]
        try:
            p = self.dataset.bondParameters(t1, t2)
        except KeyError:
            raise KeyError('No parameters for bond ' + `a1` +  '--' + `a2`)
        if p is not None:
            d = data.get('universe').distance(a1, a2)
            data.add('bonds', (i1, i2, d, p[1]))

    def addBondAngleTerm(self, data, angle, object, global_data):
        if not self.arguments[1]:
            return
        a1 = angle.a1
        a2 = angle.a2
        ca = angle.ca
        i1 = a1.index
        i2 = a2.index
        ic = ca.index
        global_data.add('excluded_pairs', Utility.normalizePair((i1, i2)))
        t1 = global_data.atom_type[a1]
        t2 = global_data.atom_type[a2]
        tc = global_data.atom_type[ca]
        try:
            p = self.dataset.bondAngleParameters(t1, tc, t2)
        except KeyError:
            raise KeyError('No parameters for angle ' + `a1` +
                            '--' + `ca` + '--' + `a2`)
        if p is not None:
            v1 = a1.position()-ca.position()
            v2 = a2.position()-ca.position()
            angle = v1.angle(v2)
            data.add('angles', (i1, ic, i2, angle) + p[1:])

    def addDihedralTerm(self, data, dihedral, object, global_data):
        if not self.arguments[2]:
            return
        a1 = dihedral.a1
        a2 = dihedral.a2
        a3 = dihedral.a3
        a4 = dihedral.a4
        i1 = a1.index
        i2 = a2.index
        i3 = a3.index
        i4 = a4.index
        global_data.add('1_4_pairs', Utility.normalizePair((i1, i4)))
        t1 = global_data.atom_type[a1]
        t2 = global_data.atom_type[a2]
        t3 = global_data.atom_type[a3]
        t4 = global_data.atom_type[a4]
        terms = self.dataset.dihedralParameters(t1, t2, t3, t4)
        if terms is not None:
            v1 = a1.position()-a2.position()
            v2 = a2.position()-a3.position()
            v3 = a4.position()-a3.position()
            a = v1.cross(v2).normal()
            b = v3.cross(v2).normal()
            cos = a*b
            sin = b.cross(a)*v2/v2.length()
            dihedral = Transformation.angleFromSineAndCosine(sin, cos)
            if dihedral > N.pi:
                dihedral -= 2.*N.pi
            for p in terms:
                if p[2] != 0.:
                    mult = p[0]
                    phase = N.fmod(N.pi-mult*dihedral, 2.*N.pi)
                    if phase < 0.:
                        phase += 2.*N.pi
                    data.add('dihedrals', (i1, i2, i3, i4,
                                           p[0], phase) + p[2:])

    def addImproperTerm(self, data, improper, object, global_data):
        if not self.arguments[2]:
            return
        a1 = improper.a1
        a2 = improper.a2
        a3 = improper.a3
        a4 = improper.a4
        i1 = a1.index
        i2 = a2.index
        i3 = a3.index
        i4 = a4.index
        t1 = global_data.atom_type[a1]
        t2 = global_data.atom_type[a2]
        t3 = global_data.atom_type[a3]
        t4 = global_data.atom_type[a4]
        terms = self.dataset.improperParameters(t1, t2, t3, t4)
        if terms is not None:
            atoms = [(t2,i2,a2), (t3,i3,a3), (t4,i4,a4)]
            atoms.sort(_order)
            i2, i3, i4 = tuple(map(lambda t: t[1], atoms))
            a2, a3, a4 = tuple(map(lambda t: t[2], atoms))
            v1 = a2.position()-a3.position()
            v2 = a3.position()-a1.position()
            v3 = a4.position()-a1.position()
            a = v1.cross(v2).normal()
            b = v3.cross(v2).normal()
            cos = a*b
            sin = b.cross(a)*v2/v2.length()
            dihedral = Transformation.angleFromSineAndCosine(sin, cos)
            if dihedral > N.pi:
                dihedral -= 2.*N.pi
            for p in terms:
                if p[2] != 0.:
                    mult = p[0]
                    phase = N.fmod(N.pi-mult*dihedral, 2.*N.pi)
                    if phase < 0.:
                        phase += 2.*N.pi
                    data.add('dihedrals', (i2, i3, i1, i4,
                                           p[0], phase) + p[2:])

def _order(a1, a2):
    ret = cmp(a1[0], a2[0])
    if ret == 0:
        ret = cmp(a1[1], a2[1])
    return ret

#
# Mid-range harmonic force field to complement bonded part
#
class MidrangeForceField(NonBondedForceField):

    def __init__(self, fc_length = 0.3, cutoff = 0.5, factor = 59908.8):
        NonBondedForceField.__init__(self, 'harmonic')
        self.arguments = (fc_length, cutoff, factor)
        self.fc_length = fc_length
        self.cutoff = cutoff
        self.factor = factor
        self.one_four_factor = 0.5

    def evaluatorParameters(self, universe, subset1, subset2, global_data):
        excluded_pairs, one_four_pairs, atom_subset = \
                        self.excludedPairs(subset1, subset2, global_data)
        if self.cutoff is None:
            cutoff = 0.
        else:
            cutoff = self.cutoff
        return {'deformation_term': {'cutoff': cutoff,
                                      'fc_length': self.fc_length,
                                      'scale_factor': self.factor,
                                      'one_four_factor': self.one_four_factor},
                 'nonbonded': {'excluded_pairs': excluded_pairs,
                               'one_four_pairs': one_four_pairs,
                               'atom_subset': atom_subset},
                }

    def evaluatorTerms(self, universe, subset1, subset2, global_data):
        param = self.evaluatorParameters(universe, subset1, subset2,
                                         global_data)['deformation_term']
        nblist, nblist_update = \
                self.nonbondedList(universe, subset1, subset2, global_data)
        ev = DeformationTerm(universe._spec, nblist, param['fc_length'],
                             param['cutoff'], param['scale_factor'],
                             param['one_four_factor'])
        return [nblist_update, ev]



[docs]class HarmonicForceField(MMAtomParameters, CompoundForceField):

    """
    Simplified harmonic force field for normal mode calculations

    This force field is made up of the bonded terms from the Amber 94
    force field with the equilibrium positions of all terms changed
    to the corresponding values in the input configuration, such that
    the input configuration becomes an energy minimum by construction.
    The nonbonded terms are replaced by a generic short-ranged
    deformation term.

    For a description of this force field, see:
     | Hinsen & Kneller, J. Chem. Phys. 24, 10766 (1999)
    For an application to DNA, see:
     | Viduna, Hinsen & Kneller, Phys. Rev. E 3, 3986 (2000)
    """

    def __init__(self, fc_length = 0.45, cutoff = 0.6, factor = 400.):
        self.arguments = (fc_length, cutoff, factor)
        self.bonded = BondForceField(1, 1, 1)
        self.midrange = MidrangeForceField(fc_length, cutoff, factor)
        apply(CompoundForceField.__init__, (self, self.bonded, self.midrange))

    description = ForceField.description



if __name__ == '__main__':

    from MMTK import *
    from MMTK.Proteins import Protein
    from MMTK.NormalModes import NormalModes
    world = InfiniteUniverse(HarmonicForceField(bonds=1,angles=1,dihedrals=1))
    world.protein = Protein('bala1')
    print world.energy()
    modes = NormalModes(world)
    for m in modes:
        print m

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Source code for MMTK.ForceFields.CalphaFF

# C-alpha force field
#
# Written by Konrad Hinsen
#

"""
C-alpha force field for protein normal mode analysis
(Elastic Network Model)
"""

__docformat__ = 'restructuredtext'

from MMTK.ForceFields.ForceField import ForceField, ForceFieldData
from MMTK import Utility
from MMTK_forcefield import NonbondedList, NonbondedListTerm
from MMTK_deformation import CalphaTerm
from Scientific import N

#
# The deformation force field class
#

[docs]class CalphaForceField(ForceField):

    """
    Effective harmonic force field for a C-alpha protein model
    """

    def __init__(self, cutoff = None, scale_factor = 1., version=1):
        """
        :param cutoff: the cutoff for pair interactions, should be
                       at least 2.5 nm. Pair interactions in periodic
                       systems are calculated using the minimum-image
                       convention; the cutoff should therefore never be
                       larger than half the smallest edge length of the
                       elementary cell.
        :type cutoff: float
        :param scale_factor: a global scaling factor
        :type scale_factor: float
        """
        ForceField.__init__(self, 'calpha')
        self.arguments = (cutoff,)
        self.cutoff = cutoff
        self.scale_factor = scale_factor
        self.version = version

    def ready(self, global_data):
        return True

    def evaluatorTerms(self, universe, subset1, subset2, global_data):
        nothing = N.zeros((0, 2), N.Int)
        if subset1 is not None:
            set1 = set(a.index for a in subset1.atomList())
            set2 = set(a.index for a in subset2.atomList())
            excluded_pairs = set(Utility.orderedPairs(list(set1-set2))) \
                             | set(Utility.orderedPairs(list(set2-set1)))
            excluded_pairs = N.array(list(excluded_pairs))
            atom_subset = list(set1 | set2)
            atom_subset.sort()
            atom_subset = N.array(atom_subset)
        else:
            atom_subset = N.array([], N.Int)
            excluded_pairs = nothing
        nbl = NonbondedList(excluded_pairs, nothing, atom_subset, universe._spec,
                            self.cutoff)
        update = NonbondedListTerm(nbl)
        cutoff = self.cutoff
        if cutoff is None:
            cutoff = 0.
        ev = CalphaTerm(universe._spec, nbl, cutoff,
                        self.scale_factor, self.version)
        return [update, ev]

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Source code for MMTK.ForceFields.DeformationFF

# Deformation force field
#
# Written by Konrad Hinsen
#

"""
Deformation force field (elastic network model)
For proteins, CalphaForceField is usually a better choice.
"""

__docformat__ = 'restructuredtext'

from MMTK.ForceFields.ForceField import ForceField, ForceFieldData
from MMTK import Utility
from MMTK_forcefield import NonbondedList, NonbondedListTerm
from MMTK_deformation import DeformationTerm
from Scientific import N

#
# The deformation force field class
#

[docs]class DeformationForceField(ForceField):

    """
    Deformation force field for protein normal mode calculations

    The pair interaction force constant has the form
    k(r)=factor*exp(-(r**2-0.01)/range**2). The default value
    for range is appropriate for a C-alpha model of a protein.
    The offset of 0.01 is a convenience for defining factor;
    with this definition, factor is the force constant for a
    distance of 0.1nm.
    """

    def __init__(self, fc_length = 0.7, cutoff = 1.2, factor = 46402.):
        """
        :param fc_length: a range parameter
        :type fc_length: float
        :param cutoff: the cutoff for pair interactions, should be
                       at least 2.5 nm. Pair interactions in periodic
                       systems are calculated using the minimum-image
                       convention; the cutoff should therefore never be
                       larger than half the smallest edge length of the
                       elementary cell.
        :type cutoff: float
        :param factor: a global scaling factor
        :type factor: float
        """
        self.arguments = (fc_length, cutoff, factor)
        ForceField.__init__(self, 'deformation')
        self.arguments = (fc_length, cutoff, factor)
        self.fc_length = fc_length
        self.cutoff = cutoff
        self.factor = factor

    def ready(self, global_data):
        return True

    def evaluatorTerms(self, universe, subset1, subset2, global_data):
        nothing = N.zeros((0, 2), N.Int)
        if subset1 is not None:
            set1 = set(a.index for a in subset1.atomList())
            set2 = set(a.index for a in subset2.atomList())
            excluded_pairs = set(Utility.orderedPairs(list(set1-set2))) \
                             | set(Utility.orderedPairs(list(set2-set1)))
            excluded_pairs = N.array(list(excluded_pairs))
            atom_subset = list(set1 | set2)
            atom_subset.sort()
            atom_subset = N.array(atom_subset)
        else:
            atom_subset = N.array([], N.Int)
            excluded_pairs = nothing
        nbl = NonbondedList(excluded_pairs, nothing, atom_subset, universe._spec,
                            self.cutoff)
        update = NonbondedListTerm(nbl)
        cutoff = self.cutoff
        if cutoff is None:
            cutoff = 0.
        ev = DeformationTerm(universe._spec, nbl, self.fc_length,
                             self.cutoff, self.factor, 1.)
        return [update, ev]

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Source code for MMTK.ForceFields.ForceFieldTest

# This module implements test functions.
#
# Written by Konrad Hinsen
#

"""
Force field consistency tests
"""

__docformat__ = 'restructuredtext'

from MMTK import Utility
from Scientific.Geometry import Vector, ex, ey, ez
from Scientific import N
import itertools

#
# Check consistency of energies and gradients.
#

[docs]def gradientTest(universe, atoms = None, delta = 0.0001):
    """
    Test gradients by comparing to numerical derivatives of the energy.

    :param universe: the universe on which the test is performed
    :type universe: :class:`~MMTK.Universe.Universe`
    :param atoms: the atoms of the universe for which the gradient
                  is tested (default: all atoms)
    :type atoms: list
    :param delta: the step size used in calculating the numerical derivatives
    :type delta: float
    """
    e0, grad = universe.energyAndGradients()
    print 'Energy: ', e0
    if atoms is None:
        atoms = universe.atomList()
    for a in atoms:
        print a
        print grad[a]
        num_grad = []
        for v in [ex, ey, ez]:
            x = a.position()
            a.setPosition(x+delta*v)
            eplus = universe.energy()
            a.setPosition(x-delta*v)
            eminus = universe.energy()
            a.setPosition(x)
            num_grad.append(0.5*(eplus-eminus)/delta)
        print Vector(num_grad)

#
# Check consistency of gradients and force constants.
#


[docs]def forceConstantTest(universe, atoms = None, delta = 0.0001):
    """
    Test force constants by comparing to the numerical derivatives
    of the gradients.

    :param universe: the universe on which the test is performed
    :type universe: :class:`~MMTK.Universe.Universe`
    :param atoms: the atoms of the universe for which the gradient
                  is tested (default: all atoms)
    :type atoms: list
    :param delta: the step size used in calculating the numerical derivatives
    :type delta: float
    """
    e0, grad0, fc = universe.energyGradientsAndForceConstants()
    if atoms is None:
        atoms = universe.atomList()
    for a1, a2 in itertools.chain(itertools.izip(atoms, atoms),
                                  Utility.pairs(atoms)):
        print a1, a2
        print fc[a1, a2]
        num_fc = []
        for v in [ex, ey, ez]:
            x = a1.position()
            a1.setPosition(x+delta*v)
            e_plus, grad_plus = universe.energyAndGradients()
            a1.setPosition(x-delta*v)
            e_minus, grad_minus = universe.energyAndGradients()
            a1.setPosition(x)
            num_fc.append(0.5*(grad_plus[a2]-grad_minus[a2])/delta)
        print N.array(map(lambda a: a.array, num_fc))

#
# Check consistency of gradients and virial
#


[docs]def virialTest(universe):
    """
    Test the virial by comparing to an explicit computation from
    positions and gradients.

    :param universe: the universe on which the test is performed
    :type universe: :class:`~MMTK.Universe.Universe`
    """
    ev = universe.energyEvaluator()
    e, grad = ev(gradients = True)
    virial = ev.lastVirial()
    conf = universe.configuration()
    print virial, -(conf*grad).sumOverParticles()


if __name__ == '__main__':

    from MMTK import *
    from MMTK.ForceFields import Amber94ForceField
    delta = 0.001
    world = InfiniteUniverse(Amber94ForceField())
    m = Molecule('water')
    m.O.translateBy(Vector(0.,0.,0.01))
    m.H1.translateBy(Vector(0.01,0.,0.))
    atoms = None
    world.addObject(m)
    gradientTest(world, atoms, delta)
    forceConstantTest(world, atoms, delta)
    virialTest(world)

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Source code for MMTK.ForceFields.LennardJonesFF

# A Lennard-Jones force fields for simple liquids
#
# Written by Konrad Hinsen
#

"""
Lennard-Jones force field for simple liquids
"""

__docformat__ = 'restructuredtext'

from MMTK.ForceFields.NonBondedInteractions import LJForceField
from Scientific.Geometry import Vector
import copy

#
# The force field class
#

[docs]class LennardJonesForceField(LJForceField):

    """
    Lennard-Jones force field

    The Lennard-Jones parameters are taken from the atom attributes
    LJ_radius and LJ_energy. The pair interaction energy has the form
    U(r)=4*LJ_energy*((LJ_radius/r)**12-(LJ_radius/r)**6).
    """

    def __init__(self, cutoff = None):
        """
        :param cutoff: a cutoff value or None, meaning no cutoff.
                       Pair interactions in periodic systems are calculated
                       using the minimum-image convention; the cutoff should
                       therefore never be larger than half the smallest edge
                       length of the elementary cell.
        :type cutoff: float
        """
        self.arguments = (cutoff, )
        LJForceField.__init__(self, 'LJ', cutoff)
        self.lj_14_factor = 1.

    def ready(self, global_data):
        return True

    def collectParameters(self, universe, global_data):
        if not hasattr(global_data, 'lj_parameters'):
            parameters = {}
            for o in universe:
                for a in o.atomList():
                    parameters[a.symbol] = (a.LJ_energy, a.LJ_radius, 0)
            global_data.lj_parameters = parameters

    def _atomType(self, object, atom, global_data):
        return atom.symbol

    def _ljParameters(self, type, global_data):
        return global_data.lj_parameters[type]

    def evaluatorParameters(self, universe, subset1, subset2, global_data):
        self.collectParameters(universe, global_data)
        return LJForceField.evaluatorParameters(self, universe, subset1,
                                                subset2, global_data)

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Source code for MMTK.ForceFields.Restraints

# Harmonic restraint terms that can be added to a force field.
#
# Written by Konrad Hinsen
#

"""
Harmonic restraint terms that can be added to any force field

Example::

 from MMTK import *
 from MMTK.ForceFields import Amber99ForceField
 from MMTK.ForceFields.Restraints import HarmonicDistanceRestraint
 
 universe = InfiniteUniverse()
 universe.protein = Protein('bala1')
 force_field = Amber99ForceField() + \
               HarmonicDistanceRestraint(universe.protein[0][1].peptide.N,
                                         universe.protein[0][1].peptide.O,
                                         0.5, 10.)
 universe.setForceField(force_field)
"""

__docformat__ = 'restructuredtext'

from MMTK.ChemicalObjects import isChemicalObject
from MMTK.ForceFields.ForceField import ForceField
from MMTK import Utility
from MMTK_forcefield import HarmonicDistanceTerm, HarmonicAngleTerm, \
                            CosineDihedralTerm
from MMTK_restraints import HarmonicCMTrapTerm, HarmonicCMDistanceTerm
from Scientific import N


[docs]class HarmonicDistanceRestraint(ForceField):

    """
    Harmonic distance restraint between two atoms
    """

    def __init__(self, obj1, obj2, distance, force_constant,
                 nb_exclusion=False):
        """
        :param obj1: the object defining center-of-mass 1
        :type obj1: :class:`~MMTK.Collections.GroupOfAtoms`
        :param obj2: the object defining center-of-mass 2
        :type obj2: :class:`~MMTK.Collections.GroupOfAtoms`
        :param distance: the distance between cm 1 and cm2 at which
                         the restraint is zero
        :type distance: float
        :param force_constant: the force constant of the restraint term.
                               The functional form of the restraint is
                               force_constant*((cm1-cm2).length()-distance)**2,
                               where cm1 and cm2 are the centrer-of-mass
                               positions of the two objects.
        :type force_constant: float
        :param nb_exclussion: if True, non-bonded interactions between
                              the restrained atoms are suppressed, as
                              for a chemical bond
        :type nb_exclussion: bool
        """
        if isinstance(obj1, int) and isinstance(obj2, int):
            # Older MMTK versions admitted only single atoms and
            # stored single indices. Support this mode for opening
            # trajectories made with those versions
            self.atom_indices_1 = [obj1]
            self.atom_indices_2 = [obj2]
        if isChemicalObject(obj1):
            obj1 = obj1.atomList()
        if isChemicalObject(obj2):
            obj2 = obj2.atomList()
        self.atom_indices_1 = self.getAtomParameterIndices(obj1)
        self.atom_indices_2 = self.getAtomParameterIndices(obj2)
        if nb_exclusion and (len(self.atom_indices_1) > 1
                             or len(self.atom_indices_2) > 1):
            raise ValueError("Non-bonded exclusion possible only "
                             "between single-atom objects")
        self.arguments = (self.atom_indices_1, self.atom_indices_2,
                          distance, force_constant, nb_exclusion)
        self.distance = distance
        self.force_constant = force_constant
        self.nb_exclusion = nb_exclusion
        ForceField.__init__(self, 'harmonic distance restraint')

    def declareDependencies(self, global_data):
        global_data.add('nb_exclusions', self.__class__)

    def evaluatorParameters(self, universe, subset1, subset2, global_data):
        if universe.is_periodic and \
                (len(self.atom_indices_1) > 1 or len(self.atom_indices_1) > 1):
            raise ValueError("Center-of-mass restraints not implemented"
                             " for periodic universes")
        ok = False
        for s1, s2 in [(subset1, subset2), (subset2, subset1)]:
            if s1 is None and s1 is None:
                ok = True
                break
            s1 = set(a.index for a in s1.atomIterator())
            diff1 = set(self.atom_indices_1).difference(s1)
            s2 = set(a.index for a in s2.atomIterator())
            diff2 = set(self.atom_indices_2).difference(s2)
            if not diff1 and not diff2:
                # Each object is in one of the subsets
                ok = True
                break
            if (diff1 and len(diff1) != len(self.atom_indices_1)) \
                    or (diff2 and len(diff2) != len(self.atom_indices_2)):
                # The subset contains some but not all of the
                # restrained atoms.
                raise ValueError("Restrained atoms partially "
                                 "in a subset")
        global_data.add('initialized', self.__class__)
        if not ok:
            # The objects are not in the subsets, so there is no
            # contribution to the total energy.
            return {'harmonic_distance_cm': []}
        if self.nb_exclusion:
            assert len(self.atom_indices_1) == 1 
            assert len(self.atom_indices_2) == 1
            i1 = self.atom_indices_1[0]
            i2 = self.atom_indices_2[0]
            global_data.add('excluded_pairs', Utility.normalizePair((i1, i2)))
        if len(self.atom_indices_1) == 1 and len(self.atom_indices_2) == 1:
            # Keep the old format for the single-atom case for best
            # compatibility with older MMTK versions.
            return {'harmonic_distance_term':
                    [(self.atom_indices_1[0], self.atom_indices_2[0],
                      self.distance, self.force_constant)]}
        else:
            return {'harmonic_distance_cm':
                    [(self.atom_indices_1, self.atom_indices_2,
                      self.distance, self.force_constant)]}

    def evaluatorTerms(self, universe, subset1, subset2, global_data):
        params = self.evaluatorParameters(universe, subset1, subset2,
                                          global_data)
        if params.has_key('harmonic_distance_term'):
            params = params['harmonic_distance_term']
            return [HarmonicDistanceTerm(universe._spec,
                                         N.array([p[:2]]),
                                         N.array([p[2:]]),
                                         self.name)
                    for p in params]
        else:
            params = params['harmonic_distance_cm']
            return [HarmonicCMDistanceTerm(universe,
                                           N.array(p[0]),
                                           N.array(p[1]),
                                           universe.masses().array,
                                           p[2],
                                           p[3])
                    for p in params]

    def description(self):
        return 'ForceFields.Restraints.' + self.__class__.__name__ + \
               `self.arguments`




[docs]class HarmonicAngleRestraint(ForceField):

    """
    Harmonic angle restraint between three atoms
    """

    def __init__(self, atom1, atom2, atom3, angle, force_constant):
        """
        :param atom1: first atom
        :type atom1: :class:`~MMTK.ChemicalObjects.Atom`
        :param atom2: second (central) atom
        :type atom2: :class:`~MMTK.ChemicalObjects.Atom`
        :param atom3: third atom
        :type atom3: :class:`~MMTK.ChemicalObjects.Atom`
        :param angle: the angle at which the restraint is zero
        :type angle: float
        :param force_constant: the force constant of the restraint term.
                               The functional form of the restraint is
                               force_constant*(phi-angle)**2, where
                               phi is the angle atom1-atom2-atom3.
        """
        self.index1, self.index2, self.index3 = \
                    self.getAtomParameterIndices((atom1, atom2, atom3))
        self.arguments = (self.index1, self.index2, self.index3,
                          angle, force_constant) 
        self.angle = angle
        self.force_constant = force_constant
        ForceField.__init__(self, 'harmonic angle restraint')

    def evaluatorParameters(self, universe, subset1, subset2, global_data):
        return {'harmonic_angle_term':
                 [(self.index1, self.index2, self.index3,
                   self.angle, self.force_constant)]}

    def evaluatorTerms(self, universe, subset1, subset2, global_data):
        params = self.evaluatorParameters(universe, subset1, subset2,
                                          global_data)['harmonic_angle_term']
        assert len(params) == 1
        indices = N.array([params[0][:3]])
        parameters = N.array([params[0][3:]])
        return [HarmonicAngleTerm(universe._spec, indices, parameters,
                                  self.name)]



[docs]class HarmonicDihedralRestraint(ForceField):

    """
    Harmonic dihedral angle restraint between four atoms
    """

    def __init__(self, atom1, atom2, atom3, atom4, dihedral, force_constant):
        """
        :param atom1: first atom
        :type atom1: :class:`~MMTK.ChemicalObjects.Atom`
        :param atom2: second (axis) atom
        :type atom2: :class:`~MMTK.ChemicalObjects.Atom`
        :param atom3: third (axis)atom
        :type atom3: :class:`~MMTK.ChemicalObjects.Atom`
        :param atom4: fourth atom
        :type atom4: :class:`~MMTK.ChemicalObjects.Atom`
        :param dihedral: the dihedral angle at which the restraint is zero
        :type dihedral: float
        :param force_constant: the force constant of the restraint term.
                               The functional form of the restraint is
                               force_constant*(phi-abs(dihedral))**2, where
                               phi is the dihedral angle
                               atom1-atom2-atom3-atom4.
        """
        self.index1, self.index2, self.index3, self.index4 = \
                   self.getAtomParameterIndices((atom1, atom2, atom3, atom4))
        self.dihedral = dihedral
        self.force_constant = force_constant
        self.arguments = (self.index1, self.index2, self.index3, self.index4,
                          dihedral, force_constant) 
        ForceField.__init__(self, 'harmonic dihedral restraint')

    def evaluatorParameters(self, universe, subset1, subset2, global_data):
        return {'cosine_dihedral_term': [(self.index1, self.index2,
                                          self.index3, self.index4,
                                          0., self.dihedral,
                                          0., self.force_constant)]}

    def evaluatorTerms(self, universe, subset1, subset2, global_data):
        params = self.evaluatorParameters(universe, subset1, subset2,
                                          global_data)['cosine_dihedral_term']
        assert len(params) == 1
        indices = N.array([params[0][:4]])
        parameters = N.array([params[0][4:]])
        return [CosineDihedralTerm(universe._spec, indices, parameters,
                                   self.name)]



[docs]class HarmonicTrapForceField(ForceField):

    """
    Harmonic potential with respect to a fixed point in space
    """

    def __init__(self, obj, center, force_constant):
        """
        :param obj: the object on whose center of mass the force field acts
        :type obj: :class:`~MMTK.Collections.GroupOfAtoms`
        :param center: the point to which the atom is attached by
                        the harmonic potential
        :type center: Scientific.Geometry.Vector
        :param force_constant: the force constant of the harmonic potential
        :type force_constant: float
        """
        if isChemicalObject(obj):
            obj = obj.atomList()
        self.atom_indices = self.getAtomParameterIndices(obj)
        self.arguments = (self.atom_indices, center, force_constant)
        ForceField.__init__(self, 'harmonic_trap')
        self.center = center
        self.force_constant = force_constant

    def ready(self, global_data):
        return True

    def evaluatorParameters(self, universe, subset1, subset2, global_data):
        if universe.is_periodic and len(self.atom_indices) > 1:
            raise ValueError("Center-of-mass restraints not implemented"
                             " for periodic universes")
        for subset in [subset1, subset2]:
            if subset is not None:
                subset = set(a.index for a in subset.atomIterator())
                diff = set(self.atom_indices).difference(subset)
                if diff:
                    if len(diff) == len(self.atom_indices):
                        # The subset doesn't contain the restrained atoms.
                        return {'harmonic_trap_cm': []}
                    else:
                        # The subset contains some but not all of the
                        # restrained atoms.
                        raise ValueError("Restrained atoms partially "
                                         "in a subset")
        return {'harmonic_trap_cm':
                [(self.atom_indices, self.center, self.force_constant)]}

    def evaluatorTerms(self, universe, subset1, subset2, global_data):
        params = self.evaluatorParameters(universe, subset1, subset2,
                                          global_data)['harmonic_trap_cm']
        return [HarmonicCMTrapTerm(universe,
                                   N.array(p[0]),
                                   universe.masses().array,
                                   p[1],
                                   p[2])
                for p in params]

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Source code for MMTK.ForceFields.SPCEFF

# SPCE force field
#
# Written by Konrad Hinsen
#

"""
SPC/E force field for water simulations
"""

__docformat__ = 'restructuredtext'

from MMTK.ForceFields import MMForceField


class SPCEParameters(object):

    atom_type_property = 'spce_atom_type'
    charge_property = 'spce_charge'
    lennard_jones_1_4 = 1.
    electrostatic_1_4 = 1.

    def ljParameters(self, type):
        if type == 'O':
            # TIP3: 0.6363936, 0.315075240657
            return (0.650169580819, 0.31655578902, 0)
        elif type == 'H':
            return (0., 0., 0)
        else:
            raise ValueError('Unknown atom type ' + type)

    # Bond and angle parameters from:
    # O. Telemann, B. Jonsson, S. Engstrom
    # Mol. Phys. 60(1), 193-203 (1987)
    def bondParameters(self, at1, at2):
        if at1 == 'O' or at2 == 'O':
            return (0.1, 463700.)
        else:
            return (0.163298086184, 0.)
    
    def bondAngleParameters(self, at1, at2, at3):
        if at2 == 'O':
            return (1.91061193216, 383.)
        else:
            return (0.615490360716, 0.)
    
    def dihedralParameters(self, at1, at2, at3, at4):
        return [(1, 0., 0.)]
    
    def improperParameters(self, at1, at2, at3, at4):
        return [(1, 0., 0.)]



[docs]class SPCEForceField(MMForceField.MMForceField):

    """
    Force field for water simulations with the SPC/E model
    """

    def __init__(self, lj_options=None, es_options=None):
        """
        :param lj_options: parameters for Lennard-Jones
                           interactions; one of:

                            * a number, specifying the cutoff
                            * None, meaning the default method
                              (no cutoff; inclusion of all
                              pairs, using the minimum-image
                              conventions for periodic universes)
                            * a dictionary with an entry "method"
                              which specifies the calculation
                              method as either "direct" (all pair
                              terms) or "cutoff", with the cutoff
                              specified by the dictionary
                              entry "cutoff".

        :param es_options: parameters for electrostatic
                           interactions; one of:

                            * a number, specifying the cutoff
                            * None, meaning the default method
                              (all pairs without cutoff for
                              non-periodic system, Ewald summation
                              for periodic systems)
                            * a dictionary with an entry "method"
                              which specifies the calculation
                              method as either "direct" (all pair
                              terms), "cutoff" (with the cutoff
                              specified by the dictionary
                              entry "cutoff"), "ewald" (Ewald
                              summation, only for periodic
                              universes), "screened" or
                              "multipole" (fast-multipole method).

        """
        MMForceField.MMForceField.__init__(self, 'SPCE', SPCEParameters(),
                                           lj_options, es_options)
        self.arguments = (lj_options, es_options)

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Source code for MMTK.FourierBasis

# Fourier basis for low-frequency normal mode calculations.
#
# Written by Konrad Hinsen
#

"""
Fourier basis for low-frequency normal mode calculations

This module provides a basis that is suitable for the
calculation of low-frequency normal modes. The basis is
derived from vector fields whose components are stationary
waves in a box surrounding the system. For a description,
see

  | K. Hinsen
  | Analysis of domain motions by approximate normal mode calculations
  | Proteins 33 (1998): 417-429
"""

from MMTK import ParticleProperties
from Scientific.Geometry import Vector
from Scientific import N


[docs]class FourierBasis(object):

    """
    Collective-motion basis for low-frequency normal mode calculations

    A FourierBasis behaves like a sequence of
    :class:`~MMTK.ParticleProperties.ParticleVector` objects. The vectors are
    B{not} orthonormal, because orthonormalization is handled
    automatically by the normal mode class.
    """

    def __init__(self, universe, cutoff):
        """
        :param universe: the universe for which the basis will be used
        :type universe: :class:`~MMTK.Universe.Universe`
        :param cutoff: the wavelength cutoff. A smaller value yields
                       a larger basis.
        :type cutoff: float
        """
        p1, p2 = universe.boundingBox()
        p2 = p2 + Vector(cutoff, cutoff, cutoff)
        l = (p2-p1).array
        n_max = (0.5*l/cutoff+0.5).astype(N.Int)

        wave_numbers = [(nx, ny, nz)
                        for nx in range(-n_max[0], n_max[0]+1)
                        for ny in range(-n_max[1], n_max[1]+1)
                        for nz in range(-n_max[2], n_max[2]+1)
                        if (nx/l[0])**2 + (ny/l[1])**2 + (nz/l[2])**2 \
                                    < 0.25/cutoff**2]

        atoms = universe.atomList()
        natoms = len(atoms)
        basis = N.zeros((3*len(wave_numbers)+3, natoms, 3), N.Float)
        cm = universe.centerOfMass()
        i = 0
        for rotation in [Vector(1.,0.,0.), Vector(0.,1.,0.), Vector(0.,0.,1.)]:
            v = ParticleProperties.ParticleVector(universe, basis[i])
            for a in atoms:
                v[a] = rotation.cross(a.position()-cm)
            i += i
        conf = universe.configuration().array-p1.array
        for n in wave_numbers:
            k = 2.*N.pi*N.array(n)/l
            w = self._w(conf[:, 0], k[0]) * self._w(conf[:, 1], k[1]) * \
                self._w(conf[:, 2], k[2])
            basis[i, :, 0] = w
            basis[i+1, :, 1] = w
            basis[i+2, :, 2] = w
            i += 3

        self.array = basis
        self.universe = universe

    __safe_for_unpickling__ = True
    __had_initargs__ = True

    def _w(self, x, k):
        if k < 0:
            return N.sin(-k*x)
        else:
            return N.cos(k*x)

    def __len__(self):
        return self.array.shape[0]

    def __getitem__(self, item):
        return ParticleProperties.ParticleVector(self.universe,
                                                 self.array[item])


# Estimate number of basis vectors for a given cutoff



[docs]def countBasisVectors(universe, cutoff):
    """
    Estimate the number of basis vectors for a given universe and cutoff

    :param universe: the universe
    :type universe: :class:`~MMTK.Universe.Universe`
    :param cutoff: the wavelength cutoff. A smaller value yields a larger basis.
    :type cutoff: float
    :returns: the number of basis vectors in a FourierBasis
    :rtype: int
    """
    p1, p2 = universe.boundingBox()
    p2 = p2 + Vector(cutoff, cutoff, cutoff)
    l = (p2-p1).array
    n_max = (0.5*l/cutoff+0.5).astype(N.Int)
    n_wave_numbers = 0
    for nx in range(-n_max[0], n_max[0]+1):
        for ny in range(-n_max[1], n_max[1]+1):
            for nz in range(-n_max[2], n_max[2]+1):
                if (nx/l[0])**2 + (ny/l[1])**2 + (nz/l[2])**2 < 0.25/cutoff**2:
                    n_wave_numbers += 1
    return 3*n_wave_numbers+3


# Estimate cutoff for a given number of basis vectors



[docs]def estimateCutoff(universe, nmodes):
    """
    Estimate the cutoff that yields a given number of basis vectors
    for a given universe.

    :param universe: the universe
    :type universe: :class:`~MMTK.Universe.Universe`
    :param nmodes: the number of basis vectors in a FourierBasis
    :type nmodes: int
    :returns: the wavelength cutoff and the precise number of basis vectors
    :rtype: tuple (float, int)
    """
    natoms = universe.numberOfCartesianCoordinates()
    if nmodes > natoms:
        nmodes = 3*natoms
        cutoff = None
    else:
        p1, p2 = universe.boundingBox()
        cutoff_max = (p2-p1).length()
        cutoff = 0.5*cutoff_max
        nmodes_opt = nmodes
        nmodes = countBasisVectors(universe, cutoff)
        while nmodes > nmodes_opt:
            cutoff += 0.1
            if cutoff > cutoff_max:
                cutoff = cutoff_max
                break
            nmodes = countBasisVectors(universe, cutoff)
        while nmodes < nmodes_opt:
            cutoff -= 0.1
            if cutoff < 0.1:
                cutoff = 0.1
                break
            nmodes = countBasisVectors(universe, cutoff)
    return cutoff, nmodes

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Source code for MMTK.Geometry

# This module defines some geometrical objects in 3D-space.
#
# Written by Konrad Hinsen
#

"""
Elementary geometrical objects and operations

There are essentially two kinds of geometrical objects: shape objects
(spheres, planes, etc.), from which intersections can be calculated,
and lattice objects, which define a regular arrangements of points.
"""

__docformat__ = 'restructuredtext'

from Scientific.Geometry import Vector
from Scientific import N


# Error type
class GeomError(Exception):
    pass

# Small number
eps = 1.e-16

#
# The base class
#

[docs]class GeometricalObject3D(object):

    """
    3D shape object

    This is an abstract base class. To create 3D objects,
    use one of its subclasses.
    """
    

[docs]    def intersectWith(self, other):
        """
        :param other: another 3D object
        :returns: a 3D object that represents the intersection with other
        """
	if self.__class__ > other.__class__:
	    self, other = other, self
	try:
	    f, switch = _intersectTable[(self.__class__, other.__class__)]
	    if switch:
		return f(other, self)
	    else:
		return f(self, other)
	except KeyError:
	    raise GeomError("Can't calculate intersection of " +
			     self.__class__.__name__ + " with " +
			     other.__class__.__name__)



[docs]    def volume(self):
        """
        :returns: the volume of the object
        :rtype: float
        """
        raise NotImplementedError



[docs]    def hasPoint(self, point):
        """
        :param point: a point in 3D space
        :type point: Scientific.Geometry.Vector
        :returns: True of the point lies on the surface of the object
        :rtype: bool
        """
	return self.distanceFrom(point) < eps

    # subclasses that enclose a volume should override this method
    # a return value of None indicates "don't know", "can't compute",
    # or "not implemented (yet)".


[docs]    def enclosesPoint(self, point):
        """
        :param point: a point in 3D space
        :type point: Scientific.Geometry.Vector
        :returns: True of the point is inside the volume of the object
        :rtype: bool
        """
        return None


_intersectTable = {}

#
# Boxes
#

[docs]class Box(GeometricalObject3D):

    """
    Rectangular box aligned with the coordinate axes
    """

    def __init__(self, corner1, corner2):
        """
        :param corner1: one corner of the box
        :type corner1: Scientific.Geometry.Vector
        :param corner2: the diagonally opposite corner
        :type corner2: Scientific.Geometry.Vector
        """
        c1 = N.minimum(corner1.array, corner2.array)
        c2 = N.maximum(corner1.array, corner2.array)
        self.corners = c1, c2

    def __repr__(self):
        return 'Box(' + `Vector(self.corners[0])` + ', ' \
               + `Vector(self.corners[1])` + ')'

    __str__ = __repr__

    def volume(self):
        c1, c2 = self.corners
        return N.multiply.reduce(c2-c1)

    def hasPoint(self, point):
        c1, c2 = self.corners
        min1 = N.minimum.reduce(N.fabs(point.array-c1))
        min2 = N.minimum.reduce(N.fabs(point.array-c2))
        return min1 < eps or min2 < eps

    def enclosesPoint(self, point):
        c1, c2 = self.corners
        out1 = N.logical_or.reduce(N.less(point.array-c1, 0))
        out2 = N.logical_or.reduce(N.less_equal(c2-point.array, 0))
        return not (out1 or out2)

    def cornerPoints(self):
        (c1x, c1y, c1z), (c2x, c2y, c2z) = self.corners
        return [Vector(c1x, c1y, c1z),
                Vector(c1x, c1y, c2z),
                Vector(c1x, c2y, c1z),
                Vector(c2x, c1y, c1z),
                Vector(c2x, c2y, c1z),
                Vector(c2x, c1y, c2z),
                Vector(c1x, c2y, c2z),
                Vector(c2x, c2y, c2z)]

#
# Spheres
#


[docs]class Sphere(GeometricalObject3D):

    """
    Sphere
    """

    def __init__(self, center, radius):
        """
        :param center: the center of the sphere
        :type center: Scientific.Geometry.Vector
        :param radius: the radius of the sphere
        :type radius: float
        """
	self.center = center
	self.radius = radius

    def __repr__(self):
        return 'Sphere(' + `self.center` + ', ' + `self.radius` + ')'
    __str__ = __repr__

    def volume(self):
	return (4.*N.pi/3.) * self.radius**3

    def hasPoint(self, point):
        return N.fabs((point-self.center).length()-self.radius) < eps

    def enclosesPoint(self, point):
        return (point - self.center).length() < self.radius

#
# Cylinders
#


[docs]class Cylinder(GeometricalObject3D):

    """
    Cylinder
    """

    def __init__(self, center1, center2, radius):
        """
        :param center1: the center of the bottom circle
        :type center1: Scientific.Geometry.Vector
        :param center2: the center of the top circle
        :type center2: Scientific.Geometry.Vector
        :param radius: the radius of the cylinder
        :type radius: float
        """
        self.center1 = center1            # center of base
        self.center2 = center2            # center of top
        self.radius = radius
        self.height = (center2-center1).length()

    def volume(self):
        return N.pi*self.radius*self.radius*self.height

    def __repr__(self):
        return 'Cylinder(' + `self.center1` + ', ' + `self.center2` + \
               ', ' + `self.radius` + ')'
    __str__ = __repr__

    def hasPoint(self, point):
        center_line = LineSegment(self.center1, self.center2)
        pt = center_line.projectionOf(point)
        if pt is None:
            return 0
        return N.fabs((point - pt).length() - self.radius) < eps

    def enclosesPoint(self, point):
        center_line = LineSegment(self.center1, self.center2)
        pt = center_line.projectionOf(point)
        if pt is None:
            return 0
        return (point - pt).length() < self.radius

#
# Planes
#


[docs]class Plane(GeometricalObject3D):

    """
    2D plane in 3D space
    """

    def __init__(self, *args):
        """
        :param args: three points (of type Scientific.Geometry.Vector)
                     that are not collinear, or a point in the plane and
                     the normal vector of the plane
        """
	if len(args) == 2:   # point, normal
	    self.normal = args[1].normal()
	    self.distance_from_zero = self.normal*args[0]
	else:                # three points
	    v1 = args[1]-args[0]
	    v2 = args[2]-args[1]
	    self.normal = (v1.cross(v2)).normal()
	    self.distance_from_zero = self.normal*args[1]

    def __repr__(self):
        return 'Plane(' + str(self.normal*self.distance_from_zero) + \
               ', ' + str(self.normal) + ')'
    __str__ = __repr__

    def distanceFrom(self, point):
	return abs(self.normal*point-self.distance_from_zero)

    def projectionOf(self, point):
        return point - (self.normal*point-self.distance_from_zero)*self.normal

    def rotate(self, axis, angle):
	point = rotatePoint(self.distance_from_zero*self.normal, axis, angle)
	normal = rotateDirection(self.normal, axis, angle)
	return Plane(point, normal)

    def volume(self):
	return 0.

#
# Infinite cones
#


[docs]class Cone(GeometricalObject3D):

    """
    Cone
    """

    def __init__(self, center, axis, angle):
        """
        :param center: the center (tip) of the cone
        :type center: Scientific.Geometry.Vector
        :param axis: the direction of the axis of rotational symmetry
        :type axis: Scientific.Geometry.Vector
        :param angle: the angle between any straight line on the cone
                      surface and the axis of symmetry
        :type angle: float
        """
	self.center = center
	self.axis = axis.normal()
	self.angle = angle

    def __repr__(self):
        return 'Cone(' + `self.center` + ', ' + `self.axis` + ',' + \
               `self.angle` + ')'
    __str__ = __repr__

    def volume(self):
	return None

#
# Circles
#


[docs]class Circle(GeometricalObject3D):

    """
    2D circle in 3D space
    """

    def __init__(self, center, normal, radius):
        """
        :param center: the center of the circle
        :type center: Scientific.Geometry.Vector
        :param normal: the normal vector of the circle's plane
        :type normal: Scientific.Geometry.Vector
        :param radius: the radius of the circle
        :type radius: float
        """
	self.center = center
	self.normal = normal
	self.radius = radius

    def planeOf(self):
        return Plane(self.center, self.normal)

    def __repr__(self):
        return 'Circle(' + `self.center` + ', ' + `self.normal` + \
               ', ' + `self.radius` + ')'
    __str__ = __repr__

    def volume(self):
        return 0.

    def distanceFrom(self, point):
        plane = self.planeOf()
        project_on_plane = plane.projectionOf(point)
        center_to_projection = project_on_plane - self.center
        if center_to_projection.length() < eps:
            return 0
        closest_point = self.center + self.radius*center_to_projection.normal()
        return (point - closest_point).length()

#
# Lines
#


[docs]class Line(GeometricalObject3D):

    """
    Line
    """

    def __init__(self, point, direction):
        """
        :param point: any point on the line
        :type point: Scientific.Geometry.Vector
        :param direction: the direction of the line
        :type direction: Scientific.Geometry.Vector
        """
	self.point = point
	self.direction = direction.normal()


[docs]    def distanceFrom(self, point):
        """
        :param point: a point in space
        :type point: Scientific.Geometry.Vector
        :returns: the smallest distance of the point from the line
        :rtype: float
        """
	d = self.point-point
	d = d - (d*self.direction)*self.direction
	return d.length()



[docs]    def projectionOf(self, point):
        """
        :param point: a point in space
        :type point: Scientific.Geometry.Vector
        :returns: the orthogonal projection of the point onto the line
        :rtype: Scientific.Geometry.Vector
        """
	d = self.point-point
	d = d - (d*self.direction)*self.direction
	return point+d



[docs]    def perpendicularVector(self, plane):
        """
        :param plane: a plane
        :type plane: Plane
        :returns: a vector in the plane perpendicular to the line
        :rtype: Scientific.Geometry.Vector
        """
        return self.direction.cross(plane.normal)


    def __repr__(self):
        return 'Line(' + `self.point` + ', ' + `self.direction` + ')'
    __str__ = __repr__

    def volume(self):
	return 0.



class LineSegment(Line):

    def __init__(self, point1, point2):
        Line.__init__(self, point1, point2 - point1)
        self.point2 = point2

    def __repr__(self):
        return 'LineSegment(' + `self.point` + ', ' + `self.point2` + ')'
    __str__ = __repr__

    def distanceFrom(self, point):
        pt = self.projectionOf(point)
        if pt is not None:
            return (pt - point).length()
        d1 = (self.point - point).length()
        d2 = (self.point2 - point).length()
        return min(d1, d2)

    def projectionOf(self, point):
        d = self.point-point
        d = d - (d*self.direction)*self.direction
        pt = point+d
        if self.isWithin(pt):
            return pt
        return None

    def isWithin(point):
        v1 = point - self.point
        v2 = point - self.point2
        if abs(v1 * v2) < eps:
            return 0
        return not Same_Dir(v1, v2)

#
# Intersection calculations
#
def _addIntersectFunction(f, class1, class2):
    switch = class1 > class2
    if switch:
	class1, class2 = class2, class1
    _intersectTable[(class1, class2)] = (f, switch)


# Box with box

def _intersectBoxBox(box1, box2):
    c1 = N.maximum(box1.corners[0], box2.corners[0])
    c2 = N.minimum(box1.corners[1], box2.corners[1])
    if N.logical_or.reduce(N.greater_equal(c1, c2)):
        return None
    return Box(Vector(c1), Vector(c2))

_addIntersectFunction(_intersectBoxBox, Box, Box)

# Sphere with sphere

def _intersectSphereSphere(sphere1, sphere2):
    r1r2 = sphere2.center-sphere1.center
    d = r1r2.length()
    if d > sphere1.radius+sphere2.radius:
	return None
    if d+min(sphere1.radius, sphere2.radius) < \
                             max(sphere1.radius, sphere2.radius):
	return None
    x = 0.5*(d**2 + sphere1.radius**2 - sphere2.radius**2)/d
    h = N.sqrt(sphere1.radius**2-x**2)
    normal = r1r2.normal()
    return Circle(sphere1.center + x*normal, normal, h)

_addIntersectFunction(_intersectSphereSphere, Sphere, Sphere)

# Sphere with cone

def _intersectSphereCone(sphere, cone):
    if sphere.center != cone.center:
	raise GeomError("Not yet implemented")
    from_center = sphere.radius*N.cos(cone.angle)
    radius = sphere.radius*N.sin(cone.angle)
    return Circle(cone.center+from_center*cone.axis, cone.axis, radius)

_addIntersectFunction(_intersectSphereCone, Sphere, Cone)

# Plane with plane

def _intersectPlanePlane(plane1, plane2):
    if abs(abs(plane1.normal*plane2.normal)-1.) < eps:
	if abs(plane1.distance_from_zero-plane2.distance_from_zero) < eps:
	    return plane1
	else:
            return None
    else:
	direction = plane1.normal.cross(plane2.normal)
	point_in_1 = plane1.distance_from_zero*plane1.normal
	point_in_both = point_in_1 - (point_in_1*plane2.normal -
				      plane2.distance_from_zero)*plane2.normal
	return Line(point_in_both, direction)

_addIntersectFunction(_intersectPlanePlane, Plane, Plane)

# Circle with plane

def _intersectCirclePlane(circle, plane):
    if abs(abs(circle.normal*plane.normal)-1.) < eps:
	if plane.hasPoint(circle.center):
	    return circle
	else:
	    return None
    else:
	line = plane.intersectWith(Plane(circle.center, circle.normal))
	x = line.distanceFrom(circle.center)
	if x > circle.radius:
	    return None
	else:
	    angle = N.arccos(x/circle.radius)
	    along_line = N.sin(angle)*circle.radius
	    normal = circle.normal.cross(line.direction)
	    if line.distanceFrom(circle.center+normal) > x:
		normal = -normal
	    return (circle.center+x*normal-along_line*line.direction,
		    circle.center+x*normal+along_line*line.direction)
	    
_addIntersectFunction(_intersectCirclePlane, Circle, Plane)

#
# Rotation
#
def rotateDirection(vector, axis, angle):
    s = N.sin(angle)
    c = N.cos(angle)
    c1 = 1-c
    try:
        axis = axis.direction
    except AttributeError:
        pass
    return s*axis.cross(vector) + c1*(axis*vector)*axis + c*vector

def rotatePoint(point, axis, angle):
    return axis.point + rotateDirection(point-axis.point, axis, angle)

#
# Lattices
#

#
# Lattice base class
#

[docs]class Lattice(object):

    """
    General lattice

    Lattices are special sequence objects that contain vectors
    (points on the lattice) or objects that are constructed as
    functions of these vectors. Lattice objects behave like
    lists, i.e. they permit indexing, length inquiry, and iteration
    by 'for'-loops. Note that the lattices represented by these
    objects are finite, they have a finite (and fixed) number
    of repetitions along each lattice vector.

    This is an abstract base class. To create lattice objects,
    use one of its subclasses.
    """

    def __init__(self, function):
	if function is not None:
	    self.elements = map(function, self.elements)

    def __getitem__(self, item):
	return self.elements[item]

    def __setitem__(self, item, value):
	self.elements[item] = value

    def __len__(self):
	return len(self.elements)

#
# General rhombic lattice
#


[docs]class RhombicLattice(Lattice):

    """
    Rhombic lattice
    """

    def __init__(self, elementary_cell, lattice_vectors, cells,
                 function=None, base=None):
        """
        :param elementary_cell: a list of points in the elementary cell
        :param lattice_vectors: a list of lattice vectors. Each lattice
                                vector defines a lattice dimension (only
                                values from one to three make sense) and
                                indicates the displacement along this
                                dimension from one cell to the next.
        :param cells: a list of integers, whose length must equal the number
                      of dimensions. Each entry specifies how often a cell is
                      repeated along this dimension.
        :param function: a function that is called for every lattice point with
                         the vector describing the point as argument. The return
                         value of this function is stored in the lattice object.
                         If the function is 'None', the vector is directly
                         stored in the lattice object.
        """
	if len(lattice_vectors) != len(cells):
	    raise TypeError('Inconsistent dimension specification')
        if base is None:
            base = Vector(0, 0, 0)
	self.dimension = len(lattice_vectors)
	self.elements = []
	self.makeLattice(elementary_cell, lattice_vectors, cells, base)
	Lattice.__init__(self, function)

    def makeLattice(self, elementary_cell, lattice_vectors, cells, base):
	if len(cells) == 0:
	    for p in elementary_cell:
		self.elements.append(p+base)
	else:
	    for i in range(cells[0]):
		self.makeLattice(elementary_cell, lattice_vectors[1:],
				 cells[1:], base+i*lattice_vectors[0])
	    
#
# Bravais lattice
#


[docs]class BravaisLattice(RhombicLattice):

    """
    Bravais lattice

    A Bravais lattice is a special case of a general rhombic lattice
    in which the elementary cell contains only one point.
    """

    def __init__(self, lattice_vectors, cells, function=None, base=None):
        """
        :param lattice_vectors: a list of lattice vectors. Each lattice
                                vector defines a lattice dimension (only
                                values from one to three make sense) and
                                indicates the displacement along this
                                dimension from one cell to the next.
        :param cells: a list of integers, whose length must equal the number
                      of dimensions. Each entry specifies how often a cell is
                      repeated along this dimension.
        :param function: a function that is called for every lattice point with
                         the vector describing the point as argument. The return
                         value of this function is stored in the lattice object.
                         If the function is 'None', the vector is directly
                         stored in the lattice object.
        """
	cell = [Vector(0,0,0)]
	RhombicLattice.__init__(self, cell, lattice_vectors, cells,
                                function, base)

#
# Simple cubic lattice
#


[docs]class SCLattice(BravaisLattice):

    """
    Simple Cubic lattice

    A Simple Cubic lattice is a special case of a Bravais lattice
    in which the elementary cell is a cube.
    """

    def __init__(self, cellsize, cells, function=None, base=None):
        """
        :param cellsize: the edge length of the elementary cell
        :type cellsize: float
        :param cells: a list of integers, whose length must equal the number
                      of dimensions. Each entry specifies how often a cell is
                      repeated along this dimension.
        :param function: a function that is called for every lattice point with
                         the vector describing the point as argument. The return
                         value of this function is stored in the lattice object.
                         If the function is 'None', the vector is directly
                         stored in the lattice object.
        """
	lattice_vectors = (cellsize*Vector(1., 0., 0.),
                           cellsize*Vector(0., 1., 0.),
                           cellsize*Vector(0., 0., 1.))
	if type(cells) != type(()):
	    cells = 3*(cells,)
	BravaisLattice.__init__(self, lattice_vectors, cells, function, base)

#
# Body-centered cubic lattice
#


[docs]class BCCLattice(RhombicLattice):

    """
    Body-Centered Cubic lattice

    A Body-Centered Cubic lattice has two points per elementary cell.
    """

    def __init__(self, cellsize, cells, function=None, base=None):
        """
        :param cellsize: the edge length of the elementary cell
        :type cellsize: float
        :param cells: a list of integers, whose length must equal the number
                      of dimensions. Each entry specifies how often a cell is
                      repeated along this dimension.
        :param function: a function that is called for every lattice point with
                         the vector describing the point as argument. The return
                         value of this function is stored in the lattice object.
                         If the function is 'None', the vector is directly
                         stored in the lattice object.
        """
        cell = [Vector(0,0,0), cellsize*Vector(0.5,0.5,0.5)]
	lattice_vectors = (cellsize*Vector(1., 0., 0.),
                           cellsize*Vector(0., 1., 0.),
                           cellsize*Vector(0., 0., 1.))
	if type(cells) != type(()):
	    cells = 3*(cells,)
	RhombicLattice.__init__(self, cell, lattice_vectors, cells,
                                function, base)

#
# Face-centered cubic lattice
#


[docs]class FCCLattice(RhombicLattice):

    """Face-Centered Cubic lattice

    A Face-Centered Cubic lattice has four points per elementary cell.
    """

    def __init__(self, cellsize, cells, function=None, base=None):
        """
        :param cellsize: the edge length of the elementary cell
        :type cellsize: float
        :param cells: a list of integers, whose length must equal the number
                      of dimensions. Each entry specifies how often a cell is
                      repeated along this dimension.
        :param function: a function that is called for every lattice point with
                         the vector describing the point as argument. The return
                         value of this function is stored in the lattice object.
                         If the function is 'None', the vector is directly
                         stored in the lattice object.
        """
        cell = [Vector(0,0,0),
                cellsize*Vector(  0,0.5,0.5),
                cellsize*Vector(0.5,  0,0.5),
                cellsize*Vector(0.5,0.5,  0)]
	lattice_vectors = (cellsize*Vector(1., 0., 0.),
                           cellsize*Vector(0., 1., 0.),
                           cellsize*Vector(0., 0., 1.))
	if type(cells) != type(()):
	    cells = 3*(cells,)
	RhombicLattice.__init__(self, cell, lattice_vectors, cells,
                                function, base)

#
# Optimal superposition of a molecule in two configurations
#


[docs]def superpositionFit(confs):
    """
    :param confs: the weight, reference position, and alternate
                  position for each atom
    :type confs: sequence of (float, Vector, Vector)
    :returns: the quaternion representing the rotation,
              the center of mass in the alternate configuraton,
              the center of mass in the reference configuration,
              and the RMS distance after the optimal superposition
    """
    w_sum = 0.
    wr_sum = N.zeros((3,), N.Float)
    for w, r_ref, r in confs:
        w_sum += w
        wr_sum += w*r_ref.array
    ref_cms = wr_sum/w_sum
    pos = N.zeros((3,), N.Float)
    possq = 0.
    cross = N.zeros((3, 3), N.Float)
    for w, r_ref, r in confs:
        w = w/w_sum
        r_ref = r_ref.array-ref_cms
        r = r.array
        pos = pos + w*r
        possq = possq + w*N.add.reduce(r*r) \
                      + w*N.add.reduce(r_ref*r_ref)
        cross = cross + w*r[:, N.NewAxis]*r_ref[N.NewAxis, :]
    k = N.zeros((4, 4), N.Float)
    k[0, 0] = -cross[0, 0]-cross[1, 1]-cross[2, 2]
    k[0, 1] = cross[1, 2]-cross[2, 1]
    k[0, 2] = cross[2, 0]-cross[0, 2]
    k[0, 3] = cross[0, 1]-cross[1, 0]
    k[1, 1] = -cross[0, 0]+cross[1, 1]+cross[2, 2]
    k[1, 2] = -cross[0, 1]-cross[1, 0]
    k[1, 3] = -cross[0, 2]-cross[2, 0]
    k[2, 2] = cross[0, 0]-cross[1, 1]+cross[2, 2]
    k[2, 3] = -cross[1, 2]-cross[2, 1]
    k[3, 3] = cross[0, 0]+cross[1, 1]-cross[2, 2]
    for i in range(1, 4):
        for j in range(i):
            k[i, j] = k[j, i]
    k = 2.*k
    for i in range(4):
        k[i, i] = k[i, i] + possq - N.add.reduce(pos*pos)
    from Scientific import LA
    e, v = LA.eigenvectors(k)
    i = N.argmin(e)
    v = v[i]
    if v[0] < 0: v = -v
    if e[i] <= 0.:
        rms = 0.
    else:
        rms = N.sqrt(e[i])
    from Scientific.Geometry import Quaternion
    return Quaternion.Quaternion(v), Vector(ref_cms), \
           Vector(pos), rms

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MMTK-2.7.9/Doc/HTML/_modules/MMTK/InternalCoordinates.html0000644000076600000240000014202112013143455023374 0ustar hinsenstaff00000000000000 MMTK.InternalCoordinates — MMTK User Guide 2.7.7 documentation

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Source code for MMTK.InternalCoordinates

# Manipulation of internal coordinates
#
# Written by Konrad Hinsen
#

"""
Manipulation of molecular configurations in terms of internal coordinates
"""

__docformat__ = 'restructuredtext'

import MMTK
from Scientific import N

#
# The abstract base class
#
class InternalCoordinate:

    def __init__(self, atoms):
        self.atoms = atoms
        self.molecule = None
        for m in atoms[0].topLevelChemicalObject().bondedUnits():
            if atoms[0] in m.atomList():
                self.molecule = m
                break
        if self.molecule is None:
            raise ValueError("inconsistent data structure")
        for a in atoms[1:]:
            if a not in self.molecule.atomList():
                raise ValueError("atoms not in the same molecule")
        self.universe = self.molecule.universe()
        if self.universe is None:
            self.universe = MMTK.InfiniteUniverse()

    def bondTest(self):
        for i in range(len(self.atoms)-1):
            if not self.atoms[i] in self.atoms[i+1].bondedTo():
                raise ValueError("no bond between %s and %s"
                                  % (self.atoms[i], self.atoms[i+1]))

    def findFragments(self, spec1, spec2, excluded_first_only=False):
        self.fragment1 = MMTK.Collection()
        self.fragment2 = MMTK.Collection()
        self.molecule.setBondAttributes()
        try:
            self.bondTest()
            for fragment, (start, excluded, error_check) \
                    in [(self.fragment1, spec1), (self.fragment2, spec2)]:
                atoms = set([start])
                first = True
                new_atoms = set([start])
                while new_atoms:
                    check = new_atoms
                    new_atoms = set()
                    for a in check:
                        for na in a.bondedTo():
                            if excluded_first_only:
                                if first:
                                    add = na not in excluded
                                else:
                                    add = True
                            else:
                                add = na not in excluded
                            if add and na not in atoms:
                                atoms.add(na)
                                new_atoms.add(na)
                    first = False
                if error_check in atoms:
                    raise ValueError("cyclic bond structure")
                for a in atoms:
                    fragment.addObject(a)
        finally:
            self.molecule.clearBondAttributes()

#
# Bond length
#

[docs]class BondLength(InternalCoordinate):

    """
    Bond length coordinate

    A BondLength object permits calculating and modifying the
    length of a bond in a molecule, under the condition that
    the bond is not part of a circular bond structure (but it
    is not a problem to have a circular bond structure elsewhere
    in the molecule). Modifying the bond length moves the parts
    of the molecule on both sides of the bond along the bond direction
    in such a way that the center of mass of the molecule does not
    change.

    The initial construction of the BondLength object can be
    expensive (the bond structure of the molecule must be
    analyzed). It is therefore advisable to keep the object
    rather than recreate it frequently. Note that if you only
    want to calculate bond lengths (no modification), the
    method Universe.distance is simpler and faster.
    """

    def __init__(self, atom1, atom2):
        """
        :param atom1: the first atom that defines the bond
        :type atom1: :class:`~MMTK.ChemicalObjects.Atom`
        :param atom2: the second atom that defines the bond
        :type atom2: :class:`~MMTK.ChemicalObjects.Atom`
        """
        InternalCoordinate.__init__(self, [atom1, atom2])
        self.findFragments((atom1, set([atom2]), atom2),
                           (atom2, set([atom1]), atom1),
                            True)


[docs]    def getValue(self, conf = None):
        """
        :param conf: a configuration (defaults to the current configuration)
        :type conf: :class:`~MMTK.ParticleProperties.Configuration`
        :returns: the length of the bond in the configuration conf
        :rtype: float
        """
        return self.universe.distance(self.atoms[0], self.atoms[1], conf)



[docs]    def setValue(self, value):
        """
        Sets the length of the bond
        :param value: the desired length of the bond
        :type value: float
        """
        v = self.universe.distanceVector(self.atoms[0], self.atoms[1])
        axis = v.normal()
        distance = value - v.length()
        m1 = self.fragment1.mass()
        m2 = self.fragment2.mass()
        d1 = -m2*distance/(m1+m2)
        d2 = m1*distance/(m1+m2)
        self.fragment1.translateBy(d1*axis)
        self.fragment2.translateBy(d2*axis)

#
# Bond angles
#


[docs]class BondAngle(InternalCoordinate):

    """
    Bond angle

    A BondAngle object permits calculating and modifying the
    angle between two bonds in a molecule, under the condition that
    the bonds are not part of a circular bond structure (but it
    is not a problem to have a circular bond structure elsewhere
    in the molecule). Modifying the bond angle rotates the parts
    of the molecule on both sides of the central atom around
    an axis passing through the central atom and perpendicular
    to the plane defined by the two bonds in such a way that
    there is no overall rotation of the molecule. The central
    atom and any other atoms bonded to it do not move.

    The initial construction of the BondAngle object can be
    expensive (the bond structure of the molecule must be
    analyzed). It is therefore advisable to keep the object
    rather than recreate it frequently. Note that if you only
    want to calculate bond angles (no modification), the
    method Universe.angle is simpler and faster.
    """

    def __init__(self, atom1, atom2, atom3):
        """
        :param atom1: the first atom that defines the angle
        :type atom1: :class:`~MMTK.ChemicalObjects.Atom`
        :param atom2: the second and central atom that defines the bond
        :type atom2: :class:`~MMTK.ChemicalObjects.Atom`
        :param atom3: the third atom that defines the bond
        :type atom3: :class:`~MMTK.ChemicalObjects.Atom`
        """
        InternalCoordinate.__init__(self, [atom1, atom2, atom3])
        excluded = set([atom2])
        self.findFragments((atom1, excluded, atom3), (atom3, excluded, atom1))


[docs]    def getValue(self, conf = None):
        """
        :param conf: a configuration (defaults to the current configuration)
        :type conf: :class:`~MMTK.ParticleProperties.Configuration`
        :returns: the size of the angle in the configuration conf
        :rtype: float
        """
        return self.universe.angle(self.atoms[0], self.atoms[1],
                                   self.atoms[2], conf)



[docs]    def setValue(self, value):
        """
        Sets the size of the angle
        :param value: the desired angle
        :type value: float
        """
        from Scientific.Geometry import delta
        v1 = self.universe.distanceVector(self.atoms[1], self.atoms[0])
        v2 = self.universe.distanceVector(self.atoms[1], self.atoms[2])
        angle = v1.angle(v2)
        if N.fabs(angle - N.pi) < 1.e-4:
            raise ValueError("angle too close to pi")
        axis = v1.cross(v2).normal()
        d = angle-value
        cm1, th1 = self.fragment1.centerAndMomentOfInertia()
        r1 = self.universe.distanceVector(self.atoms[1], cm1)
        th1 -= self.fragment1.mass()*((r1*r1)*delta-r1.dyadicProduct(r1))
        i1 = axis*(th1*axis)
        cm2, th2 = self.fragment2.centerAndMomentOfInertia()
        r2 = self.universe.distanceVector(self.atoms[1], cm2)
        th2 -= self.fragment2.mass()*((r2*r2)*delta-r2.dyadicProduct(r2))
        i2 = axis*(th2*axis)
        d1 = i2*d/(i1+i2)
        d2 = d1-d
        self.fragment1.rotateAroundAxis(self.atoms[1].position(),
                                        self.atoms[1].position()+axis,
                                        d1)
        self.fragment2.rotateAroundAxis(self.atoms[1].position(),
                                        self.atoms[1].position()+axis,
                                        d2)

#
# Dihedral angles
#


[docs]class DihedralAngle(InternalCoordinate):

    """
    Dihedral angle

    A DihedralAngle object permits calculating and modifying the
    dihedral angle defined by three consecutive bonds in a molecule,
    under the condition that the central bond is not part of a
    circular bond structure (but it is not a problem to have a
    circular bond structure elsewhere in the molecule). Modifying the
    dihedral angle rotates the parts of the molecule on both sides of
    the central bond around this central bond in such a way that there
    is no overall rotation of the molecule.

    The initial construction of the DihedralAngle object can be
    expensive (the bond structure of the molecule must be
    analyzed). It is therefore advisable to keep the object
    rather than recreate it frequently. Note that if you only
    want to calculate bond angles (no modification), the
    method Universe.dihedral is simpler and faster.
    """

    def __init__(self, atom1, atom2, atom3, atom4):
        """
        :param atom1: the first atom that defines the dihedral
        :type atom1: :class:`~MMTK.ChemicalObjects.Atom`
        :param atom2: the second atom that defines the dihedral,
                      must be on the central bond
        :type atom2: :class:`~MMTK.ChemicalObjects.Atom`
        :param atom3: the third atom that defines the dihedral,
                      must be on the central bond
        :type atom3: :class:`~MMTK.ChemicalObjects.Atom`
        :param atom4: the fourth atom that defines the dihedral
        :type atom4: :class:`~MMTK.ChemicalObjects.Atom`
        """
        InternalCoordinate.__init__(self, [atom1, atom2, atom3, atom4])
        excluded = set([atom2, atom3])
        self.findFragments((atom1, excluded, atom4), (atom4, excluded, atom1))


[docs]    def getValue(self, conf = None):
        """
        :param conf: a configuration (defaults to the current configuration)
        :type conf: :class:`~MMTK.ParticleProperties.Configuration`
        :returns: the size of the dihedral angle in the configuration conf
        :rtype: float
        """
        return self.universe.dihedral(self.atoms[0], self.atoms[1],
                                      self.atoms[2], self.atoms[3], conf)



[docs]    def setValue(self, value):
        """
        Sets the size of the dihedral
        :param value: the desired dihedral angle
        :type value: float
        """
        from Scientific.Geometry import delta
        angle  = self.universe.dihedral(self.atoms[0], self.atoms[1],
                                        self.atoms[2], self.atoms[3])
        v = self.universe.distanceVector(self.atoms[1], self.atoms[2])
        axis = v.normal()
        d = N.fmod(angle-value, 2.*N.pi)
        cm1, th1 = self.fragment1.centerAndMomentOfInertia()
        r1 = self.universe.distanceVector(self.atoms[1], cm1)
        th1 -= self.fragment1.mass()*((r1*r1)*delta-r1.dyadicProduct(r1))
        i1 = axis*(th1*axis)
        cm2, th2 = self.fragment2.centerAndMomentOfInertia()
        r2 = self.universe.distanceVector(self.atoms[2], cm2)
        th2 -= self.fragment2.mass()*((r2*r2)*delta-r2.dyadicProduct(r2))
        i2 = axis*(th2*axis)
        d1 = i2*d/(i1+i2)
        d2 = d1-d
        self.fragment1.rotateAroundAxis(self.atoms[1].position(),
                                        self.atoms[1].position()+axis,
                                        d1)
        self.fragment2.rotateAroundAxis(self.atoms[2].position(),
                                        self.atoms[2].position()+axis,
                                        d2)

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Source code for MMTK.Minimization

# This module implements energy minimizers.
#
# Written by Konrad Hinsen
#

"""
Energy minimizers
"""

__docformat__ = 'restructuredtext'

from MMTK import Features, Trajectory, Units
from MMTK_minimization import conjugateGradient, steepestDescent

#
# Minimizer base class
#
class Minimizer(Trajectory.TrajectoryGenerator):

    def __init__(self, universe, options):
	Trajectory.TrajectoryGenerator.__init__(self, universe, options)

    default_options = {'steps': 100, 'step_size': 0.02*Units.Ang,
		       'convergence': 0.01*Units.kJ/(Units.mol*Units.nm),
                       'background': False, 'threads': None,
                       'mpi_communicator': None, 'actions': []}

    available_data = ['energy', 'configuration', 'gradients']

    restart_data = ['configuration', 'energy']

    def __call__(self, options):
	raise AttributeError

#
# Steepest descent minimizer
#

[docs]class SteepestDescentMinimizer(Minimizer):

    """
    Steepest-descent minimizer

    The minimizer can handle fixed atoms, but no distance constraints.
    It is fully thread-safe.

    The minimization is started by calling the minimizer object.
    All the keyword options (see documnentation of __init__) can be
    specified either when creating the minimizer or when calling it.

    The following data categories and variables are available for
    output:

     - category "configuration": configuration and box size (for
       periodic universes)

     - category "gradients": energy gradients for each atom

     - category "energy": potential energy and
                          norm of the potential energy gradient
    """

    def __init__(self, universe, **options):
        """
        :param universe: the universe on which the integrator acts
        :type universe: :class:`~MMTK.Universe.Universe`
        :keyword steps: the number of minimization steps (default is 100)
        :type steps: int
        :keyword step_size: the initial size of a minimization step
                            (default is 0.002 nm)
        :type step_size: float
        :keyword convergence: the root-mean-square gradient length at which
                              minimization stops (default is 0.01 kJ/mol/nm)
        :type convergence: float
        :keyword actions: a list of actions to be executed periodically
                          (default is none)
        :type actions: list
        :keyword threads: the number of threads to use in energy evaluation
                          (default set by MMTK_ENERGY_THREADS)
        :type threads: int
        :keyword background: if True, the integration is executed as a
                             separate thread (default: False)
        :type background: bool
        :keyword mpi_communicator: an MPI communicator object, or None,
                                   meaning no parallelization (default: None)
        :type mpi_communicator: Scientific.MPI.MPICommunicator
        """
	Minimizer.__init__(self, universe, options)
	self.features = [Features.FixedParticleFeature,
			 Features.NoseThermostatFeature,
                         Features.AndersenBarostatFeature]

    def __call__(self, **options):
        """
        Run the minimizer. The keyword options are the same as described
        under __init__.
        """
	self.setCallOptions(options)
	Features.checkFeatures(self, self.universe)
	configuration = self.universe.configuration()
	fixed = self.universe.getAtomBooleanArray('fixed')
        nt = self.getOption('threads')
        comm = self.getOption('mpi_communicator')
	evaluator = self.universe.energyEvaluator(threads=nt,
                                                  mpi_communicator=comm)
        evaluator = evaluator.CEvaluator()
	args = (self.universe,
                configuration.array, fixed.array, evaluator,
                self.getOption('steps'), self.getOption('step_size'),
                self.getOption('convergence'), self.getActions(),
                'Steepest descent minimization with ' +
                self.optionString(['convergence', 'step_size', 'steps']))
        return self.run(steepestDescent, args)

#
# Conjugate gradient minimizer
#


[docs]class ConjugateGradientMinimizer(Minimizer):

    """
    Conjugate gradient minimizer

    The minimizer can handle fixed atoms, but no distance constraints.
    It is fully thread-safe.

    The minimization is started by calling the minimizer object.
    All the keyword options can be specified either when
    creating the minimizer or when calling it.

    The following data categories and variables are available for
    output:

     - category "configuration": configuration and box size (for
       periodic universes)

     - category "gradients": energy gradients for each atom

     - category "energy": potential energy and
                          norm of the potential energy gradient
    """

    def __init__(self, universe, **options):
        """
        :param universe: the universe on which the integrator acts
        :type universe: :class:`~MMTK.Universe.Universe`
        :keyword steps: the number of minimization steps (default is 100)
        :type steps: int
        :keyword step_size: the initial size of a minimization step
                            (default is 0.002 nm)
        :type step_size: float
        :keyword convergence: the root-mean-square gradient length at which
                              minimization stops (default is 0.01 kJ/mol/nm)
        :type convergence: float
        :keyword actions: a list of actions to be executed periodically
                          (default is none)
        :type actions: list
        :keyword threads: the number of threads to use in energy evaluation
                          (default set by MMTK_ENERGY_THREADS)
        :type threads: int
        :keyword background: if True, the integration is executed as a
                             separate thread (default: False)
        :type background: bool
        """
	Minimizer.__init__(self, universe, options)
	self.features = [Features.FixedParticleFeature,
			 Features.NoseThermostatFeature]

    def __call__(self, **options):
        """
        Run the minimizer. The keyword options are the same as described
        under __init__.
        """
	self.setCallOptions(options)
	Features.checkFeatures(self, self.universe)
	configuration = self.universe.configuration()
	fixed = self.universe.getAtomBooleanArray('fixed')
        nt = self.getOption('threads')
	evaluator = self.universe.energyEvaluator(threads=nt).CEvaluator()
	args =(self.universe,
               configuration.array, fixed.array, evaluator,
               self.getOption('steps'), self.getOption('step_size'),
               self.getOption('convergence'), self.getActions(),
               'Conjugate gradient minimization with ' +
               self.optionString(['convergence', 'step_size', 'steps']))
        return self.run(conjugateGradient, args)

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Source code for MMTK.MolecularSurface

#
# Copyright 2000 by Peter McCluskey (pcm@rahul.net).
# You may do anything you want with it, provided this notice is kept intact.
#

# This module is in part a replacement for the MolecularSurface MMTK module
# that uses the NSC code. It has a less restrictive license than the NSC code,
# and is mostly more flexible, but is probably slower and somewhat less
# accurate. It can run (slowly) without any of the C code. It has an
# additional routine surfacePointsAndGradients, which returns area, an
# "up" vector, and surface points for each atom.


# Modifications by Konrad Hinsen <hinsen@cnrs-orleans.fr>:
# - Replaced tabs by spaces
# - Added/adapted docstrings to MMTK conventions
# - Removed assignment of methods to class GroupOfAtoms
# - Use vectors in the return value of surfacePointsAndGradients
# - Replaced "math" module by "Numeric"
# - Renamed module _surface to MMTK_surface
# - Converted docstrings to epydoc
# - Changed first argument name from self to object

"""
Molecular surfaces and volumes.
"""

__docformat__ = 'restructuredtext'

from MMTK import surfm
from MMTK.Collections import Collection
from MMTK import Vector
from Scientific import N


[docs]def surfaceAndVolume(object, probe_radius = 0.):
    """
    :param object: a chemical object
    :type object: :class:`~MMTK.Collections.GroupOfAtoms`
    :param probe_radius: the distance from the vdW-radii of the atoms
                         at which the surface is computed
    :type probe_radius: float
    :returns: the molecular surface and volume of object
    :rtype: tuple
    """
    atoms = object.atomList()
    smap = surfm.surface_atoms(atoms, probe_radius, ret_fmt = 2)
    tot_a = 0
    tot_v = 0
    for a in atoms:
        atom_data = smap[a]
        tot_a = tot_a + atom_data[0]
        tot_v = tot_v + atom_data[1]
    return (tot_a, tot_v)



[docs]def surfaceAtoms(object, probe_radius = 0.):
    """
    :param object: a chemical object
    :type object: :class:`~MMTK.Collections.GroupOfAtoms`
    :param probe_radius: the distance from the vdW-radii of the atoms
                         at which the surface is computed
    :type probe_radius: float
    :returns: a dictionary that maps the surface atoms to their
              exposed surface areas
    :rtype: dict
    """
    atoms = object.atomList()
    smap = surfm.surface_atoms(atoms, probe_radius, ret_fmt = 1)
    surface_atoms = {}
    for a in atoms:
        area = smap[a]
        if area > 0.:
            # we have a surface atom
            surface_atoms[a] = area
    return surface_atoms



[docs]def surfacePointsAndGradients(object, probe_radius = 0., point_density = 258):
    """
    :param object: a chemical object
    :type object: :class:`~MMTK.Collections.GroupOfAtoms`
    :param probe_radius: the distance from the vdW-radii of the atoms
                         at which the surface is computed
    :type probe_radius: float
    :param point_density: the density of points that describe the surface
    :type point_density: int
    :returns: a dictionary that maps the surface atoms to a tuple
              containing three surface-related quantities: the exposed surface
              area, a list of points in the exposed surface, and a gradient
              vector pointing outward from the surface.
    :rtype: dict
    """
    atoms = object.atomList()
    smap = surfm.surface_atoms(atoms, probe_radius, ret_fmt = 4,
                               point_density = point_density)
    surface_data = {}
    for a in atoms:
        (area, volume, points1, grad) = smap[a]
        if area > 0.:
            # we have a surface atom
            surface_data[a] = (area, map(Vector, points1), Vector(grad))
    return surface_data



class Contact(object):

    def __init__(self, a1, a2, dist = None):
        self.a1 = a1
        self.a2 = a2
        if dist is None:
            self.dist = (a1.position() - a2.position()).length()
        else:
            self.dist = dist

    def __getitem__(self, index):
        return (self.a1, self.a2)[index]

    def __cmp__(a, b):
        return cmp(a.dist, b.dist)

    def __hash__(self):
        return (self.a1, self.a2)

    def __repr__(self):
        return 'Contact(%s, %s)' % (self.a1, self.a2)

    __str__ = __repr__


[docs]def findContacts(object1, object2, contact_factor = 1.0, cutoff = 0.0):
    """
    Identifies contacts between two molecules. A contact is defined as a pair
    of atoms whose distance is less than contact_factor*(r1+r2+cutoff),
    where r1 and r2 are the atomic van-der-Waals radii.

    :param object1: a chemical object
    :type object1: :class:`~MMTK.Collections.GroupOfAtoms`
    :param object2: a chemical object
    :type object2: :class:`~MMTK.Collections.GroupOfAtoms`
    :param contact_factor: a scale factor in the contact distance criterion
    :type contact_factor: float
    :param cutoff: a constant in the contact distance criterion
    :type cutoff: float
    :returns: a list of Contact objects that describe atomic contacts
              between object1 and object2. 
    :rtype: list
    """
    max_object1 = len(object1.atomList())
    atoms = object1.atomList() + object2.atomList()
    tup = surfm.get_atom_data(atoms, 0.0)
    max_rad = tup[0]                    # max vdW_radius
    atom_data = tup[1]
    nbors = surfm.NeighborList(atoms, contact_factor*(2*max_rad+cutoff),
                               atom_data)
    clist = []
    done = {}
    for index1 in range(len(atoms)):
        for (index2, dist2) in nbors[index1]:
            if (index1 < max_object1) != (index2 < max_object1):
                if index1 < index2:
                    a1 = atoms[index1]
                    a2 = atoms[index2]
                else:
                    a1 = atoms[index2]
                    a2 = atoms[index1]
                dist = N.sqrt(dist2)
                if dist >= contact_factor*(a1.vdW_radius + a2.vdW_radius + cutoff):
                    continue
                if not done.has_key((index1, index2)):
                    clist.append(Contact(a1, a2, dist))
                    done[(index1, index2)] = 1
    return clist


if __name__ == '__main__':
    
    from MMTK.PDB import PDBConfiguration
    from MMTK import Units
    import sys

    target_filename = sys.argv[2]
    pdb_conf1 = PDBConfiguration(target_filename)
    if sys.argv[1][:2] == '-f':
        chains = pdb_conf1.createNucleotideChains()
        molecule_names = []
        if len(chains) >= 2:
            clist = findContacts(chains[0], chains[1])
        else:
            molecule_names = []
            for (key, mol) in pdb_conf1.molecules.items():
                for o in mol:
                    molecule_names.append(o.name)
            targets = pdb_conf1.createAll(molecule_names = molecule_names)
            if len(molecule_names) > 1:
                clist = findContacts(targets[0], targets[1])
            else:
                atoms = targets.atomList()
                mid = len(atoms)/2
                clist = findContacts(Collection(atoms[:mid]),
                                     Collection(atoms[mid:]))
        print len(clist), 'contacts'
        for c in clist[:8]:
            print '%-64s %6.2f' % (c, c.dist/Units.Ang)
    else:
        target = pdb_conf1.createAll()
        if sys.argv[1][:2] == '-v':
            (a, v) = target.surfaceAndVolume()
            print 'surface area %.2f volume %.2f' \
                  % (a/(Units.Ang**2), v/(Units.Ang**3))
        elif sys.argv[1][:2] == '-a':
            smap = target.surfaceAtoms(probe_radius = 1.4*Units.Ang)
            print len(smap.keys()),'of',len(target.atomList()),'atoms on surface'
        elif sys.argv[1][:2] == '-p':
            smap = target.surfacePointsAndGradients(probe_radius = 1.4*Units.Ang)
            for (a, tup) in smap.items():
                print '%-40.40s %6.2f %5d %5.1f %5.1f %5.1f' \
                      % (a.fullName(), tup[0]/(Units.Ang**2), len(tup[1]),
                         tup[2][0], tup[2][1], tup[2][2])

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Source code for MMTK.MoleculeFactory

# A molecule factory is used to create chemical objects
# in code, without a database definition.
#
# Written by Konrad Hinsen
#

"""
Molecule factory for creating chemical objects
"""

__docformat__ = 'restructuredtext'

from MMTK import Bonds, ChemicalObjects

#
# Molecule factories store AtomTemplate and GroupTemplate objects.
#

class AtomTemplate(object):
    
    def __init__(self, element):
        self.element = element

class GroupTemplate(object):
    
    def __init__(self, name):
        self.name = name
        self.children = []
        self.names = {}
        self.attributes = {}
        self.positions = {}
        self.bonds = []
        self.locked = False

    def addAtom(self, atom_name, element):
        """
        Add an atom.

        :param atom_name: the name of the atom
        :type atom_name: str
        :param element: the chemical element symbol
        :type element: str
        """
        if self.locked:
            raise ValueError("group is locked")
        atom = AtomTemplate(element)
        self.names[atom_name] = len(self.children)
        self.children.append(atom)

    def addSubgroup(self, subgroup_name, subgroup):
        """
        Add a subgroup.

        :param subgroup_name: the name of the subgroup within this group
        :type subgroup_name: str
        :param subgroup: the subgroup type
        :type subgroup: str
        """
        if self.locked:
            raise ValueError("group is locked")
        self.names[subgroup_name] = len(self.children)
        self.children.append(subgroup)

    def addBond(self, atom1, atom2):
        """
        Add a bond.

        :param atom1: the name of the first atom
        :type atom1: str
        :param atom2: the name of the second atom
        :type atom2: str
        """
        if self.locked:
            raise ValueError("group is locked")
        self.bonds.append((self.atomNameToPath(atom1),
                           self.atomNameToPath(atom2)))

    def setAttribute(self, name, value):
        if self.locked:
            raise ValueError("group is locked")
        self.attributes[name] = value

    def setPosition(self, name, vector):
        if self.locked:
            raise ValueError("group is locked")
        self.positions[name] = vector

    def getAtomReference(self, atom_name):
        path = self.atomNameToPath(atom_name)
        object = self
        for path_element in path[:-1]:
            object =  object.children[object.names[path_element]]
        atom_index = object.names[path[-1]]
        reference = 0
        for i in range(atom_index):
            if isinstance(object.children[i], AtomTemplate):
                reference += 1
        from MMTK.Database import AtomReference
        return AtomReference(reference)

    def atomNameToPath(self, atom_name):
        atom_name = atom_name.split('.')
        object = self
        try:
            for path_element in atom_name:
                object = object.children[object.names[path_element]]
            if not isinstance(object, AtomTemplate):
                raise ValueError("no atom " + atom)
        except KeyError:
            raise ValueError("no atom " + '.'.join(atom_name))
        return atom_name

    def writeXML(self, file, memo):
        if memo.get(self.name, False):
            return
        names = len(self.children)*[None]
        for name in self.names:
            names[self.names[name]] = name
        atoms = []
        subgroups = []
        for i in range(len(self.children)):
            object = self.children[i]
            name = names[i]
            if isinstance(object, GroupTemplate):
                object.writeXML(file, memo)
                subgroups.append((name, object))
            else:
                atoms.append((name, object))
        file.write('<molecule id="%s">\n' % self.name)
        for name, subgroup in subgroups:
            file.write('  <molecule ref="%s" title="%s"/>\n'
                       % (subgroup.name, name))
        if atoms:
            file.write('  <atomArray>\n')
            for name, atom in atoms:
                file.write('    <atom title="%s" elementType="%s"/>\n'
                           % (name, atom.element))
            file.write('  </atomArray>\n')
        if self.bonds:
            file.write('  <bondArray>\n')
            for a1, a2 in self.bonds:
                file.write('    <bond atomRefs2="%s %s"/>\n'
                           % (':'.join(a1), ':'.join(a2)))
            file.write('  </bondArray>\n')
        file.write('</molecule>\n')
        memo[self.name] = True

    def getXMLAtomOrder(self):
        atom_names = []
        names = len(self.children)*[None]
        for name in self.names:
            names[self.names[name]] = name
        for i in range(len(self.children)):
            oname = names[i]
            object = self.children[i]
            if isinstance(object, GroupTemplate):
                for name in object.getXMLAtomOrder():
                    atom_names.append(oname + ':' + name)
            else:
                atom_names.append(oname)
        return atom_names

#
# The MoleculeFactory class
#

[docs]class MoleculeFactory(object):
    '''
    MoleculeFactory

    A MoleculeFactory serves to create molecules without reference to database
    definitions. Molecules and groups are defined in terms of atoms, groups, and
    bonds. Each MoleculeFactory constitutes an independent set of definitions.
    Definitions within a MoleculeFactory can refer to each other.

    Each MoleculeFactory stores a set of :class:`~MMTK.ChemicalObjects.Group`
    objects which are referred to by names. The typical operation sequence is to
    create a new group and then add atoms, bonds, and subgroups. It is also
    possible to define coordinates and arbitrary attributes (in particular for
    force fields). In the end, a finished object can be retrieved as a 
    :class:`~MMTK.ChemicalObjects.Molecule` object.
    '''

    def __init__(self):
        self.groups = {}
        self.locked_groups = {}


[docs]    def createGroup(self, name):
        """
        Create a new (initially empty) group object.
        """
        if self.groups.has_key(name):
            raise ValueError("redefinition of group " + name)
        self.groups[name] = GroupTemplate(name)



[docs]    def addSubgroup(self, group, subgroup_name, subgroup):
        """
        Add a subgroup to a group

        :param group: the name of the group
        :type group: str
        :param subgroup_name: the name of the subgroup within the group
        :type subgroup_name: str
        :param subgroup: the subgroup type
        :type subgroup: str
        """
        self.groups[group].addSubgroup(subgroup_name, self.groups[subgroup])



[docs]    def addAtom(self, group, atom_name, element):
        """
        Add an atom to a group

        :param group: the name of the group
        :type group: str
        :param atom_name: the name of the atom
        :type atom_name: str
        :param element: the chemical element symbol
        :type element: str
        """
        self.groups[group].addAtom(atom_name, element)



[docs]    def addBond(self, group, atom1, atom2):
        """
        Add a bond to a group

        :param group: the name of the group
        :type group: str
        :param atom1: the name of the first atom
        :type atom1: str
        :param atom2: the name of the second atom
        :type atom2: str
        """
        self.groups[group].addBond(atom1, atom2)


    def setAttribute(self, group, name, value):
        self.groups[group].setAttribute(name, value)

    def setPosition(self, group, atom, vector):
        self.groups[group].setPosition(atom, vector)

    def getAtomReference(self, group, atom):
        return self.groups[group].getAtomReference(atom)


[docs]    def retrieveMolecule(self, group):
        """
        :param group: the name of the group to be used as a template
        :type group: str
        :returns: a molecule defined by the contents of the group
        :rtype: :class:`~MMTK.ChemicalObjects.Molecule`
        """

        group = self.groups[group]
        return self.makeChemicalObjects(group, True)
    

    def makeChemicalObjects(self, template, top_level):
        self.groups[template.name].locked = True
        if top_level:
            if template.attributes.has_key('sequence'):
                object = ChemicalObjects.ChainMolecule(None)
            else:
                object = ChemicalObjects.Molecule(None)
        else:
            object = ChemicalObjects.Group(None)
        object.atoms = []
        object.bonds = Bonds.BondList([])
        object.groups = []
        object.type = self.groups[template.name]
        object.parent = None
        child_objects = []
        for child in template.children:
            if isinstance(child, GroupTemplate):
                group = self.makeChemicalObjects(child, False)
                object.groups.append(group)
                object.atoms.extend(group.atoms)
                object.bonds.extend(group.bonds)
                group.parent = object
                child_objects.append(group)
            else:
                atom = ChemicalObjects.Atom(child.element)
                object.atoms.append(atom)
                atom.parent = object
                child_objects.append(atom)
        for name, index in template.names.items():
            setattr(object, name, child_objects[index])
            child_objects[index].name = name
        for name, value in template.attributes.items():
            path = name.split('.')
            setattr(self.namePath(object, path[:-1]), path[-1], value)
        for atom1, atom2 in template.bonds:
            atom1 = self.namePath(object, atom1)
            atom2 = self.namePath(object, atom2)
            object.bonds.append(Bonds.Bond((atom1, atom2)))
        for name, vector in template.positions.items():
            path = name.split('.')
            self.namePath(object, path).setPosition(vector)
        return object

    def namePath(self, object, path):
        for item in path:
            object = getattr(object, item)
        return object

    def writeXML(self, file):
        file.write('<?xml version="1.0" encoding="ISO-8859-1" ' +
                   'standalone="yes"?>\n\n')
        file.write('<templates>\n\n')
        memo = {}
        for group in self.groups:
            self.groups[group].writeXML(file, memo)
        file.write('\n</templates>\n')

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© Copyright 2010, Konrad Hinsen. Created using Sphinx 1.1.3.
MMTK-2.7.9/Doc/HTML/_modules/MMTK/NormalModes/0000755000076600000240000000000012156626563020775 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/_modules/MMTK/NormalModes/BrownianModes.html0000644000076600000240000024226212013143454024423 0ustar hinsenstaff00000000000000 MMTK.NormalModes.BrownianModes — MMTK User Guide 2.7.7 documentation

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Source code for MMTK.NormalModes.BrownianModes

# Brownian normal mode calculations.
#
# Written by Konrad Hinsen
#

"""
Brownian normal modes
"""

__docformat__ = 'restructuredtext'

from MMTK import Features, Units, ParticleProperties, Random
from Scientific.Functions.Interpolation import InterpolatingFunction
from Scientific import N

from MMTK.NormalModes import Core

#
# Class for a single mode
#

[docs]class BrownianMode(Core.Mode):

    """
    Single Brownian normal mode

    Mode objects are created by indexing a :class:`~MMTK.NormalModes.BrownianModes.BrownianModes` object.
    They contain the atomic displacements corresponding to a single
    mode. In addition, the inverse of the relaxation time corresponding
    to the mode is stored in the attribute "inv_relaxation_time".

    Note: the Brownian mode vectors are not friction weighted and therefore
    not orthogonal to each other.
    """

    def __init__(self, universe, n, inv_relaxation_time, mode):
        self.inv_relaxation_time = inv_relaxation_time
        Core.Mode.__init__(self, universe, n, mode)

    def __str__(self):
        return 'Mode ' + `self.number` + \
               ' with inverse relaxation time ' \
               + `self.inv_relaxation_time`

    __repr__ = __str__

#
# Class for a full set of normal modes
#


[docs]class BrownianModes(Core.NormalModes):

    """
    Brownian modes describe the independent relaxation motions of a
    harmonic system with friction. They are obtained by diagonalizing the
    friction-weighted force constant matrix.

    In order to obtain physically reasonable normal modes, the configuration
    of the universe must correspond to a local minimum of the potential
    energy.

    Individual modes (see :class:`~MMTK.NormalModes.BrownianModes.BrownianMode`)
    can be extracted by indexing with an integer. Looping over the modes
    is possible as well.

    Brownian modes describe the independent relaxation motions of a
    harmonic system with friction. They are obtained by diagonalizing the
    friction-weighted force constant matrix.

    In order to obtain physically reasonable normal modes, the configuration
    of the universe must correspond to a local minimum of the potential
    energy.

    A BrownianModes object behaves like a sequence of modes.
    Individual modes (see :class:`~MMTK.NormalModes.BrownianMode`)
    can be extracted by indexing with an integer. Looping over the modes
    is possible as well.

    """
    features = []

    def __init__(self, universe = None, friction = None,
                 temperature = 300.*Units.K,
                 subspace = None, delta = None, sparse = False):
        """
        :param universe: the system for which the normal modes are calculated;
                         it must have a force field which provides the second
                         derivatives of the potential energy
        :type universe: :class:`~MMTK.Universe.Universe`
        :param friction: the friction coefficient for each particle.
                         Note: The friction coefficients are not mass-weighted,
                         i.e. they have the dimension of an inverse time.
        :type friction: :class:`~MMTK.ParticleProperties.ParticleScalar`
        :param temperature: the temperature for which the amplitudes of the
                            atomic displacement vectors are calculated. A
                            value of None can be specified to have no scaling
                            at all. In that case the mass-weighted norm
                            of each normal mode is one.
        :type temperature: float
        :param subspace: the basis for the subspace in which the normal modes
                         are calculated (or, more precisely, a set of vectors
                         spanning the subspace; it does not have to be
                         orthogonal). This can either be a sequence of
                         :class:`~MMTK.ParticleProperties.ParticleVector` objects
                         or a tuple of two such sequences. In the second case,
                         the subspace is defined by the space spanned by the
                         second set of vectors projected on the complement of
                         the space spanned by the first set of vectors.
                         The first set thus defines directions that are
                         excluded from the subspace.
                         The default value of None indicates a standard
                         normal mode calculation in the 3N-dimensional
                         configuration space.
        :param delta: the rms step length for numerical differentiation.
                      The default value of None indicates analytical
                      differentiation.
                      Numerical differentiation is available only when a
                      subspace basis is used as well. Instead of calculating
                      the full force constant matrix and then multiplying
                      with the subspace basis, the subspace force constant
                      matrix is obtained by numerical differentiation of the
                      energy gradients along the basis vectors of the subspace.
                      If the basis is much smaller than the full configuration
                      space, this approach needs much less memory.
        :type delta: float
        :param sparse: a flag that indicates if a sparse representation of
                       the force constant matrix is to be used. This is of
                       interest when there are no long-range interactions and
                       a subspace of smaller size then 3N is specified. In that
                       case, the calculation will use much less memory with a
                       sparse representation.
        :type sparse: bool
        """

        if universe == None:
            return
        Features.checkFeatures(self, universe)
        Core.NormalModes.__init__(self, universe, subspace, delta, sparse,
                                  ['array', 'inv_relaxation_times'])
        self.friction = friction
        self.temperature = temperature

        self.weights = N.sqrt(friction.array)
        self.weights = self.weights[:, N.NewAxis]

        self._forceConstantMatrix()
        ev = self._diagonalize()

        self.inv_relaxation_times = ev
        self.sort_index = N.argsort(self.inv_relaxation_times)
        self.array.shape = (self.nmodes, self.natoms, 3)

        self.cleanup()

    def __getitem__(self, item):
        index = self.sort_index[item]
        return BrownianMode(self.universe, item,
                            self.inv_relaxation_times[index],
                            self.array[index]/self.weights)


[docs]    def rawMode(self, item):
        """
        :param item: the index of a normal mode
        :type item: int
        :returns: the unscaled mode vector
        :rtype: :class:`~MMTK.NormalModes.BrownianModes.BrownianMode`
        """
        index = self.sort_index[item]
        return BrownianMode(self.universe, item,
                            self.inv_relaxation_times[index],
                            self.array[index])


    def fluctuations(self, first_mode=6):
        f = ParticleProperties.ParticleScalar(self.universe)
        for i in range(first_mode, self.nmodes):
            mode = self.rawMode(i)
            f += (mode*mode)/mode.inv_relaxation_time
        f = Units.k_B*self.temperature*f/self.friction
        return f


[docs]    def meanSquareDisplacement(self, subset=None, weights=None,
                               time_range = (0., None, None),
                               first_mode = 6):
        """
        :param subset: the subset of the universe used in the calculation
                       (default: the whole universe)
        :type subset: :class:`~MMTK.Collections.GroupOfAtoms`
        :param weights: the weight to be given to each atom in the average
                        (default: atomic masses)
        :type weights: :class:`~MMTK.ParticleProperties.ParticleScalar`
        :param time_range: the time values at which the mean-square
                           displacement is evaluated, specified as a
                           range tuple (first, last, step).
                           The defaults are first=0, last=
                           20 times the longest vibration perdiod,
                           and step defined such that 300 points are
                           used in total.
        :type time_range: tuple
        :param first_mode: the first mode to be taken into account for
                           the fluctuation calculation. The default value
                           of 6 is right for molecules in vacuum.
        :type first_mode: int
        :returns: the averaged mean-square displacement of the
                  atoms in subset as a function of time
        :rtype: Scientific.Functions.Interpolation.InterpolatingFunction
        """
        if subset is None:
            subset = self.universe
        if weights is None:
            weights = self.universe.masses()
        weights = weights*subset.booleanMask()
        total = weights.sumOverParticles()
        weights = weights/(total*self.friction)
        first, last, step = (time_range + (None, None))[:3]
        if last is None:
            last = 3./self.rawMode(first_mode).inv_relaxation_time
        if step is None:
            step = (last-first)/300.
        time = N.arange(first, last, step)
        msd = N.zeros(time.shape, N.Float)
        for i in range(first_mode, self.nmodes):
            mode = self.rawMode(i)
            rt = mode.inv_relaxation_time
            d = (weights*(mode*mode)).sumOverParticles()
            N.add(msd, d*(1.-N.exp(-rt*time))/rt, msd)
        N.multiply(msd, 2.*Units.k_B*self.temperature, msd)
        return InterpolatingFunction((time,), msd)



[docs]    def staticStructureFactor(self, q_range = (1., 15.), subset=None,
                              weights=None, random_vectors=15,
                              first_mode = 6):
        """
        :param q_range: the range of angular wavenumber values
        :type q_range: tuple
        :param subset: the subset of the universe used in the calculation
                       (default: the whole universe)
        :type subset: :class:`~MMTK.Collections.GroupOfAtoms`
        :param weights: the weight to be given to each atom in the average
                        (default: coherent scattering lengths)
        :type weights: :class:`~MMTK.ParticleProperties.ParticleScalar`
        :param random_vectors: the number of random direction vectors
                               used in the orientational average
        :type random_vectors: int
        :param first_mode: the first mode to be taken into account for
                           the fluctuation calculation. The default value
                           of 6 is right for molecules in vacuum.
        :type first_mode: int
        :returns: the Static Structure Factor as a
                  function of angular wavenumber
        :rtype: Scientific.Functions.Interpolation.InterpolatingFunction
        """
        if subset is None:
            subset = self.universe
        if weights is None:
            weights = self.universe.getParticleScalar('b_coherent')
        mask = subset.booleanMask()
        weights = N.repeat(weights.array, mask.array)
        weights = weights/N.sqrt(N.add.reduce(weights*weights))
        friction = N.repeat(self.friction.array, mask.array)
        r = N.repeat(self.universe.configuration().array, mask.array)

        first, last, step = (q_range+(None,))[:3]
        if step is None:
            step = (last-first)/50.
        q = N.arange(first, last, step)

        kT = Units.k_B*self.temperature
        natoms = subset.numberOfAtoms()
        sq = 0.
        random_vectors = Random.randomDirections(random_vectors)
        for v in random_vectors:
            sab = N.zeros((natoms, natoms), N.Float)
            for i in range(first_mode, self.nmodes):
                irt = self.rawMode(i).inv_relaxation_time
                d = N.repeat((self.rawMode(i)*v).array, mask.array) \
                       / N.sqrt(friction)
                sab = sab + (d[N.NewAxis,:]-d[:,N.NewAxis])**2/irt
            sab = sab[N.NewAxis,:,:]*q[:, N.NewAxis, N.NewAxis]**2
            phase = N.exp(-1.j*q[:, N.NewAxis]
                          * N.dot(r, v.array)[N.NewAxis, :]) \
                    * weights[N.NewAxis, :]
            temp = N.sum(phase[:, :, N.NewAxis]*N.exp(-0.5*kT*sab), 1)
            temp = N.sum(N.conjugate(phase)*temp, 1)
            sq = sq + temp.real
        return InterpolatingFunction((q,), sq/len(random_vectors))



[docs]    def coherentScatteringFunction(self, q, time_range = (0., None, None),
                                   subset=None, weights=None,
                                   random_vectors=15, first_mode = 6):
        """
        :param q: the angular wavenumber
        :type q: float
        :param time_range: the time values at which the mean-square
                           displacement is evaluated, specified as a
                           range tuple (first, last, step).
                           The defaults are first=0, last=
                           20 times the longest vibration perdiod,
                           and step defined such that 300 points are
                           used in total.
        :type time_range: tuple
        :param subset: the subset of the universe used in the calculation
                       (default: the whole universe)
        :type subset: :class:`~MMTK.Collections.GroupOfAtoms`
        :param weights: the weight to be given to each atom in the average
                        (default: coherent scattering lengths)
        :type weights: :class:`~MMTK.ParticleProperties.ParticleScalar`
        :param random_vectors: the number of random direction vectors
                               used in the orientational average
        :type random_vectors: int
        :param first_mode: the first mode to be taken into account for
                           the fluctuation calculation. The default value
                           of 6 is right for molecules in vacuum.
        :type first_mode: int
        :returns: the Coherent Scattering Function as a function of time
        :rtype: Scientific.Functions.Interpolation.InterpolatingFunction
        """
        if subset is None:
            subset = self.universe
        if weights is None:
            weights = self.universe.getParticleScalar('b_coherent')
        mask = subset.booleanMask()
        weights = N.repeat(weights.array, mask.array)
        weights = weights/N.sqrt(N.add.reduce(weights*weights))
        friction = N.repeat(self.friction.array, mask.array)
        r = N.repeat(self.universe.configuration().array, mask.array)

        first, last, step = (time_range + (None, None))[:3]
        if last is None:
            last = 3./self.rawMode(first_mode).inv_relaxation_time
        if step is None:
            step = (last-first)/300.
        time = N.arange(first, last, step)

        natoms = subset.numberOfAtoms()
        kT = Units.k_B*self.temperature
        fcoh = N.zeros((len(time),), N.Complex)
        random_vectors = Random.randomDirections(random_vectors)
        for v in random_vectors:
            phase = N.exp(-1.j*q*N.dot(r, v.array))
            for ai in range(natoms):
                fbt = N.zeros((natoms, len(time)), N.Float)
                for i in range(first_mode, self.nmodes):
                    irt = self.rawMode(i).inv_relaxation_time
                    d = q * N.repeat((self.rawMode(i)*v).array, mask.array) \
                        / N.sqrt(friction)
                    ft = N.exp(-irt*time)/irt
                    N.add(fbt,
                          d[ai] * d[:, N.NewAxis] * ft[N.NewAxis, :],
                          fbt)
                    N.add(fbt,
                          (-0.5/irt) * (d[ai]**2 + d[:, N.NewAxis]**2),
                          fbt)
                N.add(fcoh,
                      weights[ai]*phase[ai]
                      * N.dot(weights*N.conjugate(phase),
                              N.exp(kT*fbt)),
                      fcoh)
        return InterpolatingFunction((time,), fcoh.real/len(random_vectors))



[docs]    def incoherentScatteringFunction(self, q, time_range = (0., None, None),
                                     subset=None, random_vectors=15,
                                     first_mode = 6):
        """
        :param q: the angular wavenumber
        :type q: float
        :param time_range: the time values at which the mean-square
                           displacement is evaluated, specified as a
                           range tuple (first, last, step).
                           The defaults are first=0, last=
                           20 times the longest vibration perdiod,
                           and step defined such that 300 points are
                           used in total.
        :type time_range: tuple
        :param subset: the subset of the universe used in the calculation
                       (default: the whole universe)
        :type subset: :class:`~MMTK.Collections.GroupOfAtoms`
        :param random_vectors: the number of random direction vectors
                               used in the orientational average
        :type random_vectors: int
        :param first_mode: the first mode to be taken into account for
                           the fluctuation calculation. The default value
                           of 6 is right for molecules in vacuum.
        :type first_mode: int
        :returns: the Incoherent Scattering Function as a function of time
        :rtype: Scientific.Functions.Interpolation.InterpolatingFunction
        """
        if subset is None:
            subset = self.universe
        mask = subset.booleanMask()
        weights_inc = self.universe.getParticleScalar('b_incoherent')
        weights_inc = N.repeat(weights_inc.array**2, mask.array)
        weights_inc = weights_inc/N.add.reduce(weights_inc)
        friction = N.repeat(self.friction.array, mask.array)
        mass = N.repeat(self.universe.masses().array, mask.array)
        r = N.repeat(self.universe.configuration().array, mask.array)

        first, last, step = (time_range + (None, None))[:3]
        if last is None:
            last = 3./self.weighedMode(first_mode).inv_relaxation_time
        if step is None:
            step = (last-first)/300.
        time = N.arange(first, last, step)

        natoms = subset.numberOfAtoms()
        kT = Units.k_B*self.temperature
        finc = N.zeros((len(time),), N.Float)
        eisf = 0.
        random_vectors = Random.randomDirections(random_vectors)
        for v in random_vectors:
            phase = N.exp(-1.j*q*N.dot(r, v.array))
            faat = N.zeros((natoms, len(time)), N.Float)
            eisf_sum = N.zeros((natoms,), N.Float)
            for i in range(first_mode, self.nmodes):
                irt = self.rawMode(i).inv_relaxation_time
                d = q * N.repeat((self.rawMode(i)*v).array, mask.array) \
                    / N.sqrt(friction)
                ft = (N.exp(-irt*time)-1.)/irt
                N.add(faat,
                      d[:, N.NewAxis]**2 * ft[N.NewAxis, :],
                      faat)
                N.add(eisf_sum, -d**2/irt, eisf_sum)
            N.add(finc,
                  N.sum(weights_inc[:, N.NewAxis]
                        * N.exp(kT*faat), 0),
                  finc)
            eisf = eisf + N.sum(weights_inc*N.exp(kT*eisf_sum))
        return InterpolatingFunction((time,), finc/len(random_vectors))




[docs]    def EISF(self, q_range = (0., 15.), subset=None, weights = None,
             random_vectors = 15, first_mode = 6):
        """
        :param q_range: the range of angular wavenumber values
        :type q_range: tuple
        :param subset: the subset of the universe used in the calculation
                       (default: the whole universe)
        :type subset: :class:`~MMTK.Collections.GroupOfAtoms`
        :param weights: the weight to be given to each atom in the average
                        (default: incoherent scattering lengths)
        :type weights: :class:`~MMTK.ParticleProperties.ParticleScalar`
        :param random_vectors: the number of random direction vectors
                               used in the orientational average
        :type random_vectors: int
        :param first_mode: the first mode to be taken into account for
                           the fluctuation calculation. The default value
                           of 6 is right for molecules in vacuum.
        :type first_mode: int
        :returns: the Elastic Incoherent Structure Factor (EISF) as a
                  function of angular wavenumber
        :rtype: Scientific.Functions.Interpolation.InterpolatingFunction
        """
        if subset is None:
            subset = self.universe
        if weights is None:
            weights = self.universe.getParticleScalar('b_incoherent')
            weights = weights*weights
        weights = weights*subset.booleanMask()
        total = weights.sumOverParticles()
        weights = weights/total

        first, last, step = (q_range+(None,))[:3]
        if step is None:
            step = (last-first)/50.
        q = N.arange(first, last, step)

        f = ParticleProperties.ParticleTensor(self.universe)
        for i in range(first_mode, self.nmodes):
            mode = self.rawMode(i)
            f = f + (1./mode.inv_relaxation_time)*mode.dyadicProduct(mode)
        f = Units.k_B*self.temperature*f/self.friction

        eisf = N.zeros(q.shape, N.Float)
        random_vectors = Random.randomDirections(random_vectors)
        for v in random_vectors:
            for a in subset.atomList():
                exp = N.exp(-v*(f[a]*v))
                N.add(eisf, weights[a]*exp**(q*q), eisf)
        return InterpolatingFunction((q,), eisf/len(random_vectors))

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Source code for MMTK.NormalModes.EnergeticModes

# Energetic normal mode calculations.
#
# Written by Konrad Hinsen
#

"""
Energetic normal modes
"""

__docformat__ = 'restructuredtext'

from MMTK import Features, Units, ParticleProperties
from MMTK.NormalModes import Core
from Scientific import N

#
# Class for a single mode
#

[docs]class EnergeticMode(Core.Mode):

    """
    Single energetic normal mode

    Mode objects are created by indexing a :class:`MMTK.NormalModes.EnergeticModes.EnergeticModes` object.
    They contain the atomic displacements corresponding to a
    single mode. In addition, the force constant corresponding to the mode
    is stored in the attribute "force_constant".
    """

    def __init__(self, universe, n, force_constant, mode):
        self.force_constant = force_constant
        Core.Mode.__init__(self, universe, n, mode)

    def __str__(self):
        return 'Mode ' + `self.number` + ' with force constant ' + \
               `self.force_constant`

    __repr__ = __str__

#
# Class for a full set of normal modes
#


[docs]class EnergeticModes(Core.NormalModes):

    """
    Energetic modes describe the principal axes of an harmonic approximation
    to the potential energy surface of a system. They are obtained by
    diagonalizing the force constant matrix without prior mass-weighting.

    In order to obtain physically reasonable normal modes, the configuration
    of the universe must correspond to a local minimum of the potential
    energy.

    Individual modes (see class :class:`~MMTK.NormalModes.EnergeticModes.EnergeticMode`)
    can be extracted by indexing with an integer. Looping over the modes
    is possible as well.
    """

    features = []

    def __init__(self, universe=None, temperature = 300*Units.K,
                 subspace = None, delta = None, sparse = False):
        """
        :param universe: the system for which the normal modes are calculated;
                         it must have a force field which provides the second
                         derivatives of the potential energy
        :type universe: :class:`~MMTK.Universe.Universe`
        :param temperature: the temperature for which the amplitudes of the
                            atomic displacement vectors are calculated. A
                            value of None can be specified to have no scaling
                            at all. In that case the mass-weighted norm
                            of each normal mode is one.
        :type temperature: float
        :param subspace: the basis for the subspace in which the normal modes
                         are calculated (or, more precisely, a set of vectors
                         spanning the subspace; it does not have to be
                         orthogonal). This can either be a sequence of
                         :class:`~MMTK.ParticleProperties.ParticleVector` objects
                         or a tuple of two such sequences. In the second case,
                         the subspace is defined by the space spanned by the
                         second set of vectors projected on the complement of
                         the space spanned by the first set of vectors.
                         The first set thus defines directions that are
                         excluded from the subspace.
                         The default value of None indicates a standard
                         normal mode calculation in the 3N-dimensional
                         configuration space.
        :param delta: the rms step length for numerical differentiation.
                      The default value of None indicates analytical
                      differentiation.
                      Numerical differentiation is available only when a
                      subspace basis is used as well. Instead of calculating
                      the full force constant matrix and then multiplying
                      with the subspace basis, the subspace force constant
                      matrix is obtained by numerical differentiation of the
                      energy gradients along the basis vectors of the subspace.
                      If the basis is much smaller than the full configuration
                      space, this approach needs much less memory.
        :type delta: float
        :param sparse: a flag that indicates if a sparse representation of
                       the force constant matrix is to be used. This is of
                       interest when there are no long-range interactions and
                       a subspace of smaller size then 3N is specified. In that
                       case, the calculation will use much less memory with a
                       sparse representation.
        :type sparse: bool
        """

        if universe == None:
            return
        Features.checkFeatures(self, universe)
        Core.NormalModes.__init__(self, universe, subspace, delta, sparse,
                                  ['array', 'force_constants'])
        self.temperature = temperature

        self.weights = N.ones((1, 1), N.Float)

        self._forceConstantMatrix()
        ev = self._diagonalize()

        self.force_constants = ev
        self.sort_index = N.argsort(self.force_constants)
        self.array.shape = (self.nmodes, self.natoms, 3)

        self.cleanup()


    def __getitem__(self, item):
        index = self.sort_index[item]
        f = self.force_constants[index]
        #!!
        if self.temperature is None or item < 6:
            amplitude = 1.
        else:
            amplitude = N.sqrt(2.*self.temperature*Units.k_B
                               / self.force_constants[index])
        return EnergeticMode(self.universe, item,
                             self.force_constants[index],
                             amplitude*self.array[index])


[docs]    def rawMode(self, item):
        """
        :param item: the index of a normal mode
        :type item: int
        :returns: the unscaled mode vector
        :rtype: :class:`~MMTK.NormalModes.EnergeticModes.EnergeticMode`
        """
        index = self.sort_index[item]
        f = self.force_constants[index]
        return EnergeticMode(self.universe, item,
                             self.force_constants[index],
                             self.array[index])


    def fluctuations(self, first_mode=6):
        f = ParticleProperties.ParticleScalar(self.universe)
        for i in range(first_mode, self.nmodes):
            mode = self.rawMode(i)
            f += (mode*mode)/mode.force_constant
        if self.temperature is not None:
            f.array *= Units.k_B*self.temperature
        return f

    def anisotropicFluctuations(self, first_mode=6):
        f = ParticleProperties.ParticleTensor(self.universe)
        for i in range(first_mode, self.nmodes):
            mode = self.rawMode(i)
            array = mode.array
            f.array += (array[:, :, N.NewAxis]*array[:, N.NewAxis, :]) \
                       / mode.force_constant
        if self.temperature is not None:
            f.array *= Units.k_B*self.temperature
        return f

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Source code for MMTK.NormalModes.VibrationalModes

# Vibrational normal mode calculations.
#
# Written by Konrad Hinsen
#

"""
Vibrational normal modes
"""

__docformat__ = 'restructuredtext'

from MMTK import Features, Units, ParticleProperties
from Scientific import N

from MMTK.NormalModes import Core

#
# Class for a single mode
#

[docs]class VibrationalMode(Core.Mode):

    """
    Single vibrational normal mode

    Mode objects are created by indexing a 
    :class:`~MMTK.NormalModes.VibrationalModes.VibrationalModes` object.
    They contain the atomic displacements corresponding to a
    single mode. In addition, the frequency corresponding to the mode
    is stored in the attribute "frequency".

    Note: the normal mode vectors are *not* mass weighted, and therefore
    not orthogonal to each other.
    """

    def __init__(self, universe, n, frequency, mode):
        self.frequency = frequency
        Core.Mode.__init__(self, universe, n, mode)

    def __str__(self):
        return 'Mode ' + `self.number` + ' with frequency ' + `self.frequency`

    __repr__ = __str__

    def effectiveMassAndForceConstant(self):
        m = self.massWeightedDotProduct(self)/self.dotProduct(self)
        k = m*(2.*N.pi*self.frequency)**2
        return m, k

#
# Class for a full set of normal modes
#


[docs]class VibrationalModes(Core.NormalModes):

    """
    Vibrational modes describe the independent vibrational motions
    of a harmonic system. They are obtained by diagonalizing the mass-weighted
    force constant matrix.

    In order to obtain physically reasonable normal modes, the configuration
    of the universe must correspond to a local minimum of the potential
    energy.

    Individual modes (see :class:`~MMTK.NormalModes.VibrationalModes.VibrationalMode`)
    can be extracted by indexing with an integer. Looping over the modes
    is possible as well.

    """

    features = []

    def __init__(self, universe=None, temperature = 300.*Units.K,
                 subspace = None, delta = None, sparse = False):
        """
        :param universe: the system for which the normal modes are calculated;
                         it must have a force field which provides the second
                         derivatives of the potential energy
        :type universe: :class:`~MMTK.Universe.Universe`
        :param temperature: the temperature for which the amplitudes of the
                            atomic displacement vectors are calculated. A
                            value of None can be specified to have no scaling
                            at all. In that case the mass-weighted norm
                            of each normal mode is one.
        :type temperature: float
        :param subspace: the basis for the subspace in which the normal modes
                         are calculated (or, more precisely, a set of vectors
                         spanning the subspace; it does not have to be
                         orthogonal). This can either be a sequence of
                         :class:`~MMTK.ParticleProperties.ParticleVector` objects
                         or a tuple of two such sequences. In the second case,
                         the subspace is defined by the space spanned by the
                         second set of vectors projected on the complement of
                         the space spanned by the first set of vectors.
                         The first set thus defines directions that are
                         excluded from the subspace.
                         The default value of None indicates a standard
                         normal mode calculation in the 3N-dimensional
                         configuration space.
        :param delta: the rms step length for numerical differentiation.
                      The default value of None indicates analytical
                      differentiation.
                      Numerical differentiation is available only when a
                      subspace basis is used as well. Instead of calculating
                      the full force constant matrix and then multiplying
                      with the subspace basis, the subspace force constant
                      matrix is obtained by numerical differentiation of the
                      energy gradients along the basis vectors of the subspace.
                      If the basis is much smaller than the full configuration
                      space, this approach needs much less memory.
        :type delta: float
        :param sparse: a flag that indicates if a sparse representation of
                       the force constant matrix is to be used. This is of
                       interest when there are no long-range interactions and
                       a subspace of smaller size then 3N is specified. In that
                       case, the calculation will use much less memory with a
                       sparse representation.
        :type sparse: bool
        """

        if universe == None:
            return
        Features.checkFeatures(self, universe)
        Core.NormalModes.__init__(self, universe, subspace, delta, sparse,
                                  ['array', 'imaginary',
                                   'frequencies', 'omega_sq'])
        self.temperature = temperature

        self.weights = N.sqrt(self.universe.masses().array)
        self.weights = self.weights[:, N.NewAxis]

        self._forceConstantMatrix()
        ev = self._diagonalize()

        self.imaginary = N.less(ev, 0.)
        self.omega_sq = ev
        self.frequencies = N.sqrt(N.fabs(ev)) / (2.*N.pi)
        self.sort_index = N.argsort(self.frequencies)
        self.array.shape = (self.nmodes, self.natoms, 3)

        self.cleanup()

    def __setstate__(self, state):
        # This fixes unpickled objects from pre-2.5 versions.
        # Their modes were stored in un-weighted coordinates.
        if not state.has_key('weights'):
            weights = N.sqrt(state['universe'].masses().array)[:, N.NewAxis]
            state['weights'] = weights
            state['array'] *= weights[N.NewAxis, :, :]
            if state['temperature'] is not None:
                factor = N.sqrt(2.*state['temperature']*Units.k_B/Units.amu) / \
                         (2.*N.pi)
                freq = state['frequencies']
                for i in range(state['nmodes']):
                    index = state['sort_index'][i]
                    if index >= 6:
                        state['array'][index] *= freq[index]/factor
        self.__dict__.update(state)

    def __getitem__(self, item):
        index = self.sort_index[item]
        f = self.frequencies[index]
        if self.imaginary[index]:
            f = f*1.j
        # !!
        if self.temperature is None or item < 6:
            amplitude = 1.
        else:
            amplitude = N.sqrt(2.*self.temperature*Units.k_B/Units.amu) / \
                        (2.*N.pi*f)
        return VibrationalMode(self.universe, item, f,
                               amplitude*self.array[index]/self.weights)


[docs]    def rawMode(self, item):
        """
        :param item: the index of a normal mode
        :type item: int
        :returns: the unscaled mode vector
        :rtype: :class:`~MMTK.NormalModes.VibrationalModes.VibrationalMode`
        """
        index = self.sort_index[item]
        f = self.frequencies[index]
        if self.imaginary[index]:
            f = f*1.j
        return VibrationalMode(self.universe, item, f, self.array[index])


    def fluctuations(self, first_mode=6):
        f = ParticleProperties.ParticleScalar(self.universe)
        for i in range(first_mode, self.nmodes):
            mode = self.rawMode(i)
            f += (mode*mode)/mode.frequency**2
        f.array /= self.weights[:, 0]**2
        s = 1./(2.*N.pi)**2
        if self.temperature is not None:
            s *= Units.k_B*self.temperature
        f.array *= s
        return f

    def anisotropicFluctuations(self, first_mode=6):
        f = ParticleProperties.ParticleTensor(self.universe)
        for i in range(first_mode, self.nmodes):
            mode = self.rawMode(i)
            array = mode.array
            f.array += (array[:, :, N.NewAxis]*array[:, N.NewAxis, :]) \
                       / mode.frequency**2
        s = (2.*N.pi)**2
        if self.temperature is not None:
            s /= Units.k_B*self.temperature
        f.array /= s*self.universe.masses().array[:, N.NewAxis, N.NewAxis]
        return f


[docs]    def meanSquareDisplacement(self, subset=None, weights=None,
                               time_range = (0., None, None), tau=None,
                               first_mode = 6):
        """
        :param subset: the subset of the universe used in the calculation
                       (default: the whole universe)
        :type subset: :class:`~MMTK.Collections.GroupOfAtoms`
        :param weights: the weight to be given to each atom in the average
                        (default: atomic masses)
        :type weights: :class:`~MMTK.ParticleProperties.ParticleScalar`
        :param time_range: the time values at which the mean-square
                           displacement is evaluated, specified as a
                           range tuple (first, last, step).
                           The defaults are first=0, last=
                           20 times the longest vibration perdiod,
                           and step defined such that 300 points are
                           used in total.
        :type time_range: tuple
        :param tau: the relaxation time of an exponential damping factor
                    (default: no damping)
        :type tau: float
        :param first_mode: the first mode to be taken into account for
                           the fluctuation calculation. The default value
                           of 6 is right for molecules in vacuum.
        :type first_mode: int
        :returns: the averaged mean-square displacement of the
                  atoms in subset as a function of time
        :rtype: Scientific.Functions.Interpolation.InterpolatingFunction
        """
        if self.temperature is None:
            raise ValueError("no temperature available")
        if subset is None:
            subset = self.universe
        if weights is None:
            weights = self.universe.masses()
        weights = weights*subset.booleanMask()
        total = weights.sumOverParticles()
        weights = weights/total
        first, last, step = (time_range + (None, None))[:3]
        if last is None:
            last = 20./self[first_mode].frequency
        if step is None:
            step = (last-first)/300.
        time = N.arange(first, last, step)
        if tau is None: damping = 1.
        else: damping = N.exp(-(time/tau)**2)
        msd = N.zeros(time.shape, N.Float)
        for i in range(first_mode, self.nmodes):
            mode = self[i]
            omega = 2.*N.pi*mode.frequency
            d = (weights*(mode*mode)).sumOverParticles()
            N.add(msd, d*(1.-damping*N.cos(omega*time)), msd)
        return InterpolatingFunction((time,), msd)



[docs]    def EISF(self, q_range = (0., 15.), subset=None, weights = None,
             random_vectors = 15, first_mode = 6):
        """
        :param q_range: the range of angular wavenumber values
        :type q_range: tuple
        :param subset: the subset of the universe used in the calculation
                       (default: the whole universe)
        :type subset: :class:`~MMTK.Collections.GroupOfAtoms`
        :param weights: the weight to be given to each atom in the average
                        (default: incoherent scattering lengths)
        :type weights: :class:`~MMTK.ParticleProperties.ParticleScalar`
        :param random_vectors: the number of random direction vectors
                               used in the orientational average
        :type random_vectors: int
        :param first_mode: the first mode to be taken into account for
                           the fluctuation calculation. The default value
                           of 6 is right for molecules in vacuum.
        :type first_mode: int
        :returns: the Elastic Incoherent Structure Factor (EISF) as a
                  function of angular wavenumber
        :rtype: Scientific.Functions.Interpolation.InterpolatingFunction
        """
        if self.temperature is None:
            raise ValueError("no temperature available")
        if subset is None:
            subset = self.universe
        if weights is None:
            weights = self.universe.getParticleScalar('b_incoherent')
            weights = weights*weights
        weights = weights*subset.booleanMask()
        total = weights.sumOverParticles()
        weights = weights/total
    
        first, last, step = (q_range+(None,))[:3]
        if step is None:
            step = (last-first)/50.
        q = N.arange(first, last, step)
    
        f = MMTK.ParticleTensor(self.universe)
        for i in range(first_mode, self.nmodes):
            mode = self[i]
            f = f + mode.dyadicProduct(mode)
    
        eisf = N.zeros(q.shape, N.Float)
        for i in range(random_vectors):
            v = MMTK.Random.randomDirection()
            for a in subset.atomList():
                exp = N.exp(-v*(f[a]*v))
                N.add(eisf, weights[a]*exp**(q*q), eisf)
        return InterpolatingFunction((q,), eisf/random_vectors)



[docs]    def incoherentScatteringFunction(self, q, time_range = (0., None, None),
                                     subset=None, weights=None, tau=None,
                                     random_vectors=15, first_mode = 6):
        """
        :param q: the angular wavenumber
        :type q: float
        :param time_range: the time values at which the mean-square
                           displacement is evaluated, specified as a
                           range tuple (first, last, step).
                           The defaults are first=0, last=
                           20 times the longest vibration perdiod,
                           and step defined such that 300 points are
                           used in total.
        :type time_range: tuple
        :param subset: the subset of the universe used in the calculation
                       (default: the whole universe)
        :type subset: :class:`~MMTK.Collections.GroupOfAtoms`
        :param weights: the weight to be given to each atom in the average
                        (default: incoherent scattering lengths)
        :type weights: :class:`~MMTK.ParticleProperties.ParticleScalar`
        :param tau: the relaxation time of an exponential damping factor
                    (default: no damping)
        :type tau: float
        :param random_vectors: the number of random direction vectors
                               used in the orientational average
        :type random_vectors: int
        :param first_mode: the first mode to be taken into account for
                           the fluctuation calculation. The default value
                           of 6 is right for molecules in vacuum.
        :type first_mode: int
        :returns: the Incoherent Scattering Function as a function of time
        :rtype: Scientific.Functions.Interpolation.InterpolatingFunction
        """
        if self.temperature is None:
            raise ValueError("no temperature available")
        if subset is None:
            subset = self.universe
        if weights is None:
            weights = self.universe.getParticleScalar('b_incoherent')
            weights = weights*weights
        weights = weights*subset.booleanMask()
        total = weights.sumOverParticles()
        weights = weights/total
    
        first, last, step = (time_range + (None, None))[:3]
        if last is None:
            last = 20./self[first_mode].frequency
        if step is None:
            step = (last-first)/400.
        time = N.arange(first, last, step)
    
        if tau is None:
            damping = 1.
        else:
            damping = N.exp(-(time/tau)**2)
        finc = N.zeros(time.shape, N.Float)
        random_vectors = MMTK.Random.randomDirections(random_vectors)
        for v in random_vectors:
            for a in subset.atomList():
                msd = 0.
                for i in range(first_mode, self.nmodes):
                    mode = self[i]
                    d = (v*mode[a])**2
                    omega = 2.*N.pi*mode.frequency
                    msd = msd+d*(1.-damping*N.cos(omega*time))
                N.add(finc, weights[a]*N.exp(-q*q*msd), finc)
        return InterpolatingFunction((time,), finc/len(random_vectors))

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Source code for MMTK.NucleicAcids

# This module implements classes for nucleotide chains.
#
# Written by Konrad Hinsen
#

"""
Nucleic acid chains
"""

__docformat__ = 'restructuredtext'

from MMTK import Biopolymers, Bonds, ChemicalObjects, Collections, \
                 ConfigIO, Database, Universe, Utility
from Scientific.Geometry import Vector

from MMTK.Biopolymers import defineNucleicAcidResidue

#
# Nucleotides are special groups
#

[docs]class Nucleotide(Biopolymers.Residue):

    """
    Nucleic acid residue

    Nucleotides are a special kind of group. Like any other
    group, they are defined in the chemical database. Each residue
    has two or three subgroups ('sugar' and 'base', plus 'phosphate'
    except for 5'-terminal residues) and is usually
    connected to other residues to form a nucleotide chain. The database
    contains three variants of each residue (5'-terminal, 3'-terminal,
    non-terminal).
    """

    def __init__(self, name = None, model = 'all'):
        """
        :param name: the name of the nucleotide in the chemical database. This
                     is the full name of the residue plus the suffix
                     "_5ter" or "_3ter" for the terminal variants.
        :type name: str
        :param model: one of "all" (all-atom), "none" (no hydrogens),
                      "polar" (united-atom with only polar hydrogens),
                      "polar_charmm" (like "polar", but defining
                      polar hydrogens like in the CHARMM force field).
                      Currently the database has definitions only for "all".
        :type model: str
        """
	if name is not None:
	    blueprint = _residueBlueprint(name, model)
	    ChemicalObjects.Group.__init__(self, blueprint)
	    self.model = model
	    self._init()


[docs]    def backbone(self):
        """
        :returns: the sugar and phosphate groups
        :rtype: :class:`~MMTK.ChemicalObjects.Group`
        """
        bb = self.sugar
        if hasattr(self, 'phosphate'):
            bb = Collections.Collection([bb, self.phosphate])
        return bb



[docs]    def bases(self):
        """
        :returns: the base group
        :rtype: :class:`~MMTK.ChemicalObjects.Group`
        """
        return self.base



def _residueBlueprint(name, model):
    try:
	blueprint = _residue_blueprints[(name, model)]
    except KeyError:
	if model == 'polar':
	    name = name + '_uni'
	elif model == 'polar_charmm':
	    name = name + '_uni2'
	elif model == 'none':
	    name = name + '_noh'
	blueprint = Database.BlueprintGroup(name)
	_residue_blueprints[(name, model)] = blueprint
    return blueprint

_residue_blueprints = {}

#
# Nucleotide chains are molecules with added features.
#

[docs]class NucleotideChain(Biopolymers.ResidueChain):

    """
    Nucleotide chain

    Nucleotide chains consist of nucleotides that are linked together.
    They are a special kind of molecule, i.e. all molecule operations
    are available.

    Nucleotide chains act as sequences of residues. If n is a NucleotideChain
    object, then

     * len(n) yields the number of nucleotides

     * n[i] yields nucleotide number i

     * n[i:j] yields the subchain from nucleotide number i up to but
                 excluding nucleotide number j
    """

    def __init__(self, sequence, **properties):
        """
        :param sequence: the nucleotide sequence. This can be a string
                         containing the one-letter codes, or a list
                         of two-letter codes (a "d" or "r" for the type of
                         sugar, and the one-letter base code), or a
                         :class:`~MMTK.PDB.PDBNucleotideChain` object.
                         If a PDBNucleotideChain object is supplied, the
                         atomic positions it contains are assigned to the
                         atoms of the newly generated nucleotide chain,
                         otherwise the positions of all atoms are undefined.
        :keyword model: one of "all" (all-atom), "no_hydrogens" or "none"
                        (no hydrogens), "polar_hydrogens" or "polar"
                        (united-atom with only polar hydrogens),
                        "polar_charmm" (like "polar", but defining
                        polar hydrogens like in the CHARMM force field),
                        "polar_opls" (like "polar", but defining
                        polar hydrogens like in the latest OPLS force field).
                        Default is "all". Currently the database contains
                        definitions only for "all".
        :type model: str
        :keyword terminus_5: if True, the first residue is constructed
                             using the 5'-terminal variant, if False the
                             non-terminal version is used. Default is True.
        :type terminus_5: bool
        :keyword terminus_3: if True, the last residue is constructed
                             using the 3'-terminal variant, if False the
                             non-terminal version is used. Default is True.
        :type terminus_3: bool
        :keyword circular: if True, a bond is constructed
                           between the first and the last residue.
                           Default is False.
        :type circular: bool
        :keyword name: a name for the chain (a string)
        :type name: str
        """
	if sequence is not None:
	    hydrogens = self.binaryProperty(properties, 'hydrogens', 'all')
	    if hydrogens == 1:
		hydrogens = 'all'
	    elif hydrogens == 0:
		hydrogens = 'none'
	    term5 = self.binaryProperty(properties, 'terminus_5', True)
	    term3 = self.binaryProperty(properties, 'terminus_3', True)
	    circular = self.binaryProperty(properties, 'circular', False)
	    try:
		model = properties['model'].lower()
	    except KeyError:
		model = hydrogens
	    self.version_spec = {'hydrogens': hydrogens,
				 'terminus_5': term5,
				 'terminus_3': term3,
				 'model': model,
                                 'circular': circular}
            if isinstance(sequence[0], basestring):
		conf = None
	    else:
		conf = sequence
		sequence = [r.name for r in sequence]
	    sequence = [Biopolymers._fullName(r) for r in sequence]
            if term5:
                if sequence[0].find('5ter') == -1:
                    sequence[0] += '_5ter'
            if term3:
                if sequence[-1].find('3ter') == -1:
                    sequence[-1] += '_3ter'

            self.groups = []
            n = 0
            for residue in sequence:
                n += 1
                r = Nucleotide(residue, model)
                r.name = r.symbol + '_' + `n`
                r.sequence_number = n
                r.parent = self
                self.groups.append(r)

            self._setupChain(circular, properties, conf)

    is_nucleotide_chain = True

    def __getslice__(self, first, last):
	return NucleotideSubChain(self, self.groups[first:last])


[docs]    def backbone(self):
        """
        :returns: the sugar and phosphate groups of all nucleotides
        :rtype: :class:`~MMTK.Collections.Collection`
        """
        bb = Collections.Collection([])
        for residue in self.groups:
            try:
                bb.addObject(residue.phosphate)
            except AttributeError:
                pass
            bb.addObject(residue.sugar)
        return bb



[docs]    def bases(self):
        """
        :returns: the base groups of all nucleotides
        :rtype: :class:`~MMTK.Collections.Collection`
        """
	return Collections.Collection([r.base for r in self.groups])


    def _descriptionSpec(self):
        kwargs = ','.join([name + '=' + `self.version_spec[name]`
                           for name in sorted(self.version_spec.keys())])
	return "N", kwargs

    def _typeName(self):
        return ''.join([s.ljust(3) for s in self.sequence()])

    def _graphics(self, conf, distance_fn, model, module, options):
	if model != 'backbone':
	    return ChemicalObjects.Molecule._graphics(self, conf,
						      distance_fn, model,
						      module, options)
	color = options.get('color', 'black')
	material = module.EmissiveMaterial(color)
	objects = []
	for i in range(1, len(self.groups)-1):
	    a1 = self.groups[i].phosphate.P
	    a2 = self.groups[i+1].phosphate.P
	    p1 = a1.position(conf)
	    p2 = a2.position(conf)
	    if p1 is not None and p2 is not None:
		bond_vector = 0.5*distance_fn(a1, a2, conf)
		cut = bond_vector != 0.5*(p2-p1)
		if not cut:
		    objects.append(module.Line(p1, p2, material = material))
		else:
		    objects.append(module.Line(p1, p1+bond_vector,
					       material = material))
		    objects.append(module.Line(p2, p2-bond_vector,
					       material = material))
	return objects

#
# Subchains are created by slicing chains or extracting a chain from
# a group of connected chains.
#


[docs]class NucleotideSubChain(NucleotideChain):

    """
    A contiguous part of a nucleotide chain

    NucleotideSubChain objects are the result of slicing operations on
    NucleotideChain objects. They cannot be created directly.
    NucleotideSubChain objects permit all operations of NucleotideChain
    objects, but cannot be added to a universe.
    """

    def __init__(self, chain, groups, name = ''):
	self.groups = groups
	self.atoms = []
	self.bonds = []
	for g in self.groups:
	    self.atoms = self.atoms + g.atoms
	    self.bonds = self.bonds + g.bonds
        for i in range(len(self.groups)-1):
            self.bonds.append(Bonds.Bond((self.groups[i].sugar.O_3,
                                          self.groups[i+1].phosphate.P)))
	self.bonds = Bonds.BondList(self.bonds)
	self.name = name
	self.parent = chain.parent
	self.type = None
	self.configurations = {}
	self.part_of = chain

    is_incomplete = True

    def __repr__(self):
	if self.name == '':
	    return 'SubChain of ' + repr(self.part_of)
	else:
	    return ChemicalObjects.Molecule.__repr__(self)
    __str__ = __repr__


#
# Type check functions
#


[docs]def isNucleotideChain(x):
    "Returns True if x is a NucleotideChain."
    return hasattr(x, 'is_nucleotide_chain')

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Source code for MMTK.ParticleProperties

# This module implements classes that represent the atomic properties in a
# simulation, i.e. configurations, force vectors, etc.
#
# Written by Konrad Hinsen
#

"""
Quantities defined for each particle in a universe
"""

__docformat__ = 'restructuredtext'

from MMTK import Utility
from Scientific.Geometry import Vector, isVector, Tensor, isTensor
from Scientific.indexing import index_expression
from Scientific import N
import copy

#
# Base class for all properties defined for a universe.
#

[docs]class ParticleProperty(object):

    """
    Property defined for each particle

    This is an abstract base class; for creating instances, use one of
    its subclasses.

    ParticleProperty objects store properties that are defined per
    particle, such as mass, position, velocity, etc. The value
    corresponding to a particular atom can be retrieved or changed by
    indexing with the atom object.
    """

    def __init__(self, universe, data_rank, value_rank):
        universe.configuration()
        self.universe = universe
        self.version =  universe._version
        self.n = universe.numberOfPoints()
        self.data_rank = data_rank
        self.value_rank = value_rank

    __safe_for_unpickling__ = True
    __had_initargs__ = True

    def __len__(self):
        return self.n

    def _checkCompatibility(self, other, allow_scalar=False):
        if isParticleProperty(other):
            if other.data_rank != self.data_rank:
                raise TypeError('Incompatible types')
            if self.universe != other.universe:
                raise ValueError('Variables are for different universes')
            if self.version != other.version:
                raise ValueError("Universe version numbers do not agree")
            if self.value_rank == other.value_rank:
                return self.return_class, other.array
            elif allow_scalar and (self.value_rank==0 or other.value_rank==0):
                if self.value_rank == 0:
                    return other.return_class, other.array
                else:
                    return self.return_class, other.array
            else:
                raise ValueError("Ranks do not match")
        elif isVector(other) or isTensor(other):
            if len(other.array.shape) > self.value_rank:
                raise TypeError('Incompatible types')
            return self.return_class, other.array
        else:
            return self.return_class, other


[docs]    def zero(self):
        """
        :returns: an object of the element type (scalar, vector, etc.)
                  with the value 0.
        :rtype: element type
        """
        pass



[docs]    def sumOverParticles(self):
        """
        :returns: the sum of the values for all particles.
        :rtype: element type
        """
        pass


    def _arithmetic(self, other, op, allow_scalar=False):
        a1 = self.array
        return_class, a2 = self._checkCompatibility(other, allow_scalar)
        if type(a2) != N.ArrayType:
            a2 = N.array([a2])
        if len(a1.shape) != len(a2.shape):
            if len(a1.shape) == 1:
                a1 = a1[index_expression[...] +
                        (len(a2.shape)-1)*index_expression[N.NewAxis]]
            else:
                a2 = a2[index_expression[...] +
                        (len(a1.shape)-1)*index_expression[N.NewAxis]]
        return return_class(self.universe, op(a1, a2))

    def __add__(self, other):
        return self._arithmetic(other, N.add)

    __radd__ = __add__

    def __sub__(self, other):
        return self._arithmetic(other, N.subtract)

    def __rsub__(self, other):
        return self._arithmetic(other, lambda a, b: N.subtract(b, a))

    def __mul__(self, other):
        return self._arithmetic(other, N.multiply, True)

    __rmul__ = __mul__

    def __div__(self, other):
        return self._arithmetic(other, N.divide, True)

    def __rdiv__(self, other):
        return self._arithmetic(other, lambda a, b: N.divide(b, a), True)

    def __neg__(self):
        return self.return_class(self.universe, -self.array)

    def __copy__(self, memo = None):
        return self.__class__(self.universe, copy.copy(self.array))
    __deepcopy__ = __copy__


[docs]    def assign(self, other):
        """
        Copy all values from another compatible ParticleProperty object.

        :param other: the data source
        """
        self._checkCompatibility(other)
        self.array[:] = other.array[:]



[docs]    def scaleBy(self, factor):
        """
        Multiply all values by a factor

        :param factor: the scale factor
        :type factor: float
        """
        self.array[:] = self.array[:]*factor



[docs]    def selectAtoms(self, condition):
        """
        Return a collection containing all atoms a for which 
        condition(property[a]) is True.

        :param condition: a test function
        :type condition: callable
        """
        from MMTK.Collections import Collection
        return Collection([a for a in self.universe.atomList()
                           if condition(self[a])])


ParticleProperty.return_class = ParticleProperty

#
# One scalar per particle.
#

[docs]class ParticleScalar(ParticleProperty):

    """
    Scalar property defined for each particle

    ParticleScalar objects can be added to each other and
    multiplied with scalars.
    """

    def __init__(self, universe, data_array=None):
        """
        :param universe: the universe for which the values are defined
        :type universe: :class:`~MMTK.Universe.Universe`
        :param data_array: the data array containing the values for each
                           particle. If None, a new array containing
                           zeros is created and used. Otherwise, the
                           array myst be of shape (N,), where N is the
                           number of particles in the universe.
        :type data_array: Scientific.N.array_type
        """
        ParticleProperty.__init__(self, universe, 1, 0)
        if data_array is None:
            self.array = N.zeros((self.n,), N.Float)
        else:
            self.array = data_array
            if data_array.shape[0] != self.n:
                raise ValueError('Data incompatible with universe')

    def __getitem__(self, item):
        if not isinstance(item, int):
            item = item.index
        return self.array[item]

    def __setitem__(self, item, value):
        if not isinstance(item, int):
            item = item.index
        self.array[item] = value

    def zero(self):
        return 0.


[docs]    def maximum(self):
        """
        :returns: the highest value in the data array particle
        :rtype: float
        """
        return N.maximum.reduce(self.array)



[docs]    def minimum(self):
        """
        :returns: the smallest value in the data array particle
        :rtype: float
        """
        return N.minimum.reduce(self.array)


    def sumOverParticles(self):
        return N.add.reduce(self.array)


[docs]    def applyFunction(self, function):
        """
        :param function: a function that is applied to each data value
        :returns: a new ParticleScalar object containing the function results
        """
        return ParticleScalar(self.universe, function(self.array))



ParticleScalar.return_class = ParticleScalar

#
# One vector per particle.
#

[docs]class ParticleVector(ParticleProperty):

    """
    Vector property defined for each particle

    ParticleVector objects can be added to each other and
    multiplied with scalars or :class:`~MMTK.ParticleProperties.ParticleScalar` objects; all
    of these operations result in another ParticleVector
    object. Multiplication with a vector or another ParticleVector object
    yields a :class:`~MMTK.ParticleProperties.ParticleScalar` object containing the dot products
    for each particle. Multiplications that treat ParticleVectors
    as vectors in a 3N-dimensional space are implemented as methods.
    """

    def __init__(self, universe, data_array=None):
        """
        :param universe: the universe for which the values are defined
        :type universe: :class:`~MMTK.Universe.Universe`
        :param data_array: the data array containing the values for each
                           particle. If None, a new array containing
                           zeros is created and used. Otherwise, the
                           array myst be of shape (N,3), where N is the
                           number of particles in the universe.
        :type data_array: Scientific.N.array_type
        """
        ParticleProperty.__init__(self, universe, 1, 1)
        if data_array is None:
            self.array = N.zeros((self.n, 3), N.Float)
        else:
            self.array = data_array
            if data_array.shape[0] != self.n:
                raise ValueError('Data incompatible with universe')

    def __getitem__(self, item):
        if not isinstance(item, int):
            item = item.index
        return Vector(self.array[item])

    def __setitem__(self, item, value):
        if not isinstance(item, int):
            item = item.index
        self.array[item] = value.array

    def __mul__(self, other):
        if isParticleProperty(other):
            if self.universe != other.universe:
                raise ValueError('Variables are for different universes')
            if other.value_rank == 0:
                return ParticleVector(self.universe,
                                      self.array*other.array[:,N.NewAxis])
            elif other.value_rank == 1:
                return ParticleScalar(self.universe,
                                      N.add.reduce(self.array * \
                                                   other.array, -1))
            else:
                raise TypeError('not yet implemented')
        elif isVector(other):
            return ParticleScalar(self.universe,
                                  N.add.reduce(
                                      self.array*other.array[N.NewAxis,:],
                                      -1))
        elif isTensor(other):
            raise TypeError('not yet implemented')
        else:
            return ParticleVector(self.universe, self.array*other)

    __rmul__ = __mul__
    _product_with_vector = __mul__

    def zero(self):
        return Vector(0., 0., 0.)


[docs]    def length(self):
        """
        :returns: the length (norm) of the vector for each particle
        :rtype: :class:`~MMTK.ParticleProperties.ParticleScalar`
        """
        return ParticleScalar(self.universe,
                              N.sqrt(N.add.reduce(self.array**2,
                                                  -1)))


    def sumOverParticles(self):
        return Vector(N.add.reduce(self.array, 0))


[docs]    def norm(self):
        """
        :returns: the norm of the ParticleVector seen as a 3N-dimensional
                  vector
        :rtype: float
        """
        return N.sqrt(N.add.reduce(N.ravel(self.array**2)))

    totalNorm = norm

    def scaledToNorm(self, norm):
        f = norm/self.norm()
        return ParticleVector(self.universe, f*self.array)


[docs]    def dotProduct(self, other):
        """
        :param other: another ParticleVector
        :type other: :class:`~MMTK.ParticleProperties.ParticleVector`
        :returns: the dot product with other, treating both operands
                  as 3N-dimensional vectors.
        """
        if self.universe != other.universe:
            raise ValueError('Variables are for different universes')
        return N.add.reduce(N.ravel(self.array * other.array))



[docs]    def massWeightedNorm(self):
        """
        :returns: the mass-weighted norm of the ParticleVector seen as a
                  3N-dimensional vector
        :rtype: float
        """
        m = self.universe.masses().array
        return N.sqrt(N.sum(N.ravel(m[:, N.NewAxis] *
                                    self.array**2))
                      / N.sum(m))


    def scaledToMassWeightedNorm(self, norm):
        f = norm/self.massWeightedNorm()
        return ParticleVector(self.universe, f*self.array)


[docs]    def massWeightedDotProduct(self, other):
        """
        :param other: another ParticleVector
        :type other: :class:`~MMTK.ParticleProperties.ParticleVector`
        :returns: the mass-weighted dot product with other treating both
                  operands as 3N-dimensional vectors
        :rtype: float
        """
        if self.universe != other.universe:
            raise ValueError('Variables are for different universes')
        m = self.universe.masses().array
        return N.add.reduce(N.ravel(self.array * other.array * \
                                    m[:, N.NewAxis]))



[docs]    def dyadicProduct(self, other):
        """
        :param other: another ParticleVector
        :type other: :class:`~MMTK.ParticleProperties.ParticleVector`
        :returns: the dyadic product with other
        :rtype: :class:`~MMTK.ParticleProperties.ParticleTensor`
        """
        if self.universe != other.universe:
            raise ValueError('Variables are for different universes')
        return ParticleTensor(self.universe,
                              self.array[:, :, N.NewAxis] * \
                              other.array[:, N.NewAxis, :])


ParticleVector.return_class = ParticleVector

#
# Configuration variables: ParticleVector plus universe parameters
#

[docs]class Configuration(ParticleVector):

    """
    Configuration of a universe

    Configuration instances represent a configuration of a universe,
    consisting of positions for all atoms (like in a ParticleVector) plus
    the geometry of the universe itself, e.g. the cell shape for
    periodic universes.
    """

    def __init__(self, universe, data_array=None, cell = None):
        """
        :param universe: the universe for which the values are defined
        :type universe: :class:`~MMTK.Universe.Universe`
        :param data_array: the data array containing the values for each
                           particle. If None, a new array containing
                           zeros is created and used. Otherwise, the
                           array myst be of shape (N,3), where N is the
                           number of particles in the universe.
        :type data_array: Scientific.N.array_type
        :param cell: the cell parameters of the universe,
                     i.e. the return value of universe.cellParameters()
        """
        ParticleVector.__init__(self, universe, data_array)
        if cell is None:
            self.cell_parameters = universe.cellParameters()
        else:
            self.cell_parameters = cell

    def __add__(self, other):
        value = ParticleVector.__add__(self, other)
        return Configuration(self.universe, value.array, self.cell_parameters)

    def __sub__(self, other):
        value = ParticleVector.__sub__(self, other)
        return Configuration(self.universe, value.array, self.cell_parameters)

    def __copy__(self, memo = None):
        return self.__class__(self.universe, copy.copy(self.array),
                              copy.copy(self.cell_parameters))
    __deepcopy__ = __copy__

    def hasValidPositions(self):
        return N.logical_and.reduce(N.ravel(N.less(self.array,
                                                   Utility.undefined_limit)))

    def convertToBoxCoordinates(self):
        array = self.universe._realToBoxPointArray(self.array,
                                                   self.cell_parameters)
        self.array[:] = array

    def convertFromBoxCoordinates(self):
        array = self.universe._boxToRealPointArray(self.array,
                                                   self.cell_parameters)
        self.array[:] = array

#
# One tensor per particle.
#


[docs]class ParticleTensor(ParticleProperty):

    """
    Rank-2 tensor property defined for each particle

    ParticleTensor objects can be added to each other and
    multiplied with scalars or :class:`~MMTK.ParticleProperties.ParticleScalar` objects; all
    of these operations result in another ParticleTensor object.
    """

    def __init__(self, universe, data_array=None):
        """
        :param universe: the universe for which the values are defined
        :type universe: :class:`~MMTK.Universe.Universe`
        :param data_array: the data array containing the values for each
                           particle. If None, a new array containing
                           zeros is created and used. Otherwise, the
                           array myst be of shape (N,3,3), where N is the
                           number of particles in the universe.
        :type data_array: Scientific.N.array_type
        """
        ParticleProperty.__init__(self, universe, 1, 2)
        if data_array is None:
            self.array = N.zeros((self.n, 3, 3), N.Float)
        else:
            self.array = data_array
            if data_array.shape[0] != self.n:
                raise ValueError('Data incompatible with universe')

    def __getitem__(self, item):
        if not isinstance(item, int):
            item = item.index
        return Tensor(self.array[item])

    def __setitem__(self, item, value):
        if not isinstance(item, int):
            item = item.index
        self.array[item] = value.array

    def __mul__(self, other):
        if isParticleProperty(other):
            if self.universe != other.universe:
                raise ValueError('Variables are for different universes')
            if other.value_rank == 0:
                return ParticleTensor(self.universe,
                                      self.array*other.array[:,
                                                             N.NewAxis,
                                                             N.NewAxis])
            else:
                raise TypeError('not yet implemented')
        elif isVector(other):
            raise TypeError('not yet implemented')
        elif isTensor(other):
            raise TypeError('not yet implemented')
        else:
            return ParticleTensor(self.universe, self.array*other)

    __rmul__ = __mul__

    def zero(self):
        return Tensor([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]])

    def sumOverParticles(self):
        return Tensor(N.add.reduce(self.array, 0))

    def trace(self):
        return ParticleScalar(self.universe,
                              self.array[:, 0, 0] + self.array[:, 1, 1]
                              + self.array[:, 2, 2])


ParticleTensor.return_class = ParticleTensor

#
# One tensor per pair, symmetric.
#
class SymmetricPairTensor(ParticleProperty):

    def __init__(self, universe, data_array=None):
        """
        :param universe: the universe for which the values are defined
        :type universe: :class:`~MMTK.Universe.Universe`
        :param data_array: the data array containing the values for each
                           particle. If None, a new array containing
                           zeros is created and used. Otherwise, the
                           array myst be of shape (N,3,N,3), where N is the
                           number of particles in the universe.
        :type data_array: Scientific.N.array_type
        """
        ParticleProperty.__init__(self, universe, 2, 2)
        if data_array is None:
            self.array = N.zeros((self.n,3, self.n,3), N.Float)
        else:
            self.array = data_array
            if data_array.shape[0] != self.n or \
               data_array.shape[2] != self.n:
                raise ValueError('Data incompatible with universe')
        self.symmetrized = False

    def __getitem__(self, item):
        i1, i2 = item
        if not isinstance(i1, int):
            i1 = i1.index
        if not isinstance(i2, int):
            i2 = i2.index
        if i1 > i2:
            i1, i2 = i2, i1
            return Tensor(N.transpose(self.array[i1,:,i2,:]))
        else:
            return Tensor(self.array[i1,:,i2,:])

    def __setitem__(self, item, value):
        i1, i2 = item
        if not isinstance(i1, int):
            i1 = i1.index
        if not isinstance(i2, int):
            i2 = i2.index
        if i1 > i2:
            i1, i2 = i2, i1
            self.array[i1,:,i2,:] = value.transpose().array
        else:
            self.array[i1,:,i2,:] = value.array

    def zero(self):
        return Tensor(3*[[0., 0., 0.]])

    def symmetrize(self):
        if not self.symmetrized:
            a = self.array
            n = a.shape[0]
            a.shape = (3*n, 3*n)
            nn = 3*n
            for i in range(nn):
                for j in range(i+1, nn):
                    a[j,i] = a[i,j]
            a.shape = (n, 3, n, 3)
            self.symmetrized = True

    def __mul__(self, other):
        self.symmetrize()
        if isParticleProperty(other):
            if self.universe != other.universe:
                raise ValueError('Variables are for different universes')
            if other.value_rank == 1:
                n = self.array.shape[0]
                sa = N.reshape(self.array, (n, 3, 3*n))
                oa = N.reshape(other.array, (3*n, ))
                return ParticleVector(self.universe, N.dot(sa, oa))
            else:
                raise TypeError('not yet implemented')


SymmetricPairTensor.return_class = SymmetricPairTensor

#
# Type check function
#

[docs]def isParticleProperty(object):
    """
    :param object: any object
    :returns: True if object is a :class:`~MMTK.ParticleProperties.ParticleProperty`
    :rtype: bool
    """
    return isinstance(object, ParticleProperty)



[docs]def isConfiguration(object):
    """
    :param object: any object
    :returns: True if object is a :class:`~MMTK.ParticleProperties.Configuration`
    :rtype: bool
    """
    return isinstance(object, Configuration)

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Source code for MMTK.PDB

# This module deals with input and output of configurations in PDB format.
#
# Written by Konrad Hinsen
#

"""
PDB files

This module provides classes that represent molecules in a PDB file.
They permit various manipulations and the creation of MMTK objects.
Note that the classes defined in this module are specializations
of classed defined in Scientific.IO.PDB; the methods defined in
that module are also available.
"""

__docformat__ = 'restructuredtext'

from MMTK import ChemicalObjects, Collections, Database, Units, \
                 Universe, Utility
from Scientific.Geometry import Vector
import Scientific.IO.PDB
import copy, string

#
# The chain classes from Scientific.IO.PDB are extended by methods
# that construct MMTK objects and set configurations.
#
class PDBChain(object):

    def applyTo(self, chain, map = 'pdbmap', alt = 'pdb_alternative',
                atom_map = None):
        if len(chain) != len(self):
            raise ValueError("chain lengths do not match")
        for i in range(len(chain)):
            residue = chain[i]
            pdbmap = getattr(residue, map)
            try: altmap = getattr(residue, alt)
            except AttributeError: altmap = {}
            setResidueConfiguration(residue, self[i], pdbmap[0], altmap,
                                    atom_map)
    

[docs]class PDBPeptideChain(Scientific.IO.PDB.PeptideChain, PDBChain):

    """
    Peptide chain in a PDB file

    See the description of Scientific.IO.PDB.PeptideChain for the
    constructor and additional methods. In MMTK, PDBPeptideChain
    objects are usually obtained from a PDBConfiguration object via
    its attribute peptide_chains (see the documentation of
    Scientific.IO.PDB.Structure).
    """


[docs]    def createPeptideChain(self, model = 'all',
                           n_terminus=None, c_terminus=None):
        """
        :returns: a :class:`~MMTK.Proteins.PeptideChain` object corresponding to the
                  peptide chain in the PDB file. The parameter model
                  has the same meaning as for the PeptideChain constructor.
        :rtype: :class:`~MMTK.Proteins.PeptideChain`
        """
        self.identifyProtonation()
        from MMTK import Proteins
        properties = {'model': model}
        if self.segment_id != '':
            properties['name'] = self.segment_id
        elif self.chain_id != '':
            properties['name'] = self.chain_id
        if c_terminus is None:
            properties['c_terminus'] = self.isTerminated()
        else:
            properties['c_terminus'] = c_terminus
        if n_terminus is not None:
            properties['n_terminus'] = n_terminus
        chain = apply(Proteins.PeptideChain, (self,), properties)
        if model != 'no_hydrogens':
            chain.findHydrogenPositions()
        return chain


    def identifyProtonation(self):
        for residue in self.residues:
            if residue.name == 'HIS':
                count_hd = 0
                count_he = 0
                for atom in residue:
                    if 'HD' in atom.name:
                        count_hd += 1
                    if 'HE' in atom.name:
                        count_he += 1
                if count_hd + count_he == 0:
                    # default for crystallographic structures without hydrogens
                    residue.name = 'HIE'
                elif count_he == 2:
                    if count_hd == 2:
                        residue.name = 'HIP'
                    else:
                        residue.name = 'HIE'
                else:
                    residue.name = 'HID'
            elif residue.name == 'GLU':
                for atom in residue:
                    if 'HE' in atom.name:
                        residue.name = 'GLP'
                        break
            elif residue.name == 'ASP':
                for atom in residue:
                    if 'HD' in atom.name:
                        residue.name = 'APP'
                        break
            elif residue.name == 'LYS':
                count_hz = 0
                for atom in residue:
                    if 'HZ' in atom.name:
                        count_hz += 1
                if count_hz > 0 and count_hz < 3:
                    # for count_hz == 0 (most probably a crystallographic
                    # structure), keep LYS which is the most frequent one.
                    residue.name = 'LYP'



[docs]class PDBNucleotideChain(Scientific.IO.PDB.NucleotideChain, PDBChain):

    """
    Nucleotide chain in a PDB file

    See the description of Scientific.IO.PDB.NucleotideChain for the
    constructor and additional methods. In MMTK, PDBNucleotideChain
    objects are usually obtained from a PDBConfiguration object via
    its attribute nucleotide_chains (see the documentation of
    Scientific.IO.PDB.Structure).
    """


[docs]    def createNucleotideChain(self, model='all'):
        """
        :returns: a :class:`~MMTK.NucleicAcids.NucleotideChain` object corresponding 
                  to the nucleotide chain in the PDB file. The parameter model
                  has the same meaning as for the NucleotideChain constructor.
        :rtype: :class:`~MMTK.NucleicAcids.NucleotideChain`
        """
        from MMTK import NucleicAcids
        properties = {'model': model}
        if self.segment_id != '':
            properties['name'] = self.segment_id
        elif self.chain_id != '':
            properties['name'] = self.chain_id
        if self[0].hasPhosphate():
            properties['terminus_5'] = 0
        chain = apply(NucleicAcids.NucleotideChain, (self,), properties)
        if model != 'no_hydrogens':
            chain.findHydrogenPositions()
        return chain



[docs]class PDBMolecule(Scientific.IO.PDB.Molecule):

    """
    Molecule in a PDB file

    See the description of Scientific.IO.PDB.Molecule for the
    constructor and additional methods. In MMTK, PDBMolecule objects
    are usually obtained from a PDBConfiguration object via its
    attribute molecules (see the documentation of
    Scientific.IO.PDB.Structure). A molecule is by definition any
    residue in a PDB file that is not an amino acid or nucleotide
    residue.
    """

    def applyTo(self, molecule, map = 'pdbmap', alt = 'pdb_alternative',
                atom_map = None):
        pdbmap = getattr(molecule, map)
        try: altmap = getattr(molecule, alt)
        except AttributeError: altmap = {}
        setResidueConfiguration(molecule, self, pdbmap[0], altmap, atom_map)


[docs]    def createMolecule(self, name=None):
        """
        :returns: a :class:`~MMTK.ChemicalObjects.Molecule` object corresponding 
                  to the molecule in the PDB file. The parameter name 
                  specifies the molecule name as defined in the chemical database.
                  It can be left out for known molecules (currently
                  only water).
        :rtype: :class:`~MMTK.ChemicalObjects.Molecule`
        """
        if name is None:
            name = molecule_names[self.name]
        m = ChemicalObjects.Molecule(name)
        setConfiguration(m, [self])
        return m

#
# The structure class from Scientific.IO.PDB is extended by methods
# that construct MMTK objects and set configurations.
#


[docs]class PDBConfiguration(Scientific.IO.PDB.Structure):

    """
    Everything in a PDB file

    A PDBConfiguration object represents the full contents of a PDB
    file. It can be used to create MMTK objects for all or part
    of the molecules, or to change the configuration of an existing
    system.

    """
    
    def __init__(self, file_or_filename, model = 0, alternate_code = 'A'):
        """
        :param file_or_filename: the name of a PDB file, or a file object
        :param model: the number of the model to be used from a
                      multiple model file
        :type model: int
        :param alternate_code: the alternate code to be used for atoms that
                               have multiple positions
        :type alternate_code: str
        """

        if isinstance(file_or_filename, basestring):
            file_or_filename = Database.PDBPath(file_or_filename)
        Scientific.IO.PDB.Structure.__init__(self, file_or_filename,
                                             model, alternate_code)
        self._numberAtoms()
        self._convertUnits()

    peptide_chain_constructor = PDBPeptideChain
    nucleotide_chain_constructor = PDBNucleotideChain
    molecule_constructor = PDBMolecule

    def _numberAtoms(self):
        n = 1
        for residue in self.residues:
            for atom in residue:
                atom.number = n
                n += 1

    def _convertUnits(self):
        for residue in self.residues:
            for atom in residue:
                atom.position = atom.position*Units.Ang
                try:
                    b = atom.properties['temperature_factor']
                    atom.properties['temperature_factor'] = b*Units.Ang**2
                except KeyError:
                    pass
                try:
                    u = atom.properties['u']
                    atom.properties['u'] = u*Units.Ang**2
                except KeyError:
                    pass

        # All these attributes exist only if ScientificPython >= 2.7.5 is used.
        # The Scaling transformation was introduced with the same version,
        # so if it exists, the rest should work as well.
        try:
            from Scientific.Geometry.Transformation import Scaling
        except ImportError:
            return
        for attribute in ['a', 'b', 'c']:
            value = getattr(self, attribute)
            if value is not None:
                setattr(self, attribute, value*Units.Ang)
        for attribute in ['alpha', 'beta', 'gamma']:
            value = getattr(self, attribute)
            if value is not None:
                setattr(self, attribute, value*Units.deg)
        if self.to_fractional is not None:
            self.to_fractional = self.to_fractional*Scaling(1./Units.Ang)
            v1 = self.to_fractional(Vector(1., 0., 0.))
            v2 = self.to_fractional(Vector(0., 1., 0.))
            v3 = self.to_fractional(Vector(0., 0., 1.))
            self.reciprocal_basis = (Vector(v1[0], v2[0], v3[0]),
                                     Vector(v1[1], v2[1], v3[1]),
                                     Vector(v1[2], v2[2], v3[2]))
        else:
            self.reciprocal_basis = None
        if self.from_fractional is not None:
            self.from_fractional = Scaling(Units.Ang)*self.from_fractional
            self.basis = (self.from_fractional(Vector(1., 0., 0.)),
                          self.from_fractional(Vector(0., 1., 0.)),
                          self.from_fractional(Vector(0., 0., 1.)))
        else:
            self.basis = None
        for i in range(len(self.ncs_transformations)):
            tr = self.ncs_transformations[i]
            tr_new = Scaling(Units.Ang)*tr*Scaling(1./Units.Ang)
            tr_new.given = tr.given
            tr_new.serial = tr.serial
            self.ncs_transformations[i] = tr_new
        for i in range(len(self.cs_transformations)):
            tr = self.cs_transformations[i]
            tr_new = Scaling(Units.Ang)*tr*Scaling(1./Units.Ang)
            self.cs_transformations[i] = tr_new


[docs]    def createUnitCellUniverse(self):
        """
        Constructs an empty universe (OrthrhombicPeriodicUniverse or
        ParallelepipedicPeriodicUniverse) representing the
        unit cell of the crystal. If the PDB file does not define
        a unit cell at all, an InfiniteUniverse is returned.
        
        :returns: a universe
        :rtype: :class:`~MMTK.Universe.Universe`
        """
        if self.from_fractional is None:
            return Universe.InfiniteUniverse()
        e1 = self.from_fractional(Vector(1., 0., 0.))
        e2 = self.from_fractional(Vector(0., 1., 0.))
        e3 = self.from_fractional(Vector(0., 0., 1.))
        if abs(e1.normal()*Vector(1., 0., 0.)-1.) < 1.e-15 \
               and abs(e2.normal()*Vector(0., 1., 0.)-1.) < 1.e-15 \
               and abs(e3.normal()*Vector(0., 0., 1.)-1.) < 1.e-15:
            return \
               Universe.OrthorhombicPeriodicUniverse((e1.length(),
                                                      e2.length(),
                                                      e3.length()))
        return Universe.ParallelepipedicPeriodicUniverse((e1, e2, e3))



[docs]    def createPeptideChains(self, model='all'):
        """
        :returns: a list of :class:`~MMTK.Proteins.PeptideChain` objects, one for each
                  peptide chain in the PDB file. The parameter model
                  has the same meaning as for the PeptideChain constructor.
        :rtype: list
        """
        return [chain.createPeptideChain(model)
                for chain in self.peptide_chains]



[docs]    def createNucleotideChains(self, model='all'):
        """
        :returns: a list of :class:`~MMTK.NucleicAcids.NucleotideChain` objects, one for each
                  nucleotide chain in the PDB file. The parameter model
                  has the same meaning as for the NucleotideChain constructor.
        :rtype: list
        """
        return [chain.createNucleotideChain(model)
                for chain in self.nucleotide_chains]



[docs]    def createMolecules(self, names = None, permit_undefined=True):
        """
        :param names: If a list of molecule names (as defined in the
                      chemical database) and/or PDB residue names,
                      only molecules mentioned in this list will be
                      constructed. If a dictionary, it is used to map
                      PDB residue names to molecule names. With the
                      default (None), only water molecules are
                      built.
        :type names: list
        :param permit_undefined: If False, an exception is raised
                                 when a PDB residue is encountered for
                                 which no molecule name is supplied
                                 in names. If True, an AtomCluster
                                 object is constructed for each unknown
                                 molecule.
        :returns: a collection of :class:`~MMTK.ChemicalObjects.Molecule` objects,
                  one for each molecule in the PDB file. Each PDB residue not 
                  describing an amino acid or nucleotide residue is considered a
                  molecule.
        :rtype: :class:`~MMTK.Collections.Collection`
        """
        collection = Collections.Collection()
        mol_dicts = [molecule_names]
        if type(names) == type({}):
            mol_dicts.append(names)
            names = None
        for name in self.molecules.keys():
            full_name = None
            for dict in mol_dicts:
                full_name = dict.get(name, None)
            if names is None or name in names or full_name in names:
                if full_name is None and not permit_undefined:
                    raise ValueError("no definition for molecule " + name)
                for molecule in self.molecules[name]:
                    if full_name:
                        m = ChemicalObjects.Molecule(full_name)
                        setConfiguration(m, [molecule])
                    else:
                        pdbdict = {}
                        atoms = []
                        i = 0
                        for atom in molecule:
                            aname = atom.name
                            while aname[0] in string.digits:
                                aname = aname[1:] + aname[0]
                            try:
                                element = atom['element'].strip()
                                a = ChemicalObjects.Atom(element, name = aname)
                            except KeyError:
                                try:
                                    a = ChemicalObjects.Atom(aname[:2].strip(),
                                                             name = aname)
                                except IOError:
                                    a = ChemicalObjects.Atom(aname[:1],
                                                             name = aname)
                            a.setPosition(atom.position)
                            atoms.append(a)
                            pdbdict[atom.name] = Database.AtomReference(i)
                            i += 1
                        m = ChemicalObjects.AtomCluster(atoms, name = name)
                        if len(pdbdict) == len(molecule):
                            # pdbmap is correct only if the AtomCluster has
                            # unique atom names
                            m.pdbmap = [(name, pdbdict)]
                        setConfiguration(m, [molecule])
                    collection.addObject(m)
        return collection


    def createGroups(self, mapping):
        groups = []
        for name in self.molecules.keys():
            full_name = mapping.get(name, None)
            if full_name is not None:
                for molecule in self.molecules[name]:
                    g = ChemicalObjects.Group(full_name)
                    setConfiguration(g, [molecule], toplevel=0)
                    groups.append(g)
        return groups


[docs]    def createAll(self, molecule_names = None, permit_undefined=True):
        """
        :returns: a collection containing all objects contained in the
                  PDB file, i.e. the combination of the objects
                  returned by :func:`~MMTK.PDB.PDBConfiguration.createPeptideChains`,
                  :func:`~MMTK.PDB.PDBConfiguration.createNucleotideChains`,
                  and :func:`~MMTK.PDB.PDBConfiguration.createMolecules`.
                  The parameters have the same meaning as for
                  :func:`~MMTK.PDB.PDBConfiguration.createMolecules`.
        :rtype: :class:`~MMTK.Collectionc.Collection`
        """
        collection = Collections.Collection()
        peptide_chains = self.createPeptideChains()
        if peptide_chains:
            import Proteins
            collection.addObject(Proteins.Protein(peptide_chains))
        nucleotide_chains = self.createNucleotideChains()
        collection.addObject(nucleotide_chains)
        molecules = self.createMolecules(molecule_names, permit_undefined)
        collection.addObject(molecules)
        return collection



[docs]    def asuToUnitCell(self, asu_contents, compact=True):
        """
        :param asu_contents: the molecules in the asymmetric unit, usually
                             obtained from :func:`~MMTK.PDB.PDBConfiguration.createAll()`.
        :param compact: if True, all molecules images are shifted such that
                        their centers of mass lie inside the unit cell.
        :type compact: bool
        :returns: a collection containing all molecules in the unit cell,
                  obtained by copying and moving the molecules from the
                  asymmetric unit according to the crystallographic
                  symmetry operations.
        :rtype: :class:`~MMTK.Collections.Collection`
        """
        unit_cell_contents = Collections.Collection()
        for symop in self.cs_transformations:
            transformation = symop.asLinearTransformation()
            rotation = transformation.tensor
            translation = transformation.vector
            image = copy.deepcopy(asu_contents)
            for atom in image.atomList():
                atom.setPosition(symop(atom.position()))
            if compact:
                cm = image.centerOfMass()
                cm_fr = self.to_fractional(cm)
                cm_fr = Vector(cm_fr[0] % 1., cm_fr[1] % 1., cm_fr[2] % 1.) \
                        - Vector(0.5, 0.5, 0.5)
                cm = self.from_fractional(cm_fr)
                image.translateTo(cm)
            unit_cell_contents.addObject(image)
        return unit_cell_contents



[docs]    def applyTo(self, object, atom_map=None):
        """
        Sets the configuration of object from the coordinates in the
        PDB file. The object must be compatible with the PDB file, i.e.
        contain the same subobjects and in the same order. This is usually
        only guaranteed if the object was created by the method

        :func:`~MMTK.PDB.PDBConfiguration.createAll` from a PDB file with the same layout.
        :param object: a chemical object or collection of chemical objects
        """
        setConfiguration(object, self.residues, atom_map=atom_map)

#
# An alternative name for compatibility in Database files.
#

PDBFile = PDBConfiguration

#
# Set atom coordinates from PDB configuration.
#
def setResidueConfiguration(object, pdb_residue, pdbmap, altmap,
                            atom_map = None):
    defined = 0
    for atom in pdb_residue:
        name = atom.name
        try: name = altmap[name]
        except KeyError: pass
        try:
            pdbname = pdbmap[1][name]
        except KeyError:
            pdbname = None
            if not object.isSubsetModel():
                raise IOError('Atom '+atom.name+' of PDB residue ' +
                               pdb_residue.name+' not found in residue ' +
                               pdbmap[0] + ' of object ' + object.fullName())
        if pdbname:
            object.setPosition(pdbname, atom.position)
            try:
                object.setIndex(pdbname, atom.number-1)
            except ValueError:
                pass
            if atom_map is not None:
                atom_map[object.getAtom(pdbname)] = atom
            defined += 1
    return defined

def setConfiguration(object, pdb_residues,
                     map = 'pdbmap', alt = 'pdb_alternative',
                     atom_map = None, toplevel = True):
    defined = 0
    if hasattr(object, 'is_protein'):
        i = 0
        for chain in object:
            l = len(chain)
            defined += setConfiguration(chain, pdb_residues[i:i+l],
                                        map, alt, atom_map, False)
            i = i + l
    elif hasattr(object, 'is_chain'):
        for i in range(len(object)):
            defined += setConfiguration(object[i], pdb_residues[i:i+1],
                                        map, alt, atom_map, False)
    elif hasattr(object, map):
        pdbmap = getattr(object, map)
        try: altmap = getattr(object, alt)
        except AttributeError: altmap = {}
        nres = len(pdb_residues)
        if len(pdbmap) != nres:
            raise IOError('PDB configuration does not match object ' +
                           object.fullName())
        for i in range(nres):
            defined += setResidueConfiguration(object, pdb_residues[i],
                                               pdbmap[i], altmap, atom_map)
    elif Collections.isCollection(object):
        nres = len(pdb_residues)
        if len(object) != nres:
            raise IOError('PDB configuration does not match object ' +
                           object.fullName())
        for i in range(nres):
            defined += setConfiguration(object[i], [pdb_residues[i]],
                                        map, alt, atom_map, False)
    else:
        try:
            name = object.fullName()
        except AttributeError:
            try:
                name = object.name
            except AttributeError:
                name = '???'
        raise IOError('PDB configuration does not match object ' + name)
              
    if toplevel and defined < object.numberOfAtoms():
        name = '[unnamed object]'
        try:
            name = object.fullName()
        except: pass
        if name: name = ' in ' + name
        Utility.warning(`object.numberOfAtoms()-defined` + ' atom(s)' + name +
                        ' were not assigned (new) positions.')
    return defined


#
# Create objects from a PDB configuration.
#
molecule_names = {'HOH': 'water', 'TIP': 'water', 'TIP3': 'water',
                  'WAT': 'water', 'HEM': 'heme'}

def defineMolecule(code, name):
    if molecule_names.has_key(code):
        raise ValueError("PDB code " + code + " already used")
    molecule_names[code] = name

#
# This object represents a PDB file for output.
#

[docs]class PDBOutputFile(object):

    """
    PDB file for output

    """

    def __init__(self, filename, subformat= None):
        """
        :param filename: the name of the PDB file that is created
        :type filename: str
        :param subformat: a variant of the PDB format; these subformats
                          are defined in module Scientific.IO.PDB. The
                          default is the standard PDB format.
        :type subformat: str
        """
        self.file = Scientific.IO.PDB.PDBFile(filename, 'w', subformat)
        self.warning = False
        self.atom_sequence = []
        self.model_number = None


[docs]    def nextModel(self):
        """
        Start a new model
        """
        if self.model_number is None:
            self.model_number = 1
        else:
            self.file.writeLine('ENDMDL', '')
            self.model_number = self.model_number + 1
        self.file.writeLine('MODEL', {'serial_number': self.model_number})
        


[docs]    def write(self, object, configuration = None, tag = None):
        """
        Write  an object to the file

        :param object: the object to be written
        :type object: :class:`~MMTK.Collections.GroupOfAtoms`
        :param configuration: the configuration from which the coordinates 
                              are taken (default: current configuration)
        :type configuration: :class:`~MMTK.ParticleProperties.Configuration`
        """
        if not ChemicalObjects.isChemicalObject(object):
            for o in object:
                self.write(o, configuration)
        else:
            toplevel = tag is None
            if toplevel:
                tag = Utility.uniqueAttribute()
            if hasattr(object, 'pdbmap'):
                for residue in object.pdbmap:
                    self.file.nextResidue(residue[0], )
                    sorted_atoms = residue[1].items()
                    sorted_atoms.sort(lambda x, y:
                                      cmp(x[1].number, y[1].number))
                    for atom_name, atom in sorted_atoms:
                        atom = object.getAtom(atom)
                        p = atom.position(configuration)
                        if Utility.isDefinedPosition(p):
                            try: occ = atom.occupancy
                            except AttributeError: occ = 0.
                            try: temp = atom.temperature_factor
                            except AttributeError: temp = 0.
                            self.file.writeAtom(atom_name, p/Units.Ang,
                                                occ, temp, atom.type.symbol)
                            self.atom_sequence.append(atom)
                        else:
                            self.warning = True
                        setattr(atom, tag, None)
            else:
                if hasattr(object, 'is_protein'):
                    for chain in object:                    
                        self.write(chain, configuration, tag)
                elif hasattr(object, 'is_chain'):
                        self.file.nextChain(None, object.name)
                        for residue in object:
                            self.write(residue, configuration, tag)
                        self.file.terminateChain()
                elif hasattr(object, 'molecules'):
                    for m in object.molecules:
                        self.write(m, configuration, tag)
                elif hasattr(object, 'groups'):
                    for g in object.groups:
                        self.write(g, configuration, tag)
            if toplevel:
                for a in object.atomList():
                    if not hasattr(a, tag):
                        self.write(a, configuration, tag)
                    delattr(a, tag)



[docs]    def close(self):
        """
        Closes the file. Must be called in order to prevent data loss.
        """
        if self.model_number is not None:
            self.file.writeLine('ENDMDL', '')
        self.file.close()
        if self.warning:
            Utility.warning('Some atoms are missing in the output file ' + \
                            'because their positions are undefined.')
            self.warning = False

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MMTK-2.7.9/Doc/HTML/_modules/MMTK/PDBMoleculeFactory.html0000644000076600000240000015456712013143455023072 0ustar hinsenstaff00000000000000 MMTK.PDBMoleculeFactory — MMTK User Guide 2.7.7 documentation

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Source code for MMTK.PDBMoleculeFactory

# This module constructs molecules and universes corresponding exactly
# to the contents of a PDB file, using residue and atom names as
# given in the file.
#
# Written by Konrad Hinsen
#

"""
Molecule factory defined by a PDB file

This module permits the construction of molecular objects that correspond
exactly to the contents of a PDB file. It is used for working with
experimental data. Note that most force fields cannot be applied to the
systems generated in this way because the molecule factory does not know
any force field parameters.
"""

__docformat__ = 'restructuredtext'

import MMTK
from MMTK.MoleculeFactory import MoleculeFactory
from Scientific.Geometry import Vector, delta
from Scientific import N
import copy


[docs]class PDBMoleculeFactory(MoleculeFactory):

    """
    A PDBMoleculeFactory generates molecules and universes from the
    contents of a PDBConfiguration. Nothing is added or left out
    (except as defined by the optional residue and atom filters), and
    the atom and residue names are exactly those of the original PDB
    file.
    """

    def __init__(self, pdb_conf, residue_filter=None, atom_filter=None):
        """
        :param pdb_conf: a PDBConfiguration
        :type pdb_conf: :class:`~MMTK.PDB.PDBConfiguration`
        :param residue_filter: a function taking a residue object
                               (as defined in Scientific.IO.PDB)
                               and returning True if that residue is
                               to be kept in the molecule factory
        :type residue_filter: callable
        :param atom_filter: a function taking a residue object and an
                            atom object (as defined in Scientific.IO.PDB)     
                            and returning True if that atom is 
                            to be kept in the molecule factory
        :type atom_filter: callable
        """
        MoleculeFactory.__init__(self)

        if residue_filter is None and atom_filter is None:
            self.pdb_conf = pdb_conf
        else:
            self.pdb_conf = copy.deepcopy(pdb_conf)
            if atom_filter is not None:
                for residue in self.pdb_conf.residues:
                    delete = [atom for atom in residue
                              if not atom_filter(residue, atom)]
                    for atom in delete:
                        residue.deleteAtom(atom)
            if residue_filter is not None:
                delete = [residue for residue in self.pdb_conf.residues
                          if len(residue) == 0 or not residue_filter(residue)]
                for residue in delete:
                    self.pdb_conf.deleteResidue(residue)

        self.peptide_chains = []
        self.nucleotide_chains = []
        self.molecules = {}
        self.makeAll()


[docs]    def retrieveMolecules(self):
        """
        Constructs Molecule objects corresponding to the contents of the
        PDBConfiguration. Each peptide or nucleotide chain becomes
        one molecule. For residues that are neither amino acids nor
        nucleic acids, each residue becomes one molecule.

        Chain molecules (peptide and nucleotide chains) can be iterated
        over to retrieve the individual residues. The residues can also
        be accessed as attributes whose names are the three-letter
        residue name (upper case) followed by an underscore and the
        residue number from the PDB file (e.g. 'GLY_34').

        Each object that corresponds to a PDB residue (i.e. each
        residue in a chain and each non-chain molecule) has the
        attributes 'residue_name' and 'residue_number'. Each atom has
        the attributes 'serial_number', 'occupancy' and
        'temperature_factor'. Atoms for which a ANISOU record exists
        also have an attribute 'u' whose value is a tensor object.
        
        :returns: a list of Molecule objects
        :rtype: list
        """
        objects = [self.retrieveMolecule(o)
                   for o in self.peptide_chains
                            + self.nucleotide_chains]
        for mollist in self.molecules.values():
            objects.extend([self.retrieveMolecule(residue)
                            for residue in mollist])
        return objects



[docs]    def retrieveUniverse(self):
        """
        Constructs an empty universe (OrthrhombicPeriodicUniverse or
        ParallelepipedicPeriodicUniverse) representing the
        unit cell of the crystal. If the PDB file does not define
        a unit cell at all, an InfiniteUniverse is returned.
        
        :returns: a universe
        :rtype: :class:`~MMTK.Universe.Universe`
        """
        return self.pdb_conf.createUnitCellUniverse()



[docs]    def retrieveAsymmetricUnit(self):
        """
        Constructs a universe (OrthrhombicPeriodicUniverse or
        ParallelepipedicPeriodicUniverse) representing the
        unit cell of the crystal and adds the molecules representing
        the asymmetric unit.
        
        :returns: a universe
        :rtype: :class:`~MMTK.Universe.Universe`
        """
        universe = self.retrieveUniverse()
        universe.addObject(self.retrieveMolecules())
        return universe



[docs]    def retrieveUnitCell(self, compact=True):
        """
        Constructs a universe (OrthrhombicPeriodicUniverse or
        ParallelepipedicPeriodicUniverse) representing the
        unit cell of the crystal and adds all the molecules it
        contains, i.e. the molecules of the asymmetric unit and
        its images obtained by applying the crystallographic
        symmetry operations.

        :param compact: if True, the images are shifted such that
                        their centers of mass lie inside the unit cell.
        :type compact: bool
        :returns: a universe
        :rtype: :class:`~MMTK.Universe.Universe`
        """
        if not self.pdb_conf.cs_transformations:
            return self.retrieveAsymmetricUnit()
        universe = self.retrieveUniverse()
        asu_count = 0
        for symop in self.pdb_conf.cs_transformations:
            transformation = symop.asLinearTransformation()
            rotation = transformation.tensor
            translation = transformation.vector
            is_asu = translation.length() < 1.e-8 and \
                     N.maximum.reduce(N.ravel(N.fabs((rotation
                                                      -delta).array))) < 1.e-8
            if is_asu:
                asu_count += 1
            asu = MMTK.Collection(self.retrieveMolecules())
            for atom in asu.atomList():
                atom.setPosition(symop(atom.position()))
                if hasattr(atom, 'u'):
                    atom.u = rotation.dot(atom.u.dot(rotation.transpose()))
                    atom.u = atom.u.symmetricalPart()
                atom.in_asu = is_asu
            if compact:
                cm = asu.centerOfMass()
                cm_fr = self.pdb_conf.to_fractional(cm)
                cm_fr = Vector(cm_fr[0] % 1., cm_fr[1] % 1., cm_fr[2] % 1.) \
                        - Vector(0.5, 0.5, 0.5)
                cm = self.pdb_conf.from_fractional(cm_fr)
                asu.translateTo(cm)
            universe.addObject(asu)
        assert asu_count == 1
        return universe


    def makeAll(self):
        cystines = []
        for chain in self.pdb_conf.peptide_chains:
            chain_id = chain.chain_id
            if not chain_id:
                chain_id = 'PeptideChain'+str(len(self.peptide_chains)+1)
            for residue in chain:
                if residue.name == 'CYS' and residue.atoms.has_key('SG'):
                    cystines.append((chain_id, residue, residue.atoms['SG']))
            self.makeChain(chain, chain_id)
            self.peptide_chains.append(chain_id)
        for i in range(len(cystines)):
            for j in range(i+1, len(cystines)):
                d = (cystines[i][2].position-cystines[j][2].position).length()
                if d < 0.25:
                    if cystines[i][0] is cystines[j][0]:
                        cys1 = cystines[i][1]
                        cys2 = cystines[j][1]
                        cys1 = cys1.name + '_' + str(cys1.number)
                        cys2 = cys2.name + '_' + str(cys2.number)
                        self.addBond(cystines[i][0], cys1+'.SG', cys2+'.SG')
                    else:
                        raise NotImplemented('Inter-chain disulfide bridges'
                                             ' not yet implemented')
        for chain in self.pdb_conf.nucleotide_chains:
            chain_id = chain.chain_id
            if not chain_id:
                chain_id = 'NucleotideChain'+str(len(self.nucleotide_chains)+1)
            self.makeChain(chain, chain_id)
            self.nucleotide_chains.append(chain_id)
        for molname, mollist in self.pdb_conf.molecules.items():
            self.molecules[molname] = []
            for residue in mollist:
                base_resname = residue.name + '_' + str(residue.number)
                suffix = 1
                resname = base_resname
                done = False
                while not done:
                    try:
                        self.makeResidue(residue, resname)
                        done = True
                    except ValueError, e:
                        if str(e).split()[0] == "redefinition":
                            resname = base_resname + "_" + str(suffix)
                            suffix += 1
                        else:
                            raise
                self.molecules[molname].append(resname)

    def makeChain(self, chain, chain_id):
        groups = []
        for residue in chain:
            resname = chain_id + '_' + residue.name + '_' + str(residue.number)
            groups.append(resname)
            self.makeResidue(residue, resname)
        self.createGroup(chain_id)
        local_resnames = []
        for resname in groups:
            local_resname = '_'.join(resname.split('_')[1:])
            self.addSubgroup(chain_id, local_resname, resname)
            local_resnames.append(local_resname)
        self.setAttribute(chain_id, 'sequence', local_resnames)
        for i in range(1, len(chain)):
            if chain[i-1].number == chain[i].number-1:
                for atom1 in chain[i-1]:
                    for atom2 in chain[i]:
                        if self.assumeBond(atom1.properties['element'],
                                           atom1.position,
                                           atom2.properties['element'],
                                           atom2.position):
                            self.addBond(chain_id,
                                         local_resnames[i-1]+'.'+atom1.name,
                                         local_resnames[i]+'.'+atom2.name)
                       
    def makeResidue(self, residue, group_name):
        self.createGroup(group_name)
        self.setAttribute(group_name, 'residue_name', residue.name)
        self.setAttribute(group_name, 'residue_number', residue.number)
        atoms = []
        for atom in residue:
            atoms.append((atom.name, atom.properties['element'],
                          atom.position))
            self.addAtom(group_name, atom.name, atom.properties['element'])
            self.setPosition(group_name, atom.name, atom.position)
            self.setAttribute(group_name, atom.name+'.temperature_factor',
                              atom.properties['temperature_factor'])
            self.setAttribute(group_name, atom.name+'.occupancy',
                              atom.properties['occupancy'])
            self.setAttribute(group_name, atom.name+'.serial_number',
                              atom.properties['serial_number'])
            if atom.properties.has_key('u'):
                self.setAttribute(group_name, atom.name+'.u',
                                  atom.properties['u'])
        for i in range(len(atoms)):
            atom_i, element_i, pos_i = atoms[i]
            for j in range(i+1, len(atoms)):
                atom_j, element_j, pos_j = atoms[j]
                if self.assumeBond(element_i, pos_i, element_j, pos_j):
                    self.addBond(group_name, atom_i, atom_j)

    def assumeBond(self, element1, pos1, element2, pos2):
        if element1 > element2:
            element1, element2 = element2, element1
        try:
            d = self.bond_lengths[(element1, element2)]
            return (pos1-pos2).length() < d
        except KeyError:
            return False

    bond_lengths = {('C', 'C'): 0.16,
                    ('C', 'H'): 0.115,
                    ('C', 'N'): 0.215,
                    ('C', 'O'): 0.16,
                    ('C', 'P'): 0.2,
                    ('C', 'S'): 0.2,
                    ('H', 'H'): 0.08,
                    ('H', 'N'): 0.115,
                    ('H', 'O'): 0.115,
                    ('H', 'P'): 0.15,
                    ('H', 'S'): 0.14,
                    ('N', 'N'): 0.155,
                    ('N', 'O'): 0.15,
                    ('O', 'O'): 0.16,
                    ('O', 'P'): 0.17,
                    ('O', 'S'): 0.16,
                    ('P', 'S'): 0.2,
                    ('S', 'S'): 0.25,
                    }

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Source code for MMTK.ProteinFriction

# Friction constants for protein C-alpha models
#
# Written by Konrad Hinsen
#

"""
A friction constant model for |C_alpha| models of proteins
"""

__docformat__ = 'restructuredtext'

import MMTK.ParticleProperties
from Scientific import N


[docs]def calphaFrictionConstants(protein, set=2):
    """
    :param protein: a |C_alpha| model protein
    :type protein: :class:`~MMTK.Proteins.Protein`
    :param set: the number of a friction constant set (1, 2, 3, or 4)
    :return: the estimated friction constants for the atoms in the protein
    :rtype: :class:`~MMTK.ParticleProperties.ParticleScalar`
    """
    radius = 1.5
    atoms = protein.atomCollection()
    f = MMTK.ParticleProperties.ParticleScalar(protein.universe())
    for chain in protein:
        for residue in chain:
            a = residue.peptide.C_alpha
            m = atoms.selectShell(a.position(), radius).mass()
            d = 3.*m/(4.*N.pi*radius**3)
            if set == 1:  # linear fit to initial slope
                f[a] = max(1000., 121.2*d-8600)
            elif set == 2:  # exponential fit 400 steps
                f[a] = max(1000., 68.2*d-5160)
            elif set == 3:  # exponential fit 200 steps
                f[a] = max(1000., 38.2*d-2160)
            elif set == 4:  # expansion fit 50 steps
                f[a] = max(1000., 20.4*d-500.)
                
    return f

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MMTK-2.7.9/Doc/HTML/_modules/MMTK/Proteins.html0000644000076600000240000044036412013143455021243 0ustar hinsenstaff00000000000000 MMTK.Proteins — MMTK User Guide 2.7.7 documentation

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Source code for MMTK.Proteins

# This module implements classes for peptide chains and proteins.
#
# Written by Konrad Hinsen
#

"""
Peptide chains and proteins
"""

__docformat__ = 'restructuredtext'

from MMTK import Biopolymers, Bonds, ChemicalObjects, Collections, \
                 ConfigIO, Database, Units, Universe, Utility
from Scientific.Geometry import Vector

from MMTK.Biopolymers import defineAminoAcidResidue

#
# Residues are special groups
#

[docs]class Residue(Biopolymers.Residue):

    """
    Amino acid residue

    Amino acid residues are a special kind of group. They are defined
    in the chemical database. Each residue has two subgroups
    ('peptide' and 'sidechain') and is usually connected to other
    residues to form a peptide chain. The database contains three
    variants of each residue (N-terminal, C-terminal,
    non-terminal) and various models (all-atom, united-atom,
    |C_alpha|).
    """

    def __init__(self, name = None, model = 'all'):
        """
        :param name: the name of the residue in the chemical database. This
                     is the full name of the residue plus the suffix
                     "_nt" or "_ct" for the terminal variants.
        :type name: str
        :param model: one of "all" (all-atom), "none" (no hydrogens),
                      "polar" (united-atom with only polar hydrogens),
                      "polar_charmm" (like "polar", but defining
                      polar hydrogens like in the CHARMM force field),
                      "polar_opls" (like "polar", but defining
                      polar hydrogens like in the latest OPLS force field),
                      "calpha" (only the |C_alpha| atom).
        :type model: str
        """
        if name is not None:
            blueprint = _residueBlueprint(name, model)
            ChemicalObjects.Group.__init__(self, blueprint)
            self.model = model
            self._init()

    def _init(self):
        Biopolymers.Residue._init(self)
        # create peptide attribute for calpha model
        if self.model == 'calpha':
            self.peptide = self

    def isNTerminus(self):
        return hasattr(self.peptide, 'H_3')

    def isCTerminus(self):
        return hasattr(self.peptide, 'O_2')

    def _makeCystine(self):
        if self.model == 'calpha':
            return self
        if self.symbol.lower() != 'cys':
            raise ValueError(`self` + " is not cysteine.")
        new_residue = 'cystine_ss'
        if self.isNTerminus():
            new_residue = new_residue + '_nt'
        elif self.isCTerminus():
            new_residue = new_residue + '_ct'
        new_residue = Residue(new_residue, self.model)
        for g in ['peptide', 'sidechain']:
            g_old = getattr(self, g)
            g_new = getattr(new_residue, g)
            for a in getattr(g_new, 'atoms'):
                set_method = getattr(getattr(g_new, a.name), 'setPosition')
                set_method(getattr(getattr(g_old, a.name), 'position')())
        return new_residue

    def isSubsetModel(self):
        return self.model == 'calpha'


[docs]    def backbone(self):
        """
        :returns: the peptide group
        :rtype: :class:`~MMTK.ChemicalObjects.Group`
        """
        return self.peptide



[docs]    def sidechains(self):
        """
        :returns: the sidechain group
        :rtype: :class:`~MMTK.ChemicalObjects.Group`
        """
        return self.sidechain



[docs]    def phiPsi(self, conf = None):
        """
        :returns: the values of the backbone dihedral angles phi and psi.
        :rtype: tuple (float, float)
        """
        universe = self.universe()
        if universe is None:
            universe = Universe.InfiniteUniverse()
        C = None
        for a in self.peptide.N.bondedTo():
            if a.parent.parent != self:
                C = a
                break
        if C is None:
            phi = None
        else:
            phi = universe.dihedral(self.peptide.C, self.peptide.C_alpha,
                                    self.peptide.N, C, conf)
        N = None
        for a in self.peptide.C.bondedTo():
            if a.parent.parent != self:
                N = a
                break
        if N is None:
            psi = None
        else:
            psi = universe.dihedral(N, self.peptide.C, self.peptide.C_alpha,
                                    self.peptide.N, conf)
        return phi, psi



[docs]    def phiAngle(self):
        """
        :returns: an object representing the phi angle and allowing to modify it
        :rtype: MMTK.InternalCoordinates.DihedralAngle
        """
        from MMTK.InternalCoordinates import DihedralAngle
        C = None
        for a in self.peptide.N.bondedTo():
            if a.parent.parent != self:
                C = a
                break
        if C is None:
            raise ValueError("residue is N-terminus")
        return DihedralAngle(self.peptide.C, self.peptide.C_alpha,
                             self.peptide.N, C)



[docs]    def psiAngle(self):
        """
        :returns: an object representing the psi angle and allowing to modify it
        :rtype: MMTK.InternalCoordinates.DihedralAngle
        """
        from MMTK.InternalCoordinates import DihedralAngle
        N = None
        for a in self.peptide.C.bondedTo():
            if a.parent.parent != self:
                N = a
                break
        if N is None:
            raise ValueError("residue is C-terminus")
        return DihedralAngle(N, self.peptide.C, self.peptide.C_alpha,
                             self.peptide.N)



[docs]    def chiAngle(self):
        """
        :returns: an object representing the chi angle and allowing to modify it
        :rtype: MMTK.InternalCoordinates.DihedralAngle
        """
        from MMTK.InternalCoordinates import DihedralAngle
        try:
            C_beta = self.sidechain.C_beta
        except AttributeError:
            raise ValueError("no C_beta in sidechain")
        X = None
        for atom_name in ['C_gamma', 'C_gamma_1', 'S_gamma',
                          'O_gamma', 'O_gamma_1', 'H_beta_1']:
            try:
                X = getattr(self.sidechain, atom_name)
                break
            except AttributeError:
                pass
        if X is None:
            raise ValueError("no sidechain reference atom found")
        return DihedralAngle(self.peptide.N, self.peptide.C_alpha,
                             C_beta, X)



def _residueBlueprint(name, model):
    try:
        blueprint = _residue_blueprints[(name, model)]
    except KeyError:
        if model == 'polar':
            name = name + '_uni'
        elif model == 'polar_charmm':
            name = name + '_uni2'
        elif model == 'polar_oldopls':
            name = name + '_uni3'
        elif model == 'none':
            name = name + '_noh'
        elif model == 'calpha':
            name = name + '_calpha'
        blueprint = Database.BlueprintGroup(name)
        _residue_blueprints[(name, model)] = blueprint
    return blueprint

_residue_blueprints = {}

#
# Peptide chains are molecules with added features.
#

[docs]class PeptideChain(Biopolymers.ResidueChain):

    """
    Peptide chain

    Peptide chains consist of amino acid residues that are linked
    by peptide bonds. They are a special kind of molecule, i.e.
    all molecule operations are available.

    Peptide chains act as sequences of residues. If p is a PeptideChain
    object, then

     * len(p) yields the number of residues
     * p[i] yields residue number i
     * p[i:j] yields the subchain from residue number i up to
                 but excluding residue number j

    :param sequence: the amino acid sequence. This can be a string
                     containing the one-letter codes, or a list
                     of three-letter codes, or a
                     :class:`~MMTK.PDB.PDBPeptideChain` object.
                     If a PDBPeptideChain object is supplied, the atomic
                     positions it contains are assigned to the atoms
                     of the newly generated peptide chain, otherwise the
                     positions of all atoms are undefined.
    :keyword model: one of "all" (all-atom), "no_hydrogens" or "none"
                    (no hydrogens), "polar_hydrogens" or "polar"
                    (united-atom with only polar hydrogens),
                    "polar_charmm" (like "polar", but defining
                    polar hydrogens like in the CHARMM force field),
                    "polar_opls" (like "polar", but defining
                    polar hydrogens like in the latest OPLS force field),
                    "calpha" (only the |C_alpha| atom of each residue).
                    Default is "all".
    :type model: str
    :keyword n_terminus: if True, the first residue is constructed
                         using the N-terminal variant, if False the
                         non-terminal version is used. Default is True.
    :type n_terminus: bool
    :keyword c_terminus: if True, the last residue is constructed
                         using the C-terminal variant, if False the
                         non-terminal version is used. Default is True.
    :type c_terminus: bool
    :keyword circular: if True, a peptide bond is constructed
                       between the first and the last residues.
                       Default is False.
    :type circular: bool
    :keyword name: a name for the chain (a string)
    :type name: str

    """

    def __init__(self, sequence, **properties):
        if sequence is not None:
            model = 'all'
            if properties.has_key('model'):
                model = properties['model'].lower()
            elif properties.has_key('hydrogens'):
                model = properties['hydrogens']
                if model == 1: model = 'all'
                elif model == 0: model = 'none'
                else: model = model.lower()
            if model == 'no_hydrogens':
                model = 'none'
            elif model == 'polar_hydrogens':
                model = 'polar'
            n_term = self.binaryProperty(properties, 'n_terminus', True)
            c_term = self.binaryProperty(properties, 'c_terminus', True)
            circular = self.binaryProperty(properties, 'circular', False)
            self.version_spec = {'n_terminus': n_term,
                                 'c_terminus': c_term,
                                 'model': model,
                                 'circular': circular}
            if type(sequence[0]) == type(''):
                conf = None
                numbers = range(len(sequence))
            else:
                conf = sequence
                sequence = conf.sequence()
                numbers = [r.number for r in conf]
            sequence = map(Biopolymers._fullName, sequence)
            if model != 'calpha':
                if n_term:
                    sequence[0] = sequence[0] + '_nt'
                if c_term:
                    sequence[-1] = sequence[-1] + '_ct'

            self.groups = []
            n = 0
            for residue, number in zip(sequence, numbers):
                n = n + 1
                r = Residue(residue, model)
                r.name = r.symbol + str(number)
                r.sequence_number = n
                r.parent = self
                self.groups.append(r)

            self._setupChain(circular, properties, conf)

    is_peptide_chain = True

    def __getslice__(self, first, last):
        return SubChain(self, self.groups[first:last])


[docs]    def sequence(self):
        """
        :returns: the primary sequence as a list of three-letter
                  residue codes.
        :rtype: list
        """
        return [r.symbol for r in self.groups]



[docs]    def backbone(self):
        """
        :returns: the peptide groups of all residues
        :rtype: :class:`~MMTK.Collections.Collection`
        """
        backbone = Collections.Collection()
        for r in self.groups:
            try:
                backbone.addObject(r.peptide)
            except AttributeError:
                pass
        return backbone
    


[docs]    def sidechains(self):
        """
        :returns: the sidechain groups of all residues
        :rtype: :class:`~MMTK.Collections.Collection`
        """
        sidechains = Collections.Collection()
        for r in self.groups:
            try:
                sidechains.addObject(r.sidechain)
            except AttributeError:
                pass
        return sidechains



[docs]    def phiPsi(self, conf = None):
        """
        :returns: a list of the (phi, psi) backbone angles for each residue
        :rtype: list of tuple of float
        """
        universe = self.universe()
        if universe is None:
            universe = Universe.InfiniteUniverse()
        angles = []
        for i in range(len(self)):
            r = self[i]
            if i == 0:
                phi = None
            else:
                phi = universe.dihedral(r.peptide.C, r.peptide.C_alpha,
                                        r.peptide.N,
                                        self[i-1].peptide.C, conf)
            if i == len(self)-1:
                psi = None
            else:
                psi = universe.dihedral(self[i+1].peptide.N,
                                        r.peptide.C, r.peptide.C_alpha,
                                        r.peptide.N, conf)
            angles.append((phi, psi))
        return angles



[docs]    def replaceResidue(self, r_old, r_new):
        """
        :param r_old: the residue to be replaced (must be part of the chain)
        :type r_old: Residue
        :param r_new: the residue that replaces r_old
        :type r_new: Residue
        """
        n = self.groups.index(r_old)
        for a in r_old.atoms:
            self.atoms.remove(a)
        obsolete_bonds = []
        for b in self.bonds:
            if b.a1 in r_old.atoms or b.a2 in r_old.atoms:
                obsolete_bonds.append(b)
        for b in obsolete_bonds:
            self.bonds.remove(b)
        r_old.parent = None
        self.atoms.extend(r_new.atoms)
        self.bonds.extend(r_new.bonds)
        r_new.sequence_number = n+1
        r_new.name = r_new.symbol+`n+1`
        r_new.parent = self
        self.groups[n] = r_new
        if n > 0:
            peptide_old = self.bonds.bondsOf(r_old.peptide.N)
            if peptide_old:
                self.bonds.remove(peptide_old[0])
            if not (self.groups[n-1].isCTerminus()
                    or self.groups[n].isNTerminus()):
                # ConnectedChain objects can have N/C-terminal
                # residues inside the (virtual) chain, so the
                # test is necessary.
                self.bonds.append(Bonds.Bond((self.groups[n-1].peptide.C,
                                              self.groups[n].peptide.N)))
        if n < len(self.groups)-1:
            peptide_old = self.bonds.bondsOf(r_old.peptide.C)
            if peptide_old:
                self.bonds.remove(peptide_old[0])
            if not (self.groups[n].isCTerminus()
                    or self.groups[n+1].isNTerminus()):
                self.bonds.append(Bonds.Bond((self.groups[n].peptide.C,
                                              self.groups[n+1].peptide.N)))
        if isinstance(self.parent, ChemicalObjects.Complex):
            self.parent.recreateAtomList()
        universe = self.universe()
        if universe is not None:
            universe._changed(True)

    # add sulfur bridges between cysteine residues

    def _addSSBridges(self, bonds):
        for b in bonds:
            cys1 = b[0]
            if cys1.symbol.lower() == 'cyx':
                cys_ss1 = cys1
            else:
                cys_ss1 = cys1._makeCystine()
                self.replaceResidue(cys1, cys_ss1)
            cys2 = b[1]
            if cys2.symbol.lower() == 'cyx':
                cys_ss2 = cys2
            else:
                cys_ss2 = cys2._makeCystine()
                self.replaceResidue(cys2, cys_ss2)
            self.bonds.append(Bonds.Bond((cys_ss1.sidechain.S_gamma,
                                          cys_ss2.sidechain.S_gamma)))

    def _descriptionSpec(self):
        kwargs = ','.join([name + '=' + `self.version_spec[name]`
                           for name in sorted(self.version_spec.keys())])
	return "S", kwargs

    def _typeName(self):
        return ''.join(self.sequence())

    def _graphics(self, conf, distance_fn, model, module, options):
        if model != 'backbone':
            return ChemicalObjects.Molecule._graphics(self, conf,
                                                      distance_fn, model,
                                                      module, options)
        color = options.get('color', 'black')
        material = module.EmissiveMaterial(color)
        objects = []
        for i in range(len(self.groups)-1):
            a1 = self.groups[i].peptide.C_alpha
            a2 = self.groups[i+1].peptide.C_alpha
            p1 = a1.position(conf)
            p2 = a2.position(conf)
            if p1 is not None and p2 is not None:
                bond_vector = 0.5*distance_fn(a1, a2, conf)
                cut = bond_vector != 0.5*(p2-p1)
                if not cut:
                    objects.append(module.Line(p1, p2, material = material))
                else:
                    objects.append(module.Line(p1, p1+bond_vector,
                                               material = material))
                    objects.append(module.Line(p2, p2-bond_vector,
                                               material = material))
        return objects

#
# Subchains are created by slicing chains or extracting a chain from
# a group of connected chains.
#


[docs]class SubChain(PeptideChain):

    """
    A contiguous part of a peptide chain

    SubChain objects are the result of slicing operations on
    PeptideChain objects. They cannot be created directly.
    SubChain objects permit all operations of PeptideChain
    objects, but cannot be added to a universe.
    """

    def __init__(self, chain=None, groups=None, name = ''):
        if chain is not None:
            self.groups = groups
            self.atoms = []
            self.bonds = []
            for g in self.groups:
                self.atoms.extend(g.atoms)
                self.bonds.extend(g.bonds)
            for i in range(len(self.groups)-1):
                link1 = self.groups[i].chain_links[1]
                link2 = self.groups[i+1].chain_links[0]
                self.bonds.append(Bonds.Bond((link1, link2)))
            self.bonds = Bonds.BondList(self.bonds)
            self.name = name
            self.model = chain.model
            self.parent = chain.parent
            self.type = None
            self.configurations = {}
            self.part_of = chain

    is_incomplete = True

    def __repr__(self):
        if self.name == '':
            return 'SubChain of ' + repr(self.part_of)
        else:
            return ChemicalObjects.Molecule.__repr__(self)
    __str__ = __repr__

    def replaceResidue(self, r_old, r_new):
        for a in r_old.atoms:
            self.atoms.remove(a)
        obsolete_bonds = []
        for b in self.bonds:
            if b.a1 in r_old.atoms or b.a2 in r_old.atoms:
                obsolete_bonds.append(b)
        for b in obsolete_bonds:
            self.bonds.remove(b)
        n = self.groups.index(r_old)
        if n > 0:
            for b in self.bonds.bondsOf(r_old.peptide.N):
                self.bonds.remove(b)
        if n < len(self.groups)-1:
            for b in self.bonds.bondsOf(r_old.peptide.C):
                self.bonds.remove(b)
        PeptideChain.replaceResidue(self.part_of, r_old, r_new)
        self.groups[n] = r_new
        self.atoms.extend(r_new.atoms)
        self.bonds.extend(r_new.bonds)
        if n > 0:
            self.bonds.append(Bonds.Bond((self.groups[n-1].peptide.C,
                                          self.groups[n].peptide.N)))
        if n < len(self.groups)-1:
            self.bonds.append(Bonds.Bond((self.groups[n].peptide.C,
                                          self.groups[n+1].peptide.N)))

    def _distanceConstraintList(self):
        atoms = self.atomList()
        return [(a1, a2, d)
                for a1, a2, d in self.part_of._distanceConstraintList()
                if a1 in atoms and a2 in atoms]

    def addDistanceConstraint(self, atom1, atom2, distance):
        chain = self
        while True:
            try:
                chain = chain.part_of
            except AttributeError:
                break
        try:
            chain.distance_constraints.append((atom1, atom2, distance))
        except AttributeError:
            chain.distance_constraints = [(atom1, atom2, distance)]

    def removeDistanceConstraints(self, universe=None):
        raise NotImplementedError

#
# Connected chains are collections of peptide chains connected by s-s bridges.
#


[docs]class ConnectedChains(PeptideChain):

    """
    Peptide chains connected by disulfide bridges
    
    A group of peptide chains connected by disulfide bridges must be considered
    a single molecule due to the presence of chemical bonds. Such a molecule
    is represented by a ConnectedChains object. These objects are created
    automatically when a Protein object is assembled. They are normally
    not used directly by application programs. When a chain with disulfide
    bridges to other chains is extracted from a Protein object, the
    return value is a SubChain object that indirectly refers to a
    ConnectedChains object.
    """

    def __init__(self, chains=None):
        if chains is not None:
            self.chains = []
            self.groups = []
            self.atoms = []
            self.bonds = Bonds.BondList([])
            self.chain_names = []
            self.model = chains[0].model
            version_spec = chains[0].version_spec
            for c in chains:
                if c.version_spec['model'] != version_spec['model']:
                    raise ValueError("mixing chains of different model: " +
                                      c.version_spec['model'] + "/" +
                                      version_spec['model'])
                ng = len(self.groups)
                self.chains.append((c.name, ng, ng+len(c.groups),
                                    c.version_spec))
                self.groups.extend(c.groups)
                self.atoms.extend(c.atoms)
                self.bonds.extend(c.bonds)
                try: name = c.name
                except AttributeError: name = ''
                self.chain_names.append(name)
            for g in self.groups:
                g.parent = self
            self.name = ''
            self.parent = None
            self.type = None
            self.configurations = {}
    is_connected_chains = True

    def _finalize(self):
        for i in range(len(self.chains)):
            c = self.chains[i]
            sub_chain = SubChain(self, self.groups[c[1]:c[2]], c[0])
            sub_chain.version_spec = c[3]
            for g in sub_chain.groups:
                g.parent = sub_chain
            self.chains[i] = sub_chain

    def __len__(self):
        return len(self.chains)

    def __getitem__(self, item):
        return self.chains[item]

    def __getslice__(self, first, last):
        raise TypeError("Can't slice connected chains")

    def _graphics(self, conf, distance_fn, model, module, options):
        if model != 'backbone':
            return ChemicalObjects.Molecule._graphics(self, conf,
                                                      distance_fn, model,
                                                      module, options)
        objects = []
        for chain in self:
            objects = objects + chain._graphics(conf, distance_fn,
                                                model, module, options)
        return objects

#
# Proteins are complexes of peptide chains, connected peptide chains,
# and possibly other things.
#


[docs]class Protein(ChemicalObjects.Complex):

    """
    Protein

    A Protein object is a special kind of :class:`~MMTK.ChemicalObjects.Complex`
    object which is made up of peptide chains and possibly ligands.

    If the atoms in the peptide chains that make up a protein have
    defined positions, sulfur bridges within chains and between
    chains will be constructed automatically during protein generation
    based on a distance criterion between cystein sidechains.


    Proteins act as sequences of chains. If p is a Protein object, then

    * len(p) yields the number of chains
    * p[i] yields chain number i

    """

    def __init__(self, *items, **properties):
        """
        :param items: either a sequence of peptide chain objects, or
                      a string, which is interpreted as the name of a
                      database definition for a protein.
                      If that definition does not exist, the string
                      is taken to be the name of a PDB file, from which
                      all peptide chains are constructed and
                      assembled into a protein.
        :keyword model: one of "all" (all-atom), "no_hydrogens" or "none"
                        (no hydrogens),"polar_hydrogens" or "polar"
                        (united-atom with only polar hydrogens),
                        "polar_charmm" (like "polar", but defining
                        polar hydrogens like in the CHARMM force field),
                        "polar_opls" (like "polar", but defining
                        polar hydrogens like in the latest OPLS force field),
                        "calpha" (only the |C_alpha| atom of each residue).
                        Default is "all".
        :type model: str
        :keyword position: the center-of-mass position of the protein
        :type position: Scientific.Geometry.Vector
        :keyword name: a name for the protein
        :type name: str
        """
        if items == (None,):
            return
        self.name = ''
        if len(items) == 1 and type(items[0]) == type(''):
            try:
                filename = Database.databasePath(items[0], 'Proteins')
                found = 1
            except IOError:
                found = 0
            if found:
                blueprint = Database.BlueprintProtein(items[0])
                items = blueprint.chains
                for attr, value in vars(blueprint).items():
                    if attr not in ['type', 'chains']:
                        setattr(self, attr, value)
            else:
                import PDB
                conf = PDB.PDBConfiguration(items[0])
                model = properties.get('model', 'all')
                items = conf.createPeptideChains(model)
        molecules = []
        for i in items:
            if ChemicalObjects.isChemicalObject(i):
                molecules.append(i)
            else:
                molecules = molecules + list(i)
        for m, i in zip(molecules, range(len(molecules))):
            m._numbers = [i]
            if not m.name:
                m.name = 'chain'+`i`
        ss = self._findSSBridges(molecules)
        new_mol = {}
        for m in molecules:
            new_mol[m] = ([m],[])
        for bond in ss:
            m1 = new_mol[bond[0].topLevelChemicalObject()]
            m2 = new_mol[bond[1].topLevelChemicalObject()]
            if m1 == m2:
                m1[1].append(bond)
            else:
                combined = (m1[0] + m2[0], m1[1] + m2[1] + [bond])
                for m in combined[0]:
                    new_mol[m] = combined
        self.molecules = []
        while new_mol:
            m = new_mol.values()[0]
            for i in m[0]:
                del new_mol[i]
            bonds = m[1]
            if len(m[0]) == 1:
                m = m[0][0]
                m._addSSBridges(bonds)
            else:
                numbers = sum((i._numbers for i in m[0]), [])
                m = ConnectedChains(m[0])
                m._numbers = numbers
                m._addSSBridges(bonds)
                m._finalize()
                for c in m:
                    c.parent = self
            m.parent = self
            self.molecules.append(m)

        self.atoms = []
        self.chains = []
        for m in self.molecules:
            self.atoms.extend(m.atoms)
            if hasattr(m, 'is_connected_chains'):
                for c, name, i in zip(range(len(m)),
                                   m.chain_names, m._numbers):
                    self.chains.append((m, c, name, i))
            else:
                try: name = m.name
                except AttributeError: name = ''
                self.chains.append((m, None, name, m._numbers[0]))
        self.chains.sort(lambda c1, c2: cmp(c1[3], c2[3]))
        self.chains = map(lambda c: c[:3], self.chains)

        self.parent = None
        self.type = None
        self.configurations = {}
        try:
            self.name = properties['name']
            del properties['name']
        except KeyError: pass
        if properties.has_key('position'):
            self.translateTo(properties['position'])
            del properties['position']
        self.addProperties(properties)

        undefined = 0
        for a in self.atoms:
            if a.position() is None:
                undefined += 1
        if undefined > 0 and undefined != len(self.atoms):
            Utility.warning('Some atoms in a protein ' +
                            'have undefined positions.')

    is_protein = True

    def __len__(self):
        return len(self.chains)

    def __getitem__(self, item):
        if isinstance(item, int):
            m, c, name = self.chains[item]
        else:
            for m, c, name in self.chains:
                if name == item:
                    break
            if name != item:
                raise ValueError('No chain with name ' + item)
        if c is None:
            return m
        else:
            return m[c]


[docs]    def residuesOfType(self, *types):
        """
        :param types: a sequence of residue codes (one- or three-letter)
        :type types: sequence of str
        :returns: all residues whose type (one- or three-letter code)
                  is contained in types
        :rtype: :class:`~MMTK.Collections.Collection`
        """
        rlist = Collections.Collection([])
        for m in self.molecules:
            if isPeptideChain(m):
                rlist = rlist + apply(m.residuesOfType, types)
        return rlist



[docs]    def backbone(self):
        """
        :returns: the peptide groups of all residues in all chains
        :rtype: :class:`~MMTK.Collections.Collection`
        """
        rlist = Collections.Collection([])
        for m in self.molecules:
            if isPeptideChain(m):
                rlist = rlist + m.backbone()
        return rlist



[docs]    def sidechains(self):
        """
        :returns: the sidechain groups of all residues in all chains
        :rtype: :class:`~MMTK.Collections.Collection`
        """
        rlist = Collections.Collection([])
        for m in self.molecules:
            if isPeptideChain(m):
                rlist = rlist + m.sidechains()
        return rlist



[docs]    def residues(self):
        """
        :returns: all residues in all chains
        :rtype: :class:`~MMTK.Collections.Collection`
        """
        rlist = Collections.Collection([])
        for m in self.molecules:
            if isPeptideChain(m):
                rlist = rlist + m.residues()
        return rlist



[docs]    def phiPsi(self, conf = None):
        """
        :returns: a list of the (phi, psi) backbone angles for all residue
                  in all chains
        :rtype: list of list of tuple of float
        """
        return [chain.phiPsi(conf) for chain in self]


    _ss_bond_max = 0.25*Units.nm

    def _findSSBridges(self, molecules):
        molecules = filter(lambda m: hasattr(m, 'is_peptide_chain'), molecules)
        cys = Collections.Collection([])
        for m in molecules:
            if m.version_spec['model'] != 'calpha':
                cys = cys + m.residuesOfType('cys') + m.residuesOfType('cyx')
        s = cys.map(lambda r: r.sidechain.S_gamma)
        ns = len(s)
        ss = []
        for i in xrange(ns-1):
            for j in xrange(i+1,ns):
                r1 = s[i].position()
                r2 = s[j].position()
                if r1 and r2 and (r1-r2).length() < self._ss_bond_max:
                    ss.append((cys[i], cys[j]))
        return ss

    def _subunits(self):
        return list(self)

    def _description(self, tag, index_map, toplevel):
        if not toplevel:
            raise ValueError
        return 'l(' + `self.__class__.__name__` + ',' + `self.name` + ',[' + \
               ','.join(o._description(tag, index_map, True) for o in self) + \
               '])'

    def _graphics(self, conf, distance_fn, model, module, options):
        if model != 'backbone':
            return ChemicalObjects.Complex._graphics(self, conf, distance_fn,
                                                     model, module, options)
        objects = []
        for chain in self:
            objects.extend(chain._graphics(conf, distance_fn,
                                           model, module, options))
        return objects

#
# Type check functions
#


[docs]def isPeptideChain(x):
    """
    :param x: any object
    :returns: True if x is a peptide chain
    :rtype: bool
    """
    return hasattr(x, 'is_peptide_chain')



[docs]def isProtein(x):
    """
    :param x: any object
    :returns: True if x is a protein
    :rtype: bool
    """
    return hasattr(x, 'is_protein')

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Source code for MMTK.Random

# Functions for finding random points and orientations.
#
# Written by: Konrad Hinsen
# 

"""
Random quantities for use in molecular simulations
"""

__docformat__ = 'restructuredtext'

from Scientific.Geometry import Vector
from Scientific.Geometry.Transformation import Rotation
from Scientific import N
from MMTK import ParticleProperties, Units

try:
    numeric = N.package
except AttributeError:
    numeric = "Numeric"

RNG = None
try:
    if numeric == "Numeric":
        import RNG
    elif numeric == "NumPy":
        import numpy.oldnumeric.rng as RNG
except ImportError:
    pass


if RNG is None:

    if numeric == "Numeric":
        from RandomArray import uniform, seed
    elif numeric == "NumPy":
        from numpy.oldnumeric.random_array import uniform, seed
    random = __import__('random')
    seed(1, 1)
    random.seed(1)

    def initializeRandomNumbersFromTime():
        random.seed()
        seed(0, 0)

    def gaussian(mean, std, shape=None):
        if shape is None:
            x = random.normalvariate(0., 1.)
        else:
            x = N.zeros(shape, N.Float)
            xflat = N.ravel(x)
            for i in range(len(xflat)):
                xflat[i] = random.normalvariate(0., 1.)
        return mean + std*x

else:

    _uniform_generator = \
                    RNG.CreateGenerator(-1, RNG.UniformDistribution(0., 1.))
    _gaussian_generator = \
                    RNG.CreateGenerator(-1, RNG.NormalDistribution(0., 1.))

    def initializeRandomNumbersFromTime():
        global _uniform_generator, _gaussian_generator
        _uniform_generator = \
                       RNG.CreateGenerator(0, RNG.UniformDistribution(0., 1.))
        _gaussian_generator = \
                       RNG.CreateGenerator(0, RNG.NormalDistribution(0., 1.))

    def uniform(x1, x2, shape=None):
        if shape is None:
            x = _uniform_generator.ranf()
        else:
            n = N.multiply.reduce(shape)
            x = _uniform_generator.sample(n)
            x.shape = shape
        return x1+(x2-x1)*x

    def gaussian(mean, std, shape=None):
        if shape is None:
            x = _gaussian_generator.ranf()
        else:
            n = N.multiply.reduce(shape)
            x = _gaussian_generator.sample(n)
            x.shape = shape
        return mean+std*x

del numeric

#
# Random point in a rectangular box centered around the origin
#

[docs]def randomPointInBox(a, b = None, c = None):
    """
    :param a: the edge length of a box along the x axis
    :type a: float
    :param b: the edge length of a box along the y axis (default: a)
    :type b: float
    :param c: the edge length of a box along the z axis (default: a)
    :type c: float
    :returns: a vector drawn from a uniform distribution within a
              rectangular box with edge lengths a, b, c.
    :rtype: Scientific.Geometry.Vector
    """
    if b is None: b = a
    if c is None: c = a
    x = uniform(-0.5*a, 0.5*a)
    y = uniform(-0.5*b, 0.5*b)
    z = uniform(-0.5*c, 0.5*c)
    return Vector(x, y, z)

#
# Random point in a sphere around the origin.
#


[docs]def randomPointInSphere(r):
    """
    :param r: the radius of a sphere
    :type r: float
    :returns: a vector drawn from a uniform distribution within
              a sphere of radius r.
    :rtype: Scientific.Geometry.Vector
    """
    rsq = r*r
    while 1:
        x = N.array([uniform(-r, r), uniform(-r, r), uniform(-r, r)])
        if N.dot(x, x) < rsq: break
    return Vector(x)

#
# Random direction (unit vector).
#


[docs]def randomDirection():
    """
    :returns: a vector drawn from a uniform distribution on the surface
              of a unit sphere.
    :rtype: Scientific.Geometry.Vector
    """
    r = randomPointInSphere(1.)
    return r.normal()



[docs]def randomDirections(n):
    """
    :param n: the number of direction vectors
    :returns: a list of n vectors drawn from a uniform distribution on
              the surface of a unit sphere. If n is negative, returns
              a deterministic list of not more than -n vectors of unit
              length (useful for testing purposes).
    :rtype: list
    """
    if n < 0:
        vs = [Vector(1., 0., 0.), Vector(0., -1., 0.), Vector(0., 0., 1.),
              Vector(-1., 1., 0.).normal(), Vector(-1., 0., 1.).normal(),
              Vector(0., 1., -1.).normal(), Vector(1., -1., 1.).normal()]
        return vs[:-n]
    else:
        return [randomDirection() for i in range(n)]

#
# Random rotation.
#


[docs]def randomRotation(max_angle = N.pi):
    """
    :param max_angle: the upper limit for the rotation angle
    :type max_angle: float
    :returns: a random rotation with a uniform axis distribution
              and angles drawn from a uniform distribution between
              -max_angle and max_angle.
    :rtype: Scientific.Geometry.Transformations.Rotation
    """
    return Rotation(randomDirection(), uniform(-max_angle, max_angle))

#
# Random velocity (gaussian)
#


[docs]def randomVelocity(temperature, mass):
    """
    :param temperature: the temperature defining a Maxwell distribution
    :type temperature: float
    :param mass: the mass of a particle
    :type mass: float
    :returns: a random velocity vector for a particle of a given mass
              at a given temperature
    :rtype: Scientific.Geometry.Vector
    """
    sigma = N.sqrt((temperature*Units.k_B)/(mass*Units.amu))
    return Vector(gaussian(0., sigma, (3,)))

#
# Random ParticleVector (gaussian)
#


[docs]def randomParticleVector(universe, width):
    """
    :param universe: a universe
    :type universe: :class:`~MMTK.Universe.Universe`
    :param width: the width (standard deviation) of a Gaussian distribution
    :type width: float
    :returns: a set of vectors drawn from a Gaussian distribution
              with a given width centered  around zero.
    :rtype: :class:`~MMTK.ParticleProperties.ParticleVector`
    """
    data = gaussian(0., 0.577350269189*width, (universe.numberOfPoints(), 3))
    return ParticleProperties.ParticleVector(universe, data)

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Source code for MMTK.Solvation

# This module contains code for solvation.
#
# Written by Konrad Hinsen
#

"""
Solvation of solute molecules
"""

__docformat__ = 'restructuredtext'

from MMTK import ChemicalObjects, Units, Universe
from MMTK.MolecularSurface import surfaceAndVolume
from MMTK.Minimization import SteepestDescentMinimizer
from MMTK.Dynamics import VelocityVerletIntegrator, VelocityScaler, \
                          TranslationRemover, RotationRemover
from MMTK.Trajectory import Trajectory, TrajectoryOutput, SnapshotGenerator, \
                            StandardLogOutput
import copy

#
# Calculate the number of solvent molecules
#

[docs]def numberOfSolventMolecules(universe, solvent, density):
    """
    :param universe: a finite universe
    :type universe: :class:`~MMTK.Universe.Universe`
    :param solvent: a molecule, or the name of a molecule in the database
    :type solvent: :class:`~MMTK.ChemicalObjects.Molecule` or str
    :param density: the density of the solvent (amu/nm**3)
    :type density: float
    :returns: the number of solvent molecules that must be added to the
              universe, in addition to whatever it already contains,
              to obtain the given solvent density.
    :rtype: int
    """
    if isinstance(solvent, basestring):
	solvent = ChemicalObjects.Molecule(solvent)
    cell_volume = universe.cellVolume()
    if cell_volume is None:
	raise TypeError("universe volume is undefined")
    solute_volume = 0.
    for o in universe._objects:
	solute_volume = solute_volume + surfaceAndVolume(o)[1]
    return int(round(density*(cell_volume-solute_volume)/solvent.mass()))

#
# Add solvent to a universe containing solute molecules.
#


[docs]def addSolvent(universe, solvent, density, scale_factor=4.):
    """
    Scales up the universe and adds as many solvent molecules
    as are necessary to obtain the specified solvent density,
    taking account of the solute molecules that are already present
    in the universe. The molecules are placed at random positions
    in the scaled-up universe, but without overlaps between
    any two molecules.

    :param universe: a finite universe
    :type universe: :class:`~MMTK.Universe.Universe`
    :param solvent: a molecule, or the name of a molecule in the database
    :type solvent: :class:`~MMTK.ChemicalObjects.Molecule` or str
    :param density: the density of the solvent (amu/nm**3)
    :type density: float
    :param scale_factor: the factor by which the initial universe is
                         expanded before adding the solvent molecules
    :type scale_factor: float
    """

    # Calculate number of solvent molecules and universe size
    if isinstance(solvent, basestring):
	solvent = ChemicalObjects.Molecule(solvent)
    cell_volume = universe.cellVolume()
    if cell_volume is None:
	raise TypeError("universe volume is undefined")
    solute = copy.copy(universe._objects)
    solute_volume = 0.
    excluded_regions = []
    for o in solute:
	solute_volume = solute_volume + surfaceAndVolume(o)[1]
	excluded_regions.append(o.boundingSphere())
    n_solvent = int(round(density*(cell_volume-solute_volume)/solvent.mass()))
    solvent_volume = n_solvent*solvent.mass()/density
    cell_volume = solvent_volume + solute_volume
    universe.translateBy(-solute.position())
    universe.scaleSize((cell_volume/universe.cellVolume())**(1./3.))

    # Scale up the universe and add solvent molecules at random positions
    universe.scaleSize(scale_factor)
    universe.scale_factor = scale_factor
    for i in range(n_solvent):
	m = copy.copy(solvent)
	m.translateTo(universe.randomPoint())
	while True:
	    s = m.boundingSphere()
	    collision = False
	    for region in excluded_regions:
		if (s.center-region.center).length() < s.radius+region.radius:
		    collision = True
		    break
	    if not collision:
		break
	    m.translateTo(universe.randomPoint())
	universe.addObject(m)
	excluded_regions.append(s)

#
# Shrink the universe to its final size
#


[docs]def shrinkUniverse(universe, temperature=300.*Units.K, trajectory=None,
                   scale_factor=0.95):
    """
    Shrinks the universe, which must have been scaled up by
    :class:`~MMTK.Solvation.addSolvent`, back to its original size.
    The compression is performed in small steps, in between which
    some energy minimization and molecular dynamics steps are executed.
    The molecular dynamics is run at the given temperature, and
    an optional trajectory can be specified in which intermediate
    configurations are stored.

    :param universe: a finite universe
    :type universe: :class:`~MMTK.Universe.Universe`
    :param temperature: the temperature at which the Molecular Dynamics
                        steps are run
    :type temperature: float
    :param trajectory: the trajectory in which the progress of the
                       shrinking procedure is stored, or a filename
    :type trajectory: :class:`~MMTK.Trajectory.Trajectory` or str
    :param scale_factor: the factor by which the universe is scaled
                         at each reduction step
    :type scale_factor: float
    """

    # Set velocities and initialize trajectory output
    universe.initializeVelocitiesToTemperature(temperature)
    if trajectory is not None:
        if isinstance(trajectory, basestring):
            trajectory = Trajectory(universe, trajectory, "w",
                                    "solvation protocol")
            close_trajectory = True
        else:
            close_trajectory = False
        actions = [TrajectoryOutput(trajectory, ["configuration"], 0, None, 1)]
        snapshot = SnapshotGenerator(universe, actions=actions)
        snapshot()

    # Do some minimization and equilibration
    minimizer = SteepestDescentMinimizer(universe, step_size = 0.05*Units.Ang)
    actions = [VelocityScaler(temperature, 0.01*temperature, 0, None, 1),
               TranslationRemover(0, None, 20)]
    integrator = VelocityVerletIntegrator(universe, delta_t = 0.5*Units.fs,
                                          actions = actions)
    for i in range(5):
	minimizer(steps = 40)
    integrator(steps = 200)

    # Scale down the system in small steps
    i = 0
    while universe.scale_factor > 1.:
        if trajectory is not None and i % 1 == 0:
            snapshot()
        i = i + 1
	step_factor = max(scale_factor, 1./universe.scale_factor)
	for object in universe:
	    object.translateTo(step_factor*object.position())
	universe.scaleSize(step_factor)
	universe.scale_factor = universe.scale_factor*step_factor
	for i in range(3):
	    minimizer(steps = 10)
	integrator(steps = 50)

    del universe.scale_factor

    if trajectory is not None:
        snapshot()
        if close_trajectory:
            trajectory.close()

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Source code for MMTK.Subspace

# This module implements subspaces for motion analysis etc.
#
# Written by Konrad Hinsen
#

"""
Linear subspaces for infinitesimal motions

This module implements subspaces for infinitesimal (or finite
small-amplitude) atomic motions. They can be used in normal mode
calculations or for analyzing complex motions. For an explanation, see:

  |  K. Hinsen and G.R. Kneller
  |  Projection methods for the analysis of complex motions in macromolecules
  |  Mol. Sim. 23:275-292 (2000)
"""

__docformat__ = 'restructuredtext'

from MMTK import Utility, ParticleProperties
from Scientific.Geometry import Vector, ex, ey, ez
from Scientific import N
import copy

#
# Import LAPACK routines
#
try:
    array_package = N.package
except AttributeError:
    array_package = "Numeric"

dgesdd = None
try:
    if array_package == "Numeric":
        from lapack_lite import dgesdd, LapackError
    else:
        from numpy.linalg.lapack_lite import dgesdd, LapackError
except ImportError: pass
if dgesdd is None:
    try:
        # PyLAPACK
        from lapack_dge import dgesdd
    except ImportError: pass
if dgesdd:
    n = 1
    array = N.zeros((n, n), N.Float)
    sv = N.zeros((n,), N.Float)
    u = N.zeros((n, n), N.Float)
    vt = N.zeros((n, n), N.Float)
    work = N.zeros((1,), N.Float)
    int_types = [N.Int, N.Int8, N.Int16, N.Int32]
    try:
        int_types.append(N.Int64)
        int_types.append(N.Int128)
    except AttributeError:
        pass
    for int_type in int_types:
        iwork = N.zeros((1,), int_type)
        try:
            dgesdd('S', n, n, array, n, sv, u, n, vt, n, work, -1, iwork, 0)
            break
        except LapackError:
            pass
    del n, array, sv, u, vt, work, iwork, int_types

del array_package

#
# A set of particle vectors that define a subspace
#
class ParticleVectorSet(object):

    def __init__(self, universe, data):
        self.universe = universe
        if type(data) == N.arraytype:
            self.n = data.shape[0]
            self.array = data
        else:
            self.n = data
            self.array = N.zeros((self.n, universe.numberOfAtoms(), 3),
                                 N.Float)

    def __len__(self):
        return self.n

    def __getitem__(self, i):
        if i >= self.n:
            raise IndexError
        return ParticleProperties.ParticleVector(self.universe, self.array[i])



[docs]class Subspace(object):

    """
    Subspace of infinitesimal atomic motions
    """

    def __init__(self, universe, vectors):
        """
        :param universe: the universe for which the subspace is created
        :type universe: :class:`~MMTK.Universe.Universe`
        :param vectors: a list of :class:`~MMTK.ParticleProperties.ParticleVector`
                        objects that define the subspace. They need not be
                        orthogonal or linearly independent.
        :type vectors: list
        """
        self.universe = universe
        self.vectors = vectors
        self._basis = None

    def __add__(self, other):
        return Subspace(self.vectors+other.vectors)

    def __len__(self):
        return len(self.vectors)

    def __getitem__(self, item):
        return self.vectors[item]


[docs]    def getBasis(self):
        """
        Construct a basis for the subspace by orthonormalization of
        the input vectors using Singular Value Decomposition. The
        basis consists of a sequence of
        :class:`~MMTK.ParticleProperties.ParticleVector`
        objects that are orthonormal in configuration space.
        :returns: the basis
        """
        if self._basis is None:
            basis = N.array([v.array for v in self.vectors], N.Float)
            shape = basis.shape
            nvectors = shape[0]
            natoms = shape[1]
            basis.shape = (nvectors, 3*natoms)
            sv = N.zeros((min(nvectors, 3*natoms),), N.Float)
            min_n_m = min(3*natoms, nvectors)
            vt = N.zeros((nvectors, min_n_m), N.Float)
            work = N.zeros((1,), N.Float)
            iwork = N.zeros((8*min_n_m,), int_type)
            if 3*natoms >= nvectors:
                result = dgesdd('O', 3*natoms, nvectors, basis, 3*natoms,
                                sv, basis, 3*natoms, vt, min_n_m,
                                work, -1, iwork, 0)
                work = N.zeros((int(work[0]),), N.Float)
                result = dgesdd('O', 3*natoms, nvectors, basis, 3*natoms,
                                sv, basis, 3*natoms, vt, min_n_m,
                                work, work.shape[0], iwork, 0)
                u = basis
            else:
                u = N.zeros((min_n_m, 3*natoms), N.Float)
                result = dgesdd('S', 3*natoms, nvectors, basis, 3*natoms,
                                sv, u, 3*natoms, vt, min_n_m,
                                work, -1, iwork, 0)
                work = N.zeros((int(work[0]),), N.Float)
                result = dgesdd('S', 3*natoms, nvectors, basis, 3*natoms,
                                sv, u, 3*natoms, vt, min_n_m,
                                work, work.shape[0], iwork, 0)
            if result['info'] != 0:
                raise ValueError('Lapack SVD: ' + `result['info']`)
            svmax = N.maximum.reduce(sv)
            nvectors = N.add.reduce(N.greater(sv, 1.e-10*svmax))
            u = u[:nvectors]
            u.shape = (nvectors, natoms, 3)
            self._basis = ParticleVectorSet(self.universe, u)
        return self._basis



[docs]    def projectionOf(self, vector):
        """
        :param vector: a particle vector
        :type vector: :class:`~MMTK.ParticleProperties.ParticleVector`
        :returns: the projection of the vector onto the subspace.
        """
        vector = vector.array
        basis = self.getBasis().array
        p = N.zeros(vector.shape, N.Float)
        for bv in basis:
            N.add(N.add.reduce(N.ravel(bv*vector))*bv, p, p)
        return ParticleProperties.ParticleVector(self.universe, p)



[docs]    def projectionComplementOf(self, vector):
        """
        :param vector: a particle vector
        :type vector: :class:`~MMTK.ParticleProperties.ParticleVector`
        :returns: the projection of the vector onto the orthogonal complement
                  of the subspace.
        """
        return vector - self.projectionOf(vector)



[docs]    def complement(self):
        """
        :returns: the orthogonal complement subspace
        :rtype: :class:`~MMTK.Subspace.Subspace`
        """
        basis = []
        for i in range(self.universe.numberOfAtoms()):
            for e in [ex, ey, ez]:
                p = ParticleProperties.ParticleVector(self.universe)
                p[i] = e
                basis.append(self.projectionComplementOf(p))
        return Subspace(self.universe, basis)




[docs]class LinkedRigidBodyMotionSubspace(Subspace):
    
    """
    Subspace for the motion of linked rigid bodies

    This class describes the same subspace as RigidBodyMotionSubspace,
    and is used by the latter. For collections of several chains of
    linked rigid bodies, RigidBodyMotionSubspace is more efficient.
    """
    def __init__(self, universe, rigid_bodies):
        """
        :param universe: the universe for which the subspace is created
        :type universe: :class:`~MMTK.Universe.Universe`
        :param rigid_bodies: a list or set of rigid bodies
                             with some common atoms
        """
        ex_ey_ez = [Vector(1.,0.,0.), Vector(0.,1.,0.), Vector(0.,0.,1.)]
        # Constructs
        # 1) a list of vectors describing the rigid-body motions of each
        #    rigid body as if it were independent.
        # 2) a list of pair-distance constraint vectors for all pairs of
        #    atoms inside a rigid body.
        # The LRB subspace is constructed from the projections of the
        # first set of vectors onto the orthogonal complement of the
        # subspace generated by the second set of vectors.
        vectors = []
        c_vectors = []
        for rb in rigid_bodies:
            atoms = rb.atomList()
            for d in ex_ey_ez:
                v = ParticleProperties.ParticleVector(universe)
                for a in atoms:
                    v[a] = d
                vectors.append(v)
            if len(atoms) > 1:
                center = rb.centerOfMass()
                iv = len(vectors)-3
                for d in ex_ey_ez:
                    v = ParticleProperties.ParticleVector(universe)
                    for a in atoms:
                        v[a] = d.cross(a.position()-center)
                    for vt in vectors[iv:]:
                        v -= v.dotProduct(vt)*vt
                    if v.dotProduct(v) > 0.:
                        vectors.append(v)
            for a1, a2 in Utility.pairs(atoms):
                distance = universe.distanceVector(a1.position(),
                                                   a2.position())
                v = ParticleProperties.ParticleVector(universe)
                v[a1] = distance
                v[a2] = -distance
                c_vectors.append(v)
        if c_vectors:
            constraints = Subspace(universe, c_vectors)
            vectors = [constraints.projectionComplementOf(v)
                       for v in vectors]
        Subspace.__init__(self, universe, vectors)




[docs]class RigidMotionSubspace(Subspace):

    """
    Subspace of rigid-body motions

    A rigid-body motion subspace is the subspace which contains
    the rigid-body motions of any number of chemical objects.
    """

    def __init__(self, universe, objects):
        """
        :param universe: the universe for which the subspace is created
        :type universe: :class:`~MMTK.Universe.Universe`
        :param objects: a sequence of objects whose rigid-body motion is
                        included in the subspace
        """
        if not Utility.isSequenceObject(objects):
            objects = [objects]
        else:
            objects = copy.copy(objects)
        # Identify connected sets of linked rigid bodies and remove
        # them from the plain rigid body list.
        atom_map = {}
        for o in objects:
            for a in o.atomIterator():
                am = atom_map.get(a, [])
                am.append(o)
                atom_map[a] = am
        rb_map = {}
        for rbs in atom_map.values():
            if len(rbs) > 1:
                for rb in rbs:
                    rb_map[rb] = rb_map.get(rb, frozenset()) \
                                 .union(frozenset(rbs))
        for rb in rb_map.keys():
            objects.remove(rb)
        while True:
            changed = False
            for rbs in rb_map.values():
                for rb in rbs:
                    s = rb_map[rb]
                    rb_map[rb] = s.union(rbs)
                    if s != rb_map[rb]:
                        changed = True
            if not changed:
                break
        lrbs = frozenset(rb_map.values())

        # Generate the subspace vectors for the isolated rigid bodies.
        ex_ey_ez = [Vector(1.,0.,0.), Vector(0.,1.,0.), Vector(0.,0.,1.)]
        vectors = []
        for o in objects:
            rb_atoms = o.atomList()
            for d in ex_ey_ez:
                v = ParticleProperties.ParticleVector(universe)
                for a in rb_atoms:
                    v[a] = d
                vectors.append(v/N.sqrt(len(rb_atoms)))
            if len(rb_atoms) > 1:
                center = o.centerOfMass()
                iv = len(vectors)-3
                for d in ex_ey_ez:
                    v = ParticleProperties.ParticleVector(universe)
                    for a in rb_atoms:
                        v[a] = d.cross(a.position()-center)
                    for vt in vectors[iv:]:
                        v -= v.dotProduct(vt)*vt
                    norm_sq = N.sqrt(v.dotProduct(v))
                    if norm_sq > 0.:
                        vectors.append(v/norm_sq)

        # Generate the subspace vectors for the linked rigid bodies.
        for lrb in lrbs:
            lrb_ss = LinkedRigidBodyMotionSubspace(universe, lrb)
            for v in lrb_ss.getBasis():
                vectors.append(v)

        Subspace.__init__(self, universe, vectors)
        # The vector set is already orthonormal by construction,
        # so we can skip the lengthy SVD procedure.
        self._basis = ParticleVectorSet(universe, len(vectors))
        for i in range(len(vectors)):
            self._basis.array[i] = vectors[i].array




[docs]class PairDistanceSubspace(Subspace):

    """
    Subspace of pair-distance motions

    A pair-distance motion subspace is the subspace which contains
    the relative motions of any number of atom pairs along
    their distance vector, e.g. bond elongation between two
    bonded atoms.
    """

    def __init__(self, universe, atom_pairs):
        """
        :param universe: the universe for which the subspace is created
        :type universe: :class:`~MMTK.Universe.Universe`
        :param atom_pairs: a sequence of atom pairs whose distance-vector
                           motion is included in the subspace
        """
        vectors = []
        for atom1, atom2 in atom_pairs:
            v = ParticleProperties.ParticleVector(universe)
            distance = atom1.position()-atom2.position()
            v[atom1] = distance
            v[atom2] = -distance
            vectors.append(v)
        Subspace.__init__(self, universe, vectors)

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Source code for MMTK.Trajectory

# This module implements trajetories and trajectory generators.
#
# Written by Konrad Hinsen
#

"""
Trajectory files and their contents
"""

__docformat__ = 'restructuredtext'

from MMTK import Collections, Units, Universe, Utility, \
                 ParticleProperties, Visualization
from Scientific.Geometry import Vector
from Scientific import N
import copy, os, sys

# Report error if the netCDF module is not available.
try:
    from Scientific.IO import NetCDF
except ImportError:
    raise Utility.MMTKError("Trajectories are not available " +
                             "because the netCDF module is missing.")
#
# Trajectory class
#

[docs]class Trajectory(object):

    #
    # Trajectory cache
    #
    # This cache is maintained for better efficiency in parallel
    # processing. The cache contains all trajectories currently open
    # for reading. When a trajectory object is unpickled, a trajectory
    # from the cache is reused if possible. This means that 


    """
    Trajectory file

    The data in a trajectory file can be accessed by step or by
    variable. If t is a Trajectory object, then:

     * len(t) is the number of steps
     * t[i] is the data for step i, in the form of a dictionary that
       maps variable names to data
     * t[i:j] and t[i:j:n] return a :class:`~MMTK.Trajectory.SubTrajectory` 
       object that refers to a subset of the total number of steps 
       (no data is copied)
     * t.variable returns the value of the named variable at all
       time steps. If the variable is a simple scalar, it is read
       completely and returned as an array. If the variable contains
       data for each atom, a :class:`~MMTK.Trajectory.TrajectoryVariable` 
       object is returned from which data at specific steps can be obtained 
       by further indexing operations.

    The routines that generate trajectories decide what variables
    are used and what they contain. The most frequently used variable
    is "configuration", which stores the positions of all atoms.
    Other common variables are "time", "velocities", "temperature",
    "pressure", and various energy terms whose name end with "_energy".
    """

    def __init__(self, object, filename, mode = 'r', comment = None,
                 double_precision = False, cycle = 0, block_size = 1):
        """
        :param object: the object whose data is stored in the trajectory file.
                       This can be 'None' when opening a file for reading;
                       in that case, a universe object is constructed from the
                       description stored in the trajectory file. This universe
                       object can be accessed via the attribute 'universe'
                       of the trajectory object.
        :type object: :class:`~MMTK.ChemicalObjects.ChemicalObject`
        :param filename: the name of the trajectory file
        :type filename: str
        :param mode: one of "r" (read-only), "w" (create new file for writing),
                     or "a" (append to existing file or create if the file does
                     not exist)
        :type mode: str
        :param comment: optional comment that is stored in the file;
                        allowed only with mode="r"
        :type comment: str
        :param double_precision: if True, data in the file is stored using
                                 double precision; default is single precision.
                                 Note that all I/O via trajectory objects is
                                 double precision; conversion from and to
                                 single precision file variables is handled
                                 automatically.
        :type double_precision: bool
        :param cycle: if non-zero, a trajectory is created for a fixed number
                      of steps equal to the value of cycle, and these steps
                      are used cyclically. This is meant for restart
                      trajectories.
        :type cycle: int
        :param block_size: an optimization parameter that influences the file
                           structure and the I/O performance for very large
                           files. A block size of 1 is optimal for sequential
                           access to configurations etc., whereas a block size
                           equal to the number of steps is optimal for reading
                           coordinates or scalar variables along the time axis.
                           The default value is 1. Note that older MMTK releases
                           always used a block size of 1 and cannot handle
                           trajectories with different block sizes.
        :type block_size: int
        """
        filename = os.path.expanduser(filename)
        self.filename = filename
        self.mode = mode
        if object is None and mode == 'r':
            file = NetCDF.NetCDFFile(filename, 'r')
            description = file.variables['description'][:].tostring()
            try:
                self.block_size = file.dimensions['minor_step_number']
            except KeyError:
                self.block_size = 1
            conf = None
            cell = None
            if self.block_size == 1:
                try:
                    conf_var = file.variables['configuration']
                    conf = conf_var[0, :, :]
                except KeyError: pass
                try:
                    cell = file.variables['box_size'][0, :]
                except KeyError: pass
            else:
                try:
                    conf_var = file.variables['configuration']
                    conf = conf_var[0, :, :, 0]
                except KeyError: pass
                try:
                    cell = file.variables['box_size'][0, :, 0]
                except KeyError: pass
            file.close()
            import Skeleton
            local = {}
            skeleton = eval(description, vars(Skeleton), local)
            universe = skeleton.make({}, conf)
            universe.setCellParameters(cell)
            object = universe
            initialize = 1
        else:
            universe = object.universe()
            if universe is None:
                raise ValueError("objects not in the same universe")
            description = None
            initialize = 0
        universe.configuration()
        if object is universe:
            index_map = None
            inverse_map = None
        else:
            if mode == 'r':
                raise ValueError("can't read trajectory for a non-universe")
            index_map = N.array([a.index for a in  object.atomList()])
            inverse_map = universe.numberOfPoints()*[None]
            for i in range(len(index_map)):
                inverse_map[index_map[i]] = i
            toplevel = set()
            for o in Collections.Collection(object):
                toplevel.add(o.topLevelChemicalObject())
            object = Collections.Collection(list(toplevel))
        if description is None:
            description = universe.description(object, inverse_map)
        import MMTK_trajectory
        self.trajectory = MMTK_trajectory.Trajectory(universe, description,
                                                     index_map, filename,
                                                     mode + 's',
                                                     double_precision, cycle,
                                                     block_size)
        self.universe = universe
        self.index_map = index_map
        try:
            self.block_size = \
                       self.trajectory.file.dimensions['minor_step_number']
        except KeyError:
            self.block_size = 1
        if comment is not None:
            if mode == 'r':
                raise IOError('cannot add comment in read-only mode')
            self.trajectory.file.comment = comment
        if initialize and conf is not None:
            self.universe.setFromTrajectory(self)
        self.particle_trajectory_reader = ParticleTrajectoryReader(self)

    def __getstate__(self):
        if self.mode != 'r':
            raise ValueError("Cannot copy or pickle write-mode trajectories")
        return self.filename

    def __setstate__(self, state):
        self.__init__(None, state)


[docs]    def flush(self):
        """
        Make sure that all data that has been written to the trajectory
        is also written to the file.
        """
        self.trajectory.flush()



[docs]    def close(self):
        """
        Close the trajectory file. Must be called after writing to
        ensure that all buffered data is written to the file. No data
        access is possible after closing a file.
        """
        self.trajectory.close()


    def __len__(self):
        return self.trajectory.nsteps

    def __getitem__(self, item):
        if not isinstance(item, int):
            return SubTrajectory(self, N.arange(len(self)))[item]
        if item < 0:
            item += len(self)
        if item >= len(self):
            raise IndexError
        data = {}
        for name, var in self.trajectory.file.variables.items():
            if 'step_number' not in var.dimensions:
                continue
            if 'atom_number' in var.dimensions:
                if 'xyz' in var.dimensions:
                    array = ParticleProperties.ParticleVector(self.universe,
                                self.trajectory.readParticleVector(name, item))
                else:
                    array = ParticleProperties.ParticleScalar(self.universe,
                                self.trajectory.readParticleScalar(name, item))
            else:
                bs = self.block_size
                if bs == 1:
                    array = var[item]
                else:
                    if len(var.shape) == 2:
                        array = var[item/bs, item%bs]
                    else:
                        array = var[item/bs, ..., item%bs]
            data[name] = 0.+array
        if data.has_key('configuration'):
            box = data.get('box_size', None)
            if box is not None:
                box = box.astype(N.Float)
            conf = data['configuration']
            data['configuration'] = \
               ParticleProperties.Configuration(conf.universe, conf.array, box)
        return data

    def __getslice__(self, first, last):
        return self[(slice(first, last),)]

    def __getattr__(self, name):
        try:
            var = self.trajectory.file.variables[name]
        except KeyError:
            raise AttributeError("no variable named " + name)
        if 'atom_number' in var.dimensions:
            return TrajectoryVariable(self.universe, self, name)
        elif 'box_size_length' in var.dimensions:
            if 'minor_step_number' in var.dimensions:
                bs = N.transpose(var[:], [0, 2, 1])
                bs = N.reshape(bs, (bs.shape[0]*bs.shape[1], bs.shape[2]))
                return bs[:len(self)]
            else:
                return var[:]
        else:
            return N.ravel(N.array(var))[:len(self)]

    def defaultStep(self):
        try:
            step = int(self.trajectory.file.last_step[0])
        except AttributeError:
            step = 0
        return step


[docs]    def readParticleTrajectory(self, atom, first=0, last=None, skip=1,
                               variable = "configuration"):
        """
        Read trajectory information for a single atom but for multiple
        time steps.

        :param atom: the atom whose trajectory is requested
        :type atom: :class:`~MMTK.ChemicalObjects.Atom`
        :param first: the number of the first step to be read
        :type first: int
        :param last: the number of the first step not to be read.
                     A value of None indicates that the
                     whole trajectory should be read.
        :type last: int
        :param skip: the number of steps to skip between two steps read
        :type skip: int
        :param variable: the name of the trajectory variable to be read.
                         If the variable is "configuration", the resulting
                         trajectory is made continuous by eliminating all
                         jumps caused by periodic boundary conditions.
                         The pseudo-variable "box_coordinates" can be read
                         to obtain the values of the variable "configuration"
                         scaled to box coordinates. For non-periodic universes
                         there is no difference between box coordinates
                         and real coordinates.
        :type variable: str
        :returns: the trajectory for a single atom
        :rtype: :class:`~MMTK.Trajectory.ParticleTrajectory`
        """
        return ParticleTrajectory(self, atom, first, last, skip, variable)



[docs]    def readRigidBodyTrajectory(self, object, first=0, last=None, skip=1,
                                reference = None):
        """
        Read the positions for an object at multiple time steps
        and extract the rigid-body motion (center-of-mass position plus
        orientation as a quaternion) by an optimal-transformation fit.

        :param object: the object whose rigid-body trajectory is requested
        :type object: :class:`~MMTK.Collections.GroupOfAtoms`
        :param first: the number of the first step to be read
        :type first: int
        :param last: the number of the first step not to be read.
                     A value of None indicates that the
                     whole trajectory should be read.
        :type last: int
        :param skip: the number of steps to skip between two steps read
        :type skip: int
        :param reference: the reference configuration for the fit
        :type reference: :class:`~MMTK.ParticleProperties.Configuration`
        :returns: the trajectory for a single rigid body
        :rtype: :class:`~MMTK.Trajectory.RigidBodyTrajectory`
        """
        return RigidBodyTrajectory(self, object, first, last, skip, reference)



[docs]    def variables(self):
        """
        :returns: a list of the names of all variables that are stored
                  in the trajectory
        :rtype: list of str
        """
        vars = copy.copy(self.trajectory.file.variables.keys())
        vars.remove('step')
        try:
            vars.remove('description')
        except ValueError: pass
        return vars



[docs]    def view(self, first=0, last=None, skip=1, object = None):
        """
        Show an animation of the trajectory using an external visualization
        program.

        :param first: the number of the first step in the animation
        :type first: int
        :param last: the number of the first step not to include in the
                     animation. A value of None indicates that the
                     whole trajectory should be used.
        :type last: int
        :param skip: the number of steps to skip between two steps read
        :type skip: int
        :param object: the object to be animated, which must be in the
                       universe stored in the trajectory. None
                       stands for the whole universe.
        :type object: :class:`~MMTK.Collections.GroupOfAtoms`
        """
        Visualization.viewTrajectory(self, first, last, skip, object)


    def _boxTransformation(self, pt_in, pt_out, to_box=0):
        from MMTK_trajectory import boxTransformation
        try:
            box_size = self.trajectory.recently_read_box_size
        except AttributeError:
            return
        boxTransformation(self.universe._spec,
                          pt_in, pt_out, box_size, to_box)




[docs]class SubTrajectory(object):

    """
    Reference to a subset of a trajectory

    A SubTrajectory object is created by slicing a Trajectory object
    or another SubTrajectory object. It provides all the operations
    defined on Trajectory objects.
    """

    def __init__(self, trajectory, indices):
        self.trajectory = trajectory
        self.indices = indices
        self.universe = trajectory.universe

    def __len__(self):
        return len(self.indices)

    def __getitem__(self, item):
        if isinstance(item, int):
            return self.trajectory[int(self.indices[item])]
        else:
            return SubTrajectory(self.trajectory, self.indices[item])

    def __getslice__(self, first, last):
        return self[(slice(first, last),)]

    def __getattr__(self, name):
        return SubVariable(getattr(self.trajectory, name), self.indices)

    def readParticleTrajectory(self, atom, first=0, last=None, skip=1,
                               variable = "configuration"):
        if last is None:
            last = len(self.indices)
        indices = self.indices[first:last:skip]
        first = indices[0]
        last = indices[-1]+1
        if len(self.indices) > 1:
            skip = self.indices[1]-self.indices[0]
        else:
            skip = 1
        return self.trajectory.readParticleTrajectory(atom, first, last,
                                                      skip, variable)

    def readRigidBodyTrajectory(self, object, first=0, last=None, skip=1,
                                reference = None):
        if last is None:
            last = len(self.indices)
        indices = self.indices[first:last:skip]
        first = indices[0]
        last = indices[-1]+1
        if len(self.indices) > 1:
            skip = self.indices[1]-self.indices[0]
        else:
            skip = 1
        return RigidBodyTrajectory(self.trajectory, object,
                                   first, last, skip, reference)

    def variables(self):
        return self.trajectory.variables()

    def view(self, first=0, last=None, step=1, subset = None):
        Visualization.viewTrajectory(self, first, last, step, subset)

    def close(self):
        del self.trajectory

    def _boxTransformation(self, pt_in, pt_out, to_box=0):
        Trajectory._boxTransformation(self.trajectory, pt_in, pt_out, to_box)

#
# Trajectory variables
#


[docs]class TrajectoryVariable(object):

    """
    Variable in a trajectory

    A TrajectoryVariable object is created by extracting a variable from
    a Trajectory object if that variable contains data for each atom and
    is thus potentially large. No data is read from the trajectory file
    when a TrajectoryVariable object is created; the read operation
    takes place when the TrajectoryVariable is indexed with a specific
    step number.

    If t is a TrajectoryVariable object, then:

     * len(t) is the number of steps
     * t[i] is the data for step i, in the form of a ParticleScalar,
       a ParticleVector, or a Configuration object, depending on the
       variable
     * t[i:j] and t[i:j:n] return a SubVariable object that refers
       to a subset of the total number of steps
    """
    
    def __init__(self, universe, trajectory, name):
        self.universe = universe
        self.trajectory = trajectory
        self.name = name
        self.var = self.trajectory.trajectory.file.variables[self.name]
        if self.name == 'configuration':
            try:
                self.box_size = \
                        self.trajectory.trajectory.file.variables['box_size']
            except KeyError:
                self.box_size = None

    def __len__(self):
        return len(self.trajectory)

    def __getitem__(self, item):
        if not isinstance(item, int):
            return SubVariable(self, N.arange(len(self)))[item]
        item = int(item) # gets rid of numpy.intXX objects
        if item < 0:
            item = item + len(self.trajectory)
        if item >= len(self.trajectory):
            raise IndexError
        if self.name == 'configuration':
            if self.box_size is None:
                box = None
            elif len(self.box_size.shape) == 3:
                bs = self.trajectory.block_size
                box = self.box_size[item/bs, :, item%bs].astype(N.Float)
            else:
                box = self.box_size[item].astype(N.Float)
            array = ParticleProperties.Configuration(self.universe,
                self.trajectory.trajectory.readParticleVector(self.name, item),
                box)
        elif 'xyz' in self.var.dimensions:
            array = ParticleProperties.ParticleVector(self.universe,
                self.trajectory.trajectory.readParticleVector(self.name, item))
        else:
            array = ParticleProperties.ParticleScalar(self.universe,
                self.trajectory.trajectory.readParticleScalar(self.name, item))
        return array

    def __getslice__(self, first, last):
        return self[(slice(first, last),)]

    def average(self):
        sum = self[0]
        for value in self[1:]:
            sum = sum + value
        return sum/len(self)



[docs]class SubVariable(TrajectoryVariable):

    """
    Reference to a subset of a :class:`~MMTK.Trajectory.TrajectoryVariable`

    A SubVariable object is created by slicing a TrajectoryVariable
    object or another SubVariable object. It provides all the operations
    defined on TrajectoryVariable objects.
    """

    def __init__(self, variable, indices):
        self.variable = variable
        self.indices = indices

    def __len__(self):
        return len(self.indices)

    def __getitem__(self, item):
        if isinstance(item, int):
            return self.variable[self.indices[item]]
        else:
            return SubVariable(self.variable, self.indices[item])

    def __getslice__(self, first, last):
        return self[(slice(first, last),)]

#
# Trajectory consisting of multiple files
#


[docs]class TrajectorySet(object):

    """
    Trajectory file set

    A TrajectorySet permits to treat a sequence of trajectory files
    like a single trajectory for reading data. It behaves exactly like a
    :class:`~MMTK.Trajectory.Trajectory` object. The trajectory files must all contain data
    for the same system. The variables stored in the individual files
    need not be the same, but only variables common to all files
    can be accessed.

    Note: depending on how the sequence of trajectories was constructed,
    the first configuration of each trajectory might be the same as the
    last one in the preceding trajectory. To avoid counting it twice,
    specify (filename, 1, None, 1) for all but the first trajectory in
    the set.
    """

    def __init__(self, object, filenames):
        """
        :param object: the object whose data is stored in the trajectory files.
                       This can be (and usually is) None;
                       in that case, a universe object is constructed from the
                       description stored in the first trajectory file.
                       This universe object can be accessed via the attribute
                       universe of the trajectory set object.
        :param filenames: a list of trajectory file names or
                          (filename, first_step, last_step, increment)
                          tuples.
        """
        first = filenames[0]
        if isinstance(first, tuple):
            first = Trajectory(object, first[0])[first[1]:first[2]:first[3]]
        else:
            first = Trajectory(object, first)
        self.universe = first.universe
        self.trajectories = [first]
        self.nsteps = [0, len(first)]
        self.cell_parameters = []
        for file in filenames[1:]:
            if isinstance(file, tuple):
                t = Trajectory(self.universe, file[0])[file[1]:file[2]:file[3]]
            else:
                t = Trajectory(self.universe, file)
            self.trajectories.append(t)
            self.nsteps.append(self.nsteps[-1]+len(t))
            try:
                self.cell_parameters.append(t[0]['box_size'])
            except KeyError:
                pass
        vars = {}
        for t in self.trajectories:
            for v in t.variables():
                vars[v] = vars.get(v, 0) + 1
        self.vars = []
        for v, count in vars.items():
            if count == len(self.trajectories):
                self.vars.append(v)

    def close(self):
        for t in self.trajectories:
            t.close()

    def __len__(self):
        return self.nsteps[-1]

    def __getitem__(self, item):
        if not isinstance(item, int):
            return SubTrajectory(self, N.arange(len(self)))[item]
        if item >= len(self):
            raise IndexError
        tindex = N.add.reduce(N.greater_equal(item, self.nsteps))-1
        return self.trajectories[tindex][item-self.nsteps[tindex]]

    def __getslice__(self, first, last):
        return self[(slice(first, last),)]

    def __getattr__(self, name):
        if name not in self.vars+['step']:
            raise AttributeError("no variable named " + name)
        var = self.trajectories[0].trajectory.file.variables[name]
        if 'atom_number' in var.dimensions:
            return TrajectorySetVariable(self.universe, self, name)
        else:
            data = []
            for t in self.trajectories:
                var = t.trajectory.file.variables[name]
                data.append(N.ravel(N.array(var))[:len(t)])
            return N.concatenate(data)

    def readParticleTrajectory(self, atom, first=0, last=None, skip=1,
                               variable = "configuration"):
        total = None
        self.steps_read = []
        for i in range(len(self.trajectories)):
            if self.nsteps[i+1] <= first:
                self.steps_read.append(0)
                continue
            if last is not None and self.nsteps[i] >= last:
                break
            n = max(0, (self.nsteps[i]-first+skip-1)/skip)
            start = first+skip*n-self.nsteps[i]
            n = (self.nsteps[i+1]-first+skip-1)/skip
            stop = first+skip*n
            if last is not None:
                stop = min(stop, last)
            stop = stop-self.nsteps[i]
            if start >= 0 and start < self.nsteps[i+1]-self.nsteps[i]:
                t = self.trajectories[i]
                pt = t.readParticleTrajectory(atom, start, stop, skip,
                                              variable)
                self.steps_read.append((stop-start)/skip)
                if total is None:
                    total = pt
                else:
                    if variable == "configuration" \
                       and self.cell_parameters[0] is not None:
                        jump = pt.array[0]-total.array[-1]
                        mult = -(jump/self.cell_parameters[i-1]).astype('i')
                        if len(N.nonzero(mult)) > 0:
                            t._boxTransformation(pt.array, pt.array, 1)
                            N.add(pt.array, mult[N.NewAxis, : ],
                                        pt.array)
                            t._boxTransformation(pt.array, pt.array, 0)
                            jump = pt.array[0] - total.array[-1]
                        mask = N.less(jump,
                                            -0.5*self.cell_parameters[i-1])- \
                               N.greater(jump,
                                               0.5*self.cell_parameters[i-1])
                        if len(N.nonzero(mask)) > 0:
                            t._boxTransformation(pt.array, pt.array, 1)
                            N.add(pt.array, mask[N.NewAxis, :],
                                        pt.array)
                            t._boxTransformation(pt.array, pt.array, 0)
                    elif variable == "box_coordinates" \
                       and self.cell_parameters[0] is not None:
                        jump = pt.array[0]-total.array[-1]
                        mult = -jump.astype('i')
                        if len(N.nonzero(mult)) > 0:
                            N.add(pt.array, mult[N.NewAxis, : ],
                                        pt.array)
                            jump = pt.array[0] - total.array[-1]
                        mask = N.less(jump, -0.5)- \
                               N.greater(jump, 0.5)
                        if len(N.nonzero(mask)) > 0:
                            N.add(pt.array, mask[N.NewAxis, :],
                                        pt.array)
                    total.array = N.concatenate((total.array, pt.array))
            else:
                self.steps_read.append(0)
        return total

    def readRigidBodyTrajectory(self, object, first=0, last=None, skip=1,
                                reference = None):
        return RigidBodyTrajectory(self, object, first, last, skip, reference)

    def _boxTransformation(self, pt_in, pt_out, to_box=0):
        n = 0
        for i in range(len(self.steps_read)):
            t = self.trajectories[i]
            steps = self.steps_read[i]
            if steps > 0:
                t._boxTransformation(pt_in[n:n+steps], pt_out[n:n+steps],
                                     to_box)
            n = n + steps

    def variables(self):
        return self.vars

    def view(self, first=0, last=None, step=1, object = None):
        Visualization.viewTrajectory(self, first, last, step, object)




[docs]class TrajectorySetVariable(TrajectoryVariable):

    """
    Variable in a trajectory set

    A TrajectorySetVariable object is created by extracting a variable from
    a TrajectorySet object if that variable contains data for each atom and
    is thus potentially large. It behaves exactly like a TrajectoryVariable
    object.
    """
    
    def __init__(self, universe, trajectory_set, name):
        self.universe = universe
        self.trajectory_set = trajectory_set
        self.name = name

    def __len__(self):
        return len(self.trajectory_set)

    def __getitem__(self, item):
        if not isinstance(item, int):
            return SubVariable(self, N.arange(len(self)))[item]
        if item >= len(self.trajectory_set):
            raise IndexError
        tindex = N.add.reduce(N.greater_equal(item,
                                              self.trajectory_set.nsteps))-1
        step = item-self.trajectory_set.nsteps[tindex]
        t = self.trajectory_set.trajectories[tindex]
        return getattr(t, self.name)[step]

#
# Cache for atom trajectories
#

class ParticleTrajectoryReader(object):

    def __init__(self, trajectory):
        self.trajectory = trajectory
        self.natoms = self.trajectory.universe.numberOfAtoms()
        self._trajectory = trajectory.trajectory
        self.cache = {}
        self.cache_lifetime = 2

    def __call__(self, atom, variable, first, last, skip, correct, box):
        if isinstance(atom, int):
            index = atom
        else:
            index = atom.index
            if atom.universe() is not self.trajectory.universe:
                raise ValueError("objects not in the same universe")
        key = (index, variable, first, last, skip, correct, box)
        data, count = self.cache.get(key, (None, 0))
        if data is not None:
            self.cache[key] = (data, self.cache_lifetime)
            return data
        delete = []
        for k, value in self.cache.items():
            data, count = value
            count -= 1
            if count == 0:
                delete.append(k)
            else:
                self.cache[k] = (data, count)
        for k in delete:
            del self.cache[k]
        cache_size = min(10, max(1, 100000/max(1, len(self.trajectory))))
        natoms = min(cache_size, self.natoms-index)
        data = self._trajectory.readParticleTrajectories(index, natoms,
                                                         variable,
                                                         first, last, skip,
                                                         correct, box)
        for i in range(natoms):
            key = (index+i, variable, first, last, skip, correct, box)
            self.cache[key] = (data[i], self.cache_lifetime)
        return data[0]

#
# Single-atom trajectory
#

[docs]class ParticleTrajectory(object):

    """
    Trajectory data for a single particle

    A ParticleTrajectory object is created by calling the method
    :func:`~MMTK.Trajectory.Trajectory.readParticleTrajectory`
    on a :class:`~MMTK.Trajectory.Trajectory` object.

    If pt is a ParticleTrajectory object, then

     * len(pt) is the number of steps stored in it
     * pt[i] is the value at step i (a vector)
    """
    
    def __init__(self, trajectory, atom, first=0, last=None, skip=1,
                 variable = "configuration"):
        if last is None:
            last = len(trajectory)
        if variable == "box_coordinates":
            variable = "configuration"
            box = 1
        else:
            box = 0
        reader = trajectory.particle_trajectory_reader
        self.array = reader(atom, variable, first, last, skip,
                            variable == "configuration", box)

    def __len__(self):
        return self.array.shape[0]

    def __getitem__(self, index):
        return Vector(self.array[index])


[docs]    def translateBy(self, vector):
        """
        Adds a vector to the values at all steps. This does B{not}
        change the data in the trajectory file.

        :param vector: the vector to be added
        :type vector: Scientific.Geometry.Vector
        """
        N.add(self.array, vector.array[N.NewAxis, :], self.array)

#
# Rigid-body trajectory
#


[docs]class RigidBodyTrajectory(object):

    """
    Rigid-body trajectory data

    A RigidBodyTrajectory object is created by calling the method
    :func:`~MMTK.Trajectory.Trajectory.readRigidBodyTrajectory`
    on a :class:`~MMTK.Trajectory.Trajectory` object.

    If rbt is a RigidBodyTrajectory object, then

     * len(rbt) is the number of steps stored in it
     * rbt[i] is the value at step i (a vector for the center of mass
       and a quaternion for the orientation)
    """
    
    def __init__(self, trajectory, object, first=0, last=None, skip=1,
                 reference = None):
        self.trajectory = trajectory
        universe = trajectory.universe
        if last is None: last = len(trajectory)
        first_conf = trajectory.configuration[first]
        offset = universe.contiguousObjectOffset([object], first_conf, True)
        if reference is None:
            reference = first_conf
        reference = universe.contiguousObjectConfiguration([object], reference)
        steps = (last-first+skip-1)/skip
        mass = object.mass()
        ref_cms = object.centerOfMass(reference)
        atoms = object.atomList()

        possq = N.zeros((steps,), N.Float)
        cross = N.zeros((steps, 3, 3), N.Float)
        rcms = N.zeros((steps, 3), N.Float)

        # cms of the CONTIGUOUS object made of CONTINUOUS atom trajectories 
        for a in atoms:
            r = trajectory.readParticleTrajectory(a, first, last, skip,
                                                  "box_coordinates").array
            w = a._mass/mass
            N.add(rcms, w*r, rcms)
            if offset is not None:
                N.add(rcms, w*offset[a].array, rcms)
        
        # relative coords of the CONTIGUOUS reference
        r_ref = N.zeros((len(atoms), 3), N.Float)
        for a in range(len(atoms)):
            r_ref[a] = atoms[a].position(reference).array - ref_cms.array

        # main loop: storing data needed to fill M matrix 
        for a in range(len(atoms)):
            r = trajectory.readParticleTrajectory(atoms[a],
                                                  first, last, skip,
                                                  "box_coordinates").array
            r = r - rcms # (a-b)**2 != a**2 - b**2
            if offset is not None:
                N.add(r, offset[atoms[a]].array,r)
            trajectory._boxTransformation(r, r)
            w = atoms[a]._mass/mass
            N.add(possq, w*N.add.reduce(r*r, -1), possq)
            N.add(possq, w*N.add.reduce(r_ref[a]*r_ref[a],-1),
                        possq)
            N.add(cross, w*r[:,:,N.NewAxis]*r_ref[N.NewAxis,
                                                              a,:],cross)
        self.trajectory._boxTransformation(rcms, rcms)

        # filling matrix M (formula no 40)
        k = N.zeros((steps, 4, 4), N.Float)
        k[:, 0, 0] = -cross[:, 0, 0]-cross[:, 1, 1]-cross[:, 2, 2]
        k[:, 0, 1] = cross[:, 1, 2]-cross[:, 2, 1]
        k[:, 0, 2] = cross[:, 2, 0]-cross[:, 0, 2]
        k[:, 0, 3] = cross[:, 0, 1]-cross[:, 1, 0]
        k[:, 1, 1] = -cross[:, 0, 0]+cross[:, 1, 1]+cross[:, 2, 2]
        k[:, 1, 2] = -cross[:, 0, 1]-cross[:, 1, 0]
        k[:, 1, 3] = -cross[:, 0, 2]-cross[:, 2, 0]
        k[:, 2, 2] = cross[:, 0, 0]-cross[:, 1, 1]+cross[:, 2, 2]
        k[:, 2, 3] = -cross[:, 1, 2]-cross[:, 2, 1]
        k[:, 3, 3] = cross[:, 0, 0]+cross[:, 1, 1]-cross[:, 2, 2]
        del cross
        for i in range(1, 4):
            for j in range(i):
                k[:, i, j] = k[:, j, i]
        N.multiply(k, 2., k)
        for i in range(4):
            N.add(k[:,i,i], possq, k[:,i,i])
        del possq

        quaternions = N.zeros((steps, 4), N.Float)
        fit = N.zeros((steps,), N.Float)
        from Scientific.LA import eigenvectors
        for i in range(steps):
            e, v = eigenvectors(k[i])
            j = N.argmin(e)
            if e[j] < 0.:
                fit[i] = 0.
            else:
                fit[i] = N.sqrt(e[j])
            if v[j,0] < 0.: quaternions[i] = -v[j] # eliminate jumps
            else: quaternions[i] = v[j]
        self.fit = fit
        self.cms = rcms
        self.quaternions = quaternions

    def __len__(self):
        return self.cms.shape[0]

    def __getitem__(self, index):
        from Scientific.Geometry.Quaternion import Quaternion
        return Vector(self.cms[index]), Quaternion(self.quaternions[index])

#
# Type check for trajectory objects
#


[docs]def isTrajectory(object):
    """
    :param object: any Python object
    :returns: True if object is a trajectory
    """
    import MMTK_trajectory
    return isinstance(object, (Trajectory, MMTK_trajectory.trajectory_type))

#
# Base class for all objects that generate trajectories
#


[docs]class TrajectoryGenerator(object):

    """
    Trajectory generator base class

    This base class implements the common aspects of everything that
    generates trajectories: integrators, minimizers, etc.
    """

    def __init__(self, universe, options):
        self.universe = universe
        self.options = options

    def setCallOptions(self, options):
        self.call_options = options

    def getActions(self):
        try:
            self.actions = self.getOption('actions')
        except ValueError:
            self.actions = []
        try:
            if self.getOption('background'):
                import MMTK_state_accessor
                self.state_accessor = MMTK_state_accessor.StateAccessor()
                self.actions.append(self.state_accessor)
        except ValueError:
            pass
        try:
            steps = self.getOption('steps')
        except ValueError:
            steps = None
        return map(lambda a, t=self, s=steps: a.getSpecificationList(t, s),
                   self.actions)

    def cleanupActions(self):
        for a in self.actions:
            a.cleanup()

    def getOption(self, option):
        try:
            value = self.call_options[option]
        except KeyError:
            try:
                value = self.options[option]
            except KeyError:
                try:
                    value = self.default_options[option]
                except KeyError:
                    raise ValueError('undefined option: ' + option)
        return value

    def optionString(self, options):
        s = ''
        for o in options:
            s = s + o + '=' + `self.getOption(o)` + ', '
        return s[:-2]

    def run(self, function, args):
        if self.getOption('background'):
            import ThreadManager
            return ThreadManager.TrajectoryGeneratorThread(self.universe,
                                      function, args, self.state_accessor)
        else:
            apply(function, args)
        
#
# Trajectory action base class
#


[docs]class TrajectoryAction(object):

    """
    Trajectory action base class

    Subclasses of this base class implement the actions that can be
    inserted into trajectory generation at regular intervals.
    """

    def __init__(self, first, last, skip):
        self.first = first
        self.last = last
        self.skip = skip

    spec_type = 'function'

    def _getSpecificationList(self, trajectory_generator, steps):
        first = self.first
        last = self.last
        if first < 0:
            first = first + steps
        if last is None:
            import MMTK_trajectory
            last = MMTK_trajectory.maxint
        elif last < 0:
            last = last + steps+1
        return (self.spec_type, first, last, self.skip)

    def getSpecificationList(self, trajectory_generator, steps):
        return self._getSpecificationList(trajectory_generator, steps) \
               + (self.Cfunction, self.parameters)

    def cleanup(self):
        pass



[docs]class TrajectoryOutput(TrajectoryAction):

    """
    Trajectory output action

    A TrajectoryOutput object can be used in the action list of any
    trajectory-generating operation. It writes any of the available
    data to a trajectory file. It is possible to use several
    TrajectoryOutput objects at the same time in order to produce
    multiple trajectories from a single run.
    """

    def __init__(self, trajectory, data = None,
                 first=0, last=None, skip=1):
        """
        :param trajectory: a trajectory object or a string, which is
                           interpreted as the name of a file that is opened
                           as a trajectory in append mode
        :param data: a list of data categories. All variables provided by the
                     trajectory generator that fall in any of the listed
                     categories are written to the trajectory file. See the
                     descriptions of the trajectory generators for a list
                     of variables and categories. By default (data = None)
                     the categories "configuration", "energy",
                     "thermodynamic", and "time" are written.
        :param first: the number of the first step at which the action is run
        :type first: int
        :param last: the number of the step at which the action is suspended.
                     A value of None indicates that the action should
                     be applied indefinitely.
        :type last: int
        :param skip: the number of steps to skip between two action runs
        :type skip: int
        """
        TrajectoryAction.__init__(self, first, last, skip)
        self.destination = trajectory
        self.categories = data
        self.must_be_closed = None

    spec_type = 'trajectory'

    def getSpecificationList(self, trajectory_generator, steps):
        if type(self.destination) == type(''):
            destination = self._setupDestination(self.destination,
                                                 trajectory_generator.universe)
        else:
            destination = self.destination
        if self.categories is None:
            categories = self._defaultCategories(trajectory_generator)
        else:
            if self.categories == 'all' or self.categories == ['all']:
                categories = trajectory_generator.available_data
            else:
                categories = self.categories
                for item in categories:
                    if item not in trajectory_generator.available_data:
                        raise ValueError('data item %s is not available' % item)
        return self._getSpecificationList(trajectory_generator, steps) \
               + (destination, categories)

    def _setupDestination(self, destination, universe):
        self.must_be_closed = Trajectory(universe, destination, 'a')
        return self.must_be_closed
        
    def cleanup(self):
        if self.must_be_closed is not None:
            self.must_be_closed.close()

    def _defaultCategories(self, trajectory_generator):
        available = trajectory_generator.available_data
        return tuple(filter(lambda x, a=available: x in a, self.default_data))

    default_data = ['configuration', 'energy', 'thermodynamic', 'time']



[docs]class RestartTrajectoryOutput(TrajectoryOutput):

    """
    Restart trajectory output action

    A RestartTrajectoryOutput object is used in the action list of any
    trajectory-generating operation. It writes those variables to a
    trajectory that the trajectory generator declares as necessary
    for restarting.
    """

    def __init__(self, trajectory, skip=100, length=3):
        """
        :param trajectory: a trajectory object or a string, which is interpreted
                           as the name of a file that is opened as a trajectory
                           in append mode with a cycle length of length and
                           double-precision variables
        :param skip: the number of steps between two write operations to the
                     restart trajectory
        :type skip: int
        :param length: the number of steps stored in the restart trajectory;
                       used only if trajectory is a string
        """
        TrajectoryAction.__init__(self, 0, None, skip)
        self.destination = trajectory
        self.categories = None
        self.length = length

    def _setupDestination(self, destination, universe):
        self.must_be_closed = Trajectory(universe, destination, 'a',
                                         'Restart trajectory', 1, self.length)
        return self.must_be_closed
        
    def _defaultCategories(self, trajectory_generator):
        if trajectory_generator.restart_data is None:
            raise ValueError("Trajectory generator does not permit restart")
        return trajectory_generator.restart_data



[docs]class LogOutput(TrajectoryOutput):

    """
    Protocol file output action

    A LogOutput object can be used in the action list of any
    trajectory-generating operation. It writes any of the available
    data to a text file.
    """

    def __init__(self, file, data = None, first=0, last=None, skip=1):
        """
        :param file: a file object or a string, which is interpreted as the
                     name of a file that is opened in write mode
        :param data: a list of data categories. All variables provided by the
                     trajectory generator that fall in any of the listed
                     categories are written to the trajectory file. See the
                     descriptions of the trajectory generators for a list
                     of variables and categories. By default (data = None)
                     the categories "configuration", "energy",
                     "thermodynamic", and "time" are written.
        :param first: the number of the first step at which the action is run
        :type first: int
        :param last: the number of the step at which the action is suspended.
                     A value of None indicates that the action should
                     be applied indefinitely.
        :type last: int
        :param skip: the number of steps to skip between two action runs
        :type skip: int
        """
        TrajectoryOutput.__init__(self, file, data, first, last, skip)

    def _setupDestination(self, destination, universe):
        self.must_be_closed = open(destination, 'w')
        return self.must_be_closed

    spec_type = 'print'

    default_data = ['energy', 'time']



[docs]class StandardLogOutput(LogOutput):

    """
    Standard protocol output action

    A StandardLogOutput object can be used in the action list of any
    trajectory-generating operation. It is a specialization of
    LogOutput to the most common case and writes data in the categories
    "time" and "energy" to the standard output stream.

    :param skip: the number of steps to skip between two action runs
    :type skip: int
    """

    def __init__(self, skip=50):
        LogOutput.__init__(self, sys.stdout, None, 0, None, skip)

#
# Snapshot generator
#


[docs]class SnapshotGenerator(TrajectoryGenerator):

    """
    Trajectory generator for single steps

    A SnapshotGenerator is used for manual assembly of trajectory
    files. At each call it writes one step to the trajectory,
    using the current state of the universe (configuration, velocities, etc.)
    and data provided explicitly with the call.

    Each call to the SnapshotGenerator object produces one step.
    All the keyword options can be specified either when
    creating the generator or when calling it.
    """

    def __init__(self, universe, **options):
        """
        :param universe: the universe on which the generator acts
        :keyword data: a dictionary that supplies values for variables
                       that are not part of the universe state
                       (e.g. potential energy)
        :keyword actions: a list of actions to be executed periodically
                          (default is none)
        """
        TrajectoryGenerator.__init__(self, universe, options)
        self.available_data = []
        try:
            e, g = self.universe.energyAndGradients()
        except: pass
        else:
            self.available_data.append('energy')
            self.available_data.append('gradients')
        try:
            self.universe.configuration()
            self.available_data.append('configuration')
        except: pass
        if self.universe.cellVolume() is not None:
            self.available_data.append('thermodynamic')
        if self.universe.velocities() is not None:
            self.available_data.append('velocities')
            self.available_data.append('energy')
            self.available_data.append('thermodynamic')

    default_options = {'steps': 0, 'actions': []}

    def __call__(self, **options):
        self.setCallOptions(options)
        from MMTK_trajectory import snapshot
        data = copy.copy(options.get('data', {}))
        energy_terms = 0
        for name in data.keys():
            if name == 'time' and 'time' not in self.available_data:
                self.available_data.append('time')
            if  name[-7:] == '_energy':
                energy_terms = energy_terms + 1
                if 'energy' not in self.available_data:
                    self.available_data.append('energy')
            if (name == 'temperature' or name == 'pressure') \
               and 'thermodynamic' not in self.available_data:
                self.available_data.append('thermodynamic')
            if name == 'gradients' and 'gradients' not in self.available_data:
                self.available_data.append('gradients')
        actions = self.getActions()
        for action in actions:
            categories = action[-1]
            for c in categories:
                if c == 'energy' and not data.has_key('kinetic_energy'):
                    v = self.universe.velocities()
                    if v is not None:
                        m = self.universe.masses()
                        e = (v*v*m*0.5).sumOverParticles()
                        data['kinetic_energy'] = e
                        df = self.universe.degreesOfFreedom()
                        data['temperature'] = 2.*e/df/Units.k_B/Units.K
                if c == 'configuration':
                    if  data.has_key('configuration'):
                        data['configuration'] = data['configuration'].array
                    else:
                        data['configuration'] = \
                                         self.universe.configuration().array
                if c == 'velocities':
                    if  data.has_key('velocities'):
                        data['velocities'] = data['velocities'].array
                    else:
                        data['velocities'] = self.universe.velocities().array
                if c == 'gradients':
                    if  data.has_key('gradients'):
                        data['gradients'] = data['gradients'].array
                p = self.universe.cellParameters()
                if p is not None:
                    data['box_size'] = p
                volume = self.universe.cellVolume()
                if volume is not None:
                    data['volume'] = volume
                try:
                    m = self.universe.masses()
                    data['masses'] = m.array
                except: pass
        snapshot(self.universe, data, actions, energy_terms)

#
# Trajectory reader (not yet functional...)
#

if False:

    class TrajectoryReader(TrajectoryGenerator):

        def __init__(self, trajectory, options):
            TrajectoryGenerator.__init__(self, trajectory.universe, options)
            self.input = trajectory
            self.available_data = trajectory.variables()

        default_options = {'trajectory': None, 'log': None, 'options': []}

        def __call__(self, **options):
            self.setCallOptions(options)
            from MMTK_trajectory import readTrajectory
            readTrajectory(self.universe, self.input.trajectory,
                           [self.getOption('trajectory'),
                            self.getOption('log')] +
                           self.getOption('options'))

#
# Print information about trajectory file
#

[docs]def trajectoryInfo(filename):
    """
    :param filename: the name of a trajectory file
    :type filename: str
    :returns: a string with summarial information about the trajectory
    """
    from Scientific.IO import NetCDF
    file = NetCDF.NetCDFFile(filename, 'r')
    nsteps = file.variables['step'].shape[0]
    if 'minor_step_number' in file.dimensions.keys():
        nsteps = nsteps*file.variables['step'].shape[1]
    s = 'Information about trajectory file ' + filename + ':\n'
    try:
        s += file.comment + '\n'
    except AttributeError:
        pass
    s += `file.dimensions['atom_number']` + ' atoms\n'
    s += `nsteps` + ' steps\n'
    s += file.history
    file.close()
    return s

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© Copyright 2010, Konrad Hinsen. Created using Sphinx 1.1.3.
MMTK-2.7.9/Doc/HTML/_modules/MMTK/Universe.html0000644000076600000240000074256612013143455021251 0ustar hinsenstaff00000000000000 MMTK.Universe — MMTK User Guide 2.7.7 documentation

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Source code for MMTK.Universe

# This module implements the various types of universes
# (infinite, periodic etc.). A universe defines the
# geometry of space, the force field, and external interactions
# (boundary conditions, external fields, etc.)
#
# Written by Konrad Hinsen
#

"""
Universes
"""

__docformat__ = 'restructuredtext'

from MMTK import Bonds, ChemicalObjects, Collections, Environment, \
                 Random, Utility, ParticleProperties, Visualization
from Scientific.Geometry import Transformation
from Scientific.Geometry import Vector, isVector
from Scientific import N
import copy

try:
    import threading
    if not hasattr(threading, 'Thread'):
        threading = None
except ImportError:
    threading = None

#
# The base class for all universes.
#

[docs]class Universe(Collections.GroupOfAtoms, Visualization.Viewable):

    """
    Universe

    A universe represents a complete model of a chemical system, i.e.
    the molecules, their environment (topology, boundary conditions,
    thermostats, etc.), and optionally a force field.

    The class Universe is an abstract base class that defines
    properties common to all kinds of universes. To create universe
    objects, use one of its subclasses.

    In addition to the methods listed below, universe objects support
    the following operations (u is any universe object, o is any
    chemical object):

     * len(u) yields the number of chemical objects in the universe
     * u[i] returns object number i
     * u.name = o adds o to the universe and also makes it accessible as
       an attribute
     * del u.name removes the object that was assigned to u.name from
       the universe
    """

    def __init__(self, forcefield, properties):
        self._forcefield = forcefield
        self._evaluator = {}
        self.name = ''
        if properties.has_key('name'):
            self.name = properties['name']
            del properties['name']
        self._objects = Collections.Collection()
        self._environment = []
        self._configuration = None
        self._masses = None
        self._atom_properties = {}
        self._atoms = None
        self._bond_database = None
        self._bond_pairs = None
        self._version = 0
        self._np = None

    is_universe = True
    is_periodic = False
    is_orthogonal = False

    def __getstate__(self):
        state = copy.copy(self.__dict__)
        state['_evaluator'] = {}
        state['_configuration'] = None
        del state['_masses']
        del state['_bond_database']
        del state['_bond_pairs']
        del state['_np']
        del state['_spec']
        return state

    def __setstate__(self, state):
        state['_np'] = None
        state['_atoms'] = None
        state['_bond_database'] = None
        state['_bond_pairs'] = None
        self.__dict__['_environment'] = []
        if state.has_key('atom_properties'):
            self.__dict__['_atom_properties'] = state['atom_properties']
            del state['atom_properties']
        for attr, value in state.items():
            self.__dict__[attr] = value
        self._evaluator = {}
        self._masses = None
        self._createSpec()

    def __len__(self):
        return len(self._objects)

    def __getitem__(self, item):
        return self._objects[item]

    def __setattr__(self, attr, value):
        if attr[0] != '_' and self.__dict__.has_key(attr):
            try:
                self.removeObject(self.__dict__[attr])
            except ValueError:
                pass
        self.__dict__[attr] = value
        if attr[0] != '_' and (ChemicalObjects.isChemicalObject(value)
                               or Environment.isEnvironmentObject(value)):
            self.addObject(value)

    def __delattr__(self, attr):
        try:
            self.removeObject(self.__dict__[attr])
        except ValueError:
            pass
        del self.__dict__[attr]

    def __repr__(self):
        return self.__class__.__name__ + ' ' + self.name + ' containing ' + \
               `len(self._objects)` + ' objects.'
    __str__ = __repr__

    def __copy__(self):
        return copy.deepcopy(self)


[docs]    def objectList(self, klass = None):
        """
        :param klass: an optional class argument
        :type klass: class
        :returns: a list of all chemical objects in the universe.
                  If klass is given, only objects that are instances
                  of klass are returned.
        :rtype: list
        """
        return self._objects.objectList(klass)



[docs]    def environmentObjectList(self, klass = None):
        """
        :param klass: an optional class argument
        :type klass: class
        :returns: a list of all environment objects in the universe.
                  If klass is given, only objects that are instances
                  of klass are returned.
        :rtype: list
        """
        if klass is None:
            return self._environment
        else:
            return filter(lambda o, k=klass: o.__class__ is k,
                          self._environment)



[docs]    def atomList(self):
        """
        :returns: a list of all atoms in the universe
        :rtype: list
        """
        if self._atoms is None:
            self._atoms = self._objects.atomList()
        return self._atoms


    def atomIterator(self):
        return self._objects.atomIterator()

    def bondedUnits(self):
        return self._objects.bondedUnits()


[docs]    def universe(self):
        """
        :returns: the universe itself
        """
        return self



[docs]    def addObject(self, object, steal = False):
        """
        Adds object to the universe. If object is a Collection,
        all elements of the Collection are added to the universe.

        :param object: the object (chemical or environment) to be added
        :param steal: if True, permit stealing the object from another
                      universe, otherwise the object must not yet be
                      attached to any universe.
        :type steal: bool
        """
        if ChemicalObjects.isChemicalObject(object):
            if (not steal) and object.parent is not None:
                if isUniverse(object.parent):
                    raise ValueError(`object` +
                                      ' is already in another universe')
                else:
                    raise ValueError(`object` + ' is part of another object')
            object.parent = self
            self._objects.addObject(object)
            self._changed(True)
        elif Environment.isEnvironmentObject(object):
            for o in self._environment:
                o.checkCompatibilityWith(object)
            self._environment.append(object)
            self._changed(False)
        elif Collections.isCollection(object) \
             or Utility.isSequenceObject(object):
            for o in object:
                self.addObject(o, steal)
        else:
            raise TypeError(repr(object) + ' cannot be added to a universe')



[docs]    def removeObject(self, object):
        """
        Removes object from the universe. If object is a Collection,
        each of its elements is removed. The object to be removed must
        be in the universe.

        :param object: the object (chemical or environment) to be removed
        """
        if ChemicalObjects.isChemicalObject(object):
            if object.parent != self:
                raise ValueError(`object` + ' is not in this universe.')
            object.parent = None
            self._objects.removeObject(object)
            self._changed(True)
        elif Collections.isCollection(object) \
                 or (Utility.isSequenceObject(object)
                     # Strings are nasty because their elements are strings
                     # as well. This creates infinite recursion without
                     # this special-case handling.
                     and not isinstance(object, basestring)):
            for o in object:
                self.removeObject(o)
        elif Environment.isEnvironmentObject(object):
            self._environment.remove(object)
            self._changed(False)
        else:
            raise ValueError(`object` + ' is not in this universe.')



[docs]    def selectShell(self, point, r1, r2=0.):
        """
        :param point: a point in space
        :type point: Scientific.Geometry.Vector
        :param r1: one of the radii of a spherical shell
        :type r1: float
        :param r2: the other of the two radii of a spherical shell
        :type r2: float
        :returns: a Collection of all objects in the universe whose
                  distance from point lies between r1 and r2.
        """
        return self._objects.selectShell(point, r1, r2)



[docs]    def selectBox(self, p1, p2):
        """
        :param p1: one corner of a box in space
        :type p1: Scientific.Geometry.Vector
        :param p2: the other corner of a box in space
        :type p2: Scientific.Geometry.Vector
        :returns: a Collection of all objects in the universe that lie
                   within the box whose diagonally opposite corners are
                   given by p1 and p2.
        """
        return self._objects.selectBox(p1, p2)


    def _changed(self, system_size_changed):
        self._evaluator = {}
        self._bond_database = None
        self._version += 1
        if system_size_changed:
            if self._configuration is not None:
                for a in self.atomList():
                    a.unsetArray()
                self._configuration = None
                self._masses = None
                self._atom_properties = {}
            self._atoms = None
            self._np = None
            self._bond_pairs = None
        else:
            if self._configuration is not None:
                self._configuration.version = self._version
            if self._masses is not None:
                self._masses.version = self._version


[docs]    def acquireReadStateLock(self):
        """
        Acquire the universe read state lock. Any application that
        uses threading must acquire this lock prior to accessing the
        current state of the universe, in particular its configuration
        (particle positions). This guarantees the consistency of the
        data; while any thread holds the read state lock, no other
        thread can obtain the write state lock that permits modifying
        the state. The read state lock should be released as soon as
        possible.

        The read state lock can be acquired only if no thread holds
        the write state lock. If the read state lock cannot be
        acquired immediately, the thread will be blocked until
        it becomes available. Any number of threads can acquire
        the read state lock simultaneously.
        """
        return self._spec.stateLock(1)



[docs]    def acquireWriteStateLock(self):
        """
        Acquire the universe write state lock. Any application that
        uses threading must acquire this lock prior to modifying the
        current state of the universe, in particular its configuration
        (particle positions). This guarantees the consistency of the
        data; while any thread holds the write state lock, no other
        thread can obtain the read state lock that permits accessing
        the state. The write state lock should be released as soon as
        possible.

        The write state lock can be acquired only if no other thread
        holds either the read state lock or the write state lock. If
        the write state lock cannot be acquired immediately, the
        thread will be blocked until it becomes available.
        """
        return self._spec.stateLock(-1)



[docs]    def releaseReadStateLock(self, write=False):
        """
        Release the universe read state lock.
        """
        return self._spec.stateLock(2)



[docs]    def releaseWriteStateLock(self, write=False):
        """
        Release the universe write state lock.
        """
        return self._spec.stateLock(-2)



[docs]    def acquireConfigurationChangeLock(self, waitflag=True):
        """
        Acquire the configuration change lock. This lock should be
        acquired before starting an algorithm that changes the
        configuration continuously, e.g. minimization or molecular dynamics
        algorithms. This guarantees the proper order of execution when
        several such operations are started in succession. For example,
        when a minimization should be followed by a dynamics run,
        the use of this flag permits both operations to be started
        as background tasks which will be executed one after the other,
        permitting other threads to run in parallel.

        The configuration change lock should not be confused with
        the universe state lock. The former guarantees the proper
        sequence of long-running algorithms, whereas the latter
        guarantees the consistency of the data. A dynamics algorithm,
        for example, keeps the configuration change lock from the
        beginning to the end, but acquires the universe state lock
        only immediately before modifying configuration and velocities,
        and releases it immediately afterwards.

        :param waitflag: if true, the method waits until the lock
                         becomes available; this is the most common mode.
                         If false, the method returns immediately even
                         if another thread holds the lock.
        :type waitflag: bool
        :returns: a flag indicating if the lock was successfully
                  acquired (1) or not (0).
        :rtype: int
        """
        if waitflag:
            return self._spec.configurationChangeLock(1)
        else:
            return self._spec.configurationChangeLock(0)



[docs]    def releaseConfigurationChangeLock(self):
        """
        Releases the configuration change lock.
        """
        self._spec.configurationChangeLock(2)



[docs]    def setForceField(self, forcefield):
        """
        :param forcefield: the new forcefield for this universe
        :type forcefield: :class:`~MMTK.ForceFields.ForceField.ForceField`
        """
        self._forcefield = forcefield
        self._evaluator = {}
        self._bond_database = None


    def position(self, object, conf):
        if ChemicalObjects.isChemicalObject(object):
            return object.position(conf)
        elif isVector(object):
            return object
        else:
            return Vector(object)

    def numberOfAtoms(self):
        return self._objects.numberOfAtoms()

    def numberOfPoints(self):
        if self._np is None:
            self._np = Collections.GroupOfAtoms.numberOfPoints(self)
        return self._np

    numberOfCartesianCoordinates = numberOfPoints


[docs]    def configuration(self):
        """
        :returns: the configuration object describing the current
                  configuration of the universe. Note that this is not a
                  copy of the current state, but a reference: the positions
                  in the configuration object will change when coordinate
                  changes are applied to the universe in whatever way.
        :rtype: :class:`~MMTK.ParticleProperties.Configuration`
        """
        if self._configuration is None:
            np = self.numberOfAtoms()
            coordinates = N.zeros((np, 3), N.Float)
            index_map = {}
            redef = []
            for a in self.atomList():
                if a.index is None or a.index >= np:
                    redef.append(a)
                else:
                    if index_map.get(a.index, None) is None:
                        index_map[a.index] = a
                    else:
                        redef.append(a)
            free_indices = [i for i in xrange(np)
                            if index_map.get(i, None) is None]
            assert len(free_indices) == len(redef)
            for a, i in zip(redef, free_indices):
                a.index = i
            # At this point a.index runs from 0 to np-1 in the universe.
            for a in self.atomList():
                if a.array is None:
                    try:
                        coordinates[a.index, :] = a.pos.array
                        del a.pos
                    except AttributeError:
                        coordinates[a.index, :] = Utility.undefined
                else:
                    coordinates[a.index, :] = a.array[a.index, :]
                a.array = coordinates
            # Define configuration object.
            self._configuration = 1 # a hack to prevent endless recursion
            self._configuration = \
                         ParticleProperties.Configuration(self, coordinates)
        return self._configuration



[docs]    def copyConfiguration(self):
        """
        This operation is thread-safe; it won't return inconsistent
        data even when another thread is modifying the configuration.

        :returns: a copy of the current configuration
        :rtype: :class:`~MMTK.ParticleProperties.Configuration`
        """
        self.acquireReadStateLock()
        try:
            conf = copy.copy(self.configuration())
        finally:
            self.releaseReadStateLock()
        return conf


    def atomNames(self):
        self.configuration()
        names = self.numberOfAtoms()*[None]
        for a in self.atomList():
            names[a.index] = a.fullName()
        return names


[docs]    def setConfiguration(self, configuration, block=True):
        """
        Update the current configuration of the universe by copying
        the given input configuration.
        
        :param configuration: the new configuration
        :type configuration: :class:`~MMTK.ParticleProperties.Configuration`
        :param block: if True, the operation blocks other threads
                      from accessing the configuration before the update
                      is completed. If False, it is assumed that the
                      caller takes care of locking.
        :type block: bool
        """
        if not ParticleProperties.isConfiguration(configuration):
            raise TypeError('not a universe configuration')
        conf = self.configuration()
        if block:
            self.acquireWriteStateLock()
        try:
            conf.assign(configuration)
            self.setCellParameters(configuration.cell_parameters)
        finally:
            if block:
                self.releaseWriteStateLock()



[docs]    def addToConfiguration(self, displacement, block=True):
        """
        Update the current configuration of the universe by adding
        the given displacement vector.
        
        :param displacement: the displacement vector for each atom
        :type displacement: :class:`~MMTK.ParticleProperties.ParticleVector`
        :param block: if True, the operation blocks other threads
                      from accessing the configuration before the update
                      is completed. If False, it is assumed that the
                      caller takes care of locking.
        :type block: bool
        """
        conf = self.configuration()
        if block:
            self.acquireWriteStateLock()
        try:
            conf.assign(conf+displacement)
        finally:
            if block:
                self.releaseWriteStateLock()



[docs]    def getParticleScalar(self, name, datatype = N.Float):
        """
        :param name: the name of an atom attribute
        :type name: str
        :param datatype: the datatype of the array allocated to hold the data
        :returns: the values of the attribute 'name' for each atom
                  in the universe.
        :rtype: :class:`~MMTK.ParticleProperties.ParticleScalar`
        """
        conf = self.configuration()
        array = N.zeros((len(conf),), datatype)
        for a in self.atomList():
            array[a.index] = getattr(a, name)
        return ParticleProperties.ParticleScalar(self, array)

    getAtomScalarArray = getParticleScalar


[docs]    def getParticleBoolean(self, name):
        """
        :param name: the name of an atom attribute
        :type name: str
        :returns: the values of the boolean attribute 'name' for each atom
                  in the universe, or False for atoms that do not have
                  the attribute.
        :rtype: :class:`~MMTK.ParticleProperties.ParticleScalar`
        """
        conf = self.configuration()
        array = N.zeros((len(conf),), N.Int)
        for a in self.atomList():
            try:
                array[a.index] = getattr(a, name)
            except AttributeError: pass
        return ParticleProperties.ParticleScalar(self, array)

    getAtomBooleanArray = getParticleBoolean


[docs]    def masses(self):
        """
        :returns: the masses of all atoms in the universe
        :rtype: :class:`~MMTK.ParticleProperties.ParticleScalar`
        """
        if self._masses is None:
            self._masses = self.getParticleScalar('_mass')
        return self._masses



[docs]    def charges(self):
        """
        Return the atomic charges defined by the universe's
        force field.

        :returns: the charges of all atoms in the universe
        :rtype: :class:`~MMTK.ParticleProperties.ParticleScalar`
        """
        ff = self._forcefield
        if ff is None:
            raise ValueError("no force field defined")
        return ff.charges(self)



[docs]    def velocities(self):
        """
        :returns: the current velocities of all atoms, or None if
                  no velocities are defined. Note that this is not a
                  copy of the current state but a reference to it;
                  its data will change whenever any changes are made
                  to the current velocities.
        :rtype: :class:`~MMTK.ParticleProperties.ParticleVector`
        """
        try:
            return self._atom_properties['velocity']
        except KeyError:
            return None



[docs]    def setVelocities(self, velocities, block=True):
        """
        Update the current velocities of the universe by copying
        the given input velocities.
        
        :param velocities: the new velocities, or None to remove
                           the velocity definition from the universe
        :type velocities: :class:`~MMTK.ParticleProperties.ParticleVector`
        :param block: if True, the operation blocks other threads
                      from accessing the configuration before the update
                      is completed. If False, it is assumed that the
                      caller takes care of locking.
        :type block: bool
        """
        if velocities is None:
            try:
                del self._atom_properties['velocity']
            except KeyError:
                pass
        else:
            try:
                v = self._atom_properties['velocity']
            except KeyError:
                v = ParticleProperties.ParticleVector(self)
                self._atom_properties['velocity'] = v
            if block:
                self.acquireWriteStateLock()
            try:
                v.assign(velocities)
            finally:
                if block:
                    self.releaseWriteStateLock()



[docs]    def initializeVelocitiesToTemperature(self, temperature):
        """
        Generate random velocities for all atoms from a Boltzmann
        distribution.

        :param temperature: the reference temperature for the Boltzmann
                            distribution
        :type temperature: float
        """
        self.configuration()
        masses = self.masses()
        if self._atom_properties.has_key('velocity'):
            del self._atom_properties['velocity']
        fixed = self.getParticleBoolean('fixed')
        np = self.numberOfPoints()
        velocities = N.zeros((np, 3), N.Float)
        for i in xrange(np):
            m = masses[i]
            if m > 0. and not fixed[i]:
                velocities[i] = Random.randomVelocity(temperature,
                                                           m).array
        self._atom_properties['velocity'] = \
                          ParticleProperties.ParticleVector(self, velocities)
        self.adjustVelocitiesToConstraints()



[docs]    def scaleVelocitiesToTemperature(self, temperature, block=True):
        """
        Scale all velocities by a common factor in order to obtain
        the specified temperature.

        :param temperature: the reference temperature
        :type temperature: float
        :param block: if True, the operation blocks other threads
                      from accessing the configuration before the update
                      is completed. If False, it is assumed that the
                      caller takes care of locking.
        :type block: bool
        """
        velocities = self.velocities()
        factor = N.sqrt(temperature/self.temperature())
        if block:
            self.acquireWriteStateLock()
        try:
            velocities.scaleBy(factor)
        finally:
            if block:
                self.releaseWriteStateLock()


    def degreesOfFreedom(self):
        return GroupOfAtoms.degreesOfFreedom(self) \
               - self.numberOfDistanceConstraints()


[docs]    def distanceConstraintList(self):
        """
        :returns: the list of distance constraints
        :rtype: list
        """
        return self._objects.distanceConstraintList()



[docs]    def numberOfDistanceConstraints(self):
        """
        :returns: the number of distance constraints
        :rtype: int
        """
        return self._objects.numberOfDistanceConstraints()



[docs]    def setBondConstraints(self):
        """
        Sets distance constraints for all bonds.
        """
        self.configuration()
        self._objects.setBondConstraints(self)
        self.enforceConstraints()



[docs]    def removeDistanceConstraints(self):
        """
        Removes all distance constraints.
        """
        self._objects.removeDistanceConstraints(self)



[docs]    def enforceConstraints(self, configuration=None, velocities=None):
        """
        Enforces the previously defined distance constraints
        by modifying the configuration and velocities.

        :param configuration: the configuration in which the
                              constraints are enforced
                              (None for current configuration)
        :type configuration: :class:`~MMTK.ParticleProperties.Configuration`
        :param velocities: the velocities in which the
                              constraints are enforced
                              (None for current velocities)
        :type velocities: :class:`~MMTK.ParticleProperties.ParticleVector`
        """
        from MMTK import Dynamics
        Dynamics.enforceConstraints(self, configuration)
        self.adjustVelocitiesToConstraints(velocities)



[docs]    def adjustVelocitiesToConstraints(self, velocities=None, block=True):
        """
        Modifies the velocities to be compatible with
        the distance constraints, i.e. projects out the velocity
        components along the constrained distances.

        :param velocities: the velocities in which the
                              constraints are enforced
                              (None for current velocities)
        :type velocities: :class:`~MMTK.ParticleProperties.ParticleVector`
        :param block: if True, the operation blocks other threads
                      from accessing the configuration before the update
                      is completed. If False, it is assumed that the
                      caller takes care of locking.
        :type block: bool
        """
        from MMTK import Dynamics
        if velocities is None:
            velocities = self.velocities()
        if velocities is not None:
            if block:
                self.acquireWriteStateLock()
            try:
                Dynamics.projectVelocities(self, velocities)
            finally:
                if block:
                    self.releaseWriteStateLock()


    def bondLengthDatabase(self):
        if self._bond_database is None:
            self._bond_database = None
            if self._bond_database is None:
                ff = self._forcefield
                try:
                    self._bond_database = ff.bondLengthDatabase(self)
                except AttributeError:
                    pass
            if self._bond_database is None:
                self._bond_database = Bonds.DummyBondLengthDatabase(self)
        return self._bond_database


[docs]    def forcefield(self):
        """
        :returns: the force field
        :rtype: :class:`~MMTK.ForceFields.ForceField.ForceField`
        """
        return self._forcefield


    def energyEvaluatorParameters(self, subset1 = None, subset2 = None):
        self.configuration()
        from MMTK.ForceFields import ForceField
        ffdata = ForceField.ForceFieldData()
        return self._forcefield.evaluatorParameters(self, subset1, subset2,
                                                    ffdata)

    def energyEvaluator(self, subset1 = None, subset2 = None,
                        threads=None, mpi_communicator=None):
        if self._forcefield is None:
            raise ValueError("no force field defined")
        try:
            eval = self._evaluator[(subset1, subset2, threads)]
        except KeyError:
            from MMTK.ForceFields import ForceField
            eval = ForceField.EnergyEvaluator(self, self._forcefield,
                                              subset1, subset2,
                                              threads, mpi_communicator)
            self._evaluator[(subset1, subset2, threads)] = eval
        return eval


[docs]    def energy(self, subset1 = None, subset2 = None, small_change=False):
        """
        :param subset1: a subset of a universe, or None
        :type subset1: :class:`~MMTK.ChemicalObjects.ChemicalObject`
        :param subset2: a subset of a universe, or None
        :type subset2: :class:`~MMTK.ChemicalObjects.ChemicalObject`
        :param small_change: if True, algorithms optimized for small
                             configurational changes relative to the last
                             evaluation may be used.
        :type small_change: bool
        :returns: the potential energy of interaction between the atoms
                  in subset1 and the atoms in subset2. If subset2 is None,
                  the interactions within subset1 are calculated. It both
                  subsets are None, the potential energy of the whole
                  universe is returned.
        :rtype: float
        """
        eval = self.energyEvaluator(subset1, subset2)
        return eval(0, 0, small_change)



[docs]    def energyAndGradients(self, subset1 = None, subset2 = None,
                           small_change=False):
        """
        :returns: the energy and the energy gradients
        :rtype: (float, :class:`~MMTK.ParticleProperties.ParticleVector`)
        """
        eval = self.energyEvaluator(subset1, subset2)
        return eval(1, 0, small_change)



[docs]    def energyAndForceConstants(self, subset1 = None, subset2 = None,
                                small_change=False):
        """
        :returns: the energy and the force constants
        :rtype: (float, :class:`~MMTK.ParticleProperties.SymmetricPairTensor`)
        """
        eval = self.energyEvaluator(subset1, subset2)
        e, g, fc = eval(0, 1, small_change)
        return e, fc



[docs]    def energyGradientsAndForceConstants(self, subset1 = None, subset2 = None,
                                         small_change=False):
        """
        :returns: the energy, its gradients, and the force constants
        :rtype: (float, :class:`~MMTK.ParticleProperties.ParticleVector`,
                 :class:`~MMTK.ParticleProperties.SymmetricPairTensor`)
        """
        eval = self.energyEvaluator(subset1, subset2)
        return eval(1, 1, small_change)



[docs]    def energyTerms(self, subset1 = None, subset2 = None, small_change=False):
        """
        :returns: a dictionary containing the energy values for each
                  energy term separately. The energy terms are defined by the
                  force field.
        :rtype: dict
        """
        eval = self.energyEvaluator(subset1, subset2)
        eval(0, 0, small_change)
        return eval.lastEnergyTerms()



[docs]    def configurationDifference(self, conf1, conf2):
        """
        :param conf1: a configuration
        :type conf1: :class:`~MMTK.ParticleProperties.Configuration`
        :param conf2: a configuration
        :type conf2: :class:`~MMTK.ParticleProperties.Configuration`
        :returns: the difference vector between the two configurations
                  for each atom, taking into account the universe
                  topology (e.g. minimum-image convention).
        :rtype: :class:`~MMTK.ParticleProperties.ParticleVector`
        """
        d = conf2-conf1
        cell = conf1.cell_parameters
        if cell is not None:
            self._spec.foldCoordinatesIntoBox(d.array)
        return d
            


[docs]    def distanceVector(self, p1, p2, conf=None):
        """
        :param p1: a vector or a chemical object whose position is taken
        :param p2: a vector or a chemical object whose position is taken
        :param conf: a configuration (None for the current configuration)
        :returns: the distance vector between p1 and p2 (i.e. the
                  vector from p1 to p2) in the configuration conf,
                  taking into account the universe's topology.
        """
        p1 = self.position(p1, conf)
        p2 = self.position(p2, conf)
        if conf is None:
            return Vector(self._spec.distanceVector(p1.array, p2.array))
        else:
            cell = self._fixCellParameters(conf.cell_parameters)
            if cell is None:
                return Vector(self._spec.distanceVector(p1.array, p2.array))
            else:
                return Vector(self._spec.distanceVector(p1.array, p2.array,
                                                        cell))
            


[docs]    def distance(self, p1, p2, conf = None):
        """
        :param p1: a vector or a chemical object whose position is taken
        :param p2: a vector or a chemical object whose position is taken
        :param conf: a configuration (None for the current configuration)
        :returns: the distance between p1 and p2, i.e. the length
                  of the distance vector
        :rtype: float
        """
        return self.distanceVector(p1, p2, conf).length()



[docs]    def angle(self, p1, p2, p3, conf = None):
        """
        :param p1: a vector or a chemical object whose position is taken
        :param p2: a vector or a chemical object whose position is taken
        :param p3: a vector or a chemical object whose position is taken
        :param conf: a configuration (None for the current configuration)
        :returns: the angle between the distance vectors p1-p2 and p3-p2
        :rtype: float
        """
        v1 = self.distanceVector(p2, p1, conf)
        v2 = self.distanceVector(p2, p3, conf)
        return v1.angle(v2)



[docs]    def dihedral(self, p1, p2, p3, p4, conf = None):
        """
        :param p1: a vector or a chemical object whose position is taken
        :param p2: a vector or a chemical object whose position is taken
        :param p3: a vector or a chemical object whose position is taken
        :param p4: a vector or a chemical object whose position is taken
        :param conf: a configuration (None for the current configuration)
        :returns: the dihedral angle between the plane containing the
                  distance vectors p1-p2 and p3-p2 and the plane containing
                  the distance vectors p2-p3 and p4-p3
        :rtype: float
        """
        v1 = self.distanceVector(p2, p1, conf)
        v2 = self.distanceVector(p3, p2, conf)
        v3 = self.distanceVector(p3, p4, conf)
        a = v1.cross(v2).normal()
        b = v3.cross(v2).normal()
        cos = a*b
        sin = b.cross(a)*v2/v2.length()
        return Transformation.angleFromSineAndCosine(sin, cos)


    def _deleteAtom(self, atom):
        pass


[docs]    def basisVectors(self):
        """
        :returns: the basis vectors of the elementary cell of a periodic
                  universe, or None for a non-periodic universe
        :rtype: NoneType or list
        """
        return None



[docs]    def reciprocalBasisVectors(self):
        """
        :returns: the reciprocal basis vectors of the elementary cell of
                  a periodic universe, or None for a non-periodic universe
        :rtype: NoneType or list
        """
        return None


    def cellParameters(self):
        return None

    def setCellParameters(self, parameters):
        if parameters is not None:
            raise ValueError('incompatible cell parameters')

    def _fixCellParameters(self, cell_parameters):
        return cell_parameters


[docs]    def cellVolume(self):
        """
        :returns: the volume of the elementary cell of a periodic
                  universe, None for a non-periodic universe
        :rtype: NoneType or float
        """
        return None



[docs]    def largestDistance(self):
        """
        :returns: the largest possible distance between any two points
                  that can be represented independent of orientation, i.e. the
                  radius of the largest sphere that fits into the simulation
                  cell. Returns None if no such upper limit exists.
        :rtype: NoneType or float
        """
        return None



[docs]    def contiguousObjectOffset(self, objects = None, conf = None,
                               box_coordinates = False):
        """
        :param objects: a list of chemical objects, or None for all
                        objects in the universe
        :type objects: list
        :param conf: a configuration (None for the current configuration)
        :param box_coordinates: use box coordinates rather than real ones
        :type box_coordinates: bool
        :returns: a set of displacement vectors relative to
                  the conf which, when added to the configuration,
                  create a configuration in which none of the objects
                  is split across the edge of the elementary cell.
                  For nonperiodic universes the return value is None.
        :rtype: :class:`~MMTK.ParticleProperties.ParticleVector`
        """
        return None



[docs]    def contiguousObjectConfiguration(self, objects = None, conf = None):
        """
        :param objects: a list of chemical objects, or None for all
                        objects in the universe
        :type objects: list
        :param conf: a configuration (None for the current configuration)
        :returns: configuration conf (default: current configuration)
                  corrected by the contiguous object offsets for that
                  configuration.
        :rtype: :class:`~MMTK.ParticleProperties.Configuration`
        """
        if conf is None:
            conf = self.configuration()
        offset = self.contiguousObjectOffset(objects, conf)
        if offset is not None:
            return conf + offset
        else:
            return copy.copy(conf)



[docs]    def realToBoxCoordinates(self, vector):
        """
        Box coordinates are defined only for periodic universes;
        their components have values between -0.5 and 0.5; these
        extreme values correspond to the walls of the simulation box.

        :param vector: a point in the universe
        :returns: the box coordinate equivalent of vector, or the original
                  vector if no box coordinate system exists
        :rtype: Scientific.Geometry.Vector
        """
        return vector
    


[docs]    def boxToRealCoordinates(self, vector):
        """
        :param vector: a point in the universe expressed in box coordinates
        :returns: the real-space equivalent of vector
        :rtype: Scientific.Geometry.Vector
        """
        return vector


    def _realToBoxPointArray(self, array, parameters=None):
        return array

    def _boxToRealPointArray(self, array, parameters=None):
        return array


[docs]    def cartesianToFractional(self, vector):
        """
        Fractional coordinates are defined only for periodic universes;
        their components have values between 0. and 1.

        :param vector: a point in the universe
        :type vector: Scientific.Geometry.Vector
        :returns: the fractional coordinate equivalent of vector
        :rtype: Scientific.N.array_type
        """
        raise ValueError("Universe is not periodic")


    def cartesianToFractionalMatrix(self):
        raise ValueError("Universe is not periodic")


[docs]    def fractionalToCartesian(self, array):
        """
        Fractional coordinates are defined only for periodic universes;
        their components have values between 0. and 1.

        :param array: an array of fractional coordinates
        :type array: Scientific.N.array_type
        :returns: the real-space equivalent of vector
        :rtype: Scientific.Geometry.Vector
        """
        raise ValueError("Universe is not periodic")


    def fractionalToCartesianMatrix(self):
        raise ValueError("Universe is not periodic")

    def foldCoordinatesIntoBox(self):
        return


[docs]    def randomPoint(self):
        """
        :returns: a random point from a uniform distribution within
                  the universe. This operation is defined only for
                  finite-volume universes, e.g. periodic universes.
        :rtype: Scientific.Geometry.Vector
        """
        raise TypeError("undefined operation")



[docs]    def map(self, function):
        """
        Apply a function to all objects in the universe and
        return the list of the results. If the results are chemical
        objects, a Collection object is returned instead of a list.

        :param function: the function to be applied
        :type function: callable
        :returns: the list or collection of the results
        """
        return self._objects.map(function)


    def description(self, objects = None, index_map = None):
        if objects is None:
            objects = self
        attributes = {}
        for attr in dir(self):
            if attr[0] != '_':
                object = getattr(self, attr)
                if ChemicalObjects.isChemicalObject(object) \
                   or Environment.isEnvironmentObject(object):
                    attributes[object] = attr
        items = []
        for o in objects.objectList():
            attr = attributes.get(o, None)
            if attr is not None:
                items.append(repr(attr))
            items.append(o.description(index_map))
        for o in self._environment:
            attr = attributes.get(o, None)
            if attr is not None:
                items.append(repr(attr))
            items.append(o.description())
        try:
            classname = self.classname_for_trajectories
        except AttributeError:
            classname = self.__class__.__name__
        s = 'c(%s,[%s])' % \
            (`classname + self._descriptionArguments()`,
             ','.join(items))
        return s

    def _graphics(self, conf, distance_fn, model, module, options):
        return self._objects._graphics(conf, distance_fn, model,
                                       module, options)


[docs]    def setFromTrajectory(self, trajectory, step = None):
        """
        Set the state of the universe to the one stored in a trajectory.
        This operation is thread-safe; it blocks other threads that
        want to access the configuration or velocities while the data is
        being updated.

        :param trajectory: a trajectory object for this universe
        :type trajectory: :class:`~MMTK.Trajectory.Trajectory`
        :param step: a step number, or None for the default step
                     (0 for a standard trajectory, the last written
                     step for a restart trajectory)
        :type step: int
        """
        if step is None:
            step = trajectory.defaultStep()
        self.acquireWriteStateLock()
        try:
            self.setConfiguration(trajectory.configuration[step], False)
            vel = self.velocities()
            try:
                vel_tr = trajectory.velocities[step]
            except AttributeError:
                if vel is not None:
                    Utility.warning("velocities were not modified because " +
                                    "the trajectory does not contain " +
                                    "velocity data.")
                return
            if vel is None:
                self._atom_properties['velocity'] = vel_tr
            else:
                vel.assign(vel_tr)
        finally:
            self.releaseWriteStateLock()

    #
    # More efficient reimplementations of methods in Collections.GroupOfAtoms
    #

    def numberOfFixedAtoms(self):
        return self.getParticleBoolean('fixed').sumOverParticles()

    def degreesOfFreedom(self):
        return 3*(self.numberOfAtoms()-self.numberOfFixedAtoms()) \
               - self.numberOfDistanceConstraints()

    def mass(self):
        return self.masses().sumOverParticles()

    def centerOfMass(self, conf = None):
        m = self.masses()
        if conf is None:
            conf = self.configuration()
        return (m*conf).sumOverParticles()/m.sumOverParticles()

    def kineticEnergy(self, velocities = None):
        if velocities is None:
            velocities = self.velocities()
        return 0.5*velocities.massWeightedDotProduct(velocities)

    def momentum(self, velocities = None):
        if velocities is None:
            velocities = self.velocities()
        return (self.masses()*velocities).sumOverParticles()

    def translateBy(self, vector):
        conf = self.configuration().array
        N.add(conf, vector.array[N.NewAxis, :], conf)

    def applyTransformation(self, t):
        conf = self.configuration().array
        rot = t.rotation().tensor.array
        conf[:] = N.dot(conf, N.transpose(rot))
        N.add(conf, t.translation().vector.array[N.NewAxis, :], conf)

    def writeXML(self, file):
        file.write('<?xml version="1.0" encoding="ISO-8859-1" ' +
                   'standalone="yes"?>\n\n')
        file.write('<molecularsystem>\n\n')
        file.write('<templates>\n\n')
        memo = {'counter': 1}
        instances = []
        atoms = []
        for object in self._objects.objectList():
            instances = instances + object.writeXML(file, memo, 1)
            atoms = atoms + object.getXMLAtomOrder()
        file.write('\n</templates>\n\n')
        file.write('<universe %s>\n' % self.XMLSpec())
        for instance in instances:
            file.write('  ')
            file.write(instance)
            file.write('\n')
        conf = self.configuration()
        if conf.hasValidPositions():
            file.write('  <configuration>\n')
            file.write('    <atomArray units="units:nm"\n')
            file.write('    x3="')
            for atom in atoms:
                file.write(str(conf[atom][0]))
                file.write(' ')
            file.write('"\n')
            file.write('    y3="')
            for atom in atoms:
                file.write(str(conf[atom][1]))
                file.write(' ')
            file.write('"\n')
            file.write('    z3="')
            for atom in atoms:
                file.write(str(conf[atom][2]))
                file.write(' ')
            file.write('"\n')
            file.write('    />\n')
            file.write('  </configuration>\n')
        file.write('</universe>\n\n') 
        file.write('</molecularsystem>\n')
       

#
# Infinite universes
#


[docs]class InfiniteUniverse(Universe):

    """
    Infinite (unbounded and nonperiodic) universe.
    """

    def __init__(self, forcefield=None, **properties):
        """
        :param forcefield: a force field, or None for no force field
        :type forcefield: :class:`~MMTK.ForceFields.ForceField.ForceField`
        """
        Universe.__init__(self, forcefield, properties)
        self._createSpec()

    def CdistanceFunction(self):
        from MMTK_universe import infinite_universe_distance_function
        return infinite_universe_distance_function, N.array([0.])

    def CcorrectionFunction(self):
        from MMTK_universe import infinite_universe_correction_function
        return infinite_universe_correction_function, N.array([0.])

    def CvolumeFunction(self):
        from MMTK_universe import infinite_universe_volume_function
        return infinite_universe_volume_function, N.array([0.])

    def CboxTransformationFunction(self):
        return None, N.array([0.])

    def _createSpec(self):
        from MMTK_universe import InfiniteUniverseSpec
        self._spec = InfiniteUniverseSpec()

    def _descriptionArguments(self):
        if self._forcefield is None:
            return '()'
        else:
            return '(%s)' % self._forcefield.description()

    def XMLSpec(self):
        return 'topology="infinite"'

#
# 3D periodic universe base class
#

class Periodic3DUniverse(Universe):

    is_periodic = True

    def setVolume(self, volume):
        """
        Multiplies all edge lengths by the same factor such that the cell
        volume becomes equal to the specified value.

        :param volume: the desired volume
        :type volume: float
        """
        factor = (volume/self.cellVolume())**(1./3.)
        self.scaleSize(factor)

    def foldCoordinatesIntoBox(self):
        self._spec.foldCoordinatesIntoBox(self.configuration().array)
    
    def basisVectors(self):
        return [self.boxToRealCoordinates(Vector(1., 0., 0.)),
                self.boxToRealCoordinates(Vector(0., 1., 0.)),
                self.boxToRealCoordinates(Vector(0., 0., 1.))]

    def cartesianToFractional(self, vector):
        r1, r2, r3 = self.reciprocalBasisVectors()
        return N.array([r1*vector, r2*vector, r3*vector])

    def cartesianToFractionalMatrix(self):
        return N.array(self.reciprocalBasisVectors())

    def fractionalToCartesian(self, array):
        e1, e2, e3 = self.basisVectors()
        return array[0]*e1 + array[1]*e2 + array[2]*e3

    def fractionalToCartesianMatrix(self):
        return N.transpose(self.basisVectors())

    def randomPoint(self):
        return self.boxToRealCoordinates(Random.randomPointInBox(1., 1., 1.))

    def contiguousObjectOffset(self, objects = None, conf = None,
                               box_coordinates = 0):
        from MMTK_universe import contiguous_object_offset
        if objects is None or objects == self or objects == [self]:
            default = True
            objects = self._objects.objectList()
            pairs = self._bond_pairs
        else:
            default = False
            pairs = None
        if conf is None:
            conf = self.configuration()
        cell = self._fixCellParameters(conf.cell_parameters)
        offset = ParticleProperties.ParticleVector(self)
        if pairs is None:
            pairs = []
            for o in objects:
                new_object = True
                if ChemicalObjects.isChemicalObject(o):
                    units = o.bondedUnits()
                elif Collections.isCollection(o) or isUniverse(o):
                    units = set([u
                                 for element in o
                                 for u in element.topLevelChemicalObject()
                                                              .bondedUnits()])
                else:
                    raise ValueError(str(o) + " not a chemical object")
                for bu in units:
                    atoms = [a.index for a in bu.atomsWithDefinedPositions()]
                    mpairs = bu.traverseBondTree(lambda a: a.index)
                    mpairs = [(a1, a2) for (a1, a2) in mpairs
                              if a1 in atoms and a2 in atoms]
                    if len(mpairs) == 0:
                        mpairs = Utility.pairs(atoms)
                    new_object = False
                    pairs.extend(mpairs)
            pairs = N.array(pairs)
            if default:
                self._bond_pairs = pairs
        if cell is None:
            contiguous_object_offset(self._spec, pairs, conf.array,
                                     offset.array, box_coordinates)
        else:
            contiguous_object_offset(self._spec, pairs, conf.array,
                                     offset.array, box_coordinates, cell)
        return offset

    def _graphics(self, conf, distance_fn, model, module, options):
        objects = self._objects._graphics(conf, distance_fn, model,
                                          module, options)
        v1, v2, v3 = self.basisVectors()
        p = -0.5*(v1+v2+v3)
        color = options.get('color', 'white')
        material = module.EmissiveMaterial(color)
        objects.append(module.Line(p, p+v1, material=material))
        objects.append(module.Line(p, p+v2, material=material))
        objects.append(module.Line(p+v1, p+v1+v2, material=material))
        objects.append(module.Line(p+v2, p+v1+v2, material=material))
        objects.append(module.Line(p, p+v3, material=material))
        objects.append(module.Line(p+v1, p+v1+v3, material=material))
        objects.append(module.Line(p+v2, p+v2+v3, material=material))
        objects.append(module.Line(p+v1+v2, p+v1+v2+v3, material=material))
        objects.append(module.Line(p+v3, p+v1+v3, material=material))
        objects.append(module.Line(p+v3, p+v2+v3, material=material))
        objects.append(module.Line(p+v1+v3, p+v1+v2+v3, material=material))
        objects.append(module.Line(p+v2+v3, p+v1+v2+v3, material=material))
        return objects

#
# Orthorhombic universe with periodic boundary conditions
#

[docs]class OrthorhombicPeriodicUniverse(Periodic3DUniverse):

    """
    Periodic universe with orthorhombic elementary cell.
    """

    def __init__(self, size = None, forcefield = None, **properties):
        """
        :param size: a sequence of length three specifying the edge
                     lengths along the x, y, and z directions
        :param forcefield: a force field, or None for no force field
        :type forcefield: :class:`~MMTK.ForceFields.ForceField.ForceField`
        """
        Universe.__init__(self, forcefield, properties)
        self.data = N.zeros((3,), N.Float)
        if size is not None:
            self.setSize(size)
        self._createSpec()

    is_orthogonal = True

    def __setstate__(self, state):
        Universe.__setstate__(self, state)
        if len(self.data.shape) == 2:
            self.data = self.data[0]

    def setSize(self, size):
        self.data[:] = size


[docs]    def scaleSize(self, factor):
        """
        Multiplies all edge lengths by a factor.

        :param factor: the scale factor
        :type factor: float
        """
        self.data[:] = factor*self.data
        self._spec.foldCoordinatesIntoBox(self.configuration().array)


    def setCellParameters(self, parameters):
        if parameters is not None:
            self.data[:] = parameters

    def realToBoxCoordinates(self, vector):
        x, y, z = vector
        return Vector(x/self.data[0],
                      y/self.data[1],
                      z/self.data[2])

    def boxToRealCoordinates(self, vector):
        x, y, z = vector
        return Vector(x*self.data[0],
                      y*self.data[1],
                      z*self.data[2])

    def _realToBoxPointArray(self, array, parameters=None):
        if parameters is None:
            parameters = self.data
        if parameters.shape == (3,):
            parameters = parameters[N.NewAxis, :]
        return array/parameters

    def _boxToRealPointArray(self, array, parameters=None):
        if parameters is None:
            parameters = self.data
        if parameters.shape == (3,):
            parameters = parameters[N.NewAxis, :]
        return array*parameters

    def CdistanceFunction(self):
        from MMTK_universe import orthorhombic_universe_distance_function
        return orthorhombic_universe_distance_function, self.data

    def CcorrectionFunction(self):
        from MMTK_universe import orthorhombic_universe_correction_function
        return orthorhombic_universe_correction_function, self.data

    def CvolumeFunction(self):
        from MMTK_universe import orthorhombic_universe_volume_function
        return orthorhombic_universe_volume_function, self.data

    def CboxTransformationFunction(self):
        from MMTK_universe import orthorhombic_universe_box_transformation
        return orthorhombic_universe_box_transformation, self.data

    def cellParameters(self):
        return self.data

    def reciprocalBasisVectors(self):
        return [Vector(1., 0., 0.)/self.data[0],
                Vector(0., 1., 0.)/self.data[1],
                Vector(0., 0., 1.)/self.data[2]]

    def cellVolume(self):
        return N.multiply.reduce(self.data)

    def largestDistance(self):
        return 0.5*N.minimum.reduce(self.data)

    def _createSpec(self):
        from MMTK_universe import OrthorhombicPeriodicUniverseSpec
        self._spec = OrthorhombicPeriodicUniverseSpec(self.data)

    def _descriptionArguments(self):
        if self._forcefield is None:
            return '((0.,0.,0.),)'
        else:
            return '((0.,0.,0.),%s)' % self._forcefield.description()

    def XMLSpec(self):
        return 'topology="periodic3d" ' + \
               'cellshape="orthorhombic" ' + \
               ('cellsize="%f %f %f" ' % tuple(self.data)) + \
               'units="units:nm"'

#
# Cubic universe with periodic boundary conditions
#


[docs]class CubicPeriodicUniverse(OrthorhombicPeriodicUniverse):

    """
    Periodic universe with cubic elementary cell.
    """


[docs]    def setSize(self, size):
        """
        Set the edge length to a given value.

        :param size: the new size
        :type size: float
        """
        OrthorhombicPeriodicUniverse.setSize(self, 3*(size,))


    def _descriptionArguments(self):
        if self._forcefield is None:
            return '(0.)'
        else:
            return '(0.,%s)' % self._forcefield.description()

#
# Parallelepipedic universe with periodic boundary conditions
#


[docs]class ParallelepipedicPeriodicUniverse(Periodic3DUniverse):

    """
    Periodic universe with parallelepipedic elementary cell.
    """

    def __init__(self, shape = None, forcefield = None, **properties):
        """
        :param shape: the basis vectors
        :type shape: sequence of Scientific.Geometry.Vector
        :param forcefield: a force field, or None for no force field
        :type forcefield: :class:`~MMTK.ForceFields.ForceField.ForceField`
        """
        Universe.__init__(self, forcefield, properties)
        self.data = N.zeros((19,), N.Float)
        if shape is not None:
            self.setShape(shape)
        self._createSpec()

    is_periodic = True

    def setShape(self, shape):
        self.data[:9] = N.ravel(N.transpose([list(s) for s in shape]))
        from MMTK_universe import parallelepiped_invert
        parallelepiped_invert(self.data)


[docs]    def scaleSize(self, factor):
        """
        Multiplies all edge lengths by a factor.

        :param factor: the scale factor
        :type factor: float
        """
        self.data[:9] = factor*self.data[:9]
        from MMTK_universe import parallelepiped_invert
        parallelepiped_invert(self.data)
        self._spec.foldCoordinatesIntoBox(self.configuration().array)


    def setCellParameters(self, parameters):
        if parameters is not None:
            self.data[:9] = parameters
            from MMTK_universe import parallelepiped_invert
            parallelepiped_invert(self.data)

    def _fixCellParameters(self, cell_parameters):
        full_parameters = 0.*self.data
        full_parameters[:9] = cell_parameters
        from MMTK_universe import parallelepiped_invert
        parallelepiped_invert(full_parameters)
        return full_parameters

    def realToBoxCoordinates(self, vector):
        x, y, z = vector
        return Vector(self.data[0+9]*x + self.data[1+9]*y + self.data[2+9]*z,
                      self.data[3+9]*x + self.data[4+9]*y + self.data[5+9]*z,
                      self.data[6+9]*x + self.data[7+9]*y + self.data[8+9]*z)

    def boxToRealCoordinates(self, vector):
        x, y, z = vector
        return Vector(self.data[0]*x + self.data[1]*y + self.data[2]*z,
                      self.data[3]*x + self.data[4]*y + self.data[5]*z,
                      self.data[6]*x + self.data[7]*y + self.data[8]*z)

    def _realToBoxPointArray(self, array, parameters=None):
        if parameters is None:
            matrix = N.reshape(self.data[9:18], (1, 3, 3))
        else:
            parameters = N.concatenate([parameters, N.zeros((10,), N.Float)])
            from MMTK_universe import parallelepiped_invert
            parallelepiped_invert(parameters)
            matrix = N.reshape(parameters[9:18], (1, 3, 3))
        return N.add.reduce(matrix*array[:, N.NewAxis, :], axis=-1)

    def _boxToRealPointArray(self, array, parameters=None):
        if parameters is None:
            parameters = self.data[:9]
        matrix = N.reshape(parameters, (1, 3, 3))
        return N.add.reduce(matrix*array[:, N.NewAxis, :], axis=-1)

    def CdistanceFunction(self):
        from MMTK_universe import parallelepipedic_universe_distance_function
        return parallelepipedic_universe_distance_function, self.data

    def CcorrectionFunction(self):
        from MMTK_universe import parallelepipedic_universe_correction_function
        return parallelepipedic_universe_correction_function, self.data

    def CvolumeFunction(self):
        from MMTK_universe import parallelepipedic_universe_volume_function
        return parallelepipedic_universe_volume_function, self.data

    def CboxTransformationFunction(self):
        from MMTK_universe import parallelepipedic_universe_box_transformation
        return parallelepipedic_universe_box_transformation, self.data

    def cellParameters(self):
        return self.data[:9]

    def reciprocalBasisVectors(self):
        return [Vector(self.data[9:12]),
                Vector(self.data[12:15]),
                Vector(self.data[15:18])]

    def cellVolume(self):
        return abs(self.data[18])

    def largestDistance(self):
        return min([0.5/v.length() for v in self.reciprocalBasisVectors()])

    def _createSpec(self):
        from MMTK_universe import ParallelepipedicPeriodicUniverseSpec
        self._spec = ParallelepipedicPeriodicUniverseSpec(self.data)

    def _descriptionArguments(self):
        if self._forcefield is None:
            return '((Vector(0.,0.,0.),Vector(0.,0.,0.),Vector(0.,0.,0.)))'
        else:
            return '((Vector(0.,0.,0.),Vector(0.,0.,0.),Vector(0.,0.,0.)),%s)'\
                   % self._forcefield.description()

    def XMLSpec(self):
        return 'topology="periodic3d" ' + \
               'cellshape="parallelepipedic" ' + \
               ('cellshape="%f %f %f %f %f %f %f %f %f" '
                % tuple(self.data[:9])) + \
               'units="units:nm"'

#
# Recognition functions
#


[docs]def isUniverse(object):
    """
    :param object: any Python object
    :returns: True if object is a universe.
    """
    return isinstance(object, Universe)

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© Copyright 2010, Konrad Hinsen. Created using Sphinx 1.1.3.
MMTK-2.7.9/Doc/HTML/_modules/MMTK/Visualization.html0000644000076600000240000025462012013143455022277 0ustar hinsenstaff00000000000000 MMTK.Visualization — MMTK User Guide 2.7.7 documentation

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Source code for MMTK.Visualization

# This module contains interfaces to external visualization programs
# and a visualization base class
#
# Written by Konrad Hinsen
#

"""
Visualization of chemical objects, including animation

This module provides visualization of chemical objects and animated
visualization of normal modes and sequences of configurations, including
trajectories. Visualization depends on external visualization programs.
On Unix systems, these programs are defined by environment variables.
Under Windows NT, the system definitions for files with extension
"pdb" and "wrl" are used.

A viewer for PDB files can be defined by the environment variable
'PDBVIEWER'. For showing a PDB file, MMTK will execute a command
consisting of the value of this variable followed by a space
and the name of the PDB file.

A viewer for VRML files can be defined by the environment variable
'VRMLVIEWER'. For showing a VRML file, MMTK will execute a command
consisting of the value of this variable followed by a space
and the name of the VRML file.

Since there is no standard for launching viewers for animation,
MMTK supports only two programs: VMD and XMol. MMTK detects
these programs by inspecting the value of the environment variable
'PDBVIEWER'. This value must be the file name of the executable,
and must give "vmd" or "xmol" after stripping off an optional
directory specification.
"""

__docformat__ = 'restructuredtext'

from MMTK import Units, Utility
from Scientific import N
import subprocess, sys, tempfile, os

#
# If you want temporary files in a non-standard directory, make
# its name the value of this variable:
#
tempdir = None

#
# Identify OS
#
running_on_windows = sys.platform == 'win32'
running_on_macosx = sys.platform.startswith('darwin')
running_on_linux = sys.platform.startswith('linux')

#
# Get visualization program names
#
viewer = {}
try:
    pdbviewer = os.environ['PDBVIEWER']
    prog = os.path.split(pdbviewer)[1].lower().split('.')[0]
    viewer['pdb'] = (prog, pdbviewer)
except KeyError: pass
try:
    vrmlviewer = os.environ['VRMLVIEWER']
    prog = os.path.split(vrmlviewer)[1].lower().split('.')[0]
    viewer['vrml'] = (prog, vrmlviewer)
except KeyError: pass



[docs]def definePDBViewer(progname, exec_path):
    """
    Define the program used to view PDB files.

    :param progname: the canonical name of the PDB viewer. If it is
                     a known one (one of "vmd", "xmol", "imol"),
                     special features such as animation may be
                     available.
    :type progname: str
    :param exec_path: the path to the executable program
    :type exec_path: str
    """
    viewer['pdb'] = (progname.lower(), exec_path)



[docs]def defineVRMLiewer(progname, exec_path):
    """
    Define the program used to view VRML files.

    :param progname: the canonical name of the VRML viewer
    :type progname: str
    :param exec_path: the path to the executable program
    :type exec_path: str
    """
    viewer['vrml'] = (progname.lower(), exec_path)

#
# Visualization base class. Defines methods for general visualization
# tasks.
#


[docs]class Viewable(object):

    """
    Any viewable chemical object

    This is a mix-in class that defines a general
    visualization method for all viewable objects, i.e. chemical
    objects (atoms, molecules, etc.), collections, and universes.
    """


[docs]    def graphicsObjects(self, **options):
        """
        :keyword configuration: the configuration in which the objects
                                are drawn (default: the current configuration)
        :type configuration: :class:`~MMTK.ParticleProperties.Configuration`
        :keyword model: the graphical representation to be used (one of
                        "wireframe", "tube", "ball_and_stick", "vdw" and
                        "vdw_and_stick").  The vdw models use balls
                        with the radii taken from the atom objects.
                        Default is "wireframe".
        :type model: str
        :keyword ball_radius: the radius of the balls representing the atoms
                              in a ball_and_stick model, default: 0.03
                              This is also used in vdw and vdw_and_stick when
                              an atom does not supply a radius.
        :type ball_radius: float
        :keyword stick_radius: the radius of the sticks representing the bonds
                               in a ball_and_stick, vdw_and_stick or tube model.
                               Default: 0.02 for the tube model, 0.01 for the
                               ball_and_stick and vdw_and_stick models
        :type stick_radius: float
        :keyword graphics_module: the module in which the elementary graphics
                                  objects are defined
                                  (default: Scientific.Visualization.VRML)
        :type graphics_module: module
        :keyword color_values:  a color value for each atom which defines
                                the color via the color scale object specified
                                by the option color_scale. If no value is
                                given, the atoms' colors are taken from the
                                attribute 'color' of each atom object (default
                                values for each chemical element are provided
                                in the chemical database).
        :type  color_values: :class:`~MMTK.ParticleProperties.ParticleScalar`
        :keyword color_scale: an object that returns a color object (as defined
                              in the module Scientific.Visualization.Color)
                              when called with a number argument. Suitable
                              objects are defined by
                              Scientific.Visualization.Color.ColorScale and
                              Scientific.Visualization.Color.SymmetricColorScale.
                              The object is used only when the option
                              color_values is specified as well. The default
                              is a blue-to-red color scale that covers the
                              range of the values given in color_values.
        :type color_scale: callable
        :keyword color: a color name predefined in the module
                        Scientific.Visualization.Color. The corresponding
                        color is applied to all graphics objects that are
                        returned.
        :returns: a list of graphics objects that represent
                  the object for which the method is called.
        :rtype: list
        """
        conf = options.get('configuration', None)
        model = options.get('model', 'wireframe')
        if model == 'tube':
            model = 'ball_and_stick'
            radius = options.get('stick_radius', 0.02)
            options['stick_radius'] = radius
            options['ball_radius'] = radius
        try:
            module = options['graphics_module']
        except KeyError:
            from Scientific.Visualization import VRML
            module = VRML
        color = options.get('color', None)
        if color is None:
            color_values = options.get('color_values', None)
            if color_values is not None:
                lower = N.minimum.reduce(color_values.array)
                upper = N.maximum.reduce(color_values.array)
                options['color_scale'] = module.ColorScale((lower, upper))
        try:
            distance_fn = self.universe().distanceVector
        except AttributeError:
            from MMTK import Universe
            distance_fn = Universe.InfiniteUniverse().distanceVector
        return self._graphics(conf, distance_fn, model, module, options)


    def _atomColor(self, atom, options):
        color = options.get('color', None)
        if color is not None:
            return color
        color_values = options.get('color_values', None)
        if color_values is None:
            return atom.color
        else:
            scale = options['color_scale']
            return scale(color_values[atom])


#
# View anything viewable.
#


[docs]def view(object, *parameters):
    "Equivalent to object.view(parameters)."
    object.view(*parameters)

#
# Display an object or a collection of objects using an external
# viewing program.
#

def genericViewConfiguration(object, configuration = None, format = 'pdb',
                             label = None):
    format = format.lower()
    viewer_format = format.split('.')[0]

    tempfile.tempdir = tempdir
    filename = tempfile.mktemp()
    tempfile.tempdir = None
    if viewer_format == 'pdb':
        filename = filename + '.pdb'
    elif viewer_format == 'vrml':
        filename = filename + '.wrl'
    
    if running_on_windows:
        object.writeToFile(filename, configuration, format)
        try:
            os.startfile(filename)
        except win32api.error, error_number:
            #Looking for error 31, SE_ERR_NOASSOC, in particular
            file_type = os.path.splitext(filename)[1]
            if error_number[0]==31:
                print ('There is no program associated with .%s files,' + \
                       ' please install a suitable viewer') % file_type
            else:
                print 'Unexpected error attempting to open .%s file' % file_type
                print sys.exc_value
    elif viewer.has_key(viewer_format):
        # On Unix-like systems, give priority to a  user-specified viewer.
        object.writeToFile(filename, configuration, format)
        if os.fork() == 0:
            pipe = os.popen(viewer[format][1] + ' ' + filename + \
                            ' 1> /dev/null 2>&1', 'w')
            pipe.close()
            os.unlink(filename)
            os._exit(0)
    elif running_on_macosx:
        object.writeToFile(filename, configuration, format)
        subprocess.call(["/usr/bin/open", filename])
    elif running_on_linux:
        object.writeToFile(filename, configuration, format)
        subprocess.call(["xdg-open", filename])
    else:
        Utility.warning('No viewer for %s defined.' % viewer_format)
        return

def viewConfiguration(*args, **kwargs):
    pdbviewer, exec_path = viewer.get('pdb', (None, None))
    function = {'vmd': viewConfigurationVMD,
                'xmol': viewConfigurationXMol,
                'imol': viewConfigurationIMol} \
               .get(pdbviewer,genericViewConfiguration)
    function(*args, **kwargs)

#
# Normal mode and trajectory animation
#
def viewSequence(object, conf_list, periodic = False, label = None):
    """
    Launches an animation using an external viewer.

    :param object: the object for which the animation is displayed.
    :type object: :class:`~MMTK.Collections.GroupOfAtoms`
    :param conf_list: a sequence of configurations that define the animation
    :type conf_list: sequence
    :param periodic: if True, turn animation into a loop
    :param label: an optional text string that some interfaces
                  use to pass a description of the object to the
                  visualization system.
    :type label: str
    """
    pdbviewer, exec_path = viewer.get('pdb', (None, None))
    function = {'vmd': viewSequenceVMD,
                'xmol': viewSequenceXMol,
                'imol': viewSequenceIMol,
                None: None}[pdbviewer]
    if function is None:
        Utility.warning('No viewer with animation feature defined.')
    else:
        function(object, conf_list, periodic, label)



[docs]def viewTrajectory(trajectory, first=0, last=None, skip=1, subset = None,
                   label = None):
    """
    Launches an animation based on a trajectory using an external viewer.

    :param trajectory: the trajectory
    :type trajectory: :class:`~MMTK.Trajectory.Trajectory`
    :param first: the first trajectory step to be used
    :type first: int
    :param last: the first trajectory step NOT to be used
    :type last: int
    :param skip: the distance between two consecutive steps shown
    :type skip: int
    :param subset: the subset of the universe that is shown
                   (default: the whole universe)
    :type subset: :class:`~MMTK.Collections.GroupOfAtoms`
    :param label: an optional text string that some interfaces
                  use to pass a description of the object to the
                  visualization system.
    :type label: str
    """
    if type(trajectory) == type(''):
        from MMTK.Trajectory import Trajectory
        trajectory = Trajectory(None, trajectory, 'r')
    if last is None:
        last = len(trajectory)
    elif last < 0:
        last = len(trajectory) + last
    universe = trajectory.universe
    if subset is None:
        subset = universe
    viewSequence(subset, trajectory.configuration[first:last:skip], label)


def viewMode(mode, factor=1., subset=None, label=None):
    universe = mode.universe
    if subset is None:
        subset = universe
    conf = universe.configuration()
    viewSequence(subset, [conf, conf+factor*mode, conf, conf-factor*mode], 1,
                 label)

#
# XMol support
#    

#
# Animation with XMol.
#
viewConfigurationXMol = viewConfiguration

def viewSequenceXMol(object, conf_list, periodic = 0, label = None):
    tempfile.tempdir = tempdir
    file_list = []
    for conf in conf_list:
        file = tempfile.mktemp()
        file_list.append(file)
        object.writeToFile(file, conf, 'pdb')
    bigfile = tempfile.mktemp()
    tempfile.tempdir = None
    subprocess.call(['cat'] + file_list + ['>', bigfile])
    for file in file_list:
        os.unlink(file)
    if os.fork() == 0:
        pipe = os.popen('xmol -readFormat pdb ' + bigfile + \
                        ' 1> /dev/null 2>&1', 'w')
        pipe.close()
        os.unlink(bigfile)
        os._exit(0)


#
# VMD support
#

#
# View configuration
#
def isCalpha(object):
    from MMTK.Proteins import isProtein, isPeptideChain
    from MMTK.Universe import isUniverse
    from MMTK.Collections import isCollection
    if isProtein(object):
       chain_list = list(object)
    elif isPeptideChain(object):
        chain_list = [object]
    elif isUniverse(object) or isCollection(object):
        chain_list = []
        for element in object:
            if isProtein(element):
                chain_list = chain_list + list(element)
            elif isPeptideChain(element):
                chain_list.append(element)
            else:
                return False
    else:
        return False
    for chain in chain_list:
        try:
            if chain.model != 'calpha':
                return False
        except AttributeError:
            return False
    return True

def viewConfigurationVMD(object, configuration = None, format = 'pdb',
                      label = None):
    from MMTK import Universe
    format = format.lower()
    if format != 'pdb':
        return genericViewConfiguration(object, configuration, format)
    tempfile.tempdir = tempdir
    filename = tempfile.mktemp()
    filename_tcl = filename.replace('\\', '\\\\')
    script = tempfile.mktemp()
    script_tcl = script.replace('\\', '\\\\')
    tempfile.tempdir = None
    object.writeToFile(filename, configuration, format)
    file = open(script, 'w')
    file.write('mol load pdb ' + filename_tcl + '\n')
    if isCalpha(object):
        file.write('mol modstyle 0 all trace\n')
    file.write('color Name 1 white\n')
    file.write('color Name 2 white\n')
    file.write('color Name 3 white\n')
    if Universe.isUniverse(object):
        # add a box around periodic universes
        basis = object.basisVectors()
        if basis is not None:
            v1, v2, v3 = basis
            p = -0.5*(v1+v2+v3)
            for p1, p2 in [(p, p+v1), (p, p+v2), (p+v1, p+v1+v2),
                           (p+v2, p+v1+v2), (p, p+v3), (p+v1, p+v1+v3),
                           (p+v2, p+v2+v3), (p+v1+v2, p+v1+v2+v3),
                           (p+v3, p+v1+v3), (p+v3, p+v2+v3),
                           (p+v1+v3, p+v1+v2+v3), (p+v2+v3, p+v1+v2+v3)]:
                file.write('graphics 0 line {%f %f %f} {%f %f %f}\n' %
                           (tuple(p1/Units.Ang) + tuple(p2/Units.Ang)))
    file.write('file delete ' + filename_tcl + '\n')
    if sys.platform != 'win32':
            # Under Windows, it seems to be impossible to delete
            # the script file while it is still in use. For the moment
            # we just don't delete it at all.
        file.write('file delete ' + script_tcl + '\n')
    file.close()
    subprocess.Popen([viewer['pdb'][1], '-nt', '-e', script])

#
# Animate sequence
#
def viewSequenceVMD(object, conf_list, periodic = 0, label=None):
    tempfile.tempdir = tempdir
    script = tempfile.mktemp()
    script_tcl = script.replace('\\', '\\\\')
    np = object.numberOfPoints()
    universe = object.universe()
    if np == universe.numberOfPoints() \
       and len(conf_list) > 2:
        from MMTK import DCD
        pdbfile = tempfile.mktemp()
        pdbfile_tcl = pdbfile.replace('\\', '\\\\')
        dcdfile = tempfile.mktemp()
        dcdfile_tcl = dcdfile.replace('\\', '\\\\')
        tempfile.tempdir = None
        sequence = DCD.writePDB(universe, conf_list[0], pdbfile)
        indices = map(lambda a: a.index, sequence)
        DCD.writeDCD(conf_list[1:], dcdfile, 1./Units.Ang, indices)
        file = open(script, 'w')
        file.write('mol load pdb ' + pdbfile_tcl + '\n')
        if isCalpha(object):
            file.write('mol modstyle 0 all trace\n')
        file.write('animate read dcd ' + dcdfile_tcl + '\n')
        if periodic:
            file.write('animate style loop\n')
        else:
            file.write('animate style once\n')
        file.write('animate forward\n')
        file.write('file delete ' + pdbfile_tcl + '\n')
        file.write('file delete ' + dcdfile_tcl + '\n')
        if sys.platform != 'win32':
            # Under Windows, it seems to be impossible to delete
            # the script file while it is still in use. For the moment
            # we just don't delete it at all.
            file.write('file delete ' + script_tcl + '\n')
        file.close()
    else:
        file_list = []
        for conf in conf_list:
            file = tempfile.mktemp()
            file_list.append(file)
            object.writeToFile(file, conf, 'pdb')
        tempfile.tempdir = None
        file = open(script, 'w')
        file.write('mol load pdb ' + file_list[0] + '\n')
        for conf in file_list[1:]:
            file.write('animate read pdb ' + conf.replace('\\', '\\\\') + '\n')
        if periodic:
            file.write('animate style loop\n')
        else:
            file.write('animate style once\n')
        file.write('animate forward\n')
        for conf in file_list:
            file.write('file delete ' + conf.replace('\\', '\\\\') + '\n')
        if sys.platform != 'win32':
            # Under Windows, it seems to be impossible to delete
            # the script file while it is still in use. For the moment
            # we just don't delete it at all.
            file.write('file delete ' + script_tcl + '\n')
        file.close()
    subprocess.Popen([viewer['pdb'][1], '-nt', '-e', script])

#
# iMol support
#

#
# View configuration
#
def viewConfigurationIMol(object, configuration = None, format = 'pdb',
                      label = None):
    format = format.lower()
    if format != 'pdb':
        return genericViewConfiguration(object, configuration, format)
    tempfile.tempdir = tempdir
    filename = tempfile.mktemp() + '.pdb'
    tempfile.tempdir = None
    object.writeToFile(filename, configuration, format)
    subprocess.call(['open', '-a', prog, filename])

#
# Animate sequence
#
def viewSequenceIMol(object, conf_list, periodic = 0, label=None):
    from MMTK import PDB
    tempfile.tempdir = tempdir
    filename = tempfile.mktemp() + '.pdb'
    file = PDB.PDBOutputFile(filename)
    for conf in conf_list:
        file.nextModel()
        file.write(object, conf)
    file.close()
    tempfile.tempdir = None
    subprocess.call(['open', '-a', prog, filename])

#
# PyMOL support
#
from MMTK import PyMOL
if PyMOL.in_pymol:

    _representation = None

    def viewConfiguration(object, configuration = None, format = 'pdb',
                          label = None):
        global _representation
        if label is None:
            label = "MMTK Object"
        if _representation is not None:
            _representation.remove()
        _representation = PyMOL.Representation(object, label, configuration)
        _representation.show()
        

[docs]    def viewSequence(object, conf_list, periodic = 0, label = None):
        global _representation
        if label is None:
            label = "MMTK Object"
        if _representation is not None:
            _representation.remove()
        _representation = PyMOL.Representation(object, label,
                                               conf_list[0])
        _representation.movie(conf_list)


    genericViewMode = viewMode
    
    def viewMode(mode, factor=1., subset=None, label=None):
        from pymol import cmd
        cmd.set("movie_delay","200",log=1)
        genericViewMode(mode, factor, subset, label)

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© Copyright 2010, Konrad Hinsen. Created using Sphinx 1.1.3.
MMTK-2.7.9/Doc/HTML/_modules/MMTK/XML.html0000644000076600000240000005370712013143453020077 0ustar hinsenstaff00000000000000 MMTK.XML — MMTK User Guide 2.7.7 documentation

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Source code for MMTK.XML

# XML I/O
#
# Written by Konrad Hinsen
#

"""
XML format for describing molecular systems

Note: this format is not used by any other program at the moment. It should
be considered experimental and subject to change.
"""

__docformat__ = 'restructuredtext'

import MMTK
from MMTK.MoleculeFactory import MoleculeFactory
from xml.etree.ElementTree import iterparse
from Scientific import N



[docs]class XMLMoleculeFactory(MoleculeFactory):

    """
    XML molecule factory
    
    An XML molecule factory reads an XML specification of a molecular
    system and builds the molecule objects and universe described
    therein. The universe can be obtained through the attribute
    *universe*.
    
    :param file: the name of an XML file, or a file object
    """

    def __init__(self, file):
        MoleculeFactory.__init__(self)
        for event, element in iterparse(file):
            tag = element.tag
            ob_id = element.attrib.get('id', None)
            if tag == 'molecule' and ob_id is not None:
                self.makeGroup(element)
                element.clear()
            elif tag == 'templates':
                element.clear()
            elif tag == 'universe':
                self.makeUniverse(element)

    def makeGroup(self, element):
        group = element.attrib['id']
        self.createGroup(group)
        for molecule_element in element.findall('molecule'):
            self.addSubgroup(group, molecule_element.attrib.get('title', ''),
                             molecule_element.attrib['ref'])
        atom_array = element.find('atomArray')
        if atom_array is not None:
            for atom_element in atom_array:
                self.addAtom(group, atom_element.attrib['title'],
                             atom_element.attrib['elementType'])
        bond_array = element.find('bondArray')
        if bond_array is not None:
            for bond_element in bond_array:
                atom1, atom2 = bond_element.attrib['atomRefs2'].split()
                atom1 = '.'.join(atom1.split(':'))
                atom2 = '.'.join(atom2.split(':'))
                self.addBond(group, atom1, atom2)

    def makeUniverse(self, element):
        topology = element.attrib.get('topology', 'infinite')
        if topology == 'infinite':
            universe = MMTK.InfiniteUniverse()
        elif topology == 'periodic3d':
            cellshape = element.attrib['cellshape']
            if cellshape == 'orthorhombic':
                cellsize = element.attrib['cellsize'].split()
                units = element.attrib.get('units', 'units:nm')
                factor = getattr(MMTK.Units, units.split(':')[1])
                universe = MMTK.OrthorhombicPeriodicUniverse(tuple(
                                     [factor*float(size) for size in cellsize]))
            else:
                raise ValueError("cell shape %s not implemented" % cellshape)
        else:
            raise ValueError("topology %s not implemented" % topology)
        atom_index = 0
        for subelement in element:
            if subelement.tag == 'molecule':
                molecule = self.retrieveMolecule(subelement.attrib['ref'])
                for atom in molecule.atomList():
                    atom.index = atom_index
                    atom_index += 1
                universe.addObject(molecule)
            elif subelement.tag == 'atom':
                atom = MMTK.Atom(subelement.attrib['elementType'])
                atom.index = atom_index
                atom_index += 1
                universe.addObject(atom)
        configuration = element.find('configuration')
        if configuration is not None:
            array = configuration.find('atomArray')
            x = map(float, array.attrib['x3'].split())
            y = map(float, array.attrib['y3'].split())
            z = map(float, array.attrib['z3'].split())
            units = array.attrib.get('units', 'units:nm')
            factor = getattr(MMTK.Units, units.split(':')[1])
            array = universe.configuration().array
            array[:, 0] = x
            array[:, 1] = y
            array[:, 2] = z
            N.multiply(array, factor, array)
        self.universe = universe

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© Copyright 2010, Konrad Hinsen. Created using Sphinx 1.1.3.
MMTK-2.7.9/Doc/HTML/_sources/0000755000076600000240000000000012156626563016020 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/_sources/Examples/0000755000076600000240000000000012156626561017574 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/_sources/Examples/DNA/0000755000076600000240000000000012156626563020200 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/_sources/Examples/DNA/construction.py.txt0000644000076600000240000000027012000014157024073 0ustar hinsenstaff00000000000000:orphan: Constructing a DNA strand with a ligand ####################################### .. literalinclude:: ../../../Examples/DNA/construction.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Forcefield/0000755000076600000240000000000012156626561021636 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/_sources/Examples/Forcefield/ElectricField/0000755000076600000240000000000012156626561024334 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/_sources/Examples/Forcefield/ElectricField/Cython/0000755000076600000240000000000012156626563025602 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/_sources/Examples/Forcefield/ElectricField/Cython/ElectricField.py.txt0000644000076600000240000000032512000016335031442 0ustar hinsenstaff00000000000000:orphan: An electric field term (Python part) #################################### .. literalinclude:: ../../../../../Examples/Forcefield/ElectricField/Cython/ElectricField.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Forcefield/ElectricField/Cython/MMTK_electric_field.pyx.txt0000644000076600000240000000033412000016343032720 0ustar hinsenstaff00000000000000:orphan: An electric field term (Cython part) #################################### .. literalinclude:: ../../../../../Examples/Forcefield/ElectricField/Cython/MMTK_electric_field.pyx :language: cython :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Forcefield/ElectricField/Python/0000755000076600000240000000000012156626563025617 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/_sources/Examples/Forcefield/ElectricField/Python/ElectricField.py.txt0000644000076600000240000000035712000016353031464 0ustar hinsenstaff00000000000000:orphan: An electric field term implemented in pure Python ################################################# .. literalinclude:: ../../../../../Examples/Forcefield/ElectricField/Python/ElectricField.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Forcefield/HarmonicOscillator/0000755000076600000240000000000012156626561025432 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/_sources/Examples/Forcefield/HarmonicOscillator/Cython/0000755000076600000240000000000012156626563026700 5ustar hinsenstaff00000000000000././@LongLink0000000000000000000000000000014700000000000011217 Lustar 00000000000000MMTK-2.7.9/Doc/HTML/_sources/Examples/Forcefield/HarmonicOscillator/Cython/HarmonicOscillatorFF.py.txtMMTK-2.7.9/Doc/HTML/_sources/Examples/Forcefield/HarmonicOscillator/Cython/HarmonicOscillatorFF.py.t0000644000076600000240000000037712000016370033504 0ustar hinsenstaff00000000000000:orphan: An efficient harmonic oscillator term (Python part) ################################################### .. literalinclude:: ../../../../../Examples/Forcefield/HarmonicOscillator/Cython/HarmonicOscillatorFF.py :language: python :linenos: ././@LongLink0000000000000000000000000000015400000000000011215 Lustar 00000000000000MMTK-2.7.9/Doc/HTML/_sources/Examples/Forcefield/HarmonicOscillator/Cython/MMTK_harmonic_oscillator.pyx.txtMMTK-2.7.9/Doc/HTML/_sources/Examples/Forcefield/HarmonicOscillator/Cython/MMTK_harmonic_oscillator.0000644000076600000240000000040412000016374033537 0ustar hinsenstaff00000000000000:orphan: An efficient harmonic oscillator term (Cython part) ################################################### .. literalinclude:: ../../../../../Examples/Forcefield/HarmonicOscillator/Cython/MMTK_harmonic_oscillator.pyx :language: cython :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Forcefield/HarmonicOscillator/Python/0000755000076600000240000000000012156626563026715 5ustar hinsenstaff00000000000000././@LongLink0000000000000000000000000000014700000000000011217 Lustar 00000000000000MMTK-2.7.9/Doc/HTML/_sources/Examples/Forcefield/HarmonicOscillator/Python/HarmonicOscillatorFF.py.txtMMTK-2.7.9/Doc/HTML/_sources/Examples/Forcefield/HarmonicOscillator/Python/HarmonicOscillatorFF.py.t0000644000076600000240000000035312000016403033510 0ustar hinsenstaff00000000000000:orphan: A harmonic oscillator term in pure Python ######################################### .. literalinclude:: ../../../../../Examples/Forcefield/HarmonicOscillator/Python/HarmonicOscillatorFF.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/LangevinDynamics/0000755000076600000240000000000012156626563023031 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/_sources/Examples/LangevinDynamics/LangevinDynamics.py.txt0000644000076600000240000000033512000016420027422 0ustar hinsenstaff00000000000000:orphan: An integrator for Langevin dynamics (Python part) ################################################# .. literalinclude:: ../../../Examples/LangevinDynamics/LangevinDynamics.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/LangevinDynamics/MMTK_langevin.c.txt0000644000076600000240000000031212000016425026414 0ustar hinsenstaff00000000000000:orphan: An integrator for Langevin dynamics (C part) ############################################ .. literalinclude:: ../../../Examples/LangevinDynamics/MMTK_langevin.c :language: c :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/MDIntegrator/0000755000076600000240000000000012156626563022135 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/_sources/Examples/MDIntegrator/VelocityVerlet.pyx.txt0000644000076600000240000000027412000016435026453 0ustar hinsenstaff00000000000000:orphan: A simple Velocity Verlet integrator ################################### .. literalinclude:: ../../../Examples/MDIntegrator/VelocityVerlet.pyx :language: cython :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Miscellaneous/0000755000076600000240000000000012156626563022401 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/_sources/Examples/Miscellaneous/charge_fit.py.txt0000644000076600000240000000035012000016453025635 0ustar hinsenstaff00000000000000:orphan: Fitting point charges to an electrostatic potential surface ########################################################### .. literalinclude:: ../../../Examples/Miscellaneous/charge_fit.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Miscellaneous/construct_from_pdb.py.txt0000644000076600000240000000032212000016457027441 0ustar hinsenstaff00000000000000:orphan: Building a complete universe from a PDB file ############################################ .. literalinclude:: ../../../Examples/Miscellaneous/construct_from_pdb.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Miscellaneous/lattice.py.txt0000644000076600000240000000024712000016462025174 0ustar hinsenstaff00000000000000:orphan: Place molecules on a lattice ############################ .. literalinclude:: ../../../Examples/Miscellaneous/lattice.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Miscellaneous/vector_field.py.txt0000644000076600000240000000037012000016466026215 0ustar hinsenstaff00000000000000:orphan: Vector fields for analysis and visualization of collective motions ################################################################## .. literalinclude:: ../../../Examples/Miscellaneous/vector_field.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/MolecularDynamics/0000755000076600000240000000000012156626563023211 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/_sources/Examples/MolecularDynamics/argon.py.txt0000644000076600000240000000024412000016501025454 0ustar hinsenstaff00000000000000:orphan: Simulation of liquid argon ########################## .. literalinclude:: ../../../Examples/MolecularDynamics/argon.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/MolecularDynamics/protein.py.txt0000644000076600000240000000024012000016505026026 0ustar hinsenstaff00000000000000:orphan: Simulation of a protein ####################### .. literalinclude:: ../../../Examples/MolecularDynamics/protein.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/MolecularDynamics/restart.py.txt0000644000076600000240000000026412000016510026034 0ustar hinsenstaff00000000000000:orphan: Restarting the protein simulation ################################# .. literalinclude:: ../../../Examples/MolecularDynamics/restart.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/MolecularDynamics/solvation.py.txt0000644000076600000240000000026212000016513026367 0ustar hinsenstaff00000000000000:orphan: Solvation of a protein in water ############################### .. literalinclude:: ../../../Examples/MolecularDynamics/solvation.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/MonteCarlo/0000755000076600000240000000000012156626563021641 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/_sources/Examples/MonteCarlo/backbone.py.txt0000644000076600000240000000031212000016524024543 0ustar hinsenstaff00000000000000:orphan: Sampling a backbone-only configuration ensemble ############################################### .. literalinclude:: ../../../Examples/MonteCarlo/backbone.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/MPI/0000755000076600000240000000000012156626563020223 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/_sources/Examples/MPI/md.py.txt0000644000076600000240000000023212000016445021764 0ustar hinsenstaff00000000000000:orphan: Protein solvation in parallel ############################# .. literalinclude:: ../../../Examples/MPI/md.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/NormalModes/0000755000076600000240000000000012156626563022016 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/_sources/Examples/NormalModes/calpha_modes.py.txt0000644000076600000240000000033212000016532025574 0ustar hinsenstaff00000000000000:orphan: Slow normal modes of a protein using a C-alpha model #################################################### .. literalinclude:: ../../../Examples/NormalModes/calpha_modes.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/NormalModes/constrained_modes.py.txt0000644000076600000240000000034112000016535026660 0ustar hinsenstaff00000000000000:orphan: Normal modes of a protein using a rigid-residue model ##################################################### .. literalinclude:: ../../../Examples/NormalModes/constrained_modes.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/NormalModes/harmonic_force_field.py.txt0000644000076600000240000000035212000016537027305 0ustar hinsenstaff00000000000000:orphan: Normal modes of a protein using a simplified force field ######################################################## .. literalinclude:: ../../../Examples/NormalModes/harmonic_force_field.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/NormalModes/modes.py.txt0000644000076600000240000000023512000016543024270 0ustar hinsenstaff00000000000000:orphan: Normal modes of a protein ######################### .. literalinclude:: ../../../Examples/NormalModes/modes.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Proteins/0000755000076600000240000000000012156626563021401 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/_sources/Examples/Proteins/analysis.py.txt0000644000076600000240000000025312000016553024370 0ustar hinsenstaff00000000000000:orphan: Comparing protein configurations ################################ .. literalinclude:: ../../../Examples/Proteins/analysis.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Proteins/construction.py.txt0000644000076600000240000000030712000016557025303 0ustar hinsenstaff00000000000000:orphan: Protein construction beyond the simple cases ############################################ .. literalinclude:: ../../../Examples/Proteins/construction.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Trajectories/0000755000076600000240000000000012156626563022234 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/_sources/Examples/Trajectories/calpha_trajectory.py.txt0000644000076600000240000000034712000016567027107 0ustar hinsenstaff00000000000000:orphan: Extract a C-alpha trajectory from an all-atom trajectory ######################################################## .. literalinclude:: ../../../Examples/Trajectories/calpha_trajectory.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Trajectories/dcd_export.py.txt0000644000076600000240000000026412000016572025536 0ustar hinsenstaff00000000000000:orphan: Convert a trajectory to DCD format ################################## .. literalinclude:: ../../../Examples/Trajectories/dcd_export.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Trajectories/dcd_import.py.txt0000644000076600000240000000027012000016575025527 0ustar hinsenstaff00000000000000:orphan: Importing a trajectory in DCD format #################################### .. literalinclude:: ../../../Examples/Trajectories/dcd_import.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Trajectories/fluctuations.py.txt0000644000076600000240000000032412000017075026117 0ustar hinsenstaff00000000000000:orphan: Calculating atomic fluctuations from a trajectory ################################################# .. literalinclude:: ../../../Examples/Trajectories/fluctuations.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Trajectories/pdb_export.py.txt0000644000076600000240000000032412000017165025545 0ustar hinsenstaff00000000000000:orphan: Converting a trajectory to a sequence of PDB files ################################################## .. literalinclude:: ../../../Examples/Trajectories/pdb_export.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Trajectories/snapshot.py.txt0000644000076600000240000000026612000016601025235 0ustar hinsenstaff00000000000000:orphan: Assembling a trajectory step by step #################################### .. literalinclude:: ../../../Examples/Trajectories/snapshot.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Trajectories/trajectory_average.py.txt0000644000076600000240000000032412000016605027255 0ustar hinsenstaff00000000000000:orphan: Compute an average structure from a trajectory ############################################## .. literalinclude:: ../../../Examples/Trajectories/trajectory_average.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Trajectories/trajectory_extraction.py.txt0000644000076600000240000000027712000016611030027 0ustar hinsenstaff00000000000000:orphan: Extract a subset from a trajectory ################################## .. literalinclude:: ../../../Examples/Trajectories/trajectory_extraction.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Trajectories/view_trajectory.py.txt0000644000076600000240000000026712000016615026624 0ustar hinsenstaff00000000000000:orphan: Show an animation of a trajectory ################################# .. literalinclude:: ../../../Examples/Trajectories/view_trajectory.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Visualization/0000755000076600000240000000000012156626563022437 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/_sources/Examples/Visualization/additional_objects.py.txt0000644000076600000240000000035612000016623027426 0ustar hinsenstaff00000000000000:orphan: Adding custom graphics to a molecular system visualization ########################################################## .. literalinclude:: ../../../Examples/Visualization/additional_objects.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Visualization/graphics_data.py.txt0000644000076600000240000000032612000017321026367 0ustar hinsenstaff00000000000000:orphan: Extracting numerical values from graphics objects ################################################# .. literalinclude:: ../../../Examples/Visualization/graphics_data.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Visualization/vector_field_chimera.py.txt0000644000076600000240000000036112000017463027741 0ustar hinsenstaff00000000000000:orphan: Vector field visualization of a normal mode (using Chimera) ########################################################### .. literalinclude:: ../../../Examples/Visualization/vector_field_chimera.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/Examples/Visualization/vector_field_vmd.py.txt0000644000076600000240000000034512000017427027121 0ustar hinsenstaff00000000000000:orphan: Vector field visualization of a normal mode (using VMD) ####################################################### .. literalinclude:: ../../../Examples/Visualization/vector_field_vmd.py :language: python :linenos: MMTK-2.7.9/Doc/HTML/_sources/examples.txt0000644000076600000240000002044112000020170020342 0ustar hinsenstaff00000000000000.. _Examples: .. |C_alpha| replace:: C\ :sub:`α` Code Examples ############# One of the best ways to learn how to use a new tool is to look at examples. The examples given in this manual were adapted from real-life MMTK applications. They are also contained in the MMTK distribution (directory "Examples") for direct use and modification. The example molecules, system sizes, parameters, etc., were chosen to reduce execution time as much as possible, in order to enable you to run the examples interactively step by step to see how they work. If you plan to modify an example program for your own use, don't forget to check all parameters carefully to make sure that you obtain reasonable results. .. _Example-MolecularDynamics: - Molecular Dynamics examples - The program :doc:`argon.py ` contains a simulation of liquid argon at constant temperature and pressure. - The program :doc:`protein.py ` contains a simulation of a small (very small) protein in vacuum. - The program :doc:`restart.py ` shows how the simulation started in :doc:`protein.py ` can be continued. - The program :doc:`solvation.py ` contains the solvation of a protein by water molecules. .. _Example-MonteCarlo: - Monte-Carlo examples - The program :doc:`backbone.py <../Examples/MonteCarlo/backbone.py>` generates an ensemble of backbone configuration (C-alpha atoms only) for a protein. .. _Example-Trajectories: - Trajectory examples - The program :doc:`snapshot.py <../Examples/Trajectories/snapshot.py>` shows how a trajectory can be built up step by step from arbitrary data. - The program :doc:`dcd_import.py <../Examples/Trajectories/dcd_import.py>` converts a trajectory in DCD format (used by the programs CHARMM, X-Plor, and NAMD) to MMTK's format. - The program :doc:`dcd_export.py <../Examples/Trajectories/dcd_export.py>` converts an MMTK trajectory to DCD format (used by the programs CHARMM, X-Plor, and NAMD). - The program :doc:`pdb_export.py <../Examples/Trajectories/pdb_export.py>` converts an MMTK trajectory to a sequence of PDB files. - The program :doc:`trajectory_average.py <../Examples/Trajectories/trajectory_average.py>` calculates an average structure from a trajectory. - The program :doc:`trajectory_extraction.py <../Examples/Trajectories/trajectory_extraction.py>` reads a trajectory and writes a new one containing only a subset of the original universe. - The program :doc:`view_trajectory.py <../Examples/Trajectories/view_trajectory.py>` shows an animation of a trajectory, provided that an external molecule viewer with animation is available. - The program :doc:`calpha_trajectory.py <../Examples/Trajectories/calpha_trajectory.py>` shows how a much smaller |C_alpha|-only trajectory can be extracted from a trajectory containing one or more proteins. - The program :doc:`fluctuations.py <../Examples/Trajectories/fluctuations.py>` shows how to calculate atomic fluctuations from a trajectory file. .. _Example-NormalModes: - Normal mode examples - The program :doc:`modes.py <../Examples/NormalModes/modes.py>` contains a standard normal mode calculation for a small protein. - The program :doc:`constrained_modes.py <../Examples/NormalModes/constrained_modes.py>` contains a normal mode calculation for a small protein using a model in which each amino acid residue is rigid. - The program :doc:`calpha_modes.py <../Examples/NormalModes/calpha_modes.py>` contains a normal mode calculation for a mid-size protein using a |C_alpha| model and an elastic network model. - The program :doc:`harmonic_force_field.py <../Examples/NormalModes/harmonic_force_field.py>` contains a normal mode calculation for a protein using a detailed but still simple harmonic force field. .. _Example-Proteins: - Protein examples - The program :doc:`construction.py <../Examples/Proteins/construction.py>` shows some more complex examples of protein construction from PDB files. - The program :doc:`analysis.py <../Examples/Proteins/analysis.py>` demonstrates a few analysis techniques for comparing protein conformations. .. _Example-DNA: - DNA examples - The program :doc:`construction.py <../Examples/DNA/construction.py>` contains the construction of a DNA strand with a ligand. .. _Example-Forcefield: - Forcefield examples - Electric field term - A pure Python implementation (rather slow in general, but tolerable for a simple term like this one) is given in :doc:`Python/ElectricField.py <../Examples/Forcefield/ElectricField/Python/ElectricField.py>`. - A more efficient implementation has the evaluation code written in Cython (:doc:`Cython/MMTK_electric_field.pyx <../Examples/Forcefield/ElectricField/Cython/MMTK_electric_field.pyx>`) while the bookkeeping part remains in Python (:doc:`Cython/ElectricField.py <../Examples/Forcefield/ElectricField/Cython/ElectricField.py>`). - Harmonic oscillator term - A pure Python implementation (rather slow in general, but tolerable for a simple term like this one) is given in :doc:`Python/HarmonicOscillatorFF.py <../Examples/Forcefield/HarmonicOscillator/Python/HarmonicOscillatorFF.py>`. - A more efficient implementation has the evaluation code written in Cython (:doc:`Cython/MMTK_harmonic_oscillator.pyx <../Examples/Forcefield/HarmonicOscillator/Cython/MMTK_harmonic_oscillator.pyx>`) while the bookkeeping part remains in Python (:doc:`Cython/HarmonicOscillatorFF.py <../Examples/Forcefield/HarmonicOscillator/Cython/HarmonicOscillatorFF.py>`). .. _Example-MPI: - MPI examples (parallelization) - The program :doc:`md.py <../Examples/MPI/md.py>` contains a parallelized version of :doc:`solvation.py <../Examples/MolecularDynamics/solvation.py>`. .. _Example-MDIntegrator: - Molecular Dynamics integrators - The program :doc:`md.py <../Examples/MDIntegrator/VelocityVerlet.pyx>` illustrates how Molecular Dynamics integrators can be implemented in Cython. .. _Example-LangevinDynamics: - Langevin dynamics integrator - The programs :doc:`LangevinDynamics.py <../Examples/LangevinDynamics/LangevinDynamics.py>` and :doc:`MMTK_langevinmodule.c <../Examples/LangevinDynamics/MMTK_langevin.c>` implement a simple integrator for Langevin dynamics. It is meant as an example of how to write integrators etc. in C, but of course it can also be used directly. .. _Example-Visualization: - Visualization examples - The program :doc:`additional_objects.py <../Examples/Visualization/additional_objects.py>` describes the addition of custom graphics objects to the representation of a molecular system. - The program :doc:`vector_field_chimera.py <../Examples/Visualization/vector_field_chimera.py>` shows how to create a vector-field visualization of a normal mode using the graphics program Chimera for visualization. The program :doc:`vector_field_vmd.py <../Examples/Visualization/vector_field_vmd.py>` does the same but uses the graphics program VMD. - The program :doc:`graphics_data.py <../Examples/Visualization/graphics_data.py>` shows how to use a fake graphics module to extract numerical values from a vector field object. .. _Example-Miscellaneous: - Micellaneous examples - The example :doc:`charge_fit.py <../Examples/Miscellaneous/charge_fit.py>` demonstrates fitting point charges to an electrostatic potential energy surface. - The program :doc:`construct_from_pdb.py <../Examples/Miscellaneous/construct_from_pdb.py>` shows how a universe can be built from a PDB file in such a way that the internal atom ordering is compatible. This is important for exchanging data with other programs. - The program :doc:`lattice.py <../Examples/Miscellaneous/lattice.py>` constructs molecules placed on a lattice. - The program :doc:`vector_field.py <../Examples/Miscellaneous/vector_field.py>` shows how vector fields can be used in the analysis and visualization of collective motions. MMTK-2.7.9/Doc/HTML/_sources/glossary.txt0000644000076600000240000000330411662461637020424 0ustar hinsenstaff00000000000000Glossary ======== .. glossary:: Abstract base class A :term:`Base Class` that is not directly usable by itself, but which defines the common properties of several subclasses. Example: the class :class:`MMTK.ChemicalObjects.ChemicalObject` is an abstract base class which defines the common properties of its subclasses :class:`MMTK.ChemicalObjects.Atom`, :class:`MMTK.ChemicalObjects.Group`, :class:`MMTK.ChemicalObjects.Molecule`, :class:`MMTK.ChemicaObjects.Complex`, and :class:`MMTK.ChemicalObjects.AtomCluster`. A :term:`Mix-in class` is a special kind of abstract base class. Base class A class from which another class inherits. In most cases, the inheriting class is a specialization of the base class. For example, the class :class:`MMTK.ChemicalObjects.Molecule` is a base class of :class:`MMTK.Proteins.PeptideChain`, because peptide chains are special molecules. Another common application is the :term:`Abstract base class`. Mix-in class A class that is used as a :term:`Base class` in other classes with the sole intention of providing methods that are common to these classes. Mix-in classes cannot be used to create instances. They are a special kind of :term:`Abstract base class`. Example: class :class:`MMTK.Collections.GroupOfAtoms`. Subclass A class that has another class as its :term:`Base class`. The subclass is usually a specialization of the base class, and can use all of the methods defined in the base class. Example: class :class:`MMTK.Proteins.Residue` is a subclass of :class:`MMTK.ChemicalObjects.Group`. MMTK-2.7.9/Doc/HTML/_sources/index.txt0000644000076600000240000000104611662461637017671 0ustar hinsenstaff00000000000000.. MMTK User Guide documentation master file, created by sphinx-quickstart on Thu Oct 21 18:14:14 2010. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. MMTK User Guide =============== .. toctree:: :maxdepth: 3 mmtk glossary Code examples ============= .. toctree:: :maxdepth: 3 examples Module reference ================ .. toctree:: :maxdepth: 3 modules Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` MMTK-2.7.9/Doc/HTML/_sources/mmtk.txt0000644000076600000240000024253411777777067017561 0ustar hinsenstaff00000000000000MMTK User's Guide ################# for MMTK |version| by Konrad Hinsen .. |C_alpha| replace:: C\ :sub:`α` Introduction ############ The Molecular Modelling Toolkit (MMTK) presents a new approach to molecular simulations. It is not a "simulation program" with a certain set of functions that can be used by writing more or less flexible "input files", but a collection of library modules written in an easy-to-learn high-level programming language, `Python `_. This approach offers three important advantages: - Application programs can use the full power of a general and well-designed programming language. - Application programs can profit from the large set of other libraries that are available for Python. These may be scientific or non-scientific; for example, it is very easy to write simulation or analysis programs with graphical user interfaces (using the module Tkinter in the Python standard library), or couple scientific calculations with a Web server. - Any user can provide useful additions in separate modules, whereas adding features to a monolithic program requires at least the cooperation of the original author. To further encourage collaborative code development, MMTK uses a very unrestrictive licensing policy. MMTK is free software, just like Python. Although MMTK is copyrighted, anyone is allowed to use it for any purpose, including commercial ones, as well as to modify and redistribute it (a more precise description is given in the copyright statement that comes with the code). This manual describes version |version| of MMTK. The 2.x versions contain some incompatible changes with respect to earlier versions (1.x), most importantly a package structure that reduces the risk of name conflicts with other Python packages, and facilitates future enhancements. There are also many new features and improvements to existing functions. Using MMTK requires a basic knowledge of object-oriented programming and Python. Newcomers to this subject should have a look at the introductory section in this manual and at the `Python tutorial `_ (which also comes with the Python interpreter). There are also numerous `books on Python `_ that are useful in getting started. Even without MMTK, Python is a very useful programming language for scientific use, allowing rapid development and testing and easy interfacing to code written in low-level languages such as Fortran or C. This manual consists of several introductory chapters and a :ref:`module reference `. The introductory chapters explain how common tasks are handled with MMTK, but they do not describe all of its features, nor do they contain a full documentation of functions or classes. This information can be found in the :ref:`module -reference `, which describes all classes and functions intended for end-user applications module by module, using documentation extracted directly from the source code. References from the introductory sections to the module reference facilitate finding the relevant documentation. Overview ######## This chapter explains the basic structure of MMTK and its view of molecular systems. Every MMTK user should read it at least once. Using MMTK ========== MMTK applications are ordinary Python programs, and can be written using any standard text editor. For interactive use it is recommended to use one of the many tools available for interactive Python programming. MMTK tries to be as user-friendly as possible for interactive use. For example, lengthy calculations can usually be interrupted by typing Control-C. This will result in an error message ("Keyboard Interrupt"), but you can simply go on typing other commands. Interruption is particularly useful for energy minimization and molecular dynamics: you can interrupt the calculation at any time, look at the current state or do some analysis, and then continue. Modules ======= MMTK is a package consisting of various modules, most of them written in Python, and some in C for efficiency. The individual modules are described in the :ref:`module -reference `. The basic definitions that almost every application needs are collected in the top-level module, MMTK. The first line of most applications is therefore:: from MMTK import * The definitions that are specific to particular applications reside in submodules within the package MMTK. For example, force fields are defined in :mod:`MMTK.ForceFields`, and peptide chain and protein objects are defined in :mod:`MMTK.Proteins`. Python provides two ways to access objects in modules and submodules. The first one is importing a module and referring to objects in it, e.g.:: import MMTK import MMTK.ForceFields universe = MMTK.InfiniteUniverse(MMTK.ForceFields.Amber99ForceField()) The second method is importing all or some objects *from* a module:: from MMTK import InfiniteUniverse from MMTK.ForceFields import Amber99ForceField universe = InfiniteUniverse(Amber99ForceField()) These two import styles can also be mixed according to convience. In order to prevent any confusion, all objects are referred to by their full names in this manual. The Amber force field object is thus called :class:`MMTK.ForceFields.Amber99ForceField`. Of course the user is free to use selective imports in order to be able to use such objects with shorter names. Objects ======= MMTK is an object-oriented system. Since objects are everywhere and everything is an object, it is useful to know the most important object types and what can be done with them. All object types in MMTK have meaningful names, so it is easy to identify them in practice. The following overview contains only those objects that a user will see directly. There are many more object types used by MMTK internally, and also some less common user objects that are not mentioned here. Chemical objects ---------------- These are the objects that represent the parts of a molecular system: - atoms - groups - molecules - molecular complexes These objects form a simple hierarchy: complexes consist of molecules, molecules consist of groups and atoms, groups consist of smaller groups and atoms. All of these, except for groups, can be used directly to construct a molecular system. Groups can only be used in the definitions of other groups and molecules in the :ref:`overview-database`. A number of operations can be performed on chemical objects, which can roughly be classified into inquiry (constituent atoms, bonds, center of mass etc.) and modification (translate, rotate). There are also specialized versions of some of these objects. For example, MMTK defines proteins as special complexes, consisting of peptide chains, which are special molecules. They offer a range of special operations (such as selecting residues or constructing the positions of missing hydrogen atoms) that do not make sense for molecules in general. Collections ----------- Collection objects represent arbitrary collections of chemical objects. They are used to be able to refer to collections as single entities. For example, you might want to call all water molecules collectively "solvent". Most of the operations on chemical objects are also available for collections. Force fields ------------ Force field objects represent a precise description of force fields, i.e. a complete recipe for calculating the potential energy (and its derivatives) for a given molecular system. In other words, they specify not only the functional form of the various interactions, but also all parameters and the prescriptions for applying these parameters to an actual molecular system. Universes --------- Universes define complete molecular systems, i.e. they contain chemical objects. In addition, they describe interactions within the system (by a force field), boundary conditions, external fields, etc. Many of the operations that can be used on chemical objects can also be applied to complete universes. Minimizers and integrators -------------------------- A minimizer object is a special "machine" that can find local minima in the potential energy surface of a universe. You may consider this a function, if you wish, but of course functions are just special objects. Similarly, an integrator is a special "machine" that can determine a dynamical trajectory for a system on a given potential energy surface. Trajectories ------------ Minimizers and integrators can produce trajectories, which are special files containing a sequence of configurations and/or other related information. Of course trajectory objects can also be read for analysis. Variables --------- Variable objects (not to be confused with standard Python variables) describe quantities that have a value for each atom in a system, for example positions, masses, or energy gradients. Their most common use is for storing various configurations of a system. Normal modes ------------ Normal mode objects contain normal mode frequencies and atomic displacements for a given universe. MMTK provides three kinds of normal modes which correspond to three different physical situations: - Energetic modes represent the principal axes of the potential energy surface. They each have a force constant that is smallest for the most collective motions and highest for the most localized motions. Energetic modes are appropriate for describing the potential energy surface without reference to any particular dynamics, e.g. in flexibility analysis or Monte-Carlo sampling. - Vibrational modes represent the vibrational motions of a system that have well-defined frequencies. Their use implies that the system follows Newtonian dynamics or quantum dynamics. This is appropriate for small molecules, and for the most localized motions of macromolecules. - Brownian modes represent diffusional motions of an overdamped system. They describe systems with Brownian dynamics and are appropriate for describing the slow motions of macromolecules. Non-MMTK objects ---------------- An MMTK application program will typically also make use of objects provided by Python or Python library modules. A particularly useful library is the package Scientific, which is also used by MMTK itself. The most important objects are - numbers (integers, real number, complex numbers), provided by Python - vectors (in 3D coordinate space) provided by the module Scientific.Geometry. - character strings, provided by Python - files, provided by Python Of course MMTK applications can make use of the Python standard library or any other Python modules. For example, it is possible to write a simulation program that provides status reports via an integrated Web server, using the Python standard module SimpleHTTPServer. .. _overview-database: The chemical database ===================== For defining the chemical objects described above, MMTK uses a database of descriptions. There is a database for atoms, one for groups, etc. When you ask MMTK to make a specific chemical object, for example a water molecule, MMTK looks for the definition of water in the molecule database. A database entry contains everything there is to know about the object it defines: its constituents and their names, configurations, other names used e.g. for I/O, and all information force fields might need about the objects. MMTK comes with database entries for many common objects (water, amino acids, etc.). For other objects you will have to write the definitions yourself. as described in the section :ref:`database`. Force fields ============ MMTK contains everything necessary to use the `Amber 99 force field `_ on proteins, DNA, and water molecules. It uses the standard Amber parameter and modification file format. In addition to the Amber force field, there is a simple Lennard-Jones force field for noble gases, and a deformation force field for normal mode calculations on large proteins. MMTK was designed to make the addition of force field terms and the implementation of other force fields as easy as possible. Force field terms can be defined in Python (for ease of implementation) or in Cython, C, or Fortran (for efficiency). This is described in the developer's guide. Units ===== Since MMTK is not a black-box program, but a modular library, it is essential for it to use a consistent unit system in which, for example, the inverse of a frequency is a time, and the product of a mass and the square of a velocity is an energy, without additional conversion factors. Black-box programs can (and usually do) use a consistent unit system internally and convert to "conventional" units for input and output. The unit system of MMTK consists mostly of SI units of appropriate magnitude for molecular systems: =============== ========= **Measurement** **Units** =============== ========= Length nm --------------- --------- Time ps --------------- --------- Mass amu (g/mol) --------------- --------- Energy kJ/mol --------------- --------- Frequency THz (1/ps) --------------- --------- Temperature K --------------- --------- Charge e =============== ========= The module :mod:`MMTK.Units` contains convenient conversion constants for the units commonly used in computational chemistry. For example, a length of 2 Ã…ngström can be written as ``2*Units.Ang``, and a frequency can be printed in wavenumbers with ``print frequency/Units.invcm``. A simple example ================ The following simple example shows how a typical MMTK application might look like. It constructs a system consisting of a single water molecule and runs a short molecular dynamics trajectory. There are many alternative ways to do this; this particular one was chosen because it makes each step explicit and clear. The individual steps are explained in the remaining chapters of the manual. :: # Import the necessary MMTK definitions. from MMTK import * from MMTK.ForceFields import Amber99ForceField from MMTK.Trajectory import Trajectory, TrajectoryOutput, StandardLogOutput from MMTK.Dynamics import VelocityVerletIntegrator # Create an infinite universe (i.e. no boundaries, non-periodic). universe = InfiniteUniverse(Amber99ForceField()) # Create a water molecule in the universe. # Water is defined in the database. universe.molecule = Molecule('water') # Generate random velocities. universe.initializeVelocitiesToTemperature(300*Units.K) # Create an integrator. integrator = VelocityVerletIntegrator(universe) # Generate a trajectory trajectory = Trajectory(universe, "water.nc", "w") # Run the integrator for 50 steps of 1 fs, printing time and energy # every fifth step and writing time, energy, temperature, and the positions # of all atoms to the trajectory at each step. t_actions = [StandardLogOutput(5), \ TrajectoryOutput("time", "energy", \ "thermodynamic", "configuration"), 0, None, 1] integrator(delta_t = 1.*Units.fs, steps = 50, actions = t_actions) # Close the trajectory trajectory.close() Constructing a molecular system ############################### The construction of a complete system for simulation or analysis involves some or all of the following operations: - Creating molecules and other chemical objects. - Defining the configuration of all objects. - Defining the "surroundings" (e.g. boundary conditions). - Choosing a force field. MMTK offers a large range of functions to deal with these tasks. Creating chemical objects ========================= Chemical objects (atoms, molecules, complexes) are created from definitions in the :ref:`database`. Since these definitions contain most of the necessary information, the subsequent creation of the objects is a simple procedure. All objects are created by their class name (:class:`MMTK.ChemicalObjects.Atom`, :class:`MMTK.ChemicalObjects.Molecule`, and :class:`MMTK.ChemicalObjects.Complex`) with the name of the definition file as first parameter. Additional optional parameters can be specified to modify the object being created. The following optional parameters can be used for all object types: - name=string Specifies a name for the object. The default name is the one given in the definition file. - position=vector Specifies the position of the center of mass. The default is the origin. - configuration=string Indicates a configuration from the configuration dictionary in the definition file. The default is 'default' if such an entry exists in the configuration dictionary. Otherwise the object is created without atomic positions. Some examples with additional explanations for specific types: - ``Atom('C')`` creates a carbon atom. - ``Molecule('water', position=Vector(0.,0.,1.))`` creates a water molecule using configuration 'default' and moves the center of mass to the indicated position. Proteins, peptide chains, and nucleotide chains ----------------------------------------------- MMTK contains special support for working with proteins, peptide chains, and nucleotide chains. As described in the chapter :ref:`database`, proteins can be described by a special database definition file. However, it is often simpler to create macromolecular objects directly in an application program. The classes are :class:`MMTK.Proteins.PeptideChain`, :class:`MMTK.Proteins.Protein`, and :class:`MMTK.NucleicAcids.NucleotideChain`. Proteins can be created from definition files in the database, from previously constructed peptide chain objects, or directly from PDB files if no special manipulations are necessary. Examples: - Protein('insulin') creates a protein object for insulin from a database file. - Protein('1mbd.pdb') creates a protein object for myoglobin directly from a PDB file, but leaving out the heme group, which is not a peptide chain. Peptide chains are created from a sequence of residues, which can be a :class:`MMTK.PDB.PDBPeptideChain` object, a list of three-letter residue codes, or a string containing one-letter residue codes. In the last two cases the atomic positions are not defined. MMTK provides several models for the residues which provide different levels of detail: an all-atom model, a model without hydrogen atoms, two models containing only polar hydrogens (using different definitions of polar hydrogens), and a model containing only the |C_alpha| atoms, with each |C_alpha| atom having the mass of the entire residue. The last model is useful for conformational analyses in which only the backbone conformations are important. The construction of nucleotide chains is very similar. The residue list can be either a :class:`MMTK.PDB.PDBNucleotideChain` object or a list of two-letter residue names. The first letter of a residue name indicates the sugar type ('R' for ribose and'D' for desoxyribose), and the second letter defines the base ('A', 'C', and'G', plus 'T' for DNA and'U' for RNA). The models are the same as for peptide chains, except that the |C_alpha| model does not exist. Most frequently proteins and nucleotide chains are created from a PDB file. The PDB files often contain solvent (water) as well, and perhaps some other molecules. MMTK provides convenient functions for extracting information from PDB files and for building molecules from them in the module :mod:`MMTK.PDB`. The first step is the creation of a :class:`MMTK.PDB.PDBConfiguration` object from the PDB file: :: from MMTK.PDB import PDBConfiguration configuration = PDBConfiguration('some_file.pdb') The easiest way to generate MMTK objects for all molecules in the PDB file is then :: molecules = configuration.createAll() The result is a collection of molecules, peptide chains, and nucleotide chains, depending on the contents of the PDB files. There are also methods for modifying the PDBConfiguration before creating MMTK objects from it, and for creating objects selectively. See the documentation for the modules :class:`MMTK.PDB` and Scientific.IO.PDB for details, as well as the :ref:`Proteins ` and :ref:`DNA ` examples. Lattices -------- Sometimes it is necessary to generate objects (atoms or molecules) positioned on a lattice. To facilitate this task, MMTK defines lattice objects which are essentially sequence objects containing points or objects at points. Lattices can therefore be used like lists with indexing and for-loops. The lattice classes are :class:`MMTK.Geometry.RhombicLattice`, :class:`MMTK.Geometry.BravaisLattice`, and :class:`MMTK.Geometry.SCLattice`. Random numbers -------------- The Python standard library and the Numerical Python package provide random number generators, and more are available in seperate packages. MMTK provides some convenience functions that return more specialized random quantities: random points in a universe, random velocities, random particle displacement vectors, random orientations. These functions are defined in module :class:`MMTK.Random`. Collections ----------- Often it is useful to treat a collection of several objects as a single entity. Examples are a large number of solvent molecules surrounding a solute, or all sidechains of a protein. MMTK has special collection objects for this purpose, defined as :class:`MMTK.Collections.Collection`. Most of the methods available for molecules can also be used on collections. A variant of a collection is the partitioned collection, implemented in class :class:`MMTK.Collections.PartitionedCollection`. This class acts much like a standard collection, but groups its elements by geometrical position in small sub-boxes. As a consequence, some geometrical algorithms (e.g. pair search within a cutoff) are much faster, but other operations become somewhat slower. Creating universes ------------------ A universe describes a complete molecular system consisting of any number of chemical objects and a specification of their interactions (i.e. a force field) and surroundings: boundary conditions, external fields, thermostats, etc. The universe classes are defined in module MMTK: - :class:`MMTK.Universe.InfiniteUniverse` represents an infinite universe, without any boundary or periodic boundary conditions. - :class:`MMTK.Universe.ParallelepipedicPeriodicUniverse` represents a general periodic universe defined by three basis vectors. - :class:`MMTK.Universe.OrthorhombicPeriodicUniverse` represents a periodic universe with an orthorhombic elementary cell, whose size is defined by the three edge lengths. The edges are oriented along the axes of the coordinate system. - :class:`MMTK.Universe.CubicPeriodicUniverse` is a special case of :class:`MMTK.Universe.OrthorhombicPeriodicUniverse` in which the elementary cell is cubic. Universes are created empty; the contents are then added to them. Three types of objects can be added to a universe: chemical objects (atoms, molecules, etc.), collections, and environment objects (thermostats etc.). It is also possible to remove objects from a universe. Force fields ------------ MMTK comes with several force fields, and permits the definition of additional force fields. Force fields are defined in module :class:`MMTK.ForceFields.ForceField`. The most important built-in force field is the `Amber 99 force field `_, represented by the class :class:`MMTK.ForceFields.Amber.AmberForceField.Amber99ForceField`. It offers several strategies for electrostatic interactions, including Ewald summation and cutoff with charge neutralization and optional screening :ref:`[Wolf1999] `.. In addition to the Amber 99 force field, there is the older Amber 94 forcefield, a Lennard-Jones force field for noble gases (Class :class:`MMTK.ForceFields.LennardJonesFF`) and an Elastic Network Model force field for protein normal mode calculations (:class:`MMTK.ForceFields.DeformationFF.CalphaForceField`). Referring to objects and parts of objects ========================================= Most MMTK objects (in fact all except for atoms) have a hierarchical structure of parts of which they consist. For many operations it is necessary to access specific parts in this hierarchy. In most cases, parts are attributes with a specific name. For example, the oxygen atom in every water molecule is an attribute with the name "O". Therefore if ``w`` refers to a water molecule, then ``w.O`` refers to its oxygen atom. For a more complicated example, if ``m`` refers to a molecule that has a methyl group called "M1", then ``m.M1.C`` refers to the carbon atom of that methyl group. The names of attributes are defined in the database. Some objects consist of parts that need not have unique names, for example the elements of a collection, the residues in a peptide chain, or the chains in a protein. Such parts are accessed by indices; the objects that contain them are Python sequence types. Some examples: - Asking for the number of items: if ``c`` refers to a collection, then ``len(c)`` is the number of its elements. - Extracting an item: if ``p`` refers to a protein, then ``p[0]`` is its first peptide chain. - Iterating over items: if ``p`` refers to a peptide chain, then ``for residue in p: print residue.position()`` will print the center of mass positions of all its residues. Peptide and nucleotide chains also allow the operation of slicing: if ``p`` refers to a peptide chain, then ``p[1:-1]`` is a subchain extending from the second to the next-to-last residue. The structure of peptide and nucleotide chains ---------------------------------------------- Since peptide and nucleotide chains are not constructed from an explicit definition file in the database, it is not evident where their hierarchical structure comes from. But it is only the top-level structure that is treated in a special way. The constituents of peptide and nucleotide chains, residues, are normal group objects. The definition files for these group objects are in the MMTK standard database and can be freely inspected and even modified or overriden by an entry in a database that is listed earlier in MMTKDATABASE. Peptide chains are made up of amino acid residues, each of which is a group consisting of two other groups, one being called "peptide" and the other "sidechain". The first group contains the peptide group and the C and H atoms; everything else is contained in the sidechain. The C atom of the fifth residue of peptide chain ``p`` is therefore referred to as ``p[4].peptide.C_alpha``. Nucleotide chains are made up of nucleotide residues, each of which is a group consisting of two or three other groups. One group is called "sugar" and is either a ribose or a desoxyribose group, the second one is called "base" and is one the five standard bases. All but the first residue in a nucleotide chain also have a subgroup called "phosphate" describing the phosphate group that links neighbouring residues. Analyzing and modifying atom properties ======================================= General operations ------------------ Many inquiry and modification operations act at the atom level and can equally well be applied to any object that is made up of atoms, i.e. atoms, molecules, collections, universes, etc. These operations are defined once in a :term:`Mix-in class` called :class:`MMTK.Collections.GroupOfAtoms`, but are available for all objects for which they make sense. They include inquiry-type functions (total mass, center of mass, moment of inertia, bounding box, total kinetic energy etc.), coordinate modifications (translation, rotation, application of :ref:`transformation`) and coordinate comparisons (RMS difference, optimal fits). .. _transformation: Coordinate transformations -------------------------- The most common coordinate manipulations involve translations and rotations of specific parts of a system. It is often useful to refer to such an operation by a special kind of object, which permits the combination and analysis of transformations as well as its application to atomic positions. Transformation objects specify a general displacement consisting of a rotation around the origin of the coordinate system followed by a translation. They are defined in the module ``Scientific.Geometry``, but for convenience the module ``MMTK`` contains a reference to them as well. Transformation objects corresponding to pure translations can be created with ``Translation(displacement)``; transformation objects describing pure rotations with ``Rotation(axis, angle)`` or ``Rotation(rotation_matrix)``. Multiplication of transformation objects returns a composite transformation. The translational component of any transformation can be obtained by calling the method ``translation()``; the rotational component is obtained analogously with ``rotation()``. The displacement vector for a pure translation can be extracted with the method ``displacement()``, a tuple of axis and angle can be extracted from a pure rotation by calling ``axisAndAngle()``. .. _atom_property: Atomic property objects ----------------------- Many properties in a molecular system are defined for each individual atom: position, velocity, mass, etc. Such properties are represented in special objects, defined in module MMTK: :class:`MMTK.ParticleProperties.ParticleScalar` for scalar quantities, :class:`MMTK.ParticleProperties.ParticleVector` for vector quantities, and :class:`MMTK.ParticleProperties.ParticleTensor` for rank-2 tensors. All these objects can be indexed with an atom object to retrieve or change the corresponding value. Standard arithmetic operations are also defined, as well as some useful methods. Configurations -------------- A configuration object, represented by the class :class:`MMTK.ParticleProperties.Configuration` is a special variant of a :class:`MMTK.ParticleProperties.ParticleVector` object. In addition to the atomic coordinates of a universe, it stores geometric parameters of a universe that are subject to change, e.g. the edge lengths of the elementary cell of a periodic universe. Every universe has a current configuration, which is what all operations act on by default. It is also the configuration that is updated by minimizations, molecular dynamics, etc. The current configuration can be obtained by calling the method ``configuration()``. There are two ways to create configuration objects: by making a copy of the current configuration (with ``universe.copyConfiguration()``, or by reading a configuration from a :ref:`trajectory `. Minimization and Molecular Dynamics ################################### .. _Trajectories: Trajectories ============ Minimization and dynamics algorithms produce sequences of configurations that are often stored for later analysis. In fact, they are often the most valuable result of a lengthy simulation run. To make sure that the use of trajectory files is not limited by machine compatibility, MMTK stores trajectories in `netCDF `_ files. These files contain binary data, minimizing disk space usage, but are freely interchangeable between different machines. In addition, there are a number of programs that can perform standard operations on arbitrary netCDF files, and which can therefore be used directly on MMTK trajectory files. Finally, netCDF files are self-describing, i.e. contain all the information needed to interpret their contents. An MMTK trajectory file can thus be inspected and processed without requiring any further information. For illustrations of trajectory operations, see the :ref:`examples `. Trajectory file objects are represented by the class :class:`MMTK.Trajectory.Trajectory`. They can be opened for reading, writing, or modification. The data in trajectory files can be stored in single precision or double precision; single-precision is usually sufficient, but double-precision files are required to reproduce a given state of the system exactly. A trajectory is closed by calling the method ``close()``. If anything has been written to a trajectory, closing it is required to guarantee that all data has been written to the file. Closing a trajectory after reading is recommended in order to prevent memory leakage, but is not strictly required. Newly created trajectories can contain all objects in a universe or any subset; this is useful for limiting the amount of disk space occupied by the file by not storing uninteresting parts of the system, e.g. the solvent surrounding a protein. It is even possible to create a trajectory for a subset of the atoms in a molecule, e.g. for only the |C_alpha| atoms of a protein. The universe description that is stored in the trajectory file contains all chemical objects of which at least one atom is represented. When a trajectory is opened for reading, no universe object needs to be specified. In that case, MMTK creates a universe from the description contained in the trajectory file. This universe will contain the same objects as the one for which the trajectory file was created, but not necessarily have all the properties of the original universe (the description contains only the names and types of the objects in the universe, but not, for example, the force field). The universe can be accessed via the attribute universe of the trajectory. If the trajectory was created with partial data for some of the objects, reading data from it will set the data for the missing parts to "undefined". Analysis operations on such systems must be done very carefully. In most cases, the trajectory data will contain the atomic configurations, and in that case the "defined" atoms can be extracted with the method ``atomsWithDefinedPositions()``. MMTK trajectory files can store various data: atomic positions, velocities, energies, energy gradients etc. Each trajectory-producing algorithm offers a set of quantities from which the user can choose what to put into the trajectory. Since a detailed selection would be tedious, the data is divided into classes, e.g. the class "energy" stands for potential energy, kinetic energy, and whatever other energy-related quantities an algorithm produces. For optimizing I/O efficiency, the data layout in a trajectory file can be modified by the block_size parameter. Small block sizes favour reading or writing all data for one time step, whereas large block sizes (up to the number of steps in the trajectory) favour accessing a few values for all time steps, e.g. scalar variables like energies or trajectories for individual atoms. The default value of the block size is one. Every trajectory file contains a history of its creation. The creation of the file is logged with time and date, as well as each operation that adds data to it with parameters and the time/date of start and end. This information, together with the comment and the number of atoms and steps contained in the file, can be obtained with the function :func:`MMTK.Trajectory.trajectoryInfo`. It is possible to read data from a trajectory file that is being written to by another process. For efficiency, trajectory data is not written to the file at every time step, but only approximately every 15 minutes. Therefore the amount of data available for reading may be somewhat less than what has been produced already. Options for minimization and dynamics ===================================== Minimizers and dynamics integrators accept various optional parameter specifications. All of them are selected by keywords, have reasonable default values, and can be specified when the minimizer or integrator is created or when it is called. In addition to parameters that are specific to each algorithm, there is a general parameter ``actions`` that specifies actions that are executed periodically, including trajectory and console output. Periodic actions ---------------- Periodic actions are specified by the keyword parameter ``actions`` whose value is a list of periodic actions, which defaults to an empty list. Some of these actions are applicable to any trajectory-generating algorithm, especially the output actions. Others make sense only for specific algorithms or specific universes, e.g. the periodic rescaling of velocities during a Molecular Dynamics simulation. Each action is described by an action object. The step numbers for which an action is executed are specified by three parameters. The parameter ``first`` indicates the number of the first step for which the action is executed, and defaults to 0. The parameter ``last`` indicates the last step for which the action is executed, and default to ``None``, meaning that the action is executed indefinitely. The parameter ``skip`` speficies how many steps are skipped between two executions of the action. The default value of 1 means that the action is executed at each step. Of course an action object may have additional parameters that are specific to its action. The output actions are defined in the module :mod:`MMTK.Trajectory` and can be used with any trajectory-generating algorithm. They are: - :class:`MMTK.Trajectory.TrajectoryOutput` for writing data to a trajectory. Note that it is possible to use several trajectory output actions simultaneously to write to multiple trajectories. It is thus possible, for example, to write a short dense trajectory during a dynamics run for analyzing short-time dynamics, and simultaneously a long-time trajectory with a larger step spacing, for analyzing long-time dynamics. - :class:`MMTK.Trajectory.RestartTrajectoryOutput`, which is a specialized version of :class:`MMTK.Trajectory.TrajectoryOutput`. It writes the data that the algorithm needs in order to be restarted to a restart trajectory file. A restart trajectory is a trajectory that stores a fixed number of steps which are reused cyclically, such that it always contain the last few steps of a trajectory. - :class:`MMTK.Trajectory.LogOutput` for text output of data to a file. - :class:`MMTK.Trajectory.StandardLogOutput`, a specialized version of :class:`MMTK.Trajectory.LogOutput` that writes the data classes "time" and "energy" during the whole simulation run to standard output. The other periodic actions are meaningful only for Molecular Dynamics simulations: - :class:`MMTK.Dynamics.VelocityScaler` is used for rescaling the velocities to force the kinetic energy to the value defined by some temperature. This is usually done during initial equilibration. - :class:`MMTK.Dynamics.BarostatReset` resets the barostat coordinate to zero and is during initial equilibration of systems in the NPT ensemble. - :class:`MMTK.Dynamics.Heater` rescales the velocities like :class:`MMTK.Dynamics.VelocityScaler`, but increases the temperature step by step. - :class:`MMTK.Dynamics.TranslationRemover` subtracts the global translational velocity of the system from all individual atomic velocities. This prevents a slow but systematic energy flow into the degrees of freedom of global translation, which occurs with most MD integrators due to non-perfect conservation of momentum. - :class:`MMTK.Dynamics.RotationRemover` subtracts the global angular velocity of the system from all individual atomic velocities. This prevents a slow but systematic energy flow into the degrees of freedom of global rotation, which occurs with most MD integrators due to non-perfect conservation of angular momentum. Fixed atoms ----------- During the course of a minimization or molecular dynamics algorithm, the atoms move to different positions. It is possible to exclude specific atoms from this movement, i.e. fixing them at their initial positions. This has no influence whatsoever on energy or force calculations; the only effect is that the atoms' positions never change. Fixed atoms are specified by giving them an attribute fixed with a value of one. Atoms that do not have an attributefixed, or one with a value of zero, move according to the selected algorithm. .. _energy_minimization: Energy minimization =================== MMTK has two energy minimizers using different algorithms: steepest descent (:class:`MMTK.Minimization.SteepestDescentMinimizer`) and conjugate gradient (:class:`MMTK.Minimization.ConjugateGradientMinimizer`) . Steepest descent minimization is very inefficient if the goal is to find a local minimum of the potential energy. However, it has the advantage of always moving towards the minimum that is closest to the starting point and is therefore ideal for removing bad contacts in a unreasonably high energy configuration. For finding local minima, the conjugate gradient algorithm should be used. Both minimizers accept three specific optional parameters: - ``steps`` (an integer) to specify the maximum number of steps (default is 100) - ``step_size`` (a number) to specify an initial step length used in the search for a minimum (default is 2 pm) - ``convergence`` (a number) to specify the gradient norm (more precisely the root-mean-square length) at which the minimization should stop (default is 0.01 kJ/mol/nm) There are three classes of trajectory data: "energy" includes the potential energy and the norm of its gradient, "configuration" stands for the atomic positions, and "gradients" stands for the energy gradients at each atom position. The following example performs 100 steps of steepest descent minimization without producing any trajectory or printed output: :: from MMTK import * from MMTK.ForceFields import Amber99ForceField from MMTK.Minimization import SteepestDescentMinimizer universe = InfiniteUniverse(Amber99ForceField()) universe.protein = Protein('insulin') minimizer = SteepestDescentMinimizer(universe) minimizer(steps = 100) See also the example file :ref:`modes.py `. Molecular dynamics ================== The techniques described in this section are illustrated by several :ref:`examples `. Velocities ---------- The integration of the classical equations of motion for an atomic system requires not only positions, but also velocities for all atoms. Usually the velocities are initialized to random values drawn from a normal distribution with a variance corresponding to a certain temperature. This is done by calling the method :func:`~MMTK.Universe.Universe.initializeVelocitiesToTemperature` on a universe. Note that the velocities are assigned atom by atom; no attempt is made to remove global translation or rotation of the total system or any part of the system. During equilibration of a system, it is common to multiply all velocities by a common factor to restore the intended temperature. This can done explicitly by calling the method :func:`~MMTK.Universe.Universe.scaleVelocitiesToTemperature` on a universe, or by using the action object :class:`MMTK.Dynamics.VelocityScaler`. Distance constraints -------------------- A common technique to eliminate the fastest (usually uninteresting) degrees of freedom, permitting a larger integration time step, is the use of distance constraints on some or all chemical bonds. MMTK allows the use of distance constraints on any pair of atoms, even though constraining anything but chemical bonds is not recommended due to considerable modifications of the dynamics of the system :ref:`[vanGunsteren1982] `, :ref:`[Hinsen1995] `. MMTK permits the definition of distance constraints on all atom pairs in an object that are connected by a chemical bond by calling the method setBondConstraints. Usually this is called for a complete universe, but it can also be called for a chemical object or a collection of chemical objects. The :func:`~MMTK.ChemicalObjects.ChemicalObject.removeDistanceConstraints` removes all distance constraints from the object for which it is called. Constraints defined as described above are automatically taken into account by Molecular Dynamics integrators. It is also possible to enforce the constraints explicitly by calling the method :func:`~MMTK.Universe.Universe.enforceConstraints` for a universe. This has the effect of modifying the configuration and the velocities (if velocities exist) in order to make them compatible with the constraints. Thermostats and barostats ------------------------- A standard Molecular Dynamics integration allows time averages corresponding to the NVE ensemble, in which the number of molecules, the system volume, and the total energy are constant. This ensemble does not represent typical experimental conditions very well. Alternative ensembles are the NVT ensemble, in which the temperature is kept constant by a thermostat, and the NPT ensemble, in which temperature and pressure are kept constant by a thermostat and a barostat. To obtain these ensembles in MMTK, thermostat and barostat objects must be added to a universe. In the presence of these objects, the Molecular Dynamics integrator will use the extended-systems method for producing the correct ensemble. The classes to be used are :class:`MMTK.Environment.NoseThermostat` and :class:`MMTK.Environment.AndersenBarostat`. Integration ----------- A Molecular Dynamics integrator based on the "Velocity Verlet" algorithm :ref:`[Swope1982] `, which was extended to handle distance constraints as well as thermostats and barostats :ref:`[Kneller1996] `, is implemented by the class :class:`MMTK.Dynamics.VelocityVerletIntegrator`. It has two optional keyword parameters: - ``steps`` (an integer) to specify the number of steps (default is 100) - ``delta_t`` (a number) to specify the time step (default 1 fs) There are three classes of trajectory data: "energy" includes the potential energy and the kinetic energy, as well as the energies of thermostat and barostat coordinates if they exist, "time" stands for the time, "thermodynamic" stand for temperature and pressure, "configuration" stands for the atomic positions, "velocities" stands for the atomic velocities, and "gradients" stands for the energy gradients at each atom position. The following example performs a 1000 step dynamics integration, storing every 10th step in a trajectory file and removing the total translation and rotation every 50th step: :: from MMTK import * from MMTK.ForceFields import Amber99ForceField from MMTK.Dynamics import VelocityVerletIntegrator, \ TranslationRemover, \ RotationRemover from MMTK.Trajectory import TrajectoryOutput universe = InfiniteUniverse(Amber99ForceField()) universe.protein = Protein('insulin') universe.initializeVelocitiesToTemperature(300.*Units.K) actions = [TranslationRemover(0, None, 50), \ RotationRemover(0, None, 50), \ TrajectoryOutput("insulin.nc", \ ("configuration", "energy", "time"), \ 0, None, 10)] integrator = VelocityVerletIntegrator(universe, delta_t = 1.*Units.fs, \ actions = actions) integrator(steps = 1000) Snapshots ========= A snapshot generator allows writing the current system state to a trajectory. It works much like a zero-step minimization or dynamics run, i.e. it takes the same optional arguments for specifying the trajectory and protocol output. A snapshot generator is created using the class :class:`MMTK.Trajectory.SnapshotGenerator`. Normal modes ############ Normal mode analysis provides an analytic description of the dynamics of a system near a minimum using an harmonic approximation to the potential. Before a normal mode analysis can be started, the system must be brought to a local minimum of the potential energy by :ref:`energy minimization `, except when special force fields designed only for normal mode analysis are used (e.g. :class:`MMTK.ForceFields.CalphaFF.CalphaForceField`). See also the :ref:`Normal Modes ` examples. A standard normal mode analysis is performed by creating a normal modes object, implemented in the classes :class:`MMTK.NormalModes.EnergeticModes.EnergeticModes`, :class:`MMTK.NormalModes.VibrationalModes.VibrationalModes`, and :class:`MMTK.NormalModes.BrownianModes.BrownianModes`. A normal mode object behaves like a sequence of mode objects, which store the atomic displacement vectors corresponding to each mode and the associated force constant, vibrational frequency, or inverse relaxation time. For short-ranged potentials, it is advantageous to store the second derivatives of the potential in a sparse-matrix form and to use an iterative method to determine some or all modes. This permits the treatment of larger systems that would normally require huge amounts of memory. Another approach to deal with large systems is the restriction to low-frequency modes which are supposed to be well representable by linear combinations of a given set of basis vectors. The basis vectors can be obtained from a basis for the full Cartesian space by elimination of known fast degrees of freedom (e.g. bonds); the module :mod:`MMTK.Subspace` contains support classes for this approach. It is also possible to construct a suitable basis vector set from small-deformation vector fields (e.g. :class:`MMTK.FourierBasis.FourierBasis`). Analysis operations ################### Analysis is the most non-standard part of molecular simulations. The quantities that must be calculated depend strongly on the system and the problem under study. MMTK provides a wide range of elementary operations that inquire the state of the system, as well as several more complex analysis tools. Some of them are demonstrated in the :ref:`example ` section. Properties of chemical objects and universes ============================================ Many operations access and modify various properties of an object. They are defined for the most general type of object: anything that can be broken down to atoms, i.e. atoms, molecules, collections, universes, etc., i.e. in the class :class:`MMTK.Collections.GroupOfAtoms`. The most elementary operations are inquiries about specific properties of an object: number of atoms, total mass, center of mass, total momentum, total charge, etc. There are also operations that compare two different conformations of a system. Finally, there are special operations for analyzing conformations of peptide chains and proteins. Geometrical operations in periodic universes require special care. Whenever a distance vector between two points in a systems is evaluated, the minimum-image convention must be used in order to obtain consistent results. MMTK provides routines for finding these distance vectors as well as distances, angles, and dihedral angles between any points. Because these operations depend on the topology and geometry of the universe, they are implemented as methods in class :class:`MMTK.Universe.Universe` and its subclasses. Of course they are available for non-periodic universes as well. Universes also provide methods for obtaining :ref:`atom property ` objects that describe the state of the system (configurations, velocities, masses), and for restoring the system state from a :ref:`trajectory ` file. Energy evaluation ================= Energy evaluation requires a force field, and therefore all the methods in this section are defined only for universe objects, i.e. in class :class:`MMTK.Universe.Universe`. However, they all take an optional arguments (anything that can be broken down into atoms) that indicates for which subset of the universe the energy is to be evaluated. In addition to the potential energy, energy gradients and second derivatives (force constants) can be obtained, if the force field implements them. There is also a method that returns a dictionary containing the values for all the individual force field terms, which is often useful for analysis. Surfaces and volumes ==================== Surfaces and volumes can be analyzed for anything consisting of atoms. Both quantities are defined by assigning a radius to each atom; the surface of the resulting conglomerate of overlapping spheres is taken to be the surface of the atom group. Atom radii for surface determination are usually called "van der Waals radii", but there is no unique method for determining them. MMTK uses the values from :ref:`[Bondi1964] `. However, users can change these values for each individual atom by assigning a new value to the attribute ``vdW_radius``. The operations provided in :mod:`MMTK.MolecularSurface` include basic surface and volume calculation, determination of exposed atoms, and identification of contacts between two objects. Miscellaneous operations ######################## Saving, loading, and copying objects ==================================== MMTK provides an easy way to store (almost) arbitrary objects in files and retrieve them later. All objects of interest to users can be stored, including chemical objects, collections, universes, normal modes, configurations, etc. It is also possible to store standard Python objects such as numbers, lists, dictionaries etc., as well as practically any user-defined objects. Storage is based on the standard Python module pickle. Objects are saved with :func:`MMTK.save` and restored with :func:`MMTK.load`. If several objects are to be stored in a single file, use tuples: ``save((object1, object2), filename)`` and ``object1, object2 = load(filename)`` to retrieve the objects. Note that storing an object in a file implies storing all objects referenced by it as well, such that the size of the file can become larger than expected. For example, a configuration object contains a reference to the universe for which it is defined. Therefore storing a configuration object means storing the whole universe as well. However, nothing is ever written twice to the same file. If you store a list or a tuple containing a universe and a configuration for it, the universe is written only once. Frequently it is also useful to copy an object, such as a molecule or a configuration. There are two functions (which are actually taken from the Python standard library module copy) for this purpose, which have a somewhat different behaviour for container-type objects (lists, dictionaries, collections etc.). :func:`MMTK.copy` returns a copy of the given object. For a container object, it returns a new container object which contains the same objects as the original one. If the intention is to get a container object which contains copies of the original contents, then ``MMTK.deepcopy(object)`` should be used. For objects that are not container-type objects, there is no difference between the two functions. Exporting to specific file formats and visualization ==================================================== MMTK can write objects in specific file formats that can be used by other programs. Three file formats are supported: the PDB format, widely used in computational chemistry, the DCD format for trajectories, written by the programs CHARMM, X-Plor, and NAMDm, and read by many visualization programs, and the VRML format, understood by VRML browsers as a representation of a three-dimensional scene for visualization. MMTK also provides a more general interface that can generate graphics objects in any representation if a special module for that representation exists. In addition to facilitating the implementation of new graphics file formats, this approach also permits the addition of custom graphics elements (lines, arrows, spheres, etc.) to molecular representations. PDB, VRML, and DCD files ------------------------ Any chemical object, collection, or universe can be written to a PDB or VRML file by calling the method ``writeToFile``, defined in class :class:`MMTK.Collections.GroupOfAtoms`. PDB files are read via the class :class:`MMTK.PDB.PDBConfiguration`. DCD files can be read by a :class:`MMTK.DCD.DCDReader` object. For writing DCD files, there is the function :func:`MMTK.DCD.writeDCDPDB`, which also creates a compatible PDB file without which the DCD file could not be interpreted. Special care must be taken to ensure a correct mapping of atom numbers when reading from a DCD file. In MMTK, each atom object has a unique identity and atom numbers, also used internally for efficiency, are not strictly necessary and are not used anywhere in MMTK's application programming interface. DCD file, however, simply list coordinates sorted by atom number. For interpreting DCD files, another file must be available which allows the identification of atoms from their number and vice versa; this can for example be a PDB file. When reading DCD files, MMTK assumes that the atom order in the DCD file is identical to the internal atom numbering of the universe for which the DCD file is read. This assumption is in general valid only if the universe has been created from a PDB file that is compatible with the DCD file, without any additions or removals. Visualization and animation --------------------------- The most common need for file export is visualization. All objects that can be visualized (chemical systems and subsets thereof, normal mode objects, trajectories) provide a method view which creates temporary export files, starts a visualization program, and deletes the temporary files. Depending on the object type there are various optional parameters. MMTK also allows visualization of normal modes and trajectories using animation. Since not all visualization programs permit animation, and since there is no standard way to ask for it, animation is implemented only for the programs `XMol `_ and `VMD `_. Animation is available for normal modes, trajectories, and arbitrary sequences of configurations (see function :func:`MMTK.Visualization.viewSequence`). For more specialized needs, MMTK permits the creation of graphical representations of most of its objects via general graphics modules that have to be provided externally. Suitable modules are provided in the package Scientific.Visualization and cover VRML (version 1), VRML2 (aka VRML97), and the molecular visualization program VMD. Modules for other representations (e.g. rendering programs) can be written easily; it is recommended to use the existing modules as an example. The generation of graphics objects is handled by the method graphicsObjects, defined in the class :class:`MMTK.Visualization.Viewable`, which is a :term:`Mix-in class` that makes graphics objects generation available for all objects that define chemical systems or parts thereof, as well as for certain other objects that are viewable. The explicit generation of graphics objects permits the mixture of different graphical representations for various parts of a system, as well as the combination of MMTK-generated graphics objects with arbitrary other graphics objects, such as lines, arrows, or spheres. All graphics objects are finally combined into a scene object (also defined in the various graphics modules) in order to be displayed. See also the :ref:`visualization ` examples. Fields ====== For analyzing or visualizing atomic properties that change little over short distances, it is often convenient to represent these properties as functions of position instead of one value per atom. Functions of position are also known as fields, and mathematical techniques for the analysis of fields have proven useful in many branches of physics. Such a field can be obtained by averaging over the values corresponding to the atoms in a small region of space. MMTK provides classes for scalar and vector field in module :mod:`MMTK.Field`. See also the example :ref:`vector_field.py `. Charge fitting ============== A frequent problem in determining force field parameters is the determination of partial charges for the atoms of a molecule by fitting to the electrostatic potential around the molecule, which is obtained from quantum chemistry programs. Although this is essentially a straightforward linear least-squares problem, many procedures that are in common use do not use state-of-the-art techniques and may yield erroneous results. MMTK provides a charge fitting method that is numerically stable and allows the imposition of constraints on the charges. It is implemented in module :mod:`MMTK.ChargeFit`. See also the example :ref:`charge_fit.py `. .. _database: Constructing the database ######################### MMTK uses a database of chemical entities to define the properties of atoms, molecules, and related objects. This database consists of plain text files, more precisely short Python programs, whose names are the names of the object types. This chapter explains how to construct and manage these files. Note that the standard database already contains many definitions, in particular for proteins and nucleic acids. You do not need to read this chapter unless you want to add your own molecule definitions. MMTK's database does not have to reside in a single place. It can consist of any number of subdatabases, each of which can be a directory or a URL. Typically the database consists of at least two parts: MMTK's standard definitions and a user's personal definitions. When looking up an object type in the database, MMTK checks the value of the environment variable MMTKDATABASE. The value of this variable must be a list of subdatabase locations seperated by white space. If the variable MMTKDATABASE is not defined, MMTK uses a default value that contains the path ".mmtk/Database" in the user's home directory followed by MMTK's standard database, which resides in the directory Database within the MMTK package directory (on many Unix systems this is ``/usr/local/lib/python2.x/site-packages/MMTK``). MMTK checks the subdatabases in the order in which they are mentioned in MMTKDATABASE. Each subdatabase contains directories corresponding to the object classes, i.e. Atoms (atom definitions), Groups (group definitions), Molecules (molecule definitions), Complexes (complex definitions), Proteins (protein definitions), and PDB (Protein Data Bank files). These directories contain the definition files, whose names may not contain any upper-case letters. These file names correspond to the object types, e.g. the call ``MMTK.Molecule('Water')`` will cause MMTK to look for the file Molecules/water in the database (note that the names are converted to lower case). The remaining sections of this chapter explain how the individual definition files are constructed. Keep in mind that each file is actually a Python program, so of course standard Python syntax rules apply. Atom definitions ================ An atom definition in MMTK describes a chemical element, such as "hydrogen". This should not be confused with the "atom types" used in force field descriptions and in some modelling programs. As a consequence, it is rarely necessary to add atom definitions to MMTK. Atom definition files are short and of essentially identical format. This is the definition for carbon: :: name = 'carbon' symbol = 'C' mass = [(12, 98.90), (13.003354826, 1.10)] color = 'black' vdW_radius = 0.17 The name should be meaningful to users, but is not used by MMTK itself. The symbol, however, is used to identify chemical elements. It must be exactly equal to the symbol defined by IUPAC, including capitalization (e.g. 'Cl' for chlorine). The mass can be either a number or a list of tuples, as shown above. Each tuple defines an isotope by its mass and its percentage of occurrence; the percentages must add up to 100. The color is used for VRML output and must equal one of the color names defined in the module VRML. The van der Waals radius is used for the calculation of molecular volumes and surfaces; the values are taken from :ref:`[Bondi1964] `. An application program can create an isolated atom with ``Atom('c')`` or, specifying an initial position, with ``Atom('c', position=Vector(0.,1.,0.))``. The element name can use any combination of upper and lower case letters, which are considered equivalent. Group definitions ================= Group definitions in MMTK exist to facilitate the definition of molecules by avoiding the frequent repetition of common combinations. MMTK doesn't give any physical meaning to groups. Groups can contain atoms and other groups. Their definitions look exactly like molecule definitions; the only difference between groups and molecules is the way they are used. This is the definition of a methyl group: :: name = 'methyl group' C = Atom('C') H1 = Atom('H') H2 = Atom('H') H3 = Atom('H') bonds = [Bond(C, H1), Bond(C, H2), Bond(C, H3)] pdbmap = [('MTH', {'C': C, 'H1': H1, 'H2': H2, 'H3': H3})] amber_atom_type = {C: 'CT', H1: 'HC', H2: 'HC', H3: 'HC'} amber_charge = {C: 0., H1: 0.1, H2: 0.1, H3: 0.1} The name should be meaningful to users, but is not used by MMTK itself. The following lines create the atoms in the group and assign them to variables. These variables become attributes of whatever object uses this group; their names can be anything that is a legal Python name. The list of bonds, however, must be assigned to the name ``bonds``. The bond list is used by force fields and for visualization. The name ``pdbmap`` is used for reading and writing PDB files. Its value must be a list of tuples, where each tuple defines one PDB residue. The first element of the tuple is the residue name, which is used only for output. The second element is a dictionary that maps PDB atom names to the actual atoms. The ``pdbmap`` entry of any object can be overridden by an entry in a higher-level object. Therefore the entry for a group is only used for atoms that do not occur in the entry for a molecule that contains this group. The remaining lines in the definition file contain information specific to force fields, in this case the Amber force field. The dictionary ``amber_atom_type`` defines the atom type for each atom; the dictionary ``amber_charge`` defines the partial charges. As for ``pdbmap`` entries, these definitions can be overridden by higher-level definitions. Molecule definitions ==================== Molecules are typically used directly in application programs, but they can also be used in the definition of complexes. Molecule definitions can use atoms and groups. This is the definition of a water molecule: :: name = 'water' structure = \ " O \n" + \ " / \ \n" + \ "H H \n" O = Atom('O') H1 = Atom('H') H2 = Atom('H') bonds = [Bond(O, H1), Bond(O, H2)] pdbmap = [('HOH', {'O': O, 'H1': H1, 'H2': H2})] pdb_alternative = {'OH2': 'O'} amber_atom_type = {O: 'OW', H1: 'HW', H2: 'HW'} amber_charge = {O: -0.83400, H1: 0.41700, H2: 0.41700} configurations = { 'default': ZMatrix([[H1], \ [O, H1, 0.9572*Ang], \ [H2, O, 0.9572*Ang, H1, 104.52*deg]]) } The name should be meaningful to users, but is not used by MMTK itself. The structure is optional and not used by MMTK either. The following lines create the atoms in the group and assign them to variables. These variables become attributes of the molecule, i.e. when a water molecule is created in an application program by ``w = Molecule('water')``, then ``w.H1`` will refer to its first hydrogen atom. The names of these variables can be any legal Python names. The list of bonds, however, must be assigned to the name ``bonds``. The bond list is used by force fields and for visualization. The name ``pdbmap`` is used for reading and writing PDB files. Its value must be a list of tuples, where each tuple defines one PDB residue. The first element of the tuple is the residue name, which is used only for output. The second element is a dictionary that maps PDB atom names to the actual atoms. The ``pdbmap`` entry of any object can be overridden by an entry in a higher-level object, i.e. in the case of a molecule a complex containing it. The name ``pdb_alternative`` allows to read PDB files that use non-standard names. When a PDB atom name is not found in the ``pdbmap``, an attempt is made to translate it to another name using ``pdb_alternative``. The two following lines in the definition file contain information specific to force fields, in this case the Amber force field. The dictionary ``amber_atom_type`` defines the atom type for each atom; the dictionary ``amber_charge`` defines the partial charges. As for pdbmap entries, these definitions can be overridden by higher-level definitions. The name ``configurations`` can be defined to be a dictionary of configurations for the molecule. During the construction of a molecule, a configuration can be specified via an optional parameter, e.g. ``w = Molecule('water', configuration='default')``. The names of the configurations can be arbitrary; only the name "default" has a special meaning; it is applied by default if no other configuration is specified when constructing the molecule. If there is no default configuration, and no other configuration is explicitly specified, then the molecule is created with undefined atomic positions. There are three ways of describing configurations: - By a Z-Matrix: :: ZMatrix([[H1], \ [O, H1, 0.9572*Ang], \ [H2, O, 0.9572*Ang, H1, 104.52*deg]]) - By Cartesian coordinates: :: Cartesian({O: ( 0.004, -0.00518, 0.0), H1: (-0.092, -0.00518, 0.0), H2: ( 0.028, 0.0875, 0.0)}) - By a PDB file: :: PDBFile('water.pdb') The PDB file must be in the database subdirectory PDB, unless a full path name is specified for it. Complex definitions =================== Complexes are defined much like molecules, except that they are composed of molecules and atoms; no groups are allowed, and neither are bonds. Protein definitions =================== Protein definitions can take many different forms, depending on the source of input data and the type of information that is to be stored. For proteins it is particularly useful that database definition files are Python programs with all their flexibility. The most common way of constructing a protein is from a PDB file. This is an example for a protein definition: :: name = 'insulin' # Read the PDB file. conf = PDBConfiguration('insulin.pdb') # Construct the peptide chains. chains = conf.createPeptideChains() # Clean up del conf The name should be meaningful to users, but is not used by MMTK itself. The second command reads the sequences of all peptide chains from a PDB file. Everything which is not a peptide chain is ignored. The following line constructs a PeptideChain object (a special molecule) for each chain from the PDB sequence. This involves constructing positions for any missing hydrogen atoms. Finally, the temporary data ("conf") is deleted, otherwise it would remain in memory forever. The net result of a protein definition file is the assignment of a list of molecules (usually PeptideChain objects) to the variable "chains". MMTK then constructs a protein object from it. To use the above example, an application program would use the command ``p = Protein('insulin')``. The construction of the protein involves one nontrivial (but automatic) step: the construction of disulfide bridges for pairs of cystein residues whose sulfur atoms have a distance of less then 2.5 Ã…ngström. Threads and parallelization ########################### This chapter explains the use of threads by MMTK and MMTK's parallelization support. This is an advanced topic, and not essential for the majority MMTK applications. You need to read this chapter only if you use multiprocessor computers, or if you want to implement multi-threaded programs that use MMTK. Threads are different execution paths through a program that are executed in parallel, at least in principle; real parallel execution is possible only on multiprocessor systems. MMTK makes use of threads in two ways, which are conceptually unrelated: parallelization of energy evaluation on shared-memory multiprocessor computers, and support for multithreaded applications. Thread support is not available on all machines; you can check if yous system supports threads by starting a Python interpreter and typing import threading. If this produces an error message, then your system does not support threads, otherwise it is available in Python and also in MMTK. If you do not have thread support in Python although you know that your operating system supports threads, you might have compiled your Python interpreter without thread support; in that case, MMTK does not have thread support either. Another approach to parallelization is message passing: several processors work on a program and communicate via a fast network to share results. A standard library, called MPI (Message Passing Interface), has been developped for sharing data by message passing, and implementations are available for all parallel computers currently on the market. MMTK contains elementary support for parallelization by message passing: only the energy evaluation has been paralellized, using a data-replication strategy, which is simple but not the most efficient for large systems. MPI support is disabled by default. Enabling it involves modifying the file Src/Setup.template prior to compilation of MMTK. Furthermore, an MPI-enabled installation of ScientificPython is required, and the mpipython executable must be used instead of the standard Python interpreter. Threads and message passing can be used together to use a cluster of shared-memory machines most efficiently. However, this requires that the thread and MPI implementations being used work together; sometimes there are conflicts, for example due to the use of the same signal in both libraries. Refer to your system documentation for details. The use of threads for parallelization on shared-memory systems is very simple: Just set the environment variable MMTK_ENERGY_THREADS to the desired value. If this variable is not defined, the default value is 1, i.e. energy evaluations are performed serially. For choosing an appropriate value for this environment variable, the following points should be considered: - The number of energy evaluation threads should not be larger than the number of processors that are fully dedicated to the MMTK application. A larger number of threads does not lead to wrong results, but it can increase the total execution time. - MMTK assumes that all processors are equally fast. If you use a heteregenous multiprocessor machine, in which the processors have different speeds, you might find that the total execution time is larger than without threads. - The use of threads incurs some computational overhead. For very small systems, it is usually faster not to use threads. - Not all energy terms necessarily support threads. Of the force field terms that part of MMTK, only the multipole algorithms for electrostatic interactions does not support threads, but additional force fields defined outside MMTK might also be affected. MMTK automatically evaluates such energy terms with a single thread, such that there is no risk of getting wrong results. However, you might not get the performance you expect. - If second derivatives of the potential energy are requested, energy evaluation is handled by a single thread. An efficient implementation of multi-threaded energy evaluation would require a separate copy of the second-derivative matrix per thread. This approach needs too much memory for big systems to be feasible. Since second derivatives are almost exclusively used for normal mode calculations, which need only a single energy evaluation, multi-thread support is not particularly important anyway. Parallelization via message passing is somewhat more complicated. In the current MMTK parallelization model, all processors execute the same program and replicate all tasks, with the important exception of energy evaluation. Energy terms are divided evenly between the processors, and at the end the energy and gradient values are shared by all machines. This is the only step involving network communication. Like thread-based parallelization, message-passing parallelization does not support the evaluation of second derivatives. A special problem with message-passing systems is input and output. The MMTK application must ensure that output files are written by only one processor, and that all processors correctly access input files, especially in the case of each processor having its own disk space. See the example :ref:`md.py ` for illustration. Multithreaded applications are applications that use multiple threads in order to simplify the implementation of certain algorithms, i.e. not necessarily with the goal of profiting from multiple processors. If you plan to write a multithreaded application that uses MMTK, you should first make sure you understand threading support in Python. In particular, you should keep in mind that the global interpreter lock prevents the effective use of multiple processors by Python code; only one thread at a time can execute interpreted Python code. C code called from Python can permit other threads to execute simultaneously; MMTK does this for energy evaluation, molecular dynamics integration, energy minimization, and normal mode calculation. A general problem in multithreaded applications is access to resources that are shared among the threads. In MMTK applications, the most important shared resource is the description of the chemical systems, i.e. universe objects and their contents. Chaos would result if two threads tried to modify the state of a universe simultaneously, or even if one thread uses information that is simultaneously being modified by another thread. Synchronization is therefore a critical part of multithreaded application. MMTK provides two synchronization aids, both of which described in the documentation of the class :class:`MMTK.Universe.Universe`: the configuration change lock (methods :func:`~MMTK.Universe.Universe.acquireConfigurationChangeLock` and :func:`~MMTK.Universe.Universe.releaseConfigurationChangeLock`), and the universe state lock (methods :func:`~MMTK.Universe.Universe.acquireReadStateChangeLock`, :func:`~MMTK.Universe.Universe.releaseReadStateChangeLock`, :func:`~MMTK.Universe.Universe.acquireWriteStateChangeLock`, and :func:`~MMTK.Universe.Universe.releaseWriteStateChangeLock`). Only a few common universe operations manipulate the universe state lock in order to avoid conflicts with other threads; these methods are marked as thread-safe in the description. All other operations should only be used inside a code section that is protected by the appropriate manipulation of the state lock. The configuration change lock is less critical; it is used only by the molecular dynamics and energy minimization algorithms in MMTK. Bibliography ############ .. _Bondi1964: [Bondi1964] | A. Bondi | `van der Waals Volumes and Radii `_ | 1964 .. _Eisenhaber1993: [Eisenhaber1993] | F. Eisenhaber, P. Argos | `Improved Strategy in Analytic Surface Calculation for Molecular Systems: Handling of Singularities and Computational Efficiency `_ | 1993 .. _Eisenhaber1995: [Eisenhaber1995] | F. Eisenhaber, P. Lijnzaad, P. Argos, M. Scharf | `The Double Cubic Lattice Method: Efficient Approaches to Numerical Integration of Surface Area and Volume and to Dot Surface Contouring of Molecular Assemblies `_ | 1995 .. _Hinsen1995: [Hinsen1995] | Konrad Hinsen, Gerald R. Kneller | `Influence of constraints on the dynamics of polypeptide chains `_ | 1995 .. _Hinsen1997: [Hinsen1997] | Konrad Hinsen, Benoit Roux | `An accurate potential for simulating proton transfer in acetylacetone `_ | 1997 .. _Hinsen1998: [Hinsen1998] | Konrad Hinsen | `Analysis of domain motions by approximate normal mode calculations `_ | 1998 .. _Hinsen1999: [Hinsen1999] | Konrad Hinsen, Aline Thomas, Martin J. Field | `Analysis of domain motions in large proteins `_ | 1999 .. _Hinsen1999a: [Hinsen1999a] | Konrad Hinsen, Gerald R. Kneller | `Projection methods for the analysis of complex motions in macromolecules `_ | 1999 .. _Hinsen1999b: [Hinsen1999b] | Konrad Hinsen, Gerald R. Kneller | `A simplified force field for describing vibrational protein dynamics over the whole frequency range `_ | 1999 .. _Hinsen2000: [Hinsen2000] | Konrad Hinsen, Andrei J. Petrescu, Serge Dellerue, Marie-Claire Bellissent-Funel, Gerald R. Kneller. | `Harmonicity in slow protein dynamics `_ | 2000 .. _Kneller1990: [Kneller1990] | Gerald R. Kneller | `Superposition of molecular structures using quaternions `_ | 1990 .. _Kneller1996: [Kneller1996] | Gerald R. Kneller, Thomas Mülders | `Nosé-Andersen dynamics of partially rigid molecules: Coupling of all degrees of freedom to heat and pressure baths `_ | 1996 .. _Swope1982: [Swope1982] | W.C. Swope, H.C. Andersen, P.H. Berens, K.R. Wilson | `A computer simulation method for the calculation of equilibrium constants for the formation of physical clusters of molecules: application to small water clusters `_ | 1982 .. _vanGunsteren1982: [vanGunsteren1982] | Wilfred F. van Gunsteren, Martin Karplus | `Effect of Constraints on the Dynamics of Macromolecules `_ | 1982 .. _Viduna2000: [Viduna2000] | David Viduna, Konrad Hinsen, Gerald R. Kneller | `The influence of molecular flexibility on DNA radiosensitivity: A simulation study `_ | 2000 .. _Wolf1999: [Wolf1999] | D. Wolf, P. Keblinski, S.R. Philpot, J. Eggebrecht | `Exact method for the simulation of Coulombic systems by spherically truncated, pairwise r\ :sup:`-1` summation `_ | 1999 MMTK-2.7.9/Doc/HTML/_sources/modules.txt0000644000076600000240000001135611662461637020237 0ustar hinsenstaff00000000000000.. _Reference: .. |C_alpha| replace:: C\ :sub:`α` Module Reference ################ .. Undocumented modules __pkginfo__ AtomEnvironment CCPNDataModel ComplexEnvironment CrystalEnvironment Database GroupEnvironment Features ForceFields.Amber.AmberData ForceFields.BondedInteractions ForceFields.ForceField ForceFields.NonBondedInteractions ForceFields.MMForceField MoleculeEnvironment NormalModes.Core PDBML ProteinEnvironment PyMOL surfm tess Skeleton ThreadManager Tk Tk.ProteinVisualization Utility MMTK ===== .. automodule:: MMTK MMTK.Biopolymers ================ .. automodule:: MMTK.Biopolymers MMTK.Bonds ========== .. automodule:: MMTK.Bonds MMTK.ChargeFit ============== .. automodule:: MMTK.ChargeFit MMTK.ChemicalObjects ==================== .. automodule:: MMTK.ChemicalObjects MMTK.Collections ================ .. automodule:: MMTK.Collections MMTK.ConfigIO ============= .. automodule:: MMTK.ConfigIO MMTK.DCD ======== .. automodule:: MMTK.DCD :exclude-members: writePDB, writeDCD MMTK.Deformation ================ .. automodule:: MMTK.Deformation MMTK.Dynamics ============= .. automodule:: MMTK.Dynamics MMTK.Environment ================ .. automodule:: MMTK.Environment MMTK.Field ========== .. automodule:: MMTK.Field MMTK.ForceFields ================ MMTK.ForceFields.AmberForceField -------------------------------- .. automodule:: MMTK.ForceFields.Amber.AmberForceField :no-members: MMTK.ForceFields.Amber94ForceField ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: MMTK.ForceFields.Amber.Amber94ForceField MMTK.ForceFields.Amber99ForceField ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: MMTK.ForceFields.Amber.Amber99ForceField MMTK.ForceFields.OPLSForceField ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: MMTK.ForceFields.Amber.OPLSForceField MMTK.ForceFields.AnisotropicNetworkForceField --------------------------------------------- .. autoclass:: MMTK.ForceFields.ANMFF.AnisotropicNetworkForceField MMTK.ForceFields.CalphaForceField -------------------------------------- .. autoclass:: MMTK.ForceFields.CalphaFF.CalphaForceField MMTK.ForceFields.DeformationForceField -------------------------------------- .. autoclass:: MMTK.ForceFields.DeformationFF.DeformationForceField MMTK.ForceFields.HarmonicForceField ----------------------------------- .. autoclass:: MMTK.ForceFields.BondFF.HarmonicForceField MMTK.ForceFields.LennardJonesForceField --------------------------------------- .. autoclass:: MMTK.ForceFields.LennardJonesFF.LennardJonesForceField MMTK.ForceFields.SPCEForceField ------------------------------- .. autoclass:: MMTK.ForceFields.SPCEFF.SPCEForceField MMTK.ForceFields.ForceFieldTest ------------------------------- .. automodule:: MMTK.ForceFields.ForceFieldTest MMTK.ForceFields.Restraints --------------------------- .. automodule:: MMTK.ForceFields.Restraints MMTK.FourierBasis ================= .. automodule:: MMTK.FourierBasis MMTK.Geometry ============= .. automodule:: MMTK.Geometry MMTK.InternalCoordinates ======================== .. automodule:: MMTK.InternalCoordinates :show-inheritance: MMTK.Minimization ================= .. automodule:: MMTK.Minimization MMTK.MolecularSurface ===================== .. automodule:: MMTK.MolecularSurface MMTK.MoleculeFactory ==================== .. automodule:: MMTK.MoleculeFactory MMTK.NormalModes ================ .. automodule:: MMTK.NormalModes MMTK.NormalModes.BrownianModes ------------------------------ .. automodule:: MMTK.NormalModes.BrownianModes MMTK.NormalModes.EnergeticModes ------------------------------- .. automodule:: MMTK.NormalModes.EnergeticModes MMTK.NormalModes.VibrationalModes --------------------------------- .. automodule:: MMTK.NormalModes.VibrationalModes MMTK.NucleicAcids ================= .. automodule:: MMTK.NucleicAcids MMTK.PDB ======== .. automodule:: MMTK.PDB MMTK.PDBMoleculeFactory ----------------------- .. automodule:: MMTK.PDBMoleculeFactory MMTK.ParticleProperties ======================= .. automodule:: MMTK.ParticleProperties MMTK.ProteinFriction ==================== .. automodule:: MMTK.ProteinFriction MMTK.Proteins ============= .. automodule:: MMTK.Proteins MMTK.Random =========== .. automodule:: MMTK.Random MMTK.Solvation ============== .. automodule:: MMTK.Solvation MMTK.Subspace ============= .. automodule:: MMTK.Subspace MMTK.Trajectory =============== .. automodule:: MMTK.Trajectory MMTK.Units ========== .. automodule:: MMTK.Units MMTK.Universe ============= .. automodule:: MMTK.Universe MMTK.Visualization ================== .. automodule:: MMTK.Visualization MMTK.XML ======== .. automodule:: MMTK.XML MMTK-2.7.9/Doc/HTML/_static/0000755000076600000240000000000012156626563015624 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/_static/ajax-loader.gif0000644000076600000240000000124111761607760020477 0ustar hinsenstaff00000000000000GIF89aòÿÿÿU|ÆÖßN€U|l–®Š«¾™¶Æ!þCreated with ajaxload.info!ù !ÿ NETSCAPE2.0,3ºÜþ0ÊIkc:œN˜f E±1º™Á¶.`ÄÂqÐ-[9ݦ9 JkçH!ù ,4ºÜþNŒ! „ »°æŠDqBQT`1 `LE[¨|µußía€ ×â†C²%$*!ù ,6º2#+ÊAÈÌ”V/…côNñIBa˜«pð ̳½ƨ+YíüƒÃ2©dŸ¿!ù ,3ºb%+Ê2†‘ìœV_…‹¦ …! 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var start = 0; $.each(keywords, function() { var i = textLower.indexOf(this.toLowerCase()); if (i > -1) start = i; }); start = Math.max(start - 120, 0); var excerpt = ((start > 0) ? '...' : '') + $.trim(text.substr(start, 240)) + ((start + 240 - text.length) ? '...' : ''); var rv = $('
').text(excerpt); $.each(hlwords, function() { rv = rv.highlightText(this, 'highlighted'); }); return rv; } /** * Porter Stemmer */ var Stemmer = function() { var step2list = { ational: 'ate', tional: 'tion', enci: 'ence', anci: 'ance', izer: 'ize', bli: 'ble', alli: 'al', entli: 'ent', eli: 'e', ousli: 'ous', ization: 'ize', ation: 'ate', ator: 'ate', alism: 'al', iveness: 'ive', fulness: 'ful', ousness: 'ous', aliti: 'al', iviti: 'ive', biliti: 'ble', logi: 'log' }; var step3list = { icate: 'ic', ative: '', alize: 'al', iciti: 'ic', ical: 'ic', ful: '', ness: '' }; var c = "[^aeiou]"; // consonant var v = "[aeiouy]"; // vowel var C = c + "[^aeiouy]*"; // consonant sequence var V = v + "[aeiou]*"; // vowel sequence var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 var s_v = "^(" + C + ")?" + v; // vowel in stem this.stemWord = function (w) { var stem; var suffix; var firstch; var origword = w; if (w.length < 3) return w; var re; var re2; var re3; var re4; firstch = w.substr(0,1); if (firstch == "y") w = firstch.toUpperCase() + w.substr(1); // Step 1a re = /^(.+?)(ss|i)es$/; re2 = /^(.+?)([^s])s$/; if (re.test(w)) w = w.replace(re,"$1$2"); else if (re2.test(w)) w = w.replace(re2,"$1$2"); // Step 1b re = /^(.+?)eed$/; re2 = /^(.+?)(ed|ing)$/; if (re.test(w)) { var fp = re.exec(w); re = new RegExp(mgr0); if (re.test(fp[1])) { re = /.$/; w = w.replace(re,""); } } else if (re2.test(w)) { var fp = re2.exec(w); stem = fp[1]; re2 = new RegExp(s_v); if (re2.test(stem)) { w = stem; re2 = /(at|bl|iz)$/; re3 = new RegExp("([^aeiouylsz])\\1$"); re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); if (re2.test(w)) w = w + "e"; else if (re3.test(w)) { re = /.$/; w = w.replace(re,""); } else if (re4.test(w)) w = w + "e"; } } // Step 1c re = /^(.+?)y$/; if (re.test(w)) { var fp = re.exec(w); stem = fp[1]; re = new RegExp(s_v); if (re.test(stem)) w = stem + "i"; } // Step 2 re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; if (re.test(w)) { var fp = re.exec(w); stem = fp[1]; suffix = fp[2]; re = new RegExp(mgr0); if (re.test(stem)) w = stem + step2list[suffix]; } // Step 3 re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; if (re.test(w)) { var fp = re.exec(w); stem = fp[1]; suffix = fp[2]; re = new RegExp(mgr0); if (re.test(stem)) w = stem + step3list[suffix]; } // Step 4 re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; re2 = /^(.+?)(s|t)(ion)$/; if (re.test(w)) { var fp = re.exec(w); stem = fp[1]; re = new RegExp(mgr1); if (re.test(stem)) w = stem; } else if (re2.test(w)) { var fp = re2.exec(w); stem = fp[1] + fp[2]; re2 = new RegExp(mgr1); if (re2.test(stem)) w = stem; } // Step 5 re = /^(.+?)e$/; if (re.test(w)) { var fp = re.exec(w); stem = fp[1]; re = new RegExp(mgr1); re2 = new RegExp(meq1); re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) w = stem; } re = /ll$/; re2 = new RegExp(mgr1); if (re.test(w) && re2.test(w)) { re = /.$/; w = w.replace(re,""); } // and turn initial Y back to y if (firstch == "y") w = firstch.toLowerCase() + w.substr(1); return w; } } /** * Search Module */ var Search = { _index : null, _queued_query : null, _pulse_status : -1, init : function() { var params = $.getQueryParameters(); if (params.q) { var query = params.q[0]; $('input[name="q"]')[0].value = query; this.performSearch(query); } }, loadIndex : function(url) { $.ajax({type: "GET", url: url, data: null, success: null, dataType: "script", cache: true}); }, setIndex : function(index) { var q; this._index = index; if ((q = this._queued_query) !== null) { this._queued_query = null; Search.query(q); } }, hasIndex : function() { return this._index !== null; }, deferQuery : function(query) { this._queued_query = query; }, stopPulse : function() { this._pulse_status = 0; }, startPulse : function() { if (this._pulse_status >= 0) return; function pulse() { Search._pulse_status = (Search._pulse_status + 1) % 4; var dotString = ''; for (var i = 0; i < Search._pulse_status; i++) dotString += '.'; Search.dots.text(dotString); if (Search._pulse_status > -1) window.setTimeout(pulse, 500); }; pulse(); }, /** * perform a search for something */ performSearch : function(query) { // create the required interface elements this.out = $('#search-results'); this.title = $('

' + _('Searching') + '

').appendTo(this.out); this.dots = $('').appendTo(this.title); this.status = $('

').appendTo(this.out); this.output = $('
    ').appendTo(this.out); $('#search-progress').text(_('Preparing search...')); this.startPulse(); // index already loaded, the browser was quick! if (this.hasIndex()) this.query(query); else this.deferQuery(query); }, query : function(query) { var stopwords = ["and","then","into","it","as","are","in","if","for","no","there","their","was","is","be","to","that","but","they","not","such","with","by","a","on","these","of","will","this","near","the","or","at"]; // Stem the searchterms and add them to the correct list var stemmer = new Stemmer(); var searchterms = []; var excluded = []; var hlterms = []; var tmp = query.split(/\s+/); var objectterms = []; for (var i = 0; i < tmp.length; i++) { if (tmp[i] != "") { objectterms.push(tmp[i].toLowerCase()); } if ($u.indexOf(stopwords, tmp[i]) != -1 || tmp[i].match(/^\d+$/) || tmp[i] == "") { // skip this "word" continue; } // stem the word var word = stemmer.stemWord(tmp[i]).toLowerCase(); // select the correct list if (word[0] == '-') { var toAppend = excluded; word = word.substr(1); } else { var toAppend = searchterms; hlterms.push(tmp[i].toLowerCase()); } // only add if not already in the list if (!$.contains(toAppend, word)) toAppend.push(word); }; var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" ")); // console.debug('SEARCH: searching for:'); // console.info('required: ', searchterms); // console.info('excluded: ', excluded); // prepare search var filenames = this._index.filenames; var titles = this._index.titles; var terms = this._index.terms; var fileMap = {}; var files = null; // different result priorities var importantResults = []; var objectResults = []; var regularResults = []; var unimportantResults = []; $('#search-progress').empty(); // lookup as object for (var i = 0; i < objectterms.length; i++) { var others = [].concat(objectterms.slice(0,i), objectterms.slice(i+1, objectterms.length)) var results = this.performObjectSearch(objectterms[i], others); // Assume first word is most likely to be the object, // other words more likely to be in description. // Therefore put matches for earlier words first. // (Results are eventually used in reverse order). objectResults = results[0].concat(objectResults); importantResults = results[1].concat(importantResults); unimportantResults = results[2].concat(unimportantResults); } // perform the search on the required terms for (var i = 0; i < searchterms.length; i++) { var word = searchterms[i]; // no match but word was a required one if ((files = terms[word]) == null) break; if (files.length == undefined) { files = [files]; } // create the mapping for (var j = 0; j < files.length; j++) { var file = files[j]; if (file in fileMap) fileMap[file].push(word); else fileMap[file] = [word]; } } // now check if the files don't contain excluded terms for (var file in fileMap) { var valid = true; // check if all requirements are matched if (fileMap[file].length != searchterms.length) continue; // ensure that none of the excluded terms is in the // search result. for (var i = 0; i < excluded.length; i++) { if (terms[excluded[i]] == file || $.contains(terms[excluded[i]] || [], file)) { valid = false; break; } } // if we have still a valid result we can add it // to the result list if (valid) regularResults.push([filenames[file], titles[file], '', null]); } // delete unused variables in order to not waste // memory until list is retrieved completely delete filenames, titles, terms; // now sort the regular results descending by title regularResults.sort(function(a, b) { var left = a[1].toLowerCase(); var right = b[1].toLowerCase(); return (left > right) ? -1 : ((left < right) ? 1 : 0); }); // combine all results var results = unimportantResults.concat(regularResults) .concat(objectResults).concat(importantResults); // print the results var resultCount = results.length; function displayNextItem() { // results left, load the summary and display it if (results.length) { var item = results.pop(); var listItem = $('
  • '); if (DOCUMENTATION_OPTIONS.FILE_SUFFIX == '') { // dirhtml builder var dirname = item[0] + '/'; if (dirname.match(/\/index\/$/)) { dirname = dirname.substring(0, dirname.length-6); } else if (dirname == 'index/') { dirname = ''; } listItem.append($('').attr('href', DOCUMENTATION_OPTIONS.URL_ROOT + dirname + highlightstring + item[2]).html(item[1])); } else { // normal html builders listItem.append($('').attr('href', item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX + highlightstring + item[2]).html(item[1])); } if (item[3]) { listItem.append($(' (' + item[3] + ')')); Search.output.append(listItem); listItem.slideDown(5, function() { displayNextItem(); }); } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) { $.get(DOCUMENTATION_OPTIONS.URL_ROOT + '_sources/' + item[0] + '.txt', function(data) { if (data != '') { listItem.append($.makeSearchSummary(data, searchterms, hlterms)); Search.output.append(listItem); } listItem.slideDown(5, function() { displayNextItem(); }); }, "text"); } else { // no source available, just display title Search.output.append(listItem); listItem.slideDown(5, function() { displayNextItem(); }); } } // search finished, update title and status message else { Search.stopPulse(); Search.title.text(_('Search Results')); if (!resultCount) Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.')); else Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount)); Search.status.fadeIn(500); } } displayNextItem(); }, performObjectSearch : function(object, otherterms) { var filenames = this._index.filenames; var objects = this._index.objects; var objnames = this._index.objnames; var titles = this._index.titles; var importantResults = []; var objectResults = []; var unimportantResults = []; for (var prefix in objects) { for (var name in objects[prefix]) { var fullname = (prefix ? prefix + '.' : '') + name; if (fullname.toLowerCase().indexOf(object) > -1) { var match = objects[prefix][name]; var objname = objnames[match[1]][2]; var title = titles[match[0]]; // If more than one term searched for, we require other words to be // found in the name/title/description if (otherterms.length > 0) { var haystack = (prefix + ' ' + name + ' ' + objname + ' ' + title).toLowerCase(); var allfound = true; for (var i = 0; i < otherterms.length; i++) { if (haystack.indexOf(otherterms[i]) == -1) { allfound = false; break; } } if (!allfound) { continue; } } var descr = objname + _(', in ') + title; anchor = match[3]; if (anchor == '') anchor = fullname; else if (anchor == '-') anchor = objnames[match[1]][1] + '-' + fullname; result = [filenames[match[0]], fullname, '#'+anchor, descr]; switch (match[2]) { case 1: objectResults.push(result); break; case 0: importantResults.push(result); break; case 2: unimportantResults.push(result); break; } } } } // sort results descending objectResults.sort(function(a, b) { return (a[1] > b[1]) ? -1 : ((a[1] < b[1]) ? 1 : 0); }); importantResults.sort(function(a, b) { return (a[1] > b[1]) ? -1 : ((a[1] < b[1]) ? 1 : 0); }); unimportantResults.sort(function(a, b) { return (a[1] > b[1]) ? -1 : ((a[1] < b[1]) ? 1 : 0); }); return [importantResults, objectResults, unimportantResults] } } $(document).ready(function() { Search.init(); });MMTK-2.7.9/Doc/HTML/_static/sidebar.js0000644000076600000240000001120411761607760017570 0ustar hinsenstaff00000000000000/* * sidebar.js * ~~~~~~~~~~ * * This script makes the Sphinx sidebar collapsible. * * .sphinxsidebar contains .sphinxsidebarwrapper. 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by = unescape(document.cookie.substring(start, end)); } } } setComparator(); } /** * Show a comment div. */ function show(id) { $('#ao' + id).hide(); $('#ah' + id).show(); var context = $.extend({id: id}, opts); var popup = $(renderTemplate(popupTemplate, context)).hide(); popup.find('textarea[name="proposal"]').hide(); popup.find('a.by' + by).addClass('sel'); var form = popup.find('#cf' + id); form.submit(function(event) { event.preventDefault(); addComment(form); }); $('#s' + id).after(popup); popup.slideDown('fast', function() { getComments(id); }); } /** * Hide a comment div. */ function hide(id) { $('#ah' + id).hide(); $('#ao' + id).show(); var div = $('#sc' + id); div.slideUp('fast', function() { div.remove(); }); } /** * Perform an ajax request to get comments for a node * and insert the comments into the comments tree. */ function getComments(id) { $.ajax({ type: 'GET', url: opts.getCommentsURL, data: {node: id}, success: function(data, textStatus, request) { var ul = $('#cl' + id); var speed = 100; $('#cf' + id) .find('textarea[name="proposal"]') .data('source', data.source); if (data.comments.length === 0) { ul.html('
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div.data('comment', this); }); } /** * After adding a new comment, it must be inserted in the correct * location in the comment tree. */ function insertComment(comment) { var div = createCommentDiv(comment); // To avoid stagnating data, don't store the comments children in data. comment.children = null; div.data('comment', comment); var ul = $('#cl' + (comment.node || comment.parent)); var siblings = getChildren(ul); var li = $(document.createElement('li')); li.hide(); // Determine where in the parents children list to insert this comment. for(i=0; i < siblings.length; i++) { if (comp(comment, siblings[i]) <= 0) { $('#cd' + siblings[i].id) .parent() .before(li.html(div)); li.slideDown('fast'); return; } } // If we get here, this comment rates lower than all the others, // or it is the only comment in the list. ul.append(li.html(div)); li.slideDown('fast'); } function acceptComment(id) { $.ajax({ type: 'POST', url: opts.acceptCommentURL, data: {id: id}, success: function(data, textStatus, request) { $('#cm' + id).fadeOut('fast'); $('#cd' + id).removeClass('moderate'); }, error: function(request, textStatus, error) { showError('Oops, there was a problem accepting the comment.'); } }); } function deleteComment(id) { $.ajax({ type: 'POST', url: opts.deleteCommentURL, data: {id: id}, success: function(data, textStatus, request) { var div = $('#cd' + id); if (data == 'delete') { // Moderator mode: remove the comment and all children immediately div.slideUp('fast', function() { div.remove(); }); return; } // User mode: only mark the comment as deleted div .find('span.user-id:first') .text('[deleted]').end() .find('div.comment-text:first') .text('[deleted]').end() .find('#cm' + id + ', #dc' + id + ', #ac' + id + ', #rc' + id + ', #sp' + id + ', #hp' + id + ', #cr' + id + ', #rl' + id) .remove(); var comment = div.data('comment'); comment.username = '[deleted]'; comment.text = '[deleted]'; div.data('comment', comment); }, error: function(request, textStatus, error) { showError('Oops, there was a problem deleting the comment.'); 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Your comment will show up ' + 'once it is has been approved by a moderator.'); } // Prettify the comment rating. comment.pretty_rating = comment.rating + ' point' + (comment.rating == 1 ? '' : 's'); // Make a class (for displaying not yet moderated comments differently) comment.css_class = comment.displayed ? '' : ' moderate'; // Create a div for this comment. var context = $.extend({}, opts, comment); var div = $(renderTemplate(commentTemplate, context)); // If the user has voted on this comment, highlight the correct arrow. if (comment.vote) { var direction = (comment.vote == 1) ? 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Placeholders such as <#id#> * are not escaped. */ function renderTemplate(template, context) { var esc = $(document.createElement('div')); function handle(ph, escape) { var cur = context; $.each(ph.split('.'), function() { cur = cur[this]; }); return escape ? esc.text(cur || "").html() : cur; } return template.replace(/<([%#])([\w\.]*)\1>/g, function() { return handle(arguments[2], arguments[1] == '%' ? true : false); }); } /** Flash an error message briefly. */ function showError(message) { $(document.createElement('div')).attr({'class': 'popup-error'}) .append($(document.createElement('div')) .attr({'class': 'error-message'}).text(message)) .appendTo('body') .fadeIn("slow") .delay(2000) .fadeOut("slow"); } /** Add a link the user uses to open the comments popup. */ $.fn.comment = function() { return this.each(function() { var id = $(this).attr('id').substring(1); var count = COMMENT_METADATA[id]; var title = count + ' comment' + (count == 1 ? '' : 's'); var image = count > 0 ? opts.commentBrightImage : opts.commentImage; var addcls = count == 0 ? 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        • '; $(document).ready(function() { init(); }); })(jQuery); $(document).ready(function() { // add comment anchors for all paragraphs that are commentable $('.sphinx-has-comment').comment(); // highlight search words in search results $("div.context").each(function() { var params = $.getQueryParameters(); var terms = (params.q) ? params.q[0].split(/\s+/) : []; var result = $(this); $.each(terms, function() { result.highlightText(this.toLowerCase(), 'highlighted'); }); }); // directly open comment window if requested var anchor = document.location.hash; if (anchor.substring(0, 9) == '#comment-') { $('#ao' + anchor.substring(9)).click(); document.location.hash = '#s' + anchor.substring(9); } }); MMTK-2.7.9/Doc/HTML/Examples/0000755000076600000240000000000012156626561015752 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/Examples/DNA/0000755000076600000240000000000012156626562016355 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/Examples/DNA/construction.py.html0000644000076600000240000001441612013143446022415 0ustar hinsenstaff00000000000000 Constructing a DNA strand with a ligand — MMTK User Guide 2.7.7 documentation

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        Constructing a DNA strand with a ligand¶

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        # Create a nucleotide chain with a ligand from a PDB file.
#

from MMTK import *
from MMTK.PDB import PDBConfiguration
from MMTK.NucleicAcids import NucleotideChain
from MMTK.Visualization import view

# Load the PDB entry 110d. It contains a single DNA strand with a
# ligand (daunomycin).
configuration = PDBConfiguration('110d.pdb')

# Construct the nucleotide chain object. This also constructs positions
# for the missing hydrogens, using geometrical criteria.
chain = configuration.createNucleotideChains()[0]

# Construct the ligand. There is no definition of it in the database,
# so it can only be constructed as a collection of atoms. The second
# argument of createMolecules() is set to one in order to allow
# this use of an unknown residue.
ligand = configuration.createMolecules(['DM1'], 1)

# Put everyting in a universe and show it graphically.
universe = InfiniteUniverse()
universe.addObject(chain)
universe.addObject(ligand)

view(universe)

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        MMTK-2.7.9/Doc/HTML/Examples/Forcefield/0000755000076600000240000000000012156626561020014 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/Examples/Forcefield/ElectricField/0000755000076600000240000000000012156626561022512 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/Examples/Forcefield/ElectricField/Cython/0000755000076600000240000000000012156626562023757 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/Examples/Forcefield/ElectricField/Cython/ElectricField.py.html0000644000076600000240000002455112013143446027764 0ustar hinsenstaff00000000000000 An electric field term (Python part) — MMTK User Guide 2.7.7 documentation

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        An electric field term (Python part)¶

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        # Electric field term representing a uniform external electric field.
# can be added to any other force field

from MMTK import ParticleScalar
from MMTK.ForceFields.ForceField import ForceField
from MMTK_electric_field import ElectricFieldTerm

class ElectricField(ForceField):

    """
    Uniform electric field
    """

    def __init__(self, strength, charge_property='amber_charge'):
        """
        @param strength: the electric field vector
        @type strength: L{Scientific.Geometry.Vector}
        @charge_property: the name of the atomic property in the database
                          that is used to retrieve the atomic charges.
                          The default is 'amber_charge', the charge property
                          for Amber94 and Amber99.
        @type charge_property: C{str}
        """
        # Store arguments that recreate the force field from a pickled
        # universe or from a trajectory.
        self.arguments = (strength, charge_property)
        # Initialize the ForceField class, giving a name to this one.
        ForceField.__init__(self, 'electric_field')
        # Store the parameters for later use.
        self.strength = strength
        self.charge_property = charge_property

    # The following method is called by the energy evaluation engine
    # to inquire if this force field term has all the parameters it
    # requires. This is necessary for interdependent force field
    # terms. In our case, we just say "yes" immediately.
    def ready(self, global_data):
        return True

    # The following method is called by the energy evaluation engine
    # to obtain a list of the low-level evaluator objects (the C routines)
    # that handle the calculations.
    def evaluatorTerms(self, universe, subset1, subset2, global_data):
        # The energy for subsets is defined as consisting only
        # of interactions within that subset, so the contribution
        # of an external field is zero. Therefore we just return
        # an empty list of energy terms.
        if subset1 is not None or subset2 is not None:
            return []
        # Collect the charges into an array
        charges = ParticleScalar(universe)
        for o in universe:
            for a in o.atomList():
                charges[a] = o.getAtomProperty(a, self.charge_property)
        # Here we pass all the parameters to
        # the Cython code that handles energy calculations.
        return [ElectricFieldTerm(universe,
                                  charges.array,
                                  self.strength)]

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        An electric field term (Cython part)¶

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        # Example for a forcefield implementation in Cython.


#
# Get all the required declarations
#
include "MMTK/python.pxi"
include "MMTK/numeric.pxi"
include "MMTK/core.pxi"
include "MMTK/universe.pxi"
include 'MMTK/forcefield.pxi'

#
# The force field term implementation.
# The rules:
#
# - The class must inherit from EnergyTerm.
#
# - EnergyTerm.__init__() must be called with the arguments
#   shown here. The third argument is the name of the EnergyTerm
#   object, the fourth a tuple of the names of all the terms it
#   implements (one object can implement several terms).
#   The assignment to self.eval_func is essential, without it
#   any energy evaluation will crash.
#
# - The function "evaluate" must have exactly the parameter
#   list from this example.
#
cdef class ElectricFieldTerm(EnergyTerm):

    cdef ArrayType charges
    cdef vector3 strength

    def __init__(self, universe, charges, strength):
        EnergyTerm.__init__(self, universe,
                            "electric_field", ("electric_field",))
        self.eval_func = <void *>ElectricFieldTerm.evaluate
        self.charges = charges
        self.strength[0] = strength[0]
        self.strength[1] = strength[1]
        self.strength[2] = strength[2]

    # The function evaluate is called for every single energy
    # evaluation and should therefore be optimized for speed.
    # Its first argument is the global energy evaluator object,
    # which is needed only for parallelized energy terms.
    # The second argument is a C structure that contains all the
    # input data, in particular the particle configuration.
    # The third argument is a C structure that contains the
    # energy term fields and gradient arrays for storing the results.
    # For details, see MMTK_forcefield.pxi.
    cdef void evaluate(self, EnergyEvaluator eval,
                       energy_spec *input, energy_data *energy):
        cdef vector3 *coordinates, *gradients
        cdef double *q
        cdef double e
        cdef int natoms, i

        coordinates = <vector3 *>input.coordinates.data
        natoms = input.coordinates.dimensions[0]
        q = <double *>self.charges.data

        energy.energy_terms[self.index] = 0.
        if energy.gradients != NULL:
            gradients = <vector3 *>(<PyArrayObject *> energy.gradients).data

        for i from 0 <= i < natoms:
            e = q[i]*(self.strength[0]*coordinates[i][0]
                      + self.strength[1]*coordinates[i][1]
                      + self.strength[2]*coordinates[i][2])
            energy.energy_terms[self.index] = \
                                energy.energy_terms[self.index] + e
            energy.energy_terms[self.virial_index] = \
                                energy.energy_terms[self.virial_index] - e
            if energy.gradients != NULL:
                gradients[i][0] += q[i]*self.strength[0]
                gradients[i][1] += q[i]*self.strength[1]
                gradients[i][2] += q[i]*self.strength[2]

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        An electric field term implemented in pure Python¶

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        # Electric field term representing a uniform external electric field.
# It can be added to any other force field.

from MMTK import ParticleScalar, ParticleVector, SymmetricPairTensor
from MMTK.ForceFields.ForceField import ForceField, EnergyTerm

#
# Each force field term requires two classes. One represents the
# abstract force field (ElectricField in this example), it knows
# nothing about any molecular system to which it might be applied.
# The other one (ElectricFieldTerm) stores all the parameters required
# for energy evaluation, and provides the actual code.
#
# When a force field is evaluated for the first time for a given universe,
# the method evaluatorTerms() of the force field is called. It creates
# the EnergyTerm objects. The method evaluate() of the energy term object
# is called for every energy evaluation. Consequently, as much of the
# computation as possible should be done outside of this method.
#

class ElectricFieldTerm(EnergyTerm):

    # The __init__ method only remembers parameters. Note that
    # EnergyTerm.__init__ takes care of storing the name and the
    # universe object.
    def __init__(self, universe, strength, charges):
        EnergyTerm.__init__(self, 'electric field', universe)
        self.strength = strength
        self.charges = charges

    # This method is called for every single energy evaluation, so make
    # it as efficient as possible. The parameters do_gradients and
    # do_force_constants are flags that indicate if gradients and/or
    # force constants are requested.
    def evaluate(self, configuration, do_gradients, do_force_constants):
        results = {}
        energy = (self.charges*(configuration*self.strength)) \
                 .sumOverParticles()
        results['energy'] = energy
        results['virial'] = -energy
        if do_gradients:
            gradients = ParticleVector(self.universe)
            for atom in self.universe.atomList():
                gradients[atom] = self.charges[atom]*self.strength
            results['gradients'] = gradients
        if do_force_constants:
            # The force constants are zero -> nothing to calculate.
            results['force_constants'] = SymmetricPairTensor(self.universe)
        else:
            force_constants = None            
        return results


class ElectricField(ForceField):

    """
    Uniform electric field
    """

    def __init__(self, strength, charge_property='amber_charge'):
        """
        @param strength: the electric field vector
        @type strength: L{Scientific.Geometry.Vector}
        @charge_property: the name of the atomic property in the database
                          that is used to retrieve the atomic charges.
                          The default is 'amber_charge', the charge property
                          for Amber94 and Amber99.
        @type charge_property: C{str}
        """
        # Store arguments that recreate the force field from a pickled
        # universe or from a trajectory.
        self.arguments = (strength, charge_property)
        # Initialize the ForceField class, giving a name to this one.
        ForceField.__init__(self, 'electric_field')
        # Store the parameters for later use.
        self.strength = strength
        self.charge_property = charge_property

    # The following method is called by the energy evaluation engine
    # to inquire if this force field term has all the parameters it
    # requires. This is necessary for interdependent force field
    # terms. In our case, we just say "yes" immediately.
    def ready(self, global_data):
        return True

    # The following method is called by the energy evaluation engine
    # to obtain a list of the evaluator objects
    # that handle the calculations.
    def evaluatorTerms(self, universe, subset1, subset2, global_data):
        # The energy for subsets is defined as consisting only
        # of interactions within that subset, so the contribution
        # of an external field is zero. Therefore we just return
        # an empty list of energy terms.
        if subset1 is not None or subset2 is not None:
            return []
        # Collect the charges into an array
        charges = ParticleScalar(universe)
        for o in universe:
            for a in o.atomList():
                charges[a] = o.getAtomProperty(a, self.charge_property)
        # Here we pass all the parameters to
        # the energy term code that handles energy calculations.
        return [ElectricFieldTerm(universe, self.strength, charges)]

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        MMTK-2.7.9/Doc/HTML/Examples/Forcefield/HarmonicOscillator/0000755000076600000240000000000012156626561023610 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/Examples/Forcefield/HarmonicOscillator/Cython/0000755000076600000240000000000012156626562025055 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/Examples/Forcefield/HarmonicOscillator/Cython/HarmonicOscillatorFF.py.html0000644000076600000240000002463012013143446032372 0ustar hinsenstaff00000000000000 An efficient harmonic oscillator term (Python part) — MMTK User Guide 2.7.7 documentation

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        An efficient harmonic oscillator term (Python part)¶

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        # Harmonic potential with respect to a fixed point in space

from MMTK.ForceFields.ForceField import ForceField
from MMTK_harmonic_oscillator import HarmonicOscillatorTerm

class HarmonicOscillatorForceField(ForceField):

    """
    Harmonic potential with respect to a fixed point in space
    """

    def __init__(self, atom, center, force_constant):
        """
        @param atom: the atom on which the force field acts
        @type atom: L{MMTK.ChemicalObjects.Atom}
        @param center: the point to which the atom is attached by
                       the harmonic potential
        @type center: L{Scientific.Geometry.Vector}
        @param force_constant: the force constant of the harmonic potential
        @type force_constant: C{float}
        """
        # Get the internal index of the atom if the argument is an
        # atom object. It is the index that is stored internally,
        # and when the force field is recreated from a specification
        # in a trajectory file, it is the index that is passed instead
        # of the atom object itself. Calling this method takes care
        # of all the necessary checks and conversions.
        self.atom_index = self.getAtomParameterIndices([atom])[0]
        # Store arguments that recreate the force field from a pickled
        # universe or from a trajectory.
        self.arguments = (self.atom_index, center, force_constant)
        # Initialize the ForceField class, giving a name to this one.
        ForceField.__init__(self, 'harmonic_oscillator')
        # Store the parameters for later use.
        self.center = center
        self.force_constant = force_constant

    # The following method is called by the energy evaluation engine
    # to inquire if this force field term has all the parameters it
    # requires. This is necessary for interdependent force field
    # terms. In our case, we just say "yes" immediately.
    def ready(self, global_data):
        return True

    # The following method is called by the energy evaluation engine
    # to obtain a list of the low-level evaluator objects (the C routines)
    # that handle the calculations.
    def evaluatorTerms(self, universe, subset1, subset2, global_data):
        # The subset evaluation mechanism does not make much sense for
        # this force field, so we just signal an error if someone
        # uses it by accident.
        if subset1 is not None or subset2 is not None:
            raise ValueError("sorry, no subsets here")
        # Here we pass all the parameters to the Cython code
        # that handles energy calculations.
        return [HarmonicOscillatorTerm(universe,
                                       self.atom_index,
                                       self.center,
                                       self.force_constant)]

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        An efficient harmonic oscillator term (Cython part)¶

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        # Example for a forcefield implementation in Cython.

#
# Get all the required declarations
#
include "MMTK/python.pxi"
include "MMTK/numeric.pxi"
include "MMTK/core.pxi"
include "MMTK/universe.pxi"
include 'MMTK/forcefield.pxi'

#
# The force field term implementation.
# The rules:
#
# - The class must inherit from EnergyTerm.
#
# - EnergyTerm.__init__() must be called with the arguments
#   shown here. The third argument is the name of the EnergyTerm
#   object, the fourth a tuple of the names of all the terms it
#   implements (one object can implement several terms).
#   The assignment to self.eval_func is essential, without it
#   any energy evaluation will crash.
#
# - The function "evaluate" must have exactly the parameter
#   list given in this example.
#
cdef class HarmonicOscillatorTerm(EnergyTerm):

    cdef int atom_index
    cdef double ref_x, ref_y, ref_z
    cdef double k

    def __init__(self, universe, atom_index, reference, force_constant):
        cdef ArrayType ref_array
        EnergyTerm.__init__(self, universe,
                            "harmonic_oscillator", ("harmonic_oscillator",))
        self.eval_func = <void *>HarmonicOscillatorTerm.evaluate
        self.atom_index = atom_index
        ref_array = reference.array
        self.ref_x = (<double *>ref_array.data)[0]
        self.ref_y = (<double *>ref_array.data)[1]
        self.ref_z = (<double *>ref_array.data)[2]
        self.k = force_constant

    # The function evaluate is called for every single energy
    # evaluation and should therefore be optimized for speed.
    # Its first argument is the global energy evaluator object,
    # which is needed only for parallelized energy terms.
    # The second argument is a C structure that contains all the
    # input data, in particular the particle configuration.
    # The third argument is a C structure that contains the
    # energy term fields and gradient arrays for storing the results.
    # For details, see MMTK_forcefield.pxi.
    cdef void evaluate(self, PyFFEvaluatorObject *eval,
                       energy_spec *input, energy_data *energy):
        cdef vector3 *coordinates, *gradients
        cdef double *fc
        cdef double dx, dy, dz
        cdef int n, offset
        coordinates = <vector3 *>input.coordinates.data
        dx = coordinates[self.atom_index][0] - self.ref_x
        dy = coordinates[self.atom_index][1] - self.ref_y
        dz = coordinates[self.atom_index][2] - self.ref_z
        energy.energy_terms[self.index] = 0.5*self.k*(dx*dx + dy*dy + dz*dz)
        energy.energy_terms[self.virial_index] -= self.k*(dx*dx + dy*dy + dz*dz)
        if energy.gradients != NULL:
            gradients = <vector3 *>(<PyArrayObject *> energy.gradients).data
            gradients[self.atom_index][0] += self.k*dx
            gradients[self.atom_index][1] += self.k*dy
            gradients[self.atom_index][2] += self.k*dz
        if energy.force_constants != NULL:
            fc = <double *>(<PyArrayObject *> energy.force_constants).data
            n = (<PyArrayObject *> energy.force_constants).dimensions[0]
            offset = (9*n+3)*self.atom_index
            fc[offset + 3*n*0 + 0] += self.k
            fc[offset + 3*n*1 + 1] += self.k
            fc[offset + 3*n*2 + 2] += self.k

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        MMTK-2.7.9/Doc/HTML/Examples/Forcefield/HarmonicOscillator/Python/0000755000076600000240000000000012156626562025072 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/Examples/Forcefield/HarmonicOscillator/Python/HarmonicOscillatorFF.py.html0000644000076600000240000004123512013143446032407 0ustar hinsenstaff00000000000000 A harmonic oscillator term in pure Python — MMTK User Guide 2.7.7 documentation

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        A harmonic oscillator term in pure Python¶

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        # Harmonic potential with respect to a fixed point in space

from MMTK.ForceFields.ForceField import ForceField, EnergyTerm
from MMTK import ParticleVector, SymmetricPairTensor
from Scientific.Geometry import delta

#
# Each force field term requires two classes. One represents the
# abstract force field (HarmonicOscillator in this example), it knows
# nothing about any molecular system to which it might be applied.
# The other one (HarmonicOscillatorTerm) stores all the parameters required
# for energy evaluation, and provides the actual code.
#
# When a force field is evaluated for the first time for a given universe,
# the method evaluatorTerms() of the force field is called. It creates
# the EnergyTerm objects. The method evaluate() of the energy term object
# is called for every energy evaluation. Consequently, as much of the
# computation as possible should be done outside of this method.
#

class HarmonicOscillatorTerm(EnergyTerm):

    # The __init__ method only remembers parameters. Note that
    # EnergyTerm.__init__ takes care of storing the name and the
    # universe object.
    def __init__(self, universe, atom_index, center, force_constant):
        EnergyTerm.__init__(self, 'harmonic oscillator', universe)
        self.atom_index = atom_index
        self.center = center
        self.force_constant = force_constant

    # This method is called for every single energy evaluation, so make
    # it as efficient as possible. The parameters do_gradients and
    # do_force_constants are flags that indicate if gradients and/or
    # force constants are requested.
    def evaluate(self, configuration, do_gradients, do_force_constants):
        results = {}
        d = configuration[self.atom_index]-self.center
        results['energy'] = 0.5*self.force_constant*(d*d)
        if do_gradients:
            gradients = ParticleVector(self.universe)
            gradients[self.atom_index] = self.force_constant*d
            results['gradients'] = gradients
        if do_force_constants:
            force_constants = SymmetricPairTensor(self.universe)
            force_constants[self.atom_index, self.atom_index] = self.force_constant*delta
            results['force_constants'] = force_constants
        return results

class HarmonicOscillatorForceField(ForceField):

    """
    Harmonic potential with respect to a fixed point in space
    """

    def __init__(self, atom, center, force_constant):
        """
        @param atom: the atom on which the force field acts
        @type atom: L{MMTK.ChemicalObjects.Atom}
        @param center: the point to which the atom is attached by
                       the harmonic potential
        @type center: L{Scientific.Geometry.Vector}
        @param force_constant: the force constant of the harmonic potential
        @type force_constant: C{float}
        """
        # Get the internal index of the atom if the argument is an
        # atom object. It is the index that is stored internally,
        # and when the force field is recreated from a specification
        # in a trajectory file, it is the index that is passed instead
        # of the atom object itself. Calling this method takes care
        # of all the necessary checks and conversions.
        self.atom_index = self.getAtomParameterIndices([atom])[0]
        # Store arguments that recreate the force field from a pickled
        # universe or from a trajectory.
        self.arguments = (self.atom_index, center, force_constant)
        # Initialize the ForceField class, giving a name to this one.
        ForceField.__init__(self, 'harmonic_oscillator')
        # Store the parameters for later use.
        self.center = center
        self.force_constant = force_constant

    # The following method is called by the energy evaluation engine
    # to inquire if this force field term has all the parameters it
    # requires. This is necessary for interdependent force field
    # terms. In our case, we just say "yes" immediately.
    def ready(self, global_data):
        return True

    # The following method is called by the energy evaluation engine
    # to obtain a list of the low-level evaluator objects (the C routines)
    # that handle the calculations.
    def evaluatorTerms(self, universe, subset1, subset2, global_data):
        # The subset evaluation mechanism does not make much sense for
        # this force field, so we just signal an error if someone
        # uses it by accident.
        if subset1 is not None or subset2 is not None:
            raise ValueError("sorry, no subsets here")
        # Here we pass all the parameters to the code
        # that handles energy calculations.
        return [HarmonicOscillatorTerm(universe,
                                       self.atom_index,
                                       self.center,
                                       self.force_constant)]

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        An integrator for Langevin dynamics (Python part)¶

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        # This module implements a Langevin integrator.
#
# Written by Konrad Hinsen
#

from MMTK import Dynamics, Environment, Features, Trajectory, \
                 Units, ParticleProperties
import MMTK_langevin
import MMTK_forcefield

#
# Langevin integrator
#
class LangevinIntegrator(Dynamics.Integrator):

    def __init__(self, universe, **options):
        Dynamics.Integrator.__init__(self, universe, options)
        # Supported features: none for the moment, to keep it simple
        self.features = []

    def __call__(self, **options):
        # Process the keyword arguments
        self.setCallOptions(options)
        # Check if the universe has features not supported by the integrator
        Features.checkFeatures(self, self.universe)
        # Get the universe variables needed by the integrator
        configuration = self.universe.configuration()
        masses = self.universe.masses()
        velocities = self.universe.velocities()
        if velocities is None:
            raise ValueError("no velocities")

        # Get the friction coefficients. First check if a keyword argument
        # 'friction' was given to the integrator. Its value can be a
        # ParticleScalar or a plain number (used for all atoms). If no
        # such argument is given, collect the values of the attribute
        # 'friction' from all atoms (default is zero).
        try:
            friction = self.getOption('friction')
        except KeyError:
            friction = self.universe.getParticleScalar('friction')
        if not ParticleProperties.isParticleProperty(friction):
            var = ParticleProperties.ParticleScalar(self.universe)
            var.array[:] = friction
            friction = var

        # Construct a C evaluator object for the force field, using
        # the specified number of threads or the default value
        nt = self.getOption('threads')
        evaluator = self.universe.energyEvaluator(threads=nt).CEvaluator()

        # Run the C integrator
        MMTK_langevin.integrateLD(self.universe,
                                  configuration.array, velocities.array,
                                  masses.array, friction.array, evaluator,
                                  self.getOption('temperature'),
                                  self.getOption('delta_t'),
                                  self.getOption('steps'),
                                  self.getActions())

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        An integrator for Langevin dynamics (C part)¶

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        /* Low-level Langevin dynamics integrators
 *
 * Written by Konrad Hinsen
 */

#include "MMTK/universe.h"
#include "MMTK/forcefield.h"
#include "MMTK/trajectory.h"

#include "ranf.h"

/* Global variables */

double kB;   /* Boltzman constant */

/* Allocate and initialize Output variable descriptors */

static PyTrajectoryVariable *
get_data_descriptors(int n, PyUniverseSpecObject *universe_spec,
		     PyArrayObject *configuration, PyArrayObject *velocities,
		     PyArrayObject *gradients, PyArrayObject *masses,
		     int *ndf, double *time,
		     double *p_energy, double *k_energy,
		     double *temperature, double *pressure,
		     double *box_size)
{
  PyTrajectoryVariable *vars = (PyTrajectoryVariable *)
                               malloc((n+1)*sizeof(PyTrajectoryVariable));
  int i = 0;
  if (vars != NULL) {
    if (time != NULL && i < n) {
      vars[i].name = "time";
      vars[i].text = "Time: %lf\n";
      vars[i].unit = time_unit_name;
      vars[i].type = PyTrajectory_Scalar;
      vars[i].class = PyTrajectory_Time;
      vars[i].value.dp = time;
      i++;
    }
    if (p_energy != NULL && i < n) {
      vars[i].name = "potential_energy";
      vars[i].text = "Potential energy: %lf, ";
      vars[i].unit = energy_unit_name;
      vars[i].type = PyTrajectory_Scalar;
      vars[i].class = PyTrajectory_Energy;
      vars[i].value.dp = p_energy;
      i++;
    }
    if (k_energy != NULL && i < n) {
      vars[i].name = "kinetic_energy";
      vars[i].text = "Kinetic energy: %lf\n";
      vars[i].unit = energy_unit_name;
      vars[i].type = PyTrajectory_Scalar;
      vars[i].class = PyTrajectory_Energy;
      vars[i].value.dp = k_energy;
      i++;
    }
    if (temperature != NULL && i < n) {
      vars[i].name = "temperature";
      vars[i].text = "Temperature: %lf\n";
      vars[i].unit = temperature_unit_name;
      vars[i].type = PyTrajectory_Scalar;
      vars[i].class = PyTrajectory_Thermodynamic;
      vars[i].value.dp = temperature;
      i++;
    }
    if (pressure != NULL && i < n) {
      vars[i].name = "pressure";
      vars[i].text = "Pressure: %lf\n";
      vars[i].unit = pressure_unit_name;
      vars[i].type = PyTrajectory_Scalar;
      vars[i].class = PyTrajectory_Thermodynamic;
      vars[i].value.dp = pressure;
      i++;
    }
    if (configuration != NULL && i < n) {
      vars[i].name = "configuration";
      vars[i].text = "Configuration:\n";
      vars[i].unit = length_unit_name;
      vars[i].type = PyTrajectory_ParticleVector;
      vars[i].class = PyTrajectory_Configuration;
      vars[i].value.array = configuration;
      i++;
    }
    if (box_size != NULL && i < n) {
      vars[i].name = "box_size";
      vars[i].text = "Box size:";
      vars[i].unit = length_unit_name;
      vars[i].type = PyTrajectory_BoxSize;
      vars[i].class = PyTrajectory_Configuration;
      vars[i].value.dp = box_size;
      vars[i].length = universe_spec->geometry_data_length;
      i++;
  }
    if (velocities != NULL && i < n) {
      vars[i].name = "velocities";
      vars[i].text = "Velocities:\n";
      vars[i].unit = velocity_unit_name;
      vars[i].type = PyTrajectory_ParticleVector;
      vars[i].class = PyTrajectory_Velocities;
      vars[i].value.array = velocities;
      i++;
    }
    if (gradients != NULL && i < n) {
      vars[i].name = "gradients";
      vars[i].text = "Energy gradients:\n";
      vars[i].unit = energy_gradient_unit_name;
      vars[i].type = PyTrajectory_ParticleVector;
      vars[i].class = PyTrajectory_Gradients;
      vars[i].value.array = gradients;
      i++;
    }
    if (masses != NULL && i < n) {
      vars[i].name = "masses";
      vars[i].text = "Masses:\n";
      vars[i].unit = mass_unit_name;
      vars[i].type = PyTrajectory_ParticleScalar;
      vars[i].class = PyTrajectory_Internal;
      vars[i].value.array = masses;
      i++;
    }
    if (ndf != NULL && i < n) {
      vars[i].name = "degrees_of_freedom";
      vars[i].text = "Degrees of freedom: %d\n";
      vars[i].unit = "";
      vars[i].type = PyTrajectory_IntScalar;
      vars[i].class = PyTrajectory_Internal;
      vars[i].value.ip = ndf;
      i++;
    }
    vars[i].name = NULL;
  }
  return vars;
}

/* Generate random numbers with bivariate gaussian distribution */
static void
gaussian_bivariate(double *r1, double *r2,
		   double s1, double s2, double c12, double c12x)
{
  double v1, v2, s;
  do {
    v1 = 2.*Ranf()-1.;
    v2 = 2.*Ranf()-1.;
    s = v1*v1 + v2*v2;
  } while (s >= 1. || s == 0.);
  s = sqrt(-2.*log(s)/s);
  v1 *= s;
  v2 *= s;
  *r1 = s1*v1;
  *r2 = s2*(c12*v1+c12x*v2);
}

/* Generate random forces */
static void
random_forces(vector3 *rx, vector3 *rv, double *friction, double *mass,
	      int atoms, double temperature, double delta_t)
{
  int i, j;
  for (i = 0; i < atoms; i++) {
    if (friction[i] < 1.e-8) {
      rx[i][0] = rx[i][1] = rx[i][2] = 0.;
      rv[i][0] = rv[i][1] = rv[i][2] = 0.;
    }
    else {
      double ft = delta_t*friction[i]/mass[i];
      double expft = exp(-ft);
      double ktm = kB*temperature/mass[i];
      double s1 = delta_t*sqrt(ktm*(2.-(3.+(expft-4.)*expft)/ft)/ft);
      double s2 = sqrt(ktm*(1.-sqr(expft)));
      double c12 = delta_t*ktm*(1.+expft*(expft-2.))/(ft*s1*s2);
      double c12x = sqrt(1.-c12*c12);
      for (j = 0; j < 3; j++)
	gaussian_bivariate(&rx[i][j], &rv[i][j], s1, s2, c12, c12x);
    }
  }
}

/* Langevin dynamics integrator */

static PyObject *
integrateLD(PyObject *dummy, PyObject *args)
{
  /* The parameters passed from the Python code */
  PyObject *universe;                /* universe */
  PyArrayObject *configuration;      /* array of positions */
  PyArrayObject *velocities;         /* array of velocities */
  PyArrayObject *masses;             /* array of masses */
  PyArrayObject *friction;           /* array of friction coefficients */
  PyListObject *spec_list;           /* list of periodic actions */
  PyFFEvaluatorObject *evaluator;    /* energy evaluator */
  double ext_temp;                   /* temperature of the heat bath */
  double delta_t;                    /* time step */
  int steps;                         /* number of steps */

  /* Other variables, see below for explanations */
  PyThreadState *this_thread;
  PyUniverseSpecObject *universe_spec;
  PyArrayObject *gradients, *random1, *random2;
  PyTrajectoryOutputSpec *output;
  PyTrajectoryVariable *data_descriptors = NULL;
  vector3 *x, *v, *g, *rx, *rv;
  double *m, *f;
  energy_data p_energy;
  double time, k_energy, temperature, volume, pressure;
  int atoms, df;
  int pressure_available;
  int i, j;

  /* Get arguments passed from Python code */
  if (!PyArg_ParseTuple(args, "OO!O!O!O!O!ddiO!", &universe,
			&PyArray_Type, &configuration,
			&PyArray_Type, &velocities,
			&PyArray_Type, &masses,
			&PyArray_Type, &friction,
			&PyFFEvaluator_Type, &evaluator,
			&ext_temp, &delta_t, &steps,
			&PyList_Type, &spec_list))
    return NULL;

  /* Obtain the universe specification */
  universe_spec = (PyUniverseSpecObject *)
                   PyObject_GetAttrString(universe, "_spec");
  if (universe_spec == NULL)
    return NULL;

  /* Create the array for energy gradients */
#if defined(NUMPY)
  gradients = (PyArrayObject *)PyArray_Copy(configuration);
#else
  gradients = (PyArrayObject *)PyArray_FromDims(configuration->nd,
						configuration->dimensions,
						PyArray_DOUBLE);
#endif
  if (gradients == NULL)
    return NULL;

  /* Create the arrays for random forces */
#if defined(NUMPY)
  random1 = (PyArrayObject *)PyArray_Copy(configuration);
#else
  random1 = (PyArrayObject *)PyArray_FromDims(configuration->nd,
					      configuration->dimensions,
					      PyArray_DOUBLE);
#endif
  if (random1 == NULL) {
    Py_DECREF(gradients);
    return NULL;
  }
#if defined(NUMPY)
  random2 = (PyArrayObject *)PyArray_Copy(configuration);
#else
  random2 = (PyArrayObject *)PyArray_FromDims(configuration->nd,
					      configuration->dimensions,
					      PyArray_DOUBLE);
#endif
  if (random2 == NULL) {
    Py_DECREF(gradients);
    Py_DECREF(random1);
    return NULL;
  }

  /* Set some convenient variables */
  atoms = configuration->dimensions[0];    /* number of atoms */
  x = (vector3 *)configuration->data;      /* pointer to positions */
  v = (vector3 *)velocities->data;         /* pointer to velocities */
  g = (vector3 *)gradients->data;          /* pointer to energy gradients */
  rx = (vector3 *)random1->data;           /* pointer to random moves */
  rv = (vector3 *)random2->data;           /* pointer to random velocities */
  f = (double *)friction->data;            /* pointer to friction constants */
  m = (double *)masses->data;              /* pointer to masses */
  df = 3*atoms;                            /* number of degrees of freedom */

  /* Initial coordinate correction (for periodic universes etc.) */
  universe_spec->correction_function(x, atoms, universe_spec->geometry_data);

  /* Initial force calculation */
  p_energy.gradients = (PyObject *)gradients;
  p_energy.gradient_fn = NULL;
  p_energy.force_constants = NULL;
  p_energy.fc_fn = NULL;
  Py_BEGIN_ALLOW_THREADS;
  PyUniverseSpec_StateLock(universe_spec, 1);
  (*evaluator->eval_func)(evaluator, &p_energy, configuration, 0);
  PyUniverseSpec_StateLock(universe_spec, 2);
  Py_END_ALLOW_THREADS;
  if (p_energy.error)
    goto error2;

  /* Check if pressure can be calculated (i.e. the force field implementation
     provides the virial and the universe has a finite volume) */
  Py_BEGIN_ALLOW_THREADS;
  PyUniverseSpec_StateLock(universe_spec, 1);
  volume = universe_spec->volume_function(1., universe_spec->geometry_data);
  PyUniverseSpec_StateLock(universe_spec, 2);
  Py_END_ALLOW_THREADS;
  pressure_available = volume > 0. && p_energy.virial_available;

  /* Initialize trajectory output and periodic actions */
  data_descriptors =
    get_data_descriptors(10 + pressure_available,
			 universe_spec,
			 configuration, velocities,
			 gradients, masses, &df,
			 &time, &p_energy.energy, &k_energy,
			 &temperature,
			 pressure_available ? &pressure:NULL,
			 (universe_spec->geometry_data_length > 0) ?
			           universe_spec->geometry_data : NULL);
  if (data_descriptors == NULL)
    goto error2;
  output = PyTrajectory_OutputSpecification(universe, spec_list,
					    "Langevin Dynamics",
					    data_descriptors);
  if (output == NULL)
    goto error2;

  /* Allow parallel threads and get the universe write state lock */
  this_thread = PyEval_SaveThread();
  PyUniverseSpec_StateLock(universe_spec, -1);

  /*
   * Main integration loop
   */
  time  = 0.;
  for (i = 0; i < steps; i++) {

    /* Calculation of thermodynamic properties */
    k_energy = 0.;
    for (j = 0; j < atoms; j++)
      k_energy += m[j]*dot(v[j], v[j]);
    k_energy *= 0.5;
    temperature = 2.*k_energy/(df*kB);
    pressure = 2.*k_energy+p_energy.virial/(3.*volume);

    /* Trajectory and log output */
    if (PyTrajectory_Output(output, i, data_descriptors, &this_thread) == -1) {
      PyUniverseSpec_StateLock(universe_spec, -2);
      PyEval_RestoreThread(this_thread);
      goto error;
    }

    /* Calculation of random forces */
    random_forces(rx, rv, f, m, atoms, ext_temp, delta_t);

    /* First part of integration step */
    for (j = 0; j < atoms; j++) {
      double ft = delta_t*f[j]/m[j];
      double c0 = 1.+ft*(-1.+ft*(0.5+ft*(-1./6.+ft/24.)));
      double c1 = 1.+ft*(-0.5+ft*(1./6.-ft/24.));
      double c2 = 0.5+ft*(-1./6.+ft/24.);
      x[j][0] += delta_t*(c1*v[j][0]-c2*delta_t*g[j][0]/m[j])+rx[j][0];
      x[j][1] += delta_t*(c1*v[j][1]-c2*delta_t*g[j][1]/m[j])+rx[j][1];
      x[j][2] += delta_t*(c1*v[j][2]-c2*delta_t*g[j][2]/m[j])+rx[j][2];
      v[j][0] = c0*v[j][0]+(c2-c1)*delta_t*g[j][0]/m[j]+rv[j][0];
      v[j][1] = c0*v[j][1]+(c2-c1)*delta_t*g[j][1]/m[j]+rv[j][1];
      v[j][2] = c0*v[j][2]+(c2-c1)*delta_t*g[j][2]/m[j]+rv[j][2];
    }

    /* Coordinate correction (for periodic universes etc.) */
    universe_spec->correction_function(x, atoms, universe_spec->geometry_data);

    /* Mid-step force evaluation */
    PyUniverseSpec_StateLock(universe_spec, -2);
    PyUniverseSpec_StateLock(universe_spec, 1);
    (*evaluator->eval_func)(evaluator, &p_energy, configuration, 1);
    PyUniverseSpec_StateLock(universe_spec, 2);
    if (p_energy.error) {
      PyEval_RestoreThread(this_thread);
      goto error;
    }
    PyUniverseSpec_StateLock(universe_spec, -1);

    /* Second part of integration step */
    for (j = 0; j < atoms; j++) {
      double ft = delta_t*f[j]/m[j];
      double c2 = 0.5+ft*(-1./6.+ft/24.);
      v[j][0] -= c2*delta_t*g[j][0]/m[j];
      v[j][1] -= c2*delta_t*g[j][1]/m[j];
      v[j][2] -= c2*delta_t*g[j][2]/m[j];
    }

    /* The End - next time step! */
    time += delta_t;
  }
  /** End of main integration loop **/

  /* Final thermodynamic property evaluation */
  k_energy = 0.;
  for (j = 0; j < atoms; j++)
    k_energy += m[j]*dot(v[j], v[j]);
  k_energy *= 0.5;
  temperature = 2.*k_energy/(df*kB);
  pressure = 2.*k_energy+p_energy.virial/(3.*volume);

  /* Final trajectory and log output */
  if (PyTrajectory_Output(output, i, data_descriptors, &this_thread) == -1) {
    PyUniverseSpec_StateLock(universe_spec, -2);
    PyEval_RestoreThread(this_thread);
    goto error;
  }

  /* Cleanup */
  PyUniverseSpec_StateLock(universe_spec, -2);
  PyEval_RestoreThread(this_thread);
  PyTrajectory_OutputFinish(output, i, 0, 1, data_descriptors);
  free(data_descriptors);
  Py_DECREF(gradients);
  Py_DECREF(random1);
  Py_DECREF(random2);
  Py_INCREF(Py_None);
  return Py_None;

  /* Error return */
error:
  PyTrajectory_OutputFinish(output, i, 1, 1, data_descriptors);
error2:
  free(data_descriptors);
  Py_DECREF(gradients);
  Py_DECREF(random1);
  Py_DECREF(random2);
  return NULL;
}


/*
 * List of functions defined in the module
 */

static PyMethodDef langevin_methods[] = {
  {"integrateLD", integrateLD, 1},
  {NULL, NULL}		/* sentinel */
};

/* Initialization function for the module */

void
initMMTK_langevin()
{
  PyObject *m, *dict;
  PyObject *universe, *trajectory, *forcefield, *units;

  /* Create the module and add the functions */
  m = Py_InitModule("MMTK_langevin", langevin_methods);
  dict = PyModule_GetDict(m);

  /* Import the array module */
  import_array();

  /* Import MMTK modules */
  import_MMTK_universe();
  import_MMTK_forcefield();
  import_MMTK_trajectory();

  /* Get the Boltzman constant factor from MMTK.Units */
  units = PyImport_ImportModule("MMTK.Units");
  if (units != NULL) {
    PyObject *module_dict = PyModule_GetDict(units);
    PyObject *factor = PyDict_GetItemString(module_dict, "k_B");
    kB = PyFloat_AsDouble(factor);
  }

  /* Check for errors */
  if (PyErr_Occurred())
    Py_FatalError("can't initialize module MMTK_langevin");
}

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        MMTK-2.7.9/Doc/HTML/Examples/MDIntegrator/0000755000076600000240000000000012156626562020312 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/Examples/MDIntegrator/VelocityVerlet.pyx.html0000644000076600000240000007105512013143447024773 0ustar hinsenstaff00000000000000 A simple Velocity Verlet integrator — MMTK User Guide 2.7.7 documentation

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        A simple Velocity Verlet integrator¶

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        # This module implements a Velocity Verlet integrator.
#
# Written by Konrad Hinsen
#

import numpy as N
cimport numpy as N
import cython

cimport MMTK_trajectory_generator
from MMTK import Units, ParticleProperties
import MMTK_trajectory
import MMTK_forcefield

include "MMTK/python.pxi"
include "MMTK/numeric.pxi"
include "MMTK/core.pxi"
include "MMTK/universe.pxi"
include "MMTK/trajectory.pxi"
include "MMTK/forcefield.pxi"


#
# Velocity Verlet integrator
#
cdef class VelocityVerletIntegrator(MMTK_trajectory_generator.EnergyBasedTrajectoryGenerator):

    """
    Velocity-Verlet molecular dynamics integrator

    The integrator is fully thread-safe.

    The integration is started by calling the integrator object.
    All the keyword options (see documnentation of __init__) can be
    specified either when creating the integrator or when calling it.

    The following data categories and variables are available for
    output:

     - category "time": time

     - category "configuration": configuration and box size (for
       periodic universes)

     - category "velocities": atomic velocities

     - category "gradients": energy gradients for each atom

     - category "energy": potential and kinetic energy, plus
       extended-system energy terms if a thermostat and/or barostat
       are used
    """

    def __init__(self, universe, **options):
        """
        @param universe: the universe on which the integrator acts
        @type universe: L{MMTK.Universe}
        @keyword steps: the number of integration steps (default is 100)
        @type steps: C{int}
        @keyword delta_t: the time step (default is 1 fs)
        @type delta_t: C{float}
        @keyword actions: a list of actions to be executed periodically
                          (default is none)
        @type actions: C{list}
        @keyword threads: the number of threads to use in energy evaluation
                          (default set by MMTK_ENERGY_THREADS)
        @type threads: C{int}
        @keyword background: if True, the integration is executed as a
                             separate thread (default: False)
        @type background: C{bool}
        """
        MMTK_trajectory_generator.EnergyBasedTrajectoryGenerator.__init__(
            self, universe, options, "Velocity Verlet integrator")
        # Supported features: none for the moment, to keep it simple
        self.features = []

    default_options = {'first_step': 0, 'steps': 100, 'delta_t': 1.*Units.fs,
                       'background': False, 'threads': None,
                       'actions': []}

    available_data = ['configuration', 'velocities', 'gradients',
                      'energy', 'time']

    restart_data = ['configuration', 'velocities', 'energy']

    # Cython compiler directives set for efficiency:
    # - No bound checks on index operations
    # - No support for negative indices
    # - Division uses C semantics
    @cython.boundscheck(False)
    @cython.wraparound(False)
    @cython.cdivision(True)
    cdef start(self):
        cdef N.ndarray[double, ndim=2] x, v, g, dv
        cdef N.ndarray[double, ndim=1] m
        cdef energy_data energy
        cdef double time, delta_t, ke
        cdef int natoms, nsteps, step
        cdef Py_ssize_t i, j, k
    
        # Check if velocities have been initialized
        if self.universe.velocities() is None:
            raise ValueError("no velocities")

        # Gather state variables and parameters
        configuration = self.universe.configuration()
        velocities = self.universe.velocities()
        gradients = ParticleProperties.ParticleVector(self.universe)
        masses = self.universe.masses()
        delta_t = self.getOption('delta_t')
        nsteps = self.getOption('steps')
        natoms = self.universe.numberOfAtoms()
    
        # For efficiency, the Cython code works at the array
        # level rather than at the ParticleProperty level.
        x = configuration.array
        v = velocities.array
        g = gradients.array
        m = masses.array
        dv = N.zeros((natoms, 3), N.float)

        # Ask for energy gradients to be calculated and stored in
        # the array g. Force constants are not requested.
        energy.gradients = <void *>g
        energy.gradient_fn = NULL
        energy.force_constants = NULL
        energy.fc_fn = NULL

        # Declare the variables accessible to trajectory actions.
        self.declareTrajectoryVariable_double(
            &time, "time", "Time: %lf\n", time_unit_name, PyTrajectory_Time)
        self.declareTrajectoryVariable_array(
            v, "velocities", "Velocities:\n", velocity_unit_name,
            PyTrajectory_Velocities)
        self.declareTrajectoryVariable_array(
            g, "gradients", "Energy gradients:\n", energy_gradient_unit_name,
            PyTrajectory_Gradients)
        self.declareTrajectoryVariable_double(
            &energy.energy,"potential_energy", "Potential energy: %lf\n",
            energy_unit_name, PyTrajectory_Energy)
        self.declareTrajectoryVariable_double(
            &ke, "kinetic_energy", "Kinetic energy: %lf\n",
            energy_unit_name, PyTrajectory_Energy)
        self.initializeTrajectoryActions()

        # Acquire the write lock of the universe. This is necessary to
        # make sure that the integrator's modifications to positions
        # and velocities are synchronized with other threads that
        # attempt to use or modify these same values.
        #
        # Note that the write lock will be released temporarily
        # for trajectory actions. It will also be converted to
        # a read lock temporarily for energy evaluation. This
        # is taken care of automatically by the respective methods
        # of class EnergyBasedTrajectoryGenerator.
        self.acquireWriteLock()

        # Preparation: Calculate initial half-step accelerations
        # and run the trajectory actions on the initial state.
        ke = 0.
        self.foldCoordinatesIntoBox()
        self.calculateEnergies(x, &energy, 0)
        for i in range(natoms):
            for j in range(3):
                dv[i, j] = -0.5*delta_t*g[i, j]/m[i]
                ke += 0.5*m[i]*v[i, j]*v[i, j]
        self.trajectoryActions(step)

        # Main integration loop
        time = 0.
        for step in range(nsteps):
            # First half-step
            for i in range(natoms):
                for j in range(3):
                    v[i, j] += dv[i, j]
                    x[i, j] += delta_t*v[i, j]
            # Mid-step energy calculation
            self.foldCoordinatesIntoBox()
            self.calculateEnergies(x, &energy, 1)
            # Second half-step
            ke = 0.
            for i in range(natoms):
                for j in range(3):
                    dv[i, j] = -0.5*delta_t*g[i, j]/m[i]
                    v[i, j] += dv[i, j]
                    ke += 0.5*m[i]*v[i, j]*v[i, j]
            time += delta_t
            self.trajectoryActions(step)

        # Release the write lock.
        self.releaseWriteLock()

        # Finalize all trajectory actions (close files etc.)
        self.finalizeTrajectoryActions(nsteps)

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        MMTK-2.7.9/Doc/HTML/Examples/Miscellaneous/0000755000076600000240000000000012156626562020556 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/Examples/Miscellaneous/charge_fit.py.html0000644000076600000240000002132712013143447024157 0ustar hinsenstaff00000000000000 Fitting point charges to an electrostatic potential surface — MMTK User Guide 2.7.7 documentation

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        Fitting point charges to an electrostatic potential surface¶

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        # This program demonstrates how point charges can be fitted to
# known values of the electrostatic potential energy surface.
# In a real application, these values would come from an
# ab-initio calculation.
#
from MMTK import *
from MMTK.ChargeFit import ChargeFit, TotalChargeConstraint, \
                           evaluationPoints

# Construct a small system of two atoms.
a1 = Atom('C', position=Vector(-0.05,0.,0.))
a2 = Atom('C', position=Vector( 0.05,0.,0.))
system = Collection(a1, a2)

# Define charges. Note that their sum is not zero.
a1.charge = -0.75
a2.charge = 0.76

# Calculate the electrostatic potential energy of the two charges.
# This is obviously unrealistic; in a real application, the potential
# energy surface would not be due to known point charges, but come
# from ab-initio calculations.
points = []
for r in evaluationPoints(system, 50):
    p = 0.
    for atom in system.atomList():
        p = p + atom.charge/(r-atom.position()).length()
    points.append((r, p*Units.electrostatic_energy))

# Define a charge constraint that ensures neutrality of the total system.
constraints = [TotalChargeConstraint(system, 0.)]

# Perform the fit and print the two fitted charges. They are not equal
# to the input charges due to the neutrality constraint.
fit = ChargeFit(system, points, constraints)
print fit[a1], fit[a2]

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        MMTK-2.7.9/Doc/HTML/Examples/Miscellaneous/construct_from_pdb.py.html0000644000076600000240000001223312013143447025754 0ustar hinsenstaff00000000000000 Building a complete universe from a PDB file — MMTK User Guide 2.7.7 documentation

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        Building a complete universe from a PDB file¶

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        # This example shows how a universe can be built from a PDB file in such
# a way that all objects in the PDB file are represented as well as
# possible, using AtomCluster objects when nothing more specific can
# be constructed.
#
# This procedure is the only way to construct a universe that uses the
# same internal atom order as the PDB file, which is important for
# data exchange with other programs.

from MMTK import *
from MMTK.PDB import PDBConfiguration

configuration = PDBConfiguration('some_file.pdb')
universe = InfiniteUniverse()
universe.addObject(configuration.createAll(None, 1))

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        MMTK-2.7.9/Doc/HTML/Examples/Miscellaneous/lattice.py.html0000644000076600000240000001477012013143447023515 0ustar hinsenstaff00000000000000 Place molecules on a lattice — MMTK User Guide 2.7.7 documentation

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        Place molecules on a lattice¶

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        # Example: water molecules on a lattice
#
# This example creates a system of water molecules whose centers
# of mass are positioned simple cubic lattice consisting of
# 3x4x5 cells, i.e. there are 60 water molecules in total.
# The molecules are put into a suitably sized periodic universe.
#

from MMTK import *
from Scientific.Geometry.Objects3D import SCLattice
from MMTK.Visualization import view

# Define parameters of the lattice
edge_length = 0.5*Units.nm
lattice_size = (3, 4, 5)

# Construct the universe
universe = OrthorhombicPeriodicUniverse((edge_length*lattice_size[0],
                                         edge_length*lattice_size[1],
                                         edge_length*lattice_size[2]))

# Add the water molecules
for point in SCLattice(edge_length, lattice_size):
    universe.addObject(Molecule('water', position = point))

# Visualization
view(universe)

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        MMTK-2.7.9/Doc/HTML/Examples/Miscellaneous/vector_field.py.html0000644000076600000240000002227312013143447024532 0ustar hinsenstaff00000000000000 Vector fields for analysis and visualization of collective motions — MMTK User Guide 2.7.7 documentation

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        Vector fields for analysis and visualization of collective motions¶

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        # Example: Vector field analysis of the slowest normal mode of insulin.
#
# This example shows the use of vector fields in the analysis and
# visualization of collective motions. For a more elaborate application,
# see
#       A. Thomas, K. Hinsen, M.J. Field, D. Perahia
#       Tertiary and quaternary confrmational changes in aspartate
#       transcarbamylase: a normal mode study.
#       Proteins, 34(1), 96-112 (1999)
#
from MMTK import *
from MMTK.Proteins import Protein
from MMTK.ForceFields import DeformationForceField
from MMTK.NormalModes import NormalModes
from MMTK.Field import AtomicVectorField
from Scientific.Visualization import VRML2 as VRML

# Construct system
universe = InfiniteUniverse(DeformationForceField())
universe.protein = Protein('insulin.pdb', model='calpha')

# Calculate normal modes
modes = NormalModes(universe)

# Create the vector field corresponding to the first non-zero mode
field = AtomicVectorField(universe, 0.5, modes[6])

# Calculate the curl of the vector field. The curl indicated the
# local rotational motion; the direction of the curl vector is
# the rotation axis, and its length is proportional to the amount
# of the rotation.
curl = field.curl()

# Create graphics objects for the protein and the vector fields
graphics = universe.graphicsObjects(model='backbone', graphics_module=VRML) + \
           field.graphicsObjects(color='yellow',
                                 scale=80., range=(0., 0.01),
                                 graphics_module=VRML) + \
           curl.graphicsObjects(color='red',
                                 scale=80., range=(0., 0.01),
                                 graphics_module=VRML)

# Show graphics in a VRML browser
VRML.Scene(graphics).view()

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        MMTK-2.7.9/Doc/HTML/Examples/MolecularDynamics/0000755000076600000240000000000012156626562021366 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/Examples/MolecularDynamics/argon.py.html0000644000076600000240000003373112013143447024004 0ustar hinsenstaff00000000000000 Simulation of liquid argon — MMTK User Guide 2.7.7 documentation

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        Simulation of liquid argon¶

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        # Constant-temperature constant-pressure MD simulation of Argon.
#

from MMTK import *
from MMTK.ForceFields import LennardJonesForceField
from MMTK.Environment import NoseThermostat, AndersenBarostat
from MMTK.Trajectory import Trajectory, TrajectoryOutput, LogOutput
from MMTK.Dynamics import VelocityVerletIntegrator, VelocityScaler, \
                          TranslationRemover, BarostatReset
import string
from Scientific.IO.TextFile import TextFile

# Open the configuration file and read the box size.
conf_file = TextFile('argon.conf.gz')
lx, ly, lz = map(string.atof, string.split(conf_file.readline()))

# Construct a periodic universe using a Lennard-Jones (noble gas) force field
# with a cutoff of 15 Angstrom.
universe = OrthorhombicPeriodicUniverse((lx*Units.Ang,
                                         ly*Units.Ang, lz*Units.Ang),
                                        LennardJonesForceField(15.*Units.Ang))
# Read the atom positions and construct the atoms.
while 1:
    line = conf_file.readline()
    if not line: break
    x, y, z = map(string.atof, string.split(line))
    universe.addObject(Atom('Ar', position=Vector(x*Units.Ang,
                                                  y*Units.Ang, z*Units.Ang)))

# Define thermodynamic parameters.
temperature = 94.4*Units.K
pressure = 1.*Units.atm

# Add thermostat and barostat.
universe.thermostat = NoseThermostat(temperature)
universe.barostat = AndersenBarostat(pressure)

# Initialize velocities.
universe.initializeVelocitiesToTemperature(temperature)

# Create trajectory and integrator.
trajectory = Trajectory(universe, "argon_npt.nc", "w", "Argon NPT test")
integrator = VelocityVerletIntegrator(universe, delta_t=10*Units.fs)

# Periodical actions for trajectory output and text log output.
output_actions = [TrajectoryOutput(trajectory,
                                   ('configuration', 'energy', 'thermodynamic',
                                    'time', 'auxiliary'), 0, None, 20),
                  LogOutput("argon.log", ('time', 'energy'), 0, None, 100)]

# Do some equilibration steps, rescaling velocities and resetting the
# barostat in regular intervals.
integrator(steps = 2000,
           actions = [TranslationRemover(0, None, 100),
                      VelocityScaler(temperature, 0.1*temperature,
                                     0, None, 100),
                      BarostatReset(100)] + output_actions)

# Do some "production" steps.
integrator(steps = 2000,
           actions = [TranslationRemover(0, None, 100)] + output_actions)

# Close the trajectory.
trajectory.close()

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        MMTK-2.7.9/Doc/HTML/Examples/MolecularDynamics/protein.py.html0000644000076600000240000003272512013143447024360 0ustar hinsenstaff00000000000000 Simulation of a protein — MMTK User Guide 2.7.7 documentation

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        Simulation of a protein¶

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        # A simple Molecular Dynamics simulation.
#

from MMTK import *
from MMTK.Proteins import Protein
from MMTK.ForceFields import Amber99ForceField
from MMTK.Dynamics import VelocityVerletIntegrator, Heater, \
                          TranslationRemover, RotationRemover
from MMTK.Visualization import view
from MMTK.Trajectory import Trajectory, TrajectoryOutput, \
                            RestartTrajectoryOutput, StandardLogOutput, \
                            trajectoryInfo

# Define system
universe = InfiniteUniverse(Amber99ForceField(mod_files=['frcmod.ff99SB']))
universe.protein = Protein('bala1')

# Initialize velocities
universe.initializeVelocitiesToTemperature(50.*Units.K)
print 'Temperature: ', universe.temperature()
print 'Momentum: ', universe.momentum()
print 'Angular momentum: ', universe.angularMomentum()

# Create integrator
integrator = VelocityVerletIntegrator(universe, delta_t=1.*Units.fs)

# Heating and equilibration
integrator(steps=1000,
                    # Heat from 50 K to 300 K applying a temperature
                    # change of 0.5 K/fs; scale velocities at every step.
	   actions=[Heater(50.*Units.K, 300.*Units.K, 0.5*Units.K/Units.fs,
                           0, None, 1),
                    # Remove global translation every 50 steps.
		    TranslationRemover(0, None, 50),
                    # Remove global rotation every 50 steps.
		    RotationRemover(0, None, 50),
                    # Log output to screen every 100 steps.
                    StandardLogOutput(100)])

# "Production" run
trajectory = Trajectory(universe, "bala1.nc", "w", "A simple test case")
integrator(steps=100,
                      # Remove global translation every 50 steps.
           actions = [TranslationRemover(0, None, 50),
                      # Remove global rotation every 50 steps.
                      RotationRemover(0, None, 50),
                      # Write every second step to the trajectory file.
                      TrajectoryOutput(trajectory, ("time", "energy",
                                                    "thermodynamic",
                                                    "configuration"),
                                       0, None, 2),
                      # Write restart data every fifth step.
                      RestartTrajectoryOutput("restart.nc", 5),
                      # Log output to screen every 10 steps.
                      StandardLogOutput(10)])
trajectory.close()

# Print information about the trajectory file
print "Information about the trajectory file 'bala1.nc':"
print trajectoryInfo('bala1.nc')

# Reopen trajectory file
trajectory = Trajectory(universe, "bala1.nc", "r")

# Read step 10 and display configuration
step10 = trajectory[10]
view(universe, step10['configuration'])

# Print the kinetic energy along the trajectory
print "Kinetic energy along trajectory:"
print trajectory.kinetic_energy

trajectory.close()

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        MMTK-2.7.9/Doc/HTML/Examples/MolecularDynamics/restart.py.html0000644000076600000240000002366612013143447024370 0ustar hinsenstaff00000000000000 Restarting the protein simulation — MMTK User Guide 2.7.7 documentation

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        Restarting the protein simulation¶

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        # Continuation of the simulation from protein.py using the restart
# trajectory.
#
# Note: Restart trajectories don't differ much from ordinary
# trajectories; they have a fixed number of steps that are reused
# cyclically, but otherwise they are plain trajectory files.
# As a consequence, you can restart a molecular dynamics run
# from any trajectory that contains positions and velocities,
# with only a minor modification mentioned below.
#

from MMTK import *
from MMTK.Proteins import Protein
from MMTK.ForceFields import Amber94ForceField
from MMTK.Dynamics import VelocityVerletIntegrator, Heater, \
                          TranslationRemover, RotationRemover
from MMTK.Visualization import view
from MMTK.Trajectory import Trajectory, TrajectoryOutput, \
                            RestartTrajectoryOutput, StandardLogOutput, \
                            trajectoryInfo

# Define system
universe = InfiniteUniverse(Amber94ForceField())
universe.protein = Protein('bala1')

# Initialize system state from the restart trajectory
universe.setFromTrajectory(Trajectory(universe, "restart.nc"))

# Note: for restarting from a standard trajectory, use:
#
#   universe.setFromTrajectory(Trajectory(universe, "trajectory.nc"), -1)
#
# in order to load the last step (default would be the first one).

# Create integrator
integrator = VelocityVerletIntegrator(universe, delta_t=1.*Units.fs)

# Do some more MD steps.
trajectory = Trajectory(universe, "bala1.nc", "a")
integrator(steps=100,
                      # Remove global translation every 50 steps.
           actions = [TranslationRemover(0, None, 50),
                      # Remove global rotation every 50 steps.
                      RotationRemover(0, None, 50),
                      # Write every second step to the trajectory file.
                      TrajectoryOutput(trajectory, ("time", "energy",
                                                    "thermodynamic",
                                                    "configuration"),
                                       0, None, 2),
                      # Write restart data every fifth step.
                      RestartTrajectoryOutput("restart.nc", 5),
                      # Log output to screen every 10 steps.
                      StandardLogOutput(10)])
trajectory.close()

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        MMTK-2.7.9/Doc/HTML/Examples/MolecularDynamics/solvation.py.html0000644000076600000240000003743512013143447024721 0ustar hinsenstaff00000000000000 Solvation of a protein in water — MMTK User Guide 2.7.7 documentation

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        Solvation of a protein in water¶

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        # Solvation of a protein with water.
#
# The solvation procedure consists of three steps:
#
# - The universe containing the protein is scaled up, and the required
#   number of water molecules is added at random positions, but without
#   any overlap between molecules. The system is so dilute that random
#   placements are easily possible.
#
# - The universe is slowly scaled down do its original size, with
#   each scaling step followed by some energy minimization and
#   molecular dynamics steps.
#
# - A molecular dynamics run at constant pressure and temperature is
#   used to put the system into a well-defined thermodynamic state.
#

from MMTK import *
from MMTK.Proteins import Protein
from MMTK.ForceFields import Amber94ForceField
from MMTK.Environment import NoseThermostat, AndersenBarostat
from MMTK.Trajectory import Trajectory, TrajectoryOutput, LogOutput
from MMTK.Dynamics import VelocityVerletIntegrator, VelocityScaler, \
                          BarostatReset, TranslationRemover
import MMTK.Solvation

# Create the solute.
protein = Protein('bala1')

# Put the solvent in a standard configuration: center of mass at the
# coordinate origin, principal axes of inertia parallel to the coordinate axes.
protein.normalizeConfiguration()

# Define density, pressure,  and temperature of the solvent.
water_density = 1.*Units.g/Units.cm**3
temperature = 300.*Units.K
pressure = 1.*Units.atm

# Calculate the box size as the boundary box of the protein plus an
# offset. Note: a much larger offset should be used in real applications.
box = protein.boundingBox()
box = box[1]-box[0]+Vector(0.5, 0.5, 0.5)

# Create a periodic universe. The force field is intentionally created with
# a rather small cutoff to speed up the solvation process.
universe = OrthorhombicPeriodicUniverse(tuple(box),
                                        Amber94ForceField(1., 1.))
universe.protein = protein

# Find the number of solvent molecules.
print MMTK.Solvation.numberOfSolventMolecules(universe,'water',water_density),\
      "water molecules will be added"

# Scale up the universe and add the solvent molecules.
MMTK.Solvation.addSolvent(universe, 'water', water_density)
print "Solvent molecules have been added, now shrinking universe..."

# Shrink the universe back to its original size, thereby compressing
# the solvent to its real density.
MMTK.Solvation.shrinkUniverse(universe, temperature, 'solvation.nc')
print "Universe has been compressed, now equilibrating..."

# Set a better force field and add thermostat and barostat.
#
# Note: For efficiency, optimized Ewald parameters should be used
# in a real application. The barostat relaxation time must be
# adjusted to the system size; it should be chosen smaller than
# for a realistic simulation in order to reach the final
# volume faster.
universe.setForceField(Amber94ForceField(1.4, {'method': 'ewald'}))
universe.thermostat = NoseThermostat(temperature)
universe.barostat = AndersenBarostat(pressure, 0.1*Units.ps)

# Create an integrator and a trajectory.
integrator = VelocityVerletIntegrator(universe, delta_t=1.*Units.fs)

trajectory = Trajectory(universe, "equilibration.nc", "w",
                        "Equilibration (NPT ensemble)")

# Start an NPT integration with periodic rescaling of velocities
# and resetting of the barostat. The number of steps required to
# reach a stable volume depends strongly on the system!
output_actions = [TrajectoryOutput(trajectory,
                                   ('configuration', 'energy', 'thermodynamic',
                                    'time', 'auxiliary'), 0, None, 100),
                  LogOutput("equilibration.log", ('time', 'energy'),
                            0, None, 100)]
integrator(steps = 1000,
           actions = [TranslationRemover(0, None, 200),
                      BarostatReset(0, None, 20),
                      VelocityScaler(temperature, 0., 0, None, 20)]
           + output_actions)

# Close the equilibration trajectory
trajectory.close()

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        MMTK-2.7.9/Doc/HTML/Examples/MonteCarlo/0000755000076600000240000000000012156626562020016 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/Examples/MonteCarlo/backbone.py.html0000644000076600000240000002363012013143447023067 0ustar hinsenstaff00000000000000 Sampling a backbone-only configuration ensemble — MMTK User Guide 2.7.7 documentation

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        Sampling a backbone-only configuration ensemble¶

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        # Generate an ensemble of backbone configurations for a protein using
# a simplified protein model which consists only of the C_alpha atoms.
# The ensemble is written to a trajectory file.
#

from MMTK import *
from MMTK.Proteins import Protein
from MMTK.ForceFields import CalphaForceField
from MMTK.NormalModes import NormalModes
from MMTK.Random import gaussian
from MMTK.Trajectory import Trajectory, SnapshotGenerator, TrajectoryOutput

# Construct the system. The scaling factor of 0.1 for the CalphaForceField
# was determined empirically for a C-phycocyanin dimer at 300 K; its value
# is expected to depend at least on the protein and the temperature,
# but the order of magnitude should be right for mid-sized proteins.
universe = InfiniteUniverse(CalphaForceField(2.5, 0.1))
universe.protein = Protein('insulin.pdb', model='calpha')

# Set all masses to the same value, since we don't want
# mass-weighted sampling.
for atom in universe.atomList():
    atom.setMass(1.)

# Calculate normal modes. Note that the normal modes are automatically
# scaled to their vibrational amplitudes at a given temperature.
modes = NormalModes(universe, 300.*Units.K)

# Create trajectory
trajectory = Trajectory(universe, "insulin_backbone.nc", "w",
                        "Monte-Carlo sampling for insulin backbone")

# Create the snapshot generator
snapshot = SnapshotGenerator(universe,
                             actions = [TrajectoryOutput(trajectory,
                                                         ["all"], 0, None, 1)])

# Generate an ensemble of 100 configurations. The scaling factor for
# each mode is half the vibrational amplitude, which explains the
# factor 0.5.
minimum = copy(universe.configuration())
for i in range(100):
    conf = minimum
    for j in range(6, len(modes)):
        conf = conf + gaussian(0., 0.5)*modes[j]
    universe.setConfiguration(conf)
    snapshot()

# Close trajectory
trajectory.close()

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        Protein solvation in parallel¶

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        # A simple Molecular Dynamics simulation using MPI.
#
# Note: the system simulated here is much too small to profit from
# parallelization, and multiple processors will actually slow down the
# simulation. This program is meant to be an illustration, not a
# real-life application.
#
# This example does exactly the same as the one in
# MolecularDynamics/protein.py. Compare the two to see what must be
# changed to use MPI parallelization.
#

from MMTK import *
from MMTK.Proteins import Protein
from MMTK.ForceFields import Amber94ForceField
from MMTK.Dynamics import VelocityVerletIntegrator, Heater, \
                          TranslationRemover, RotationRemover
from MMTK.Visualization import view
from MMTK.Trajectory import Trajectory, TrajectoryOutput, \
                            RestartTrajectoryOutput, StandardLogOutput, \
                            trajectoryInfo
from Scientific.MPI import world

# Define the system and write it to a file, on one processor only.
if world.rank == 0:
    universe = InfiniteUniverse(Amber94ForceField())
    universe.protein = Protein('bala1')
    universe.initializeVelocitiesToTemperature(50.*Units.K)
    save(universe, 'md_mpi.setup')

# Synchronize and load the universe definition on all processors.
world.barrier()
universe = load('md_mpi.setup')

# Note: Creating the universe in parallel on all processors is possible
# if the construction is purely deterministic, but with the assignment
# of random velocities, it cannot be guaranteed that all processors will
# end up with the same system state.


# Create integrator, specifying the MPI communicator to be used
# in energy evaluations
integrator = VelocityVerletIntegrator(universe, delta_t=1.*Units.fs,
                                      mpi_communicator = world)

# Define output actions for one processor only
if world.rank == 0:
    # Log output to console every 100 steps for processor 0.
    output_actions = [StandardLogOutput(100)]
else:
    # Nothing for the other processors
    output_actions = []

# Heating and equilibration
integrator(steps=1000,
                    # Heat from 50 K to 300 K applying a temperature
                    # change of 0.5 K/fs; scale velocities at every step.
	   actions=[Heater(50.*Units.K, 300.*Units.K, 0.5*Units.K/Units.fs,
                           0, None, 1),
                    # Remove global translation every 50 steps.
		    TranslationRemover(0, None, 50),
                    # Remove global rotation every 50 steps.
		    RotationRemover(0, None, 50)] + output_actions)

# Synchronize
world.barrier()

# Define output actions for one processor only
if world.rank == 0:
    trajectory = Trajectory(universe, "bala1.nc", "w", "A simple test case")
    # Trajectory, restart, and log output for processor 0.
    output_actions = [RestartTrajectoryOutput("restart.nc", 5),
                      TrajectoryOutput(trajectory, ("time", "energy",
                                                    "thermodynamic",
                                                    "configuration"),
                                       0, None, 2),
                      StandardLogOutput(10)]
else:
    # Nothing for the other processors.
    output_actions = []

# "Production" run
integrator(steps=100,
                      # Remove global translation every 50 steps.
           actions = [TranslationRemover(0, None, 50),
                      # Remove global rotation every 50 steps.
                      RotationRemover(0, None, 50)] + output_actions)

# Close trajectory
if world.rank == 0:
    trajectory.close()

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        MMTK-2.7.9/Doc/HTML/Examples/NormalModes/0000755000076600000240000000000012156626562020173 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/Examples/NormalModes/calpha_modes.py.html0000644000076600000240000002434412013143447024122 0ustar hinsenstaff00000000000000 Slow normal modes of a protein using a C-alpha model — MMTK User Guide 2.7.7 documentation

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        Slow normal modes of a protein using a C-alpha model¶

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        # This example shows how to calculate approximate low-frequency
# modes for big proteins. For a description of the techniques,
# see
#
#    K. Hinsen
#    Analysis of domain motions by approximate normal mode calculations
#    Proteins 33, 417 (1998)
#
# and
#
#    K. Hinsen, A.J. Petrescu, S. Dellerue, M.C. Bellissent-Funel, G.R. Kneller
#    Harmonicity in slow protein dynamics
#    Chem. Phys. 261, 25 (2000)
#

from MMTK import *
from MMTK.Proteins import Protein
from MMTK.ForceFields import CalphaForceField
from MMTK.FourierBasis import FourierBasis, estimateCutoff
from MMTK.NormalModes import EnergeticModes
from MMTK.Visualization import view
import pylab

# Construct system
universe = InfiniteUniverse(CalphaForceField(2.5))
universe.protein = Protein('insulin.pdb', model='calpha')

# Find a reasonable basis set size and cutoff
nbasis = max(10, universe.numberOfAtoms()/5)
cutoff, nbasis = estimateCutoff(universe, nbasis)
print "Calculating %d low-frequency modes." % nbasis

if cutoff is None:
    # Do full normal mode calculation
    modes = EnergeticModes(universe, 300.*Units.K)
else:
    # Do subspace mode calculation with Fourier basis
    subspace = FourierBasis(universe, cutoff)
    modes = EnergeticModes(universe, 300.*Units.K, subspace)

# Plot the atomic fluctuations in the first three non-zero modes
# for chain A
chain = universe.protein[0]
pylab.xticks(range(len(chain)),
             [r.name for r in chain])
for i in range(6, 9):
    f = modes[i]*modes[i]
    pylab.plot([f[residue.peptide.C_alpha] for residue in chain])

# Show animation of the first non-trivial mode
view(modes[6], 15.)

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        MMTK-2.7.9/Doc/HTML/Examples/NormalModes/constrained_modes.py.html0000644000076600000240000002403612013143447025201 0ustar hinsenstaff00000000000000 Normal modes of a protein using a rigid-residue model — MMTK User Guide 2.7.7 documentation

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        Normal modes of a protein using a rigid-residue model¶

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        # A normal mode calculation in the subspace of residue rigid-body motion.
#

from MMTK import *
from MMTK.Proteins import Protein
from MMTK.ForceFields import Amber94ForceField
from MMTK.NormalModes import VibrationalModes
from MMTK.Subspace import RigidMotionSubspace
from MMTK.Minimization import ConjugateGradientMinimizer
from MMTK.Trajectory import StandardLogOutput

from Scientific import N

# Construct system
universe = InfiniteUniverse(Amber94ForceField())
universe.protein = Protein('bala1')

# Minimize
minimizer = ConjugateGradientMinimizer(universe,
                                       actions=[StandardLogOutput(50)])
minimizer(convergence = 1.e-3, steps = 10000)

# Set up the subspace: rigid-body translation and rotation for each residue
subspace = RigidMotionSubspace(universe, universe.protein.residues())

# Calculate normal modes
modes = VibrationalModes(universe, subspace=subspace)

# Calculate full  modes for comparison
full_modes = VibrationalModes(universe, subspace=None)

# Compare the modes. For each reduced mode, find the full mode with
# the most similar displacement vector. Print both frequencies and
# the overlap of the displacement vectors.
masses = universe.masses()
for i in range(6, len(modes)):
    m1 = modes[i]
    overlap = []
    for m2 in full_modes:
	o = m1.massWeightedDotProduct(m2) / \
	    N.sqrt(m1.massWeightedDotProduct(m1)) / \
	    N.sqrt(m2.massWeightedDotProduct(m2))
	overlap.append(abs(o))
    best = N.argsort(overlap)[-1]
    print m1.frequency, full_modes[best].frequency, overlap[best]

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        MMTK-2.7.9/Doc/HTML/Examples/NormalModes/harmonic_force_field.py.html0000644000076600000240000001402612013143447025620 0ustar hinsenstaff00000000000000 Normal modes of a protein using a simplified force field — MMTK User Guide 2.7.7 documentation

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        Normal modes of a protein using a simplified force field¶

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        # Normal mode calculation using a simplified harmonic force field.
# For a description of the force field, see
#
#     K. Hinsen & G.R. Kneller
#     A simplified force field for describing vibrational protein dynamics
#     over the whole frequency range
#     J. Chem. Phys. 111, 10766 (1999)
#

from MMTK import *
from MMTK.Proteins import Protein
from MMTK.ForceFields import HarmonicForceField
from MMTK.NormalModes import VibrationalModes
from MMTK.Visualization import view

# Construct system
universe = InfiniteUniverse(HarmonicForceField())
universe.protein = Protein('bala1')

# Calculate normal modes
modes = VibrationalModes(universe)

# Print frequencies
for mode in modes:
    print mode

# Show animation of the first non-trivial mode 
view(modes[6])

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        MMTK-2.7.9/Doc/HTML/Examples/NormalModes/modes.py.html0000644000076600000240000001477612013143447022622 0ustar hinsenstaff00000000000000 Normal modes of a protein — MMTK User Guide 2.7.7 documentation

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        Normal modes of a protein¶

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        # Standard normal mode calculation.
#

from MMTK import *
from MMTK.Proteins import Protein
from MMTK.ForceFields import Amber94ForceField
from MMTK.NormalModes import VibrationalModes
from MMTK.Minimization import ConjugateGradientMinimizer
from MMTK.Trajectory import StandardLogOutput
from MMTK.Visualization import view

# Construct system
universe = InfiniteUniverse(Amber94ForceField())
universe.protein = Protein('bala1')

# Minimize
minimizer = ConjugateGradientMinimizer(universe,
                                       actions=[StandardLogOutput(50)])
minimizer(convergence = 1.e-3, steps = 10000)

# Calculate normal modes
modes = VibrationalModes(universe)

# Print frequencies
for mode in modes:
    print mode

# Show animation of the first non-trivial mode 
view(modes[6])

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        MMTK-2.7.9/Doc/HTML/Examples/Proteins/0000755000076600000240000000000012156626562017556 5ustar hinsenstaff00000000000000MMTK-2.7.9/Doc/HTML/Examples/Proteins/analysis.py.html0000644000076600000240000005232512013143447022711 0ustar hinsenstaff00000000000000 Comparing protein configurations — MMTK User Guide 2.7.7 documentation

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        Comparing protein configurations¶

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        # This example demonstrates some of the analysis features of MMTK.
# Two protein structures from the PDB are compared in some detail.
#
# Disclaimer: I know nothing about this protein, and the analysis
# I present here may be of no biological interest.
#

from MMTK import *
from MMTK.PDB import PDBConfiguration
from MMTK.Proteins import PeptideChain

# First we read the two PDB files.

configuration1 = PDBConfiguration('4q21.pdb.gz')
configuration2 = PDBConfiguration('6q21.pdb.gz')

# The first file contains a monomer, the second one a tetramer in
# which each chain is almost identical to the monomer from the first
# file. We have to cut off the last (incomplete) residue from the
# monomer and the last three residues of each chain of the tetramers
# to get matching sequences. We'll just deal with one of the chains of
# the tetramer here.

monomer = configuration1.peptide_chains[0]
monomer.removeResidues(-1, None)
tetramer_1 = configuration2.peptide_chains[0]
tetramer_1.removeResidues(-3, None)

# Next we build a PeptideChain for the monomer. We have to specify
# that we have no C terminus (missing) and no hydrogens.

chain = PeptideChain(monomer, model='no_hydrogens', c_terminus=0)

# How big is this beast? For a quick guess, print the size of the smallest
# rectangular box containing it:

p1, p2 = chain.boundingBox()
print "Size of a bounding box: %4.2f x %4.2f x %4.2f nm" % tuple(p2-p1)

# Formalities: we define a universe and put the chain inside.
# Then we make a copy of the configuration of the universe for later use.
# This is necessary because configurations are defined only for
# universes (for various good reasons...).

universe = InfiniteUniverse()
universe.addObject(chain)
monomer_configuration = copy(universe.configuration())

# Next we change the conformation of the protein to that of the first
# tetramer chain.

tetramer_1.applyTo(chain)

# Now we want to get rid of the difference in position and orientation
# of the two configurations, so we calculate the transformation that
# minimizes the RMS difference.

transformation, rms = chain.findTransformation(monomer_configuration)

# That's the transformation *from* the current *to* the monomer
# configuration. Let's analyze it:

print "Translation/rotation fit:"
print "  RMS difference:", rms

translation = transformation.translation()
rotation = transformation.rotation()
axis, angle = rotation.axisAndAngle()

print "  Rotation axis: ", axis
print "  Rotation angle: ", angle
print "  Translation: ", translation.displacement()

# Note that order is important: the rotation (around the origin) is
# applied first, and then the translation. If you want to do it the
# other way round, the translation displacement must be different.

# Note also the units: distances in nanometers, angles in radians!
# If you insist on Angstrom and degrees, you'll have to type a bit more:

print "  Rotation angle in degrees: ", angle/Units.deg
print "  Translation in Angstrom: ", translation.displacement()/Units.Ang

# Just for fun we'll do it again, but minimizing the RMS for the
# C_alpha atoms only:

c_alphas = chain.residues().map(lambda residue: residue.peptide.C_alpha)
calpha_transformation, rms = c_alphas.findTransformation(monomer_configuration)

print "C_alpha fit:"
print "  RMS difference:", rms

translation = calpha_transformation.translation()
rotation = calpha_transformation.rotation()
axis, angle = rotation.axisAndAngle()

print "  Rotation axis: ", axis
print "  Rotation angle: ", angle
print "  Translation: ", translation.displacement()

# As expected there is little difference. Now we apply the transformation
# to the current configuration to align it with the reference configuration:

chain.applyTransformation(transformation)

# Let's calculate the local deformation due to the change in conformation
# for each atom. Small values indicate rigid regions and large values
# indicate flexible regions. The visualization requires a VRML viewer.
# Reference: K. Hinsen, A. Thomas, M.J. Field:  Proteins 34, 369 (1999)

from MMTK.Deformation import FiniteDeformationFunction
from Scientific.Visualization import VRML
difference = monomer_configuration-universe.configuration()
deformation_function = FiniteDeformationFunction(universe, 0.3, 0.6)
deformation = deformation_function(difference)
graphics = universe.graphicsObjects(color_values = deformation,
                                    graphics_module = VRML)
VRML.Scene(graphics).view()

# The deformation shows a small rigid region and a much larger
# more flexible region.

# Action: We show an animation of the two configurations. This requires
# an animation-capable external viewer, e.g. VMD.

from MMTK.Visualization import viewSequence
viewSequence(chain, [universe.configuration(), monomer_configuration])

# Interesting: there's a floppy loop with residues Gln61, Glu62, and Glu63
# at the tip. It seems to do a funny rotation relative to a closeby helix
# (His94-Lys101) when switching between the two configurations. Let's
# find out what this rotation is!

# First, we define the parts of the chain we are interested in. We don't
# want the sidechains to mess up the picture, so we use just the backbone:

tip = chain[60:64].backbone()
helix = chain[93:101].backbone()

# Then we align the two configurations to make the helix match as well
# as possible:

transformation, rms = helix.findTransformation(monomer_configuration)
chain.applyTransformation(transformation)

# Note that we *fit* only the helix, but *apply* the transformation
# to the whole chain.

# And now another fit for the tip of the loop. This time we don't want
# to apply the transformation, but print out its characteristics:

transformation, rms = tip.findTransformation(monomer_configuration)

print "Movement of the tip of the loop:"
print "  RMS difference:", rms

translation = transformation.translation()
rotation = transformation.rotation()
axis, angle = rotation.axisAndAngle()

print "  Rotation axis: ", axis
print "  Rotation angle: ", angle
print "  Translation: ", translation.displacement()

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        Protein construction beyond the simple cases¶

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        # This file contains some examples for non-standard generation of
# protein objects from PDB files.
#

from MMTK import *
from MMTK.PDB import PDBConfiguration
from MMTK.Proteins import PeptideChain, Protein

#
# First problem: construct an all-atom model from a structure without
# hydrogens. This is the standard problem when using an all-atom force
# field with crystallographic structures.
#
# Note: the simple solution in this case is just
#       insulin = Protein('insulin.pdb')
# but the explicit form shown below is necessary when any kind of
# modification is required.
#
# Load the PDB file.
configuration = PDBConfiguration('insulin.pdb')

# Construct the peptide chain objects. This also constructs positions
# for any missing hydrogens, using geometrical criteria.
chains = configuration.createPeptideChains()

# Make the protein object.
insulin = Protein(chains)

# Write out the structure with hydrogens to a new file - we will use
# it as an input example later on.
insulin.writeToFile('insulin_with_h.pdb')


#
# Second problem: read a file with hydrogens and create a structure
# without them. This is useful for analysis; if you don't need the
# hydrogens, processing is faster without them. Or you might want
# to compare structures with and without hydrogens.
#
# Load the PDB file.
configuration = PDBConfiguration('./insulin_with_h.pdb')

# Delete hydrogens.
configuration.deleteHydrogens()

# Construct the peptide chain objects without hydrogens.
chains = configuration.createPeptideChains(model = 'no_hydrogens')

# Make the protein object
insulin = Protein(chains)

#
# Third problem: cut off three residues from the start of the second
# chain before constructing the protein. This is useful for comparing
# incomplete structures.
#
# Load the PDB file.
configuration = PDBConfiguration('insulin.pdb')

# Cut off the first three residues of the third chain.
configuration.peptide_chains[2].removeResidues(0, 3)

# Mark the second chain as modified
for chain in configuration.peptide_chains:
    chain.modified = 0
configuration.peptide_chains[2].modified = 1

# Construct the peptide chain objects without hydrogens. For
# modified chains, don't use the N-terminal form for the fist residue.
chains = []
for chain in configuration.peptide_chains:
    if chain.modified:
	chains.append(PeptideChain(chain, model='no_hydrogens', n_terminus=0))
    else:
	chains.append(PeptideChain(chain, model='no_hydrogens'))

# Make the protein object.
insulin = Protein(chains)


#
# Stop here, since the last "problem" is just an illustration,
# you can't run it directly because there is no "special residue"
# definition.
#
if 0:

    #
    # Fourth problem: construct a protein with a non-standard residue.
    # The new residue's name is 'XXX' and its definition is stored in the
    # MMTK group database under the name 'special_residue'.
    #
    from MMTK.Proteins import defineAminoAcidResidue
    defineAminoAcidResidue('special_residue', 'xxx')
    protein = Protein('special_protein.pdb')

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        Extract a C-alpha trajectory from an all-atom trajectory¶

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        # This program reads a trajecory containing one or more proteins
# and extracts a trajectory for a C-alpha model, which it stores in
# a new trajectory. This trajectory is smaller and easier to analyze
# or visualize. Moreover, the global (rigid-body) motion of the
# protein(s) is eliminated from the trajectory.
#
# Note: You cannot run this example without having a suitable
#       trajectory file, whose name you should put in place of
#       "full_trajectory.nc" below.
#

from MMTK import *
from MMTK.Trajectory import Trajectory, TrajectoryOutput, SnapshotGenerator
from MMTK.Proteins import Protein, PeptideChain
from Scientific import N

# Open the input trajectory.
trajectory = Trajectory(None, 'full_trajectory.nc')
universe = trajectory.universe

# Choose all objects of class "Protein"
proteins = universe.objectList(Protein)

# Construct an initial configuration in which the proteins are contiguous.
conf = universe.contiguousObjectConfiguration(proteins)

# Construct a C_alpha representation and a mapping from the "real"
# C_alpha atoms to their counterparts in the reduced model.
universe_calpha = InfiniteUniverse()
index = 0
map = []
for protein in proteins:
    chains_calpha = []
    for chain in protein:
        chains_calpha.append(PeptideChain(chain.sequence(),
                                          model='calpha'))
    protein_calpha = Protein(chains_calpha)
    universe_calpha.addObject(protein_calpha)
    for i in range(len(protein)):
        chain = protein[i]
        chain_calpha = protein_calpha[i]
        for j in range(len(chain)):
            r = conf[chain[j].peptide.C_alpha]
            chain_calpha[j].peptide.C_alpha.setPosition(r)
            chain_calpha[j].peptide.C_alpha.index = index
            map.append(chain[j].peptide.C_alpha.index)
            index = index + 1
universe_calpha.configuration()

# Open the new trajectory for just the interesting objects.
trajectory_calpha = Trajectory(universe_calpha, 'calpha_trajectory.nc', 'w')

# Make a snapshot generator for saving.
snapshot = SnapshotGenerator(universe_calpha,
                             actions = [TrajectoryOutput(trajectory_calpha,
                                                         None, 0, None, 1)])

# Loop over all steps, eliminate rigid-body motion with reference to
# the initial configuration, and save the configurations to the new
# trajectory.
first = 1
for step in trajectory:
    conf = universe.contiguousObjectConfiguration(proteins,
                                                  step['configuration'])
    conf_calpha = Configuration(universe_calpha, N.take(conf.array, map),
                                None)
    universe_calpha.setConfiguration(conf_calpha)
    if first:
        initial_conf_calpha = copy(conf_calpha)
        first = 0
    else:
        tr, rms = universe_calpha.findTransformation(initial_conf_calpha)
        universe_calpha.applyTransformation(tr)
    del step['step']
    del step['configuration']
    try:
        del step['box_size']
    except KeyError: pass
    try:
        del step['velocities']
    except KeyError: pass
    snapshot(data = step)

# Close both trajectories.
trajectory.close()
trajectory_calpha.close()

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        MMTK-2.7.9/Doc/HTML/Examples/Trajectories/dcd_export.py.html0000644000076600000240000001240612013143447024050 0ustar hinsenstaff00000000000000 Convert a trajectory to DCD format — MMTK User Guide 2.7.7 documentation

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        Convert a trajectory to DCD format¶

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        # Conversion of an MMTK trajectory to DCD format (CHARMM/XPlor).
# A PDB file with the same atom order is created at the same time,
# because otherwise it would be impossible to interpret the data
# in the DCD file.
#
# Note: In order to create the trajectory file "rotation.nc" which is
#       read by this example, you must run the example snapshot.py.
#

from MMTK import *
from MMTK.Trajectory import Trajectory
from MMTK.DCD import writeDCDPDB


trajectory = Trajectory(None, "rotation.nc")
universe = trajectory.universe

writeDCDPDB(trajectory.configuration, 'rotation.dcd', 'rotation.pdb')

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        Importing a trajectory in DCD format¶

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        # Conversion of a DCD trajectory (CHARMM/XPlor) to MMTK's trajectory format.
#
# Note: In order to create the files "rotation.dcd" and "rotation.pdb" which
#       are read by this example, you must run the example dcd_export.py.
#

from MMTK import *
from MMTK.Proteins import Protein
from MMTK.PDB import PDBConfiguration
from MMTK.Trajectory import Trajectory, TrajectoryOutput
from MMTK.DCD import DCDReader

# Create the universe.
universe = InfiniteUniverse()

# Create all objects from the PDB file. The PDB file *must* match the
# the DCD file (same atom order), and you *must* create all objects
# defined in the PDB file, otherwise interpretation of the DCD file
# is not possible.
conf = PDBConfiguration('rotation.pdb')
chain = conf.peptide_chains[0].createPeptideChain(c_terminus=1)
universe.addObject(Protein([chain]))

# Create the trajectory object for output.
t = Trajectory(universe, "rotation_from_dcd.nc", "w", "Converted from DCD")

# Create a DCD reader that reads all configurations.
dcd_reader = DCDReader(universe, dcd_file = 'rotation.dcd',
                       actions = [TrajectoryOutput(t, "all", 0, None, 1)])
# Run the reader...
dcd_reader()
# ... and close the output trajectory.
t.close()

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        MMTK-2.7.9/Doc/HTML/Examples/Trajectories/fluctuations.py.html0000644000076600000240000002106512013143447024436 0ustar hinsenstaff00000000000000 Calculating atomic fluctuations from a trajectory — MMTK User Guide 2.7.7 documentation

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        Calculating atomic fluctuations from a trajectory¶

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        # Calculate atomic fluctuations from a trajectory.
#
# The trajectory file contains a protein. The fluctuations are calculated
# for all atoms, and then the average over the C-alpha atoms is determined.
# Note that calculating the fluctuations for only the C-alpha atoms is
# more complicated and no faster.
#
# This example illustrates:
# 1) Reading trajectory files
# 2) Selecting parts of a system
# 3) Calculating trajectory averages
#

from MMTK import *
from MMTK.Trajectory import Trajectory
from MMTK.Proteins import Protein

# Open the trajectory, use every tenth step
trajectory = Trajectory(None, 'lysozyme.nc')[::10]
universe = trajectory.universe

# Calculate the average conformation
average = ParticleVector(universe)
for step in trajectory:
    average += step['configuration']
average /= len(trajectory)

# Calculate the fluctiations for all atoms
fluctuations = ParticleScalar(universe)
for step in trajectory:
    d = step['configuration']-average
    fluctuations += d*d
fluctuations /= len(trajectory)

# Collect the C-alpha atoms
calphas = Collection()
for protein in universe.objectList(Protein):
    for chain in protein:
        for residue in chain:
            calphas.addObject(residue.peptide.C_alpha)

# Average over the C-alpha atoms
calpha_mask = calphas.booleanMask()
print (fluctuations*calpha_mask).sumOverParticles()/calphas.numberOfAtoms()

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        MMTK-2.7.9/Doc/HTML/Examples/Trajectories/pdb_export.py.html0000644000076600000240000001244412013143447024065 0ustar hinsenstaff00000000000000 Converting a trajectory to a sequence of PDB files — MMTK User Guide 2.7.7 documentation

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        Converting a trajectory to a sequence of PDB files¶

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        # Conversion of an MMTK trajectory into a sequence of PDB files.
#
# Note: In order to create the trajectory file "rotation.nc" which is
#       read by this example, you must run the example snapshot.py.
#

from MMTK import *
from MMTK.Trajectory import Trajectory

trajectory = Trajectory(None, "rotation.nc")
universe = trajectory.universe

for i, conf in enumerate(trajectory.configuration):
    universe.writeToFile("conf%d.pdb" % i, conf)

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        MMTK-2.7.9/Doc/HTML/Examples/Trajectories/snapshot.py.html0000644000076600000240000001623312013143447023556 0ustar hinsenstaff00000000000000 Assembling a trajectory step by step — MMTK User Guide 2.7.7 documentation

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        Assembling a trajectory step by step¶

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        # "Manual" creation of a trajectory using the snapshot generator.
#

from MMTK import *
from MMTK.Proteins import Protein
from MMTK.Trajectory import Trajectory, SnapshotGenerator, TrajectoryOutput

# Construct system
universe = InfiniteUniverse()
universe.protein = Protein('bala1')

# Transformation to be applied between the steps
transformation = Rotation(Vector(0.,0.,1.), 1.*Units.deg)

# Create trajectory
trajectory = Trajectory(universe, "rotation.nc", "w", "A rotating molecule")

# Create the snapshot generator
snapshot = SnapshotGenerator(universe,
                             actions = [TrajectoryOutput(trajectory,
                                                         ["all"], 0, None, 1)])

# Perform rotations and write the configurations
snapshot()
for i in range(20):
    universe.protein.applyTransformation(transformation)
    snapshot()

# Close trajectory
trajectory.close()

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        Compute an average structure from a trajectory¶

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        # This program reads a trajecory and calculates the average
# configuration, which is then visualized. This is not a particularly
# useful operation, but it illustrates the use of trajectories and
# configuration variables.
#
# Note: In order to create the trajectory file "bala1.nc" which is
#       read by this example, you must run the example
#       MolecularDynamics/protein.py!
#

from MMTK import *
from MMTK.Trajectory import Trajectory
from MMTK.Visualization import view

# Open the input trajectory.
trajectory = Trajectory(None, 'bala1.nc')
universe = trajectory.universe

# Create a zeroed particle vector object.
sum = ParticleVector(universe)

# Loop over all steps and add the configurations to the sum.
for configuration in trajectory.configuration:
    sum = sum + configuration

# Divide by the number of steps.
sum = sum/len(trajectory)

# Visualize the average.
view(universe, sum)

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        Extract a subset from a trajectory¶

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        # This program reads a trajecory for a solvated protein and
# extracts just the protein data, which it stores in a new
# trajectory. This trajectory is smaller and easier to analyze
# or visualize.
#
# Note: You cannot run this example without having a suitable
#       trajectory file, whose name you should put in place of
#       "full_trajectory.nc" below.
#

from MMTK import *
from MMTK.Trajectory import Trajectory, TrajectoryOutput, SnapshotGenerator

# Open the input trajectory.
full = Trajectory(None, 'full_trajectory.nc')

# Collect the items you want to keep. Here we keep everything but
# water molecules.
universe = full.universe
keep = Collection()
for object in universe:
    try:
        is_water = object.type.name == 'water'
    except AttributeError:
        is_water = 0
    if not is_water:
        keep.addObject(object)

# Open the new trajectory for just the interesting objects.
subset = Trajectory(keep, 'subset_trajectory.nc', 'w')

# Make a snapshot generator for saving.
snapshot = SnapshotGenerator(universe,
                             actions = [TrajectoryOutput(subset, None,
                                                         0, None, 1)])

# Loop over all steps and save them to the new trajectory.
for configuration in full.configuration:
    universe.setConfiguration(configuration)
    snapshot()

# Close both trajectories.
full.close()
subset.close()

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        Show an animation of a trajectory¶

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        # This program will show an animation of the trajectory file given as
# an argument. Optional parameters indicate the configurations to be
# included (first, last, step).
#

from MMTK.Visualization import viewTrajectory
import string, sys

first, last, step = 0, None, 1
argc = len(sys.argv)
if argc > 2:
    first = string.atoi(sys.argv[2])
    if argc > 3:
	last = string.atoi(sys.argv[3])
	if argc > 4:
	    step = string.atoi(sys.argv[4])

viewTrajectory(sys.argv[1], first, last, step)

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        Adding custom graphics to a molecular system visualization¶

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        # Generate a protein backbone representation plus an indication
# of the principal axes of inertia by arrows.
#
from MMTK import *
from MMTK.Proteins import Protein
from Scientific import N, LA

# Import the graphics module. Substitute any other graphics
# module name to make the example use that module.
from Scientific.Visualization import VRML2; module = VRML2

# Create the protein and find its center of mass and tensor of inertia.
protein = Protein('insulin')
center, inertia = protein.centerAndMomentOfInertia()

# Diagonalize the inertia tensor and scale the axes to a suitable length.
mass = protein.mass()
diagonal, directions = LA.eigenvectors(inertia.array)
diagonal = N.sqrt(diagonal/mass)

# Generate the backbone graphics objects.
graphics = protein.graphicsObjects(graphics_module = module,
                                   model = 'backbone', color = 'red')

# Add an atomic wireframe representation of all valine sidechains
valines = protein.residuesOfType('val')
sidechains = valines.map(lambda r: r.sidechain)
graphics = graphics + sidechains.graphicsObjects(graphics_module = module,
                                                 model='wireframe',
                                                 color = 'blue')

# Add three arrows corresponding to the principal axes of inertia.
for length, axis in map(None, diagonal, directions):
    graphics.append(module.Arrow(center, center+length*Vector(axis), 0.02,
                                 material=module.EmissiveMaterial('green')))

# Display everything using a VRML browser.
scene = module.Scene(graphics)
scene.view()

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        Extracting numerical values from graphics objects¶

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        # This example shows how the graphics generator functions in MMTK
# can be redirected to give access to the numerical values.
# It extracts the data about the arrows in an AtomicVectorField.
# The trick is to write a highly specialized graphics "module",
# which here is a fake module - in fact any object with the right
# attributes will do.

from MMTK import *
from MMTK.Proteins import Protein
from MMTK.NormalModes import NormalModes
from MMTK.ForceFields import CalphaForceField
from MMTK.Field import AtomicVectorField

# Use generic color handling routines
import Scientific.Visualization.Color

# The fake graphics module - a class.
class DummyGraphics:

    def __init__(self):
        self.arrows = []

    def Color(self, rgb):
        return Scientific.Visualization.Color.Color(rgb)

    def ColorByName(self, name):
        return Scientific.Visualization.Color.ColorByName(name)

    def ColorScale(self, range):
        return Scientific.Visualization.Color.ColorScale(range)
    
    def SymmetricColorScale(self, range):
        return Scientific.Visualization.Color.SymmetricColorScale(range)

    def Sphere(self, center, radius, **attributes):
        pass

    def Cube(self, center, edge_length, **attributes):
        pass

    def Cylinder(self, point1, point2, radius, **attributes):
        pass

    def Cone(self, point1, point2, radius, **attributes):
        pass

    def Line(self, point1, point2, **attributes):
        pass

    # Only arrow data is stored for later extraction
    def Arrow(self, point1, point2, radius, **attributes):
        self.arrows.append((point1, point2))

    def Material(self, **attributes):
        return None

    def DiffuseMaterial(self, color):
        return None

    def EmissiveMaterial(self, color):
        return None

# Create the system
universe = InfiniteUniverse(CalphaForceField())
universe.protein = Protein('insulin', model='calpha')
# Calculate the normal modes
modes = NormalModes(universe)

# Generate the vector field for the first non-zero mode
vector_field = AtomicVectorField(universe, 0.5, modes[6])

# Generate the graphics data
graphics = DummyGraphics()
vector_field.graphicsObjects(graphics_module = graphics)

# Print the arrow coordinates
for point1, point2 in graphics.arrows:
    print point1, point2

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        Vector field visualization of a normal mode (using Chimera)¶

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        # Example: Vector field visualization of the slowest normal mode of insulin.
#
# This example shows the use of vector fields in the analysis and
# visualization of collective motions. For a more elaborate application,
# see
#       A. Thomas, K. Hinsen, M.J. Field, D. Perahia
#       Tertiary and quaternary confrmational changes in aspartate
#       transcarbamylase: a normal mode study.
#       Proteins, 34(1), 96-112 (1999)
#
from MMTK import *
from MMTK.Proteins import Protein
from MMTK.NormalModes import EnergeticModes
from MMTK.ForceFields import CalphaForceField
from MMTK.Field import AtomicVectorField
from MMTK.Database import PDBPath
from Scientific.Visualization import Chimera
import chimera

# Load the molecule into Chimera
filename = PDBPath("insulin.pdb")
chimera.runCommand("open pdb:%s" % filename)

# Create a simulation universe containing the protein
universe = InfiniteUniverse(CalphaForceField())
universe.addObject(Protein(filename, model='calpha'))

# Calculate normal modes
modes = EnergeticModes(universe)

# Create the vector field corresponding to the first non-zero mode
# using a grid spacing of 0.5 nm.
field = AtomicVectorField(universe, 0.5, modes[6])

# Create graphics objects for the vector fields.
# The arrows are yellow and their lengths are multiplied by 80.
# Only displacement vectors of lengths between 0. and 0.01 will be shown.
# (This restriction is not necessary for this example, but used for
#  illustration.)
graphics = field.graphicsObjects(color='yellow',
                                 scale=80., range=(0., 0.01),
                                 graphics_module=Chimera)

# Display in Chimera. The scale factor is necessary because MMTK
# uses nm whereas Chimera works in Angstrom.
Chimera.Scene(graphics, name="first mode",
              scale=1./Units.Ang).view()

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        Vector field visualization of a normal mode (using VMD)¶

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        # Example: Vector field visualization of the slowest normal mode of insulin.
#
# This example shows the use of vector fields in the analysis and
# visualization of collective motions. For a more elaborate application,
# see
#       A. Thomas, K. Hinsen, M.J. Field, D. Perahia
#       Tertiary and quaternary confrmational changes in aspartate
#       transcarbamylase: a normal mode study.
#       Proteins, 34(1), 96-112 (1999)
#
from MMTK import *
from MMTK.Proteins import Protein
from MMTK.NormalModes import EnergeticModes
from MMTK.ForceFields import CalphaForceField
from MMTK.Field import AtomicVectorField
from Scientific.Visualization import VMD

# Create a simulation universe containing the protein
universe = InfiniteUniverse(CalphaForceField())
universe.addObject(Protein("insulin.pdb", model='calpha'))

# Calculate normal modes
modes = EnergeticModes(universe)

# Create the vector field corresponding to the first non-zero mode
# using a grid spacing of 0.5 nm.
field = AtomicVectorField(universe, 0.5, modes[6])

# Create graphics objects for the vector fields.
# The arrows are yellow and their lengths are multiplied by 80.
# Only displacement vectors of lengths between 0. and 0.01 will be shown.
# (This restriction is not necessary for this example, but used for
#  illustration.)
graphics = field.graphicsObjects(color='yellow',
                                 scale=80., range=(0., 0.01),
                                 graphics_module=VMD)

# Create graphics object for the protein.
graphics = graphics + [VMD.Molecules(universe)]

# Launch VMD.
VMD.Scene(graphics, scale=1./Units.Ang).view()

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        Code Examples¶

        One of the best ways to learn how to use a new tool is to look at examples. The examples given in this manual were adapted from real-life MMTK applications. They are also contained in the MMTK distribution (directory “Examples”) for direct use and modification.

        The example molecules, system sizes, parameters, etc., were chosen to reduce execution time as much as possible, in order to enable you to run the examples interactively step by step to see how they work. If you plan to modify an example program for your own use, don’t forget to check all parameters carefully to make sure that you obtain reasonable results.

        • Molecular Dynamics examples
          • The program argon.py contains a simulation of liquid argon at constant temperature and pressure.
          • The program protein.py contains a simulation of a small (very small) protein in vacuum.
          • The program restart.py shows how the simulation started in protein.py can be continued.
          • The program solvation.py contains the solvation of a protein by water molecules.
        • Monte-Carlo examples
          • The program backbone.py generates an ensemble of backbone configuration (C-alpha atoms only) for a protein.
        • Trajectory examples
          • The program snapshot.py shows how a trajectory can be built up step by step from arbitrary data.
          • The program dcd_import.py converts a trajectory in DCD format (used by the programs CHARMM, X-Plor, and NAMD) to MMTK’s format.
          • The program dcd_export.py converts an MMTK trajectory to DCD format (used by the programs CHARMM, X-Plor, and NAMD).
          • The program pdb_export.py converts an MMTK trajectory to a sequence of PDB files.
          • The program trajectory_average.py calculates an average structure from a trajectory.
          • The program trajectory_extraction.py reads a trajectory and writes a new one containing only a subset of the original universe.
          • The program view_trajectory.py shows an animation of a trajectory, provided that an external molecule viewer with animation is available.
          • The program calpha_trajectory.py shows how a much smaller Cα-only trajectory can be extracted from a trajectory containing one or more proteins.
          • The program fluctuations.py shows how to calculate atomic fluctuations from a trajectory file.
        • Normal mode examples
        • The program modes.py contains a standard normal mode calculation for a small protein.
        • The program constrained_modes.py contains a normal mode calculation for a small protein using a model in which each amino acid residue is rigid.
        • The program calpha_modes.py contains a normal mode calculation for a mid-size protein using a Cα model and an elastic network model.
        • The program harmonic_force_field.py contains a normal mode calculation for a protein using a detailed but still simple harmonic force field.
        • Protein examples
          • The program construction.py shows some more complex examples of protein construction from PDB files.
          • The program analysis.py demonstrates a few analysis techniques for comparing protein conformations.
        • DNA examples
          • The program construction.py contains the construction of a DNA strand with a ligand.
        • MPI examples (parallelization)
        • Molecular Dynamics integrators
          • The program md.py illustrates how Molecular Dynamics integrators can be implemented in Cython.
        • Langevin dynamics integrator
          • The programs LangevinDynamics.py and MMTK_langevinmodule.c implement a simple integrator for Langevin dynamics. It is meant as an example of how to write integrators etc. in C, but of course it can also be used directly.
        • Visualization examples
        • The program additional_objects.py describes the addition of custom graphics objects to the representation of a molecular system.
        • The program vector_field_chimera.py shows how to create a vector-field visualization of a normal mode using the graphics program Chimera for visualization. The program vector_field_vmd.py does the same but uses the graphics program VMD.
        • The program graphics_data.py shows how to use a fake graphics module to extract numerical values from a vector field object.
        • Micellaneous examples
          • The example charge_fit.py demonstrates fitting point charges to an electrostatic potential energy surface.
          • The program construct_from_pdb.py shows how a universe can be built from a PDB file in such a way that the internal atom ordering is compatible. This is important for exchanging data with other programs.
          • The program lattice.py constructs molecules placed on a lattice.
          • The program vector_field.py shows how vector fields can be used in the analysis and visualization of collective motions.

        Previous topic

        Glossary

        Next topic

        Module Reference

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        Index

        A | B | C | D | E | F | G | H | I | K | L | M | N | O | P | R | S | T | U | V | W | X | Z

        A

        Abstract base class
        acquireConfigurationChangeLock() (MMTK.Universe.Universe method)
        acquireReadStateLock() (MMTK.Universe.Universe method)
        acquireWriteStateLock() (MMTK.Universe.Universe method)
        addAtom() (MMTK.MoleculeFactory.MoleculeFactory method)
        addBond() (MMTK.MoleculeFactory.MoleculeFactory method)
        addObject() (MMTK.Collections.Collection method)
        (MMTK.Universe.Universe method)
        addSolvent() (in module MMTK.Solvation)
        addSubgroup() (MMTK.MoleculeFactory.MoleculeFactory method)
        addToConfiguration() (MMTK.Universe.Universe method)
        adjustVelocitiesToConstraints() (MMTK.Universe.Universe method)
        Amber94ForceField (class in MMTK.ForceFields.Amber)
        Amber99ForceField (class in MMTK.ForceFields.Amber)
        AndersenBarostat (class in MMTK.Environment)
        angle() (MMTK.Universe.Universe method)
        angularMomentum() (MMTK.Collections.GroupOfAtoms method)
        angularVelocity() (MMTK.Collections.GroupOfAtoms method)
        AnisotropicNetworkForceField (class in MMTK.ForceFields.ANMFF)
        applyFunction() (MMTK.ParticleProperties.ParticleScalar method)
        applyTo() (MMTK.ConfigIO.Cartesian method)
        (MMTK.ConfigIO.ZMatrix method)
        (MMTK.PDB.PDBConfiguration method)
        applyTransformation() (MMTK.Collections.GroupOfAtoms method)
        assign() (MMTK.ParticleProperties.ParticleProperty method)
        asuToUnitCell() (MMTK.PDB.PDBConfiguration method)
        Atom (class in MMTK.ChemicalObjects)
        AtomCluster (class in MMTK.ChemicalObjects)
        atomCollection() (MMTK.Collections.GroupOfAtoms method)
        AtomicField (class in MMTK.Field)
        AtomicScalarField (class in MMTK.Field)
        AtomicVectorField (class in MMTK.Field)
        atomIterator() (MMTK.ChemicalObjects.ChemicalObject method)
        atomList() (MMTK.ChemicalObjects.ChemicalObject method)
        (MMTK.Collections.Collection method)
        (MMTK.Universe.Universe method)
        atomsWithDefinedPositions() (MMTK.Collections.GroupOfAtoms method)

        B

        backbone() (MMTK.NucleicAcids.Nucleotide method)
        (MMTK.NucleicAcids.NucleotideChain method)
        (MMTK.Proteins.PeptideChain method)
        (MMTK.Proteins.Protein method)
        (MMTK.Proteins.Residue method)
        BarostatReset (class in MMTK.Dynamics)
        Base class
        bases() (MMTK.NucleicAcids.Nucleotide method)
        (MMTK.NucleicAcids.NucleotideChain method)
        basisVectors() (MMTK.Universe.Universe method)
        BCCLattice (class in MMTK.Geometry)
        Bond (class in MMTK.Bonds)
        BondAngle (class in MMTK.Bonds)
        (class in MMTK.InternalCoordinates)
        BondAngleList (class in MMTK.Bonds)
        bondedTo() (MMTK.ChemicalObjects.Atom method)
        bondedUnits() (MMTK.ChemicalObjects.ChemicalObject method)
        BondLength (class in MMTK.InternalCoordinates)
        booleanMask() (MMTK.Collections.GroupOfAtoms method)
        boundingBox() (MMTK.Collections.GroupOfAtoms method)
        boundingSphere() (MMTK.Collections.GroupOfAtoms method)
        Box (class in MMTK.Geometry)
        boxToRealCoordinates() (MMTK.Universe.Universe method)
        BravaisLattice (class in MMTK.Geometry)
        BrownianMode (class in MMTK.NormalModes.BrownianModes)
        BrownianModes (class in MMTK.NormalModes.BrownianModes)

        C

        CalphaForceField (class in MMTK.ForceFields.CalphaFF)
        calphaFrictionConstants() (in module MMTK.ProteinFriction)
        Cartesian (class in MMTK.ConfigIO)
        cartesianToFractional() (MMTK.Universe.Universe method)
        cellVolume() (MMTK.Universe.Universe method)
        centerAndMomentOfInertia() (MMTK.Collections.GroupOfAtoms method)
        centerOfMass() (MMTK.ChemicalObjects.Atom method)
        (MMTK.Collections.GroupOfAtoms method)
        ChainMolecule (class in MMTK.ChemicalObjects)
        charge() (MMTK.Collections.GroupOfAtoms method)
        ChargeFit (class in MMTK.ChargeFit)
        charges() (MMTK.Universe.Universe method)
        ChemicalObject (class in MMTK.ChemicalObjects)
        chiAngle() (MMTK.Proteins.Residue method)
        Circle (class in MMTK.Geometry)
        close() (MMTK.PDB.PDBOutputFile method)
        (MMTK.Trajectory.Trajectory method)
        coherentScatteringFunction() (MMTK.NormalModes.BrownianModes.BrownianModes method)
        Collection (class in MMTK.Collections)
        complement() (MMTK.Subspace.Subspace method)
        Complex (class in MMTK.ChemicalObjects)
        CompositeChemicalObject (class in MMTK.ChemicalObjects)
        Cone (class in MMTK.Geometry)
        Configuration (class in MMTK.ParticleProperties)
        configuration() (MMTK.Universe.Universe method)
        configurationDifference() (MMTK.Universe.Universe method)
        ConjugateGradientMinimizer (class in MMTK.Minimization)
        ConnectedChains (class in MMTK.Proteins)
        contiguousObjectConfiguration() (MMTK.Universe.Universe method)
        contiguousObjectOffset() (MMTK.Universe.Universe method)
        copyConfiguration() (MMTK.Universe.Universe method)
        countBasisVectors() (in module MMTK.FourierBasis)
        createAll() (MMTK.PDB.PDBConfiguration method)
        createGroup() (MMTK.MoleculeFactory.MoleculeFactory method)
        createMolecule() (MMTK.PDB.PDBMolecule method)
        createMolecules() (MMTK.PDB.PDBConfiguration method)
        createNucleotideChain() (MMTK.PDB.PDBNucleotideChain method)
        createNucleotideChains() (MMTK.PDB.PDBConfiguration method)
        createPeptideChain() (MMTK.PDB.PDBPeptideChain method)
        createPeptideChains() (MMTK.PDB.PDBConfiguration method)
        createUnitCellUniverse() (MMTK.PDB.PDBConfiguration method)
        CubicPeriodicUniverse (class in MMTK.Universe)
        curl() (MMTK.Field.AtomicVectorField method)
        Cylinder (class in MMTK.Geometry)

        D

        DCDReader (class in MMTK.DCD)
        defineAminoAcidResidue() (in module MMTK.Biopolymers)
        defineNucleicAcidResidue() (in module MMTK.Biopolymers)
        definePDBViewer() (in module MMTK.Visualization)
        defineVRMLiewer() (in module MMTK.Visualization)
        DeformationEnergyFunction (class in MMTK.Deformation)
        DeformationForceField (class in MMTK.ForceFields.DeformationFF)
        DeformationFunction (class in MMTK.Deformation)
        DeformationReducer (class in MMTK.Deformation)
        degreesOfFreedom() (MMTK.ChemicalObjects.ChemicalObject method)
        (MMTK.Collections.GroupOfAtoms method)
        dihedral() (MMTK.Universe.Universe method)
        DihedralAngle (class in MMTK.Bonds)
        (class in MMTK.InternalCoordinates)
        DihedralAngleList (class in MMTK.Bonds)
        dihedralAngles() (MMTK.Bonds.BondAngleList method)
        dipole() (MMTK.Collections.GroupOfAtoms method)
        displacementUnderTransformation() (MMTK.Collections.GroupOfAtoms method)
        distance() (MMTK.Universe.Universe method)
        distanceConstraintList() (MMTK.ChemicalObjects.ChemicalObject method)
        (MMTK.Collections.Collection method)
        (MMTK.Universe.Universe method)
        distanceFrom() (MMTK.Geometry.Line method)
        distanceVector() (MMTK.Universe.Universe method)
        divergence() (MMTK.Field.AtomicVectorField method)
        dotProduct() (MMTK.ParticleProperties.ParticleVector method)
        dyadicProduct() (MMTK.ParticleProperties.ParticleVector method)

        E

        EISF() (MMTK.NormalModes.BrownianModes.BrownianModes method)
        (MMTK.NormalModes.VibrationalModes.VibrationalModes method)
        enclosesPoint() (MMTK.Geometry.GeometricalObject3D method)
        EnergeticMode (class in MMTK.NormalModes.EnergeticModes)
        EnergeticModes (class in MMTK.NormalModes.EnergeticModes)
        energy() (MMTK.Universe.Universe method)
        energyAndForceConstants() (MMTK.Universe.Universe method)
        energyAndGradients() (MMTK.Universe.Universe method)
        energyGradientsAndForceConstants() (MMTK.Universe.Universe method)
        energyTerms() (MMTK.Universe.Universe method)
        enforceConstraints() (MMTK.Universe.Universe method)
        environmentObjectList() (MMTK.Universe.Universe method)
        EqualityConstraint (class in MMTK.ChargeFit)
        estimateCutoff() (in module MMTK.FourierBasis)
        evaluationPoints() (in module MMTK.ChargeFit)

        F

        FCCLattice (class in MMTK.Geometry)
        findContacts() (in module MMTK.MolecularSurface)
        findHydrogenPositions() (MMTK.ChemicalObjects.Molecule method)
        findTransformation() (MMTK.Collections.GroupOfAtoms method)
        FiniteDeformationEnergyFunction (class in MMTK.Deformation)
        FiniteDeformationFunction (class in MMTK.Deformation)
        FiniteDeformationReducer (class in MMTK.Deformation)
        flush() (MMTK.Trajectory.Trajectory method)
        forceConstantTest() (in module MMTK.ForceFields.ForceFieldTest)
        forcefield() (MMTK.Universe.Universe method)
        FourierBasis (class in MMTK.FourierBasis)
        fractionalToCartesian() (MMTK.Universe.Universe method)
        fullName() (MMTK.ChemicalObjects.ChemicalObject method)

        G

        GeometricalObject3D (class in MMTK.Geometry)
        getAtomBooleanArray() (MMTK.Universe.Universe method)
        getAtomProperty() (MMTK.ChemicalObjects.ChemicalObject method)
        getAtomScalarArray() (MMTK.Universe.Universe method)
        getBasis() (MMTK.Subspace.Subspace method)
        getParticleBoolean() (MMTK.Universe.Universe method)
        getParticleScalar() (MMTK.Universe.Universe method)
        getValue() (MMTK.InternalCoordinates.BondAngle method)
        (MMTK.InternalCoordinates.BondLength method)
        (MMTK.InternalCoordinates.DihedralAngle method)
        gradient() (MMTK.Field.AtomicScalarField method)
        gradientTest() (in module MMTK.ForceFields.ForceFieldTest)
        graphicsObjects() (MMTK.Visualization.Viewable method)
        Group (class in MMTK.ChemicalObjects)
        GroupOfAtoms (class in MMTK.Collections)

        H

        HarmonicAngleRestraint (class in MMTK.ForceFields.Restraints)
        HarmonicDihedralRestraint (class in MMTK.ForceFields.Restraints)
        HarmonicDistanceRestraint (class in MMTK.ForceFields.Restraints)
        HarmonicForceField (class in MMTK.ForceFields.BondFF)
        HarmonicTrapForceField (class in MMTK.ForceFields.Restraints)
        hasAtom() (MMTK.Bonds.Bond method)
        hasPoint() (MMTK.Geometry.GeometricalObject3D method)
        Heater (class in MMTK.Dynamics)

        I

        incoherentScatteringFunction() (MMTK.NormalModes.BrownianModes.BrownianModes method)
        (MMTK.NormalModes.VibrationalModes.VibrationalModes method)
        InfiniteUniverse (class in MMTK.Universe)
        initializeVelocitiesToTemperature() (MMTK.Universe.Universe method)
        intersectWith() (MMTK.Geometry.GeometricalObject3D method)
        isChemicalObject() (in module MMTK.ChemicalObjects)
        isCollection() (in module MMTK.Collections)
        isConfiguration() (in module MMTK.ParticleProperties)
        isNucleotideChain() (in module MMTK.NucleicAcids)
        isParticleProperty() (in module MMTK.ParticleProperties)
        isPeptideChain() (in module MMTK.Proteins)
        isProtein() (in module MMTK.Proteins)
        isTrajectory() (in module MMTK.Trajectory)
        isUniverse() (in module MMTK.Universe)

        K

        kineticEnergy() (MMTK.Collections.GroupOfAtoms method)

        L

        laplacian() (MMTK.Field.AtomicScalarField method)
        (MMTK.Field.AtomicVectorField method)
        largestDistance() (MMTK.Universe.Universe method)
        Lattice (class in MMTK.Geometry)
        length() (MMTK.Field.AtomicVectorField method)
        (MMTK.ParticleProperties.ParticleVector method)
        LennardJonesForceField (class in MMTK.ForceFields.LennardJonesFF)
        Line (class in MMTK.Geometry)
        LinkedRigidBodyMotionSubspace (class in MMTK.Subspace)
        LogOutput (class in MMTK.Trajectory)

        M

        map() (MMTK.Collections.Collection method)
        (MMTK.Universe.Universe method)
        mass() (MMTK.Collections.GroupOfAtoms method)
        masses() (MMTK.Universe.Universe method)
        massWeightedDotProduct() (MMTK.ParticleProperties.ParticleVector method)
        massWeightedNorm() (MMTK.ParticleProperties.ParticleVector method)
        maximum() (MMTK.ParticleProperties.ParticleScalar method)
        meanSquareDisplacement() (MMTK.NormalModes.BrownianModes.BrownianModes method)
        (MMTK.NormalModes.VibrationalModes.VibrationalModes method)
        minimum() (MMTK.ParticleProperties.ParticleScalar method)
        Mix-in class
        MMTK (module)
        MMTK.Biopolymers (module)
        MMTK.Bonds (module)
        MMTK.ChargeFit (module)
        MMTK.ChemicalObjects (module)
        MMTK.Collections (module)
        MMTK.ConfigIO (module)
        MMTK.DCD (module)
        MMTK.Deformation (module)
        MMTK.Dynamics (module)
        MMTK.Environment (module)
        MMTK.Field (module)
        MMTK.ForceFields.Amber.AmberForceField (module)
        MMTK.ForceFields.ForceFieldTest (module)
        MMTK.ForceFields.Restraints (module)
        MMTK.FourierBasis (module)
        MMTK.Geometry (module)
        MMTK.InternalCoordinates (module)
        MMTK.Minimization (module)
        MMTK.MolecularSurface (module)
        MMTK.MoleculeFactory (module)
        MMTK.NormalModes (module)
        MMTK.NormalModes.BrownianModes (module)
        MMTK.NormalModes.EnergeticModes (module)
        MMTK.NormalModes.VibrationalModes (module)
        MMTK.NucleicAcids (module)
        MMTK.ParticleProperties (module)
        MMTK.PDB (module)
        MMTK.PDBMoleculeFactory (module)
        MMTK.ProteinFriction (module)
        MMTK.Proteins (module)
        MMTK.Random (module)
        MMTK.Solvation (module)
        MMTK.Subspace (module)
        MMTK.Trajectory (module)
        MMTK.Units (module)
        MMTK.Universe (module)
        MMTK.Visualization (module)
        MMTK.XML (module)
        Molecule (class in MMTK.ChemicalObjects)
        molecule_constructor (MMTK.PDB.PDBConfiguration attribute)
        MoleculeFactory (class in MMTK.MoleculeFactory)
        momentum() (MMTK.Collections.GroupOfAtoms method)

        N

        nextModel() (MMTK.PDB.PDBOutputFile method)
        nextResidue() (MMTK.Biopolymers.Residue method)
        norm() (MMTK.ParticleProperties.ParticleVector method)
        normalizeConfiguration() (MMTK.Collections.GroupOfAtoms method)
        NormalizedDeformationEnergyFunction (class in MMTK.Deformation)
        NormalizedDeformationFunction (class in MMTK.Deformation)
        normalizePosition() (MMTK.Collections.GroupOfAtoms method)
        normalizingTransformation() (MMTK.Collections.GroupOfAtoms method)
        NoseThermostat (class in MMTK.Environment)
        Nucleotide (class in MMTK.NucleicAcids)
        nucleotide_chain_constructor (MMTK.PDB.PDBConfiguration attribute)
        NucleotideChain (class in MMTK.NucleicAcids)
        NucleotideSubChain (class in MMTK.NucleicAcids)
        numberOfAtoms() (MMTK.Collections.Collection method)
        (MMTK.Collections.GroupOfAtoms method)
        numberOfCartesianCoordinates() (MMTK.Collections.GroupOfAtoms method)
        numberOfDistanceConstraints() (MMTK.ChemicalObjects.ChemicalObject method)
        (MMTK.Collections.Collection method)
        (MMTK.Universe.Universe method)
        numberOfFixedAtoms() (MMTK.Collections.GroupOfAtoms method)
        numberOfPoints() (MMTK.Collections.GroupOfAtoms method)
        numberOfSolventMolecules() (in module MMTK.Solvation)

        O

        objectList() (MMTK.Collections.Collection method)
        (MMTK.Universe.Universe method)
        OPLSForceField (class in MMTK.ForceFields.Amber)
        OrthorhombicPeriodicUniverse (class in MMTK.Universe)
        otherAtom() (MMTK.Bonds.Bond method)
        otherBond() (MMTK.Bonds.BondAngle method)

        P

        PairDistanceSubspace (class in MMTK.Subspace)
        pairsWithinCutoff() (MMTK.Collections.PartitionedCollection method)
        ParallelepipedicPeriodicUniverse (class in MMTK.Universe)
        ParticleProperty (class in MMTK.ParticleProperties)
        ParticleScalar (class in MMTK.ParticleProperties)
        ParticleTensor (class in MMTK.ParticleProperties)
        ParticleTrajectory (class in MMTK.Trajectory)
        particleValues() (MMTK.Field.AtomicField method)
        ParticleVector (class in MMTK.ParticleProperties)
        PartitionedAtomCollection (class in MMTK.Collections)
        PartitionedCollection (class in MMTK.Collections)
        partitions() (MMTK.Collections.PartitionedCollection method)
        PDBConfiguration (class in MMTK.PDB)
        PDBFile (in module MMTK.PDB)
        PDBMolecule (class in MMTK.PDB)
        PDBMoleculeFactory (class in MMTK.PDBMoleculeFactory)
        PDBNucleotideChain (class in MMTK.PDB)
        PDBOutputFile (class in MMTK.PDB)
        PDBPeptideChain (class in MMTK.PDB)
        peptide_chain_constructor (MMTK.PDB.PDBConfiguration attribute)
        PeptideChain (class in MMTK.Proteins)
        perpendicularVector() (MMTK.Geometry.Line method)
        phiAngle() (MMTK.Proteins.Residue method)
        phiPsi() (MMTK.Proteins.PeptideChain method)
        (MMTK.Proteins.Protein method)
        (MMTK.Proteins.Residue method)
        Plane (class in MMTK.Geometry)
        position() (MMTK.ChemicalObjects.Atom method)
        (MMTK.Collections.GroupOfAtoms method)
        precedingResidue() (MMTK.Biopolymers.Residue method)
        projectionComplementOf() (MMTK.Subspace.Subspace method)
        projectionOf() (MMTK.Geometry.Line method)
        (MMTK.Subspace.Subspace method)
        Protein (class in MMTK.Proteins)
        psiAngle() (MMTK.Proteins.Residue method)

        R

        randomDirection() (in module MMTK.Random)
        randomDirections() (in module MMTK.Random)
        randomParticleVector() (in module MMTK.Random)
        randomPoint() (MMTK.Universe.Universe method)
        randomPointInBox() (in module MMTK.Random)
        randomPointInSphere() (in module MMTK.Random)
        randomRotation() (in module MMTK.Random)
        randomVelocity() (in module MMTK.Random)
        rawMode() (MMTK.NormalModes.BrownianModes.BrownianModes method)
        (MMTK.NormalModes.EnergeticModes.EnergeticModes method)
        (MMTK.NormalModes.VibrationalModes.VibrationalModes method)
        readParticleTrajectory() (MMTK.Trajectory.Trajectory method)
        readRigidBodyTrajectory() (MMTK.Trajectory.Trajectory method)
        realToBoxCoordinates() (MMTK.Universe.Universe method)
        reciprocalBasisVectors() (MMTK.Universe.Universe method)
        releaseConfigurationChangeLock() (MMTK.Universe.Universe method)
        releaseReadStateLock() (MMTK.Universe.Universe method)
        releaseWriteStateLock() (MMTK.Universe.Universe method)
        removeDistanceConstraints() (MMTK.ChemicalObjects.ChemicalObject method)
        (MMTK.Collections.Collection method)
        (MMTK.Universe.Universe method)
        removeObject() (MMTK.Collections.Collection method)
        (MMTK.Universe.Universe method)
        replaceResidue() (MMTK.Proteins.PeptideChain method)
        Residue (class in MMTK.Biopolymers)
        (class in MMTK.Proteins)
        ResidueChain (class in MMTK.Biopolymers)
        residues() (MMTK.Biopolymers.ResidueChain method)
        (MMTK.Proteins.Protein method)
        residuesOfType() (MMTK.Biopolymers.ResidueChain method)
        (MMTK.Proteins.Protein method)
        RestartTrajectoryOutput (class in MMTK.Trajectory)
        retrieveAsymmetricUnit() (MMTK.PDBMoleculeFactory.PDBMoleculeFactory method)
        retrieveMolecule() (MMTK.MoleculeFactory.MoleculeFactory method)
        retrieveMolecules() (MMTK.PDBMoleculeFactory.PDBMoleculeFactory method)
        retrieveUnitCell() (MMTK.PDBMoleculeFactory.PDBMoleculeFactory method)
        retrieveUniverse() (MMTK.PDBMoleculeFactory.PDBMoleculeFactory method)
        return_class (MMTK.ParticleProperties.ParticleProperty attribute)
        (MMTK.ParticleProperties.ParticleScalar attribute)
        (MMTK.ParticleProperties.ParticleTensor attribute)
        (MMTK.ParticleProperties.ParticleVector attribute)
        RhombicLattice (class in MMTK.Geometry)
        RigidBodyTrajectory (class in MMTK.Trajectory)
        RigidMotionSubspace (class in MMTK.Subspace)
        rmsDifference() (MMTK.Collections.GroupOfAtoms method)
        rotateAroundAxis() (MMTK.Collections.GroupOfAtoms method)
        rotateAroundCenter() (MMTK.Collections.GroupOfAtoms method)
        rotateAroundOrigin() (MMTK.Collections.GroupOfAtoms method)
        rotationalConstants() (MMTK.Collections.GroupOfAtoms method)
        RotationRemover (class in MMTK.Dynamics)

        S

        scaleBy() (MMTK.ParticleProperties.ParticleProperty method)
        scaleSize() (MMTK.Universe.OrthorhombicPeriodicUniverse method)
        (MMTK.Universe.ParallelepipedicPeriodicUniverse method)
        scaleVelocitiesToTemperature() (MMTK.Universe.Universe method)
        SCLattice (class in MMTK.Geometry)
        selectAtoms() (MMTK.ParticleProperties.ParticleProperty method)
        selectBox() (MMTK.Collections.Collection method)
        (MMTK.Universe.Universe method)
        selectShell() (MMTK.Collections.Collection method)
        (MMTK.Universe.Universe method)
        sequence() (MMTK.Biopolymers.ResidueChain method)
        (MMTK.Proteins.PeptideChain method)
        setBondConstraints() (MMTK.ChemicalObjects.ChemicalObject method)
        (MMTK.Collections.Collection method)
        (MMTK.Universe.Universe method)
        setConfiguration() (MMTK.Universe.Universe method)
        setForceField() (MMTK.Universe.Universe method)
        setFromTrajectory() (MMTK.Universe.Universe method)
        setMass() (MMTK.ChemicalObjects.Atom method)
        setPosition() (MMTK.ChemicalObjects.Atom method)
        setRigidBodyConstraints() (MMTK.ChemicalObjects.ChemicalObject method)
        setSize() (MMTK.Universe.CubicPeriodicUniverse method)
        setValue() (MMTK.InternalCoordinates.BondAngle method)
        (MMTK.InternalCoordinates.BondLength method)
        (MMTK.InternalCoordinates.DihedralAngle method)
        setVelocities() (MMTK.Universe.Universe method)
        shrinkUniverse() (in module MMTK.Solvation)
        sidechains() (MMTK.Proteins.PeptideChain method)
        (MMTK.Proteins.Protein method)
        (MMTK.Proteins.Residue method)
        SnapshotGenerator (class in MMTK.Trajectory)
        SPCEForceField (class in MMTK.ForceFields.SPCEFF)
        Sphere (class in MMTK.Geometry)
        StandardLogOutput (class in MMTK.Trajectory)
        staticStructureFactor() (MMTK.NormalModes.BrownianModes.BrownianModes method)
        SteepestDescentMinimizer (class in MMTK.Minimization)
        SubChain (class in MMTK.Proteins)
        Subclass
        Subspace (class in MMTK.Subspace)
        SubTrajectory (class in MMTK.Trajectory)
        SubVariable (class in MMTK.Trajectory)
        sumOverParticles() (MMTK.ParticleProperties.ParticleProperty method)
        superpositionFit() (in module MMTK.Geometry)
        surfaceAndVolume() (in module MMTK.MolecularSurface)
        surfaceAtoms() (in module MMTK.MolecularSurface)
        surfacePointsAndGradients() (in module MMTK.MolecularSurface)

        T

        temperature() (MMTK.Collections.GroupOfAtoms method)
        topLevelChemicalObject() (MMTK.ChemicalObjects.ChemicalObject method)
        TotalChargeConstraint (class in MMTK.ChargeFit)
        totalNorm() (MMTK.ParticleProperties.ParticleVector method)
        Trajectory (class in MMTK.Trajectory)
        TrajectoryAction (class in MMTK.Trajectory)
        TrajectoryGenerator (class in MMTK.Trajectory)
        trajectoryInfo() (in module MMTK.Trajectory)
        TrajectoryOutput (class in MMTK.Trajectory)
        TrajectorySet (class in MMTK.Trajectory)
        TrajectorySetVariable (class in MMTK.Trajectory)
        TrajectoryVariable (class in MMTK.Trajectory)
        translateBy() (MMTK.Collections.GroupOfAtoms method)
        (MMTK.Trajectory.ParticleTrajectory method)
        translateTo() (MMTK.ChemicalObjects.Atom method)
        (MMTK.Collections.GroupOfAtoms method)
        TranslationRemover (class in MMTK.Dynamics)

        U

        Universe (class in MMTK.Universe)
        universe() (MMTK.ChemicalObjects.ChemicalObject method)
        (MMTK.Collections.Collection method)
        (MMTK.Collections.GroupOfAtoms method)
        (MMTK.Universe.Universe method)

        V

        variables() (MMTK.Trajectory.Trajectory method)
        velocities() (MMTK.Universe.Universe method)
        VelocityScaler (class in MMTK.Dynamics)
        VelocityVerletIntegrator (class in MMTK.Dynamics)
        VibrationalMode (class in MMTK.NormalModes.VibrationalModes)
        VibrationalModes (class in MMTK.NormalModes.VibrationalModes)
        view() (in module MMTK.Visualization)
        (MMTK.Collections.GroupOfAtoms method)
        (MMTK.Field.AtomicField method)
        (MMTK.Trajectory.Trajectory method)
        Viewable (class in MMTK.Visualization)
        viewSequence() (in module MMTK.Visualization)
        viewTrajectory() (in module MMTK.Visualization)
        virialTest() (in module MMTK.ForceFields.ForceFieldTest)
        volume() (MMTK.Geometry.GeometricalObject3D method)

        W

        write() (MMTK.PDB.PDBOutputFile method)
        writeDCDPDB() (in module MMTK.DCD)
        writeToFile() (MMTK.Collections.GroupOfAtoms method)
        (MMTK.Field.AtomicField method)
        writeVelocityDCDPDB() (in module MMTK.DCD)

        X

        XMLMoleculeFactory (class in MMTK.XML)

        Z

        zero() (MMTK.ParticleProperties.ParticleProperty method)
        ZMatrix (class in MMTK.ConfigIO)

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        Glossary¶

        Abstract base class
        A Base Class that is not directly usable by itself, but which defines the common properties of several subclasses. Example: the class MMTK.ChemicalObjects.ChemicalObject is an abstract base class which defines the common properties of its subclasses MMTK.ChemicalObjects.Atom, MMTK.ChemicalObjects.Group, MMTK.ChemicalObjects.Molecule, MMTK.ChemicaObjects.Complex, and MMTK.ChemicalObjects.AtomCluster. A Mix-in class is a special kind of abstract base class.
        Base class
        A class from which another class inherits. In most cases, the inheriting class is a specialization of the base class. For example, the class MMTK.ChemicalObjects.Molecule is a base class of MMTK.Proteins.PeptideChain, because peptide chains are special molecules. Another common application is the Abstract base class.
        Mix-in class
        A class that is used as a Base class in other classes with the sole intention of providing methods that are common to these classes. Mix-in classes cannot be used to create instances. They are a special kind of Abstract base class. Example: class MMTK.Collections.GroupOfAtoms.
        Subclass
        A class that has another class as its Base class. The subclass is usually a specialization of the base class, and can use all of the methods defined in the base class. Example: class MMTK.Proteins.Residue is a subclass of MMTK.ChemicalObjects.Group.

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        MMTK User’s Guide¶

        for MMTK 2.7.7

        by Konrad Hinsen

        Introduction¶

        The Molecular Modelling Toolkit (MMTK) presents a new approach to molecular simulations. It is not a “simulation program” with a certain set of functions that can be used by writing more or less flexible “input files”, but a collection of library modules written in an easy-to-learn high-level programming language, Python. This approach offers three important advantages:

        • Application programs can use the full power of a general and well-designed programming language.
        • Application programs can profit from the large set of other libraries that are available for Python. These may be scientific or non-scientific; for example, it is very easy to write simulation or analysis programs with graphical user interfaces (using the module Tkinter in the Python standard library), or couple scientific calculations with a Web server.
        • Any user can provide useful additions in separate modules, whereas adding features to a monolithic program requires at least the cooperation of the original author.

        To further encourage collaborative code development, MMTK uses a very unrestrictive licensing policy. MMTK is free software, just like Python. Although MMTK is copyrighted, anyone is allowed to use it for any purpose, including commercial ones, as well as to modify and redistribute it (a more precise description is given in the copyright statement that comes with the code).

        This manual describes version 2.7.7 of MMTK. The 2.x versions contain some incompatible changes with respect to earlier versions (1.x), most importantly a package structure that reduces the risk of name conflicts with other Python packages, and facilitates future enhancements. There are also many new features and improvements to existing functions.

        Using MMTK requires a basic knowledge of object-oriented programming and Python. Newcomers to this subject should have a look at the introductory section in this manual and at the Python tutorial (which also comes with the Python interpreter). There are also numerous books on Python that are useful in getting started. Even without MMTK, Python is a very useful programming language for scientific use, allowing rapid development and testing and easy interfacing to code written in low-level languages such as Fortran or C.

        This manual consists of several introductory chapters and a module reference. The introductory chapters explain how common tasks are handled with MMTK, but they do not describe all of its features, nor do they contain a full documentation of functions or classes. This information can be found in the module -reference, which describes all classes and functions intended for end-user applications module by module, using documentation extracted directly from the source code. References from the introductory sections to the module reference facilitate finding the relevant documentation.

        Overview¶

        This chapter explains the basic structure of MMTK and its view of molecular systems. Every MMTK user should read it at least once.

        Using MMTK¶

        MMTK applications are ordinary Python programs, and can be written using any standard text editor. For interactive use it is recommended to use one of the many tools available for interactive Python programming.

        MMTK tries to be as user-friendly as possible for interactive use. For example, lengthy calculations can usually be interrupted by typing Control-C. This will result in an error message (“Keyboard Interrupt”), but you can simply go on typing other commands. Interruption is particularly useful for energy minimization and molecular dynamics: you can interrupt the calculation at any time, look at the current state or do some analysis, and then continue.

        Modules¶

        MMTK is a package consisting of various modules, most of them written in Python, and some in C for efficiency. The individual modules are described in the module -reference. The basic definitions that almost every application needs are collected in the top-level module, MMTK. The first line of most applications is therefore:

        from MMTK import *

        The definitions that are specific to particular applications reside in submodules within the package MMTK. For example, force fields are defined in MMTK.ForceFields, and peptide chain and protein objects are defined in MMTK.Proteins.

        Python provides two ways to access objects in modules and submodules. The first one is importing a module and referring to objects in it, e.g.:

        import MMTK
import MMTK.ForceFields
universe = MMTK.InfiniteUniverse(MMTK.ForceFields.Amber99ForceField())

        The second method is importing all or some objects from a module:

        from MMTK import InfiniteUniverse
from MMTK.ForceFields import Amber99ForceField
universe = InfiniteUniverse(Amber99ForceField())

        These two import styles can also be mixed according to convience. In order to prevent any confusion, all objects are referred to by their full names in this manual. The Amber force field object is thus called MMTK.ForceFields.Amber99ForceField. Of course the user is free to use selective imports in order to be able to use such objects with shorter names.

        Objects¶

        MMTK is an object-oriented system. Since objects are everywhere and everything is an object, it is useful to know the most important object types and what can be done with them. All object types in MMTK have meaningful names, so it is easy to identify them in practice. The following overview contains only those objects that a user will see directly. There are many more object types used by MMTK internally, and also some less common user objects that are not mentioned here.

        Chemical objects¶

        These are the objects that represent the parts of a molecular system:

        • atoms
        • groups
        • molecules
        • molecular complexes

        These objects form a simple hierarchy: complexes consist of molecules, molecules consist of groups and atoms, groups consist of smaller groups and atoms. All of these, except for groups, can be used directly to construct a molecular system. Groups can only be used in the definitions of other groups and molecules in the The chemical database.

        A number of operations can be performed on chemical objects, which can roughly be classified into inquiry (constituent atoms, bonds, center of mass etc.) and modification (translate, rotate).

        There are also specialized versions of some of these objects. For example, MMTK defines proteins as special complexes, consisting of peptide chains, which are special molecules. They offer a range of special operations (such as selecting residues or constructing the positions of missing hydrogen atoms) that do not make sense for molecules in general.

        Collections¶

        Collection objects represent arbitrary collections of chemical objects. They are used to be able to refer to collections as single entities. For example, you might want to call all water molecules collectively “solvent”. Most of the operations on chemical objects are also available for collections.

        Force fields¶

        Force field objects represent a precise description of force fields, i.e. a complete recipe for calculating the potential energy (and its derivatives) for a given molecular system. In other words, they specify not only the functional form of the various interactions, but also all parameters and the prescriptions for applying these parameters to an actual molecular system.

        Universes¶

        Universes define complete molecular systems, i.e. they contain chemical objects. In addition, they describe interactions within the system (by a force field), boundary conditions, external fields, etc. Many of the operations that can be used on chemical objects can also be applied to complete universes.

        Minimizers and integrators¶

        A minimizer object is a special “machine” that can find local minima in the potential energy surface of a universe. You may consider this a function, if you wish, but of course functions are just special objects. Similarly, an integrator is a special “machine” that can determine a dynamical trajectory for a system on a given potential energy surface.

        Trajectories¶

        Minimizers and integrators can produce trajectories, which are special files containing a sequence of configurations and/or other related information. Of course trajectory objects can also be read for analysis.

        Variables¶

        Variable objects (not to be confused with standard Python variables) describe quantities that have a value for each atom in a system, for example positions, masses, or energy gradients. Their most common use is for storing various configurations of a system.

        Normal modes¶

        Normal mode objects contain normal mode frequencies and atomic displacements for a given universe. MMTK provides three kinds of normal modes which correspond to three different physical situations:

        • Energetic modes represent the principal axes of the potential energy surface. They each have a force constant that is smallest for the most collective motions and highest for the most localized motions. Energetic modes are appropriate for describing the potential energy surface without reference to any particular dynamics, e.g. in flexibility analysis or Monte-Carlo sampling.
        • Vibrational modes represent the vibrational motions of a system that have well-defined frequencies. Their use implies that the system follows Newtonian dynamics or quantum dynamics. This is appropriate for small molecules, and for the most localized motions of macromolecules.
        • Brownian modes represent diffusional motions of an overdamped system. They describe systems with Brownian dynamics and are appropriate for describing the slow motions of macromolecules.

        Non-MMTK objects¶

        An MMTK application program will typically also make use of objects provided by Python or Python library modules. A particularly useful library is the package Scientific, which is also used by MMTK itself. The most important objects are

        • numbers (integers, real number, complex numbers), provided by Python
        • vectors (in 3D coordinate space) provided by the module Scientific.Geometry.
        • character strings, provided by Python
        • files, provided by Python

        Of course MMTK applications can make use of the Python standard library or any other Python modules. For example, it is possible to write a simulation program that provides status reports via an integrated Web server, using the Python standard module SimpleHTTPServer.

        The chemical database¶

        For defining the chemical objects described above, MMTK uses a database of descriptions. There is a database for atoms, one for groups, etc. When you ask MMTK to make a specific chemical object, for example a water molecule, MMTK looks for the definition of water in the molecule database. A database entry contains everything there is to know about the object it defines: its constituents and their names, configurations, other names used e.g. for I/O, and all information force fields might need about the objects.

        MMTK comes with database entries for many common objects (water, amino acids, etc.). For other objects you will have to write the definitions yourself. as described in the section Constructing the database.

        Force fields¶

        MMTK contains everything necessary to use the Amber 99 force field on proteins, DNA, and water molecules. It uses the standard Amber parameter and modification file format. In addition to the Amber force field, there is a simple Lennard-Jones force field for noble gases, and a deformation force field for normal mode calculations on large proteins.

        MMTK was designed to make the addition of force field terms and the implementation of other force fields as easy as possible. Force field terms can be defined in Python (for ease of implementation) or in Cython, C, or Fortran (for efficiency). This is described in the developer’s guide.

        Units¶

        Since MMTK is not a black-box program, but a modular library, it is essential for it to use a consistent unit system in which, for example, the inverse of a frequency is a time, and the product of a mass and the square of a velocity is an energy, without additional conversion factors. Black-box programs can (and usually do) use a consistent unit system internally and convert to “conventional” units for input and output.

        The unit system of MMTK consists mostly of SI units of appropriate magnitude for molecular systems:

        Measurement Units
        Length nm
        Time ps
        Mass amu (g/mol)
        Energy kJ/mol
        Frequency THz (1/ps)
        Temperature K
        Charge e

        The module MMTK.Units contains convenient conversion constants for the units commonly used in computational chemistry. For example, a length of 2 Ångström can be written as 2*Units.Ang, and a frequency can be printed in wavenumbers with print frequency/Units.invcm.

        A simple example¶

        The following simple example shows how a typical MMTK application might look like. It constructs a system consisting of a single water molecule and runs a short molecular dynamics trajectory. There are many alternative ways to do this; this particular one was chosen because it makes each step explicit and clear. The individual steps are explained in the remaining chapters of the manual.

        # Import the necessary MMTK definitions.
from MMTK import *
from MMTK.ForceFields import Amber99ForceField
from MMTK.Trajectory import Trajectory, TrajectoryOutput, StandardLogOutput
from MMTK.Dynamics import VelocityVerletIntegrator
# Create an infinite universe (i.e. no boundaries, non-periodic).
universe = InfiniteUniverse(Amber99ForceField())
# Create a water molecule in the universe.
# Water is defined in the database.
universe.molecule = Molecule('water')
# Generate random velocities.
universe.initializeVelocitiesToTemperature(300*Units.K)
# Create an integrator.
integrator = VelocityVerletIntegrator(universe)
# Generate a trajectory
trajectory = Trajectory(universe, "water.nc", "w")
# Run the integrator for 50 steps of 1 fs, printing time and energy
# every fifth step and writing time, energy, temperature, and the positions
# of all atoms to the trajectory at each step.
t_actions = [StandardLogOutput(5), \
             TrajectoryOutput("time", "energy", \
             "thermodynamic", "configuration"), 0, None, 1]
integrator(delta_t = 1.*Units.fs, steps = 50, actions = t_actions)
# Close the trajectory
trajectory.close()

        Constructing a molecular system¶

        The construction of a complete system for simulation or analysis involves some or all of the following operations:

        • Creating molecules and other chemical objects.
        • Defining the configuration of all objects.
        • Defining the “surroundings” (e.g. boundary conditions).
        • Choosing a force field.

        MMTK offers a large range of functions to deal with these tasks.

        Creating chemical objects¶

        Chemical objects (atoms, molecules, complexes) are created from definitions in the Constructing the database. Since these definitions contain most of the necessary information, the subsequent creation of the objects is a simple procedure.

        All objects are created by their class name (MMTK.ChemicalObjects.Atom, MMTK.ChemicalObjects.Molecule, and MMTK.ChemicalObjects.Complex) with the name of the definition file as first parameter. Additional optional parameters can be specified to modify the object being created. The following optional parameters can be used for all object types:

        • name=string Specifies a name for the object. The default name is the one given in the definition file.
        • position=vector Specifies the position of the center of mass. The default is the origin.
        • configuration=string Indicates a configuration from the configuration dictionary in the definition file. The default is ‘default’ if such an entry exists in the configuration dictionary. Otherwise the object is created without atomic positions.

        Some examples with additional explanations for specific types:

        • Atom('C') creates a carbon atom.
        • Molecule('water', position=Vector(0.,0.,1.)) creates a water molecule using configuration ‘default’ and moves the center of mass to the indicated position.

        Proteins, peptide chains, and nucleotide chains¶

        MMTK contains special support for working with proteins, peptide chains, and nucleotide chains. As described in the chapter Constructing the database, proteins can be described by a special database definition file. However, it is often simpler to create macromolecular objects directly in an application program. The classes are MMTK.Proteins.PeptideChain, MMTK.Proteins.Protein, and MMTK.NucleicAcids.NucleotideChain.

        Proteins can be created from definition files in the database, from previously constructed peptide chain objects, or directly from PDB files if no special manipulations are necessary.

        Examples:

        • Protein(‘insulin’) creates a protein object for insulin from a database file.
        • Protein(‘1mbd.pdb’) creates a protein object for myoglobin directly from a PDB file, but leaving out the heme group, which is not a peptide chain.

        Peptide chains are created from a sequence of residues, which can be a MMTK.PDB.PDBPeptideChain object, a list of three-letter residue codes, or a string containing one-letter residue codes. In the last two cases the atomic positions are not defined. MMTK provides several models for the residues which provide different levels of detail: an all-atom model, a model without hydrogen atoms, two models containing only polar hydrogens (using different definitions of polar hydrogens), and a model containing only the Cα atoms, with each Cα atom having the mass of the entire residue. The last model is useful for conformational analyses in which only the backbone conformations are important.

        The construction of nucleotide chains is very similar. The residue list can be either a MMTK.PDB.PDBNucleotideChain object or a list of two-letter residue names. The first letter of a residue name indicates the sugar type (‘R’ for ribose and’D’ for desoxyribose), and the second letter defines the base (‘A’, ‘C’, and’G’, plus ‘T’ for DNA and’U’ for RNA). The models are the same as for peptide chains, except that the Cα model does not exist.

        Most frequently proteins and nucleotide chains are created from a PDB file. The PDB files often contain solvent (water) as well, and perhaps some other molecules. MMTK provides convenient functions for extracting information from PDB files and for building molecules from them in the module MMTK.PDB. The first step is the creation of a MMTK.PDB.PDBConfiguration object from the PDB file:

        from MMTK.PDB import PDBConfiguration
configuration = PDBConfiguration('some_file.pdb')

        The easiest way to generate MMTK objects for all molecules in the PDB file is then

        molecules = configuration.createAll()

        The result is a collection of molecules, peptide chains, and nucleotide chains, depending on the contents of the PDB files. There are also methods for modifying the PDBConfiguration before creating MMTK objects from it, and for creating objects selectively. See the documentation for the modules MMTK.PDB and Scientific.IO.PDB for details, as well as the Proteins and DNA examples.

        Lattices¶

        Sometimes it is necessary to generate objects (atoms or molecules) positioned on a lattice. To facilitate this task, MMTK defines lattice objects which are essentially sequence objects containing points or objects at points. Lattices can therefore be used like lists with indexing and for-loops. The lattice classes are MMTK.Geometry.RhombicLattice, MMTK.Geometry.BravaisLattice, and MMTK.Geometry.SCLattice.

        Random numbers¶

        The Python standard library and the Numerical Python package provide random number generators, and more are available in seperate packages. MMTK provides some convenience functions that return more specialized random quantities: random points in a universe, random velocities, random particle displacement vectors, random orientations. These functions are defined in module MMTK.Random.

        Collections¶

        Often it is useful to treat a collection of several objects as a single entity. Examples are a large number of solvent molecules surrounding a solute, or all sidechains of a protein. MMTK has special collection objects for this purpose, defined as MMTK.Collections.Collection. Most of the methods available for molecules can also be used on collections.

        A variant of a collection is the partitioned collection, implemented in class MMTK.Collections.PartitionedCollection. This class acts much like a standard collection, but groups its elements by geometrical position in small sub-boxes. As a consequence, some geometrical algorithms (e.g. pair search within a cutoff) are much faster, but other operations become somewhat slower.

        Creating universes¶

        A universe describes a complete molecular system consisting of any number of chemical objects and a specification of their interactions (i.e. a force field) and surroundings: boundary conditions, external fields, thermostats, etc. The universe classes are defined in module MMTK:

        Universes are created empty; the contents are then added to them. Three types of objects can be added to a universe: chemical objects (atoms, molecules, etc.), collections, and environment objects (thermostats etc.). It is also possible to remove objects from a universe.

        Force fields¶

        MMTK comes with several force fields, and permits the definition of additional force fields. Force fields are defined in module MMTK.ForceFields.ForceField. The most important built-in force field is the Amber 99 force field, represented by the class MMTK.ForceFields.Amber.AmberForceField.Amber99ForceField. It offers several strategies for electrostatic interactions, including Ewald summation and cutoff with charge neutralization and optional screening [Wolf1999]..

        In addition to the Amber 99 force field, there is the older Amber 94 forcefield, a Lennard-Jones force field for noble gases (Class MMTK.ForceFields.LennardJonesFF) and an Elastic Network Model force field for protein normal mode calculations (MMTK.ForceFields.DeformationFF.CalphaForceField).

        Referring to objects and parts of objects¶

        Most MMTK objects (in fact all except for atoms) have a hierarchical structure of parts of which they consist. For many operations it is necessary to access specific parts in this hierarchy.

        In most cases, parts are attributes with a specific name. For example, the oxygen atom in every water molecule is an attribute with the name “O”. Therefore if w refers to a water molecule, then w.O refers to its oxygen atom. For a more complicated example, if m refers to a molecule that has a methyl group called “M1”, then m.M1.C refers to the carbon atom of that methyl group. The names of attributes are defined in the database.

        Some objects consist of parts that need not have unique names, for example the elements of a collection, the residues in a peptide chain, or the chains in a protein. Such parts are accessed by indices; the objects that contain them are Python sequence types. Some examples:

        • Asking for the number of items: if c refers to a collection, then len(c) is the number of its elements.
        • Extracting an item: if p refers to a protein, then p[0] is its first peptide chain.
        • Iterating over items: if p refers to a peptide chain, then for residue in p: print residue.position() will print the center of mass positions of all its residues.

        Peptide and nucleotide chains also allow the operation of slicing: if p refers to a peptide chain, then p[1:-1] is a subchain extending from the second to the next-to-last residue.

        The structure of peptide and nucleotide chains¶

        Since peptide and nucleotide chains are not constructed from an explicit definition file in the database, it is not evident where their hierarchical structure comes from. But it is only the top-level structure that is treated in a special way. The constituents of peptide and nucleotide chains, residues, are normal group objects. The definition files for these group objects are in the MMTK standard database and can be freely inspected and even modified or overriden by an entry in a database that is listed earlier in MMTKDATABASE.

        Peptide chains are made up of amino acid residues, each of which is a group consisting of two other groups, one being called “peptide” and the other “sidechain”. The first group contains the peptide group and the C and H atoms; everything else is contained in the sidechain. The C atom of the fifth residue of peptide chain p is therefore referred to as p[4].peptide.C_alpha.

        Nucleotide chains are made up of nucleotide residues, each of which is a group consisting of two or three other groups. One group is called “sugar” and is either a ribose or a desoxyribose group, the second one is called “base” and is one the five standard bases. All but the first residue in a nucleotide chain also have a subgroup called “phosphate” describing the phosphate group that links neighbouring residues.

        Analyzing and modifying atom properties¶

        General operations¶

        Many inquiry and modification operations act at the atom level and can equally well be applied to any object that is made up of atoms, i.e. atoms, molecules, collections, universes, etc. These operations are defined once in a Mix-in class called MMTK.Collections.GroupOfAtoms, but are available for all objects for which they make sense. They include inquiry-type functions (total mass, center of mass, moment of inertia, bounding box, total kinetic energy etc.), coordinate modifications (translation, rotation, application of Coordinate transformations) and coordinate comparisons (RMS difference, optimal fits).

        Coordinate transformations¶

        The most common coordinate manipulations involve translations and rotations of specific parts of a system. It is often useful to refer to such an operation by a special kind of object, which permits the combination and analysis of transformations as well as its application to atomic positions.

        Transformation objects specify a general displacement consisting of a rotation around the origin of the coordinate system followed by a translation. They are defined in the module Scientific.Geometry, but for convenience the module MMTK contains a reference to them as well. Transformation objects corresponding to pure translations can be created with Translation(displacement); transformation objects describing pure rotations with Rotation(axis, angle) or Rotation(rotation_matrix). Multiplication of transformation objects returns a composite transformation.

        The translational component of any transformation can be obtained by calling the method translation(); the rotational component is obtained analogously with rotation(). The displacement vector for a pure translation can be extracted with the method displacement(), a tuple of axis and angle can be extracted from a pure rotation by calling axisAndAngle().

        Atomic property objects¶

        Many properties in a molecular system are defined for each individual atom: position, velocity, mass, etc. Such properties are represented in special objects, defined in module MMTK: MMTK.ParticleProperties.ParticleScalar for scalar quantities, MMTK.ParticleProperties.ParticleVector for vector quantities, and MMTK.ParticleProperties.ParticleTensor for rank-2 tensors. All these objects can be indexed with an atom object to retrieve or change the corresponding value. Standard arithmetic operations are also defined, as well as some useful methods.

        Configurations¶

        A configuration object, represented by the class MMTK.ParticleProperties.Configuration is a special variant of a MMTK.ParticleProperties.ParticleVector object. In addition to the atomic coordinates of a universe, it stores geometric parameters of a universe that are subject to change, e.g. the edge lengths of the elementary cell of a periodic universe. Every universe has a current configuration, which is what all operations act on by default. It is also the configuration that is updated by minimizations, molecular dynamics, etc. The current configuration can be obtained by calling the method configuration().

        There are two ways to create configuration objects: by making a copy of the current configuration (with universe.copyConfiguration(), or by reading a configuration from a trajectory.

        Minimization and Molecular Dynamics¶

        Trajectories¶

        Minimization and dynamics algorithms produce sequences of configurations that are often stored for later analysis. In fact, they are often the most valuable result of a lengthy simulation run. To make sure that the use of trajectory files is not limited by machine compatibility, MMTK stores trajectories in netCDF files. These files contain binary data, minimizing disk space usage, but are freely interchangeable between different machines. In addition, there are a number of programs that can perform standard operations on arbitrary netCDF files, and which can therefore be used directly on MMTK trajectory files. Finally, netCDF files are self-describing, i.e. contain all the information needed to interpret their contents. An MMTK trajectory file can thus be inspected and processed without requiring any further information.

        For illustrations of trajectory operations, see the examples.

        Trajectory file objects are represented by the class MMTK.Trajectory.Trajectory. They can be opened for reading, writing, or modification. The data in trajectory files can be stored in single precision or double precision; single-precision is usually sufficient, but double-precision files are required to reproduce a given state of the system exactly.

        A trajectory is closed by calling the method close(). If anything has been written to a trajectory, closing it is required to guarantee that all data has been written to the file. Closing a trajectory after reading is recommended in order to prevent memory leakage, but is not strictly required.

        Newly created trajectories can contain all objects in a universe or any subset; this is useful for limiting the amount of disk space occupied by the file by not storing uninteresting parts of the system, e.g. the solvent surrounding a protein. It is even possible to create a trajectory for a subset of the atoms in a molecule, e.g. for only the Cα atoms of a protein. The universe description that is stored in the trajectory file contains all chemical objects of which at least one atom is represented.

        When a trajectory is opened for reading, no universe object needs to be specified. In that case, MMTK creates a universe from the description contained in the trajectory file. This universe will contain the same objects as the one for which the trajectory file was created, but not necessarily have all the properties of the original universe (the description contains only the names and types of the objects in the universe, but not, for example, the force field). The universe can be accessed via the attribute universe of the trajectory.

        If the trajectory was created with partial data for some of the objects, reading data from it will set the data for the missing parts to “undefined”. Analysis operations on such systems must be done very carefully. In most cases, the trajectory data will contain the atomic configurations, and in that case the “defined” atoms can be extracted with the method atomsWithDefinedPositions().

        MMTK trajectory files can store various data: atomic positions, velocities, energies, energy gradients etc. Each trajectory-producing algorithm offers a set of quantities from which the user can choose what to put into the trajectory. Since a detailed selection would be tedious, the data is divided into classes, e.g. the class “energy” stands for potential energy, kinetic energy, and whatever other energy-related quantities an algorithm produces.

        For optimizing I/O efficiency, the data layout in a trajectory file can be modified by the block_size parameter. Small block sizes favour reading or writing all data for one time step, whereas large block sizes (up to the number of steps in the trajectory) favour accessing a few values for all time steps, e.g. scalar variables like energies or trajectories for individual atoms. The default value of the block size is one.

        Every trajectory file contains a history of its creation. The creation of the file is logged with time and date, as well as each operation that adds data to it with parameters and the time/date of start and end. This information, together with the comment and the number of atoms and steps contained in the file, can be obtained with the function MMTK.Trajectory.trajectoryInfo().

        It is possible to read data from a trajectory file that is being written to by another process. For efficiency, trajectory data is not written to the file at every time step, but only approximately every 15 minutes. Therefore the amount of data available for reading may be somewhat less than what has been produced already.

        Options for minimization and dynamics¶

        Minimizers and dynamics integrators accept various optional parameter specifications. All of them are selected by keywords, have reasonable default values, and can be specified when the minimizer or integrator is created or when it is called. In addition to parameters that are specific to each algorithm, there is a general parameter actions that specifies actions that are executed periodically, including trajectory and console output.

        Periodic actions¶

        Periodic actions are specified by the keyword parameter actions whose value is a list of periodic actions, which defaults to an empty list. Some of these actions are applicable to any trajectory-generating algorithm, especially the output actions. Others make sense only for specific algorithms or specific universes, e.g. the periodic rescaling of velocities during a Molecular Dynamics simulation.

        Each action is described by an action object. The step numbers for which an action is executed are specified by three parameters. The parameter first indicates the number of the first step for which the action is executed, and defaults to 0. The parameter last indicates the last step for which the action is executed, and default to None, meaning that the action is executed indefinitely. The parameter skip speficies how many steps are skipped between two executions of the action. The default value of 1 means that the action is executed at each step. Of course an action object may have additional parameters that are specific to its action.

        The output actions are defined in the module MMTK.Trajectory and can be used with any trajectory-generating algorithm. They are:

        • MMTK.Trajectory.TrajectoryOutput for writing data to a trajectory. Note that it is possible to use several trajectory output actions simultaneously to write to multiple trajectories. It is thus possible, for example, to write a short dense trajectory during a dynamics run for analyzing short-time dynamics, and simultaneously a long-time trajectory with a larger step spacing, for analyzing long-time dynamics.
        • MMTK.Trajectory.RestartTrajectoryOutput, which is a specialized version of MMTK.Trajectory.TrajectoryOutput. It writes the data that the algorithm needs in order to be restarted to a restart trajectory file. A restart trajectory is a trajectory that stores a fixed number of steps which are reused cyclically, such that it always contain the last few steps of a trajectory.
        • MMTK.Trajectory.LogOutput for text output of data to a file.
        • MMTK.Trajectory.StandardLogOutput, a specialized version of MMTK.Trajectory.LogOutput that writes the data classes “time” and “energy” during the whole simulation run to standard output.

        The other periodic actions are meaningful only for Molecular Dynamics simulations:

        • MMTK.Dynamics.VelocityScaler is used for rescaling the velocities to force the kinetic energy to the value defined by some temperature. This is usually done during initial equilibration.
        • MMTK.Dynamics.BarostatReset resets the barostat coordinate to zero and is during initial equilibration of systems in the NPT ensemble.
        • MMTK.Dynamics.Heater rescales the velocities like MMTK.Dynamics.VelocityScaler, but increases the temperature step by step.
        • MMTK.Dynamics.TranslationRemover subtracts the global translational velocity of the system from all individual atomic velocities. This prevents a slow but systematic energy flow into the degrees of freedom of global translation, which occurs with most MD integrators due to non-perfect conservation of momentum.
        • MMTK.Dynamics.RotationRemover subtracts the global angular velocity of the system from all individual atomic velocities. This prevents a slow but systematic energy flow into the degrees of freedom of global rotation, which occurs with most MD integrators due to non-perfect conservation of angular momentum.

        Fixed atoms¶

        During the course of a minimization or molecular dynamics algorithm, the atoms move to different positions. It is possible to exclude specific atoms from this movement, i.e. fixing them at their initial positions. This has no influence whatsoever on energy or force calculations; the only effect is that the atoms’ positions never change. Fixed atoms are specified by giving them an attribute fixed with a value of one. Atoms that do not have an attributefixed, or one with a value of zero, move according to the selected algorithm.

        Energy minimization¶

        MMTK has two energy minimizers using different algorithms: steepest descent (MMTK.Minimization.SteepestDescentMinimizer) and conjugate gradient (MMTK.Minimization.ConjugateGradientMinimizer) . Steepest descent minimization is very inefficient if the goal is to find a local minimum of the potential energy. However, it has the advantage of always moving towards the minimum that is closest to the starting point and is therefore ideal for removing bad contacts in a unreasonably high energy configuration. For finding local minima, the conjugate gradient algorithm should be used.

        Both minimizers accept three specific optional parameters:

        • steps (an integer) to specify the maximum number of steps (default is 100)
        • step_size (a number) to specify an initial step length used in the search for a minimum (default is 2 pm)
        • convergence (a number) to specify the gradient norm (more precisely the root-mean-square length) at which the minimization should stop (default is 0.01 kJ/mol/nm)

        There are three classes of trajectory data: “energy” includes the potential energy and the norm of its gradient, “configuration” stands for the atomic positions, and “gradients” stands for the energy gradients at each atom position.

        The following example performs 100 steps of steepest descent minimization without producing any trajectory or printed output:

        from MMTK import *
from MMTK.ForceFields import Amber99ForceField
from MMTK.Minimization import SteepestDescentMinimizer
universe = InfiniteUniverse(Amber99ForceField())
universe.protein = Protein('insulin')
minimizer = SteepestDescentMinimizer(universe)
minimizer(steps = 100)

        See also the example file modes.py.

        Molecular dynamics¶

        The techniques described in this section are illustrated by several examples.

        Velocities¶

        The integration of the classical equations of motion for an atomic system requires not only positions, but also velocities for all atoms. Usually the velocities are initialized to random values drawn from a normal distribution with a variance corresponding to a certain temperature. This is done by calling the method initializeVelocitiesToTemperature() on a universe. Note that the velocities are assigned atom by atom; no attempt is made to remove global translation or rotation of the total system or any part of the system.

        During equilibration of a system, it is common to multiply all velocities by a common factor to restore the intended temperature. This can done explicitly by calling the method scaleVelocitiesToTemperature() on a universe, or by using the action object MMTK.Dynamics.VelocityScaler.

        Distance constraints¶

        A common technique to eliminate the fastest (usually uninteresting) degrees of freedom, permitting a larger integration time step, is the use of distance constraints on some or all chemical bonds. MMTK allows the use of distance constraints on any pair of atoms, even though constraining anything but chemical bonds is not recommended due to considerable modifications of the dynamics of the system [vanGunsteren1982], [Hinsen1995].

        MMTK permits the definition of distance constraints on all atom pairs in an object that are connected by a chemical bond by calling the method setBondConstraints. Usually this is called for a complete universe, but it can also be called for a chemical object or a collection of chemical objects. The removeDistanceConstraints() removes all distance constraints from the object for which it is called.

        Constraints defined as described above are automatically taken into account by Molecular Dynamics integrators. It is also possible to enforce the constraints explicitly by calling the method enforceConstraints() for a universe. This has the effect of modifying the configuration and the velocities (if velocities exist) in order to make them compatible with the constraints.

        Thermostats and barostats¶

        A standard Molecular Dynamics integration allows time averages corresponding to the NVE ensemble, in which the number of molecules, the system volume, and the total energy are constant. This ensemble does not represent typical experimental conditions very well. Alternative ensembles are the NVT ensemble, in which the temperature is kept constant by a thermostat, and the NPT ensemble, in which temperature and pressure are kept constant by a thermostat and a barostat. To obtain these ensembles in MMTK, thermostat and barostat objects must be added to a universe. In the presence of these objects, the Molecular Dynamics integrator will use the extended-systems method for producing the correct ensemble. The classes to be used are MMTK.Environment.NoseThermostat and MMTK.Environment.AndersenBarostat.

        Integration¶

        A Molecular Dynamics integrator based on the “Velocity Verlet” algorithm [Swope1982], which was extended to handle distance constraints as well as thermostats and barostats [Kneller1996], is implemented by the class MMTK.Dynamics.VelocityVerletIntegrator. It has two optional keyword parameters:

        • steps (an integer) to specify the number of steps (default is 100)
        • delta_t (a number) to specify the time step (default 1 fs)

        There are three classes of trajectory data: “energy” includes the potential energy and the kinetic energy, as well as the energies of thermostat and barostat coordinates if they exist, “time” stands for the time, “thermodynamic” stand for temperature and pressure, “configuration” stands for the atomic positions, “velocities” stands for the atomic velocities, and “gradients” stands for the energy gradients at each atom position.

        The following example performs a 1000 step dynamics integration, storing every 10th step in a trajectory file and removing the total translation and rotation every 50th step:

        from MMTK import *
from MMTK.ForceFields import Amber99ForceField
from MMTK.Dynamics import VelocityVerletIntegrator, \
                          TranslationRemover, \
                          RotationRemover
from MMTK.Trajectory import TrajectoryOutput
universe = InfiniteUniverse(Amber99ForceField())
universe.protein = Protein('insulin')
universe.initializeVelocitiesToTemperature(300.*Units.K)
actions = [TranslationRemover(0, None, 50), \
           RotationRemover(0, None, 50), \
           TrajectoryOutput("insulin.nc", \
           ("configuration", "energy", "time"), \
           0, None, 10)]
integrator = VelocityVerletIntegrator(universe, delta_t = 1.*Units.fs, \
                                      actions = actions)
integrator(steps = 1000)

        Snapshots¶

        A snapshot generator allows writing the current system state to a trajectory. It works much like a zero-step minimization or dynamics run, i.e. it takes the same optional arguments for specifying the trajectory and protocol output. A snapshot generator is created using the class MMTK.Trajectory.SnapshotGenerator.

        Normal modes¶

        Normal mode analysis provides an analytic description of the dynamics of a system near a minimum using an harmonic approximation to the potential. Before a normal mode analysis can be started, the system must be brought to a local minimum of the potential energy by energy minimization, except when special force fields designed only for normal mode analysis are used (e.g. MMTK.ForceFields.CalphaFF.CalphaForceField). See also the Normal Modes examples.

        A standard normal mode analysis is performed by creating a normal modes object, implemented in the classes MMTK.NormalModes.EnergeticModes.EnergeticModes, MMTK.NormalModes.VibrationalModes.VibrationalModes, and MMTK.NormalModes.BrownianModes.BrownianModes. A normal mode object behaves like a sequence of mode objects, which store the atomic displacement vectors corresponding to each mode and the associated force constant, vibrational frequency, or inverse relaxation time.

        For short-ranged potentials, it is advantageous to store the second derivatives of the potential in a sparse-matrix form and to use an iterative method to determine some or all modes. This permits the treatment of larger systems that would normally require huge amounts of memory.

        Another approach to deal with large systems is the restriction to low-frequency modes which are supposed to be well representable by linear combinations of a given set of basis vectors. The basis vectors can be obtained from a basis for the full Cartesian space by elimination of known fast degrees of freedom (e.g. bonds); the module MMTK.Subspace contains support classes for this approach. It is also possible to construct a suitable basis vector set from small-deformation vector fields (e.g. MMTK.FourierBasis.FourierBasis).

        Analysis operations¶

        Analysis is the most non-standard part of molecular simulations. The quantities that must be calculated depend strongly on the system and the problem under study. MMTK provides a wide range of elementary operations that inquire the state of the system, as well as several more complex analysis tools. Some of them are demonstrated in the example section.

        Properties of chemical objects and universes¶

        Many operations access and modify various properties of an object. They are defined for the most general type of object: anything that can be broken down to atoms, i.e. atoms, molecules, collections, universes, etc., i.e. in the class MMTK.Collections.GroupOfAtoms.

        The most elementary operations are inquiries about specific properties of an object: number of atoms, total mass, center of mass, total momentum, total charge, etc. There are also operations that compare two different conformations of a system. Finally, there are special operations for analyzing conformations of peptide chains and proteins.

        Geometrical operations in periodic universes require special care. Whenever a distance vector between two points in a systems is evaluated, the minimum-image convention must be used in order to obtain consistent results. MMTK provides routines for finding these distance vectors as well as distances, angles, and dihedral angles between any points. Because these operations depend on the topology and geometry of the universe, they are implemented as methods in class MMTK.Universe.Universe and its subclasses. Of course they are available for non-periodic universes as well.

        Universes also provide methods for obtaining atom property objects that describe the state of the system (configurations, velocities, masses), and for restoring the system state from a trajectory file.

        Energy evaluation¶

        Energy evaluation requires a force field, and therefore all the methods in this section are defined only for universe objects, i.e. in class MMTK.Universe.Universe. However, they all take an optional arguments (anything that can be broken down into atoms) that indicates for which subset of the universe the energy is to be evaluated. In addition to the potential energy, energy gradients and second derivatives (force constants) can be obtained, if the force field implements them. There is also a method that returns a dictionary containing the values for all the individual force field terms, which is often useful for analysis.

        Surfaces and volumes¶

        Surfaces and volumes can be analyzed for anything consisting of atoms. Both quantities are defined by assigning a radius to each atom; the surface of the resulting conglomerate of overlapping spheres is taken to be the surface of the atom group. Atom radii for surface determination are usually called “van der Waals radii”, but there is no unique method for determining them. MMTK uses the values from [Bondi1964]. However, users can change these values for each individual atom by assigning a new value to the attribute vdW_radius.

        The operations provided in MMTK.MolecularSurface include basic surface and volume calculation, determination of exposed atoms, and identification of contacts between two objects.

        Miscellaneous operations¶

        Saving, loading, and copying objects¶

        MMTK provides an easy way to store (almost) arbitrary objects in files and retrieve them later. All objects of interest to users can be stored, including chemical objects, collections, universes, normal modes, configurations, etc. It is also possible to store standard Python objects such as numbers, lists, dictionaries etc., as well as practically any user-defined objects. Storage is based on the standard Python module pickle.

        Objects are saved with MMTK.save() and restored with MMTK.load(). If several objects are to be stored in a single file, use tuples: save((object1, object2), filename) and object1, object2 = load(filename) to retrieve the objects.

        Note that storing an object in a file implies storing all objects referenced by it as well, such that the size of the file can become larger than expected. For example, a configuration object contains a reference to the universe for which it is defined. Therefore storing a configuration object means storing the whole universe as well. However, nothing is ever written twice to the same file. If you store a list or a tuple containing a universe and a configuration for it, the universe is written only once.

        Frequently it is also useful to copy an object, such as a molecule or a configuration. There are two functions (which are actually taken from the Python standard library module copy) for this purpose, which have a somewhat different behaviour for container-type objects (lists, dictionaries, collections etc.). MMTK.copy() returns a copy of the given object. For a container object, it returns a new container object which contains the same objects as the original one. If the intention is to get a container object which contains copies of the original contents, then MMTK.deepcopy(object) should be used. For objects that are not container-type objects, there is no difference between the two functions.

        Exporting to specific file formats and visualization¶

        MMTK can write objects in specific file formats that can be used by other programs. Three file formats are supported: the PDB format, widely used in computational chemistry, the DCD format for trajectories, written by the programs CHARMM, X-Plor, and NAMDm, and read by many visualization programs, and the VRML format, understood by VRML browsers as a representation of a three-dimensional scene for visualization. MMTK also provides a more general interface that can generate graphics objects in any representation if a special module for that representation exists. In addition to facilitating the implementation of new graphics file formats, this approach also permits the addition of custom graphics elements (lines, arrows, spheres, etc.) to molecular representations.

        PDB, VRML, and DCD files¶

        Any chemical object, collection, or universe can be written to a PDB or VRML file by calling the method writeToFile, defined in class MMTK.Collections.GroupOfAtoms. PDB files are read via the class MMTK.PDB.PDBConfiguration. DCD files can be read by a MMTK.DCD.DCDReader object. For writing DCD files, there is the function MMTK.DCD.writeDCDPDB(), which also creates a compatible PDB file without which the DCD file could not be interpreted.

        Special care must be taken to ensure a correct mapping of atom numbers when reading from a DCD file. In MMTK, each atom object has a unique identity and atom numbers, also used internally for efficiency, are not strictly necessary and are not used anywhere in MMTK’s application programming interface. DCD file, however, simply list coordinates sorted by atom number. For interpreting DCD files, another file must be available which allows the identification of atoms from their number and vice versa; this can for example be a PDB file.

        When reading DCD files, MMTK assumes that the atom order in the DCD file is identical to the internal atom numbering of the universe for which the DCD file is read. This assumption is in general valid only if the universe has been created from a PDB file that is compatible with the DCD file, without any additions or removals.

        Visualization and animation¶

        The most common need for file export is visualization. All objects that can be visualized (chemical systems and subsets thereof, normal mode objects, trajectories) provide a method view which creates temporary export files, starts a visualization program, and deletes the temporary files. Depending on the object type there are various optional parameters.

        MMTK also allows visualization of normal modes and trajectories using animation. Since not all visualization programs permit animation, and since there is no standard way to ask for it, animation is implemented only for the programs XMol and VMD. Animation is available for normal modes, trajectories, and arbitrary sequences of configurations (see function MMTK.Visualization.viewSequence()).

        For more specialized needs, MMTK permits the creation of graphical representations of most of its objects via general graphics modules that have to be provided externally. Suitable modules are provided in the package Scientific.Visualization and cover VRML (version 1), VRML2 (aka VRML97), and the molecular visualization program VMD. Modules for other representations (e.g. rendering programs) can be written easily; it is recommended to use the existing modules as an example. The generation of graphics objects is handled by the method graphicsObjects, defined in the class MMTK.Visualization.Viewable, which is a Mix-in class that makes graphics objects generation available for all objects that define chemical systems or parts thereof, as well as for certain other objects that are viewable.

        The explicit generation of graphics objects permits the mixture of different graphical representations for various parts of a system, as well as the combination of MMTK-generated graphics objects with arbitrary other graphics objects, such as lines, arrows, or spheres. All graphics objects are finally combined into a scene object (also defined in the various graphics modules) in order to be displayed. See also the visualization examples.

        Fields¶

        For analyzing or visualizing atomic properties that change little over short distances, it is often convenient to represent these properties as functions of position instead of one value per atom. Functions of position are also known as fields, and mathematical techniques for the analysis of fields have proven useful in many branches of physics. Such a field can be obtained by averaging over the values corresponding to the atoms in a small region of space. MMTK provides classes for scalar and vector field in module MMTK.Field. See also the example vector_field.py.

        Charge fitting¶

        A frequent problem in determining force field parameters is the determination of partial charges for the atoms of a molecule by fitting to the electrostatic potential around the molecule, which is obtained from quantum chemistry programs. Although this is essentially a straightforward linear least-squares problem, many procedures that are in common use do not use state-of-the-art techniques and may yield erroneous results. MMTK provides a charge fitting method that is numerically stable and allows the imposition of constraints on the charges. It is implemented in module MMTK.ChargeFit. See also the example charge_fit.py.

        Constructing the database¶

        MMTK uses a database of chemical entities to define the properties of atoms, molecules, and related objects. This database consists of plain text files, more precisely short Python programs, whose names are the names of the object types. This chapter explains how to construct and manage these files. Note that the standard database already contains many definitions, in particular for proteins and nucleic acids. You do not need to read this chapter unless you want to add your own molecule definitions.

        MMTK’s database does not have to reside in a single place. It can consist of any number of subdatabases, each of which can be a directory or a URL. Typically the database consists of at least two parts: MMTK’s standard definitions and a user’s personal definitions. When looking up an object type in the database, MMTK checks the value of the environment variable MMTKDATABASE. The value of this variable must be a list of subdatabase locations seperated by white space. If the variable MMTKDATABASE is not defined, MMTK uses a default value that contains the path ”.mmtk/Database” in the user’s home directory followed by MMTK’s standard database, which resides in the directory Database within the MMTK package directory (on many Unix systems this is /usr/local/lib/python2.x/site-packages/MMTK). MMTK checks the subdatabases in the order in which they are mentioned in MMTKDATABASE.

        Each subdatabase contains directories corresponding to the object classes, i.e. Atoms (atom definitions), Groups (group definitions), Molecules (molecule definitions), Complexes (complex definitions), Proteins (protein definitions), and PDB (Protein Data Bank files). These directories contain the definition files, whose names may not contain any upper-case letters. These file names correspond to the object types, e.g. the call MMTK.Molecule('Water') will cause MMTK to look for the file Molecules/water in the database (note that the names are converted to lower case).

        The remaining sections of this chapter explain how the individual definition files are constructed. Keep in mind that each file is actually a Python program, so of course standard Python syntax rules apply.

        Atom definitions¶

        An atom definition in MMTK describes a chemical element, such as “hydrogen”. This should not be confused with the “atom types” used in force field descriptions and in some modelling programs. As a consequence, it is rarely necessary to add atom definitions to MMTK.

        Atom definition files are short and of essentially identical format. This is the definition for carbon:

        name = 'carbon'
symbol = 'C'
mass = [(12, 98.90), (13.003354826, 1.10)]
color = 'black'
vdW_radius = 0.17

        The name should be meaningful to users, but is not used by MMTK itself. The symbol, however, is used to identify chemical elements. It must be exactly equal to the symbol defined by IUPAC, including capitalization (e.g. ‘Cl’ for chlorine). The mass can be either a number or a list of tuples, as shown above. Each tuple defines an isotope by its mass and its percentage of occurrence; the percentages must add up to 100. The color is used for VRML output and must equal one of the color names defined in the module VRML. The van der Waals radius is used for the calculation of molecular volumes and surfaces; the values are taken from [Bondi1964].

        An application program can create an isolated atom with Atom('c') or, specifying an initial position, with Atom('c', position=Vector(0.,1.,0.)). The element name can use any combination of upper and lower case letters, which are considered equivalent.

        Group definitions¶

        Group definitions in MMTK exist to facilitate the definition of molecules by avoiding the frequent repetition of common combinations. MMTK doesn’t give any physical meaning to groups. Groups can contain atoms and other groups. Their definitions look exactly like molecule definitions; the only difference between groups and molecules is the way they are used.

        This is the definition of a methyl group:

        name = 'methyl group'
C  = Atom('C')
H1 = Atom('H')
H2 = Atom('H')
H3 = Atom('H')
bonds = [Bond(C, H1), Bond(C, H2), Bond(C, H3)]
pdbmap = [('MTH', {'C': C, 'H1': H1, 'H2': H2, 'H3': H3})]
amber_atom_type = {C: 'CT', H1: 'HC', H2: 'HC', H3: 'HC'}
amber_charge = {C: 0., H1: 0.1, H2: 0.1, H3: 0.1}

        The name should be meaningful to users, but is not used by MMTK itself. The following lines create the atoms in the group and assign them to variables. These variables become attributes of whatever object uses this group; their names can be anything that is a legal Python name. The list of bonds, however, must be assigned to the name bonds. The bond list is used by force fields and for visualization.

        The name pdbmap is used for reading and writing PDB files. Its value must be a list of tuples, where each tuple defines one PDB residue. The first element of the tuple is the residue name, which is used only for output. The second element is a dictionary that maps PDB atom names to the actual atoms. The pdbmap entry of any object can be overridden by an entry in a higher-level object. Therefore the entry for a group is only used for atoms that do not occur in the entry for a molecule that contains this group.

        The remaining lines in the definition file contain information specific to force fields, in this case the Amber force field. The dictionary amber_atom_type defines the atom type for each atom; the dictionary amber_charge defines the partial charges. As for pdbmap entries, these definitions can be overridden by higher-level definitions.

        Molecule definitions¶

        Molecules are typically used directly in application programs, but they can also be used in the definition of complexes. Molecule definitions can use atoms and groups.

        This is the definition of a water molecule:

        name = 'water'
structure = \
"  O   \n" + \
" / \  \n" + \
"H   H \n"
O  = Atom('O')
H1 = Atom('H')
H2 = Atom('H')
bonds = [Bond(O, H1), Bond(O, H2)]
pdbmap = [('HOH', {'O': O, 'H1': H1, 'H2': H2})]
pdb_alternative = {'OH2': 'O'}
amber_atom_type = {O: 'OW', H1: 'HW', H2: 'HW'}
amber_charge = {O: -0.83400, H1: 0.41700, H2: 0.41700}
configurations = {
'default': ZMatrix([[H1], \
                   [O,  H1,  0.9572*Ang], \
                   [H2, O,   0.9572*Ang,  H1,  104.52*deg]])
}

        The name should be meaningful to users, but is not used by MMTK itself. The structure is optional and not used by MMTK either. The following lines create the atoms in the group and assign them to variables. These variables become attributes of the molecule, i.e. when a water molecule is created in an application program by w = Molecule('water'), then w.H1 will refer to its first hydrogen atom. The names of these variables can be any legal Python names. The list of bonds, however, must be assigned to the name bonds. The bond list is used by force fields and for visualization.

        The name pdbmap is used for reading and writing PDB files. Its value must be a list of tuples, where each tuple defines one PDB residue. The first element of the tuple is the residue name, which is used only for output. The second element is a dictionary that maps PDB atom names to the actual atoms. The pdbmap entry of any object can be overridden by an entry in a higher-level object, i.e. in the case of a molecule a complex containing it. The name pdb_alternative allows to read PDB files that use non-standard names. When a PDB atom name is not found in the pdbmap, an attempt is made to translate it to another name using pdb_alternative.

        The two following lines in the definition file contain information specific to force fields, in this case the Amber force field. The dictionary amber_atom_type defines the atom type for each atom; the dictionary amber_charge defines the partial charges. As for pdbmap entries, these definitions can be overridden by higher-level definitions.

        The name configurations can be defined to be a dictionary of configurations for the molecule. During the construction of a molecule, a configuration can be specified via an optional parameter, e.g. w = Molecule('water', configuration='default'). The names of the configurations can be arbitrary; only the name “default” has a special meaning; it is applied by default if no other configuration is specified when constructing the molecule. If there is no default configuration, and no other configuration is explicitly specified, then the molecule is created with undefined atomic positions.

        There are three ways of describing configurations:

        • By a Z-Matrix:

          ZMatrix([[H1], \
         [O,  H1,  0.9572*Ang], \
         [H2, O,   0.9572*Ang,  H1,  104.52*deg]])

        • By Cartesian coordinates:

          Cartesian({O:  ( 0.004, -0.00518, 0.0),
H1: (-0.092, -0.00518, 0.0),
H2: ( 0.028,  0.0875,  0.0)})

        • By a PDB file:

          PDBFile('water.pdb')

          The PDB file must be in the database subdirectory PDB, unless a full path name is specified for it.

        Complex definitions¶

        Complexes are defined much like molecules, except that they are composed of molecules and atoms; no groups are allowed, and neither are bonds.

        Protein definitions¶

        Protein definitions can take many different forms, depending on the source of input data and the type of information that is to be stored. For proteins it is particularly useful that database definition files are Python programs with all their flexibility.

        The most common way of constructing a protein is from a PDB file. This is an example for a protein definition:

        name = 'insulin'
# Read the PDB file.
conf = PDBConfiguration('insulin.pdb')
# Construct the peptide chains.
chains = conf.createPeptideChains()
# Clean up
del conf

        The name should be meaningful to users, but is not used by MMTK itself. The second command reads the sequences of all peptide chains from a PDB file. Everything which is not a peptide chain is ignored. The following line constructs a PeptideChain object (a special molecule) for each chain from the PDB sequence. This involves constructing positions for any missing hydrogen atoms. Finally, the temporary data (“conf”) is deleted, otherwise it would remain in memory forever.

        The net result of a protein definition file is the assignment of a list of molecules (usually PeptideChain objects) to the variable “chains”. MMTK then constructs a protein object from it. To use the above example, an application program would use the command p = Protein('insulin'). The construction of the protein involves one nontrivial (but automatic) step: the construction of disulfide bridges for pairs of cystein residues whose sulfur atoms have a distance of less then 2.5 Ã…ngström.

        Threads and parallelization¶

        This chapter explains the use of threads by MMTK and MMTK’s parallelization support. This is an advanced topic, and not essential for the majority MMTK applications. You need to read this chapter only if you use multiprocessor computers, or if you want to implement multi-threaded programs that use MMTK.

        Threads are different execution paths through a program that are executed in parallel, at least in principle; real parallel execution is possible only on multiprocessor systems. MMTK makes use of threads in two ways, which are conceptually unrelated: parallelization of energy evaluation on shared-memory multiprocessor computers, and support for multithreaded applications. Thread support is not available on all machines; you can check if yous system supports threads by starting a Python interpreter and typing import threading. If this produces an error message, then your system does not support threads, otherwise it is available in Python and also in MMTK. If you do not have thread support in Python although you know that your operating system supports threads, you might have compiled your Python interpreter without thread support; in that case, MMTK does not have thread support either.

        Another approach to parallelization is message passing: several processors work on a program and communicate via a fast network to share results. A standard library, called MPI (Message Passing Interface), has been developped for sharing data by message passing, and implementations are available for all parallel computers currently on the market. MMTK contains elementary support for parallelization by message passing: only the energy evaluation has been paralellized, using a data-replication strategy, which is simple but not the most efficient for large systems. MPI support is disabled by default. Enabling it involves modifying the file Src/Setup.template prior to compilation of MMTK. Furthermore, an MPI-enabled installation of ScientificPython is required, and the mpipython executable must be used instead of the standard Python interpreter.

        Threads and message passing can be used together to use a cluster of shared-memory machines most efficiently. However, this requires that the thread and MPI implementations being used work together; sometimes there are conflicts, for example due to the use of the same signal in both libraries. Refer to your system documentation for details.

        The use of threads for parallelization on shared-memory systems is very simple: Just set the environment variable MMTK_ENERGY_THREADS to the desired value. If this variable is not defined, the default value is 1, i.e. energy evaluations are performed serially. For choosing an appropriate value for this environment variable, the following points should be considered:

        • The number of energy evaluation threads should not be larger than the number of processors that are fully dedicated to the MMTK application. A larger number of threads does not lead to wrong results, but it can increase the total execution time.
        • MMTK assumes that all processors are equally fast. If you use a heteregenous multiprocessor machine, in which the processors have different speeds, you might find that the total execution time is larger than without threads.
        • The use of threads incurs some computational overhead. For very small systems, it is usually faster not to use threads.
        • Not all energy terms necessarily support threads. Of the force field terms that part of MMTK, only the multipole algorithms for electrostatic interactions does not support threads, but additional force fields defined outside MMTK might also be affected. MMTK automatically evaluates such energy terms with a single thread, such that there is no risk of getting wrong results. However, you might not get the performance you expect.
        • If second derivatives of the potential energy are requested, energy evaluation is handled by a single thread. An efficient implementation of multi-threaded energy evaluation would require a separate copy of the second-derivative matrix per thread. This approach needs too much memory for big systems to be feasible. Since second derivatives are almost exclusively used for normal mode calculations, which need only a single energy evaluation, multi-thread support is not particularly important anyway.

        Parallelization via message passing is somewhat more complicated. In the current MMTK parallelization model, all processors execute the same program and replicate all tasks, with the important exception of energy evaluation. Energy terms are divided evenly between the processors, and at the end the energy and gradient values are shared by all machines. This is the only step involving network communication. Like thread-based parallelization, message-passing parallelization does not support the evaluation of second derivatives.

        A special problem with message-passing systems is input and output. The MMTK application must ensure that output files are written by only one processor, and that all processors correctly access input files, especially in the case of each processor having its own disk space. See the example md.py for illustration.

        Multithreaded applications are applications that use multiple threads in order to simplify the implementation of certain algorithms, i.e. not necessarily with the goal of profiting from multiple processors. If you plan to write a multithreaded application that uses MMTK, you should first make sure you understand threading support in Python. In particular, you should keep in mind that the global interpreter lock prevents the effective use of multiple processors by Python code; only one thread at a time can execute interpreted Python code. C code called from Python can permit other threads to execute simultaneously; MMTK does this for energy evaluation, molecular dynamics integration, energy minimization, and normal mode calculation.

        A general problem in multithreaded applications is access to resources that are shared among the threads. In MMTK applications, the most important shared resource is the description of the chemical systems, i.e. universe objects and their contents. Chaos would result if two threads tried to modify the state of a universe simultaneously, or even if one thread uses information that is simultaneously being modified by another thread. Synchronization is therefore a critical part of multithreaded application. MMTK provides two synchronization aids, both of which described in the documentation of the class MMTK.Universe.Universe: the configuration change lock (methods acquireConfigurationChangeLock() and releaseConfigurationChangeLock()), and the universe state lock (methods acquireReadStateChangeLock(), releaseReadStateChangeLock(), acquireWriteStateChangeLock(), and releaseWriteStateChangeLock()). Only a few common universe operations manipulate the universe state lock in order to avoid conflicts with other threads; these methods are marked as thread-safe in the description. All other operations should only be used inside a code section that is protected by the appropriate manipulation of the state lock. The configuration change lock is less critical; it is used only by the molecular dynamics and energy minimization algorithms in MMTK.

        Bibliography¶

        [Bondi1964]

        [Eisenhaber1993]

        [Eisenhaber1995]

        [Hinsen1995]

        [Hinsen1997]

        [Hinsen1998]

        [Hinsen1999]

        Konrad Hinsen, Aline Thomas, Martin J. Field
        Analysis of domain motions in large proteins
        1999

        [Hinsen1999a]

        [Hinsen1999b]

        [Hinsen2000]

        Konrad Hinsen, Andrei J. Petrescu, Serge Dellerue, Marie-Claire Bellissent-Funel, Gerald R. Kneller.
        Harmonicity in slow protein dynamics
        2000

        [Kneller1990]

        [Kneller1996]

        [Swope1982]

        [vanGunsteren1982]

        Wilfred F. van Gunsteren, Martin Karplus
        Effect of Constraints on the Dynamics of Macromolecules
        1982

        [Viduna2000]

        [Wolf1999]

        D. Wolf, P. Keblinski, S.R. Philpot, J. Eggebrecht
        Exact method for the simulation of Coulombic systems by spherically truncated, pairwise r:sup:-1` summation <http://dx.doi.org/10.1063/1.478738>`_
        1999

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        MMTK-2.7.9/Doc/HTML/modules.html0000644000076600000240000222452412013143453016527 0ustar hinsenstaff00000000000000 Module Reference — MMTK User Guide 2.7.7 documentation

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        Module Reference¶

        MMTK¶

        MMTK is the base module of the Molecular Modelling Toolkit. It contains the most common objects and all submodules. As a convenience to the user, it also imports some commonly used objects from other libraries:

        • Vector from Scientific.Geometry
        • Translation and Rotation from Scientific.Geometry.Transformation
        • copy and deepcopy from copy
        • stdin, stdout, and stderr from sys

        MMTK.Biopolymers¶

        Base classes for proteins and nucleic acids

        class MMTK.Biopolymers.Residue(group_spec, _memo=None, **properties)[source]¶

        Bases: MMTK.ChemicalObjects.Group

        Base class for aminoacid and nucleic acid residues

        Parameters:
        • group_spec (str) – a string (not case sensitive) that specifies the group name in the chemical database
        • position (Scientific.Geometry.Vector) – the position of the center of mass of the group
        • name (str) – a name given to the group
        nextResidue()[source]¶

        :returns the next residue in the chain

        precedingResidue()[source]¶

        :returns the preceding residue in the chain

        class MMTK.Biopolymers.ResidueChain(molecule_spec, _memo=None, **properties)[source]¶

        Bases: MMTK.ChemicalObjects.Molecule

        Chain of residues

        Base class for peptide chains and nucleotide chains

        Parameters:
        • molecule_spec (str) – a string (not case sensitive) that specifies the molecule name in the chemical database
        • position (Scientific.Geometry.Vector) – the position of the center of mass of the molecule
        • name (str) – a name given to the molecule
        • configuration (str) – the name of a configuration listed in the database definition of the molecule, which is used to initialize the atom positions. If no configuration is specified, the configuration named “default” will be used, if it exists. Otherwise the atom positions are undefined.
        residues()[source]¶
        Returns:a collection containing all residues
        Return type:Collection
        residuesOfType(*types)[source]¶
        Parameters:types (str) – residue type codes
        Returns:a collection that contains all residues whose type (residue code) is contained in types
        Return type:Collection
        sequence()[source]¶
        Returns:the residue sequence as a list of residue codes
        Return type:list of str
        MMTK.Biopolymers.defineAminoAcidResidue(full_name, code3, code1=None)[source]¶

        Add a non-standard amino acid residue to the internal residue table. Once added to the residue table, the new residue can be used like any of the standard residues in the creation of peptide chains.

        Parameters:
        • full_name (str) – the name of the group definition in the chemical database
        • code3 (str) – the three-letter residue code
        • code1 (str) – an optionel one-letter residue code
        MMTK.Biopolymers.defineNucleicAcidResidue(full_name, code)[source]¶

        Add a non-standard nucleic acid residue to the internal residue table. Once added to the residue table, the new residue can be used like any of the standard residues in the creation of nucleotide chains.

        Parameters:
        • full_name (str) – the name of the group definition in the chemical database
        • code – the residue code

        MMTK.Bonds¶

        Bonds, bond lists, bond angle lists, and dihedral angle lists

        The classes in this module are normally not used directly from client code. They are used by the classes in ChemicalObjects and ForceField.

        class MMTK.Bonds.Bond(blueprint, memo=None)[source]¶

        Bases: object

        Chemical bond

        A bond links two atoms (attributes a1 and a2)

        hasAtom(a)[source]¶
        Parameters:a (Atom) – an atom
        Returns:True if a participates in the bond
        otherAtom(a)[source]¶
        Parameters:a (Atom) – an atom involved in the bond
        Returns:the atom at the other end of the bond
        Return type:Atom
        Raises ValueError:
         if a does not belong to the bond
        class MMTK.Bonds.BondAngle(b1, b2, ca)[source]¶

        Bases: object

        Bond angle

        A bond angle is the angle between two bonds that share a common atom. It is defined by two bond objects (attributes b1 and b2) and an atom object (the common atom, attribute ca).

        otherBond(bond)[source]¶
        Parameters:bond (Bond) – a bond involved in the angle
        Returns:the other bond involved in the angle
        Return type:Bond
        Raises ValueError:
         if bond does not belong to the angle
        class MMTK.Bonds.BondAngleList(angles)[source]¶

        Bases: object

        Bond angle list

        dihedralAngles()[source]¶
        Returns:a list of all dihedral angles that can be formed from the bond angles in the list
        Return type:DihedralAngleList
        class MMTK.Bonds.DihedralAngle(ba1, ba2, cb)[source]¶

        Bases: object

        Dihedral angle

        A dihedral angle is the angle between two planes that are defined by BondAngle objects (attributes ba1 and ba2) and their common bond (attribute cb).

        There are proper dihedrals (four atoms linked by three bonds in sequence) and improper dihedrals (a central atom linked to three surrounding atoms by three bonds). The boolean attribute improper indicates whether a dihedral is an improper one.

        class MMTK.Bonds.DihedralAngleList(dihedrals)[source]¶

        Bases: object

        Dihedral angle list

        MMTK.ChargeFit¶

        Fit of point chages to an electrostatic potential surface

        This module implements a numerically stable method (based on Singular Value Decomposition) to fit point charges to values of an electrostatic potential surface. Two types of constraints are avaiable: a constraint on the total charge of the system or a subset of the system, and constraints that force the charges of several atoms to be equal. There is also a utility function that selects suitable evaluation points for the electrostatic potential surface. For the potential evaluation itself, some quantum chemistry program is needed.

        The charge fitting method is described in:

        K. Hinsen and B. Roux,
        An accurate potential for simulating proton transfer in acetylacetone,
        J. Comp. Chem. 18, 1997: 368

        See also Examples/Miscellaneous/charge_fit.py.

        class MMTK.ChargeFit.ChargeFit(system, points, constraints=None)[source]¶

        Bases: object

        Fit of point charges to an electrostatic potential surface

        A ChargeFit object acts like a dictionary that stores the fitted charge value for each atom in the system.

        Parameters:
        • system – any chemical object (usually a molecule)
        • points – a list of point/potential pairs (a vector for the evaluation point, a number for the potential), or a dictionary whose keys are Configuration objects and whose values are lists of point/potential pairs. The latter case permits combined fits for several conformations of the system.
        • constraints – an optional list of constraint objects (TotalChargeConstraint and/or EqualityConstraint objects). If the constraints are inconsistent, a warning is printed and the result will satisfy the constraints only in a least-squares sense.
        class MMTK.ChargeFit.EqualityConstraint(atom1, atom2)[source]¶

        Bases: object

        Constraint forcing two charges to be equal

        To be used with ChargeFit

        Any atom may occur in more than one EqualityConstraint object, in order to keep the charges of more than two atoms equal.

        Parameters:
        • atom1 (Atom) – the first atom in the equality relation
        • atom2 (Atom) – the second atom in the equality relation
        class MMTK.ChargeFit.TotalChargeConstraint(system, charge)[source]¶

        Bases: object

        Constraint on the total system charge

        To be used with ChargeFit

        Parameters:
        • system – any chamical object whose total charge is to be constrained
        • charge (number) – the total charge value
        MMTK.ChargeFit.evaluationPoints(system, n, smallest=0.3, largest=0.5)[source]¶

        Generate points in space around a molecule that are suitable for potential evaluation in view of a subsequent charge fit. The points are chosen at random and uniformly in a shell around the system.

        Parameters:
        • system – the chemical object for which the charges will be fitted
        • n – the number of evaluation points to be generated
        • smallest – the smallest allowed distance of any evaluation point from any non-hydrogen atom
        • largest – the largest allowed value for the distance from an evaluation point to the nearest atom
        Returns:

        a list of evaluation points
        Return type:

        list of Scientific.Geometry.Vector

        MMTK.ChemicalObjects¶

        Atoms, groups, molecules and similar objects

        class MMTK.ChemicalObjects.Atom(atom_spec, _memo=None, **properties)[source]¶

        Bases: MMTK.ChemicalObjects.ChemicalObject

        Atom

        Parameters:
        • atom_spec (str) – a string (not case sensitive) specifying the chemical element
        • position (Scientific.Geometry.Vector) – the position of the atom
        • name (str) – a name given to the atom
        bondedTo()[source]¶
        Returns:a list of all atoms to which a chemical bond exists.
        Return type:list
        centerOfMass(conf=None)¶
        Returns:the position in configuration conf. If conf is ‘None’, use the current configuration. If the atom has not been assigned a position, the return value is None.
        Return type:Scientific.Geometry.Vector
        position(conf=None)[source]¶
        Returns:the position in configuration conf. If conf is ‘None’, use the current configuration. If the atom has not been assigned a position, the return value is None.
        Return type:Scientific.Geometry.Vector
        setMass(mass)[source]¶

        Changes the mass of the atom.

        Parameters:mass (float) – the mass
        setPosition(position)[source]¶

        Changes the position of the atom.

        Parameters:position (Scientific.Geometry.Vector) – the new position
        translateTo(position)¶

        Changes the position of the atom.

        Parameters:position (Scientific.Geometry.Vector) – the new position
        class MMTK.ChemicalObjects.AtomCluster(atoms, **properties)[source]¶

        Bases: MMTK.ChemicalObjects.CompositeChemicalObject, MMTK.ChemicalObjects.ChemicalObject

        An agglomeration of atoms

        An atom cluster acts like a molecule without any bonds or atom properties. It can be used to represent a group of atoms that are known to form a chemical unit but whose chemical properties are not sufficiently known to define a molecule.

        Parameters:
        • atoms (list) – a list of atoms in the cluster
        • position (Scientific.Geometry.Vector) – the position of the center of mass of the cluster
        • name (str) – a name given to the cluster
        class MMTK.ChemicalObjects.ChainMolecule(molecule_spec, _memo=None, **properties)[source]¶

        Bases: MMTK.ChemicalObjects.Molecule

        ChainMolecule

        ChainMolecules are generated by a MoleculeFactory from templates that have a sequence attribute.

        Parameters:
        • molecule_spec (str) – a string (not case sensitive) that specifies the molecule name in the chemical database
        • position (Scientific.Geometry.Vector) – the position of the center of mass of the molecule
        • name (str) – a name given to the molecule
        • configuration (str) – the name of a configuration listed in the database definition of the molecule, which is used to initialize the atom positions. If no configuration is specified, the configuration named “default” will be used, if it exists. Otherwise the atom positions are undefined.
        class MMTK.ChemicalObjects.ChemicalObject(blueprint, memo)[source]¶

        Bases: MMTK.Collections.GroupOfAtoms, MMTK.Visualization.Viewable

        General chemical object

        This is an abstract base class that implements methods which are applicable to any chemical object (atom, molecule, etc.).

        atomIterator()[source]¶
        Returns:an iterator over all atoms in the object
        Return type:iterator
        atomList()[source]¶
        Returns:a list containing all atoms in the object
        Return type:list
        bondedUnits()[source]¶
        Returns:the largest subobjects which can contain bonds. There are no bonds between any of the subobjects in the list.
        Return type:list
        degreesOfFreedom()[source]¶
        Returns:the number of degrees of freedom of the object
        Return type:int
        distanceConstraintList()[source]¶
        Returns:the distance constraints of the object
        Return type:list
        fullName()[source]¶
        Returns:the full name of the object. The full name consists of the proper name of the object preceded by the full name of its parent separated by a dot.
        Return type:str
        getAtomProperty(atom, property)[source]¶

        Retrieve atom properties from the chemical database.

        Note: the property is first looked up in the database entry for the object on which the method is called. If the lookup fails, the complete hierarchy from the atom to the top-level object is constructed and traversed starting from the top-level object until the property is found. This permits database entries for higher-level objects to override property definitions in its constituents.

        At the atom level, the property is retrieved from an attribute with the same name. This means that properties at the atom level can be defined both in the chemical database and for each atom individually by assignment to the attribute.

        Returns:the value of the specified property for the given atom from the chemical database.
        numberOfDistanceConstraints()[source]¶
        Returns:the number of distance constraints of the object
        Return type:int
        removeDistanceConstraints(universe=None)[source]¶

        Removes all distance constraints.

        setBondConstraints(universe=None)[source]¶

        Sets distance constraints for all bonds.

        setRigidBodyConstraints(universe=None)[source]¶

        Sets distance constraints that make the object fully rigid.

        topLevelChemicalObject()[source]¶

        Returns the highest-level chemical object of which the current object is a part.

        universe()[source]¶
        Returns:the universe to which the object belongs, or None if the object does not belong to any universe
        Return type:Universe
        class MMTK.ChemicalObjects.Complex(complex_spec, _memo=None, **properties)[source]¶

        Bases: MMTK.ChemicalObjects.CompositeChemicalObject, MMTK.ChemicalObjects.ChemicalObject

        Complex

        A complex is an assembly of molecules that are not connected by chemical bonds.

        Parameters:
        • complex_spec (str) – a string (not case sensitive) that specifies the complex name in the chemical database
        • position (Scientific.Geometry.Vector) – the position of the center of mass of the complex
        • name (str) – a name given to the complex
        • configuration (str) – the name of a configuration listed in the database definition of the complex, which is used to initialize the atom positions. If no configuration is specified, the configuration named “default” will be used, if it exists. Otherwise the atom positions are undefined.
        class MMTK.ChemicalObjects.CompositeChemicalObject(properties)[source]¶

        Bases: object

        Chemical object containing subobjects

        This is an abstract base class that implements methods which can be used with any composite chemical object, i.e. any chemical object that is not an atom.

        class MMTK.ChemicalObjects.Group(group_spec, _memo=None, **properties)[source]¶

        Bases: MMTK.ChemicalObjects.CompositeChemicalObject, MMTK.ChemicalObjects.ChemicalObject

        Group of bonded atoms

        Groups can contain atoms and other groups, and link them by chemical bonds. They are used to represent functional groups or any other part of a molecule that has a well-defined identity.

        Groups cannot be created in application programs, but only in database definitions for molecules or through a MoleculeFactory.

        Parameters:
        • group_spec (str) – a string (not case sensitive) that specifies the group name in the chemical database
        • position (Scientific.Geometry.Vector) – the position of the center of mass of the group
        • name (str) – a name given to the group
        class MMTK.ChemicalObjects.Molecule(molecule_spec, _memo=None, **properties)[source]¶

        Bases: MMTK.ChemicalObjects.CompositeChemicalObject, MMTK.ChemicalObjects.ChemicalObject

        Molecule

        Molecules consist of atoms and groups linked by bonds.

        Parameters:
        • molecule_spec (str) – a string (not case sensitive) that specifies the molecule name in the chemical database
        • position (Scientific.Geometry.Vector) – the position of the center of mass of the molecule
        • name (str) – a name given to the molecule
        • configuration (str) – the name of a configuration listed in the database definition of the molecule, which is used to initialize the atom positions. If no configuration is specified, the configuration named “default” will be used, if it exists. Otherwise the atom positions are undefined.
        findHydrogenPositions()[source]¶

        Find reasonable positions for hydrogen atoms that have no position assigned.

        This method uses a heuristic approach based on standard geometry data. It was developed for proteins and DNA and may not give good results for other molecules. It raises an exception if presented with a topology it cannot handle.

        MMTK.ChemicalObjects.isChemicalObject(object)[source]¶
        Returns:True if object is a chemical object

        MMTK.Collections¶

        Collections of chemical objects

        class MMTK.Collections.Collection(*objects)[source]¶

        Bases: MMTK.Collections.GroupOfAtoms, MMTK.Visualization.Viewable

        Collection of chemical objects

        Collections permit the grouping of arbitrary chemical objects (atoms, molecules, etc.) into one object for the purpose of analysis or manipulation.

        Collections permit length inquiry, item extraction by indexing, and iteration, like any Python sequence object. Two collections can be added to yield a collection that contains the combined elements.

        Parameters:objects – a chemical object or a sequence of chemical objects that define the initial content of the collection.
        addObject(object)[source]¶

        Add objects to the collection.

        Parameters:object – the object(s) to be added. If it is another collection or a list, all of its elements are added
        atomList()[source]¶
        Returns:a list containing all atoms of all objects in the collection
        Return type:list
        distanceConstraintList()[source]¶
        Returns:the list of distance constraints
        Return type:list
        map(function)[source]¶

        Apply a function to all objects in the collection and return the list of the results. If the results are chemical objects, a Collection object is returned instead of a list.

        Parameters:function (callable) – the function to be applied
        Returns:the list or collection of the results
        numberOfAtoms()[source]¶
        Returns:the total number of atoms in the objects of the collection
        Return type:int
        numberOfDistanceConstraints()[source]¶
        Returns:the number of distance constraints
        objectList(type=None)[source]¶

        Make a list of all objects in the collection that are instances of a specific type or of one of its subtypes.

        Parameters:type – the type that serves as a filter. If None, all objects are returned
        Returns:the objects that match the given type
        Return type:list
        removeDistanceConstraints(universe=None)[source]¶

        Remove all distance constraints

        removeObject(object)[source]¶

        Remove an object or a list or collection of objects from the collection. The object(s) to be removed must be elements of the collection.

        Parameters:object – the object to be removed, or a list or collection of objects whose elements are to be removed
        Raises ValueError:
         if the object is not an element of the collection
        selectBox(p1, p2)[source]¶

        Select objects in a rectangular volume

        Parameters:
        • p1 (Scientific.Geometry.Vector) – one corner of the rectangular volume
        • p2 (Scientific.Geometry.Vector) – the other corner of the rectangular volume
        Returns:

        a collection of all elements that lie within the rectangular volume
        Return type:

        Collection
        selectShell(point, r1, r2=0.0)[source]¶

        Select objects in a spherical shell around a central point.

        Parameters:
        • point (Scientific.Geometry.Vector) – the center of the spherical shell
        • r1 (float) – inner or outer radius of the shell
        • r2 (float) – inner or outer radius of the shell (default: 0.)
        Returns:

        a collection of all elements whose distance from point is between r1 and r2
        Return type:

        Collection
        setBondConstraints(universe=None)[source]¶

        Set distance constraints for all bonds

        universe()[source]¶
        Returns:the universe of which all objects in the collection are part. If no such universe exists, the return value is None
        Return type:Universe
        class MMTK.Collections.GroupOfAtoms[source]¶

        Bases: object

        Anything that consists of atoms

        A mix-in class that defines a large set of operations which are common to all objects that consist of atoms, i.e. any subset of a chemical system. Examples are atoms, molecules, collections, or universes.

        angularMomentum(velocities=None, conf=None)[source]¶
        Parameters:
        • velocities (ParticleVector) – a set of velocities for all atoms, or None for the current velocities
        • conf (Configuration) – a configuration object, or None for the current configuration
        Returns:

        the angluar momentum
        Return type:

        Scientific.Geometry.Vector
        angularVelocity(velocities=None, conf=None)[source]¶
        Parameters:
        • velocities (ParticleVector) – a set of velocities for all atoms, or None for the current velocities
        • conf (Configuration) – a configuration object, or None for the current configuration
        Returns:

        the angluar velocity
        Return type:

        Scientific.Geometry.Vector
        applyTransformation(t)[source]¶

        Apply a transformation to the object

        Parameters:t (Scientific.Geometry.Transformation) – the transformation to be applied
        atomCollection()[source]¶
        Returns:a collection containing all atoms in the object
        atomsWithDefinedPositions(conf=None)[source]¶
        Parameters:conf (Configuration or NoneType) – a configuration object, or None for the current configuration
        Returns:a collection of all atoms that have a position in the given configuration
        booleanMask()[source]¶
        Returns:a ParticleScalar object that contains a value of 1 for each atom that is in the object and a value of 0 for all other atoms in the universe
        Return type:ParticleScalar
        boundingBox(conf=None)[source]¶
        Parameters:conf (Configuration or NoneType) – a configuration object, or None for the current configuration
        Returns:two opposite corners of a bounding box around the object. The bounding box is the smallest rectangular bounding box with edges parallel to the coordinate axes.
        Return type:tuple of two Scientific.Geometry.Vector
        boundingSphere(conf=None)[source]¶
        Parameters:conf (Configuration or NoneType) – a configuration object, or None for the current configuration
        Returns:a sphere that contains all atoms in the object. This is B{not} the minimal bounding sphere, just B{some} bounding sphere.
        Return type:Scientific.Geometry.Objects3D.Sphere
        centerAndMomentOfInertia(conf=None)[source]¶
        Parameters:conf (Configuration or NoneType) – a configuration object, or None for the current configuration
        Returns:the center of mass and the moment of inertia tensor in the given configuration
        centerOfMass(conf=None)[source]¶
        Parameters:conf (Configuration or NoneType) – a configuration object, or None for the current configuration
        Returns:the center of mass in the given configuration
        Return type:Scientific.Geometry.Vector
        charge()[source]¶
        Returns:the total charge of the object. This is defined only for objects that are part of a universe with a force field that defines charges.
        Return type:float
        degreesOfFreedom()[source]¶
        Returns:the number of mechanical degrees of freedom
        Return type:int
        dipole(reference=None)[source]¶
        Returns:the total dipole moment of the object. This is defined only for objects that are part of a universe with a force field that defines charges.
        Return type:Scientific.Geometry.Vector
        displacementUnderTransformation(t)[source]¶
        Parameters:t (Scientific.Geometry.Transformation) – the transformation to be applied
        Returns:the displacement vectors for the atoms in the object that correspond to the transformation t.
        Return type:ParticleVector
        findTransformation(conf1, conf2=None)[source]¶
        Parameters:
        • conf1 (Configuration) – a configuration object
        • conf2 (Configuration or NoneType) – a configuration object, or None for the current configuration
        Returns:

        the linear transformation that, when applied to the object in configuration conf1, minimizes the RMS distance to the conformation in conf2, and the minimal RMS distance. If conf2 is None, returns the transformation from the current configuration to conf1 and the associated RMS distance.
        kineticEnergy(velocities=None)[source]¶
        Parameters:velocities (ParticleVector) – a set of velocities for all atoms, or None for the current velocities
        Returns:the kinetic energy
        Return type:float
        mass()[source]¶
        Returns:the total mass
        Return type:float
        momentum(velocities=None)[source]¶
        Parameters:velocities (ParticleVector) – a set of velocities for all atoms, or None for the current velocities
        Returns:the momentum
        Return type:Scientific.Geometry.Vector
        normalizeConfiguration(repr=None)[source]¶

        Apply a linear transformation such that the center of mass of the object is translated to the coordinate origin and its principal axes of inertia become parallel to the three coordinate axes.

        Parameters:repr – the specific representation for axis alignment: - Ir : x y z <–> b c a - IIr : x y z <–> c a b - IIIr : x y z <–> a b c - Il : x y z <–> c b a - IIl : x y z <–> a c b - IIIl : x y z <–> b a c
        normalizePosition()[source]¶

        Translate the center of mass to the coordinate origin

        normalizingTransformation(repr=None)[source]¶

        Calculate a linear transformation that shifts the center of mass of the object to the coordinate origin and makes its principal axes of inertia parallel to the three coordinate axes.

        Parameters:repr – the specific representation for axis alignment: Ir : x y z <–> b c a IIr : x y z <–> c a b IIIr : x y z <–> a b c Il : x y z <–> c b a IIl : x y z <–> a c b IIIl : x y z <–> b a c
        Returns:the normalizing transformation
        Return type:Scientific.Geometry.Transformation.RigidBodyTransformation
        numberOfAtoms()[source]¶
        Returns:the number of atoms
        Return type:int
        numberOfCartesianCoordinates()¶
        Returns:the number of geometrical points that define the object. It is currently equal to the number of atoms, but could be different e.g. for quantum systems, in which each atom is described by a wave function or a path integral.
        Return type:int
        numberOfFixedAtoms()[source]¶
        Returns:the number of atoms that are fixed, i.e. that cannot move
        Return type:int
        numberOfPoints()[source]¶
        Returns:the number of geometrical points that define the object. It is currently equal to the number of atoms, but could be different e.g. for quantum systems, in which each atom is described by a wave function or a path integral.
        Return type:int
        position(conf=None)¶
        Parameters:conf (Configuration or NoneType) – a configuration object, or None for the current configuration
        Returns:the center of mass in the given configuration
        Return type:Scientific.Geometry.Vector
        rmsDifference(conf1, conf2=None)[source]¶
        Parameters:
        • conf1 (Configuration) – a configuration object
        • conf2 (Configuration or NoneType) – a configuration object, or None for the current configuration
        Returns:

        the RMS (root-mean-square) difference between the conformations of the object in two universe configurations, conf1 and conf2
        Return type:

        float
        rotateAroundAxis(point1, point2, angle)[source]¶

        Rotate the object arond an axis specified by two points

        Parameters:
        • point1 (Scientific.Geometry.Vector) – the first point
        • point2 (Scientific.Geometry.Vector) – the second point
        • angle (float) – the rotation angle (in radians)
        rotateAroundCenter(axis_direction, angle)[source]¶

        Rotate the object around an axis that passes through its center of mass.

        Parameters:
        • axis_direction (Scientific.Geometry.Vector) – the direction of the axis of rotation
        • angle (float) – the rotation angle (in radians)
        rotateAroundOrigin(axis_direction, angle)[source]¶

        Rotate the object around an axis that passes through the coordinate origin.

        Parameters:
        • axis_direction (Scientific.Geometry.Vector) – the direction of the axis of rotation
        • angle (float) – the rotation angle (in radians)
        rotationalConstants(conf=None)[source]¶
        Parameters:conf (Configuration or NoneType) – a configuration object, or None for the current configuration
        Returns:a sorted array of rotational constants A, B, C in internal units
        temperature(velocities=None)[source]¶
        Parameters:velocities (ParticleVector) – a set of velocities for all atoms, or None for the current velocities
        Returns:the temperature
        Return type:float
        translateBy(vector)[source]¶

        Translate the object by a displacement vector

        Parameters:vector (Scientific.Geometry.Vector) – the displacement vector
        translateTo(position)[source]¶

        Translate the object such that its center of mass is at position :param position: the final position :type position: Scientific.Geometry.Vector

        universe()[source]¶
        Returns:the universe of which the object is part. For an object that is not part of a universe, the result is None
        Return type:Universe
        view(configuration=None, format='pdb')[source]¶

        Start an external viewer for the object in the given configuration.

        Parameters:
        • configuration (Configuration) – the configuration to be visualized
        • format – ‘pdb’ (for running $PDBVIEWER) or ‘vrml’ (for running $VRMLVIEWER). An optional subformat specification can be added, see writeToFile for the details.
        writeToFile(filename, configuration=None, format=None)[source]¶

        Write a representation of the object in a given configuration to a file.

        Parameters:
        • filename (str) – the name of the file
        • configuration (Configuration or NoneType) – a configuration object, or None for the current configuration
        • format (str) – ‘pdb’ or ‘vrml’ (default: guess from filename) A subformat specification can be added, separated by a dot. Subformats of ‘vrml’ are ‘wireframe’ (default), ‘ball_and_stick’, ‘highlight’ (like ‘wireframe’, but with a small sphere for all atoms that have an attribute ‘highlight’ with a non-zero value), and ‘charge’ (wireframe plus small spheres for the atoms whose color indicates the charge on a red-to-green color scale)
        class MMTK.Collections.PartitionedAtomCollection(*args)[source]¶

        Bases: MMTK.Collections.PartitionedCollection

        Partitioned collection of atoms

        PartitionedAtomCollection objects behave like PartitionedCollection atoms, except that they store only atoms. When a composite chemical object is added, its atoms are stored instead.

        class MMTK.Collections.PartitionedCollection(partition_size, *objects)[source]¶

        Bases: MMTK.Collections.Collection

        Collection with cubic partitions

        A PartitionedCollection differs from a plain Collection by sorting its elements into small cubic cells. This makes adding objects slower, but geometrical operations like selectShell become much faster for a large number of objects.

        Parameters:
        • partition_size – the edge length of the cubic cells
        • objects – a chemical object or a sequence of chemical objects that define the initial content of the collection.
        pairsWithinCutoff(cutoff)[source]¶
        Parameters:cutoff – a cutoff for pair distances
        Returns:a list containing all pairs of objects in the collection whose center-of-mass distance is less than the cutoff
        Return type:list
        partitions()[source]¶
        Returns:a list of cubic partitions. Each partition is specified by a tuple containing two vectors (describing the diagonally opposite corners) and the list of objects in the partition.
        Return type:list
        MMTK.Collections.isCollection(object)[source]¶
        Parameters:object – any Python object
        Returns:True if the object is a Collection

        MMTK.ConfigIO¶

        I/O of molecular configurations

        Input: Z-Matrix and Cartesian Output: VRML PDB files are handled in PDB.

        class MMTK.ConfigIO.Cartesian(data)[source]¶

        Bases: object

        Cartesian specification of a molecule conformation

        Cartesian objects can be used in chemical database entries to specify molecule conformations by Cartesian coordinates.

        Parameters:data – a dictionary mapping atoms to tuples of length three that define its Cartesian coordinates
        applyTo(object)[source]¶

        Define the positions of the atoms in a chemical object by the stored coordinates.

        Parameters:object – the object to which the coordinates are applied
        class MMTK.ConfigIO.ZMatrix(data)[source]¶

        Bases: object

        Z-Matrix specification of a molecule conformation

        ZMatrix objects can be used in chemical database entries to specify molecule conformations by internal coordinates. With the exception of the three first atoms, each atom is defined relative to three previously atoms by a distance, an angle, and a dihedral angle.

        Parameters:data –

        a list of atom definitions. Each atom definition (except for the first three ones) is a list containing seven elements:

        • the atom to be defined
        • a previously defined atom and the distance to it
        • another previously defined atom and the angle to it
        • a third previously defined atom and the dihedral angle to it

        The definition of the first atom contains only the first element, the second atom needs the first three elements, and the third atom is defined by the first five elements.
        applyTo(object)[source]¶

        Define the positions of the atoms in a chemical object by the internal coordinates of the Z-Matrix.

        Parameters:object – the object to which the Z-Matrix is applied

        MMTK.DCD¶

        Reading and writing of DCD trajectory files

        The DCD format for trajectories is used by CHARMM, X-Plor, and NAMD. It can be read by various visualization programs.

        The DCD format is defined as a binary (unformatted) Fortran format and is therefore platform-dependent.

        class MMTK.DCD.DCDReader(universe, **options)[source]¶

        Bases: MMTK.Trajectory.TrajectoryGenerator

        Reader for DCD trajectories (CHARMM/X-Plor/NAMD)

        A DCDReader reads a DCD trajectory and “plays back” the data as if it were generated directly by an integrator. The universe for which the DCD file is read must be perfectly compatible with the data in the file, including an identical internal atom numbering. This can be guaranteed only if the universe was created from a PDB file that is compatible with the DCD file without leaving out any part of the system.

        Reading is started by calling the reader object. The following data categories and variables are available for output:

        • category “time”: time
        • category “configuration”: configuration
        Parameters:
        • universe – the universe for which the information from the trajectory file is read
        • options – keyword options
        • dcd_file – the name of the DCD trajecory file to be read
        • actions – a list of actions to be executed periodically (default is none)
        MMTK.DCD.writeDCDPDB(conf_list, dcd_file_name, pdb_file_name, delta_t=0.1)[source]¶

        Write a sequence of configurations to a DCD file and generate a compatible PDB file.

        Parameters:
        • conf_list (sequence of Configuration) – the sequence of configurations
        • dcd_file_name (str) – the name of the DCD file
        • pdb_file_name (str) – the name of the PDB file
        • delta_t (float) – the time step between two configurations
        MMTK.DCD.writeVelocityDCDPDB(vel_list, dcd_file_name, pdb_file_name, delta_t=0.1)[source]¶

        Write a sequence of velocity particle vectors to a DCD file and generate a compatible PDB file.

        Parameters:
        • vel_list (sequence of ParticleVector) – the sequence of velocity particle vectors
        • dcd_file_name (str) – the name of the DCD file
        • pdb_file_name (str) – the name of the PDB file
        • delta_t (float) – the time step between two velocity sets

        MMTK.Deformation¶

        Deformation energies in proteins

        This module implements deformational energies for use in the analysis of motions and conformational changes in macromolecules. A description of the techniques can be found in the following articles:

        K. Hinsen
        Analysis of domain motions by approximate normal mode calculations
        Proteins 33 (1998): 417-429
        K. Hinsen, A. Thomas, M.J. Field
        Analysis of domain motions in large proteins
        Proteins 34 (1999): 369-382
        class MMTK.Deformation.DeformationEnergyFunction(universe, fc_length=0.7, cutoff=1.2, factor=46402.0, form='exponential')[source]¶

        Bases: MMTK.Deformation.DeformationEvaluationFunction

        Infinite-displacement deformation energy function

        The deformation energy is the sum of the deformation values over all atoms of a system.

        The default values are appropriate for a Cα model of a protein with the global scaling described in the reference cited above.

        A DeformationEnergyFunction is called with one or two parameters. The first parameter is a ParticleVector object containing the infinitesimal displacements of the atoms for which the deformation energy is to be evaluated. The optional second argument can be set to a non-zero value to request the gradients of the energy in addition to the energy itself. In that case there are two return values (energy and the gradients in a ParticleVector object), otherwise only the energy is returned.

        Parameters:
        • universe (Universe) – the universe for which the deformation function is defined
        • fc_length (float) – the range parameter r_0 in the pair interaction term
        • cutoff (float) – the cutoff used in the deformation calculation
        • factor (float) – a global scaling factor
        • form (str) – the functional form (‘exponential’ or ‘calpha’)
        class MMTK.Deformation.DeformationFunction(universe, fc_length=0.7, cutoff=1.2, factor=46402.0, form='exponential')[source]¶

        Bases: MMTK.Deformation.DeformationEvaluationFunction

        Infinite-displacement deformation function

        The default values are appropriate for a Cα model of a protein with the global scaling described in the reference cited above.

        A DeformationFunction object must be called with a single parameter, which is a ParticleVector object containing the infinitesimal displacements of the atoms for which the deformation is to be evaluated. The return value is a ParticleScalar object containing the deformation value for each atom.

        Parameters:
        • universe (Universe) – the universe for which the deformation function is defined
        • fc_length (float) – the range parameter r_0 in the pair interaction term
        • cutoff (float) – the cutoff used in the deformation calculation
        • factor (float) – a global scaling factor
        • form (str) – the functional form (‘exponential’ or ‘calpha’)
        class MMTK.Deformation.DeformationReducer(universe, fc_length=0.7, cutoff=1.2, factor=46402.0, form='exponential')[source]¶

        Bases: MMTK.Deformation.DeformationEvaluationFunction

        Iterative reduction of the deformation energy

        The default values are appropriate for a Cα model of a protein with the global scaling described in the reference cited above.

        A DeformationReducer is called with two arguments. The first is a ParticleVector containing the initial infinitesimal displacements for all atoms. The second is an integer indicating the number of iterations. The result is a modification of the displacements by steepest-descent minimization of the deformation energy.

        Parameters:
        • universe (Universe) – the universe for which the deformation function is defined
        • fc_length (float) – the range parameter r_0 in the pair interaction term
        • cutoff (float) – the cutoff used in the deformation calculation
        • factor (float) – a global scaling factor
        • form (str) – the functional form (‘exponential’ or ‘calpha’)
        class MMTK.Deformation.FiniteDeformationEnergyFunction(universe, fc_length=0.7, cutoff=1.2, factor=46402.0, form='exponential')[source]¶

        Bases: MMTK.Deformation.DeformationEvaluationFunction

        Finite-displacement deformation energy function

        The deformation energy is the sum of the deformation values over all atoms of a system.

        The default values are appropriate for a Cα model of a protein with the global scaling described in the reference cited above.

        A FiniteDeformationEnergyFunction is called with one or two parameters. The first parameter is a ParticleVector object containing the alternate configuration of the universe for which the deformation energy is to be evaluated. The optional second argument can be set to a non-zero value to request the gradients of the energy in addition to the energy itself. In that case there are two return values (energy and the gradients in a ParticleVector object), otherwise only the energy is returned.

        Parameters:
        • universe (Universe) – the universe for which the deformation function is defined
        • fc_length (float) – the range parameter r_0 in the pair interaction term
        • cutoff (float) – the cutoff used in the deformation calculation
        • factor (float) – a global scaling factor
        • form (str) – the functional form (‘exponential’ or ‘calpha’)
        class MMTK.Deformation.FiniteDeformationFunction(universe, fc_length=0.7, cutoff=1.2, factor=46402.0, form='exponential')[source]¶

        Bases: MMTK.Deformation.DeformationEvaluationFunction

        Finite-displacement deformation function

        The default values are appropriate for a Cα model of a protein with the global scaling described in the reference cited above.

        A FiniteDeformationFunction object must be called with a single parameter, which is a Configuration or a ParticleVector object containing the alternate configuration of the universe for which the deformation is to be evaluated. The return value is a ParticleScalar object containing the deformation value for each atom.

        Parameters:
        • universe (Universe) – the universe for which the deformation function is defined
        • fc_length (float) – the range parameter r_0 in the pair interaction term
        • cutoff (float) – the cutoff used in the deformation calculation
        • factor (float) – a global scaling factor
        • form (str) – the functional form (‘exponential’ or ‘calpha’)
        class MMTK.Deformation.FiniteDeformationReducer(universe, fc_length=0.7, cutoff=1.2, factor=46402.0, form='exponential')[source]¶

        Bases: MMTK.Deformation.DeformationEvaluationFunction

        Iterative reduction of the finite-displacement deformation energy

        The default values are appropriate for a Cα model of a protein with the global scaling described in the reference cited above.

        A FiniteDeformationReducer is called with two arguments. The first is a ParticleVector or Configuration containing the alternate configuration for which the deformation energy is evaluated. The second is the RMS distance that defines the termination condition. The return value a configuration that differs from the input configuration by approximately the specified RMS distance, and which is obtained by iterative steepest-descent minimization of the finite-displacement deformation energy.

        Parameters:
        • universe (Universe) – the universe for which the deformation function is defined
        • fc_length (float) – the range parameter r_0 in the pair interaction term
        • cutoff (float) – the cutoff used in the deformation calculation
        • factor (float) – a global scaling factor
        • form (str) – the functional form (‘exponential’ or ‘calpha’)
        class MMTK.Deformation.NormalizedDeformationEnergyFunction(universe, fc_length=0.7, cutoff=1.2, factor=46402.0, form='exponential')[source]¶

        Bases: MMTK.Deformation.DeformationEnergyFunction

        Normalized infinite-displacement deformation energy function

        The normalized deformation energy is the sum of the normalized deformation values over all atoms of a system.

        The default values are appropriate for a Cα model of a protein with the global scaling described in the reference cited above. The normalization is defined by equation 10 of reference 1.

        A NormalizedDeformationEnergyFunction is called with one or two parameters. The first parameter is a ParticleVector object containing the infinitesimal displacements of the atoms for which the deformation energy is to be evaluated. The optional second argument can be set to a non-zero value to request the gradients of the energy in addition to the energy itself. In that case there are two return values (energy and the gradients in a ParticleVector object), otherwise only the energy is returned.

        Parameters:
        • universe (Universe) – the universe for which the deformation function is defined
        • fc_length (float) – the range parameter r_0 in the pair interaction term
        • cutoff (float) – the cutoff used in the deformation calculation
        • factor (float) – a global scaling factor
        • form (str) – the functional form (‘exponential’ or ‘calpha’)
        class MMTK.Deformation.NormalizedDeformationFunction(*args, **kwargs)[source]¶

        Bases: MMTK.Deformation.DeformationFunction

        Normalized infinite-displacement deformation function

        The default values are appropriate for a Cα model of a protein with the global scaling described in the reference cited above. The normalization is defined by equation 10 of reference 1 (see above).

        A NormalizedDeformationFunction object must be called with a single parameter, which is a ParticleVector object containing the infinitesimal displacements of the atoms for which the deformation is to be evaluated. The return value is a ParticleScalar object containing the deformation value for each atom.

        MMTK.Dynamics¶

        Molecular Dynamics integrators

        class MMTK.Dynamics.BarostatReset(first=0, last=None, skip=1)[source]¶

        Bases: MMTK.Trajectory.TrajectoryAction

        Barostat reset action

        A BarostatReset object is used in the action list of a VelocityVerletIntegrator. It resets the barostat coordinate to zero.

        Parameters:
        • first (int) – the number of the first step at which the action is executed
        • last (int) – the number of the first step at which the action is no longer executed. A value of None indicates that the action should be executed indefinitely.
        • skip (int) – the number of steps to skip between two applications of the action
        class MMTK.Dynamics.Heater(temp1, temp2, gradient, first=0, last=None, skip=1)[source]¶

        Bases: MMTK.Trajectory.TrajectoryAction

        Periodic heating action

        A Heater object us used in the action list of a VelocityVerletIntegrator. It scales the velocities to a temperature that increases over time.

        Parameters:
        • temp1 (float) – the temperature value to which the velocities should be scaled initially
        • temp2 (float) – the final temperature value to which the velocities should be scaled
        • gradient (float) – the temperature gradient (in K/ps)
        • first (int) – the number of the first step at which the action is executed
        • last (int) – the number of the first step at which the action is no longer executed. A value of None indicates that the action should be executed indefinitely.
        • skip (int) – the number of steps to skip between two applications of the action
        class MMTK.Dynamics.RotationRemover(first=0, last=None, skip=1)[source]¶

        Bases: MMTK.Trajectory.TrajectoryAction

        Action that eliminates global rotation

        A RotationRemover object is used in the action list of a VelocityVerletIntegrator. It adjusts the atomic velocities such that the total angular momentum is zero.

        Parameters:
        • first (int) – the number of the first step at which the action is executed
        • last (int) – the number of the first step at which the action is no longer executed. A value of None indicates that the action should be executed indefinitely.
        • skip (int) – the number of steps to skip between two applications of the action
        class MMTK.Dynamics.TranslationRemover(first=0, last=None, skip=1)[source]¶

        Bases: MMTK.Trajectory.TrajectoryAction

        Action that eliminates global translation

        A TranslationRemover object is used in the action list of a VelocityVerletIntegrator. It subtracts the total velocity of the system from each atomic velocity.

        Parameters:
        • first (int) – the number of the first step at which the action is executed
        • last (int) – the number of the first step at which the action is no longer executed. A value of None indicates that the action should be executed indefinitely.
        • skip (int) – the number of steps to skip between two applications of the action
        class MMTK.Dynamics.VelocityScaler(temperature, window=0.0, first=0, last=None, skip=1)[source]¶

        Bases: MMTK.Trajectory.TrajectoryAction

        Periodic velocity scaling action

        A VelocityScaler object is used in the action list of a VelocityVerletIntegrator. It rescales all atomic velocities by a common factor to make the temperature of the system equal to a predefined value.

        Parameters:
        • temperature (float) – the temperature value to which the velocities should be scaled
        • window (float) – the deviation from the ideal temperature that is tolerated in either direction before rescaling takes place
        • first (int) – the number of the first step at which the action is executed
        • last (int) – the number of the first step at which the action is no longer executed. A value of None indicates that the action should be executed indefinitely.
        • skip (int) – the number of steps to skip between two applications of the action
        class MMTK.Dynamics.VelocityVerletIntegrator(universe, **options)[source]¶

        Bases: MMTK.Dynamics.Integrator

        Velocity-Verlet molecular dynamics integrator

        The integrator can handle fixed atoms, distance constraints, a thermostat, and a barostat, as well as any combination. It is fully thread-safe.

        The integration is started by calling the integrator object. All the keyword options (see documnentation of __init__) can be specified either when creating the integrator or when calling it.

        The following data categories and variables are available for output:

        • category “time”: time
        • category “configuration”: configuration and box size (for periodic universes)
        • category “velocities”: atomic velocities
        • category “gradients”: energy gradients for each atom
        • category “energy”: potential and kinetic energy, plus extended-system energy terms if a thermostat and/or barostat are used
        • category “thermodynamic”: temperature, volume (if a barostat is used) and pressure
        • category “auxiliary”: extended-system coordinates if a thermostat and/or barostat are used
        Parameters:
        • universe (Universe) – the universe on which the integrator acts
        • steps (int) – the number of integration steps (default is 100)
        • delta_t (float) – the time step (default is 1 fs)
        • actions (list) – a list of actions to be executed periodically (default is none)
        • threads (int) – the number of threads to use in energy evaluation (default set by MMTK_ENERGY_THREADS)
        • background (bool) – if True, the integration is executed as a separate thread (default: False)
        • mpi_communicator (Scientific.MPI.MPICommunicator) – an MPI communicator object, or None, meaning no parallelization (default: None)

        MMTK.Environment¶

        Environment objects

        Environment objects are objects that define a simulation system without being composed of atoms. Examples are thermostats, barostats, external fields, etc.

        class MMTK.Environment.AndersenBarostat(pressure, relaxation_time=1.5)[source]¶

        Bases: MMTK.Environment.EnvironmentObject

        Andersen barostat for Molecular Dynamics

        A barostat object can be added to a universe and will then together with a thermostat object modify the integration algorithm to a simulation of an NPT ensemble.

        Parameters:
        • pressure (float) – the pressure set by the barostat
        • relaxation_time (float) – the relaxation time of the barostat coordinate
        class MMTK.Environment.NoseThermostat(temperature, relaxation_time=0.2)[source]¶

        Bases: MMTK.Environment.EnvironmentObject

        Nose thermostat for Molecular Dynamics

        A thermostat object can be added to a universe and will then modify the integration algorithm to a simulation of an NVT ensemble.

        Parameters:
        • temperature (float) – the temperature set by the thermostat
        • relaxation_time (float) – the relaxation time of the thermostat coordinate

        MMTK.Field¶

        Scalar and vector fields in molecular systems

        This module defines field objects that are useful in the analysis and visualization of collective motions in molecular systems. Atomic quantities characterizing collective motions vary slowly in space, and can be considered functions of position instead of values per atom. Functions of position are called fields, and mathematical techniques for the analysis of fields have proven useful in many branches of physics. Fields can be described numerically by values on a regular grid. In addition to permitting the application of vector analysis methods to atomic quantities, the introduction of fields is a valuable visualization aid, because information defined on a coarse regular grid can be added to a picture of a molecular system without overloading it.

        class MMTK.Field.AtomicField(system, grid_size, values)[source]¶

        Bases: object

        A field whose values are determined by atomic quantities

        This is an abstract base class. To create field objects, use one of its subclasses.

        particleValues()[source]¶
        Returns:the values of the field at the positions of the atoms
        Return type:ParticleProperty
        view(scale=1.0, length_scale=1.0, color=None)[source]¶

        Shows a graphical representation of the field using a VRML viewer.

        Parameters:
        • scale (float) – scale factor applied to all field values
        • color (Scientific.Visualization.Color) – the color for all graphics objects
        writeToFile(filename, scale=1.0, length_scale=1.0, color=None)[source]¶

        Writes a graphical representation of the field to a VRML file.

        Parameters:
        • filename (str) – the name of the destination file
        • scale (float) – scale factor applied to all field values
        • color (Scientific.Visualization.Color) – the color for all graphics objects
        class MMTK.Field.AtomicScalarField(system, grid_size, values)[source]¶

        Bases: MMTK.Field.AtomicField, MMTK.Visualization.Viewable

        Scalar field defined by atomic quantities

        For visualization, scalar fields are represented by a small cube on each grid point whose color indicates the field’s value on a symmetric red-to-green color scale defined by the range of the field values.

        Additional keyword options exist for graphics object generation:
        • scale=factor, to multiply all field values by a factor
        • range=(min, max), to eliminate graphics objects for values that are smaller than min or larger than max
        Parameters:
        • system – any subset of a molecular system
        • grid_size (float) – the spacing of a cubic grid on which the field values are defined. The value for a point is obtained by averaging the atomic quantities over all atoms in a cube centered on the point.
        • values (ParticleScalar) – the atomic values that define the field
        gradient()[source]¶
        Returns:the gradient of the field
        Return type:AtomicVectorField
        laplacian()[source]¶
        Returns:the laplacian of the field
        Return type:AtomicScalarField
        class MMTK.Field.AtomicVectorField(system, grid_size, values)[source]¶

        Bases: MMTK.Field.AtomicField, MMTK.Visualization.Viewable

        Vector field defined by atomic quantities

        For visualization, scalar fields are represented by a small arrow on each grid point. The arrow starts at the grid point and represents the vector value at that point.

        Additional keyword options exist for graphics object generation:
        • scale=factor, to multiply all field values by a factor
        • diameter=number, to define the diameter of the arrow objects (default: 1.)
        • range=(min, max), to eliminate graphics objects for values that are smaller than min or larger than max
        • color=string, to define the color of the arrows by a color name
        Parameters:
        • system – any subset of a molecular system
        • grid_size (float) – the spacing of a cubic grid on which the field values are defined. The value for a point is obtained by averaging the atomic quantities over all atoms in a cube centered on the point.
        • values (ParticleVector) – the atomic values that define the field
        curl()[source]¶
        Returns:the curl of the field
        Return type:AtomicVectorField
        divergence()[source]¶
        Returns:the divergence of the field
        Return type:AtomicScalarField
        laplacian()[source]¶
        Returns:the laplacian of the field
        Return type:AtomicVectorField
        length()[source]¶
        Returns:a field of the length of the field vectors
        Return type:AtomicScalarField

        MMTK.ForceFields¶

        MMTK.ForceFields.AmberForceField¶

        Amber force field implementation

        General comments about parameters for Lennard-Jones and electrostatic interactions:

        Pair interactions in periodic systems are calculated using the minimum-image convention; the cutoff should therefore never be larger than half the smallest edge length of the elementary cell.

        For Lennard-Jones interactions, all terms for pairs whose distance exceeds the cutoff are set to zero, without any form of correction. For electrostatic interactions, a charge-neutralizing surface charge density is added around the cutoff sphere in order to reduce cutoff effects (see Wolf et al., J. Chem. Phys. 17, 8254 (1999)).

        For Ewald summation, there are some additional parameters that can be specified by dictionary entries:

        • “beta” specifies the Ewald screening parameter

        • “real_cutoff” specifies the cutoff for the real-space sum.

          It should be significantly larger than 1/beta to ensure that the neglected terms are small.

        • “reciprocal_cutoff” specifies the cutoff for the reciprocal-space sum.

          Note that, like the real-space cutoff, this is a distance; it describes the smallest wavelength of plane waves to take into account. Consequently, a smaller value means a more precise (and more expensive) calculation.

        MMTK provides default values for these parameter which are calculated as a function of the system size. However, these parameters are exaggerated in most cases of practical interest and can lead to excessive calculation times for large systems. It is preferable to determine suitable values empirically for the specific system to be simulated.

        The method “screened” uses the real-space part of the Ewald sum with a charge-neutralizing surface charge density around the cutoff sphere, and no reciprocal sum (see article cited above). It requires the specification of the dictionary entries “cutoff” and “beta”.

        MMTK.ForceFields.Amber94ForceField¶

        class MMTK.ForceFields.Amber.Amber94ForceField(lj_options=None, es_options=None, bonded_scale_factor=1.0, **kwargs)¶

        Bases: MMTK.ForceFields.MMForceField.MMForceField

        Amber 94 force field

        Parameters:
        • lj_options –

          parameters for Lennard-Jones interactions; one of:

          • a number, specifying the cutoff
          • None, meaning the default method (no cutoff; inclusion of all pairs, using the minimum-image conventions for periodic universes)
          • a dictionary with an entry “method” which specifies the calculation method as either “direct” (all pair terms) or “cutoff”, with the cutoff specified by the dictionary entry “cutoff”.
        • es_options –

          parameters for electrostatic interactions; one of:

          • a number, specifying the cutoff
          • None, meaning the default method (all pairs without cutoff for non-periodic system, Ewald summation for periodic systems)
          • a dictionary with an entry “method” which specifies the calculation method as either “direct” (all pair terms), “cutoff” (with the cutoff specified by the dictionary entry “cutoff”), “ewald” (Ewald summation, only for periodic universes), or “screened”.
        • mod_files – a list of parameter modification files. The file format is the one defined by AMBER. Each item in the list can be either a file object or a filename, filenames are looked up first relative to the current directory and then relative to the directory containing MMTK’s AMBER parameter files.

        MMTK.ForceFields.Amber99ForceField¶

        class MMTK.ForceFields.Amber.Amber99ForceField(lj_options=None, es_options=None, bonded_scale_factor=1.0, **kwargs)¶

        Bases: MMTK.ForceFields.MMForceField.MMForceField

        Amber 99 force field

        Parameters:
        • lj_options –

          parameters for Lennard-Jones interactions; one of:

          • a number, specifying the cutoff
          • None, meaning the default method (no cutoff; inclusion of all pairs, using the minimum-image conventions for periodic universes)
          • a dictionary with an entry “method” which specifies the calculation method as either “direct” (all pair terms) or “cutoff”, with the cutoff specified by the dictionary entry “cutoff”.
        • es_options –

          parameters for electrostatic interactions; one of:

          • a number, specifying the cutoff
          • None, meaning the default method (all pairs without cutoff for non-periodic system, Ewald summation for periodic systems)
          • a dictionary with an entry “method” which specifies the calculation method as either “direct” (all pair terms), “cutoff” (with the cutoff specified by the dictionary entry “cutoff”), “ewald” (Ewald summation, only for periodic universes), or “screened”.
        • mod_files – a list of parameter modification files. The file format is the one defined by AMBER. Each item in the list can be either a file object or a filename, filenames are looked up first relative to the current directory and then relative to the directory containing MMTK’s AMBER parameter files.

        MMTK.ForceFields.OPLSForceField¶

        class MMTK.ForceFields.Amber.OPLSForceField(lj_options=None, es_options=None, bonded_scale_factor=1.0, **kwargs)¶

        Bases: MMTK.ForceFields.MMForceField.MMForceField

        MMTK.ForceFields.AnisotropicNetworkForceField¶

        class MMTK.ForceFields.ANMFF.AnisotropicNetworkForceField(cutoff=None, scale_factor=1.0)[source]¶

        Bases: MMTK.ForceFields.ForceField.ForceField

        Effective harmonic force field for an ANM protein model

        Parameters:
        • cutoff (float) – the cutoff for pair interactions. Pair interactions in periodic systems are calculated using the minimum-image convention; the cutoff should therefore never be larger than half the smallest edge length of the elementary cell.
        • scale_factor (float) – a global scaling factor

        MMTK.ForceFields.CalphaForceField¶

        class MMTK.ForceFields.CalphaFF.CalphaForceField(cutoff=None, scale_factor=1.0, version=1)[source]¶

        Bases: MMTK.ForceFields.ForceField.ForceField

        Effective harmonic force field for a C-alpha protein model

        Parameters:
        • cutoff (float) – the cutoff for pair interactions, should be at least 2.5 nm. Pair interactions in periodic systems are calculated using the minimum-image convention; the cutoff should therefore never be larger than half the smallest edge length of the elementary cell.
        • scale_factor (float) – a global scaling factor

        MMTK.ForceFields.DeformationForceField¶

        class MMTK.ForceFields.DeformationFF.DeformationForceField(fc_length=0.7, cutoff=1.2, factor=46402.0)[source]¶

        Bases: MMTK.ForceFields.ForceField.ForceField

        Deformation force field for protein normal mode calculations

        The pair interaction force constant has the form k(r)=factor*exp(-(r**2-0.01)/range**2). The default value for range is appropriate for a C-alpha model of a protein. The offset of 0.01 is a convenience for defining factor; with this definition, factor is the force constant for a distance of 0.1nm.

        Parameters:
        • fc_length (float) – a range parameter
        • cutoff (float) – the cutoff for pair interactions, should be at least 2.5 nm. Pair interactions in periodic systems are calculated using the minimum-image convention; the cutoff should therefore never be larger than half the smallest edge length of the elementary cell.
        • factor (float) – a global scaling factor

        MMTK.ForceFields.HarmonicForceField¶

        class MMTK.ForceFields.BondFF.HarmonicForceField(fc_length=0.45, cutoff=0.6, factor=400.0)[source]¶

        Bases: MMTK.ForceFields.MMForceField.MMAtomParameters, MMTK.ForceFields.ForceField.CompoundForceField

        Simplified harmonic force field for normal mode calculations

        This force field is made up of the bonded terms from the Amber 94 force field with the equilibrium positions of all terms changed to the corresponding values in the input configuration, such that the input configuration becomes an energy minimum by construction. The nonbonded terms are replaced by a generic short-ranged deformation term.

        For a description of this force field, see:
        Hinsen & Kneller, J. Chem. Phys. 24, 10766 (1999)
        For an application to DNA, see:
        Viduna, Hinsen & Kneller, Phys. Rev. E 3, 3986 (2000)

        MMTK.ForceFields.LennardJonesForceField¶

        class MMTK.ForceFields.LennardJonesFF.LennardJonesForceField(cutoff=None)[source]¶

        Bases: MMTK.ForceFields.NonBondedInteractions.LJForceField

        Lennard-Jones force field

        The Lennard-Jones parameters are taken from the atom attributes LJ_radius and LJ_energy. The pair interaction energy has the form U(r)=4*LJ_energy*((LJ_radius/r)**12-(LJ_radius/r)**6).

        Parameters:cutoff (float) – a cutoff value or None, meaning no cutoff. Pair interactions in periodic systems are calculated using the minimum-image convention; the cutoff should therefore never be larger than half the smallest edge length of the elementary cell.

        MMTK.ForceFields.SPCEForceField¶

        class MMTK.ForceFields.SPCEFF.SPCEForceField(lj_options=None, es_options=None)[source]¶

        Bases: MMTK.ForceFields.MMForceField.MMForceField

        Force field for water simulations with the SPC/E model

        Parameters:
        • lj_options –

          parameters for Lennard-Jones interactions; one of:

          • a number, specifying the cutoff
          • None, meaning the default method (no cutoff; inclusion of all pairs, using the minimum-image conventions for periodic universes)
          • a dictionary with an entry “method” which specifies the calculation method as either “direct” (all pair terms) or “cutoff”, with the cutoff specified by the dictionary entry “cutoff”.
        • es_options –

          parameters for electrostatic interactions; one of:

          • a number, specifying the cutoff
          • None, meaning the default method (all pairs without cutoff for non-periodic system, Ewald summation for periodic systems)
          • a dictionary with an entry “method” which specifies the calculation method as either “direct” (all pair terms), “cutoff” (with the cutoff specified by the dictionary entry “cutoff”), “ewald” (Ewald summation, only for periodic universes), “screened” or “multipole” (fast-multipole method).

        MMTK.ForceFields.ForceFieldTest¶

        Force field consistency tests

        MMTK.ForceFields.ForceFieldTest.forceConstantTest(universe, atoms=None, delta=0.0001)[source]¶

        Test force constants by comparing to the numerical derivatives of the gradients.

        Parameters:
        • universe (Universe) – the universe on which the test is performed
        • atoms (list) – the atoms of the universe for which the gradient is tested (default: all atoms)
        • delta (float) – the step size used in calculating the numerical derivatives
        MMTK.ForceFields.ForceFieldTest.gradientTest(universe, atoms=None, delta=0.0001)[source]¶

        Test gradients by comparing to numerical derivatives of the energy.

        Parameters:
        • universe (Universe) – the universe on which the test is performed
        • atoms (list) – the atoms of the universe for which the gradient is tested (default: all atoms)
        • delta (float) – the step size used in calculating the numerical derivatives
        MMTK.ForceFields.ForceFieldTest.virialTest(universe)[source]¶

        Test the virial by comparing to an explicit computation from positions and gradients.

        Parameters:universe (Universe) – the universe on which the test is performed

        MMTK.ForceFields.Restraints¶

        Harmonic restraint terms that can be added to any force field

        Example:

        from MMTK import *
from MMTK.ForceFields import Amber99ForceField
from MMTK.ForceFields.Restraints import HarmonicDistanceRestraint

universe = InfiniteUniverse()
universe.protein = Protein('bala1')
force_field = Amber99ForceField() +                HarmonicDistanceRestraint(universe.protein[0][1].peptide.N,
                                        universe.protein[0][1].peptide.O,
                                        0.5, 10.)
universe.setForceField(force_field)
        class MMTK.ForceFields.Restraints.HarmonicAngleRestraint(atom1, atom2, atom3, angle, force_constant)[source]¶

        Bases: MMTK.ForceFields.ForceField.ForceField

        Harmonic angle restraint between three atoms

        Parameters:
        • atom1 (Atom) – first atom
        • atom2 (Atom) – second (central) atom
        • atom3 (Atom) – third atom
        • angle (float) – the angle at which the restraint is zero
        • force_constant – the force constant of the restraint term. The functional form of the restraint is force_constant*(phi-angle)**2, where phi is the angle atom1-atom2-atom3.
        class MMTK.ForceFields.Restraints.HarmonicDihedralRestraint(atom1, atom2, atom3, atom4, dihedral, force_constant)[source]¶

        Bases: MMTK.ForceFields.ForceField.ForceField

        Harmonic dihedral angle restraint between four atoms

        Parameters:
        • atom1 (Atom) – first atom
        • atom2 (Atom) – second (axis) atom
        • atom3 (Atom) – third (axis)atom
        • atom4 (Atom) – fourth atom
        • dihedral (float) – the dihedral angle at which the restraint is zero
        • force_constant – the force constant of the restraint term. The functional form of the restraint is force_constant*(phi-abs(dihedral))**2, where phi is the dihedral angle atom1-atom2-atom3-atom4.
        class MMTK.ForceFields.Restraints.HarmonicDistanceRestraint(obj1, obj2, distance, force_constant, nb_exclusion=False)[source]¶

        Bases: MMTK.ForceFields.ForceField.ForceField

        Harmonic distance restraint between two atoms

        Parameters:
        • obj1 (GroupOfAtoms) – the object defining center-of-mass 1
        • obj2 (GroupOfAtoms) – the object defining center-of-mass 2
        • distance (float) – the distance between cm 1 and cm2 at which the restraint is zero
        • force_constant (float) – the force constant of the restraint term. The functional form of the restraint is force_constant*((cm1-cm2).length()-distance)**2, where cm1 and cm2 are the centrer-of-mass positions of the two objects.
        • nb_exclussion (bool) – if True, non-bonded interactions between the restrained atoms are suppressed, as for a chemical bond
        class MMTK.ForceFields.Restraints.HarmonicTrapForceField(obj, center, force_constant)[source]¶

        Bases: MMTK.ForceFields.ForceField.ForceField

        Harmonic potential with respect to a fixed point in space

        Parameters:
        • obj (GroupOfAtoms) – the object on whose center of mass the force field acts
        • center (Scientific.Geometry.Vector) – the point to which the atom is attached by the harmonic potential
        • force_constant (float) – the force constant of the harmonic potential

        MMTK.FourierBasis¶

        Fourier basis for low-frequency normal mode calculations

        This module provides a basis that is suitable for the calculation of low-frequency normal modes. The basis is derived from vector fields whose components are stationary waves in a box surrounding the system. For a description, see

        K. Hinsen
        Analysis of domain motions by approximate normal mode calculations
        Proteins 33 (1998): 417-429
        class MMTK.FourierBasis.FourierBasis(universe, cutoff)[source]¶

        Bases: object

        Collective-motion basis for low-frequency normal mode calculations

        A FourierBasis behaves like a sequence of ParticleVector objects. The vectors are B{not} orthonormal, because orthonormalization is handled automatically by the normal mode class.

        Parameters:
        • universe (Universe) – the universe for which the basis will be used
        • cutoff (float) – the wavelength cutoff. A smaller value yields a larger basis.
        MMTK.FourierBasis.countBasisVectors(universe, cutoff)[source]¶

        Estimate the number of basis vectors for a given universe and cutoff

        Parameters:
        • universe (Universe) – the universe
        • cutoff (float) – the wavelength cutoff. A smaller value yields a larger basis.
        Returns:

        the number of basis vectors in a FourierBasis
        Return type:

        int
        MMTK.FourierBasis.estimateCutoff(universe, nmodes)[source]¶

        Estimate the cutoff that yields a given number of basis vectors for a given universe.

        Parameters:
        • universe (Universe) – the universe
        • nmodes (int) – the number of basis vectors in a FourierBasis
        Returns:

        the wavelength cutoff and the precise number of basis vectors
        Return type:

        tuple (float, int)

        MMTK.Geometry¶

        Elementary geometrical objects and operations

        There are essentially two kinds of geometrical objects: shape objects (spheres, planes, etc.), from which intersections can be calculated, and lattice objects, which define a regular arrangements of points.

        class MMTK.Geometry.BCCLattice(cellsize, cells, function=None, base=None)[source]¶

        Bases: MMTK.Geometry.RhombicLattice

        Body-Centered Cubic lattice

        A Body-Centered Cubic lattice has two points per elementary cell.

        Parameters:
        • cellsize (float) – the edge length of the elementary cell
        • cells – a list of integers, whose length must equal the number of dimensions. Each entry specifies how often a cell is repeated along this dimension.
        • function – a function that is called for every lattice point with the vector describing the point as argument. The return value of this function is stored in the lattice object. If the function is ‘None’, the vector is directly stored in the lattice object.
        class MMTK.Geometry.Box(corner1, corner2)[source]¶

        Bases: MMTK.Geometry.GeometricalObject3D

        Rectangular box aligned with the coordinate axes

        Parameters:
        • corner1 (Scientific.Geometry.Vector) – one corner of the box
        • corner2 (Scientific.Geometry.Vector) – the diagonally opposite corner
        class MMTK.Geometry.BravaisLattice(lattice_vectors, cells, function=None, base=None)[source]¶

        Bases: MMTK.Geometry.RhombicLattice

        Bravais lattice

        A Bravais lattice is a special case of a general rhombic lattice in which the elementary cell contains only one point.

        Parameters:
        • lattice_vectors – a list of lattice vectors. Each lattice vector defines a lattice dimension (only values from one to three make sense) and indicates the displacement along this dimension from one cell to the next.
        • cells – a list of integers, whose length must equal the number of dimensions. Each entry specifies how often a cell is repeated along this dimension.
        • function – a function that is called for every lattice point with the vector describing the point as argument. The return value of this function is stored in the lattice object. If the function is ‘None’, the vector is directly stored in the lattice object.
        class MMTK.Geometry.Circle(center, normal, radius)[source]¶

        Bases: MMTK.Geometry.GeometricalObject3D

        2D circle in 3D space

        Parameters:
        • center (Scientific.Geometry.Vector) – the center of the circle
        • normal (Scientific.Geometry.Vector) – the normal vector of the circle’s plane
        • radius (float) – the radius of the circle
        class MMTK.Geometry.Cone(center, axis, angle)[source]¶

        Bases: MMTK.Geometry.GeometricalObject3D

        Cone

        Parameters:
        • center (Scientific.Geometry.Vector) – the center (tip) of the cone
        • axis (Scientific.Geometry.Vector) – the direction of the axis of rotational symmetry
        • angle (float) – the angle between any straight line on the cone surface and the axis of symmetry
        class MMTK.Geometry.Cylinder(center1, center2, radius)[source]¶

        Bases: MMTK.Geometry.GeometricalObject3D

        Cylinder

        Parameters:
        • center1 (Scientific.Geometry.Vector) – the center of the bottom circle
        • center2 (Scientific.Geometry.Vector) – the center of the top circle
        • radius (float) – the radius of the cylinder
        class MMTK.Geometry.FCCLattice(cellsize, cells, function=None, base=None)[source]¶

        Bases: MMTK.Geometry.RhombicLattice

        Face-Centered Cubic lattice

        A Face-Centered Cubic lattice has four points per elementary cell.

        Parameters:
        • cellsize (float) – the edge length of the elementary cell
        • cells – a list of integers, whose length must equal the number of dimensions. Each entry specifies how often a cell is repeated along this dimension.
        • function – a function that is called for every lattice point with the vector describing the point as argument. The return value of this function is stored in the lattice object. If the function is ‘None’, the vector is directly stored in the lattice object.
        class MMTK.Geometry.GeometricalObject3D[source]¶

        Bases: object

        3D shape object

        This is an abstract base class. To create 3D objects, use one of its subclasses.

        enclosesPoint(point)[source]¶
        Parameters:point (Scientific.Geometry.Vector) – a point in 3D space
        Returns:True of the point is inside the volume of the object
        Return type:bool
        hasPoint(point)[source]¶
        Parameters:point (Scientific.Geometry.Vector) – a point in 3D space
        Returns:True of the point lies on the surface of the object
        Return type:bool
        intersectWith(other)[source]¶
        Parameters:other – another 3D object
        Returns:a 3D object that represents the intersection with other
        volume()[source]¶
        Returns:the volume of the object
        Return type:float
        class MMTK.Geometry.Lattice(function)[source]¶

        Bases: object

        General lattice

        Lattices are special sequence objects that contain vectors (points on the lattice) or objects that are constructed as functions of these vectors. Lattice objects behave like lists, i.e. they permit indexing, length inquiry, and iteration by ‘for’-loops. Note that the lattices represented by these objects are finite, they have a finite (and fixed) number of repetitions along each lattice vector.

        This is an abstract base class. To create lattice objects, use one of its subclasses.

        class MMTK.Geometry.Line(point, direction)[source]¶

        Bases: MMTK.Geometry.GeometricalObject3D

        Line

        Parameters:
        • point (Scientific.Geometry.Vector) – any point on the line
        • direction (Scientific.Geometry.Vector) – the direction of the line
        distanceFrom(point)[source]¶
        Parameters:point (Scientific.Geometry.Vector) – a point in space
        Returns:the smallest distance of the point from the line
        Return type:float
        perpendicularVector(plane)[source]¶
        Parameters:plane (Plane) – a plane
        Returns:a vector in the plane perpendicular to the line
        Return type:Scientific.Geometry.Vector
        projectionOf(point)[source]¶
        Parameters:point (Scientific.Geometry.Vector) – a point in space
        Returns:the orthogonal projection of the point onto the line
        Return type:Scientific.Geometry.Vector
        class MMTK.Geometry.Plane(*args)[source]¶

        Bases: MMTK.Geometry.GeometricalObject3D

        2D plane in 3D space

        Parameters:args – three points (of type Scientific.Geometry.Vector) that are not collinear, or a point in the plane and the normal vector of the plane
        class MMTK.Geometry.RhombicLattice(elementary_cell, lattice_vectors, cells, function=None, base=None)[source]¶

        Bases: MMTK.Geometry.Lattice

        Rhombic lattice

        Parameters:
        • elementary_cell – a list of points in the elementary cell
        • lattice_vectors – a list of lattice vectors. Each lattice vector defines a lattice dimension (only values from one to three make sense) and indicates the displacement along this dimension from one cell to the next.
        • cells – a list of integers, whose length must equal the number of dimensions. Each entry specifies how often a cell is repeated along this dimension.
        • function – a function that is called for every lattice point with the vector describing the point as argument. The return value of this function is stored in the lattice object. If the function is ‘None’, the vector is directly stored in the lattice object.
        class MMTK.Geometry.SCLattice(cellsize, cells, function=None, base=None)[source]¶

        Bases: MMTK.Geometry.BravaisLattice

        Simple Cubic lattice

        A Simple Cubic lattice is a special case of a Bravais lattice in which the elementary cell is a cube.

        Parameters:
        • cellsize (float) – the edge length of the elementary cell
        • cells – a list of integers, whose length must equal the number of dimensions. Each entry specifies how often a cell is repeated along this dimension.
        • function – a function that is called for every lattice point with the vector describing the point as argument. The return value of this function is stored in the lattice object. If the function is ‘None’, the vector is directly stored in the lattice object.
        class MMTK.Geometry.Sphere(center, radius)[source]¶

        Bases: MMTK.Geometry.GeometricalObject3D

        Sphere

        Parameters:
        • center (Scientific.Geometry.Vector) – the center of the sphere
        • radius (float) – the radius of the sphere
        MMTK.Geometry.superpositionFit(confs)[source]¶
        Parameters:confs (sequence of (float, Vector, Vector)) – the weight, reference position, and alternate position for each atom
        Returns:the quaternion representing the rotation, the center of mass in the alternate configuraton, the center of mass in the reference configuration, and the RMS distance after the optimal superposition

        MMTK.InternalCoordinates¶

        Manipulation of molecular configurations in terms of internal coordinates

        class MMTK.InternalCoordinates.BondAngle(atom1, atom2, atom3)[source]¶

        Bases: MMTK.InternalCoordinates.InternalCoordinate

        Bond angle

        A BondAngle object permits calculating and modifying the angle between two bonds in a molecule, under the condition that the bonds are not part of a circular bond structure (but it is not a problem to have a circular bond structure elsewhere in the molecule). Modifying the bond angle rotates the parts of the molecule on both sides of the central atom around an axis passing through the central atom and perpendicular to the plane defined by the two bonds in such a way that there is no overall rotation of the molecule. The central atom and any other atoms bonded to it do not move.

        The initial construction of the BondAngle object can be expensive (the bond structure of the molecule must be analyzed). It is therefore advisable to keep the object rather than recreate it frequently. Note that if you only want to calculate bond angles (no modification), the method Universe.angle is simpler and faster.

        Parameters:
        • atom1 (Atom) – the first atom that defines the angle
        • atom2 (Atom) – the second and central atom that defines the bond
        • atom3 (Atom) – the third atom that defines the bond
        getValue(conf=None)[source]¶
        Parameters:conf (Configuration) – a configuration (defaults to the current configuration)
        Returns:the size of the angle in the configuration conf
        Return type:float
        setValue(value)[source]¶

        Sets the size of the angle :param value: the desired angle :type value: float

        class MMTK.InternalCoordinates.BondLength(atom1, atom2)[source]¶

        Bases: MMTK.InternalCoordinates.InternalCoordinate

        Bond length coordinate

        A BondLength object permits calculating and modifying the length of a bond in a molecule, under the condition that the bond is not part of a circular bond structure (but it is not a problem to have a circular bond structure elsewhere in the molecule). Modifying the bond length moves the parts of the molecule on both sides of the bond along the bond direction in such a way that the center of mass of the molecule does not change.

        The initial construction of the BondLength object can be expensive (the bond structure of the molecule must be analyzed). It is therefore advisable to keep the object rather than recreate it frequently. Note that if you only want to calculate bond lengths (no modification), the method Universe.distance is simpler and faster.

        Parameters:
        • atom1 (Atom) – the first atom that defines the bond
        • atom2 (Atom) – the second atom that defines the bond
        getValue(conf=None)[source]¶
        Parameters:conf (Configuration) – a configuration (defaults to the current configuration)
        Returns:the length of the bond in the configuration conf
        Return type:float
        setValue(value)[source]¶

        Sets the length of the bond :param value: the desired length of the bond :type value: float

        class MMTK.InternalCoordinates.DihedralAngle(atom1, atom2, atom3, atom4)[source]¶

        Bases: MMTK.InternalCoordinates.InternalCoordinate

        Dihedral angle

        A DihedralAngle object permits calculating and modifying the dihedral angle defined by three consecutive bonds in a molecule, under the condition that the central bond is not part of a circular bond structure (but it is not a problem to have a circular bond structure elsewhere in the molecule). Modifying the dihedral angle rotates the parts of the molecule on both sides of the central bond around this central bond in such a way that there is no overall rotation of the molecule.

        The initial construction of the DihedralAngle object can be expensive (the bond structure of the molecule must be analyzed). It is therefore advisable to keep the object rather than recreate it frequently. Note that if you only want to calculate bond angles (no modification), the method Universe.dihedral is simpler and faster.

        Parameters:
        • atom1 (Atom) – the first atom that defines the dihedral
        • atom2 (Atom) – the second atom that defines the dihedral, must be on the central bond
        • atom3 (Atom) – the third atom that defines the dihedral, must be on the central bond
        • atom4 (Atom) – the fourth atom that defines the dihedral
        getValue(conf=None)[source]¶
        Parameters:conf (Configuration) – a configuration (defaults to the current configuration)
        Returns:the size of the dihedral angle in the configuration conf
        Return type:float
        setValue(value)[source]¶

        Sets the size of the dihedral :param value: the desired dihedral angle :type value: float

        MMTK.Minimization¶

        Energy minimizers

        class MMTK.Minimization.ConjugateGradientMinimizer(universe, **options)[source]¶

        Bases: MMTK.Minimization.Minimizer

        Conjugate gradient minimizer

        The minimizer can handle fixed atoms, but no distance constraints. It is fully thread-safe.

        The minimization is started by calling the minimizer object. All the keyword options can be specified either when creating the minimizer or when calling it.

        The following data categories and variables are available for output:

        • category “configuration”: configuration and box size (for periodic universes)

        • category “gradients”: energy gradients for each atom

        • category “energy”: potential energy and

          norm of the potential energy gradient

        Parameters:
        • universe (Universe) – the universe on which the integrator acts
        • steps (int) – the number of minimization steps (default is 100)
        • step_size (float) – the initial size of a minimization step (default is 0.002 nm)
        • convergence (float) – the root-mean-square gradient length at which minimization stops (default is 0.01 kJ/mol/nm)
        • actions (list) – a list of actions to be executed periodically (default is none)
        • threads (int) – the number of threads to use in energy evaluation (default set by MMTK_ENERGY_THREADS)
        • background (bool) – if True, the integration is executed as a separate thread (default: False)
        class MMTK.Minimization.SteepestDescentMinimizer(universe, **options)[source]¶

        Bases: MMTK.Minimization.Minimizer

        Steepest-descent minimizer

        The minimizer can handle fixed atoms, but no distance constraints. It is fully thread-safe.

        The minimization is started by calling the minimizer object. All the keyword options (see documnentation of __init__) can be specified either when creating the minimizer or when calling it.

        The following data categories and variables are available for output:

        • category “configuration”: configuration and box size (for periodic universes)

        • category “gradients”: energy gradients for each atom

        • category “energy”: potential energy and

          norm of the potential energy gradient

        Parameters:
        • universe (Universe) – the universe on which the integrator acts
        • steps (int) – the number of minimization steps (default is 100)
        • step_size (float) – the initial size of a minimization step (default is 0.002 nm)
        • convergence (float) – the root-mean-square gradient length at which minimization stops (default is 0.01 kJ/mol/nm)
        • actions (list) – a list of actions to be executed periodically (default is none)
        • threads (int) – the number of threads to use in energy evaluation (default set by MMTK_ENERGY_THREADS)
        • background (bool) – if True, the integration is executed as a separate thread (default: False)
        • mpi_communicator (Scientific.MPI.MPICommunicator) – an MPI communicator object, or None, meaning no parallelization (default: None)

        MMTK.MolecularSurface¶

        Molecular surfaces and volumes.

        MMTK.MolecularSurface.findContacts(object1, object2, contact_factor=1.0, cutoff=0.0)[source]¶

        Identifies contacts between two molecules. A contact is defined as a pair of atoms whose distance is less than contact_factor*(r1+r2+cutoff), where r1 and r2 are the atomic van-der-Waals radii.

        Parameters:
        • object1 (GroupOfAtoms) – a chemical object
        • object2 (GroupOfAtoms) – a chemical object
        • contact_factor (float) – a scale factor in the contact distance criterion
        • cutoff (float) – a constant in the contact distance criterion
        Returns:

        a list of Contact objects that describe atomic contacts between object1 and object2.
        Return type:

        list
        MMTK.MolecularSurface.surfaceAndVolume(object, probe_radius=0.0)[source]¶
        Parameters:
        • object (GroupOfAtoms) – a chemical object
        • probe_radius (float) – the distance from the vdW-radii of the atoms at which the surface is computed
        Returns:

        the molecular surface and volume of object
        Return type:

        tuple
        MMTK.MolecularSurface.surfaceAtoms(object, probe_radius=0.0)[source]¶
        Parameters:
        • object (GroupOfAtoms) – a chemical object
        • probe_radius (float) – the distance from the vdW-radii of the atoms at which the surface is computed
        Returns:

        a dictionary that maps the surface atoms to their exposed surface areas
        Return type:

        dict
        MMTK.MolecularSurface.surfacePointsAndGradients(object, probe_radius=0.0, point_density=258)[source]¶
        Parameters:
        • object (GroupOfAtoms) – a chemical object
        • probe_radius (float) – the distance from the vdW-radii of the atoms at which the surface is computed
        • point_density (int) – the density of points that describe the surface
        Returns:

        a dictionary that maps the surface atoms to a tuple containing three surface-related quantities: the exposed surface area, a list of points in the exposed surface, and a gradient vector pointing outward from the surface.
        Return type:

        dict

        MMTK.MoleculeFactory¶

        Molecule factory for creating chemical objects

        class MMTK.MoleculeFactory.MoleculeFactory[source]¶

        Bases: object

        MoleculeFactory

        A MoleculeFactory serves to create molecules without reference to database definitions. Molecules and groups are defined in terms of atoms, groups, and bonds. Each MoleculeFactory constitutes an independent set of definitions. Definitions within a MoleculeFactory can refer to each other.

        Each MoleculeFactory stores a set of Group objects which are referred to by names. The typical operation sequence is to create a new group and then add atoms, bonds, and subgroups. It is also possible to define coordinates and arbitrary attributes (in particular for force fields). In the end, a finished object can be retrieved as a Molecule object.

        addAtom(group, atom_name, element)[source]¶

        Add an atom to a group

        Parameters:
        • group (str) – the name of the group
        • atom_name (str) – the name of the atom
        • element (str) – the chemical element symbol
        addBond(group, atom1, atom2)[source]¶

        Add a bond to a group

        Parameters:
        • group (str) – the name of the group
        • atom1 (str) – the name of the first atom
        • atom2 (str) – the name of the second atom
        addSubgroup(group, subgroup_name, subgroup)[source]¶

        Add a subgroup to a group

        Parameters:
        • group (str) – the name of the group
        • subgroup_name (str) – the name of the subgroup within the group
        • subgroup (str) – the subgroup type
        createGroup(name)[source]¶

        Create a new (initially empty) group object.

        retrieveMolecule(group)[source]¶
        Parameters:group (str) – the name of the group to be used as a template
        Returns:a molecule defined by the contents of the group
        Return type:Molecule

        MMTK.NormalModes¶

        Normal modes

        MMTK.NormalModes.BrownianModes¶

        Brownian normal modes

        class MMTK.NormalModes.BrownianModes.BrownianMode(universe, n, inv_relaxation_time, mode)[source]¶

        Bases: MMTK.NormalModes.Core.Mode

        Single Brownian normal mode

        Mode objects are created by indexing a BrownianModes object. They contain the atomic displacements corresponding to a single mode. In addition, the inverse of the relaxation time corresponding to the mode is stored in the attribute “inv_relaxation_time”.

        Note: the Brownian mode vectors are not friction weighted and therefore not orthogonal to each other.

        class MMTK.NormalModes.BrownianModes.BrownianModes(universe=None, friction=None, temperature=300.0, subspace=None, delta=None, sparse=False)[source]¶

        Bases: MMTK.NormalModes.Core.NormalModes

        Brownian modes describe the independent relaxation motions of a harmonic system with friction. They are obtained by diagonalizing the friction-weighted force constant matrix.

        In order to obtain physically reasonable normal modes, the configuration of the universe must correspond to a local minimum of the potential energy.

        Individual modes (see BrownianMode) can be extracted by indexing with an integer. Looping over the modes is possible as well.

        Brownian modes describe the independent relaxation motions of a harmonic system with friction. They are obtained by diagonalizing the friction-weighted force constant matrix.

        In order to obtain physically reasonable normal modes, the configuration of the universe must correspond to a local minimum of the potential energy.

        A BrownianModes object behaves like a sequence of modes. Individual modes (see BrownianMode) can be extracted by indexing with an integer. Looping over the modes is possible as well.

        Parameters:
        • universe (Universe) – the system for which the normal modes are calculated; it must have a force field which provides the second derivatives of the potential energy
        • friction (ParticleScalar) – the friction coefficient for each particle. Note: The friction coefficients are not mass-weighted, i.e. they have the dimension of an inverse time.
        • temperature (float) – the temperature for which the amplitudes of the atomic displacement vectors are calculated. A value of None can be specified to have no scaling at all. In that case the mass-weighted norm of each normal mode is one.
        • subspace – the basis for the subspace in which the normal modes are calculated (or, more precisely, a set of vectors spanning the subspace; it does not have to be orthogonal). This can either be a sequence of ParticleVector objects or a tuple of two such sequences. In the second case, the subspace is defined by the space spanned by the second set of vectors projected on the complement of the space spanned by the first set of vectors. The first set thus defines directions that are excluded from the subspace. The default value of None indicates a standard normal mode calculation in the 3N-dimensional configuration space.
        • delta (float) – the rms step length for numerical differentiation. The default value of None indicates analytical differentiation. Numerical differentiation is available only when a subspace basis is used as well. Instead of calculating the full force constant matrix and then multiplying with the subspace basis, the subspace force constant matrix is obtained by numerical differentiation of the energy gradients along the basis vectors of the subspace. If the basis is much smaller than the full configuration space, this approach needs much less memory.
        • sparse (bool) – a flag that indicates if a sparse representation of the force constant matrix is to be used. This is of interest when there are no long-range interactions and a subspace of smaller size then 3N is specified. In that case, the calculation will use much less memory with a sparse representation.
        EISF(q_range=(0.0, 15.0), subset=None, weights=None, random_vectors=15, first_mode=6)[source]¶
        Parameters:
        • q_range (tuple) – the range of angular wavenumber values
        • subset (GroupOfAtoms) – the subset of the universe used in the calculation (default: the whole universe)
        • weights (ParticleScalar) – the weight to be given to each atom in the average (default: incoherent scattering lengths)
        • random_vectors (int) – the number of random direction vectors used in the orientational average
        • first_mode (int) – the first mode to be taken into account for the fluctuation calculation. The default value of 6 is right for molecules in vacuum.
        Returns:

        the Elastic Incoherent Structure Factor (EISF) as a function of angular wavenumber
        Return type:

        Scientific.Functions.Interpolation.InterpolatingFunction
        coherentScatteringFunction(q, time_range=(0.0, None, None), subset=None, weights=None, random_vectors=15, first_mode=6)[source]¶
        Parameters:
        • q (float) – the angular wavenumber
        • time_range (tuple) – the time values at which the mean-square displacement is evaluated, specified as a range tuple (first, last, step). The defaults are first=0, last= 20 times the longest vibration perdiod, and step defined such that 300 points are used in total.
        • subset (GroupOfAtoms) – the subset of the universe used in the calculation (default: the whole universe)
        • weights (ParticleScalar) – the weight to be given to each atom in the average (default: coherent scattering lengths)
        • random_vectors (int) – the number of random direction vectors used in the orientational average
        • first_mode (int) – the first mode to be taken into account for the fluctuation calculation. The default value of 6 is right for molecules in vacuum.
        Returns:

        the Coherent Scattering Function as a function of time
        Return type:

        Scientific.Functions.Interpolation.InterpolatingFunction
        incoherentScatteringFunction(q, time_range=(0.0, None, None), subset=None, random_vectors=15, first_mode=6)[source]¶
        Parameters:
        • q (float) – the angular wavenumber
        • time_range (tuple) – the time values at which the mean-square displacement is evaluated, specified as a range tuple (first, last, step). The defaults are first=0, last= 20 times the longest vibration perdiod, and step defined such that 300 points are used in total.
        • subset (GroupOfAtoms) – the subset of the universe used in the calculation (default: the whole universe)
        • random_vectors (int) – the number of random direction vectors used in the orientational average
        • first_mode (int) – the first mode to be taken into account for the fluctuation calculation. The default value of 6 is right for molecules in vacuum.
        Returns:

        the Incoherent Scattering Function as a function of time
        Return type:

        Scientific.Functions.Interpolation.InterpolatingFunction
        meanSquareDisplacement(subset=None, weights=None, time_range=(0.0, None, None), first_mode=6)[source]¶
        Parameters:
        • subset (GroupOfAtoms) – the subset of the universe used in the calculation (default: the whole universe)
        • weights (ParticleScalar) – the weight to be given to each atom in the average (default: atomic masses)
        • time_range (tuple) – the time values at which the mean-square displacement is evaluated, specified as a range tuple (first, last, step). The defaults are first=0, last= 20 times the longest vibration perdiod, and step defined such that 300 points are used in total.
        • first_mode (int) – the first mode to be taken into account for the fluctuation calculation. The default value of 6 is right for molecules in vacuum.
        Returns:

        the averaged mean-square displacement of the atoms in subset as a function of time
        Return type:

        Scientific.Functions.Interpolation.InterpolatingFunction
        rawMode(item)[source]¶
        Parameters:item (int) – the index of a normal mode
        Returns:the unscaled mode vector
        Return type:BrownianMode
        staticStructureFactor(q_range=(1.0, 15.0), subset=None, weights=None, random_vectors=15, first_mode=6)[source]¶
        Parameters:
        • q_range (tuple) – the range of angular wavenumber values
        • subset (GroupOfAtoms) – the subset of the universe used in the calculation (default: the whole universe)
        • weights (ParticleScalar) – the weight to be given to each atom in the average (default: coherent scattering lengths)
        • random_vectors (int) – the number of random direction vectors used in the orientational average
        • first_mode (int) – the first mode to be taken into account for the fluctuation calculation. The default value of 6 is right for molecules in vacuum.
        Returns:

        the Static Structure Factor as a function of angular wavenumber
        Return type:

        Scientific.Functions.Interpolation.InterpolatingFunction

        MMTK.NormalModes.EnergeticModes¶

        Energetic normal modes

        class MMTK.NormalModes.EnergeticModes.EnergeticMode(universe, n, force_constant, mode)[source]¶

        Bases: MMTK.NormalModes.Core.Mode

        Single energetic normal mode

        Mode objects are created by indexing a MMTK.NormalModes.EnergeticModes.EnergeticModes object. They contain the atomic displacements corresponding to a single mode. In addition, the force constant corresponding to the mode is stored in the attribute “force_constant”.

        class MMTK.NormalModes.EnergeticModes.EnergeticModes(universe=None, temperature=300, subspace=None, delta=None, sparse=False)[source]¶

        Bases: MMTK.NormalModes.Core.NormalModes

        Energetic modes describe the principal axes of an harmonic approximation to the potential energy surface of a system. They are obtained by diagonalizing the force constant matrix without prior mass-weighting.

        In order to obtain physically reasonable normal modes, the configuration of the universe must correspond to a local minimum of the potential energy.

        Individual modes (see class EnergeticMode) can be extracted by indexing with an integer. Looping over the modes is possible as well.

        Parameters:
        • universe (Universe) – the system for which the normal modes are calculated; it must have a force field which provides the second derivatives of the potential energy
        • temperature (float) – the temperature for which the amplitudes of the atomic displacement vectors are calculated. A value of None can be specified to have no scaling at all. In that case the mass-weighted norm of each normal mode is one.
        • subspace – the basis for the subspace in which the normal modes are calculated (or, more precisely, a set of vectors spanning the subspace; it does not have to be orthogonal). This can either be a sequence of ParticleVector objects or a tuple of two such sequences. In the second case, the subspace is defined by the space spanned by the second set of vectors projected on the complement of the space spanned by the first set of vectors. The first set thus defines directions that are excluded from the subspace. The default value of None indicates a standard normal mode calculation in the 3N-dimensional configuration space.
        • delta (float) – the rms step length for numerical differentiation. The default value of None indicates analytical differentiation. Numerical differentiation is available only when a subspace basis is used as well. Instead of calculating the full force constant matrix and then multiplying with the subspace basis, the subspace force constant matrix is obtained by numerical differentiation of the energy gradients along the basis vectors of the subspace. If the basis is much smaller than the full configuration space, this approach needs much less memory.
        • sparse (bool) – a flag that indicates if a sparse representation of the force constant matrix is to be used. This is of interest when there are no long-range interactions and a subspace of smaller size then 3N is specified. In that case, the calculation will use much less memory with a sparse representation.
        rawMode(item)[source]¶
        Parameters:item (int) – the index of a normal mode
        Returns:the unscaled mode vector
        Return type:EnergeticMode

        MMTK.NormalModes.VibrationalModes¶

        Vibrational normal modes

        class MMTK.NormalModes.VibrationalModes.VibrationalMode(universe, n, frequency, mode)[source]¶

        Bases: MMTK.NormalModes.Core.Mode

        Single vibrational normal mode

        Mode objects are created by indexing a VibrationalModes object. They contain the atomic displacements corresponding to a single mode. In addition, the frequency corresponding to the mode is stored in the attribute “frequency”.

        Note: the normal mode vectors are not mass weighted, and therefore not orthogonal to each other.

        class MMTK.NormalModes.VibrationalModes.VibrationalModes(universe=None, temperature=300.0, subspace=None, delta=None, sparse=False)[source]¶

        Bases: MMTK.NormalModes.Core.NormalModes

        Vibrational modes describe the independent vibrational motions of a harmonic system. They are obtained by diagonalizing the mass-weighted force constant matrix.

        In order to obtain physically reasonable normal modes, the configuration of the universe must correspond to a local minimum of the potential energy.

        Individual modes (see VibrationalMode) can be extracted by indexing with an integer. Looping over the modes is possible as well.

        Parameters:
        • universe (Universe) – the system for which the normal modes are calculated; it must have a force field which provides the second derivatives of the potential energy
        • temperature (float) – the temperature for which the amplitudes of the atomic displacement vectors are calculated. A value of None can be specified to have no scaling at all. In that case the mass-weighted norm of each normal mode is one.
        • subspace – the basis for the subspace in which the normal modes are calculated (or, more precisely, a set of vectors spanning the subspace; it does not have to be orthogonal). This can either be a sequence of ParticleVector objects or a tuple of two such sequences. In the second case, the subspace is defined by the space spanned by the second set of vectors projected on the complement of the space spanned by the first set of vectors. The first set thus defines directions that are excluded from the subspace. The default value of None indicates a standard normal mode calculation in the 3N-dimensional configuration space.
        • delta (float) – the rms step length for numerical differentiation. The default value of None indicates analytical differentiation. Numerical differentiation is available only when a subspace basis is used as well. Instead of calculating the full force constant matrix and then multiplying with the subspace basis, the subspace force constant matrix is obtained by numerical differentiation of the energy gradients along the basis vectors of the subspace. If the basis is much smaller than the full configuration space, this approach needs much less memory.
        • sparse (bool) – a flag that indicates if a sparse representation of the force constant matrix is to be used. This is of interest when there are no long-range interactions and a subspace of smaller size then 3N is specified. In that case, the calculation will use much less memory with a sparse representation.
        EISF(q_range=(0.0, 15.0), subset=None, weights=None, random_vectors=15, first_mode=6)[source]¶
        Parameters:
        • q_range (tuple) – the range of angular wavenumber values
        • subset (GroupOfAtoms) – the subset of the universe used in the calculation (default: the whole universe)
        • weights (ParticleScalar) – the weight to be given to each atom in the average (default: incoherent scattering lengths)
        • random_vectors (int) – the number of random direction vectors used in the orientational average
        • first_mode (int) – the first mode to be taken into account for the fluctuation calculation. The default value of 6 is right for molecules in vacuum.
        Returns:

        the Elastic Incoherent Structure Factor (EISF) as a function of angular wavenumber
        Return type:

        Scientific.Functions.Interpolation.InterpolatingFunction
        incoherentScatteringFunction(q, time_range=(0.0, None, None), subset=None, weights=None, tau=None, random_vectors=15, first_mode=6)[source]¶
        Parameters:
        • q (float) – the angular wavenumber
        • time_range (tuple) – the time values at which the mean-square displacement is evaluated, specified as a range tuple (first, last, step). The defaults are first=0, last= 20 times the longest vibration perdiod, and step defined such that 300 points are used in total.
        • subset (GroupOfAtoms) – the subset of the universe used in the calculation (default: the whole universe)
        • weights (ParticleScalar) – the weight to be given to each atom in the average (default: incoherent scattering lengths)
        • tau (float) – the relaxation time of an exponential damping factor (default: no damping)
        • random_vectors (int) – the number of random direction vectors used in the orientational average
        • first_mode (int) – the first mode to be taken into account for the fluctuation calculation. The default value of 6 is right for molecules in vacuum.
        Returns:

        the Incoherent Scattering Function as a function of time
        Return type:

        Scientific.Functions.Interpolation.InterpolatingFunction
        meanSquareDisplacement(subset=None, weights=None, time_range=(0.0, None, None), tau=None, first_mode=6)[source]¶
        Parameters:
        • subset (GroupOfAtoms) – the subset of the universe used in the calculation (default: the whole universe)
        • weights (ParticleScalar) – the weight to be given to each atom in the average (default: atomic masses)
        • time_range (tuple) – the time values at which the mean-square displacement is evaluated, specified as a range tuple (first, last, step). The defaults are first=0, last= 20 times the longest vibration perdiod, and step defined such that 300 points are used in total.
        • tau (float) – the relaxation time of an exponential damping factor (default: no damping)
        • first_mode (int) – the first mode to be taken into account for the fluctuation calculation. The default value of 6 is right for molecules in vacuum.
        Returns:

        the averaged mean-square displacement of the atoms in subset as a function of time
        Return type:

        Scientific.Functions.Interpolation.InterpolatingFunction
        rawMode(item)[source]¶
        Parameters:item (int) – the index of a normal mode
        Returns:the unscaled mode vector
        Return type:VibrationalMode

        MMTK.NucleicAcids¶

        Nucleic acid chains

        class MMTK.NucleicAcids.Nucleotide(name=None, model='all')[source]¶

        Bases: MMTK.Biopolymers.Residue

        Nucleic acid residue

        Nucleotides are a special kind of group. Like any other group, they are defined in the chemical database. Each residue has two or three subgroups (‘sugar’ and ‘base’, plus ‘phosphate’ except for 5’-terminal residues) and is usually connected to other residues to form a nucleotide chain. The database contains three variants of each residue (5’-terminal, 3’-terminal, non-terminal).

        Parameters:
        • name (str) – the name of the nucleotide in the chemical database. This is the full name of the residue plus the suffix “_5ter” or “_3ter” for the terminal variants.
        • model (str) – one of “all” (all-atom), “none” (no hydrogens), “polar” (united-atom with only polar hydrogens), “polar_charmm” (like “polar”, but defining polar hydrogens like in the CHARMM force field). Currently the database has definitions only for “all”.
        backbone()[source]¶
        Returns:the sugar and phosphate groups
        Return type:Group
        bases()[source]¶
        Returns:the base group
        Return type:Group
        class MMTK.NucleicAcids.NucleotideChain(sequence, **properties)[source]¶

        Bases: MMTK.Biopolymers.ResidueChain

        Nucleotide chain

        Nucleotide chains consist of nucleotides that are linked together. They are a special kind of molecule, i.e. all molecule operations are available.

        Nucleotide chains act as sequences of residues. If n is a NucleotideChain object, then

        • len(n) yields the number of nucleotides

        • n[i] yields nucleotide number i

        • n[i:j] yields the subchain from nucleotide number i up to but

          excluding nucleotide number j

        Parameters:
        • sequence – the nucleotide sequence. This can be a string containing the one-letter codes, or a list of two-letter codes (a “d” or “r” for the type of sugar, and the one-letter base code), or a PDBNucleotideChain object. If a PDBNucleotideChain object is supplied, the atomic positions it contains are assigned to the atoms of the newly generated nucleotide chain, otherwise the positions of all atoms are undefined.
        • model (str) – one of “all” (all-atom), “no_hydrogens” or “none” (no hydrogens), “polar_hydrogens” or “polar” (united-atom with only polar hydrogens), “polar_charmm” (like “polar”, but defining polar hydrogens like in the CHARMM force field), “polar_opls” (like “polar”, but defining polar hydrogens like in the latest OPLS force field). Default is “all”. Currently the database contains definitions only for “all”.
        • terminus_5 (bool) – if True, the first residue is constructed using the 5’-terminal variant, if False the non-terminal version is used. Default is True.
        • terminus_3 (bool) – if True, the last residue is constructed using the 3’-terminal variant, if False the non-terminal version is used. Default is True.
        • circular (bool) – if True, a bond is constructed between the first and the last residue. Default is False.
        • name (str) – a name for the chain (a string)
        backbone()[source]¶
        Returns:the sugar and phosphate groups of all nucleotides
        Return type:Collection
        bases()[source]¶
        Returns:the base groups of all nucleotides
        Return type:Collection
        class MMTK.NucleicAcids.NucleotideSubChain(chain, groups, name='')[source]¶

        Bases: MMTK.NucleicAcids.NucleotideChain

        A contiguous part of a nucleotide chain

        NucleotideSubChain objects are the result of slicing operations on NucleotideChain objects. They cannot be created directly. NucleotideSubChain objects permit all operations of NucleotideChain objects, but cannot be added to a universe.

        MMTK.NucleicAcids.isNucleotideChain(x)[source]¶

        Returns True if x is a NucleotideChain.

        MMTK.PDB¶

        PDB files

        This module provides classes that represent molecules in a PDB file. They permit various manipulations and the creation of MMTK objects. Note that the classes defined in this module are specializations of classed defined in Scientific.IO.PDB; the methods defined in that module are also available.

        class MMTK.PDB.PDBConfiguration(file_or_filename, model=0, alternate_code='A')[source]¶

        Bases: Scientific.IO.PDB.Structure

        Everything in a PDB file

        A PDBConfiguration object represents the full contents of a PDB file. It can be used to create MMTK objects for all or part of the molecules, or to change the configuration of an existing system.

        Parameters:
        • file_or_filename – the name of a PDB file, or a file object
        • model (int) – the number of the model to be used from a multiple model file
        • alternate_code (str) – the alternate code to be used for atoms that have multiple positions
        applyTo(object, atom_map=None)[source]¶

        Sets the configuration of object from the coordinates in the PDB file. The object must be compatible with the PDB file, i.e. contain the same subobjects and in the same order. This is usually only guaranteed if the object was created by the method

        createAll() from a PDB file with the same layout. :param object: a chemical object or collection of chemical objects

        asuToUnitCell(asu_contents, compact=True)[source]¶
        Parameters:
        • asu_contents – the molecules in the asymmetric unit, usually obtained from createAll().
        • compact (bool) – if True, all molecules images are shifted such that their centers of mass lie inside the unit cell.
        Returns:

        a collection containing all molecules in the unit cell, obtained by copying and moving the molecules from the asymmetric unit according to the crystallographic symmetry operations.
        Return type:

        Collection
        createAll(molecule_names=None, permit_undefined=True)[source]¶
        Returns:a collection containing all objects contained in the PDB file, i.e. the combination of the objects returned by createPeptideChains(), createNucleotideChains(), and createMolecules(). The parameters have the same meaning as for createMolecules().
        Return type:Collection
        createMolecules(names=None, permit_undefined=True)[source]¶
        Parameters:
        • names (list) – If a list of molecule names (as defined in the chemical database) and/or PDB residue names, only molecules mentioned in this list will be constructed. If a dictionary, it is used to map PDB residue names to molecule names. With the default (None), only water molecules are built.
        • permit_undefined – If False, an exception is raised when a PDB residue is encountered for which no molecule name is supplied in names. If True, an AtomCluster object is constructed for each unknown molecule.
        Returns:

        a collection of Molecule objects, one for each molecule in the PDB file. Each PDB residue not describing an amino acid or nucleotide residue is considered a molecule.
        Return type:

        Collection
        createNucleotideChains(model='all')[source]¶
        Returns:a list of NucleotideChain objects, one for each nucleotide chain in the PDB file. The parameter model has the same meaning as for the NucleotideChain constructor.
        Return type:list
        createPeptideChains(model='all')[source]¶
        Returns:a list of PeptideChain objects, one for each peptide chain in the PDB file. The parameter model has the same meaning as for the PeptideChain constructor.
        Return type:list
        createUnitCellUniverse()[source]¶

        Constructs an empty universe (OrthrhombicPeriodicUniverse or ParallelepipedicPeriodicUniverse) representing the unit cell of the crystal. If the PDB file does not define a unit cell at all, an InfiniteUniverse is returned.

        Returns:a universe
        Return type:Universe
        molecule_constructor¶

        alias of PDBMolecule

        nucleotide_chain_constructor¶

        alias of PDBNucleotideChain

        peptide_chain_constructor¶

        alias of PDBPeptideChain

        MMTK.PDB.PDBFile¶

        alias of PDBConfiguration

        class MMTK.PDB.PDBMolecule(name, atoms=None, number=None)[source]¶

        Bases: Scientific.IO.PDB.Molecule

        Molecule in a PDB file

        See the description of Scientific.IO.PDB.Molecule for the constructor and additional methods. In MMTK, PDBMolecule objects are usually obtained from a PDBConfiguration object via its attribute molecules (see the documentation of Scientific.IO.PDB.Structure). A molecule is by definition any residue in a PDB file that is not an amino acid or nucleotide residue.

        @param name: the name of the group @type name: C{str} @param atoms: a list of atoms (or C{None} for no atoms) @type atoms: C{list} or C{NoneType} @param number: the PDB residue number (or C{None}) @type number: C{int} or C{NoneType}

        createMolecule(name=None)[source]¶
        Returns:a Molecule object corresponding to the molecule in the PDB file. The parameter name specifies the molecule name as defined in the chemical database. It can be left out for known molecules (currently only water).
        Return type:Molecule
        class MMTK.PDB.PDBNucleotideChain(residues=None, chain_id=None, segment_id=None)[source]¶

        Bases: Scientific.IO.PDB.NucleotideChain, MMTK.PDB.PDBChain

        Nucleotide chain in a PDB file

        See the description of Scientific.IO.PDB.NucleotideChain for the constructor and additional methods. In MMTK, PDBNucleotideChain objects are usually obtained from a PDBConfiguration object via its attribute nucleotide_chains (see the documentation of Scientific.IO.PDB.Structure).

        @param residues: a list of residue objects, or C{None} meaning
        that the chain is initially empty

        @type residues: C{list} or C{NoneType} @param chain_id: a one-letter chain identifier or C{None} @type chain_id: C{str} or C{NoneType} @param segment_id: a multi-character segment identifier or C{None} @type segment_id: C{str} or C{NoneType}

        createNucleotideChain(model='all')[source]¶
        Returns:a NucleotideChain object corresponding to the nucleotide chain in the PDB file. The parameter model has the same meaning as for the NucleotideChain constructor.
        Return type:NucleotideChain
        class MMTK.PDB.PDBOutputFile(filename, subformat=None)[source]¶

        Bases: object

        PDB file for output

        Parameters:
        • filename (str) – the name of the PDB file that is created
        • subformat (str) – a variant of the PDB format; these subformats are defined in module Scientific.IO.PDB. The default is the standard PDB format.
        close()[source]¶

        Closes the file. Must be called in order to prevent data loss.

        nextModel()[source]¶

        Start a new model

        write(object, configuration=None, tag=None)[source]¶

        Write an object to the file

        Parameters:
        • object (GroupOfAtoms) – the object to be written
        • configuration (Configuration) – the configuration from which the coordinates are taken (default: current configuration)
        class MMTK.PDB.PDBPeptideChain(residues=None, chain_id=None, segment_id=None)[source]¶

        Bases: Scientific.IO.PDB.PeptideChain, MMTK.PDB.PDBChain

        Peptide chain in a PDB file

        See the description of Scientific.IO.PDB.PeptideChain for the constructor and additional methods. In MMTK, PDBPeptideChain objects are usually obtained from a PDBConfiguration object via its attribute peptide_chains (see the documentation of Scientific.IO.PDB.Structure).

        @param residues: a list of residue objects, or C{None} meaning
        that the chain is initially empty

        @type residues: C{list} or C{NoneType} @param chain_id: a one-letter chain identifier or C{None} @type chain_id: C{str} or C{NoneType} @param segment_id: a multi-character segment identifier or C{None} @type segment_id: C{str} or C{NoneType}

        createPeptideChain(model='all', n_terminus=None, c_terminus=None)[source]¶
        Returns:a PeptideChain object corresponding to the peptide chain in the PDB file. The parameter model has the same meaning as for the PeptideChain constructor.
        Return type:PeptideChain

        MMTK.PDBMoleculeFactory¶

        Molecule factory defined by a PDB file

        This module permits the construction of molecular objects that correspond exactly to the contents of a PDB file. It is used for working with experimental data. Note that most force fields cannot be applied to the systems generated in this way because the molecule factory does not know any force field parameters.

        class MMTK.PDBMoleculeFactory.PDBMoleculeFactory(pdb_conf, residue_filter=None, atom_filter=None)[source]¶

        Bases: MMTK.MoleculeFactory.MoleculeFactory

        A PDBMoleculeFactory generates molecules and universes from the contents of a PDBConfiguration. Nothing is added or left out (except as defined by the optional residue and atom filters), and the atom and residue names are exactly those of the original PDB file.

        Parameters:
        • pdb_conf (PDBConfiguration) – a PDBConfiguration
        • residue_filter (callable) – a function taking a residue object (as defined in Scientific.IO.PDB) and returning True if that residue is to be kept in the molecule factory
        • atom_filter (callable) – a function taking a residue object and an atom object (as defined in Scientific.IO.PDB) and returning True if that atom is to be kept in the molecule factory
        retrieveAsymmetricUnit()[source]¶

        Constructs a universe (OrthrhombicPeriodicUniverse or ParallelepipedicPeriodicUniverse) representing the unit cell of the crystal and adds the molecules representing the asymmetric unit.

        Returns:a universe
        Return type:Universe
        retrieveMolecules()[source]¶

        Constructs Molecule objects corresponding to the contents of the PDBConfiguration. Each peptide or nucleotide chain becomes one molecule. For residues that are neither amino acids nor nucleic acids, each residue becomes one molecule.

        Chain molecules (peptide and nucleotide chains) can be iterated over to retrieve the individual residues. The residues can also be accessed as attributes whose names are the three-letter residue name (upper case) followed by an underscore and the residue number from the PDB file (e.g. ‘GLY_34’).

        Each object that corresponds to a PDB residue (i.e. each residue in a chain and each non-chain molecule) has the attributes ‘residue_name’ and ‘residue_number’. Each atom has the attributes ‘serial_number’, ‘occupancy’ and ‘temperature_factor’. Atoms for which a ANISOU record exists also have an attribute ‘u’ whose value is a tensor object.

        Returns:a list of Molecule objects
        Return type:list
        retrieveUnitCell(compact=True)[source]¶

        Constructs a universe (OrthrhombicPeriodicUniverse or ParallelepipedicPeriodicUniverse) representing the unit cell of the crystal and adds all the molecules it contains, i.e. the molecules of the asymmetric unit and its images obtained by applying the crystallographic symmetry operations.

        Parameters:compact (bool) – if True, the images are shifted such that their centers of mass lie inside the unit cell.
        Returns:a universe
        Return type:Universe
        retrieveUniverse()[source]¶

        Constructs an empty universe (OrthrhombicPeriodicUniverse or ParallelepipedicPeriodicUniverse) representing the unit cell of the crystal. If the PDB file does not define a unit cell at all, an InfiniteUniverse is returned.

        Returns:a universe
        Return type:Universe

        MMTK.ParticleProperties¶

        Quantities defined for each particle in a universe

        class MMTK.ParticleProperties.Configuration(universe, data_array=None, cell=None)[source]¶

        Bases: MMTK.ParticleProperties.ParticleVector

        Configuration of a universe

        Configuration instances represent a configuration of a universe, consisting of positions for all atoms (like in a ParticleVector) plus the geometry of the universe itself, e.g. the cell shape for periodic universes.

        Parameters:
        • universe (Universe) – the universe for which the values are defined
        • data_array (Scientific.N.array_type) – the data array containing the values for each particle. If None, a new array containing zeros is created and used. Otherwise, the array myst be of shape (N,3), where N is the number of particles in the universe.
        • cell – the cell parameters of the universe, i.e. the return value of universe.cellParameters()
        class MMTK.ParticleProperties.ParticleProperty(universe, data_rank, value_rank)[source]¶

        Bases: object

        Property defined for each particle

        This is an abstract base class; for creating instances, use one of its subclasses.

        ParticleProperty objects store properties that are defined per particle, such as mass, position, velocity, etc. The value corresponding to a particular atom can be retrieved or changed by indexing with the atom object.

        assign(other)[source]¶

        Copy all values from another compatible ParticleProperty object.

        Parameters:other – the data source
        return_class¶

        alias of ParticleProperty

        scaleBy(factor)[source]¶

        Multiply all values by a factor

        Parameters:factor (float) – the scale factor
        selectAtoms(condition)[source]¶

        Return a collection containing all atoms a for which condition(property[a]) is True.

        Parameters:condition (callable) – a test function
        sumOverParticles()[source]¶
        Returns:the sum of the values for all particles.
        Return type:element type
        zero()[source]¶
        Returns:an object of the element type (scalar, vector, etc.) with the value 0.
        Return type:element type
        class MMTK.ParticleProperties.ParticleScalar(universe, data_array=None)[source]¶

        Bases: MMTK.ParticleProperties.ParticleProperty

        Scalar property defined for each particle

        ParticleScalar objects can be added to each other and multiplied with scalars.

        Parameters:
        • universe (Universe) – the universe for which the values are defined
        • data_array (Scientific.N.array_type) – the data array containing the values for each particle. If None, a new array containing zeros is created and used. Otherwise, the array myst be of shape (N,), where N is the number of particles in the universe.
        applyFunction(function)[source]¶
        Parameters:function – a function that is applied to each data value
        Returns:a new ParticleScalar object containing the function results
        maximum()[source]¶
        Returns:the highest value in the data array particle
        Return type:float
        minimum()[source]¶
        Returns:the smallest value in the data array particle
        Return type:float
        return_class¶

        alias of ParticleScalar

        class MMTK.ParticleProperties.ParticleTensor(universe, data_array=None)[source]¶

        Bases: MMTK.ParticleProperties.ParticleProperty

        Rank-2 tensor property defined for each particle

        ParticleTensor objects can be added to each other and multiplied with scalars or ParticleScalar objects; all of these operations result in another ParticleTensor object.

        Parameters:
        • universe (Universe) – the universe for which the values are defined
        • data_array (Scientific.N.array_type) – the data array containing the values for each particle. If None, a new array containing zeros is created and used. Otherwise, the array myst be of shape (N,3,3), where N is the number of particles in the universe.
        return_class¶

        alias of ParticleTensor

        class MMTK.ParticleProperties.ParticleVector(universe, data_array=None)[source]¶

        Bases: MMTK.ParticleProperties.ParticleProperty

        Vector property defined for each particle

        ParticleVector objects can be added to each other and multiplied with scalars or ParticleScalar objects; all of these operations result in another ParticleVector object. Multiplication with a vector or another ParticleVector object yields a ParticleScalar object containing the dot products for each particle. Multiplications that treat ParticleVectors as vectors in a 3N-dimensional space are implemented as methods.

        Parameters:
        • universe (Universe) – the universe for which the values are defined
        • data_array (Scientific.N.array_type) – the data array containing the values for each particle. If None, a new array containing zeros is created and used. Otherwise, the array myst be of shape (N,3), where N is the number of particles in the universe.
        dotProduct(other)[source]¶
        Parameters:other (ParticleVector) – another ParticleVector
        Returns:the dot product with other, treating both operands as 3N-dimensional vectors.
        dyadicProduct(other)[source]¶
        Parameters:other (ParticleVector) – another ParticleVector
        Returns:the dyadic product with other
        Return type:ParticleTensor
        length()[source]¶
        Returns:the length (norm) of the vector for each particle
        Return type:ParticleScalar
        massWeightedDotProduct(other)[source]¶
        Parameters:other (ParticleVector) – another ParticleVector
        Returns:the mass-weighted dot product with other treating both operands as 3N-dimensional vectors
        Return type:float
        massWeightedNorm()[source]¶
        Returns:the mass-weighted norm of the ParticleVector seen as a 3N-dimensional vector
        Return type:float
        norm()[source]¶
        Returns:the norm of the ParticleVector seen as a 3N-dimensional vector
        Return type:float
        return_class¶

        alias of ParticleVector

        totalNorm()¶
        Returns:the norm of the ParticleVector seen as a 3N-dimensional vector
        Return type:float
        MMTK.ParticleProperties.isConfiguration(object)[source]¶
        Parameters:object – any object
        Returns:True if object is a Configuration
        Return type:bool
        MMTK.ParticleProperties.isParticleProperty(object)[source]¶
        Parameters:object – any object
        Returns:True if object is a ParticleProperty
        Return type:bool

        MMTK.ProteinFriction¶

        A friction constant model for Cα models of proteins

        MMTK.ProteinFriction.calphaFrictionConstants(protein, set=2)[source]¶
        Parameters:
        • protein (Protein) – a Cα model protein
        • set – the number of a friction constant set (1, 2, 3, or 4)
        Returns:

        the estimated friction constants for the atoms in the protein
        Return type:

        ParticleScalar

        MMTK.Proteins¶

        Peptide chains and proteins

        class MMTK.Proteins.ConnectedChains(chains=None)[source]¶

        Bases: MMTK.Proteins.PeptideChain

        Peptide chains connected by disulfide bridges

        A group of peptide chains connected by disulfide bridges must be considered a single molecule due to the presence of chemical bonds. Such a molecule is represented by a ConnectedChains object. These objects are created automatically when a Protein object is assembled. They are normally not used directly by application programs. When a chain with disulfide bridges to other chains is extracted from a Protein object, the return value is a SubChain object that indirectly refers to a ConnectedChains object.

        class MMTK.Proteins.PeptideChain(sequence, **properties)[source]¶

        Bases: MMTK.Biopolymers.ResidueChain

        Peptide chain

        Peptide chains consist of amino acid residues that are linked by peptide bonds. They are a special kind of molecule, i.e. all molecule operations are available.

        Peptide chains act as sequences of residues. If p is a PeptideChain object, then

        • len(p) yields the number of residues

        • p[i] yields residue number i

        • p[i:j] yields the subchain from residue number i up to

          but excluding residue number j

        Parameters:
        • sequence – the amino acid sequence. This can be a string containing the one-letter codes, or a list of three-letter codes, or a PDBPeptideChain object. If a PDBPeptideChain object is supplied, the atomic positions it contains are assigned to the atoms of the newly generated peptide chain, otherwise the positions of all atoms are undefined.
        • model (str) – one of “all” (all-atom), “no_hydrogens” or “none” (no hydrogens), “polar_hydrogens” or “polar” (united-atom with only polar hydrogens), “polar_charmm” (like “polar”, but defining polar hydrogens like in the CHARMM force field), “polar_opls” (like “polar”, but defining polar hydrogens like in the latest OPLS force field), “calpha” (only the Cα atom of each residue). Default is “all”.
        • n_terminus (bool) – if True, the first residue is constructed using the N-terminal variant, if False the non-terminal version is used. Default is True.
        • c_terminus (bool) – if True, the last residue is constructed using the C-terminal variant, if False the non-terminal version is used. Default is True.
        • circular (bool) – if True, a peptide bond is constructed between the first and the last residues. Default is False.
        • name (str) – a name for the chain (a string)
        backbone()[source]¶
        Returns:the peptide groups of all residues
        Return type:Collection
        phiPsi(conf=None)[source]¶
        Returns:a list of the (phi, psi) backbone angles for each residue
        Return type:list of tuple of float
        replaceResidue(r_old, r_new)[source]¶
        Parameters:
        • r_old (Residue) – the residue to be replaced (must be part of the chain)
        • r_new (Residue) – the residue that replaces r_old
        sequence()[source]¶
        Returns:the primary sequence as a list of three-letter residue codes.
        Return type:list
        sidechains()[source]¶
        Returns:the sidechain groups of all residues
        Return type:Collection
        class MMTK.Proteins.Protein(*items, **properties)[source]¶

        Bases: MMTK.ChemicalObjects.Complex

        Protein

        A Protein object is a special kind of Complex object which is made up of peptide chains and possibly ligands.

        If the atoms in the peptide chains that make up a protein have defined positions, sulfur bridges within chains and between chains will be constructed automatically during protein generation based on a distance criterion between cystein sidechains.

        Proteins act as sequences of chains. If p is a Protein object, then

        • len(p) yields the number of chains
        • p[i] yields chain number i
        Parameters:
        • items – either a sequence of peptide chain objects, or a string, which is interpreted as the name of a database definition for a protein. If that definition does not exist, the string is taken to be the name of a PDB file, from which all peptide chains are constructed and assembled into a protein.
        • model (str) – one of “all” (all-atom), “no_hydrogens” or “none” (no hydrogens),”polar_hydrogens” or “polar” (united-atom with only polar hydrogens), “polar_charmm” (like “polar”, but defining polar hydrogens like in the CHARMM force field), “polar_opls” (like “polar”, but defining polar hydrogens like in the latest OPLS force field), “calpha” (only the Cα atom of each residue). Default is “all”.
        • position (Scientific.Geometry.Vector) – the center-of-mass position of the protein
        • name (str) – a name for the protein
        backbone()[source]¶
        Returns:the peptide groups of all residues in all chains
        Return type:Collection
        phiPsi(conf=None)[source]¶
        Returns:a list of the (phi, psi) backbone angles for all residue in all chains
        Return type:list of list of tuple of float
        residues()[source]¶
        Returns:all residues in all chains
        Return type:Collection
        residuesOfType(*types)[source]¶
        Parameters:types (sequence of str) – a sequence of residue codes (one- or three-letter)
        Returns:all residues whose type (one- or three-letter code) is contained in types
        Return type:Collection
        sidechains()[source]¶
        Returns:the sidechain groups of all residues in all chains
        Return type:Collection
        class MMTK.Proteins.Residue(name=None, model='all')[source]¶

        Bases: MMTK.Biopolymers.Residue

        Amino acid residue

        Amino acid residues are a special kind of group. They are defined in the chemical database. Each residue has two subgroups (‘peptide’ and ‘sidechain’) and is usually connected to other residues to form a peptide chain. The database contains three variants of each residue (N-terminal, C-terminal, non-terminal) and various models (all-atom, united-atom, Cα).

        Parameters:
        • name (str) – the name of the residue in the chemical database. This is the full name of the residue plus the suffix “_nt” or “_ct” for the terminal variants.
        • model (str) – one of “all” (all-atom), “none” (no hydrogens), “polar” (united-atom with only polar hydrogens), “polar_charmm” (like “polar”, but defining polar hydrogens like in the CHARMM force field), “polar_opls” (like “polar”, but defining polar hydrogens like in the latest OPLS force field), “calpha” (only the Cα atom).
        backbone()[source]¶
        Returns:the peptide group
        Return type:Group
        chiAngle()[source]¶
        Returns:an object representing the chi angle and allowing to modify it
        Return type:MMTK.InternalCoordinates.DihedralAngle
        phiAngle()[source]¶
        Returns:an object representing the phi angle and allowing to modify it
        Return type:MMTK.InternalCoordinates.DihedralAngle
        phiPsi(conf=None)[source]¶
        Returns:the values of the backbone dihedral angles phi and psi.
        Return type:tuple (float, float)
        psiAngle()[source]¶
        Returns:an object representing the psi angle and allowing to modify it
        Return type:MMTK.InternalCoordinates.DihedralAngle
        sidechains()[source]¶
        Returns:the sidechain group
        Return type:Group
        class MMTK.Proteins.SubChain(chain=None, groups=None, name='')[source]¶

        Bases: MMTK.Proteins.PeptideChain

        A contiguous part of a peptide chain

        SubChain objects are the result of slicing operations on PeptideChain objects. They cannot be created directly. SubChain objects permit all operations of PeptideChain objects, but cannot be added to a universe.

        MMTK.Proteins.isPeptideChain(x)[source]¶
        Parameters:x – any object
        Returns:True if x is a peptide chain
        Return type:bool
        MMTK.Proteins.isProtein(x)[source]¶
        Parameters:x – any object
        Returns:True if x is a protein
        Return type:bool

        MMTK.Random¶

        Random quantities for use in molecular simulations

        MMTK.Random.randomDirection()[source]¶
        Returns:a vector drawn from a uniform distribution on the surface of a unit sphere.
        Return type:Scientific.Geometry.Vector
        MMTK.Random.randomDirections(n)[source]¶
        Parameters:n – the number of direction vectors
        Returns:a list of n vectors drawn from a uniform distribution on the surface of a unit sphere. If n is negative, returns a deterministic list of not more than -n vectors of unit length (useful for testing purposes).
        Return type:list
        MMTK.Random.randomParticleVector(universe, width)[source]¶
        Parameters:
        • universe (Universe) – a universe
        • width (float) – the width (standard deviation) of a Gaussian distribution
        Returns:

        a set of vectors drawn from a Gaussian distribution with a given width centered around zero.
        Return type:

        ParticleVector
        MMTK.Random.randomPointInBox(a, b=None, c=None)[source]¶
        Parameters:
        • a (float) – the edge length of a box along the x axis
        • b (float) – the edge length of a box along the y axis (default: a)
        • c (float) – the edge length of a box along the z axis (default: a)
        Returns:

        a vector drawn from a uniform distribution within a rectangular box with edge lengths a, b, c.
        Return type:

        Scientific.Geometry.Vector
        MMTK.Random.randomPointInSphere(r)[source]¶
        Parameters:r (float) – the radius of a sphere
        Returns:a vector drawn from a uniform distribution within a sphere of radius r.
        Return type:Scientific.Geometry.Vector
        MMTK.Random.randomRotation(max_angle=3.141592653589793)[source]¶
        Parameters:max_angle (float) – the upper limit for the rotation angle
        Returns:a random rotation with a uniform axis distribution and angles drawn from a uniform distribution between -max_angle and max_angle.
        Return type:Scientific.Geometry.Transformations.Rotation
        MMTK.Random.randomVelocity(temperature, mass)[source]¶
        Parameters:
        • temperature (float) – the temperature defining a Maxwell distribution
        • mass (float) – the mass of a particle
        Returns:

        a random velocity vector for a particle of a given mass at a given temperature
        Return type:

        Scientific.Geometry.Vector

        MMTK.Solvation¶

        Solvation of solute molecules

        MMTK.Solvation.addSolvent(universe, solvent, density, scale_factor=4.0)[source]¶

        Scales up the universe and adds as many solvent molecules as are necessary to obtain the specified solvent density, taking account of the solute molecules that are already present in the universe. The molecules are placed at random positions in the scaled-up universe, but without overlaps between any two molecules.

        Parameters:
        • universe (Universe) – a finite universe
        • solvent (Molecule or str) – a molecule, or the name of a molecule in the database
        • density (float) – the density of the solvent (amu/nm**3)
        • scale_factor (float) – the factor by which the initial universe is expanded before adding the solvent molecules
        MMTK.Solvation.numberOfSolventMolecules(universe, solvent, density)[source]¶
        Parameters:
        • universe (Universe) – a finite universe
        • solvent (Molecule or str) – a molecule, or the name of a molecule in the database
        • density (float) – the density of the solvent (amu/nm**3)
        Returns:

        the number of solvent molecules that must be added to the universe, in addition to whatever it already contains, to obtain the given solvent density.
        Return type:

        int
        MMTK.Solvation.shrinkUniverse(universe, temperature=300.0, trajectory=None, scale_factor=0.95)[source]¶

        Shrinks the universe, which must have been scaled up by addSolvent, back to its original size. The compression is performed in small steps, in between which some energy minimization and molecular dynamics steps are executed. The molecular dynamics is run at the given temperature, and an optional trajectory can be specified in which intermediate configurations are stored.

        Parameters:
        • universe (Universe) – a finite universe
        • temperature (float) – the temperature at which the Molecular Dynamics steps are run
        • trajectory (Trajectory or str) – the trajectory in which the progress of the shrinking procedure is stored, or a filename
        • scale_factor (float) – the factor by which the universe is scaled at each reduction step

        MMTK.Subspace¶

        Linear subspaces for infinitesimal motions

        This module implements subspaces for infinitesimal (or finite small-amplitude) atomic motions. They can be used in normal mode calculations or for analyzing complex motions. For an explanation, see:

        K. Hinsen and G.R. Kneller
        Projection methods for the analysis of complex motions in macromolecules
        Mol. Sim. 23:275-292 (2000)
        class MMTK.Subspace.LinkedRigidBodyMotionSubspace(universe, rigid_bodies)[source]¶

        Bases: MMTK.Subspace.Subspace

        Subspace for the motion of linked rigid bodies

        This class describes the same subspace as RigidBodyMotionSubspace, and is used by the latter. For collections of several chains of linked rigid bodies, RigidBodyMotionSubspace is more efficient.

        Parameters:
        • universe (Universe) – the universe for which the subspace is created
        • rigid_bodies – a list or set of rigid bodies with some common atoms
        class MMTK.Subspace.PairDistanceSubspace(universe, atom_pairs)[source]¶

        Bases: MMTK.Subspace.Subspace

        Subspace of pair-distance motions

        A pair-distance motion subspace is the subspace which contains the relative motions of any number of atom pairs along their distance vector, e.g. bond elongation between two bonded atoms.

        Parameters:
        • universe (Universe) – the universe for which the subspace is created
        • atom_pairs – a sequence of atom pairs whose distance-vector motion is included in the subspace
        class MMTK.Subspace.RigidMotionSubspace(universe, objects)[source]¶

        Bases: MMTK.Subspace.Subspace

        Subspace of rigid-body motions

        A rigid-body motion subspace is the subspace which contains the rigid-body motions of any number of chemical objects.

        Parameters:
        • universe (Universe) – the universe for which the subspace is created
        • objects – a sequence of objects whose rigid-body motion is included in the subspace
        class MMTK.Subspace.Subspace(universe, vectors)[source]¶

        Bases: object

        Subspace of infinitesimal atomic motions

        Parameters:
        • universe (Universe) – the universe for which the subspace is created
        • vectors (list) – a list of ParticleVector objects that define the subspace. They need not be orthogonal or linearly independent.
        complement()[source]¶
        Returns:the orthogonal complement subspace
        Return type:Subspace
        getBasis()[source]¶

        Construct a basis for the subspace by orthonormalization of the input vectors using Singular Value Decomposition. The basis consists of a sequence of ParticleVector objects that are orthonormal in configuration space. :returns: the basis

        projectionComplementOf(vector)[source]¶
        Parameters:vector (ParticleVector) – a particle vector
        Returns:the projection of the vector onto the orthogonal complement of the subspace.
        projectionOf(vector)[source]¶
        Parameters:vector (ParticleVector) – a particle vector
        Returns:the projection of the vector onto the subspace.

        MMTK.Trajectory¶

        Trajectory files and their contents

        class MMTK.Trajectory.LogOutput(file, data=None, first=0, last=None, skip=1)[source]¶

        Bases: MMTK.Trajectory.TrajectoryOutput

        Protocol file output action

        A LogOutput object can be used in the action list of any trajectory-generating operation. It writes any of the available data to a text file.

        Parameters:
        • file – a file object or a string, which is interpreted as the name of a file that is opened in write mode
        • data – a list of data categories. All variables provided by the trajectory generator that fall in any of the listed categories are written to the trajectory file. See the descriptions of the trajectory generators for a list of variables and categories. By default (data = None) the categories “configuration”, “energy”, “thermodynamic”, and “time” are written.
        • first (int) – the number of the first step at which the action is run
        • last (int) – the number of the step at which the action is suspended. A value of None indicates that the action should be applied indefinitely.
        • skip (int) – the number of steps to skip between two action runs
        class MMTK.Trajectory.ParticleTrajectory(trajectory, atom, first=0, last=None, skip=1, variable='configuration')[source]¶

        Bases: object

        Trajectory data for a single particle

        A ParticleTrajectory object is created by calling the method readParticleTrajectory() on a Trajectory object.

        If pt is a ParticleTrajectory object, then

        • len(pt) is the number of steps stored in it
        • pt[i] is the value at step i (a vector)
        translateBy(vector)[source]¶

        Adds a vector to the values at all steps. This does B{not} change the data in the trajectory file.

        Parameters:vector (Scientific.Geometry.Vector) – the vector to be added
        class MMTK.Trajectory.RestartTrajectoryOutput(trajectory, skip=100, length=3)[source]¶

        Bases: MMTK.Trajectory.TrajectoryOutput

        Restart trajectory output action

        A RestartTrajectoryOutput object is used in the action list of any trajectory-generating operation. It writes those variables to a trajectory that the trajectory generator declares as necessary for restarting.

        Parameters:
        • trajectory – a trajectory object or a string, which is interpreted as the name of a file that is opened as a trajectory in append mode with a cycle length of length and double-precision variables
        • skip (int) – the number of steps between two write operations to the restart trajectory
        • length – the number of steps stored in the restart trajectory; used only if trajectory is a string
        class MMTK.Trajectory.RigidBodyTrajectory(trajectory, object, first=0, last=None, skip=1, reference=None)[source]¶

        Bases: object

        Rigid-body trajectory data

        A RigidBodyTrajectory object is created by calling the method readRigidBodyTrajectory() on a Trajectory object.

        If rbt is a RigidBodyTrajectory object, then

        • len(rbt) is the number of steps stored in it
        • rbt[i] is the value at step i (a vector for the center of mass and a quaternion for the orientation)
        class MMTK.Trajectory.SnapshotGenerator(universe, **options)[source]¶

        Bases: MMTK.Trajectory.TrajectoryGenerator

        Trajectory generator for single steps

        A SnapshotGenerator is used for manual assembly of trajectory files. At each call it writes one step to the trajectory, using the current state of the universe (configuration, velocities, etc.) and data provided explicitly with the call.

        Each call to the SnapshotGenerator object produces one step. All the keyword options can be specified either when creating the generator or when calling it.

        Parameters:
        • universe – the universe on which the generator acts
        • data – a dictionary that supplies values for variables that are not part of the universe state (e.g. potential energy)
        • actions – a list of actions to be executed periodically (default is none)
        class MMTK.Trajectory.StandardLogOutput(skip=50)[source]¶

        Bases: MMTK.Trajectory.LogOutput

        Standard protocol output action

        A StandardLogOutput object can be used in the action list of any trajectory-generating operation. It is a specialization of LogOutput to the most common case and writes data in the categories “time” and “energy” to the standard output stream.

        Parameters:skip (int) – the number of steps to skip between two action runs
        class MMTK.Trajectory.SubTrajectory(trajectory, indices)[source]¶

        Bases: object

        Reference to a subset of a trajectory

        A SubTrajectory object is created by slicing a Trajectory object or another SubTrajectory object. It provides all the operations defined on Trajectory objects.

        class MMTK.Trajectory.SubVariable(variable, indices)[source]¶

        Bases: MMTK.Trajectory.TrajectoryVariable

        Reference to a subset of a TrajectoryVariable

        A SubVariable object is created by slicing a TrajectoryVariable object or another SubVariable object. It provides all the operations defined on TrajectoryVariable objects.

        class MMTK.Trajectory.Trajectory(object, filename, mode='r', comment=None, double_precision=False, cycle=0, block_size=1)[source]¶

        Bases: object

        Trajectory file

        The data in a trajectory file can be accessed by step or by variable. If t is a Trajectory object, then:

        • len(t) is the number of steps
        • t[i] is the data for step i, in the form of a dictionary that maps variable names to data
        • t[i:j] and t[i:j:n] return a SubTrajectory object that refers to a subset of the total number of steps (no data is copied)
        • t.variable returns the value of the named variable at all time steps. If the variable is a simple scalar, it is read completely and returned as an array. If the variable contains data for each atom, a TrajectoryVariable object is returned from which data at specific steps can be obtained by further indexing operations.

        The routines that generate trajectories decide what variables are used and what they contain. The most frequently used variable is “configuration”, which stores the positions of all atoms. Other common variables are “time”, “velocities”, “temperature”, “pressure”, and various energy terms whose name end with “_energy”.

        Parameters:
        • object (ChemicalObject) – the object whose data is stored in the trajectory file. This can be ‘None’ when opening a file for reading; in that case, a universe object is constructed from the description stored in the trajectory file. This universe object can be accessed via the attribute ‘universe’ of the trajectory object.
        • filename (str) – the name of the trajectory file
        • mode (str) – one of “r” (read-only), “w” (create new file for writing), or “a” (append to existing file or create if the file does not exist)
        • comment (str) – optional comment that is stored in the file; allowed only with mode=”r”
        • double_precision (bool) – if True, data in the file is stored using double precision; default is single precision. Note that all I/O via trajectory objects is double precision; conversion from and to single precision file variables is handled automatically.
        • cycle (int) – if non-zero, a trajectory is created for a fixed number of steps equal to the value of cycle, and these steps are used cyclically. This is meant for restart trajectories.
        • block_size (int) – an optimization parameter that influences the file structure and the I/O performance for very large files. A block size of 1 is optimal for sequential access to configurations etc., whereas a block size equal to the number of steps is optimal for reading coordinates or scalar variables along the time axis. The default value is 1. Note that older MMTK releases always used a block size of 1 and cannot handle trajectories with different block sizes.
        close()[source]¶

        Close the trajectory file. Must be called after writing to ensure that all buffered data is written to the file. No data access is possible after closing a file.

        flush()[source]¶

        Make sure that all data that has been written to the trajectory is also written to the file.

        readParticleTrajectory(atom, first=0, last=None, skip=1, variable='configuration')[source]¶

        Read trajectory information for a single atom but for multiple time steps.

        Parameters:
        • atom (Atom) – the atom whose trajectory is requested
        • first (int) – the number of the first step to be read
        • last (int) – the number of the first step not to be read. A value of None indicates that the whole trajectory should be read.
        • skip (int) – the number of steps to skip between two steps read
        • variable (str) – the name of the trajectory variable to be read. If the variable is “configuration”, the resulting trajectory is made continuous by eliminating all jumps caused by periodic boundary conditions. The pseudo-variable “box_coordinates” can be read to obtain the values of the variable “configuration” scaled to box coordinates. For non-periodic universes there is no difference between box coordinates and real coordinates.
        Returns:

        the trajectory for a single atom
        Return type:

        ParticleTrajectory
        readRigidBodyTrajectory(object, first=0, last=None, skip=1, reference=None)[source]¶

        Read the positions for an object at multiple time steps and extract the rigid-body motion (center-of-mass position plus orientation as a quaternion) by an optimal-transformation fit.

        Parameters:
        • object (GroupOfAtoms) – the object whose rigid-body trajectory is requested
        • first (int) – the number of the first step to be read
        • last (int) – the number of the first step not to be read. A value of None indicates that the whole trajectory should be read.
        • skip (int) – the number of steps to skip between two steps read
        • reference (Configuration) – the reference configuration for the fit
        Returns:

        the trajectory for a single rigid body
        Return type:

        RigidBodyTrajectory
        variables()[source]¶
        Returns:a list of the names of all variables that are stored in the trajectory
        Return type:list of str
        view(first=0, last=None, skip=1, object=None)[source]¶

        Show an animation of the trajectory using an external visualization program.

        Parameters:
        • first (int) – the number of the first step in the animation
        • last (int) – the number of the first step not to include in the animation. A value of None indicates that the whole trajectory should be used.
        • skip (int) – the number of steps to skip between two steps read
        • object (GroupOfAtoms) – the object to be animated, which must be in the universe stored in the trajectory. None stands for the whole universe.
        class MMTK.Trajectory.TrajectoryAction(first, last, skip)[source]¶

        Bases: object

        Trajectory action base class

        Subclasses of this base class implement the actions that can be inserted into trajectory generation at regular intervals.

        class MMTK.Trajectory.TrajectoryGenerator(universe, options)[source]¶

        Bases: object

        Trajectory generator base class

        This base class implements the common aspects of everything that generates trajectories: integrators, minimizers, etc.

        class MMTK.Trajectory.TrajectoryOutput(trajectory, data=None, first=0, last=None, skip=1)[source]¶

        Bases: MMTK.Trajectory.TrajectoryAction

        Trajectory output action

        A TrajectoryOutput object can be used in the action list of any trajectory-generating operation. It writes any of the available data to a trajectory file. It is possible to use several TrajectoryOutput objects at the same time in order to produce multiple trajectories from a single run.

        Parameters:
        • trajectory – a trajectory object or a string, which is interpreted as the name of a file that is opened as a trajectory in append mode
        • data – a list of data categories. All variables provided by the trajectory generator that fall in any of the listed categories are written to the trajectory file. See the descriptions of the trajectory generators for a list of variables and categories. By default (data = None) the categories “configuration”, “energy”, “thermodynamic”, and “time” are written.
        • first (int) – the number of the first step at which the action is run
        • last (int) – the number of the step at which the action is suspended. A value of None indicates that the action should be applied indefinitely.
        • skip (int) – the number of steps to skip between two action runs
        class MMTK.Trajectory.TrajectorySet(object, filenames)[source]¶

        Bases: object

        Trajectory file set

        A TrajectorySet permits to treat a sequence of trajectory files like a single trajectory for reading data. It behaves exactly like a Trajectory object. The trajectory files must all contain data for the same system. The variables stored in the individual files need not be the same, but only variables common to all files can be accessed.

        Note: depending on how the sequence of trajectories was constructed, the first configuration of each trajectory might be the same as the last one in the preceding trajectory. To avoid counting it twice, specify (filename, 1, None, 1) for all but the first trajectory in the set.

        Parameters:
        • object – the object whose data is stored in the trajectory files. This can be (and usually is) None; in that case, a universe object is constructed from the description stored in the first trajectory file. This universe object can be accessed via the attribute universe of the trajectory set object.
        • filenames – a list of trajectory file names or (filename, first_step, last_step, increment) tuples.
        class MMTK.Trajectory.TrajectorySetVariable(universe, trajectory_set, name)[source]¶

        Bases: MMTK.Trajectory.TrajectoryVariable

        Variable in a trajectory set

        A TrajectorySetVariable object is created by extracting a variable from a TrajectorySet object if that variable contains data for each atom and is thus potentially large. It behaves exactly like a TrajectoryVariable object.

        class MMTK.Trajectory.TrajectoryVariable(universe, trajectory, name)[source]¶

        Bases: object

        Variable in a trajectory

        A TrajectoryVariable object is created by extracting a variable from a Trajectory object if that variable contains data for each atom and is thus potentially large. No data is read from the trajectory file when a TrajectoryVariable object is created; the read operation takes place when the TrajectoryVariable is indexed with a specific step number.

        If t is a TrajectoryVariable object, then:

        • len(t) is the number of steps
        • t[i] is the data for step i, in the form of a ParticleScalar, a ParticleVector, or a Configuration object, depending on the variable
        • t[i:j] and t[i:j:n] return a SubVariable object that refers to a subset of the total number of steps
        MMTK.Trajectory.isTrajectory(object)[source]¶
        Parameters:object – any Python object
        Returns:True if object is a trajectory
        MMTK.Trajectory.trajectoryInfo(filename)[source]¶
        Parameters:filename (str) – the name of a trajectory file
        Returns:a string with summarial information about the trajectory

        MMTK.Units¶

        Units and physical constants

        This module defines constants and prefactors that convert from various units to MMTK’s internal unit system. There are also some common physical constants.

        MMTK’s unit system is defined by:

        • nm for length
        • ps for time
        • amu (g/mol) for mass
        • the charge of a proton for charge
        • rad for angles

        All formulas that do not contain electrical quantities (charge, electric/magnetic field, ...) have the same form in this unit system as in the SI system. The energy unit that results from the choices given above is kJ/mol.

        The constants defined in this module are:

        • SI Prefixes: ato, femto, pico, nano, micro, milli, centi, deci,

          deca, hecto, kilo, mega, giga, tera, peta

        • Length units: m, cm, mm, nm, pm, fm, Ang, Bohr

        • Angle units: rad, deg

        • Volume units: l

        • Time units: s, ns, ps, fs

        • Frequency units: Hz, invcm (wavenumbers)

        • Mass units: amu, g, kg

        • Quantity-of-matter units: mol

        • Energy units: J, kJ, cal (thermochemical), kcal, Hartree, Bohr

        • Temperature units: K

        • Force units: N, dyn

        • Pressure units: Pa, MPa, GPa, bar, kbar, atm

        • Electrostatic units: C, A, V, D, eV, e

        • Physical constants:

          • c (speed of light)
          • Nav (Avogadro number)
          • h (Planck constant)
          • hbar (Planck constant divided by 2*Pi)
          • k_B (Boltzmann constant)
          • eps0 (permittivity of vacuum)
          • me (electron mass)

        • Other:

          • akma_time (the time unit in the DCD trajectory format)
          • electrostatic_energy (the prefactor in Coulomb’s law)

        MMTK.Universe¶

        Universes

        class MMTK.Universe.CubicPeriodicUniverse(size=None, forcefield=None, **properties)[source]¶

        Bases: MMTK.Universe.OrthorhombicPeriodicUniverse

        Periodic universe with cubic elementary cell.

        Parameters:
        • size – a sequence of length three specifying the edge lengths along the x, y, and z directions
        • forcefield (ForceField) – a force field, or None for no force field
        setSize(size)[source]¶

        Set the edge length to a given value.

        Parameters:size (float) – the new size
        class MMTK.Universe.InfiniteUniverse(forcefield=None, **properties)[source]¶

        Bases: MMTK.Universe.Universe

        Infinite (unbounded and nonperiodic) universe.

        Parameters:forcefield (ForceField) – a force field, or None for no force field
        class MMTK.Universe.OrthorhombicPeriodicUniverse(size=None, forcefield=None, **properties)[source]¶

        Bases: MMTK.Universe.Periodic3DUniverse

        Periodic universe with orthorhombic elementary cell.

        Parameters:
        • size – a sequence of length three specifying the edge lengths along the x, y, and z directions
        • forcefield (ForceField) – a force field, or None for no force field
        scaleSize(factor)[source]¶

        Multiplies all edge lengths by a factor.

        Parameters:factor (float) – the scale factor
        class MMTK.Universe.ParallelepipedicPeriodicUniverse(shape=None, forcefield=None, **properties)[source]¶

        Bases: MMTK.Universe.Periodic3DUniverse

        Periodic universe with parallelepipedic elementary cell.

        Parameters:
        • shape (sequence of Scientific.Geometry.Vector) – the basis vectors
        • forcefield (ForceField) – a force field, or None for no force field
        scaleSize(factor)[source]¶

        Multiplies all edge lengths by a factor.

        Parameters:factor (float) – the scale factor
        class MMTK.Universe.Universe(forcefield, properties)[source]¶

        Bases: MMTK.Collections.GroupOfAtoms, MMTK.Visualization.Viewable

        Universe

        A universe represents a complete model of a chemical system, i.e. the molecules, their environment (topology, boundary conditions, thermostats, etc.), and optionally a force field.

        The class Universe is an abstract base class that defines properties common to all kinds of universes. To create universe objects, use one of its subclasses.

        In addition to the methods listed below, universe objects support the following operations (u is any universe object, o is any chemical object):

        • len(u) yields the number of chemical objects in the universe
        • u[i] returns object number i
        • u.name = o adds o to the universe and also makes it accessible as an attribute
        • del u.name removes the object that was assigned to u.name from the universe
        acquireConfigurationChangeLock(waitflag=True)[source]¶

        Acquire the configuration change lock. This lock should be acquired before starting an algorithm that changes the configuration continuously, e.g. minimization or molecular dynamics algorithms. This guarantees the proper order of execution when several such operations are started in succession. For example, when a minimization should be followed by a dynamics run, the use of this flag permits both operations to be started as background tasks which will be executed one after the other, permitting other threads to run in parallel.

        The configuration change lock should not be confused with the universe state lock. The former guarantees the proper sequence of long-running algorithms, whereas the latter guarantees the consistency of the data. A dynamics algorithm, for example, keeps the configuration change lock from the beginning to the end, but acquires the universe state lock only immediately before modifying configuration and velocities, and releases it immediately afterwards.

        Parameters:waitflag (bool) – if true, the method waits until the lock becomes available; this is the most common mode. If false, the method returns immediately even if another thread holds the lock.
        Returns:a flag indicating if the lock was successfully acquired (1) or not (0).
        Return type:int
        acquireReadStateLock()[source]¶

        Acquire the universe read state lock. Any application that uses threading must acquire this lock prior to accessing the current state of the universe, in particular its configuration (particle positions). This guarantees the consistency of the data; while any thread holds the read state lock, no other thread can obtain the write state lock that permits modifying the state. The read state lock should be released as soon as possible.

        The read state lock can be acquired only if no thread holds the write state lock. If the read state lock cannot be acquired immediately, the thread will be blocked until it becomes available. Any number of threads can acquire the read state lock simultaneously.

        acquireWriteStateLock()[source]¶

        Acquire the universe write state lock. Any application that uses threading must acquire this lock prior to modifying the current state of the universe, in particular its configuration (particle positions). This guarantees the consistency of the data; while any thread holds the write state lock, no other thread can obtain the read state lock that permits accessing the state. The write state lock should be released as soon as possible.

        The write state lock can be acquired only if no other thread holds either the read state lock or the write state lock. If the write state lock cannot be acquired immediately, the thread will be blocked until it becomes available.

        addObject(object, steal=False)[source]¶

        Adds object to the universe. If object is a Collection, all elements of the Collection are added to the universe.

        Parameters:
        • object – the object (chemical or environment) to be added
        • steal (bool) – if True, permit stealing the object from another universe, otherwise the object must not yet be attached to any universe.
        addToConfiguration(displacement, block=True)[source]¶

        Update the current configuration of the universe by adding the given displacement vector.

        Parameters:
        • displacement (ParticleVector) – the displacement vector for each atom
        • block (bool) – if True, the operation blocks other threads from accessing the configuration before the update is completed. If False, it is assumed that the caller takes care of locking.
        adjustVelocitiesToConstraints(velocities=None, block=True)[source]¶

        Modifies the velocities to be compatible with the distance constraints, i.e. projects out the velocity components along the constrained distances.

        Parameters:
        • velocities (ParticleVector) – the velocities in which the constraints are enforced (None for current velocities)
        • block (bool) – if True, the operation blocks other threads from accessing the configuration before the update is completed. If False, it is assumed that the caller takes care of locking.
        angle(p1, p2, p3, conf=None)[source]¶
        Parameters:
        • p1 – a vector or a chemical object whose position is taken
        • p2 – a vector or a chemical object whose position is taken
        • p3 – a vector or a chemical object whose position is taken
        • conf – a configuration (None for the current configuration)
        Returns:

        the angle between the distance vectors p1-p2 and p3-p2
        Return type:

        float
        atomList()[source]¶
        Returns:a list of all atoms in the universe
        Return type:list
        basisVectors()[source]¶
        Returns:the basis vectors of the elementary cell of a periodic universe, or None for a non-periodic universe
        Return type:NoneType or list
        boxToRealCoordinates(vector)[source]¶
        Parameters:vector – a point in the universe expressed in box coordinates
        Returns:the real-space equivalent of vector
        Return type:Scientific.Geometry.Vector
        cartesianToFractional(vector)[source]¶

        Fractional coordinates are defined only for periodic universes; their components have values between 0. and 1.

        Parameters:vector (Scientific.Geometry.Vector) – a point in the universe
        Returns:the fractional coordinate equivalent of vector
        Return type:Scientific.N.array_type
        cellVolume()[source]¶
        Returns:the volume of the elementary cell of a periodic universe, None for a non-periodic universe
        Return type:NoneType or float
        charges()[source]¶

        Return the atomic charges defined by the universe’s force field.

        Returns:the charges of all atoms in the universe
        Return type:ParticleScalar
        configuration()[source]¶
        Returns:the configuration object describing the current configuration of the universe. Note that this is not a copy of the current state, but a reference: the positions in the configuration object will change when coordinate changes are applied to the universe in whatever way.
        Return type:Configuration
        configurationDifference(conf1, conf2)[source]¶
        Parameters:
        Returns:

        the difference vector between the two configurations for each atom, taking into account the universe topology (e.g. minimum-image convention).
        Return type:

        ParticleVector
        contiguousObjectConfiguration(objects=None, conf=None)[source]¶
        Parameters:
        • objects (list) – a list of chemical objects, or None for all objects in the universe
        • conf – a configuration (None for the current configuration)
        Returns:

        configuration conf (default: current configuration) corrected by the contiguous object offsets for that configuration.
        Return type:

        Configuration
        contiguousObjectOffset(objects=None, conf=None, box_coordinates=False)[source]¶
        Parameters:
        • objects (list) – a list of chemical objects, or None for all objects in the universe
        • conf – a configuration (None for the current configuration)
        • box_coordinates (bool) – use box coordinates rather than real ones
        Returns:

        a set of displacement vectors relative to the conf which, when added to the configuration, create a configuration in which none of the objects is split across the edge of the elementary cell. For nonperiodic universes the return value is None.
        Return type:

        ParticleVector
        copyConfiguration()[source]¶

        This operation is thread-safe; it won’t return inconsistent data even when another thread is modifying the configuration.

        Returns:a copy of the current configuration
        Return type:Configuration
        dihedral(p1, p2, p3, p4, conf=None)[source]¶
        Parameters:
        • p1 – a vector or a chemical object whose position is taken
        • p2 – a vector or a chemical object whose position is taken
        • p3 – a vector or a chemical object whose position is taken
        • p4 – a vector or a chemical object whose position is taken
        • conf – a configuration (None for the current configuration)
        Returns:

        the dihedral angle between the plane containing the distance vectors p1-p2 and p3-p2 and the plane containing the distance vectors p2-p3 and p4-p3
        Return type:

        float
        distance(p1, p2, conf=None)[source]¶
        Parameters:
        • p1 – a vector or a chemical object whose position is taken
        • p2 – a vector or a chemical object whose position is taken
        • conf – a configuration (None for the current configuration)
        Returns:

        the distance between p1 and p2, i.e. the length of the distance vector
        Return type:

        float
        distanceConstraintList()[source]¶
        Returns:the list of distance constraints
        Return type:list
        distanceVector(p1, p2, conf=None)[source]¶
        Parameters:
        • p1 – a vector or a chemical object whose position is taken
        • p2 – a vector or a chemical object whose position is taken
        • conf – a configuration (None for the current configuration)
        Returns:

        the distance vector between p1 and p2 (i.e. the vector from p1 to p2) in the configuration conf, taking into account the universe’s topology.
        energy(subset1=None, subset2=None, small_change=False)[source]¶
        Parameters:
        • subset1 (ChemicalObject) – a subset of a universe, or None
        • subset2 (ChemicalObject) – a subset of a universe, or None
        • small_change (bool) – if True, algorithms optimized for small configurational changes relative to the last evaluation may be used.
        Returns:

        the potential energy of interaction between the atoms in subset1 and the atoms in subset2. If subset2 is None, the interactions within subset1 are calculated. It both subsets are None, the potential energy of the whole universe is returned.
        Return type:

        float
        energyAndForceConstants(subset1=None, subset2=None, small_change=False)[source]¶
        Returns:the energy and the force constants
        Return type:(float, SymmetricPairTensor)
        energyAndGradients(subset1=None, subset2=None, small_change=False)[source]¶
        Returns:the energy and the energy gradients
        Return type:(float, ParticleVector)
        energyGradientsAndForceConstants(subset1=None, subset2=None, small_change=False)[source]¶
        Returns:the energy, its gradients, and the force constants
        Return type:(float, ParticleVector, SymmetricPairTensor)
        energyTerms(subset1=None, subset2=None, small_change=False)[source]¶
        Returns:a dictionary containing the energy values for each energy term separately. The energy terms are defined by the force field.
        Return type:dict
        enforceConstraints(configuration=None, velocities=None)[source]¶

        Enforces the previously defined distance constraints by modifying the configuration and velocities.

        Parameters:
        • configuration (Configuration) – the configuration in which the constraints are enforced (None for current configuration)
        • velocities (ParticleVector) – the velocities in which the constraints are enforced (None for current velocities)
        environmentObjectList(klass=None)[source]¶
        Parameters:klass (class) – an optional class argument
        Returns:a list of all environment objects in the universe. If klass is given, only objects that are instances of klass are returned.
        Return type:list
        forcefield()[source]¶
        Returns:the force field
        Return type:ForceField
        fractionalToCartesian(array)[source]¶

        Fractional coordinates are defined only for periodic universes; their components have values between 0. and 1.

        Parameters:array (Scientific.N.array_type) – an array of fractional coordinates
        Returns:the real-space equivalent of vector
        Return type:Scientific.Geometry.Vector
        getAtomBooleanArray(name)¶
        Parameters:name (str) – the name of an atom attribute
        Returns:the values of the boolean attribute ‘name’ for each atom in the universe, or False for atoms that do not have the attribute.
        Return type:ParticleScalar
        getAtomScalarArray(name, datatype='d')¶
        Parameters:
        • name (str) – the name of an atom attribute
        • datatype – the datatype of the array allocated to hold the data
        Returns:

        the values of the attribute ‘name’ for each atom in the universe.
        Return type:

        ParticleScalar
        getParticleBoolean(name)[source]¶
        Parameters:name (str) – the name of an atom attribute
        Returns:the values of the boolean attribute ‘name’ for each atom in the universe, or False for atoms that do not have the attribute.
        Return type:ParticleScalar
        getParticleScalar(name, datatype='d')[source]¶
        Parameters:
        • name (str) – the name of an atom attribute
        • datatype – the datatype of the array allocated to hold the data
        Returns:

        the values of the attribute ‘name’ for each atom in the universe.
        Return type:

        ParticleScalar
        initializeVelocitiesToTemperature(temperature)[source]¶

        Generate random velocities for all atoms from a Boltzmann distribution.

        Parameters:temperature (float) – the reference temperature for the Boltzmann distribution
        largestDistance()[source]¶
        Returns:the largest possible distance between any two points that can be represented independent of orientation, i.e. the radius of the largest sphere that fits into the simulation cell. Returns None if no such upper limit exists.
        Return type:NoneType or float
        map(function)[source]¶

        Apply a function to all objects in the universe and return the list of the results. If the results are chemical objects, a Collection object is returned instead of a list.

        Parameters:function (callable) – the function to be applied
        Returns:the list or collection of the results
        masses()[source]¶
        Returns:the masses of all atoms in the universe
        Return type:ParticleScalar
        numberOfDistanceConstraints()[source]¶
        Returns:the number of distance constraints
        Return type:int
        objectList(klass=None)[source]¶
        Parameters:klass (class) – an optional class argument
        Returns:a list of all chemical objects in the universe. If klass is given, only objects that are instances of klass are returned.
        Return type:list
        randomPoint()[source]¶
        Returns:a random point from a uniform distribution within the universe. This operation is defined only for finite-volume universes, e.g. periodic universes.
        Return type:Scientific.Geometry.Vector
        realToBoxCoordinates(vector)[source]¶

        Box coordinates are defined only for periodic universes; their components have values between -0.5 and 0.5; these extreme values correspond to the walls of the simulation box.

        Parameters:vector – a point in the universe
        Returns:the box coordinate equivalent of vector, or the original vector if no box coordinate system exists
        Return type:Scientific.Geometry.Vector
        reciprocalBasisVectors()[source]¶
        Returns:the reciprocal basis vectors of the elementary cell of a periodic universe, or None for a non-periodic universe
        Return type:NoneType or list
        releaseConfigurationChangeLock()[source]¶

        Releases the configuration change lock.

        releaseReadStateLock(write=False)[source]¶

        Release the universe read state lock.

        releaseWriteStateLock(write=False)[source]¶

        Release the universe write state lock.

        removeDistanceConstraints()[source]¶

        Removes all distance constraints.

        removeObject(object)[source]¶

        Removes object from the universe. If object is a Collection, each of its elements is removed. The object to be removed must be in the universe.

        Parameters:object – the object (chemical or environment) to be removed
        scaleVelocitiesToTemperature(temperature, block=True)[source]¶

        Scale all velocities by a common factor in order to obtain the specified temperature.

        Parameters:
        • temperature (float) – the reference temperature
        • block (bool) – if True, the operation blocks other threads from accessing the configuration before the update is completed. If False, it is assumed that the caller takes care of locking.
        selectBox(p1, p2)[source]¶
        Parameters:
        • p1 (Scientific.Geometry.Vector) – one corner of a box in space
        • p2 (Scientific.Geometry.Vector) – the other corner of a box in space
        Returns:

        a Collection of all objects in the universe that lie within the box whose diagonally opposite corners are given by p1 and p2.
        selectShell(point, r1, r2=0.0)[source]¶
        Parameters:
        • point (Scientific.Geometry.Vector) – a point in space
        • r1 (float) – one of the radii of a spherical shell
        • r2 (float) – the other of the two radii of a spherical shell
        Returns:

        a Collection of all objects in the universe whose distance from point lies between r1 and r2.
        setBondConstraints()[source]¶

        Sets distance constraints for all bonds.

        setConfiguration(configuration, block=True)[source]¶

        Update the current configuration of the universe by copying the given input configuration.

        Parameters:
        • configuration (Configuration) – the new configuration
        • block (bool) – if True, the operation blocks other threads from accessing the configuration before the update is completed. If False, it is assumed that the caller takes care of locking.
        setForceField(forcefield)[source]¶
        Parameters:forcefield (ForceField) – the new forcefield for this universe
        setFromTrajectory(trajectory, step=None)[source]¶

        Set the state of the universe to the one stored in a trajectory. This operation is thread-safe; it blocks other threads that want to access the configuration or velocities while the data is being updated.

        Parameters:
        • trajectory (Trajectory) – a trajectory object for this universe
        • step (int) – a step number, or None for the default step (0 for a standard trajectory, the last written step for a restart trajectory)
        setVelocities(velocities, block=True)[source]¶

        Update the current velocities of the universe by copying the given input velocities.

        Parameters:
        • velocities (ParticleVector) – the new velocities, or None to remove the velocity definition from the universe
        • block (bool) – if True, the operation blocks other threads from accessing the configuration before the update is completed. If False, it is assumed that the caller takes care of locking.
        universe()[source]¶
        Returns:the universe itself
        velocities()[source]¶
        Returns:the current velocities of all atoms, or None if no velocities are defined. Note that this is not a copy of the current state but a reference to it; its data will change whenever any changes are made to the current velocities.
        Return type:ParticleVector
        MMTK.Universe.isUniverse(object)[source]¶
        Parameters:object – any Python object
        Returns:True if object is a universe.

        MMTK.Visualization¶

        Visualization of chemical objects, including animation

        This module provides visualization of chemical objects and animated visualization of normal modes and sequences of configurations, including trajectories. Visualization depends on external visualization programs. On Unix systems, these programs are defined by environment variables. Under Windows NT, the system definitions for files with extension “pdb” and “wrl” are used.

        A viewer for PDB files can be defined by the environment variable ‘PDBVIEWER’. For showing a PDB file, MMTK will execute a command consisting of the value of this variable followed by a space and the name of the PDB file.

        A viewer for VRML files can be defined by the environment variable ‘VRMLVIEWER’. For showing a VRML file, MMTK will execute a command consisting of the value of this variable followed by a space and the name of the VRML file.

        Since there is no standard for launching viewers for animation, MMTK supports only two programs: VMD and XMol. MMTK detects these programs by inspecting the value of the environment variable ‘PDBVIEWER’. This value must be the file name of the executable, and must give “vmd” or “xmol” after stripping off an optional directory specification.

        class MMTK.Visualization.Viewable[source]¶

        Bases: object

        Any viewable chemical object

        This is a mix-in class that defines a general visualization method for all viewable objects, i.e. chemical objects (atoms, molecules, etc.), collections, and universes.

        graphicsObjects(**options)[source]¶
        Parameters:
        • configuration (Configuration) – the configuration in which the objects are drawn (default: the current configuration)
        • model (str) – the graphical representation to be used (one of “wireframe”, “tube”, “ball_and_stick”, “vdw” and “vdw_and_stick”). The vdw models use balls with the radii taken from the atom objects. Default is “wireframe”.
        • ball_radius (float) – the radius of the balls representing the atoms in a ball_and_stick model, default: 0.03 This is also used in vdw and vdw_and_stick when an atom does not supply a radius.
        • stick_radius (float) – the radius of the sticks representing the bonds in a ball_and_stick, vdw_and_stick or tube model. Default: 0.02 for the tube model, 0.01 for the ball_and_stick and vdw_and_stick models
        • graphics_module (module) – the module in which the elementary graphics objects are defined (default: Scientific.Visualization.VRML)
        • color_values (ParticleScalar) – a color value for each atom which defines the color via the color scale object specified by the option color_scale. If no value is given, the atoms’ colors are taken from the attribute ‘color’ of each atom object (default values for each chemical element are provided in the chemical database).
        • color_scale (callable) – an object that returns a color object (as defined in the module Scientific.Visualization.Color) when called with a number argument. Suitable objects are defined by Scientific.Visualization.Color.ColorScale and Scientific.Visualization.Color.SymmetricColorScale. The object is used only when the option color_values is specified as well. The default is a blue-to-red color scale that covers the range of the values given in color_values.
        • color – a color name predefined in the module Scientific.Visualization.Color. The corresponding color is applied to all graphics objects that are returned.
        Returns:

        a list of graphics objects that represent the object for which the method is called.
        Return type:

        list
        MMTK.Visualization.definePDBViewer(progname, exec_path)[source]¶

        Define the program used to view PDB files.

        Parameters:
        • progname (str) – the canonical name of the PDB viewer. If it is a known one (one of “vmd”, “xmol”, “imol”), special features such as animation may be available.
        • exec_path (str) – the path to the executable program
        MMTK.Visualization.defineVRMLiewer(progname, exec_path)[source]¶

        Define the program used to view VRML files.

        Parameters:
        • progname (str) – the canonical name of the VRML viewer
        • exec_path (str) – the path to the executable program
        MMTK.Visualization.view(object, *parameters)[source]¶

        Equivalent to object.view(parameters).

        MMTK.Visualization.viewSequence(object, conf_list, periodic=False, label=None)[source]¶

        Launches an animation using an external viewer.

        Parameters:
        • object (GroupOfAtoms) – the object for which the animation is displayed.
        • conf_list (sequence) – a sequence of configurations that define the animation
        • periodic – if True, turn animation into a loop
        • label (str) – an optional text string that some interfaces use to pass a description of the object to the visualization system.
        MMTK.Visualization.viewTrajectory(trajectory, first=0, last=None, skip=1, subset=None, label=None)[source]¶

        Launches an animation based on a trajectory using an external viewer.

        Parameters:
        • trajectory (Trajectory) – the trajectory
        • first (int) – the first trajectory step to be used
        • last (int) – the first trajectory step NOT to be used
        • skip (int) – the distance between two consecutive steps shown
        • subset (GroupOfAtoms) – the subset of the universe that is shown (default: the whole universe)
        • label (str) – an optional text string that some interfaces use to pass a description of the object to the visualization system.

        MMTK.XML¶

        XML format for describing molecular systems

        Note: this format is not used by any other program at the moment. It should be considered experimental and subject to change.

        class MMTK.XML.XMLMoleculeFactory(file)[source]¶

        Bases: MMTK.MoleculeFactory.MoleculeFactory

        XML molecule factory

        An XML molecule factory reads an XML specification of a molecular system and builds the molecule objects and universe described therein. The universe can be obtained through the attribute universe.

        Parameters:file – the name of an XML file, or a file object

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        Python Module Index

        m
         
        m
        MMTK
            MMTK.Biopolymers
            MMTK.Bonds
            MMTK.ChargeFit
            MMTK.ChemicalObjects
            MMTK.Collections
            MMTK.ConfigIO
            MMTK.DCD
            MMTK.Deformation
            MMTK.Dynamics
            MMTK.Environment
            MMTK.Field
            MMTK.ForceFields.Amber.AmberForceField
            MMTK.ForceFields.ForceFieldTest
            MMTK.ForceFields.Restraints
            MMTK.FourierBasis
            MMTK.Geometry
            MMTK.InternalCoordinates
            MMTK.Minimization
            MMTK.MolecularSurface
            MMTK.MoleculeFactory
            MMTK.NormalModes
            MMTK.NormalModes.BrownianModes
            MMTK.NormalModes.EnergeticModes
            MMTK.NormalModes.VibrationalModes
            MMTK.NucleicAcids
            MMTK.ParticleProperties
            MMTK.PDB
            MMTK.PDBMoleculeFactory
            MMTK.ProteinFriction
            MMTK.Proteins
            MMTK.Random
            MMTK.Solvation
            MMTK.Subspace
            MMTK.Trajectory
            MMTK.Units
            MMTK.Universe
            MMTK.Visualization
            MMTK.XML

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        MMTK-2.7.9/Doc/HTML/searchindex.js0000644000076600000240000017226412013143456017030 0ustar 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0ustar hinsenstaff00000000000000.. MMTK User Guide documentation master file, created by sphinx-quickstart on Thu Oct 21 18:14:14 2010. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. MMTK User Guide =============== .. toctree:: :maxdepth: 3 mmtk glossary Code examples ============= .. toctree:: :maxdepth: 3 examples Module reference ================ .. toctree:: :maxdepth: 3 modules Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` MMTK-2.7.9/Doc/make_example_template.py0000644000076600000240000000035111662461637020326 0ustar hinsenstaff00000000000000import sys template = """ ############################################## .. literalinclude:: ../../../%s :language: python :linenos: """ script_name = sys.argv[1] file(script_name+'.rst', 'w').write(template % script_name) MMTK-2.7.9/Doc/Makefile0000644000076600000240000001103711662461637015074 0ustar hinsenstaff00000000000000# Makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = $(HOME)/pydev/bin/sphinx-build PAPER = a4 BUILDDIR = _build # Internal variables. 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The manual pages are in $(BUILDDIR)/man." changes: $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes @echo @echo "The overview file is in $(BUILDDIR)/changes." linkcheck: $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck @echo @echo "Link check complete; look for any errors in the above output " \ "or in $(BUILDDIR)/linkcheck/output.txt." doctest: $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest @echo "Testing of doctests in the sources finished, look at the " \ "results in $(BUILDDIR)/doctest/output.txt." MMTK-2.7.9/Doc/mmtk.rst0000644000076600000240000024253412117632051015130 0ustar hinsenstaff00000000000000MMTK User's Guide ################# for MMTK |version| by Konrad Hinsen .. |C_alpha| replace:: C\ :sub:`α` Introduction ############ The Molecular Modelling Toolkit (MMTK) presents a new approach to molecular simulations. It is not a "simulation program" with a certain set of functions that can be used by writing more or less flexible "input files", but a collection of library modules written in an easy-to-learn high-level programming language, `Python `_. This approach offers three important advantages: - Application programs can use the full power of a general and well-designed programming language. - Application programs can profit from the large set of other libraries that are available for Python. These may be scientific or non-scientific; for example, it is very easy to write simulation or analysis programs with graphical user interfaces (using the module Tkinter in the Python standard library), or couple scientific calculations with a Web server. - Any user can provide useful additions in separate modules, whereas adding features to a monolithic program requires at least the cooperation of the original author. To further encourage collaborative code development, MMTK uses a very unrestrictive licensing policy. MMTK is free software, just like Python. Although MMTK is copyrighted, anyone is allowed to use it for any purpose, including commercial ones, as well as to modify and redistribute it (a more precise description is given in the copyright statement that comes with the code). This manual describes version |version| of MMTK. The 2.x versions contain some incompatible changes with respect to earlier versions (1.x), most importantly a package structure that reduces the risk of name conflicts with other Python packages, and facilitates future enhancements. There are also many new features and improvements to existing functions. Using MMTK requires a basic knowledge of object-oriented programming and Python. Newcomers to this subject should have a look at the introductory section in this manual and at the `Python tutorial `_ (which also comes with the Python interpreter). There are also numerous `books on Python `_ that are useful in getting started. Even without MMTK, Python is a very useful programming language for scientific use, allowing rapid development and testing and easy interfacing to code written in low-level languages such as Fortran or C. This manual consists of several introductory chapters and a :ref:`module reference `. The introductory chapters explain how common tasks are handled with MMTK, but they do not describe all of its features, nor do they contain a full documentation of functions or classes. This information can be found in the :ref:`module -reference `, which describes all classes and functions intended for end-user applications module by module, using documentation extracted directly from the source code. References from the introductory sections to the module reference facilitate finding the relevant documentation. Overview ######## This chapter explains the basic structure of MMTK and its view of molecular systems. Every MMTK user should read it at least once. Using MMTK ========== MMTK applications are ordinary Python programs, and can be written using any standard text editor. For interactive use it is recommended to use one of the many tools available for interactive Python programming. MMTK tries to be as user-friendly as possible for interactive use. For example, lengthy calculations can usually be interrupted by typing Control-C. This will result in an error message ("Keyboard Interrupt"), but you can simply go on typing other commands. Interruption is particularly useful for energy minimization and molecular dynamics: you can interrupt the calculation at any time, look at the current state or do some analysis, and then continue. Modules ======= MMTK is a package consisting of various modules, most of them written in Python, and some in C for efficiency. The individual modules are described in the :ref:`module -reference `. The basic definitions that almost every application needs are collected in the top-level module, MMTK. The first line of most applications is therefore:: from MMTK import * The definitions that are specific to particular applications reside in submodules within the package MMTK. For example, force fields are defined in :mod:`MMTK.ForceFields`, and peptide chain and protein objects are defined in :mod:`MMTK.Proteins`. Python provides two ways to access objects in modules and submodules. The first one is importing a module and referring to objects in it, e.g.:: import MMTK import MMTK.ForceFields universe = MMTK.InfiniteUniverse(MMTK.ForceFields.Amber99ForceField()) The second method is importing all or some objects *from* a module:: from MMTK import InfiniteUniverse from MMTK.ForceFields import Amber99ForceField universe = InfiniteUniverse(Amber99ForceField()) These two import styles can also be mixed according to convience. In order to prevent any confusion, all objects are referred to by their full names in this manual. The Amber force field object is thus called :class:`MMTK.ForceFields.Amber99ForceField`. Of course the user is free to use selective imports in order to be able to use such objects with shorter names. Objects ======= MMTK is an object-oriented system. Since objects are everywhere and everything is an object, it is useful to know the most important object types and what can be done with them. All object types in MMTK have meaningful names, so it is easy to identify them in practice. The following overview contains only those objects that a user will see directly. There are many more object types used by MMTK internally, and also some less common user objects that are not mentioned here. Chemical objects ---------------- These are the objects that represent the parts of a molecular system: - atoms - groups - molecules - molecular complexes These objects form a simple hierarchy: complexes consist of molecules, molecules consist of groups and atoms, groups consist of smaller groups and atoms. All of these, except for groups, can be used directly to construct a molecular system. Groups can only be used in the definitions of other groups and molecules in the :ref:`overview-database`. A number of operations can be performed on chemical objects, which can roughly be classified into inquiry (constituent atoms, bonds, center of mass etc.) and modification (translate, rotate). There are also specialized versions of some of these objects. For example, MMTK defines proteins as special complexes, consisting of peptide chains, which are special molecules. They offer a range of special operations (such as selecting residues or constructing the positions of missing hydrogen atoms) that do not make sense for molecules in general. Collections ----------- Collection objects represent arbitrary collections of chemical objects. They are used to be able to refer to collections as single entities. For example, you might want to call all water molecules collectively "solvent". Most of the operations on chemical objects are also available for collections. Force fields ------------ Force field objects represent a precise description of force fields, i.e. a complete recipe for calculating the potential energy (and its derivatives) for a given molecular system. In other words, they specify not only the functional form of the various interactions, but also all parameters and the prescriptions for applying these parameters to an actual molecular system. Universes --------- Universes define complete molecular systems, i.e. they contain chemical objects. In addition, they describe interactions within the system (by a force field), boundary conditions, external fields, etc. Many of the operations that can be used on chemical objects can also be applied to complete universes. Minimizers and integrators -------------------------- A minimizer object is a special "machine" that can find local minima in the potential energy surface of a universe. You may consider this a function, if you wish, but of course functions are just special objects. Similarly, an integrator is a special "machine" that can determine a dynamical trajectory for a system on a given potential energy surface. Trajectories ------------ Minimizers and integrators can produce trajectories, which are special files containing a sequence of configurations and/or other related information. Of course trajectory objects can also be read for analysis. Variables --------- Variable objects (not to be confused with standard Python variables) describe quantities that have a value for each atom in a system, for example positions, masses, or energy gradients. Their most common use is for storing various configurations of a system. Normal modes ------------ Normal mode objects contain normal mode frequencies and atomic displacements for a given universe. MMTK provides three kinds of normal modes which correspond to three different physical situations: - Energetic modes represent the principal axes of the potential energy surface. They each have a force constant that is smallest for the most collective motions and highest for the most localized motions. Energetic modes are appropriate for describing the potential energy surface without reference to any particular dynamics, e.g. in flexibility analysis or Monte-Carlo sampling. - Vibrational modes represent the vibrational motions of a system that have well-defined frequencies. Their use implies that the system follows Newtonian dynamics or quantum dynamics. This is appropriate for small molecules, and for the most localized motions of macromolecules. - Brownian modes represent diffusional motions of an overdamped system. They describe systems with Brownian dynamics and are appropriate for describing the slow motions of macromolecules. Non-MMTK objects ---------------- An MMTK application program will typically also make use of objects provided by Python or Python library modules. A particularly useful library is the package Scientific, which is also used by MMTK itself. The most important objects are - numbers (integers, real number, complex numbers), provided by Python - vectors (in 3D coordinate space) provided by the module Scientific.Geometry. - character strings, provided by Python - files, provided by Python Of course MMTK applications can make use of the Python standard library or any other Python modules. For example, it is possible to write a simulation program that provides status reports via an integrated Web server, using the Python standard module SimpleHTTPServer. .. _overview-database: The chemical database ===================== For defining the chemical objects described above, MMTK uses a database of descriptions. There is a database for atoms, one for groups, etc. When you ask MMTK to make a specific chemical object, for example a water molecule, MMTK looks for the definition of water in the molecule database. A database entry contains everything there is to know about the object it defines: its constituents and their names, configurations, other names used e.g. for I/O, and all information force fields might need about the objects. MMTK comes with database entries for many common objects (water, amino acids, etc.). For other objects you will have to write the definitions yourself. as described in the section :ref:`database`. Force fields ============ MMTK contains everything necessary to use the `Amber 99 force field `_ on proteins, DNA, and water molecules. It uses the standard Amber parameter and modification file format. In addition to the Amber force field, there is a simple Lennard-Jones force field for noble gases, and a deformation force field for normal mode calculations on large proteins. MMTK was designed to make the addition of force field terms and the implementation of other force fields as easy as possible. Force field terms can be defined in Python (for ease of implementation) or in Cython, C, or Fortran (for efficiency). This is described in the developer's guide. Units ===== Since MMTK is not a black-box program, but a modular library, it is essential for it to use a consistent unit system in which, for example, the inverse of a frequency is a time, and the product of a mass and the square of a velocity is an energy, without additional conversion factors. Black-box programs can (and usually do) use a consistent unit system internally and convert to "conventional" units for input and output. The unit system of MMTK consists mostly of SI units of appropriate magnitude for molecular systems: =============== ========= **Measurement** **Units** =============== ========= Length nm --------------- --------- Time ps --------------- --------- Mass amu (g/mol) --------------- --------- Energy kJ/mol --------------- --------- Frequency THz (1/ps) --------------- --------- Temperature K --------------- --------- Charge e =============== ========= The module :mod:`MMTK.Units` contains convenient conversion constants for the units commonly used in computational chemistry. For example, a length of 2 Ã…ngström can be written as ``2*Units.Ang``, and a frequency can be printed in wavenumbers with ``print frequency/Units.invcm``. A simple example ================ The following simple example shows how a typical MMTK application might look like. It constructs a system consisting of a single water molecule and runs a short molecular dynamics trajectory. There are many alternative ways to do this; this particular one was chosen because it makes each step explicit and clear. The individual steps are explained in the remaining chapters of the manual. :: # Import the necessary MMTK definitions. from MMTK import * from MMTK.ForceFields import Amber99ForceField from MMTK.Trajectory import Trajectory, TrajectoryOutput, StandardLogOutput from MMTK.Dynamics import VelocityVerletIntegrator # Create an infinite universe (i.e. no boundaries, non-periodic). universe = InfiniteUniverse(Amber99ForceField()) # Create a water molecule in the universe. # Water is defined in the database. universe.molecule = Molecule('water') # Generate random velocities. universe.initializeVelocitiesToTemperature(300*Units.K) # Create an integrator. integrator = VelocityVerletIntegrator(universe) # Generate a trajectory trajectory = Trajectory(universe, "water.nc", "w") # Run the integrator for 50 steps of 1 fs, printing time and energy # every fifth step and writing time, energy, temperature, and the positions # of all atoms to the trajectory at each step. t_actions = [StandardLogOutput(5), \ TrajectoryOutput("time", "energy", \ "thermodynamic", "configuration"), 0, None, 1] integrator(delta_t = 1.*Units.fs, steps = 50, actions = t_actions) # Close the trajectory trajectory.close() Constructing a molecular system ############################### The construction of a complete system for simulation or analysis involves some or all of the following operations: - Creating molecules and other chemical objects. - Defining the configuration of all objects. - Defining the "surroundings" (e.g. boundary conditions). - Choosing a force field. MMTK offers a large range of functions to deal with these tasks. Creating chemical objects ========================= Chemical objects (atoms, molecules, complexes) are created from definitions in the :ref:`database`. Since these definitions contain most of the necessary information, the subsequent creation of the objects is a simple procedure. All objects are created by their class name (:class:`MMTK.ChemicalObjects.Atom`, :class:`MMTK.ChemicalObjects.Molecule`, and :class:`MMTK.ChemicalObjects.Complex`) with the name of the definition file as first parameter. Additional optional parameters can be specified to modify the object being created. The following optional parameters can be used for all object types: - name=string Specifies a name for the object. The default name is the one given in the definition file. - position=vector Specifies the position of the center of mass. The default is the origin. - configuration=string Indicates a configuration from the configuration dictionary in the definition file. The default is 'default' if such an entry exists in the configuration dictionary. Otherwise the object is created without atomic positions. Some examples with additional explanations for specific types: - ``Atom('C')`` creates a carbon atom. - ``Molecule('water', position=Vector(0.,0.,1.))`` creates a water molecule using configuration 'default' and moves the center of mass to the indicated position. Proteins, peptide chains, and nucleotide chains ----------------------------------------------- MMTK contains special support for working with proteins, peptide chains, and nucleotide chains. As described in the chapter :ref:`database`, proteins can be described by a special database definition file. However, it is often simpler to create macromolecular objects directly in an application program. The classes are :class:`MMTK.Proteins.PeptideChain`, :class:`MMTK.Proteins.Protein`, and :class:`MMTK.NucleicAcids.NucleotideChain`. Proteins can be created from definition files in the database, from previously constructed peptide chain objects, or directly from PDB files if no special manipulations are necessary. Examples: - Protein('insulin') creates a protein object for insulin from a database file. - Protein('1mbd.pdb') creates a protein object for myoglobin directly from a PDB file, but leaving out the heme group, which is not a peptide chain. Peptide chains are created from a sequence of residues, which can be a :class:`MMTK.PDB.PDBPeptideChain` object, a list of three-letter residue codes, or a string containing one-letter residue codes. In the last two cases the atomic positions are not defined. MMTK provides several models for the residues which provide different levels of detail: an all-atom model, a model without hydrogen atoms, two models containing only polar hydrogens (using different definitions of polar hydrogens), and a model containing only the |C_alpha| atoms, with each |C_alpha| atom having the mass of the entire residue. The last model is useful for conformational analyses in which only the backbone conformations are important. The construction of nucleotide chains is very similar. The residue list can be either a :class:`MMTK.PDB.PDBNucleotideChain` object or a list of two-letter residue names. The first letter of a residue name indicates the sugar type ('R' for ribose and'D' for desoxyribose), and the second letter defines the base ('A', 'C', and'G', plus 'T' for DNA and'U' for RNA). The models are the same as for peptide chains, except that the |C_alpha| model does not exist. Most frequently proteins and nucleotide chains are created from a PDB file. The PDB files often contain solvent (water) as well, and perhaps some other molecules. MMTK provides convenient functions for extracting information from PDB files and for building molecules from them in the module :mod:`MMTK.PDB`. The first step is the creation of a :class:`MMTK.PDB.PDBConfiguration` object from the PDB file: :: from MMTK.PDB import PDBConfiguration configuration = PDBConfiguration('some_file.pdb') The easiest way to generate MMTK objects for all molecules in the PDB file is then :: molecules = configuration.createAll() The result is a collection of molecules, peptide chains, and nucleotide chains, depending on the contents of the PDB files. There are also methods for modifying the PDBConfiguration before creating MMTK objects from it, and for creating objects selectively. See the documentation for the modules :class:`MMTK.PDB` and Scientific.IO.PDB for details, as well as the :ref:`Proteins ` and :ref:`DNA ` examples. Lattices -------- Sometimes it is necessary to generate objects (atoms or molecules) positioned on a lattice. To facilitate this task, MMTK defines lattice objects which are essentially sequence objects containing points or objects at points. Lattices can therefore be used like lists with indexing and for-loops. The lattice classes are :class:`MMTK.Geometry.RhombicLattice`, :class:`MMTK.Geometry.BravaisLattice`, and :class:`MMTK.Geometry.SCLattice`. Random numbers -------------- The Python standard library and the Numerical Python package provide random number generators, and more are available in seperate packages. MMTK provides some convenience functions that return more specialized random quantities: random points in a universe, random velocities, random particle displacement vectors, random orientations. These functions are defined in module :class:`MMTK.Random`. Collections ----------- Often it is useful to treat a collection of several objects as a single entity. Examples are a large number of solvent molecules surrounding a solute, or all sidechains of a protein. MMTK has special collection objects for this purpose, defined as :class:`MMTK.Collections.Collection`. Most of the methods available for molecules can also be used on collections. A variant of a collection is the partitioned collection, implemented in class :class:`MMTK.Collections.PartitionedCollection`. This class acts much like a standard collection, but groups its elements by geometrical position in small sub-boxes. As a consequence, some geometrical algorithms (e.g. pair search within a cutoff) are much faster, but other operations become somewhat slower. Creating universes ------------------ A universe describes a complete molecular system consisting of any number of chemical objects and a specification of their interactions (i.e. a force field) and surroundings: boundary conditions, external fields, thermostats, etc. The universe classes are defined in module MMTK: - :class:`MMTK.Universe.InfiniteUniverse` represents an infinite universe, without any boundary or periodic boundary conditions. - :class:`MMTK.Universe.ParallelepipedicPeriodicUniverse` represents a general periodic universe defined by three basis vectors. - :class:`MMTK.Universe.OrthorhombicPeriodicUniverse` represents a periodic universe with an orthorhombic elementary cell, whose size is defined by the three edge lengths. The edges are oriented along the axes of the coordinate system. - :class:`MMTK.Universe.CubicPeriodicUniverse` is a special case of :class:`MMTK.Universe.OrthorhombicPeriodicUniverse` in which the elementary cell is cubic. Universes are created empty; the contents are then added to them. Three types of objects can be added to a universe: chemical objects (atoms, molecules, etc.), collections, and environment objects (thermostats etc.). It is also possible to remove objects from a universe. Force fields ------------ MMTK comes with several force fields, and permits the definition of additional force fields. Force fields are defined in module :class:`MMTK.ForceFields.ForceField`. The most important built-in force field is the `Amber 99 force field `_, represented by the class :class:`MMTK.ForceFields.Amber.AmberForceField.Amber99ForceField`. It offers several strategies for electrostatic interactions, including Ewald summation and cutoff with charge neutralization and optional screening :ref:`[Wolf1999] `.. In addition to the Amber 99 force field, there is the older Amber 94 forcefield, a Lennard-Jones force field for noble gases (Class :class:`MMTK.ForceFields.LennardJonesFF`) and an Elastic Network Model force field for protein normal mode calculations (:class:`MMTK.ForceFields.DeformationFF.CalphaForceField`). Referring to objects and parts of objects ========================================= Most MMTK objects (in fact all except for atoms) have a hierarchical structure of parts of which they consist. For many operations it is necessary to access specific parts in this hierarchy. In most cases, parts are attributes with a specific name. For example, the oxygen atom in every water molecule is an attribute with the name "O". Therefore if ``w`` refers to a water molecule, then ``w.O`` refers to its oxygen atom. For a more complicated example, if ``m`` refers to a molecule that has a methyl group called "M1", then ``m.M1.C`` refers to the carbon atom of that methyl group. The names of attributes are defined in the database. Some objects consist of parts that need not have unique names, for example the elements of a collection, the residues in a peptide chain, or the chains in a protein. Such parts are accessed by indices; the objects that contain them are Python sequence types. Some examples: - Asking for the number of items: if ``c`` refers to a collection, then ``len(c)`` is the number of its elements. - Extracting an item: if ``p`` refers to a protein, then ``p[0]`` is its first peptide chain. - Iterating over items: if ``p`` refers to a peptide chain, then ``for residue in p: print residue.position()`` will print the center of mass positions of all its residues. Peptide and nucleotide chains also allow the operation of slicing: if ``p`` refers to a peptide chain, then ``p[1:-1]`` is a subchain extending from the second to the next-to-last residue. The structure of peptide and nucleotide chains ---------------------------------------------- Since peptide and nucleotide chains are not constructed from an explicit definition file in the database, it is not evident where their hierarchical structure comes from. But it is only the top-level structure that is treated in a special way. The constituents of peptide and nucleotide chains, residues, are normal group objects. The definition files for these group objects are in the MMTK standard database and can be freely inspected and even modified or overriden by an entry in a database that is listed earlier in MMTKDATABASE. Peptide chains are made up of amino acid residues, each of which is a group consisting of two other groups, one being called "peptide" and the other "sidechain". The first group contains the peptide group and the C and H atoms; everything else is contained in the sidechain. The C atom of the fifth residue of peptide chain ``p`` is therefore referred to as ``p[4].peptide.C_alpha``. Nucleotide chains are made up of nucleotide residues, each of which is a group consisting of two or three other groups. One group is called "sugar" and is either a ribose or a desoxyribose group, the second one is called "base" and is one the five standard bases. All but the first residue in a nucleotide chain also have a subgroup called "phosphate" describing the phosphate group that links neighbouring residues. Analyzing and modifying atom properties ======================================= General operations ------------------ Many inquiry and modification operations act at the atom level and can equally well be applied to any object that is made up of atoms, i.e. atoms, molecules, collections, universes, etc. These operations are defined once in a :term:`Mix-in class` called :class:`MMTK.Collections.GroupOfAtoms`, but are available for all objects for which they make sense. They include inquiry-type functions (total mass, center of mass, moment of inertia, bounding box, total kinetic energy etc.), coordinate modifications (translation, rotation, application of :ref:`transformation`) and coordinate comparisons (RMS difference, optimal fits). .. _transformation: Coordinate transformations -------------------------- The most common coordinate manipulations involve translations and rotations of specific parts of a system. It is often useful to refer to such an operation by a special kind of object, which permits the combination and analysis of transformations as well as its application to atomic positions. Transformation objects specify a general displacement consisting of a rotation around the origin of the coordinate system followed by a translation. They are defined in the module ``Scientific.Geometry``, but for convenience the module ``MMTK`` contains a reference to them as well. Transformation objects corresponding to pure translations can be created with ``Translation(displacement)``; transformation objects describing pure rotations with ``Rotation(axis, angle)`` or ``Rotation(rotation_matrix)``. Multiplication of transformation objects returns a composite transformation. The translational component of any transformation can be obtained by calling the method ``translation()``; the rotational component is obtained analogously with ``rotation()``. The displacement vector for a pure translation can be extracted with the method ``displacement()``, a tuple of axis and angle can be extracted from a pure rotation by calling ``axisAndAngle()``. .. _atom_property: Atomic property objects ----------------------- Many properties in a molecular system are defined for each individual atom: position, velocity, mass, etc. Such properties are represented in special objects, defined in module MMTK: :class:`MMTK.ParticleProperties.ParticleScalar` for scalar quantities, :class:`MMTK.ParticleProperties.ParticleVector` for vector quantities, and :class:`MMTK.ParticleProperties.ParticleTensor` for rank-2 tensors. All these objects can be indexed with an atom object to retrieve or change the corresponding value. Standard arithmetic operations are also defined, as well as some useful methods. Configurations -------------- A configuration object, represented by the class :class:`MMTK.ParticleProperties.Configuration` is a special variant of a :class:`MMTK.ParticleProperties.ParticleVector` object. In addition to the atomic coordinates of a universe, it stores geometric parameters of a universe that are subject to change, e.g. the edge lengths of the elementary cell of a periodic universe. Every universe has a current configuration, which is what all operations act on by default. It is also the configuration that is updated by minimizations, molecular dynamics, etc. The current configuration can be obtained by calling the method ``configuration()``. There are two ways to create configuration objects: by making a copy of the current configuration (with ``universe.copyConfiguration()``, or by reading a configuration from a :ref:`trajectory `. Minimization and Molecular Dynamics ################################### .. _Trajectories: Trajectories ============ Minimization and dynamics algorithms produce sequences of configurations that are often stored for later analysis. In fact, they are often the most valuable result of a lengthy simulation run. To make sure that the use of trajectory files is not limited by machine compatibility, MMTK stores trajectories in `netCDF `_ files. These files contain binary data, minimizing disk space usage, but are freely interchangeable between different machines. In addition, there are a number of programs that can perform standard operations on arbitrary netCDF files, and which can therefore be used directly on MMTK trajectory files. Finally, netCDF files are self-describing, i.e. contain all the information needed to interpret their contents. An MMTK trajectory file can thus be inspected and processed without requiring any further information. For illustrations of trajectory operations, see the :ref:`examples `. Trajectory file objects are represented by the class :class:`MMTK.Trajectory.Trajectory`. They can be opened for reading, writing, or modification. The data in trajectory files can be stored in single precision or double precision; single-precision is usually sufficient, but double-precision files are required to reproduce a given state of the system exactly. A trajectory is closed by calling the method ``close()``. If anything has been written to a trajectory, closing it is required to guarantee that all data has been written to the file. Closing a trajectory after reading is recommended in order to prevent memory leakage, but is not strictly required. Newly created trajectories can contain all objects in a universe or any subset; this is useful for limiting the amount of disk space occupied by the file by not storing uninteresting parts of the system, e.g. the solvent surrounding a protein. It is even possible to create a trajectory for a subset of the atoms in a molecule, e.g. for only the |C_alpha| atoms of a protein. The universe description that is stored in the trajectory file contains all chemical objects of which at least one atom is represented. When a trajectory is opened for reading, no universe object needs to be specified. In that case, MMTK creates a universe from the description contained in the trajectory file. This universe will contain the same objects as the one for which the trajectory file was created, but not necessarily have all the properties of the original universe (the description contains only the names and types of the objects in the universe, but not, for example, the force field). The universe can be accessed via the attribute universe of the trajectory. If the trajectory was created with partial data for some of the objects, reading data from it will set the data for the missing parts to "undefined". Analysis operations on such systems must be done very carefully. In most cases, the trajectory data will contain the atomic configurations, and in that case the "defined" atoms can be extracted with the method ``atomsWithDefinedPositions()``. MMTK trajectory files can store various data: atomic positions, velocities, energies, energy gradients etc. Each trajectory-producing algorithm offers a set of quantities from which the user can choose what to put into the trajectory. Since a detailed selection would be tedious, the data is divided into classes, e.g. the class "energy" stands for potential energy, kinetic energy, and whatever other energy-related quantities an algorithm produces. For optimizing I/O efficiency, the data layout in a trajectory file can be modified by the block_size parameter. Small block sizes favour reading or writing all data for one time step, whereas large block sizes (up to the number of steps in the trajectory) favour accessing a few values for all time steps, e.g. scalar variables like energies or trajectories for individual atoms. The default value of the block size is one. Every trajectory file contains a history of its creation. The creation of the file is logged with time and date, as well as each operation that adds data to it with parameters and the time/date of start and end. This information, together with the comment and the number of atoms and steps contained in the file, can be obtained with the function :func:`MMTK.Trajectory.trajectoryInfo`. It is possible to read data from a trajectory file that is being written to by another process. For efficiency, trajectory data is not written to the file at every time step, but only approximately every 15 minutes. Therefore the amount of data available for reading may be somewhat less than what has been produced already. Options for minimization and dynamics ===================================== Minimizers and dynamics integrators accept various optional parameter specifications. All of them are selected by keywords, have reasonable default values, and can be specified when the minimizer or integrator is created or when it is called. In addition to parameters that are specific to each algorithm, there is a general parameter ``actions`` that specifies actions that are executed periodically, including trajectory and console output. Periodic actions ---------------- Periodic actions are specified by the keyword parameter ``actions`` whose value is a list of periodic actions, which defaults to an empty list. Some of these actions are applicable to any trajectory-generating algorithm, especially the output actions. Others make sense only for specific algorithms or specific universes, e.g. the periodic rescaling of velocities during a Molecular Dynamics simulation. Each action is described by an action object. The step numbers for which an action is executed are specified by three parameters. The parameter ``first`` indicates the number of the first step for which the action is executed, and defaults to 0. The parameter ``last`` indicates the last step for which the action is executed, and default to ``None``, meaning that the action is executed indefinitely. The parameter ``skip`` speficies how many steps are skipped between two executions of the action. The default value of 1 means that the action is executed at each step. Of course an action object may have additional parameters that are specific to its action. The output actions are defined in the module :mod:`MMTK.Trajectory` and can be used with any trajectory-generating algorithm. They are: - :class:`MMTK.Trajectory.TrajectoryOutput` for writing data to a trajectory. Note that it is possible to use several trajectory output actions simultaneously to write to multiple trajectories. It is thus possible, for example, to write a short dense trajectory during a dynamics run for analyzing short-time dynamics, and simultaneously a long-time trajectory with a larger step spacing, for analyzing long-time dynamics. - :class:`MMTK.Trajectory.RestartTrajectoryOutput`, which is a specialized version of :class:`MMTK.Trajectory.TrajectoryOutput`. It writes the data that the algorithm needs in order to be restarted to a restart trajectory file. A restart trajectory is a trajectory that stores a fixed number of steps which are reused cyclically, such that it always contain the last few steps of a trajectory. - :class:`MMTK.Trajectory.LogOutput` for text output of data to a file. - :class:`MMTK.Trajectory.StandardLogOutput`, a specialized version of :class:`MMTK.Trajectory.LogOutput` that writes the data classes "time" and "energy" during the whole simulation run to standard output. The other periodic actions are meaningful only for Molecular Dynamics simulations: - :class:`MMTK.Dynamics.VelocityScaler` is used for rescaling the velocities to force the kinetic energy to the value defined by some temperature. This is usually done during initial equilibration. - :class:`MMTK.Dynamics.BarostatReset` resets the barostat coordinate to zero and is during initial equilibration of systems in the NPT ensemble. - :class:`MMTK.Dynamics.Heater` rescales the velocities like :class:`MMTK.Dynamics.VelocityScaler`, but increases the temperature step by step. - :class:`MMTK.Dynamics.TranslationRemover` subtracts the global translational velocity of the system from all individual atomic velocities. This prevents a slow but systematic energy flow into the degrees of freedom of global translation, which occurs with most MD integrators due to non-perfect conservation of momentum. - :class:`MMTK.Dynamics.RotationRemover` subtracts the global angular velocity of the system from all individual atomic velocities. This prevents a slow but systematic energy flow into the degrees of freedom of global rotation, which occurs with most MD integrators due to non-perfect conservation of angular momentum. Fixed atoms ----------- During the course of a minimization or molecular dynamics algorithm, the atoms move to different positions. It is possible to exclude specific atoms from this movement, i.e. fixing them at their initial positions. This has no influence whatsoever on energy or force calculations; the only effect is that the atoms' positions never change. Fixed atoms are specified by giving them an attribute fixed with a value of one. Atoms that do not have an attributefixed, or one with a value of zero, move according to the selected algorithm. .. _energy_minimization: Energy minimization =================== MMTK has two energy minimizers using different algorithms: steepest descent (:class:`MMTK.Minimization.SteepestDescentMinimizer`) and conjugate gradient (:class:`MMTK.Minimization.ConjugateGradientMinimizer`) . Steepest descent minimization is very inefficient if the goal is to find a local minimum of the potential energy. However, it has the advantage of always moving towards the minimum that is closest to the starting point and is therefore ideal for removing bad contacts in a unreasonably high energy configuration. For finding local minima, the conjugate gradient algorithm should be used. Both minimizers accept three specific optional parameters: - ``steps`` (an integer) to specify the maximum number of steps (default is 100) - ``step_size`` (a number) to specify an initial step length used in the search for a minimum (default is 2 pm) - ``convergence`` (a number) to specify the gradient norm (more precisely the root-mean-square length) at which the minimization should stop (default is 0.01 kJ/mol/nm) There are three classes of trajectory data: "energy" includes the potential energy and the norm of its gradient, "configuration" stands for the atomic positions, and "gradients" stands for the energy gradients at each atom position. The following example performs 100 steps of steepest descent minimization without producing any trajectory or printed output: :: from MMTK import * from MMTK.ForceFields import Amber99ForceField from MMTK.Minimization import SteepestDescentMinimizer universe = InfiniteUniverse(Amber99ForceField()) universe.protein = Protein('insulin') minimizer = SteepestDescentMinimizer(universe) minimizer(steps = 100) See also the example file :ref:`modes.py `. Molecular dynamics ================== The techniques described in this section are illustrated by several :ref:`examples `. Velocities ---------- The integration of the classical equations of motion for an atomic system requires not only positions, but also velocities for all atoms. Usually the velocities are initialized to random values drawn from a normal distribution with a variance corresponding to a certain temperature. This is done by calling the method :func:`~MMTK.Universe.Universe.initializeVelocitiesToTemperature` on a universe. Note that the velocities are assigned atom by atom; no attempt is made to remove global translation or rotation of the total system or any part of the system. During equilibration of a system, it is common to multiply all velocities by a common factor to restore the intended temperature. This can done explicitly by calling the method :func:`~MMTK.Universe.Universe.scaleVelocitiesToTemperature` on a universe, or by using the action object :class:`MMTK.Dynamics.VelocityScaler`. Distance constraints -------------------- A common technique to eliminate the fastest (usually uninteresting) degrees of freedom, permitting a larger integration time step, is the use of distance constraints on some or all chemical bonds. MMTK allows the use of distance constraints on any pair of atoms, even though constraining anything but chemical bonds is not recommended due to considerable modifications of the dynamics of the system :ref:`[vanGunsteren1982] `, :ref:`[Hinsen1995] `. MMTK permits the definition of distance constraints on all atom pairs in an object that are connected by a chemical bond by calling the method setBondConstraints. Usually this is called for a complete universe, but it can also be called for a chemical object or a collection of chemical objects. The :func:`~MMTK.ChemicalObjects.ChemicalObject.removeDistanceConstraints` removes all distance constraints from the object for which it is called. Constraints defined as described above are automatically taken into account by Molecular Dynamics integrators. It is also possible to enforce the constraints explicitly by calling the method :func:`~MMTK.Universe.Universe.enforceConstraints` for a universe. This has the effect of modifying the configuration and the velocities (if velocities exist) in order to make them compatible with the constraints. Thermostats and barostats ------------------------- A standard Molecular Dynamics integration allows time averages corresponding to the NVE ensemble, in which the number of molecules, the system volume, and the total energy are constant. This ensemble does not represent typical experimental conditions very well. Alternative ensembles are the NVT ensemble, in which the temperature is kept constant by a thermostat, and the NPT ensemble, in which temperature and pressure are kept constant by a thermostat and a barostat. To obtain these ensembles in MMTK, thermostat and barostat objects must be added to a universe. In the presence of these objects, the Molecular Dynamics integrator will use the extended-systems method for producing the correct ensemble. The classes to be used are :class:`MMTK.Environment.NoseThermostat` and :class:`MMTK.Environment.AndersenBarostat`. Integration ----------- A Molecular Dynamics integrator based on the "Velocity Verlet" algorithm :ref:`[Swope1982] `, which was extended to handle distance constraints as well as thermostats and barostats :ref:`[Kneller1996] `, is implemented by the class :class:`MMTK.Dynamics.VelocityVerletIntegrator`. It has two optional keyword parameters: - ``steps`` (an integer) to specify the number of steps (default is 100) - ``delta_t`` (a number) to specify the time step (default 1 fs) There are three classes of trajectory data: "energy" includes the potential energy and the kinetic energy, as well as the energies of thermostat and barostat coordinates if they exist, "time" stands for the time, "thermodynamic" stand for temperature and pressure, "configuration" stands for the atomic positions, "velocities" stands for the atomic velocities, and "gradients" stands for the energy gradients at each atom position. The following example performs a 1000 step dynamics integration, storing every 10th step in a trajectory file and removing the total translation and rotation every 50th step: :: from MMTK import * from MMTK.ForceFields import Amber99ForceField from MMTK.Dynamics import VelocityVerletIntegrator, \ TranslationRemover, \ RotationRemover from MMTK.Trajectory import TrajectoryOutput universe = InfiniteUniverse(Amber99ForceField()) universe.protein = Protein('insulin') universe.initializeVelocitiesToTemperature(300.*Units.K) actions = [TranslationRemover(0, None, 50), \ RotationRemover(0, None, 50), \ TrajectoryOutput("insulin.nc", \ ("configuration", "energy", "time"), \ 0, None, 10)] integrator = VelocityVerletIntegrator(universe, delta_t = 1.*Units.fs, \ actions = actions) integrator(steps = 1000) Snapshots ========= A snapshot generator allows writing the current system state to a trajectory. It works much like a zero-step minimization or dynamics run, i.e. it takes the same optional arguments for specifying the trajectory and protocol output. A snapshot generator is created using the class :class:`MMTK.Trajectory.SnapshotGenerator`. Normal modes ############ Normal mode analysis provides an analytic description of the dynamics of a system near a minimum using an harmonic approximation to the potential. Before a normal mode analysis can be started, the system must be brought to a local minimum of the potential energy by :ref:`energy minimization `, except when special force fields designed only for normal mode analysis are used (e.g. :class:`MMTK.ForceFields.CalphaFF.CalphaForceField`). See also the :ref:`Normal Modes ` examples. A standard normal mode analysis is performed by creating a normal modes object, implemented in the classes :class:`MMTK.NormalModes.EnergeticModes.EnergeticModes`, :class:`MMTK.NormalModes.VibrationalModes.VibrationalModes`, and :class:`MMTK.NormalModes.BrownianModes.BrownianModes`. A normal mode object behaves like a sequence of mode objects, which store the atomic displacement vectors corresponding to each mode and the associated force constant, vibrational frequency, or inverse relaxation time. For short-ranged potentials, it is advantageous to store the second derivatives of the potential in a sparse-matrix form and to use an iterative method to determine some or all modes. This permits the treatment of larger systems that would normally require huge amounts of memory. Another approach to deal with large systems is the restriction to low-frequency modes which are supposed to be well representable by linear combinations of a given set of basis vectors. The basis vectors can be obtained from a basis for the full Cartesian space by elimination of known fast degrees of freedom (e.g. bonds); the module :mod:`MMTK.Subspace` contains support classes for this approach. It is also possible to construct a suitable basis vector set from small-deformation vector fields (e.g. :class:`MMTK.FourierBasis.FourierBasis`). Analysis operations ################### Analysis is the most non-standard part of molecular simulations. The quantities that must be calculated depend strongly on the system and the problem under study. MMTK provides a wide range of elementary operations that inquire the state of the system, as well as several more complex analysis tools. Some of them are demonstrated in the :ref:`example ` section. Properties of chemical objects and universes ============================================ Many operations access and modify various properties of an object. They are defined for the most general type of object: anything that can be broken down to atoms, i.e. atoms, molecules, collections, universes, etc., i.e. in the class :class:`MMTK.Collections.GroupOfAtoms`. The most elementary operations are inquiries about specific properties of an object: number of atoms, total mass, center of mass, total momentum, total charge, etc. There are also operations that compare two different conformations of a system. Finally, there are special operations for analyzing conformations of peptide chains and proteins. Geometrical operations in periodic universes require special care. Whenever a distance vector between two points in a systems is evaluated, the minimum-image convention must be used in order to obtain consistent results. MMTK provides routines for finding these distance vectors as well as distances, angles, and dihedral angles between any points. Because these operations depend on the topology and geometry of the universe, they are implemented as methods in class :class:`MMTK.Universe.Universe` and its subclasses. Of course they are available for non-periodic universes as well. Universes also provide methods for obtaining :ref:`atom property ` objects that describe the state of the system (configurations, velocities, masses), and for restoring the system state from a :ref:`trajectory ` file. Energy evaluation ================= Energy evaluation requires a force field, and therefore all the methods in this section are defined only for universe objects, i.e. in class :class:`MMTK.Universe.Universe`. However, they all take an optional arguments (anything that can be broken down into atoms) that indicates for which subset of the universe the energy is to be evaluated. In addition to the potential energy, energy gradients and second derivatives (force constants) can be obtained, if the force field implements them. There is also a method that returns a dictionary containing the values for all the individual force field terms, which is often useful for analysis. Surfaces and volumes ==================== Surfaces and volumes can be analyzed for anything consisting of atoms. Both quantities are defined by assigning a radius to each atom; the surface of the resulting conglomerate of overlapping spheres is taken to be the surface of the atom group. Atom radii for surface determination are usually called "van der Waals radii", but there is no unique method for determining them. MMTK uses the values from :ref:`[Bondi1964] `. However, users can change these values for each individual atom by assigning a new value to the attribute ``vdW_radius``. The operations provided in :mod:`MMTK.MolecularSurface` include basic surface and volume calculation, determination of exposed atoms, and identification of contacts between two objects. Miscellaneous operations ######################## Saving, loading, and copying objects ==================================== MMTK provides an easy way to store (almost) arbitrary objects in files and retrieve them later. All objects of interest to users can be stored, including chemical objects, collections, universes, normal modes, configurations, etc. It is also possible to store standard Python objects such as numbers, lists, dictionaries etc., as well as practically any user-defined objects. Storage is based on the standard Python module pickle. Objects are saved with :func:`MMTK.save` and restored with :func:`MMTK.load`. If several objects are to be stored in a single file, use tuples: ``save((object1, object2), filename)`` and ``object1, object2 = load(filename)`` to retrieve the objects. Note that storing an object in a file implies storing all objects referenced by it as well, such that the size of the file can become larger than expected. For example, a configuration object contains a reference to the universe for which it is defined. Therefore storing a configuration object means storing the whole universe as well. However, nothing is ever written twice to the same file. If you store a list or a tuple containing a universe and a configuration for it, the universe is written only once. Frequently it is also useful to copy an object, such as a molecule or a configuration. There are two functions (which are actually taken from the Python standard library module copy) for this purpose, which have a somewhat different behaviour for container-type objects (lists, dictionaries, collections etc.). :func:`MMTK.copy` returns a copy of the given object. For a container object, it returns a new container object which contains the same objects as the original one. If the intention is to get a container object which contains copies of the original contents, then ``MMTK.deepcopy(object)`` should be used. For objects that are not container-type objects, there is no difference between the two functions. Exporting to specific file formats and visualization ==================================================== MMTK can write objects in specific file formats that can be used by other programs. Three file formats are supported: the PDB format, widely used in computational chemistry, the DCD format for trajectories, written by the programs CHARMM, X-Plor, and NAMDm, and read by many visualization programs, and the VRML format, understood by VRML browsers as a representation of a three-dimensional scene for visualization. MMTK also provides a more general interface that can generate graphics objects in any representation if a special module for that representation exists. In addition to facilitating the implementation of new graphics file formats, this approach also permits the addition of custom graphics elements (lines, arrows, spheres, etc.) to molecular representations. PDB, VRML, and DCD files ------------------------ Any chemical object, collection, or universe can be written to a PDB or VRML file by calling the method ``writeToFile``, defined in class :class:`MMTK.Collections.GroupOfAtoms`. PDB files are read via the class :class:`MMTK.PDB.PDBConfiguration`. DCD files can be read by a :class:`MMTK.DCD.DCDReader` object. For writing DCD files, there is the function :func:`MMTK.DCD.writeDCDPDB`, which also creates a compatible PDB file without which the DCD file could not be interpreted. Special care must be taken to ensure a correct mapping of atom numbers when reading from a DCD file. In MMTK, each atom object has a unique identity and atom numbers, also used internally for efficiency, are not strictly necessary and are not used anywhere in MMTK's application programming interface. DCD file, however, simply list coordinates sorted by atom number. For interpreting DCD files, another file must be available which allows the identification of atoms from their number and vice versa; this can for example be a PDB file. When reading DCD files, MMTK assumes that the atom order in the DCD file is identical to the internal atom numbering of the universe for which the DCD file is read. This assumption is in general valid only if the universe has been created from a PDB file that is compatible with the DCD file, without any additions or removals. Visualization and animation --------------------------- The most common need for file export is visualization. All objects that can be visualized (chemical systems and subsets thereof, normal mode objects, trajectories) provide a method view which creates temporary export files, starts a visualization program, and deletes the temporary files. Depending on the object type there are various optional parameters. MMTK also allows visualization of normal modes and trajectories using animation. Since not all visualization programs permit animation, and since there is no standard way to ask for it, animation is implemented only for the programs `XMol `_ and `VMD `_. Animation is available for normal modes, trajectories, and arbitrary sequences of configurations (see function :func:`MMTK.Visualization.viewSequence`). For more specialized needs, MMTK permits the creation of graphical representations of most of its objects via general graphics modules that have to be provided externally. Suitable modules are provided in the package Scientific.Visualization and cover VRML (version 1), VRML2 (aka VRML97), and the molecular visualization program VMD. Modules for other representations (e.g. rendering programs) can be written easily; it is recommended to use the existing modules as an example. The generation of graphics objects is handled by the method graphicsObjects, defined in the class :class:`MMTK.Visualization.Viewable`, which is a :term:`Mix-in class` that makes graphics objects generation available for all objects that define chemical systems or parts thereof, as well as for certain other objects that are viewable. The explicit generation of graphics objects permits the mixture of different graphical representations for various parts of a system, as well as the combination of MMTK-generated graphics objects with arbitrary other graphics objects, such as lines, arrows, or spheres. All graphics objects are finally combined into a scene object (also defined in the various graphics modules) in order to be displayed. See also the :ref:`visualization ` examples. Fields ====== For analyzing or visualizing atomic properties that change little over short distances, it is often convenient to represent these properties as functions of position instead of one value per atom. Functions of position are also known as fields, and mathematical techniques for the analysis of fields have proven useful in many branches of physics. Such a field can be obtained by averaging over the values corresponding to the atoms in a small region of space. MMTK provides classes for scalar and vector field in module :mod:`MMTK.Field`. See also the example :ref:`vector_field.py `. Charge fitting ============== A frequent problem in determining force field parameters is the determination of partial charges for the atoms of a molecule by fitting to the electrostatic potential around the molecule, which is obtained from quantum chemistry programs. Although this is essentially a straightforward linear least-squares problem, many procedures that are in common use do not use state-of-the-art techniques and may yield erroneous results. MMTK provides a charge fitting method that is numerically stable and allows the imposition of constraints on the charges. It is implemented in module :mod:`MMTK.ChargeFit`. See also the example :ref:`charge_fit.py `. .. _database: Constructing the database ######################### MMTK uses a database of chemical entities to define the properties of atoms, molecules, and related objects. This database consists of plain text files, more precisely short Python programs, whose names are the names of the object types. This chapter explains how to construct and manage these files. Note that the standard database already contains many definitions, in particular for proteins and nucleic acids. You do not need to read this chapter unless you want to add your own molecule definitions. MMTK's database does not have to reside in a single place. It can consist of any number of subdatabases, each of which can be a directory or a URL. Typically the database consists of at least two parts: MMTK's standard definitions and a user's personal definitions. When looking up an object type in the database, MMTK checks the value of the environment variable MMTKDATABASE. The value of this variable must be a list of subdatabase locations seperated by white space. If the variable MMTKDATABASE is not defined, MMTK uses a default value that contains the path ".mmtk/Database" in the user's home directory followed by MMTK's standard database, which resides in the directory Database within the MMTK package directory (on many Unix systems this is ``/usr/local/lib/python2.x/site-packages/MMTK``). MMTK checks the subdatabases in the order in which they are mentioned in MMTKDATABASE. Each subdatabase contains directories corresponding to the object classes, i.e. Atoms (atom definitions), Groups (group definitions), Molecules (molecule definitions), Complexes (complex definitions), Proteins (protein definitions), and PDB (Protein Data Bank files). These directories contain the definition files, whose names may not contain any upper-case letters. These file names correspond to the object types, e.g. the call ``MMTK.Molecule('Water')`` will cause MMTK to look for the file Molecules/water in the database (note that the names are converted to lower case). The remaining sections of this chapter explain how the individual definition files are constructed. Keep in mind that each file is actually a Python program, so of course standard Python syntax rules apply. Atom definitions ================ An atom definition in MMTK describes a chemical element, such as "hydrogen". This should not be confused with the "atom types" used in force field descriptions and in some modelling programs. As a consequence, it is rarely necessary to add atom definitions to MMTK. Atom definition files are short and of essentially identical format. This is the definition for carbon: :: name = 'carbon' symbol = 'C' mass = [(12, 98.90), (13.003354826, 1.10)] color = 'black' vdW_radius = 0.17 The name should be meaningful to users, but is not used by MMTK itself. The symbol, however, is used to identify chemical elements. It must be exactly equal to the symbol defined by IUPAC, including capitalization (e.g. 'Cl' for chlorine). The mass can be either a number or a list of tuples, as shown above. Each tuple defines an isotope by its mass and its percentage of occurrence; the percentages must add up to 100. The color is used for VRML output and must equal one of the color names defined in the module VRML. The van der Waals radius is used for the calculation of molecular volumes and surfaces; the values are taken from :ref:`[Bondi1964] `. An application program can create an isolated atom with ``Atom('c')`` or, specifying an initial position, with ``Atom('c', position=Vector(0.,1.,0.))``. The element name can use any combination of upper and lower case letters, which are considered equivalent. Group definitions ================= Group definitions in MMTK exist to facilitate the definition of molecules by avoiding the frequent repetition of common combinations. MMTK doesn't give any physical meaning to groups. Groups can contain atoms and other groups. Their definitions look exactly like molecule definitions; the only difference between groups and molecules is the way they are used. This is the definition of a methyl group: :: name = 'methyl group' C = Atom('C') H1 = Atom('H') H2 = Atom('H') H3 = Atom('H') bonds = [Bond(C, H1), Bond(C, H2), Bond(C, H3)] pdbmap = [('MTH', {'C': C, 'H1': H1, 'H2': H2, 'H3': H3})] amber_atom_type = {C: 'CT', H1: 'HC', H2: 'HC', H3: 'HC'} amber_charge = {C: 0., H1: 0.1, H2: 0.1, H3: 0.1} The name should be meaningful to users, but is not used by MMTK itself. The following lines create the atoms in the group and assign them to variables. These variables become attributes of whatever object uses this group; their names can be anything that is a legal Python name. The list of bonds, however, must be assigned to the name ``bonds``. The bond list is used by force fields and for visualization. The name ``pdbmap`` is used for reading and writing PDB files. Its value must be a list of tuples, where each tuple defines one PDB residue. The first element of the tuple is the residue name, which is used only for output. The second element is a dictionary that maps PDB atom names to the actual atoms. The ``pdbmap`` entry of any object can be overridden by an entry in a higher-level object. Therefore the entry for a group is only used for atoms that do not occur in the entry for a molecule that contains this group. The remaining lines in the definition file contain information specific to force fields, in this case the Amber force field. The dictionary ``amber_atom_type`` defines the atom type for each atom; the dictionary ``amber_charge`` defines the partial charges. As for ``pdbmap`` entries, these definitions can be overridden by higher-level definitions. Molecule definitions ==================== Molecules are typically used directly in application programs, but they can also be used in the definition of complexes. Molecule definitions can use atoms and groups. This is the definition of a water molecule: :: name = 'water' structure = \ " O \n" + \ " / \ \n" + \ "H H \n" O = Atom('O') H1 = Atom('H') H2 = Atom('H') bonds = [Bond(O, H1), Bond(O, H2)] pdbmap = [('HOH', {'O': O, 'H1': H1, 'H2': H2})] pdb_alternative = {'OH2': 'O'} amber_atom_type = {O: 'OW', H1: 'HW', H2: 'HW'} amber_charge = {O: -0.83400, H1: 0.41700, H2: 0.41700} configurations = { 'default': ZMatrix([[H1], \ [O, H1, 0.9572*Ang], \ [H2, O, 0.9572*Ang, H1, 104.52*deg]]) } The name should be meaningful to users, but is not used by MMTK itself. The structure is optional and not used by MMTK either. The following lines create the atoms in the group and assign them to variables. These variables become attributes of the molecule, i.e. when a water molecule is created in an application program by ``w = Molecule('water')``, then ``w.H1`` will refer to its first hydrogen atom. The names of these variables can be any legal Python names. The list of bonds, however, must be assigned to the name ``bonds``. The bond list is used by force fields and for visualization. The name ``pdbmap`` is used for reading and writing PDB files. Its value must be a list of tuples, where each tuple defines one PDB residue. The first element of the tuple is the residue name, which is used only for output. The second element is a dictionary that maps PDB atom names to the actual atoms. The ``pdbmap`` entry of any object can be overridden by an entry in a higher-level object, i.e. in the case of a molecule a complex containing it. The name ``pdb_alternative`` allows to read PDB files that use non-standard names. When a PDB atom name is not found in the ``pdbmap``, an attempt is made to translate it to another name using ``pdb_alternative``. The two following lines in the definition file contain information specific to force fields, in this case the Amber force field. The dictionary ``amber_atom_type`` defines the atom type for each atom; the dictionary ``amber_charge`` defines the partial charges. As for pdbmap entries, these definitions can be overridden by higher-level definitions. The name ``configurations`` can be defined to be a dictionary of configurations for the molecule. During the construction of a molecule, a configuration can be specified via an optional parameter, e.g. ``w = Molecule('water', configuration='default')``. The names of the configurations can be arbitrary; only the name "default" has a special meaning; it is applied by default if no other configuration is specified when constructing the molecule. If there is no default configuration, and no other configuration is explicitly specified, then the molecule is created with undefined atomic positions. There are three ways of describing configurations: - By a Z-Matrix: :: ZMatrix([[H1], \ [O, H1, 0.9572*Ang], \ [H2, O, 0.9572*Ang, H1, 104.52*deg]]) - By Cartesian coordinates: :: Cartesian({O: ( 0.004, -0.00518, 0.0), H1: (-0.092, -0.00518, 0.0), H2: ( 0.028, 0.0875, 0.0)}) - By a PDB file: :: PDBFile('water.pdb') The PDB file must be in the database subdirectory PDB, unless a full path name is specified for it. Complex definitions =================== Complexes are defined much like molecules, except that they are composed of molecules and atoms; no groups are allowed, and neither are bonds. Protein definitions =================== Protein definitions can take many different forms, depending on the source of input data and the type of information that is to be stored. For proteins it is particularly useful that database definition files are Python programs with all their flexibility. The most common way of constructing a protein is from a PDB file. This is an example for a protein definition: :: name = 'insulin' # Read the PDB file. conf = PDBConfiguration('insulin.pdb') # Construct the peptide chains. chains = conf.createPeptideChains() # Clean up del conf The name should be meaningful to users, but is not used by MMTK itself. The second command reads the sequences of all peptide chains from a PDB file. Everything which is not a peptide chain is ignored. The following line constructs a PeptideChain object (a special molecule) for each chain from the PDB sequence. This involves constructing positions for any missing hydrogen atoms. Finally, the temporary data ("conf") is deleted, otherwise it would remain in memory forever. The net result of a protein definition file is the assignment of a list of molecules (usually PeptideChain objects) to the variable "chains". MMTK then constructs a protein object from it. To use the above example, an application program would use the command ``p = Protein('insulin')``. The construction of the protein involves one nontrivial (but automatic) step: the construction of disulfide bridges for pairs of cystein residues whose sulfur atoms have a distance of less then 2.5 Ã…ngström. Threads and parallelization ########################### This chapter explains the use of threads by MMTK and MMTK's parallelization support. This is an advanced topic, and not essential for the majority MMTK applications. You need to read this chapter only if you use multiprocessor computers, or if you want to implement multi-threaded programs that use MMTK. Threads are different execution paths through a program that are executed in parallel, at least in principle; real parallel execution is possible only on multiprocessor systems. MMTK makes use of threads in two ways, which are conceptually unrelated: parallelization of energy evaluation on shared-memory multiprocessor computers, and support for multithreaded applications. Thread support is not available on all machines; you can check if yous system supports threads by starting a Python interpreter and typing import threading. If this produces an error message, then your system does not support threads, otherwise it is available in Python and also in MMTK. If you do not have thread support in Python although you know that your operating system supports threads, you might have compiled your Python interpreter without thread support; in that case, MMTK does not have thread support either. Another approach to parallelization is message passing: several processors work on a program and communicate via a fast network to share results. A standard library, called MPI (Message Passing Interface), has been developped for sharing data by message passing, and implementations are available for all parallel computers currently on the market. MMTK contains elementary support for parallelization by message passing: only the energy evaluation has been paralellized, using a data-replication strategy, which is simple but not the most efficient for large systems. MPI support is disabled by default. Enabling it involves modifying the file Src/Setup.template prior to compilation of MMTK. Furthermore, an MPI-enabled installation of ScientificPython is required, and the mpipython executable must be used instead of the standard Python interpreter. Threads and message passing can be used together to use a cluster of shared-memory machines most efficiently. However, this requires that the thread and MPI implementations being used work together; sometimes there are conflicts, for example due to the use of the same signal in both libraries. Refer to your system documentation for details. The use of threads for parallelization on shared-memory systems is very simple: Just set the environment variable MMTK_ENERGY_THREADS to the desired value. If this variable is not defined, the default value is 1, i.e. energy evaluations are performed serially. For choosing an appropriate value for this environment variable, the following points should be considered: - The number of energy evaluation threads should not be larger than the number of processors that are fully dedicated to the MMTK application. A larger number of threads does not lead to wrong results, but it can increase the total execution time. - MMTK assumes that all processors are equally fast. If you use a heteregenous multiprocessor machine, in which the processors have different speeds, you might find that the total execution time is larger than without threads. - The use of threads incurs some computational overhead. For very small systems, it is usually faster not to use threads. - Not all energy terms necessarily support threads. Of the force field terms that part of MMTK, only the multipole algorithms for electrostatic interactions does not support threads, but additional force fields defined outside MMTK might also be affected. MMTK automatically evaluates such energy terms with a single thread, such that there is no risk of getting wrong results. However, you might not get the performance you expect. - If second derivatives of the potential energy are requested, energy evaluation is handled by a single thread. An efficient implementation of multi-threaded energy evaluation would require a separate copy of the second-derivative matrix per thread. This approach needs too much memory for big systems to be feasible. Since second derivatives are almost exclusively used for normal mode calculations, which need only a single energy evaluation, multi-thread support is not particularly important anyway. Parallelization via message passing is somewhat more complicated. In the current MMTK parallelization model, all processors execute the same program and replicate all tasks, with the important exception of energy evaluation. Energy terms are divided evenly between the processors, and at the end the energy and gradient values are shared by all machines. This is the only step involving network communication. Like thread-based parallelization, message-passing parallelization does not support the evaluation of second derivatives. A special problem with message-passing systems is input and output. The MMTK application must ensure that output files are written by only one processor, and that all processors correctly access input files, especially in the case of each processor having its own disk space. See the example :ref:`md.py ` for illustration. Multithreaded applications are applications that use multiple threads in order to simplify the implementation of certain algorithms, i.e. not necessarily with the goal of profiting from multiple processors. If you plan to write a multithreaded application that uses MMTK, you should first make sure you understand threading support in Python. In particular, you should keep in mind that the global interpreter lock prevents the effective use of multiple processors by Python code; only one thread at a time can execute interpreted Python code. C code called from Python can permit other threads to execute simultaneously; MMTK does this for energy evaluation, molecular dynamics integration, energy minimization, and normal mode calculation. A general problem in multithreaded applications is access to resources that are shared among the threads. In MMTK applications, the most important shared resource is the description of the chemical systems, i.e. universe objects and their contents. Chaos would result if two threads tried to modify the state of a universe simultaneously, or even if one thread uses information that is simultaneously being modified by another thread. Synchronization is therefore a critical part of multithreaded application. MMTK provides two synchronization aids, both of which described in the documentation of the class :class:`MMTK.Universe.Universe`: the configuration change lock (methods :func:`~MMTK.Universe.Universe.acquireConfigurationChangeLock` and :func:`~MMTK.Universe.Universe.releaseConfigurationChangeLock`), and the universe state lock (methods :func:`~MMTK.Universe.Universe.acquireReadStateChangeLock`, :func:`~MMTK.Universe.Universe.releaseReadStateChangeLock`, :func:`~MMTK.Universe.Universe.acquireWriteStateChangeLock`, and :func:`~MMTK.Universe.Universe.releaseWriteStateChangeLock`). Only a few common universe operations manipulate the universe state lock in order to avoid conflicts with other threads; these methods are marked as thread-safe in the description. All other operations should only be used inside a code section that is protected by the appropriate manipulation of the state lock. The configuration change lock is less critical; it is used only by the molecular dynamics and energy minimization algorithms in MMTK. Bibliography ############ .. _Bondi1964: [Bondi1964] | A. Bondi | `van der Waals Volumes and Radii `_ | 1964 .. _Eisenhaber1993: [Eisenhaber1993] | F. Eisenhaber, P. Argos | `Improved Strategy in Analytic Surface Calculation for Molecular Systems: Handling of Singularities and Computational Efficiency `_ | 1993 .. _Eisenhaber1995: [Eisenhaber1995] | F. Eisenhaber, P. Lijnzaad, P. Argos, M. Scharf | `The Double Cubic Lattice Method: Efficient Approaches to Numerical Integration of Surface Area and Volume and to Dot Surface Contouring of Molecular Assemblies `_ | 1995 .. _Hinsen1995: [Hinsen1995] | Konrad Hinsen, Gerald R. Kneller | `Influence of constraints on the dynamics of polypeptide chains `_ | 1995 .. _Hinsen1997: [Hinsen1997] | Konrad Hinsen, Benoit Roux | `An accurate potential for simulating proton transfer in acetylacetone `_ | 1997 .. _Hinsen1998: [Hinsen1998] | Konrad Hinsen | `Analysis of domain motions by approximate normal mode calculations `_ | 1998 .. _Hinsen1999: [Hinsen1999] | Konrad Hinsen, Aline Thomas, Martin J. Field | `Analysis of domain motions in large proteins `_ | 1999 .. _Hinsen1999a: [Hinsen1999a] | Konrad Hinsen, Gerald R. Kneller | `Projection methods for the analysis of complex motions in macromolecules `_ | 1999 .. _Hinsen1999b: [Hinsen1999b] | Konrad Hinsen, Gerald R. Kneller | `A simplified force field for describing vibrational protein dynamics over the whole frequency range `_ | 1999 .. _Hinsen2000: [Hinsen2000] | Konrad Hinsen, Andrei J. Petrescu, Serge Dellerue, Marie-Claire Bellissent-Funel, Gerald R. Kneller. | `Harmonicity in slow protein dynamics `_ | 2000 .. _Kneller1990: [Kneller1990] | Gerald R. Kneller | `Superposition of molecular structures using quaternions `_ | 1990 .. _Kneller1996: [Kneller1996] | Gerald R. Kneller, Thomas Mülders | `Nosé-Andersen dynamics of partially rigid molecules: Coupling of all degrees of freedom to heat and pressure baths `_ | 1996 .. _Swope1982: [Swope1982] | W.C. Swope, H.C. Andersen, P.H. Berens, K.R. Wilson | `A computer simulation method for the calculation of equilibrium constants for the formation of physical clusters of molecules: application to small water clusters `_ | 1982 .. _vanGunsteren1982: [vanGunsteren1982] | Wilfred F. van Gunsteren, Martin Karplus | `Effect of Constraints on the Dynamics of Macromolecules `_ | 1982 .. _Viduna2000: [Viduna2000] | David Viduna, Konrad Hinsen, Gerald R. Kneller | `The influence of molecular flexibility on DNA radiosensitivity: A simulation study `_ | 2000 .. _Wolf1999: [Wolf1999] | D. Wolf, P. Keblinski, S.R. Philpot, J. Eggebrecht | `Exact method for the simulation of Coulombic systems by spherically truncated, pairwise r\ :sup:`-1` summation `_ | 1999 MMTK-2.7.9/Doc/modules.rst0000644000076600000240000001135612117632051015624 0ustar hinsenstaff00000000000000.. _Reference: .. |C_alpha| replace:: C\ :sub:`α` Module Reference ################ .. Undocumented modules __pkginfo__ AtomEnvironment CCPNDataModel ComplexEnvironment CrystalEnvironment Database GroupEnvironment Features ForceFields.Amber.AmberData ForceFields.BondedInteractions ForceFields.ForceField ForceFields.NonBondedInteractions ForceFields.MMForceField MoleculeEnvironment NormalModes.Core PDBML ProteinEnvironment PyMOL surfm tess Skeleton ThreadManager Tk Tk.ProteinVisualization Utility MMTK ===== .. automodule:: MMTK MMTK.Biopolymers ================ .. automodule:: MMTK.Biopolymers MMTK.Bonds ========== .. automodule:: MMTK.Bonds MMTK.ChargeFit ============== .. automodule:: MMTK.ChargeFit MMTK.ChemicalObjects ==================== .. automodule:: MMTK.ChemicalObjects MMTK.Collections ================ .. automodule:: MMTK.Collections MMTK.ConfigIO ============= .. automodule:: MMTK.ConfigIO MMTK.DCD ======== .. automodule:: MMTK.DCD :exclude-members: writePDB, writeDCD MMTK.Deformation ================ .. automodule:: MMTK.Deformation MMTK.Dynamics ============= .. automodule:: MMTK.Dynamics MMTK.Environment ================ .. automodule:: MMTK.Environment MMTK.Field ========== .. automodule:: MMTK.Field MMTK.ForceFields ================ MMTK.ForceFields.AmberForceField -------------------------------- .. automodule:: MMTK.ForceFields.Amber.AmberForceField :no-members: MMTK.ForceFields.Amber94ForceField ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: MMTK.ForceFields.Amber.Amber94ForceField MMTK.ForceFields.Amber99ForceField ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: MMTK.ForceFields.Amber.Amber99ForceField MMTK.ForceFields.OPLSForceField ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. autoclass:: MMTK.ForceFields.Amber.OPLSForceField MMTK.ForceFields.AnisotropicNetworkForceField --------------------------------------------- .. autoclass:: MMTK.ForceFields.ANMFF.AnisotropicNetworkForceField MMTK.ForceFields.CalphaForceField -------------------------------------- .. autoclass:: MMTK.ForceFields.CalphaFF.CalphaForceField MMTK.ForceFields.DeformationForceField -------------------------------------- .. autoclass:: MMTK.ForceFields.DeformationFF.DeformationForceField MMTK.ForceFields.HarmonicForceField ----------------------------------- .. autoclass:: MMTK.ForceFields.BondFF.HarmonicForceField MMTK.ForceFields.LennardJonesForceField --------------------------------------- .. autoclass:: MMTK.ForceFields.LennardJonesFF.LennardJonesForceField MMTK.ForceFields.SPCEForceField ------------------------------- .. autoclass:: MMTK.ForceFields.SPCEFF.SPCEForceField MMTK.ForceFields.ForceFieldTest ------------------------------- .. automodule:: MMTK.ForceFields.ForceFieldTest MMTK.ForceFields.Restraints --------------------------- .. automodule:: MMTK.ForceFields.Restraints MMTK.FourierBasis ================= .. automodule:: MMTK.FourierBasis MMTK.Geometry ============= .. automodule:: MMTK.Geometry MMTK.InternalCoordinates ======================== .. automodule:: MMTK.InternalCoordinates :show-inheritance: MMTK.Minimization ================= .. automodule:: MMTK.Minimization MMTK.MolecularSurface ===================== .. automodule:: MMTK.MolecularSurface MMTK.MoleculeFactory ==================== .. automodule:: MMTK.MoleculeFactory MMTK.NormalModes ================ .. automodule:: MMTK.NormalModes MMTK.NormalModes.BrownianModes ------------------------------ .. automodule:: MMTK.NormalModes.BrownianModes MMTK.NormalModes.EnergeticModes ------------------------------- .. automodule:: MMTK.NormalModes.EnergeticModes MMTK.NormalModes.VibrationalModes --------------------------------- .. automodule:: MMTK.NormalModes.VibrationalModes MMTK.NucleicAcids ================= .. automodule:: MMTK.NucleicAcids MMTK.PDB ======== .. automodule:: MMTK.PDB MMTK.PDBMoleculeFactory ----------------------- .. automodule:: MMTK.PDBMoleculeFactory MMTK.ParticleProperties ======================= .. automodule:: MMTK.ParticleProperties MMTK.ProteinFriction ==================== .. automodule:: MMTK.ProteinFriction MMTK.Proteins ============= .. automodule:: MMTK.Proteins MMTK.Random =========== .. automodule:: MMTK.Random MMTK.Solvation ============== .. automodule:: MMTK.Solvation MMTK.Subspace ============= .. automodule:: MMTK.Subspace MMTK.Trajectory =============== .. automodule:: MMTK.Trajectory MMTK.Units ========== .. automodule:: MMTK.Units MMTK.Universe ============= .. automodule:: MMTK.Universe MMTK.Visualization ================== .. automodule:: MMTK.Visualization MMTK.XML ======== .. automodule:: MMTK.XML MMTK-2.7.9/Doc/tviewer.10000644000076600000240000000051311662461637015200 0ustar hinsenstaff00000000000000.Dd September 21, 2011 .Dt TVIEWER 1 .Sh NAME .Nm tviewer .Nd MMTK trajectory viewer .Sh SYNOPSIS .Nm .Ar trajectory_file .Sh DESCRIPTION tviewer plots energies and thermodynamic quantities stored in an MMTK trajectory. It can also extract configurations in PDB format and run a visualization program to display an animation. .Pp MMTK-2.7.9/Examples/0000755000076600000240000000000012156626561014501 5ustar hinsenstaff00000000000000MMTK-2.7.9/Examples/DNA/0000755000076600000240000000000012156626563015105 5ustar hinsenstaff00000000000000MMTK-2.7.9/Examples/DNA/construction.py0000644000076600000240000000170011662461637020207 0ustar hinsenstaff00000000000000# Create a nucleotide chain with a ligand from a PDB file. # from MMTK import * from MMTK.PDB import PDBConfiguration from MMTK.NucleicAcids import NucleotideChain from MMTK.Visualization import view # Load the PDB entry 110d. It contains a single DNA strand with a # ligand (daunomycin). configuration = PDBConfiguration('110d.pdb') # Construct the nucleotide chain object. This also constructs positions # for the missing hydrogens, using geometrical criteria. chain = configuration.createNucleotideChains()[0] # Construct the ligand. There is no definition of it in the database, # so it can only be constructed as a collection of atoms. The second # argument of createMolecules() is set to one in order to allow # this use of an unknown residue. ligand = configuration.createMolecules(['DM1'], 1) # Put everyting in a universe and show it graphically. universe = InfiniteUniverse() universe.addObject(chain) universe.addObject(ligand) view(universe) MMTK-2.7.9/Examples/Forcefield/0000755000076600000240000000000012156626563016545 5ustar hinsenstaff00000000000000MMTK-2.7.9/Examples/Forcefield/ElectricField/0000755000076600000240000000000012156626561021241 5ustar hinsenstaff00000000000000MMTK-2.7.9/Examples/Forcefield/ElectricField/C/0000755000076600000240000000000012156626563021425 5ustar hinsenstaff00000000000000MMTK-2.7.9/Examples/Forcefield/ElectricField/C/ElectricField.py0000644000076600000240000000513412117632051024462 0ustar hinsenstaff00000000000000# Electric field term representing a uniform external electric field. # can be added to any other force field from MMTK import ParticleScalar from MMTK.ForceFields.ForceField import ForceField from MMTK_electric_field import ElectricFieldTerm class ElectricField(ForceField): """ Uniform electric field """ def __init__(self, strength, charge_property='amber_charge'): """ @param strength: the electric field vector @type strength: L{Scientific.Geometry.Vector} @charge_property: the name of the atomic property in the database that is used to retrieve the atomic charges. The default is 'amber_charge', the charge property for Amber94 and Amber99. @type charge_property: C{str} """ # Store arguments that recreate the force field from a pickled # universe or from a trajectory. self.arguments = (strength, charge_property) # Initialize the ForceField class, giving a name to this one. ForceField.__init__(self, 'electric_field') # Store the parameters for later use. self.strength = strength self.charge_property = charge_property # The following method is called by the energy evaluation engine # to inquire if this force field term has all the parameters it # requires. This is necessary for interdependent force field # terms. In our case, we just say "yes" immediately. def ready(self, global_data): return True # The following method is called by the energy evaluation engine # to obtain a list of the low-level evaluator objects (the C routines) # that handle the calculations. def evaluatorTerms(self, universe, subset1, subset2, global_data): # The energy for subsets is defined as consisting only # of interactions within that subset, so the contribution # of an external field is zero. Therefore we just return # an empty list of energy terms. if subset1 is not None or subset2 is not None: return [] # Collect the charges into an array charges = ParticleScalar(universe) for o in universe: for a in o.atomList(): charges[a] = o.getAtomProperty(a, self.charge_property) # Here we pass all the parameters as "simple" data types to # the C code that handles energy calculations. return [ElectricFieldTerm(universe._spec, charges.array, self.strength[0], self.strength[1], self.strength[2])] MMTK-2.7.9/Examples/Forcefield/ElectricField/C/MMTK_electric_field.c0000644000076600000240000001451511662461637025364 0ustar hinsenstaff00000000000000/* C routines for ElectricField.py */ #include "MMTK/universe.h" #include "MMTK/forcefield.h" #include "MMTK/forcefield_private.h" /* This function does the actual energy (and gradient) calculation. Everything else is just bookkeeping. */ static void ef_evaluator(PyFFEnergyTermObject *self, PyFFEvaluatorObject *eval, energy_spec *input, energy_data *energy) /* The four parameters are pointers to structures that are defined in MMTK/forcefield.h. PyFFEnergyTermObject: All data relevant to this particular energy term. PyFFEvaluatorObject: Data referring to the global energy evaluation process, e.g. parallelization options. Not used here. energy_spec: Input parameters for this routine, i.e. atom positions and parallelization parameters. energy_data: Storage for the results (energy terms, gradients, second derivatives). */ { vector3 *coordinates = (vector3 *)input->coordinates->data; int natoms = input->coordinates->dimensions[0]; vector3 *g; double ex = self->param[0]; /* electric field x */ double ey = self->param[1]; /* electric field y */ double ez = self->param[2]; /* electric field z */ PyArrayObject *charge_array = (PyArrayObject *)self->data[0]; double *charges = (double *)charge_array->data; /* atomic charges */ int atom_index; /* energy_terms is an array because each routine could compute several terms that should logically be kept apart. For example, a single routine calculates Lennard-Jones and electrostatic interactions in a single iteration over the nonbonded list. The separation of terms is only done for the benefit of user code (universe.energyTerms()) returns the value of each term separately), the total energy is always the sum of all terms. Here we have only one energy term, which is initialized to zero. Note that the virial is also stored in the array energy_terms, at the index self->virial_index. However, there is only one virial term for the whole system, it is not added up term by term. Therefore we don't set it to zero, we just add to it in the loop. */ energy->energy_terms[self->index] = 0.; /* Add the gradient contribution to the global gradient array. It would be a serious error to use '=' instead of '+=' here, in that case all previously calculated forces would be erased. If energy_gradients is NULL, then the calling routine does not want gradients, and didn't provide storage for them. Second derivatives are not calculated because they are zero. */ if (energy->gradients != NULL) g = (vector3 *)((PyArrayObject*)energy->gradients)->data; for (atom_index = 0; atom_index < natoms; atom_index++) { double q = charges[atom_index]; double e = q*(ex*coordinates[atom_index][0] + ey*coordinates[atom_index][1] + ez*coordinates[atom_index][2]); energy->energy_terms[self->index] += e; energy->energy_terms[self->virial_index] -= e; if (energy->gradients != NULL) { g[atom_index][0] += q*ex; g[atom_index][1] += q*ey; g[atom_index][2] += q*ez; } } } /* A utility function that allocates memory for a copy of a string */ static char * allocstring(char *string) { char *memory = (char *)malloc(strlen(string)+1); if (memory != NULL) strcpy(memory, string); return memory; } /* The next function is meant to be called from Python. It creates the energy term object at the C level and stores all the parameters in there in a form that is convient to access for the C routine above. This is the routine that is imported into and called by the Python module, ElectricField.py. */ static PyObject * ElectricFieldTerm(PyObject *dummy, PyObject *args) { PyFFEnergyTermObject *self; PyArrayObject *charges; double x, y, z; /* Create a new energy term object and return if the creation fails. */ self = PyFFEnergyTerm_New(); if (self == NULL) return NULL; /* Convert the parameters to C data types. */ if (!PyArg_ParseTuple(args, "O!O!ddd", &PyUniverseSpec_Type, &self->universe_spec, &PyArray_Type, &charges, &x, &y, &z)) return NULL; /* We keep a reference to the universe_spec in the newly created energy term object, so we have to increase the reference count. */ Py_INCREF(self->universe_spec); /* A pointer to the evaluation routine. */ self->eval_func = ef_evaluator; /* The name of the energy term object. */ self->evaluator_name = "electric_field"; /* The names of the individual energy terms - just one here. */ self->term_names[0] = allocstring("electric_field"); if (self->term_names[0] == NULL) return PyErr_NoMemory(); self->nterms = 1; /* self->param is a storage area for parameters. Note that there are only 40 slots (double) there. */ self->param[0] = x; self->param[1] = y; self->param[2] = z; /* self->data is the other storage area for parameters. There are 40 Python object slots there */ self->data[0] = (PyObject *)charges; Py_INCREF(charges); /* Return the energy term object. */ return (PyObject *)self; } /* This is a list of all Python-callable functions defined in this module. Each list entry consists of the name of the function object in the module, the C routine that implements it, and a "1" signalling new-style parameter passing conventions (only veterans care about the alternatives). The list is terminated by a NULL entry. */ static PyMethodDef functions[] = { {"ElectricFieldTerm", ElectricFieldTerm, 1}, {NULL, NULL} /* sentinel */ }; /* The initialization function for the module. This is the only function that must be publicly visible, everything else should be declared static to prevent name clashes with other modules. The name of this function must be "init" followed by the module name. */ DL_EXPORT(void) initMMTK_electric_field(void) { PyObject *m; /* Create the module and add the functions. */ m = Py_InitModule("MMTK_electric_field", functions); /* Import the array module. */ #ifdef import_array import_array(); #endif /* Import MMTK modules. */ import_MMTK_universe(); import_MMTK_forcefield(); /* Check for errors. */ if (PyErr_Occurred()) Py_FatalError("can't initialize module MMTK_electric_field"); } MMTK-2.7.9/Examples/Forcefield/ElectricField/C/setup.py0000644000076600000240000000164711662461637023147 0ustar hinsenstaff00000000000000# Use this script to compile the C module by running # # python setup.py build_ext --inplace # # Then run "python test.py" from distutils.core import setup, Extension import os, sys compile_args = [] include_dirs = ['../../../../Include'] from Scientific import N try: num_package = N.package except AttributeError: num_package = "Numeric" if num_package == "NumPy": compile_args.append("-DNUMPY=1") import numpy.distutils.misc_util include_dirs.extend(numpy.distutils.misc_util.get_numpy_include_dirs()) setup (name = "ElectricField", version = "1.0", description = "Electric field term for MMTK", py_modules = ['ElectricField'], ext_modules = [Extension('MMTK_electric_field', ['MMTK_electric_field.c'], extra_compile_args = compile_args, include_dirs=include_dirs)] ) MMTK-2.7.9/Examples/Forcefield/ElectricField/C/test.py0000644000076600000240000000117411662461637022761 0ustar hinsenstaff00000000000000from MMTK import * from ElectricField import ElectricField from MMTK.ForceFields.ForceFieldTest import gradientTest universe = InfiniteUniverse() universe.atom1 = Atom('C', position=Vector(0., 0., 1.)) universe.atom1.test_charge = 1. universe.atom2 = Atom('C', position=Vector(0., 0., 0.)) universe.atom2.test_charge = -0.2 universe.setForceField(ElectricField(0.5*(Units.V/Units.m) * Vector(0., 0., 1.), 'test_charge')) print universe.energyTerms() e, g = universe.energyAndGradients() print g[universe.atom1] print g[universe.atom2] gradientTest(universe) MMTK-2.7.9/Examples/Forcefield/ElectricField/Cython/0000755000076600000240000000000012156626563022507 5ustar hinsenstaff00000000000000MMTK-2.7.9/Examples/Forcefield/ElectricField/Cython/ElectricField.py0000644000076600000240000000503512117632051025544 0ustar hinsenstaff00000000000000# Electric field term representing a uniform external electric field. # can be added to any other force field from MMTK import ParticleScalar from MMTK.ForceFields.ForceField import ForceField from MMTK_electric_field import ElectricFieldTerm class ElectricField(ForceField): """ Uniform electric field """ def __init__(self, strength, charge_property='amber_charge'): """ @param strength: the electric field vector @type strength: L{Scientific.Geometry.Vector} @charge_property: the name of the atomic property in the database that is used to retrieve the atomic charges. The default is 'amber_charge', the charge property for Amber94 and Amber99. @type charge_property: C{str} """ # Store arguments that recreate the force field from a pickled # universe or from a trajectory. self.arguments = (strength, charge_property) # Initialize the ForceField class, giving a name to this one. ForceField.__init__(self, 'electric_field') # Store the parameters for later use. self.strength = strength self.charge_property = charge_property # The following method is called by the energy evaluation engine # to inquire if this force field term has all the parameters it # requires. This is necessary for interdependent force field # terms. In our case, we just say "yes" immediately. def ready(self, global_data): return True # The following method is called by the energy evaluation engine # to obtain a list of the low-level evaluator objects (the C routines) # that handle the calculations. def evaluatorTerms(self, universe, subset1, subset2, global_data): # The energy for subsets is defined as consisting only # of interactions within that subset, so the contribution # of an external field is zero. Therefore we just return # an empty list of energy terms. if subset1 is not None or subset2 is not None: return [] # Collect the charges into an array charges = ParticleScalar(universe) for o in universe: for a in o.atomList(): charges[a] = o.getAtomProperty(a, self.charge_property) # Here we pass all the parameters to # the Cython code that handles energy calculations. return [ElectricFieldTerm(universe, charges.array, self.strength)] MMTK-2.7.9/Examples/Forcefield/ElectricField/Cython/MMTK_electric_field.pyx0000644000076600000240000000566511662461637027052 0ustar hinsenstaff00000000000000# Example for a forcefield implementation in Cython. # # Get all the required declarations # include "MMTK/python.pxi" include "MMTK/numeric.pxi" include "MMTK/core.pxi" include "MMTK/universe.pxi" include 'MMTK/forcefield.pxi' # # The force field term implementation. # The rules: # # - The class must inherit from EnergyTerm. # # - EnergyTerm.__init__() must be called with the arguments # shown here. The third argument is the name of the EnergyTerm # object, the fourth a tuple of the names of all the terms it # implements (one object can implement several terms). # The assignment to self.eval_func is essential, without it # any energy evaluation will crash. # # - The function "evaluate" must have exactly the parameter # list from this example. # cdef class ElectricFieldTerm(EnergyTerm): cdef ArrayType charges cdef vector3 strength def __init__(self, universe, charges, strength): EnergyTerm.__init__(self, universe, "electric_field", ("electric_field",)) self.eval_func = ElectricFieldTerm.evaluate self.charges = charges self.strength[0] = strength[0] self.strength[1] = strength[1] self.strength[2] = strength[2] # The function evaluate is called for every single energy # evaluation and should therefore be optimized for speed. # Its first argument is the global energy evaluator object, # which is needed only for parallelized energy terms. # The second argument is a C structure that contains all the # input data, in particular the particle configuration. # The third argument is a C structure that contains the # energy term fields and gradient arrays for storing the results. # For details, see MMTK_forcefield.pxi. cdef void evaluate(self, EnergyEvaluator eval, energy_spec *input, energy_data *energy): cdef vector3 *coordinates, *gradients cdef double *q cdef double e cdef int natoms, i coordinates = input.coordinates.data natoms = input.coordinates.dimensions[0] q = self.charges.data energy.energy_terms[self.index] = 0. if energy.gradients != NULL: gradients = ( energy.gradients).data for i from 0 <= i < natoms: e = q[i]*(self.strength[0]*coordinates[i][0] + self.strength[1]*coordinates[i][1] + self.strength[2]*coordinates[i][2]) energy.energy_terms[self.index] = \ energy.energy_terms[self.index] + e energy.energy_terms[self.virial_index] = \ energy.energy_terms[self.virial_index] - e if energy.gradients != NULL: gradients[i][0] += q[i]*self.strength[0] gradients[i][1] += q[i]*self.strength[1] gradients[i][2] += q[i]*self.strength[2] MMTK-2.7.9/Examples/Forcefield/ElectricField/Cython/setup.py0000644000076600000240000000177411662461637024232 0ustar hinsenstaff00000000000000# Use this script to compile the C module by running # # python setup.py build_ext --inplace # # Then run "python test.py" from distutils.core import setup, Extension from Cython.Distutils import build_ext import os, sys compile_args = [] include_dirs = ['../../../../Include'] from Scientific import N try: num_package = N.package except AttributeError: num_package = "Numeric" if num_package == "NumPy": compile_args.append("-DNUMPY=1") import numpy.distutils.misc_util include_dirs.extend(numpy.distutils.misc_util.get_numpy_include_dirs()) setup (name = "ElectricField", version = "1.0", description = "Electric field term for MMTK", py_modules = ['ElectricField'], ext_modules = [Extension('MMTK_electric_field', ['MMTK_electric_field.pyx'], extra_compile_args = compile_args, include_dirs=include_dirs)], cmdclass = {'build_ext': build_ext} ) MMTK-2.7.9/Examples/Forcefield/ElectricField/Cython/test.py0000644000076600000240000000117411662461637024043 0ustar hinsenstaff00000000000000from MMTK import * from ElectricField import ElectricField from MMTK.ForceFields.ForceFieldTest import gradientTest universe = InfiniteUniverse() universe.atom1 = Atom('C', position=Vector(0., 0., 1.)) universe.atom1.test_charge = 1. universe.atom2 = Atom('C', position=Vector(0., 0., 0.)) universe.atom2.test_charge = -0.2 universe.setForceField(ElectricField(0.5*(Units.V/Units.m) * Vector(0., 0., 1.), 'test_charge')) print universe.energyTerms() e, g = universe.energyAndGradients() print g[universe.atom1] print g[universe.atom2] gradientTest(universe) MMTK-2.7.9/Examples/Forcefield/ElectricField/Python/0000755000076600000240000000000012156626563022524 5ustar hinsenstaff00000000000000MMTK-2.7.9/Examples/Forcefield/ElectricField/Python/ElectricField.py0000644000076600000240000001072312117632051025561 0ustar hinsenstaff00000000000000# Electric field term representing a uniform external electric field. # It can be added to any other force field. from MMTK import ParticleScalar, ParticleVector, SymmetricPairTensor from MMTK.ForceFields.ForceField import ForceField, EnergyTerm # # Each force field term requires two classes. One represents the # abstract force field (ElectricField in this example), it knows # nothing about any molecular system to which it might be applied. # The other one (ElectricFieldTerm) stores all the parameters required # for energy evaluation, and provides the actual code. # # When a force field is evaluated for the first time for a given universe, # the method evaluatorTerms() of the force field is called. It creates # the EnergyTerm objects. The method evaluate() of the energy term object # is called for every energy evaluation. Consequently, as much of the # computation as possible should be done outside of this method. # class ElectricFieldTerm(EnergyTerm): # The __init__ method only remembers parameters. Note that # EnergyTerm.__init__ takes care of storing the name and the # universe object. def __init__(self, universe, strength, charges): EnergyTerm.__init__(self, 'electric field', universe) self.strength = strength self.charges = charges # This method is called for every single energy evaluation, so make # it as efficient as possible. The parameters do_gradients and # do_force_constants are flags that indicate if gradients and/or # force constants are requested. def evaluate(self, configuration, do_gradients, do_force_constants): results = {} energy = (self.charges*(configuration*self.strength)) \ .sumOverParticles() results['energy'] = energy results['virial'] = -energy if do_gradients: gradients = ParticleVector(self.universe) for atom in self.universe.atomList(): gradients[atom] = self.charges[atom]*self.strength results['gradients'] = gradients if do_force_constants: # The force constants are zero -> nothing to calculate. results['force_constants'] = SymmetricPairTensor(self.universe) else: force_constants = None return results class ElectricField(ForceField): """ Uniform electric field """ def __init__(self, strength, charge_property='amber_charge'): """ @param strength: the electric field vector @type strength: L{Scientific.Geometry.Vector} @charge_property: the name of the atomic property in the database that is used to retrieve the atomic charges. The default is 'amber_charge', the charge property for Amber94 and Amber99. @type charge_property: C{str} """ # Store arguments that recreate the force field from a pickled # universe or from a trajectory. self.arguments = (strength, charge_property) # Initialize the ForceField class, giving a name to this one. ForceField.__init__(self, 'electric_field') # Store the parameters for later use. self.strength = strength self.charge_property = charge_property # The following method is called by the energy evaluation engine # to inquire if this force field term has all the parameters it # requires. This is necessary for interdependent force field # terms. In our case, we just say "yes" immediately. def ready(self, global_data): return True # The following method is called by the energy evaluation engine # to obtain a list of the evaluator objects # that handle the calculations. def evaluatorTerms(self, universe, subset1, subset2, global_data): # The energy for subsets is defined as consisting only # of interactions within that subset, so the contribution # of an external field is zero. Therefore we just return # an empty list of energy terms. if subset1 is not None or subset2 is not None: return [] # Collect the charges into an array charges = ParticleScalar(universe) for o in universe: for a in o.atomList(): charges[a] = o.getAtomProperty(a, self.charge_property) # Here we pass all the parameters to # the energy term code that handles energy calculations. return [ElectricFieldTerm(universe, self.strength, charges)] MMTK-2.7.9/Examples/Forcefield/ElectricField/Python/test.py0000644000076600000240000000117411662461637024060 0ustar hinsenstaff00000000000000from MMTK import * from ElectricField import ElectricField from MMTK.ForceFields.ForceFieldTest import gradientTest universe = InfiniteUniverse() universe.atom1 = Atom('C', position=Vector(0., 0., 1.)) universe.atom1.test_charge = 1. universe.atom2 = Atom('C', position=Vector(0., 0., 0.)) universe.atom2.test_charge = -0.2 universe.setForceField(ElectricField(0.5*(Units.V/Units.m) * Vector(0., 0., 1.), 'test_charge')) print universe.energyTerms() e, g = universe.energyAndGradients() print g[universe.atom1] print g[universe.atom2] gradientTest(universe) MMTK-2.7.9/Examples/Forcefield/HarmonicOscillator/0000755000076600000240000000000012156626561022337 5ustar hinsenstaff00000000000000MMTK-2.7.9/Examples/Forcefield/HarmonicOscillator/C/0000755000076600000240000000000012156626563022523 5ustar hinsenstaff00000000000000MMTK-2.7.9/Examples/Forcefield/HarmonicOscillator/C/HarmonicOscillatorFF.py0000644000076600000240000000550112117632051027070 0ustar hinsenstaff00000000000000# Harmonic potential with respect to a fixed point in space from MMTK.ForceFields.ForceField import ForceField from MMTK_harmonic_oscillator import HarmonicOscillatorTerm class HarmonicOscillatorForceField(ForceField): """ Harmonic potential with respect to a fixed point in space """ def __init__(self, atom, center, force_constant): """ @param atom: the atom on which the force field acts @type atom: L{MMTK.ChemicalObjects.Atom} @param center: the point to which the atom is attached by the harmonic potential @type center: L{Scientific.Geometry.Vector} @param force_constant: the force constant of the harmonic potential @type force_constant: C{float} """ # Get the internal index of the atom if the argument is an # atom object. It is the index that is stored internally, # and when the force field is recreated from a specification # in a trajectory file, it is the index that is passed instead # of the atom object itself. Calling this method takes care # of all the necessary checks and conversions. self.atom_index = self.getAtomParameterIndices([atom])[0] # Store arguments that recreate the force field from a pickled # universe or from a trajectory. self.arguments = (self.atom_index, center, force_constant) # Initialize the ForceField class, giving a name to this one. ForceField.__init__(self, 'harmonic_oscillator') # Store the parameters for later use. self.center = center self.force_constant = force_constant # The following method is called by the energy evaluation engine # to inquire if this force field term has all the parameters it # requires. This is necessary for interdependent force field # terms. In our case, we just say "yes" immediately. def ready(self, global_data): return True # The following method is called by the energy evaluation engine # to obtain a list of the low-level evaluator objects (the C routines) # that handle the calculations. def evaluatorTerms(self, universe, subset1, subset2, global_data): # The subset evaluation mechanism does not make much sense for # this force field, so we just signal an error if someone # uses it by accident. if subset1 is not None or subset2 is not None: raise ValueError("sorry, no subsets here") # Here we pass all the parameters as "simple" data types to # the C code that handles energy calculations. return [HarmonicOscillatorTerm(universe._spec, self.atom_index, self.center[0], self.center[1], self.center[2], self.force_constant)] MMTK-2.7.9/Examples/Forcefield/HarmonicOscillator/C/MMTK_harmonic_oscillator.c0000644000076600000240000001452111662461637027555 0ustar hinsenstaff00000000000000/* C routines for HarmonicOscillatorFF.py */ #include "MMTK/universe.h" #include "MMTK/forcefield.h" #include "MMTK/forcefield_private.h" /* This function does the actual energy (and gradient) calculation. Everything else is just bookkeeping. */ static void harmonic_evaluator(PyFFEnergyTermObject *self, PyFFEvaluatorObject *eval, energy_spec *input, energy_data *energy) /* The four parameters are pointers to structures that are defined in MMTK/forcefield.h. PyFFEnergyTermObject: All data relevant to this particular energy term. PyFFEvaluatorObject: Data referring to the global energy evaluation process, e.g. parallelization options. Not used here. energy_spec: Input parameters for this routine, i.e. atom positions and parallelization parameters. energy_data: Storage for the results (energy terms, gradients, second derivatives). */ { vector3 *coordinates = (vector3 *)input->coordinates->data; double x = self->param[0]; /* reference point x */ double y = self->param[1]; /* reference point y */ double z = self->param[2]; /* reference point z */ double k = self->param[3]; /* force constant */ int atom_index = (int)self->param[4]; /* atom index */ double dx = coordinates[atom_index][0] - x; double dy = coordinates[atom_index][1] - y; double dz = coordinates[atom_index][2] - z; /* energy_terms is an array because each routine could compute several terms that should logically be kept apart. For example, a single routine calculates Lennard-Jones and electrostatic interactions in a single iteration over the nonbonded list. The separation of terms is only done for the benefit of user code (universe.energyTerms()) returns the value of each term separately), the total energy is always the sum of all terms. Here we have only one energy term. */ energy->energy_terms[self->index] = 0.5*k*(dx*dx + dy*dy + dz*dz); energy->energy_terms[self->virial_index] -= k*(dx*dx + dy*dy + dz*dz); /* If only the energy is asked for, stop here. */ if (energy->gradients == NULL && energy->force_constants == NULL) return; /* Add the gradient contribution to the global gradient array. It would be a serious error to use '=' instead of '+=' here, in that case all previously calculated forces would be erased. */ if (energy->gradients != NULL) { vector3 *g = (vector3 *)((PyArrayObject*)energy->gradients)->data; g[atom_index][0] += k*dx; g[atom_index][1] += k*dy; g[atom_index][2] += k*dz; } /* Add the force constant contribution to the global force constant array. It would be a serious error to use '=' instead of '+=' here, in that case all previously calculated forces would be erased. */ if (energy->force_constants != NULL) { double *fc = (double *)((PyArrayObject*)energy->force_constants)->data; int n = ((PyArrayObject *)energy->force_constants)->dimensions[0]; fc += (9*n+3)*atom_index; fc[3*n*0+0] += k; fc[3*n*1+1] += k; fc[3*n*2+2] += k; } } /* A utility function that allocates memory for a copy of a string */ static char * allocstring(char *string) { char *memory = (char *)malloc(strlen(string)+1); if (memory != NULL) strcpy(memory, string); return memory; } /* The next function is meant to be called from Python. It creates the energy term object at the C level and stores all the parameters in there in a form that is convient to access for the C routine above. This is the routine that is imported into and called by the Python module, HarmonicOscillatorFF.py. */ static PyObject * HarmonicOscillatorTerm(PyObject *dummy, PyObject *args) { PyFFEnergyTermObject *self; int atom_index; double x, y, z; double force_constant; /* Create a new energy term object and return if the creation fails. */ self = PyFFEnergyTerm_New(); if (self == NULL) return NULL; /* Convert the parameters to C data types. */ if (!PyArg_ParseTuple(args, "O!idddd", &PyUniverseSpec_Type, &self->universe_spec, &atom_index, &x, &y, &z, &force_constant)) return NULL; /* We keep a reference to the universe_spec in the newly created energy term object, so we have to increase the reference count. */ Py_INCREF(self->universe_spec); /* A pointer to the evaluation routine. */ self->eval_func = harmonic_evaluator; /* The name of the energy term object. */ self->evaluator_name = "harmonic_oscillator"; /* The names of the individual energy terms - just one here. */ self->term_names[0] = allocstring("harmonic_oscillator"); if (self->term_names[0] == NULL) return PyErr_NoMemory(); self->nterms = 1; /* self->param is a storage area for parameters. Note that there are only 40 slots (double) there, if you need more space, you can use self->data, an array for up to 40 Python object pointers. */ self->param[0] = x; self->param[1] = y; self->param[2] = z; self->param[3] = force_constant; self->param[4] = (double) atom_index; /* Return the energy term object. */ return (PyObject *)self; } /* This is a list of all Python-callable functions defined in this module. Each list entry consists of the name of the function object in the module, the C routine that implements it, and a "1" signalling new-style parameter passing conventions (only veterans care about the alternatives). The list is terminated by a NULL entry. */ static PyMethodDef functions[] = { {"HarmonicOscillatorTerm", HarmonicOscillatorTerm, 1}, {NULL, NULL} /* sentinel */ }; /* The initialization function for the module. This is the only function that must be publicly visible, everything else should be declared static to prevent name clashes with other modules. The name of this function must be "init" followed by the module name. */ DL_EXPORT(void) initMMTK_harmonic_oscillator(void) { PyObject *m; /* Create the module and add the functions. */ m = Py_InitModule("MMTK_harmonic_oscillator", functions); /* Import the array module. */ #ifdef import_array import_array(); #endif /* Import MMTK modules. */ import_MMTK_universe(); import_MMTK_forcefield(); /* Check for errors. */ if (PyErr_Occurred()) Py_FatalError("can't initialize module MMTK_harmonic_oscillator"); } MMTK-2.7.9/Examples/Forcefield/HarmonicOscillator/C/setup.py0000644000076600000240000000172711662461637024244 0ustar hinsenstaff00000000000000# Use this script to compile the C module by running # # python setup.py build_ext --inplace # # Then run "python test.py" from distutils.core import setup, Extension import os, sys compile_args = [] include_dirs = ['../../../../Include'] from Scientific import N try: num_package = N.package except AttributeError: num_package = "Numeric" if num_package == "NumPy": compile_args.append("-DNUMPY=1") import numpy.distutils.misc_util include_dirs.extend(numpy.distutils.misc_util.get_numpy_include_dirs()) setup (name = "HarmonicOscillatorForceField", version = "1.0", description = "Harmonic Oscillator forcefield term for MMTK", py_modules = ['HarmonicOscillatorFF'], ext_modules = [Extension('MMTK_harmonic_oscillator', ['MMTK_harmonic_oscillator.c'], extra_compile_args = compile_args, include_dirs=include_dirs)] ) MMTK-2.7.9/Examples/Forcefield/HarmonicOscillator/C/test.py0000644000076600000240000000121312117632051024033 0ustar hinsenstaff00000000000000from MMTK import * from HarmonicOscillatorFF import HarmonicOscillatorForceField from MMTK.ForceFields.ForceFieldTest import gradientTest, forceConstantTest, virialTest universe = InfiniteUniverse() universe.atom1 = Atom('C', position=Vector(0., 0., 1.05)) universe.atom2 = Atom('C', position=Vector(0., 1.05, 0.)) ff1 = HarmonicOscillatorForceField(universe.atom1, Vector(0., 0., 0.), 100.) ff2 = HarmonicOscillatorForceField(universe.atom2, Vector(0., 0., 0.), 100.) universe.setForceField(ff1+ff2) e, g = universe.energyAndGradients() print universe.energyTerms() print e gradientTest(universe) forceConstantTest(universe) virialTest(universe) MMTK-2.7.9/Examples/Forcefield/HarmonicOscillator/Cython/0000755000076600000240000000000012156626563023605 5ustar hinsenstaff00000000000000MMTK-2.7.9/Examples/Forcefield/HarmonicOscillator/Cython/HarmonicOscillatorFF.py0000644000076600000240000000540612117632051030156 0ustar hinsenstaff00000000000000# Harmonic potential with respect to a fixed point in space from MMTK.ForceFields.ForceField import ForceField from MMTK_harmonic_oscillator import HarmonicOscillatorTerm class HarmonicOscillatorForceField(ForceField): """ Harmonic potential with respect to a fixed point in space """ def __init__(self, atom, center, force_constant): """ @param atom: the atom on which the force field acts @type atom: L{MMTK.ChemicalObjects.Atom} @param center: the point to which the atom is attached by the harmonic potential @type center: L{Scientific.Geometry.Vector} @param force_constant: the force constant of the harmonic potential @type force_constant: C{float} """ # Get the internal index of the atom if the argument is an # atom object. It is the index that is stored internally, # and when the force field is recreated from a specification # in a trajectory file, it is the index that is passed instead # of the atom object itself. Calling this method takes care # of all the necessary checks and conversions. self.atom_index = self.getAtomParameterIndices([atom])[0] # Store arguments that recreate the force field from a pickled # universe or from a trajectory. self.arguments = (self.atom_index, center, force_constant) # Initialize the ForceField class, giving a name to this one. ForceField.__init__(self, 'harmonic_oscillator') # Store the parameters for later use. self.center = center self.force_constant = force_constant # The following method is called by the energy evaluation engine # to inquire if this force field term has all the parameters it # requires. This is necessary for interdependent force field # terms. In our case, we just say "yes" immediately. def ready(self, global_data): return True # The following method is called by the energy evaluation engine # to obtain a list of the low-level evaluator objects (the C routines) # that handle the calculations. def evaluatorTerms(self, universe, subset1, subset2, global_data): # The subset evaluation mechanism does not make much sense for # this force field, so we just signal an error if someone # uses it by accident. if subset1 is not None or subset2 is not None: raise ValueError("sorry, no subsets here") # Here we pass all the parameters to the Cython code # that handles energy calculations. return [HarmonicOscillatorTerm(universe, self.atom_index, self.center, self.force_constant)] MMTK-2.7.9/Examples/Forcefield/HarmonicOscillator/Cython/MMTK_harmonic_oscillator.pyx0000644000076600000240000000632411662461637031237 0ustar hinsenstaff00000000000000# Example for a forcefield implementation in Cython. # # Get all the required declarations # include "MMTK/python.pxi" include "MMTK/numeric.pxi" include "MMTK/core.pxi" include "MMTK/universe.pxi" include 'MMTK/forcefield.pxi' # # The force field term implementation. # The rules: # # - The class must inherit from EnergyTerm. # # - EnergyTerm.__init__() must be called with the arguments # shown here. The third argument is the name of the EnergyTerm # object, the fourth a tuple of the names of all the terms it # implements (one object can implement several terms). # The assignment to self.eval_func is essential, without it # any energy evaluation will crash. # # - The function "evaluate" must have exactly the parameter # list given in this example. # cdef class HarmonicOscillatorTerm(EnergyTerm): cdef int atom_index cdef double ref_x, ref_y, ref_z cdef double k def __init__(self, universe, atom_index, reference, force_constant): cdef ArrayType ref_array EnergyTerm.__init__(self, universe, "harmonic_oscillator", ("harmonic_oscillator",)) self.eval_func = HarmonicOscillatorTerm.evaluate self.atom_index = atom_index ref_array = reference.array self.ref_x = (ref_array.data)[0] self.ref_y = (ref_array.data)[1] self.ref_z = (ref_array.data)[2] self.k = force_constant # The function evaluate is called for every single energy # evaluation and should therefore be optimized for speed. # Its first argument is the global energy evaluator object, # which is needed only for parallelized energy terms. # The second argument is a C structure that contains all the # input data, in particular the particle configuration. # The third argument is a C structure that contains the # energy term fields and gradient arrays for storing the results. # For details, see MMTK_forcefield.pxi. cdef void evaluate(self, PyFFEvaluatorObject *eval, energy_spec *input, energy_data *energy): cdef vector3 *coordinates, *gradients cdef double *fc cdef double dx, dy, dz cdef int n, offset coordinates = input.coordinates.data dx = coordinates[self.atom_index][0] - self.ref_x dy = coordinates[self.atom_index][1] - self.ref_y dz = coordinates[self.atom_index][2] - self.ref_z energy.energy_terms[self.index] = 0.5*self.k*(dx*dx + dy*dy + dz*dz) energy.energy_terms[self.virial_index] -= self.k*(dx*dx + dy*dy + dz*dz) if energy.gradients != NULL: gradients = ( energy.gradients).data gradients[self.atom_index][0] += self.k*dx gradients[self.atom_index][1] += self.k*dy gradients[self.atom_index][2] += self.k*dz if energy.force_constants != NULL: fc = ( energy.force_constants).data n = ( energy.force_constants).dimensions[0] offset = (9*n+3)*self.atom_index fc[offset + 3*n*0 + 0] += self.k fc[offset + 3*n*1 + 1] += self.k fc[offset + 3*n*2 + 2] += self.k MMTK-2.7.9/Examples/Forcefield/HarmonicOscillator/Cython/setup.py0000644000076600000240000000206111662461637025316 0ustar hinsenstaff00000000000000# Use this script to compile the Cython module by running # # python setup.py build_ext --inplace # # Then run "python test.py" from distutils.core import setup, Extension from Cython.Distutils import build_ext import os, sys compile_args = [] include_dirs = ['../../../../Include'] from Scientific import N try: num_package = N.package except AttributeError: num_package = "Numeric" if num_package == "NumPy": compile_args.append("-DNUMPY=1") import numpy.distutils.misc_util include_dirs.extend(numpy.distutils.misc_util.get_numpy_include_dirs()) setup (name = "HarmonicOscillatorForceField", version = "1.0", description = "Harmonic Oscillator forcefield term for MMTK", py_modules = ['HarmonicOscillatorFF'], ext_modules = [Extension('MMTK_harmonic_oscillator', ['MMTK_harmonic_oscillator.pyx'], extra_compile_args = compile_args, include_dirs=include_dirs)], cmdclass = {'build_ext': build_ext} ) MMTK-2.7.9/Examples/Forcefield/HarmonicOscillator/Cython/test.py0000644000076600000240000000121311662461637025133 0ustar hinsenstaff00000000000000from MMTK import * from HarmonicOscillatorFF import HarmonicOscillatorForceField from MMTK.ForceFields.ForceFieldTest import gradientTest, forceConstantTest, virialTest universe = InfiniteUniverse() universe.atom1 = Atom('C', position=Vector(0., 0., 1.05)) universe.atom2 = Atom('C', position=Vector(0., 1.05, 0.)) ff1 = HarmonicOscillatorForceField(universe.atom1, Vector(0., 0., 0.), 100.) ff2 = HarmonicOscillatorForceField(universe.atom2, Vector(0., 0., 0.), 100.) universe.setForceField(ff1+ff2) e, g = universe.energyAndGradients() print universe.energyTerms() print e gradientTest(universe) forceConstantTest(universe) virialTest(universe) MMTK-2.7.9/Examples/Forcefield/HarmonicOscillator/Python/0000755000076600000240000000000012156626564023623 5ustar hinsenstaff00000000000000MMTK-2.7.9/Examples/Forcefield/HarmonicOscillator/Python/HarmonicOscillatorFF.py0000644000076600000240000001144612117632051030174 0ustar hinsenstaff00000000000000# Harmonic potential with respect to a fixed point in space from MMTK.ForceFields.ForceField import ForceField, EnergyTerm from MMTK import ParticleVector, SymmetricPairTensor from Scientific.Geometry import delta # # Each force field term requires two classes. One represents the # abstract force field (HarmonicOscillator in this example), it knows # nothing about any molecular system to which it might be applied. # The other one (HarmonicOscillatorTerm) stores all the parameters required # for energy evaluation, and provides the actual code. # # When a force field is evaluated for the first time for a given universe, # the method evaluatorTerms() of the force field is called. It creates # the EnergyTerm objects. The method evaluate() of the energy term object # is called for every energy evaluation. Consequently, as much of the # computation as possible should be done outside of this method. # class HarmonicOscillatorTerm(EnergyTerm): # The __init__ method only remembers parameters. Note that # EnergyTerm.__init__ takes care of storing the name and the # universe object. def __init__(self, universe, atom_index, center, force_constant): EnergyTerm.__init__(self, 'harmonic oscillator', universe) self.atom_index = atom_index self.center = center self.force_constant = force_constant # This method is called for every single energy evaluation, so make # it as efficient as possible. The parameters do_gradients and # do_force_constants are flags that indicate if gradients and/or # force constants are requested. def evaluate(self, configuration, do_gradients, do_force_constants): results = {} d = configuration[self.atom_index]-self.center results['energy'] = 0.5*self.force_constant*(d*d) if do_gradients: gradients = ParticleVector(self.universe) gradients[self.atom_index] = self.force_constant*d results['gradients'] = gradients if do_force_constants: force_constants = SymmetricPairTensor(self.universe) force_constants[self.atom_index, self.atom_index] = self.force_constant*delta results['force_constants'] = force_constants return results class HarmonicOscillatorForceField(ForceField): """ Harmonic potential with respect to a fixed point in space """ def __init__(self, atom, center, force_constant): """ @param atom: the atom on which the force field acts @type atom: L{MMTK.ChemicalObjects.Atom} @param center: the point to which the atom is attached by the harmonic potential @type center: L{Scientific.Geometry.Vector} @param force_constant: the force constant of the harmonic potential @type force_constant: C{float} """ # Get the internal index of the atom if the argument is an # atom object. It is the index that is stored internally, # and when the force field is recreated from a specification # in a trajectory file, it is the index that is passed instead # of the atom object itself. Calling this method takes care # of all the necessary checks and conversions. self.atom_index = self.getAtomParameterIndices([atom])[0] # Store arguments that recreate the force field from a pickled # universe or from a trajectory. self.arguments = (self.atom_index, center, force_constant) # Initialize the ForceField class, giving a name to this one. ForceField.__init__(self, 'harmonic_oscillator') # Store the parameters for later use. self.center = center self.force_constant = force_constant # The following method is called by the energy evaluation engine # to inquire if this force field term has all the parameters it # requires. This is necessary for interdependent force field # terms. In our case, we just say "yes" immediately. def ready(self, global_data): return True # The following method is called by the energy evaluation engine # to obtain a list of the low-level evaluator objects (the C routines) # that handle the calculations. def evaluatorTerms(self, universe, subset1, subset2, global_data): # The subset evaluation mechanism does not make much sense for # this force field, so we just signal an error if someone # uses it by accident. if subset1 is not None or subset2 is not None: raise ValueError("sorry, no subsets here") # Here we pass all the parameters to the code # that handles energy calculations. return [HarmonicOscillatorTerm(universe, self.atom_index, self.center, self.force_constant)] MMTK-2.7.9/Examples/Forcefield/HarmonicOscillator/Python/mdtest.py0000644000076600000240000000123211662461637025472 0ustar hinsenstaff00000000000000from MMTK import * from HarmonicOscillatorFF import HarmonicOscillatorForceField from MMTK.Dynamics import VelocityVerletIntegrator from MMTK.Trajectory import TrajectoryOutput universe = InfiniteUniverse() universe.atom1 = Atom('C', position=Vector(0., 0., 1.05)) universe.atom2 = Atom('C', position=Vector(0., 1.05, 0.)) ff1 = HarmonicOscillatorForceField(universe.atom1, Vector(0., 0., 1.), 100.) ff2 = HarmonicOscillatorForceField(universe.atom2, Vector(0., 1., 0.), 100.) universe.setForceField(ff1+ff2) universe.initializeVelocitiesToTemperature(300.*Units.K) integrator = VelocityVerletIntegrator(universe, delta_t = 1.0*Units.fs) integrator(steps = 1000) MMTK-2.7.9/Examples/Forcefield/HarmonicOscillator/Python/test.py0000644000076600000240000000115211662461637025152 0ustar hinsenstaff00000000000000from MMTK import * from HarmonicOscillatorFF import HarmonicOscillatorForceField from MMTK.ForceFields.ForceFieldTest import gradientTest, forceConstantTest universe = InfiniteUniverse() universe.atom1 = Atom('C', position=Vector(0., 0., 1.05)) universe.atom2 = Atom('C', position=Vector(0., 1.05, 0.)) ff1 = HarmonicOscillatorForceField(universe.atom1, Vector(0., 0., 1.), 100.) ff2 = HarmonicOscillatorForceField(universe.atom2, Vector(0., 1., 0.), 100.) universe.setForceField(ff1+ff2) e, g = universe.energyAndGradients() print universe.energyTerms() print e gradientTest(universe) forceConstantTest(universe) MMTK-2.7.9/Examples/Forcefield/README0000644000076600000240000000366511662461637017437 0ustar hinsenstaff00000000000000These examples show how new force field terms are implemented. The HarmonicOscillator example implements a harmonic potential that ties an individual atom to a specific point in space. The ElectricField example implements an external electric field that acts on all charges. Three implementations are provided for each example, one in Python, one in C and one in Cython. The Python and Cython examples require Python 2.2 (or later), Cython 0.123 (or later), and MMTK 2.5.7 (or later). The C examples require Python 1.5.2 (or later) and MMTK 2.0 (or later). The Python versions are the easiest to write, test, and understand, but they are also by far the slowest. For simple energy calculations like in these two exemples, a Python version may be sufficient. In most cases, a Cython or C implementation will be necessary, but a Python version can be very useful in designing, testing, and debugging an equivalent Cython or C implementation. Cython is a compiled version of Python that permits adding C type declarations for gaining speed. If all variables have C type declarations, then Cython code is as fast as hand-written C code. However, as the examples show, Cython code is much more compact, mostly because Cython handles the bookkeeping behind Python extension types automatically. I expect Cython to become the tool of choice for writing C extensions in the future, so I do recommend everyone to have a look at it. For more information about Cython, and for downloading it, see http://www.cython.org/. As the examples make clear, a force field term always consists of two parts: the force field term itself, always implemented in Python, and a corresponding energy evaluator term, implemented in Python, C, or Cython. The energy evaluator part is what is called for each energy evaluation. The force field part is only executed once, to prepare the parameters. For efficiency reasons, as much work as possible should therefore be done in the force field part. MMTK-2.7.9/Examples/LangevinDynamics/0000755000076600000240000000000012156626564017737 5ustar hinsenstaff00000000000000MMTK-2.7.9/Examples/LangevinDynamics/example.py0000644000076600000240000000406511662461637021750 0ustar hinsenstaff00000000000000# An example for LangevinDynamics # from MMTK import * from MMTK.Proteins import Protein from MMTK.ForceFields import Amber94ForceField from MMTK.Dynamics import TranslationRemover, RotationRemover, VelocityScaler from MMTK.Trajectory import Trajectory, TrajectoryOutput, StandardLogOutput from LangevinDynamics import LangevinIntegrator # Define system universe = InfiniteUniverse(Amber94ForceField()) universe.protein = Protein('bala1') temperature = 300.*Units.K # Initialize velocities universe.initializeVelocitiesToTemperature(temperature) # Set friction coefficients friction = universe.masses()*0.01/Units.ps # Create integrator integrator = LangevinIntegrator(universe, delta_t=1.*Units.fs, friction=friction, temperature=temperature) # Equilibration integrator(steps=10000, actions=[# Scale velocities every 50 steps. VelocityScaler(temperature, 0.1*temperature, 0, None, 50), # Remove global translation every 50 steps. TranslationRemover(0, None, 50), # Remove global rotation every 50 steps. RotationRemover(0, None, 50), # Log output to screen every 500 steps. StandardLogOutput(500)]) # "Production" run trajectory = Trajectory(universe, "langevin.nc", "w", "Langevin test") integrator(steps=10000, actions = [# Remove global translation every 50 steps. TranslationRemover(0, None, 50), # Remove global rotation every 50 steps. RotationRemover(0, None, 50), # Write every fifth step to the trajectory file. TrajectoryOutput(trajectory, ("time", "energy", "thermodynamic", "configuration"), 0, None, 10), # Log output to screen every 100 steps. StandardLogOutput(100)]) trajectory.close() MMTK-2.7.9/Examples/LangevinDynamics/LangevinDynamics.py0000644000076600000240000000460311662461637023546 0ustar hinsenstaff00000000000000# This module implements a Langevin integrator. # # Written by Konrad Hinsen # from MMTK import Dynamics, Environment, Features, Trajectory, \ Units, ParticleProperties import MMTK_langevin import MMTK_forcefield # # Langevin integrator # class LangevinIntegrator(Dynamics.Integrator): def __init__(self, universe, **options): Dynamics.Integrator.__init__(self, universe, options) # Supported features: none for the moment, to keep it simple self.features = [] def __call__(self, **options): # Process the keyword arguments self.setCallOptions(options) # Check if the universe has features not supported by the integrator Features.checkFeatures(self, self.universe) # Get the universe variables needed by the integrator configuration = self.universe.configuration() masses = self.universe.masses() velocities = self.universe.velocities() if velocities is None: raise ValueError("no velocities") # Get the friction coefficients. First check if a keyword argument # 'friction' was given to the integrator. Its value can be a # ParticleScalar or a plain number (used for all atoms). If no # such argument is given, collect the values of the attribute # 'friction' from all atoms (default is zero). try: friction = self.getOption('friction') except KeyError: friction = self.universe.getParticleScalar('friction') if not ParticleProperties.isParticleProperty(friction): var = ParticleProperties.ParticleScalar(self.universe) var.array[:] = friction friction = var # Construct a C evaluator object for the force field, using # the specified number of threads or the default value nt = self.getOption('threads') evaluator = self.universe.energyEvaluator(threads=nt).CEvaluator() # Run the C integrator MMTK_langevin.integrateLD(self.universe, configuration.array, velocities.array, masses.array, friction.array, evaluator, self.getOption('temperature'), self.getOption('delta_t'), self.getOption('steps'), self.getActions()) MMTK-2.7.9/Examples/LangevinDynamics/MMTK_langevin.c0000644000076600000240000003500011662461637022533 0ustar hinsenstaff00000000000000/* Low-level Langevin dynamics integrators * * Written by Konrad Hinsen */ #include "MMTK/universe.h" #include "MMTK/forcefield.h" #include "MMTK/trajectory.h" #include "ranf.h" /* Global variables */ double kB; /* Boltzman constant */ /* Allocate and initialize Output variable descriptors */ static PyTrajectoryVariable * get_data_descriptors(int n, PyUniverseSpecObject *universe_spec, PyArrayObject *configuration, PyArrayObject *velocities, PyArrayObject *gradients, PyArrayObject *masses, int *ndf, double *time, double *p_energy, double *k_energy, double *temperature, double *pressure, double *box_size) { PyTrajectoryVariable *vars = (PyTrajectoryVariable *) malloc((n+1)*sizeof(PyTrajectoryVariable)); int i = 0; if (vars != NULL) { if (time != NULL && i < n) { vars[i].name = "time"; vars[i].text = "Time: %lf\n"; vars[i].unit = time_unit_name; vars[i].type = PyTrajectory_Scalar; vars[i].class = PyTrajectory_Time; vars[i].value.dp = time; i++; } if (p_energy != NULL && i < n) { vars[i].name = "potential_energy"; vars[i].text = "Potential energy: %lf, "; vars[i].unit = energy_unit_name; vars[i].type = PyTrajectory_Scalar; vars[i].class = PyTrajectory_Energy; vars[i].value.dp = p_energy; i++; } if (k_energy != NULL && i < n) { vars[i].name = "kinetic_energy"; vars[i].text = "Kinetic energy: %lf\n"; vars[i].unit = energy_unit_name; vars[i].type = PyTrajectory_Scalar; vars[i].class = PyTrajectory_Energy; vars[i].value.dp = k_energy; i++; } if (temperature != NULL && i < n) { vars[i].name = "temperature"; vars[i].text = "Temperature: %lf\n"; vars[i].unit = temperature_unit_name; vars[i].type = PyTrajectory_Scalar; vars[i].class = PyTrajectory_Thermodynamic; vars[i].value.dp = temperature; i++; } if (pressure != NULL && i < n) { vars[i].name = "pressure"; vars[i].text = "Pressure: %lf\n"; vars[i].unit = pressure_unit_name; vars[i].type = PyTrajectory_Scalar; vars[i].class = PyTrajectory_Thermodynamic; vars[i].value.dp = pressure; i++; } if (configuration != NULL && i < n) { vars[i].name = "configuration"; vars[i].text = "Configuration:\n"; vars[i].unit = length_unit_name; vars[i].type = PyTrajectory_ParticleVector; vars[i].class = PyTrajectory_Configuration; vars[i].value.array = configuration; i++; } if (box_size != NULL && i < n) { vars[i].name = "box_size"; vars[i].text = "Box size:"; vars[i].unit = length_unit_name; vars[i].type = PyTrajectory_BoxSize; vars[i].class = PyTrajectory_Configuration; vars[i].value.dp = box_size; vars[i].length = universe_spec->geometry_data_length; i++; } if (velocities != NULL && i < n) { vars[i].name = "velocities"; vars[i].text = "Velocities:\n"; vars[i].unit = velocity_unit_name; vars[i].type = PyTrajectory_ParticleVector; vars[i].class = PyTrajectory_Velocities; vars[i].value.array = velocities; i++; } if (gradients != NULL && i < n) { vars[i].name = "gradients"; vars[i].text = "Energy gradients:\n"; vars[i].unit = energy_gradient_unit_name; vars[i].type = PyTrajectory_ParticleVector; vars[i].class = PyTrajectory_Gradients; vars[i].value.array = gradients; i++; } if (masses != NULL && i < n) { vars[i].name = "masses"; vars[i].text = "Masses:\n"; vars[i].unit = mass_unit_name; vars[i].type = PyTrajectory_ParticleScalar; vars[i].class = PyTrajectory_Internal; vars[i].value.array = masses; i++; } if (ndf != NULL && i < n) { vars[i].name = "degrees_of_freedom"; vars[i].text = "Degrees of freedom: %d\n"; vars[i].unit = ""; vars[i].type = PyTrajectory_IntScalar; vars[i].class = PyTrajectory_Internal; vars[i].value.ip = ndf; i++; } vars[i].name = NULL; } return vars; } /* Generate random numbers with bivariate gaussian distribution */ static void gaussian_bivariate(double *r1, double *r2, double s1, double s2, double c12, double c12x) { double v1, v2, s; do { v1 = 2.*Ranf()-1.; v2 = 2.*Ranf()-1.; s = v1*v1 + v2*v2; } while (s >= 1. || s == 0.); s = sqrt(-2.*log(s)/s); v1 *= s; v2 *= s; *r1 = s1*v1; *r2 = s2*(c12*v1+c12x*v2); } /* Generate random forces */ static void random_forces(vector3 *rx, vector3 *rv, double *friction, double *mass, int atoms, double temperature, double delta_t) { int i, j; for (i = 0; i < atoms; i++) { if (friction[i] < 1.e-8) { rx[i][0] = rx[i][1] = rx[i][2] = 0.; rv[i][0] = rv[i][1] = rv[i][2] = 0.; } else { double ft = delta_t*friction[i]/mass[i]; double expft = exp(-ft); double ktm = kB*temperature/mass[i]; double s1 = delta_t*sqrt(ktm*(2.-(3.+(expft-4.)*expft)/ft)/ft); double s2 = sqrt(ktm*(1.-sqr(expft))); double c12 = delta_t*ktm*(1.+expft*(expft-2.))/(ft*s1*s2); double c12x = sqrt(1.-c12*c12); for (j = 0; j < 3; j++) gaussian_bivariate(&rx[i][j], &rv[i][j], s1, s2, c12, c12x); } } } /* Langevin dynamics integrator */ static PyObject * integrateLD(PyObject *dummy, PyObject *args) { /* The parameters passed from the Python code */ PyObject *universe; /* universe */ PyArrayObject *configuration; /* array of positions */ PyArrayObject *velocities; /* array of velocities */ PyArrayObject *masses; /* array of masses */ PyArrayObject *friction; /* array of friction coefficients */ PyListObject *spec_list; /* list of periodic actions */ PyFFEvaluatorObject *evaluator; /* energy evaluator */ double ext_temp; /* temperature of the heat bath */ double delta_t; /* time step */ int steps; /* number of steps */ /* Other variables, see below for explanations */ PyThreadState *this_thread; PyUniverseSpecObject *universe_spec; PyArrayObject *gradients, *random1, *random2; PyTrajectoryOutputSpec *output; PyTrajectoryVariable *data_descriptors = NULL; vector3 *x, *v, *g, *rx, *rv; double *m, *f; energy_data p_energy; double time, k_energy, temperature, volume, pressure; int atoms, df; int pressure_available; int i, j; /* Get arguments passed from Python code */ if (!PyArg_ParseTuple(args, "OO!O!O!O!O!ddiO!", &universe, &PyArray_Type, &configuration, &PyArray_Type, &velocities, &PyArray_Type, &masses, &PyArray_Type, &friction, &PyFFEvaluator_Type, &evaluator, &ext_temp, &delta_t, &steps, &PyList_Type, &spec_list)) return NULL; /* Obtain the universe specification */ universe_spec = (PyUniverseSpecObject *) PyObject_GetAttrString(universe, "_spec"); if (universe_spec == NULL) return NULL; /* Create the array for energy gradients */ #if defined(NUMPY) gradients = (PyArrayObject *)PyArray_Copy(configuration); #else gradients = (PyArrayObject *)PyArray_FromDims(configuration->nd, configuration->dimensions, PyArray_DOUBLE); #endif if (gradients == NULL) return NULL; /* Create the arrays for random forces */ #if defined(NUMPY) random1 = (PyArrayObject *)PyArray_Copy(configuration); #else random1 = (PyArrayObject *)PyArray_FromDims(configuration->nd, configuration->dimensions, PyArray_DOUBLE); #endif if (random1 == NULL) { Py_DECREF(gradients); return NULL; } #if defined(NUMPY) random2 = (PyArrayObject *)PyArray_Copy(configuration); #else random2 = (PyArrayObject *)PyArray_FromDims(configuration->nd, configuration->dimensions, PyArray_DOUBLE); #endif if (random2 == NULL) { Py_DECREF(gradients); Py_DECREF(random1); return NULL; } /* Set some convenient variables */ atoms = configuration->dimensions[0]; /* number of atoms */ x = (vector3 *)configuration->data; /* pointer to positions */ v = (vector3 *)velocities->data; /* pointer to velocities */ g = (vector3 *)gradients->data; /* pointer to energy gradients */ rx = (vector3 *)random1->data; /* pointer to random moves */ rv = (vector3 *)random2->data; /* pointer to random velocities */ f = (double *)friction->data; /* pointer to friction constants */ m = (double *)masses->data; /* pointer to masses */ df = 3*atoms; /* number of degrees of freedom */ /* Initial coordinate correction (for periodic universes etc.) */ universe_spec->correction_function(x, atoms, universe_spec->geometry_data); /* Initial force calculation */ p_energy.gradients = (PyObject *)gradients; p_energy.gradient_fn = NULL; p_energy.force_constants = NULL; p_energy.fc_fn = NULL; Py_BEGIN_ALLOW_THREADS; PyUniverseSpec_StateLock(universe_spec, 1); (*evaluator->eval_func)(evaluator, &p_energy, configuration, 0); PyUniverseSpec_StateLock(universe_spec, 2); Py_END_ALLOW_THREADS; if (p_energy.error) goto error2; /* Check if pressure can be calculated (i.e. the force field implementation provides the virial and the universe has a finite volume) */ Py_BEGIN_ALLOW_THREADS; PyUniverseSpec_StateLock(universe_spec, 1); volume = universe_spec->volume_function(1., universe_spec->geometry_data); PyUniverseSpec_StateLock(universe_spec, 2); Py_END_ALLOW_THREADS; pressure_available = volume > 0. && p_energy.virial_available; /* Initialize trajectory output and periodic actions */ data_descriptors = get_data_descriptors(10 + pressure_available, universe_spec, configuration, velocities, gradients, masses, &df, &time, &p_energy.energy, &k_energy, &temperature, pressure_available ? &pressure:NULL, (universe_spec->geometry_data_length > 0) ? universe_spec->geometry_data : NULL); if (data_descriptors == NULL) goto error2; output = PyTrajectory_OutputSpecification(universe, spec_list, "Langevin Dynamics", data_descriptors); if (output == NULL) goto error2; /* Allow parallel threads and get the universe write state lock */ this_thread = PyEval_SaveThread(); PyUniverseSpec_StateLock(universe_spec, -1); /* * Main integration loop */ time = 0.; for (i = 0; i < steps; i++) { /* Calculation of thermodynamic properties */ k_energy = 0.; for (j = 0; j < atoms; j++) k_energy += m[j]*dot(v[j], v[j]); k_energy *= 0.5; temperature = 2.*k_energy/(df*kB); pressure = 2.*k_energy+p_energy.virial/(3.*volume); /* Trajectory and log output */ if (PyTrajectory_Output(output, i, data_descriptors, &this_thread) == -1) { PyUniverseSpec_StateLock(universe_spec, -2); PyEval_RestoreThread(this_thread); goto error; } /* Calculation of random forces */ random_forces(rx, rv, f, m, atoms, ext_temp, delta_t); /* First part of integration step */ for (j = 0; j < atoms; j++) { double ft = delta_t*f[j]/m[j]; double c0 = 1.+ft*(-1.+ft*(0.5+ft*(-1./6.+ft/24.))); double c1 = 1.+ft*(-0.5+ft*(1./6.-ft/24.)); double c2 = 0.5+ft*(-1./6.+ft/24.); x[j][0] += delta_t*(c1*v[j][0]-c2*delta_t*g[j][0]/m[j])+rx[j][0]; x[j][1] += delta_t*(c1*v[j][1]-c2*delta_t*g[j][1]/m[j])+rx[j][1]; x[j][2] += delta_t*(c1*v[j][2]-c2*delta_t*g[j][2]/m[j])+rx[j][2]; v[j][0] = c0*v[j][0]+(c2-c1)*delta_t*g[j][0]/m[j]+rv[j][0]; v[j][1] = c0*v[j][1]+(c2-c1)*delta_t*g[j][1]/m[j]+rv[j][1]; v[j][2] = c0*v[j][2]+(c2-c1)*delta_t*g[j][2]/m[j]+rv[j][2]; } /* Coordinate correction (for periodic universes etc.) */ universe_spec->correction_function(x, atoms, universe_spec->geometry_data); /* Mid-step force evaluation */ PyUniverseSpec_StateLock(universe_spec, -2); PyUniverseSpec_StateLock(universe_spec, 1); (*evaluator->eval_func)(evaluator, &p_energy, configuration, 1); PyUniverseSpec_StateLock(universe_spec, 2); if (p_energy.error) { PyEval_RestoreThread(this_thread); goto error; } PyUniverseSpec_StateLock(universe_spec, -1); /* Second part of integration step */ for (j = 0; j < atoms; j++) { double ft = delta_t*f[j]/m[j]; double c2 = 0.5+ft*(-1./6.+ft/24.); v[j][0] -= c2*delta_t*g[j][0]/m[j]; v[j][1] -= c2*delta_t*g[j][1]/m[j]; v[j][2] -= c2*delta_t*g[j][2]/m[j]; } /* The End - next time step! */ time += delta_t; } /** End of main integration loop **/ /* Final thermodynamic property evaluation */ k_energy = 0.; for (j = 0; j < atoms; j++) k_energy += m[j]*dot(v[j], v[j]); k_energy *= 0.5; temperature = 2.*k_energy/(df*kB); pressure = 2.*k_energy+p_energy.virial/(3.*volume); /* Final trajectory and log output */ if (PyTrajectory_Output(output, i, data_descriptors, &this_thread) == -1) { PyUniverseSpec_StateLock(universe_spec, -2); PyEval_RestoreThread(this_thread); goto error; } /* Cleanup */ PyUniverseSpec_StateLock(universe_spec, -2); PyEval_RestoreThread(this_thread); PyTrajectory_OutputFinish(output, i, 0, 1, data_descriptors); free(data_descriptors); Py_DECREF(gradients); Py_DECREF(random1); Py_DECREF(random2); Py_INCREF(Py_None); return Py_None; /* Error return */ error: PyTrajectory_OutputFinish(output, i, 1, 1, data_descriptors); error2: free(data_descriptors); Py_DECREF(gradients); Py_DECREF(random1); Py_DECREF(random2); return NULL; } /* * List of functions defined in the module */ static PyMethodDef langevin_methods[] = { {"integrateLD", integrateLD, 1}, {NULL, NULL} /* sentinel */ }; /* Initialization function for the module */ void initMMTK_langevin() { PyObject *m, *dict; PyObject *universe, *trajectory, *forcefield, *units; /* Create the module and add the functions */ m = Py_InitModule("MMTK_langevin", langevin_methods); dict = PyModule_GetDict(m); /* Import the array module */ import_array(); /* Import MMTK modules */ import_MMTK_universe(); import_MMTK_forcefield(); import_MMTK_trajectory(); /* Get the Boltzman constant factor from MMTK.Units */ units = PyImport_ImportModule("MMTK.Units"); if (units != NULL) { PyObject *module_dict = PyModule_GetDict(units); PyObject *factor = PyDict_GetItemString(module_dict, "k_B"); kB = PyFloat_AsDouble(factor); } /* Check for errors */ if (PyErr_Occurred()) Py_FatalError("can't initialize module MMTK_langevin"); } MMTK-2.7.9/Examples/LangevinDynamics/pmath_rng.c0000644000076600000240000003741311662461637022071 0ustar hinsenstaff00000000000000/* pmath_rng.c * * This set of routines reproduces the Cray RANF family of random * number routines bit-for-bit. Most of the routines here were * written by Jim Rathkopf, CP-Division, LLNL, and modified by * William C. Biester, B-Division. LLNL. They were subsequently * modified for use in PMATH by Fred N. Fritsch, LC, LLNL. * * This version contains only the generator proper (equivalent to PM_RANF8 * in the official PMATH version) and makes the seed and multiplier * directly accessible as arrays of doubles. This was modified for use by * the Basis and Python module ranf.c, which contains its own seed and * multiplier management routines. (FNF -- 9/4/96) */ /* ----------------------------------------------------------------------- Copyright (c) 1993,1994,1995,1996. The Regents of the University of California. All rights reserved. This work was produced at the University of California, Lawrence Livermore National Laboratory (UC LLNL) under contract no. W-7405-ENG-48 between the U.S. Department of Energy (DOE) and The Regents of the University of California (University) for the operation of UC LLNL. Copyright is reserved to the University for purposes of controlled dissemination, commercialization through formal licensing, or other disposition under terms of Contract 48; DOE policies, regulations and orders; and U.S. statutes. DISCLAIMER This document was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the University of California nor any of their employees, makes any warranty, express or implied, or assumes any liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately-owned rights. Reference herein to any specific commercial products, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or the University of California, and shall not be used for advertising or product endorsement purposes. */ /* ----------------------------------------------------------------------- * * Service routines defined: * PM_16to24 - Convert 3 16-bit shorts to 2 24-bit doubles * PM_24to16 - Convert 2 24-bit doubles to 3 16-bit shorts * PM_GSeed - Get the seed as two 24-bit doubles * PM_SSeed - Set the seed from two 24-bit doubles (unsafe) * PM_GMult - Get the multiplier as two 24-bit doubles * PM_SMult - Set the multiplier from two 24-bit doubles (unsafe) * * User-callable routines defined: * PM_RANF - The generator itself * * Currently responsible person: * Fred N. Fritsch * Computer Applications Organization * LCPD, ICF Group * Lawrence Livermore National Laboratory * fnf@llnl.gov * * Modifications: * (See individual SLATEC-format prologues for detailed modification records.) * 03-09-93 Integrated Jim's routines into my library system. (WCB) * 10-21-93 Modified to provide RANF8 for PMATH. (FNF) * 10-22-93 Added code for RNFCNT. (FNF) * 11-09-93 Added code for RNMSET. (FNF) * 12-15-93 Modified to process bytes of seeds and multipliers in * in the reverse order, to agree with the way they come * from the Cray. (FNF) * 03-15-94 Changed general pm_binds.h to pm_rnfset.h. (FNF) * 04-25-94 Added SLATEC-format prologues. (FNF) * 09-26-95 Simplified code and improved internal documentation. * This included removing unnecessary internal procedures * setseed and seed48. (FNF) * 09-27-95 Simplified further by eliminating internal procedure * rnset and no longer used variable Multiplier. Also, * moved PM_RANF8 so that it appears first. (FNF) * 09-28-95 Modified behavior of RNMSET to agree with MATHLIB. (FNF) * 10-02-95 Eliminated unions via calls to CV16TO64 and CV64TO16. * Most conditional compilations are now in the rand48_* * procedures. (FNF) * 10-05-95 Corrected "overflow" problem to make code work on Cray. * Removed some unnecessary code in the process. (FNF) * 10-09-95 Removed conditional compiles. (No longer needed after * change of 10-02-95.) (FNF) * 08-27-96 Removed code for PM_RNSSET, PM_RNMSET, PM_RNSGET, PM_RNFCNT. * Changed name PM_RANF8 to PM_RANF. Added simple interface * routines to get/set seed and multiplier. Removed dependency * on CV16TO64 and CV64TO16. Eliminated Fortran bindings. (FNF) * 09-04-96 Improved descriptions of the get/set routines. (FNF) * 09-05-96 Added definitions of internal procedures PM_16to24 and * PM_24to16 (see ranf.h). (FNF) * ----------------------------------------------------------------------- */ /* Get includes, typedefs, and protypes for ranf.c and pmath_rng.c */ #include "ranf.h" #define BASE24 16777216.0 /* 2^24 */ #define TWO8 256.0 /* 2^8 */ #define TWO16 65536.0 /* 2^16 */ #define TWO48 281474976710656.0 /* 2^48 */ /* ------------------------------------------------------ The following static global variables retain the state of the random number generator between calls. ------------------------------------------------------ */ /* The default values to reproduce Cray values */ static double dseed[2] = {16555217.0, 9732691.0} ; static double dmult[2] = {15184245.0, 2651554.0} ; static double dadder = 0.0; /* This is a relic from drand48. */ /* -------------------------------------- Define in-line mod function. -------------------------------------- */ #define MODF(r,m) (r - (floor(r/m))*m) /* ----------------------------------------- Define PMATH RNG internal procedures. ----------------------------------------- */ /* PM_16to24 -- Take a number stored in 3 16-bit unsigned shorts and move it * to 2 doubles each containing 24 bits of data. * * This is a local function. * * Calling sequence: * u16 x16[3]; * double x24[2]; * PM_16to24 (x16, x24); * * x16 : the 3 16-bit unsigned shorts. * x24 := the 2 doubles of 24 bits returned. * * Return value: * none. * * Note: * This is the same as PMATH internal procedure rand48_16to24 except * that the order of the entries in x16 have been reversed and its * type declaration has been changed to u16. It is intended for use * by user-callable routines that set seeds or multipliers for users. */ void PM_16to24(u16 x16[3], double x24[2]) { double t0, t1, t2, t1u, t1l; t0 = (double) x16[0]; t1 = (double) x16[1]; t2 = (double) x16[2]; t1u = floor(t1/TWO8); t1l = t1 -t1u*TWO8; x24[0] = t0 + t1l*TWO16; x24[1] = t1u + t2*TWO8; #ifdef RAN_DEBUG fprintf(stderr,"PM_16to24: x16 = %04x %04x %04x\n",x16[2],x16[1],x16[0]); fprintf(stderr,"PM_16to24: x24 = %.1f %.1f\n",x24[1],x24[0]); #endif return; } /* PM_24to16 -- Take a number stored in 2 doubles each containing 24 bits * of data and move it to 3 16-bit unsigned shorts. * * Calling sequence: * double x24[2]; * u16 x16[3]; * PM_24to16 (x24, x16); * * x24 : the 2 doubles of 24 bits. * x16 := the 3 16-bit unsigned shorts returned. * * Return value: * none. * * Note: * This is the same as PMATH internal procedure rand48_24to16 except * that the order of the entries in x16 have been reversed and its * type declaration has been changed to u16. It is intended for use * by user-callable routines that get seeds or multipliers for users. */ void PM_24to16(double x24[2], u16 x16[3]) { double t0u, t0l, t1u, t1l; t0u = floor(x24[0]/TWO16); t0l = x24[0] - t0u*TWO16; x16[0] = (u16)t0l; t1u = floor(x24[1]/TWO8); x16[2] = (u16)t1u; t1l = x24[1] - t1u*TWO8; x16[1] = (u16)(t0u + t1l*TWO8); #ifdef RAN_DEBUG fprintf(stderr,"PM_24to16: x24 = %.1f %.1f\n",x24[1],x24[0]); fprintf(stderr,"PM_24to16: x16 = %04x %04x %04x\n",x16[2],x16[1],x16[0]); #endif return; } /* End of internal procedure definitions. ----------------------------------------- */ /* -------------------------------------- Define service routines. -------------------------------------- */ /* PM_GSeed -- Get the seed as 2 doubles each containing 24 bits of data. * * Calling sequence: * double myseed[2]; * PM_GSeed(myseed); * * myseed := the 2 doubles of 24 bits returned. * * Return value: * none. */ void PM_GSeed(double seedout[2]) { seedout[0] = dseed[0]; seedout[1] = dseed[1]; #ifdef RAN_DEBUG fprintf(stderr,"PM_GSeed: seed = %.1f %.1f\n", dseed[1], dseed[0]); #endif return; } /* PM_SSeed -- Set the seed from 2 doubles each containing 24 bits of data. * * Calling sequence: * double myseed[2]; * PM_SSeed(myseed); * * myseed : the 2 doubles of 24 bits to be used as the new seed. * * Return value: * none. * * Caution: * This routine does not check for valid seeds! It is intended for * use in a safe interface such as Setranf (in ranf.c). * The elements of the myseed array are the coefficients in the base * 2**24 representation of the odd 48-bit integer * seed = myseed[1]*2**24 + myseed[0], * so myseed must satisfy: * (1) myseed[0] and myseed[1] are integers, stored in doubles; * (2) 0 < myseed[0] < 2**24, 0 <= myseed[1] < 2**24; * (3) myseed[0] is odd. */ void PM_SSeed(double seedin[2]) { dseed[0] = seedin[0]; dseed[1] = seedin[1]; #ifdef RAN_DEBUG fprintf(stderr,"PM_SSeed: seed = %.1f %.1f\n", dseed[1], dseed[0]); #endif return; } /* PM_GMult -- Get the multiplier as 2 doubles each containing 24 bits * of data. * * Calling sequence: * double mymult[2]; * PM_GMult(mymult); * * mymult := the 2 doubles of 24 bits returned. * * Return value: * none. */ void PM_GMult(double multout[2]) { multout[0] = dmult[0]; multout[1] = dmult[1]; #ifdef RAN_DEBUG fprintf(stderr,"PM_GMult: mult = %.1f %.1f\n", dmult[1], dmult[0]); #endif return; } /* PM_SMult -- Set the multiplier from 2 doubles each containing 24 bits * of data. * * Calling sequence: * double mymult[2]; * PM_SMult(mymult); * * mymult : the 2 doubles of 24 bits to be used as the new multiplier. * * Return value: * none. * * Caution: * This routine does not check for valid multipliers! It is intended for * use in a safe interface such as Setmult (in ranf.c). * The elements of the mymult array are the coefficients in the base * 2**24 representation of the odd 48-bit integer * mult = mymult[1]*2**24 + mymult[0]. * Since we require 1 < mult < 2**46, mymult must satisfy: * (1) mymult[0] and mymult[1] are integers, stored in doubles; * (2) 1 < mymult[0] < 2**24, 0 <= mymult[1] < 2**22; * (3) mymult[0] is odd. */ void PM_SMult(double multin[2]) { dmult[0] = multin[0]; dmult[1] = multin[1]; #ifdef RAN_DEBUG fprintf(stderr,"PM_SMult: mult = %.1f %.1f\n", dmult[1], dmult[0]); #endif return; } /* -------------------------------------- Define the generator itself. -------------------------------------- */ /*DECK PM_RANF C***BEGIN PROLOGUE PM_RANF C***PURPOSE Uniform random-number generator. C The pseudorandom numbers generated by C function PM_RANF C are uniformly distributed in the open interval (0,1). C***LIBRARY PMATH C***CATEGORY L6A21 C***TYPE REAL*8 (PM_RANF-8) C***KEYWORDS RANDOM NUMBER GENERATION, UNIFORM DISTRIBUTION C***AUTHOR Rathkopf, Jim, (LLNL/CP-Division) C Fritsch, Fred N., (LLNL/LC/MSS) C***DESCRIPTION C (Portable version of Cray MATHLIB routine RANF.) C (Equivalent to Fortran-callable RANF8.) C *Usage: C double r; C r = PM_RANF(); C C *Function Return Values: C r A random number between 0 and 1. C C *Description: C PM_RANF generates pseudorandom numbers lying strictly between 0 C and 1. Each call to PM_RANF produces a different value, until the C sequence cycles after 2**46 calls. C C PM_RANF is a linear congruential pseudorandom-number generator. C The default starting seed is C SEED = 4510112377116321(oct) = 948253fc9cd1(hex). C The multiplier is 1207264271730565(oct) = 2875a2e7b175(hex). C C *See Also: C This is the same generator used by the Fortran generator family C SRANF/DRANF/RANF8. C The starting seed for PM_RANF may be set via PM_SSeed. C The current PM_RANF seed may be obtained from PM_GSeed. C The current PM_RANF multiplier may be obtained from PM_GMult. C The PM_RANF multiplier may be set via PM_SMult (changing the C multiplier is not recommended). C C***ROUTINES CALLED (NONE) C***REVISION HISTORY (YYMMDD) C 930308 DATE WRITTEN C (Date from Biester's math_rnf.c.) C 931021 Changed name from B_RANF to PM_RANF8. (FNF) C 940425 Added SLATEC-format prologue. (FNF) C 950922 Re-ordered code to eliminate old EMULRAND routine. (FNF) C 951005 Eliminated unnecessary upper part computations. C Added fmod invocations in t2_48 computation to avoid C generating numbers too large for Cray floating point. (FNF) C 951020 Replaced fmod with macro MODF to avoid library calls. (FNF) C 951025 Removed MODF from first term of t2_48. (FNF) C 960827 Changed name from PM_RANF8 to PM_RANF, eliminated reference C counter, and removed Fortran binding. (FNF) C***END PROLOGUE PM_RANF C C*Internal notes: C C This routine generates pseudo-random numbers through a 48-bit C linear congruential algorithm. This emulates the drand48 library C of random number generators available on many, but not all, C UNIX machines. C C Algorithm: C x(n+1) = (a*x(n) + c) mod m C C where the defaults for the standard UNIX rand48 are: C C double name hexdecimal decimal C x: seed -dseed[0],[1] 1234abcd330e 20017429951246 C a: multiplier -dmult[0],[1] 5deece66d 25214903917 C c: adder -dadder b 11 C m: 2**48 1000000000000 281474976710656 C C 24-bit defaults (decimal) (lower bits listed first) C x: dseed[0],[1] = 13447950.0, 1193131.0 C a: dmult[0],[1] = 15525485.0, 1502.0 C c: dadder = 11.0 C C The Cray defaults used in this code are: C C double name hexdecimal octal C x: seed -dseed[0],[1] 948253fc9cd1 4510112377116321 C a: multiplier -dmult[0],[1] 2875a2e7b175 1207264271730565 C c: adder -dadder 0 0 C m: 2**48 1000000000000 10000000000000000 C C 24-bit defaults (decimal) (lower bits listed first) C x: dseed[0],[1] = 16555217.0, 9732691.0 C a: dmult[0],[1] = 15184245.0, 2651554.0 C c: dadder = 0.0 C C Return value: C double random number such that 0.0< d <1.0 C C**End */ double PM_RANF () { double t1_48, t2_48, t1_24[2], t2_24; double d; /* perform 48-bit arithmetic using two part data */ t1_48 = dmult[0]*dseed[0] + dadder; t2_48 = dmult[1]*dseed[0] + MODF(dmult[0]*dseed[1],BASE24); /* First term safe, since dmult[1] < 2**22 for default mult */ t1_24[1] = floor(t1_48/BASE24); /* upper part */ t1_24[0] = t1_48 - t1_24[1]*BASE24; /* lower part */ t2_24 = MODF(t2_48, BASE24); /* lower part */ t2_48 = t2_24 + t1_24[1]; t2_24 = MODF(t2_48, BASE24); /* discard anything over 2**24 */ d = (dseed[0] + dseed[1]*BASE24)/TWO48; dseed[0] = t1_24[0]; dseed[1] = t2_24; return (d); } MMTK-2.7.9/Examples/LangevinDynamics/ranf.c0000644000076600000240000002436511662461637021042 0ustar hinsenstaff00000000000000/* ranf.c The routines below implement an interface based on the drand48 family of random number routines, but with seed handling and default starting seed compatible with the CRI MATHLIB random number generator (RNG) family "ranf". */ /* ----------------------------------------------------------------------- * * User-callable routines defined: * Seedranf - Set seed from 32-bit integer * Mixranf - Set seed, with options; return seed * Getranf - Get 48-bit seed in integer array * Setranf - Set seed from 48-bit integer * Getmult - Get 48-bit multiplier in integer array * Setmult - Set multiplier from 48-bit integer * Ranf - The generator itself * * Currently responsible person: * Fred N. Fritsch * Computer Applications Organization * LCPD, ICF Group * Lawrence Livermore National Laboratory * fnf@llnl.gov * * Revision history: * (yymmdd) * 91???? DATE WRITTEN * This started with the ranf.c that was checked out of the * Basis repository in August 1996. It was written by Lee * Busby to provide an interface for Basis to the drand48 * family of generators that is compatible with the CRI * MATHLIB ranf. Following are the relevant cvs log entries. * 950511 Added back seedranf, setranf, getranf, mixranf from older * Basis version. (LB) * 950530 Changed type of u32 from long to int, which is currently ok * on all our supported platforms. (LB) * 960823 Revised to use the portable PMATH generator instead. (FNF) * 960903 Added new routines Getmult and Setmult. (FNF) * 960904 Changed declarations for 48-bit quantities from pointers to * two-element arrays, to be consistent with actual usage, and * brought internal documentation up to LDOC standard. (FNF) * 960905 Moved definitions of internal procedures PM_16to24 and * PM_24to16 to pmath_rng.c (see ranf.h). (FNF) * 960911 Corrected some problems with return values from Mixranf. * Also improved calling sequence descriptions for Seedranf and * Mixranf. (FNF) * 960916 Eliminated state variable ranf_started. Since the PMATH * family has the Cray default starting values built in, this * is no longer needed. (FNF) * 961011 Modifed Setranf and Setmult to work OK on Cray's when given * values saved on a 32-bit workstation. (FNF) * 961212 Corrected dimension error in Getmult. (FNF per E. Brooks) * * ----------------------------------------------------------------------- */ #ifdef __cplusplus extern "C" { #endif /* Get includes, typedefs, and protypes for ranf.c and pmath_rng.c */ #include "ranf.h" /* Seedranf - Reset the RNG, given a new (32-bit) seed value. * * Calling sequence: * u32 s; * Seedranf(&s); * * s : (pointer to) the new (32-bit) seed value. * * Return value: * none. * * Note: * The upper 16 bits of the 48-bit seed are set to zero and then * Setranf is called with the result. This is provided primarily * as a user convenience. */ void Seedranf(u32 *s) { u32 s48[2]; s48[0] = *s; s48[1] = 0x0; #ifdef RAN_DEBUG fprintf(stderr,"Seedranf set 48-bit seed %08x %08x\n",s48[1],s48[0]); #endif (void)Setranf(s48); } /* Mixranf - Reset the RNG with a new 48-bit seed, with options. * * Calling sequence: * int s; * u32 s48[2]; * Mixranf(&s,s48); * * s : the value used to determine the new seed, as follows: * s < 0 ==> Use the default initial seed value. * s = 0 ==> Set a "random" value for the seed from the * system clock. * s > 0 ==> Set seed directly (32 bits only). * s48 := the new 48-bit seed, returned in an array of u32's. * (The upper 16 bits of s48[1] will be zero.) * * Return value: * none. * * Note: * In case of a positive s, all of s48[1] will be zero. In order to set * a seed with more than 32 bits, use Setranf directly. * * Portability: * The structs timeval and timezone and routine gettimeofday are required * when s=0. These are generally available in . */ void Mixranf(int *s,u32 s48[2]) { if(*s < 0){ /* Set default initial value */ s48[0] = s48[1] = 0; Setranf(s48); Getranf(s48); /* Return default seed */ }else if(*s == 0){ /* Use the clock */ int i; #ifdef __MWERKS__ /* use this for the Macintosh */ time_t theTime; UnsignedWide tv_usec; (void)time(&theTime); Microseconds(&tv_usec); /* the microsecs since start up */ s48[0] = (u32)theTime; s48[1] = (u32)tv_usec.lo; #else #if defined(_WIN32) // suggestions welcome for something better! // one of these is from start of job, the other time of day time_t long_time; clock_t clock_time; time(&long_time); clock_time = clock(); s48[0] = (u32)long_time; s48[1] = (u32)clock_time; #else struct timeval tv; struct timezone tz; #if !defined(__sgi) int gettimeofday(); #endif (void)gettimeofday(&tv,&tz); s48[0] = (u32)tv.tv_sec; s48[1] = (u32)tv.tv_usec; #endif /* !_WIN32 */ #endif /* !__MWERKS__ */ Setranf(s48); for(i=0;i<10;i++) (void)Ranf(); /* Discard first 10 numbers */ Getranf(s48); /* Return seed after these 10 calls */ }else{ /* s > 0, so set seed directly */ s48[0] = (u32)*s; s48[1] = 0x0; Setranf(s48); Getranf(s48); /* Return (possibly) corrected value of seed */ } #ifdef RAN_DEBUG fprintf(stderr,"Mixranf set 48-bit seed %08x %08x\n",s48[1],s48[0]); #endif } /* Getranf - Get the 48-bit seed value. * * Calling sequence: * u32 s48[2]; * Getranf(s48); * * s48 := the current 48-bit seed, stored in an array of u32's. * (The upper 16 bits of s48[1] will be zero.) * * Return value: * none. */ void Getranf(u32 s48[2]) { u16 p[3]; double pm_seed[2]; /* Get the PMATH seed in an array of doubles. */ PM_GSeed(pm_seed); /* Convert it to an array of u16's */ PM_24to16(pm_seed, p); /* Now put all the bits in the right place. The picture below shows s48[0], the rightmost 16 bits of s48[1], and all 16 bits of p[0], p[1], and p[2] as aligned after assignment. --------------------------------------------------------------------- | s48[1] | s48[0] | --------------------------------------------------------------------- 15 0|31 16|15 0 --------------------------------------------------------------------- | p[2] | p[1] | p[0] | --------------------------------------------------------------------- */ s48[0] = p[1]; s48[0] = (s48[0] << 16) + p[0]; s48[1] = p[2]; #ifdef RAN_DEBUG fprintf(stderr,"Getranf returns %08x %08x\n",s48[1],s48[0]); #endif } /* Setranf - Reset the RNG seed from a 48-bit integer value. * * Calling sequence: * u32 s48[2]; * Setranf(s48); * * s48 : the new 48-bit seed, contained in an array of u32's. * If s48 is all zeros, set the default starting value, * 948253fc9cd1 (hex). * * Return value: * none. * * Note: * A 1 is masked into the lowest bit position to make sure the seed * is odd. (The upper 16 bits of s48[1] will be ignored if nonzero.) */ void Setranf(u32 s48[2]) { u16 p[3]; double pm_seed[2]; #ifdef RAN_DEBUG fprintf(stderr,"Setranf called with s48 = %08x %08x\n",s48[1],s48[0]); #endif if(s48[0] == 0 && s48[1] == 0){ /* Set default starting value */ s48[0] = 0x53fc9cd1; s48[1] = 0x9482; } /* Store seed as 3 u16's. */ p[0] = (s48[0] & 0xffff) | 0x1; /* Force an odd number */ p[1] = (s48[0] >> 16) & 0xffff; /* Protect against sign extension on Cray*/ p[2] = s48[1]; /* Convert to PMATH seed. */ PM_16to24(p, pm_seed); /* Now reset the seed. */ PM_SSeed(pm_seed); #ifdef RAN_DEBUG fprintf(stderr,"Leaving Setranf\n"); #endif } /* Getmult - Get the current 48-bit multiplier. * * Calling sequence: * u32 m48[2]; * Getmult(m48); * * m48 := the current 48-bit multiplier, stored in an array of u32's. * (The upper 16 bits of m48[1] will be zero.) * * Return value: * none. */ void Getmult(u32 m48[2]) { u16 p[3]; double pm_mult[2]; /* Get the PMATH multiplier in an array of doubles. */ PM_GMult(pm_mult); /* Convert it to an array of u16's */ PM_24to16(pm_mult, p); /* Now put all the bits in the right place. (See Getranf for picture.) */ m48[0] = p[1]; m48[0] = (m48[0] << 16) + p[0]; m48[1] = p[2]; #ifdef RAN_DEBUG fprintf(stderr,"Getmult returns %08x %08x\n",m48[1],m48[0]); #endif } /* Setmult - Reset the RNG multiplier from a 48-bit integer value. * * Calling sequence: * u32 m48[2]; * Setmult(m48); * * m48 : the new 48-bit multiplier, contained in an array of u32's. * If m48 is all zeros, set the default starting value, * 2875a2e7b175 (hex). * * Return value: * none. * * Note: * A 1 is masked into the lowest bit position to make sure the value * is odd. (The upper 18 bits of m48[1] will be ignored if nonzero.) */ void Setmult(u32 *m48) { u16 p[3]; double pm_mult[2]; #ifdef RAN_DEBUG fprintf(stderr,"Setmult called with m48 = %08x %08x\n",m48[1],m48[0]); #endif if(m48[0] == 0 && m48[1] == 0){ /* Set default starting value */ m48[0] = 0xa2e7b175; m48[1] = 0x2875; } /* Store multiplier as 3 u16's. */ p[0] = (m48[0] & 0xffff) | 0x1; /* Force an odd number */ p[1] = (m48[0] >> 16) & 0xffff; /* Protect against sign extension on Cray*/ p[2] = m48[1] & 0x3fff; /* Force mult < 2**26 */ /* Convert to PMATH multiplier. */ PM_16to24(p, pm_mult); /* Now reset the multiplier. */ PM_SMult(pm_mult); #ifdef RAN_DEBUG fprintf(stderr,"Leaving Setmult\n"); #endif } /* Ranf - Uniform random number generator. * * Calling sequence: * f64 value; * value = Ranf(); * * Return value: * value := the next number in the ranf sequence. * * Note: * The Ranf function is just a shell around PM_RANF. Since the * PMATH generator has the Cray default starting values built in, * no initialization is needed. */ f64 Ranf() { return(PM_RANF()); } #ifdef __cplusplus } #endif MMTK-2.7.9/Examples/LangevinDynamics/ranf.h0000644000076600000240000000344011662461637021036 0ustar hinsenstaff00000000000000#include #include #ifdef RAN_DEBUG #include #endif #ifdef __MWERKS__ /*#include */ #include #include #else #if defined(_WIN32) #include #else #include #endif #endif typedef unsigned int u32; typedef unsigned short int u16; typedef double f64; /* Prototypes for routines defined in ranf.c */ #ifdef __STDC__ void Seedranf(u32 *s); /* Set seed from 32-bit integer */ void Mixranf(int *s, u32 s48[2]); /* Set seed, with options; return seed */ void Getranf(u32 s48[2]); /* Get 48-bit seed in integer array */ void Setranf(u32 s48[2]); /* Set seed from 48-bit integer */ void Getmult(u32 m48[2]); /* Get 48-bit multiplier in integer array */ void Setmult(u32 m48[2]); /* Set multiplier from 48-bit integer */ f64 Ranf(); /* The generator itself */ #else void Seedranf(); void Mixranf(); void Getranf(); void Setranf(); void Getmult(); void Setmult(); f64 Ranf(); #endif /* Prototypes for routines defined in pmath_rng.c */ #ifdef __STDC__ void PM_16to24(u16 x16[3], double x24[2]); /* Convert 3 16-bit shorts to 2 24-bit doubles */ void PM_24to16(double x24[2], u16 x16[3]); /* Convert 2 24-bit doubles to 3 16-bit shorts */ void PM_GSeed(double seedout[2]); /* Get the current seed */ void PM_SSeed(double seedin[2]); /* Reset the seed (unsafe) */ void PM_GMult(double multout[2]); /* Get the current multiplier */ void PM_SMult(double multin[2]); /* Reset the multiplier (unsafe) */ f64 PM_RANF(); /* The generator itself */ #else void PM_16to24(); void PM_24to16(); void PM_GSeed(); void PM_SSeed(); void PM_GMult(); void PM_SMult(); f64 PM_RANF(); #endif MMTK-2.7.9/Examples/LangevinDynamics/README0000644000076600000240000000141511662461637020617 0ustar hinsenstaff00000000000000The files in this directory are provided as an illustration of how to implement molecular dynamics integrators and similar algorithms. The Langevin integrator is therefore intentionally kept simple; it supports neither fixed atoms nor distance constraints. The algorithm is taken from Allen&Tildesley (Eq. 9.24). The core of the integrator is written in C (file MMTK_langevinmodule.c), with a bit of Python code for parameter extraction, error checking, etc. (LangevinDynamics.py). The files ranf.c, ranf.h, and pmath_rng.c contain a random number generator and are taken without modification from the random number module for Python written at LLNL. To compile and link the C module, type python setup.py build_ext --inplace To run the example, type python example.py MMTK-2.7.9/Examples/LangevinDynamics/setup.py0000644000076600000240000000241211662461637021447 0ustar hinsenstaff00000000000000#!/usr/bin/env python from distutils.core import setup, Extension import os, sys compile_args = [] include_dirs = ['.'] from Scientific import N try: num_package = N.package except AttributeError: num_package = "Numeric" if num_package == "NumPy": compile_args.append("-DNUMPY=1") if sys.platform == 'win32': include_dirs.append(os.path.join(sys.prefix, "Lib/site-packages/numpy/core/include")) else: include_dirs.append(os.path.join(sys.prefix, "lib/python%s.%s/site-packages/numpy/core/include" % sys.version_info [:2])) setup (name = "MMTK-LangevinDynamics", version = "2.7", description = "Langevin dynamics module for the Molecular Modelling Toolkit", author = "Konrad Hinsen", author_email = "hinsen@cnrs-orleans.fr", url = "http://dirac.cnrs-orleans.fr/MMTK/", license = "CeCILL-C", py_modules = ['LangevinDynamics'], ext_modules = [Extension('MMTK_langevin', ['./MMTK_langevin.c', 'ranf.c', 'pmath_rng.c'], extra_compile_args = compile_args, include_dirs=include_dirs), ], ) MMTK-2.7.9/Examples/MDIntegrator/0000755000076600000240000000000012156626564017043 5ustar hinsenstaff00000000000000MMTK-2.7.9/Examples/MDIntegrator/README0000644000076600000240000000071711662461637017727 0ustar hinsenstaff00000000000000The files in this directory are provided as an illustration of how to implement molecular dynamics integrators and similar algorithms in Cython. The Velocity Verlet integrator provided as an example is intentionally kept simple; it supports neither fixed atoms nor distance constraints, and has neither a thermostat nor a barostat. To compile and link the C module, type python setup.py build_ext --inplace To run the test script, type python test.py MMTK-2.7.9/Examples/MDIntegrator/setup.py0000644000076600000240000000174312117632051020543 0ustar hinsenstaff00000000000000#!/usr/bin/env python from distutils.core import setup, Extension from Cython.Distutils import build_ext import numpy.distutils.misc_util import os, sys compile_args = ['-g', '-O0'] include_dirs = ['.', '../../Include'] from Scientific import N assert N.package == "NumPy" compile_args.append("-DNUMPY=1") include_dirs.extend(numpy.distutils.misc_util.get_numpy_include_dirs()) setup (name = "MMTK-VelocityVerlet", version = "2.7", description = "Velocity Verlet for the Molecular Modelling Toolkit", author = "Konrad Hinsen", author_email = "hinsen@cnrs-orleans.fr", url = "http://dirac.cnrs-orleans.fr/MMTK/", license = "CeCILL-C", ext_modules = [Extension('VelocityVerlet', ['./VelocityVerlet.pyx'], extra_compile_args = compile_args, include_dirs=include_dirs), ], cmdclass = {'build_ext': build_ext}, ) MMTK-2.7.9/Examples/MDIntegrator/test.py0000644000076600000240000000533411662461637020400 0ustar hinsenstaff00000000000000from MMTK import * from MMTK.Proteins import Protein from MMTK.ForceFields import Amber99ForceField from VelocityVerlet import VelocityVerletIntegrator from MMTK.Dynamics import Heater, TranslationRemover, RotationRemover from MMTK.Trajectory import Trajectory, TrajectoryOutput, \ RestartTrajectoryOutput, StandardLogOutput import time # Define system universe = InfiniteUniverse(Amber99ForceField(mod_files=['frcmod.ff99SB'])) universe.protein = Protein('bala1') # Initialize velocities universe.initializeVelocitiesToTemperature(50.*Units.K) print 'Temperature: ', universe.temperature() print 'Momentum: ', universe.momentum() print 'Angular momentum: ', universe.angularMomentum() # Create integrator integrator = VelocityVerletIntegrator(universe, delta_t=1.*Units.fs) # Heating and equilibration integrator(steps=1000, # Heat from 50 K to 300 K applying a temperature # change of 0.5 K/fs; scale velocities at every step. actions=[Heater(50.*Units.K, 300.*Units.K, 0.5*Units.K/Units.fs, 0, None, 1), # Remove global translation every 50 steps. TranslationRemover(0, None, 50), # Remove global rotation every 50 steps. RotationRemover(0, None, 50), # Log output to screen every 100 steps. StandardLogOutput(100)]) # "Production" run trajectory = Trajectory(universe, "bala1.nc", "w", "A simple test case") integrator(steps=100, # Remove global translation every 50 steps. actions = [TranslationRemover(0, None, 50), # Remove global rotation every 50 steps. RotationRemover(0, None, 50), # Write every second step to the trajectory file. TrajectoryOutput(trajectory, ("time", "energy", "configuration"), 0, None, 2), # Write restart data every fifth step. RestartTrajectoryOutput("restart.nc", 5), # Log output to screen every 10 steps. StandardLogOutput(10)]) trajectory.close() print "Starting background thread..." thread = integrator(steps = 10000, background = True, actions = [TranslationRemover(0, None, 50), RotationRemover(0, None, 50)]) print "Monitoring progress:" while thread.is_alive(): state = thread.copyState() if state is not None: print "CPU time:", time.clock() print "Simulation time:", state['time'] print "Potential energy:", state['potential_energy'] time.sleep(0.1) MMTK-2.7.9/Examples/MDIntegrator/VelocityVerlet.pyx0000644000076600000240000001604211662461637022567 0ustar hinsenstaff00000000000000# This module implements a Velocity Verlet integrator. # # Written by Konrad Hinsen # import numpy as N cimport numpy as N import cython cimport MMTK_trajectory_generator from MMTK import Units, ParticleProperties import MMTK_trajectory import MMTK_forcefield include "MMTK/python.pxi" include "MMTK/numeric.pxi" include "MMTK/core.pxi" include "MMTK/universe.pxi" include "MMTK/trajectory.pxi" include "MMTK/forcefield.pxi" # # Velocity Verlet integrator # cdef class VelocityVerletIntegrator(MMTK_trajectory_generator.EnergyBasedTrajectoryGenerator): """ Velocity-Verlet molecular dynamics integrator The integrator is fully thread-safe. The integration is started by calling the integrator object. All the keyword options (see documnentation of __init__) can be specified either when creating the integrator or when calling it. The following data categories and variables are available for output: - category "time": time - category "configuration": configuration and box size (for periodic universes) - category "velocities": atomic velocities - category "gradients": energy gradients for each atom - category "energy": potential and kinetic energy, plus extended-system energy terms if a thermostat and/or barostat are used """ def __init__(self, universe, **options): """ @param universe: the universe on which the integrator acts @type universe: L{MMTK.Universe} @keyword steps: the number of integration steps (default is 100) @type steps: C{int} @keyword delta_t: the time step (default is 1 fs) @type delta_t: C{float} @keyword actions: a list of actions to be executed periodically (default is none) @type actions: C{list} @keyword threads: the number of threads to use in energy evaluation (default set by MMTK_ENERGY_THREADS) @type threads: C{int} @keyword background: if True, the integration is executed as a separate thread (default: False) @type background: C{bool} """ MMTK_trajectory_generator.EnergyBasedTrajectoryGenerator.__init__( self, universe, options, "Velocity Verlet integrator") # Supported features: none for the moment, to keep it simple self.features = [] default_options = {'first_step': 0, 'steps': 100, 'delta_t': 1.*Units.fs, 'background': False, 'threads': None, 'actions': []} available_data = ['configuration', 'velocities', 'gradients', 'energy', 'time'] restart_data = ['configuration', 'velocities', 'energy'] # Cython compiler directives set for efficiency: # - No bound checks on index operations # - No support for negative indices # - Division uses C semantics @cython.boundscheck(False) @cython.wraparound(False) @cython.cdivision(True) cdef start(self): cdef N.ndarray[double, ndim=2] x, v, g, dv cdef N.ndarray[double, ndim=1] m cdef energy_data energy cdef double time, delta_t, ke cdef int natoms, nsteps, step cdef Py_ssize_t i, j, k # Check if velocities have been initialized if self.universe.velocities() is None: raise ValueError("no velocities") # Gather state variables and parameters configuration = self.universe.configuration() velocities = self.universe.velocities() gradients = ParticleProperties.ParticleVector(self.universe) masses = self.universe.masses() delta_t = self.getOption('delta_t') nsteps = self.getOption('steps') natoms = self.universe.numberOfAtoms() # For efficiency, the Cython code works at the array # level rather than at the ParticleProperty level. x = configuration.array v = velocities.array g = gradients.array m = masses.array dv = N.zeros((natoms, 3), N.float) # Ask for energy gradients to be calculated and stored in # the array g. Force constants are not requested. energy.gradients = g energy.gradient_fn = NULL energy.force_constants = NULL energy.fc_fn = NULL # Declare the variables accessible to trajectory actions. self.declareTrajectoryVariable_double( &time, "time", "Time: %lf\n", time_unit_name, PyTrajectory_Time) self.declareTrajectoryVariable_array( v, "velocities", "Velocities:\n", velocity_unit_name, PyTrajectory_Velocities) self.declareTrajectoryVariable_array( g, "gradients", "Energy gradients:\n", energy_gradient_unit_name, PyTrajectory_Gradients) self.declareTrajectoryVariable_double( &energy.energy,"potential_energy", "Potential energy: %lf\n", energy_unit_name, PyTrajectory_Energy) self.declareTrajectoryVariable_double( &ke, "kinetic_energy", "Kinetic energy: %lf\n", energy_unit_name, PyTrajectory_Energy) self.initializeTrajectoryActions() # Acquire the write lock of the universe. This is necessary to # make sure that the integrator's modifications to positions # and velocities are synchronized with other threads that # attempt to use or modify these same values. # # Note that the write lock will be released temporarily # for trajectory actions. It will also be converted to # a read lock temporarily for energy evaluation. This # is taken care of automatically by the respective methods # of class EnergyBasedTrajectoryGenerator. self.acquireWriteLock() # Preparation: Calculate initial half-step accelerations # and run the trajectory actions on the initial state. ke = 0. self.foldCoordinatesIntoBox() self.calculateEnergies(x, &energy, 0) for i in range(natoms): for j in range(3): dv[i, j] = -0.5*delta_t*g[i, j]/m[i] ke += 0.5*m[i]*v[i, j]*v[i, j] self.trajectoryActions(step) # Main integration loop time = 0. for step in range(nsteps): # First half-step for i in range(natoms): for j in range(3): v[i, j] += dv[i, j] x[i, j] += delta_t*v[i, j] # Mid-step energy calculation self.foldCoordinatesIntoBox() self.calculateEnergies(x, &energy, 1) # Second half-step ke = 0. for i in range(natoms): for j in range(3): dv[i, j] = -0.5*delta_t*g[i, j]/m[i] v[i, j] += dv[i, j] ke += 0.5*m[i]*v[i, j]*v[i, j] time += delta_t self.trajectoryActions(step) # Release the write lock. self.releaseWriteLock() # Finalize all trajectory actions (close files etc.) self.finalizeTrajectoryActions(nsteps) MMTK-2.7.9/Examples/Miscellaneous/0000755000076600000240000000000012156626564017307 5ustar hinsenstaff00000000000000MMTK-2.7.9/Examples/Miscellaneous/bad_contacts.py0000644000076600000240000000236311662461637022310 0ustar hinsenstaff00000000000000# Find bad contacts in a protein structure # # This script identifies that atoms in a protein structure # that have the highest forces acting on them after a few # steps of minimization. # # Note that there are no bad contacts in the example protein. # The gradient norm values printed by this script can thus be # used as a reference for what is normal. # from MMTK import * from MMTK.Proteins import Protein from MMTK.ForceFields import Amber99ForceField from MMTK.Minimization import SteepestDescentMinimizer from MMTK.Trajectory import StandardLogOutput # Construct system universe = InfiniteUniverse(Amber99ForceField()) universe.protein = Protein('bala1') # Minimize minimizer = SteepestDescentMinimizer(universe, actions=[StandardLogOutput(20)]) minimizer(convergence = 1.e-3, steps = 100) # Calculate gradients and their lengths energy, gradients = universe.energyAndGradients() gradient_lengths = gradients.length() # Make a list of all atoms sorted by decreasing gradient length atoms = sorted(universe.atomList(), key = lambda a: gradient_lengths[a], reverse = True) # Print the five atoms with the highest forces acting on them for a in atoms[:5]: print a, gradient_lengths[a] MMTK-2.7.9/Examples/Miscellaneous/ccpn_data_model.py0000644000076600000240000000102411662461637022751 0ustar hinsenstaff00000000000000# An example of the use of the CCPN Data Model interface. # Note that this interface is a first draft and thus likely to change! from MMTK.CCPNDataModel import CCPNMolSystemAdapter from memops.general.Io import loadXmlProjectFile project = loadXmlProjectFile(file="/Users/hinsen/Programs/CCPN/projects/Test.xml") mol_system = project.molSystems[0] adapter = CCPNMolSystemAdapter(mol_system) objects = adapter.makeAllMolecules() for o in objects: print o.numberOfAtoms() print o.atomsWithDefinedPositions().centerOfMass() MMTK-2.7.9/Examples/Miscellaneous/charge_fit.py0000644000076600000240000000250311662461637021753 0ustar hinsenstaff00000000000000# This program demonstrates how point charges can be fitted to # known values of the electrostatic potential energy surface. # In a real application, these values would come from an # ab-initio calculation. # from MMTK import * from MMTK.ChargeFit import ChargeFit, TotalChargeConstraint, \ evaluationPoints # Construct a small system of two atoms. a1 = Atom('C', position=Vector(-0.05,0.,0.)) a2 = Atom('C', position=Vector( 0.05,0.,0.)) system = Collection(a1, a2) # Define charges. Note that their sum is not zero. a1.charge = -0.75 a2.charge = 0.76 # Calculate the electrostatic potential energy of the two charges. # This is obviously unrealistic; in a real application, the potential # energy surface would not be due to known point charges, but come # from ab-initio calculations. points = [] for r in evaluationPoints(system, 50): p = 0. for atom in system.atomList(): p = p + atom.charge/(r-atom.position()).length() points.append((r, p*Units.electrostatic_energy)) # Define a charge constraint that ensures neutrality of the total system. constraints = [TotalChargeConstraint(system, 0.)] # Perform the fit and print the two fitted charges. They are not equal # to the input charges due to the neutrality constraint. fit = ChargeFit(system, points, constraints) print fit[a1], fit[a2] MMTK-2.7.9/Examples/Miscellaneous/construct_from_pdb.py0000644000076600000240000000112511662461637023553 0ustar hinsenstaff00000000000000# This example shows how a universe can be built from a PDB file in such # a way that all objects in the PDB file are represented as well as # possible, using AtomCluster objects when nothing more specific can # be constructed. # # This procedure is the only way to construct a universe that uses the # same internal atom order as the PDB file, which is important for # data exchange with other programs. from MMTK import * from MMTK.PDB import PDBConfiguration configuration = PDBConfiguration('some_file.pdb') universe = InfiniteUniverse() universe.addObject(configuration.createAll(None, 1)) MMTK-2.7.9/Examples/Miscellaneous/crystal.py0000644000076600000240000000500411662461637021340 0ustar hinsenstaff00000000000000# This example shows how to construct the unit cell and images of the # unit cell from the information in a PDB file. # # Note that this will not necessarily work with any PDB file. Many files # use non-crystallographic symmetry information in a non-standard way. # This is usually explained in REMARK records, but those cannot be # evaluated automatically. # from MMTK import * from MMTK.PDB import PDBConfiguration # A utility function that creates an image of an object by making # a copy and applying a transformation to the copy. def makeImage(object, transformation): image = deepcopy(object) for atom in image.atomList(): atom.setPosition(transformation(atom.position())) return image # Read PDB configuration and create MMTK objects for all peptide chains. # A C-alpha model is used to reduce the system size. You can remove # 'model="calpha"' to get an all-atom model, but for insulin this will # create more than 380000 atoms for the 27-unit-cell crystal! conf = PDBConfiguration('insulin.pdb') chains = Collection(conf.createPeptideChains(model="calpha")) # Apply non-crystallographic symmetries to construct the asymmetric unit asu = Collection(chains) for so in conf.ncs_transformations: if not so.given: image = makeImage(chains, so) asu.addObject(image) # Apply crystallographic symmetries to construct the unit cell # Note that the list of crystallographic symmetries includes the # identity transformation, so the unmodified asu is not added # to the unit cell. cell = Collection() for so in conf.cs_transformations: image = makeImage(asu, so) cell.addObject(image) # Translate the image such that its center of mass is # located inside the unit cell. cm = image.centerOfMass() cm_fract = conf.to_fractional(cm) cm_fract = Vector(cm_fract[0] % 1., cm_fract[1] % 1., cm_fract[2] % 1.) cm = conf.from_fractional(cm_fract) image.translateTo(cm) # Write the unit cell to a PDB file cell.writeToFile('insulin_unit_cell.pdb') # Construct the base vectors of the unit cell e1 = conf.from_fractional(Vector(1., 0., 0.)) e2 = conf.from_fractional(Vector(0., 1., 0.)) e3 = conf.from_fractional(Vector(0., 0., 1.)) # Add the 26 nearest neighbour images of the unit cell universe = InfiniteUniverse() for i in [-1, 0, 1]: for j in [-1, 0, 1]: for k in [-1, 0, 1]: image = deepcopy(cell) image.translateBy(i*e1+j*e2+k*e3) universe.addObject(image) # Write the crystal to a PDB file universe.writeToFile('insulin_crystal.pdb') MMTK-2.7.9/Examples/Miscellaneous/crystal_periodic.py0000644000076600000240000000265011662461637023222 0ustar hinsenstaff00000000000000# This example shows how to construct a periodic universe containing # the crystallographic unit cell from the information in a PDB file. # # Note that this will not necessarily work with any PDB file. Many files # use non-crystallographic symmetry information in a non-standard way. # This is usually explained in REMARK records, but those cannot be # evaluated automatically. # from MMTK import * from MMTK.PDB import PDBConfiguration from MMTK.Proteins import Protein # Read PDB configuration and create MMTK objects for all peptide chains. # A C-alpha model is used to reduce the system size. You can remove # 'model="calpha"' to get an all-atom model. conf = PDBConfiguration('insulin.pdb') chains = Collection(conf.createPeptideChains(model="calpha")) # Copy and transform the objects representing the asymmetric unit in order # to obtain the contents of the unit cell. chains = conf.asuToUnitCell(chains) # Construct a periodic universe representing the unit cell. universe = conf.createUnitCellUniverse() # Add each chain as one protein. If the unit cell contains multimers, # the chains must be combined into protein objects by hand, # as no corresponding information can be extracted from the PDB file. for chain in chains: universe.addObject(Protein(chain)) # If VMD has been defined as the PDB viewer, this will not only show # the molecules, but also the shape of the universe (which # equals the unit cell). universe.view() MMTK-2.7.9/Examples/Miscellaneous/force_field_parameters.py0000644000076600000240000000373111662461637024350 0ustar hinsenstaff00000000000000# Obtain the force field parameters for a given universe. # from MMTK import * from MMTK.Proteins import Protein from MMTK.ForceFields import Amber99ForceField # Define system universe = InfiniteUniverse(Amber99ForceField()) universe.protein = Protein('bala1') # Get energy term parameters parameters = universe.energyEvaluatorParameters() documentation = { "nonbonded": """ excluded_pairs and one_four_pairs: lists of pairs of atom indices atom_subset: list of atom indices, or None for 'all atoms' """, "lennard_jones": """ type: array of atom type numbers epsilon_sigma: 3D array. The first two indices are the atom type numbers of the two atoms, the third index is 0 for epsilon and 1 for sigma type_names: the atom type names from the force field (for documentation) """, "electrostatic": """ charge: array of partial charges """, "harmonic_distance_term": """ a list of tuples (atom_index1, atom_index2, length, force_constant) """, "harmonic_angle_term": """ a list of tuples (atom_index1, atom_index2, atom_index3, angle, force_constant) """, "cosine_dihedral_term": """ a list of tuples (atom_index1, atom_index2, atom_index3, atom_index4, multiplicity, angle, force_constant) """, } # Print the parameters in a readable format for p_type in parameters: p = parameters[p_type] print p_type print len(p_type)*'-' print documentation[p_type] if isinstance(p, list): print "[" for data in p: print " ", data, "," print " ]" elif isinstance(p, dict): print "{" for key, value in p.items(): s = repr(value) if len(key) + len(s) < 75: print " %s: %s," % (key, value) else: print " %s:" % key print " %s," % value print "}" else: print p print MMTK-2.7.9/Examples/Miscellaneous/lattice.py0000644000076600000240000000160011662461637021302 0ustar hinsenstaff00000000000000# Example: water molecules on a lattice # # This example creates a system of water molecules whose centers # of mass are positioned simple cubic lattice consisting of # 3x4x5 cells, i.e. there are 60 water molecules in total. # The molecules are put into a suitably sized periodic universe. # from MMTK import * from Scientific.Geometry.Objects3D import SCLattice from MMTK.Visualization import view # Define parameters of the lattice edge_length = 0.5*Units.nm lattice_size = (3, 4, 5) # Construct the universe universe = OrthorhombicPeriodicUniverse((edge_length*lattice_size[0], edge_length*lattice_size[1], edge_length*lattice_size[2])) # Add the water molecules for point in SCLattice(edge_length, lattice_size): universe.addObject(Molecule('water', position = point)) # Visualization view(universe) MMTK-2.7.9/Examples/Miscellaneous/molecule_factory.py0000644000076600000240000000444611662461637023224 0ustar hinsenstaff00000000000000# Create molecules from scratch # # This example shows how a molecular system (SPCE water) can be set up using # molecule factories instead of the molecular database. # from MMTK import * from MMTK.MoleculeFactory import MoleculeFactory from MMTK.ForceFields import SPCEForceField # Create a new molecule factory. factory = MoleculeFactory() # Create the empty molecule. There is no distinction between groups # and molecules at this level. Everything is a building block. factory.createGroup('water') # Add the atoms. factory.addAtom('water', 'O', 'O') factory.addAtom('water', 'H1', 'H') factory.addAtom('water', 'H2', 'H') # Add the bonds. factory.addBond('water', 'O', 'H1') factory.addBond('water', 'O', 'H2') # Define the atom positions factory.setPosition('water', 'O', Vector(0., 0., 0.00655616814675)) factory.setPosition('water', 'H1', Vector(-0.0756950327264, 0., -0.0520320595151)) factory.setPosition('water', 'H2', Vector(0.0756950327264, 0., -0.0520320595151)) # Define atom types and charges for the SPCE force field. factory.setAttribute('water', 'O.spce_atom_type', 'O') factory.setAttribute('water', 'H1.spce_atom_type', 'H') factory.setAttribute('water', 'H2.spce_atom_type', 'H') factory.setAttribute('water', 'O.spce_charge', -0.8476) factory.setAttribute('water', 'H1.spce_charge', 0.4238) factory.setAttribute('water', 'H2.spce_charge', 0.4238) # Define the PDB atom name map factory.setAttribute('water', 'pdbmap', [('HOH', {'O': factory.getAtomReference('water', 'O'), 'H1': factory.getAtomReference('water', 'H1'), 'H2': factory.getAtomReference('water', 'H2')})]) # Create the universe and add water molecules. universe = OrthorhombicPeriodicUniverse((5., 5., 5.), SPCEForceField()) universe.addObject(factory.retrieveMolecule('water')) universe.addObject(factory.retrieveMolecule('water')) universe[0].translateBy(Vector(1., 0., 0.)) universe[1].translateBy(Vector(0., 1., 0.)) # Print energy terms print universe.energyTerms() # Write the factory to an XML file factory.writeXML(file('water_factory.xml', 'w')) # Write the universe to an XML file universe.writeXML(file('water_universe.xml', 'w')) # Write the universe to a PDB file universe.writeToFile('water_universe.pdb') MMTK-2.7.9/Examples/Miscellaneous/read_xml.py0000644000076600000240000000041711662461637021455 0ustar hinsenstaff00000000000000# Read a universe from an XML file produced by the writeXML() method. # # The file used here is produced by running the example molecule_factory.py # from MMTK.XML import XMLMoleculeFactory factory = XMLMoleculeFactory('water_universe.xml') universe = factory.universe MMTK-2.7.9/Examples/Miscellaneous/thermal_fluctuations.py0000644000076600000240000000302011662461637024107 0ustar hinsenstaff00000000000000# Retrieve atomic fluctuation information from a PDBConfiguration object. # from MMTK import * from MMTK.PDB import PDBConfiguration from Scientific import N # Read a PDB file containing ANISOU records. conf = PDBConfiguration('1G66.pdb') # By passing the applyTo methods of MMTK.PDB a dictionary argument # atom_map, one obtains a dictionary from MMTK atom objects to the # corresponding PDB atom objects. This dictionary can be used to # retrieve additional atom data from the PDB file. atom_map = {} for c in conf.peptide_chains: # Create a PeptideChain object chain = c.createPeptideChain() # Retrieve the atom_map dictionary # Note: this also redefines the configuration, but in this # application that makes no difference since it is the same # that was defined in the previous line. c.applyTo(chain, atom_map=atom_map) # Print the B factor and the trace of the anisotropic displacement # tensor for each atom for which both are available. They should # be equal, but due to the limited precision in PDB files # there can be small differences. for atom in chain.atomList(): try: pdb_atom = atom_map[atom] except KeyError: print atom, " not in PDB file" continue b_factor = pdb_atom.properties['temperature_factor'] * Units.Ang**2 try: u_trace = pdb_atom.properties['u'].trace()/3. * Units.Ang**2 except KeyError: u_trace = "[no ANISOU record]" print atom, b_factor/(8*N.pi**2), u_trace MMTK-2.7.9/Examples/Miscellaneous/two_models.py0000644000076600000240000000172211662461637022036 0ustar hinsenstaff00000000000000# Set up an all-atom model and a C-alpha model for the same protein, # with a facility for copying configurations back and forth. # from MMTK import * from MMTK.Proteins import Protein # Create an all-atom universe and a c-alpha universe universe_aa = InfiniteUniverse() universe_ca = InfiniteUniverse() # Add the protein to both universes protein_aa = Protein('insulin.pdb') universe_aa.addObject(protein_aa) protein_ca = Protein('insulin.pdb', model='calpha') universe_ca.addObject(protein_ca) # Set up a dictionary mapping aa atoms to ca atoms atom_map = {} for ichain in range(len(protein_aa)): chain_aa = protein_aa[ichain] chain_ca = protein_ca[ichain] for iresidue in range(len(chain_aa)): ca_aa = chain_aa[iresidue].peptide.C_alpha ca_ca = chain_ca[iresidue].peptide.C_alpha atom_map[ca_aa] = ca_ca # Copy the aa configuration to the ca configuration for ca_aa, ca_ca in atom_map.items(): ca_ca.setPosition(ca_aa.position()) MMTK-2.7.9/Examples/Miscellaneous/vector_field.py0000644000076600000240000000332011662461637022323 0ustar hinsenstaff00000000000000# Example: Vector field analysis of the slowest normal mode of insulin. # # This example shows the use of vector fields in the analysis and # visualization of collective motions. For a more elaborate application, # see # A. Thomas, K. Hinsen, M.J. Field, D. Perahia # Tertiary and quaternary confrmational changes in aspartate # transcarbamylase: a normal mode study. # Proteins, 34(1), 96-112 (1999) # from MMTK import * from MMTK.Proteins import Protein from MMTK.ForceFields import DeformationForceField from MMTK.NormalModes import NormalModes from MMTK.Field import AtomicVectorField from Scientific.Visualization import VRML2 as VRML # Construct system universe = InfiniteUniverse(DeformationForceField()) universe.protein = Protein('insulin.pdb', model='calpha') # Calculate normal modes modes = NormalModes(universe) # Create the vector field corresponding to the first non-zero mode field = AtomicVectorField(universe, 0.5, modes[6]) # Calculate the curl of the vector field. The curl indicated the # local rotational motion; the direction of the curl vector is # the rotation axis, and its length is proportional to the amount # of the rotation. curl = field.curl() # Create graphics objects for the protein and the vector fields graphics = universe.graphicsObjects(model='backbone', graphics_module=VRML) + \ field.graphicsObjects(color='yellow', scale=80., range=(0., 0.01), graphics_module=VRML) + \ curl.graphicsObjects(color='red', scale=80., range=(0., 0.01), graphics_module=VRML) # Show graphics in a VRML browser VRML.Scene(graphics).view() MMTK-2.7.9/Examples/MolecularDynamics/0000755000076600000240000000000012156626564020117 5ustar hinsenstaff00000000000000MMTK-2.7.9/Examples/MolecularDynamics/argon.conf.gz0000644000076600000240000004333711662461637022524 0ustar hinsenstaff00000000000000‹7Ì6argon.conf]ë±ëÊn„ÿß(N[5ïGÎ?%÷-‰ 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ÒûÅÖX—q‘t.þØ(B#±S\E[%°ÙÕ%k‰¿y–•èƒ>–„² 5­ï ¡½•Ûãä&Rv%z#=R¦¨y¢I æÊsñŽ)R5õÆŒ³u|Rƒr€Mz7µÈßôzr,‰¢ó¶éIdÛIÂÖZÒ¿è‹$Ž+†m…š×%…ÌГ´0×r,ϸ28W0S¡²‹´WæÔ1âw5²ú¬#¥:õÜnÞ_pf?å÷ßZŒ…hËæ”·)\o'N¢vƒÑèn ¥ãqå JC¾ÔêéLJéëç±ÉSŽѵ‰NíI£¡fvº~ž‹ f–WbïlŒñ®=öBrÙw~«Ö­îwž3Tš§£©ÃÓÐæÂz:v3ëÉ© Gw]~€…m¡æ㇅Ie¹:2&‡îõb%d~­£Í$S }4õ­NŠbïÞdwXÑßmĬ™)ö†cþ^íÿuÚ¶ øÍMMTK-2.7.9/Examples/MolecularDynamics/argon.py0000644000076600000240000000504011662461637021575 0ustar hinsenstaff00000000000000# Constant-temperature constant-pressure MD simulation of Argon. # from MMTK import * from MMTK.ForceFields import LennardJonesForceField from MMTK.Environment import NoseThermostat, AndersenBarostat from MMTK.Trajectory import Trajectory, TrajectoryOutput, LogOutput from MMTK.Dynamics import VelocityVerletIntegrator, VelocityScaler, \ TranslationRemover, BarostatReset import string from Scientific.IO.TextFile import TextFile # Open the configuration file and read the box size. conf_file = TextFile('argon.conf.gz') lx, ly, lz = map(string.atof, string.split(conf_file.readline())) # Construct a periodic universe using a Lennard-Jones (noble gas) force field # with a cutoff of 15 Angstrom. universe = OrthorhombicPeriodicUniverse((lx*Units.Ang, ly*Units.Ang, lz*Units.Ang), LennardJonesForceField(15.*Units.Ang)) # Read the atom positions and construct the atoms. while 1: line = conf_file.readline() if not line: break x, y, z = map(string.atof, string.split(line)) universe.addObject(Atom('Ar', position=Vector(x*Units.Ang, y*Units.Ang, z*Units.Ang))) # Define thermodynamic parameters. temperature = 94.4*Units.K pressure = 1.*Units.atm # Add thermostat and barostat. universe.thermostat = NoseThermostat(temperature) universe.barostat = AndersenBarostat(pressure) # Initialize velocities. universe.initializeVelocitiesToTemperature(temperature) # Create trajectory and integrator. trajectory = Trajectory(universe, "argon_npt.nc", "w", "Argon NPT test") integrator = VelocityVerletIntegrator(universe, delta_t=10*Units.fs) # Periodical actions for trajectory output and text log output. output_actions = [TrajectoryOutput(trajectory, ('configuration', 'energy', 'thermodynamic', 'time', 'auxiliary'), 0, None, 20), LogOutput("argon.log", ('time', 'energy'), 0, None, 100)] # Do some equilibration steps, rescaling velocities and resetting the # barostat in regular intervals. integrator(steps = 2000, actions = [TranslationRemover(0, None, 100), VelocityScaler(temperature, 0.1*temperature, 0, None, 100), BarostatReset(100)] + output_actions) # Do some "production" steps. integrator(steps = 2000, actions = [TranslationRemover(0, None, 100)] + output_actions) # Close the trajectory. trajectory.close() MMTK-2.7.9/Examples/MolecularDynamics/md_in_python.py0000644000076600000240000000476611662461637023174 0ustar hinsenstaff00000000000000# A Velocity-Verlet integrator implemented in Python. # Use this as a starting point for modified integrators. # from MMTK import * from MMTK.Proteins import Protein from MMTK.ForceFields import Amber99ForceField from MMTK.Trajectory import Trajectory, TrajectoryOutput, SnapshotGenerator # Velocity Verlet integrator in Python def doVelocityVerletSteps(delta_t, nsteps, equilibration_temperature = None, equilibration_frequency = 1): configuration = universe.configuration() velocities = universe.velocities() gradients = ParticleVector(universe) inv_masses = 1./universe.masses() evaluator = universe.energyEvaluator() energy, gradients = evaluator(gradients) dv = -0.5*delta_t*gradients*inv_masses time = 0. snapshot(data={'time': time, 'potential_energy': energy}) for step in range(nsteps): velocities += dv configuration += delta_t*velocities universe.setConfiguration(configuration) energy, gradients = evaluator(gradients) dv = -0.5*delta_t*gradients*inv_masses velocities += dv universe.setVelocities(velocities) time += delta_t snapshot(data={'time': time, 'potential_energy': energy}) if equilibration_temperature is not None \ and step % equilibration_frequency == 0: universe.scaleVelocitiesToTemperature(equilibration_temperature) # Define system universe = InfiniteUniverse(Amber99ForceField()) universe.protein = Protein('bala1') # Create trajectory and snapshot generator trajectory = Trajectory(universe, "md_trajectory.nc", "w", "Generated by a Python integrator") snapshot = SnapshotGenerator(universe, actions = [TrajectoryOutput(trajectory, ["all"], 0, None, 1)]) # Initialize velocities universe.initializeVelocitiesToTemperature(50.*Units.K) # Heat and equilibrate for temperature in [50., 100., 200., 300.]: doVelocityVerletSteps(delta_t = 1.*Units.fs, nsteps = 500, equilibration_temperature = temperature*Units.K, equilibration_frequency = 1) doVelocityVerletSteps(delta_t = 1.*Units.fs, nsteps = 500, equilibration_temperature = 300*Units.K, equilibration_frequency = 10) # Production run doVelocityVerletSteps(delta_t = 1.*Units.fs, nsteps = 5000) trajectory.close() MMTK-2.7.9/Examples/MolecularDynamics/protein.py0000644000076600000240000000556211662461637022160 0ustar hinsenstaff00000000000000# A simple Molecular Dynamics simulation. # from MMTK import * from MMTK.Proteins import Protein from MMTK.ForceFields import Amber99ForceField from MMTK.Dynamics import VelocityVerletIntegrator, Heater, \ TranslationRemover, RotationRemover from MMTK.Visualization import view from MMTK.Trajectory import Trajectory, TrajectoryOutput, \ RestartTrajectoryOutput, StandardLogOutput, \ trajectoryInfo # Define system universe = InfiniteUniverse(Amber99ForceField(mod_files=['frcmod.ff99SB'])) universe.protein = Protein('bala1') # Initialize velocities universe.initializeVelocitiesToTemperature(50.*Units.K) print 'Temperature: ', universe.temperature() print 'Momentum: ', universe.momentum() print 'Angular momentum: ', universe.angularMomentum() # Create integrator integrator = VelocityVerletIntegrator(universe, delta_t=1.*Units.fs) # Heating and equilibration integrator(steps=1000, # Heat from 50 K to 300 K applying a temperature # change of 0.5 K/fs; scale velocities at every step. actions=[Heater(50.*Units.K, 300.*Units.K, 0.5*Units.K/Units.fs, 0, None, 1), # Remove global translation every 50 steps. TranslationRemover(0, None, 50), # Remove global rotation every 50 steps. RotationRemover(0, None, 50), # Log output to screen every 100 steps. StandardLogOutput(100)]) # "Production" run trajectory = Trajectory(universe, "bala1.nc", "w", "A simple test case") integrator(steps=100, # Remove global translation every 50 steps. actions = [TranslationRemover(0, None, 50), # Remove global rotation every 50 steps. RotationRemover(0, None, 50), # Write every second step to the trajectory file. TrajectoryOutput(trajectory, ("time", "energy", "thermodynamic", "configuration"), 0, None, 2), # Write restart data every fifth step. RestartTrajectoryOutput("restart.nc", 5), # Log output to screen every 10 steps. StandardLogOutput(10)]) trajectory.close() # Print information about the trajectory file print "Information about the trajectory file 'bala1.nc':" print trajectoryInfo('bala1.nc') # Reopen trajectory file trajectory = Trajectory(universe, "bala1.nc", "r") # Read step 10 and display configuration step10 = trajectory[10] view(universe, step10['configuration']) # Print the kinetic energy along the trajectory print "Kinetic energy along trajectory:" print trajectory.kinetic_energy trajectory.close() MMTK-2.7.9/Examples/MolecularDynamics/restart.py0000644000076600000240000000441311662461637022156 0ustar hinsenstaff00000000000000# Continuation of the simulation from protein.py using the restart # trajectory. # # Note: Restart trajectories don't differ much from ordinary # trajectories; they have a fixed number of steps that are reused # cyclically, but otherwise they are plain trajectory files. # As a consequence, you can restart a molecular dynamics run # from any trajectory that contains positions and velocities, # with only a minor modification mentioned below. # from MMTK import * from MMTK.Proteins import Protein from MMTK.ForceFields import Amber94ForceField from MMTK.Dynamics import VelocityVerletIntegrator, Heater, \ TranslationRemover, RotationRemover from MMTK.Visualization import view from MMTK.Trajectory import Trajectory, TrajectoryOutput, \ RestartTrajectoryOutput, StandardLogOutput, \ trajectoryInfo # Define system universe = InfiniteUniverse(Amber94ForceField()) universe.protein = Protein('bala1') # Initialize system state from the restart trajectory universe.setFromTrajectory(Trajectory(universe, "restart.nc")) # Note: for restarting from a standard trajectory, use: # # universe.setFromTrajectory(Trajectory(universe, "trajectory.nc"), -1) # # in order to load the last step (default would be the first one). # Create integrator integrator = VelocityVerletIntegrator(universe, delta_t=1.*Units.fs) # Do some more MD steps. trajectory = Trajectory(universe, "bala1.nc", "a") integrator(steps=100, # Remove global translation every 50 steps. actions = [TranslationRemover(0, None, 50), # Remove global rotation every 50 steps. RotationRemover(0, None, 50), # Write every second step to the trajectory file. TrajectoryOutput(trajectory, ("time", "energy", "thermodynamic", "configuration"), 0, None, 2), # Write restart data every fifth step. RestartTrajectoryOutput("restart.nc", 5), # Log output to screen every 10 steps. StandardLogOutput(10)]) trajectory.close() MMTK-2.7.9/Examples/MolecularDynamics/solvation.py0000644000076600000240000000764211662461637022517 0ustar hinsenstaff00000000000000# Solvation of a protein with water. # # The solvation procedure consists of three steps: # # - The universe containing the protein is scaled up, and the required # number of water molecules is added at random positions, but without # any overlap between molecules. The system is so dilute that random # placements are easily possible. # # - The universe is slowly scaled down do its original size, with # each scaling step followed by some energy minimization and # molecular dynamics steps. # # - A molecular dynamics run at constant pressure and temperature is # used to put the system into a well-defined thermodynamic state. # from MMTK import * from MMTK.Proteins import Protein from MMTK.ForceFields import Amber94ForceField from MMTK.Environment import NoseThermostat, AndersenBarostat from MMTK.Trajectory import Trajectory, TrajectoryOutput, LogOutput from MMTK.Dynamics import VelocityVerletIntegrator, VelocityScaler, \ BarostatReset, TranslationRemover import MMTK.Solvation # Create the solute. protein = Protein('bala1') # Put the solvent in a standard configuration: center of mass at the # coordinate origin, principal axes of inertia parallel to the coordinate axes. protein.normalizeConfiguration() # Define density, pressure, and temperature of the solvent. water_density = 1.*Units.g/Units.cm**3 temperature = 300.*Units.K pressure = 1.*Units.atm # Calculate the box size as the boundary box of the protein plus an # offset. Note: a much larger offset should be used in real applications. box = protein.boundingBox() box = box[1]-box[0]+Vector(0.5, 0.5, 0.5) # Create a periodic universe. The force field is intentionally created with # a rather small cutoff to speed up the solvation process. universe = OrthorhombicPeriodicUniverse(tuple(box), Amber94ForceField(1., 1.)) universe.protein = protein # Find the number of solvent molecules. print MMTK.Solvation.numberOfSolventMolecules(universe,'water',water_density),\ "water molecules will be added" # Scale up the universe and add the solvent molecules. MMTK.Solvation.addSolvent(universe, 'water', water_density) print "Solvent molecules have been added, now shrinking universe..." # Shrink the universe back to its original size, thereby compressing # the solvent to its real density. MMTK.Solvation.shrinkUniverse(universe, temperature, 'solvation.nc') print "Universe has been compressed, now equilibrating..." # Set a better force field and add thermostat and barostat. # # Note: For efficiency, optimized Ewald parameters should be used # in a real application. The barostat relaxation time must be # adjusted to the system size; it should be chosen smaller than # for a realistic simulation in order to reach the final # volume faster. universe.setForceField(Amber94ForceField(1.4, {'method': 'ewald'})) universe.thermostat = NoseThermostat(temperature) universe.barostat = AndersenBarostat(pressure, 0.1*Units.ps) # Create an integrator and a trajectory. integrator = VelocityVerletIntegrator(universe, delta_t=1.*Units.fs) trajectory = Trajectory(universe, "equilibration.nc", "w", "Equilibration (NPT ensemble)") # Start an NPT integration with periodic rescaling of velocities # and resetting of the barostat. The number of steps required to # reach a stable volume depends strongly on the system! output_actions = [TrajectoryOutput(trajectory, ('configuration', 'energy', 'thermodynamic', 'time', 'auxiliary'), 0, None, 100), LogOutput("equilibration.log", ('time', 'energy'), 0, None, 100)] integrator(steps = 1000, actions = [TranslationRemover(0, None, 200), BarostatReset(0, None, 20), VelocityScaler(temperature, 0., 0, None, 20)] + output_actions) # Close the equilibration trajectory trajectory.close() MMTK-2.7.9/Examples/MolecularDynamics/solvent.py0000644000076600000240000000612111662461637022162 0ustar hinsenstaff00000000000000# Example: Create a cubic water box # # This example requires only trivial modification to perform solvatation # of another molecule. Simply put the molecule into the universe before # the water molecule is added, and put its bounding sphere on the list # of excluded regions. And don't forget to calculate the final box # size (real_size) correctly. # from MMTK import * from MMTK.ForceFields import Amber94ForceField from MMTK.Trajectory import Trajectory, TrajectoryOutput from MMTK.Minimization import SteepestDescentMinimizer from MMTK.Dynamics import VelocityVerletIntegrator, VelocityScaler, \ TranslationRemover from MMTK.Random import randomPointInBox # Set the number of molecules and the temperature n_molecules = 20 temperature = 300.*Units.K # Calculate the size of the box density = 1.*Units.g/(Units.cm)**3 number_density = density/Molecule('water').mass() real_size = (n_molecules/number_density)**(1./3.) # Create the universe with a larger initial size current_size = 5.*real_size world = CubicPeriodicUniverse(current_size, Amber94ForceField(1.2*Units.nm, {'method': 'ewald'})) # Add solvent molecules at random positions, avoiding the neighbourhood # of previously placed molecules excluded_regions = [] for i in range(n_molecules): m = Molecule('water', position = randomPointInBox(current_size)) while 1: s = m.boundingSphere() collision = 0 for region in excluded_regions: if s.intersectWith(region) is not None: collision = 1 break if not collision: break m.translateTo(randomPointInBox(current_size)) world.addObject(m) excluded_regions.append(s) # Reduce energy minimizer = SteepestDescentMinimizer(world, step_size = 0.05*Units.Ang) minimizer(steps = 100) # Set velocities and equilibrate for a while world.initializeVelocitiesToTemperature(temperature) integrator = VelocityVerletIntegrator(world, actions=[VelocityScaler(300., 10.), TranslationRemover()]) integrator(steps = 200) # Scale down the system in small steps while current_size > real_size: scale_factor = max(0.95, real_size/current_size) for object in world: object.translateTo(scale_factor*object.position()) current_size = scale_factor*current_size world.setSize(current_size) print 'Current size: ', current_size stdout.flush() minimizer(steps = 100) integrator(steps = 200) save(world, 'water'+`n_molecules`+'.intermediate.setup') # Final equilibration trajectory = Trajectory(world, 'water.nc', 'w', 'Final equilibration') integrator(steps = 1000, actions = [TrajectoryOutput(trajectory, ("time", "energy", "thermodynamic", "configuration"), 0, None, 10)]) trajectory.close() # Save final system save(world, 'water'+`n_molecules`+'.setup') MMTK-2.7.9/Examples/MonteCarlo/0000755000076600000240000000000012156626564016547 5ustar hinsenstaff00000000000000MMTK-2.7.9/Examples/MonteCarlo/backbone.py0000644000076600000240000000362411662461637020671 0ustar hinsenstaff00000000000000# Generate an ensemble of backbone configurations for a protein using # a simplified protein model which consists only of the C_alpha atoms. # The ensemble is written to a trajectory file. # from MMTK import * from MMTK.Proteins import Protein from MMTK.ForceFields import CalphaForceField from MMTK.NormalModes import NormalModes from MMTK.Random import gaussian from MMTK.Trajectory import Trajectory, SnapshotGenerator, TrajectoryOutput # Construct the system. The scaling factor of 0.1 for the CalphaForceField # was determined empirically for a C-phycocyanin dimer at 300 K; its value # is expected to depend at least on the protein and the temperature, # but the order of magnitude should be right for mid-sized proteins. universe = InfiniteUniverse(CalphaForceField(2.5, 0.1)) universe.protein = Protein('insulin.pdb', model='calpha') # Set all masses to the same value, since we don't want # mass-weighted sampling. for atom in universe.atomList(): atom.setMass(1.) # Calculate normal modes. Note that the normal modes are automatically # scaled to their vibrational amplitudes at a given temperature. modes = NormalModes(universe, 300.*Units.K) # Create trajectory trajectory = Trajectory(universe, "insulin_backbone.nc", "w", "Monte-Carlo sampling for insulin backbone") # Create the snapshot generator snapshot = SnapshotGenerator(universe, actions = [TrajectoryOutput(trajectory, ["all"], 0, None, 1)]) # Generate an ensemble of 100 configurations. The scaling factor for # each mode is half the vibrational amplitude, which explains the # factor 0.5. minimum = copy(universe.configuration()) for i in range(100): conf = minimum for j in range(6, len(modes)): conf = conf + gaussian(0., 0.5)*modes[j] universe.setConfiguration(conf) snapshot() # Close trajectory trajectory.close() MMTK-2.7.9/Examples/MPI/0000755000076600000240000000000012156626564015131 5ustar hinsenstaff00000000000000MMTK-2.7.9/Examples/MPI/md.py0000644000076600000240000000677611662461637016122 0ustar hinsenstaff00000000000000# A simple Molecular Dynamics simulation using MPI. # # Note: the system simulated here is much too small to profit from # parallelization, and multiple processors will actually slow down the # simulation. This program is meant to be an illustration, not a # real-life application. # # This example does exactly the same as the one in # MolecularDynamics/protein.py. Compare the two to see what must be # changed to use MPI parallelization. # from MMTK import * from MMTK.Proteins import Protein from MMTK.ForceFields import Amber94ForceField from MMTK.Dynamics import VelocityVerletIntegrator, Heater, \ TranslationRemover, RotationRemover from MMTK.Visualization import view from MMTK.Trajectory import Trajectory, TrajectoryOutput, \ RestartTrajectoryOutput, StandardLogOutput, \ trajectoryInfo from Scientific.MPI import world # Define the system and write it to a file, on one processor only. if world.rank == 0: universe = InfiniteUniverse(Amber94ForceField()) universe.protein = Protein('bala1') universe.initializeVelocitiesToTemperature(50.*Units.K) save(universe, 'md_mpi.setup') # Synchronize and load the universe definition on all processors. world.barrier() universe = load('md_mpi.setup') # Note: Creating the universe in parallel on all processors is possible # if the construction is purely deterministic, but with the assignment # of random velocities, it cannot be guaranteed that all processors will # end up with the same system state. # Create integrator, specifying the MPI communicator to be used # in energy evaluations integrator = VelocityVerletIntegrator(universe, delta_t=1.*Units.fs, mpi_communicator = world) # Define output actions for one processor only if world.rank == 0: # Log output to console every 100 steps for processor 0. output_actions = [StandardLogOutput(100)] else: # Nothing for the other processors output_actions = [] # Heating and equilibration integrator(steps=1000, # Heat from 50 K to 300 K applying a temperature # change of 0.5 K/fs; scale velocities at every step. actions=[Heater(50.*Units.K, 300.*Units.K, 0.5*Units.K/Units.fs, 0, None, 1), # Remove global translation every 50 steps. TranslationRemover(0, None, 50), # Remove global rotation every 50 steps. RotationRemover(0, None, 50)] + output_actions) # Synchronize world.barrier() # Define output actions for one processor only if world.rank == 0: trajectory = Trajectory(universe, "bala1.nc", "w", "A simple test case") # Trajectory, restart, and log output for processor 0. output_actions = [RestartTrajectoryOutput("restart.nc", 5), TrajectoryOutput(trajectory, ("time", "energy", "thermodynamic", "configuration"), 0, None, 2), StandardLogOutput(10)] else: # Nothing for the other processors. output_actions = [] # "Production" run integrator(steps=100, # Remove global translation every 50 steps. actions = [TranslationRemover(0, None, 50), # Remove global rotation every 50 steps. RotationRemover(0, None, 50)] + output_actions) # Close trajectory if world.rank == 0: trajectory.close() MMTK-2.7.9/Examples/NormalModes/0000755000076600000240000000000012156626564016724 5ustar hinsenstaff00000000000000MMTK-2.7.9/Examples/NormalModes/brownian_modes.py0000644000076600000240000000217311662461637022306 0ustar hinsenstaff00000000000000# This example shows how to calculate Brownian modes # for big proteins. For a description of the techniques, see # # K. Hinsen, A.J. Petrescu, S. Dellerue, M.C. Bellissent-Funel, G.R. Kneller # Harmonicity in slow protein dynamics # Chem. Phys. 261, 25 (2000) # from MMTK import * from MMTK.Proteins import Protein from MMTK.ForceFields import CalphaForceField from MMTK.ProteinFriction import calphaFrictionConstants from MMTK.NormalModes import BrownianModes # Construct system universe = InfiniteUniverse(CalphaForceField(2.5)) universe.protein = Protein('insulin.pdb', model='calpha') # Calculate Brownian modes modes = BrownianModes(universe, calphaFrictionConstants(universe.protein), 300.*Units.K) # Print relaxation times for the slowest ten modes # # Note: realistic time scales can be expected only if the potential # has been scaled by a protein-specific factor such that the fluctuation # amplitudes are approximated well. That factor must in practice be # obtained from experiment. See the publication cited above for details. # for i in range(6, 16): print i, 1./modes[i].inv_relaxation_time MMTK-2.7.9/Examples/NormalModes/calpha_modes.py0000644000076600000240000000313511662461637021716 0ustar hinsenstaff00000000000000# This example shows how to calculate approximate low-frequency # modes for big proteins. For a description of the techniques, # see # # K. Hinsen # Analysis of domain motions by approximate normal mode calculations # Proteins 33, 417 (1998) # # and # # K. Hinsen, A.J. Petrescu, S. Dellerue, M.C. Bellissent-Funel, G.R. Kneller # Harmonicity in slow protein dynamics # Chem. Phys. 261, 25 (2000) # from MMTK import * from MMTK.Proteins import Protein from MMTK.ForceFields import CalphaForceField from MMTK.FourierBasis import FourierBasis, estimateCutoff from MMTK.NormalModes import EnergeticModes from MMTK.Visualization import view import pylab # Construct system universe = InfiniteUniverse(CalphaForceField(2.5)) universe.protein = Protein('insulin.pdb', model='calpha') # Find a reasonable basis set size and cutoff nbasis = max(10, universe.numberOfAtoms()/5) cutoff, nbasis = estimateCutoff(universe, nbasis) print "Calculating %d low-frequency modes." % nbasis if cutoff is None: # Do full normal mode calculation modes = EnergeticModes(universe, 300.*Units.K) else: # Do subspace mode calculation with Fourier basis subspace = FourierBasis(universe, cutoff) modes = EnergeticModes(universe, 300.*Units.K, subspace) # Plot the atomic fluctuations in the first three non-zero modes # for chain A chain = universe.protein[0] pylab.xticks(range(len(chain)), [r.name for r in chain]) for i in range(6, 9): f = modes[i]*modes[i] pylab.plot([f[residue.peptide.C_alpha] for residue in chain]) # Show animation of the first non-trivial mode view(modes[6], 15.) MMTK-2.7.9/Examples/NormalModes/conformational_change_analysis.py0000644000076600000240000000336411662461637025526 0ustar hinsenstaff00000000000000# Analyze a conformational change in terms of the contributions of # each normal mode. # # Note: download 1SU4 and 1T5T from the PDB before running this script from MMTK import * from MMTK.PDB import PDBConfiguration from MMTK.Proteins import Protein from MMTK.ForceFields import CalphaForceField from MMTK.NormalModes import EnergeticModes import pylab import numpy # Make a universe for the first configuration universe = InfiniteUniverse(CalphaForceField()) universe.protein = Protein('1SU4.pdb', model='calpha') conf_1SU4 = universe.copyConfiguration() # Apply the second configuration and do a rigid-body superposition fit PDBConfiguration('1T5T.pdb').applyTo(universe.protein) tr, rms = universe.findTransformation(conf_1SU4) universe.applyTransformation(tr) conf_1T5T = universe.copyConfiguration() # Set first configuration and calculate normal modes universe.setConfiguration(conf_1SU4) modes = EnergeticModes(universe, 300.*Units.K) # Calculate the normalized difference vector diff = (conf_1T5T - conf_1SU4).scaledToNorm(1.) # Calculate the squared overlaps for all modes mode_numbers = numpy.arange(6, len(modes)) overlaps = [modes.rawMode(i).dotProduct(diff)**2 for i in mode_numbers] # First plot: show that the cumulative sum converges to 1. This means that # the squared overlaps can be considered as a percentage that each mode # contributes to the conformational change. pylab.figure(1) pylab.plot(mode_numbers+1, numpy.add.accumulate(overlaps)) pylab.xlabel('mode number') pylab.ylabel('cumulative squared overlap') # Second plot: show the contribution of each of the first 100 modes # to the conformational change. pylab.figure(2) pylab.plot(mode_numbers[:100]+1, overlaps[:100]) pylab.xlabel('mode number') pylab.ylabel('squared overlap') MMTK-2.7.9/Examples/NormalModes/constrained_modes.py0000644000076600000240000000302311662461637022773 0ustar hinsenstaff00000000000000# A normal mode calculation in the subspace of residue rigid-body motion. # from MMTK import * from MMTK.Proteins import Protein from MMTK.ForceFields import Amber94ForceField from MMTK.NormalModes import VibrationalModes from MMTK.Subspace import RigidMotionSubspace from MMTK.Minimization import ConjugateGradientMinimizer from MMTK.Trajectory import StandardLogOutput from Scientific import N # Construct system universe = InfiniteUniverse(Amber94ForceField()) universe.protein = Protein('bala1') # Minimize minimizer = ConjugateGradientMinimizer(universe, actions=[StandardLogOutput(50)]) minimizer(convergence = 1.e-3, steps = 10000) # Set up the subspace: rigid-body translation and rotation for each residue subspace = RigidMotionSubspace(universe, universe.protein.residues()) # Calculate normal modes modes = VibrationalModes(universe, subspace=subspace) # Calculate full modes for comparison full_modes = VibrationalModes(universe, subspace=None) # Compare the modes. For each reduced mode, find the full mode with # the most similar displacement vector. Print both frequencies and # the overlap of the displacement vectors. masses = universe.masses() for i in range(6, len(modes)): m1 = modes[i] overlap = [] for m2 in full_modes: o = m1.massWeightedDotProduct(m2) / \ N.sqrt(m1.massWeightedDotProduct(m1)) / \ N.sqrt(m2.massWeightedDotProduct(m2)) overlap.append(abs(o)) best = N.argsort(overlap)[-1] print m1.frequency, full_modes[best].frequency, overlap[best] MMTK-2.7.9/Examples/NormalModes/harmonic_force_field.py0000644000076600000240000000137711662461637023426 0ustar hinsenstaff00000000000000# Normal mode calculation using a simplified harmonic force field. # For a description of the force field, see # # K. Hinsen & G.R. Kneller # A simplified force field for describing vibrational protein dynamics # over the whole frequency range # J. Chem. Phys. 111, 10766 (1999) # from MMTK import * from MMTK.Proteins import Protein from MMTK.ForceFields import HarmonicForceField from MMTK.NormalModes import VibrationalModes from MMTK.Visualization import view # Construct system universe = InfiniteUniverse(HarmonicForceField()) universe.protein = Protein('bala1') # Calculate normal modes modes = VibrationalModes(universe) # Print frequencies for mode in modes: print mode # Show animation of the first non-trivial mode view(modes[6]) MMTK-2.7.9/Examples/NormalModes/mode_analysis.py0000644000076600000240000000461411662461637022131 0ustar hinsenstaff00000000000000from MMTK import * from MMTK.Subspace import Subspace from Scientific.Statistics.Histogram import WeightedHistogram, Histogram from Scientific import N # # You ned to provide a mode file as input for this script; the # line below is just an illustration for how to load one. A mode # file is created by writing a NormalModes object to a file # using MMTK.save(). # modes = load('~/proteins/lysozyme/lysozyme.umodes') universe = modes.universe protein = universe.protein protein.model = 'all' protein[0].model = 'all' helices = [] for chain in protein: dihedrals = chain.phiPsi()[1:-1] dihedrals_with_index = zip(range(1, 1+len(dihedrals)), dihedrals) helix_indices = [index for index, (phi, psi) in dihedrals_with_index if 4.5 < phi < 5.8 and 5. < psi < 6.] helix_indices = N.array(helix_indices) breaks = N.repeat(N.arange(len(helix_indices)-1), (helix_indices[1:]-helix_indices[:-1]) > 1) breaks = N.concatenate(([0], breaks + 1)) backbone = chain.backbone() for i in range(len(breaks)-1): residues = N.take(backbone, helix_indices[breaks[i]:breaks[i+1]]) helices.append(Collection(list(residues))) helices = [h for h in helices if len(h) > 4] #Collection(helices).view() residue_motion_vectors = [] helix_motion_vectors = [] for helix in helices: end_to_end = helix[0].centerOfMass()-helix[-1].centerOfMass() cms, inertia = helix.centerAndMomentOfInertia() moments, axes = inertia.diagonalization() axes = [a.asVector() for a in axes] helix_axis = axes[N.argmax([abs(end_to_end*v) for v in axes])] hmv = ParticleVector(universe) helix_motion_vectors.append(hmv) for residue in helix: mv = ParticleVector(universe) mv[residue.C_alpha] = helix_axis.cross(residue.C_alpha.position()-cms) hmv[residue.C_alpha] = helix_axis.cross(residue.C_alpha.position()-cms) residue_motion_vectors.append(mv) residue_subspace = Subspace(universe, residue_motion_vectors) helix_subspace = Subspace(universe, helix_motion_vectors) frequencies = [] projections = [] for m in modes[6:]: frequencies.append(m.frequency) ms = m.scaledToNorm(1.) projections.append(residue_subspace.projectionOf(ms).norm()**2 - helix_subspace.projectionOf(ms).norm()**2) histo = WeightedHistogram(frequencies, projections, 100) plot(histo) plot(Histogram(frequencies, 100)) MMTK-2.7.9/Examples/NormalModes/modes.py0000644000076600000240000000142411662461637020405 0ustar hinsenstaff00000000000000# Standard normal mode calculation. # from MMTK import * from MMTK.Proteins import Protein from MMTK.ForceFields import Amber94ForceField from MMTK.NormalModes import VibrationalModes from MMTK.Minimization import ConjugateGradientMinimizer from MMTK.Trajectory import StandardLogOutput from MMTK.Visualization import view # Construct system universe = InfiniteUniverse(Amber94ForceField()) universe.protein = Protein('bala1') # Minimize minimizer = ConjugateGradientMinimizer(universe, actions=[StandardLogOutput(50)]) minimizer(convergence = 1.e-3, steps = 10000) # Calculate normal modes modes = VibrationalModes(universe) # Print frequencies for mode in modes: print mode # Show animation of the first non-trivial mode view(modes[6]) MMTK-2.7.9/Examples/NormalModes/subset_modes.py0000644000076600000240000000261411662461637021774 0ustar hinsenstaff00000000000000# A normal mode calculation in the subspace of a part of the total system. # from MMTK import * from MMTK.Proteins import Protein from MMTK.ForceFields import Amber94ForceField from MMTK.NormalModes import VibrationalModes from MMTK.Subspace import Subspace from MMTK.Minimization import ConjugateGradientMinimizer from MMTK.Trajectory import StandardLogOutput from Scientific import N # Construct system universe = InfiniteUniverse(Amber94ForceField()) universe.protein1 = Protein('bala1') universe.protein2 = Protein('bala1') universe.protein2.translateBy(Vector(1., 0., 0.)) # Define normal mode subspace class SubsetSubspace(Subspace): def __init__(self, universe, objects): vectors = [] for a in objects.atomList(): for d in [Vector(1.,0.,0.), Vector(0.,1.,0.), Vector(0.,0.,1.)]: v = ParticleProperties.ParticleVector(universe) v[a] = d vectors.append(v) Subspace.__init__(self, universe, vectors) subspace = SubsetSubspace(universe, universe.protein1) # Minimize minimizer = ConjugateGradientMinimizer(universe, actions=[StandardLogOutput(50)]) minimizer(convergence = 1.e-3, steps = 10000) # Calculate normal modes modes = VibrationalModes(universe, subspace=subspace) # Print frequencies for mode in modes: print mode # Show animation of the first mode modes[0].view() MMTK-2.7.9/Examples/Proteins/0000755000076600000240000000000012156626564016307 5ustar hinsenstaff00000000000000MMTK-2.7.9/Examples/Proteins/analysis.py0000644000076600000240000001366211662461637020513 0ustar hinsenstaff00000000000000# This example demonstrates some of the analysis features of MMTK. # Two protein structures from the PDB are compared in some detail. # # Disclaimer: I know nothing about this protein, and the analysis # I present here may be of no biological interest. # from MMTK import * from MMTK.PDB import PDBConfiguration from MMTK.Proteins import PeptideChain # First we read the two PDB files. configuration1 = PDBConfiguration('4q21.pdb.gz') configuration2 = PDBConfiguration('6q21.pdb.gz') # The first file contains a monomer, the second one a tetramer in # which each chain is almost identical to the monomer from the first # file. We have to cut off the last (incomplete) residue from the # monomer and the last three residues of each chain of the tetramers # to get matching sequences. We'll just deal with one of the chains of # the tetramer here. monomer = configuration1.peptide_chains[0] monomer.removeResidues(-1, None) tetramer_1 = configuration2.peptide_chains[0] tetramer_1.removeResidues(-3, None) # Next we build a PeptideChain for the monomer. We have to specify # that we have no C terminus (missing) and no hydrogens. chain = PeptideChain(monomer, model='no_hydrogens', c_terminus=0) # How big is this beast? For a quick guess, print the size of the smallest # rectangular box containing it: p1, p2 = chain.boundingBox() print "Size of a bounding box: %4.2f x %4.2f x %4.2f nm" % tuple(p2-p1) # Formalities: we define a universe and put the chain inside. # Then we make a copy of the configuration of the universe for later use. # This is necessary because configurations are defined only for # universes (for various good reasons...). universe = InfiniteUniverse() universe.addObject(chain) monomer_configuration = copy(universe.configuration()) # Next we change the conformation of the protein to that of the first # tetramer chain. tetramer_1.applyTo(chain) # Now we want to get rid of the difference in position and orientation # of the two configurations, so we calculate the transformation that # minimizes the RMS difference. transformation, rms = chain.findTransformation(monomer_configuration) # That's the transformation *from* the current *to* the monomer # configuration. Let's analyze it: print "Translation/rotation fit:" print " RMS difference:", rms translation = transformation.translation() rotation = transformation.rotation() axis, angle = rotation.axisAndAngle() print " Rotation axis: ", axis print " Rotation angle: ", angle print " Translation: ", translation.displacement() # Note that order is important: the rotation (around the origin) is # applied first, and then the translation. If you want to do it the # other way round, the translation displacement must be different. # Note also the units: distances in nanometers, angles in radians! # If you insist on Angstrom and degrees, you'll have to type a bit more: print " Rotation angle in degrees: ", angle/Units.deg print " Translation in Angstrom: ", translation.displacement()/Units.Ang # Just for fun we'll do it again, but minimizing the RMS for the # C_alpha atoms only: c_alphas = chain.residues().map(lambda residue: residue.peptide.C_alpha) calpha_transformation, rms = c_alphas.findTransformation(monomer_configuration) print "C_alpha fit:" print " RMS difference:", rms translation = calpha_transformation.translation() rotation = calpha_transformation.rotation() axis, angle = rotation.axisAndAngle() print " Rotation axis: ", axis print " Rotation angle: ", angle print " Translation: ", translation.displacement() # As expected there is little difference. Now we apply the transformation # to the current configuration to align it with the reference configuration: chain.applyTransformation(transformation) # Let's calculate the local deformation due to the change in conformation # for each atom. Small values indicate rigid regions and large values # indicate flexible regions. The visualization requires a VRML viewer. # Reference: K. Hinsen, A. Thomas, M.J. Field: Proteins 34, 369 (1999) from MMTK.Deformation import FiniteDeformationFunction from Scientific.Visualization import VRML difference = monomer_configuration-universe.configuration() deformation_function = FiniteDeformationFunction(universe, 0.3, 0.6) deformation = deformation_function(difference) graphics = universe.graphicsObjects(color_values = deformation, graphics_module = VRML) VRML.Scene(graphics).view() # The deformation shows a small rigid region and a much larger # more flexible region. # Action: We show an animation of the two configurations. This requires # an animation-capable external viewer, e.g. VMD. from MMTK.Visualization import viewSequence viewSequence(chain, [universe.configuration(), monomer_configuration]) # Interesting: there's a floppy loop with residues Gln61, Glu62, and Glu63 # at the tip. It seems to do a funny rotation relative to a closeby helix # (His94-Lys101) when switching between the two configurations. Let's # find out what this rotation is! # First, we define the parts of the chain we are interested in. We don't # want the sidechains to mess up the picture, so we use just the backbone: tip = chain[60:64].backbone() helix = chain[93:101].backbone() # Then we align the two configurations to make the helix match as well # as possible: transformation, rms = helix.findTransformation(monomer_configuration) chain.applyTransformation(transformation) # Note that we *fit* only the helix, but *apply* the transformation # to the whole chain. # And now another fit for the tip of the loop. This time we don't want # to apply the transformation, but print out its characteristics: transformation, rms = tip.findTransformation(monomer_configuration) print "Movement of the tip of the loop:" print " RMS difference:", rms translation = transformation.translation() rotation = transformation.rotation() axis, angle = rotation.axisAndAngle() print " Rotation axis: ", axis print " Rotation angle: ", angle print " Translation: ", translation.displacement() MMTK-2.7.9/Examples/Proteins/analysis_calpha.py0000644000076600000240000001314211662461637022014 0ustar hinsenstaff00000000000000# This is a slightly modified version of analysis.py. It uses only the # C-alpha atoms of the peptide chains, and it discards the primary sequence # information. This makes it faster (fewer atoms) and suitable for comparing # proteins of similar fold but with different primary sequences. # from MMTK import * from MMTK.PDB import PDBConfiguration from MMTK.Proteins import PeptideChain # First we read the two PDB files. configuration1 = PDBConfiguration('4q21.pdb.gz') configuration2 = PDBConfiguration('6q21.pdb.gz') # Set all residue names to GLY. This permits the comparison # and superposition of proteins with different sequences. Since # a C-alpha model is used, the side chains are thrown away anyway. for conf in [configuration1, configuration2]: for chain in conf.peptide_chains: for residue in chain: residue.name = 'GLY' # The first file contains a monomer, the second one a tetramer in # which each chain is almost identical to the monomer from the first # file. We have to cut off the last (incomplete) residue from the # monomer and the last three residues of each chain of the tetramers # to get matching sequences. We'll just deal with one of the chains of # the tetramer here. monomer = configuration1.peptide_chains[0] monomer.removeResidues(-1, None) tetramer_1 = configuration2.peptide_chains[0] tetramer_1.removeResidues(-3, None) # Next we build a PeptideChain for the monomer. We use a C-alpha # model, which is sufficient for the purpose of this script. chain = PeptideChain(monomer, model='calpha') # How big is this beast? For a quick guess, print the size of the smallest # rectangular box containing it: p1, p2 = chain.boundingBox() print "Size of a bounding box: %4.2f x %4.2f x %4.2f nm" % tuple(p2-p1) # Formalities: we define a universe and put the chain inside. # Then we make a copy of the configuration of the universe for later use. # This is necessary because configurations are defined only for # universes (for various good reasons...). universe = InfiniteUniverse() universe.addObject(chain) monomer_configuration = copy(universe.configuration()) # Next we change the conformation of the protein to that of the first # tetramer chain. tetramer_1.applyTo(chain) # Now we want to get rid of the difference in position and orientation # of the two configurations, so we calculate the transformation that # minimizes the RMS difference. transformation, rms = chain.findTransformation(monomer_configuration) # That's the transformation *from* the current *to* the monomer # configuration. Let's analyze it: print "Translation/rotation fit:" print " RMS difference:", rms translation = transformation.translation() rotation = transformation.rotation() axis, angle = rotation.axisAndAngle() print " Rotation axis: ", axis print " Rotation angle: ", angle print " Translation: ", translation.displacement() # Note that order is important: the rotation (around the origin) is # applied first, and then the translation. If you want to do it the # other way round, the translation displacement must be different. # Note also the units: distances in nanometers, angles in radians! # If you insist on Angstrom and degrees, you'll have to type a bit more: print " Rotation angle in degrees: ", angle/Units.deg print " Translation in Angstrom: ", translation.displacement()/Units.Ang # Now we apply the transformation to the current configuration to # align it with the reference configuration: chain.applyTransformation(transformation) # Let's calculate the local deformation due to the change in conformation # for each atom. Small values indicate rigid regions and large values # indicate flexible regions. The visualization requires a VRML viewer. # Reference: K. Hinsen, A. Thomas, M.J. Field: Proteins 34, 369 (1999) from MMTK.Deformation import FiniteDeformationFunction from Scientific.Visualization import VRML difference = monomer_configuration-universe.configuration() deformation_function = FiniteDeformationFunction(universe, 0.3, 0.6) deformation = deformation_function(difference) graphics = universe.graphicsObjects(color_values = deformation, graphics_module = VRML) VRML.Scene(graphics).view() # The deformation shows a small rigid region and a much larger # more flexible region. # Action: We show an animation of the two configurations. This requires # an animation-capable external viewer, e.g. VMD. from MMTK.Visualization import viewSequence viewSequence(chain, [universe.configuration(), monomer_configuration]) # Interesting: there's a floppy loop with residues Gln61, Glu62, and Glu63 # at the tip. It seems to do a funny rotation relative to a closeby helix # (His94-Lys101) when switching between the two configurations. Let's # find out what this rotation is! # First, we define the parts of the chain we are interested in. tip = chain[60:64] helix = chain[93:101] # Then we align the two configurations to make the helix match as well # as possible: transformation, rms = helix.findTransformation(monomer_configuration) chain.applyTransformation(transformation) # Note that we *fit* only the helix, but *apply* the transformation # to the whole chain. # And now another fit for the tip of the loop. This time we don't want # to apply the transformation, but print out its characteristics: transformation, rms = tip.findTransformation(monomer_configuration) print "Movement of the tip of the loop:" print " RMS difference:", rms translation = transformation.translation() rotation = transformation.rotation() axis, angle = rotation.axisAndAngle() print " Rotation axis: ", axis print " Rotation angle: ", angle print " Translation: ", translation.displacement() MMTK-2.7.9/Examples/Proteins/construction.py0000644000076600000240000000571111662461637021416 0ustar hinsenstaff00000000000000# This file contains some examples for non-standard generation of # protein objects from PDB files. # from MMTK import * from MMTK.PDB import PDBConfiguration from MMTK.Proteins import PeptideChain, Protein # # First problem: construct an all-atom model from a structure without # hydrogens. This is the standard problem when using an all-atom force # field with crystallographic structures. # # Note: the simple solution in this case is just # insulin = Protein('insulin.pdb') # but the explicit form shown below is necessary when any kind of # modification is required. # # Load the PDB file. configuration = PDBConfiguration('insulin.pdb') # Construct the peptide chain objects. This also constructs positions # for any missing hydrogens, using geometrical criteria. chains = configuration.createPeptideChains() # Make the protein object. insulin = Protein(chains) # Write out the structure with hydrogens to a new file - we will use # it as an input example later on. insulin.writeToFile('insulin_with_h.pdb') # # Second problem: read a file with hydrogens and create a structure # without them. This is useful for analysis; if you don't need the # hydrogens, processing is faster without them. Or you might want # to compare structures with and without hydrogens. # # Load the PDB file. configuration = PDBConfiguration('./insulin_with_h.pdb') # Delete hydrogens. configuration.deleteHydrogens() # Construct the peptide chain objects without hydrogens. chains = configuration.createPeptideChains(model = 'no_hydrogens') # Make the protein object insulin = Protein(chains) # # Third problem: cut off three residues from the start of the second # chain before constructing the protein. This is useful for comparing # incomplete structures. # # Load the PDB file. configuration = PDBConfiguration('insulin.pdb') # Cut off the first three residues of the third chain. configuration.peptide_chains[2].removeResidues(0, 3) # Mark the second chain as modified for chain in configuration.peptide_chains: chain.modified = 0 configuration.peptide_chains[2].modified = 1 # Construct the peptide chain objects without hydrogens. For # modified chains, don't use the N-terminal form for the fist residue. chains = [] for chain in configuration.peptide_chains: if chain.modified: chains.append(PeptideChain(chain, model='no_hydrogens', n_terminus=0)) else: chains.append(PeptideChain(chain, model='no_hydrogens')) # Make the protein object. insulin = Protein(chains) # # Stop here, since the last "problem" is just an illustration, # you can't run it directly because there is no "special residue" # definition. # if 0: # # Fourth problem: construct a protein with a non-standard residue. # The new residue's name is 'XXX' and its definition is stored in the # MMTK group database under the name 'special_residue'. # from MMTK.Proteins import defineAminoAcidResidue defineAminoAcidResidue('special_residue', 'xxx') protein = Protein('special_protein.pdb') MMTK-2.7.9/Examples/Proteins/rotamers.py0000644000076600000240000000207711662461637020522 0ustar hinsenstaff00000000000000# Modify sidechain dihedral angles # from MMTK import * from MMTK.Proteins import Protein from MMTK.Trajectory import Trajectory, SnapshotGenerator, TrajectoryOutput from Scientific import N # Construct system: lysozyme in vaccuum universe = InfiniteUniverse() universe.protein = Protein('~/hao/proteins/PDB/193l.pdb') # Select residues to rotate # (this particular choice here is completely arbitrary) residues = [universe.protein[0][i] for i in [11, 35, 68, 110]] # Create trajectory trajectory = Trajectory(universe, "rotamers.nc", "w", "Sidechain rotations") # Create the snapshot generator snapshot = SnapshotGenerator(universe, actions = [TrajectoryOutput(trajectory, ["all"], 0, None, 1)]) # Perform sidechain rotations and write the configurations snapshot() for residue in residues: print residue chi = residue.chiAngle() for angle in N.arange(-N.pi, N.pi, N.pi/10.): chi.setValue(angle) print angle snapshot() # Close trajectory trajectory.close() MMTK-2.7.9/Examples/Trajectories/0000755000076600000240000000000012156626564017142 5ustar hinsenstaff00000000000000MMTK-2.7.9/Examples/Trajectories/calpha_trajectory.py0000644000076600000240000000621311662461637023213 0ustar hinsenstaff00000000000000# This program reads a trajecory containing one or more proteins # and extracts a trajectory for a C-alpha model, which it stores in # a new trajectory. This trajectory is smaller and easier to analyze # or visualize. Moreover, the global (rigid-body) motion of the # protein(s) is eliminated from the trajectory. # # Note: You cannot run this example without having a suitable # trajectory file, whose name you should put in place of # "full_trajectory.nc" below. # from MMTK import * from MMTK.Trajectory import Trajectory, TrajectoryOutput, SnapshotGenerator from MMTK.Proteins import Protein, PeptideChain from Scientific import N # Open the input trajectory. trajectory = Trajectory(None, 'full_trajectory.nc') universe = trajectory.universe # Choose all objects of class "Protein" proteins = universe.objectList(Protein) # Construct an initial configuration in which the proteins are contiguous. conf = universe.contiguousObjectConfiguration(proteins) # Construct a C_alpha representation and a mapping from the "real" # C_alpha atoms to their counterparts in the reduced model. universe_calpha = InfiniteUniverse() index = 0 map = [] for protein in proteins: chains_calpha = [] for chain in protein: chains_calpha.append(PeptideChain(chain.sequence(), model='calpha')) protein_calpha = Protein(chains_calpha) universe_calpha.addObject(protein_calpha) for i in range(len(protein)): chain = protein[i] chain_calpha = protein_calpha[i] for j in range(len(chain)): r = conf[chain[j].peptide.C_alpha] chain_calpha[j].peptide.C_alpha.setPosition(r) chain_calpha[j].peptide.C_alpha.index = index map.append(chain[j].peptide.C_alpha.index) index = index + 1 universe_calpha.configuration() # Open the new trajectory for just the interesting objects. trajectory_calpha = Trajectory(universe_calpha, 'calpha_trajectory.nc', 'w') # Make a snapshot generator for saving. snapshot = SnapshotGenerator(universe_calpha, actions = [TrajectoryOutput(trajectory_calpha, None, 0, None, 1)]) # Loop over all steps, eliminate rigid-body motion with reference to # the initial configuration, and save the configurations to the new # trajectory. first = 1 for step in trajectory: conf = universe.contiguousObjectConfiguration(proteins, step['configuration']) conf_calpha = Configuration(universe_calpha, N.take(conf.array, map), None) universe_calpha.setConfiguration(conf_calpha) if first: initial_conf_calpha = copy(conf_calpha) first = 0 else: tr, rms = universe_calpha.findTransformation(initial_conf_calpha) universe_calpha.applyTransformation(tr) del step['step'] del step['configuration'] try: del step['box_size'] except KeyError: pass try: del step['velocities'] except KeyError: pass snapshot(data = step) # Close both trajectories. trajectory.close() trajectory_calpha.close() MMTK-2.7.9/Examples/Trajectories/dcd_export.py0000644000076600000240000000112711662461637021647 0ustar hinsenstaff00000000000000# Conversion of an MMTK trajectory to DCD format (CHARMM/XPlor). # A PDB file with the same atom order is created at the same time, # because otherwise it would be impossible to interpret the data # in the DCD file. # # Note: In order to create the trajectory file "rotation.nc" which is # read by this example, you must run the example snapshot.py. # from MMTK import * from MMTK.Trajectory import Trajectory from MMTK.DCD import writeDCDPDB trajectory = Trajectory(None, "rotation.nc") universe = trajectory.universe writeDCDPDB(trajectory.configuration, 'rotation.dcd', 'rotation.pdb') MMTK-2.7.9/Examples/Trajectories/dcd_import.py0000644000076600000240000000231011662461637021633 0ustar hinsenstaff00000000000000# Conversion of a DCD trajectory (CHARMM/XPlor) to MMTK's trajectory format. # # Note: In order to create the files "rotation.dcd" and "rotation.pdb" which # are read by this example, you must run the example dcd_export.py. # from MMTK import * from MMTK.Proteins import Protein from MMTK.PDB import PDBConfiguration from MMTK.Trajectory import Trajectory, TrajectoryOutput from MMTK.DCD import DCDReader # Create the universe. universe = InfiniteUniverse() # Create all objects from the PDB file. The PDB file *must* match the # the DCD file (same atom order), and you *must* create all objects # defined in the PDB file, otherwise interpretation of the DCD file # is not possible. conf = PDBConfiguration('rotation.pdb') chain = conf.peptide_chains[0].createPeptideChain(c_terminus=1) universe.addObject(Protein([chain])) # Create the trajectory object for output. t = Trajectory(universe, "rotation_from_dcd.nc", "w", "Converted from DCD") # Create a DCD reader that reads all configurations. dcd_reader = DCDReader(universe, dcd_file = 'rotation.dcd', actions = [TrajectoryOutput(t, "all", 0, None, 1)]) # Run the reader... dcd_reader() # ... and close the output trajectory. t.close() MMTK-2.7.9/Examples/Trajectories/fluctuations.py0000644000076600000240000000254111662461637022235 0ustar hinsenstaff00000000000000# Calculate atomic fluctuations from a trajectory. # # The trajectory file contains a protein. The fluctuations are calculated # for all atoms, and then the average over the C-alpha atoms is determined. # Note that calculating the fluctuations for only the C-alpha atoms is # more complicated and no faster. # # This example illustrates: # 1) Reading trajectory files # 2) Selecting parts of a system # 3) Calculating trajectory averages # from MMTK import * from MMTK.Trajectory import Trajectory from MMTK.Proteins import Protein # Open the trajectory, use every tenth step trajectory = Trajectory(None, 'lysozyme.nc')[::10] universe = trajectory.universe # Calculate the average conformation average = ParticleVector(universe) for step in trajectory: average += step['configuration'] average /= len(trajectory) # Calculate the fluctiations for all atoms fluctuations = ParticleScalar(universe) for step in trajectory: d = step['configuration']-average fluctuations += d*d fluctuations /= len(trajectory) # Collect the C-alpha atoms calphas = Collection() for protein in universe.objectList(Protein): for chain in protein: for residue in chain: calphas.addObject(residue.peptide.C_alpha) # Average over the C-alpha atoms calpha_mask = calphas.booleanMask() print (fluctuations*calpha_mask).sumOverParticles()/calphas.numberOfAtoms() MMTK-2.7.9/Examples/Trajectories/pdb_export.py0000644000076600000240000000067511662461637021671 0ustar hinsenstaff00000000000000# Conversion of an MMTK trajectory into a sequence of PDB files. # # Note: In order to create the trajectory file "rotation.nc" which is # read by this example, you must run the example snapshot.py. # from MMTK import * from MMTK.Trajectory import Trajectory trajectory = Trajectory(None, "rotation.nc") universe = trajectory.universe for i, conf in enumerate(trajectory.configuration): universe.writeToFile("conf%d.pdb" % i, conf) MMTK-2.7.9/Examples/Trajectories/snapshot.py0000644000076600000240000000160711662461637021356 0ustar hinsenstaff00000000000000# "Manual" creation of a trajectory using the snapshot generator. # from MMTK import * from MMTK.Proteins import Protein from MMTK.Trajectory import Trajectory, SnapshotGenerator, TrajectoryOutput # Construct system universe = InfiniteUniverse() universe.protein = Protein('bala1') # Transformation to be applied between the steps transformation = Rotation(Vector(0.,0.,1.), 1.*Units.deg) # Create trajectory trajectory = Trajectory(universe, "rotation.nc", "w", "A rotating molecule") # Create the snapshot generator snapshot = SnapshotGenerator(universe, actions = [TrajectoryOutput(trajectory, ["all"], 0, None, 1)]) # Perform rotations and write the configurations snapshot() for i in range(20): universe.protein.applyTransformation(transformation) snapshot() # Close trajectory trajectory.close() MMTK-2.7.9/Examples/Trajectories/trajectory_average.py0000644000076600000240000000161111662461637023372 0ustar hinsenstaff00000000000000# This program reads a trajecory and calculates the average # configuration, which is then visualized. This is not a particularly # useful operation, but it illustrates the use of trajectories and # configuration variables. # # Note: In order to create the trajectory file "bala1.nc" which is # read by this example, you must run the example # MolecularDynamics/protein.py! # from MMTK import * from MMTK.Trajectory import Trajectory from MMTK.Visualization import view # Open the input trajectory. trajectory = Trajectory(None, 'bala1.nc') universe = trajectory.universe # Create a zeroed particle vector object. sum = ParticleVector(universe) # Loop over all steps and add the configurations to the sum. for configuration in trajectory.configuration: sum = sum + configuration # Divide by the number of steps. sum = sum/len(trajectory) # Visualize the average. view(universe, sum) MMTK-2.7.9/Examples/Trajectories/trajectory_extraction.py0000644000076600000240000000257111662461637024146 0ustar hinsenstaff00000000000000# This program reads a trajecory for a solvated protein and # extracts just the protein data, which it stores in a new # trajectory. This trajectory is smaller and easier to analyze # or visualize. # # Note: You cannot run this example without having a suitable # trajectory file, whose name you should put in place of # "full_trajectory.nc" below. # from MMTK import * from MMTK.Trajectory import Trajectory, TrajectoryOutput, SnapshotGenerator # Open the input trajectory. full = Trajectory(None, 'full_trajectory.nc') # Collect the items you want to keep. Here we keep everything but # water molecules. universe = full.universe keep = Collection() for object in universe: try: is_water = object.type.name == 'water' except AttributeError: is_water = 0 if not is_water: keep.addObject(object) # Open the new trajectory for just the interesting objects. subset = Trajectory(keep, 'subset_trajectory.nc', 'w') # Make a snapshot generator for saving. snapshot = SnapshotGenerator(universe, actions = [TrajectoryOutput(subset, None, 0, None, 1)]) # Loop over all steps and save them to the new trajectory. for configuration in full.configuration: universe.setConfiguration(configuration) snapshot() # Close both trajectories. full.close() subset.close() MMTK-2.7.9/Examples/Trajectories/view_trajectory.py0000644000076600000240000000075311662461637022740 0ustar hinsenstaff00000000000000# This program will show an animation of the trajectory file given as # an argument. Optional parameters indicate the configurations to be # included (first, last, step). # from MMTK.Visualization import viewTrajectory import string, sys first, last, step = 0, None, 1 argc = len(sys.argv) if argc > 2: first = string.atoi(sys.argv[2]) if argc > 3: last = string.atoi(sys.argv[3]) if argc > 4: step = string.atoi(sys.argv[4]) viewTrajectory(sys.argv[1], first, last, step) MMTK-2.7.9/Examples/Visualization/0000755000076600000240000000000012156626564017345 5ustar hinsenstaff00000000000000MMTK-2.7.9/Examples/Visualization/additional_objects.py0000644000076600000240000000310211662461637023533 0ustar hinsenstaff00000000000000# Generate a protein backbone representation plus an indication # of the principal axes of inertia by arrows. # from MMTK import * from MMTK.Proteins import Protein from Scientific import N, LA # Import the graphics module. Substitute any other graphics # module name to make the example use that module. from Scientific.Visualization import VRML2; module = VRML2 # Create the protein and find its center of mass and tensor of inertia. protein = Protein('insulin') center, inertia = protein.centerAndMomentOfInertia() # Diagonalize the inertia tensor and scale the axes to a suitable length. mass = protein.mass() diagonal, directions = LA.eigenvectors(inertia.array) diagonal = N.sqrt(diagonal/mass) # Generate the backbone graphics objects. graphics = protein.graphicsObjects(graphics_module = module, model = 'backbone', color = 'red') # Add an atomic wireframe representation of all valine sidechains valines = protein.residuesOfType('val') sidechains = valines.map(lambda r: r.sidechain) graphics = graphics + sidechains.graphicsObjects(graphics_module = module, model='wireframe', color = 'blue') # Add three arrows corresponding to the principal axes of inertia. for length, axis in map(None, diagonal, directions): graphics.append(module.Arrow(center, center+length*Vector(axis), 0.02, material=module.EmissiveMaterial('green'))) # Display everything using a VRML browser. scene = module.Scene(graphics) scene.view() MMTK-2.7.9/Examples/Visualization/graphics_data.py0000644000076600000240000000437511662461637022520 0ustar hinsenstaff00000000000000# This example shows how the graphics generator functions in MMTK # can be redirected to give access to the numerical values. # It extracts the data about the arrows in an AtomicVectorField. # The trick is to write a highly specialized graphics "module", # which here is a fake module - in fact any object with the right # attributes will do. from MMTK import * from MMTK.Proteins import Protein from MMTK.NormalModes import NormalModes from MMTK.ForceFields import CalphaForceField from MMTK.Field import AtomicVectorField # Use generic color handling routines import Scientific.Visualization.Color # The fake graphics module - a class. class DummyGraphics: def __init__(self): self.arrows = [] def Color(self, rgb): return Scientific.Visualization.Color.Color(rgb) def ColorByName(self, name): return Scientific.Visualization.Color.ColorByName(name) def ColorScale(self, range): return Scientific.Visualization.Color.ColorScale(range) def SymmetricColorScale(self, range): return Scientific.Visualization.Color.SymmetricColorScale(range) def Sphere(self, center, radius, **attributes): pass def Cube(self, center, edge_length, **attributes): pass def Cylinder(self, point1, point2, radius, **attributes): pass def Cone(self, point1, point2, radius, **attributes): pass def Line(self, point1, point2, **attributes): pass # Only arrow data is stored for later extraction def Arrow(self, point1, point2, radius, **attributes): self.arrows.append((point1, point2)) def Material(self, **attributes): return None def DiffuseMaterial(self, color): return None def EmissiveMaterial(self, color): return None # Create the system universe = InfiniteUniverse(CalphaForceField()) universe.protein = Protein('insulin', model='calpha') # Calculate the normal modes modes = NormalModes(universe) # Generate the vector field for the first non-zero mode vector_field = AtomicVectorField(universe, 0.5, modes[6]) # Generate the graphics data graphics = DummyGraphics() vector_field.graphicsObjects(graphics_module = graphics) # Print the arrow coordinates for point1, point2 in graphics.arrows: print point1, point2 MMTK-2.7.9/Examples/Visualization/vector_field_chimera.py0000644000076600000240000000344611662461637024062 0ustar hinsenstaff00000000000000# Example: Vector field visualization of the slowest normal mode of insulin. # # This example shows the use of vector fields in the analysis and # visualization of collective motions. For a more elaborate application, # see # A. Thomas, K. Hinsen, M.J. Field, D. Perahia # Tertiary and quaternary confrmational changes in aspartate # transcarbamylase: a normal mode study. # Proteins, 34(1), 96-112 (1999) # from MMTK import * from MMTK.Proteins import Protein from MMTK.NormalModes import EnergeticModes from MMTK.ForceFields import CalphaForceField from MMTK.Field import AtomicVectorField from MMTK.Database import PDBPath from Scientific.Visualization import Chimera import chimera # Load the molecule into Chimera filename = PDBPath("insulin.pdb") chimera.runCommand("open pdb:%s" % filename) # Create a simulation universe containing the protein universe = InfiniteUniverse(CalphaForceField()) universe.addObject(Protein(filename, model='calpha')) # Calculate normal modes modes = EnergeticModes(universe) # Create the vector field corresponding to the first non-zero mode # using a grid spacing of 0.5 nm. field = AtomicVectorField(universe, 0.5, modes[6]) # Create graphics objects for the vector fields. # The arrows are yellow and their lengths are multiplied by 80. # Only displacement vectors of lengths between 0. and 0.01 will be shown. # (This restriction is not necessary for this example, but used for # illustration.) graphics = field.graphicsObjects(color='yellow', scale=80., range=(0., 0.01), graphics_module=Chimera) # Display in Chimera. The scale factor is necessary because MMTK # uses nm whereas Chimera works in Angstrom. Chimera.Scene(graphics, name="first mode", scale=1./Units.Ang).view() MMTK-2.7.9/Examples/Visualization/vector_field_vmd.py0000644000076600000240000000312711662461637023234 0ustar hinsenstaff00000000000000# Example: Vector field visualization of the slowest normal mode of insulin. # # This example shows the use of vector fields in the analysis and # visualization of collective motions. For a more elaborate application, # see # A. Thomas, K. Hinsen, M.J. Field, D. Perahia # Tertiary and quaternary confrmational changes in aspartate # transcarbamylase: a normal mode study. # Proteins, 34(1), 96-112 (1999) # from MMTK import * from MMTK.Proteins import Protein from MMTK.NormalModes import EnergeticModes from MMTK.ForceFields import CalphaForceField from MMTK.Field import AtomicVectorField from Scientific.Visualization import VMD # Create a simulation universe containing the protein universe = InfiniteUniverse(CalphaForceField()) universe.addObject(Protein("insulin.pdb", model='calpha')) # Calculate normal modes modes = EnergeticModes(universe) # Create the vector field corresponding to the first non-zero mode # using a grid spacing of 0.5 nm. field = AtomicVectorField(universe, 0.5, modes[6]) # Create graphics objects for the vector fields. # The arrows are yellow and their lengths are multiplied by 80. # Only displacement vectors of lengths between 0. and 0.01 will be shown. # (This restriction is not necessary for this example, but used for # illustration.) graphics = field.graphicsObjects(color='yellow', scale=80., range=(0., 0.01), graphics_module=VMD) # Create graphics object for the protein. graphics = graphics + [VMD.Molecules(universe)] # Launch VMD. VMD.Scene(graphics, scale=1./Units.Ang).view() MMTK-2.7.9/Examples/Visualization/vrml2_cameras_vdw.py0000644000076600000240000001427711662461637023346 0ustar hinsenstaff00000000000000# This example demonstrates using MMTK and the Scientific # Python VRML2 (VRML97) visualisation module for drawing a # protein from several different angles using the camera object. # # The camera object is not available in the VRML, VPython # and VMD visualisation modules. # # The new space filling van der Waals representation (model='vdw') # is also demonstrated. [Introduced in June 2004, should be # available in the next release after Scientific Python 2.4.6] # # Written by Peter Cock # p.j.a.cock [at] warwick.ac.uk # Molecular Organisation and Assembly in Cells (MOAC) Doctoral # Training Centre, University of Warwick, CV4 7AL, UK # # from MMTK import * from MMTK.Proteins import Protein # Import the graphics module. You can substitute other graphics # module names to make the example use that module, but not # everything supports the camera object, used for view points from Scientific.Visualization import VRML2; visualization_module = VRML2 #In fact VRML, VPython and VMD do not support cameras:- #from Scientific.Visualization import VPython; visualization_module = VPython #from Scientific.Visualization import VRML; visualization_module = VRML #from Scientific.Visualization import VMD; visualization_module = VMD print "Loading protein file" #This can be any name from the MMTK database, or a PDB file. protein = Protein('insulin') #Note that by default this looks for all the atoms. Some PDB files #don't include the hydrogens, in which case MMTK will guess them. #Some PDB files don't even have the positions of the side chains, #and MMTK will not be able to guess their locations. print "Finding centre of mass and moment of inertia" center, inertia = protein.centerAndMomentOfInertia() print "Creating view points" #Use the centre of mass as the centre of the picture, with the camera #pulled back from it. # #X and Y are the axis of the screen #Y is in (-ve) and out (+ve) of the screen # #I am rotating the camera about the y-axis (which is vertical in screen) #in units of pi/2 in order to give four views (Front, Right, Back, Left) distance_away = 8.0 front_cam = visualization_module.Camera(position=[center[0],center[1],center[2]+distance_away], description="Front") right_cam = visualization_module.Camera(position=[center[0]+distance_away,center[1],center[2]], orientation=(Vector(0, 1, 0),3.14159*0.5), description="Right") back_cam = visualization_module.Camera(position=[center[0],center[1],center[2]-distance_away], orientation=(Vector(0, 1, 0),3.14159), description="Back") left_cam = visualization_module.Camera(position=[center[0]-distance_away,center[1],center[2]], orientation=(Vector(0, 1, 0),3.14159*1.5), description="Left") #Other visualisation methods ("models") you might like to try:- model_name = 'vdw' #model_name = 'tube' #model_name = 'ball_and_stick' print "Creating " + model_name + " graphic of Protein" #You can also specify colour for the whole molecule if you want, #otherwise the individual atom colours are used (Oxygen = Red, #Hydrogen = White, Sulphur = Yellow etc) graphics = protein.graphicsObjects(graphics_module = visualization_module, model = model_name) print "Creating arrows below protein" #In order to help visualise what the camera objects are doing, I #am adding some arrows in the x-z planes above and below the molecule. # #The red arrows point towards the "front" camera, green towards the #"right" camera, blue towards the "back" camera and yellow towards the #"left" camera. # d = 2.0 #distance from centre of mass (above or below) l = 2.0 #length of each arrow graphics.append(visualization_module.Arrow(center + Vector(0,-d,0), center + Vector(0,-d,l), 0.1, material=visualization_module.DiffuseMaterial('red'))) graphics.append(visualization_module.Arrow(center + Vector(0,-d,0), center + Vector(l,-d,0), 0.1, material=visualization_module.DiffuseMaterial('green'))) graphics.append(visualization_module.Arrow(center + Vector(0,-d,0), center + Vector(0,-d,-l), 0.1, material=visualization_module.DiffuseMaterial('blue'))) graphics.append(visualization_module.Arrow(center + Vector(0,-d,0), center + Vector(-l,-d,0), 0.1, material=visualization_module.DiffuseMaterial('yellow'))) print "Creating arrows above protein" graphics.append(visualization_module.Arrow(center + Vector(0,+d,0), center + Vector(0,+d,l), 0.1, material=visualization_module.DiffuseMaterial('red'))) graphics.append(visualization_module.Arrow(center + Vector(0,+d,0), center + Vector(l,+d,0), 0.1, material=visualization_module.DiffuseMaterial('green'))) graphics.append(visualization_module.Arrow(center + Vector(0,+d,0), center + Vector(0,+d,-l), 0.1, material=visualization_module.DiffuseMaterial('blue'))) graphics.append(visualization_module.Arrow(center + Vector(0,+d,0), center + Vector(-l,+d,0), 0.1, material=visualization_module.DiffuseMaterial('yellow'))) #print "Viewing front of " + model_name + " model..." #visualization_module.Scene(graphics, cameras=[front_cam]).view() #print "Viewing right of " + model_name + " model..." #visualization_module.Scene(graphics, cameras=[right_cam]).view() #print "Viewing back of " + model_name + " model..." #visualization_module.Scene(graphics, cameras=[back_cam]).view() #print "Viewing left of " + model_name + " model..." #visualization_module.Scene(graphics, cameras=[left_cam]).view() print "Viewing " + model_name + " model with all four cameras..." visualization_module.Scene(graphics, cameras=[front_cam,right_cam,back_cam,left_cam]).view() #Your VRML2 viewing program should let you switch between the #four cameras (and show you their names "Front", "Right", etc). # #For example, on Windows, using GLView 4.4 this is done using #the menu "Camera","Viewpoints" (or shortcut key F6) #Available here: http://home.snafu.de/hg/ print "Done" MMTK-2.7.9/Include/0000755000076600000240000000000012156626564014311 5ustar hinsenstaff00000000000000MMTK-2.7.9/Include/MMTK/0000755000076600000240000000000012156626564015061 5ustar hinsenstaff00000000000000MMTK-2.7.9/Include/MMTK/arrayobject.h0000644000076600000240000000013711662461637017537 0ustar hinsenstaff00000000000000#if defined(NUMPY) #include "numpy/oldnumeric.h" #else #include "Numeric/arrayobject.h" #endif MMTK-2.7.9/Include/MMTK/core.h0000644000076600000240000000734711662461637016174 0ustar hinsenstaff00000000000000/* * General include file for MMTK C modules. * * Written by Konrad Hinsen */ #ifndef MMTK_H #include "Python.h" #include "MMTK/arrayobject.h" /* Provide Py_ssize_t for Python < 2.5 */ #if PY_VERSION_HEX < 0x02050000 #if !defined(PY_SSIZE_T_COMPATIBILITY) #define PY_SSIZE_T_COMPATIBILITY #if !defined(NUMPY) typedef int Py_ssize_t; #endif #define PY_SSIZE_T_MAX INT_MAX #define PY_SSIZE_T_MIN INT_MIN #define PyInt_FromSsize_t(n) PyInt_FromLong(n) typedef Py_ssize_t (*lenfunc)(PyObject *); typedef PyObject *(*ssizeargfunc)(PyObject *, Py_ssize_t); typedef PyObject *(*ssizessizeargfunc)(PyObject *, Py_ssize_t, Py_ssize_t); typedef int(*ssizeobjargproc)(PyObject *, Py_ssize_t, PyObject *); typedef int(*ssizessizeobjargproc)(PyObject *, Py_ssize_t, Py_ssize_t, PyObject *); typedef Py_ssize_t (*readbufferproc)(PyObject *, Py_ssize_t, void **); typedef Py_ssize_t (*writebufferproc)(PyObject *, Py_ssize_t, void **); typedef Py_ssize_t (*segcountproc)(PyObject *, Py_ssize_t *); typedef Py_ssize_t (*charbufferproc)(PyObject *, Py_ssize_t, char **); #endif #endif /* MinGW doesn't have this */ #ifndef M_PI #define M_PI 3.14159265358979323846 #endif /* Configuration parameters */ /* max. number of threads that can try to access the same universe in parallel (energy evaluation threads don't count) */ #define MMTK_MAX_THREADS 10 /* Type definitions */ typedef double vector3[3]; typedef double tensor3[3][3]; /* Useful general macros */ #define sqr(x) ((x)*(x)) #define cube(x) ((x)*(x)*(x)) #define vector_add(v1, v2, f) \ { int i_; for (i_=0; i_<3; i_++) (v1)[i_] += (f)*(v2)[i_]; } #define vector_length_sq(r) (sqr(r[0])+sqr(r[1])+sqr(r[2])) #define vector_length(r) (sqrt(vector_length_sq(r))) #define dot(v1, v2) ((v1)[0]*(v2)[0] + (v1)[1]*(v2)[1] + (v1)[2]*(v2)[2]) #define cross(v, v1, v2) \ { v[0] = (v1)[1]*(v2)[2]-(v1)[2]*(v2)[1]; \ v[1] = (v1)[2]*(v2)[0]-(v1)[0]*(v2)[2]; \ v[2] = (v1)[0]*(v2)[1]-(v1)[1]*(v2)[0];} #define vector_scale(v, f) { v[0]*=f; v[1]*=f; v[2]*=f; } #define vector_copy(v1, v2) { (v1)[0]=(v2)[0]; (v1)[1]=(v2)[1]; \ (v1)[2]=(v2)[2]; } #define vector_changesign(v) { v[0]=-v[0]; v[1]=-v[1]; v[2]=-v[2]; } #define tensor_product(t, v1, v2, f) \ { int i_,j_; for (i_=0;i_<3;i_++) for (j_=0;j_<3;j_++) \ (t)[i_][j_]=(f)*(v1)[i_]*(v2)[j_]; } #define symmetric_tensor_product(t, v1, v2, f) \ { int i_,j_; for (i_=0;i_<3;i_++) for (j_=0;j_<3;j_++) \ t[i_][j_] = f*((v1)[i_]*(v2)[j_]+(v1)[j_]*(v2)[i_]); } #define tensor_copy(t1, t2) \ { int i_,j_; for (i_=0;i_<3;i_++) for (j_=0;j_<3;j_++) \ (t1)[i_][j_] = (t2)[i_][j_]; } #define tensor_scale(t1, f) \ { int i_,j_; for (i_=0;i_<3;i_++) for (j_=0;j_<3;j_++) (t1)[i_][j_] *= f; } #define tensor_add(t1, t2, f) \ { int i_,j_; for (i_=0;i_<3;i_++) for (j_=0;j_<3;j_++) \ (t1)[i_][j_] += (f)*t2[i_][j_]; } #define tensor_changesign(t) { vector_changesign(t[0]); \ vector_changesign(t[1]); \ vector_changesign(t[2]); } #define tensor_transpose(t) { double temp_; \ temp_=t[0][1]; t[0][1]=t[1][0]; t[1][0]=temp_; \ temp_=t[0][2]; t[0][2]=t[2][0]; t[2][0]=temp_; \ temp_=t[1][2]; t[1][2]=t[2][1]; t[2][1]=temp_; } /* Constants */ /* Numbers larger than 'undefined_limit' are considered to be undefined in * certain situations. This is used e.g. to represent unknown positions. * The value 'undefined' is used to mark numbers as undefined. The difference * between these two values should make sure that undefined values are * recognized across floating point representation boundaries. */ #define undefined_limit 1.e30; #define undefined 1.e31; #define MMTK_H #endif MMTK-2.7.9/Include/MMTK/core.pxi0000644000076600000240000000014311662461637016530 0ustar hinsenstaff00000000000000cdef extern from "MMTK/core.h": ctypedef double vector3[3] ctypedef double tensor3[3][3] MMTK-2.7.9/Include/MMTK/forcefield.h0000644000076600000240000002720412117632051017322 0ustar hinsenstaff00000000000000/* Include file for C force field calculations. * * Written by Konrad Hinsen */ #ifndef MMTK_FORCEFIELD_H /* Common include files */ #include "MMTK/core.h" #include "MMTK/universe.h" #include #ifdef WITH_THREAD #include "pythread.h" #ifdef HAVE_UNISTD_H #include #endif #ifdef _POSIX_THREADS #include #endif #endif #ifdef WITH_MPI #include "Scientific/mpimodule.h" #endif /* Configurable parameters */ #define MMTK_MAX_TERMS 5 /* Max. energy terms in an energy term object */ #define MMTK_MAX_DATA 40 /* Max. data slots in an energy term object */ /* Global variables */ extern double electrostatic_energy_factor; /* Type definitions */ typedef struct { PyArrayObject *coordinates; int natoms; int thread_id, proc_id, slice_id, nthreads, nprocs, nslices; int small_change; } energy_spec; struct ffedata; struct ffterm; struct ffeval; typedef int gradient_function(struct ffedata *energy, int i, vector3 gradient); typedef int fc_function(struct ffedata *energy, int i, int j, tensor3 fc, double r_sq); typedef void ff_eterm_function(struct ffterm *term, struct ffeval *evaluator, energy_spec *input, struct ffedata *energy); typedef void ff_eval_function(struct ffeval *evaluator, struct ffedata *energy, PyArrayObject *configuration, int small_change); typedef void energy_retrieval_function(PyObject *, PyObject *); typedef struct ffedata { PyObject *gradients; gradient_function *gradient_fn; PyObject *force_constants; fc_function *fc_fn; double *energy_terms; double energy; double virial; int virial_available; int error; } energy_data; #ifdef WITH_THREAD typedef struct { #ifdef _POSIX_THREADS pthread_mutex_t lock; pthread_cond_t cond; #else PyThread_type_lock lock; #endif int n; } barrierinfo; typedef struct { struct ffeval *evaluator; PyThread_type_lock lock; energy_spec input; energy_data energy; int with_gradients; int exit, stop, done; } threadinfo; #endif /* Energy term object structure */ typedef struct ffterm { PyObject_HEAD PyObject *user_info; PyUniverseSpecObject *universe_spec; ff_eterm_function *eval_func; char *evaluator_name; char *term_names[MMTK_MAX_TERMS]; PyObject *data[MMTK_MAX_DATA]; void *scratch; double param[MMTK_MAX_DATA]; int index, virial_index, barrier_index; int nterms, nbarriers; int n; int threaded, parallelized, thread_safe; } PyFFEnergyTermObject; /* Evaluator object structure */ typedef struct ffeval { PyObject_HEAD ff_eval_function *eval_func; PyArrayObject *terms; PyUniverseSpecObject *universe_spec; PyArrayObject *energy_terms_array; double *energy_terms; void *scratch; #ifdef WITH_THREAD PyThreadState *tstate_save; PyThread_type_lock global_lock; barrierinfo *binfo; #endif #ifdef WITH_MPI PyMPICommunicatorObject *communicator; double *energy_parts; double *gradient_parts; #endif int nterms, ntermobjects; int nthreads, nprocs, nslices, proc_id; } PyFFEvaluatorObject; /* Non-bonded list object structure */ #define NBLIST_NEIGHBORS 5 #define NBLIST_NEIGHBOR_DIMENSION (2*NBLIST_NEIGHBORS+1) typedef struct { int *atoms; int ix, iy, iz; int n, i; } nbbox; enum nblist_iterator_states { nblist_start, nblist_continue, nblist_finished, nblist_start_excluded, nblist_continue_excluded, nblist_start_14, nblist_continue_14 } ; struct nblist_iterator { nbbox *box1, *box2; int ibox, jbox, ineighbor, i, j, a1, a2; Py_ssize_t n; int state; }; typedef struct { PyObject_HEAD struct nblist_iterator iterator; PyObject *excluded_pairs; PyObject *one_four_pairs; PyObject *atom_subset; PyUniverseSpecObject *universe_spec; vector3 *lastx; int *box_number; int *box_atoms; nbbox *boxes; int box_count[3]; int nboxes; int allocated_boxes; int neighbors[NBLIST_NEIGHBOR_DIMENSION*NBLIST_NEIGHBOR_DIMENSION *NBLIST_NEIGHBOR_DIMENSION][3]; int nneighbors; double cutoff; } PyNonbondedListObject; /* Sparse force constant matrix object */ struct pair_fc { tensor3 fc; int i, j; }; struct pair_descr { int diffij; int index; }; struct pair_descr_list { struct pair_descr *list; int nalloc; int nused; }; typedef struct sparse_fc { PyObject_HEAD struct pair_fc *data; struct pair_descr_list *index; Py_ssize_t nalloc; Py_ssize_t nused; int natoms; fc_function *fc_fn; double cutoff_sq; } PySparseFCObject; /* * C API */ /* Type definitions */ #define PyFFEnergyTerm_Type_NUM 0 #define PyFFEvaluator_Type_NUM 1 #define PyNonbondedList_Type_NUM 2 #define PySparseFC_Type_NUM 3 /* Create sparce force constant matrix */ #define PySparseFC_New_RET PySparseFCObject * #define PySparseFC_New_PROTO Py_PROTO((int natoms, int nalloc)) #define PySparseFC_New_NUM 4 /* Zero sparce force constant matrix */ #define PySparseFC_Zero_RET void #define PySparseFC_Zero_PROTO Py_PROTO((PySparseFCObject *fc)) #define PySparseFC_Zero_NUM 5 /* Find a pair entry in a sparce force constant matrix */ #define PySparseFC_Find_RET double * #define PySparseFC_Find_PROTO Py_PROTO((PySparseFCObject *fc, int i, int j)) #define PySparseFC_Find_NUM 6 /* Add a pair contribution to a sparce force constant matrix */ #define PySparseFC_AddTerm_RET int #define PySparseFC_AddTerm_PROTO Py_PROTO((PySparseFCObject *fc, \ int i, int j, double *term)) #define PySparseFC_AddTerm_NUM 7 /* Copy data to an array */ #define PySparseFC_CopyToArray_RET void #define PySparseFC_CopyToArray_PROTO Py_PROTO((PySparseFCObject *fc, \ double *data, int lastdim, \ int from1, int to1, \ int from2, int to2)) #define PySparseFC_CopyToArray_NUM 8 /* Extract data as an array */ #define PySparseFC_AsArray_RET PyObject * #define PySparseFC_AsArray_PROTO Py_PROTO((PySparseFCObject *fc, \ int from1, int to1, \ int from2, int to2)) #define PySparseFC_AsArray_NUM 9 /* Multiply with a vector */ #define PySparseFC_VectorMultiply_RET void #define PySparseFC_VectorMultiply_PROTO Py_PROTO((PySparseFCObject *fc, \ double *result, \ double *vector, \ int from_i, int to_i, \ int from_j, int to_j)) #define PySparseFC_VectorMultiply_NUM 10 /* Create energy term */ #define PyFFEnergyTerm_New_RET PyFFEnergyTermObject * #define PyFFEnergyTerm_New_PROTO Py_PROTO((void)) #define PyFFEnergyTerm_New_NUM 11 /* Create force field evaluator */ #define PyFFEvaluator_New_RET PyFFEvaluatorObject * #define PyFFEvaluator_New_PROTO Py_PROTO((void)) #define PyFFEvaluator_New_NUM 12 /* Scale by weight vector */ #define PySparseFC_Scale_RET void #define PySparseFC_Scale_PROTO Py_PROTO((PySparseFCObject *fc, \ PyArrayObject *factors)) #define PySparseFC_Scale_NUM 13 /* Update nonbonded list */ #define PyNonbondedListUpdate_RET int #define PyNonbondedListUpdate_PROTO Py_PROTO((PyNonbondedListObject *nblist, \ int natoms, double *coordinates,\ double *geometry_data)) #define PyNonbondedListUpdate_NUM 14 /* Iterate over nonbonded list */ #define PyNonbondedListIterate_RET int #define PyNonbondedListIterate_PROTO Py_PROTO((PyNonbondedListObject *nblist, \ struct nblist_iterator \ *iterator)) #define PyNonbondedListIterate_NUM 15 /* Total number of C API pointers */ #define PyFF_API_pointers 16 #ifdef _FORCEFIELD_MODULE /* Type object declarations */ extern PyTypeObject PyFFEnergyTerm_Type; extern PyTypeObject PyFFEvaluator_Type; extern PyTypeObject PyNonbondedList_Type; extern PyTypeObject PySparseFC_Type; /* Type check macros */ #define PyFFEnergyTerm_Check(op) ((op)->ob_type == &PyFFEnergyTerm_Type) #define PyFFEvaluator_Check(op) ((op)->ob_type == &PyFFEvaluator_Type) #define PyNonbondedList_Check(op) ((op)->ob_type == &PyNonbondedList_Type) #define PySparseFC_Check(op) ((op)->ob_type == &PySparseFC_Type) /* C API function declarations */ extern PySparseFC_New_RET PySparseFC_New PySparseFC_New_PROTO; extern PySparseFC_Zero_RET PySparseFC_Zero PySparseFC_Zero_PROTO; extern PySparseFC_Find_RET PySparseFC_Find PySparseFC_Find_PROTO; extern PySparseFC_AddTerm_RET PySparseFC_AddTerm PySparseFC_AddTerm_PROTO; extern PySparseFC_CopyToArray_RET PySparseFC_CopyToArray \ PySparseFC_CopyToArray_PROTO; extern PySparseFC_AsArray_RET PySparseFC_AsArray PySparseFC_AsArray_PROTO; extern PySparseFC_VectorMultiply_RET PySparseFC_VectorMultiply \ PySparseFC_VectorMultiply_PROTO; extern PySparseFC_Scale_RET PySparseFC_Scale PySparseFC_Scale_PROTO; extern PyFFEnergyTerm_New_RET PyFFEnergyTerm_New PyFFEnergyTerm_New_PROTO; extern PyFFEvaluator_New_RET PyFFEvaluator_New PyFFEvaluator_New_PROTO; extern PyNonbondedListUpdate_RET PyNonbondedListUpdate \ PyNonbondedListUpdate_PROTO; extern PyNonbondedListIterate_RET PyNonbondedListIterate \ PyNonbondedListIterate_PROTO; #else /* C API address pointer */ static void **PyFF_API; /* Type definitions */ #define PyFFEnergyTerm_Type *(PyTypeObject *)PyFF_API[PyFFEnergyTerm_Type_NUM] #define PyFFEvaluator_Type *(PyTypeObject *)PyFF_API[PyFFEvaluator_Type_NUM] #define PyNonbondedList_Type *(PyTypeObject *)PyFF_API[PyNonbondedList_Type_NUM] #define PySparseFC_Type *(PyTypeObject *)PyFF_API[PySparseFC_Type_NUM] /* Type check macros */ #define PyFFEnergyTerm_Check(op) \ ((op)->ob_type == (PyTypeObject *)PyFF_API[PyFFEnergyTerm_Type_NUM]) #define PyFFEvaluator_Check(op) \ ((op)->ob_type == (PyTypeObject *)PyFF_API[PyFFEvaluator_Type_NUM]) #define PyNonbondedList_Check(op) \ ((op)->ob_type == (PyTypeObject *)PyFF_API[PyNonbondedList_Type_NUM]) #define PySparseFC_Check(op) \ ((op)->ob_type == (PyTypeObject *)PyFF_API[PySparseFC_Type_NUM]) /* C API function declarations */ #define PySparseFC_New \ (*(PySparseFC_New_RET (*)PySparseFC_New_PROTO) \ PyFF_API[PySparseFC_New_NUM]) #define PySparseFC_Zero \ (*(PySparseFC_Zero_RET (*)PySparseFC_Zero_PROTO) \ PyFF_API[PySparseFC_Zero_NUM]) #define PySparseFC_Find \ (*(PySparseFC_Find_RET (*)PySparseFC_Find_PROTO) \ PyFF_API[PySparseFC_Find_NUM]) #define PySparseFC_AddTerm \ (*(PySparseFC_AddTerm_RET (*)PySparseFC_AddTerm_PROTO) \ PyFF_API[PySparseFC_AddTerm_NUM]) #define PySparseFC_CopyToArray \ (*(PySparseFC_CopyToArray_RET (*)PySparseFC_CopyToArray_PROTO) \ PyFF_API[PySparseFC_CopyToArray_NUM]) #define PySparseFC_AsArray \ (*(PySparseFC_AsArray_RET (*)PySparseFC_AsArray_PROTO) \ PyFF_API[PySparseFC_AsArray_NUM]) #define PySparseFC_VectorMultiply \ (*(PySparseFC_VectorMultiply_RET (*)PySparseFC_VectorMultiply_PROTO) \ PyFF_API[PySparseFC_VectorMultiply_NUM]) #define PyFFEnergyTerm_New \ (*(PyFFEnergyTerm_New_RET (*)PyFFEnergyTerm_New_PROTO) \ PyFF_API[PyFFEnergyTerm_New_NUM]) #define PyFFEvaluator_New \ (*(PyFFEvaluator_New_RET (*)PyFFEvaluator_New_PROTO) \ PyFF_API[PyFFEvaluator_New_NUM]) #define PySparseFC_Scale \ (*(PySparseFC_Scale_RET (*)PySparseFC_Scale_PROTO) \ PyFF_API[PySparseFC_Scale_NUM]) #define PyNonbondedListUpdate \ (*(PyNonbondedListUpdate_RET (*)PyNonbondedListUpdate_PROTO) \ PyFF_API[PyNonbondedListUpdate_NUM]) #define PyNonbondedListIterate \ (*(PyNonbondedListIterate_RET (*)PyNonbondedListIterate_PROTO) \ PyFF_API[PyNonbondedListIterate_NUM]) #endif /* Import macro */ #define import_MMTK_forcefield() \ { \ PyObject *module = PyImport_ImportModule("MMTK_forcefield"); \ if (module != NULL) { \ PyObject *module_dict = PyModule_GetDict(module); \ PyObject *c_api_object = PyDict_GetItemString(module_dict, "_C_API"); \ if (PyCObject_Check(c_api_object)) { \ PyFF_API = (void **)PyCObject_AsVoidPtr(c_api_object); \ } \ } \ } #define MMTK_FORCEFIELD_H #endif MMTK-2.7.9/Include/MMTK/forcefield.pxi0000644000076600000240000000717111662461637017712 0ustar hinsenstaff00000000000000cdef extern from "MMTK/forcefield.h": void import_MMTK_forcefield() # This structure combines all the input data required by an # energy calculation. ctypedef struct energy_spec: # particle configuration PyArrayObject *coordinates # number of particles int natoms # thread number (only for multi-threaded energy terms) int thread_id # processor number (only for parallelized energy terms) int proc_id # slice number (only for parallelized energy terms) # A slice is the part of the total that one thread on # one processor works on. The number of slices is # the number of processors times the number of threads # per processor, where one processor is really one node # in a parallel system, i.e. a multiprocessor node with # shared memory is counted as *one* processor. int slice_id # number of threads (only for multi-threaded energy terms) int nthreads # number of processors (or rather nodes) int nprocs # number of slices int nslices # Set to 1 if the input configuration is close to the one of # the previous call. This is only a hint for choosing the best # algorithm. int small_change ctypedef struct energy_data ctypedef int gradient_function(energy_data *energy, int i, vector3 gradient) ctypedef int fc_function(energy_data *energy, int i, int j, tensor3 fc, double r_sq) ctypedef struct energy_data: # An array or a sparse gradient storage. PyObject *gradients # A pointer to a function that gets/sets the gradient components, # used when gradients is NOT an array. gradient_function *gradient_fn # An array or a sparse force constant matrix. PyObject *force_constants # A pointer to a function that gets/sets the force constant components, # used when force_constannt is NOT an array. fc_function *fc_fn # An array of energy term values. Each energy term should # access only its alloted slots in there, plus the virial # term indicated by self->virial_index double *energy_terms # The total energy, calculated after all energy terms # have been evaluated. Don't touch inside energy term routines double energy # The virial. The same comment applies. Energy terms should add # their contribution to energy_terms[self->virial_index]. double virial # If a term cannot calculate a virial, it should set this # variable to 0, in which case the total virial is considered # invalid and pressure-related calculations are not possible. int virial_available # Set this to 1 if there is an error during processing. int error ctypedef class MMTK_forcefield.EnergyTerm [object PyFFEnergyTermObject]: cdef int index cdef int virial_index cdef void *eval_func cdef int nterms cdef char *evaluator_name cdef char **term_names ctypedef struct PyFFEvaluatorObject ctypedef void ff_eval_function(PyFFEvaluatorObject *evaluator, energy_data *ed, PyArrayObject *configuration, int small_change) nogil ctypedef struct PyFFEvaluatorObject: ff_eval_function eval_func double *energy_terms PyThreadState *tstate_save cdef void **PyFF_API import_MMTK_forcefield() MMTK-2.7.9/Include/MMTK/forcefield_private.h0000644000076600000240000000322411662461637021066 0ustar hinsenstaff00000000000000/* Private include file for C force field calculations. * * Written by Konrad Hinsen */ #ifndef MMTK_FORCEFIELD_PRIVATE_H #include "MMTK/universe.h" #undef GRADIENTFN /* Global variables */ extern distance_fn *distance_vector_pointer; extern distance_fn *orthorhombic_distance_vector_pointer; extern distance_fn *parallelepipedic_distance_vector_pointer; /* Functions in MMTK/forcefieldmodule.c */ void add_pair_fc(energy_data *energy, int i, int j, vector3 dr, double r_sq, double f1, double f2); #ifdef WITH_THREAD /* Remove a macro from Linux 2.6 that is in the way */ #ifdef barrier #undef barrier #endif void barrier(barrierinfo *binfo, int thread_id, int nthreads); #endif /* Functions in bonded.c */ ff_eterm_function harmonic_bond_evaluator; ff_eterm_function harmonic_angle_evaluator; ff_eterm_function cosine_dihedral_evaluator; /* Functions in nonbonded.c */ int nblist_update(PyNonbondedListObject *nblist, int natoms, double *coordinates, double *geometry_data); int nblist_iterate(PyNonbondedListObject *nblist, struct nblist_iterator *iterator); ff_eterm_function nonbonded_evaluator; ff_eterm_function lennard_jones_evaluator; ff_eterm_function electrostatic_evaluator; ff_eterm_function es_mp_evaluator; ff_eterm_function lj_es_evaluator; ff_eterm_function es_ewald_evaluator; /* Functions in ewald.c */ int init_kvectors(box_fn *box_transformation_fn, double *universe_data, int natoms, long *kmax, double cutoff_sq, void *scratch, int nvect); /* Functions in sparsefc.c */ PyObject * SparseForceConstants(PyObject *dummy, PyObject *args); fc_function sparse_fc_function; #define MMTK_FORCEFIELD_PRIVATE_H #endif MMTK-2.7.9/Include/MMTK/numeric.pxi0000644000076600000240000000172211662461637017246 0ustar hinsenstaff00000000000000cdef extern from "MMTK/arrayobject.h": void import_array() cdef enum Pyarray_TYPES: PyArray_CHAR, PyArray_UBYTE, PyArray_SBYTE, PyArray_SHORT, PyArray_USHORT, PyArray_INT, PyArray_UINT, PyArray_LONG, PyArray_FLOAT, PyArray_DOUBLE, PyArray_CFLOAT, PyArray_CDOUBLE, PyArray_OBJECT, PyArray_NTYPES, PyArray_NOTYPE struct PyArray_Descr: int type_num, elsize char type ctypedef struct PyArrayObject: char *data int nd int *dimensions, *strides PyObject *base PyArray_Descr *descr int flags ctypedef class Scientific.N.ArrayType [object PyArrayObject]: cdef char *data cdef int nd cdef int *dimensions, *strides cdef object base cdef PyArray_Descr *descr cdef int flags object PyArray_FromDims(int n_dimensions, int dimensions[], int item_type) import_array() MMTK-2.7.9/Include/MMTK/python.pxi0000644000076600000240000000030312117632051017101 0ustar hinsenstaff00000000000000cdef extern from "Python.h": ctypedef void PyObject ctypedef struct PyThreadState cdef PyThreadState *PyEval_SaveThread() cdef void PyEval_RestoreThread(PyThreadState *thread) MMTK-2.7.9/Include/MMTK/readdcd.h0000644000076600000240000000505411662461637016623 0ustar hinsenstaff00000000000000/*************************************************************************** *cr *cr (C) Copyright 1995 The Board of Trustees of the *cr University of Illinois *cr All Rights Reserved *cr ***************************************************************************/ /*************************************************************************** * RCS INFORMATION: * * $RCSfile: ReadDCD.h,v $ * $Author: billh $ $Locker: $ $State: Exp $ * $Revision: 1.3 $ $Date: 1995/05/11 23:42:25 $ * *************************************************************************** * DESCRIPTION: * * C routines to read and write binary DCD files (which use the goofy * FORTRAN UNFORMATTED format). These routines are courtesy of * Mark Nelson (autographs available upon request and $1 tip). * ***************************************************************************/ #ifndef READ_DCD_H #define READ_DCD_H #include #ifdef HAVE_MALLOC_H #include #endif #include #include #include #include /* DEFINE ERROR CODES THAT MAY BE RETURNED BY DCD ROUTINES */ #define DCD_DNE -2 /* DCD file does not exist */ #define DCD_OPENFAILED -3 /* Open of DCD file failed */ #define DCD_BADREAD -4 /* read call on DCD file failed */ #define DCD_BADEOF -5 /* premature EOF found in DCD file */ #define DCD_BADFORMAT -6 /* format of DCD file is wrong */ #define DCD_FILEEXISTS -7 /* output file already exists */ #define DCD_BADMALLOC -8 /* malloc failed */ /* FUNCTION ALLUSIONS */ FILE *open_dcd_read(const char *); /* Open a DCD file for reading */ int read_dcdheader(FILE *, int*, int*, int*, int*, float*, int*, int**); /* Read the DCD header */ int read_dcdstep(FILE *, int, float*, float*, float*, int, int, int*); /* Read a timestep's values */ FILE *open_dcd_write(char *); /* Open a DCD file for writing */ int write_dcdstep(FILE *, int, float *, float *, float *); /* Write out a timesteps values */ int write_dcdheader(FILE *, char*, int, int, int, int, double); /* Write a dcd header */ void close_dcd_read(FILE *, int, int *); /* Close a dcd file open for reading */ void close_dcd_write(FILE *); /* Close a dcd file open for writing */ #endif /* ! DCDLIB_H */ MMTK-2.7.9/Include/MMTK/trajectory.h0000644000076600000240000001432411662461637017423 0ustar hinsenstaff00000000000000/* * Include file for trajectory objects * * Written by Konrad Hinsen */ #ifndef MMTK_TRAJECTORY_H /* General include files */ #include "MMTK/core.h" #include "Scientific/netcdfmodule.h" #ifdef USE_NETCDF_H_FROM_SCIENTIFIC # include "Scientific/netcdf.h" #else # include "netcdf.h" #endif #include /* Unit names */ #define length_unit_name "nanometer" #define volume_unit_name "nanometer3" #define time_unit_name "picosecond" #define frequency_unit_name "picosecond-1" #define frequency_square_unit_name "picosecond-2" #define velocity_unit_name "nanometer picosecond-1" #define mass_unit_name "atomic_mass_unit" #define energy_unit_name "kilojoule mole-1" #define energy_gradient_unit_name "kilojoule mole-1 nanometer-1" #define temperature_unit_name "kelvin" #define pressure_unit_name "kilojoule mole-1 nanometer-3" /* Trajectory object structure */ typedef struct { PyObject_HEAD PyObject *universe; PyArrayObject *index_map; PyNetCDFFileObject *file; PyNetCDFVariableObject *var_step; PyArrayObject *sbuffer, *vbuffer; PyArrayObject *box_buffer; clock_t last_flush; int box_buffer_first, box_buffer_last, box_buffer_skip; int floattype; int natoms, trajectory_atoms; int steps; int block_size; int cycle; int first_step; int write; } PyTrajectoryObject; extern PyTypeObject PyTrajectory_Type; /* Trajectory variable types */ enum PyTrajectory_VariableTypes { PyTrajectory_Scalar, PyTrajectory_ParticleScalar, PyTrajectory_ParticleVector, PyTrajectory_IntScalar, PyTrajectory_BoxSize }; enum PyTrajectory_DataClass { PyTrajectory_Configuration = 1, PyTrajectory_Velocities = 2, PyTrajectory_Gradients = 4, PyTrajectory_Energy = 8, PyTrajectory_Thermodynamic = 16, PyTrajectory_Time = 32, PyTrajectory_Internal = 64, PyTrajectory_Auxiliary = 128 }; typedef struct { char *name; char *text; char *unit; union data { int *ip; double *dp; PyArrayObject *array; } value; int length; int type; int class; int modified; } PyTrajectoryVariable; /* Trajectory function declaration */ typedef int trajectory_fn(PyTrajectoryVariable *, PyObject *, int step, void **scratch, PyObject *universe); /* Output specification structure */ typedef struct { PyObject *destination; PyObject **variables; trajectory_fn *function; PyObject *parameters; PyObject *universe; void *scratch; int first; int last; int frequency; int type; int close; int what; } PyTrajectoryOutputSpec; /* * C API functions */ /* Type definitions */ #define PyTrajectory_Type_NUM 0 /* Open a trajectory file */ #define PyTrajectory_Open_RET PyTrajectoryObject * #define PyTrajectory_Open_PROTO Py_PROTO((PyObject *universe, \ PyObject *description, \ PyArrayObject *index_map, \ char *filename, char *mode, \ int floatttype, int cycle, \ int block_size)) #define PyTrajectory_Open_NUM 1 /* Close a trajectory file */ #define PyTrajectory_Close_RET int #define PyTrajectory_Close_PROTO Py_PROTO((PyTrajectoryObject *trajectory)) #define PyTrajectory_Close_NUM 2 /* Prepare output */ #define PyTrajectory_OutputSpecification_RET PyTrajectoryOutputSpec * #define PyTrajectory_OutputSpecification_PROTO \ Py_PROTO((PyObject *universe, PyListObject *spec_list, char *description,\ PyTrajectoryVariable *data)) #define PyTrajectory_OutputSpecification_NUM 3 /* Finish output */ #define PyTrajectory_OutputFinish_RET void #define PyTrajectory_OutputFinish_PROTO \ Py_PROTO((PyTrajectoryOutputSpec *spec, int step, int error_flag,\ int time_stamp_flag, PyTrajectoryVariable *data)) #define PyTrajectory_OutputFinish_NUM 4 /* Do output */ #define PyTrajectory_Output_RET int #define PyTrajectory_Output_PROTO \ Py_PROTO((PyTrajectoryOutputSpec *spec, int step, \ PyTrajectoryVariable *data, PyThreadState **thread)) #define PyTrajectory_Output_NUM 5 /* Total number of C API pointers */ #define PyTrajectory_API_pointers 6 #ifdef _TRAJECTORY_MODULE /* Type object declarations */ extern PyTypeObject PyTrajectory_Type; /* Type check macros */ #define PyTrajectory_Check(op) ((op)->ob_type == &PyTrajectory_Type) /* C API function declarations */ static PyTrajectory_Open_RET PyTrajectory_Open PyTrajectory_Open_PROTO; static PyTrajectory_Close_RET PyTrajectory_Close PyTrajectory_Close_PROTO; static PyTrajectory_OutputSpecification_RET PyTrajectory_OutputSpecification \ PyTrajectory_OutputSpecification_PROTO; static PyTrajectory_OutputFinish_RET PyTrajectory_OutputFinish \ PyTrajectory_OutputFinish_PROTO; static PyTrajectory_Output_RET PyTrajectory_Output \ PyTrajectory_Output_PROTO; #else /* C API address pointer */ static void **PyTrajectory_API; /* Type check macros */ #define PyTrajectory_Check(op) \ ((op)->ob_type == (PyTypeObject *)PyTrajectory_API[PyTrajectory_Type_NUM]) /* C API function declarations */ #define PyTrajectory_Open \ (*(PyTrajectory_Open_RET (*)PyTrajectory_Open_PROTO) \ PyTrajectory_API[PyTrajectory_Open_NUM]) #define PyTrajectory_Close \ (*(PyTrajectory_Close_RET (*)PyTrajectory_Close_PROTO) \ PyTrajectory_API[PyTrajectory_Close_NUM]) #define PyTrajectory_OutputSpecification \ (*(PyTrajectory_OutputSpecification_RET \ (*)PyTrajectory_OutputSpecification_PROTO) \ PyTrajectory_API[PyTrajectory_OutputSpecification_NUM]) #define PyTrajectory_OutputFinish \ (*(PyTrajectory_OutputFinish_RET \ (*)PyTrajectory_OutputFinish_PROTO) \ PyTrajectory_API[PyTrajectory_OutputFinish_NUM]) #define PyTrajectory_Output \ (*(PyTrajectory_Output_RET \ (*)PyTrajectory_Output_PROTO) \ PyTrajectory_API[PyTrajectory_Output_NUM]) #endif /* Import macro */ #define import_MMTK_trajectory() \ { \ PyObject *module = PyImport_ImportModule("MMTK_trajectory"); \ if (module != NULL) { \ PyObject *module_dict = PyModule_GetDict(module); \ PyObject *c_api_object = PyDict_GetItemString(module_dict, "_C_API"); \ if (PyCObject_Check(c_api_object)) { \ PyTrajectory_API = (void **)PyCObject_AsVoidPtr(c_api_object); \ } \ } \ } #define MMTK_TRAJECTORY_H #endif MMTK-2.7.9/Include/MMTK/trajectory.pxi0000644000076600000240000000407411662461637017775 0ustar hinsenstaff00000000000000cdef extern from "Python.h": ctypedef PyObject PyListObject cdef extern from "MMTK/trajectory.h": void import_MMTK_trajectory() cdef enum PyTrajectory_VariableTypes: PyTrajectory_Scalar PyTrajectory_ParticleScalar PyTrajectory_ParticleVector PyTrajectory_IntScalar PyTrajectory_BoxSize cdef enum PyTrajectory_DataClass: PyTrajectory_Configuration PyTrajectory_Velocities PyTrajectory_Gradients PyTrajectory_Energy PyTrajectory_Thermodynamic PyTrajectory_Time PyTrajectory_Internal PyTrajectory_Auxiliary cdef union data: int *ip double *dp PyArrayObject *array ctypedef struct PyTrajectoryVariable: char *name char *text char *unit data value int length int type int data_class "class" int modified cdef char *length_unit_name cdef char *volume_unit_name cdef char *time_unit_name cdef char *frequency_unit_name cdef char *frequency_square_unit_name cdef char *velocity_unit_name cdef char *mass_unit_name cdef char *energy_unit_name cdef char *energy_gradient_unit_name cdef char *temperature_unit_name cdef char *pressure_unit_name ctypedef struct PyTrajectoryOutputSpec cdef PyTrajectoryOutputSpec *PyTrajectory_OutputSpecification(object universe, PyListObject *spec_list, char *description, PyTrajectoryVariable *data) cdef void PyTrajectory_OutputFinish(PyTrajectoryOutputSpec *spec, int step, int error_flag, int time_stamp_flag, PyTrajectoryVariable *data) cdef int PyTrajectory_Output(PyTrajectoryOutputSpec *spec, int step, PyTrajectoryVariable *data, PyThreadState **thread) cdef void **PyTrajectory_API import_MMTK_trajectory() MMTK-2.7.9/Include/MMTK/universe.h0000644000076600000240000001057411662461637017100 0ustar hinsenstaff00000000000000/* Include file for C universe-related functions. * * Written by Konrad Hinsen */ #ifndef MMTK_UNIVERSE_H #include "MMTK/core.h" #ifdef WITH_THREAD #include "pythread.h" #endif /* Type definitions */ typedef void distance_fn(vector3 d, vector3 r1, vector3 r2, double *data); typedef void correction_fn(vector3 *x, int natoms, double *data); typedef double volume_fn(double scale_factor, double *data); typedef void box_fn(vector3 *x, vector3 *b, int n, double *data, int to_box); typedef void bounding_box_fn(vector3 *box1, vector3 *box2, vector3 *x, int n, double *data); /* Universe specification object structure */ typedef struct { PyObject_HEAD PyArrayObject *geometry; double *geometry_data; distance_fn *distance_function; correction_fn *correction_function; volume_fn *volume_function; box_fn *box_function; box_fn *trajectory_function; bounding_box_fn *bounding_box_function; #ifdef WITH_THREAD PyThread_type_lock configuration_change_lock; PyThread_type_lock main_state_lock; PyThread_type_lock state_wait_lock[MMTK_MAX_THREADS]; int state_access_type[MMTK_MAX_THREADS]; int state_access; int waiting_threads; #endif int is_periodic; int is_orthogonal; int geometry_data_length; } PyUniverseSpecObject; /* Macro definitions */ /* Distance vector in infinite universe */ #define distance_vector_1(d, r1, r2, data) \ { \ d[0] = r2[0]-r1[0]; \ d[1] = r2[1]-r1[1]; \ d[2] = r2[2]-r1[2]; \ } /* Distance vector in orthorhombic universe */ #define distance_vector_2(d, r1, r2, data) \ { \ double xh = 0.5*(data)[0]; \ double yh = 0.5*(data)[1]; \ double zh = 0.5*(data)[2]; \ d[0] = r2[0]-r1[0]; \ if (d[0] > xh) d[0] -= (data)[0]; \ if (d[0] <= -xh) d[0] += (data)[0]; \ d[1] = r2[1]-r1[1]; \ if (d[1] > yh) d[1] -= (data)[1]; \ if (d[1] <= -yh) d[1] += (data)[1]; \ d[2] = r2[2]-r1[2]; \ if (d[2] > zh) d[2] -= (data)[2]; \ if (d[2] <= -zh) d[2] += (data)[2]; \ } /* Distance vector in parallelepipedic universe */ #define distance_vector_3(d, r1, r2, data) \ { \ double dx = (r2)[0]-(r1)[0]; \ double dy = (r2)[1]-(r1)[1]; \ double dz = (r2)[2]-(r1)[2]; \ double dfx = (data)[0+9]*dx + (data)[1+9]*dy + (data)[2+9]*dz; \ double dfy = (data)[3+9]*dx + (data)[4+9]*dy + (data)[5+9]*dz; \ double dfz = (data)[6+9]*dx + (data)[7+9]*dy + (data)[8+9]*dz; \ if (dfx > 0.5) dfx -= 1.; \ if (dfx <= -0.5) dfx += 1.; \ if (dfy > 0.5) dfy -= 1.; \ if (dfy <= -0.5) dfy += 1.; \ if (dfz > 0.5) dfz -= 1.; \ if (dfz <= -0.5) dfz += 1.; \ d[0] = (data)[0]*dfx + (data)[1]*dfy + (data)[2]*dfz; \ d[1] = (data)[3]*dfx + (data)[4]*dfy + (data)[5]*dfz; \ d[2] = (data)[6]*dfx + (data)[7]*dfy + (data)[8]*dfz; \ } /* * C API */ /* Type definitions */ #define PyUniverseSpec_Type_NUM 0 /* Universe state lock access */ #define PyUniverseSpec_StateLock_RET int #define PyUniverseSpec_StateLock_PROTO \ Py_PROTO((PyUniverseSpecObject *universe, int action)) #define PyUniverseSpec_StateLock_NUM 1 /* Total number of C API pointers */ #define PyUniverse_API_pointers 2 #ifdef _UNIVERSE_MODULE /* Type object declarations */ extern PyTypeObject PyUniverseSpec_Type; /* Type check macros */ #define PyUniverseSpec_Check(op) ((op)->ob_type == &PyUniverseSpec_Type) /* C API function declarations */ extern PyUniverseSpec_StateLock_RET PyUniverseSpec_StateLock \ PyUniverseSpec_StateLock_PROTO; #else /* C API address pointer */ static void **PyUniverse_API; /* Type definitions */ #define PyUniverseSpec_Type *(PyTypeObject *) \ PyUniverse_API[PyUniverseSpec_Type_NUM] /* Type check macros */ #define PyUniverseSpec_Check(op) \ ((op)->ob_type == (PyTypeObject *)PyUniverse_API[PyUniverseSpec_Type_NUM]) /* C API function declarations */ #define PyUniverseSpec_StateLock \ (*(PyUniverseSpec_StateLock_RET (*)PyUniverseSpec_StateLock_PROTO) \ PyUniverse_API[PyUniverseSpec_StateLock_NUM]) /* Import macro */ #define import_MMTK_universe() \ { \ PyObject *module = PyImport_ImportModule("MMTK_universe"); \ if (module != NULL) { \ PyObject *module_dict = PyModule_GetDict(module); \ PyObject *c_api_object = PyDict_GetItemString(module_dict, "_C_API"); \ if (PyCObject_Check(c_api_object)) { \ PyUniverse_API = (void **)PyCObject_AsVoidPtr(c_api_object); \ } \ } \ } #endif #define MMTK_UNIVERSE_H #endif MMTK-2.7.9/Include/MMTK/universe.pxi0000644000076600000240000000224311662461637017443 0ustar hinsenstaff00000000000000cdef extern from "MMTK/universe.h": void import_MMTK_universe() ctypedef void distance_fn(vector3 d, vector3 r1, vector3 r2, double *data) nogil ctypedef void correction_fn(vector3 *x, int natoms, double *data) nogil ctypedef double volume_fn(double scale_factor, double *data) nogil ctypedef void box_fn(vector3 *x, vector3 *b, int n, double *data, int to_box) nogil ctypedef void bounding_box_fn(vector3 *box1, vector3 *box2, vector3 *x, int n, double *data) nogil ctypedef struct PyUniverseSpecObject: PyArrayObject *geometry double *geometry_data distance_fn *distance_function correction_fn *correction_function volume_fn *volume_function box_fn *box_function box_fn *trajectory_function bounding_box_fn *bounding_box_function int is_periodic int is_orthogonal int geometry_data_length cdef int PyUniverseSpec_StateLock(PyUniverseSpecObject *universe, int action) nogil cdef void **PyUniverse_API import_MMTK_universe() MMTK-2.7.9/Include/MMTK_trajectory_action.pxd0000644000076600000240000000123011662461637021374 0ustar hinsenstaff00000000000000# Trajectory actions in Cython # # Written by Konrad Hinsen # include "MMTK/python.pxi" include "MMTK/numeric.pxi" include "MMTK/core.pxi" include "MMTK/universe.pxi" include 'MMTK/trajectory.pxi' cdef class TrajectoryAction: cdef long first cdef long last cdef long skip cdef int initialize(self, object universe, long generator_id, PyTrajectoryVariable *dynamic_data) cdef int finalize(self, object universe, long generator_id, PyTrajectoryVariable *dynamic_data) cdef int apply(self, object universe, long generator_id, int step, PyTrajectoryVariable *dynamic_data) MMTK-2.7.9/Include/MMTK_trajectory_generator.pxd0000644000076600000240000000450112117632051022073 0ustar hinsenstaff00000000000000# Trajectory generators in Cython # # Written by Konrad Hinsen # cimport numpy as N include "MMTK/python.pxi" include "MMTK/numeric.pxi" include "MMTK/core.pxi" include "MMTK/universe.pxi" include "MMTK/trajectory.pxi" include "MMTK/forcefield.pxi" cdef extern from "stdlib.h": ctypedef long size_t cdef void *malloc(size_t size) cdef void free(void *ptr) cdef class TrajectoryGenerator(object): cdef readonly universe, options, name cdef readonly call_options cdef readonly features cdef readonly actions cdef public state_accessor cdef PyTrajectoryVariable *tvars cdef PyTrajectoryOutputSpec *tspec cdef PyUniverseSpecObject *universe_spec cdef int natoms, df cdef N.ndarray conf_array cdef int lock_state cdef void declareTrajectoryVariable_double(self, double * var, char *name, char *text, char*unit, int data_class) except * cdef void declareTrajectoryVariable_int(self, int * var, char *name, char *text, char*unit, int data_class) except * cdef void declareTrajectoryVariable_array(self, N.ndarray array, char*name, char *text, char*unit, int data_class) except * cdef void declareTrajectoryVariable_box(self, double * var, int l) except * cdef void _addTrajectoryVariable(self, PyTrajectoryVariable v) except * cdef void initializeTrajectoryActions(self) except * cdef void finalizeTrajectoryActions(self, int last_step, int error=?) \ except * cdef int trajectoryActions(self, int step) except -1 cdef void foldCoordinatesIntoBox(self) nogil cdef void acquireReadLock(self) nogil cdef void releaseReadLock(self) nogil cdef void acquireWriteLock(self) nogil cdef void releaseWriteLock(self) nogil cdef start(self) cdef class EnergyBasedTrajectoryGenerator(TrajectoryGenerator): cdef evaluator_object cdef c_evaluator_object cdef PyFFEvaluatorObject *evaluator cdef void calculateEnergies(self, N.ndarray[double, ndim=2] conf_array, energy_data *energy, int small_change=?) \ except * MMTK-2.7.9/LICENCE0000644000076600000240000005314611662461637013723 0ustar hinsenstaff00000000000000 CONTRAT DE LICENCE DE LOGICIEL LIBRE CeCILL-C Avertissement Ce contrat est une licence de logiciel libre issue d'une concertation entre ses auteurs afin que le respect de deux grands principes préside à sa rédaction: * d'une part, le respect des principes de diffusion des logiciels libres: accès au code source, droits étendus conférés aux utilisateurs, * d'autre part, la désignation d'un droit applicable, le droit français, auquel elle est conforme, tant au regard du droit de la responsabilité civile que du droit de la propriété intellectuelle et de la protection qu'il offre aux auteurs et titulaires des droits patrimoniaux sur un logiciel. Les auteurs de la licence CeCILL-C (pour Ce[a] C[nrs] I[nria] L[ogiciel] L[ibre]) sont: Commissariat à l'Energie Atomique - CEA, établissement public de recherche à caractère scientifique, technique et industriel, dont le siège est situé 25 rue Leblanc, immeuble Le Ponant D, 75015 Paris. Centre National de la Recherche Scientifique - CNRS, établissement public à caractère scientifique et technologique, dont le siège est situé 3 rue Michel-Ange, 75794 Paris cedex 16. Institut National de Recherche en Informatique et en Automatique - INRIA, établissement public à caractère scientifique et technologique, dont le siège est situé Domaine de Voluceau, Rocquencourt, BP 105, 78153 Le Chesnay cedex. Préambule Ce contrat est une licence de logiciel libre dont l'objectif est de conférer aux utilisateurs la liberté de modifier et de réutiliser le logiciel régi par cette licence. L'exercice de cette liberté est assorti d'une obligation de remettre à la disposition de la communauté les modifications apportées au code source du logiciel afin de contribuer à son évolution. L'accessibilité au code source et les droits de copie, de modification et de redistribution qui découlent de ce contrat ont pour contrepartie de n'offrir aux utilisateurs qu'une garantie limitée et de ne faire peser sur l'auteur du logiciel, le titulaire des droits patrimoniaux et les concédants successifs qu'une responsabilité restreinte. A cet égard l'attention de l'utilisateur est attirée sur les risques associés au chargement, à l'utilisation, à la modification et/ou au développement et à la reproduction du logiciel par l'utilisateur étant donné sa spécificité de logiciel libre, qui peut le rendre complexe à manipuler et qui le réserve donc à des développeurs ou des professionnels avertis possédant des connaissances informatiques approfondies. Les utilisateurs sont donc invités à charger et tester l'adéquation du logiciel à leurs besoins dans des conditions permettant d'assurer la sécurité de leurs systèmes et/ou de leurs données et, plus généralement, à l'utiliser et l'exploiter dans les mêmes conditions de sécurité. Ce contrat peut être reproduit et diffusé librement, sous réserve de le conserver en l'état, sans ajout ni suppression de clauses. Ce contrat est susceptible de s'appliquer à tout logiciel dont le titulaire des droits patrimoniaux décide de soumettre l'exploitation aux dispositions qu'il contient. Article 1 - DEFINITIONS Dans ce contrat, les termes suivants, lorsqu'ils seront écrits avec une lettre capitale, auront la signification suivante: Contrat: désigne le présent contrat de licence, ses éventuelles versions postérieures et annexes. Logiciel: désigne le logiciel sous sa forme de Code Objet et/ou de Code Source et le cas échéant sa documentation, dans leur état au moment de l'acceptation du Contrat par le Licencié. Logiciel Initial: désigne le Logiciel sous sa forme de Code Source et éventuellement de Code Objet et le cas échéant sa documentation, dans leur état au moment de leur première diffusion sous les termes du Contrat. Logiciel Modifié: désigne le Logiciel modifié par au moins une Contribution Intégrée. Code Source: désigne l'ensemble des instructions et des lignes de programme du Logiciel et auquel l'accès est nécessaire en vue de modifier le Logiciel. Code Objet: désigne les fichiers binaires issus de la compilation du Code Source. Titulaire: désigne le ou les détenteurs des droits patrimoniaux d'auteur sur le Logiciel Initial. Licencié: désigne le ou les utilisateurs du Logiciel ayant accepté le Contrat. Contributeur: désigne le Licencié auteur d'au moins une Contribution Intégrée. Concédant: désigne le Titulaire ou toute personne physique ou morale distribuant le Logiciel sous le Contrat. Contribution Intégrée: désigne l'ensemble des modifications, corrections, traductions, adaptations et/ou nouvelles fonctionnalités intégrées dans le Code Source par tout Contributeur. Module Lié: désigne un ensemble de fichiers sources y compris leur documentation qui, sans modification du Code Source, permet de réaliser des fonctionnalités ou services supplémentaires à ceux fournis par le Logiciel. Logiciel Dérivé: désigne toute combinaison du Logiciel, modifié ou non, et d'un Module Lié. Parties: désigne collectivement le Licencié et le Concédant. Ces termes s'entendent au singulier comme au pluriel. Article 2 - OBJET Le Contrat a pour objet la concession par le Concédant au Licencié d'une licence non exclusive, cessible et mondiale du Logiciel telle que définie ci-après à l'article 5 pour toute la durée de protection des droits portant sur ce Logiciel. Article 3 - ACCEPTATION 3.1 L'acceptation par le Licencié des termes du Contrat est réputée acquise du fait du premier des faits suivants: * (i) le chargement du Logiciel par tout moyen notamment par téléchargement à partir d'un serveur distant ou par chargement à partir d'un support physique; * (ii) le premier exercice par le Licencié de l'un quelconque des droits concédés par le Contrat. 3.2 Un exemplaire du Contrat, contenant notamment un avertissement relatif aux spécificités du Logiciel, à la restriction de garantie et à la limitation à un usage par des utilisateurs expérimentés a été mis à disposition du Licencié préalablement à son acceptation telle que définie à l'article 3.1 ci dessus et le Licencié reconnaît en avoir pris connaissance. Article 4 - ENTREE EN VIGUEUR ET DUREE 4.1 ENTREE EN VIGUEUR Le Contrat entre en vigueur à la date de son acceptation par le Licencié telle que définie en 3.1. 4.2 DUREE Le Contrat produira ses effets pendant toute la durée légale de protection des droits patrimoniaux portant sur le Logiciel. Article 5 - ETENDUE DES DROITS CONCEDES Le Concédant concède au Licencié, qui accepte, les droits suivants sur le Logiciel pour toutes destinations et pour la durée du Contrat dans les conditions ci-après détaillées. Par ailleurs, si le Concédant détient ou venait à détenir un ou plusieurs brevets d'invention protégeant tout ou partie des fonctionnalités du Logiciel ou de ses composants, il s'engage à ne pas opposer les éventuels droits conférés par ces brevets aux Licenciés successifs qui utiliseraient, exploiteraient ou modifieraient le Logiciel. En cas de cession de ces brevets, le Concédant s'engage à faire reprendre les obligations du présent alinéa aux cessionnaires. 5.1 DROIT D'UTILISATION Le Licencié est autorisé à utiliser le Logiciel, sans restriction quant aux domaines d'application, étant ci-après précisé que cela comporte: 1. la reproduction permanente ou provisoire du Logiciel en tout ou partie par tout moyen et sous toute forme. 2. le chargement, l'affichage, l'exécution, ou le stockage du Logiciel sur tout support. 3. la possibilité d'en observer, d'en étudier, ou d'en tester le fonctionnement afin de déterminer les idées et principes qui sont à la base de n'importe quel élément de ce Logiciel; et ceci, lorsque le Licencié effectue toute opération de chargement, d'affichage, d'exécution, de transmission ou de stockage du Logiciel qu'il est en droit d'effectuer en vertu du Contrat. 5.2 DROIT DE MODIFICATION Le droit de modification comporte le droit de traduire, d'adapter, d'arranger ou d'apporter toute autre modification au Logiciel et le droit de reproduire le logiciel en résultant. Il comprend en particulier le droit de créer un Logiciel Dérivé. Le Licencié est autorisé à apporter toute modification au Logiciel sous réserve de mentionner, de façon explicite, son nom en tant qu'auteur de cette modification et la date de création de celle-ci. 5.3 DROIT DE DISTRIBUTION Le droit de distribution comporte notamment le droit de diffuser, de transmettre et de communiquer le Logiciel au public sur tout support et par tout moyen ainsi que le droit de mettre sur le marché à titre onéreux ou gratuit, un ou des exemplaires du Logiciel par tout procédé. Le Licencié est autorisé à distribuer des copies du Logiciel, modifié ou non, à des tiers dans les conditions ci-après détaillées. 5.3.1 DISTRIBUTION DU LOGICIEL SANS MODIFICATION Le Licencié est autorisé à distribuer des copies conformes du Logiciel, sous forme de Code Source ou de Code Objet, à condition que cette distribution respecte les dispositions du Contrat dans leur totalité et soit accompagnée: 1. d'un exemplaire du Contrat, 2. d'un avertissement relatif à la restriction de garantie et de responsabilité du Concédant telle que prévue aux articles 8 et 9, et que, dans le cas où seul le Code Objet du Logiciel est redistribué, le Licencié permette un accès effectif au Code Source complet du Logiciel pendant au moins toute la durée de sa distribution du Logiciel, étant entendu que le coût additionnel d'acquisition du Code Source ne devra pas excéder le simple coût de transfert des données. 5.3.2 DISTRIBUTION DU LOGICIEL MODIFIE Lorsque le Licencié apporte une Contribution Intégrée au Logiciel, les conditions de distribution du Logiciel Modifié en résultant sont alors soumises à l'intégralité des dispositions du Contrat. Le Licencié est autorisé à distribuer le Logiciel Modifié sous forme de code source ou de code objet, à condition que cette distribution respecte les dispositions du Contrat dans leur totalité et soit accompagnée: 1. d'un exemplaire du Contrat, 2. d'un avertissement relatif à la restriction de garantie et de responsabilité du Concédant telle que prévue aux articles 8 et 9, et que, dans le cas où seul le code objet du Logiciel Modifié est redistribué, le Licencié permette un accès effectif à son code source complet pendant au moins toute la durée de sa distribution du Logiciel Modifié, étant entendu que le coût additionnel d'acquisition du code source ne devra pas excéder le simple coût de transfert des données. 5.3.3 DISTRIBUTION DU LOGICIEL DERIVE Lorsque le Licencié crée un Logiciel Dérivé, ce Logiciel Dérivé peut être distribué sous un contrat de licence autre que le présent Contrat à condition de respecter les obligations de mention des droits sur le Logiciel telles que définies à l'article 6.4. Dans le cas où la création du Logiciel Dérivé a nécessité une modification du Code Source le licencié s'engage à ce que: 1. le Logiciel Modifié correspondant à cette modification soit régi par le présent Contrat, 2. les Contributions Intégrées dont le Logiciel Modifié résulte soient clairement identifiées et documentées, 3. le Licencié permette un accès effectif au code source du Logiciel Modifié, pendant au moins toute la durée de la distribution du Logiciel Dérivé, de telle sorte que ces modifications puissent être reprises dans une version ultérieure du Logiciel, étant entendu que le coût additionnel d'acquisition du code source du Logiciel Modifié ne devra pas excéder le simple coût du transfert des données. 5.3.4 COMPATIBILITE AVEC LA LICENCE CeCILL Lorsqu'un Logiciel Modifié contient une Contribution Intégrée soumise au contrat de licence CeCILL, ou lorsqu'un Logiciel Dérivé contient un Module Lié soumis au contrat de licence CeCILL, les stipulations prévues au troisième item de l'article 6.4 sont facultatives. Article 6 - PROPRIETE INTELLECTUELLE 6.1 SUR LE LOGICIEL INITIAL Le Titulaire est détenteur des droits patrimoniaux sur le Logiciel Initial. Toute utilisation du Logiciel Initial est soumise au respect des conditions dans lesquelles le Titulaire a choisi de diffuser son oeuvre et nul autre n'a la faculté de modifier les conditions de diffusion de ce Logiciel Initial. Le Titulaire s'engage à ce que le Logiciel Initial reste au moins régi par le Contrat et ce, pour la durée visée à l'article 4.2. 6.2 SUR LES CONTRIBUTIONS INTEGREES Le Licencié qui a développé une Contribution Intégrée est titulaire sur celle-ci des droits de propriété intellectuelle dans les conditions définies par la législation applicable. 6.3 SUR LES MODULES LIES Le Licencié qui a développé un Module Lié est titulaire sur celui-ci des droits de propriété intellectuelle dans les conditions définies par la législation applicable et reste libre du choix du contrat régissant sa diffusion dans les conditions définies à l'article 5.3.3. 6.4 MENTIONS DES DROITS Le Licencié s'engage expressément: 1. à ne pas supprimer ou modifier de quelque manière que ce soit les mentions de propriété intellectuelle apposées sur le Logiciel; 2. à reproduire à l'identique lesdites mentions de propriété intellectuelle sur les copies du Logiciel modifié ou non; 3. à faire en sorte que l'utilisation du Logiciel, ses mentions de propriété intellectuelle et le fait qu'il est régi par le Contrat soient indiqués dans un texte facilement accessible notamment depuis l'interface de tout Logiciel Dérivé. Le Licencié s'engage à ne pas porter atteinte, directement ou indirectement, aux droits de propriété intellectuelle du Titulaire et/ou des Contributeurs sur le Logiciel et à prendre, le cas échéant, à l'égard de son personnel toutes les mesures nécessaires pour assurer le respect des dits droits de propriété intellectuelle du Titulaire et/ou des Contributeurs. Article 7 - SERVICES ASSOCIES 7.1 Le Contrat n'oblige en aucun cas le Concédant à la réalisation de prestations d'assistance technique ou de maintenance du Logiciel. Cependant le Concédant reste libre de proposer ce type de services. Les termes et conditions d'une telle assistance technique et/ou d'une telle maintenance seront alors déterminés dans un acte séparé. Ces actes de maintenance et/ou assistance technique n'engageront que la seule responsabilité du Concédant qui les propose. 7.2 De même, tout Concédant est libre de proposer, sous sa seule responsabilité, à ses licenciés une garantie, qui n'engagera que lui, lors de la redistribution du Logiciel et/ou du Logiciel Modifié et ce, dans les conditions qu'il souhaite. Cette garantie et les modalités financières de son application feront l'objet d'un acte séparé entre le Concédant et le Licencié. Article 8 - RESPONSABILITE 8.1 Sous réserve des dispositions de l'article 8.2, le Licencié a la faculté, sous réserve de prouver la faute du Concédant concerné, de solliciter la réparation du préjudice direct qu'il subirait du fait du Logiciel et dont il apportera la preuve. 8.2 La responsabilité du Concédant est limitée aux engagements pris en application du Contrat et ne saurait être engagée en raison notamment: (i) des dommages dus à l'inexécution, totale ou partielle, de ses obligations par le Licencié, (ii) des dommages directs ou indirects découlant de l'utilisation ou des performances du Logiciel subis par le Licencié et (iii) plus généralement d'un quelconque dommage indirect. En particulier, les Parties conviennent expressément que tout préjudice financier ou commercial (par exemple perte de données, perte de bénéfices, perte d'exploitation, perte de clientèle ou de commandes, manque à gagner, trouble commercial quelconque) ou toute action dirigée contre le Licencié par un tiers, constitue un dommage indirect et n'ouvre pas droit à réparation par le Concédant. Article 9 - GARANTIE 9.1 Le Licencié reconnaît que l'état actuel des connaissances scientifiques et techniques au moment de la mise en circulation du Logiciel ne permet pas d'en tester et d'en vérifier toutes les utilisations ni de détecter l'existence d'éventuels défauts. L'attention du Licencié a été attirée sur ce point sur les risques associés au chargement, à l'utilisation, la modification et/ou au développement et à la reproduction du Logiciel qui sont réservés à des utilisateurs avertis. Il relève de la responsabilité du Licencié de contrôler, par tous moyens, l'adéquation du produit à ses besoins, son bon fonctionnement et de s'assurer qu'il ne causera pas de dommages aux personnes et aux biens. 9.2 Le Concédant déclare de bonne foi être en droit de concéder l'ensemble des droits attachés au Logiciel (comprenant notamment les droits visés à l'article 5). 9.3 Le Licencié reconnaît que le Logiciel est fourni "en l'état" par le Concédant sans autre garantie, expresse ou tacite, que celle prévue à l'article 9.2 et notamment sans aucune garantie sur sa valeur commerciale, son caractère sécurisé, innovant ou pertinent. En particulier, le Concédant ne garantit pas que le Logiciel est exempt d'erreur, qu'il fonctionnera sans interruption, qu'il sera compatible avec l'équipement du Licencié et sa configuration logicielle ni qu'il remplira les besoins du Licencié. 9.4 Le Concédant ne garantit pas, de manière expresse ou tacite, que le Logiciel ne porte pas atteinte à un quelconque droit de propriété intellectuelle d'un tiers portant sur un brevet, un logiciel ou sur tout autre droit de propriété. Ainsi, le Concédant exclut toute garantie au profit du Licencié contre les actions en contrefaçon qui pourraient être diligentées au titre de l'utilisation, de la modification, et de la redistribution du Logiciel. Néanmoins, si de telles actions sont exercées contre le Licencié, le Concédant lui apportera son aide technique et juridique pour sa défense. Cette aide technique et juridique est déterminée au cas par cas entre le Concédant concerné et le Licencié dans le cadre d'un protocole d'accord. Le Concédant dégage toute responsabilité quant à l'utilisation de la dénomination du Logiciel par le Licencié. Aucune garantie n'est apportée quant à l'existence de droits antérieurs sur le nom du Logiciel et sur l'existence d'une marque. Article 10 - RESILIATION 10.1 En cas de manquement par le Licencié aux obligations mises à sa charge par le Contrat, le Concédant pourra résilier de plein droit le Contrat trente (30) jours après notification adressée au Licencié et restée sans effet. 10.2 Le Licencié dont le Contrat est résilié n'est plus autorisé à utiliser, modifier ou distribuer le Logiciel. Cependant, toutes les licences qu'il aura concédées antérieurement à la résiliation du Contrat resteront valides sous réserve qu'elles aient été effectuées en conformité avec le Contrat. Article 11 - DISPOSITIONS DIVERSES 11.1 CAUSE EXTERIEURE Aucune des Parties ne sera responsable d'un retard ou d'une défaillance d'exécution du Contrat qui serait dû à un cas de force majeure, un cas fortuit ou une cause extérieure, telle que, notamment, le mauvais fonctionnement ou les interruptions du réseau électrique ou de télécommunication, la paralysie du réseau liée à une attaque informatique, l'intervention des autorités gouvernementales, les catastrophes naturelles, les dégâts des eaux, les tremblements de terre, le feu, les explosions, les grèves et les conflits sociaux, l'état de guerre... 11.2 Le fait, par l'une ou l'autre des Parties, d'omettre en une ou plusieurs occasions de se prévaloir d'une ou plusieurs dispositions du Contrat, ne pourra en aucun cas impliquer renonciation par la Partie intéressée à s'en prévaloir ultérieurement. 11.3 Le Contrat annule et remplace toute convention antérieure, écrite ou orale, entre les Parties sur le même objet et constitue l'accord entier entre les Parties sur cet objet. Aucune addition ou modification aux termes du Contrat n'aura d'effet à l'égard des Parties à moins d'être faite par écrit et signée par leurs représentants dûment habilités. 11.4 Dans l'hypothèse où une ou plusieurs des dispositions du Contrat s'avèrerait contraire à une loi ou à un texte applicable, existants ou futurs, cette loi ou ce texte prévaudrait, et les Parties feraient les amendements nécessaires pour se conformer à cette loi ou à ce texte. Toutes les autres dispositions resteront en vigueur. De même, la nullité, pour quelque raison que ce soit, d'une des dispositions du Contrat ne saurait entraîner la nullité de l'ensemble du Contrat. 11.5 LANGUE Le Contrat est rédigé en langue française et en langue anglaise, ces deux versions faisant également foi. Article 12 - NOUVELLES VERSIONS DU CONTRAT 12.1 Toute personne est autorisée à copier et distribuer des copies de ce Contrat. 12.2 Afin d'en préserver la cohérence, le texte du Contrat est protégé et ne peut être modifié que par les auteurs de la licence, lesquels se réservent le droit de publier périodiquement des mises à jour ou de nouvelles versions du Contrat, qui posséderont chacune un numéro distinct. Ces versions ultérieures seront susceptibles de prendre en compte de nouvelles problématiques rencontrées par les logiciels libres. 12.3 Tout Logiciel diffusé sous une version donnée du Contrat ne pourra faire l'objet d'une diffusion ultérieure que sous la même version du Contrat ou une version postérieure. Article 13 - LOI APPLICABLE ET COMPETENCE TERRITORIALE 13.1 Le Contrat est régi par la loi française. Les Parties conviennent de tenter de régler à l'amiable les différends ou litiges qui viendraient à se produire par suite ou à l'occasion du Contrat. 13.2 A défaut d'accord amiable dans un délai de deux (2) mois à compter de leur survenance et sauf situation relevant d'une procédure d'urgence, les différends ou litiges seront portés par la Partie la plus diligente devant les Tribunaux compétents de Paris. Version 1.0 du 2006-09-05. MMTK-2.7.9/LICENSE0000644000076600000240000005254711662461637013747 0ustar hinsenstaff00000000000000 CeCILL-C FREE SOFTWARE LICENSE AGREEMENT Notice This Agreement is a Free Software license agreement that is the result of discussions between its authors in order to ensure compliance with the two main principles guiding its drafting: * firstly, compliance with the principles governing the distribution of Free Software: access to source code, broad rights granted to users, * secondly, the election of a governing law, French law, with which it is conformant, both as regards the law of torts and intellectual property law, and the protection that it offers to both authors and holders of the economic rights over software. The authors of the CeCILL-C (for Ce[a] C[nrs] I[nria] L[ogiciel] L[ibre]) license are: Commissariat à l'Energie Atomique - CEA, a public scientific, technical and industrial research establishment, having its principal place of business at 25 rue Leblanc, immeuble Le Ponant D, 75015 Paris, France. Centre National de la Recherche Scientifique - CNRS, a public scientific and technological establishment, having its principal place of business at 3 rue Michel-Ange, 75794 Paris cedex 16, France. Institut National de Recherche en Informatique et en Automatique - INRIA, a public scientific and technological establishment, having its principal place of business at Domaine de Voluceau, Rocquencourt, BP 105, 78153 Le Chesnay cedex, France. Preamble The purpose of this Free Software license agreement is to grant users the right to modify and re-use the software governed by this license. The exercising of this right is conditional upon the obligation to make available to the community the modifications made to the source code of the software so as to contribute to its evolution. In consideration of access to the source code and the rights to copy, modify and redistribute granted by the license, users are provided only with a limited warranty and the software's author, the holder of the economic rights, and the successive licensors only have limited liability. In this respect, the risks associated with loading, using, modifying and/or developing or reproducing the software by the user are brought to the user's attention, given its Free Software status, which may make it complicated to use, with the result that its use is reserved for developers and experienced professionals having in-depth computer knowledge. Users are therefore encouraged to load and test the suitability of the software as regards their requirements in conditions enabling the security of their systems and/or data to be ensured and, more generally, to use and operate it in the same conditions of security. This Agreement may be freely reproduced and published, provided it is not altered, and that no provisions are either added or removed herefrom. This Agreement may apply to any or all software for which the holder of the economic rights decides to submit the use thereof to its provisions. Article 1 - DEFINITIONS For the purpose of this Agreement, when the following expressions commence with a capital letter, they shall have the following meaning: Agreement: means this license agreement, and its possible subsequent versions and annexes. Software: means the software in its Object Code and/or Source Code form and, where applicable, its documentation, "as is" when the Licensee accepts the Agreement. Initial Software: means the Software in its Source Code and possibly its Object Code form and, where applicable, its documentation, "as is" when it is first distributed under the terms and conditions of the Agreement. Modified Software: means the Software modified by at least one Integrated Contribution. Source Code: means all the Software's instructions and program lines to which access is required so as to modify the Software. Object Code: means the binary files originating from the compilation of the Source Code. Holder: means the holder(s) of the economic rights over the Initial Software. Licensee: means the Software user(s) having accepted the Agreement. Contributor: means a Licensee having made at least one Integrated Contribution. Licensor: means the Holder, or any other individual or legal entity, who distributes the Software under the Agreement. Integrated Contribution: means any or all modifications, corrections, translations, adaptations and/or new functions integrated into the Source Code by any or all Contributors. Related Module: means a set of sources files including their documentation that, without modification to the Source Code, enables supplementary functions or services in addition to those offered by the Software. Derivative Software: means any combination of the Software, modified or not, and of a Related Module. Parties: mean both the Licensee and the Licensor. These expressions may be used both in singular and plural form. Article 2 - PURPOSE The purpose of the Agreement is the grant by the Licensor to the Licensee of a non-exclusive, transferable and worldwide license for the Software as set forth in Article 5 hereinafter for the whole term of the protection granted by the rights over said Software. Article 3 - ACCEPTANCE 3.1 The Licensee shall be deemed as having accepted the terms and conditions of this Agreement upon the occurrence of the first of the following events: * (i) loading the Software by any or all means, notably, by downloading from a remote server, or by loading from a physical medium; * (ii) the first time the Licensee exercises any of the rights granted hereunder. 3.2 One copy of the Agreement, containing a notice relating to the characteristics of the Software, to the limited warranty, and to the fact that its use is restricted to experienced users has been provided to the Licensee prior to its acceptance as set forth in Article 3.1 hereinabove, and the Licensee hereby acknowledges that it has read and understood it. Article 4 - EFFECTIVE DATE AND TERM 4.1 EFFECTIVE DATE The Agreement shall become effective on the date when it is accepted by the Licensee as set forth in Article 3.1. 4.2 TERM The Agreement shall remain in force for the entire legal term of protection of the economic rights over the Software. Article 5 - SCOPE OF RIGHTS GRANTED The Licensor hereby grants to the Licensee, who accepts, the following rights over the Software for any or all use, and for the term of the Agreement, on the basis of the terms and conditions set forth hereinafter. Besides, if the Licensor owns or comes to own one or more patents protecting all or part of the functions of the Software or of its components, the Licensor undertakes not to enforce the rights granted by these patents against successive Licensees using, exploiting or modifying the Software. If these patents are transferred, the Licensor undertakes to have the transferees subscribe to the obligations set forth in this paragraph. 5.1 RIGHT OF USE The Licensee is authorized to use the Software, without any limitation as to its fields of application, with it being hereinafter specified that this comprises: 1. permanent or temporary reproduction of all or part of the Software by any or all means and in any or all form. 2. loading, displaying, running, or storing the Software on any or all medium. 3. entitlement to observe, study or test its operation so as to determine the ideas and principles behind any or all constituent elements of said Software. This shall apply when the Licensee carries out any or all loading, displaying, running, transmission or storage operation as regards the Software, that it is entitled to carry out hereunder. 5.2 RIGHT OF MODIFICATION The right of modification includes the right to translate, adapt, arrange, or make any or all modifications to the Software, and the right to reproduce the resulting software. It includes, in particular, the right to create a Derivative Software. The Licensee is authorized to make any or all modification to the Software provided that it includes an explicit notice that it is the author of said modification and indicates the date of the creation thereof. 5.3 RIGHT OF DISTRIBUTION In particular, the right of distribution includes the right to publish, transmit and communicate the Software to the general public on any or all medium, and by any or all means, and the right to market, either in consideration of a fee, or free of charge, one or more copies of the Software by any means. The Licensee is further authorized to distribute copies of the modified or unmodified Software to third parties according to the terms and conditions set forth hereinafter. 5.3.1 DISTRIBUTION OF SOFTWARE WITHOUT MODIFICATION The Licensee is authorized to distribute true copies of the Software in Source Code or Object Code form, provided that said distribution complies with all the provisions of the Agreement and is accompanied by: 1. a copy of the Agreement, 2. a notice relating to the limitation of both the Licensor's warranty and liability as set forth in Articles 8 and 9, and that, in the event that only the Object Code of the Software is redistributed, the Licensee allows effective access to the full Source Code of the Software at a minimum during the entire period of its distribution of the Software, it being understood that the additional cost of acquiring the Source Code shall not exceed the cost of transferring the data. 5.3.2 DISTRIBUTION OF MODIFIED SOFTWARE When the Licensee makes an Integrated Contribution to the Software, the terms and conditions for the distribution of the resulting Modified Software become subject to all the provisions of this Agreement. The Licensee is authorized to distribute the Modified Software, in source code or object code form, provided that said distribution complies with all the provisions of the Agreement and is accompanied by: 1. a copy of the Agreement, 2. a notice relating to the limitation of both the Licensor's warranty and liability as set forth in Articles 8 and 9, and that, in the event that only the object code of the Modified Software is redistributed, the Licensee allows effective access to the full source code of the Modified Software at a minimum during the entire period of its distribution of the Modified Software, it being understood that the additional cost of acquiring the source code shall not exceed the cost of transferring the data. 5.3.3 DISTRIBUTION OF DERIVATIVE SOFTWARE When the Licensee creates Derivative Software, this Derivative Software may be distributed under a license agreement other than this Agreement, subject to compliance with the requirement to include a notice concerning the rights over the Software as defined in Article 6.4. In the event the creation of the Derivative Software required modification of the Source Code, the Licensee undertakes that: 1. the resulting Modified Software will be governed by this Agreement, 2. the Integrated Contributions in the resulting Modified Software will be clearly identified and documented, 3. the Licensee will allow effective access to the source code of the Modified Software, at a minimum during the entire period of distribution of the Derivative Software, such that such modifications may be carried over in a subsequent version of the Software; it being understood that the additional cost of purchasing the source code of the Modified Software shall not exceed the cost of transferring the data. 5.3.4 COMPATIBILITY WITH THE CeCILL LICENSE When a Modified Software contains an Integrated Contribution subject to the CeCILL license agreement, or when a Derivative Software contains a Related Module subject to the CeCILL license agreement, the provisions set forth in the third item of Article 6.4 are optional. Article 6 - INTELLECTUAL PROPERTY 6.1 OVER THE INITIAL SOFTWARE The Holder owns the economic rights over the Initial Software. Any or all use of the Initial Software is subject to compliance with the terms and conditions under which the Holder has elected to distribute its work and no one shall be entitled to modify the terms and conditions for the distribution of said Initial Software. 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No supplement or modification to the terms and conditions hereof shall be effective as between the Parties unless it is made in writing and signed by their duly authorized representatives. 11.4 In the event that one or more of the provisions hereof were to conflict with a current or future applicable act or legislative text, said act or legislative text shall prevail, and the Parties shall make the necessary amendments so as to comply with said act or legislative text. All other provisions shall remain effective. Similarly, invalidity of a provision of the Agreement, for any reason whatsoever, shall not cause the Agreement as a whole to be invalid. 11.5 LANGUAGE The Agreement is drafted in both French and English and both versions are deemed authentic. Article 12 - NEW VERSIONS OF THE AGREEMENT 12.1 Any person is authorized to duplicate and distribute copies of this Agreement. 12.2 So as to ensure coherence, the wording of this Agreement is protected and may only be modified by the authors of the License, who reserve the right to periodically publish updates or new versions of the Agreement, each with a separate number. These subsequent versions may address new issues encountered by Free Software. 12.3 Any Software distributed under a given version of the Agreement may only be subsequently distributed under the same version of the Agreement or a subsequent version. Article 13 - GOVERNING LAW AND JURISDICTION 13.1 The Agreement is governed by French law. The Parties agree to endeavor to seek an amicable solution to any disagreements or disputes that may arise during the performance of the Agreement. 13.2 Failing an amicable solution within two (2) months as from their occurrence, and unless emergency proceedings are necessary, the disagreements or disputes shall be referred to the Paris Courts having jurisdiction, by the more diligent Party. Version 1.0 dated 2006-09-05. MMTK-2.7.9/MANIFEST.in0000644000076600000240000000341712117632051014452 0ustar hinsenstaff00000000000000include README LICENSE LICENCE MANIFEST.in include tviewer include MMTK/ForceFields/force_fields include MMTK/ForceFields/Amber/*parm* include MMTK/ForceFields/Amber/frcmod* include MMTK/Database/Atoms/* include MMTK/Database/Groups/* include MMTK/Database/Molecules/* include MMTK/Database/Proteins/* include MMTK/Database/PDB/* recursive-include Include *.pxi recursive-include Include *.pxd include Src/*.c include Src/*.i include Src/*.h include Src/*.pyx include Src/*opt recursive-include Include *.h exclude test/* exclude Test/* include Tests/*.py include config/*.c include Doc/CHANGELOG include Doc/*.rst include Doc/Makefile include Doc/conf.py include Doc/make_example_template.py include Doc/tviewer.1 recursive-include Doc/Examples * recursive-include Doc/HTML * recursive-include Examples *.py recursive-include Examples *.c recursive-include Examples *.pyx include Examples/Forcefield/README exclude Examples/Forcefield/HarmonicOscillator/Cython/*.c exclude Examples/Forcefield/ElectricField/Cython/*.c include Examples/MolecularDynamics/argon.conf.gz include Examples/LangevinDynamics/*.c include Examples/LangevinDynamics/ranf.h include Examples/LangevinDynamics/README include Examples/MDIntegrator/README prune Examples/Forcefield/HarmonicOscillator/C/build prune Examples/Forcefield/HarmonicOscillator/C/dist prune Examples/Forcefield/HarmonicOscillator/Cython/build prune Examples/Forcefield/HarmonicOscillator/Cython/dist prune Examples/Forcefield/ElectricField/C/build prune Examples/Forcefield/ElectricField/C/dist prune Examples/Forcefield/ElectricField/Cython/build prune Examples/Forcefield/ElectricField/Cython/dist prune Examples/MDIntegrator/build prune Examples/MDIntegrator/dist recursive-include Tools *.py include MMTK/Tools/TrajectoryViewer/README exclude *~ exclude ._* MMTK-2.7.9/MMTK/0000755000076600000240000000000012156626564013476 5ustar hinsenstaff00000000000000MMTK-2.7.9/MMTK/__init__.py0000644000076600000240000000632012117632051015571 0ustar hinsenstaff00000000000000# MMTK initialization # # Written by Konrad Hinsen # """ MMTK is the base module of the Molecular Modelling Toolkit. It contains the most common objects and all submodules. As a convenience to the user, it also imports some commonly used objects from other libraries: * ``Vector`` from ``Scientific.Geometry`` * ``Translation`` and ``Rotation`` from ``Scientific.Geometry.Transformation`` * ``copy`` and ``deepcopy`` from ``copy`` * ``stdin``, ``stdout``, and ``stderr`` from ``sys`` """ __docformat__ = 'restructuredtext' # # Package information # from __pkginfo__ import __version__ # # Add shared library path to sys.path # import os, sys sys.path.append(os.path.join(os.path.split(__file__)[0], sys.platform)) del os del sys # # General library modules, for convenience # from Scientific.Geometry import Vector from Scientific.Geometry.Transformation import Translation, Rotation from copy import copy, deepcopy from sys import stdin, stdout, stderr # # MMTK core modules # from ThreadManager import activeThreads, waitForThreads from Universe import InfiniteUniverse, OrthorhombicPeriodicUniverse, \ CubicPeriodicUniverse, ParallelepipedicPeriodicUniverse from ParticleProperties import ParticleScalar, ParticleVector, \ ParticleTensor, SymmetricPairTensor, \ Configuration from ChemicalObjects import Atom, Molecule, Complex, AtomCluster from Collections import Collection, PartitionedCollection, \ PartitionedAtomCollection from Utility import save, load import Units # Pretend that certain object are defined here for documentation purposes import sys if sys.modules.has_key('pythondoc'): InfiniteUniverse.__module__ = 'MMTK' OrthorhombicPeriodicUniverse.__module__ = 'MMTK' CubicPeriodicUniverse.__module__ = 'MMTK' ParticleScalar.__module__ = 'MMTK' ParticleVector.__module__ = 'MMTK' ParticleTensor.__module__ = 'MMTK' SymmetricPairTensor.__module__ = 'MMTK' Configuration.__module__ = 'MMTK' Atom.__module__ = 'MMTK' Molecule.__module__ = 'MMTK' Complex.__module__ = 'MMTK' AtomCluster.__module__ = 'MMTK' Collection.__module__ = 'MMTK' PartitionedCollection.__module__ = 'MMTK' PartitionedAtomCollection.__module__ = 'MMTK' Units.__module__ = 'MMTK' save.func_globals['__name__'] = 'MMTK' load.func_globals['__name__'] = 'MMTK' del sys save.func_globals['virtual_module'] = 'MMTK' load.func_globals['virtual_module'] = 'MMTK' ## import string ## load.func_globals['__file__'] = string.replace(load.func_globals['__file__'], ## 'Utility', '__init__') ## save.func_globals['__file__'] = string.replace(load.func_globals['__file__'], ## 'Utility', '__init__') ## del string # Execute user-defined initialization code import os, sys filename = os.path.expanduser('~/.mmtk/init.py') if os.path.exists(filename): definitions = {} execfile(filename, globals(), definitions) if definitions.has_key('export'): mod = sys.modules['MMTK'] for name, value in definitions['export'].items(): setattr(mod, name, value) del filename del os del sys MMTK-2.7.9/MMTK/__pkginfo__.py0000644000076600000240000000010712156625400016263 0ustar hinsenstaff00000000000000# Data used both in the package and by setup.py __version__ = '2.7.9' MMTK-2.7.9/MMTK/AtomEnvironment.py0000644000076600000240000000015711662461637017177 0ustar hinsenstaff00000000000000# This module defines the environment in which atom definition files # are executed. from MMTK.Units import * MMTK-2.7.9/MMTK/Biopolymers.py0000644000076600000240000002405612041510260016335 0ustar hinsenstaff00000000000000# This module implements the base classes for proteins and # nucleic acid chains. # # Written by Konrad Hinsen # """ Base classes for proteins and nucleic acids """ from MMTK import Bonds, ChemicalObjects, Collections, Database, PDB import Scientific.IO.PDB class Residue(ChemicalObjects.Group): """ Base class for aminoacid and nucleic acid residues """ def __setstate__(self, state): self.__dict__.update(state) try: self.model = self.hydrogens except AttributeError: pass self._init() def _init(self): # construct PDB map and alternative names type = self.type if not hasattr(type, 'pdbmap'): pdb_dict = {} type.pdbmap = [(type.symbol, pdb_dict)] offset = 0 for g in type.groups: for name, atom in g.pdbmap[0][1].items(): pdb_dict[name] = Database.AtomReference(atom.number + \ offset) offset = offset + len(g.atoms) if not hasattr(type, 'pdb_alternative'): alt_dict = {} for g in type.groups: if hasattr(g, 'pdb_alternative'): for key, value in g.pdb_alternative.items(): alt_dict[key] = value setattr(type, 'pdb_alternative', alt_dict) def _residueThroughLinkAtom(self, link_atom): if link_atom is None: return None levels = 0 obj = link_atom while obj is not None and obj != self: obj = obj.parent levels += 1 for atom in link_atom.bondedTo(): if atom.parent is not link_atom.parent: obj = atom while levels > 0: obj = obj.parent levels -= 1 return obj return None def precedingResidue(self): """ :returns the preceding residue in the chain """ return self._residueThroughLinkAtom(self.chain_links[0]) def nextResidue(self): """ :returns the next residue in the chain """ return self._residueThroughLinkAtom(self.chain_links[1]) class ResidueChain(ChemicalObjects.Molecule): """ Chain of residues Base class for peptide chains and nucleotide chains """ is_chain = 1 def _setupChain(self, circular, properties, conf): self.atoms = [] self.bonds = [] for g in self.groups: self.atoms.extend(g.atoms) self.bonds.extend(g.bonds) for i in range(len(self.groups)-1): link1 = self.groups[i].chain_links[1] link2 = self.groups[i+1].chain_links[0] self.bonds.append(Bonds.Bond((link1, link2))) if circular: link1 = self.groups[-1].chain_links[1] link2 = self.groups[0].chain_links[0] self.bonds.append(Bonds.Bond((link1, link2))) self.bonds = Bonds.BondList(self.bonds) self.parent = None self.type = None self.configurations = {} try: self.name = properties['name'] del properties['name'] except KeyError: self.name = '' if conf: conf.applyTo(self) try: self.translateTo(properties['position']) del properties['position'] except KeyError: pass self.addProperties(properties) def __len__(self): return len(self.groups) def __getitem__(self, item): return self.groups[item] def __setitem__(self, item, value): self.replaceResidue(self.groups[item], value) def residuesOfType(self, *types): """ :param types: residue type codes :type types: str :returns: a collection that contains all residues whose type (residue code) is contained in types :rtype: :class:`~MMTK.Collections.Collection` """ types = [t.lower() for t in types] rlist = [r for r in self.groups if r.type.symbol.lower() in types] return Collections.Collection(rlist) def residues(self): """ :returns: a collection containing all residues :rtype: :class:`~MMTK.Collections.Collection` """ return Collections.Collection(self.groups) def sequence(self): """ :returns: the residue sequence as a list of residue codes :rtype: list of str """ return [r.type.symbol.lower() for r in self.groups] # # Find the full name of a residue # def _fullName(residue): residue = residue.lower() try: return _aa_residue_names[residue] except KeyError: return _na_residue_names[residue] _aa_residue_names = {'ala': 'alanine', 'a': 'alanine', 'arg': 'arginine', 'r': 'arginine', 'asn': 'asparagine', 'n': 'asparagine', 'asp': 'aspartic_acid', 'd': 'aspartic_acid', 'cys': 'cysteine', 'c': 'cysteine', 'gln': 'glutamine', 'q': 'glutamine', 'glu': 'glutamic_acid', 'e': 'glutamic_acid', 'gly': 'glycine', 'g': 'glycine', 'his': 'histidine', 'h': 'histidine', 'ile': 'isoleucine', 'i': 'isoleucine', 'leu': 'leucine', 'l': 'leucine', 'lys': 'lysine', 'k': 'lysine', 'met': 'methionine', 'm': 'methionine', 'phe': 'phenylalanine', 'f': 'phenylalanine', 'pro': 'proline', 'p': 'proline', 'ser': 'serine', 's': 'serine', 'thr': 'threonine', 't': 'threonine', 'trp': 'tryptophan', 'w': 'tryptophan', 'tyr': 'tyrosine', 'y': 'tyrosine', 'val': 'valine', 'v': 'valine', 'cyx': 'cystine_ss', 'cym': 'cysteine_with_negative_charge', 'app': 'aspartic_acid_neutral', 'glp': 'glutamic_acid_neutral', 'hsd': 'histidine_deltah', 'hse': 'histidine_epsilonh', 'hsp': 'histidine_plus', 'hid': 'histidine_deltah', 'hie': 'histidine_epsilonh', 'hip': 'histidine_plus', 'lyp': 'lysine_neutral', 'ace': 'ace_beginning', 'nme': 'nmethyl', 'nhe': 'amide', } _na_residue_names = {'da': 'd-adenosine', 'da5': 'd-adenosine_5ter', 'da3': 'd-adenosine_3ter', 'dan': 'd-adenosine_5ter_3ter', 'dc': 'd-cytosine', 'dc5': 'd-cytosine_5ter', 'dc3': 'd-cytosine_3ter', 'dcn': 'd-cytosine_5ter_3ter', 'dg': 'd-guanosine', 'dg5': 'd-guanosine_5ter', 'dg3': 'd-guanosine_3ter', 'dgn': 'd-guanosine_5ter_3ter', 'dt': 'd-thymine', 'dt5': 'd-thymine_5ter', 'dt3': 'd-thymine_3ter', 'dtn': 'd-thymine_5ter_3ter', 'ra': 'r-adenosine', 'ra5': 'r-adenosine_5ter', 'ra3': 'r-adenosine_3ter', 'ran': 'r-adenosine_5ter_3ter', 'rc': 'r-cytosine', 'rc5': 'r-cytosine_5ter', 'rc3': 'r-cytosine_3ter', 'rcn': 'r-cytosine_5ter_3ter', 'rg': 'r-guanosine', 'rg5': 'r-guanosine_5ter', 'rg3': 'r-guanosine_3ter', 'rgn': 'r-guanosine_5ter_3ter', 'ru': 'r-uracil', 'ru5': 'r-uracil_5ter', 'ru3': 'r-uracil_3ter', 'run': 'r-uracil_5ter_3ter', } for code in _aa_residue_names: if len(code) == 3: Scientific.IO.PDB.defineAminoAcidResidue(code) for code in _na_residue_names: Scientific.IO.PDB.defineNucleicAcidResidue(code) # # Add a residue to the residue list # def defineAminoAcidResidue(full_name, code3, code1 = None): """ Add a non-standard amino acid residue to the internal residue table. Once added to the residue table, the new residue can be used like any of the standard residues in the creation of peptide chains. :param full_name: the name of the group definition in the chemical database :type full_name: str :param code3: the three-letter residue code :type code3: str :param code1: an optionel one-letter residue code :type code1: str """ code3 = code3.lower() if code1 is not None: code1 = code1.lower() if _aa_residue_names.has_key(code3) or _na_residue_names.has_key(code3): raise ValueError("residue name " + code3 + " already used") if _aa_residue_names.has_key(code1): raise ValueError("residue name " + code1 + " already used") _aa_residue_names[code3] = full_name if code1 is not None: _aa_residue_names[code1] = full_name Scientific.IO.PDB.defineAminoAcidResidue(code3) def defineNucleicAcidResidue(full_name, code): """ Add a non-standard nucleic acid residue to the internal residue table. Once added to the residue table, the new residue can be used like any of the standard residues in the creation of nucleotide chains. :param full_name: the name of the group definition in the chemical database :type full_name: str :param code: the residue code :type code3: str """ code = code.lower() if _aa_residue_names.has_key(code) or _na_residue_names.has_key(code): raise ValueError("residue name " + code + " already used") _na_residue_names[code] = full_name Scientific.IO.PDB.defineNucleicAcidResidue(code) MMTK-2.7.9/MMTK/Bonds.py0000644000076600000240000003161511662461637015122 0ustar hinsenstaff00000000000000# This module implements classes that represent lists of bonds, # bond angles, and dihedral angles. # # Written by Konrad Hinsen # """ Bonds, bond lists, bond angle lists, and dihedral angle lists The classes in this module are normally not used directly from client code. They are used by the classes in ChemicalObjects and ForceField. """ __docformat__ = 'restructuredtext' from MMTK import Database, Utility from copy import copy # # Bonds # # Bond objects are created from the specifications in the data base. # class Bond(object): """ Chemical bond A bond links two atoms (attributes a1 and a2) """ def __init__(self, blueprint, memo = None): if type(blueprint) is type(()): self.a1 = blueprint[0] self.a2 = blueprint[1] else: self.a1 = Database.instantiate(blueprint.a1, memo) self.a2 = Database.instantiate(blueprint.a2, memo) if Utility.uniqueID(self.a2) < Utility.uniqueID(self.a1): self.a1, self.a2 = self.a2, self.a1 Utility.uniqueID.registerObject(self) blueprintclass = Database.BlueprintBond __safe_for_unpickling__ = 1 __had_initargs__ = 1 def __repr__(self): return 'Bond(' + `self.a1` + ', ' + `self.a2` + ')' __str__ = __repr__ def hasAtom(self, a): """ :param a: an atom :type a: :class:`~MMTK.ChemicalObjects.Atom` :returns: True if a participates in the bond """ return a is self.a1 or a is self.a2 def otherAtom(self, a): """ :param a: an atom involved in the bond :type a: :class:`~MMTK.ChemicalObjects.Atom` :returns: the atom at the other end of the bond :rtype: :class:`~MMTK.ChemicalObjects.Atom` :raises ValueError: if a does not belong to the bond """ if a is self.a1: return self.a2 elif a is self.a2: return self.a1 else: raise ValueError('atom not in bond') def _graphics(self, conf, distance_fn, model, module, options): objects = [] if model == 'ball_and_stick' or model == 'vdw_and_stick': radius = options.get('stick_radius', 0.01) elif model == 'wireframe': radius = None else: return [] p1 = self.a1.position(conf) p2 = self.a2.position(conf) if p1 is not None and p2 is not None: bond_vector = 0.5*distance_fn(self.a1, self.a2, conf) cut = bond_vector != 0.5*(p2-p1) color1 = self.a1._atomColor(self.a1, options) color2 = self.a2._atomColor(self.a2, options) material1 = module.EmissiveMaterial(color1) material2 = module.EmissiveMaterial(color2) if color1 == color2 and not cut: if radius is None: objects.append(module.Line(p1, p2, material = material1)) else: objects.append(module.Cylinder(p1, p2, radius, material = material1)) else: if radius is None: objects.append(module.Line(p1, p1+bond_vector, material = material1)) objects.append(module.Line(p2, p2-bond_vector, material = material2)) else: objects.append(module.Cylinder(p1, p1+bond_vector, radius, material = material1)) objects.append(module.Cylinder(p2, p2-bond_vector, radius, material = material2)) return objects Database.registerInstanceClass(Bond.blueprintclass, Bond) # # Bond angles # class BondAngle(object): """ Bond angle A bond angle is the angle between two bonds that share a common atom. It is defined by two bond objects (attributes b1 and b2) and an atom object (the common atom, attribute ca). """ def __init__(self, b1, b2, ca): self.b1 = b1 # bond 1 self.b2 = b2 # bond 2 self.ca = ca # common atom if Utility.uniqueID(self.b2) < Utility.uniqueID(self.b1): self.b1, self.b2 = self.b2, self.b1 self.a1 = b1.otherAtom(ca) self.a2 = b2.otherAtom(ca) Utility.uniqueID.registerObject(self) def __repr__(self): return 'BondAngle(' + `self.a1` +',' + `self.ca` +','+ `self.a2` +')' def otherBond(self, bond): """ :param bond: a bond involved in the angle :type bond: :class:`~MMTK.Bonds.Bond` :returns: the other bond involved in the angle :rtype: :class:`~MMTK.Bonds.Bond` :raises ValueError: if bond does not belong to the angle """ if bond is self.b1: return self.b2 elif bond is self.b2: return self.b1 else: raise ValueError('bond not in bond angle') # # Dihedral angles # class DihedralAngle(object): """ Dihedral angle A dihedral angle is the angle between two planes that are defined by BondAngle objects (attributes ba1 and ba2) and their common bond (attribute cb). There are proper dihedrals (four atoms linked by three bonds in sequence) and improper dihedrals (a central atom linked to three surrounding atoms by three bonds). The boolean attribute improper indicates whether a dihedral is an improper one. """ def __init__(self, ba1, ba2, cb): self.ba1 = ba1 # bond angle 1 self.ba2 = ba2 # bond angle 2 # cb is the common bond, i.e. the central bond for a proper dihedral if Utility.uniqueID(self.ba2) < Utility.uniqueID(self.ba1): self.ba1, self.ba2 = self.ba2, self.ba1 self.improper = (self.ba1.ca is self.ba2.ca) if self.improper: self.b1 = self.ba1.otherBond(cb) self.b2 = cb self.b3 = self.ba2.otherBond(cb) self.a1 = self.ba1.ca # central atom self.a2 = self.b1.otherAtom(self.ba1.ca) self.a3 = cb.otherAtom(self.ba1.ca) self.a4 = self.b3.otherAtom(self.ba2.ca) # each improper dihedral will come in three versions; # identify an arbitrary unique one for constructing the list self.normalized = Utility.uniqueID(cb) < Utility.uniqueID(self.b1)\ and Utility.uniqueID(cb) < \ Utility.uniqueID(self.b3) else: self.b1 = self.ba1.otherBond(cb) self.b2 = cb self.b3 = self.ba2.otherBond(cb) self.a1 = self.b1.otherAtom(self.ba1.ca) self.a2 = self.ba1.ca # these two are self.a3 = self.ba2.ca # on the common bond self.a4 = self.b3.otherAtom(self.ba2.ca) self.normalized = self.a1 is not self.a4 def __repr__(self): if self.improper: return 'ImproperDihedral(' + `self.a1` +','+ `self.a2` +','+ \ `self.a3` +','+ `self.a4` +')' else: return 'Dihedral(' + `self.a1` +','+ `self.a2` +','+ \ `self.a3` +','+ `self.a4` +')' # # Bond lists # # Bond lists can create bond angle and dihedral angle lists # for themselves. These are cached for efficiency. The cached # copy is deleted whenever the bond list is modified. # class BondList(list): def __init__(self, initlist=None): list.__init__(self, initlist) self._clearCache() __safe_for_unpickling__ = 1 def _clearCache(self): self.bond_angles = None self.dihedral_angles = None def __getinitargs__(self): return (None,) def __getstate__(self): self._clearCache() return self.__dict__ def __setitem__(self, i, item): list.__setitem__(self, i, item) self._clearCache() def __setslice__(self, i, j, data): list.__setslice__(self, i, j, data) self._clearCache() def __delslice__(self, i, j): list.__delslice__(self, i, j) self._clearCache() def append(self, item): list.append(self, item) self._clearCache() def extend(self, data): list.extend(self, data) self._clearCache() def insert(self, i, item): list.insert(i, item) self._clearCache() def remove(self, item): list.remove(self, item) self._clearCache() def bondAngles(self): """ :returns: a list of all bond angles that can be formed from the bonds in the list :rtype: :class:`~MMTK.Bonds.BondAngleList` """ if self.bond_angles is None: # find all atoms that are involved in more than one bond bonds = {} atom_list = [] for bond in self: try: bl = bonds[bond.a1] except KeyError: bl = [] bonds[bond.a1] = bl atom_list.append(bond.a1) bl.append(bond) try: bl = bonds[bond.a2] except KeyError: bl = [] bonds[bond.a2] = bl atom_list.append(bond.a2) bl.append(bond) angles = [] for atom in atom_list: # each pair of bonds at the same atom defines a bond angle for p in Utility.pairs(bonds[atom]): angles.append(BondAngle(p[0], p[1], atom)) self.bond_angles = BondAngleList(angles) return self.bond_angles def dihedralAngles(self): """ :returns: a list of all dihedral angles that can be formed from the bonds in the list :rtype: :class:`~MMTK.Bonds.DihedralAngleList` """ if self.dihedral_angles is None: self.dihedral_angles = self.bondAngles().dihedralAngles() return self.dihedral_angles def bondedTo(self, atom): """ :param atom: an atom :type atom: :class:`~MMTK.ChemicalObjects.Atom` :returns: a list of all atoms to which the given atom is bound :rtype: list """ return [b.otherAtom(atom) for b in self if b.hasAtom(atom)] def bondsOf(self, atom): """ :param atom: an atom :type atom: :class:`~MMTK.ChemicalObjects.Atom` :returns: a list of all bonds in which the given atom is involved :rtype: list """ return [b for b in self if b.hasAtom(atom)] def setBondAttributes(self): """ Create an attribute in all atoms of all bonds that points to the bonded atom. :note: Bond attributes are only set temporarily for optimization purposes. """ for b in self: b.a1.setBondAttribute(b.a2) b.a2.setBondAttribute(b.a1) # # Bond angle lists # class BondAngleList(object): """ Bond angle list """ def __init__(self, angles): self.data = angles def __repr__(self): return repr(self.data) def __len__(self): return len(self.data) def __getitem__(self, i): return self.data[i] def dihedralAngles(self): """ :returns: a list of all dihedral angles that can be formed from the bond angles in the list :rtype: :class:`~MMTK.Bonds.DihedralAngleList` """ # find all bonds that are involved in more than one bond angle angles = {} bond_list = [] for angle in self.data: try: al = angles[angle.b1] except KeyError: al = [] angles[angle.b1] = al bond_list.append(angle.b1) al.append(angle) try: al = angles[angle.b2] except KeyError: al = [] angles[angle.b2] = al bond_list.append(angle.b2) al.append(angle) dihedrals = [] for bond in bond_list: # each pair of bond angles with a common bond defines a dihedral for p in Utility.pairs(angles[bond]): d = DihedralAngle(p[0], p[1], bond) if d.normalized: dihedrals.append(d) return DihedralAngleList(dihedrals) # # Dihedral angle lists # class DihedralAngleList(object): """ Dihedral angle list """ def __init__(self, dihedrals): self.data = dihedrals def __repr__(self): return repr(self.data) def __len__(self): return len(self.data) def __getitem__(self, i): return self.data[i] # # Dummy bond length database, for constraints without a force field # class DummyBondLengthDatabase(object): def __init__(self, universe): pass def bondLength(self, bond): return None def bondAngle(self, angle): return None MMTK-2.7.9/MMTK/CCPNDataModel.py0000644000076600000240000003244311662461637016353 0ustar hinsenstaff00000000000000# Interface to the CCPN Data Model # # Warning: this is a first draft and thus likely to change! # At the moment, it is a one-way interface that creates MMTK objects # from CCPN MolSystems and MolStructures. # # Written by Konrad Hinsen # """Interface to the CCPN Data Model The CCPN Data Model was created to facilitate exchange between programs working with macromolecules and related experimental data. For more information, see http://www.ccpn.ac.uk/datamodel/datamodel.html Both the CCPN Data Model and MMTK's internal data model are object-oriented representations of molecular systems and they share many common features. However, there are important differences in terminology that can easily create confusion. Here is a rough comparison chart between the central classes in both models. The equivalences are of course not perfect. CCPN MMTK ==== ==== Molecule MoleculeType MolResidue BlueprintGroup ChemCompVar GroupType MolSystem set of Molecules without coordinates MolSystem.Chain Molecule without coordinates MolStructure set of Molecules with coordinates MolStructure.CoordChain Molecule with coordinates An important difference is that in the CCPN model, all molecules are chains of residues. Non-polymeric molecules are represented as chains containing a single residue. """ from MMTK.MoleculeFactory import MoleculeFactory from MMTK import Units, Vector, Collection from ccp.api.molecule.MolSystem import MolSystem, MolStructure import sys class CCPNMoleculeFactory(MoleculeFactory): """A MoleculeFactory built from the contents of a CCPN MolSystem. """ def __init__(self, mol_system): MoleculeFactory.__init__(self) self.mol_system = mol_system self.group_name_mapping = {} self.peptide_chains = {} self.makeAllGroups() def retrieveMoleculeForChain(self, mol_system_chain): group_name = self.group_name_mapping[mol_system_chain] if self.peptide_chains.has_key(group_name): return self.retrievePeptideChain(self.peptide_chains[group_name], mol_system_chain) else: return self.retrieveMolecule(group_name) def retrievePeptideChain(self, sequence, mol_system_chain): from MMTK.Proteins import PeptideChain, Residue chain = PeptideChain(None) n_terminus = \ mol_system_chain.residues[0].molResidue.chemCompVar.linking == "start" c_terminus = \ mol_system_chain.residues[-1].molResidue.chemCompVar.linking == "end" chain.version_spec = {'n_terminus': n_terminus, 'c_terminus': c_terminus, 'model': 'all', 'circular': False} numbers = [r.seqId for r in mol_system_chain.residues] chain.groups = [] n = 0 for residue, number in zip(sequence, numbers): n = n + 1 r = Residue(residue, 'all') r.name = r.symbol + str(number) r.sequence_number = n r.parent = chain chain.groups.append(r) chain._setupChain(False, {}, None) return chain def makeAllGroups(self): for chain in self.mol_system.chains: if chain.molecule.molType == "protein": self.registerPeptideSequence(chain) else: residues = chain.residues for residue in residues: chem_comp_var = residue.molResidue.chemCompVar self.makeGroupFromChemCompVar(chem_comp_var) mol_type = chain.molecule.molType if len(residues) > 1: self.makeGroupFromChain(chain) self.group_name_mapping[chain] = chain.molecule.name else: group_name = self.groupNameFromChemCompVar(chem_comp_var) self.group_name_mapping[chain] = group_name def registerPeptideSequence(self, chain): chain_name = chain.molecule.name self.group_name_mapping[chain] = chain_name sequence = [] for residue in chain.residues: chem_comp_var = residue.molResidue.chemCompVar rd, pd, sd = self.processDescriptor(chem_comp_var.descriptor) # The first branch is a quick hack for testing the rest of the # code without having to create a project with realistic # protonation states. if False: pd = '' res_name = self.peptide_mapping.get(chem_comp_var.chemComp.ccpCode, None) if isinstance(res_name, dict): for name in res_name.values(): if name is not None: res_name = name break else: res_name = self.peptide_mapping.get(chem_comp_var.chemComp.ccpCode, None) if isinstance(res_name, dict): if rd and sd: label = ';'.join((rd, sd)) elif rd: label = rd else: label = sd res_name = res_name.get(label, None) if res_name is None \ or pd: # MMTK has no variants of the peptide group raise ValueError("Unknown residue %s %s" % (chem_comp_var.chemComp.ccpCode, chem_comp_var.descriptor)) try: res_name = res_name + \ self.peptide_linking[chem_comp_var.linking] except KeyError: raise ValueError("Unknown linking %s" % chem_comp_var.linking) sequence.append(res_name) self.peptide_chains[chain_name] = sequence def processDescriptor(self, descriptor): residue = [] peptide = [] sidechain = [] for part in descriptor.split(';'): if ':' not in part: residue.append(part) else: label, atoms = part.split(':') atoms = atoms.split(',') for atom in atoms: if len(atom) > 1 and atom[1] in 'BGDEZH': sidechain.append(label[0] + atom) else: peptide.append(label[0] + atom) residue.sort() peptide.sort() sidechain.sort() return ','.join(residue), ','.join(peptide), ','.join(sidechain) peptide_mapping = { 'ALA': 'alanine', 'ARG': {'pHH12': 'arginine', 'dHH12': None}, 'ASN': {'neutral': 'asparagine', 'lND2': None}, 'ASP': {'pHD2': 'aspartic_acid_neutral', 'dHD2': 'aspartic_acid'}, 'CYS': {'pHG': 'cysteine', 'dHG': 'cysteine_with_negative_charge', 'lSG': 'cystine_ss'}, 'GLN': 'glutamine', 'GLU': {'pHE2': 'glutamic_acid_neutral', 'dHE2': 'glutamic_acid'}, 'GLY': 'glycine', 'HIS': {'pHD1,pHE2': 'histidine_plus', 'dHD1,pHE2': 'histidine_epsilonh', 'dHE1,pHD1': 'histidine_deltah'}, 'ILE': 'isoleucine', 'LEU': 'leucine', 'LYS': {'pHZ3': 'lysine', 'dHZ3': 'lysine_neutral'}, 'MET': 'methionine', 'PHE': 'phenylalanine', 'PRO': 'proline', 'SER': {'pHG': 'serine', 'dHG': None}, 'THR': {'pHG1': 'threonine', 'dHG1': None}, 'TRP': {'pHE1': 'tryptophan', 'dHE1': None}, 'TYR': {'pHH': 'tyrosine', 'dHH': None,}, 'VAL': 'valine', } peptide_linking = { 'start': '_nt', 'middle': '', 'end': '_ct', } def groupNameFromChemCompVar(self, chem_comp_var): group_name = chem_comp_var.name descriptor = chem_comp_var.descriptor if descriptor != "neutral": group_name = group_name + '/' + descriptor linking = chem_comp_var.linking if linking != "none": group_name = group_name + '/' + linking return group_name def makeGroupFromChemCompVar(self, chem_comp_var): group_name = self.groupNameFromChemCompVar(chem_comp_var) if self.groups.has_key(group_name): return self.createGroup(group_name) for atom in chem_comp_var.findAllChemAtoms(className="ChemAtom"): self.addAtom(group_name, atom.name, atom.elementSymbol) for bond in chem_comp_var.chemBonds: atoms = bond.findAllChemAtoms(className="ChemAtom") if len(atoms) < 2: continue atom1 = atoms[0].name atom2 = atoms[1].name self.addBond(group_name, atom1, atom2) def makeGroupFromChain(self, chain): group_name = chain.molecule.name if self.groups.has_key(group_name): return self.createGroup(group_name) residue_ids = [] for residue in chain.residues: chem_comp_var = residue.molResidue.chemCompVar residue_name = self.groupNameFromChemCompVar(chem_comp_var) residue_id = residue.ccpCode + str(residue.seqId) self.addSubgroup(group_name, residue_id, residue_name) residue_ids.append(residue_id) for link in chain.molecule.molResLinks: le1, le2 = link.molResLinkEnds la1 = le1.linkEnd.boundChemAtom la2 = le2.linkEnd.boundChemAtom s1 = le1.parent.serial s2 = le2.parent.serial self.addBond(group_name, "%s.%s" % (residue_ids[s1-1], la1.name), "%s.%s" % (residue_ids[s2-1], la2.name)) class CoordMapping(dict): """Mapoing from MolSystem.Atom objects to tuples of MolStructure.Coord objects taken from a corresponding MolStructure. """ def __init__(self, mol_structure): for chain in mol_structure.coordChains: for residue in chain.residues: for atom in residue.atoms: self[atom.atom] = atom.coords def __getitem__(self, key): return self.get(key, ()) class CCPNMolSystemAdapter(object): """Interface between a CCPN MolSystem and corresponding MMTK objects. At the moment, this is one-way only (MMTK objects can be generated from CCPN MolSystems), but ultimately it will be two-way. """ def __init__(self, mol_system): self.mol_system = mol_system self.mol_structures = mol_system.molStructures self.factory = CCPNMoleculeFactory(self.mol_system) self.coord_mappings = [CoordMapping(ms) for ms in self.mol_structures] self.ccpn_to_mmtk_atom_map = {} self.mmtk_to_ccpn_atom_map = {} self.ccpn_to_mmtk_molecule_map = {} self.mmtk_to_ccpn_molecule_map = {} def makeMolecule(self, ccpn_chain): molecule = self.factory.retrieveMoleculeForChain(ccpn_chain) self.ccpn_to_mmtk_molecule_map[ccpn_chain] = molecule self.mmtk_to_ccpn_molecule_map[molecule] = ccpn_chain residues = ccpn_chain.residues if ccpn_chain.molecule.molType == "protein": for i in range(len(residues)): residue = residues[i] my_residue = molecule[i] self.makeAtomMaps(residue, my_residue) else: for residue in residues: if len(residues) == 1: my_residue = molecule else: residue_id = residue.ccpCode + str(residue.seqId) my_residue = getattr(molecule, residue_id) for atom in residue.atoms: my_atom = getattr(my_residue, atom.name) self.ccpn_to_mmtk_atom_map[atom] = my_atom self.mmtk_to_ccpn_atom_map[my_atom] = atom if len(self.coord_mappings) == 1: self.assignPositions([molecule]) return molecule def makeAtomMaps(self, ccpn_residue, my_residue): pdbmap = my_residue.pdbmap altmap = my_residue.pdb_alternative for atom in ccpn_residue.atoms: name = atom.name name = altmap.get(name, name) atom_ref = pdbmap[0][1][name] my_atom = my_residue.atoms[atom_ref.number] self.ccpn_to_mmtk_atom_map[atom] = my_atom self.mmtk_to_ccpn_atom_map[my_atom] = atom def makeAllMolecules(self): molecules = Collection() for ccpn_chain in self.mol_system.chains: molecules.addObject(self.makeMolecule(ccpn_chain)) return molecules def assignPositions(self, molecules, mol_structure_index=0): mapping = self.coord_mappings[mol_structure_index] for molecule in molecules: for atom in molecule.atomList(): try: coords = mapping[self.mmtk_to_ccpn_atom_map[atom]] except KeyError: coords = None if coords: position = Vector(coords[0].x, coords[0].y, coords[0].z) atom.setPosition(position*Units.Ang) MMTK-2.7.9/MMTK/ChargeFit.py0000644000076600000240000002250011662461637015702 0ustar hinsenstaff00000000000000# This module contains code for charge fitting. # # Written by Konrad Hinsen # """ Fit of point chages to an electrostatic potential surface This module implements a numerically stable method (based on Singular Value Decomposition) to fit point charges to values of an electrostatic potential surface. Two types of constraints are avaiable: a constraint on the total charge of the system or a subset of the system, and constraints that force the charges of several atoms to be equal. There is also a utility function that selects suitable evaluation points for the electrostatic potential surface. For the potential evaluation itself, some quantum chemistry program is needed. The charge fitting method is described in: | K. Hinsen and B. Roux, | An accurate potential for simulating proton transfer in acetylacetone, | J. Comp. Chem. 18, 1997: 368 See also Examples/Miscellaneous/charge_fit.py. """ __docformat__ = 'restructuredtext' from MMTK import Random, Units, Utility from Scientific.Geometry import Vector from Scientific import N from Scientific import LA class ChargeFit(object): """ Fit of point charges to an electrostatic potential surface A ChargeFit object acts like a dictionary that stores the fitted charge value for each atom in the system. """ def __init__(self, system, points, constraints = None): """ :param system: any chemical object (usually a molecule) :param points: a list of point/potential pairs (a vector for the evaluation point, a number for the potential), or a dictionary whose keys are Configuration objects and whose values are lists of point/potential pairs. The latter case permits combined fits for several conformations of the system. :param constraints: an optional list of constraint objects (:class:`~MMTK.ChargeFit.TotalChargeConstraint` and/or :class:`~MMTK.ChargeFit.EqualityConstraint` objects). If the constraints are inconsistent, a warning is printed and the result will satisfy the constraints only in a least-squares sense. """ self.atoms = system.atomList() if type(points) != type({}): points = {None: points} if constraints is not None: constraints = ChargeConstraintSet(self.atoms, constraints) npoints = sum([len(v) for v in points.values()]) natoms = len(self.atoms) if npoints < natoms: raise ValueError("Not enough data points for fit") m = N.zeros((npoints, natoms), N.Float) phi = N.zeros((npoints,), N.Float) i = 0 for conf, pointlist in points.items(): for r, p in pointlist: for j in range(natoms): m[i, j] = 1./(r-self.atoms[j].position(conf)).length() phi[i] = p i = i + 1 m = m*Units.electrostatic_energy m_test = m phi_test = phi if constraints is not None: phi -= N.dot(m, constraints.bi_c) m = N.dot(m, constraints.p) c_rank = constraints.rank else: c_rank = 0 u, s, vt = LA.singular_value_decomposition(m) s_test = s[:len(s)-c_rank] cutoff = 1.e-10*N.maximum.reduce(s_test) nonzero = N.repeat(s_test, N.not_equal(s_test, 0.)) self.rank = len(nonzero) self.condition = N.maximum.reduce(nonzero) / \ N.minimum.reduce(nonzero) self.effective_rank = N.add.reduce(N.greater(s, cutoff)) if self.effective_rank < self.rank: self.effective_condition = N.maximum.reduce(nonzero) / cutoff else: self.effective_condition = self.condition if self.effective_rank < natoms-c_rank: Utility.warning('Not all charges are uniquely determined' + ' by the available data') for i in range(natoms): if s[i] > cutoff: s[i] = 1./s[i] else: s[i] = 0. q = N.dot(N.transpose(vt), s*N.dot(N.transpose(u)[:natoms, :], phi)) if constraints is not None: q = constraints.bi_c + N.dot(constraints.p, q) deviation = N.dot(m_test, q)-phi_test self.rms_error = N.sqrt(N.dot(deviation, deviation)) deviation = N.fabs(deviation/phi_test) self.relative_rms_error = N.sqrt(N.dot(deviation, deviation)) self.charges = {} for i in range(natoms): self.charges[self.atoms[i]] = q[i] def __getitem__(self, item): return self.charges[item] class TotalChargeConstraint(object): """ Constraint on the total system charge To be used with :class:`~MMTK.ChargeFit.ChargeFit` """ def __init__(self, system, charge): """ :param system: any chamical object whose total charge is to be constrained :param charge: the total charge value :type charge: number """ self.atoms = system.atomList() self.charge = charge def __len__(self): return 1 def setCoefficients(self, atoms, b, c, i): for a in self.atoms: j = atoms.index(a) b[i, j] = 1. c[i] = self.charge class EqualityConstraint(object): """ Constraint forcing two charges to be equal To be used with :class:`~MMTK.ChargeFit.ChargeFit` Any atom may occur in more than one EqualityConstraint object, in order to keep the charges of more than two atoms equal. """ def __init__(self, atom1, atom2): """ :param atom1: the first atom in the equality relation :type atom1: :class:`~MMTK.ChemicalObjects.Atom` :param atom2: the second atom in the equality relation :type atom2: :class:`~MMTK.ChemicalObjects.Atom` """ self.a1 = atom1 self.a2 = atom2 def __len__(self): return 1 def setCoefficients(self, atoms, b, c, i): b[i, atoms.index(self.a1)] = 1. b[i, atoms.index(self.a2)] = -1. c[i] = 0. class ChargeConstraintSet(object): def __init__(self, atoms, constraints): self.atoms = atoms natoms = len(self.atoms) nconst = sum([len(c) for c in constraints]) b = N.zeros((nconst, natoms), N.Float) c = N.zeros((nconst,), N.Float) i = 0 for cons in constraints: cons.setCoefficients(self.atoms, b, c, i) i = i + len(cons) u, s, vt = LA.singular_value_decomposition(b) self.rank = 0 for i in range(min(natoms, nconst)): if s[i] > 0.: self.rank = self.rank + 1 self.b = b self.bi = LA.generalized_inverse(b) self.p = N.identity(natoms)-N.dot(self.bi, self.b) self.c = c self.bi_c = N.dot(self.bi, c) c_test = N.dot(self.b, self.bi_c) if N.add.reduce((c_test-c)**2)/nconst > 1.e-12: Utility.warning("The charge constraints are inconsistent." " They will be applied as a least-squares" " condition.") def evaluationPoints(system, n, smallest = 0.3, largest = 0.5): """ Generate points in space around a molecule that are suitable for potential evaluation in view of a subsequent charge fit. The points are chosen at random and uniformly in a shell around the system. :param system: the chemical object for which the charges will be fitted :param n: the number of evaluation points to be generated :param smallest: the smallest allowed distance of any evaluation point from any non-hydrogen atom :param largest: the largest allowed value for the distance from an evaluation point to the nearest atom :returns: a list of evaluation points :rtype: list of Scientific.Geometry.Vector """ atoms = system.atomList() p1, p2 = system.boundingBox() margin = Vector(largest, largest, largest) p1 -= margin p2 += margin a, b, c = tuple(p2-p1) offset = 0.5*Vector(a, b, c) points = [] while len(points) < n: p = p1 + Random.randomPointInBox(a, b, c) + offset m = 2*largest ok = 1 for atom in atoms: d = (p-atom.position()).length() m = min(m, d) if d < smallest and atom.symbol != 'H': ok = 0 if not ok: break if ok and m <= largest: points.append(p) return points if __name__ == '__main__': from MMTK import * a1 = Atom('C', position=Vector(-0.05,0.,0.)) a2 = Atom('C', position=Vector( 0.05,0.,0.)) system = Collection(a1, a2) a1.charge = -0.75 a2.charge = 0.15 points = [] for r in evaluationPoints(system, 50): p = 0. for atom in system.atomList(): p = p + atom.charge/(r-atom.position()).length() points.append((r, p*Units.electrostatic_energy)) constraints = [TotalChargeConstraint(system, 0.)] constraints = [EqualityConstraint(a1, a2)] constraints = None f = ChargeFit(system, points, constraints) print f[a1], a1.charge print f[a2], a2.charge MMTK-2.7.9/MMTK/ChemicalObjects.py0000644000076600000240000012561612117632051017063 0ustar hinsenstaff00000000000000# This module implements classes that represent atoms, molecules, and # complexes. They are made as copies from blueprints in the database. # # Written by Konrad Hinsen # """ Atoms, groups, molecules and similar objects """ __docformat__ = 'restructuredtext' from MMTK import Bonds, Collections, Database, \ Units, Utility, Visualization from Scientific.Geometry import Vector from Scientific.Geometry import Objects3D from Scientific import N import copy # # The base class for all chemical objects. # class ChemicalObject(Collections.GroupOfAtoms, Visualization.Viewable): """ General chemical object This is an abstract base class that implements methods which are applicable to any chemical object (atom, molecule, etc.). """ def __init__(self, blueprint, memo): if isinstance(blueprint, basestring): blueprint = self.blueprintclass(blueprint) self.type = blueprint.type if hasattr(blueprint, 'name'): self.name = blueprint.name if memo is None: memo = {} memo[id(blueprint)] = self for attr in blueprint.instance: setattr(self, attr, Database.instantiate(getattr(blueprint, attr), memo)) is_chemical_object = True is_incomplete = False is_modified = False __safe_for_unpickling__ = True __had_initargs__ = True def __hash__(self): return id(self) def __getattr__(self, attr): if attr[:1] == '_' or attr[:3] == 'is_': raise AttributeError else: return getattr(self.type, attr) def isSubsetModel(self): return False def addProperties(self, properties): if properties: for item in properties.items(): if hasattr(self, item[0]) and item[0] != 'name': raise TypeError('attribute '+item[0]+' already defined') setattr(self, item[0], item[1]) def binaryProperty(self, properties, name, default): value = default try: value = properties[name] del properties[name] except KeyError: pass return value def topLevelChemicalObject(self): """Returns the highest-level chemical object of which the current object is a part.""" if self.parent is None or not isChemicalObject(self.parent): return self else: return self.parent.topLevelChemicalObject() def universe(self): """ :returns: the universe to which the object belongs, or None if the object does not belong to any universe :rtype: :class:`~MMTK.Universe.Universe` """ if self.parent is None: return None else: return self.parent.universe() def bondedUnits(self): """ :returns: the largest subobjects which can contain bonds. There are no bonds between any of the subobjects in the list. :rtype: list """ return [self] def atomList(self): """ :returns: a list containing all atoms in the object :rtype: list """ pass def atomIterator(self): """ :returns: an iterator over all atoms in the object :rtype: iterator """ pass def fullName(self): """ :returns: the full name of the object. The full name consists of the proper name of the object preceded by the full name of its parent separated by a dot. :rtype: str """ if self.parent is None or not isChemicalObject(self.parent): return self.name else: return self.parent.fullName() + '.' + self.name def degreesOfFreedom(self): """ :returns: the number of degrees of freedom of the object :rtype: int """ return Collections.GroupOfAtoms.degreesOfFreedom(self) \ - self.numberOfDistanceConstraints() def distanceConstraintList(self): """ :returns: the distance constraints of the object :rtype: list """ return [] def _distanceConstraintList(self): return [] def traverseBondTree(self, function = None): return [] def numberOfDistanceConstraints(self): """ :returns: the number of distance constraints of the object :rtype: int """ return 0 def setBondConstraints(self, universe=None): """ Sets distance constraints for all bonds. """ pass def removeDistanceConstraints(self, universe=None): """ Removes all distance constraints. """ pass def setRigidBodyConstraints(self, universe = None): """ Sets distance constraints that make the object fully rigid. """ if universe is None: universe = self.universe() if universe is None: import Universe universe = Universe.InfiniteUniverse() atoms = self.atomList() if len(atoms) > 1: self.addDistanceConstraint(atoms[0], atoms[1], universe.distance(atoms[0], atoms[1])) if len(atoms) > 2: self.addDistanceConstraint(atoms[0], atoms[2], universe.distance(atoms[0], atoms[2])) self.addDistanceConstraint(atoms[1], atoms[2], universe.distance(atoms[1], atoms[2])) if len(atoms) > 3: for a in atoms[3:]: self.addDistanceConstraint(atoms[0], a, universe.distance(atoms[0], a)) self.addDistanceConstraint(atoms[1], a, universe.distance(atoms[1], a)) self.addDistanceConstraint(atoms[2], a, universe.distance(atoms[2], a)) def getAtomProperty(self, atom, property): """ Retrieve atom properties from the chemical database. Note: the property is first looked up in the database entry for the object on which the method is called. If the lookup fails, the complete hierarchy from the atom to the top-level object is constructed and traversed starting from the top-level object until the property is found. This permits database entries for higher-level objects to override property definitions in its constituents. At the atom level, the property is retrieved from an attribute with the same name. This means that properties at the atom level can be defined both in the chemical database and for each atom individually by assignment to the attribute. :returns: the value of the specified property for the given atom from the chemical database. """ def writeXML(self, file, memo, toplevel=1): if self.type is None: name = 'm' + `memo['counter']` memo['counter'] = memo['counter'] + 1 memo[id(self)] = name atoms = copy.copy(self.atoms) bonds = copy.copy(self.bonds) for group in self.groups: group.writeXML(file, memo, 0) for atom in group.atoms: atoms.remove(atom) for bond in group.bonds: bonds.remove(bond) file.write('\n' % name) for group in self.groups: file.write(' \n' % (memo[id(group.type)], group.name)) if atoms: file.write(' \n') for atom in atoms: file.write(' \n' % (atom.name, atom.type.symbol)) file.write(' \n') if bonds: file.write(' \n') for bond in bonds: a1n = self.relativeName(bond.a1) a2n = self.relativeName(bond.a2) file.write(' \n' % (a1n, a2n)) file.write(' \n') file.write('\n') else: name = self.name self.type.writeXML(file, memo) atom_names = self.type.getXMLAtomOrder() if toplevel: return ['' % name] else: return None def getXMLAtomOrder(self): if self.type is None: atoms = [] for group in self.groups: atoms.extend(group.getXMLAtomOrder()) for atom in self.atoms: if atom not in atoms: atoms.append(atom) else: atom_names = self.type.getXMLAtomOrder() atoms = [] for name in atom_names: parts = name.split(':') object = self for attr in parts: object = getattr(object, attr) atoms.append(object) return atoms def relativeName(self, object): name = '' while object is not None and object != self: name = object.name + ':' + name object = object.parent return name[:-1] def relativeName_unused(self, object): name = '' while object is not None and object != self: for attr, value in object.parent.__dict__.items(): if value is object: name = attr + ':' + name break object = object.parent return name[:-1] def description(self, index_map = None): tag = Utility.uniqueAttribute() s = self._description(tag, index_map, 1) for a in self.atomList(): delattr(a, tag) return s def __repr__(self): return self.__class__.__name__ + ' ' + self.fullName() __str__ = __repr__ def __copy__(self): return copy.deepcopy(self, {id(self.parent): None}) # Type check def isChemicalObject(object): """ :returns: True if object is a chemical object """ return hasattr(object, 'is_chemical_object') # # The second base class for all composite chemical objects. # class CompositeChemicalObject(object): """ Chemical object containing subobjects This is an abstract base class that implements methods which can be used with any composite chemical object, i.e. any chemical object that is not an atom. """ def __init__(self, properties): if properties.has_key('configuration'): conf = properties['configuration'] self.configurations[conf].applyTo(self) del properties['configuration'] elif hasattr(self, 'configurations') and \ self.configurations.has_key('default'): self.configurations['default'].applyTo(self) if properties.has_key('position'): self.translateTo(properties['position']) del properties['position'] self.addProperties(properties) def atomList(self): return self.atoms def atomIterator(self): return iter(self.atoms) def setPosition(self, atom, position): if atom.__class__ is Database.AtomReference: atom = self.atoms[atom.number] atom.setPosition(position) def setIndex(self, atom, index): if atom.__class__ is Database.AtomReference: atom = self.atoms[atom.number] atom.setIndex(index) def getAtom(self, atom): if atom.__class__ is Database.AtomReference: atom = self.atoms[atom.number] return atom def getReference(self, atom): if atom.__class__ is Database.AtomReference: return atom return Database.AtomReference(self.atoms.index(atom)) def getAtomProperty(self, atom, property, levels = None): try: return atom.__dict__[property] except KeyError: try: return getattr(self, property)[self.getReference(atom)] except (AttributeError, KeyError): if levels is None: object = atom levels = [] while object != self: levels.append(object) object = object.parent if not levels: raise KeyError('Property ' + property + ' not defined for ', `atom`) return levels[-1].getAtomProperty(atom, property, levels[:-1]) def deleteUndefinedAtoms(self): delete = [a for a in self.atoms if a.position() is None] for a in delete: a.delete() def _deleteAtom(self, atom): self.atoms.remove(atom) self.is_modified = True self.type = None if self.parent is not None: self.parent._deleteAtom(atom) def distanceConstraintList(self): dc = self._distanceConstraintList() for o in self._subunits(): dc = dc + o._distanceConstraintList() return dc def _distanceConstraintList(self): try: return self.distance_constraints except AttributeError: return [] def numberOfDistanceConstraints(self): n = len(self._distanceConstraintList()) + \ sum(len(o._distanceConstraintList()) for o in self._subunits()) return n def setBondConstraints(self, universe=None): if universe is None: universe = self.universe() bond_database = universe.bondLengthDatabase() for o in self.bondedUnits(): o._setBondConstraints(universe, bond_database) def _setBondConstraints(self, universe, bond_database): self.distance_constraints = [] for bond in self.bonds: d = bond_database.bondLength(bond) if d is None: d = universe.distance(bond.a1, bond.a2) self.addDistanceConstraint(bond.a1, bond.a2, d) def addDistanceConstraint(self, atom1, atom2, distance): try: self.distance_constraints.append((atom1, atom2, distance)) except AttributeError: self.distance_constraints = [(atom1, atom2, distance)] def removeDistanceConstraints(self, universe=None): try: del self.distance_constraints except AttributeError: pass for o in self._subunits(): o.removeDistanceConstraints() def traverseBondTree(self, function = None): self.setBondAttributes() todo = [self.atoms[0]] done = {todo[0]: True} bonds = [] while todo: next_todo = [] for atom in todo: bonded = atom.bondedTo() for other in bonded: if not done.get(other, False): if function is None: bonds.append((atom, other)) else: bonds.append((function(atom), function(other))) next_todo.append(other) done[other] = True todo = next_todo self.clearBondAttributes() return bonds def _description(self, tag, index_map, toplevel): letter, kwargs = self._descriptionSpec() s = [letter, '(', `self.name`, ',['] s.extend([o._description(tag, index_map, 0) + ',' for o in self._subunits()]) s.extend([a._description(tag, index_map, 0) + ',' for a in self.atoms if not hasattr(a, tag)]) s.append(']') if toplevel: s.extend([',', `self._typeName()`]) if kwargs is not None: s.extend([',', kwargs]) constraints = self._distanceConstraintList() if constraints: s.append(',dc=[') if index_map is None: s.extend(['(%d,%d,%f),' % (c[0].index, c[1].index, c[2]) for c in constraints]) else: s.extend(['(%d,%d,%f),' % (index_map[c[0].index], index_map[c[1].index], c[2]) for c in constraints]) s.append(']') s.append(')') return ''.join(s) def _typeName(self): return self.type.name def _graphics(self, conf, distance_fn, model, module, options): glist = [] for bu in self.bondedUnits(): for a in bu.atomList(): glist.extend(a._graphics(conf, distance_fn, model, module, options)) if hasattr(bu, 'bonds'): for b in bu.bonds: glist.extend(b._graphics(conf, distance_fn, model, module, options)) return glist # # The classes for atoms, groups, molecules, and complexes. # class Atom(ChemicalObject): """ Atom """ def __init__(self, atom_spec, _memo = None, **properties): """ :param atom_spec: a string (not case sensitive) specifying the chemical element :type atom_spec: str :keyword position: the position of the atom :type position: Scientific.Geometry.Vector :keyword name: a name given to the atom :type name: str """ Utility.uniqueID.registerObject(self) ChemicalObject.__init__(self, atom_spec, _memo) self._mass = self.type.average_mass self.array = None self.index = None if properties.has_key('position'): self.setPosition(properties['position']) del properties['position'] self.addProperties(properties) blueprintclass = Database.BlueprintAtom def __getstate__(self): state = copy.copy(self.__dict__) if self.array is not None: state['array'] = None state['pos'] = Vector(self.array[self.index,:]) return state def atomList(self): return [self] def atomIterator(self): yield self def setPosition(self, position): """ Changes the position of the atom. :param position: the new position :type position: Scientific.Geometry.Vector """ if position is None: if self.array is None: try: del self.pos except AttributeError: pass else: self.array[self.index,0] = Utility.undefined self.array[self.index,1] = Utility.undefined self.array[self.index,2] = Utility.undefined else: if self.array is None: self.pos = position else: self.array[self.index,0] = position[0] self.array[self.index,1] = position[1] self.array[self.index,2] = position[2] translateTo = setPosition def position(self, conf = None): """ :returns: the position in configuration conf. If conf is 'None', use the current configuration. If the atom has not been assigned a position, the return value is None. :rtype: Scientific.Geometry.Vector """ if conf is None: if self.array is None: try: return self.pos except AttributeError: return None else: if N.logical_or.reduce( N.greater(self.array[self.index, :], Utility.undefined_limit)): return None else: return Vector(self.array[self.index,:]) else: return conf[self] centerOfMass = position def setMass(self, mass): """ Changes the mass of the atom. :param mass: the mass :type mass: float """ self._mass = mass universe = self.universe() if universe is not None: universe._changed(True) def getAtom(self, atom): return self def translateBy(self, vector): if self.array is None: self.pos = self.pos + vector else: self.array[self.index,0] = self.array[self.index,0] + vector[0] self.array[self.index,1] = self.array[self.index,1] + vector[1] self.array[self.index,2] = self.array[self.index,2] + vector[2] def numberOfPoints(self): return 1 numberOfCartesianCoordinates = numberOfPoints def setIndex(self, index): if self.index is not None and self.index != index: raise ValueError('Wrong atom index') self.index = index def setArray(self, index): self.index = index self.array = None def getArray(self): return self.array def unsetArray(self): self.pos = self.position() self.array = None def setBondAttribute(self, atom): try: self.bonded_to__.append(atom) except AttributeError: self.bonded_to__ = [atom] def clearBondAttribute(self): try: del self.bonded_to__ except AttributeError: pass def bondedTo(self): """ :returns: a list of all atoms to which a chemical bond exists. :rtype: list """ try: return self.bonded_to__ except AttributeError: if self.parent is None or not isChemicalObject(self.parent): return [] else: return self.parent.bondedTo(self) def delete(self): if self.parent is not None: self.parent._deleteAtom(self) def getAtomProperty(self, atom, property, levels = None): if self != atom: raise ValueError("Wrong atom") return getattr(self, property) def _description(self, tag, index_map, toplevel): setattr(self, tag, None) if index_map is None: index = self.index else: index = index_map[self.index] if toplevel: return 'A(' + `self.name` + ',' + `index` + ',' + \ `self.symbol` + ')' else: return 'A(' + `self.name` + ',' + `index` + ')' def _graphics(self, conf, distance_fn, model, module, options): #PJC change: if model == 'ball_and_stick': color = self._atomColor(self, options) material = module.DiffuseMaterial(color) radius = options.get('ball_radius', 0.03) return [module.Sphere(self.position(), radius, material=material)] elif model == 'vdw' or model == 'vdw_and_stick': color = self._atomColor(self, options) material = module.DiffuseMaterial(color) try: radius = self.vdW_radius except: radius = options.get('ball_radius', 0.03) return [module.Sphere(self.position(), radius, material=material)] else: return [] if model != 'ball_and_stick': return [] color = self._atomColor(self, options) material = module.DiffuseMaterial(color) radius = options.get('ball_radius', 0.03) return [module.Sphere(self.position(), radius, material=material)] def writeXML(self, file, memo, toplevel=1): return ['' % (self.name, self.type.symbol)] def getXMLAtomOrder(self): return [self] class Group(CompositeChemicalObject, ChemicalObject): """ Group of bonded atoms Groups can contain atoms and other groups, and link them by chemical bonds. They are used to represent functional groups or any other part of a molecule that has a well-defined identity. Groups cannot be created in application programs, but only in database definitions for molecules or through a MoleculeFactory. """ def __init__(self, group_spec, _memo = None, **properties): """ :param group_spec: a string (not case sensitive) that specifies the group name in the chemical database :type group_spec: str :keyword position: the position of the center of mass of the group :type position: Scientific.Geometry.Vector :keyword name: a name given to the group :type name: str """ if group_spec is not None: # group_spec is None when called from MoleculeFactory ChemicalObject.__init__(self, group_spec, _memo) self.addProperties(properties) blueprintclass = Database.BlueprintGroup is_incomplete = True def bondedTo(self, atom): if self.parent is None or not isChemicalObject(self.parent): return [] else: return self.parent.bondedTo(atom) def setBondAttributes(self): pass def clearBondAttributes(self): pass def _subunits(self): return self.groups def _descriptionSpec(self): return "G", None class Molecule(CompositeChemicalObject, ChemicalObject): """Molecule Molecules consist of atoms and groups linked by bonds. """ def __init__(self, molecule_spec, _memo = None, **properties): """ :param molecule_spec: a string (not case sensitive) that specifies the molecule name in the chemical database :type molecule_spec: str :keyword position: the position of the center of mass of the molecule :type position: Scientific.Geometry.Vector :keyword name: a name given to the molecule :type name: str :keyword configuration: the name of a configuration listed in the database definition of the molecule, which is used to initialize the atom positions. If no configuration is specified, the configuration named "default" will be used, if it exists. Otherwise the atom positions are undefined. :type configuration: str """ if molecule_spec is not None: # molecule_spec is None when called from MoleculeFactory ChemicalObject.__init__(self, molecule_spec, _memo) properties = copy.copy(properties) CompositeChemicalObject.__init__(self, properties) self.bonds = Bonds.BondList(self.bonds) blueprintclass = Database.BlueprintMolecule def bondedTo(self, atom): return self.bonds.bondedTo(atom) def setBondAttributes(self): self.bonds.setBondAttributes() def clearBondAttributes(self): for a in self.atoms: a.clearBondAttribute() def _subunits(self): return self.groups def _descriptionSpec(self): return "M", None def addGroup(self, group, bond_atom_pairs): for a1, a2 in bond_atom_pairs: o1 = a1.topLevelChemicalObject() o2 = a2.topLevelChemicalObject() if set([o1, o2]) != set([self, group]): raise ValueError("bond %s-%s outside object" % (str(a1), str(a2))) self.groups.append(group) self.atoms = self.atoms + group.atoms group.parent = self self.clearBondAttributes() for a1, a2 in bond_atom_pairs: self.bonds.append(Bonds.Bond((a1, a2))) for b in group.bonds: self.bonds.append(b) # construct positions of missing hydrogens def findHydrogenPositions(self): """ Find reasonable positions for hydrogen atoms that have no position assigned. This method uses a heuristic approach based on standard geometry data. It was developed for proteins and DNA and may not give good results for other molecules. It raises an exception if presented with a topology it cannot handle. """ self.setBondAttributes() try: unknown = {} for a in self.atoms: if a.position() is None: if a.symbol != 'H': raise ValueError('position of ' + a.fullName() + \ ' is undefined') bonded = a.bondedTo()[0] unknown.setdefault(bonded, []).append(a) for a, list in unknown.items(): bonded = a.bondedTo() n = len(bonded) known = [b for b in bonded if b.position() is not None] nb = len(list) try: method = self._h_methods[a.symbol][n][nb] except KeyError: raise ValueError("Can't handle this yet: " + a.symbol + ' with ' + `n` + ' bonds (' + a.fullName() + ').') method(self, a, known, list) finally: self.clearBondAttributes() # default C-H bond length and X-C-H angle _ch_bond = 1.09*Units.Ang _hch_angle = N.arccos(-1./3.)*Units.rad _nh_bond = 1.03*Units.Ang _hnh_angle = 120.*Units.deg _oh_bond = 0.95*Units.Ang _coh_angle = 114.9*Units.deg _sh_bond = 1.007*Units.Ang _csh_angle = 96.5*Units.deg def _C4oneH(self, atom, known, unknown): r = atom.position() n0 = (known[0].position()-r).normal() n1 = (known[1].position()-r).normal() n2 = (known[2].position()-r).normal() n3 = (n0 + n1 + n2).normal() unknown[0].setPosition(r-self._ch_bond*n3) def _C4twoH(self, atom, known, unknown): r = atom.position() r1 = known[0].position() r2 = known[1].position() plane = Objects3D.Plane(r, r1, r2) axis = -((r1-r)+(r2-r)).normal() plane = plane.rotate(Objects3D.Line(r, axis), 90.*Units.deg) cone = Objects3D.Cone(r, axis, 0.5*self._hch_angle) sphere = Objects3D.Sphere(r, self._ch_bond) circle = sphere.intersectWith(cone) points = circle.intersectWith(plane) unknown[0].setPosition(points[0]) unknown[1].setPosition(points[1]) def _C4threeH(self, atom, known, unknown): self._tetrahedralH(atom, known, unknown, self._ch_bond) def _C3oneH(self, atom, known, unknown): r = atom.position() n1 = (known[0].position()-r).normal() n2 = (known[1].position()-r).normal() n3 = -(n1 + n2).normal() unknown[0].setPosition(r+self._ch_bond*n3) def _C3twoH(self, atom, known, unknown): r = atom.position() r1 = known[0].position() others = filter(lambda a: a.symbol != 'H', known[0].bondedTo()) r2 = others[0].position() try: plane = Objects3D.Plane(r, r1, r2) except ZeroDivisionError: # We get here if all three points are colinear. # Add a small random displacement as a fix. from MMTK.Random import randomPointInSphere plane = Objects3D.Plane(r, r1, r2 + randomPointInSphere(0.001)) axis = (r-r1).normal() cone = Objects3D.Cone(r, axis, 0.5*self._hch_angle) sphere = Objects3D.Sphere(r, self._ch_bond) circle = sphere.intersectWith(cone) points = circle.intersectWith(plane) unknown[0].setPosition(points[0]) unknown[1].setPosition(points[1]) def _C2oneH(self, atom, known, unknown): r = atom.position() r1 = known[0].position() x = r + self._ch_bond * (r - r1).normal() unknown[0].setPosition(x) def _N2oneH(self, atom, known, unknown): r = atom.position() r1 = known[0].position() others = filter(lambda a: a.symbol != 'H', known[0].bondedTo()) r2 = others[0].position() try: plane = Objects3D.Plane(r, r1, r2) except ZeroDivisionError: # We get here when all three points are colinear. # Add a small random displacement as a fix. from MMTK.Random import randomPointInSphere plane = Objects3D.Plane(r, r1, r2 + randomPointInSphere(0.001)) axis = (r-r1).normal() cone = Objects3D.Cone(r, axis, 0.5*self._hch_angle) sphere = Objects3D.Sphere(r, self._nh_bond) circle = sphere.intersectWith(cone) points = circle.intersectWith(plane) unknown[0].setPosition(points[0]) def _N3oneH(self, atom, known, unknown): r = atom.position() n1 = (known[0].position()-r).normal() n2 = (known[1].position()-r).normal() n3 = -(n1 + n2).normal() unknown[0].setPosition(r+self._nh_bond*n3) def _N3twoH(self, atom, known, unknown): r = atom.position() r1 = known[0].position() others = filter(lambda a: a.symbol != 'H', known[0].bondedTo()) r2 = others[0].position() plane = Objects3D.Plane(r, r1, r2) axis = (r-r1).normal() cone = Objects3D.Cone(r, axis, 0.5*self._hnh_angle) sphere = Objects3D.Sphere(r, self._nh_bond) circle = sphere.intersectWith(cone) points = circle.intersectWith(plane) unknown[0].setPosition(points[0]) unknown[1].setPosition(points[1]) def _N4threeH(self, atom, known, unknown): self._tetrahedralH(atom, known, unknown, self._nh_bond) def _N4twoH(self, atom, known, unknown): r = atom.position() r1 = known[0].position() r2 = known[1].position() plane = Objects3D.Plane(r, r1, r2) axis = -((r1-r)+(r2-r)).normal() plane = plane.rotate(Objects3D.Line(r, axis), 90.*Units.deg) cone = Objects3D.Cone(r, axis, 0.5*self._hnh_angle) sphere = Objects3D.Sphere(r, self._nh_bond) circle = sphere.intersectWith(cone) points = circle.intersectWith(plane) unknown[0].setPosition(points[0]) unknown[1].setPosition(points[1]) def _N4oneH(self, atom, known, unknown): r = atom.position() n0 = (known[0].position()-r).normal() n1 = (known[1].position()-r).normal() n2 = (known[2].position()-r).normal() n3 = (n0 + n1 + n2).normal() unknown[0].setPosition(r-self._nh_bond*n3) def _O2(self, atom, known, unknown): others = known[0].bondedTo() for a in others: r = a.position() if a != atom and r is not None: break dihedral = 180.*Units.deg self._findPosition(unknown[0], atom.position(), known[0].position(), r, self._oh_bond, self._coh_angle, dihedral) def _S2(self, atom, known, unknown): c2 = filter(lambda a: a.symbol == 'C', known[0].bondedTo())[0] self._findPosition(unknown[0], atom.position(), known[0].position(), c2.position(), self._sh_bond, self._csh_angle, 180.*Units.deg) def _tetrahedralH(self, atom, known, unknown, bond): r = atom.position() n = (known[0].position()-r).normal() cone = Objects3D.Cone(r, n, N.arccos(-1./3.)) sphere = Objects3D.Sphere(r, bond) circle = sphere.intersectWith(cone) others = filter(lambda a: a.symbol != 'H', known[0].bondedTo()) others.remove(atom) other = others[0] ref = (Objects3D.Plane(circle.center, circle.normal) \ .projectionOf(other.position())-circle.center).normal() p0 = circle.center + ref*circle.radius p0 = Objects3D.rotatePoint(p0, Objects3D.Line(circle.center, circle.normal), 60.*Units.deg) p1 = Objects3D.rotatePoint(p0, Objects3D.Line(circle.center, circle.normal), 120.*Units.deg) p2 = Objects3D.rotatePoint(p1, Objects3D.Line(circle.center, circle.normal), 120.*Units.deg) unknown[0].setPosition(p0) unknown[1].setPosition(p1) unknown[2].setPosition(p2) def _findPosition(self, unknown, a1, a2, a3, bond, angle, dihedral): sphere = Objects3D.Sphere(a1, bond) cone = Objects3D.Cone(a1, a2-a1, angle) plane = Objects3D.Plane(a3, a2, a1) plane = plane.rotate(Objects3D.Line(a1, a2-a1), dihedral) points = sphere.intersectWith(cone).intersectWith(plane) for p in points: if (a1-a2).cross(p-a1)*(plane.normal) > 0: unknown.setPosition(p) break _h_methods = {'C': {4: {3: _C4threeH, 2: _C4twoH, 1: _C4oneH}, 3: {2: _C3twoH, 1: _C3oneH}, 2: {1: _C2oneH}}, 'N': {4: {3: _N4threeH, 2: _N4twoH, 1: _N4oneH}, 3: {2: _N3twoH, 1: _N3oneH}, 2: {1: _N2oneH}}, 'O': {2: {1: _O2}}, 'S': {2: {1: _S2}}, } class ChainMolecule(Molecule): """ ChainMolecule ChainMolecules are generated by a MoleculeFactory from templates that have a sequence attribute. """ def __len__(self): return len(self.sequence) def __getitem__(self, item): return getattr(self, self.sequence[item]) class Crystal(CompositeChemicalObject, ChemicalObject): def __init__(self, blueprint, _memo = None, **properties): ChemicalObject.__init__(self, blueprint, _memo) properties = copy.copy(properties) CompositeChemicalObject.__init__(self, properties) self.bonds = Bonds.BondList(self.bonds) blueprintclass = Database.BlueprintCrystal def _subunits(self): return self.groups def _descriptionSpec(self): return "X", None class Complex(CompositeChemicalObject, ChemicalObject): """ Complex A complex is an assembly of molecules that are not connected by chemical bonds. """ def __init__(self, complex_spec, _memo = None, **properties): """ :param complex_spec: a string (not case sensitive) that specifies the complex name in the chemical database :type complex_spec: str :keyword position: the position of the center of mass of the complex :type position: Scientific.Geometry.Vector :keyword name: a name given to the complex :type name: str :keyword configuration: the name of a configuration listed in the database definition of the complex, which is used to initialize the atom positions. If no configuration is specified, the configuration named "default" will be used, if it exists. Otherwise the atom positions are undefined. :type configuration: str """ ChemicalObject.__init__(self, complex_spec, _memo) properties = copy.copy(properties) CompositeChemicalObject.__init__(self, properties) blueprintclass = Database.BlueprintComplex def recreateAtomList(self): self.atoms = [] for m in self.molecules: self.atoms.extend(m.atoms) def bondedUnits(self): return self.molecules def setBondAttributes(self): for m in self.molecules: m.setBondAttributes() def clearBondAttributes(self): for m in self.molecules: m.clearBondAttributes() def _subunits(self): return self.molecules def _descriptionSpec(self): return "C", None def writeXML(self, file, memo, toplevel=1): if self.type is None: name = 'm' + `memo['counter']` memo['counter'] = memo['counter'] + 1 memo[id(self)] = name for molecule in self.molecules: molecule.writeXML(file, memo, 0) file.write('\n' % name) for molecule in self.molecules: file.write(' \n' % memo[id(molecule)]) file.write('\n') else: ChemicalObject.writeXML(self, file, memo, toplevel) if toplevel: return ['' % name] else: return None def getXMLAtomOrder(self): if self.type is None: atoms = [] for molecule in self.molecules: atoms.extend(molecule.getXMLAtomOrder()) return atoms else: return ChemicalObject.getXMLAtomOrder(self) Database.registerInstanceClass(Atom.blueprintclass, Atom) Database.registerInstanceClass(Group.blueprintclass, Group) Database.registerInstanceClass(Molecule.blueprintclass, Molecule) Database.registerInstanceClass(Crystal.blueprintclass, Crystal) Database.registerInstanceClass(Complex.blueprintclass, Complex) class AtomCluster(CompositeChemicalObject, ChemicalObject): """ An agglomeration of atoms An atom cluster acts like a molecule without any bonds or atom properties. It can be used to represent a group of atoms that are known to form a chemical unit but whose chemical properties are not sufficiently known to define a molecule. """ def __init__(self, atoms, **properties): """ :param atoms: a list of atoms in the cluster :type atoms: list :keyword position: the position of the center of mass of the cluster :type position: Scientific.Geometry.Vector :keyword name: a name given to the cluster :type name: str """ self.atoms = list(atoms) self.parent = None self.name = '' self.type = None for a in self.atoms: if a.parent is not None: raise ValueError(repr(a)+' is part of ' + repr(a.parent)) a.parent = self if a.name != '': setattr(self, a.name, a) properties = copy.copy(properties) CompositeChemicalObject.__init__(self, properties) self.bonds = Bonds.BondList([]) def bondedTo(self, atom): return [] def setBondAttributes(self): pass def clearBondAttributes(self): pass def _subunits(self): return [] def _description(self, tag, index_map, toplevel): s = 'AC(' + `self.name` + ',[' for a in self.atoms: s = s + a._description(tag, index_map, 1) + ',' s = s + ']' constraints = self._distanceConstraintList() if constraints: s = s + ',dc=[' if index_map is None: for c in constraints: s = s + '(%d,%d,%f),' % (c[0].index, c[1].index, c[2]) else: for c in constraints: s = s + '(%d,%d,%f),' % (index_map[c[0].index], index_map[c[1].index], c[2]) s = s + ']' return s + ')' MMTK-2.7.9/MMTK/Collections.py0000644000076600000240000011471112117632051016314 0ustar hinsenstaff00000000000000# This module defines collections of chemical objects. # # Written by Konrad Hinsen # """ Collections of chemical objects """ __docformat__ = 'restructuredtext' from MMTK import Utility, Units, ParticleProperties, Visualization from MMTK.Geometry import superpositionFit from Scientific.Geometry import Vector, Tensor, Objects3D from Scientific.Geometry import Transformation from Scientific import N import copy, itertools, types # # This class defines groups of atoms. It is used as a base class # for anything containing atoms, including chemical objects, collections, # universes etc., but it can't be used directly. All its subclasses # must define a method atomList() that returns a list of all their atoms. # class GroupOfAtoms(object): """ Anything that consists of atoms A mix-in class that defines a large set of operations which are common to all objects that consist of atoms, i.e. any subset of a chemical system. Examples are atoms, molecules, collections, or universes. """ def numberOfAtoms(self): """ :returns: the number of atoms :rtype: int """ return len(self.atomList()) def numberOfPoints(self): """ :returns: the number of geometrical points that define the object. It is currently equal to the number of atoms, but could be different e.g. for quantum systems, in which each atom is described by a wave function or a path integral. :rtype: int """ return sum([a.numberOfPoints() for a in self.atomIterator()]) numberOfCartesianCoordinates = numberOfPoints def numberOfFixedAtoms(self): """ :returns: the number of atoms that are fixed, i.e. that cannot move :rtype: int """ n = 0 for a in self.atomIterator(): try: if a.fixed: n = n + 1 except AttributeError: pass return n def degreesOfFreedom(self): """ :returns: the number of mechanical degrees of freedom :rtype: int """ return 3*(self.numberOfAtoms()-self.numberOfFixedAtoms()) def atomCollection(self): """ :returns: a collection containing all atoms in the object """ return Collection(self.atomList()) def atomsWithDefinedPositions(self, conf = None): """ :param conf: a configuration object, or None for the current configuration :type conf: :class:`~MMTK.ParticleProperties.Configuration` or NoneType :returns: a collection of all atoms that have a position in the given configuration """ return Collection([a for a in self.atomIterator() if Utility.isDefinedPosition(a.position(conf))]) def mass(self): """ :returns: the total mass :rtype: float """ return sum(a._mass for a in self.atomIterator()) def centerOfMass(self, conf = None): """ :param conf: a configuration object, or None for the current configuration :type conf: :class:`~MMTK.ParticleProperties.Configuration` or NoneType :returns: the center of mass in the given configuration :rtype: Scientific.Geometry.Vector """ offset = None universe = self.universe() if universe is not None: offset = universe.contiguousObjectOffset([self], conf) m = 0. mr = Vector(0.,0.,0.) if offset is None: for a in self.atomIterator(): m += a._mass mr += a._mass * a.position(conf) else: for a in self.atomIterator(): m += a._mass mr += a._mass * (a.position(conf)+offset[a]) return mr/m position = centerOfMass def centerAndMomentOfInertia(self, conf = None): """ :param conf: a configuration object, or None for the current configuration :type conf: :class:`~MMTK.ParticleProperties.Configuration` or NoneType :returns: the center of mass and the moment of inertia tensor in the given configuration """ from Scientific.Geometry import delta offset = None universe = self.universe() if universe is not None: offset = universe.contiguousObjectOffset([self], conf) m = 0. mr = Vector(0.,0.,0.) t = Tensor(3*[3*[0.]]) for a in self.atomIterator(): ma = a._mass if offset is None: r = a.position(conf) else: r = a.position(conf)+offset[a] m += ma mr += ma*r t += ma*r.dyadicProduct(r) cm = mr/m t -= m*cm.dyadicProduct(cm) t = t.trace()*delta - t return cm, t def rotationalConstants(self, conf=None): """ :param conf: a configuration object, or None for the current configuration :type conf: :class:`~MMTK.ParticleProperties.Configuration` or NoneType :returns: a sorted array of rotational constants A, B, C in internal units """ com, i = self.centerAndMomentOfInertia(conf) pmi = i.eigenvalues() return N.sort(Units.h / (8.*N.pi*N.pi*pmi))[::-1] def boundingBox(self, conf = None): """ :param conf: a configuration object, or None for the current configuration :type conf: :class:`~MMTK.ParticleProperties.Configuration` or NoneType :returns: two opposite corners of a bounding box around the object. The bounding box is the smallest rectangular bounding box with edges parallel to the coordinate axes. :rtype: tuple of two Scientific.Geometry.Vector """ atoms = self.atomList() min = atoms[0].position(conf).array max = min for a in atoms[1:]: r = a.position(conf).array min = N.minimum(min, r) max = N.maximum(max, r) return Vector(min), Vector(max) def boundingSphere(self, conf = None): """ :param conf: a configuration object, or None for the current configuration :type conf: :class:`~MMTK.ParticleProperties.Configuration` or NoneType :returns: a sphere that contains all atoms in the object. This is B{not} the minimal bounding sphere, just B{some} bounding sphere. :rtype: Scientific.Geometry.Objects3D.Sphere """ atoms = self.atomList() center = sum((a.position(conf) for a in atoms), Vector(0., 0., 0.)) / len(atoms) r = 0. for a in atoms: r = max(r, (a.position(conf)-center).length()) return Objects3D.Sphere(center, r) def rmsDifference(self, conf1, conf2 = None): """ :param conf1: a configuration object :type conf1: :class:`~MMTK.ParticleProperties.Configuration` :param conf2: a configuration object, or None for the current configuration :type conf2: :class:`~MMTK.ParticleProperties.Configuration` or NoneType :returns: the RMS (root-mean-square) difference between the conformations of the object in two universe configurations, conf1 and conf2 :rtype: float """ universe = conf1.universe m = 0. rms = 0. for a in self.atomIterator(): ma = a._mass dr = universe.distanceVector(a.position(conf1), a.position(conf2)) m += ma rms += ma*dr*dr return N.sqrt(rms/m) def findTransformationAsQuaternion(self, conf1, conf2 = None): universe = self.universe() if universe.is_periodic: raise ValueError("superposition in periodic universe " "is not defined") if conf1.universe != universe: raise ValueError("conformation is for a different universe") if conf2 is None: conf2 = conf1 conf1 = universe.configuration() else: if conf2.universe != universe: raise ValueError("conformation is for a different universe") weights = universe.masses() return superpositionFit([(weights[a], conf1[a], conf2[a]) for a in self.atomIterator()]) def findTransformation(self, conf1, conf2 = None): """ :param conf1: a configuration object :type conf1: :class:`~MMTK.ParticleProperties.Configuration` :param conf2: a configuration object, or None for the current configuration :type conf2: :class:`~MMTK.ParticleProperties.Configuration` or NoneType :returns: the linear transformation that, when applied to the object in configuration conf1, minimizes the RMS distance to the conformation in conf2, and the minimal RMS distance. If conf2 is None, returns the transformation from the current configuration to conf1 and the associated RMS distance. """ q, cm1, cm2, rms = self.findTransformationAsQuaternion(conf1, conf2) return Transformation.Translation(cm2) * \ q.asRotation() * \ Transformation.Translation(-cm1), \ rms def translateBy(self, vector): """ Translate the object by a displacement vector :param vector: the displacement vector :type vector: Scientific.Geometry.Vector """ for a in self.atomIterator(): a.translateBy(vector) def translateTo(self, position): """ Translate the object such that its center of mass is at position :param position: the final position :type position: Scientific.Geometry.Vector """ self.translateBy(position-self.centerOfMass()) def normalizePosition(self): """ Translate the center of mass to the coordinate origin """ self.translateTo(Vector(0., 0., 0.)) def normalizeConfiguration(self, repr=None): """ Apply a linear transformation such that the center of mass of the object is translated to the coordinate origin and its principal axes of inertia become parallel to the three coordinate axes. :param repr: the specific representation for axis alignment: - Ir : x y z <--> b c a - IIr : x y z <--> c a b - IIIr : x y z <--> a b c - Il : x y z <--> c b a - IIl : x y z <--> a c b - IIIl : x y z <--> b a c """ transformation = self.normalizingTransformation(repr) self.applyTransformation(transformation) def normalizingTransformation(self, repr=None): """ Calculate a linear transformation that shifts the center of mass of the object to the coordinate origin and makes its principal axes of inertia parallel to the three coordinate axes. :param repr: the specific representation for axis alignment: Ir : x y z <--> b c a IIr : x y z <--> c a b IIIr : x y z <--> a b c Il : x y z <--> c b a IIl : x y z <--> a c b IIIl : x y z <--> b a c :returns: the normalizing transformation :rtype: Scientific.Geometry.Transformation.RigidBodyTransformation """ from Scientific.LA import determinant cm, inertia = self.centerAndMomentOfInertia() ev, diag = inertia.diagonalization() if determinant(diag.array) < 0: diag.array[0] = -diag.array[0] if repr != None: seq = N.argsort(ev) if repr == 'Ir': seq = N.array([seq[1], seq[2], seq[0]]) elif repr == 'IIr': seq = N.array([seq[2], seq[0], seq[1]]) elif repr == 'Il': seq = N.seq[2::-1] elif repr == 'IIl': seq[1:3] = N.array([seq[2], seq[1]]) elif repr == 'IIIl': seq[0:2] = N.array([seq[1], seq[0]]) elif repr != 'IIIr': print 'unknown representation' diag.array = N.take(diag.array, seq) return Transformation.Rotation(diag)*Transformation.Translation(-cm) def applyTransformation(self, t): """ Apply a transformation to the object :param t: the transformation to be applied :type t: Scientific.Geometry.Transformation """ for a in self.atomIterator(): a.setPosition(t(a.position())) def displacementUnderTransformation(self, t): """ :param t: the transformation to be applied :type t: Scientific.Geometry.Transformation :returns: the displacement vectors for the atoms in the object that correspond to the transformation t. :rtype: :class:`~MMTK.ParticleProperties.ParticleVector` """ d = ParticleProperties.ParticleVector(self.universe()) for a in self.atomIterator(): r = a.position() d[a] = t(r)-r return d def rotateAroundCenter(self, axis_direction, angle): """ Rotate the object around an axis that passes through its center of mass. :param axis_direction: the direction of the axis of rotation :type axis_direction: Scientific.Geometry.Vector :param angle: the rotation angle (in radians) :type angle: float """ cm = self.centerOfMass() t = Transformation.Translation(cm) * \ Transformation.Rotation(axis_direction, angle) * \ Transformation.Translation(-cm) self.applyTransformation(t) def rotateAroundOrigin(self, axis_direction, angle): """ Rotate the object around an axis that passes through the coordinate origin. :param axis_direction: the direction of the axis of rotation :type axis_direction: Scientific.Geometry.Vector :param angle: the rotation angle (in radians) :type angle: float """ self.applyTransformation(Transformation.Rotation(axis_direction, angle)) def rotateAroundAxis(self, point1, point2, angle): """ Rotate the object arond an axis specified by two points :param point1: the first point :type point1: Scientific.Geometry.Vector :param point2: the second point :type point2: Scientific.Geometry.Vector :param angle: the rotation angle (in radians) :type angle: float """ tr1 = Transformation.Translation(-point1) tr2 = Transformation.Rotation(point2-point1, angle) tr3 = tr1.inverse() self.applyTransformation(tr3*tr2*tr1) def writeToFile(self, filename, configuration = None, format = None): """ Write a representation of the object in a given configuration to a file. :param filename: the name of the file :type filename: str :param configuration: a configuration object, or None for the current configuration :type configuration: :class:`~MMTK.ParticleProperties.Configuration` or NoneType :param format: 'pdb' or 'vrml' (default: guess from filename) A subformat specification can be added, separated by a dot. Subformats of 'vrml' are 'wireframe' (default), 'ball_and_stick', 'highlight' (like 'wireframe', but with a small sphere for all atoms that have an attribute 'highlight' with a non-zero value), and 'charge' (wireframe plus small spheres for the atoms whose color indicates the charge on a red-to-green color scale) :type format: str """ from MMTK import ConfigIO universe = self.universe() if universe is not None: configuration = universe.contiguousObjectConfiguration( [self], configuration) file = ConfigIO.OutputFile(filename, format) file.write(self, configuration) file.close() def view(self, configuration = None, format = 'pdb'): """ Start an external viewer for the object in the given configuration. :param configuration: the configuration to be visualized :type configuration: :class:`~MMTK.ParticleProperties.Configuration` :param format: 'pdb' (for running $PDBVIEWER) or 'vrml' (for running $VRMLVIEWER). An optional subformat specification can be added, see :class:`~MMTK.Collections.GroupOfAtoms.writeToFile` for the details. """ universe = self.universe() if universe is not None: configuration = universe.contiguousObjectConfiguration([self], configuration) Visualization.viewConfiguration(self, configuration, format) def kineticEnergy(self, velocities = None): """ :param velocities: a set of velocities for all atoms, or None for the current velocities :type velocities: :class:`~MMTK.ParticleProperties.ParticleVector` :returns: the kinetic energy :rtype: float """ if velocities is None: velocities = self.atomList()[0].universe().velocities() energy = 0. for a in self.atomIterator(): v = velocities[a] energy = energy + a._mass*(v*v) return 0.5*energy def temperature(self, velocities = None): """ :param velocities: a set of velocities for all atoms, or None for the current velocities :type velocities: :class:`~MMTK.ParticleProperties.ParticleVector` :returns: the temperature :rtype: float """ energy = self.kineticEnergy(velocities) return 2.*energy/(self.degreesOfFreedom()*Units.k_B) def momentum(self, velocities = None): """ :param velocities: a set of velocities for all atoms, or None for the current velocities :type velocities: :class:`~MMTK.ParticleProperties.ParticleVector` :returns: the momentum :rtype: Scientific.Geometry.Vector """ if velocities is None: velocities = self.atomList()[0].universe().velocities() return sum((a._mass*velocities[a] for a in self.atomIterator()), Vector(0., 0., 0.)) def angularMomentum(self, velocities = None, conf = None): """ :param velocities: a set of velocities for all atoms, or None for the current velocities :type velocities: :class:`~MMTK.ParticleProperties.ParticleVector` :param conf: a configuration object, or None for the current configuration :type conf: :class:`~MMTK.ParticleProperties.Configuration` :returns: the angluar momentum :rtype: Scientific.Geometry.Vector """ if velocities is None: velocities = self.atomList()[0].universe().velocities() cm = self.centerOfMass(conf) return sum((a._mass*a.position(conf).cross(velocities[a]) for a in self.atomIterator()), Vector(0., 0., 0.)) def angularVelocity(self, velocities = None, conf = None): """ :param velocities: a set of velocities for all atoms, or None for the current velocities :type velocities: :class:`~MMTK.ParticleProperties.ParticleVector` :param conf: a configuration object, or None for the current configuration :type conf: :class:`~MMTK.ParticleProperties.Configuration` :returns: the angluar velocity :rtype: Scientific.Geometry.Vector """ if velocities is None: velocities = self.atomList()[0].universe().velocities() cm, inertia = self.centerAndMomentOfInertia(conf) l = sum((a._mass*a.position(conf).cross(velocities[a]) for a in self.atomIterator()), Vector(0., 0., 0.)) return inertia.inverse()*l def universe(self): """ :returns: the universe of which the object is part. For an object that is not part of a universe, the result is None :rtype: :class:`~MMTK.Universe.Universe` """ atoms = self.atomList() if not atoms: return None universe = atoms[0].universe() for a in atoms[1:]: if a.universe() is not universe: return None return universe def charge(self): """ :returns: the total charge of the object. This is defined only for objects that are part of a universe with a force field that defines charges. :rtype: float """ return self.universe().forcefield().charge(self) def dipole(self, reference = None): """ :returns: the total dipole moment of the object. This is defined only for objects that are part of a universe with a force field that defines charges. :rtype: Scientific.Geometry.Vector """ return self.universe().forcefield().dipole(self, reference) def booleanMask(self): """ :returns: a ParticleScalar object that contains a value of 1 for each atom that is in the object and a value of 0 for all other atoms in the universe :rtype: :class:`~MMTK.ParticleProperties.ParticleScalar` """ universe = self.universe() if universe is None: raise ValueError("object not in a universe") array = N.zeros((universe.numberOfAtoms(),), N.Int) mask = ParticleProperties.ParticleScalar(universe, array) for a in self.atomIterator(): mask[a] = 1 return mask # # This class defines a general collection that can contain # chemical objects and other collections. # class Collection(GroupOfAtoms, Visualization.Viewable): """ Collection of chemical objects Collections permit the grouping of arbitrary chemical objects (atoms, molecules, etc.) into one object for the purpose of analysis or manipulation. Collections permit length inquiry, item extraction by indexing, and iteration, like any Python sequence object. Two collections can be added to yield a collection that contains the combined elements. """ def __init__(self, *objects): """ :param objects: a chemical object or a sequence of or a generator yielding chemical objects that define the initial content of the collection. """ self.objects = [] if len(objects) == 1 and isinstance(objects[0], types.GeneratorType): for obj in objects[0]: self.addObject(obj) else: self.addObject(objects) is_collection = 1 def addObject(self, object): """ Add objects to the collection. :param object: the object(s) to be added. If it is another collection or a list, all of its elements are added """ from MMTK.ChemicalObjects import isChemicalObject if isChemicalObject(object): self.addChemicalObject(object) elif isCollection(object): self.addChemicalObjectList(object.objectList()) elif Utility.isSequenceObject(object): if object and isChemicalObject(object[0]): self.addChemicalObjectList(list(object)) else: for o in object: self.addObject(o) else: raise TypeError('Wrong object type in collection') def addChemicalObject(self, object): self.objects.append(object) def addChemicalObjectList(self, list): self.objects.extend(list) def removeObject(self, object): """ Remove an object or a list or collection of objects from the collection. The object(s) to be removed must be elements of the collection. :param object: the object to be removed, or a list or collection of objects whose elements are to be removed :raises ValueError: if the object is not an element of the collection """ from MMTK.ChemicalObjects import isChemicalObject if isChemicalObject(object): self.removeChemicalObject(object) elif isCollection(object) or Utility.isSequenceObject(object): for o in object: self.removeObject(o) else: raise ValueError('Object not in this collection') def removeChemicalObject(self, object): try: self.objects.remove(object) except ValueError: raise ValueError('Object not in this collection') def selectShell(self, point, r1, r2=0.): """ Select objects in a spherical shell around a central point. :param point: the center of the spherical shell :type point: Scientific.Geometry.Vector :param r1: inner or outer radius of the shell :type r1: float :param r2: inner or outer radius of the shell (default: 0.) :type r2: float :returns: a collection of all elements whose distance from point is between r1 and r2 :rtype: :class:`~MMTK.Collections.Collection` """ if r1 > r2: r1, r2 = r2, r1 universe = self.universe() in_shell = [] for o in self.objects: for a in o.atomIterator(): r = universe.distance(a.position(), point) if r >= r1 and r <= r2: in_shell.append(o) break return Collection(in_shell) def selectBox(self, p1, p2): """ Select objects in a rectangular volume :param p1: one corner of the rectangular volume :type p1: Scientific.Geometry.Vector :param p2: the other corner of the rectangular volume :type p2: Scientific.Geometry.Vector :returns: a collection of all elements that lie within the rectangular volume :rtype: :class:`~MMTK.Collections.Collection` """ x1 = N.minimum(p1.array, p2.array) x2 = N.maximum(p1.array, p2.array) in_box = [] for o in self.objects: r = o.position().array if N.logical_and.reduce( \ N.logical_and(N.less_equal(x1, r), N.less(r, x2))): in_box.append(o) return Collection(in_box) def objectList(self, type = None): """ Make a list of all objects in the collection that are instances of a specific type or of one of its subtypes. :param type: the type that serves as a filter. If None, all objects are returned :returns: the objects that match the given type :rtype: list """ if type is None: return self.objects else: return [o for o in self.objects if isinstance(o, type)] def atomList(self): """ :returns: a list containing all atoms of all objects in the collection :rtype: list """ atoms = [] for o in self.objectList(): atoms.extend(o.atomList()) return atoms def atomIterator(self): return itertools.chain(*(o.atomIterator() for o in self.objects)) def numberOfAtoms(self): """ :returns: the total number of atoms in the objects of the collection :rtype: int """ return sum(o.numberOfAtoms() for o in self.objectList()) def universe(self): """ :returns: the universe of which all objects in the collection are part. If no such universe exists, the return value is None :rtype: :class:`~MMTK.Universe.Universe` """ if not self.objects: return None universe = self.objects[0].universe() for o in self.objects[1:]: if o.universe() is not universe: return None return universe def __len__(self): """ :returns: the number of objects in the collection :rtype: int """ return len(self.objects) def __getitem__(self, item): """ :param item: an index into the object list :type item: int :returns: the object with the given index """ return self.objects[item] def __iter__(self): return self.objects.__iter__() def __add__(self, other): return Collection(self.objectList(), other.objectList()) def __str__(self): return "Collection of %d objects" % len(self.objects) def map(self, function): """ Apply a function to all objects in the collection and return the list of the results. If the results are chemical objects, a Collection object is returned instead of a list. :param function: the function to be applied :type function: callable :returns: the list or collection of the results """ from MMTK.ChemicalObjects import isChemicalObject list = [function(o) for o in self.objectList()] if list and isChemicalObject(list[0]): return Collection(list) else: return list def bondedUnits(self): bu = [] for o in self.objects: bu = bu + o.bondedUnits() return bu def degreesOfFreedom(self): return GroupOfAtoms.degreesOfFreedom(self) \ - self.numberOfDistanceConstraints() def distanceConstraintList(self): """ :returns: the list of distance constraints :rtype: list """ dc = [] for o in self.objects: dc.extend(o.distanceConstraintList()) return dc def numberOfDistanceConstraints(self): """ :returns: the number of distance constraints """ return sum(o.numberOfDistanceConstraints() for o in self.objects) def setBondConstraints(self, universe=None): """ Set distance constraints for all bonds """ if universe is None: universe = self.universe() for o in self.objects: o.setBondConstraints(universe) def removeDistanceConstraints(self, universe=None): """ Remove all distance constraints """ if universe is None: universe = self.universe() for o in self.objects: o.removeDistanceConstraints(universe) def _graphics(self, conf, distance_fn, model, module, options): gobs = [] for o in self.objects: gobs.extend(o._graphics(conf, distance_fn, model, module, options)) return gobs def __copy__(self): return self.__class__(copy.copy(self.objects)) # type check for collections def isCollection(object): """ :param object: any Python object :returns: True if the object is a :class:`~MMTK.Collections.Collection` """ return hasattr(object, 'is_collection') # # This class defines a partitioned collection. Such collections # divide their objects into cubic boxes according to their positions. # It is then possible to find potential neighbours much more efficiently. # class PartitionedCollection(Collection): """ Collection with cubic partitions A PartitionedCollection differs from a plain Collection by sorting its elements into small cubic cells. This makes adding objects slower, but geometrical operations like selectShell become much faster for a large number of objects. """ def __init__(self, partition_size, *objects): """ :param partition_size: the edge length of the cubic cells :param objects: a chemical object or a sequence of chemical objects that define the initial content of the collection. """ self.partition_size = 1.*partition_size self.undefined = [] self.partition = {} self.addObject(objects) def addChemicalObject(self, object): p = object.position() if p is None: self.undefined.append(object) else: index = self.partitionIndex(p) try: partition = self.partition[index] except KeyError: partition = [] self.partition[index] = partition partition.append(object) self.all = None def addChemicalObjectList(self, list): for object in list: self.addChemicalObject(object) def removeChemicalObject(self, object): p = object.position() if p is None: self.undefined.remove(object) else: index = self.partitionIndex(p) try: partition = self.partition[index] except KeyError: raise ValueError('Object not in this collection') try: partition.remove(object) except ValueError: raise ValueError('Object not in this collection') self.all = None def partitionIndex(self, x): return (int(N.floor(x[0]/self.partition_size)), int(N.floor(x[1]/self.partition_size)), int(N.floor(x[2]/self.partition_size))) def objectList(self): return sum(self.partition.values(), [self.undefined]) def __len__(self): return sum(len(p) for p in self.partition.values()) + \ len(self.undefined) def __getitem__(self, item): if self.all is None: self.all = self.objectList() if item >= len(self.all): self.all = None raise IndexError return self.all[item] def __copy__(self): return self.__class__(self.partition_size, copy.copy(self.objectList())) def partitions(self): """ :returns: a list of cubic partitions. Each partition is specified by a tuple containing two vectors (describing the diagonally opposite corners) and the list of objects in the partition. :rtype: list """ list = [] for index, objects in self.partition.items(): min = Vector(index)*self.partition_size max = min + Vector(3*[self.partition_size]) list.append((min, max, objects)) return list def selectCube(self, point, edge): x = int(round(point[0]/self.partition_size)) y = int(round(point[1]/self.partition_size)) z = int(round(point[2]/self.partition_size)) d = (Vector(x, y, z)*self.partition_size-point).length() n = int(N.ceil((edge + d)/(2.*self.partition_size))) objects = [] for nx in range(-n, n): for ny in range(-n, n): for nz in range(-n, n): try: objects.append(self.partition[(nx+x, ny+y, nz+z)]) except KeyError: pass return Collection(objects) def selectShell(self, point, min, max=0): if min > max: min, max = max, min objects = Collection() minsq = min**2 maxsq = max**2 for index in self.partition.keys(): d1 = self.partition_size*N.array(index) - point.array d2 = d1 + self.partition_size dmin = (d1 > 0.)*d1 - (d2 < 0.)*d2 dminsq = N.add.reduce(dmin**2) dmaxsq = N.add.reduce(N.maximum(d1**2, d2**2)) if dminsq >= minsq and dmaxsq <= maxsq: objects.addObject(self.partition[index]) elif dmaxsq >= minsq and dminsq <= maxsq: o = Collection(self.partition[index]).selectShell(point, min, max) objects.addObject(o) return objects def pairsWithinCutoff(self, cutoff): """ :param cutoff: a cutoff for pair distances :returns: a list containing all pairs of objects in the collection whose center-of-mass distance is less than the cutoff :rtype: list """ pairs = [] positions = {} for index, objects in self.partition.items(): pos = map(lambda o: o.position(), objects) positions[index] = pos for o1, o2 in Utility.pairs(zip(objects, pos)): if (o2[1]-o1[1]).length() <= cutoff: pairs.append((o1[0], o2[0])) partition_cutoff = int(N.floor((cutoff/self.partition_size)**2)) ones = N.array([1,1,1]) zeros = N.array([0,0,0]) keys = self.partition.keys() for i in range(len(keys)): p1 = keys[i] for j in range(i+1, len(keys)): p2 = keys[j] d = N.maximum(abs(N.array(p2)-N.array(p1)) - ones, zeros) if N.add.reduce(d*d) <= partition_cutoff: for o1, pos1 in zip(self.partition[p1], positions[p1]): for o2, pos2 in zip(self.partition[p2], positions[p2]): if (pos2-pos1).length() <= cutoff: pairs.append((o1, o2)) return pairs # # A special form of partitioned collection that stores the atoms # of all objects that are added to it. # class PartitionedAtomCollection(PartitionedCollection): """ Partitioned collection of atoms PartitionedAtomCollection objects behave like PartitionedCollection atoms, except that they store only atoms. When a composite chemical object is added, its atoms are stored instead. """ def __init__(*args): apply(PartitionedCollection.__init__, args) def addChemicalObject(self, object): for atom in object.atomIterator(): PartitionedCollection.addChemicalObject(self, atom) def removeChemicalObject(self, object): for atom in object.atomIterator(): PartitionedCollection.removeChemicalObject(self, atom) MMTK-2.7.9/MMTK/ComplexEnvironment.py0000644000076600000240000000045211662461637017704 0ustar hinsenstaff00000000000000# This module defines the environment in which molecule definition files # are executed. from MMTK.Database import BlueprintAtom, BlueprintMolecule from MMTK.ConfigIO import Cartesian, ZMatrix from MMTK.PDB import PDBFile from MMTK.Units import * Atom = BlueprintAtom Molecule = BlueprintMolecule MMTK-2.7.9/MMTK/ConfigIO.py0000644000076600000240000003047312117632051015475 0ustar hinsenstaff00000000000000# This module deals with input and output of configurations. # # Written by Konrad Hinsen # """ I/O of molecular configurations Input: Z-Matrix and Cartesian Output: VRML PDB files are handled in :class:`~MMTK.PDB`. """ __docformat__ = 'restructuredtext' from MMTK import PDB, Units, Utility from Scientific.Geometry.Objects3D import Sphere, Cone, Plane, Line, \ rotatePoint from Scientific.Geometry import Vector from Scientific.Visualization import VRML from Scientific import N import os # # This class represents a Z-Matrix. Z-Matrix data consists of a list # with one element for each atom being defined. Each entry is a # list containing the data defining the atom. # class ZMatrix(object): """ Z-Matrix specification of a molecule conformation ZMatrix objects can be used in chemical database entries to specify molecule conformations by internal coordinates. With the exception of the three first atoms, each atom is defined relative to three previously atoms by a distance, an angle, and a dihedral angle. """ def __init__(self, data): """ :param data: a list of atom definitions. Each atom definition (except for the first three ones) is a list containing seven elements: - the atom to be defined - a previously defined atom and the distance to it - another previously defined atom and the angle to it - a third previously defined atom and the dihedral angle to it The definition of the first atom contains only the first element, the second atom needs the first three elements, and the third atom is defined by the first five elements. """ self.data = data self.coordinates = {} _substitute = True def findPositions(self): # First atom at origin self.coordinates[self.data[0][0]] = Vector(0,0,0) # Second atom along x-axis self.coordinates[self.data[1][0]] = Vector(self.data[1][2],0,0) # Third atom in xy-plane try: pos1 = self.coordinates[self.data[2][1]] except KeyError: raise ValueError("atom %d has no defined position" % self.data[2][1].number) try: pos2 = self.coordinates[self.data[2][3]] except KeyError: raise ValueError("atom %d has no defined position" % self.data[2][3].number) sphere = Sphere(pos1, self.data[2][2]) cone = Cone(pos1, pos2-pos1, self.data[2][4]) plane = Plane(Vector(0,0,0), Vector(0,0,1)) points = sphere.intersectWith(cone).intersectWith(plane) self.coordinates[self.data[2][0]] = points[0] # All following atoms defined by distance + angle + dihedral for entry in self.data[3:]: try: pos1 = self.coordinates[entry[1]] except KeyError: raise ValueError("atom %d has no defined position" % entry[1].number) try: pos2 = self.coordinates[entry[3]] except KeyError: raise ValueError("atom %d has no defined position" % entry[3].number) try: pos3 = self.coordinates[entry[5]] except KeyError: raise ValueError("atom %d has no defined position" % entry[5].number) distance = entry[2] angle = entry[4] dihedral = entry[6] sphere = Sphere(pos1, distance) cone = Cone(pos1, pos2-pos1, angle) plane123 = Plane(pos3, pos2, pos1) points = sphere.intersectWith(cone).intersectWith(plane123) for p in points: if Plane(pos2, pos1, p).normal * plane123.normal > 0: break p = rotatePoint(p, Line(pos1, pos2-pos1), dihedral) self.coordinates[entry[0]] = p def applyTo(self, object): """ Define the positions of the atoms in a chemical object by the internal coordinates of the Z-Matrix. :param object: the object to which the Z-Matrix is applied """ if not len(self.coordinates): self.findPositions() for entry in self.coordinates.items(): object.setPosition(entry[0], entry[1]) object.normalizePosition() # # This class represents a dictionary of Cartesian positions # class Cartesian(object): """ Cartesian specification of a molecule conformation Cartesian objects can be used in chemical database entries to specify molecule conformations by Cartesian coordinates. """ def __init__(self, data): """ :param data: a dictionary mapping atoms to tuples of length three that define its Cartesian coordinates """ self.dict = data _substitute = True def applyTo(self, object): """ Define the positions of the atoms in a chemical object by the stored coordinates. :param object: the object to which the coordinates are applied """ for a, r in self.dict.items(): object.setPosition(a, Vector(r[0], r[1], r[2])) # # VRML output # class VRMLWireframeFile(VRML.VRMLFile): def __init__(self, filename, color_values = None): VRML.VRMLFile.__init__(self, filename, 'w') self.warning = 0 self.color_values = color_values if self.color_values is not None: lower = N.minimum.reduce(color_values.array) upper = N.maximum.reduce(color_values.array) self.color_scale = VRML.ColorScale((lower, upper)) def write(self, object, configuration = None, distance = None): from MMTK.ChemicalObjects import isChemicalObject from MMTK.Universe import InfiniteUniverse if distance is None: try: distance = object.universe().distanceVector except AttributeError: distance = InfiniteUniverse().distanceVector if not isChemicalObject(object): for o in object: self.write(o, configuration, distance) else: for bu in object.bondedUnits(): for a in bu.atomList(): self.writeAtom(a, configuration) if hasattr(bu, 'bonds'): for b in bu.bonds: self.writeBond(b, configuration, distance) def close(self): VRML.VRMLFile.close(self) if self.warning: Utility.warning('Some atoms are missing in the output file ' + \ 'because their positions are undefined.') self.warning = 0 def atomColor(self, atom): if self.color_values is None: return atom.color else: return self.color_scale(self.color_values[atom]) def writeAtom(self, atom, configuration): pass def writeBond(self, bond, configuration, distance): p1 = bond.a1.position(configuration) p2 = bond.a2.position(configuration) if p1 is not None and p2 is not None: bond_vector = 0.5*distance(bond.a1, bond.a2, configuration) cut = bond_vector != 0.5*(p2-p1) color1 = self.atomColor(bond.a1) color2 = self.atomColor(bond.a2) material1 = VRML.EmissiveMaterial(color1) material2 = VRML.EmissiveMaterial(color2) if color1 == color2 and not cut: c = VRML.Line(p1, p2, material = material1) c.writeToFile(self) else: c = VRML.Line(p1, p1+bond_vector, material = material1) c.writeToFile(self) c = VRML.Line(p2, p2-bond_vector, material = material2) c.writeToFile(self) class VRMLHighlight(VRMLWireframeFile): def writeAtom(self, atom, configuration): try: highlight = atom.highlight except AttributeError: highlight = 0 if highlight: p = atom.position(configuration) if p is None: self.warning = 1 else: s = VRML.Sphere(p, 0.1*Units.Ang, material = VRML.DiffuseMaterial(atom.color), reuse = 1) s.writeToFile(self) class VRMLBallAndStickFile(VRMLWireframeFile): def writeAtom(self, atom, configuration): p = atom.position(configuration) if p is None: self.warning = 1 else: color = self.atomColor(atom) s = VRML.Sphere(p, 0.1*Units.Ang, material = VRML.DiffuseMaterial(color), reuse = 1) s.writeToFile(self) def writeBond(self, bond, configuration, distance): p1 = bond.a1.position(configuration) p2 = bond.a2.position(configuration) if p1 is not None and p2 is not None: bond_vector = 0.5*distance(bond.a1, bond.a2, configuration) cut = bond_vector != 0.5*(p2-p1) color1 = self.atomColor(bond.a1) color2 = self.atomColor(bond.a2) material1 = VRML.EmissiveMaterial(color1) material2 = VRML.EmissiveMaterial(color2) if color1 == color2 and not cut: c = VRML.Cylinder(p1, p2, 0.03*Units.Ang, material = material1) c.writeToFile(self) else: c = VRML.Cylinder(p1, p1+bond_vector, 0.03*Units.Ang, material = material1) c.writeToFile(self) c = VRML.Cylinder(p2, p2-bond_vector, 0.03*Units.Ang, material = material2) c.writeToFile(self) class VRMLChargeFile(VRMLWireframeFile): color_scale = VRML.SymmetricColorScale(1.) def writeAtom(self, atom, configuration): p = atom.position(configuration) c = atom.charge() c = max(min(c, 1.), -1.) if p is None: self.warning = 1 else: s = VRML.Sphere(p, 0.1*Units.Ang, material = VRML.Material(diffuse_color = self.color_scale(c))) s.writeToFile(self) bond_material = VRML.DiffuseMaterial('black') def writeBond(self, bond, configuration, distance): p1 = bond.a1.position(configuration) p2 = bond.a2.position(configuration) if p1 is not None and p2 is not None: bond_vector = 0.5*distance(bond.a1, bond.a2, configuration) cut = bond_vector != 0.5*(p2-p1) if not cut: c = VRML.Line(p1, p2, material = self.bond_material) c.writeToFile(self) else: c = VRML.Line(p1, p1+bond_vector, material = self.bond_material) c.writeToFile(self) c = VRML.Line(p2, p2-bond_vector, material = self.bond_material) c.writeToFile(self) VRMLFile = VRMLWireframeFile # # Recognize some standard file types by their extensions # def fileFormatFromExtension(filename): filename, ext = os.path.splitext(filename) if ext in _file_compressions: filename, ext = os.path.splitext(filename) try: return _file_formats[ext] except KeyError: raise IOError('Unknown file format') _file_formats = {'.pdb': 'pdb', '.wrl': 'vrml'} _file_compressions = ['.gz', '.Z'] # # Output file for a specified format # def OutputFile(filename, format = None): if format is None: format = fileFormatFromExtension(filename) format = tuple(format.split('.')) try: return _file_types[format](filename) except KeyError: if len(format) == 1: return _file_types[format[0]](filename) else: _file_types[format[0]](filename, format[1]) _file_types = {'pdb': PDB.PDBOutputFile, ('vrml',): VRMLFile, ('vrml', 'wireframe'): VRMLWireframeFile, ('vrml', 'highlight'): VRMLHighlight, ('vrml', 'ball_and_stick'): VRMLBallAndStickFile, ('vrml', 'charge'): VRMLChargeFile} MMTK-2.7.9/MMTK/CrystalEnvironment.py0000644000076600000240000000062011662461637017713 0ustar hinsenstaff00000000000000# This module defines the environment in which crystal definition files # are executed. from MMTK.Database import BlueprintAtom, BlueprintGroup, BlueprintMolecule, \ BlueprintBond from MMTK.ConfigIO import Cartesian, ZMatrix from MMTK.PDB import PDBFile from MMTK.Units import * Atom = BlueprintAtom Group = BlueprintGroup Molecule = BlueprintMolecule Bond = BlueprintBond MMTK-2.7.9/MMTK/Database/0000755000076600000240000000000012156626561015177 5ustar hinsenstaff00000000000000MMTK-2.7.9/MMTK/Database/Atoms/0000755000076600000240000000000012156626565016266 5ustar hinsenstaff00000000000000MMTK-2.7.9/MMTK/Database/Atoms/al0000644000076600000240000000032611662461637016604 0ustar hinsenstaff00000000000000name = 'aluminum' symbol = 'Al' mass = 26.9815 color = 'green' vdW_radius = 0.22 # half-assed guess b_coherent = 3.4492*fm b_incoherent = 0.2554*fm b_coherent_deut = 3.4492*fm b_incoherent_deut = 0.2554*fm MMTK-2.7.9/MMTK/Database/Atoms/alanine0000644000076600000240000000032011662461637017611 0ustar hinsenstaff00000000000000name = 'alanine_calpha' symbol = 'C' mass = 71.079018 color = 'black' vdW_radius = 0.17 b_coherent = 17.965276*fm b_incoherent = 129.181000*fm b_coherent_deut = 21.805667*fm b_incoherent_deut = 23.206000*fm MMTK-2.7.9/MMTK/Database/Atoms/ar0000644000076600000240000000033411662461637016611 0ustar hinsenstaff00000000000000name = 'argon' symbol = 'Ar' mass = 39.948 color = 'green' vdW_radius = 0.24 LJ_radius = 3.4*Ang LJ_energy = 120*K*k_B b_coherent = 1.909*fm b_incoherent = 0.*fm b_coherent_deut = 1.909*fm b_incoherent_deut = 0.*fm MMTK-2.7.9/MMTK/Database/Atoms/arginine0000644000076600000240000000032211662461637020000 0ustar hinsenstaff00000000000000name = 'arginine_calpha' symbol = 'C' mass = 157.196106 color = 'black' vdW_radius = 0.17 b_coherent = 28.753600*fm b_incoherent = 338.495000*fm b_coherent_deut = 34.984040*fm b_incoherent_deut = 62.960000*fm MMTK-2.7.9/MMTK/Database/Atoms/asparagine0000644000076600000240000000032411662461637020320 0ustar hinsenstaff00000000000000name = 'asparagine_calpha' symbol = 'C' mass = 114.104059 color = 'black' vdW_radius = 0.17 b_coherent = 22.385936*fm b_incoherent = 156.924000*fm b_coherent_deut = 26.161294*fm b_incoherent_deut = 29.754000*fm MMTK-2.7.9/MMTK/Database/Atoms/aspartic_acid0000644000076600000240000000032711662461637020777 0ustar hinsenstaff00000000000000name = 'aspartic_acid_calpha' symbol = 'C' mass = 114.080689 color = 'black' vdW_radius = 0.17 b_coherent = 20.502390*fm b_incoherent = 104.249000*fm b_coherent_deut = 23.292419*fm b_incoherent_deut = 19.469000*fm MMTK-2.7.9/MMTK/Database/Atoms/au0000644000076600000240000000025011662461637016611 0ustar hinsenstaff00000000000000name = 'gold' symbol = 'Au' mass = 196.97 color = 'green' b_coherent = 7.6322*fm b_incoherent = 1.8498*fm b_coherent_deut = 7.6322*fm b_incoherent_deut = 1.8498*fmMMTK-2.7.9/MMTK/Database/Atoms/br0000644000076600000240000000015612041510260016567 0ustar hinsenstaff00000000000000name = 'bromine' symbol = 'Br' mass = [(78.9183, 50.69), (80.9163, 49.31)] color = 'brown' vdW_radius = 0.185 MMTK-2.7.9/MMTK/Database/Atoms/c0000644000076600000240000000032411662461637016430 0ustar hinsenstaff00000000000000name = 'carbon' symbol = 'C' mass = [(12, 98.90), (13.003354826, 1.10)] color = 'black' vdW_radius = 0.17 b_coherent = 6.648*fm b_incoherent = 0.285*fm b_coherent_deut = 6.648*fm b_incoherent_deut = 0.285*fm MMTK-2.7.9/MMTK/Database/Atoms/ca0000644000076600000240000000051111662461637016567 0ustar hinsenstaff00000000000000name = 'calcium' symbol = 'Ca' mass = [(39.9625906, 96.941), (41.9586176, 0.647), (42.9587662, 0.135), (43.9554806, 2.086), (45.953689, 0.004), (47.952533, 0.187)] color = 'red' b_coherent = 4.70*fm b_incoherent = 0.063*fm b_coherent_deut = 4.70*fm b_incoherent_deut = 0.063*fm MMTK-2.7.9/MMTK/Database/Atoms/ch0000644000076600000240000000045711662461637016607 0ustar hinsenstaff00000000000000name = 'CH' symbol = 'C' mass = 12.0110369031 + 1.00797597651 color = 'black' vdW_radius = 0.17 from Scientific import N b_coherent = (6.648 + (-3.741))*fm b_incoherent = N.sqrt(0.285**2 + 25.217**2)*fm b_coherent_deut = (6.648 + (6.674))*fm b_incoherent_deut = N.sqrt(0.285**2 + 4.022**2)*fm del N MMTK-2.7.9/MMTK/Database/Atoms/ch20000644000076600000240000000047011662461637016664 0ustar hinsenstaff00000000000000name = 'CH2' symbol = 'C' mass = 12.0110369031 + 2*1.00797597651 color = 'black' vdW_radius = 0.17 from Scientific import N b_coherent = (6.648 + 2*(-3.741))*fm b_incoherent = N.sqrt(0.285**2 + 2*25.217**2)*fm b_coherent_deut = (6.648 + 2*6.674)*fm b_incoherent_deut = N.sqrt(0.285**2 + 2*4.022**2)*fm del N MMTK-2.7.9/MMTK/Database/Atoms/ch30000644000076600000240000000047011662461637016665 0ustar hinsenstaff00000000000000name = 'CH3' symbol = 'C' mass = 12.0110369031 + 3*1.00797597651 color = 'black' vdW_radius = 0.17 from Scientific import N b_coherent = (6.648 + 3*(-3.741))*fm b_incoherent = N.sqrt(0.285**2 + 3*25.217**2)*fm b_coherent_deut = (6.648 + 3*6.674)*fm b_incoherent_deut = N.sqrt(0.285**2 + 3*4.022**2)*fm del N MMTK-2.7.9/MMTK/Database/Atoms/cl0000644000076600000240000000033611662461637016607 0ustar hinsenstaff00000000000000name = 'chlorine' symbol = 'Cl' mass = [(34.968852721, 75.77), (36.96590262, 24.23)] color = 'green' vdW_radius = 0.175 b_coherent = 9.5792*fm b_incoherent = 6.*fm b_coherent_deut = 9.5792*fm b_incoherent_deut = 6.*fm MMTK-2.7.9/MMTK/Database/Atoms/cs0000644000076600000240000000025211662461637016613 0ustar hinsenstaff00000000000000name = 'cesium' symbol = 'Cs' mass = 132.90 color = 'blue' b_coherent = 5.4189*fm b_incoherent = 1.2927*fm b_coherent_deut = 5.4189*fm b_incoherent_deut = 1.2927*fm MMTK-2.7.9/MMTK/Database/Atoms/cysteine0000644000076600000240000000032211662461637020027 0ustar hinsenstaff00000000000000name = 'cysteine_calpha' symbol = 'C' mass = 103.143407 color = 'black' vdW_radius = 0.17 b_coherent = 18.189463*fm b_incoherent = 129.369000*fm b_coherent_deut = 21.990737*fm b_incoherent_deut = 23.394000*fm MMTK-2.7.9/MMTK/Database/Atoms/cystine_ss0000644000076600000240000000032111662461637020366 0ustar hinsenstaff00000000000000name = 'cystine_calpha' symbol = 'C' mass = 102.135431 color = 'black' vdW_radius = 0.17 b_coherent = 17.800603*fm b_incoherent = 104.152000*fm b_coherent_deut = 20.953526*fm b_incoherent_deut = 19.372000*fm MMTK-2.7.9/MMTK/Database/Atoms/d0000644000076600000240000000027711662461637016440 0ustar hinsenstaff00000000000000name = 'deuterium' symbol = 'D' mass = 2.014101779 color = 'white' vdW_radius = 0.12 b_coherent = 6.674*fm b_incoherent = 4.022*fm b_coherent_deut = 6.674*fm b_incoherent_deut = 4.022*fm MMTK-2.7.9/MMTK/Database/Atoms/deut.py0000644000076600000240000000213011662461637017573 0ustar hinsenstaff00000000000000from MMTK import * from MMTK.ChemicalObjects import Group from Scientific.N import sqrt residues = ['alanine', 'arginine', 'asparagine', 'aspartic_acid', 'cysteine', 'cystine_ss', 'glutamic_acid', 'glutamine', 'glycine', 'histidine', 'histidine_deltah', 'histidine_epsilonh', 'histidine_plus', 'isoleucine', 'leucine', 'lysine', 'methionine', 'phenylalanine', 'proline', 'serine', 'threonine', 'tryptophan', 'tyrosine', 'valine'] for r in residues: binc = 0. binc_deut = 0. bcoh_sq = 0. bcoh_deut_sq = 0. mass = 0. for a in Group(r).atomList(): binc = binc + a.b_incoherent/Units.fm binc_deut = binc_deut + a.b_incoherent_deut/Units.fm bcoh_sq = bcoh_sq + (a.b_coherent/Units.fm)**2 bcoh_deut_sq = bcoh_deut_sq + (a.b_coherent_deut/Units.fm)**2 mass = mass + a.mass() print r print "b_coherent = %f*fm" % sqrt(bcoh_sq) print "b_incoherent = %f*fm" % binc print "b_coherent_deut = %f*fm" % sqrt(bcoh_deut_sq) print "b_incoherent_deut = %f*fm" % binc_deut MMTK-2.7.9/MMTK/Database/Atoms/f0000644000076600000240000000012411662461637016431 0ustar hinsenstaff00000000000000name = 'fluorine' symbol = 'F' mass = 18.998 color = 'yellow' vdW_radius = 0.171 MMTK-2.7.9/MMTK/Database/Atoms/fe0000644000076600000240000000037711662461637016610 0ustar hinsenstaff00000000000000name = 'iron' symbol = 'Fe' mass = [(53.9396127, 5.845), (55.9349393, 91.754), (56.9353958, 2.119), (57.9332773, 0.282)] color = 'red' vdW_radius = None b_coherent = 9.45*fm b_incoherent = 0.*fm b_coherent_deut = 9.45*fm b_incoherent_deut = 0.*fm MMTK-2.7.9/MMTK/Database/Atoms/ge0000644000076600000240000000025411662461637016603 0ustar hinsenstaff00000000000000name = 'germanium' symbol = 'Ge' mass = 72.64 color = 'blue' b_coherent = 8.1856*fm b_incoherent = 1.1968*fm b_coherent_deut = 8.1856*fm b_incoherent_deut = 1.1968*fm MMTK-2.7.9/MMTK/Database/Atoms/glutamic_acid0000644000076600000240000000032711662461637020776 0ustar hinsenstaff00000000000000name = 'glutamic_acid_calpha' symbol = 'C' mass = 128.107678 color = 'black' vdW_radius = 0.17 b_coherent = 22.193109*fm b_incoherent = 154.968000*fm b_coherent_deut = 25.996485*fm b_incoherent_deut = 27.798000*fm MMTK-2.7.9/MMTK/Database/Atoms/glutamine0000644000076600000240000000032311662461637020172 0ustar hinsenstaff00000000000000name = 'glutamine_calpha' symbol = 'C' mass = 128.131048 color = 'black' vdW_radius = 0.17 b_coherent = 23.944023*fm b_incoherent = 207.643000*fm b_coherent_deut = 28.595345*fm b_incoherent_deut = 38.083000*fm MMTK-2.7.9/MMTK/Database/Atoms/glycine0000644000076600000240000000031711662461637017642 0ustar hinsenstaff00000000000000name = 'glycine_calpha' symbol = 'C' mass = 57.052030 color = 'black' vdW_radius = 0.17 b_coherent = 15.829247*fm b_incoherent = 78.462000*fm b_coherent_deut = 18.498829*fm b_incoherent_deut = 14.877000*fm MMTK-2.7.9/MMTK/Database/Atoms/h0000644000076600000240000000034211662461637016435 0ustar hinsenstaff00000000000000name = 'hydrogen' symbol = 'H' mass = [(1.007825035, 99.985), (2.014101779, 0.015)] color = 'white' vdW_radius = 0.12 b_coherent = -3.741*fm b_incoherent = 25.217*fm b_coherent_deut = 6.674*fm b_incoherent_deut = 4.022*fm MMTK-2.7.9/MMTK/Database/Atoms/he0000644000076600000240000000027211662461637016604 0ustar hinsenstaff00000000000000name = 'helium' symbol = 'He' mass = 4.0026 color = 'white' vdW_radius = 0.14 b_coherent = -3.741*fm b_incoherent = 25.217*fm b_coherent_deut = 6.674*fm b_incoherent_deut = 4.022*fm MMTK-2.7.9/MMTK/Database/Atoms/hg0000644000076600000240000000025211662461637016604 0ustar hinsenstaff00000000000000name = 'mercury' symbol = 'Hg' mass = 200.6 color = 'blue' b_coherent = 12.691*fm b_incoherent = 7.2471*fm b_coherent_deut = 12.691*fm b_incoherent_deut = 7.2471*fm MMTK-2.7.9/MMTK/Database/Atoms/histidine0000644000076600000240000000033211662461637020165 0ustar hinsenstaff00000000000000name = 'histidine_deltah_calpha' symbol = 'C' mass = 137.141527 color = 'black' vdW_radius = 0.17 b_coherent = 25.618529*fm b_incoherent = 184.952000*fm b_coherent_deut = 29.498125*fm b_incoherent_deut = 36.587000*fm MMTK-2.7.9/MMTK/Database/Atoms/histidine_deltah0000644000076600000240000000033211662461637021506 0ustar hinsenstaff00000000000000name = 'histidine_deltah_calpha' symbol = 'C' mass = 137.141527 color = 'black' vdW_radius = 0.17 b_coherent = 25.618529*fm b_incoherent = 184.952000*fm b_coherent_deut = 29.498125*fm b_incoherent_deut = 36.587000*fm MMTK-2.7.9/MMTK/Database/Atoms/histidine_epsilonh0000644000076600000240000000033411662461637022070 0ustar hinsenstaff00000000000000name = 'histidine_epsilonh_calpha' symbol = 'C' mass = 137.141527 color = 'black' vdW_radius = 0.17 b_coherent = 25.618529*fm b_incoherent = 184.952000*fm b_coherent_deut = 29.498125*fm b_incoherent_deut = 36.587000*fm MMTK-2.7.9/MMTK/Database/Atoms/histidine_plus0000644000076600000240000000033011662461637021226 0ustar hinsenstaff00000000000000name = 'histidine_plus_calpha' symbol = 'C' mass = 138.149503 color = 'black' vdW_radius = 0.17 b_coherent = 25.890232*fm b_incoherent = 210.169000*fm b_coherent_deut = 30.243704*fm b_incoherent_deut = 40.609000*fm MMTK-2.7.9/MMTK/Database/Atoms/i0000644000076600000240000000013712041510260016413 0ustar hinsenstaff00000000000000name = 'iodine' symbol = 'I' mass = [(126.90447, 100.00),] color = 'purple' vdW_radius = 0.198 MMTK-2.7.9/MMTK/Database/Atoms/in0000644000076600000240000000025111662461637016613 0ustar hinsenstaff00000000000000name = 'indium' symbol = 'In' mass = 114.8 color = 'blue' b_coherent = 4.0684*fm b_incoherent = 2.0730*fm b_coherent_deut = 4.0684*fm b_incoherent_deut = 2.0730*fm MMTK-2.7.9/MMTK/Database/Atoms/isoleucine0000644000076600000240000000032411662461637020345 0ustar hinsenstaff00000000000000name = 'isoleucine_calpha' symbol = 'C' mass = 113.159985 color = 'black' vdW_radius = 0.17 b_coherent = 23.223035*fm b_incoherent = 281.338000*fm b_coherent_deut = 29.585951*fm b_incoherent_deut = 48.193000*fm MMTK-2.7.9/MMTK/Database/Atoms/k0000644000076600000240000000014711662461637016443 0ustar hinsenstaff00000000000000name = 'potassium' symbol = 'K' mass = 39.102 color = 'green' vdW_radius = 0.25 # half-assed guess MMTK-2.7.9/MMTK/Database/Atoms/leucine0000644000076600000240000000032111662461637017627 0ustar hinsenstaff00000000000000name = 'leucine_calpha' symbol = 'C' mass = 113.159985 color = 'black' vdW_radius = 0.17 b_coherent = 23.223035*fm b_incoherent = 281.338000*fm b_coherent_deut = 29.585951*fm b_incoherent_deut = 48.193000*fm MMTK-2.7.9/MMTK/Database/Atoms/lp0000644000076600000240000000016211662461637016621 0ustar hinsenstaff00000000000000# Dummy atom type used in Amber91 name = 'lone pair' symbol = 'LP' mass = 3. color = 'yellow' vdW_radius = 0. MMTK-2.7.9/MMTK/Database/Atoms/lysine0000644000076600000240000000032011662461637017505 0ustar hinsenstaff00000000000000name = 'lysine_calpha' symbol = 'C' mass = 129.182660 color = 'black' vdW_radius = 0.17 b_coherent = 25.569308*fm b_incoherent = 334.013000*fm b_coherent_deut = 32.417635*fm b_incoherent_deut = 58.478000*fm MMTK-2.7.9/MMTK/Database/Atoms/methionine0000644000076600000240000000032411662461637020345 0ustar hinsenstaff00000000000000name = 'methionine_calpha' symbol = 'C' mass = 131.197384 color = 'black' vdW_radius = 0.17 b_coherent = 21.799740*fm b_incoherent = 230.807000*fm b_coherent_deut = 27.388929*fm b_incoherent_deut = 40.052000*fm MMTK-2.7.9/MMTK/Database/Atoms/mg0000644000076600000240000000024511662461637016613 0ustar hinsenstaff00000000000000name = 'magnesium' symbol = 'Mg' mass = 24.30 color = 'red' b_coherent = 5.375*fm b_incoherent = 0.079*fm b_coherent_deut = 5.375*fm b_incoherent_deut = 0.079*fm MMTK-2.7.9/MMTK/Database/Atoms/n0000644000076600000240000000027711662461637016452 0ustar hinsenstaff00000000000000name = 'nitrogen' symbol = 'N' mass = 14.006723 color = 'blue' vdW_radius = 0.155000 b_coherent = 9.300*fm b_incoherent = 2.241*fm b_coherent_deut = 9.300*fm b_incoherent_deut = 2.241*fm MMTK-2.7.9/MMTK/Database/Atoms/na0000644000076600000240000000027411662461637016610 0ustar hinsenstaff00000000000000name = 'sodium' symbol = 'Na' mass = [(22.99, 100)] color = 'blue' vdW_radius = 0.227 b_coherent = 3.63*fm b_incoherent = 3.59*fm b_coherent_deut = 3.63*fm b_incoherent_deut = 3.59*fm MMTK-2.7.9/MMTK/Database/Atoms/ne0000644000076600000240000000033711662461637016614 0ustar hinsenstaff00000000000000name = 'neon' symbol = 'Ne' mass = 20.180 color = 'green' vdW_radius = 0.154 LJ_radius = 2.789*Ang LJ_energy = 36.8*K*k_B b_coherent = 4.610*fm b_incoherent = 0.*fm b_coherent_deut = 4.610*fm b_incoherent_deut = 0.*fm MMTK-2.7.9/MMTK/Database/Atoms/nh0000644000076600000240000000044111662461637016613 0ustar hinsenstaff00000000000000name = 'NH' symbol = 'N' mass = 15.014699 color = 'blue' vdW_radius = 0.155000 from Scientific import N b_coherent = (9.300 + 1*(-3.741))*fm b_incoherent = N.sqrt(2.241**2 + 1*25.217**2)*fm b_coherent_deut = (9.300 + 1*6.674)*fm b_incoherent_deut = N.sqrt(2.241**2 + 1*4.022**2)*fm del N MMTK-2.7.9/MMTK/Database/Atoms/nh20000644000076600000240000000044211662461637016676 0ustar hinsenstaff00000000000000name = 'NH2' symbol = 'N' mass = 16.022675 color = 'blue' vdW_radius = 0.155000 from Scientific import N b_coherent = (9.300 + 2*(-3.741))*fm b_incoherent = N.sqrt(2.241**2 + 2*25.217**2)*fm b_coherent_deut = (9.300 + 2*6.674)*fm b_incoherent_deut = N.sqrt(2.241**2 + 2*4.022**2)*fm del N MMTK-2.7.9/MMTK/Database/Atoms/nh30000644000076600000240000000044211662461637016677 0ustar hinsenstaff00000000000000name = 'NH3' symbol = 'N' mass = 17.030651 color = 'blue' vdW_radius = 0.155000 from Scientific import N b_coherent = (9.300 + 3*(-3.741))*fm b_incoherent = N.sqrt(2.241**2 + 3*25.217**2)*fm b_coherent_deut = (9.300 + 3*6.674)*fm b_incoherent_deut = N.sqrt(2.241**2 + 3*4.022**2)*fm del N MMTK-2.7.9/MMTK/Database/Atoms/o0000644000076600000240000000035311662461637016446 0ustar hinsenstaff00000000000000name = 'oxygen' symbol = 'O' mass = [(15.99491463, 99.762), (16.9991312, 0.038), (17.9991603, 0.200)] color = 'red' vdW_radius = 0.152 b_coherent = 5.805*fm b_incoherent = 0.*fm b_coherent_deut = 5.805*fm b_incoherent_deut = 0.*fm MMTK-2.7.9/MMTK/Database/Atoms/oh0000644000076600000240000000032111662461637016611 0ustar hinsenstaff00000000000000name = 'OH' symbol = 'O' mass = 17.007281 color = 'red' vdW_radius = 0.152000 b_coherent = (5.805 + 1*(-3.741))*fm b_incoherent = 25.217*fm b_coherent_deut = (5.805 + 1*6.674)*fm b_incoherent_deut = 4.022*fm MMTK-2.7.9/MMTK/Database/Atoms/p0000644000076600000240000000030411662461637016443 0ustar hinsenstaff00000000000000name = 'phosphor' symbol = 'P' mass = [(30.9737620, 100)] color = 'green' vdW_radius = 0.18 b_coherent = 5.13*fm b_incoherent = 0.235*fm b_coherent_deut = 5.13*fm b_incoherent_deut = 0.235*fm MMTK-2.7.9/MMTK/Database/Atoms/pb0000644000076600000240000000024711662461637016613 0ustar hinsenstaff00000000000000name = 'lead' symbol = 'Pb' mass = 207.2 color = 'blue' b_coherent = 9.4048*fm b_incoherent = 0.1545*fm b_coherent_deut = 9.4048*fm b_incoherent_deut = 0.1545*fm MMTK-2.7.9/MMTK/Database/Atoms/phenylalanine0000644000076600000240000000032711662461637021040 0ustar hinsenstaff00000000000000name = 'phenylalanine_calpha' symbol = 'C' mass = 147.177144 color = 'black' vdW_radius = 0.17 b_coherent = 25.375320*fm b_incoherent = 231.759000*fm b_coherent_deut = 30.312236*fm b_incoherent_deut = 41.004000*fm MMTK-2.7.9/MMTK/Database/Atoms/proline0000644000076600000240000000032011662461637017652 0ustar hinsenstaff00000000000000name = 'proline_calpha' symbol = 'C' mass = 97.117044 color = 'black' vdW_radius = 0.17 b_coherent = 20.955503*fm b_incoherent = 180.185000*fm b_coherent_deut = 25.553150*fm b_incoherent_deut = 31.820000*fm MMTK-2.7.9/MMTK/Database/Atoms/rb0000644000076600000240000000025311662461637016612 0ustar hinsenstaff00000000000000name = 'rubidium' symbol = 'Rb' mass = 85.47 color = 'blue' b_coherent = 5.0293*fm b_incoherent = 1.9947*fm b_coherent_deut = 5.0293*fm b_incoherent_deut = 1.9947*fm MMTK-2.7.9/MMTK/Database/Atoms/s0000644000076600000240000000041011662461637016444 0ustar hinsenstaff00000000000000name = 'sulphur' symbol = 'S' mass = [(31.97207070, 95.02), (32.97145843, 0.75), (33.96786665, 4.21), (35.9670806, 0.02)] color = 'yellow' vdW_radius = 0.18 b_coherent = 2.847*fm b_incoherent = 0.188*fm b_coherent_deut = 2.847*fm b_incoherent_deut = 0.188*fm MMTK-2.7.9/MMTK/Database/Atoms/serine0000644000076600000240000000031711662461637017475 0ustar hinsenstaff00000000000000name = 'serine_calpha' symbol = 'C' mass = 87.078323 color = 'black' vdW_radius = 0.17 b_coherent = 18.879861*fm b_incoherent = 129.181000*fm b_coherent_deut = 22.565131*fm b_incoherent_deut = 23.206000*fm MMTK-2.7.9/MMTK/Database/Atoms/sh0000644000076600000240000000052311662461637016621 0ustar hinsenstaff00000000000000name = 'SH' symbol = 'S' mass = 33.072364 color = 'yellow' vdW_radius = 0.180000 b_coherent = -0.894*fm b_incoherent = 25.218*fm from Scientific import N b_coherent = (2.847 + 1*(-3.741))*fm b_incoherent = N.sqrt(0.188**2 + 1*25.217**2)*fm b_coherent_deut = (2.847 + 1*6.674)*fm b_incoherent_deut = N.sqrt(0.188**2 + 1*4.022**2)*fm del N MMTK-2.7.9/MMTK/Database/Atoms/si0000644000076600000240000000030511662461637016620 0ustar hinsenstaff00000000000000name = 'silicon' symbol = 'Si' mass = 28.0855 color = 'black' vdW_radius = 0.225 b_coherent = 4.15071*fm b_incoherent = 0.17841*fm b_coherent_deut = 4.15071*fm b_incoherent_deut = 0.17841*fm MMTK-2.7.9/MMTK/Database/Atoms/sr0000644000076600000240000000024711662461637016636 0ustar hinsenstaff00000000000000name = 'strontium' symbol = 'Sr' mass = 87.62 color = 'grey' b_coherent = 7.020*fm b_incoherent = 0.069*fm b_coherent_deut = 7.020*fm b_incoherent_deut = 0.069*fm MMTK-2.7.9/MMTK/Database/Atoms/threonine0000644000076600000240000000032311662461637020200 0ustar hinsenstaff00000000000000name = 'threonine_calpha' symbol = 'C' mass = 101.105312 color = 'black' vdW_radius = 0.17 b_coherent = 20.703508*fm b_incoherent = 179.900000*fm b_coherent_deut = 25.346905*fm b_incoherent_deut = 31.535000*fm MMTK-2.7.9/MMTK/Database/Atoms/ti0000644000076600000240000000025311662461637016623 0ustar hinsenstaff00000000000000name = 'titanium' symbol = 'Ti' mass = 47.87 color = 'blue' b_coherent = 3.4376*fm b_incoherent = 4.7790*fm b_coherent_deut = 3.4376*fm b_incoherent_deut = 4.7790*fm MMTK-2.7.9/MMTK/Database/Atoms/tl0000644000076600000240000000025211662461637016625 0ustar hinsenstaff00000000000000name = 'thalium' symbol = 'Tl' mass = 11.85 color = 'blue' b_coherent = 7.7015*fm b_incoherent = 1.2927*fm b_coherent_deut = 7.7015*fm b_incoherent_deut = 1.2927*fm MMTK-2.7.9/MMTK/Database/Atoms/tryptophan0000644000076600000240000000032411662461637020416 0ustar hinsenstaff00000000000000name = 'tryptophan_calpha' symbol = 'C' mass = 186.213917 color = 'black' vdW_radius = 0.17 b_coherent = 28.857993*fm b_incoherent = 259.787000*fm b_coherent_deut = 33.738046*fm b_incoherent_deut = 47.837000*fm MMTK-2.7.9/MMTK/Database/Atoms/tyrosine0000644000076600000240000000032211662461637020060 0ustar hinsenstaff00000000000000name = 'tyrosine_calpha' symbol = 'C' mass = 163.176449 color = 'black' vdW_radius = 0.17 b_coherent = 26.030845*fm b_incoherent = 231.759000*fm b_coherent_deut = 30.863079*fm b_incoherent_deut = 41.004000*fm MMTK-2.7.9/MMTK/Database/Atoms/valine0000644000076600000240000000031711662461637017466 0ustar hinsenstaff00000000000000name = 'valine_calpha' symbol = 'C' mass = 99.132996 color = 'black' vdW_radius = 0.17 b_coherent = 21.613035*fm b_incoherent = 230.619000*fm b_coherent_deut = 27.240559*fm b_incoherent_deut = 39.864000*fm MMTK-2.7.9/MMTK/Database/Atoms/zn0000644000076600000240000000026411662461637016640 0ustar hinsenstaff00000000000000name = 'zinc' symbol = 'Zn' mass = [(63.9291448, 48.6), (65.9260347, 27.9), (66.9271291, 4.1), (67.9248459, 18.8), (69.925325, 0.6)] color = 'orange' vdW_radius = 0.139 MMTK-2.7.9/MMTK/Database/Groups/0000755000076600000240000000000012156626571016457 5ustar hinsenstaff00000000000000MMTK-2.7.9/MMTK/Database/Groups/ace_beginning_nt0000644000076600000240000000122612041514571021641 0ustar hinsenstaff00000000000000name = 'ace_beginning' symbol = 'ACE' CH3 = Atom('C') HH31 = Atom('H') HH32 = Atom('H') HH33 = Atom('H') CBond = Atom('C') O = Atom('O') bonds = [Bond(CH3, HH31), Bond(CH3, HH32), Bond(CH3, HH33), Bond(CH3, CBond), Bond(CBond, O)] chain_links = [None, CBond] amber_atom_type = {CH3: 'CT', HH31: 'HC', HH32: 'HC', HH33: 'HC', CBond: 'C', O: 'O'} amber_charge = {CH3: -0.3662, HH31: 0.1123, HH32: 0.1123, HH33: 0.1123, CBond: 0.5972, O: -0.5679} pdbmap = [('ACE', {'CH3': CH3, '1HH3': HH31, '2HH3': HH32, '3HH3': HH33, 'C': CBond, 'O': O})] pdb_alternative = {'HH31': '1HH3', 'HH32': '2HH3', 'HH33': '3HH3'} amber12_atom_type = amber_atom_type MMTK-2.7.9/MMTK/Database/Groups/ace_beginning_nt_noh0000644000076600000240000000044011662461637022516 0ustar hinsenstaff00000000000000name = 'ace_beginning' symbol = 'ACE' CH3 = Atom('CH3') CBond = Atom('C') O = Atom('O') bonds = [Bond(CH3, CBond), Bond(CBond, O)] chain_links = [None, CBond] amber_atom_type = {CH3: 'CT', CBond: 'C', O: 'O'} pdbmap = [('ACE', {'CH3': CH3, 'C': CBond, 'O': O})] pdb_alternative = {} MMTK-2.7.9/MMTK/Database/Groups/adenine0000644000076600000240000000157112041514571017776 0ustar hinsenstaff00000000000000name ='adenine' N_9 = Atom('N') C_8 = Atom('C') H_8 = Atom('H') N_7 = Atom('N') C_5 = Atom('C') C_6 = Atom('C') N_6 = Atom('N') H_61 = Atom('H') H_62 = Atom('H') N_1 = Atom('N') C_2 = Atom('C') H_2 = Atom('H') N_3 = Atom('N') C_4 = Atom('C') bonds = [Bond(C_8, N_9), Bond(H_8, C_8), Bond(N_7, C_8), Bond(C_5, N_7), Bond(C_6, C_5), Bond(N_6, C_6), Bond(H_61, N_6), Bond(H_62, N_6), Bond(N_1, C_6), Bond(C_2, N_1), Bond(H_2, C_2), Bond(N_3, C_2), Bond(C_4, N_3), Bond(C_4, C_5), Bond(C_4, N_9), ] pdbmap = [('A', {"H62": H_62, "H61": H_61, "C8": C_8, "C6": C_6, "C4": C_4, "C5": C_5, "C2": C_2, "N3": N_3, "H8": H_8, "N1": N_1, "N7": N_7, "N6": N_6, "N9": N_9, "H2": H_2, })] amber_atom_type = {H_62: 'H', H_61: 'H', C_8: 'CK', C_6: 'CA', C_4: 'CB', C_5: 'CB', C_2: 'CQ', N_3: 'NC', H_8: 'H5', N_1: 'NC', N_7: 'NB', N_6: 'N2', N_9: 'N*', H_2: 'H5', } amber12_atom_type = amber_atom_type MMTK-2.7.9/MMTK/Database/Groups/ala_sidechain0000644000076600000240000000071612041514571021137 0ustar hinsenstaff00000000000000C_beta = Atom('c') H_beta_3 = Atom('h') H_beta_2 = Atom('h') H_beta_1 = Atom('h') bonds = [Bond(H_beta_1, C_beta), Bond(H_beta_2, C_beta), Bond(H_beta_3, C_beta), ] pdbmap = [('ALA', {'2HB': H_beta_2, '3HB': H_beta_3, 'CB': C_beta, '1HB': H_beta_1, }, ), ] pdb_alternative = {'HB2': '2HB', 'HB3': '3HB', 'HB1': '1HB', } amber_atom_type = {H_beta_3: 'HC', C_beta: 'CT', H_beta_2: 'HC', H_beta_1: 'HC', } name = 'ala_sidechain' amber12_atom_type = amber_atom_type MMTK-2.7.9/MMTK/Database/Groups/ala_sidechain_noh0000644000076600000240000000017011662461637022011 0ustar hinsenstaff00000000000000C_beta = Atom('CH3') pdbmap = [('ALA', {'CB': C_beta, }, ), ] amber_atom_type = {C_beta: 'CT', } name = 'ala_sidechain' MMTK-2.7.9/MMTK/Database/Groups/ala_sidechain_uni0000644000076600000240000000027411662461637022025 0ustar hinsenstaff00000000000000C_beta = Atom('CH3') pdbmap = [('ALA', {'CB': C_beta, }, ), ] name = 'ala_sidechain' opls_atom_type = { C_beta: 'C3' } opls_charge = { C_beta: 0.0 } amber91_atom_type = {C_beta: 'C3', } MMTK-2.7.9/MMTK/Database/Groups/ala_sidechain_uni20000644000076600000240000000017211662461637022104 0ustar hinsenstaff00000000000000C_beta = Atom('CH3') pdbmap = [('ALA', {'CB': C_beta, }, ), ] amber91_atom_type = {C_beta: 'C3', } name = 'ala_sidechain' MMTK-2.7.9/MMTK/Database/Groups/ala_sidechain_uni30000644000076600000240000000027411662461637022110 0ustar hinsenstaff00000000000000C_beta = Atom('CH3') pdbmap = [('ALA', {'CB': C_beta, }, ), ] name = 'ala_sidechain' opls_atom_type = { C_beta: 'C3' } opls_charge = { C_beta: 0.0 } amber91_atom_type = {C_beta: 'C3', } MMTK-2.7.9/MMTK/Database/Groups/alanine0000644000076600000240000000067011777301631020007 0ustar hinsenstaff00000000000000sidechain = Group('ala_sidechain') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ala' amber_charge = {sidechain.H_beta_3: 0.0603, sidechain.C_beta: -0.1825, peptide.N: -0.4157, peptide.H: 0.2719, peptide.C: 0.5973, peptide.C_alpha: 0.0337, sidechain.H_beta_1: 0.0603, sidechain.H_beta_2: 0.0603, peptide.O: -0.5679, peptide.H_alpha: 0.0823, } name = 'alanine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/alanine_calpha0000644000076600000240000000020011662461637021312 0ustar hinsenstaff00000000000000symbol = 'Ala' name = 'alanine' C_alpha = Atom('alanine') pdbmap = [('ALA', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/alanine_ct0000644000076600000240000000071511777301631020475 0ustar hinsenstaff00000000000000sidechain = Group('ala_sidechain') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ala' amber_charge = {peptide.O_2: -0.8055, peptide.C_alpha: -0.1747, peptide.H: 0.2681, peptide.H_alpha: 0.1067, sidechain.H_beta_1: 0.0764, sidechain.H_beta_2: 0.0764, peptide.O: -0.8055, sidechain.H_beta_3: 0.0764, sidechain.C_beta: -0.2093, peptide.N: -0.3821, peptide.C: 0.7731, } name = 'alanine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/alanine_ct_noh0000644000076600000240000000052511662461637021346 0ustar hinsenstaff00000000000000sidechain = Group('ala_sidechain_noh') peptide = Group('peptide_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ala' amber_charge = {peptide.O_2: -0.8055, peptide.N: -0.3821, peptide.O: -0.8055, peptide.C: 0.7731, sidechain.C_beta: -0.2093, peptide.C_alpha: -0.1747, } name = 'alanine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/alanine_ct_uni0000644000076600000240000000054011662461637021352 0ustar hinsenstaff00000000000000sidechain = Group('ala_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ala' amber91_charge = {peptide.O_2: -0.706, sidechain.C_beta: 0.031, peptide.C_alpha: 0.209, peptide.H: 0.248, peptide.C: 0.444, peptide.O: -0.706, peptide.N: -0.52, } name = 'alanine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/alanine_ct_uni20000644000076600000240000000054011662461637021434 0ustar hinsenstaff00000000000000sidechain = Group('ala_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ala' amber91_charge = {peptide.O_2: -0.706, sidechain.C_beta: 0.031, peptide.C_alpha: 0.209, peptide.H: 0.248, peptide.N: -0.52, peptide.O: -0.706, peptide.C: 0.444, } name = 'alanine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/alanine_ct_uni30000644000076600000240000000054011662461637021435 0ustar hinsenstaff00000000000000sidechain = Group('ala_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ala' amber91_charge = {peptide.O_2: -0.706, sidechain.C_beta: 0.031, peptide.C_alpha: 0.209, peptide.H: 0.248, peptide.C: 0.444, peptide.O: -0.706, peptide.N: -0.52, } name = 'alanine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/alanine_noh0000644000076600000240000000050011662461637020651 0ustar hinsenstaff00000000000000sidechain = Group('ala_sidechain_noh') peptide = Group('peptide_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ala' amber_charge = {peptide.N: -0.4157, peptide.C_alpha: 0.0337, peptide.C: 0.5973, sidechain.C_beta: -0.1825, peptide.O: -0.5679, } name = 'alanine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/alanine_nt0000644000076600000240000000073311777301631020510 0ustar hinsenstaff00000000000000sidechain = Group('ala_sidechain') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ala' amber_charge = {peptide.O: -0.5722, peptide.H_3: 0.1997, peptide.C: 0.6163, peptide.H_2: 0.1997, sidechain.C_beta: -0.0597, sidechain.H_beta_2: 0.03, sidechain.H_beta_3: 0.03, peptide.H_alpha: 0.0889, sidechain.H_beta_1: 0.03, peptide.H_1: 0.1997, peptide.N: 0.1414, peptide.C_alpha: 0.0962, } name = 'alanine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/alanine_nt_ct_noh0000644000076600000240000000027311662461637022047 0ustar hinsenstaff00000000000000sidechain = Group('ala_sidechain_noh') peptide = Group('peptide_nt_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ala' name = 'alanine' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/alanine_nt_noh0000644000076600000240000000047511662461637021365 0ustar hinsenstaff00000000000000sidechain = Group('ala_sidechain_noh') peptide = Group('peptide_nt_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ala' amber_charge = {peptide.C: 0.6163, peptide.O: -0.5722, peptide.N: 0.1414, sidechain.C_beta: -0.0597, peptide.C_alpha: 0.0962, } name = 'alanine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/alanine_nt_uni0000644000076600000240000000056311662461637021372 0ustar hinsenstaff00000000000000sidechain = Group('ala_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ala' amber91_charge = {peptide.H_2: 0.312, peptide.N: -0.263, peptide.O: -0.5, peptide.H_3: 0.312, sidechain.C_beta: 0.031, peptide.C_alpha: 0.27, peptide.H_1: 0.312, peptide.C: 0.526, } name = 'alanine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/alanine_nt_uni20000644000076600000240000000056311662461637021454 0ustar hinsenstaff00000000000000sidechain = Group('ala_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ala' amber91_charge = {peptide.H_2: 0.312, peptide.H_1: 0.312, sidechain.C_beta: 0.031, peptide.C: 0.526, peptide.N: -0.263, peptide.C_alpha: 0.27, peptide.O: -0.5, peptide.H_3: 0.312, } name = 'alanine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/alanine_nt_uni30000644000076600000240000000056311662461637021455 0ustar hinsenstaff00000000000000sidechain = Group('ala_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ala' amber91_charge = {peptide.H_2: 0.312, peptide.N: -0.263, peptide.O: -0.5, peptide.H_3: 0.312, sidechain.C_beta: 0.031, peptide.C_alpha: 0.27, peptide.H_1: 0.312, peptide.C: 0.526, } name = 'alanine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/alanine_uni0000644000076600000240000000051311662461637020664 0ustar hinsenstaff00000000000000sidechain = Group('ala_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ala' name = 'alanine' chain_links = [peptide.N, peptide.C] amber91_charge = {peptide.O: -0.5, peptide.H: 0.248, sidechain.C_beta: 0.031, peptide.C: 0.526, peptide.N: -0.52, peptide.C_alpha: 0.215, } MMTK-2.7.9/MMTK/Database/Groups/alanine_uni20000644000076600000240000000051311662461637020746 0ustar hinsenstaff00000000000000sidechain = Group('ala_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ala' amber91_charge = {peptide.O: -0.5, sidechain.C_beta: 0.031, peptide.C_alpha: 0.215, peptide.C: 0.526, peptide.N: -0.52, peptide.H: 0.248, } name = 'alanine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/alanine_uni30000644000076600000240000000051311662461637020747 0ustar hinsenstaff00000000000000sidechain = Group('ala_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ala' name = 'alanine' chain_links = [peptide.N, peptide.C] amber91_charge = {peptide.O: -0.5, peptide.H: 0.248, sidechain.C_beta: 0.031, peptide.C: 0.526, peptide.N: -0.52, peptide.C_alpha: 0.215, } MMTK-2.7.9/MMTK/Database/Groups/amide0000644000076600000240000000045711662461637017470 0ustar hinsenstaff00000000000000N = Atom('N') H_1 = Atom('H') H_2 = Atom('H') bonds = [Bond(N,H_1), Bond(N,H_2), ] name = 'amide' symbol = 'Nhe' pdbmap = [('NHE', {'N': N, 'HN1': H_1, 'HN2': H_2, }, ), ] opls_atom_type = {N: 'N2', H_1: 'H2', H_2: 'H2', } opls_charge = {N: -0.463, H_1: 0.2315, H_2: 0.2315, } chain_links = [N,None] MMTK-2.7.9/MMTK/Database/Groups/amide_uni0000644000076600000240000000045711662461637020343 0ustar hinsenstaff00000000000000N = Atom('N') H_1 = Atom('H') H_2 = Atom('H') bonds = [Bond(N,H_1), Bond(N,H_2), ] name = 'amide' symbol = 'Nhe' pdbmap = [('NHE', {'N': N, 'HN1': H_1, 'HN2': H_2, }, ), ] opls_atom_type = {N: 'N2', H_1: 'H2', H_2: 'H2', } opls_charge = {N: -0.463, H_1: 0.2315, H_2: 0.2315, } chain_links = [N,None] MMTK-2.7.9/MMTK/Database/Groups/amide_uni30000644000076600000240000000045711662461637020426 0ustar hinsenstaff00000000000000N = Atom('N') H_1 = Atom('H') H_2 = Atom('H') bonds = [Bond(N,H_1), Bond(N,H_2), ] name = 'amide' symbol = 'Nhe' pdbmap = [('NHE', {'N': N, 'HN1': H_1, 'HN2': H_2, }, ), ] opls_atom_type = {N: 'N2', H_1: 'H2', H_2: 'H2', } opls_charge = {N: -0.463, H_1: 0.2315, H_2: 0.2315, } chain_links = [N,None] MMTK-2.7.9/MMTK/Database/Groups/arg_sidechain0000644000076600000240000000377212041514571021160 0ustar hinsenstaff00000000000000H_beta_3 = Atom('h') H_delta_2 = Atom('h') H_delta_3 = Atom('h') C_zeta = Atom('c') C_delta = Atom('c') C_beta = Atom('c') H_gamma_3 = Atom('h') H_gamma_2 = Atom('h') H_epsilon = Atom('h') H_eta_1_1 = Atom('h') C_gamma = Atom('c') H_eta_2_1 = Atom('h') H_eta_2_2 = Atom('h') H_eta_1_2 = Atom('h') N_epsilon = Atom('n') N_eta_2 = Atom('n') H_beta_2 = Atom('h') N_eta_1 = Atom('n') bonds = [Bond(H_beta_2, C_beta), Bond(H_beta_3, C_beta), Bond(C_gamma, C_beta), Bond(H_gamma_2, C_gamma), Bond(H_gamma_3, C_gamma), Bond(C_delta, C_gamma), Bond(H_delta_2, C_delta), Bond(H_delta_3, C_delta), Bond(N_epsilon, C_delta), Bond(H_epsilon, N_epsilon), Bond(C_zeta, N_epsilon), Bond(N_eta_1, C_zeta), Bond(H_eta_1_1, N_eta_1), Bond(H_eta_1_2, N_eta_1), Bond(N_eta_2, C_zeta), Bond(H_eta_2_1, N_eta_2), Bond(H_eta_2_2, N_eta_2), ] name = 'arg_sidechain' pdbmap = [('ARG', {'1HH1': H_eta_1_1, '1HH2': H_eta_2_1, '3HG': H_gamma_3, '3HD': H_delta_3, '3HB': H_beta_3, 'HE': H_epsilon, '2HG': H_gamma_2, 'NE': N_epsilon, '2HB': H_beta_2, 'NH2': N_eta_2, 'NH1': N_eta_1, 'CZ': C_zeta, 'CD': C_delta, 'CG': C_gamma, 'CB': C_beta, '2HD': H_delta_2, '2HH1': H_eta_1_2, '2HH2': H_eta_2_2, }, ), ] pdb_alternative = {'1HB': '3HB', '1HD': '3HD', '1HG': '3HG', 'HB2': '2HB', 'HB3': '3HB', 'HB1': '3HB', 'HH11': '1HH1', 'HH12': '2HH1', 'HH21': '1HH2', 'HH22': '2HH2', 'HG1': '3HG', 'HG3': '3HG', 'HG2': '2HG', 'HD1': '3HD', 'HD2': '2HD', 'HD3': '3HD', } amber_atom_type = {H_eta_2_2: 'H', H_eta_1_1: 'H', H_delta_2: 'H1', H_beta_3: 'HC', N_epsilon: 'N2', C_delta: 'CT', N_eta_1: 'N2', H_epsilon: 'H', C_zeta: 'CA', H_eta_1_2: 'H', N_eta_2: 'N2', C_beta: 'CT', H_gamma_2: 'HC', H_gamma_3: 'HC', H_beta_2: 'HC', H_eta_2_1: 'H', C_gamma: 'CT', H_delta_3: 'H1', } amber12_atom_type = {H_eta_2_2: 'H', H_eta_1_1: 'H', H_delta_2: 'H1', H_beta_3: 'HC', N_epsilon: 'N2', C_delta: 'C8', N_eta_1: 'N2', H_epsilon: 'H', C_zeta: 'CA', H_eta_1_2: 'H', N_eta_2: 'N2', C_beta: 'C8', H_gamma_2: 'HC', H_gamma_3: 'HC', H_beta_2: 'HC', H_eta_2_1: 'H', C_gamma: 'C8', H_delta_3: 'H1', } MMTK-2.7.9/MMTK/Database/Groups/arg_sidechain_noh0000644000076600000240000000111511662461637022025 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta = Atom('CH2') C_gamma = Atom('CH2') C_zeta = Atom('C') N_epsilon = Atom('NH') N_eta_1 = Atom('NH2') N_eta_2 = Atom('NH2') bonds = [Bond(C_gamma, C_beta), Bond(C_delta, C_gamma), Bond(N_epsilon, C_delta), Bond(C_zeta, N_epsilon), Bond(N_eta_1, C_zeta), Bond(N_eta_2, C_zeta), ] pdbmap = [('ARG', {'CD': C_delta, 'NE': N_epsilon, 'CG': C_gamma, 'CB': C_beta, 'NH2': N_eta_2, 'NH1': N_eta_1, 'CZ': C_zeta, }, ), ] amber_atom_type = {N_eta_2: 'N2', C_delta: 'CT', C_zeta: 'CA', N_eta_1: 'N2', C_beta: 'CT', C_gamma: 'CT', N_epsilon: 'N2', } name = 'arg_sidechain' MMTK-2.7.9/MMTK/Database/Groups/arg_sidechain_uni0000644000076600000240000000315211662461637022037 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta = Atom('CH2') C_gamma = Atom('CH2') C_zeta = Atom('C') H_epsilon = Atom('H') H_eta_1_1 = Atom('H') H_eta_1_2 = Atom('H') H_eta_2_1 = Atom('H') H_eta_2_2 = Atom('H') N_epsilon = Atom('N') N_eta_1 = Atom('N') N_eta_2 = Atom('N') bonds = [Bond(C_gamma, C_beta), Bond(C_delta, C_gamma), Bond(N_epsilon, C_delta), Bond(H_epsilon, N_epsilon), Bond(C_zeta, N_epsilon), Bond(N_eta_1, C_zeta), Bond(H_eta_1_1, N_eta_1), Bond(H_eta_1_2, N_eta_1), Bond(N_eta_2, C_zeta), Bond(H_eta_2_1, N_eta_2), Bond(H_eta_2_2, N_eta_2), ] amber91_atom_type = {H_epsilon: 'H3', H_eta_2_2: 'H3', H_eta_1_1: 'H3', H_eta_2_1: 'H3', C_delta: 'C2', C_zeta: 'CA', N_eta_1: 'N2', H_eta_1_2: 'H3', N_eta_2: 'N2', C_beta: 'C2', C_gamma: 'C2', N_epsilon: 'N2', } opls_atom_type = {H_epsilon: 'H3', H_eta_2_2: 'H3', H_eta_1_1: 'H3', H_eta_2_1: 'H3', C_delta: 'C2', C_zeta: 'C4', N_eta_1: 'N2', H_eta_1_2: 'H3', N_eta_2: 'N2', C_beta: 'C2', C_gamma: 'C2', N_epsilon: 'N2', } opls_charge = {H_epsilon: .44, H_eta_2_2: .46, H_eta_1_1: .46, H_eta_2_1: .46, C_delta: .31, C_zeta: .64, N_eta_1: -.8, H_eta_1_2: .46, N_eta_2: -.8, C_beta: 0.0, C_gamma: .07, N_epsilon: -.7, } name = 'arg_sidechain' pdbmap = [('ARG', {'HE': H_epsilon, '1HH1': H_eta_1_1, '1HH2': H_eta_2_1, 'NH2': N_eta_2, 'NH1': N_eta_1, 'CZ': C_zeta, 'CD': C_delta, 'NE': N_epsilon, 'CG': C_gamma, 'CB': C_beta, '2HH1': H_eta_1_2, '2HH2': H_eta_2_2, }, ), ] pdb_alternative = {'HH11': '1HH1', '1HN1': '1HH1', 'HH12': '2HH1', '2HN1': '2HH1', 'HH21': '1HH2', '1HN2': '1HH2', 'HH22': '2HH2', '2HN2': '2HH2', 'HNE': 'HE', 'HN11': '1HN1', 'HN21': '1HN2', 'HN12': '2HN1', 'HN22': '2HN2'} MMTK-2.7.9/MMTK/Database/Groups/arg_sidechain_uni20000644000076600000240000000210311662461637022114 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta = Atom('CH2') C_gamma = Atom('CH2') C_zeta = Atom('C') H_epsilon = Atom('H') H_eta_1_1 = Atom('H') H_eta_1_2 = Atom('H') H_eta_2_1 = Atom('H') H_eta_2_2 = Atom('H') N_epsilon = Atom('N') N_eta_1 = Atom('N') N_eta_2 = Atom('N') bonds = [Bond(C_gamma, C_beta), Bond(C_delta, C_gamma), Bond(N_epsilon, C_delta), Bond(H_epsilon, N_epsilon), Bond(C_zeta, N_epsilon), Bond(N_eta_1, C_zeta), Bond(H_eta_1_1, N_eta_1), Bond(H_eta_1_2, N_eta_1), Bond(N_eta_2, C_zeta), Bond(H_eta_2_1, N_eta_2), Bond(H_eta_2_2, N_eta_2), ] amber91_atom_type = {H_epsilon: 'H3', H_eta_2_2: 'H3', H_eta_1_1: 'H3', H_eta_2_1: 'H3', C_delta: 'C2', C_zeta: 'CA', N_eta_1: 'N2', H_eta_1_2: 'H3', N_eta_2: 'N2', C_beta: 'C2', C_gamma: 'C2', N_epsilon: 'N2', } name = 'arg_sidechain' pdbmap = [('ARG', {'HE': H_epsilon, '1HH1': H_eta_1_1, '1HH2': H_eta_2_1, 'NH2': N_eta_2, 'NH1': N_eta_1, 'CZ': C_zeta, 'CD': C_delta, 'NE': N_epsilon, 'CG': C_gamma, 'CB': C_beta, '2HH1': H_eta_1_2, '2HH2': H_eta_2_2, }, ), ] pdb_alternative = {'HH11': '1HH1', 'HH12': '2HH1', 'HH21': '1HH2', 'HH22': '2HH2', } MMTK-2.7.9/MMTK/Database/Groups/arg_sidechain_uni30000644000076600000240000000315211662461637022122 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta = Atom('CH2') C_gamma = Atom('CH2') C_zeta = Atom('C') H_epsilon = Atom('H') H_eta_1_1 = Atom('H') H_eta_1_2 = Atom('H') H_eta_2_1 = Atom('H') H_eta_2_2 = Atom('H') N_epsilon = Atom('N') N_eta_1 = Atom('N') N_eta_2 = Atom('N') bonds = [Bond(C_gamma, C_beta), Bond(C_delta, C_gamma), Bond(N_epsilon, C_delta), Bond(H_epsilon, N_epsilon), Bond(C_zeta, N_epsilon), Bond(N_eta_1, C_zeta), Bond(H_eta_1_1, N_eta_1), Bond(H_eta_1_2, N_eta_1), Bond(N_eta_2, C_zeta), Bond(H_eta_2_1, N_eta_2), Bond(H_eta_2_2, N_eta_2), ] amber91_atom_type = {H_epsilon: 'H3', H_eta_2_2: 'H3', H_eta_1_1: 'H3', H_eta_2_1: 'H3', C_delta: 'C2', C_zeta: 'CA', N_eta_1: 'N2', H_eta_1_2: 'H3', N_eta_2: 'N2', C_beta: 'C2', C_gamma: 'C2', N_epsilon: 'N2', } opls_atom_type = {H_epsilon: 'H3', H_eta_2_2: 'H3', H_eta_1_1: 'H3', H_eta_2_1: 'H3', C_delta: 'C2', C_zeta: 'C4', N_eta_1: 'N2', H_eta_1_2: 'H3', N_eta_2: 'N2', C_beta: 'C2', C_gamma: 'C2', N_epsilon: 'N2', } opls_charge = {H_epsilon: .44, H_eta_2_2: .46, H_eta_1_1: .46, H_eta_2_1: .46, C_delta: .31, C_zeta: .64, N_eta_1: -.8, H_eta_1_2: .46, N_eta_2: -.8, C_beta: 0.0, C_gamma: .07, N_epsilon: -.7, } name = 'arg_sidechain' pdbmap = [('ARG', {'HE': H_epsilon, '1HH1': H_eta_1_1, '1HH2': H_eta_2_1, 'NH2': N_eta_2, 'NH1': N_eta_1, 'CZ': C_zeta, 'CD': C_delta, 'NE': N_epsilon, 'CG': C_gamma, 'CB': C_beta, '2HH1': H_eta_1_2, '2HH2': H_eta_2_2, }, ), ] pdb_alternative = {'HH11': '1HH1', '1HN1': '1HH1', 'HH12': '2HH1', '2HN1': '2HH1', 'HH21': '1HH2', '1HN2': '1HH2', 'HH22': '2HH2', '2HN2': '2HH2', 'HNE': 'HE', 'HN11': '1HN1', 'HN21': '1HN2', 'HN12': '2HN1', 'HN22': '2HN2'} MMTK-2.7.9/MMTK/Database/Groups/arginine0000644000076600000240000000150711777301631020174 0ustar hinsenstaff00000000000000sidechain = Group('arg_sidechain') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Arg' amber_charge = {sidechain.H_epsilon: 0.3456, sidechain.N_eta_1: -0.8627, sidechain.H_beta_3: 0.0327, sidechain.C_delta: 0.0486, sidechain.N_eta_2: -0.8627, sidechain.H_beta_2: 0.0327, sidechain.N_epsilon: -0.5295, sidechain.H_eta_1_1: 0.4478, sidechain.H_eta_2_1: 0.4478, sidechain.C_beta: -0.0007, peptide.O: -0.5894, sidechain.C_gamma: 0.039, peptide.H_alpha: 0.156, peptide.N: -0.3479, sidechain.H_eta_2_2: 0.4478, sidechain.H_eta_1_2: 0.4478, sidechain.C_zeta: 0.8076, peptide.C_alpha: -0.2637, peptide.H: 0.2747, sidechain.H_gamma_3: 0.0285, sidechain.H_delta_2: 0.0687, sidechain.H_delta_3: 0.0687, sidechain.H_gamma_2: 0.0285, peptide.C: 0.7341, } name = 'arginine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/arginine_calpha0000644000076600000240000000020211662461637021501 0ustar hinsenstaff00000000000000symbol = 'Arg' name = 'arginine' C_alpha = Atom('arginine') pdbmap = [('ARG', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/arginine_ct0000644000076600000240000000153511777301631020663 0ustar hinsenstaff00000000000000sidechain = Group('arg_sidechain') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Arg' amber_charge = {sidechain.H_delta_3: 0.0468, sidechain.H_eta_2_1: 0.4493, sidechain.H_gamma_3: 0.0185, peptide.O_2: -0.8266, sidechain.C_beta: -0.0374, sidechain.N_eta_1: -0.8737, peptide.C: 0.8557, sidechain.H_epsilon: 0.3479, sidechain.N_eta_2: -0.8737, peptide.C_alpha: -0.3068, sidechain.H_delta_2: 0.0468, sidechain.C_zeta: 0.8368, peptide.H: 0.2764, peptide.H_alpha: 0.1447, sidechain.H_beta_3: 0.0371, sidechain.C_gamma: 0.0744, peptide.N: -0.3481, sidechain.H_eta_1_2: 0.4493, sidechain.H_gamma_2: 0.0185, sidechain.H_beta_2: 0.0371, sidechain.N_epsilon: -0.5564, sidechain.H_eta_2_2: 0.4493, sidechain.H_eta_1_1: 0.4493, sidechain.C_delta: 0.1114, peptide.O: -0.8266, } name = 'arginine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/arginine_ct_noh0000644000076600000240000000077411662461637021541 0ustar hinsenstaff00000000000000sidechain = Group('arg_sidechain_noh') peptide = Group('peptide_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Arg' amber_charge = {sidechain.N_epsilon: -0.5564, peptide.N: -0.3481, peptide.C_alpha: -0.3068, sidechain.C_delta: 0.1114, sidechain.C_gamma: 0.0744, peptide.C: 0.8557, sidechain.N_eta_2: -0.8737, peptide.O_2: -0.8266, sidechain.C_beta: -0.0374, peptide.O: -0.8266, sidechain.C_zeta: 0.8368, sidechain.N_eta_1: -0.8737, } name = 'arginine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/arginine_ct_uni0000644000076600000240000000122311662461637021536 0ustar hinsenstaff00000000000000sidechain = Group('arg_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Arg' amber91_charge = {peptide.N: -0.52, peptide.O: -0.706, peptide.C: 0.444, sidechain.N_eta_2: -0.6345, peptide.H: 0.248, sidechain.H_eta_1_1: 0.3615, sidechain.C_beta: 0.049, sidechain.N_eta_1: -0.6345, sidechain.H_epsilon: 0.294, sidechain.C_gamma: 0.058, sidechain.H_eta_1_2: 0.3615, sidechain.C_delta: 0.111, sidechain.H_eta_2_2: 0.3615, peptide.C_alpha: 0.231, peptide.O_2: -0.706, sidechain.C_zeta: 0.813, sidechain.H_eta_2_1: 0.3615, sidechain.N_epsilon: -0.493, } name = 'arginine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/arginine_ct_uni20000644000076600000240000000122311662461637021620 0ustar hinsenstaff00000000000000sidechain = Group('arg_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Arg' amber91_charge = {sidechain.H_eta_1_1: 0.3615, peptide.H: 0.248, peptide.C_alpha: 0.231, sidechain.N_eta_1: -0.6345, peptide.N: -0.52, sidechain.H_epsilon: 0.294, sidechain.C_delta: 0.111, peptide.C: 0.444, sidechain.C_zeta: 0.813, sidechain.H_eta_2_2: 0.3615, peptide.O: -0.706, sidechain.C_gamma: 0.058, sidechain.C_beta: 0.049, peptide.O_2: -0.706, sidechain.H_eta_2_1: 0.3615, sidechain.H_eta_1_2: 0.3615, sidechain.N_eta_2: -0.6345, sidechain.N_epsilon: -0.493, } name = 'arginine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/arginine_ct_uni30000644000076600000240000000122311662461637021621 0ustar hinsenstaff00000000000000sidechain = Group('arg_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Arg' amber91_charge = {peptide.N: -0.52, peptide.O: -0.706, peptide.C: 0.444, sidechain.N_eta_2: -0.6345, peptide.H: 0.248, sidechain.H_eta_1_1: 0.3615, sidechain.C_beta: 0.049, sidechain.N_eta_1: -0.6345, sidechain.H_epsilon: 0.294, sidechain.C_gamma: 0.058, sidechain.H_eta_1_2: 0.3615, sidechain.C_delta: 0.111, sidechain.H_eta_2_2: 0.3615, peptide.C_alpha: 0.231, peptide.O_2: -0.706, sidechain.C_zeta: 0.813, sidechain.H_eta_2_1: 0.3615, sidechain.N_epsilon: -0.493, } name = 'arginine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/arginine_noh0000644000076600000240000000074711662461637021053 0ustar hinsenstaff00000000000000sidechain = Group('arg_sidechain_noh') peptide = Group('peptide_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Arg' amber_charge = {sidechain.C_beta: -0.0007, sidechain.N_eta_2: -0.8627, sidechain.C_zeta: 0.8076, peptide.C: 0.7341, peptide.O: -0.5894, sidechain.N_eta_1: -0.8627, peptide.C_alpha: -0.2637, sidechain.C_delta: 0.0486, sidechain.C_gamma: 0.039, sidechain.N_epsilon: -0.5295, peptide.N: -0.3479, } name = 'arginine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/arginine_nt0000644000076600000240000000156111777301631020675 0ustar hinsenstaff00000000000000sidechain = Group('arg_sidechain') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Arg' amber_charge = {sidechain.N_eta_2: -0.8693, sidechain.H_eta_1_2: 0.4494, sidechain.H_gamma_3: 0.0309, sidechain.C_delta: 0.0935, peptide.H_alpha: 0.1242, sidechain.H_eta_1_1: 0.4494, sidechain.H_gamma_2: 0.0309, sidechain.H_delta_2: 0.0527, sidechain.H_beta_2: 0.0226, peptide.H_2: 0.2083, peptide.C_alpha: -0.0223, sidechain.H_beta_3: 0.0226, sidechain.C_zeta: 0.8281, peptide.O: -0.60130, peptide.C: 0.7214, sidechain.C_gamma: 0.0236, sidechain.N_epsilon: -0.565, sidechain.C_beta: 0.0118, sidechain.H_eta_2_2: 0.4494, sidechain.H_delta_3: 0.0527, sidechain.N_eta_1: -0.8693, peptide.H_3: 0.2083, sidechain.H_eta_2_1: 0.4494, peptide.N: 0.1305, sidechain.H_epsilon: 0.3592, peptide.H_1: 0.2083, } name = 'arginine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/arginine_nt_ct_noh0000644000076600000240000000027411662461637022235 0ustar hinsenstaff00000000000000sidechain = Group('arg_sidechain_noh') peptide = Group('peptide_nt_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Arg' name = 'arginine' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/arginine_nt_noh0000644000076600000240000000073211662461637021546 0ustar hinsenstaff00000000000000sidechain = Group('arg_sidechain_noh') peptide = Group('peptide_nt_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Arg' amber_charge = {sidechain.N_eta_2: -0.624, sidechain.C_beta: -0.08, sidechain.N_epsilon: -0.324, sidechain.N_eta_1: -0.624, peptide.C_alpha: 0.151, peptide.C: 0.616, sidechain.C_gamma: -0.103, sidechain.C_delta: -0.228, sidechain.C_zeta: 0.76, peptide.O: -0.504, peptide.N: -0.263, } name = 'arginine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/arginine_nt_uni0000644000076600000240000000124711662461637021557 0ustar hinsenstaff00000000000000sidechain = Group('arg_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Arg' amber91_charge = {sidechain.N_eta_2: -0.6345, sidechain.H_eta_2_1: 0.3615, peptide.C_alpha: 0.292, sidechain.H_eta_1_1: 0.3615, sidechain.C_zeta: 0.813, peptide.C: 0.526, sidechain.N_eta_1: -0.6345, sidechain.H_eta_2_2: 0.3615, peptide.H_3: 0.312, sidechain.H_epsilon: 0.294, peptide.H_2: 0.312, sidechain.C_gamma: 0.058, peptide.O: -0.5, sidechain.C_beta: 0.049, sidechain.N_epsilon: -0.493, peptide.H_1: 0.312, sidechain.H_eta_1_2: 0.3615, peptide.N: -0.263, sidechain.C_delta: 0.111, } name = 'arginine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/arginine_nt_uni20000644000076600000240000000124711662461637021641 0ustar hinsenstaff00000000000000sidechain = Group('arg_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Arg' amber91_charge = {peptide.H_1: 0.312, sidechain.H_eta_2_1: 0.3615, peptide.H_3: 0.312, peptide.H_2: 0.312, sidechain.N_epsilon: -0.493, peptide.C: 0.526, sidechain.H_epsilon: 0.294, sidechain.H_eta_2_2: 0.3615, sidechain.H_eta_1_1: 0.3615, sidechain.N_eta_2: -0.6345, sidechain.C_gamma: 0.058, sidechain.H_eta_1_2: 0.3615, sidechain.N_eta_1: -0.6345, peptide.O: -0.5, sidechain.C_delta: 0.111, sidechain.C_beta: 0.049, sidechain.C_zeta: 0.813, peptide.C_alpha: 0.292, peptide.N: -0.263, } name = 'arginine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/arginine_nt_uni30000644000076600000240000000124711662461637021642 0ustar hinsenstaff00000000000000sidechain = Group('arg_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Arg' amber91_charge = {sidechain.N_eta_2: -0.6345, sidechain.H_eta_2_1: 0.3615, peptide.C_alpha: 0.292, sidechain.H_eta_1_1: 0.3615, sidechain.C_zeta: 0.813, peptide.C: 0.526, sidechain.N_eta_1: -0.6345, sidechain.H_eta_2_2: 0.3615, peptide.H_3: 0.312, sidechain.H_epsilon: 0.294, peptide.H_2: 0.312, sidechain.C_gamma: 0.058, peptide.O: -0.5, sidechain.C_beta: 0.049, sidechain.N_epsilon: -0.493, peptide.H_1: 0.312, sidechain.H_eta_1_2: 0.3615, peptide.N: -0.263, sidechain.C_delta: 0.111, } name = 'arginine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/arginine_uni0000644000076600000240000000117611662461637021057 0ustar hinsenstaff00000000000000sidechain = Group('arg_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Arg' amber91_charge = {sidechain.H_eta_2_1: 0.3615, peptide.O: -0.5, peptide.C: 0.526, sidechain.H_eta_1_1: 0.3615, sidechain.H_epsilon: 0.294, sidechain.H_eta_1_2: 0.3615, sidechain.C_delta: 0.111, sidechain.C_gamma: 0.058, sidechain.N_eta_1: -0.6345, sidechain.C_zeta: 0.813, sidechain.N_epsilon: -0.493, sidechain.N_eta_2: -0.6345, sidechain.C_beta: 0.049, peptide.N: -0.52, sidechain.H_eta_2_2: 0.3615, peptide.H: 0.248, peptide.C_alpha: 0.237, } name = 'arginine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/arginine_uni20000644000076600000240000000117611662461637021141 0ustar hinsenstaff00000000000000sidechain = Group('arg_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Arg' amber91_charge = {peptide.C_alpha: 0.237, peptide.H: 0.248, sidechain.C_zeta: 0.813, peptide.C: 0.526, sidechain.H_epsilon: 0.294, sidechain.N_epsilon: -0.493, peptide.O: -0.5, sidechain.C_delta: 0.111, sidechain.C_beta: 0.049, sidechain.H_eta_1_2: 0.3615, sidechain.N_eta_1: -0.6345, sidechain.C_gamma: 0.058, sidechain.N_eta_2: -0.6345, sidechain.H_eta_2_1: 0.3615, sidechain.H_eta_2_2: 0.3615, sidechain.H_eta_1_1: 0.3615, peptide.N: -0.52, } name = 'arginine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/arginine_uni30000644000076600000240000000117611662461637021142 0ustar hinsenstaff00000000000000sidechain = Group('arg_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Arg' amber91_charge = {sidechain.H_eta_2_1: 0.3615, peptide.O: -0.5, peptide.C: 0.526, sidechain.H_eta_1_1: 0.3615, sidechain.H_epsilon: 0.294, sidechain.H_eta_1_2: 0.3615, sidechain.C_delta: 0.111, sidechain.C_gamma: 0.058, sidechain.N_eta_1: -0.6345, sidechain.C_zeta: 0.813, sidechain.N_epsilon: -0.493, sidechain.N_eta_2: -0.6345, sidechain.C_beta: 0.049, peptide.N: -0.52, sidechain.H_eta_2_2: 0.3615, peptide.H: 0.248, peptide.C_alpha: 0.237, } name = 'arginine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/asn_sidechain0000644000076600000240000000206412041514571021161 0ustar hinsenstaff00000000000000H_delta_2_1 = Atom('h') H_delta_2_2 = Atom('h') O_delta_1 = Atom('o') N_delta_2 = Atom('n') C_gamma = Atom('c') C_beta = Atom('c') H_beta_3 = Atom('h') H_beta_2 = Atom('h') bonds = [Bond(H_beta_2, C_beta), Bond(H_beta_3, C_beta), Bond(C_gamma, C_beta), Bond(O_delta_1, C_gamma), Bond(N_delta_2, C_gamma), Bond(H_delta_2_1, N_delta_2), Bond(H_delta_2_2, N_delta_2), ] pdbmap = [('ASN', {'3HB': H_beta_3, 'CG': C_gamma, 'ND2': N_delta_2, 'CB': C_beta, 'OD1': O_delta_1, '2HD2': H_delta_2_2, '2HB': H_beta_2, '1HD2': H_delta_2_1, }, ), ] amber_atom_type = {O_delta_1: 'O', H_delta_2_2: 'H', H_beta_3: 'HC', N_delta_2: 'N', H_beta_2: 'HC', H_delta_2_1: 'H', C_beta: 'CT', C_gamma: 'C', } pdb_alternative = {'1HB': '3HB', 'HB2': '2HB', 'HB3': '3HB', 'HB1': '3HB', 'HD22': '2HD2', 'HD21': '1HD2', } amber12_atom_type = {O_delta_1: 'O', H_delta_2_2: 'H', H_beta_3: 'HC', N_delta_2: 'N', H_beta_2: 'HC', H_delta_2_1: 'H', C_beta: '2C', C_gamma: 'C', } pdb_alternative = {'1HB': '3HB', 'HB2': '2HB', 'HB3': '3HB', 'HB1': '3HB', 'HD22': '2HD2', 'HD21': '1HD2', } name = 'asn_sidechain' MMTK-2.7.9/MMTK/Database/Groups/asn_sidechain_noh0000644000076600000240000000056111662461637022041 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_gamma = Atom('C') N_delta_2 = Atom('NH2') O_delta_1 = Atom('O') bonds = [Bond(C_gamma, C_beta), Bond(O_delta_1, C_gamma), Bond(N_delta_2, C_gamma), ] pdbmap = [('ASN', {'CG': C_gamma, 'ND2': N_delta_2, 'OD1': O_delta_1, 'CB': C_beta, }, ), ] amber_atom_type = {N_delta_2: 'N', C_gamma: 'C', O_delta_1: 'O', C_beta: 'CT', } name = 'asn_sidechain' MMTK-2.7.9/MMTK/Database/Groups/asn_sidechain_uni0000644000076600000240000000157111662461637022052 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_gamma = Atom('C') H_delta_2_1 = Atom('H') H_delta_2_2 = Atom('H') N_delta_2 = Atom('N') O_delta_1 = Atom('O') bonds = [Bond(C_gamma, C_beta), Bond(O_delta_1, C_gamma), Bond(N_delta_2, C_gamma), Bond(H_delta_2_1, N_delta_2), Bond(H_delta_2_2, N_delta_2), ] pdbmap = [('ASN', {'CG': C_gamma, 'ND2': N_delta_2, 'OD1': O_delta_1, 'CB': C_beta, '2HD2': H_delta_2_2, '1HD2': H_delta_2_1, }, ), ] pdb_alternative = {'HD21': '1HD2', 'HD22': '2HD2', 'HB1': '3HB', '1HND': '1HD2', '2HND': '2HD2' } name = 'asn_sidechain' opls_atom_type = { C_beta: 'C2', C_gamma: 'C', O_delta_1: 'O', N_delta_2: 'N', H_delta_2_1: 'H', H_delta_2_2: 'H' } opls_charge = { C_beta: 0.0, C_gamma: 0.5, O_delta_1: -0.5, N_delta_2: -0.85, H_delta_2_1: 0.425, H_delta_2_2: 0.425 } amber91_atom_type = {H_delta_2_2: 'H', C_gamma: 'C', N_delta_2: 'N', H_delta_2_1: 'H', C_beta: 'C2', O_delta_1: 'O', } MMTK-2.7.9/MMTK/Database/Groups/asn_sidechain_uni20000644000076600000240000000115611662461637022133 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_gamma = Atom('C') H_delta_2_1 = Atom('H') H_delta_2_2 = Atom('H') N_delta_2 = Atom('N') O_delta_1 = Atom('O') bonds = [Bond(C_gamma, C_beta), Bond(O_delta_1, C_gamma), Bond(N_delta_2, C_gamma), Bond(H_delta_2_1, N_delta_2), Bond(H_delta_2_2, N_delta_2), ] amber91_atom_type = {H_delta_2_2: 'H', C_gamma: 'C', N_delta_2: 'N', H_delta_2_1: 'H', C_beta: 'C2', O_delta_1: 'O', } pdbmap = [('ASN', {'CG': C_gamma, 'ND2': N_delta_2, 'OD1': O_delta_1, 'CB': C_beta, '2HD2': H_delta_2_2, '1HD2': H_delta_2_1, }, ), ] pdb_alternative = {'HD21': '1HD2', 'HD22': '2HD2', 'HB1': '3HB', } name = 'asn_sidechain' MMTK-2.7.9/MMTK/Database/Groups/asn_sidechain_uni30000644000076600000240000000157111662461637022135 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_gamma = Atom('C') H_delta_2_1 = Atom('H') H_delta_2_2 = Atom('H') N_delta_2 = Atom('N') O_delta_1 = Atom('O') bonds = [Bond(C_gamma, C_beta), Bond(O_delta_1, C_gamma), Bond(N_delta_2, C_gamma), Bond(H_delta_2_1, N_delta_2), Bond(H_delta_2_2, N_delta_2), ] pdbmap = [('ASN', {'CG': C_gamma, 'ND2': N_delta_2, 'OD1': O_delta_1, 'CB': C_beta, '2HD2': H_delta_2_2, '1HD2': H_delta_2_1, }, ), ] pdb_alternative = {'HD21': '1HD2', 'HD22': '2HD2', 'HB1': '3HB', '1HND': '1HD2', '2HND': '2HD2' } name = 'asn_sidechain' opls_atom_type = { C_beta: 'C2', C_gamma: 'C', O_delta_1: 'O', N_delta_2: 'N', H_delta_2_1: 'H', H_delta_2_2: 'H' } opls_charge = { C_beta: 0.0, C_gamma: 0.5, O_delta_1: -0.5, N_delta_2: -0.85, H_delta_2_1: 0.425, H_delta_2_2: 0.425 } amber91_atom_type = {H_delta_2_2: 'H', C_gamma: 'C', N_delta_2: 'N', H_delta_2_1: 'H', C_beta: 'C2', O_delta_1: 'O', } MMTK-2.7.9/MMTK/Database/Groups/asp_sidechain0000644000076600000240000000131612041514571021162 0ustar hinsenstaff00000000000000O_delta_1 = Atom('o') O_delta_2 = Atom('o') C_gamma = Atom('c') C_beta = Atom('c') H_beta_3 = Atom('h') H_beta_2 = Atom('h') bonds = [Bond(H_beta_2, C_beta), Bond(H_beta_3, C_beta), Bond(C_gamma, C_beta), Bond(O_delta_1, C_gamma), Bond(O_delta_2, C_gamma), ] pdbmap = [('ASP', {'CG': C_gamma, '2HB': H_beta_2, 'OD2': O_delta_2, 'OD1': O_delta_1, '3HB': H_beta_3, 'CB': C_beta, }, ), ] amber_atom_type = {O_delta_1: 'O2', O_delta_2: 'O2', H_beta_2: 'HC', C_gamma: 'C', C_beta: 'CT', H_beta_3: 'HC', } amber12_atom_type = {O_delta_1: 'O2', O_delta_2: 'O2', H_beta_2: 'HC', C_gamma: 'CO', C_beta: '2C', H_beta_3: 'HC', } pdb_alternative = {'1HB': '3HB', 'HB2': '2HB', 'HB3': '3HB', 'HB1': '3HB', } name = 'asp_sidechain' MMTK-2.7.9/MMTK/Database/Groups/asp_sidechain_neutral0000644000076600000240000000147712041514571022724 0ustar hinsenstaff00000000000000O_delta_1 = Atom('o') O_delta_2 = Atom('o') H_delta_2 = Atom('h') C_gamma = Atom('c') C_beta = Atom('c') H_beta_3 = Atom('h') H_beta_2 = Atom('h') bonds = [Bond(H_beta_2, C_beta), Bond(H_beta_3, C_beta), Bond(C_gamma, C_beta), Bond(O_delta_1, C_gamma), Bond(O_delta_2, C_gamma), Bond(O_delta_2, H_delta_2), ] pdbmap = [('', {'CG': C_gamma, '2HB': H_beta_2, 'OD2': O_delta_2, 'OD1': O_delta_1, '3HB': H_beta_3, 'CB': C_beta, 'HD2': H_delta_2}, ), ] amber_atom_type = {O_delta_1: 'O', O_delta_2: 'OH', H_beta_2: 'HC', C_gamma: 'C', C_beta: 'CT', H_beta_3: 'HC', H_delta_2: 'HO',} amber12_atom_type = {O_delta_1: 'O', O_delta_2: 'OH', H_beta_2: 'HC', C_gamma: 'C', C_beta: '2C', H_beta_3: 'HC', H_delta_2: 'HO',} pdb_alternative = {'1HB': '3HB', 'HB2': '2HB', 'HB3': '3HB', 'HB1': '3HB', '2HD': 'HD2',} name = 'asp_sidechain_neutral' MMTK-2.7.9/MMTK/Database/Groups/asp_sidechain_noh0000644000076600000240000000056111662461637022043 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_gamma = Atom('C') O_delta_1 = Atom('O') O_delta_2 = Atom('O') bonds = [Bond(C_gamma, C_beta), Bond(O_delta_1, C_gamma), Bond(O_delta_2, C_gamma), ] pdbmap = [('ASP', {'OD2': O_delta_2, 'CG': C_gamma, 'CB': C_beta, 'OD1': O_delta_1, }, ), ] amber_atom_type = {C_beta: 'CT', O_delta_1: 'O2', C_gamma: 'C', O_delta_2: 'O2', } name = 'asp_sidechain' MMTK-2.7.9/MMTK/Database/Groups/asp_sidechain_uni0000644000076600000240000000107211662461637022050 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_gamma = Atom('C') O_delta_1 = Atom('O') O_delta_2 = Atom('O') bonds = [Bond(C_gamma, C_beta), Bond(O_delta_1, C_gamma), Bond(O_delta_2, C_gamma), ] pdbmap = [('ASP', {'CG': C_gamma, 'OD2': O_delta_2, 'OD1': O_delta_1, 'CB': C_beta, }, ), ] pdb_alternative = {'HB1': '3HB', } amber91_atom_type = {C_beta: 'C2', O_delta_2: 'O2', C_gamma: 'C', O_delta_1: 'O2', } opls_atom_type = {C_beta: 'C2', O_delta_2: 'O2', C_gamma: 'C', O_delta_1: 'O2', } opls_charge = {C_beta: -0.1, O_delta_2: -.8, C_gamma: .7, O_delta_1: -.8, } name = 'asp_sidechain' MMTK-2.7.9/MMTK/Database/Groups/asp_sidechain_uni20000644000076600000240000000062611662461637022136 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_gamma = Atom('C') O_delta_1 = Atom('O') O_delta_2 = Atom('O') bonds = [Bond(C_gamma, C_beta), Bond(O_delta_1, C_gamma), Bond(O_delta_2, C_gamma), ] pdbmap = [('ASP', {'CG': C_gamma, 'OD2': O_delta_2, 'OD1': O_delta_1, 'CB': C_beta, }, ), ] pdb_alternative = {'HB1': '3HB', } amber91_atom_type = {C_beta: 'C2', O_delta_2: 'O2', C_gamma: 'C', O_delta_1: 'O2', } name = 'asp_sidechain' MMTK-2.7.9/MMTK/Database/Groups/asp_sidechain_uni30000644000076600000240000000107211662461637022133 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_gamma = Atom('C') O_delta_1 = Atom('O') O_delta_2 = Atom('O') bonds = [Bond(C_gamma, C_beta), Bond(O_delta_1, C_gamma), Bond(O_delta_2, C_gamma), ] pdbmap = [('ASP', {'CG': C_gamma, 'OD2': O_delta_2, 'OD1': O_delta_1, 'CB': C_beta, }, ), ] pdb_alternative = {'HB1': '3HB', } amber91_atom_type = {C_beta: 'C2', O_delta_2: 'O2', C_gamma: 'C', O_delta_1: 'O2', } opls_atom_type = {C_beta: 'C2', O_delta_2: 'O2', C_gamma: 'C', O_delta_1: 'O2', } opls_charge = {C_beta: -0.1, O_delta_2: -.8, C_gamma: .7, O_delta_1: -.8, } name = 'asp_sidechain' MMTK-2.7.9/MMTK/Database/Groups/asparagine0000644000076600000240000000106311777301631020507 0ustar hinsenstaff00000000000000sidechain = Group('asn_sidechain') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asn' amber_charge = {sidechain.H_delta_2_1: 0.4196, peptide.N: -0.4157, peptide.H: 0.2719, peptide.O: -0.5679, peptide.H_alpha: 0.1048, sidechain.H_delta_2_2: 0.4196, peptide.C: 0.5973, peptide.C_alpha: 0.0143, sidechain.N_delta_2: -0.9191, sidechain.C_beta: -0.2041, sidechain.O_delta_1: -0.5931, sidechain.H_beta_3: 0.0797, sidechain.H_beta_2: 0.0797, sidechain.C_gamma: 0.713, } name = 'asparagine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/asparagine_calpha0000644000076600000240000000020611662461637022023 0ustar hinsenstaff00000000000000symbol = 'Asn' name = 'asparagine' C_alpha = Atom('asparagine') pdbmap = [('ASN', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/asparagine_ct0000644000076600000240000000110411777301631021171 0ustar hinsenstaff00000000000000sidechain = Group('asn_sidechain') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asn' amber_charge = {sidechain.H_beta_3: 0.1023, peptide.O: -0.8147, sidechain.H_delta_2_1: 0.415, sidechain.O_delta_1: -0.601, peptide.C: 0.805, sidechain.H_beta_2: 0.1023, peptide.C_alpha: -0.208, peptide.N: -0.3821, sidechain.N_delta_2: -0.9084, sidechain.C_beta: -0.2299, peptide.O_2: -0.8147, peptide.H_alpha: 0.1358, sidechain.H_delta_2_2: 0.415, peptide.H: 0.2681, sidechain.C_gamma: 0.7153, } name = 'asparagine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/asparagine_ct_noh0000644000076600000240000000065411662461637022054 0ustar hinsenstaff00000000000000sidechain = Group('asn_sidechain_noh') peptide = Group('peptide_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asn' amber_charge = {sidechain.N_delta_2: -0.9084, peptide.C_alpha: -0.208, peptide.O: -0.8147, sidechain.C_beta: -0.2299, peptide.O_2: -0.8147, peptide.C: 0.805, peptide.N: -0.3821, sidechain.C_gamma: 0.7153, sidechain.O_delta_1: -0.601, } name = 'asparagine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/asparagine_ct_uni0000644000076600000240000000076211662461637022063 0ustar hinsenstaff00000000000000sidechain = Group('asn_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asn' amber91_charge = {sidechain.C_beta: 0.003, sidechain.C_gamma: 0.675, sidechain.H_delta_2_1: 0.344, peptide.N: -0.52, peptide.O: -0.706, sidechain.N_delta_2: -0.867, peptide.O_2: -0.706, peptide.C_alpha: 0.211, peptide.H: 0.248, peptide.C: 0.444, sidechain.H_delta_2_2: 0.344, sidechain.O_delta_1: -0.47, } name = 'asparagine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/asparagine_ct_uni20000644000076600000240000000076211662461637022145 0ustar hinsenstaff00000000000000sidechain = Group('asn_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asn' amber91_charge = {sidechain.H_delta_2_1: 0.344, peptide.N: -0.52, peptide.H: 0.248, sidechain.H_delta_2_2: 0.344, peptide.O: -0.706, sidechain.C_gamma: 0.675, sidechain.C_beta: 0.003, peptide.C_alpha: 0.211, peptide.C: 0.444, sidechain.O_delta_1: -0.47, sidechain.N_delta_2: -0.867, peptide.O_2: -0.706, } name = 'asparagine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/asparagine_ct_uni30000644000076600000240000000076211662461637022146 0ustar hinsenstaff00000000000000sidechain = Group('asn_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asn' amber91_charge = {sidechain.C_beta: 0.003, sidechain.C_gamma: 0.675, sidechain.H_delta_2_1: 0.344, peptide.N: -0.52, peptide.O: -0.706, sidechain.N_delta_2: -0.867, peptide.O_2: -0.706, peptide.C_alpha: 0.211, peptide.H: 0.248, peptide.C: 0.444, sidechain.H_delta_2_2: 0.344, sidechain.O_delta_1: -0.47, } name = 'asparagine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/asparagine_noh0000644000076600000240000000063111662461637021361 0ustar hinsenstaff00000000000000sidechain = Group('asn_sidechain_noh') peptide = Group('peptide_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asn' amber_charge = {peptide.N: -0.4157, sidechain.C_gamma: 0.713, peptide.O: -0.5679, peptide.C: 0.5973, sidechain.N_delta_2: -0.9191, peptide.C_alpha: 0.0143, sidechain.O_delta_1: -0.5931, sidechain.C_beta: -0.2041, } name = 'asparagine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/asparagine_nt0000644000076600000240000000113511777301631021210 0ustar hinsenstaff00000000000000sidechain = Group('asn_sidechain') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asn' amber_charge = {peptide.C_alpha: 0.0368, sidechain.H_beta_3: 0.0515, peptide.O: -0.5722, sidechain.H_beta_2: 0.0515, peptide.C: 0.6163, sidechain.O_delta_1: -0.5744, sidechain.H_delta_2_1: 0.4097, peptide.H_1: 0.1921, sidechain.C_beta: -0.0283, sidechain.H_delta_2_2: 0.4097, peptide.H_alpha: 0.1231, peptide.N: 0.1801, peptide.H_3: 0.1921, sidechain.N_delta_2: -0.8634, sidechain.C_gamma: 0.5833, peptide.H_2: 0.1921, } name = 'asparagine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/asparagine_nt_ct_noh0000644000076600000240000000027611662461637022555 0ustar hinsenstaff00000000000000sidechain = Group('asn_sidechain_noh') peptide = Group('peptide_nt_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asn' name = 'asparagine' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/asparagine_nt_noh0000644000076600000240000000062711662461637022067 0ustar hinsenstaff00000000000000sidechain = Group('asn_sidechain_noh') peptide = Group('peptide_nt_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asn' amber_charge = {sidechain.C_gamma: 0.5833, peptide.C: 0.6163, sidechain.C_beta: -0.0283, sidechain.O_delta_1: -0.5744, sidechain.N_delta_2: -0.8634, peptide.N: 0.1801, peptide.C_alpha: 0.0368, peptide.O: -0.5722, } name = 'asparagine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/asparagine_nt_uni0000644000076600000240000000100611662461637022066 0ustar hinsenstaff00000000000000sidechain = Group('asn_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asn' amber91_charge = {sidechain.H_delta_2_1: 0.344, peptide.H_2: 0.312, sidechain.C_beta: 0.003, peptide.C_alpha: 0.272, peptide.O: -0.5, peptide.C: 0.526, peptide.H_3: 0.312, sidechain.O_delta_1: -0.47, peptide.N: -0.263, sidechain.H_delta_2_2: 0.344, peptide.H_1: 0.312, sidechain.C_gamma: 0.675, sidechain.N_delta_2: -0.867, } name = 'asparagine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/asparagine_nt_uni20000644000076600000240000000100611662461637022150 0ustar hinsenstaff00000000000000sidechain = Group('asn_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asn' amber91_charge = {peptide.H_1: 0.312, peptide.C: 0.526, sidechain.C_beta: 0.003, sidechain.H_delta_2_2: 0.344, peptide.N: -0.263, sidechain.C_gamma: 0.675, sidechain.N_delta_2: -0.867, sidechain.O_delta_1: -0.47, sidechain.H_delta_2_1: 0.344, peptide.H_2: 0.312, peptide.H_3: 0.312, peptide.C_alpha: 0.272, peptide.O: -0.5, } name = 'asparagine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/asparagine_nt_uni30000644000076600000240000000100611662461637022151 0ustar hinsenstaff00000000000000sidechain = Group('asn_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asn' amber91_charge = {sidechain.H_delta_2_1: 0.344, peptide.H_2: 0.312, sidechain.C_beta: 0.003, peptide.C_alpha: 0.272, peptide.O: -0.5, peptide.C: 0.526, peptide.H_3: 0.312, sidechain.O_delta_1: -0.47, peptide.N: -0.263, sidechain.H_delta_2_2: 0.344, peptide.H_1: 0.312, sidechain.C_gamma: 0.675, sidechain.N_delta_2: -0.867, } name = 'asparagine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/asparagine_uni0000644000076600000240000000073511662461637021375 0ustar hinsenstaff00000000000000sidechain = Group('asn_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asn' name = 'asparagine' chain_links = [peptide.N, peptide.C] amber91_charge = {peptide.H: 0.248, sidechain.H_delta_2_1: 0.344, sidechain.C_gamma: 0.675, sidechain.C_beta: 0.003, peptide.O: -0.5, peptide.C: 0.526, sidechain.N_delta_2: -0.867, sidechain.O_delta_1: -0.47, peptide.C_alpha: 0.217, sidechain.H_delta_2_2: 0.344, peptide.N: -0.52, } MMTK-2.7.9/MMTK/Database/Groups/asparagine_uni20000644000076600000240000000073511662461637021457 0ustar hinsenstaff00000000000000sidechain = Group('asn_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asn' amber91_charge = {peptide.H: 0.248, sidechain.H_delta_2_1: 0.344, sidechain.H_delta_2_2: 0.344, peptide.C_alpha: 0.217, sidechain.C_beta: 0.003, sidechain.O_delta_1: -0.47, sidechain.N_delta_2: -0.867, peptide.N: -0.52, sidechain.C_gamma: 0.675, peptide.O: -0.5, peptide.C: 0.526, } name = 'asparagine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/asparagine_uni30000644000076600000240000000073511662461637021460 0ustar hinsenstaff00000000000000sidechain = Group('asn_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asn' name = 'asparagine' chain_links = [peptide.N, peptide.C] amber91_charge = {peptide.H: 0.248, sidechain.H_delta_2_1: 0.344, sidechain.C_gamma: 0.675, sidechain.C_beta: 0.003, peptide.O: -0.5, peptide.C: 0.526, sidechain.N_delta_2: -0.867, sidechain.O_delta_1: -0.47, peptide.C_alpha: 0.217, sidechain.H_delta_2_2: 0.344, peptide.N: -0.52, } MMTK-2.7.9/MMTK/Database/Groups/aspartic_acid0000644000076600000240000000077211777301631021171 0ustar hinsenstaff00000000000000sidechain = Group('asp_sidechain') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asp' amber_charge = {peptide.C: 0.5366, peptide.H_alpha: 0.088, peptide.O: -0.5819, sidechain.H_beta_3: -0.0122, sidechain.H_beta_2: -0.0122, peptide.N: -0.5163, sidechain.O_delta_1: -0.8014, sidechain.C_gamma: 0.7994, sidechain.O_delta_2: -0.8014, peptide.H: 0.2936, peptide.C_alpha: 0.0381, sidechain.C_beta: -0.0303, } name = 'aspartic_acid' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/aspartic_acid_calpha0000644000076600000240000000021411662461637022476 0ustar hinsenstaff00000000000000symbol = 'Asp' name = 'aspartic_acid' C_alpha = Atom('aspartic_acid') pdbmap = [('ASP', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/aspartic_acid_ct0000644000076600000240000000102011777301631021642 0ustar hinsenstaff00000000000000sidechain = Group('asp_sidechain') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asp' amber_charge = {sidechain.C_gamma: 0.8851, peptide.O: -0.7887, sidechain.H_beta_3: -0.0212, peptide.H_alpha: 0.1046, peptide.N: -0.5192, peptide.C: 0.7256, sidechain.O_delta_2: -0.8162, peptide.H: 0.3055, peptide.O_2: -0.7887, sidechain.C_beta: -0.0677, peptide.C_alpha: -0.1817, sidechain.O_delta_1: -0.8162, sidechain.H_beta_2: -0.0212, } name = 'aspartic_acid' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/aspartic_acid_ct_noh0000644000076600000240000000066211662461637022527 0ustar hinsenstaff00000000000000sidechain = Group('asp_sidechain_noh') peptide = Group('peptide_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asp' amber_charge = {sidechain.O_delta_2: -0.8162, sidechain.C_beta: -0.0677, peptide.N: -0.5192, peptide.C_alpha: -0.1817, sidechain.O_delta_1: -0.8162, peptide.O: -0.7887, sidechain.C_gamma: 0.8851, peptide.C: 0.7256, peptide.O_2: -0.7887, } name = 'aspartic_acid' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/aspartic_acid_ct_uni0000644000076600000240000000067111662461637022536 0ustar hinsenstaff00000000000000sidechain = Group('asp_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asp' amber91_charge = {sidechain.O_delta_2: -0.706, sidechain.C_beta: -0.208, sidechain.O_delta_1: -0.706, peptide.H: 0.248, peptide.O_2: -0.706, peptide.N: -0.52, peptide.C: 0.444, sidechain.C_gamma: 0.62, peptide.O: -0.706, peptide.C_alpha: 0.24, } name = 'aspartic_acid' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/aspartic_acid_ct_uni20000644000076600000240000000067111662461637022620 0ustar hinsenstaff00000000000000sidechain = Group('asp_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asp' amber91_charge = {peptide.N: -0.52, sidechain.C_beta: -0.208, peptide.C: 0.444, sidechain.O_delta_2: -0.706, peptide.O_2: -0.706, peptide.O: -0.706, peptide.C_alpha: 0.24, sidechain.O_delta_1: -0.706, sidechain.C_gamma: 0.62, peptide.H: 0.248, } name = 'aspartic_acid' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/aspartic_acid_ct_uni30000644000076600000240000000067111662461637022621 0ustar hinsenstaff00000000000000sidechain = Group('asp_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asp' amber91_charge = {sidechain.O_delta_2: -0.706, sidechain.C_beta: -0.208, sidechain.O_delta_1: -0.706, peptide.H: 0.248, peptide.O_2: -0.706, peptide.N: -0.52, peptide.C: 0.444, sidechain.C_gamma: 0.62, peptide.O: -0.706, peptide.C_alpha: 0.24, } name = 'aspartic_acid' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/aspartic_acid_neutral0000644000076600000240000000106111777301631022713 0ustar hinsenstaff00000000000000sidechain = Group('asp_sidechain_neutral') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'App' amber_charge = {peptide.C: 0.59730, peptide.H_alpha: 0.08640, peptide.O: -0.56790, sidechain.H_beta_3: 0.04880, sidechain.H_beta_2: 0.04880, peptide.N: -0.41570, sidechain.O_delta_1: -0.55540, sidechain.C_gamma: 0.64620, sidechain.O_delta_2: -0.63760, peptide.H: 0.27190, peptide.C_alpha: 0.03410, sidechain.C_beta: -0.03160, sidechain.H_delta_2: 0.47470} name = 'aspartic_acid_neutral' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/aspartic_acid_neutral_calpha0000644000076600000240000000021411662461637024230 0ustar hinsenstaff00000000000000symbol = 'Asp' name = 'aspartic_acid' C_alpha = Atom('aspartic_acid') pdbmap = [('ASP', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/aspartic_acid_noh0000644000076600000240000000063511662461637022041 0ustar hinsenstaff00000000000000sidechain = Group('asp_sidechain_noh') peptide = Group('peptide_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asp' amber_charge = {sidechain.C_gamma: 0.7994, sidechain.O_delta_2: -0.8014, sidechain.O_delta_1: -0.8014, peptide.O: -0.5819, sidechain.C_beta: -0.0303, peptide.N: -0.5163, peptide.C: 0.5366, peptide.C_alpha: 0.0381, } name = 'aspartic_acid' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/aspartic_acid_nt0000644000076600000240000000103611777301631021664 0ustar hinsenstaff00000000000000sidechain = Group('asp_sidechain') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asp' amber_charge = {peptide.H_3: 0.22, peptide.H_1: 0.22, peptide.H_alpha: 0.1141, sidechain.H_beta_3: -0.0169, sidechain.O_delta_2: -0.8084, sidechain.C_beta: -0.0235, sidechain.O_delta_1: -0.8084, peptide.N: 0.0782, peptide.C: 0.5621, peptide.O: -0.5889, sidechain.C_gamma: 0.8194, sidechain.H_beta_2: -0.0169, peptide.H_2: 0.22, peptide.C_alpha: 0.0292, } name = 'aspartic_acid' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/aspartic_acid_nt_ct_noh0000644000076600000240000000030111662461637023216 0ustar hinsenstaff00000000000000sidechain = Group('asp_sidechain_noh') peptide = Group('peptide_nt_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asp' name = 'aspartic_acid' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/aspartic_acid_nt_noh0000644000076600000240000000063211662461637022537 0ustar hinsenstaff00000000000000sidechain = Group('asp_sidechain_noh') peptide = Group('peptide_nt_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asp' amber_charge = {sidechain.O_delta_1: -0.8084, sidechain.C_gamma: 0.8194, peptide.O: -0.5889, peptide.N: 0.0782, sidechain.O_delta_2: -0.8084, sidechain.C_beta: -0.0235, peptide.C: 0.5621, peptide.C_alpha: 0.0292, } name = 'aspartic_acid' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/aspartic_acid_nt_uni0000644000076600000240000000071611662461637022551 0ustar hinsenstaff00000000000000sidechain = Group('asp_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asp' amber91_charge = {peptide.N: -0.263, sidechain.O_delta_1: -0.706, sidechain.O_delta_2: -0.706, peptide.H_1: 0.312, sidechain.C_gamma: 0.62, peptide.C: 0.526, peptide.O: -0.5, sidechain.C_beta: -0.208, peptide.C_alpha: 0.301, peptide.H_3: 0.312, peptide.H_2: 0.312, } name = 'aspartic_acid' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/aspartic_acid_nt_uni20000644000076600000240000000071611662461637022633 0ustar hinsenstaff00000000000000sidechain = Group('asp_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asp' amber91_charge = {sidechain.O_delta_1: -0.706, peptide.N: -0.263, peptide.C: 0.526, sidechain.C_beta: -0.208, peptide.O: -0.5, peptide.H_1: 0.312, sidechain.O_delta_2: -0.706, peptide.H_3: 0.312, peptide.C_alpha: 0.301, sidechain.C_gamma: 0.62, peptide.H_2: 0.312, } name = 'aspartic_acid' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/aspartic_acid_nt_uni30000644000076600000240000000071611662461637022634 0ustar hinsenstaff00000000000000sidechain = Group('asp_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asp' amber91_charge = {peptide.N: -0.263, sidechain.O_delta_1: -0.706, sidechain.O_delta_2: -0.706, peptide.H_1: 0.312, sidechain.C_gamma: 0.62, peptide.C: 0.526, peptide.O: -0.5, sidechain.C_beta: -0.208, peptide.C_alpha: 0.301, peptide.H_3: 0.312, peptide.H_2: 0.312, } name = 'aspartic_acid' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/aspartic_acid_uni0000644000076600000240000000064511662461637022051 0ustar hinsenstaff00000000000000sidechain = Group('asp_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asp' amber91_charge = {peptide.C_alpha: 0.246, peptide.H: 0.248, sidechain.O_delta_1: -0.706, peptide.O: -0.5, sidechain.O_delta_2: -0.706, peptide.N: -0.52, sidechain.C_gamma: 0.62, sidechain.C_beta: -0.208, peptide.C: 0.526, } name = 'aspartic_acid' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/aspartic_acid_uni20000644000076600000240000000064511662461637022133 0ustar hinsenstaff00000000000000sidechain = Group('asp_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asp' amber91_charge = {sidechain.C_beta: -0.208, sidechain.C_gamma: 0.62, peptide.C_alpha: 0.246, peptide.N: -0.52, sidechain.O_delta_1: -0.706, sidechain.O_delta_2: -0.706, peptide.H: 0.248, peptide.C: 0.526, peptide.O: -0.5, } name = 'aspartic_acid' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/aspartic_acid_uni30000644000076600000240000000064511662461637022134 0ustar hinsenstaff00000000000000sidechain = Group('asp_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Asp' amber91_charge = {peptide.C_alpha: 0.246, peptide.H: 0.248, sidechain.O_delta_1: -0.706, peptide.O: -0.5, sidechain.O_delta_2: -0.706, peptide.N: -0.52, sidechain.C_gamma: 0.62, sidechain.C_beta: -0.208, peptide.C: 0.526, } name = 'aspartic_acid' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/bound_heme0000644000076600000240000001461111662461637020513 0ustar hinsenstaff00000000000000name = 'heme_bound_to_two_cysteines' symbol = 'heme' FE = Atom('Fe') NA = Atom('N') C1A = Atom('C') C2A = Atom('C') CAA = Atom('C') HP71 = Atom('H') HP72 = Atom('H') CBA = Atom('C') HP73 = Atom('H') HP74 = Atom('H') CGA = Atom('C') O1A = Atom('O') O2A = Atom('O') C3A = Atom('C') CMA = Atom('C') HM81 = Atom('H') HM82 = Atom('H') HM83 = Atom('H') C4A = Atom('C') CHB = Atom('C') HDM = Atom('H') C1B = Atom('C') NB = Atom('N') C2B = Atom('C') CMB = Atom('C') HM11 = Atom('H') HM12 = Atom('H') HM13 = Atom('H') C3B = Atom('C') CAB = Atom('C') CBB = Atom('C') HVC2 = Atom('H') HVT2 = Atom('H') C4B = Atom('C') CHC = Atom('C') HAM = Atom('H') C1C = Atom('C') NC = Atom('N') C2C = Atom('C') CMC = Atom('C') HM31 = Atom('H') HM32 = Atom('H') HM33 = Atom('H') C3C = Atom('C') CAC = Atom('C') CBC = Atom('C') HVC4 = Atom('H') HVT4 = Atom('H') C4C = Atom('C') CHD = Atom('C') HBM = Atom('H') C1D = Atom('C') ND = Atom('N') C2D = Atom('C') CMD = Atom('C') HM51 = Atom('H') HM52 = Atom('H') HM53 = Atom('H') C3D = Atom('C') C4D = Atom('C') CHA = Atom('C') HGM = Atom('H') CAD = Atom('C') HP61 = Atom('H') HP62 = Atom('H') CBD = Atom('C') HP63 = Atom('H') HP64 = Atom('H') CGD = Atom('C') O1D = Atom('O') O2D = Atom('O') bonds = [Bond(NA, FE), Bond(C1A, NA), Bond(C2A, C1A), Bond(CAA, C2A), Bond(HP71, CAA), Bond(HP72, CAA), Bond(CBA, CAA), Bond(HP73, CBA), Bond(HP74, CBA), Bond(CGA, CBA), Bond(O1A, CGA), Bond(O2A, CGA), Bond(C3A, C2A), Bond(CMA, C3A), Bond(HM81, CMA), Bond(HM82, CMA), Bond(HM83, CMA), Bond(C4A, C3A), Bond(CHB, C4A), Bond(HDM, CHB), Bond(C1B, CHB), Bond(NB, C1B), Bond(C2B, C1B), Bond(CMB, C2B), Bond(HM11, CMB), Bond(HM12, CMB), Bond(HM13, CMB), Bond(C3B, C2B), Bond(CAB, C3B), Bond(CBB, CAB), Bond(HVC2, CBB), Bond(HVT2, CBB), Bond(C4B, C3B), Bond(CHC, C4B), Bond(HAM, CHC), Bond(C1C, CHC), Bond(NC, C1C), Bond(C2C, C1C), Bond(CMC, C2C), Bond(HM31, CMC), Bond(HM32, CMC), Bond(HM33, CMC), Bond(C3C, C2C), Bond(CAC, C3C), Bond(CBC, CAC), Bond(HVC4, CBC), Bond(HVT4, CBC), Bond(C4C, C3C), Bond(CHD, C4C), Bond(HBM, CHD), Bond(C1D, CHD), Bond(ND, C1D), Bond(C2D, C1D), Bond(CMD, C2D), Bond(HM51, CMD), Bond(HM52, CMD), Bond(HM53, CMD), Bond(C3D, C2D), Bond(C4D, C3D), Bond(CHA, C4D), Bond(HGM, CHA), Bond(CAD, C3D), Bond(HP61, CAD), Bond(HP62, CAD), Bond(CBD, CAD), Bond(HP63, CBD), Bond(HP64, CBD), Bond(CGD, CBD), Bond(O1D, CGD), Bond(O2D, CGD), Bond(NA, C4A), Bond(FE, NB), Bond(FE, NC), Bond(FE, ND), Bond(NB, C4B), Bond(NC, C4C), Bond(ND, C4D), Bond(C1A, CHA)] pdbmap = [('HEM', {'FE': FE, 'CHA': CHA, 'CHB': CHB, 'CHC': CHC, 'CHD': CHD, 'NA': NA, 'C1A': C1A, 'C2A': C2A, 'C3A': C3A, 'C4A': C4A, 'CMA': CMA, 'CAA': CAA, 'CBA': CBA, 'CGA': CGA, 'O1A': O1A, 'O2A': O2A, 'NB': NB, 'C1B': C1B, 'C2B': C2B, 'C3B': C3B, 'C4B': C4B, 'CMB': CMB, 'CAB': CAB, 'CBB': CBB, 'NC': NC, 'C1C': C1C, 'C2C': C2C, 'C3C': C3C, 'C4C': C4C, 'CMC': CMC, 'CAC': CAC, 'CBC': CBC, 'ND': ND, 'C1D': C1D, 'C2D': C2D, 'C3D': C3D, 'C4D': C4D, 'CMD': CMD, 'CAD': CAD, 'CBD': CBD, 'CGD': CGD, 'O1D': O1D, 'O2D': O2D, '1HP7': HP71, '2HP7': HP72, '3HP7': HP73, '4HP7': HP74, '1HM8': HM81, '2HM8': HM82, '3HM8': HM83, 'HDM': HDM, '1HM1': HM11, '2HM1': HM12, '3HM1': HM13, '2HVC': HVC2, '2HVT': HVT2, 'HAM': HAM, '1HM3': HM31, '2HM3': HM32, '3HM3': HM33, '4HVC': HVC4, '4HVT': HVT4, 'HBM': HBM, '1HM5': HM51, '2HM5': HM52, '3HM5': HM53, 'HGM': HGM, '1HP6': HP61, '2HP6': HP62, '3HP6': HP63, '4HP6': HP64})] amber_atom_type = {FE: 'FE', NA: 'NP', C1A: 'CC', C2A: 'CB', CAA: 'CT', HP71: 'HC', HP72: 'HC', CBA: 'CT', HP73: 'HC', HP74: 'HC', CGA: 'C', O1A: 'O2', O2A: 'O2', C3A: 'CB', CMA: 'CT', HM81: 'HC', HM82: 'HC', HM83: 'HC', C4A: 'CC', CHB: 'CD', HDM: 'HC', C1B: 'CC', NB: 'NO', C2B: 'CB', CMB: 'CT', HM11: 'HC', HM12: 'HC', HM13: 'HC', C3B: 'CB', CAB: 'CY', CBB: 'CX', HVC2: 'HC', HVT2: 'HC', C4B: 'CC', CHC: 'CD', HAM: 'HC', C1C: 'CC', NC: 'NP', C2C: 'CB', CMC: 'CT', HM31: 'HC', HM32: 'HC', HM33: 'HC', C3C: 'CB', CAC: 'CY', CBC: 'CX', HVC4: 'HC', HVT4: 'HC', C4C: 'CC', CHD: 'CD', HBM: 'HC', C1D: 'CC', ND: 'NO', C2D: 'CB', CMD: 'CT', HM51: 'HC', HM52: 'HC', HM53: 'HC', C3D: 'CB', C4D: 'CC', CHA: 'CD', HGM: 'HC', CAD: 'CT', HP61: 'HC', HP62: 'HC', CBD: 'CT', HP63: 'HC', HP64: 'HC', CGD: 'C', O1D: 'O2', O2D: 'O2'} amber_charge = { FE: 0.2500, NA: -0.1800, C1A: 0.0300, C2A: -0.0200, CAA: -0.1600, HP71: 0.1000, HP72: 0.1000, CBA: -0.3000, HP73: 0.1000, HP74: 0.1000, CGA: 0.3000, O1A: -0.5000, O2A: -0.5000, C3A: 0.0200, CMA: -0.2650, HM81: 0.0750, HM82: 0.0750, HM83: 0.0750, C4A: 0.0200, CHB: -0.1100, HDM: 0.1500, C1B: 0.0300, NB: -0.1800, C2B: 0.0200, CMB: -0.2650, HM11: 0.0750, HM12: 0.0750, HM13: 0.0750, C3B: -0.0500, CAB: -0.1300, CBB: -0.3000, HVC2: 0.1000, HVT2: 0.1000, C4B: 0.0200, CHC: -0.1100, HAM: 0.1500, C1C: 0.0300, NC: -0.1800, C2C: 0.0200, CMC: -0.2650, HM31: 0.0750, HM32: 0.0750, HM33: 0.0750, C3C: -0.0500, CAC: -0.1200, CBC: -0.3000, HVC4: 0.1000, HVT4: 0.1000, C4C: 0.0200, CHD: -0.1100, HBM: 0.1500, C1D: 0.0300, ND: -0.1800, C2D: 0.0200, CMD: -0.2650, HM51: 0.0750, HM52: 0.0750, HM53: 0.0750, C3D: -0.0200, C4D: 0.0200, CHA: -0.1100, HGM: 0.1500, CAD: -0.1600, HP61: 0.1000, HP62: 0.1000, CBD: -0.3000, HP63: 0.1000, HP64: 0.1000, CGD: 0.3000, O1D: -0.5000, O2D: -0.5000, } MMTK-2.7.9/MMTK/Database/Groups/cym_sidechain0000644000076600000240000000070712041514571021172 0ustar hinsenstaff00000000000000name ='cym_sidechain' C_beta = Atom('C') S_gamma = Atom('S') H_beta_3 = Atom('H') H_beta_2 = Atom('H') bonds = [Bond(H_beta_3, C_beta), Bond(H_beta_2, C_beta), Bond(S_gamma, C_beta), ] pdbmap = [('CYM', {'CB': C_beta, '3HB': H_beta_3, '2HB': H_beta_2, 'SG': S_gamma, })] pdb_alternative = {'1HB': '3HB', 'HB3': '3HB', 'HB2': '2HB'} amber_atom_type = {C_beta: 'CT', H_beta_3: 'H1', H_beta_2: 'H1', S_gamma: 'SH', } amber12_atom_type = amber_atom_type MMTK-2.7.9/MMTK/Database/Groups/cys_sidechain0000644000076600000240000000115512041514571021176 0ustar hinsenstaff00000000000000S_gamma = Atom('s') H_gamma = Atom('h') C_beta = Atom('c') H_beta_3 = Atom('h') H_beta_2 = Atom('h') bonds = [Bond(H_beta_2, C_beta), Bond(H_beta_3, C_beta), Bond(S_gamma, C_beta), Bond(H_gamma, S_gamma), ] pdbmap = [('CYS', {'HG': H_gamma, 'SG': S_gamma, '2HB': H_beta_2, 'CB': C_beta, '3HB': H_beta_3, }, ), ] amber_atom_type = {S_gamma: 'SH', C_beta: 'CT', H_beta_2: 'H1', H_gamma: 'HS', H_beta_3: 'H1', } amber12_atom_type = {S_gamma: 'SH', C_beta: '2C', H_beta_2: 'H1', H_gamma: 'HS', H_beta_3: 'H1', } pdb_alternative = {'1HB': '3HB', 'HG1': 'HG', 'HB3': '3HB', 'HB1': '3HB', 'HB2': '2HB', } name = 'cys_sidechain' MMTK-2.7.9/MMTK/Database/Groups/cys_sidechain_noh0000644000076600000240000000031511662461637022053 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') S_gamma = Atom('SH') bonds = [Bond(S_gamma, C_beta), ] pdbmap = [('CYS', {'CB': C_beta, 'SG': S_gamma, }, ), ] amber_atom_type = {C_beta: 'CT', S_gamma: 'SH', } name = 'cys_sidechain' MMTK-2.7.9/MMTK/Database/Groups/cys_sidechain_uni0000644000076600000240000000060311662461637022062 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') H_gamma = Atom('H') S_gamma = Atom('S') bonds = [Bond(S_gamma, C_beta), Bond(H_gamma, S_gamma), ] pdbmap = [('CYS', { 'HG': H_gamma, 'SG': S_gamma, 'CB': C_beta, }, ), ] pdb_alternative = {'HG1': 'HG', 'HB1': '3HB', } opls_atom_type = { C_beta: 'C2', S_gamma: 'SH', H_gamma: 'HS', } opls_charge = { C_beta: .18, S_gamma: -.45, H_gamma: .27, } name = 'cys_sidechain' MMTK-2.7.9/MMTK/Database/Groups/cys_sidechain_uni20000644000076600000240000000023311662461637022143 0ustar hinsenstaff00000000000000S_gamma = Atom('sh') C_beta = Atom('ch2') bonds = [Bond(S_gamma, C_beta), ] pdbmap = [('CYS', {'CB': C_beta, 'SG': S_gamma, }, ), ] name = 'cys_sidechain' MMTK-2.7.9/MMTK/Database/Groups/cys_sidechain_uni30000644000076600000240000000060311662461637022145 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') H_gamma = Atom('H') S_gamma = Atom('S') bonds = [Bond(S_gamma, C_beta), Bond(H_gamma, S_gamma), ] pdbmap = [('CYS', { 'HG': H_gamma, 'SG': S_gamma, 'CB': C_beta, }, ), ] pdb_alternative = {'HG1': 'HG', 'HB1': '3HB', } opls_atom_type = { C_beta: 'C2', S_gamma: 'SH', H_gamma: 'HS', } opls_charge = { C_beta: .18, S_gamma: -.45, H_gamma: .27, } name = 'cys_sidechain' MMTK-2.7.9/MMTK/Database/Groups/cysteine0000644000076600000240000000072411777301631020223 0ustar hinsenstaff00000000000000sidechain = Group('cys_sidechain') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cys' amber_charge = {peptide.O: -0.5679, sidechain.H_beta_2: 0.1112, sidechain.H_beta_3: 0.1112, peptide.H_alpha: 0.1124, peptide.H: 0.2719, peptide.N: -0.4157, sidechain.H_gamma: 0.1933, sidechain.S_gamma: -0.3119, peptide.C_alpha: 0.0213, sidechain.C_beta: -0.1231, peptide.C: 0.5973, } name = 'cysteine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/cysteine_calpha0000644000076600000240000000020211662461637021530 0ustar hinsenstaff00000000000000symbol = 'Cys' name = 'cysteine' C_alpha = Atom('cysteine') pdbmap = [('CYS', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/cysteine_ct0000644000076600000240000000075111777301631020711 0ustar hinsenstaff00000000000000sidechain = Group('cys_sidechain') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cys' amber_charge = {sidechain.H_gamma: 0.2068, peptide.O_2: -0.7981, peptide.N: -0.3821, peptide.H_alpha: 0.1396, peptide.C_alpha: -0.1635, peptide.O: -0.7981, sidechain.S_gamma: -0.3102, sidechain.C_beta: -0.1996, peptide.H: 0.2681, sidechain.H_beta_3: 0.1437, peptide.C: 0.7497, sidechain.H_beta_2: 0.1437, } name = 'cysteine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/cysteine_ct_noh0000644000076600000240000000056211662461637021563 0ustar hinsenstaff00000000000000sidechain = Group('cys_sidechain_noh') peptide = Group('peptide_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cys' amber_charge = {peptide.O: -0.7981, peptide.C: 0.7497, sidechain.S_gamma: -0.3102, peptide.O_2: -0.7981, peptide.C_alpha: -0.1635, sidechain.C_beta: -0.1996, peptide.N: -0.3821, } name = 'cysteine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/cysteine_ct_uni0000644000076600000240000000070211662461637021566 0ustar hinsenstaff00000000000000sidechain = Group('cys_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cys' amber91_charge = {sidechain.S_gamma: 0.827, sidechain.H_gamma: 0.135, peptide.C: 0.444, sidechain.LP_1: -0.481, peptide.O: -0.706, peptide.O_2: -0.706, sidechain.C_beta: 0.1, peptide.N: -0.52, peptide.C_alpha: 0.14, sidechain.LP_2: -0.481, peptide.H: 0.248, } name = 'cysteine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/cysteine_ct_uni20000644000076600000240000000030011662461637021642 0ustar hinsenstaff00000000000000sidechain = Group('cys_sidechain_uni2') peptide = Group('peptide_ct_uni2') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cys' name = 'cysteine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/cysteine_ct_uni30000644000076600000240000000070211662461637021651 0ustar hinsenstaff00000000000000sidechain = Group('cys_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cys' amber91_charge = {sidechain.S_gamma: 0.827, sidechain.H_gamma: 0.135, peptide.C: 0.444, sidechain.LP_1: -0.481, peptide.O: -0.706, peptide.O_2: -0.706, sidechain.C_beta: 0.1, peptide.N: -0.52, peptide.C_alpha: 0.14, sidechain.LP_2: -0.481, peptide.H: 0.248, } name = 'cysteine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/cysteine_noh0000644000076600000240000000053511662461637021075 0ustar hinsenstaff00000000000000sidechain = Group('cys_sidechain_noh') peptide = Group('peptide_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cys' amber_charge = {peptide.C_alpha: 0.0213, peptide.C: 0.5973, peptide.O: -0.5679, sidechain.S_gamma: -0.3119, peptide.N: -0.4157, sidechain.C_beta: -0.1231, } name = 'cysteine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/cysteine_nt0000644000076600000240000000077511777301631020732 0ustar hinsenstaff00000000000000sidechain = Group('cys_sidechain') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cys' amber_charge = {peptide.H_3: 0.2023, peptide.H_2: 0.2023, sidechain.H_gamma: 0.1975, peptide.C_alpha: 0.0927, peptide.H_alpha: 0.1411, peptide.C: 0.6123, sidechain.H_beta_2: 0.1188, peptide.O: -0.5713, sidechain.S_gamma: -0.3298, sidechain.C_beta: -0.1195, peptide.N: 0.1325, peptide.H_1: 0.2023, sidechain.H_beta_3: 0.1188, } name = 'cysteine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/cysteine_nt_ct_noh0000644000076600000240000000027411662461637022264 0ustar hinsenstaff00000000000000sidechain = Group('cys_sidechain_noh') peptide = Group('peptide_nt_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cys' name = 'cysteine' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/cysteine_nt_noh0000644000076600000240000000053211662461637021573 0ustar hinsenstaff00000000000000sidechain = Group('cys_sidechain_noh') peptide = Group('peptide_nt_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cys' amber_charge = {peptide.N: 0.1325, sidechain.S_gamma: -0.3298, sidechain.C_beta: -0.1195, peptide.O: -0.5713, peptide.C: 0.6123, peptide.C_alpha: 0.0927, } name = 'cysteine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/cysteine_nt_uni0000644000076600000240000000072711662461637021610 0ustar hinsenstaff00000000000000sidechain = Group('cys_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cys' amber91_charge = {sidechain.S_gamma: 0.827, sidechain.LP_2: -0.481, peptide.H_1: 0.312, peptide.C: 0.526, sidechain.C_beta: 0.1, peptide.N: -0.263, peptide.C_alpha: 0.201, peptide.O: -0.5, peptide.H_3: 0.312, peptide.H_2: 0.312, sidechain.LP_1: -0.481, sidechain.H_gamma: 0.135, } name = 'cysteine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/cysteine_nt_uni20000644000076600000240000000030011662461637021655 0ustar hinsenstaff00000000000000sidechain = Group('cys_sidechain_uni2') peptide = Group('peptide_nt_uni2') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cys' name = 'cysteine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/cysteine_nt_uni30000644000076600000240000000072711662461637021673 0ustar hinsenstaff00000000000000sidechain = Group('cys_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cys' amber91_charge = {sidechain.S_gamma: 0.827, sidechain.LP_2: -0.481, peptide.H_1: 0.312, peptide.C: 0.526, sidechain.C_beta: 0.1, peptide.N: -0.263, peptide.C_alpha: 0.201, peptide.O: -0.5, peptide.H_3: 0.312, peptide.H_2: 0.312, sidechain.LP_1: -0.481, sidechain.H_gamma: 0.135, } name = 'cysteine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/cysteine_uni0000644000076600000240000000065611662461637021110 0ustar hinsenstaff00000000000000sidechain = Group('cys_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cys' amber91_charge = {peptide.N: -0.52, peptide.H: 0.248, sidechain.S_gamma: 0.827, sidechain.LP_1: -0.481, peptide.C: 0.526, sidechain.LP_2: -0.481, peptide.C_alpha: 0.146, peptide.O: -0.5, sidechain.H_gamma: 0.135, sidechain.C_beta: 0.1, } name = 'cysteine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/cysteine_uni20000644000076600000240000000030211662461637021156 0ustar hinsenstaff00000000000000sidechain = Group('cys_sidechain_uni2') peptide = Group('peptide_uni2') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cys' name = 'cysteine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/cysteine_uni30000644000076600000240000000065611662461637021173 0ustar hinsenstaff00000000000000sidechain = Group('cys_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cys' amber91_charge = {peptide.N: -0.52, peptide.H: 0.248, sidechain.S_gamma: 0.827, sidechain.LP_1: -0.481, peptide.C: 0.526, sidechain.LP_2: -0.481, peptide.C_alpha: 0.146, peptide.O: -0.5, sidechain.H_gamma: 0.135, sidechain.C_beta: 0.1, } name = 'cysteine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/cysteine_with_negative_charge0000644000076600000240000000070611777301631024451 0ustar hinsenstaff00000000000000name = 'cysteine_with_negative_charge' symbol = 'Cym' peptide = Group('peptide') sidechain = Group('cym_sidechain') bonds = [Bond(peptide.C_alpha, sidechain.C_beta)] amber_charge = {peptide.N: -0.463, peptide.H: 0.252, peptide.C_alpha: 0.035, peptide.H_alpha: 0.048, peptide.C: 0.616, peptide.O: -0.504, sidechain.C_beta: -0.736, sidechain.H_beta_3: 0.244, sidechain.H_beta_2: 0.244, sidechain.S_gamma: -0.736, } chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/cystine_ss0000644000076600000240000000067011777301631020563 0ustar hinsenstaff00000000000000sidechain = Group('cyx_sidechain') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cyx' amber_charge = {sidechain.C_beta: -0.079, sidechain.H_beta_2: 0.091, peptide.H: 0.2719, peptide.O: -0.5679, peptide.H_alpha: 0.0766, peptide.C: 0.5973, sidechain.S_gamma: -0.1081, peptide.N: -0.4157, sidechain.H_beta_3: 0.091, peptide.C_alpha: 0.0429, } name = 'cystine_ss' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/cystine_ss_calpha0000644000076600000240000000020611662461637022074 0ustar hinsenstaff00000000000000symbol = 'Cyx' name = 'cystine_ss' C_alpha = Atom('cystine_ss') pdbmap = [('CYX', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/cystine_ss_ct0000644000076600000240000000073211777301631021250 0ustar hinsenstaff00000000000000sidechain = Group('cyx_sidechain') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cyx' amber_charge = {peptide.O_2: -0.8041, sidechain.H_beta_3: 0.1228, sidechain.C_beta: -0.1943, peptide.N: -0.3821, sidechain.S_gamma: -0.0529, peptide.H_alpha: 0.0938, peptide.O: -0.8041, peptide.H: 0.2681, sidechain.H_beta_2: 0.1228, peptide.C_alpha: -0.1318, peptide.C: 0.7618, } name = 'cystine (s-s_bridge)' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/cystine_ss_ct_noh0000644000076600000240000000057611662461637022130 0ustar hinsenstaff00000000000000sidechain = Group('cyx_sidechain_noh') peptide = Group('peptide_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cyx' amber_charge = {peptide.O: -0.8041, sidechain.C_beta: -0.1943, peptide.C: 0.7618, peptide.N: -0.3821, peptide.O_2: -0.8041, sidechain.S_gamma: -0.0529, peptide.C_alpha: -0.1318, } name = 'cystine (s-s_bridge)' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/cystine_ss_ct_uni0000644000076600000240000000067111662461637022133 0ustar hinsenstaff00000000000000sidechain = Group('cyx_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cyx' amber91_charge = {peptide.N: -0.52, sidechain.LP_2: -0.4045, sidechain.S_gamma: 0.824, peptide.H: 0.248, sidechain.C_beta: 0.143, peptide.C: 0.444, sidechain.LP_1: -0.4045, peptide.O_2: -0.706, peptide.C_alpha: 0.082, peptide.O: -0.706, } name = 'cystine (s-s_bridge)' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/cystine_ss_ct_uni20000644000076600000240000000031411662461637022207 0ustar hinsenstaff00000000000000sidechain = Group('cyx_sidechain_uni2') peptide = Group('peptide_ct_uni2') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cyx' name = 'cystine (s-s_bridge)' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/cystine_ss_ct_uni30000644000076600000240000000067111662461637022216 0ustar hinsenstaff00000000000000sidechain = Group('cyx_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cyx' amber91_charge = {peptide.N: -0.52, sidechain.LP_2: -0.4045, sidechain.S_gamma: 0.824, peptide.H: 0.248, sidechain.C_beta: 0.143, peptide.C: 0.444, sidechain.LP_1: -0.4045, peptide.O_2: -0.706, peptide.C_alpha: 0.082, peptide.O: -0.706, } name = 'cystine (s-s_bridge)' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/cystine_ss_noh0000644000076600000240000000055011662461637021432 0ustar hinsenstaff00000000000000sidechain = Group('cyx_sidechain_noh') peptide = Group('peptide_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cyx' amber_charge = {sidechain.S_gamma: -0.1081, peptide.O: -0.5679, peptide.C_alpha: 0.0429, sidechain.C_beta: -0.079, peptide.N: -0.4157, peptide.C: 0.5973, } name = 'cystine (s-s_bridge)' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/cystine_ss_nt0000644000076600000240000000075411777301631021267 0ustar hinsenstaff00000000000000sidechain = Group('cyx_sidechain') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cyx' amber_charge = {peptide.N: 0.2069, sidechain.H_beta_3: 0.068, sidechain.C_beta: -0.0277, peptide.C: 0.6123, peptide.O: -0.5713, peptide.H_2: 0.1815, peptide.H_alpha: 0.0922, peptide.H_1: 0.1815, peptide.H_3: 0.1815, peptide.C_alpha: 0.1055, sidechain.S_gamma: -0.0984, sidechain.H_beta_2: 0.068, } name = 'cystine (s-s_bridge)' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/cystine_ss_nt_ct_noh0000644000076600000240000000031011662461637022613 0ustar hinsenstaff00000000000000sidechain = Group('cyx_sidechain_noh') peptide = Group('peptide_nt_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cyx' name = 'cystine (s-s_bridge)' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/cystine_ss_nt_noh0000644000076600000240000000054611662461637022140 0ustar hinsenstaff00000000000000sidechain = Group('cyx_sidechain_noh') peptide = Group('peptide_nt_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cyx' amber_charge = {peptide.C_alpha: 0.1055, peptide.C: 0.6123, peptide.N: 0.2069, peptide.O: -0.5713, sidechain.C_beta: -0.0277, sidechain.S_gamma: -0.0984, } name = 'cystine (s-s_bridge)' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/cystine_ss_nt_uni0000644000076600000240000000071511662461637022145 0ustar hinsenstaff00000000000000sidechain = Group('cyx_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cyx' amber91_charge = {peptide.H_2: 0.312, peptide.H_1: 0.312, sidechain.C_beta: 0.143, peptide.C: 0.526, peptide.N: -0.263, sidechain.LP_2: -0.4045, sidechain.LP_1: -0.4045, peptide.C_alpha: 0.143, sidechain.S_gamma: 0.824, peptide.O: -0.5, peptide.H_3: 0.312, } name = 'cystine (s-s_bridge)' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/cystine_ss_nt_uni20000644000076600000240000000031411662461637022222 0ustar hinsenstaff00000000000000sidechain = Group('cyx_sidechain_uni2') peptide = Group('peptide_nt_uni2') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cyx' name = 'cystine (s-s_bridge)' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/cystine_ss_nt_uni30000644000076600000240000000071511662461637022230 0ustar hinsenstaff00000000000000sidechain = Group('cyx_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cyx' amber91_charge = {peptide.H_2: 0.312, peptide.H_1: 0.312, sidechain.C_beta: 0.143, peptide.C: 0.526, peptide.N: -0.263, sidechain.LP_2: -0.4045, sidechain.LP_1: -0.4045, peptide.C_alpha: 0.143, sidechain.S_gamma: 0.824, peptide.O: -0.5, peptide.H_3: 0.312, } name = 'cystine (s-s_bridge)' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/cystine_ss_uni0000644000076600000240000000064411662461637021445 0ustar hinsenstaff00000000000000sidechain = Group('cyx_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cyx' amber91_charge = {sidechain.LP_2: -0.4045, peptide.O: -0.5, peptide.C_alpha: 0.088, peptide.C: 0.526, sidechain.C_beta: 0.143, sidechain.S_gamma: 0.824, sidechain.LP_1: -0.4045, peptide.H: 0.248, peptide.N: -0.52, } name = 'cystine (s-s_bridge)' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/cystine_ss_uni20000644000076600000240000000031611662461637021523 0ustar hinsenstaff00000000000000sidechain = Group('cyx_sidechain_uni2') peptide = Group('peptide_uni2') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cyx' name = 'cystine (s-s_bridge)' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/cystine_ss_uni30000644000076600000240000000064411662461637021530 0ustar hinsenstaff00000000000000sidechain = Group('cyx_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Cyx' amber91_charge = {sidechain.LP_2: -0.4045, peptide.O: -0.5, peptide.C_alpha: 0.088, peptide.C: 0.526, sidechain.C_beta: 0.143, sidechain.S_gamma: 0.824, sidechain.LP_1: -0.4045, peptide.H: 0.248, peptide.N: -0.52, } name = 'cystine (s-s_bridge)' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/cytosine0000644000076600000240000000137512041514571020232 0ustar hinsenstaff00000000000000name ='cytosine' N_1 = Atom('N') C_6 = Atom('C') H_6 = Atom('H') C_5 = Atom('C') H_5 = Atom('H') C_4 = Atom('C') N_4 = Atom('N') H_41 = Atom('H') H_42 = Atom('H') N_3 = Atom('N') C_2 = Atom('C') O_2 = Atom('O') bonds = [Bond(C_6, N_1), Bond(H_6, C_6), Bond(C_5, C_6), Bond(H_5, C_5), Bond(C_4, C_5), Bond(N_4, C_4), Bond(H_41, N_4), Bond(H_42, N_4), Bond(N_3, C_4), Bond(C_2, N_3), Bond(O_2, C_2), Bond(C_2, N_1), ] pdbmap = [('C', {"C6": C_6, "H41": H_41, "H42": H_42, "C5": C_5, "C2": C_2, "C4": C_4, "N3": N_3, "N1": N_1, "N4": N_4, "O2": O_2, "H5": H_5, "H6": H_6, })] amber_atom_type = {C_6: 'CM', H_41: 'H', H_42: 'H', C_5: 'CM', C_2: 'C', C_4: 'CA', N_3: 'NC', N_1: 'N*', N_4: 'N2', O_2: 'O', H_5: 'HA', H_6: 'H4', } amber12_atom_type = amber_atom_type MMTK-2.7.9/MMTK/Database/Groups/cyx_sidechain0000644000076600000240000000100312041514571021173 0ustar hinsenstaff00000000000000S_gamma = Atom('s') C_beta = Atom('c') H_beta_3 = Atom('h') H_beta_2 = Atom('h') bonds = [Bond(H_beta_2, C_beta), Bond(H_beta_3, C_beta), Bond(S_gamma, C_beta), ] pdbmap = [('CYX', {'SG': S_gamma, '2HB': H_beta_2, '3HB': H_beta_3, 'CB': C_beta, }, ), ] pdb_alternative = {'HB2': '2HB', 'HB3': '3HB', 'HB1': '3HB', '1HB': '3HB'} amber_atom_type = {H_beta_3: 'H1', S_gamma: 'S', C_beta: 'CT', H_beta_2: 'H1', } amber12_atom_type = {H_beta_3: 'H1', S_gamma: 'S', C_beta: '2C', H_beta_2: 'H1', } name = 'cyx_sidechain' MMTK-2.7.9/MMTK/Database/Groups/cyx_sidechain_noh0000644000076600000240000000031311662461637022056 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') S_gamma = Atom('S') bonds = [Bond(S_gamma, C_beta), ] pdbmap = [('CYX', {'CB': C_beta, 'SG': S_gamma, }, ), ] amber_atom_type = {C_beta: 'CT', S_gamma: 'S', } name = 'cyx_sidechain' MMTK-2.7.9/MMTK/Database/Groups/cyx_sidechain_uni0000644000076600000240000000036711662461637022076 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') S_gamma = Atom('S') bonds = [Bond(S_gamma, C_beta), ] opls_atom_type = { S_gamma: 'S', C_beta: 'CQ', } opls_charge = { S_gamma: -.3, C_beta: .3, } pdbmap = [('CYX', {'SG': S_gamma, 'CB': C_beta, }, ), ] name = 'cyx_sidechain' MMTK-2.7.9/MMTK/Database/Groups/cyx_sidechain_uni20000644000076600000240000000031511662461637022151 0ustar hinsenstaff00000000000000S_gamma = Atom('s') C_beta = Atom('ch2') bonds = [Bond(S_gamma, C_beta), ] amber91_atom_type = {S_gamma: 'S', C_beta: 'C2', } pdbmap = [('CYX', {'CB': C_beta, 'SG': S_gamma, }, ), ] name = 'cyx_sidechain' MMTK-2.7.9/MMTK/Database/Groups/cyx_sidechain_uni30000644000076600000240000000036711662461637022161 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') S_gamma = Atom('S') bonds = [Bond(S_gamma, C_beta), ] opls_atom_type = { S_gamma: 'S', C_beta: 'CQ', } opls_charge = { S_gamma: -.3, C_beta: .3, } pdbmap = [('CYX', {'SG': S_gamma, 'CB': C_beta, }, ), ] name = 'cyx_sidechain' MMTK-2.7.9/MMTK/Database/Groups/d-adenosine0000644000076600000240000000255011777301631020565 0ustar hinsenstaff00000000000000name ='d-adenosine' symbol ='DA' phosphate = Group('na_phosphate') sugar = Group('desoxyribose') base = Group('adenine') bonds = [Bond(sugar.O_5, phosphate.P), Bond(base.N_9, sugar.C_1), ] chain_links = [phosphate.P, sugar.O_3] amber_charge = {phosphate.P: 1.1659, phosphate.O_1: -0.7761, phosphate.O_2: -0.7761, sugar.O_5: -0.4954, sugar.C_5: -0.0069, sugar.H_51: 0.0754, sugar.H_52: 0.0754, sugar.C_4: 0.1629, sugar.H_4: 0.1176, sugar.O_4: -0.3691, sugar.C_1: 0.0431, sugar.H_1: 0.1838, base.N_9: -0.0268, base.C_8: 0.1607, base.H_8: 0.1877, base.N_7: -0.6175, base.C_5: 0.0725, base.C_6: 0.6897, base.N_6: -0.9123, base.H_61: 0.4167, base.H_62: 0.4167, base.N_1: -0.7624, base.C_2: 0.5716, base.H_2: 0.0598, base.N_3: -0.7417, base.C_4: 0.3800, sugar.C_3: 0.0713, sugar.H_3: 0.0985, sugar.C_2: -0.0854, sugar.H_21: 0.0718, sugar.H_22: 0.0718, sugar.O_3: -0.5232, } MMTK-2.7.9/MMTK/Database/Groups/d-adenosine_3ter0000644000076600000240000000263211777301631021523 0ustar hinsenstaff00000000000000name ='d-adenosine_3ter' symbol ='DA3' phosphate = Group('na_phosphate') sugar = Group('desoxyribose_3ter') base = Group('adenine') bonds = [Bond(sugar.O_5, phosphate.P), Bond(base.N_9, sugar.C_1), ] chain_links = [phosphate.P, None] amber_charge = {phosphate.P: 1.1659, phosphate.O_1: -0.7761, phosphate.O_2: -0.7761, sugar.O_5: -0.4954, sugar.C_5: -0.0069, sugar.H_51: 0.0754, sugar.H_52: 0.0754, sugar.C_4: 0.1629, sugar.H_4: 0.1176, sugar.O_4: -0.3691, sugar.C_1: 0.0431, sugar.H_1: 0.1838, base.N_9: -0.0268, base.C_8: 0.1607, base.H_8: 0.1877, base.N_7: -0.6175, base.C_5: 0.0725, base.C_6: 0.6897, base.N_6: -0.9123, base.H_61: 0.4167, base.H_62: 0.4167, base.N_1: -0.7624, base.C_2: 0.5716, base.H_2: 0.0598, base.N_3: -0.7417, base.C_4: 0.3800, sugar.C_3: 0.0713, sugar.H_3: 0.0985, sugar.C_2: -0.0854, sugar.H_21: 0.0718, sugar.H_22: 0.0718, sugar.O_3: -0.6549, sugar.H_3_terminal: 0.4396, } MMTK-2.7.9/MMTK/Database/Groups/d-adenosine_5ter0000644000076600000240000000234311777301631021524 0ustar hinsenstaff00000000000000name ='d-adenosine_5ter' symbol ='DA5' sugar = Group('desoxyribose_5ter') base = Group('adenine') bonds = [Bond(base.N_9, sugar.C_1), ] chain_links = [None, sugar.O_3] amber_charge = {sugar.H_5_terminal: 0.4422, sugar.O_5: -0.6318, sugar.C_5: -0.0069, sugar.H_51: 0.0754, sugar.H_52: 0.0754, sugar.C_4: 0.1629, sugar.H_4: 0.1176, sugar.O_4: -0.3691, sugar.C_1: 0.0431, sugar.H_1: 0.1838, base.N_9: -0.0268, base.C_8: 0.1607, base.H_8: 0.1877, base.N_7: -0.6175, base.C_5: 0.0725, base.C_6: 0.6897, base.N_6: -0.9123, base.H_61: 0.4167, base.H_62: 0.4167, base.N_1: -0.7624, base.C_2: 0.5716, base.H_2: 0.0598, base.N_3: -0.7417, base.C_4: 0.3800, sugar.C_3: 0.0713, sugar.H_3: 0.0985, sugar.C_2: -0.0854, sugar.H_21: 0.0718, sugar.H_22: 0.0718, sugar.O_3: -0.5232, } MMTK-2.7.9/MMTK/Database/Groups/d-adenosine_5ter_3ter0000644000076600000240000000242411777301631022461 0ustar hinsenstaff00000000000000name ='d-adenosine_5ter_3ter' symbol ='DAN' sugar = Group('desoxyribose_5ter_3ter') base = Group('adenine') bonds = [Bond(base.N_9, sugar.C_1), ] chain_links = [None, None] amber_charge = {sugar.H_5_terminal: 0.4422, sugar.O_5: -0.6318, sugar.C_5: -0.0069, sugar.H_51: 0.0754, sugar.H_52: 0.0754, sugar.C_4: 0.1629, sugar.H_4: 0.1176, sugar.O_4: -0.3691, sugar.C_1: 0.0431, sugar.H_1: 0.1838, base.N_9: -0.0268, base.C_8: 0.1607, base.H_8: 0.1877, base.N_7: -0.6175, base.C_5: 0.0725, base.C_6: 0.6897, base.N_6: -0.9123, base.H_61: 0.4167, base.H_62: 0.4167, base.N_1: -0.7624, base.C_2: 0.5716, base.H_2: 0.0598, base.N_3: -0.7417, base.C_4: 0.3800, sugar.C_3: 0.0713, sugar.H_3: 0.0985, sugar.C_2: -0.0854, sugar.H_21: 0.0718, sugar.H_22: 0.0718, sugar.O_3: -0.6549, sugar.H_3_terminal: 0.4396, } MMTK-2.7.9/MMTK/Database/Groups/d-cytosine0000644000076600000240000000244611777301631020461 0ustar hinsenstaff00000000000000name ='d-cytosine' symbol ='DC' phosphate = Group('na_phosphate') sugar = Group('desoxyribose') base = Group('cytosine') bonds = [Bond(sugar.O_5, phosphate.P), Bond(base.N_1, sugar.C_1), ] chain_links = [phosphate.P, sugar.O_3] amber_charge = {phosphate.P: 1.1659, phosphate.O_1: -0.7761, phosphate.O_2: -0.7761, sugar.O_5: -0.4954, sugar.C_5: -0.0069, sugar.H_51: 0.0754, sugar.H_52: 0.0754, sugar.C_4: 0.1629, sugar.H_4: 0.1176, sugar.O_4: -0.3691, sugar.C_1: -0.0116, sugar.H_1: 0.1963, base.N_1: -0.0339, base.C_6: -0.0183, base.H_6: 0.2293, base.C_5: -0.5222, base.H_5: 0.1863, base.C_4: 0.8439, base.N_4: -0.9773, base.H_41: 0.4314, base.H_42: 0.4314, base.N_3: -0.7748, base.C_2: 0.7959, base.O_2: -0.6548, sugar.C_3: 0.0713, sugar.H_3: 0.0985, sugar.C_2: -0.0854, sugar.H_21: 0.0718, sugar.H_22: 0.0718, sugar.O_3: -0.5232, } MMTK-2.7.9/MMTK/Database/Groups/d-cytosine_3ter0000644000076600000240000000253011777301631021410 0ustar hinsenstaff00000000000000name ='d-cytosine_3ter' symbol ='DC3' phosphate = Group('na_phosphate') sugar = Group('desoxyribose_3ter') base = Group('cytosine') bonds = [Bond(sugar.O_5, phosphate.P), Bond(base.N_1, sugar.C_1), ] chain_links = [phosphate.P, None] amber_charge = {phosphate.P: 1.1659, phosphate.O_1: -0.7761, phosphate.O_2: -0.7761, sugar.O_5: -0.4954, sugar.C_5: -0.0069, sugar.H_51: 0.0754, sugar.H_52: 0.0754, sugar.C_4: 0.1629, sugar.H_4: 0.1176, sugar.O_4: -0.3691, sugar.C_1: -0.0116, sugar.H_1: 0.1963, base.N_1: -0.0339, base.C_6: -0.0183, base.H_6: 0.2293, base.C_5: -0.5222, base.H_5: 0.1863, base.C_4: 0.8439, base.N_4: -0.9773, base.H_41: 0.4314, base.H_42: 0.4314, base.N_3: -0.7748, base.C_2: 0.7959, base.O_2: -0.6548, sugar.C_3: 0.0713, sugar.H_3: 0.0985, sugar.C_2: -0.0854, sugar.H_21: 0.0718, sugar.H_22: 0.0718, sugar.O_3: -0.6549, sugar.H_3_terminal: 0.4396, } MMTK-2.7.9/MMTK/Database/Groups/d-cytosine_5ter0000644000076600000240000000224111777301631021411 0ustar hinsenstaff00000000000000name ='d-cytosine_5ter' symbol ='DC5' sugar = Group('desoxyribose_5ter') base = Group('cytosine') bonds = [Bond(base.N_1, sugar.C_1), ] chain_links = [None, sugar.O_3] amber_charge = {sugar.H_5_terminal: 0.4422, sugar.O_5: -0.6318, sugar.C_5: -0.0069, sugar.H_51: 0.0754, sugar.H_52: 0.0754, sugar.C_4: 0.1629, sugar.H_4: 0.1176, sugar.O_4: -0.3691, sugar.C_1: -0.0116, sugar.H_1: 0.1963, base.N_1: -0.0339, base.C_6: -0.0183, base.H_6: 0.2293, base.C_5: -0.5222, base.H_5: 0.1863, base.C_4: 0.8439, base.N_4: -0.9773, base.H_41: 0.4314, base.H_42: 0.4314, base.N_3: -0.7748, base.C_2: 0.7959, base.O_2: -0.6548, sugar.C_3: 0.0713, sugar.H_3: 0.0985, sugar.C_2: -0.0854, sugar.H_21: 0.0718, sugar.H_22: 0.0718, sugar.O_3: -0.5232, } MMTK-2.7.9/MMTK/Database/Groups/d-cytosine_5ter_3ter0000644000076600000240000000232211777301631022346 0ustar hinsenstaff00000000000000name ='d-cytosine_5ter_3ter' symbol ='DCN' sugar = Group('desoxyribose_5ter_3ter') base = Group('cytosine') bonds = [Bond(base.N_1, sugar.C_1), ] chain_links = [None, None] amber_charge = {sugar.H_5_terminal: 0.4422, sugar.O_5: -0.6318, sugar.C_5: -0.0069, sugar.H_51: 0.0754, sugar.H_52: 0.0754, sugar.C_4: 0.1629, sugar.H_4: 0.1176, sugar.O_4: -0.3691, sugar.C_1: -0.0116, sugar.H_1: 0.1963, base.N_1: -0.0339, base.C_6: -0.0183, base.H_6: 0.2293, base.C_5: -0.5222, base.H_5: 0.1863, base.C_4: 0.8439, base.N_4: -0.9773, base.H_41: 0.4314, base.H_42: 0.4314, base.N_3: -0.7748, base.C_2: 0.7959, base.O_2: -0.6548, sugar.C_3: 0.0713, sugar.H_3: 0.0985, sugar.C_2: -0.0854, sugar.H_21: 0.0718, sugar.H_22: 0.0718, sugar.O_3: -0.6549, sugar.H_3_terminal: 0.4396, } MMTK-2.7.9/MMTK/Database/Groups/d-guanosine0000644000076600000240000000261311777301631020610 0ustar hinsenstaff00000000000000name ='d-guanosine' symbol ='DG' phosphate = Group('na_phosphate') sugar = Group('desoxyribose') base = Group('guanine') bonds = [Bond(sugar.O_5, phosphate.P), Bond(base.N_9, sugar.C_1), ] chain_links = [phosphate.P, sugar.O_3] amber_charge = {phosphate.P: 1.1659, phosphate.O_1: -0.7761, phosphate.O_2: -0.7761, sugar.O_5: -0.4954, sugar.C_5: -0.0069, sugar.H_51: 0.0754, sugar.H_52: 0.0754, sugar.C_4: 0.1629, sugar.H_4: 0.1176, sugar.O_4: -0.3691, sugar.C_1: 0.0358, sugar.H_1: 0.1746, base.N_9: 0.0577, base.C_8: 0.0736, base.H_8: 0.1997, base.N_7: -0.5725, base.C_5: 0.1991, base.C_6: 0.4918, base.O_6: -0.5699, base.N_1: -0.5053, base.H_1: 0.3520, base.C_2: 0.7432, base.N_2: -0.9230, base.H_21: 0.4235, base.H_22: 0.4235, base.N_3: -0.6636, base.C_4: 0.1814, sugar.C_3: 0.0713, sugar.H_3: 0.0985, sugar.C_2: -0.0854, sugar.H_21: 0.0718, sugar.H_22: 0.0718, sugar.O_3: -0.5232, } MMTK-2.7.9/MMTK/Database/Groups/d-guanosine_3ter0000644000076600000240000000267411777301631021554 0ustar hinsenstaff00000000000000name ='d-guanosine_3ter' symbol ='DG3' phosphate = Group('na_phosphate') sugar = Group('desoxyribose_3ter') base = Group('guanine') bonds = [Bond(sugar.O_5, phosphate.P), Bond(base.N_9, sugar.C_1), ] chain_links = [phosphate.P, None] amber_charge = {phosphate.P: 1.1659, phosphate.O_1: -0.7761, phosphate.O_2: -0.7761, sugar.O_5: -0.4954, sugar.C_5: -0.0069, sugar.H_51: 0.0754, sugar.H_52: 0.0754, sugar.C_4: 0.1629, sugar.H_4: 0.1176, sugar.O_4: -0.3691, sugar.C_1: 0.0358, sugar.H_1: 0.1746, base.N_9: 0.0577, base.C_8: 0.0736, base.H_8: 0.1997, base.N_7: -0.5725, base.C_5: 0.1991, base.C_6: 0.4918, base.O_6: -0.5699, base.N_1: -0.5053, base.H_1: 0.3520, base.C_2: 0.7432, base.N_2: -0.9230, base.H_21: 0.4235, base.H_22: 0.4235, base.N_3: -0.6636, base.C_4: 0.1814, sugar.C_3: 0.0713, sugar.H_3: 0.0985, sugar.C_2: -0.0854, sugar.H_21: 0.0718, sugar.H_22: 0.0718, sugar.O_3: -0.6549, sugar.H_3_terminal: 0.4396, } MMTK-2.7.9/MMTK/Database/Groups/d-guanosine_5ter0000644000076600000240000000240511777301631021546 0ustar hinsenstaff00000000000000name ='d-guanosine_5ter' symbol ='DG5' sugar = Group('desoxyribose_5ter') base = Group('guanine') bonds = [Bond(base.N_9, sugar.C_1), ] chain_links = [None, sugar.O_3] amber_charge = {sugar.H_5_terminal: 0.4422, sugar.O_5: -0.6318, sugar.C_5: -0.0069, sugar.H_51: 0.0754, sugar.H_52: 0.0754, sugar.C_4: 0.1629, sugar.H_4: 0.1176, sugar.O_4: -0.3691, sugar.C_1: 0.0358, sugar.H_1: 0.1746, base.N_9: 0.0577, base.C_8: 0.0736, base.H_8: 0.1997, base.N_7: -0.5725, base.C_5: 0.1991, base.C_6: 0.4918, base.O_6: -0.5699, base.N_1: -0.5053, base.H_1: 0.3520, base.C_2: 0.7432, base.N_2: -0.9230, base.H_21: 0.4235, base.H_22: 0.4235, base.N_3: -0.6636, base.C_4: 0.1814, sugar.C_3: 0.0713, sugar.H_3: 0.0985, sugar.C_2: -0.0854, sugar.H_21: 0.0718, sugar.H_22: 0.0718, sugar.O_3: -0.5232, } MMTK-2.7.9/MMTK/Database/Groups/d-guanosine_5ter_3ter0000644000076600000240000000246611777301631022512 0ustar hinsenstaff00000000000000name ='d-guanosine_5ter_3ter' symbol ='DGN' sugar = Group('desoxyribose_5ter_3ter') base = Group('guanine') bonds = [Bond(base.N_9, sugar.C_1), ] chain_links = [None, None] amber_charge = {sugar.H_5_terminal: 0.4422, sugar.O_5: -0.6318, sugar.C_5: -0.0069, sugar.H_51: 0.0754, sugar.H_52: 0.0754, sugar.C_4: 0.1629, sugar.H_4: 0.1176, sugar.O_4: -0.3691, sugar.C_1: 0.0358, sugar.H_1: 0.1746, base.N_9: 0.0577, base.C_8: 0.0736, base.H_8: 0.1997, base.N_7: -0.5725, base.C_5: 0.1991, base.C_6: 0.4918, base.O_6: -0.5699, base.N_1: -0.5053, base.H_1: 0.3520, base.C_2: 0.7432, base.N_2: -0.9230, base.H_21: 0.4235, base.H_22: 0.4235, base.N_3: -0.6636, base.C_4: 0.1814, sugar.C_3: 0.0713, sugar.H_3: 0.0985, sugar.C_2: -0.0854, sugar.H_21: 0.0718, sugar.H_22: 0.0718, sugar.O_3: -0.6549, sugar.H_3_terminal: 0.4396, } MMTK-2.7.9/MMTK/Database/Groups/d-thymine0000644000076600000240000000255011777301631020275 0ustar hinsenstaff00000000000000name ='d-thymine' symbol ='DT' phosphate = Group('na_phosphate') sugar = Group('desoxyribose') base = Group('thymine') bonds = [Bond(sugar.O_5, phosphate.P), Bond(base.N_1, sugar.C_1), ] chain_links = [phosphate.P, sugar.O_3] amber_charge = {phosphate.P: 1.1659, phosphate.O_1: -0.7761, phosphate.O_2: -0.7761, sugar.O_5: -0.4954, sugar.C_5: -0.0069, sugar.H_51: 0.0754, sugar.H_52: 0.0754, sugar.C_4: 0.1629, sugar.H_4: 0.1176, sugar.O_4: -0.3691, sugar.C_1: 0.0680, sugar.H_1: 0.1804, base.N_1: -0.0239, base.C_6: -0.2209, base.H_6: 0.2607, base.C_5: 0.0025, base.C_7: -0.2269, base.H_71: 0.0770, base.H_72: 0.0770, base.H_73: 0.0770, base.C_4: 0.5194, base.O_4: -0.5563, base.N_3: -0.4340, base.H_3: 0.3420, base.C_2: 0.5677, base.O_2: -0.5881, sugar.C_3: 0.0713, sugar.H_3: 0.0985, sugar.C_2: -0.0854, sugar.H_21: 0.0718, sugar.H_22: 0.0718, sugar.O_3: -0.5232, } MMTK-2.7.9/MMTK/Database/Groups/d-thymine_3ter0000644000076600000240000000263211777301631021233 0ustar hinsenstaff00000000000000name ='d-thymine_3ter' symbol ='DT3' phosphate = Group('na_phosphate') sugar = Group('desoxyribose_3ter') base = Group('thymine') bonds = [Bond(sugar.O_5, phosphate.P), Bond(base.N_1, sugar.C_1), ] chain_links = [phosphate.P, None] amber_charge = {phosphate.P: 1.1659, phosphate.O_1: -0.7761, phosphate.O_2: -0.7761, sugar.O_5: -0.4954, sugar.C_5: -0.0069, sugar.H_51: 0.0754, sugar.H_52: 0.0754, sugar.C_4: 0.1629, sugar.H_4: 0.1176, sugar.O_4: -0.3691, sugar.C_1: 0.0680, sugar.H_1: 0.1804, base.N_1: -0.0239, base.C_6: -0.2209, base.H_6: 0.2607, base.C_5: 0.0025, base.C_7: -0.2269, base.H_71: 0.0770, base.H_72: 0.0770, base.H_73: 0.0770, base.C_4: 0.5194, base.O_4: -0.5563, base.N_3: -0.4340, base.H_3: 0.3420, base.C_2: 0.5677, base.O_2: -0.5881, sugar.C_3: 0.0713, sugar.H_3: 0.0985, sugar.C_2: -0.0854, sugar.H_21: 0.0718, sugar.H_22: 0.0718, sugar.O_3: -0.6549, sugar.H_3_terminal: 0.4396, } MMTK-2.7.9/MMTK/Database/Groups/d-thymine_5ter0000644000076600000240000000234311777301631021234 0ustar hinsenstaff00000000000000name ='d-thymine_5ter' symbol ='DT5' sugar = Group('desoxyribose_5ter') base = Group('thymine') bonds = [Bond(base.N_1, sugar.C_1), ] chain_links = [None, sugar.O_3] amber_charge = {sugar.H_5_terminal: 0.4422, sugar.O_5: -0.6318, sugar.C_5: -0.0069, sugar.H_51: 0.0754, sugar.H_52: 0.0754, sugar.C_4: 0.1629, sugar.H_4: 0.1176, sugar.O_4: -0.3691, sugar.C_1: 0.0680, sugar.H_1: 0.1804, base.N_1: -0.0239, base.C_6: -0.2209, base.H_6: 0.2607, base.C_5: 0.0025, base.C_7: -0.2269, base.H_71: 0.0770, base.H_72: 0.0770, base.H_73: 0.0770, base.C_4: 0.5194, base.O_4: -0.5563, base.N_3: -0.4340, base.H_3: 0.3420, base.C_2: 0.5677, base.O_2: -0.5881, sugar.C_3: 0.0713, sugar.H_3: 0.0985, sugar.C_2: -0.0854, sugar.H_21: 0.0718, sugar.H_22: 0.0718, sugar.O_3: -0.5232, } MMTK-2.7.9/MMTK/Database/Groups/d-thymine_5ter_3ter0000644000076600000240000000242411777301631022171 0ustar hinsenstaff00000000000000name ='d-thymine_5ter_3ter' symbol ='DTN' sugar = Group('desoxyribose_5ter_3ter') base = Group('thymine') bonds = [Bond(base.N_1, sugar.C_1), ] chain_links = [None, None] amber_charge = {sugar.H_5_terminal: 0.4422, sugar.O_5: -0.6318, sugar.C_5: -0.0069, sugar.H_51: 0.0754, sugar.H_52: 0.0754, sugar.C_4: 0.1629, sugar.H_4: 0.1176, sugar.O_4: -0.3691, sugar.C_1: 0.0680, sugar.H_1: 0.1804, base.N_1: -0.0239, base.C_6: -0.2209, base.H_6: 0.2607, base.C_5: 0.0025, base.C_7: -0.2269, base.H_71: 0.0770, base.H_72: 0.0770, base.H_73: 0.0770, base.C_4: 0.5194, base.O_4: -0.5563, base.N_3: -0.4340, base.H_3: 0.3420, base.C_2: 0.5677, base.O_2: -0.5881, sugar.C_3: 0.0713, sugar.H_3: 0.0985, sugar.C_2: -0.0854, sugar.H_21: 0.0718, sugar.H_22: 0.0718, sugar.O_3: -0.6549, sugar.H_3_terminal: 0.4396, } MMTK-2.7.9/MMTK/Database/Groups/desoxyribose0000644000076600000240000000251212041514571021106 0ustar hinsenstaff00000000000000name ='desoxyribose' O_5 = Atom('O') C_5 = Atom('C') H_51 = Atom('H') H_52 = Atom('H') C_4 = Atom('C') H_4 = Atom('H') O_4 = Atom('O') C_1 = Atom('C') H_1 = Atom('H') C_3 = Atom('C') H_3 = Atom('H') C_2 = Atom('C') H_21 = Atom('H') H_22 = Atom('H') O_3 = Atom('O') bonds = [Bond(C_5, O_5), Bond(H_51, C_5), Bond(H_52, C_5), Bond(C_4, C_5), Bond(H_4, C_4), Bond(O_4, C_4), Bond(C_1, O_4), Bond(H_1, C_1), Bond(C_3, C_4), Bond(H_3, C_3), Bond(C_2, C_3), Bond(H_21, C_2), Bond(H_22, C_2), Bond(O_3, C_3), Bond(C_1, C_2), ] pdbmap = [('D', {"1H5*": H_51, "2H5*": H_52, "C4*": C_4, "C5*": C_5, "C2*": C_2, "C3*": C_3, "C1*": C_1, "1H2*": H_21, "H1*": H_1, "O3*": O_3, "H3*": H_3, "2H2*": H_22, "H4*": H_4, "O4*": O_4, "O5*": O_5, })] pdb_alternative = {"1H5'": "1H5*", "2H5'": "2H5*", "C4'": "C4*", "C5'": "C5*", "C2'": "C2*", "C3'": "C3*", "C1'": "C1*", "1H2'": "1H2*", "H1'": "H1*", "O3'": "O3*", "H3'": "H3*", "2H2'": "2H2*", "H4'": "H4*", "O4'": "O4*", "O5'": "O5*",} amber_atom_type = {H_51: 'H1', H_52: 'H1', C_4: 'CT', C_5: 'CT', C_2: 'CT', C_3: 'CT', C_1: 'CT', H_21: 'HC', H_1: 'H2', O_3: 'OS', H_3: 'H1', H_22: 'HC', H_4: 'H1', O_4: 'OS', O_5: 'OS', } amber12_atom_type = {H_51: 'H1', H_52: 'H1', C_4: 'CT', C_5: 'CI', C_2: 'CT', C_3: 'CT', C_1: 'CT', H_21: 'HC', H_1: 'H2', O_3: 'OS', H_3: 'H1', H_22: 'HC', H_4: 'H1', O_4: 'OS', O_5: 'OS', } MMTK-2.7.9/MMTK/Database/Groups/desoxyribose_3ter0000644000076600000240000000277712041514571022060 0ustar hinsenstaff00000000000000name ='desoxyribose_3ter' O_5 = Atom('O') C_5 = Atom('C') H_51 = Atom('H') H_52 = Atom('H') C_4 = Atom('C') H_4 = Atom('H') O_4 = Atom('O') C_1 = Atom('C') H_1 = Atom('H') C_3 = Atom('C') H_3 = Atom('H') C_2 = Atom('C') H_21 = Atom('H') H_22 = Atom('H') O_3 = Atom('O') H_3_terminal = Atom('H') bonds = [Bond(C_5, O_5), Bond(H_51, C_5), Bond(H_52, C_5), Bond(C_4, C_5), Bond(H_4, C_4), Bond(O_4, C_4), Bond(C_1, O_4), Bond(H_1, C_1), Bond(C_3, C_4), Bond(H_3, C_3), Bond(C_2, C_3), Bond(H_21, C_2), Bond(H_22, C_2), Bond(O_3, C_3), Bond(H_3_terminal, O_3), Bond(C_1, C_2), ] pdbmap = [('D', {"1H5*": H_51, "2H5*": H_52, "C4*": C_4, "C5*": C_5, "C2*": C_2, "C3*": C_3, "C1*": C_1, "1H2*": H_21, "H1*": H_1, "O3*": O_3, "H3*": H_3, "H3T": H_3_terminal, "2H2*": H_22, "H4*": H_4, "O4*": O_4, "O5*": O_5, })] pdb_alternative = {"1H5'": "1H5*", "H5'1": "1H5*", "2H5'": "2H5*", "H5'2": "2H5*", "C4'": "C4*", "C5'": "C5*", "C2'": "C2*", "C3'": "C3*", "C1'": "C1*", "1H2'": "1H2*", "H2'1": "1H2*", "H1'": "H1*", "O3'": "O3*", "H3'": "H3*", "2H2'": "2H2*", "H2'2": "2H2*", "H4'": "H4*", "O4'": "O4*", "O5'": "O5*",} amber_atom_type = {H_51: 'H1', H_52: 'H1', C_4: 'CT', C_5: 'CT', C_2: 'CT', C_3: 'CT', C_1: 'CT', H_21: 'HC', H_1: 'H2', O_3: 'OH', H_3: 'H1', H_3_terminal: 'HO', H_22: 'HC', H_4: 'H1', O_4: 'OS', O_5: 'OS', } amber12_atom_type = {H_51: 'H1', H_52: 'H1', C_4: 'CT', C_5: 'CI', C_2: 'CT', C_3: 'CT', C_1: 'CT', H_21: 'HC', H_1: 'H2', O_3: 'OH', H_3: 'H1', H_3_terminal: 'HO', H_22: 'HC', H_4: 'H1', O_4: 'OS', O_5: 'OS', } MMTK-2.7.9/MMTK/Database/Groups/desoxyribose_5ter0000644000076600000240000000277712041514571022062 0ustar hinsenstaff00000000000000name ='desoxyribose_5ter' H_5_terminal = Atom('H') O_5 = Atom('O') C_5 = Atom('C') H_51 = Atom('H') H_52 = Atom('H') C_4 = Atom('C') H_4 = Atom('H') O_4 = Atom('O') C_1 = Atom('C') H_1 = Atom('H') C_3 = Atom('C') H_3 = Atom('H') C_2 = Atom('C') H_21 = Atom('H') H_22 = Atom('H') O_3 = Atom('O') bonds = [Bond(O_5, H_5_terminal), Bond(C_5, O_5), Bond(H_51, C_5), Bond(H_52, C_5), Bond(C_4, C_5), Bond(H_4, C_4), Bond(O_4, C_4), Bond(C_1, O_4), Bond(H_1, C_1), Bond(C_3, C_4), Bond(H_3, C_3), Bond(C_2, C_3), Bond(H_21, C_2), Bond(H_22, C_2), Bond(O_3, C_3), Bond(C_1, C_2), ] pdbmap = [('D', {"2H5*": H_52, "1H5*": H_51, "C5*": C_5, "C2*": C_2, "C3*": C_3, "C1*": C_1, "H5T": H_5_terminal, "C4*": C_4, "1H2*": H_21, "H1*": H_1, "O3*": O_3, "H3*": H_3, "2H2*": H_22, "H4*": H_4, "O4*": O_4, "O5*": O_5, })] pdb_alternative = {"1H5'": "1H5*", "H5'1": "1H5*", "2H5'": "2H5*", "H5'2": "2H5*", "C4'": "C4*", "C5'": "C5*", "C2'": "C2*", "C3'": "C3*", "C1'": "C1*", "1H2'": "1H2*", "H2'1": "1H2*", "H1'": "H1*", "O3'": "O3*", "H3'": "H3*", "2H2'": "2H2*", "H2'2": "2H2*", "H4'": "H4*", "O4'": "O4*", "O5'": "O5*",} amber_atom_type = {H_52: 'H1', H_51: 'H1', C_5: 'CT', C_2: 'CT', C_3: 'CT', C_1: 'CT', H_5_terminal: 'HO', C_4: 'CT', H_21: 'HC', H_1: 'H2', O_3: 'OS', H_3: 'H1', H_22: 'HC', H_4: 'H1', O_4: 'OS', O_5: 'OH', } amber12_atom_type = {H_52: 'H1', H_51: 'H1', C_5: 'CI', C_2: 'CT', C_3: 'CT', C_1: 'CT', H_5_terminal: 'HO', C_4: 'CT', H_21: 'HC', H_1: 'H2', O_3: 'OS', H_3: 'H1', H_22: 'HC', H_4: 'H1', O_4: 'OS', O_5: 'OH', } MMTK-2.7.9/MMTK/Database/Groups/desoxyribose_5ter_3ter0000644000076600000240000000316312041514571023005 0ustar hinsenstaff00000000000000name ='desoxyribose_5ter_3ter' H_5_terminal = Atom('H') O_5 = Atom('O') C_5 = Atom('C') H_51 = Atom('H') H_52 = Atom('H') C_4 = Atom('C') H_4 = Atom('H') O_4 = Atom('O') C_1 = Atom('C') H_1 = Atom('H') C_3 = Atom('C') H_3 = Atom('H') C_2 = Atom('C') H_21 = Atom('H') H_22 = Atom('H') O_3 = Atom('O') H_3_terminal = Atom('H') bonds = [Bond(O_5, H_5_terminal), Bond(C_5, O_5), Bond(H_51, C_5), Bond(H_52, C_5), Bond(C_4, C_5), Bond(H_4, C_4), Bond(O_4, C_4), Bond(C_1, O_4), Bond(H_1, C_1), Bond(C_3, C_4), Bond(H_3, C_3), Bond(C_2, C_3), Bond(H_21, C_2), Bond(H_22, C_2), Bond(O_3, C_3), Bond(H_3_terminal, O_3), Bond(C_1, C_2), ] pdbmap = [('D', {"2H5*": H_52, "1H5*": H_51, "C5*": C_5, "C2*": C_2, "C3*": C_3, "C1*": C_1, "H5T": H_5_terminal, "C4*": C_4, "1H2*": H_21, "H1*": H_1, "O3*": O_3, "H3*": H_3, "H3T": H_3_terminal, "2H2*": H_22, "H4*": H_4, "O4*": O_4, "O5*": O_5, })] pdb_alternative = {"1H5'": "1H5*", "H5'1": "1H5*", "2H5'": "2H5*", "H5'2": "2H5*", "C4'": "C4*", "C5'": "C5*", "C2'": "C2*", "C3'": "C3*", "C1'": "C1*", "1H2'": "1H2*", "H2'1": "1H2*", "H1'": "H1*", "O3'": "O3*", "H3'": "H3*", "2H2'": "2H2*", "H2'2": "2H2*", "H4'": "H4*", "O4'": "O4*", "O5'": "O5*",} amber_atom_type = {H_52: 'H1', H_51: 'H1', C_5: 'CT', C_2: 'CT', C_3: 'CT', C_1: 'CT', H_5_terminal: 'HO', C_4: 'CT', H_21: 'HC', H_1: 'H2', O_3: 'OH', H_3: 'H1', H_3_terminal: 'HO', H_22: 'HC', H_4: 'H1', O_4: 'OS', O_5: 'OH', } amber12_atom_type = {H_52: 'H1', H_51: 'H1', C_5: 'CI', C_2: 'CT', C_3: 'CT', C_1: 'CT', H_5_terminal: 'HO', C_4: 'CT', H_21: 'HC', H_1: 'H2', O_3: 'OH', H_3: 'H1', H_3_terminal: 'HO', H_22: 'HC', H_4: 'H1', O_4: 'OS', O_5: 'OH', } MMTK-2.7.9/MMTK/Database/Groups/gln_sidechain0000644000076600000240000000251412041514571021160 0ustar hinsenstaff00000000000000O_epsilon_1 = Atom('o') H_epsilon_2_2 = Atom('h') H_epsilon_2_1 = Atom('h') C_delta = Atom('c') C_beta = Atom('c') H_gamma_3 = Atom('h') H_gamma_2 = Atom('h') C_gamma = Atom('c') N_epsilon_2 = Atom('n') H_beta_3 = Atom('h') H_beta_2 = Atom('h') bonds = [Bond(H_beta_2, C_beta), Bond(H_beta_3, C_beta), Bond(C_gamma, C_beta), Bond(H_gamma_2, C_gamma), Bond(H_gamma_3, C_gamma), Bond(C_delta, C_gamma), Bond(O_epsilon_1, C_delta), Bond(N_epsilon_2, C_delta), Bond(H_epsilon_2_1, N_epsilon_2), Bond(H_epsilon_2_2, N_epsilon_2), ] name = 'gln_sidechain' pdbmap = [('GLN', {'2HG': H_gamma_2, 'NE2': N_epsilon_2, '2HB': H_beta_2, '3HG': H_gamma_3, '3HB': H_beta_3, 'CD': C_delta, 'CG': C_gamma, '2HE2': H_epsilon_2_2, 'OE1': O_epsilon_1, 'CB': C_beta, '1HE2': H_epsilon_2_1, }, ), ] pdb_alternative = {'1HB': '3HB', '1HG': '3HG', 'HG1': '3HG', 'HB3': '3HB', 'HG3': '3HG', 'HB1': '3HB', 'HE21': '1HE2', 'HE22': '2HE2', 'HG2': '2HG', 'HB2': '2HB', } amber_atom_type = {H_gamma_3: 'HC', H_epsilon_2_1: 'H', C_gamma: 'CT', C_delta: 'C', C_beta: 'CT', H_beta_3: 'HC', H_epsilon_2_2: 'H', N_epsilon_2: 'N', O_epsilon_1: 'O', H_gamma_2: 'HC', H_beta_2: 'HC', } amber12_atom_type = {H_gamma_3: 'HC', H_epsilon_2_1: 'H', C_gamma: '2C', C_delta: 'C', C_beta: '2C', H_beta_3: 'HC', H_epsilon_2_2: 'H', N_epsilon_2: 'N', O_epsilon_1: 'O', H_gamma_2: 'HC', H_beta_2: 'HC', } MMTK-2.7.9/MMTK/Database/Groups/gln_sidechain_noh0000644000076600000240000000071511662461637022041 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta = Atom('C') C_gamma = Atom('CH2') N_epsilon_2 = Atom('NH2') O_epsilon_1 = Atom('O') bonds = [Bond(C_gamma, C_beta), Bond(C_delta, C_gamma), Bond(O_epsilon_1, C_delta), Bond(N_epsilon_2, C_delta), ] pdbmap = [('GLN', {'CG': C_gamma, 'NE2': N_epsilon_2, 'OE1': O_epsilon_1, 'CB': C_beta, 'CD': C_delta, }, ), ] amber_atom_type = {C_beta: 'CT', C_gamma: 'CT', C_delta: 'C', N_epsilon_2: 'N', O_epsilon_1: 'O', } name = 'gln_sidechain' MMTK-2.7.9/MMTK/Database/Groups/gln_sidechain_uni0000644000076600000240000000210111662461637022037 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta = Atom('C') C_gamma = Atom('CH2') H_epsilon_2_1 = Atom('H') H_epsilon_2_2 = Atom('H') N_epsilon_2 = Atom('N') O_epsilon_1 = Atom('O') bonds = [Bond(C_gamma, C_beta), Bond(C_delta, C_gamma), Bond(O_epsilon_1, C_delta), Bond(N_epsilon_2, C_delta), Bond(H_epsilon_2_1, N_epsilon_2), Bond(H_epsilon_2_2, N_epsilon_2), ] pdbmap = [('GLN', {'CD': C_delta, 'NE2': N_epsilon_2, '2HE2': H_epsilon_2_2, 'OE1': O_epsilon_1, 'CG': C_gamma, '1HE2': H_epsilon_2_1, 'CB': C_beta, }, ), ] pdb_alternative = {'HG1': '3HG', 'HB1': '3HB', 'HE21': '1HE2', 'HE22': '2HE2', 'HNE1': '1HE2', '1HNE': '1HE2', 'HNE2': '2HE2', '2HNE': '2HE2'} amber91_atom_type = {C_gamma: 'C2', C_delta: 'C', O_epsilon_1: 'O', N_epsilon_2: 'N', H_epsilon_2_1: 'H', H_epsilon_2_2: 'H', C_beta: 'C2', } name = 'gln_sidechain' opls_atom_type = {C_gamma: 'C2', C_delta: 'C', O_epsilon_1: 'O', N_epsilon_2: 'N', H_epsilon_2_1: 'H', H_epsilon_2_2: 'H', C_beta: 'C2', } opls_charge = {C_gamma: 0.0, C_delta: .5, O_epsilon_1: -0.5, N_epsilon_2: -.85, H_epsilon_2_1: .425, H_epsilon_2_2: .425, C_beta: 0.0, } MMTK-2.7.9/MMTK/Database/Groups/gln_sidechain_uni20000644000076600000240000000135411662461637022132 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta = Atom('C') C_gamma = Atom('CH2') H_epsilon_2_1 = Atom('H') H_epsilon_2_2 = Atom('H') N_epsilon_2 = Atom('N') O_epsilon_1 = Atom('O') bonds = [Bond(C_gamma, C_beta), Bond(C_delta, C_gamma), Bond(O_epsilon_1, C_delta), Bond(N_epsilon_2, C_delta), Bond(H_epsilon_2_1, N_epsilon_2), Bond(H_epsilon_2_2, N_epsilon_2), ] pdbmap = [('GLN', {'CD': C_delta, 'NE2': N_epsilon_2, '2HE2': H_epsilon_2_2, 'OE1': O_epsilon_1, 'CG': C_gamma, '1HE2': H_epsilon_2_1, 'CB': C_beta, }, ), ] pdb_alternative = {'HG1': '3HG', 'HB1': '3HB', 'HE21': '1HE2', 'HE22': '2HE2', } amber91_atom_type = {C_gamma: 'C2', C_delta: 'C', O_epsilon_1: 'O', N_epsilon_2: 'N', H_epsilon_2_1: 'H', H_epsilon_2_2: 'H', C_beta: 'C2', } name = 'gln_sidechain' MMTK-2.7.9/MMTK/Database/Groups/gln_sidechain_uni30000644000076600000240000000210111662461637022122 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta = Atom('C') C_gamma = Atom('CH2') H_epsilon_2_1 = Atom('H') H_epsilon_2_2 = Atom('H') N_epsilon_2 = Atom('N') O_epsilon_1 = Atom('O') bonds = [Bond(C_gamma, C_beta), Bond(C_delta, C_gamma), Bond(O_epsilon_1, C_delta), Bond(N_epsilon_2, C_delta), Bond(H_epsilon_2_1, N_epsilon_2), Bond(H_epsilon_2_2, N_epsilon_2), ] pdbmap = [('GLN', {'CD': C_delta, 'NE2': N_epsilon_2, '2HE2': H_epsilon_2_2, 'OE1': O_epsilon_1, 'CG': C_gamma, '1HE2': H_epsilon_2_1, 'CB': C_beta, }, ), ] pdb_alternative = {'HG1': '3HG', 'HB1': '3HB', 'HE21': '1HE2', 'HE22': '2HE2', 'HNE1': '1HE2', '1HNE': '1HE2', 'HNE2': '2HE2', '2HNE': '2HE2'} amber91_atom_type = {C_gamma: 'C2', C_delta: 'C', O_epsilon_1: 'O', N_epsilon_2: 'N', H_epsilon_2_1: 'H', H_epsilon_2_2: 'H', C_beta: 'C2', } name = 'gln_sidechain' opls_atom_type = {C_gamma: 'C2', C_delta: 'C', O_epsilon_1: 'O', N_epsilon_2: 'N', H_epsilon_2_1: 'H', H_epsilon_2_2: 'H', C_beta: 'C2', } opls_charge = {C_gamma: 0.0, C_delta: .5, O_epsilon_1: -0.5, N_epsilon_2: -.85, H_epsilon_2_1: .425, H_epsilon_2_2: .425, C_beta: 0.0, } MMTK-2.7.9/MMTK/Database/Groups/glu_sidechain0000644000076600000240000000207312041514571021167 0ustar hinsenstaff00000000000000O_epsilon_1 = Atom('o') O_epsilon_2 = Atom('o') H_gamma_3 = Atom('h') H_gamma_2 = Atom('h') C_gamma = Atom('c') C_delta = Atom('c') C_beta = Atom('c') H_beta_3 = Atom('h') H_beta_2 = Atom('h') bonds = [Bond(H_beta_2, C_beta), Bond(H_beta_3, C_beta), Bond(C_gamma, C_beta), Bond(H_gamma_2, C_gamma), Bond(H_gamma_3, C_gamma), Bond(C_delta, C_gamma), Bond(O_epsilon_1, C_delta), Bond(O_epsilon_2, C_delta), ] pdbmap = [('GLU', {'2HG': H_gamma_2, '2HB': H_beta_2, '3HG': H_gamma_3, '3HB': H_beta_3, 'CD': C_delta, 'CG': C_gamma, 'OE1': O_epsilon_1, 'CB': C_beta, 'OE2': O_epsilon_2, }, ), ] amber_atom_type = {C_delta: 'C', O_epsilon_2: 'O2', C_gamma: 'CT', H_beta_3: 'HC', H_gamma_3: 'HC', O_epsilon_1: 'O2', H_gamma_2: 'HC', C_beta: 'CT', H_beta_2: 'HC', } amber12_atom_type = {C_delta: 'CO', O_epsilon_2: 'O2', C_gamma: '2C', H_beta_3: 'HC', H_gamma_3: 'HC', O_epsilon_1: 'O2', H_gamma_2: 'HC', C_beta: '2C', H_beta_2: 'HC', } pdb_alternative = {'1HB': '3HB', '1HG': '3HG', 'HG1': '3HG', 'HB3': '3HB', 'HG3': '3HG', 'HB1': '3HB', 'HG2': '2HG', 'HB2': '2HB', } name = 'glu_sidechain' MMTK-2.7.9/MMTK/Database/Groups/glu_sidechain_neutral0000644000076600000240000000227212041514571022722 0ustar hinsenstaff00000000000000O_epsilon_1 = Atom('o') O_epsilon_2 = Atom('o') H_epsilon_2 = Atom('h') H_gamma_3 = Atom('h') H_gamma_2 = Atom('h') C_gamma = Atom('c') C_delta = Atom('c') C_beta = Atom('c') H_beta_3 = Atom('h') H_beta_2 = Atom('h') bonds = [Bond(H_beta_2, C_beta), Bond(H_beta_3, C_beta), Bond(C_gamma, C_beta), Bond(H_gamma_2, C_gamma), Bond(H_gamma_3, C_gamma), Bond(C_delta, C_gamma), Bond(O_epsilon_1, C_delta), Bond(O_epsilon_2, C_delta), Bond(O_epsilon_2,H_epsilon_2),] pdbmap = [('GLP', {'2HG': H_gamma_2, '2HB': H_beta_2, '3HG': H_gamma_3, '3HB': H_beta_3, 'CD': C_delta, 'CG': C_gamma, 'OE1': O_epsilon_1, 'CB': C_beta, 'OE2': O_epsilon_2, 'HE2': H_epsilon_2}, ), ] amber_atom_type = {C_delta: 'C', O_epsilon_2: 'OH', C_gamma: 'CT', H_beta_3: 'HC', H_gamma_3: 'HC', O_epsilon_1: 'O', H_gamma_2: 'HC', C_beta: 'CT', H_beta_2: 'HC', H_epsilon_2: 'HO',} amber12_atom_type = {C_delta: 'C', O_epsilon_2: 'OH', C_gamma: '2C', H_beta_3: 'HC', H_gamma_3: 'HC', O_epsilon_1: 'O', H_gamma_2: 'HC', C_beta: '2C', H_beta_2: 'HC', H_epsilon_2: 'HO',} pdb_alternative = {'1HB': '3HB', '1HG': '3HG', 'HG1': '3HG', 'HB3': '3HB', 'HG3': '3HG', 'HB1': '3HB', 'HG2': '2HG', 'HB2': '2HB', '2HE': 'HE2', } name = 'glu_sidechain_neutral' MMTK-2.7.9/MMTK/Database/Groups/glu_sidechain_noh0000644000076600000240000000071511662461637022050 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta = Atom('C') C_gamma = Atom('CH2') O_epsilon_1 = Atom('O') O_epsilon_2 = Atom('O') bonds = [Bond(C_gamma, C_beta), Bond(C_delta, C_gamma), Bond(O_epsilon_1, C_delta), Bond(O_epsilon_2, C_delta), ] pdbmap = [('GLU', {'OE1': O_epsilon_1, 'CG': C_gamma, 'CB': C_beta, 'OE2': O_epsilon_2, 'CD': C_delta, }, ), ] amber_atom_type = {C_delta: 'C', O_epsilon_1: 'O2', O_epsilon_2: 'O2', C_gamma: 'CT', C_beta: 'CT', } name = 'glu_sidechain' MMTK-2.7.9/MMTK/Database/Groups/glu_sidechain_uni0000644000076600000240000000130411662461637022052 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta = Atom('C') C_gamma = Atom('CH2') O_epsilon_1 = Atom('O') O_epsilon_2 = Atom('O') bonds = [Bond(C_gamma, C_beta), Bond(C_delta, C_gamma), Bond(O_epsilon_1, C_delta), Bond(O_epsilon_2, C_delta), ] pdbmap = [('GLU', {'CD': C_delta, 'CG': C_gamma, 'OE1': O_epsilon_1, 'CB': C_beta, 'OE2': O_epsilon_2, }, ), ] pdb_alternative = {'HG1': '3HG', 'HB1': '3HB', } name = 'glu_sidechain' opls_atom_type = {C_beta: 'C2', C_gamma: 'C2', C_delta: 'C', O_epsilon_1: 'O2', O_epsilon_2: 'O2' } opls_charge = {C_beta: 0.0, C_gamma: -0.1, C_delta: 0.7, O_epsilon_1: -0.8, O_epsilon_2: -0.8 } amber91_atom_type = {C_beta: 'C2', O_epsilon_2: 'O2', O_epsilon_1: 'O2', C_gamma: 'C2', C_delta: 'C', } MMTK-2.7.9/MMTK/Database/Groups/glu_sidechain_uni20000644000076600000240000000100011662461637022125 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta = Atom('C') C_gamma = Atom('CH2') O_epsilon_1 = Atom('O') O_epsilon_2 = Atom('O') bonds = [Bond(C_gamma, C_beta), Bond(C_delta, C_gamma), Bond(O_epsilon_1, C_delta), Bond(O_epsilon_2, C_delta), ] amber91_atom_type = {C_beta: 'C2', O_epsilon_2: 'O2', O_epsilon_1: 'O2', C_gamma: 'C2', C_delta: 'C', } pdbmap = [('GLU', {'CD': C_delta, 'CG': C_gamma, 'OE1': O_epsilon_1, 'CB': C_beta, 'OE2': O_epsilon_2, }, ), ] pdb_alternative = {'HG1': '3HG', 'HB1': '3HB', } name = 'glu_sidechain' MMTK-2.7.9/MMTK/Database/Groups/glu_sidechain_uni30000644000076600000240000000130411662461637022135 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta = Atom('C') C_gamma = Atom('CH2') O_epsilon_1 = Atom('O') O_epsilon_2 = Atom('O') bonds = [Bond(C_gamma, C_beta), Bond(C_delta, C_gamma), Bond(O_epsilon_1, C_delta), Bond(O_epsilon_2, C_delta), ] pdbmap = [('GLU', {'CD': C_delta, 'CG': C_gamma, 'OE1': O_epsilon_1, 'CB': C_beta, 'OE2': O_epsilon_2, }, ), ] pdb_alternative = {'HG1': '3HG', 'HB1': '3HB', } name = 'glu_sidechain' opls_atom_type = {C_beta: 'C2', C_gamma: 'C2', C_delta: 'C', O_epsilon_1: 'O2', O_epsilon_2: 'O2' } opls_charge = {C_beta: 0.0, C_gamma: -0.1, C_delta: 0.7, O_epsilon_1: -0.8, O_epsilon_2: -0.8 } amber91_atom_type = {C_beta: 'C2', O_epsilon_2: 'O2', O_epsilon_1: 'O2', C_gamma: 'C2', C_delta: 'C', } MMTK-2.7.9/MMTK/Database/Groups/glutamic_acid0000644000076600000240000000112411777301631021160 0ustar hinsenstaff00000000000000sidechain = Group('glu_sidechain') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Glu' amber_charge = {sidechain.O_epsilon_1: -0.8188, peptide.N: -0.5163, sidechain.C_beta: 0.056, sidechain.C_gamma: 0.0136, peptide.O: -0.5819, peptide.C: 0.5366, peptide.C_alpha: 0.0397, peptide.H_alpha: 0.1105, sidechain.H_beta_3: -0.0173, sidechain.O_epsilon_2: -0.8188, peptide.H: 0.2936, sidechain.C_delta: 0.8054, sidechain.H_gamma_3: -0.0425, sidechain.H_beta_2: -0.0173, sidechain.H_gamma_2: -0.0425, } name = 'glutamic_acid' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glutamic_acid_calpha0000644000076600000240000000021411662461637022475 0ustar hinsenstaff00000000000000symbol = 'Glu' name = 'glutamic_acid' C_alpha = Atom('glutamic_acid') pdbmap = [('GLU', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/glutamic_acid_ct0000644000076600000240000000114511777301631021651 0ustar hinsenstaff00000000000000sidechain = Group('glu_sidechain') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Glu' amber_charge = {peptide.C_alpha: -0.2059, sidechain.H_gamma_2: -0.0548, peptide.H: 0.3055, sidechain.H_beta_3: -0.0078, sidechain.C_beta: 0.0071, peptide.N: -0.5192, peptide.O: -0.793, sidechain.C_gamma: 0.0675, peptide.H_alpha: 0.1399, sidechain.C_delta: 0.8183, sidechain.O_epsilon_2: -0.822, sidechain.H_gamma_3: -0.0548, peptide.C: 0.742, sidechain.O_epsilon_1: -0.822, sidechain.H_beta_2: -0.0078, peptide.O_2: -0.793, } name = 'glutamic_acid' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/glutamic_acid_ct_noh0000644000076600000240000000071311662461637022523 0ustar hinsenstaff00000000000000sidechain = Group('glu_sidechain_noh') peptide = Group('peptide_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Glu' amber_charge = {sidechain.O_epsilon_2: -0.822, sidechain.O_epsilon_1: -0.822, peptide.C_alpha: -0.2059, sidechain.C_gamma: 0.0675, peptide.N: -0.5192, peptide.O: -0.793, sidechain.C_delta: 0.8183, sidechain.C_beta: 0.0071, peptide.O_2: -0.793, peptide.C: 0.742, } name = 'glutamic_acid' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/glutamic_acid_ct_uni0000644000076600000240000000072511662461637022535 0ustar hinsenstaff00000000000000sidechain = Group('glu_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Glu' amber91_charge = {sidechain.C_gamma: -0.208, peptide.N: -0.52, peptide.H: 0.248, peptide.C: 0.444, peptide.O: -0.706, sidechain.C_delta: 0.62, sidechain.O_epsilon_1: -0.706, sidechain.O_epsilon_2: -0.706, peptide.O_2: -0.706, sidechain.C_beta: 0.0, peptide.C_alpha: 0.24, } name = 'glutamic_acid' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/glutamic_acid_ct_uni20000644000076600000240000000072511662461637022617 0ustar hinsenstaff00000000000000sidechain = Group('glu_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Glu' amber91_charge = {peptide.O: -0.706, sidechain.C_beta: 0.0, peptide.C: 0.444, sidechain.O_epsilon_1: -0.706, peptide.H: 0.248, peptide.O_2: -0.706, sidechain.C_delta: 0.62, peptide.C_alpha: 0.24, sidechain.O_epsilon_2: -0.706, sidechain.C_gamma: -0.208, peptide.N: -0.52, } name = 'glutamic_acid' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/glutamic_acid_ct_uni30000644000076600000240000000072511662461637022620 0ustar hinsenstaff00000000000000sidechain = Group('glu_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Glu' amber91_charge = {sidechain.C_gamma: -0.208, peptide.N: -0.52, peptide.H: 0.248, peptide.C: 0.444, peptide.O: -0.706, sidechain.C_delta: 0.62, sidechain.O_epsilon_1: -0.706, sidechain.O_epsilon_2: -0.706, peptide.O_2: -0.706, sidechain.C_beta: 0.0, peptide.C_alpha: 0.24, } name = 'glutamic_acid' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/glutamic_acid_neutral0000644000076600000240000000122111777301631022710 0ustar hinsenstaff00000000000000sidechain = Group('glu_sidechain_neutral') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Glp' amber_charge = {sidechain.O_epsilon_1: -0.58380, peptide.N: -0.41570, sidechain.C_beta: -0.00710, sidechain.C_gamma: -0.01740, peptide.O: -0.56790, peptide.C: 0.59730, peptide.C_alpha: 0.01450, peptide.H_alpha: 0.07790, sidechain.H_beta_3: 0.02560, sidechain.O_epsilon_2: -0.65110, peptide.H: 0.27190, sidechain.C_delta: 0.68010, sidechain.H_gamma_3: 0.04300, sidechain.H_beta_2: 0.02560, sidechain.H_gamma_2: 0.04300, sidechain.H_epsilon_2: 0.46410 } name = 'glutamic_acid_neutral' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glutamic_acid_neutral_calpha0000644000076600000240000000021411662461637024227 0ustar hinsenstaff00000000000000symbol = 'Glu' name = 'glutamic_acid' C_alpha = Atom('glutamic_acid') pdbmap = [('GLU', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/glutamic_acid_noh0000644000076600000240000000067211662461637022041 0ustar hinsenstaff00000000000000sidechain = Group('glu_sidechain_noh') peptide = Group('peptide_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Glu' amber_charge = {sidechain.O_epsilon_1: -0.8188, peptide.C_alpha: 0.0397, sidechain.C_beta: 0.056, peptide.N: -0.5163, sidechain.O_epsilon_2: -0.8188, sidechain.C_delta: 0.8054, sidechain.C_gamma: 0.0136, peptide.C: 0.5366, peptide.O: -0.5819, } name = 'glutamic_acid' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glutamic_acid_nt0000644000076600000240000000117711777301631021671 0ustar hinsenstaff00000000000000sidechain = Group('glu_sidechain') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Glu' amber_charge = {sidechain.C_gamma: -0.0236, peptide.H_2: 0.2391, sidechain.C_beta: 0.0909, sidechain.H_beta_3: -0.0232, peptide.C_alpha: 0.0588, sidechain.H_beta_2: -0.0232, peptide.O: -0.5889, sidechain.C_delta: 0.8087, peptide.C: 0.5621, sidechain.H_gamma_3: -0.0315, sidechain.O_epsilon_2: -0.8189, peptide.N: 0.0017, peptide.H_alpha: 0.1202, sidechain.H_gamma_2: -0.0315, peptide.H_3: 0.2391, sidechain.O_epsilon_1: -0.8189, peptide.H_1: 0.2391, } name = 'glutamic_acid' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glutamic_acid_nt_ct_noh0000644000076600000240000000030111662461637023215 0ustar hinsenstaff00000000000000sidechain = Group('glu_sidechain_noh') peptide = Group('peptide_nt_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Glu' name = 'glutamic_acid' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/glutamic_acid_nt_noh0000644000076600000240000000067111662461637022541 0ustar hinsenstaff00000000000000sidechain = Group('glu_sidechain_noh') peptide = Group('peptide_nt_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Glu' amber_charge = {peptide.O: -0.5889, peptide.N: 0.0017, sidechain.C_gamma: -0.0236, peptide.C_alpha: 0.0588, sidechain.C_beta: 0.0909, peptide.C: 0.5621, sidechain.O_epsilon_2: -0.8189, sidechain.O_epsilon_1: -0.8189, sidechain.C_delta: 0.8087, } name = 'glutamic_acid' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glutamic_acid_nt_uni0000644000076600000240000000075211662461637022550 0ustar hinsenstaff00000000000000sidechain = Group('glu_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Glu' amber91_charge = {peptide.O: -0.5, peptide.C: 0.526, peptide.C_alpha: 0.301, peptide.N: -0.263, sidechain.C_delta: 0.62, peptide.H_1: 0.312, sidechain.O_epsilon_1: -0.706, peptide.H_2: 0.312, sidechain.O_epsilon_2: -0.706, peptide.H_3: 0.312, sidechain.C_gamma: -0.208, sidechain.C_beta: 0.0, } name = 'glutamic_acid' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glutamic_acid_nt_uni20000644000076600000240000000075211662461637022632 0ustar hinsenstaff00000000000000sidechain = Group('glu_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Glu' amber91_charge = {peptide.N: -0.263, peptide.H_2: 0.312, peptide.H_3: 0.312, peptide.C_alpha: 0.301, peptide.C: 0.526, sidechain.C_gamma: -0.208, peptide.H_1: 0.312, sidechain.C_delta: 0.62, sidechain.O_epsilon_2: -0.706, peptide.O: -0.5, sidechain.C_beta: 0.0, sidechain.O_epsilon_1: -0.706, } name = 'glutamic_acid' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glutamic_acid_nt_uni30000644000076600000240000000075211662461637022633 0ustar hinsenstaff00000000000000sidechain = Group('glu_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Glu' amber91_charge = {peptide.O: -0.5, peptide.C: 0.526, peptide.C_alpha: 0.301, peptide.N: -0.263, sidechain.C_delta: 0.62, peptide.H_1: 0.312, sidechain.O_epsilon_1: -0.706, peptide.H_2: 0.312, sidechain.O_epsilon_2: -0.706, peptide.H_3: 0.312, sidechain.C_gamma: -0.208, sidechain.C_beta: 0.0, } name = 'glutamic_acid' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glutamic_acid_uni0000644000076600000240000000070111662461637022041 0ustar hinsenstaff00000000000000sidechain = Group('glu_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Glu' name = 'glutamic_acid' chain_links = [peptide.N, peptide.C] amber91_charge = {sidechain.C_beta: 0.0, peptide.C: 0.526, peptide.C_alpha: 0.246, sidechain.C_gamma: -0.208, sidechain.O_epsilon_2: -0.706, sidechain.O_epsilon_1: -0.706, peptide.O: -0.5, sidechain.C_delta: 0.62, peptide.N: -0.52, peptide.H: 0.248, } MMTK-2.7.9/MMTK/Database/Groups/glutamic_acid_uni20000644000076600000240000000070111662461637022123 0ustar hinsenstaff00000000000000sidechain = Group('glu_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Glu' amber91_charge = {peptide.O: -0.5, sidechain.O_epsilon_1: -0.706, peptide.C: 0.526, sidechain.C_beta: 0.0, sidechain.O_epsilon_2: -0.706, sidechain.C_delta: 0.62, sidechain.C_gamma: -0.208, peptide.H: 0.248, peptide.N: -0.52, peptide.C_alpha: 0.246, } name = 'glutamic_acid' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glutamic_acid_uni30000644000076600000240000000070111662461637022124 0ustar hinsenstaff00000000000000sidechain = Group('glu_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Glu' name = 'glutamic_acid' chain_links = [peptide.N, peptide.C] amber91_charge = {sidechain.C_beta: 0.0, peptide.C: 0.526, peptide.C_alpha: 0.246, sidechain.C_gamma: -0.208, sidechain.O_epsilon_2: -0.706, sidechain.O_epsilon_1: -0.706, peptide.O: -0.5, sidechain.C_delta: 0.62, peptide.N: -0.52, peptide.H: 0.248, } MMTK-2.7.9/MMTK/Database/Groups/glutamine0000644000076600000240000000122111777301631020356 0ustar hinsenstaff00000000000000sidechain = Group('gln_sidechain') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Gln' amber_charge = {peptide.N: -0.4157, sidechain.N_epsilon_2: -0.9407, sidechain.C_beta: -0.0036, sidechain.H_gamma_3: 0.0352, sidechain.H_beta_3: 0.0171, sidechain.O_epsilon_1: -0.6086, peptide.O: -0.5679, peptide.H: 0.2719, sidechain.C_delta: 0.6951, sidechain.C_gamma: -0.0645, sidechain.H_epsilon_2_1: 0.4251, peptide.C_alpha: -0.0031, sidechain.H_epsilon_2_2: 0.4251, peptide.H_alpha: 0.085, sidechain.H_beta_2: 0.0171, peptide.C: 0.5973, sidechain.H_gamma_2: 0.0352, } name = 'glutamine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glutamine_calpha0000644000076600000240000000020411662461637021674 0ustar hinsenstaff00000000000000symbol = 'Gln' name = 'glutamine' C_alpha = Atom('glutamine') pdbmap = [('GLN', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/glutamine_ct0000644000076600000240000000124511777301631021052 0ustar hinsenstaff00000000000000sidechain = Group('gln_sidechain') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Gln' amber_charge = {sidechain.H_gamma_2: 0.0203, peptide.N: -0.3821, sidechain.H_epsilon_2_1: 0.4304, peptide.O_2: -0.8042, sidechain.O_epsilon_1: -0.6098, sidechain.C_gamma: -0.021, peptide.C: 0.7775, sidechain.C_beta: -0.0664, sidechain.H_beta_3: 0.0452, sidechain.H_gamma_3: 0.0203, peptide.C_alpha: -0.2248, sidechain.C_delta: 0.7093, sidechain.N_epsilon_2: -0.9574, peptide.O: -0.8042, sidechain.H_epsilon_2_2: 0.4304, sidechain.H_beta_2: 0.0452, peptide.H: 0.2681, peptide.H_alpha: 0.1232, } name = 'glutamine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/glutamine_ct_noh0000644000076600000240000000071511662461637021725 0ustar hinsenstaff00000000000000sidechain = Group('gln_sidechain_noh') peptide = Group('peptide_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Gln' amber_charge = {peptide.O_2: -0.8042, sidechain.C_gamma: -0.021, sidechain.O_epsilon_1: -0.6098, sidechain.C_beta: -0.0664, sidechain.C_delta: 0.7093, peptide.C_alpha: -0.2248, peptide.N: -0.3821, peptide.O: -0.8042, sidechain.N_epsilon_2: -0.9574, peptide.C: 0.7775, } name = 'glutamine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/glutamine_ct_uni0000644000076600000240000000102411662461637021726 0ustar hinsenstaff00000000000000sidechain = Group('gln_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Gln' amber91_charge = {peptide.N: -0.52, peptide.H: 0.248, sidechain.H_epsilon_2_2: 0.344, peptide.O: -0.706, sidechain.C_gamma: -0.043, peptide.C_alpha: 0.204, sidechain.C_beta: 0.053, sidechain.H_epsilon_2_1: 0.344, peptide.C: 0.444, sidechain.O_epsilon_1: -0.47, sidechain.C_delta: 0.675, sidechain.N_epsilon_2: -0.867, peptide.O_2: -0.706, } name = 'glutamine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/glutamine_ct_uni20000644000076600000240000000102411662461637022010 0ustar hinsenstaff00000000000000sidechain = Group('gln_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Gln' amber91_charge = {sidechain.O_epsilon_1: -0.47, peptide.C: 0.444, sidechain.H_epsilon_2_2: 0.344, peptide.O: -0.706, sidechain.H_epsilon_2_1: 0.344, peptide.C_alpha: 0.204, peptide.H: 0.248, sidechain.C_beta: 0.053, peptide.O_2: -0.706, peptide.N: -0.52, sidechain.C_delta: 0.675, sidechain.N_epsilon_2: -0.867, sidechain.C_gamma: -0.043, } name = 'glutamine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/glutamine_ct_uni30000644000076600000240000000102411662461637022011 0ustar hinsenstaff00000000000000sidechain = Group('gln_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Gln' amber91_charge = {peptide.N: -0.52, peptide.H: 0.248, sidechain.H_epsilon_2_2: 0.344, peptide.O: -0.706, sidechain.C_gamma: -0.043, peptide.C_alpha: 0.204, sidechain.C_beta: 0.053, sidechain.H_epsilon_2_1: 0.344, peptide.C: 0.444, sidechain.O_epsilon_1: -0.47, sidechain.C_delta: 0.675, sidechain.N_epsilon_2: -0.867, peptide.O_2: -0.706, } name = 'glutamine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/glutamine_noh0000644000076600000240000000067211662461637021241 0ustar hinsenstaff00000000000000sidechain = Group('gln_sidechain_noh') peptide = Group('peptide_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Gln' amber_charge = {peptide.C: 0.5973, sidechain.N_epsilon_2: -0.9407, peptide.O: -0.5679, sidechain.O_epsilon_1: -0.6086, sidechain.C_gamma: -0.0645, sidechain.C_delta: 0.6951, peptide.C_alpha: -0.0031, peptide.N: -0.4157, sidechain.C_beta: -0.0036, } name = 'glutamine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glutamine_nt0000644000076600000240000000126711777301631021071 0ustar hinsenstaff00000000000000sidechain = Group('gln_sidechain') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Gln' amber_charge = {peptide.H_1: 0.1996, sidechain.C_gamma: -0.0903, peptide.H_alpha: 0.1015, sidechain.H_gamma_3: 0.0331, sidechain.O_epsilon_1: -0.6133, peptide.H_3: 0.1996, peptide.H_2: 0.1996, sidechain.H_beta_3: 0.005, sidechain.N_epsilon_2: -1.0031, peptide.O: -0.5713, sidechain.C_beta: 0.0651, sidechain.H_epsilon_2_2: 0.4429, sidechain.H_gamma_2: 0.0331, sidechain.H_beta_2: 0.005, peptide.C_alpha: 0.0536, sidechain.C_delta: 0.7354, peptide.N: 0.1493, sidechain.H_epsilon_2_1: 0.4429, peptide.C: 0.6123, } name = 'glutamine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glutamine_nt_ct_noh0000644000076600000240000000027511662461637022427 0ustar hinsenstaff00000000000000sidechain = Group('gln_sidechain_noh') peptide = Group('peptide_nt_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Gln' name = 'glutamine' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/glutamine_nt_noh0000644000076600000240000000066511662461637021744 0ustar hinsenstaff00000000000000sidechain = Group('gln_sidechain_noh') peptide = Group('peptide_nt_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Gln' amber_charge = {peptide.N: 0.1493, peptide.C: 0.6123, peptide.O: -0.5713, sidechain.C_delta: 0.7354, peptide.C_alpha: 0.0536, sidechain.N_epsilon_2: -1.0031, sidechain.O_epsilon_1: -0.6133, sidechain.C_beta: 0.0651, sidechain.C_gamma: -0.0903, } name = 'glutamine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glutamine_nt_uni0000644000076600000240000000105011662461637021740 0ustar hinsenstaff00000000000000sidechain = Group('gln_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Gln' amber91_charge = {sidechain.H_epsilon_2_2: 0.344, peptide.H_1: 0.312, peptide.N: -0.263, peptide.C: 0.526, sidechain.C_gamma: -0.043, sidechain.N_epsilon_2: -0.867, sidechain.C_delta: 0.675, sidechain.H_epsilon_2_1: 0.344, peptide.C_alpha: 0.265, peptide.O: -0.5, sidechain.O_epsilon_1: -0.47, sidechain.C_beta: 0.053, peptide.H_3: 0.312, peptide.H_2: 0.312, } name = 'glutamine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glutamine_nt_uni20000644000076600000240000000105011662461637022022 0ustar hinsenstaff00000000000000sidechain = Group('gln_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Gln' amber91_charge = {peptide.H_3: 0.312, sidechain.N_epsilon_2: -0.867, peptide.O: -0.5, sidechain.C_beta: 0.053, sidechain.H_epsilon_2_1: 0.344, peptide.H_2: 0.312, sidechain.C_gamma: -0.043, peptide.N: -0.263, peptide.C: 0.526, peptide.C_alpha: 0.265, sidechain.C_delta: 0.675, sidechain.H_epsilon_2_2: 0.344, sidechain.O_epsilon_1: -0.47, peptide.H_1: 0.312, } name = 'glutamine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glutamine_nt_uni30000644000076600000240000000105011662461637022023 0ustar hinsenstaff00000000000000sidechain = Group('gln_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Gln' amber91_charge = {sidechain.H_epsilon_2_2: 0.344, peptide.H_1: 0.312, peptide.N: -0.263, peptide.C: 0.526, sidechain.C_gamma: -0.043, sidechain.N_epsilon_2: -0.867, sidechain.C_delta: 0.675, sidechain.H_epsilon_2_1: 0.344, peptide.C_alpha: 0.265, peptide.O: -0.5, sidechain.O_epsilon_1: -0.47, sidechain.C_beta: 0.053, peptide.H_3: 0.312, peptide.H_2: 0.312, } name = 'glutamine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glutamine_uni0000644000076600000240000000077611662461637021255 0ustar hinsenstaff00000000000000sidechain = Group('gln_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Gln' amber91_charge = {sidechain.C_beta: 0.053, sidechain.H_epsilon_2_2: 0.344, sidechain.H_epsilon_2_1: 0.344, sidechain.O_epsilon_1: -0.47, peptide.O: -0.5, peptide.N: -0.52, sidechain.C_gamma: -0.043, sidechain.N_epsilon_2: -0.867, peptide.H: 0.248, peptide.C: 0.526, sidechain.C_delta: 0.675, peptide.C_alpha: 0.21, } name = 'glutamine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glutamine_uni20000644000076600000240000000077611662461637021337 0ustar hinsenstaff00000000000000sidechain = Group('gln_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Gln' amber91_charge = {peptide.C: 0.526, sidechain.C_delta: 0.675, sidechain.H_epsilon_2_1: 0.344, sidechain.O_epsilon_1: -0.47, peptide.N: -0.52, peptide.H: 0.248, peptide.O: -0.5, sidechain.H_epsilon_2_2: 0.344, sidechain.C_beta: 0.053, sidechain.N_epsilon_2: -0.867, peptide.C_alpha: 0.21, sidechain.C_gamma: -0.043, } name = 'glutamine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glutamine_uni30000644000076600000240000000077611662461637021340 0ustar hinsenstaff00000000000000sidechain = Group('gln_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Gln' amber91_charge = {sidechain.C_beta: 0.053, sidechain.H_epsilon_2_2: 0.344, sidechain.H_epsilon_2_1: 0.344, sidechain.O_epsilon_1: -0.47, peptide.O: -0.5, peptide.N: -0.52, sidechain.C_gamma: -0.043, sidechain.N_epsilon_2: -0.867, peptide.H: 0.248, peptide.C: 0.526, sidechain.C_delta: 0.675, peptide.C_alpha: 0.21, } name = 'glutamine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/gly_nt_sidechain0000644000076600000240000000034612041514571021675 0ustar hinsenstaff00000000000000H_alpha_3 = Atom('h') pdbmap = [('GLY', {'3HA': H_alpha_3, }, ), ] pdb_alternative = {'1HA': '3HA', 'HA3': '3HA', 'HA1': '3HA', } amber_atom_type = {H_alpha_3: 'HP', } name = 'gly_nt_sidechain' amber12_atom_type = amber_atom_type MMTK-2.7.9/MMTK/Database/Groups/gly_nt_sidechain_noh0000644000076600000240000000011211662461637022544 0ustar hinsenstaff00000000000000pdbmap = [('GLY', {}, ), ] amber_atom_type = {} name = 'gly_nt_sidechain' MMTK-2.7.9/MMTK/Database/Groups/gly_nt_sidechain_uni0000644000076600000240000000011411662461637022555 0ustar hinsenstaff00000000000000amber91_atom_type = {} pdbmap = [('GLY', {}, ), ] name = 'gly_nt_sidechain' MMTK-2.7.9/MMTK/Database/Groups/gly_nt_sidechain_uni20000644000076600000240000000011411662461637022637 0ustar hinsenstaff00000000000000amber91_atom_type = {} pdbmap = [('GLY', {}, ), ] name = 'gly_nt_sidechain' MMTK-2.7.9/MMTK/Database/Groups/gly_nt_sidechain_uni30000644000076600000240000000011411662461637022640 0ustar hinsenstaff00000000000000amber91_atom_type = {} pdbmap = [('GLY', {}, ), ] name = 'gly_nt_sidechain' MMTK-2.7.9/MMTK/Database/Groups/gly_sidechain0000644000076600000240000000034312041514571021171 0ustar hinsenstaff00000000000000H_alpha_3 = Atom('h') pdbmap = [('GLY', {'3HA': H_alpha_3, }, ), ] pdb_alternative = {'1HA': '3HA', 'HA3': '3HA', 'HA1': '3HA', } amber_atom_type = {H_alpha_3: 'H1', } name = 'gly_sidechain' amber12_atom_type = amber_atom_type MMTK-2.7.9/MMTK/Database/Groups/gly_sidechain_noh0000644000076600000240000000010711662461637022047 0ustar hinsenstaff00000000000000pdbmap = [('GLY', {}, ), ] amber_atom_type = {} name = 'gly_sidechain' MMTK-2.7.9/MMTK/Database/Groups/gly_sidechain_uni0000644000076600000240000000020011662461637022050 0ustar hinsenstaff00000000000000amber91_atom_type = {} opls_atom_type = {} pdbmap = [('GLY', {}, ), ] pdb_alternative = {'HA1': '3HA', } name = 'gly_sidechain' MMTK-2.7.9/MMTK/Database/Groups/gly_sidechain_uni20000644000076600000240000000015411662461637022142 0ustar hinsenstaff00000000000000amber91_atom_type = {} pdbmap = [('GLY', {}, ), ] pdb_alternative = {'HA1': '3HA', } name = 'gly_sidechain' MMTK-2.7.9/MMTK/Database/Groups/gly_sidechain_uni30000644000076600000240000000020011662461637022133 0ustar hinsenstaff00000000000000amber91_atom_type = {} opls_atom_type = {} pdbmap = [('GLY', {}, ), ] pdb_alternative = {'HA1': '3HA', } name = 'gly_sidechain' MMTK-2.7.9/MMTK/Database/Groups/glycine0000644000076600000240000000055211777301631020031 0ustar hinsenstaff00000000000000sidechain = Group('gly_sidechain') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.H_alpha_3), ] symbol = 'Gly' amber_charge = {peptide.H_alpha: 0.0698, sidechain.H_alpha_3: 0.0698, peptide.N: -0.4157, peptide.C_alpha: -0.0252, peptide.O: -0.5679, peptide.H: 0.2719, peptide.C: 0.5973, } name = 'glycine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glycine_calpha0000644000076600000240000000020011662461637021335 0ustar hinsenstaff00000000000000symbol = 'Gly' name = 'glycine' C_alpha = Atom('glycine') pdbmap = [('GLY', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/glycine_ct0000644000076600000240000000057611777301631020525 0ustar hinsenstaff00000000000000sidechain = Group('gly_sidechain') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.H_alpha_3), ] symbol = 'Gly' amber_charge = {peptide.C_alpha: -0.2493, peptide.H_alpha: 0.1056, peptide.O: -0.7855, peptide.C: 0.7231, peptide.H: 0.2681, peptide.O_2: -0.7855, sidechain.H_alpha_3: 0.1056, peptide.N: -0.3821, } name = 'glycine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/glycine_ct_noh0000644000076600000240000000040611662461637021367 0ustar hinsenstaff00000000000000sidechain = Group('gly_sidechain_noh') peptide = Group('peptide_ct_noh') symbol = 'Gly' amber_charge = {peptide.N: -0.3821, peptide.O_2: -0.7855, peptide.O: -0.7855, peptide.C: 0.7231, peptide.C_alpha: -0.2493, } name = 'glycine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/glycine_ct_uni0000644000076600000240000000043211662461637021375 0ustar hinsenstaff00000000000000sidechain = Group('gly_sidechain_uni') peptide = Group('peptide_glycine_ct_uni') symbol = 'Gly' amber91_charge = {peptide.C_alpha: 0.24, peptide.N: -0.52, peptide.O_2: -0.706, peptide.C: 0.444, peptide.O: -0.706, peptide.H: 0.248, } name = 'glycine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/glycine_ct_uni20000644000076600000240000000042211662461637021456 0ustar hinsenstaff00000000000000sidechain = Group('gly_sidechain_uni') peptide = Group('peptide_ct_uni') symbol = 'Gly' amber91_charge = {peptide.O_2: -0.706, peptide.O: -0.706, peptide.H: 0.248, peptide.C: 0.444, peptide.N: -0.52, peptide.C_alpha: 0.24, } name = 'glycine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/glycine_ct_uni30000644000076600000240000000043211662461637021460 0ustar hinsenstaff00000000000000sidechain = Group('gly_sidechain_uni') peptide = Group('peptide_glycine_ct_uni') symbol = 'Gly' amber91_charge = {peptide.C_alpha: 0.24, peptide.N: -0.52, peptide.O_2: -0.706, peptide.C: 0.444, peptide.O: -0.706, peptide.H: 0.248, } name = 'glycine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/glycine_noh0000644000076600000240000000036211662461637020702 0ustar hinsenstaff00000000000000sidechain = Group('gly_sidechain_noh') peptide = Group('peptide_noh') symbol = 'Gly' amber_charge = {peptide.C_alpha: -0.0252, peptide.C: 0.5973, peptide.O: -0.5679, peptide.N: -0.4157, } name = 'glycine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glycine_nt0000644000076600000240000000062411777301631020532 0ustar hinsenstaff00000000000000sidechain = Group('gly_nt_sidechain') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.H_alpha_3), ] symbol = 'Gly' amber_charge = {peptide.H_3: 0.1642, peptide.O: -0.5722, peptide.C_alpha: -0.01, peptide.N: 0.2943, peptide.H_1: 0.1642, peptide.C: 0.6163, sidechain.H_alpha_3: 0.0895, peptide.H_2: 0.1642, peptide.H_alpha: 0.0895, } name = 'glycine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glycine_nt_ct_noh0000644000076600000240000000021211662461637022063 0ustar hinsenstaff00000000000000sidechain = Group('gly_nt_sidechain_noh') peptide = Group('peptide_nt_ct_noh') symbol = 'Gly' name = 'glycine' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/glycine_nt_noh0000644000076600000240000000036011662461637021401 0ustar hinsenstaff00000000000000sidechain = Group('gly_nt_sidechain_noh') peptide = Group('peptide_nt_noh') symbol = 'Gly' amber_charge = {peptide.C: 0.6163, peptide.O: -0.5722, peptide.C_alpha: -0.01, peptide.N: 0.2943, } name = 'glycine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glycine_nt_uni0000644000076600000240000000045711662461637021417 0ustar hinsenstaff00000000000000sidechain = Group('gly_sidechain_uni') peptide = Group('peptide_glycine_nt_uni') symbol = 'Gly' name = 'glycine' chain_links = [None, peptide.C] amber91_charge = {peptide.N: -0.263, peptide.H_1: 0.312, peptide.H_3: 0.312, peptide.C: 0.526, peptide.H_2: 0.312, peptide.O: -0.5, peptide.C_alpha: 0.301, } MMTK-2.7.9/MMTK/Database/Groups/glycine_nt_uni20000644000076600000240000000045211662461637021474 0ustar hinsenstaff00000000000000sidechain = Group('gly_nt_sidechain_uni') peptide = Group('peptide_nt_uni') symbol = 'Gly' amber91_charge = {peptide.H_2: 0.312, peptide.H_3: 0.312, peptide.N: -0.263, peptide.H_1: 0.312, peptide.O: -0.5, peptide.C: 0.526, peptide.C_alpha: 0.301, } name = 'glycine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glycine_nt_uni30000644000076600000240000000045711662461637021502 0ustar hinsenstaff00000000000000sidechain = Group('gly_sidechain_uni') peptide = Group('peptide_glycine_nt_uni') symbol = 'Gly' name = 'glycine' chain_links = [None, peptide.C] amber91_charge = {peptide.N: -0.263, peptide.H_1: 0.312, peptide.H_3: 0.312, peptide.C: 0.526, peptide.H_2: 0.312, peptide.O: -0.5, peptide.C_alpha: 0.301, } MMTK-2.7.9/MMTK/Database/Groups/glycine_uni0000644000076600000240000000073711662461637020717 0ustar hinsenstaff00000000000000sidechain = Group('gly_sidechain_uni') peptide = Group('peptide_glycine_uni') symbol = 'Gly' name = 'glycine' chain_links = [peptide.N, peptide.C] amber91_charge = {peptide.N: -0.52, peptide.H: 0.248, peptide.O: -0.5, peptide.C: 0.526, peptide.C_alpha: 0.246, } opls_atom_type = {peptide.N: 'N', peptide.H: 'H', peptide.C_alpha: 'CQ', peptide.C: 'C', peptide.O: 'O', } opls_charge = {peptide.N: -0.57, peptide.H: 0.37, peptide.C_alpha: 0.2, peptide.C: 0.5, peptide.O: -0.5, } MMTK-2.7.9/MMTK/Database/Groups/glycine_uni20000644000076600000240000000037611662461637021000 0ustar hinsenstaff00000000000000sidechain = Group('gly_sidechain_uni') peptide = Group('peptide_uni') symbol = 'Gly' amber91_charge = {peptide.C_alpha: 0.246, peptide.O: -0.5, peptide.N: -0.52, peptide.C: 0.526, peptide.H: 0.248, } name = 'glycine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/glycine_uni30000644000076600000240000000073711662461637021002 0ustar hinsenstaff00000000000000sidechain = Group('gly_sidechain_uni') peptide = Group('peptide_glycine_uni') symbol = 'Gly' name = 'glycine' chain_links = [peptide.N, peptide.C] amber91_charge = {peptide.N: -0.52, peptide.H: 0.248, peptide.O: -0.5, peptide.C: 0.526, peptide.C_alpha: 0.246, } opls_atom_type = {peptide.N: 'N', peptide.H: 'H', peptide.C_alpha: 'CQ', peptide.C: 'C', peptide.O: 'O', } opls_charge = {peptide.N: -0.57, peptide.H: 0.37, peptide.C_alpha: 0.2, peptide.C: 0.5, peptide.O: -0.5, } MMTK-2.7.9/MMTK/Database/Groups/guanine0000644000076600000240000000165512041514571020024 0ustar hinsenstaff00000000000000name ='guanine' N_9 = Atom('N') C_8 = Atom('C') H_8 = Atom('H') N_7 = Atom('N') C_5 = Atom('C') C_6 = Atom('C') O_6 = Atom('O') N_1 = Atom('N') H_1 = Atom('H') C_2 = Atom('C') N_2 = Atom('N') H_21 = Atom('H') H_22 = Atom('H') N_3 = Atom('N') C_4 = Atom('C') bonds = [Bond(C_8, N_9), Bond(H_8, C_8), Bond(N_7, C_8), Bond(C_5, N_7), Bond(C_6, C_5), Bond(O_6, C_6), Bond(N_1, C_6), Bond(H_1, N_1), Bond(C_2, N_1), Bond(N_2, C_2), Bond(H_21, N_2), Bond(H_22, N_2), Bond(N_3, C_2), Bond(C_4, N_3), Bond(C_4, C_5), Bond(C_4, N_9), ] pdbmap = [('G', {"N2": N_2, "C8": C_8, "C6": C_6, "C4": C_4, "C5": C_5, "C2": C_2, "N3": N_3, "H8": H_8, "N1": N_1, "N7": N_7, "H22": H_22, "H1": H_1, "N9": N_9, "O6": O_6, "H21": H_21, })] amber_atom_type = {N_2: 'N2', C_8: 'CK', C_6: 'C', C_4: 'CB', C_5: 'CB', C_2: 'CA', N_3: 'NC', H_8: 'H5', N_1: 'NA', N_7: 'NB', H_22: 'H', H_1: 'H', N_9: 'N*', O_6: 'O', H_21: 'H', } amber12_atom_type = amber_atom_type MMTK-2.7.9/MMTK/Database/Groups/hid_sidechain0000644000076600000240000000221112041514571021136 0ustar hinsenstaff00000000000000H_epsilon_1 = Atom('h') C_epsilon_1 = Atom('c') H_delta_1 = Atom('h') H_delta_2 = Atom('h') N_delta_1 = Atom('n') C_delta_2 = Atom('c') C_beta = Atom('c') C_gamma = Atom('c') N_epsilon_2 = Atom('n') H_beta_3 = Atom('h') H_beta_2 = Atom('h') bonds = [Bond(H_beta_2, C_beta), Bond(H_beta_3, C_beta), Bond(C_gamma, C_beta), Bond(N_delta_1, C_gamma), Bond(H_delta_1, N_delta_1), Bond(C_epsilon_1, N_delta_1), Bond(H_epsilon_1, C_epsilon_1), Bond(N_epsilon_2, C_epsilon_1), Bond(C_delta_2, N_epsilon_2), Bond(H_delta_2, C_delta_2), Bond(C_gamma, C_delta_2), ] name = 'hid_sidechain' pdbmap = [('HID', {'2HD': H_delta_2, 'NE2': N_epsilon_2, '2HB': H_beta_2, '1HD': H_delta_1, 'ND1': N_delta_1, '1HE': H_epsilon_1, 'CE1': C_epsilon_1, '3HB': H_beta_3, 'CG': C_gamma, 'CB': C_beta, 'CD2': C_delta_2, }, ), ] pdb_alternative = {'1HB': '3HB', 'HB2': '2HB', 'HB3': '3HB', 'HE1': '1HE', 'HB1': '3HB', 'HD2': '2HD', 'HD1': '1HD', } amber_atom_type = {C_delta_2: 'CV', N_epsilon_2: 'NB', C_gamma: 'CC', H_delta_2: 'H4', C_beta: 'CT', H_epsilon_1: 'H5', H_delta_1: 'H', C_epsilon_1: 'CR', H_beta_3: 'HC', H_beta_2: 'HC', N_delta_1: 'NA', } amber12_atom_type = amber_atom_type MMTK-2.7.9/MMTK/Database/Groups/hid_sidechain_noh0000644000076600000240000000111711662461637022022 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_2 = Atom('CH') C_epsilon_1 = Atom('CH') C_gamma = Atom('C') N_delta_1 = Atom('NH') N_epsilon_2 = Atom('N') bonds = [Bond(C_gamma, C_beta), Bond(N_delta_1, C_gamma), Bond(C_epsilon_1, N_delta_1), Bond(N_epsilon_2, C_epsilon_1), Bond(C_delta_2, N_epsilon_2), Bond(C_gamma, C_delta_2), ] pdbmap = [('HID', {'NE2': N_epsilon_2, 'CG': C_gamma, 'CD2': C_delta_2, 'CE1': C_epsilon_1, 'CB': C_beta, 'ND1': N_delta_1, }, ), ] amber_atom_type = {N_delta_1: 'NA', C_beta: 'CT', C_delta_2: 'CV', N_epsilon_2: 'NB', C_gamma: 'CC', C_epsilon_1: 'CR', } name = 'hid_sidechain' MMTK-2.7.9/MMTK/Database/Groups/hid_sidechain_uni0000644000076600000240000000210411662461637022026 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_2 = Atom('CH') C_epsilon_1 = Atom('CH') C_gamma = Atom('C') N_delta_1 = Atom('N') H_delta_1 = Atom('H') H_delta_2 = Atom('H') N_epsilon_2 = Atom('N') H_epsilon_1 = Atom('H') bonds = [Bond(C_gamma, C_beta), Bond(N_delta_1, C_gamma), Bond(H_delta_1, N_delta_1), Bond(C_epsilon_1, N_delta_1), Bond(N_epsilon_2, C_epsilon_1), Bond(C_delta_2, N_epsilon_2), Bond(C_gamma, C_delta_2), Bond(C_delta_2,H_delta_2), Bond(H_epsilon_1,C_epsilon_1)] pdbmap = [('HID', {'CG': C_gamma, 'CE1': C_epsilon_1, 'CB': C_beta, 'ND1': N_delta_1, '1HD': H_delta_1, 'CD2': C_delta_2, 'NE2': N_epsilon_2, '2HD': H_delta_2, '1HE': H_epsilon_1 }, ), ] pdb_alternative = {'HD1': '1HD', 'HB1': '3HB', 'HND': '1HD'} name = 'hid_sidechain' opls_atom_type = { C_beta: 'C2', C_gamma: 'CK', N_delta_1: 'NA', H_delta_1: 'H', C_epsilon_1: 'CK', N_epsilon_2: 'NB', C_delta_2: 'CK', H_epsilon_1: 'HK', H_delta_2: 'HK' } opls_charge = { C_beta: .115, C_gamma: 0.015, N_delta_1: -0.57, H_delta_1: 0.42, C_epsilon_1: 0.295, N_epsilon_2: -0.49, C_delta_2: -.015, H_epsilon_1: .115, H_delta_2: .115 } MMTK-2.7.9/MMTK/Database/Groups/hid_sidechain_uni20000644000076600000240000000132511662461637022114 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_2 = Atom('CH') C_epsilon_1 = Atom('CH') C_gamma = Atom('C') H_delta_1 = Atom('H') N_delta_1 = Atom('N') N_epsilon_2 = Atom('N') bonds = [Bond(C_gamma, C_beta), Bond(N_delta_1, C_gamma), Bond(H_delta_1, N_delta_1), Bond(C_epsilon_1, N_delta_1), Bond(N_epsilon_2, C_epsilon_1), Bond(C_delta_2, N_epsilon_2), Bond(C_gamma, C_delta_2), ] pdb_alternative = {'HD1': '1HD', 'HB1': '3HB', } pdbmap = [('HID', {'CG': C_gamma, 'CE1': C_epsilon_1, 'CB': C_beta, 'ND1': N_delta_1, '1HD': H_delta_1, 'CD2': C_delta_2, 'NE2': N_epsilon_2, }, ), ] amber91_atom_type = {C_gamma: 'CC', C_epsilon_1: 'CP', N_delta_1: 'NA', H_delta_1: 'H', C_delta_2: 'CF', N_epsilon_2: 'NB', C_beta: 'C2', } name = 'hid_sidechain' MMTK-2.7.9/MMTK/Database/Groups/hid_sidechain_uni30000644000076600000240000000176711662461637022127 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_2 = Atom('CH') C_epsilon_1 = Atom('CH') C_gamma = Atom('C') H_delta_1 = Atom('H') N_delta_1 = Atom('N') N_epsilon_2 = Atom('N') bonds = [Bond(C_gamma, C_beta), Bond(N_delta_1, C_gamma), Bond(H_delta_1, N_delta_1), Bond(C_epsilon_1, N_delta_1), Bond(N_epsilon_2, C_epsilon_1), Bond(C_delta_2, N_epsilon_2), Bond(C_gamma, C_delta_2), ] pdb_alternative = {'HD1': '1HD', 'HB1': '3HB', 'HND': '1HD' } pdbmap = [('HDU', {'CG': C_gamma, 'CE1': C_epsilon_1, 'CB': C_beta, 'ND1': N_delta_1, '1HD': H_delta_1, 'CD2': C_delta_2, 'NE2': N_epsilon_2, }, ), ] name = 'hdu_sidechain' opls_atom_type = { C_beta: 'C2', C_gamma: 'CC', N_delta_1: 'NA', H_delta_1: 'H', C_epsilon_1: 'CP', N_epsilon_2: 'NB', C_delta_2: 'CF' } opls_charge = { C_beta: 0.0, C_gamma: 0.13, N_delta_1: -0.57, H_delta_1: 0.42, C_epsilon_1: 0.41, N_epsilon_2: -0.49, C_delta_2: 0.1 } amber91_atom_type = {C_gamma: 'CC', C_epsilon_1: 'CP', N_delta_1: 'NA', H_delta_1: 'H', C_delta_2: 'CF', N_epsilon_2: 'NB', C_beta: 'C2', } MMTK-2.7.9/MMTK/Database/Groups/hie_sidechain0000644000076600000240000000222312041514571021142 0ustar hinsenstaff00000000000000H_epsilon_2 = Atom('h') H_epsilon_1 = Atom('h') C_epsilon_1 = Atom('c') H_delta_2 = Atom('h') N_delta_1 = Atom('n') C_delta_2 = Atom('c') C_beta = Atom('c') C_gamma = Atom('c') N_epsilon_2 = Atom('n') H_beta_3 = Atom('h') H_beta_2 = Atom('h') bonds = [Bond(H_beta_2, C_beta), Bond(H_beta_3, C_beta), Bond(C_gamma, C_beta), Bond(N_delta_1, C_gamma), Bond(C_epsilon_1, N_delta_1), Bond(H_epsilon_1, C_epsilon_1), Bond(N_epsilon_2, C_epsilon_1), Bond(H_epsilon_2, N_epsilon_2), Bond(C_delta_2, N_epsilon_2), Bond(H_delta_2, C_delta_2), Bond(C_gamma, C_delta_2), ] name = 'hie_sidechain' pdbmap = [('HIE', {'2HD': H_delta_2, 'NE2': N_epsilon_2, '2HB': H_beta_2, 'ND1': N_delta_1, '1HE': H_epsilon_1, 'CE1': C_epsilon_1, '2HE': H_epsilon_2, '3HB': H_beta_3, 'CG': C_gamma, 'CB': C_beta, 'CD2': C_delta_2, }, ), ] pdb_alternative = {'1HB': '3HB', 'HB2': '2HB', 'HB3': '3HB', 'HE1': '1HE', 'HB1': '3HB', 'HD2': '2HD', 'HE2': '2HE', } amber_atom_type = {H_epsilon_2: 'H', H_epsilon_1: 'H5', H_beta_3: 'HC', H_beta_2: 'HC', N_delta_1: 'NB', C_delta_2: 'CW', C_gamma: 'CC', C_beta: 'CT', N_epsilon_2: 'NA', H_delta_2: 'H4', C_epsilon_1: 'CR', } amber12_atom_type = amber_atom_type MMTK-2.7.9/MMTK/Database/Groups/hie_sidechain_noh0000644000076600000240000000111711662461637022023 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_2 = Atom('CH') C_epsilon_1 = Atom('CH') C_gamma = Atom('C') N_delta_1 = Atom('N') N_epsilon_2 = Atom('NH') bonds = [Bond(C_gamma, C_beta), Bond(N_delta_1, C_gamma), Bond(C_epsilon_1, N_delta_1), Bond(N_epsilon_2, C_epsilon_1), Bond(C_delta_2, N_epsilon_2), Bond(C_gamma, C_delta_2), ] pdbmap = [('HIE', {'NE2': N_epsilon_2, 'CG': C_gamma, 'CD2': C_delta_2, 'CE1': C_epsilon_1, 'CB': C_beta, 'ND1': N_delta_1, }, ), ] amber_atom_type = {C_gamma: 'CC', N_delta_1: 'NB', N_epsilon_2: 'NA', C_epsilon_1: 'CR', C_beta: 'CT', C_delta_2: 'CW', } name = 'hie_sidechain' MMTK-2.7.9/MMTK/Database/Groups/hie_sidechain_uni0000644000076600000240000000221011662461637022025 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_2 = Atom('CH') C_epsilon_1 = Atom('CH') C_gamma = Atom('C') H_epsilon_2 = Atom('H') N_delta_1 = Atom('N') N_epsilon_2 = Atom('N') H_epsilon_1 = Atom('H') H_delta_2 = Atom('H') bonds = [Bond(C_gamma, C_beta), Bond(N_delta_1, C_gamma), Bond(C_epsilon_1, N_delta_1), Bond(N_epsilon_2, C_epsilon_1), Bond(H_epsilon_2, N_epsilon_2), Bond(C_delta_2, N_epsilon_2), Bond(C_gamma, C_delta_2), Bond(C_epsilon_1,H_epsilon_1), Bond(C_delta_2,H_delta_2)] pdbmap = [('HIE', {'CG': C_gamma, 'CB': C_beta, 'ND1': N_delta_1, 'CE1': C_epsilon_1, '2HE': H_epsilon_2, 'CD2': C_delta_2, 'NE2': N_epsilon_2, '1HE': H_epsilon_1, '2HD': H_delta_2 }, ), ] pdb_alternative = {'1HB': '3HB', 'HB2': '2HB', 'HB3': '3HB', 'HE1': '1HE', 'HB1': '3HB', 'HD2': '2HD', 'HE2': '2HE', 'HNE': '2HE'} opls_atom_type = {C_epsilon_1: 'CK', C_delta_2: 'CK', N_delta_1: 'NB', H_epsilon_2: 'H', C_beta: 'C2', C_gamma: 'CK', N_epsilon_2: 'NA', H_epsilon_1: 'HK', H_delta_2: 'HK' } opls_charge = {C_epsilon_1: .295, C_delta_2: .015, N_delta_1: -.49, H_epsilon_2: .42, C_beta: .115, C_gamma: -.015, N_epsilon_2: -.57, H_epsilon_1: .115, H_delta_2: .115 } name = 'hie_sidechain' MMTK-2.7.9/MMTK/Database/Groups/hie_sidechain_uni20000644000076600000240000000133711662461637022120 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_2 = Atom('CH') C_epsilon_1 = Atom('CH') C_gamma = Atom('C') H_epsilon_2 = Atom('H') N_delta_1 = Atom('N') N_epsilon_2 = Atom('N') bonds = [Bond(C_gamma, C_beta), Bond(N_delta_1, C_gamma), Bond(C_epsilon_1, N_delta_1), Bond(N_epsilon_2, C_epsilon_1), Bond(H_epsilon_2, N_epsilon_2), Bond(C_delta_2, N_epsilon_2), Bond(C_gamma, C_delta_2), ] pdb_alternative = {'HE2': '2HE', 'HB1': '3HB', } pdbmap = [('HIE', {'CG': C_gamma, 'CB': C_beta, 'ND1': N_delta_1, 'CE1': C_epsilon_1, '2HE': H_epsilon_2, 'CD2': C_delta_2, 'NE2': N_epsilon_2, }, ), ] amber91_atom_type = {C_epsilon_1: 'CP', C_delta_2: 'CG', N_delta_1: 'NB', H_epsilon_2: 'H', C_beta: 'C2', C_gamma: 'CC', N_epsilon_2: 'NA', } name = 'hie_sidechain' MMTK-2.7.9/MMTK/Database/Groups/hie_sidechain_uni30000644000076600000240000000153711662461637022123 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_2 = Atom('CH') C_epsilon_1 = Atom('CH') C_gamma = Atom('C') H_epsilon_2 = Atom('H') N_delta_1 = Atom('N') N_epsilon_2 = Atom('N') bonds = [Bond(C_gamma, C_beta), Bond(N_delta_1, C_gamma), Bond(C_epsilon_1, N_delta_1), Bond(N_epsilon_2, C_epsilon_1), Bond(H_epsilon_2, N_epsilon_2), Bond(C_delta_2, N_epsilon_2), Bond(C_gamma, C_delta_2), ] pdb_alternative = {'HE2': '2HE', 'HB1': '3HB', } pdbmap = [('HEU', {'CG': C_gamma, 'CB': C_beta, 'ND1': N_delta_1, 'CE1': C_epsilon_1, '2HE': H_epsilon_2, 'CD2': C_delta_2, 'NE2': N_epsilon_2, }, ), ] opls_atom_type = {C_epsilon_1: 'CP', C_delta_2: 'CG', N_delta_1: 'NB', H_epsilon_2: 'H', C_beta: 'C2', C_gamma: 'CC', N_epsilon_2: 'NA', } opls_charge = {C_epsilon_1: .41, C_delta_2: .13, N_delta_1: -.49, H_epsilon_2: .42, C_beta: 0.0, C_gamma: .1, N_epsilon_2: -.57, } name = 'heu_sidechain' MMTK-2.7.9/MMTK/Database/Groups/hip_sidechain0000644000076600000240000000236512041514571021164 0ustar hinsenstaff00000000000000H_epsilon_2 = Atom('h') H_epsilon_1 = Atom('h') C_beta = Atom('c') C_epsilon_1 = Atom('c') H_delta_1 = Atom('h') H_delta_2 = Atom('h') N_delta_1 = Atom('n') C_gamma = Atom('c') N_epsilon_2 = Atom('n') C_delta_2 = Atom('c') H_beta_3 = Atom('h') H_beta_2 = Atom('h') bonds = [Bond(H_beta_2, C_beta), Bond(H_beta_3, C_beta), Bond(C_gamma, C_beta), Bond(N_delta_1, C_gamma), Bond(H_delta_1, N_delta_1), Bond(C_epsilon_1, N_delta_1), Bond(H_epsilon_1, C_epsilon_1), Bond(N_epsilon_2, C_epsilon_1), Bond(H_epsilon_2, N_epsilon_2), Bond(C_delta_2, N_epsilon_2), Bond(H_delta_2, C_delta_2), Bond(C_gamma, C_delta_2), ] name = 'hip_sidechain' pdbmap = [('HIP', {'2HD': H_delta_2, 'NE2': N_epsilon_2, '2HB': H_beta_2, '1HD': H_delta_1, 'ND1': N_delta_1, '1HE': H_epsilon_1, 'CE1': C_epsilon_1, '2HE': H_epsilon_2, '3HB': H_beta_3, 'CG': C_gamma, 'CB': C_beta, 'CD2': C_delta_2, }, ), ] pdb_alternative = {'1HB': '3HB', 'HB2': '2HB', 'HB3': '3HB', 'HB1': '3HB', 'HD1': '1HD', 'HD2': '2HD', 'HE1': '1HE', 'HE2': '2HE', } amber_atom_type = {H_beta_2: 'HC', N_delta_1: 'NA', N_epsilon_2: 'NA', C_beta: 'CT', H_delta_2: 'H4', H_epsilon_1: 'H5', H_beta_3: 'HC', C_epsilon_1: 'CR', C_delta_2: 'CW', C_gamma: 'CC', H_epsilon_2: 'H', H_delta_1: 'H', } amber12_atom_type = amber_atom_type MMTK-2.7.9/MMTK/Database/Groups/hip_sidechain_noh0000644000076600000240000000112011662461637022030 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_2 = Atom('CH') C_epsilon_1 = Atom('CH') C_gamma = Atom('C') N_delta_1 = Atom('NH') N_epsilon_2 = Atom('NH') bonds = [Bond(C_gamma, C_beta), Bond(N_delta_1, C_gamma), Bond(C_epsilon_1, N_delta_1), Bond(N_epsilon_2, C_epsilon_1), Bond(C_delta_2, N_epsilon_2), Bond(C_gamma, C_delta_2), ] pdbmap = [('HIP', {'NE2': N_epsilon_2, 'CG': C_gamma, 'CD2': C_delta_2, 'CE1': C_epsilon_1, 'CB': C_beta, 'ND1': N_delta_1, }, ), ] amber_atom_type = {C_epsilon_1: 'CR', C_beta: 'CT', N_delta_1: 'NA', C_delta_2: 'CW', N_epsilon_2: 'NA', C_gamma: 'CC', } name = 'hip_sidechain' MMTK-2.7.9/MMTK/Database/Groups/hip_sidechain_uni0000644000076600000240000000226411662461637022051 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_2 = Atom('CH') C_epsilon_1 = Atom('CH') C_gamma = Atom('C') H_delta_1 = Atom('H') H_epsilon_2 = Atom('H') N_delta_1 = Atom('N') N_epsilon_2 = Atom('N') H_epsilon_1 = Atom('H') H_delta_2 = Atom('H') bonds = [Bond(C_gamma, C_beta), Bond(N_delta_1, C_gamma), Bond(H_delta_1, N_delta_1), Bond(C_epsilon_1, N_delta_1), Bond(N_epsilon_2, C_epsilon_1), Bond(H_epsilon_2, N_epsilon_2), Bond(C_delta_2, N_epsilon_2), Bond(C_gamma, C_delta_2), Bond(C_epsilon_1,H_epsilon_1),Bond(C_delta_2,H_delta_2) ] pdb_alternative = {'HE2': '2HE', 'HD1': '1HD', 'HB1': '3HB', 'HNE': '1HE'} pdbmap = [('HIP', {'NE2': N_epsilon_2, 'CE1': C_epsilon_1, 'CB': C_beta, 'ND1': N_delta_1, 'CG': C_gamma, '1HD': H_delta_1, '1HE': H_epsilon_2, 'CD2': C_delta_2, H_epsilon_1: '1HE', H_delta_2: '2HD' }, ), ] opls_atom_type = {N_delta_1: 'NA', H_epsilon_2: 'H', H_delta_1: 'H', C_beta: 'C2', C_epsilon_1: 'CK', C_gamma: 'CK', C_delta_2: 'CK', N_epsilon_2: 'NA', H_delta_2: 'HK', H_epsilon_1: 'HK' } opls_charge = {N_delta_1: -.54, H_epsilon_2: .46, H_delta_1: .46, C_beta: .115, C_epsilon_1: .385, C_gamma: .215, C_delta_2: .215, N_epsilon_2: -.54, H_delta_2: .115, H_epsilon_1: .115 } name = 'hip_sidechain' MMTK-2.7.9/MMTK/Database/Groups/hip_sidechain_uni20000644000076600000240000000150111662461637022124 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_2 = Atom('CH') C_epsilon_1 = Atom('CH') C_gamma = Atom('C') H_delta_1 = Atom('H') H_epsilon_2 = Atom('H') N_delta_1 = Atom('N') N_epsilon_2 = Atom('N') bonds = [Bond(C_gamma, C_beta), Bond(N_delta_1, C_gamma), Bond(H_delta_1, N_delta_1), Bond(C_epsilon_1, N_delta_1), Bond(N_epsilon_2, C_epsilon_1), Bond(H_epsilon_2, N_epsilon_2), Bond(C_delta_2, N_epsilon_2), Bond(C_gamma, C_delta_2), ] pdb_alternative = {'HE2': '2HE', 'HD1': '1HD', 'HB1': '3HB', } pdbmap = [('HIP', {'NE2': N_epsilon_2, 'CE1': C_epsilon_1, 'CB': C_beta, 'ND1': N_delta_1, 'CG': C_gamma, '1HD': H_delta_1, '2HE': H_epsilon_2, 'CD2': C_delta_2, }, ), ] amber91_atom_type = {N_delta_1: 'NA', H_epsilon_2: 'H', H_delta_1: 'H', C_beta: 'C2', C_epsilon_1: 'CP', C_gamma: 'CC', C_delta_2: 'CG', N_epsilon_2: 'NA', } name = 'hip_sidechain' MMTK-2.7.9/MMTK/Database/Groups/hip_sidechain_uni30000644000076600000240000000172111662461637022131 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_2 = Atom('CH') C_epsilon_1 = Atom('CH') C_gamma = Atom('C') H_delta_1 = Atom('H') H_epsilon_2 = Atom('H') N_delta_1 = Atom('N') N_epsilon_2 = Atom('N') bonds = [Bond(C_gamma, C_beta), Bond(N_delta_1, C_gamma), Bond(H_delta_1, N_delta_1), Bond(C_epsilon_1, N_delta_1), Bond(N_epsilon_2, C_epsilon_1), Bond(H_epsilon_2, N_epsilon_2), Bond(C_delta_2, N_epsilon_2), Bond(C_gamma, C_delta_2), ] pdb_alternative = {'HE2': '2HE', 'HD1': '1HD', 'HB1': '3HB', } pdbmap = [('HPU', {'NE2': N_epsilon_2, 'CE1': C_epsilon_1, 'CB': C_beta, 'ND1': N_delta_1, 'CG': C_gamma, '1HD': H_delta_1, '2HE': H_epsilon_2, 'CD2': C_delta_2, }, ), ] opls_atom_type = {N_delta_1: 'NA', H_epsilon_2: 'H', H_delta_1: 'H', C_beta: 'C2', C_epsilon_1: 'CP', C_gamma: 'CC', C_delta_2: 'CG', N_epsilon_2: 'NA', } opls_charge = {N_delta_1: -.54, H_epsilon_2: .46, H_delta_1: .46, C_beta: 0.0, C_epsilon_1: .5, C_gamma: .33, C_delta_2: .33, N_epsilon_2: -.54, } name = 'hpu_sidechain' MMTK-2.7.9/MMTK/Database/Groups/histidine0000644000076600000240000000122411777301631020354 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'His' amber_charge = {sidechain.N_delta_1: -0.3811, sidechain.H_delta_1: 0.3649, peptide.O: -0.5679, sidechain.C_beta: -0.0462, sidechain.H_epsilon_1: 0.1392, sidechain.C_epsilon_1: 0.2057, sidechain.H_beta_2: 0.0402, peptide.N: -0.4157, sidechain.H_delta_2: 0.1147, peptide.C_alpha: 0.0188, sidechain.H_beta_3: 0.0402, sidechain.N_epsilon_2: -0.5727, sidechain.C_delta_2: 0.1292, peptide.H_alpha: 0.0881, peptide.C: 0.5973, sidechain.C_gamma: -0.0266, peptide.H: 0.2719, } name = 'histidine_deltah' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_calpha0000644000076600000240000000022211662461637021667 0ustar hinsenstaff00000000000000symbol = 'His' name = 'histidine_deltah' C_alpha = Atom('histidine_deltah') pdbmap = [('HIS', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/histidine_ct0000644000076600000240000000124611777301631021046 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'His' amber_charge = {sidechain.H_delta_2: 0.1241, sidechain.H_beta_3: 0.0565, peptide.O_2: -0.8016, sidechain.C_beta: -0.1046, peptide.H: 0.2681, sidechain.C_epsilon_1: 0.1925, peptide.C: 0.7615, sidechain.C_delta_2: 0.1001, peptide.N: -0.3821, sidechain.H_delta_1: 0.3755, sidechain.H_beta_2: 0.0565, peptide.O: -0.8016, sidechain.H_epsilon_1: 0.1418, peptide.C_alpha: -0.1739, sidechain.N_epsilon_2: -0.5629, sidechain.N_delta_1: -0.3892, sidechain.C_gamma: 0.0293, peptide.H_alpha: 0.11, } name = 'histidine_deltah' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/histidine_ct_noh0000644000076600000240000000076311662461637021723 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain_noh') peptide = Group('peptide_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'His' amber_charge = {peptide.C_alpha: -0.1739, peptide.C: 0.7615, sidechain.N_delta_1: -0.3892, sidechain.N_epsilon_2: -0.5629, sidechain.C_delta_2: 0.1001, sidechain.C_gamma: 0.0293, peptide.O_2: -0.8016, peptide.O: -0.8016, sidechain.C_beta: -0.1046, sidechain.C_epsilon_1: 0.1925, peptide.N: -0.3821, } name = 'histidine_deltah' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/histidine_ct_uni0000644000076600000240000000077411662461637021734 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'His' amber91_charge = {sidechain.C_epsilon_1: 0.384, sidechain.C_beta: 0.06, peptide.N: -0.52, sidechain.N_delta_1: -0.444, sidechain.H_delta_1: 0.32, sidechain.N_epsilon_2: -0.527, sidechain.C_delta_2: 0.145, peptide.H: 0.248, peptide.C: 0.526, sidechain.C_gamma: 0.089, peptide.O: -0.5, peptide.C_alpha: 0.219, } name = 'histidine_deltah' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/histidine_ct_uni20000644000076600000240000000077411662461637022016 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'His' amber91_charge = {sidechain.N_epsilon_2: -0.527, sidechain.C_epsilon_1: 0.384, sidechain.N_delta_1: -0.444, peptide.C: 0.526, sidechain.C_gamma: 0.089, peptide.O: -0.5, sidechain.C_delta_2: 0.145, peptide.H: 0.248, peptide.C_alpha: 0.219, peptide.N: -0.52, sidechain.H_delta_1: 0.32, sidechain.C_beta: 0.06, } name = 'histidine_deltah' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/histidine_deltah0000644000076600000240000000122411777301631021675 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsd' amber_charge = {peptide.C_alpha: 0.0188, sidechain.C_gamma: -0.0266, sidechain.C_epsilon_1: 0.2057, sidechain.N_delta_1: -0.3811, sidechain.H_delta_1: 0.3649, sidechain.C_beta: -0.0462, sidechain.H_beta_3: 0.0402, sidechain.H_delta_2: 0.1147, peptide.H_alpha: 0.0881, sidechain.H_beta_2: 0.0402, sidechain.H_epsilon_1: 0.1392, sidechain.C_delta_2: 0.1292, peptide.N: -0.4157, peptide.H: 0.2719, peptide.C: 0.5973, sidechain.N_epsilon_2: -0.5727, peptide.O: -0.5679, } name = 'histidine_deltah' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_deltah_calpha0000644000076600000240000000022211662461637023210 0ustar hinsenstaff00000000000000symbol = 'Hsd' name = 'histidine_deltah' C_alpha = Atom('histidine_deltah') pdbmap = [('HSD', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/histidine_deltah_ct0000644000076600000240000000124611777301631022367 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsd' amber_charge = {sidechain.H_delta_1: 0.3755, sidechain.N_epsilon_2: -0.5629, peptide.C: 0.7615, sidechain.H_beta_2: 0.0565, sidechain.C_delta_2: 0.1001, peptide.O: -0.8016, sidechain.N_delta_1: -0.3892, sidechain.C_beta: -0.1046, sidechain.C_gamma: 0.0293, peptide.C_alpha: -0.1739, peptide.H: 0.2681, sidechain.H_delta_2: 0.1241, peptide.O_2: -0.8016, sidechain.H_epsilon_1: 0.1418, sidechain.H_beta_3: 0.0565, peptide.N: -0.3821, sidechain.C_epsilon_1: 0.1925, peptide.H_alpha: 0.11, } name = 'histidine_deltah' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/histidine_deltah_ct_noh0000644000076600000240000000076311662461637023244 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain_noh') peptide = Group('peptide_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsd' amber_charge = {sidechain.C_epsilon_1: 0.1925, peptide.O_2: -0.8016, peptide.O: -0.8016, peptide.C_alpha: -0.1739, peptide.C: 0.7615, sidechain.C_delta_2: 0.1001, sidechain.N_delta_1: -0.3892, sidechain.N_epsilon_2: -0.5629, sidechain.C_beta: -0.1046, peptide.N: -0.3821, sidechain.C_gamma: 0.0293, } name = 'histidine_deltah' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/histidine_deltah_ct_uni0000644000076600000240000000102311662461637023241 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsd' amber91_charge = {sidechain.C_delta_2: 0.145, peptide.N: -0.52, sidechain.C_gamma: 0.089, sidechain.C_beta: 0.06, peptide.O: -0.706, peptide.O_2: -0.706, sidechain.N_delta_1: -0.444, sidechain.C_epsilon_1: 0.384, peptide.C: 0.444, sidechain.N_epsilon_2: -0.527, peptide.H: 0.248, sidechain.H_delta_1: 0.32, peptide.C_alpha: 0.213, } name = 'histidine_deltah' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/histidine_deltah_ct_uni20000644000076600000240000000102311662461637023323 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsd' amber91_charge = {sidechain.N_epsilon_2: -0.527, peptide.C: 0.444, peptide.H: 0.248, sidechain.H_delta_1: 0.32, peptide.O_2: -0.706, peptide.C_alpha: 0.213, sidechain.C_gamma: 0.089, peptide.N: -0.52, sidechain.C_beta: 0.06, peptide.O: -0.706, sidechain.C_delta_2: 0.145, sidechain.N_delta_1: -0.444, sidechain.C_epsilon_1: 0.384, } name = 'histidine_deltah' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/histidine_deltah_noh0000644000076600000240000000073711662461637022557 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain_noh') peptide = Group('peptide_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsd' amber_charge = {sidechain.C_delta_2: 0.1292, sidechain.N_delta_1: -0.3811, sidechain.C_gamma: -0.0266, peptide.C_alpha: 0.0188, sidechain.C_epsilon_1: 0.2057, peptide.N: -0.4157, peptide.O: -0.5679, sidechain.C_beta: -0.0462, peptide.C: 0.5973, sidechain.N_epsilon_2: -0.5727, } name = 'histidine_deltah' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_deltah_nt0000644000076600000240000000127411777301631022403 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsd' amber_charge = {sidechain.C_gamma: -0.0399, peptide.C: 0.6123, peptide.H_3: 0.1963, sidechain.H_delta_2: 0.1299, peptide.N: 0.1542, sidechain.H_epsilon_1: 0.1385, sidechain.H_beta_3: 0.0209, sidechain.C_epsilon_1: 0.2127, peptide.C_alpha: 0.0964, sidechain.C_beta: 0.0259, sidechain.H_delta_1: 0.3632, sidechain.N_epsilon_2: -0.5711, sidechain.H_beta_2: 0.0209, peptide.O: -0.5713, sidechain.N_delta_1: -0.3819, peptide.H_alpha: 0.0958, sidechain.C_delta_2: 0.1046, peptide.H_1: 0.1963, peptide.H_2: 0.1963, } name = 'histidine_deltah' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_deltah_nt_ct_noh0000644000076600000240000000030411662461637023734 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain_noh') peptide = Group('peptide_nt_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsd' name = 'histidine_deltah' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/histidine_deltah_nt_noh0000644000076600000240000000073311662461637023254 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain_noh') peptide = Group('peptide_nt_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsd' amber_charge = {peptide.C_alpha: 0.0964, sidechain.C_beta: 0.0259, sidechain.C_epsilon_1: 0.2127, sidechain.C_gamma: -0.0399, sidechain.C_delta_2: 0.1046, peptide.N: 0.1542, peptide.O: -0.5713, sidechain.N_delta_1: -0.3819, peptide.C: 0.6123, sidechain.N_epsilon_2: -0.5711, } name = 'histidine_deltah' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_deltah_nt_uni0000644000076600000240000000104711662461637023262 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsd' amber91_charge = {sidechain.N_delta_1: -0.444, peptide.O: -0.5, sidechain.N_epsilon_2: -0.527, sidechain.C_gamma: 0.089, peptide.H_1: 0.312, sidechain.C_beta: 0.06, peptide.C: 0.526, sidechain.H_delta_1: 0.32, sidechain.C_delta_2: 0.145, peptide.N: -0.263, sidechain.C_epsilon_1: 0.384, peptide.H_2: 0.312, peptide.H_3: 0.312, peptide.C_alpha: 0.274, } name = 'histidine_deltah' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_deltah_nt_uni20000644000076600000240000000104711662461637023344 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsd' amber91_charge = {peptide.N: -0.263, sidechain.C_epsilon_1: 0.384, peptide.H_1: 0.312, peptide.O: -0.5, sidechain.C_gamma: 0.089, peptide.H_2: 0.312, sidechain.N_epsilon_2: -0.527, sidechain.H_delta_1: 0.32, sidechain.C_beta: 0.06, peptide.C: 0.526, sidechain.C_delta_2: 0.145, peptide.C_alpha: 0.274, peptide.H_3: 0.312, sidechain.N_delta_1: -0.444, } name = 'histidine_deltah' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_deltah_uni0000644000076600000240000000077611662461637022571 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsd' name = 'histidine_deltah' chain_links = [peptide.N, peptide.C] amber91_charge = {peptide.O: -0.5, peptide.N: -0.52, sidechain.H_delta_1: 0.32, sidechain.C_beta: 0.06, peptide.C_alpha: 0.219, sidechain.C_gamma: 0.089, sidechain.C_delta_2: 0.145, sidechain.N_epsilon_2: -0.527, sidechain.N_delta_1: -0.444, sidechain.C_epsilon_1: 0.384, peptide.C: 0.526, peptide.H: 0.248, } MMTK-2.7.9/MMTK/Database/Groups/histidine_deltah_uni20000644000076600000240000000077611662461637022653 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsd' amber91_charge = {sidechain.C_epsilon_1: 0.384, peptide.C: 0.526, sidechain.C_gamma: 0.089, sidechain.N_epsilon_2: -0.527, sidechain.C_beta: 0.06, sidechain.H_delta_1: 0.32, peptide.O: -0.5, sidechain.N_delta_1: -0.444, sidechain.C_delta_2: 0.145, peptide.H: 0.248, peptide.C_alpha: 0.219, peptide.N: -0.52, } name = 'histidine_deltah' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_deltah_uni30000644000076600000240000000077711662461637022655 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain_uni3') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hid' name = 'histidine_deltah' chain_links = [peptide.N, peptide.C] amber91_charge = {peptide.O: -0.5, peptide.N: -0.52, sidechain.H_delta_1: 0.32, sidechain.C_beta: 0.06, peptide.C_alpha: 0.219, sidechain.C_gamma: 0.089, sidechain.C_delta_2: 0.145, sidechain.N_epsilon_2: -0.527, sidechain.N_delta_1: -0.444, sidechain.C_epsilon_1: 0.384, peptide.C: 0.526, peptide.H: 0.248, } MMTK-2.7.9/MMTK/Database/Groups/histidine_epsilonh0000644000076600000240000000123011777301631022252 0ustar hinsenstaff00000000000000sidechain = Group('hie_sidechain') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hse' amber_charge = {peptide.H_alpha: 0.136, peptide.C_alpha: -0.0581, sidechain.H_epsilon_2: 0.3339, sidechain.H_beta_3: 0.0367, sidechain.H_beta_2: 0.0367, peptide.C: 0.5973, sidechain.H_delta_2: 0.1862, sidechain.N_epsilon_2: -0.2795, peptide.O: -0.5679, sidechain.C_epsilon_1: 0.1635, sidechain.H_epsilon_1: 0.1435, peptide.H: 0.2719, sidechain.C_beta: -0.0074, sidechain.C_delta_2: -0.2207, sidechain.N_delta_1: -0.5432, sidechain.C_gamma: 0.1868, peptide.N: -0.4157, } name = 'histidine_epsilonh' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_epsilonh_calpha0000644000076600000240000000022611662461637023574 0ustar hinsenstaff00000000000000symbol = 'Hse' name = 'histidine_epsilonh' C_alpha = Atom('histidine_epsilonh') pdbmap = [('HSE', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/histidine_epsilonh_ct0000644000076600000240000000125211777301631022744 0ustar hinsenstaff00000000000000sidechain = Group('hie_sidechain') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hse' amber_charge = {sidechain.H_beta_2: 0.062, peptide.N: -0.3821, peptide.O: -0.8065, sidechain.H_epsilon_1: 0.1448, peptide.C_alpha: -0.2699, sidechain.N_epsilon_2: -0.267, peptide.C: 0.7916, peptide.O_2: -0.8065, sidechain.H_beta_3: 0.062, peptide.H: 0.2681, sidechain.H_delta_2: 0.1957, sidechain.C_delta_2: -0.2588, sidechain.C_beta: -0.1068, sidechain.H_epsilon_2: 0.3319, sidechain.N_delta_1: -0.5517, sidechain.C_gamma: 0.2724, sidechain.C_epsilon_1: 0.1558, peptide.H_alpha: 0.165, } name = 'histidine_epsilon-h' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/histidine_epsilonh_ct_noh0000644000076600000240000000076611662461637023627 0ustar hinsenstaff00000000000000sidechain = Group('hie_sidechain_noh') peptide = Group('peptide_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hse' amber_charge = {sidechain.C_beta: -0.1068, sidechain.C_epsilon_1: 0.1558, sidechain.C_delta_2: -0.2588, sidechain.N_epsilon_2: -0.267, peptide.O: -0.8065, sidechain.N_delta_1: -0.5517, sidechain.C_gamma: 0.2724, peptide.N: -0.3821, peptide.O_2: -0.8065, peptide.C: 0.7916, peptide.C_alpha: -0.2699, } name = 'histidine_epsilon-h' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/histidine_epsilonh_ct_uni0000644000076600000240000000103011662461637023617 0ustar hinsenstaff00000000000000sidechain = Group('hie_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hse' amber91_charge = {peptide.O: -0.706, sidechain.H_epsilon_2: 0.32, peptide.C_alpha: 0.213, sidechain.N_delta_1: -0.527, sidechain.C_epsilon_1: 0.384, sidechain.C_beta: 0.06, peptide.N: -0.52, peptide.O_2: -0.706, sidechain.C_delta_2: 0.122, sidechain.C_gamma: 0.112, peptide.H: 0.248, peptide.C: 0.444, sidechain.N_epsilon_2: -0.444, } name = 'histidine_epsilon-h' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/histidine_epsilonh_ct_uni20000644000076600000240000000103011662461637023701 0ustar hinsenstaff00000000000000sidechain = Group('hie_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hse' amber91_charge = {peptide.N: -0.52, sidechain.C_gamma: 0.112, sidechain.C_beta: 0.06, peptide.O: -0.706, sidechain.C_delta_2: 0.122, sidechain.N_delta_1: -0.527, sidechain.C_epsilon_1: 0.384, peptide.C: 0.444, sidechain.N_epsilon_2: -0.444, peptide.H: 0.248, sidechain.H_epsilon_2: 0.32, peptide.O_2: -0.706, peptide.C_alpha: 0.213, } name = 'histidine_epsilon-h' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/histidine_epsilonh_noh0000644000076600000240000000074211662461637023133 0ustar hinsenstaff00000000000000sidechain = Group('hie_sidechain_noh') peptide = Group('peptide_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hse' amber_charge = {peptide.C_alpha: -0.0581, sidechain.C_epsilon_1: 0.1635, sidechain.N_delta_1: -0.5432, peptide.O: -0.5679, sidechain.C_delta_2: -0.2207, peptide.C: 0.5973, sidechain.C_beta: -0.0074, sidechain.N_epsilon_2: -0.2795, sidechain.C_gamma: 0.1868, peptide.N: -0.4157, } name = 'histidine_epsilonh' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_epsilonh_nt0000644000076600000240000000127611777301631022765 0ustar hinsenstaff00000000000000sidechain = Group('hie_sidechain') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hse' amber_charge = {sidechain.H_beta_2: 0.0223, peptide.H_1: 0.2016, peptide.H_3: 0.2016, sidechain.C_delta_2: -0.2349, peptide.C: 0.6123, sidechain.N_delta_1: -0.5579, sidechain.H_beta_3: 0.0223, sidechain.C_gamma: 0.174, peptide.O: -0.5713, peptide.C_alpha: 0.0236, sidechain.H_delta_2: 0.1963, sidechain.C_beta: 0.0489, peptide.H_2: 0.2016, sidechain.H_epsilon_1: 0.1397, sidechain.H_epsilon_2: 0.3324, peptide.N: 0.1472, sidechain.N_epsilon_2: -0.2781, sidechain.C_epsilon_1: 0.1804, peptide.H_alpha: 0.138, } name = 'histidine_epsilonh' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_epsilonh_nt_ct_noh0000644000076600000240000000030611662461637024316 0ustar hinsenstaff00000000000000sidechain = Group('hie_sidechain_noh') peptide = Group('peptide_nt_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hse' name = 'histidine_epsilonh' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/histidine_epsilonh_nt_noh0000644000076600000240000000073411662461637023635 0ustar hinsenstaff00000000000000sidechain = Group('hie_sidechain_noh') peptide = Group('peptide_nt_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hse' amber_charge = {peptide.C: 0.6123, sidechain.C_gamma: 0.174, peptide.C_alpha: 0.0236, sidechain.C_delta_2: -0.2349, peptide.O: -0.5713, sidechain.C_beta: 0.0489, sidechain.C_epsilon_1: 0.1804, peptide.N: 0.1472, sidechain.N_delta_1: -0.5579, sidechain.N_epsilon_2: -0.2781, } name = 'histidine_epsilonh' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_epsilonh_nt_uni0000644000076600000240000000105311662461637023637 0ustar hinsenstaff00000000000000sidechain = Group('hie_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hse' amber91_charge = {sidechain.N_epsilon_2: -0.444, peptide.H_3: 0.312, sidechain.H_epsilon_2: 0.32, sidechain.C_beta: 0.06, peptide.N: -0.263, peptide.H_1: 0.312, sidechain.C_gamma: 0.112, peptide.H_2: 0.312, sidechain.C_delta_2: 0.122, peptide.C: 0.526, sidechain.N_delta_1: -0.527, peptide.C_alpha: 0.274, sidechain.C_epsilon_1: 0.384, peptide.O: -0.5, } name = 'histidine_epsilonh' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_epsilonh_nt_uni20000644000076600000240000000105311662461637023721 0ustar hinsenstaff00000000000000sidechain = Group('hie_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hse' amber91_charge = {sidechain.H_epsilon_2: 0.32, peptide.C_alpha: 0.274, sidechain.C_epsilon_1: 0.384, peptide.O: -0.5, peptide.H_2: 0.312, sidechain.C_beta: 0.06, sidechain.C_delta_2: 0.122, peptide.N: -0.263, sidechain.N_delta_1: -0.527, sidechain.N_epsilon_2: -0.444, peptide.C: 0.526, sidechain.C_gamma: 0.112, peptide.H_3: 0.312, peptide.H_1: 0.312, } name = 'histidine_epsilonh' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_epsilonh_uni0000644000076600000240000000100211662461637023130 0ustar hinsenstaff00000000000000sidechain = Group('hie_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hse' amber91_charge = {peptide.C: 0.526, sidechain.C_beta: 0.06, sidechain.C_gamma: 0.112, peptide.N: -0.52, peptide.C_alpha: 0.219, sidechain.C_epsilon_1: 0.384, sidechain.C_delta_2: 0.122, peptide.O: -0.5, peptide.H: 0.248, sidechain.N_delta_1: -0.527, sidechain.H_epsilon_2: 0.32, sidechain.N_epsilon_2: -0.444, } name = 'histidine_epsilonh' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_epsilonh_uni20000644000076600000240000000100211662461637023212 0ustar hinsenstaff00000000000000sidechain = Group('hie_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hse' amber91_charge = {sidechain.C_delta_2: 0.122, peptide.H: 0.248, peptide.C_alpha: 0.219, sidechain.H_epsilon_2: 0.32, peptide.N: -0.52, peptide.O: -0.5, sidechain.C_epsilon_1: 0.384, sidechain.N_delta_1: -0.527, sidechain.C_gamma: 0.112, sidechain.C_beta: 0.06, peptide.C: 0.526, sidechain.N_epsilon_2: -0.444, } name = 'histidine_epsilonh' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_epsilonh_uni30000644000076600000240000000100311662461637023214 0ustar hinsenstaff00000000000000sidechain = Group('hie_sidechain_uni3') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hie' amber91_charge = {peptide.C: 0.526, sidechain.C_beta: 0.06, sidechain.C_gamma: 0.112, peptide.N: -0.52, peptide.C_alpha: 0.219, sidechain.C_epsilon_1: 0.384, sidechain.C_delta_2: 0.122, peptide.O: -0.5, peptide.H: 0.248, sidechain.N_delta_1: -0.527, sidechain.H_epsilon_2: 0.32, sidechain.N_epsilon_2: -0.444, } name = 'histidine_epsilonh' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_noh0000644000076600000240000000073711662461637021236 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain_noh') peptide = Group('peptide_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'His' amber_charge = {sidechain.N_delta_1: -0.3811, sidechain.C_gamma: -0.0266, sidechain.C_delta_2: 0.1292, sidechain.N_epsilon_2: -0.5727, peptide.N: -0.4157, sidechain.C_epsilon_1: 0.2057, sidechain.C_beta: -0.0462, peptide.C_alpha: 0.0188, peptide.O: -0.5679, peptide.C: 0.5973, } name = 'histidine_deltah' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_nt0000644000076600000240000000127411777301631021062 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'His' amber_charge = {sidechain.C_gamma: -0.0399, sidechain.C_epsilon_1: 0.2127, peptide.C: 0.6123, sidechain.H_delta_2: 0.1299, peptide.H_alpha: 0.0958, sidechain.H_epsilon_1: 0.1385, sidechain.H_beta_3: 0.0209, peptide.H_1: 0.1963, sidechain.C_delta_2: 0.1046, peptide.O: -0.5713, sidechain.H_delta_1: 0.3632, sidechain.N_epsilon_2: -0.5711, sidechain.H_beta_2: 0.0209, peptide.H_2: 0.1963, peptide.C_alpha: 0.0964, peptide.H_3: 0.1963, peptide.N: 0.1542, sidechain.N_delta_1: -0.3819, sidechain.C_beta: 0.0259, } name = 'histidine_deltah' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_nt_ct_noh0000644000076600000240000000030411662461637022413 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain_noh') peptide = Group('peptide_nt_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'His' name = 'histidine_deltah' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/histidine_nt_noh0000644000076600000240000000073311662461637021733 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain_noh') peptide = Group('peptide_nt_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'His' amber_charge = {sidechain.C_delta_2: 0.1046, sidechain.N_epsilon_2: -0.5711, sidechain.C_epsilon_1: 0.2127, peptide.N: 0.1542, sidechain.C_gamma: -0.0399, sidechain.C_beta: 0.0259, peptide.O: -0.5713, peptide.C_alpha: 0.0964, peptide.C: 0.6123, sidechain.N_delta_1: -0.3819, } name = 'histidine_deltah' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_nt_uni0000644000076600000240000000104711662461637021741 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'His' amber91_charge = {sidechain.C_delta_2: 0.145, peptide.H_3: 0.312, peptide.H_1: 0.312, sidechain.H_delta_1: 0.32, peptide.C: 0.526, sidechain.C_beta: 0.06, sidechain.C_epsilon_1: 0.384, peptide.N: -0.263, sidechain.N_delta_1: -0.444, sidechain.C_gamma: 0.089, peptide.C_alpha: 0.274, peptide.O: -0.5, peptide.H_2: 0.312, sidechain.N_epsilon_2: -0.527, } name = 'histidine_deltah' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_nt_uni20000644000076600000240000000104711662461637022023 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'His' amber91_charge = {sidechain.C_gamma: 0.089, peptide.H_2: 0.312, sidechain.H_delta_1: 0.32, peptide.C_alpha: 0.274, peptide.O: -0.5, sidechain.C_epsilon_1: 0.384, peptide.C: 0.526, sidechain.C_delta_2: 0.145, sidechain.N_epsilon_2: -0.527, peptide.H_3: 0.312, peptide.N: -0.263, peptide.H_1: 0.312, sidechain.N_delta_1: -0.444, sidechain.C_beta: 0.06, } name = 'histidine_deltah' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_plus0000644000076600000240000000126111777301631021420 0ustar hinsenstaff00000000000000sidechain = Group('hip_sidechain') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsp' amber_charge = {sidechain.C_epsilon_1: -0.017, sidechain.N_epsilon_2: -0.1718, sidechain.H_beta_3: 0.081, peptide.N: -0.3479, peptide.C: 0.7341, peptide.H: 0.2747, sidechain.H_delta_2: 0.2317, sidechain.N_delta_1: -0.1513, sidechain.C_gamma: -0.0012, sidechain.H_beta_2: 0.081, sidechain.H_epsilon_1: 0.2681, sidechain.C_beta: -0.0414, peptide.C_alpha: -0.1354, sidechain.H_epsilon_2: 0.3911, sidechain.C_delta_2: -0.1141, sidechain.H_delta_1: 0.3866, peptide.H_alpha: 0.1212, peptide.O: -0.5894, } name = 'histidine_plus' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_plus_calpha0000644000076600000240000000021611662461637022735 0ustar hinsenstaff00000000000000symbol = 'Hsp' name = 'histidine_plus' C_alpha = Atom('histidine_plus') pdbmap = [('HSP', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/histidine_plus_ct0000644000076600000240000000130511777301631022105 0ustar hinsenstaff00000000000000sidechain = Group('hip_sidechain') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsp' amber_charge = {sidechain.C_epsilon_1: -0.0251, sidechain.H_epsilon_1: 0.2694, sidechain.N_epsilon_2: -0.1683, peptide.N: -0.3481, sidechain.N_delta_1: -0.1501, sidechain.C_beta: -0.08, sidechain.H_delta_1: 0.3883, sidechain.C_gamma: 0.0298, peptide.H_alpha: 0.1115, peptide.C: 0.8032, peptide.C_alpha: -0.1445, sidechain.H_beta_3: 0.0868, sidechain.H_epsilon_2: 0.3913, sidechain.C_delta_2: -0.1256, sidechain.H_beta_2: 0.0868, sidechain.H_delta_2: 0.2336, peptide.O_2: -0.8177, peptide.O: -0.8177, peptide.H: 0.2764, } name = 'histidine_plus' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/histidine_plus_ct_noh0000644000076600000240000000076111662461637022764 0ustar hinsenstaff00000000000000sidechain = Group('hip_sidechain_noh') peptide = Group('peptide_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsp' amber_charge = {peptide.N: -0.3481, sidechain.N_delta_1: -0.1501, peptide.O_2: -0.8177, sidechain.C_gamma: 0.0298, sidechain.C_delta_2: -0.1256, peptide.C_alpha: -0.1445, sidechain.C_epsilon_1: -0.0251, sidechain.N_epsilon_2: -0.1683, sidechain.C_beta: -0.08, peptide.C: 0.8032, peptide.O: -0.8177, } name = 'histidine_plus' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/histidine_plus_ct_uni0000644000076600000240000000106111662461637022765 0ustar hinsenstaff00000000000000sidechain = Group('hip_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsp' amber91_charge = {sidechain.N_delta_1: -0.613, sidechain.C_epsilon_1: 0.719, peptide.O_2: -0.706, sidechain.C_gamma: 0.103, sidechain.N_epsilon_2: -0.686, peptide.N: -0.52, peptide.C_alpha: 0.189, sidechain.C_beta: 0.211, sidechain.C_delta_2: 0.353, peptide.O: -0.706, peptide.C: 0.444, sidechain.H_delta_1: 0.478, peptide.H: 0.248, sidechain.H_epsilon_2: 0.486, } name = 'histidine_plus' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/histidine_plus_ct_uni20000644000076600000240000000106111662461637023047 0ustar hinsenstaff00000000000000sidechain = Group('hip_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsp' amber91_charge = {sidechain.C_beta: 0.211, peptide.H: 0.248, sidechain.H_epsilon_2: 0.486, peptide.N: -0.52, peptide.O_2: -0.706, sidechain.H_delta_1: 0.478, peptide.C_alpha: 0.189, peptide.C: 0.444, peptide.O: -0.706, sidechain.N_epsilon_2: -0.686, sidechain.C_gamma: 0.103, sidechain.C_delta_2: 0.353, sidechain.N_delta_1: -0.613, sidechain.C_epsilon_1: 0.719, } name = 'histidine_plus' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/histidine_plus_noh0000644000076600000240000000073711662461637022301 0ustar hinsenstaff00000000000000sidechain = Group('hip_sidechain_noh') peptide = Group('peptide_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsp' amber_charge = {peptide.O: -0.5894, peptide.N: -0.3479, sidechain.C_beta: -0.0414, peptide.C: 0.7341, sidechain.C_delta_2: -0.1141, sidechain.C_epsilon_1: -0.017, sidechain.N_epsilon_2: -0.1718, peptide.C_alpha: -0.1354, sidechain.C_gamma: -0.0012, sidechain.N_delta_1: -0.1513, } name = 'histidine_plus' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_plus_nt0000644000076600000240000000133111777301631022117 0ustar hinsenstaff00000000000000sidechain = Group('hip_sidechain') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsp' amber_charge = {peptide.H_3: 0.1704, peptide.H_alpha: 0.1047, sidechain.H_beta_3: 0.0531, sidechain.H_delta_2: 0.2495, peptide.C_alpha: 0.0581, sidechain.C_epsilon_1: -0.0011, sidechain.H_epsilon_1: 0.2645, peptide.H_2: 0.1704, sidechain.C_delta_2: -0.1433, peptide.N: 0.256, sidechain.H_epsilon_2: 0.3921, sidechain.C_gamma: -0.0236, sidechain.N_delta_1: -0.151, sidechain.H_delta_1: 0.3821, peptide.C: 0.7214, peptide.H_1: 0.1704, sidechain.H_beta_2: 0.0531, sidechain.N_epsilon_2: -0.1739, peptide.O: -0.6013, sidechain.C_beta: 0.0484, } name = 'histidine_plus' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_plus_nt_ct_noh0000644000076600000240000000030211662461637023454 0ustar hinsenstaff00000000000000sidechain = Group('hip_sidechain_noh') peptide = Group('peptide_nt_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsp' name = 'histidine_plus' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/histidine_plus_nt_noh0000644000076600000240000000072111662461637022773 0ustar hinsenstaff00000000000000sidechain = Group('hip_sidechain_noh') peptide = Group('peptide_nt_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsp' amber_charge = {peptide.C_alpha: 0.151, sidechain.C_delta_2: -0.037, sidechain.C_gamma: 0.058, sidechain.N_epsilon_2: -0.058, peptide.O: -0.504, sidechain.C_beta: -0.098, peptide.C: 0.616, peptide.N: -0.263, sidechain.N_delta_1: -0.058, sidechain.C_epsilon_1: 0.114, } name = 'histidine_plus' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_plus_nt_uni0000644000076600000240000000110411662461637022776 0ustar hinsenstaff00000000000000sidechain = Group('hip_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsp' amber91_charge = {sidechain.C_epsilon_1: 0.719, sidechain.C_gamma: 0.103, peptide.O: -0.5, sidechain.C_beta: 0.211, peptide.H_1: 0.312, sidechain.N_epsilon_2: -0.686, peptide.H_2: 0.312, sidechain.C_delta_2: 0.353, peptide.C: 0.526, sidechain.H_epsilon_2: 0.486, peptide.C_alpha: 0.25, sidechain.N_delta_1: -0.613, peptide.N: -0.263, sidechain.H_delta_1: 0.478, peptide.H_3: 0.312, } name = 'histidine_plus' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_plus_nt_uni20000644000076600000240000000110411662461637023060 0ustar hinsenstaff00000000000000sidechain = Group('hip_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsp' amber91_charge = {sidechain.C_epsilon_1: 0.719, peptide.H_1: 0.312, sidechain.C_delta_2: 0.353, sidechain.H_delta_1: 0.478, sidechain.C_gamma: 0.103, peptide.O: -0.5, peptide.C: 0.526, peptide.C_alpha: 0.25, sidechain.H_epsilon_2: 0.486, sidechain.N_delta_1: -0.613, sidechain.C_beta: 0.211, sidechain.N_epsilon_2: -0.686, peptide.H_2: 0.312, peptide.N: -0.263, peptide.H_3: 0.312, } name = 'histidine_plus' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_plus_uni0000644000076600000240000000103411662461637022277 0ustar hinsenstaff00000000000000sidechain = Group('hip_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsp' amber91_charge = {sidechain.N_epsilon_2: -0.686, peptide.O: -0.5, sidechain.C_beta: 0.211, peptide.H: 0.248, sidechain.N_delta_1: -0.613, peptide.N: -0.52, sidechain.C_delta_2: 0.353, sidechain.C_epsilon_1: 0.719, peptide.C: 0.526, sidechain.H_epsilon_2: 0.486, sidechain.H_delta_1: 0.478, peptide.C_alpha: 0.195, sidechain.C_gamma: 0.103, } name = 'histidine_plus' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_plus_uni20000644000076600000240000000103411662461637022361 0ustar hinsenstaff00000000000000sidechain = Group('hip_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hsp' amber91_charge = {sidechain.C_epsilon_1: 0.719, peptide.C: 0.526, sidechain.C_gamma: 0.103, peptide.C_alpha: 0.195, sidechain.H_delta_1: 0.478, sidechain.H_epsilon_2: 0.486, sidechain.C_beta: 0.211, peptide.N: -0.52, peptide.O: -0.5, sidechain.N_delta_1: -0.613, peptide.H: 0.248, sidechain.C_delta_2: 0.353, sidechain.N_epsilon_2: -0.686, } name = 'histidine_plus' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_plus_uni30000644000076600000240000000103511662461637022363 0ustar hinsenstaff00000000000000sidechain = Group('hip_sidechain_uni3') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Hip' amber91_charge = {sidechain.N_epsilon_2: -0.686, peptide.O: -0.5, sidechain.C_beta: 0.211, peptide.H: 0.248, sidechain.N_delta_1: -0.613, peptide.N: -0.52, sidechain.C_delta_2: 0.353, sidechain.C_epsilon_1: 0.719, peptide.C: 0.526, sidechain.H_epsilon_2: 0.486, sidechain.H_delta_1: 0.478, peptide.C_alpha: 0.195, sidechain.C_gamma: 0.103, } name = 'histidine_plus' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_uni0000644000076600000240000000077611662461637021250 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'His' amber91_charge = {sidechain.C_gamma: 0.089, sidechain.N_epsilon_2: -0.527, sidechain.C_beta: 0.06, peptide.O: -0.5, sidechain.C_epsilon_1: 0.384, peptide.C: 0.526, peptide.H: 0.248, sidechain.N_delta_1: -0.444, peptide.N: -0.52, sidechain.H_delta_1: 0.32, peptide.C_alpha: 0.219, sidechain.C_delta_2: 0.145, } name = 'histidine_deltah' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/histidine_uni20000644000076600000240000000077611662461637021332 0ustar hinsenstaff00000000000000sidechain = Group('hid_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'His' amber91_charge = {peptide.N: -0.52, sidechain.N_delta_1: -0.444, sidechain.C_beta: 0.06, sidechain.C_gamma: 0.089, peptide.H: 0.248, peptide.C: 0.526, sidechain.C_delta_2: 0.145, peptide.O: -0.5, sidechain.H_delta_1: 0.32, sidechain.C_epsilon_1: 0.384, peptide.C_alpha: 0.219, sidechain.N_epsilon_2: -0.527, } name = 'histidine_deltah' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/ile_sidechain0000644000076600000240000000323412041514571021151 0ustar hinsenstaff00000000000000H_beta = Atom('h') C_gamma_2 = Atom('c') H_gamma_1_2 = Atom('h') H_gamma_1_3 = Atom('h') C_gamma_1 = Atom('c') H_delta_1_2 = Atom('h') H_delta_1_3 = Atom('h') H_delta_1_1 = Atom('h') C_beta = Atom('c') H_gamma_2_1 = Atom('h') H_gamma_2_2 = Atom('h') H_gamma_2_3 = Atom('h') C_delta_1 = Atom('c') bonds = [Bond(H_beta, C_beta), Bond(C_gamma_2, C_beta), Bond(H_gamma_2_1, C_gamma_2), Bond(H_gamma_2_2, C_gamma_2), Bond(H_gamma_2_3, C_gamma_2), Bond(C_gamma_1, C_beta), Bond(H_gamma_1_2, C_gamma_1), Bond(H_gamma_1_3, C_gamma_1), Bond(C_delta_1, C_gamma_1), Bond(H_delta_1_1, C_delta_1), Bond(H_delta_1_2, C_delta_1), Bond(H_delta_1_3, C_delta_1), ] name = 'ile_sidechain' pdbmap = [('ILE', {'1HD1': H_delta_1_1, '3HG1': H_gamma_1_3, 'CG2': C_gamma_2, 'HB': H_beta, 'CD1': C_delta_1, '1HG2': H_gamma_2_1, '3HD1': H_delta_1_3, '2HD1': H_delta_1_2, '3HG2': H_gamma_2_3, 'CB': C_beta, 'CG1': C_gamma_1, '2HG1': H_gamma_1_2, '2HG2': H_gamma_2_2, }, ), ] pdb_alternative = {'1HG1': '3HG1', 'HG22': '2HG2', 'HG23': '3HG2', 'HD11': '1HD1', 'HG21': '1HG2', 'HD12': '2HD1', 'HD13': '3HD1', 'HG13': '3HG1', 'HG12': '2HG1', 'HG11': '3HG1', 'CD': 'CD1', 'HD1': '1HD1', 'HD2': '2HD1', 'HD3': '3HD1', } amber_atom_type = {H_delta_1_2: 'HC', H_delta_1_1: 'HC', C_gamma_2: 'CT', H_gamma_2_1: 'HC', H_gamma_1_2: 'HC', H_gamma_2_3: 'HC', H_delta_1_3: 'HC', H_beta: 'HC', C_beta: 'CT', H_gamma_1_3: 'HC', H_gamma_2_2: 'HC', C_delta_1: 'CT', C_gamma_1: 'CT', } amber12_atom_type = {H_delta_1_2: 'HC', H_delta_1_1: 'HC', C_gamma_2: 'CT', H_gamma_2_1: 'HC', H_gamma_1_2: 'HC', H_gamma_2_3: 'HC', H_delta_1_3: 'HC', H_beta: 'HC', C_beta: '3C', H_gamma_1_3: 'HC', H_gamma_2_2: 'HC', C_delta_1: 'CT', C_gamma_1: '2C', } MMTK-2.7.9/MMTK/Database/Groups/ile_sidechain_noh0000644000076600000240000000064311662461637022032 0ustar hinsenstaff00000000000000C_beta = Atom('CH') C_delta_1 = Atom('CH3') C_gamma_1 = Atom('CH2') C_gamma_2 = Atom('CH3') bonds = [Bond(C_gamma_2, C_beta), Bond(C_gamma_1, C_beta), Bond(C_delta_1, C_gamma_1), ] pdbmap = [('ILE', {'CD1': C_delta_1, 'CG2': C_gamma_2, 'CB': C_beta, 'CG1': C_gamma_1, }, ), ] pdb_alternative = {'CD': 'CD1', } amber_atom_type = {C_delta_1: 'CT', C_beta: 'CT', C_gamma_2: 'CT', C_gamma_1: 'CT', } name = 'ile_sidechain' MMTK-2.7.9/MMTK/Database/Groups/ile_sidechain_uni0000644000076600000240000000121011662461637022030 0ustar hinsenstaff00000000000000C_beta = Atom('CH') C_delta_1 = Atom('CH3') C_gamma_1 = Atom('CH2') C_gamma_2 = Atom('CH3') bonds = [Bond(C_gamma_2, C_beta), Bond(C_gamma_1, C_beta), Bond(C_delta_1, C_gamma_1), ] pdbmap = [('ILE', {'CD1': C_delta_1, 'CG2': C_gamma_2, 'CB': C_beta, 'CG1': C_gamma_1, }, ), ] pdb_alternative = {'CD': 'CD1', 'HD1': '1HD1', 'HD2': '2HD1', 'HD3': '3HD1', 'HG11': '3HG1', } name = 'ile_sidechain' opls_atom_type = { C_beta: 'CZ', C_gamma_2: 'C3', C_gamma_1: 'C2', C_delta_1: 'CV', } opls_charge = { C_beta: 0.0, C_gamma_2: 0.0, C_gamma_1: 0.0, C_delta_1: 0.0 } amber91_atom_type = {C_gamma_2: 'C3', C_delta_1: 'C3', C_beta: 'CH', C_gamma_1: 'C2', } MMTK-2.7.9/MMTK/Database/Groups/ile_sidechain_uni20000644000076600000240000000074211662461637022123 0ustar hinsenstaff00000000000000C_beta = Atom('CH') C_delta_1 = Atom('CH3') C_gamma_1 = Atom('CH2') C_gamma_2 = Atom('CH3') bonds = [Bond(C_gamma_2, C_beta), Bond(C_gamma_1, C_beta), Bond(C_delta_1, C_gamma_1), ] amber91_atom_type = {C_gamma_2: 'C3', C_delta_1: 'C3', C_beta: 'CH', C_gamma_1: 'C2', } pdbmap = [('ILE', {'CD1': C_delta_1, 'CG2': C_gamma_2, 'CB': C_beta, 'CG1': C_gamma_1, }, ), ] pdb_alternative = {'CD': 'CD1', 'HD1': '1HD1', 'HD2': '2HD1', 'HD3': '3HD1', 'HG11': '3HG1', } name = 'ile_sidechain' MMTK-2.7.9/MMTK/Database/Groups/ile_sidechain_uni30000644000076600000240000000121011662461637022113 0ustar hinsenstaff00000000000000C_beta = Atom('CH') C_delta_1 = Atom('CH3') C_gamma_1 = Atom('CH2') C_gamma_2 = Atom('CH3') bonds = [Bond(C_gamma_2, C_beta), Bond(C_gamma_1, C_beta), Bond(C_delta_1, C_gamma_1), ] pdbmap = [('ILE', {'CD1': C_delta_1, 'CG2': C_gamma_2, 'CB': C_beta, 'CG1': C_gamma_1, }, ), ] pdb_alternative = {'CD': 'CD1', 'HD1': '1HD1', 'HD2': '2HD1', 'HD3': '3HD1', 'HG11': '3HG1', } name = 'ile_sidechain' opls_atom_type = { C_beta: 'CZ', C_gamma_2: 'C3', C_gamma_1: 'C2', C_delta_1: 'CV', } opls_charge = { C_beta: 0.0, C_gamma_2: 0.0, C_gamma_1: 0.0, C_delta_1: 0.0 } amber91_atom_type = {C_gamma_2: 'C3', C_delta_1: 'C3', C_beta: 'CH', C_gamma_1: 'C2', } MMTK-2.7.9/MMTK/Database/Groups/isoleucine0000644000076600000240000000132111777301631020531 0ustar hinsenstaff00000000000000sidechain = Group('ile_sidechain') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ile' amber_charge = {sidechain.H_beta: 0.0187, sidechain.H_gamma_2_3: 0.0882, sidechain.H_delta_1_2: 0.0186, peptide.H_alpha: 0.0869, sidechain.H_gamma_2_1: 0.0882, sidechain.C_gamma_1: -0.043, sidechain.C_delta_1: -0.066, sidechain.H_delta_1_3: 0.0186, peptide.H: 0.2719, sidechain.H_gamma_1_2: 0.0236, sidechain.C_gamma_2: -0.3204, sidechain.H_delta_1_1: 0.0186, sidechain.H_gamma_1_3: 0.0236, peptide.C: 0.5973, sidechain.C_beta: 0.1303, peptide.N: -0.4157, peptide.C_alpha: -0.0597, sidechain.H_gamma_2_2: 0.0882, peptide.O: -0.5679, } name = 'isoleucine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/isoleucine_calpha0000644000076600000240000000020611662461637022050 0ustar hinsenstaff00000000000000symbol = 'Ile' name = 'isoleucine' C_alpha = Atom('isoleucine') pdbmap = [('ILE', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/isoleucine_ct0000644000076600000240000000134311777301631021223 0ustar hinsenstaff00000000000000sidechain = Group('ile_sidechain') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ile' amber_charge = {peptide.O_2: -0.819, sidechain.C_gamma_1: -0.0323, peptide.N: -0.3821, peptide.H_alpha: 0.1375, sidechain.H_gamma_2_2: 0.1021, peptide.H: 0.2681, sidechain.C_beta: 0.0363, sidechain.H_gamma_1_3: 0.0321, sidechain.C_delta_1: -0.0699, sidechain.H_delta_1_3: 0.0196, sidechain.H_beta: 0.0766, sidechain.H_gamma_1_2: 0.0321, peptide.C: 0.8343, sidechain.H_delta_1_2: 0.0196, peptide.O: -0.819, sidechain.H_gamma_2_3: 0.1021, sidechain.H_delta_1_1: 0.0196, sidechain.C_gamma_2: -0.3498, peptide.C_alpha: -0.31, sidechain.H_gamma_2_1: 0.1021, } name = 'isoleucine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/isoleucine_ct_noh0000644000076600000240000000065511662461637022102 0ustar hinsenstaff00000000000000sidechain = Group('ile_sidechain_noh') peptide = Group('peptide_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ile' amber_charge = {peptide.C_alpha: -0.31, peptide.O: -0.819, sidechain.C_delta_1: -0.0699, sidechain.C_gamma_1: -0.0323, sidechain.C_beta: 0.0363, peptide.N: -0.3821, peptide.O_2: -0.819, sidechain.C_gamma_2: -0.3498, peptide.C: 0.8343, } name = 'isoleucine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/isoleucine_ct_uni0000644000076600000240000000066711662461637022114 0ustar hinsenstaff00000000000000sidechain = Group('ile_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ile' amber91_charge = {peptide.O: -0.706, sidechain.C_beta: 0.03, sidechain.C_gamma_2: 0.001, sidechain.C_delta_1: -0.001, peptide.O_2: -0.706, peptide.H: 0.248, peptide.C: 0.444, peptide.N: -0.52, sidechain.C_gamma_1: 0.017, peptide.C_alpha: 0.193, } name = 'isoleucine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/isoleucine_ct_uni20000644000076600000240000000066711662461637022176 0ustar hinsenstaff00000000000000sidechain = Group('ile_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ile' amber91_charge = {sidechain.C_beta: 0.03, sidechain.C_delta_1: -0.001, peptide.C_alpha: 0.193, sidechain.C_gamma_2: 0.001, peptide.N: -0.52, sidechain.C_gamma_1: 0.017, peptide.H: 0.248, peptide.C: 0.444, peptide.O: -0.706, peptide.O_2: -0.706, } name = 'isoleucine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/isoleucine_ct_uni30000644000076600000240000000066711662461637022177 0ustar hinsenstaff00000000000000sidechain = Group('ile_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ile' amber91_charge = {peptide.O: -0.706, sidechain.C_beta: 0.03, sidechain.C_gamma_2: 0.001, sidechain.C_delta_1: -0.001, peptide.O_2: -0.706, peptide.H: 0.248, peptide.C: 0.444, peptide.N: -0.52, sidechain.C_gamma_1: 0.017, peptide.C_alpha: 0.193, } name = 'isoleucine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/isoleucine_noh0000644000076600000240000000063311662461637021410 0ustar hinsenstaff00000000000000sidechain = Group('ile_sidechain_noh') peptide = Group('peptide_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ile' amber_charge = {peptide.O: -0.5679, peptide.C: 0.5973, sidechain.C_gamma_1: -0.043, sidechain.C_gamma_2: -0.3204, sidechain.C_delta_1: -0.066, sidechain.C_beta: 0.1303, peptide.N: -0.4157, peptide.C_alpha: -0.0597, } name = 'isoleucine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/isoleucine_nt0000644000076600000240000000137211777301631021240 0ustar hinsenstaff00000000000000sidechain = Group('ile_sidechain') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ile' amber_charge = {sidechain.H_delta_1_3: 0.0226, sidechain.H_gamma_2_3: 0.0947, peptide.O: -0.5713, sidechain.C_gamma_1: -0.0387, sidechain.C_gamma_2: -0.372, sidechain.C_beta: 0.1885, sidechain.H_gamma_2_1: 0.0947, peptide.H_1: 0.2329, sidechain.H_delta_1_2: 0.0226, peptide.C: 0.6123, sidechain.H_gamma_1_2: 0.0201, sidechain.C_delta_1: -0.0908, peptide.H_alpha: 0.1031, sidechain.H_beta: 0.0213, peptide.C_alpha: 0.0257, sidechain.H_delta_1_1: 0.0226, sidechain.H_gamma_1_3: 0.0201, peptide.N: 0.0311, peptide.H_2: 0.2329, sidechain.H_gamma_2_2: 0.0947, peptide.H_3: 0.2329, } name = 'isoleucine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/isoleucine_nt_ct_noh0000644000076600000240000000027611662461637022602 0ustar hinsenstaff00000000000000sidechain = Group('ile_sidechain_noh') peptide = Group('peptide_nt_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ile' name = 'isoleucine' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/isoleucine_nt_noh0000644000076600000240000000063011662461637022106 0ustar hinsenstaff00000000000000sidechain = Group('ile_sidechain_noh') peptide = Group('peptide_nt_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ile' amber_charge = {peptide.C: 0.6123, sidechain.C_gamma_1: -0.0387, peptide.O: -0.5713, peptide.N: 0.0311, sidechain.C_gamma_2: -0.372, sidechain.C_beta: 0.1885, peptide.C_alpha: 0.0257, sidechain.C_delta_1: -0.0908, } name = 'isoleucine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/isoleucine_nt_uni0000644000076600000240000000071311662461637022117 0ustar hinsenstaff00000000000000sidechain = Group('ile_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ile' amber91_charge = {peptide.N: -0.263, sidechain.C_gamma_2: 0.001, peptide.O: -0.5, sidechain.C_beta: 0.03, sidechain.C_gamma_1: 0.017, peptide.H_1: 0.312, peptide.C_alpha: 0.254, peptide.H_2: 0.312, sidechain.C_delta_1: -0.001, peptide.H_3: 0.312, peptide.C: 0.526, } name = 'isoleucine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/isoleucine_nt_uni20000644000076600000240000000071311662461637022201 0ustar hinsenstaff00000000000000sidechain = Group('ile_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ile' amber91_charge = {sidechain.C_gamma_2: 0.001, sidechain.C_delta_1: -0.001, sidechain.C_gamma_1: 0.017, sidechain.C_beta: 0.03, peptide.C: 0.526, peptide.H_2: 0.312, peptide.H_3: 0.312, peptide.N: -0.263, peptide.O: -0.5, peptide.C_alpha: 0.254, peptide.H_1: 0.312, } name = 'isoleucine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/isoleucine_nt_uni30000644000076600000240000000071311662461637022202 0ustar hinsenstaff00000000000000sidechain = Group('ile_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ile' amber91_charge = {peptide.N: -0.263, sidechain.C_gamma_2: 0.001, peptide.O: -0.5, sidechain.C_beta: 0.03, sidechain.C_gamma_1: 0.017, peptide.H_1: 0.312, peptide.C_alpha: 0.254, peptide.H_2: 0.312, sidechain.C_delta_1: -0.001, peptide.H_3: 0.312, peptide.C: 0.526, } name = 'isoleucine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/isoleucine_uni0000644000076600000240000000064211662461637021417 0ustar hinsenstaff00000000000000sidechain = Group('ile_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ile' name = 'isoleucine' chain_links = [peptide.N, peptide.C] amber91_charge = {peptide.H: 0.248, sidechain.C_gamma_2: 0.001, peptide.C: 0.526, peptide.N: -0.52, sidechain.C_gamma_1: 0.017, sidechain.C_delta_1: -0.001, peptide.O: -0.5, peptide.C_alpha: 0.199, sidechain.C_beta: 0.03, } MMTK-2.7.9/MMTK/Database/Groups/isoleucine_uni20000644000076600000240000000064211662461637021501 0ustar hinsenstaff00000000000000sidechain = Group('ile_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ile' amber91_charge = {peptide.H: 0.248, sidechain.C_beta: 0.03, peptide.N: -0.52, peptide.C_alpha: 0.199, peptide.O: -0.5, sidechain.C_gamma_2: 0.001, sidechain.C_delta_1: -0.001, sidechain.C_gamma_1: 0.017, peptide.C: 0.526, } name = 'isoleucine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/isoleucine_uni30000644000076600000240000000064211662461637021502 0ustar hinsenstaff00000000000000sidechain = Group('ile_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ile' name = 'isoleucine' chain_links = [peptide.N, peptide.C] amber91_charge = {peptide.H: 0.248, sidechain.C_gamma_2: 0.001, peptide.C: 0.526, peptide.N: -0.52, sidechain.C_gamma_1: 0.017, sidechain.C_delta_1: -0.001, peptide.O: -0.5, peptide.C_alpha: 0.199, sidechain.C_beta: 0.03, } MMTK-2.7.9/MMTK/Database/Groups/leu_sidechain0000644000076600000240000000305612041514571021167 0ustar hinsenstaff00000000000000H_gamma = Atom('h') H_delta_1_2 = Atom('h') H_delta_1_3 = Atom('h') H_delta_1_1 = Atom('h') C_beta = Atom('c') H_delta_2_1 = Atom('h') H_delta_2_3 = Atom('h') H_delta_2_2 = Atom('h') C_gamma = Atom('c') C_delta_2 = Atom('c') C_delta_1 = Atom('c') H_beta_3 = Atom('h') H_beta_2 = Atom('h') bonds = [Bond(H_beta_2, C_beta), Bond(H_beta_3, C_beta), Bond(C_gamma, C_beta), Bond(H_gamma, C_gamma), Bond(C_delta_1, C_gamma), Bond(H_delta_1_1, C_delta_1), Bond(H_delta_1_2, C_delta_1), Bond(H_delta_1_3, C_delta_1), Bond(C_delta_2, C_gamma), Bond(H_delta_2_1, C_delta_2), Bond(H_delta_2_2, C_delta_2), Bond(H_delta_2_3, C_delta_2), ] name = 'leu_sidechain' pdbmap = [('LEU', {'HG': H_gamma, '2HB': H_beta_2, 'CD2': C_delta_2, 'CD1': C_delta_1, '3HD2': H_delta_2_3, '3HB': H_beta_3, '3HD1': H_delta_1_3, '2HD1': H_delta_1_2, 'CG': C_gamma, '1HD1': H_delta_1_1, 'CB': C_beta, '2HD2': H_delta_2_2, '1HD2': H_delta_2_1, }, ), ] pdb_alternative = {'1HB': '3HB', 'HB2': '2HB', 'HB3': '3HB', 'HB1': '3HB', 'HD11': '1HD1', 'HD12': '2HD1', 'HD13': '3HD1', 'HD21': '1HD2', 'HD23': '3HD2', 'HD22': '2HD2', } amber_atom_type = {H_delta_1_1: 'HC', H_delta_1_3: 'HC', H_gamma: 'HC', C_delta_1: 'CT', H_beta_2: 'HC', C_beta: 'CT', C_delta_2: 'CT', H_delta_1_2: 'HC', H_delta_2_1: 'HC', H_beta_3: 'HC', C_gamma: 'CT', H_delta_2_3: 'HC', H_delta_2_2: 'HC', } amber12_atom_type = {H_delta_1_1: 'HC', H_delta_1_3: 'HC', H_gamma: 'HC', C_delta_1: 'CT', H_beta_2: 'HC', C_beta: '2C', C_delta_2: 'CT', H_delta_1_2: 'HC', H_delta_2_1: 'HC', H_beta_3: 'HC', C_gamma: '3C', H_delta_2_3: 'HC', H_delta_2_2: 'HC', } MMTK-2.7.9/MMTK/Database/Groups/leu_sidechain_noh0000644000076600000240000000056711662461637022053 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_1 = Atom('CH3') C_delta_2 = Atom('CH3') C_gamma = Atom('CH') bonds = [Bond(C_gamma, C_beta), Bond(C_delta_1, C_gamma), Bond(C_delta_2, C_gamma), ] pdbmap = [('LEU', {'CD2': C_delta_2, 'CD1': C_delta_1, 'CG': C_gamma, 'CB': C_beta, }, ), ] amber_atom_type = {C_gamma: 'CT', C_delta_1: 'CT', C_delta_2: 'CT', C_beta: 'CT', } name = 'leu_sidechain' MMTK-2.7.9/MMTK/Database/Groups/leu_sidechain_uni0000644000076600000240000000107511662461637022055 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_1 = Atom('CH3') C_delta_2 = Atom('CH3') C_gamma = Atom('CH') bonds = [Bond(C_gamma, C_beta), Bond(C_delta_1, C_gamma), Bond(C_delta_2, C_gamma), ] pdbmap = [('LEU', {'CD2': C_delta_2, 'CD1': C_delta_1, 'CG': C_gamma, 'CB': C_beta, }, ), ] pdb_alternative = {'HB1': '3HB', } name = 'leu_sidechain' opls_atom_type = { C_beta: 'C2', C_gamma: 'CZ', C_delta_1: 'C3', C_delta_2: 'C3' } opls_charge = { C_beta: 0.0, C_gamma: 0.0, C_delta_1: 0.0, C_delta_2: 0.0 } amber91_atom_type = {C_delta_2: 'C3', C_gamma: 'CH', C_beta: 'C2', C_delta_1: 'C3', } MMTK-2.7.9/MMTK/Database/Groups/leu_sidechain_uni20000644000076600000240000000063411662461637022137 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_1 = Atom('CH3') C_delta_2 = Atom('CH3') C_gamma = Atom('CH') bonds = [Bond(C_gamma, C_beta), Bond(C_delta_1, C_gamma), Bond(C_delta_2, C_gamma), ] pdbmap = [('LEU', {'CD2': C_delta_2, 'CD1': C_delta_1, 'CG': C_gamma, 'CB': C_beta, }, ), ] pdb_alternative = {'HB1': '3HB', } amber91_atom_type = {C_delta_2: 'C3', C_gamma: 'CH', C_beta: 'C2', C_delta_1: 'C3', } name = 'leu_sidechain' MMTK-2.7.9/MMTK/Database/Groups/leu_sidechain_uni30000644000076600000240000000107511662461637022140 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_1 = Atom('CH3') C_delta_2 = Atom('CH3') C_gamma = Atom('CH') bonds = [Bond(C_gamma, C_beta), Bond(C_delta_1, C_gamma), Bond(C_delta_2, C_gamma), ] pdbmap = [('LEU', {'CD2': C_delta_2, 'CD1': C_delta_1, 'CG': C_gamma, 'CB': C_beta, }, ), ] pdb_alternative = {'HB1': '3HB', } name = 'leu_sidechain' opls_atom_type = { C_beta: 'C2', C_gamma: 'CZ', C_delta_1: 'C3', C_delta_2: 'C3' } opls_charge = { C_beta: 0.0, C_gamma: 0.0, C_delta_1: 0.0, C_delta_2: 0.0 } amber91_atom_type = {C_delta_2: 'C3', C_gamma: 'CH', C_beta: 'C2', C_delta_1: 'C3', } MMTK-2.7.9/MMTK/Database/Groups/leucine0000644000076600000240000000127011777301631020021 0ustar hinsenstaff00000000000000sidechain = Group('leu_sidechain') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Leu' amber_charge = {sidechain.H_delta_2_2: 0.1, sidechain.H_beta_2: 0.0457, sidechain.C_gamma: 0.3531, peptide.O: -0.5679, sidechain.C_beta: -0.1102, sidechain.H_delta_1_2: 0.1, peptide.H: 0.2719, peptide.C: 0.5973, peptide.H_alpha: 0.0922, sidechain.C_delta_2: -0.4121, sidechain.H_delta_2_1: 0.1, sidechain.H_delta_2_3: 0.1, peptide.N: -0.4157, sidechain.H_beta_3: 0.0457, sidechain.H_gamma: -0.0361, sidechain.H_delta_1_3: 0.1, peptide.C_alpha: -0.0518, sidechain.C_delta_1: -0.4121, sidechain.H_delta_1_1: 0.1, } name = 'leucine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/leucine_calpha0000644000076600000240000000020011662461637021327 0ustar hinsenstaff00000000000000symbol = 'Leu' name = 'leucine' C_alpha = Atom('leucine') pdbmap = [('LEU', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/leucine_ct0000644000076600000240000000133611777301631020512 0ustar hinsenstaff00000000000000sidechain = Group('leu_sidechain') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Leu' amber_charge = {peptide.N: -0.3821, sidechain.H_beta_3: 0.0974, sidechain.C_gamma: 0.3706, peptide.C: 0.8326, sidechain.H_delta_2_3: 0.1038, peptide.H: 0.2681, sidechain.H_delta_1_3: 0.1038, peptide.C_alpha: -0.2847, peptide.H_alpha: 0.1346, sidechain.C_delta_1: -0.4163, sidechain.C_delta_2: -0.4163, sidechain.C_beta: -0.2469, sidechain.H_delta_1_1: 0.1038, peptide.O: -0.8199, sidechain.H_delta_2_2: 0.1038, sidechain.H_delta_1_2: 0.1038, sidechain.H_beta_2: 0.0974, sidechain.H_delta_2_1: 0.1038, sidechain.H_gamma: -0.0374, peptide.O_2: -0.8199, } name = 'leucine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/leucine_ct_noh0000644000076600000240000000065411662461637021366 0ustar hinsenstaff00000000000000sidechain = Group('leu_sidechain_noh') peptide = Group('peptide_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Leu' amber_charge = {sidechain.C_beta: -0.2469, peptide.N: -0.3821, peptide.C: 0.8326, peptide.O: -0.8199, peptide.C_alpha: -0.2847, sidechain.C_delta_2: -0.4163, peptide.O_2: -0.8199, sidechain.C_delta_1: -0.4163, sidechain.C_gamma: 0.3706, } name = 'leucine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/leucine_ct_uni0000644000076600000240000000066411662461637021376 0ustar hinsenstaff00000000000000sidechain = Group('leu_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Leu' amber91_charge = {peptide.H: 0.248, peptide.C_alpha: 0.198, sidechain.C_delta_2: -0.014, sidechain.C_gamma: 0.054, peptide.O_2: -0.706, sidechain.C_delta_1: -0.014, peptide.N: -0.52, peptide.C: 0.444, sidechain.C_beta: 0.016, peptide.O: -0.706, } name = 'leucine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/leucine_ct_uni20000644000076600000240000000066411662461637021460 0ustar hinsenstaff00000000000000sidechain = Group('leu_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Leu' amber91_charge = {sidechain.C_gamma: 0.054, sidechain.C_delta_1: -0.014, peptide.C: 0.444, sidechain.C_beta: 0.016, peptide.H: 0.248, peptide.N: -0.52, peptide.O_2: -0.706, sidechain.C_delta_2: -0.014, peptide.C_alpha: 0.198, peptide.O: -0.706, } name = 'leucine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/leucine_ct_uni30000644000076600000240000000066411662461637021461 0ustar hinsenstaff00000000000000sidechain = Group('leu_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Leu' amber91_charge = {peptide.H: 0.248, peptide.C_alpha: 0.198, sidechain.C_delta_2: -0.014, sidechain.C_gamma: 0.054, peptide.O_2: -0.706, sidechain.C_delta_1: -0.014, peptide.N: -0.52, peptide.C: 0.444, sidechain.C_beta: 0.016, peptide.O: -0.706, } name = 'leucine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/leucine_noh0000644000076600000240000000063011662461637020672 0ustar hinsenstaff00000000000000sidechain = Group('leu_sidechain_noh') peptide = Group('peptide_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Leu' amber_charge = {peptide.C: 0.5973, peptide.O: -0.5679, sidechain.C_gamma: 0.3531, peptide.N: -0.4157, peptide.C_alpha: -0.0518, sidechain.C_delta_2: -0.4121, sidechain.C_beta: -0.1102, sidechain.C_delta_1: -0.4121, } name = 'leucine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/leucine_nt0000644000076600000240000000135211777301631020523 0ustar hinsenstaff00000000000000sidechain = Group('leu_sidechain') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Leu' amber_charge = {sidechain.H_gamma: -0.038, sidechain.H_beta_3: 0.0256, sidechain.C_gamma: 0.3421, sidechain.H_delta_2_1: 0.098, peptide.N: 0.101, sidechain.C_beta: -0.0244, sidechain.H_delta_1_1: 0.098, sidechain.H_delta_1_3: 0.098, sidechain.C_delta_1: -0.4106, peptide.C: 0.6123, peptide.H_3: 0.2148, peptide.C_alpha: 0.0104, sidechain.C_delta_2: -0.4104, sidechain.H_beta_2: 0.0256, sidechain.H_delta_2_2: 0.098, peptide.H_alpha: 0.1053, sidechain.H_delta_1_2: 0.098, sidechain.H_delta_2_3: 0.098, peptide.H_1: 0.2148, peptide.O: -0.5713, peptide.H_2: 0.2148, } name = 'leucine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/leucine_nt_ct_noh0000644000076600000240000000027311662461637022064 0ustar hinsenstaff00000000000000sidechain = Group('leu_sidechain_noh') peptide = Group('peptide_nt_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Leu' name = 'leucine' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/leucine_nt_noh0000644000076600000240000000062311662461637021375 0ustar hinsenstaff00000000000000sidechain = Group('leu_sidechain_noh') peptide = Group('peptide_nt_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Leu' amber_charge = {sidechain.C_gamma: 0.3421, peptide.C_alpha: 0.0104, sidechain.C_delta_1: -0.4106, peptide.O: -0.5713, peptide.N: 0.101, peptide.C: 0.6123, sidechain.C_delta_2: -0.4104, sidechain.C_beta: -0.0244, } name = 'leucine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/leucine_nt_uni0000644000076600000240000000071011662461637021401 0ustar hinsenstaff00000000000000sidechain = Group('leu_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Leu' amber91_charge = {sidechain.C_delta_1: -0.014, peptide.C_alpha: 0.259, peptide.H_1: 0.312, sidechain.C_delta_2: -0.014, peptide.N: -0.263, sidechain.C_beta: 0.016, peptide.C: 0.526, sidechain.C_gamma: 0.054, peptide.O: -0.5, peptide.H_2: 0.312, peptide.H_3: 0.312, } name = 'leucine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/leucine_nt_uni20000644000076600000240000000071011662461637021463 0ustar hinsenstaff00000000000000sidechain = Group('leu_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Leu' amber91_charge = {peptide.H_2: 0.312, peptide.N: -0.263, sidechain.C_delta_2: -0.014, peptide.C: 0.526, sidechain.C_gamma: 0.054, sidechain.C_delta_1: -0.014, peptide.H_1: 0.312, peptide.O: -0.5, sidechain.C_beta: 0.016, peptide.H_3: 0.312, peptide.C_alpha: 0.259, } name = 'leucine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/leucine_nt_uni30000644000076600000240000000071011662461637021464 0ustar hinsenstaff00000000000000sidechain = Group('leu_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Leu' amber91_charge = {sidechain.C_delta_1: -0.014, peptide.C_alpha: 0.259, peptide.H_1: 0.312, sidechain.C_delta_2: -0.014, peptide.N: -0.263, sidechain.C_beta: 0.016, peptide.C: 0.526, sidechain.C_gamma: 0.054, peptide.O: -0.5, peptide.H_2: 0.312, peptide.H_3: 0.312, } name = 'leucine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/leucine_uni0000644000076600000240000000063711662461637020710 0ustar hinsenstaff00000000000000sidechain = Group('leu_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Leu' name = 'leucine' chain_links = [peptide.N, peptide.C] amber91_charge = {peptide.O: -0.5, sidechain.C_beta: 0.016, sidechain.C_delta_1: -0.014, sidechain.C_delta_2: -0.014, peptide.C_alpha: 0.204, sidechain.C_gamma: 0.054, peptide.H: 0.248, peptide.C: 0.526, peptide.N: -0.52, } MMTK-2.7.9/MMTK/Database/Groups/leucine_uni20000644000076600000240000000063711662461637020772 0ustar hinsenstaff00000000000000sidechain = Group('leu_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Leu' amber91_charge = {peptide.C_alpha: 0.204, sidechain.C_gamma: 0.054, peptide.O: -0.5, peptide.N: -0.52, sidechain.C_delta_1: -0.014, sidechain.C_delta_2: -0.014, peptide.H: 0.248, sidechain.C_beta: 0.016, peptide.C: 0.526, } name = 'leucine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/leucine_uni30000644000076600000240000000063711662461637020773 0ustar hinsenstaff00000000000000sidechain = Group('leu_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Leu' name = 'leucine' chain_links = [peptide.N, peptide.C] amber91_charge = {peptide.O: -0.5, sidechain.C_beta: 0.016, sidechain.C_delta_1: -0.014, sidechain.C_delta_2: -0.014, peptide.C_alpha: 0.204, sidechain.C_gamma: 0.054, peptide.H: 0.248, peptide.C: 0.526, peptide.N: -0.52, } MMTK-2.7.9/MMTK/Database/Groups/lys_sidechain0000644000076600000240000000355712041514571021217 0ustar hinsenstaff00000000000000H_epsilon_2 = Atom('h') H_epsilon_3 = Atom('h') C_epsilon = Atom('c') C_beta = Atom('c') H_delta_2 = Atom('h') H_delta_3 = Atom('h') C_delta = Atom('c') N_zeta = Atom('n') H_gamma_3 = Atom('h') H_gamma_2 = Atom('h') C_gamma = Atom('c') H_zeta_3 = Atom('h') H_zeta_2 = Atom('h') H_zeta_1 = Atom('h') H_beta_3 = Atom('h') H_beta_2 = Atom('h') bonds = [Bond(H_beta_2, C_beta), Bond(H_beta_3, C_beta), Bond(C_gamma, C_beta), Bond(H_gamma_2, C_gamma), Bond(H_gamma_3, C_gamma), Bond(C_delta, C_gamma), Bond(H_delta_2, C_delta), Bond(H_delta_3, C_delta), Bond(C_epsilon, C_delta), Bond(H_epsilon_2, C_epsilon), Bond(H_epsilon_3, C_epsilon), Bond(N_zeta, C_epsilon), Bond(H_zeta_1, N_zeta), Bond(H_zeta_2, N_zeta), Bond(H_zeta_3, N_zeta), ] name = 'lys_sidechain' pdbmap = [('LYS', {'2HG': H_gamma_2, '2HD': H_delta_2, '2HE': H_epsilon_2, '2HB': H_beta_2, '3HG': H_gamma_3, '3HE': H_epsilon_3, '3HD': H_delta_3, 'NZ': N_zeta, 'CD': C_delta, 'CE': C_epsilon, 'CG': C_gamma, '3HZ': H_zeta_3, 'CB': C_beta, '1HZ': H_zeta_1, '2HZ': H_zeta_2, '3HB': H_beta_3, }, ), ] pdb_alternative = {'1HB': '3HB', '1HG': '3HG', '1HD': '3HD', '1HE': '3HE', 'HB2': '2HB', 'HB3': '3HB', 'HB1': '3HB', 'HZ2': '2HZ', 'HZ3': '3HZ', 'HD3': '3HD', 'HZ1': '1HZ', 'HG1': '3HG', 'HD2': '2HD', 'HG3': '3HG', 'HG2': '2HG', 'HE3': '3HE', 'HD1': '3HD', 'HE1': '3HE', 'HE2': '2HE', } amber_atom_type = {H_gamma_2: 'HC', H_gamma_3: 'HC', C_delta: 'CT', N_zeta: 'N3', H_epsilon_3: 'HP', C_beta: 'CT', H_epsilon_2: 'HP', H_zeta_2: 'H', H_zeta_3: 'H', H_beta_3: 'HC', H_delta_2: 'HC', H_beta_2: 'HC', C_gamma: 'CT', H_zeta_1: 'H', H_delta_3: 'HC', C_epsilon: 'CT', } amber12_atom_type = {H_gamma_2: 'HC', H_gamma_3: 'HC', C_delta: 'C8', N_zeta: 'N3', H_epsilon_3: 'HP', C_beta: 'C8', H_epsilon_2: 'HP', H_zeta_2: 'H', H_zeta_3: 'H', H_beta_3: 'HC', H_delta_2: 'HC', H_beta_2: 'HC', C_gamma: 'C8', H_zeta_1: 'H', H_delta_3: 'HC', C_epsilon: 'C8', } MMTK-2.7.9/MMTK/Database/Groups/lys_sidechain_neutral0000644000076600000240000000352012041514571022737 0ustar hinsenstaff00000000000000H_epsilon_2 = Atom('h') H_epsilon_3 = Atom('h') C_epsilon = Atom('c') C_beta = Atom('c') H_delta_2 = Atom('h') H_delta_3 = Atom('h') C_delta = Atom('c') N_zeta = Atom('n') H_gamma_3 = Atom('h') H_gamma_2 = Atom('h') C_gamma = Atom('c') H_zeta_3 = Atom('h') H_zeta_2 = Atom('h') H_beta_3 = Atom('h') H_beta_2 = Atom('h') bonds = [Bond(H_beta_2, C_beta), Bond(H_beta_3, C_beta), Bond(C_gamma, C_beta), Bond(H_gamma_2, C_gamma), Bond(H_gamma_3, C_gamma), Bond(C_delta, C_gamma), Bond(H_delta_2, C_delta), Bond(H_delta_3, C_delta), Bond(C_epsilon, C_delta), Bond(H_epsilon_2, C_epsilon), Bond(H_epsilon_3, C_epsilon), Bond(N_zeta, C_epsilon), Bond(H_zeta_2, N_zeta), Bond(H_zeta_3, N_zeta) ] name = 'lys_sidechain_neutral' pdbmap = [('LYP', {'2HG': H_gamma_2, '2HD': H_delta_2, '2HE': H_epsilon_2, '2HB': H_beta_2, '3HG': H_gamma_3, '3HE': H_epsilon_3, '3HD': H_delta_3, 'NZ': N_zeta, 'CD': C_delta, 'CE': C_epsilon, 'CG': C_gamma, '3HZ': H_zeta_3, 'CB': C_beta, '2HZ': H_zeta_2, '3HB': H_beta_3, }, ), ] pdb_alternative = {'1HB': '3HB', '1HG': '3HG', '1HD': '3HD', '1HE': '3HE', 'HB2': '2HB', 'HB3': '3HB', 'HB1': '3HB', 'HZ2': '2HZ', 'HZ3': '3HZ', 'HD3': '3HD', 'HZ1': '1HZ', 'HG1': '3HG', 'HD2': '2HD', 'HG3': '3HG', 'HG2': '2HG', 'HE3': '3HE', 'HD1': '3HD', 'HE1': '3HE', 'HE2': '2HE', } amber_atom_type = {H_gamma_2: 'HC', H_gamma_3: 'HC', C_delta: 'CT', N_zeta: 'N3', H_epsilon_3: 'HP', C_beta: 'CT', H_epsilon_2: 'HP', H_zeta_2: 'H', H_zeta_3: 'H', H_beta_3: 'HC', H_delta_2: 'HC', H_beta_2: 'HC', C_gamma: 'CT', H_delta_3: 'HC', C_epsilon: 'CT', } amber12_atom_type = {H_gamma_2: 'HC', H_gamma_3: 'HC', C_delta: 'C8', N_zeta: 'N3', H_epsilon_3: 'HP', C_beta: 'C8', H_epsilon_2: 'HP', H_zeta_2: 'H', H_zeta_3: 'H', H_beta_3: 'HC', H_delta_2: 'HC', H_beta_2: 'HC', C_gamma: 'C8', H_delta_3: 'HC', C_epsilon: 'C8', } MMTK-2.7.9/MMTK/Database/Groups/lys_sidechain_noh0000644000076600000240000000067011662461637022070 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta = Atom('CH2') C_epsilon = Atom('CH2') C_gamma = Atom('CH2') N_zeta = Atom('NH3') bonds = [Bond(C_gamma, C_beta), Bond(C_delta, C_gamma), Bond(C_epsilon, C_delta), Bond(N_zeta, C_epsilon), ] pdbmap = [('LYS', {'CD': C_delta, 'CE': C_epsilon, 'CG': C_gamma, 'NZ': N_zeta, 'CB': C_beta, }, ), ] name = 'lys_sidechain' amber_atom_type = {C_gamma: 'CT', C_beta: 'CT', C_delta: 'CT', C_epsilon: 'CT', N_zeta: 'N3', } MMTK-2.7.9/MMTK/Database/Groups/lys_sidechain_uni0000644000076600000240000000214111662461637022072 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta = Atom('CH2') C_epsilon = Atom('CH2') C_gamma = Atom('CH2') H_zeta_1 = Atom('H') H_zeta_2 = Atom('H') H_zeta_3 = Atom('H') N_zeta = Atom('N') bonds = [Bond(C_gamma, C_beta), Bond(C_delta, C_gamma), Bond(C_epsilon, C_delta), Bond(N_zeta, C_epsilon), Bond(H_zeta_1, N_zeta), Bond(H_zeta_2, N_zeta), Bond(H_zeta_3, N_zeta), ] pdb_alternative = {'HG1': '3HG', 'HB1': '3HB', 'HZ3': '3HZ', 'HZ2': '2HZ', 'HD1': '3HD', 'HE1': '3HE', 'HZ1': '1HZ', '1HNZ': '1HZ', '2HNZ': '2HZ', '3HNZ': '3HZ' } pdbmap = [('LYS', {'CD': C_delta, 'CE': C_epsilon, 'CG': C_gamma, '3HZ': H_zeta_3, 'CB': C_beta, '1HZ': H_zeta_1, '2HZ': H_zeta_2, 'NZ': N_zeta, }, ), ] name = 'lys_sidechain' opls_atom_type = { C_beta: 'C2', C_gamma: 'C2', C_delta: 'C2', C_epsilon: 'C2', N_zeta: 'N3', H_zeta_1: 'H3', H_zeta_2: 'H3', H_zeta_3: 'H3' } opls_charge = { C_beta: 0.0, C_gamma: 0.0, C_delta: 0.0, C_epsilon: 0.31, N_zeta: -0.3, H_zeta_1: 0.33, H_zeta_2: 0.33, H_zeta_3: 0.33 } amber91_atom_type = {N_zeta: 'N3', C_gamma: 'C2', H_zeta_2: 'H3', C_delta: 'C2', H_zeta_3: 'H3', H_zeta_1: 'H3', C_epsilon: 'C2', C_beta: 'C2', } MMTK-2.7.9/MMTK/Database/Groups/lys_sidechain_uni20000644000076600000240000000143111662461637022155 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta = Atom('CH2') C_epsilon = Atom('CH2') C_gamma = Atom('CH2') H_zeta_1 = Atom('H') H_zeta_2 = Atom('H') H_zeta_3 = Atom('H') N_zeta = Atom('N') bonds = [Bond(C_gamma, C_beta), Bond(C_delta, C_gamma), Bond(C_epsilon, C_delta), Bond(N_zeta, C_epsilon), Bond(H_zeta_1, N_zeta), Bond(H_zeta_2, N_zeta), Bond(H_zeta_3, N_zeta), ] pdb_alternative = {'HG1': '3HG', 'HB1': '3HB', 'HZ3': '3HZ', 'HZ2': '2HZ', 'HD1': '3HD', 'HE1': '3HE', 'HZ1': '1HZ', } pdbmap = [('LYS', {'CD': C_delta, 'CE': C_epsilon, 'CG': C_gamma, '3HZ': H_zeta_3, 'CB': C_beta, '1HZ': H_zeta_1, '2HZ': H_zeta_2, 'NZ': N_zeta, }, ), ] amber91_atom_type = {N_zeta: 'N3', C_gamma: 'C2', H_zeta_2: 'H3', C_delta: 'C2', H_zeta_3: 'H3', H_zeta_1: 'H3', C_epsilon: 'C2', C_beta: 'C2', } name = 'lys_sidechain' MMTK-2.7.9/MMTK/Database/Groups/lys_sidechain_uni30000644000076600000240000000214111662461637022155 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta = Atom('CH2') C_epsilon = Atom('CH2') C_gamma = Atom('CH2') H_zeta_1 = Atom('H') H_zeta_2 = Atom('H') H_zeta_3 = Atom('H') N_zeta = Atom('N') bonds = [Bond(C_gamma, C_beta), Bond(C_delta, C_gamma), Bond(C_epsilon, C_delta), Bond(N_zeta, C_epsilon), Bond(H_zeta_1, N_zeta), Bond(H_zeta_2, N_zeta), Bond(H_zeta_3, N_zeta), ] pdb_alternative = {'HG1': '3HG', 'HB1': '3HB', 'HZ3': '3HZ', 'HZ2': '2HZ', 'HD1': '3HD', 'HE1': '3HE', 'HZ1': '1HZ', '1HNZ': '1HZ', '2HNZ': '2HZ', '3HNZ': '3HZ' } pdbmap = [('LYS', {'CD': C_delta, 'CE': C_epsilon, 'CG': C_gamma, '3HZ': H_zeta_3, 'CB': C_beta, '1HZ': H_zeta_1, '2HZ': H_zeta_2, 'NZ': N_zeta, }, ), ] name = 'lys_sidechain' opls_atom_type = { C_beta: 'C2', C_gamma: 'C2', C_delta: 'C2', C_epsilon: 'C2', N_zeta: 'N3', H_zeta_1: 'H3', H_zeta_2: 'H3', H_zeta_3: 'H3' } opls_charge = { C_beta: 0.0, C_gamma: 0.0, C_delta: 0.0, C_epsilon: 0.31, N_zeta: -0.3, H_zeta_1: 0.33, H_zeta_2: 0.33, H_zeta_3: 0.33 } amber91_atom_type = {N_zeta: 'N3', C_gamma: 'C2', H_zeta_2: 'H3', C_delta: 'C2', H_zeta_3: 'H3', H_zeta_1: 'H3', C_epsilon: 'C2', C_beta: 'C2', } MMTK-2.7.9/MMTK/Database/Groups/lysine0000644000076600000240000000141211777301631017676 0ustar hinsenstaff00000000000000sidechain = Group('lys_sidechain') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Lys' amber_charge = {sidechain.C_delta: -0.0479, peptide.O: -0.5894, sidechain.H_delta_3: 0.0621, sidechain.H_delta_2: 0.0621, sidechain.H_gamma_2: 0.0103, peptide.N: -0.3479, sidechain.H_zeta_3: 0.34, sidechain.N_zeta: -0.3854, sidechain.H_epsilon_2: 0.1135, peptide.H_alpha: 0.1426, sidechain.C_beta: -0.0094, sidechain.H_epsilon_3: 0.1135, sidechain.H_beta_3: 0.0362, sidechain.H_zeta_1: 0.34, sidechain.C_epsilon: -0.0143, peptide.C_alpha: -0.24, sidechain.C_gamma: 0.0187, sidechain.H_beta_2: 0.0362, peptide.H: 0.2747, sidechain.H_zeta_2: 0.34, sidechain.H_gamma_3: 0.0103, peptide.C: 0.7341, } name = 'lysine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/lysine_calpha0000644000076600000240000000017611662461637021222 0ustar hinsenstaff00000000000000symbol = 'Lys' name = 'lysine' C_alpha = Atom('lysine') pdbmap = [('LYS', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/lysine_ct0000644000076600000240000000144611777301631020373 0ustar hinsenstaff00000000000000sidechain = Group('lys_sidechain') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Lys' amber_charge = {sidechain.C_beta: -0.0538, peptide.H_alpha: 0.1438, sidechain.H_delta_2: 0.0611, sidechain.N_zeta: -0.3741, peptide.N: -0.3481, sidechain.H_epsilon_3: 0.1121, sidechain.C_delta: -0.0392, peptide.C: 0.8488, peptide.C_alpha: -0.2903, sidechain.H_delta_3: 0.0611, sidechain.H_beta_2: 0.0482, sidechain.H_zeta_2: 0.3374, sidechain.H_zeta_3: 0.3374, peptide.O_2: -0.8252, peptide.H: 0.2764, sidechain.C_gamma: 0.0227, sidechain.H_gamma_3: 0.0134, sidechain.H_zeta_1: 0.3374, sidechain.H_beta_3: 0.0482, sidechain.C_epsilon: -0.0176, sidechain.H_epsilon_2: 0.1121, sidechain.H_gamma_2: 0.0134, peptide.O: -0.8252, } name = 'lysine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/lysine_ct_noh0000644000076600000240000000070411662461637021241 0ustar hinsenstaff00000000000000sidechain = Group('lys_sidechain_noh') peptide = Group('peptide_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Lys' amber_charge = {peptide.N: -0.3481, sidechain.C_gamma: 0.0227, sidechain.N_zeta: -0.3741, sidechain.C_delta: -0.0392, peptide.C: 0.8488, peptide.O_2: -0.8252, peptide.O: -0.8252, sidechain.C_epsilon: -0.0176, sidechain.C_beta: -0.0538, peptide.C_alpha: -0.2903, } name = 'lysine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/lysine_ct_uni0000644000076600000240000000103211662461637021243 0ustar hinsenstaff00000000000000sidechain = Group('lys_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Lys' amber91_charge = {peptide.O_2: -0.706, peptide.O: -0.706, peptide.C_alpha: 0.221, sidechain.C_beta: 0.039, sidechain.N_zeta: -0.272, sidechain.H_zeta_1: 0.311, sidechain.H_zeta_3: 0.311, peptide.H: 0.248, sidechain.H_zeta_2: 0.311, peptide.C: 0.444, sidechain.C_epsilon: 0.218, sidechain.C_gamma: 0.053, peptide.N: -0.52, sidechain.C_delta: 0.048, } name = 'lysine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/lysine_ct_uni20000644000076600000240000000103211662461637021325 0ustar hinsenstaff00000000000000sidechain = Group('lys_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Lys' amber91_charge = {peptide.N: -0.52, sidechain.H_zeta_1: 0.311, sidechain.H_zeta_2: 0.311, sidechain.C_epsilon: 0.218, sidechain.C_beta: 0.039, sidechain.C_gamma: 0.053, peptide.O: -0.706, sidechain.C_delta: 0.048, peptide.C_alpha: 0.221, peptide.O_2: -0.706, peptide.H: 0.248, peptide.C: 0.444, sidechain.H_zeta_3: 0.311, sidechain.N_zeta: -0.272, } name = 'lysine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/lysine_ct_uni30000644000076600000240000000103211662461637021326 0ustar hinsenstaff00000000000000sidechain = Group('lys_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Lys' amber91_charge = {peptide.O_2: -0.706, peptide.O: -0.706, peptide.C_alpha: 0.221, sidechain.C_beta: 0.039, sidechain.N_zeta: -0.272, sidechain.H_zeta_1: 0.311, sidechain.H_zeta_3: 0.311, peptide.H: 0.248, sidechain.H_zeta_2: 0.311, peptide.C: 0.444, sidechain.C_epsilon: 0.218, sidechain.C_gamma: 0.053, peptide.N: -0.52, sidechain.C_delta: 0.048, } name = 'lysine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/lysine_neutral0000644000076600000240000000153611777301631021437 0ustar hinsenstaff00000000000000sidechain = Group('lys_sidechain_neutral') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Lyp' amber_charge = { sidechain.C_delta: -0.03768, sidechain.H_delta_3: 0.01155, sidechain.H_delta_2: 0.01155, sidechain.H_gamma_2: 0.01041, sidechain.H_zeta_3: 0.38604, sidechain.N_zeta: -1.03581, sidechain.H_epsilon_2: -0.03358, sidechain.C_beta: -0.04845, sidechain.H_epsilon_3: -0.03358, sidechain.H_beta_3: 0.034, sidechain.C_epsilon: 0.32604, sidechain.C_gamma: 0.06612, sidechain.H_beta_2: 0.034, sidechain.H_zeta_2: 0.38604, sidechain.H_gamma_3: 0.01041, peptide.C: 0.59730, peptide.H_alpha: 0.09940, peptide.O: -0.56790, peptide.H: 0.27190, peptide.C_alpha: -0.07206,peptide.N: -0.41570,} name = 'lysine_neutral' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/lysine_neutral_calpha0000644000076600000240000000017611662461637022754 0ustar hinsenstaff00000000000000symbol = 'Lys' name = 'lysine' C_alpha = Atom('lysine') pdbmap = [('LYS', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/lysine_neutral_ct0000644000076600000240000000161311777301631022121 0ustar hinsenstaff00000000000000sidechain = Group('lys_sidechain_neutral') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Lyp' amber_charge = { sidechain.C_delta: -0.03768, sidechain.H_delta_3: 0.01155, sidechain.H_delta_2: 0.01155, sidechain.H_gamma_2: 0.01041, sidechain.H_zeta_3: 0.38604, sidechain.N_zeta: -1.03581, sidechain.H_epsilon_2: -0.03358, sidechain.C_beta: -0.04845, sidechain.H_epsilon_3: -0.03358, sidechain.H_beta_3: 0.034, sidechain.C_epsilon: 0.32604, sidechain.C_gamma: 0.06612, sidechain.H_beta_2: 0.034, sidechain.H_zeta_2: 0.38604, sidechain.H_gamma_3: 0.01041, peptide.O: -0.8252, peptide.H_alpha: 0.1438, peptide.N: -0.3481, peptide.O_2: -0.8252, peptide.H: 0.2764, peptide.C: 0.8488, peptide.C_alpha: -0.2903, } name = 'lysine_neutral' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/lysine_neutral_noh0000644000076600000240000000065611662461637022313 0ustar hinsenstaff00000000000000sidechain = Group('lys_sidechain_noh') peptide = Group('peptide_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Lys' amber_charge = {peptide.N: -0.3479, peptide.C: 0.7341, peptide.O: -0.5894, sidechain.C_gamma: 0.0187, sidechain.C_delta: -0.0479, peptide.C_alpha: -0.24, sidechain.N_zeta: -0.3854, sidechain.C_beta: -0.0094, sidechain.C_epsilon: -0.0143, } name = 'lysine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/lysine_neutral_nt0000644000076600000240000000163611777301631022141 0ustar hinsenstaff00000000000000sidechain = Group('lys_sidechain_neutral') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Lyp' amber_charge = { sidechain.C_delta: -0.03768, sidechain.H_delta_3: 0.01155, sidechain.H_delta_2: 0.01155, sidechain.H_gamma_2: 0.01041, sidechain.H_zeta_3: 0.38604, sidechain.N_zeta: -1.03581, sidechain.H_epsilon_2: -0.03358, sidechain.C_beta: -0.04845, sidechain.H_epsilon_3: -0.03358, sidechain.H_beta_3: 0.034, sidechain.C_epsilon: 0.32604, sidechain.C_gamma: 0.06612, sidechain.H_beta_2: 0.034, sidechain.H_zeta_2: 0.38604, sidechain.H_gamma_3: 0.01041, peptide.C_alpha: -0.0015, peptide.H_alpha: 0.118, peptide.H_1: 0.2165, peptide.H_2: 0.2165, peptide.C: 0.7214, peptide.H_3: 0.2165, peptide.O: -0.6013, peptide.N: 0.0966,} name = 'lysine_neutral' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/lysine_noh0000644000076600000240000000065611662461637020561 0ustar hinsenstaff00000000000000sidechain = Group('lys_sidechain_noh') peptide = Group('peptide_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Lys' amber_charge = {peptide.N: -0.3479, peptide.C: 0.7341, peptide.O: -0.5894, sidechain.C_gamma: 0.0187, sidechain.C_delta: -0.0479, peptide.C_alpha: -0.24, sidechain.N_zeta: -0.3854, sidechain.C_beta: -0.0094, sidechain.C_epsilon: -0.0143, } name = 'lysine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/lysine_nt0000644000076600000240000000147211777301631020405 0ustar hinsenstaff00000000000000sidechain = Group('lys_sidechain') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Lys' amber_charge = {peptide.H_2: 0.2165, sidechain.C_delta: -0.0608, peptide.C: 0.7214, sidechain.N_zeta: -0.3764, sidechain.H_beta_3: 0.0283, sidechain.H_beta_2: 0.0283, sidechain.H_zeta_2: 0.3382, sidechain.H_delta_3: 0.0633, sidechain.H_delta_2: 0.0633, sidechain.C_gamma: -0.0048, sidechain.H_zeta_1: 0.3382, sidechain.H_epsilon_3: 0.1171, sidechain.C_beta: 0.0212, peptide.H_3: 0.2165, sidechain.H_epsilon_2: 0.1171, sidechain.H_gamma_2: 0.0121, peptide.O: -0.6013, sidechain.C_epsilon: -0.0181, sidechain.H_zeta_3: 0.3382, peptide.N: 0.0966, sidechain.H_gamma_3: 0.0121, peptide.C_alpha: -0.0015, peptide.H_alpha: 0.118, peptide.H_1: 0.2165, } name = 'lysine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/lysine_nt_ct_noh0000644000076600000240000000027211662461637021742 0ustar hinsenstaff00000000000000sidechain = Group('lys_sidechain_noh') peptide = Group('peptide_nt_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Lys' name = 'lysine' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/lysine_nt_noh0000644000076600000240000000064311662461637021256 0ustar hinsenstaff00000000000000sidechain = Group('lys_sidechain_noh') peptide = Group('peptide_nt_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Lys' amber_charge = {sidechain.C_gamma: -0.16, peptide.C: 0.616, sidechain.N_zeta: -0.138, peptide.N: -0.263, sidechain.C_beta: -0.098, peptide.O: -0.504, sidechain.C_epsilon: -0.038, peptide.C_alpha: 0.151, sidechain.C_delta: -0.18, } name = 'lysine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/lysine_nt_uni0000644000076600000240000000105611662461637021264 0ustar hinsenstaff00000000000000sidechain = Group('lys_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Lys' amber91_charge = {sidechain.C_epsilon: 0.218, peptide.C_alpha: 0.282, sidechain.C_gamma: 0.053, peptide.O: -0.5, peptide.H_2: 0.312, sidechain.H_zeta_3: 0.311, sidechain.C_delta: 0.048, sidechain.N_zeta: -0.272, peptide.N: -0.263, sidechain.H_zeta_2: 0.311, peptide.C: 0.526, sidechain.C_beta: 0.039, sidechain.H_zeta_1: 0.311, peptide.H_3: 0.312, peptide.H_1: 0.312, } name = 'lysine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/lysine_nt_uni20000644000076600000240000000105611662461637021346 0ustar hinsenstaff00000000000000sidechain = Group('lys_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Lys' amber91_charge = {sidechain.N_zeta: -0.272, sidechain.H_zeta_2: 0.311, peptide.O: -0.5, peptide.H_3: 0.312, sidechain.C_gamma: 0.053, sidechain.C_delta: 0.048, peptide.N: -0.263, sidechain.H_zeta_3: 0.311, peptide.C: 0.526, sidechain.C_beta: 0.039, sidechain.H_zeta_1: 0.311, sidechain.C_epsilon: 0.218, peptide.C_alpha: 0.282, peptide.H_2: 0.312, peptide.H_1: 0.312, } name = 'lysine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/lysine_nt_uni30000644000076600000240000000105611662461637021347 0ustar hinsenstaff00000000000000sidechain = Group('lys_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Lys' amber91_charge = {sidechain.C_epsilon: 0.218, peptide.C_alpha: 0.282, sidechain.C_gamma: 0.053, peptide.O: -0.5, peptide.H_2: 0.312, sidechain.H_zeta_3: 0.311, sidechain.C_delta: 0.048, sidechain.N_zeta: -0.272, peptide.N: -0.263, sidechain.H_zeta_2: 0.311, peptide.C: 0.526, sidechain.C_beta: 0.039, sidechain.H_zeta_1: 0.311, peptide.H_3: 0.312, peptide.H_1: 0.312, } name = 'lysine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/lysine_uni0000644000076600000240000000100511662461637020555 0ustar hinsenstaff00000000000000sidechain = Group('lys_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Lys' name = 'lysine' chain_links = [peptide.N, peptide.C] amber91_charge = {peptide.O: -0.5, sidechain.C_gamma: 0.053, sidechain.N_zeta: -0.272, sidechain.H_zeta_1: 0.311, peptide.C_alpha: 0.227, peptide.C: 0.526, peptide.H: 0.248, sidechain.C_delta: 0.048, peptide.N: -0.52, sidechain.C_beta: 0.039, sidechain.C_epsilon: 0.218, sidechain.H_zeta_3: 0.311, sidechain.H_zeta_2: 0.311, } MMTK-2.7.9/MMTK/Database/Groups/lysine_uni20000644000076600000240000000100511662461637020637 0ustar hinsenstaff00000000000000sidechain = Group('lys_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Lys' amber91_charge = {peptide.N: -0.52, sidechain.C_beta: 0.039, peptide.H: 0.248, sidechain.C_epsilon: 0.218, peptide.O: -0.5, sidechain.H_zeta_1: 0.311, sidechain.N_zeta: -0.272, sidechain.H_zeta_2: 0.311, peptide.C: 0.526, sidechain.H_zeta_3: 0.311, sidechain.C_gamma: 0.053, sidechain.C_delta: 0.048, peptide.C_alpha: 0.227, } name = 'lysine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/lysine_uni30000644000076600000240000000100511662461637020640 0ustar hinsenstaff00000000000000sidechain = Group('lys_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Lys' name = 'lysine' chain_links = [peptide.N, peptide.C] amber91_charge = {peptide.O: -0.5, sidechain.C_gamma: 0.053, sidechain.N_zeta: -0.272, sidechain.H_zeta_1: 0.311, peptide.C_alpha: 0.227, peptide.C: 0.526, peptide.H: 0.248, sidechain.C_delta: 0.048, peptide.N: -0.52, sidechain.C_beta: 0.039, sidechain.C_epsilon: 0.218, sidechain.H_zeta_3: 0.311, sidechain.H_zeta_2: 0.311, } MMTK-2.7.9/MMTK/Database/Groups/met_sidechain0000644000076600000240000000247312041514571021171 0ustar hinsenstaff00000000000000H_epsilon_2 = Atom('h') H_epsilon_3 = Atom('h') C_epsilon = Atom('c') H_epsilon_1 = Atom('h') S_delta = Atom('s') C_beta = Atom('c') H_gamma_3 = Atom('h') H_gamma_2 = Atom('h') C_gamma = Atom('c') H_beta_3 = Atom('h') H_beta_2 = Atom('h') bonds = [Bond(H_beta_2, C_beta), Bond(H_beta_3, C_beta), Bond(C_gamma, C_beta), Bond(H_gamma_2, C_gamma), Bond(H_gamma_3, C_gamma), Bond(S_delta, C_gamma), Bond(C_epsilon, S_delta), Bond(H_epsilon_1, C_epsilon), Bond(H_epsilon_2, C_epsilon), Bond(H_epsilon_3, C_epsilon), ] name = 'met_sidechain' pdbmap = [('MET', {'SD': S_delta, '2HG': H_gamma_2, '2HE': H_epsilon_2, '2HB': H_beta_2, '1HE': H_epsilon_1, '3HG': H_gamma_3, '3HE': H_epsilon_3, '3HB': H_beta_3, 'CE': C_epsilon, 'CG': C_gamma, 'CB': C_beta, }, ), ] pdb_alternative = {'1HB': '3HB', '1HG': '3HG', 'HG1': '3HG', 'HB3': '3HB', 'HG3': '3HG', 'HB1': '3HB', 'HE3': '3HE', 'HE2': '2HE', 'HE1': '1HE', 'HG2': '2HG', 'HB2': '2HB', } amber_atom_type = {H_epsilon_1: 'H1', H_epsilon_3: 'H1', H_epsilon_2: 'H1', C_epsilon: 'CT', S_delta: 'S', H_gamma_3: 'H1', H_gamma_2: 'H1', H_beta_2: 'HC', H_beta_3: 'HC', C_gamma: 'CT', C_beta: 'CT', } amber12_atom_type = {H_epsilon_1: 'H1', H_epsilon_3: 'H1', H_epsilon_2: 'H1', C_epsilon: 'CT', S_delta: 'S', H_gamma_3: 'H1', H_gamma_2: 'H1', H_beta_2: 'HC', H_beta_3: 'HC', C_gamma: '2C', C_beta: '2C', } MMTK-2.7.9/MMTK/Database/Groups/met_sidechain_noh0000644000076600000240000000055311662461637022046 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_epsilon = Atom('CH3') C_gamma = Atom('CH2') S_delta = Atom('S') bonds = [Bond(C_gamma, C_beta), Bond(S_delta, C_gamma), Bond(C_epsilon, S_delta), ] pdbmap = [('MET', {'SD': S_delta, 'CE': C_epsilon, 'CG': C_gamma, 'CB': C_beta, }, ), ] amber_atom_type = {C_beta: 'CT', C_gamma: 'CT', C_epsilon: 'CT', S_delta: 'S', } name = 'met_sidechain' MMTK-2.7.9/MMTK/Database/Groups/met_sidechain_uni0000644000076600000240000000075411662461637022060 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_epsilon = Atom('CH3') C_gamma = Atom('CH2') S_delta = Atom('S') bonds = [Bond(C_gamma, C_beta), Bond(S_delta, C_gamma), Bond(C_epsilon, S_delta) ] pdbmap = [('MET', {'SD': S_delta, 'CE': C_epsilon, 'CG': C_gamma, 'CB': C_beta, }, ), ] pdb_alternative = {'HG1': '3HG', 'HB1': '3HB', } name = 'met_sidechain' opls_atom_type = { C_beta: 'C2', C_gamma: 'CQ', S_delta: 'S', C_epsilon: 'CW' } opls_charge = { C_beta: 0.0, C_gamma: 0.235, S_delta: -0.47, C_epsilon: 0.235 } MMTK-2.7.9/MMTK/Database/Groups/met_sidechain_uni20000644000076600000240000000043211662461637022133 0ustar hinsenstaff00000000000000C_epsilon = Atom('ch3') S_delta = Atom('s') C_beta = Atom('ch2') C_gamma = Atom('ch2') bonds = [Bond(C_gamma, C_beta), Bond(S_delta, C_gamma), Bond(C_epsilon, S_delta), ] pdbmap = [('MET', {'SD': S_delta, 'CE': C_epsilon, 'CG': C_gamma, 'CB': C_beta, }, ), ] name = 'met_sidechain' MMTK-2.7.9/MMTK/Database/Groups/met_sidechain_uni30000644000076600000240000000075411662461637022143 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_epsilon = Atom('CH3') C_gamma = Atom('CH2') S_delta = Atom('S') bonds = [Bond(C_gamma, C_beta), Bond(S_delta, C_gamma), Bond(C_epsilon, S_delta) ] pdbmap = [('MET', {'SD': S_delta, 'CE': C_epsilon, 'CG': C_gamma, 'CB': C_beta, }, ), ] pdb_alternative = {'HG1': '3HG', 'HB1': '3HB', } name = 'met_sidechain' opls_atom_type = { C_beta: 'C2', C_gamma: 'CQ', S_delta: 'S', C_epsilon: 'CW' } opls_charge = { C_beta: 0.0, C_gamma: 0.235, S_delta: -0.47, C_epsilon: 0.235 } MMTK-2.7.9/MMTK/Database/Groups/methionine0000644000076600000240000000121011777301631020526 0ustar hinsenstaff00000000000000sidechain = Group('met_sidechain') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Met' amber_charge = {sidechain.H_beta_2: 0.0241, sidechain.C_epsilon: -0.0536, peptide.H_alpha: 0.088, sidechain.H_epsilon_1: 0.0684, peptide.N: -0.4157, sidechain.H_epsilon_3: 0.0684, peptide.C_alpha: -0.0237, sidechain.H_gamma_3: 0.044, sidechain.C_gamma: 0.0018, sidechain.H_gamma_2: 0.044, peptide.C: 0.5973, peptide.H: 0.2719, sidechain.H_epsilon_2: 0.0684, sidechain.S_delta: -0.2737, peptide.O: -0.5679, sidechain.C_beta: 0.0342, sidechain.H_beta_3: 0.0241, } name = 'methionine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/methionine_calpha0000644000076600000240000000020611662461637022050 0ustar hinsenstaff00000000000000symbol = 'Met' name = 'methionine' C_alpha = Atom('methionine') pdbmap = [('MET', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/methionine_ct0000644000076600000240000000123611777301631021224 0ustar hinsenstaff00000000000000sidechain = Group('met_sidechain') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Met' amber_charge = {peptide.C: 0.8013, sidechain.H_gamma_3: 0.0317, peptide.H_alpha: 0.1277, sidechain.S_delta: -0.2692, peptide.N: -0.3821, sidechain.C_epsilon: -0.0376, sidechain.H_epsilon_2: 0.0625, sidechain.C_beta: -0.0236, sidechain.H_gamma_2: 0.0317, sidechain.H_beta_3: 0.048, sidechain.H_beta_2: 0.048, peptide.O: -0.8105, sidechain.C_gamma: 0.0492, sidechain.H_epsilon_1: 0.0625, peptide.H: 0.2681, sidechain.H_epsilon_3: 0.0625, peptide.O_2: -0.8105, peptide.C_alpha: -0.2597, } name = 'methionine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/methionine_ct_noh0000644000076600000240000000065511662461637022102 0ustar hinsenstaff00000000000000sidechain = Group('met_sidechain_noh') peptide = Group('peptide_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Met' amber_charge = {sidechain.S_delta: -0.2692, peptide.O: -0.8105, peptide.O_2: -0.8105, peptide.C_alpha: -0.2597, sidechain.C_gamma: 0.0492, peptide.C: 0.8013, sidechain.C_beta: -0.0236, peptide.N: -0.3821, sidechain.C_epsilon: -0.0376, } name = 'methionine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/methionine_ct_uni0000644000076600000240000000074211662461637022106 0ustar hinsenstaff00000000000000sidechain = Group('met_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Met' amber91_charge = {peptide.O_2: -0.706, sidechain.C_gamma: 0.09, sidechain.C_epsilon: 0.007, sidechain.LP_1: -0.381, peptide.N: -0.52, peptide.C_alpha: 0.131, peptide.O: -0.706, sidechain.C_beta: 0.037, peptide.H: 0.248, sidechain.LP_2: -0.381, peptide.C: 0.444, sidechain.S_delta: 0.737, } name = 'methionine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/methionine_ct_uni20000644000076600000240000000030211662461637022160 0ustar hinsenstaff00000000000000sidechain = Group('met_sidechain_uni2') peptide = Group('peptide_ct_uni2') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Met' name = 'methionine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/methionine_ct_uni30000644000076600000240000000074211662461637022171 0ustar hinsenstaff00000000000000sidechain = Group('met_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Met' amber91_charge = {peptide.O_2: -0.706, sidechain.C_gamma: 0.09, sidechain.C_epsilon: 0.007, sidechain.LP_1: -0.381, peptide.N: -0.52, peptide.C_alpha: 0.131, peptide.O: -0.706, sidechain.C_beta: 0.037, peptide.H: 0.248, sidechain.LP_2: -0.381, peptide.C: 0.444, sidechain.S_delta: 0.737, } name = 'methionine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/methionine_noh0000644000076600000240000000063011662461637021405 0ustar hinsenstaff00000000000000sidechain = Group('met_sidechain_noh') peptide = Group('peptide_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Met' amber_charge = {sidechain.S_delta: -0.2737, peptide.O: -0.5679, peptide.N: -0.4157, sidechain.C_beta: 0.0342, sidechain.C_gamma: 0.0018, peptide.C_alpha: -0.0237, sidechain.C_epsilon: -0.0536, peptide.C: 0.5973, } name = 'methionine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/methionine_nt0000644000076600000240000000126311777301631021237 0ustar hinsenstaff00000000000000sidechain = Group('met_sidechain') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Met' amber_charge = {sidechain.H_epsilon_2: 0.0597, sidechain.H_epsilon_3: 0.0597, sidechain.H_gamma_2: 0.0292, sidechain.H_epsilon_1: 0.0597, sidechain.C_gamma: 0.0334, peptide.H_2: 0.1984, sidechain.C_beta: 0.0865, peptide.N: 0.1592, peptide.C: 0.6123, peptide.H_alpha: 0.1116, sidechain.H_beta_2: 0.0125, sidechain.C_epsilon: -0.0341, peptide.C_alpha: 0.0221, sidechain.H_gamma_3: 0.0292, sidechain.S_delta: -0.2774, peptide.H_1: 0.1984, peptide.O: -0.5713, peptide.H_3: 0.1984, sidechain.H_beta_3: 0.0125, } name = 'methionine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/methionine_nt_ct_noh0000644000076600000240000000027611662461637022602 0ustar hinsenstaff00000000000000sidechain = Group('met_sidechain_noh') peptide = Group('peptide_nt_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Met' name = 'methionine' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/methionine_nt_noh0000644000076600000240000000062411662461637022111 0ustar hinsenstaff00000000000000sidechain = Group('met_sidechain_noh') peptide = Group('peptide_nt_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Met' amber_charge = {sidechain.S_delta: -0.2774, peptide.O: -0.5713, peptide.C_alpha: 0.0221, sidechain.C_gamma: 0.0334, peptide.N: 0.1592, sidechain.C_epsilon: -0.0341, peptide.C: 0.6123, sidechain.C_beta: 0.0865, } name = 'methionine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/methionine_nt_uni0000644000076600000240000000076611662461637022127 0ustar hinsenstaff00000000000000sidechain = Group('met_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Met' amber91_charge = {sidechain.LP_1: -0.381, peptide.H_3: 0.312, peptide.H_1: 0.312, sidechain.C_epsilon: 0.007, peptide.C: 0.526, peptide.O: -0.5, peptide.N: -0.263, sidechain.C_beta: 0.037, sidechain.S_delta: 0.737, peptide.C_alpha: 0.192, sidechain.LP_2: -0.381, peptide.H_2: 0.312, sidechain.C_gamma: 0.09, } name = 'methionine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/methionine_nt_uni20000644000076600000240000000030211662461637022173 0ustar hinsenstaff00000000000000sidechain = Group('met_sidechain_uni2') peptide = Group('peptide_nt_uni2') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Met' name = 'methionine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/methionine_nt_uni30000644000076600000240000000076611662461637022212 0ustar hinsenstaff00000000000000sidechain = Group('met_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Met' amber91_charge = {sidechain.LP_1: -0.381, peptide.H_3: 0.312, peptide.H_1: 0.312, sidechain.C_epsilon: 0.007, peptide.C: 0.526, peptide.O: -0.5, peptide.N: -0.263, sidechain.C_beta: 0.037, sidechain.S_delta: 0.737, peptide.C_alpha: 0.192, sidechain.LP_2: -0.381, peptide.H_2: 0.312, sidechain.C_gamma: 0.09, } name = 'methionine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/methionine_uni0000644000076600000240000000073011662461637021415 0ustar hinsenstaff00000000000000sidechain = Group('met_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Met' name = 'methionine' chain_links = [peptide.N, peptide.C] # amber91_charge = {sidechain.LP_1 ???: -0.381, peptide.O: -0.5, peptide.N: -0.52, peptide.C_alpha: 0.137, sidechain.C_beta: 0.037, sidechain.S_delta: 0.737, sidechain.LP_2 ???: -0.381, sidechain.C_epsilon: 0.007, sidechain.C_gamma: 0.09, peptide.H: 0.248, peptide.C: 0.526, } MMTK-2.7.9/MMTK/Database/Groups/methionine_uni20000644000076600000240000000030411662461637021474 0ustar hinsenstaff00000000000000sidechain = Group('met_sidechain_uni2') peptide = Group('peptide_uni2') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Met' name = 'methionine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/methionine_uni30000644000076600000240000000073011662461637021500 0ustar hinsenstaff00000000000000sidechain = Group('met_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Met' name = 'methionine' chain_links = [peptide.N, peptide.C] # amber91_charge = {sidechain.LP_1 ???: -0.381, peptide.O: -0.5, peptide.N: -0.52, peptide.C_alpha: 0.137, sidechain.C_beta: 0.037, sidechain.S_delta: 0.737, sidechain.LP_2 ???: -0.381, sidechain.C_epsilon: 0.007, sidechain.C_gamma: 0.09, peptide.H: 0.248, peptide.C: 0.526, } MMTK-2.7.9/MMTK/Database/Groups/methyl0000644000076600000240000000047311662461637017711 0ustar hinsenstaff00000000000000name = 'methyl group' valence = 1 C = Atom('C') H1 = Atom('H') H2 = Atom('H') H3 = Atom('H') bonds = [Bond(C, H1), Bond(C, H2), Bond(C, H3)] pdbmap = [('MTH', {'C': C, 'H1': H1, 'H2': H2, 'H3': H3})] amber_atom_type = {C: 'CT', H1: 'HC', H2: 'HC', H3: 'HC'} amber_charge = {C: 0., H1: 0.1, H2: 0.1, H3: 0.1} MMTK-2.7.9/MMTK/Database/Groups/na_phosphate0000644000076600000240000000042712041514571021043 0ustar hinsenstaff00000000000000P = Atom('P') O_1 = Atom('O') O_2 = Atom('O') bonds = [Bond(O_1, P), Bond(O_2, P), ] pdbmap = [('NAP', {"OP2": O_2, "P": P, "OP1": O_1, })] pdb_alternative = {'O1P': 'OP1', 'O2P': 'OP2'} amber_atom_type = {O_2: 'O2', P: 'P', O_1: 'O2', } amber12_atom_type = amber_atom_type MMTK-2.7.9/MMTK/Database/Groups/nmethyl_ct0000644000076600000240000000121712041514571020536 0ustar hinsenstaff00000000000000name = 'nmethyl' symbol = 'NME' NBond = Atom('N') H = Atom('H') CH3 = Atom('C') HH31 = Atom('H') HH32 = Atom('H') HH33 = Atom('H') bonds = [Bond(NBond, H), Bond(NBond, CH3), Bond(CH3, HH31), Bond(CH3, HH32), Bond(CH3, HH33)] chain_links = [NBond, None] amber_atom_type = {CH3: 'CT', HH31: 'H1', HH32: 'H1', HH33: 'H1', NBond: 'N', H: 'H'} amber_charge = {CH3: -0.149, HH31: 0.0976, HH32: 0.0976, HH33: 0.0976, NBond: -0.4157, H: 0.2719} pdbmap = [('NME', {'CH3': CH3, '1HH3': HH31, '2HH3': HH32, '3HH3': HH33, 'N': NBond, 'H': H})] pdb_alternative = {'HH31': '1HH3', 'HH32': '2HH3', 'HH33': '3HH3'} amber12_atom_type = amber_atom_type MMTK-2.7.9/MMTK/Database/Groups/nmethyl_ct_noh0000644000076600000240000000036711662461637021423 0ustar hinsenstaff00000000000000name = 'nmethyl' symbol = 'NME' NBond = Atom('NH') CH3 = Atom('CH3') bonds = [Bond(NBond, CH3)] chain_links = [NBond, None] amber_atom_type = {CH3: 'CT', NBond: 'N'} pdbmap = [('NME', {'CH3': CH3, 'N': NBond})] pdb_alternative = {'C': 'CH3'} MMTK-2.7.9/MMTK/Database/Groups/peptide0000644000076600000240000000101012041514571020011 0ustar hinsenstaff00000000000000H_alpha = Atom('h') C_alpha = Atom('c') N = Atom('n') O = Atom('o') H = Atom('h') C = Atom('c') bonds = [Bond(N, H), Bond(N, C_alpha), Bond(C_alpha, H_alpha), Bond(C_alpha, C), Bond(C, O), ] pdbmap = [('', {'N': N, 'O': O, 'HA': H_alpha, 'CA': C_alpha, 'H': H, 'C': C, }, ), ] amber_atom_type = {C: 'C', O: 'O', H_alpha: 'H1', C_alpha: 'CT', N: 'N', H: 'H', } amber12_atom_type = {C: 'C', O: 'O', H_alpha: 'H1', C_alpha: 'CX', N: 'N', H: 'H', } pdb_alternative = {'HA2': 'HA', '2HA': 'HA', 'HN': 'H', } name = 'peptide' MMTK-2.7.9/MMTK/Database/Groups/peptide_ct0000644000076600000240000000130512117632042020504 0ustar hinsenstaff00000000000000H_alpha = Atom('h') C_alpha = Atom('c') O = Atom('o') O_2 = Atom('o') H = Atom('h') N = Atom('n') C = Atom('c') bonds = [Bond(N, H), Bond(N, C_alpha), Bond(C_alpha, H_alpha), Bond(C_alpha, C), Bond(C, O), Bond(C, O_2), ] pdbmap = [('', {'N': N, 'O': O, 'HA': H_alpha, 'CA': C_alpha, 'H': H, 'OXT': O_2, 'C': C, }, ), ] amber_atom_type = {C: 'C', H: 'H', H_alpha: 'H1', O: 'O2', C_alpha: 'CT', O_2: 'O2', N: 'N', } amber12_atom_type = {C: 'C', H: 'H', H_alpha: 'H1', O: 'O2', C_alpha: 'CX', O_2: 'O2', N: 'N', } pdb_alternative = {'O1': 'O', 'O2': 'OXT', 'OC1': 'O', 'OC2': 'OXT', 'HN': 'H', 'OT': 'OXT', 'OT2': 'OXT', 'OT1': 'O', "O'": 'O', "O''": 'OXT', 'HA2': 'HA', '2HA': 'HA'} name = 'peptide C terminus' MMTK-2.7.9/MMTK/Database/Groups/peptide_ct_noh0000644000076600000240000000070112117632042021347 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') N = Atom('NH') O = Atom('O') O_2 = Atom('O') bonds = [Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), Bond(C, O_2), ] pdbmap = [('', {'C': C, 'O': O, 'CA': C_alpha, 'N': N, 'OXT': O_2, }, ), ] amber_atom_type = {C: 'C', N: 'N', O: 'O2', C_alpha: 'CT', O_2: 'O2', } pdb_alternative = {'O1': 'O', 'O2': 'OXT', 'HN': 'H', 'OT': 'OXT', 'OT2': 'OXT', 'OT1': 'O', 'OC2': 'OXT', 'OC1': 'O', } name = 'peptide C terminus' MMTK-2.7.9/MMTK/Database/Groups/peptide_ct_uni0000644000076600000240000000120312117632042021354 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') H = Atom('H') N = Atom('N') O = Atom('O') O_2 = Atom('O') bonds = [Bond(N, H), Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), Bond(C, O_2), ] pdbmap = [('', {'N': N, 'O': O, 'CA': C_alpha, 'C': C, 'H': H, 'OXT': O_2, }, ), ] amber91_atom_type = {C_alpha: 'CH', O: 'O2', N: 'N', C: 'C', O_2: 'O2', H: 'H', } opls_atom_type = {C_alpha: 'CH', O: 'O2', N: 'N', C: 'C', O_2: 'O2', H: 'H', } opls_charge = {C_alpha: .1, O: -.8, N: -.57, C: .7, O_2: -.8, H: .37, } pdb_alternative = {'O1': 'O', 'O2': 'OXT', 'HN': 'H', 'OT': 'OXT', 'OT2': 'OXT', 'OT1': 'O', 'OC2': 'OXT', 'OC1': 'O', } name = 'peptide C terminus' MMTK-2.7.9/MMTK/Database/Groups/peptide_ct_uni20000644000076600000240000000075412117632042021450 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') H = Atom('H') N = Atom('N') O = Atom('O') O_2 = Atom('O') bonds = [Bond(N, H), Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), Bond(C, O_2), ] pdbmap = [('', {'N': N, 'O': O, 'CA': C_alpha, 'C': C, 'H': H, 'OXT': O_2, }, ), ] amber91_atom_type = {C_alpha: 'CH', O: 'O2', N: 'N', O_2: 'O2', C: 'C', H: 'H', } pdb_alternative = {'O1': 'O', 'O2': 'OXT', 'HN': 'H', 'OT': 'OXT', 'OT2': 'OXT', 'OT1': 'O', 'OC2': 'OXT', 'OC1': 'O', } name = 'peptide C terminus' MMTK-2.7.9/MMTK/Database/Groups/peptide_ct_uni30000644000076600000240000000120312117632042021437 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') H = Atom('H') N = Atom('N') O = Atom('O') O_2 = Atom('O') bonds = [Bond(N, H), Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), Bond(C, O_2), ] pdbmap = [('', {'N': N, 'O': O, 'CA': C_alpha, 'C': C, 'H': H, 'OXT': O_2, }, ), ] amber91_atom_type = {C_alpha: 'CH', O: 'O2', N: 'N', C: 'C', O_2: 'O2', H: 'H', } opls_atom_type = {C_alpha: 'CH', O: 'O2', N: 'N', C: 'C', O_2: 'O2', H: 'H', } opls_charge = {C_alpha: .1, O: -.8, N: -.57, C: .7, O_2: -.8, H: .37, } pdb_alternative = {'O1': 'O', 'O2': 'OXT', 'HN': 'H', 'OT': 'OXT', 'OT2': 'OXT', 'OT1': 'O', 'OC2': 'OXT', 'OC1': 'O', } name = 'peptide C terminus' MMTK-2.7.9/MMTK/Database/Groups/peptide_glycine_ct_uni0000644000076600000240000000103311662461637023105 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH2') H = Atom('H') N = Atom('N') O = Atom('O') O_2 = Atom('O') bonds = [Bond(N, H), Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), Bond(C, O_2), ] pdbmap = [('', {'N': N, 'O': O, 'CA': C_alpha, 'C': C, 'H': H, 'OXT': O_2, }, ), ] pdb_alternative = {'O1': 'O', 'O2': 'OXT', 'HN': 'H', 'OT': 'OXT', 'OT2': 'OXT', 'OT1': 'O', } name = 'peptide C terminus' opls_atom_type = {N: 'N', H: 'H', C_alpha: 'CQ', C: 'C', O: 'O', O_2: 'O' } opls_charge = {N: -0.57, H: 0.37, C_alpha: 0.1, C: 0.8, O: -0.8, O_2: -0.8 } MMTK-2.7.9/MMTK/Database/Groups/peptide_glycine_ct_uni30000644000076600000240000000103311662461637023170 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH2') H = Atom('H') N = Atom('N') O = Atom('O') O_2 = Atom('O') bonds = [Bond(N, H), Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), Bond(C, O_2), ] pdbmap = [('', {'N': N, 'O': O, 'CA': C_alpha, 'C': C, 'H': H, 'OXT': O_2, }, ), ] pdb_alternative = {'O1': 'O', 'O2': 'OXT', 'HN': 'H', 'OT': 'OXT', 'OT2': 'OXT', 'OT1': 'O', } name = 'peptide C terminus' opls_atom_type = {N: 'N', H: 'H', C_alpha: 'CQ', C: 'C', O: 'O', O_2: 'O' } opls_charge = {N: -0.57, H: 0.37, C_alpha: 0.1, C: 0.8, O: -0.8, O_2: -0.8 } MMTK-2.7.9/MMTK/Database/Groups/peptide_glycine_nt_uni0000644000076600000240000000131611662461637023124 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH2') H_1 = Atom('H') H_2 = Atom('H') H_3 = Atom('H') N = Atom('N') O = Atom('O') bonds = [Bond(N, H_1), Bond(N, H_2), Bond(N, H_3), Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), ] pdbmap = [('', {'N': N, 'O': O, 'CA': C_alpha, '1HT': H_1, 'HN2': H_2, 'HN3': H_3, 'C': C, }, ), ] pdb_alternative = {'HT1': '1HT', 'HT2': '2HT', 'HT3': '3HT', 'H1': '1HT', 'H3': '3HT', 'H2': '2HT', 'HN1': '1HT', 'HN2': '2HT', 'HN3': '3HT', '1HN': '1HT', '2HN': '2HT', '3HN': '3HT' } name = 'peptide N terminus' opls_atom_type = {N: 'N3', H_1: 'H3', H_2: 'H3', H_3: 'H3', C_alpha: 'CQ', C: 'C', O: 'O', } opls_charge = {N: -0.30, H_1: 0.33, H_2: 0.33, H_3: 0.33, C_alpha: 0.31, C: 0.5, O: -0.5, } MMTK-2.7.9/MMTK/Database/Groups/peptide_glycine_nt_uni30000644000076600000240000000131611662461637023207 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH2') H_1 = Atom('H') H_2 = Atom('H') H_3 = Atom('H') N = Atom('N') O = Atom('O') bonds = [Bond(N, H_1), Bond(N, H_2), Bond(N, H_3), Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), ] pdbmap = [('', {'N': N, 'O': O, 'CA': C_alpha, '1HT': H_1, 'HN2': H_2, 'HN3': H_3, 'C': C, }, ), ] pdb_alternative = {'HT1': '1HT', 'HT2': '2HT', 'HT3': '3HT', 'H1': '1HT', 'H3': '3HT', 'H2': '2HT', 'HN1': '1HT', 'HN2': '2HT', 'HN3': '3HT', '1HN': '1HT', '2HN': '2HT', '3HN': '3HT' } name = 'peptide N terminus' opls_atom_type = {N: 'N3', H_1: 'H3', H_2: 'H3', H_3: 'H3', C_alpha: 'CQ', C: 'C', O: 'O', } opls_charge = {N: -0.30, H_1: 0.33, H_2: 0.33, H_3: 0.33, C_alpha: 0.31, C: 0.5, O: -0.5, } MMTK-2.7.9/MMTK/Database/Groups/peptide_glycine_uni0000644000076600000240000000064311662461637022425 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH2') H = Atom('H') N = Atom('N') O = Atom('O') bonds = [Bond(N, H), Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), ] pdbmap = [('', {'N': N, 'O': O, 'CA': C_alpha, 'H': H, 'C': C, }, ), ] pdb_alternative = {'HA2': 'HA', 'HN': 'H', } name = 'peptide' opls_atom_type = {N: 'N', H: 'H', C_alpha: 'CQ', C: 'C', O: 'O', } opls_charge = {N: -0.57, H: 0.37, C_alpha: 0.2, C: 0.5, O: -0.5, } MMTK-2.7.9/MMTK/Database/Groups/peptide_glycine_uni30000644000076600000240000000064311662461637022510 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH2') H = Atom('H') N = Atom('N') O = Atom('O') bonds = [Bond(N, H), Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), ] pdbmap = [('', {'N': N, 'O': O, 'CA': C_alpha, 'H': H, 'C': C, }, ), ] pdb_alternative = {'HA2': 'HA', 'HN': 'H', } name = 'peptide' opls_atom_type = {N: 'N', H: 'H', C_alpha: 'CQ', C: 'C', O: 'O', } opls_charge = {N: -0.57, H: 0.37, C_alpha: 0.2, C: 0.5, O: -0.5, } MMTK-2.7.9/MMTK/Database/Groups/peptide_noh0000644000076600000240000000044711662461637020706 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') N = Atom('NH') O = Atom('O') bonds = [Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), ] pdbmap = [('', {'N': N, 'O': O, 'CA': C_alpha, 'C': C, }, ), ] amber_atom_type = {O: 'O', C: 'C', N: 'N', C_alpha: 'CT', } pdb_alternative = {'HN': 'H', } name = 'peptide' MMTK-2.7.9/MMTK/Database/Groups/peptide_nt0000644000076600000240000000166312041514571020530 0ustar hinsenstaff00000000000000H_alpha = Atom('h') H_1 = Atom('h') C_alpha = Atom('c') O = Atom('o') H_3 = Atom('h') H_2 = Atom('h') N = Atom('n') C = Atom('c') bonds = [Bond(N, H_1), Bond(N, H_2), Bond(N, H_3), Bond(N, C_alpha), Bond(C_alpha, H_alpha), Bond(C_alpha, C), Bond(C, O), ] pdbmap = [('', {'N': N, 'O': O, '2HT': H_2, 'HA': H_alpha, 'CA': C_alpha, '1HT': H_1, '3HT': H_3, 'C': C, }, ), ] name = 'peptide N terminus' amber_atom_type = {H_3: 'H', C: 'C', O: 'O', H_alpha: 'HP', H_1: 'H', C_alpha: 'CT', H_2: 'H', N: 'N3', } amber12_atom_type = {H_3: 'H', C: 'C', O: 'O', H_alpha: 'HP', H_1: 'H', C_alpha: 'CX', H_2: 'H', N: 'N3', } #PJC change, added 1H,2H,3H to copy H1,H2,H3 #pdb_alternative = {'HT1': '1HT', 'HT2': '2HT', 'HT3': '3HT', 'H1': '1HT', 'H3': '3HT', 'H2': '2HT', '2HA': 'HA', 'HA2': 'HA'} pdb_alternative = {'HT1': '1HT', 'HT2': '2HT', 'HT3': '3HT', 'H1': '1HT', '1H': '1HT', 'H3': '3HT','3H': '3HT', 'H2': '2HT', '2H': '2HT', '2HA': 'HA', 'HA2': 'HA'}MMTK-2.7.9/MMTK/Database/Groups/peptide_nt_ct0000644000076600000240000000162112117632042021206 0ustar hinsenstaff00000000000000H_alpha = Atom('h') H_1 = Atom('h') C_alpha = Atom('c') O = Atom('o') O_2 = Atom('o') H_3 = Atom('h') H_2 = Atom('h') N = Atom('n') C = Atom('c') bonds = [Bond(N, H_1), Bond(N, H_2), Bond(N, H_3), Bond(N, C_alpha), Bond(C_alpha, H_alpha), Bond(C_alpha, C), Bond(C, O), Bond(C, O_2), ] pdbmap = [('', {'N': N, 'O': O, '2HT': H_2, 'HA': H_alpha, 'CA': C_alpha, '1HT': H_1, '3HT': H_3, 'C': C, 'OXT': O_2, }, ), ] name = 'peptide N terminus/C terminus' amber_atom_type = {H_3: 'H', C: 'C', O: 'O', H_alpha: 'HP', H_1: 'H', C_alpha: 'CT', H_2: 'H', N: 'N3', O_2: 'O2',} amber12_atom_type = {H_3: 'H', C: 'C', O: 'O', H_alpha: 'HP', H_1: 'H', C_alpha: 'CX', H_2: 'H', N: 'N3', O_2: 'O2',} pdb_alternative = {'HT1': '1HT', 'HT2': '2HT', 'HT3': '3HT', 'H1': '1HT', 'H3': '3HT', 'H2': '2HT', '2HA': 'HA', 'HA2': 'HA', 'O2': 'OXT', 'OT': 'OXT', 'OT2': 'OXT', 'OT1': 'O', 'OC2': 'OXT', 'OC1': 'O', "O'": 'O', "O''": 'OXT'} MMTK-2.7.9/MMTK/Database/Groups/peptide_nt_ct_noh0000644000076600000240000000066111662461637022073 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') N = Atom('NH3') O = Atom('O') O_2 = Atom('O') bonds = [Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), Bond(C, O_2), ] pdbmap = [('', {'N': N, 'O': O, 'CA': C_alpha, 'C': C, 'OXT': O_2}, ), ] amber_atom_type = {N: 'N3', O: 'O', C: 'C', C_alpha: 'CT',O_2: 'O2'} pdb_alternative = {'H1': '1HT', 'H3': '3HT', 'H2': '2HT', 'O2': 'OXT', 'OT': 'OXT', 'OT2': 'OXT'} name = 'peptide N terminus/C terminus' MMTK-2.7.9/MMTK/Database/Groups/peptide_nt_noh0000644000076600000240000000052011662461637021377 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') N = Atom('NH3') O = Atom('O') bonds = [Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), ] pdbmap = [('', {'N': N, 'O': O, 'CA': C_alpha, 'C': C, }, ), ] amber_atom_type = {N: 'N3', O: 'O', C: 'C', C_alpha: 'CT', } pdb_alternative = {'H1': '1HT', 'H3': '3HT', 'H2': '2HT', } name = 'peptide N terminus' MMTK-2.7.9/MMTK/Database/Groups/peptide_nt_uni0000644000076600000240000000174011662461637021413 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') H_1 = Atom('H') H_2 = Atom('H') H_3 = Atom('H') N = Atom('N') O = Atom('O') bonds = [Bond(N, H_1), Bond(N, H_2), Bond(N, H_3), Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), ] pdbmap = [('', {'N': N, 'O': O, '2HT': H_2, 'CA': C_alpha, '1HT': H_1, '3HT': H_3, 'C': C, }, ), ] #PJC change, added 1H,2H,3H to copy H1,H2,H3 #pdb_alternative = {'HT1': '1HT', 'HT2': '2HT', 'HT3': '3HT', 'H1': '1HT', 'H3': '3HT', 'H2': '2HT', 'HN1': '1HT', 'HN2': '2HT', 'HN3': '3HT' } pdb_alternative = {'HT1': '1HT', 'HT2': '2HT', 'HT3': '3HT', 'H1': '1HT', '1H': '1HT', 'H3': '3HT','3H': '3HT', 'H2': '2HT','2H': '2HT', 'HN1': '1HT', 'HN2': '2HT', 'HN3': '3HT' } name = 'peptide N terminus' opls_atom_type = {H_1: 'H3', N: 'N3', H_2: 'H3', H_3: 'H3', C_alpha: 'CQ', C: 'C', O: 'O', } opls_charge = {H_1: 0.33, N: -0.3, H_2: 0.33, H_3: 0.33, C_alpha: 0.31, C: 0.5, O: -0.5, } amber91_atom_type = {O: 'O', H_2: 'H3', H_3: 'H3', C_alpha: 'CH', H_1: 'H3', N: 'N3', C: 'C', } MMTK-2.7.9/MMTK/Database/Groups/peptide_nt_uni20000644000076600000240000000132411662461637021473 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') H_1 = Atom('H') H_2 = Atom('H') H_3 = Atom('H') N = Atom('N') O = Atom('O') bonds = [Bond(N, H_1), Bond(N, H_2), Bond(N, H_3), Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), ] pdbmap = [('', {'N': N, 'O': O, '2HT': H_2, 'CA': C_alpha, '1HT': H_1, '3HT': H_3, 'C': C, }, ), ] #PJC change, added 1H,2H,3H to copy H1,H2,H3 #pdb_alternative = {'HT1': '1HT', 'HT2': '2HT', 'HT3': '3HT', 'H1': '1HT', 'H3': '3HT', 'H2': '2HT', } pdb_alternative = {'HT1': '1HT', 'HT2': '2HT', 'HT3': '3HT', 'H1': '1HT', '1H': '1HT', 'H3': '3HT', '3H': '3HT', 'H2': '2HT','2H': '2HT' } amber91_atom_type = {H_1: 'H3', H_3: 'H3', C: 'C', N: 'N3', O: 'O', C_alpha: 'CH', H_2: 'H3', } name = 'peptide N terminus' MMTK-2.7.9/MMTK/Database/Groups/peptide_nt_uni30000644000076600000240000000174011662461637021476 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') H_1 = Atom('H') H_2 = Atom('H') H_3 = Atom('H') N = Atom('N') O = Atom('O') bonds = [Bond(N, H_1), Bond(N, H_2), Bond(N, H_3), Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), ] pdbmap = [('', {'N': N, 'O': O, '2HT': H_2, 'CA': C_alpha, '1HT': H_1, '3HT': H_3, 'C': C, }, ), ] #PJC change, added 1H,2H,3H to copy H1,H2,H3 #pdb_alternative = {'HT1': '1HT', 'HT2': '2HT', 'HT3': '3HT', 'H1': '1HT', 'H3': '3HT', 'H2': '2HT', 'HN1': '1HT', 'HN2': '2HT', 'HN3': '3HT' } pdb_alternative = {'HT1': '1HT', 'HT2': '2HT', 'HT3': '3HT', 'H1': '1HT', '1H': '1HT', 'H3': '3HT','3H': '3HT', 'H2': '2HT','2H': '2HT', 'HN1': '1HT', 'HN2': '2HT', 'HN3': '3HT' } name = 'peptide N terminus' opls_atom_type = {H_1: 'H3', N: 'N3', H_2: 'H3', H_3: 'H3', C_alpha: 'CQ', C: 'C', O: 'O', } opls_charge = {H_1: 0.33, N: -0.3, H_2: 0.33, H_3: 0.33, C_alpha: 0.31, C: 0.5, O: -0.5, } amber91_atom_type = {O: 'O', H_2: 'H3', H_3: 'H3', C_alpha: 'CH', H_1: 'H3', N: 'N3', C: 'C', } MMTK-2.7.9/MMTK/Database/Groups/peptide_proline0000644000076600000240000000070712041514571021555 0ustar hinsenstaff00000000000000H_alpha = Atom('h') C_alpha = Atom('c') N = Atom('n') O = Atom('o') C = Atom('c') bonds = [Bond(N, C_alpha), Bond(C_alpha, H_alpha), Bond(C_alpha, C), Bond(C, O), ] pdbmap = [('', {'N': N, 'O': O, 'HA': H_alpha, 'CA': C_alpha, 'C': C, }, ), ] pdb_alternative = {'HN': 'H', } name = 'peptide in proline' amber_atom_type = {N: 'N', H_alpha: 'H1', O: 'O', C: 'C', C_alpha: 'CT', } amber12_atom_type = {N: 'N', H_alpha: 'H1', O: 'O', C: 'C', C_alpha: 'CX', } MMTK-2.7.9/MMTK/Database/Groups/peptide_proline_ct0000644000076600000240000000115012041514571022234 0ustar hinsenstaff00000000000000H_alpha = Atom('h') C_alpha = Atom('c') N = Atom('n') O = Atom('o') O_2 = Atom('o') C = Atom('c') bonds = [Bond(N, C_alpha), Bond(C_alpha, H_alpha), Bond(C_alpha, C), Bond(C, O), Bond(C, O_2), ] pdbmap = [('', {'N': N, 'O': O, 'OXT': O_2, 'HA': H_alpha, 'CA': C_alpha, 'C': C, }, ), ] pdb_alternative = {'O1': 'O', 'O2': 'OXT', 'HN': 'H', 'OT': 'O', 'OT2': 'OXT', 'OT1': 'O', "O'": 'O', "O''": 'OXT'} name = 'peptide in proline C terminus' amber_atom_type = {C: 'C', O_2: 'O2', H_alpha: 'H1', N: 'N', O: 'O2', C_alpha: 'CT', } amber12_atom_type = {C: 'C', O_2: 'O2', H_alpha: 'H1', N: 'N', O: 'O2', C_alpha: 'CX', } MMTK-2.7.9/MMTK/Database/Groups/peptide_proline_ct_noh0000644000076600000240000000066111662461637023122 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') N = Atom('N') O = Atom('O') O_2 = Atom('O') bonds = [Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), Bond(C, O_2), ] pdbmap = [('', {'N': N, 'O': O, 'OXT': O_2, 'CA': C_alpha, 'C': C, }, ), ] amber_atom_type = {N: 'N', C: 'C', O_2: 'O2', O: 'O2', C_alpha: 'CT', } pdb_alternative = {'O1': 'O', 'O2': 'OXT', 'HN': 'H', 'OT': 'OXT', 'OT2': 'OXT', 'OT1': 'O', } name = 'peptide in proline C terminus' MMTK-2.7.9/MMTK/Database/Groups/peptide_proline_ct_uni0000644000076600000240000000107211662461637023126 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') N = Atom('N') O = Atom('O') O_2 = Atom('O') bonds = [Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), Bond(C, O_2), ] pdbmap = [('', {'N': N, 'O': O, 'OXT': O_2, 'CA': C_alpha, 'C': C, }, ), ] pdb_alternative = {'O1': 'O', 'O2': 'OXT', 'HN': 'H', 'OT': 'O', 'OT2': 'OXT', 'OT1': 'O', } amber91_atom_type = {O_2: 'O2', O: 'O2', C: 'C', N: 'N', C_alpha: 'CH', } opls_atom_type = {O_2: 'O2', O: 'O2', C: 'C', N: 'N', C_alpha: 'CH', } opls_charge = {O_2: -.8, O: -.8, C: .7, N: -.57, C_alpha: .185, } name = 'peptide in proline C terminus' MMTK-2.7.9/MMTK/Database/Groups/peptide_proline_ct_uni20000644000076600000240000000066111662461637023213 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') N = Atom('N') O = Atom('O') O_2 = Atom('O') bonds = [Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), Bond(C, O_2), ] pdbmap = [('', {'N': N, 'O': O, 'OXT': O_2, 'CA': C_alpha, 'C': C, }, ), ] pdb_alternative = {'O1': 'O', 'O2': 'OXT', 'HN': 'H', 'OT': 'O', 'OT2': 'OXT', 'OT1': 'O', } amber91_atom_type = {O_2: 'O2', O: 'O2', C: 'C', N: 'N', C_alpha: 'CH', } name = 'peptide in proline C terminus' MMTK-2.7.9/MMTK/Database/Groups/peptide_proline_ct_uni30000644000076600000240000000107211662461637023211 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') N = Atom('N') O = Atom('O') O_2 = Atom('O') bonds = [Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), Bond(C, O_2), ] pdbmap = [('', {'N': N, 'O': O, 'OXT': O_2, 'CA': C_alpha, 'C': C, }, ), ] pdb_alternative = {'O1': 'O', 'O2': 'OXT', 'HN': 'H', 'OT': 'O', 'OT2': 'OXT', 'OT1': 'O', } amber91_atom_type = {O_2: 'O2', O: 'O2', C: 'C', N: 'N', C_alpha: 'CH', } opls_atom_type = {O_2: 'O2', O: 'O2', C: 'C', N: 'N', C_alpha: 'CH', } opls_charge = {O_2: -.8, O: -.8, C: .7, N: -.57, C_alpha: .185, } name = 'peptide in proline C terminus' MMTK-2.7.9/MMTK/Database/Groups/peptide_proline_noh0000644000076600000240000000046111662461637022432 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') N = Atom('N') O = Atom('O') bonds = [Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), ] pdbmap = [('', {'N': N, 'O': O, 'CA': C_alpha, 'C': C, }, ), ] amber_atom_type = {C_alpha: 'CT', O: 'O', C: 'C', N: 'N', } pdb_alternative = {'HN': 'H', } name = 'peptide in proline' MMTK-2.7.9/MMTK/Database/Groups/peptide_proline_nt0000644000076600000240000000114612041514571022254 0ustar hinsenstaff00000000000000H_alpha = Atom('h') C_alpha = Atom('c') O = Atom('o') H_3 = Atom('h') H_2 = Atom('h') N = Atom('n') C = Atom('c') bonds = [Bond(N, H_2), Bond(N, H_3), Bond(N, C_alpha), Bond(C_alpha, H_alpha), Bond(C_alpha, C), Bond(C, O), ] name = 'peptide in proline N terminus' pdbmap = [('', {'N': N, 'O': O, '2H': H_2, 'HA': H_alpha, 'CA': C_alpha, '3H': H_3, 'C': C, }, ), ] pdb_alternative = {'H3': '3H', 'H2': '2H', 'H': '3H', } amber_atom_type = {O: 'O', C: 'C', C_alpha: 'CT', H_3: 'H', N: 'N3', H_2: 'H', H_alpha: 'HP', } amber12_atom_type = {O: 'O', C: 'C', C_alpha: 'CX', H_3: 'H', N: 'N3', H_2: 'H', H_alpha: 'HP', } MMTK-2.7.9/MMTK/Database/Groups/peptide_proline_nt_ct_noh0000644000076600000240000000067511662461637023630 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') N = Atom('NH2') O = Atom('O') O_2 = Atom('O') bonds = [Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), Bond(C, O_2), ] pdbmap = [('', {'N': N, 'O': O, 'CA': C_alpha, 'C': C, 'OXT': O_2}, ), ] amber_atom_type = {O: 'O', N: 'N3', C_alpha: 'CT', C: 'C', O_2: 'O2'} pdb_alternative = {'H1': '1HT', 'H3': '3HT', 'H2': '2HT', 'O2': 'OXT', 'OT': 'OXT', 'OT2': 'OXT'} name = 'peptide in proline N terminus/C terminus' MMTK-2.7.9/MMTK/Database/Groups/peptide_proline_nt_noh0000644000076600000240000000043711662461637023136 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') N = Atom('NH2') O = Atom('O') bonds = [Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), ] pdbmap = [('', {'N': N, 'O': O, 'CA': C_alpha, 'C': C, }, ), ] amber_atom_type = {O: 'O', N: 'N3', C_alpha: 'CT', C: 'C', } name = 'peptide in proline N terminus' MMTK-2.7.9/MMTK/Database/Groups/peptide_proline_nt_uni0000644000076600000240000000112111662461637023134 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') H_2 = Atom('H') H_3 = Atom('H') N = Atom('N') O = Atom('O') bonds = [Bond(N, H_2), Bond(N, H_3), Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), ] pdbmap = [('', {'N': N, 'O': O, '2H': H_2, 'CA': C_alpha, '3H': H_3, 'C': C, }, ), ] pdb_alternative = {'H3': '3H', 'H2': '2H', } amber91_atom_type = {C: 'C', N: 'N3', O: 'O', C_alpha: 'CH', H_2: 'H3', H_3: 'H3', } opls_atom_type = {C: 'C', N: 'N3', O: 'O', C_alpha: 'CH', H_2: 'H3', H_3: 'H3', } opls_charge = {C: .5, N: -0.3, O: -.5, C_alpha: .33, H_2: .33, H_3: .33, } name = 'peptide in proline N terminus' MMTK-2.7.9/MMTK/Database/Groups/peptide_proline_nt_uni20000644000076600000240000000066411662461637023231 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') H_2 = Atom('H') H_3 = Atom('H') N = Atom('N') O = Atom('O') bonds = [Bond(N, H_2), Bond(N, H_3), Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), ] pdbmap = [('', {'N': N, 'O': O, '2H': H_2, 'CA': C_alpha, '3H': H_3, 'C': C, }, ), ] pdb_alternative = {'H3': '3H', 'H2': '2H', } amber91_atom_type = {C: 'C', N: 'N3', O: 'O', C_alpha: 'CH', H_2: 'H3', H_3: 'H3', } name = 'peptide in proline N terminus' MMTK-2.7.9/MMTK/Database/Groups/peptide_proline_nt_uni30000644000076600000240000000112111662461637023217 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') H_2 = Atom('H') H_3 = Atom('H') N = Atom('N') O = Atom('O') bonds = [Bond(N, H_2), Bond(N, H_3), Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), ] pdbmap = [('', {'N': N, 'O': O, '2H': H_2, 'CA': C_alpha, '3H': H_3, 'C': C, }, ), ] pdb_alternative = {'H3': '3H', 'H2': '2H', } amber91_atom_type = {C: 'C', N: 'N3', O: 'O', C_alpha: 'CH', H_2: 'H3', H_3: 'H3', } opls_atom_type = {C: 'C', N: 'N3', O: 'O', C_alpha: 'CH', H_2: 'H3', H_3: 'H3', } opls_charge = {C: .5, N: -0.3, O: -.5, C_alpha: .33, H_2: .33, H_3: .33, } name = 'peptide in proline N terminus' MMTK-2.7.9/MMTK/Database/Groups/peptide_proline_uni0000644000076600000240000000064611662461637022446 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') N = Atom('N') O = Atom('O') bonds = [Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), ] pdbmap = [('', {'N': N, 'O': O, 'CA': C_alpha, 'C': C, }, ), ] pdb_alternative = {'HN': 'H', } amber91_atom_type = {C: 'C', N: 'N', O: 'O', C_alpha: 'CH', } opls_atom_type = {C: 'C', N: 'N', O: 'O', C_alpha: 'CH', } opls_charge = {C: .5, N: -.57, O: -.5, C_alpha: .285, } name = 'peptide in proline' MMTK-2.7.9/MMTK/Database/Groups/peptide_proline_uni20000644000076600000240000000046311662461637022525 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') N = Atom('N') O = Atom('O') bonds = [Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), ] pdbmap = [('', {'N': N, 'O': O, 'CA': C_alpha, 'C': C, }, ), ] pdb_alternative = {'HN': 'H', } amber91_atom_type = {C: 'C', N: 'N', O: 'O', C_alpha: 'CH', } name = 'peptide in proline' MMTK-2.7.9/MMTK/Database/Groups/peptide_proline_uni30000644000076600000240000000064611662461637022531 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') N = Atom('N') O = Atom('O') bonds = [Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), ] pdbmap = [('', {'N': N, 'O': O, 'CA': C_alpha, 'C': C, }, ), ] pdb_alternative = {'HN': 'H', } amber91_atom_type = {C: 'C', N: 'N', O: 'O', C_alpha: 'CH', } opls_atom_type = {C: 'C', N: 'N', O: 'O', C_alpha: 'CH', } opls_charge = {C: .5, N: -.57, O: -.5, C_alpha: .285, } name = 'peptide in proline' MMTK-2.7.9/MMTK/Database/Groups/peptide_uni0000644000076600000240000000074711662461637020720 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') H = Atom('H') N = Atom('N') O = Atom('O') bonds = [Bond(N, H), Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), ] pdbmap = [('', {'N': N, 'O': O, 'CA': C_alpha, 'H': H, 'C': C, }, ), ] pdb_alternative = {'HA2': 'HA', 'HN': 'H', } name = 'peptide' opls_atom_type = {N: 'N', H: 'H', C_alpha: 'CH', C: 'C', O: 'O', } opls_charge = {N: -0.57, H: 0.37, C_alpha: 0.2, C: 0.5, O: -0.5, } amber91_atom_type = {N: 'N', O: 'O', C_alpha: 'CH', C: 'C', H: 'H', } MMTK-2.7.9/MMTK/Database/Groups/peptide_uni20000644000076600000240000000053711662461637020777 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') H = Atom('H') N = Atom('N') O = Atom('O') bonds = [Bond(N, H), Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), ] pdbmap = [('', {'N': N, 'O': O, 'CA': C_alpha, 'H': H, 'C': C, }, ), ] amber91_atom_type = {O: 'O', N: 'N', C: 'C', H: 'H', C_alpha: 'CH', } pdb_alternative = {'HA2': 'HA', 'HN': 'H', } name = 'peptide' MMTK-2.7.9/MMTK/Database/Groups/peptide_uni30000644000076600000240000000074711662461637021003 0ustar hinsenstaff00000000000000C = Atom('C') C_alpha = Atom('CH') H = Atom('H') N = Atom('N') O = Atom('O') bonds = [Bond(N, H), Bond(N, C_alpha), Bond(C_alpha, C), Bond(C, O), ] pdbmap = [('', {'N': N, 'O': O, 'CA': C_alpha, 'H': H, 'C': C, }, ), ] pdb_alternative = {'HA2': 'HA', 'HN': 'H', } name = 'peptide' opls_atom_type = {N: 'N', H: 'H', C_alpha: 'CH', C: 'C', O: 'O', } opls_charge = {N: -0.57, H: 0.37, C_alpha: 0.2, C: 0.5, O: -0.5, } amber91_atom_type = {N: 'N', O: 'O', C_alpha: 'CH', C: 'C', H: 'H', } MMTK-2.7.9/MMTK/Database/Groups/phe_sidechain0000644000076600000240000000260112041514571021151 0ustar hinsenstaff00000000000000H_epsilon_2 = Atom('h') H_epsilon_1 = Atom('h') C_beta = Atom('c') C_epsilon_1 = Atom('c') H_delta_1 = Atom('h') H_delta_2 = Atom('h') C_epsilon_2 = Atom('c') C_zeta = Atom('c') C_gamma = Atom('c') C_delta_2 = Atom('c') C_delta_1 = Atom('c') H_beta_3 = Atom('h') H_zeta = Atom('h') H_beta_2 = Atom('h') bonds = [Bond(H_beta_2, C_beta), Bond(H_beta_3, C_beta), Bond(C_gamma, C_beta), Bond(C_delta_1, C_gamma), Bond(H_delta_1, C_delta_1), Bond(C_epsilon_1, C_delta_1), Bond(H_epsilon_1, C_epsilon_1), Bond(C_zeta, C_epsilon_1), Bond(H_zeta, C_zeta), Bond(C_epsilon_2, C_zeta), Bond(H_epsilon_2, C_epsilon_2), Bond(C_delta_2, C_epsilon_2), Bond(H_delta_2, C_delta_2), Bond(C_delta_2, C_gamma), ] name = 'phe_sidechain' pdbmap = [('PHE', {'2HD': H_delta_2, '2HE': H_epsilon_2, '2HB': H_beta_2, '1HD': H_delta_1, '1HE': H_epsilon_1, 'CE1': C_epsilon_1, 'CE2': C_epsilon_2, '3HB': H_beta_3, 'CZ': C_zeta, 'CG': C_gamma, 'CD1': C_delta_1, 'CB': C_beta, 'CD2': C_delta_2, 'HZ': H_zeta, }, ), ] pdb_alternative = {'1HB': '3HB', 'HB2': '2HB', 'HB3': '3HB', 'HB1': '3HB', 'HD1': '1HD', 'HD2': '2HD', 'HE1': '1HE', 'HE2': '2HE', } amber_atom_type = {H_epsilon_2: 'HA', H_epsilon_1: 'HA', C_gamma: 'CA', H_delta_1: 'HA', H_beta_2: 'HC', C_delta_2: 'CA', C_beta: 'CT', H_zeta: 'HA', C_delta_1: 'CA', H_delta_2: 'HA', H_beta_3: 'HC', C_zeta: 'CA', C_epsilon_1: 'CA', C_epsilon_2: 'CA', } amber12_atom_type = amber_atom_type MMTK-2.7.9/MMTK/Database/Groups/phe_sidechain_noh0000644000076600000240000000122611662461637022033 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_1 = Atom('CH') C_delta_2 = Atom('CH') C_epsilon_1 = Atom('CH') C_epsilon_2 = Atom('CH') C_gamma = Atom('C') C_zeta = Atom('CH') bonds = [Bond(C_gamma, C_beta), Bond(C_delta_1, C_gamma), Bond(C_epsilon_1, C_delta_1), Bond(C_zeta, C_epsilon_1), Bond(C_epsilon_2, C_zeta), Bond(C_delta_2, C_epsilon_2), Bond(C_delta_2, C_gamma), ] pdbmap = [('PHE', {'CD1': C_delta_1, 'CG': C_gamma, 'CB': C_beta, 'CE1': C_epsilon_1, 'CE2': C_epsilon_2, 'CD2': C_delta_2, 'CZ': C_zeta, }, ), ] amber_atom_type = {C_epsilon_1: 'CA', C_gamma: 'CA', C_epsilon_2: 'CA', C_delta_2: 'CA', C_delta_1: 'CA', C_beta: 'CT', C_zeta: 'CA', } name = 'phe_sidechain' MMTK-2.7.9/MMTK/Database/Groups/phe_sidechain_uni0000644000076600000240000000255211662461637022045 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_1 = Atom('C') C_delta_2 = Atom('C') C_epsilon_1 = Atom('C') C_epsilon_2 = Atom('C') C_gamma = Atom('C') C_zeta = Atom('C') H_delta_1 = Atom('H') H_delta_2 = Atom('H') H_epsilon_1 = Atom('H') H_epsilon_2 = Atom('H') H_zeta = Atom('H') bonds = [Bond(C_gamma, C_beta), Bond(C_delta_1, C_gamma), Bond(C_epsilon_1, C_delta_1), Bond(C_zeta, C_epsilon_1), Bond(C_epsilon_2, C_zeta), Bond(C_delta_2, C_epsilon_2), Bond(C_delta_2, C_gamma), Bond(C_delta_1,H_delta_1), Bond(C_delta_2,H_delta_2), Bond(C_epsilon_1,H_epsilon_1), Bond(C_epsilon_2,H_epsilon_2), Bond(C_zeta,H_zeta)] pdbmap = [('PHE', {'2HD': H_delta_2, '2HE': H_epsilon_2, '1HD': H_delta_1, '1HE': H_epsilon_1, 'CE1': C_epsilon_1, 'CE2': C_epsilon_2, 'CZ': C_zeta, 'CG': C_gamma, 'CD1': C_delta_1, 'CB': C_beta, 'CD2': C_delta_2, 'HZ': H_zeta, }, ), ] pdb_alternative = {'HD1': '1HD', 'HD2': '2HD', 'HE1': '1HE', 'HE2': '2HE', } name = 'phe_sidechain' opls_atom_type = { C_beta: 'C2', C_gamma: 'CK', C_delta_1: 'CK', C_epsilon_1: 'CK', C_zeta: 'CK', C_epsilon_2: 'CK', C_delta_2: 'CK', H_delta_1: 'HK', H_delta_2: 'HK', H_epsilon_1: 'HK', H_epsilon_2: 'HK', H_zeta: 'HK' } opls_charge = { C_beta: .115, C_gamma: -.115, C_delta_1: -.115, C_epsilon_1: -.115, C_zeta: -.115, C_epsilon_2: -.115, C_delta_2: -.115, H_delta_1: .115, H_delta_2: .115, H_epsilon_1: .115, H_epsilon_2: .115, H_zeta: .115 } MMTK-2.7.9/MMTK/Database/Groups/phe_sidechain_uni20000644000076600000240000000127311662461637022126 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_1 = Atom('CH') C_delta_2 = Atom('CH') C_epsilon_1 = Atom('CH') C_epsilon_2 = Atom('CH') C_gamma = Atom('C') C_zeta = Atom('CH') bonds = [Bond(C_gamma, C_beta), Bond(C_delta_1, C_gamma), Bond(C_epsilon_1, C_delta_1), Bond(C_zeta, C_epsilon_1), Bond(C_epsilon_2, C_zeta), Bond(C_delta_2, C_epsilon_2), Bond(C_delta_2, C_gamma), ] pdbmap = [('PHE', {'CG': C_gamma, 'CB': C_beta, 'CE2': C_epsilon_2, 'CE1': C_epsilon_1, 'CD1': C_delta_1, 'CD2': C_delta_2, 'CZ': C_zeta, }, ), ] pdb_alternative = {'HB1': '3HB', } amber91_atom_type = {C_delta_2: 'CD', C_epsilon_1: 'CD', C_gamma: 'CA', C_epsilon_2: 'CD', C_delta_1: 'CD', C_beta: 'C2', C_zeta: 'CD', } name = 'phe_sidechain' MMTK-2.7.9/MMTK/Database/Groups/phe_sidechain_uni30000644000076600000240000000170011662461637022122 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_1 = Atom('CH') C_delta_2 = Atom('CH') C_epsilon_1 = Atom('CH') C_epsilon_2 = Atom('CH') C_gamma = Atom('C') C_zeta = Atom('CH') bonds = [Bond(C_gamma, C_beta), Bond(C_delta_1, C_gamma), Bond(C_epsilon_1, C_delta_1), Bond(C_zeta, C_epsilon_1), Bond(C_epsilon_2, C_zeta), Bond(C_delta_2, C_epsilon_2), Bond(C_delta_2, C_gamma), ] pdbmap = [('PHU', {'CG': C_gamma, 'CB': C_beta, 'CE2': C_epsilon_2, 'CE1': C_epsilon_1, 'CD1': C_delta_1, 'CD2': C_delta_2, 'CZ': C_zeta, }, ), ] pdb_alternative = {'HB1': '3HB', } name = 'phu_sidechain' opls_atom_type = { C_beta: 'C2', C_gamma: 'CA', C_delta_1: 'CD', C_epsilon_1: 'CD', C_zeta: 'CD', C_epsilon_2: 'CD', C_delta_2: 'CD' } opls_charge = { C_beta: 0.0, C_gamma: 0.0, C_delta_1: 0.0, C_epsilon_1: 0.0, C_zeta: 0.0, C_epsilon_2: 0.0, C_delta_2: 0.0 } amber91_atom_type = {C_delta_2: 'CD', C_epsilon_1: 'CD', C_gamma: 'CA', C_epsilon_2: 'CD', C_delta_1: 'CD', C_beta: 'C2', C_zeta: 'CD', } MMTK-2.7.9/MMTK/Database/Groups/phenylalanine0000644000076600000240000000134311777301631021225 0ustar hinsenstaff00000000000000sidechain = Group('phe_sidechain') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Phe' amber_charge = {peptide.N: -0.4157, sidechain.C_delta_2: -0.1256, sidechain.H_zeta: 0.1297, peptide.H: 0.2719, sidechain.C_epsilon_1: -0.1704, peptide.H_alpha: 0.0978, sidechain.H_epsilon_1: 0.143, sidechain.C_gamma: 0.0118, sidechain.H_epsilon_2: 0.143, sidechain.H_beta_2: 0.0295, peptide.O: -0.5679, sidechain.H_beta_3: 0.0295, sidechain.C_epsilon_2: -0.1704, peptide.C_alpha: -0.0024, sidechain.H_delta_1: 0.133, sidechain.C_zeta: -0.1072, sidechain.H_delta_2: 0.133, sidechain.C_beta: -0.0343, peptide.C: 0.5973, sidechain.C_delta_1: -0.1256, } name = 'phenylalanine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/phenylalanine_calpha0000644000076600000240000000021411662461637022537 0ustar hinsenstaff00000000000000symbol = 'Phe' name = 'phenylalanine' C_alpha = Atom('phenylalanine') pdbmap = [('PHE', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/phenylalanine_ct0000644000076600000240000000136511777301631021717 0ustar hinsenstaff00000000000000sidechain = Group('phe_sidechain') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Phe' amber_charge = {sidechain.H_delta_2: 0.1408, sidechain.C_epsilon_2: -0.1847, sidechain.C_delta_2: -0.13, sidechain.H_beta_2: 0.0443, peptide.O: -0.8026, peptide.C: 0.766, sidechain.C_gamma: 0.0552, sidechain.H_epsilon_2: 0.1461, peptide.H_alpha: 0.1098, peptide.N: -0.3821, peptide.C_alpha: -0.1825, sidechain.H_epsilon_1: 0.1461, sidechain.H_delta_1: 0.1408, sidechain.C_zeta: -0.0944, sidechain.C_beta: -0.0959, sidechain.C_delta_1: -0.13, peptide.H: 0.2681, sidechain.H_zeta: 0.128, sidechain.H_beta_3: 0.0443, sidechain.C_epsilon_1: -0.1847, peptide.O_2: -0.8026, } name = 'phenylalanine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/phenylalanine_ct_noh0000644000076600000240000000101011662461637022554 0ustar hinsenstaff00000000000000sidechain = Group('phe_sidechain_noh') peptide = Group('peptide_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Phe' amber_charge = {peptide.C: 0.766, sidechain.C_delta_1: -0.13, sidechain.C_beta: -0.0959, sidechain.C_gamma: 0.0552, sidechain.C_delta_2: -0.13, sidechain.C_epsilon_1: -0.1847, peptide.O_2: -0.8026, sidechain.C_epsilon_2: -0.1847, sidechain.C_zeta: -0.0944, peptide.C_alpha: -0.1825, peptide.O: -0.8026, peptide.N: -0.3821, } name = 'phenylalanine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/phenylalanine_ct_uni0000644000076600000240000000102011662461637022564 0ustar hinsenstaff00000000000000sidechain = Group('phe_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Phe' amber91_charge = {sidechain.C_beta: 0.038, peptide.O_2: -0.706, peptide.O: -0.706, peptide.C_alpha: 0.208, sidechain.C_delta_1: -0.011, sidechain.C_delta_2: -0.011, peptide.N: -0.52, sidechain.C_gamma: 0.011, peptide.C: 0.444, sidechain.C_epsilon_2: 0.004, peptide.H: 0.248, sidechain.C_zeta: -0.003, sidechain.C_epsilon_1: 0.004, } name = 'phenylalanine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/phenylalanine_ct_uni20000644000076600000240000000102011662461637022646 0ustar hinsenstaff00000000000000sidechain = Group('phe_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Phe' amber91_charge = {sidechain.C_epsilon_1: 0.004, sidechain.C_delta_1: -0.011, peptide.H: 0.248, sidechain.C_epsilon_2: 0.004, peptide.C: 0.444, peptide.C_alpha: 0.208, sidechain.C_zeta: -0.003, sidechain.C_delta_2: -0.011, peptide.O_2: -0.706, peptide.O: -0.706, peptide.N: -0.52, sidechain.C_gamma: 0.011, sidechain.C_beta: 0.038, } name = 'phenylalanine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/phenylalanine_noh0000644000076600000240000000077111662461637022103 0ustar hinsenstaff00000000000000sidechain = Group('phe_sidechain_noh') peptide = Group('peptide_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Phe' amber_charge = {sidechain.C_beta: -0.0343, sidechain.C_epsilon_2: -0.1704, peptide.C_alpha: -0.0024, sidechain.C_zeta: -0.1072, sidechain.C_delta_1: -0.1256, peptide.O: -0.5679, sidechain.C_delta_2: -0.1256, peptide.N: -0.4157, sidechain.C_epsilon_1: -0.1704, sidechain.C_gamma: 0.0118, peptide.C: 0.5973, } name = 'phenylalanine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/phenylalanine_nt0000644000076600000240000000141511777301631021726 0ustar hinsenstaff00000000000000sidechain = Group('phe_sidechain') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Phe' amber_charge = {peptide.H_3: 0.1921, sidechain.H_epsilon_2: 0.1433, sidechain.C_delta_2: -0.1391, sidechain.H_delta_1: 0.1374, sidechain.H_epsilon_1: 0.1433, sidechain.H_beta_2: 0.0104, peptide.H_1: 0.1921, sidechain.C_zeta: -0.1208, sidechain.C_beta: 0.033, sidechain.C_epsilon_1: -0.1602, sidechain.C_gamma: 0.0031, sidechain.C_epsilon_2: -0.1603, sidechain.C_delta_1: -0.1392, peptide.C: 0.6123, peptide.N: 0.1737, peptide.C_alpha: 0.0733, sidechain.H_delta_2: 0.1374, sidechain.H_beta_3: 0.0104, peptide.H_alpha: 0.1041, peptide.H_2: 0.1921, sidechain.H_zeta: 0.1329, peptide.O: -0.5713, } name = 'phenylalanine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/phenylalanine_nt_ct_noh0000644000076600000240000000030111662461637023257 0ustar hinsenstaff00000000000000sidechain = Group('phe_sidechain_noh') peptide = Group('peptide_nt_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Phe' name = 'phenylalanine' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/phenylalanine_nt_noh0000644000076600000240000000076311662461637022605 0ustar hinsenstaff00000000000000sidechain = Group('phe_sidechain_noh') peptide = Group('peptide_nt_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Phe' amber_charge = {sidechain.C_epsilon_1: -0.1602, sidechain.C_delta_2: -0.1391, peptide.C_alpha: 0.0733, sidechain.C_epsilon_2: -0.1603, sidechain.C_delta_1: -0.1392, peptide.N: 0.1737, peptide.O: -0.5713, peptide.C: 0.6123, sidechain.C_beta: 0.033, sidechain.C_gamma: 0.0031, sidechain.C_zeta: -0.1208, } name = 'phenylalanine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/phenylalanine_nt_uni0000644000076600000240000000104411662461637022605 0ustar hinsenstaff00000000000000sidechain = Group('phe_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Phe' amber91_charge = {sidechain.C_delta_1: -0.011, peptide.O: -0.5, peptide.H_2: 0.312, peptide.C: 0.526, sidechain.C_delta_2: -0.011, sidechain.C_epsilon_2: 0.004, peptide.C_alpha: 0.269, peptide.H_1: 0.312, sidechain.C_gamma: 0.011, peptide.N: -0.263, sidechain.C_zeta: -0.003, sidechain.C_beta: 0.038, sidechain.C_epsilon_1: 0.004, peptide.H_3: 0.312, } name = 'phenylalanine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/phenylalanine_nt_uni20000644000076600000240000000104411662461637022667 0ustar hinsenstaff00000000000000sidechain = Group('phe_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Phe' amber91_charge = {sidechain.C_zeta: -0.003, peptide.H_2: 0.312, peptide.H_1: 0.312, sidechain.C_delta_2: -0.011, sidechain.C_gamma: 0.011, peptide.C_alpha: 0.269, sidechain.C_delta_1: -0.011, sidechain.C_beta: 0.038, peptide.H_3: 0.312, sidechain.C_epsilon_2: 0.004, peptide.O: -0.5, peptide.N: -0.263, sidechain.C_epsilon_1: 0.004, peptide.C: 0.526, } name = 'phenylalanine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/phenylalanine_uni0000644000076600000240000000077311662461637022114 0ustar hinsenstaff00000000000000sidechain = Group('phe_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Phe' name = 'phenylalanine' chain_links = [peptide.N, peptide.C] amber91_charge = {sidechain.C_epsilon_2: 0.004, peptide.C: 0.526, peptide.N: -0.52, sidechain.C_delta_1: -0.011, peptide.O: -0.5, sidechain.C_delta_2: -0.011, sidechain.C_zeta: -0.003, peptide.H: 0.248, sidechain.C_gamma: 0.011, peptide.C_alpha: 0.214, sidechain.C_epsilon_1: 0.004, sidechain.C_beta: 0.038, } MMTK-2.7.9/MMTK/Database/Groups/phenylalanine_uni20000644000076600000240000000077311662461637022176 0ustar hinsenstaff00000000000000sidechain = Group('phe_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Phe' amber91_charge = {peptide.C_alpha: 0.214, sidechain.C_beta: 0.038, sidechain.C_epsilon_2: 0.004, peptide.C: 0.526, peptide.H: 0.248, sidechain.C_delta_1: -0.011, sidechain.C_epsilon_1: 0.004, peptide.N: -0.52, sidechain.C_zeta: -0.003, sidechain.C_gamma: 0.011, peptide.O: -0.5, sidechain.C_delta_2: -0.011, } name = 'phenylalanine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/phenylalanine_uni30000644000076600000240000000077411662461637022200 0ustar hinsenstaff00000000000000sidechain = Group('phe_sidechain_uni3') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Phe' name = 'phenylalanine' chain_links = [peptide.N, peptide.C] amber91_charge = {sidechain.C_epsilon_2: 0.004, peptide.C: 0.526, peptide.N: -0.52, sidechain.C_delta_1: -0.011, peptide.O: -0.5, sidechain.C_delta_2: -0.011, sidechain.C_zeta: -0.003, peptide.H: 0.248, sidechain.C_gamma: 0.011, peptide.C_alpha: 0.214, sidechain.C_epsilon_1: 0.004, sidechain.C_beta: 0.038, } MMTK-2.7.9/MMTK/Database/Groups/pro_sidechain0000644000076600000240000000173512041514571021204 0ustar hinsenstaff00000000000000H_gamma_3 = Atom('h') H_gamma_2 = Atom('h') H_delta_2 = Atom('h') H_delta_3 = Atom('h') C_gamma = Atom('c') C_delta = Atom('c') C_beta = Atom('c') H_beta_2 = Atom('h') H_beta_3 = Atom('h') bonds = [Bond(H_delta_3, C_delta), Bond(H_delta_2, C_delta), Bond(C_gamma, C_delta), Bond(H_gamma_2, C_gamma), Bond(H_gamma_3, C_gamma), Bond(C_beta, C_gamma), Bond(H_beta_2, C_beta), Bond(H_beta_3, C_beta), ] pdb_alternative = {'1HB': '3HB', '1HD': '3HD', '1HG': '3HG', 'HG1': '3HG', 'HB3': '3HB', 'HG3': '3HG', 'HB1': '3HB', 'HD1': '3HD', 'HD2': '2HD', 'HD3': '3HD', 'HG2': '2HG', 'HB2': '2HB', } pdbmap = [('PRO', {'2HG': H_gamma_2, '2HD': H_delta_2, '2HB': H_beta_2, '3HG': H_gamma_3, '3HD': H_delta_3, '3HB': H_beta_3, 'CD': C_delta, 'CG': C_gamma, 'CB': C_beta, }, ), ] amber_atom_type = {C_gamma: 'CT', H_beta_2: 'HC', C_delta: 'CT', H_gamma_2: 'HC', C_beta: 'CT', H_beta_3: 'HC', H_delta_2: 'H1', H_gamma_3: 'HC', H_delta_3: 'H1', } name = 'pro_sidechain' amber12_atom_type = amber_atom_type MMTK-2.7.9/MMTK/Database/Groups/pro_sidechain_noh0000644000076600000240000000043211662461637022055 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta = Atom('CH2') C_gamma = Atom('CH2') bonds = [Bond(C_gamma, C_delta), Bond(C_beta, C_gamma), ] pdbmap = [('PRO', {'CD': C_delta, 'CB': C_beta, 'CG': C_gamma, }, ), ] amber_atom_type = {C_beta: 'CT', C_gamma: 'CT', C_delta: 'CT', } name = 'pro_sidechain' MMTK-2.7.9/MMTK/Database/Groups/pro_sidechain_nt0000644000076600000240000000173512041514571021705 0ustar hinsenstaff00000000000000H_gamma_3 = Atom('h') H_gamma_2 = Atom('h') H_delta_2 = Atom('h') H_delta_3 = Atom('h') C_gamma = Atom('c') C_delta = Atom('c') C_beta = Atom('c') H_beta_2 = Atom('h') H_beta_3 = Atom('h') bonds = [Bond(H_delta_3, C_delta), Bond(H_delta_2, C_delta), Bond(C_gamma, C_delta), Bond(H_gamma_2, C_gamma), Bond(H_gamma_3, C_gamma), Bond(C_beta, C_gamma), Bond(H_beta_2, C_beta), Bond(H_beta_3, C_beta), ] pdb_alternative = {'1HB': '3HB', '1HD': '3HD', '1HG': '3HG', 'HG1': '3HG', 'HB3': '3HB', 'HG3': '3HG', 'HB1': '3HB', 'HD1': '3HD', 'HD2': '2HD', 'HD3': '3HD', 'HG2': '2HG', 'HB2': '2HB', } pdbmap = [('PRO', {'2HG': H_gamma_2, '2HD': H_delta_2, '2HB': H_beta_2, '3HG': H_gamma_3, '3HD': H_delta_3, '3HB': H_beta_3, 'CD': C_delta, 'CG': C_gamma, 'CB': C_beta, }, ), ] amber_atom_type = {C_gamma: 'CT', H_beta_2: 'HC', C_delta: 'CT', H_gamma_2: 'HC', C_beta: 'CT', H_beta_3: 'HC', H_delta_2: 'HP', H_gamma_3: 'HC', H_delta_3: 'HP', } name = 'pro_sidechain' amber12_atom_type = amber_atom_type MMTK-2.7.9/MMTK/Database/Groups/pro_sidechain_uni0000644000076600000240000000072611662461637022072 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta = Atom('CH2') C_gamma = Atom('CH2') bonds = [Bond(C_gamma, C_delta), Bond(C_beta, C_gamma), ] pdbmap = [('PRO', {'CD': C_delta, 'CB': C_beta, 'CG': C_gamma, }, ), ] pdb_alternative = {'HG1': '3HG', 'HD3': '1HD', 'HB1': '3HB', } amber91_atom_type = {C_delta: 'C2', C_gamma: 'C2', C_beta: 'C2', } opls_atom_type = {C_delta: 'CQ', C_gamma: 'C2', C_beta: 'C2', } opls_charge = {C_delta: .285, C_gamma: 0.0, C_beta: 0.0, } name = 'pro_sidechain' MMTK-2.7.9/MMTK/Database/Groups/pro_sidechain_uni20000644000076600000240000000053311662461637022150 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta = Atom('CH2') C_gamma = Atom('CH2') bonds = [Bond(C_gamma, C_delta), Bond(C_beta, C_gamma), ] pdbmap = [('PRO', {'CD': C_delta, 'CB': C_beta, 'CG': C_gamma, }, ), ] pdb_alternative = {'HG1': '3HG', 'HD3': '1HD', 'HB1': '3HB', } amber91_atom_type = {C_delta: 'C2', C_gamma: 'C2', C_beta: 'C2', } name = 'pro_sidechain' MMTK-2.7.9/MMTK/Database/Groups/pro_sidechain_uni30000644000076600000240000000072611662461637022155 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta = Atom('CH2') C_gamma = Atom('CH2') bonds = [Bond(C_gamma, C_delta), Bond(C_beta, C_gamma), ] pdbmap = [('PRO', {'CD': C_delta, 'CB': C_beta, 'CG': C_gamma, }, ), ] pdb_alternative = {'HG1': '3HG', 'HD3': '1HD', 'HB1': '3HB', } amber91_atom_type = {C_delta: 'C2', C_gamma: 'C2', C_beta: 'C2', } opls_atom_type = {C_delta: 'CQ', C_gamma: 'C2', C_beta: 'C2', } opls_charge = {C_delta: .285, C_gamma: 0.0, C_beta: 0.0, } name = 'pro_sidechain' MMTK-2.7.9/MMTK/Database/Groups/proline0000644000076600000240000000113711777301631020047 0ustar hinsenstaff00000000000000sidechain = Group('pro_sidechain') peptide = Group('peptide_proline') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), Bond(peptide.N, sidechain.C_delta), ] symbol = 'Pro' amber_charge = {peptide.H_alpha: 0.0641, sidechain.H_gamma_2: 0.0213, sidechain.C_beta: -0.007, sidechain.H_delta_2: 0.0391, peptide.C: 0.5896, sidechain.H_delta_3: 0.0391, sidechain.H_beta_2: 0.0253, sidechain.H_beta_3: 0.0253, sidechain.C_delta: 0.0192, peptide.N: -0.2548, peptide.O: -0.5748, sidechain.H_gamma_3: 0.0213, peptide.C_alpha: -0.0266, sidechain.C_gamma: 0.0189, } name = 'proline' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/proline_calpha0000644000076600000240000000020011662461637021353 0ustar hinsenstaff00000000000000symbol = 'Pro' name = 'proline' C_alpha = Atom('proline') pdbmap = [('PRO', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/proline_ct0000644000076600000240000000116411777301631020535 0ustar hinsenstaff00000000000000sidechain = Group('pro_sidechain') peptide = Group('peptide_proline_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), Bond(peptide.N, sidechain.C_delta), ] symbol = 'Pro' amber_charge = {sidechain.H_delta_2: 0.0331, sidechain.H_beta_2: 0.0381, sidechain.C_delta: 0.0434, peptide.C_alpha: -0.1336, peptide.N: -0.2802, sidechain.C_beta: -0.0543, sidechain.H_gamma_2: 0.0172, sidechain.H_beta_3: 0.0381, sidechain.C_gamma: 0.0466, peptide.H_alpha: 0.0776, sidechain.H_gamma_3: 0.0172, sidechain.H_delta_3: 0.0331, peptide.O_2: -0.7697, peptide.O: -0.7697, peptide.C: 0.6631, } name = 'proline' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/proline_ct_noh0000644000076600000240000000066711662461637021416 0ustar hinsenstaff00000000000000sidechain = Group('pro_sidechain_noh') peptide = Group('peptide_proline_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), Bond(peptide.N, sidechain.C_delta), ] symbol = 'Pro' amber_charge = {peptide.O_2: -0.7697, peptide.C: 0.6631, sidechain.C_delta: 0.0434, sidechain.C_gamma: 0.0466, peptide.O: -0.7697, sidechain.C_beta: -0.0543, peptide.N: -0.2802, peptide.C_alpha: -0.1336, } name = 'proline' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/proline_ct_uni0000644000076600000240000000065711662461637021424 0ustar hinsenstaff00000000000000sidechain = Group('pro_sidechain_uni') peptide = Group('peptide_proline_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), Bond(peptide.N, sidechain.C_delta), ] symbol = 'Pro' amber91_charge = {peptide.C_alpha: 0.112, sidechain.C_delta: 0.084, peptide.O_2: -0.706, peptide.C: 0.444, sidechain.C_gamma: 0.03, sidechain.C_beta: -0.001, peptide.N: -0.257, peptide.O: -0.706, } name = 'proline' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/proline_ct_uni20000644000076600000240000000065711662461637021506 0ustar hinsenstaff00000000000000sidechain = Group('pro_sidechain_uni') peptide = Group('peptide_proline_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), Bond(peptide.N, sidechain.C_delta), ] symbol = 'Pro' amber91_charge = {peptide.O: -0.706, sidechain.C_delta: 0.084, peptide.O_2: -0.706, sidechain.C_gamma: 0.03, sidechain.C_beta: -0.001, peptide.N: -0.257, peptide.C_alpha: 0.112, peptide.C: 0.444, } name = 'proline' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/proline_ct_uni30000644000076600000240000000065711662461637021507 0ustar hinsenstaff00000000000000sidechain = Group('pro_sidechain_uni') peptide = Group('peptide_proline_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), Bond(peptide.N, sidechain.C_delta), ] symbol = 'Pro' amber91_charge = {peptide.C_alpha: 0.112, sidechain.C_delta: 0.084, peptide.O_2: -0.706, peptide.C: 0.444, sidechain.C_gamma: 0.03, sidechain.C_beta: -0.001, peptide.N: -0.257, peptide.O: -0.706, } name = 'proline' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/proline_noh0000644000076600000240000000064211662461637020721 0ustar hinsenstaff00000000000000sidechain = Group('pro_sidechain_noh') peptide = Group('peptide_proline_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), Bond(peptide.N, sidechain.C_delta), ] symbol = 'Pro' amber_charge = {sidechain.C_beta: -0.007, peptide.O: -0.5748, sidechain.C_gamma: 0.0189, peptide.C: 0.5896, sidechain.C_delta: 0.0192, peptide.C_alpha: -0.0266, peptide.N: -0.2548, } name = 'proline' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/proline_nt0000644000076600000240000000115211777301631020545 0ustar hinsenstaff00000000000000sidechain = Group('pro_sidechain_nt') peptide = Group('peptide_proline_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), Bond(peptide.N, sidechain.C_delta), ] symbol = 'Pro' amber_charge = {peptide.H_3: 0.312, sidechain.H_beta_2: 0.1, peptide.N: -0.202, peptide.C: 0.526, sidechain.H_gamma_3: 0.1, sidechain.C_gamma: -0.121, sidechain.H_beta_3: 0.1, sidechain.C_beta: -0.115, peptide.C_alpha: 0.1, sidechain.H_gamma_2: 0.1, peptide.H_alpha: 0.1, sidechain.C_delta: -0.012, sidechain.H_delta_3: 0.1, peptide.O: -0.5, sidechain.H_delta_2: 0.1, peptide.H_2: 0.312, } name = 'proline' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/proline_nt_ct_noh0000644000076600000240000000034711662461637022112 0ustar hinsenstaff00000000000000sidechain = Group('pro_sidechain_noh') peptide = Group('peptide_proline_nt_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), Bond(peptide.N, sidechain.C_delta), ] symbol = 'Pro' name = 'proline' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/proline_nt_noh0000644000076600000240000000062711662461637021425 0ustar hinsenstaff00000000000000sidechain = Group('pro_sidechain_noh') peptide = Group('peptide_proline_nt_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), Bond(peptide.N, sidechain.C_delta), ] symbol = 'Pro' amber_charge = {sidechain.C_beta: -0.115, sidechain.C_gamma: -0.121, peptide.C_alpha: 0.1, sidechain.C_delta: -0.012, peptide.O: -0.5, peptide.N: -0.202, peptide.C: 0.526, } name = 'proline' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/proline_nt_uni0000644000076600000240000000070111662461637021425 0ustar hinsenstaff00000000000000sidechain = Group('pro_sidechain_uni') peptide = Group('peptide_proline_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), Bond(peptide.N, sidechain.C_delta), ] symbol = 'Pro' amber91_charge = {peptide.H_3: 0.312, peptide.H_2: 0.312, sidechain.C_delta: 0.191, peptide.C: 0.596, sidechain.C_gamma: -0.011, peptide.O: -0.451, sidechain.C_beta: 0.0, peptide.C_alpha: 0.212, peptide.N: -0.161, } name = 'proline' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/proline_nt_uni20000644000076600000240000000070111662461637021507 0ustar hinsenstaff00000000000000sidechain = Group('pro_sidechain_uni') peptide = Group('peptide_proline_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), Bond(peptide.N, sidechain.C_delta), ] symbol = 'Pro' amber91_charge = {peptide.C_alpha: 0.212, peptide.O: -0.451, sidechain.C_delta: 0.191, peptide.N: -0.161, peptide.H_2: 0.312, peptide.H_3: 0.312, peptide.C: 0.596, sidechain.C_beta: 0.0, sidechain.C_gamma: -0.011, } name = 'proline' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/proline_nt_uni30000644000076600000240000000070111662461637021510 0ustar hinsenstaff00000000000000sidechain = Group('pro_sidechain_uni') peptide = Group('peptide_proline_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), Bond(peptide.N, sidechain.C_delta), ] symbol = 'Pro' amber91_charge = {peptide.H_3: 0.312, peptide.H_2: 0.312, sidechain.C_delta: 0.191, peptide.C: 0.596, sidechain.C_gamma: -0.011, peptide.O: -0.451, sidechain.C_beta: 0.0, peptide.C_alpha: 0.212, peptide.N: -0.161, } name = 'proline' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/proline_uni0000644000076600000240000000063311662461637020730 0ustar hinsenstaff00000000000000sidechain = Group('pro_sidechain_uni') peptide = Group('peptide_proline_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), Bond(peptide.N, sidechain.C_delta), ] symbol = 'Pro' amber91_charge = {sidechain.C_beta: -0.001, peptide.C_alpha: 0.112, peptide.N: -0.257, sidechain.C_delta: 0.084, sidechain.C_gamma: 0.036, peptide.C: 0.526, peptide.O: -0.5, } name = 'proline' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/proline_uni20000644000076600000240000000063311662461637021012 0ustar hinsenstaff00000000000000sidechain = Group('pro_sidechain_uni') peptide = Group('peptide_proline_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), Bond(peptide.N, sidechain.C_delta), ] symbol = 'Pro' amber91_charge = {peptide.C: 0.526, peptide.O: -0.5, sidechain.C_gamma: 0.036, peptide.C_alpha: 0.112, sidechain.C_beta: -0.001, peptide.N: -0.257, sidechain.C_delta: 0.084, } name = 'proline' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/proline_uni30000644000076600000240000000063311662461637021013 0ustar hinsenstaff00000000000000sidechain = Group('pro_sidechain_uni') peptide = Group('peptide_proline_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), Bond(peptide.N, sidechain.C_delta), ] symbol = 'Pro' amber91_charge = {sidechain.C_beta: -0.001, peptide.C_alpha: 0.112, peptide.N: -0.257, sidechain.C_delta: 0.084, sidechain.C_gamma: 0.036, peptide.C: 0.526, peptide.O: -0.5, } name = 'proline' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/r-adenosine0000644000076600000240000000313611777301631020604 0ustar hinsenstaff00000000000000name ='r-adenosine' symbol ='RA' phosphate = Group('na_phosphate') sugar = Group('ribose') base = Group('adenine') bonds = [Bond(sugar.O_5, phosphate.P), Bond(base.N_9, sugar.C_1), ] chain_links = [phosphate.P, sugar.O_3] amber_charge = {phosphate.P: 1.1662, phosphate.O_1: -0.7760, phosphate.O_2: -0.7760, sugar.O_5: -0.4989, sugar.C_5: 0.0558, sugar.H_51: 0.0679, sugar.H_52: 0.0679, sugar.C_4: 0.1065, sugar.H_4: 0.1174, sugar.O_4: -0.3548, sugar.C_1: 0.0394, sugar.H_1: 0.2007, base.N_9: -0.0251, base.C_8: 0.2006, base.H_8: 0.1553, base.N_7: -0.6073, base.C_5: 0.0515, base.C_6: 0.7009, base.N_6: -0.9019, base.H_61: 0.4115, base.H_62: 0.4115, base.N_1: -0.7615, base.C_2: 0.5875, base.H_2: 0.0473, base.N_3: -0.6997, base.C_4: 0.3053, sugar.C_3: 0.2022, sugar.H_3: 0.0615, sugar.C_2: 0.0670, sugar.H_21: 0.0972, sugar.O_2: -0.6139, sugar.H_O2: 0.4186, sugar.O_3: -0.5246, } MMTK-2.7.9/MMTK/Database/Groups/r-adenosine_3ter0000644000076600000240000000347011777301631021542 0ustar hinsenstaff00000000000000name ='r-adenosine_3ter' symbol ='RA3' phosphate = Group('na_phosphate') sugar = Group('ribose_3ter') base = Group('adenine') bonds = [Bond(sugar.O_5, phosphate.P), Bond(base.N_9, sugar.C_1), ] chain_links = [phosphate.P, None] amber_charge = {phosphate.P: 1.1662, phosphate.O_1: -0.7760, phosphate.O_2: -0.7760, sugar.O_5: -0.4989, sugar.C_5: 0.0558, sugar.H_51: 0.0679, sugar.H_52: 0.0679, sugar.C_4: 0.1065, sugar.H_4: 0.1174, sugar.O_4: -0.3548, sugar.C_1: 0.0394, sugar.H_1: 0.2007, base.N_9: -0.0251, base.C_8: 0.2006, base.H_8: 0.1553, base.N_7: -0.6073, base.C_5: 0.0515, base.C_6: 0.7009, base.N_6: -0.9019, base.H_61: 0.4115, base.H_62: 0.4115, base.N_1: -0.7615, base.C_2: 0.5875, base.H_2: 0.0473, base.N_3: -0.6997, base.C_4: 0.3053, sugar.C_3: 0.2022, sugar.H_3: 0.0615, sugar.C_2: 0.0670, sugar.H_21: 0.0972, sugar.O_2: -0.6139, sugar.H_O2: 0.4186, sugar.O_3: -0.6541, sugar.H_3_terminal: 0.4376, } MMTK-2.7.9/MMTK/Database/Groups/r-adenosine_5ter0000644000076600000240000000305311777301631021541 0ustar hinsenstaff00000000000000name ='r-adenosine_5ter' symbol ='RA5' sugar = Group('ribose_5ter') base = Group('adenine') bonds = [Bond(base.N_9, sugar.C_1), ] chain_links = [None, sugar.O_3] amber_charge = {sugar.H_5_terminal: 0.4295, sugar.O_5: -0.6223, sugar.C_5: 0.0558, sugar.H_51: 0.0679, sugar.H_52: 0.0679, sugar.C_4: 0.1065, sugar.H_4: 0.1174, sugar.O_4: -0.3548, sugar.C_1: 0.0394, sugar.H_1: 0.2007, base.N_9: -0.0251, base.C_8: 0.2006, base.H_8: 0.1553, base.N_7: -0.6073, base.C_5: 0.0515, base.C_6: 0.7009, base.N_6: -0.9019, base.H_61: 0.4115, base.H_62: 0.4115, base.N_1: -0.7615, base.C_2: 0.5875, base.H_2: 0.0473, base.N_3: -0.6997, base.C_4: 0.3053, sugar.C_3: 0.2022, sugar.H_3: 0.0615, sugar.C_2: 0.0670, sugar.H_21: 0.0972, sugar.O_2: -0.6139, sugar.H_O2: 0.4186, sugar.O_3: -0.5246, } MMTK-2.7.9/MMTK/Database/Groups/r-adenosine_5ter_3ter0000644000076600000240000000323511777301631022500 0ustar hinsenstaff00000000000000name ='r-adenosine_5ter_3ter' symbol ='RAN' sugar = Group('ribose_5ter_3ter') base = Group('adenine') bonds = [Bond(base.N_9, sugar.C_1), ] chain_links = [None, None] amber_charge = {sugar.H_5_terminal: 0.4295, sugar.O_5: -0.6223, sugar.C_5: 0.0558, sugar.H_51: 0.0679, sugar.H_52: 0.0679, sugar.C_4: 0.1065, sugar.H_4: 0.1174, sugar.O_4: -0.3548, sugar.C_1: 0.0394, sugar.H_1: 0.2007, base.N_9: -0.0251, base.C_8: 0.2006, base.H_8: 0.1553, base.N_7: -0.6073, base.C_5: 0.0515, base.C_6: 0.7009, base.N_6: -0.9019, base.H_61: 0.4115, base.H_62: 0.4115, base.N_1: -0.7615, base.C_2: 0.5875, base.H_2: 0.0473, base.N_3: -0.6997, base.C_4: 0.3053, sugar.C_3: 0.2022, sugar.H_3: 0.0615, sugar.C_2: 0.0670, sugar.H_21: 0.0972, sugar.O_2: -0.6139, sugar.H_O2: 0.4186, sugar.O_3: -0.6541, sugar.H_3_terminal: 0.4376, } MMTK-2.7.9/MMTK/Database/Groups/r-cytosine0000644000076600000240000000275311777301631020500 0ustar hinsenstaff00000000000000name ='r-cytosine' symbol ='RC' phosphate = Group('na_phosphate') sugar = Group('ribose') base = Group('cytosine') bonds = [Bond(sugar.O_5, phosphate.P), Bond(base.N_1, sugar.C_1), ] chain_links = [phosphate.P, sugar.O_3] amber_charge = {phosphate.P: 1.1662, phosphate.O_1: -0.7760, phosphate.O_2: -0.7760, sugar.O_5: -0.4989, sugar.C_5: 0.0558, sugar.H_51: 0.0679, sugar.H_52: 0.0679, sugar.C_4: 0.1065, sugar.H_4: 0.1174, sugar.O_4: -0.3548, sugar.C_1: 0.0066, sugar.H_1: 0.2029, base.N_1: -0.0484, base.C_6: 0.0053, base.H_6: 0.1958, base.C_5: -0.5215, base.H_5: 0.1928, base.C_4: 0.8185, base.N_4: -0.9530, base.H_41: 0.4234, base.H_42: 0.4234, base.N_3: -0.7584, base.C_2: 0.7538, base.O_2: -0.6252, sugar.C_3: 0.2022, sugar.H_3: 0.0615, sugar.C_2: 0.0670, sugar.H_21: 0.0972, sugar.O_2: -0.6139, sugar.H_O2: 0.4186, sugar.O_3: -0.5246, } MMTK-2.7.9/MMTK/Database/Groups/r-cytosine_3ter0000644000076600000240000000327211777301631021432 0ustar hinsenstaff00000000000000name ='r-cytosine_3ter' symbol ='RC3' phosphate = Group('na_phosphate') sugar = Group('ribose_3ter') base = Group('cytosine') bonds = [Bond(sugar.O_5, phosphate.P), Bond(base.N_1, sugar.C_1), ] chain_links = [phosphate.P, None] amber_charge = {phosphate.P: 1.1662, phosphate.O_1: -0.7760, phosphate.O_2: -0.7760, sugar.O_5: -0.4989, sugar.C_5: 0.0558, sugar.H_51: 0.0679, sugar.H_52: 0.0679, sugar.C_4: 0.1065, sugar.H_4: 0.1174, sugar.O_4: -0.3548, sugar.C_1: 0.0066, sugar.H_1: 0.2029, base.N_1: -0.0484, base.C_6: 0.0053, base.H_6: 0.1958, base.C_5: -0.5215, base.H_5: 0.1928, base.C_4: 0.8185, base.N_4: -0.9530, base.H_41: 0.4234, base.H_42: 0.4234, base.N_3: -0.7584, base.C_2: 0.7538, base.O_2: -0.6252, sugar.C_3: 0.2022, sugar.H_3: 0.0615, sugar.C_2: 0.0670, sugar.H_21: 0.0972, sugar.O_2: -0.6139, sugar.H_O2: 0.4186, sugar.O_3: -0.6541, sugar.H_3_terminal: 0.4376, } MMTK-2.7.9/MMTK/Database/Groups/r-cytosine_5ter0000644000076600000240000000272111777301631021432 0ustar hinsenstaff00000000000000name ='r-cytosine_5ter' symbol ='RC5' sugar = Group('ribose_5ter') base = Group('cytosine') bonds = [Bond(base.N_1, sugar.C_1), ] chain_links = [None, sugar.O_3] amber_charge = {sugar.H_5_terminal: 0.4295, sugar.O_5: -0.6223, sugar.C_5: 0.0558, sugar.H_51: 0.0679, sugar.H_52: 0.0679, sugar.C_4: 0.1065, sugar.H_4: 0.1174, sugar.O_4: -0.3548, sugar.C_1: 0.0066, sugar.H_1: 0.2029, base.N_1: -0.0484, base.C_6: 0.0053, base.H_6: 0.1958, base.C_5: -0.5215, base.H_5: 0.1928, base.C_4: 0.8185, base.N_4: -0.9530, base.H_41: 0.4234, base.H_42: 0.4234, base.N_3: -0.7584, base.C_2: 0.7538, base.O_2: -0.6252, sugar.C_3: 0.2022, sugar.H_3: 0.0615, sugar.C_2: 0.0670, sugar.H_21: 0.0972, sugar.O_2: -0.6139, sugar.H_O2: 0.4186, sugar.O_3: -0.5246, } MMTK-2.7.9/MMTK/Database/Groups/r-cytosine_5ter_3ter0000644000076600000240000000304111777301631022363 0ustar hinsenstaff00000000000000name ='r-cytosine_5ter_3ter' symbol ='RCN' sugar = Group('ribose_5ter_3ter') base = Group('cytosine') bonds = [Bond(base.N_1, sugar.C_1), ] chain_links = [None, None] amber_charge = {sugar.H_5_terminal: 0.4295, sugar.O_5: -0.6223, sugar.C_5: 0.0558, sugar.H_51: 0.0679, sugar.H_52: 0.0679, sugar.C_4: 0.1065, sugar.H_4: 0.1174, sugar.O_4: -0.3548, sugar.C_1: 0.0066, sugar.H_1: 0.2029, base.N_1: -0.0484, base.C_6: 0.0053, base.H_6: 0.1958, base.C_5: -0.5215, base.H_5: 0.1928, base.C_4: 0.8185, base.N_4: -0.9530, base.H_41: 0.4234, base.H_42: 0.4234, base.N_3: -0.7584, base.C_2: 0.7538, base.O_2: -0.6252, sugar.C_3: 0.2022, sugar.H_3: 0.0615, sugar.C_2: 0.0670, sugar.H_21: 0.0972, sugar.O_2: -0.6139, sugar.H_O2: 0.4186, sugar.O_3: -0.6541, sugar.H_3_terminal: 0.4376, } MMTK-2.7.9/MMTK/Database/Groups/r-guanosine0000644000076600000240000000314611777301631020630 0ustar hinsenstaff00000000000000name ='r-guanosine' symbol ='RG' phosphate = Group('na_phosphate') sugar = Group('ribose') base = Group('guanine') bonds = [Bond(sugar.O_5, phosphate.P), Bond(base.N_9, sugar.C_1), ] chain_links = [phosphate.P, sugar.O_3] amber_charge = {phosphate.P: 1.1662, phosphate.O_1: -0.7760, phosphate.O_2: -0.7760, sugar.O_5: -0.4989, sugar.C_5: 0.0558, sugar.H_51: 0.0679, sugar.H_52: 0.0679, sugar.C_4: 0.1065, sugar.H_4: 0.1174, sugar.O_4: -0.3548, sugar.C_1: 0.0191, sugar.H_1: 0.2006, base.N_9: 0.0492, base.C_8: 0.1374, base.H_8: 0.1640, base.N_7: -0.5709, base.C_5: 0.1744, base.C_6: 0.4770, base.O_6: -0.5597, base.N_1: -0.4787, base.H_1: 0.3424, base.C_2: 0.7657, base.N_2: -0.9672, base.H_21: 0.4364, base.H_22: 0.4364, base.N_3: -0.6323, base.C_4: 0.1222, sugar.C_3: 0.2022, sugar.H_3: 0.0615, sugar.C_2: 0.0670, sugar.H_21: 0.0972, sugar.O_2: -0.6139, sugar.H_O2: 0.4186, sugar.O_3: -0.5246, } MMTK-2.7.9/MMTK/Database/Groups/r-guanosine_3ter0000644000076600000240000000354711777301631021572 0ustar hinsenstaff00000000000000name ='r-guanosine_3ter' symbol ='RG3' phosphate = Group('na_phosphate') sugar = Group('ribose_3ter') base = Group('guanine') bonds = [Bond(sugar.O_5, phosphate.P), Bond(base.N_9, sugar.C_1), ] chain_links = [phosphate.P, None] amber_charge = {phosphate.P: 1.1662, phosphate.O_1: -0.7760, phosphate.O_2: -0.7760, sugar.O_5: -0.4989, sugar.C_5: 0.0558, sugar.H_51: 0.0679, sugar.H_52: 0.0679, sugar.C_4: 0.1065, sugar.H_4: 0.1174, sugar.O_4: -0.3548, sugar.C_1: 0.0191, sugar.H_1: 0.2006, base.N_9: 0.0492, base.C_8: 0.1374, base.H_8: 0.1640, base.N_7: -0.5709, base.C_5: 0.1744, base.C_6: 0.4770, base.O_6: -0.5597, base.N_1: -0.4787, base.H_1: 0.3424, base.C_2: 0.7657, base.N_2: -0.9672, base.H_21: 0.4364, base.H_22: 0.4364, base.N_3: -0.6323, base.C_4: 0.1222, sugar.C_3: 0.2022, sugar.H_3: 0.0615, sugar.C_2: 0.0670, sugar.H_21: 0.0972, sugar.O_2: -0.6139, sugar.H_O2: 0.4186, sugar.O_3: -0.6541, sugar.H_3_terminal: 0.4376, } MMTK-2.7.9/MMTK/Database/Groups/r-guanosine_5ter0000644000076600000240000000317011777301631021564 0ustar hinsenstaff00000000000000name ='r-guanosine_5ter' symbol ='RG5' sugar = Group('ribose_5ter') base = Group('guanine') bonds = [Bond(base.N_9, sugar.C_1), ] chain_links = [None, sugar.O_3] amber_charge = {sugar.H_5_terminal: 0.4295, sugar.O_5: -0.6223, sugar.C_5: 0.0558, sugar.H_51: 0.0679, sugar.H_52: 0.0679, sugar.C_4: 0.1065, sugar.H_4: 0.1174, sugar.O_4: -0.3548, sugar.C_1: 0.0191, sugar.H_1: 0.2006, base.N_9: 0.0492, base.C_8: 0.1374, base.H_8: 0.1640, base.N_7: -0.5709, base.C_5: 0.1744, base.C_6: 0.4770, base.O_6: -0.5597, base.N_1: -0.4787, base.H_1: 0.3424, base.C_2: 0.7657, base.N_2: -0.9672, base.H_21: 0.4364, base.H_22: 0.4364, base.N_3: -0.6323, base.C_4: 0.1222, sugar.C_3: 0.2022, sugar.H_3: 0.0615, sugar.C_2: 0.0670, sugar.H_21: 0.0972, sugar.O_2: -0.6139, sugar.H_O2: 0.4186, sugar.O_3: -0.5246, } MMTK-2.7.9/MMTK/Database/Groups/r-guanosine_5ter_3ter0000644000076600000240000000325311777301631022523 0ustar hinsenstaff00000000000000name ='r-guanosine_5ter_3ter' symbol ='RGN' sugar = Group('ribose_5ter_3ter') base = Group('guanine') bonds = [Bond(base.N_9, sugar.C_1), ] chain_links = [None, None] amber_charge = {sugar.H_5_terminal: 0.4295, sugar.O_5: -0.6223, sugar.C_5: 0.0558, sugar.H_51: 0.0679, sugar.H_52: 0.0679, sugar.C_4: 0.1065, sugar.H_4: 0.1174, sugar.O_4: -0.3548, sugar.C_1: 0.0191, sugar.H_1: 0.2006, base.N_9: 0.0492, base.C_8: 0.1374, base.H_8: 0.1640, base.N_7: -0.5709, base.C_5: 0.1744, base.C_6: 0.4770, base.O_6: -0.5597, base.N_1: -0.4787, base.H_1: 0.3424, base.C_2: 0.7657, base.N_2: -0.9672, base.H_21: 0.4364, base.H_22: 0.4364, base.N_3: -0.6323, base.C_4: 0.1222, sugar.C_3: 0.2022, sugar.H_3: 0.0615, sugar.C_2: 0.0670, sugar.H_21: 0.0972, sugar.O_2: -0.6139, sugar.H_O2: 0.4186, sugar.O_3: -0.6541, sugar.H_3_terminal: 0.4376, } MMTK-2.7.9/MMTK/Database/Groups/r-uracil0000644000076600000240000000267611777301631020126 0ustar hinsenstaff00000000000000name ='r-uracil' symbol ='RU' phosphate = Group('na_phosphate') sugar = Group('ribose') base = Group('uracil') bonds = [Bond(sugar.O_5, phosphate.P), Bond(base.N_1, sugar.C_1), ] chain_links = [phosphate.P, sugar.O_3] amber_charge = {phosphate.P: 1.1662, phosphate.O_1: -0.7760, phosphate.O_2: -0.7760, sugar.O_5: -0.4989, sugar.C_5: 0.0558, sugar.H_51: 0.0679, sugar.H_52: 0.0679, sugar.C_4: 0.1065, sugar.H_4: 0.1174, sugar.O_4: -0.3548, sugar.C_1: 0.0674, sugar.H_1: 0.1824, base.N_1: 0.0418, base.C_6: -0.1126, base.H_6: 0.2188, base.C_5: -0.3635, base.H_5: 0.1811, base.C_4: 0.5952, base.O_4: -0.5761, base.N_3: -0.3549, base.H_3: 0.3154, base.C_2: 0.4687, base.O_2: -0.5477, sugar.C_3: 0.2022, sugar.H_3: 0.0615, sugar.C_2: 0.0670, sugar.H_21: 0.0972, sugar.O_2: -0.6139, sugar.H_O2: 0.4186, sugar.O_3: -0.5246, } MMTK-2.7.9/MMTK/Database/Groups/r-uracil_3ter0000644000076600000240000000321011777301631021044 0ustar hinsenstaff00000000000000name ='r-uracil_3ter' symbol ='RU3' phosphate = Group('na_phosphate') sugar = Group('ribose_3ter') base = Group('uracil') bonds = [Bond(sugar.O_5, phosphate.P), Bond(base.N_1, sugar.C_1), ] chain_links = [phosphate.P, None] amber_charge = {phosphate.P: 1.1662, phosphate.O_1: -0.7760, phosphate.O_2: -0.7760, sugar.O_5: -0.4989, sugar.C_5: 0.0558, sugar.H_51: 0.0679, sugar.H_52: 0.0679, sugar.C_4: 0.1065, sugar.H_4: 0.1174, sugar.O_4: -0.3548, sugar.C_1: 0.0674, sugar.H_1: 0.1824, base.N_1: 0.0418, base.C_6: -0.1126, base.H_6: 0.2188, base.C_5: -0.3635, base.H_5: 0.1811, base.C_4: 0.5952, base.O_4: -0.5761, base.N_3: -0.3549, base.H_3: 0.3154, base.C_2: 0.4687, base.O_2: -0.5477, sugar.C_3: 0.2022, sugar.H_3: 0.0615, sugar.C_2: 0.0670, sugar.H_21: 0.0972, sugar.O_2: -0.6139, sugar.H_O2: 0.4186, sugar.O_3: -0.6541, sugar.H_3_terminal: 0.4376, } MMTK-2.7.9/MMTK/Database/Groups/r-uracil_5ter0000644000076600000240000000264011777301631021054 0ustar hinsenstaff00000000000000name ='r-uracil_5ter' symbol ='RU5' sugar = Group('ribose_5ter') base = Group('uracil') bonds = [Bond(base.N_1, sugar.C_1), ] chain_links = [None, sugar.O_3] amber_charge = {sugar.H_5_terminal: 0.4295, sugar.O_5: -0.6223, sugar.C_5: 0.0558, sugar.H_51: 0.0679, sugar.H_52: 0.0679, sugar.C_4: 0.1065, sugar.H_4: 0.1174, sugar.O_4: -0.3548, sugar.C_1: 0.0674, sugar.H_1: 0.1824, base.N_1: 0.0418, base.C_6: -0.1126, base.H_6: 0.2188, base.C_5: -0.3635, base.H_5: 0.1811, base.C_4: 0.5952, base.O_4: -0.5761, base.N_3: -0.3549, base.H_3: 0.3154, base.C_2: 0.4687, base.O_2: -0.5477, sugar.C_3: 0.2022, sugar.H_3: 0.0615, sugar.C_2: 0.0670, sugar.H_21: 0.0972, sugar.O_2: -0.6139, sugar.H_O2: 0.4186, sugar.O_3: -0.5246, } MMTK-2.7.9/MMTK/Database/Groups/r-uracil_5ter_3ter0000644000076600000240000000275711777301631022022 0ustar hinsenstaff00000000000000name ='r-uracil_5ter_3ter' symbol ='RUN' sugar = Group('ribose_5ter_3ter') base = Group('uracil') bonds = [Bond(base.N_1, sugar.C_1), ] chain_links = [None, None] amber_charge = {sugar.H_5_terminal: 0.4295, sugar.O_5: -0.6223, sugar.C_5: 0.0558, sugar.H_51: 0.0679, sugar.H_52: 0.0679, sugar.C_4: 0.1065, sugar.H_4: 0.1174, sugar.O_4: -0.3548, sugar.C_1: 0.0674, sugar.H_1: 0.1824, base.N_1: 0.0418, base.C_6: -0.1126, base.H_6: 0.2188, base.C_5: -0.3635, base.H_5: 0.1811, base.C_4: 0.5952, base.O_4: -0.5761, base.N_3: -0.3549, base.H_3: 0.3154, base.C_2: 0.4687, base.O_2: -0.5477, sugar.C_3: 0.2022, sugar.H_3: 0.0615, sugar.C_2: 0.0670, sugar.H_21: 0.0972, sugar.O_2: -0.6139, sugar.H_O2: 0.4186, sugar.O_3: -0.6541, sugar.H_3_terminal: 0.4376, } MMTK-2.7.9/MMTK/Database/Groups/ribose0000644000076600000240000000264412041514571017660 0ustar hinsenstaff00000000000000name ='ribose' O_5 = Atom('O') C_5 = Atom('C') H_51 = Atom('H') H_52 = Atom('H') C_4 = Atom('C') H_4 = Atom('H') O_4 = Atom('O') C_1 = Atom('C') H_1 = Atom('H') C_3 = Atom('C') H_3 = Atom('H') C_2 = Atom('C') H_21 = Atom('H') O_2 = Atom('O') H_O2 = Atom('H') O_3 = Atom('O') bonds = [Bond(C_5, O_5), Bond(H_51, C_5), Bond(H_52, C_5), Bond(C_4, C_5), Bond(H_4, C_4), Bond(O_4, C_4), Bond(C_1, O_4), Bond(H_1, C_1), Bond(C_3, C_4), Bond(H_3, C_3), Bond(C_2, C_3), Bond(H_21, C_2), Bond(O_2, C_2), Bond(H_O2, O_2), Bond(O_3, C_3), Bond(C_1, C_2), ] pdbmap = [('R', {"1H5*": H_51, "2HO*": H_O2, "2H5*": H_52, "C4*": C_4, "C5*": C_5, "C2*": C_2, "C3*": C_3, "C1*": C_1, "O2*": O_2, "1H2*": H_21, "H1*": H_1, "O3*": O_3, "H3*": H_3, "H4*": H_4, "O4*": O_4, "O5*": O_5, })] pdb_alternative = {"1H5'": "1H5*", "H2'": "2HO*", "2HO'": "2HO*", "2H5'": "2H5*", "C4'": "C4*", "C5'": "C5*", "C2'": "C2*", "C3'": "C3*", "C1'": "C1*", "O2'": "O2*", "1H2'": "1H2*", "H1'": "H1*", "O3'": "O3*", "H3'": "H3*", "H4'": "H4*", "O4'": "O4*", "O5'": "O5*", } amber_atom_type = {H_51: 'H1', H_O2: 'HO', H_52: 'H1', C_4: 'CT', C_5: 'CT', C_2: 'CT', C_3: 'CT', C_1: 'CT', O_2: 'OH', H_21: 'H1', H_1: 'H2', O_3: 'OS', H_3: 'H1', H_4: 'H1', O_4: 'OS', O_5: 'OS', } amber12_atom_type = {H_51: 'H1', H_O2: 'HO', H_52: 'H1', C_4: 'CT', C_5: 'CI', C_2: 'CT', C_3: 'CT', C_1: 'CT', O_2: 'OH', H_21: 'H1', H_1: 'H2', O_3: 'OS', H_3: 'H1', H_4: 'H1', O_4: 'OS', O_5: 'OS', } MMTK-2.7.9/MMTK/Database/Groups/ribose_3ter0000644000076600000240000000302612041514571020610 0ustar hinsenstaff00000000000000name ='ribose_3ter' O_5 = Atom('O') C_5 = Atom('C') H_51 = Atom('H') H_52 = Atom('H') C_4 = Atom('C') H_4 = Atom('H') O_4 = Atom('O') C_1 = Atom('C') H_1 = Atom('H') C_3 = Atom('C') H_3 = Atom('H') C_2 = Atom('C') H_21 = Atom('H') O_2 = Atom('O') H_O2 = Atom('H') O_3 = Atom('O') H_3_terminal = Atom('H') bonds = [Bond(C_5, O_5), Bond(H_51, C_5), Bond(H_52, C_5), Bond(C_4, C_5), Bond(H_4, C_4), Bond(O_4, C_4), Bond(C_1, O_4), Bond(H_1, C_1), Bond(C_3, C_4), Bond(H_3, C_3), Bond(C_2, C_3), Bond(H_21, C_2), Bond(O_2, C_2), Bond(H_O2, O_2), Bond(O_3, C_3), Bond(H_3_terminal, O_3), Bond(C_1, C_2), ] pdbmap = [('R', {"1H5*": H_51, "2HO*": H_O2, "2H5*": H_52, "C4*": C_4, "C5*": C_5, "C2*": C_2, "C3*": C_3, "C1*": C_1, "O2*": O_2, "1H2*": H_21, "H1*": H_1, "O3*": O_3, "H3*": H_3, "H3T": H_3_terminal, "H4*": H_4, "O4*": O_4, "O5*": O_5, })] pdb_alternative = {"1H5'": "1H5*", "H2'": "2HO*", "2HO'": "2HO*", "2H5'": "2H5*", "C4'": "C4*", "C5'": "C5*", "C2'": "C2*", "C3'": "C3*", "C1'": "C1*", "O2'": "O2*", "1H2'": "1H2*", "H1'": "H1*", "O3'": "O3*", "H3'": "H3*", "H4'": "H4*", "O4'": "O4*", "O5'": "O5*"} amber_atom_type = {H_51: 'H1', H_O2: 'HO', H_52: 'H1', C_4: 'CT', C_5: 'CT', C_2: 'CT', C_3: 'CT', C_1: 'CT', O_2: 'OH', H_21: 'H1', H_1: 'H2', O_3: 'OH', H_3: 'H1', H_3_terminal: 'HO', H_4: 'H1', O_4: 'OS', O_5: 'OS', } amber12_atom_type = {H_51: 'H1', H_O2: 'HO', H_52: 'H1', C_4: 'CT', C_5: 'CI', C_2: 'CT', C_3: 'CT', C_1: 'CT', O_2: 'OH', H_21: 'H1', H_1: 'H2', O_3: 'OH', H_3: 'H1', H_3_terminal: 'HO', H_4: 'H1', O_4: 'OS', O_5: 'OS', } MMTK-2.7.9/MMTK/Database/Groups/ribose_5ter0000644000076600000240000000302512041514571020611 0ustar hinsenstaff00000000000000name ='ribose_5ter' H_5_terminal = Atom('H') O_5 = Atom('O') C_5 = Atom('C') H_51 = Atom('H') H_52 = Atom('H') C_4 = Atom('C') H_4 = Atom('H') O_4 = Atom('O') C_1 = Atom('C') H_1 = Atom('H') C_3 = Atom('C') H_3 = Atom('H') C_2 = Atom('C') H_21 = Atom('H') O_2 = Atom('O') H_O2 = Atom('H') O_3 = Atom('O') bonds = [Bond(O_5, H_5_terminal), Bond(C_5, O_5), Bond(H_51, C_5), Bond(H_52, C_5), Bond(C_4, C_5), Bond(H_4, C_4), Bond(O_4, C_4), Bond(C_1, O_4), Bond(H_1, C_1), Bond(C_3, C_4), Bond(H_3, C_3), Bond(C_2, C_3), Bond(H_21, C_2), Bond(O_2, C_2), Bond(H_O2, O_2), Bond(O_3, C_3), Bond(C_1, C_2), ] pdbmap = [('R', {"2HO*": H_O2, "2H5*": H_52, "1H5*": H_51, "C5*": C_5, "C2*": C_2, "C3*": C_3, "C1*": C_1, "H5T": H_5_terminal, "O2*": O_2, "C4*": C_4, "1H2*": H_21, "H1*": H_1, "O3*": O_3, "H3*": H_3, "H4*": H_4, "O4*": O_4, "O5*": O_5, })] pdb_alternative = {"H2'": "2HO*", "2HO'": "2HO*", "2H5'": "2H5*", "1H5'": "1H5*", "C5'": "C5*", "C2'": "C2*", "C3'": "C3*", "C1'": "C1*", "O2'": "O2*", "C4'": "C4*", "1H2'": "1H2*", "H1'": "H1*", "O3'": "O3*", "H3'": "H3*", "H4'": "H4*", "O4'": "O4*", "O5'": "O5*"} amber_atom_type = {H_O2: 'HO', H_52: 'H1', H_51: 'H1', C_5: 'CT', C_2: 'CT', C_3: 'CT', C_1: 'CT', H_5_terminal: 'HO', O_2: 'OH', C_4: 'CT', H_21: 'H1', H_1: 'H2', O_3: 'OS', H_3: 'H1', H_4: 'H1', O_4: 'OS', O_5: 'OH', } amber12_atom_type = {H_O2: 'HO', H_52: 'H1', H_51: 'H1', C_5: 'CI', C_2: 'CT', C_3: 'CT', C_1: 'CT', H_5_terminal: 'HO', O_2: 'OH', C_4: 'CT', H_21: 'H1', H_1: 'H2', O_3: 'OS', H_3: 'H1', H_4: 'H1', O_4: 'OS', O_5: 'OH', } MMTK-2.7.9/MMTK/Database/Groups/ribose_5ter_3ter0000644000076600000240000000317112041514571021550 0ustar hinsenstaff00000000000000name ='ribose_5ter_3ter' H_5_terminal = Atom('H') O_5 = Atom('O') C_5 = Atom('C') H_51 = Atom('H') H_52 = Atom('H') C_4 = Atom('C') H_4 = Atom('H') O_4 = Atom('O') C_1 = Atom('C') H_1 = Atom('H') C_3 = Atom('C') H_3 = Atom('H') C_2 = Atom('C') H_21 = Atom('H') O_2 = Atom('O') H_O2 = Atom('H') O_3 = Atom('O') H_3_terminal = Atom('H') bonds = [Bond(O_5, H_5_terminal), Bond(C_5, O_5), Bond(H_51, C_5), Bond(H_52, C_5), Bond(C_4, C_5), Bond(H_4, C_4), Bond(O_4, C_4), Bond(C_1, O_4), Bond(H_1, C_1), Bond(C_3, C_4), Bond(H_3, C_3), Bond(C_2, C_3), Bond(H_21, C_2), Bond(O_2, C_2), Bond(H_O2, O_2), Bond(O_3, C_3), Bond(H_3_terminal, O_3), Bond(C_1, C_2), ] pdbmap = [('R', {"2HO*": H_O2, "2H5*": H_52, "1H5*": H_51, "C5*": C_5, "C2*": C_2, "C3*": C_3, "C1*": C_1, "H5T": H_5_terminal, "O2*": O_2, "C4*": C_4, "1H2*": H_21, "H1*": H_1, "O3*": O_3, "H3*": H_3, "H3T": H_3_terminal, "H4*": H_4, "O4*": O_4, "O5*": O_5, })] pdb_alternative = {"H2'": "2HO*", "2HO'": "2HO*", "2H5'": "2H5*", "1H5'": "1H5*", "C5'": "C5*", "C2'": "C2*", "C3'": "C3*", "C1'": "C1*", "O2'": "O2*", "C4'": "C4*", "H1'": "H1*", "O3'": "O3*", "H3'": "H3*", "H4'": "H4*", "O4'": "O4*", "O5'": "O5*"} amber_atom_type = {H_O2: 'HO', H_52: 'H1', H_51: 'H1', C_5: 'CT', C_2: 'CT', C_3: 'CT', C_1: 'CT', H_5_terminal: 'HO', O_2: 'OH', C_4: 'CT', H_21: 'H1', H_1: 'H2', O_3: 'OH', H_3: 'H1', H_3_terminal: 'HO', H_4: 'H1', O_4: 'OS', O_5: 'OH', } amber12_atom_type = {H_O2: 'HO', H_52: 'H1', H_51: 'H1', C_5: 'CI', C_2: 'CT', C_3: 'CT', C_1: 'CT', H_5_terminal: 'HO', O_2: 'OH', C_4: 'CT', H_21: 'H1', H_1: 'H2', O_3: 'OH', H_3: 'H1', H_3_terminal: 'HO', H_4: 'H1', O_4: 'OS', O_5: 'OH', } MMTK-2.7.9/MMTK/Database/Groups/ser_sidechain0000644000076600000240000000115512041514571021171 0ustar hinsenstaff00000000000000H_gamma = Atom('h') O_gamma = Atom('o') H_beta_3 = Atom('h') C_beta = Atom('c') H_beta_2 = Atom('h') bonds = [Bond(H_beta_2, C_beta), Bond(H_beta_3, C_beta), Bond(O_gamma, C_beta), Bond(H_gamma, O_gamma), ] pdbmap = [('SER', {'HG': H_gamma, '2HB': H_beta_2, 'CB': C_beta, 'OG': O_gamma, '3HB': H_beta_3, }, ), ] pdb_alternative = {'1HB': '3HB', 'HG1': 'HG', 'HB3': '3HB', 'HB1': '3HB', 'HB2': '2HB', } amber_atom_type = {H_gamma: 'HO', H_beta_3: 'H1', O_gamma: 'OH', C_beta: 'CT', H_beta_2: 'H1', } amber12_atom_type = {H_gamma: 'HO', H_beta_3: 'H1', O_gamma: 'OH', C_beta: '2C', H_beta_2: 'H1', } name = 'ser_sidechain' MMTK-2.7.9/MMTK/Database/Groups/ser_sidechain_noh0000644000076600000240000000035711662461637022054 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') O_gamma = Atom('OH') bonds = [Bond(O_gamma, C_beta), ] pdbmap = [('SER', {'CB': C_beta, 'OG': O_gamma, }, ), ] pdb_alternative = {'HG1': 'HG', } amber_atom_type = {O_gamma: 'OH', C_beta: 'CT', } name = 'ser_sidechain' MMTK-2.7.9/MMTK/Database/Groups/ser_sidechain_uni0000644000076600000240000000072511662461637022062 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') H_gamma = Atom('H') O_gamma = Atom('O') bonds = [Bond(O_gamma, C_beta), Bond(H_gamma, O_gamma), ] pdbmap = [('SER', {'HG': H_gamma, 'CB': C_beta, 'OG': O_gamma, }, ), ] pdb_alternative = {'HG1': 'HG', 'HB1': '3HB', 'HOG': 'HG' } name = 'ser_sidechain' opls_atom_type = { C_beta: 'C2', O_gamma: 'OH', H_gamma: 'HO' } opls_charge = { C_beta: 0.265, O_gamma: -0.7, H_gamma: 0.435 } amber91_atom_type = {H_gamma: 'HO', O_gamma: 'OH', C_beta: 'C2', } MMTK-2.7.9/MMTK/Database/Groups/ser_sidechain_uni20000644000076600000240000000051011662461637022134 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') H_gamma = Atom('H') O_gamma = Atom('O') bonds = [Bond(O_gamma, C_beta), Bond(H_gamma, O_gamma), ] amber91_atom_type = {H_gamma: 'HO', O_gamma: 'OH', C_beta: 'C2', } pdbmap = [('SER', {'HG': H_gamma, 'CB': C_beta, 'OG': O_gamma, }, ), ] pdb_alternative = {'HG1': 'HG', 'HB1': '3HB', } name = 'ser_sidechain' MMTK-2.7.9/MMTK/Database/Groups/ser_sidechain_uni30000644000076600000240000000072511662461637022145 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') H_gamma = Atom('H') O_gamma = Atom('O') bonds = [Bond(O_gamma, C_beta), Bond(H_gamma, O_gamma), ] pdbmap = [('SER', {'HG': H_gamma, 'CB': C_beta, 'OG': O_gamma, }, ), ] pdb_alternative = {'HG1': 'HG', 'HB1': '3HB', 'HOG': 'HG' } name = 'ser_sidechain' opls_atom_type = { C_beta: 'C2', O_gamma: 'OH', H_gamma: 'HO' } opls_charge = { C_beta: 0.265, O_gamma: -0.7, H_gamma: 0.435 } amber91_atom_type = {H_gamma: 'HO', O_gamma: 'OH', C_beta: 'C2', } MMTK-2.7.9/MMTK/Database/Groups/serine0000644000076600000240000000072211777301631017663 0ustar hinsenstaff00000000000000sidechain = Group('ser_sidechain') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ser' amber_charge = {peptide.N: -0.4157, peptide.C: 0.5973, peptide.H: 0.2719, peptide.O: -0.5679, sidechain.H_beta_3: 0.0352, sidechain.H_gamma: 0.4275, sidechain.C_beta: 0.2117, peptide.C_alpha: -0.0249, sidechain.H_beta_2: 0.0352, sidechain.O_gamma: -0.6546, peptide.H_alpha: 0.0843, } name = 'serine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/serine_calpha0000644000076600000240000000017611662461637021204 0ustar hinsenstaff00000000000000symbol = 'Ser' name = 'serine' C_alpha = Atom('serine') pdbmap = [('SER', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/serine_ct0000644000076600000240000000074611777301631020357 0ustar hinsenstaff00000000000000sidechain = Group('ser_sidechain') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ser' amber_charge = {sidechain.H_gamma: 0.4474, peptide.C_alpha: -0.2722, peptide.C: 0.8113, peptide.H_alpha: 0.1304, peptide.O: -0.8132, peptide.N: -0.3821, sidechain.H_beta_3: 0.0813, sidechain.H_beta_2: 0.0813, peptide.O_2: -0.8132, peptide.H: 0.2681, sidechain.C_beta: 0.1123, sidechain.O_gamma: -0.6514, } name = 'serine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/serine_ct_noh0000644000076600000240000000055711662461637021231 0ustar hinsenstaff00000000000000sidechain = Group('ser_sidechain_noh') peptide = Group('peptide_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ser' amber_charge = {sidechain.O_gamma: -0.6514, peptide.O_2: -0.8132, peptide.O: -0.8132, peptide.C: 0.8113, peptide.C_alpha: -0.2722, sidechain.C_beta: 0.1123, peptide.N: -0.3821, } name = 'serine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/serine_ct_uni0000644000076600000240000000062211662461637021231 0ustar hinsenstaff00000000000000sidechain = Group('ser_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ser' amber91_charge = {sidechain.H_gamma: 0.31, peptide.H: 0.248, sidechain.O_gamma: -0.55, peptide.N: -0.52, peptide.O_2: -0.706, peptide.C: 0.444, sidechain.C_beta: 0.194, peptide.C_alpha: 0.286, peptide.O: -0.706, } name = 'serine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/serine_ct_uni20000644000076600000240000000062211662461637021313 0ustar hinsenstaff00000000000000sidechain = Group('ser_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ser' amber91_charge = {sidechain.C_beta: 0.194, peptide.H: 0.248, peptide.N: -0.52, peptide.C_alpha: 0.286, sidechain.H_gamma: 0.31, peptide.O: -0.706, sidechain.O_gamma: -0.55, peptide.C: 0.444, peptide.O_2: -0.706, } name = 'serine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/serine_ct_uni30000644000076600000240000000062211662461637021314 0ustar hinsenstaff00000000000000sidechain = Group('ser_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ser' amber91_charge = {sidechain.H_gamma: 0.31, peptide.H: 0.248, sidechain.O_gamma: -0.55, peptide.N: -0.52, peptide.O_2: -0.706, peptide.C: 0.444, sidechain.C_beta: 0.194, peptide.C_alpha: 0.286, peptide.O: -0.706, } name = 'serine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/serine_noh0000644000076600000240000000053311662461637020535 0ustar hinsenstaff00000000000000sidechain = Group('ser_sidechain_noh') peptide = Group('peptide_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ser' amber_charge = {peptide.O: -0.5679, peptide.C_alpha: -0.0249, peptide.N: -0.4157, sidechain.O_gamma: -0.6546, peptide.C: 0.5973, sidechain.C_beta: 0.2117, } name = 'serine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/serine_nt0000644000076600000240000000077211777301631020371 0ustar hinsenstaff00000000000000sidechain = Group('ser_sidechain') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ser' amber_charge = {peptide.H_3: 0.1898, sidechain.H_gamma: 0.4239, sidechain.H_beta_3: 0.0273, peptide.N: 0.1849, peptide.C: 0.6163, sidechain.O_gamma: -0.6714, sidechain.H_beta_2: 0.0273, peptide.H_alpha: 0.0782, sidechain.C_beta: 0.2596, peptide.O: -0.5722, peptide.C_alpha: 0.0567, peptide.H_2: 0.1898, peptide.H_1: 0.1898, } name = 'serine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/serine_nt_ct_noh0000644000076600000240000000027211662461637021724 0ustar hinsenstaff00000000000000sidechain = Group('ser_sidechain_noh') peptide = Group('peptide_nt_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ser' name = 'serine' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/serine_nt_noh0000644000076600000240000000052711662461637021241 0ustar hinsenstaff00000000000000sidechain = Group('ser_sidechain_noh') peptide = Group('peptide_nt_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ser' amber_charge = {peptide.N: 0.1849, peptide.O: -0.5722, peptide.C: 0.6163, sidechain.C_beta: 0.2596, peptide.C_alpha: 0.0567, sidechain.O_gamma: -0.6714, } name = 'serine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/serine_nt_uni0000644000076600000240000000064611662461637021252 0ustar hinsenstaff00000000000000sidechain = Group('ser_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ser' amber91_charge = {peptide.H_2: 0.312, peptide.C: 0.526, sidechain.O_gamma: -0.55, peptide.H_3: 0.312, peptide.C_alpha: 0.347, peptide.O: -0.5, sidechain.C_beta: 0.194, peptide.H_1: 0.312, sidechain.H_gamma: 0.31, peptide.N: -0.263, } name = 'serine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/serine_nt_uni20000644000076600000240000000064611662461637021334 0ustar hinsenstaff00000000000000sidechain = Group('ser_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ser' amber91_charge = {sidechain.O_gamma: -0.55, peptide.H_3: 0.312, peptide.H_2: 0.312, peptide.N: -0.263, peptide.C: 0.526, sidechain.H_gamma: 0.31, peptide.O: -0.5, sidechain.C_beta: 0.194, peptide.H_1: 0.312, peptide.C_alpha: 0.347, } name = 'serine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/serine_nt_uni30000644000076600000240000000064611662461637021335 0ustar hinsenstaff00000000000000sidechain = Group('ser_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ser' amber91_charge = {peptide.H_2: 0.312, peptide.C: 0.526, sidechain.O_gamma: -0.55, peptide.H_3: 0.312, peptide.C_alpha: 0.347, peptide.O: -0.5, sidechain.C_beta: 0.194, peptide.H_1: 0.312, sidechain.H_gamma: 0.31, peptide.N: -0.263, } name = 'serine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/serine_uni0000644000076600000240000000057511662461637020552 0ustar hinsenstaff00000000000000sidechain = Group('ser_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ser' name = 'serine' chain_links = [peptide.N, peptide.C] amber91_charge = {peptide.C_alpha: 0.292, peptide.O: -0.5, peptide.H: 0.248, peptide.N: -0.52, sidechain.O_gamma: -0.55, sidechain.H_gamma: 0.31, sidechain.C_beta: 0.194, peptide.C: 0.526, } MMTK-2.7.9/MMTK/Database/Groups/serine_uni20000644000076600000240000000057511662461637020634 0ustar hinsenstaff00000000000000sidechain = Group('ser_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ser' amber91_charge = {peptide.N: -0.52, peptide.C_alpha: 0.292, peptide.H: 0.248, peptide.O: -0.5, sidechain.C_beta: 0.194, sidechain.H_gamma: 0.31, sidechain.O_gamma: -0.55, peptide.C: 0.526, } name = 'serine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/serine_uni30000644000076600000240000000057511662461637020635 0ustar hinsenstaff00000000000000sidechain = Group('ser_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Ser' name = 'serine' chain_links = [peptide.N, peptide.C] amber91_charge = {peptide.C_alpha: 0.292, peptide.O: -0.5, peptide.H: 0.248, peptide.N: -0.52, sidechain.O_gamma: -0.55, sidechain.H_gamma: 0.31, sidechain.C_beta: 0.194, peptide.C: 0.526, } MMTK-2.7.9/MMTK/Database/Groups/sodium0000644000076600000240000000042511662461637017704 0ustar hinsenstaff00000000000000name= 'sodium' symbol = 'Sod' Sod = Atom('na') amber_atom_type = { Sod: 'IP', } pdbmap = [ ('SOD', {'NA': Sod} ) ] amber_charge = { Sod: 1.0, } configurations = {'default': Cartesian({Sod: (0.0,0.0,0.0),})} MMTK-2.7.9/MMTK/Database/Groups/thr_sidechain0000644000076600000240000000171312041514571021175 0ustar hinsenstaff00000000000000H_gamma_2_3 = Atom('h') H_beta = Atom('h') O_gamma_1 = Atom('o') H_gamma_1 = Atom('h') C_gamma_2 = Atom('c') C_beta = Atom('c') H_gamma_2_1 = Atom('h') H_gamma_2_2 = Atom('h') bonds = [Bond(H_beta, C_beta), Bond(C_gamma_2, C_beta), Bond(H_gamma_2_1, C_gamma_2), Bond(H_gamma_2_2, C_gamma_2), Bond(H_gamma_2_3, C_gamma_2), Bond(O_gamma_1, C_beta), Bond(H_gamma_1, O_gamma_1), ] pdbmap = [('THR', {'OG1': O_gamma_1, 'HB': H_beta, 'CG2': C_gamma_2, '3HG2': H_gamma_2_3, 'CB': C_beta, '1HG': H_gamma_1, '1HG2': H_gamma_2_1, '2HG2': H_gamma_2_2, }, ), ] amber_atom_type = {O_gamma_1: 'OH', C_gamma_2: 'CT', H_gamma_2_1: 'HC', H_gamma_2_2: 'HC', H_gamma_1: 'HO', C_beta: 'CT', H_beta: 'H1', H_gamma_2_3: 'HC', } amber12_atom_type = {O_gamma_1: 'OH', C_gamma_2: 'CT', H_gamma_2_1: 'HC', H_gamma_2_2: 'HC', H_gamma_1: 'HO', C_beta: '3C', H_beta: 'H1', H_gamma_2_3: 'HC', } pdb_alternative = {'HG1': '1HG', 'HG22': '2HG2', 'HG23': '3HG2', 'HG21': '1HG2', } name = 'thr_sidechain' MMTK-2.7.9/MMTK/Database/Groups/thr_sidechain_noh0000644000076600000240000000045111662461637022053 0ustar hinsenstaff00000000000000C_beta = Atom('CH') C_gamma_2 = Atom('CH3') O_gamma_1 = Atom('OH') bonds = [Bond(C_gamma_2, C_beta), Bond(O_gamma_1, C_beta), ] pdbmap = [('THR', {'CG2': C_gamma_2, 'CB': C_beta, 'OG1': O_gamma_1, }, ), ] amber_atom_type = {O_gamma_1: 'OH', C_beta: 'CT', C_gamma_2: 'CT', } name = 'thr_sidechain' MMTK-2.7.9/MMTK/Database/Groups/thr_sidechain_uni0000644000076600000240000000112411662461637022060 0ustar hinsenstaff00000000000000C_beta = Atom('CH') C_gamma_2 = Atom('CH3') H_gamma_1 = Atom('H') O_gamma_1 = Atom('O') bonds = [Bond(C_gamma_2, C_beta), Bond(O_gamma_1, C_beta), Bond(H_gamma_1, O_gamma_1), ] pdbmap = [('THR', {'1HG': H_gamma_1, 'OG1': O_gamma_1, 'CG2': C_gamma_2, 'CB': C_beta, }, ), ] pdb_alternative = {'HG1': '1HG', 'HOG': '1HG' } amber91_atom_type = {H_gamma_1: 'HO', C_gamma_2: 'C3', C_beta: 'CH', O_gamma_1: 'OH', } opls_atom_type = {H_gamma_1: 'HO', C_gamma_2: 'C3', C_beta: 'CZ', O_gamma_1: 'OH', } opls_charge = {H_gamma_1: .435, C_gamma_2: 0.0, C_beta: .265, O_gamma_1: -.7, } name = 'thr_sidechain' MMTK-2.7.9/MMTK/Database/Groups/thr_sidechain_uni20000644000076600000240000000064211662461637022146 0ustar hinsenstaff00000000000000C_beta = Atom('CH') C_gamma_2 = Atom('CH3') H_gamma_1 = Atom('H') O_gamma_1 = Atom('O') bonds = [Bond(C_gamma_2, C_beta), Bond(O_gamma_1, C_beta), Bond(H_gamma_1, O_gamma_1), ] pdbmap = [('THR', {'1HG': H_gamma_1, 'OG1': O_gamma_1, 'CG2': C_gamma_2, 'CB': C_beta, }, ), ] pdb_alternative = {'HG1': '1HG', } amber91_atom_type = {H_gamma_1: 'HO', C_gamma_2: 'C3', C_beta: 'CH', O_gamma_1: 'OH', } name = 'thr_sidechain' MMTK-2.7.9/MMTK/Database/Groups/thr_sidechain_uni30000644000076600000240000000112411662461637022143 0ustar hinsenstaff00000000000000C_beta = Atom('CH') C_gamma_2 = Atom('CH3') H_gamma_1 = Atom('H') O_gamma_1 = Atom('O') bonds = [Bond(C_gamma_2, C_beta), Bond(O_gamma_1, C_beta), Bond(H_gamma_1, O_gamma_1), ] pdbmap = [('THR', {'1HG': H_gamma_1, 'OG1': O_gamma_1, 'CG2': C_gamma_2, 'CB': C_beta, }, ), ] pdb_alternative = {'HG1': '1HG', 'HOG': '1HG' } amber91_atom_type = {H_gamma_1: 'HO', C_gamma_2: 'C3', C_beta: 'CH', O_gamma_1: 'OH', } opls_atom_type = {H_gamma_1: 'HO', C_gamma_2: 'C3', C_beta: 'CZ', O_gamma_1: 'OH', } opls_charge = {H_gamma_1: .435, C_gamma_2: 0.0, C_beta: .265, O_gamma_1: -.7, } name = 'thr_sidechain' MMTK-2.7.9/MMTK/Database/Groups/threonine0000644000076600000240000000106611777301631020373 0ustar hinsenstaff00000000000000sidechain = Group('thr_sidechain') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Thr' amber_charge = {sidechain.C_beta: 0.3654, peptide.O: -0.5679, sidechain.H_beta: 0.0043, peptide.H: 0.2719, peptide.H_alpha: 0.1007, sidechain.H_gamma_2_2: 0.0642, peptide.C: 0.5973, peptide.N: -0.4157, sidechain.H_gamma_2_3: 0.0642, sidechain.H_gamma_2_1: 0.0642, sidechain.O_gamma_1: -0.6761, sidechain.H_gamma_1: 0.4102, peptide.C_alpha: -0.0389, sidechain.C_gamma_2: -0.2438, } name = 'threonine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/threonine_calpha0000644000076600000240000000020411662461637021702 0ustar hinsenstaff00000000000000symbol = 'Thr' name = 'threonine' C_alpha = Atom('threonine') pdbmap = [('THR', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/threonine_ct0000644000076600000240000000111011777301631021047 0ustar hinsenstaff00000000000000sidechain = Group('thr_sidechain') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Thr' amber_charge = {sidechain.H_gamma_2_2: 0.0586, sidechain.C_gamma_2: -0.1853, sidechain.H_gamma_2_3: 0.0586, peptide.H_alpha: 0.1207, sidechain.H_gamma_1: 0.4119, peptide.H: 0.2681, peptide.O_2: -0.8044, peptide.C: 0.781, sidechain.H_beta: 0.0078, sidechain.C_beta: 0.3025, peptide.N: -0.3821, peptide.C_alpha: -0.242, sidechain.H_gamma_2_1: 0.0586, sidechain.O_gamma_1: -0.6496, peptide.O: -0.8044, } name = 'threonine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/threonine_ct_noh0000644000076600000240000000062011662461637021726 0ustar hinsenstaff00000000000000sidechain = Group('thr_sidechain_noh') peptide = Group('peptide_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Thr' amber_charge = {peptide.O_2: -0.8044, sidechain.C_gamma_2: -0.1853, peptide.C_alpha: -0.242, peptide.C: 0.781, sidechain.O_gamma_1: -0.6496, peptide.O: -0.8044, sidechain.C_beta: 0.3025, peptide.N: -0.3821, } name = 'threonine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/threonine_ct_uni0000644000076600000240000000066511662461637021746 0ustar hinsenstaff00000000000000sidechain = Group('thr_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Thr' amber91_charge = {sidechain.H_gamma_1: 0.31, peptide.O: -0.706, peptide.O_2: -0.706, peptide.C_alpha: 0.262, peptide.C: 0.444, sidechain.C_gamma_2: 0.007, sidechain.O_gamma_1: -0.55, sidechain.C_beta: 0.211, peptide.H: 0.248, peptide.N: -0.52, } name = 'threonine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/threonine_ct_uni20000644000076600000240000000066511662461637022030 0ustar hinsenstaff00000000000000sidechain = Group('thr_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Thr' amber91_charge = {peptide.O: -0.706, sidechain.H_gamma_1: 0.31, sidechain.C_beta: 0.211, sidechain.O_gamma_1: -0.55, peptide.C_alpha: 0.262, peptide.H: 0.248, sidechain.C_gamma_2: 0.007, peptide.N: -0.52, peptide.O_2: -0.706, peptide.C: 0.444, } name = 'threonine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/threonine_ct_uni30000644000076600000240000000066511662461637022031 0ustar hinsenstaff00000000000000sidechain = Group('thr_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Thr' amber91_charge = {sidechain.H_gamma_1: 0.31, peptide.O: -0.706, peptide.O_2: -0.706, peptide.C_alpha: 0.262, peptide.C: 0.444, sidechain.C_gamma_2: 0.007, sidechain.O_gamma_1: -0.55, sidechain.C_beta: 0.211, peptide.H: 0.248, peptide.N: -0.52, } name = 'threonine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/threonine_noh0000644000076600000240000000057611662461637021252 0ustar hinsenstaff00000000000000sidechain = Group('thr_sidechain_noh') peptide = Group('peptide_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Thr' amber_charge = {sidechain.C_gamma_2: -0.2438, peptide.C: 0.5973, sidechain.C_beta: 0.3654, sidechain.O_gamma_1: -0.6761, peptide.O: -0.5679, peptide.N: -0.4157, peptide.C_alpha: -0.0389, } name = 'threonine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/threonine_nt0000644000076600000240000000113711777301631021073 0ustar hinsenstaff00000000000000sidechain = Group('thr_sidechain') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Thr' amber_charge = {peptide.H_3: 0.1934, sidechain.H_gamma_2_1: 0.0627, sidechain.H_gamma_1: 0.407, sidechain.H_gamma_2_3: 0.06270, peptide.H_alpha: 0.1087, sidechain.H_gamma_2_2: 0.0627, peptide.O: -0.5722, peptide.C: 0.6163, sidechain.H_beta: -0.0323, sidechain.C_gamma_2: -0.2554, sidechain.O_gamma_1: -0.6764, peptide.H_2: 0.1934, peptide.H_1: 0.1934, sidechain.C_beta: 0.4514, peptide.N: 0.1812, peptide.C_alpha: 0.0034, } name = 'threonine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/threonine_nt_ct_noh0000644000076600000240000000027511662461637022435 0ustar hinsenstaff00000000000000sidechain = Group('thr_sidechain_noh') peptide = Group('peptide_nt_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Thr' name = 'threonine' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/threonine_nt_noh0000644000076600000240000000056211662461637021746 0ustar hinsenstaff00000000000000sidechain = Group('thr_sidechain_noh') peptide = Group('peptide_nt_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Thr' amber_charge = {peptide.C_alpha: 0.151, sidechain.O_gamma_1: -0.55, peptide.N: -0.263, peptide.C: 0.616, peptide.O: -0.504, sidechain.C_beta: 0.17, sidechain.C_gamma_2: -0.191, } name = 'threonine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/threonine_nt_uni0000644000076600000240000000071111662461637021751 0ustar hinsenstaff00000000000000sidechain = Group('thr_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Thr' amber91_charge = {sidechain.C_gamma_2: 0.007, peptide.H_2: 0.312, peptide.C: 0.526, sidechain.C_beta: 0.211, sidechain.H_gamma_1: 0.31, peptide.N: -0.263, sidechain.O_gamma_1: -0.55, peptide.C_alpha: 0.323, peptide.H_3: 0.312, peptide.O: -0.5, peptide.H_1: 0.312, } name = 'threonine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/threonine_nt_uni20000644000076600000240000000071111662461637022033 0ustar hinsenstaff00000000000000sidechain = Group('thr_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Thr' amber91_charge = {sidechain.C_beta: 0.211, peptide.C: 0.526, sidechain.O_gamma_1: -0.55, peptide.N: -0.263, peptide.H_1: 0.312, sidechain.H_gamma_1: 0.31, peptide.C_alpha: 0.323, peptide.O: -0.5, peptide.H_3: 0.312, sidechain.C_gamma_2: 0.007, peptide.H_2: 0.312, } name = 'threonine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/threonine_nt_uni30000644000076600000240000000071111662461637022034 0ustar hinsenstaff00000000000000sidechain = Group('thr_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Thr' amber91_charge = {sidechain.C_gamma_2: 0.007, peptide.H_2: 0.312, peptide.C: 0.526, sidechain.C_beta: 0.211, sidechain.H_gamma_1: 0.31, peptide.N: -0.263, sidechain.O_gamma_1: -0.55, peptide.C_alpha: 0.323, peptide.H_3: 0.312, peptide.O: -0.5, peptide.H_1: 0.312, } name = 'threonine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/threonine_uni0000644000076600000240000000064011662461637021251 0ustar hinsenstaff00000000000000sidechain = Group('thr_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Thr' amber91_charge = {sidechain.O_gamma_1: -0.55, peptide.C: 0.526, sidechain.H_gamma_1: 0.31, sidechain.C_gamma_2: 0.007, peptide.H: 0.248, sidechain.C_beta: 0.211, peptide.N: -0.52, peptide.O: -0.5, peptide.C_alpha: 0.268, } name = 'threonine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/threonine_uni20000644000076600000240000000064011662461637021333 0ustar hinsenstaff00000000000000sidechain = Group('thr_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Thr' amber91_charge = {peptide.N: -0.52, sidechain.C_gamma_2: 0.007, sidechain.H_gamma_1: 0.31, sidechain.O_gamma_1: -0.55, peptide.O: -0.5, peptide.C: 0.526, sidechain.C_beta: 0.211, peptide.H: 0.248, peptide.C_alpha: 0.268, } name = 'threonine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/threonine_uni30000644000076600000240000000064011662461637021334 0ustar hinsenstaff00000000000000sidechain = Group('thr_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Thr' amber91_charge = {sidechain.O_gamma_1: -0.55, peptide.C: 0.526, sidechain.H_gamma_1: 0.31, sidechain.C_gamma_2: 0.007, peptide.H: 0.248, sidechain.C_beta: 0.211, peptide.N: -0.52, peptide.O: -0.5, peptide.C_alpha: 0.268, } name = 'threonine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/thymine0000644000076600000240000000166612041514572020056 0ustar hinsenstaff00000000000000name ='thymine' N_1 = Atom('N') C_6 = Atom('C') H_6 = Atom('H') C_5 = Atom('C') C_7 = Atom('C') H_71 = Atom('H') H_72 = Atom('H') H_73 = Atom('H') C_4 = Atom('C') O_4 = Atom('O') N_3 = Atom('N') H_3 = Atom('H') C_2 = Atom('C') O_2 = Atom('O') bonds = [Bond(C_6, N_1), Bond(H_6, C_6), Bond(C_5, C_6), Bond(C_7, C_5), Bond(H_71, C_7), Bond(H_72, C_7), Bond(H_73, C_7), Bond(C_4, C_5), Bond(O_4, C_4), Bond(N_3, C_4), Bond(H_3, N_3), Bond(C_2, N_3), Bond(O_2, C_2), Bond(C_2, N_1), ] pdbmap = [('T', {"C6": C_6, "C7": C_7, "C4": C_4, "C5": C_5, "H71": H_71, "H73": H_73, "H72": H_72, "N3": N_3, "C2": C_2, "N1": N_1, "O2": O_2, "H3": H_3, "O4": O_4, "H6": H_6, })] pdb_alternative = {'C5M': 'C7', 'H51': 'H71', 'H52': 'H72', 'H53': 'H73'} amber_atom_type = {C_6: 'CM', C_7: 'CT', C_4: 'C', C_5: 'CM', H_71: 'HC', H_73: 'HC', H_72: 'HC', N_3: 'NA', C_2: 'C', N_1: 'N*', O_2: 'O', H_3: 'H', O_4: 'O', H_6: 'H4', } amber12_atom_type = amber_atom_type MMTK-2.7.9/MMTK/Database/Groups/trp_sidechain0000644000076600000240000000343612041514572021212 0ustar hinsenstaff00000000000000C_zeta_2 = Atom('c') C_zeta_3 = Atom('c') H_epsilon_3 = Atom('h') H_epsilon_1 = Atom('h') C_beta = Atom('c') H_delta_1 = Atom('h') C_epsilon_3 = Atom('c') C_epsilon_2 = Atom('c') C_delta_2 = Atom('c') C_eta_2 = Atom('c') H_eta_2 = Atom('h') N_epsilon_1 = Atom('n') C_gamma = Atom('c') H_zeta_3 = Atom('h') H_zeta_2 = Atom('h') C_delta_1 = Atom('c') H_beta_3 = Atom('h') H_beta_2 = Atom('h') bonds = [Bond(H_beta_2, C_beta), Bond(H_beta_3, C_beta), Bond(C_gamma, C_beta), Bond(C_delta_1, C_gamma), Bond(H_delta_1, C_delta_1), Bond(N_epsilon_1, C_delta_1), Bond(H_epsilon_1, N_epsilon_1), Bond(C_epsilon_2, N_epsilon_1), Bond(C_zeta_2, C_epsilon_2), Bond(H_zeta_2, C_zeta_2), Bond(C_eta_2, C_zeta_2), Bond(H_eta_2, C_eta_2), Bond(C_zeta_3, C_eta_2), Bond(H_zeta_3, C_zeta_3), Bond(C_epsilon_3, C_zeta_3), Bond(H_epsilon_3, C_epsilon_3), Bond(C_delta_2, C_epsilon_3), Bond(C_gamma, C_delta_2), Bond(C_epsilon_2, C_delta_2), ] name = 'trp_sidechain' pdbmap = [('TRP', {'NE1': N_epsilon_1, '1HD': H_delta_1, '3HE': H_epsilon_3, 'CH2': C_eta_2, '3HB': H_beta_3, '3HZ': H_zeta_3, 'CB': C_beta, 'CD1': C_delta_1, '2HB': H_beta_2, '1HE': H_epsilon_1, 'CD2': C_delta_2, 'CE2': C_epsilon_2, 'CE3': C_epsilon_3, '2HH': H_eta_2, 'CG': C_gamma, 'CZ3': C_zeta_3, 'CZ2': C_zeta_2, '2HZ': H_zeta_2, }, ), ] pdb_alternative = {'1HB': '3HB', '1HZ': '3HZ', 'HB2': '2HB', 'HB3': '3HB', 'HB1': '3HB', 'HD1': '1HD', 'HZ2': '2HZ', 'HZ3': '3HZ', 'HE1': '1HE', 'HE3': '3HE', 'HH2': '2HH', } amber_atom_type = {C_gamma: 'C*', H_zeta_3: 'HA', C_zeta_3: 'CA', H_epsilon_3: 'HA', C_zeta_2: 'CA', H_delta_1: 'H4', H_beta_2: 'HC', H_zeta_2: 'HA', C_epsilon_3: 'CA', N_epsilon_1: 'NA', H_eta_2: 'HA', C_eta_2: 'CA', H_epsilon_1: 'H', C_epsilon_2: 'CN', H_beta_3: 'HC', C_beta: 'CT', C_delta_2: 'CB', C_delta_1: 'CW', } amber12_atom_type = amber_atom_type MMTK-2.7.9/MMTK/Database/Groups/trp_sidechain_noh0000644000076600000240000000167211662461637022071 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_1 = Atom('CH') C_delta_2 = Atom('C') C_epsilon_2 = Atom('C') C_epsilon_3 = Atom('CH') C_eta_2 = Atom('CH') C_gamma = Atom('C') C_zeta_2 = Atom('CH') C_zeta_3 = Atom('CH') N_epsilon_1 = Atom('NH') bonds = [Bond(C_gamma, C_beta), Bond(C_delta_1, C_gamma), Bond(N_epsilon_1, C_delta_1), Bond(C_epsilon_2, N_epsilon_1), Bond(C_zeta_2, C_epsilon_2), Bond(C_eta_2, C_zeta_2), Bond(C_zeta_3, C_eta_2), Bond(C_epsilon_3, C_zeta_3), Bond(C_delta_2, C_epsilon_3), Bond(C_gamma, C_delta_2), Bond(C_epsilon_2, C_delta_2), ] amber_atom_type = {C_delta_2: 'CB', C_epsilon_3: 'CA', C_delta_1: 'CW', C_zeta_2: 'CA', C_epsilon_2: 'CN', C_gamma: 'C*', C_eta_2: 'CA', C_zeta_3: 'CA', N_epsilon_1: 'NA', C_beta: 'CT', } pdbmap = [('TRP', {'NE1': N_epsilon_1, 'CG': C_gamma, 'CB': C_beta, 'CZ3': C_zeta_3, 'CZ2': C_zeta_2, 'CD2': C_delta_2, 'CD1': C_delta_1, 'CE3': C_epsilon_3, 'CE2': C_epsilon_2, 'CH2': C_eta_2, }, ), ] name = 'trp_sidechain' MMTK-2.7.9/MMTK/Database/Groups/trp_sidechain_uni0000644000076600000240000000313111662461637022070 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_1 = Atom('CH') C_delta_2 = Atom('C') C_epsilon_2 = Atom('C') C_epsilon_3 = Atom('CH') C_eta_2 = Atom('CH') C_gamma = Atom('C') C_zeta_2 = Atom('CH') C_zeta_3 = Atom('CH') H_epsilon_1 = Atom('H') N_epsilon_1 = Atom('N') H_delta_1 = Atom('H') H_epsilon_3 = Atom('H') H_zeta_2 = Atom('H') H_zeta_3 = Atom('H') H_eta_2 = Atom('H') bonds = [Bond(C_gamma, C_beta), Bond(C_delta_1, C_gamma), Bond(N_epsilon_1, C_delta_1), Bond(H_epsilon_1, N_epsilon_1), Bond(C_epsilon_2, N_epsilon_1), Bond(C_zeta_2, C_epsilon_2), Bond(C_eta_2, C_zeta_2), Bond(C_zeta_3, C_eta_2), Bond(C_epsilon_3, C_zeta_3), Bond(C_delta_2, C_epsilon_3), Bond(C_gamma, C_delta_2), Bond(C_epsilon_2, C_delta_2), Bond(C_delta_1,H_delta_1), Bond(C_zeta_2,H_zeta_2), Bond(C_zeta_3,H_zeta_3), Bond(C_eta_2,H_eta_2), Bond(C_epsilon_3,H_epsilon_3) ] opls_atom_type = {C_gamma: 'CK', H_epsilon_1: 'H', C_eta_2: 'CK', C_zeta_3: 'CK', C_epsilon_2: 'CN', N_epsilon_1: 'NA', C_zeta_2: 'CK', C_delta_2: 'CB', C_delta_1: 'CK', C_epsilon_3: 'CK', C_beta: 'C2', H_delta_1: 'HK', H_epsilon_3: 'HK', H_zeta_2: 'HK', H_zeta_3: 'HK', H_eta_2: 'HK' } name = 'trp_sidechain' pdbmap = [('TRP', {'NE1': N_epsilon_1, '1HD': H_delta_1, '3HE': H_epsilon_3, 'CH2': C_eta_2, '3HZ': H_zeta_3, 'CB': C_beta, 'CD1': C_delta_1, '1HE': H_epsilon_1, 'CD2': C_delta_2, 'CE2': C_epsilon_2, 'CE3': C_epsilon_3, '2HH': H_eta_2, 'CG': C_gamma, 'CZ3': C_zeta_3, 'CZ2': C_zeta_2, '2HZ': H_zeta_2, }, ), ] pdb_alternative = {'1HZ': '3HZ', 'HD1': '1HD', 'HD': '1HD', 'HZ2': '2HZ', 'HZ3': '3HZ', 'HE1': '1HE', 'HE3': '3HE', 'HH2': '2HH', 'HNE': '1HE', 'HH': '2HH', 'HE': '3HE'} MMTK-2.7.9/MMTK/Database/Groups/trp_sidechain_uni20000644000076600000240000000211211662461637022150 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_1 = Atom('CH') C_delta_2 = Atom('C') C_epsilon_2 = Atom('C') C_epsilon_3 = Atom('CH') C_eta_2 = Atom('CH') C_gamma = Atom('C') C_zeta_2 = Atom('CH') C_zeta_3 = Atom('CH') H_epsilon_1 = Atom('H') N_epsilon_1 = Atom('N') bonds = [Bond(C_gamma, C_beta), Bond(C_delta_1, C_gamma), Bond(N_epsilon_1, C_delta_1), Bond(H_epsilon_1, N_epsilon_1), Bond(C_epsilon_2, N_epsilon_1), Bond(C_zeta_2, C_epsilon_2), Bond(C_eta_2, C_zeta_2), Bond(C_zeta_3, C_eta_2), Bond(C_epsilon_3, C_zeta_3), Bond(C_delta_2, C_epsilon_3), Bond(C_gamma, C_delta_2), Bond(C_epsilon_2, C_delta_2), ] amber91_atom_type = {C_gamma: 'C*', H_epsilon_1: 'H', C_eta_2: 'CD', C_zeta_3: 'CD', C_epsilon_2: 'CN', N_epsilon_1: 'NA', C_zeta_2: 'CD', C_delta_2: 'CB', C_delta_1: 'CG', C_epsilon_3: 'CD', C_beta: 'C2', } name = 'trp_sidechain' pdbmap = [('TRP', {'NE1': N_epsilon_1, '1HE': H_epsilon_1, 'CD2': C_delta_2, 'CD1': C_delta_1, 'CE3': C_epsilon_3, 'CH2': C_eta_2, 'CZ3': C_zeta_3, 'CG': C_gamma, 'CB': C_beta, 'CZ2': C_zeta_2, 'CE2': C_epsilon_2, }, ), ] pdb_alternative = {'HE1': '1HE', 'HB1': '3HB', } MMTK-2.7.9/MMTK/Database/Groups/trp_sidechain_uni30000644000076600000240000000242011662461637022153 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_1 = Atom('CH') C_delta_2 = Atom('C') C_epsilon_2 = Atom('C') C_epsilon_3 = Atom('CH') C_eta_2 = Atom('CH') C_gamma = Atom('C') C_zeta_2 = Atom('CH') C_zeta_3 = Atom('CH') H_epsilon_1 = Atom('H') N_epsilon_1 = Atom('N') bonds = [Bond(C_gamma, C_beta), Bond(C_delta_1, C_gamma), Bond(N_epsilon_1, C_delta_1), Bond(H_epsilon_1, N_epsilon_1), Bond(C_epsilon_2, N_epsilon_1), Bond(C_zeta_2, C_epsilon_2), Bond(C_eta_2, C_zeta_2), Bond(C_zeta_3, C_eta_2), Bond(C_epsilon_3, C_zeta_3), Bond(C_delta_2, C_epsilon_3), Bond(C_gamma, C_delta_2), Bond(C_epsilon_2, C_delta_2), ] opls_atom_type = {C_gamma: 'C*', H_epsilon_1: 'H', C_eta_2: 'CD', C_zeta_3: 'CD', C_epsilon_2: 'CN', N_epsilon_1: 'NA', C_zeta_2: 'CD', C_delta_2: 'CB', C_delta_1: 'CG', C_epsilon_3: 'CD', C_beta: 'C2', } opls_charge = {C_gamma: -.055, H_epsilon_1: .42, C_eta_2: 0.0, C_zeta_3: .0, C_epsilon_2: .13, N_epsilon_1: -.57, C_zeta_2: 0., C_delta_2: -.055, C_delta_1: .13, C_epsilon_3: 0.0, C_beta: 0.0, } name = 'tru_sidechain' pdbmap = [('TRU', {'NE1': N_epsilon_1, '1HE': H_epsilon_1, 'CD2': C_delta_2, 'CD1': C_delta_1, 'CE3': C_epsilon_3, 'CH2': C_eta_2, 'CZ3': C_zeta_3, 'CG': C_gamma, 'CB': C_beta, 'CZ2': C_zeta_2, 'CE2': C_epsilon_2, }, ), ] pdb_alternative = {'HE1': '1HE', 'HB1': '3HB', } MMTK-2.7.9/MMTK/Database/Groups/tryptophan0000644000076600000240000000152611777301631020611 0ustar hinsenstaff00000000000000sidechain = Group('trp_sidechain') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Trp' amber_charge = {peptide.C_alpha: -0.0275, sidechain.C_delta_1: -0.1638, peptide.H: 0.2719, sidechain.H_delta_1: 0.2062, sidechain.C_epsilon_3: -0.2387, sidechain.C_zeta_3: -0.1972, peptide.O: -0.5679, sidechain.C_delta_2: 0.1243, sidechain.C_zeta_2: -0.2601, sidechain.H_zeta_2: 0.1572, sidechain.C_gamma: -0.1415, peptide.N: -0.4157, peptide.H_alpha: 0.1123, sidechain.H_beta_2: 0.0339, peptide.C: 0.5973, sidechain.C_beta: -0.005, sidechain.H_zeta_3: 0.1447, sidechain.C_epsilon_2: 0.138, sidechain.N_epsilon_1: -0.3418, sidechain.C_eta_2: -0.1134, sidechain.H_epsilon_3: 0.17, sidechain.H_eta_2: 0.1417, sidechain.H_beta_3: 0.0339, sidechain.H_epsilon_1: 0.3412, } name = 'tryptophan' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/tryptophan_calpha0000644000076600000240000000020611662461637022121 0ustar hinsenstaff00000000000000symbol = 'Trp' name = 'tryptophan' C_alpha = Atom('tryptophan') pdbmap = [('TRP', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/tryptophan_ct0000644000076600000240000000155511777301631021301 0ustar hinsenstaff00000000000000sidechain = Group('trp_sidechain') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Trp' amber_charge = {sidechain.C_epsilon_3: -0.1837, peptide.C: 0.7658, peptide.H_alpha: 0.1272, sidechain.C_eta_2: -0.102, peptide.O_2: -0.8011, sidechain.C_delta_1: -0.1808, sidechain.C_zeta_3: -0.2287, sidechain.H_epsilon_1: 0.3413, peptide.O: -0.8011, sidechain.N_epsilon_1: -0.3316, sidechain.C_delta_2: 0.1078, peptide.N: -0.3821, sidechain.H_beta_3: 0.0497, peptide.C_alpha: -0.2084, sidechain.C_beta: -0.0742, sidechain.H_zeta_3: 0.1507, sidechain.H_beta_2: 0.0497, sidechain.C_epsilon_2: 0.1222, peptide.H: 0.2681, sidechain.H_zeta_2: 0.1567, sidechain.H_eta_2: 0.1401, sidechain.C_gamma: -0.0796, sidechain.H_delta_1: 0.2043, sidechain.H_epsilon_3: 0.1491, sidechain.C_zeta_2: -0.2594, } name = 'tryptophan' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/tryptophan_ct_noh0000644000076600000240000000114311662461637022144 0ustar hinsenstaff00000000000000sidechain = Group('trp_sidechain_noh') peptide = Group('peptide_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Trp' amber_charge = {sidechain.C_epsilon_3: -0.1837, peptide.O_2: -0.8011, sidechain.C_beta: -0.0742, sidechain.C_delta_2: 0.1078, sidechain.C_zeta_3: -0.2287, sidechain.C_eta_2: -0.102, sidechain.C_delta_1: -0.1808, peptide.C: 0.7658, peptide.O: -0.8011, sidechain.C_gamma: -0.0796, sidechain.N_epsilon_1: -0.3316, sidechain.C_zeta_2: -0.2594, sidechain.C_epsilon_2: 0.1222, peptide.N: -0.3821, peptide.C_alpha: -0.2084, } name = 'tryptophan' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/tryptophan_ct_uni0000644000076600000240000000117411662461637022157 0ustar hinsenstaff00000000000000sidechain = Group('trp_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Trp' amber91_charge = {sidechain.C_epsilon_3: 0.145, sidechain.C_eta_2: 0.034, sidechain.H_epsilon_1: 0.294, peptide.C_alpha: 0.242, sidechain.C_beta: 0.02, sidechain.C_delta_2: -0.275, sidechain.C_zeta_3: -0.082, peptide.N: -0.52, sidechain.C_delta_1: 0.117, peptide.O_2: -0.706, sidechain.C_gamma: 0.046, sidechain.N_epsilon_1: -0.33, sidechain.C_zeta_2: 0.029, peptide.H: 0.248, sidechain.C_epsilon_2: 0.0, peptide.C: 0.444, peptide.O: -0.706, } name = 'tryptophan' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/tryptophan_ct_uni20000644000076600000240000000117411662461637022241 0ustar hinsenstaff00000000000000sidechain = Group('trp_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Trp' amber91_charge = {peptide.N: -0.52, sidechain.C_delta_1: 0.117, sidechain.C_delta_2: -0.275, sidechain.C_epsilon_2: 0.0, sidechain.C_zeta_3: -0.082, sidechain.C_beta: 0.02, sidechain.C_epsilon_3: 0.145, peptide.H: 0.248, peptide.C: 0.444, sidechain.C_zeta_2: 0.029, peptide.O_2: -0.706, peptide.O: -0.706, sidechain.C_eta_2: 0.034, sidechain.N_epsilon_1: -0.33, peptide.C_alpha: 0.242, sidechain.C_gamma: 0.046, sidechain.H_epsilon_1: 0.294, } name = 'tryptophan' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/tryptophan_noh0000644000076600000240000000111611662461637021456 0ustar hinsenstaff00000000000000sidechain = Group('trp_sidechain_noh') peptide = Group('peptide_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Trp' amber_charge = {sidechain.C_epsilon_3: -0.2387, peptide.N: -0.4157, sidechain.C_eta_2: -0.1134, peptide.C: 0.5973, sidechain.C_delta_1: -0.1638, sidechain.C_zeta_2: -0.2601, sidechain.C_zeta_3: -0.1972, sidechain.C_epsilon_2: 0.138, peptide.C_alpha: -0.0275, sidechain.N_epsilon_1: -0.3418, peptide.O: -0.5679, sidechain.C_beta: -0.005, sidechain.C_gamma: -0.1415, sidechain.C_delta_2: 0.1243, } name = 'tryptophan' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/tryptophan_nt0000644000076600000240000000157711777301631021320 0ustar hinsenstaff00000000000000sidechain = Group('trp_sidechain') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Trp' amber_charge = {sidechain.C_zeta_2: -0.271, peptide.H_3: 0.1888, sidechain.H_beta_3: 0.0222, peptide.C: 0.6123, peptide.H_2: 0.1888, sidechain.C_zeta_3: -0.2034, sidechain.H_beta_2: 0.0222, sidechain.N_epsilon_1: -0.3444, sidechain.C_gamma: -0.1654, sidechain.C_epsilon_2: 0.1575, peptide.H_1: 0.1888, peptide.N: 0.1913, sidechain.C_eta_2: -0.108, sidechain.C_delta_2: 0.1132, peptide.O: -0.5713, sidechain.H_epsilon_3: 0.1646, sidechain.H_eta_2: 0.1411, sidechain.C_beta: 0.0543, sidechain.H_delta_1: 0.2195, sidechain.H_zeta_2: 0.1589, sidechain.C_epsilon_3: -0.2265, sidechain.H_epsilon_1: 0.3412, peptide.C_alpha: 0.0421, sidechain.C_delta_1: -0.1788, sidechain.H_zeta_3: 0.1458, peptide.H_alpha: 0.1162, } name = 'tryptophan' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/tryptophan_nt_ct_noh0000644000076600000240000000027611662461637022653 0ustar hinsenstaff00000000000000sidechain = Group('trp_sidechain_noh') peptide = Group('peptide_nt_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Trp' name = 'tryptophan' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/tryptophan_nt_noh0000644000076600000240000000111111662461637022152 0ustar hinsenstaff00000000000000sidechain = Group('trp_sidechain_noh') peptide = Group('peptide_nt_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Trp' amber_charge = {sidechain.C_zeta_2: -0.271, sidechain.C_beta: 0.0543, sidechain.N_epsilon_1: -0.3444, sidechain.C_eta_2: -0.108, sidechain.C_delta_1: -0.1788, sidechain.C_delta_2: 0.1132, sidechain.C_gamma: -0.1654, peptide.O: -0.5713, sidechain.C_epsilon_3: -0.2265, peptide.C: 0.6123, peptide.N: 0.1913, sidechain.C_zeta_3: -0.2034, sidechain.C_epsilon_2: 0.1575, peptide.C_alpha: 0.0421, } name = 'tryptophan' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/tryptophan_nt_uni0000644000076600000240000000122011662461637022162 0ustar hinsenstaff00000000000000sidechain = Group('trp_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Trp' amber91_charge = {sidechain.C_beta: 0.02, sidechain.C_gamma: 0.046, peptide.H_1: 0.312, sidechain.C_eta_2: 0.034, sidechain.C_delta_1: 0.117, peptide.C: 0.526, peptide.C_alpha: 0.303, sidechain.C_epsilon_2: 0.0, sidechain.H_epsilon_1: 0.294, sidechain.C_delta_2: -0.275, peptide.N: -0.263, sidechain.C_zeta_3: -0.082, peptide.H_3: 0.312, sidechain.C_zeta_2: 0.029, peptide.O: -0.5, peptide.H_2: 0.312, sidechain.C_epsilon_3: 0.145, sidechain.N_epsilon_1: -0.33, } name = 'tryptophan' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/tryptophan_nt_uni20000644000076600000240000000122011662461637022244 0ustar hinsenstaff00000000000000sidechain = Group('trp_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Trp' amber91_charge = {peptide.H_1: 0.312, peptide.C_alpha: 0.303, sidechain.N_epsilon_1: -0.33, peptide.N: -0.263, sidechain.C_epsilon_3: 0.145, peptide.H_3: 0.312, sidechain.C_zeta_2: 0.029, peptide.C: 0.526, sidechain.C_eta_2: 0.034, peptide.O: -0.5, sidechain.H_epsilon_1: 0.294, sidechain.C_gamma: 0.046, sidechain.C_delta_2: -0.275, sidechain.C_delta_1: 0.117, sidechain.C_epsilon_2: 0.0, sidechain.C_beta: 0.02, sidechain.C_zeta_3: -0.082, peptide.H_2: 0.312, } name = 'tryptophan' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/tryptophan_uni0000644000076600000240000000114711662461637021471 0ustar hinsenstaff00000000000000sidechain = Group('trp_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Trp' amber91_charge = {sidechain.C_eta_2: 0.034, sidechain.C_epsilon_2: 0.0, peptide.C: 0.526, sidechain.C_zeta_2: 0.029, peptide.O: -0.5, sidechain.N_epsilon_1: -0.33, sidechain.C_zeta_3: -0.082, sidechain.C_beta: 0.02, peptide.C_alpha: 0.248, peptide.H: 0.248, sidechain.H_epsilon_1: 0.294, sidechain.C_gamma: 0.046, peptide.N: -0.52, sidechain.C_epsilon_3: 0.145, sidechain.C_delta_2: -0.275, sidechain.C_delta_1: 0.117, } name = 'tryptophan' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/tryptophan_uni20000644000076600000240000000114711662461637021553 0ustar hinsenstaff00000000000000sidechain = Group('trp_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Trp' amber91_charge = {sidechain.C_zeta_3: -0.082, peptide.O: -0.5, sidechain.H_epsilon_1: 0.294, peptide.C_alpha: 0.248, sidechain.C_epsilon_2: 0.0, sidechain.C_gamma: 0.046, peptide.N: -0.52, sidechain.C_zeta_2: 0.029, sidechain.C_eta_2: 0.034, sidechain.C_delta_1: 0.117, sidechain.C_delta_2: -0.275, sidechain.C_epsilon_3: 0.145, sidechain.C_beta: 0.02, peptide.H: 0.248, peptide.C: 0.526, sidechain.N_epsilon_1: -0.33, } name = 'tryptophan' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/tryptophan_uni30000644000076600000240000000115011662461637021546 0ustar hinsenstaff00000000000000sidechain = Group('trp_sidechain_uni3') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Trp' amber91_charge = {sidechain.C_eta_2: 0.034, sidechain.C_epsilon_2: 0.0, peptide.C: 0.526, sidechain.C_zeta_2: 0.029, peptide.O: -0.5, sidechain.N_epsilon_1: -0.33, sidechain.C_zeta_3: -0.082, sidechain.C_beta: 0.02, peptide.C_alpha: 0.248, peptide.H: 0.248, sidechain.H_epsilon_1: 0.294, sidechain.C_gamma: 0.046, peptide.N: -0.52, sidechain.C_epsilon_3: 0.145, sidechain.C_delta_2: -0.275, sidechain.C_delta_1: 0.117, } name = 'tryptophan' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/tyr_sidechain0000644000076600000240000000267412041514572021226 0ustar hinsenstaff00000000000000H_epsilon_2 = Atom('h') H_epsilon_1 = Atom('h') C_beta = Atom('c') C_epsilon_1 = Atom('c') H_delta_1 = Atom('h') H_delta_2 = Atom('h') C_epsilon_2 = Atom('c') O_eta = Atom('o') H_beta_3 = Atom('h') C_zeta = Atom('c') C_gamma = Atom('c') C_delta_2 = Atom('c') H_eta = Atom('h') C_delta_1 = Atom('c') H_beta_2 = Atom('h') bonds = [Bond(H_beta_2, C_beta), Bond(H_beta_3, C_beta), Bond(C_gamma, C_beta), Bond(C_delta_1, C_gamma), Bond(H_delta_1, C_delta_1), Bond(C_epsilon_1, C_delta_1), Bond(H_epsilon_1, C_epsilon_1), Bond(C_zeta, C_epsilon_1), Bond(O_eta, C_zeta), Bond(H_eta, O_eta), Bond(C_epsilon_2, C_zeta), Bond(H_epsilon_2, C_epsilon_2), Bond(C_delta_2, C_epsilon_2), Bond(H_delta_2, C_delta_2), Bond(C_delta_2, C_gamma), ] name = 'tyr_sidechain' pdbmap = [('TYR', {'2HD': H_delta_2, '2HE': H_epsilon_2, '2HB': H_beta_2, '1HD': H_delta_1, '1HE': H_epsilon_1, 'CE2': C_epsilon_2, 'CE1': C_epsilon_1, 'CD1': C_delta_1, 'HH': H_eta, 'CZ': C_zeta, 'CG': C_gamma, '3HB': H_beta_3, 'CB': C_beta, 'OH': O_eta, 'CD2': C_delta_2, }, ), ] pdb_alternative = {'1HB': '3HB', 'HB2': '2HB', 'HB3': '3HB', 'HB1': '3HB', 'HD1': '1HD', 'HD2': '2HD', 'HE1': '1HE', 'HE2': '2HE', } amber_atom_type = {H_delta_1: 'HA', H_beta_2: 'HC', C_epsilon_1: 'CA', H_beta_3: 'HC', H_delta_2: 'HA', O_eta: 'OH', C_gamma: 'CA', H_eta: 'HO', C_delta_2: 'CA', C_zeta: 'C', C_epsilon_2: 'CA', C_delta_1: 'CA', H_epsilon_2: 'HA', C_beta: 'CT', H_epsilon_1: 'HA', } amber12_atom_type = amber_atom_type MMTK-2.7.9/MMTK/Database/Groups/tyr_sidechain_noh0000644000076600000240000000132611662461637022076 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_1 = Atom('CH') C_delta_2 = Atom('CH') C_epsilon_1 = Atom('CH') C_epsilon_2 = Atom('CH') C_gamma = Atom('C') C_zeta = Atom('C') O_eta = Atom('OH') bonds = [Bond(C_gamma, C_beta), Bond(C_delta_1, C_gamma), Bond(C_epsilon_1, C_delta_1), Bond(C_zeta, C_epsilon_1), Bond(O_eta, C_zeta), Bond(C_epsilon_2, C_zeta), Bond(C_delta_2, C_epsilon_2), Bond(C_delta_2, C_gamma), ] pdbmap = [('TYR', {'CD1': C_delta_1, 'CG': C_gamma, 'CB': C_beta, 'OH': O_eta, 'CE1': C_epsilon_1, 'CE2': C_epsilon_2, 'CD2': C_delta_2, 'CZ': C_zeta, }, ), ] name = 'tyr_sidechain' amber_atom_type = {C_epsilon_2: 'CA', O_eta: 'OH', C_beta: 'CT', C_delta_2: 'CA', C_gamma: 'CA', C_epsilon_1: 'CA', C_zeta: 'C', C_delta_1: 'CA', } MMTK-2.7.9/MMTK/Database/Groups/tyr_sidechain_uni0000644000076600000240000000271511662461637022110 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_1 = Atom('CH') C_delta_2 = Atom('CH') C_epsilon_1 = Atom('CH') C_epsilon_2 = Atom('CH') C_gamma = Atom('C') C_zeta = Atom('C') H_eta = Atom('H') O_eta = Atom('O') H_delta_1 = Atom('H') H_delta_2 = Atom('H') H_epsilon_1 = Atom('H') H_epsilon_2 = Atom('H') bonds = [Bond(C_gamma, C_beta), Bond(C_delta_1, C_gamma), Bond(C_epsilon_1, C_delta_1), Bond(C_zeta, C_epsilon_1), Bond(O_eta, C_zeta), Bond(H_eta, O_eta), Bond(C_epsilon_2, C_zeta), Bond(C_delta_2, C_epsilon_2), Bond(C_delta_2, C_gamma), Bond(C_delta_1,H_delta_1), Bond(C_delta_2,H_delta_2), Bond(C_epsilon_1,H_epsilon_1), Bond(C_epsilon_2,H_epsilon_2)] opls_atom_type = {C_gamma: 'CK', C_zeta: 'CK', C_epsilon_2: 'CK', C_delta_2: 'CK', C_delta_1: 'CK', H_eta: 'HO', C_beta: 'C2', O_eta: 'OH', C_epsilon_1: 'CK', H_epsilon_1: 'HK', H_epsilon_2: 'HK', H_delta_1: 'HK', H_delta_2: 'HK' } opls_charge = {C_gamma: -.115, C_zeta: .150, C_epsilon_2: -.115, C_delta_2: -.115, C_delta_1: -.115, H_eta: .435, C_beta: .115, O_eta: -.585, C_epsilon_1: -.115, H_epsilon_1: .115, H_epsilon_2: .115, H_delta_1: .115, H_delta_2: .115 } name = 'tyr_sidechain' pdbmap = [('TYR', {'2HD': H_delta_2, '2HE': H_epsilon_2, '1HD': H_delta_1, '1HE': H_epsilon_1, 'CE2': C_epsilon_2, 'CE1': C_epsilon_1, 'CD1': C_delta_1, 'HH': H_eta, 'CZ': C_zeta, 'CG': C_gamma, 'CB': C_beta, 'OH': O_eta, 'CD2': C_delta_2, }, ), ] pdb_alternative = {'HD1': '1HD', 'HD2': '2HD', 'HE1': '1HE', 'HE2': '2HE', 'HOH': 'HH'} MMTK-2.7.9/MMTK/Database/Groups/tyr_sidechain_uni20000644000076600000240000000147211662461637022171 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_1 = Atom('CH') C_delta_2 = Atom('CH') C_epsilon_1 = Atom('CH') C_epsilon_2 = Atom('CH') C_gamma = Atom('C') C_zeta = Atom('C') H_eta = Atom('H') O_eta = Atom('O') bonds = [Bond(C_gamma, C_beta), Bond(C_delta_1, C_gamma), Bond(C_epsilon_1, C_delta_1), Bond(C_zeta, C_epsilon_1), Bond(O_eta, C_zeta), Bond(H_eta, O_eta), Bond(C_epsilon_2, C_zeta), Bond(C_delta_2, C_epsilon_2), Bond(C_delta_2, C_gamma), ] pdbmap = [('TYR', {'CG': C_gamma, 'CB': C_beta, 'CE2': C_epsilon_2, 'OH': O_eta, 'CE1': C_epsilon_1, 'CD1': C_delta_1, 'HH': H_eta, 'CD2': C_delta_2, 'CZ': C_zeta, }, ), ] amber91_atom_type = {C_gamma: 'CA', C_zeta: 'C', C_epsilon_2: 'CD', C_delta_2: 'CD', C_delta_1: 'CD', H_eta: 'HO', C_beta: 'C2', O_eta: 'OH', C_epsilon_1: 'CD', } pdb_alternative = {'HB1': '3HB', } name = 'tyr_sidechain' MMTK-2.7.9/MMTK/Database/Groups/tyr_sidechain_uni30000644000076600000240000000216711662461637022174 0ustar hinsenstaff00000000000000C_beta = Atom('CH2') C_delta_1 = Atom('CH') C_delta_2 = Atom('CH') C_epsilon_1 = Atom('CH') C_epsilon_2 = Atom('CH') C_gamma = Atom('C') C_zeta = Atom('C') H_eta = Atom('H') O_eta = Atom('O') bonds = [Bond(C_gamma, C_beta), Bond(C_delta_1, C_gamma), Bond(C_epsilon_1, C_delta_1), Bond(C_zeta, C_epsilon_1), Bond(O_eta, C_zeta), Bond(H_eta, O_eta), Bond(C_epsilon_2, C_zeta), Bond(C_delta_2, C_epsilon_2), Bond(C_delta_2, C_gamma), ] pdbmap = [('TYU', {'CG': C_gamma, 'CB': C_beta, 'CE2': C_epsilon_2, 'OH': O_eta, 'CE1': C_epsilon_1, 'CD1': C_delta_1, 'HOH': H_eta, 'CD2': C_delta_2, 'CZ': C_zeta, }, ), ] amber91_atom_type = {C_gamma: 'CA', C_zeta: 'C', C_epsilon_2: 'CD', C_delta_2: 'CD', C_delta_1: 'CD', H_eta: 'HO', C_beta: 'C2', O_eta: 'OH', C_epsilon_1: 'CD', } opls_atom_type = {C_gamma: 'CA', C_zeta: 'CA', C_epsilon_2: 'CD', C_delta_2: 'CD', C_delta_1: 'CD', H_eta: 'HO', C_beta: 'C2', O_eta: 'OH', C_epsilon_1: 'CD', } opls_charge = {C_gamma: 0.0, C_zeta: .265, C_epsilon_2: 0.0, C_delta_2: 0.0, C_delta_1: 0.0, H_eta: .435, C_beta: 0.0, O_eta: -.7, C_epsilon_1: 0.0, } pdb_alternative = {'HB1': '3HB', } name = 'tyu_sidechain' MMTK-2.7.9/MMTK/Database/Groups/tyrosine0000644000076600000240000000137311777301631020255 0ustar hinsenstaff00000000000000sidechain = Group('tyr_sidechain') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Tyr' amber_charge = {peptide.H_alpha: 0.0876, sidechain.H_delta_2: 0.1699, sidechain.C_epsilon_1: -0.2341, sidechain.C_epsilon_2: -0.2341, sidechain.H_delta_1: 0.1699, sidechain.H_beta_3: 0.0295, peptide.O: -0.5679, sidechain.O_eta: -0.5579, sidechain.H_epsilon_1: 0.1656, peptide.H: 0.2719, sidechain.H_beta_2: 0.0295, peptide.C: 0.5973, sidechain.C_delta_2: -0.1906, peptide.N: -0.4157, sidechain.C_gamma: -0.0011, peptide.C_alpha: -0.0014, sidechain.H_epsilon_2: 0.1656, sidechain.C_zeta: 0.3226, sidechain.H_eta: 0.3992, sidechain.C_delta_1: -0.1906, sidechain.C_beta: -0.0152, } name = 'tyrosine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/tyrosine_calpha0000644000076600000240000000020211662461637021561 0ustar hinsenstaff00000000000000symbol = 'Tyr' name = 'tyrosine' C_alpha = Atom('tyrosine') pdbmap = [('TYR', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/tyrosine_ct0000644000076600000240000000141011777301631020733 0ustar hinsenstaff00000000000000sidechain = Group('tyr_sidechain') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Tyr' amber_charge = {sidechain.H_delta_2: 0.178, sidechain.H_delta_1: 0.178, sidechain.H_epsilon_2: 0.1673, sidechain.H_epsilon_1: 0.1673, sidechain.C_delta_2: -0.1922, peptide.O_2: -0.807, sidechain.H_beta_3: 0.049, sidechain.C_gamma: 0.0243, sidechain.H_eta: 0.4017, sidechain.C_zeta: 0.3395, sidechain.C_beta: -0.0752, peptide.C: 0.7817, sidechain.C_epsilon_1: -0.2458, peptide.H_alpha: 0.1092, peptide.C_alpha: -0.2015, sidechain.C_epsilon_2: -0.2458, sidechain.H_beta_2: 0.049, sidechain.O_eta: -0.5643, peptide.O: -0.807, sidechain.C_delta_1: -0.1922, peptide.H: 0.2681, peptide.N: -0.3821, } name = 'tyrosine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/tyrosine_ct_noh0000644000076600000240000000103711662461637021612 0ustar hinsenstaff00000000000000sidechain = Group('tyr_sidechain_noh') peptide = Group('peptide_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Tyr' amber_charge = {sidechain.O_eta: -0.5643, peptide.N: -0.3821, sidechain.C_delta_2: -0.1922, sidechain.C_beta: -0.0752, peptide.C: 0.7817, peptide.O_2: -0.807, peptide.C_alpha: -0.2015, sidechain.C_delta_1: -0.1922, sidechain.C_gamma: 0.0243, sidechain.C_epsilon_2: -0.2458, peptide.O: -0.807, sidechain.C_epsilon_1: -0.2458, sidechain.C_zeta: 0.3395, } name = 'tyrosine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/tyrosine_ct_uni0000644000076600000240000000107111662461637021617 0ustar hinsenstaff00000000000000sidechain = Group('tyr_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Tyr' amber91_charge = {peptide.N: -0.52, sidechain.C_delta_2: -0.035, sidechain.C_zeta: -0.121, sidechain.C_beta: 0.022, peptide.H: 0.248, sidechain.C_epsilon_1: 0.1, peptide.O_2: -0.706, sidechain.H_eta: 0.339, sidechain.C_delta_1: -0.035, peptide.C_alpha: 0.239, peptide.O: -0.706, sidechain.O_eta: -0.368, peptide.C: 0.444, sidechain.C_epsilon_2: 0.1, sidechain.C_gamma: -0.001, } name = 'tyrosine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/tyrosine_ct_uni20000644000076600000240000000107111662461637021701 0ustar hinsenstaff00000000000000sidechain = Group('tyr_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Tyr' amber91_charge = {sidechain.C_epsilon_2: 0.1, sidechain.C_gamma: -0.001, sidechain.H_eta: 0.339, peptide.H: 0.248, sidechain.O_eta: -0.368, peptide.O_2: -0.706, sidechain.C_zeta: -0.121, sidechain.C_delta_1: -0.035, peptide.N: -0.52, peptide.C_alpha: 0.239, peptide.O: -0.706, sidechain.C_beta: 0.022, peptide.C: 0.444, sidechain.C_delta_2: -0.035, sidechain.C_epsilon_1: 0.1, } name = 'tyrosine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/tyrosine_noh0000644000076600000240000000101611662461637021121 0ustar hinsenstaff00000000000000sidechain = Group('tyr_sidechain_noh') peptide = Group('peptide_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Tyr' amber_charge = {sidechain.C_epsilon_1: -0.2341, sidechain.C_delta_2: -0.1906, sidechain.O_eta: -0.5579, sidechain.C_zeta: 0.3226, sidechain.C_beta: -0.0152, sidechain.C_gamma: -0.0011, peptide.N: -0.4157, peptide.O: -0.5679, peptide.C: 0.5973, peptide.C_alpha: -0.0014, sidechain.C_delta_1: -0.1906, sidechain.C_epsilon_2: -0.2341, } name = 'tyrosine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/tyrosine_nt0000644000076600000240000000143411777301631020754 0ustar hinsenstaff00000000000000sidechain = Group('tyr_sidechain') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Tyr' amber_charge = {peptide.H_3: 0.1873, sidechain.H_epsilon_2: 0.165, peptide.C: 0.6123, sidechain.C_epsilon_2: -0.2239, peptide.H_alpha: 0.0983, sidechain.C_epsilon_1: -0.2239, sidechain.H_epsilon_1: 0.165, sidechain.H_delta_2: 0.172, sidechain.H_delta_1: 0.172, peptide.H_2: 0.1873, peptide.O: -0.5713, sidechain.C_beta: 0.0659, peptide.C_alpha: 0.057, sidechain.C_delta_1: -0.2002, sidechain.H_eta: 0.4001, sidechain.C_gamma: -0.0205, sidechain.H_beta_2: 0.0102, sidechain.O_eta: -0.5578, sidechain.C_zeta: 0.3139, sidechain.H_beta_3: 0.0102, peptide.H_1: 0.1873, sidechain.C_delta_2: -0.2002, peptide.N: 0.194, } name = 'tyrosine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/tyrosine_nt_ct_noh0000644000076600000240000000027411662461637022315 0ustar hinsenstaff00000000000000sidechain = Group('tyr_sidechain_noh') peptide = Group('peptide_nt_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Tyr' name = 'tyrosine' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/tyrosine_nt_noh0000644000076600000240000000100711662461637021622 0ustar hinsenstaff00000000000000sidechain = Group('tyr_sidechain_noh') peptide = Group('peptide_nt_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Tyr' amber_charge = {peptide.C_alpha: 0.057, peptide.N: 0.194, peptide.C: 0.6123, sidechain.C_gamma: -0.0205, sidechain.C_epsilon_1: -0.2239, sidechain.C_epsilon_2: -0.2239, peptide.O: -0.5713, sidechain.O_eta: -0.5578, sidechain.C_zeta: 0.3139, sidechain.C_beta: 0.0659, sidechain.C_delta_2: -0.2002, sidechain.C_delta_1: -0.2002, } name = 'tyrosine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/tyrosine_nt_uni0000644000076600000240000000111311662461637021627 0ustar hinsenstaff00000000000000sidechain = Group('tyr_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Tyr' amber91_charge = {sidechain.C_epsilon_2: 0.1, sidechain.O_eta: -0.368, peptide.H_1: 0.312, sidechain.C_beta: 0.022, sidechain.C_gamma: -0.001, sidechain.C_delta_1: -0.035, peptide.C: 0.526, peptide.H_3: 0.312, peptide.N: -0.263, sidechain.C_delta_2: -0.035, sidechain.H_eta: 0.339, sidechain.C_epsilon_1: 0.1, peptide.C_alpha: 0.3, peptide.O: -0.5, peptide.H_2: 0.312, sidechain.C_zeta: -0.121, } name = 'tyrosine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/tyrosine_nt_uni20000644000076600000240000000111311662461637021711 0ustar hinsenstaff00000000000000sidechain = Group('tyr_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Tyr' amber91_charge = {peptide.N: -0.263, sidechain.C_delta_2: -0.035, sidechain.C_zeta: -0.121, peptide.C_alpha: 0.3, sidechain.H_eta: 0.339, peptide.C: 0.526, sidechain.C_epsilon_2: 0.1, peptide.H_3: 0.312, sidechain.O_eta: -0.368, sidechain.C_epsilon_1: 0.1, sidechain.C_beta: 0.022, peptide.O: -0.5, sidechain.C_delta_1: -0.035, peptide.H_2: 0.312, sidechain.C_gamma: -0.001, peptide.H_1: 0.312, } name = 'tyrosine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/tyrosine_uni0000644000076600000240000000104411662461637021131 0ustar hinsenstaff00000000000000sidechain = Group('tyr_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Tyr' amber91_charge = {sidechain.C_delta_2: -0.035, sidechain.C_delta_1: -0.035, sidechain.C_zeta: -0.121, peptide.N: -0.52, sidechain.H_eta: 0.339, sidechain.C_epsilon_1: 0.1, peptide.H: 0.248, sidechain.C_epsilon_2: 0.1, sidechain.C_beta: 0.022, peptide.C_alpha: 0.245, peptide.C: 0.526, peptide.O: -0.5, sidechain.O_eta: -0.368, sidechain.C_gamma: -0.001, } name = 'tyrosine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/tyrosine_uni20000644000076600000240000000104411662461637021213 0ustar hinsenstaff00000000000000sidechain = Group('tyr_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Tyr' amber91_charge = {sidechain.H_eta: 0.339, sidechain.C_gamma: -0.001, peptide.N: -0.52, sidechain.C_delta_2: -0.035, sidechain.C_delta_1: -0.035, sidechain.C_epsilon_2: 0.1, peptide.C_alpha: 0.245, peptide.H: 0.248, sidechain.O_eta: -0.368, sidechain.C_zeta: -0.121, peptide.O: -0.5, sidechain.C_epsilon_1: 0.1, sidechain.C_beta: 0.022, peptide.C: 0.526, } name = 'tyrosine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/tyrosine_uni30000644000076600000240000000104511662461637021215 0ustar hinsenstaff00000000000000sidechain = Group('tyr_sidechain_uni3') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Tyr' amber91_charge = {sidechain.C_delta_2: -0.035, sidechain.C_delta_1: -0.035, sidechain.C_zeta: -0.121, peptide.N: -0.52, sidechain.H_eta: 0.339, sidechain.C_epsilon_1: 0.1, peptide.H: 0.248, sidechain.C_epsilon_2: 0.1, sidechain.C_beta: 0.022, peptide.C_alpha: 0.245, peptide.C: 0.526, peptide.O: -0.5, sidechain.O_eta: -0.368, sidechain.C_gamma: -0.001, } name = 'tyrosine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/uracil0000644000076600000240000000144112041514571017646 0ustar hinsenstaff00000000000000name ='uracil' N_1 = Atom('N') C_6 = Atom('C') H_6 = Atom('H') C_5 = Atom('C') H_5 = Atom('H') C_4 = Atom('C') O_4 = Atom('O') N_3 = Atom('N') H_3 = Atom('H') C_2 = Atom('C') O_2 = Atom('O') bonds = [Bond(C_6, N_1), Bond(H_6, C_6), Bond(C_5, C_6), Bond(H_5, C_5), Bond(C_4, C_5), Bond(O_4, C_4), Bond(N_3, C_4), Bond(H_3, N_3), Bond(C_2, N_3), Bond(O_2, C_2), Bond(C_2, N_1), ] pdbmap = [('U', {"N3": N_3, "C2": C_2, "N1": N_1, "H3": H_3, "C6": C_6, "C4": C_4, "C5": C_5, "H5": H_5, "O2": O_2, "O4": O_4, "H6": H_6, })] amber_atom_type = {N_3: 'NA', C_2: 'C', N_1: 'N*', H_3: 'H', C_6: 'CM', C_4: 'C', C_5: 'CM', H_5: 'HA', O_2: 'O', O_4: 'O', H_6: 'H4', } amber12_atom_type = {N_3: 'NA', C_2: 'C', N_1: 'N*', H_3: 'H', C_6: 'CS', C_4: 'C', C_5: 'CS', H_5: 'HA', O_2: 'O', O_4: 'O', H_6: 'H4', } MMTK-2.7.9/MMTK/Database/Groups/val_sidechain0000644000076600000240000000233212041514571021160 0ustar hinsenstaff00000000000000H_gamma_2_3 = Atom('h') H_beta = Atom('h') C_gamma_2 = Atom('c') H_gamma_1_2 = Atom('h') H_gamma_1_3 = Atom('h') C_gamma_1 = Atom('c') H_gamma_1_1 = Atom('h') C_beta = Atom('c') H_gamma_2_1 = Atom('h') H_gamma_2_2 = Atom('h') bonds = [Bond(H_beta, C_beta), Bond(C_gamma_1, C_beta), Bond(H_gamma_1_1, C_gamma_1), Bond(H_gamma_1_2, C_gamma_1), Bond(H_gamma_1_3, C_gamma_1), Bond(C_gamma_2, C_beta), Bond(H_gamma_2_1, C_gamma_2), Bond(H_gamma_2_2, C_gamma_2), Bond(H_gamma_2_3, C_gamma_2), ] name = 'val_sidechain' pdbmap = [('VAL', {'3HG1': H_gamma_1_3, 'CG2': C_gamma_2, 'HB': H_beta, '1HG1': H_gamma_1_1, '1HG2': H_gamma_2_1, '3HG2': H_gamma_2_3, 'CB': C_beta, 'CG1': C_gamma_1, '2HG1': H_gamma_1_2, '2HG2': H_gamma_2_2, }, ), ] pdb_alternative = {'HG13': '3HG1', 'HG23': '3HG2', 'HG22': '2HG2', 'HG12': '2HG1', 'HG11': '1HG1', 'HG21': '1HG2', } amber_atom_type = {C_gamma_2: 'CT', H_gamma_1_1: 'HC', C_beta: 'CT', C_gamma_1: 'CT', H_gamma_2_3: 'HC', H_gamma_2_2: 'HC', H_beta: 'HC', H_gamma_2_1: 'HC', H_gamma_1_3: 'HC', H_gamma_1_2: 'HC', } amber12_atom_type = {C_gamma_2: 'CT', H_gamma_1_1: 'HC', C_beta: '3C', C_gamma_1: 'CT', H_gamma_2_3: 'HC', H_gamma_2_2: 'HC', H_beta: 'HC', H_gamma_2_1: 'HC', H_gamma_1_3: 'HC', H_gamma_1_2: 'HC', } MMTK-2.7.9/MMTK/Database/Groups/val_sidechain_noh0000644000076600000240000000045211662461637022041 0ustar hinsenstaff00000000000000C_beta = Atom('CH') C_gamma_1 = Atom('CH3') C_gamma_2 = Atom('CH3') bonds = [Bond(C_gamma_1, C_beta), Bond(C_gamma_2, C_beta), ] pdbmap = [('VAL', {'CG2': C_gamma_2, 'CB': C_beta, 'CG1': C_gamma_1, }, ), ] amber_atom_type = {C_gamma_2: 'CT', C_gamma_1: 'CT', C_beta: 'CT', } name = 'val_sidechain' MMTK-2.7.9/MMTK/Database/Groups/val_sidechain_uni0000644000076600000240000000066011662461637022051 0ustar hinsenstaff00000000000000C_beta = Atom('CH') C_gamma_1 = Atom('CH3') C_gamma_2 = Atom('CH3') bonds = [Bond(C_gamma_1, C_beta), Bond(C_gamma_2, C_beta), ] pdbmap = [('VAL', {'CG2': C_gamma_2, 'CB': C_beta, 'CG1': C_gamma_1, }, ), ] name = 'val_sidechain' opls_atom_type = { C_beta: 'CZ', C_gamma_1: 'C3', C_gamma_2: 'C3' } opls_charge = { C_beta: 0.0, C_gamma_1: 0.0, C_gamma_2: 0.0 } amber91_atom_type = {C_gamma_2: 'C3', C_gamma_1: 'C3', C_beta: 'CH', } MMTK-2.7.9/MMTK/Database/Groups/val_sidechain_uni20000644000076600000240000000045411662461637022134 0ustar hinsenstaff00000000000000C_beta = Atom('CH') C_gamma_1 = Atom('CH3') C_gamma_2 = Atom('CH3') bonds = [Bond(C_gamma_1, C_beta), Bond(C_gamma_2, C_beta), ] pdbmap = [('VAL', {'CG2': C_gamma_2, 'CB': C_beta, 'CG1': C_gamma_1, }, ), ] amber91_atom_type = {C_gamma_2: 'C3', C_gamma_1: 'C3', C_beta: 'CH', } name = 'val_sidechain' MMTK-2.7.9/MMTK/Database/Groups/val_sidechain_uni30000644000076600000240000000066011662461637022134 0ustar hinsenstaff00000000000000C_beta = Atom('CH') C_gamma_1 = Atom('CH3') C_gamma_2 = Atom('CH3') bonds = [Bond(C_gamma_1, C_beta), Bond(C_gamma_2, C_beta), ] pdbmap = [('VAL', {'CG2': C_gamma_2, 'CB': C_beta, 'CG1': C_gamma_1, }, ), ] name = 'val_sidechain' opls_atom_type = { C_beta: 'CZ', C_gamma_1: 'C3', C_gamma_2: 'C3' } opls_charge = { C_beta: 0.0, C_gamma_1: 0.0, C_gamma_2: 0.0 } amber91_atom_type = {C_gamma_2: 'C3', C_gamma_1: 'C3', C_beta: 'CH', } MMTK-2.7.9/MMTK/Database/Groups/valine0000644000076600000240000000116411777301631017655 0ustar hinsenstaff00000000000000sidechain = Group('val_sidechain') peptide = Group('peptide') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Val' amber_charge = {sidechain.C_gamma_2: -0.3192, sidechain.C_beta: 0.2985, peptide.O: -0.5679, sidechain.H_gamma_1_1: 0.0791, peptide.H_alpha: 0.0969, sidechain.H_gamma_1_3: 0.0791, sidechain.H_gamma_2_2: 0.0791, peptide.H: 0.2719, sidechain.C_gamma_1: -0.3192, peptide.C: 0.5973, sidechain.H_gamma_1_2: 0.0791, sidechain.H_beta: -0.0297, peptide.C_alpha: -0.0875, sidechain.H_gamma_2_3: 0.0791, sidechain.H_gamma_2_1: 0.0791, peptide.N: -0.4157, } name = 'valine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/valine_calpha0000644000076600000240000000017611662461637021175 0ustar hinsenstaff00000000000000symbol = 'Val' name = 'valine' C_alpha = Atom('valine') pdbmap = [('VAL', {'CA': C_alpha}),] chain_links = [C_alpha, C_alpha] MMTK-2.7.9/MMTK/Database/Groups/valine_ct0000644000076600000240000000120511777301631020337 0ustar hinsenstaff00000000000000sidechain = Group('val_sidechain') peptide = Group('peptide_ct') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Val' amber_charge = {peptide.C_alpha: -0.3438, sidechain.H_gamma_2_1: 0.0836, peptide.O: -0.8173, sidechain.H_gamma_1_2: 0.0836, sidechain.H_gamma_1_3: 0.0836, sidechain.H_gamma_2_2: 0.0836, sidechain.H_gamma_1_1: 0.0836, peptide.C: 0.835, sidechain.H_gamma_2_3: 0.0836, sidechain.C_gamma_1: -0.3064, peptide.H: 0.2681, sidechain.H_beta: 0.0308, peptide.O_2: -0.8173, sidechain.C_gamma_2: -0.3064, sidechain.C_beta: 0.194, peptide.N: -0.3821, peptide.H_alpha: 0.1438, } name = 'valine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/valine_ct_noh0000644000076600000240000000061511662461637021215 0ustar hinsenstaff00000000000000sidechain = Group('val_sidechain_noh') peptide = Group('peptide_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Val' amber_charge = {sidechain.C_gamma_2: -0.3064, peptide.N: -0.3821, peptide.C: 0.835, peptide.O_2: -0.8173, peptide.O: -0.8173, sidechain.C_beta: 0.194, peptide.C_alpha: -0.3438, sidechain.C_gamma_1: -0.3064, } name = 'valine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/valine_ct_uni0000644000076600000240000000062711662461637021227 0ustar hinsenstaff00000000000000sidechain = Group('val_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Val' amber91_charge = {peptide.N: -0.52, peptide.H: 0.248, peptide.O_2: -0.706, peptide.C: 0.444, sidechain.C_gamma_1: 0.006, sidechain.C_gamma_2: 0.006, sidechain.C_beta: 0.033, peptide.C_alpha: 0.195, peptide.O: -0.706, } name = 'valine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/valine_ct_uni20000644000076600000240000000062711662461637021311 0ustar hinsenstaff00000000000000sidechain = Group('val_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Val' amber91_charge = {peptide.H: 0.248, sidechain.C_gamma_2: 0.006, peptide.C: 0.444, sidechain.C_gamma_1: 0.006, peptide.N: -0.52, peptide.O: -0.706, sidechain.C_beta: 0.033, peptide.C_alpha: 0.195, peptide.O_2: -0.706, } name = 'valine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/valine_ct_uni30000644000076600000240000000062711662461637021312 0ustar hinsenstaff00000000000000sidechain = Group('val_sidechain_uni') peptide = Group('peptide_ct_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Val' amber91_charge = {peptide.N: -0.52, peptide.H: 0.248, peptide.O_2: -0.706, peptide.C: 0.444, sidechain.C_gamma_1: 0.006, sidechain.C_gamma_2: 0.006, sidechain.C_beta: 0.033, peptide.C_alpha: 0.195, peptide.O: -0.706, } name = 'valine' chain_links = [peptide.N, None] MMTK-2.7.9/MMTK/Database/Groups/valine_noh0000644000076600000240000000057311662461637020532 0ustar hinsenstaff00000000000000sidechain = Group('val_sidechain_noh') peptide = Group('peptide_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Val' amber_charge = {peptide.C: 0.5973, peptide.C_alpha: -0.0875, sidechain.C_gamma_2: -0.3192, sidechain.C_beta: 0.2985, peptide.O: -0.5679, sidechain.C_gamma_1: -0.3192, peptide.N: -0.4157, } name = 'valine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/valine_nt0000644000076600000240000000123511777301631020355 0ustar hinsenstaff00000000000000sidechain = Group('val_sidechain') peptide = Group('peptide_nt') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Val' amber_charge = {sidechain.H_gamma_2_1: 0.0735, sidechain.H_gamma_2_2: 0.0735, peptide.O: -0.5722, sidechain.H_gamma_1_2: 0.0735, sidechain.H_gamma_2_3: 0.0735, peptide.H_3: 0.2272, peptide.H_2: 0.2272, sidechain.H_gamma_1_1: 0.0735, sidechain.C_gamma_1: -0.3129, peptide.C: 0.6163, peptide.N: 0.0577, sidechain.C_gamma_2: -0.3129, peptide.C_alpha: -0.0054, peptide.H_1: 0.2272, sidechain.H_beta: -0.0221, peptide.H_alpha: 0.1093, sidechain.H_gamma_1_3: 0.0735, sidechain.C_beta: 0.3196, } name = 'valine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/valine_nt_ct_noh0000644000076600000240000000027211662461637021715 0ustar hinsenstaff00000000000000sidechain = Group('val_sidechain_noh') peptide = Group('peptide_nt_ct_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Val' name = 'valine' chain_links = [None, None] MMTK-2.7.9/MMTK/Database/Groups/valine_nt_noh0000644000076600000240000000057011662461637021230 0ustar hinsenstaff00000000000000sidechain = Group('val_sidechain_noh') peptide = Group('peptide_nt_noh') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Val' amber_charge = {sidechain.C_beta: 0.3196, peptide.C: 0.6163, peptide.N: 0.0577, sidechain.C_gamma_1: -0.3129, sidechain.C_gamma_2: -0.3129, peptide.O: -0.5722, peptide.C_alpha: -0.0054, } name = 'valine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/valine_nt_uni0000644000076600000240000000065311662461637021241 0ustar hinsenstaff00000000000000sidechain = Group('val_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Val' amber91_charge = {peptide.O: -0.5, peptide.C_alpha: 0.256, sidechain.C_gamma_2: 0.006, sidechain.C_beta: 0.033, peptide.C: 0.526, sidechain.C_gamma_1: 0.006, peptide.H_1: 0.312, peptide.H_2: 0.312, peptide.N: -0.263, peptide.H_3: 0.312, } name = 'valine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/valine_nt_uni20000644000076600000240000000065311662461637021323 0ustar hinsenstaff00000000000000sidechain = Group('val_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Val' amber91_charge = {peptide.N: -0.263, peptide.O: -0.5, peptide.H_2: 0.312, sidechain.C_gamma_2: 0.006, sidechain.C_gamma_1: 0.006, peptide.H_1: 0.312, peptide.H_3: 0.312, sidechain.C_beta: 0.033, peptide.C_alpha: 0.256, peptide.C: 0.526, } name = 'valine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/valine_nt_uni30000644000076600000240000000065311662461637021324 0ustar hinsenstaff00000000000000sidechain = Group('val_sidechain_uni') peptide = Group('peptide_nt_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Val' amber91_charge = {peptide.O: -0.5, peptide.C_alpha: 0.256, sidechain.C_gamma_2: 0.006, sidechain.C_beta: 0.033, peptide.C: 0.526, sidechain.C_gamma_1: 0.006, peptide.H_1: 0.312, peptide.H_2: 0.312, peptide.N: -0.263, peptide.H_3: 0.312, } name = 'valine' chain_links = [None, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/valine_uni0000644000076600000240000000060211662461637020532 0ustar hinsenstaff00000000000000sidechain = Group('val_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Val' name = 'valine' chain_links = [peptide.N, peptide.C] amber91_charge = {sidechain.C_gamma_2: 0.006, peptide.O: -0.5, peptide.H: 0.248, peptide.C_alpha: 0.201, peptide.N: -0.52, sidechain.C_gamma_1: 0.006, peptide.C: 0.526, sidechain.C_beta: 0.033, } MMTK-2.7.9/MMTK/Database/Groups/valine_uni20000644000076600000240000000060211662461637020614 0ustar hinsenstaff00000000000000sidechain = Group('val_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Val' amber91_charge = {peptide.H: 0.248, peptide.N: -0.52, peptide.C: 0.526, sidechain.C_beta: 0.033, sidechain.C_gamma_1: 0.006, peptide.C_alpha: 0.201, sidechain.C_gamma_2: 0.006, peptide.O: -0.5, } name = 'valine' chain_links = [peptide.N, peptide.C] MMTK-2.7.9/MMTK/Database/Groups/valine_uni30000644000076600000240000000060211662461637020615 0ustar hinsenstaff00000000000000sidechain = Group('val_sidechain_uni') peptide = Group('peptide_uni') bonds = [Bond(peptide.C_alpha, sidechain.C_beta), ] symbol = 'Val' name = 'valine' chain_links = [peptide.N, peptide.C] amber91_charge = {sidechain.C_gamma_2: 0.006, peptide.O: -0.5, peptide.H: 0.248, peptide.C_alpha: 0.201, peptide.N: -0.52, sidechain.C_gamma_1: 0.006, peptide.C: 0.526, sidechain.C_beta: 0.033, } MMTK-2.7.9/MMTK/Database/Molecules/0000755000076600000240000000000012156626571017130 5ustar hinsenstaff00000000000000MMTK-2.7.9/MMTK/Database/Molecules/heme0000644000076600000240000001477711662461637020012 0ustar hinsenstaff00000000000000name = 'heme' FE = Atom('Fe') NA = Atom('N') C1A = Atom('C') C2A = Atom('C') CAA = Atom('C') HP71 = Atom('H') HP72 = Atom('H') CBA = Atom('C') HP73 = Atom('H') HP74 = Atom('H') CGA = Atom('C') O1A = Atom('O') O2A = Atom('O') C3A = Atom('C') CMA = Atom('C') HM81 = Atom('H') HM82 = Atom('H') HM83 = Atom('H') C4A = Atom('C') CHB = Atom('C') HDM = Atom('H') C1B = Atom('C') NB = Atom('N') C2B = Atom('C') CMB = Atom('C') HM11 = Atom('H') HM12 = Atom('H') HM13 = Atom('H') C3B = Atom('C') CAB = Atom('C') HV2 = Atom('H') CBB = Atom('C') HVC2 = Atom('H') HVT2 = Atom('H') C4B = Atom('C') CHC = Atom('C') HAM = Atom('H') C1C = Atom('C') NC = Atom('N') C2C = Atom('C') CMC = Atom('C') HM31 = Atom('H') HM32 = Atom('H') HM33 = Atom('H') C3C = Atom('C') CAC = Atom('C') HV4 = Atom('H') CBC = Atom('C') HVC4 = Atom('H') HVT4 = Atom('H') C4C = Atom('C') CHD = Atom('C') HBM = Atom('H') C1D = Atom('C') ND = Atom('N') C2D = Atom('C') CMD = Atom('C') HM51 = Atom('H') HM52 = Atom('H') HM53 = Atom('H') C3D = Atom('C') C4D = Atom('C') CHA = Atom('C') HGM = Atom('H') CAD = Atom('C') HP61 = Atom('H') HP62 = Atom('H') CBD = Atom('C') HP63 = Atom('H') HP64 = Atom('H') CGD = Atom('C') O1D = Atom('O') O2D = Atom('O') bonds = [Bond(NA, FE), Bond(C1A, NA), Bond(C2A, C1A), Bond(CAA, C2A), Bond(HP71, CAA), Bond(HP72, CAA), Bond(CBA, CAA), Bond(HP73, CBA), Bond(HP74, CBA), Bond(CGA, CBA), Bond(O1A, CGA), Bond(O2A, CGA), Bond(C3A, C2A), Bond(CMA, C3A), Bond(HM81, CMA), Bond(HM82, CMA), Bond(HM83, CMA), Bond(C4A, C3A), Bond(CHB, C4A), Bond(HDM, CHB), Bond(C1B, CHB), Bond(NB, C1B), Bond(C2B, C1B), Bond(CMB, C2B), Bond(HM11, CMB), Bond(HM12, CMB), Bond(HM13, CMB), Bond(C3B, C2B), Bond(CAB, C3B), Bond(HV2, CAB), Bond(CBB, CAB), Bond(HVC2, CBB), Bond(HVT2, CBB), Bond(C4B, C3B), Bond(CHC, C4B), Bond(HAM, CHC), Bond(C1C, CHC), Bond(NC, C1C), Bond(C2C, C1C), Bond(CMC, C2C), Bond(HM31, CMC), Bond(HM32, CMC), Bond(HM33, CMC), Bond(C3C, C2C), Bond(CAC, C3C), Bond(HV4, CAC), Bond(CBC, CAC), Bond(HVC4, CBC), Bond(HVT4, CBC), Bond(C4C, C3C), Bond(CHD, C4C), Bond(HBM, CHD), Bond(C1D, CHD), Bond(ND, C1D), Bond(C2D, C1D), Bond(CMD, C2D), Bond(HM51, CMD), Bond(HM52, CMD), Bond(HM53, CMD), Bond(C3D, C2D), Bond(C4D, C3D), Bond(CHA, C4D), Bond(HGM, CHA), Bond(CAD, C3D), Bond(HP61, CAD), Bond(HP62, CAD), Bond(CBD, CAD), Bond(HP63, CBD), Bond(HP64, CBD), Bond(CGD, CBD), Bond(O1D, CGD), Bond(O2D, CGD), Bond(NA, C4A), Bond(FE, NB), Bond(FE, NC), Bond(FE, ND), Bond(NB, C4B), Bond(NC, C4C), Bond(ND, C4D), Bond(C1A, CHA)] pdbmap = [('HEM', {'FE': FE, 'CHA': CHA, 'CHB': CHB, 'CHC': CHC, 'CHD': CHD, 'N A': NA, 'C1A': C1A, 'C2A': C2A, 'C3A': C3A, 'C4A': C4A, 'CMA': CMA, 'CAA': CAA, 'CBA': CBA, 'CGA': CGA, 'O1A': O1A, 'O2A': O2A, 'N B': NB, 'C1B': C1B, 'C2B': C2B, 'C3B': C3B, 'C4B': C4B, 'CMB': CMB, 'CAB': CAB, 'CBB': CBB, 'N C': NC, 'C1C': C1C, 'C2C': C2C, 'C3C': C3C, 'C4C': C4C, 'CMC': CMC, 'CAC': CAC, 'CBC': CBC, 'N D': ND, 'C1D': C1D, 'C2D': C2D, 'C3D': C3D, 'C4D': C4D, 'CMD': CMD, 'CAD': CAD, 'CBD': CBD, 'CGD': CGD, 'O1D': O1D, 'O2D': O2D, '1HP7': HP71, '2HP7': HP72, '3HP7': HP73, '4HP7': HP74, '1HM8': HM81, '2HM8': HM82, '3HM8': HM83, 'HDM': HDM, '1HM1': HM11, '2HM1': HM12, '3HM1': HM13, '2HV': HV2, '2HVC': HVC2, '2HVT': HVT2, 'HAM': HAM, '1HM3': HM31, '2HM3': HM32, '3HM3': HM33, '4HV': HV4, '4HVC': HVC4, '4HVT': HVT4, 'HBM': HBM, '1HM5': HM51, '2HM5': HM52, '3HM5': HM53, 'HGM': HGM, '1HP6': HP61, '2HP6': HP62, '3HP6': HP63, '4HP6': HP64})] amber_atom_type = {FE: 'FE', NA: 'NP', C1A: 'CC', C2A: 'CB', CAA: 'CT', HP71: 'HC', HP72: 'HC', CBA: 'CT', HP73: 'HC', HP74: 'HC', CGA: 'C', O1A: 'O2', O2A: 'O2', C3A: 'CB', CMA: 'CT', HM81: 'HC', HM82: 'HC', HM83: 'HC', C4A: 'CC', CHB: 'CD', HDM: 'HC', C1B: 'CC', NB: 'NO', C2B: 'CB', CMB: 'CT', HM11: 'HC', HM12: 'HC', HM13: 'HC', C3B: 'CB', CAB: 'CY', HV2: 'HC', CBB: 'CX', HVC2: 'HC', HVT2: 'HC', C4B: 'CC', CHC: 'CD', HAM: 'HC', C1C: 'CC', NC: 'NP', C2C: 'CB', CMC: 'CT', HM31: 'HC', HM32: 'HC', HM33: 'HC', C3C: 'CB', CAC: 'CY', HV4: 'HC', CBC: 'CX', HVC4: 'HC', HVT4: 'HC', C4C: 'CC', CHD: 'CD', HBM: 'HC', C1D: 'CC', ND: 'NO', C2D: 'CB', CMD: 'CT', HM51: 'HC', HM52: 'HC', HM53: 'HC', C3D: 'CB', C4D: 'CC', CHA: 'CD', HGM: 'HC', CAD: 'CT', HP61: 'HC', HP62: 'HC', CBD: 'CT', HP63: 'HC', HP64: 'HC', CGD: 'C', O1D: 'O2', O2D: 'O2'} amber_charge = { FE: 0.2500, NA: -0.1800, C1A: 0.0300, C2A: -0.0200, CAA: -0.1600, HP71: 0.1000, HP72: 0.1000, CBA: -0.3000, HP73: 0.1000, HP74: 0.1000, CGA: 0.3000, O1A: -0.5000, O2A: -0.5000, C3A: 0.0200, CMA: -0.2650, HM81: 0.0750, HM82: 0.0750, HM83: 0.0750, C4A: 0.0200, CHB: -0.1100, HDM: 0.1500, C1B: 0.0300, NB: -0.1800, C2B: 0.0200, CMB: -0.2650, HM11: 0.0750, HM12: 0.0750, HM13: 0.0750, C3B: -0.0500, CAB: -0.1300, HV2: 0.1500, CBB: -0.3000, HVC2: 0.1000, HVT2: 0.1000, C4B: 0.0200, CHC: -0.1100, HAM: 0.1500, C1C: 0.0300, NC: -0.1800, C2C: 0.0200, CMC: -0.2650, HM31: 0.0750, HM32: 0.0750, HM33: 0.0750, C3C: -0.0500, CAC: -0.1200, HV4: 0.1500, CBC: -0.3000, HVC4: 0.1000, HVT4: 0.1000, C4C: 0.0200, CHD: -0.1100, HBM: 0.1500, C1D: 0.0300, ND: -0.1800, C2D: 0.0200, CMD: -0.2650, HM51: 0.0750, HM52: 0.0750, HM53: 0.0750, C3D: -0.0200, C4D: 0.0200, CHA: -0.1100, HGM: 0.1500, CAD: -0.1600, HP61: 0.1000, HP62: 0.1000, CBD: -0.3000, HP63: 0.1000, HP64: 0.1000, CGD: 0.3000, O1D: -0.5000, O2D: -0.5000, } MMTK-2.7.9/MMTK/Database/Molecules/water0000644000076600000240000000144511662461637020202 0ustar hinsenstaff00000000000000name = 'water' structure = \ " O\n" + \ " / \\\n" + \ "H H\n" O = Atom('O') H1 = Atom('H') H2 = Atom('H') bonds = [Bond(O, H1), Bond(O, H2), Bond(H1, H2)] pdbmap = [('HOH', {'O': O, 'H1': H1, 'H2': H2})] pdb_alternative = {'OH2': 'O', '1H': 'H1', '2H': 'H2'} amber_atom_type = {O: 'OW', H1: 'HW', H2: 'HW'} amber_charge = {O: -0.83400, H1: 0.41700, H2: 0.41700} opls_atom_type = {O: 'OT', H1: 'HT', H2: 'HT'} opls_charge = {O: -0.83400, H1: 0.41700, H2: 0.41700} spce_atom_type = {O: 'O', H1: 'H', H2: 'H'} spce_charge = {O: -0.8476, H1: 0.4238, H2: 0.4238} configurations = { 'default': Cartesian({H1: (-0.0756950327264, 0., -0.0520320595151), H2: (0.0756950327264, 0., -0.0520320595151), O: (0., 0., 0.00655616814675)}) } MMTK-2.7.9/MMTK/Database/PDB/0000755000076600000240000000000012156626571015605 5ustar hinsenstaff00000000000000MMTK-2.7.9/MMTK/Database/PDB/110d.pdb0000644000076600000240000006056411662461640016747 0ustar hinsenstaff00000000000000HEADER DEOXYRIBONUCLEIC ACID 21-JAN-93 110D 110D COMPND /DNA$ (5'-$D(*CP*GP*GP*CP*CP*G)-3') COMPLEX WITH DAUNOMYCIN 110D SOURCE SYNTHETIC 110D AUTHOR G.A.LEONARD,T.W.HAMBLEY,K.MCAULEY-HECHT,T.BROWN,W.N.HUNTER 110D REVDAT 1 15-APR-93 110D 0 110D JRNL AUTH G.A.LEONARD,T.W.HAMBLEY,K.MCAULEY-HECHT,T.BROWN, 110D JRNL AUTH 2 W.N.HUNTER 110D JRNL TITL ANTHRACYCLINE BINDING TO UNFAVOURABLE BASE-PAIR 110D JRNL TITL 2 TRIPLET SITES: THE STRUCTURES OF THE 110D JRNL TITL 3 DNA(SLASH)ANTHRACYCLINE COMPLEXES 110D JRNL TITL 4 D(CGGCCG)(SLASH)DAUNOMYCIN AND 110D JRNL TITL 5 D(TGGCCA)(SLASH)ADRIAMYCIN 110D JRNL REF TO BE PUBLISHED 110D JRNL REFN 353 110D REMARK 1 110D REMARK 2 110D REMARK 2 RESOLUTION. 1.9 ANGSTROMS. 110D REMARK 3 110D REMARK 3 REFINEMENT. BY THE KONNERT-HENDRICKSON REFINEMENT WITH 110D REMARK 3 PROGRAM *NUCLSQ* (E.WESTHOF, P.DUMAS, D.MORAS, 110D REMARK 3 J.MOL.BIOL., V. 184, P. 119, 1985). IN ADDITION, A 110D REMARK 3 SIMULATED ANNEALING PROTOCOL (A.T.BRUENGER, M.KARPLUS, 110D REMARK 3 G.A.PETSKO, ACTA. CRYSTALLOGR.,V. A45, P. 50, 1989, AND 110D REMARK 3 A.T.BRUENGER, X-PLOR V2.1 MANUAL, YALE UNIVERSITY PRESS, 110D REMARK 3 NEW HAVEN, CT, 1990) WAS EMPLOYED. THE MINIMUM B-VALUE 110D REMARK 3 SET TO BE 2.0 ANGSTROMS**2. THE R VALUE IS 0.208 FOR 110D REMARK 3 1108 REFLECTIONS IN THE RESOLUTION RANGE 7.0 TO 1.90 110D REMARK 3 ANGSTROMS WITH FOBS .GT. 2.0* SIGMA(FOBS). 110D REMARK 3 110D REMARK 3 RMS DEVIATIONS FROM IDEAL VALUES (THE VALUES OF 110D REMARK 3 SIGMA, IN PARENTHESES, ARE THE INPUT ESTIMATED 110D REMARK 3 STANDARD DEVIATIONS THAT DETERMINE THE RELATIVE 110D REMARK 3 WEIGHTS OF THE CORRESPONDING RESTRAINTS) 110D REMARK 3 DISTANCE RESTRAINTS (ANGSTROMS) 110D REMARK 3 SUGAR-BASE BOND DISTANCE 0.022(0.017) 110D REMARK 3 SUGAR-BASE BOND ANGLE DISTANCE 0.054(0.030) 110D REMARK 3 PHOSPHATE BOND DISTANCE 0.040(0.025) 110D REMARK 3 PHOSPHATE BOND ANGLE DISTANCE 0.062(0.030) 110D REMARK 3 PLANE RESTRAINT (ANGSTROMS) 0.016(0.015) 110D REMARK 3 NON-BOUNDED CONTACT RESTRAINTS (ANGSTROMS) 110D REMARK 3 SINGLE TORSION CONTACT 0.069(0.063) 110D REMARK 3 MULTIPLE TORSION CONTACT 0.135(0.063) 110D REMARK 3 HYDROGEN BONDS (0.063) 110D REMARK 3 ISOTROPIC THERMAL FACTOR RESTRAINTS (ANGSTROMS**2) 110D REMARK 3 SUGAR-BASE BOND 9.9(8.0) 110D REMARK 3 SUGAR-BASE ANGLE 12.4(12.0) 110D REMARK 3 PHOSPHATE BOND 12.6(12.0) 110D REMARK 3 PHOSPHATE BOND ANGLE 14.24(12.0) 110D REMARK 4 110D REMARK 4 THE MOLECULE CONSISTS OF TWO CHAINS OF DNA AND TWO 110D REMARK 4 DAUNOMYCIN MOLECULES. THIS ENTRY REPRESENTS THE 110D REMARK 4 CRYSTALLOGRAPHIC ASYMMETRIC UNIT WHICH CONSISTS OF ONE 110D REMARK 4 CHAIN OF DNA AND ONE DRUG MOLECULE. TO GENERATE THE 110D REMARK 4 COMPLETE MOLECULE APPLY THE FOLLOWING SYMMETRY OPERATION 110D REMARK 4 TO THE COORDINATES IN THIS ENTRY 110D REMARK 4 110D REMARK 4 0.0 -1.0 0.0 0.0 110D REMARK 4 -1.0 0.0 0.0 0.0 110D REMARK 4 0.0 0.0 -1.0 26.675 110D REMARK 4 110D REMARK 5 110D REMARK 5 THE PROTEIN DATA BANK HAS ADOPTED THE SACCHARIDE CHEMISTS 110D REMARK 5 NOMENCLATURE FOR ATOMS OF THE DEOXYRIBOSE MOIETY RATHER 110D REMARK 5 THAN THAT OF THE NUCLEOSIDE CHEMISTS. THE RING OXYGEN 110D REMARK 5 ATOM IS LABELLED O4* INSTEAD OF O1*. 110D SEQRES 1 A 6 C G G C C G 110D HET DM1 7 38 DAUNOMYCIN 110D FORMUL 2 DM1 C27 H30 N1 O10 110D FORMUL 3 HOH *49(H2 O1) 110D CRYST1 28.070 28.070 53.350 90.00 90.00 90.00 P 41 21 2 8 110D ORIGX1 1.000000 0.000000 0.000000 0.00000 110D ORIGX2 0.000000 1.000000 0.000000 0.00000 110D ORIGX3 0.000000 0.000000 1.000000 0.00000 110D SCALE1 0.035625 0.000000 0.000000 0.00000 110D SCALE2 0.000000 0.035625 0.000000 0.00000 110D SCALE3 0.000000 0.000000 0.018744 0.00000 110D ATOM 1 O5* C A 1 -19.029 20.418 23.202 1.00 33.16 110D ATOM 2 C5* C A 1 -18.133 21.255 23.991 1.00 45.58 110D ATOM 3 C4* C A 1 -16.884 20.415 24.146 1.00 31.91 110D ATOM 4 O4* C A 1 -17.008 19.183 24.781 1.00 34.69 110D ATOM 5 C3* C A 1 -16.118 20.132 22.850 1.00 51.36 110D ATOM 6 O3* C A 1 -15.253 21.145 22.594 1.00 63.33 110D ATOM 7 C2* C A 1 -15.596 18.714 22.978 1.00 44.64 110D ATOM 8 C1* C A 1 -15.947 18.260 24.376 1.00 21.42 110D ATOM 9 N1 C A 1 -16.429 16.895 24.472 1.00 16.02 110D ATOM 10 C2 C A 1 -15.517 15.882 24.701 1.00 12.97 110D ATOM 11 O2 C A 1 -14.318 16.042 24.813 1.00 14.18 110D ATOM 12 N3 C A 1 -15.958 14.602 24.776 1.00 6.27 110D ATOM 13 C4 C A 1 -17.308 14.327 24.728 1.00 5.01 110D ATOM 14 N4 C A 1 -17.740 13.089 24.888 1.00 5.11 110D ATOM 15 C5 C A 1 -18.257 15.368 24.520 1.00 24.25 110D ATOM 16 C6 C A 1 -17.774 16.595 24.397 1.00 26.82 110D ATOM 17 P G A 2 -14.515 22.133 21.676 1.00 75.83 110D ATOM 18 O1P G A 2 -13.707 22.973 22.642 1.00 65.44 110D ATOM 19 O2P G A 2 -15.377 23.085 20.865 1.00 69.51 110D ATOM 20 O5* G A 2 -13.678 21.103 20.732 1.00 58.22 110D ATOM 21 C5* G A 2 -12.463 20.494 21.127 1.00 33.03 110D ATOM 22 C4* G A 2 -11.899 19.697 19.969 1.00 22.35 110D ATOM 23 O4* G A 2 -12.805 18.655 19.862 1.00 21.87 110D ATOM 24 C3* G A 2 -11.820 20.295 18.571 1.00 32.60 110D ATOM 25 O3* G A 2 -10.599 19.860 17.926 1.00 48.60 110D ATOM 26 C2* G A 2 -13.151 19.834 17.904 1.00 21.39 110D ATOM 27 C1* G A 2 -13.086 18.414 18.475 1.00 24.79 110D ATOM 28 N9 G A 2 -14.257 17.547 18.411 1.00 12.46 110D ATOM 29 C8 G A 2 -15.568 17.892 18.395 1.00 14.49 110D ATOM 30 N7 G A 2 -16.348 16.831 18.320 1.00 15.19 110D ATOM 31 C5 G A 2 -15.565 15.714 18.267 1.00 4.73 110D ATOM 32 C6 G A 2 -15.809 14.344 18.171 1.00 9.56 110D ATOM 33 O6 G A 2 -16.912 13.855 18.091 1.00 6.46 110D ATOM 34 N1 G A 2 -14.740 13.504 18.171 1.00 7.94 110D ATOM 35 C2 G A 2 -13.471 14.063 18.235 1.00 2.34 110D ATOM 36 N2 G A 2 -12.429 13.213 18.240 1.00 2.00 110D ATOM 37 N3 G A 2 -13.154 15.346 18.326 1.00 20.84 110D ATOM 38 C4 G A 2 -14.251 16.137 18.347 1.00 17.35 110D ATOM 39 P G A 3 -9.836 20.881 16.976 1.00 54.31 110D ATOM 40 O1P G A 3 -10.546 20.755 15.669 1.00 69.40 110D ATOM 41 O2P G A 3 -9.920 22.268 17.563 1.00 16.66 110D ATOM 42 O5* G A 3 -8.311 20.561 16.939 1.00 23.11 110D ATOM 43 C5* G A 3 -7.860 19.537 17.835 1.00 15.18 110D ATOM 44 C4* G A 3 -7.489 18.380 16.912 1.00 27.56 110D ATOM 45 O4* G A 3 -8.609 17.496 16.848 1.00 20.36 110D ATOM 46 C3* G A 3 -7.144 18.745 15.471 1.00 15.85 110D ATOM 47 O3* G A 3 -6.125 17.926 15.002 1.00 28.99 110D ATOM 48 C2* G A 3 -8.483 18.459 14.709 1.00 23.83 110D ATOM 49 C1* G A 3 -8.974 17.257 15.466 1.00 15.37 110D ATOM 50 N9 G A 3 -10.428 17.103 15.295 1.00 10.44 110D ATOM 51 C8 G A 3 -11.447 17.956 15.279 1.00 8.67 110D ATOM 52 N7 G A 3 -12.668 17.387 15.146 1.00 6.41 110D ATOM 53 C5 G A 3 -12.376 16.034 15.109 1.00 12.31 110D ATOM 54 C6 G A 3 -13.232 14.916 15.002 1.00 8.94 110D ATOM 55 O6 G A 3 -14.456 14.759 14.885 1.00 14.38 110D ATOM 56 N1 G A 3 -12.522 13.740 15.077 1.00 2.00 110D ATOM 57 C2 G A 3 -11.172 13.670 15.135 1.00 7.33 110D ATOM 58 N2 G A 3 -10.779 12.429 15.093 1.00 2.00 110D ATOM 59 N3 G A 3 -10.333 14.653 15.237 1.00 12.94 110D ATOM 60 C4 G A 3 -11.012 15.823 15.210 1.00 12.53 110D ATOM 61 P C A 4 -5.106 18.100 13.802 1.00 27.47 110D ATOM 62 O1P C A 4 -5.134 19.481 13.359 1.00 33.52 110D ATOM 63 O2P C A 4 -3.840 17.693 14.613 1.00 11.86 110D ATOM 64 O5* C A 4 -5.359 16.766 12.932 1.00 19.02 110D ATOM 65 C5* C A 4 -4.806 15.500 13.412 1.00 9.30 110D ATOM 66 C4* C A 4 -5.673 14.450 12.793 1.00 22.38 110D ATOM 67 O4* C A 4 -7.015 14.672 13.017 1.00 19.58 110D ATOM 68 C3* C A 4 -5.535 14.504 11.278 1.00 41.63 110D ATOM 69 O3* C A 4 -4.749 13.384 10.894 1.00 62.12 110D ATOM 70 C2* C A 4 -6.936 14.442 10.761 1.00 29.96 110D ATOM 71 C1* C A 4 -7.773 14.032 11.961 1.00 9.37 110D ATOM 72 N1 C A 4 -9.126 14.641 11.838 1.00 17.15 110D ATOM 73 C2 C A 4 -10.271 13.827 11.764 1.00 7.04 110D ATOM 74 O2 C A 4 -10.220 12.575 11.796 1.00 21.58 110D ATOM 75 N3 C A 4 -11.534 14.425 11.636 1.00 5.28 110D ATOM 76 C4 C A 4 -11.629 15.773 11.598 1.00 2.00 110D ATOM 77 N4 C A 4 -12.828 16.311 11.454 1.00 16.96 110D ATOM 78 C5 C A 4 -10.456 16.542 11.646 1.00 4.22 110D ATOM 79 C6 C A 4 -9.283 15.955 11.780 1.00 12.02 110D ATOM 80 P C A 5 -3.719 13.443 9.651 1.00 47.78 110D ATOM 81 O1P C A 5 -3.222 14.855 9.635 1.00 62.69 110D ATOM 82 O2P C A 5 -2.650 12.480 9.918 1.00 59.71 110D ATOM 83 O5* C A 5 -4.682 13.053 8.456 1.00 48.62 110D ATOM 84 C5* C A 5 -4.929 11.632 8.227 1.00 35.27 110D ATOM 85 C4* C A 5 -6.288 11.697 7.544 1.00 16.85 110D ATOM 86 O4* C A 5 -6.973 12.704 8.323 1.00 35.13 110D ATOM 87 C3* C A 5 -6.302 12.261 6.130 1.00 29.47 110D ATOM 88 O3* C A 5 -6.973 11.497 5.127 1.00 23.41 110D ATOM 89 C2* C A 5 -6.872 13.676 6.343 1.00 14.93 110D ATOM 90 C1* C A 5 -7.913 13.286 7.416 1.00 5.42 110D ATOM 91 N1 C A 5 -8.814 14.377 7.773 1.00 11.14 110D ATOM 92 C2 C A 5 -10.189 14.055 7.954 1.00 7.75 110D ATOM 93 O2 C A 5 -10.672 12.884 7.906 1.00 15.37 110D ATOM 94 N3 C A 5 -11.017 15.102 8.253 1.00 8.62 110D ATOM 95 C4 C A 5 -10.594 16.317 8.381 1.00 3.00 110D ATOM 96 N4 C A 5 -11.492 17.266 8.632 1.00 7.52 110D ATOM 97 C5 C A 5 -9.190 16.646 8.179 1.00 4.57 110D ATOM 98 C6 C A 5 -8.337 15.652 7.901 1.00 5.04 110D ATOM 99 P G A 6 -6.072 10.470 4.172 1.00 23.74 110D ATOM 100 O1P G A 6 -5.154 11.273 3.398 1.00 10.84 110D ATOM 101 O2P G A 6 -5.468 9.300 4.951 1.00 42.39 110D ATOM 102 O5* G A 6 -7.424 9.875 3.441 1.00 22.53 110D ATOM 103 C5* G A 6 -7.756 8.497 3.777 1.00 42.63 110D ATOM 104 C4* G A 6 -8.615 8.283 2.502 1.00 45.74 110D ATOM 105 O4* G A 6 -9.580 9.325 2.705 1.00 29.19 110D ATOM 106 C3* G A 6 -7.871 8.755 1.243 1.00 46.79 110D ATOM 107 O3* G A 6 -6.908 7.902 0.656 1.00 53.97 110D ATOM 108 C2* G A 6 -9.041 9.207 0.389 1.00 35.66 110D ATOM 109 C1* G A 6 -10.010 9.799 1.398 1.00 22.35 110D ATOM 110 N9 G A 6 -10.102 11.279 1.440 1.00 9.59 110D ATOM 111 C8 G A 6 -9.041 12.137 1.467 1.00 2.00 110D ATOM 112 N7 G A 6 -9.395 13.381 1.499 1.00 12.93 110D ATOM 113 C5 G A 6 -10.810 13.339 1.478 1.00 7.35 110D ATOM 114 C6 G A 6 -11.781 14.383 1.510 1.00 5.47 110D ATOM 115 O6 G A 6 -11.649 15.613 1.590 1.00 10.55 110D ATOM 116 N1 G A 6 -13.072 13.909 1.499 1.00 14.41 110D ATOM 117 C2 G A 6 -13.384 12.589 1.435 1.00 2.00 110D ATOM 118 N2 G A 6 -14.647 12.224 1.387 1.00 5.70 110D ATOM 119 N3 G A 6 -12.449 11.618 1.403 1.00 4.38 110D ATOM 120 C4 G A 6 -11.220 12.067 1.435 1.00 2.15 110D TER 121 G A 6 110D HETATM 122 C1 DM1 7 -12.056 18.591 5.250 1.00 6.70 110D HETATM 123 C2 DM1 7 -10.824 19.307 5.244 1.00 18.47 110D HETATM 124 C3 DM1 7 -9.634 18.703 5.111 1.00 25.90 110D HETATM 125 C4 DM1 7 -9.575 17.297 4.967 1.00 26.46 110D HETATM 126 C5 DM1 7 -10.784 15.113 4.801 1.00 2.00 110D HETATM 127 C6 DM1 7 -12.126 13.061 4.679 1.00 12.48 110D HETATM 128 C7 DM1 7 -13.325 10.883 4.684 1.00 11.64 110D HETATM 129 C8 DM1 7 -14.759 10.254 4.647 1.00 20.27 110D HETATM 130 C9 DM1 7 -16.067 10.872 5.271 1.00 18.74 110D HETATM 131 C10 DM1 7 -15.916 12.379 4.956 1.00 13.25 110D HETATM 132 C11 DM1 7 -14.566 14.459 4.967 1.00 18.61 110D HETATM 133 C12 DM1 7 -13.333 16.573 5.116 1.00 10.58 110D HETATM 134 C13 DM1 7 -17.499 10.372 5.042 1.00 10.36 110D HETATM 135 C14 DM1 7 -17.530 8.980 5.538 1.00 4.91 110D HETATM 136 C15 DM1 7 -11.977 17.227 5.111 1.00 16.51 110D HETATM 137 C16 DM1 7 -10.793 16.547 4.967 1.00 19.40 110D HETATM 138 C17 DM1 7 -12.098 14.445 4.823 1.00 3.59 110D HETATM 139 C18 DM1 7 -13.308 15.121 4.946 1.00 15.20 110D HETATM 140 C19 DM1 7 -14.568 13.039 4.801 1.00 12.01 110D HETATM 141 C20 DM1 7 -13.392 12.401 4.663 1.00 9.72 110D HETATM 142 C21 DM1 7 -7.186 17.462 4.769 1.00 29.21 110D HETATM 143 O4 DM1 7 -8.429 16.679 4.764 1.00 27.22 110D HETATM 144 O5 DM1 7 -9.723 14.501 4.583 1.00 18.84 110D HETATM 145 O6 DM1 7 -11.017 12.295 4.407 1.00 17.33 110D HETATM 146 O7 DM1 7 -12.328 10.728 5.714 1.00 25.47 110D HETATM 147 O9 DM1 7 -15.433 10.933 6.594 1.00 33.94 110D HETATM 148 O11 DM1 7 -15.753 15.065 5.170 1.00 20.29 110D HETATM 149 O12 DM1 7 -14.403 17.187 5.330 1.00 17.03 110D HETATM 150 O13 DM1 7 -18.389 10.998 4.444 1.00 20.60 110D HETATM 151 C1* DM1 7 -11.643 9.524 5.591 1.00 32.49 110D HETATM 152 C2* DM1 7 -11.088 9.485 7.005 1.00 43.28 110D HETATM 153 C3* DM1 7 -12.210 9.047 7.965 1.00 41.62 110D HETATM 154 C4* DM1 7 -12.486 7.607 7.576 1.00 52.51 110D HETATM 155 C5* DM1 7 -13.005 7.520 6.178 1.00 54.71 110D HETATM 156 C6* DM1 7 -12.968 6.060 5.586 1.00 42.32 110D HETATM 157 O5* DM1 7 -12.345 8.348 5.207 1.00 57.65 110D HETATM 158 O4* DM1 7 -11.503 6.734 8.093 1.00 56.38 110D HETATM 159 N3* DM1 7 -11.725 9.179 9.363 1.00 55.77 110D HETATM 160 O HOH 8 -4.985 18.063 19.265 1.00 20.71 110D HETATM 161 O HOH 9 -3.046 17.078 17.766 1.00 39.77 110D HETATM 162 O HOH 10 -13.824 25.504 21.415 1.00 73.90 110D HETATM 163 O HOH 11 -5.799 17.198 1.483 1.00 80.88 110D HETATM 164 O HOH 12 -9.005 20.325 8.536 1.00 33.45 110D HETATM 165 O HOH 13 -1.300 16.727 13.631 1.00 66.63 110D HETATM 166 O HOH 14 -19.360 18.234 21.095 1.00 62.30 110D HETATM 167 O HOH 15 -21.218 15.554 21.244 1.00 78.98 110D HETATM 168 O HOH 16 -11.307 24.401 12.484 1.00 57.11 110D HETATM 169 O HOH 17 -6.069 7.613 6.770 1.00 35.93 110D HETATM 170 O HOH 18 -6.801 7.565 8.936 1.00 29.27 110D HETATM 171 O HOH 19 -8.210 10.571 13.780 1.00 25.77 110D HETATM 172 O HOH 20 -6.770 25.022 16.010 1.00 47.20 110D HETATM 173 O HOH 21 -15.722 17.656 11.481 1.00 77.65 110D HETATM 174 O HOH 22 -0.609 20.820 12.431 1.00 31.06 110D HETATM 175 O HOH 23 -13.109 19.088 11.812 1.00 36.78 110D HETATM 176 O HOH 24 -12.943 24.769 6.669 1.00 56.93 110D HETATM 177 O HOH 25 -1.639 11.801 15.279 1.00 37.15 110D HETATM 178 O HOH 26 -16.710 21.288 15.925 1.00 59.23 110D HETATM 179 O HOH 27 -5.524 18.549 8.189 1.00 29.66 110D HETATM 180 O HOH 28 -6.537 18.439 10.899 1.00 37.14 110D HETATM 181 O HOH 29 -7.627 23.531 20.764 1.00 49.24 110D HETATM 182 O HOH 30 -15.551 19.335 15.061 1.00 39.68 110D HETATM 183 O HOH 31 -9.086 20.867 11.678 1.00 77.39 110D HETATM 184 O HOH 32 -15.045 24.180 12.217 1.00 76.41 110D HETATM 185 O HOH 33 -12.031 24.261 21.927 1.00 63.95 110D HETATM 186 O HOH 34 -12.157 19.104 24.797 1.00 61.02 110D HETATM 187 O HOH 35 -17.948 24.536 12.756 1.00 49.72 110D HETATM 188 O HOH 36 -7.702 15.152 2.193 1.00 14.96 110D HETATM 189 O HOH 37 -2.543 14.596 5.682 1.00 43.64 110D HETATM 190 O HOH 38 -1.847 17.642 9.512 1.00 72.07 110D HETATM 191 O HOH 39 -4.250 12.121 16.181 1.00 40.44 110D HETATM 192 O HOH 40 -21.695 13.263 23.943 1.00 49.88 110D HETATM 193 O HOH 41 -4.213 16.314 7.464 1.00 56.44 110D HETATM 194 O HOH 42 -18.299 19.006 11.678 1.00 58.51 110D HETATM 195 O HOH 43 -12.536 27.172 6.679 1.00 46.51 110D HETATM 196 O HOH 44 -13.120 24.758 19.270 1.00 56.31 110D HETATM 197 O HOH 45 -23.736 13.468 26.270 1.00 64.33 110D HETATM 198 O HOH 46 -8.053 5.620 11.721 1.00 32.37 110D HETATM 199 O HOH 47 -4.300 14.726 16.752 1.00 38.37 110D HETATM 200 O HOH 48 -14.577 24.067 8.643 1.00 40.98 110D HETATM 201 O HOH 49 -17.530 23.138 16.923 1.00 38.00 110D HETATM 202 O HOH 50 -10.004 4.839 8.285 1.00 45.25 110D HETATM 203 O HOH 51 -19.422 22.324 11.481 1.00 58.01 110D HETATM 204 O HOH 52 -14.992 24.803 17.280 1.00 43.21 110D HETATM 205 O HOH 53 -8.671 10.330 10.798 1.00 71.15 110D HETATM 206 O HOH 54 -12.090 21.213 25.453 1.00 79.85 110D HETATM 207 O HOH 55 -0.516 18.689 11.481 1.00 95.39 110D HETATM 208 O HOH 56 -11.604 22.504 6.077 1.00 40.59 110D CONECT 122 123 136 110D CONECT 123 122 124 110D CONECT 124 123 125 110D CONECT 125 124 137 143 110D CONECT 126 137 138 144 110D CONECT 127 138 141 145 110D CONECT 128 129 141 146 110D CONECT 129 128 130 110D CONECT 130 129 131 134 147 110D CONECT 131 130 140 110D CONECT 132 139 140 148 110D CONECT 133 136 139 149 110D CONECT 134 130 135 150 110D CONECT 135 134 110D CONECT 136 122 133 137 110D CONECT 137 125 126 136 110D CONECT 138 126 127 139 110D CONECT 139 132 133 138 110D CONECT 140 131 132 141 110D CONECT 141 127 128 140 110D CONECT 142 143 110D CONECT 143 125 142 110D CONECT 144 126 110D CONECT 145 127 110D CONECT 146 128 151 110D CONECT 147 130 110D CONECT 148 132 110D CONECT 149 133 110D CONECT 150 134 110D CONECT 151 146 152 157 110D CONECT 152 151 153 110D CONECT 153 152 154 159 110D CONECT 154 153 155 158 110D CONECT 155 154 156 157 110D CONECT 156 155 110D CONECT 157 151 155 110D CONECT 158 154 110D CONECT 159 153 110D MASTER 51 0 1 0 0 0 0 6 207 1 38 1 110D END 110D MMTK-2.7.9/MMTK/Database/PDB/2YCC.pdb0000644000076600000240000027120311662461640016774 0ustar hinsenstaff00000000000000HEADER ELECTRON TRANSPORT (HEME PROTEIN) 29-JAN-91 2YCC 2YCC 2 COMPND CYTOCHROME $C (ISOZYME 1) (OXIDIZED) (MUTANT WITH CYS 102 2YCC 3 COMPND 2 REPLACED BY THR) (/C102T$) 2YCC 4 SOURCE BAKER'S YEAST (SACCHAROMYCES $CEREVISIAE) 2YCC 5 AUTHOR A.M.BERGHUIS,G.D.BRAYER 2YCC 6 REVDAT 1 15-JUL-92 2YCC 0 2YCC 7 JRNL AUTH A.M.BERGHUIS,G.D.BRAYER 2YCC 8 JRNL TITL OXIDATION STATE-DEPENDENT CONFORMATIONAL CHANGES 2YCC 9 JRNL TITL 2 IN CYTOCHROME $C 2YCC 10 JRNL REF J.MOL.BIOL. V. 223 959 1992 2YCC 11 JRNL REFN ASTM JMOBAK UK ISSN 0022-2836 070 2YCC 12 REMARK 1 2YCC 13 REMARK 1 REFERENCE 1 2YCC 14 REMARK 1 AUTH G.V.LOUIE,G.D.BRAYER 2YCC 15 REMARK 1 TITL HIGH-RESOLUTION REFINEMENT OF YEAST 2YCC 16 REMARK 1 TITL 2 ISO-1-*CYTOCHROME $C AND COMPARISONS WITH OTHER 2YCC 17 REMARK 1 TITL 3 EUKARYOTIC CYTOCHROMES $C 2YCC 18 REMARK 1 REF J.MOL.BIOL. V. 214 527 1990 2YCC 19 REMARK 1 REFN ASTM JMOBAK UK ISSN 0022-2836 070 2YCC 20 REMARK 1 REFERENCE 2 2YCC 21 REMARK 1 AUTH G.V.LOUIE,G.D.BRAYER 2YCC 22 REMARK 1 TITL A POLYPEPTIDE CHAIN-REFOLDING EVENT OCCURS IN THE 2YCC 23 REMARK 1 TITL 2 GLY82 VARIANT OF YEAST ISO-1-CYTOCHROME $C 2YCC 24 REMARK 1 REF J.MOL.BIOL. V. 210 313 1989 2YCC 25 REMARK 1 REFN ASTM JMOBAK UK ISSN 0022-2836 070 2YCC 26 REMARK 1 REFERENCE 3 2YCC 27 REMARK 1 AUTH C.J.LEUNG,B.T.NALL,G.D.BRAYER 2YCC 28 REMARK 1 TITL CRYSTALLIZATION OF YEAST ISO-2-CYTOCHROME $C USING 2YCC 29 REMARK 1 TITL 2 A NOVEL HAIR SEEDING TECHNIQUE 2YCC 30 REMARK 1 REF J.MOL.BIOL. V. 206 783 1989 2YCC 31 REMARK 1 REFN ASTM JMOBAK UK ISSN 0022-2836 070 2YCC 32 REMARK 1 REFERENCE 4 2YCC 33 REMARK 1 AUTH G.V.LOUIE,W.L.B.HUTCHEON,G.D.BRAYER 2YCC 34 REMARK 1 TITL YEAST ISO-1-*CYTOCHROME $C. A 2.8 ANGSTROM 2YCC 35 REMARK 1 TITL 2 RESOLUTION THREE-DIMENSIONAL STRUCTURE 2YCC 36 REMARK 1 TITL 3 DETERMINATION 2YCC 37 REMARK 1 REF J.MOL.BIOL. V. 199 295 1988 2YCC 38 REMARK 1 REFN ASTM JMOBAK UK ISSN 0022-2836 070 2YCC 39 REMARK 1 REFERENCE 5 2YCC 40 REMARK 1 AUTH G.V.LOUIE,G.J.PIELAK,M.SMITH,G.D.BRAYER 2YCC 41 REMARK 1 TITL ROLE OF PHENYLALANINE-82 IN YEAST 2YCC 42 REMARK 1 TITL 2 ISO-1-CYTOCHROME $C AND REMOTE CONFORMATIONAL 2YCC 43 REMARK 1 TITL 3 CHANGES INDUCED BY A SERINE RESIDUE AT THIS 2YCC 44 REMARK 1 TITL 4 POSITION 2YCC 45 REMARK 1 REF BIOCHEMISTRY V. 27 7870 1988 2YCC 46 REMARK 1 REFN ASTM BICHAW US ISSN 0006-2960 033 2YCC 47 REMARK 2 2YCC 48 REMARK 2 RESOLUTION. 1.9 ANGSTROMS. 2YCC 49 REMARK 3 2YCC 50 REMARK 3 REFINEMENT. BY THE RESTRAINED LEAST-SQUARES PROCEDURE OF J. 2YCC 51 REMARK 3 KONNERT AND W. HENDRICKSON (PROGRAM *PROLSQ*). THE R 2YCC 52 REMARK 3 VALUE IS 0.197. THE RMS DEVIATION FROM IDEALITY OF THE 2YCC 53 REMARK 3 BOND LENGTHS IS 0.022 ANGSTROMS. THE RMS DEVIATION FROM 2YCC 54 REMARK 3 IDEALITY OF THE BOND ANGLE DISTANCES IS 0.046 ANGSTROMS. 2YCC 55 REMARK 4 2YCC 56 REMARK 4 THE AMINO ACID NUMBERING SCHEME IS BASED ON AN ALIGNMENT TO 2YCC 57 REMARK 4 THE SEQUENCE OF VERTEBRATE CYTOCHROME C'S. THE N-TERMINAL 2YCC 58 REMARK 4 RESIDUE IS NUMBERED -5 AND THE C-TERMINAL RESIDUE IS 2YCC 59 REMARK 4 NUMBERED 103. 2YCC 60 REMARK 5 2YCC 61 REMARK 5 THE NUMBERING OF SOLVENT MOLECULES IN OXIDIZED YEAST 2YCC 62 REMARK 5 ISO-1-CYTOCHROME $C IS IDENTICAL TO THAT OF THE REDUCED 2YCC 63 REMARK 5 FORM (PROTEIN DATA BANK ENTRY 1YCC). WATER MOLECULES NOT 2YCC 64 REMARK 5 FOUND IN REDUCED YEAST ISO-1-CYTOCHROME $C HAVE BEEN 2YCC 65 REMARK 5 ASSIGNED NUMBERS STARTING WITH 300. 2YCC 66 REMARK 6 2YCC 67 REMARK 6 TO OVERLAY THE YEAST ISO-1-CYTOCHROME C OXIDIZED STRUCTURE 2YCC 68 REMARK 6 ON TOP OF THE REDUCED FORM (PROTEIN DATA BANK ENTRY 1YCC) 2YCC 69 REMARK 6 THE FOLLOWING ROTATION MATRIX PLUS TRANSLATION VECTOR 2YCC 70 REMARK 6 SHOULD BE APPLIED TO THE COORDINATES IN THIS ENTRY: 2YCC 71 REMARK 6 2YCC 72 REMARK 6 0.99998 0.00077 0.00605 0.02034 2YCC 73 REMARK 6 -0.00079 0.99999 0.00348 -0.00593 2YCC 74 REMARK 6 -0.00605 -0.00348 0.99998 0.09970 2YCC 75 REMARK 7 2YCC 76 REMARK 7 THIS PROTEIN HAS BEEN STABILIZED FOR ANALYSES BY THE 2YCC 77 REMARK 7 MUTATION OF CYSTEINE 102 TO A THREONINE RESIDUE. 2YCC 78 REMARK 8 2YCC 79 REMARK 8 RESIDUE 72 IS EPSILON-N-TRIMETHYLLYSINE. THE THREE METHYL 2YCC 80 REMARK 8 CARBONS FOR THIS RESIDUE ARE PRESENT AS HETATMS WITH 2YCC 81 REMARK 8 RESIDUE NAME TML (FOLLOWING THE HEME). RESIDUE 72 IS 2YCC 82 REMARK 8 IDENTIFIED AS LYS ON THE ATOM AND SEQRES RECORDS. 2YCC 83 REMARK 9 2YCC 84 REMARK 9 THE END OF THE 50 HELIX (RESIDUE 55) IS DISTORTED. 2YCC 85 REMARK 10 2YCC 86 REMARK 10 RESIDUES IN SHEET S1 FORM A HIGHLY DISTORTED BETA TYPE 2YCC 87 REMARK 10 CONFORMATION. 2YCC 88 REMARK 11 2YCC 89 REMARK 11 IN TURN T5 THE H-BOND IS MEDIATED THROUGH A WATER MOLECULE. 2YCC 90 REMARK 12 2YCC 91 REMARK 12 IN ORDER TO CORRECTLY INDICATE IDENTICAL ATOMS BETWEEN THE 2YCC 92 REMARK 12 COORDINATE ENTRIES OF THE REDUCED AND OXIDIZED FORMS OF 2YCC 93 REMARK 12 YEAST ISO-1 CYTOCHROME C (PROTEIN DATA BANK ENTRIES 1YCC 2YCC 94 REMARK 12 AND 2YCC, RESPECTIVELY), THE SIDE-CHAIN ATOM NAMES OF 2YCC 95 REMARK 12 SEVERAL RESIDUES CANNOT FOLLOW IUPAC/IUB SPECIFICATIONS 2YCC 96 REMARK 12 WITH RESPECT TO THE DESIGNATION OF 1 AND 2 FOR BRANCHED 2YCC 97 REMARK 12 SIDE CHAINS. THE AFFECTED RESIDUES ARE GLU -4, GLU 21, 2YCC 98 REMARK 12 ARG 38, GLU 44, TYR 46, ASP 50, GLU 61, TYR 67, PHE 82, AND 2YCC 99 REMARK 12 ASP 90. 2YCC 100 SEQRES 1 107 THR GLU PHE LYS ALA GLY SER ALA LYS LYS GLY ALA THR 2YCC 101 SEQRES 2 107 LEU PHE LYS THR ARG CYS LEU GLN CYS HIS THR VAL GLU 2YCC 102 SEQRES 3 107 LYS GLY GLY PRO HIS LYS VAL GLY PRO ASN LEU HIS GLY 2YCC 103 SEQRES 4 107 ILE PHE GLY ARG HIS SER GLY GLN ALA GLU GLY TYR SER 2YCC 104 SEQRES 5 107 TYR THR ASP ALA ASN ILE LYS LYS ASN VAL LEU TRP ASP 2YCC 105 SEQRES 6 107 GLU ASN ASN MET SER GLU TYR LEU THR ASN PRO LYS LYS 2YCC 106 SEQRES 7 107 TYR ILE PRO GLY THR LYS MET ALA PHE GLY GLY LEU LYS 2YCC 107 SEQRES 8 107 LYS GLU LYS ASP ARG ASN ASP LEU ILE THR TYR LEU LYS 2YCC 108 SEQRES 9 107 LYS ALA THR GLU 2YCC 109 FTNOTE 1 2YCC 110 FTNOTE 1 RESIDUE LYS 72 AND TML 72 FORM EPSILON-N-TRIMETHYLLYSINE. 2YCC 111 FTNOTE 1 SEE REMARK 8 ABOVE. 2YCC 112 FTNOTE 2 2YCC 113 FTNOTE 2 THE HEME GROUP IS COVALENTLY ATTACHED TO THE PROTEIN 2YCC 114 FTNOTE 2 VIA THIOETHER BONDS TO THE SG ATOMS OF CYS 14 AND CYS 17. 2YCC 115 FTNOTE 3 2YCC 116 FTNOTE 3 IN ORDER TO CORRECTLY INDICATE IDENTICAL ATOMS BETWEEN THE 2YCC 117 FTNOTE 3 COORDINATE ENTRIES OF THE REDUCED AND OXIDIZED FORMS OF 2YCC 118 FTNOTE 3 YEAST ISO-1 CYTOCHROME C (PROTEIN DATA BANK ENTRIES 1YCC 2YCC 119 FTNOTE 3 AND 2YCC, RESPECTIVELY), THE SIDE-CHAIN ATOM NAMES OF 2YCC 120 FTNOTE 3 SEVERAL RESIDUES CANNOT FOLLOW IUPAC/IUB SPECIFICATIONS 2YCC 121 FTNOTE 3 WITH RESPECT TO THE DESIGNATION OF 1 AND 2 FOR BRANCHED 2YCC 122 FTNOTE 3 SIDE CHAINS. THE AFFECTED RESIDUES ARE GLU -4, GLU 21, 2YCC 123 FTNOTE 3 ARG 38, GLU 44, TYR 46, ASP 50, GLU 61, TYR 67, PHE 82, AND 2YCC 124 FTNOTE 3 ASP 90. 2YCC 125 HET HEM 104 43 PROTOPROPHYRIN IX CONTAINS FE(III) 2YCC 126 HET TML 72 3 METHYL PART OF EPS-N-TRIMETHYLLYSINE 72 2YCC 127 HET SO4 117 5 SULFATE GROUP 2YCC 128 FORMUL 2 HEM C34 H30 N4 O4 ---- FE1 +++ 2YCC 129 FORMUL 3 TML C3 H9 2YCC 130 FORMUL 4 SO4 O4 S1 -- 2YCC 131 FORMUL 5 HOH *61(H2 O1) 2YCC 132 HELIX 1 NT SER 2 CYS 14 1 2YCC 133 HELIX 2 50 THR 49 LYS 55 1 RESIDUE 55 DISTORTED 2YCC 134 HELIX 3 60 ASP 60 ASN 70 1 2YCC 135 HELIX 4 70 ASN 70 ILE 75 1 2YCC 136 HELIX 5 CT LYS 87 THR 102 1 2YCC 137 SHEET 1 S1 2 GLY 37 SER 40 0 2YCC 138 SHEET 2 S1 2 VAL 57 TRP 59 -1 O VAL 57 N SER 40 2YCC 139 TURN 1 T1 GLU 21 GLY 24 TYPE II 2YCC 140 TURN 2 T2 LYS 27 GLY 29 GAMMA TURN 2YCC 141 TURN 3 T3 LEU 32 ILE 35 TYPE II 2YCC 142 TURN 4 T4 ILE 35 ARG 38 TYPE II 2YCC 143 TURN 5 T5 ALA 43 TYR 46 TYPE II (DISTORTED) 2YCC 144 TURN 6 T6 ILE 75 THR 78 TYPE II 2YCC 145 CRYST1 36.470 36.470 137.240 90.00 90.00 90.00 P 43 21 2 8 2YCC 146 ORIGX1 1.000000 0.000000 0.000000 0.00000 2YCC 147 ORIGX2 0.000000 1.000000 0.000000 0.00000 2YCC 148 ORIGX3 0.000000 0.000000 1.000000 0.00000 2YCC 149 SCALE1 0.027420 0.000000 0.000000 0.00000 2YCC 150 SCALE2 0.000000 0.027420 0.000000 0.00000 2YCC 151 SCALE3 0.000000 0.000000 0.007287 0.00000 2YCC 152 ATOM 1 N THR -5 3.573 9.273 -8.420 1.00 54.63 2YCC 153 ATOM 2 CA THR -5 3.196 10.594 -8.987 1.00 54.96 2YCC 154 ATOM 3 C THR -5 1.763 10.904 -8.560 1.00 54.76 2YCC 155 ATOM 4 O THR -5 0.761 10.786 -9.272 1.00 54.39 2YCC 156 ATOM 5 CB THR -5 3.488 10.725 -10.523 1.00 55.24 2YCC 157 ATOM 6 OG1 THR -5 3.724 12.143 -10.803 1.00 54.48 2YCC 158 ATOM 7 CG2 THR -5 2.450 10.118 -11.472 1.00 54.71 2YCC 159 ATOM 8 N GLU -4 1.741 11.326 -7.287 1.00 54.41 3 2YCC 160 ATOM 9 CA GLU -4 0.508 11.657 -6.573 1.00 53.07 3 2YCC 161 ATOM 10 C GLU -4 0.644 12.924 -5.753 1.00 51.13 3 2YCC 162 ATOM 11 O GLU -4 0.218 12.978 -4.587 1.00 51.46 3 2YCC 163 ATOM 12 CB GLU -4 0.088 10.504 -5.640 1.00 54.53 3 2YCC 164 ATOM 13 CG GLU -4 0.181 9.083 -6.181 1.00 56.26 3 2YCC 165 ATOM 14 CD GLU -4 0.475 7.997 -5.184 1.00 57.45 3 2YCC 166 ATOM 15 OE1 GLU -4 1.529 7.368 -5.137 1.00 58.15 3 2YCC 167 ATOM 16 OE2 GLU -4 -0.483 7.782 -4.399 1.00 58.27 3 2YCC 168 ATOM 17 N PHE -3 1.247 13.928 -6.352 1.00 48.82 2YCC 169 ATOM 18 CA PHE -3 1.441 15.252 -5.764 1.00 46.71 2YCC 170 ATOM 19 C PHE -3 1.824 16.249 -6.883 1.00 45.50 2YCC 171 ATOM 20 O PHE -3 2.961 16.165 -7.410 1.00 45.83 2YCC 172 ATOM 21 CB PHE -3 2.474 15.351 -4.623 1.00 44.39 2YCC 173 ATOM 22 CG PHE -3 2.759 16.794 -4.289 1.00 44.13 2YCC 174 ATOM 23 CD1 PHE -3 1.888 17.509 -3.453 1.00 44.23 2YCC 175 ATOM 24 CD2 PHE -3 3.817 17.469 -4.883 1.00 43.63 2YCC 176 ATOM 25 CE1 PHE -3 2.088 18.838 -3.140 1.00 42.80 2YCC 177 ATOM 26 CE2 PHE -3 4.023 18.831 -4.600 1.00 43.66 2YCC 178 ATOM 27 CZ PHE -3 3.150 19.508 -3.734 1.00 42.73 2YCC 179 ATOM 28 N LYS -2 0.927 17.180 -7.132 1.00 43.13 2YCC 180 ATOM 29 CA LYS -2 1.270 18.182 -8.173 1.00 41.44 2YCC 181 ATOM 30 C LYS -2 1.383 19.545 -7.505 1.00 39.21 2YCC 182 ATOM 31 O LYS -2 0.622 19.884 -6.576 1.00 39.25 2YCC 183 ATOM 32 CB LYS -2 0.331 18.153 -9.340 1.00 43.90 2YCC 184 ATOM 33 CG LYS -2 0.613 17.101 -10.407 1.00 45.24 2YCC 185 ATOM 34 CD LYS -2 0.551 17.634 -11.822 1.00 46.60 2YCC 186 ATOM 35 CE LYS -2 -0.821 18.086 -12.271 1.00 47.98 2YCC 187 ATOM 36 NZ LYS -2 -1.828 17.000 -12.103 1.00 49.34 2YCC 188 ATOM 37 N ALA -1 2.350 20.310 -7.969 1.00 36.34 2YCC 189 ATOM 38 CA ALA -1 2.644 21.641 -7.488 1.00 33.72 2YCC 190 ATOM 39 C ALA -1 1.460 22.591 -7.735 1.00 32.37 2YCC 191 ATOM 40 O ALA -1 0.967 22.694 -8.854 1.00 30.82 2YCC 192 ATOM 41 CB ALA -1 3.880 22.196 -8.216 1.00 33.15 2YCC 193 ATOM 42 N GLY 1 1.078 23.240 -6.650 1.00 30.86 2YCC 194 ATOM 43 CA GLY 1 0.013 24.237 -6.640 1.00 29.34 2YCC 195 ATOM 44 C GLY 1 0.698 25.622 -6.667 1.00 28.16 2YCC 196 ATOM 45 O GLY 1 1.770 25.800 -7.269 1.00 28.56 2YCC 197 ATOM 46 N SER 2 0.080 26.577 -6.007 1.00 27.14 2YCC 198 ATOM 47 CA SER 2 0.536 27.955 -5.885 1.00 25.53 2YCC 199 ATOM 48 C SER 2 1.337 28.184 -4.590 1.00 23.90 2YCC 200 ATOM 49 O SER 2 0.920 27.818 -3.511 1.00 23.88 2YCC 201 ATOM 50 CB SER 2 -0.569 28.984 -5.963 1.00 26.26 2YCC 202 ATOM 51 OG SER 2 -0.211 30.259 -5.455 1.00 26.57 2YCC 203 ATOM 52 N ALA 3 2.497 28.847 -4.834 1.00 21.77 2YCC 204 ATOM 53 CA ALA 3 3.340 29.267 -3.727 1.00 18.16 2YCC 205 ATOM 54 C ALA 3 2.527 30.292 -2.939 1.00 15.73 2YCC 206 ATOM 55 O ALA 3 2.259 30.058 -1.736 1.00 14.69 2YCC 207 ATOM 56 CB ALA 3 4.677 29.845 -4.160 1.00 18.89 2YCC 208 ATOM 57 N LYS 4 2.117 31.373 -3.589 1.00 14.19 2YCC 209 ATOM 58 CA LYS 4 1.303 32.418 -2.940 1.00 13.20 2YCC 210 ATOM 59 C LYS 4 0.118 31.851 -2.190 1.00 12.30 2YCC 211 ATOM 60 O LYS 4 -0.316 32.272 -1.089 1.00 9.46 2YCC 212 ATOM 61 CB LYS 4 0.704 33.374 -4.035 1.00 13.48 2YCC 213 ATOM 62 CG LYS 4 1.867 34.290 -4.478 1.00 16.09 2YCC 214 ATOM 63 CD LYS 4 1.354 35.277 -5.519 1.00 18.73 2YCC 215 ATOM 64 CE LYS 4 2.303 35.459 -6.712 1.00 19.59 2YCC 216 ATOM 65 NZ LYS 4 3.683 34.964 -6.543 1.00 16.07 2YCC 217 ATOM 66 N LYS 5 -0.499 30.856 -2.875 1.00 11.43 2YCC 218 ATOM 67 CA LYS 5 -1.657 30.214 -2.231 1.00 9.58 2YCC 219 ATOM 68 C LYS 5 -1.188 29.605 -0.892 1.00 10.73 2YCC 220 ATOM 69 O LYS 5 -1.778 29.915 0.167 1.00 11.41 2YCC 221 ATOM 70 CB LYS 5 -2.306 29.182 -3.129 1.00 9.36 2YCC 222 ATOM 71 CG LYS 5 -3.779 28.866 -2.759 1.00 8.46 2YCC 223 ATOM 72 CD LYS 5 -4.714 30.014 -2.761 1.00 7.85 2YCC 224 ATOM 73 CE LYS 5 -5.737 30.122 -3.909 1.00 9.21 2YCC 225 ATOM 74 NZ LYS 5 -6.272 31.556 -4.022 1.00 5.36 2YCC 226 ATOM 75 N GLY 6 -0.163 28.742 -1.006 1.00 8.96 2YCC 227 ATOM 76 CA GLY 6 0.403 28.062 0.134 1.00 10.24 2YCC 228 ATOM 77 C GLY 6 0.801 28.923 1.353 1.00 8.92 2YCC 229 ATOM 78 O GLY 6 0.523 28.438 2.490 1.00 8.65 2YCC 230 ATOM 79 N ALA 7 1.398 30.078 1.093 1.00 7.47 2YCC 231 ATOM 80 CA ALA 7 1.884 31.072 1.974 1.00 7.34 2YCC 232 ATOM 81 C ALA 7 0.823 31.596 2.953 1.00 8.63 2YCC 233 ATOM 82 O ALA 7 1.360 32.000 4.029 1.00 10.44 2YCC 234 ATOM 83 CB ALA 7 2.382 32.376 1.290 1.00 5.77 2YCC 235 ATOM 84 N THR 8 -0.449 31.589 2.610 1.00 7.23 2YCC 236 ATOM 85 CA THR 8 -1.581 32.083 3.365 1.00 5.93 2YCC 237 ATOM 86 C THR 8 -2.373 31.018 4.168 1.00 5.54 2YCC 238 ATOM 87 O THR 8 -3.004 31.384 5.214 1.00 2.80 2YCC 239 ATOM 88 CB THR 8 -2.596 33.059 2.717 1.00 6.87 2YCC 240 ATOM 89 OG1 THR 8 -3.346 32.527 1.582 1.00 8.18 2YCC 241 ATOM 90 CG2 THR 8 -1.917 34.409 2.343 1.00 6.22 2YCC 242 ATOM 91 N LEU 9 -2.269 29.815 3.686 1.00 5.87 2YCC 243 ATOM 92 CA LEU 9 -2.887 28.698 4.473 1.00 7.51 2YCC 244 ATOM 93 C LEU 9 -1.897 28.569 5.671 1.00 9.02 2YCC 245 ATOM 94 O LEU 9 -2.347 28.344 6.772 1.00 11.41 2YCC 246 ATOM 95 CB LEU 9 -2.870 27.371 3.742 1.00 11.76 2YCC 247 ATOM 96 CG LEU 9 -4.091 26.754 3.115 1.00 13.31 2YCC 248 ATOM 97 CD1 LEU 9 -4.232 27.263 1.642 1.00 13.68 2YCC 249 ATOM 98 CD2 LEU 9 -3.930 25.236 3.097 1.00 13.36 2YCC 250 ATOM 99 N PHE 10 -0.604 28.555 5.337 1.00 6.86 2YCC 251 ATOM 100 CA PHE 10 0.536 28.409 6.245 1.00 3.95 2YCC 252 ATOM 101 C PHE 10 0.378 29.448 7.369 1.00 3.74 2YCC 253 ATOM 102 O PHE 10 0.374 29.094 8.536 1.00 2.00 2YCC 254 ATOM 103 CB PHE 10 1.948 28.362 5.585 1.00 2.94 2YCC 255 ATOM 104 CG PHE 10 2.917 27.955 6.749 1.00 4.48 2YCC 256 ATOM 105 CD1 PHE 10 3.429 28.964 7.562 1.00 2.36 2YCC 257 ATOM 106 CD2 PHE 10 3.217 26.603 7.019 1.00 2.00 2YCC 258 ATOM 107 CE1 PHE 10 4.261 28.635 8.613 1.00 2.00 2YCC 259 ATOM 108 CE2 PHE 10 4.006 26.254 8.086 1.00 2.13 2YCC 260 ATOM 109 CZ PHE 10 4.588 27.253 8.867 1.00 2.00 2YCC 261 ATOM 110 N LYS 11 0.170 30.706 6.986 1.00 2.37 2YCC 262 ATOM 111 CA LYS 11 -0.141 31.775 7.890 1.00 6.59 2YCC 263 ATOM 112 C LYS 11 -1.446 31.389 8.637 1.00 6.65 2YCC 264 ATOM 113 O LYS 11 -1.420 31.249 9.823 1.00 7.25 2YCC 265 ATOM 114 CB LYS 11 -0.502 33.166 7.229 1.00 4.98 2YCC 266 ATOM 115 CG LYS 11 0.732 33.429 6.322 1.00 4.37 2YCC 267 ATOM 116 CD LYS 11 0.834 34.893 5.891 1.00 6.78 2YCC 268 ATOM 117 CE LYS 11 1.536 34.989 4.494 1.00 3.56 2YCC 269 ATOM 118 NZ LYS 11 1.832 36.491 4.360 1.00 6.06 2YCC 270 ATOM 119 N THR 12 -2.534 31.170 7.960 1.00 7.06 2YCC 271 ATOM 120 CA THR 12 -3.812 30.827 8.521 1.00 6.45 2YCC 272 ATOM 121 C THR 12 -3.797 29.505 9.225 1.00 8.15 2YCC 273 ATOM 122 O THR 12 -4.490 29.448 10.224 1.00 6.55 2YCC 274 ATOM 123 CB THR 12 -5.073 31.034 7.569 1.00 9.22 2YCC 275 ATOM 124 OG1 THR 12 -5.020 30.157 6.375 1.00 2.18 2YCC 276 ATOM 125 CG2 THR 12 -5.356 32.539 7.286 1.00 5.95 2YCC 277 ATOM 126 N ARG 13 -3.013 28.505 8.859 1.00 7.79 2YCC 278 ATOM 127 CA ARG 13 -3.025 27.192 9.441 1.00 9.66 2YCC 279 ATOM 128 C ARG 13 -1.841 26.568 10.154 1.00 7.22 2YCC 280 ATOM 129 O ARG 13 -2.136 25.497 10.783 1.00 6.06 2YCC 281 ATOM 130 CB ARG 13 -3.434 26.050 8.395 1.00 11.45 2YCC 282 ATOM 131 CG ARG 13 -4.691 26.383 7.669 1.00 17.16 2YCC 283 ATOM 132 CD ARG 13 -5.554 25.293 7.205 1.00 18.92 2YCC 284 ATOM 133 NE ARG 13 -6.147 24.519 8.258 1.00 22.21 2YCC 285 ATOM 134 CZ ARG 13 -6.749 23.343 8.297 1.00 22.67 2YCC 286 ATOM 135 NH1 ARG 13 -6.937 22.540 7.251 1.00 22.73 2YCC 287 ATOM 136 NH2 ARG 13 -7.133 22.912 9.517 1.00 21.53 2YCC 288 ATOM 137 N CYS 14 -0.684 27.106 10.153 1.00 6.36 2 2YCC 289 ATOM 138 CA CYS 14 0.509 26.514 10.746 1.00 7.44 2 2YCC 290 ATOM 139 C CYS 14 1.324 27.613 11.497 1.00 7.27 2 2YCC 291 ATOM 140 O CYS 14 2.209 27.204 12.300 1.00 9.46 2 2YCC 292 ATOM 141 CB CYS 14 1.425 25.980 9.666 1.00 6.25 2 2YCC 293 ATOM 142 SG CYS 14 0.731 24.990 8.275 1.00 2.69 2 2YCC 294 ATOM 143 N LEU 15 1.148 28.870 11.245 1.00 5.30 2YCC 295 ATOM 144 CA LEU 15 1.938 29.915 11.949 1.00 8.21 2YCC 296 ATOM 145 C LEU 15 1.750 29.937 13.452 1.00 9.38 2YCC 297 ATOM 146 O LEU 15 2.749 30.213 14.228 1.00 11.34 2YCC 298 ATOM 147 CB LEU 15 1.645 31.258 11.287 1.00 8.28 2YCC 299 ATOM 148 CG LEU 15 2.278 32.531 11.766 1.00 6.34 2YCC 300 ATOM 149 CD1 LEU 15 3.830 32.346 11.848 1.00 6.76 2YCC 301 ATOM 150 CD2 LEU 15 1.926 33.617 10.768 1.00 6.70 2YCC 302 ATOM 151 N GLN 16 0.532 29.697 13.862 1.00 7.14 2YCC 303 ATOM 152 CA GLN 16 0.146 29.610 15.293 1.00 5.63 2YCC 304 ATOM 153 C GLN 16 1.212 28.810 16.014 1.00 5.84 2YCC 305 ATOM 154 O GLN 16 1.572 29.202 17.160 1.00 5.60 2YCC 306 ATOM 155 CB GLN 16 -1.184 28.870 15.457 1.00 4.56 2YCC 307 ATOM 156 CG GLN 16 -1.440 28.267 16.866 1.00 3.40 2YCC 308 ATOM 157 CD GLN 16 -2.797 27.677 17.133 1.00 8.20 2YCC 309 ATOM 158 OE1 GLN 16 -3.659 27.573 16.190 1.00 6.71 2YCC 310 ATOM 159 NE2 GLN 16 -3.019 27.232 18.407 1.00 4.81 2YCC 311 ATOM 160 N CYS 17 1.636 27.713 15.312 1.00 5.36 2 2YCC 312 ATOM 161 CA CYS 17 2.660 26.994 16.171 1.00 4.17 2 2YCC 313 ATOM 162 C CYS 17 3.977 26.732 15.557 1.00 4.59 2 2YCC 314 ATOM 163 O CYS 17 4.778 25.909 16.186 1.00 6.43 2 2YCC 315 ATOM 164 CB CYS 17 2.016 25.617 16.516 1.00 6.22 2 2YCC 316 ATOM 165 SG CYS 17 0.261 25.584 17.049 1.00 2.00 2 2YCC 317 ATOM 166 N HIS 18 4.260 27.361 14.441 1.00 5.28 2YCC 318 ATOM 167 CA HIS 18 5.517 27.017 13.683 1.00 4.87 2YCC 319 ATOM 168 C HIS 18 6.094 28.181 12.902 1.00 3.17 2YCC 320 ATOM 169 O HIS 18 5.424 29.173 12.527 1.00 3.56 2YCC 321 ATOM 170 CB HIS 18 5.103 25.971 12.606 1.00 3.78 2YCC 322 ATOM 171 CG HIS 18 4.950 24.533 12.949 1.00 2.00 2YCC 323 ATOM 172 ND1 HIS 18 5.927 23.619 13.093 1.00 3.02 2YCC 324 ATOM 173 CD2 HIS 18 3.790 23.816 13.027 1.00 6.76 2YCC 325 ATOM 174 CE1 HIS 18 5.408 22.419 13.340 1.00 5.70 2YCC 326 ATOM 175 NE2 HIS 18 4.054 22.479 13.301 1.00 2.46 2YCC 327 ATOM 176 N THR 19 7.399 28.055 12.698 1.00 3.58 2YCC 328 ATOM 177 CA THR 19 8.263 28.955 11.951 1.00 3.66 2YCC 329 ATOM 178 C THR 19 8.931 28.035 10.862 1.00 3.88 2YCC 330 ATOM 179 O THR 19 9.065 26.859 11.157 1.00 3.22 2YCC 331 ATOM 180 CB THR 19 9.347 29.795 12.683 1.00 2.00 2YCC 332 ATOM 181 OG1 THR 19 10.238 28.790 13.288 1.00 2.00 2YCC 333 ATOM 182 CG2 THR 19 8.777 30.692 13.814 1.00 2.00 2YCC 334 ATOM 183 N VAL 20 9.209 28.618 9.743 1.00 5.85 2YCC 335 ATOM 184 CA VAL 20 9.714 28.033 8.515 1.00 8.63 2YCC 336 ATOM 185 C VAL 20 11.145 28.426 8.164 1.00 10.57 2YCC 337 ATOM 186 O VAL 20 11.956 27.821 7.388 1.00 13.44 2YCC 338 ATOM 187 CB VAL 20 8.752 28.484 7.351 1.00 8.87 2YCC 339 ATOM 188 CG1 VAL 20 7.243 28.411 7.643 1.00 7.27 2YCC 340 ATOM 189 CG2 VAL 20 9.101 29.914 6.899 1.00 7.14 2YCC 341 ATOM 190 N GLU 21 11.465 29.581 8.698 1.00 13.50 3 2YCC 342 ATOM 191 CA GLU 21 12.724 30.294 8.473 1.00 15.20 3 2YCC 343 ATOM 192 C GLU 21 13.941 29.762 9.179 1.00 15.44 3 2YCC 344 ATOM 193 O GLU 21 13.890 29.632 10.406 1.00 15.94 3 2YCC 345 ATOM 194 CB GLU 21 12.524 31.761 8.935 1.00 17.64 3 2YCC 346 ATOM 195 CG GLU 21 11.147 32.210 9.357 1.00 19.90 3 2YCC 347 ATOM 196 CD GLU 21 10.864 32.532 10.780 1.00 21.78 3 2YCC 348 ATOM 197 OE1 GLU 21 9.787 32.291 11.308 1.00 21.61 3 2YCC 349 ATOM 198 OE2 GLU 21 11.774 33.078 11.460 1.00 23.34 3 2YCC 350 ATOM 199 N LYS 22 15.061 29.588 8.485 1.00 15.40 2YCC 351 ATOM 200 CA LYS 22 16.283 29.074 9.146 1.00 15.40 2YCC 352 ATOM 201 C LYS 22 16.701 29.989 10.281 1.00 13.10 2YCC 353 ATOM 202 O LYS 22 16.931 31.203 10.057 1.00 14.56 2YCC 354 ATOM 203 CB LYS 22 17.378 28.729 8.187 1.00 14.91 2YCC 355 ATOM 204 CG LYS 22 18.735 29.407 8.289 1.00 17.36 2YCC 356 ATOM 205 CD LYS 22 19.759 28.478 8.929 1.00 20.25 2YCC 357 ATOM 206 CE LYS 22 20.922 29.227 9.612 1.00 20.97 2YCC 358 ATOM 207 NZ LYS 22 22.170 28.801 8.903 1.00 22.10 2YCC 359 ATOM 208 N GLY 23 16.891 29.447 11.482 1.00 12.09 2YCC 360 ATOM 209 CA GLY 23 17.352 30.434 12.515 1.00 10.27 2YCC 361 ATOM 210 C GLY 23 16.122 31.152 13.084 1.00 9.84 2YCC 362 ATOM 211 O GLY 23 16.215 32.143 13.843 1.00 9.49 2YCC 363 ATOM 212 N GLY 24 15.007 30.563 12.731 1.00 7.77 2YCC 364 ATOM 213 CA GLY 24 13.677 30.912 13.179 1.00 8.92 2YCC 365 ATOM 214 C GLY 24 13.498 29.959 14.425 1.00 10.04 2YCC 366 ATOM 215 O GLY 24 14.235 28.944 14.511 1.00 10.02 2YCC 367 ATOM 216 N PRO 25 12.636 30.415 15.327 1.00 8.97 2YCC 368 ATOM 217 CA PRO 25 12.380 29.663 16.547 1.00 8.81 2YCC 369 ATOM 218 C PRO 25 11.428 28.522 16.571 1.00 9.14 2YCC 370 ATOM 219 O PRO 25 10.439 28.269 15.811 1.00 7.76 2YCC 371 ATOM 220 CB PRO 25 11.874 30.757 17.544 1.00 7.63 2YCC 372 ATOM 221 CG PRO 25 11.273 31.792 16.611 1.00 9.78 2YCC 373 ATOM 222 CD PRO 25 11.939 31.677 15.271 1.00 9.04 2YCC 374 ATOM 223 N HIS 26 11.750 27.698 17.631 1.00 8.02 2YCC 375 ATOM 224 CA HIS 26 10.863 26.536 17.900 1.00 10.13 2YCC 376 ATOM 225 C HIS 26 9.734 27.197 18.781 1.00 9.07 2YCC 377 ATOM 226 O HIS 26 10.168 28.013 19.614 1.00 6.89 2YCC 378 ATOM 227 CB HIS 26 11.401 25.378 18.705 1.00 10.94 2YCC 379 ATOM 228 CG HIS 26 12.407 24.558 17.957 1.00 13.03 2YCC 380 ATOM 229 ND1 HIS 26 12.489 24.250 16.633 1.00 11.74 2YCC 381 ATOM 230 CD2 HIS 26 13.423 23.886 18.590 1.00 13.80 2YCC 382 ATOM 231 CE1 HIS 26 13.506 23.399 16.463 1.00 13.94 2YCC 383 ATOM 232 NE2 HIS 26 14.107 23.161 17.694 1.00 13.83 2YCC 384 ATOM 233 N LYS 27 8.523 26.778 18.536 1.00 8.39 2YCC 385 ATOM 234 CA LYS 27 7.380 27.352 19.303 1.00 9.24 2YCC 386 ATOM 235 C LYS 27 6.566 26.203 19.874 1.00 9.45 2YCC 387 ATOM 236 O LYS 27 7.259 25.382 20.561 1.00 11.43 2YCC 388 ATOM 237 CB LYS 27 6.555 28.316 18.552 1.00 10.41 2YCC 389 ATOM 238 CG LYS 27 7.181 29.425 17.695 1.00 8.76 2YCC 390 ATOM 239 CD LYS 27 6.155 30.461 17.417 1.00 10.32 2YCC 391 ATOM 240 CE LYS 27 4.935 29.938 16.596 1.00 10.41 2YCC 392 ATOM 241 NZ LYS 27 4.578 31.100 15.706 1.00 10.76 2YCC 393 ATOM 242 N VAL 28 5.264 26.128 19.557 1.00 6.13 2YCC 394 ATOM 243 CA VAL 28 4.553 24.941 20.091 1.00 4.92 2YCC 395 ATOM 244 C VAL 28 5.109 23.714 19.393 1.00 3.22 2YCC 396 ATOM 245 O VAL 28 5.634 22.701 19.937 1.00 2.99 2YCC 397 ATOM 246 CB VAL 28 3.035 25.108 19.912 1.00 2.60 2YCC 398 ATOM 247 CG1 VAL 28 2.343 23.771 20.294 1.00 2.15 2YCC 399 ATOM 248 CG2 VAL 28 2.456 26.236 20.740 1.00 2.16 2YCC 400 ATOM 249 N GLY 29 4.992 23.823 18.066 1.00 2.00 2YCC 401 ATOM 250 CA GLY 29 5.560 22.755 17.206 1.00 2.08 2YCC 402 ATOM 251 C GLY 29 6.944 23.486 16.890 1.00 2.97 2YCC 403 ATOM 252 O GLY 29 7.040 24.669 17.168 1.00 2.25 2YCC 404 ATOM 253 N PRO 30 7.815 22.675 16.316 1.00 2.00 2YCC 405 ATOM 254 CA PRO 30 9.200 23.064 16.011 1.00 2.09 2YCC 406 ATOM 255 C PRO 30 9.414 23.915 14.780 1.00 2.00 2YCC 407 ATOM 256 O PRO 30 8.405 23.962 14.082 1.00 2.00 2YCC 408 ATOM 257 CB PRO 30 9.809 21.657 15.764 1.00 2.00 2YCC 409 ATOM 258 CG PRO 30 8.627 20.995 15.028 1.00 2.25 2YCC 410 ATOM 259 CD PRO 30 7.534 21.235 16.041 1.00 2.00 2YCC 411 ATOM 260 N ASN 31 10.621 24.525 14.661 1.00 2.00 2YCC 412 ATOM 261 CA ASN 31 10.681 25.315 13.363 1.00 2.00 2YCC 413 ATOM 262 C ASN 31 10.930 24.205 12.299 1.00 2.00 2YCC 414 ATOM 263 O ASN 31 11.691 23.171 12.503 1.00 2.00 2YCC 415 ATOM 264 CB ASN 31 11.598 26.514 13.377 1.00 2.00 2YCC 416 ATOM 265 CG ASN 31 12.254 26.902 12.083 1.00 2.23 2YCC 417 ATOM 266 OD1 ASN 31 13.072 26.120 11.473 1.00 5.97 2YCC 418 ATOM 267 ND2 ASN 31 11.968 28.063 11.431 1.00 2.03 2YCC 419 ATOM 268 N LEU 32 10.336 24.403 11.128 1.00 2.00 2YCC 420 ATOM 269 CA LEU 32 10.424 23.411 10.058 1.00 2.49 2YCC 421 ATOM 270 C LEU 32 11.546 23.660 9.013 1.00 6.79 2YCC 422 ATOM 271 O LEU 32 11.650 22.825 8.025 1.00 5.22 2YCC 423 ATOM 272 CB LEU 32 9.037 23.403 9.306 1.00 5.07 2YCC 424 ATOM 273 CG LEU 32 7.825 23.156 10.219 1.00 5.91 2YCC 425 ATOM 274 CD1 LEU 32 6.496 23.589 9.564 1.00 7.69 2YCC 426 ATOM 275 CD2 LEU 32 7.757 21.689 10.642 1.00 3.42 2YCC 427 ATOM 276 N HIS 33 12.331 24.737 9.212 1.00 6.81 2YCC 428 ATOM 277 CA HIS 33 13.354 24.982 8.145 1.00 8.71 2YCC 429 ATOM 278 C HIS 33 14.221 23.726 7.980 1.00 10.43 2YCC 430 ATOM 279 O HIS 33 14.905 23.206 8.887 1.00 11.51 2YCC 431 ATOM 280 CB HIS 33 14.205 26.218 8.203 1.00 9.13 2YCC 432 ATOM 281 CG HIS 33 15.197 26.460 7.098 1.00 10.69 2YCC 433 ATOM 282 ND1 HIS 33 15.068 27.389 6.090 1.00 12.47 2YCC 434 ATOM 283 CD2 HIS 33 16.373 25.799 6.877 1.00 9.97 2YCC 435 ATOM 284 CE1 HIS 33 16.167 27.313 5.262 1.00 9.35 2YCC 436 ATOM 285 NE2 HIS 33 16.965 26.332 5.716 1.00 8.94 2YCC 437 ATOM 286 N GLY 34 14.269 23.335 6.706 1.00 9.11 2YCC 438 ATOM 287 CA GLY 34 14.999 22.229 6.222 1.00 8.33 2YCC 439 ATOM 288 C GLY 34 14.262 20.936 6.495 1.00 7.21 2YCC 440 ATOM 289 O GLY 34 14.864 19.880 6.530 1.00 6.00 2YCC 441 ATOM 290 N ILE 35 12.972 20.987 6.643 1.00 9.55 2YCC 442 ATOM 291 CA ILE 35 12.235 19.717 6.881 1.00 11.94 2YCC 443 ATOM 292 C ILE 35 12.371 18.686 5.816 1.00 13.32 2YCC 444 ATOM 293 O ILE 35 12.370 17.474 6.111 1.00 14.40 2YCC 445 ATOM 294 CB ILE 35 10.730 20.114 7.152 1.00 13.71 2YCC 446 ATOM 295 CG1 ILE 35 9.990 18.873 7.710 1.00 16.02 2YCC 447 ATOM 296 CG2 ILE 35 10.035 20.837 6.031 1.00 17.29 2YCC 448 ATOM 297 CD1 ILE 35 10.541 18.447 9.134 1.00 16.34 2YCC 449 ATOM 298 N PHE 36 12.446 19.100 4.527 1.00 14.49 2YCC 450 ATOM 299 CA PHE 36 12.511 18.167 3.396 1.00 13.00 2YCC 451 ATOM 300 C PHE 36 13.800 17.405 3.333 1.00 14.39 2YCC 452 ATOM 301 O PHE 36 14.912 17.956 3.323 1.00 14.77 2YCC 453 ATOM 302 CB PHE 36 11.985 18.775 2.057 1.00 13.65 2YCC 454 ATOM 303 CG PHE 36 10.495 19.030 2.170 1.00 9.85 2YCC 455 ATOM 304 CD1 PHE 36 9.634 17.915 2.239 1.00 12.79 2YCC 456 ATOM 305 CD2 PHE 36 9.977 20.303 2.299 1.00 9.02 2YCC 457 ATOM 306 CE1 PHE 36 8.264 18.052 2.396 1.00 12.36 2YCC 458 ATOM 307 CE2 PHE 36 8.598 20.497 2.500 1.00 9.18 2YCC 459 ATOM 308 CZ PHE 36 7.748 19.386 2.521 1.00 10.71 2YCC 460 ATOM 309 N GLY 37 13.642 16.082 3.337 1.00 14.37 2YCC 461 ATOM 310 CA GLY 37 14.724 15.110 3.312 1.00 14.52 2YCC 462 ATOM 311 C GLY 37 15.070 14.552 4.692 1.00 13.93 2YCC 463 ATOM 312 O GLY 37 15.738 13.505 4.810 1.00 11.40 2YCC 464 ATOM 313 N ARG 38 14.652 15.310 5.705 1.00 13.45 3 2YCC 465 ATOM 314 CA ARG 38 14.934 14.891 7.108 1.00 12.48 3 2YCC 466 ATOM 315 C ARG 38 13.736 14.036 7.538 1.00 12.01 3 2YCC 467 ATOM 316 O ARG 38 12.650 14.137 6.913 1.00 8.38 3 2YCC 468 ATOM 317 CB ARG 38 15.414 15.919 8.061 1.00 14.40 3 2YCC 469 ATOM 318 CG ARG 38 14.824 16.328 9.349 1.00 13.97 3 2YCC 470 ATOM 319 CD ARG 38 15.504 17.512 10.041 1.00 14.21 3 2YCC 471 ATOM 320 NE ARG 38 14.425 18.450 10.416 1.00 13.30 3 2YCC 472 ATOM 321 CZ ARG 38 14.368 19.754 10.329 1.00 12.44 3 2YCC 473 ATOM 322 NH1 ARG 38 13.310 20.481 10.649 1.00 10.13 3 2YCC 474 ATOM 323 NH2 ARG 38 15.461 20.429 9.912 1.00 15.68 3 2YCC 475 ATOM 324 N HIS 39 14.134 13.186 8.453 1.00 12.02 2YCC 476 ATOM 325 CA HIS 39 13.317 12.182 9.150 1.00 12.56 2YCC 477 ATOM 326 C HIS 39 12.662 12.931 10.337 1.00 13.62 2YCC 478 ATOM 327 O HIS 39 13.265 13.872 10.880 1.00 13.88 2YCC 479 ATOM 328 CB HIS 39 14.122 11.032 9.796 1.00 15.45 2YCC 480 ATOM 329 CG HIS 39 14.960 10.286 8.798 1.00 16.24 2YCC 481 ATOM 330 ND1 HIS 39 16.172 10.722 8.338 1.00 18.77 2YCC 482 ATOM 331 CD2 HIS 39 14.681 9.132 8.117 1.00 19.22 2YCC 483 ATOM 332 CE1 HIS 39 16.615 9.838 7.408 1.00 19.86 2YCC 484 ATOM 333 NE2 HIS 39 15.726 8.872 7.274 1.00 20.02 2YCC 485 ATOM 334 N SER 40 11.478 12.487 10.662 1.00 12.50 2YCC 486 ATOM 335 CA SER 40 10.697 13.079 11.735 1.00 12.33 2YCC 487 ATOM 336 C SER 40 11.445 12.901 13.084 1.00 14.33 2YCC 488 ATOM 337 O SER 40 11.993 11.807 13.402 1.00 13.24 2YCC 489 ATOM 338 CB SER 40 9.368 12.322 11.842 1.00 10.77 2YCC 490 ATOM 339 OG SER 40 9.762 11.047 12.515 1.00 2.26 2YCC 491 ATOM 340 N GLY 41 11.351 13.979 13.849 1.00 13.98 2YCC 492 ATOM 341 CA GLY 41 11.897 14.125 15.191 1.00 14.02 2YCC 493 ATOM 342 C GLY 41 13.338 14.533 15.316 1.00 13.96 2YCC 494 ATOM 343 O GLY 41 13.970 14.085 16.262 1.00 13.18 2YCC 495 ATOM 344 N GLN 42 13.902 15.328 14.392 1.00 14.65 2YCC 496 ATOM 345 CA GLN 42 15.351 15.635 14.528 1.00 14.33 2YCC 497 ATOM 346 C GLN 42 15.741 17.044 14.862 1.00 13.53 2YCC 498 ATOM 347 O GLN 42 16.964 17.295 15.078 1.00 10.79 2YCC 499 ATOM 348 CB GLN 42 16.127 15.030 13.356 1.00 17.60 2YCC 500 ATOM 349 CG GLN 42 16.110 13.551 13.145 1.00 16.88 2YCC 501 ATOM 350 CD GLN 42 17.163 12.798 13.909 1.00 17.36 2YCC 502 ATOM 351 OE1 GLN 42 16.856 11.911 14.692 1.00 14.58 2YCC 503 ATOM 352 NE2 GLN 42 18.427 13.187 13.678 1.00 18.65 2YCC 504 ATOM 353 N ALA 43 14.805 17.976 14.962 1.00 13.47 2YCC 505 ATOM 354 CA ALA 43 15.066 19.347 15.379 1.00 14.71 2YCC 506 ATOM 355 C ALA 43 15.591 19.258 16.857 1.00 15.50 2YCC 507 ATOM 356 O ALA 43 14.829 18.750 17.697 1.00 14.01 2YCC 508 ATOM 357 CB ALA 43 13.922 20.347 15.445 1.00 15.61 2YCC 509 ATOM 358 N GLU 44 16.820 19.768 16.972 1.00 14.83 3 2YCC 510 ATOM 359 CA GLU 44 17.474 19.791 18.287 1.00 14.81 3 2YCC 511 ATOM 360 C GLU 44 16.965 21.064 19.031 1.00 13.76 3 2YCC 512 ATOM 361 O GLU 44 16.953 22.205 18.485 1.00 10.67 3 2YCC 513 ATOM 362 CB GLU 44 18.985 19.779 18.058 1.00 16.75 3 2YCC 514 ATOM 363 CG GLU 44 19.822 21.011 18.288 1.00 17.49 3 2YCC 515 ATOM 364 CD GLU 44 21.276 20.782 18.663 1.00 19.88 3 2YCC 516 ATOM 365 OE1 GLU 44 21.769 20.720 19.811 1.00 18.83 3 2YCC 517 ATOM 366 OE2 GLU 44 21.918 20.651 17.577 1.00 18.92 3 2YCC 518 ATOM 367 N GLY 45 16.576 20.855 20.260 1.00 13.22 2YCC 519 ATOM 368 CA GLY 45 16.046 21.786 21.239 1.00 10.80 2YCC 520 ATOM 369 C GLY 45 14.517 21.649 21.297 1.00 11.58 2YCC 521 ATOM 370 O GLY 45 13.819 22.531 21.864 1.00 10.84 2YCC 522 ATOM 371 N TYR 46 13.993 20.568 20.692 1.00 11.23 3 2YCC 523 ATOM 372 CA TYR 46 12.526 20.415 20.730 1.00 10.87 3 2YCC 524 ATOM 373 C TYR 46 12.151 19.098 21.343 1.00 12.47 3 2YCC 525 ATOM 374 O TYR 46 12.807 18.045 21.134 1.00 12.87 3 2YCC 526 ATOM 375 CB TYR 46 11.940 20.706 19.342 1.00 10.33 3 2YCC 527 ATOM 376 CG TYR 46 10.390 20.755 19.449 1.00 9.18 3 2YCC 528 ATOM 377 CD1 TYR 46 9.687 21.935 19.687 1.00 7.97 3 2YCC 529 ATOM 378 CD2 TYR 46 9.728 19.545 19.380 1.00 4.75 3 2YCC 530 ATOM 379 CE1 TYR 46 8.288 21.894 19.765 1.00 9.69 3 2YCC 531 ATOM 380 CE2 TYR 46 8.366 19.519 19.566 1.00 5.86 3 2YCC 532 ATOM 381 CZ TYR 46 7.637 20.645 19.693 1.00 7.30 3 2YCC 533 ATOM 382 OH TYR 46 6.261 20.500 19.794 1.00 6.57 3 2YCC 534 ATOM 383 N SER 47 11.129 19.140 22.237 1.00 13.83 2YCC 535 ATOM 384 CA SER 47 10.739 17.915 22.918 1.00 15.58 2YCC 536 ATOM 385 C SER 47 9.519 17.350 22.215 1.00 16.59 2YCC 537 ATOM 386 O SER 47 8.378 17.515 22.629 1.00 18.55 2YCC 538 ATOM 387 CB SER 47 10.669 18.053 24.435 1.00 16.89 2YCC 539 ATOM 388 OG SER 47 10.436 16.748 25.011 1.00 16.23 2YCC 540 ATOM 389 N TYR 48 9.821 16.632 21.173 1.00 17.23 2YCC 541 ATOM 390 CA TYR 48 8.946 15.886 20.272 1.00 18.45 2YCC 542 ATOM 391 C TYR 48 8.421 14.684 21.079 1.00 18.79 2YCC 543 ATOM 392 O TYR 48 9.217 14.151 21.866 1.00 18.61 2YCC 544 ATOM 393 CB TYR 48 9.716 15.366 19.068 1.00 16.03 2YCC 545 ATOM 394 CG TYR 48 10.301 16.063 17.911 1.00 16.74 2YCC 546 ATOM 395 CD1 TYR 48 9.572 16.465 16.777 1.00 18.49 2YCC 547 ATOM 396 CD2 TYR 48 11.699 16.351 17.856 1.00 18.93 2YCC 548 ATOM 397 CE1 TYR 48 10.143 17.083 15.650 1.00 16.62 2YCC 549 ATOM 398 CE2 TYR 48 12.310 16.999 16.784 1.00 18.13 2YCC 550 ATOM 399 CZ TYR 48 11.516 17.331 15.666 1.00 19.19 2YCC 551 ATOM 400 OH TYR 48 12.142 17.928 14.626 1.00 18.31 2YCC 552 ATOM 401 N THR 49 7.166 14.317 20.925 1.00 20.79 2YCC 553 ATOM 402 CA THR 49 6.613 13.146 21.599 1.00 21.03 2YCC 554 ATOM 403 C THR 49 7.363 11.887 21.071 1.00 22.89 2YCC 555 ATOM 404 O THR 49 7.925 11.897 19.955 1.00 22.34 2YCC 556 ATOM 405 CB THR 49 5.089 12.831 21.328 1.00 19.57 2YCC 557 ATOM 406 OG1 THR 49 5.072 12.358 19.926 1.00 18.47 2YCC 558 ATOM 407 CG2 THR 49 4.059 13.886 21.631 1.00 19.02 2YCC 559 ATOM 408 N ASP 50 7.216 10.828 21.856 1.00 24.14 3 2YCC 560 ATOM 409 CA ASP 50 7.779 9.518 21.618 1.00 25.55 3 2YCC 561 ATOM 410 C ASP 50 7.351 9.066 20.229 1.00 26.03 3 2YCC 562 ATOM 411 O ASP 50 8.171 8.594 19.448 1.00 26.93 3 2YCC 563 ATOM 412 CB ASP 50 7.399 8.468 22.691 1.00 26.77 3 2YCC 564 ATOM 413 CG ASP 50 8.585 8.256 23.626 1.00 29.19 3 2YCC 565 ATOM 414 OD1 ASP 50 8.523 8.178 24.881 1.00 29.53 3 2YCC 566 ATOM 415 OD2 ASP 50 9.692 8.228 23.015 1.00 30.21 3 2YCC 567 ATOM 416 N ALA 51 6.068 9.284 20.036 1.00 26.35 2YCC 568 ATOM 417 CA ALA 51 5.381 8.934 18.803 1.00 27.99 2YCC 569 ATOM 418 C ALA 51 6.102 9.427 17.560 1.00 28.99 2YCC 570 ATOM 419 O ALA 51 6.232 8.621 16.571 1.00 29.47 2YCC 571 ATOM 420 CB ALA 51 3.927 9.426 18.796 1.00 26.98 2YCC 572 ATOM 421 N ASN 52 6.479 10.715 17.626 1.00 29.22 2YCC 573 ATOM 422 CA ASN 52 7.156 11.296 16.473 1.00 29.86 2YCC 574 ATOM 423 C ASN 52 8.346 10.417 16.031 1.00 30.33 2YCC 575 ATOM 424 O ASN 52 8.379 9.828 14.930 1.00 30.42 2YCC 576 ATOM 425 CB ASN 52 7.539 12.775 16.617 1.00 30.01 2YCC 577 ATOM 426 CG ASN 52 8.042 13.271 15.223 1.00 30.21 2YCC 578 ATOM 427 OD1 ASN 52 9.247 13.013 14.900 1.00 30.47 2YCC 579 ATOM 428 ND2 ASN 52 7.148 13.832 14.471 1.00 27.64 2YCC 580 ATOM 429 N ILE 53 9.295 10.465 16.921 1.00 30.18 2YCC 581 ATOM 430 CA ILE 53 10.603 9.824 16.925 1.00 30.68 2YCC 582 ATOM 431 C ILE 53 10.622 8.422 16.312 1.00 30.14 2YCC 583 ATOM 432 O ILE 53 11.408 8.124 15.389 1.00 28.39 2YCC 584 ATOM 433 CB ILE 53 11.219 10.006 18.386 1.00 29.99 2YCC 585 ATOM 434 CG1 ILE 53 12.196 11.203 18.404 1.00 28.74 2YCC 586 ATOM 435 CG2 ILE 53 11.928 8.779 19.021 1.00 31.15 2YCC 587 ATOM 436 CD1 ILE 53 11.816 12.376 19.328 1.00 29.42 2YCC 588 ATOM 437 N LYS 54 9.771 7.564 16.834 1.00 30.58 2YCC 589 ATOM 438 CA LYS 54 9.589 6.175 16.483 1.00 31.04 2YCC 590 ATOM 439 C LYS 54 8.962 5.959 15.111 1.00 31.87 2YCC 591 ATOM 440 O LYS 54 9.224 4.903 14.497 1.00 31.52 2YCC 592 ATOM 441 CB LYS 54 8.750 5.440 17.524 1.00 31.15 2YCC 593 ATOM 442 CG LYS 54 9.498 5.118 18.824 1.00 30.88 2YCC 594 ATOM 443 CD LYS 54 9.835 6.395 19.618 1.00 29.84 2YCC 595 ATOM 444 CE LYS 54 9.336 6.207 21.046 1.00 29.76 2YCC 596 ATOM 445 NZ LYS 54 10.050 4.990 21.592 1.00 30.43 2YCC 597 ATOM 446 N LYS 55 8.198 6.947 14.709 1.00 32.65 2YCC 598 ATOM 447 CA LYS 55 7.525 6.928 13.396 1.00 34.30 2YCC 599 ATOM 448 C LYS 55 8.654 6.774 12.349 1.00 35.23 2YCC 600 ATOM 449 O LYS 55 8.742 5.862 11.517 1.00 35.58 2YCC 601 ATOM 450 CB LYS 55 6.744 8.225 13.130 1.00 33.66 2YCC 602 ATOM 451 CG LYS 55 6.157 8.344 11.717 1.00 32.22 2YCC 603 ATOM 452 CD LYS 55 4.988 7.392 11.518 1.00 32.11 2YCC 604 ATOM 453 CE LYS 55 4.450 7.228 10.106 1.00 29.29 2YCC 605 ATOM 454 NZ LYS 55 3.580 5.998 10.083 1.00 25.12 2YCC 606 ATOM 455 N ASN 56 9.523 7.749 12.507 1.00 35.56 2YCC 607 ATOM 456 CA ASN 56 10.711 7.965 11.738 1.00 36.85 2YCC 608 ATOM 457 C ASN 56 10.467 8.141 10.240 1.00 36.65 2YCC 609 ATOM 458 O ASN 56 11.102 7.482 9.415 1.00 36.38 2YCC 610 ATOM 459 CB ASN 56 11.775 6.889 12.096 1.00 36.86 2YCC 611 ATOM 460 CG ASN 56 13.104 7.570 11.714 1.00 36.89 2YCC 612 ATOM 461 OD1 ASN 56 13.651 7.194 10.683 1.00 38.26 2YCC 613 ATOM 462 ND2 ASN 56 13.450 8.583 12.501 1.00 37.09 2YCC 614 ATOM 463 N VAL 57 9.597 9.094 9.933 1.00 36.86 2YCC 615 ATOM 464 CA VAL 57 9.217 9.433 8.550 1.00 35.72 2YCC 616 ATOM 465 C VAL 57 10.248 10.304 7.841 1.00 35.00 2YCC 617 ATOM 466 O VAL 57 10.753 11.220 8.500 1.00 34.87 2YCC 618 ATOM 467 CB VAL 57 7.843 10.157 8.675 1.00 36.45 2YCC 619 ATOM 468 CG1 VAL 57 7.498 11.083 7.532 1.00 36.90 2YCC 620 ATOM 469 CG2 VAL 57 6.712 9.145 8.828 1.00 38.03 2YCC 621 ATOM 470 N LEU 58 10.467 10.025 6.558 1.00 33.50 2YCC 622 ATOM 471 CA LEU 58 11.402 10.850 5.711 1.00 31.85 2YCC 623 ATOM 472 C LEU 58 10.458 11.937 5.100 1.00 29.70 2YCC 624 ATOM 473 O LEU 58 9.339 11.638 4.653 1.00 28.48 2YCC 625 ATOM 474 CB LEU 58 12.160 9.974 4.789 1.00 33.66 2YCC 626 ATOM 475 CG LEU 58 13.565 10.029 4.244 1.00 35.20 2YCC 627 ATOM 476 CD1 LEU 58 13.673 10.611 2.829 1.00 32.86 2YCC 628 ATOM 477 CD2 LEU 58 14.505 10.819 5.158 1.00 34.48 2YCC 629 ATOM 478 N TRP 59 10.890 13.185 5.185 1.00 27.59 2YCC 630 ATOM 479 CA TRP 59 10.134 14.348 4.748 1.00 25.12 2YCC 631 ATOM 480 C TRP 59 10.414 14.852 3.326 1.00 24.38 2YCC 632 ATOM 481 O TRP 59 11.346 15.582 2.928 1.00 22.70 2YCC 633 ATOM 482 CB TRP 59 10.130 15.441 5.814 1.00 24.00 2YCC 634 ATOM 483 CG TRP 59 9.498 15.205 7.151 1.00 21.73 2YCC 635 ATOM 484 CD1 TRP 59 10.029 15.387 8.380 1.00 21.20 2YCC 636 ATOM 485 CD2 TRP 59 8.169 14.705 7.389 1.00 21.31 2YCC 637 ATOM 486 NE1 TRP 59 9.130 15.048 9.377 1.00 20.73 2YCC 638 ATOM 487 CE2 TRP 59 7.992 14.613 8.782 1.00 19.96 2YCC 639 ATOM 488 CE3 TRP 59 7.140 14.289 6.514 1.00 18.66 2YCC 640 ATOM 489 CZ2 TRP 59 6.801 14.150 9.351 1.00 21.28 2YCC 641 ATOM 490 CZ3 TRP 59 6.000 13.822 7.057 1.00 19.72 2YCC 642 ATOM 491 CH2 TRP 59 5.790 13.747 8.466 1.00 20.86 2YCC 643 ATOM 492 N ASP 60 9.472 14.436 2.496 1.00 23.48 2YCC 644 ATOM 493 CA ASP 60 9.313 14.788 1.068 1.00 23.01 2YCC 645 ATOM 494 C ASP 60 7.812 15.125 0.916 1.00 22.64 2YCC 646 ATOM 495 O ASP 60 6.974 14.920 1.813 1.00 22.65 2YCC 647 ATOM 496 CB ASP 60 9.857 13.807 0.053 1.00 21.22 2YCC 648 ATOM 497 CG ASP 60 9.187 12.429 0.195 1.00 19.21 2YCC 649 ATOM 498 OD1 ASP 60 7.976 12.324 0.358 1.00 16.53 2YCC 650 ATOM 499 OD2 ASP 60 10.023 11.481 0.149 1.00 19.30 2YCC 651 ATOM 500 N GLU 61 7.505 15.765 -0.162 1.00 23.07 3 2YCC 652 ATOM 501 CA GLU 61 6.227 16.277 -0.595 1.00 22.68 3 2YCC 653 ATOM 502 C GLU 61 5.048 15.342 -0.517 1.00 21.98 3 2YCC 654 ATOM 503 O GLU 61 3.906 15.758 -0.309 1.00 19.61 3 2YCC 655 ATOM 504 CB GLU 61 6.360 16.755 -2.064 1.00 24.83 3 2YCC 656 ATOM 505 CG GLU 61 7.238 15.910 -2.957 1.00 26.54 3 2YCC 657 ATOM 506 CD GLU 61 6.982 15.902 -4.441 1.00 27.58 3 2YCC 658 ATOM 507 OE1 GLU 61 6.454 14.948 -4.994 1.00 26.72 3 2YCC 659 ATOM 508 OE2 GLU 61 7.455 16.917 -5.018 1.00 28.05 3 2YCC 660 ATOM 509 N ASN 62 5.359 14.056 -0.638 1.00 22.88 2YCC 661 ATOM 510 CA ASN 62 4.331 13.003 -0.653 1.00 22.26 2YCC 662 ATOM 511 C ASN 62 3.879 12.518 0.693 1.00 21.47 2YCC 663 ATOM 512 O ASN 62 2.646 12.477 0.975 1.00 21.88 2YCC 664 ATOM 513 CB ASN 62 4.815 11.930 -1.621 1.00 24.87 2YCC 665 ATOM 514 CG ASN 62 5.120 12.504 -3.006 1.00 25.70 2YCC 666 ATOM 515 OD1 ASN 62 6.066 13.308 -3.136 1.00 27.13 2YCC 667 ATOM 516 ND2 ASN 62 4.289 12.124 -3.952 1.00 27.49 2YCC 668 ATOM 517 N ASN 63 4.828 12.107 1.485 1.00 21.12 2YCC 669 ATOM 518 CA ASN 63 4.635 11.610 2.907 1.00 20.45 2YCC 670 ATOM 519 C ASN 63 3.799 12.694 3.608 1.00 17.78 2YCC 671 ATOM 520 O ASN 63 2.687 12.560 4.174 1.00 17.06 2YCC 672 ATOM 521 CB ASN 63 6.029 11.406 3.471 1.00 22.94 2YCC 673 ATOM 522 CG ASN 63 6.408 10.282 4.360 1.00 25.80 2YCC 674 ATOM 523 OD1 ASN 63 5.848 10.061 5.495 1.00 27.42 2YCC 675 ATOM 524 ND2 ASN 63 7.429 9.519 3.953 1.00 26.31 2YCC 676 ATOM 525 N MET 64 4.342 13.857 3.407 1.00 17.06 2YCC 677 ATOM 526 CA MET 64 3.917 15.165 3.863 1.00 17.55 2YCC 678 ATOM 527 C MET 64 2.485 15.535 3.471 1.00 17.47 2YCC 679 ATOM 528 O MET 64 1.925 16.471 4.139 1.00 17.45 2YCC 680 ATOM 529 CB MET 64 4.958 16.201 3.465 1.00 19.09 2YCC 681 ATOM 530 CG MET 64 4.572 17.592 3.795 1.00 22.23 2YCC 682 ATOM 531 SD MET 64 4.394 17.687 5.607 1.00 28.88 2YCC 683 ATOM 532 CE MET 64 5.901 18.661 5.982 1.00 26.41 2YCC 684 ATOM 533 N SER 65 1.985 14.860 2.474 1.00 15.73 2YCC 685 ATOM 534 CA SER 65 0.580 15.151 2.007 1.00 15.24 2YCC 686 ATOM 535 C SER 65 -0.359 14.208 2.733 1.00 16.09 2YCC 687 ATOM 536 O SER 65 -1.417 14.622 3.274 1.00 14.80 2YCC 688 ATOM 537 CB SER 65 0.628 15.173 0.489 1.00 14.66 2YCC 689 ATOM 538 OG SER 65 -0.623 15.079 -0.154 1.00 12.06 2YCC 690 ATOM 539 N GLU 66 0.031 12.927 2.877 1.00 17.84 2YCC 691 ATOM 540 CA GLU 66 -0.845 12.037 3.672 1.00 21.97 2YCC 692 ATOM 541 C GLU 66 -0.866 12.615 5.108 1.00 22.10 2YCC 693 ATOM 542 O GLU 66 -1.928 13.043 5.621 1.00 23.28 2YCC 694 ATOM 543 CB GLU 66 -0.496 10.588 3.720 1.00 23.40 2YCC 695 ATOM 544 CG GLU 66 -0.393 9.776 2.443 1.00 27.52 2YCC 696 ATOM 545 CD GLU 66 1.021 9.581 1.963 1.00 28.77 2YCC 697 ATOM 546 OE1 GLU 66 1.769 10.485 1.676 1.00 29.19 2YCC 698 ATOM 547 OE2 GLU 66 1.327 8.359 1.883 1.00 30.99 2YCC 699 ATOM 548 N TYR 67 0.265 12.737 5.757 1.00 23.34 3 2YCC 700 ATOM 549 CA TYR 67 0.389 13.304 7.117 1.00 22.52 3 2YCC 701 ATOM 550 C TYR 67 -0.553 14.504 7.308 1.00 20.96 3 2YCC 702 ATOM 551 O TYR 67 -1.230 14.665 8.318 1.00 18.19 3 2YCC 703 ATOM 552 CB TYR 67 1.860 13.723 7.373 1.00 25.07 3 2YCC 704 ATOM 553 CG TYR 67 2.104 14.421 8.690 1.00 27.38 3 2YCC 705 ATOM 554 CD1 TYR 67 2.627 15.725 8.687 1.00 29.17 3 2YCC 706 ATOM 555 CD2 TYR 67 1.764 13.849 9.936 1.00 27.48 3 2YCC 707 ATOM 556 CE1 TYR 67 2.823 16.395 9.915 1.00 28.66 3 2YCC 708 ATOM 557 CE2 TYR 67 1.960 14.496 11.153 1.00 26.38 3 2YCC 709 ATOM 558 CZ TYR 67 2.499 15.775 11.129 1.00 28.51 3 2YCC 710 ATOM 559 OH TYR 67 2.702 16.464 12.290 1.00 27.04 3 2YCC 711 ATOM 560 N LEU 68 -0.521 15.346 6.270 1.00 19.60 2YCC 712 ATOM 561 CA LEU 68 -1.280 16.563 6.172 1.00 18.86 2YCC 713 ATOM 562 C LEU 68 -2.755 16.275 6.046 1.00 19.49 2YCC 714 ATOM 563 O LEU 68 -3.539 17.108 6.518 1.00 19.24 2YCC 715 ATOM 564 CB LEU 68 -0.625 17.581 5.191 1.00 18.43 2YCC 716 ATOM 565 CG LEU 68 0.547 18.322 5.908 1.00 18.52 2YCC 717 ATOM 566 CD1 LEU 68 1.408 19.154 4.962 1.00 17.56 2YCC 718 ATOM 567 CD2 LEU 68 -0.031 19.131 7.070 1.00 16.99 2YCC 719 ATOM 568 N THR 69 -3.077 15.112 5.503 1.00 19.15 2YCC 720 ATOM 569 CA THR 69 -4.492 14.681 5.399 1.00 20.25 2YCC 721 ATOM 570 C THR 69 -5.094 14.455 6.769 1.00 20.81 2YCC 722 ATOM 571 O THR 69 -6.193 14.923 7.087 1.00 21.48 2YCC 723 ATOM 572 CB THR 69 -4.571 13.418 4.450 1.00 18.97 2YCC 724 ATOM 573 OG1 THR 69 -4.076 14.013 3.190 1.00 19.96 2YCC 725 ATOM 574 CG2 THR 69 -5.880 12.722 4.239 1.00 16.96 2YCC 726 ATOM 575 N ASN 70 -4.323 13.821 7.651 1.00 21.84 2YCC 727 ATOM 576 CA ASN 70 -4.741 13.461 9.017 1.00 21.95 2YCC 728 ATOM 577 C ASN 70 -3.527 12.969 9.821 1.00 20.83 2YCC 729 ATOM 578 O ASN 70 -3.240 11.754 9.841 1.00 19.93 2YCC 730 ATOM 579 CB ASN 70 -5.791 12.327 8.883 1.00 22.77 2YCC 731 ATOM 580 CG ASN 70 -6.717 12.356 10.091 1.00 23.00 2YCC 732 ATOM 581 OD1 ASN 70 -6.246 11.980 11.194 1.00 23.69 2YCC 733 ATOM 582 ND2 ASN 70 -7.964 12.756 9.829 1.00 21.90 2YCC 734 ATOM 583 N PRO 71 -2.865 13.947 10.438 1.00 19.87 2YCC 735 ATOM 584 CA PRO 71 -1.639 13.705 11.212 1.00 19.51 2YCC 736 ATOM 585 C PRO 71 -1.702 12.602 12.252 1.00 18.37 2YCC 737 ATOM 586 O PRO 71 -0.906 11.660 12.155 1.00 16.87 2YCC 738 ATOM 587 CB PRO 71 -1.247 15.045 11.811 1.00 19.19 2YCC 739 ATOM 588 CG PRO 71 -1.889 16.059 10.859 1.00 20.82 2YCC 740 ATOM 589 CD PRO 71 -3.189 15.376 10.385 1.00 20.32 2YCC 741 ATOM 590 N LYS 72 -2.599 12.802 13.212 1.00 19.80 1 2YCC 742 ATOM 591 CA LYS 72 -2.821 11.878 14.345 1.00 21.32 1 2YCC 743 ATOM 592 C LYS 72 -3.029 10.477 13.783 1.00 21.29 1 2YCC 744 ATOM 593 O LYS 72 -2.651 9.470 14.409 1.00 23.46 1 2YCC 745 ATOM 594 CB LYS 72 -3.903 12.250 15.381 1.00 20.57 1 2YCC 746 ATOM 595 CG LYS 72 -3.715 13.640 16.014 1.00 19.78 1 2YCC 747 ATOM 596 CD LYS 72 -4.505 13.870 17.288 1.00 20.40 1 2YCC 748 ATOM 597 CE LYS 72 -4.090 15.196 17.972 1.00 19.05 1 2YCC 749 ATOM 598 NZ LYS 72 -3.940 15.059 19.441 1.00 18.74 1 2YCC 750 ATOM 599 N LYS 73 -3.547 10.474 12.566 1.00 20.59 2YCC 751 ATOM 600 CA LYS 73 -3.779 9.242 11.808 1.00 20.10 2YCC 752 ATOM 601 C LYS 73 -2.454 8.749 11.234 1.00 19.76 2YCC 753 ATOM 602 O LYS 73 -2.154 7.553 11.432 1.00 20.45 2YCC 754 ATOM 603 CB LYS 73 -4.864 9.333 10.746 1.00 16.84 2YCC 755 ATOM 604 CG LYS 73 -5.216 7.922 10.219 1.00 14.76 2YCC 756 ATOM 605 CD LYS 73 -6.501 7.365 10.831 1.00 12.87 2YCC 757 ATOM 606 CE LYS 73 -6.239 5.889 11.180 1.00 13.19 2YCC 758 ATOM 607 NZ LYS 73 -6.932 5.017 10.192 1.00 7.65 2YCC 759 ATOM 608 N TYR 74 -1.618 9.553 10.631 1.00 19.90 2YCC 760 ATOM 609 CA TYR 74 -0.315 9.143 10.090 1.00 18.91 2YCC 761 ATOM 610 C TYR 74 0.643 8.699 11.223 1.00 19.07 2YCC 762 ATOM 611 O TYR 74 1.319 7.657 11.032 1.00 16.61 2YCC 763 ATOM 612 CB TYR 74 0.309 10.172 9.149 1.00 21.30 2YCC 764 ATOM 613 CG TYR 74 1.386 9.610 8.221 1.00 22.26 2YCC 765 ATOM 614 CD1 TYR 74 1.029 8.963 7.028 1.00 23.10 2YCC 766 ATOM 615 CD2 TYR 74 2.737 9.739 8.485 1.00 23.33 2YCC 767 ATOM 616 CE1 TYR 74 1.964 8.428 6.156 1.00 23.64 2YCC 768 ATOM 617 CE2 TYR 74 3.695 9.230 7.619 1.00 23.16 2YCC 769 ATOM 618 CZ TYR 74 3.319 8.562 6.465 1.00 24.66 2YCC 770 ATOM 619 OH TYR 74 4.300 8.056 5.622 1.00 24.14 2YCC 771 ATOM 620 N ILE 75 0.632 9.475 12.291 1.00 18.74 2YCC 772 ATOM 621 CA ILE 75 1.370 9.269 13.537 1.00 19.36 2YCC 773 ATOM 622 C ILE 75 0.543 9.352 14.823 1.00 18.77 2YCC 774 ATOM 623 O ILE 75 0.535 10.351 15.589 1.00 18.47 2YCC 775 ATOM 624 CB ILE 75 2.713 10.100 13.676 1.00 20.01 2YCC 776 ATOM 625 CG1 ILE 75 3.216 10.497 12.249 1.00 19.62 2YCC 777 ATOM 626 CG2 ILE 75 3.805 9.302 14.430 1.00 16.83 2YCC 778 ATOM 627 CD1 ILE 75 3.674 11.932 12.017 1.00 14.85 2YCC 779 ATOM 628 N PRO 76 -0.174 8.267 15.114 1.00 17.70 2YCC 780 ATOM 629 CA PRO 76 -0.982 8.184 16.359 1.00 17.02 2YCC 781 ATOM 630 C PRO 76 -0.021 8.273 17.525 1.00 16.21 2YCC 782 ATOM 631 O PRO 76 0.889 7.430 17.715 1.00 16.73 2YCC 783 ATOM 632 CB PRO 76 -1.693 6.852 16.254 1.00 17.11 2YCC 784 ATOM 633 CG PRO 76 -0.657 6.036 15.485 1.00 18.35 2YCC 785 ATOM 634 CD PRO 76 -0.264 7.018 14.336 1.00 18.20 2YCC 786 ATOM 635 N GLY 77 -0.156 9.351 18.294 1.00 15.47 2YCC 787 ATOM 636 CA GLY 77 0.776 9.550 19.423 1.00 13.41 2YCC 788 ATOM 637 C GLY 77 1.151 11.032 19.429 1.00 13.73 2YCC 789 ATOM 638 O GLY 77 1.258 11.606 20.562 1.00 14.73 2YCC 790 ATOM 639 N THR 78 1.224 11.583 18.219 1.00 11.09 2YCC 791 ATOM 640 CA THR 78 1.637 12.953 17.965 1.00 6.97 2YCC 792 ATOM 641 C THR 78 0.893 14.017 18.741 1.00 6.53 2YCC 793 ATOM 642 O THR 78 -0.357 14.055 18.895 1.00 6.46 2YCC 794 ATOM 643 CB THR 78 1.700 13.323 16.432 1.00 4.72 2YCC 795 ATOM 644 OG1 THR 78 1.968 14.796 16.328 1.00 2.00 2YCC 796 ATOM 645 CG2 THR 78 0.312 13.101 15.746 1.00 2.00 2YCC 797 ATOM 646 N LYS 79 1.708 14.938 19.264 1.00 5.22 2YCC 798 ATOM 647 CA LYS 79 0.988 16.011 20.023 1.00 6.23 2YCC 799 ATOM 648 C LYS 79 0.515 17.045 19.027 1.00 8.33 2YCC 800 ATOM 649 O LYS 79 0.060 18.136 19.375 1.00 5.60 2YCC 801 ATOM 650 CB LYS 79 1.865 16.562 21.105 1.00 8.82 2YCC 802 ATOM 651 CG LYS 79 3.277 17.034 20.688 1.00 5.40 2YCC 803 ATOM 652 CD LYS 79 3.946 17.539 21.992 1.00 4.04 2YCC 804 ATOM 653 CE LYS 79 5.417 17.823 21.853 1.00 5.74 2YCC 805 ATOM 654 NZ LYS 79 5.823 18.935 22.789 1.00 8.64 2YCC 806 ATOM 655 N MET 80 0.569 16.704 17.690 1.00 9.39 2YCC 807 ATOM 656 CA MET 80 0.095 17.731 16.765 1.00 9.46 2YCC 808 ATOM 657 C MET 80 -1.419 17.832 16.838 1.00 10.39 2YCC 809 ATOM 658 O MET 80 -2.042 16.854 16.370 1.00 10.52 2YCC 810 ATOM 659 CB MET 80 0.554 17.546 15.329 1.00 11.94 2YCC 811 ATOM 660 CG MET 80 0.132 18.822 14.601 1.00 11.80 2YCC 812 ATOM 661 SD MET 80 1.236 19.077 13.157 1.00 11.54 2YCC 813 ATOM 662 CE MET 80 -0.102 19.562 12.055 1.00 11.77 2YCC 814 ATOM 663 N ALA 81 -1.887 18.972 17.334 1.00 12.35 2YCC 815 ATOM 664 CA ALA 81 -3.341 19.252 17.427 1.00 14.32 2YCC 816 ATOM 665 C ALA 81 -3.735 19.961 16.127 1.00 15.69 2YCC 817 ATOM 666 O ALA 81 -3.696 21.183 15.963 1.00 15.35 2YCC 818 ATOM 667 CB ALA 81 -3.798 20.081 18.641 1.00 11.93 2YCC 819 ATOM 668 N PHE 82 -4.083 19.113 15.179 1.00 19.95 3 2YCC 820 ATOM 669 CA PHE 82 -4.478 19.601 13.816 1.00 23.12 3 2YCC 821 ATOM 670 C PHE 82 -5.182 18.417 13.138 1.00 23.86 3 2YCC 822 ATOM 671 O PHE 82 -4.611 17.290 13.139 1.00 22.83 3 2YCC 823 ATOM 672 CB PHE 82 -3.268 20.119 13.030 1.00 24.93 3 2YCC 824 ATOM 673 CG PHE 82 -3.642 20.743 11.705 1.00 24.97 3 2YCC 825 ATOM 674 CD1 PHE 82 -3.495 20.010 10.530 1.00 25.02 3 2YCC 826 ATOM 675 CD2 PHE 82 -4.152 22.025 11.661 1.00 25.46 3 2YCC 827 ATOM 676 CE1 PHE 82 -3.824 20.562 9.313 1.00 24.59 3 2YCC 828 ATOM 677 CE2 PHE 82 -4.496 22.612 10.438 1.00 26.03 3 2YCC 829 ATOM 678 CZ PHE 82 -4.324 21.845 9.260 1.00 24.62 3 2YCC 830 ATOM 679 N GLY 83 -6.407 18.769 12.731 1.00 22.67 2YCC 831 ATOM 680 CA GLY 83 -7.258 17.763 12.091 1.00 21.93 2YCC 832 ATOM 681 C GLY 83 -6.661 17.362 10.751 1.00 21.68 2YCC 833 ATOM 682 O GLY 83 -6.838 16.203 10.332 1.00 21.72 2YCC 834 ATOM 683 N GLY 84 -5.999 18.318 10.115 1.00 22.70 2YCC 835 ATOM 684 CA GLY 84 -5.351 18.093 8.774 1.00 21.25 2YCC 836 ATOM 685 C GLY 84 -5.965 19.174 7.823 1.00 20.82 2YCC 837 ATOM 686 O GLY 84 -6.569 20.149 8.304 1.00 20.66 2YCC 838 ATOM 687 N LEU 85 -5.823 18.905 6.558 1.00 17.62 2YCC 839 ATOM 688 CA LEU 85 -6.312 19.652 5.387 1.00 16.59 2YCC 840 ATOM 689 C LEU 85 -6.993 18.613 4.461 1.00 15.34 2YCC 841 ATOM 690 O LEU 85 -6.226 17.781 3.960 1.00 14.70 2YCC 842 ATOM 691 CB LEU 85 -5.055 20.291 4.791 1.00 14.92 2YCC 843 ATOM 692 CG LEU 85 -4.870 21.782 4.968 1.00 15.28 2YCC 844 ATOM 693 CD1 LEU 85 -3.422 22.165 4.773 1.00 16.60 2YCC 845 ATOM 694 CD2 LEU 85 -5.741 22.552 3.942 1.00 16.16 2YCC 846 ATOM 695 N LYS 86 -8.302 18.643 4.317 1.00 16.13 2YCC 847 ATOM 696 CA LYS 86 -9.132 17.694 3.592 1.00 17.30 2YCC 848 ATOM 697 C LYS 86 -8.958 17.600 2.074 1.00 16.00 2YCC 849 ATOM 698 O LYS 86 -9.165 16.525 1.496 1.00 17.32 2YCC 850 ATOM 699 CB LYS 86 -10.642 17.823 3.778 1.00 21.55 2YCC 851 ATOM 700 CG LYS 86 -11.248 18.190 5.107 1.00 26.68 2YCC 852 ATOM 701 CD LYS 86 -12.396 19.194 4.985 1.00 29.36 2YCC 853 ATOM 702 CE LYS 86 -11.952 20.608 4.672 1.00 30.47 2YCC 854 ATOM 703 NZ LYS 86 -12.009 21.530 5.819 1.00 33.57 2YCC 855 ATOM 704 N LYS 87 -8.683 18.708 1.446 1.00 14.40 2YCC 856 ATOM 705 CA LYS 87 -8.492 18.857 0.014 1.00 13.93 2YCC 857 ATOM 706 C LYS 87 -7.074 18.754 -0.468 1.00 13.81 2YCC 858 ATOM 707 O LYS 87 -6.110 19.351 0.032 1.00 12.76 2YCC 859 ATOM 708 CB LYS 87 -9.050 20.222 -0.410 1.00 15.78 2YCC 860 ATOM 709 CG LYS 87 -10.558 20.129 -0.579 1.00 18.25 2YCC 861 ATOM 710 CD LYS 87 -11.332 21.247 0.135 1.00 19.62 2YCC 862 ATOM 711 CE LYS 87 -12.688 20.653 0.571 1.00 20.31 2YCC 863 ATOM 712 NZ LYS 87 -13.387 21.653 1.409 1.00 23.40 2YCC 864 ATOM 713 N GLU 88 -7.019 17.962 -1.551 1.00 14.56 2YCC 865 ATOM 714 CA GLU 88 -5.744 17.678 -2.272 1.00 13.82 2YCC 866 ATOM 715 C GLU 88 -5.069 18.952 -2.740 1.00 14.14 2YCC 867 ATOM 716 O GLU 88 -3.852 19.172 -2.500 1.00 16.31 2YCC 868 ATOM 717 CB GLU 88 -6.099 16.841 -3.457 1.00 15.15 2YCC 869 ATOM 718 CG GLU 88 -5.149 15.788 -4.030 1.00 14.37 2YCC 870 ATOM 719 CD GLU 88 -5.698 15.583 -5.465 1.00 17.17 2YCC 871 ATOM 720 OE1 GLU 88 -6.811 15.055 -5.591 1.00 14.51 2YCC 872 ATOM 721 OE2 GLU 88 -4.804 16.104 -6.177 1.00 13.40 2YCC 873 ATOM 722 N LYS 89 -5.844 19.794 -3.405 1.00 12.92 2YCC 874 ATOM 723 CA LYS 89 -5.451 21.097 -3.920 1.00 13.52 2YCC 875 ATOM 724 C LYS 89 -4.741 22.010 -2.904 1.00 12.55 2YCC 876 ATOM 725 O LYS 89 -3.748 22.708 -3.200 1.00 13.54 2YCC 877 ATOM 726 CB LYS 89 -6.637 21.809 -4.602 1.00 11.32 2YCC 878 ATOM 727 CG LYS 89 -7.588 22.531 -3.628 1.00 11.70 2YCC 879 ATOM 728 CD LYS 89 -8.710 23.328 -4.358 1.00 13.10 2YCC 880 ATOM 729 CE LYS 89 -9.898 22.348 -4.574 1.00 13.89 2YCC 881 ATOM 730 NZ LYS 89 -9.466 21.411 -5.673 1.00 10.45 2YCC 882 ATOM 731 N ASP 90 -5.306 22.145 -1.729 1.00 13.98 3 2YCC 883 ATOM 732 CA ASP 90 -4.865 22.930 -0.558 1.00 12.59 3 2YCC 884 ATOM 733 C ASP 90 -3.564 22.344 0.005 1.00 11.35 3 2YCC 885 ATOM 734 O ASP 90 -2.595 22.986 0.453 1.00 9.76 3 2YCC 886 ATOM 735 CB ASP 90 -5.989 22.984 0.455 1.00 13.44 3 2YCC 887 ATOM 736 CG ASP 90 -7.123 23.916 0.169 1.00 15.20 3 2YCC 888 ATOM 737 OD1 ASP 90 -8.257 23.401 0.051 1.00 16.76 3 2YCC 889 ATOM 738 OD2 ASP 90 -6.892 25.148 0.109 1.00 14.77 3 2YCC 890 ATOM 739 N ARG 91 -3.541 21.025 -0.086 1.00 11.67 2YCC 891 ATOM 740 CA ARG 91 -2.359 20.269 0.352 1.00 12.32 2YCC 892 ATOM 741 C ARG 91 -1.193 20.669 -0.571 1.00 12.59 2YCC 893 ATOM 742 O ARG 91 -0.124 21.177 -0.164 1.00 14.69 2YCC 894 ATOM 743 CB ARG 91 -2.658 18.774 0.333 1.00 12.74 2YCC 895 ATOM 744 CG ARG 91 -3.865 18.412 1.239 1.00 12.68 2YCC 896 ATOM 745 CD ARG 91 -3.694 17.097 1.955 1.00 10.62 2YCC 897 ATOM 746 NE ARG 91 -4.583 16.113 1.331 1.00 9.64 2YCC 898 ATOM 747 CZ ARG 91 -4.315 15.252 0.428 1.00 7.05 2YCC 899 ATOM 748 NH1 ARG 91 -3.095 15.117 -0.060 1.00 10.12 2YCC 900 ATOM 749 NH2 ARG 91 -5.265 14.411 -0.030 1.00 13.05 2YCC 901 ATOM 750 N ASN 92 -1.458 20.501 -1.864 1.00 10.91 2YCC 902 ATOM 751 CA ASN 92 -0.528 20.774 -2.960 1.00 8.32 2YCC 903 ATOM 752 C ASN 92 0.132 22.147 -2.794 1.00 6.93 2YCC 904 ATOM 753 O ASN 92 1.383 22.323 -2.703 1.00 9.30 2YCC 905 ATOM 754 CB ASN 92 -1.271 20.509 -4.289 1.00 8.64 2YCC 906 ATOM 755 CG ASN 92 -1.515 19.004 -4.499 1.00 7.43 2YCC 907 ATOM 756 OD1 ASN 92 -0.876 18.216 -3.878 1.00 2.65 2YCC 908 ATOM 757 ND2 ASN 92 -2.439 18.720 -5.457 1.00 8.86 2YCC 909 ATOM 758 N ASP 93 -0.718 23.131 -2.712 1.00 5.17 2YCC 910 ATOM 759 CA ASP 93 -0.447 24.547 -2.527 1.00 2.83 2YCC 911 ATOM 760 C ASP 93 0.486 24.769 -1.364 1.00 3.37 2YCC 912 ATOM 761 O ASP 93 1.744 25.125 -1.537 1.00 2.04 2YCC 913 ATOM 762 CB ASP 93 -1.846 25.235 -2.376 1.00 4.87 2YCC 914 ATOM 763 CG ASP 93 -2.551 25.404 -3.759 1.00 6.10 2YCC 915 ATOM 764 OD1 ASP 93 -2.072 25.003 -4.825 1.00 2.63 2YCC 916 ATOM 765 OD2 ASP 93 -3.700 25.878 -3.699 1.00 4.04 2YCC 917 ATOM 766 N LEU 94 0.001 24.400 -0.162 1.00 2.00 2YCC 918 ATOM 767 CA LEU 94 0.942 24.588 1.005 1.00 2.00 2YCC 919 ATOM 768 C LEU 94 2.270 23.862 0.852 1.00 2.00 2YCC 920 ATOM 769 O LEU 94 3.341 24.476 1.104 1.00 2.76 2YCC 921 ATOM 770 CB LEU 94 0.200 24.468 2.315 1.00 2.00 2YCC 922 ATOM 771 CG LEU 94 1.034 24.472 3.581 1.00 2.00 2YCC 923 ATOM 772 CD1 LEU 94 1.983 25.638 3.895 1.00 2.00 2YCC 924 ATOM 773 CD2 LEU 94 -0.102 24.425 4.680 1.00 2.00 2YCC 925 ATOM 774 N ILE 95 2.356 22.599 0.517 1.00 2.00 2YCC 926 ATOM 775 CA ILE 95 3.659 21.887 0.303 1.00 3.04 2YCC 927 ATOM 776 C ILE 95 4.569 22.651 -0.688 1.00 3.59 2YCC 928 ATOM 777 O ILE 95 5.850 22.713 -0.599 1.00 2.26 2YCC 929 ATOM 778 CB ILE 95 3.379 20.423 -0.178 1.00 6.78 2YCC 930 ATOM 779 CG1 ILE 95 2.869 19.620 1.055 1.00 7.76 2YCC 931 ATOM 780 CG2 ILE 95 4.652 19.645 -0.757 1.00 8.57 2YCC 932 ATOM 781 CD1 ILE 95 2.380 18.182 0.698 1.00 8.20 2YCC 933 ATOM 782 N THR 96 3.965 23.186 -1.704 1.00 2.59 2YCC 934 ATOM 783 CA THR 96 4.731 23.938 -2.755 1.00 4.36 2YCC 935 ATOM 784 C THR 96 5.300 25.082 -1.938 1.00 6.14 2YCC 936 ATOM 785 O THR 96 6.484 25.461 -2.022 1.00 7.79 2YCC 937 ATOM 786 CB THR 96 3.839 24.261 -4.000 1.00 2.00 2YCC 938 ATOM 787 OG1 THR 96 3.024 23.042 -4.296 1.00 2.00 2YCC 939 ATOM 788 CG2 THR 96 4.683 24.703 -5.195 1.00 2.00 2YCC 940 ATOM 789 N TYR 97 4.406 25.610 -1.111 1.00 5.68 2YCC 941 ATOM 790 CA TYR 97 4.767 26.699 -0.187 1.00 7.34 2YCC 942 ATOM 791 C TYR 97 5.895 26.202 0.703 1.00 8.47 2YCC 943 ATOM 792 O TYR 97 6.949 26.857 0.894 1.00 7.11 2YCC 944 ATOM 793 CB TYR 97 3.619 27.248 0.699 1.00 6.99 2YCC 945 ATOM 794 CG TYR 97 4.155 28.330 1.626 1.00 8.14 2YCC 946 ATOM 795 CD1 TYR 97 4.633 29.548 1.246 1.00 9.19 2YCC 947 ATOM 796 CD2 TYR 97 4.328 27.923 3.007 1.00 10.83 2YCC 948 ATOM 797 CE1 TYR 97 5.265 30.369 2.170 1.00 9.53 2YCC 949 ATOM 798 CE2 TYR 97 4.903 28.779 3.958 1.00 9.32 2YCC 950 ATOM 799 CZ TYR 97 5.366 30.022 3.521 1.00 9.74 2YCC 951 ATOM 800 OH TYR 97 5.893 30.892 4.442 1.00 9.50 2YCC 952 ATOM 801 N LEU 98 5.599 24.989 1.209 1.00 8.07 2YCC 953 ATOM 802 CA LEU 98 6.639 24.517 2.187 1.00 8.38 2YCC 954 ATOM 803 C LEU 98 7.934 24.275 1.419 1.00 8.00 2YCC 955 ATOM 804 O LEU 98 9.040 24.684 1.902 1.00 5.33 2YCC 956 ATOM 805 CB LEU 98 5.945 23.593 3.186 1.00 8.58 2YCC 957 ATOM 806 CG LEU 98 5.138 24.384 4.265 1.00 9.98 2YCC 958 ATOM 807 CD1 LEU 98 4.079 23.476 4.843 1.00 12.24 2YCC 959 ATOM 808 CD2 LEU 98 6.122 24.729 5.411 1.00 10.23 2YCC 960 ATOM 809 N LYS 99 7.715 23.662 0.281 1.00 8.55 2YCC 961 ATOM 810 CA LYS 99 8.719 23.315 -0.710 1.00 9.14 2YCC 962 ATOM 811 C LYS 99 9.754 24.438 -0.755 1.00 9.82 2YCC 963 ATOM 812 O LYS 99 10.956 24.302 -0.562 1.00 9.32 2YCC 964 ATOM 813 CB LYS 99 8.220 23.195 -2.190 1.00 7.26 2YCC 965 ATOM 814 CG LYS 99 9.389 22.587 -3.011 1.00 8.84 2YCC 966 ATOM 815 CD LYS 99 9.808 23.028 -4.341 1.00 12.08 2YCC 967 ATOM 816 CE LYS 99 9.284 24.268 -5.003 1.00 10.94 2YCC 968 ATOM 817 NZ LYS 99 7.845 24.450 -4.705 1.00 9.23 2YCC 969 ATOM 818 N LYS 100 9.122 25.575 -1.005 1.00 12.13 2YCC 970 ATOM 819 CA LYS 100 9.840 26.849 -1.169 1.00 13.69 2YCC 971 ATOM 820 C LYS 100 10.538 27.369 0.054 1.00 14.06 2YCC 972 ATOM 821 O LYS 100 11.781 27.316 0.157 1.00 12.05 2YCC 973 ATOM 822 CB LYS 100 8.823 27.864 -1.672 1.00 16.79 2YCC 974 ATOM 823 CG LYS 100 9.131 28.334 -3.145 1.00 19.51 2YCC 975 ATOM 824 CD LYS 100 8.356 29.680 -3.246 1.00 18.68 2YCC 976 ATOM 825 CE LYS 100 9.064 30.777 -2.470 1.00 19.55 2YCC 977 ATOM 826 NZ LYS 100 8.210 32.015 -2.453 1.00 19.95 2YCC 978 ATOM 827 N ALA 101 9.721 27.920 1.007 1.00 13.45 2YCC 979 ATOM 828 CA ALA 101 10.352 28.518 2.182 1.00 15.42 2YCC 980 ATOM 829 C ALA 101 11.216 27.668 3.060 1.00 15.69 2YCC 981 ATOM 830 O ALA 101 12.274 28.282 3.461 1.00 17.05 2YCC 982 ATOM 831 CB ALA 101 9.469 29.464 2.994 1.00 11.45 2YCC 983 ATOM 832 N THR 102 10.925 26.464 3.441 1.00 17.37 2YCC 984 ATOM 833 CA THR 102 11.811 25.715 4.375 1.00 17.25 2YCC 985 ATOM 834 C THR 102 13.217 25.482 3.806 1.00 17.58 2YCC 986 ATOM 835 O THR 102 14.097 24.857 4.436 1.00 18.14 2YCC 987 ATOM 836 CB THR 102 11.302 24.272 4.735 1.00 16.90 2YCC 988 ATOM 837 OG1 THR 102 11.276 23.632 3.421 1.00 16.42 2YCC 989 ATOM 838 CG2 THR 102 10.053 23.969 5.540 1.00 15.38 2YCC 990 ATOM 839 N GLU 103 13.458 25.927 2.614 1.00 15.69 2YCC 991 ATOM 840 CA GLU 103 14.666 25.691 1.897 1.00 16.58 2YCC 992 ATOM 841 C GLU 103 15.919 26.451 2.239 1.00 14.87 2YCC 993 ATOM 842 O GLU 103 16.981 25.770 2.432 1.00 12.46 2YCC 994 ATOM 843 CB GLU 103 14.370 25.923 0.368 1.00 17.57 2YCC 995 ATOM 844 CG GLU 103 14.821 24.763 -0.508 1.00 21.62 2YCC 996 ATOM 845 CD GLU 103 16.159 24.186 -0.163 1.00 23.62 2YCC 997 ATOM 846 OE1 GLU 103 17.264 24.670 -0.435 1.00 26.67 2YCC 998 ATOM 847 OE2 GLU 103 15.916 23.184 0.554 1.00 25.06 2YCC 999 ATOM 848 OXT GLU 103 15.837 27.676 2.230 1.00 15.49 2YCC1000 TER 849 GLU 103 2YCC1001 HETATM 850 FE HEM 104 2.859 20.878 13.068 1.00 8.58 2 2YCC1002 HETATM 851 CHA HEM 104 5.033 18.463 14.164 1.00 8.10 2 2YCC1003 HETATM 852 CHB HEM 104 3.970 20.640 9.964 1.00 6.80 2 2YCC1004 HETATM 853 CHC HEM 104 0.188 22.667 12.154 1.00 4.70 2 2YCC1005 HETATM 854 CHD HEM 104 2.353 21.815 16.332 1.00 4.66 2 2YCC1006 HETATM 855 N A HEM 104 4.272 19.780 12.241 1.00 7.82 2 2YCC1007 HETATM 856 C1A HEM 104 5.031 18.786 12.831 1.00 8.03 2 2YCC1008 HETATM 857 C2A HEM 104 5.811 18.131 11.813 1.00 8.27 2 2YCC1009 HETATM 858 C3A HEM 104 5.573 18.720 10.635 1.00 7.49 2 2YCC1010 HETATM 859 C4A HEM 104 4.593 19.756 10.856 1.00 8.65 2 2YCC1011 HETATM 860 CMA HEM 104 6.089 18.320 9.245 1.00 7.85 2 2YCC1012 HETATM 861 CAA HEM 104 6.775 16.949 12.134 1.00 7.60 2 2YCC1013 HETATM 862 CBA HEM 104 8.046 17.365 12.817 1.00 7.16 2 2YCC1014 HETATM 863 CGA HEM 104 9.401 16.826 12.588 1.00 5.53 2 2YCC1015 HETATM 864 O1A HEM 104 10.363 17.601 12.447 1.00 4.79 2 2YCC1016 HETATM 865 O2A HEM 104 9.663 15.600 12.727 1.00 6.79 2 2YCC1017 HETATM 866 N B HEM 104 2.096 21.441 11.331 1.00 4.53 2 2YCC1018 HETATM 867 C1B HEM 104 2.826 21.425 10.142 1.00 4.56 2 2YCC1019 HETATM 868 C2B HEM 104 2.058 22.193 9.152 1.00 3.90 2 2YCC1020 HETATM 869 C3B HEM 104 1.024 22.754 9.774 1.00 3.20 2 2YCC1021 HETATM 870 C4B HEM 104 1.069 22.315 11.155 1.00 3.81 2 2YCC1022 HETATM 871 CMB HEM 104 2.471 22.315 7.671 1.00 2.43 2 2YCC1023 HETATM 872 CAB HEM 104 -0.101 23.691 9.226 1.00 3.34 2 2YCC1024 HETATM 873 CBB HEM 104 -1.329 23.209 8.531 1.00 4.19 2 2YCC1025 HETATM 874 N C HEM 104 1.535 22.042 14.054 1.00 4.64 2 2YCC1026 HETATM 875 C1C HEM 104 0.493 22.670 13.508 1.00 4.40 2 2YCC1027 HETATM 876 C2C HEM 104 -0.278 23.372 14.540 1.00 5.95 2 2YCC1028 HETATM 877 C3C HEM 104 0.357 23.194 15.699 1.00 4.68 2 2YCC1029 HETATM 878 C4C HEM 104 1.486 22.301 15.418 1.00 6.20 2 2YCC1030 HETATM 879 CMC HEM 104 -1.587 24.194 14.241 1.00 2.64 2 2YCC1031 HETATM 880 CAC HEM 104 -0.067 23.771 17.080 1.00 6.83 2 2YCC1032 HETATM 881 CBC HEM 104 -1.583 23.793 17.449 1.00 3.20 2 2YCC1033 HETATM 882 N D HEM 104 3.544 20.242 14.887 1.00 6.79 2 2YCC1034 HETATM 883 C1D HEM 104 3.159 20.742 16.114 1.00 6.50 2 2YCC1035 HETATM 884 C2D HEM 104 3.788 19.942 17.154 1.00 6.32 2 2YCC1036 HETATM 885 C3D HEM 104 4.519 19.025 16.560 1.00 5.90 2 2YCC1037 HETATM 886 C4D HEM 104 4.359 19.148 15.127 1.00 7.71 2 2YCC1038 HETATM 887 CMD HEM 104 3.522 20.093 18.683 1.00 7.10 2 2YCC1039 HETATM 888 CAD HEM 104 5.330 17.880 17.254 1.00 5.93 2 2YCC1040 HETATM 889 CBD HEM 104 4.429 16.665 17.400 1.00 3.07 2 2YCC1041 HETATM 890 CGD HEM 104 4.897 15.616 18.376 1.00 3.35 2 2YCC1042 HETATM 891 O1D HEM 104 5.891 15.925 19.084 1.00 2.15 2 2YCC1043 HETATM 892 O2D HEM 104 4.206 14.536 18.405 1.00 2.63 2 2YCC1044 HETATM 893 CH1 TML 72 -3.252 13.816 19.802 1.00 14.68 1 2YCC1045 HETATM 894 CH2 TML 72 -3.248 16.235 20.042 1.00 18.45 1 2YCC1046 HETATM 895 CH3 TML 72 -5.376 15.084 19.969 1.00 18.93 1 2YCC1047 HETATM 896 O HOH 106 18.098 12.969 5.472 1.00 2.12 2YCC1048 HETATM 897 O HOH 107 -8.600 20.403 9.732 1.00 30.02 2YCC1049 HETATM 898 O HOH 110 8.345 26.268 14.859 1.00 11.24 2YCC1050 HETATM 899 O HOH 112 7.396 15.353 24.499 1.00 10.77 2YCC1051 HETATM 900 O HOH 113 11.758 21.535 23.424 1.00 38.75 2YCC1052 HETATM 901 S SO4 117 2.575 31.442 -7.570 1.00 32.90 2YCC1053 HETATM 902 O1 SO4 117 2.593 31.815 -6.130 1.00 33.56 2YCC1054 HETATM 903 O2 SO4 117 1.247 31.580 -8.223 1.00 32.86 2YCC1055 HETATM 904 O3 SO4 117 3.562 32.342 -8.274 1.00 34.17 2YCC1056 HETATM 905 O4 SO4 117 3.054 29.998 -7.677 1.00 34.14 2YCC1057 HETATM 906 O HOH 121 12.878 16.287 12.156 1.00 9.89 2YCC1058 HETATM 907 O HOH 122 -0.957 21.122 18.548 1.00 16.31 2YCC1059 HETATM 908 O HOH 123 12.467 1.669 18.496 1.00 27.98 2YCC1060 HETATM 909 O HOH 124 12.372 13.084 1.083 1.00 2.00 2YCC1061 HETATM 910 O HOH 125 3.796 28.911 19.093 1.00 26.06 2YCC1062 HETATM 911 O HOH 126 -2.397 26.913 13.858 1.00 20.88 2YCC1063 HETATM 912 O HOH 127 -1.225 7.857 4.525 1.00 25.77 2YCC1064 HETATM 913 O HOH 128 7.479 21.661 22.275 1.00 8.78 2YCC1065 HETATM 914 O HOH 129 17.808 17.993 21.625 1.00 35.77 2YCC1066 HETATM 915 O HOH 130 18.872 15.228 17.890 1.00 16.58 2YCC1067 HETATM 916 O HOH 133 17.816 26.896 10.733 1.00 3.62 2YCC1068 HETATM 917 O HOH 135 -8.990 18.665 -3.623 1.00 14.00 2YCC1069 HETATM 918 O HOH 136 -8.416 13.447 2.003 1.00 7.21 2YCC1070 HETATM 919 O HOH 138 6.078 10.636 24.888 1.00 11.83 2YCC1071 HETATM 920 O HOH 142 -5.808 11.506 -4.701 1.00 29.31 2YCC1072 HETATM 921 O HOH 150 -2.873 9.426 -4.984 1.00 11.39 2YCC1073 HETATM 922 O HOH 151 -11.089 25.458 0.000 1.00 67.04 2YCC1074 HETATM 923 O HOH 152 9.185 19.946 -7.097 1.00 19.69 2YCC1075 HETATM 924 O HOH 153 14.812 14.235 18.903 1.00 13.61 2YCC1076 HETATM 925 O HOH 154 14.630 28.891 0.436 1.00 2.00 2YCC1077 HETATM 926 O HOH 155 20.659 12.084 14.351 1.00 18.37 2YCC1078 HETATM 927 O HOH 157 -15.846 16.579 6.149 1.00 2.00 2YCC1079 HETATM 928 O HOH 158 -10.175 20.660 2.571 1.00 2.00 2YCC1080 HETATM 929 O HOH 160 -5.417 26.604 -2.257 1.00 26.96 2YCC1081 HETATM 930 O HOH 161 -2.445 10.387 0.022 1.00 36.99 2YCC1082 HETATM 931 O HOH 163 16.618 12.470 0.634 1.00 2.00 2YCC1083 HETATM 932 O HOH 166 3.626 16.213 14.736 1.00 2.00 2YCC1084 HETATM 933 O HOH 167 -2.358 29.327 12.406 1.00 2.00 2YCC1085 HETATM 934 O HOH 168 11.585 20.185 12.725 1.00 2.00 2YCC1086 HETATM 935 O HOH 171 12.792 25.291 -3.308 1.00 29.18 2YCC1087 HETATM 936 O HOH 172 -6.191 14.096 14.649 1.00 13.77 2YCC1088 HETATM 937 O HOH 175 22.261 8.887 12.068 1.00 5.99 2YCC1089 HETATM 938 O HOH 177 1.246 9.352 -1.015 1.00 15.04 2YCC1090 HETATM 939 O HOH 182 17.126 18.973 1.479 1.00 28.01 2YCC1091 HETATM 940 O HOH 183 -9.516 21.121 5.907 1.00 2.00 2YCC1092 HETATM 941 O HOH 185 12.167 14.222 22.328 1.00 17.97 2YCC1093 HETATM 942 O HOH 189 10.975 27.566 22.404 1.00 34.72 2YCC1094 HETATM 943 O HOH 192 -4.806 25.012 -5.514 1.00 6.50 2YCC1095 HETATM 944 O HOH 195 7.874 30.745 10.055 1.00 2.19 2YCC1096 HETATM 945 O HOH 196 13.826 21.516 3.511 1.00 22.43 2YCC1097 HETATM 946 O HOH 197 18.378 11.960 8.110 1.00 20.76 2YCC1098 HETATM 947 O HOH 198 12.154 24.220 22.800 1.00 25.39 2YCC1099 HETATM 948 O HOH 200 2.481 5.828 15.364 1.00 36.62 2YCC1100 HETATM 949 O HOH 213 20.030 9.869 11.904 1.00 46.74 2YCC1101 HETATM 950 O HOH 218 6.227 33.920 11.908 1.00 11.64 2YCC1102 HETATM 951 O HOH 300 18.972 20.503 5.897 1.00 16.65 2YCC1103 HETATM 952 O HOH 301 7.438 1.774 13.827 1.00 42.29 2YCC1104 HETATM 953 O HOH 302 4.797 2.300 -4.767 1.00 47.10 2YCC1105 HETATM 954 O HOH 303 -7.662 17.173 7.774 1.00 26.48 2YCC1106 HETATM 955 O HOH 304 24.157 21.354 20.048 1.00 19.94 2YCC1107 HETATM 956 O HOH 305 -7.593 10.446 5.706 1.00 20.63 2YCC1108 HETATM 957 O HOH 306 15.524 17.327 22.072 1.00 53.75 2YCC1109 HETATM 958 O HOH 307 20.872 16.892 14.609 1.00 46.43 2YCC1110 HETATM 959 O HOH 308 11.152 16.815 -4.551 1.00 2.00 2YCC1111 HETATM 960 O HOH 309 12.652 29.563 22.369 1.00 2.00 2YCC1112 HETATM 961 O HOH 310 6.734 21.555 -9.615 1.00 4.30 2YCC1113 CONECT 142 141 872 2YCC1114 CONECT 165 164 880 2YCC1115 CONECT 175 173 174 850 2YCC1116 CONECT 598 597 893 894 895 2YCC1117 CONECT 661 660 662 850 2YCC1118 CONECT 850 175 661 855 866 2YCC1119 CONECT 850 874 882 2YCC1120 CONECT 851 856 886 2YCC1121 CONECT 852 859 867 2YCC1122 CONECT 853 870 875 2YCC1123 CONECT 854 878 883 2YCC1124 CONECT 855 850 856 859 2YCC1125 CONECT 856 851 855 857 2YCC1126 CONECT 857 856 858 861 2YCC1127 CONECT 858 857 859 860 2YCC1128 CONECT 859 852 855 858 2YCC1129 CONECT 860 858 2YCC1130 CONECT 861 857 862 2YCC1131 CONECT 862 861 863 2YCC1132 CONECT 863 862 864 865 2YCC1133 CONECT 864 863 2YCC1134 CONECT 865 863 2YCC1135 CONECT 866 850 867 870 2YCC1136 CONECT 867 852 866 868 2YCC1137 CONECT 868 867 869 871 2YCC1138 CONECT 869 868 870 872 2YCC1139 CONECT 870 853 866 869 2YCC1140 CONECT 871 868 2YCC1141 CONECT 872 142 869 873 2YCC1142 CONECT 873 872 2YCC1143 CONECT 874 850 875 878 2YCC1144 CONECT 875 853 874 876 2YCC1145 CONECT 876 875 877 879 2YCC1146 CONECT 877 876 878 880 2YCC1147 CONECT 878 854 874 877 2YCC1148 CONECT 879 876 2YCC1149 CONECT 880 165 877 881 2YCC1150 CONECT 881 880 2YCC1151 CONECT 882 850 883 886 2YCC1152 CONECT 883 854 882 884 2YCC1153 CONECT 884 883 885 887 2YCC1154 CONECT 885 884 886 888 2YCC1155 CONECT 886 851 882 885 2YCC1156 CONECT 887 884 2YCC1157 CONECT 888 885 889 2YCC1158 CONECT 889 888 890 2YCC1159 CONECT 890 889 891 892 2YCC1160 CONECT 891 890 2YCC1161 CONECT 892 890 2YCC1162 CONECT 893 598 2YCC1163 CONECT 894 598 2YCC1164 CONECT 895 598 2YCC1165 CONECT 901 902 903 904 905 2YCC1166 CONECT 902 901 2YCC1167 CONECT 903 901 2YCC1168 CONECT 904 901 2YCC1169 CONECT 905 901 2YCC1170 MASTER 88 16 3 5 2 6 0 6 960 1 57 9 2YCC1171 END 2YCC1172 MMTK-2.7.9/MMTK/Database/PDB/bALA1.pdb0000644000076600000240000000324211662461640017110 0ustar hinsenstaff00000000000000ATOM 1 CH3 ACE 1 -2.160 0.537 0.930 0.00 0.00 1BLO ATOM 2 HH31 ACE 1 -2.466 0.003 0.005 0.00 0.00 1BLO ATOM 3 HH32 ACE 1 -2.562 1.572 0.910 0.00 0.00 1BLO ATOM 4 HH33 ACE 1 -2.562 0.001 1.816 0.00 0.00 1BLO ATOM 5 C ACE 1 -0.672 0.582 1.009 0.00 0.00 1BLO ATOM 6 O ACE 1 -0.105 1.128 1.954 0.00 0.00 1BLO ATOM 7 N ALA 2 0.000 0.000 0.000 0.00 0.00 1BLO ATOM 8 H ALA 2 -0.453 -0.444 -0.769 0.00 0.00 1BLO ATOM 9 CA ALA 2 1.459 0.000 0.000 0.00 0.00 1BLO ATOM 10 HA ALA 2 1.812 -0.497 0.897 0.00 0.00 1BLO ATOM 11 C ALA 2 2.096 1.401 0.000 0.00 0.00 1BLO ATOM 12 O ALA 2 3.318 1.542 0.000 0.00 0.00 1BLO ATOM 13 CB ALA 2 1.949 -0.834 -1.207 0.00 0.00 1BLO ATOM 14 HB1 ALA 2 1.514 -1.854 -1.155 0.00 0.00 1BLO ATOM 15 HB2 ALA 2 1.628 -0.373 -2.167 0.00 0.00 1BLO ATOM 16 HB3 ALA 2 3.056 -0.932 -1.214 0.00 0.00 1BLO ATOM 17 N NME 3 1.248 2.445 0.000 0.00 0.00 1BLO ATOM 18 H NME 3 0.257 2.340 0.000 0.00 0.00 1BLO ATOM 19 CH3 NME 3 1.759 3.781 0.000 0.00 0.00 1BLO ATOM 20 HH31 NME 3 2.381 3.940 -0.907 0.00 0.00 1BLO ATOM 21 HH32 NME 3 0.914 4.502 0.000 0.00 0.00 1BLO ATOM 22 HH33 NME 3 2.381 3.940 0.907 0.00 0.00 1BLO END MMTK-2.7.9/MMTK/Database/PDB/insulin.pdb0000644000076600000240000034351711662461640017765 0ustar hinsenstaff00000000000000HEADER HORMONE 10-JUL-89 4INS 4INSA 1 COMPND INSULIN 4INS 4 SOURCE PIG (SUS $SCROFA) 4INS 5 AUTHOR G.G.DODSON,E.J.DODSON,D.C.HODGKIN,N.W.ISAACS,M.VIJAYAN 4INS 6 REVDAT 3 31-JUL-94 4INSB 3 HETATM 4INSB 1 REVDAT 2 15-JUL-93 4INSA 1 HEADER 4INSA 2 REVDAT 1 15-APR-90 4INS 0 4INS 7 SPRSDE 15-APR-90 4INS 1INS 4INS 8 REMARK 1 4INS 9 REMARK 1 REFERENCE 1 4INS 10 REMARK 1 AUTH E.N.BAKER,T.L.BLUNDELL,J.F.CUTFIELD,S.M.CUTFIELD, 4INS 11 REMARK 1 AUTH 2 E.J.DODSON,G.G.DODSON,D.M.CROWFOOT HODGKIN, 4INS 12 REMARK 1 AUTH 3 R.E.HUBBARD,N.W.ISAACS,C.D.REYNOLDS,K.SAKABE, 4INS 13 REMARK 1 AUTH 4 N.SAKABE,N.M.VIJAYAN 4INS 14 REMARK 1 TITL THE STRUCTURE OF 2ZN PIG INSULIN CRYSTALS AT 1.5 4INS 15 REMARK 1 TITL 2 ANGSTROMS RESOLUTION 4INS 16 REMARK 1 REF PHILOS.TRANS.R.SOC.LONDON, V. 319 369 1988 4INS 17 REMARK 1 REF 2 SER.B 4INS 18 REMARK 1 REFN ASTM PTRBAE UK ISSN 0080-4622 441 4INS 19 REMARK 1 REFERENCE 2 4INS 20 REMARK 1 AUTH J.BORDAS,G.G.DODSON,H.GREWE,M.H.J.KOCH,B.KREBS, 4INS 21 REMARK 1 AUTH 2 J.RANDALL 4INS 22 REMARK 1 TITL A COMPARATIVE ASSESSMENT OF THE ZINC-PROTEIN 4INS 23 REMARK 1 TITL 2 COORDINATION IN 2*ZN-INSULIN AS DETERMINED BY X-RAY 4INS 24 REMARK 1 TITL 3 ABSORPTION FINE STRUCTURE (/EXAFS$) AND X-RAY 4INS 25 REMARK 1 TITL 4 CRYSTALLOGRAPHY 4INS 26 REMARK 1 REF PROC.R.SOC.LONDON,SER.B V. 219 21 1983 4INS 27 REMARK 1 REFN ASTM PRLBA4 UK ISSN 0080-4649 338 4INS 28 REMARK 1 REFERENCE 3 4INS 29 REMARK 1 AUTH E.J.DODSON,G.G.DODSON,D.C.HODGKIN,C.D.REYNOLDS 4INS 30 REMARK 1 TITL STRUCTURAL RELATIONSHIPS IN THE TWO-ZINC INSULIN 4INS 31 REMARK 1 TITL 2 HEXAMER 4INS 32 REMARK 1 REF CAN.J.BIOCHEM. V. 57 469 1979 4INS 33 REMARK 1 REFN ASTM CJBIAE CN ISSN 0008-4018 415 4INS 34 REMARK 1 REFERENCE 4 4INS 35 REMARK 1 AUTH N.W.ISAACS,R.C.AGARWAL 4INS 36 REMARK 1 TITL EXPERIENCE WITH FAST FOURIER LEAST SQUARES IN THE 4INS 37 REMARK 1 TITL 2 REFINEMENT OF THE CRYSTAL STRUCTURE OF RHOMBOHEDRAL 4INS 38 REMARK 1 TITL 3 2-*ZINC INSULIN AT 1.5 ANGSTROMS RESOLUTION 4INS 39 REMARK 1 REF ACTA CRYSTALLOGR.,SECT.A V. 34 782 1978 4INS 40 REMARK 1 REFN ASTM ACACBN DK ISSN 0567-7394 108 4INS 41 REMARK 1 REFERENCE 5 4INS 42 REMARK 1 AUTH G.BENTLEY,G.DODSON,A.LEWITOVA 4INS 43 REMARK 1 TITL RHOMBOHEDRAL INSULIN CRYSTAL TRANSFORMATION 4INS 44 REMARK 1 REF J.MOL.BIOL. V. 126 871 1978 4INS 45 REMARK 1 REFN ASTM JMOBAK UK ISSN 0022-2836 070 4INS 46 REMARK 1 REFERENCE 6 4INS 47 REMARK 1 AUTH E.J.DODSON,N.W.ISAACS,J.S.ROLLETT 4INS 48 REMARK 1 TITL A METHOD FOR FITTING SATISFACTORY MODELS TO SETS OF 4INS 49 REMARK 1 TITL 2 ATOMIC POSITIONS IN PROTEIN STRUCTURE REFINEMENTS 4INS 50 REMARK 1 REF ACTA CRYSTALLOGR.,SECT.A V. 32 311 1976 4INS 51 REMARK 1 REFN ASTM ACACBN DK ISSN 0567-7394 108 4INS 52 REMARK 1 REFERENCE 7 4INS 53 REMARK 1 AUTH D.C.HODGKIN 4INS 54 REMARK 1 TITL VARIETIES OF INSULIN 4INS 55 REMARK 1 REF J.ENDOCRINOL. V. 63 1 1974 4INS 56 REMARK 1 REFN ASTM JOENAK UK ISSN 0022-0795 907 4INS 57 REMARK 1 REFERENCE 8 4INS 58 REMARK 1 AUTH D.C.HODGKIN 4INS 59 REMARK 1 TITL THE STRUCTURE OF INSULIN 4INS 60 REMARK 1 REF DAN.TIDSSKR.FARM. V. 46 1 1972 4INS 61 REMARK 1 REFN ASTM DTFAAN DK ISSN 0011-6513 168 4INS 62 REMARK 1 REFERENCE 9 4INS 63 REMARK 1 AUTH T.BLUNDELL,G.DODSON,D.HODGKIN,D.MERCOLA 4INS 64 REMARK 1 TITL INSULIN. THE STRUCTURE IN THE CRYSTAL AND ITS 4INS 65 REMARK 1 TITL 2 REFLECTION IN CHEMISTRY AND BIOLOGY 4INS 66 REMARK 1 REF ADV.PROTEIN CHEM. V. 26 279 1972 4INS 67 REMARK 1 REFN ASTM APCHA2 US ISSN 0065-3233 433 4INS 68 REMARK 1 REFERENCE 10 4INS 69 REMARK 1 AUTH T.L.BLUNDELL,J.F.CUTFIELD,E.J.DODSON,G.G.DODSON, 4INS 70 REMARK 1 AUTH 2 D.C.HODGKIN,D.A.MERCOLA 4INS 71 REMARK 1 TITL THE CRYSTAL STRUCTURE OF RHOMBOHEDRAL 2 ZINC 4INS 72 REMARK 1 TITL 2 INSULIN 4INS 73 REMARK 1 REF COLD SPRING HARBOR SYMP. V. 36 233 1972 4INS 74 REMARK 1 REF 2 QUANT.BIOL. 4INS 75 REMARK 1 REFN ASTM CSHSAZ US ISSN 0091-7451 421 4INS 76 REMARK 1 REFERENCE 11 4INS 77 REMARK 1 AUTH T.L.BLUNDELL,J.F.CUTFIELD,S.M.CUTFIELD,E.J.DODSON, 4INS 78 REMARK 1 AUTH 2 G.G.DODSON,D.C.HODGKIN,D.A.MERCOLA,M.VIJAYAN 4INS 79 REMARK 1 TITL ATOMIC POSITIONS IN RHOMBOHEDRAL 2-*ZINC INSULIN 4INS 80 REMARK 1 TITL 2 CRYSTALS 4INS 81 REMARK 1 REF NATURE V. 231 506 1971 4INS 82 REMARK 1 REFN ASTM NATUAS UK ISSN 0028-0836 006 4INS 83 REMARK 1 REFERENCE 12 4INS 84 REMARK 1 AUTH T.L.BLUNDELL,G.G.DODSON,E.DODSON,D.C.HODGKIN, 4INS 85 REMARK 1 AUTH 2 M.VIJAYAN 4INS 86 REMARK 1 TITL X-*RAY ANALYSIS AND THE STRUCTURE OF INSULIN 4INS 87 REMARK 1 REF RECENT PROG.HORM.RES. V. 27 1 1971 4INS 88 REMARK 1 REFN ASTM RPHRA6 US ISSN 0079-9963 908 4INS 89 REMARK 1 REFERENCE 13 4INS 90 REMARK 1 AUTH E.N.BAKER,G.DODSON 4INS 91 REMARK 1 TITL X-RAY DIFFRACTION DATA ON SOME CRYSTALLINE 4INS 92 REMARK 1 TITL 2 VARIETIES OF INSULIN 4INS 93 REMARK 1 REF J.MOL.BIOL. V. 54 605 1970 4INS 94 REMARK 1 REFN ASTM JMOBAK UK ISSN 0022-2836 070 4INS 95 REMARK 1 REFERENCE 14 4INS 96 REMARK 1 AUTH M.J.ADAMS,T.L.BLUNDELL,E.J.DODSON,G.G.DODSON, 4INS 97 REMARK 1 AUTH 2 M.VIJAYAN,E.N.BAKER,M.M.HARDING,D.C.HODGKIN, 4INS 98 REMARK 1 AUTH 3 B.RIMMER,S.SHEAT 4INS 99 REMARK 1 TITL STRUCTURE OF RHOMBOHEDRAL 2 ZINC INSULIN CRYSTALS 4INS 100 REMARK 1 REF NATURE V. 224 491 1969 4INS 101 REMARK 1 REFN ASTM NATUAS UK ISSN 0028-0836 006 4INS 102 REMARK 1 REFERENCE 15 4INS 103 REMARK 1 EDIT M.O.DAYHOFF 4INS 104 REMARK 1 REF ATLAS OF PROTEIN SEQUENCE V. 5 187 1972 4INS 105 REMARK 1 REF 2 AND STRUCTURE (DATA SECTION) 4INS 106 REMARK 1 PUBL NATIONAL BIOMEDICAL RESEARCH FOUNDATION, 4INS 107 REMARK 1 PUBL 2 SILVER SPRING,MD. 4INS 108 REMARK 1 REFN ISBN 0-912466-02-2 435 4INS 109 REMARK 2 4INS 110 REMARK 2 RESOLUTION. 1.5 ANGSTROMS. 4INS 111 REMARK 3 4INS 112 REMARK 3 REFINEMENT. BY THE RESTRAINED LEAST-SQUARES PROCEDURE OF J. 4INS 113 REMARK 3 KONNERT AND W. HENDRICKSON (PROGRAM *PROLSQ*). THE R 4INS 114 REMARK 3 VALUE IS 0.153. THE RMS DEVIATION FROM IDEALITY OF THE 4INS 115 REMARK 3 BOND LENGTHS IS 0.005 ANGSTROMS. THE RMS DEVIATION FROM 4INS 116 REMARK 3 IDEALITY OF THE BOND ANGLES IS 5.9 DEGREES. 4INS 117 REMARK 4 4INS 118 REMARK 4 SOLVENT MOLECULES ARE INCLUDED IN THE REFINEMENT 4INS 119 REMARK 4 CALCULATIONS. A COMPLETE SET OF SOLVENT COORDINATES IS 4INS 120 REMARK 4 INCLUDED IN THIS ENTRY. 4INS 121 REMARK 5 4INS 122 REMARK 5 THE CRYSTALLOGRAPHIC ASYMMETRIC UNIT OF INSULIN CONSISTS OF 4INS 123 REMARK 5 TWO INSULIN MOLECULES EACH CONSISTING OF TWO CHAINS. THIS 4INS 124 REMARK 5 ENTRY PRESENTS COORDINATES FOR MOLECULES I (CHAIN 4INS 125 REMARK 5 INDICATORS *A* AND *B*) AND II (CHAIN INDICATORS *C* AND 4INS 126 REMARK 5 *D*). THE QUASI-TWO-FOLD AXIS THAT TRANSFORMS MOLECULE I 4INS 127 REMARK 5 INTO MOLECULE II IS GIVEN IN THE *MTRIX* RECORDS BELOW. 4INS 128 REMARK 5 APPLYING THE THREE-FOLD CRYSTALLOGRAPHIC AXIS YIELDS A 4INS 129 REMARK 5 HEXAMER AROUND THE AXIS. THERE ARE TWO ZINC IONS SITUATED 4INS 130 REMARK 5 ON THIS THREE-FOLD AXIS. COORDINATES FOR THE ZINC IONS AND 4INS 131 REMARK 5 SOME WATER MOLECULES ARE INCLUDED BELOW WITH A BLANK CHAIN 4INS 132 REMARK 5 INDICATOR. 4INS 133 REMARK 6 4INS 134 REMARK 6 SITES *D1* AND *D2* ARE THE DIMER-FORMING RESIDUES IN 4INS 135 REMARK 6 MOLECULES I AND II RESPECTIVELY. SITES *H1* AND *H2* ARE 4INS 136 REMARK 6 THE HEXAMER-FORMING RESIDUES IN MOLECULES I AND II 4INS 137 REMARK 6 RESPECTIVELY. SITES *SI1* AND *SI2* ARE THE 4INS 138 REMARK 6 SURFACE-INVARIANT RESIDUES IN MOLECULES I AND II, 4INS 139 REMARK 6 RESPECTIVELY, THAT ARE NOT INVOLVED IN DIMERIZATION. 4INS 140 REMARK 6 RESIDUE GLU A 4 IS INVARIANT AS A CARBOXYLIC ACID. 4INS 141 REMARK 6 RESIDUES HIS B 5 AND ARG B22 ARE INVARIANT IN INSULINS OF 4INS 142 REMARK 6 HIGH POTENCY ONLY. 4INS 143 REMARK 7 4INS 144 REMARK 7 THERE ARE TWO COORDINATION SITES IN THE HEXAMER. SITE 4INS 145 REMARK 7 *ZN1* COMPRISES RESIDUE HIS B 10 AND WATER HOH 4201 AND 4INS 146 REMARK 7 THEIR TWO CRYSTALLOGRAPHICALLY-RELATED EQUIVALENTS. SITE 4INS 147 REMARK 7 *ZN2* COMPRISES RESIDUE HIS D 10 AND WATER HOH 4513 AND 4INS 148 REMARK 7 THEIR TWO CRYSTALLOGRAPHICALLY-RELATED EQUIVALENTS. SITE 4INS 149 REMARK 7 *ZN1* IS OCTAHEDRALLY COORDINATED AROUND ZN1 AND SITE *ZN2* 4INS 150 REMARK 7 IS OCTAHEDRALLY COORDINATED AROUND ZN2. THE TWO SITES ARE 4INS 151 REMARK 7 VERY SIMILAR. 4INS 152 REMARK 7 BECAUSE THE COORDINATES OF THE SYMMETRY-RELATED ATOMS ARE 4INS 153 REMARK 7 NOT INCLUDED IN THIS ENTRY THE COMPLETE CONNECTIVITY OF 4INS 154 REMARK 7 ATOMS ZN1 AND ZN2 CANNOT BE SPECIFIED. PARTIAL 4INS 155 REMARK 7 CONNECTIVITY IS GIVEN BY 4INS 156 REMARK 7 CONECT 247 245 246 832 4INS 157 REMARK 7 CONECT 661 659 660 833 4INS 158 REMARK 7 CONECT 832 247 851 ... ... ... ... 4INS 159 REMARK 7 CONECT 833 661 895 ... ... ... ... 4INS 160 REMARK 7 CONECT 851 832 4INS 161 REMARK 7 CONECT 895 833 4INS 162 REMARK 7 . 4INS 163 REMARK 7 . 4INS 164 REMARK 7 . 4INS 165 REMARK 8 4INS 166 REMARK 8 SOME RESIDUES ARE APPARENTLY DISORDERED BUT DIFFICULT TO 4INS 167 REMARK 8 DESCRIBE IN TERMS OF ATOMIC POSITIONS. ALA B 30 IS ONE OF 4INS 168 REMARK 8 THESE RESIDUES. 4INS 169 REMARK 9 4INSA 3 REMARK 9 CORRECTION. CORRECT DEPOSITION DATE ON HEADER RECORD. 4INSA 4 REMARK 9 15-JUL-93. 4INSA 5 REMARK 10 4INSB 2 REMARK 10 CORRECTION. MOVE RESIDUE NUMBERS FOR HOH ATOMS TO THE 4INSB 3 REMARK 10 CORRECT COLUMNS. 31-JUL-94. 4INSB 4 SEQRES 1 A 21 GLY ILE VAL GLU GLN CYS CYS THR SER ILE CYS SER LEU 4INS 170 SEQRES 2 A 21 TYR GLN LEU GLU ASN TYR CYS ASN 4INS 171 SEQRES 1 B 30 PHE VAL ASN GLN HIS LEU CYS GLY SER HIS LEU VAL GLU 4INS 172 SEQRES 2 B 30 ALA LEU TYR LEU VAL CYS GLY GLU ARG GLY PHE PHE TYR 4INS 173 SEQRES 3 B 30 THR PRO LYS ALA 4INS 174 SEQRES 1 C 21 GLY ILE VAL GLU GLN CYS CYS THR SER ILE CYS SER LEU 4INS 175 SEQRES 2 C 21 TYR GLN LEU GLU ASN TYR CYS ASN 4INS 176 SEQRES 1 D 30 PHE VAL ASN GLN HIS LEU CYS GLY SER HIS LEU VAL GLU 4INS 177 SEQRES 2 D 30 ALA LEU TYR LEU VAL CYS GLY GLU ARG GLY PHE PHE TYR 4INS 178 SEQRES 3 D 30 THR PRO LYS ALA 4INS 179 FTNOTE 1 4INS 180 FTNOTE 1 THE QUASI-TWO-FOLD SYMMETRY BREAKS DOWN MOST SERIOUSLY AT 4INS 181 FTNOTE 1 RESIDUES 4INS 182 FTNOTE 1 GLY A 1 TO GLN A 5 AND GLY C 1 TO GLN C 5 4INS 183 FTNOTE 1 HIS B 5 AND HIS D 5 4INS 184 FTNOTE 1 PHE B 25 AND PHE D 25 4INS 185 FTNOTE 2 4INS 186 FTNOTE 2 THE FOLLOWING RESIDUES ARE DISORDERED - GLN B 4, VAL B 12, 4INS 187 FTNOTE 2 GLU B 21, ARG B 22, ARG D 22, LYS D 29. 4INS 188 FTNOTE 3 4INS 189 FTNOTE 3 SEE REMARK 8. 4INS 190 HET ZN 1 1 ZINC ION ON 3-FOLD CRYSTAL AXIS 4INS 191 HET ZN 2 1 ZINC ION ON 3-FOLD CRYSTAL AXIS 4INS 192 FORMUL 5 ZN 2(ZN1 ++) 4INS 193 FORMUL 6 HOH *350(H2 O1) 4INS 194 HELIX 1 A11 GLY A 1 ILE A 10 1 VAL 203 O H-BONDED TO HOH 4INS 195 HELIX 2 A12 SER A 12 GLU A 17 5 CNTCTS MOSTLY GT 3A,NOT IDEAL 4INS 196 HELIX 3 B11 SER B 9 GLY B 20 1 CYS 67 GLY 68, 3(10) CONTACTS 4INS 197 HELIX 4 A21 GLY C 1 ILE C 10 1 NOT IDEAL ALPH,SOME PI CNTCTS 4INS 198 HELIX 5 A22 SER C 12 GLU C 17 5 CNTCTS MOSTLY GT 3A,NOT IDEAL 4INS 199 HELIX 6 B21 SER D 9 GLY D 20 1 CYS 67,GLY 68, 3(10) CONTACTS 4INS 200 SHEET 1 B 2 PHE B 24 TYR B 26 0 4INS 201 SHEET 2 B 2 PHE D 24 TYR D 26 -1 N PHE B 24 O TYR D 26 4INS 202 TURN 1 1B1 CYS B 19 ARG B 22 4INS 203 TURN 2 1B2 GLY B 20 GLY B 23 4INS 204 TURN 3 2B1 CYS D 19 ARG D 22 4INS 205 TURN 4 2B2 GLY D 20 GLY D 23 4INS 206 SSBOND 1 CYS A 6 CYS A 11 4INS 207 SSBOND 2 CYS C 6 CYS C 11 4INS 208 SSBOND 3 CYS A 7 CYS B 7 4INS 209 SSBOND 4 CYS A 20 CYS B 19 4INS 210 SSBOND 5 CYS C 7 CYS D 7 4INS 211 SSBOND 6 CYS C 20 CYS D 19 4INS 212 SITE 1 D1 5 VAL B 12 TYR B 16 PHE B 24 PHE B 25 4INS 213 SITE 2 D1 5 TYR B 26 4INS 214 SITE 1 D2 5 VAL D 12 TYR D 16 PHE D 24 PHE D 25 4INS 215 SITE 2 D2 5 TYR D 26 4INS 216 SITE 1 H1 7 LEU A 13 TYR A 14 PHE B 1 GLU B 13 4INS 217 SITE 2 H1 7 ALA B 14 LEU B 17 VAL B 18 4INS 218 SITE 1 H2 7 LEU C 13 TYR C 14 PHE D 1 GLU D 13 4INS 219 SITE 2 H2 7 ALA D 14 LEU D 17 VAL D 18 4INS 220 SITE 1 SI1 7 GLY A 1 GLU A 4 GLN A 5 CYS A 7 4INS 221 SITE 2 SI1 7 TYR A 19 ASN A 21 CYS B 7 4INS 222 SITE 1 SI2 7 GLY C 1 GLU C 4 GLN C 5 CYS C 7 4INS 223 SITE 2 SI2 7 TYR C 19 ASN C 21 CYS D 7 4INS 224 CRYST1 82.500 82.500 34.000 90.00 90.00 120.00 R 3 18 4INS 225 ORIGX1 1.000000 0.000000 0.000000 0.00000 4INS 226 ORIGX2 0.000000 1.000000 0.000000 0.00000 4INS 227 ORIGX3 0.000000 0.000000 1.000000 0.00000 4INS 228 SCALE1 0.012121 0.006998 0.000000 0.00000 4INS 229 SCALE2 0.000000 0.013996 0.000000 0.00000 4INS 230 SCALE3 0.000000 0.000000 0.029412 0.00000 4INS 231 MTRIX1 1 -0.878620 -0.476960 0.023050 0.00000 1 4INS 232 MTRIX2 1 -0.477430 0.878370 -0.022860 0.00000 1 4INS 233 MTRIX3 1 -0.009350 -0.031090 -0.999470 0.00000 1 4INS 234 ATOM 1 N GLY A 1 -8.863 16.944 14.289 1.00 21.88 1 4INS 235 ATOM 2 CA GLY A 1 -9.929 17.026 13.244 1.00 22.85 1 4INS 236 ATOM 3 C GLY A 1 -10.051 15.625 12.618 1.00 43.92 1 4INS 237 ATOM 4 O GLY A 1 -9.782 14.728 13.407 1.00 25.22 1 4INS 238 ATOM 5 N ILE A 2 -10.333 15.531 11.332 1.00 26.28 1 4INS 239 ATOM 6 CA ILE A 2 -10.488 14.266 10.600 1.00 20.84 1 4INS 240 ATOM 7 C ILE A 2 -9.367 13.302 10.658 1.00 11.81 1 4INS 241 ATOM 8 O ILE A 2 -9.580 12.092 10.969 1.00 20.31 1 4INS 242 ATOM 9 CB ILE A 2 -10.883 14.493 9.095 1.00 40.00 1 4INS 243 ATOM 10 CG1 ILE A 2 -11.579 13.146 8.697 1.00 36.74 1 4INS 244 ATOM 11 CG2 ILE A 2 -9.741 14.794 8.140 1.00 23.02 1 4INS 245 ATOM 12 CD1 ILE A 2 -12.813 13.031 9.640 1.00 26.69 1 4INS 246 ATOM 13 N VAL A 3 -8.133 13.759 10.483 1.00 16.57 1 4INS 247 ATOM 14 CA VAL A 3 -6.966 12.901 10.576 1.00 15.75 1 4INS 248 ATOM 15 C VAL A 3 -6.892 12.161 11.922 1.00 22.09 1 4INS 249 ATOM 16 O VAL A 3 -6.547 10.990 12.037 1.00 24.52 1 4INS 250 ATOM 17 CB VAL A 3 -5.697 13.708 10.225 1.00 21.34 1 4INS 251 ATOM 18 CG1 VAL A 3 -4.382 12.960 10.448 1.00 32.48 1 4INS 252 ATOM 19 CG2 VAL A 3 -5.842 14.209 8.777 1.00 26.35 1 4INS 253 ATOM 20 N GLU A 4 -7.043 13.019 12.935 1.00 16.58 1 4INS 254 ATOM 21 CA GLU A 4 -6.889 12.474 14.295 1.00 15.32 1 4INS 255 ATOM 22 C GLU A 4 -8.004 11.558 14.610 1.00 16.88 1 4INS 256 ATOM 23 O GLU A 4 -7.888 10.474 15.128 1.00 23.30 1 4INS 257 ATOM 24 CB GLU A 4 -6.809 13.691 15.266 1.00 17.11 1 4INS 258 ATOM 25 CG GLU A 4 -5.615 14.565 14.951 1.00 21.45 1 4INS 259 ATOM 26 CD GLU A 4 -5.704 15.457 13.735 1.00 21.59 1 4INS 260 ATOM 27 OE1 GLU A 4 -6.757 15.959 13.377 1.00 23.43 1 4INS 261 ATOM 28 OE2 GLU A 4 -4.568 15.569 13.179 1.00 25.36 1 4INS 262 ATOM 29 N GLN A 5 -9.199 12.048 14.356 1.00 15.69 1 4INS 263 ATOM 30 CA GLN A 5 -10.407 11.299 14.630 1.00 12.38 1 4INS 264 ATOM 31 C GLN A 5 -10.431 9.940 13.980 1.00 19.86 1 4INS 265 ATOM 32 O GLN A 5 -10.815 8.931 14.542 1.00 16.83 1 4INS 266 ATOM 33 CB GLN A 5 -11.594 12.130 14.152 1.00 21.13 1 4INS 267 ATOM 34 CG GLN A 5 -12.860 11.374 14.561 1.00 22.06 1 4INS 268 ATOM 35 CD GLN A 5 -13.946 11.901 13.634 1.00 42.02 1 4INS 269 ATOM 36 OE1 GLN A 5 -13.908 13.027 13.169 1.00 55.10 1 4INS 270 ATOM 37 NE2 GLN A 5 -14.943 11.030 13.351 1.00 27.27 1 4INS 271 ATOM 38 N CYS A 6 -10.033 9.815 12.695 1.00 13.19 4INS 272 ATOM 39 CA CYS A 6 -10.050 8.518 12.065 1.00 12.63 4INS 273 ATOM 40 C CYS A 6 -9.105 7.520 12.667 1.00 9.95 4INS 274 ATOM 41 O CYS A 6 -9.395 6.288 12.666 1.00 14.22 4INS 275 ATOM 42 CB CYS A 6 -9.660 8.673 10.559 1.00 12.54 4INS 276 ATOM 43 SG CYS A 6 -10.925 9.459 9.579 1.00 13.00 4INS 277 ATOM 44 N CYS A 7 -8.018 7.992 13.171 1.00 10.84 4INS 278 ATOM 45 CA CYS A 7 -6.964 7.186 13.808 1.00 17.02 4INS 279 ATOM 46 C CYS A 7 -7.236 6.948 15.358 1.00 13.71 4INS 280 ATOM 47 O CYS A 7 -7.061 5.782 15.768 1.00 19.28 4INS 281 ATOM 48 CB CYS A 7 -5.578 7.826 13.656 1.00 20.24 4INS 282 ATOM 49 SG CYS A 7 -4.181 6.819 14.134 1.00 13.80 4INS 283 ATOM 50 N THR A 8 -7.655 7.937 16.058 1.00 12.57 4INS 284 ATOM 51 CA THR A 8 -7.862 7.732 17.520 1.00 19.99 4INS 285 ATOM 52 C THR A 8 -9.143 6.997 17.870 1.00 26.34 4INS 286 ATOM 53 O THR A 8 -9.189 6.157 18.795 1.00 25.43 4INS 287 ATOM 54 CB THR A 8 -7.728 9.055 18.386 1.00 20.77 4INS 288 ATOM 55 OG1 THR A 8 -8.889 9.918 18.117 1.00 26.76 4INS 289 ATOM 56 CG2 THR A 8 -6.334 9.700 18.196 1.00 26.50 4INS 290 ATOM 57 N SER A 9 -10.170 7.350 17.058 1.00 20.01 4INS 291 ATOM 58 CA SER A 9 -11.509 6.803 17.121 1.00 16.88 4INS 292 ATOM 59 C SER A 9 -11.796 5.981 15.856 1.00 12.70 4INS 293 ATOM 60 O SER A 9 -11.139 5.010 15.473 1.00 17.60 4INS 294 ATOM 61 CB SER A 9 -12.331 8.067 17.439 1.00 19.52 4INS 295 ATOM 62 OG SER A 9 -13.674 7.774 17.650 1.00 32.34 4INS 296 ATOM 63 N ILE A 10 -12.883 6.382 15.159 1.00 15.34 4INS 297 ATOM 64 CA ILE A 10 -13.350 5.723 13.932 1.00 20.23 4INS 298 ATOM 65 C ILE A 10 -13.969 6.902 13.106 1.00 17.50 4INS 299 ATOM 66 O ILE A 10 -14.355 7.922 13.623 1.00 16.60 4INS 300 ATOM 67 CB ILE A 10 -14.366 4.524 14.047 1.00 19.39 4INS 301 ATOM 68 CG1 ILE A 10 -15.702 4.874 14.742 1.00 22.05 4INS 302 ATOM 69 CG2 ILE A 10 -13.711 3.300 14.723 1.00 23.30 4INS 303 ATOM 70 CD1 ILE A 10 -16.702 3.722 15.005 1.00 42.11 4INS 304 ATOM 71 N CYS A 11 -14.080 6.685 11.767 1.00 12.14 4INS 305 ATOM 72 CA CYS A 11 -14.665 7.679 10.880 1.00 11.24 4INS 306 ATOM 73 C CYS A 11 -15.301 6.881 9.766 1.00 12.17 4INS 307 ATOM 74 O CYS A 11 -14.962 5.692 9.528 1.00 21.14 4INS 308 ATOM 75 CB CYS A 11 -13.695 8.702 10.417 1.00 13.03 4INS 309 ATOM 76 SG CYS A 11 -12.375 8.019 9.385 1.00 13.60 4INS 310 ATOM 77 N SER A 12 -16.233 7.557 9.095 1.00 11.37 4INS 311 ATOM 78 CA SER A 12 -16.999 6.978 8.005 1.00 9.91 4INS 312 ATOM 79 C SER A 12 -16.563 7.644 6.726 1.00 7.40 4INS 313 ATOM 80 O SER A 12 -15.967 8.753 6.711 1.00 9.67 4INS 314 ATOM 81 CB SER A 12 -18.516 7.183 8.084 1.00 16.64 4INS 315 ATOM 82 OG SER A 12 -18.869 8.543 7.881 1.00 17.14 4INS 316 ATOM 83 N LEU A 13 -16.852 6.914 5.612 1.00 11.35 4INS 317 ATOM 84 CA LEU A 13 -16.530 7.444 4.259 1.00 11.35 4INS 318 ATOM 85 C LEU A 13 -17.317 8.715 4.030 1.00 12.55 4INS 319 ATOM 86 O LEU A 13 -16.835 9.521 3.226 1.00 11.78 4INS 320 ATOM 87 CB LEU A 13 -16.774 6.348 3.232 1.00 11.66 4INS 321 ATOM 88 CG LEU A 13 -15.940 5.046 3.316 1.00 18.12 4INS 322 ATOM 89 CD1 LEU A 13 -16.050 4.197 2.018 1.00 18.76 4INS 323 ATOM 90 CD2 LEU A 13 -14.471 5.320 3.537 1.00 17.26 4INS 324 ATOM 91 N TYR A 14 -18.491 8.790 4.629 1.00 10.84 4INS 325 ATOM 92 CA TYR A 14 -19.282 10.035 4.368 1.00 10.75 4INS 326 ATOM 93 C TYR A 14 -18.639 11.228 4.963 1.00 12.81 4INS 327 ATOM 94 O TYR A 14 -18.706 12.298 4.341 1.00 15.11 4INS 328 ATOM 95 CB TYR A 14 -20.746 9.900 4.799 1.00 12.90 4INS 329 ATOM 96 CG TYR A 14 -21.463 8.764 4.079 1.00 18.23 4INS 330 ATOM 97 CD1 TYR A 14 -22.110 9.123 2.891 1.00 18.95 4INS 331 ATOM 98 CD2 TYR A 14 -21.461 7.440 4.475 1.00 15.42 4INS 332 ATOM 99 CE1 TYR A 14 -22.767 8.167 2.086 1.00 18.15 4INS 333 ATOM 100 CE2 TYR A 14 -22.118 6.436 3.676 1.00 14.31 4INS 334 ATOM 101 CZ TYR A 14 -22.738 6.856 2.556 1.00 15.47 4INS 335 ATOM 102 OH TYR A 14 -23.436 5.926 1.716 1.00 24.86 4INS 336 ATOM 103 N GLN A 15 -17.945 11.100 6.091 1.00 9.63 4INS 337 ATOM 104 CA GLN A 15 -17.178 12.138 6.774 1.00 9.40 4INS 338 ATOM 105 C GLN A 15 -16.012 12.543 5.900 1.00 10.52 4INS 339 ATOM 106 O GLN A 15 -15.611 13.717 5.722 1.00 14.25 4INS 340 ATOM 107 CB GLN A 15 -16.774 11.841 8.205 1.00 13.89 4INS 341 ATOM 108 CG GLN A 15 -17.894 11.668 9.206 1.00 17.53 4INS 342 ATOM 109 CD GLN A 15 -17.524 11.056 10.515 1.00 28.21 4INS 343 ATOM 110 OE1 GLN A 15 -16.865 10.027 10.598 1.00 20.14 4INS 344 ATOM 111 NE2 GLN A 15 -17.994 11.650 11.624 1.00 30.25 4INS 345 ATOM 112 N LEU A 16 -15.352 11.525 5.325 1.00 12.99 4INS 346 ATOM 113 CA LEU A 16 -14.185 11.826 4.470 1.00 11.19 4INS 347 ATOM 114 C LEU A 16 -14.605 12.634 3.249 1.00 15.54 4INS 348 ATOM 115 O LEU A 16 -13.767 13.398 2.745 1.00 16.01 4INS 349 ATOM 116 CB LEU A 16 -13.588 10.521 4.060 1.00 12.67 4INS 350 ATOM 117 CG LEU A 16 -12.954 9.717 5.182 1.00 13.07 4INS 351 ATOM 118 CD1 LEU A 16 -12.115 8.571 4.602 1.00 16.61 4INS 352 ATOM 119 CD2 LEU A 16 -12.041 10.559 6.028 1.00 16.50 4INS 353 ATOM 120 N GLU A 17 -15.779 12.420 2.759 1.00 17.50 4INS 354 ATOM 121 CA GLU A 17 -16.223 13.179 1.589 1.00 17.72 4INS 355 ATOM 122 C GLU A 17 -16.171 14.693 1.811 1.00 19.21 4INS 356 ATOM 123 O GLU A 17 -16.118 15.466 0.803 1.00 18.48 4INS 357 ATOM 124 CB GLU A 17 -17.645 12.862 1.215 1.00 17.38 4INS 358 ATOM 125 CG GLU A 17 -17.885 11.629 0.360 1.00 27.97 4INS 359 ATOM 126 CD GLU A 17 -19.225 11.667 -0.391 1.00 26.70 4INS 360 ATOM 127 OE1 GLU A 17 -20.201 11.466 0.276 1.00 29.93 4INS 361 ATOM 128 OE2 GLU A 17 -19.127 11.873 -1.643 1.00 34.66 4INS 362 ATOM 129 N ASN A 18 -16.094 15.074 3.104 1.00 15.10 4INS 363 ATOM 130 CA ASN A 18 -16.029 16.534 3.332 1.00 18.85 4INS 364 ATOM 131 C ASN A 18 -14.703 17.131 2.954 1.00 18.46 4INS 365 ATOM 132 O ASN A 18 -14.545 18.377 2.834 1.00 19.68 4INS 366 ATOM 133 CB ASN A 18 -16.489 16.934 4.738 1.00 20.66 4INS 367 ATOM 134 CG ASN A 18 -17.868 16.338 5.142 1.00 29.79 4INS 368 ATOM 135 OD1 ASN A 18 -18.813 16.053 4.382 1.00 34.48 4INS 369 ATOM 136 ND2 ASN A 18 -17.991 16.168 6.452 1.00 36.00 4INS 370 ATOM 137 N TYR A 19 -13.697 16.327 2.738 1.00 15.68 4INS 371 ATOM 138 CA TYR A 19 -12.358 16.724 2.380 1.00 14.19 4INS 372 ATOM 139 C TYR A 19 -12.154 16.695 0.899 1.00 13.20 4INS 373 ATOM 140 O TYR A 19 -11.010 17.038 0.480 1.00 16.12 4INS 374 ATOM 141 CB TYR A 19 -11.364 15.840 3.178 1.00 14.35 4INS 375 ATOM 142 CG TYR A 19 -11.586 16.223 4.634 1.00 21.24 4INS 376 ATOM 143 CD1 TYR A 19 -10.853 17.300 5.129 1.00 26.61 4INS 377 ATOM 144 CD2 TYR A 19 -12.562 15.703 5.445 1.00 19.21 4INS 378 ATOM 145 CE1 TYR A 19 -11.084 17.801 6.393 1.00 27.80 4INS 379 ATOM 146 CE2 TYR A 19 -12.833 16.207 6.714 1.00 23.98 4INS 380 ATOM 147 CZ TYR A 19 -12.081 17.267 7.187 1.00 34.08 4INS 381 ATOM 148 OH TYR A 19 -12.227 17.849 8.400 1.00 37.96 4INS 382 ATOM 149 N CYS A 20 -13.057 16.313 0.077 1.00 13.05 4INS 383 ATOM 150 CA CYS A 20 -12.838 16.309 -1.389 1.00 18.69 4INS 384 ATOM 151 C CYS A 20 -12.984 17.799 -1.802 1.00 19.09 4INS 385 ATOM 152 O CYS A 20 -13.588 18.579 -1.084 1.00 19.31 4INS 386 ATOM 153 CB CYS A 20 -13.850 15.490 -2.157 1.00 15.99 4INS 387 ATOM 154 SG CYS A 20 -13.923 13.761 -1.584 1.00 12.90 4INS 388 ATOM 155 N ASN A 21 -12.380 18.063 -2.909 1.00 17.63 4INS 389 ATOM 156 CA ASN A 21 -12.404 19.399 -3.608 1.00 25.23 4INS 390 ATOM 157 C ASN A 21 -13.642 19.696 -4.447 1.00 34.82 4INS 391 ATOM 158 O ASN A 21 -14.146 18.703 -4.956 1.00 31.24 4INS 392 ATOM 159 CB ASN A 21 -11.228 19.392 -4.521 1.00 19.06 4INS 393 ATOM 160 CG ASN A 21 -10.020 20.283 -4.456 1.00 40.71 4INS 394 ATOM 161 OD1 ASN A 21 -10.067 21.380 -5.083 1.00 68.22 4INS 395 ATOM 162 ND2 ASN A 21 -9.004 19.667 -3.808 1.00 39.69 4INS 396 ATOM 163 OXT ASN A 21 -13.881 20.890 -4.604 1.00 41.83 4INS 397 TER 164 ASN A 21 4INS 398 ATOM 165 N PHE B 1 -21.768 1.132 3.577 1.00 25.87 4INS 399 ATOM 166 CA PHE B 1 -20.374 1.368 4.053 1.00 24.30 4INS 400 ATOM 167 C PHE B 1 -20.341 1.145 5.585 1.00 39.74 4INS 401 ATOM 168 O PHE B 1 -21.423 1.141 6.173 1.00 38.10 4INS 402 ATOM 169 CB PHE B 1 -19.806 2.718 3.624 1.00 22.51 4INS 403 ATOM 170 CG PHE B 1 -19.924 2.916 2.131 1.00 16.52 4INS 404 ATOM 171 CD1 PHE B 1 -20.067 4.204 1.618 1.00 35.58 4INS 405 ATOM 172 CD2 PHE B 1 -19.709 1.873 1.262 1.00 20.86 4INS 406 ATOM 173 CE1 PHE B 1 -20.093 4.444 0.243 1.00 52.66 4INS 407 ATOM 174 CE2 PHE B 1 -19.824 2.067 -0.123 1.00 51.46 4INS 408 ATOM 175 CZ PHE B 1 -20.011 3.332 -0.631 1.00 42.63 4INS 409 ATOM 176 N VAL B 2 -19.104 0.899 6.027 1.00 21.12 4INS 410 ATOM 177 CA VAL B 2 -18.754 0.598 7.406 1.00 36.74 4INS 411 ATOM 178 C VAL B 2 -17.780 1.656 7.965 1.00 23.52 4INS 412 ATOM 179 O VAL B 2 -17.104 2.328 7.197 1.00 19.56 4INS 413 ATOM 180 CB VAL B 2 -18.048 -0.765 7.638 1.00 30.58 4INS 414 ATOM 181 CG1 VAL B 2 -18.993 -1.953 7.609 1.00 25.73 4INS 415 ATOM 182 CG2 VAL B 2 -16.776 -0.916 6.799 1.00 22.31 4INS 416 ATOM 183 N ASN B 3 -17.741 1.753 9.278 1.00 13.38 4INS 417 ATOM 184 CA ASN B 3 -16.872 2.691 9.950 1.00 13.94 4INS 418 ATOM 185 C ASN B 3 -15.457 2.100 9.881 1.00 15.03 4INS 419 ATOM 186 O ASN B 3 -15.312 0.857 9.926 1.00 24.85 4INS 420 ATOM 187 CB ASN B 3 -17.272 3.010 11.382 1.00 25.01 4INS 421 ATOM 188 CG ASN B 3 -18.513 3.844 11.511 1.00 49.04 4INS 422 ATOM 189 OD1 ASN B 3 -18.658 4.774 10.724 1.00 34.50 4INS 423 ATOM 190 ND2 ASN B 3 -19.333 3.415 12.473 1.00 35.00 4INS 424 ATOM 191 N GLN B 4 -14.509 3.031 9.767 1.00 12.52 2 4INS 425 ATOM 192 CA GLN B 4 -13.137 2.542 9.571 1.00 22.69 2 4INS 426 ATOM 193 C GLN B 4 -12.213 3.224 10.580 1.00 13.29 2 4INS 427 ATOM 194 O GLN B 4 -12.347 4.333 11.118 1.00 20.53 2 4INS 428 ATOM 195 CB GLN B 4 -12.666 2.760 8.116 1.00 39.18 2 4INS 429 ATOM 196 CG AGLN B 4 -13.007 1.731 7.035 0.60 11.45 2 4INS 430 ATOM 197 CG BGLN B 4 -12.978 1.763 6.996 0.40 37.30 2 4INS 431 ATOM 198 CD AGLN B 4 -12.270 0.520 6.830 0.60 12.42 2 4INS 432 ATOM 199 CD BGLN B 4 -14.070 2.781 6.746 0.40 32.97 2 4INS 433 ATOM 200 OE1AGLN B 4 -12.812 -0.612 6.494 0.60 17.67 2 4INS 434 ATOM 201 OE1BGLN B 4 -14.059 3.957 7.112 0.40 40.00 2 4INS 435 ATOM 202 NE2AGLN B 4 -10.898 0.624 6.949 0.60 28.94 2 4INS 436 ATOM 203 NE2BGLN B 4 -15.108 2.179 6.165 0.40 35.67 2 4INS 437 ATOM 204 N HIS B 5 -11.158 2.442 10.837 1.00 13.02 1 4INS 438 ATOM 205 CA HIS B 5 -10.083 3.000 11.779 1.00 17.05 1 4INS 439 ATOM 206 C HIS B 5 -8.855 3.149 10.899 1.00 10.95 1 4INS 440 ATOM 207 O HIS B 5 -8.284 2.166 10.380 1.00 17.14 1 4INS 441 ATOM 208 CB HIS B 5 -9.982 1.956 12.877 1.00 22.24 1 4INS 442 ATOM 209 CG HIS B 5 -8.934 2.400 13.860 1.00 25.74 1 4INS 443 ATOM 210 ND1 HIS B 5 -8.072 1.535 14.436 1.00 35.32 1 4INS 444 ATOM 211 CD2 HIS B 5 -8.637 3.596 14.329 1.00 28.02 1 4INS 445 ATOM 212 CE1 HIS B 5 -7.275 2.240 15.211 1.00 28.73 1 4INS 446 ATOM 213 NE2 HIS B 5 -7.571 3.509 15.150 1.00 30.21 1 4INS 447 ATOM 214 N LEU B 6 -8.529 4.400 10.604 1.00 11.30 4INS 448 ATOM 215 CA LEU B 6 -7.468 4.709 9.611 1.00 11.13 4INS 449 ATOM 216 C LEU B 6 -6.399 5.604 10.158 1.00 11.03 4INS 450 ATOM 217 O LEU B 6 -6.695 6.779 10.484 1.00 13.66 4INS 451 ATOM 218 CB LEU B 6 -8.231 5.398 8.411 1.00 14.13 4INS 452 ATOM 219 CG LEU B 6 -9.251 4.634 7.563 1.00 13.39 4INS 453 ATOM 220 CD1 LEU B 6 -10.017 5.598 6.671 1.00 14.70 4INS 454 ATOM 221 CD2 LEU B 6 -8.620 3.517 6.767 1.00 18.25 4INS 455 ATOM 222 N CYS B 7 -5.180 5.069 10.115 1.00 10.06 4INS 456 ATOM 223 CA CYS B 7 -4.058 5.835 10.569 1.00 10.70 4INS 457 ATOM 224 C CYS B 7 -3.033 5.982 9.484 1.00 13.26 4INS 458 ATOM 225 O CYS B 7 -2.955 5.198 8.573 1.00 19.10 4INS 459 ATOM 226 CB CYS B 7 -3.434 5.105 11.762 1.00 15.88 4INS 460 ATOM 227 SG CYS B 7 -4.523 5.099 13.246 1.00 16.40 4INS 461 ATOM 228 N GLY B 8 -2.181 6.993 9.540 1.00 12.37 4INS 462 ATOM 229 CA GLY B 8 -1.070 7.261 8.632 1.00 12.72 4INS 463 ATOM 230 C GLY B 8 -1.465 7.317 7.204 1.00 13.24 4INS 464 ATOM 231 O GLY B 8 -2.470 7.884 6.744 1.00 11.92 4INS 465 ATOM 232 N SER B 9 -0.609 6.582 6.429 1.00 11.74 4INS 466 ATOM 233 CA SER B 9 -0.863 6.544 4.980 1.00 15.89 4INS 467 ATOM 234 C SER B 9 -2.183 5.870 4.578 1.00 9.73 4INS 468 ATOM 235 O SER B 9 -2.649 6.111 3.528 1.00 10.43 4INS 469 ATOM 236 CB SER B 9 0.309 5.921 4.206 1.00 17.74 4INS 470 ATOM 237 OG SER B 9 0.534 4.626 4.735 1.00 17.37 4INS 471 ATOM 238 N HIS B 10 -2.721 5.100 5.451 1.00 10.19 4INS 472 ATOM 239 CA HIS B 10 -3.940 4.379 5.188 1.00 7.66 4INS 473 ATOM 240 C HIS B 10 -5.081 5.431 5.075 1.00 10.17 4INS 474 ATOM 241 O HIS B 10 -6.021 5.163 4.291 1.00 10.92 4INS 475 ATOM 242 CB HIS B 10 -4.234 3.316 6.228 1.00 9.55 4INS 476 ATOM 243 CG HIS B 10 -3.192 2.269 6.364 1.00 9.55 4INS 477 ATOM 244 ND1 HIS B 10 -3.043 1.310 5.423 1.00 15.86 4INS 478 ATOM 245 CD2 HIS B 10 -2.289 1.991 7.311 1.00 8.47 4INS 479 ATOM 246 CE1 HIS B 10 -2.078 0.573 5.774 1.00 10.65 4INS 480 ATOM 247 NE2 HIS B 10 -1.589 0.939 6.878 1.00 9.41 4INS 481 ATOM 248 N LEU B 11 -5.016 6.497 5.810 1.00 8.93 4INS 482 ATOM 249 CA LEU B 11 -6.071 7.518 5.617 1.00 9.64 4INS 483 ATOM 250 C LEU B 11 -5.967 8.182 4.279 1.00 7.89 4INS 484 ATOM 251 O LEU B 11 -6.969 8.462 3.666 1.00 9.74 4INS 485 ATOM 252 CB LEU B 11 -5.860 8.541 6.740 1.00 6.93 4INS 486 ATOM 253 CG LEU B 11 -6.949 9.607 6.783 1.00 14.50 4INS 487 ATOM 254 CD1 LEU B 11 -8.376 9.229 6.627 1.00 18.34 4INS 488 ATOM 255 CD2 LEU B 11 -6.742 10.309 8.115 1.00 20.70 4INS 489 ATOM 256 N VAL B 12 -4.751 8.449 3.799 1.00 10.12 2 4INS 490 ATOM 257 CA VAL B 12 -4.579 9.057 2.495 1.00 8.05 2 4INS 491 ATOM 258 C VAL B 12 -5.050 8.131 1.372 1.00 8.14 2 4INS 492 ATOM 259 O VAL B 12 -5.595 8.653 0.398 1.00 11.63 2 4INS 493 ATOM 260 CB VAL B 12 -3.153 9.538 2.230 1.00 11.54 2 4INS 494 ATOM 261 CG1 VAL B 12 -2.809 10.670 3.148 1.00 17.75 2 4INS 495 ATOM 262 CG2AVAL B 12 -2.822 9.786 0.799 0.50 4.68 2 4INS 496 ATOM 263 CG2BVAL B 12 -1.963 8.655 2.123 0.50 10.87 2 4INS 497 ATOM 264 N GLU B 13 -4.906 6.880 1.502 1.00 6.12 4INS 498 ATOM 265 CA GLU B 13 -5.432 5.946 0.542 1.00 8.88 4INS 499 ATOM 266 C GLU B 13 -6.966 6.014 0.472 1.00 12.22 4INS 500 ATOM 267 O GLU B 13 -7.578 6.035 -0.614 1.00 11.15 4INS 501 ATOM 268 CB GLU B 13 -4.996 4.506 0.854 1.00 12.65 4INS 502 ATOM 269 CG GLU B 13 -3.497 4.444 0.582 1.00 15.60 4INS 503 ATOM 270 CD GLU B 13 -3.246 3.857 -0.794 1.00 53.85 4INS 504 ATOM 271 OE1 GLU B 13 -4.238 3.643 -1.500 1.00 33.68 4INS 505 ATOM 272 OE2 GLU B 13 -2.114 3.611 -1.126 1.00 47.24 4INS 506 ATOM 273 N ALA B 14 -7.659 6.004 1.637 1.00 7.15 4INS 507 ATOM 274 CA ALA B 14 -9.061 6.164 1.719 1.00 7.29 4INS 508 ATOM 275 C ALA B 14 -9.563 7.482 1.051 1.00 6.80 4INS 509 ATOM 276 O ALA B 14 -10.595 7.468 0.346 1.00 11.10 4INS 510 ATOM 277 CB ALA B 14 -9.604 6.039 3.106 1.00 12.06 4INS 511 ATOM 278 N LEU B 15 -8.876 8.580 1.321 1.00 6.72 4INS 512 ATOM 279 CA LEU B 15 -9.224 9.854 0.717 1.00 13.51 4INS 513 ATOM 280 C LEU B 15 -9.111 9.815 -0.829 1.00 14.62 4INS 514 ATOM 281 O LEU B 15 -9.956 10.390 -1.496 1.00 12.32 4INS 515 ATOM 282 CB LEU B 15 -8.317 10.981 1.327 1.00 9.71 4INS 516 ATOM 283 CG LEU B 15 -8.755 11.581 2.649 1.00 8.92 4INS 517 ATOM 284 CD1 LEU B 15 -7.682 12.475 3.236 1.00 14.49 4INS 518 ATOM 285 CD2 LEU B 15 -10.096 12.235 2.460 1.00 12.03 4INS 519 ATOM 286 N TYR B 16 -8.050 9.147 -1.297 1.00 8.65 4INS 520 ATOM 287 CA TYR B 16 -7.838 8.961 -2.686 1.00 8.75 4INS 521 ATOM 288 C TYR B 16 -8.999 8.175 -3.284 1.00 11.14 4INS 522 ATOM 289 O TYR B 16 -9.508 8.504 -4.371 1.00 14.34 4INS 523 ATOM 290 CB TYR B 16 -6.494 8.247 -3.047 1.00 7.72 4INS 524 ATOM 291 CG TYR B 16 -6.271 8.027 -4.522 1.00 10.81 4INS 525 ATOM 292 CD1 TYR B 16 -6.450 6.784 -5.047 1.00 17.09 4INS 526 ATOM 293 CD2 TYR B 16 -6.009 9.104 -5.338 1.00 12.64 4INS 527 ATOM 294 CE1 TYR B 16 -6.354 6.581 -6.467 1.00 17.76 4INS 528 ATOM 295 CE2 TYR B 16 -5.898 8.958 -6.741 1.00 13.94 4INS 529 ATOM 296 CZ TYR B 16 -6.110 7.692 -7.259 1.00 17.34 4INS 530 ATOM 297 OH TYR B 16 -5.925 7.520 -8.594 1.00 25.34 4INS 531 ATOM 298 N LEU B 17 -9.428 7.109 -2.664 1.00 8.68 4INS 532 ATOM 299 CA LEU B 17 -10.566 6.290 -3.167 1.00 8.83 4INS 533 ATOM 300 C LEU B 17 -11.861 7.087 -3.142 1.00 10.95 4INS 534 ATOM 301 O LEU B 17 -12.650 7.046 -4.073 1.00 15.67 4INS 535 ATOM 302 CB LEU B 17 -10.665 5.052 -2.327 1.00 10.82 4INS 536 ATOM 303 CG LEU B 17 -9.594 4.104 -2.924 1.00 28.76 4INS 537 ATOM 304 CD1 LEU B 17 -9.136 3.067 -1.933 1.00 30.52 4INS 538 ATOM 305 CD2 LEU B 17 -10.280 3.540 -4.157 1.00 34.91 4INS 539 ATOM 306 N VAL B 18 -12.123 7.786 -2.036 1.00 8.85 4INS 540 ATOM 307 CA VAL B 18 -13.351 8.545 -1.933 1.00 8.77 4INS 541 ATOM 308 C VAL B 18 -13.433 9.713 -2.873 1.00 9.77 4INS 542 ATOM 309 O VAL B 18 -14.472 9.937 -3.457 1.00 16.86 4INS 543 ATOM 310 CB VAL B 18 -13.604 8.974 -0.463 1.00 14.39 4INS 544 ATOM 311 CG1 VAL B 18 -14.784 9.899 -0.282 1.00 11.72 4INS 545 ATOM 312 CG2 VAL B 18 -13.862 7.763 0.393 1.00 12.58 4INS 546 ATOM 313 N CYS B 19 -12.422 10.518 -2.958 1.00 9.03 4INS 547 ATOM 314 CA CYS B 19 -12.433 11.758 -3.756 1.00 8.88 4INS 548 ATOM 315 C CYS B 19 -11.994 11.555 -5.212 1.00 14.69 4INS 549 ATOM 316 O CYS B 19 -12.410 12.303 -6.126 1.00 16.46 4INS 550 ATOM 317 CB CYS B 19 -11.558 12.719 -3.005 1.00 11.19 4INS 551 ATOM 318 SG CYS B 19 -12.040 13.127 -1.344 1.00 10.10 4INS 552 ATOM 319 N GLY B 20 -11.149 10.609 -5.463 1.00 17.12 4INS 553 ATOM 320 CA GLY B 20 -10.685 10.359 -6.851 1.00 21.59 4INS 554 ATOM 321 C GLY B 20 -10.275 11.650 -7.524 1.00 21.38 4INS 555 ATOM 322 O GLY B 20 -9.494 12.483 -7.053 1.00 20.41 4INS 556 ATOM 323 N GLU B 21 -10.784 11.844 -8.710 1.00 29.76 2 4INS 557 ATOM 324 CA GLU B 21 -10.398 13.043 -9.501 1.00 24.44 2 4INS 558 ATOM 325 C GLU B 21 -10.898 14.356 -9.065 1.00 19.21 2 4INS 559 ATOM 326 O GLU B 21 -10.430 15.331 -9.665 1.00 19.00 2 4INS 560 ATOM 327 CB GLU B 21 -10.776 12.724 -10.968 1.00 28.66 2 4INS 561 ATOM 328 CG AGLU B 21 -12.310 12.519 -11.045 0.50 56.11 2 4INS 562 ATOM 329 CG BGLU B 21 -9.804 13.415 -11.966 0.50 75.35 2 4INS 563 ATOM 330 CD AGLU B 21 -12.707 11.349 -11.901 0.50 62.47 2 4INS 564 ATOM 331 CD BGLU B 21 -9.689 13.292 -13.466 0.50 52.68 2 4INS 565 ATOM 332 OE1AGLU B 21 -12.515 10.193 -11.564 0.50 48.34 2 4INS 566 ATOM 333 OE1BGLU B 21 -10.540 12.768 -14.159 0.50 50.28 2 4INS 567 ATOM 334 OE2AGLU B 21 -13.225 11.772 -12.958 0.50 49.92 2 4INS 568 ATOM 335 OE2BGLU B 21 -8.505 13.537 -13.781 0.50 27.33 2 4INS 569 ATOM 336 N ARG B 22 -11.703 14.491 -8.034 1.00 15.49 2 4INS 570 ATOM 337 CA ARG B 22 -12.089 15.732 -7.456 1.00 16.44 2 4INS 571 ATOM 338 C ARG B 22 -10.797 16.222 -6.745 1.00 17.70 2 4INS 572 ATOM 339 O ARG B 22 -10.636 17.458 -6.608 1.00 21.36 2 4INS 573 ATOM 340 CB ARG B 22 -13.234 15.678 -6.464 1.00 21.99 2 4INS 574 ATOM 341 CG ARG B 22 -14.645 15.427 -7.037 1.00 57.89 2 4INS 575 ATOM 342 CD ARG B 22 -15.675 15.167 -5.960 1.00 31.23 2 4INS 576 ATOM 343 NE AARG B 22 -15.739 16.404 -5.124 0.50 16.46 2 4INS 577 ATOM 344 NE BARG B 22 -15.629 13.808 -5.271 0.50 17.69 2 4INS 578 ATOM 345 CZ AARG B 22 -16.608 16.581 -4.143 0.50 39.57 2 4INS 579 ATOM 346 CZ BARG B 22 -16.379 13.225 -4.283 0.50 33.09 2 4INS 580 ATOM 347 NH1AARG B 22 -16.743 17.672 -3.416 0.50 20.14 2 4INS 581 ATOM 348 NH1BARG B 22 -16.987 14.046 -3.392 0.50 51.50 2 4INS 582 ATOM 349 NH2AARG B 22 -17.405 15.551 -3.871 0.50 35.40 2 4INS 583 ATOM 350 NH2BARG B 22 -16.705 11.943 -4.184 0.50 19.59 2 4INS 584 ATOM 351 N GLY B 23 -10.007 15.246 -6.287 1.00 19.18 4INS 585 ATOM 352 CA GLY B 23 -8.844 15.673 -5.491 1.00 11.89 4INS 586 ATOM 353 C GLY B 23 -9.339 15.932 -4.075 1.00 12.83 4INS 587 ATOM 354 O GLY B 23 -10.524 15.922 -3.626 1.00 14.47 4INS 588 ATOM 355 N PHE B 24 -8.343 16.165 -3.187 1.00 12.54 4INS 589 ATOM 356 CA PHE B 24 -8.584 16.432 -1.765 1.00 10.08 4INS 590 ATOM 357 C PHE B 24 -7.488 17.220 -1.123 1.00 10.77 4INS 591 ATOM 358 O PHE B 24 -6.411 17.409 -1.657 1.00 10.93 4INS 592 ATOM 359 CB PHE B 24 -8.754 15.111 -1.032 1.00 3.80 4INS 593 ATOM 360 CG PHE B 24 -7.638 14.114 -1.034 1.00 5.98 4INS 594 ATOM 361 CD1 PHE B 24 -7.488 13.202 -2.069 1.00 5.61 4INS 595 ATOM 362 CD2 PHE B 24 -6.667 14.205 -0.036 1.00 8.93 4INS 596 ATOM 363 CE1 PHE B 24 -6.375 12.338 -2.106 1.00 14.64 4INS 597 ATOM 364 CE2 PHE B 24 -5.573 13.387 0.027 1.00 11.74 4INS 598 ATOM 365 CZ PHE B 24 -5.457 12.470 -1.008 1.00 9.78 4INS 599 ATOM 366 N PHE B 25 -7.717 17.612 0.116 1.00 14.20 1 4INS 600 ATOM 367 CA PHE B 25 -6.813 18.302 1.052 1.00 12.03 1 4INS 601 ATOM 368 C PHE B 25 -6.569 17.356 2.221 1.00 12.69 1 4INS 602 ATOM 369 O PHE B 25 -7.485 16.788 2.757 1.00 15.22 1 4INS 603 ATOM 370 CB PHE B 25 -7.387 19.633 1.684 1.00 17.25 1 4INS 604 ATOM 371 CG PHE B 25 -7.105 20.689 0.637 1.00 30.38 1 4INS 605 ATOM 372 CD1 PHE B 25 -7.842 20.802 -0.543 1.00 61.20 1 4INS 606 ATOM 373 CD2 PHE B 25 -6.003 21.541 0.896 1.00 56.90 1 4INS 607 ATOM 374 CE1 PHE B 25 -7.445 21.790 -1.461 1.00 29.52 1 4INS 608 ATOM 375 CE2 PHE B 25 -5.648 22.564 -0.027 1.00 40.31 1 4INS 609 ATOM 376 CZ PHE B 25 -6.382 22.681 -1.235 1.00 30.38 1 4INS 610 ATOM 377 N TYR B 26 -5.345 17.202 2.583 1.00 11.25 4INS 611 ATOM 378 CA TYR B 26 -4.996 16.333 3.717 1.00 10.42 4INS 612 ATOM 379 C TYR B 26 -4.445 17.350 4.714 1.00 15.08 4INS 613 ATOM 380 O TYR B 26 -3.350 17.906 4.518 1.00 14.52 4INS 614 ATOM 381 CB TYR B 26 -3.949 15.288 3.319 1.00 9.51 4INS 615 ATOM 382 CG TYR B 26 -3.404 14.530 4.474 1.00 12.61 4INS 616 ATOM 383 CD1 TYR B 26 -4.243 13.688 5.178 1.00 20.50 4INS 617 ATOM 384 CD2 TYR B 26 -2.105 14.676 4.857 1.00 13.88 4INS 618 ATOM 385 CE1 TYR B 26 -3.652 12.967 6.246 1.00 16.38 4INS 619 ATOM 386 CE2 TYR B 26 -1.577 14.010 5.941 1.00 10.97 4INS 620 ATOM 387 CZ TYR B 26 -2.347 13.149 6.642 1.00 11.71 4INS 621 ATOM 388 OH TYR B 26 -1.853 12.447 7.734 1.00 16.39 4INS 622 ATOM 389 N THR B 27 -5.287 17.537 5.752 1.00 15.47 4INS 623 ATOM 390 CA THR B 27 -4.902 18.555 6.772 1.00 20.05 4INS 624 ATOM 391 C THR B 27 -4.834 18.053 8.250 1.00 15.09 4INS 625 ATOM 392 O THR B 27 -5.825 18.282 8.943 1.00 22.56 4INS 626 ATOM 393 CB THR B 27 -5.856 19.825 6.753 1.00 26.34 4INS 627 ATOM 394 OG1ATHR B 27 -7.228 19.328 6.558 0.50 39.91 4INS 628 ATOM 395 OG1BTHR B 27 -5.691 20.478 5.511 0.50 29.43 4INS 629 ATOM 396 CG2ATHR B 27 -5.505 20.781 5.606 0.50 34.53 4INS 630 ATOM 397 CG2BTHR B 27 -5.413 20.850 7.858 0.50 34.13 4INS 631 ATOM 398 N PRO B 28 -3.702 17.548 8.603 1.00 18.26 4INS 632 ATOM 399 CA PRO B 28 -3.494 17.055 9.954 1.00 21.56 4INS 633 ATOM 400 C PRO B 28 -3.306 18.220 10.892 1.00 22.68 4INS 634 ATOM 401 O PRO B 28 -3.072 19.330 10.484 1.00 21.93 4INS 635 ATOM 402 CB PRO B 28 -2.249 16.209 9.808 1.00 21.60 4INS 636 ATOM 403 CG PRO B 28 -1.544 16.617 8.595 1.00 21.39 4INS 637 ATOM 404 CD PRO B 28 -2.526 17.320 7.778 1.00 14.32 4INS 638 ATOM 405 N LYS B 29 -3.452 17.975 12.175 1.00 26.27 4INS 639 ATOM 406 CA LYS B 29 -3.227 18.941 13.307 1.00 23.17 4INS 640 ATOM 407 C LYS B 29 -1.707 18.995 13.459 1.00 52.81 4INS 641 ATOM 408 O LYS B 29 -1.026 17.919 13.406 1.00 39.37 4INS 642 ATOM 409 CB LYS B 29 -3.764 18.417 14.615 1.00 22.26 4INS 643 ATOM 410 CG LYS B 29 -3.990 19.385 15.801 1.00 48.01 4INS 644 ATOM 411 CD LYS B 29 -5.153 18.811 16.622 1.00 37.36 4INS 645 ATOM 412 CE LYS B 29 -5.067 18.493 18.087 1.00 53.09 4INS 646 ATOM 413 NZ LYS B 29 -4.208 19.418 18.841 1.00 61.16 4INS 647 ATOM 414 N ALA B 30 -1.166 20.052 13.779 1.00 53.30 3 4INS 648 ATOM 415 CA ALA B 30 0.148 20.539 13.902 1.00 45.30 3 4INS 649 ATOM 416 C ALA B 30 0.991 20.467 15.167 1.00 50.30 3 4INS 650 ATOM 417 O ALA B 30 0.427 20.710 16.268 1.00 62.63 3 4INS 651 ATOM 418 CB ALA B 30 0.033 22.113 13.690 1.00 53.30 3 4INS 652 ATOM 419 OXT ALA B 30 2.226 20.205 15.000 1.00 76.30 3 4INS 653 TER 420 ALA B 30 4INS 654 ATOM 421 N GLY C 1 -0.643 19.956 -14.073 1.00 26.16 1 4INS 655 ATOM 422 CA GLY C 1 -0.389 20.033 -12.615 1.00 30.96 1 4INS 656 ATOM 423 C GLY C 1 0.447 18.825 -12.180 1.00 33.76 1 4INS 657 ATOM 424 O GLY C 1 1.216 18.311 -13.006 1.00 21.35 1 4INS 658 ATOM 425 N ILE C 2 0.244 18.434 -10.942 1.00 23.96 1 4INS 659 ATOM 426 CA ILE C 2 1.003 17.290 -10.393 1.00 15.36 1 4INS 660 ATOM 427 C ILE C 2 0.946 16.025 -11.185 1.00 13.59 1 4INS 661 ATOM 428 O ILE C 2 1.971 15.359 -11.278 1.00 17.19 1 4INS 662 ATOM 429 CB ILE C 2 0.491 17.013 -8.931 1.00 16.47 1 4INS 663 ATOM 430 CG1 ILE C 2 1.539 16.143 -8.164 1.00 15.91 1 4INS 664 ATOM 431 CG2 ILE C 2 -0.969 16.533 -8.863 1.00 17.06 1 4INS 665 ATOM 432 CD1 ILE C 2 1.081 15.828 -6.720 1.00 19.56 1 4INS 666 ATOM 433 N VAL C 3 -0.179 15.655 -11.786 1.00 13.55 1 4INS 667 ATOM 434 CA VAL C 3 -0.278 14.455 -12.591 1.00 17.58 1 4INS 668 ATOM 435 C VAL C 3 0.590 14.454 -13.881 1.00 21.18 1 4INS 669 ATOM 436 O VAL C 3 1.245 13.451 -14.197 1.00 19.26 1 4INS 670 ATOM 437 CB VAL C 3 -1.709 14.136 -12.915 1.00 26.49 1 4INS 671 ATOM 438 CG1 VAL C 3 -1.808 12.868 -13.745 1.00 30.71 1 4INS 672 ATOM 439 CG2 VAL C 3 -2.567 13.989 -11.662 1.00 16.85 1 4INS 673 ATOM 440 N GLU C 4 0.621 15.590 -14.497 1.00 24.23 1 4INS 674 ATOM 441 CA GLU C 4 1.481 15.743 -15.686 1.00 23.06 1 4INS 675 ATOM 442 C GLU C 4 2.966 15.744 -15.343 1.00 19.78 1 4INS 676 ATOM 443 O GLU C 4 3.805 15.198 -16.061 1.00 29.30 1 4INS 677 ATOM 444 CB GLU C 4 1.063 17.059 -16.361 1.00 24.43 1 4INS 678 ATOM 445 CG GLU C 4 -0.349 16.900 -16.964 1.00 19.87 1 4INS 679 ATOM 446 CD GLU C 4 -1.541 17.024 -16.030 1.00 27.93 1 4INS 680 ATOM 447 OE1 GLU C 4 -2.429 16.169 -16.211 1.00 37.08 1 4INS 681 ATOM 448 OE2 GLU C 4 -1.527 17.967 -15.181 1.00 27.92 1 4INS 682 ATOM 449 N GLN C 5 3.289 16.386 -14.236 1.00 17.90 1 4INS 683 ATOM 450 CA GLN C 5 4.695 16.445 -13.800 1.00 17.90 1 4INS 684 ATOM 451 C GLN C 5 5.206 15.104 -13.307 1.00 23.62 1 4INS 685 ATOM 452 O GLN C 5 6.331 14.684 -13.578 1.00 26.25 1 4INS 686 ATOM 453 CB GLN C 5 4.820 17.553 -12.780 1.00 22.64 1 4INS 687 ATOM 454 CG GLN C 5 4.373 18.969 -13.199 1.00 32.56 1 4INS 688 ATOM 455 CD GLN C 5 5.405 19.755 -12.404 1.00 59.85 1 4INS 689 ATOM 456 OE1 GLN C 5 6.478 19.979 -12.947 1.00 50.22 1 4INS 690 ATOM 457 NE2 GLN C 5 4.976 19.940 -11.148 1.00 56.46 1 4INS 691 ATOM 458 N CYS C 6 4.386 14.408 -12.514 1.00 20.45 4INS 692 ATOM 459 CA CYS C 6 4.949 13.166 -11.921 1.00 20.95 4INS 693 ATOM 460 C CYS C 6 4.472 11.875 -12.436 1.00 18.67 4INS 694 ATOM 461 O CYS C 6 5.127 10.851 -12.293 1.00 17.98 4INS 695 ATOM 462 CB CYS C 6 4.732 13.264 -10.354 1.00 16.76 4INS 696 ATOM 463 SG CYS C 6 5.416 14.719 -9.509 1.00 20.00 4INS 697 ATOM 464 N CYS C 7 3.347 11.802 -13.063 1.00 15.93 4INS 698 ATOM 465 CA CYS C 7 2.858 10.515 -13.612 1.00 10.31 4INS 699 ATOM 466 C CYS C 7 3.090 10.422 -15.112 1.00 27.34 4INS 700 ATOM 467 O CYS C 7 3.601 9.455 -15.603 1.00 24.77 4INS 701 ATOM 468 CB CYS C 7 1.348 10.332 -13.329 1.00 17.60 4INS 702 ATOM 469 SG CYS C 7 0.602 8.899 -13.976 1.00 19.30 4INS 703 ATOM 470 N THR C 8 2.691 11.395 -15.898 1.00 22.31 4INS 704 ATOM 471 CA THR C 8 2.912 11.356 -17.382 1.00 20.22 4INS 705 ATOM 472 C THR C 8 4.408 11.508 -17.641 1.00 29.24 4INS 706 ATOM 473 O THR C 8 5.051 10.716 -18.357 1.00 25.27 4INS 707 ATOM 474 CB THR C 8 1.993 12.487 -17.997 1.00 28.76 4INS 708 ATOM 475 OG1 THR C 8 0.590 12.071 -17.868 1.00 41.93 4INS 709 ATOM 476 CG2 THR C 8 2.536 12.795 -19.370 1.00 39.10 4INS 710 ATOM 477 N SER C 9 4.957 12.549 -16.969 1.00 23.04 4INS 711 ATOM 478 CA SER C 9 6.438 12.783 -17.031 1.00 23.81 4INS 712 ATOM 479 C SER C 9 7.038 12.104 -15.796 1.00 22.97 4INS 713 ATOM 480 O SER C 9 6.505 11.080 -15.351 1.00 28.36 4INS 714 ATOM 481 CB SER C 9 6.838 14.249 -17.258 1.00 22.96 4INS 715 ATOM 482 OG SER C 9 8.266 14.523 -17.319 1.00 30.96 4INS 716 ATOM 483 N ILE C 10 8.157 12.532 -15.274 1.00 18.48 4INS 717 ATOM 484 CA ILE C 10 8.820 12.068 -14.116 1.00 14.18 4INS 718 ATOM 485 C ILE C 10 9.162 13.268 -13.217 1.00 22.06 4INS 719 ATOM 486 O ILE C 10 9.398 14.336 -13.807 1.00 21.37 4INS 720 ATOM 487 CB ILE C 10 10.082 11.219 -14.296 1.00 14.27 4INS 721 ATOM 488 CG1 ILE C 10 11.168 12.058 -14.941 1.00 17.05 4INS 722 ATOM 489 CG2 ILE C 10 9.771 9.901 -15.075 1.00 23.12 4INS 723 ATOM 490 CD1 ILE C 10 12.447 11.250 -15.181 1.00 28.86 4INS 724 ATOM 491 N CYS C 11 9.158 13.001 -11.897 1.00 17.73 4INS 725 ATOM 492 CA CYS C 11 9.488 14.178 -11.063 1.00 13.63 4INS 726 ATOM 493 C CYS C 11 10.301 13.733 -9.869 1.00 24.12 4INS 727 ATOM 494 O CYS C 11 10.238 12.518 -9.595 1.00 27.63 4INS 728 ATOM 495 CB CYS C 11 8.284 14.934 -10.501 1.00 24.47 4INS 729 ATOM 496 SG CYS C 11 7.324 13.971 -9.301 1.00 22.60 4INS 730 ATOM 497 N SER C 12 10.941 14.682 -9.241 1.00 20.64 4INS 731 ATOM 498 CA SER C 12 11.739 14.434 -8.095 1.00 19.89 4INS 732 ATOM 499 C SER C 12 10.978 14.612 -6.784 1.00 26.49 4INS 733 ATOM 500 O SER C 12 9.925 15.241 -6.843 1.00 17.94 4INS 734 ATOM 501 CB SER C 12 12.896 15.478 -7.911 1.00 22.13 4INS 735 ATOM 502 OG SER C 12 12.280 16.737 -7.889 1.00 25.55 4INS 736 ATOM 503 N LEU C 13 11.522 14.165 -5.654 1.00 20.92 4INS 737 ATOM 504 CA LEU C 13 10.827 14.392 -4.422 1.00 18.59 4INS 738 ATOM 505 C LEU C 13 10.811 15.860 -4.099 1.00 17.62 4INS 739 ATOM 506 O LEU C 13 9.865 16.311 -3.427 1.00 17.56 4INS 740 ATOM 507 CB LEU C 13 11.451 13.616 -3.261 1.00 24.54 4INS 741 ATOM 508 CG LEU C 13 11.620 12.137 -3.475 1.00 37.60 4INS 742 ATOM 509 CD1 LEU C 13 12.386 11.586 -2.273 1.00 42.94 4INS 743 ATOM 510 CD2 LEU C 13 10.209 11.562 -3.628 1.00 33.13 4INS 744 ATOM 511 N TYR C 14 11.756 16.642 -4.538 1.00 19.39 4INS 745 ATOM 512 CA TYR C 14 11.756 18.053 -4.253 1.00 15.81 4INS 746 ATOM 513 C TYR C 14 10.632 18.665 -4.977 1.00 16.49 4INS 747 ATOM 514 O TYR C 14 10.098 19.673 -4.517 1.00 24.31 4INS 748 ATOM 515 CB TYR C 14 13.061 18.792 -4.698 1.00 23.42 4INS 749 ATOM 516 CG TYR C 14 14.244 18.334 -3.884 1.00 22.68 4INS 750 ATOM 517 CD1 TYR C 14 14.406 18.926 -2.643 1.00 37.43 4INS 751 ATOM 518 CD2 TYR C 14 15.065 17.294 -4.321 1.00 44.52 4INS 752 ATOM 519 CE1 TYR C 14 15.469 18.455 -1.806 1.00 35.18 4INS 753 ATOM 520 CE2 TYR C 14 16.138 16.837 -3.558 1.00 39.13 4INS 754 ATOM 521 CZ TYR C 14 16.305 17.440 -2.304 1.00 43.24 4INS 755 ATOM 522 OH TYR C 14 17.325 17.067 -1.472 1.00 40.09 4INS 756 ATOM 523 N GLN C 15 10.286 18.189 -6.156 1.00 16.44 4INS 757 ATOM 524 CA GLN C 15 9.175 18.706 -6.908 1.00 13.87 4INS 758 ATOM 525 C GLN C 15 7.940 18.402 -6.108 1.00 16.45 4INS 759 ATOM 526 O GLN C 15 7.042 19.228 -6.047 1.00 17.74 4INS 760 ATOM 527 CB GLN C 15 9.021 18.202 -8.378 1.00 19.83 4INS 761 ATOM 528 CG GLN C 15 10.045 18.695 -9.368 1.00 27.50 4INS 762 ATOM 529 CD GLN C 15 10.002 18.165 -10.771 1.00 46.46 4INS 763 ATOM 530 OE1 GLN C 15 10.459 17.057 -11.040 1.00 43.32 4INS 764 ATOM 531 NE2 GLN C 15 9.456 18.889 -11.761 1.00 35.38 4INS 765 ATOM 532 N LEU C 16 7.841 17.245 -5.491 1.00 18.47 4INS 766 ATOM 533 CA LEU C 16 6.580 16.984 -4.783 1.00 17.18 4INS 767 ATOM 534 C LEU C 16 6.311 17.904 -3.587 1.00 21.36 4INS 768 ATOM 535 O LEU C 16 5.195 18.069 -3.079 1.00 17.34 4INS 769 ATOM 536 CB LEU C 16 6.542 15.527 -4.227 1.00 17.15 4INS 770 ATOM 537 CG LEU C 16 6.500 14.520 -5.341 1.00 17.53 4INS 771 ATOM 538 CD1 LEU C 16 6.477 13.227 -4.593 1.00 22.82 4INS 772 ATOM 539 CD2 LEU C 16 5.117 14.493 -5.991 1.00 18.33 4INS 773 ATOM 540 N GLU C 17 7.389 18.427 -3.040 1.00 16.47 4INS 774 ATOM 541 CA GLU C 17 7.353 19.319 -1.959 1.00 10.93 4INS 775 ATOM 542 C GLU C 17 6.614 20.601 -2.282 1.00 11.20 4INS 776 ATOM 543 O GLU C 17 6.183 21.310 -1.386 1.00 13.69 4INS 777 ATOM 544 CB GLU C 17 8.751 19.800 -1.584 1.00 16.99 4INS 778 ATOM 545 CG GLU C 17 9.178 18.980 -0.408 1.00 22.69 4INS 779 ATOM 546 CD GLU C 17 10.397 19.524 0.269 1.00 26.38 4INS 780 ATOM 547 OE1 GLU C 17 11.270 20.013 -0.342 1.00 26.42 4INS 781 ATOM 548 OE2 GLU C 17 10.358 19.291 1.470 1.00 30.68 4INS 782 ATOM 549 N ASN C 18 6.466 20.844 -3.575 1.00 13.42 4INS 783 ATOM 550 CA ASN C 18 5.707 22.010 -4.006 1.00 12.91 4INS 784 ATOM 551 C ASN C 18 4.238 21.879 -3.683 1.00 14.96 4INS 785 ATOM 552 O ASN C 18 3.506 22.887 -3.698 1.00 17.72 4INS 786 ATOM 553 CB ASN C 18 5.894 22.279 -5.506 1.00 16.62 4INS 787 ATOM 554 CG ASN C 18 7.286 22.755 -5.884 1.00 20.64 4INS 788 ATOM 555 OD1 ASN C 18 7.761 22.375 -6.927 1.00 28.41 4INS 789 ATOM 556 ND2 ASN C 18 7.836 23.540 -4.999 1.00 26.53 4INS 790 ATOM 557 N TYR C 19 3.683 20.717 -3.420 1.00 10.42 4INS 791 ATOM 558 CA TYR C 19 2.315 20.489 -3.110 1.00 11.44 4INS 792 ATOM 559 C TYR C 19 2.035 20.284 -1.641 1.00 19.22 4INS 793 ATOM 560 O TYR C 19 0.884 20.008 -1.330 1.00 14.52 4INS 794 ATOM 561 CB TYR C 19 1.745 19.342 -3.938 1.00 13.89 4INS 795 ATOM 562 CG TYR C 19 1.995 19.579 -5.394 1.00 13.15 4INS 796 ATOM 563 CD1 TYR C 19 1.176 20.360 -6.185 1.00 15.85 4INS 797 ATOM 564 CD2 TYR C 19 3.101 19.022 -6.004 1.00 20.18 4INS 798 ATOM 565 CE1 TYR C 19 1.482 20.590 -7.526 1.00 15.80 4INS 799 ATOM 566 CE2 TYR C 19 3.427 19.257 -7.342 1.00 20.94 4INS 800 ATOM 567 CZ TYR C 19 2.600 20.077 -8.111 1.00 16.07 4INS 801 ATOM 568 OH TYR C 19 2.930 20.198 -9.438 1.00 28.20 4INS 802 ATOM 569 N CYS C 20 2.997 20.523 -0.779 1.00 15.85 4INS 803 ATOM 570 CA CYS C 20 2.816 20.515 0.660 1.00 12.75 4INS 804 ATOM 571 C CYS C 20 2.167 21.848 1.080 1.00 18.29 4INS 805 ATOM 572 O CYS C 20 2.424 22.842 0.449 1.00 26.63 4INS 806 ATOM 573 CB CYS C 20 4.116 20.329 1.465 1.00 12.02 4INS 807 ATOM 574 SG CYS C 20 5.109 18.925 1.079 1.00 13.70 4INS 808 ATOM 575 N ASN C 21 1.396 21.899 2.168 1.00 17.14 4INS 809 ATOM 576 CA ASN C 21 0.814 23.152 2.677 1.00 22.18 4INS 810 ATOM 577 C ASN C 21 1.879 23.650 3.681 1.00 31.46 4INS 811 ATOM 578 O ASN C 21 2.937 22.991 3.836 1.00 44.77 4INS 812 ATOM 579 CB ASN C 21 -0.433 22.757 3.387 1.00 21.67 4INS 813 ATOM 580 CG ASN C 21 -1.636 22.166 2.742 1.00 29.17 4INS 814 ATOM 581 OD1 ASN C 21 -2.062 22.468 1.647 1.00 33.63 4INS 815 ATOM 582 ND2 ASN C 21 -2.242 21.277 3.591 1.00 33.21 4INS 816 ATOM 583 OXT ASN C 21 1.575 24.585 4.398 1.00 52.38 4INS 817 TER 584 ASN C 21 4INS 818 ATOM 585 N PHE D 1 18.330 11.816 -3.893 1.00 61.50 4INS 819 ATOM 586 CA PHE D 1 17.047 11.272 -4.371 1.00 28.75 4INS 820 ATOM 587 C PHE D 1 17.165 10.885 -5.854 1.00 45.77 4INS 821 ATOM 588 O PHE D 1 18.169 11.137 -6.550 1.00 49.85 4INS 822 ATOM 589 CB PHE D 1 15.801 12.158 -4.041 1.00 30.01 4INS 823 ATOM 590 CG PHE D 1 16.035 12.424 -2.571 1.00 31.74 4INS 824 ATOM 591 CD1 PHE D 1 15.980 11.294 -1.745 1.00 39.79 4INS 825 ATOM 592 CD2 PHE D 1 16.425 13.629 -2.070 1.00 31.26 4INS 826 ATOM 593 CE1 PHE D 1 16.219 11.375 -0.405 1.00 31.66 4INS 827 ATOM 594 CE2 PHE D 1 16.706 13.719 -0.715 1.00 53.74 4INS 828 ATOM 595 CZ PHE D 1 16.609 12.612 0.115 1.00 37.15 4INS 829 ATOM 596 N VAL D 2 16.117 10.168 -6.274 1.00 33.71 4INS 830 ATOM 597 CA VAL D 2 16.006 9.666 -7.682 1.00 24.16 4INS 831 ATOM 598 C VAL D 2 14.648 10.218 -8.239 1.00 33.79 4INS 832 ATOM 599 O VAL D 2 13.733 10.588 -7.495 1.00 22.26 4INS 833 ATOM 600 CB VAL D 2 16.128 8.144 -7.911 1.00 27.80 4INS 834 ATOM 601 CG1 VAL D 2 17.529 7.562 -7.875 1.00 85.81 4INS 835 ATOM 602 CG2 VAL D 2 15.239 7.326 -6.976 1.00 36.43 4INS 836 ATOM 603 N ASN D 3 14.564 10.315 -9.558 1.00 19.91 4INS 837 ATOM 604 CA ASN D 3 13.383 10.694 -10.332 1.00 33.51 4INS 838 ATOM 605 C ASN D 3 12.449 9.525 -10.618 1.00 27.19 4INS 839 ATOM 606 O ASN D 3 12.946 8.479 -11.058 1.00 35.28 4INS 840 ATOM 607 CB ASN D 3 13.966 11.427 -11.571 1.00 28.03 4INS 841 ATOM 608 CG ASN D 3 14.214 12.869 -11.102 1.00 36.72 4INS 842 ATOM 609 OD1 ASN D 3 14.425 13.813 -11.829 1.00 71.83 4INS 843 ATOM 610 ND2 ASN D 3 14.114 13.118 -9.791 1.00 69.95 4INS 844 ATOM 611 N GLN D 4 11.128 9.652 -10.488 1.00 38.71 4INS 845 ATOM 612 CA GLN D 4 10.224 8.523 -10.782 1.00 30.37 4INS 846 ATOM 613 C GLN D 4 8.833 8.864 -11.345 1.00 13.92 4INS 847 ATOM 614 O GLN D 4 8.361 10.028 -11.197 1.00 25.56 4INS 848 ATOM 615 CB GLN D 4 9.983 7.921 -9.378 1.00 38.84 4INS 849 ATOM 616 CG GLN D 4 10.350 6.429 -9.194 1.00 40.71 4INS 850 ATOM 617 CD GLN D 4 10.515 6.248 -7.678 1.00 31.29 4INS 851 ATOM 618 OE1 GLN D 4 10.490 5.138 -7.268 1.00 47.62 4INS 852 ATOM 619 NE2 GLN D 4 10.697 7.383 -6.997 1.00 30.17 4INS 853 ATOM 620 N HIS D 5 8.291 7.803 -12.000 1.00 13.87 1 4INS 854 ATOM 621 CA HIS D 5 6.903 7.919 -12.482 1.00 16.22 1 4INS 855 ATOM 622 C HIS D 5 6.029 7.528 -11.252 1.00 16.61 1 4INS 856 ATOM 623 O HIS D 5 6.115 6.355 -10.822 1.00 21.77 1 4INS 857 ATOM 624 CB HIS D 5 6.427 7.086 -13.663 1.00 24.93 1 4INS 858 ATOM 625 CG HIS D 5 6.855 7.450 -15.054 1.00 32.91 1 4INS 859 ATOM 626 ND1 HIS D 5 6.285 8.338 -15.876 1.00 31.86 1 4INS 860 ATOM 627 CD2 HIS D 5 7.867 6.966 -15.822 1.00 31.55 1 4INS 861 ATOM 628 CE1 HIS D 5 6.866 8.458 -17.068 1.00 28.50 1 4INS 862 ATOM 629 NE2 HIS D 5 7.838 7.587 -17.015 1.00 18.29 1 4INS 863 ATOM 630 N LEU D 6 5.259 8.452 -10.792 1.00 11.23 4INS 864 ATOM 631 CA LEU D 6 4.302 8.232 -9.639 1.00 22.97 4INS 865 ATOM 632 C LEU D 6 2.854 8.566 -10.048 1.00 11.62 4INS 866 ATOM 633 O LEU D 6 2.505 9.705 -10.335 1.00 14.32 4INS 867 ATOM 634 CB LEU D 6 4.815 9.069 -8.489 1.00 16.96 4INS 868 ATOM 635 CG LEU D 6 6.253 8.828 -7.985 1.00 18.92 4INS 869 ATOM 636 CD1 LEU D 6 6.828 10.000 -7.271 1.00 17.11 4INS 870 ATOM 637 CD2 LEU D 6 6.091 7.594 -7.091 1.00 18.40 4INS 871 ATOM 638 N CYS D 7 2.119 7.495 -10.108 1.00 13.70 4INS 872 ATOM 639 CA CYS D 7 0.745 7.570 -10.582 1.00 18.64 4INS 873 ATOM 640 C CYS D 7 -0.288 7.058 -9.564 1.00 17.90 4INS 874 ATOM 641 O CYS D 7 0.016 6.138 -8.853 1.00 18.63 4INS 875 ATOM 642 CB CYS D 7 0.607 6.723 -11.869 1.00 31.44 4INS 876 ATOM 643 SG CYS D 7 1.635 7.350 -13.232 1.00 20.00 4INS 877 ATOM 644 N GLY D 8 -1.440 7.618 -9.644 1.00 19.94 4INS 878 ATOM 645 CA GLY D 8 -2.580 7.155 -8.776 1.00 18.12 4INS 879 ATOM 646 C GLY D 8 -2.305 7.237 -7.329 1.00 8.63 4INS 880 ATOM 647 O GLY D 8 -1.914 8.246 -6.763 1.00 13.74 4INS 881 ATOM 648 N SER D 9 -2.656 6.101 -6.694 1.00 11.65 4INS 882 ATOM 649 CA SER D 9 -2.483 5.997 -5.227 1.00 12.39 4INS 883 ATOM 650 C SER D 9 -1.032 6.120 -4.867 1.00 8.86 4INS 884 ATOM 651 O SER D 9 -0.760 6.584 -3.762 1.00 9.84 4INS 885 ATOM 652 CB SER D 9 -3.264 4.870 -4.568 1.00 14.54 4INS 886 ATOM 653 OG SER D 9 -2.756 3.670 -5.015 1.00 21.40 4INS 887 ATOM 654 N HIS D 10 -0.166 5.698 -5.800 1.00 8.91 4INS 888 ATOM 655 CA HIS D 10 1.270 5.805 -5.452 1.00 9.09 4INS 889 ATOM 656 C HIS D 10 1.755 7.227 -5.317 1.00 7.69 4INS 890 ATOM 657 O HIS D 10 2.658 7.481 -4.533 1.00 11.42 4INS 891 ATOM 658 CB HIS D 10 2.092 5.048 -6.482 1.00 8.95 4INS 892 ATOM 659 CG HIS D 10 1.707 3.599 -6.615 1.00 8.78 4INS 893 ATOM 660 ND1 HIS D 10 1.951 2.701 -5.608 1.00 13.99 4INS 894 ATOM 661 CD2 HIS D 10 1.059 2.922 -7.561 1.00 11.16 4INS 895 ATOM 662 CE1 HIS D 10 1.536 1.530 -6.025 1.00 8.79 4INS 896 ATOM 663 NE2 HIS D 10 0.948 1.628 -7.159 1.00 11.60 4INS 897 ATOM 664 N LEU D 11 1.142 8.109 -6.070 1.00 9.45 4INS 898 ATOM 665 CA LEU D 11 1.434 9.541 -6.053 1.00 9.98 4INS 899 ATOM 666 C LEU D 11 1.027 10.155 -4.729 1.00 5.91 4INS 900 ATOM 667 O LEU D 11 1.818 10.853 -4.084 1.00 8.83 4INS 901 ATOM 668 CB LEU D 11 0.815 10.196 -7.249 1.00 10.51 4INS 902 ATOM 669 CG LEU D 11 1.105 11.705 -7.294 1.00 13.37 4INS 903 ATOM 670 CD1 LEU D 11 2.526 12.118 -7.270 1.00 11.72 4INS 904 ATOM 671 CD2 LEU D 11 0.404 12.225 -8.564 1.00 16.22 4INS 905 ATOM 672 N VAL D 12 -0.188 9.816 -4.302 1.00 6.52 4INS 906 ATOM 673 CA VAL D 12 -0.534 10.395 -2.938 1.00 6.95 4INS 907 ATOM 674 C VAL D 12 0.207 9.758 -1.787 1.00 5.55 4INS 908 ATOM 675 O VAL D 12 0.467 10.475 -0.761 1.00 8.60 4INS 909 ATOM 676 CB VAL D 12 -2.065 10.355 -2.790 1.00 14.07 4INS 910 ATOM 677 CG1 VAL D 12 -2.687 11.271 -3.899 1.00 16.64 4INS 911 ATOM 678 CG2 VAL D 12 -2.636 9.010 -2.737 1.00 14.03 4INS 912 ATOM 679 N GLU D 13 0.681 8.537 -1.890 1.00 8.61 4INS 913 ATOM 680 CA GLU D 13 1.509 7.903 -0.866 1.00 7.25 4INS 914 ATOM 681 C GLU D 13 2.815 8.631 -0.804 1.00 7.02 4INS 915 ATOM 682 O GLU D 13 3.403 8.915 0.274 1.00 11.89 4INS 916 ATOM 683 CB GLU D 13 1.770 6.435 -1.051 1.00 11.84 4INS 917 ATOM 684 CG GLU D 13 0.458 5.634 -0.816 1.00 27.71 4INS 918 ATOM 685 CD GLU D 13 0.536 4.937 0.492 1.00 62.15 4INS 919 ATOM 686 OE1 GLU D 13 1.499 5.094 1.172 1.00 28.12 4INS 920 ATOM 687 OE2 GLU D 13 -0.429 4.258 0.726 1.00 32.97 4INS 921 ATOM 688 N ALA D 14 3.390 9.046 -1.888 1.00 9.58 4INS 922 ATOM 689 CA ALA D 14 4.643 9.836 -1.985 1.00 10.00 4INS 923 ATOM 690 C ALA D 14 4.495 11.218 -1.377 1.00 10.02 4INS 924 ATOM 691 O ALA D 14 5.373 11.718 -0.675 1.00 12.34 4INS 925 ATOM 692 CB ALA D 14 5.151 9.884 -3.366 1.00 9.23 4INS 926 ATOM 693 N LEU D 15 3.340 11.836 -1.609 1.00 8.93 4INS 927 ATOM 694 CA LEU D 15 3.014 13.121 -1.053 1.00 9.97 4INS 928 ATOM 695 C LEU D 15 2.968 13.020 0.450 1.00 16.23 4INS 929 ATOM 696 O LEU D 15 3.462 13.914 1.142 1.00 11.63 4INS 930 ATOM 697 CB LEU D 15 1.719 13.654 -1.641 1.00 9.29 4INS 931 ATOM 698 CG LEU D 15 1.771 14.340 -2.979 1.00 10.49 4INS 932 ATOM 699 CD1 LEU D 15 0.398 14.483 -3.556 1.00 12.99 4INS 933 ATOM 700 CD2 LEU D 15 2.487 15.663 -2.804 1.00 15.87 4INS 934 ATOM 701 N TYR D 16 2.313 11.974 0.938 1.00 11.90 4INS 935 ATOM 702 CA TYR D 16 2.254 11.791 2.374 1.00 8.84 4INS 936 ATOM 703 C TYR D 16 3.662 11.710 3.013 1.00 11.08 4INS 937 ATOM 704 O TYR D 16 3.925 12.338 4.014 1.00 10.74 4INS 938 ATOM 705 CB TYR D 16 1.409 10.545 2.657 1.00 8.68 4INS 939 ATOM 706 CG TYR D 16 1.408 10.201 4.150 1.00 9.93 4INS 940 ATOM 707 CD1 TYR D 16 0.611 10.879 5.091 1.00 13.27 4INS 941 ATOM 708 CD2 TYR D 16 2.232 9.163 4.535 1.00 10.34 4INS 942 ATOM 709 CE1 TYR D 16 0.657 10.554 6.429 1.00 11.61 4INS 943 ATOM 710 CE2 TYR D 16 2.284 8.846 5.894 1.00 17.86 4INS 944 ATOM 711 CZ TYR D 16 1.518 9.554 6.843 1.00 13.23 4INS 945 ATOM 712 OH TYR D 16 1.664 9.081 8.177 1.00 17.80 4INS 946 ATOM 713 N LEU D 17 4.478 10.910 2.349 1.00 9.51 4INS 947 ATOM 714 CA LEU D 17 5.832 10.719 2.819 1.00 8.94 4INS 948 ATOM 715 C LEU D 17 6.672 11.993 2.879 1.00 12.51 4INS 949 ATOM 716 O LEU D 17 7.395 12.298 3.853 1.00 13.68 4INS 950 ATOM 717 CB LEU D 17 6.573 9.683 1.932 1.00 12.01 4INS 951 ATOM 718 CG LEU D 17 7.784 9.141 2.606 1.00 26.75 4INS 952 ATOM 719 CD1 LEU D 17 7.274 8.084 3.613 1.00 27.77 4INS 953 ATOM 720 CD2 LEU D 17 8.533 8.579 1.435 1.00 36.25 4INS 954 ATOM 721 N VAL D 18 6.585 12.773 1.784 1.00 10.21 4INS 955 ATOM 722 CA VAL D 18 7.348 14.029 1.661 1.00 8.25 4INS 956 ATOM 723 C VAL D 18 6.789 15.099 2.514 1.00 8.01 4INS 957 ATOM 724 O VAL D 18 7.560 15.914 3.063 1.00 14.17 4INS 958 ATOM 725 CB VAL D 18 7.357 14.409 0.176 1.00 8.24 4INS 959 ATOM 726 CG1 VAL D 18 7.683 15.903 0.006 1.00 18.17 4INS 960 ATOM 727 CG2 VAL D 18 8.321 13.582 -0.638 1.00 16.22 4INS 961 ATOM 728 N CYS D 19 5.469 15.224 2.565 1.00 8.80 4INS 962 ATOM 729 CA CYS D 19 4.790 16.327 3.282 1.00 10.89 4INS 963 ATOM 730 C CYS D 19 4.550 16.076 4.760 1.00 20.41 4INS 964 ATOM 731 O CYS D 19 4.771 17.057 5.513 1.00 17.13 4INS 965 ATOM 732 CB CYS D 19 3.540 16.778 2.546 1.00 11.71 4INS 966 ATOM 733 SG CYS D 19 3.846 17.363 0.909 1.00 11.30 4INS 967 ATOM 734 N GLY D 20 4.108 14.860 5.076 1.00 15.31 4INS 968 ATOM 735 CA GLY D 20 3.841 14.553 6.475 1.00 18.10 4INS 969 ATOM 736 C GLY D 20 2.815 15.479 7.177 1.00 19.19 4INS 970 ATOM 737 O GLY D 20 1.669 15.721 6.770 1.00 18.72 4INS 971 ATOM 738 N GLU D 21 3.387 15.976 8.278 1.00 20.01 4INS 972 ATOM 739 CA GLU D 21 2.659 16.847 9.244 1.00 23.69 4INS 973 ATOM 740 C GLU D 21 2.237 18.138 8.636 1.00 19.09 4INS 974 ATOM 741 O GLU D 21 1.246 18.727 9.109 1.00 24.03 4INS 975 ATOM 742 CB GLU D 21 3.575 17.120 10.476 1.00 22.76 4INS 976 ATOM 743 CG GLU D 21 5.075 17.192 10.046 1.00 53.97 4INS 977 ATOM 744 CD GLU D 21 6.099 17.832 10.938 1.00 78.86 4INS 978 ATOM 745 OE1 GLU D 21 5.775 18.563 11.863 1.00 73.07 4INS 979 ATOM 746 OE2 GLU D 21 7.300 17.568 10.645 1.00 72.74 4INS 980 ATOM 747 N ARG D 22 2.952 18.555 7.566 1.00 15.75 2 4INS 981 ATOM 748 CA ARG D 22 2.552 19.835 6.996 1.00 21.02 2 4INS 982 ATOM 749 C ARG D 22 1.231 19.779 6.228 1.00 30.79 2 4INS 983 ATOM 750 O ARG D 22 0.558 20.740 5.955 1.00 22.02 2 4INS 984 ATOM 751 CB ARG D 22 3.613 20.247 5.973 1.00 29.63 2 4INS 985 ATOM 752 CG ARG D 22 5.024 20.325 6.450 1.00 16.81 2 4INS 986 ATOM 753 CD ARG D 22 5.952 20.706 5.309 1.00 32.57 2 4INS 987 ATOM 754 NE AARG D 22 5.584 21.912 4.554 0.50 23.88 2 4INS 988 ATOM 755 NE BARG D 22 6.663 19.518 4.827 0.50 24.77 2 4INS 989 ATOM 756 CZ AARG D 22 6.163 22.431 3.476 0.50 34.85 2 4INS 990 ATOM 757 CZ BARG D 22 7.482 19.684 3.777 0.50 11.98 2 4INS 991 ATOM 758 NH1AARG D 22 5.819 23.547 2.845 0.50 18.26 2 4INS 992 ATOM 759 NH1BARG D 22 7.547 20.848 3.124 0.50 20.33 2 4INS 993 ATOM 760 NH2AARG D 22 7.190 21.770 2.930 0.50 20.80 2 4INS 994 ATOM 761 NH2BARG D 22 8.196 18.646 3.318 0.50 20.38 2 4INS 995 ATOM 762 N GLY D 23 0.914 18.555 5.836 1.00 17.93 4INS 996 ATOM 763 CA GLY D 23 -0.237 18.302 4.993 1.00 12.82 4INS 997 ATOM 764 C GLY D 23 0.087 18.615 3.514 1.00 11.10 4INS 998 ATOM 765 O GLY D 23 1.137 19.102 3.198 1.00 12.49 4INS 999 ATOM 766 N PHE D 24 -0.903 18.290 2.672 1.00 11.76 4INS1000 ATOM 767 CA PHE D 24 -0.774 18.497 1.209 1.00 10.21 4INS1001 ATOM 768 C PHE D 24 -2.127 18.688 0.607 1.00 9.65 4INS1002 ATOM 769 O PHE D 24 -3.142 18.470 1.204 1.00 12.03 4INS1003 ATOM 770 CB PHE D 24 0.000 17.400 0.578 1.00 9.41 4INS1004 ATOM 771 CG PHE D 24 -0.602 16.046 0.597 1.00 9.89 4INS1005 ATOM 772 CD1 PHE D 24 -0.280 15.182 1.648 1.00 12.55 4INS1006 ATOM 773 CD2 PHE D 24 -1.497 15.657 -0.387 1.00 12.14 4INS1007 ATOM 774 CE1 PHE D 24 -0.863 13.901 1.714 1.00 19.03 4INS1008 ATOM 775 CE2 PHE D 24 -2.049 14.388 -0.368 1.00 12.53 4INS1009 ATOM 776 CZ PHE D 24 -1.737 13.486 0.692 1.00 14.23 4INS1010 ATOM 777 N PHE D 25 -2.070 19.069 -0.693 1.00 8.89 1 4INS1011 ATOM 778 CA PHE D 25 -3.285 19.185 -1.469 1.00 10.03 1 4INS1012 ATOM 779 C PHE D 25 -3.021 18.326 -2.724 1.00 9.84 1 4INS1013 ATOM 780 O PHE D 25 -1.918 18.362 -3.216 1.00 12.46 1 4INS1014 ATOM 781 CB PHE D 25 -3.686 20.613 -1.925 1.00 10.32 1 4INS1015 ATOM 782 CG PHE D 25 -2.567 21.455 -2.479 1.00 14.41 1 4INS1016 ATOM 783 CD1 PHE D 25 -2.435 21.546 -3.850 1.00 20.47 1 4INS1017 ATOM 784 CD2 PHE D 25 -1.647 22.070 -1.651 1.00 14.83 1 4INS1018 ATOM 785 CE1 PHE D 25 -1.470 22.321 -4.476 1.00 13.89 1 4INS1019 ATOM 786 CE2 PHE D 25 -0.608 22.775 -2.271 1.00 20.12 1 4INS1020 ATOM 787 CZ PHE D 25 -0.558 22.880 -3.660 1.00 13.72 1 4INS1021 ATOM 788 N TYR D 26 -3.962 17.546 -3.192 1.00 10.77 4INS1022 ATOM 789 CA TYR D 26 -3.915 16.723 -4.366 1.00 8.24 4INS1023 ATOM 790 C TYR D 26 -5.013 17.222 -5.272 1.00 11.68 4INS1024 ATOM 791 O TYR D 26 -6.209 17.040 -5.011 1.00 12.49 4INS1025 ATOM 792 CB TYR D 26 -4.090 15.233 -4.010 1.00 12.91 4INS1026 ATOM 793 CG TYR D 26 -4.159 14.291 -5.196 1.00 13.37 4INS1027 ATOM 794 CD1 TYR D 26 -5.379 13.599 -5.455 1.00 12.94 4INS1028 ATOM 795 CD2 TYR D 26 -3.138 14.227 -6.110 1.00 12.82 4INS1029 ATOM 796 CE1 TYR D 26 -5.491 12.770 -6.582 1.00 18.45 4INS1030 ATOM 797 CE2 TYR D 26 -3.289 13.401 -7.216 1.00 14.97 4INS1031 ATOM 798 CZ TYR D 26 -4.390 12.679 -7.390 1.00 16.87 4INS1032 ATOM 799 OH TYR D 26 -4.411 11.849 -8.508 1.00 19.99 4INS1033 ATOM 800 N THR D 27 -4.639 17.933 -6.342 1.00 10.07 4INS1034 ATOM 801 CA THR D 27 -5.563 18.555 -7.264 1.00 15.24 4INS1035 ATOM 802 C THR D 27 -5.254 18.120 -8.704 1.00 16.10 4INS1036 ATOM 803 O THR D 27 -4.545 18.821 -9.404 1.00 18.07 4INS1037 ATOM 804 CB THR D 27 -5.536 20.114 -7.048 1.00 16.40 4INS1038 ATOM 805 OG1 THR D 27 -4.178 20.585 -6.974 1.00 19.93 4INS1039 ATOM 806 CG2 THR D 27 -6.181 20.408 -5.709 1.00 16.74 4INS1040 ATOM 807 N PRO D 28 -5.766 17.035 -9.149 1.00 11.98 4INS1041 ATOM 808 CA PRO D 28 -5.571 16.537 -10.477 1.00 13.55 4INS1042 ATOM 809 C PRO D 28 -6.105 17.452 -11.585 1.00 21.64 4INS1043 ATOM 810 O PRO D 28 -5.471 17.453 -12.635 1.00 24.41 4INS1044 ATOM 811 CB PRO D 28 -6.192 15.163 -10.479 1.00 21.77 4INS1045 ATOM 812 CG PRO D 28 -6.813 14.874 -9.135 1.00 18.60 4INS1046 ATOM 813 CD PRO D 28 -6.687 16.109 -8.343 1.00 11.54 4INS1047 ATOM 814 N LYS D 29 -7.103 18.249 -11.346 1.00 18.85 2 4INS1048 ATOM 815 CA LYS D 29 -7.647 19.148 -12.397 1.00 21.86 2 4INS1049 ATOM 816 C LYS D 29 -6.782 20.313 -12.719 1.00 25.32 2 4INS1050 ATOM 817 O LYS D 29 -6.944 20.988 -13.739 1.00 28.67 2 4INS1051 ATOM 818 CB LYS D 29 -9.020 19.689 -11.971 1.00 37.73 2 4INS1052 ATOM 819 CG ALYS D 29 -10.118 18.678 -12.365 0.50 26.24 2 4INS1053 ATOM 820 CG BLYS D 29 -9.869 20.535 -12.933 0.50 51.60 2 4INS1054 ATOM 821 CD ALYS D 29 -11.261 18.659 -11.369 0.50 25.69 2 4INS1055 ATOM 822 CD BLYS D 29 -11.094 21.211 -12.318 0.50 26.73 2 4INS1056 ATOM 823 CE ALYS D 29 -12.315 17.615 -11.670 0.50 22.42 2 4INS1057 ATOM 824 CE BLYS D 29 -12.076 21.723 -13.345 0.50 41.26 2 4INS1058 ATOM 825 NZ ALYS D 29 -13.648 18.108 -11.182 0.50 39.07 2 4INS1059 ATOM 826 NZ BLYS D 29 -13.280 22.348 -12.731 0.50 38.89 2 4INS1060 ATOM 827 N ALA D 30 -5.874 20.552 -11.809 1.00 17.54 4INS1061 ATOM 828 CA ALA D 30 -5.022 21.709 -11.876 1.00 15.58 4INS1062 ATOM 829 C ALA D 30 -3.942 21.534 -12.910 1.00 35.26 4INS1063 ATOM 830 O ALA D 30 -3.347 20.451 -13.025 1.00 37.09 4INS1064 ATOM 831 CB ALA D 30 -4.392 21.978 -10.527 1.00 32.86 4INS1065 ATOM 832 OXT ALA D 30 -3.822 22.597 -13.538 1.00 43.22 4INS1066 TER 833 ALA D 30 4INS1067 HETATM 834 ZN ZN 1 -0.002 -0.004 7.891 0.33 10.40 4INS1068 HETATM 835 ZN ZN 2 0.000 0.000 -8.039 0.33 11.00 4INS1069 HETATM 836 O HOH 18 1.208 0.917 -0.239 1.00 44.11 4INSB 5 HETATM 837 O HOH 19 26.674 0.029 0.118 1.00 64.26 4INSB 6 HETATM 838 O HOH 11 13.443 19.181 0.629 1.00 37.18 4INSB 7 HETATM 839 O HOH 12 8.897 22.662 0.833 0.50 72.59 4INSB 8 HETATM 840 O HOH 13 5.430 4.632 0.353 0.50 68.27 4INSB 9 HETATM 841 O HOH 14 -9.600 22.800 0.340 1.00 86.30 4INSB 10 HETATM 842 O HOH 21 -11.312 20.399 0.771 1.00 76.27 4INSB 11 HETATM 843 O HOH 22 10.779 23.846 1.400 0.50 44.66 4INSB 12 HETATM 844 O HOH 15 14.910 22.149 0.107 1.00 64.34 4INSB 13 HETATM 845 O HOH 41 3.208 6.718 2.188 1.00 44.14 4INSB 14 HETATM 846 O HOH 42 15.228 18.988 2.773 1.00 47.95 4INSB 15 HETATM 847 O HOH 51 1.948 1.415 2.000 1.00 83.31 4INSB 16 HETATM 848 O HOH 52 -12.203 23.328 2.558 0.50 53.25 4INSB 17 HETATM 849 O HOH 61 3.289 3.437 2.898 1.00 29.40 4INSB 18 HETATM 850 O HOH 62 -0.463 3.085 2.990 1.00 35.04 4INSB 19 HETATM 851 O HOH 63 10.311 22.834 3.121 0.50 53.93 4INSB 20 HETATM 852 O HOH 64 10.183 24.198 3.002 0.50 47.80 4INSB 21 HETATM 853 O HOH 65 23.650 0.900 3.000 1.00 86.30 4INSB 22 HETATM 854 O HOH 71 8.256 18.783 3.330 0.50 14.13 4INSB 23 HETATM 855 O HOH 72 0.000 0.000 2.721 0.33 78.41 4INSB 24 HETATM 856 O HOH 73 -5.500 23.300 3.200 1.00 76.30 4INSB 25 HETATM 857 O HOH 74 -10.414 20.888 3.177 1.00 46.17 4INSB 26 HETATM 858 O HOH 75 5.786 4.688 3.296 1.00 79.24 4INSB 27 HETATM 859 O HOH 76 12.041 23.097 3.478 0.50 31.09 4INSB 28 HETATM 860 O HOH 101 -9.400 22.600 4.900 0.50 52.19 4INSB 29 HETATM 861 O HOH 91 9.781 13.999 4.347 1.00 38.00 4INSB 30 HETATM 862 O HOH 92 14.487 19.051 4.963 0.50 47.26 4INSB 31 HETATM 863 O HOH 94 24.800 2.000 4.680 1.00 50.76 4INSB 32 HETATM 864 O HOH 95 1.700 14.430 4.490 1.00 60.70 4INSB 33 HETATM 865 O HOH 111 -7.624 16.101 5.527 1.00 25.99 4INSB 34 HETATM 866 O HOH 113 4.071 5.888 5.145 1.00 60.78 4INSB 35 HETATM 867 O HOH 112 12.744 23.426 5.604 0.50 40.09 4INSB 36 HETATM 868 O HOH 115 10.354 21.084 4.829 0.50 33.95 4INSB 37 HETATM 869 O HOH 116 12.369 21.475 5.162 0.50 61.18 4INSB 38 HETATM 870 O HOH 122 -9.100 20.600 6.100 0.50 60.00 4INSB 39 HETATM 871 O HOH 123 -8.768 19.973 5.675 0.50 51.16 4INSB 40 HETATM 872 O HOH 131 13.100 13.724 6.029 1.00 21.23 4INSB 41 HETATM 873 O HOH 132 -7.991 22.590 6.582 0.50 57.20 4INSB 42 HETATM 874 O HOH 133 -12.272 21.574 6.100 1.00 55.48 4INSB 43 HETATM 875 O HOH 141 -2.652 21.385 6.534 1.00 64.51 4INSB 44 HETATM 876 O HOH 142 25.212 3.031 6.890 1.00 49.58 4INSB 45 HETATM 877 O HOH 143 10.600 10.100 6.800 0.50 50.00 4INSB 46 HETATM 878 O HOH 146 11.191 23.508 6.621 0.50 37.26 4INSB 47 HETATM 879 O HOH 151 14.528 16.192 7.070 1.00 51.29 4INSB 48 HETATM 880 O HOH 152 1.492 23.231 6.892 0.50 27.00 4INSB 49 HETATM 881 O HOH 155 14.502 18.882 6.980 0.50 59.76 4INSB 50 HETATM 882 O HOH 156 -9.484 23.456 7.067 0.50 41.28 4INSB 51 HETATM 883 O HOH 161 1.228 4.480 7.603 1.00 34.42 4INSB 52 HETATM 884 O HOH 162 0.172 22.959 7.609 0.50 29.31 4INSB 53 HETATM 885 O HOH 164 10.079 20.626 8.067 0.52 33.28 4INSB 54 HETATM 886 O HOH 144 8.276 22.353 6.634 0.50 71.16 4INSB 55 HETATM 887 O HOH 145 11.200 20.200 6.480 0.50 43.34 4INSB 56 HETATM 888 O HOH 147 9.467 21.709 6.550 0.50 60.52 4INSB 57 HETATM 889 O HOH 165 12.911 19.857 7.882 0.50 41.83 4INSB 58 HETATM 890 O HOH 166 5.794 10.580 7.551 0.50 18.82 4INSB 59 HETATM 891 O HOH 171 -3.193 10.008 8.356 1.00 20.33 4INSB 60 HETATM 892 O HOH 172 3.540 5.186 7.514 1.00 50.94 4INSB 61 HETATM 893 O HOH 173 -2.600 23.100 8.100 0.50 37.93 4INSB 62 HETATM 894 O HOH 174 24.906 0.400 8.400 1.00 59.59 4INSB 63 HETATM 895 O HOH 175 22.962 4.308 7.971 1.00 75.46 4INSB 64 HETATM 896 O HOH 181 3.162 12.350 8.534 1.00 60.02 4INSB 65 HETATM 897 O HOH 182 6.986 23.533 8.608 0.50 42.86 4INSB 66 HETATM 898 O HOH 183 7.747 22.514 8.736 0.50 51.31 4INSB 67 HETATM 899 O HOH 184 11.350 23.300 9.170 1.00 50.61 4INSB 68 HETATM 900 O HOH 185 13.640 22.500 8.400 1.00 61.09 4INSB 69 HETATM 901 O HOH 186 5.402 7.037 8.018 1.00 76.30 4INSB 70 HETATM 902 O HOH 191 -9.443 20.240 8.418 0.50 46.63 4INSB 71 HETATM 903 O HOH 193 21.200 5.800 9.000 1.00 46.66 4INSB 72 HETATM 904 O HOH 194 -0.559 20.337 9.227 0.50 31.04 4INSB 73 HETATM 905 O HOH 201 -0.074 1.535 9.452 1.00 25.97 4INSB 74 HETATM 906 O HOH 202 18.617 13.600 9.207 1.00 29.22 4INSB 75 HETATM 907 O HOH 203 0.475 13.399 9.000 1.00 61.33 4INSB 76 HETATM 908 O HOH 204 2.956 7.356 9.255 1.00 37.48 4INSB 77 HETATM 909 O HOH 205 11.907 19.705 9.066 0.50 35.59 4INSB 78 HETATM 910 O HOH 206 16.094 16.073 9.932 1.00 48.31 4INSB 79 HETATM 911 O HOH 207 -8.473 18.398 9.204 0.50 34.10 4INSB 80 HETATM 912 O HOH 208 13.482 17.840 9.054 1.00 66.41 4INSB 81 HETATM 913 O HOH 209 4.832 22.586 10.697 0.50 41.13 4INSB 82 HETATM 914 O HOH 211 4.499 3.274 9.801 1.00 21.36 4INSB 83 HETATM 915 O HOH 212 9.900 19.400 10.176 1.00 57.45 4INSB 84 HETATM 916 O HOH 213 -9.814 18.858 10.172 0.50 36.40 4INSB 85 HETATM 917 O HOH 214 8.300 21.900 9.770 0.50 62.82 4INSB 86 HETATM 918 O HOH 215 5.531 14.046 9.251 1.00 53.12 4INSB 87 HETATM 919 O HOH 216 0.500 21.500 9.739 0.50 40.73 4INSB 88 HETATM 920 O HOH 217 24.500 1.839 10.261 0.50 44.08 4INSB 89 HETATM 921 O HOH 218 14.403 20.407 9.881 0.25 62.98 4INSB 90 HETATM 922 O HOH 219 -9.503 22.800 10.020 0.50 40.98 4INSB 91 HETATM 923 O HOH 221 -5.367 9.301 10.390 1.00 29.29 4INSB 92 HETATM 924 O HOH 222 5.099 10.014 10.625 1.00 39.93 4INSB 93 HETATM 925 O HOH 223 3.402 22.242 10.235 0.50 38.69 4INSB 94 HETATM 926 O HOH 224 0.786 10.457 10.424 0.50 27.11 4INSB 95 HETATM 927 O HOH 225 7.099 20.641 10.117 0.50 45.52 4INSB 96 HETATM 928 O HOH 226 1.327 5.583 11.052 0.50 56.24 4INSB 97 HETATM 929 O HOH 227 -11.769 21.611 10.650 0.50 56.71 4INSB 98 HETATM 930 O HOH 228 -9.990 20.281 10.186 0.50 56.19 4INSB 99 HETATM 931 O HOH 231 -7.077 17.022 11.114 1.00 24.63 4INSB100 HETATM 932 O HOH 232 10.114 17.143 11.048 1.00 38.47 4INSB101 HETATM 933 O HOH 233 13.122 21.921 10.724 0.50 29.51 4INSB102 HETATM 934 O HOH 234 -8.631 22.492 11.840 0.50 43.99 4INSB103 HETATM 935 O HOH 235 24.000 4.400 11.200 1.00 63.61 4INSB104 HETATM 936 O HOH 241 0.397 4.490 11.763 0.50 23.04 4INSB105 HETATM 937 O HOH 242 20.200 6.301 11.100 1.00 62.49 4INSB106 HETATM 938 O HOH 243 -6.059 22.694 11.492 1.00 61.96 4INSB107 HETATM 939 O HOH 244 2.400 0.700 11.139 1.00 63.37 4INSB108 HETATM 940 O HOH 245 1.431 8.001 10.900 1.00 61.63 4INSB109 HETATM 941 O HOH 246 0.589 13.731 11.296 0.50 60.20 4INSB110 HETATM 942 O HOH 247 -0.216 11.635 11.240 0.50 52.12 4INSB111 HETATM 943 O HOH 249 3.575 13.150 11.256 0.50 53.19 4INSB112 HETATM 944 O HOH 251 -0.023 -0.033 11.206 0.33 21.05 4INSB113 HETATM 945 O HOH 252 3.725 4.937 12.004 1.00 60.07 4INSB114 HETATM 946 O HOH 253 9.246 23.287 11.503 1.00 45.58 4INSB115 HETATM 947 O HOH 254 -5.435 20.514 11.630 1.00 51.70 4INSB116 HETATM 948 O HOH 255 13.713 17.818 11.963 1.00 66.30 4INSB117 HETATM 949 O HOH 256 16.125 12.139 12.240 1.00 50.03 4INSB118 HETATM 950 O HOH 257 12.904 20.420 11.491 0.50 30.82 4INSB119 HETATM 951 O HOH 258 22.345 2.177 11.300 1.00 53.69 4INSB120 HETATM 952 O HOH 261 -1.821 8.684 12.044 1.00 36.04 4INSB121 HETATM 953 O HOH 262 2.938 2.622 12.785 1.00 45.76 4INSB122 HETATM 954 O HOH 263 3.200 8.800 12.200 1.00 58.95 4INSB123 HETATM 955 O HOH 271 6.250 11.753 12.889 1.00 50.22 4INSB124 HETATM 956 O HOH 272 12.623 23.254 12.208 0.50 36.01 4INSB125 HETATM 957 O HOH 273 6.083 21.437 12.952 0.50 45.78 4INSB126 HETATM 958 O HOH 274 15.629 16.552 13.704 1.00 54.02 4INSB127 HETATM 959 O HOH 275 4.207 21.400 12.867 0.50 42.63 4INSB128 HETATM 960 O HOH 276 1.400 10.000 13.205 1.00 62.38 4INSB129 HETATM 961 O HOH 281 -1.805 14.585 13.563 1.00 36.72 4INSB130 HETATM 962 O HOH 282 10.681 15.953 13.460 1.00 37.62 4INSB131 HETATM 963 O HOH 283 3.526 17.510 13.393 0.50 58.59 4INSB132 HETATM 964 O HOH 284 4.989 14.660 13.137 1.00 52.11 4INSB133 HETATM 965 O HOH 285 -3.059 10.929 12.998 0.50 54.91 4INSB134 HETATM 966 O HOH 286 -8.655 24.051 2.000 1.00 56.40 4INSB135 HETATM 967 O HOH 287 17.589 14.886 12.994 0.50 46.92 4INSB136 HETATM 968 O HOH 288 -2.700 22.200 12.300 1.00 69.61 4INSB137 HETATM 969 O HOH 289 -7.400 20.900 13.300 1.00 82.49 4INSB138 HETATM 970 O HOH 291 7.840 13.593 13.240 1.00 36.77 4INSB139 HETATM 971 O HOH 277 8.436 16.103 13.124 0.50 66.40 4INSB140 HETATM 972 O HOH 292 20.767 2.758 14.127 0.50 49.50 4INSB141 HETATM 973 O HOH 278 19.461 3.175 13.236 0.50 43.30 4INSB142 HETATM 974 O HOH 293 10.600 21.200 12.800 1.00 65.41 4INSB143 HETATM 975 O HOH 294 17.091 12.037 14.424 1.00 66.30 4INSB144 HETATM 976 O HOH 295 26.230 1.151 12.982 0.50 42.97 4INSB145 HETATM 977 O HOH 296 1.000 16.700 13.200 1.00 45.00 4INSB146 HETATM 978 O HOH 297 -1.841 2.626 14.104 1.00 47.47 4INSB147 HETATM 979 O HOH 298 8.200 19.886 13.338 1.00 59.62 4INSB148 HETATM 980 O HOH 299 3.947 18.704 13.926 0.50 87.22 4INSB149 HETATM 981 O HOH 301 3.000 8.250 14.500 1.00 39.38 4INSB150 HETATM 982 O HOH 306 23.669 1.157 13.223 1.00 49.56 4INSB151 HETATM 983 O HOH 307 6.706 17.656 14.200 1.00 62.44 4INSB152 HETATM 984 O HOH 308 -10.436 21.800 14.703 1.00 82.89 4INSB153 HETATM 985 O HOH 302 21.660 8.100 14.400 1.00 78.85 4INSB154 HETATM 986 O HOH 303 16.002 14.165 14.204 0.50 36.21 4INSB155 HETATM 987 O HOH 311 18.624 9.971 14.472 1.00 68.09 4INSB156 HETATM 988 O HOH 312 12.345 18.363 14.320 1.00 61.52 4INSB157 HETATM 989 O HOH 314 15.375 13.331 15.234 0.50 38.87 4INSB158 HETATM 990 O HOH 315 8.900 16.074 15.111 0.50 56.02 4INSB159 HETATM 991 O HOH 316 -8.811 19.800 14.800 1.00 84.33 4INSB160 HETATM 992 O HOH 319 9.800 20.500 15.000 0.50 37.74 4INSB161 HETATM 993 O HOH 321 -0.629 5.214 14.504 1.00 52.63 4INSB162 HETATM 994 O HOH 305 0.063 7.371 13.477 0.50 44.43 4INSB163 HETATM 995 O HOH 322 4.331 21.351 15.200 1.00 48.37 4INSB164 HETATM 996 O HOH 324 20.471 6.314 15.326 0.50 42.47 4INSB165 HETATM 997 O HOH 325 15.956 10.347 15.577 1.00 73.09 4INSB166 HETATM 998 O HOH 326 4.166 16.122 15.117 1.00 64.59 4INSB167 HETATM 999 O HOH 331 -3.496 10.462 15.323 1.00 41.57 4INSB168 HETATM 1000 O HOH 333 22.179 1.588 16.322 1.00 60.14 4INSB169 HETATM 1001 O HOH 334 7.227 21.575 16.034 1.00 79.51 4INSB170 HETATM 1002 O HOH 335 24.987 3.024 15.000 0.50 77.50 4INSB171 HETATM 1003 O HOH 341 0.000 0.000 15.940 0.33 48.19 4INSB172 HETATM 1004 O HOH 342 2.058 1.124 15.886 0.50 57.48 4INSB173 HETATM 1005 O HOH 343 -1.885 13.663 16.148 1.00 61.43 4INSB174 HETATM 1006 O HOH 344 -3.119 22.865 15.776 1.00 59.24 4INSB175 HETATM 1007 O HOH 345 19.200 4.000 16.256 1.00 52.18 4INSB176 HETATM 1008 O HOH 346 14.044 21.773 15.730 0.50 35.08 4INSB177 HETATM 1009 O HOH 347 1.900 3.400 16.729 1.00 42.63 4INSB178 HETATM 1010 O HOH 349 14.400 17.800 15.886 0.50 52.96 4INSB179 HETATM 1011 O HOH 336 13.700 15.600 15.000 1.00 65.01 4INSB180 HETATM 1012 O HOH 351 2.100 6.200 15.800 1.00 73.63 4INSB181 HETATM 1013 O HOH 352 13.105 18.675 16.692 0.50 57.75 4INSB182 HETATM 1014 O HOH 355 5.384 1.947 16.328 1.00 52.83 4INSB183 HETATM 1015 O HOH 356 19.387 1.649 16.538 1.00 38.74 4INSB184 HETATM 1016 O HOH 357 6.570 18.429 16.505 1.00 50.65 4INSB185 HETATM 1017 O HOH 358 -11.760 21.100 16.700 0.50 40.14 4INSB186 HETATM 1018 O HOH 359 18.269 7.536 16.565 1.00 60.44 4INSB187 HETATM 1019 O HOH 350 16.961 12.601 17.169 1.00 81.43 4INSB188 HETATM 1020 O HOH 354 19.266 10.576 17.790 1.00 63.02 4INSB189 HETATM 1021 O HOH 361 -2.564 5.638 17.137 1.00 48.33 4INSB190 HETATM 1022 O HOH 362 -3.814 12.169 17.184 1.00 40.38 4INSB191 HETATM 1023 O HOH 363 21.336 8.539 16.849 1.00 46.39 4INSB192 HETATM 1024 O HOH 364 11.361 23.312 16.935 1.00 56.56 4INSB193 HETATM 1025 O HOH 365 11.482 20.115 16.180 0.50 36.42 4INSB194 HETATM 1026 O HOH 366 10.431 19.608 16.704 0.50 61.59 4INSB195 HETATM 1027 O HOH 367 -0.459 10.100 16.800 1.00 96.03 4INSB196 HETATM 1028 O HOH 368 -5.600 22.740 16.755 0.50 60.98 4INSB197 HETATM 1029 O HOH 369 22.701 4.882 16.533 0.50 59.04 4INSB198 HETATM 1030 O HOH 360 -0.559 7.125 16.712 1.00 55.39 4INSB199 HETATM 1031 O HOH 370 22.349 3.895 16.628 0.50 44.55 4INSB200 HETATM 1032 O HOH 379 24.254 2.529 17.221 0.50 64.98 4INSB201 HETATM 1033 O HOH 371 -8.270 20.622 17.537 0.50 29.13 4INSB202 HETATM 1034 O HOH 376 -7.614 21.771 17.400 0.50 39.65 4INSB203 HETATM 1035 O HOH 372 17.313 15.074 17.581 1.00 53.72 4INSB204 HETATM 1036 O HOH 374 -1.700 21.227 17.206 1.00 50.32 4INSB205 HETATM 1037 O HOH 375 16.509 9.562 17.812 1.00 54.16 4INSB206 HETATM 1038 O HOH 377 -7.800 16.300 17.668 1.00 75.77 4INSB207 HETATM 1039 O HOH 381 4.677 20.377 17.882 1.00 49.25 4INSB208 HETATM 1040 O HOH 382 17.535 3.747 17.792 1.00 51.88 4INSB209 HETATM 1041 O HOH 383 -2.265 8.874 17.970 1.00 48.81 4INSB210 HETATM 1042 O HOH 386 -8.894 18.338 17.943 1.00 51.71 4INSB211 HETATM 1043 O HOH 387 0.500 3.000 18.600 0.50 35.00 4INSB212 HETATM 1044 O HOH 388 6.000 4.100 17.800 1.00 50.00 4INSB213 HETATM 1045 O HOH 390 2.700 3.900 19.000 0.50 40.00 4INSB214 HETATM 1046 O HOH 391 2.992 5.257 18.400 0.50 40.00 4INSB215 HETATM 1047 O HOH 389 22.320 6.616 18.376 1.00 76.30 4INSB216 HETATM 1048 O HOH 378 14.300 15.147 17.400 0.50 41.41 4INSB217 HETATM 1049 O HOH 392 12.379 14.000 18.485 1.00 69.21 4INSB218 HETATM 1050 O HOH 394 26.585 1.034 18.302 1.00 72.39 4INSB219 HETATM 1051 O HOH 395 15.048 0.586 18.649 0.50 27.91 4INSB220 HETATM 1052 O HOH 396 24.664 3.165 18.640 0.50 55.98 4INSB221 HETATM 1053 O HOH 397 16.000 6.400 18.500 1.00 75.03 4INSB222 HETATM 1054 O HOH 401 -4.837 15.671 19.009 1.00 33.67 4INSB223 HETATM 1055 O HOH 402 -1.199 22.838 19.138 1.00 36.10 4INSB224 HETATM 1056 O HOH 403 1.686 21.194 18.890 1.00 36.62 4INSB225 HETATM 1057 O HOH 404 7.587 19.898 18.900 1.00 74.10 4INSB226 HETATM 1058 O HOH 405 23.636 0.400 18.433 0.50 38.58 4INSB227 HETATM 1059 O HOH 407 7.200 0.000 19.364 1.00 80.00 4INSB228 HETATM 1060 O HOH 408 12.325 20.187 18.584 1.00 76.30 4INSB229 HETATM 1061 O HOH 409 8.868 21.918 18.717 1.00 88.44 4INSB230 HETATM 1062 O HOH 411 -5.300 12.855 19.148 1.00 51.00 4INSB231 HETATM 1063 O HOH 412 4.000 0.889 19.161 1.00 56.19 4INSB232 HETATM 1064 O HOH 413 11.500 16.200 18.500 1.00 54.59 4INSB233 HETATM 1065 O HOH 414 15.033 14.600 19.135 0.50 40.58 4INSB234 HETATM 1066 O HOH 415 16.700 11.300 19.500 0.50 53.18 4INSB235 HETATM 1067 O HOH 421 4.304 23.913 8.300 1.00 56.36 4INSB236 HETATM 1068 O HOH 422 17.595 8.307 19.856 1.00 43.95 4INSB237 HETATM 1069 O HOH 423 8.460 17.052 19.897 1.00 58.87 4INSB238 HETATM 1070 O HOH 424 11.600 18.400 20.000 1.00 60.00 4INSB239 HETATM 1071 O HOH 425 -9.409 23.116 19.633 1.00 66.30 4INSB240 HETATM 1072 O HOH 428 7.200 3.000 19.668 1.00 79.22 4INSB241 HETATM 1073 O HOH 429 21.000 4.000 19.956 1.00 59.41 4INSB242 HETATM 1074 O HOH 420 -0.800 4.800 19.700 1.00 50.00 4INSB243 HETATM 1075 O HOH 431 13.592 4.017 20.054 1.00 43.20 4INSB244 HETATM 1076 O HOH 432 -0.400 2.600 20.000 0.50 40.00 4INSB245 HETATM 1077 O HOH 433 19.480 6.217 19.797 1.00 62.99 4INSB246 HETATM 1078 O HOH 435 20.876 1.574 20.116 0.50 41.62 4INSB247 HETATM 1079 O HOH 437 18.870 3.203 20.295 1.00 65.25 4INSB248 HETATM 1080 O HOH 438 22.043 8.448 20.602 0.50 38.02 4INSB249 HETATM 1081 O HOH 439 15.420 7.740 20.813 1.00 61.77 4INSB250 HETATM 1082 O HOH 441 14.400 19.900 20.800 1.00 46.45 4INSB251 HETATM 1083 O HOH 442 8.900 4.300 20.900 1.00 66.62 4INSB252 HETATM 1084 O HOH 443 12.900 7.700 20.600 1.00 50.81 4INSB253 HETATM 1085 O HOH 444 23.373 3.907 20.784 0.50 37.81 4INSB254 HETATM 1086 O HOH 445 22.600 5.200 21.000 0.50 50.00 4INSB255 HETATM 1087 O HOH 446 11.000 20.300 21.100 0.50 50.00 4INSB256 HETATM 1088 O HOH 447 3.848 22.050 20.436 1.00 73.68 4INSB257 HETATM 1089 O HOH 448 15.900 13.400 20.500 0.50 37.02 4INSB258 HETATM 1090 O HOH 449 15.000 15.900 20.409 0.50 51.72 4INSB259 HETATM 1091 O HOH 451 -5.648 12.110 21.371 1.00 36.62 4INSB260 HETATM 1092 O HOH 453 -3.499 9.530 21.143 1.00 58.40 4INSB261 HETATM 1093 O HOH 452 11.455 5.672 21.400 0.50 45.30 4INSB262 HETATM 1094 O HOH 455 19.300 11.200 21.251 1.00 93.71 4INSB263 HETATM 1095 O HOH 456 12.867 15.311 21.383 1.00104.59 4INSB264 HETATM 1096 O HOH 450 16.200 18.500 20.516 0.50 57.42 4INSB265 HETATM 1097 O HOH 459 17.900 14.200 21.400 0.50 54.11 4INSB266 HETATM 1098 O HOH 461 -2.438 17.554 21.488 1.00 27.54 4INSB267 HETATM 1099 O HOH 462 2.426 2.789 21.667 0.50 37.21 4INSB268 HETATM 1100 O HOH 473 5.591 3.514 22.644 1.00 66.58 4INSB269 HETATM 1101 O HOH 474 21.490 9.784 21.789 0.50 37.24 4INSB270 HETATM 1102 O HOH 475 22.459 7.099 21.906 0.50 54.11 4INSB271 HETATM 1103 O HOH 476 23.362 5.246 22.116 0.50 53.14 4INSB272 HETATM 1104 O HOH 477 10.026 5.995 22.154 0.50 36.85 4INSB273 HETATM 1105 O HOH 481 16.230 16.756 22.900 1.00 51.60 4INSB274 HETATM 1106 O HOH 482 7.950 1.500 22.400 1.00 74.32 4INSB275 HETATM 1107 O HOH 484 0.000 0.000 22.203 0.33 26.53 4INSB276 HETATM 1108 O HOH 485 6.000 0.331 22.809 1.00 81.99 4INSB277 HETATM 1109 O HOH 486 2.400 1.900 22.950 0.50 53.75 4INSB278 HETATM 1110 O HOH 487 16.826 9.360 22.915 1.00 64.50 4INSB279 HETATM 1111 O HOH 488 10.028 0.600 22.667 1.00 50.88 4INSB280 HETATM 1112 O HOH 491 1.801 22.097 22.791 1.00 33.66 4INSB281 HETATM 1113 O HOH 492 -2.158 10.172 22.900 1.00 34.73 4INSB282 HETATM 1114 O HOH 493 8.566 22.210 23.275 1.00 52.52 4INSB283 HETATM 1115 O HOH 494 19.041 7.317 22.965 1.00 45.49 4INSB284 HETATM 1116 O HOH 496 13.154 17.896 23.121 0.50 58.99 4INSB285 HETATM 1117 O HOH 497 25.120 2.792 23.670 1.00 49.60 4INSB286 HETATM 1118 O HOH 498 0.848 2.630 23.300 0.50 51.96 4INSB287 HETATM 1119 O HOH 501 12.799 4.914 23.300 1.00 45.19 4INSB288 HETATM 1120 O HOH 502 14.100 1.511 23.000 1.00 54.91 4INSB289 HETATM 1121 O HOH 503 18.000 5.200 23.400 0.50 40.00 4INSB290 HETATM 1122 O HOH 504 20.460 5.246 23.195 0.50 42.74 4INSB291 HETATM 1123 O HOH 505 18.500 13.238 23.926 0.50 60.34 4INSB292 HETATM 1124 O HOH 506 9.469 4.007 23.263 0.50 65.39 4INSB293 HETATM 1125 O HOH 511 3.330 4.805 23.757 1.00 24.03 4INSB294 HETATM 1126 O HOH 512 -6.414 11.027 24.091 1.00 36.40 4INSB295 HETATM 1127 O HOH 514 -1.798 3.543 24.064 1.00 80.64 4INSB296 HETATM 1128 O HOH 513 1.468 0.225 24.184 1.00 35.46 4INSB297 HETATM 1129 O HOH 515 -12.014 22.141 24.296 1.00 66.63 4INSB298 HETATM 1130 O HOH 516 13.052 19.509 23.128 0.50 62.84 4INSB299 HETATM 1131 O HOH 517 -9.519 22.726 24.412 1.00 71.89 4INSB300 HETATM 1132 O HOH 521 9.900 22.890 25.259 1.00 65.26 4INSB301 HETATM 1133 O HOH 522 16.495 17.126 26.000 1.00 40.00 4INSB302 HETATM 1134 O HOH 524 21.700 5.400 24.514 0.50 50.22 4INSB303 HETATM 1135 O HOH 525 6.642 20.679 24.898 1.00 58.56 4INSB304 HETATM 1136 O HOH 526 20.692 8.311 24.457 0.50 48.03 4INSB305 HETATM 1137 O HOH 531 -1.763 20.077 25.007 1.00 21.36 4INSB306 HETATM 1138 O HOH 532 -8.670 18.367 25.265 1.00 27.41 4INSB307 HETATM 1139 O HOH 533 7.700 4.223 25.055 1.00 33.74 4INSB308 HETATM 1140 O HOH 535 19.581 11.070 25.093 1.00 65.09 4INSB309 HETATM 1141 O HOH 536 -2.718 10.300 25.150 1.00 73.39 4INSB310 HETATM 1142 O HOH 537 22.450 8.700 25.159 0.50 48.15 4INSB311 HETATM 1143 O HOH 541 -1.293 22.963 25.513 1.00 29.91 4INSB312 HETATM 1144 O HOH 542 12.210 21.699 25.848 0.50 44.27 4INSB313 HETATM 1145 O HOH 527 11.500 21.600 24.157 0.50 70.69 4INSB314 HETATM 1146 O HOH 544 25.803 0.095 26.600 1.00 56.59 4INSB315 HETATM 1147 O HOH 545 23.392 1.295 25.732 1.00 48.54 4INSB316 HETATM 1148 O HOH 546 12.194 3.114 25.000 1.00 76.30 4INSB317 HETATM 1149 O HOH 551 4.708 1.340 26.039 1.00 30.59 4INSB318 HETATM 1150 O HOH 552 14.388 19.737 25.636 1.00 64.76 4INSB319 HETATM 1151 O HOH 561 5.419 3.667 26.434 1.00 31.12 4INSB320 HETATM 1152 O HOH 562 23.512 4.500 26.629 1.00 66.30 4INSB321 HETATM 1153 O HOH 563 -10.096 20.400 26.037 1.00 73.01 4INSB322 HETATM 1154 O HOH 571 -9.600 23.170 26.887 1.00 62.82 4INSB323 HETATM 1155 O HOH 572 16.389 14.064 27.171 1.00 74.53 4INSB324 HETATM 1156 O HOH 574 18.700 14.000 26.532 1.00 79.38 4INSB325 HETATM 1157 O HOH 582 10.926 10.684 26.940 1.00 44.80 4INSB326 HETATM 1158 O HOH 583 12.500 21.800 27.254 0.50 47.63 4INSB327 HETATM 1159 O HOH 581 15.038 21.545 5.268 1.00 55.92 4INSB328 HETATM 1160 O HOH 591 -1.739 18.453 27.705 1.00 12.21 4INSB329 HETATM 1161 O HOH 592 27.000 1.000 28.300 1.00 70.92 4INSB330 HETATM 1162 O HOH 593 20.879 8.934 27.397 0.50 49.09 4INSB331 HETATM 1163 O HOH 601 5.301 3.962 28.700 0.60 31.54 4INSB332 HETATM 1164 O HOH 602 13.794 13.317 28.059 1.00 74.41 4INSB333 HETATM 1165 O HOH 603 6.578 4.370 28.779 0.40 28.91 4INSB334 HETATM 1166 O HOH 611 19.254 14.024 28.832 1.00 55.11 4INSB335 HETATM 1167 O HOH 612 14.279 1.568 28.963 1.00 65.08 4INSB336 HETATM 1168 O HOH 622 14.080 9.849 29.479 1.00 27.58 4INSB337 HETATM 1169 O HOH 621 23.137 6.636 29.293 0.50 55.90 4INSB338 HETATM 1170 O HOH 641 18.586 7.949 30.401 0.50 17.96 4INSB339 HETATM 1171 O HOH 631 10.475 22.332 30.034 1.00 41.35 4INSB340 HETATM 1172 O HOH 632 14.200 22.300 30.301 1.00 67.11 4INSB341 HETATM 1173 O HOH 642 4.564 5.544 30.131 1.00 37.24 4INSB342 HETATM 1174 O HOH 651 0.000 0.000 30.941 0.33 24.09 4INSB343 HETATM 1175 O HOH 652 3.559 3.107 30.844 1.00 34.61 4INSB344 HETATM 1176 O HOH 653 -1.317 2.461 30.833 1.00 27.74 4INSB345 HETATM 1177 O HOH 671 6.900 3.468 31.640 1.00 38.60 4INSB346 HETATM 1178 O HOH 672 1.484 2.379 31.639 1.00 81.34 4INSB347 HETATM 1179 O HOH 673 -11.700 22.918 32.100 1.00 83.01 4INSB348 HETATM 1180 O HOH 674 12.200 21.561 31.604 1.00 55.36 4INSB349 HETATM 1181 O HOH 691 25.195 1.527 33.000 1.00 77.73 4INSB350 HETATM 1182 O HOH 701 11.090 22.753 33.200 1.00 96.30 4INSB351 HETATM 1183 O HOH 702 3.296 3.651 33.200 1.00 86.30 4INSB352 HETATM 1184 O HOH 711 24.237 4.743 33.829 1.00 38.87 4INSB353 HETATM 1185 O HOH 721 6.358 23.611 -0.018 1.00 94.84 4INSB354 CONECT 43 42 76 4INS1420 CONECT 49 48 227 4INS1421 CONECT 76 43 75 4INS1422 CONECT 154 153 318 4INS1423 CONECT 227 49 226 4INS1424 CONECT 318 154 317 4INS1425 CONECT 463 462 496 4INS1426 CONECT 469 468 643 4INS1427 CONECT 496 463 495 4INS1428 CONECT 574 573 733 4INS1429 CONECT 643 469 642 4INS1430 CONECT 733 574 732 4INS1431 MASTER 167 11 2 6 2 4 12 9 1181 4 12 10 4INSB355 END 4INS1433 MMTK-2.7.9/MMTK/Database/Proteins/0000755000076600000240000000000012156626571017003 5ustar hinsenstaff00000000000000MMTK-2.7.9/MMTK/Database/Proteins/bala10000644000076600000240000000015611662461640017703 0ustar hinsenstaff00000000000000name = 'bALA1' conf = PDBConfiguration('bALA1.pdb') chains = map(PeptideChain, conf.peptide_chains) del conf MMTK-2.7.9/MMTK/Database/Proteins/cytochrome-c0000644000076600000240000000106511662461640021317 0ustar hinsenstaff00000000000000name = "Cytochrome C" conf = PDBConfiguration('2YCC.pdb') # construct the peptide chains chains = conf.createPeptideChains() # construct the isolated heme group heme = conf.createGroups({'HEM': 'bound_heme'})[0] # convert cysteins to cystines chains[0][18]._makeCystine() chains[0][21]._makeCystine() # attach the heme group chains[0].addGroup(heme, [(heme.CAB, chains[0][18].sidechain.S_gamma), (heme.CAC, chains[0][21].sidechain.S_gamma)]) # assign positions to undefined hydrogen atoms chains[0].findHydrogenPositions() del conf MMTK-2.7.9/MMTK/Database/Proteins/insulin0000644000076600000240000000025011662461640020377 0ustar hinsenstaff00000000000000name = 'insulin' # Read the PDB file. conf = PDBConfiguration('insulin.pdb') # Construct the peptide chains. chains = conf.createPeptideChains() # Clean up del conf MMTK-2.7.9/MMTK/Database/Proteins/insulin_calpha0000644000076600000240000000027011662461640021711 0ustar hinsenstaff00000000000000name = 'insulin' # Read the PDB file. conf = PDBConfiguration('insulin.pdb') # Construct the peptide chains. chains = conf.createPeptideChains(model = 'calpha') # Clean up del conf MMTK-2.7.9/MMTK/Database.py0000644000076600000240000004350212041510260015532 0ustar hinsenstaff00000000000000# This module manages the chemical database. # # Written by Konrad Hinsen # """ Management of the chemical database The database contains definitions for atoms, groups, molecules, and complexes. Each definition is a Python file that is executed in the global environment of the module 'XEnvironment' (X standing for 'Atom', 'Group', 'Molecule', 'Complex', 'Protein', or 'Crystal'). Definitions made in that file will end up as attributes of an object that is later used as a blueprint to create chemical objects. """ __docformat__ = 'restructuredtext' from MMTK import Utility import copy import os import sys # # Find database path # try: path = os.environ['MMTKDATABASE'].split() except KeyError: path = ['~/.mmtk/Database', os.path.join(os.path.split(__file__)[0], 'Database')] for i in range(len(path)): if not Utility.isURL(path[i]): path[i] = os.path.expanduser(path[i]) # # Some miscellaneous functions for use by other modules # def databasePath(filename, directory, try_direct = False): if Utility.isURL(filename): return filename filename = os.path.expanduser(filename) if try_direct and os.path.exists(filename): return os.path.normcase(filename) entries = [] if os.path.split(filename)[0] == '': for p in path: if Utility.isURL(p): url = Utility.joinURL(p, directory+'/'+filename) if Utility.checkURL(url): entries.append(url) else: full_name = os.path.join(os.path.join(p, directory), filename) if os.path.exists(full_name): entries.append(os.path.normcase(full_name)) if len(entries) == 0: raise IOError("Database entry %s/%s not found" % (directory, filename)) else: if len(entries) > 1: Utility.warning("multiple database entries for %s/%s, using first one" % (directory, filename)) for e in entries: sys.stderr.write(e+'\n') return entries[0] def PDBPath(filename): return databasePath(filename, 'PDB', True) def addDatabaseDirectory(directory): "Add a directory to the database search path" if not Utility.isURL(directory): directory = os.path.expanduser(directory) path.append(directory) # # The class that represents a database. There will be one instance # for atoms, one for groups etc. # class Database(object): def __init__(self, directory, type_constructor): self.directory = directory self.type_constructor = type_constructor self.types = {} def findType(self, name): name = name.lower() if not self.types.has_key(name): filename = databasePath(name, self.directory, False) self.types[name] = self.type_constructor(filename, name) return self.types[name] # # The base class for all the type classes. It defines how definitions # are loaded from the database. # class ChemicalObjectType(object): def __init__(self, filename, database_name, module, instancevars): self.filename = filename self.database_name = database_name file_text = Utility.readURL(filename) newvars = {} exec file_text in vars(module), newvars for name, value in newvars.items(): setattr(self, name, value) self.parent = None if not hasattr(self, 'instance'): self.instance = [] for attr in instancevars+('parent',): if not hasattr(self, attr): setattr(self, attr, []) if attr not in self.instance: self.instance.append(attr) attributes = vars(self).items() attributes.sort(lambda a, b: cmp(a[0], b[0])) for name, object in attributes: if hasattr(object, 'is_instance_var'): if name not in self.instance: self.instance.append(name) object.parent = self object.name = name if hasattr(object, 'object_list'): getattr(self, object.object_list).append(object) is_chemical_object_type = True def setReferences(self): atom_refs = [] for i in range(len(self.atoms)): atom_refs.append(AtomReference(i)) for attr in vars(self).items(): if attr[0] not in self.instance: setattr(self, attr[0], Utility.substitute(getattr(self, attr[0]), self.atoms, atom_refs)) # Type objects are singletons, they are never copied def __copy__(self, memo = None): return self __deepcopy__ = __copy__ # Pickle support. When pickled/unpickled through # MMTK.Utility.save and MMTK.Utility.load, type objects # are treated as external persistent objects. This is handled # by a specialized Pickler and Unpickler (see MMTK.Utility) # and the _restoreId() methods in the subclasses of # ChemicalObjectType. This mechanism maintains the singleton # nature of type objects. # When pickling using the unmodified pickle/cPickle routines, # there is no way to prevent type objects from being duplicated # at unpickle time. The best we can do is to make the newly created # type object a clone of the singleton object. This is handled # by the __setstate__ and __getstate__ methods that follow def __getstate__(self): return self._restoreId() def __setstate__(self, state): import sys singleton_type = eval("MMTK."+state, sys.modules) self.__dict__.update(singleton_type.__dict__) def writeXML(self, file, memo): if memo.get(id(self), None) is not None: return try: name = self.name except AttributeError: name = 'm' + `memo['counter']` memo['counter'] = memo['counter'] + 1 memo[id(self)] = name atoms = copy.copy(self.atoms) bonds = copy.copy(self.bonds) for group in self.groups: group.type.writeXML(file, memo) for atom in group.atoms: atoms.remove(atom) for bond in group.bonds: bonds.remove(bond) file.write('\n' % name) for group in self.groups: file.write(' \n' % (memo[id(group.type)], group.name)) if atoms: file.write(' \n') for atom in atoms: file.write(' \n' % (atom.name, atom.type.symbol)) file.write(' \n') if bonds: file.write(' \n') for bond in bonds: a1n = self.relativeName(bond.a1) a2n = self.relativeName(bond.a2) file.write(' \n' % (a1n, a2n)) file.write(' \n') file.write('\n') def getXMLAtomOrder(self): atoms = copy.copy(self.atoms) atom_names = [] for group in self.groups: for atom in group.atoms: atoms.remove(atom) group_name = self.relativeName(group) for atom in group.type.getXMLAtomOrder(): atom_names.append(group_name + ':' + atom) for atom in atoms: atom_names.append(self.relativeName(atom)) return atom_names def relativeName(self, object): name = '' while object is not None and object != self: for attr, value in object.parent.__dict__.items(): if value is object: name = attr + ':' + name break object = object.parent return name[:-1] # # Atom type class # class AtomType(ChemicalObjectType): error = 'AtomTypeError' def __init__(self, filename, database_name): from MMTK import AtomEnvironment ChemicalObjectType.__init__(self, filename, database_name, AtomEnvironment, ()) if not isinstance(self.mass, list): self.mass = [(self.mass, 100.)] total_probability = sum([m[1] for m in self.mass]) if abs(total_probability-100.) > 1.e-4: raise self.error('Inconsistent mass specification: ' + `total_probability-100.` + ' percent missing') self.average_mass = sum([m[0]*m[1] for m in self.mass])/100 if not hasattr(self, 'pdbmap'): name = self.symbol.upper() self.pdbmap = [(name, {name: None})] def _restoreId(self): return 'Database.atom_types.findType("' + \ self.database_name + '")' def writeXML(self, file, memo): file.write('\n') def getXMLAtomOrder(self): return [self.name] # # Group type class # class GroupType(ChemicalObjectType): error = 'GroupTypeError' def __init__(self, filename, database_name): from MMTK import GroupEnvironment ChemicalObjectType.__init__(self, filename, database_name, GroupEnvironment, ('atoms', 'groups', 'bonds', 'chain_links')) for g in self.groups: self.atoms = self.atoms + g.atoms self.bonds = self.bonds + g.bonds self.setReferences() def _restoreId(self): return 'Database.group_types.findType("' + \ self.database_name + '")' # # Molecule type class # class MoleculeType(ChemicalObjectType): error = 'MoleculeTypeError' def __init__(self, filename, database_name): from MMTK import MoleculeEnvironment ChemicalObjectType.__init__(self, filename, database_name, MoleculeEnvironment, ('atoms', 'groups', 'bonds')) for g in self.groups: self.atoms = self.atoms + g.atoms self.bonds = self.bonds + g.bonds self.setReferences() def _restoreId(self): return 'Database.molecule_types.findType("' + \ self.database_name + '")' # # Crystal type class # class CrystalType(ChemicalObjectType): error = 'CrystalTypeError' def __init__(self, filename, database_name): from MMTK import CrystalEnvironment ChemicalObjectType.__init__(self, filename, database_name, CrystalEnvironment, ('atoms', 'groups', 'molecules', 'bonds')) for g in self.groups: self.atoms = self.atoms + g.atoms self.bonds = self.bonds + g.bonds for m in self.molecules: self.atoms = self.atoms + m.atoms self.bonds = self.bonds + m.bonds self.setReferences() def _restoreId(self): return 'Database.crystal_types.findType("' + \ self.database_name + '")' # # Complex type class # class ComplexType(ChemicalObjectType): error = 'ComplexTypeError' def __init__(self, filename, database_name): import ComplexEnvironment ChemicalObjectType.__init__(self, filename, database_name, ComplexEnvironment, ('atoms', 'molecules')) for m in self.molecules: self.atoms = self.atoms + m.atoms self.setReferences() def _restoreId(self): return 'Database.complex_types.findType("' + \ self.database_name + '")' # # An atom reference object is substituted for all references to # atom objects that are not in instance variables. A reference # object contains only the number of the atom in the list of # atoms of its parent object. # class AtomReference(object): def __init__(self, number): self.number = number def increaseBy(self, offset): self.number = self.number + offset def __repr__(self): return '' __str__ = __repr__ def __eq__(self, other): if isinstance(other, AtomReference): return self.number == other.number else: return NotImplemented def __ne__(self, other): if isinstance(other, AtomReference): return self.number != other.number else: return NotImplemented def __hash__(self): return hash(self.number) # # The base class for all types that just contain references to # the file names. These are for objects that tend to be big # and used only in small numbers. # class ReferenceType(object): def __init__(self, filename, database_name, environment): self.filename = filename self.database_name = database_name self.environment = environment def createObject(self, newvars): file_text = Utility.readURL(self.filename) exec file_text in vars(self.environment), newvars class ProteinType(ReferenceType): def __init__(self, filename, database_name): from MMTK import ProteinEnvironment ReferenceType.__init__(self, filename, database_name, ProteinEnvironment) def _restoreId(self): return 'Database.protein_types.findType("' + \ self.database_name + '")' # # The five databases. # atom_types = Database('Atoms', AtomType) group_types = Database('Groups', GroupType) molecule_types = Database('Molecules', MoleculeType) crystal_types = Database('Crystals', CrystalType) complex_types = Database('Complexes', ComplexType) protein_types = Database('Proteins', ProteinType) # # The following classes represent the chemical objects # in the type blueprints. They contain no information # in addition to references to an object type. They # are needed only to establish a test of identity; # e.g. each hydrogen atom in a molecule must be represented # by a different object. # class BlueprintObject(object): def __init__(self, original, database, memo): if isinstance(original, basestring): original = database.findType(original) self.type = original elif hasattr(original, 'is_blueprint'): self.type = original.type if hasattr(original, 'name'): self.name = original.name else: self.type = original if memo is None: memo = {} memo[id(original)] = self for attr in self.type.instance: setattr(self, attr, _blueprintCopy(getattr(original, attr), memo)) is_instance_var = True is_blueprint = True def __getattr__(self, attr): return getattr(self.type, attr) def __copy__(self, memo = None): return self __deepcopy__ = __copy__ class BlueprintAtom(BlueprintObject): def __init__(self, type, memo = None): BlueprintObject.__init__(self, type, atom_types, memo) object_list = 'atoms' class BlueprintGroup(BlueprintObject): def __init__(self, type, memo = None): BlueprintObject.__init__(self, type, group_types, memo) object_list = 'groups' class BlueprintMolecule(BlueprintObject): def __init__(self, type, memo = None): BlueprintObject.__init__(self, type, molecule_types, memo) object_list = 'molecules' class BlueprintCrystal(BlueprintObject): def __init__(self, type, memo = None): BlueprintObject.__init__(self, type, crystal_types, memo) object_list = 'crystals' class BlueprintComplex(BlueprintObject): def __init__(self, type, memo = None): BlueprintObject.__init__(self, type, complex_types, memo) object_list = 'complexes' # # Blueprint class corresponding to ReferenceType # class ReferenceBlueprint(object): def __init__(self, original, database): if isinstance(original, basestring): self.type = database.findType(original) elif hasattr(original, 'is_blueprint'): self.type = original.type else: self.type = type self.type.createObject(self.__dict__) is_blueprint = True class BlueprintProtein(ReferenceBlueprint): def __init__(self, type): ReferenceBlueprint.__init__(self, type, protein_types) # # This function copies the appropriate attributes of # a blueprint object. # def _blueprintCopy(object, memo): key = id(object) try: return memo[key] except KeyError: pass if isinstance(object, list): return [_blueprintCopy(o, memo) for o in object] if hasattr(object, 'is_blueprint'): new = object.__class__(object, memo) elif hasattr(object, '_blueprintCopy'): new = object._blueprintCopy(memo) else: new = object memo[key] = new return new # # The bond class just keeps track of the two atoms involved. # class BlueprintBond(object): def __init__(self, a1, a2): self.a1 = a1 self.a2 = a2 is_instance_var = True def _blueprintCopy(self, memo): return BlueprintBond(_blueprintCopy(self.a1, memo), _blueprintCopy(self.a2, memo)) object_list = 'bonds' def __copy__(self, memo = None): return self __deepcopy__ = __copy__ # # The function "instantiate" returns the real object corresponding # to a given blueprint object. A list of previously instantiated # objects is kept to make sure that references to the same blueprint # object don't create several real objects. # def instantiate(blueprint, memo): key = id(blueprint) try: return memo[key] except KeyError: pass if isinstance(blueprint, list): newobject = [instantiate(e, memo) for e in blueprint] else: try: newobject = _instanceclass[blueprint.__class__](blueprint, memo) except KeyError: newobject = blueprint memo[key] = newobject return newobject # # Register the instanceclasses for each blueprintclass # def registerInstanceClass(blueprint, instance): _instanceclass[blueprint] = instance _instanceclass = {} MMTK-2.7.9/MMTK/DCD.py0000644000076600000240000001203112117632051014420 0ustar hinsenstaff00000000000000# This module implements a DCD reader/writer # # Written by Lutz Ehrlich # Adapted to MMTK conventions by Konrad Hinsen """ Reading and writing of DCD trajectory files The DCD format for trajectories is used by CHARMM, X-Plor, and NAMD. It can be read by various visualization programs. The DCD format is defined as a binary (unformatted) Fortran format and is therefore platform-dependent. """ __docformat__ = 'restructuredtext' import MMTK_DCD from MMTK import PDB, Trajectory, Units from Scientific import N class DCDReader(Trajectory.TrajectoryGenerator): """ Reader for DCD trajectories (CHARMM/X-Plor/NAMD) A DCDReader reads a DCD trajectory and "plays back" the data as if it were generated directly by an integrator. The universe for which the DCD file is read must be perfectly compatible with the data in the file, including an identical internal atom numbering. This can be guaranteed only if the universe was created from a PDB file that is compatible with the DCD file without leaving out any part of the system. Reading is started by calling the reader object. The following data categories and variables are available for output: * category "time": time * category "configuration": configuration """ default_options = {} available_data = ['configuration', 'time'] restart_data = None def __init__(self, universe, **options): """ :param universe: the universe for which the information from the trajectory file is read :param options: keyword options :keyword dcd_file: the name of the DCD trajecory file to be read :keyword actions: a list of actions to be executed periodically (default is none) """ Trajectory.TrajectoryGenerator.__init__(self, universe, options) def __call__(self, **options): self.setCallOptions(options) configuration = self.universe.configuration() MMTK_DCD.readDCD(self.universe, configuration.array, self.getActions(), self.getOption('dcd_file')) def writeDCD(vector_list, dcd_file_name, factor, atom_order=None, delta_t=0.1, conf_flag=1): universe = vector_list[0].universe natoms = universe.numberOfPoints() if atom_order is None: atom_order = N.arrayrange(natoms) else: atom_order = N.array(atom_order) i_start = 0 # always start at frame 0 n_savc = 1 # save every frame fd = MMTK_DCD.writeOpenDCD(dcd_file_name, natoms, len(vector_list), i_start, n_savc, delta_t) for vector in vector_list: if conf_flag: vector = universe.contiguousObjectConfiguration(None, vector) array = factor*vector.array x = N.take(array[:, 0], atom_order).astype(N.Float16) y = N.take(array[:, 1], atom_order).astype(N.Float16) z = N.take(array[:, 2], atom_order).astype(N.Float16) MMTK_DCD.writeDCDStep(fd, x, y, z) MMTK_DCD.writeCloseDCD(fd) def writePDB(universe, configuration, pdb_file_name): offset = None if universe is not None: configuration = universe.contiguousObjectConfiguration(None, configuration) pdb = PDB.PDBOutputFile(pdb_file_name, 'xplor') pdb.write(universe, configuration) sequence = pdb.atom_sequence pdb.close() return sequence def writeDCDPDB(conf_list, dcd_file_name, pdb_file_name, delta_t=0.1): """ Write a sequence of configurations to a DCD file and generate a compatible PDB file. :param conf_list: the sequence of configurations :type conf_list: sequence of :class:`~MMTK.ParticleProperties.Configuration` :param dcd_file_name: the name of the DCD file :type dcd_file_name: str :param pdb_file_name: the name of the PDB file :type pdb_file_name: str :param delta_t: the time step between two configurations :type delta_t: float """ universe = conf_list[0].universe sequence = writePDB(universe, conf_list[0], pdb_file_name) indices = map(lambda a: a.index, sequence) writeDCD(conf_list, dcd_file_name, 1./Units.Ang, indices, delta_t, 1) def writeVelocityDCDPDB(vel_list, dcd_file_name, pdb_file_name, delta_t=0.1): """ Write a sequence of velocity particle vectors to a DCD file and generate a compatible PDB file. :param vel_list: the sequence of velocity particle vectors :type vel_list: sequence of :class:`~MMTK.ParticleProperties.ParticleVector` :param dcd_file_name: the name of the DCD file :type dcd_file_name: str :param pdb_file_name: the name of the PDB file :type pdb_file_name: str :param delta_t: the time step between two velocity sets :type delta_t: float """ universe = vel_list[0].universe sequence = writePDB(universe, universe.configuration(), pdb_file_name) indices = map(lambda a: a.index, sequence) writeDCD(vel_list, dcd_file_name, 1./(Units.Ang/Units.akma_time), indices, delta_t, 0) MMTK-2.7.9/MMTK/Deformation.py0000644000076600000240000004242711662461640016321 0ustar hinsenstaff00000000000000# Deformation energy module # # Written by Konrad Hinsen # """ Deformation energies in proteins This module implements deformational energies for use in the analysis of motions and conformational changes in macromolecules. A description of the techniques can be found in the following articles: | K. Hinsen | Analysis of domain motions by approximate normal mode calculations | Proteins 33 (1998): 417-429 | K. Hinsen, A. Thomas, M.J. Field | Analysis of domain motions in large proteins | Proteins 34 (1999): 369-382 """ __docformat__ = 'restructuredtext' try: from MMTK_forcefield import NonbondedList from MMTK_deformation import deformation, reduceDeformation, \ reduceFiniteDeformation except ImportError: pass from MMTK import ParticleProperties from Scientific import N # # Deformation energy evaluations # class DeformationEvaluationFunction(object): def __init__(self, universe, fc_length = 0.7, cutoff = 1.2, factor = 46402., form = 'exponential'): self.universe = universe self.fc_length = fc_length self.cutoff = cutoff self.factor = factor nothing = N.zeros((0,2), N.Int) self.pairs = NonbondedList(nothing, nothing, nothing, universe._spec, cutoff) self.pairs.update(universe.configuration().array) self.normalize = 0 try: self.version = self.forms.index(form) except ValueError: raise ValueError("unknown functional form") forms = ['exponential', 'calpha'] def newConfiguration(self): self.pairs.update(self.universe.configuration().array) class DeformationFunction(DeformationEvaluationFunction): """ Infinite-displacement deformation function The default values are appropriate for a |C_alpha| model of a protein with the global scaling described in the reference cited above. A DeformationFunction object must be called with a single parameter, which is a ParticleVector object containing the infinitesimal displacements of the atoms for which the deformation is to be evaluated. The return value is a ParticleScalar object containing the deformation value for each atom. """ def __init__(self, universe, fc_length = 0.7, cutoff = 1.2, factor = 46402., form = 'exponential'): """ :param universe: the universe for which the deformation function is defined :type universe: :class:`~MMTK.Universe.Universe` :param fc_length: the range parameter r_0 in the pair interaction term :type fc_length: float :param cutoff: the cutoff used in the deformation calculation :type cutoff: float :param factor: a global scaling factor :type factor: float :param form: the functional form ('exponential' or 'calpha') :type form: str """ DeformationEvaluationFunction.__init__(self, universe, fc_length, cutoff, factor, form) def __call__(self, vector): conf = self.universe.configuration() r = ParticleProperties.ParticleScalar(self.universe) l = deformation(conf.array, vector.array, self.pairs, None, r.array, self.cutoff, self.fc_length, self.factor, self.normalize, 0, self.version) return r class NormalizedDeformationFunction(DeformationFunction): """ Normalized infinite-displacement deformation function The default values are appropriate for a |C_alpha| model of a protein with the global scaling described in the reference cited above. The normalization is defined by equation 10 of reference 1 (see above). A NormalizedDeformationFunction object must be called with a single parameter, which is a ParticleVector object containing the infinitesimal displacements of the atoms for which the deformation is to be evaluated. The return value is a ParticleScalar object containing the deformation value for each atom. """ def __init__(self, universe, fc_length = 0.7, cutoff = 1.2, factor = 46402., form = 'exponential'): """ :param universe: the universe for which the deformation function is defined :type universe: :class:`~MMTK.Universe.Universe` :param fc_length: the range parameter r_0 in the pair interaction term :type fc_length: float :param cutoff: the cutoff used in the deformation calculation :type cutoff: float :param factor: a global scaling factor :type factor: float :param form: the functional form ('exponential' or 'calpha') :type form: str """ DeformationFunction.__init__(self, universe, fc_length, cutoff, factor, form) def __init__(self, *args, **kwargs): apply(DeformationFunction.__init__, (self, ) + args, kwargs) self.normalize = 1 class FiniteDeformationFunction(DeformationEvaluationFunction): """ Finite-displacement deformation function The default values are appropriate for a |C_alpha| model of a protein with the global scaling described in the reference cited above. A FiniteDeformationFunction object must be called with a single parameter, which is a Configuration or a ParticleVector object containing the alternate configuration of the universe for which the deformation is to be evaluated. The return value is a ParticleScalar object containing the deformation value for each atom. """ def __init__(self, universe, fc_length = 0.7, cutoff = 1.2, factor = 46402., form = 'exponential'): """ :param universe: the universe for which the deformation function is defined :type universe: :class:`~MMTK.Universe.Universe` :param fc_length: the range parameter r_0 in the pair interaction term :type fc_length: float :param cutoff: the cutoff used in the deformation calculation :type cutoff: float :param factor: a global scaling factor :type factor: float :param form: the functional form ('exponential' or 'calpha') :type form: str """ DeformationEvaluationFunction.__init__(self, universe, fc_length, cutoff, factor, form) def __call__(self, vector): conf = self.universe.configuration() vector = vector-conf r = ParticleProperties.ParticleScalar(self.universe) l = deformation(conf.array, vector.array, self.pairs, None, r.array, self.cutoff, self.fc_length, self.factor, 0, 1, self.version) return r class DeformationEnergyFunction(DeformationEvaluationFunction): """ Infinite-displacement deformation energy function The deformation energy is the sum of the deformation values over all atoms of a system. The default values are appropriate for a |C_alpha| model of a protein with the global scaling described in the reference cited above. A DeformationEnergyFunction is called with one or two parameters. The first parameter is a ParticleVector object containing the infinitesimal displacements of the atoms for which the deformation energy is to be evaluated. The optional second argument can be set to a non-zero value to request the gradients of the energy in addition to the energy itself. In that case there are two return values (energy and the gradients in a ParticleVector object), otherwise only the energy is returned. """ def __init__(self, universe, fc_length = 0.7, cutoff = 1.2, factor = 46402., form = 'exponential'): """ :param universe: the universe for which the deformation function is defined :type universe: :class:`~MMTK.Universe.Universe` :param fc_length: the range parameter r_0 in the pair interaction term :type fc_length: float :param cutoff: the cutoff used in the deformation calculation :type cutoff: float :param factor: a global scaling factor :type factor: float :param form: the functional form ('exponential' or 'calpha') :type form: str """ DeformationEvaluationFunction.__init__(self, universe, fc_length, cutoff, factor, form) def __call__(self, vector, gradients = None): conf = self.universe.configuration() g = None if gradients is not None: if ParticleProperties.isParticleProperty(gradients): g = gradients elif isinstance(gradients, N.array_type): g = ParticleProperties.ParticleVector(self.universe, gradients) elif gradients: g = ParticleProperties.ParticleVector(self.universe) if g is None: g_array = None else: g_array = g.array l = deformation(conf.array, vector.array, self.pairs, g_array, None, self.cutoff, self.fc_length, self.factor, self.normalize, 0, self.version) if g is None: return l else: return l, g class NormalizedDeformationEnergyFunction(DeformationEnergyFunction): """ Normalized infinite-displacement deformation energy function The normalized deformation energy is the sum of the normalized deformation values over all atoms of a system. The default values are appropriate for a |C_alpha| model of a protein with the global scaling described in the reference cited above. The normalization is defined by equation 10 of reference 1. A NormalizedDeformationEnergyFunction is called with one or two parameters. The first parameter is a ParticleVector object containing the infinitesimal displacements of the atoms for which the deformation energy is to be evaluated. The optional second argument can be set to a non-zero value to request the gradients of the energy in addition to the energy itself. In that case there are two return values (energy and the gradients in a ParticleVector object), otherwise only the energy is returned. """ def __init__(self, universe, fc_length = 0.7, cutoff = 1.2, factor = 46402., form = 'exponential'): """ :param universe: the universe for which the deformation function is defined :type universe: :class:`~MMTK.Universe.Universe` :param fc_length: the range parameter r_0 in the pair interaction term :type fc_length: float :param cutoff: the cutoff used in the deformation calculation :type cutoff: float :param factor: a global scaling factor :type factor: float :param form: the functional form ('exponential' or 'calpha') :type form: str """ DeformationEnergyFunction.__init__(self, universe, fc_length, cutoff, factor, form) self.normalize = 1 class FiniteDeformationEnergyFunction(DeformationEvaluationFunction): """ Finite-displacement deformation energy function The deformation energy is the sum of the deformation values over all atoms of a system. The default values are appropriate for a |C_alpha| model of a protein with the global scaling described in the reference cited above. A FiniteDeformationEnergyFunction is called with one or two parameters. The first parameter is a ParticleVector object containing the alternate configuration of the universe for which the deformation energy is to be evaluated. The optional second argument can be set to a non-zero value to request the gradients of the energy in addition to the energy itself. In that case there are two return values (energy and the gradients in a ParticleVector object), otherwise only the energy is returned. """ def __init__(self, universe, fc_length = 0.7, cutoff = 1.2, factor = 46402., form = 'exponential'): """ :param universe: the universe for which the deformation function is defined :type universe: :class:`~MMTK.Universe.Universe` :param fc_length: the range parameter r_0 in the pair interaction term :type fc_length: float :param cutoff: the cutoff used in the deformation calculation :type cutoff: float :param factor: a global scaling factor :type factor: float :param form: the functional form ('exponential' or 'calpha') :type form: str """ DeformationEvaluationFunction.__init__(self, universe, fc_length, cutoff, factor, form) def __call__(self, vector, gradients = None): conf = self.universe.configuration() g = None if gradients is not None: if ParticleProperties.isParticleProperty(gradients): g = gradients elif isinstance(gradients, N.array_type): g = ParticleProperties.ParticleVector(self.universe, gradients) elif gradients: g = ParticleProperties.ParticleVector(self.universe) if g is None: g_array = None else: g_array = g.array l = deformation(conf.array, vector.array, self.pairs, g_array, None, self.cutoff, self.fc_length, self.factor, 0, 1, self.version) if g is None: return l else: return l, g # # Deformation energy minimization # class DeformationReducer(DeformationEvaluationFunction): """ Iterative reduction of the deformation energy The default values are appropriate for a |C_alpha| model of a protein with the global scaling described in the reference cited above. A DeformationReducer is called with two arguments. The first is a ParticleVector containing the initial infinitesimal displacements for all atoms. The second is an integer indicating the number of iterations. The result is a modification of the displacements by steepest-descent minimization of the deformation energy. """ def __init__(self, universe, fc_length = 0.7, cutoff = 1.2, factor = 46402., form = 'exponential'): """ :param universe: the universe for which the deformation function is defined :type universe: :class:`~MMTK.Universe.Universe` :param fc_length: the range parameter r_0 in the pair interaction term :type fc_length: float :param cutoff: the cutoff used in the deformation calculation :type cutoff: float :param factor: a global scaling factor :type factor: float :param form: the functional form ('exponential' or 'calpha') :type form: str """ DeformationEvaluationFunction.__init__(self, universe, fc_length, cutoff, factor, form) def __call__(self, vector, niter): conf = self.universe.configuration() reduceDeformation(conf.array, vector.array, self.pairs, self.cutoff, self.fc_length, self.factor, niter, self.version) class FiniteDeformationReducer(DeformationEvaluationFunction): """ Iterative reduction of the finite-displacement deformation energy The default values are appropriate for a |C_alpha| model of a protein with the global scaling described in the reference cited above. A FiniteDeformationReducer is called with two arguments. The first is a ParticleVector or Configuration containing the alternate configuration for which the deformation energy is evaluated. The second is the RMS distance that defines the termination condition. The return value a configuration that differs from the input configuration by approximately the specified RMS distance, and which is obtained by iterative steepest-descent minimization of the finite-displacement deformation energy. """ def __init__(self, universe, fc_length = 0.7, cutoff = 1.2, factor = 46402., form = 'exponential'): """ :param universe: the universe for which the deformation function is defined :type universe: :class:`~MMTK.Universe.Universe` :param fc_length: the range parameter r_0 in the pair interaction term :type fc_length: float :param cutoff: the cutoff used in the deformation calculation :type cutoff: float :param factor: a global scaling factor :type factor: float :param form: the functional form ('exponential' or 'calpha') :type form: str """ DeformationEvaluationFunction.__init__(self, universe, fc_length, cutoff, factor, form) def __call__(self, vector, rms_reduction): conf = self.universe.configuration() vector = vector-conf reduceFiniteDeformation(conf.array, vector.array, self.pairs, self.cutoff, self.fc_length, self.factor, rms_reduction, self.version) return conf+vector MMTK-2.7.9/MMTK/Dynamics.py0000644000076600000240000003276512117632051015615 0ustar hinsenstaff00000000000000# This module implements MD integrators # # Written by Konrad Hinsen # """ Molecular Dynamics integrators """ __docformat__ = 'restructuredtext' from MMTK import Environment, Features, Trajectory, Units import MMTK_dynamics from Scientific import N # # Integrator base class # class Integrator(Trajectory.TrajectoryGenerator): def __init__(self, universe, options): Trajectory.TrajectoryGenerator.__init__(self, universe, options) default_options = {'first_step': 0, 'steps': 100, 'delta_t': 1.*Units.fs, 'background': False, 'threads': None, 'mpi_communicator': None, 'actions': []} available_data = ['configuration', 'velocities', 'gradients', 'energy', 'thermodynamic', 'time', 'auxiliary'] restart_data = ['configuration', 'velocities', 'energy', 'thermodynamic', 'auxiliary'] def __call__(self, options): raise AttributeError # # Velocity-Verlet integrator # class VelocityVerletIntegrator(Integrator): """ Velocity-Verlet molecular dynamics integrator The integrator can handle fixed atoms, distance constraints, a thermostat, and a barostat, as well as any combination. It is fully thread-safe. The integration is started by calling the integrator object. All the keyword options (see documnentation of __init__) can be specified either when creating the integrator or when calling it. The following data categories and variables are available for output: - category "time": time - category "configuration": configuration and box size (for periodic universes) - category "velocities": atomic velocities - category "gradients": energy gradients for each atom - category "energy": potential and kinetic energy, plus extended-system energy terms if a thermostat and/or barostat are used - category "thermodynamic": temperature, volume (if a barostat is used) and pressure - category "auxiliary": extended-system coordinates if a thermostat and/or barostat are used """ def __init__(self, universe, **options): """ :param universe: the universe on which the integrator acts :type universe: :class:`~MMTK.Universe.Universe` :keyword steps: the number of integration steps (default is 100) :type steps: int :keyword delta_t: the time step (default is 1 fs) :type delta_t: float :keyword actions: a list of actions to be executed periodically (default is none) :type actions: list :keyword threads: the number of threads to use in energy evaluation (default set by MMTK_ENERGY_THREADS) :type threads: int :keyword background: if True, the integration is executed as a separate thread (default: False) :type background: bool :keyword mpi_communicator: an MPI communicator object, or None, meaning no parallelization (default: None) :type mpi_communicator: Scientific.MPI.MPICommunicator """ Integrator.__init__(self, universe, options) self.features = [Features.FixedParticleFeature, Features.NoseThermostatFeature, Features.AndersenBarostatFeature, Features.DistanceConstraintsFeature] def __call__(self, **options): """ Run the integrator. The keyword options are the same as described under __init__. """ self.setCallOptions(options) used_features = Features.checkFeatures(self, self.universe) configuration = self.universe.configuration() velocities = self.universe.velocities() if velocities is None: raise ValueError("no velocities") masses = self.universe.masses() fixed = self.universe.getAtomBooleanArray('fixed') nt = self.getOption('threads') comm = self.getOption('mpi_communicator') evaluator = self.universe.energyEvaluator(threads=nt, mpi_communicator=comm) evaluator = evaluator.CEvaluator() constraints, const_distances_sq, c_blocks = \ _constraintArrays(self.universe) type = 'NVE' if Features.NoseThermostatFeature in used_features: type = 'NVT' thermostat = self.universe.environmentObjectList( Environment.NoseThermostat)[0] t_parameters = thermostat.parameters t_coordinates = thermostat.coordinates else: t_parameters = N.zeros((0,), N.Float) t_coordinates = N.zeros((2,), N.Float) if Features.AndersenBarostatFeature in used_features: if self.universe.cellVolume() is None: raise ValueError("Barostat requires finite volume universe") if type == 'NVE': type = 'NPH' else: type = 'NPT' barostat = self.universe.environmentObjectList( Environment.AndersenBarostat)[0] b_parameters = barostat.parameters b_coordinates = barostat.coordinates else: b_parameters = N.zeros((0,), N.Float) b_coordinates = N.zeros((1,), N.Float) args = (self.universe, configuration.array, velocities.array, masses.array, fixed.array, evaluator, constraints, const_distances_sq, c_blocks, t_parameters, t_coordinates, b_parameters, b_coordinates, self.getOption('delta_t'), self.getOption('first_step'), self.getOption('steps'), self.getActions(), type + ' dynamics trajectory with ' + self.optionString(['delta_t', 'steps'])) return self.run(MMTK_dynamics.integrateVV, args) # # Velocity scaling, removal of global translation/rotation # class VelocityScaler(Trajectory.TrajectoryAction): """ Periodic velocity scaling action A VelocityScaler object is used in the action list of a VelocityVerletIntegrator. It rescales all atomic velocities by a common factor to make the temperature of the system equal to a predefined value. """ def __init__(self, temperature, window=0., first=0, last=None, skip=1): """ :param temperature: the temperature value to which the velocities should be scaled :type temperature: float :param window: the deviation from the ideal temperature that is tolerated in either direction before rescaling takes place :type window: float :param first: the number of the first step at which the action is executed :type first: int :param last: the number of the first step at which the action is no longer executed. A value of None indicates that the action should be executed indefinitely. :type last: int :param skip: the number of steps to skip between two applications of the action :type skip: int """ Trajectory.TrajectoryAction.__init__(self, first, last, skip) self.parameters = N.array([temperature, window], N.Float) self.Cfunction = MMTK_dynamics.scaleVelocities class Heater(Trajectory.TrajectoryAction): """ Periodic heating action A Heater object us used in the action list of a VelocityVerletIntegrator. It scales the velocities to a temperature that increases over time. """ def __init__(self, temp1, temp2, gradient, first=0, last=None, skip=1): """ :param temp1: the temperature value to which the velocities should be scaled initially :type temp1: float :param temp2: the final temperature value to which the velocities should be scaled :type temp2: float :param gradient: the temperature gradient (in K/ps) :type gradient: float :param first: the number of the first step at which the action is executed :type first: int :param last: the number of the first step at which the action is no longer executed. A value of None indicates that the action should be executed indefinitely. :type last: int :param skip: the number of steps to skip between two applications of the action :type skip: int """ Trajectory.TrajectoryAction.__init__(self, first, last, skip) self.parameters = N.array([temp1, temp2, gradient], N.Float) self.Cfunction = MMTK_dynamics.heat class BarostatReset(Trajectory.TrajectoryAction): """ Barostat reset action A BarostatReset object is used in the action list of a VelocityVerletIntegrator. It resets the barostat coordinate to zero. """ def __init__(self, first=0, last=None, skip=1): """ :param first: the number of the first step at which the action is executed :type first: int :param last: the number of the first step at which the action is no longer executed. A value of None indicates that the action should be executed indefinitely. :type last: int :param skip: the number of steps to skip between two applications of the action :type skip: int """ Trajectory.TrajectoryAction.__init__(self, first, last, skip) self.parameters = N.zeros((0,), N.Float) self.Cfunction = MMTK_dynamics.resetBarostat class TranslationRemover(Trajectory.TrajectoryAction): """ Action that eliminates global translation A TranslationRemover object is used in the action list of a VelocityVerletIntegrator. It subtracts the total velocity of the system from each atomic velocity. """ def __init__(self, first=0, last=None, skip=1): """ :param first: the number of the first step at which the action is executed :type first: int :param last: the number of the first step at which the action is no longer executed. A value of None indicates that the action should be executed indefinitely. :type last: int :param skip: the number of steps to skip between two applications of the action :type skip: int """ Trajectory.TrajectoryAction.__init__(self, first, last, skip) self.parameters = None self.Cfunction = MMTK_dynamics.removeTranslation class RotationRemover(Trajectory.TrajectoryAction): """ Action that eliminates global rotation A RotationRemover object is used in the action list of a VelocityVerletIntegrator. It adjusts the atomic velocities such that the total angular momentum is zero. """ def __init__(self, first=0, last=None, skip=1): """ :param first: the number of the first step at which the action is executed :type first: int :param last: the number of the first step at which the action is no longer executed. A value of None indicates that the action should be executed indefinitely. :type last: int :param skip: the number of steps to skip between two applications of the action :type skip: int """ Trajectory.TrajectoryAction.__init__(self, first, last, skip) self.parameters = None self.Cfunction = MMTK_dynamics.removeRotation # # Construct constraint arrays # def _constraintArrays(universe): nc = universe.numberOfDistanceConstraints() constraints = N.zeros((nc, 2), N.Int) const_distances_sq = N.zeros((nc, ), N.Float) if nc > 0: i = 0 c_blocks = [0] for o in universe.objectList(): for c in o.distanceConstraintList(): constraints[i, 0] = c[0].index constraints[i, 1] = c[1].index const_distances_sq[i] = c[2]*c[2] i = i + 1 c_blocks.append(i) c_blocks = N.array(c_blocks) else: c_blocks = N.zeros((1,), N.Int) return constraints, const_distances_sq, c_blocks # # Enforce distance constraints for current configuration # def enforceConstraints(universe, configuration=None): constraints, const_distances_sq, c_blocks = _constraintArrays(universe) if len(constraints) == 0: return if configuration is None: configuration = universe.configuration() MMTK_dynamics.enforceConstraints(universe._spec, configuration.array, universe.masses().array, constraints, const_distances_sq, c_blocks) # # Project velocity vector onto the constraint surface # def projectVelocities(universe, velocities): constraints, const_distances_sq, c_blocks = _constraintArrays(universe) if len(constraints) == 0: return MMTK_dynamics.projectVelocities(universe._spec, universe.configuration().array, velocities.array, universe.masses().array, constraints, const_distances_sq, c_blocks) MMTK-2.7.9/MMTK/Environment.py0000644000076600000240000000555012117632051016342 0ustar hinsenstaff00000000000000# This module defines environment objects for universes. # # Written by Konrad Hinsen # """ Environment objects Environment objects are objects that define a simulation system without being composed of atoms. Examples are thermostats, barostats, external fields, etc. """ __docformat__ = 'restructuredtext' from Scientific import N # # The environment object base class # class EnvironmentObject(object): is_environment_object = 1 def checkCompatibilityWith(self, other): pass def description(self): return "o('Environment." + self.__class__.__name__ + \ `tuple(self.parameters)` + "')" # Type check def isEnvironmentObject(object): return hasattr(object, 'is_environment_object') # # Nose thermostat class # class NoseThermostat(EnvironmentObject): """ Nose thermostat for Molecular Dynamics A thermostat object can be added to a universe and will then modify the integration algorithm to a simulation of an NVT ensemble. """ def __init__(self, temperature, relaxation_time = 0.2): """ :param temperature: the temperature set by the thermostat :type temperature: float :param relaxation_time: the relaxation time of the thermostat coordinate :type relaxation_time: float """ self.arguments = (temperature, relaxation_time) self.parameters = N.array([temperature, relaxation_time]) self.coordinates = N.array([0., 0.]) def setTemperature(self, temperature): self.parameters[0] = temperature def setRelaxationTime(self, t): self.parameters[1] = t def checkCompatibilityWith(self, other): if other.__class__ is NoseThermostat: raise ValueError("the universe already has a thermostat") # # Andersen barostat class # class AndersenBarostat(EnvironmentObject): """ Andersen barostat for Molecular Dynamics A barostat object can be added to a universe and will then together with a thermostat object modify the integration algorithm to a simulation of an NPT ensemble. """ def __init__(self, pressure, relaxation_time = 1.5): """ :param pressure: the pressure set by the barostat :type pressure: float :param relaxation_time: the relaxation time of the barostat coordinate :type relaxation_time: float """ self.arguments = (pressure, relaxation_time) self.parameters = N.array([pressure, relaxation_time]) self.coordinates = N.array([0.]) def setPressure(self, pressure): self.parameters[0] = pressure def setRelaxationTime(self, t): self.parameters[1] = t def checkCompatibilityWith(self, other): if other.__class__ is AndersenBarostat: raise ValueError("the universe already has a barostat") MMTK-2.7.9/MMTK/Features.py0000644000076600000240000000542212117632051015612 0ustar hinsenstaff00000000000000# This module contains feature management classes. # # Written by Konrad Hinsen # from MMTK import Environment import string # Each feature class represents a feature that a certain universe # might or might not have: fixed atoms, constraints, thermostats, etc. # Each non-trivial algorithm that works on a universe keeps a list of # features it can handle. This arrangement ensures a minimal # compatibility test between universes and algorithms. # The feature list stores all defined features. _all = [] # The feature base class ensures that each feature is a singleton. class Feature(object): def __init__(self): for f in _all: if f.__class__ == self.__class__: raise ValueError("feature alredy defined") _all.append(self) # Method to be redefined by subclasses def isInUniverse(self, universe): raise TypeError("must be defined in subclass") # # Fixed particle feature # class FixedParticleFeatureClass(Feature): def isInUniverse(self, universe): fixed = universe.getAtomBooleanArray('fixed') return fixed.sumOverParticles() > 0 description = 'fixed particles' FixedParticleFeature = FixedParticleFeatureClass() # # Distance constraints feature # class DistanceConstraintsFeatureClass(Feature): def isInUniverse(self, universe): return universe.numberOfDistanceConstraints() > 0 description = 'distance constraints' DistanceConstraintsFeature = DistanceConstraintsFeatureClass() # # Nose thermostat feature # class NoseThermostatFeatureClass(Feature): def isInUniverse(self, universe): for o in universe._environment: if o.__class__ is Environment.NoseThermostat: return True return False description = 'Nose thermostat' NoseThermostatFeature = NoseThermostatFeatureClass() # # Andersen barostat feature # class AndersenBarostatFeatureClass(Feature): def isInUniverse(self, universe): for o in universe._environment: if o.__class__ is Environment.AndersenBarostat: return True return False description = 'Andersen barostat' AndersenBarostatFeature = AndersenBarostatFeatureClass() # # Return feature list for a universe. # def getFeatureList(universe): return [f for f in _all if f.isInUniverse(universe)] # # Check that a feature list contains everything necessary for a universe. # def checkFeatures(algorithm, universe): universe_features = set(getFeatureList(universe)) unsupported = universe_features.difference(set(algorithm.features)) if unsupported: f = '\n'.join([f.description for f in unsupported]) raise ValueError(algorithm.__class__.__name__ + " does not support the following features:\n" + f) return universe_features MMTK-2.7.9/MMTK/Field.py0000644000076600000240000003155012041510260015051 0ustar hinsenstaff00000000000000# This module defines scalar and vector fields in molecular systems # # Written by Konrad Hinsen # """ Scalar and vector fields in molecular systems This module defines field objects that are useful in the analysis and visualization of collective motions in molecular systems. Atomic quantities characterizing collective motions vary slowly in space, and can be considered functions of position instead of values per atom. Functions of position are called fields, and mathematical techniques for the analysis of fields have proven useful in many branches of physics. Fields can be described numerically by values on a regular grid. In addition to permitting the application of vector analysis methods to atomic quantities, the introduction of fields is a valuable visualization aid, because information defined on a coarse regular grid can be added to a picture of a molecular system without overloading it. """ __docformat__ = 'restructuredtext' from MMTK import Collections, ParticleProperties, Visualization from Scientific.Visualization import Color from Scientific.Geometry import Vector, TensorAnalysis from Scientific import N class AtomicField(object): """ A field whose values are determined by atomic quantities This is an abstract base class. To create field objects, use one of its subclasses. """ def __init__(self, system, grid_size, values): self.system = system if isinstance(grid_size, tuple): self.box, self.field, self.points, self.colors = grid_size else: if values.data_rank != 1: raise TypeError("data not a particle variable") self.box = Collections.PartitionedAtomCollection(grid_size, system) self._setField(values) def _setField(self, data): points = [] values = [] colors = [] default_color = Color.ColorByName('black') for min, max, atoms in self.box.partitions(): center = 0.5*(min+max) points.append(center.array) v = data.zero() total_weight = 0. color = default_color for a in atoms: d = a.position()-center weight = N.exp(-d*d/self.box.partition_size**2) v = v + weight*data[a] total_weight = total_weight + weight c = default_color try: c = a.color except AttributeError: pass if isinstance(c, basestring): c = Color.ColorByName(c) color = color + c values.append(v/total_weight) colors.append(color) min = N.minimum.reduce(points) max = N.maximum.reduce(points) axes = (N.arange(min[0], max[0]+1, self.box.partition_size), N.arange(min[1], max[1]+1, self.box.partition_size), N.arange(min[2], max[2]+1, self.box.partition_size)) array = N.zeros(tuple(map(len, axes)) + data.value_rank*(3,), N.Float) inside = N.zeros(tuple(map(len, axes)), N.Float) for p, v in zip(points, values): indices = N.floor((p-min)/self.box.partition_size+0.5) indices = tuple(indices.astype(N.Int)) array[indices] = v inside[indices] = 1. self.field = self.field_class(axes, array, data.zero()) inside = TensorAnalysis.ScalarField(axes, inside).gradient().length() self.points = [] self.colors = [] for i in range(len(points)): p = points[i] test = 0 try: test = apply(inside, tuple(p)) > 1.e-10 except ValueError: pass if test: self.points.append(p) self.colors.append(colors[i]) def __call__(self, point): return self.field(point) def particleValues(self): """ :returns: the values of the field at the positions of the atoms :rtype: :class:`~MMTK.ParticleProperties.ParticleProperty` """ universe = self.system.universe() rank = self.field.rank if rank == 0: v = ParticleProperties.ParticleScalar(universe) elif rank == 1: v = ParticleProperties.ParticleVector(universe) else: raise ValueError("no appropriate return type") for a in self.system.atomList(): v[a] = self.field(a.position()) return v def writeToFile(self, filename, scale = 1., length_scale=1., color = None): """ Writes a graphical representation of the field to a VRML file. :param filename: the name of the destination file :type filename: str :param scale: scale factor applied to all field values :type scale: float :param color: the color for all graphics objects :type color: Scientific.Visualization.Color """ from Scientific.Visualization import VRML2 objects = self.graphicsObjects(scale=scale, length_scale=length_scale, color=color, graphics_module=VRML2) VRML2.Scene(objects).writeToFile(filename) def view(self, scale = 1., length_scale = 1., color = None): """ Shows a graphical representation of the field using a VRML viewer. :param scale: scale factor applied to all field values :type scale: float :param color: the color for all graphics objects :type color: Scientific.Visualization.Color """ from Scientific.Visualization import VRML2 objects = self.graphicsObjects(scale=scale, length_scale=length_scale, color=color, graphics_module=VRML2) VRML2.Scene(objects).view() class AtomicScalarField(AtomicField, Visualization.Viewable): """ Scalar field defined by atomic quantities For visualization, scalar fields are represented by a small cube on each grid point whose color indicates the field's value on a symmetric red-to-green color scale defined by the range of the field values. Additional keyword options exist for graphics object generation: - scale=factor, to multiply all field values by a factor - range=(min, max), to eliminate graphics objects for values that are smaller than min or larger than max """ def __init__(self, system, grid_size, values): """ :param system: any subset of a molecular system :param grid_size: the spacing of a cubic grid on which the field values are defined. The value for a point is obtained by averaging the atomic quantities over all atoms in a cube centered on the point. :type grid_size: float :param values: the atomic values that define the field :type values: :class:`~MMTK.ParticleProperties.ParticleScalar` """ if values is not None and values.value_rank != 0: raise TypeError("data not a vector field") self.field_class = TensorAnalysis.ScalarField AtomicField.__init__(self, system, grid_size, values) def gradient(self): """ :returns: the gradient of the field :rtype: :class:`~MMTK.Field.AtomicVectorField` """ field = self.field.gradient() return AtomicVectorField(self.system, (self.box, field, self.points, self.colors), None) def laplacian(self): """ :returns: the laplacian of the field :rtype: :class:`~MMTK.Field.AtomicScalarField` """ field = self.field.laplacian() return AtomicScalarField(self.system, (self.box, field, self.points, self.colors), None) def _graphics(self, conf, distance_fn, model, module, options): scale = options.get('scale', 1.) range = options.get('range', (None, None)) length_scale = options.get('length_scale', 1.) lower, upper = range size = self.box.partition_size/10. objects = [] color_scale = module.SymmetricColorScale(1.) for p, c in zip(self.points, self.colors): p = tuple(p) v = apply(self.field, p) if (lower is None or v > lower) and (upper is None or v < upper): p = apply(Vector, p) v = scale*v if v < -1. or v > 1.: m = module.DiffuseMaterial('black') else: m = module.Material(diffuse_color = color_scale(scale*v)) objects.append(module.Cube(length_scale*p, length_scale*size, material = m)) return objects class AtomicVectorField(AtomicField, Visualization.Viewable): """ Vector field defined by atomic quantities For visualization, scalar fields are represented by a small arrow on each grid point. The arrow starts at the grid point and represents the vector value at that point. Additional keyword options exist for graphics object generation: - scale=factor, to multiply all field values by a factor - diameter=number, to define the diameter of the arrow objects (default: 1.) - range=(min, max), to eliminate graphics objects for values that are smaller than min or larger than max - color=string, to define the color of the arrows by a color name """ def __init__(self, system, grid_size, values): """ :param system: any subset of a molecular system :param grid_size: the spacing of a cubic grid on which the field values are defined. The value for a point is obtained by averaging the atomic quantities over all atoms in a cube centered on the point. :type grid_size: float :param values: the atomic values that define the field :type values: :class:`~MMTK.ParticleProperties.ParticleVector` """ if values is not None and values.value_rank != 1: raise TypeError("data not a vector field") self.field_class = TensorAnalysis.VectorField AtomicField.__init__(self, system, grid_size, values) def length(self): """ :returns: a field of the length of the field vectors :rtype: :class:`~MMTK.Field.AtomicScalarField` """ field = self.field.length() return AtomicScalarField(self.system, (self.box, field, self.points, self.colors), None) def divergence(self): """ :returns: the divergence of the field :rtype: :class:`~MMTK.Field.AtomicScalarField` """ field = self.field.divergence() return AtomicScalarField(self.system, (self.box, field, self.points, self.colors), None) def curl(self): """ :returns: the curl of the field :rtype: :class:`~MMTK.Field.AtomicVectorField` """ field = self.field.curl() return AtomicVectorField(self.system, (self.box, field, self.points, self.colors), None) def laplacian(self): """ :returns: the laplacian of the field :rtype: :class:`~MMTK.Field.AtomicVectorField` """ field = self.field.curl() return AtomicVectorField(self.system, (self.box, field, self.points, self.colors), None) def _graphics(self, conf, distance_fn, model, module, options): scale = options.get('scale', 1.) diameter = options.get('diameter', 1.) color = options.get('color', None) range = options.get('range', (None, None)) length_scale = options.get('length_scale', 1.) lower, upper = range size = diameter*self.box.partition_size/50. if color is not None: color = module.ColorByName(color) objects = [] materials = {} for p, c in zip(self.points, self.colors): p = tuple(p) v = apply(self.field, p) lv = v.length() if (lower is None or lv > lower) and (upper is None or lv < upper): p = apply(Vector, p) if color is not None: c = color try: m = materials[c] except KeyError: m = module.Material(diffuse_color = c) materials[c] = m objects.append(module.Arrow(length_scale*p, length_scale*(p+scale*v), length_scale*size, material = m)) return objects MMTK-2.7.9/MMTK/ForceFields/0000755000076600000240000000000012156626571015661 5ustar hinsenstaff00000000000000MMTK-2.7.9/MMTK/ForceFields/__init__.py0000644000076600000240000000200611662461640017763 0ustar hinsenstaff00000000000000# Force field initialization # # Written by Konrad Hinsen # """ Force fields """ __docformat__ = 'restructuredtext' import os, string, sys class _ForceFieldLoader(object): def __init__(self, module, object): self.module = module self.object = object self.globals = globals() __safe_for_unpickling__ = True def __call__(self, *args, **kw): ffc = getattr(__import__(self.module, self.globals), self.object) ffc.description = _description return apply(ffc, args, kw) def _description(self): return 'ForceFields.' + self.__class__.__name__ + `self.arguments` ff_list = open(os.path.join(os.path.split(__file__)[0], 'force_fields')).readlines() for line in ff_list: line = string.split(line) exec line[0] + "=_ForceFieldLoader(line[1], line[2])" try: default_energy_threads = string.atoi(os.environ['MMTK_ENERGY_THREADS']) except KeyError: default_energy_threads = 1 del os del string del sys del ff_list del line MMTK-2.7.9/MMTK/ForceFields/Amber/0000755000076600000240000000000012156626571016707 5ustar hinsenstaff00000000000000MMTK-2.7.9/MMTK/ForceFields/Amber/__init__.py0000644000076600000240000000043212041514572021005 0ustar hinsenstaff00000000000000# Amber forcefield initialization # # Written by Konrad Hinsen # """ Amber force fields """ __docformat__ = 'restructuredtext' from AmberForceField import Amber12SBForceField, Amber99ForceField, \ Amber94ForceField, Amber91ForceField, OPLSForceField MMTK-2.7.9/MMTK/ForceFields/Amber/AmberData.py0000644000076600000240000003545712064347316021112 0ustar hinsenstaff00000000000000# This module handles input and application of Amber parameter files. # # Written by Konrad Hinsen # from MMTK import Units from Scientific.IO.FortranFormat import FortranFormat, FortranLine from Scientific.IO.TextFile import TextFile from Scientific.DictWithDefault import DictWithDefault amber_energy_unit = Units.kcal/Units.mol amber_length_unit = Units.Ang amber_angle_unit = Units.deg amber_fc_angle_unit = Units.rad # # The force field parameter file # class AmberParameters(object): def __init__(self, file, modifications=[]): if isinstance(file, basestring): file = TextFile(file) title = file.readline()[:-1] self.atom_types = DictWithDefault(None) self._readAtomTypes(file) format = FortranFormat('20(A2,2X)') done = False while not done: l = FortranLine(file.readline()[:-1], format) for entry in l: name = _normalizeName(entry) if len(name) == 0: done = True break try: # ignore errors for now self.atom_types[name].hydrophylic = True except: pass self.bonds = {} self._readBondParameters(file) self.bond_angles = {} self._readAngleParameters(file) self.dihedrals = {} self.dihedrals_2 = {} self._readDihedralParameters(file) self.impropers = {} self.impropers_1 = {} self.impropers_2 = {} self._readImproperParameters(file) self.hbonds = {} self._readHbondParameters(file) self.lj_equivalent = {} format = FortranFormat('20(A2,2X)') while True: l = FortranLine(file.readline()[:-1], format) if l.isBlank(): break name1 = _normalizeName(l[0]) for s in l[1:]: name2 = _normalizeName(s) self.lj_equivalent[name2] = name1 self.ljpar_sets = {} while True: l = FortranLine(file.readline()[:-1], 'A4,6X,A2') if l[0] == 'END ': break set_name = _normalizeName(l[0]) ljpar_set = AmberLJParameterSet(set_name, l[1]) self.ljpar_sets[set_name] = ljpar_set self._readLJParameters(file, ljpar_set) file.close() for mod, ljname in modifications: if isinstance(mod, basestring): file = TextFile(mod) else: file = mod title = file.readline()[:-1] blank = file.readline()[:-1] while True: keyword = file.readline() if not keyword: break keyword = keyword.strip()[:4] if keyword == 'MASS': self._readAtomTypes(file) elif keyword == 'BOND': self._readBondParameters(file) elif keyword == 'ANGL': self._readAngleParameters(file) elif keyword == 'DIHE': self._readDihedralParameters(file) elif keyword == 'IMPR': self._readImproperParameters(file) elif keyword == 'HBON': self._readHbondParameters(file) elif keyword == 'NONB': self._readLJParameters(file, self.ljpar_sets[ljname]) def _readAtomTypes(self, file): format = FortranFormat('A2,1X,F10.2') while True: l = FortranLine(file.readline()[:-1], format) if l.isBlank(): break name = _normalizeName(l[0]) self.atom_types[name] = AmberAtomType(name, l[1]) def _readBondParameters(self, file): format = FortranFormat('A2,1X,A2,2F10.2') while True: l = FortranLine(file.readline()[:-1], format) if l.isBlank(): break name1 = _normalizeName(l[0]) name2 = _normalizeName(l[1]) name1, name2 = _sort(name1, name2) self.bonds[(name1, name2)] = \ AmberBondParameters(self.atom_types[name1], self.atom_types[name2], l[2], l[3]) def _readAngleParameters(self, file): format = FortranFormat('A2,1X,A2,1X,A2,2F10.2') while True: l = FortranLine(file.readline()[:-1], format) if l.isBlank(): break name1 = _normalizeName(l[0]) name2 = _normalizeName(l[1]) name3 = _normalizeName(l[2]) name1, name3 = _sort(name1, name3) self.bond_angles[(name1, name2, name3)] = \ AmberBondAngleParameters(self.atom_types[name1], self.atom_types[name2], self.atom_types[name3], l[3], l[4]) def _readDihedralParameters(self, file): append = None while True: line = file.readline().rstrip() if not line: break names, params = line[:11], line[11:] params = params.strip().split()[:4] divf = int(params[0]) k = float(params[1]) delta = float(params[2]) n = float(params[3]) if append is not None: append.addTerm(divf, k, delta, n) if n >= 0: append = None else: name1, name2, name3, name4 = [_normalizeName(name) for name in names.split('-')] name1, name2, name3, name4 = _sort4(name1, name2, name3, name4) p = AmberDihedralParameters(self.atom_types[name1], self.atom_types[name2], self.atom_types[name3], self.atom_types[name4], divf, k, delta, n) if name1 == 'X' and name4 == 'X': self.dihedrals_2[(name2, name3)] = p else: self.dihedrals[(name1, name2, name3, name4)] = p if n < 0: append = p def _readImproperParameters(self, file): format = FortranFormat('A2,1X,A2,1X,A2,1X,A2,I4,3F15.2') while True: l = FortranLine(file.readline()[:-1], format) if l.isBlank(): break name1 = _normalizeName(l[0]) name2 = _normalizeName(l[1]) name3 = _normalizeName(l[2]) name4 = _normalizeName(l[3]) if name1 == 'X': if name2 == 'X': name1 = name3 name2 = name4 name3 = 'X' name4 = 'X' else: name1 = name3 name2, name3 = _sort(name2, name4) name4 = 'X' else: name1, name2, name3, name4 = \ (name3, ) + _sort3(name1, name2, name4) p = AmberImproperParameters(self.atom_types[name1], self.atom_types[name2], self.atom_types[name3], self.atom_types[name4], l[4], l[5], l[6], l[7]) if name4 == 'X': if name3 == 'X': self.impropers_2[(name1, name2)] = p else: self.impropers_1[(name1, name2, name3)] = p else: self.impropers[(name1, name2, name3, name4)] = p def _readHbondParameters(self, file): format = FortranFormat('2X,A2,2X,A2,2X,2F10.2') while True: l = FortranLine(file.readline()[:-1], format) if l.isBlank(): break name1 = _normalizeName(l[0]) name2 = _normalizeName(l[1]) name1, name2 = _sort(name1, name2) self.hbonds[(name1, name2)] = \ AmberHbondParameters(self.atom_types[name1], self.atom_types[name2], l[2], l[3]) def _readLJParameters(self, file, ljpar_set): format = FortranFormat('2X,A2,6X,3F10.6') while True: l = FortranLine(file.readline()[:-1], format) if l.isBlank(): break name = _normalizeName(l[0]) ljpar_set.addEntry(name, l[1], l[2], l[3]) def bondParameters(self, name1, name2): name1 = _normalizeName(name1) name2 = _normalizeName(name2) name1, name2 = _sort(name1, name2) p = self.bonds[(name1, name2)] return (p.l*amber_length_unit, p.k*amber_energy_unit/amber_length_unit**2) def bondAngleParameters(self, name1, name2, name3): name1 = _normalizeName(name1) name2 = _normalizeName(name2) name3 = _normalizeName(name3) name1, name3 = _sort(name1, name3) p = self.bond_angles[(name1, name2, name3)] return (p.angle*amber_angle_unit, p.k*amber_energy_unit/amber_fc_angle_unit) def dihedralParameters(self, name1, name2, name3, name4): name1 = _normalizeName(name1) name2 = _normalizeName(name2) name3 = _normalizeName(name3) name4 = _normalizeName(name4) name1, name2, name3, name4 = _sort4(name1, name2, name3, name4) try: p = self.dihedrals[(name1, name2, name3, name4)] except KeyError: try: p = self.dihedrals_2[(name2, name3)] except KeyError: p = None if p is None: return p else: return map(lambda p: (p[2], p[1]*amber_angle_unit, p[0]*amber_energy_unit), p.terms) def improperParameters(self, name1, name2, name3, name4): name1 = _normalizeName(name1) name2 = _normalizeName(name2) name3 = _normalizeName(name3) name4 = _normalizeName(name4) name2, name3, name4 = _sort3(name2, name3, name4) p = None try: p = self.impropers[(name1, name2, name3, name4)] except KeyError: for names in [(name2, name3), (name2, name4), (name3, name4)]: try: p = self.impropers_1[(name1,) + names] except KeyError: pass if p is None: for name in [name2, name3, name4]: try: p = self.impropers_2[(name1, name)] except KeyError: pass if p is None: return p else: return map(lambda p: (p[2], p[1]*amber_angle_unit, p[0]*amber_energy_unit), p.terms) def ljParameters(self, name, parameter_set = None): if parameter_set is None: ljp = self.default_ljpar_set else: ljp = self.ljpar_sets[parameter_set] try: name = self.lj_equivalent[name] except KeyError: pass if ljp.type == 'RE': p = ljp.getEntry(name) return (p[1]*amber_energy_unit, 1.78179743628*p[0]*amber_length_unit, 0) elif ljp.type == 'AC': p = ljp.getEntry(name) a = p[0]*amber_energy_unit*amber_length_unit**12 c = p[1]*amber_energy_unit*amber_length_unit**6 if c == 0.: eps = 0. else: eps = 0.25*c*c/a if a == 0.: sigma = 0. else: sigma = (a/c)**(1./6.) return (eps, sigma, 1) else: raise ValueError('Type ' + ljp.type + ' not supported.') # # Atom type class # class AmberAtomType(object): def __init__(self, name, mass): self.name = name self.mass = mass self.hydrophylic = False # # Bond parameter class # class AmberBondParameters(object): def __init__(self, a1, a2, k, l): self.a1 = a1 self.a2 = a2 self.k = k self.l = l # # Bond angle parameter class # class AmberBondAngleParameters(object): def __init__(self, a1, a2, a3, k, angle): self.a1 = a1 self.a2 = a2 self.a3 = a3 self.k = k self.angle = angle # # Dihedral angle parameter class # class AmberDihedralParameters(object): def __init__(self, a1, a2, a3, a4, divf, k, delta, n): self.a1 = a1 self.a2 = a2 self.a3 = a3 self.a4 = a4 self.terms = [(k/divf, delta, int(abs(n)))] def addTerm(self, divf, k, delta, n): self.terms.append((k/divf, delta, int(abs(n)))) def __repr__(self): if self.a1 is None and self.a4 is None: return 'X-' + self.a2.name + '-' + self.a3.name + '-X' else: return self.a1.name + '-' + self.a2.name + '-' + \ self.a3.name + '-' + self.a4.name __str__ = __repr__ # # Improper dihedral angle parameter class # class AmberImproperParameters(object): def __init__(self, a1, a2, a3, a4, divf, k, delta, n): self.a1 = a1 self.a2 = a2 self.a3 = a3 self.a4 = a4 self.terms = [(0.5*k, delta, int(abs(n)))] def addTerm(self, divf, k, delta, n): self.terms.append((0.5*k, delta, int(abs(n)))) def __repr__(self): if self.a4 is None: if self.a3 is None: return self.a1.name + '-' + self.a2.name + '-X-X' else: return self.a1.name + '-' + self.a2.name + '-' + \ self.a3.name + '-X' else: return self.a1.name + '-' + self.a2.name + '-' + \ self.a3.name + '-' + self.a4.name __str__ = __repr__ # # H-bond parameter class # class AmberHbondParameters(object): def __init__(self, a1, a2, a, b): self.a1 = a1 self.a2 = a2 self.a = a self.b = b # # Lennard-Jones parameter sets # class AmberLJParameterSet(object): def __init__(self, name, type): self.name = name self.type = type self.entries = {} def addEntry(self, name, p1, p2, p3): if self.type == 'SK': self.entries[name] = (p1, p2, p3) else: self.entries[name] = (p1, p2) def getEntry(self, name): return self.entries[name] # # Utility functions # def _normalizeName(name): return name.strip() def _sort(s1, s2): if s2 < s1: return s2, s1 else: return s1, s2 def _sort3(s1, s2, s3): if s2 < s1: s1, s2 = s2, s1 if s3 < s2: s2, s3 = s3, s2 if s2 < s1: s1, s2 = s2, s1 return s1, s2, s3 def _sort4(s1, s2, s3, s4): if s3 < s2: return s4, s3, s2, s1 else: return s1, s2, s3, s4 MMTK-2.7.9/MMTK/ForceFields/Amber/AmberForceField.py0000644000076600000240000004453512117632051022230 0ustar hinsenstaff00000000000000# This file provides the Amber force field, using Amber parameter files. # # Written by Konrad Hinsen # """ Amber force field implementation General comments about parameters for Lennard-Jones and electrostatic interactions: Pair interactions in periodic systems are calculated using the minimum-image convention; the cutoff should therefore never be larger than half the smallest edge length of the elementary cell. For Lennard-Jones interactions, all terms for pairs whose distance exceeds the cutoff are set to zero, without any form of correction. For electrostatic interactions, a charge-neutralizing surface charge density is added around the cutoff sphere in order to reduce cutoff effects (see Wolf et al., J. Chem. Phys. 17, 8254 (1999)). For Ewald summation, there are some additional parameters that can be specified by dictionary entries: * "beta" specifies the Ewald screening parameter * "real_cutoff" specifies the cutoff for the real-space sum. It should be significantly larger than 1/beta to ensure that the neglected terms are small. * "reciprocal_cutoff" specifies the cutoff for the reciprocal-space sum. Note that, like the real-space cutoff, this is a distance; it describes the smallest wavelength of plane waves to take into account. Consequently, a smaller value means a more precise (and more expensive) calculation. MMTK provides default values for these parameter which are calculated as a function of the system size. However, these parameters are exaggerated in most cases of practical interest and can lead to excessive calculation times for large systems. It is preferable to determine suitable values empirically for the specific system to be simulated. The method "screened" uses the real-space part of the Ewald sum with a charge-neutralizing surface charge density around the cutoff sphere, and no reciprocal sum (see article cited above). It requires the specification of the dictionary entries "cutoff" and "beta". """ __docformat__ = 'restructuredtext' from MMTK.ForceFields import MMForceField from MMTK.ForceFields.Amber import AmberData import os # # Read parameter files # Amber12SB = None Amber99 = None Amber94 = None Amber91 = None OPLS = None this_directory = os.path.split(__file__)[0] def fullModFilePath(modfile): if not isinstance(modfile, basestring): return modfile if os.path.exists(modfile): return modfile return os.path.join(this_directory, os.path.basename(modfile)) def readAmber12SB(main_file = None, mod_files = None): global Amber12SB if main_file is None and mod_files is None: if Amber12SB is None: paramfile = os.path.join(this_directory, "parm10.dat") modfile = os.path.join(this_directory, "frcmod.ff12SB") Amber12SB = AmberData.AmberParameters(paramfile, [(modfile, 'MOD4')]) Amber12SB.lennard_jones_1_4 = 0.5 Amber12SB.electrostatic_1_4 = 1./1.2 Amber12SB.default_ljpar_set = Amber12SB.ljpar_sets['MOD4'] Amber12SB.atom_type_property = 'amber12_atom_type' Amber12SB.charge_property = 'amber_charge' return Amber12SB else: if main_file is None: main_file = os.path.join(this_directory, "parm10.dat") mod_files = [(fullModFilePath(mf), 'MOD4') for mf in mod_files] mod_files.insert(0, (os.path.join(this_directory, "frcmod.ff12SB"), 'MOD4')) params = AmberData.AmberParameters(main_file, mod_files) params.lennard_jones_1_4 = 0.5 params.electrostatic_1_4 = 1./1.2 params.default_ljpar_set = params.ljpar_sets['MOD4'] params.atom_type_property = 'amber12_atom_type' params.charge_property = 'amber_charge' return params def readAmber94(main_file = None, mod_files = None): global Amber94 if main_file is None and mod_files is None: if Amber94 is None: paramfile = os.path.join(this_directory, "parm94.dat") modfile = os.path.join(this_directory, "frcmod.heme_ff94") Amber94 = AmberData.AmberParameters(paramfile, [(modfile, 'MOD4')]) Amber94.lennard_jones_1_4 = 0.5 Amber94.electrostatic_1_4 = 1./1.2 Amber94.default_ljpar_set = Amber94.ljpar_sets['MOD4'] Amber94.atom_type_property = 'amber_atom_type' Amber94.charge_property = 'amber_charge' return Amber94 else: if main_file is None: main_file = os.path.join(this_directory, "parm94.dat") mod_files = map(lambda mf: (fullModFilePath(mf), 'MOD4'), mod_files) params = AmberData.AmberParameters(main_file, mod_files) params.lennard_jones_1_4 = 0.5 params.electrostatic_1_4 = 1./1.2 params.default_ljpar_set = params.ljpar_sets['MOD4'] params.atom_type_property = 'amber_atom_type' params.charge_property = 'amber_charge' return params def readAmber99(main_file = None, mod_files = None): global Amber99 if main_file is None and mod_files is None: if Amber99 is None: paramfile = os.path.join(this_directory, "parm99.dat") Amber99 = AmberData.AmberParameters(paramfile) Amber99.lennard_jones_1_4 = 0.5 Amber99.electrostatic_1_4 = 1./1.2 Amber99.default_ljpar_set = Amber99.ljpar_sets['MOD4'] Amber99.atom_type_property = 'amber_atom_type' Amber99.charge_property = 'amber_charge' return Amber99 else: if main_file is None: main_file = os.path.join(this_directory, "parm99.dat") mod_files = map(lambda mf: (fullModFilePath(mf), 'MOD4'), mod_files) params = AmberData.AmberParameters(main_file, mod_files) params.lennard_jones_1_4 = 0.5 params.electrostatic_1_4 = 1./1.2 params.default_ljpar_set = params.ljpar_sets['MOD4'] params.atom_type_property = 'amber_atom_type' params.charge_property = 'amber_charge' return params def readAmber91(): global Amber91 if Amber91 is None: Amber91 = AmberData.AmberParameters(os.path.join(this_directory, "parm91.dat")) Amber91.lennard_jones_1_4 = 0.5 Amber91.electrostatic_1_4 = 0.5 Amber91.default_ljpar_set = Amber91.ljpar_sets['STDA'] Amber91.atom_type_property = 'amber91_atom_type' Amber91.charge_property = 'amber91_charge' def readOPLS(main_file = None, mod_files = None): global OPLS if main_file is None and mod_files is None: if OPLS is None: paramfile = os.path.join(this_directory, "opls_parm.dat") OPLS = AmberData.AmberParameters(paramfile) OPLS.lennard_jones_1_4 = 0.125 OPLS.electrostatic_1_4 = 0.5 OPLS.default_ljpar_set = OPLS.ljpar_sets['OPLS'] OPLS.atom_type_property = 'opls_atom_type' OPLS.charge_property = 'opls_charge' return OPLS else: if main_file is None: main_file = os.path.join(this_directory, "opls_parm.dat") mod_files = map(lambda mf: (fullModFilePath(mf), 'OPLS'), mod_files) params = AmberData.AmberParameters(main_file, mod_files) params.lennard_jones_1_4 = 0.125 params.electrostatic_1_4 = 0.5 params.default_ljpar_set = params.ljpar_sets['OPLS'] params.atom_type_property = 'opls_atom_type' params.charge_property = 'opls_charge' return params # # The total force field # class Amber94ForceField(MMForceField.MMForceField): """ Amber 94 force field """ def __init__(self, lj_options = None, es_options = None, bonded_scale_factor = 1., **kwargs): """ :param lj_options: parameters for Lennard-Jones interactions; one of: * a number, specifying the cutoff * None, meaning the default method (no cutoff; inclusion of all pairs, using the minimum-image conventions for periodic universes) * a dictionary with an entry "method" which specifies the calculation method as either "direct" (all pair terms) or "cutoff", with the cutoff specified by the dictionary entry "cutoff". :param es_options: parameters for electrostatic interactions; one of: * a number, specifying the cutoff * None, meaning the default method (all pairs without cutoff for non-periodic system, Ewald summation for periodic systems) * a dictionary with an entry "method" which specifies the calculation method as either "direct" (all pair terms), "cutoff" (with the cutoff specified by the dictionary entry "cutoff"), "ewald" (Ewald summation, only for periodic universes), or "screened". :keyword mod_files: a list of parameter modification files. The file format is the one defined by AMBER. Each item in the list can be either a file object or a filename, filenames are looked up first relative to the current directory and then relative to the directory containing MMTK's AMBER parameter files. """ main_file = kwargs.get('parameter_file', None) mod_files = kwargs.get('mod_files', None) parameters = readAmber94(main_file, mod_files) MMForceField.MMForceField.__init__(self, 'Amber94', parameters, lj_options, es_options, bonded_scale_factor) self.arguments = (lj_options, es_options, bonded_scale_factor) AmberForceField = Amber94ForceField class Amber99ForceField(MMForceField.MMForceField): """ Amber 99 force field """ def __init__(self, lj_options = None, es_options = None, bonded_scale_factor = 1., **kwargs): """ :param lj_options: parameters for Lennard-Jones interactions; one of: * a number, specifying the cutoff * None, meaning the default method (no cutoff; inclusion of all pairs, using the minimum-image conventions for periodic universes) * a dictionary with an entry "method" which specifies the calculation method as either "direct" (all pair terms) or "cutoff", with the cutoff specified by the dictionary entry "cutoff". :param es_options: parameters for electrostatic interactions; one of: * a number, specifying the cutoff * None, meaning the default method (all pairs without cutoff for non-periodic system, Ewald summation for periodic systems) * a dictionary with an entry "method" which specifies the calculation method as either "direct" (all pair terms), "cutoff" (with the cutoff specified by the dictionary entry "cutoff"), "ewald" (Ewald summation, only for periodic universes), or "screened". :keyword mod_files: a list of parameter modification files. The file format is the one defined by AMBER. Each item in the list can be either a file object or a filename, filenames are looked up first relative to the current directory and then relative to the directory containing MMTK's AMBER parameter files. """ main_file = kwargs.get('parameter_file', None) mod_files = kwargs.get('mod_files', None) parameters = readAmber99(main_file, mod_files) MMForceField.MMForceField.__init__(self, 'Amber99', parameters, lj_options, es_options, bonded_scale_factor) self.arguments = (lj_options, es_options, bonded_scale_factor) class Amber12SBForceField(MMForceField.MMForceField): """ Amber force field ff12SB """ def __init__(self, lj_options = None, es_options = None, bonded_scale_factor = 1., **kwargs): """ :param lj_options: parameters for Lennard-Jones interactions; one of: * a number, specifying the cutoff * None, meaning the default method (no cutoff; inclusion of all pairs, using the minimum-image conventions for periodic universes) * a dictionary with an entry "method" which specifies the calculation method as either "direct" (all pair terms) or "cutoff", with the cutoff specified by the dictionary entry "cutoff". :param es_options: parameters for electrostatic interactions; one of: * a number, specifying the cutoff * None, meaning the default method (all pairs without cutoff for non-periodic system, Ewald summation for periodic systems) * a dictionary with an entry "method" which specifies the calculation method as either "direct" (all pair terms), "cutoff" (with the cutoff specified by the dictionary entry "cutoff"), "ewald" (Ewald summation, only for periodic universes), or "screened". :keyword mod_files: a list of parameter modification files. The file format is the one defined by AMBER. Each item in the list can be either a file object or a filename, filenames are looked up first relative to the current directory and then relative to the directory containing MMTK's AMBER parameter files. """ main_file = kwargs.get('parameter_file', None) mod_files = kwargs.get('mod_files', None) parameters = readAmber12SB(main_file, mod_files) MMForceField.MMForceField.__init__(self, 'Amber12SB', parameters, lj_options, es_options, bonded_scale_factor) self.arguments = (lj_options, es_options, bonded_scale_factor) class Amber91ForceField(MMForceField.MMForceField): def __init__(self, lj_options = None, es_options = None, bonded_scale_factor=1., **kwargs): readAmber91() MMForceField.MMForceField.__init__(self, 'Amber91', Amber91, lj_options, es_options, bonded_scale_factor) self.arguments = (lj_options, es_options, bonded_scale_factor) class OPLSForceField(MMForceField.MMForceField): def __init__(self, lj_options = None, es_options = None, bonded_scale_factor = 1., **kwargs): main_file = kwargs.get('parameter_file', None) mod_files = kwargs.get('mod_files', None) parameters = readOPLS(main_file, mod_files) MMForceField.MMForceField.__init__(self, 'OPLS', parameters, lj_options, es_options, bonded_scale_factor) self.arguments = (lj_options, es_options, bonded_scale_factor) # # The following classes provides access to individual terms of the # Amber force field. They are not used normally, but can be useful # for testing. # # # Bonded interactions # class AmberBondedForceField(MMForceField.MMBondedForceField): def __init__(self): readAmber94() MMForceField.MMBondedForceField.__init__(self, 'Amber_bonded', Amber94) self.arguments = () # # Nonbonded interactions # class AmberLJForceField(MMForceField.MMLJForceField): def __init__(self, cutoff = None): readAmber94() MMForceField.MMLJForceField.__init__(self, 'Amber_LJ', Amber94, cutoff) self.arguments = (cutoff,) class AmberESForceField(MMForceField.MMESForceField): def __init__(self, cutoff = None): readAmber94() MMForceField.MMESForceField.__init__(self, 'Amber_ES', Amber94, cutoff) self.arguments = (cutoff,) class AmberEwaldESForceField(MMForceField.MMEwaldESForceField): def __init__(self, options = {}): readAmber94() MMForceField.MMEwaldForceESField.__init__(self, 'Amber_ES', Amber94, options) self.arguments = (options,) class AmberNonbondedForceField(MMForceField.MMNonbondedForceField): def __init__(self, lj_options = None, es_options = None): readAmber94() MMForceField.MMNonbondedForceField.__init__(self, 'Amber_NB', Amber94, lj_options, es_options) self.arguments = (lj_options, es_options) MMTK-2.7.9/MMTK/ForceFields/Amber/frcmod.ff12SB0000644000076600000240000006125312041514571021062 0ustar hinsenstaff00000000000000ff12SB protein backbone and sidechain parameters MASS CO 12.01 0.616 ! sp2 C carboxylate group 2C 12.01 0.878 sp3 aliphatic C with two (duo) heavy atoms 3C 12.01 0.878 sp3 aliphatic C with three (tres) heavy atoms C8 12.01 0.878 sp3 aliphatic C basic AA side chain BOND C -2C 317.0 1.5220 C*-2C 317.0 1.4950 C8-C8 310.0 1.5260 C8-CX 310.0 1.5260 C8-H1 340.0 1.0900 C8-HC 340.0 1.0900 C8-HP 340.0 1.0900 C8-N2 337.0 1.4630 C8-N3 367.0 1.4710 CA-2C 317.0 1.5100 CC-2C 317.0 1.5040 CO-O2 656.0 1.2500 CO-2C 317.0 1.5220 CT-2C 310.0 1.5260 CT-3C 310.0 1.5260 CX-2C 310.0 1.5260 CX-3C 310.0 1.5260 H1-2C 340.0 1.0900 H1-3C 340.0 1.0900 HC-2C 340.0 1.0900 HC-3C 340.0 1.0900 OH-2C 320.0 1.4100 OH-3C 320.0 1.4100 S -2C 227.0 1.8100 SH-2C 237.0 1.8100 2C-2C 310.0 1.5260 2C-3C 310.0 1.5260 ANGL N -C -2C 70.0 116.60 O -C -2C 80.0 120.40 OH-C -2C 80.0 110.00 CB-C*-2C 70.0 128.60 CW-C*-2C 70.0 125.00 C8-C8-C8 40.0 109.50 C8-C8-CX 40.0 109.50 C8-C8-H1 50.0 109.50 C8-C8-HC 50.0 109.50 C8-C8-HP 50.0 109.50 C8-C8-N2 80.0 111.20 C8-C8-N3 80.0 111.20 CX-C8-HC 50.0 109.50 H1-C8-H1 35.0 109.50 H1-C8-N2 50.0 109.50 HC-C8-HC 35.0 109.50 HP-C8-HP 35.0 109.50 HP-C8-N3 50.0 109.50 CA-CA-2C 70.0 120.00 CV-CC-2C 70.0 120.00 CW-CC-2C 70.0 120.00 NA-CC-2C 70.0 120.00 NB-CC-2C 70.0 120.00 O2-CO-O2 80.0 126.00 O2-CO-2C 70.0 117.00 HC-CT-2C 50.0 109.50 HC-CT-3C 50.0 109.50 C -CX-C8 63.0 111.10 C -CX-2C 63.0 111.10 C -CX-3C 63.0 111.10 C8-CX-H1 50.0 109.50 C8-CX-N 80.0 109.70 C8-CX-N3 80.0 111.20 H1-CX-2C 50.0 109.50 H1-CX-3C 50.0 109.50 HP-CX-C8 50.0 109.50 changed based on NMA nmodes (was CT-CT-HP) HP-CX-CG 50.0 109.50 changed based on NMA nmodes (was CT-CT-HP) HP-CX-2C 50.0 109.50 changed based on NMA nmodes (was CT-CT-HP) HP-CX-3C 50.0 109.50 changed based on NMA nmodes (was CT-CT-HP) N -CX-2C 80.0 109.70 N -CX-3C 80.0 109.70 N3-CX-2C 80.0 111.20 N3-CX-3C 80.0 111.20 C8-N2-CA 50.0 123.20 C8-N2-H 50.0 118.40 C8-N3-H 50.0 109.50 HO-OH-2C 55.0 108.50 HO-OH-3C 55.0 108.50 CT-S -2C 62.0 98.90 2C-S -S 68.0 103.70 HS-SH-2C 43.0 96.00 C -2C-CX 63.0 111.10 C -2C-HC 50.0 109.50 C -2C-2C 63.0 111.10 C*-2C-CX 63.0 115.60 C*-2C-HC 50.0 109.50 CA-2C-CX 63.0 114.00 CA-2C-HC 50.0 109.50 CC-2C-CX 63.0 113.10 CC-2C-HC 50.0 109.50 CO-2C-CX 63.0 111.10 CO-2C-HC 50.0 109.50 CO-2C-2C 63.0 111.10 CT-2C-HC 50.0 109.50 CT-2C-3C 40.0 109.50 CX-2C-H1 50.0 109.50 CX-2C-HC 50.0 109.50 CX-2C-OH 50.0 109.50 CX-2C-S 50.0 114.70 CX-2C-SH 50.0 108.60 CX-2C-2C 40.0 109.50 CX-2C-3C 40.0 109.50 H1-2C-H1 35.0 109.50 H1-2C-OH 50.0 109.50 H1-2C-S 50.0 109.50 H1-2C-SH 50.0 109.50 H1-2C-2C 50.0 109.50 HC-2C-HC 35.0 109.50 HC-2C-2C 50.0 109.50 HC-2C-3C 50.0 109.50 S -2C-2C 50.0 114.70 CT-3C-CT 40.0 109.50 CT-3C-CX 40.0 109.50 CT-3C-H1 50.0 109.50 CT-3C-HC 50.0 109.50 CT-3C-OH 50.0 109.50 CT-3C-2C 40.0 109.50 CX-3C-H1 50.0 109.50 CX-3C-HC 50.0 109.50 CX-3C-OH 50.0 109.50 CX-3C-2C 40.0 109.50 H1-3C-OH 50.0 109.50 HC-3C-2C 50.0 109.50 DIHE C8-CX-N -C 1 0.000 0.0 -4. phi' C8-CX-N -C 1 0.800 0.0 -3. C8-CX-N -C 1 1.800 0.0 -2. C8-CX-N -C 1 2.000 0.0 1. CT-CX-N -C 1 0.000 0.0 -4. CT-CX-N -C 1 0.800 0.0 -3. CT-CX-N -C 1 1.800 0.0 -2. CT-CX-N -C 1 2.000 0.0 1. 2C-CX-N -C 1 0.000 0.0 -4. 2C-CX-N -C 1 0.800 0.0 -3. 2C-CX-N -C 1 1.800 0.0 -2. 2C-CX-N -C 1 2.000 0.0 1. 3C-CX-N -C 1 0.000 0.0 -4. 3C-CX-N -C 1 0.800 0.0 -3. 3C-CX-N -C 1 1.800 0.0 -2. 3C-CX-N -C 1 2.000 0.0 1. N -C -CX-C8 1 0.000 0.0 -4. psi' N -C -CX-C8 1 0.400 0.0 -3. N -C -CX-C8 1 0.200 0.0 -2. N -C -CX-C8 1 0.200 0.0 1. N -C -CX-CT 1 0.000 0.0 -4. N -C -CX-CT 1 0.400 0.0 -3. N -C -CX-CT 1 0.200 0.0 -2. N -C -CX-CT 1 0.200 0.0 1. N -C -CX-2C 1 0.000 0.0 -4. N -C -CX-2C 1 0.400 0.0 -3. N -C -CX-2C 1 0.200 0.0 -2. N -C -CX-2C 1 0.200 0.0 1. N -C -CX-3C 1 0.000 0.0 -4. N -C -CX-3C 1 0.400 0.0 -3. N -C -CX-3C 1 0.200 0.0 -2. N -C -CX-3C 1 0.200 0.0 1. N -C -2C-HC 1 0.000 0.0 2. JCC,7,(1986),230 (X -C -CT-X ) O -C -2C-HC 1 0.080 180.0 -3. Junmei et al, 1999 (HC-CT-C -O ) O -C -2C-HC 1 0.000 0.0 -2. O -C -2C-HC 1 0.800 0.0 1. OH-C -2C-HC 1 0.000 0.0 2. CB-C*-2C-HC 1 0.000 0.0 2. JCC,7,(1986),230 (X -C*-CT-X ) CW-C*-2C-HC 1 0.000 0.0 2. X -C8-C8-X 9 1.400 0.0 3. JCC,7,(1986),230 (X -CT-CT-X ) C8-C8-C8-HC 1 0.160 0.0 3. Junmei et al, 1999 (was HC-CT-CT-CT) CX-C8-C8-HC 1 0.160 0.0 3. HC-C8-C8-HC 1 0.150 0.0 3. Junmei et al, 1999 (HC-CT-CT-HC) X -C8-CX-X 9 1.400 0.0 3. X -C8-N2-X 6 0.000 0.0 3. JCC,7,(1986),230 (X -CT-N2-X ) X -C8-N3-X 9 1.400 0.0 3. JCC,7,(1986),230 (was X -CT-N3-X ) X -CA-2C-X 1 0.000 0.0 2. JCC,7,(1986),230 (was X -CA-CT-X ) 2C-CC-CV-H4 1 5.150 180.0 2. 2C-CC-CV-NB 1 5.150 180.0 2. 2C-CC-CW-H4 1 5.375 180.0 2. 2C-CC-CW-NA 1 5.375 180.0 2. 2C-CC-NA-CR 1 1.400 180.0 2. 2C-CC-NA-H 1 1.400 180.0 2. 2C-CC-NB-CR 1 2.400 180.0 2. CV-CC-2C-HC 1 0.000 0.0 2. CW-CC-2C-HC 1 0.000 0.0 2. NA-CC-2C-HC 1 0.000 0.0 2. NB-CC-2C-HC 1 0.000 0.0 2. O2-CO-2C-HC 1 0.000 0.0 2. H1-CT-S -2C 3 1.000 0.0 3. HC-CT-2C-HC 1 0.150 0.0 3. HC-CT-2C-3C 1 0.160 0.0 3. HC-CT-3C-CT 1 0.160 0.0 3. HC-CT-3C-CX 1 0.160 0.0 3. HC-CT-3C-H1 9 1.400 0.0 3. HC-CT-3C-HC 1 0.150 0.0 3. HC-CT-3C-OH 1 0.000 0.0 -3. HC-CT-3C-OH 1 0.250 0.0 1. HC-CT-3C-2C 1 0.160 0.0 3. X -CX-2C-X 9 1.400 0.0 3. H1-CX-2C-OH 1 0.000 0.0 -3. H1-CX-2C-OH 1 0.250 0.0 1. X -CX-3C-X 9 1.400 0.0 3. H1-CX-3C-OH 1 0.000 0.0 -3. H1-CX-3C-OH 1 0.250 0.0 1. HO-OH-2C-H1 3 0.500 0.0 3. JCC,7,(1986),230 HO-OH-3C-H1 3 0.500 0.0 3. EP-S -S -2C 1 0.000 0.0 3. cyx (dac) X -S -2C-X 3 1.000 0.0 3. JCC,7,(1986),230 (X -CT-S -X ) X -SH-2C-X 3 0.750 0.0 3. JCC,7,(1986),230 (X -CT-SH-X ) X -2C-2C-X 9 1.400 0.0 3. CX-2C-2C-HC 1 0.160 0.0 3. Junmei et al, 1999 HC-2C-2C-HC 1 0.150 0.0 3. Junmei et al, 1999 CT-2C-3C-HC 1 0.160 0.0 3. Junmei et al, 1999 CX-2C-3C-HC 1 0.160 0.0 3. HC-2C-3C-CT 1 0.160 0.0 3. HC-2C-3C-CX 1 0.160 0.0 3. HC-2C-3C-HC 1 0.150 0.0 3. C -CX-C8-C8 1 0.261 0.0 -4. Arg Lys C -CX-C8-C8 1 0.580 180.0 -3. C -CX-C8-C8 1 0.058 180.0 -2. C -CX-C8-C8 1 0.339 180.0 1. N -CX-C8-C8 1 0.218 0.0 -4. N -CX-C8-C8 1 0.942 0.0 -3. N -CX-C8-C8 1 0.186 180.0 -2. N -CX-C8-C8 1 0.008 180.0 1. N3-CX-C8-C8 1 0.218 0.0 -4. N3-CX-C8-C8 1 0.942 0.0 -3. N3-CX-C8-C8 1 0.186 180.0 -2. N3-CX-C8-C8 1 0.008 180.0 1. CX-C8-C8-C8 1 0.040 12.2 -4. CX-C8-C8-C8 1 0.100 -6.9 -3. CX-C8-C8-C8 1 0.127 174.2 -2. CX-C8-C8-C8 1 0.327 -178.9 1. C8-C8-C8-N2 1 0.000 83.4 -4. C8-C8-C8-N2 1 0.156 -0.0 -3. C8-C8-C8-N2 1 0.000 145.1 -2. C8-C8-C8-N2 1 0.000 -135.6 1. C8-C8-N2-H 1 0.085 0.0 -4. C8-C8-N2-H 1 0.614 180.0 -3. C8-C8-N2-H 1 1.290 0.0 -2. C8-C8-N2-H 1 1.066 180.0 1. C8-C8-N2-CA 1 0.002 180.0 -4. C8-C8-N2-CA 1 0.735 180.0 -3. C8-C8-N2-CA 1 1.176 180.0 -2. C8-C8-N2-CA 1 1.155 180.0 1. C8-N2-CA-N2 1 0.000 0.0 -4. C8-N2-CA-N2 1 2.400 180.0 2. H -N2-CA-N2 1 0.000 0.0 -4. H -N2-CA-N2 1 2.400 180.0 2. C8-C8-C8-C8 1 0.033 4.6 -4. C8-C8-C8-C8 1 0.129 -1.6 -3. C8-C8-C8-C8 1 0.159 179.7 -2. C8-C8-C8-C8 1 0.295 -179.8 1. C8-C8-C8-N3 1 0.107 -16.8 -4. C8-C8-C8-N3 1 0.369 -4.6 -3. C8-C8-C8-N3 1 0.087 15.5 -2. C8-C8-C8-N3 1 0.199 174.0 1. C8-C8-N3-H 9 1.400 0.0 3. C -CX-CT-CC 1 0.090 0.0 -4. His C -CX-CT-CC 1 0.431 180.0 -3. C -CX-CT-CC 1 0.148 0.0 -2. C -CX-CT-CC 1 0.379 180.0 1. N -CX-CT-CC 1 0.146 0.0 -4. N -CX-CT-CC 1 0.399 0.0 -3. N -CX-CT-CC 1 0.029 0.0 -2. N -CX-CT-CC 1 0.571 180.0 1. N3-CX-CT-CC 1 0.146 0.0 -4. N3-CX-CT-CC 1 0.399 0.0 -3. N3-CX-CT-CC 1 0.029 0.0 -2. N3-CX-CT-CC 1 0.571 180.0 1. CX-CT-CC-NA 1 0.030 0.0 -4. CX-CT-CC-NA 1 0.573 0.0 -3. CX-CT-CC-NA 1 1.063 0.0 -2. CX-CT-CC-NA 1 0.827 0.0 1. CX-CT-CC-CV 1 0.059 0.0 -4. CX-CT-CC-CV 1 0.031 180.0 -3. CX-CT-CC-CV 1 0.411 180.0 -2. CX-CT-CC-CV 1 0.466 0.0 1. CX-CT-CC-NB 1 0.119 180.0 -4. CX-CT-CC-NB 1 0.621 0.0 -3. CX-CT-CC-NB 1 1.483 0.0 -2. CX-CT-CC-NB 1 1.185 0.0 1. CX-CT-CC-CW 1 0.217 0.0 -4. CX-CT-CC-CW 1 0.086 180.0 -3. CX-CT-CC-CW 1 1.070 180.0 -2. CX-CT-CC-CW 1 0.644 0.0 1. C -CX-2C-CO 1 0.060 0.0 -4. Asp C -CX-2C-CO 1 4.415 180.0 -3. C -CX-2C-CO 1 0.688 180.0 -2. C -CX-2C-CO 1 0.765 180.0 1. N -CX-2C-CO 1 0.022 0.0 -4. N -CX-2C-CO 1 4.430 0.0 -3. N -CX-2C-CO 1 0.325 180.0 -2. N -CX-2C-CO 1 2.524 180.0 1. N3-CX-2C-CO 1 0.022 0.0 -4. N3-CX-2C-CO 1 4.430 0.0 -3. N3-CX-2C-CO 1 0.325 180.0 -2. N3-CX-2C-CO 1 2.524 180.0 1. CX-2C-CO-O2 1 0.004 -161.2 -4. Asp chi2 CX-2C-CO-O2 1 0.832 -176.2 2. C -CX-2C-C 1 0.003 0.0 -4. Ashn C -CX-2C-C 1 1.028 180.0 -3. C -CX-2C-C 1 0.205 180.0 -2. C -CX-2C-C 1 0.668 0.0 1. N -CX-2C-C 1 0.008 0.0 -4. N -CX-2C-C 1 0.856 0.0 -3. N -CX-2C-C 1 0.199 180.0 -2. N -CX-2C-C 1 0.702 180.0 1. N3-CX-2C-C 1 0.008 0.0 -4. N3-CX-2C-C 1 0.856 0.0 -3. N3-CX-2C-C 1 0.199 180.0 -2. N3-CX-2C-C 1 0.702 180.0 1. CX-2C-C -O 1 0.118 0.0 -4. Ashn chi2 CX-2C-C -O 1 0.057 180.0 -3. CX-2C-C -O 1 0.431 0.0 -2. CX-2C-C -O 1 1.283 0.0 1. CX-2C-C -OH 1 0.157 180.0 -4. Ash chi2 CX-2C-C -OH 1 0.173 180.0 -3. CX-2C-C -OH 1 1.255 180.0 -2. CX-2C-C -OH 1 0.119 0.0 1. CX-2C-C -N 1 0.156 180.0 -4. Asn chi2 CX-2C-C -N 1 0.346 180.0 -3. CX-2C-C -N 1 0.904 180.0 -2. CX-2C-C -N 1 0.857 0.0 1. C -CX-CT-CA 1 0.121 0.0 -4. Phe Tyr C -CX-CT-CA 1 5.728 180.0 -3. C -CX-CT-CA 1 0.462 180.0 -2. C -CX-CT-CA 1 0.056 180.0 1. N -CX-CT-CA 1 0.011 0.0 -4. N -CX-CT-CA 1 5.998 0.0 -3. N -CX-CT-CA 1 0.536 180.0 -2. N -CX-CT-CA 1 0.140 180.0 1. N3-CX-CT-CA 1 0.011 0.0 -4. N3-CX-CT-CA 1 5.998 0.0 -3. N3-CX-CT-CA 1 0.536 180.0 -2. N3-CX-CT-CA 1 0.140 180.0 1. CX-CT-CA-CA 1 0.126 -21.2 -4. Phe Tyr chi2 CX-CT-CA-CA 1 0.459 -2.9 2. CA-C -OH-HO 1 0.028 -3.3 -4. Tyr oh CA-C -OH-HO 1 0.858 179.6 2. C -CX-2C-3C 1 0.183 0.0 -4. Leu C -CX-2C-3C 1 0.283 0.0 -3. C -CX-2C-3C 1 0.841 180.0 -2. C -CX-2C-3C 1 0.099 0.0 1. N -CX-2C-3C 1 0.040 180.0 -4. N -CX-2C-3C 1 0.381 180.0 -3. N -CX-2C-3C 1 0.288 180.0 -2. N -CX-2C-3C 1 0.153 180.0 1. CX-2C-3C-CT 1 0.149 0.0 -4. Leu chi2 CX-2C-3C-CT 1 0.164 0.0 -3. CX-2C-3C-CT 1 0.024 180.0 -2. CX-2C-3C-CT 1 0.543 0.0 1. C -CX-3C-CT 1 0.138 0.0 -4. Ile Thr Val C -CX-3C-CT 1 1.747 0.0 -3. C -CX-3C-CT 1 0.421 180.0 -2. C -CX-3C-CT 1 0.490 180.0 1. C -CX-3C-2C 1 0.074 180.0 -4. C -CX-3C-2C 1 2.566 180.0 -3. C -CX-3C-2C 1 1.145 180.0 -2. C -CX-3C-2C 1 0.679 180.0 1. N -CX-3C-CT 1 0.010 180.0 -4. N -CX-3C-CT 1 1.610 180.0 -3. N -CX-3C-CT 1 0.394 180.0 -2. N -CX-3C-CT 1 0.153 180.0 1. N -CX-3C-2C 1 0.286 180.0 -4. N -CX-3C-2C 1 1.848 0.0 -3. N -CX-3C-2C 1 0.908 180.0 -2. N -CX-3C-2C 1 0.371 180.0 1. N3-CX-3C-CT 1 0.010 180.0 -4. N3-CX-3C-CT 1 1.610 180.0 -3. N3-CX-3C-CT 1 0.394 180.0 -2. N3-CX-3C-CT 1 0.153 180.0 1. N3-CX-3C-2C 1 0.286 180.0 -4. N3-CX-3C-2C 1 1.848 0.0 -3. N3-CX-3C-2C 1 0.908 180.0 -2. N3-CX-3C-2C 1 0.371 180.0 1. CX-3C-2C-CT 1 0.262 0.0 -4. CX-3C-2C-CT 1 0.265 180.0 -3. CX-3C-2C-CT 1 0.014 180.0 -2. CX-3C-2C-CT 1 0.085 180.0 1. CT-3C-2C-CT 1 0.262 0.0 -4. CT-3C-2C-CT 1 0.309 0.0 -3. CT-3C-2C-CT 1 0.021 180.0 -2. CT-3C-2C-CT 1 0.068 0.0 1. C -CX-3C-OH 1 0.039 0.0 -4. C -CX-3C-OH 1 3.146 0.0 -3. C -CX-3C-OH 1 0.522 180.0 -2. C -CX-3C-OH 1 1.373 180.0 1. N -CX-3C-OH 1 0.203 180.0 -4. N -CX-3C-OH 1 3.007 180.0 -3. N -CX-3C-OH 1 0.710 180.0 -2. N -CX-3C-OH 1 0.441 180.0 1. N3-CX-3C-OH 1 0.203 180.0 -4. N3-CX-3C-OH 1 3.007 180.0 -3. N3-CX-3C-OH 1 0.710 180.0 -2. N3-CX-3C-OH 1 0.441 180.0 1. CX-3C-OH-HO 1 0.012 0.0 -4. CX-3C-OH-HO 1 0.106 0.0 -3. CX-3C-OH-HO 1 0.090 180.0 -2. CX-3C-OH-HO 1 1.023 0.0 1. CT-3C-OH-HO 1 0.010 0.0 -4. CT-3C-OH-HO 1 0.220 0.0 -3. CT-3C-OH-HO 1 0.208 180.0 -2. CT-3C-OH-HO 1 0.767 0.0 1. C -CX-2C-OH 1 0.098 0.0 -4. Ser C -CX-2C-OH 1 0.715 180.0 -3. C -CX-2C-OH 1 0.053 0.0 -2. C -CX-2C-OH 1 0.761 180.0 1. N -CX-2C-OH 1 0.092 0.0 -4. N -CX-2C-OH 1 1.231 0.0 -3. N -CX-2C-OH 1 0.119 0.0 -2. N -CX-2C-OH 1 0.730 0.0 1. N3-CX-2C-OH 1 0.092 0.0 -4. Ser chi2 N3-CX-2C-OH 1 1.231 0.0 -3. N3-CX-2C-OH 1 0.119 0.0 -2. N3-CX-2C-OH 1 0.730 0.0 1. CX-2C-OH-HO 1 0.021 -26.1 -4. Ser oh CX-2C-OH-HO 1 0.184 -14.2 -3. CX-2C-OH-HO 1 0.279 -15.7 -2. CX-2C-OH-HO 1 0.251 -13.3 1. C -CX-CT-C* 1 0.085 0.0 -4. Trp C -CX-CT-C* 1 2.242 180.0 -3. C -CX-CT-C* 1 0.206 180.0 -2. C -CX-CT-C* 1 0.050 180.0 1. N -CX-CT-C* 1 0.016 0.0 -4. N -CX-CT-C* 1 2.475 0.0 -3. N -CX-CT-C* 1 0.410 180.0 -2. N -CX-CT-C* 1 0.003 0.0 1. N3-CX-CT-C* 1 0.016 0.0 -4. N3-CX-CT-C* 1 2.475 0.0 -3. N3-CX-CT-C* 1 0.410 180.0 -2. N3-CX-CT-C* 1 0.003 0.0 1. CX-CT-C*-CW 1 0.175 180.0 -4. Trp chi2 CX-CT-C*-CW 1 1.029 0.0 -3. CX-CT-C*-CW 1 1.141 0.0 -2. CX-CT-C*-CW 1 4.853 180.0 1. CX-CT-C*-CB 1 0.073 0.0 -4. CX-CT-C*-CB 1 1.880 0.0 -3. CX-CT-C*-CB 1 0.909 0.0 -2. CX-CT-C*-CB 1 2.848 180.0 1. C -CX-2C-SH 1 0.143 180.0 -4. Cys C -CX-2C-SH 1 1.561 180.0 -3. C -CX-2C-SH 1 0.026 0.0 -2. C -CX-2C-SH 1 0.634 180.0 1. N -CX-2C-SH 1 0.198 180.0 -4. N -CX-2C-SH 1 1.893 0.0 -3. N -CX-2C-SH 1 0.244 180.0 -2. N -CX-2C-SH 1 0.175 0.0 1. N3-CX-2C-SH 1 0.198 180.0 -4. Cys chi2 N3-CX-2C-SH 1 1.893 0.0 -3. N3-CX-2C-SH 1 0.244 180.0 -2. N3-CX-2C-SH 1 0.175 0.0 1. CX-2C-SH-HS 1 0.016 -123.6 -4. Cys sh CX-2C-SH-HS 1 0.202 -6.1 -3. CX-2C-SH-HS 1 0.613 -10.7 -2. CX-2C-SH-HS 1 0.297 -26.8 1. C -CX-2C-S 2 0.239 180.0 -4. Cyx C -CX-2C-S 2 1.953 180.0 -3. C -CX-2C-S 2 0.102 180.0 -2. C -CX-2C-S 2 0.421 0.0 1. N -CX-2C-S 2 0.550 180.0 -4. N -CX-2C-S 2 2.486 0.0 -3. N -CX-2C-S 2 0.240 180.0 -2. N -CX-2C-S 2 0.692 0.0 1. N3-CX-2C-S 2 0.550 180.0 -4. N3-CX-2C-S 2 2.486 0.0 -3. N3-CX-2C-S 2 0.240 180.0 -2. N3-CX-2C-S 2 0.692 0.0 1. CX-2C-S -S 2 0.386 137.8 -4. Cyx chi2 CX-2C-S -S 2 0.337 12.0 -3. CX-2C-S -S 2 1.166 -40.1 -2. CX-2C-S -S 2 1.389 -112.7 1. 2C-S -S -2C 1 0.193 32.5 -4. Cyx disulf 2C-S -S -2C 1 0.957 -0.5 -3. 2C-S -S -2C 1 4.542 1.5 -2. 2C-S -S -2C 1 0.543 -25.0 1. C -CX-2C-2C 1 0.122 0.0 -4. Glh Gln Glu Met C -CX-2C-2C 1 0.615 180.0 -3. C -CX-2C-2C 1 0.375 180.0 -2. C -CX-2C-2C 1 0.440 180.0 1. N -CX-2C-2C 1 0.099 0.0 -4. N -CX-2C-2C 1 0.906 0.0 -3. N -CX-2C-2C 1 0.173 180.0 -2. N -CX-2C-2C 1 0.098 180.0 1. N3-CX-2C-2C 1 0.099 0.0 -4. N3-CX-2C-2C 1 0.906 0.0 -3. N3-CX-2C-2C 1 0.173 180.0 -2. N3-CX-2C-2C 1 0.098 180.0 1. CX-2C-2C-C 1 0.188 42.8 -4. CX-2C-2C-C 1 0.380 172.0 -3. CX-2C-2C-C 1 0.125 -29.9 -2. CX-2C-2C-C 1 0.217 -158.6 1. 2C-2C-C -O 1 0.505 180.0 -4. 2C-2C-C -O 1 1.117 0.0 -3. 2C-2C-C -O 1 0.007 180.0 -2. 2C-2C-C -O 1 2.453 180.0 1. 2C-2C-C -OH 1 0.466 0.0 -4. 2C-2C-C -OH 1 1.130 0.0 -3. 2C-2C-C -OH 1 1.104 180.0 -2. 2C-2C-C -OH 1 3.212 180.0 1. 2C-2C-C -N 1 0.594 0.0 -4. 2C-2C-C -N 1 1.043 0.0 -3. 2C-2C-C -N 1 0.871 180.0 -2. 2C-2C-C -N 1 3.013 180.0 1. CX-2C-2C-CO 1 0.060 -169.5 -4. CX-2C-2C-CO 1 0.580 179.2 -3. CX-2C-2C-CO 1 0.181 177.9 -2. CX-2C-2C-CO 1 1.353 176.4 1. 2C-2C-CO-O2 1 0.101 114.7 -4. 2C-2C-CO-O2 1 0.893 -178.9 2. CX-2C-2C-S 1 0.151 86.9 -4. CX-2C-2C-S 1 0.151 79.7 -3. CX-2C-2C-S 1 0.238 8.3 -2. CX-2C-2C-S 1 0.391 -3.8 1. 2C-2C-S -CT 1 0.086 -38.3 -4. 2C-2C-S -CT 1 0.401 1.0 -3. 2C-2C-S -CT 1 0.442 2.3 -2. 2C-2C-S -CT 1 0.242 176.2 1. IMPR X -O2-CO-O2 10.5 180. 2. 2C-O -C -OH 10.5 180. 2. CA-CA-CA-2C 1.1 180. 2. NONB 2C 1.9080 0.1094 Spellmeyer 3C 1.9080 0.1094 Spellmeyer C8 1.9080 0.1094 Spellmeyer CO 1.9080 0.0860 OPLS MMTK-2.7.9/MMTK/ForceFields/Amber/frcmod.ff99SB0000644000076600000240000000217211700655442021100 0ustar hinsenstaff00000000000000Modification/update of parm99.dat (Hornak & Simmerling) MASS BOND ANGL DIHEDRAL C -N -CT-C 1 0.00 0.0 -4. four amplitudes and C -N -CT-C 1 0.42 0.0 -3. phases for phi C -N -CT-C 1 0.27 0.0 -2. C -N -CT-C 1 0.00 0.0 1. N -CT-C -N 1 0.00 0.0 -4. four amplitudes and N -CT-C -N 1 0.55 180.0 -3. phases for psi N -CT-C -N 1 1.58 180.0 -2. N -CT-C -N 1 0.45 180.0 1. CT-CT-N -C 1 0.00 0.0 -4. four amplitudes and CT-CT-N -C 1 0.40 0.0 -3. phases for phi' CT-CT-N -C 1 2.00 0.0 -2. CT-CT-N -C 1 2.00 0.0 1. CT-CT-C -N 1 0.00 0.0 -4. four amplitudes and CT-CT-C -N 1 0.40 0.0 -3. phases for psi' CT-CT-C -N 1 0.20 0.0 -2. CT-CT-C -N 1 0.20 0.0 1. NONB MMTK-2.7.9/MMTK/ForceFields/Amber/frcmod.ff99SBildn0000644000076600000240000001142412041514571021743 0ustar hinsenstaff00000000000000Modification/update of parm99.dat (Hornak & Simmerling) + ILDN corrections MASS C3 12.01 new atom types for ILDN patch C4 12.01 C5 12.01 C6 12.01 BOND C3-HC 340.0 1.090 C4-HC 340.0 1.090 CT-C3 310.0 1.526 CT-C4 310.0 1.526 C6-O2 656.0 1.250 CC-O2 656.0 1.250 C5-O 570.0 1.229 C5-N 490.0 1.335 C5-CT 317.0 1.522 C6-CT 317.0 1.522 ANGL CT-C5-O 80.0 120.40 CT-C6-O2 70.0 117.00 CT-CC-O2 70.0 117.00 CT-C5-N 70.0 116.60 N -C5-O 80.0 122.90 O2-C6-O2 80.0 126.00 O2-CC-O2 80.0 126.00 HC-C3-HC 35.0 109.50 C5-CT-HC 50.0 109.50 C6-CT-HC 50.0 109.50 C5-CT-CT 63.0 111.10 C6-CT-CT 63.0 111.10 CT-C4-CT 40.0 109.50 CT-CT-C4 40.0 109.50 C4-CT-HC 50.0 109.50 CT-C4-HC 50.0 109.50 CT-CT-C3 40.0 109.50 CT-C3-HC 50.0 109.50 C3-CT-HC 50.0 109.50 C5-N -H 50.0 120.00 DIHEDRAL C -N -CT-C 1 0.00 0.0 -4. four amplitudes and C -N -CT-C 1 0.42 0.0 -3. phases for phi C -N -CT-C 1 0.27 0.0 -2. C -N -CT-C 1 0.00 0.0 1. N -CT-C -N 1 0.00 0.0 -4. four amplitudes and N -CT-C -N 1 0.55 180.0 -3. phases for psi N -CT-C -N 1 1.58 180.0 -2. N -CT-C -N 1 0.45 180.0 1. CT-CT-N -C 1 0.00 0.0 -4. four amplitudes and CT-CT-N -C 1 0.40 0.0 -3. phases for phi' CT-CT-N -C 1 2.00 0.0 -2. CT-CT-N -C 1 2.00 0.0 1. CT-CT-C -N 1 0.00 0.0 -4. four amplitudes and CT-CT-C -N 1 0.40 0.0 -3. phases for psi' CT-CT-C -N 1 0.20 0.0 -2. CT-CT-C -N 1 0.20 0.0 1. CT-CT-CT-C3 1 0.1800 0.0 -3. ILDN CT-CT-CT-C3 1 0.2500 180.0 -2. CT-CT-CT-C3 1 0.2000 180.0 1. CT-C4-CT-CT 1 0.1800 0.0 -3. CT-C4-CT-CT 1 0.2500 180.0 -2. CT-C4-CT-CT 1 0.2000 180.0 1. HC-CT-CT-C3 1 0.1600 0.0 3. HC-CT-CT-C4 1 0.1600 0.0 3. HC-C4-CT-HC 1 0.1500 0.0 3. HC-CT-C5-O 1 0.0800 180.0 -3. HC-CT-C5-O 1 0.8000 0.0 1. H -N -C5-O 1 2.5000 180.0 -2. H -N -C5-O 1 2.0000 0.0 1. HC-CT-C3-HC 1 0.1500 0.0 3. N -CT-CT-C3 1 0.8459 180.0 -2. N -CT-CT-C3 1 0.1951 0.0 1. C -CT-CT-C4 1 0.1348 0.0 -3. C -CT-CT-C4 1 0.3583 180.0 -2. C -CT-CT-C4 1 0.5714 0.0 1. HC-C3-CT-CT 1 0.1600 0.0 3. HC-C4-CT-CT 1 0.1600 0.0 3. HC-CT-C4-CT 1 0.1600 0.0 3. N -CT-CT-C6 1 0.21273765 180.0 -6. N -CT-CT-C6 1 0.2317 0.0 -5. N -CT-CT-C6 1 0.4227 0.0 -4. N -CT-CT-C6 1 0.0068 180.0 -3. N -CT-CT-C6 1 1.1903 180.0 -2. N -CT-CT-C6 1 2.6352 180.0 1. C -CT-CT-C5 1 0.100721 180.0 -6. C -CT-CT-C5 1 0.1042 0.0 -5. C -CT-CT-C5 1 0.4172 180.0 -4. C -CT-CT-C5 1 0.1185 0.0 -3. C -CT-CT-C5 1 0.5957 180.0 -2. C -CT-CT-C5 1 0.5707 0.0 1. X -C5-N -X 1 2.5000 180.0 2. N -C5-CT-CT 1 0.106053 180.0 -6. N -C5-CT-CT 1 0.1298 0.0 -5. N -C5-CT-CT 1 0.1003 0.0 -4. N -C5-CT-CT 1 0.0354 180.0 -3. N -C5-CT-CT 1 0.1810 180.0 -2. N -C5-CT-CT 1 1.0463 180.0 1. O2-C6-CT-CT 1 0.01325289 180.0 -6. O2-C6-CT-CT 1 0.1376 180.0 -4. O2-C6-CT-CT 1 0.4432 180.0 2. X -C -CT-X 1 0.0000 0.0 2. X -C5-CT-X 1 0.0000 0.0 2. X -C6-CT-X 1 0.0000 0.0 2. IMPROP C5-CT-N -O 1.1000 180.0 2. C5-CT-N -H 1.1000 180.0 2. X -O2-C6-O2 10.500 180.0 2. X -X -C5-O 10.500 180.0 2. NONB C3 1.9080 0.1094 Spellmeyer C4 1.9080 0.1094 Spellmeyer C5 1.9080 0.0860 OPLS C6 1.9080 0.0860 OPLS MMTK-2.7.9/MMTK/ForceFields/Amber/frcmod.ff99SBnmr0000644000076600000240000000221512041514571021607 0ustar hinsenstaff00000000000000From D.W. Li and R. Bruschweiler, Angew. Chem. Int. Eng. Ed. 49:6778, 2010 MASS BOND ANGL DIHEDRAL C -N -CT-C 1 0.41 0.0 -4. four amplitudes and C -N -CT-C 1 0.52 0.0 -3. phases for phi C -N -CT-C 1 0.11 0.0 -2. C -N -CT-C 1 0.00 0.0 1. N -CT-C -N 1 0.13 0.0 -4. four amplitudes and N -CT-C -N 1 0.75 180.0 -3. phases for psi N -CT-C -N 1 1.21 180.0 -2. N -CT-C -N 1 0.64 180.0 1. CT-CT-N -C 1 0.32 0.0 -4. four amplitudes and CT-CT-N -C 1 0.13 0.0 -3. phases for phi' CT-CT-N -C 1 1.63 0.0 -2. CT-CT-N -C 1 2.43 0.0 1. CT-CT-C -N 1 0.25 0.0 -4. four amplitudes and CT-CT-C -N 1 0.70 0.0 -3. phases for psi' CT-CT-C -N 1 0.31 0.0 -2. CT-CT-C -N 1 0.41 180.0 1. NONB MMTK-2.7.9/MMTK/ForceFields/Amber/frcmod.ff99SP0000644000076600000240000000032011700655442021107 0ustar hinsenstaff00000000000000from Sorin & Pande, Biophys J. 88: 2472-2493, 2005. Modifies parm99. MASS BOND ANGL DIHEDRAL C -N -CT-C 1 0.200 180.000 -2. C -N -CT-C 1 0.000 0.000 1. NONB MMTK-2.7.9/MMTK/ForceFields/Amber/frcmod.heme_ff940000644000076600000240000000541112041514225021633 0ustar hinsenstaff00000000000000Force field modifcations for all-atom heme MASS NP 14.01 NO 14.01 CY 12.0 CX 12.0 CD 12.0 FE 55.0 LC 12.01 LO 16.00 BOND FE-NP 50.000 2.01000 FE-LO 200.000 1.78 FE-LC 200.000 1.75000 LC-LO 1000.000 1.12000 FE-NB 60.000 2.01000 FE-NO 50.000 2.01000 NP-CC 316.000 1.38400 NO-CC 316.000 1.38400 CC-CB 273.000 1.44400 CC-CD 391.000 1.39100 CB-CB 418.000 1.35700 CB-CT 297.000 1.50100 CB-CY 297.000 1.50100 CD-HC 338.000 1.09000 CY-HC 340.000 1.08000 CX-HC 340.000 1.08000 CY-CX 570.000 1.34000 LO-LO 848.000 1.25000 unbound O2 ligand ANGLE NB-FE-NP 50.000 90.000 NB-FE-NO 50.000 90.000 NB-FE-LC 0.000 90.000 NB-FE-LO 0.000 90.000 NP-FE-NP 0.000 90.000 NO-FE-NO 0.000 90.000 NO-FE-NP 50.000 90.000 NO-FE-LC 45.000 90.000 NO-FE-LO 45.000 90.000 NP-FE-LC 45.000 90.000 NP-FE-LO 45.000 90.000 FE-NP-CC 30.000 127.400 FE-NO-CC 30.000 127.400 NO-CC-CB 70.000 110.300 NP-CC-CB 70.000 110.300 NO-CC-CD 70.000 125.500 NP-CC-CD 70.000 125.500 CC-NP-CC 70.000 105.400 CC-NO-CC 70.000 105.400 CC-CB-CB 70.000 107.000 CC-CD-CC 70.000 124.100 CB-CC-CD 70.000 125.400 CC-CB-CT 70.000 124.900 CB-CB-CT 70.000 128.200 CC-CD-HC 30.000 118.000 CR-NB-FE 0.000 110.000 CV-NB-FE 0.000 110.000 CB-CT-HC 35.000 109.500 CB-CB-CY 70.000 128.200 CB-CY-HC 35.000 120.000 CB-CY-CX 70.000 120.000 CY-CX-HC 35.000 120.000 CC-CB-CY 70.000 124.900 CX-CY-HC 35.000 120.000 CB-CT-CT 63.000 114.000 HC-CX-HC 35.000 120.000 FE-LC-LO 35.000 180.000 FE-LO-LO 35.000 135.000 LP-SH-LP 600.000 160.000 DIHEDRAL X -NB-FE-X 1 0.000 0.000 2.000 X -NO-FE-X 1 0.000 180.000 2.000 X -NP-FE-X 1 0.000 180.000 2.000 X -NO-CC-X 4 5.700 180.000 2.000 X -NP-CC-X 4 5.700 180.000 2.000 X -CC-CB-X 4 3.150 180.000 2.000 X -CB-CB-X 4 21.500 180.000 2.000 X -CD-CC-X 4 7.900 180.000 2.000 X -CB-CT-X 1 0.000 180.000 2.000 X -CB-CY-X 4 0.000 180.000 2.000 X -CY-CX-X 4 30.000 180.000 2.000 X -FE-LO-X 1 0.0 180.0 2.0 IMPROPER X -X -CC-X 0 1.000 180.000 2.000 X -X -CB-X 0 1.000 180.000 2.000 X -X -NP-X 0 1.000 180.000 2.000 X -X -NO-X 0 1.000 180.000 2.000 NONBON FE 1.20000 0.05000 0.00000 LO 1.60000 0.20000 0.00000 LC 1.85 0.12 0.0 MMTK-2.7.9/MMTK/ForceFields/Amber/opls_parm.dat0000644000076600000240000021727212041514537021377 0ustar hinsenstaff00000000000000 from Jorgensen UNITED ATOM FORCE FIELD FOR DNA AND PROTEINS BR 79.90 C 12.01 CA 13.02 CB 12.01 CC 12.01 ? CD 13.02 ? CE 13.02 CF 13.02 CH 13.02 CK 12.01 OPLS (JTR, 6-29-89) CZ 13.02 OPLS (JTR, 12/11/85) C2 14.03 CQ 14.03 OPLS (JTR 12-11-85) C3 15.03 CV 15.03 OPLS (JTR 12-11-85) CW 15.03 OPLS (JTR 12-11-85) C* 12.01 CT 12.01 CY 12.01 CX 12.01 CJ 12.01 CI 12.01 CM 12.01 CN 12.01 CG 12.01 CP 12.01 C4 12.01 OPLS (JTR 09-29-86), central in arg CL 35.45 F 19.00 H 1.008 HC 1.008 HK 1.008 OPLS (JTR, 6-29-89) HO 1.008 HT 1.008 H4 1.008 HS 1.008 HW 1.008 H2 1.008 H3 1.008 I 126.9 IM 35.45 ASSUMED TO BE CL- IP 131.0 ASSUMED TO BE NA+(H2O)6 M1 999. UNDEFINED M2 999. UNDEFINED N 14.01 NA 14.01 NB 14.01 NC 14.01 NT 14.01 N2 14.01 N3 14.01 N* 14.01 NP 14.01 NO 14.01 O 16.00 OT 16.00 O4 16.00 OW 16. W 16. OH 16.00 OM 16.00 OS 16.00 O2 16.00 P 30.97 S 32.06 SH 32.06 SF 32.06 FE 55.85 S* 32.06 CU 63.55 LP 12.00 WHY? M4 12.00 WHY? ZN 65.38 C H HO H2 H3 IM IP N NA NB NC N2 NT N2 N3 N* O OH OM OS P O2 OT O4 M4 ZN CB-C3 300. 1.51 HEME CB-CV 300. 1.51 HEME OPLS (JTR 12-11-85) CB-CW 300. 1.51 HEME OPLS (JTR 12-11-85) FE-W 70. 2.01 HEME CC-NO 180. 1.38 HEME CC-NP 180. 1.38 HEME FE-NO 70. 2.00 HEME FE-NP 70. 2.00 HEME NB-FE 70. 2.05 HEME CB-C2 450. 1.51 HEME CB-CQ 450. 1.51 HEME OPLS (JTR 12-11-85) CB-CC 460. 1.44 HEME CC-CD 250. 1.40 HEME CB-CY 450. 1.51 HEME CY-CX 450. 1.35 HEME LP-S 600. 0.679 LP-SH 600. 0.679 C -CB 447. 1.419 GUA C -CD 469. 1.40 TYR C -CH 317. 1.522 AA C -CZ 317. 1.522 AA, OPLS (JTR 12/11/85) C -CJ 410. 1.444 URA C -CM 410. 1.444 THY C -CT 317. 1.522 C -C2 317. 1.522 GLY,ASP,GLU C -CQ 317. 1.522 GLY,ASP,GLU OPLS (JTR 12-11-85) C -C3 317. 1.522 END C -CV 317. 1.522 END OPLS (JTR 12-11-85) C -CW 317. 1.522 END OPLS (JTR 12-11-85) C -N 490. 1.335 AA C -NA 418. 1.388 URAGUA C4-N2 481. 1.340 ARG OPLS (JTR 09-22-86) C -NC 457. 1.358 CYT C -N* 424. 1.383 CYT,URA C -O 570. 1.229 URAGUA,CYT,AA C -OH 450. 1.364 TYR CA-OH 450. 1.364 TYR OPLS (JTR 10-25-86) CK-OH 450. 1.364 TYR OPLS (JTR 9/13/91) C -O2 656. 1.25 GLU,ASP CA-CB 469. 1.404 ADE CA-CD 469. 1.40 PHE,TYR CA-CK 469. 1.40 PHE,TYR OPLS (JTR 6-29-89) C3-CK 469. 1.40 CRESOL OPLS (JTR 7-24-91) CA-CJ 427. 1.433 CYT CA-C2 317. 1.51 PHE,TYR CK-C2 317. 1.51 PHE,TYR OPLS (JTR 6-29-89) CA-CQ 317. 1.51 PHE,TYR OPLS (JTR 12-11-85) CA-NA 427. 1.381 GUA CA-NC 483. 1.339 ADE,GUA,CYT CA-N2 481. 1.340 ARG CB-CB 520. 1.370 ADE,GUA CB-CD 469. 1.40 TRP CB-CN 447. 1.419 TRP CB-C* 388. 1.459 TRP CB-CK 388. 1.459 TRP OPLS (JTR 10-22-90) CB-NB 414. 1.391 ADE,GUA,HIS CB-NC 461. 1.354 ADE,GUA CB-N* 436. 1.374 ADE,GUA CC-CF 512. 1.375 HIS CC-CG 518. 1.371 HIS CC-C2 317. 1.504 HIS CC-CQ 317. 1.504 HIS OPLS (JTR 12-11-85) CC-NA 422. 1.385 HIS CK-NA 422. 1.385 HIS OPLS (JTR 10-22-90) CC-NB 410. 1.394 ADE,GUA,HIS CK-NB 488. 1.335 HIS(MOD) OPLS (JTR 10-22-90) CD-CD 469. 1.40 TRP,TYR,PHE CK-CK 469. 1.40 TRP,TYR,PHE OPLS (JTR 6-29-89) CD-CN 469. 1.40 TRP CK-CN 469. 1.40 TRP OPLS (JTR 10-22-90) CE-NB 529. 1.304 ADE,GUA CE-N* 440. 1.371 ADE,GUA CF-NB 410. 1.394 ADE,GUA,HIS CG-NA 427. 1.381 TRP,HIS CG-C* 546. 1.352 TRP CN-NA 428. 1.38 TRP CH-CH 260. 1.526 SUG(as in CH-C2),ILE CH-CZ 260. 1.526 SUG(as in CH-C2),ILE OPLS (JTR 12/11/85) CZ-CZ 260. 1.526 SUG(as in CH-C2),ILE OPLS (JTR 12/11/85) CH-C2 260. 1.526 AA,SUG CH-CQ 260. 1.526 AA,SUG OPLS (JTR 12-11-85) CZ-C2 260. 1.526 AA,SUG OPLS (JTR 12/11/85) CZ-CQ 260. 1.526 AA,SUG OPLS (JTR 12/11/85) CH-C3 260. 1.526 ALA CH-CV 260. 1.526 ALA OPLS (JTR 12-11-85) CH-CW 260. 1.526 ALA OPLS (JTR 12-11-85) CZ-C3 260. 1.526 ALA OPLS (JTR 12/11/85) CZ-CV 260. 1.526 ALA OPLS (JTR 12/11/85) CZ-CW 260. 1.526 ALA OPLS (JTR 12/11/85) CH-N 337. 1.449 AA CZ-N 337. 1.449 AA OPLS (JTR 12/11/85) CH-NT 367. 1.471 AA CZ-NT 367. 1.471 AA OPLS (JTR 12/11/85) CH-N* 337. 1.475 ADE,GUA,CYT,URA CZ-N* 337. 1.475 ADE,GUA,CYT,URA OPLS (JTR 12/11/85) CH-OH 386. 1.425 RSUG,THR CZ-OH 386. 1.425 RSUG,THR OPLS (JTR 12/11/85) CH-OS 320. 1.425 SUG CZ-OS 320. 1.425 SUG OPLS (JTR 12/11/85) CI-NC 502. 1.324 ADE CJ-CJ 549. 1.350 URA,CYT CJ-CM 560. 1.343 THY CJ-N* 448. 1.365 URA,CYT CP-NA 477. 1.343 HIS CP-NB 488. 1.335 HIS(MOD) CT-CT 310. 1.526 CT-N 337. 1.449 CT-HC 331. 1.09 C2-C2 260. 1.526 AA(OL) C2-CQ 260. 1.526 AA(OL) OPLS (JTR 12-11-85) CQ-CQ 260. 1.526 AA(OL) OPLS (JTR 12-11-85) C2-C3 260. 1.526 ILE(OL) C2-CV 260. 1.526 ILE(OL) OPLS (JTR 12-11-85) C2-CW 260. 1.526 ILE(OL) OPLS (JTR 12-11-85) CQ-C3 260. 1.526 ILE(OL) OPLS (JTR 12-11-85) CQ-CV 260. 1.526 ILE(OL) OPLS (JTR 12-11-85) CQ-CW 260. 1.526 ILE(OL) OPLS (JTR 12-11-85) C2-N2 337. 1.463 ARG(OL) CQ-N2 337. 1.463 ARG(OL) OPLS (JTR 12-11-85) C3-N2 337. 1.463 ARG(OL) CV-N2 337. 1.463 ARG(OL) OPLS (JTR 12-11-85) CW-N2 337. 1.463 ARG(OL) OPLS (JTR 12-11-85) C2-C* 317. 1.495 TRP(OL) CQ-C* 317. 1.495 TRP(OL) OPLS (JTR 12-11-85) C2-N 337. 1.449 GLY(OL) CQ-N 337. 1.449 GLY(OL) OPLS (JTR 12-11-85) C2-NT 367. 1.471 GLY CQ-NT 367. 1.471 GLY OPLS (JTR 12-11-85) C3-N3 367. 1.471 CV-N3 367. 1.471 OPLS (JTR 12-11-85) CW-N3 367. 1.471 OPLS (JTR 12-11-85) C2-N3 367. 1.471 LYS(OL) CQ-N3 367. 1.471 LYS(OL) OPLS (JTR 12-11-85) CH-N3 367. 1.471 LYS(OL) CZ-N3 367. 1.471 LYS(OL) OPLS (JTR 12/11/85) C2-OS 320. 1.425 SUG(OL) CQ-OS 320. 1.425 SUG(OL) OPLS (JTR 12-11-85) C2-OH 386. 1.425 SUG(OL),SER CQ-OH 386. 1.425 SUG(OL),SER OPLS (JTR 12-11-85) C3-OH 386. 1.425 SUG(OL),SER CV-OH 386. 1.425 SUG(OL),SER OPLS (JTR 12-11-85) CW-OH 386. 1.425 SUG(OL),SER OPLS (JTR 12-11-85) C2-S 222. 1.81 CYX(OL) CQ-S 222. 1.81 CYX(OL) OPLS (JTR 12-11-85) C2-SH 222. 1.81 CYS(OL) CQ-SH 222. 1.81 CYS(OL) OPLS (JTR 12-11-85) C3-SH 222. 1.81 CYS(OL) C3-S 222. 1.81 MET(OL) C3-CM 317. 1.51 THY(use std C-C) C3-N 337. 1.449 TEST!!!! C3-N* 337. 1.475 9 methyl bases C3-OS 320. 1.425 DMP CV-SH 222. 1.81 CYS(OL) CV-S 222. 1.81 MET(OL) CV-CM 317. 1.51 THY(use std C-C) CV-N 337. 1.449 TEST!!!! CV-N* 337. 1.475 9 methyl bases CV-OS 320. 1.425 DMP CW-SH 222. 1.81 CYS(OL) CW-S 222. 1.81 MET(OL) CW-CM 317. 1.51 THY(use std C-C) CW-N 337. 1.449 TEST!!!! CW-N* 337. 1.475 9 methyl bases CW-OS 320. 1.425 DMP HK-CK 340. 1.08 TYR,PHE OPLS (JTR 6-29-89) H -N 434. 1.01 AA H -N2 434. 1.01 URA,GUA,HIS H -NA 434. 1.01 URA,GUA,HIS HO-OS 553. 0.96 SUG(OL) HO-OH 553. 0.96 SUG(OL) HT-OT 6000. 0.9572 TIP3P MODEL OPLS (JTR 12-11-85) HT-HT 6000. 1.5174 TIP3P MODEL OPLS (JTR 3-19-85) H4-O4 6000. 0.9572 TIP4P MODEL OPLS (JTR 12-11-85) M4-O4 6000. 0.15 TIP4P MODEL OPLS (JTR 12-11-85) H2-N 434. 1.01 AA H2-N2 434. 1.01 ADE,GUA,CYT,GLN,ASN,ARG H3-N2 434. 1.01 ADE,GUA,CYT,GLN,ASN,ARG H2-NT 434. 1.01 AA(END) H3-N3 434. 1.01 LYS(OL) HS-SH 274. 1.336 CYS(OL) OS-P 230. 1.61 SUG(OL) O2-P 525. 1.48 SUG(OL) OH-P 230. 1.61 SUG(OL) S -S 166. 2.038 CYX(OL) SCHERAGA CP-NB-FE 130. 124.8 HEME NB-FE-W 30. 180.0 HEME NP-FE-W 30. 90.0 HEME NO-FE-W 30. 90.0 HEME CF-NB-FE 130. 124.8 HEME NB-FE-NP 55. 90.0 HEME NB-FE-NO 55. 90.0 HEME FE-NP-CC 50. 127.3 HEME FE-NO-CC 50. 127.3 HEME NP-CC-CD 70. 125.5 HEME NO-CC-CD 70. 125.5 HEME NP-CC-CB 70. 110.3 HEME NO-CC-CB 70. 110.3 HEME NP-FE-NO 55. 90.0 HEME NP-FE-NP 0. 180.0 HEME NB-FE-NB 0. 180.0 HEME NO-FE-NO 0. 180.0 HEME CC-CB-C2 70. 126.5 HEME CC-CB-CQ 70. 126.5 HEME OPLS (JTR 12-11-85) CC-CB-CB 80. 107.0 HEME CC-NP-CC 50. 105.4 HEME CC-NO-CC 50. 105.4 HEME CC-CD-CC 70. 124.4 HEME CB-C2-C2 46.5 112.0 HEME CB-CQ-C2 46.5 112.0 HEME OPLS (JTR 12-11-85) CB-C2-CQ 46.5 112.0 HEME OPLS (JTR 12-11-85) CB-CQ-CQ 46.5 112.0 HEME OPLS (JTR 12-11-85) CB-CB-C3 70. 126.5 HEME CB-CB-CV 70. 126.5 HEME OPLS (JTR 12-11-85) CB-CB-CW 70. 126.5 HEME OPLS (JTR 12-11-85) CB-CC-CD 70. 124.2 HEME C3-CB-CC 70. 126.5 HEME CV-CB-CC 70. 126.5 HEME OPLS (JTR 12-11-85) CW-CB-CC 70. 126.5 HEME OPLS (JTR 12-11-85) CB-CB-CY 70. 120.0 HEME C2-CB-CB 70. 126.5 HEME CQ-CB-CB 70. 126.5 HEME OPLS (JTR 12-11-85) CB-CY-CX 50. 120.0 HEME CY-CB-CC 70. 120.0 END OF HEME ANGLES C2-N2-H3 35. 118.4 DOHE END GROUP FOR PROTEINS CQ-N2-H3 35. 118.4 SAME, OPLS (JTR 12-11-85) CA-N2-H3 35. 120.0 DOHE END GROUP FOR PROTEINS C4-N2-H3 35. 120.0 ARG OPLS (JTR 09-22-86) H3-N2-H3 35. 120.0 DOHE END GROUP FOR PROTEINS C2-C -OH 50. 115.0 DOHE END GROUP FOR PROTEINS CQ-C -OH 50. 115.0 SAME, OPLS (JTR 12-11-85) O -C -OH 80. 126.0 DOHE END GROUP FOR PROTEINS LP-S -S 150. 96.7 LP-S -C2 150. 96.7 LP-S -CQ 150. 96.7 OPLS (JTR 12-11-85) LP-S -C3 150. 96.7 LP-S -CV 150. 96.7 OPLS (JTR 12-11-85) LP-S -CW 150. 96.7 OPLS (JTR 12-11-85) LP-SH-C2 150. 96.7 LP-SH-CQ 150. 96.7 OPLS (JTR 12-11-85) LP-SH-C3 150. 96.7 LP-SH-CV 150. 96.7 OPLS (JTR 12-11-85) LP-SH-CW 150. 96.7 OPLS (JTR 12-11-85) LP-SH-HS 150. 96.7 LP-S -LP 0. 160.0 LP-SH-LP 0. 160.0 HS-SH-HS 35. 92.07 CH-C2-S 50. 114.7 CYX SCHERAGA JPC 79,1428 CH-CQ-S 50. 114.7 CYX SCH JPC79,1428 OPLS (JTR 12-11-85 CZ-C2-S 50. 114.7 CYX SCHERAGA JPC79,1428 OPLS(JTR12/11/ CZ-CQ-S 50. 114.7 CYX SCHERAGA JPC79,1428 OPLS(JTR12/11/ CH-C2-SH 50.0 108.6 CYS CH-CQ-SH 50.0 108.6 CYS OPLS (JTR 12-11-85) CZ-C2-SH 50.0 108.6 CYS OPLS (JTR 12/11/85) CZ-CQ-SH 50.0 108.6 CYS OPLS (JTR 12/11/85) C2-C2-S 50. 114.7 MET SCHERAGA JPC 79,1428 C2-CQ-S 50. 114.7 MET ABOVE, OPLS (JTR 12-11-85) CQ-C2-S 50. 114.7 MET ABOVE, OPLS (JTR 12-11-85) CQ-CQ-S 50. 114.7 MET ABOVE,OPLS (JTR 12-11-85) C2-S -C3 62. 98.9 MET(OL) C2-S -CV 62. 98.9 MET(OL) OPLS (JTR 12-11-85) C2-S -CW 62. 98.9 MET(OL) OPLS (JTR 12-11-85) CW-S -CW 62. 98.9 MET(OL) OPLS (JTR 1-7-87) CQ-S -C3 62. 98.9 MET(OL) OPLS (JTR 12-11-85) CQ-S -CV 62. 98.9 MET(OL) OPLS (JTR 12-11-85) CQ-S -CW 62. 98.9 MET(OL) OPLS (JTR 12-11-85) C2-S -S 68. 103.7 CYX(OL) SCHERAGA JPC 79,1428 CQ-S -S 68. 103.7 CYX(OL) ABOVE, OPLS (JTR 12-11-85) C3-S -S 68. 103.7 CYX(OL) SCHERAGA JPC 79,1428 CV-S -S 68. 103.7 CYX(OL) ABOVE, OPLS (JTR 12-11-85) CW-S -S 68. 103.7 CYX(OL) ABOVE, OPLS (JTR 12-11-85) C2-SH-HS 44. 96.0 CYS(OL) CQ-SH-HS 44. 96.0 CYS(OL) OPLS (JTR 12-11-85) C3-SH-HS 44. 96.0 CYS(OL) CV-SH-HS 44. 96.0 CYS(OL) OPLS (JTR 12-11-85) CW-SH-HS 44. 96.0 CYS(OL) OPLS (JTR 12-11-85) CB-C -O 80. 128.8 GUA CB-C -NA 70. 111.3 GUA CD-C -OH 70. 120. TYR(OL) GELIN CD-CA-OH 70. 120. TYR(OL) OPLS (JTR 10-25-86) CD-C -CD 85. 120. TYR(OL) GELIN CH-C -O 80. 120.4 AA(OL) CZ-C -O 80. 120.4 AA(OL) OPLS (JTR 12/11/85) CH-C -OH 70. 115. ACID(OL) SCH JPC 79,2379 CZ-C -OH 70. 115. ACID(OL) SCH JPC 79,2379 OPLS(JTR 12/11/ CH-C -O2 65. 117. AA(OL) SCH JPC 79,2379 CZ-C -O2 65. 117. AA(OL)SCH JPC 79,2379 OPLS (JTR 12/11/85 CH-C -N 70. 116.6 AA(OL) CZ-C -N 70. 116.6 AA(OL) OPLS (JTR 12/11/85) CJ-C -O 80. 125.3 URA CJ-C -NA 70. 114.1 URA CM-C -O 80. 125.3 THY CM-C -NA 70. 114.1 THY CT-C -N 70. 116.6 CT-C -O 80. 120.4 C2-C -N 70. 116.6 GLY GELIN CQ-C -N 70. 116.6 GLY GELIN, OPLS (JTR 12-11-85) C2-C -O 80. 120.4 ASN(OL) GELIN CQ-C -O 80. 120.4 ASN(OL) GELIN, OPLS (JTR 12-11-85) C2-C -O2 70. 117. GLU(OL) SCH JPC 79,2379 CQ-C -O2 70. 117. GLU(OL) SCH JPC79,2379/ OPLS (JTR 12-11-8 C3-C -O2 70. 117. GLU(OL) SCH JPC 79,2379 C3-C -O 80. 120.4 ACET(OL) C3-C -N 70. 116.6 ACET(OL) BENEDETTI CV-C -O2 70. 117. GLU(OL)SCHJPC79,2379 OPLS (JTR 12-11-85) CV-C -O 80. 120.4 ACET(OL) OPLS (JTR 12-11-85) CV-C -N 70. 116.6 ACET(OL) BENEDETTI OPLS (JTR 12/11/85) CW-C -O2 70. 117. GLU(OL)SCHJPC79,2379 OPLS (JTR 12-11-85) CW-C -O 80. 120.4 ACET(OL) OPLS (JTR 12-11-85) CW-C -N 70. 116.6 ACET(OL) BENEDETTI OPLS (JTR 12/11/85) N -C -O 80. 122.9 AA(OL) N*-C -O 80. 120.9 URA,CYT NA-C -O 80. 120.6 URA(2),GUA NA-C -N* 70. 115.4 URA NC-C -O 80. 122.5 CYT NC-C -N* 70. 118.6 CYT O2-C -O2 80. 126.0 GLU(OL) SCH JPC 79,2379 O -C -O 80. 126.0 GLU(OL) SCH JPC 79,2379 CB-CA-N2 70. 123.5 ADE CB-CA-NC 70. 117.3 ADE CD-CA-CD 85. 120. PHE(OL) CD-CA-C2 70. 120. PHE(OL) CD-CA-CQ 70. 120. PHE(OL) OPLS (JTR 12-11-85) CJ-CA-N2 70. 120.1 CYT CJ-CA-NC 70. 121.5 CYT NC-CA-N2 70. 119.8 ADE,GUA N2-CA-N2 70. 120. ARG(OL) N2-C4-N2 70. 120. ARG(OL) OPLS (JTR 09-22-86) NA-CA-NC 70. 123.3 GUA NA-CA-N2 70. 116.0 GUA C -CB-NB 70. 130. GUA C -CB-CB 85. 119.2 GUA CA-CB-NB 70. 132.4 ADE CA-CB-CB 85. 117.3 ADE CB-CB-NC 70. 127.7 GUA,ADE CB-CB-NB 70. 110.4 GUA,ADE CB-CB-N* 70. 106.2 GUA,ADE CD-CB-CN 85. 116.2 TRP CK-CB-CN 85. 116.2 TRP OPLS (JTR 10-22-90) CD-CB-C* 85. 134.9 TRP(OL) CK-CB-CK 85. 134.9 TRP(OL) OPLS (JTR 10-22-90) CN-CB-C* 85. 108.8 TRP(OL) CN-CB-CK 85. 108.8 TRP(OL) OPLS (JTR 10-22-90) NC-CB-N* 70. 126.0 GUA,ADE C2-CC-NA 70. 122.2 HIS(OL) C2-CK-NA 70. 122.2 HIS(OL), OPLS (JTR 10-22-90) CQ-CC-NA 70. 122.2 HIS(OL), OPLS (JTR 12-11-85) C2-CC-CF 70. 131.9 HIS(OL) CQ-CC-CF 70. 131.9 HIS(OL), OPLS (JTR 12-11-85) C2-CC-CG 70. 129.05 HIS(OL) CQ-CC-CG 70. 129.05 HIS(OL), OPLS (JTR 12-11-85) C2-CC-NB 70. 121.05 HIS(OL) C2-CK-NB 70. 121.05 HIS(OL), OPLS (JTR 12-11-90) CQ-CC-NB 70. 121.05 HIS(OL), OPLS (JTR 12-11-85) CG-CC-NB 70. 109.9 HIS(OL) CF-CC-NA 70. 105.9 HIS(OL) CF-CC-NA 70. 105.9 HIS(OL) CG-CC-NA 70. 108.75 HIS(OL) CK-CK-NA 70. 108.75 HIS(OL) OPLS (JTR 10-22-90) CA-CD-CD 85. 120. PHE CD-CD-CD 85. 120. PHE(OL) CD-CD-CN 85. 120. TRP(OL) CB-CD-CD 85. 120. TRP(OL) CB-CK-CK 85. 120. TRP(OL) OPLS (JTR 10-22-90) CD-CD-C 85. 120. TYR(OL) N*-CE-NB 70. 113.9 ADE,GUA NB-CP-NA 70. 111.6 HIS(OL) NB-CK-NA 70. 111.6 HIS(OL) OPLS (JTR 10-22-90) NA-CP-NA 70. 110.75 HISP(OL) NA-CK-NA 70. 110.75 HISP(OL) OPLS (JTR 10-22-90) CC-CF-NB 70. 109.9 HIS(OL) CK-CK-NB 70. 109.9 HIS(OL) OPLS (JTR 10-22-90) CC-CG-NA 70. 105.9 HIS(OL) C*-CG-NA 70. 108.7 TRP(OL) NC-CI-NC 70. 129.1 ADE CA-CJ-CJ 85. 117.0 CYT CJ-CJ-N* 70. 121.2 CYT C -CJ-CJ 85. 120.7 URA CJ-CJ-N* 70. 121.2 URA CM-CJ-N* 70. 121.2 THY C -CM-CJ 85. 120.7 THY C -CM-C3 85. 119.7 THY C -CM-CV 85. 119.7 THY OPLS (JTR 12-11-85) C -CM-CW 85. 119.7 THY OPLS (JTR 12-11-85) CJ-CM-C3 85. 119.7 THY CJ-CM-CV 85. 119.7 THY OPLS (JTR 12-11-85) CJ-CM-CW 85. 119.7 THY OPLS (JTR 12-11-85) CB-CN-CD 85. 122.7 TRP CB-CN-CK 85. 122.7 TRP OPLS (JTR 10-22-90) CB-CN-NA 70. 104.4 CD-CN-NA 70. 132.8 TRP(OL) CK-CN-NA 70. 132.8 TRP(OL) OPLS (JTR 10-22-90) CH-CH-C2 63.0 111.5 SUG,ILE CH-CH-CQ 63.0 111.5 SUG,ILE OPLS (JTR 12-11-85) CH-CZ-C2 63.0 111.5 SUG,ILE OPLS (JTR 12/11/85) CH-CZ-CQ 63.0 111.5 SUG,ILE OPLS (JTR 12/11/85) CZ-CH-C2 63.0 111.5 SUG,ILE OPLS (JTR 12/11/85) CZ-CH-CQ 63.0 111.5 SUG,ILE OPLS (JTR 12/11/85) CZ-CZ-C2 63.0 111.5 SUG,ILE OPLS (JTR 12/11/85) CZ-CZ-CQ 63.0 111.5 SUG,ILE OPLS (JTR 12/11/85) C -CH-CH 63.0 111.1 ILE C -CH-CZ 63.0 111.1 ILE OPLS (JTR 12/11/85) C -CZ-CH 63.0 111.1 ILE OPLS (JTR 12/11/85) C -CZ-CZ 63.0 111.1 ILE OPLS (JTR 12/11/85) C -CH-C2 63.0 111.1 AA C -CH-CQ 63.0 111.1 AA OPLS (JTR 12-11-85) C -CZ-C2 63.0 111.1 AA OPLS (JTR 12/11/85) C -CZ-CQ 63.0 111.1 AA OPLS (JTR 12/11/85) C -CH-C3 63.0 111.1 ALA C -CH-CV 63.0 111.1 ALA OPLS (JTR 12-11-85) C -CH-CW 63.0 111.1 ALA OPLS (JTR 12-11-85) C -CZ-C3 63.0 111.1 ALA OPLS (JTR 12/11/85) C -CZ-CV 63.0 111.1 ALA OPLS (JTR 12/11/85) C -CZ-CW 63.0 111.1 ALA OPLS (JTR 12/11/85) C -CH-N 63.0 110.1 AA WORK DONE ON 6/23/82 C -CZ-N 63.0 110.1 AA 6/23/82, OPLS (JTR 12/11/85) C -CH-N3 63.0 110.1 AA WORK DONE ON 6/23/82 C -CZ-N3 63.0 110.1 AA 6/23/82, OPLS (JTR 12/11/85) CH-CH-C3 63.0 111.5 ILE CH-CZ-C3 63.0 111.5 ILE OPLS (JTR 12/11/85) CZ-CH-C3 63.0 111.5 ILE OPLS (JTR 12/11/85) CZ-CZ-C3 63.0 111.5 ILE OPLS (JTR 12/11/85) CH-CH-CV 63.0 111.5 ILE OPLS (JTR 12-11-85) CH-CZ-CV 63.0 111.5 ILE OPLS (JTR 12/11/85) CZ-CH-CV 63.0 111.5 ILE OPLS (JTR 12/11/85) CZ-CZ-CV 63.0 111.5 ILE OPLS (JTR 12/11/85) CH-CH-CW 63.0 111.5 ILE OPLS (JTR 12-11-85) CH-CZ-CW 63.0 111.5 ILE OPLS (JTR 12/11/85) CZ-CH-CW 63.0 111.5 ILE OPLS (JTR 12/11/85) CZ-CZ-CW 63.0 111.5 ILE OPLS (JTR 12/11/85) CH-CH-N* 80.0 109.5 SUG CH-CZ-N* 80.0 109.5 SUG OPLS (JTR 12/11/85) CZ-CH-N* 80.0 109.5 SUG OPLS (JTR 12/11/85) CZ-CZ-N* 80.0 109.5 SUG OPLS (JTR 12/11/85) CH-CH-C2 63.0 111.5 SUG,ILE CH-CH-CQ 63.0 111.5 SUG,ILE OPLS (JTR 12-11-85) CH-CZ-C2 63.0 111.5 SUG,ILE OPLS (JTR 12/11/85) CH-CZ-CQ 63.0 111.5 SUG,ILE OPLS (JTR 12/11/85) CZ-CH-C2 63.0 111.5 SUG,ILE OPLS (JTR 12/11/85) CZ-CH-CQ 63.0 111.5 SUG,ILE OPLS (JTR 12/11/85) CZ-CZ-C2 63.0 111.5 SUG,ILE OPLS (JTR 12/11/85) CZ-CZ-CQ 63.0 111.5 SUG,ILE OPLS (JTR 12/11/85) CH-CH-CH 63.0 111.5 SUG CH-CH-CZ 63.0 111.5 SUG OPLS (JTR 12/11/85) CH-CZ-CH 63.0 111.5 SUG OPLS (JTR 12/11/85) CZ-CZ-CH 63.0 111.5 SUG OPLS (JTR 12/11/85) CZ-CH-CZ 63.0 111.5 SUG OPLS (JTR 12/11/85) CZ-CZ-CZ 63.0 111.5 SUG OPLS (JTR 12/11/85) CH-CH-OS 80.0 109.5 SUG CH-CZ-OS 80.0 109.5 SUG OPLS (JTR 12/11/85) CZ-CH-OS 80.0 109.5 SUG OPLS (JTR 12/11/85) CZ-CZ-OS 80.0 109.5 SUG OPLS (JTR 12/11/85) CH-CH-OH 80.0 109.5 THR,end sugar CH-CZ-OH 80.0 109.5 THR,end sugar OPLS (JTR 12/11/85) CZ-CH-OH 80.0 109.5 THR,end sugar OPLS (JTR 12/11/85) CZ-CZ-OH 80.0 109.5 THR,end sugar OPLS (JTR 12/11/85) N -CH-C2 80.0 109.7 ALA JACS 94, 2657 N -CH-CQ 80.0 109.7 ALA JACS94,2657 OPLS (JTR 12-11-85) N -CZ-C2 80.0 109.7 ALA JACS94,2657 OPLS (JTR 12/11/85) N -CZ-CQ 80.0 109.7 ALA JACS94,2657 OPLS (JTR 12/11/85) N -CH-CH 80.0 109.7 ILE JACS 94, 2657 N -CH-CZ 80.0 109.7 ILE JACS94,2657 OPLS (JTR 12/11/85) N -CZ-CH 80.0 109.7 ILE JACS94,2657 OPLS (JTR 12/11/85) N -CZ-CZ 80.0 109.7 ILE JACS94,2657 OPLS (JTR 12/11/85) C2-CH-C3 63.0 111.5 LEU CQ-CH-C3 63.0 111.5 LEU OPLS (JTR 12-11-85) C2-CZ-C3 63.0 111.5 LEU OPLS (JTR 12/11/85) CQ-CZ-C3 63.0 111.5 LEU OPLS (JTR 12/11/85) C2-CH-CV 63.0 111.5 LEU OPLS (JTR 12-11-85) CQ-CH-CV 63.0 111.5 LEU OPLS (JTR 12-11-85) C2-CZ-CV 63.0 111.5 LEU OPLS (JTR 12/11/85) CQ-CZ-CV 63.0 111.5 LEU OPLS (JTR 12/11/85) C2-CH-CW 63.0 111.5 LEU OPLS (JTR 12-11-85) CQ-CH-CW 63.0 111.5 LEU OPLS (JTR 12-11-85) C2-CZ-CW 63.0 111.5 LEU OPLS (JTR 12/11/85) CQ-CZ-CW 63.0 111.5 LEU OPLS (JTR 12/11/85) C2-CH-N* 80.0 109.5 SUG CQ-CH-N* 80.0 109.5 SUG OPLS (JTR 12-11-85) C2-CZ-N* 80.0 109.5 SUG OPLS (JTR 12/11/85) CQ-CZ-N* 80.0 109.5 SUG OPLS (JTR 12/11/85) C2-CH-OS 80.0 109.5 SUG CQ-CH-OS 80.0 109.5 SUG OPLS (JTR 12-11-85) C2-CZ-OS 80.0 109.5 SUG OPLS (JTR 12/11/85) CQ-CZ-OS 80.0 109.5 SUG OPLS (JTR 12/11/85) C2-CH-OH 80.0 109.5 SUG (END GROUP) CQ-CH-OH 80.0 109.5 SUG (END GROUP) OPLS (JTR 12-11-85) C2-CZ-OH 80.0 109.5 SUG (END GROUP) OPLS (JTR 12/11/85) CQ-CZ-OH 80.0 109.5 SUG (END GROUP) OPLS (JTR 12/11/85) C3-CH-OH 80.0 109.5 THR CV-CH-OH 80.0 109.5 THR OPLS (JTR 12-11-85) CW-CH-OH 80.0 109.5 THR OPLS (JTR 12-11-85) C3-CZ-OH 80.0 109.5 THR OPLS (JTR 12/11/85) CV-CZ-OH 80.0 109.5 THR OPLS (JTR 12/11/85) CW-CZ-OH 80.0 109.5 THR OPLS (JTR 12/11/85) C3-CH-C3 63.0 111.5 VAL C3-CH-CV 63.0 111.5 VAL OPLS (JTR 12-11-85) C3-CH-CW 63.0 111.5 VAL OPLS (JTR 12-11-85) CV-CH-CW 63.0 111.5 VAL OPLS (JTR 12-11-85) CV-CH-CV 63.0 111.5 VAL OPLS (JTR 12-11-85) CW-CH-CW 63.0 111.5 VAL OPLS (JTR 12-11-85) C3-CZ-C3 63.0 111.5 VAL OPLS (JTR 12/11/85) C3-CZ-CV 63.0 111.5 VAL OPLS (JTR 12-11-85) C3-CZ-CW 63.0 111.5 VAL OPLS (JTR 12-11-85) CV-CZ-CW 63.0 111.5 VAL OPLS (JTR 12-11-85) CV-CZ-CV 63.0 111.5 VAL OPLS (JTR 12-11-85) CW-CZ-CW 63.0 111.5 VAL OPLS (JTR 12-11-85) C3-CH-N 80. 109.5 ** CV-CH-N 80. 109.5 ** OPLS (JTR 12-11-85) CW-CH-N 80. 109.5 ** OPLS (JTR 12-11-85) C3-CZ-N 80. 109.5 ** OPLS (JTR 12/11/85) CV-CZ-N 80. 109.5 ** OPLS (JTR 12/11/85) CW-CZ-N 80. 109.5 ** OPLS (JTR 12/11/85) NT-CH-C 80.0 109.7 AA NT-CZ-C 80.0 109.7 AA OPLS (JTR 12/11/85) NT-CH-C2 80.0 109.7 AA NT-CH-CQ 80.0 109.7 AA OPLS (JTR 12-11-85) NT-CZ-C2 80.0 109.7 AA OPLS (JTR 12/11/85) NT-CZ-CQ 80.0 109.7 AA OPLS (JTR 12/11/85) NT-CH-C3 80.0 109.7 AA NT-CH-CV 80.0 109.7 AA OPLS (JTR 12-11-85) NT-CH-CW 80.0 109.7 AA OPLS (JTR 12-11-85) NT-CZ-C3 80.0 109.7 AA OPLS (JTR 12-11-85) NT-CZ-CV 80.0 109.7 AA OPLS (JTR 12-11-85) NT-CZ-CW 80.0 109.7 AA OPLS (JTR 12-11-85) NT-CH-CH 80.0 109.7 AA NT-CH-CZ 80.0 109.7 AA OPLS (JTR 12-11-85) NT-CZ-CH 80.0 109.7 AA OPLS (JTR 12-11-85) NT-CZ-CZ 80.0 109.7 AA OPLS (JTR 12-11-85) N3-CH-C2 80.0 109.7 AA by analogy to NT-CH-C2 N3-CH-CQ 80.0 109.7 AA ABOVE, OPLS (JTR 12-11-85) N3-CH-C3 80.0 109.7 AA ABOVE, OPLS (JTR 12-11-85) N3-CZ-C2 80.0 109.7 AA above, OPLS (JTR 12-11-85) N3-CZ-CQ 80.0 109.7 AA above, OPLS (JTR 12-11-85) N*-CH-OS 80.0 109.5 SUG N*-CZ-OS 80.0 109.5 SUG OPLS (JTR 12-11-85) CH-C2-C* 63.0 115.6 TRP(OL) CH-CQ-C* 63.0 115.6 TRP(OL) OPLS (JTR 12-11-85) CZ-C2-C* 63.0 115.6 TRP(OL) OPLS (JTR 12-11-85) CZ-CQ-C* 63.0 115.6 TRP(OL) OPLS (JTR 12-11-85) C2-C2-N3 80.0 111.2 LYS(OL) JCP 76, 1439 C2-CQ-N3 80.0 111.2 LYS(OL),ABOVE, OPLS (JTR 12-11-85) CQ-C2-N3 80.0 111.2 LYS(OL),ABOVE, OPLS (JTR 12-11-85) CQ-CQ-N3 80.0 111.2 LYS(OL),ABOVE, OPLS (JTR 12-11-85) CH-CH-N3 80.0 111.2 LYS(OL) JCP 76, 1439 CH-CZ-N3 80.0 111.2 LYS(OL) JCP76,1439 OPLS (JTR 12-11-85) CZ-CH-N3 80.0 111.2 LYS(OL) JCP76,1439 OPLS (JTR 12-11-85) CZ-CZ-N3 80.0 111.2 LYS(OL) JCP76,1439 OPLS (JTR 12-11-85) C -C2-C2 63.0 112.4 GLU C -C2-CQ 63.0 112.4 GLU OPLS (JTR 12-11-85) C -CQ-C2 63.0 112.4 GLU OPLS (JTR 12-11-85) C -CQ-CQ 63.0 112.4 GLU OPLS (JTR 12-11-85) C -C2-CH 63.0 112.4 ASP C -CQ-CH 63.0 112.4 ASP OPLS (JTR 12-11-85) C -C2-CZ 63.0 112.4 ASP OPLS (JTR 12-11-85) C -CQ-CZ 63.0 112.4 ASP OPLS (JTR 12-11-85) C -C2-N 80.0 110.3 GLY WORK DONE ON 6/23/82 C -CQ-N 80.0 110.3 GLY ABOVE, OPLS (JTR 12-11-85) CC-C2-CH 63.0 113.1 HIS(OL) CC-CQ-CH 63.0 113.1 HIS(OL) OPLS (JTR 12-11-85) CC-C2-CZ 63.0 113.1 HIS(OL) OPLS (JTR 12-11-85) CC-CQ-CZ 63.0 113.1 HIS(OL) OPLS (JTR 12-11-85) CH-C2-CA 63. 114. PHE(OL) SCH JPC 79,2379 CH-CQ-CA 63. 114. PHE(OL)SCHJPC79,2379 OPLS (JTR 12-11-8 CZ-C2-CA 63. 114. PHE(OL) SCHJPC79,2379 OPLS (JTR 12-11- CZ-CQ-CA 63. 114. PHE(OL) SCHJPC79,2379 OPLS (JTR 12-11- CH-C2-CK 63. 114.0 PHE,TYR OPLS (JTR 6-29-89) CK-CK-HK 35. 120.0 PHE,TYR OPLS (JTR 6-29-89) CK-CK-HK 35. 120.0 PHE,TYR OPLS (JTR 6-29-89) CN-CK-HK 35. 120.0 PHE,TYR OPLS (JTR 10-22-90) CB-CK-HK 35. 120.0 PHE,TYR OPLS (JTR 10-22-90) CA-CK-HK 35. 120.0 PHE,TYR OPLS (JTR 6-29-89) C3-CK-CK 70. 120.0 CRESOL OPLS (JTR 7-24-91) C2-CK-CK 70. 120.0 PHE,TYR OPLS (JTR 6-29-89) CK-CK-CK 85. 120.0 PHE,TYR OPLS (JTR 6-29-89) CN-CK-CK 85. 120.0 PHE,TYR OPLS (JTR 10-22-90) CK-CK-CA 85. 120.0 PHE,TYR OPLS (JTR 6-29-89) CK-CA-CK 85. 120.0 PHE,TYR OPLS (JTR 6-29-89) CK-CA-OH 70. 120.0 PHE,TYR OPLS (JTR 6-29-89) CK-CK-OH 70. 120.0 PHE,TYR OPLS (JTR 9/13/91) CH-C2-CH 63.0 112.4 SUG,LEU CH-CQ-CH 63.0 112.4 SUG,LEU OPLS (JTR 12-11-85) CH-C2-CZ 63.0 112.4 SUG,LEU OPLS (JTR 12-11-85) CH-CQ-CZ 63.0 112.4 SUG,LEU OPLS (JTR 12-11-85) CZ-C2-CZ 63.0 112.4 SUG,LEU OPLS (JTR 12-11-85) CZ-CQ-CZ 63.0 112.4 SUG,LEU OPLS (JTR 12-11-85) CH-C2-C2 63.0 112.4 MET CH-C2-CQ 63.0 112.4 MET OPLS (JTR 12-11-85) CH-CQ-C2 63.0 112.4 MET OPLS (JTR 12-11-85) CH-CQ-CQ 63.0 112.4 MET OPLS (JTR 12-11-85) CZ-C2-C2 63.0 112.4 MET OPLS (JTR 12-11-85) CZ-CQ-C2 63.0 112.4 MET OPLS (JTR 12-11-85) CZ-C2-CQ 63.0 112.4 MET OPLS (JTR 12-11-85) CZ-CQ-CQ 63.0 112.4 MET OPLS (JTR 12-11-85) CH-C2-C3 63.0 112.4 ILE CH-C2-CV 63.0 112.4 ILE OPLS (JTR 12-11-85) CH-C2-CW 63.0 112.4 ILE OPLS (JTR 12-11-85) CH-CQ-C3 63.0 112.4 ILE OPLS (JTR 12-11-85) CH-CQ-CV 63.0 112.4 ILE OPLS (JTR 12-11-85) CH-CQ-CW 63.0 112.4 ILE OPLS (JTR 12-11-85) CZ-C2-C3 63.0 112.4 ILE OPLS (JTR 12-11-85) CZ-C2-CV 63.0 112.4 ILE OPLS (JTR 12-11-85) CZ-C2-CW 63.0 112.4 ILE OPLS (JTR 12-11-85) CZ-CQ-C3 63.0 112.4 ILE OPLS (JTR 12-11-85) CZ-CQ-CV 63.0 112.4 ILE OPLS (JTR 12-11-85) CZ-CQ-CW 63.0 112.4 ILE OPLS (JTR 12-11-85) CV-C2-OH 80.0 109.5 OPLS (JTR 06-09-87) CH-C2-OH 80.0 109.5 SER,end sugar CH-CQ-OH 80.0 109.5 SER,end sugar OPLS (JTR 12-11-85) CZ-C2-OH 80.0 109.5 SER,end sugar OPLS (JTR 12-11-85) CZ-CQ-OH 80.0 109.5 SER,end sugar OPLS (JTR 12-11-85) CH-C2-OS 80.0 109.5 SUG CH-CQ-OS 80.0 109.5 SUG OPLS (JTR 12-11-85) CZ-C2-OS 80.0 109.5 SUG OPLS (JTR 12-11-85) CZ-CQ-OS 80.0 109.5 SUG OPLS (JTR 12-11-85) C3-C2-C2 63.0 112.4 CV-C2-C2 63.0 112.4 OPLS CW-C2-C2 63.0 112.4 OPLS C2-C2-C2 63.0 112.4 PRO,LYS C2-C2-CQ 63.0 112.4 PRO,LYS OPLS (JTR 12-11-85) C2-CQ-C2 63.0 112.4 PRO,LYS OPLS (JTR 12-11-85) C2-CQ-CQ 63.0 112.4 PRO,LYS OPLS (JTR 12-11-85) CQ-CQ-C2 63.0 112.4 PRO,LYS OPLS (JTR 12-11-85) CQ-C2-CQ 63.0 112.4 PRO,LYS OPLS (JTR 12-11-85) CQ-CQ-CQ 63.0 112.4 PRO,LYS OPLS (JTR 12-11-85) C2-C2-N 80.0 111.2 PRO JCP 76,1439 C2-CQ-N 80.0 111.2 PRO JCP 76,1439 OPLS (JTR 12-11-85) CQ-C2-N 80.0 111.2 PRO JCP 76,1439 OPLS (JTR 12-11-85) CQ-CQ-N 80.0 111.2 PRO JCP 76,1439 OPLS (JTR 12-11-85) C2-C2-N2 80.0 111.2 ARG JCP 76, 1439 C2-CQ-N2 80.0 111.2 ARG JCP 76,1439 OPLS (JTR 12-11-85) CQ-C2-N2 80.0 111.2 ARG JCP 76,1439 OPLS (JTR 12-11-85) CQ-CQ-N2 80.0 111.2 ARG JCP 76,1439 OPLS (JTR 12-11-85) C2-C2-OS 80.0 109.5 THF fit C2-CQ-OS 80.0 109.5 THF fit OPLS (JTR 12-11-85) CQ-C2-OS 80.0 109.5 THF fit OPLS (JTR 12-11-85) CQ-CQ-OS 80.0 109.5 THF fit OPLS (JTR 12-11-85) C3-C2-OS 80.0 109.5 MEE CV-C2-OS 80.0 109.5 MEE OPLS (JTR 12-11-85) CW-C2-OS 80.0 109.5 MEE OPLS (JTR 12-11-85) C3-CQ-OS 80.0 109.5 MEE OPLS (JTR 12-11-85) CV-CQ-OS 80.0 109.5 MEE OPLS (JTR 12-11-85) CW-CQ-OS 80.0 109.5 MEE OPLS (JTR 12-11-85) NT-C2-C 80.0 111.2 GLY JCP 76, 1439 NT-CQ-C 80.0 111.2 GLYJCP 76,1439 OPLS (JTR 12-11-85) N3-C2-C 80.0 111.2 CHARGED N-TERM GLY (ANALOGY W/ABOVE) N3-CQ-C 80.0 111.2 CHARGED N-TERM GLY (ANALOGY W/ABOVE) NT-C2-C2 80.0 111.2 PRO JCP 76, 1439 NT-C2-CQ 80.0 111.2 PROJCP76,1439 OPLS (JTR 12-11-85) NT-CQ-C2 80.0 111.2 PROJCP76,1439 OPLS (JTR 12-11-85) NT-CQ-CQ 80.0 111.2 PROJCP76,1439 OPLS (JTR 12-11-85) CB-C*-C2 70. 128.6 TRP(OL) CB-CK-C2 70. 128.6 TRP(OL) OPLS (JTR 10-22-90) CG-C*-C2 70. 125. TRP(OL) OPLS (JTR 12-11-85) CG-C*-CQ 70. 125. TRP(OL) OPLS (JTR 12-11-85) CB-C*-CG 85. 106.4 TRP(OL) CK-CK-CG 85. 106.4 TRP(OL) OPLS (JTR 10-22-90) C -N -H 35. 119.8 AA(OL) C -N -CH 50. 121.9 AA(OL) C -N -CZ 50. 121.9 AA(OL) OPLS (JTR 12-11-85) CH-N -H 38. 118.4 AA(OL) CZ-N -H 38. 118.4 AA(OL) OPLS (JTR 12-11-85) C -N -C2 50. 121.9 PRO(OL) C -N -CQ 50. 121.9 PRO(OL) OPLS (JTR 12-11-85) C -N -C3 50. 121.9 TEST!!!!!!!! C -N -CV 50. 121.9 TEST!!!!!!!! OPLS (JTR 12-11-85) C -N -CW 50. 121.9 TEST!!!!!!!! OPLS (JTR 12-11-85) C -N -CT 50. 121.9 C2-N -H 38. 118.4 AA(OL) CQ-N -H 38. 118.4 AA(OL) OPLS (JTR 12-11-85) C3-N -H 38. 118.4 TEST!!!!!!! CV-N -H 38. 118.4 TEST!!!!!!! OPLS (JTR 12-11-85) CW-N -H 38. 118.4 TEST!!!!!!! OPLS (JTR 12-11-85) CT-N -H 38. 118.4 CH-N -C2 50. 118. PRO(OL)DETAR JACS 99,1232 CH-N -CQ 50. 118. PRO(OL)above, OPLS (JTR 12-11-85) CZ-N -C2 50. 118. PRO(OL)above, OPLS (JTR 12-11-85) CZ-N -CQ 50. 118. PRO(OL)above, OPLS (JTR 12-11-85) H -N -H 35. 120. ADE,CYT,GUA,GLN,ASN ** H3-N -H3 35. 120. ADE,CYT,GUA,GLN,ASN ** C -N -H2 35. 120. GLN,ASN ** C -N*-CJ 70. 121.6 URA,CYT C -N*-CH 70. 117.6 URA,CYT C -N*-CZ 70. 117.6 URA,CYT OPLS (JTR 12-11-85) CJ-N*-CH 70. 121.2 URA,CYT CJ-N*-CZ 70. 121.2 URA,CYT OPLS (JTR 12-11-85) CB-N*-CE 70. 105.4 GUA,ADE CE-N*-C3 70. 128.8 Methylated purines CE-N*-CV 70. 128.8 Methylated purines, OPLS (JTR 12-11-85 CE-N*-CW 70. 128.8 Methylated purines, OPLS (JTR 12-11-85 CE-N*-CH 70. 128.8 GUA,ADE CE-N*-CZ 70. 128.8 GUA,ADE OPLS (JTR 12-11-85) CB-N*-C3 70. 125.8 9 methylated guan,aden CB-N*-CV 70. 125.8 9 me-gua,ade, OPLS (JTR 12-11-85) CB-N*-CW 70. 125.8 9 me-gua,ade, OPLS (JTR 12-11-85) CB-N*-CH 70. 125.8 GUA,ADE CB-N*-CZ 70. 125.8 GUA,ADE OPLS (JTR 12-11-85) C -NA-H 35. 116.8 GUA,URA(2) C -NA-CA 70. 125.2 GUA C -NA-C 70. 126.4 URA CA-NA-H 35. 118.0 GUA CC-NA-CP 70. 107.3 HIS(OL) CK-NA-CK 70. 107.3 HIS(OL) OPLS (JTR 10-22-90) CC-NA-H 35. 126.35 HIS(OL) CK-NA-H 35. 126.35 HIS(OL) OPLS (JTR 10-22-90) CG-NA-H 35. 126.35 HIS(OL) CN-NA-CG 70. 111.6 TRP(OL) CN-NA-CK 70. 111.6 TRP(OL) OPLS (JTR 10-22-90) CN-NA-H 35. 124.2 TRP CP-NA-CG 70. 107.3 HIS(OL) CP-NA-H 35. 126.35 HIS(OL) CB-NB-CE 70. 103.8 GUA,ADE CF-NB-CP 70. 105.3 HIS(OL) CK-NB-CK 70. 105.3 HIS(OL) OPLS (JTR 10-22-90) CC-NB-CP 70. 105.3 HIS(OL) C -NC-CA 70. 120.5 CYT CA-NC-CI 70. 118.6 ADE CA-NC-CB 70. 112.2 GUA CB-NC-CI 70. 111.0 ADE CA-N2-C2 50. 123.2 ARG(OL) C4-N2-C2 50. 123.2 ARG(OL) OPLS (JTR 09-22-86) CA-N2-CQ 50. 123.2 ARG(OL), OPLS (JTR 12-11-85) CA-N2-C3 50. 123.2 ARG(OL) CA-N2-CV 50. 123.2 ARG(OL), OPLS (JTR 12-11-85) CA-N2-CW 50. 123.2 ARG(OL), OPLS (JTR 12-11-85) CA-N2-H 35. 120. ARG(OL) CA-N2-H2 35. 120. ADE,CYT,GUA,ARG CA-N2-H3 35. 120. ARG C2-N2-H2 35. 118.4 ARG(OL) CQ-N2-H2 35. 118.4 ARG(OL) OPLS (JTR 12-11-85) C2-N2-H3 35. 118.4 ARG CQ-N2-H3 35. 118.4 ARG OPLS (JTR 12-11-85) C3-N2-H2 35. 118.4 ARG(OL) CV-N2-H2 35. 118.4 ARG(OL), OPLS (JTR 12-11-85) CW-N2-H2 35. 118.4 ARG(OL), OPLS (JTR 12-11-85) H2-N2-H2 35. 120. ADE,CYT,GUA,GLN,ASN,ARG H3-N2-H3 35. 120. ARG C3-N3-H3 35. 109.5 CV-N3-H3 35. 109.5 OPLS (JTR 12-11-85) CW-N3-H3 35. 109.5 OPLS (JTR 12-11-85) C2-N3-H3 35. 109.5 LYS CQ-N3-H3 35. 109.5 LYS OPLS (JTR 12-11-85) CH-N3-H3 35. 109.5 LYS CZ-N3-H3 35. 109.5 LYS OPLS (JTR 12-11-85) H3-N3-H3 35. 109.5 LYS C2-NT-H2 35. 109.5 AA CQ-NT-H2 35. 109.5 AA OPLS (JTR 12-11-85) CH-NT-H2 35. 109.5 GLY CZ-NT-H2 35. 109.5 GLY OPLS (JTR 12-11-85) H2-NT-H2 35. 109.5 AA(END) HO-OH-HO 47. 104.5 HT-OT-HT 600. 104.52 TIP3P MODEL OPLS (JTR 12-11-85) H4-O4-H4 600. 104.52 TIP4P MODEL OPLS (JTR 12-11-85) H4-O4-M4 600. 52.26 TIP4P MODEL OPLS (JTR 12-11-85) CH-OH-HO 55.0 108.5 THR(OL),SUG CZ-OH-HO 55.0 108.5 THR(OL),SUG OPLS (JTR 12-11-85) C2-OH-HO 55.0 108.5 SUG,SER(OL,mod) CQ-OH-HO 55.0 108.5 SUG,SER(OL,mod) OPLS (JTR 12-11-85) C3-OH-HO 55.0 108.5 SUG,SER(OL,mod) CV-OH-HO 55.0 108.5 SUG,SER(OL,mod) OPLS (JTR 12-11-85) CW-OH-HO 55.0 108.5 SUG,SER(OL,mod) OPLS (JTR 12-11-85) HO-OH-P 45.0 108.5 SUG(OL) CH-OS-CH 100.0 111.8 SUG(dme based) CH-OS-CZ 100.0 111.8 SUG(dme based) OPLS (JTR 12-11-85) CZ-OS-CZ 100.0 111.8 SUG(dme based) OPLS (JTR 12-11-85) C2-OS-C2 100.0 111.8 DME based CQ-OS-C2 100.0 111.8 DME based OPLS (JTR 12-11-85) CQ-OS-CQ 100.0 111.8 DME based OPLS (JTR 12-11-85) C2-OS-C3 100.0 111.8 DME based C2-OS-CV 100.0 111.8 DME based OPLS (JTR 12-11-85) C2-OS-CW 100.0 111.8 DME based OPLS (JTR 12-11-85) CQ-OS-C3 100.0 111.8 DME based OPLS (JTR 12-11-85) CQ-OS-CV 100.0 111.8 DME based OPLS (JTR 12-11-85) CQ-OS-CW 100.0 111.8 DME based OPLS (JTR 12-11-85) C3-OS-P 100.0 120.5 DMPhos based CV-OS-P 100.0 120.5 DMPhos based OPLS (JTR 12-11-85) CW-OS-P 100.0 120.5 DMPhos based OPLS (JTR 12-11-85) CH-OS-P 100.0 120.5 SUG CZ-OS-P 100.0 120.5 SUG OPLS (JTR 12-11-85) C2-OS-P 100.0 120.5 SUG(OL) CQ-OS-P 100.0 120.5 SUG(OL) OPLS (JTR 12-11-85) C2-OS-HO 55.0 108.5 SUG CQ-OS-HO 55.0 108.5 SUG OPLS (JTR 12-11-85) CH-OS-HO 55.0 108.5 SUG CZ-OS-HO 55.0 108.5 SUG OPLS (JTR 12-11-85) OS-P -O2 100.0 108.23 SUG(OL) OS-P -OS 45.0 102.6 SUG(OL) O2-P -O2 140.0 119.9 SUG(OL) OS-P -OH 45.0 102.6 SUG(OL) OH-P -O2 45.0 108.23 SUG(OL) C -OH-HO 35. 113.0 TYR(PHENOL) HARMONY MEOH CA-OH-HO 35. 113.0 TYR(PHENOL) OPLS (JTR 10-25-86) CK-OH-HO 35. 113.0 TYR(PHENOL) OPLS (JTR 9-13-91) NA-CK-HK 35. 120.0 HIS, OPLS (JTR 10-22-90) NB-CK-HK 35. 120.0 HIS, OPLS (JTR 10-22-90) X -FE-NB-X 1 0.0 0.0 2. X -FE-NP-X 1 0.0 0. 2. X -FE-NO-X 1 0.0 0. 2. X -NP-CC-X 4 6.0 180.0 2. X -NO-CC-X 4 6.0 180.0 2. X -CB-CC-X 4 20.0 180.0 2. X -CC-CD-X 2 10.0 180.0 2. X -CB-C2-X 2 1.5 0. 3. X -CB-CQ-X 2 1.5 0. 3. X -NP-CC-X 4 6.0 180.0 2. X -CB-CY-X 2 5.0 180.0 2. X -C -CB-X 4 4.4 180. 2. X -C -CD-X 2 5.3 180. 2. X -C -CH-X 4 0.0 0. 2. X -C -CZ-X 4 0.0 0. 2. X -C -CJ-X 2 3.1 180. 2. X -C -CM-X 4 3.1 180. 2. X -C -CT-X 6 0.0 0. 2. X -C -N -X 4 10.0 180. 2. X -C -N*-X 4 5.8 180. 2. X -C -N2-X 4 6.8 180. 2. X -C4-N2-X 4 6.8 180. 2. X -C -NA-X 4 5.4 180. 2. X -C -NC-X 2 8.0 180. 2. X -C -OH-X 2 1.8 180. 2. X -CA-OH-X 2 1.8 180. 2. X -CK-OH-X 2 1.8 180. 2. X -C*-C2-X 2 0.0 0. 2. X -C*-CQ-X 2 0.0 0. 2. X -C*-CB-X 4 2.4 180. 2. X -C*-CG-X 2 23.6 180. 2. X -CA-CB-X 4 5.1 180. 2. X -CA-CD-X 2 5.3 180. 2. X -CA-CJ-X 2 3.7 180. 2. X -CA-C2-X 2 0.0 0. 2. X -CA-CQ-X 2 0.0 0. 2. X -CA-NA-X 4 6.0 180. 2. X -CA-NC-X 2 9.6 180. 2. X -CA-N2-X 4 6.8 180. 2. X -CB-CB-X 4 16.3 180. 2. X -CB-CD-X 2 5.3 180. 2. X -CB-CN-X 4 4.4 180. 2. X -CB-NB-X 2 5.1 180. 2. X -CB-N*-X 4 6.6 180. 2. X -CB-NC-X 2 8.3 180. 2. X -CC-CF-X 2 14.3 180. 2. X -CC-CG-X 2 15.9 180. 2. X -CC-NA-X 4 5.6 180. 2. X -CC-NB-X 2 4.8 180. 2. X -CK-NA-X 4 5.6 180. 2. X -CK-NB-X 2 4.8 180. 2. X -CD-CD-X 1 5.3 180. 2. X -CD-CN-X 2 5.3 180. 2. X -CE-NB-X 1 20.0 180. 2. X -CF-NB-X 1 4.8 180. 2. X -CG-NA-X 2 6.0 180. 2. X -CI-NC-X 1 13.5 180. 2. X -CJ-CJ-X 1 24.4 180. 2. X -CJ-CM-X 2 27.2 180. 2. X -CN-NA-X 4 6.1 180. 2. X -CP-NA-X 2 9.3 180. 2. X -CP-NB-X 1 10.0 180. 2. X -CT-CT-X 9 2.0 0. 3. X -CT-N -X 6 0.0 0. 3. X -CH-CH-X 4 2.0 0. 3. X -CH-CZ-X 4 2.0 0. 3. X -CZ-CZ-X 4 2.0 0. 3. X -CH-C2-X 2 2.0 0. 3. X -CH-CQ-X 2 2.0 0. 3. X -CZ-C2-X 2 2.0 0. 3. X -CZ-CQ-X 2 2.0 0. 3. X -CH-N -X 4 0.0 0. 3. X -CZ-N -X 4 0.0 0. 3. X -CH-N*-X 4 0.0 0. 2. X -CZ-N*-X 4 0.0 0. 2. X -CH-OH-X 2 0.5 0. 3. X -CZ-OH-X 2 0.5 0. 3. X -CH-OS-X 2 1.45 0. 3. X -CZ-OS-X 2 1.45 0. 3. X -C2-N3-X 3 1.4 0. 3. X -CQ-N3-X 3 1.4 0. 3. X -CH-N3-X 6 1.4 0. 3. X -CZ-N3-X 6 1.4 0. 3. X -C2-CC-X 2 0.0 0. 2. X -CQ-CC-X 2 0.0 0. 2. X -C2-C -X 2 0.0 180. 3. X -CQ-C -X 2 0.0 180. 3. X -C2-C2-X 1 2.0 0. 3. X -CQ-C2-X 1 2.0 0. 3. X -CQ-CQ-X 1 2.0 0. 3. X -C2-N -X 2 0.0 0. 3. X -CQ-N -X 2 0.0 0. 3. X -C2-S -X 1 1.0 0. 3. X -CQ-S -X 1 1.0 0. 3. X -C2-SH-X 1 0.75 0. 3. X -CQ-SH-X 1 0.75 0. 3. X -C2-N2-X 1 0.0 0. 3. X -CQ-N2-X 1 0.0 0. 3. X -C2-OH-X 1 0.5 0. 3. X -CQ-OH-X 1 0.5 0. 3. X -C2-OS-X 1 1.45 0. 3. X -CQ-OS-X 1 1.45 0. 3. X -N*-CJ-X 2 7.4 180. 2. X -NT-C2-X 2 1.0 0. 3. X -NT-CQ-X 2 1.0 0. 3. X -NT-CH-X 4 1.0 0. 3. X -NT-CZ-X 4 1.0 0. 3. X -OH-P -X 3 0.75 0. 3. X -OS-P -X 3 0.75 0. 3. X -N*-CE-X 2 6.7 180. 2. X -C2-CK-X 2 0. 0. 2. X -CK-CK-X 4 5.3 180. 2. X -CK-CA-X 4 5.3 180. 2. X -CK-CN-X 4 5.3 180. 2. X -CK-CB-X 4 5.3 180. 2. C2-C2-S -LP 1 0. 0. 3. C2-CQ-S -LP 1 0. 0. 3. CQ-C2-S -LP 1 0. 0. 3. CQ-CQ-S -LP 1 0. 0. 3. CH-C2-SH-LP 1 0. 0. 3. CH-CQ-SH-LP 1 0. 0. 3. CZ-C2-SH-LP 1 0. 0. 3. CZ-CQ-SH-LP 1 0. 0. 3. LP-S -S -LP 1 0. 0. 3. LP-S -S -C2 1 0. 0. 3. LP-S -S -CQ 1 0. 0. 3. LP-S -S -C3 1 0. 0. 3. LP-S -S -CV 1 0. 0. 3. LP-S -S -CW 1 0. 0. 3. O -C -C2-N 1 0.2 180. 3. O -C -CQ-N 1 0.2 180. 3. O -C -CH-N 1 0.1 180. 3. O -C -CZ-N 1 0.1 180. 3. O -C -CH-C2 1 0.1 180. 3. O -C -CH-CQ 1 0.1 180. 3. O -C -CZ-C2 1 0.1 180. 3. O -C -CZ-CQ 1 0.1 180. 3. O -C -CH-CH 1 0.1 180. 3. O -C -CH-CZ 1 0.1 180. 3. O -C -CZ-CH 1 0.1 180. 3. O -C -CZ-CZ 1 0.1 180. 3. HC-CT-C -O 1 0.067 180. 3. CT-CT-C -O 1 0.067 180. 3. N -CT-C -O 1 0.067 180. 3. H -N -C -O 1 2.5 180. -2. H -N -C -O 1 0.65 0. 1. C2-S -S -C2 1 3.5 0. -2. C2-S -S -C2 1 0.6 0. 3. C2-S -S -CQ 1 3.5 0. -2. C2-S -S -CQ 1 0.6 0. 3. CQ-S -S -CQ 1 3.5 0. -2. CQ-S -S -CQ 1 0.6 0. 3. C3-S -S -C3 1 3.5 0. -2. C3-S -S -C3 1 0.6 0. 3. C3-S -S -CV 1 3.5 0. -2. C3-S -S -CV 1 0.6 0. 3. CV-S -S -CV 1 3.5 0. -2. CV-S -S -CV 1 0.6 0. 3. C3-S -S -CW 1 3.5 0. -2. C3-S -S -CW 1 0.6 0. 3. CW-S -S -CW 1 3.5 0. -2. CW-S -S -CW 1 0.6 0. 3. CV-S -S -CW 1 3.5 0. -2. CV-S -S -CW 1 0.6 0. 3. OS-CH-CH-OS 1 0.5 0. -3. OS-CH-CH-OS 1 0.5 0. 2. OS-CH-CZ-OS 1 0.5 0. -3. OS-CH-CZ-OS 1 0.5 0. 2. OS-CZ-CH-OS 1 0.5 0. -3. OS-CZ-CH-OS 1 0.5 0. 2. OS-CZ-CZ-OS 1 0.5 0. -3. OS-CZ-CZ-OS 1 0.5 0. 2. OS-C2-CH-OS 1 1.0 0. -3. OS-C2-CH-OS 1 0.5 0. 2. OS-CQ-CH-OS 1 1.0 0. -3. OS-CQ-CH-OS 1 0.5 0. 2. OS-C2-CZ-OS 1 1.0 0. -3. OS-C2-CZ-OS 1 0.5 0. 2. OS-CQ-CZ-OS 1 1.0 0. -3. OS-CQ-CZ-OS 1 0.5 0. 2. OS-CH-CH-OH 1 0.5 0. -3. OS-CH-CH-OH 1 0.5 0. 2. OS-CH-CZ-OH 1 0.5 0. -3. OS-CH-CZ-OH 1 0.5 0. 2. OS-CZ-CH-OH 1 0.5 0. -3. OS-CZ-CH-OH 1 0.5 0. 2. OS-CZ-CZ-OH 1 0.5 0. -3. OS-CZ-CZ-OH 1 0.5 0. 2. OS-CH-C2-OH 1 1.0 0. -3. OS-CH-C2-OH 1 0.5 0. 2. OS-CH-CQ-OH 1 1.0 0. -3. OS-CH-CQ-OH 1 0.5 0. 2. OS-CZ-C2-OH 1 1.0 0. -3. OS-CZ-C2-OH 1 0.5 0. 2. OS-CZ-CQ-OH 1 1.0 0. -3. OS-CZ-CQ-OH 1 0.5 0. 2. OS-C2-CH-OH 1 1.0 0. -3. OS-C2-CH-OH 1 0.5 0. 2. OS-CQ-CH-OH 1 1.0 0. -3. OS-CQ-CH-OH 1 0.5 0. 2. OS-C2-CZ-OH 1 1.0 0. -3. OS-C2-CZ-OH 1 0.5 0. 2. OS-CQ-CZ-OH 1 1.0 0. -3. OS-CQ-CZ-OH 1 0.5 0. 2. OS-C2-C2-OH 1 2.0 0. -3. OS-C2-C2-OH 1 0.5 0. 2. OS-C2-CQ-OH 1 2.0 0. -3. OS-C2-CQ-OH 1 0.5 0. 2. OS-CQ-C2-OH 1 2.0 0. -3. OS-CQ-C2-OH 1 0.5 0. 2. OS-CQ-CQ-OH 1 2.0 0. -3. OS-CQ-CQ-OH 1 0.5 0. 2. OS-C2-C2-OS 1 2.0 0. -3. OS-C2-C2-OS 1 0.5 0. 2. OS-C2-CQ-OS 1 2.0 0. -3. OS-C2-CQ-OS 1 0.5 0. 2. OS-CQ-CQ-OS 1 2.0 0. -3. OS-CQ-CQ-OS 1 0.5 0. 2. OH-CH-CH-OH 1 0.5 0. -3. OH-CH-CH-OH 1 0.5 0. 2. OH-CH-CZ-OH 1 0.5 0. -3. OH-CH-CZ-OH 1 0.5 0. 2. OH-CZ-CZ-OH 1 0.5 0. -3. OH-CZ-CZ-OH 1 0.5 0. 2. OH-C2-C2-OH 1 2.0 0. -3. OH-C2-C2-OH 1 0.5 0. 2. OH-C2-CQ-OH 1 2.0 0. -3. OH-C2-CQ-OH 1 0.5 0. 2. OH-CQ-CQ-OH 1 2.0 0. -3. OH-CQ-CQ-OH 1 0.5 0. 2. OH-C2-CH-OH 1 1.0 0. -3. OH-C2-CH-OH 1 0.5 0. 2. OH-CQ-CH-OH 1 1.0 0. -3. OH-CQ-CH-OH 1 0.5 0. 2. OH-C2-CZ-OH 1 1.0 0. -3. OH-C2-CZ-OH 1 0.5 0. 2. OH-CQ-CZ-OH 1 1.0 0. -3. OH-CQ-CZ-OH 1 0.5 0. 2. C2-OS-C2-C2 1 1.45 0. -3. C2-OS-C2-C2 1 0.10 0. 2. C2-OS-C2-CQ 1 1.45 0. -3. C2-OS-C2-CQ 1 0.10 0. 2. C2-OS-CQ-C2 1 1.45 0. -3. C2-OS-CQ-C2 1 0.10 0. 2. CQ-OS-C2-C2 1 1.45 0. -3. CQ-OS-C2-C2 1 0.10 0. 2. C2-OS-CQ-CQ 1 1.45 0. -3. C2-OS-CQ-CQ 1 0.10 0. 2. CQ-OS-CQ-C2 1 1.45 0. -3. CQ-OS-CQ-C2 1 0.10 0. 2. CQ-OS-C2-CQ 1 1.45 0. -3. CQ-OS-C2-CQ 1 0.10 0. 2. CQ-OS-CQ-CQ 1 1.45 0. -3. CQ-OS-CQ-CQ 1 0.10 0. 2. C3-OS-C2-C3 1 1.45 0. -3. C3-OS-C2-C3 1 0.10 0. 2. C3-OS-C2-CV 1 1.45 0. -3. C3-OS-C2-CV 1 0.10 0. 2. CV-OS-C2-C3 1 1.45 0. -3. CV-OS-C2-C3 1 0.10 0. 2. CV-OS-C2-CV 1 1.45 0. -3. CV-OS-C2-CV 1 0.10 0. 2. C3-OS-C2-CW 1 1.45 0. -3. C3-OS-C2-CW 1 0.10 0. 2. CW-OS-C2-C3 1 1.45 0. -3. CW-OS-C2-C3 1 0.10 0. 2. CW-OS-C2-CW 1 1.45 0. -3. CW-OS-C2-CW 1 0.10 0. 2. CV-OS-C2-CW 1 1.45 0. -3. CV-OS-C2-CW 1 0.10 0. 2. CW-OS-C2-CV 1 1.45 0. -3. CW-OS-C2-CV 1 0.10 0. 2. CH-OS-CH-C2 1 0.725 0. -3. CH-OS-CH-C2 1 0.1 0. 2. CZ-OS-CZ-C2 1 0.725 0. -3. CZ-OS-CZ-C2 1 0.1 0. 2. CH-OS-CZ-C2 1 0.725 0. -3. CH-OS-CZ-C2 1 0.1 0. 2. CZ-OS-CH-C2 1 0.725 0. -3. CZ-OS-CH-C2 1 0.1 0. 2. C3-OS-CQ-C3 1 1.45 0. -3. C3-OS-CQ-C3 1 0.10 0. 2. C3-OS-CQ-CV 1 1.45 0. -3. C3-OS-CQ-CV 1 0.10 0. 2. CV-OS-CQ-C3 1 1.45 0. -3. CV-OS-CQ-C3 1 0.10 0. 2. CV-OS-CQ-CV 1 1.45 0. -3. CV-OS-CQ-CV 1 0.10 0. 2. C3-OS-CQ-CW 1 1.45 0. -3. C3-OS-CQ-CW 1 0.10 0. 2. CW-OS-CQ-C3 1 1.45 0. -3. CW-OS-CQ-C3 1 0.10 0. 2. CW-OS-CQ-CW 1 1.45 0. -3. CW-OS-CQ-CW 1 0.10 0. 2. CV-OS-CQ-CW 1 1.45 0. -3. CV-OS-CQ-CW 1 0.10 0. 2. CW-OS-CQ-CV 1 1.45 0. -3. CW-OS-CQ-CV 1 0.10 0. 2. CH-OS-CH-CQ 1 0.725 0. -3. CH-OS-CH-CQ 1 0.1 0. 2. CZ-OS-CZ-CQ 1 0.725 0. -3. CZ-OS-CZ-CQ 1 0.1 0. 2. CH-OS-CZ-CQ 1 0.725 0. -3. CH-OS-CZ-CQ 1 0.1 0. 2. CZ-OS-CH-CQ 1 0.725 0. -3. CZ-OS-CH-CQ 1 0.1 0. 2. CH-OS-CH-CH 1 0.725 0. -3. CH-OS-CH-CH 1 0.1 0. 2. CZ-OS-CZ-CH 1 0.725 0. -3. CZ-OS-CZ-CH 1 0.1 0. 2. CH-OS-CZ-CZ 1 0.725 0. -3. CH-OS-CZ-CZ 1 0.1 0. 2. CZ-OS-CH-CZ 1 0.725 0. -3. CZ-OS-CH-CZ 1 0.1 0. 2. CZ-OS-CH-CH 1 0.725 0. -3. CZ-OS-CH-CH 1 0.1 0. 2. CH-OS-CH-CZ 1 0.725 0. -3. CH-OS-CH-CZ 1 0.1 0. 2. CH-OS-CZ-CH 1 0.725 0. -3. CH-OS-CZ-CH 1 0.1 0. 2. CZ-OS-CZ-CZ 1 0.725 0. -3. CZ-OS-CZ-CZ 1 0.1 0. 2. C2-OS-CH-C2 1 0.725 0. -3. C2-OS-CH-C2 1 0.1 0. 2. C2-OS-CZ-C2 1 0.725 0. -3. C2-OS-CZ-C2 1 0.1 0. 2. C2-OS-CH-CQ 1 0.725 0. -3. C2-OS-CH-CQ 1 0.1 0. 2. C2-OS-CZ-CQ 1 0.725 0. -3. C2-OS-CZ-CQ 1 0.1 0. 2. CQ-OS-CH-C2 1 0.725 0. -3. CQ-OS-CH-C2 1 0.1 0. 2. CQ-OS-CZ-C2 1 0.725 0. -3. CQ-OS-CZ-C2 1 0.1 0. 2. CQ-OS-CH-CQ 1 0.725 0. -3. CQ-OS-CH-CQ 1 0.1 0. 2. CQ-OS-CZ-CQ 1 0.725 0. -3. CQ-OS-CZ-CQ 1 0.1 0. 2. C2-OS-CH-C3 1 0.725 0. -3. C2-OS-CH-C3 1 0.1 0. 2. CQ-OS-CH-C3 1 0.725 0. -3. CQ-OS-CH-C3 1 0.1 0. 2. C2-OS-CZ-C3 1 0.725 0. -3. C2-OS-CZ-C3 1 0.1 0. 2. CQ-OS-CZ-C3 1 0.725 0. -3. CQ-OS-CZ-C3 1 0.1 0. 2. C2-OS-CH-CV 1 0.725 0. -3. C2-OS-CH-CV 1 0.1 0. 2. CQ-OS-CH-CV 1 0.725 0. -3. CQ-OS-CH-CV 1 0.1 0. 2. C2-OS-CZ-CV 1 0.725 0. -3. C2-OS-CZ-CV 1 0.1 0. 2. CQ-OS-CZ-CV 1 0.725 0. -3. CQ-OS-CZ-CV 1 0.1 0. 2. C2-OS-CH-CW 1 0.725 0. -3. C2-OS-CH-CW 1 0.1 0. 2. CQ-OS-CH-CW 1 0.725 0. -3. CQ-OS-CH-CW 1 0.1 0. 2. C2-OS-CZ-CW 1 0.725 0. -3. C2-OS-CZ-CW 1 0.1 0. 2. CQ-OS-CZ-CW 1 0.725 0. -3. CQ-OS-CZ-CW 1 0.1 0. 2. C3-OS-CH-C3 1 0.725 0. -3. C3-OS-CH-C3 1 0.1 0. 2. C3-OS-CH-CV 1 0.725 0. -3. C3-OS-CH-CV 1 0.1 0. 2. CV-OS-CH-C3 1 0.725 0. -3. CV-OS-CH-C3 1 0.1 0. 2. CV-OS-CH-CV 1 0.725 0. -3. CV-OS-CH-CV 1 0.1 0. 2. C3-OS-CH-CW 1 0.725 0. -3. C3-OS-CH-CW 1 0.1 0. 2. CW-OS-CH-C3 1 0.725 0. -3. CW-OS-CH-C3 1 0.1 0. 2. CW-OS-CH-CW 1 0.725 0. -3. CW-OS-CH-CW 1 0.1 0. 2. CV-OS-CH-CW 1 0.725 0. -3. CV-OS-CH-CW 1 0.1 0. 2. CW-OS-CH-CV 1 0.725 0. -3. CW-OS-CH-CV 1 0.1 0. 2. C3-OS-CZ-C3 1 0.725 0. -3. C3-OS-CZ-C3 1 0.1 0. 2. C3-OS-CZ-CV 1 0.725 0. -3. C3-OS-CZ-CV 1 0.1 0. 2. CV-OS-CZ-C3 1 0.725 0. -3. CV-OS-CZ-C3 1 0.1 0. 2. CV-OS-CZ-CV 1 0.725 0. -3. CV-OS-CZ-CV 1 0.1 0. 2. C3-OS-CZ-CW 1 0.725 0. -3. C3-OS-CZ-CW 1 0.1 0. 2. CW-OS-CZ-C3 1 0.725 0. -3. CW-OS-CZ-C3 1 0.1 0. 2. CW-OS-CZ-CW 1 0.725 0. -3. CW-OS-CZ-CW 1 0.1 0. 2. CV-OS-CZ-CW 1 0.725 0. -3. CV-OS-CZ-CW 1 0.1 0. 2. CW-OS-CZ-CV 1 0.725 0. -3. CW-OS-CZ-CV 1 0.1 0. 2. C2-OS-C2-C3 1 0.725 0. -3. C2-OS-C2-C3 1 0.1 0. 2. C2-OS-CQ-C3 1 0.725 0. -3. C2-OS-CQ-C3 1 0.1 0. 2. CQ-OS-C2-C3 1 0.725 0. -3. CQ-OS-C2-C3 1 0.1 0. 2. CQ-OS-CQ-C3 1 0.725 0. -3. CQ-OS-CQ-C3 1 0.1 0. 2. C2-OS-C2-CV 1 0.725 0. -3. C2-OS-C2-CV 1 0.1 0. 2. C2-OS-CQ-CV 1 0.725 0. -3. C2-OS-CQ-CV 1 0.1 0. 2. CQ-OS-C2-CV 1 0.725 0. -3. CQ-OS-C2-CV 1 0.1 0. 2. CQ-OS-CQ-CV 1 0.725 0. -3. CQ-OS-CQ-CV 1 0.1 0. 2. C2-OS-C2-CW 1 0.725 0. -3. C2-OS-C2-CW 1 0.1 0. 2. C2-OS-CQ-CW 1 0.725 0. -3. C2-OS-CQ-CW 1 0.1 0. 2. CQ-OS-C2-CW 1 0.725 0. -3. CQ-OS-C2-CW 1 0.1 0. 2. CQ-OS-CQ-CW 1 0.725 0. -3. CQ-OS-CQ-CW 1 0.1 0. 2. CH-OS-CH-N* 1 0.725 0. -3. CH-OS-CH-N* 1 0.000 0. 2. CH-OS-CZ-N* 1 0.725 0. -3. CH-OS-CZ-N* 1 0.000 0. 2. CZ-OS-CH-N* 1 0.725 0. -3. CZ-OS-CH-N* 1 0.000 0. 2. CZ-OS-CZ-N* 1 0.725 0. -3. CZ-OS-CZ-N* 1 0.000 0. 2. OH-P -OS-C2 1 .25 0. -3. OH-P -OS-C2 1 .75 0. 2. OH-P -OS-CQ 1 .25 0. -3. OH-P -OS-CQ 1 .75 0. 2. OH-P -OS-CH 1 .25 0. -3. OH-P -OS-CH 1 .75 0. 2. OH-P -OS-CZ 1 .25 0. -3. OH-P -OS-CZ 1 .75 0. 2. OS-P -OS-CH 1 .25 0. -3. OS-P -OS-CH 1 .75 0. 2. OS-P -OS-CZ 1 .25 0. -3. OS-P -OS-CZ 1 .75 0. 2. OS-P -OS-C2 1 .25 0. -3. OS-P -OS-C2 1 .75 0. 2. OS-P -OS-CQ 1 .25 0. -3. OS-P -OS-CQ 1 .75 0. 2. OS-P -OS-C3 1 .25 0. -3. OS-P -OS-C3 1 .75 0. 2. OH-P -OS-C3 1 .25 0. -3. OH-P -OS-C3 1 .75 0. 2. OS-P -OS-CV 1 .25 0. -3. OS-P -OS-CV 1 .75 0. 2. OH-P -OS-CV 1 .25 0. -3. OH-P -OS-CV 1 .75 0. 2. OS-P -OS-CW 1 .25 0. -3. OS-P -OS-CW 1 .75 0. 2. OH-P -OS-CW 1 .25 0. -3. OH-P -OS-CW 1 .75 0. 2. X -CC-CC-X 0.0 180. 2. X -X -NT-H2 1.0 180. 2. X -X -CK-HK 2. 180. 2. X -CH-NT-C 14.0 180.0 3. X -CZ-NT-C 14.0 180.0 3. X -CH-N3-C 14.0 180.0 3. X -CZ-N3-C 14.0 180.0 3. X -CB-NO-X 0.0 180.0 2. X -CB-NP-X 0.0 180.0 2. X -CB-CC-X 0.0 180.0 2. CH-CH-C -N3 7.0 180. 3. CH-CZ-C -N3 7.0 180. 3. CZ-CH-C -N3 7.0 180. 3. CZ-CZ-C -N3 7.0 180. 3. C2-CH-C -N3 7.0 180. 3. CQ-CH-C -N3 7.0 180. 3. C2-CZ-C -N3 7.0 180. 3. CQ-CZ-C -N3 7.0 180. 3. C3-CH-CA-C3 7.0 180. 3. C3-CH-CA-CV 7.0 180. 3. CV-CH-CA-C3 7.0 180. 3. C3-CH-CA-CW 7.0 180. 3. CW-CH-CA-C3 7.0 180. 3. CV-CH-CA-CW 7.0 180. 3. CW-CH-CA-CV 7.0 180. 3. CV-CH-CA-CV 7.0 180. 3. CW-CH-CA-CW 7.0 180. 3. C3-CZ-CA-C3 7.0 180. 3. C3-CZ-CA-CV 7.0 180. 3. CV-CZ-CA-C3 7.0 180. 3. C3-CZ-CA-CW 7.0 180. 3. CW-CZ-CA-C3 7.0 180. 3. CV-CZ-CA-CW 7.0 180. 3. CW-CZ-CA-CV 7.0 180. 3. CV-CZ-CA-CV 7.0 180. 3. CW-CZ-CA-CW 7.0 180. 3. X -C2-CH-X 14.0 180. 3. X -CQ-CH-X 14.0 180. 3. X -C2-CZ-X 14.0 180. 3. X -CQ-CZ-X 14.0 180. 3. X -CH-CH-X 14.0 180. 3. X -CH-CZ-X 14.0 180. 3. X -CZ-CZ-X 14.0 180. 3. X -X -N -H 1.0 180. 2. X -X -NA-H 1.0 180. 2. X -X -C -O 10.5 180. 2. X -O2-C -O2 10.5 180. 2. X -CH-N -C 14. 180. 3. X -CZ-N -C 14. 180. 3. X -H2-N -H2 1.0 180. 2. X -N2-CA-N2 10.5 180. 2. X -N2-C -N2 10.5 180. 2. X -N2-C4-N2 10.5 180. 2. X -CA-NA-X 7.0 180.0 2. X -CH-N -C2 1.0 180. 2. X -CH-N -CQ 1.0 180. 2. X -CZ-N -C2 1.0 180. 2. X -CZ-N -CQ 1.0 180. 2. HT OT 0000. 0000. 4. C2 CX C CM CD CE CI CJ CY HO H2 H3 N N2 N* NT NP NO OH W S SH FE STDU RE H 1.00 .020 HC 1.375 .038 HO 1.00 .020 H2 1.00 .020 H3 1.00 .020 HS 1.00 .020 OW 1.80 0.12 OH 1.65 0.15 OS 1.65 0.15 O 1.6 0.20 O2 1.6 0.20 CH 1.85 0.09 CZ 1.85 0.09 C2 1.925 0.12 CQ 1.925 0.12 C3 2.00 0.15 CV 2.00 0.15 CW 2.00 0.15 C 1.85 0.120 C* 1.85 0.120 CB 1.85 0.120 CC 1.85 0.120 CN 1.85 0.120 CA 1.85 0.120 C4 1.85 0.120 CD 1.85 0.12 CF 1.85 0.12 CG 1.85 0.12 CP 1.85 0.12 N 1.75 0.16 NA 1.75 0.16 NB 1.75 0.16 NC 1.75 0.16 NT 1.85 0.12 IM 2.00 0.30 IP 1.60 0.05 N3 1.85 0.08 S 2.00 0.20 SH 2.00 0.20 P 2.10 0.20 CU 1.2 0.05 CT 1.80 0.06 I 2.35 0.40 LP 1.2 0.016 ZN 1.85 0.06 STUB RE H 1.00 .020 HC 1.375 .038 HO 1.00 .020 H2 1.00 .020 H3 1.00 .020 HS 1.00 .020 OW 1.80 0.12 OH 1.65 0.15 OS 1.65 0.15 O 1.6 0.20 O2 1.6 0.20 CH 2.385 0.049 CZ 2.385 0.049 C2 2.235 0.114 C3 2.165 0.181 CV 2.165 0.181 CW 2.165 0.181 C 1.85 0.120 C* 1.85 0.120 CB 1.85 0.120 CC 1.85 0.120 CN 1.85 0.120 CA 1.85 0.120 C4 1.85 0.120 CD 1.85 0.12 CF 1.85 0.12 CG 1.85 0.12 CP 1.85 0.12 N 1.75 0.16 NA 1.75 0.16 NB 1.75 0.16 NC 1.75 0.16 NT 1.85 0.12 IM 2.00 0.30 IP 1.60 0.05 N3 1.85 0.08 S 2.00 0.20 SH 2.00 0.20 P 2.10 0.20 CU 1.2 0.05 CT 1.80 0.06 I 2.35 0.40 LP 1.2 0.016 ZN 1.85 0.06 OPLS AC H 0.00 0.00 HO 0.00 0.00 H3 0.00 0.00 HS 0.00 0.00 HT 0.00 0.00 H4 0.00 0.00 HK 4841.3 24.1 OH 476600.0 569.3 OS 361380.0 495.7 O 380000.0 565.0 O2 380000.0 565.0 OT 582100.0 595.0 O4 600000.0 610.0 CK 1121800.0 560.4 CH 2901000.0 963.5 CZ 3393700.0 1042.1 C2 5934600.0 1673.7 CQ 4279000.0 1421.2 C3 8171500.0 2286.9 C4 3366.8 25.9 CV 8801400.0 2482.1 CW 6164700.0 2047.4 C 3248100.0 1168.0 CA 3402700.0 1223.6 CD 3402700.0 1223.6 CP 4485400.0 1613.0 CF 4485400.0 1613.0 CC 4485400.0 1613.0 CN 4485400.0 1613.0 CG 4485400.0 1613.0 C* 4485400.0 1613.0 CB 4485400.0 1613.0 CT 1813150.0 602.2 N 944300.0 801.3 NA 944300.0 801.3 NB 944300.0 801.3 N2 944300.0 801.3 N3 944300.0 801.3 S 4006300.0 2001.6 M4 0.0 0.0 ZN 395000.0 307.9 CL 26000420.3 3500.0511 END MMTK-2.7.9/MMTK/ForceFields/Amber/parm10.dat0000644000076600000240000016145412041514571020501 0ustar hinsenstaff00000000000000PARM99 + frcmod.ff99SB + frcmod.parmbsc0 + OL3 for RNA C 12.01 0.616 ! sp2 C carbonyl group CA 12.01 0.360 sp2 C pure aromatic (benzene) CB 12.01 0.360 sp2 aromatic C, 5&6 membered ring junction CC 12.01 0.360 sp2 aromatic C, 5 memb. ring HIS CD 12.01 0.360 sp2 C atom in the middle of: C=CD-CD=C CI 12.01 parmbsc0 CK 12.01 0.360 sp2 C 5 memb.ring in purines (dA,dG) CP 12.01 0.360 sp2 C 5 memb.ring in purines (G) CM 12.01 0.360 sp2 C pyrimidines in pos. 5 & 6 (dT,dC) CS 12.01 0.360 sp2 C pyrimidines in pos. 5 & 6 (U) CN 12.01 0.360 sp2 C aromatic 5&6 memb.ring junct.(TRP) CQ 12.01 0.360 sp2 C in 5 mem.ring of purines between 2 N CR 12.01 0.360 sp2 arom as CQ but in HIS CT 12.01 0.878 sp3 aliphatic C CV 12.01 0.360 sp2 arom. 5 memb.ring w/1 N and 1 H (HIS) CW 12.01 0.360 sp2 arom. 5 memb.ring w/1 N-H and 1 H (HIS) C* 12.01 0.360 sp2 arom. 5 memb.ring w/1 subst. (TRP) CX 12.01 0.360 protein C-alpha (new to ff10) CY 12.01 0.360 nitrile C (Howard et al.JCC,16,243,1995) CZ 12.01 0.360 sp C (Howard et al.JCC,16,243,1995) C5 12.01 0.360 sp2 C 5 memb.ring in purines (A) C4 12.01 0.360 sp2 C pyrimidines in pos. 5 & 6 (C) C0 40.08 calcium H 1.008 0.161 H bonded to nitrogen atoms HC 1.008 0.135 H aliph. bond. to C without electrwd.group H1 1.008 0.135 H aliph. bond. to C with 1 electrwd. group H2 1.008 0.135 H aliph. bond. to C with 2 electrwd.groups H3 1.008 0.135 H aliph. bond. to C with 3 eletrwd.groups HA 1.008 0.167 H arom. bond. to C without elctrwd. groups H4 1.008 0.167 H arom. bond. to C with 1 electrwd. group H5 1.008 0.167 H arom.at C with 2 elctrwd. gr,+HCOO group HO 1.008 0.135 hydroxyl group HS 1.008 0.135 hydrogen bonded to sulphur (pol?) HW 1.008 0.000 H in TIP3P water HP 1.008 0.135 H bonded to C next to positively charged gr HZ 1.008 0.161 H bond sp C (Howard et al.JCC,16,243,1995) F 19.00 0.320 fluorine (not fluoride!) Cl 35.45 1.910 chlorine (Applequist) (not chloride!) Br 79.90 2.880 bromine (Applequist) (not bromide!) I 126.9 4.690 iodine (Applequist) (not iodide!) MG 24.305 0.120 magnesium N 14.01 0.530 sp2 nitrogen in amide groups NA 14.01 0.530 sp2 N in 5 memb.ring w/H atom (HIS) NB 14.01 0.530 sp2 N in 5 memb.ring w/LP (HIS,ADE,GUA) NC 14.01 0.530 sp2 N in 6 memb.ring w/LP (ADE,GUA) N2 14.01 0.530 sp2 N in amino groups N3 14.01 0.530 sp3 N for charged amino groups (Lys, etc) NT 14.01 0.530 sp3 N for amino groups amino groups N* 14.01 0.530 sp2 N NY 14.01 0.530 nitrile N (Howard et al.JCC,16,243,1995) O 16.00 0.434 carbonyl group oxygen O2 16.00 0.434 carboxyl and phosphate group oxygen OW 16.00 0.000 oxygen in TIP3P water OH 16.00 0.465 oxygen in hydroxyl group OS 16.00 0.465 ether and ester oxygen OP 16.00 0.465 2- phosphate oxygen P 30.97 1.538 phosphate,pol:JACS,112,8543,90,K.J.Miller S 32.06 2.900 S in disulfide linkage,pol:JPC,102,2399,98 SH 32.06 2.900 S in cystine CU 63.55 copper FE 55.00 iron Zn 65.4 Zn2+ EP 0.00 0.000 extra point C H HO N NA NB NC N2 NT N2 N3 N* O OH OS P O2 OW-HW 553.0 0.9572 ! TIP3P water HW-HW 553.0 1.5136 TIP3P water C -C 310.0 1.525 Junmei et al, 1999 C -CA 469.0 1.409 JCC,7,(1986),230; (not used any more in TYR) C -CB 447.0 1.419 JCC,7,(1986),230; GUA C -CM 410.0 1.444 JCC,7,(1986),230; THY,URA C -CS 410.0 1.444 JCC,7,(1986),230; THY,URA C -CT 317.0 1.522 JCC,7,(1986),230; AA C -CX 317.0 1.522 JCC,7,(1986),230; AA (was C-CT) C -N 490.0 1.335 JCC,7,(1986),230; AA C -N* 424.0 1.383 JCC,7,(1986),230; CYT,URA C -NA 418.0 1.388 JCC,7,(1986),230; GUA.URA C -NC 457.0 1.358 JCC,7,(1986),230; CYT C -O 570.0 1.229 JCC,7,(1986),230; AA,CYT,GUA,THY,URA C -O2 656.0 1.250 JCC,7,(1986),230; GLU,ASP C -OH 450.0 1.364 JCC,7,(1986),230; (not used any more for TYR) C -OS 450.0 1.323 Junmei et al, 1999 C -H4 367.0 1.080 Junmei et al, 1999 C -H5 367.0 1.080 Junmei et al, 1999 CA-CA 469.0 1.400 JCC,7,(1986),230; BENZENE,PHE,TRP,TYR CA-CB 469.0 1.404 JCC,7,(1986),230; ADE,TRP CA-CM 427.0 1.433 JCC,7,(1986),230; CYT CA-CS 427.0 1.433 JCC,7,(1986),230; CYT CA-CN 469.0 1.400 JCC,7,(1986),230; TRP CA-CT 317.0 1.510 JCC,7,(1986),230; PHE,TYR CA-HA 367.0 1.080 changed from 340. bsd on C6H6 nmodes; PHE,TRP,TYR CA-H4 367.0 1.080 changed from 340. bsd on C6H6 nmodes; no assigned CA-N2 481.0 1.340 JCC,7,(1986),230; ARG,CYT,GUA CA-NA 427.0 1.381 JCC,7,(1986),230; GUA CA-NC 483.0 1.339 JCC,7,(1986),230; ADE,CYT,GUA CA-OH 450.0 1.364 substituted for C-OH in tyr CB-CB 520.0 1.370 JCC,7,(1986),230; ADE,GUA CB-N* 436.0 1.374 JCC,7,(1986),230; ADE,GUA CB-NB 414.0 1.391 JCC,7,(1986),230; ADE,GUA CB-NC 461.0 1.354 JCC,7,(1986),230; ADE,GUA CD-HA 367.0 1.080 Junmei et al, 1999 CD-CD 469.0 1.400 Junmei et al, 1999 CD-CM 549.0 1.350 Junmei et al, 1999 CD-CS 549.0 1.350 Junmei et al, 1999 CD-CT 317.0 1.510 Junmei et al, 1999 CK-H5 367.0 1.080 changed from 340. bsd on C6H6 nmodes; ADE,GUA CK-N* 440.0 1.371 JCC,7,(1986),230; ADE,GUA CK-NB 529.0 1.304 JCC,7,(1986),230; ADE,GUA CP-H5 367.0 1.080 changed from 340. bsd on C6H6 nmodes; ADE,GUA CP-N* 440.0 1.371 JCC,7,(1986),230; ADE,GUA CP-NB 529.0 1.304 JCC,7,(1986),230; ADE,GUA CM-CM 549.0 1.350 JCC,7,(1986),230; CYT,THY,URA CM-CT 317.0 1.510 JCC,7,(1986),230; THY CM-HA 367.0 1.080 changed from 340. bsd on C6H6 nmodes; CYT,URA CM-H4 367.0 1.080 changed from 340. bsd on C6H6 nmodes; CYT,URA CM-H5 367.0 1.080 changed from 340. bsd on C6H6 nmodes; not assigned CM-N* 448.0 1.365 JCC,7,(1986),230; CYT,THY,URA CM-OS 480.0 1.240 Junmei et al, 1999 CS-CS 549.0 1.350 JCC,7,(1986),230; CYT,THY,URA CS-CT 317.0 1.510 JCC,7,(1986),230; THY CS-HA 367.0 1.080 changed from 340. bsd on C6H6 nmodes; CYT,URA CS-H4 367.0 1.080 changed from 340. bsd on C6H6 nmodes; CYT,URA CS-H5 367.0 1.080 changed from 340. bsd on C6H6 nmodes; not assigned CS-N* 448.0 1.365 JCC,7,(1986),230; CYT,THY,URA CS-OS 480.0 1.240 Junmei et al, 1999 CQ-H5 367.0 1.080 changed from 340. bsd on C6H6 nmodes; ADE CQ-NC 502.0 1.324 JCC,7,(1986),230; ADE CT-CT 310.0 1.526 JCC,7,(1986),230; AA, SUGARS CX-CT 310.0 1.526 JCC,7,(1986),230; AA, SUGARS (was CT-CT) CT-HC 340.0 1.090 changed from 331 bsd on NMA nmodes; AA, SUGARS CT-H1 340.0 1.090 changed from 331 bsd on NMA nmodes; AA, RIBOSE CX-H1 340.0 1.090 changed from 331 bsd on NMA nmodes; AA, RIBOSE (was CT-H1) CT-H2 340.0 1.090 changed from 331 bsd on NMA nmodes; SUGARS CT-H3 340.0 1.090 changed from 331 bsd on NMA nmodes; not assigned CT-HP 340.0 1.090 changed from 331; AA-lysine, methyl ammonium cation CX-HP 340.0 1.090 changed from 331; AA-lysine, methyl ammonium cation (was CT-HP) CT-N* 337.0 1.475 JCC,7,(1986),230; ADE,CYT,GUA,THY,URA CT-N2 337.0 1.463 JCC,7,(1986),230; ARG CT-OH 320.0 1.410 JCC,7,(1986),230; SUGARS CT-OS 320.0 1.410 JCC,7,(1986),230; NUCLEIC ACIDS C*-HC 367.0 1.080 changed from 340. bsd on C6H6 nmodes, not needed AA C*-CB 388.0 1.459 JCC,7,(1986),230; TRP C*-CT 317.0 1.495 JCC,7,(1986),230; TRP C*-CW 546.0 1.352 JCC,7,(1986),230; TRP CB-CN 447.0 1.419 JCC,7,(1986),230; TRP CC-CT 317.0 1.504 JCC,7,(1986),230; HIS CC-CV 512.0 1.375 JCC,7,(1986),230; HIS(delta) CC-CW 518.0 1.371 JCC,7,(1986),230; HIS(epsilon) CC-NA 422.0 1.385 JCC,7,(1986),230; HIS CC-NB 410.0 1.394 JCC,7,(1986),230; HIS CN-NA 428.0 1.380 JCC,7,(1986),230; TRP CR-H5 367.0 1.080 changed from 340. bsd on C6H6 nmodes;HIS CR-NA 477.0 1.343 JCC,7,(1986),230; HIS CR-NB 488.0 1.335 JCC,7,(1986),230; HIS CT-N 337.0 1.449 JCC,7,(1986),230; AA CX-N 337.0 1.449 JCC,7,(1986),230; AA (was CT-N) CT-N3 367.0 1.471 JCC,7,(1986),230; LYS CX-N3 367.0 1.471 JCC,7,(1986),230; LYS (was CT-N3) CT-NT 367.0 1.471 for neutral amines CT-S 227.0 1.810 changed from 222.0 based on dimethylS nmodes CT-SH 237.0 1.810 changed from 222.0 based on methanethiol nmodes CT-CY 400.0 1.458 Howard et al JCC.16,243,1995 CT-CZ 400.0 1.459 Howard et al JCC,16,243,1995 CV-H4 367.0 1.080 changed from 340. bsd on C6H6 nmodes; HIS CV-NB 410.0 1.394 JCC,7,(1986),230; HIS CW-H4 367.0 1.080 changed from 340. bsd on C6H6 nmodes;HIS(epsilon,+) CW-NA 427.0 1.381 JCC,7,(1986),230; HIS,TRP CY-NY 600.0 1.150 Howard et al JCC,16,243,1995 CZ-CZ 600.0 1.206 Howard et al JCC,16,243,1995 CZ-HZ 400.0 1.056 Howard et al JCC,16,243,1995 OP-P 525.0 1.480 JCC,7,(1986),230; NA PHOSPHATES O2-P 525.0 1.480 JCC,7,(1986),230; NA PHOSPHATES OH-P 230.0 1.610 JCC,7,(1986),230; NA PHOSPHATES OS-P 230.0 1.610 JCC,7,(1986),230; NA PHOSPHATES NA-P 250.0 1.840 phosphohistidine H -N2 434.0 1.010 JCC,7,(1986),230; ADE,CYT,GUA,ARG H -N* 434.0 1.010 for plain unmethylated bases ADE,CYT,GUA,ARG H -NA 434.0 1.010 JCC,7,(1986),230; GUA,URA,HIS H -N 434.0 1.010 JCC,7,(1986),230; AA H -N3 434.0 1.010 JCC,7,(1986),230; LYS H -NT 434.0 1.010 for neutral amines HO-OH 553.0 0.960 JCC,7,(1986),230; SUGARS,SER,TYR HO-OS 553.0 0.960 JCC,7,(1986),230; NUCLEOTIDE ENDS HS-SH 274.0 1.336 JCC,7,(1986),230; CYS S -S 166.0 2.038 JCC,7,(1986),230; CYX (SCHERAGA) F -CT 367.0 1.380 JCC,13,(1992),963;CF4; R0=1.332 FOR CHF3 Cl-CT 232.0 1.766 6-31g* opt Br-CT 159.0 1.944 Junmei et al,99 I -CT 148.0 2.166 Junmei et al,99 F -CA 386.0 1.359 Junmei et al,99 Cl-CA 193.0 1.727 Junmei et al,99 I -CA 171.0 2.075 Junmei et al,99 Br-CA 172.0 1.890 Junmei et al,99 EP-O 600.0 0.200 or 0.35 EP-OH 600.0 0.200 or 0.35 EP-OS 600.0 0.200 or 0.35 EP-N3 600.0 0.200 or 0.35 EP-NT 600.0 0.200 or 0.35 EP-NB 600.0 0.200 or 0.35 histidines, nucleic acids EP-NC 600.0 0.200 or 0.35 nucleic acids EP-S 600.0 0.700 cys,cyx,met EP-SH 600.0 0.700 cys,cyx CI-H1 340.0 1.090 parmbsc0 CI-CT 310.0 1.526 parmbsc0 OS-CI 320.0 1.410 parmbsc0 OH-CI 320.0 1.410 parmbsc0 C5-H5 367.0 1.080 changed from 340. bsd on C6H6 nmodes; ADE,GUA C5-N* 440.0 1.371 JCC,7,(1986),230; ADE,GUA C5-NB 529.0 1.304 JCC,7,(1986),230; ADE,GUA C -C4 410.0 1.444 JCC,7,(1986),230; THY,URA CA-C4 427.0 1.433 JCC,7,(1986),230; CYT C4-C4 549.0 1.350 JCC,7,(1986),230; CYT,THY,URA C4-CT 317.0 1.510 JCC,7,(1986),230; THY C4-HA 367.0 1.080 changed from 340. bsd on C6H6 nmodes; CYT,URA C4-H4 367.0 1.080 changed from 340. bsd on C6H6 nmodes; CYT,URA C4-N* 448.0 1.365 JCC,7,(1986),230; CYT,THY,URA HW-OW-HW 100. 104.52 TIP3P water HW-HW-OW 0. 127.74 (found in crystallographic water with 3 bonds) C -C -O 80.0 120.00 Junmei et al, 1999 acrolein C -C -OH 80.0 120.00 Junmei et al, 1999 CA-C -CA 63.0 120.00 changed from 85.0 bsd on C6H6 nmodes; AA CA-C -OH 70.0 120.00 AA (not used in tyr) CA-C -OS 70.0 120.00 phosphotyrosine CC-NA-P 76.7 125.10 phosphohistidine CR-NA-P 76.7 125.10 phosphohistidine NA-P -OP 42.9 102.38 phosphohistidine CB-C -NA 70.0 111.30 NA CB-C -O 80.0 128.80 CM-C -NA 70.0 114.10 CM-C -O 80.0 125.30 CS-C -NA 70.0 114.10 CS-C -O 80.0 125.30 CT-C -O 80.0 120.40 CX-C -O 80.0 120.40 (was CT-C-O) CT-C -O2 70.0 117.00 CX-C -O2 70.0 117.00 (was CT-C -O2) CT-C -N 70.0 116.60 AA general CX-C -N 70.0 116.60 AA general (was CT-C-N) CT-C -CT 63.0 117.00 Junmei et al, 1999 CT-C -OS 80.0 115.00 Junmei et al, 1999 CT-C -OH 80.0 110.00 Junmei et al, 1999 CX-C -OH 80.0 110.00 Junmei et al, 1999 (was CT-C-OH) N*-C -NA 70.0 115.40 N*-C -NC 70.0 118.60 N*-C -O 80.0 120.90 NA-C -O 80.0 120.60 NC-C -O 80.0 122.50 N -C -O 80.0 122.90 AA general O -C -O 80.0 126.00 AA COO- terminal residues O -C -OH 80.0 120.00 (check with Junmei for: theta0:120.0?) O -C -OS 80.0 125.00 Junmei et al, 1999 O2-C -O2 80.0 126.00 AA GLU (SCH JPC 79,2379) H4-C -C 50.0 120.00 Junmei et al, 1999 H4-C -CM 50.0 115.00 Junmei et al, 1999 H4-C -CS 50.0 115.00 Junmei et al, 1999 H4-C -CT 50.0 115.00 Junmei et al, 1999 H4-C -O 50.0 120.00 Junmei et al, 1999 H4-C -OH 50.0 120.00 Junmei et al, 1999 H5-C -N 50.0 120.00 Junmei et al, 1999 H5-C -O 50.0 119.00 Junmei et al, 1999 H5-C -OH 50.0 107.00 Junmei et al, 1999 H5-C -OS 50.0 107.00 Junmei et al, 1999 C -CA-CA 63.0 120.00 changed from 85.0 bsd on C6H6 nmodes C -CA-HA 50.0 120.00 AA (not used in tyr) CA-CA-CA 63.0 120.00 changed from 85.0 bsd on C6H6 nmodes CA-CA-CB 63.0 120.00 changed from 85.0 bsd on C6H6 nmodes CA-CA-CT 70.0 120.00 CA-CA-HA 50.0 120.00 CA-CA-H4 50.0 120.00 CA-CA-OH 70.0 120.00 replacement in tyr CA-CA-CN 63.0 120.00 changed from 85.0 bsd on C6H6 nmodes; AA trp CB-CA-HA 50.0 120.00 CB-CA-H4 50.0 120.00 CB-CA-N2 70.0 123.50 CB-CA-NC 70.0 117.30 CM-CA-N2 70.0 120.10 CM-CA-NC 70.0 121.50 CS-CA-N2 70.0 120.10 CS-CA-NC 70.0 121.50 CN-CA-HA 50.0 120.00 AA trp NA-CA-NC 70.0 123.30 N2-CA-NA 70.0 116.00 N2-CA-NC 70.0 119.30 N2-CA-N2 70.0 120.00 AA arg F -CA-CA 70.0 121.00 Junmei et al,99 Cl-CA-CA 70.0 118.80 Junmei et al,99 Br-CA-CA 70.0 118.80 Junmei et al,99 I -CA-CA 70.0 118.80 Junmei et al,99 C -CB-CB 63.0 119.20 changed from 85.0 bsd on C6H6 nmodes; NA gua C -CB-NB 70.0 130.00 CA-CB-CB 63.0 117.30 changed from 85.0 bsd on C6H6 nmodes; NA ade CA-CB-NB 70.0 132.40 CB-CB-N* 70.0 106.20 CB-CB-NB 70.0 110.40 CB-CB-NC 70.0 127.70 C*-CB-CA 63.0 134.90 changed from 85.0 bsd on C6H6 nmodes; AA trp C*-CB-CN 63.0 108.80 changed from 85.0 bsd on C6H6 nmodes; AA trp CA-CB-CN 63.0 116.20 changed from 85.0 bsd on C6H6 nmodes; AA trp N*-CB-NC 70.0 126.20 CD-CD-CM 63.0 120.00 Junmei et al, 1999 CD-CD-CS 63.0 120.00 Junmei et al, 1999 CD-CD-CT 70.0 120.00 Junmei et al, 1999 CM-CD-CT 70.0 120.00 Junmei et al, 1999 CS-CD-CT 70.0 120.00 Junmei et al, 1999 HA-CD-HA 35.0 119.00 Junmei et al, 1999 HA-CD-CD 50.0 120.00 Junmei et al, 1999 HA-CD-CM 50.0 120.00 Junmei et al, 1999 HA-CD-CS 50.0 120.00 Junmei et al, 1999 H5-CK-N* 50.0 123.05 H5-CK-NB 50.0 123.05 N*-CK-NB 70.0 113.90 H5-CP-N* 50.0 123.05 H5-CP-NB 50.0 123.05 N*-CP-NB 70.0 113.90 C -CM-CM 63.0 120.70 changed from 85.0 bsd on C6H6 nmodes; NA thy C -CM-CT 70.0 119.70 C -CM-HA 50.0 119.70 C -CM-H4 50.0 119.70 CA-CM-CM 63.0 117.00 changed from 85.0 bsd on C6H6 nmodes; NA cyt CA-CM-HA 50.0 123.30 CA-CM-H4 50.0 123.30 CM-CM-CT 70.0 119.70 CM-CM-HA 50.0 119.70 CM-CM-H4 50.0 119.70 CM-CM-N* 70.0 121.20 CM-CM-OS 80.0 125.00 Junmei et al, 1999 H4-CM-N* 50.0 119.10 H4-CM-OS 50.0 113.00 Junmei et al, 1999 HA-CM-HA 35.0 120.00 Junmei et al, 1999 HA-CM-CD 50.0 120.00 Junmei et al, 1999 HA-CM-CT 50.0 120.00 Junmei et al, 1999 C -CS-CS 63.0 120.70 changed from 85.0 bsd on C6H6 nmodes; NA thy C -CS-CT 70.0 119.70 C -CS-HA 50.0 119.70 C -CS-H4 50.0 119.70 CA-CS-CS 63.0 117.00 changed from 85.0 bsd on C6H6 nmodes; NA cyt CA-CS-HA 50.0 123.30 CA-CS-H4 50.0 123.30 CM-CS-CT 70.0 119.70 CS-CS-HA 50.0 119.70 CS-CS-H4 50.0 119.70 CS-CS-N* 70.0 121.20 CS-CS-OS 80.0 125.00 Junmei et al, 1999 H4-CS-N* 50.0 119.10 H4-CS-OS 50.0 113.00 Junmei et al, 1999 HA-CS-HA 35.0 120.00 Junmei et al, 1999 HA-CS-CD 50.0 120.00 Junmei et al, 1999 HA-CS-CT 50.0 120.00 Junmei et al, 1999 NC-CQ-NC 70.0 129.10 H5-CQ-NC 50.0 115.45 H1-CT-H1 35.0 109.50 H1-CX-H1 35.0 109.50 (was H1-CT-H1) H1-CT-N* 50.0 109.50 changed based on NMA nmodes H1-CT-OH 50.0 109.50 changed based on NMA nmodes H1-CT-OS 50.0 109.50 changed based on NMA nmodes H1-CT-CM 50.0 109.50 Junmei et al, 1999 H1-CT-CS 50.0 109.50 Junmei et al, 1999 H1-CT-CY 50.0 110.00 Junmei et al, 1999 H1-CT-CZ 50.0 110.00 Junmei et al, 1999 H1-CT-N 50.0 109.50 AA general changed based on NMA nmodes H1-CX-N 50.0 109.50 AA general (was H1-CT-N) H1-CT-S 50.0 109.50 AA cys changed based on NMA nmodes H1-CT-SH 50.0 109.50 AA cyx changed based on NMA nmodes H1-CT-N2 50.0 109.50 AA arg changed based on NMA nmodes H1-CT-NT 50.0 109.50 neutral amines H2-CT-H2 35.0 109.50 AA lys H2-CT-N* 50.0 109.50 changed based on NMA nmodes H2-CT-OS 50.0 109.50 changed based on NMA nmodes HP-CT-HP 35.0 109.50 AA lys, ch3nh4+ HP-CX-HP 35.0 109.50 AA lys, ch3nh4+ (was HP-CT-HP) HP-CT-N3 50.0 109.50 AA lys, ch3nh3+, changed based on NMA nmodes HP-CX-N3 50.0 109.50 AA lys, ch3nh3+, changed based on NMA nmodes (was HP-CT-N3) HC-CT-HC 35.0 109.50 HC-CT-CM 50.0 109.50 changed based on NMA nmodes HC-CT-CS 50.0 109.50 changed based on NMA nmodes HC-CT-CD 50.0 109.50 Junmei et al, 1999 HC-CT-CZ 50.0 110.00 Junmei et al, 1999 C -CT-H1 50.0 109.50 AA general changed based on NMA nmodes C -CX-H1 50.0 109.50 AA general (was C-CT-H1) C -CT-HP 50.0 109.50 AA zwitterion changed based on NMA nmodes C -CX-HP 50.0 109.50 AA zwitterion changed based on NMA nmodes (was C -CT-HP) C -CT-HC 50.0 109.50 AA gln changed based on NMA nmodes C -CT-N 63.0 110.10 AA general C -CX-N 63.0 110.10 AA general (was C-CT-N) C -CT-N3 80.0 111.20 AA amino terminal residues C -CX-N3 80.0 111.20 AA amino terminal residues (was C -CT-N3) C -CT-CT 63.0 111.10 AA general C -CT-CX 63.0 111.10 AA general (was C -CT-CT) C -CX-CT 63.0 111.10 AA general (was C-CT-CT) C -CT-OS 60.0 109.50 Junmei et al, 1999 CA-CT-HC 50.0 109.50 AA tyr changed based on NMA nmodes CC-CT-CT 63.0 113.10 AA his CC-CT-CX 63.0 113.10 AA his (was CC-CT-CT) CC-CT-HC 50.0 109.50 AA his changed based on NMA nmodes CM-CT-CT 63.0 111.00 Junmei et al, 1999 (last change: Mar24,99) CM-CT-OS 50.0 109.50 Junmei et al, 1999 CS-CT-CT 63.0 111.00 Junmei et al, 1999 (last change: Mar24,99) CS-CT-OS 50.0 109.50 Junmei et al, 1999 CT-CT-CT 40.0 109.50 CT-CT-CX 40.0 109.50 (was CT-CT-CT) CT-CT-HC 50.0 109.50 changed based on NMA nmodes CX-CT-HC 50.0 109.50 changed based on NMA nmodes (was CT-CT-HC) CT-CT-H1 50.0 109.50 changed based on NMA nmodes CT-CX-H1 50.0 109.50 changed based on NMA nmodes (was CT-CT-H1) CX-CT-H1 50.0 109.50 changed based on NMA nmodes (was CT-CT-H1) CT-CT-H2 50.0 109.50 changed based on NMA nmodes CT-CT-HP 50.0 109.50 changed based on NMA nmodes CT-CX-HP 50.0 109.50 changed based on NMA nmodes (was CT-CT-HP) CT-CT-N* 50.0 109.50 CT-CT-OH 50.0 109.50 CX-CT-OH 50.0 109.50 (was CT-CT-OH) CT-CT-OS 50.0 109.50 CT-CT-S 50.0 114.70 AA cyx (SCHERAGA JPC 79,1428) CX-CT-S 50.0 114.70 AA cyx (SCHERAGA JPC 79,1428) (was CT-CT-S ) CT-CT-SH 50.0 108.60 AA cys CX-CT-SH 50.0 108.60 AA cys (was CT-CT-SH) CT-CT-CA 63.0 114.00 AA phe tyr (SCH JPC 79,2379) CX-CT-CA 63.0 114.00 AA phe tyr (SCH JPC 79,2379) (was CT-CT-CA) CT-CT-N2 80.0 111.20 AA arg (JCP 76, 1439) CT-CT-N 80.0 109.70 AA ala, general (JACS 94, 2657) CT-CX-N 80.0 109.70 AA ala, general (was CT-CT-N) CT-CT-N3 80.0 111.20 AA lys (JCP 76, 1439) CT-CX-N3 80.0 111.20 AA lys (JCP 76, 1439) (was CT-CT-N3) CT-CT-NT 80.0 111.20 neutral amines CT-CT-CY 63.0 110.00 Junmei et al, 1999 CT-CT-CZ 63.0 110.00 Junmei et al, 1999 C*-CT-CT 63.0 115.60 AA trp C*-CT-CX 63.0 115.60 AA trp (was C*-CT-CT) C*-CT-HC 50.0 109.50 AA trp changed based on NMA nmodes OS-CT-OS 160.0 101.00 Junmei et al, 1999 OS-CT-CY 50.0 110.00 Junmei et al, 1999 OS-CT-CZ 50.0 110.00 Junmei et al, 1999 OS-CT-N* 50.0 109.50 F -CT-F 77.0 109.10 JCC,13,(1992),963; F -CT-H1 50.0 109.50 JCC,13,(1992),963; F -CT-CT 50.0 109.00 F -CT-H2 50.0 109.50 Cl-CT-CT 50.0 108.50 (6-31g* opt value) Cl-CT-H1 50.0 108.50 (6-31g* opt value) Br-CT-CT 50.0 108.00 Junmei et al 99 Br-CT-H1 50.0 106.50 Junmei et al 99 I -CT-CT 50.0 106.00 Junmei et al,99 CT-CC-NA 70.0 120.00 AA his CT-CC-CV 70.0 120.00 AA his CT-CC-NB 70.0 120.00 AA his CV-CC-NA 70.0 120.00 AA his CW-CC-NA 70.0 120.00 AA his CW-CC-NB 70.0 120.00 AA his CT-CC-CW 70.0 120.00 AA his H5-CR-NA 50.0 120.00 AA his H5-CR-NB 50.0 120.00 AA his NA-CR-NA 70.0 120.00 AA his NA-CR-NB 70.0 120.00 AA his CC-CV-H4 50.0 120.00 AA his CC-CV-NB 70.0 120.00 AA his H4-CV-NB 50.0 120.00 AA his CC-CW-H4 50.0 120.00 AA his CC-CW-NA 70.0 120.00 AA his C*-CW-H4 50.0 120.00 AA trp C*-CW-NA 70.0 108.70 AA trp H4-CW-NA 50.0 120.00 AA his CB-C*-CT 70.0 128.60 AA trp CB-C*-CW 63.0 106.40 changed from 85.0 bsd on C6H6 nmodes; AA trp CT-C*-CW 70.0 125.00 AA trp CA-CN-CB 63.0 122.70 changed from 85.0 bsd on C6H6 nmodes; AA trp CA-CN-NA 70.0 132.80 AA trp CB-CN-NA 70.0 104.40 AA trp CT-CY-NY 80.0 180.00 Junmei et al, 1999 CT-CZ-CZ 80.0 180.00 Junmei et al, 1999 CZ-CZ-HZ 50.0 180.00 Junmei et al, 1999 C -N -CT 50.0 121.90 AA general C -N -CX 50.0 121.90 AA general (was C-N-CT) C -N -H 50.0 120.00 AA general, gln, asn,changed based on NMA nmodes CT-N -H 50.0 118.04 AA general, changed based on NMA nmodes CX-N -H 50.0 118.04 AA general, (was CT-N-H) CT-N -CT 50.0 118.00 AA pro (DETAR JACS 99,1232) CT-N -CX 50.0 118.00 AA pro (DETAR JACS 99,1232) (was CT-N -CT) H -N -H 35.0 120.00 ade,cyt,gua,gln,asn ** C -N*-CM 70.0 121.60 C -N*-CS 70.0 121.60 C -N*-CT 70.0 117.60 C -N*-H 50.0 119.20 changed based on NMA nmodes CB-N*-CK 70.0 105.40 CB-N*-CP 70.0 105.40 CB-N*-CT 70.0 125.80 CB-N*-H 50.0 125.80 for unmethylated n.a. bases,chngd bsd NMA nmodes CK-N*-CT 70.0 128.80 CK-N*-H 50.0 128.80 for unmethylated n.a. bases,chngd bsd NMA nmodes CP-N*-CT 70.0 128.80 CP-N*-H 50.0 128.80 for unmethylated n.a. bases,chngd bsd NMA nmodes CM-N*-CT 70.0 121.20 CM-N*-H 50.0 121.20 for unmethylated n.a. bases,chngd bsd NMA nmodes CS-N*-CT 70.0 121.20 CS-N*-H 50.0 121.20 for unmethylated n.a. bases,chngd bsd NMA nmodes CA-N2-H 50.0 120.00 CA-N2-CT 50.0 123.20 AA arg CT-N2-H 50.0 118.40 AA arg H -N2-H 35.0 120.00 CT-N3-H 50.0 109.50 AA lys, changed based on NMA nmodes CX-N3-H 50.0 109.50 AA lys, changed based on NMA nmodes (was CT-N3-H ) CT-N3-CT 50.0 109.50 AA pro/nt CT-N3-CX 50.0 109.50 AA pro/nt (was CT-N3-CT) H -N3-H 35.0 109.50 AA lys, AA(end) CT-NT-H 50.0 109.50 neutral amines CT-NT-CT 50.0 109.50 neutral amines H -NT-H 35.0 109.50 neutral amines C -NA-C 70.0 126.40 C -NA-CA 70.0 125.20 C -NA-H 50.0 116.80 changed based on NMA nmodes CA-NA-H 50.0 118.00 changed based on NMA nmodes CC-NA-CR 70.0 120.00 AA his CC-NA-H 50.0 120.00 AA his, changed based on NMA nmodes CR-NA-CW 70.0 120.00 AA his CR-NA-H 50.0 120.00 AA his, changed based on NMA nmodes CW-NA-H 50.0 120.00 AA his, changed based on NMA nmodes CN-NA-CW 70.0 111.60 AA trp CN-NA-H 50.0 123.10 AA trp, changed based on NMA nmodes CB-NB-CK 70.0 103.80 CB-NB-CP 70.0 103.80 CC-NB-CR 70.0 117.00 AA his CR-NB-CV 70.0 117.00 AA his C -NC-CA 70.0 120.50 CA-NC-CB 70.0 112.20 CA-NC-CQ 70.0 118.60 CB-NC-CQ 70.0 111.00 C -OH-HO 50.0 113.00 (not used in tyr anymore) CA-OH-HO 50.0 113.00 replacement in tyr CT-OH-HO 55.0 108.50 HO-OH-P 45.0 108.50 C -OS-CT 60.0 117.00 Junmei et al, 1999 CM-OS-CT 60.0 117.00 Junmei et al, 1999 CS-OS-CT 60.0 117.00 Junmei et al, 1999 CT-OS-CT 60.0 109.50 CT-OS-P 100.0 120.50 C -OS-P 100.0 120.50 phosphotyrosine P -OS-P 100.0 120.50 O2-P -OH 45.0 108.23 O2-P -O2 140.0 119.90 OP-P -OP 140.0 119.90 OP-P -OS 100.0 108.23 O2-P -OS 100.0 108.23 OH-P -OS 45.0 102.60 OS-P -OS 45.0 102.60 CT-S -CT 62.0 98.90 AA met CT-S -S 68.0 103.70 AA cyx (SCHERAGA JPC 79,1428) CT-SH-HS 43.0 96.00 changed from 44.0 based on methanethiol nmodes HS-SH-HS 35.0 92.07 AA cys CB-NB-EP 150.0 126.0 NA CC-NB-EP 150.0 126.0 his,NA CK-NB-EP 150.0 126.0 NA CP-NB-EP 150.0 126.0 NA CR-NB-EP 150.0 126.0 his,NA CV-NB-EP 150.0 126.0 his,NA C -NC-EP 150.0 120.0 NA CA-NC-EP 150.0 120.0 NA CB-NC-EP 150.0 120.0 NA CQ-NC-EP 150.0 120.0 NA CT-N3-EP 150.0 109.5 in neutral lysine H -N3-EP 150.0 109.5 in neutral lysine CT-NT-EP 150.0 109.5 H -NT-EP 150.0 109.5 C -O -EP 150.0 120.0 EP-O -EP 150.0 120.0 C -OH-EP 150.0 120.0 CT-OH-EP 150.0 109.5 HO-OH-EP 150.0 109.5 EP-OH-EP 150.0 109.5 C -OS-EP 150.0 109.5 CM-OS-EP 150.0 109.5 methyl vinyl ether CS-OS-EP 150.0 109.5 methyl vinyl ether CT-OS-EP 150.0 109.5 EP-OS-EP 150.0 109.5 CT-S -EP 150.0 90.0 cys,cyx,met CT-SH-EP 150.0 90.0 cys,cyx,met P -OS-EP 150.0 109.5 NA EP-S -EP 150.0 180.0 cys,cyx,met EP-SH-EP 150.0 180.0 cys,cyx,met HS-SH-EP 150.0 90.0 cys H1-CI-CT 50.0 109.50 parmbsc0 H1-CI-H1 35.0 109.50 parmbsc0 CI-CT-H1 50.0 109.50 parmbsc0 CI-CT-OS 50.0 109.50 parmbsc0 CI-CT-CT 40.0 109.50 parmbsc0 OS-CI-H1 50.0 109.50 parmbsc0 OS-CI-CT 50.0 109.50 parmbsc0 P -OS-CI 100.0 120.50 parmbsc0 OH-CI-H1 50.0 109.50 parmbsc0 OH-CI-CT 50.0 109.50 parmbsc0 HO-OH-CI 55.0 108.50 parmbsc0 H5-C5-N* 50.0 123.05 H5-C5-NB 50.0 123.05 N*-C5-NB 70.0 113.90 CB-N*-C5 70.0 105.40 C5-N*-CT 70.0 128.80 CB-NB-C5 70.0 103.80 C4-C -NA 70.0 114.10 C4-C -O 80.0 125.30 C4-CA-N2 70.0 120.10 C4-CA-NC 70.0 121.50 C -C4-C4 63.0 120.70 changed from 85.0 bsd on C6H6 nmodes; NA thy C -C4-CT 70.0 119.70 C -C4-HA 50.0 119.70 C -C4-H4 50.0 119.70 CA-C4-C4 63.0 117.00 changed from 85.0 bsd on C6H6 nmodes; NA cyt CA-C4-HA 50.0 123.30 CA-C4-H4 50.0 123.30 C4-C4-CT 70.0 119.70 C4-C4-HA 50.0 119.70 C4-C4-H4 50.0 119.70 C4-C4-N* 70.0 121.20 H4-C4-N* 50.0 119.10 H1-CT-C4 50.0 109.50 Junmei et al, 1999 HC-CT-C4 50.0 109.50 changed based on NMA nmodes C -N*-C4 70.0 121.60 C4-N*-CT 70.0 121.20 EP-S -S 150.0 96.70 AA cyx (dac, from parm91 LP-S-S) X -C -C -X 4 14.50 180.0 2. Junmei et al, 1999 X -C -CA-X 4 14.50 180.0 2. intrpol.bsd.on C6H6 X -C -CB-X 4 12.00 180.0 2. intrpol.bsd.on C6H6 X -C -CM-X 4 8.70 180.0 2. intrpol.bsd.on C6H6 X -C -CS-X 4 8.70 180.0 2. intrpol.bsd.on C6H6 X -C -CT-X 6 0.00 0.0 2. JCC,7,(1986),230 X -C -CX-X 6 0.00 0.0 2. JCC,7,(1986),230 (was X -C -CT-X ) X -C -N -X 4 10.00 180.0 2. AA,NMA X -C -N*-X 4 5.80 180.0 2. JCC,7,(1986),230 X -C -NA-X 4 5.40 180.0 2. JCC,7,(1986),230 X -C -NC-X 2 8.00 180.0 2. JCC,7,(1986),230 X -C -O -X 4 11.20 180.0 2. Junmei et al, 1999 X -C -OH-X 2 4.60 180.0 2. Junmei et al, 1999 X -C -OS-X 2 5.40 180.0 2. Junmei et al, 1999 X -CA-CA-X 4 14.50 180.0 2. intrpol.bsd.on C6H6 X -CA-CB-X 4 14.00 180.0 2. intrpol.bsd.on C6H6 X -CA-CM-X 4 10.20 180.0 2. intrpol.bsd.on C6H6 X -CA-CS-X 4 10.20 180.0 2. intrpol.bsd.on C6H6 X -CA-CN-X 4 14.50 180.0 2. reinterpolated 93' X -CA-CT-X 6 0.00 0.0 2. JCC,7,(1986),230 X -CA-N2-X 4 9.60 180.0 2. reinterpolated 93' X -CA-NA-X 4 6.00 180.0 2. JCC,7,(1986),230 X -CA-NC-X 2 9.60 180.0 2. JCC,7,(1986),230 X -CA-OH-X 2 1.80 180.0 2. Junmei et al, 99 X -CB-CB-X 4 21.80 180.0 2. intrpol.bsd.on C6H6 X -CB-CN-X 4 12.00 180.0 2. reinterpolated 93' X -CB-N*-X 4 6.60 180.0 2. JCC,7,(1986),230 X -CB-NB-X 2 5.10 180.0 2. JCC,7,(1986),230 X -CB-NC-X 2 8.30 180.0 2. JCC,7,(1986),230 X -CC-CT-X 6 0.00 0.0 2. JCC,7,(1986),230 X -CC-CV-X 4 20.60 180.0 2. intrpol.bsd.on C6H6 X -CC-CW-X 4 21.50 180.0 2. intrpol.bsd.on C6H6 X -CC-NA-X 4 5.60 180.0 2. JCC,7,(1986),230 X -CC-NB-X 2 4.80 180.0 2. JCC,7,(1986),230 X -CD-CD-X 4 4.00 180.0 2. Junmei et al, 1999 X -CD-CT-X 6 0.00 0.0 2. Junmei et al, 1999 X -CD-CM-X 4 26.60 180.0 2. Junmei et al, 1999 X -CD-CS-X 4 26.60 180.0 2. Junmei et al, 1999 X -CK-N*-X 4 6.80 180.0 2. JCC,7,(1986),230 X -CK-NB-X 2 20.00 180.0 2. JCC,7,(1986),230 X -CP-N*-X 4 6.80 180.0 2. JCC,7,(1986),230 X -CP-NB-X 2 20.00 180.0 2. JCC,7,(1986),230 X -CM-CM-X 4 26.60 180.0 2. intrpol.bsd.on C6H6 X -CM-CT-X 6 0.00 0.0 3. JCC,7,(1986),230 X -CM-N*-X 4 7.40 180.0 2. JCC,7,(1986),230 X -CM-OS-X 2 2.10 180.0 2. Junmei et al, 1999 X -CS-CS-X 4 26.60 180.0 2. intrpol.bsd.on C6H6 X -CS-CT-X 6 0.00 0.0 3. JCC,7,(1986),230 X -CS-N*-X 4 7.40 180.0 2. JCC,7,(1986),230 X -CS-OS-X 2 2.10 180.0 2. Junmei et al, 1999 X -CN-NA-X 4 6.10 180.0 2. reinterpolated 93' X -CQ-NC-X 2 13.60 180.0 2. JCC,7,(1986),230 X -CT-CT-X 9 1.40 0.0 3. JCC,7,(1986),230 X -CT-CX-X 9 1.40 0.0 3. JCC,7,(1986),230 (was X -CT-CT-X ) X -CT-CY-X 3 0.00 0.0 1. Junmei et al, 1999 X -CT-CZ-X 3 0.00 0.0 1. Junmei et al, 1999 X -CT-N -X 6 0.00 0.0 2. JCC,7,(1986),230 X -CX-N -X 6 0.00 0.0 2. JCC,7,(1986),230 (was X -CT-N -X ) X -CT-N*-X 6 0.00 0.0 2. JCC,7,(1986),230 X -CT-N2-X 6 0.00 0.0 3. JCC,7,(1986),230 X -CT-NT-X 6 1.80 0.0 3. Junmei et al, 1999 X -CT-N3-X 9 1.40 0.0 3. JCC,7,(1986),230 X -CX-N3-X 9 1.40 0.0 3. JCC,7,(1986),230 (was X -CT-N3-X ) X -CT-OH-X 3 0.50 0.0 3. JCC,7,(1986),230 X -CT-OS-X 3 1.15 0.0 3. JCC,7,(1986),230 X -CT-S -X 3 1.00 0.0 3. JCC,7,(1986),230 X -CT-SH-X 3 0.75 0.0 3. JCC,7,(1986),230 X -C*-CB-X 4 6.70 180.0 2. intrpol.bsd.onC6H6aa X -C*-CT-X 6 0.00 0.0 2. JCC,7,(1986),230 X -C*-CW-X 4 26.10 180.0 2. intrpol.bsd.on C6H6 X -CR-NA-X 4 9.30 180.0 2. JCC,7,(1986),230 X -CR-NB-X 2 10.00 180.0 2. JCC,7,(1986),230 X -CV-NB-X 2 4.80 180.0 2. JCC,7,(1986),230 X -CW-NA-X 4 6.00 180.0 2. JCC,7,(1986),230 X -OH-P -X 3 0.75 0.0 3. JCC,7,(1986),230 X -OS-P -X 3 0.75 0.0 3. JCC,7,(1986),230 X -CI-OS-X 3 1.150 0.0 3. X -CI-OH-X 3 0.500 0.0 3. X -CI-CT-X 9 1.400 0.0 3. X -C5-N*-X 4 6.80 180.0 2. JCC,7,(1986),230 X -C5-NB-X 2 20.00 180.0 2. JCC,7,(1986),230 X -C -C4-X 4 8.70 180.0 2. intrpol.bsd.on C6H6 X -CA-C4-X 4 10.20 180.0 2. intrpol.bsd.on C6H6 X -C4-C4-X 4 26.60 180.0 2. intrpol.bsd.on C6H6 X -C4-CT-X 6 0.00 0.0 3. JCC,7,(1986),230 X -C4-N*-X 4 7.40 180.0 2. JCC,7,(1986),230 CT-OS-CT-CI 1 0.383 0.0 -3. CT-OS-CT-CI 1 0.100 180.0 2. H1-CI-CT-OS 1 0.250 0.0 1. H1-CI-CT-OH 1 0.250 0.0 1. H1-CT-CI-OS 1 0.250 0.0 1. H1-CT-CI-OH 1 0.250 0.0 1. CI-CT-CT-CT 1 0.180 0.0 -3. CI-CT-CT-CT 1 0.250 180.0 -2. CI-CT-CT-CT 1 0.200 180.0 1. OP-P -OS-CA 1 0.0 180.0 1. phosphotyrosine CC-NA-P -OP 1 0.24 0.0 3. phosphohistidine CR-NA-P -OP 1 0.00 0.0 3. phosphohistidine OS-P -OS-CI 1 0.185181 31.79508 -1. alfa OS-P -OS-CI 1 1.256531 351.95960 -2. alfa OS-P -OS-CI 1 0.354858 357.24748 3. alfa OH-P -OS-CI 1 0.185181 31.79508 -1. alfa OH-P -OS-CI 1 1.256531 351.95960 -2. alfa OH-P -OS-CI 1 0.354858 357.24748 3. alfa CT-CT-CI-OS 1 1.178040 190.97653 -1. gamma CT-CT-CI-OS 1 0.092102 295.63279 -2. gamma CT-CT-CI-OS 1 0.962830 348.09535 3. gamma CT-CT-CI-OH 1 1.178040 190.97653 -1. gamma CT-CT-CI-OH 1 0.092102 295.63279 -2. gamma CT-CT-CI-OH 1 0.962830 348.09535 3. gamma HC-CT-C4-C4 1 0.38 180.0 -3. Junmei et al, 1999 HC-CT-C4-C4 1 1.15 0.0 1. Junmei et al, 1999 C4-C4-C -O 1 2.175 180.0 -2. Junmei et al, 1999 C4-C4-C -O 1 0.30 0.0 3. Junmei et al, 1999 OS-CT-N*-C5 1 0.96561 68.7902 -1. ol3 chi ade OS-CT-N*-C5 1 1.07403 15.6360 -2. ol3 chi ade OS-CT-N*-C5 1 0.45754 171.5787 -3. ol3 chi ade OS-CT-N*-C5 1 0.30917 19.0921 4. ol3 chi ade OS-CT-N*-CP 1 0.70510 74.7558 -1. ol3 chi gua OS-CT-N*-CP 1 1.06546 6.2286 -2. ol3 chi gua OS-CT-N*-CP 1 0.44273 168.6503 -3. ol3 chi gua OS-CT-N*-CP 1 0.25602 3.9746 4. ol3 chi gua OS-CT-N*-C4 1 1.22506 146.9892 -1. ol3 chi cyt OS-CT-N*-C4 1 1.63459 16.4766 -2. ol3 chi cyt OS-CT-N*-C4 1 0.93747 185.8774 -3. ol3 chi cyt OS-CT-N*-C4 1 0.31033 32.1590 4. ol3 chi cyt OS-CT-N*-CS 1 1.02514 149.8583 -1. ol3 chi ura OS-CT-N*-CS 1 1.74876 16.7648 -2. ol3 chi ura OS-CT-N*-CS 1 0.58150 179.3474 -3. ol3 chi ura OS-CT-N*-CS 1 0.35148 16.0016 4. ol3 chi ura C -N -CX-C 1 0.00 0.0 -4. four amplitudes and C -N -CX-C 1 0.42 0.0 -3. phases for phi C -N -CX-C 1 0.27 0.0 -2. C -N -CX-C 1 0.00 0.0 1. N -CX-C -N 1 0.00 0.0 -4. four amplitudes and N -CX-C -N 1 0.55 180.0 -3. phases for psi N -CX-C -N 1 1.58 180.0 -2. N -CX-C -N 1 0.45 180.0 1. CT-CT-N -C 1 0.00 0.0 -4. four amplitudes and CT-CT-N -C 1 0.40 0.0 -3. phases for phi' CT-CT-N -C 1 2.00 0.0 -2. CT-CT-N -C 1 2.00 0.0 1. CT-CX-N -C 1 0.00 0.0 -4. four amplitudes and CT-CX-N -C 1 0.40 0.0 -3. phases for phi' CT-CX-N -C 1 2.00 0.0 -2. CT-CX-N -C 1 2.00 0.0 1. CT-CT-C -N 1 0.00 0.0 -4. four amplitudes and CT-CT-C -N 1 0.40 0.0 -3. phases for psi' CT-CT-C -N 1 0.20 0.0 -2. CT-CT-C -N 1 0.20 0.0 1. CT-CX-C -N 1 0.00 0.0 -4. four amplitudes and CT-CX-C -N 1 0.40 0.0 -3. phases for psi' CT-CX-C -N 1 0.20 0.0 -2. CT-CX-C -N 1 0.20 0.0 1. CX-CT-C -N 1 0.00 0.0 -4. four amplitudes and CX-CT-C -N 1 0.40 0.0 -3. phases for psi' CX-CT-C -N 1 0.20 0.0 -2. CX-CT-C -N 1 0.20 0.0 1. H -N -C -O 1 2.50 180.0 -2. JCC,7,(1986),230 H -N -C -O 1 2.00 0.0 1. J.C.cistrans-NMA DE CT-S -S -CT 1 3.50 0.0 -2. JCC,7,(1986),230 CT-S -S -CT 1 0.60 0.0 3. JCC,7,(1986),230 OH-P -OS-CT 1 0.25 0.0 -3. JCC,7,(1986),230 OH-P -OS-CT 1 1.20 0.0 2. gg> ene.631g*/mp2 OS-P -OS-CT 1 0.25 0.0 -3. JCC,7,(1986),230 OS-P -OS-CT 1 1.20 0.0 2. gg> ene.631g*/mp2 H1-CT-C -O 1 0.80 0.0 -1. Junmei et al, 1999 H1-CT-C -O 1 0.00 0.0 -2. Explicit of wild card X-C-CT-X H1-CT-C -O 1 0.08 180.0 3. Junmei et al, 1999 H1-CX-C -O 1 0.80 0.0 -1. Junmei et al, 1999 (was H1-CT-C -O ) H1-CX-C -O 1 0.00 0.0 -2. Explicit of wild card X-C-CT-X H1-CX-C -O 1 0.08 180.0 3. Junmei et al, 1999 (was H1-CT-C -O ) HC-CT-C -O 1 0.80 0.0 -1. Junmei et al, 1999 HC-CT-C -O 1 0.00 0.0 -2. Explicit of wild card X-C-CT-X HC-CT-C -O 1 0.08 180.0 3. Junmei et al, 1999 HC-CT-CT-HC 1 0.15 0.0 3. Junmei et al, 1999 HC-CT-CT-CT 1 0.16 0.0 3. Junmei et al, 1999 HC-CT-CT-CX 1 0.16 0.0 3. Junmei et al, 1999 (was HC-CT-CT-CT) HC-CT-CM-CM 1 0.38 180.0 -3. Junmei et al, 1999 HC-CT-CM-CM 1 1.15 0.0 1. Junmei et al, 1999 HC-CT-CS-CS 1 0.38 180.0 -3. Junmei et al, 1999 HC-CT-CS-CS 1 1.15 0.0 1. Junmei et al, 1999 HO-OH-CT-CT 1 0.16 0.0 -3. Junmei et al, 1999 HO-OH-CT-CT 1 0.25 0.0 1. Junmei et al, 1999 HO-OH-CT-CX 1 0.16 0.0 -3. Junmei et al, 1999 (was HO-OH-CT-CT) HO-OH-CT-CX 1 0.25 0.0 1. Junmei et al, 1999 (was HO-OH-CT-CT) HO-OH-C -O 1 2.30 180.0 -2. Junmei et al, 1999 HO-OH-C -O 1 1.90 0.0 1. Junmei et al, 1999 CM-CM-C -O 1 2.175 180.0 -2. Junmei et al, 1999 CM-CM-C -O 1 0.30 0.0 3. Junmei et al, 1999 CT-CM-CM-CT 1 6.65 180.0 -2. Junmei et al, 1999 CT-CM-CM-CT 1 1.90 180.0 1. Junmei et al, 1999 CS-CS-C -O 1 2.175 180.0 -2. Junmei et al, 1999 CS-CS-C -O 1 0.30 0.0 3. Junmei et al, 1999 CT-CS-CS-CT 1 6.65 180.0 -2. Junmei et al, 1999 CT-CS-CS-CT 1 1.90 180.0 1. Junmei et al, 1999 CT-CT-CT-CT 1 0.18 0.0 -3. Junmei et al, 1999 CT-CT-CT-CT 1 0.25 180.0 -2. Junmei et al, 1999 CT-CT-CT-CT 1 0.20 180.0 1. Junmei et al, 1999 CX-CT-CT-CT 1 0.18 0.0 -3. Junmei et al, 1999 (was CT-CT-CT-CT) CX-CT-CT-CT 1 0.25 180.0 -2. Junmei et al, 1999 (was CT-CT-CT-CT) CX-CT-CT-CT 1 0.20 180.0 1. Junmei et al, 1999 (was CT-CT-CT-CT) CT-CT-NT-CT 1 0.30 0.0 -3. Junmei et al, 1999 CT-CT-NT-CT 1 0.48 180.0 2. Junmei et al, 1999 CT-CT-OS-CT 1 0.383 0.0 -3. CT-CT-OS-CT 1 0.1 180.0 2. CT-CT-OS-C 1 0.383 0.0 -3. Junmei et al, 1999 CT-CT-OS-C 1 0.80 180.0 1. Junmei et al, 1999 CT-OS-CT-OS 1 0.10 0.0 -3. Junmei et al, 1999 CT-OS-CT-OS 1 0.85 180.0 -2. Junmei et al, 1999 CT-OS-CT-OS 1 1.35 180.0 1. Junmei et al, 1999 CT-OS-CT-N* 1 0.383 0.0 -3. parm98.dat, TC,PC,PAK CT-OS-CT-N* 1 0.65 0.0 2. Piotr et al. CT-CZ-CZ-HZ 1 0.00 0.0 1. Junmei et al, 1999 O -C -OS-CT 1 2.70 180.0 -2. Junmei et al, 1999 O -C -OS-CT 1 1.40 180.0 1. Junmei et al, 1999 OS-CT-N*-CK 1 0.00 000.0 -2. parm98, TC,PC,PAK OS-CT-N*-CK 1 2.50 0.0 1. parm98, TC,PC,PAK OS-CT-N*-CM 1 0.00 000.0 -2. parm98, TC,PC,PAK OS-CT-N*-CM 1 2.50 0.0 1. parm98, TC,PC,PAK OS-CT-CT-OS 1 0.144 0.0 -3. parm98, TC,PC,PAK OS-CT-CT-OS 1 1.175 0.0 2. Piotr et al. OS-CT-CT-OH 1 0.144 0.0 -3. parm98, TC,PC,PAK OS-CT-CT-OH 1 1.175 0.0 2. parm98, TC,PC,PAK OH-CT-CT-OH 1 0.144 0.0 -3. parm98, TC,PC,PAK OH-CT-CT-OH 1 1.175 0.0 2. parm98, TC,PC,PAK F -CT-CT-F 1 0.000 0.0 -3. JCC,7,(1986),230 F -CT-CT-F 1 1.20 180.0 1. Junmei et al, 1999 Cl-CT-CT-Cl 1 0.000 0.0 -3. JCC,7,(1986),230 Cl-CT-CT-Cl 1 0.45 180.0 1. Junmei et al, 1999 Br-CT-CT-Br 1 0.000 0.0 -3. JCC,7,(1986),230 Br-CT-CT-Br 1 0.00 180.0 1. Junmei et al, 1999 H1-CT-CT-OS 1 0.000 0.0 -3. JCC,7,(1986),230 H1-CT-CT-OS 1 0.25 0.0 1. Junmei et al, 1999 H1-CT-CT-OH 1 0.000 0.0 -3. JCC,7,(1986),230 H1-CT-CT-OH 1 0.25 0.0 1. Junmei et al, 1999 H1-CX-CT-OH 1 0.000 0.0 -3. JCC,7,(1986),230 (was H1-CT-CT-OH) H1-CX-CT-OH 1 0.25 0.0 1. Junmei et al, 1999 (was H1-CT-CT-OH) H1-CT-CT-F 1 0.000 0.0 -3. JCC,7,(1986),230 H1-CT-CT-F 1 0.19 0.0 1. Junmei et al, 1999 H1-CT-CT-Cl 1 0.000 0.0 -3. JCC,7,(1986),230 H1-CT-CT-Cl 1 0.25 0.0 1. Junmei et al, 1999 H1-CT-CT-Br 1 0.000 0.0 -3. JCC,7,(1986),230 H1-CT-CT-Br 1 0.55 0.0 1. Junmei et al, 1999 HC-CT-CT-OS 1 0.000 0.0 -3. JCC,7,(1986),230 HC-CT-CT-OS 1 0.25 0.0 1. Junmei et al, 1999 HC-CT-CT-OH 1 0.000 0.0 -3. JCC,7,(1986),230 HC-CT-CT-OH 1 0.25 0.0 1. Junmei et al, 1999 HC-CT-CT-F 1 0.000 0.0 -3. JCC,7,(1986),230 HC-CT-CT-F 1 0.19 0.0 1. Junmei et al, 1999 HC-CT-CT-Cl 1 0.000 0.0 -3. JCC,7,(1986),230 HC-CT-CT-Cl 1 0.25 0.0 1. Junmei et al, 1999 HC-CT-CT-Br 1 0.000 0.0 -3. JCC,7,(1986),230 HC-CT-CT-Br 1 0.55 0.0 1. Junmei et al, 1999 H1-CT-NT-EP 1 0.000 0.000 3.000 CT-CT-NT-EP 1 0.000 0.000 3.000 CT-C -N -EP 1 0.000 180.000 2.000 O -C -N -EP 1 0.000 180.000 2.000 H1-CT-OH-EP 1 0.000 0.000 3.000 CT-CT-OH-EP 1 0.000 0.000 3.000 H1-CT-OS-EP 1 0.000 0.000 3.000 H2-CT-OS-EP 1 0.000 0.000 3.000 CT-CT-OS-EP 1 0.000 0.000 3.000 CM-CM-OS-EP 1 0.000 180.000 2.000 HA-CM-OS-EP 1 0.000 180.000 2.000 H4-CM-OS-EP 1 0.000 180.000 2.000 CS-CS-OS-EP 1 0.000 180.000 2.000 HA-CS-OS-EP 1 0.000 180.000 2.000 H4-CS-OS-EP 1 0.000 180.000 2.000 N -CT-CT-OH 1 0.000 0.000 -1. HYP N -CT-CT-OH 1 1.490 0.000 -2. HYP N -CT-CT-OH 1 0.156 0.000 -3. HYP N -CT-CT-OH 1 0.000 0.000 4. HYP EP-S -S -CT 1 0.00 0.0 3. cyx (dac) EP-S -S -EP 1 0.00 0.0 3. cyx (dac) X -X -C -O 10.5 180. 2. JCC,7,(1986),230 X -O2-C -O2 10.5 180. 2. JCC,7,(1986),230 X -X -N -H 1.0 180. 2. JCC,7,(1986),230 X -X -N2-H 1.0 180. 2. JCC,7,(1986),230 X -X -NA-H 1.0 180. 2. JCC,7,(1986),230 X -N2-CA-N2 10.5 180. 2. JCC,7,(1986),230 X -CT-N -CT 1.0 180. 2. JCC,7,(1986),230 X -CT-N -CX 1.0 180. 2. JCC,7,(1986),230 (was X -CT-N -CT) X -X -CA-HA 1.1 180. 2. bsd.on C6H6 nmodes X -X -CW-H4 1.1 180. 2. X -X -CR-H5 1.1 180. 2. X -X -CV-H4 1.1 180. 2. X -X -CQ-H5 1.1 180. 2. X -X -CK-H5 1.1 180. 2. X -X -CP-H5 1.1 180. 2. X -X -CM-H4 1.1 180. 2. X -X -CM-HA 1.1 180. 2. X -X -CS-H4 1.1 180. 2. X -X -CS-HA 1.1 180. 2. X -X -CA-H4 1.1 180. 2. bsd.on C6H6 nmodes X -X -CA-H5 1.1 180. 2. bsd.on C6H6 nmodes CB-CK-N*-CT 1.0 180. 2. CB-CP-N*-CT 1.0 180. 2. C -CM-N*-CT 1.0 180. 2. dac guess, 9/94 C -CS-N*-CT 1.0 180. 2. dac guess, 9/94 C -CS-CM-CT 1.1 180. 2. CT-O -C -OH 10.5 180. 2. CT-CV-CC-NA 1.1 180. 2. CT-CW-CC-NB 1.1 180. 2. CT-CW-CC-NA 1.1 180. 2. CB-CT-C*-CW 1.1 180. 2. CA-CA-CA-CT 1.1 180. 2. C -CM-CM-CT 1.1 180. 2. dac guess, 9/94 C -CS-CS-CT 1.1 180. 2. dac guess, 9/94 CM-N2-CA-NC 1.1 180. 2. dac guess, 9/94 CS-N2-CA-NC 1.1 180. 2. dac guess, 9/94 CB-N2-CA-NC 1.1 180. 2. dac, 10/94 N2-NA-CA-NC 1.1 180. 2. dac, 10/94 CA-CA-C -OH 1.1 180. 2. (not used in tyr!) CA-CA-CA-OH 1.1 180. 2. in tyr H5-O -C -OH 1.1 180. 2. Junmei et al.1999 H5-O -C -OS 1.1 180. 2. CM-CT-CM-HA 1.1 180. 2. Junmei et al.1999 CS-CT-CS-HA 1.1 180. 2. Junmei et al.1999 Br-CA-CA-CA 1.1 180. 2. Junmei et al.1999 CM-H4-C -O 1.1 180. 2. Junmei et al.1999 CS-H4-C -O 1.1 180. 2. Junmei et al.1999 C -CT-N -H 1.1 180. 2. Junmei et al.1999 C -CX-N -H 1.1 180. 2. Junmei et al.1999 (was C -CT-N -H ) C -CT-N -O 1.1 180. 2. Junmei et al.1999 X -X -C5-H5 1.1 180. 2. CB-C5-N*-CT 1.0 180. 2. X -X -C4-H4 1.1 180. 2. X -X -C4-HA 1.1 180. 2. C -C4-N*-CT 1.0 180. 2. dac guess, 9/94 C -C4-C4-CT 1.1 180. 2. dac guess, 9/94 C4-N2-CA-NC 1.1 180. 2. dac guess, 9/94 CA-CA-C -OS 1.1 180. 2. phosphotyrosine CR-CC-NA-P 1.1 180. 2. phosphohistine HW OW 0000. 0000. 4. flag for fast water N NA N2 N* NC NB NT NY C* CA CB CC CD CK CM CN CQ CR CV CW CY CZ CP CS MOD4 RE H 0.6000 0.0157 !Ferguson base pair geom. HO 0.0000 0.0000 OPLS Jorgensen, JACS,110,(1988),1657 HS 0.6000 0.0157 W. Cornell CH3SH --> CH3OH FEP HC 1.4870 0.0157 OPLS H1 1.3870 0.0157 Veenstra et al JCC,8,(1992),963 H2 1.2870 0.0157 Veenstra et al JCC,8,(1992),963 H3 1.1870 0.0157 Veenstra et al JCC,8,(1992),963 HP 1.1000 0.0157 Veenstra et al JCC,8,(1992),963 HA 1.4590 0.0150 Spellmeyer H4 1.4090 0.0150 Spellmeyer, one electrowithdr. neighbor H5 1.3590 0.0150 Spellmeyer, two electrowithdr. neighbor HW 0.0000 0.0000 TIP3P water model HZ 1.4590 0.0150 H bonded to sp C (Howard et al JCC 16) O 1.6612 0.2100 OPLS O2 1.6612 0.2100 OPLS OW 1.7683 0.1520 TIP3P water model OH 1.7210 0.2104 OPLS OS 1.6837 0.1700 OPLS ether OP 1.8500 0.1700 Steinbrecher/Latzer for 2- phosphate C* 1.9080 0.0860 Spellmeyer CI 1.9080 0.1094 parmbsc0 C5 1.9080 0.0860 Spellmeyer C4 1.9080 0.0860 Spellmeyer CT 1.9080 0.1094 Spellmeyer CX 1.9080 0.1094 Spellmeyer (was CT) C 1.9080 0.0860 OPLS N 1.8240 0.1700 OPLS N3 1.8240 0.1700 OPLS S 2.0000 0.2500 W. Cornell CH3SH and CH3SCH3 FEP's SH 2.0000 0.2500 W. Cornell CH3SH and CH3SCH3 FEP's P 2.1000 0.2000 JCC,7,(1986),230; MG 0.7926 0.8947 Mg2+ Aqvist JPC 1990,94,8021.(adapted) C0 1.7131 0.459789 Ca2+ Aqvist JPC 1990,94,8021.(adapted) Zn 1.10 0.0125 Zn2+, Merz,PAK, JACS,113,8262,(1991) EP 0.00 0.0000 lone pair END MMTK-2.7.9/MMTK/ForceFields/Amber/parm91.dat0000644000076600000240000010763412041514537020514 0ustar hinsenstaff00000000000000PARM91 All-Atom Force Field with TIP3P Water, New LP params, cations BR 79.90 C 12.01 CA 12.01 CB 12.01 CC 12.01 ? CD 13.02 ? CE 13.02 CF 13.02 CG 13.02 CH 13.02 CI 13.02 CJ 13.02 CK 12.01 CM 12.01 CN 12.01 CP 13.02 CQ 12.01 ? CR 12.01 CT 12.01 CV 12.01 CW 12.01 CX 14.03 CY 13.02 ? CZ 14.03 C2 14.03 C3 15.03 C* 12.01 CL 35.45 C0 40.08 c0 is calcium F 19.00 H 1.008 HC 1.008 HO 1.008 HS 1.008 HW 1.008 H2 1.008 H3 1.008 H4 1.008 I 126.9 IM 35.45 ASSUMED TO BE CL- IP 22.99 ASSUMED TO BE NA+ IB 131.0 'big ion w/ waters' for vacuum (Na+, 6H2O) M1 999. UNDEFINED M2 999. UNDEFINED MG 24.305 magnesium N 14.01 NA 14.01 NB 14.01 NZ 14.01 NP 14.01 NO 14.01 NC 14.01 NT 14.01 N2 14.01 N3 14.01 N* 14.01 O 16.00 W 16.00 OW 16. OH 16.00 OM 16.00 OS 16.00 O2 16.00 P 30.97 QC 132.9 qc is cesium QK 39.10 qk is potassium QL 6.94 ql is lithium QN 22.99 qn is sodium QR 85.47 qr is rubidium S 32.06 SH 32.06 SF 32.06 S* 32.06 CU 63.55 FE 55.0 LP 3.0 C H HO H2 H3 N NA NB NC N2 NT N2 N3 N* O OH OS P O2 MG OW-HW 553. 0.9572 HW-HW 553. 1.5136 C*-HC 340. 1.08 C -C2 317. 1.522 GLY,ASP,GLU C -C3 317. 1.522 END C -CA 469. 1.40 TYR C -CB 447. 1.419 GUA C -CD 469. 1.40 TYR C -CH 317. 1.522 AA C -CJ 410. 1.444 URA C -CM 410. 1.444 THY C -CT 317. 1.522 C -N 490. 1.335 AA C -N* 424. 1.383 CYT,URA C -NA 418. 1.388 URAGUA C -NC 457. 1.358 CYT C -O 570. 1.229 URAGUA,CYT,AA C -O2 656. 1.25 GLU,ASP C -OH 450. 1.364 TYR C*-C2 317. 1.495 TRP(OL) C*-CB 388. 1.459 TRP C*-CG 546. 1.352 TRP C*-CT 317. 1.495 TRP(OL) C*-CW 546. 1.352 TRP C2-C2 260. 1.526 AA(OL) C2-C3 260. 1.526 ILE(OL) C2-CA 317. 1.51 PHE,TYR C2-CC 317. 1.504 HIS C2-CH 260. 1.526 AA,SUG C2-N 337. 1.449 GLY(OL) C2-N2 337. 1.463 ARG(OL) C2-N3 367. 1.471 LYS(OL) CH-N3 367. 1.471 C2-NT 367. 1.471 GLY C2-OH 386. 1.425 SUG(OL),SER C2-OS 320. 1.425 SUG(OL) C2-S 222. 1.81 CYX(OL) C2-SH 222. 1.81 CYS(OL) C3-CH 260. 1.526 ALA C3-CM 317. 1.51 THY(use std C-C) C3-N 337. 1.449 TEST!!!! C3-N* 337. 1.475 9 methyl bases C3-N2 337. 1.463 ARG(OL) C3-N3 367. 1.471 C3-OH 386. 1.425 SUG(OL),SER C3-OS 320. 1.425 DMP C3-S 222. 1.81 MET(OL) C3-SH 222. 1.81 CYS(OL) CA-CA 469. 1.40 TRP,TYR,PHE CA-CB 469. 1.404 ADE CQ-CB 469. 1.404 2-amino purine CA-CD 469. 1.40 PHE,TYR CA-CJ 427. 1.433 CYT CA-CM 427. 1.433 CA-CN 469. 1.40 TRP CA-CT 317. 1.51 PHE,TYR CA-HC 340. 1.08 CA-N2 481. 1.340 ARG CA-NA 427. 1.381 GUA CA-NC 483. 1.339 ADE,GUA,CYT CB-CB 520. 1.370 ADE,GUA CB-CD 469. 1.40 TRP CB-CN 447. 1.419 TRP CB-N* 436. 1.374 ADE,GUA CB-NB 414. 1.391 ADE,GUA,HIS CB-NC 461. 1.354 ADE,GUA CC-CF 512. 1.375 HIS CC-CG 518. 1.371 HIS CC-CT 317. 1.504 HIS CC-CV 512. 1.375 HIS CC-CW 518. 1.371 HIS CC-NA 422. 1.385 HIS CC-NB 410. 1.394 ADE,GUA,HIS CD-CD 469. 1.40 TRP,TYR,PHE CD-CN 469. 1.40 TRP CE-N* 440. 1.371 ADE,GUA CE-NB 529. 1.304 ADE,GUA CF-NB 410. 1.394 ADE,GUA,HIS CG-NA 427. 1.381 TRP,HIS CH-CH 260. 1.526 SUG(as in CH-C2),ILE CH-N 337. 1.449 AA CH-N* 337. 1.475 ADE,GUA,CYT,URA CH-NT 367. 1.471 AA CH-OH 386. 1.425 RSUG,THR CH-OS 320. 1.425 SUG CI-NC 502. 1.324 ADE CJ-CJ 549. 1.350 URA,CYT CJ-CM 549. 1.350 THY CJ-N* 448. 1.365 URA,CYT CK-HC 340. 1.08 CK-N* 440. 1.371 CK-NB 529. 1.304 CM-CM 549. 1.350 CM-CT 317. 1.51 CM-HC 340. 1.08 CM-N* 448. 1.365 CN-NA 428. 1.38 TRP CP-NA 477. 1.343 HIS CP-NB 488. 1.335 HIS(MOD) CQ-HC 340. 1.08 CQ-NC 502. 1.324 CR-HC 340. 1.08 CR-NA 477. 1.343 HIS CR-NB 488. 1.335 HIS(MOD) CT-CT 310. 1.526 CT-HC 331. 1.09 CT-N 337. 1.449 CT-N* 337. 1.475 CT-N2 337. 1.463 ARG(OL) CT-N3 367. 1.471 LYS(OL) CT-OH 320. 1.41 CT-OS 320. 1.41 CT-S 222. 1.81 CYX(OL) CT-SH 222. 1.81 CYS(OL) CV-HC 340. 1.08 CV-NB 410. 1.394 ADE,GUA,HIS CW-HC 340. 1.08 CW-NA 427. 1.381 TRP,HIS H -N 434. 1.01 AA H -N2 434. 1.01 URA,GUA,HIS H -NA 434. 1.01 URA,GUA,HIS H2-N 434. 1.01 AA H2-N2 434. 1.01 ADE,GUA,CYT,GLN,ASN,ARG H2-NT 434. 1.01 AA(END) H3-N2 434. 1.01 ADE,GUA,CYT,GLN,ASN,ARG H3-N3 434. 1.01 LYS(OL) HO-OH 553. 0.96 SUG(OL) HO-OS 553. 0.96 SUG(OL) HS-SH 274. 1.336 CYS(OL) LP-S 600. 0.679 LP-SH 600. 0.679 O2-P 525. 1.48 SUG(OL) OH-P 230. 1.61 SUG(OL) OS-P 230. 1.61 SUG(OL) S -S 166. 2.038 CYX(OL) SCHERAGA HW-OW-HW 100. 104.52 HC-C*-CW 35. 126.8 HC-C*-CB 35. 126.8 C2-C -N 70. 116.6 GLY GELIN C2-C -O 80. 120.4 ASN(OL) GELIN C2-C -O2 70. 117. GLU(OL) SCH JPC 79,2379 C3-C -N 70. 116.6 ACET(OL) BENEDETTI C3-C -O 80. 120.4 ACET(OL) C3-C -O2 70. 117. GLU(OL) SCH JPC 79,2379 CA-C -CA 85. 120. TYR(OL) GELIN CA-C -OH 70. 120. TYR(OL) GELIN CB-C -NA 70. 111.3 GUA CB-C -O 80. 128.8 GUA CD-C -CD 85. 120. TYR(OL) GELIN CD-C -OH 70. 120. TYR(OL) GELIN CH-C -N 70. 116.6 AA(OL) CH-C -O 80. 120.4 AA(OL) CH-C -O2 65. 117. AA(OL) SCH JPC 79,2379 CH-C -OH 70. 115. ACID(OL) SCH JPC 79,2379 CJ-C -NA 70. 114.1 URA CJ-C -O 80. 125.3 URA CM-C -NA 70. 114.1 THY CM-C -O 80. 125.3 THY CT-C -N 70. 116.6 CT-C -O 80. 120.4 CT-C -O2 70. 117. GLU(OL) SCH JPC 79,2379 N -C -O 80. 122.9 AA(OL) N*-C -NA 70. 115.4 URA N*-C -NC 70. 118.6 CYT N*-C -O 80. 120.9 URA,CYT NA-C -O 80. 120.6 URA(2),GUA NC-C -O 80. 122.5 CYT O -C -O 80. 126.0 COO- terminal residues O2-C -O2 80. 126.0 GLU(OL) SCH JPC 79,2379 C2-C*-CB 70. 128.6 TRP(OL) C2-C*-CG 70. 125. TRP(OL) C2-C*-CW 70. 125. TRP(OL) CB-C*-CG 85. 106.4 TRP(OL) CB-C*-CT 70. 128.6 TRP(OL) CB-C*-CW 85. 106.4 TRP(OL) CT-C*-CW 70. 125. TRP(OL) C -C2-C2 63.0 112.4 GLU C -C2-CH 63.0 112.4 ASP C -C2-N 80.0 110.3 GLY WORK DONE ON 6/23/82 C -C2-NT 80.0 111.2 GLY JCP 76, 1439 C*-C2-CH 63.0 115.6 TRP(OL) C2-C2-C2 63.0 112.4 PRO,LYS C2-C2-CH 63.0 112.4 MET C2-C2-N 80.0 111.2 PRO JCP 76, 1439 C2-C2-N2 80.0 111.2 ARG JCP 76, 1439 C2-C2-N3 80.0 111.2 LYS(OL) JCP 76, 1439 C2-C2-NT 80.0 111.2 PRO JCP 76, 1439 C2-C2-OS 80.0 109.5 THF fit C2-C2-S 50. 114.7 MET SCHERAGA JPC 79,1428 C3-C2-CH 63.0 112.4 ILE C3-C2-OS 80.0 109.5 MEE CA-C2-CH 63. 114. PHE(OL) SCH JPC 79,2379 CC-C2-CH 63.0 113.1 HIS(OL) CH-C2-CH 63.0 112.4 SUG,LEU CH-C2-OH 80.0 109.5 SER,end sugar CH-C2-OS 80.0 109.5 SUG CH-C2-S 50. 114.7 CYX SCHERAGA JPC 79,1428 CH-C2-SH 50.0 108.6 CYS C -CA-CA 85. 120. TYR(OL) C -CA-HC 35. 120.0 C2-CA-CA 70. 120. PHE(OL) C2-CA-CD 70. 120. PHE(OL) CA-CA-CA 85. 120. PHE(OL) CA-CA-CB 85. 120. TRP(OL) CA-CA-CN 85. 120. TRP(OL) CA-CA-CT 70. 120. PHE(OL) CA-CA-HC 35. 120.0 CB-CA-HC 35. 120.0 CB-CQ-HC 35. 120.0 2-amino purine CB-CA-N2 70. 123.5 ADE CB-CA-NC 70. 117.3 ADE CB-CQ-NC 70. 117.3 2-amino purine CD-CA-CD 85. 120. PHE(OL) CJ-CA-N2 70. 120.1 CYT CJ-CA-NC 70. 121.5 CYT CM-CA-N2 70. 120.1 CM-CA-NC 70. 121.5 CN-CA-HC 35. 120.0 N2-CA-N2 70. 120. ARG(OL) N2-CA-NA 70. 116.0 GUA N2-CA-NC 70. 119.3 ADE,GUA NA-CA-NC 70. 123.3 GUA C -CB-CB 85. 119.2 GUA C -CB-NB 70. 130. GUA C*-CB-CA 85. 134.9 TRP(OL) C*-CB-CD 85. 134.9 TRP(OL) C*-CB-CN 85. 108.8 TRP(OL) CA-CB-CB 85. 117.3 ADE CQ-CB-CB 85. 117.3 2-amino purine CA-CB-CN 85. 116.2 TRP CA-CB-NB 70. 132.4 ADE CQ-CB-NB 70. 132.4 2-amino purine CB-CB-N* 70. 106.2 GUA,ADE CB-CB-NB 70. 110.4 GUA,ADE CB-CB-NC 70. 127.7 GUA,ADE CD-CB-CN 85. 116.2 TRP N*-CB-NC 70. 126.2 GUA,ADE C2-CC-CF 70. 131.9 HIS(OL) C2-CC-CG 70. 129.05 HIS(OL) C2-CC-CV 70. 131.9 HIS(OL) C2-CC-CW 70. 129.05 HIS(OL) C2-CC-NA 70. 122.2 HIS(OL) C2-CC-NB 70. 121.05 HIS(OL) CF-CC-NA 70. 105.9 HIS(OL) CG-CC-NA 70. 108.75 HIS(OL) CG-CC-NB 70. 109.9 HIS(OL) CT-CC-CV 70. 120.00 HIS(OL) CT-CC-CW 70. 120.00 HIS(OL) CT-CC-NA 70. 120.00 HIS(OL) CT-CC-NB 70. 120.00 HIS(OL) CV-CC-NA 70. 120.00 HIS(OL) CW-CC-NA 70. 120.00 HIS(OL) CW-CC-NB 70. 120.00 HIS(OL) C -CD-CD 85. 120. TYR(OL) CA-CD-CD 85. 120. PHE CB-CD-CD 85. 120. TRP(OL) CD-CD-CD 85. 120. PHE(OL) CD-CD-CN 85. 120. TRP(OL) N*-CE-NB 70. 113.9 ADE,GUA CC-CF-NB 70. 109.9 HIS(OL) C*-CG-NA 70. 108.7 TRP(OL) CC-CG-NA 70. 105.9 HIS(OL) C -CH-C2 63.0 111.1 AA C -CH-C3 63.0 111.1 ALA C -CH-CH 63.0 111.1 ILE C -CH-N 63.0 110.1 AA WORK DONE ON 6/23/82 C -CH-NT 80.0 109.7 AA C -CH-N3 80.0 109.7 AA C2-CH-C3 63.0 111.5 LEU C2-CH-CH 63.0 111.5 SUG,ILE C2-CH-N 80.0 109.7 ALA JACS 94, 2657 C2-CH-N* 80.0 109.5 SUG C2-CH-NT 80.0 109.7 AA C2-CH-OH 80.0 109.5 SUG (END GROUP) C2-CH-OS 80.0 109.5 SUG C3-CH-C3 63.0 111.5 VAL C3-CH-CH 63.0 111.5 ILE C3-CH-N 80. 109.5 ** C3-CH-NT 80.0 109.7 AA C3-CH-OH 80.0 109.5 THR CH-CH-CH 63.0 111.5 SUG CH-CH-N 80.0 109.7 ILE JACS 94, 2657 CH-CH-N* 80.0 109.5 SUG CH-CH-NT 80.0 109.7 AA CH-CH-N3 80.0 109.7 AA CH-CH-OH 80.0 109.5 THR,end sugar CH-CH-OS 80.0 109.5 SUG N*-CH-OS 80.0 109.5 SUG NC-CI-NC 70. 129.1 ADE C -CJ-CJ 85. 120.7 URA CA-CJ-CJ 85. 117.0 CYT CJ-CJ-N* 70. 121.2 CYT CM-CJ-N* 70. 121.2 THY HC-CK-N* 35. 123.05 HC-CK-NB 35. 123.05 N*-CK-NB 70. 113.9 C -CM-C3 85. 119.7 THY C -CM-CJ 85. 120.7 THY C -CM-CM 85. 120.7 C -CM-CT 70. 119.7 C -CM-HC 35. 119.7 C3-CM-CJ 85. 119.7 THY CA-CM-CM 85. 117.0 CA-CM-HC 35. 123.3 CJ-CM-CT 85. 119.7 CM-CM-CT 70. 119.7 CM-CM-HC 35. 119.7 CM-CM-N* 70. 121.2 HC-CM-N* 35. 119.1 CA-CN-CB 85. 122.7 TRP CA-CN-NA 70. 132.8 TRP(OL) CB-CN-CD 85. 122.7 TRP CB-CN-NA 70. 104.4 CD-CN-NA 70. 132.8 TRP(OL) NA-CP-NA 70. 110.75 HISP(OL) NA-CP-NB 70. 111.6 HIS(OL) HC-CQ-NC 35. 115.45 NC-CQ-NC 70. 129.1 NC-CA-NC 70. 129.1 2-amino purine HC-CR-NA 35. 120.0 HC-CR-NB 35. 120.0 NA-CR-NA 70. 120.00 HISP(OL) NA-CR-NB 70. 120.00 HIS(OL) C -CT-CT 63.0 111.1 AA C -CT-HC 35. 109.5 C -CT-N 63.0 110.1 AA WORK DONE ON 6/23/82 C*-CT-CT 63.0 115.6 TRP(OL) C*-CT-HC 35. 109.5 CA-CT-CT 63. 114. PHE(OL) SCH JPC 79,2379 CA-CT-HC 35. 109.5 CC-CT-CT 63.0 113.1 HIS(OL) CC-CT-HC 35. 109.5 CM-CT-HC 35. 109.5 CT-CT-CT 40. 109.5 CT-CT-HC 35. 109.5 CT-CT-N 80.0 109.7 ALA JACS 94, 2657 CT-CT-N* 50. 109.5 CT-CT-N2 80.0 111.2 ARG JCP 76, 1439 C -CT-N3 80.0 111.2 Amino terminal residues CT-CT-N3 80.0 111.2 LYS(OL) JCP 76, 1439 CT-CT-OH 50. 109.5 CT-CT-OS 50. 109.5 CT-CT-S 50. 114.7 CYX SCHERAGA JPC 79,1428 CT-CT-SH 50.0 108.6 CYS HC-CT-HC 35. 109.5 HC-CT-N 35. 109.5 HC-CT-N* 35. 109.5 HC-CT-N2 35. 109.5 HC-CT-N3 35. 109.5 HC-CT-OH 35. 109.5 HC-CT-OS 35.0 109.5 SUG HC-CT-S 35. 109.5 HC-CT-SH 35. 109.5 N*-CT-OS 50. 109.5 CC-CV-HC 35. 120.0 CC-CV-NB 70. 120.00 HIS(OL) HC-CV-NB 35. 120.0 C*-CW-HC 35. 120.0 C*-CW-NA 70. 108.7 TRP(OL) CC-CW-HC 35. 120.0 CC-CW-NA 70. 120.00 HIS(OL) HC-CW-NA 35. 120.0 C -N -C2 50. 121.9 PRO(OL) C -N -C3 50. 121.9 TEST!!!!!!!! C -N -CH 50. 121.9 AA(OL) C -N -CT 50. 121.9 C -N -H 35. 119.8 AA(OL) C -N -H2 35. 120. GLN,ASN ** C2-N -CH 50. 118. PRO(OL) DETAR JACS 99,1232 C2-N -H 38. 118.4 AA(OL) C3-N -H 38. 118.4 TEST!!!!!!! CH-N -H 38. 118.4 AA(OL) CT-N -CT 50. 118. PRO(OL) DETAR JACS 99,1232 CT-N -H 38. 118.4 H -N -H 35. 120. ADE,CYT,GUA,GLN,ASN ** H3-N -H3 35. 120. ADE,CYT,GUA,GLN,ASN ** C -N*-CH 70. 117.6 URA,CYT C -N*-CJ 70. 121.6 URA,CYT C -N*-CM 70. 121.6 C -N*-CT 70. 117.6 C -N*-H 35. 119.2 C3-N*-CB 70. 125.8 9 methylated guan,aden C3-N*-CE 70. 128.8 Methylated purines C3-N*-CK 70. 128.8 CB-N*-CE 70. 105.4 GUA,ADE CB-N*-CH 70. 125.8 GUA,ADE CB-N*-CK 70. 105.4 CB-N*-CT 70. 125.8 CB-N*-H 35. 127.3 CE-N*-CH 70. 128.8 GUA,ADE CE-N*-CT 70. 128.8 CE-N*-H 35. 127.3 CH-N*-CJ 70. 121.2 URA,CYT CH-N*-CK 70. 128.8 CJ-N*-CT 70. 121.2 CJ-N*-H 35. 119.2 CK-N*-CT 70. 128.8 CM-N*-CT 70. 121.2 CM-N*-H 35. 119.2 C2-N2-CA 50. 123.2 ARG(OL) C2-N2-H2 35. 118.4 ARG(OL) C2-N2-H3 35. 118.4 ARG(OL) C3-N2-CA 50. 123.2 ARG(OL) C3-N2-H2 35. 118.4 ARG(OL) CA-N2-CT 50. 123.2 ARG(OL) CA-N2-H 35. 120. ARG(OL) CA-N2-H2 35. 120. ADE,CYT,GUA,ARG CA-N2-H3 35. 120. ADE,CYT,GUA,ARG CT-N2-H3 35. 118.4 ARG(OL) H2-N2-H2 35. 120. ADE,CYT,GUA,GLN,ASN,ARG H3-N2-H3 35. 120. ADE,CYT,GUA,GLN,ASN,ARG C2-N3-H3 35. 109.5 LYS C3-N3-H3 35. 109.5 CT-N3-H3 35. 109.5 LYS CH-N3-H3 35. 109.5 LYS H3-N3-H3 35. 109.5 LYS CT-N3-CT 50. 113.0 proline j.phys chem 1979 p 2361 C -NA-C 70. 126.4 URA C -NA-CA 70. 125.2 GUA C -NA-H 35. 116.8 GUA,URA(2) CA-NA-H 35. 118.0 GUA CC-NA-CP 70. 107.30 HIS(OL) CC-NA-CR 70. 120.00 HIS(OL) CC-NA-H 35. 120.00 HIS(OL) CG-NA-CN 70. 111.6 TRP(OL) CG-NA-CP 70. 107.3 HIS(OL) CG-NA-CR 70. 107.3 HIS(OL) CG-NA-H 35. 126.35 HIS(OL) CN-NA-CW 70. 111.6 TRP(OL) CN-NA-H 35. 123.1 TRP CP-NA-H 35. 126.35 HIS(OL) CR-NA-CW 70. 120.00 HIS(OL) CR-NA-H 35. 120.00 HIS(OL) CW-NA-H 35. 120.00 HIS(OL) CB-NB-CE 70. 103.8 GUA,ADE CB-NB-CK 70. 103.8 CC-NB-CP 70. 105.3 HIS(OL) CC-NB-CR 70. 117.0 HIS(OL) CF-NB-CP 70. 105.3 HIS(OL) CF-NB-CR 70. 105.3 HIS(OL) CR-NB-CV 70. 117.0 HIS(OL) C -NC-CA 70. 120.5 CYT CA-NC-CB 70. 112.2 GUA CA-NC-CI 70. 118.6 ADE CA-NC-CQ 70. 118.6 CB-NC-CI 70. 111.0 ADE CB-NC-CQ 70. 111.0 C2-NT-H2 35. 109.5 AA CH-NT-H2 35. 109.5 GLY H2-NT-H2 35. 109.5 AA(END) C -OH-HO 35. 113.0 TYR(PHENOL) HARMONY MEOH C2-OH-HO 55.0 108.5 SUG,SER(OL,mod) C3-OH-HO 55.0 108.5 SUG,SER(OL,mod) CH-OH-HO 55.0 108.5 THR(OL),SUG CT-OH-HO 55. 108.5 ! End of SJW input for CT types HO-OH-HO 47. 104.5 HO-OH-P 45.0 108.5 SUG(OL) C2-OS-C2 100.0 111.8 DME based C2-OS-C3 100.0 111.8 DME based C2-OS-HO 55.0 108.5 SUG C2-OS-P 100.0 120.5 SUG(OL) C3-OS-P 100.0 120.5 DMPhos based CH-OS-CH 100.0 111.8 SUG(dme based) CH-OS-HO 55.0 108.5 SUG CH-OS-P 100.0 120.5 SUG CT-OS-CT 60. 109.5 CT-OS-P 100. 120.5 O -C -O2 80. 126.0 adk O2-P -O2 140.0 119.9 SUG(OL) O2-P -OH 45.0 108.23 SUG(OL) O2-P -OS 100.0 108.23 SUG(OL) OH-P -OS 45.0 102.6 SUG(OL) OS-P -OS 45.0 102.6 SUG(OL) C2-S -C3 62. 98.9 MET(OL) C2-S -LP 150. 96.7 C2-S -S 68. 103.7 CYX(OL) SCHERAGA JPC 79,1428 C3-S -LP 150. 96.7 C3-S -S 68. 103.7 CYX(OL) SCHERAGA JPC 79,1428 CT-S -CT 62. 98.9 MET(OL) CT-S -LP 150. 96.7 CT-S -S 68. 103.7 CYX(OL) SCHERAGA JPC 79,1428 LP-S -LP 150. 160.0 LP-S -S 150. 96.7 C2-SH-HS 44. 96.0 CYS(OL) C2-SH-LP 150. 96.7 C3-SH-HS 44. 96.0 CYS(OL) C3-SH-LP 150. 96.7 CT-SH-HS 44. 96.0 CYS(OL) CT-SH-LP 150. 96.7 HS-SH-HS 35. 92.07 HS-SH-LP 150. 96.7 LP-SH-LP 150. 160.0 P -OS-P 100. 120.5 X -C -C2-X 2 0.0 180. 3. X -C -CA-X 4 5.3 180. 2. X -C -CB-X 4 4.4 180. 2. X -C -CD-X 2 5.3 180. 2. X -C -CH-X 4 0.0 0. 2. X -C -CJ-X 2 3.1 180. 2. X -C -CM-X 4 3.1 180. 2. X -C -CT-X 6 0.0 0. 2. X -C -N -X 4 10.0 180. 2. X -C -N*-X 4 5.8 180. 2. X -C -NA-X 4 5.4 180. 2. X -C -NC-X 2 8.0 180. 2. X -C -OH-X 2 1.8 180. 2. X -C*-C2-X 2 0.0 0. 2. X -C*-CB-X 4 4.8 180. 2. X -C*-CG-X 2 23.6 180. 2. X -C*-CT-X 6 0.0 0. 2. X -C*-CW-X 4 23.6 180. 2. X -C2-C2-X 1 2.0 0. 3. X -C2-CA-X 2 0.0 0. 2. X -C2-CC-X 2 0.0 0. 2. X -C2-CH-X 2 2.0 0. 3. X -C2-N -X 2 0.0 0. 3. X -C2-N2-X 2 0.0 0. 3. X -C2-N3-X 3 1.4 0. 3. X -C2-NT-X 2 1.0 0. 3. X -C2-N3-X 2 1.0 0. 3. X -C2-OH-X 1 0.5 0. 3. X -C2-OS-X 1 1.45 0. 3. X -C2-S -X 1 1.0 0. 3. X -C2-SH-X 1 0.75 0. 3. X -CA-CA-X 4 5.3 180. 2. X -CA-CB-X 4 10.2 180. 2. X -CQ-CB-X 4 10.2 180. 2. X -CA-CD-X 2 5.3 180. 2. X -CA-CJ-X 2 3.7 180. 2. X -CA-CM-X 4 3.7 180. 2. X -CA-CN-X 4 10.6 180. 2. X -CA-CT-X 6 0.0 0. 2. X -CA-N2-X 4 6.8 180. 2. X -CA-NA-X 4 6.0 180. 2. X -CA-NC-X 2 9.6 180. 2. X -CB-CB-X 4 16.3 180. 2. X -CB-CD-X 4 5.3 180. 2. X -CB-CN-X 4 20.0 180. 2. X -CB-N*-X 4 6.6 180. 2. X -CB-NB-X 2 5.1 180. 2. X -CB-NC-X 2 8.3 180. 2. X -CC-CF-X 2 14.3 180. 2. X -CC-CG-X 2 15.9 180. 2. X -CC-CT-X 6 0.0 0. 2. X -CC-CV-X 4 14.3 180. 2. X -CC-CW-X 4 15.9 180. 2. X -CC-NA-X 4 5.6 180. 2. X -CC-NB-X 2 4.8 180. 2. X -CD-CD-X 1 5.3 180. 2. X -CD-CN-X 2 5.3 180. 2. X -CE-N*-X 2 6.7 180. 2. X -CE-NB-X 1 20.0 180. 2. X -CF-NB-X 1 4.8 180. 2. X -CG-NA-X 2 6.0 180. 2. X -CH-CH-X 4 2.0 0. 3. X -CH-N -X 4 0.0 0. 3. X -CH-N*-X 4 0.0 0. 2. X -CH-NT-X 4 1.0 0. 3. X -CH-N3-X 4 1.0 0. 3. X -CH-OH-X 2 0.5 0. 3. X -CH-OS-X 2 1.45 0. 3. X -CI-NC-X 1 13.5 180. 2. X -CJ-CJ-X 1 24.4 180. 2. X -CJ-CM-X 2 24.4 180. 2. X -CJ-N*-X 2 7.4 180. 2. X -CK-N*-X 4 6.7 180. 2. X -CK-NB-X 2 20. 180. 2. X -CM-CM-X 4 24.4 180. 2. X -CM-CT-X 6 0.0 0. 3. X -CM-N*-X 4 7.4 180. 2. X -CN-NA-X 4 12.2 180. 2. X -CP-NA-X 2 9.3 180. 2. X -CP-NB-X 1 10.0 180. 2. X -CQ-NC-X 2 13.5 180. 2. X -CR-NA-X 4 9.3 180. 2. X -CR-NB-X 2 10.0 180. 2. X -CT-CT-X 9 1.3 0. 3. X -CT-N -X 6 0.0 0. 3. X -CT-N*-X 6 0.0 0. 2. X -CT-N2-X 6 0.0 0. 3. X -CT-N3-X 9 1.4 0. 3. X -CT-OH-X 3 0.5 0. 3. X -CT-OS-X 3 1.15 0. 3. X -CT-S -X 3 1.0 0. 3. X -CT-SH-X 3 0.75 0. 3. X -CV-NB-X 2 4.8 180. 2. X -CW-NA-X 4 6.0 180. 2. X -OH-P -X 3 0.75 0. 3. X -OS-P -X 3 0.75 0. 3. C2-C2-S -LP 1 0. 0. 3. CH-C2-SH-LP 1 0. 0. 3. LP-S -S -LP 1 0. 0. 3. LP-S -S -C2 1 0. 0. 3. LP-S -S -CT 1 0. 0. 3. O -C -C2-N 1 0.2 180. 3. O -C -CH-N 1 0.1 180. 3. O -C -CH-C2 1 0.1 180. 3. O -C -CH-CH 1 0.1 180. 3. HC-CT-C -O 1 0.067 180. 3. CT-CT-C -O 1 0.067 180. 3. N -CT-C -O 1 0.067 180. 3. H -N -C -O 1 2.5 180. -2. H -N -C -O 1 0.65 0. 1. C2-S -S -C2 1 3.5 0. -2. C2-S -S -C2 1 0.6 0. 3. CT-S -S -CT 1 3.5 0. -2. CT-S -S -CT 1 0.6 0. 3. OS-CH-CH-OS 1 0.5 0. -3. OS-CH-CH-OS 1 0.5 0. 2. OS-CT-CT-OS 1 0.144 0. -3. OS-CT-CT-OS 1 0.5 0. 2. OS-CT-CT-OH 1 0.144 0. -3. OS-CT-CT-OH 1 0.5 0. 2. OH-CT-CT-OH 1 0.144 0. -3. OH-CT-CT-OH 1 0.5 0. 2. CT-CT-OS-CT 1 0.383 0. -3. CT-CT-OS-CT 1 0.2 180. 2. OS-C2-CH-OS 1 1.0 0. -3. OS-C2-CH-OS 1 0.5 0. 2. OS-CH-CH-OH 1 0.5 0. -3. OS-CH-CH-OH 1 0.5 0. 2. OS-CH-C2-OH 1 1.0 0. -3. OS-CH-C2-OH 1 0.5 0. 2. OS-C2-CH-OH 1 1.0 0. -3. OS-C2-CH-OH 1 0.5 0. 2. OS-C2-C2-OH 1 2.0 0. -3. OS-C2-C2-OH 1 0.5 0. 2. OS-C2-C2-OS 1 2.0 0. -3. OS-C2-C2-OS 1 0.5 0. 2. OH-CH-CH-OH 1 0.5 0. -3. OH-CH-CH-OH 1 0.5 0. 2. OH-C2-C2-OH 1 2.0 0. -3. OH-C2-C2-OH 1 0.5 0. 2. OH-C2-CH-OH 1 1.0 0. -3. OH-C2-CH-OH 1 0.5 0. 2. C2-OS-C2-C2 1 1.45 0. -3. C2-OS-C2-C2 1 0.10 0. 2. C3-OS-C2-C3 1 1.45 0. -3. C3-OS-C2-C3 1 0.10 0. 2. CH-OS-CH-C2 1 0.725 0. -3. CH-OS-CH-C2 1 0.1 0. 2. CH-OS-CH-CH 1 0.725 0. -3. CH-OS-CH-CH 1 0.1 0. 2. C2-OS-CH-C2 1 0.725 0. -3. C2-OS-CH-C2 1 0.1 0. 2. C2-OS-CH-C3 1 0.725 0. -3. C2-OS-CH-C3 1 0.1 0. 2. C3-OS-CH-C3 1 0.725 0. -3. C3-OS-CH-C3 1 0.1 0. 2. C2-OS-C2-C3 1 0.725 0. -3. C2-OS-C2-C3 1 0.1 0. 2. CH-OS-CH-N* 1 0.725 0. -3. CH-OS-CH-N* 1 0.000 0. 2. OH-P -OS-C2 1 .25 0. -3. OH-P -OS-C2 1 .75 0. 2. OH-P -OS-CH 1 .25 0. -3. OH-P -OS-CH 1 .75 0. 2. OS-P -OS-CH 1 .25 0. -3. OS-P -OS-CH 1 .75 0. 2. OS-P -OS-C2 1 .25 0. -3. OS-P -OS-C2 1 .75 0. 2. OS-P -OS-C3 1 .25 0. -3. OS-P -OS-C3 1 .75 0. 2. OH-P -OS-C3 1 .25 0. -3. OH-P -OS-C3 1 .75 0. 2. OH-P -OS-CT 1 .25 0. -3. OH-P -OS-CT 1 .75 0. 2. OS-P -OS-CT 1 .25 0. -3. OS-P -OS-CT 1 .75 0. 2. HC-CM-CM-HC 1 1.71 180. 2. C -CM-CM-HC 1 6.59 180. 2. N*-CM-CM-HC 1 6.59 180. 2. N*-CM-CM-C 1 9.51 180. 2. N*-CM-CM-CT 1 6.59 180. 2. HC-CM-CM-CT 1 1.71 180. 2. N*-CM-CM-CA 1 9.51 180. 2. CA-CM-CM-HC 1 6.59 180. 2. X -C2-CH-X 14.0 180. 3. X -CH-CH-X 14.0 180. 3. X -X -N -H 1.0 180. 2. X -X -N2-H3 1.0 180. 2. X -X -N2-H2 1.0 180. 2. X -X -NA-H 1.0 180. 2. X -X -C -O 10.5 180. 2. X -O2-C -O2 10.5 180. 2. X -CH-N -C 14. 180. 3. X -CH-N3-C 14. 180. 3. X -H2-N -H2 1.0 180. 2. X -N2-CA-N2 10.5 180. 2. X -CH-N -C2 1.0 180. 2. X -CT-N -CT 1.0 180. 2. X -X -CA-HC 2. 180. 2. X -X -C*-HC 0. 180. 2. X -X -CW-HC 0. 180. 2. X -X -CN-CB 0. 180. 2. X -X -CB-CN 0. 180. 2. X -X -CB-C* 0. 180. 2. X -X -CB-CA 0. 180. 2. X -X -CN-CA 0. 180. 2. X -X -CN-NA 0. 180. 2. H2-CH-N2-H2 0.0 180. 3. C3-CH-NT-C 14.0 180. 3. CH-CH-C -N3 7.0 180. 3. C2-CH-C -N3 7.0 180. 3. C3-CH-CA-C3 7.0 180. 3. C*-NA-CA-CA 0. 180. 2. H NB 7557. 2385. 4. H NC 10238. 3071. 4. H O 7557. 2385. 4. H O2 4019. 1409. 4. H OH 7557. 2385. 4. H OS 7557. 2385. 4. H OW 7557. 2385. 4. H S 265720. 35429. 4. H SH 265720. 35429. 4. H2 NB 7557. 2385. 4. H2 NC 10238. 3071. 4. H2 O 10238. 3071. 4. H2 O2 4019. 1409. 4. H2 OH 7557. 2385. 4. H2 OS 7557. 2385. 4. H2 OW 7557. 2385. 4. H2 S 265720. 35429. 4. H2 SH 265720. 35429. 4. H3 NB 4019. 1409. 4. H3 NC 4019. 1409. 4. H3 O 4019. 1409. 4. H3 O2 4019. 1409. 4. H3 OH 4019. 1409. 4. H3 OS 4019. 1409. 4. H3 OW 4019. 1409. 4. H3 S 7557. 2385. 4. H3 SH 7557. 2385. 4. HO NB 7557. 2385. 4. HO NC 10238. 3071. 4. HO O 7557. 2385. 4. HO O2 4019. 1409. 4. HO OH 7557. 2385. 4. HO OS 7557. 2385. 4. HO OW 7557. 2385. 4. HO S 265720. 35429. 4. HO SH 265720. 35429. 4. HS NB 14184. 3082. 4. HS NC 14184. 3082. 4. HS O 14184. 3082. 4. HS O2 4019. 1409. 4. HS OH 14184. 3082. 4. HS OS 14184. 3082. 4. HS OW 14184. 3082. 4. HS S 265720. 35429. 4. HS SH 265720. 35429. 4. HW NB 7557. 2385. 4. HW NC 10238. 3071. 4. HW O 7557. 2385. 4. HW O2 4019. 1409. 4. HW OH 7557. 2385. 4. HW OS 7557. 2385. 4. HW OW 0000. 0000. 4. HW S 265720. 35429. 4. HW SH 265720. 35429. 4. N NA N2 N* NP NC NO C C* CA CB CC CN CM CK CQ CD CE CF CG CP CI CJ CW CV CR CA C CX CY STDA RE H 1.00 0.020 HO 1.00 0.020 HS 1.00 0.020 H2 1.00 0.020 H3 1.00 0.020 HW 0.00 0.0 HC 1.54 0.01 OH 1.65 0.15 OS 1.65 0.15 O 1.6 0.20 O2 1.6 0.20 OW 1.768 0.152 CT 1.80 0.06 CH 1.85 0.09 C2 1.925 0.12 C3 2.00 0.150 C 1.85 0.120 C0 1.60 0.10 MG 1.17 0.10 N 1.75 0.16 NB 1.75 0.16 NC 1.75 0.16 NT 1.85 0.12 N3 1.85 0.08 QC 3.40 0.000081 QK 2.66 0.00033 QL 1.14 0.018 QN 1.87 0.0028 IP 1.87 0.0028 QR 2.96 0.00017 S 2.00 0.20 SH 2.00 0.20 P 2.10 0.20 LP 1.2 0.016 IB 5.0 0.1 solvated ion for vacuum approximation STUB RE H 1.00 0.020 HC 1.375 0.038 HO 1.00 0.020 H2 1.00 0.020 H3 1.00 0.020 HS 1.00 0.020 OW 1.80 0.12 OH 1.65 0.15 OS 1.65 0.15 O 1.6 0.20 O2 1.6 0.20 CH 2.385 0.049 C2 2.235 0.114 C3 2.165 0.181 C 1.85 0.120 CD 1.85 0.12 N 1.75 0.16 NB 1.75 0.16 NC 1.75 0.16 NT 1.85 0.12 IM 5.00 0.10 IP 5.00 0.10 N3 1.85 0.08 S 2.00 0.20 SH 2.00 0.20 P 2.10 0.20 CU 1.2 0.05 CT 1.80 0.06 I 2.35 0.40 LP 1.2 0.016 END MMTK-2.7.9/MMTK/ForceFields/Amber/parm94.dat0000644000076600000240000006574612041514537020526 0ustar hinsenstaff00000000000000PARM94 for DNA, RNA and proteins with TIP3P Water. USE SCEE=1.2 in energy progs BR 79.90 ! bromine C 12.01 sp2 C carbonyl group CA 12.01 sp2 C pure aromatic (benzene) CB 12.01 sp2 aromatic C, 5&6 membered ring junction CC 12.01 sp2 aromatic C, 5 memb. ring HIS CK 12.01 sp2 C 5 memb.ring in purines CM 12.01 sp2 C pyrimidines in pos. 5 & 6 CN 12.01 sp2 C aromatic 5&6 memb.ring junct.(TRP) CQ 12.01 sp2 C in 5 mem.ring of purines between 2 N CR 12.01 sp2 arom as CQ but in HIS CT 12.01 sp3 aliphatic C CV 12.01 sp2 arom. 5 memb.ring w/1 N and 1 H (HIS) CW 12.01 sp2 arom. 5 memb.ring w/1 N-H and 1 H (HIS) C* 12.01 sp2 arom. 5 memb.ring w/1 subst. (TRP) C0 40.08 calcium F 19.00 fluorine H 1.008 H bonded to nitrogen atoms HC 1.008 H aliph. bond. to C without electrwd.group H1 1.008 H aliph. bond. to C with 1 electrwd. group H2 1.008 H aliph. bond. to C with 2 electrwd.groups H3 1.008 H aliph. bond. to C with 3 eletrwd.groups HA 1.008 H arom. bond. to C without elctrwd. groups H4 1.008 H arom. bond. to C with 1 electrwd. group H5 1.008 H arom. bond. to C with 2 electrwd. groups HO 1.008 hydroxyl group HS 1.008 hydrogen bonded to sulphur HW 1.008 H in TIP3P water HP 1.008 H bonded to C next to positively charged gr I 126.9 iodine IM 35.45 assumed to be Cl- IP 22.99 assumed to be Na+ IB 131.0 'big ion w/ waters' for vacuum (Na+, 6H2O) MG 24.305 magnesium N 14.01 sp2 nitrogen in amide groups NA 14.01 sp2 N in 5 memb.ring w/H atom (HIS) NB 14.01 sp2 N in 5 memb.ring w/LP (HIS,ADE,GUA) NC 14.01 sp2 N in 6 memb.ring w/LP (ADE,GUA) N2 14.01 sp2 N in amino groups N3 14.01 sp3 N for charged amino groups (Lys, etc) N* 14.01 sp2 N O 16.00 carbonyl group oxygen OW 16.00 oxygen in TIP3P water OH 16.00 oxygen in hydroxyl group OS 16.00 ether and ester oxygen O2 16.00 carboxyl and phosphate group oxygen P 30.97 phosphate S 32.06 sulphur in disulfide linkage SH 32.06 sulphur in cystine CU 63.55 copper FE 55.00 iron Li 6.94 lithium K 39.10 potassium Rb 85.47 rubidium Cs 132.91 cesium EP 3.0 "extra point" (mass is ignored) C H HO N NA NB NC N2 NT N2 N3 N* O OH OS P O2 OW-HW 553.0 0.9572 TIP3P water HW-HW 553.0 1.5136 TIP3P water C -CA 469.0 1.409 JCC,7,(1986),230; TYR C -CB 447.0 1.419 JCC,7,(1986),230; GUA C -CM 410.0 1.444 JCC,7,(1986),230; THY,URA C -CT 317.0 1.522 JCC,7,(1986),230; AA C -N* 424.0 1.383 JCC,7,(1986),230; CYT,URA C -NA 418.0 1.388 JCC,7,(1986),230; GUA.URA C -NC 457.0 1.358 JCC,7,(1986),230; CYT C -O 570.0 1.229 JCC,7,(1986),230; AA,CYT,GUA,THY,URA C -O2 656.0 1.250 JCC,7,(1986),230; GLU,ASP C -OH 450.0 1.364 JCC,7,(1986),230; TYR CA-CA 469.0 1.400 JCC,7,(1986),230; BENZENE,PHE,TRP,TYR CA-CB 469.0 1.404 JCC,7,(1986),230; ADE,TRP CA-CM 427.0 1.433 JCC,7,(1986),230; CYT CA-CT 317.0 1.510 JCC,7,(1986),230; PHE,TYR CA-HA 367.0 1.080 changed from 340. bsd on C6H6 nmodes; PHE,TRP,TYR CA-H4 367.0 1.080 changed from 340. bsd on C6H6 nmodes; no assigned CA-N2 481.0 1.340 JCC,7,(1986),230; ARG,CYT,GUA CA-NA 427.0 1.381 JCC,7,(1986),230; GUA CA-NC 483.0 1.339 JCC,7,(1986),230; ADE,CYT,GUA CB-CB 520.0 1.370 JCC,7,(1986),230; ADE,GUA CB-N* 436.0 1.374 JCC,7,(1986),230; ADE,GUA CB-NB 414.0 1.391 JCC,7,(1986),230; ADE,GUA CB-NC 461.0 1.354 JCC,7,(1986),230; ADE,GUA CK-H5 367.0 1.080 changed from 340. bsd on C6H6 nmodes; ADE,GUA CK-N* 440.0 1.371 JCC,7,(1986),230; ADE,GUA CK-NB 529.0 1.304 JCC,7,(1986),230; ADE,GUA CM-CM 549.0 1.350 JCC,7,(1986),230; CYT,THY,URA CM-CT 317.0 1.510 JCC,7,(1986),230; THY CM-HA 367.0 1.080 changed from 340. bsd on C6H6 nmodes; CYT,URA CM-H4 367.0 1.080 changed from 340. bsd on C6H6 nmodes; CYT,URA CM-H5 367.0 1.080 changed from 340. bsd on C6H6 nmodes; not assigned CM-N* 448.0 1.365 JCC,7,(1986),230; CYT,THY,URA CQ-H5 367.0 1.080 changed from 340. bsd on C6H6 nmodes; ADE CQ-NC 502.0 1.324 JCC,7,(1986),230; ADE CT-CT 310.0 1.526 JCC,7,(1986),230; AA, SUGARS CT-HC 340.0 1.090 changed from 331 bsd on NMA nmodes; AA, SUGARS CT-H1 340.0 1.090 changed from 331 bsd on NMA nmodes; AA, RIBOSE CT-H2 340.0 1.090 changed from 331 bsd on NMA nmodes; SUGARS CT-H3 340.0 1.090 changed from 331 bsd on NMA nmodes; not assigned CT-HP 340.0 1.090 changed from 331; AA-lysine, methyl ammonium cation CT-N* 337.0 1.475 JCC,7,(1986),230; ADE,CYT,GUA,THY,URA CT-N2 337.0 1.463 JCC,7,(1986),230; ARG CT-OH 320.0 1.410 JCC,7,(1986),230; SUGARS CT-OS 320.0 1.410 JCC,7,(1986),230; NUCLEIC ACIDS H -N2 434.0 1.010 JCC,7,(1986),230; ADE,CYT,GUA,ARG H -N* 434.0 1.010 for plain unmethylated bases ADE,CYT,GUA,ARG H -NA 434.0 1.010 JCC,7,(1986),230; GUA,URA,HIS HO-OH 553.0 0.960 JCC,7,(1986),230; SUGARS,SER,TYR HO-OS 553.0 0.960 JCC,7,(1986),230; NUCLEOTIDE ENDS O2-P 525.0 1.480 JCC,7,(1986),230; NA PHOSPHATES OH-P 230.0 1.610 JCC,7,(1986),230; NA PHOSPHATES OS-P 230.0 1.610 JCC,7,(1986),230; NA PHOSPHATES C*-HC 367.0 1.080 changed from 340. bsd on C6H6 nmodes, not needed AA C -N 490.0 1.335 JCC,7,(1986),230; AA C*-CB 388.0 1.459 JCC,7,(1986),230; TRP C*-CT 317.0 1.495 JCC,7,(1986),230; TRP C*-CW 546.0 1.352 JCC,7,(1986),230; TRP CA-CN 469.0 1.400 JCC,7,(1986),230; TRP CB-CN 447.0 1.419 JCC,7,(1986),230; TRP CC-CT 317.0 1.504 JCC,7,(1986),230; HIS CC-CV 512.0 1.375 JCC,7,(1986),230; HIS(delta) CC-CW 518.0 1.371 JCC,7,(1986),230; HIS(epsilon) CC-NA 422.0 1.385 JCC,7,(1986),230; HIS CC-NB 410.0 1.394 JCC,7,(1986),230; HIS CN-NA 428.0 1.380 JCC,7,(1986),230; TRP CR-H5 367.0 1.080 changed from 340. bsd on C6H6 nmodes;HIS CR-NA 477.0 1.343 JCC,7,(1986),230; HIS CR-NB 488.0 1.335 JCC,7,(1986),230; HIS CT-N 337.0 1.449 JCC,7,(1986),230; AA CT-N3 367.0 1.471 JCC,7,(1986),230; LYS CT-S 227.0 1.810 changed from 222.0 based on dimethylS nmodes CT-SH 237.0 1.810 changed from 222.0 based on methanethiol nmodes CV-H4 367.0 1.080 changed from 340. bsd on C6H6 nmodes; HIS CV-NB 410.0 1.394 JCC,7,(1986),230; HIS CW-H4 367.0 1.080 changed from 340. bsd on C6H6 nmodes;HIS(epsilon,+) CW-NA 427.0 1.381 JCC,7,(1986),230; HIS,TRP H -N 434.0 1.010 JCC,7,(1986),230; AA H -N3 434.0 1.010 JCC,7,(1986),230; LYS HS-SH 274.0 1.336 JCC,7,(1986),230; CYS S -S 166.0 2.038 JCC,7,(1986),230; CYX (SCHERAGA) CT-F 367.0 1.380 JCC,13,(1992),963;CF4; R0=1.332 FOR CHF3 HW-OW-HW 100. 104.52 TIP3P water HW-HW-OW 0. 127.74 (found in crystallographic water with 3 bonds) CB-C -NA 70.0 111.30 NA CB-C -O 80.0 128.80 CM-C -NA 70.0 114.10 CM-C -O 80.0 125.30 CT-C -O 80.0 120.40 CT-C -O2 70.0 117.00 CT-C -OH 70.0 117.00 N*-C -NA 70.0 115.40 N*-C -NC 70.0 118.60 N*-C -O 80.0 120.90 NA-C -O 80.0 120.60 NC-C -O 80.0 122.50 CT-C -N 70.0 116.60 AA general N -C -O 80.0 122.90 AA general O -C -O 80.0 126.00 AA COO- terminal residues O2-C -O2 80.0 126.00 AA GLU (SCH JPC 79,2379) O -C -OH 80.0 126.00 CA-C -CA 63.0 120.00 changed from 85.0 bsd on C6H6 nmodes; AA tyr CA-C -OH 70.0 120.00 AA tyr C -CA-CA 63.0 120.00 changed from 85.0 bsd on C6H6 nmodes CA-CA-CA 63.0 120.00 changed from 85.0 bsd on C6H6 nmodes CA-CA-CB 63.0 120.00 changed from 85.0 bsd on C6H6 nmodes CA-CA-CT 70.0 120.00 CA-CA-HA 35.0 120.00 CA-CA-H4 35.0 120.00 CB-CA-HA 35.0 120.00 CB-CA-H4 35.0 120.00 CB-CA-N2 70.0 123.50 CB-CA-NC 70.0 117.30 CM-CA-N2 70.0 120.10 CM-CA-NC 70.0 121.50 N2-CA-NA 70.0 116.00 N2-CA-NC 70.0 119.30 NA-CA-NC 70.0 123.30 C -CA-HA 35.0 120.00 AA tyr N2-CA-N2 70.0 120.00 AA arg CN-CA-HA 35.0 120.00 AA trp CA-CA-CN 63.0 120.00 changed from 85.0 bsd on C6H6 nmodes; AA trp C -CB-CB 63.0 119.20 changed from 85.0 bsd on C6H6 nmodes; NA gua C -CB-NB 70.0 130.00 CA-CB-CB 63.0 117.30 changed from 85.0 bsd on C6H6 nmodes; NA ade CA-CB-NB 70.0 132.40 CB-CB-N* 70.0 106.20 CB-CB-NB 70.0 110.40 CB-CB-NC 70.0 127.70 N*-CB-NC 70.0 126.20 C*-CB-CA 63.0 134.90 changed from 85.0 bsd on C6H6 nmodes; AA trp C*-CB-CN 63.0 108.80 changed from 85.0 bsd on C6H6 nmodes; AA trp CA-CB-CN 63.0 116.20 changed from 85.0 bsd on C6H6 nmodes; AA trp H5-CK-N* 35.0 123.05 H5-CK-NB 35.0 123.05 N*-CK-NB 70.0 113.90 C -CM-CM 63.0 120.70 changed from 85.0 bsd on C6H6 nmodes; NA thy C -CM-CT 70.0 119.70 C -CM-HA 35.0 119.70 C -CM-H4 35.0 119.70 CA-CM-CM 63.0 117.00 changed from 85.0 bsd on C6H6 nmodes; NA cyt CA-CM-HA 35.0 123.30 CA-CM-H4 35.0 123.30 CM-CM-CT 70.0 119.70 CM-CM-HA 35.0 119.70 CM-CM-H4 35.0 119.70 CM-CM-N* 70.0 121.20 H4-CM-N* 35.0 119.10 H5-CQ-NC 35.0 115.45 NC-CQ-NC 70.0 129.10 CM-CT-HC 50.0 109.50 changed based on NMA nmodes CT-CT-CT 40.0 109.50 CT-CT-HC 50.0 109.50 changed based on NMA nmodes CT-CT-H1 50.0 109.50 changed based on NMA nmodes CT-CT-H2 50.0 109.50 changed based on NMA nmodes CT-CT-HP 50.0 109.50 changed based on NMA nmodes CT-CT-N* 50.0 109.50 CT-CT-OH 50.0 109.50 CT-CT-OS 50.0 109.50 HC-CT-HC 35.0 109.50 H1-CT-H1 35.0 109.50 HP-CT-HP 35.0 109.50 AA lys, ch3nh4+ H2-CT-N* 50.0 109.50 changed based on NMA nmodes H1-CT-N* 50.0 109.50 changed based on NMA nmodes H1-CT-OH 50.0 109.50 changed based on NMA nmodes H1-CT-OS 50.0 109.50 changed based on NMA nmodes H2-CT-OS 50.0 109.50 changed based on NMA nmodes N*-CT-OS 50.0 109.50 H1-CT-N 50.0 109.50 AA general changed based on NMA nmodes C -CT-H1 50.0 109.50 AA general changed based on NMA nmodes C -CT-HP 50.0 109.50 AA zwitterion changed based on NMA nmodes H1-CT-S 50.0 109.50 AA cys changed based on NMA nmodes H1-CT-SH 50.0 109.50 AA cyx changed based on NMA nmodes CT-CT-S 50.0 114.70 AA cyx (SCHERAGA JPC 79,1428) CT-CT-SH 50.0 108.60 AA cys H2-CT-H2 35.0 109.50 AA lys H1-CT-N2 50.0 109.50 AA arg changed based on NMA nmodes HP-CT-N3 50.0 109.50 AA lys, ch3nh3+, changed based on NMA nmodes CA-CT-CT 63.0 114.00 AA phe tyr (SCH JPC 79,2379) C -CT-HC 50.0 109.50 AA gln changed based on NMA nmodes C -CT-N 63.0 110.10 AA general CT-CT-N2 80.0 111.20 AA arg (JCP 76, 1439) CT-CT-N 80.0 109.70 AA ala, general (JACS 94, 2657) C -CT-CT 63.0 111.10 AA general CA-CT-HC 50.0 109.50 AA tyr changed based on NMA nmodes CT-CT-N3 80.0 111.20 AA lys (JCP 76, 1439) CC-CT-CT 63.0 113.10 AA his CC-CT-HC 50.0 109.50 AA his changed based on NMA nmodes C -CT-N3 80.0 111.20 AA amino terminal residues C*-CT-CT 63.0 115.60 AA trp C*-CT-HC 50.0 109.50 AA trp changed based on NMA nmodes CT-CC-NA 70.0 120.00 AA his CT-CC-CV 70.0 120.00 AA his CT-CC-NB 70.0 120.00 AA his CV-CC-NA 70.0 120.00 AA his CW-CC-NA 70.0 120.00 AA his CW-CC-NB 70.0 120.00 AA his CT-CC-CW 70.0 120.00 AA his H5-CR-NA 35.0 120.00 AA his H5-CR-NB 35.0 120.00 AA his NA-CR-NA 70.0 120.00 AA his NA-CR-NB 70.0 120.00 AA his CC-CV-H4 35.0 120.00 AA his CC-CV-NB 70.0 120.00 AA his H4-CV-NB 35.0 120.00 AA his CC-CW-H4 35.0 120.00 AA his CC-CW-NA 70.0 120.00 AA his H4-CW-NA 35.0 120.00 AA his C*-CW-H4 35.0 120.00 AA trp C*-CW-NA 70.0 108.70 AA trp CT-C*-CW 70.0 125.00 AA trp CB-C*-CT 70.0 128.60 AA trp CB-C*-CW 63.0 106.40 changed from 85.0 bsd on C6H6 nmodes; AA trp CA-CN-NA 70.0 132.80 AA trp CB-CN-NA 70.0 104.40 AA trp CA-CN-CB 63.0 122.70 changed from 85.0 bsd on C6H6 nmodes; AA trp C -N -CT 50.0 121.90 AA general C -N -H 30.0 120.00 AA general, gln, asn,changed based on NMA nmodes CT-N -H 30.0 118.04 AA general, changed based on NMA nmodes CT-N -CT 50.0 118.00 AA pro (DETAR JACS 99,1232) H -N -H 35.0 120.00 ade,cyt,gua,gln,asn ** C -N*-CM 70.0 121.60 C -N*-CT 70.0 117.60 C -N*-H 30.0 119.20 changed based on NMA nmodes CB-N*-CK 70.0 105.40 CB-N*-CT 70.0 125.80 CB-N*-H 30.0 125.80 for unmethylated n.a. bases,chngd bsd NMA nmodes CK-N*-CT 70.0 128.80 CK-N*-H 30.0 128.80 for unmethylated n.a. bases,chngd bsd NMA nmodes CM-N*-CT 70.0 121.20 CM-N*-H 30.0 121.20 for unmethylated n.a. bases,chngd bsd NMA nmodes CA-N2-H 35.0 120.00 H -N2-H 35.0 120.00 CT-N2-H 35.0 118.40 AA arg CA-N2-CT 50.0 123.20 AA arg CT-N3-H 50.0 109.50 AA lys, changed based on NMA nmodes CT-N3-CT 50.0 109.50 AA pro/nt H -N3-H 35.0 109.50 AA lys, AA(end) C -NA-C 70.0 126.40 C -NA-CA 70.0 125.20 C -NA-H 30.0 116.80 changed based on NMA nmodes CA-NA-H 30.0 118.00 changed based on NMA nmodes CC-NA-CR 70.0 120.00 AA his CC-NA-H 30.0 120.00 AA his, changed based on NMA nmodes CR-NA-CW 70.0 120.00 AA his CR-NA-H 30.0 120.00 AA his, changed based on NMA nmodes CW-NA-H 30.0 120.00 AA his, changed based on NMA nmodes CN-NA-CW 70.0 111.60 AA trp CN-NA-H 30.0 123.10 AA trp, changed based on NMA nmodes CB-NB-CK 70.0 103.80 CC-NB-CR 70.0 117.00 AA his CR-NB-CV 70.0 117.00 AA his C -NC-CA 70.0 120.50 CA-NC-CB 70.0 112.20 CA-NC-CQ 70.0 118.60 CB-NC-CQ 70.0 111.00 C -OH-HO 35.0 113.00 CT-OH-HO 55.0 108.50 HO-OH-P 45.0 108.50 CT-OS-CT 60.0 109.50 CT-OS-P 100.0 120.50 P -OS-P 100.0 120.50 O2-P -OH 45.0 108.23 O2-P -O2 140.0 119.90 O2-P -OS 100.0 108.23 OH-P -OS 45.0 102.60 OS-P -OS 45.0 102.60 CT-S -CT 62.0 98.90 AA met CT-S -S 68.0 103.70 AA cyx (SCHERAGA JPC 79,1428) CT-SH-HS 43.0 96.00 changed from 44.0 based on methanethiol nmodes HS-SH-HS 35.0 92.07 AA cys F -CT-F 77.0 109.10 JCC,13,(1992),963; F -CT-H1 35.0 109.50 JCC,13,(1992),963; X -C -CA-X 4 14.50 180.0 2. intrpol.bsd.on C6H6 X -C -CB-X 4 12.00 180.0 2. intrpol.bsd.on C6H6 X -C -CM-X 4 8.70 180.0 2. intrpol.bsd.on C6H6 X -C -N*-X 4 5.80 180.0 2. JCC,7,(1986),230 X -C -NA-X 4 5.40 180.0 2. JCC,7,(1986),230 X -C -NC-X 2 8.00 180.0 2. JCC,7,(1986),230 X -C -OH-X 2 1.80 180.0 2. JCC,7,(1986),230 X -C -CT-X 4 0.00 0.0 2. JCC,7,(1986),230 X -CA-CA-X 4 14.50 180.0 2. intrpol.bsd.on C6H6 X -CA-CB-X 4 14.00 180.0 2. intrpol.bsd.on C6H6 X -CA-CM-X 4 10.20 180.0 2. intrpol.bsd.on C6H6 X -CA-CT-X 6 0.00 0.0 2. JCC,7,(1986),230 X -CA-N2-X 4 9.60 180.0 2. reinterpolated 93' X -CA-NA-X 4 6.00 180.0 2. JCC,7,(1986),230 X -CA-NC-X 2 9.60 180.0 2. JCC,7,(1986),230 X -CB-CB-X 4 21.80 180.0 2. intrpol.bsd.on C6H6 X -CB-N*-X 4 6.60 180.0 2. JCC,7,(1986),230 X -CB-NB-X 2 5.10 180.0 2. JCC,7,(1986),230 X -CB-NC-X 2 8.30 180.0 2. JCC,7,(1986),230 X -CK-N*-X 4 6.80 180.0 2. JCC,7,(1986),230 X -CK-NB-X 2 20.00 180.0 2. JCC,7,(1986),230 X -CM-CM-X 4 26.60 180.0 2. intrpol.bsd.on C6H6 X -CM-CT-X 6 0.00 0.0 3. JCC,7,(1986),230 X -CM-N*-X 4 7.40 180.0 2. JCC,7,(1986),230 X -CQ-NC-X 2 13.60 180.0 2. JCC,7,(1986),230 X -CT-CT-X 9 1.40 0.0 3. JCC,7,(1986),230 X -CT-N -X 6 0.00 0.0 2. JCC,7,(1986),230 X -CT-N*-X 6 0.00 0.0 2. JCC,7,(1986),230 X -CT-N2-X 6 0.00 0.0 3. JCC,7,(1986),230 X -CT-OH-X 3 0.50 0.0 3. JCC,7,(1986),230 X -CT-OS-X 3 1.15 0.0 3. JCC,7,(1986),230 X -OH-P -X 3 0.75 0.0 3. JCC,7,(1986),230 X -OS-P -X 3 0.75 0.0 3. JCC,7,(1986),230 X -C -N -X 4 10.00 180.0 2. AA|check Wendy?&NMA X -CT-N3-X 9 1.40 0.0 3. JCC,7,(1986),230 X -CT-S -X 3 1.00 0.0 3. JCC,7,(1986),230 X -CT-SH-X 3 0.75 0.0 3. JCC,7,(1986),230 X -C*-CB-X 4 6.70 180.0 2. intrpol.bsd.onC6H6aa X -C*-CT-X 6 0.00 0.0 2. JCC,7,(1986),230 X -C*-CW-X 4 26.10 180.0 2. intrpol.bsd.on C6H6 X -CA-CN-X 4 14.50 180.0 2. reinterpolated 93' X -CB-CN-X 4 12.00 180.0 2. reinterpolated 93' X -CC-CT-X 6 0.00 0.0 2. JCC,7,(1986),230 X -CC-CV-X 4 20.60 180.0 2. intrpol.bsd.on C6H6 X -CC-CW-X 4 21.50 180.0 2. intrpol.bsd.on C6H6 X -CC-NA-X 4 5.60 180.0 2. JCC,7,(1986),230 X -CC-NB-X 2 4.80 180.0 2. JCC,7,(1986),230 X -CN-NA-X 4 6.10 180.0 2. reinterpolated 93' X -CR-NA-X 4 9.30 180.0 2. JCC,7,(1986),230 X -CR-NB-X 2 10.00 180.0 2. JCC,7,(1986),230 X -CV-NB-X 2 4.80 180.0 2. JCC,7,(1986),230 X -CW-NA-X 4 6.00 180.0 2. JCC,7,(1986),230 CT-CT-OS-CT 1 0.383 0.0 -3. CT-CT-OS-CT 1 0.1 180.0 2. C -N -CT-C 1 0.20 180.0 2. N -CT-C -N 1 0.40 180.0 -4. N -CT-C -N 1 1.35 180.0 -2. N -CT-C -N 1 0.75 180.0 1. CT-CT-N -C 1 0.50 180.0 -4. CT-CT-N -C 1 0.15 180.0 -3. CT-CT-N -C 1 0.53 0.0 1. CT-CT-C -N 1 0.100 0.0 -4. CT-CT-C -N 1 0.07 0.0 2. H -N -C -O 1 2.50 180.0 -2. JCC,7,(1986),230 H -N -C -O 1 2.00 0.0 1. J.C.cistrans-NMA DE CT-S -S -CT 1 3.50 0.0 -2. JCC,7,(1986),230 CT-S -S -CT 1 0.60 0.0 3. JCC,7,(1986),230 OS-CT-CT-OS 1 0.144 0.0 -3. JCC,7,(1986),230 OS-CT-CT-OS 1 1.00 0.0 2. pucker anal (93') OS-CT-CT-OH 1 0.144 0.0 -3. JCC,7,(1986),230 OS-CT-CT-OH 1 1.00 0.0 2. pucker anal (93') OH-CT-CT-OH 1 0.144 0.0 -3. JCC,7,(1986),230 OH-CT-CT-OH 1 1.00 0.0 2. check glicolWC? puc OH-P -OS-CT 1 0.25 0.0 -3. JCC,7,(1986),230 OH-P -OS-CT 1 1.20 0.0 2. gg> ene.631g*/mp2 OS-P -OS-CT 1 0.25 0.0 -3. JCC,7,(1986),230 OS-P -OS-CT 1 1.20 0.0 2. gg> ene.631g*/mp2 OS-CT-N*-CK 1 0.50 180.0 -2. sugar frag calc (PC) OS-CT-N*-CK 1 2.50 0.0 1. sugar frag calc (PC) OS-CT-N*-CM 1 0.50 180.0 -2. sugar frag calc (PC) OS-CT-N*-CM 1 2.50 0.0 1. sugar frag calc (PC) X -X -C -O 10.5 180. 2. JCC,7,(1986),230 X -O2-C -O2 10.5 180. 2. JCC,7,(1986),230 X -X -N -H 1.0 180. 2. JCC,7,(1986),230 X -X -N2-H 1.0 180. 2. JCC,7,(1986),230 X -X -NA-H 1.0 180. 2. JCC,7,(1986),230 X -N2-CA-N2 10.5 180. 2. JCC,7,(1986),230 X -CT-N -CT 1.0 180. 2. JCC,7,(1986),230 X -X -CA-HA 1.1 180. 2. bsd.on C6H6 nmodes X -X -CW-H4 1.1 180. 2. X -X -CR-H5 1.1 180. 2. X -X -CV-H4 1.1 180. 2. X -X -CQ-H5 1.1 180. 2. X -X -CK-H5 1.1 180. 2. X -X -CM-H4 1.1 180. 2. X -X -CM-HA 1.1 180. 2. X -X -CA-H4 1.1 180. 2. bsd.on C6H6 nmodes X -X -CA-H5 1.1 180. 2. bsd.on C6H6 nmodes CB-CK-N*-CT 1.0 180. 2. C -CM-N*-CT 1.0 180. 2. dac guess, 9/94 C -CM-CM-CT 1.1 180. 2. CT-O -C -OH 10.5 180. 2. CT-CV-CC-NA 1.1 180. 2. CT-CW-CC-NB 1.1 180. 2. CT-CW-CC-NA 1.1 180. 2. CB-CT-C*-CW 1.1 180. 2. CA-CA-CA-CT 1.1 180. 2. C -CM-CM-CT 1.1 180. 2. dac guess, 9/94 CM-N2-CA-NC 1.1 180. 2. dac guess, 9/94 CB-N2-CA-NC 1.1 180. 2. dac, 10/94 N2-NA-CA-NC 1.1 180. 2. dac, 10/94 CA-CA-C -OH 1.1 180. 2. HW OW 0000. 0000. 4. flag for fast water N NA N2 N* NC NB NP NO C C* CA CB CC CN CM CK CQ CW CV CR CX CY CD MOD4 RE H 0.6000 0.0157 !Ferguson base pair geom. HO 0.0000 0.0000 OPLS Jorgensen, JACS,110,(1988),1657 HS 0.6000 0.0157 W. Cornell CH3SH --> CH3OH FEP HC 1.4870 0.0157 OPLS H1 1.3870 0.0157 Veenstra et al JCC,8,(1992),963 H2 1.2870 0.0157 Veenstra et al JCC,8,(1992),963 H3 1.1870 0.0157 Veenstra et al JCC,8,(1992),963 HP 1.1000 0.0157 Veenstra et al JCC,8,(1992),963 HA 1.4590 0.0150 Spellmeyer H4 1.4090 0.0150 Spellmeyer, one electrowithdr. neighbor H5 1.3590 0.0150 Spellmeyer, two electrowithdr. neighbor HW 0.0000 0.0000 TIP3P water model O 1.6612 0.2100 OPLS O2 1.6612 0.2100 OPLS OW 1.7682 0.1521 TIP3P water model OH 1.7210 0.2104 OPLS OS 1.6837 0.1700 OPLS ether CT 1.9080 0.1094 Spellmeyer C 1.9080 0.0860 OPLS N 1.8240 0.1700 OPLS N3 1.8240 0.1700 OPLS S 2.0000 0.2500 W. Cornell CH3SH and CH3SCH3 FEP's SH 2.0000 0.2500 W. Cornell CH3SH and CH3SCH3 FEP's P 2.1000 0.2000 JCC,7,(1986),230; IM 2.47 0.1 Cl- Smith & Dang, JCP 1994,100:5,3757 Li 1.1370 0.0183 Li+ Aqvist JPC 1990,94,8021. (adapted) IP 1.8680 0.00277 Na+ Aqvist JPC 1990,94,8021. (adapted) K 2.6580 0.000328 K+ Aqvist JPC 1990,94,8021. (adapted) Rb 2.9560 0.00017 Rb+ Aqvist JPC 1990,94,8021. (adapted) Cs 3.3950 0.0000806 Cs+ Aqvist JPC 1990,94,8021. (adapted) I 2.35 0.40 JCC,7,(1986),230; F 1.75 0.061 Gough et al. JCC 13,(1992),963. IB 5.0 0.1 solvated ion for vacuum approximation EP 0.0 0.0 generic "extra point" END END MMTK-2.7.9/MMTK/ForceFields/Amber/parm99.dat0000644000076600000240000012271212041514537020516 0ustar hinsenstaff00000000000000PARM99 for DNA,RNA,AA, organic molecules, TIP3P wat. Polariz.& LP incl.02/04/99 C 12.01 0.616 ! sp2 C carbonyl group CA 12.01 0.360 sp2 C pure aromatic (benzene) CB 12.01 0.360 sp2 aromatic C, 5&6 membered ring junction CC 12.01 0.360 sp2 aromatic C, 5 memb. ring HIS CD 12.01 0.360 sp2 C atom in the middle of: C=CD-CD=C CK 12.01 0.360 sp2 C 5 memb.ring in purines CM 12.01 0.360 sp2 C pyrimidines in pos. 5 & 6 CN 12.01 0.360 sp2 C aromatic 5&6 memb.ring junct.(TRP) CQ 12.01 0.360 sp2 C in 5 mem.ring of purines between 2 N CR 12.01 0.360 sp2 arom as CQ but in HIS CT 12.01 0.878 sp3 aliphatic C CV 12.01 0.360 sp2 arom. 5 memb.ring w/1 N and 1 H (HIS) CW 12.01 0.360 sp2 arom. 5 memb.ring w/1 N-H and 1 H (HIS) C* 12.01 0.360 sp2 arom. 5 memb.ring w/1 subst. (TRP) CY 12.01 0.360 nitrile C (Howard et al.JCC,16,243,1995) CZ 12.01 0.360 sp C (Howard et al.JCC,16,243,1995) C0 40.08 calcium H 1.008 0.161 H bonded to nitrogen atoms HC 1.008 0.135 H aliph. bond. to C without electrwd.group H1 1.008 0.135 H aliph. bond. to C with 1 electrwd. group H2 1.008 0.135 H aliph. bond. to C with 2 electrwd.groups H3 1.008 0.135 H aliph. bond. to C with 3 eletrwd.groups HA 1.008 0.167 H arom. bond. to C without elctrwd. groups H4 1.008 0.167 H arom. bond. to C with 1 electrwd. group H5 1.008 0.167 H arom.at C with 2 elctrwd. gr,+HCOO group HO 1.008 0.135 hydroxyl group HS 1.008 0.135 hydrogen bonded to sulphur (pol?) HW 1.008 0.000 H in TIP3P water HP 1.008 0.135 H bonded to C next to positively charged gr HZ 1.008 0.161 H bond sp C (Howard et al.JCC,16,243,1995) F 19.00 0.320 fluorine Cl 35.45 1.910 chlorine (Applequist) Br 79.90 2.880 bromine (Applequist) I 126.9 4.690 iodine (Applequist) IM 35.45 3.235 assumed to be Cl- (ion minus) IB 131.0 'big ion w/ waters' for vacuum (Na+, 6H2O) MG 24.305 0.120 magnesium N 14.01 0.530 sp2 nitrogen in amide groups NA 14.01 0.530 sp2 N in 5 memb.ring w/H atom (HIS) NB 14.01 0.530 sp2 N in 5 memb.ring w/LP (HIS,ADE,GUA) NC 14.01 0.530 sp2 N in 6 memb.ring w/LP (ADE,GUA) N2 14.01 0.530 sp2 N in amino groups N3 14.01 0.530 sp3 N for charged amino groups (Lys, etc) NT 14.01 0.530 sp3 N for amino groups amino groups N* 14.01 0.530 sp2 N NY 14.01 0.530 nitrile N (Howard et al.JCC,16,243,1995) O 16.00 0.434 carbonyl group oxygen O2 16.00 0.434 carboxyl and phosphate group oxygen OW 16.00 0.000 oxygen in TIP3P water OH 16.00 0.465 oxygen in hydroxyl group OS 16.00 0.465 ether and ester oxygen P 30.97 1.538 phosphate,pol:JACS,112,8543,90,K.J.Miller S 32.06 2.900 S in disulfide linkage,pol:JPC,102,2399,98 SH 32.06 2.900 S in cystine CU 63.55 copper FE 55.00 iron Li 6.94 0.029 lithium, ions pol:J.PhysC,11,1541,(1978) IP 22.99 0.250 assumed to be Na+ (ion plus) Na 22.99 0.250 Na+, ions pol:J.PhysC,11,1541,(1978) K 39.10 1.060 potassium Rb 85.47 rubidium Cs 132.91 cesium Zn 65.4 Zn2+ LP 3.00 0.000 lone pair C H HO N NA NB NC N2 NT N2 N3 N* O OH OS P O2 OW-HW 553.0 0.9572 ! TIP3P water HW-HW 553.0 1.5136 TIP3P water C -C 310.0 1.525 Junmei et al, 1999 C -CA 469.0 1.409 JCC,7,(1986),230; (not used any more in TYR) C -CB 447.0 1.419 JCC,7,(1986),230; GUA C -CM 410.0 1.444 JCC,7,(1986),230; THY,URA C -CT 317.0 1.522 JCC,7,(1986),230; AA C -N 490.0 1.335 JCC,7,(1986),230; AA C -N* 424.0 1.383 JCC,7,(1986),230; CYT,URA C -NA 418.0 1.388 JCC,7,(1986),230; GUA.URA C -NC 457.0 1.358 JCC,7,(1986),230; CYT C -O 570.0 1.229 JCC,7,(1986),230; AA,CYT,GUA,THY,URA C -O2 656.0 1.250 JCC,7,(1986),230; GLU,ASP C -OH 450.0 1.364 JCC,7,(1986),230; (not used any more for TYR) C -OS 450.0 1.323 Junmei et al, 1999 C -H4 367.0 1.080 Junmei et al, 1999 C -H5 367.0 1.080 Junmei et al, 1999 CA-CA 469.0 1.400 JCC,7,(1986),230; BENZENE,PHE,TRP,TYR CA-CB 469.0 1.404 JCC,7,(1986),230; ADE,TRP CA-CM 427.0 1.433 JCC,7,(1986),230; CYT CA-CN 469.0 1.400 JCC,7,(1986),230; TRP CA-CT 317.0 1.510 JCC,7,(1986),230; PHE,TYR CA-HA 367.0 1.080 changed from 340. bsd on C6H6 nmodes; PHE,TRP,TYR CA-H4 367.0 1.080 changed from 340. bsd on C6H6 nmodes; no assigned CA-N2 481.0 1.340 JCC,7,(1986),230; ARG,CYT,GUA CA-NA 427.0 1.381 JCC,7,(1986),230; GUA CA-NC 483.0 1.339 JCC,7,(1986),230; ADE,CYT,GUA CA-OH 450.0 1.364 substituted for C-OH in tyr CB-CB 520.0 1.370 JCC,7,(1986),230; ADE,GUA CB-N* 436.0 1.374 JCC,7,(1986),230; ADE,GUA CB-NB 414.0 1.391 JCC,7,(1986),230; ADE,GUA CB-NC 461.0 1.354 JCC,7,(1986),230; ADE,GUA CD-HA 367.0 1.080 Junmei et al, 1999 CD-CD 469.0 1.400 Junmei et al, 1999 CD-CM 549.0 1.350 Junmei et al, 1999 CD-CT 317.0 1.510 Junmei et al, 1999 CK-H5 367.0 1.080 changed from 340. bsd on C6H6 nmodes; ADE,GUA CK-N* 440.0 1.371 JCC,7,(1986),230; ADE,GUA CK-NB 529.0 1.304 JCC,7,(1986),230; ADE,GUA CM-CM 549.0 1.350 JCC,7,(1986),230; CYT,THY,URA CM-CT 317.0 1.510 JCC,7,(1986),230; THY CM-HA 367.0 1.080 changed from 340. bsd on C6H6 nmodes; CYT,URA CM-H4 367.0 1.080 changed from 340. bsd on C6H6 nmodes; CYT,URA CM-H5 367.0 1.080 changed from 340. bsd on C6H6 nmodes; not assigned CM-N* 448.0 1.365 JCC,7,(1986),230; CYT,THY,URA CM-OS 480.0 1.240 Junmei et al, 1999 CQ-H5 367.0 1.080 changed from 340. bsd on C6H6 nmodes; ADE CQ-NC 502.0 1.324 JCC,7,(1986),230; ADE CT-CT 310.0 1.526 JCC,7,(1986),230; AA, SUGARS CT-HC 340.0 1.090 changed from 331 bsd on NMA nmodes; AA, SUGARS CT-H1 340.0 1.090 changed from 331 bsd on NMA nmodes; AA, RIBOSE CT-H2 340.0 1.090 changed from 331 bsd on NMA nmodes; SUGARS CT-H3 340.0 1.090 changed from 331 bsd on NMA nmodes; not assigned CT-HP 340.0 1.090 changed from 331; AA-lysine, methyl ammonium cation CT-N* 337.0 1.475 JCC,7,(1986),230; ADE,CYT,GUA,THY,URA CT-N2 337.0 1.463 JCC,7,(1986),230; ARG CT-OH 320.0 1.410 JCC,7,(1986),230; SUGARS CT-OS 320.0 1.410 JCC,7,(1986),230; NUCLEIC ACIDS C*-HC 367.0 1.080 changed from 340. bsd on C6H6 nmodes, not needed AA C*-CB 388.0 1.459 JCC,7,(1986),230; TRP C*-CT 317.0 1.495 JCC,7,(1986),230; TRP C*-CW 546.0 1.352 JCC,7,(1986),230; TRP CB-CN 447.0 1.419 JCC,7,(1986),230; TRP CC-CT 317.0 1.504 JCC,7,(1986),230; HIS CC-CV 512.0 1.375 JCC,7,(1986),230; HIS(delta) CC-CW 518.0 1.371 JCC,7,(1986),230; HIS(epsilon) CC-NA 422.0 1.385 JCC,7,(1986),230; HIS CC-NB 410.0 1.394 JCC,7,(1986),230; HIS CN-NA 428.0 1.380 JCC,7,(1986),230; TRP CR-H5 367.0 1.080 changed from 340. bsd on C6H6 nmodes;HIS CR-NA 477.0 1.343 JCC,7,(1986),230; HIS CR-NB 488.0 1.335 JCC,7,(1986),230; HIS CT-N 337.0 1.449 JCC,7,(1986),230; AA CT-N3 367.0 1.471 JCC,7,(1986),230; LYS CT-NT 367.0 1.471 for neutral amines CT-S 227.0 1.810 changed from 222.0 based on dimethylS nmodes CT-SH 237.0 1.810 changed from 222.0 based on methanethiol nmodes CT-CY 400.0 1.458 Howard et al JCC.16,243,1995 CT-CZ 400.0 1.459 Howard et al JCC,16,243,1995 CV-H4 367.0 1.080 changed from 340. bsd on C6H6 nmodes; HIS CV-NB 410.0 1.394 JCC,7,(1986),230; HIS CW-H4 367.0 1.080 changed from 340. bsd on C6H6 nmodes;HIS(epsilon,+) CW-NA 427.0 1.381 JCC,7,(1986),230; HIS,TRP CY-NY 600.0 1.150 Howard et al JCC,16,243,1995 CZ-CZ 600.0 1.206 Howard et al JCC,16,243,1995 CZ-HZ 400.0 1.056 Howard et al JCC,16,243,1995 O2-P 525.0 1.480 JCC,7,(1986),230; NA PHOSPHATES OH-P 230.0 1.610 JCC,7,(1986),230; NA PHOSPHATES OS-P 230.0 1.610 JCC,7,(1986),230; NA PHOSPHATES H -N2 434.0 1.010 JCC,7,(1986),230; ADE,CYT,GUA,ARG H -N* 434.0 1.010 for plain unmethylated bases ADE,CYT,GUA,ARG H -NA 434.0 1.010 JCC,7,(1986),230; GUA,URA,HIS H -N 434.0 1.010 JCC,7,(1986),230; AA H -N3 434.0 1.010 JCC,7,(1986),230; LYS H -NT 434.0 1.010 for neutral amines HO-OH 553.0 0.960 JCC,7,(1986),230; SUGARS,SER,TYR HO-OS 553.0 0.960 JCC,7,(1986),230; NUCLEOTIDE ENDS HS-SH 274.0 1.336 JCC,7,(1986),230; CYS S -S 166.0 2.038 JCC,7,(1986),230; CYX (SCHERAGA) F -CT 367.0 1.380 JCC,13,(1992),963;CF4; R0=1.332 FOR CHF3 Cl-CT 232.0 1.766 6-31g* opt Br-CT 159.0 1.944 Junmei et al,99 I -CT 148.0 2.166 Junmei et al,99 F -CA 386.0 1.359 Junmei et al,99 Cl-CA 193.0 1.727 Junmei et al,99 I -CA 171.0 2.075 Junmei et al,99 Br-CA 172.0 1.890 Junmei et al,99 LP-O 600.0 0.200 or 0.35 LP-OH 600.0 0.200 or 0.35 LP-OS 600.0 0.200 or 0.35 LP-N3 600.0 0.200 or 0.35 LP-NT 600.0 0.200 or 0.35 LP-NB 600.0 0.200 or 0.35 histidines, nucleic acids LP-NC 600.0 0.200 or 0.35 nucleic acids LP-S 600.0 0.700 cys,cyx,met LP-SH 600.0 0.700 cys,cyx HW-OW-HW 100. 104.52 TIP3P water HW-HW-OW 0. 127.74 (found in crystallographic water with 3 bonds) C -C -O 80.0 120.00 Junmei et al, 1999 acrolein C -C -OH 80.0 120.00 Junmei et al, 1999 CA-C -CA 63.0 120.00 changed from 85.0 bsd on C6H6 nmodes; AA CA-C -OH 70.0 120.00 AA (not used in tyr) CB-C -NA 70.0 111.30 NA CB-C -O 80.0 128.80 CM-C -NA 70.0 114.10 CM-C -O 80.0 125.30 CT-C -O 80.0 120.40 CT-C -O2 70.0 117.00 CT-C -N 70.0 116.60 AA general CT-C -CT 63.0 117.00 Junmei et al, 1999 CT-C -OS 80.0 115.00 Junmei et al, 1999 CT-C -OH 80.0 110.00 Junmei et al, 1999 N*-C -NA 70.0 115.40 N*-C -NC 70.0 118.60 N*-C -O 80.0 120.90 NA-C -O 80.0 120.60 NC-C -O 80.0 122.50 N -C -O 80.0 122.90 AA general O -C -O 80.0 126.00 AA COO- terminal residues O -C -OH 80.0 120.00 (check with Junmei for: theta0:120.0?) O -C -OS 80.0 125.00 Junmei et al, 1999 O2-C -O2 80.0 126.00 AA GLU (SCH JPC 79,2379) H4-C -C 50.0 120.00 Junmei et al, 1999 H4-C -CM 50.0 115.00 Junmei et al, 1999 H4-C -CT 50.0 115.00 Junmei et al, 1999 H4-C -O 50.0 120.00 Junmei et al, 1999 H4-C -OH 50.0 120.00 Junmei et al, 1999 H5-C -N 50.0 120.00 Junmei et al, 1999 H5-C -O 50.0 119.00 Junmei et al, 1999 H5-C -OH 50.0 107.00 Junmei et al, 1999 H5-C -OS 50.0 107.00 Junmei et al, 1999 C -CA-CA 63.0 120.00 changed from 85.0 bsd on C6H6 nmodes C -CA-HA 50.0 120.00 AA (not used in tyr) CA-CA-CA 63.0 120.00 changed from 85.0 bsd on C6H6 nmodes CA-CA-CB 63.0 120.00 changed from 85.0 bsd on C6H6 nmodes CA-CA-CT 70.0 120.00 CA-CA-HA 50.0 120.00 CA-CA-H4 50.0 120.00 CA-CA-OH 70.0 120.00 replacement in tyr CA-CA-CN 63.0 120.00 changed from 85.0 bsd on C6H6 nmodes; AA trp CB-CA-HA 50.0 120.00 CB-CA-H4 50.0 120.00 CB-CA-N2 70.0 123.50 CB-CA-NC 70.0 117.30 CM-CA-N2 70.0 120.10 CM-CA-NC 70.0 121.50 CN-CA-HA 50.0 120.00 AA trp NA-CA-NC 70.0 123.30 N2-CA-NA 70.0 116.00 N2-CA-NC 70.0 119.30 N2-CA-N2 70.0 120.00 AA arg F -CA-CA 70.0 121.00 Junmei et al,99 Cl-CA-CA 70.0 118.80 Junmei et al,99 Br-CA-CA 70.0 118.80 Junmei et al,99 I -CA-CA 70.0 118.80 Junmei et al,99 C -CB-CB 63.0 119.20 changed from 85.0 bsd on C6H6 nmodes; NA gua C -CB-NB 70.0 130.00 CA-CB-CB 63.0 117.30 changed from 85.0 bsd on C6H6 nmodes; NA ade CA-CB-NB 70.0 132.40 CB-CB-N* 70.0 106.20 CB-CB-NB 70.0 110.40 CB-CB-NC 70.0 127.70 C*-CB-CA 63.0 134.90 changed from 85.0 bsd on C6H6 nmodes; AA trp C*-CB-CN 63.0 108.80 changed from 85.0 bsd on C6H6 nmodes; AA trp CA-CB-CN 63.0 116.20 changed from 85.0 bsd on C6H6 nmodes; AA trp N*-CB-NC 70.0 126.20 CD-CD-CM 63.0 120.00 Junmei et al, 1999 CD-CD-CT 70.0 120.00 Junmei et al, 1999 CM-CD-CT 70.0 120.00 Junmei et al, 1999 HA-CD-HA 35.0 119.00 Junmei et al, 1999 HA-CD-CD 50.0 120.00 Junmei et al, 1999 HA-CD-CM 50.0 120.00 Junmei et al, 1999 H5-CK-N* 50.0 123.05 H5-CK-NB 50.0 123.05 N*-CK-NB 70.0 113.90 C -CM-CM 63.0 120.70 changed from 85.0 bsd on C6H6 nmodes; NA thy C -CM-CT 70.0 119.70 C -CM-HA 50.0 119.70 C -CM-H4 50.0 119.70 CA-CM-CM 63.0 117.00 changed from 85.0 bsd on C6H6 nmodes; NA cyt CA-CM-HA 50.0 123.30 CA-CM-H4 50.0 123.30 CM-CM-CT 70.0 119.70 CM-CM-HA 50.0 119.70 CM-CM-H4 50.0 119.70 CM-CM-N* 70.0 121.20 CM-CM-OS 80.0 125.00 Junmei et al, 1999 H4-CM-N* 50.0 119.10 H4-CM-OS 50.0 113.00 Junmei et al, 1999 HA-CM-HA 35.0 120.00 Junmei et al, 1999 HA-CM-CD 50.0 120.00 Junmei et al, 1999 HA-CM-CT 50.0 120.00 Junmei et al, 1999 NC-CQ-NC 70.0 129.10 H5-CQ-NC 50.0 115.45 H1-CT-H1 35.0 109.50 H1-CT-N* 50.0 109.50 changed based on NMA nmodes H1-CT-OH 50.0 109.50 changed based on NMA nmodes H1-CT-OS 50.0 109.50 changed based on NMA nmodes H1-CT-CM 50.0 109.50 Junmei et al, 1999 H1-CT-CY 50.0 110.00 Junmei et al, 1999 H1-CT-CZ 50.0 110.00 Junmei et al, 1999 H1-CT-N 50.0 109.50 AA general changed based on NMA nmodes H1-CT-S 50.0 109.50 AA cys changed based on NMA nmodes H1-CT-SH 50.0 109.50 AA cyx changed based on NMA nmodes H1-CT-N2 50.0 109.50 AA arg changed based on NMA nmodes H1-CT-NT 50.0 109.50 neutral amines H2-CT-H2 35.0 109.50 AA lys H2-CT-N* 50.0 109.50 changed based on NMA nmodes H2-CT-OS 50.0 109.50 changed based on NMA nmodes HP-CT-HP 35.0 109.50 AA lys, ch3nh4+ HP-CT-N3 50.0 109.50 AA lys, ch3nh3+, changed based on NMA nmodes HC-CT-HC 35.0 109.50 HC-CT-CM 50.0 109.50 changed based on NMA nmodes HC-CT-CD 50.0 109.50 Junmei et al, 1999 HC-CT-CZ 50.0 110.00 Junmei et al, 1999 C -CT-H1 50.0 109.50 AA general changed based on NMA nmodes C -CT-HP 50.0 109.50 AA zwitterion changed based on NMA nmodes C -CT-HC 50.0 109.50 AA gln changed based on NMA nmodes C -CT-N 63.0 110.10 AA general C -CT-N3 80.0 111.20 AA amino terminal residues C -CT-CT 63.0 111.10 AA general C -CT-OS 60.0 109.50 Junmei et al, 1999 CA-CT-HC 50.0 109.50 AA tyr changed based on NMA nmodes CC-CT-CT 63.0 113.10 AA his CC-CT-HC 50.0 109.50 AA his changed based on NMA nmodes CM-CT-CT 63.0 111.00 Junmei et al, 1999 (last change: Mar24,99) CM-CT-OS 50.0 109.50 Junmei et al, 1999 CT-CT-CT 40.0 109.50 CT-CT-HC 50.0 109.50 changed based on NMA nmodes CT-CT-H1 50.0 109.50 changed based on NMA nmodes CT-CT-H2 50.0 109.50 changed based on NMA nmodes CT-CT-HP 50.0 109.50 changed based on NMA nmodes CT-CT-N* 50.0 109.50 CT-CT-OH 50.0 109.50 CT-CT-OS 50.0 109.50 CT-CT-S 50.0 114.70 AA cyx (SCHERAGA JPC 79,1428) CT-CT-SH 50.0 108.60 AA cys CT-CT-CA 63.0 114.00 AA phe tyr (SCH JPC 79,2379) CT-CT-N2 80.0 111.20 AA arg (JCP 76, 1439) CT-CT-N 80.0 109.70 AA ala, general (JACS 94, 2657) CT-CT-N3 80.0 111.20 AA lys (JCP 76, 1439) CT-CT-NT 80.0 111.20 neutral amines CT-CT-CY 63.0 110.00 Junmei et al, 1999 CT-CT-CZ 63.0 110.00 Junmei et al, 1999 C*-CT-CT 63.0 115.60 AA trp C*-CT-HC 50.0 109.50 AA trp changed based on NMA nmodes OS-CT-OS 160.0 101.00 Junmei et al, 1999 OS-CT-CY 50.0 110.00 Junmei et al, 1999 OS-CT-CZ 50.0 110.00 Junmei et al, 1999 OS-CT-N* 50.0 109.50 F -CT-F 77.0 109.10 JCC,13,(1992),963; F -CT-H1 50.0 109.50 JCC,13,(1992),963; F -CT-CT 50.0 109.00 F -CT-H2 50.0 109.50 Cl-CT-CT 50.0 108.50 (6-31g* opt value) Cl-CT-H1 50.0 108.50 (6-31g* opt value) Br-CT-CT 50.0 108.00 Junmei et al 99 Br-CT-H1 50.0 106.50 Junmei et al 99 I -CT-CT 50.0 106.00 Junmei et al,99 CT-CC-NA 70.0 120.00 AA his CT-CC-CV 70.0 120.00 AA his CT-CC-NB 70.0 120.00 AA his CV-CC-NA 70.0 120.00 AA his CW-CC-NA 70.0 120.00 AA his CW-CC-NB 70.0 120.00 AA his CT-CC-CW 70.0 120.00 AA his H5-CR-NA 50.0 120.00 AA his H5-CR-NB 50.0 120.00 AA his NA-CR-NA 70.0 120.00 AA his NA-CR-NB 70.0 120.00 AA his CC-CV-H4 50.0 120.00 AA his CC-CV-NB 70.0 120.00 AA his H4-CV-NB 50.0 120.00 AA his CC-CW-H4 50.0 120.00 AA his CC-CW-NA 70.0 120.00 AA his C*-CW-H4 50.0 120.00 AA trp C*-CW-NA 70.0 108.70 AA trp H4-CW-NA 50.0 120.00 AA his CB-C*-CT 70.0 128.60 AA trp CB-C*-CW 63.0 106.40 changed from 85.0 bsd on C6H6 nmodes; AA trp CT-C*-CW 70.0 125.00 AA trp CA-CN-CB 63.0 122.70 changed from 85.0 bsd on C6H6 nmodes; AA trp CA-CN-NA 70.0 132.80 AA trp CB-CN-NA 70.0 104.40 AA trp CT-CY-NY 80.0 180.00 Junmei et al, 1999 CT-CZ-CZ 80.0 180.00 Junmei et al, 1999 CZ-CZ-HZ 50.0 180.00 Junmei et al, 1999 C -N -CT 50.0 121.90 AA general C -N -H 50.0 120.00 AA general, gln, asn,changed based on NMA nmodes CT-N -H 50.0 118.04 AA general, changed based on NMA nmodes CT-N -CT 50.0 118.00 AA pro (DETAR JACS 99,1232) H -N -H 35.0 120.00 ade,cyt,gua,gln,asn ** C -N*-CM 70.0 121.60 C -N*-CT 70.0 117.60 C -N*-H 50.0 119.20 changed based on NMA nmodes CB-N*-CK 70.0 105.40 CB-N*-CT 70.0 125.80 CB-N*-H 50.0 125.80 for unmethylated n.a. bases,chngd bsd NMA nmodes CK-N*-CT 70.0 128.80 CK-N*-H 50.0 128.80 for unmethylated n.a. bases,chngd bsd NMA nmodes CM-N*-CT 70.0 121.20 CM-N*-H 50.0 121.20 for unmethylated n.a. bases,chngd bsd NMA nmodes CA-N2-H 50.0 120.00 CA-N2-CT 50.0 123.20 AA arg CT-N2-H 50.0 118.40 AA arg H -N2-H 35.0 120.00 CT-N3-H 50.0 109.50 AA lys, changed based on NMA nmodes CT-N3-CT 50.0 109.50 AA pro/nt H -N3-H 35.0 109.50 AA lys, AA(end) CT-NT-H 50.0 109.50 neutral amines CT-NT-CT 50.0 109.50 neutral amines H -NT-H 35.0 109.50 neutral amines C -NA-C 70.0 126.40 C -NA-CA 70.0 125.20 C -NA-H 50.0 116.80 changed based on NMA nmodes CA-NA-H 50.0 118.00 changed based on NMA nmodes CC-NA-CR 70.0 120.00 AA his CC-NA-H 50.0 120.00 AA his, changed based on NMA nmodes CR-NA-CW 70.0 120.00 AA his CR-NA-H 50.0 120.00 AA his, changed based on NMA nmodes CW-NA-H 50.0 120.00 AA his, changed based on NMA nmodes CN-NA-CW 70.0 111.60 AA trp CN-NA-H 50.0 123.10 AA trp, changed based on NMA nmodes CB-NB-CK 70.0 103.80 CC-NB-CR 70.0 117.00 AA his CR-NB-CV 70.0 117.00 AA his C -NC-CA 70.0 120.50 CA-NC-CB 70.0 112.20 CA-NC-CQ 70.0 118.60 CB-NC-CQ 70.0 111.00 C -OH-HO 50.0 113.00 (not used in tyr anymore) CA-OH-HO 50.0 113.00 replacement in tyr CT-OH-HO 55.0 108.50 HO-OH-P 45.0 108.50 C -OS-CT 60.0 117.00 Junmei et al, 1999 CM-OS-CT 60.0 117.00 Junmei et al, 1999 CT-OS-CT 60.0 109.50 CT-OS-P 100.0 120.50 P -OS-P 100.0 120.50 O2-P -OH 45.0 108.23 O2-P -O2 140.0 119.90 O2-P -OS 100.0 108.23 OH-P -OS 45.0 102.60 OS-P -OS 45.0 102.60 CT-S -CT 62.0 98.90 AA met CT-S -S 68.0 103.70 AA cyx (SCHERAGA JPC 79,1428) CT-SH-HS 43.0 96.00 changed from 44.0 based on methanethiol nmodes HS-SH-HS 35.0 92.07 AA cys CB-NB-LP 150.0 126.0 NA CC-NB-LP 150.0 126.0 his,NA CK-NB-LP 150.0 126.0 NA CR-NB-LP 150.0 126.0 his,NA CV-NB-LP 150.0 126.0 his,NA C -NC-LP 150.0 120.0 NA CA-NC-LP 150.0 120.0 NA CB-NC-LP 150.0 120.0 NA CQ-NC-LP 150.0 120.0 NA CT-N3-LP 150.0 109.5 in neutral lysine H -N3-LP 150.0 109.5 in neutral lysine CT-NT-LP 150.0 109.5 H -NT-LP 150.0 109.5 C -O -LP 150.0 120.0 LP-O -LP 150.0 120.0 C -OH-LP 150.0 120.0 CT-OH-LP 150.0 109.5 HO-OH-LP 150.0 109.5 LP-OH-LP 150.0 109.5 C -OS-LP 150.0 109.5 CM-OS-LP 150.0 109.5 methyl vinyl ether CT-OS-LP 150.0 109.5 LP-OS-LP 150.0 109.5 CT-S -LP 150.0 90.0 cys,cyx,met CT-SH-LP 150.0 90.0 cys,cyx,met P -OS-LP 150.0 109.5 NA LP-S -LP 150.0 180.0 cys,cyx,met LP-SH-LP 150.0 180.0 cys,cyx,met HS-SH-LP 150.0 90.0 cys X -C -C -X 4 14.50 180.0 2. Junmei et al, 1999 X -C -CA-X 4 14.50 180.0 2. intrpol.bsd.on C6H6 X -C -CB-X 4 12.00 180.0 2. intrpol.bsd.on C6H6 X -C -CM-X 4 8.70 180.0 2. intrpol.bsd.on C6H6 X -C -CT-X 6 0.00 0.0 2. JCC,7,(1986),230 X -C -N -X 4 10.00 180.0 2. AA,NMA X -C -N*-X 4 5.80 180.0 2. JCC,7,(1986),230 X -C -NA-X 4 5.40 180.0 2. JCC,7,(1986),230 X -C -NC-X 2 8.00 180.0 2. JCC,7,(1986),230 X -C -O -X 4 11.20 180.0 2. Junmei et al, 1999 X -C -OH-X 2 4.60 180.0 2. Junmei et al, 1999 X -C -OS-X 2 5.40 180.0 2. Junmei et al, 1999 X -CA-CA-X 4 14.50 180.0 2. intrpol.bsd.on C6H6 X -CA-CB-X 4 14.00 180.0 2. intrpol.bsd.on C6H6 X -CA-CM-X 4 10.20 180.0 2. intrpol.bsd.on C6H6 X -CA-CN-X 4 14.50 180.0 2. reinterpolated 93' X -CA-CT-X 6 0.00 0.0 2. JCC,7,(1986),230 X -CA-N2-X 4 9.60 180.0 2. reinterpolated 93' X -CA-NA-X 4 6.00 180.0 2. JCC,7,(1986),230 X -CA-NC-X 2 9.60 180.0 2. JCC,7,(1986),230 X -CA-OH-X 2 1.80 180.0 2. Junmei et al, 99 X -CB-CB-X 4 21.80 180.0 2. intrpol.bsd.on C6H6 X -CB-CN-X 4 12.00 180.0 2. reinterpolated 93' X -CB-N*-X 4 6.60 180.0 2. JCC,7,(1986),230 X -CB-NB-X 2 5.10 180.0 2. JCC,7,(1986),230 X -CB-NC-X 2 8.30 180.0 2. JCC,7,(1986),230 X -CC-CT-X 6 0.00 0.0 2. JCC,7,(1986),230 X -CC-CV-X 4 20.60 180.0 2. intrpol.bsd.on C6H6 X -CC-CW-X 4 21.50 180.0 2. intrpol.bsd.on C6H6 X -CC-NA-X 4 5.60 180.0 2. JCC,7,(1986),230 X -CC-NB-X 2 4.80 180.0 2. JCC,7,(1986),230 X -CD-CD-X 4 4.00 180.0 2. Junmei et al, 1999 X -CD-CT-X 6 0.00 0.0 2. Junmei et al, 1999 X -CD-CM-X 4 26.60 180.0 2. Junmei et al, 1999 X -CK-N*-X 4 6.80 180.0 2. JCC,7,(1986),230 X -CK-NB-X 2 20.00 180.0 2. JCC,7,(1986),230 X -CM-CM-X 4 26.60 180.0 2. intrpol.bsd.on C6H6 X -CM-CT-X 6 0.00 0.0 3. JCC,7,(1986),230 X -CM-N*-X 4 7.40 180.0 2. JCC,7,(1986),230 X -CM-OS-X 2 2.10 180.0 2. Junmei et al, 1999 X -CN-NA-X 4 6.10 180.0 2. reinterpolated 93' X -CQ-NC-X 2 13.60 180.0 2. JCC,7,(1986),230 X -CT-CT-X 9 1.40 0.0 3. JCC,7,(1986),230 X -CT-CY-X 3 0.00 0.0 1. Junmei et al, 1999 X -CT-CZ-X 3 0.00 0.0 1. Junmei et al, 1999 X -CT-N -X 6 0.00 0.0 2. JCC,7,(1986),230 X -CT-N*-X 6 0.00 0.0 2. JCC,7,(1986),230 X -CT-N2-X 6 0.00 0.0 3. JCC,7,(1986),230 X -CT-NT-X 6 1.80 0.0 3. Junmei et al, 1999 X -CT-N3-X 9 1.40 0.0 3. JCC,7,(1986),230 X -CT-OH-X 3 0.50 0.0 3. JCC,7,(1986),230 X -CT-OS-X 3 1.15 0.0 3. JCC,7,(1986),230 X -CT-S -X 3 1.00 0.0 3. JCC,7,(1986),230 X -CT-SH-X 3 0.75 0.0 3. JCC,7,(1986),230 X -C*-CB-X 4 6.70 180.0 2. intrpol.bsd.onC6H6aa X -C*-CT-X 6 0.00 0.0 2. JCC,7,(1986),230 X -C*-CW-X 4 26.10 180.0 2. intrpol.bsd.on C6H6 X -CR-NA-X 4 9.30 180.0 2. JCC,7,(1986),230 X -CR-NB-X 2 10.00 180.0 2. JCC,7,(1986),230 X -CV-NB-X 2 4.80 180.0 2. JCC,7,(1986),230 X -CW-NA-X 4 6.00 180.0 2. JCC,7,(1986),230 X -OH-P -X 3 0.75 0.0 3. JCC,7,(1986),230 X -OS-P -X 3 0.75 0.0 3. JCC,7,(1986),230 N -CT-C -N 1 1.700 180.000 -1. N -CT-C -N 1 2.000 180.000 2. C -N -CT-C 1 0.850 180.000 -2. C -N -CT-C 1 0.800 0.000 1. CT-CT-N -C 1 0.50 180.0 -4. phi,psi,parm94 CT-CT-N -C 1 0.15 180.0 -3. phi,psi,parm94 CT-CT-N -C 1 0.00 0.0 -2. JCC,7,(1986),230 CT-CT-N -C 1 0.53 0.0 1. phi,psi,parm94 CT-CT-C -N 1 0.100 0.0 -4. phi,psi,parm94 CT-CT-C -N 1 0.07 0.0 2. phi,psi,parm94 H -N -C -O 1 2.50 180.0 -2. JCC,7,(1986),230 H -N -C -O 1 2.00 0.0 1. J.C.cistrans-NMA DE CT-S -S -CT 1 3.50 0.0 -2. JCC,7,(1986),230 CT-S -S -CT 1 0.60 0.0 3. JCC,7,(1986),230 OH-P -OS-CT 1 0.25 0.0 -3. JCC,7,(1986),230 OH-P -OS-CT 1 1.20 0.0 2. gg> ene.631g*/mp2 OS-P -OS-CT 1 0.25 0.0 -3. JCC,7,(1986),230 OS-P -OS-CT 1 1.20 0.0 2. gg> ene.631g*/mp2 H1-CT-C -O 1 0.80 0.0 -1. Junmei et al, 1999 H1-CT-C -O 1 0.00 0.0 -2. Explicit of wild card X-C-CT-X H1-CT-C -O 1 0.08 180.0 3. Junmei et al, 1999 HC-CT-C -O 1 0.80 0.0 -1. Junmei et al, 1999 HC-CT-C -O 1 0.00 0.0 -2. Explicit of wild card X-C-CT-X HC-CT-C -O 1 0.08 180.0 3. Junmei et al, 1999 HC-CT-CT-HC 1 0.15 0.0 3. Junmei et al, 1999 HC-CT-CT-CT 1 0.16 0.0 3. Junmei et al, 1999 HC-CT-CM-CM 1 0.38 180.0 -3. Junmei et al, 1999 HC-CT-CM-CM 1 1.15 0.0 1. Junmei et al, 1999 HO-OH-CT-CT 1 0.16 0.0 -3. Junmei et al, 1999 HO-OH-CT-CT 1 0.25 0.0 1. Junmei et al, 1999 HO-OH-C -O 1 2.30 180.0 -2. Junmei et al, 1999 HO-OH-C -O 1 1.90 0.0 1. Junmei et al, 1999 CM-CM-C -O 1 2.175 180.0 -2. Junmei et al, 1999 CM-CM-C -O 1 0.30 0.0 3. Junmei et al, 1999 CT-CM-CM-CT 1 6.65 180.0 -2. Junmei et al, 1999 CT-CM-CM-CT 1 1.90 180.0 1. Junmei et al, 1999 CT-CT-CT-CT 1 0.18 0.0 -3. Junmei et al, 1999 CT-CT-CT-CT 1 0.25 180.0 -2. Junmei et al, 1999 CT-CT-CT-CT 1 0.20 180.0 1. Junmei et al, 1999 CT-CT-NT-CT 1 0.30 0.0 -3. Junmei et al, 1999 CT-CT-NT-CT 1 0.48 180.0 2. Junmei et al, 1999 CT-CT-OS-CT 1 0.383 0.0 -3. CT-CT-OS-CT 1 0.1 180.0 2. CT-CT-OS-C 1 0.383 0.0 -3. Junmei et al, 1999 CT-CT-OS-C 1 0.80 180.0 1. Junmei et al, 1999 CT-OS-CT-OS 1 0.10 0.0 -3. Junmei et al, 1999 CT-OS-CT-OS 1 0.85 180.0 -2. Junmei et al, 1999 CT-OS-CT-OS 1 1.35 180.0 1. Junmei et al, 1999 CT-OS-CT-N* 1 0.383 0.0 -3. parm98.dat, TC,PC,PAK CT-OS-CT-N* 1 0.65 0.0 2. Piotr et al. CT-CZ-CZ-HZ 1 0.00 0.0 1. Junmei et al, 1999 O -C -OS-CT 1 2.70 180.0 -2. Junmei et al, 1999 O -C -OS-CT 1 1.40 180.0 1. Junmei et al, 1999 OS-CT-N*-CK 1 0.00 000.0 -2. parm98, TC,PC,PAK OS-CT-N*-CK 1 2.50 0.0 1. parm98, TC,PC,PAK OS-CT-N*-CM 1 0.00 000.0 -2. parm98, TC,PC,PAK OS-CT-N*-CM 1 2.50 0.0 1. parm98, TC,PC,PAK OS-CT-CT-OS 1 0.144 0.0 -3. parm98, TC,PC,PAK OS-CT-CT-OS 1 1.175 0.0 2. Piotr et al. OS-CT-CT-OH 1 0.144 0.0 -3. parm98, TC,PC,PAK OS-CT-CT-OH 1 1.175 0.0 2. parm98, TC,PC,PAK OH-CT-CT-OH 1 0.144 0.0 -3. parm98, TC,PC,PAK OH-CT-CT-OH 1 1.175 0.0 2. parm98, TC,PC,PAK F -CT-CT-F 1 0.000 0.0 -3. JCC,7,(1986),230 F -CT-CT-F 1 1.20 180.0 1. Junmei et al, 1999 Cl-CT-CT-Cl 1 0.000 0.0 -3. JCC,7,(1986),230 Cl-CT-CT-Cl 1 0.45 180.0 1. Junmei et al, 1999 Br-CT-CT-Br 1 0.000 0.0 -3. JCC,7,(1986),230 Br-CT-CT-Br 1 0.00 180.0 1. Junmei et al, 1999 H1-CT-CT-OS 1 0.000 0.0 -3. JCC,7,(1986),230 H1-CT-CT-OS 1 0.25 0.0 1. Junmei et al, 1999 H1-CT-CT-OH 1 0.000 0.0 -3. JCC,7,(1986),230 H1-CT-CT-OH 1 0.25 0.0 1. Junmei et al, 1999 H1-CT-CT-F 1 0.000 0.0 -3. JCC,7,(1986),230 H1-CT-CT-F 1 0.19 0.0 1. Junmei et al, 1999 H1-CT-CT-Cl 1 0.000 0.0 -3. JCC,7,(1986),230 H1-CT-CT-Cl 1 0.25 0.0 1. Junmei et al, 1999 H1-CT-CT-Br 1 0.000 0.0 -3. JCC,7,(1986),230 H1-CT-CT-Br 1 0.55 0.0 1. Junmei et al, 1999 HC-CT-CT-OS 1 0.000 0.0 -3. JCC,7,(1986),230 HC-CT-CT-OS 1 0.25 0.0 1. Junmei et al, 1999 HC-CT-CT-OH 1 0.000 0.0 -3. JCC,7,(1986),230 HC-CT-CT-OH 1 0.25 0.0 1. Junmei et al, 1999 HC-CT-CT-F 1 0.000 0.0 -3. JCC,7,(1986),230 HC-CT-CT-F 1 0.19 0.0 1. Junmei et al, 1999 HC-CT-CT-Cl 1 0.000 0.0 -3. JCC,7,(1986),230 HC-CT-CT-Cl 1 0.25 0.0 1. Junmei et al, 1999 HC-CT-CT-Br 1 0.000 0.0 -3. JCC,7,(1986),230 HC-CT-CT-Br 1 0.55 0.0 1. Junmei et al, 1999 H1-CT-NT-LP 1 0.000 0.000 3.000 CT-CT-NT-LP 1 0.000 0.000 3.000 CT-C -N -LP 1 0.000 180.000 2.000 O -C -N -LP 1 0.000 180.000 2.000 H1-CT-OH-LP 1 0.000 0.000 3.000 CT-CT-OH-LP 1 0.000 0.000 3.000 H1-CT-OS-LP 1 0.000 0.000 3.000 H2-CT-OS-LP 1 0.000 0.000 3.000 CT-CT-OS-LP 1 0.000 0.000 3.000 CM-CM-OS-LP 1 0.000 180.000 2.000 HA-CM-OS-LP 1 0.000 180.000 2.000 H4-CM-OS-LP 1 0.000 180.000 2.000 X -X -C -O 10.5 180. 2. JCC,7,(1986),230 X -O2-C -O2 10.5 180. 2. JCC,7,(1986),230 X -X -N -H 1.0 180. 2. JCC,7,(1986),230 X -X -N2-H 1.0 180. 2. JCC,7,(1986),230 X -X -NA-H 1.0 180. 2. JCC,7,(1986),230 X -N2-CA-N2 10.5 180. 2. JCC,7,(1986),230 X -CT-N -CT 1.0 180. 2. JCC,7,(1986),230 X -X -CA-HA 1.1 180. 2. bsd.on C6H6 nmodes X -X -CW-H4 1.1 180. 2. X -X -CR-H5 1.1 180. 2. X -X -CV-H4 1.1 180. 2. X -X -CQ-H5 1.1 180. 2. X -X -CK-H5 1.1 180. 2. X -X -CM-H4 1.1 180. 2. X -X -CM-HA 1.1 180. 2. X -X -CA-H4 1.1 180. 2. bsd.on C6H6 nmodes X -X -CA-H5 1.1 180. 2. bsd.on C6H6 nmodes CB-CK-N*-CT 1.0 180. 2. C -CM-N*-CT 1.0 180. 2. dac guess, 9/94 CT-O -C -OH 10.5 180. 2. CT-CV-CC-NA 1.1 180. 2. CT-CW-CC-NB 1.1 180. 2. CT-CW-CC-NA 1.1 180. 2. CB-CT-C*-CW 1.1 180. 2. CA-CA-CA-CT 1.1 180. 2. C -CM-CM-CT 1.1 180. 2. dac guess, 9/94 CM-N2-CA-NC 1.1 180. 2. dac guess, 9/94 CB-N2-CA-NC 1.1 180. 2. dac, 10/94 N2-NA-CA-NC 1.1 180. 2. dac, 10/94 CA-CA-C -OH 1.1 180. 2. (not used in tyr!) CA-CA-CA-OH 1.1 180. 2. in tyr H5-O -C -OH 1.1 180. 2. Junmei et al.1999 H5-O -C -OS 1.1 180. 2. CM-CT-CM-HA 1.1 180. 2. Junmei et al.1999 Br-CA-CA-CA 1.1 180. 2. Junmei et al.1999 CM-H4-C -O 1.1 180. 2. Junmei et al.1999 C -CT-N -H 1.1 180. 2. Junmei et al.1999 C -CT-N -O 1.1 180. 2. Junmei et al.1999 HW OW 0000. 0000. 4. flag for fast water N NA N2 N* NC NB NT NY C* CA CB CC CD CK CM CN CQ CR CV CW CY CZ MOD4 RE H 0.6000 0.0157 !Ferguson base pair geom. HO 0.0000 0.0000 OPLS Jorgensen, JACS,110,(1988),1657 HS 0.6000 0.0157 W. Cornell CH3SH --> CH3OH FEP HC 1.4870 0.0157 OPLS H1 1.3870 0.0157 Veenstra et al JCC,8,(1992),963 H2 1.2870 0.0157 Veenstra et al JCC,8,(1992),963 H3 1.1870 0.0157 Veenstra et al JCC,8,(1992),963 HP 1.1000 0.0157 Veenstra et al JCC,8,(1992),963 HA 1.4590 0.0150 Spellmeyer H4 1.4090 0.0150 Spellmeyer, one electrowithdr. neighbor H5 1.3590 0.0150 Spellmeyer, two electrowithdr. neighbor HW 0.0000 0.0000 TIP3P water model HZ 1.4590 0.0150 H bonded to sp C (Howard et al JCC 16) O 1.6612 0.2100 OPLS O2 1.6612 0.2100 OPLS OW 1.7683 0.1520 TIP3P water model OH 1.7210 0.2104 OPLS OS 1.6837 0.1700 OPLS ether C* 1.9080 0.0860 Spellmeyer CT 1.9080 0.1094 Spellmeyer C 1.9080 0.0860 OPLS N 1.8240 0.1700 OPLS N3 1.8240 0.1700 OPLS S 2.0000 0.2500 W. Cornell CH3SH and CH3SCH3 FEP's SH 2.0000 0.2500 W. Cornell CH3SH and CH3SCH3 FEP's P 2.1000 0.2000 JCC,7,(1986),230; IM 2.47 0.1 Cl- Smith & Dang, JCP 1994,100:5,3757 Li 1.1370 0.0183 Li+ Aqvist JPC 1990,94,8021. (adapted) IP 1.8680 0.00277 Na+ Aqvist JPC 1990,94,8021. (adapted) Na 1.8680 0.00277 Na+ Aqvist JPC 1990,94,8021. (adapted) K 2.6580 0.000328 K+ Aqvist JPC 1990,94,8021. (adapted) Rb 2.9560 0.00017 Rb+ Aqvist JPC 1990,94,8021. (adapted) Cs 3.3950 0.0000806 Cs+ Aqvist JPC 1990,94,8021. (adapted) MG 0.7926 0.8947 Mg2+ Aqvist JPC 1990,94,8021.(adapted) C0 1.7131 0.459789 Ca2+ Aqvist JPC 1990,94,8021.(adapted) Zn 1.10 0.0125 Zn2+, Merz,PAK, JACS,113,8262,(1991) F 1.75 0.061 Gough et al. JCC 13,(1992),963. Cl 1.948 0.265 Fox, JPCB,102,8070,(98),flex.mdl CHCl3 Br 2.22 0.320 Junmei(?) I 2.35 0.40 JCC,7,(1986),230; IB 5.0 0.1 solvated ion for vacuum approximation LP 0.00 0.0000 lone pair END #################################################### Polarizabilities: Mg2+ 0.120 F- 0.9743 additional parameters of LP H1-CT-NT-LP 1 0.000 0.000 3.000 CT-CT-NT-LP 1 0.000 0.000 3.000 CT-C -N -LP 1 0.000 180.000 2.000 O -C -N -LP 1 0.000 180.000 2.000 H1-CT-OH-LP 1 0.000 0.000 3.000 CT-CT-OH-LP 1 0.000 0.000 3.000 H1-CT-OS-LP 1 0.000 0.000 3.000 H2-CT-OS-LP 1 0.000 0.000 3.000 CT-CT-OS-LP 1 0.000 0.000 3.000 CM-CM-OS-LP 1 0.000 180.000 2.000 HA-CM-OS-LP 1 0.000 180.000 2.000 H4-CM-OS-LP 1 0.000 180.000 2.000 MMTK-2.7.9/MMTK/ForceFields/ANMFF.py0000644000076600000240000000437612117632051017057 0ustar hinsenstaff00000000000000# Anisotropic network force field # # Written by Konrad Hinsen # """ Anisotropic Network Model """ __docformat__ = 'restructuredtext' from MMTK.ForceFields.ForceField import ForceField, ForceFieldData from MMTK import Utility from MMTK_forcefield import NonbondedList, NonbondedListTerm from MMTK_deformation import ANTerm from Scientific import N # # The deformation force field class # class AnisotropicNetworkForceField(ForceField): """ Effective harmonic force field for an ANM protein model """ def __init__(self, cutoff = None, scale_factor = 1.): """ :param cutoff: the cutoff for pair interactions. Pair interactions in periodic systems are calculated using the minimum-image convention; the cutoff should therefore never be larger than half the smallest edge length of the elementary cell. :type cutoff: float :param scale_factor: a global scaling factor :type scale_factor: float """ ForceField.__init__(self, 'anisotropic_network') self.arguments = (cutoff,) self.cutoff = cutoff self.scale_factor = scale_factor def ready(self, global_data): return True def evaluatorTerms(self, universe, subset1, subset2, global_data): nothing = N.zeros((0, 2), N.Int) if subset1 is not None: set1 = set(a.index for a in subset1.atomList()) set2 = set(a.index for a in subset2.atomList()) excluded_pairs = set(Utility.orderedPairs(list(set1-set2))) \ | set(Utility.orderedPairs(list(set2-set1))) excluded_pairs = N.array(list(excluded_pairs)) atom_subset = list(set1 | set2) atom_subset.sort() atom_subset = N.array(atom_subset) else: atom_subset = N.array([], N.Int) excluded_pairs = nothing nbl = NonbondedList(excluded_pairs, nothing, atom_subset, universe._spec, self.cutoff) update = NonbondedListTerm(nbl) cutoff = self.cutoff if cutoff is None: cutoff = 0. ev = ANTerm(universe._spec, nbl, cutoff, self.scale_factor) return [update, ev] MMTK-2.7.9/MMTK/ForceFields/BondedInteractions.py0000644000076600000240000001540312041514572022002 0ustar hinsenstaff00000000000000# This module implements classes that represent force fields # for bonded interactions. # # Written by Konrad Hinsen # from MMTK.ForceFields.ForceField import ForceField, ForceFieldData from MMTK import Utility from Scientific.Geometry import Vector from Scientific import N # # The base class BondedForceField provides the common # functionality for all bonded interactions. The derived # classes have to deal with determining functional forms # and parameters and providing the evaluation code # class BondedForceField(ForceField): def __init__(self, name): ForceField.__init__(self, name) self.type = 'bonded' def declareDependencies(self, global_data): global_data.add('nb_exclusions', self.__class__) def evaluatorParameters(self, universe, subset1, subset2, global_data): data = ForceFieldData() data.set('universe', universe) if subset1 is not None: label1 = Utility.uniqueAttribute() label2 = Utility.uniqueAttribute() for atom in subset1.atomList(): setattr(atom, label1, None) for atom in subset2.atomList(): setattr(atom, label2, None) for o in universe: for bu in o.bondedUnits(): if not hasattr(bu, 'bonds'): continue options = {'bonds': True, 'bond_angles': True, 'dihedrals': True, 'impropers': True} self.getOptions(bu, options) if options['bonds']: if subset1 is None: for bond in bu.bonds: self.addBondTerm(data, bond, bu, global_data) else: for bond in bu.bonds: atoms = [bond.a1, bond.a2] if _checkSubset(atoms, label1, label2): self.addBondTerm(data, bond, bu, global_data) if options['bond_angles']: if subset1 is None: for angle in bu.bonds.bondAngles(): self.addBondAngleTerm(data, angle, bu, global_data) else: for angle in bu.bonds.bondAngles(): atoms = [angle.a1, angle.a2, angle.ca] if _checkSubset(atoms, label1, label2): self.addBondAngleTerm(data, angle, bu, global_data) d = options['dihedrals'] i = options['impropers'] if d or i: if subset1 is None: for angle in bu.bonds.dihedralAngles(): if angle.improper and i: self.addImproperTerm(data, angle, bu, global_data) elif not angle.improper and d: self.addDihedralTerm(data, angle, bu, global_data) else: for angle in bu.bonds.dihedralAngles(): atoms = [angle.a1, angle.a2, angle.a3, angle.a4] if _checkSubset(atoms, label1, label2): if angle.improper and i: self.addImproperTerm(data, angle, bu, global_data) elif not angle.improper and d: self.addDihedralTerm(data, angle, bu, global_data) if subset1 is not None: for atom in subset1.atomList(): delattr(atom, label1) for atom in subset2.atomList(): delattr(atom, label2) global_data.add('initialized', self.__class__) return {'harmonic_distance_term': data.get('bonds'), 'harmonic_angle_term': data.get('angles'), 'cosine_dihedral_term': data.get('dihedrals')} def evaluatorTerms(self, universe, subset1, subset2, global_data): from MMTK_forcefield import HarmonicDistanceTerm, HarmonicAngleTerm, \ CosineDihedralTerm param = self.evaluatorParameters(universe, subset1, subset2, global_data) eval_list = [] bonds = param['harmonic_distance_term'] if bonds: indices = N.array(map(lambda b: b[:2], bonds)) parameters = N.array(map(lambda b: b[2:], bonds)) eval_list.append(HarmonicDistanceTerm(universe._spec, indices, parameters)) angles = param['harmonic_angle_term'] if angles: indices = N.array(map(lambda a: a[:3], angles)) parameters = N.array(map(lambda a: a[3:], angles)) eval_list.append(HarmonicAngleTerm(universe._spec, indices, parameters)) dihedrals = param['cosine_dihedral_term'] if dihedrals: def _dihedral_parameters(p): return [p[4], N.cos(p[5]), N.sin(p[5]), p[6]] indices = N.array(map(lambda d: d[:4], dihedrals)) parameters = N.array(map(_dihedral_parameters, dihedrals)) eval_list.append(CosineDihedralTerm(universe._spec, indices, parameters)) return eval_list def bondedForceFields(self): return [self] # The following methods must be overridden by derived classes. def addBondTerm(self, data, bond, object, global_data): raise AttributeError def addBondAngleTerm(self, data, angle, object, global_data): raise AttributeError def addDihedralTerm(self, data, dihedral, object, global_data): raise AttributeError def addImproperTerm(self, data, improper, object, global_data): raise AttributeError # The following methods are recommended for derived classes. # They allow to read out the force field specification, e.g. for # interfacing to other programs. def bonds(self, global_data): raise AttributeError def angles(self, global_data): raise AttributeError def dihedrals(self, global_data): raise AttributeError # Check if an energy term matches the specified atom subset def _checkSubset(atoms, label1, label2): s1 = False s2 = False for a in atoms: flag = False if hasattr(a, label1): s1 = True flag = True if hasattr(a, label2): s2 = True flag = True if not flag: return False return s1 and s2 MMTK-2.7.9/MMTK/ForceFields/BondFF.py0000644000076600000240000002061711662461640017332 0ustar hinsenstaff00000000000000# Detailed harmonic force field for proteins # # Written by Konrad Hinsen # """ Simplified harmonic force field for normal mode calculations """ __docformat__ = 'restructuredtext' from MMTK.ForceFields.NonBondedInteractions import NonBondedForceField from MMTK.ForceFields.Amber.AmberForceField import AmberBondedForceField from MMTK.ForceFields.MMForceField import MMAtomParameters from MMTK.ForceFields.ForceField import ForceField, CompoundForceField from MMTK import Utility from MMTK_deformation import DeformationTerm from Scientific.Geometry import Transformation from Scientific import N class BondForceField(AmberBondedForceField): def __init__(self, bonds=True, angles=True, dihedrals=True): AmberBondedForceField.__init__(self) self.arguments = (bonds, angles, dihedrals) self.bonded = self def addBondTerm(self, data, bond, object, global_data): if not self.arguments[0]: return a1 = bond.a1 a2 = bond.a2 i1 = a1.index i2 = a2.index global_data.add('excluded_pairs', Utility.normalizePair((i1, i2))) t1 = global_data.atom_type[a1] t2 = global_data.atom_type[a2] try: p = self.dataset.bondParameters(t1, t2) except KeyError: raise KeyError('No parameters for bond ' + `a1` + '--' + `a2`) if p is not None: d = data.get('universe').distance(a1, a2) data.add('bonds', (i1, i2, d, p[1])) def addBondAngleTerm(self, data, angle, object, global_data): if not self.arguments[1]: return a1 = angle.a1 a2 = angle.a2 ca = angle.ca i1 = a1.index i2 = a2.index ic = ca.index global_data.add('excluded_pairs', Utility.normalizePair((i1, i2))) t1 = global_data.atom_type[a1] t2 = global_data.atom_type[a2] tc = global_data.atom_type[ca] try: p = self.dataset.bondAngleParameters(t1, tc, t2) except KeyError: raise KeyError('No parameters for angle ' + `a1` + '--' + `ca` + '--' + `a2`) if p is not None: v1 = a1.position()-ca.position() v2 = a2.position()-ca.position() angle = v1.angle(v2) data.add('angles', (i1, ic, i2, angle) + p[1:]) def addDihedralTerm(self, data, dihedral, object, global_data): if not self.arguments[2]: return a1 = dihedral.a1 a2 = dihedral.a2 a3 = dihedral.a3 a4 = dihedral.a4 i1 = a1.index i2 = a2.index i3 = a3.index i4 = a4.index global_data.add('1_4_pairs', Utility.normalizePair((i1, i4))) t1 = global_data.atom_type[a1] t2 = global_data.atom_type[a2] t3 = global_data.atom_type[a3] t4 = global_data.atom_type[a4] terms = self.dataset.dihedralParameters(t1, t2, t3, t4) if terms is not None: v1 = a1.position()-a2.position() v2 = a2.position()-a3.position() v3 = a4.position()-a3.position() a = v1.cross(v2).normal() b = v3.cross(v2).normal() cos = a*b sin = b.cross(a)*v2/v2.length() dihedral = Transformation.angleFromSineAndCosine(sin, cos) if dihedral > N.pi: dihedral -= 2.*N.pi for p in terms: if p[2] != 0.: mult = p[0] phase = N.fmod(N.pi-mult*dihedral, 2.*N.pi) if phase < 0.: phase += 2.*N.pi data.add('dihedrals', (i1, i2, i3, i4, p[0], phase) + p[2:]) def addImproperTerm(self, data, improper, object, global_data): if not self.arguments[2]: return a1 = improper.a1 a2 = improper.a2 a3 = improper.a3 a4 = improper.a4 i1 = a1.index i2 = a2.index i3 = a3.index i4 = a4.index t1 = global_data.atom_type[a1] t2 = global_data.atom_type[a2] t3 = global_data.atom_type[a3] t4 = global_data.atom_type[a4] terms = self.dataset.improperParameters(t1, t2, t3, t4) if terms is not None: atoms = [(t2,i2,a2), (t3,i3,a3), (t4,i4,a4)] atoms.sort(_order) i2, i3, i4 = tuple(map(lambda t: t[1], atoms)) a2, a3, a4 = tuple(map(lambda t: t[2], atoms)) v1 = a2.position()-a3.position() v2 = a3.position()-a1.position() v3 = a4.position()-a1.position() a = v1.cross(v2).normal() b = v3.cross(v2).normal() cos = a*b sin = b.cross(a)*v2/v2.length() dihedral = Transformation.angleFromSineAndCosine(sin, cos) if dihedral > N.pi: dihedral -= 2.*N.pi for p in terms: if p[2] != 0.: mult = p[0] phase = N.fmod(N.pi-mult*dihedral, 2.*N.pi) if phase < 0.: phase += 2.*N.pi data.add('dihedrals', (i2, i3, i1, i4, p[0], phase) + p[2:]) def _order(a1, a2): ret = cmp(a1[0], a2[0]) if ret == 0: ret = cmp(a1[1], a2[1]) return ret # # Mid-range harmonic force field to complement bonded part # class MidrangeForceField(NonBondedForceField): def __init__(self, fc_length = 0.3, cutoff = 0.5, factor = 59908.8): NonBondedForceField.__init__(self, 'harmonic') self.arguments = (fc_length, cutoff, factor) self.fc_length = fc_length self.cutoff = cutoff self.factor = factor self.one_four_factor = 0.5 def evaluatorParameters(self, universe, subset1, subset2, global_data): excluded_pairs, one_four_pairs, atom_subset = \ self.excludedPairs(subset1, subset2, global_data) if self.cutoff is None: cutoff = 0. else: cutoff = self.cutoff return {'deformation_term': {'cutoff': cutoff, 'fc_length': self.fc_length, 'scale_factor': self.factor, 'one_four_factor': self.one_four_factor}, 'nonbonded': {'excluded_pairs': excluded_pairs, 'one_four_pairs': one_four_pairs, 'atom_subset': atom_subset}, } def evaluatorTerms(self, universe, subset1, subset2, global_data): param = self.evaluatorParameters(universe, subset1, subset2, global_data)['deformation_term'] nblist, nblist_update = \ self.nonbondedList(universe, subset1, subset2, global_data) ev = DeformationTerm(universe._spec, nblist, param['fc_length'], param['cutoff'], param['scale_factor'], param['one_four_factor']) return [nblist_update, ev] class HarmonicForceField(MMAtomParameters, CompoundForceField): """ Simplified harmonic force field for normal mode calculations This force field is made up of the bonded terms from the Amber 94 force field with the equilibrium positions of all terms changed to the corresponding values in the input configuration, such that the input configuration becomes an energy minimum by construction. The nonbonded terms are replaced by a generic short-ranged deformation term. For a description of this force field, see: | Hinsen & Kneller, J. Chem. Phys. 24, 10766 (1999) For an application to DNA, see: | Viduna, Hinsen & Kneller, Phys. Rev. E 3, 3986 (2000) """ def __init__(self, fc_length = 0.45, cutoff = 0.6, factor = 400.): self.arguments = (fc_length, cutoff, factor) self.bonded = BondForceField(1, 1, 1) self.midrange = MidrangeForceField(fc_length, cutoff, factor) apply(CompoundForceField.__init__, (self, self.bonded, self.midrange)) description = ForceField.description if __name__ == '__main__': from MMTK import * from MMTK.Proteins import Protein from MMTK.NormalModes import NormalModes world = InfiniteUniverse(HarmonicForceField(bonds=1,angles=1,dihedrals=1)) world.protein = Protein('bala1') print world.energy() modes = NormalModes(world) for m in modes: print m MMTK-2.7.9/MMTK/ForceFields/CalphaFF.py0000644000076600000240000000463112117632051017626 0ustar hinsenstaff00000000000000# C-alpha force field # # Written by Konrad Hinsen # """ C-alpha force field for protein normal mode analysis (Elastic Network Model) """ __docformat__ = 'restructuredtext' from MMTK.ForceFields.ForceField import ForceField, ForceFieldData from MMTK import Utility from MMTK_forcefield import NonbondedList, NonbondedListTerm from MMTK_deformation import CalphaTerm from Scientific import N # # The deformation force field class # class CalphaForceField(ForceField): """ Effective harmonic force field for a C-alpha protein model """ def __init__(self, cutoff = None, scale_factor = 1., version=1): """ :param cutoff: the cutoff for pair interactions, should be at least 2.5 nm. Pair interactions in periodic systems are calculated using the minimum-image convention; the cutoff should therefore never be larger than half the smallest edge length of the elementary cell. :type cutoff: float :param scale_factor: a global scaling factor :type scale_factor: float """ ForceField.__init__(self, 'calpha') self.arguments = (cutoff,) self.cutoff = cutoff self.scale_factor = scale_factor self.version = version def ready(self, global_data): return True def evaluatorTerms(self, universe, subset1, subset2, global_data): nothing = N.zeros((0, 2), N.Int) if subset1 is not None: set1 = set(a.index for a in subset1.atomList()) set2 = set(a.index for a in subset2.atomList()) excluded_pairs = set(Utility.orderedPairs(list(set1-set2))) \ | set(Utility.orderedPairs(list(set2-set1))) excluded_pairs = N.array(list(excluded_pairs)) atom_subset = list(set1 | set2) atom_subset.sort() atom_subset = N.array(atom_subset) else: atom_subset = N.array([], N.Int) excluded_pairs = nothing nbl = NonbondedList(excluded_pairs, nothing, atom_subset, universe._spec, self.cutoff) update = NonbondedListTerm(nbl) cutoff = self.cutoff if cutoff is None: cutoff = 0. ev = CalphaTerm(universe._spec, nbl, cutoff, self.scale_factor, self.version) return [update, ev] MMTK-2.7.9/MMTK/ForceFields/DeformationFF.py0000644000076600000240000000564012117632051020706 0ustar hinsenstaff00000000000000# Deformation force field # # Written by Konrad Hinsen # """ Deformation force field (elastic network model) For proteins, CalphaForceField is usually a better choice. """ __docformat__ = 'restructuredtext' from MMTK.ForceFields.ForceField import ForceField, ForceFieldData from MMTK import Utility from MMTK_forcefield import NonbondedList, NonbondedListTerm from MMTK_deformation import DeformationTerm from Scientific import N # # The deformation force field class # class DeformationForceField(ForceField): """ Deformation force field for protein normal mode calculations The pair interaction force constant has the form k(r)=factor*exp(-(r**2-0.01)/range**2). The default value for range is appropriate for a C-alpha model of a protein. The offset of 0.01 is a convenience for defining factor; with this definition, factor is the force constant for a distance of 0.1nm. """ def __init__(self, fc_length = 0.7, cutoff = 1.2, factor = 46402.): """ :param fc_length: a range parameter :type fc_length: float :param cutoff: the cutoff for pair interactions, should be at least 2.5 nm. Pair interactions in periodic systems are calculated using the minimum-image convention; the cutoff should therefore never be larger than half the smallest edge length of the elementary cell. :type cutoff: float :param factor: a global scaling factor :type factor: float """ self.arguments = (fc_length, cutoff, factor) ForceField.__init__(self, 'deformation') self.arguments = (fc_length, cutoff, factor) self.fc_length = fc_length self.cutoff = cutoff self.factor = factor def ready(self, global_data): return True def evaluatorTerms(self, universe, subset1, subset2, global_data): nothing = N.zeros((0, 2), N.Int) if subset1 is not None: set1 = set(a.index for a in subset1.atomList()) set2 = set(a.index for a in subset2.atomList()) excluded_pairs = set(Utility.orderedPairs(list(set1-set2))) \ | set(Utility.orderedPairs(list(set2-set1))) excluded_pairs = N.array(list(excluded_pairs)) atom_subset = list(set1 | set2) atom_subset.sort() atom_subset = N.array(atom_subset) else: atom_subset = N.array([], N.Int) excluded_pairs = nothing nbl = NonbondedList(excluded_pairs, nothing, atom_subset, universe._spec, self.cutoff) update = NonbondedListTerm(nbl) cutoff = self.cutoff if cutoff is None: cutoff = 0. ev = DeformationTerm(universe._spec, nbl, self.fc_length, self.cutoff, self.factor, 1.) return [update, ev] MMTK-2.7.9/MMTK/ForceFields/force_fields0000644000076600000240000000120612041514572020215 0ustar hinsenstaff00000000000000Amber94ForceField Amber Amber94ForceField Amber99ForceField Amber Amber99ForceField Amber12SBForceField Amber Amber12SBForceField OPLSForceField Amber OPLSForceField LennardJonesForceField LennardJonesFF LennardJonesForceField DeformationForceField DeformationFF DeformationForceField CalphaForceField CalphaFF CalphaForceField SPCEForceField SPCEFF SPCEForceField HarmonicForceField BondFF HarmonicForceField AnisotropicNetworkForceField ANMFF AnisotropicNetworkForceField MMTK-2.7.9/MMTK/ForceFields/ForceField.py0000644000076600000240000003100512117632051020217 0ustar hinsenstaff00000000000000# This module implements classes that represent general force fields # and force field evaluators. # # Written by Konrad Hinsen # from MMTK import ParticleProperties, Universe, Utility from Scientific import N import copy, itertools, operator from MMTK_energy_term import PyEnergyTerm as EnergyTerm # Class definitions # # The base class ForceField contains common operations for all force fields # class ForceField(object): def __init__(self, name): self.name = name self.type = None def __reduce__(self): return type(self), self.arguments def evaluatorParameters(self, universe, subset1, subset2, global_data): # must be defined by derived classes raise NotImplementedError def evaluatorTerms(self, universe, subset1, subset2, global_data): # must be defined by derived classes raise NotImplementedError def __add__(self, other): return CompoundForceField(self, other) def getOptions(self, object, options): attr = self.type + '_options' if hasattr(object, attr): for key, value in getattr(object, attr).items(): options[key] = value attr = self.name + '_options' if hasattr(object, attr): for key, value in getattr(object, attr).items(): options[key] = value def ready(self, global_data): return True def declareDependencies(self, global_data): pass def bondedForceFields(self): return [] def bondLengthDatabase(self, universe): return None def description(self): return self.__class__.__module__ + '.' + \ self.__class__.__name__ + `self.arguments` def getAtomParameterIndices(self, atoms): universe = None indices = [] for a in atoms: if isinstance(a, int): indices.append(a) else: if universe is None: universe = a.universe() universe.configuration() else: if universe is not a.universe(): raise ValueError("Atom parameters %s are not all " "in the same universe" % repr(atoms)) indices.append(a.index) return tuple(indices) # # A CompoundForceField represents the sum of its component force fields # class CompoundForceField(ForceField): def __init__(self, *args): self.fflist = list(args) self.name = '*' self.type = 'compound' self.arguments = args def declareDependencies(self, global_data): for ff in self.fflist: ff.declareDependencies(global_data) def flatFFList(self): flat_list = [] for ff in self.fflist: if isinstance(ff, CompoundForceField): flat_list.extend(ff.flatFFList()) else: flat_list.append(ff) return flat_list # def evaluatorParameters(self, system, subset1, subset2, global_data): # self.declareDependencies() # parameters = {} # remaining = self.flatFFList() # while remaining: # done = [] # for ff in remaining: # if ff.ready(global_data): # params = ff.evaluatorParameters(system, subset1, subset2, # global_data) # if not isinstance(params, dict): # raise ValueError("evaluator parameters are not a dict") # for key, value in params.items(): # parameters[key] = _combine(value, # parameters.get(key, None)) # done.append(ff) # if not done: # raise TypeError("Cyclic force field dependence") # for ff in done: # remaining.remove(ff) # return parameters # def evaluatorTerms(self, system, subset1, subset2, global_data): # self.declareDependencies() # eval_objects = [] # remaining = self.flatFFList() # while remaining: # done = [] # for ff in remaining: # if ff.ready(global_data): # terms = ff.evaluatorTerms(system, subset1, subset2, # global_data) # if not isinstance(terms, list): # raise ValueError("evaluator term list not a list") # eval_objects.extend(terms) # done.append(ff) # if not done: # raise TypeError("Cyclic force field dependence") # for ff in done: # remaining.remove(ff) # return eval_objects def evaluatorParameters(self, system, subset1, subset2, global_data): parameters = {} def add_item(params): if not isinstance(params, dict): raise ValueError("evaluator parameters are not a dict") for key, value in params.items(): parameters[key] = _combine(value, parameters.get(key, None)) self._compoundEvaluator(system, subset1, subset2, global_data, 'evaluatorParameters', add_item) return parameters def evaluatorTerms(self, system, subset1, subset2, global_data): eval_objects = [] def add_item(terms): if not isinstance(terms, list): raise ValueError("evaluator term list not a list") eval_objects.extend(terms) self._compoundEvaluator(system, subset1, subset2, global_data, 'evaluatorTerms', add_item) return eval_objects def _compoundEvaluator(self, system, subset1, subset2, global_data, method, add_item): self.declareDependencies(global_data) remaining = self.flatFFList() while remaining: done = [] for ff in remaining: if ff.ready(global_data): item = getattr(ff, method)(system, subset1, subset2, global_data) add_item(item) done.append(ff) if not done: raise TypeError("Cyclic force field dependence") for ff in done: remaining.remove(ff) def bondedForceFields(self): return reduce(operator.add, [f.bondedForceFields() for f in self.fflist]) def description(self): return '+'.join([f.description() for f in self.fflist]) def _combine(params1, params2): if params1 is None: return params2 if params2 is None: return params1 if isinstance(params1, list) and isinstance(params2, list): return params1 + params2 if params1 == params2: return params1 raise TypeError, "_combine not implemented yet" # # This class serves to define data containers used in # force field initialization. # class ForceFieldData(object): def __init__(self): self.dict = {} def add(self, tag, value): try: self.dict[tag].append(value) except KeyError: self.dict[tag] = [value] def get(self, tag): try: return self.dict[tag] except KeyError: return [] def set(self, tag, value): self.dict[tag] = value # Type check functions def isForceField(x): return isinstance(x, ForceField) def isCompoundForceField(x): return isinstance(x, CompoundForceField) # # Force field evaluator support functions. # These functions are meant to be used with force field evaluators # implemented in Python using automatic derivatives. They put the # first and second derivative information into the right places. # def addToGradients(coordinates, indices, vectors): for index, vector in zip(indices, vectors): coordinates[index] += vector.array def addToForceConstants(total_fc, indices, small_fc): indices = zip(indices, range(len(indices))) for i1, i2 in itertools.chain(itertools.izip(indices, indices), Utility.pairs(indices)): ii1, jj1 = i1 ii2, jj2 = i2 if ii1 > ii2: ii1, ii2 = ii2, ii1 jj1, jj2 = jj2, jj1 jj1 = 3*jj1 jj2 = 3*jj2 total_fc[ii1,:,ii2,:] += small_fc[jj1:jj1+3, jj2:jj2+3] # # High-level energy evaluator (i.e. the Python interface) # class EnergyEvaluator(object): def __init__(self, universe, force_field, subset1=None, subset2=None, threads=None, mpi_communicator=None): if not Universe.isUniverse(universe): raise TypeError("energy evaluator defined only for universes") self.universe = universe self.universe_version = self.universe._version self.ff = force_field self.configuration = self.universe.configuration() self.global_data = ForceFieldData() if subset1 is not None and subset2 is None: subset2 = subset1 terms = self.ff.evaluatorTerms(self.universe, subset1, subset2, self.global_data) if not isinstance(terms, list): raise ValueError("evaluator term list not a list") from MMTK_forcefield import Evaluator if threads is None: import MMTK.ForceFields threads = MMTK.ForceFields.default_energy_threads; self.evaluator = Evaluator(N.array(terms), threads, mpi_communicator) def checkUniverseVersion(self): if self.universe_version != self.universe._version: raise ValueError('the universe has been modified') def CEvaluator(self): return self.evaluator def __call__(self, gradients = None, force_constants = None, small_change=False): self.checkUniverseVersion() args = [self.configuration.array] if ParticleProperties.isParticleProperty(gradients): args.append(gradients.array) elif isinstance(gradients, N.array_type): gradients = \ ParticleProperties.ParticleVector(self.universe, gradients) args.append(gradients.array) elif gradients: gradients = ParticleProperties.ParticleVector(self.universe, None) args.append(gradients.array) else: args.append(None) if ParticleProperties.isParticleProperty(force_constants): args.append(force_constants.array) elif isinstance(force_constants, N.array_type): force_constants = \ ParticleProperties.SymmetricPairTensor(self.universe, force_constants) args.append(force_constants.array) else: sparse_type = None try: from MMTK_forcefield import SparseForceConstants sparse_type = type(SparseForceConstants(2, 2)) except ImportError: pass if isinstance(force_constants, sparse_type): args.append(force_constants) elif force_constants: force_constants = \ ParticleProperties.SymmetricPairTensor(self.universe, None) args.append(force_constants.array) else: args.append(None) args.append(small_change) self.universe.acquireReadStateLock() try: energy = apply(self.evaluator, tuple(args)) finally: self.universe.releaseReadStateLock() if force_constants: return energy, gradients, force_constants elif gradients: return energy, gradients else: return energy def lastEnergyTerms(self): dict = {} values = self.evaluator.last_energy_values i = 0 for term_object in self.evaluator: for name in term_object.term_names: dict[name] = dict.get(name, 0.) + values[i] i = i + 1 for name, value in dict.items(): index = name.find('/') if index >= 0: category = name[:index] dict[category] = dict.get(category, 0.) + value for name, value in dict.items(): if '/' in name and value == 0.: del dict[name] return dict def lastVirial(self): return self.evaluator.last_energy_values[-1] MMTK-2.7.9/MMTK/ForceFields/ForceFieldTest.py0000644000076600000240000000677412117632051021076 0ustar hinsenstaff00000000000000# This module implements test functions. # # Written by Konrad Hinsen # """ Force field consistency tests """ __docformat__ = 'restructuredtext' from MMTK import Utility from Scientific.Geometry import Vector, ex, ey, ez from Scientific import N import itertools # # Check consistency of energies and gradients. # def gradientTest(universe, atoms = None, delta = 0.0001): """ Test gradients by comparing to numerical derivatives of the energy. :param universe: the universe on which the test is performed :type universe: :class:`~MMTK.Universe.Universe` :param atoms: the atoms of the universe for which the gradient is tested (default: all atoms) :type atoms: list :param delta: the step size used in calculating the numerical derivatives :type delta: float """ e0, grad = universe.energyAndGradients() print 'Energy: ', e0 if atoms is None: atoms = universe.atomList() for a in atoms: print a print grad[a] num_grad = [] for v in [ex, ey, ez]: x = a.position() a.setPosition(x+delta*v) eplus = universe.energy() a.setPosition(x-delta*v) eminus = universe.energy() a.setPosition(x) num_grad.append(0.5*(eplus-eminus)/delta) print Vector(num_grad) # # Check consistency of gradients and force constants. # def forceConstantTest(universe, atoms = None, delta = 0.0001): """ Test force constants by comparing to the numerical derivatives of the gradients. :param universe: the universe on which the test is performed :type universe: :class:`~MMTK.Universe.Universe` :param atoms: the atoms of the universe for which the gradient is tested (default: all atoms) :type atoms: list :param delta: the step size used in calculating the numerical derivatives :type delta: float """ e0, grad0, fc = universe.energyGradientsAndForceConstants() if atoms is None: atoms = universe.atomList() for a1, a2 in itertools.chain(itertools.izip(atoms, atoms), Utility.pairs(atoms)): print a1, a2 print fc[a1, a2] num_fc = [] for v in [ex, ey, ez]: x = a1.position() a1.setPosition(x+delta*v) e_plus, grad_plus = universe.energyAndGradients() a1.setPosition(x-delta*v) e_minus, grad_minus = universe.energyAndGradients() a1.setPosition(x) num_fc.append(0.5*(grad_plus[a2]-grad_minus[a2])/delta) print N.array(map(lambda a: a.array, num_fc)) # # Check consistency of gradients and virial # def virialTest(universe): """ Test the virial by comparing to an explicit computation from positions and gradients. :param universe: the universe on which the test is performed :type universe: :class:`~MMTK.Universe.Universe` """ ev = universe.energyEvaluator() e, grad = ev(gradients = True) virial = ev.lastVirial() conf = universe.configuration() print virial, -(conf*grad).sumOverParticles() if __name__ == '__main__': from MMTK import * from MMTK.ForceFields import Amber94ForceField delta = 0.001 world = InfiniteUniverse(Amber94ForceField()) m = Molecule('water') m.O.translateBy(Vector(0.,0.,0.01)) m.H1.translateBy(Vector(0.01,0.,0.)) atoms = None world.addObject(m) gradientTest(world, atoms, delta) forceConstantTest(world, atoms, delta) virialTest(world) MMTK-2.7.9/MMTK/ForceFields/LennardJonesFF.py0000644000076600000240000000372211662461640021030 0ustar hinsenstaff00000000000000# A Lennard-Jones force fields for simple liquids # # Written by Konrad Hinsen # """ Lennard-Jones force field for simple liquids """ __docformat__ = 'restructuredtext' from MMTK.ForceFields.NonBondedInteractions import LJForceField from Scientific.Geometry import Vector import copy # # The force field class # class LennardJonesForceField(LJForceField): """ Lennard-Jones force field The Lennard-Jones parameters are taken from the atom attributes LJ_radius and LJ_energy. The pair interaction energy has the form U(r)=4*LJ_energy*((LJ_radius/r)**12-(LJ_radius/r)**6). """ def __init__(self, cutoff = None): """ :param cutoff: a cutoff value or None, meaning no cutoff. Pair interactions in periodic systems are calculated using the minimum-image convention; the cutoff should therefore never be larger than half the smallest edge length of the elementary cell. :type cutoff: float """ self.arguments = (cutoff, ) LJForceField.__init__(self, 'LJ', cutoff) self.lj_14_factor = 1. def ready(self, global_data): return True def collectParameters(self, universe, global_data): if not hasattr(global_data, 'lj_parameters'): parameters = {} for o in universe: for a in o.atomList(): parameters[a.symbol] = (a.LJ_energy, a.LJ_radius, 0) global_data.lj_parameters = parameters def _atomType(self, object, atom, global_data): return atom.symbol def _ljParameters(self, type, global_data): return global_data.lj_parameters[type] def evaluatorParameters(self, universe, subset1, subset2, global_data): self.collectParameters(universe, global_data) return LJForceField.evaluatorParameters(self, universe, subset1, subset2, global_data) MMTK-2.7.9/MMTK/ForceFields/MMForceField.py0000644000076600000240000003667412117632051020472 0ustar hinsenstaff00000000000000# This module provides the basic mechanism for Molecular-Mechanics-type # force fields that use the standard terms (bonds, angles, dihedrals, # Lennard-Jones, electrostatic). An implementation of a specific force # field (e.g. Amber94) only needs to provide access to its parameters. # # The implementation is based on the following assumptions about the # force field: # # - Each atom has an atom type, taken from the database, which determines # bonded and Lennard-Jones interactions. The specific force field has # to provide the mapping from types to force field parameters. # - Each atom has a partial charge, taken from the database, which # is used for the electrostatic term. # # Each specific force field must supply a paramater set object to # the generic force field classes defined here. This object provides # method that return the parameters for each force field term. # # The only concrete force field that has been implemented is Amber 94/99; # implementing other MM force fields will probably require minor # modifications to this module. # # Written by Konrad Hinsen # from MMTK.ForceFields.BondedInteractions import BondedForceField from MMTK.ForceFields.NonBondedInteractions import \ NonBondedForceField, LJForceField, ElectrostaticForceField, \ ESEwaldForceField from MMTK.ForceFields.ForceField import ForceField, CompoundForceField, \ ForceFieldData from MMTK import ParticleProperties, Utility from Scientific.Geometry import Vector import copy # # Mix-in class that ensures availability of atom parameters. # class MMAtomParameters(object): def collectAtomTypesAndIndices(self, universe, global_data): if not hasattr(global_data, 'atom_type'): type = {} for o in universe: for a in o.atomList(): type[a] = o.getAtomProperty(a, self.dataset .atom_type_property) global_data.atom_type = type def collectCharges(self, universe, global_data): if not hasattr(global_data, 'charge'): charge = {} for o in universe: for a in o.atomList(): charge[a] = o.getAtomProperty(a, self.dataset .charge_property) global_data.charge = charge def _atomType(self, object, atom, global_data): return global_data.atom_type[atom] def _charge(self, object, atom, global_data): return global_data.charge[atom] def _ljParameters(self, type, global_data): return self.dataset.ljParameters(type) # # Bonded interactions # class MMBondedForceField(MMAtomParameters, BondedForceField): def __init__(self, name, parameters, scale_factor=1.): self.dataset = parameters self.scale_factor = scale_factor BondedForceField.__init__(self, name) self.arguments = (name, parameters, scale_factor) def evaluatorParameters(self, universe, subset1, subset2, global_data): self.collectAtomTypesAndIndices(universe, global_data) return BondedForceField.evaluatorParameters(self, universe, subset1, subset2, global_data) def addBondTerm(self, data, bond, object, global_data): a1 = bond.a1 a2 = bond.a2 i1 = a1.index i2 = a2.index global_data.add('excluded_pairs', Utility.normalizePair((i1, i2))) t1 = global_data.atom_type[a1] t2 = global_data.atom_type[a2] try: p = self.dataset.bondParameters(t1, t2) except KeyError: raise KeyError(('No parameters for bond %s (atom type %s)' + ' - %s (atom type %s)') % (str(a1), t1, str(a2), t2)) if p is not None and p[1] != 0.: data.add('bonds', (i1, i2, p[0], p[1]*self.scale_factor)) def addBondAngleTerm(self, data, angle, object, global_data): a1 = angle.a1 a2 = angle.a2 ca = angle.ca i1 = a1.index i2 = a2.index ic = ca.index global_data.add('excluded_pairs', Utility.normalizePair((i1, i2))) t1 = global_data.atom_type[a1] t2 = global_data.atom_type[a2] tc = global_data.atom_type[ca] try: p = self.dataset.bondAngleParameters(t1, tc, t2) except KeyError: raise KeyError(('No parameters for angle %s (atom type %s)' + ' - %s (atom type %s) - %s (atom type %s)') % (str(a1), t1, str(ca), tc, str(a2), t2)) if p is not None and p[1] != 0.: data.add('angles', (i1, ic, i2, p[0], p[1]*self.scale_factor)) def addDihedralTerm(self, data, dihedral, object, global_data): a1 = dihedral.a1 a2 = dihedral.a2 a3 = dihedral.a3 a4 = dihedral.a4 i1 = a1.index i2 = a2.index i3 = a3.index i4 = a4.index global_data.add('1_4_pairs', Utility.normalizePair((i1, i4))) t1 = global_data.atom_type[a1] t2 = global_data.atom_type[a2] t3 = global_data.atom_type[a3] t4 = global_data.atom_type[a4] terms = self.dataset.dihedralParameters(t1, t2, t3, t4) if terms is not None: for p in terms: if p[2] != 0.: data.add('dihedrals', (i1, i2, i3, i4, p[0], p[1], p[2]*self.scale_factor)) def addImproperTerm(self, data, improper, object, global_data): a1 = improper.a1 a2 = improper.a2 a3 = improper.a3 a4 = improper.a4 i1 = a1.index i2 = a2.index i3 = a3.index i4 = a4.index t1 = global_data.atom_type[a1] t2 = global_data.atom_type[a2] t3 = global_data.atom_type[a3] t4 = global_data.atom_type[a4] terms = self.dataset.improperParameters(t1, t2, t3, t4) if terms is not None: atoms = [(t2,i2), (t3,i3), (t4,i4)] atoms.sort(_order) i2, i3, i4 = tuple(map(lambda t: t[1], atoms)) for p in terms: if p[2] != 0.: data.add('dihedrals', (i1, i2, i3, i4, p[0], p[1], p[2]*self.scale_factor)) def bondLengthDatabase(self, universe): return MMBondLengthDatabase(self, universe, self.dataset) def bonds(self, global_data): return self.data.get('bonds') def angles(self, global_data): return self.data.get('angles') def dihedrals(self, global_data): return self.data.get('dihedrals') def _order(a1, a2): ret = cmp(a1[0], a2[0]) if ret == 0: ret = cmp(a1[1], a2[1]) return ret # # Nonbonded interactions # class MMLJForceField(MMAtomParameters, LJForceField): def __init__(self, name, parameters, cutoff, scale_factor=1.): self.dataset = parameters LJForceField.__init__(self, name, cutoff, scale_factor) self.lj_14_factor = self.dataset.lennard_jones_1_4 self.arguments = (name, parameters, cutoff, scale_factor) def evaluatorParameters(self, universe, subset1, subset2, global_data): self.collectAtomTypesAndIndices(universe, global_data) return LJForceField.evaluatorParameters(self, universe, subset1, subset2, global_data) class MMESForceField(MMAtomParameters, ElectrostaticForceField): def __init__(self, name, parameters, cutoff, scale_factor=1.): self.dataset = parameters ElectrostaticForceField.__init__(self, name, cutoff, scale_factor) self.es_14_factor = self.dataset.electrostatic_1_4 self.arguments = (name, parameters, cutoff, scale_factor) def evaluatorParameters(self, universe, subset1, subset2, global_data): self.collectCharges(universe, global_data) return ElectrostaticForceField.evaluatorParameters(self, universe, subset1, subset2, global_data) class MMEwaldESForceField(MMAtomParameters, ESEwaldForceField): def __init__(self, name, parameters, options): self.dataset = parameters ESEwaldForceField.__init__(self, name, options) self.es_14_factor = self.dataset.electrostatic_1_4 self.arguments = (name, parameters, options) def evaluatorParameters(self, universe, subset1, subset2, global_data): self.collectCharges(universe, global_data) return ESEwaldForceField.evaluatorParameters(self, universe, subset1, subset2, global_data) class MMNonbondedForceField(MMAtomParameters, NonBondedForceField): def __init__(self, name, parameters, lj_options, es_options): self.name = name self.dataset = parameters if lj_options is None: self.lj_options = {} elif type(lj_options) != type({}): self.lj_options = {'method': 'cutoff', 'cutoff': lj_options} else: self.lj_options = copy.copy(lj_options) if es_options is None: self.es_options = {} elif type(es_options) != type({}): self.es_options = {'method': 'cutoff', 'cutoff': es_options} else: self.es_options = copy.copy(es_options) self.arguments = (name, parameters, lj_options, es_options) def charge(self, atoms): total = 0. for a in atoms.atomList(): charge = a.topLevelChemicalObject() \ .getAtomProperty(a, self.dataset.charge_property) total = total + charge return total def charges(self, universe): q = ParticleProperties.ParticleScalar(universe) for object in universe: for atom in object.atomList(): q[atom] = object.getAtomProperty(atom, self.dataset.charge_property) return q def dipole(self, atoms, reference = None): if reference is None: reference = atoms.centerOfMass() total = Vector(0., 0., 0.) for a in atoms.atomList(): charge = a.topLevelChemicalObject() \ .getAtomProperty(a, self.dataset.charge_property) total = total + charge*(a.position()-reference) return total def _getLJForceField(self, universe): lj_method = self.lj_options.get('method', 'direct') lj_scale_factor = self.lj_options.get('scale_factor', 1.) if lj_method == 'direct': return MMLJForceField(self.name, self.dataset, None, lj_scale_factor) elif lj_method == 'cutoff': return MMLJForceField(self.name, self.dataset, self.lj_options['cutoff'], lj_scale_factor) else: raise ValueError("Unknown LJ method: " + lj_method) def _getESForceField(self, universe): if universe.is_periodic: es_method = 'ewald' else: es_method = 'direct' es_method = self.es_options.get('method', es_method) es_scale_factor = self.es_options.get('scale_factor', 1.) if es_method == 'ewald': return MMEwaldESForceField(self.name, self.dataset, self.es_options) elif es_method == 'screened': options = copy.copy(self.es_options) options['method'] = 'ewald' options['real_cutoff'] = options['cutoff'] options['no_reciprocal_sum'] = 1 del options['cutoff'] return MMEwaldESForceField(self.name, self.dataset, options) elif es_method == 'multipole': return MMMPESForceField(self.name, self.dataset, self.es_options) elif es_method == 'direct': return MMESForceField(self.name, self.dataset, None, es_scale_factor) elif es_method == 'cutoff': return MMESForceField(self.name, self.dataset, self.es_options['cutoff'], es_scale_factor) else: raise ValueError("Unknown electrostatics method: " + es_method) def evaluatorParameters(self, universe, subset1, subset2, global_data): self.collectAtomTypesAndIndices(universe, global_data) self.collectCharges(universe, global_data) lj = self._getLJForceField(universe) es = self._getESForceField(universe) compound = CompoundForceField(lj, es) return compound.evaluatorParameters(universe, subset1, subset2, global_data) def evaluatorTerms(self, universe, subset1, subset2, global_data): self.collectAtomTypesAndIndices(universe, global_data) self.collectCharges(universe, global_data) lj = self._getLJForceField(universe) es = self._getESForceField(universe) compound = CompoundForceField(lj, es) return compound.evaluatorTerms(universe, subset1, subset2, global_data) # # The total force field # class MMForceField(MMAtomParameters, CompoundForceField): def __init__(self, name, parameters, lj_options, es_options, bonded_scale_factor = 1.): self.dataset = parameters self.bonded = MMBondedForceField(name, parameters, bonded_scale_factor) self.nonbonded = MMNonbondedForceField(name, parameters, lj_options, es_options) apply(CompoundForceField.__init__, (self, self.bonded, self.nonbonded)) self.arguments = (name, parameters, lj_options, es_options, bonded_scale_factor) def evaluatorParameters(self, universe, subset1, subset2, global_data): self.collectAtomTypesAndIndices(universe, global_data) return CompoundForceField.evaluatorParameters(self, universe, subset1, subset2, global_data) def charge(self, atoms): return self.nonbonded.charge(atoms) def charges(self, universe): return self.nonbonded.charges(universe) def dipole(self, atoms, reference = None): return self.nonbonded.dipole(atoms, reference) def bondLengthDatabase(self, universe): return self.bonded.bondLengthDatabase(universe) description = ForceField.description # # The bond length database (used for constraining bond lengths) # class MMBondLengthDatabase: def __init__(self, ff, universe, parameter_set): self.data = ForceFieldData() ff.collectAtomTypesAndIndices(universe, self.data) self.parameters = parameter_set def bondLength(self, bond): a1 = bond.a1 a2 = bond.a2 t1 = self.data.atom_type[a1] t2 = self.data.atom_type[a2] try: return self.parameters.bondParameters(t1, t2)[0] except KeyError: return None def bondAngle(self, angle): a1 = angle.a1 a2 = angle.a2 ca = angle.ca t1 = self.data.atom_type[a1] t2 = self.data.atom_type[a2] tc = self.data.atom_type[ca] try: return self.parameters.bondAngleParameters(t1, tc, t2)[0] except KeyError: return None MMTK-2.7.9/MMTK/ForceFields/NonBondedInteractions.py0000644000076600000240000003210212117632051022445 0ustar hinsenstaff00000000000000# This module implements classes that represent force fields # for non-bonded interactions # # Written by Konrad Hinsen # from MMTK import Units, Utility from MMTK.ForceFields.ForceField import ForceField from Scientific.Geometry import Vector from Scientific import N # Class definitions # # The base class NonBondedForceField provides the common # functionality for all non-bonded interactions. The derived # classes have to deal with determining functional forms # and parameters and providing the evaluation code # class NonBondedForceField(ForceField): def __init__(self, name): ForceField.__init__(self, name) self.type = 'nonbonded' def ready(self, global_data): return all([klass in global_data.get('initialized') for klass in global_data.get('nb_exclusions')]) def excludedPairs(self, subset1, subset2, global_data): if 'excluded_pairs' not in global_data.get('initialized'): excluded_pairs = set(global_data.get('excluded_pairs')) if subset1 is not None: set1 = set(a.index for a in subset1.atomList()) set2 = set(a.index for a in subset2.atomList()) excluded_pairs |= set(Utility.orderedPairs(list(set1-set2))) excluded_pairs |= set(Utility.orderedPairs(list(set2-set1))) atom_subset = list(set1 | set2) atom_subset.sort() else: atom_subset = None global_data.set('atom_subset', atom_subset) global_data.set('excluded_pairs', list(excluded_pairs)) one_four_pairs = set(global_data.get('1_4_pairs')) \ - excluded_pairs global_data.set('1_4_pairs', list(one_four_pairs)) global_data.add('initialized', 'excluded_pairs') return global_data.get('excluded_pairs'), \ global_data.get('1_4_pairs'), \ global_data.get('atom_subset') def nonbondedList(self, universe, subset1, subset2, global_data): try: from MMTK_forcefield import NonbondedList, NonbondedListTerm except ImportError: return None, None nbl = None update = None if 'nonbondedlist' in global_data.get('initialized'): nbl, update, cutoff = global_data.get('nonbondedlist') if nbl is None: excluded_pairs, one_four_pairs, atom_subset = \ self.excludedPairs(subset1, subset2, global_data) excluded_pairs = N.array(excluded_pairs) one_four_pairs = N.array(one_four_pairs) if atom_subset is not None: atom_subset = N.array(atom_subset) else: atom_subset = N.array([], N.Int) nbl = NonbondedList(excluded_pairs, one_four_pairs, atom_subset, universe._spec, self.cutoff) update = NonbondedListTerm(nbl) update.info = False global_data.set('nonbondedlist', (nbl, update, self.cutoff)) global_data.add('initialized', 'nonbondedlist') else: if cutoff is not None and \ (self.cutoff is None or self.cutoff > cutoff): nbl.setCutoff(self.cutoff) return nbl, update # the following methods must be overridden by derived classes def evaluatorTerms(self, universe, subset1, subset2, global_data): raise AttributeError # # Lennard-Jones force field # class LJForceField(NonBondedForceField): def __init__(self, name, cutoff, scale_factor=1.): NonBondedForceField.__init__(self, name) self.cutoff = cutoff self.scale_factor = scale_factor def evaluatorParameters(self, universe, subset1, subset2, global_data): n = universe.numberOfPoints() lj_type = -N.ones((n,), N.Int) atom_types = {} atom_type_names = [] for o in universe: for a in o.atomList(): t = self._atomType(o, a, global_data) if not atom_types.has_key(t): atom_types[t] = len(atom_types) atom_type_names.append(t) lj_type[a.index] = atom_types[t] n_types = len(atom_types) eps_sigma = N.zeros((n_types, n_types, 2), N.Float) for t, i in atom_types.items(): eps, sigma, mix = self._ljParameters(t, global_data) eps *= self.scale_factor eps_sigma[i, i] = N.array([eps, sigma]) eps = eps_sigma[:, :, 0] sigma = eps_sigma[:, :, 1] for i in range(n_types): for j in range(i+1, n_types): eps[i,j] = N.sqrt(eps[i,i]*eps[j,j]) eps[j,i] = eps[i,j] if mix == 0: sigma[i,j] = 0.5*(sigma[i,i]+sigma[j,j]) elif mix == 1: sigma[i,j] = N.sqrt(sigma[i,i]*sigma[j,j]) else: raise ValueError("unknown Lennard-Jones mixing rule") sigma[j,i] = sigma[i,j] global_data.lj_type = lj_type global_data.lj_14_factor = self.lj_14_factor global_data.eps_sigma = eps_sigma if self.cutoff is None: cutoff = 0. else: cutoff = self.cutoff excluded_pairs, one_four_pairs, atom_subset = \ self.excludedPairs(subset1, subset2, global_data) return {'lennard_jones': {'type': lj_type, 'type_names': atom_type_names, 'epsilon_sigma': eps_sigma, 'one_four_factor': self.lj_14_factor, 'cutoff': cutoff}, 'nonbonded': {'excluded_pairs': excluded_pairs, 'one_four_pairs': one_four_pairs, 'atom_subset': atom_subset} } def evaluatorTerms(self, universe, subset1, subset2, global_data): params = self.evaluatorParameters(universe, subset1, subset2, global_data)['lennard_jones'] nblist, update = \ self.nonbondedList(universe, subset1, subset2, global_data) from MMTK_forcefield import LennardJonesTerm ev = LennardJonesTerm(universe._spec, nblist, params['epsilon_sigma'], params['type'], params['cutoff'], params['one_four_factor']) update.addTerm(ev, 0); if update.info: return [ev] else: update.info = True return [update, ev] # the following methods must be overridden by derived classes def _atomType(self, o, a, global_data): raise AttributeError def _ljParameters(self, t, global_data): raise AttributeError # # Electrostatic force field # class ElectrostaticForceField(NonBondedForceField): def __init__(self, name, cutoff, scale_factor=1.): NonBondedForceField.__init__(self, name) self.cutoff = cutoff self.scale_factor = scale_factor def evaluatorParameters(self, universe, subset1, subset2, global_data): n = universe.numberOfPoints() charge = N.zeros((n,), N.Float) for o in universe: for a in o.atomList(): charge[a.index] = self._charge(o, a, global_data) charge = charge*N.sqrt(self.scale_factor) if self.cutoff is None: cutoff = 0. else: cutoff = self.cutoff excluded_pairs, one_four_pairs, atom_subset = \ self.excludedPairs(subset1, subset2, global_data) return {'electrostatic': {'algorithm':'direct', 'charge': charge, 'cutoff': cutoff, 'one_four_factor': self.es_14_factor}, 'nonbonded': {'excluded_pairs': excluded_pairs, 'one_four_pairs': one_four_pairs, 'atom_subset': atom_subset} } def evaluatorTerms(self, universe, subset1, subset2, global_data): params = self.evaluatorParameters(universe, subset1, subset2, global_data)['electrostatic'] assert params['algorithm'] == 'direct' nblist, update = \ self.nonbondedList(universe, subset1, subset2, global_data) from MMTK_forcefield import ElectrostaticTerm ev = ElectrostaticTerm(universe._spec, nblist, params['charge'], params['cutoff'], params['one_four_factor']) update.addTerm(ev, 1) if update.info: return [ev] else: update.info = True return [update, ev] # the following method must be overridden by derived classes def _charge(self, o, a, global_data): raise AttributeError # # Ewald evaluator for electrostatic interactions # class ESEwaldForceField(NonBondedForceField): def __init__(self, name, options = {}): NonBondedForceField.__init__(self, name) self.cutoff = options.get('real_cutoff', None) self.scale_factor = options.get('scale_factor', 1.) self.options = options for key in options.keys(): if key not in self.known_options: raise ValueError(key + " is not a recognized option") known_options = ['beta', 'real_cutoff', 'cutoff', 'reciprocal_cutoff', 'ewald_precision', 'no_reciprocal_sum', 'method', 'scale_factor'] def evaluatorParameters(self, universe, subset1, subset2, global_data): rsum = not self.options.get('no_reciprocal_sum', False) if not universe.is_periodic and rsum: raise ValueError("Ewald method accepts only periodic universes") n = universe.numberOfPoints() charge = N.zeros((n,), N.Float) for o in universe: for a in o.atomList(): charge[a.index] = self._charge(o, a, global_data) charge = charge*N.sqrt(self.scale_factor) precision = self.options.get('ewald_precision', 1.e-6) p = N.sqrt(-N.log(precision)) if rsum: beta_opt = N.sqrt(N.pi) * \ (5.*universe.numberOfAtoms()/universe.cellVolume()**2)**(1./6.) max_cutoff = universe.largestDistance() beta_opt = max(p/max_cutoff, beta_opt) else: beta_opt = 0.01 options = {} options['beta'] = beta_opt options['real_cutoff'] = p/beta_opt options['reciprocal_cutoff'] = N.pi/(beta_opt*p) options['no_reciprocal_sum'] = False for key, value in self.options.items(): options[key] = value kx, ky, kz = [1./(rbv.length()*options['reciprocal_cutoff']) for rbv in universe.reciprocalBasisVectors()] kmax = N.ceil([kx, ky, kz]).astype(N.Int) excluded_pairs, one_four_pairs, atom_subset = \ self.excludedPairs(subset1, subset2, global_data) if atom_subset is not None: raise ValueError("Ewald summation not available for subsets") if options['no_reciprocal_sum']: kcutoff_sq = 0. else: kcutoff_sq = (2.*N.pi/options['reciprocal_cutoff'])**2 return {'electrostatic': {'algorithm': 'ewald', 'charge': charge, 'real_cutoff': options['real_cutoff'], 'k_cutoff_sq': kcutoff_sq, 'beta': options['beta'], 'k_max': kmax, 'one_four_factor': self.es_14_factor}, 'nonbonded': {'excluded_pairs': excluded_pairs, 'one_four_pairs': one_four_pairs, 'atom_subset': atom_subset} } def evaluatorTerms(self, universe, subset1, subset2, global_data): params = self.evaluatorParameters(universe, subset1, subset2, global_data)['electrostatic'] assert params['algorithm'] == 'ewald' nblist, update = \ self.nonbondedList(universe, subset1, subset2, global_data) if params['k_cutoff_sq'] == 0.: shape = N.zeros((3, 3), N.Float) else: shape = universe.basisVectors() if shape is None: raise ValueError("Ewald evaluator needs periodic universe") shape = N.array(shape, N.Float) from MMTK_forcefield import EsEwaldTerm ev = EsEwaldTerm(universe._spec, shape, nblist, params['charge'], params['real_cutoff'], params['k_cutoff_sq'], params['k_max'], params['one_four_factor'], params['beta']) update.addTerm(ev, 2) if update.info: return [ev] else: update.info = True return [update, ev] # the following methods must be overridden by derived classes def _charge(self, o, a, global_data): raise AttributeError MMTK-2.7.9/MMTK/ForceFields/Restraints.py0000644000076600000240000003272512117632051020365 0ustar hinsenstaff00000000000000# Harmonic restraint terms that can be added to a force field. # # Written by Konrad Hinsen # """ Harmonic restraint terms that can be added to any force field Example:: from MMTK import * from MMTK.ForceFields import Amber99ForceField from MMTK.ForceFields.Restraints import HarmonicDistanceRestraint universe = InfiniteUniverse() universe.protein = Protein('bala1') force_field = Amber99ForceField() + \ HarmonicDistanceRestraint(universe.protein[0][1].peptide.N, universe.protein[0][1].peptide.O, 0.5, 10.) universe.setForceField(force_field) """ __docformat__ = 'restructuredtext' from MMTK.ChemicalObjects import isChemicalObject from MMTK.Collections import isCollection from MMTK.ForceFields.ForceField import ForceField from MMTK import Utility from MMTK_forcefield import HarmonicDistanceTerm, HarmonicAngleTerm, \ CosineDihedralTerm from MMTK_restraints import HarmonicCMTrapTerm, HarmonicCMDistanceTerm from Scientific import N class HarmonicDistanceRestraint(ForceField): """ Harmonic distance restraint between two atoms """ def __init__(self, obj1, obj2, distance, force_constant, nb_exclusion=False): """ :param obj1: the object defining center-of-mass 1 :type obj1: :class:`~MMTK.Collections.GroupOfAtoms` :param obj2: the object defining center-of-mass 2 :type obj2: :class:`~MMTK.Collections.GroupOfAtoms` :param distance: the distance between cm 1 and cm2 at which the restraint is zero :type distance: float :param force_constant: the force constant of the restraint term. The functional form of the restraint is force_constant*((cm1-cm2).length()-distance)**2, where cm1 and cm2 are the centrer-of-mass positions of the two objects. :type force_constant: float :param nb_exclussion: if True, non-bonded interactions between the restrained atoms are suppressed, as for a chemical bond :type nb_exclussion: bool """ if isinstance(obj1, int) and isinstance(obj2, int): # Older MMTK versions admitted only single atoms and # stored single indices. Support this mode for opening # trajectories made with those versions self.atom_indices_1 = [obj1] self.atom_indices_2 = [obj2] if isChemicalObject(obj1) or isCollection(obj1): obj1 = obj1.atomList() if isChemicalObject(obj2) or isCollection(obj2): obj2 = obj2.atomList() self.atom_indices_1 = self.getAtomParameterIndices(obj1) self.atom_indices_2 = self.getAtomParameterIndices(obj2) if nb_exclusion and (len(self.atom_indices_1) > 1 or len(self.atom_indices_2) > 1): raise ValueError("Non-bonded exclusion possible only " "between single-atom objects") self.arguments = (self.atom_indices_1, self.atom_indices_2, distance, force_constant, nb_exclusion) self.distance = distance self.force_constant = force_constant self.nb_exclusion = nb_exclusion ForceField.__init__(self, 'harmonic distance restraint') def declareDependencies(self, global_data): global_data.add('nb_exclusions', self.__class__) def evaluatorParameters(self, universe, subset1, subset2, global_data): if universe.is_periodic and \ (len(self.atom_indices_1) > 1 or len(self.atom_indices_1) > 1): raise ValueError("Center-of-mass restraints not implemented" " for periodic universes") ok = False for s1, s2 in [(subset1, subset2), (subset2, subset1)]: if s1 is None and s1 is None: ok = True break s1 = set(a.index for a in s1.atomIterator()) diff1 = set(self.atom_indices_1).difference(s1) s2 = set(a.index for a in s2.atomIterator()) diff2 = set(self.atom_indices_2).difference(s2) if not diff1 and not diff2: # Each object is in one of the subsets ok = True break if (diff1 and len(diff1) != len(self.atom_indices_1)) \ or (diff2 and len(diff2) != len(self.atom_indices_2)): # The subset contains some but not all of the # restrained atoms. raise ValueError("Restrained atoms partially " "in a subset") global_data.add('initialized', self.__class__) if not ok: # The objects are not in the subsets, so there is no # contribution to the total energy. return {'harmonic_distance_cm': []} if self.nb_exclusion: assert len(self.atom_indices_1) == 1 assert len(self.atom_indices_2) == 1 i1 = self.atom_indices_1[0] i2 = self.atom_indices_2[0] global_data.add('excluded_pairs', Utility.normalizePair((i1, i2))) if len(self.atom_indices_1) == 1 and len(self.atom_indices_2) == 1: # Keep the old format for the single-atom case for best # compatibility with older MMTK versions. return {'harmonic_distance_term': [(self.atom_indices_1[0], self.atom_indices_2[0], self.distance, self.force_constant)]} else: return {'harmonic_distance_cm': [(self.atom_indices_1, self.atom_indices_2, self.distance, self.force_constant)]} def evaluatorTerms(self, universe, subset1, subset2, global_data): params = self.evaluatorParameters(universe, subset1, subset2, global_data) if params.has_key('harmonic_distance_term'): params = params['harmonic_distance_term'] return [HarmonicDistanceTerm(universe._spec, N.array([p[:2]]), N.array([p[2:]]), self.name) for p in params] else: params = params['harmonic_distance_cm'] return [HarmonicCMDistanceTerm(universe, N.array(p[0]), N.array(p[1]), universe.masses().array, p[2], p[3]) for p in params] def description(self): return 'ForceFields.Restraints.' + self.__class__.__name__ + \ `self.arguments` class HarmonicAngleRestraint(ForceField): """ Harmonic angle restraint between three atoms """ def __init__(self, atom1, atom2, atom3, angle, force_constant): """ :param atom1: first atom :type atom1: :class:`~MMTK.ChemicalObjects.Atom` :param atom2: second (central) atom :type atom2: :class:`~MMTK.ChemicalObjects.Atom` :param atom3: third atom :type atom3: :class:`~MMTK.ChemicalObjects.Atom` :param angle: the angle at which the restraint is zero :type angle: float :param force_constant: the force constant of the restraint term. The functional form of the restraint is force_constant*(phi-angle)**2, where phi is the angle atom1-atom2-atom3. """ self.index1, self.index2, self.index3 = \ self.getAtomParameterIndices((atom1, atom2, atom3)) self.arguments = (self.index1, self.index2, self.index3, angle, force_constant) self.angle = angle self.force_constant = force_constant ForceField.__init__(self, 'harmonic angle restraint') def evaluatorParameters(self, universe, subset1, subset2, global_data): return {'harmonic_angle_term': [(self.index1, self.index2, self.index3, self.angle, self.force_constant)]} def evaluatorTerms(self, universe, subset1, subset2, global_data): params = self.evaluatorParameters(universe, subset1, subset2, global_data)['harmonic_angle_term'] assert len(params) == 1 indices = N.array([params[0][:3]]) parameters = N.array([params[0][3:]]) return [HarmonicAngleTerm(universe._spec, indices, parameters, self.name)] class HarmonicDihedralRestraint(ForceField): """ Harmonic dihedral angle restraint between four atoms """ def __init__(self, atom1, atom2, atom3, atom4, dihedral, force_constant): """ :param atom1: first atom :type atom1: :class:`~MMTK.ChemicalObjects.Atom` :param atom2: second (axis) atom :type atom2: :class:`~MMTK.ChemicalObjects.Atom` :param atom3: third (axis)atom :type atom3: :class:`~MMTK.ChemicalObjects.Atom` :param atom4: fourth atom :type atom4: :class:`~MMTK.ChemicalObjects.Atom` :param dihedral: the dihedral angle at which the restraint is zero :type dihedral: float :param force_constant: the force constant of the restraint term. The functional form of the restraint is force_constant*(phi-abs(dihedral))**2, where phi is the dihedral angle atom1-atom2-atom3-atom4. """ self.index1, self.index2, self.index3, self.index4 = \ self.getAtomParameterIndices((atom1, atom2, atom3, atom4)) self.dihedral = dihedral self.force_constant = force_constant self.arguments = (self.index1, self.index2, self.index3, self.index4, dihedral, force_constant) ForceField.__init__(self, 'harmonic dihedral restraint') def evaluatorParameters(self, universe, subset1, subset2, global_data): return {'cosine_dihedral_term': [(self.index1, self.index2, self.index3, self.index4, 0., self.dihedral, 0., self.force_constant)]} def evaluatorTerms(self, universe, subset1, subset2, global_data): params = self.evaluatorParameters(universe, subset1, subset2, global_data)['cosine_dihedral_term'] assert len(params) == 1 indices = N.array([params[0][:4]]) parameters = N.array([params[0][4:]]) return [CosineDihedralTerm(universe._spec, indices, parameters, self.name)] class HarmonicTrapForceField(ForceField): """ Harmonic potential with respect to a fixed point in space """ def __init__(self, obj, center, force_constant): """ :param obj: the object on whose center of mass the force field acts :type obj: :class:`~MMTK.Collections.GroupOfAtoms` :param center: the point to which the atom is attached by the harmonic potential :type center: Scientific.Geometry.Vector :param force_constant: the force constant of the harmonic potential :type force_constant: float """ if isChemicalObject(obj): obj = obj.atomList() self.atom_indices = self.getAtomParameterIndices(obj) self.arguments = (self.atom_indices, center, force_constant) ForceField.__init__(self, 'harmonic_trap') self.center = center self.force_constant = force_constant def ready(self, global_data): return True def evaluatorParameters(self, universe, subset1, subset2, global_data): if universe.is_periodic and len(self.atom_indices) > 1: raise ValueError("Center-of-mass restraints not implemented" " for periodic universes") for subset in [subset1, subset2]: if subset is not None: subset = set(a.index for a in subset.atomIterator()) diff = set(self.atom_indices).difference(subset) if diff: if len(diff) == len(self.atom_indices): # The subset doesn't contain the restrained atoms. return {'harmonic_trap_cm': []} else: # The subset contains some but not all of the # restrained atoms. raise ValueError("Restrained atoms partially " "in a subset") return {'harmonic_trap_cm': [(self.atom_indices, self.center, self.force_constant)]} def evaluatorTerms(self, universe, subset1, subset2, global_data): params = self.evaluatorParameters(universe, subset1, subset2, global_data)['harmonic_trap_cm'] return [HarmonicCMTrapTerm(universe, N.array(p[0]), universe.masses().array, p[1], p[2]) for p in params] MMTK-2.7.9/MMTK/ForceFields/SPCEFF.py0000644000076600000240000000653511662461640017205 0ustar hinsenstaff00000000000000# SPCE force field # # Written by Konrad Hinsen # """ SPC/E force field for water simulations """ __docformat__ = 'restructuredtext' from MMTK.ForceFields import MMForceField class SPCEParameters(object): atom_type_property = 'spce_atom_type' charge_property = 'spce_charge' lennard_jones_1_4 = 1. electrostatic_1_4 = 1. def ljParameters(self, type): if type == 'O': # TIP3: 0.6363936, 0.315075240657 return (0.650169580819, 0.31655578902, 0) elif type == 'H': return (0., 0., 0) else: raise ValueError('Unknown atom type ' + type) # Bond and angle parameters from: # O. Telemann, B. Jonsson, S. Engstrom # Mol. Phys. 60(1), 193-203 (1987) def bondParameters(self, at1, at2): if at1 == 'O' or at2 == 'O': return (0.1, 463700.) else: return (0.163298086184, 0.) def bondAngleParameters(self, at1, at2, at3): if at2 == 'O': return (1.91061193216, 383.) else: return (0.615490360716, 0.) def dihedralParameters(self, at1, at2, at3, at4): return [(1, 0., 0.)] def improperParameters(self, at1, at2, at3, at4): return [(1, 0., 0.)] class SPCEForceField(MMForceField.MMForceField): """ Force field for water simulations with the SPC/E model """ def __init__(self, lj_options=None, es_options=None): """ :param lj_options: parameters for Lennard-Jones interactions; one of: * a number, specifying the cutoff * None, meaning the default method (no cutoff; inclusion of all pairs, using the minimum-image conventions for periodic universes) * a dictionary with an entry "method" which specifies the calculation method as either "direct" (all pair terms) or "cutoff", with the cutoff specified by the dictionary entry "cutoff". :param es_options: parameters for electrostatic interactions; one of: * a number, specifying the cutoff * None, meaning the default method (all pairs without cutoff for non-periodic system, Ewald summation for periodic systems) * a dictionary with an entry "method" which specifies the calculation method as either "direct" (all pair terms), "cutoff" (with the cutoff specified by the dictionary entry "cutoff"), "ewald" (Ewald summation, only for periodic universes), "screened" or "multipole" (fast-multipole method). """ MMForceField.MMForceField.__init__(self, 'SPCE', SPCEParameters(), lj_options, es_options) self.arguments = (lj_options, es_options) MMTK-2.7.9/MMTK/FourierBasis.py0000644000076600000240000001216111662461640016437 0ustar hinsenstaff00000000000000# Fourier basis for low-frequency normal mode calculations. # # Written by Konrad Hinsen # """ Fourier basis for low-frequency normal mode calculations This module provides a basis that is suitable for the calculation of low-frequency normal modes. The basis is derived from vector fields whose components are stationary waves in a box surrounding the system. For a description, see | K. Hinsen | Analysis of domain motions by approximate normal mode calculations | Proteins 33 (1998): 417-429 """ from MMTK import ParticleProperties from Scientific.Geometry import Vector from Scientific import N class FourierBasis(object): """ Collective-motion basis for low-frequency normal mode calculations A FourierBasis behaves like a sequence of :class:`~MMTK.ParticleProperties.ParticleVector` objects. The vectors are B{not} orthonormal, because orthonormalization is handled automatically by the normal mode class. """ def __init__(self, universe, cutoff): """ :param universe: the universe for which the basis will be used :type universe: :class:`~MMTK.Universe.Universe` :param cutoff: the wavelength cutoff. A smaller value yields a larger basis. :type cutoff: float """ p1, p2 = universe.boundingBox() p2 = p2 + Vector(cutoff, cutoff, cutoff) l = (p2-p1).array n_max = (0.5*l/cutoff+0.5).astype(N.Int) wave_numbers = [(nx, ny, nz) for nx in range(-n_max[0], n_max[0]+1) for ny in range(-n_max[1], n_max[1]+1) for nz in range(-n_max[2], n_max[2]+1) if (nx/l[0])**2 + (ny/l[1])**2 + (nz/l[2])**2 \ < 0.25/cutoff**2] atoms = universe.atomList() natoms = len(atoms) basis = N.zeros((3*len(wave_numbers)+3, natoms, 3), N.Float) cm = universe.centerOfMass() i = 0 for rotation in [Vector(1.,0.,0.), Vector(0.,1.,0.), Vector(0.,0.,1.)]: v = ParticleProperties.ParticleVector(universe, basis[i]) for a in atoms: v[a] = rotation.cross(a.position()-cm) i += i conf = universe.configuration().array-p1.array for n in wave_numbers: k = 2.*N.pi*N.array(n)/l w = self._w(conf[:, 0], k[0]) * self._w(conf[:, 1], k[1]) * \ self._w(conf[:, 2], k[2]) basis[i, :, 0] = w basis[i+1, :, 1] = w basis[i+2, :, 2] = w i += 3 self.array = basis self.universe = universe __safe_for_unpickling__ = True __had_initargs__ = True def _w(self, x, k): if k < 0: return N.sin(-k*x) else: return N.cos(k*x) def __len__(self): return self.array.shape[0] def __getitem__(self, item): return ParticleProperties.ParticleVector(self.universe, self.array[item]) # Estimate number of basis vectors for a given cutoff def countBasisVectors(universe, cutoff): """ Estimate the number of basis vectors for a given universe and cutoff :param universe: the universe :type universe: :class:`~MMTK.Universe.Universe` :param cutoff: the wavelength cutoff. A smaller value yields a larger basis. :type cutoff: float :returns: the number of basis vectors in a FourierBasis :rtype: int """ p1, p2 = universe.boundingBox() p2 = p2 + Vector(cutoff, cutoff, cutoff) l = (p2-p1).array n_max = (0.5*l/cutoff+0.5).astype(N.Int) n_wave_numbers = 0 for nx in range(-n_max[0], n_max[0]+1): for ny in range(-n_max[1], n_max[1]+1): for nz in range(-n_max[2], n_max[2]+1): if (nx/l[0])**2 + (ny/l[1])**2 + (nz/l[2])**2 < 0.25/cutoff**2: n_wave_numbers += 1 return 3*n_wave_numbers+3 # Estimate cutoff for a given number of basis vectors def estimateCutoff(universe, nmodes): """ Estimate the cutoff that yields a given number of basis vectors for a given universe. :param universe: the universe :type universe: :class:`~MMTK.Universe.Universe` :param nmodes: the number of basis vectors in a FourierBasis :type nmodes: int :returns: the wavelength cutoff and the precise number of basis vectors :rtype: tuple (float, int) """ natoms = universe.numberOfCartesianCoordinates() if nmodes > natoms: nmodes = 3*natoms cutoff = None else: p1, p2 = universe.boundingBox() cutoff_max = (p2-p1).length() cutoff = 0.5*cutoff_max nmodes_opt = nmodes nmodes = countBasisVectors(universe, cutoff) while nmodes > nmodes_opt: cutoff += 0.1 if cutoff > cutoff_max: cutoff = cutoff_max break nmodes = countBasisVectors(universe, cutoff) while nmodes < nmodes_opt: cutoff -= 0.1 if cutoff < 0.1: cutoff = 0.1 break nmodes = countBasisVectors(universe, cutoff) return cutoff, nmodes MMTK-2.7.9/MMTK/Geometry.py0000644000076600000240000006036612137470030015636 0ustar hinsenstaff00000000000000# This module defines some geometrical objects in 3D-space. # # Written by Konrad Hinsen # """ Elementary geometrical objects and operations There are essentially two kinds of geometrical objects: shape objects (spheres, planes, etc.), from which intersections can be calculated, and lattice objects, which define a regular arrangements of points. """ __docformat__ = 'restructuredtext' from Scientific.Geometry import Vector from Scientific import N # Error type class GeomError(Exception): pass # Small number eps = 1.e-16 # # The base class # class GeometricalObject3D(object): """ 3D shape object This is an abstract base class. To create 3D objects, use one of its subclasses. """ def intersectWith(self, other): """ :param other: another 3D object :returns: a 3D object that represents the intersection with other """ if self.__class__ > other.__class__: self, other = other, self try: f, switch = _intersectTable[(self.__class__, other.__class__)] if switch: return f(other, self) else: return f(self, other) except KeyError: raise GeomError("Can't calculate intersection of " + self.__class__.__name__ + " with " + other.__class__.__name__) def volume(self): """ :returns: the volume of the object :rtype: float """ raise NotImplementedError def hasPoint(self, point): """ :param point: a point in 3D space :type point: Scientific.Geometry.Vector :returns: True of the point lies on the surface of the object :rtype: bool """ return self.distanceFrom(point) < eps # subclasses that enclose a volume should override this method # a return value of None indicates "don't know", "can't compute", # or "not implemented (yet)". def enclosesPoint(self, point): """ :param point: a point in 3D space :type point: Scientific.Geometry.Vector :returns: True of the point is inside the volume of the object :rtype: bool """ return None _intersectTable = {} # # Boxes # class Box(GeometricalObject3D): """ Rectangular box aligned with the coordinate axes """ def __init__(self, corner1, corner2): """ :param corner1: one corner of the box :type corner1: Scientific.Geometry.Vector :param corner2: the diagonally opposite corner :type corner2: Scientific.Geometry.Vector """ c1 = N.minimum(corner1.array, corner2.array) c2 = N.maximum(corner1.array, corner2.array) self.corners = c1, c2 def __repr__(self): return 'Box(' + `Vector(self.corners[0])` + ', ' \ + `Vector(self.corners[1])` + ')' __str__ = __repr__ def volume(self): c1, c2 = self.corners return N.multiply.reduce(c2-c1) def hasPoint(self, point): c1, c2 = self.corners min1 = N.minimum.reduce(N.fabs(point.array-c1)) min2 = N.minimum.reduce(N.fabs(point.array-c2)) return min1 < eps or min2 < eps def enclosesPoint(self, point): c1, c2 = self.corners out1 = N.logical_or.reduce(N.less(point.array-c1, 0)) out2 = N.logical_or.reduce(N.less_equal(c2-point.array, 0)) return not (out1 or out2) def cornerPoints(self): (c1x, c1y, c1z), (c2x, c2y, c2z) = self.corners return [Vector(c1x, c1y, c1z), Vector(c1x, c1y, c2z), Vector(c1x, c2y, c1z), Vector(c2x, c1y, c1z), Vector(c2x, c2y, c1z), Vector(c2x, c1y, c2z), Vector(c1x, c2y, c2z), Vector(c2x, c2y, c2z)] # # Spheres # class Sphere(GeometricalObject3D): """ Sphere """ def __init__(self, center, radius): """ :param center: the center of the sphere :type center: Scientific.Geometry.Vector :param radius: the radius of the sphere :type radius: float """ self.center = center self.radius = radius def __repr__(self): return 'Sphere(' + `self.center` + ', ' + `self.radius` + ')' __str__ = __repr__ def volume(self): return (4.*N.pi/3.) * self.radius**3 def hasPoint(self, point): return N.fabs((point-self.center).length()-self.radius) < eps def enclosesPoint(self, point): return (point - self.center).length() < self.radius # # Cylinders # class Cylinder(GeometricalObject3D): """ Cylinder """ def __init__(self, center1, center2, radius): """ :param center1: the center of the bottom circle :type center1: Scientific.Geometry.Vector :param center2: the center of the top circle :type center2: Scientific.Geometry.Vector :param radius: the radius of the cylinder :type radius: float """ self.center1 = center1 # center of base self.center2 = center2 # center of top self.radius = radius self.height = (center2-center1).length() def volume(self): return N.pi*self.radius*self.radius*self.height def __repr__(self): return 'Cylinder(' + `self.center1` + ', ' + `self.center2` + \ ', ' + `self.radius` + ')' __str__ = __repr__ def hasPoint(self, point): center_line = LineSegment(self.center1, self.center2) pt = center_line.projectionOf(point) if pt is None: return 0 return N.fabs((point - pt).length() - self.radius) < eps def enclosesPoint(self, point): center_line = LineSegment(self.center1, self.center2) pt = center_line.projectionOf(point) if pt is None: return 0 return (point - pt).length() < self.radius # # Planes # class Plane(GeometricalObject3D): """ 2D plane in 3D space """ def __init__(self, *args): """ :param args: three points (of type Scientific.Geometry.Vector) that are not collinear, or a point in the plane and the normal vector of the plane """ if len(args) == 2: # point, normal self.normal = args[1].normal() self.distance_from_zero = self.normal*args[0] else: # three points v1 = args[1]-args[0] v2 = args[2]-args[1] self.normal = (v1.cross(v2)).normal() self.distance_from_zero = self.normal*args[1] def __repr__(self): return 'Plane(' + str(self.normal*self.distance_from_zero) + \ ', ' + str(self.normal) + ')' __str__ = __repr__ def distanceFrom(self, point): return abs(self.normal*point-self.distance_from_zero) def projectionOf(self, point): return point - (self.normal*point-self.distance_from_zero)*self.normal def rotate(self, axis, angle): point = rotatePoint(self.distance_from_zero*self.normal, axis, angle) normal = rotateDirection(self.normal, axis, angle) return Plane(point, normal) def volume(self): return 0. # # Infinite cones # class Cone(GeometricalObject3D): """ Cone """ def __init__(self, center, axis, angle): """ :param center: the center (tip) of the cone :type center: Scientific.Geometry.Vector :param axis: the direction of the axis of rotational symmetry :type axis: Scientific.Geometry.Vector :param angle: the angle between any straight line on the cone surface and the axis of symmetry :type angle: float """ self.center = center self.axis = axis.normal() self.angle = angle def __repr__(self): return 'Cone(' + `self.center` + ', ' + `self.axis` + ',' + \ `self.angle` + ')' __str__ = __repr__ def volume(self): return None # # Circles # class Circle(GeometricalObject3D): """ 2D circle in 3D space """ def __init__(self, center, normal, radius): """ :param center: the center of the circle :type center: Scientific.Geometry.Vector :param normal: the normal vector of the circle's plane :type normal: Scientific.Geometry.Vector :param radius: the radius of the circle :type radius: float """ self.center = center self.normal = normal self.radius = radius def planeOf(self): return Plane(self.center, self.normal) def __repr__(self): return 'Circle(' + `self.center` + ', ' + `self.normal` + \ ', ' + `self.radius` + ')' __str__ = __repr__ def volume(self): return 0. def distanceFrom(self, point): plane = self.planeOf() project_on_plane = plane.projectionOf(point) center_to_projection = project_on_plane - self.center if center_to_projection.length() < eps: return 0 closest_point = self.center + self.radius*center_to_projection.normal() return (point - closest_point).length() # # Lines # class Line(GeometricalObject3D): """ Line """ def __init__(self, point, direction): """ :param point: any point on the line :type point: Scientific.Geometry.Vector :param direction: the direction of the line :type direction: Scientific.Geometry.Vector """ self.point = point self.direction = direction.normal() def distanceFrom(self, point): """ :param point: a point in space :type point: Scientific.Geometry.Vector :returns: the smallest distance of the point from the line :rtype: float """ d = self.point-point d = d - (d*self.direction)*self.direction return d.length() def projectionOf(self, point): """ :param point: a point in space :type point: Scientific.Geometry.Vector :returns: the orthogonal projection of the point onto the line :rtype: Scientific.Geometry.Vector """ d = self.point-point d = d - (d*self.direction)*self.direction return point+d def perpendicularVector(self, plane): """ :param plane: a plane :type plane: Plane :returns: a vector in the plane perpendicular to the line :rtype: Scientific.Geometry.Vector """ return self.direction.cross(plane.normal) def __repr__(self): return 'Line(' + `self.point` + ', ' + `self.direction` + ')' __str__ = __repr__ def volume(self): return 0. class LineSegment(Line): def __init__(self, point1, point2): Line.__init__(self, point1, point2 - point1) self.point2 = point2 def __repr__(self): return 'LineSegment(' + `self.point` + ', ' + `self.point2` + ')' __str__ = __repr__ def distanceFrom(self, point): pt = self.projectionOf(point) if pt is not None: return (pt - point).length() d1 = (self.point - point).length() d2 = (self.point2 - point).length() return min(d1, d2) def projectionOf(self, point): d = self.point-point d = d - (d*self.direction)*self.direction pt = point+d if self.isWithin(pt): return pt return None def isWithin(point): v1 = point - self.point v2 = point - self.point2 if abs(v1 * v2) < eps: return 0 return not Same_Dir(v1, v2) # # Intersection calculations # def _addIntersectFunction(f, class1, class2): switch = class1 > class2 if switch: class1, class2 = class2, class1 _intersectTable[(class1, class2)] = (f, switch) # Box with box def _intersectBoxBox(box1, box2): c1 = N.maximum(box1.corners[0], box2.corners[0]) c2 = N.minimum(box1.corners[1], box2.corners[1]) if N.logical_or.reduce(N.greater_equal(c1, c2)): return None return Box(Vector(c1), Vector(c2)) _addIntersectFunction(_intersectBoxBox, Box, Box) # Sphere with sphere def _intersectSphereSphere(sphere1, sphere2): r1r2 = sphere2.center-sphere1.center d = r1r2.length() if d > sphere1.radius+sphere2.radius: return None if d+min(sphere1.radius, sphere2.radius) < \ max(sphere1.radius, sphere2.radius): return None x = 0.5*(d**2 + sphere1.radius**2 - sphere2.radius**2)/d h = N.sqrt(sphere1.radius**2-x**2) normal = r1r2.normal() return Circle(sphere1.center + x*normal, normal, h) _addIntersectFunction(_intersectSphereSphere, Sphere, Sphere) # Sphere with cone def _intersectSphereCone(sphere, cone): if sphere.center != cone.center: raise GeomError("Not yet implemented") from_center = sphere.radius*N.cos(cone.angle) radius = sphere.radius*N.sin(cone.angle) return Circle(cone.center+from_center*cone.axis, cone.axis, radius) _addIntersectFunction(_intersectSphereCone, Sphere, Cone) # Plane with plane def _intersectPlanePlane(plane1, plane2): if abs(abs(plane1.normal*plane2.normal)-1.) < eps: if abs(plane1.distance_from_zero-plane2.distance_from_zero) < eps: return plane1 else: return None else: direction = plane1.normal.cross(plane2.normal) point_in_1 = plane1.distance_from_zero*plane1.normal point_in_both = point_in_1 - (point_in_1*plane2.normal - plane2.distance_from_zero)*plane2.normal return Line(point_in_both, direction) _addIntersectFunction(_intersectPlanePlane, Plane, Plane) # Circle with plane def _intersectCirclePlane(circle, plane): if abs(abs(circle.normal*plane.normal)-1.) < eps: if plane.hasPoint(circle.center): return circle else: return None else: line = plane.intersectWith(Plane(circle.center, circle.normal)) x = line.distanceFrom(circle.center) if x > circle.radius: return None else: angle = N.arccos(x/circle.radius) along_line = N.sin(angle)*circle.radius normal = circle.normal.cross(line.direction) if line.distanceFrom(circle.center+normal) > x: normal = -normal return (circle.center+x*normal-along_line*line.direction, circle.center+x*normal+along_line*line.direction) _addIntersectFunction(_intersectCirclePlane, Circle, Plane) # # Rotation # def rotateDirection(vector, axis, angle): s = N.sin(angle) c = N.cos(angle) c1 = 1-c try: axis = axis.direction except AttributeError: pass return s*axis.cross(vector) + c1*(axis*vector)*axis + c*vector def rotatePoint(point, axis, angle): return axis.point + rotateDirection(point-axis.point, axis, angle) # # Lattices # # # Lattice base class # class Lattice(object): """ General lattice Lattices are special sequence objects that contain vectors (points on the lattice) or objects that are constructed as functions of these vectors. Lattice objects behave like lists, i.e. they permit indexing, length inquiry, and iteration by 'for'-loops. Note that the lattices represented by these objects are finite, they have a finite (and fixed) number of repetitions along each lattice vector. This is an abstract base class. To create lattice objects, use one of its subclasses. """ def __init__(self, function): if function is not None: self.elements = map(function, self.elements) def __getitem__(self, item): return self.elements[item] def __setitem__(self, item, value): self.elements[item] = value def __len__(self): return len(self.elements) # # General rhombic lattice # class RhombicLattice(Lattice): """ Rhombic lattice """ def __init__(self, elementary_cell, lattice_vectors, cells, function=None, base=None): """ :param elementary_cell: a list of points in the elementary cell :param lattice_vectors: a list of lattice vectors. Each lattice vector defines a lattice dimension (only values from one to three make sense) and indicates the displacement along this dimension from one cell to the next. :param cells: a list of integers, whose length must equal the number of dimensions. Each entry specifies how often a cell is repeated along this dimension. :param function: a function that is called for every lattice point with the vector describing the point as argument. The return value of this function is stored in the lattice object. If the function is 'None', the vector is directly stored in the lattice object. """ if len(lattice_vectors) != len(cells): raise TypeError('Inconsistent dimension specification') if base is None: base = Vector(0, 0, 0) self.dimension = len(lattice_vectors) self.elements = [] self.makeLattice(elementary_cell, lattice_vectors, cells, base) Lattice.__init__(self, function) def makeLattice(self, elementary_cell, lattice_vectors, cells, base): if len(cells) == 0: for p in elementary_cell: self.elements.append(p+base) else: for i in range(cells[0]): self.makeLattice(elementary_cell, lattice_vectors[1:], cells[1:], base+i*lattice_vectors[0]) # # Bravais lattice # class BravaisLattice(RhombicLattice): """ Bravais lattice A Bravais lattice is a special case of a general rhombic lattice in which the elementary cell contains only one point. """ def __init__(self, lattice_vectors, cells, function=None, base=None): """ :param lattice_vectors: a list of lattice vectors. Each lattice vector defines a lattice dimension (only values from one to three make sense) and indicates the displacement along this dimension from one cell to the next. :param cells: a list of integers, whose length must equal the number of dimensions. Each entry specifies how often a cell is repeated along this dimension. :param function: a function that is called for every lattice point with the vector describing the point as argument. The return value of this function is stored in the lattice object. If the function is 'None', the vector is directly stored in the lattice object. """ cell = [Vector(0,0,0)] RhombicLattice.__init__(self, cell, lattice_vectors, cells, function, base) # # Simple cubic lattice # class SCLattice(BravaisLattice): """ Simple Cubic lattice A Simple Cubic lattice is a special case of a Bravais lattice in which the elementary cell is a cube. """ def __init__(self, cellsize, cells, function=None, base=None): """ :param cellsize: the edge length of the elementary cell :type cellsize: float :param cells: a list of integers, whose length must equal the number of dimensions. Each entry specifies how often a cell is repeated along this dimension. :param function: a function that is called for every lattice point with the vector describing the point as argument. The return value of this function is stored in the lattice object. If the function is 'None', the vector is directly stored in the lattice object. """ lattice_vectors = (cellsize*Vector(1., 0., 0.), cellsize*Vector(0., 1., 0.), cellsize*Vector(0., 0., 1.)) if type(cells) != type(()): cells = 3*(cells,) BravaisLattice.__init__(self, lattice_vectors, cells, function, base) # # Body-centered cubic lattice # class BCCLattice(RhombicLattice): """ Body-Centered Cubic lattice A Body-Centered Cubic lattice has two points per elementary cell. """ def __init__(self, cellsize, cells, function=None, base=None): """ :param cellsize: the edge length of the elementary cell :type cellsize: float :param cells: a list of integers, whose length must equal the number of dimensions. Each entry specifies how often a cell is repeated along this dimension. :param function: a function that is called for every lattice point with the vector describing the point as argument. The return value of this function is stored in the lattice object. If the function is 'None', the vector is directly stored in the lattice object. """ cell = [Vector(0,0,0), cellsize*Vector(0.5,0.5,0.5)] lattice_vectors = (cellsize*Vector(1., 0., 0.), cellsize*Vector(0., 1., 0.), cellsize*Vector(0., 0., 1.)) if type(cells) != type(()): cells = 3*(cells,) RhombicLattice.__init__(self, cell, lattice_vectors, cells, function, base) # # Face-centered cubic lattice # class FCCLattice(RhombicLattice): """Face-Centered Cubic lattice A Face-Centered Cubic lattice has four points per elementary cell. """ def __init__(self, cellsize, cells, function=None, base=None): """ :param cellsize: the edge length of the elementary cell :type cellsize: float :param cells: a list of integers, whose length must equal the number of dimensions. Each entry specifies how often a cell is repeated along this dimension. :param function: a function that is called for every lattice point with the vector describing the point as argument. The return value of this function is stored in the lattice object. If the function is 'None', the vector is directly stored in the lattice object. """ cell = [Vector(0,0,0), cellsize*Vector( 0,0.5,0.5), cellsize*Vector(0.5, 0,0.5), cellsize*Vector(0.5,0.5, 0)] lattice_vectors = (cellsize*Vector(1., 0., 0.), cellsize*Vector(0., 1., 0.), cellsize*Vector(0., 0., 1.)) if type(cells) != type(()): cells = 3*(cells,) RhombicLattice.__init__(self, cell, lattice_vectors, cells, function, base) # # Optimal superposition of a molecule in two configurations # def superpositionFit(confs): """ :param confs: the weight, reference position, and alternate position for each atom :type confs: sequence of (float, Vector, Vector) :returns: the quaternion representing the rotation, the center of mass in the reference configuration, the center of mass in the alternate configuraton, and the RMS distance after the optimal superposition """ w_sum = 0. wr_sum = N.zeros((3,), N.Float) for w, r_ref, r in confs: w_sum += w wr_sum += w*r_ref.array ref_cms = wr_sum/w_sum pos = N.zeros((3,), N.Float) possq = 0. cross = N.zeros((3, 3), N.Float) for w, r_ref, r in confs: w = w/w_sum r_ref = r_ref.array-ref_cms r = r.array pos = pos + w*r possq = possq + w*N.add.reduce(r*r) \ + w*N.add.reduce(r_ref*r_ref) cross = cross + w*r[:, N.NewAxis]*r_ref[N.NewAxis, :] k = N.zeros((4, 4), N.Float) k[0, 0] = -cross[0, 0]-cross[1, 1]-cross[2, 2] k[0, 1] = cross[1, 2]-cross[2, 1] k[0, 2] = cross[2, 0]-cross[0, 2] k[0, 3] = cross[0, 1]-cross[1, 0] k[1, 1] = -cross[0, 0]+cross[1, 1]+cross[2, 2] k[1, 2] = -cross[0, 1]-cross[1, 0] k[1, 3] = -cross[0, 2]-cross[2, 0] k[2, 2] = cross[0, 0]-cross[1, 1]+cross[2, 2] k[2, 3] = -cross[1, 2]-cross[2, 1] k[3, 3] = cross[0, 0]+cross[1, 1]-cross[2, 2] for i in range(1, 4): for j in range(i): k[i, j] = k[j, i] k = 2.*k for i in range(4): k[i, i] = k[i, i] + possq - N.add.reduce(pos*pos) from Scientific import LA e, v = LA.eigenvectors(k) i = N.argmin(e) v = v[i] if v[0] < 0: v = -v if e[i] <= 0.: rms = 0. else: rms = N.sqrt(e[i]) from Scientific.Geometry import Quaternion return Quaternion.Quaternion(v), Vector(ref_cms), \ Vector(pos), rms MMTK-2.7.9/MMTK/GroupEnvironment.py0000644000076600000240000000037011662461640017362 0ustar hinsenstaff00000000000000# This module defines the environment in which group definition files # are executed. from MMTK.Database import BlueprintAtom, BlueprintGroup, BlueprintBond from MMTK.Units import * Atom = BlueprintAtom Group = BlueprintGroup Bond = BlueprintBond MMTK-2.7.9/MMTK/InternalCoordinates.py0000644000076600000240000002736711662461640020027 0ustar hinsenstaff00000000000000# Manipulation of internal coordinates # # Written by Konrad Hinsen # """ Manipulation of molecular configurations in terms of internal coordinates """ __docformat__ = 'restructuredtext' import MMTK from Scientific import N # # The abstract base class # class InternalCoordinate: def __init__(self, atoms): self.atoms = atoms self.molecule = None for m in atoms[0].topLevelChemicalObject().bondedUnits(): if atoms[0] in m.atomList(): self.molecule = m break if self.molecule is None: raise ValueError("inconsistent data structure") for a in atoms[1:]: if a not in self.molecule.atomList(): raise ValueError("atoms not in the same molecule") self.universe = self.molecule.universe() if self.universe is None: self.universe = MMTK.InfiniteUniverse() def bondTest(self): for i in range(len(self.atoms)-1): if not self.atoms[i] in self.atoms[i+1].bondedTo(): raise ValueError("no bond between %s and %s" % (self.atoms[i], self.atoms[i+1])) def findFragments(self, spec1, spec2, excluded_first_only=False): self.fragment1 = MMTK.Collection() self.fragment2 = MMTK.Collection() self.molecule.setBondAttributes() try: self.bondTest() for fragment, (start, excluded, error_check) \ in [(self.fragment1, spec1), (self.fragment2, spec2)]: atoms = set([start]) first = True new_atoms = set([start]) while new_atoms: check = new_atoms new_atoms = set() for a in check: for na in a.bondedTo(): if excluded_first_only: if first: add = na not in excluded else: add = True else: add = na not in excluded if add and na not in atoms: atoms.add(na) new_atoms.add(na) first = False if error_check in atoms: raise ValueError("cyclic bond structure") for a in atoms: fragment.addObject(a) finally: self.molecule.clearBondAttributes() # # Bond length # class BondLength(InternalCoordinate): """ Bond length coordinate A BondLength object permits calculating and modifying the length of a bond in a molecule, under the condition that the bond is not part of a circular bond structure (but it is not a problem to have a circular bond structure elsewhere in the molecule). Modifying the bond length moves the parts of the molecule on both sides of the bond along the bond direction in such a way that the center of mass of the molecule does not change. The initial construction of the BondLength object can be expensive (the bond structure of the molecule must be analyzed). It is therefore advisable to keep the object rather than recreate it frequently. Note that if you only want to calculate bond lengths (no modification), the method Universe.distance is simpler and faster. """ def __init__(self, atom1, atom2): """ :param atom1: the first atom that defines the bond :type atom1: :class:`~MMTK.ChemicalObjects.Atom` :param atom2: the second atom that defines the bond :type atom2: :class:`~MMTK.ChemicalObjects.Atom` """ InternalCoordinate.__init__(self, [atom1, atom2]) self.findFragments((atom1, set([atom2]), atom2), (atom2, set([atom1]), atom1), True) def getValue(self, conf = None): """ :param conf: a configuration (defaults to the current configuration) :type conf: :class:`~MMTK.ParticleProperties.Configuration` :returns: the length of the bond in the configuration conf :rtype: float """ return self.universe.distance(self.atoms[0], self.atoms[1], conf) def setValue(self, value): """ Sets the length of the bond :param value: the desired length of the bond :type value: float """ v = self.universe.distanceVector(self.atoms[0], self.atoms[1]) axis = v.normal() distance = value - v.length() m1 = self.fragment1.mass() m2 = self.fragment2.mass() d1 = -m2*distance/(m1+m2) d2 = m1*distance/(m1+m2) self.fragment1.translateBy(d1*axis) self.fragment2.translateBy(d2*axis) # # Bond angles # class BondAngle(InternalCoordinate): """ Bond angle A BondAngle object permits calculating and modifying the angle between two bonds in a molecule, under the condition that the bonds are not part of a circular bond structure (but it is not a problem to have a circular bond structure elsewhere in the molecule). Modifying the bond angle rotates the parts of the molecule on both sides of the central atom around an axis passing through the central atom and perpendicular to the plane defined by the two bonds in such a way that there is no overall rotation of the molecule. The central atom and any other atoms bonded to it do not move. The initial construction of the BondAngle object can be expensive (the bond structure of the molecule must be analyzed). It is therefore advisable to keep the object rather than recreate it frequently. Note that if you only want to calculate bond angles (no modification), the method Universe.angle is simpler and faster. """ def __init__(self, atom1, atom2, atom3): """ :param atom1: the first atom that defines the angle :type atom1: :class:`~MMTK.ChemicalObjects.Atom` :param atom2: the second and central atom that defines the bond :type atom2: :class:`~MMTK.ChemicalObjects.Atom` :param atom3: the third atom that defines the bond :type atom3: :class:`~MMTK.ChemicalObjects.Atom` """ InternalCoordinate.__init__(self, [atom1, atom2, atom3]) excluded = set([atom2]) self.findFragments((atom1, excluded, atom3), (atom3, excluded, atom1)) def getValue(self, conf = None): """ :param conf: a configuration (defaults to the current configuration) :type conf: :class:`~MMTK.ParticleProperties.Configuration` :returns: the size of the angle in the configuration conf :rtype: float """ return self.universe.angle(self.atoms[0], self.atoms[1], self.atoms[2], conf) def setValue(self, value): """ Sets the size of the angle :param value: the desired angle :type value: float """ from Scientific.Geometry import delta v1 = self.universe.distanceVector(self.atoms[1], self.atoms[0]) v2 = self.universe.distanceVector(self.atoms[1], self.atoms[2]) angle = v1.angle(v2) if N.fabs(angle - N.pi) < 1.e-4: raise ValueError("angle too close to pi") axis = v1.cross(v2).normal() d = angle-value cm1, th1 = self.fragment1.centerAndMomentOfInertia() r1 = self.universe.distanceVector(self.atoms[1], cm1) th1 -= self.fragment1.mass()*((r1*r1)*delta-r1.dyadicProduct(r1)) i1 = axis*(th1*axis) cm2, th2 = self.fragment2.centerAndMomentOfInertia() r2 = self.universe.distanceVector(self.atoms[1], cm2) th2 -= self.fragment2.mass()*((r2*r2)*delta-r2.dyadicProduct(r2)) i2 = axis*(th2*axis) d1 = i2*d/(i1+i2) d2 = d1-d self.fragment1.rotateAroundAxis(self.atoms[1].position(), self.atoms[1].position()+axis, d1) self.fragment2.rotateAroundAxis(self.atoms[1].position(), self.atoms[1].position()+axis, d2) # # Dihedral angles # class DihedralAngle(InternalCoordinate): """ Dihedral angle A DihedralAngle object permits calculating and modifying the dihedral angle defined by three consecutive bonds in a molecule, under the condition that the central bond is not part of a circular bond structure (but it is not a problem to have a circular bond structure elsewhere in the molecule). Modifying the dihedral angle rotates the parts of the molecule on both sides of the central bond around this central bond in such a way that there is no overall rotation of the molecule. The initial construction of the DihedralAngle object can be expensive (the bond structure of the molecule must be analyzed). It is therefore advisable to keep the object rather than recreate it frequently. Note that if you only want to calculate bond angles (no modification), the method Universe.dihedral is simpler and faster. """ def __init__(self, atom1, atom2, atom3, atom4): """ :param atom1: the first atom that defines the dihedral :type atom1: :class:`~MMTK.ChemicalObjects.Atom` :param atom2: the second atom that defines the dihedral, must be on the central bond :type atom2: :class:`~MMTK.ChemicalObjects.Atom` :param atom3: the third atom that defines the dihedral, must be on the central bond :type atom3: :class:`~MMTK.ChemicalObjects.Atom` :param atom4: the fourth atom that defines the dihedral :type atom4: :class:`~MMTK.ChemicalObjects.Atom` """ InternalCoordinate.__init__(self, [atom1, atom2, atom3, atom4]) excluded = set([atom2, atom3]) self.findFragments((atom1, excluded, atom4), (atom4, excluded, atom1)) def getValue(self, conf = None): """ :param conf: a configuration (defaults to the current configuration) :type conf: :class:`~MMTK.ParticleProperties.Configuration` :returns: the size of the dihedral angle in the configuration conf :rtype: float """ return self.universe.dihedral(self.atoms[0], self.atoms[1], self.atoms[2], self.atoms[3], conf) def setValue(self, value): """ Sets the size of the dihedral :param value: the desired dihedral angle :type value: float """ from Scientific.Geometry import delta angle = self.universe.dihedral(self.atoms[0], self.atoms[1], self.atoms[2], self.atoms[3]) v = self.universe.distanceVector(self.atoms[1], self.atoms[2]) axis = v.normal() d = N.fmod(angle-value, 2.*N.pi) cm1, th1 = self.fragment1.centerAndMomentOfInertia() r1 = self.universe.distanceVector(self.atoms[1], cm1) th1 -= self.fragment1.mass()*((r1*r1)*delta-r1.dyadicProduct(r1)) i1 = axis*(th1*axis) cm2, th2 = self.fragment2.centerAndMomentOfInertia() r2 = self.universe.distanceVector(self.atoms[2], cm2) th2 -= self.fragment2.mass()*((r2*r2)*delta-r2.dyadicProduct(r2)) i2 = axis*(th2*axis) d1 = i2*d/(i1+i2) d2 = d1-d self.fragment1.rotateAroundAxis(self.atoms[1].position(), self.atoms[1].position()+axis, d1) self.fragment2.rotateAroundAxis(self.atoms[2].position(), self.atoms[2].position()+axis, d2) MMTK-2.7.9/MMTK/Minimization.py0000644000076600000240000001542712117632051016511 0ustar hinsenstaff00000000000000# This module implements energy minimizers. # # Written by Konrad Hinsen # """ Energy minimizers """ __docformat__ = 'restructuredtext' from MMTK import Features, Trajectory, Units from MMTK_minimization import conjugateGradient, steepestDescent # # Minimizer base class # class Minimizer(Trajectory.TrajectoryGenerator): def __init__(self, universe, options): Trajectory.TrajectoryGenerator.__init__(self, universe, options) default_options = {'steps': 100, 'step_size': 0.02*Units.Ang, 'convergence': 0.01*Units.kJ/(Units.mol*Units.nm), 'background': False, 'threads': None, 'mpi_communicator': None, 'actions': []} available_data = ['energy', 'configuration', 'gradients'] restart_data = ['configuration', 'energy'] def __call__(self, options): raise AttributeError # # Steepest descent minimizer # class SteepestDescentMinimizer(Minimizer): """ Steepest-descent minimizer The minimizer can handle fixed atoms, but no distance constraints. It is fully thread-safe. The minimization is started by calling the minimizer object. All the keyword options (see documnentation of __init__) can be specified either when creating the minimizer or when calling it. The following data categories and variables are available for output: - category "configuration": configuration and box size (for periodic universes) - category "gradients": energy gradients for each atom - category "energy": potential energy and norm of the potential energy gradient """ def __init__(self, universe, **options): """ :param universe: the universe on which the integrator acts :type universe: :class:`~MMTK.Universe.Universe` :keyword steps: the number of minimization steps (default is 100) :type steps: int :keyword step_size: the initial size of a minimization step (default is 0.002 nm) :type step_size: float :keyword convergence: the root-mean-square gradient length at which minimization stops (default is 0.01 kJ/mol/nm) :type convergence: float :keyword actions: a list of actions to be executed periodically (default is none) :type actions: list :keyword threads: the number of threads to use in energy evaluation (default set by MMTK_ENERGY_THREADS) :type threads: int :keyword background: if True, the integration is executed as a separate thread (default: False) :type background: bool :keyword mpi_communicator: an MPI communicator object, or None, meaning no parallelization (default: None) :type mpi_communicator: Scientific.MPI.MPICommunicator """ Minimizer.__init__(self, universe, options) self.features = [Features.FixedParticleFeature, Features.NoseThermostatFeature, Features.AndersenBarostatFeature] def __call__(self, **options): """ Run the minimizer. The keyword options are the same as described under __init__. """ self.setCallOptions(options) Features.checkFeatures(self, self.universe) configuration = self.universe.configuration() fixed = self.universe.getAtomBooleanArray('fixed') nt = self.getOption('threads') comm = self.getOption('mpi_communicator') evaluator = self.universe.energyEvaluator(threads=nt, mpi_communicator=comm) evaluator = evaluator.CEvaluator() args = (self.universe, configuration.array, fixed.array, evaluator, self.getOption('steps'), self.getOption('step_size'), self.getOption('convergence'), self.getActions(), 'Steepest descent minimization with ' + self.optionString(['convergence', 'step_size', 'steps'])) return self.run(steepestDescent, args) # # Conjugate gradient minimizer # class ConjugateGradientMinimizer(Minimizer): """ Conjugate gradient minimizer The minimizer can handle fixed atoms, but no distance constraints. It is fully thread-safe. The minimization is started by calling the minimizer object. All the keyword options can be specified either when creating the minimizer or when calling it. The following data categories and variables are available for output: - category "configuration": configuration and box size (for periodic universes) - category "gradients": energy gradients for each atom - category "energy": potential energy and norm of the potential energy gradient """ def __init__(self, universe, **options): """ :param universe: the universe on which the integrator acts :type universe: :class:`~MMTK.Universe.Universe` :keyword steps: the number of minimization steps (default is 100) :type steps: int :keyword step_size: the initial size of a minimization step (default is 0.002 nm) :type step_size: float :keyword convergence: the root-mean-square gradient length at which minimization stops (default is 0.01 kJ/mol/nm) :type convergence: float :keyword actions: a list of actions to be executed periodically (default is none) :type actions: list :keyword threads: the number of threads to use in energy evaluation (default set by MMTK_ENERGY_THREADS) :type threads: int :keyword background: if True, the integration is executed as a separate thread (default: False) :type background: bool """ Minimizer.__init__(self, universe, options) self.features = [Features.FixedParticleFeature, Features.NoseThermostatFeature] def __call__(self, **options): """ Run the minimizer. The keyword options are the same as described under __init__. """ self.setCallOptions(options) Features.checkFeatures(self, self.universe) configuration = self.universe.configuration() fixed = self.universe.getAtomBooleanArray('fixed') nt = self.getOption('threads') evaluator = self.universe.energyEvaluator(threads=nt).CEvaluator() args =(self.universe, configuration.array, fixed.array, evaluator, self.getOption('steps'), self.getOption('step_size'), self.getOption('convergence'), self.getActions(), 'Conjugate gradient minimization with ' + self.optionString(['convergence', 'step_size', 'steps'])) return self.run(conjugateGradient, args) MMTK-2.7.9/MMTK/MolecularSurface.py0000644000076600000240000001747111662461640017307 0ustar hinsenstaff00000000000000# # Copyright 2000 by Peter McCluskey (pcm@rahul.net). # You may do anything you want with it, provided this notice is kept intact. # # This module is in part a replacement for the MolecularSurface MMTK module # that uses the NSC code. It has a less restrictive license than the NSC code, # and is mostly more flexible, but is probably slower and somewhat less # accurate. It can run (slowly) without any of the C code. It has an # additional routine surfacePointsAndGradients, which returns area, an # "up" vector, and surface points for each atom. # Modifications by Konrad Hinsen : # - Replaced tabs by spaces # - Added/adapted docstrings to MMTK conventions # - Removed assignment of methods to class GroupOfAtoms # - Use vectors in the return value of surfacePointsAndGradients # - Replaced "math" module by "Numeric" # - Renamed module _surface to MMTK_surface # - Converted docstrings to epydoc # - Changed first argument name from self to object """ Molecular surfaces and volumes. """ __docformat__ = 'restructuredtext' from MMTK import surfm from MMTK.Collections import Collection from MMTK import Vector from Scientific import N def surfaceAndVolume(object, probe_radius = 0.): """ :param object: a chemical object :type object: :class:`~MMTK.Collections.GroupOfAtoms` :param probe_radius: the distance from the vdW-radii of the atoms at which the surface is computed :type probe_radius: float :returns: the molecular surface and volume of object :rtype: tuple """ atoms = object.atomList() smap = surfm.surface_atoms(atoms, probe_radius, ret_fmt = 2) tot_a = 0 tot_v = 0 for a in atoms: atom_data = smap[a] tot_a = tot_a + atom_data[0] tot_v = tot_v + atom_data[1] return (tot_a, tot_v) def surfaceAtoms(object, probe_radius = 0.): """ :param object: a chemical object :type object: :class:`~MMTK.Collections.GroupOfAtoms` :param probe_radius: the distance from the vdW-radii of the atoms at which the surface is computed :type probe_radius: float :returns: a dictionary that maps the surface atoms to their exposed surface areas :rtype: dict """ atoms = object.atomList() smap = surfm.surface_atoms(atoms, probe_radius, ret_fmt = 1) surface_atoms = {} for a in atoms: area = smap[a] if area > 0.: # we have a surface atom surface_atoms[a] = area return surface_atoms def surfacePointsAndGradients(object, probe_radius = 0., point_density = 258): """ :param object: a chemical object :type object: :class:`~MMTK.Collections.GroupOfAtoms` :param probe_radius: the distance from the vdW-radii of the atoms at which the surface is computed :type probe_radius: float :param point_density: the density of points that describe the surface :type point_density: int :returns: a dictionary that maps the surface atoms to a tuple containing three surface-related quantities: the exposed surface area, a list of points in the exposed surface, and a gradient vector pointing outward from the surface. :rtype: dict """ atoms = object.atomList() smap = surfm.surface_atoms(atoms, probe_radius, ret_fmt = 4, point_density = point_density) surface_data = {} for a in atoms: (area, volume, points1, grad) = smap[a] if area > 0.: # we have a surface atom surface_data[a] = (area, map(Vector, points1), Vector(grad)) return surface_data class Contact(object): def __init__(self, a1, a2, dist = None): self.a1 = a1 self.a2 = a2 if dist is None: self.dist = (a1.position() - a2.position()).length() else: self.dist = dist def __getitem__(self, index): return (self.a1, self.a2)[index] def __cmp__(a, b): return cmp(a.dist, b.dist) def __hash__(self): return (self.a1, self.a2) def __repr__(self): return 'Contact(%s, %s)' % (self.a1, self.a2) __str__ = __repr__ def findContacts(object1, object2, contact_factor = 1.0, cutoff = 0.0): """ Identifies contacts between two molecules. A contact is defined as a pair of atoms whose distance is less than contact_factor*(r1+r2+cutoff), where r1 and r2 are the atomic van-der-Waals radii. :param object1: a chemical object :type object1: :class:`~MMTK.Collections.GroupOfAtoms` :param object2: a chemical object :type object2: :class:`~MMTK.Collections.GroupOfAtoms` :param contact_factor: a scale factor in the contact distance criterion :type contact_factor: float :param cutoff: a constant in the contact distance criterion :type cutoff: float :returns: a list of Contact objects that describe atomic contacts between object1 and object2. :rtype: list """ max_object1 = len(object1.atomList()) atoms = object1.atomList() + object2.atomList() tup = surfm.get_atom_data(atoms, 0.0) max_rad = tup[0] # max vdW_radius atom_data = tup[1] nbors = surfm.NeighborList(atoms, contact_factor*(2*max_rad+cutoff), atom_data) clist = [] done = {} for index1 in range(len(atoms)): for (index2, dist2) in nbors[index1]: if (index1 < max_object1) != (index2 < max_object1): if index1 < index2: a1 = atoms[index1] a2 = atoms[index2] else: a1 = atoms[index2] a2 = atoms[index1] dist = N.sqrt(dist2) if dist >= contact_factor*(a1.vdW_radius + a2.vdW_radius + cutoff): continue if not done.has_key((index1, index2)): clist.append(Contact(a1, a2, dist)) done[(index1, index2)] = 1 return clist if __name__ == '__main__': from MMTK.PDB import PDBConfiguration from MMTK import Units import sys target_filename = sys.argv[2] pdb_conf1 = PDBConfiguration(target_filename) if sys.argv[1][:2] == '-f': chains = pdb_conf1.createNucleotideChains() molecule_names = [] if len(chains) >= 2: clist = findContacts(chains[0], chains[1]) else: molecule_names = [] for (key, mol) in pdb_conf1.molecules.items(): for o in mol: molecule_names.append(o.name) targets = pdb_conf1.createAll(molecule_names = molecule_names) if len(molecule_names) > 1: clist = findContacts(targets[0], targets[1]) else: atoms = targets.atomList() mid = len(atoms)/2 clist = findContacts(Collection(atoms[:mid]), Collection(atoms[mid:])) print len(clist), 'contacts' for c in clist[:8]: print '%-64s %6.2f' % (c, c.dist/Units.Ang) else: target = pdb_conf1.createAll() if sys.argv[1][:2] == '-v': (a, v) = target.surfaceAndVolume() print 'surface area %.2f volume %.2f' \ % (a/(Units.Ang**2), v/(Units.Ang**3)) elif sys.argv[1][:2] == '-a': smap = target.surfaceAtoms(probe_radius = 1.4*Units.Ang) print len(smap.keys()),'of',len(target.atomList()),'atoms on surface' elif sys.argv[1][:2] == '-p': smap = target.surfacePointsAndGradients(probe_radius = 1.4*Units.Ang) for (a, tup) in smap.items(): print '%-40.40s %6.2f %5d %5.1f %5.1f %5.1f' \ % (a.fullName(), tup[0]/(Units.Ang**2), len(tup[1]), tup[2][0], tup[2][1], tup[2][2]) MMTK-2.7.9/MMTK/MoleculeEnvironment.py0000644000076600000240000000050511662461640020033 0ustar hinsenstaff00000000000000# This module defines the environment in which molecule definition files # are executed. from MMTK.Database import BlueprintAtom, BlueprintGroup, BlueprintBond from MMTK.ConfigIO import Cartesian, ZMatrix from MMTK.PDB import PDBFile from MMTK.Units import * Atom = BlueprintAtom Group = BlueprintGroup Bond = BlueprintBond MMTK-2.7.9/MMTK/MoleculeFactory.py0000644000076600000240000002437511662461640017151 0ustar hinsenstaff00000000000000# A molecule factory is used to create chemical objects # in code, without a database definition. # # Written by Konrad Hinsen # """ Molecule factory for creating chemical objects """ __docformat__ = 'restructuredtext' from MMTK import Bonds, ChemicalObjects # # Molecule factories store AtomTemplate and GroupTemplate objects. # class AtomTemplate(object): def __init__(self, element): self.element = element class GroupTemplate(object): def __init__(self, name): self.name = name self.children = [] self.names = {} self.attributes = {} self.positions = {} self.bonds = [] self.locked = False def addAtom(self, atom_name, element): """ Add an atom. :param atom_name: the name of the atom :type atom_name: str :param element: the chemical element symbol :type element: str """ if self.locked: raise ValueError("group is locked") atom = AtomTemplate(element) self.names[atom_name] = len(self.children) self.children.append(atom) def addSubgroup(self, subgroup_name, subgroup): """ Add a subgroup. :param subgroup_name: the name of the subgroup within this group :type subgroup_name: str :param subgroup: the subgroup type :type subgroup: str """ if self.locked: raise ValueError("group is locked") self.names[subgroup_name] = len(self.children) self.children.append(subgroup) def addBond(self, atom1, atom2): """ Add a bond. :param atom1: the name of the first atom :type atom1: str :param atom2: the name of the second atom :type atom2: str """ if self.locked: raise ValueError("group is locked") self.bonds.append((self.atomNameToPath(atom1), self.atomNameToPath(atom2))) def setAttribute(self, name, value): if self.locked: raise ValueError("group is locked") self.attributes[name] = value def setPosition(self, name, vector): if self.locked: raise ValueError("group is locked") self.positions[name] = vector def getAtomReference(self, atom_name): path = self.atomNameToPath(atom_name) object = self for path_element in path[:-1]: object = object.children[object.names[path_element]] atom_index = object.names[path[-1]] reference = 0 for i in range(atom_index): if isinstance(object.children[i], AtomTemplate): reference += 1 from MMTK.Database import AtomReference return AtomReference(reference) def atomNameToPath(self, atom_name): atom_name = atom_name.split('.') object = self try: for path_element in atom_name: object = object.children[object.names[path_element]] if not isinstance(object, AtomTemplate): raise ValueError("no atom " + atom) except KeyError: raise ValueError("no atom " + '.'.join(atom_name)) return atom_name def writeXML(self, file, memo): if memo.get(self.name, False): return names = len(self.children)*[None] for name in self.names: names[self.names[name]] = name atoms = [] subgroups = [] for i in range(len(self.children)): object = self.children[i] name = names[i] if isinstance(object, GroupTemplate): object.writeXML(file, memo) subgroups.append((name, object)) else: atoms.append((name, object)) file.write('\n' % self.name) for name, subgroup in subgroups: file.write(' \n' % (subgroup.name, name)) if atoms: file.write(' \n') for name, atom in atoms: file.write(' \n' % (name, atom.element)) file.write(' \n') if self.bonds: file.write(' \n') for a1, a2 in self.bonds: file.write(' \n' % (':'.join(a1), ':'.join(a2))) file.write(' \n') file.write('\n') memo[self.name] = True def getXMLAtomOrder(self): atom_names = [] names = len(self.children)*[None] for name in self.names: names[self.names[name]] = name for i in range(len(self.children)): oname = names[i] object = self.children[i] if isinstance(object, GroupTemplate): for name in object.getXMLAtomOrder(): atom_names.append(oname + ':' + name) else: atom_names.append(oname) return atom_names # # The MoleculeFactory class # class MoleculeFactory(object): ''' MoleculeFactory A MoleculeFactory serves to create molecules without reference to database definitions. Molecules and groups are defined in terms of atoms, groups, and bonds. Each MoleculeFactory constitutes an independent set of definitions. Definitions within a MoleculeFactory can refer to each other. Each MoleculeFactory stores a set of :class:`~MMTK.ChemicalObjects.Group` objects which are referred to by names. The typical operation sequence is to create a new group and then add atoms, bonds, and subgroups. It is also possible to define coordinates and arbitrary attributes (in particular for force fields). In the end, a finished object can be retrieved as a :class:`~MMTK.ChemicalObjects.Molecule` object. ''' def __init__(self): self.groups = {} self.locked_groups = {} def createGroup(self, name): """ Create a new (initially empty) group object. """ if self.groups.has_key(name): raise ValueError("redefinition of group " + name) self.groups[name] = GroupTemplate(name) def addSubgroup(self, group, subgroup_name, subgroup): """ Add a subgroup to a group :param group: the name of the group :type group: str :param subgroup_name: the name of the subgroup within the group :type subgroup_name: str :param subgroup: the subgroup type :type subgroup: str """ self.groups[group].addSubgroup(subgroup_name, self.groups[subgroup]) def addAtom(self, group, atom_name, element): """ Add an atom to a group :param group: the name of the group :type group: str :param atom_name: the name of the atom :type atom_name: str :param element: the chemical element symbol :type element: str """ self.groups[group].addAtom(atom_name, element) def addBond(self, group, atom1, atom2): """ Add a bond to a group :param group: the name of the group :type group: str :param atom1: the name of the first atom :type atom1: str :param atom2: the name of the second atom :type atom2: str """ self.groups[group].addBond(atom1, atom2) def setAttribute(self, group, name, value): self.groups[group].setAttribute(name, value) def setPosition(self, group, atom, vector): self.groups[group].setPosition(atom, vector) def getAtomReference(self, group, atom): return self.groups[group].getAtomReference(atom) def retrieveMolecule(self, group): """ :param group: the name of the group to be used as a template :type group: str :returns: a molecule defined by the contents of the group :rtype: :class:`~MMTK.ChemicalObjects.Molecule` """ group = self.groups[group] return self.makeChemicalObjects(group, True) def makeChemicalObjects(self, template, top_level): self.groups[template.name].locked = True if top_level: if template.attributes.has_key('sequence'): object = ChemicalObjects.ChainMolecule(None) else: object = ChemicalObjects.Molecule(None) else: object = ChemicalObjects.Group(None) object.atoms = [] object.bonds = Bonds.BondList([]) object.groups = [] object.type = self.groups[template.name] object.parent = None child_objects = [] for child in template.children: if isinstance(child, GroupTemplate): group = self.makeChemicalObjects(child, False) object.groups.append(group) object.atoms.extend(group.atoms) object.bonds.extend(group.bonds) group.parent = object child_objects.append(group) else: atom = ChemicalObjects.Atom(child.element) object.atoms.append(atom) atom.parent = object child_objects.append(atom) for name, index in template.names.items(): setattr(object, name, child_objects[index]) child_objects[index].name = name for name, value in template.attributes.items(): path = name.split('.') setattr(self.namePath(object, path[:-1]), path[-1], value) for atom1, atom2 in template.bonds: atom1 = self.namePath(object, atom1) atom2 = self.namePath(object, atom2) object.bonds.append(Bonds.Bond((atom1, atom2))) for name, vector in template.positions.items(): path = name.split('.') self.namePath(object, path).setPosition(vector) return object def namePath(self, object, path): for item in path: object = getattr(object, item) return object def writeXML(self, file): file.write('\n\n') file.write('\n\n') memo = {} for group in self.groups: self.groups[group].writeXML(file, memo) file.write('\n\n') MMTK-2.7.9/MMTK/NormalModes/0000755000076600000240000000000012156626572015715 5ustar hinsenstaff00000000000000MMTK-2.7.9/MMTK/NormalModes/__init__.py0000644000076600000240000000241511662461640020022 0ustar hinsenstaff00000000000000# Normal modes package """ Normal modes """ __docformat__ = 'restructuredtext' from MMTK.NormalModes.EnergeticModes import EnergeticModes from MMTK.NormalModes.VibrationalModes import VibrationalModes from MMTK.NormalModes.BrownianModes import BrownianModes # Backwards compatibility NormalModes = VibrationalModes class SubspaceNormalModes(VibrationalModes): def __init__(self, universe=None, basis=None, temperature=300.): VibrationalModes.__init__(self, universe, temperature, basis) class FiniteDifferenceSubspaceNormalModes(VibrationalModes): def __init__(self, universe=None, basis=None, delta=0.0001, temperature=300.): VibrationalModes.__init__(self, universe, temperature, basis, delta) class SparseMatrixNormalModes(VibrationalModes): def __init__(self, universe=None, nmodes=None, temperature=300): VibrationalModes.__init__(self, universe, temperature, nmodes, None, True) class SparseMatrixSubspaceNormalModes(VibrationalModes): def __init__(self, universe=None, basis=None, temperature=300.): VibrationalModes.__init__(self, universe, temperature, basis, None, True) MMTK-2.7.9/MMTK/NormalModes/BrownianModes.py0000644000076600000240000005315212117632051021026 0ustar hinsenstaff00000000000000# Brownian normal mode calculations. # # Written by Konrad Hinsen # """ Brownian normal modes """ __docformat__ = 'restructuredtext' from MMTK import Features, Units, ParticleProperties, Random from Scientific.Functions.Interpolation import InterpolatingFunction from Scientific import N from MMTK.NormalModes import Core # # Class for a single mode # class BrownianMode(Core.Mode): """ Single Brownian normal mode Mode objects are created by indexing a :class:`~MMTK.NormalModes.BrownianModes.BrownianModes` object. They contain the atomic displacements corresponding to a single mode. In addition, the inverse of the relaxation time corresponding to the mode is stored in the attribute "inv_relaxation_time". Note: the Brownian mode vectors are not friction weighted and therefore not orthogonal to each other. """ def __init__(self, universe, n, inv_relaxation_time, mode): self.inv_relaxation_time = inv_relaxation_time Core.Mode.__init__(self, universe, n, mode) def __str__(self): return 'Mode ' + `self.number` + \ ' with inverse relaxation time ' \ + `self.inv_relaxation_time` __repr__ = __str__ # # Class for a full set of normal modes # class BrownianModes(Core.NormalModes): """ Brownian modes describe the independent relaxation motions of a harmonic system with friction. They are obtained by diagonalizing the friction-weighted force constant matrix. In order to obtain physically reasonable normal modes, the configuration of the universe must correspond to a local minimum of the potential energy. Individual modes (see :class:`~MMTK.NormalModes.BrownianModes.BrownianMode`) can be extracted by indexing with an integer. Looping over the modes is possible as well. Brownian modes describe the independent relaxation motions of a harmonic system with friction. They are obtained by diagonalizing the friction-weighted force constant matrix. In order to obtain physically reasonable normal modes, the configuration of the universe must correspond to a local minimum of the potential energy. A BrownianModes object behaves like a sequence of modes. Individual modes (see :class:`~MMTK.NormalModes.BrownianMode`) can be extracted by indexing with an integer. Looping over the modes is possible as well. """ features = [] def __init__(self, universe = None, friction = None, temperature = 300.*Units.K, subspace = None, delta = None, sparse = False): """ :param universe: the system for which the normal modes are calculated; it must have a force field which provides the second derivatives of the potential energy :type universe: :class:`~MMTK.Universe.Universe` :param friction: the friction coefficient for each particle. Note: The friction coefficients are not mass-weighted, i.e. they have the dimension of an inverse time. :type friction: :class:`~MMTK.ParticleProperties.ParticleScalar` :param temperature: the temperature for which the amplitudes of the atomic displacement vectors are calculated. A value of None can be specified to have no scaling at all. In that case the mass-weighted norm of each normal mode is one. :type temperature: float :param subspace: the basis for the subspace in which the normal modes are calculated (or, more precisely, a set of vectors spanning the subspace; it does not have to be orthogonal). This can either be a sequence of :class:`~MMTK.ParticleProperties.ParticleVector` objects or a tuple of two such sequences. In the second case, the subspace is defined by the space spanned by the second set of vectors projected on the complement of the space spanned by the first set of vectors. The first set thus defines directions that are excluded from the subspace. The default value of None indicates a standard normal mode calculation in the 3N-dimensional configuration space. :param delta: the rms step length for numerical differentiation. The default value of None indicates analytical differentiation. Numerical differentiation is available only when a subspace basis is used as well. Instead of calculating the full force constant matrix and then multiplying with the subspace basis, the subspace force constant matrix is obtained by numerical differentiation of the energy gradients along the basis vectors of the subspace. If the basis is much smaller than the full configuration space, this approach needs much less memory. :type delta: float :param sparse: a flag that indicates if a sparse representation of the force constant matrix is to be used. This is of interest when there are no long-range interactions and a subspace of smaller size then 3N is specified. In that case, the calculation will use much less memory with a sparse representation. :type sparse: bool """ if universe == None: return Features.checkFeatures(self, universe) Core.NormalModes.__init__(self, universe, subspace, delta, sparse, ['array', 'inv_relaxation_times']) self.friction = friction self.temperature = temperature self.weights = N.sqrt(friction.array) self.weights = self.weights[:, N.NewAxis] self._forceConstantMatrix() ev = self._diagonalize() self.inv_relaxation_times = ev self.sort_index = N.argsort(self.inv_relaxation_times) self.array.shape = (self.nmodes, self.natoms, 3) self.cleanup() def __getitem__(self, item): index = self.sort_index[item] return BrownianMode(self.universe, item, self.inv_relaxation_times[index], self.array[index]/self.weights) def rawMode(self, item): """ :param item: the index of a normal mode :type item: int :returns: the unscaled mode vector :rtype: :class:`~MMTK.NormalModes.BrownianModes.BrownianMode` """ index = self.sort_index[item] return BrownianMode(self.universe, item, self.inv_relaxation_times[index], self.array[index]) def fluctuations(self, first_mode=6): f = ParticleProperties.ParticleScalar(self.universe) for i in range(first_mode, self.nmodes): mode = self.rawMode(i) f += (mode*mode)/mode.inv_relaxation_time f = Units.k_B*self.temperature*f/self.friction return f def meanSquareDisplacement(self, subset=None, weights=None, time_range = (0., None, None), first_mode = 6): """ :param subset: the subset of the universe used in the calculation (default: the whole universe) :type subset: :class:`~MMTK.Collections.GroupOfAtoms` :param weights: the weight to be given to each atom in the average (default: atomic masses) :type weights: :class:`~MMTK.ParticleProperties.ParticleScalar` :param time_range: the time values at which the mean-square displacement is evaluated, specified as a range tuple (first, last, step). The defaults are first=0, last= 20 times the longest vibration perdiod, and step defined such that 300 points are used in total. :type time_range: tuple :param first_mode: the first mode to be taken into account for the fluctuation calculation. The default value of 6 is right for molecules in vacuum. :type first_mode: int :returns: the averaged mean-square displacement of the atoms in subset as a function of time :rtype: Scientific.Functions.Interpolation.InterpolatingFunction """ if subset is None: subset = self.universe if weights is None: weights = self.universe.masses() weights = weights*subset.booleanMask() total = weights.sumOverParticles() weights = weights/(total*self.friction) first, last, step = (time_range + (None, None))[:3] if last is None: last = 3./self.rawMode(first_mode).inv_relaxation_time if step is None: step = (last-first)/300. time = N.arange(first, last, step) msd = N.zeros(time.shape, N.Float) for i in range(first_mode, self.nmodes): mode = self.rawMode(i) rt = mode.inv_relaxation_time d = (weights*(mode*mode)).sumOverParticles() N.add(msd, d*(1.-N.exp(-rt*time))/rt, msd) N.multiply(msd, 2.*Units.k_B*self.temperature, msd) return InterpolatingFunction((time,), msd) def staticStructureFactor(self, q_range = (1., 15.), subset=None, weights=None, random_vectors=15, first_mode = 6): """ :param q_range: the range of angular wavenumber values :type q_range: tuple :param subset: the subset of the universe used in the calculation (default: the whole universe) :type subset: :class:`~MMTK.Collections.GroupOfAtoms` :param weights: the weight to be given to each atom in the average (default: coherent scattering lengths) :type weights: :class:`~MMTK.ParticleProperties.ParticleScalar` :param random_vectors: the number of random direction vectors used in the orientational average :type random_vectors: int :param first_mode: the first mode to be taken into account for the fluctuation calculation. The default value of 6 is right for molecules in vacuum. :type first_mode: int :returns: the Static Structure Factor as a function of angular wavenumber :rtype: Scientific.Functions.Interpolation.InterpolatingFunction """ if subset is None: subset = self.universe if weights is None: weights = self.universe.getParticleScalar('b_coherent') mask = subset.booleanMask() weights = N.repeat(weights.array, mask.array) weights = weights/N.sqrt(N.add.reduce(weights*weights)) friction = N.repeat(self.friction.array, mask.array) r = N.repeat(self.universe.configuration().array, mask.array) first, last, step = (q_range+(None,))[:3] if step is None: step = (last-first)/50. q = N.arange(first, last, step) kT = Units.k_B*self.temperature natoms = subset.numberOfAtoms() sq = 0. random_vectors = Random.randomDirections(random_vectors) for v in random_vectors: sab = N.zeros((natoms, natoms), N.Float) for i in range(first_mode, self.nmodes): irt = self.rawMode(i).inv_relaxation_time d = N.repeat((self.rawMode(i)*v).array, mask.array) \ / N.sqrt(friction) sab = sab + (d[N.NewAxis,:]-d[:,N.NewAxis])**2/irt sab = sab[N.NewAxis,:,:]*q[:, N.NewAxis, N.NewAxis]**2 phase = N.exp(-1.j*q[:, N.NewAxis] * N.dot(r, v.array)[N.NewAxis, :]) \ * weights[N.NewAxis, :] temp = N.sum(phase[:, :, N.NewAxis]*N.exp(-0.5*kT*sab), 1) temp = N.sum(N.conjugate(phase)*temp, 1) sq = sq + temp.real return InterpolatingFunction((q,), sq/len(random_vectors)) def coherentScatteringFunction(self, q, time_range = (0., None, None), subset=None, weights=None, random_vectors=15, first_mode = 6): """ :param q: the angular wavenumber :type q: float :param time_range: the time values at which the mean-square displacement is evaluated, specified as a range tuple (first, last, step). The defaults are first=0, last= 20 times the longest vibration perdiod, and step defined such that 300 points are used in total. :type time_range: tuple :param subset: the subset of the universe used in the calculation (default: the whole universe) :type subset: :class:`~MMTK.Collections.GroupOfAtoms` :param weights: the weight to be given to each atom in the average (default: coherent scattering lengths) :type weights: :class:`~MMTK.ParticleProperties.ParticleScalar` :param random_vectors: the number of random direction vectors used in the orientational average :type random_vectors: int :param first_mode: the first mode to be taken into account for the fluctuation calculation. The default value of 6 is right for molecules in vacuum. :type first_mode: int :returns: the Coherent Scattering Function as a function of time :rtype: Scientific.Functions.Interpolation.InterpolatingFunction """ if subset is None: subset = self.universe if weights is None: weights = self.universe.getParticleScalar('b_coherent') mask = subset.booleanMask() weights = N.repeat(weights.array, mask.array) weights = weights/N.sqrt(N.add.reduce(weights*weights)) friction = N.repeat(self.friction.array, mask.array) r = N.repeat(self.universe.configuration().array, mask.array) first, last, step = (time_range + (None, None))[:3] if last is None: last = 3./self.rawMode(first_mode).inv_relaxation_time if step is None: step = (last-first)/300. time = N.arange(first, last, step) natoms = subset.numberOfAtoms() kT = Units.k_B*self.temperature fcoh = N.zeros((len(time),), N.Complex) random_vectors = Random.randomDirections(random_vectors) for v in random_vectors: phase = N.exp(-1.j*q*N.dot(r, v.array)) for ai in range(natoms): fbt = N.zeros((natoms, len(time)), N.Float) for i in range(first_mode, self.nmodes): irt = self.rawMode(i).inv_relaxation_time d = q * N.repeat((self.rawMode(i)*v).array, mask.array) \ / N.sqrt(friction) ft = N.exp(-irt*time)/irt N.add(fbt, d[ai] * d[:, N.NewAxis] * ft[N.NewAxis, :], fbt) N.add(fbt, (-0.5/irt) * (d[ai]**2 + d[:, N.NewAxis]**2), fbt) N.add(fcoh, weights[ai]*phase[ai] * N.dot(weights*N.conjugate(phase), N.exp(kT*fbt)), fcoh) return InterpolatingFunction((time,), fcoh.real/len(random_vectors)) def incoherentScatteringFunction(self, q, time_range = (0., None, None), subset=None, random_vectors=15, first_mode = 6): """ :param q: the angular wavenumber :type q: float :param time_range: the time values at which the mean-square displacement is evaluated, specified as a range tuple (first, last, step). The defaults are first=0, last= 20 times the longest vibration perdiod, and step defined such that 300 points are used in total. :type time_range: tuple :param subset: the subset of the universe used in the calculation (default: the whole universe) :type subset: :class:`~MMTK.Collections.GroupOfAtoms` :param random_vectors: the number of random direction vectors used in the orientational average :type random_vectors: int :param first_mode: the first mode to be taken into account for the fluctuation calculation. The default value of 6 is right for molecules in vacuum. :type first_mode: int :returns: the Incoherent Scattering Function as a function of time :rtype: Scientific.Functions.Interpolation.InterpolatingFunction """ if subset is None: subset = self.universe mask = subset.booleanMask() weights_inc = self.universe.getParticleScalar('b_incoherent') weights_inc = N.repeat(weights_inc.array**2, mask.array) weights_inc = weights_inc/N.add.reduce(weights_inc) friction = N.repeat(self.friction.array, mask.array) mass = N.repeat(self.universe.masses().array, mask.array) r = N.repeat(self.universe.configuration().array, mask.array) first, last, step = (time_range + (None, None))[:3] if last is None: last = 3./self.weighedMode(first_mode).inv_relaxation_time if step is None: step = (last-first)/300. time = N.arange(first, last, step) natoms = subset.numberOfAtoms() kT = Units.k_B*self.temperature finc = N.zeros((len(time),), N.Float) eisf = 0. random_vectors = Random.randomDirections(random_vectors) for v in random_vectors: phase = N.exp(-1.j*q*N.dot(r, v.array)) faat = N.zeros((natoms, len(time)), N.Float) eisf_sum = N.zeros((natoms,), N.Float) for i in range(first_mode, self.nmodes): irt = self.rawMode(i).inv_relaxation_time d = q * N.repeat((self.rawMode(i)*v).array, mask.array) \ / N.sqrt(friction) ft = (N.exp(-irt*time)-1.)/irt N.add(faat, d[:, N.NewAxis]**2 * ft[N.NewAxis, :], faat) N.add(eisf_sum, -d**2/irt, eisf_sum) N.add(finc, N.sum(weights_inc[:, N.NewAxis] * N.exp(kT*faat), 0), finc) eisf = eisf + N.sum(weights_inc*N.exp(kT*eisf_sum)) return InterpolatingFunction((time,), finc/len(random_vectors)) def EISF(self, q_range = (0., 15.), subset=None, weights = None, random_vectors = 15, first_mode = 6): """ :param q_range: the range of angular wavenumber values :type q_range: tuple :param subset: the subset of the universe used in the calculation (default: the whole universe) :type subset: :class:`~MMTK.Collections.GroupOfAtoms` :param weights: the weight to be given to each atom in the average (default: incoherent scattering lengths) :type weights: :class:`~MMTK.ParticleProperties.ParticleScalar` :param random_vectors: the number of random direction vectors used in the orientational average :type random_vectors: int :param first_mode: the first mode to be taken into account for the fluctuation calculation. The default value of 6 is right for molecules in vacuum. :type first_mode: int :returns: the Elastic Incoherent Structure Factor (EISF) as a function of angular wavenumber :rtype: Scientific.Functions.Interpolation.InterpolatingFunction """ if subset is None: subset = self.universe if weights is None: weights = self.universe.getParticleScalar('b_incoherent') weights = weights*weights weights = weights*subset.booleanMask() total = weights.sumOverParticles() weights = weights/total first, last, step = (q_range+(None,))[:3] if step is None: step = (last-first)/50. q = N.arange(first, last, step) f = ParticleProperties.ParticleTensor(self.universe) for i in range(first_mode, self.nmodes): mode = self.rawMode(i) f = f + (1./mode.inv_relaxation_time)*mode.dyadicProduct(mode) f = Units.k_B*self.temperature*f/self.friction eisf = N.zeros(q.shape, N.Float) random_vectors = Random.randomDirections(random_vectors) for v in random_vectors: for a in subset.atomList(): exp = N.exp(-v*(f[a]*v)) N.add(eisf, weights[a]*exp**(q*q), eisf) return InterpolatingFunction((q,), eisf/len(random_vectors)) MMTK-2.7.9/MMTK/NormalModes/Core.py0000644000076600000240000004042012041510260017132 0ustar hinsenstaff00000000000000# Common aspects of normal mode calculations. # # Written by Konrad Hinsen # __docformat__ = 'restructuredtext' from MMTK import Units, ParticleProperties, Visualization from Scientific import N import copy # # Import LAPACK routines or equivalents # try: array_package = N.package except AttributeError: array_package = "Numeric" eigh = None if array_package == "NumPy": # Use symeig (http://mdp-toolkit.sourceforge.net/symeig.html) # if it is installed and if NumPy is used (symeig works only # with NumPy). Symeig uses the LAPACK routine dsyevr, which # uses less memory than dsyevd. It is also said to be faster, # but in my tests on the Mac it turned out to be slightly slower. try: from scipy.linalg import eigh except ImportError: pass dsyevd = None dgesdd = None try: if array_package == "Numeric": from lapack_lite import dsyevd, dgesdd, LapackError else: from numpy.linalg.lapack_lite import dsyevd, dgesdd, LapackError except ImportError: pass if dsyevd is None: try: # PyLAPACK from lapack_dsy import dsyevd, LapackError except ImportError: pass if dgesdd is None: try: # PyLAPACK from lapack_dge import dgesdd except ImportError: pass if dsyevd is None: from Scientific.LA import Heigenvectors if dsyevd: n = 1 array = N.zeros((n, n), N.Float) ev = N.zeros((n,), N.Float) work = N.zeros((1,), N.Float) int_types = [N.Int, N.Int8, N.Int16, N.Int32] try: int_types.append(N.Int64) int_types.append(N.Int128) except AttributeError: pass for int_type in int_types: iwork = N.zeros((1,), int_type) try: dsyevd('V', 'L', n, array, n, ev, work, -1, iwork, -1, 0) break except LapackError: pass del n, array, ev, work, iwork, int_types del array_package # # Class for a single mode # class Mode(ParticleProperties.ParticleVector): def __init__(self, universe, n, mode): self.number = n ParticleProperties.ParticleVector.__init__(self, universe, mode) return_class = ParticleProperties.ParticleVector def view(self, factor=1., subset=None): """ Start an animation of the mode. See :class:`~MMTK.Visualization` for the configuration of external viewers. :param factor: a scaling factor for the amplitude of the motion :type factor: float :param subset: the subset of the universe to be shown (default: the whole universe) :type subset: :class:`~MMTK.Collections.GroupOfAtoms` """ Visualization.viewMode(self, factor, subset) # # Class for a full set of normal modes # This is an abstract base class, the real classes are # EnergeticModes, VibrationalModes, and BrownianModes # class NormalModes(object): def __init__(self, universe, basis, delta, sparse, arrays): self.universe = universe self.basis = basis if sparse: if isinstance(basis, int): self.sparse = basis self.basis = None else: self.sparse = -1 else: self.sparse = None self.delta = delta self._internal_arrays = arrays def cleanup(self): del self.basis if self.sparse is not None: del self.fc def __len__(self): return self.nmodes def __getslice__(self, first, last): last = min(last, self.nmodes) return [self[i] for i in range(first, last)] def reduceToRange(self, first, last): """ Discards all modes outside a given range of mode numbers. This is done to reduce memory requirements, especially before saving the modesto a file. :param first: the number of the first mode to be kept :param last: the number of the last mode to be kept - 1 """ junk1 = list(self.sort_index[:first]) junk2 = list(self.sort_index[last:]) junk1.sort() junk2.sort() if junk1 == range(0, first) and \ junk2 == range(last, len(self.sort_index)): # This is the most frequent case. It can be handled # without copying the mode array. for array in self._internal_arrays: setattr(self, array, getattr(self, array)[first:last]) self.sort_index = self.sort_index[first:last]-first else: keep = self.sort_index[first:last] for array in self._internal_arrays: setattr(self, array, N.take(getattr(self, array), keep)) self.sort_index = N.arange(0, last-first) self.nmodes = last-first def fluctuations(self, first_mode=6): """ :param first_mode: the first mode to be taken into account for the fluctuation calculation. The default value of 6 is right for molecules in vacuum. :type first_mode: int :returns: the thermal fluctuations for each atom in the universe :rtype: :class:`~MMTK.ParticleProperties.ParticleScalar` """ raise NotImplementedError def anisotropicFluctuations(self, first_mode=6): """ :param first_mode: the first mode to be taken into account for the fluctuation calculation. The default value of 6 is right for molecules in vacuum. :type first_mode: int :returns: the anisotropic thermal fluctuations for each atom in the universe :rtype: :class:`~MMTK.ParticleProperties.ParticleTensor` """ raise NotImplementedError def _forceConstantMatrix(self): if self.basis is not None: self._setupBasis() if self.delta is not None: self.nmodes = len(self.basis) self.array = N.zeros((self.nmodes, self.nmodes), N.Float) conf = copy.copy(self.universe.configuration()) conf_array = conf.array self.natoms = len(conf_array) small_change = 0 for i in range(self.nmodes): d = self.delta*N.reshape(self.basis[i], (self.natoms, 3))/self.weights conf_array[:] = conf_array+d self.universe.setConfiguration(conf) energy, gradients1 = self.universe.energyAndGradients( None, None, small_change) small_change = 1 conf_array[:] = conf_array-2.*d self.universe.setConfiguration(conf) energy, gradients2 = self.universe.energyAndGradients( None, None, small_change) conf_array[:] = conf_array+d v = (gradients1.array-gradients2.array) / \ (2.*self.delta*self.weights) self.array[i] = N.dot(self.basis, N.ravel(v)) self.universe.setConfiguration(conf) self.array = 0.5*(self.array+N.transpose(self.array)) return elif self.sparse is not None: from MMTK_forcefield import SparseForceConstants nmodes, natoms = self.basis.shape natoms = natoms/3 self.nmodes = nmodes self.natoms = natoms fc = SparseForceConstants(natoms, 5*natoms) eval = self.universe.energyEvaluator() energy, g, fc = eval(0, fc, 0) self.array = N.zeros((nmodes, nmodes), N.Float) for i in range(nmodes): v = N.reshape(self.basis[i], (natoms, 3))/self.weights v = fc.multiplyVector(v) v = v/self.weights v.shape = (3*natoms,) self.array[i, :] = N.dot(self.basis, v) self.sparse = None return if self.sparse is None: energy, force_constants = self.universe.energyAndForceConstants() self.array = force_constants.array self.natoms = self.array.shape[0] self.nmodes = 3*self.natoms N.divide(self.array, self.weights[N.NewAxis, N.NewAxis, :, :], self.array) N.divide(self.array, self.weights[:, :, N.NewAxis, N.NewAxis], self.array) self.array.shape = 2*(self.nmodes,) else: from MMTK_forcefield import SparseForceConstants self.natoms = self.universe.numberOfCartesianCoordinates() fc = SparseForceConstants(self.natoms, 5*self.natoms) eval = self.universe.energyEvaluator() energy, g, fc = eval(0, fc, 0) self.fc = fc self.fc.scale(1./self.weights[:, 0]) if self.basis is not None: _symmetrize(self.array) self.array = N.dot(N.dot(self.basis, self.array), N.transpose(self.basis)) self.nmodes = self.array.shape[0] def _diagonalize(self): if self.sparse is not None: from MMTK_sparseev import sparseMatrixEV eigenvalues, eigenvectors = sparseMatrixEV(self.fc, self.nmodes) self.array = eigenvectors[:self.nmodes] return eigenvalues # Calculate eigenvalues and eigenvectors of self.array if eigh is not None: _symmetrize(self.array) ev, modes = eigh(self.array, overwrite_a=True) self.array = N.transpose(modes) elif dsyevd is None: ev, modes = Heigenvectors(self.array) ev = ev.real modes = modes.real self.array = modes else: ev = N.zeros((self.nmodes,), N.Float) work = N.zeros((1,), N.Float) iwork = N.zeros((1,), int_type) results = dsyevd('V', 'L', self.nmodes, self.array, self.nmodes, ev, work, -1, iwork, -1, 0) lwork = int(work[0]) liwork = iwork[0] work = N.zeros((lwork,), N.Float) iwork = N.zeros((liwork,), int_type) results = dsyevd('V', 'L', self.nmodes, self.array, self.nmodes, ev, work, lwork, iwork, liwork, 0) if results['info'] > 0: raise ValueError('Eigenvalue calculation did not converge') if self.basis is not None: self.array = N.dot(self.array, self.basis) return ev def _setupBasis(self): if isinstance(self.basis, tuple): excluded, basis = self.basis else: excluded = [] basis = self.basis nexcluded = len(excluded) nmodes = len(basis) ntotal = nexcluded + nmodes natoms = len(basis[0]) sv = N.zeros((min(ntotal, 3*natoms),), N.Float) work_size = N.zeros((1,), N.Float) if nexcluded > 0: self.basis = N.zeros((ntotal, 3*natoms), N.Float) for i in range(nexcluded): self.basis[i] = N.ravel(excluded[i].array*self.weights) min_n_m = min(3*natoms, nexcluded) vt = N.zeros((nexcluded, min_n_m), N.Float) iwork = N.zeros((8*min_n_m,), int_type) if 3*natoms >= nexcluded: result = dgesdd('O', 3*natoms, nexcluded, self.basis, 3*natoms, sv, self.basis, 3*natoms, vt, min_n_m, work_size, -1, iwork, 0) work = N.zeros((int(work_size[0]),), N.Float) result = dgesdd('O', 3*natoms, nexcluded, self.basis, 3*natoms, sv, self.basis, 3*natoms, vt, min_n_m, work, work.shape[0], iwork, 0) else: u = N.zeros((min_n_m, 3*natoms), N.Float) result = dgesdd('S', 3*natoms, nexcluded, self.basis, 3*natoms, sv, u, 3*natoms, vt, min_n_m, work_size, -1, iwork, 0) work = N.zeros((int(work_size[0]),), N.Float) result = dgesdd('S', 3*natoms, nexcluded, self.basis, 3*natoms, sv, u, 3*natoms, vt, min_n_m, work, work.shape[0], iwork, 0) self.basis[:min_n_m] = u if result['info'] != 0: raise ValueError('Lapack SVD: ' + `result['info']`) svmax = N.maximum.reduce(sv) nexcluded = N.add.reduce(N.greater(sv, 1.e-10*svmax)) ntotal = nexcluded + nmodes for i in range(nmodes): self.basis[i+nexcluded] = N.ravel(basis[i].array*self.weights) min_n_m = min(3*natoms, ntotal) vt = N.zeros((ntotal, min_n_m), N.Float) if 3*natoms >= ntotal: result = dgesdd('O', 3*natoms, ntotal, self.basis, 3*natoms, sv, self.basis, 3*natoms, vt, min_n_m, work_size, -1, iwork, 0) if int(work_size[0]) > work.shape[0]: work = N.zeros((int(work_size[0]),), N.Float) result = dgesdd('O', 3*natoms, ntotal, self.basis, 3*natoms, sv, self.basis, 3*natoms, vt, min_n_m, work, work.shape[0], iwork, 0) else: u = N.zeros((min_n_m, 3*natoms), N.Float) result = dgesdd('S', 3*natoms, ntotal, self.basis, 3*natoms, sv, u, 3*natoms, vt, min_n_m, work_size, -1, iwork, 0) if int(work_size[0]) > work.shape[0]: work = N.zeros((int(work_size[0]),), N.Float) result = dgesdd('S', 3*natoms, ntotal, self.basis, 3*natoms, sv, u, 3*natoms, vt, min_n_m, work, work.shape[0], iwork, 0) self.basis[:min_n_m] = u if result['info'] != 0: raise ValueError('Lapack SVD: ' + `result['info']`) svmax = N.maximum.reduce(sv) ntotal = N.add.reduce(N.greater(sv, 1.e-10*svmax)) nmodes = ntotal - nexcluded else: if hasattr(self.basis, 'may_modify') and \ hasattr(self.basis, 'array'): self.basis = self.basis.array else: self.basis = N.array(map(lambda v: v.array, basis)) N.multiply(self.basis, self.weights, self.basis) self.basis.shape = (nmodes, 3*natoms) min_n_m = min(3*natoms, nmodes) vt = N.zeros((nmodes, min_n_m), N.Float) iwork = N.zeros((8*min_n_m,), int_type) if 3*natoms >= nmodes: result = dgesdd('O', 3*natoms, nmodes, self.basis, 3*natoms, sv, self.basis, 3*natoms, vt, min_n_m, work_size, -1, iwork, 0) work = N.zeros((int(work_size[0]),), N.Float) result = dgesdd('O', 3*natoms, nmodes, self.basis, 3*natoms, sv, self.basis, 3*natoms, vt, min_n_m, work, work.shape[0], iwork, 0) else: u = N.zeros((min_n_m, 3*natoms), N.Float) result = dgesdd('S', 3*natoms, nmodes, self.basis, 3*natoms, sv, u, 3*natoms, vt, min_n_m, work_size, -1, iwork, 0) work = N.zeros((int(work_size[0]),), N.Float) result = dgesdd('S', 3*natoms, nmodes, self.basis, 3*natoms, sv, u, 3*natoms, vt, min_n_m, work, work.shape[0], iwork, 0) self.basis[:min_n_m] = u if result['info'] != 0: raise ValueError('Lapack SVD: ' + `result['info']`) svmax = N.maximum.reduce(sv) nmodes = N.add.reduce(N.greater(sv, 1.e-10*svmax)) ntotal = nmodes self.basis = self.basis[nexcluded:ntotal, :] # # Helper functions # # Fill in the lower triangle of an upper-triangle symmetric matrix def _symmetrize(a): n = a.shape[0] for i in range(n): for j in range(i+1, n): a[j,i] = a[i,j] MMTK-2.7.9/MMTK/NormalModes/EnergeticModes.py0000644000076600000240000001566412117632051021162 0ustar hinsenstaff00000000000000# Energetic normal mode calculations. # # Written by Konrad Hinsen # """ Energetic normal modes """ __docformat__ = 'restructuredtext' from MMTK import Features, Units, ParticleProperties from MMTK.NormalModes import Core from Scientific import N # # Class for a single mode # class EnergeticMode(Core.Mode): """ Single energetic normal mode Mode objects are created by indexing a :class:`MMTK.NormalModes.EnergeticModes.EnergeticModes` object. They contain the atomic displacements corresponding to a single mode. In addition, the force constant corresponding to the mode is stored in the attribute "force_constant". """ def __init__(self, universe, n, force_constant, mode): self.force_constant = force_constant Core.Mode.__init__(self, universe, n, mode) def __str__(self): return 'Mode ' + `self.number` + ' with force constant ' + \ `self.force_constant` __repr__ = __str__ # # Class for a full set of normal modes # class EnergeticModes(Core.NormalModes): """ Energetic modes describe the principal axes of an harmonic approximation to the potential energy surface of a system. They are obtained by diagonalizing the force constant matrix without prior mass-weighting. In order to obtain physically reasonable normal modes, the configuration of the universe must correspond to a local minimum of the potential energy. Individual modes (see class :class:`~MMTK.NormalModes.EnergeticModes.EnergeticMode`) can be extracted by indexing with an integer. Looping over the modes is possible as well. """ features = [] def __init__(self, universe=None, temperature = 300*Units.K, subspace = None, delta = None, sparse = False): """ :param universe: the system for which the normal modes are calculated; it must have a force field which provides the second derivatives of the potential energy :type universe: :class:`~MMTK.Universe.Universe` :param temperature: the temperature for which the amplitudes of the atomic displacement vectors are calculated. A value of None can be specified to have no scaling at all. In that case the mass-weighted norm of each normal mode is one. :type temperature: float :param subspace: the basis for the subspace in which the normal modes are calculated (or, more precisely, a set of vectors spanning the subspace; it does not have to be orthogonal). This can either be a sequence of :class:`~MMTK.ParticleProperties.ParticleVector` objects or a tuple of two such sequences. In the second case, the subspace is defined by the space spanned by the second set of vectors projected on the complement of the space spanned by the first set of vectors. The first set thus defines directions that are excluded from the subspace. The default value of None indicates a standard normal mode calculation in the 3N-dimensional configuration space. :param delta: the rms step length for numerical differentiation. The default value of None indicates analytical differentiation. Numerical differentiation is available only when a subspace basis is used as well. Instead of calculating the full force constant matrix and then multiplying with the subspace basis, the subspace force constant matrix is obtained by numerical differentiation of the energy gradients along the basis vectors of the subspace. If the basis is much smaller than the full configuration space, this approach needs much less memory. :type delta: float :param sparse: a flag that indicates if a sparse representation of the force constant matrix is to be used. This is of interest when there are no long-range interactions and a subspace of smaller size then 3N is specified. In that case, the calculation will use much less memory with a sparse representation. :type sparse: bool """ if universe == None: return Features.checkFeatures(self, universe) Core.NormalModes.__init__(self, universe, subspace, delta, sparse, ['array', 'force_constants']) self.temperature = temperature self.weights = N.ones((1, 1), N.Float) self._forceConstantMatrix() ev = self._diagonalize() self.force_constants = ev self.sort_index = N.argsort(self.force_constants) self.array.shape = (self.nmodes, self.natoms, 3) self.cleanup() def __getitem__(self, item): index = self.sort_index[item] f = self.force_constants[index] #!! if self.temperature is None or item < 6: amplitude = 1. else: amplitude = N.sqrt(2.*self.temperature*Units.k_B / self.force_constants[index]) return EnergeticMode(self.universe, item, self.force_constants[index], amplitude*self.array[index]) def rawMode(self, item): """ :param item: the index of a normal mode :type item: int :returns: the unscaled mode vector :rtype: :class:`~MMTK.NormalModes.EnergeticModes.EnergeticMode` """ index = self.sort_index[item] f = self.force_constants[index] return EnergeticMode(self.universe, item, self.force_constants[index], self.array[index]) def fluctuations(self, first_mode=6): f = ParticleProperties.ParticleScalar(self.universe) for i in range(first_mode, self.nmodes): mode = self.rawMode(i) f += (mode*mode)/mode.force_constant if self.temperature is not None: f.array *= Units.k_B*self.temperature return f def anisotropicFluctuations(self, first_mode=6): f = ParticleProperties.ParticleTensor(self.universe) for i in range(first_mode, self.nmodes): mode = self.rawMode(i) array = mode.array f.array += (array[:, :, N.NewAxis]*array[:, N.NewAxis, :]) \ / mode.force_constant if self.temperature is not None: f.array *= Units.k_B*self.temperature return f MMTK-2.7.9/MMTK/NormalModes/VibrationalModes.py0000644000076600000240000004043612117632051021522 0ustar hinsenstaff00000000000000# Vibrational normal mode calculations. # # Written by Konrad Hinsen # """ Vibrational normal modes """ __docformat__ = 'restructuredtext' from MMTK import Features, Units, ParticleProperties from Scientific import N from MMTK.NormalModes import Core # # Class for a single mode # class VibrationalMode(Core.Mode): """ Single vibrational normal mode Mode objects are created by indexing a :class:`~MMTK.NormalModes.VibrationalModes.VibrationalModes` object. They contain the atomic displacements corresponding to a single mode. In addition, the frequency corresponding to the mode is stored in the attribute "frequency". Note: the normal mode vectors are *not* mass weighted, and therefore not orthogonal to each other. """ def __init__(self, universe, n, frequency, mode): self.frequency = frequency Core.Mode.__init__(self, universe, n, mode) def __str__(self): return 'Mode ' + `self.number` + ' with frequency ' + `self.frequency` __repr__ = __str__ def effectiveMassAndForceConstant(self): m = self.massWeightedDotProduct(self)/self.dotProduct(self) k = m*(2.*N.pi*self.frequency)**2 return m, k # # Class for a full set of normal modes # class VibrationalModes(Core.NormalModes): """ Vibrational modes describe the independent vibrational motions of a harmonic system. They are obtained by diagonalizing the mass-weighted force constant matrix. In order to obtain physically reasonable normal modes, the configuration of the universe must correspond to a local minimum of the potential energy. Individual modes (see :class:`~MMTK.NormalModes.VibrationalModes.VibrationalMode`) can be extracted by indexing with an integer. Looping over the modes is possible as well. """ features = [] def __init__(self, universe=None, temperature = 300.*Units.K, subspace = None, delta = None, sparse = False): """ :param universe: the system for which the normal modes are calculated; it must have a force field which provides the second derivatives of the potential energy :type universe: :class:`~MMTK.Universe.Universe` :param temperature: the temperature for which the amplitudes of the atomic displacement vectors are calculated. A value of None can be specified to have no scaling at all. In that case the mass-weighted norm of each normal mode is one. :type temperature: float :param subspace: the basis for the subspace in which the normal modes are calculated (or, more precisely, a set of vectors spanning the subspace; it does not have to be orthogonal). This can either be a sequence of :class:`~MMTK.ParticleProperties.ParticleVector` objects or a tuple of two such sequences. In the second case, the subspace is defined by the space spanned by the second set of vectors projected on the complement of the space spanned by the first set of vectors. The first set thus defines directions that are excluded from the subspace. The default value of None indicates a standard normal mode calculation in the 3N-dimensional configuration space. :param delta: the rms step length for numerical differentiation. The default value of None indicates analytical differentiation. Numerical differentiation is available only when a subspace basis is used as well. Instead of calculating the full force constant matrix and then multiplying with the subspace basis, the subspace force constant matrix is obtained by numerical differentiation of the energy gradients along the basis vectors of the subspace. If the basis is much smaller than the full configuration space, this approach needs much less memory. :type delta: float :param sparse: a flag that indicates if a sparse representation of the force constant matrix is to be used. This is of interest when there are no long-range interactions and a subspace of smaller size then 3N is specified. In that case, the calculation will use much less memory with a sparse representation. :type sparse: bool """ if universe == None: return Features.checkFeatures(self, universe) Core.NormalModes.__init__(self, universe, subspace, delta, sparse, ['array', 'imaginary', 'frequencies', 'omega_sq']) self.temperature = temperature self.weights = N.sqrt(self.universe.masses().array) self.weights = self.weights[:, N.NewAxis] self._forceConstantMatrix() ev = self._diagonalize() self.imaginary = N.less(ev, 0.) self.omega_sq = ev self.frequencies = N.sqrt(N.fabs(ev)) / (2.*N.pi) self.sort_index = N.argsort(self.frequencies) self.array.shape = (self.nmodes, self.natoms, 3) self.cleanup() def __setstate__(self, state): # This fixes unpickled objects from pre-2.5 versions. # Their modes were stored in un-weighted coordinates. if not state.has_key('weights'): weights = N.sqrt(state['universe'].masses().array)[:, N.NewAxis] state['weights'] = weights state['array'] *= weights[N.NewAxis, :, :] if state['temperature'] is not None: factor = N.sqrt(2.*state['temperature']*Units.k_B/Units.amu) / \ (2.*N.pi) freq = state['frequencies'] for i in range(state['nmodes']): index = state['sort_index'][i] if index >= 6: state['array'][index] *= freq[index]/factor self.__dict__.update(state) def __getitem__(self, item): index = self.sort_index[item] f = self.frequencies[index] if self.imaginary[index]: f = f*1.j # !! if self.temperature is None or item < 6: amplitude = 1. else: amplitude = N.sqrt(2.*self.temperature*Units.k_B/Units.amu) / \ (2.*N.pi*f) return VibrationalMode(self.universe, item, f, amplitude*self.array[index]/self.weights) def rawMode(self, item): """ :param item: the index of a normal mode :type item: int :returns: the unscaled mode vector :rtype: :class:`~MMTK.NormalModes.VibrationalModes.VibrationalMode` """ index = self.sort_index[item] f = self.frequencies[index] if self.imaginary[index]: f = f*1.j return VibrationalMode(self.universe, item, f, self.array[index]) def fluctuations(self, first_mode=6): f = ParticleProperties.ParticleScalar(self.universe) for i in range(first_mode, self.nmodes): mode = self.rawMode(i) f += (mode*mode)/mode.frequency**2 f.array /= self.weights[:, 0]**2 s = 1./(2.*N.pi)**2 if self.temperature is not None: s *= Units.k_B*self.temperature f.array *= s return f def anisotropicFluctuations(self, first_mode=6): f = ParticleProperties.ParticleTensor(self.universe) for i in range(first_mode, self.nmodes): mode = self.rawMode(i) array = mode.array f.array += (array[:, :, N.NewAxis]*array[:, N.NewAxis, :]) \ / mode.frequency**2 s = (2.*N.pi)**2 if self.temperature is not None: s /= Units.k_B*self.temperature f.array /= s*self.universe.masses().array[:, N.NewAxis, N.NewAxis] return f def meanSquareDisplacement(self, subset=None, weights=None, time_range = (0., None, None), tau=None, first_mode = 6): """ :param subset: the subset of the universe used in the calculation (default: the whole universe) :type subset: :class:`~MMTK.Collections.GroupOfAtoms` :param weights: the weight to be given to each atom in the average (default: atomic masses) :type weights: :class:`~MMTK.ParticleProperties.ParticleScalar` :param time_range: the time values at which the mean-square displacement is evaluated, specified as a range tuple (first, last, step). The defaults are first=0, last= 20 times the longest vibration perdiod, and step defined such that 300 points are used in total. :type time_range: tuple :param tau: the relaxation time of an exponential damping factor (default: no damping) :type tau: float :param first_mode: the first mode to be taken into account for the fluctuation calculation. The default value of 6 is right for molecules in vacuum. :type first_mode: int :returns: the averaged mean-square displacement of the atoms in subset as a function of time :rtype: Scientific.Functions.Interpolation.InterpolatingFunction """ if self.temperature is None: raise ValueError("no temperature available") if subset is None: subset = self.universe if weights is None: weights = self.universe.masses() weights = weights*subset.booleanMask() total = weights.sumOverParticles() weights = weights/total first, last, step = (time_range + (None, None))[:3] if last is None: last = 20./self[first_mode].frequency if step is None: step = (last-first)/300. time = N.arange(first, last, step) if tau is None: damping = 1. else: damping = N.exp(-(time/tau)**2) msd = N.zeros(time.shape, N.Float) for i in range(first_mode, self.nmodes): mode = self[i] omega = 2.*N.pi*mode.frequency d = (weights*(mode*mode)).sumOverParticles() N.add(msd, d*(1.-damping*N.cos(omega*time)), msd) return InterpolatingFunction((time,), msd) def EISF(self, q_range = (0., 15.), subset=None, weights = None, random_vectors = 15, first_mode = 6): """ :param q_range: the range of angular wavenumber values :type q_range: tuple :param subset: the subset of the universe used in the calculation (default: the whole universe) :type subset: :class:`~MMTK.Collections.GroupOfAtoms` :param weights: the weight to be given to each atom in the average (default: incoherent scattering lengths) :type weights: :class:`~MMTK.ParticleProperties.ParticleScalar` :param random_vectors: the number of random direction vectors used in the orientational average :type random_vectors: int :param first_mode: the first mode to be taken into account for the fluctuation calculation. The default value of 6 is right for molecules in vacuum. :type first_mode: int :returns: the Elastic Incoherent Structure Factor (EISF) as a function of angular wavenumber :rtype: Scientific.Functions.Interpolation.InterpolatingFunction """ if self.temperature is None: raise ValueError("no temperature available") if subset is None: subset = self.universe if weights is None: weights = self.universe.getParticleScalar('b_incoherent') weights = weights*weights weights = weights*subset.booleanMask() total = weights.sumOverParticles() weights = weights/total first, last, step = (q_range+(None,))[:3] if step is None: step = (last-first)/50. q = N.arange(first, last, step) f = MMTK.ParticleTensor(self.universe) for i in range(first_mode, self.nmodes): mode = self[i] f = f + mode.dyadicProduct(mode) eisf = N.zeros(q.shape, N.Float) for i in range(random_vectors): v = MMTK.Random.randomDirection() for a in subset.atomList(): exp = N.exp(-v*(f[a]*v)) N.add(eisf, weights[a]*exp**(q*q), eisf) return InterpolatingFunction((q,), eisf/random_vectors) def incoherentScatteringFunction(self, q, time_range = (0., None, None), subset=None, weights=None, tau=None, random_vectors=15, first_mode = 6): """ :param q: the angular wavenumber :type q: float :param time_range: the time values at which the mean-square displacement is evaluated, specified as a range tuple (first, last, step). The defaults are first=0, last= 20 times the longest vibration perdiod, and step defined such that 300 points are used in total. :type time_range: tuple :param subset: the subset of the universe used in the calculation (default: the whole universe) :type subset: :class:`~MMTK.Collections.GroupOfAtoms` :param weights: the weight to be given to each atom in the average (default: incoherent scattering lengths) :type weights: :class:`~MMTK.ParticleProperties.ParticleScalar` :param tau: the relaxation time of an exponential damping factor (default: no damping) :type tau: float :param random_vectors: the number of random direction vectors used in the orientational average :type random_vectors: int :param first_mode: the first mode to be taken into account for the fluctuation calculation. The default value of 6 is right for molecules in vacuum. :type first_mode: int :returns: the Incoherent Scattering Function as a function of time :rtype: Scientific.Functions.Interpolation.InterpolatingFunction """ if self.temperature is None: raise ValueError("no temperature available") if subset is None: subset = self.universe if weights is None: weights = self.universe.getParticleScalar('b_incoherent') weights = weights*weights weights = weights*subset.booleanMask() total = weights.sumOverParticles() weights = weights/total first, last, step = (time_range + (None, None))[:3] if last is None: last = 20./self[first_mode].frequency if step is None: step = (last-first)/400. time = N.arange(first, last, step) if tau is None: damping = 1. else: damping = N.exp(-(time/tau)**2) finc = N.zeros(time.shape, N.Float) random_vectors = MMTK.Random.randomDirections(random_vectors) for v in random_vectors: for a in subset.atomList(): msd = 0. for i in range(first_mode, self.nmodes): mode = self[i] d = (v*mode[a])**2 omega = 2.*N.pi*mode.frequency msd = msd+d*(1.-damping*N.cos(omega*time)) N.add(finc, weights[a]*N.exp(-q*q*msd), finc) return InterpolatingFunction((time,), finc/len(random_vectors)) MMTK-2.7.9/MMTK/NucleicAcids.py0000644000076600000240000002360312041510260016354 0ustar hinsenstaff00000000000000# This module implements classes for nucleotide chains. # # Written by Konrad Hinsen # """ Nucleic acid chains """ __docformat__ = 'restructuredtext' from MMTK import Biopolymers, Bonds, ChemicalObjects, Collections, \ ConfigIO, Database, Universe, Utility from Scientific.Geometry import Vector from MMTK.Biopolymers import defineNucleicAcidResidue # # Nucleotides are special groups # class Nucleotide(Biopolymers.Residue): """ Nucleic acid residue Nucleotides are a special kind of group. Like any other group, they are defined in the chemical database. Each residue has two or three subgroups ('sugar' and 'base', plus 'phosphate' except for 5'-terminal residues) and is usually connected to other residues to form a nucleotide chain. The database contains three variants of each residue (5'-terminal, 3'-terminal, non-terminal). """ def __init__(self, name = None, model = 'all'): """ :param name: the name of the nucleotide in the chemical database. This is the full name of the residue plus the suffix "_5ter" or "_3ter" for the terminal variants. :type name: str :param model: one of "all" (all-atom), "none" (no hydrogens), "polar" (united-atom with only polar hydrogens), "polar_charmm" (like "polar", but defining polar hydrogens like in the CHARMM force field). Currently the database has definitions only for "all". :type model: str """ if name is not None: blueprint = _residueBlueprint(name, model) ChemicalObjects.Group.__init__(self, blueprint) self.model = model self._init() def backbone(self): """ :returns: the sugar and phosphate groups :rtype: :class:`~MMTK.ChemicalObjects.Group` """ bb = self.sugar if hasattr(self, 'phosphate'): bb = Collections.Collection([bb, self.phosphate]) return bb def bases(self): """ :returns: the base group :rtype: :class:`~MMTK.ChemicalObjects.Group` """ return self.base def _residueBlueprint(name, model): try: blueprint = _residue_blueprints[(name, model)] except KeyError: if model == 'polar': name = name + '_uni' elif model == 'polar_charmm': name = name + '_uni2' elif model == 'none': name = name + '_noh' blueprint = Database.BlueprintGroup(name) _residue_blueprints[(name, model)] = blueprint return blueprint _residue_blueprints = {} # # Nucleotide chains are molecules with added features. # class NucleotideChain(Biopolymers.ResidueChain): """ Nucleotide chain Nucleotide chains consist of nucleotides that are linked together. They are a special kind of molecule, i.e. all molecule operations are available. Nucleotide chains act as sequences of residues. If n is a NucleotideChain object, then * len(n) yields the number of nucleotides * n[i] yields nucleotide number i * n[i:j] yields the subchain from nucleotide number i up to but excluding nucleotide number j """ def __init__(self, sequence, **properties): """ :param sequence: the nucleotide sequence. This can be a string containing the one-letter codes, or a list of two-letter codes (a "d" or "r" for the type of sugar, and the one-letter base code), or a :class:`~MMTK.PDB.PDBNucleotideChain` object. If a PDBNucleotideChain object is supplied, the atomic positions it contains are assigned to the atoms of the newly generated nucleotide chain, otherwise the positions of all atoms are undefined. :keyword model: one of "all" (all-atom), "no_hydrogens" or "none" (no hydrogens), "polar_hydrogens" or "polar" (united-atom with only polar hydrogens), "polar_charmm" (like "polar", but defining polar hydrogens like in the CHARMM force field), "polar_opls" (like "polar", but defining polar hydrogens like in the latest OPLS force field). Default is "all". Currently the database contains definitions only for "all". :type model: str :keyword terminus_5: if True, the first residue is constructed using the 5'-terminal variant, if False the non-terminal version is used. Default is True. :type terminus_5: bool :keyword terminus_3: if True, the last residue is constructed using the 3'-terminal variant, if False the non-terminal version is used. Default is True. :type terminus_3: bool :keyword circular: if True, a bond is constructed between the first and the last residue. Default is False. :type circular: bool :keyword name: a name for the chain (a string) :type name: str """ if sequence is not None: hydrogens = self.binaryProperty(properties, 'hydrogens', 'all') if hydrogens == 1: hydrogens = 'all' elif hydrogens == 0: hydrogens = 'none' term5 = self.binaryProperty(properties, 'terminus_5', True) term3 = self.binaryProperty(properties, 'terminus_3', True) circular = self.binaryProperty(properties, 'circular', False) try: model = properties['model'].lower() except KeyError: model = hydrogens self.version_spec = {'hydrogens': hydrogens, 'terminus_5': term5, 'terminus_3': term3, 'model': model, 'circular': circular} if isinstance(sequence[0], basestring): conf = None else: conf = sequence sequence = [r.name for r in sequence] sequence = [Biopolymers._fullName(r) for r in sequence] if term5: if sequence[0].find('5ter') == -1: sequence[0] += '_5ter' if term3: if sequence[-1].find('3ter') == -1: sequence[-1] += '_3ter' self.groups = [] n = 0 for residue in sequence: n += 1 r = Nucleotide(residue, model) r.name = r.symbol + '_' + `n` r.sequence_number = n r.parent = self self.groups.append(r) self._setupChain(circular, properties, conf) is_nucleotide_chain = True def __getslice__(self, first, last): return NucleotideSubChain(self, self.groups[first:last]) def backbone(self): """ :returns: the sugar and phosphate groups of all nucleotides :rtype: :class:`~MMTK.Collections.Collection` """ bb = Collections.Collection([]) for residue in self.groups: try: bb.addObject(residue.phosphate) except AttributeError: pass bb.addObject(residue.sugar) return bb def bases(self): """ :returns: the base groups of all nucleotides :rtype: :class:`~MMTK.Collections.Collection` """ return Collections.Collection([r.base for r in self.groups]) def _descriptionSpec(self): kwargs = ','.join([name + '=' + `self.version_spec[name]` for name in sorted(self.version_spec.keys())]) return "N", kwargs def _typeName(self): return ''.join([s.ljust(3) for s in self.sequence()]) def _graphics(self, conf, distance_fn, model, module, options): if model != 'backbone': return ChemicalObjects.Molecule._graphics(self, conf, distance_fn, model, module, options) color = options.get('color', 'black') material = module.EmissiveMaterial(color) objects = [] for i in range(1, len(self.groups)-1): a1 = self.groups[i].phosphate.P a2 = self.groups[i+1].phosphate.P p1 = a1.position(conf) p2 = a2.position(conf) if p1 is not None and p2 is not None: bond_vector = 0.5*distance_fn(a1, a2, conf) cut = bond_vector != 0.5*(p2-p1) if not cut: objects.append(module.Line(p1, p2, material = material)) else: objects.append(module.Line(p1, p1+bond_vector, material = material)) objects.append(module.Line(p2, p2-bond_vector, material = material)) return objects # # Subchains are created by slicing chains or extracting a chain from # a group of connected chains. # class NucleotideSubChain(NucleotideChain): """ A contiguous part of a nucleotide chain NucleotideSubChain objects are the result of slicing operations on NucleotideChain objects. They cannot be created directly. NucleotideSubChain objects permit all operations of NucleotideChain objects, but cannot be added to a universe. """ def __init__(self, chain, groups, name = ''): self.groups = groups self.atoms = [] self.bonds = [] for g in self.groups: self.atoms = self.atoms + g.atoms self.bonds = self.bonds + g.bonds for i in range(len(self.groups)-1): self.bonds.append(Bonds.Bond((self.groups[i].sugar.O_3, self.groups[i+1].phosphate.P))) self.bonds = Bonds.BondList(self.bonds) self.name = name self.parent = chain.parent self.type = None self.configurations = {} self.part_of = chain is_incomplete = True def __repr__(self): if self.name == '': return 'SubChain of ' + repr(self.part_of) else: return ChemicalObjects.Molecule.__repr__(self) __str__ = __repr__ # # Type check functions # def isNucleotideChain(x): "Returns True if x is a NucleotideChain." return hasattr(x, 'is_nucleotide_chain') MMTK-2.7.9/MMTK/ParticleProperties.py0000644000076600000240000005261112117632051017656 0ustar hinsenstaff00000000000000# This module implements classes that represent the atomic properties in a # simulation, i.e. configurations, force vectors, etc. # # Written by Konrad Hinsen # """ Quantities defined for each particle in a universe """ __docformat__ = 'restructuredtext' from MMTK import Utility from Scientific.Geometry import Vector, isVector, Tensor, isTensor from Scientific.indexing import index_expression from Scientific import N import copy # # Base class for all properties defined for a universe. # class ParticleProperty(object): """ Property defined for each particle This is an abstract base class; for creating instances, use one of its subclasses. ParticleProperty objects store properties that are defined per particle, such as mass, position, velocity, etc. The value corresponding to a particular atom can be retrieved or changed by indexing with the atom object. """ def __init__(self, universe, data_rank, value_rank): universe.configuration() self.universe = universe self.version = universe._version self.n = universe.numberOfPoints() self.data_rank = data_rank self.value_rank = value_rank __safe_for_unpickling__ = True __had_initargs__ = True def __len__(self): return self.n def _checkCompatibility(self, other, allow_scalar=False): if isParticleProperty(other): if other.data_rank != self.data_rank: raise TypeError('Incompatible types') if self.universe != other.universe: raise ValueError('Variables are for different universes') if self.version != other.version: raise ValueError("Universe version numbers do not agree") if self.value_rank == other.value_rank: return self.return_class, other.array elif allow_scalar and (self.value_rank==0 or other.value_rank==0): if self.value_rank == 0: return other.return_class, other.array else: return self.return_class, other.array else: raise ValueError("Ranks do not match") elif isVector(other) or isTensor(other): if len(other.array.shape) > self.value_rank: raise TypeError('Incompatible types') return self.return_class, other.array else: return self.return_class, other def zero(self): """ :returns: an object of the element type (scalar, vector, etc.) with the value 0. :rtype: element type """ pass def sumOverParticles(self): """ :returns: the sum of the values for all particles. :rtype: element type """ pass def _arithmetic(self, other, op, allow_scalar=False): a1 = self.array return_class, a2 = self._checkCompatibility(other, allow_scalar) if type(a2) != N.ArrayType: a2 = N.array([a2]) if len(a1.shape) != len(a2.shape): if len(a1.shape) == 1: a1 = a1[index_expression[...] + (len(a2.shape)-1)*index_expression[N.NewAxis]] else: a2 = a2[index_expression[...] + (len(a1.shape)-1)*index_expression[N.NewAxis]] return return_class(self.universe, op(a1, a2)) def __add__(self, other): return self._arithmetic(other, N.add) __radd__ = __add__ def __sub__(self, other): return self._arithmetic(other, N.subtract) def __rsub__(self, other): return self._arithmetic(other, lambda a, b: N.subtract(b, a)) def __mul__(self, other): return self._arithmetic(other, N.multiply, True) __rmul__ = __mul__ def __div__(self, other): return self._arithmetic(other, N.divide, True) def __rdiv__(self, other): return self._arithmetic(other, lambda a, b: N.divide(b, a), True) def __neg__(self): return self.return_class(self.universe, -self.array) def __copy__(self, memo = None): return self.__class__(self.universe, copy.copy(self.array)) __deepcopy__ = __copy__ def assign(self, other): """ Copy all values from another compatible ParticleProperty object. :param other: the data source """ self._checkCompatibility(other) self.array[:] = other.array[:] def scaleBy(self, factor): """ Multiply all values by a factor :param factor: the scale factor :type factor: float """ self.array[:] = self.array[:]*factor def selectAtoms(self, condition): """ Return a collection containing all atoms a for which condition(property[a]) is True. :param condition: a test function :type condition: callable """ from MMTK.Collections import Collection return Collection([a for a in self.universe.atomList() if condition(self[a])]) ParticleProperty.return_class = ParticleProperty # # One scalar per particle. # class ParticleScalar(ParticleProperty): """ Scalar property defined for each particle ParticleScalar objects can be added to each other and multiplied with scalars. """ def __init__(self, universe, data_array=None): """ :param universe: the universe for which the values are defined :type universe: :class:`~MMTK.Universe.Universe` :param data_array: the data array containing the values for each particle. If None, a new array containing zeros is created and used. Otherwise, the array myst be of shape (N,), where N is the number of particles in the universe. :type data_array: Scientific.N.array_type """ ParticleProperty.__init__(self, universe, 1, 0) if data_array is None: self.array = N.zeros((self.n,), N.Float) else: self.array = data_array if data_array.shape[0] != self.n: raise ValueError('Data incompatible with universe') def __getitem__(self, item): if not isinstance(item, int): item = item.index return self.array[item] def __setitem__(self, item, value): if not isinstance(item, int): item = item.index self.array[item] = value def zero(self): return 0. def maximum(self): """ :returns: the highest value in the data array particle :rtype: float """ return N.maximum.reduce(self.array) def minimum(self): """ :returns: the smallest value in the data array particle :rtype: float """ return N.minimum.reduce(self.array) def sumOverParticles(self): return N.add.reduce(self.array) def applyFunction(self, function): """ :param function: a function that is applied to each data value :returns: a new ParticleScalar object containing the function results """ return ParticleScalar(self.universe, function(self.array)) ParticleScalar.return_class = ParticleScalar # # One vector per particle. # class ParticleVector(ParticleProperty): """ Vector property defined for each particle ParticleVector objects can be added to each other and multiplied with scalars or :class:`~MMTK.ParticleProperties.ParticleScalar` objects; all of these operations result in another ParticleVector object. Multiplication with a vector or another ParticleVector object yields a :class:`~MMTK.ParticleProperties.ParticleScalar` object containing the dot products for each particle. Multiplications that treat ParticleVectors as vectors in a 3N-dimensional space are implemented as methods. """ def __init__(self, universe, data_array=None): """ :param universe: the universe for which the values are defined :type universe: :class:`~MMTK.Universe.Universe` :param data_array: the data array containing the values for each particle. If None, a new array containing zeros is created and used. Otherwise, the array myst be of shape (N,3), where N is the number of particles in the universe. :type data_array: Scientific.N.array_type """ ParticleProperty.__init__(self, universe, 1, 1) if data_array is None: self.array = N.zeros((self.n, 3), N.Float) else: self.array = data_array if data_array.shape[0] != self.n: raise ValueError('Data incompatible with universe') def __getitem__(self, item): if not isinstance(item, int): item = item.index return Vector(self.array[item]) def __setitem__(self, item, value): if not isinstance(item, int): item = item.index self.array[item] = value.array def __mul__(self, other): if isParticleProperty(other): if self.universe != other.universe: raise ValueError('Variables are for different universes') if other.value_rank == 0: return ParticleVector(self.universe, self.array*other.array[:,N.NewAxis]) elif other.value_rank == 1: return ParticleScalar(self.universe, N.add.reduce(self.array * \ other.array, -1)) else: raise TypeError('not yet implemented') elif isVector(other): return ParticleScalar(self.universe, N.add.reduce( self.array*other.array[N.NewAxis,:], -1)) elif isTensor(other): raise TypeError('not yet implemented') else: return ParticleVector(self.universe, self.array*other) __rmul__ = __mul__ _product_with_vector = __mul__ def zero(self): return Vector(0., 0., 0.) def length(self): """ :returns: the length (norm) of the vector for each particle :rtype: :class:`~MMTK.ParticleProperties.ParticleScalar` """ return ParticleScalar(self.universe, N.sqrt(N.add.reduce(self.array**2, -1))) def sumOverParticles(self): return Vector(N.add.reduce(self.array, 0)) def norm(self): """ :returns: the norm of the ParticleVector seen as a 3N-dimensional vector :rtype: float """ return N.sqrt(N.add.reduce(N.ravel(self.array**2))) totalNorm = norm def scaledToNorm(self, norm): f = norm/self.norm() return ParticleVector(self.universe, f*self.array) def dotProduct(self, other): """ :param other: another ParticleVector :type other: :class:`~MMTK.ParticleProperties.ParticleVector` :returns: the dot product with other, treating both operands as 3N-dimensional vectors. """ if self.universe != other.universe: raise ValueError('Variables are for different universes') return N.add.reduce(N.ravel(self.array * other.array)) def massWeightedNorm(self): """ :returns: the mass-weighted norm of the ParticleVector seen as a 3N-dimensional vector :rtype: float """ m = self.universe.masses().array return N.sqrt(N.sum(N.ravel(m[:, N.NewAxis] * self.array**2)) / N.sum(m)) def scaledToMassWeightedNorm(self, norm): f = norm/self.massWeightedNorm() return ParticleVector(self.universe, f*self.array) def massWeightedDotProduct(self, other): """ :param other: another ParticleVector :type other: :class:`~MMTK.ParticleProperties.ParticleVector` :returns: the mass-weighted dot product with other treating both operands as 3N-dimensional vectors :rtype: float """ if self.universe != other.universe: raise ValueError('Variables are for different universes') m = self.universe.masses().array return N.add.reduce(N.ravel(self.array * other.array * \ m[:, N.NewAxis])) def dyadicProduct(self, other): """ :param other: another ParticleVector :type other: :class:`~MMTK.ParticleProperties.ParticleVector` :returns: the dyadic product with other :rtype: :class:`~MMTK.ParticleProperties.ParticleTensor` """ if self.universe != other.universe: raise ValueError('Variables are for different universes') return ParticleTensor(self.universe, self.array[:, :, N.NewAxis] * \ other.array[:, N.NewAxis, :]) ParticleVector.return_class = ParticleVector # # Configuration variables: ParticleVector plus universe parameters # class Configuration(ParticleVector): """ Configuration of a universe Configuration instances represent a configuration of a universe, consisting of positions for all atoms (like in a ParticleVector) plus the geometry of the universe itself, e.g. the cell shape for periodic universes. """ def __init__(self, universe, data_array=None, cell = None): """ :param universe: the universe for which the values are defined :type universe: :class:`~MMTK.Universe.Universe` :param data_array: the data array containing the values for each particle. If None, a new array containing zeros is created and used. Otherwise, the array myst be of shape (N,3), where N is the number of particles in the universe. :type data_array: Scientific.N.array_type :param cell: the cell parameters of the universe, i.e. the return value of universe.cellParameters() """ ParticleVector.__init__(self, universe, data_array) if cell is None: self.cell_parameters = universe.cellParameters() else: self.cell_parameters = cell def __add__(self, other): value = ParticleVector.__add__(self, other) return Configuration(self.universe, value.array, self.cell_parameters) def __sub__(self, other): value = ParticleVector.__sub__(self, other) return Configuration(self.universe, value.array, self.cell_parameters) def __copy__(self, memo = None): return self.__class__(self.universe, copy.copy(self.array), copy.copy(self.cell_parameters)) __deepcopy__ = __copy__ def hasValidPositions(self): return N.logical_and.reduce(N.ravel(N.less(self.array, Utility.undefined_limit))) def convertToBoxCoordinates(self): array = self.universe._realToBoxPointArray(self.array, self.cell_parameters) self.array[:] = array def convertFromBoxCoordinates(self): array = self.universe._boxToRealPointArray(self.array, self.cell_parameters) self.array[:] = array # # One tensor per particle. # class ParticleTensor(ParticleProperty): """ Rank-2 tensor property defined for each particle ParticleTensor objects can be added to each other and multiplied with scalars or :class:`~MMTK.ParticleProperties.ParticleScalar` objects; all of these operations result in another ParticleTensor object. """ def __init__(self, universe, data_array=None): """ :param universe: the universe for which the values are defined :type universe: :class:`~MMTK.Universe.Universe` :param data_array: the data array containing the values for each particle. If None, a new array containing zeros is created and used. Otherwise, the array myst be of shape (N,3,3), where N is the number of particles in the universe. :type data_array: Scientific.N.array_type """ ParticleProperty.__init__(self, universe, 1, 2) if data_array is None: self.array = N.zeros((self.n, 3, 3), N.Float) else: self.array = data_array if data_array.shape[0] != self.n: raise ValueError('Data incompatible with universe') def __getitem__(self, item): if not isinstance(item, int): item = item.index return Tensor(self.array[item]) def __setitem__(self, item, value): if not isinstance(item, int): item = item.index self.array[item] = value.array def __mul__(self, other): if isParticleProperty(other): if self.universe != other.universe: raise ValueError('Variables are for different universes') if other.value_rank == 0: return ParticleTensor(self.universe, self.array*other.array[:, N.NewAxis, N.NewAxis]) else: raise TypeError('not yet implemented') elif isVector(other): raise TypeError('not yet implemented') elif isTensor(other): raise TypeError('not yet implemented') else: return ParticleTensor(self.universe, self.array*other) __rmul__ = __mul__ def zero(self): return Tensor([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]) def sumOverParticles(self): return Tensor(N.add.reduce(self.array, 0)) def trace(self): return ParticleScalar(self.universe, self.array[:, 0, 0] + self.array[:, 1, 1] + self.array[:, 2, 2]) ParticleTensor.return_class = ParticleTensor # # One tensor per pair, symmetric. # class SymmetricPairTensor(ParticleProperty): def __init__(self, universe, data_array=None): """ :param universe: the universe for which the values are defined :type universe: :class:`~MMTK.Universe.Universe` :param data_array: the data array containing the values for each particle. If None, a new array containing zeros is created and used. Otherwise, the array myst be of shape (N,3,N,3), where N is the number of particles in the universe. :type data_array: Scientific.N.array_type """ ParticleProperty.__init__(self, universe, 2, 2) if data_array is None: self.array = N.zeros((self.n,3, self.n,3), N.Float) else: self.array = data_array if data_array.shape[0] != self.n or \ data_array.shape[2] != self.n: raise ValueError('Data incompatible with universe') self.symmetrized = False def __getitem__(self, item): i1, i2 = item if not isinstance(i1, int): i1 = i1.index if not isinstance(i2, int): i2 = i2.index if i1 > i2: i1, i2 = i2, i1 return Tensor(N.transpose(self.array[i1,:,i2,:])) else: return Tensor(self.array[i1,:,i2,:]) def __setitem__(self, item, value): i1, i2 = item if not isinstance(i1, int): i1 = i1.index if not isinstance(i2, int): i2 = i2.index if i1 > i2: i1, i2 = i2, i1 self.array[i1,:,i2,:] = value.transpose().array else: self.array[i1,:,i2,:] = value.array def zero(self): return Tensor(3*[[0., 0., 0.]]) def symmetrize(self): if not self.symmetrized: a = self.array n = a.shape[0] a.shape = (3*n, 3*n) nn = 3*n for i in range(nn): for j in range(i+1, nn): a[j,i] = a[i,j] a.shape = (n, 3, n, 3) self.symmetrized = True def __mul__(self, other): self.symmetrize() if isParticleProperty(other): if self.universe != other.universe: raise ValueError('Variables are for different universes') if other.value_rank == 1: n = self.array.shape[0] sa = N.reshape(self.array, (n, 3, 3*n)) oa = N.reshape(other.array, (3*n, )) return ParticleVector(self.universe, N.dot(sa, oa)) else: raise TypeError('not yet implemented') SymmetricPairTensor.return_class = SymmetricPairTensor # # Type check function # def isParticleProperty(object): """ :param object: any object :returns: True if object is a :class:`~MMTK.ParticleProperties.ParticleProperty` :rtype: bool """ return isinstance(object, ParticleProperty) def isConfiguration(object): """ :param object: any object :returns: True if object is a :class:`~MMTK.ParticleProperties.Configuration` :rtype: bool """ return isinstance(object, Configuration) MMTK-2.7.9/MMTK/PDB.py0000644000076600000240000006764212117632051014455 0ustar hinsenstaff00000000000000# This module deals with input and output of configurations in PDB format. # # Written by Konrad Hinsen # """ PDB files This module provides classes that represent molecules in a PDB file. They permit various manipulations and the creation of MMTK objects. Note that the classes defined in this module are specializations of classed defined in Scientific.IO.PDB; the methods defined in that module are also available. """ __docformat__ = 'restructuredtext' from MMTK import ChemicalObjects, Collections, Database, Units, \ Universe, Utility from Scientific.Geometry import Vector import Scientific.IO.PDB import copy, string # # The chain classes from Scientific.IO.PDB are extended by methods # that construct MMTK objects and set configurations. # class PDBChain(object): def applyTo(self, chain, map = 'pdbmap', alt = 'pdb_alternative', atom_map = None): if len(chain) != len(self): raise ValueError("chain lengths do not match") for i in range(len(chain)): residue = chain[i] pdbmap = getattr(residue, map) try: altmap = getattr(residue, alt) except AttributeError: altmap = {} setResidueConfiguration(residue, self[i], pdbmap[0], altmap, atom_map) class PDBPeptideChain(Scientific.IO.PDB.PeptideChain, PDBChain): """ Peptide chain in a PDB file See the description of Scientific.IO.PDB.PeptideChain for the constructor and additional methods. In MMTK, PDBPeptideChain objects are usually obtained from a PDBConfiguration object via its attribute peptide_chains (see the documentation of Scientific.IO.PDB.Structure). """ def createPeptideChain(self, model = 'all', n_terminus=None, c_terminus=None): """ :returns: a :class:`~MMTK.Proteins.PeptideChain` object corresponding to the peptide chain in the PDB file. The parameter model has the same meaning as for the PeptideChain constructor. :rtype: :class:`~MMTK.Proteins.PeptideChain` """ self.identifyProtonation() from MMTK import Proteins properties = {'model': model} if self.segment_id != '': properties['name'] = self.segment_id elif self.chain_id != '': properties['name'] = self.chain_id if c_terminus is None: properties['c_terminus'] = self.isTerminated() else: properties['c_terminus'] = c_terminus if n_terminus is not None: properties['n_terminus'] = n_terminus chain = apply(Proteins.PeptideChain, (self,), properties) if model != 'no_hydrogens': chain.findHydrogenPositions() return chain def identifyProtonation(self): for residue in self.residues: if residue.name == 'HIS': count_hd = 0 count_he = 0 for atom in residue: if 'HD' in atom.name: count_hd += 1 if 'HE' in atom.name: count_he += 1 if count_hd + count_he == 0: # default for crystallographic structures without hydrogens residue.name = 'HIE' elif count_he == 2: if count_hd == 2: residue.name = 'HIP' else: residue.name = 'HIE' else: residue.name = 'HID' elif residue.name == 'GLU': for atom in residue: if 'HE' in atom.name: residue.name = 'GLP' break elif residue.name == 'ASP': for atom in residue: if 'HD' in atom.name: residue.name = 'APP' break elif residue.name == 'LYS': count_hz = 0 for atom in residue: if 'HZ' in atom.name: count_hz += 1 if count_hz > 0 and count_hz < 3: # for count_hz == 0 (most probably a crystallographic # structure), keep LYS which is the most frequent one. residue.name = 'LYP' class PDBNucleotideChain(Scientific.IO.PDB.NucleotideChain, PDBChain): """ Nucleotide chain in a PDB file See the description of Scientific.IO.PDB.NucleotideChain for the constructor and additional methods. In MMTK, PDBNucleotideChain objects are usually obtained from a PDBConfiguration object via its attribute nucleotide_chains (see the documentation of Scientific.IO.PDB.Structure). """ def createNucleotideChain(self, model='all'): """ :returns: a :class:`~MMTK.NucleicAcids.NucleotideChain` object corresponding to the nucleotide chain in the PDB file. The parameter model has the same meaning as for the NucleotideChain constructor. :rtype: :class:`~MMTK.NucleicAcids.NucleotideChain` """ from MMTK import NucleicAcids properties = {'model': model} if self.segment_id != '': properties['name'] = self.segment_id elif self.chain_id != '': properties['name'] = self.chain_id if self[0].hasPhosphate(): properties['terminus_5'] = 0 chain = apply(NucleicAcids.NucleotideChain, (self,), properties) if model != 'no_hydrogens': chain.findHydrogenPositions() return chain class PDBMolecule(Scientific.IO.PDB.Molecule): """ Molecule in a PDB file See the description of Scientific.IO.PDB.Molecule for the constructor and additional methods. In MMTK, PDBMolecule objects are usually obtained from a PDBConfiguration object via its attribute molecules (see the documentation of Scientific.IO.PDB.Structure). A molecule is by definition any residue in a PDB file that is not an amino acid or nucleotide residue. """ def applyTo(self, molecule, map = 'pdbmap', alt = 'pdb_alternative', atom_map = None): pdbmap = getattr(molecule, map) try: altmap = getattr(molecule, alt) except AttributeError: altmap = {} setResidueConfiguration(molecule, self, pdbmap[0], altmap, atom_map) def createMolecule(self, name=None): """ :returns: a :class:`~MMTK.ChemicalObjects.Molecule` object corresponding to the molecule in the PDB file. The parameter name specifies the molecule name as defined in the chemical database. It can be left out for known molecules (currently only water). :rtype: :class:`~MMTK.ChemicalObjects.Molecule` """ if name is None: name = molecule_names[self.name] m = ChemicalObjects.Molecule(name) setConfiguration(m, [self]) return m # # The structure class from Scientific.IO.PDB is extended by methods # that construct MMTK objects and set configurations. # class PDBConfiguration(Scientific.IO.PDB.Structure): """ Everything in a PDB file A PDBConfiguration object represents the full contents of a PDB file. It can be used to create MMTK objects for all or part of the molecules, or to change the configuration of an existing system. """ def __init__(self, file_or_filename, model = 0, alternate_code = 'A'): """ :param file_or_filename: the name of a PDB file, or a file object :param model: the number of the model to be used from a multiple model file :type model: int :param alternate_code: the alternate code to be used for atoms that have multiple positions :type alternate_code: str """ if isinstance(file_or_filename, basestring): file_or_filename = Database.PDBPath(file_or_filename) Scientific.IO.PDB.Structure.__init__(self, file_or_filename, model, alternate_code) self._numberAtoms() self._convertUnits() peptide_chain_constructor = PDBPeptideChain nucleotide_chain_constructor = PDBNucleotideChain molecule_constructor = PDBMolecule def _numberAtoms(self): n = 1 for residue in self.residues: for atom in residue: atom.number = n n += 1 def _convertUnits(self): for residue in self.residues: for atom in residue: atom.position = atom.position*Units.Ang try: b = atom.properties['temperature_factor'] atom.properties['temperature_factor'] = b*Units.Ang**2 except KeyError: pass try: u = atom.properties['u'] atom.properties['u'] = u*Units.Ang**2 except KeyError: pass # All these attributes exist only if ScientificPython >= 2.7.5 is used. # The Scaling transformation was introduced with the same version, # so if it exists, the rest should work as well. try: from Scientific.Geometry.Transformation import Scaling except ImportError: return for attribute in ['a', 'b', 'c']: value = getattr(self, attribute) if value is not None: setattr(self, attribute, value*Units.Ang) for attribute in ['alpha', 'beta', 'gamma']: value = getattr(self, attribute) if value is not None: setattr(self, attribute, value*Units.deg) if self.to_fractional is not None: self.to_fractional = self.to_fractional*Scaling(1./Units.Ang) v1 = self.to_fractional(Vector(1., 0., 0.)) v2 = self.to_fractional(Vector(0., 1., 0.)) v3 = self.to_fractional(Vector(0., 0., 1.)) self.reciprocal_basis = (Vector(v1[0], v2[0], v3[0]), Vector(v1[1], v2[1], v3[1]), Vector(v1[2], v2[2], v3[2])) else: self.reciprocal_basis = None if self.from_fractional is not None: self.from_fractional = Scaling(Units.Ang)*self.from_fractional self.basis = (self.from_fractional(Vector(1., 0., 0.)), self.from_fractional(Vector(0., 1., 0.)), self.from_fractional(Vector(0., 0., 1.))) else: self.basis = None for i in range(len(self.ncs_transformations)): tr = self.ncs_transformations[i] tr_new = Scaling(Units.Ang)*tr*Scaling(1./Units.Ang) tr_new.given = tr.given tr_new.serial = tr.serial self.ncs_transformations[i] = tr_new for i in range(len(self.cs_transformations)): tr = self.cs_transformations[i] tr_new = Scaling(Units.Ang)*tr*Scaling(1./Units.Ang) self.cs_transformations[i] = tr_new def createUnitCellUniverse(self): """ Constructs an empty universe (OrthrhombicPeriodicUniverse or ParallelepipedicPeriodicUniverse) representing the unit cell of the crystal. If the PDB file does not define a unit cell at all, an InfiniteUniverse is returned. :returns: a universe :rtype: :class:`~MMTK.Universe.Universe` """ if self.from_fractional is None: return Universe.InfiniteUniverse() e1 = self.from_fractional(Vector(1., 0., 0.)) e2 = self.from_fractional(Vector(0., 1., 0.)) e3 = self.from_fractional(Vector(0., 0., 1.)) if abs(e1.normal()*Vector(1., 0., 0.)-1.) < 1.e-15 \ and abs(e2.normal()*Vector(0., 1., 0.)-1.) < 1.e-15 \ and abs(e3.normal()*Vector(0., 0., 1.)-1.) < 1.e-15: return \ Universe.OrthorhombicPeriodicUniverse((e1.length(), e2.length(), e3.length())) return Universe.ParallelepipedicPeriodicUniverse((e1, e2, e3)) def createPeptideChains(self, model='all'): """ :returns: a list of :class:`~MMTK.Proteins.PeptideChain` objects, one for each peptide chain in the PDB file. The parameter model has the same meaning as for the PeptideChain constructor. :rtype: list """ return [chain.createPeptideChain(model) for chain in self.peptide_chains] def createNucleotideChains(self, model='all'): """ :returns: a list of :class:`~MMTK.NucleicAcids.NucleotideChain` objects, one for each nucleotide chain in the PDB file. The parameter model has the same meaning as for the NucleotideChain constructor. :rtype: list """ return [chain.createNucleotideChain(model) for chain in self.nucleotide_chains] def createMolecules(self, names = None, permit_undefined=True): """ :param names: If a list of molecule names (as defined in the chemical database) and/or PDB residue names, only molecules mentioned in this list will be constructed. If a dictionary, it is used to map PDB residue names to molecule names. With the default (None), only water molecules are built. :type names: list :param permit_undefined: If False, an exception is raised when a PDB residue is encountered for which no molecule name is supplied in names. If True, an AtomCluster object is constructed for each unknown molecule. :returns: a collection of :class:`~MMTK.ChemicalObjects.Molecule` objects, one for each molecule in the PDB file. Each PDB residue not describing an amino acid or nucleotide residue is considered a molecule. :rtype: :class:`~MMTK.Collections.Collection` """ collection = Collections.Collection() mol_dicts = [molecule_names] if type(names) == type({}): mol_dicts.append(names) names = None for name in self.molecules.keys(): full_name = None for dict in mol_dicts: full_name = dict.get(name, None) if names is None or name in names or full_name in names: if full_name is None and not permit_undefined: raise ValueError("no definition for molecule " + name) for molecule in self.molecules[name]: if full_name: m = ChemicalObjects.Molecule(full_name) setConfiguration(m, [molecule]) else: pdbdict = {} atoms = [] i = 0 for atom in molecule: aname = atom.name while aname[0] in string.digits: aname = aname[1:] + aname[0] try: element = atom['element'].strip() a = ChemicalObjects.Atom(element, name = aname) except KeyError: try: a = ChemicalObjects.Atom(aname[:2].strip(), name = aname) except IOError: a = ChemicalObjects.Atom(aname[:1], name = aname) a.setPosition(atom.position) atoms.append(a) pdbdict[atom.name] = Database.AtomReference(i) i += 1 m = ChemicalObjects.AtomCluster(atoms, name = name) if len(pdbdict) == len(molecule): # pdbmap is correct only if the AtomCluster has # unique atom names m.pdbmap = [(name, pdbdict)] setConfiguration(m, [molecule]) collection.addObject(m) return collection def createGroups(self, mapping): groups = [] for name in self.molecules.keys(): full_name = mapping.get(name, None) if full_name is not None: for molecule in self.molecules[name]: g = ChemicalObjects.Group(full_name) setConfiguration(g, [molecule], toplevel=0) groups.append(g) return groups def createAll(self, molecule_names = None, permit_undefined=True): """ :returns: a collection containing all objects contained in the PDB file, i.e. the combination of the objects returned by :func:`~MMTK.PDB.PDBConfiguration.createPeptideChains`, :func:`~MMTK.PDB.PDBConfiguration.createNucleotideChains`, and :func:`~MMTK.PDB.PDBConfiguration.createMolecules`. The parameters have the same meaning as for :func:`~MMTK.PDB.PDBConfiguration.createMolecules`. :rtype: :class:`~MMTK.Collectionc.Collection` """ collection = Collections.Collection() peptide_chains = self.createPeptideChains() if peptide_chains: import Proteins collection.addObject(Proteins.Protein(peptide_chains)) nucleotide_chains = self.createNucleotideChains() collection.addObject(nucleotide_chains) molecules = self.createMolecules(molecule_names, permit_undefined) collection.addObject(molecules) return collection def asuToUnitCell(self, asu_contents, compact=True): """ :param asu_contents: the molecules in the asymmetric unit, usually obtained from :func:`~MMTK.PDB.PDBConfiguration.createAll()`. :param compact: if True, all molecules images are shifted such that their centers of mass lie inside the unit cell. :type compact: bool :returns: a collection containing all molecules in the unit cell, obtained by copying and moving the molecules from the asymmetric unit according to the crystallographic symmetry operations. :rtype: :class:`~MMTK.Collections.Collection` """ unit_cell_contents = Collections.Collection() for symop in self.cs_transformations: transformation = symop.asLinearTransformation() rotation = transformation.tensor translation = transformation.vector image = copy.deepcopy(asu_contents) for atom in image.atomList(): atom.setPosition(symop(atom.position())) if compact: cm = image.centerOfMass() cm_fr = self.to_fractional(cm) cm_fr = Vector(cm_fr[0] % 1., cm_fr[1] % 1., cm_fr[2] % 1.) \ - Vector(0.5, 0.5, 0.5) cm = self.from_fractional(cm_fr) image.translateTo(cm) unit_cell_contents.addObject(image) return unit_cell_contents def applyTo(self, object, atom_map=None): """ Sets the configuration of object from the coordinates in the PDB file. The object must be compatible with the PDB file, i.e. contain the same subobjects and in the same order. This is usually only guaranteed if the object was created by the method :func:`~MMTK.PDB.PDBConfiguration.createAll` from a PDB file with the same layout. :param object: a chemical object or collection of chemical objects """ setConfiguration(object, self.residues, atom_map=atom_map) # # An alternative name for compatibility in Database files. # PDBFile = PDBConfiguration # # Set atom coordinates from PDB configuration. # def setResidueConfiguration(object, pdb_residue, pdbmap, altmap, atom_map = None): defined = 0 for atom in pdb_residue: name = atom.name try: name = altmap[name] except KeyError: pass try: pdbname = pdbmap[1][name] except KeyError: pdbname = None if not object.isSubsetModel(): raise IOError('Atom '+atom.name+' of PDB residue ' + pdb_residue.name+' not found in residue ' + pdbmap[0] + ' of object ' + object.fullName()) if pdbname: object.setPosition(pdbname, atom.position) try: object.setIndex(pdbname, atom.number-1) except ValueError: pass if atom_map is not None: atom_map[object.getAtom(pdbname)] = atom defined += 1 return defined def setConfiguration(object, pdb_residues, map = 'pdbmap', alt = 'pdb_alternative', atom_map = None, toplevel = True): defined = 0 if hasattr(object, 'is_protein'): i = 0 for chain in object: l = len(chain) defined += setConfiguration(chain, pdb_residues[i:i+l], map, alt, atom_map, False) i = i + l elif hasattr(object, 'is_chain'): for i in range(len(object)): defined += setConfiguration(object[i], pdb_residues[i:i+1], map, alt, atom_map, False) elif hasattr(object, map): pdbmap = getattr(object, map) try: altmap = getattr(object, alt) except AttributeError: altmap = {} nres = len(pdb_residues) if len(pdbmap) != nres: raise IOError('PDB configuration does not match object ' + object.fullName()) for i in range(nres): defined += setResidueConfiguration(object, pdb_residues[i], pdbmap[i], altmap, atom_map) elif Collections.isCollection(object): nres = len(pdb_residues) if len(object) != nres: raise IOError('PDB configuration does not match object ' + object.fullName()) for i in range(nres): defined += setConfiguration(object[i], [pdb_residues[i]], map, alt, atom_map, False) else: try: name = object.fullName() except AttributeError: try: name = object.name except AttributeError: name = '???' raise IOError('PDB configuration does not match object ' + name) if toplevel and defined < object.numberOfAtoms(): name = '[unnamed object]' try: name = object.fullName() except: pass if name: name = ' in ' + name Utility.warning(`object.numberOfAtoms()-defined` + ' atom(s)' + name + ' were not assigned (new) positions.') return defined # # Create objects from a PDB configuration. # molecule_names = {'HOH': 'water', 'TIP': 'water', 'TIP3': 'water', 'WAT': 'water', 'HEM': 'heme'} def defineMolecule(code, name): if molecule_names.has_key(code): raise ValueError("PDB code " + code + " already used") molecule_names[code] = name # # This object represents a PDB file for output. # class PDBOutputFile(object): """ PDB file for output """ def __init__(self, filename, subformat= None): """ :param filename: the name of the PDB file that is created :type filename: str :param subformat: a variant of the PDB format; these subformats are defined in module Scientific.IO.PDB. The default is the standard PDB format. :type subformat: str """ self.file = Scientific.IO.PDB.PDBFile(filename, 'w', subformat) self.warning = False self.atom_sequence = [] self.model_number = None def nextModel(self): """ Start a new model """ if self.model_number is None: self.model_number = 1 else: self.file.writeLine('ENDMDL', '') self.model_number = self.model_number + 1 self.file.writeLine('MODEL', {'serial_number': self.model_number}) def write(self, object, configuration = None, tag = None): """ Write an object to the file :param object: the object to be written :type object: :class:`~MMTK.Collections.GroupOfAtoms` :param configuration: the configuration from which the coordinates are taken (default: current configuration) :type configuration: :class:`~MMTK.ParticleProperties.Configuration` """ if not ChemicalObjects.isChemicalObject(object): for o in object: self.write(o, configuration) else: toplevel = tag is None if toplevel: tag = Utility.uniqueAttribute() if hasattr(object, 'pdbmap'): for residue in object.pdbmap: self.file.nextResidue(residue[0], ) sorted_atoms = residue[1].items() sorted_atoms.sort(lambda x, y: cmp(x[1].number, y[1].number)) for atom_name, atom in sorted_atoms: atom = object.getAtom(atom) p = atom.position(configuration) if Utility.isDefinedPosition(p): try: occ = atom.occupancy except AttributeError: occ = 0. try: temp = atom.temperature_factor except AttributeError: temp = 0. self.file.writeAtom(atom_name, p/Units.Ang, occ, temp, atom.type.symbol) self.atom_sequence.append(atom) else: self.warning = True setattr(atom, tag, None) else: if hasattr(object, 'is_protein'): for chain in object: self.write(chain, configuration, tag) elif hasattr(object, 'is_chain'): self.file.nextChain(None, object.name) for residue in object: self.write(residue, configuration, tag) self.file.terminateChain() elif hasattr(object, 'molecules'): for m in object.molecules: self.write(m, configuration, tag) elif hasattr(object, 'groups'): for g in object.groups: self.write(g, configuration, tag) if toplevel: for a in object.atomList(): if not hasattr(a, tag): self.write(a, configuration, tag) delattr(a, tag) def close(self): """ Closes the file. Must be called in order to prevent data loss. """ if self.model_number is not None: self.file.writeLine('ENDMDL', '') self.file.close() if self.warning: Utility.warning('Some atoms are missing in the output file ' + \ 'because their positions are undefined.') self.warning = False MMTK-2.7.9/MMTK/PDBML.py0000644000076600000240000001753011662461640014705 0ustar hinsenstaff00000000000000# Read PDBML files # # Written by Konrad Hinsen # """ Experimental PDBML reader. It doesn't treat water molecules correctly yet (it creates a single molecule object with all atoms of all water molecules). """ #import elementtree.ElementTree as ET import cElementTree as ET from Scientific.Geometry import Vector from Scientific.IO.PDB import Atom, AminoAcidResidue, NucleotideResidue, \ amino_acids, nucleic_acids from Scientific.IO.TextFile import TextFile import MMTK.PDB class PDBConfiguration(MMTK.PDB.PDBConfiguration): def __init__(self, filename, model = 1, alternate_code = 'A'): self.filename = filename self.model = model self.alternate = alternate_code self.residues = [] self.objects = [] self.peptide_chains = [] self.nucleotide_chains = [] self.molecules = {} self.prefix = None self.chem_comp = None self.entity_names = None self.entities = None self.chain_entities = None self.chains = None self.nonchains = None self.atoms = {} self.parseFile(TextFile(filename)) self._numberAtoms() def parseFile(self, file): current_comp_id = None current_seq_id = None current_asym_id = None current_chain = None current_residue = None for event, element in ET.iterparse(file): tag = element.tag if self.prefix is None: self.prefix = tag[:tag.find('}')+1] if tag == self.prefix+"atom_site": atom_spec = self.parseAtom(element) if (atom_spec['alt_id'] is None or atom_spec['alt_id'] == self.alternate) \ and atom_spec['model'] == self.model: atom = Atom(atom_spec['name'], atom_spec['position'], element=atom_spec['element'], occupancy=atom_spec['occupancy'], temperature_factor=atom_spec['beta']) self.atoms[atom_spec['atom_id']] = atom if atom_spec['asym_id'] != current_asym_id: # start new chain or molecule entity = self.entities[atom_spec['entity_id']] residue_name = atom_spec['comp_id'] if entity['type'] == 'polymer': if residue_name in amino_acids: current_chain = MMTK.PDB.PDBPeptideChain(chain_id = atom_spec['asym_id'], segment_id = '') self.peptide_chains.append(current_chain) elif residue_name in nucleic_acids: current_chain = MMTK.PDB.PDBNucleotideChain(chain_id = atom_spec['asym_id'], segment_id = '') self.nucleotide_chains.append(current_chain) else: raise ValueError('Unknown polymer type' + 'containing residue ' + residue_name) current_comp_id = None current_residue = None else: current_chain = None current_residue = MMTK.PDB.PDBMolecule(residue_name) current_comp_id = residue_name current_seq_id = atom_spec['seq_id'] mol_list = self.molecules.get(residue_name, []) mol_list.append(current_residue) self.molecules[residue_name] = mol_list self.residues.append(current_residue) current_asym_id = atom_spec['asym_id'] if atom_spec['comp_id'] != current_comp_id or \ atom_spec['seq_id'] != current_seq_id: # start a new residue current_comp_id = atom_spec['comp_id'] current_seq_id = atom_spec['seq_id'] if current_comp_id in amino_acids: current_residue = AminoAcidResidue(current_comp_id, [], current_seq_id) elif current_comp_id in nucleic_acids: current_residue = NucleotideResidue(current_comp_id, [], current_seq_id) else: raise ValueError('Unknown residue ' + residue_name) current_chain.addResidue(current_residue) self.residues.append(current_residue) if current_residue is not None: # Ultimately, this should never be None, but for # now we skip whatever we can't handle yet current_residue.addAtom(atom) element.clear() elif tag == self.prefix+'chem_compCategory': self.chem_comp = element elif tag == self.prefix+'pdbx_entity_nameCategory': self.entity_names = element elif tag == self.prefix+'entityCategory': self.entities = {} for e in element: entity_id = e.attrib['id'] entity_def = {} for field in e: entity_def[field.tag[len(self.prefix):]] = field.text self.entities[entity_id] = entity_def elif tag == self.prefix+'entity_poly_seqCategory': self.chain_entities = None elif tag == self.prefix+'pdbx_poly_seq_schemeCategory': self.chains = element elif tag == self.prefix+'pdbx_nonpoly_schemeCategory': self.nonchains = element elif tag == self.prefix+'atom_siteCategory': # This event happens when all atoms have been treated element.clear() def parseAtom(self, element): atom_spec = { 'atom_id': element.get('id', None), 'element': element.find(self.prefix+'type_symbol').text, 'name': element.find(self.prefix+'label_atom_id').text, 'alt_id': element.find(self.prefix+'label_alt_id').text, 'model': int(element.find(self.prefix+'pdbx_PDB_model_num').text), 'comp_id': element.find(self.prefix+'label_comp_id').text, 'asym_id': element.find(self.prefix+'label_asym_id').text, 'entity_id': element.find(self.prefix+'label_entity_id').text, 'position': Vector(float(element.find(self.prefix+'Cartn_x').text), float(element.find(self.prefix+'Cartn_y').text), float(element.find(self.prefix+'Cartn_z').text)), 'occupancy': float(element.find(self.prefix+'occupancy').text), 'beta': float(element.find(self.prefix+'B_iso_or_equiv').text), } seq_id = element.find(self.prefix+'label_seq_id').text if seq_id is None: atom_spec['seq_id'] = None else: atom_spec['seq_id'] = int(seq_id) return atom_spec if __name__ == '__main__': #c = PDBConfiguration("/Users/hinsen/Desktop/1BTY.xml") #c_ref = MMTK.PDB.PDBConfiguration("/Users/hinsen/Desktop/1BTY.pdb") #c = PDBConfiguration("/Users/hinsen/Desktop/5CYT.xml") #c_ref = MMTK.PDB.PDBConfiguration("/Users/hinsen/Desktop/5CYT.pdb") c = PDBConfiguration("/Users/hinsen/Desktop/2BG9.xml") MMTK-2.7.9/MMTK/PDBMoleculeFactory.py0000644000076600000240000003175311662461640017475 0ustar hinsenstaff00000000000000# This module constructs molecules and universes corresponding exactly # to the contents of a PDB file, using residue and atom names as # given in the file. # # Written by Konrad Hinsen # """ Molecule factory defined by a PDB file This module permits the construction of molecular objects that correspond exactly to the contents of a PDB file. It is used for working with experimental data. Note that most force fields cannot be applied to the systems generated in this way because the molecule factory does not know any force field parameters. """ __docformat__ = 'restructuredtext' import MMTK from MMTK.MoleculeFactory import MoleculeFactory from Scientific.Geometry import Vector, delta from Scientific import N import copy class PDBMoleculeFactory(MoleculeFactory): """ A PDBMoleculeFactory generates molecules and universes from the contents of a PDBConfiguration. Nothing is added or left out (except as defined by the optional residue and atom filters), and the atom and residue names are exactly those of the original PDB file. """ def __init__(self, pdb_conf, residue_filter=None, atom_filter=None): """ :param pdb_conf: a PDBConfiguration :type pdb_conf: :class:`~MMTK.PDB.PDBConfiguration` :param residue_filter: a function taking a residue object (as defined in Scientific.IO.PDB) and returning True if that residue is to be kept in the molecule factory :type residue_filter: callable :param atom_filter: a function taking a residue object and an atom object (as defined in Scientific.IO.PDB) and returning True if that atom is to be kept in the molecule factory :type atom_filter: callable """ MoleculeFactory.__init__(self) if residue_filter is None and atom_filter is None: self.pdb_conf = pdb_conf else: self.pdb_conf = copy.deepcopy(pdb_conf) if atom_filter is not None: for residue in self.pdb_conf.residues: delete = [atom for atom in residue if not atom_filter(residue, atom)] for atom in delete: residue.deleteAtom(atom) if residue_filter is not None: delete = [residue for residue in self.pdb_conf.residues if len(residue) == 0 or not residue_filter(residue)] for residue in delete: self.pdb_conf.deleteResidue(residue) self.peptide_chains = [] self.nucleotide_chains = [] self.molecules = {} self.makeAll() def retrieveMolecules(self): """ Constructs Molecule objects corresponding to the contents of the PDBConfiguration. Each peptide or nucleotide chain becomes one molecule. For residues that are neither amino acids nor nucleic acids, each residue becomes one molecule. Chain molecules (peptide and nucleotide chains) can be iterated over to retrieve the individual residues. The residues can also be accessed as attributes whose names are the three-letter residue name (upper case) followed by an underscore and the residue number from the PDB file (e.g. 'GLY_34'). Each object that corresponds to a PDB residue (i.e. each residue in a chain and each non-chain molecule) has the attributes 'residue_name' and 'residue_number'. Each atom has the attributes 'serial_number', 'occupancy' and 'temperature_factor'. Atoms for which a ANISOU record exists also have an attribute 'u' whose value is a tensor object. :returns: a list of Molecule objects :rtype: list """ objects = [self.retrieveMolecule(o) for o in self.peptide_chains + self.nucleotide_chains] for mollist in self.molecules.values(): objects.extend([self.retrieveMolecule(residue) for residue in mollist]) return objects def retrieveUniverse(self): """ Constructs an empty universe (OrthrhombicPeriodicUniverse or ParallelepipedicPeriodicUniverse) representing the unit cell of the crystal. If the PDB file does not define a unit cell at all, an InfiniteUniverse is returned. :returns: a universe :rtype: :class:`~MMTK.Universe.Universe` """ return self.pdb_conf.createUnitCellUniverse() def retrieveAsymmetricUnit(self): """ Constructs a universe (OrthrhombicPeriodicUniverse or ParallelepipedicPeriodicUniverse) representing the unit cell of the crystal and adds the molecules representing the asymmetric unit. :returns: a universe :rtype: :class:`~MMTK.Universe.Universe` """ universe = self.retrieveUniverse() universe.addObject(self.retrieveMolecules()) return universe def retrieveUnitCell(self, compact=True): """ Constructs a universe (OrthrhombicPeriodicUniverse or ParallelepipedicPeriodicUniverse) representing the unit cell of the crystal and adds all the molecules it contains, i.e. the molecules of the asymmetric unit and its images obtained by applying the crystallographic symmetry operations. :param compact: if True, the images are shifted such that their centers of mass lie inside the unit cell. :type compact: bool :returns: a universe :rtype: :class:`~MMTK.Universe.Universe` """ if not self.pdb_conf.cs_transformations: return self.retrieveAsymmetricUnit() universe = self.retrieveUniverse() asu_count = 0 for symop in self.pdb_conf.cs_transformations: transformation = symop.asLinearTransformation() rotation = transformation.tensor translation = transformation.vector is_asu = translation.length() < 1.e-8 and \ N.maximum.reduce(N.ravel(N.fabs((rotation -delta).array))) < 1.e-8 if is_asu: asu_count += 1 asu = MMTK.Collection(self.retrieveMolecules()) for atom in asu.atomList(): atom.setPosition(symop(atom.position())) if hasattr(atom, 'u'): atom.u = rotation.dot(atom.u.dot(rotation.transpose())) atom.u = atom.u.symmetricalPart() atom.in_asu = is_asu if compact: cm = asu.centerOfMass() cm_fr = self.pdb_conf.to_fractional(cm) cm_fr = Vector(cm_fr[0] % 1., cm_fr[1] % 1., cm_fr[2] % 1.) \ - Vector(0.5, 0.5, 0.5) cm = self.pdb_conf.from_fractional(cm_fr) asu.translateTo(cm) universe.addObject(asu) assert asu_count == 1 return universe def makeAll(self): cystines = [] for chain in self.pdb_conf.peptide_chains: chain_id = chain.chain_id if not chain_id: chain_id = 'PeptideChain'+str(len(self.peptide_chains)+1) for residue in chain: if residue.name == 'CYS' and residue.atoms.has_key('SG'): cystines.append((chain_id, residue, residue.atoms['SG'])) self.makeChain(chain, chain_id) self.peptide_chains.append(chain_id) for i in range(len(cystines)): for j in range(i+1, len(cystines)): d = (cystines[i][2].position-cystines[j][2].position).length() if d < 0.25: if cystines[i][0] is cystines[j][0]: cys1 = cystines[i][1] cys2 = cystines[j][1] cys1 = cys1.name + '_' + str(cys1.number) cys2 = cys2.name + '_' + str(cys2.number) self.addBond(cystines[i][0], cys1+'.SG', cys2+'.SG') else: raise NotImplemented('Inter-chain disulfide bridges' ' not yet implemented') for chain in self.pdb_conf.nucleotide_chains: chain_id = chain.chain_id if not chain_id: chain_id = 'NucleotideChain'+str(len(self.nucleotide_chains)+1) self.makeChain(chain, chain_id) self.nucleotide_chains.append(chain_id) for molname, mollist in self.pdb_conf.molecules.items(): self.molecules[molname] = [] for residue in mollist: base_resname = residue.name + '_' + str(residue.number) suffix = 1 resname = base_resname done = False while not done: try: self.makeResidue(residue, resname) done = True except ValueError, e: if str(e).split()[0] == "redefinition": resname = base_resname + "_" + str(suffix) suffix += 1 else: raise self.molecules[molname].append(resname) def makeChain(self, chain, chain_id): groups = [] for residue in chain: resname = chain_id + '_' + residue.name + '_' + str(residue.number) groups.append(resname) self.makeResidue(residue, resname) self.createGroup(chain_id) local_resnames = [] for resname in groups: local_resname = '_'.join(resname.split('_')[1:]) self.addSubgroup(chain_id, local_resname, resname) local_resnames.append(local_resname) self.setAttribute(chain_id, 'sequence', local_resnames) for i in range(1, len(chain)): if chain[i-1].number == chain[i].number-1: for atom1 in chain[i-1]: for atom2 in chain[i]: if self.assumeBond(atom1.properties['element'], atom1.position, atom2.properties['element'], atom2.position): self.addBond(chain_id, local_resnames[i-1]+'.'+atom1.name, local_resnames[i]+'.'+atom2.name) def makeResidue(self, residue, group_name): self.createGroup(group_name) self.setAttribute(group_name, 'residue_name', residue.name) self.setAttribute(group_name, 'residue_number', residue.number) atoms = [] for atom in residue: atoms.append((atom.name, atom.properties['element'], atom.position)) self.addAtom(group_name, atom.name, atom.properties['element']) self.setPosition(group_name, atom.name, atom.position) self.setAttribute(group_name, atom.name+'.temperature_factor', atom.properties['temperature_factor']) self.setAttribute(group_name, atom.name+'.occupancy', atom.properties['occupancy']) self.setAttribute(group_name, atom.name+'.serial_number', atom.properties['serial_number']) if atom.properties.has_key('u'): self.setAttribute(group_name, atom.name+'.u', atom.properties['u']) for i in range(len(atoms)): atom_i, element_i, pos_i = atoms[i] for j in range(i+1, len(atoms)): atom_j, element_j, pos_j = atoms[j] if self.assumeBond(element_i, pos_i, element_j, pos_j): self.addBond(group_name, atom_i, atom_j) def assumeBond(self, element1, pos1, element2, pos2): if element1 > element2: element1, element2 = element2, element1 try: d = self.bond_lengths[(element1, element2)] return (pos1-pos2).length() < d except KeyError: return False bond_lengths = {('C', 'C'): 0.16, ('C', 'H'): 0.115, ('C', 'N'): 0.215, ('C', 'O'): 0.16, ('C', 'P'): 0.2, ('C', 'S'): 0.2, ('H', 'H'): 0.08, ('H', 'N'): 0.115, ('H', 'O'): 0.115, ('H', 'P'): 0.15, ('H', 'S'): 0.14, ('N', 'N'): 0.155, ('N', 'O'): 0.15, ('O', 'O'): 0.16, ('O', 'P'): 0.17, ('O', 'S'): 0.16, ('P', 'S'): 0.2, ('S', 'S'): 0.25, } MMTK-2.7.9/MMTK/ProteinEnvironment.py0000644000076600000240000000056311662461640017712 0ustar hinsenstaff00000000000000# This module defines the environment in which protein definition files # are executed. from MMTK.Proteins import PeptideChain from MMTK.Collections import Collection from MMTK.PDB import PDBConfiguration from MMTK.Units import * from Scientific.Geometry import Vector from Scientific.Geometry.Transformation import Translation, Rotation from copy import copy, deepcopy MMTK-2.7.9/MMTK/ProteinFriction.py0000644000076600000240000000252611662461640017164 0ustar hinsenstaff00000000000000# Friction constants for protein C-alpha models # # Written by Konrad Hinsen # """ A friction constant model for |C_alpha| models of proteins """ __docformat__ = 'restructuredtext' import MMTK.ParticleProperties from Scientific import N def calphaFrictionConstants(protein, set=2): """ :param protein: a |C_alpha| model protein :type protein: :class:`~MMTK.Proteins.Protein` :param set: the number of a friction constant set (1, 2, 3, or 4) :return: the estimated friction constants for the atoms in the protein :rtype: :class:`~MMTK.ParticleProperties.ParticleScalar` """ radius = 1.5 atoms = protein.atomCollection() f = MMTK.ParticleProperties.ParticleScalar(protein.universe()) for chain in protein: for residue in chain: a = residue.peptide.C_alpha m = atoms.selectShell(a.position(), radius).mass() d = 3.*m/(4.*N.pi*radius**3) if set == 1: # linear fit to initial slope f[a] = max(1000., 121.2*d-8600) elif set == 2: # exponential fit 400 steps f[a] = max(1000., 68.2*d-5160) elif set == 3: # exponential fit 200 steps f[a] = max(1000., 38.2*d-2160) elif set == 4: # expansion fit 50 steps f[a] = max(1000., 20.4*d-500.) return f MMTK-2.7.9/MMTK/Proteins.py0000644000076600000240000010203311662461640015643 0ustar hinsenstaff00000000000000# This module implements classes for peptide chains and proteins. # # Written by Konrad Hinsen # """ Peptide chains and proteins """ __docformat__ = 'restructuredtext' from MMTK import Biopolymers, Bonds, ChemicalObjects, Collections, \ ConfigIO, Database, Units, Universe, Utility from Scientific.Geometry import Vector from MMTK.Biopolymers import defineAminoAcidResidue # # Residues are special groups # class Residue(Biopolymers.Residue): """ Amino acid residue Amino acid residues are a special kind of group. They are defined in the chemical database. Each residue has two subgroups ('peptide' and 'sidechain') and is usually connected to other residues to form a peptide chain. The database contains three variants of each residue (N-terminal, C-terminal, non-terminal) and various models (all-atom, united-atom, |C_alpha|). """ def __init__(self, name = None, model = 'all'): """ :param name: the name of the residue in the chemical database. This is the full name of the residue plus the suffix "_nt" or "_ct" for the terminal variants. :type name: str :param model: one of "all" (all-atom), "none" (no hydrogens), "polar" (united-atom with only polar hydrogens), "polar_charmm" (like "polar", but defining polar hydrogens like in the CHARMM force field), "polar_opls" (like "polar", but defining polar hydrogens like in the latest OPLS force field), "calpha" (only the |C_alpha| atom). :type model: str """ if name is not None: blueprint = _residueBlueprint(name, model) ChemicalObjects.Group.__init__(self, blueprint) self.model = model self._init() def _init(self): Biopolymers.Residue._init(self) # create peptide attribute for calpha model if self.model == 'calpha': self.peptide = self def isNTerminus(self): return hasattr(self.peptide, 'H_3') def isCTerminus(self): return hasattr(self.peptide, 'O_2') def _makeCystine(self): if self.model == 'calpha': return self if self.symbol.lower() != 'cys': raise ValueError(`self` + " is not cysteine.") new_residue = 'cystine_ss' if self.isNTerminus(): new_residue = new_residue + '_nt' elif self.isCTerminus(): new_residue = new_residue + '_ct' new_residue = Residue(new_residue, self.model) for g in ['peptide', 'sidechain']: g_old = getattr(self, g) g_new = getattr(new_residue, g) for a in getattr(g_new, 'atoms'): set_method = getattr(getattr(g_new, a.name), 'setPosition') set_method(getattr(getattr(g_old, a.name), 'position')()) return new_residue def isSubsetModel(self): return self.model == 'calpha' def backbone(self): """ :returns: the peptide group :rtype: :class:`~MMTK.ChemicalObjects.Group` """ return self.peptide def sidechains(self): """ :returns: the sidechain group :rtype: :class:`~MMTK.ChemicalObjects.Group` """ return self.sidechain def phiPsi(self, conf = None): """ :returns: the values of the backbone dihedral angles phi and psi. :rtype: tuple (float, float) """ universe = self.universe() if universe is None: universe = Universe.InfiniteUniverse() C = None for a in self.peptide.N.bondedTo(): if a.parent.parent != self: C = a break if C is None: phi = None else: phi = universe.dihedral(self.peptide.C, self.peptide.C_alpha, self.peptide.N, C, conf) N = None for a in self.peptide.C.bondedTo(): if a.parent.parent != self: N = a break if N is None: psi = None else: psi = universe.dihedral(N, self.peptide.C, self.peptide.C_alpha, self.peptide.N, conf) return phi, psi def phiAngle(self): """ :returns: an object representing the phi angle and allowing to modify it :rtype: MMTK.InternalCoordinates.DihedralAngle """ from MMTK.InternalCoordinates import DihedralAngle C = None for a in self.peptide.N.bondedTo(): if a.parent.parent != self: C = a break if C is None: raise ValueError("residue is N-terminus") return DihedralAngle(self.peptide.C, self.peptide.C_alpha, self.peptide.N, C) def psiAngle(self): """ :returns: an object representing the psi angle and allowing to modify it :rtype: MMTK.InternalCoordinates.DihedralAngle """ from MMTK.InternalCoordinates import DihedralAngle N = None for a in self.peptide.C.bondedTo(): if a.parent.parent != self: N = a break if N is None: raise ValueError("residue is C-terminus") return DihedralAngle(N, self.peptide.C, self.peptide.C_alpha, self.peptide.N) def chiAngle(self): """ :returns: an object representing the chi angle and allowing to modify it :rtype: MMTK.InternalCoordinates.DihedralAngle """ from MMTK.InternalCoordinates import DihedralAngle try: C_beta = self.sidechain.C_beta except AttributeError: raise ValueError("no C_beta in sidechain") X = None for atom_name in ['C_gamma', 'C_gamma_1', 'S_gamma', 'O_gamma', 'O_gamma_1', 'H_beta_1']: try: X = getattr(self.sidechain, atom_name) break except AttributeError: pass if X is None: raise ValueError("no sidechain reference atom found") return DihedralAngle(self.peptide.N, self.peptide.C_alpha, C_beta, X) def _residueBlueprint(name, model): try: blueprint = _residue_blueprints[(name, model)] except KeyError: if model == 'polar': name = name + '_uni' elif model == 'polar_charmm': name = name + '_uni2' elif model == 'polar_oldopls': name = name + '_uni3' elif model == 'none': name = name + '_noh' elif model == 'calpha': name = name + '_calpha' blueprint = Database.BlueprintGroup(name) _residue_blueprints[(name, model)] = blueprint return blueprint _residue_blueprints = {} # # Peptide chains are molecules with added features. # class PeptideChain(Biopolymers.ResidueChain): """ Peptide chain Peptide chains consist of amino acid residues that are linked by peptide bonds. They are a special kind of molecule, i.e. all molecule operations are available. Peptide chains act as sequences of residues. If p is a PeptideChain object, then * len(p) yields the number of residues * p[i] yields residue number i * p[i:j] yields the subchain from residue number i up to but excluding residue number j :param sequence: the amino acid sequence. This can be a string containing the one-letter codes, or a list of three-letter codes, or a :class:`~MMTK.PDB.PDBPeptideChain` object. If a PDBPeptideChain object is supplied, the atomic positions it contains are assigned to the atoms of the newly generated peptide chain, otherwise the positions of all atoms are undefined. :keyword model: one of "all" (all-atom), "no_hydrogens" or "none" (no hydrogens), "polar_hydrogens" or "polar" (united-atom with only polar hydrogens), "polar_charmm" (like "polar", but defining polar hydrogens like in the CHARMM force field), "polar_opls" (like "polar", but defining polar hydrogens like in the latest OPLS force field), "calpha" (only the |C_alpha| atom of each residue). Default is "all". :type model: str :keyword n_terminus: if True, the first residue is constructed using the N-terminal variant, if False the non-terminal version is used. Default is True. :type n_terminus: bool :keyword c_terminus: if True, the last residue is constructed using the C-terminal variant, if False the non-terminal version is used. Default is True. :type c_terminus: bool :keyword circular: if True, a peptide bond is constructed between the first and the last residues. Default is False. :type circular: bool :keyword name: a name for the chain (a string) :type name: str """ def __init__(self, sequence, **properties): if sequence is not None: model = 'all' if properties.has_key('model'): model = properties['model'].lower() elif properties.has_key('hydrogens'): model = properties['hydrogens'] if model == 1: model = 'all' elif model == 0: model = 'none' else: model = model.lower() if model == 'no_hydrogens': model = 'none' elif model == 'polar_hydrogens': model = 'polar' n_term = self.binaryProperty(properties, 'n_terminus', True) c_term = self.binaryProperty(properties, 'c_terminus', True) circular = self.binaryProperty(properties, 'circular', False) self.version_spec = {'n_terminus': n_term, 'c_terminus': c_term, 'model': model, 'circular': circular} if type(sequence[0]) == type(''): conf = None numbers = range(len(sequence)) else: conf = sequence sequence = conf.sequence() numbers = [r.number for r in conf] sequence = map(Biopolymers._fullName, sequence) if model != 'calpha': if n_term: sequence[0] = sequence[0] + '_nt' if c_term: sequence[-1] = sequence[-1] + '_ct' self.groups = [] n = 0 for residue, number in zip(sequence, numbers): n = n + 1 r = Residue(residue, model) r.name = r.symbol + str(number) r.sequence_number = n r.parent = self self.groups.append(r) self._setupChain(circular, properties, conf) is_peptide_chain = True def __getslice__(self, first, last): return SubChain(self, self.groups[first:last]) def sequence(self): """ :returns: the primary sequence as a list of three-letter residue codes. :rtype: list """ return [r.symbol for r in self.groups] def backbone(self): """ :returns: the peptide groups of all residues :rtype: :class:`~MMTK.Collections.Collection` """ backbone = Collections.Collection() for r in self.groups: try: backbone.addObject(r.peptide) except AttributeError: pass return backbone def sidechains(self): """ :returns: the sidechain groups of all residues :rtype: :class:`~MMTK.Collections.Collection` """ sidechains = Collections.Collection() for r in self.groups: try: sidechains.addObject(r.sidechain) except AttributeError: pass return sidechains def phiPsi(self, conf = None): """ :returns: a list of the (phi, psi) backbone angles for each residue :rtype: list of tuple of float """ universe = self.universe() if universe is None: universe = Universe.InfiniteUniverse() angles = [] for i in range(len(self)): r = self[i] if i == 0: phi = None else: phi = universe.dihedral(r.peptide.C, r.peptide.C_alpha, r.peptide.N, self[i-1].peptide.C, conf) if i == len(self)-1: psi = None else: psi = universe.dihedral(self[i+1].peptide.N, r.peptide.C, r.peptide.C_alpha, r.peptide.N, conf) angles.append((phi, psi)) return angles def replaceResidue(self, r_old, r_new): """ :param r_old: the residue to be replaced (must be part of the chain) :type r_old: Residue :param r_new: the residue that replaces r_old :type r_new: Residue """ n = self.groups.index(r_old) for a in r_old.atoms: self.atoms.remove(a) obsolete_bonds = [] for b in self.bonds: if b.a1 in r_old.atoms or b.a2 in r_old.atoms: obsolete_bonds.append(b) for b in obsolete_bonds: self.bonds.remove(b) r_old.parent = None self.atoms.extend(r_new.atoms) self.bonds.extend(r_new.bonds) r_new.sequence_number = n+1 r_new.name = r_new.symbol+`n+1` r_new.parent = self self.groups[n] = r_new if n > 0: peptide_old = self.bonds.bondsOf(r_old.peptide.N) if peptide_old: self.bonds.remove(peptide_old[0]) if not (self.groups[n-1].isCTerminus() or self.groups[n].isNTerminus()): # ConnectedChain objects can have N/C-terminal # residues inside the (virtual) chain, so the # test is necessary. self.bonds.append(Bonds.Bond((self.groups[n-1].peptide.C, self.groups[n].peptide.N))) if n < len(self.groups)-1: peptide_old = self.bonds.bondsOf(r_old.peptide.C) if peptide_old: self.bonds.remove(peptide_old[0]) if not (self.groups[n].isCTerminus() or self.groups[n+1].isNTerminus()): self.bonds.append(Bonds.Bond((self.groups[n].peptide.C, self.groups[n+1].peptide.N))) if isinstance(self.parent, ChemicalObjects.Complex): self.parent.recreateAtomList() universe = self.universe() if universe is not None: universe._changed(True) # add sulfur bridges between cysteine residues def _addSSBridges(self, bonds): for b in bonds: cys1 = b[0] if cys1.symbol.lower() == 'cyx': cys_ss1 = cys1 else: cys_ss1 = cys1._makeCystine() self.replaceResidue(cys1, cys_ss1) cys2 = b[1] if cys2.symbol.lower() == 'cyx': cys_ss2 = cys2 else: cys_ss2 = cys2._makeCystine() self.replaceResidue(cys2, cys_ss2) self.bonds.append(Bonds.Bond((cys_ss1.sidechain.S_gamma, cys_ss2.sidechain.S_gamma))) def _descriptionSpec(self): kwargs = ','.join([name + '=' + `self.version_spec[name]` for name in sorted(self.version_spec.keys())]) return "S", kwargs def _typeName(self): return ''.join(self.sequence()) def _graphics(self, conf, distance_fn, model, module, options): if model != 'backbone': return ChemicalObjects.Molecule._graphics(self, conf, distance_fn, model, module, options) color = options.get('color', 'black') material = module.EmissiveMaterial(color) objects = [] for i in range(len(self.groups)-1): a1 = self.groups[i].peptide.C_alpha a2 = self.groups[i+1].peptide.C_alpha p1 = a1.position(conf) p2 = a2.position(conf) if p1 is not None and p2 is not None: bond_vector = 0.5*distance_fn(a1, a2, conf) cut = bond_vector != 0.5*(p2-p1) if not cut: objects.append(module.Line(p1, p2, material = material)) else: objects.append(module.Line(p1, p1+bond_vector, material = material)) objects.append(module.Line(p2, p2-bond_vector, material = material)) return objects # # Subchains are created by slicing chains or extracting a chain from # a group of connected chains. # class SubChain(PeptideChain): """ A contiguous part of a peptide chain SubChain objects are the result of slicing operations on PeptideChain objects. They cannot be created directly. SubChain objects permit all operations of PeptideChain objects, but cannot be added to a universe. """ def __init__(self, chain=None, groups=None, name = ''): if chain is not None: self.groups = groups self.atoms = [] self.bonds = [] for g in self.groups: self.atoms.extend(g.atoms) self.bonds.extend(g.bonds) for i in range(len(self.groups)-1): link1 = self.groups[i].chain_links[1] link2 = self.groups[i+1].chain_links[0] self.bonds.append(Bonds.Bond((link1, link2))) self.bonds = Bonds.BondList(self.bonds) self.name = name self.model = chain.model self.parent = chain.parent self.type = None self.configurations = {} self.part_of = chain is_incomplete = True def __repr__(self): if self.name == '': return 'SubChain of ' + repr(self.part_of) else: return ChemicalObjects.Molecule.__repr__(self) __str__ = __repr__ def replaceResidue(self, r_old, r_new): for a in r_old.atoms: self.atoms.remove(a) obsolete_bonds = [] for b in self.bonds: if b.a1 in r_old.atoms or b.a2 in r_old.atoms: obsolete_bonds.append(b) for b in obsolete_bonds: self.bonds.remove(b) n = self.groups.index(r_old) if n > 0: for b in self.bonds.bondsOf(r_old.peptide.N): self.bonds.remove(b) if n < len(self.groups)-1: for b in self.bonds.bondsOf(r_old.peptide.C): self.bonds.remove(b) PeptideChain.replaceResidue(self.part_of, r_old, r_new) self.groups[n] = r_new self.atoms.extend(r_new.atoms) self.bonds.extend(r_new.bonds) if n > 0: self.bonds.append(Bonds.Bond((self.groups[n-1].peptide.C, self.groups[n].peptide.N))) if n < len(self.groups)-1: self.bonds.append(Bonds.Bond((self.groups[n].peptide.C, self.groups[n+1].peptide.N))) def _distanceConstraintList(self): atoms = self.atomList() return [(a1, a2, d) for a1, a2, d in self.part_of._distanceConstraintList() if a1 in atoms and a2 in atoms] def addDistanceConstraint(self, atom1, atom2, distance): chain = self while True: try: chain = chain.part_of except AttributeError: break try: chain.distance_constraints.append((atom1, atom2, distance)) except AttributeError: chain.distance_constraints = [(atom1, atom2, distance)] def removeDistanceConstraints(self, universe=None): raise NotImplementedError # # Connected chains are collections of peptide chains connected by s-s bridges. # class ConnectedChains(PeptideChain): """ Peptide chains connected by disulfide bridges A group of peptide chains connected by disulfide bridges must be considered a single molecule due to the presence of chemical bonds. Such a molecule is represented by a ConnectedChains object. These objects are created automatically when a Protein object is assembled. They are normally not used directly by application programs. When a chain with disulfide bridges to other chains is extracted from a Protein object, the return value is a SubChain object that indirectly refers to a ConnectedChains object. """ def __init__(self, chains=None): if chains is not None: self.chains = [] self.groups = [] self.atoms = [] self.bonds = Bonds.BondList([]) self.chain_names = [] self.model = chains[0].model version_spec = chains[0].version_spec for c in chains: if c.version_spec['model'] != version_spec['model']: raise ValueError("mixing chains of different model: " + c.version_spec['model'] + "/" + version_spec['model']) ng = len(self.groups) self.chains.append((c.name, ng, ng+len(c.groups), c.version_spec)) self.groups.extend(c.groups) self.atoms.extend(c.atoms) self.bonds.extend(c.bonds) try: name = c.name except AttributeError: name = '' self.chain_names.append(name) for g in self.groups: g.parent = self self.name = '' self.parent = None self.type = None self.configurations = {} is_connected_chains = True def _finalize(self): for i in range(len(self.chains)): c = self.chains[i] sub_chain = SubChain(self, self.groups[c[1]:c[2]], c[0]) sub_chain.version_spec = c[3] for g in sub_chain.groups: g.parent = sub_chain self.chains[i] = sub_chain def __len__(self): return len(self.chains) def __getitem__(self, item): return self.chains[item] def __getslice__(self, first, last): raise TypeError("Can't slice connected chains") def _graphics(self, conf, distance_fn, model, module, options): if model != 'backbone': return ChemicalObjects.Molecule._graphics(self, conf, distance_fn, model, module, options) objects = [] for chain in self: objects = objects + chain._graphics(conf, distance_fn, model, module, options) return objects # # Proteins are complexes of peptide chains, connected peptide chains, # and possibly other things. # class Protein(ChemicalObjects.Complex): """ Protein A Protein object is a special kind of :class:`~MMTK.ChemicalObjects.Complex` object which is made up of peptide chains and possibly ligands. If the atoms in the peptide chains that make up a protein have defined positions, sulfur bridges within chains and between chains will be constructed automatically during protein generation based on a distance criterion between cystein sidechains. Proteins act as sequences of chains. If p is a Protein object, then * len(p) yields the number of chains * p[i] yields chain number i """ def __init__(self, *items, **properties): """ :param items: either a sequence of peptide chain objects, or a string, which is interpreted as the name of a database definition for a protein. If that definition does not exist, the string is taken to be the name of a PDB file, from which all peptide chains are constructed and assembled into a protein. :keyword model: one of "all" (all-atom), "no_hydrogens" or "none" (no hydrogens),"polar_hydrogens" or "polar" (united-atom with only polar hydrogens), "polar_charmm" (like "polar", but defining polar hydrogens like in the CHARMM force field), "polar_opls" (like "polar", but defining polar hydrogens like in the latest OPLS force field), "calpha" (only the |C_alpha| atom of each residue). Default is "all". :type model: str :keyword position: the center-of-mass position of the protein :type position: Scientific.Geometry.Vector :keyword name: a name for the protein :type name: str """ if items == (None,): return self.name = '' if len(items) == 1 and type(items[0]) == type(''): try: filename = Database.databasePath(items[0], 'Proteins') found = 1 except IOError: found = 0 if found: blueprint = Database.BlueprintProtein(items[0]) items = blueprint.chains for attr, value in vars(blueprint).items(): if attr not in ['type', 'chains']: setattr(self, attr, value) else: import PDB conf = PDB.PDBConfiguration(items[0]) model = properties.get('model', 'all') items = conf.createPeptideChains(model) molecules = [] for i in items: if ChemicalObjects.isChemicalObject(i): molecules.append(i) else: molecules = molecules + list(i) for m, i in zip(molecules, range(len(molecules))): m._numbers = [i] if not m.name: m.name = 'chain'+`i` ss = self._findSSBridges(molecules) new_mol = {} for m in molecules: new_mol[m] = ([m],[]) for bond in ss: m1 = new_mol[bond[0].topLevelChemicalObject()] m2 = new_mol[bond[1].topLevelChemicalObject()] if m1 == m2: m1[1].append(bond) else: combined = (m1[0] + m2[0], m1[1] + m2[1] + [bond]) for m in combined[0]: new_mol[m] = combined self.molecules = [] while new_mol: m = new_mol.values()[0] for i in m[0]: del new_mol[i] bonds = m[1] if len(m[0]) == 1: m = m[0][0] m._addSSBridges(bonds) else: numbers = sum((i._numbers for i in m[0]), []) m = ConnectedChains(m[0]) m._numbers = numbers m._addSSBridges(bonds) m._finalize() for c in m: c.parent = self m.parent = self self.molecules.append(m) self.atoms = [] self.chains = [] for m in self.molecules: self.atoms.extend(m.atoms) if hasattr(m, 'is_connected_chains'): for c, name, i in zip(range(len(m)), m.chain_names, m._numbers): self.chains.append((m, c, name, i)) else: try: name = m.name except AttributeError: name = '' self.chains.append((m, None, name, m._numbers[0])) self.chains.sort(lambda c1, c2: cmp(c1[3], c2[3])) self.chains = map(lambda c: c[:3], self.chains) self.parent = None self.type = None self.configurations = {} try: self.name = properties['name'] del properties['name'] except KeyError: pass if properties.has_key('position'): self.translateTo(properties['position']) del properties['position'] self.addProperties(properties) undefined = 0 for a in self.atoms: if a.position() is None: undefined += 1 if undefined > 0 and undefined != len(self.atoms): Utility.warning('Some atoms in a protein ' + 'have undefined positions.') is_protein = True def __len__(self): return len(self.chains) def __getitem__(self, item): if isinstance(item, int): m, c, name = self.chains[item] else: for m, c, name in self.chains: if name == item: break if name != item: raise ValueError('No chain with name ' + item) if c is None: return m else: return m[c] def residuesOfType(self, *types): """ :param types: a sequence of residue codes (one- or three-letter) :type types: sequence of str :returns: all residues whose type (one- or three-letter code) is contained in types :rtype: :class:`~MMTK.Collections.Collection` """ rlist = Collections.Collection([]) for m in self.molecules: if isPeptideChain(m): rlist = rlist + apply(m.residuesOfType, types) return rlist def backbone(self): """ :returns: the peptide groups of all residues in all chains :rtype: :class:`~MMTK.Collections.Collection` """ rlist = Collections.Collection([]) for m in self.molecules: if isPeptideChain(m): rlist = rlist + m.backbone() return rlist def sidechains(self): """ :returns: the sidechain groups of all residues in all chains :rtype: :class:`~MMTK.Collections.Collection` """ rlist = Collections.Collection([]) for m in self.molecules: if isPeptideChain(m): rlist = rlist + m.sidechains() return rlist def residues(self): """ :returns: all residues in all chains :rtype: :class:`~MMTK.Collections.Collection` """ rlist = Collections.Collection([]) for m in self.molecules: if isPeptideChain(m): rlist = rlist + m.residues() return rlist def phiPsi(self, conf = None): """ :returns: a list of the (phi, psi) backbone angles for all residue in all chains :rtype: list of list of tuple of float """ return [chain.phiPsi(conf) for chain in self] _ss_bond_max = 0.25*Units.nm def _findSSBridges(self, molecules): molecules = filter(lambda m: hasattr(m, 'is_peptide_chain'), molecules) cys = Collections.Collection([]) for m in molecules: if m.version_spec['model'] != 'calpha': cys = cys + m.residuesOfType('cys') + m.residuesOfType('cyx') s = cys.map(lambda r: r.sidechain.S_gamma) ns = len(s) ss = [] for i in xrange(ns-1): for j in xrange(i+1,ns): r1 = s[i].position() r2 = s[j].position() if r1 and r2 and (r1-r2).length() < self._ss_bond_max: ss.append((cys[i], cys[j])) return ss def _subunits(self): return list(self) def _description(self, tag, index_map, toplevel): if not toplevel: raise ValueError return 'l(' + `self.__class__.__name__` + ',' + `self.name` + ',[' + \ ','.join(o._description(tag, index_map, True) for o in self) + \ '])' def _graphics(self, conf, distance_fn, model, module, options): if model != 'backbone': return ChemicalObjects.Complex._graphics(self, conf, distance_fn, model, module, options) objects = [] for chain in self: objects.extend(chain._graphics(conf, distance_fn, model, module, options)) return objects # # Type check functions # def isPeptideChain(x): """ :param x: any object :returns: True if x is a peptide chain :rtype: bool """ return hasattr(x, 'is_peptide_chain') def isProtein(x): """ :param x: any object :returns: True if x is a protein :rtype: bool """ return hasattr(x, 'is_protein') MMTK-2.7.9/MMTK/PyMOL.py0000644000076600000240000002172211662461640015005 0ustar hinsenstaff00000000000000# PyMOL interface # # Note: this is still in a rather experimental state. # # Written by Konrad Hinsen # import sys in_pymol = sys.modules.has_key('pymol') del sys if in_pymol: try: from pymol import cmd from chempy import Atom, Bond, Molecule from chempy.models import Indexed except ImportError: in_pymol = False import Units, Utility from Scientific.Geometry import Vector import traceback # # Create a PyMOL representation from an MMTK object # class Representation: def __init__(self, object, name = 'MMTK_model', configuration = None, b_values = None): self.object = object self.universe = object.universe() self.name = name self.model = Indexed() self.index_map = {} self.atoms = [] chain_id_number = 0 in_chain = True for o in object.bondedUnits(): if Utility.isSequenceObject(o): groups = [(g, g.name) for g in o] if not in_chain: chain_id_number = (chain_id_number+1) % len(self.chain_ids) in_chain = True else: groups = [(o, o.name)] in_chain = False residue_number = 1 for g, g_name in groups: for a in g.atomList(): atom = Atom() atom.symbol = a.symbol atom.name = a.name atom.resi_number = residue_number atom.chain = self.chain_ids[chain_id_number] if b_values is not None: atom.b = b_values[a] atom.resn = g_name self.model.atom.append(atom) self.index_map[a] = len(self.atoms) self.atoms.append(a) residue_number = residue_number + 1 if in_chain: chain_id_number = (chain_id_number+1) % len(self.chain_ids) try: bonds = o.bonds except AttributeError: bonds = [] for b in bonds: bond = Bond() bond.index = [self.index_map[b.a1], self.index_map[b.a2]] self.model.bond.append(bond) self._setCoordinates(configuration) chain_ids = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' def _setCoordinates(self, configuration): if configuration is None: for i in range(len(self.atoms)): self.model.atom[i].coord = \ list(self.atoms[i].position()/Units.Ang) else: for i in range(len(self.atoms)): self.model.atom[i].coord = \ list(configuration[self.atoms[i]]/Units.Ang) def show(self): auto_zoom = cmd.get("auto_zoom") cmd.set("auto_zoom", 0) cmd.load_model(self.model, self.name) cmd.set("auto_zoom", auto_zoom) def remove(self): cmd.delete(self.name) def update(self, configuration=None): try: cmd.set("suspend_updates","1") self._setCoordinates(configuration) cmd.load_model(self.model, self.name, 1) except: cmd.set("suspend_updates","0") traceback.print_exc() cmd.set("suspend_updates","0") cmd.refresh() def movie(self, configurations): n = 1 cmd.feedback("disable","executive","actions") auto_zoom = cmd.get("auto_zoom") cmd.set("auto_zoom", 0) for conf in configurations: self._setCoordinates(conf) cmd.load_model(self.model, self.name, n) n = n + 1 cmd.set("auto_zoom", auto_zoom) cmd.feedback("enable","executive","actions") cmd.mplay() # # Create an MMTK object from a PyMOL model # # Note: PyMOL models are closer in structure to a PDB file than # to an MMTK object. Some guessing is therefore required to make # the conversion, and that can go wrong. At the moment, this works # for standard proteins but it requires a lot of polishing or testing # before it could be used for arbitrary systems. Still, it is useful # as it is. # # This interface might change in future releases. Consider it experimental. # def scanModel(pymol_model): segment_dict = {} segment_list = [] for atom in pymol_model.atom: seg_id = atom.segi + '_' + atom.chain if seg_id not in segment_list: segment_list.append(seg_id) res_num = atom.resi_number residue_dict = segment_dict.get(seg_id, {}) segment_dict[seg_id] = residue_dict res_name, atom_list = residue_dict.get(res_num, (atom.resn, [])) residue_dict[res_num] = res_name, atom_list atom_list.append((atom.name, Vector(atom.coord)*Units.Ang)) segments = [] for seg_id in segment_list: residue_dict = segment_dict[seg_id] residue_list = residue_dict.items() residue_list.sort(lambda a, b: cmp(a[0], b[0])) segments.append((seg_id, [r[1] for r in residue_list])) return segments def buildCalphaModel(pymol_model): import Collections, Proteins data = scanModel(pymol_model) chains = [] for seg_id, segment in data: amino_acids = [] nucleic_acids = [] for res_name, residue in segment: res_name = res_name.lower() if res_name in amino_acid_names_ca: amino_acids.append((res_name, residue)) if amino_acids: chain = Proteins.PeptideChain([item[0] for item in amino_acids], model='calpha') for residue, res_data in zip(chain, amino_acids): pymol_atom_list = res_data[1] for atom_name, atom_pos in pymol_atom_list: if atom_name.strip().lower() == 'ca': residue.peptide.C_alpha.setPosition(atom_pos) chains.append(chain) return Proteins.Protein(chains) def buildAllAtomModel(pymol_model): import ChemicalObjects, Collections, Proteins data = scanModel(pymol_model) all = Collections.Collection() peptide_chains = [] for seg_id, segment in data: amino_acids = [] nucleic_acids = [] for res_name, residue in segment: res_name = res_name.lower().strip() if res_name in amino_acid_names: amino_acids.append((res_name, residue)) elif res_name in nucleic_acid_names: nucleic_acids.append((res_name, residue)) elif res_name == 'hoh': if len(residue) == 3: m = ChemicalObjects.Molecule('water') for atom_name, atom_pos in residue: atom = getattr(m, atom_name) atom.setPosition(atom_pos) elif len(residue) == 1 and residue[0][0] == 'O': m = ChemicalObjects.Atom('O') m.setPosition(residue[0][1]) all.addObject(m) else: print "Skipping unknown residue", res_name if amino_acids: chain = Proteins.PeptideChain([item[0] for item in amino_acids]) for residue, res_data in zip(chain, amino_acids): pdbmap = residue.pdbmap[0][1] try: altmap = residue.pdb_alternative except AttributeError: altmap = {} pymol_atom_list = res_data[1] atom_list = residue.atomList() for atom_name, atom_pos in pymol_atom_list: try: atom_name = altmap[atom_name] except KeyError: pass try: atom = atom_list[pdbmap[atom_name].number] except KeyError: print "No atom named " + atom_name atom.setPosition(atom_pos) print atom_name, atom.name chain.findHydrogenPositions() peptide_chains.append(chain) if peptide_chains: all.addObject(Proteins.Protein(peptide_chains)) return all amino_acid_names = ['ala', 'arg', 'asn', 'asp', 'cys', 'gln', 'glu', 'gly', 'his', 'ile', 'leu', 'lys', 'met', 'phe', 'pro', 'ser', 'thr', 'trp', 'tyr', 'val', 'cyx', 'hsd', 'hse', 'hsp', 'hid', 'hie', 'hip', 'ace', 'nme', 'nhe'] amino_acid_names_ca = ['ala', 'arg', 'asn', 'asp', 'cys', 'gln', 'glu', 'gly', 'his', 'ile', 'leu', 'lys', 'met', 'phe', 'pro', 'ser', 'thr', 'trp', 'tyr', 'val', 'cyx', 'hsd', 'hse', 'hsp', 'hid', 'hie', 'hip'] nucleic_acid_names = ['da', 'da5', 'da3', 'dan', 'dc', 'dc5', 'dc3', 'dcn', 'dg', 'dg5', 'dg3', 'dgn', 'dt', 'dt5', 'dt3', 'dtn', 'ra', 'ra5', 'ra3', 'ran', 'rc', 'rc5', 'rc3', 'rcn', 'rg', 'rg5', 'rg3', 'rgn', 'ru', 'ru5', 'ru3', 'run'] MMTK-2.7.9/MMTK/Random.py0000644000076600000240000001361211662461640015264 0ustar hinsenstaff00000000000000# Functions for finding random points and orientations. # # Written by: Konrad Hinsen # """ Random quantities for use in molecular simulations """ __docformat__ = 'restructuredtext' from Scientific.Geometry import Vector from Scientific.Geometry.Transformation import Rotation from Scientific import N from MMTK import ParticleProperties, Units try: numeric = N.package except AttributeError: numeric = "Numeric" RNG = None try: if numeric == "Numeric": import RNG elif numeric == "NumPy": import numpy.oldnumeric.rng as RNG except ImportError: pass if RNG is None: if numeric == "Numeric": from RandomArray import uniform, seed elif numeric == "NumPy": from numpy.oldnumeric.random_array import uniform, seed random = __import__('random') seed(1, 1) random.seed(1) def initializeRandomNumbersFromTime(): random.seed() seed(0, 0) def gaussian(mean, std, shape=None): if shape is None: x = random.normalvariate(0., 1.) else: x = N.zeros(shape, N.Float) xflat = N.ravel(x) for i in range(len(xflat)): xflat[i] = random.normalvariate(0., 1.) return mean + std*x else: _uniform_generator = \ RNG.CreateGenerator(-1, RNG.UniformDistribution(0., 1.)) _gaussian_generator = \ RNG.CreateGenerator(-1, RNG.NormalDistribution(0., 1.)) def initializeRandomNumbersFromTime(): global _uniform_generator, _gaussian_generator _uniform_generator = \ RNG.CreateGenerator(0, RNG.UniformDistribution(0., 1.)) _gaussian_generator = \ RNG.CreateGenerator(0, RNG.NormalDistribution(0., 1.)) def uniform(x1, x2, shape=None): if shape is None: x = _uniform_generator.ranf() else: n = N.multiply.reduce(shape) x = _uniform_generator.sample(n) x.shape = shape return x1+(x2-x1)*x def gaussian(mean, std, shape=None): if shape is None: x = _gaussian_generator.ranf() else: n = N.multiply.reduce(shape) x = _gaussian_generator.sample(n) x.shape = shape return mean+std*x del numeric # # Random point in a rectangular box centered around the origin # def randomPointInBox(a, b = None, c = None): """ :param a: the edge length of a box along the x axis :type a: float :param b: the edge length of a box along the y axis (default: a) :type b: float :param c: the edge length of a box along the z axis (default: a) :type c: float :returns: a vector drawn from a uniform distribution within a rectangular box with edge lengths a, b, c. :rtype: Scientific.Geometry.Vector """ if b is None: b = a if c is None: c = a x = uniform(-0.5*a, 0.5*a) y = uniform(-0.5*b, 0.5*b) z = uniform(-0.5*c, 0.5*c) return Vector(x, y, z) # # Random point in a sphere around the origin. # def randomPointInSphere(r): """ :param r: the radius of a sphere :type r: float :returns: a vector drawn from a uniform distribution within a sphere of radius r. :rtype: Scientific.Geometry.Vector """ rsq = r*r while 1: x = N.array([uniform(-r, r), uniform(-r, r), uniform(-r, r)]) if N.dot(x, x) < rsq: break return Vector(x) # # Random direction (unit vector). # def randomDirection(): """ :returns: a vector drawn from a uniform distribution on the surface of a unit sphere. :rtype: Scientific.Geometry.Vector """ r = randomPointInSphere(1.) return r.normal() def randomDirections(n): """ :param n: the number of direction vectors :returns: a list of n vectors drawn from a uniform distribution on the surface of a unit sphere. If n is negative, returns a deterministic list of not more than -n vectors of unit length (useful for testing purposes). :rtype: list """ if n < 0: vs = [Vector(1., 0., 0.), Vector(0., -1., 0.), Vector(0., 0., 1.), Vector(-1., 1., 0.).normal(), Vector(-1., 0., 1.).normal(), Vector(0., 1., -1.).normal(), Vector(1., -1., 1.).normal()] return vs[:-n] else: return [randomDirection() for i in range(n)] # # Random rotation. # def randomRotation(max_angle = N.pi): """ :param max_angle: the upper limit for the rotation angle :type max_angle: float :returns: a random rotation with a uniform axis distribution and angles drawn from a uniform distribution between -max_angle and max_angle. :rtype: Scientific.Geometry.Transformations.Rotation """ return Rotation(randomDirection(), uniform(-max_angle, max_angle)) # # Random velocity (gaussian) # def randomVelocity(temperature, mass): """ :param temperature: the temperature defining a Maxwell distribution :type temperature: float :param mass: the mass of a particle :type mass: float :returns: a random velocity vector for a particle of a given mass at a given temperature :rtype: Scientific.Geometry.Vector """ sigma = N.sqrt((temperature*Units.k_B)/(mass*Units.amu)) return Vector(gaussian(0., sigma, (3,))) # # Random ParticleVector (gaussian) # def randomParticleVector(universe, width): """ :param universe: a universe :type universe: :class:`~MMTK.Universe.Universe` :param width: the width (standard deviation) of a Gaussian distribution :type width: float :returns: a set of vectors drawn from a Gaussian distribution with a given width centered around zero. :rtype: :class:`~MMTK.ParticleProperties.ParticleVector` """ data = gaussian(0., 0.577350269189*width, (universe.numberOfPoints(), 3)) return ParticleProperties.ParticleVector(universe, data) MMTK-2.7.9/MMTK/Skeleton.py0000644000076600000240000001223712117632051015622 0ustar hinsenstaff00000000000000# This module handles the skeleton descriptions stored in trajectory files. # # Written by Konrad Hinsen # import MMTK import MMTK.Environment import MMTK.ForceFields import copy, sys, types # # Atoms # class A: def __init__(self, name, index, type = None): self.name = name self.index = index self.type = type def make(self, info, conf = None): atom = MMTK.Atom(self.type, name = self.name) self.assignIndex(atom, info, conf) return atom def assignIndex(self, atom, info, conf): atom.setIndex(self.index) info[self.index] = atom if conf is not None and self.index is not None: atom.setPosition(MMTK.Vector(conf[self.index])) # # Composite chemical objects # class Composite: def __init__(self, name, list, type = None, **kwargs): self.name = name self.list = list self.type = type self.kwargs = kwargs def make(self, info, conf = None): object = self._class(self.type, name=self.name) for sub in self.list: sub.assignIndex(getattr(object, sub.name), info, conf) if self.kwargs.has_key('dc'): for a1, a2, d in self.kwargs['dc']: object.addDistanceConstraint(info[a1], info[a2], d) return object def assignIndex(self, object, info, conf): for sub in self.list: sub.assignIndex(getattr(object, sub.name), info, conf) class G(Composite): pass class M(Composite): _class = MMTK.Molecule class C(Composite): _class = MMTK.Complex class AC(Composite): def make(self, info, conf = None): atoms = map(lambda a, i=info, c=conf: a.make(i, c), self.list) return MMTK.AtomCluster(atoms, name = self.name) #class X(Composite): # _class = MMTK.Crystal class S(Composite): def make(self, info, conf = None): import MMTK.Proteins n_residues = len(self.type)/3 residues = [self.type[3*i:3*i+3] for i in range(n_residues)] self.kwargs['name'] = self.name chain = apply(MMTK.Proteins.PeptideChain, (residues,), self.kwargs) for i in range(len(self.list)): self.list[i].assignIndex(chain[i], info, conf) chain[i].name = self.list[i].name return chain class N(Composite): def make(self, info, conf = None): import MMTK.NucleicAcids n_residues = len(self.type)/3 residues = [self.type[3*i:3*i+3].strip() for i in range(n_residues)] self.kwargs['name'] = self.name chain = apply(MMTK.NucleicAcids.NucleotideChain, (residues,), self.kwargs) for i in range(len(self.list)): self.list[i].assignIndex(chain[i], info, conf) return chain # # Collections and universes # class c: def __init__(self, creation, objects): self.creation = creation self.objects = objects def make(self, info, conf = None): collection = _evalString(self.creation) attr = None for o in self.objects: if isinstance(o, basestring): attr = o elif attr: setattr(collection, attr, o.make(info, conf)) attr = None else: collection.addObject(o.make(info, conf)) return collection # # Objects constructed from a list of other objects (e.g. proteins) # class l: def __init__(self, class_name, name, objects): self.class_name = class_name self.objects = objects self.name = name def make(self, info, conf = None): import MMTK.Proteins classes = {'Protein': MMTK.Proteins.Protein} return classes[self.class_name] \ (map(lambda o, i=info, c=conf: o.make(i, c), self.objects), name = self.name) # # Objects without subobjects # class o: def __init__(self, creation): self.creation = creation def make(self, info, conf = None): return _evalString(self.creation) # # Evaluate description string # In case of a NameError, suppose the missing name is the name of a # module, import that module, and try again. In case of an AttributeError, # suppose that the missing attribute is a subpackage, import that subpackage, # and try again. # def _evalString(description): local = {} namespace = copy.copy(vars(MMTK)) namespace['MMTK'] = MMTK imported = ['MMTK', 'MMTK.ForceFields'] done = False while not done: try: o = eval(description, namespace, local) done = True except NameError, exception: name = str(exception).split("'")[1] __import__(name) namespace[name] = sys.modules[name] imported.append(name) except AttributeError, exception: if str(exception).split("'")[1] == "module": name = str(exception).split("'")[3] for m in imported: try: module_name = "%s.%s" % (m, name) __import__(module_name) imported.append(module_name) except ImportError: pass else: raise return o MMTK-2.7.9/MMTK/Solvation.py0000644000076600000240000001513512041510260016005 0ustar hinsenstaff00000000000000# This module contains code for solvation. # # Written by Konrad Hinsen # """ Solvation of solute molecules """ __docformat__ = 'restructuredtext' from MMTK import ChemicalObjects, Units, Universe from MMTK.MolecularSurface import surfaceAndVolume from MMTK.Minimization import SteepestDescentMinimizer from MMTK.Dynamics import VelocityVerletIntegrator, VelocityScaler, \ TranslationRemover, RotationRemover from MMTK.Trajectory import Trajectory, TrajectoryOutput, SnapshotGenerator, \ StandardLogOutput import copy # # Calculate the number of solvent molecules # def numberOfSolventMolecules(universe, solvent, density): """ :param universe: a finite universe :type universe: :class:`~MMTK.Universe.Universe` :param solvent: a molecule, or the name of a molecule in the database :type solvent: :class:`~MMTK.ChemicalObjects.Molecule` or str :param density: the density of the solvent (amu/nm**3) :type density: float :returns: the number of solvent molecules that must be added to the universe, in addition to whatever it already contains, to obtain the given solvent density. :rtype: int """ if isinstance(solvent, basestring): solvent = ChemicalObjects.Molecule(solvent) cell_volume = universe.cellVolume() if cell_volume is None: raise TypeError("universe volume is undefined") solute_volume = 0. for o in universe._objects: solute_volume = solute_volume + surfaceAndVolume(o)[1] return int(round(density*(cell_volume-solute_volume)/solvent.mass())) # # Add solvent to a universe containing solute molecules. # def addSolvent(universe, solvent, density, scale_factor=4.): """ Scales up the universe and adds as many solvent molecules as are necessary to obtain the specified solvent density, taking account of the solute molecules that are already present in the universe. The molecules are placed at random positions in the scaled-up universe, but without overlaps between any two molecules. :param universe: a finite universe :type universe: :class:`~MMTK.Universe.Universe` :param solvent: a molecule, or the name of a molecule in the database :type solvent: :class:`~MMTK.ChemicalObjects.Molecule` or str :param density: the density of the solvent (amu/nm**3) :type density: float :param scale_factor: the factor by which the initial universe is expanded before adding the solvent molecules :type scale_factor: float """ # Calculate number of solvent molecules and universe size if isinstance(solvent, basestring): solvent = ChemicalObjects.Molecule(solvent) cell_volume = universe.cellVolume() if cell_volume is None: raise TypeError("universe volume is undefined") solute = copy.copy(universe._objects) solute_volume = 0. excluded_regions = [] for o in solute: solute_volume = solute_volume + surfaceAndVolume(o)[1] excluded_regions.append(o.boundingSphere()) n_solvent = int(round(density*(cell_volume-solute_volume)/solvent.mass())) solvent_volume = n_solvent*solvent.mass()/density cell_volume = solvent_volume + solute_volume universe.translateBy(-solute.position()) universe.scaleSize((cell_volume/universe.cellVolume())**(1./3.)) # Scale up the universe and add solvent molecules at random positions universe.scaleSize(scale_factor) universe.scale_factor = scale_factor for i in range(n_solvent): m = copy.copy(solvent) m.translateTo(universe.randomPoint()) while True: s = m.boundingSphere() collision = False for region in excluded_regions: if (s.center-region.center).length() < s.radius+region.radius: collision = True break if not collision: break m.translateTo(universe.randomPoint()) universe.addObject(m) excluded_regions.append(s) # # Shrink the universe to its final size # def shrinkUniverse(universe, temperature=300.*Units.K, trajectory=None, scale_factor=0.95): """ Shrinks the universe, which must have been scaled up by :class:`~MMTK.Solvation.addSolvent`, back to its original size. The compression is performed in small steps, in between which some energy minimization and molecular dynamics steps are executed. The molecular dynamics is run at the given temperature, and an optional trajectory can be specified in which intermediate configurations are stored. :param universe: a finite universe :type universe: :class:`~MMTK.Universe.Universe` :param temperature: the temperature at which the Molecular Dynamics steps are run :type temperature: float :param trajectory: the trajectory in which the progress of the shrinking procedure is stored, or a filename :type trajectory: :class:`~MMTK.Trajectory.Trajectory` or str :param scale_factor: the factor by which the universe is scaled at each reduction step :type scale_factor: float """ # Set velocities and initialize trajectory output universe.initializeVelocitiesToTemperature(temperature) if trajectory is not None: if isinstance(trajectory, basestring): trajectory = Trajectory(universe, trajectory, "w", "solvation protocol") close_trajectory = True else: close_trajectory = False actions = [TrajectoryOutput(trajectory, ["configuration"], 0, None, 1)] snapshot = SnapshotGenerator(universe, actions=actions) snapshot() # Do some minimization and equilibration minimizer = SteepestDescentMinimizer(universe, step_size = 0.05*Units.Ang) actions = [VelocityScaler(temperature, 0.01*temperature, 0, None, 1), TranslationRemover(0, None, 20)] integrator = VelocityVerletIntegrator(universe, delta_t = 0.5*Units.fs, actions = actions) for i in range(5): minimizer(steps = 40) integrator(steps = 200) # Scale down the system in small steps i = 0 while universe.scale_factor > 1.: if trajectory is not None and i % 1 == 0: snapshot() i = i + 1 step_factor = max(scale_factor, 1./universe.scale_factor) for object in universe: object.translateTo(step_factor*object.position()) universe.scaleSize(step_factor) universe.scale_factor = universe.scale_factor*step_factor for i in range(3): minimizer(steps = 10) integrator(steps = 50) del universe.scale_factor if trajectory is not None: snapshot() if close_trajectory: trajectory.close() MMTK-2.7.9/MMTK/Subspace.py0000644000076600000240000003227012117632051015602 0ustar hinsenstaff00000000000000# This module implements subspaces for motion analysis etc. # # Written by Konrad Hinsen # """ Linear subspaces for infinitesimal motions This module implements subspaces for infinitesimal (or finite small-amplitude) atomic motions. They can be used in normal mode calculations or for analyzing complex motions. For an explanation, see: | K. Hinsen and G.R. Kneller | Projection methods for the analysis of complex motions in macromolecules | Mol. Sim. 23:275-292 (2000) """ __docformat__ = 'restructuredtext' from MMTK import Utility, ParticleProperties from Scientific.Geometry import Vector, ex, ey, ez from Scientific import N import copy # # Import LAPACK routines # try: array_package = N.package except AttributeError: array_package = "Numeric" dgesdd = None try: if array_package == "Numeric": from lapack_lite import dgesdd, LapackError else: from numpy.linalg.lapack_lite import dgesdd, LapackError except ImportError: pass if dgesdd is None: try: # PyLAPACK from lapack_dge import dgesdd except ImportError: pass if dgesdd: n = 1 array = N.zeros((n, n), N.Float) sv = N.zeros((n,), N.Float) u = N.zeros((n, n), N.Float) vt = N.zeros((n, n), N.Float) work = N.zeros((1,), N.Float) int_types = [N.Int, N.Int8, N.Int16, N.Int32] try: int_types.append(N.Int64) int_types.append(N.Int128) except AttributeError: pass for int_type in int_types: iwork = N.zeros((1,), int_type) try: dgesdd('S', n, n, array, n, sv, u, n, vt, n, work, -1, iwork, 0) break except LapackError: pass del n, array, sv, u, vt, work, iwork, int_types del array_package # # A set of particle vectors that define a subspace # class ParticleVectorSet(object): def __init__(self, universe, data): self.universe = universe if type(data) == N.arraytype: self.n = data.shape[0] self.array = data else: self.n = data self.array = N.zeros((self.n, universe.numberOfAtoms(), 3), N.Float) def __len__(self): return self.n def __getitem__(self, i): if i >= self.n: raise IndexError return ParticleProperties.ParticleVector(self.universe, self.array[i]) class Subspace(object): """ Subspace of infinitesimal atomic motions """ def __init__(self, universe, vectors): """ :param universe: the universe for which the subspace is created :type universe: :class:`~MMTK.Universe.Universe` :param vectors: a list of :class:`~MMTK.ParticleProperties.ParticleVector` objects that define the subspace. They need not be orthogonal or linearly independent. :type vectors: list """ self.universe = universe self.vectors = vectors self._basis = None def __add__(self, other): return Subspace(self.vectors+other.vectors) def __len__(self): return len(self.vectors) def __getitem__(self, item): return self.vectors[item] def getBasis(self): """ Construct a basis for the subspace by orthonormalization of the input vectors using Singular Value Decomposition. The basis consists of a sequence of :class:`~MMTK.ParticleProperties.ParticleVector` objects that are orthonormal in configuration space. :returns: the basis """ if self._basis is None: basis = N.array([v.array for v in self.vectors], N.Float) shape = basis.shape nvectors = shape[0] natoms = shape[1] basis.shape = (nvectors, 3*natoms) sv = N.zeros((min(nvectors, 3*natoms),), N.Float) min_n_m = min(3*natoms, nvectors) vt = N.zeros((nvectors, min_n_m), N.Float) work = N.zeros((1,), N.Float) iwork = N.zeros((8*min_n_m,), int_type) if 3*natoms >= nvectors: result = dgesdd('O', 3*natoms, nvectors, basis, 3*natoms, sv, basis, 3*natoms, vt, min_n_m, work, -1, iwork, 0) work = N.zeros((int(work[0]),), N.Float) result = dgesdd('O', 3*natoms, nvectors, basis, 3*natoms, sv, basis, 3*natoms, vt, min_n_m, work, work.shape[0], iwork, 0) u = basis else: u = N.zeros((min_n_m, 3*natoms), N.Float) result = dgesdd('S', 3*natoms, nvectors, basis, 3*natoms, sv, u, 3*natoms, vt, min_n_m, work, -1, iwork, 0) work = N.zeros((int(work[0]),), N.Float) result = dgesdd('S', 3*natoms, nvectors, basis, 3*natoms, sv, u, 3*natoms, vt, min_n_m, work, work.shape[0], iwork, 0) if result['info'] != 0: raise ValueError('Lapack SVD: ' + `result['info']`) svmax = N.maximum.reduce(sv) nvectors = N.add.reduce(N.greater(sv, 1.e-10*svmax)) u = u[:nvectors] u.shape = (nvectors, natoms, 3) self._basis = ParticleVectorSet(self.universe, u) return self._basis def projectionOf(self, vector): """ :param vector: a particle vector :type vector: :class:`~MMTK.ParticleProperties.ParticleVector` :returns: the projection of the vector onto the subspace. """ vector = vector.array basis = self.getBasis().array p = N.zeros(vector.shape, N.Float) for bv in basis: N.add(N.add.reduce(N.ravel(bv*vector))*bv, p, p) return ParticleProperties.ParticleVector(self.universe, p) def projectionComplementOf(self, vector): """ :param vector: a particle vector :type vector: :class:`~MMTK.ParticleProperties.ParticleVector` :returns: the projection of the vector onto the orthogonal complement of the subspace. """ return vector - self.projectionOf(vector) def complement(self): """ :returns: the orthogonal complement subspace :rtype: :class:`~MMTK.Subspace.Subspace` """ basis = [] for i in range(self.universe.numberOfAtoms()): for e in [ex, ey, ez]: p = ParticleProperties.ParticleVector(self.universe) p[i] = e basis.append(self.projectionComplementOf(p)) return Subspace(self.universe, basis) class LinkedRigidBodyMotionSubspace(Subspace): """ Subspace for the motion of linked rigid bodies This class describes the same subspace as RigidBodyMotionSubspace, and is used by the latter. For collections of several chains of linked rigid bodies, RigidBodyMotionSubspace is more efficient. """ def __init__(self, universe, rigid_bodies): """ :param universe: the universe for which the subspace is created :type universe: :class:`~MMTK.Universe.Universe` :param rigid_bodies: a list or set of rigid bodies with some common atoms """ ex_ey_ez = [Vector(1.,0.,0.), Vector(0.,1.,0.), Vector(0.,0.,1.)] # Constructs # 1) a list of vectors describing the rigid-body motions of each # rigid body as if it were independent. # 2) a list of pair-distance constraint vectors for all pairs of # atoms inside a rigid body. # The LRB subspace is constructed from the projections of the # first set of vectors onto the orthogonal complement of the # subspace generated by the second set of vectors. vectors = [] c_vectors = [] for rb in rigid_bodies: atoms = rb.atomList() for d in ex_ey_ez: v = ParticleProperties.ParticleVector(universe) for a in atoms: v[a] = d vectors.append(v) if len(atoms) > 1: center = rb.centerOfMass() iv = len(vectors)-3 for d in ex_ey_ez: v = ParticleProperties.ParticleVector(universe) for a in atoms: v[a] = d.cross(a.position()-center) for vt in vectors[iv:]: v -= v.dotProduct(vt)*vt if v.dotProduct(v) > 0.: vectors.append(v) for a1, a2 in Utility.pairs(atoms): distance = universe.distanceVector(a1.position(), a2.position()) v = ParticleProperties.ParticleVector(universe) v[a1] = distance v[a2] = -distance c_vectors.append(v) if c_vectors: constraints = Subspace(universe, c_vectors) vectors = [constraints.projectionComplementOf(v) for v in vectors] Subspace.__init__(self, universe, vectors) class RigidMotionSubspace(Subspace): """ Subspace of rigid-body motions A rigid-body motion subspace is the subspace which contains the rigid-body motions of any number of chemical objects. """ def __init__(self, universe, objects): """ :param universe: the universe for which the subspace is created :type universe: :class:`~MMTK.Universe.Universe` :param objects: a sequence of objects whose rigid-body motion is included in the subspace """ if not Utility.isSequenceObject(objects): objects = [objects] else: objects = copy.copy(objects) # Identify connected sets of linked rigid bodies and remove # them from the plain rigid body list. atom_map = {} for o in objects: for a in o.atomIterator(): am = atom_map.get(a, []) am.append(o) atom_map[a] = am rb_map = {} for rbs in atom_map.values(): if len(rbs) > 1: for rb in rbs: rb_map[rb] = rb_map.get(rb, frozenset()) \ .union(frozenset(rbs)) for rb in rb_map.keys(): objects.remove(rb) while True: changed = False for rbs in rb_map.values(): for rb in rbs: s = rb_map[rb] rb_map[rb] = s.union(rbs) if s != rb_map[rb]: changed = True if not changed: break lrbs = frozenset(rb_map.values()) # Generate the subspace vectors for the isolated rigid bodies. ex_ey_ez = [Vector(1.,0.,0.), Vector(0.,1.,0.), Vector(0.,0.,1.)] vectors = [] for o in objects: rb_atoms = o.atomList() for d in ex_ey_ez: v = ParticleProperties.ParticleVector(universe) for a in rb_atoms: v[a] = d vectors.append(v/N.sqrt(len(rb_atoms))) if len(rb_atoms) > 1: center = o.centerOfMass() iv = len(vectors)-3 for d in ex_ey_ez: v = ParticleProperties.ParticleVector(universe) for a in rb_atoms: v[a] = d.cross(a.position()-center) for vt in vectors[iv:]: v -= v.dotProduct(vt)*vt norm_sq = N.sqrt(v.dotProduct(v)) if norm_sq > 0.: vectors.append(v/norm_sq) # Generate the subspace vectors for the linked rigid bodies. for lrb in lrbs: lrb_ss = LinkedRigidBodyMotionSubspace(universe, lrb) for v in lrb_ss.getBasis(): vectors.append(v) Subspace.__init__(self, universe, vectors) # The vector set is already orthonormal by construction, # so we can skip the lengthy SVD procedure. self._basis = ParticleVectorSet(universe, len(vectors)) for i in range(len(vectors)): self._basis.array[i] = vectors[i].array class PairDistanceSubspace(Subspace): """ Subspace of pair-distance motions A pair-distance motion subspace is the subspace which contains the relative motions of any number of atom pairs along their distance vector, e.g. bond elongation between two bonded atoms. """ def __init__(self, universe, atom_pairs): """ :param universe: the universe for which the subspace is created :type universe: :class:`~MMTK.Universe.Universe` :param atom_pairs: a sequence of atom pairs whose distance-vector motion is included in the subspace """ vectors = [] for atom1, atom2 in atom_pairs: v = ParticleProperties.ParticleVector(universe) distance = atom1.position()-atom2.position() v[atom1] = distance v[atom2] = -distance vectors.append(v) Subspace.__init__(self, universe, vectors) MMTK-2.7.9/MMTK/surfm.py0000755000076600000240000001740311662461640015205 0ustar hinsenstaff00000000000000# # Copyright 2000 by Peter McCluskey (pcm@rahul.net). # You may do anything you want with it, provided this notice is kept intact. # """ surface_atoms(atoms, solvent_radius = 0., point_density = 258, ret_fmt = 0) point_density must be 2**(2*N) + 2, where N > 1 (258 and 1026 seem best). Returns a dictionary with an item for each input atom. These choices for ret_fmt specify what the dictionary will hold for each atom: 1: area 2: (area, volume) 3: (area, volume, points) 4: (area, volume, points, dir_tuple) points is a list of 3-tuples describing coordinates of points on a solvent-accesible surface (zero to point_density points per atom). dir_tuple is a 3-tuple giving crude estimate of the direction which is locally "up", i.e. normal to the surface. It is calculated by comparing the atom's position with the average position of an atom's accesible surface points. The algorithm used is based on the method described in this paper: Eisenhaber, Frank, et al. "The Double Cubic Lattice Method: Efficient Approaches to Numerical Integration of Surface Area and Volume and to Dot Surface Contouring of Molecular Assemblies", Journal of Computational Chemistry, Vol. 16, pp 273-284 (1995). I have taken a few shortcuts which probably make it a bit less accurate than what the paper describes (in particular, I used a simple tesselation algorithm without fully understanding the one described in the paper). """ import sys, math try: import MMTK_surface have_c_code = 1 except ImportError, msg: import tess have_c_code = 0 class NeighborList: """ This class is designed to efficiently find lists of atoms which are within a distance "radius" of each other. Constructor: NeighborList(|atoms|, |radius|, |atom_data|) Arguments: |atoms| - list of MMTK Atom |radius| - max distance between neighboring atoms |atom_data| - data returned from get_atom_data """ def __init__(self, atoms, radius, atom_data): boxes = {} self.box_size = 2*radius for i in range(len(atoms)): f = self.box_size pos = atom_data[i] key = '%d %d %d' % (int(math.floor(pos[0]/f)), int(math.floor(pos[1]/f)), int(math.floor(pos[2]/f))) if boxes.has_key(key): boxes[key].append(i) else: boxes[key] = [i] self.boxes = boxes self.atoms = atoms self.atom_data = atom_data def __len__(self): return len(self.atoms) def __getitem__(self, i): """Returns a list of tuples describing the neighbors of the ith atom in the atom list. Each tuple has the index of the atom which neighbors atom i, followed by the square of the distance between atoms. """ boxes = self.boxes if have_c_code: return MMTK_surface.FindNeighborsOfAtom(self.atoms, i, boxes, self.box_size, self.atom_data) max_dist_2 = self.box_size**2 pos1 = self.atom_data[i] f = self.box_size key_tup = (int(math.floor(pos1[0]/f)), int(math.floor(pos1[1]/f)), int(math.floor(pos1[2]/f))) nlist = [] for x in (-1, 0, 1): for y in (-1, 0, 1): for z in (-1, 0, 1): key2 = '%d %d %d' % (key_tup[0]+x, key_tup[1]+y, key_tup[2]+z) if boxes.has_key(key2): for i2 in boxes[key2]: if i2 != i: apos = self.atom_data[i2] vaax = apos[0] - pos1[0] vaay = apos[1] - pos1[1] vaaz = apos[2] - pos1[2] d2 = vaax*vaax + vaay*vaay + vaaz*vaaz if d2 > max_dist_2: continue nlist.append((i2, d2)) return nlist def get_atom_data(atoms, solvent_radius): atom_data = [None] * len(atoms) max_rad = 0 sumx = 0 sumy = 0 sumz = 0 for i in range(len(atoms)): a = atoms[i] vdw = a.vdW_radius pos1 = a.position() atom_data[i] = (pos1[0], pos1[1], pos1[2], vdw + solvent_radius) sumx = sumx + atom_data[i][0] sumy = sumy + atom_data[i][1] sumz = sumz + atom_data[i][2] max_rad = max(max_rad, vdw + solvent_radius) return (max_rad, atom_data, (sumx/len(atoms), sumy/len(atoms), sumz/len(atoms))) def _xlate_results(points1, points_unit, point_density, tot_rad, pos1, ret_fmt, cent): area = 4*math.pi*(tot_rad**2)*len(points1)/point_density if 0: print 'area %6.1f %5.1f %5.3f' \ % (area/(Units.Ang**2), tot_rad/Units.Ang, len(points1)/float(point_density)) if ret_fmt >= 2: sumx = 0 sumy = 0 sumz = 0 for p in points_unit: sumx = sumx + p[0] sumy = sumy + p[1] sumz = sumz + p[2] n = max(1, len(points1)) vconst = 4/3.0*math.pi/point_density dotp1 = (pos1[0] - cent[0])*sumx + (pos1[1] - cent[1])*sumy \ + (pos1[2] - cent[2])*sumz volume = vconst*((tot_rad**2)*dotp1 + (tot_rad**3)*len(points1)) if ret_fmt == 2: return (area, volume) if ret_fmt == 4: grad = (sumx/n, sumy/n, sumz/n) return (area, volume, points1, grad) return (area, volume, points1) else: return area def atom_surf(nbors, i, atom_data, pos1, tot_rad, point_density, tess1, \ ret_unit_points = 1): if have_c_code: return MMTK_surface.surface1atom(nbors, i, atom_data, pos1, tot_rad, \ point_density, ret_unit_points) else: rad1sq = tot_rad*tot_rad rad1_2 = 2*tot_rad data = [] for (index, d2) in nbors[i]: apos = atom_data[index] vaax = apos[0] - pos1[0] vaay = apos[1] - pos1[1] vaaz = apos[2] - pos1[2] thresh = (d2 + rad1sq - (apos[3]**2)) / rad1_2 data.append((vaax, vaay, vaaz, thresh)) points_unit = [] points1 = [] for pt1 in tess1: buried = 0 for (vx, vy, vz, thresh) in data: if vx*pt1[0] + vy*pt1[1] + vz*pt1[2] > thresh: buried = 1 break if buried == 0: points1.append((tot_rad*pt1[0] + pos1[0], tot_rad*pt1[1] + pos1[1], tot_rad*pt1[2] + pos1[2])) points_unit.append(pt1) return (points1, points_unit) def surface_atoms(atoms, solvent_radius = 0., point_density = 258, ret_fmt = 0): surf_points = {} (max_rad, atom_data, cent) = get_atom_data(atoms, solvent_radius) if have_c_code: tess1 = None else: tess1 = tess.tesselate(point_density) if len(tess1) != point_density: raise ValueError('point_density invalid, must be 2**(2*N) + 2, ' + 'where N > 1') nbors = NeighborList(atoms, solvent_radius + max_rad, atom_data) for i in range(len(atoms)): a1 = atoms[i] pos1 = atom_data[i] tot_rad = pos1[3] (points1, points_unit) = atom_surf(nbors, i, atom_data, pos1, tot_rad, point_density, tess1, ret_fmt >= 2) surf_points[a1] = _xlate_results(points1, points_unit, point_density, tot_rad, pos1, ret_fmt, cent) return surf_points MMTK-2.7.9/MMTK/tess.py0000755000076600000240000000505511662461640015027 0ustar hinsenstaff00000000000000# # Copyright 2000 by Peter McCluskey (pcm@rahul.net). # You may do anything you want with it, provided this notice is kept intact. # """ tesselate(num_points) Returns a list of num_points points spread fairly evenly over the unit sphere. The points are 3-tuples of floats. num_points should be 2**(2*N) + 2, where N > 1. The C code in MMTK_surfacemodule.c does the same thing faster, so this code should normally be used only by people who can't compile C code. """ from math import sqrt, atan2, pi def _normalize(x, y, z): length = sqrt(x*x + y*y + z*z) return (x/length, y/length, z/length) def tess_triangle(pts, npts, r, pt_dict): v0 = pts[0] v1 = pts[1] v2 = pts[2] new_pt0 = _normalize(v0[0] + v1[0], v0[1] + v1[1], v0[2] + v1[2]) new_pt1 = _normalize(v1[0] + v2[0], v1[1] + v2[1], v1[2] + v2[2]) new_pt2 = _normalize(v2[0] + v0[0], v2[1] + v0[1], v2[2] + v0[2]) n4 = npts/4 if n4 <= 3: # other 2 points in pts will be created as pts[0] of # another triangle, except for top level points for p in (pts[0], new_pt0, new_pt1, new_pt2): key = '%.6f,%.6f,%.6f' % p if not pt_dict.has_key(key): pt_dict[key] = p r.append(p) else: tess_triangle((pts[0], new_pt0, new_pt2), n4, r, pt_dict) tess_triangle((new_pt0, pts[1], new_pt1), n4, r, pt_dict) tess_triangle((new_pt0, new_pt1, new_pt2), n4, r, pt_dict) tess_triangle((new_pt1, pts[2], new_pt2), n4, r, pt_dict) def tesselate(num_points): north = (0.0, 0.0, 1.0) south = (0.0, 0.0, -1.0) noon = (1.0, 0.0, 0.0) night = (-1.0, 0.0, 0.0) dawn = (0.0, 1.0, 0.0) dusk = (0.0, -1.0, 0.0) npts = int((num_points - 2)/4) pt_dict = {} r = [] for p in [north, south, noon, night, dusk, dawn]: key = '%.6f,%.6f,%.6f' % p pt_dict[key] = p r.append(p) tess_triangle((north, dawn, noon), npts, r, pt_dict) tess_triangle((north, noon, dusk), npts, r, pt_dict) tess_triangle((north, dusk, night), npts, r, pt_dict) tess_triangle((north, night, dawn), npts, r, pt_dict) tess_triangle((south, dawn, night), npts, r, pt_dict) tess_triangle((south, night, dusk), npts, r, pt_dict) tess_triangle((south, dusk, noon), npts, r, pt_dict) tess_triangle((south, noon, dawn), npts, r, pt_dict) return r if __name__ == '__main__': r = tesselate(1026) print len(r), 'points' if len(r) < 1000: for pt in r: print '%6.1f %6.1f %6.1f' % (pt[0], pt[1], pt[2]) MMTK-2.7.9/MMTK/ThreadManager.py0000644000076600000240000000341111662461640016542 0ustar hinsenstaff00000000000000# Thread manager for long-running threads (MD etc.) # # Written by Konrad Hinsen # try: import threading if not hasattr(threading, 'Thread'): threading = None except ImportError: threading = None _threads = [] def registerThread(thread): _threads.append(thread) _cleanup() def activeThreads(): _cleanup() return _threads def waitForThreads(): while _threads: _threads[0].join() _cleanup() def _cleanup(): i = 0 while i < len(_threads): if _threads[i].isAlive(): i = i + 1 else: del _threads[i] if threading: import MMTK_state_accessor class TrajectoryGeneratorThread(threading.Thread): def __init__(self, universe, target, args, state_accessor): threading.Thread.__init__(self, group = None, name = 'MMTK thread', target = target, args = args) self.universe = universe self.function = (target, args) self.state_accessor = state_accessor self.start() registerThread(self) def run(self): target, args = self.function self.universe.acquireConfigurationChangeLock() target(*args) self.universe.releaseConfigurationChangeLock() def copyState(self): if self.state_accessor is None: return None else: return self.state_accessor.copyState() else: # a fake thread class that raises an exception class TrajectoryGeneratorThread(object): def __init__(self, universe, target, args, kwargs): raise OSError("background processing not available") MMTK-2.7.9/MMTK/Tk/0000755000076600000240000000000012156626572014053 5ustar hinsenstaff00000000000000MMTK-2.7.9/MMTK/Tk/__init__.py0000644000076600000240000000002311662461640016151 0ustar hinsenstaff00000000000000# Package MMTK.Tk MMTK-2.7.9/MMTK/Tk/ProteinVisualization.py0000644000076600000240000000741611662461640020631 0ustar hinsenstaff00000000000000# Display proteins on a TkVisualizationCanvas from Scientific import N as Numeric class ProteinBackboneGraphics: def __init__(self, protein, conf = None, color = None): self.protein = protein if conf is None: conf = protein.universe().configuration() self.points = conf.array if color is None: color = {} for a in protein.atomList(): color[a.index] = a.color elif type(color) == type(''): color_name = color color = {} for a in protein.atomList(): color[a.index] = color_name self.colors = color segments = [] for chain in protein: segment = [] segments.append(segment) last = None for residue in chain: atom = residue.peptide.C_alpha if last is None: segment.append(atom.index) last = atom else: d = (last.position()-atom.position()).length() if d < 0.4: segment.append(atom.index) last = atom else: last = None segment = [] segments.append(segment) if len(segments[-1]) == 0: del segments[-1] self.segments = segments def project(self, axis, plane): self.depth = Numeric.dot(self.points, axis) self.projection = Numeric.dot(self.points, plane) def boundingBoxPlane(self): return Numeric.minimum.reduce(self.projection), \ Numeric.maximum.reduce(self.projection) def scaleAndShift(self, scale=1, shift=0): self.scaled = scale*self.projection+shift def lines(self): lines = [] depths = [] for segment in self.segments: for i in range(len(segment)-1): i1 = segment[i] i2 = segment[i+1] p1 = self.scaled[i1] p2 = self.scaled[i2] c1 = self.colors[i1] c2 = self.colors[i2] depths.append(min(self.depth[i1], self.depth[i2])) if c1 == c2: lines.append((p1[0], p1[1], p2[0], p2[1], c1, 1)) else: pc = 0.5*(p1+p2) lines.append((p1[0], p1[1], pc[0], pc[1], c1, 1)) lines.append((pc[0], pc[1], p2[0], p2[1], c2, 1)) depths.append(depths[-1]) return lines, depths if __name__ == '__main__': pdbfile = '~/proteins/PDB/crambin.pdb' from mmtk import * from Tkinter import * from TkVisualizationCanvas import VisualizationCanvas universe = InfiniteUniverse() sequence = PDBFile(pdbfile).readSequenceWithConfiguration() chains = [] for chain in sequence: model = PeptideChain(chain, model='calpha') chains.append(model) universe.protein = Protein(chains) clist = ['red', 'green', 'blue'] colors = {} universe.configuration() for atom in universe.atomList(): colors[atom.index] = clist[atom.index % len(clist)] graphics = ProteinBackboneGraphics(universe.protein, None, colors) window = Frame() window.pack(fill=BOTH, expand=YES) c = VisualizationCanvas(window, "100m", "100m", relief=SUNKEN, border=2) c.pack(side=TOP, fill=BOTH, expand=YES) c.draw(graphics) Button(window, text='Draw', command=lambda o=graphics: c.draw(o)).pack(side=LEFT) Button(window, text='Clear', command=c.clear).pack(side=LEFT) Button(window, text='Redraw', command=c.redraw).pack(side=LEFT) Button(window, text='Quit', command=window.quit).pack(side=RIGHT) window.mainloop() MMTK-2.7.9/MMTK/Tools/0000755000076600000240000000000012156626572014575 5ustar hinsenstaff00000000000000MMTK-2.7.9/MMTK/Tools/__init__.py0000644000076600000240000000001511662461640016674 0ustar hinsenstaff00000000000000# Empty file MMTK-2.7.9/MMTK/Tools/TrajectoryViewer/0000755000076600000240000000000012156626572020105 5ustar hinsenstaff00000000000000MMTK-2.7.9/MMTK/Tools/TrajectoryViewer/__init__.py0000644000076600000240000000001511662461640022204 0ustar hinsenstaff00000000000000# Empty file MMTK-2.7.9/MMTK/Tools/TrajectoryViewer/manager.py0000644000076600000240000000351311662461640022065 0ustar hinsenstaff00000000000000# Command script for managing trajectory files in a networked environment. # Requires that a TrajectoryServer is running on each participating machine. # # Written by Konrad Hinsen # import Pyro.core, Pyro.naming, Pyro.errors from TrajectoryManager import TrajectoryManager import os, socket, string, sys manager = TrajectoryManager() def show_usage(): sys.stderr.write('Usage: tmanager command [arguments]\n') sys.stderr.write(' Commands:\n') sys.stderr.write(' list\n') sys.stderr.write(' status\n') sys.stderr.write(' publish filename\n') sys.stderr.write(' unpublish filename\n') sys.stderr.write(' servers\n') sys.stderr.write(' shutdown\n') sys.exit(1) def list_trajectories(): files = manager.trajectoryList() for file in files: sys.stdout.write(file+'\n') sys.stdout.flush() def trajectory_status(): files = manager.trajectoryList() for file in files: inspector = manager.trajectoryInspector(file) inspector.reopen() sys.stdout.write('%s\n %d steps\n' % (file, inspector.numberOfSteps())) sys.stdout.flush() def list_servers(): servers = manager.serverList() for server in servers: sys.stdout.write(server+'\n') sys.stdout.flush() def quit(): manager = find_manager() if manager is None: return manager.stop() commands = {'list': list_trajectories, 'status': trajectory_status, 'publish': manager.publish, 'unpublish': manager.unpublish, 'servers': list_servers, 'shutdown': manager.stopServer} if len(sys.argv) < 2: show_usage() try: function = commands[sys.argv[1]] except KeyError: show_usage() try: apply(function, tuple(sys.argv[2:])) except TypeError: show_usage() MMTK-2.7.9/MMTK/Tools/TrajectoryViewer/README0000644000076600000240000000173411662461640020764 0ustar hinsenstaff00000000000000This directory contains a useful tool for inspecting trajectories. Just type python TrajectoryViewer.py some_trajectory.nc and play around with the menus. Animation requires an external viewer that supports animation and which is supported by MMTK. Currently only VMD and XMol meet those requirements. There is a rather experimental support for inspecting remote trajectories, i.e. trajectories that reside on other machines. I use this on a cluster of Linux PCs and haven't tested it on anything else. To use this feature, you must have Pyro, which is available at http://www.xs4all.nl/~asz00557/ap/pyro.html You must configure Pyro properly on all the machines and start a Pyro nameserver before you can use the remote trajectory facility, which can be controlled by the script manager.py. After a trajectory has been published (this must be done on the machine where it resides), it can be accessed from all other machines that have access to the same Pyro namespace. MMTK-2.7.9/MMTK/Tools/TrajectoryViewer/test.py0000644000076600000240000000101711662461640021427 0ustar hinsenstaff00000000000000import Pyro.core, Pyro.naming import socket, string Pyro.core.initClient(0) locator = Pyro.naming.NameServerLocator() pyro_ns = locator.getNS() def inspector(trajectory): colon = string.find(trajectory, ':') ip_address = socket.gethostbyname(trajectory[:colon]) name = ip_address+':MMTK:trajectory:'+trajectory[colon+1:] uri = pyro_ns.resolve(name) return Pyro.core.getProxyForURI(uri) i = inspector('p-101.para:/scratch/hinsen/lysozyme_equilibration_100bar_300K.nc') step = i.readScalarVariable('step') MMTK-2.7.9/MMTK/Tools/TrajectoryViewer/Tkwindow.py0000644000076600000240000000216411662461640022262 0ustar hinsenstaff00000000000000import Tkinter _root = Tkinter.Tk() _mainloop_started = 0 _open_windows = [] class Tkwindow(Tkinter.Toplevel): def __init__(self, master): if master is None: master = _root else: master.addDependent(self) Tkinter.Toplevel.__init__(self, master) self.protocol("WM_DELETE_WINDOW", self.close) self._dependents = [] def open(self): global _mainloop_started _open_windows.append(self) if not _mainloop_started: _root.withdraw() _root.mainloop() _mainloop_started = 1 def close(self): for window in self._dependents: window.close() self.destroy() if self.master is not _root: self.master.removeDependent(self) _open_windows.remove(self) if not _open_windows: _root.destroy() def addDependent(self, window): self._dependents.append(window) def removeDependent(self, window): self._dependents.remove(window) if __name__ == '__main__': class Test(Tkwindow): def __init__(self, master = None): Tkwindow.__init__(self, master) Tkinter.Button(self, text='Clone', command=self.clone).pack() self.open() def clone(self): Test() Test() MMTK-2.7.9/MMTK/Tools/TrajectoryViewer/TrajectoryInspector.py0000644000076600000240000000514211662461640024470 0ustar hinsenstaff00000000000000# This class provides access to selected data from a trajectory # via an interface that can be accessed remotely by Pyro. It # can be used by programs that wish to inspect a trajectory that # resides on a different machine. # # Written by Konrad Hinsen # from Scientific.IO.NetCDF import NetCDFFile from Scientific import N as Numeric class TrajectoryInspector: def __init__(self, filename): self.filename = filename self.file = NetCDFFile(self.filename, 'r') try: self.block_size = self.file.dimensions['minor_step_number'] except KeyError: self.block_size = 1 self._countSteps() def close(self): self.file.close() def reopen(self): self.file.close() self.file = NetCDFFile(self.filename, 'r') self._countSteps() def _countSteps(self): if self.block_size == 1: self.nsteps = self.file.variables['step'].shape[0] else: blocks = self.file.variables['step'].shape[0] last_block = self.file.variables['step'][blocks-1] unused = Numeric.sum(Numeric.equal(last_block, -2147483647)) self.nsteps = blocks*self.block_size-unused def comment(self): try: return self.file.comment except AttributeError: return '' def history(self): try: return self.file.history except AttributeError: return '' def description(self): return self.file.variables['description'][:].tostring() def numberOfAtoms(self): return self.file.dimensions['atom_number'] def numberOfSteps(self): return self.nsteps def variableNames(self): return self.file.variables.keys() def readScalarVariable(self, name, first=0, last=None, step=1): if last is None: last = self.nsteps variable = self.file.variables[name] if self.block_size > 1: variable = Numeric.ravel(variable[:, :]) return variable[first:last:step] def readConfiguration(self, index): if self.block_size == 1: try: cell = self.file.variables['box_size'][index] except KeyError: cell = None return cell, self.file.variables['configuration'][index] else: i1 = index / self.block_size i2 = index % self.block_size try: cell = self.file.variables['box_size'][i1, :, i2] except KeyError: cell = None return cell, self.file.variables['configuration'][i1, :, :, i2] MMTK-2.7.9/MMTK/Tools/TrajectoryViewer/TrajectoryManager.py0000644000076600000240000000521711662461640024077 0ustar hinsenstaff00000000000000import Pyro.core, Pyro.naming, Pyro.errors import os, socket, string server_script = '~/mmtk2/Tools/TrajectoryViewer/TrajectoryServer.py' class TrajectoryManager: def __init__(self): Pyro.core.initClient(0) locator = Pyro.naming.NameServerLocator() self.pyro_ns = locator.getNS() self.my_name = socket.gethostname() self.my_address = socket.gethostbyname(self.my_name) def server(self): try: uri = self.pyro_ns.resolve(self.my_address+':MMTK:server') except Pyro.errors.NamingError: return None return Pyro.core.getProxyForURI(uri) def startServer(self): import time os.system('python ' + server_script + ' > /dev/null 2>&1 &') time.sleep(2) def stopServer(self): server = self.server() server.stop() def serverList(self): servers = [] for name in self.pyro_ns.status().keys(): fields = string.split(name, ':') if len(fields) == 3 and fields[1] == 'MMTK' \ and fields[2] == 'server': ip_address = fields[0] hostname = socket.gethostbyaddr(ip_address)[0] servers.append(hostname) return servers def publish(self, filename): filename = os.path.abspath(filename) server = self.server() if server is None: self.startServer() server = self.server() if server is None: raise OSError("couldn't start server") server.publishTrajectory(filename) def unpublish(self, filename): filename = os.path.abspath(filename) server = self.server() if server is None: raise OSError("no server available") server.unpublishTrajectory(filename) def trajectoryList(self): trajectories = [] for name in self.pyro_ns.status().keys(): fields = string.split(name, ':') if len(fields) == 4 and fields[1] == 'MMTK' \ and fields[2] == 'trajectory': ip_address = fields[0] filename = fields[3] hostname = socket.gethostbyaddr(ip_address)[0] trajectories.append("%s:%s" % (hostname, filename)) return trajectories def trajectoryInspector(self, name): colon = string.find(name, ':') if colon < 0: raise ValueError("not a remote trajectory name") ip_address = socket.gethostbyname(name[:colon]) pyro_name = ip_address+':MMTK:trajectory:'+name[colon+1:] uri = self.pyro_ns.resolve(pyro_name) return Pyro.core.getProxyForURI(uri) MMTK-2.7.9/MMTK/Tools/TrajectoryViewer/TrajectoryServer.py0000644000076600000240000000366311662461640023776 0ustar hinsenstaff00000000000000# Pyro-based trajectory server # # Written by Konrad Hinsen # from TrajectoryInspector import TrajectoryInspector from Scientific import N as Numeric # to enable pickling of arrays import Pyro.core, Pyro.naming import socket, sys class PyroTrajectoryInspector(Pyro.core.ObjBase, TrajectoryInspector): def __init__(self, filename): Pyro.core.ObjBase.__init__(self) TrajectoryInspector.__init__(self, filename) def close(self): TrajectoryInspector.close(self) PyroDaemon.disconnect(self) class TrajectoryServer(Pyro.core.ObjBase): def __init__(self, hostname, ip_address): self.hostname = hostname self.ip_address = ip_address self.files = {} self.exit = 0 Pyro.core.ObjBase.__init__(self) def publishTrajectory(self, filename): inspector = PyroTrajectoryInspector(filename) self.files[filename] = inspector PyroDaemon.connect(inspector, ip_address + ':MMTK:trajectory:' + filename) def unpublishTrajectory(self, filename): try: inspector = self.files[filename] except KeyError: raise OSError("file not published") del self.files[filename] inspector.close() def stop(self): for filename, inspector in self.files.items(): del self.files[filename] inspector.close() PyroDaemon.disconnect(self) self.exit = 1 Pyro.core.initServer() PyroDaemon = Pyro.core.Daemon() hostname = socket.gethostname() ip_address = socket.gethostbyname(hostname) locator = Pyro.naming.NameServerLocator() try: pyro_ns = locator.getNS() except (PyroError,socket.error),x: pyro_ns = locator.getNS(host=hostname) PyroDaemon.useNameServer(pyro_ns) server = TrajectoryServer(hostname, ip_address) PyroDaemon.connect(server, ip_address + ':MMTK:server') while 1: PyroDaemon.handleRequests(10.) if server.exit: break MMTK-2.7.9/MMTK/Tools/TrajectoryViewer/TrajectoryViewer.py0000644000076600000240000011723412117632051023761 0ustar hinsenstaff00000000000000# Trajectory viewer for MMTK trajectory files # # Written by Konrad Hinsen # from Tkinter import * from Tkwindow import Tkwindow from Scientific.TkWidgets import FloatEntry, IntEntry, StatusBar, ModalDialog from Scientific.TkWidgets.TkPlotCanvas import PlotCanvas,PolyLine,PlotGraphics from Scientific.TkWidgets.TkVisualizationCanvas import VisualizationCanvas, \ VisualizationGraphics, PolyLine3D from MMTK.Tk.ProteinVisualization import ProteinBackboneGraphics import Dialog, FileDialog from Scientific.DictWithDefault import DictWithDefault from Scientific.IO import ArrayIO from Scientific import N as Numeric import os, string, tempfile, time import MMTK, MMTK.Trajectory from TrajectoryInspector import TrajectoryInspector try: import Pyro.core, Pyro.naming, Pyro.errors use_pyro = 1 except ImportError: use_pyro = 0 if use_pyro: from TrajectoryManager import TrajectoryManager try: tmanager = TrajectoryManager() except Pyro.errors.PyroError: tmanager = None else: tmanager = None # # Utility functions # # # Plotting via external program # plot_program = None try: plot_program = os.environ['PLOTPROGRAM'] except KeyError: pass def externalPlot(data): if plot_program is not None: filename = tempfile.mktemp() ArrayIO.writeArray(data, filename) if os.fork() == 0: pipe = os.popen(plot_program + ' ' + filename + \ ' 1> /dev/null 2>&1', 'w') pipe.close() os.unlink(filename) os._exit(0) # # Sort variables into categories for display # _category = { 'energy': 'energy', 'temperature': 'thermodynamic', 'pressure': 'thermodynamic', 'volume': 'thermodynamic', 'configuration': 'configuration', 'coordinate': 'auxiliary', 'masses': 'masses', 'time': 'time', } def categorizeVariables(variable_names): categories = DictWithDefault([]) for name in variable_names: words = string.split(name, '_') try: c = _category[words[-1]] except KeyError: c = '' if c: categories[c].append(name) for list in categories.values(): list.sort() if categories.has_key('energy'): list = categories['energy'] for variable in ['kinetic_energy', 'potential_energy']: if variable in list: list.remove(variable) list = [variable] + list categories['energy'] = list return categories # # Determine 'nice' plot range for a given value interval # def plotRange(lower, upper): range = upper-lower if range == 0.: return lower-0.5, upper+0.5 log = Numeric.log10(range) power = Numeric.floor(log) fraction = log-power if fraction <= 0.05: power = power-1 grid = 10.**power lower = lower - lower % grid mod = upper % grid if mod != 0: upper = upper - mod + grid return lower, upper # # Simple text display window # class TextViewer(Toplevel): def __init__(self, master, text, title=''): Toplevel.__init__(self, master) self.title(title) Label(self, text=text, justify=LEFT).pack(side=TOP) button_bar = Frame(self) button_bar.pack(side=BOTTOM) Button(button_bar, text='Close', command=self.destroy).pack(side=RIGHT) # # Multiple plot window # class PlotWindow(Toplevel): def __init__(self, master, title): Toplevel.__init__(self, master) self.title(title) self.plotlist = [] self.protocol("WM_DELETE_WINDOW", self.close) def close(self): for plot in self.plotlist: self.master._unregisterPlot(plot) self.destroy() def registerPlot(self, plot): self.plotlist.append(plot) self.master._registerPlot(plot) def setSelection(self, plot): if self.master.selection is not None: i1, i2 = self.master.selection plot.select((self.master.time[i1], self.master.time[i2])) # # Standard plot window for independent quantities # class PlotViewer(PlotWindow): def __init__(self, master, title, time, inspector, names): PlotWindow.__init__(self, master, title) self.time = time self.step = max(1, len(time)/300) self.setup(time, inspector, names) def setup(self, time, inspector, names): time = time[::self.step] for n in names: data = Numeric.zeros((len(time), 2), Numeric.Float) data[:, 0] = time data[:, 1] = Numeric.array(inspector.readScalarVariable(n, 0, None, self.step)) self.plotBox(n, data) def plotBox(self, name, data, data_range=None): box = Frame(self, border=2, relief=SUNKEN) box.pack(side=TOP, fill=BOTH, expand=YES) frame = Frame(box, background='grey') frame.pack(side=TOP, fill=X, expand=NO) Label(frame, text=string.capitalize(string.join( string.split(name , '_'), ' ')), background='grey').pack(side=LEFT) if data_range is None: min = Numeric.minimum.reduce(data[:,1]) max = Numeric.maximum.reduce(data[:,1]) min, max = plotRange(min, max) else: min, max = data_range plot_objects = [] plot_data = data time = plot_data[:,0] jumps = Numeric.repeat(Numeric.arange(len(time)-1), Numeric.less(time[1:], time[:-1]))+1 for i in self.master.restarts: plot_objects.append(PolyLine([(self.time[i], min), (self.time[i], max)], color='black', stipple='gray25')) plot_objects.insert(0, PolyLine(plot_data, color = 'red')) plot = PlotCanvas(box, 400, 100, zoom=1, select=self.master._selectRange) plot.pack(side=LEFT, fill=BOTH, expand=YES) plot.draw(PlotGraphics(plot_objects), 'automatic', (min, max)) plot.bind('', lambda event, d=plot_data: externalPlot(d)) self.registerPlot(plot) self.setSelection(plot) # # Special plot window for energy terms: uniform scale and # display of sum. # class EnergyViewer(PlotViewer): def setup(self, time, inspector, names): time = time[::self.step] sum = 0. range = 0. for n in names: data = Numeric.array(inspector.readScalarVariable(n, 0, None, self.step)) sum = sum + data upper = Numeric.maximum.reduce(data) lower = Numeric.minimum.reduce(data) lower, upper = plotRange(lower, upper) range = max(range, upper-lower) upper = Numeric.maximum.reduce(sum) lower = Numeric.minimum.reduce(sum) lower, upper = plotRange(lower, upper) range = 0.5*max(range, upper-lower) for n in names: data = Numeric.array(inspector.readScalarVariable(n, 0, None, self.step)) data = Numeric.transpose(Numeric.array([time, data])) mean = Numeric.add.reduce(data[:, 1])/len(data) self.plotBox(n, data, (mean-range, mean+range)) if len(names) > 1: data = Numeric.transpose(Numeric.array([time, sum])) mean = Numeric.add.reduce(data[:, 1])/len(data) self.plotBox('Sum', data, (mean-range, mean+range)) # # Plot window for mode projections # class ModeProjectionViewer(PlotWindow): def __init__(self, master, mode_projector, indices): from Scientific.Statistics import mean, standardDeviation title = "Normal mode projections (Temperature: %5.1f K)" \ % mode_projector.temperature PlotWindow.__init__(self, master, title) self.mode_projector = mode_projector plot_range = 0. for i in indices: series = mode_projector[i][:, 1] average = mean(series) deviation = standardDeviation(series) lower = Numeric.minimum.reduce(series) lower = min(lower, average-deviation) upper = Numeric.maximum.reduce(series) upper = max(upper, average+deviation) lower, upper = plotRange(lower, upper) plot_range = max(plot_range, upper-lower) nmodes = mode_projector.numberOfModes() for i in range(len(indices)): if indices[i] < 0: indices[i] = nmodes+indices[i] next_indices = [] step = indices[1] - indices[0] i = indices[-1] while len(next_indices) < 4: i = i + step if i >= nmodes: break next_indices.append(i) previous_indices = [] i = indices[0] while len(previous_indices) < 4: i = i - step if i < 6: break previous_indices.insert(0, i) if next_indices or previous_indices: frame = Frame(self) frame.pack(side=BOTTOM, fill=X, expand=YES) if previous_indices: Button(frame, text="Previous", command=lambda s=self, pi=previous_indices: s.modeProjection(pi)).pack(side=LEFT) if next_indices: Button(frame, text="Next", command=lambda s=self, ni=next_indices: s.modeProjection(ni)).pack(side=RIGHT) for i in range(len(indices)): number = indices[i] data = mode_projector[number] fluctuation = self.mode_projector.amplitude(number) average = mean(data[:, 1]) deviation = standardDeviation(data[:, 1]) box = Frame(self, border=2, relief=SUNKEN) box.pack(side=TOP, fill=BOTH, expand=YES) frame = Frame(box, background='grey') frame.pack(side=TOP, fill=X, expand=NO) Label(frame, text="Mode %d" % number, background='grey').pack(side=LEFT) Button(frame, text="Animation", background='grey', command=lambda s=self, n=number: s.animateMode(n, 1.)).pack(side=RIGHT) Button(frame, text="View", background='grey', command=lambda s=self, n=number: s.viewMode(n)).pack(side=RIGHT) minv = Numeric.minimum.reduce(data[:,1]) maxv = Numeric.maximum.reduce(data[:,1]) minv, maxv = plotRange(minv, maxv) middle = 0.5*(maxv+minv) if maxv-minv < plot_range: minv = middle-0.5*plot_range maxv = middle+0.5*plot_range plot_objects = [PolyLine([(data[1, 0], average-fluctuation), (data[1, 0], average+fluctuation)], color='blue', width=3), PolyLine([(data[-1, 0], average-deviation), (data[-1, 0], average+deviation)], color='green', width=3)] plot_objects.insert(0, PolyLine(data, color = 'red')) plot = PlotCanvas(box, 400, 100, zoom=1, select=self.master._selectRange) plot.pack(side=LEFT, fill=BOTH, expand=YES) plot.draw(PlotGraphics(plot_objects), 'automatic', (minv, maxv)) plot.bind('', lambda event, d=data: externalPlot(d)) self.registerPlot(plot) self.setSelection(plot) def viewMode(self, number): amplitude = self.mode_projector.amplitude(number) ModeViewer(self, amplitude*self.mode_projector.modes[number], number) def animateMode(self, number, scale): mode = self.mode_projector.modes[number] amplitude = self.mode_projector.amplitude(number) mode.view(scale*amplitude) def modeProjection(self, indices): self.master.modeProjection(indices) class RBProjectionViewer(PlotWindow): def __init__(self, master, rb_projector): PlotWindow.__init__(self, master, "Rigid-body motion projections") plot_range = 0. for i in range(6): series = rb_projector[i][:, 1] lower = Numeric.minimum.reduce(series) upper = Numeric.maximum.reduce(series) lower, upper = plotRange(lower, upper) plot_range = max(plot_range, upper-lower) for column, offset, type in [(0, 0, "Translation "), (1, 3, "Rotation ")]: for i in range(3): data = rb_projector[i+offset] box = Frame(self, border=2, relief=SUNKEN) box.grid(row=i, column=column, sticky=N+W+E+S) frame = Frame(box, background='grey') frame.pack(side=TOP, fill=X, expand=NO) Label(frame, text=type + ["X", "Y", "Z"][i], background='grey').pack(side=LEFT) minv = Numeric.minimum.reduce(data[:,1]) maxv = Numeric.maximum.reduce(data[:,1]) minv, maxv = plotRange(minv, maxv) middle = 0.5*(maxv+minv) if maxv-minv < plot_range: minv = middle-0.5*plot_range maxv = middle+0.5*plot_range plot_objects = [PolyLine(data, color = 'red')] plot = PlotCanvas(box, 400, 100, zoom=1, select=self.master._selectRange) plot.pack(side=LEFT, fill=BOTH, expand=YES) plot.draw(PlotGraphics(plot_objects), 'automatic', (minv, maxv)) plot.bind('', lambda event, d=data: externalPlot(d)) self.registerPlot(plot) self.setSelection(plot) # # Entry dialog for temperature # class TemperatureDialog(ModalDialog): def body(self, master): self.entry = FloatEntry(master, "Enter temperature: ", 300., 0., None) self.entry.pack() return self.entry def apply(self): self.result = self.entry.get() # # Normal mode displacement viewer window # class ModeViewer(Toplevel): def __init__(self, master, mode, number): Toplevel.__init__(self, master) self.title("Mode %d" % number) self.mode = mode self.scale = 10. frame = Frame(self) frame.pack(side=TOP, fill=X, expand=YES) self.scale_entry = FloatEntry(frame, "Enhancement factor: ", 10., 0., None) self.scale_entry.pack(side=LEFT) self.scale_entry.bind('', self.draw) Button(frame, text="Animation", command=self.animation).pack(side=RIGHT) self.canvas = VisualizationCanvas(self, width=400, height=400, background='#BBB', relief=SUNKEN, border=2) self.canvas.pack(side=BOTTOM, fill=BOTH, expand=Y) self.draw() def draw(self, event=None): self.canvas.clear() graphics = [] conf = self.mode.universe.configuration() offset = self.scale_entry.get()*self.mode conf_plus = conf+offset conf_minus = conf-offset for protein in self.mode.universe: graphics.append(ProteinBackboneGraphics(protein, conf_plus, color='green')) graphics.append(ProteinBackboneGraphics(protein, conf_minus, color='red')) for a in protein.atomList(): p1 = conf_plus[a] p2 = conf_minus[a] graphics.append(PolyLine3D([p1.array, p2.array], color = 'blue')) self.canvas.draw(VisualizationGraphics(graphics)) def animation(self): from MMTK.Visualization import viewSequence scale = self.scale_entry.get() conf = self.mode.universe.configuration() offset = scale*self.mode viewSequence(self.mode.universe, [conf, conf+offset, conf, conf-offset], 1) # # 2D mode viewer # class ModeViewer2D(Toplevel): def __init__(self, master, mode_projector): Toplevel.__init__(self, master) self.title("2D Mode Projection") self.mode_projector = mode_projector frame = Frame(self) frame.pack(side=TOP, fill=BOTH, expand=YES) self.xnum = IntEntry(frame, "X mode number: ", 6, 6, mode_projector.numberOfModes()-1) self.xnum.pack(side=LEFT) self.xnum.bind('', self.draw) self.ynum = IntEntry(frame, " Y mode number: ", 7, 6, mode_projector.numberOfModes()-1) self.ynum.pack(side=LEFT) self.ynum.bind('', self.draw) self.plot = PlotCanvas(self, 400, 400) self.plot.pack(side=TOP, fill=BOTH, expand=YES) self.draw() def draw(self, event=None): xnum = self.xnum.get() ynum = self.ynum.get() self.mode_projector.calculateProjections([xnum, ynum]) data = self.mode_projector[ynum] data[:, 0] = self.mode_projector[xnum][:, 1] self.plot.clear() self.plot.draw(PolyLine(data, color = 'red')) # # 3D mode viewer # class ModeViewer3D(Toplevel): def __init__(self, master, mode_projector): Toplevel.__init__(self, master) self.title("3D Mode Projection") self.mode_projector = mode_projector frame = Frame(self) frame.pack(side=TOP, fill=BOTH, expand=YES) self.xnum = IntEntry(frame, "X mode number: ", 6, 6, mode_projector.numberOfModes()-1, fg='red') self.xnum.pack(side=TOP) self.xnum.bind('', self.draw) self.ynum = IntEntry(frame, "Y mode number: ", 7, 6, mode_projector.numberOfModes()-1, fg='yellow') self.ynum.pack(side=TOP) self.ynum.bind('', self.draw) self.znum = IntEntry(frame, "Z mode number: ", 8, 6, mode_projector.numberOfModes()-1, fg='green') self.znum.pack(side=TOP) self.znum.bind('', self.draw) self.plot = VisualizationCanvas(self, 400, 400) self.plot.pack(side=TOP, fill=BOTH, expand=YES) self.draw() def draw(self, event=None): xnum = self.xnum.get() ynum = self.ynum.get() znum = self.znum.get() self.mode_projector.calculateProjections([xnum, ynum, znum]) x = self.mode_projector[xnum] data = Numeric.zeros((len(x), 3), Numeric.Float) data[:, 0] = x[:, 1] data[:, 1] = self.mode_projector[ynum][:, 1] data[:, 2] = self.mode_projector[znum][:, 1] minv = Numeric.minimum.reduce(data) maxv = Numeric.maximum.reduce(data) scale = maxv-minv reference = minv-0.05*scale xaxis = PolyLine3D([reference, reference+scale*Numeric.array([0.2, 0., 0.])], color='red') yaxis = PolyLine3D([reference, reference+scale*Numeric.array([0., 0.2, 0.])], color='yellow') zaxis = PolyLine3D([reference, reference+scale*Numeric.array([0., 0., 0.2])], color='green') graphics = [PolyLine3D(data, color = 'blue'), xaxis, yaxis, zaxis] self.plot.clear() self.plot.draw(VisualizationGraphics(graphics)) # # Trajectory viewer window # class TrajectoryViewer(Tkwindow): def __init__(self, master, trajectory): Tkwindow.__init__(self, master) self.filename = None if type(trajectory) == type(''): if string.find(trajectory, ':') >= 0 and tmanager is not None: self.filename = trajectory self.inspector = tmanager.trajectoryInspector(trajectory) self.inspector.reopen() else: self.filename = trajectory self.inspector = TrajectoryInspector(trajectory) else: self.inspector = TrajectoryInspector(trajectory) if self.filename is not None: self.title(self.filename) self.description = self.inspector.description() self.universe = None self.categories = categorizeVariables(self.inspector.variableNames()) step_number = Numeric.array(self.inspector.readScalarVariable('step')) jumps = Numeric.less(step_number[1:]-step_number[:-1], 0) self.restarts = Numeric.repeat(Numeric.arange(len(jumps)), jumps)+1 self.restarts = list(self.restarts) try: self.time = \ Numeric.array(self.inspector.readScalarVariable('time')) jumps = Numeric.repeat(Numeric.arange(len(self.time)-1), Numeric.less(self.time[1:], self.time[:-1]))+1 if len(jumps) > 0: for jump in jumps[::-1]: dt = self.time[jump-1] + self.time[jump+1] \ - 2*self.time[jump] try: # Numeric typecode = self.time.typecode() except AttributeError: # numpy typecode = self.time.dtype.char self.time[jump:] = (self.time[jump:] + dt).astype(typecode) except KeyError: self.time = 1.*Numeric.arange(self.inspector.numberOfSteps()) self.plotlist = [] self.selection = None self._createMenu() self._createMainBox() self.open() def _createMainBox(self): self.status = StatusBar(self) self.status.pack(side=BOTTOM, anchor=W, fill=BOTH) if self.filename is None: text = [] else: text = ['Trajectory file: ' + self.filename, ''] comment = self.inspector.comment() if len(comment) > 0: text.append(comment) text.append('') natoms = self.inspector.numberOfAtoms() nsteps = self.inspector.numberOfSteps() text.append(`natoms` + ' atoms, ' + `nsteps` + ' steps') text.append('') Label(self, text=string.join(text, '\n'), justify=LEFT).pack(side=TOP) animation = Frame(self) animation.pack(side=LEFT) Label(animation, text = 'First: ').grid(row=0, column=0) self.first_step = Entry(animation) self.first_step.grid(row=0, column=1) Label(animation, text = 'Last: ').grid(row=1, column=0) self.last_step = Entry(animation) self.last_step.grid(row=1, column=1) Label(animation, text = 'Skip: ').grid(row=2, column=0) self.skip_step = Entry(animation) self.skip_step.grid(row=2, column=1) self.first_step.insert(0, '1') self.last_step.insert(0, `nsteps`) self.skip_step.insert(0, `max(1, nsteps/100)`) button_box = Frame(animation) button_box.grid(column=3, row=0, rowspan=3) Button(button_box, text = 'Start Animation', command=self._animation).pack(side=TOP, fill=X) Button(button_box, text = 'Export PDB', command=self._export).pack(side=TOP, fill=X) def _createMenu(self): #menu_bar = Menu(self) menu_bar = Frame(self, relief=RAISED, bd=2) menu_bar.pack(side=TOP, fill=X) self._createFileMenu(menu_bar) self._createViewMenu(menu_bar) if string.find(self.description, "l('Protein'") >= 0: self._createProteinMenu(menu_bar) def _createFileMenu(self, menu_bar): menu_button = Menubutton(menu_bar, text='File', underline=0) menu_button.pack(side=LEFT) menu = Menu(menu_button) #menu = Menu(menu_bar, tearoff=0) menu.add_command(label='Open...', command=self._open) if tmanager is not None: menu.add_command(label='Open remote...', command=self._openRemote) menu.add_separator() menu.add_command(label='Close', command=self.close) menu.add_command(label='Quit', command=self.quit) menu_button['menu'] = menu def _createViewMenu(self, menu_bar): menu_button = Menubutton(menu_bar, text='View', underline=0) menu_button.pack(side=LEFT) menu = Menu(menu_button) #menu = Menu(menu_bar, tearoff=0) categories = self.categories.keys() categories = filter(lambda c, cl=categories: c in cl, ['energy', 'thermodynamic', 'auxiliary']) for category in categories: menu.add_command(label=string.capitalize(category), command=lambda s=self, c=category: s._viewData(c)) menu.add_separator() menu.add_command(label='History', command=self._showhistory) menu_button['menu'] = menu def _createProteinMenu(self, menu_bar): menu_button = Menubutton(menu_bar, text='Proteins', underline=0) menu_button.pack(side=LEFT) menu = Menu(menu_button) #menu = Menu(menu_bar, tearoff=0) menu.add_command(label='Rigid-body motion', command=self._rbProjection) menu.add_separator() menu.add_command(label='Mode projection overview', command=self._projectionOverview) menu.add_command(label='Low-frequency modes projection', command=self._projectionLow) menu.add_command(label='High-frequency modes projection', command=self._projectionHigh) menu.add_separator() menu.add_command(label='2D projection', command=self._modeProjection2D) menu.add_command(label='3D projection', command=self._modeProjection3D) menu_button['menu'] = menu def _missing(self): Dialog.Dialog(self, title='Undefined operation', text='This operation is not yet implemented.', bitmap='warning', default=0, strings = ('Cancel',)) def _open(self): fd = FileDialog.LoadFileDialog(self) file = fd.go(key='OpenTrajectory', pattern='*.nc') if file is not None: TrajectoryViewer(None, file) def _openRemote(self): window = Tkwindow(self) window.title("Remote trajectories") self.remote_window = window frame = Frame(window) frame.pack(side=TOP, expand=YES, fill=BOTH) scrollbar = Scrollbar(frame, orient=VERTICAL) self.remote = Listbox(frame, selectmode=EXTENDED, relief=SUNKEN, width=60, height=10, yscrollcommand=scrollbar.set) for name in tmanager.trajectoryList(): self.remote.insert(END, name) scrollbar.config(command=self.remote.yview) scrollbar.pack(side=RIGHT, fill=Y) self.remote.pack(side=TOP, fill=BOTH, expand=YES) button_bar = Frame(window) button_bar.pack(side=BOTTOM, fill=X, expand=YES) Button(button_bar, text='Open', command=self._openRemoteFiles).pack(side=LEFT) Button(button_bar, text='Cancel', command=window.destroy).pack(side=RIGHT) window.open() def _openRemoteFiles(self): items = self.remote.curselection() try: items = map(string.atoi, items) except ValueError: pass files = [] for item in items: files.append(self.remote.get(item)) self.remote_window.destroy() for file in files: TrajectoryViewer(None, file) def _registerPlot(self, plot_canvas): self.plotlist.append(plot_canvas) def _unregisterPlot(self, plot_canvas): try: self.plotlist.remove(plot_canvas) except ValueError: pass def _selectRange(self, range): if range is None: first = 0 last = self.inspector.numberOfSteps() self.selection = None else: first = Numeric.sum(Numeric.less(self.time, range[0])) last = Numeric.sum(Numeric.less(self.time, range[1])) self.selection = (first, last) skip = max(1, (last-first)/100) self.first_step.delete(0, END) self.first_step.insert(0, `first`) self.last_step.delete(0, END) self.last_step.insert(0, `last`) self.skip_step.delete(0, END) self.skip_step.insert(0, `skip`) for plot_canvas in self.plotlist: if range is None: plot_canvas.select(None) else: plot_canvas.select((self.time[first], self.time[last])) def _showhistory(self): TextViewer(self, self.inspector.history(), 'History') def _viewData(self, category): variable_names = self.categories[category][:] if category == 'configuration': self._missing() elif category == 'energy': EnergyViewer(self, string.capitalize(category), self.time, self.inspector, variable_names) else: PlotViewer(self, string.capitalize(category), self.time, self.inspector, variable_names) def _makeUniverse(self): import MMTK.Skeleton import MMTK.ForceFields if self.universe is None: self.status.set("Constructing model") local = {} skeleton = eval(self.description, vars(MMTK.Skeleton), local) self.universe = skeleton.make({}) self.mode_projector = None self.rb_projector = None self.temperature = None self.status.clear() def _animation(self): first = string.atoi(self.first_step.get())-1 last = string.atoi(self.last_step.get()) skip = string.atoi(self.skip_step.get()) self._makeUniverse() from MMTK.Visualization import viewSequence viewSequence(self.universe, ConfigurationFactory(self.universe, self.inspector, range(first, last, skip))) def _export(self): first = string.atoi(self.first_step.get())-1 last = string.atoi(self.last_step.get()) skip = string.atoi(self.skip_step.get()) self._makeUniverse() numbers = range(first, last, skip) conf = ConfigurationFactory(self.universe, self.inspector, numbers) for i in range(len(numbers)): n = numbers[i] self.universe.writeToFile("conf%d.pdb" % n, conf[i], 'pdb') def _rbProjection(self): self._makeUniverse() if self.rb_projector is None: last = self.inspector.numberOfSteps() skip = max(1, last/500) traj = ConfigurationFactory(self.universe, self.inspector, range(0, last, skip)) time = self.time[0:last:skip] self.status.set("Calculating projections") self.rb_projector = RBProjector(traj, time) self.rb_projector.calculateProjections(range(6)) self.status.clear() RBProjectionViewer(self, self.rb_projector) def _projectionOverview(self): self.modeProjection(None) def _projectionLow(self): self.modeProjection([6, 7, 8, 9]) def _projectionHigh(self): self.modeProjection([-4, -3, -2, -1]) def modeProjection(self, indices): self._makeModeProjector() if indices is None: nmodes = self.mode_projector.numberOfModes()-6 skip = max(2, nmodes/3)-1 indices = range(6, 6+min(nmodes, 4*skip), skip) self.status.set("Calculating projections") self.mode_projector.calculateProjections(indices) self.status.clear() ModeProjectionViewer(self, self.mode_projector, indices) def _makeModeProjector(self): self._makeUniverse() if self.mode_projector is None: self._getTemperature() if self.temperature is None: return self.status.set("Calculating normal modes") last = self.inspector.numberOfSteps() skip = max(1, last/500) traj = ConfigurationFactory(self.universe, self.inspector, range(0, last, skip)) time = self.time[0:last:skip] self.mode_projector = ModeProjector(traj, time, self.temperature) self.status.clear() def _getTemperature(self): try: t = self.inspector.readScalarVariable('temperature', 0, None, 1) self.temperature = Numeric.add.reduce(t)/len(t) except KeyError: window = TemperatureDialog(self) self.temperature = window.result def _modeProjection2D(self): self._makeModeProjector() ModeViewer2D(self, self.mode_projector) def _modeProjection3D(self): self._makeModeProjector() ModeViewer3D(self, self.mode_projector) class ConfigurationFactory: def __init__(self, universe, inspector, indices): self.universe = universe self.inspector = inspector self.indices = indices def __len__(self): return len(self.indices) def __getitem__(self, item): index = self.indices[item] cell, conf = self.inspector.readConfiguration(index) if cell is not None: cell = cell.astype(Numeric.Float) conf = conf.astype(Numeric.Float) conf = MMTK.Configuration(self.universe, conf, cell) self.universe.setConfiguration(conf) return conf def __getslice__(self, first, last): return ConfigurationFactory(self.universe, self.inspector, self.indices[first:last]) class ModeProjector: def __init__(self, trajectory, time, temperature): from MMTK.NormalModes import EnergeticModes self.raw_trajectory = trajectory self.trajectory = None self.time = time self.temperature = temperature self.cache = {} self._makeUniverse() self.modes = EnergeticModes(self.universe_calpha, temperature=None) def numberOfModes(self): return len(self.modes) def _makeUniverse(self): from MMTK.Proteins import Protein, PeptideChain from MMTK.ForceFields import CalphaForceField self.universe = self.raw_trajectory.universe self.proteins = self.universe.objectList(Protein) universe_calpha = MMTK.InfiniteUniverse(CalphaForceField(2.5)) calpha_map = {} for protein in self.proteins: chains_calpha = [] for chain in protein: chains_calpha.append(PeptideChain(chain.sequence(), model='calpha')) protein_calpha = Protein(chains_calpha) universe_calpha.addObject(protein_calpha) for i in range(len(protein)): chain = protein[i] chain_calpha = protein_calpha[i] for j in range(len(chain)): calpha_map[chain[j].peptide.C_alpha] = \ chain_calpha[j].peptide.C_alpha chain_calpha[j].peptide.C_alpha.setMass(1.) conf = self.raw_trajectory[0] for atom, calpha in calpha_map.items(): calpha.setPosition(conf[atom]) self.universe_calpha = universe_calpha universe_calpha.configuration() self.map = universe_calpha.numberOfAtoms()*[None] for atom, calpha in calpha_map.items(): self.map[calpha.index] = atom.index def _extractTrajectory(self): if self.trajectory is not None: return self.trajectory = [] universe = self.universe universe_calpha = self.universe_calpha conf_calpha_0 = universe_calpha.configuration() for conf in self.raw_trajectory: conf = self.universe.contiguousObjectConfiguration(self.proteins, conf) array = Numeric.take(conf.array, self.map) conf_calpha = MMTK.ParticleVector(universe_calpha, array) tr, rms = universe_calpha.findTransformation(conf_calpha) tr = tr.inverse() for a in universe_calpha.atomList(): conf_calpha[a] = tr(conf_calpha[a]) conf_calpha = conf_calpha-conf_calpha_0 self.trajectory.append(conf_calpha) def calculateProjections(self, indices): calc = [] for i in indices: if not self.cache.has_key(i): calc.append(i) if not calc: return self._extractTrajectory() proj = [] for conf_calpha in self.trajectory: p = [] for i in calc: p.append(conf_calpha.dotProduct(self.modes[i])) proj.append(p) proj = Numeric.transpose(Numeric.array(proj)) for i in range(len(calc)): self.cache[calc[i]] = proj[i] def __getitem__(self, item): try: series = self.cache[item] except KeyError: self.calculateProjections([item]) series = self.cache[item] return Numeric.transpose(Numeric.array([self.time, series])) def amplitude(self, number): force_constant = self.modes[number].force_constant return Numeric.sqrt(self.temperature*MMTK.Units.k_B/force_constant) class RBProjector(ModeProjector): def __init__(self, trajectory, time): self.raw_trajectory = trajectory self.time = time self.cache = {} self.modes = [] self._makeUniverse() for i in range(3): v = MMTK.ParticleVector(self.universe_calpha) v.array[:, i] = 1. self.modes.append(v.scaledToNorm(1.)) for e in [MMTK.Vector(1., 0., 0.), MMTK.Vector(0., 1., 0.), MMTK.Vector(0., 0., 1.)]: v = MMTK.ParticleVector(self.universe_calpha) for a in self.universe_calpha.atomList(): v[a] = e.cross(a.position()) self.modes.append(v.scaledToNorm(1.)) def calculateProjections(self, indices): calc = [] for i in indices: if not self.cache.has_key(i): calc.append(i) if not calc: return universe = self.universe universe_calpha = self.universe_calpha conf_calpha_0 = universe_calpha.configuration() proj = [] for conf in self.raw_trajectory: conf = self.universe.contiguousObjectConfiguration(self.proteins, conf) array = Numeric.take(conf, self.map) conf_calpha = MMTK.ParticleVector(universe_calpha, array) conf_calpha = conf_calpha-conf_calpha_0 p = [] for i in calc: p.append(conf_calpha.dotProduct(self.modes[i])) proj.append(p) proj = Numeric.transpose(Numeric.array(proj)) for i in range(len(calc)): self.cache[calc[i]] = proj[i] def amplitude(self, number): raise ValueError if __name__ == '__main__': import sys if len(sys.argv) < 2: sys.stderr.write('No trajectory specified\n') sys.exit() for trajectory in sys.argv[1:]: TrajectoryViewer(None, trajectory) MMTK-2.7.9/MMTK/Trajectory.py0000644000076600000240000015241612117632051016170 0ustar hinsenstaff00000000000000# This module implements trajetories and trajectory generators. # # Written by Konrad Hinsen # """ Trajectory files and their contents """ __docformat__ = 'restructuredtext' from MMTK import Collections, Units, Universe, Utility, \ ParticleProperties, Visualization from Scientific.Geometry import Vector from Scientific import N import copy, os, sys # Report error if the netCDF module is not available. try: from Scientific.IO import NetCDF except ImportError: raise Utility.MMTKError("Trajectories are not available " + "because the netCDF module is missing.") # # Trajectory class # class Trajectory(object): # # Trajectory cache # # This cache is maintained for better efficiency in parallel # processing. The cache contains all trajectories currently open # for reading. When a trajectory object is unpickled, a trajectory # from the cache is reused if possible. This means that """ Trajectory file The data in a trajectory file can be accessed by step or by variable. If t is a Trajectory object, then: * len(t) is the number of steps * t[i] is the data for step i, in the form of a dictionary that maps variable names to data * t[i:j] and t[i:j:n] return a :class:`~MMTK.Trajectory.SubTrajectory` object that refers to a subset of the total number of steps (no data is copied) * t.variable returns the value of the named variable at all time steps. If the variable is a simple scalar, it is read completely and returned as an array. If the variable contains data for each atom, a :class:`~MMTK.Trajectory.TrajectoryVariable` object is returned from which data at specific steps can be obtained by further indexing operations. The routines that generate trajectories decide what variables are used and what they contain. The most frequently used variable is "configuration", which stores the positions of all atoms. Other common variables are "time", "velocities", "temperature", "pressure", and various energy terms whose name end with "_energy". """ def __init__(self, object, filename, mode = 'r', comment = None, double_precision = False, cycle = 0, block_size = 1): """ :param object: the object whose data is stored in the trajectory file. This can be 'None' when opening a file for reading; in that case, a universe object is constructed from the description stored in the trajectory file. This universe object can be accessed via the attribute 'universe' of the trajectory object. :type object: :class:`~MMTK.ChemicalObjects.ChemicalObject` :param filename: the name of the trajectory file :type filename: str :param mode: one of "r" (read-only), "w" (create new file for writing), or "a" (append to existing file or create if the file does not exist) :type mode: str :param comment: optional comment that is stored in the file; allowed only with mode="r" :type comment: str :param double_precision: if True, data in the file is stored using double precision; default is single precision. Note that all I/O via trajectory objects is double precision; conversion from and to single precision file variables is handled automatically. :type double_precision: bool :param cycle: if non-zero, a trajectory is created for a fixed number of steps equal to the value of cycle, and these steps are used cyclically. This is meant for restart trajectories. :type cycle: int :param block_size: an optimization parameter that influences the file structure and the I/O performance for very large files. A block size of 1 is optimal for sequential access to configurations etc., whereas a block size equal to the number of steps is optimal for reading coordinates or scalar variables along the time axis. The default value is 1. Note that older MMTK releases always used a block size of 1 and cannot handle trajectories with different block sizes. :type block_size: int """ filename = os.path.expanduser(filename) self.filename = filename self.mode = mode if object is None and mode == 'r': file = NetCDF.NetCDFFile(filename, 'r') description = file.variables['description'][:].tostring() try: self.block_size = file.dimensions['minor_step_number'] except KeyError: self.block_size = 1 conf = None cell = None if self.block_size == 1: try: conf_var = file.variables['configuration'] conf = conf_var[0, :, :] except KeyError: pass try: cell = file.variables['box_size'][0, :] except KeyError: pass else: try: conf_var = file.variables['configuration'] conf = conf_var[0, :, :, 0] except KeyError: pass try: cell = file.variables['box_size'][0, :, 0] except KeyError: pass file.close() import Skeleton local = {} skeleton = eval(description, vars(Skeleton), local) universe = skeleton.make({}, conf) universe.setCellParameters(cell) object = universe initialize = 1 else: universe = object.universe() if universe is None: raise ValueError("objects not in the same universe") description = None initialize = 0 universe.configuration() if object is universe: index_map = None inverse_map = None else: if mode == 'r': raise ValueError("can't read trajectory for a non-universe") index_map = N.array([a.index for a in object.atomList()]) inverse_map = universe.numberOfPoints()*[None] for i in range(len(index_map)): inverse_map[index_map[i]] = i toplevel = set() for o in Collections.Collection(object): toplevel.add(o.topLevelChemicalObject()) object = Collections.Collection(list(toplevel)) if description is None: description = universe.description(object, inverse_map) import MMTK_trajectory self.trajectory = MMTK_trajectory.Trajectory(universe, description, index_map, filename, mode + 's', double_precision, cycle, block_size) self.universe = universe self.index_map = index_map try: self.block_size = \ self.trajectory.file.dimensions['minor_step_number'] except KeyError: self.block_size = 1 if comment is not None: if mode == 'r': raise IOError('cannot add comment in read-only mode') self.trajectory.file.comment = comment if initialize and conf is not None: self.universe.setFromTrajectory(self) self.particle_trajectory_reader = ParticleTrajectoryReader(self) def __getstate__(self): if self.mode != 'r': raise ValueError("Cannot copy or pickle write-mode trajectories") return self.filename def __setstate__(self, state): self.__init__(None, state) def flush(self): """ Make sure that all data that has been written to the trajectory is also written to the file. """ self.trajectory.flush() def close(self): """ Close the trajectory file. Must be called after writing to ensure that all buffered data is written to the file. No data access is possible after closing a file. """ self.trajectory.close() def __len__(self): return self.trajectory.nsteps def __getitem__(self, item): if not isinstance(item, int): return SubTrajectory(self, N.arange(len(self)))[item] if item < 0: item += len(self) if item >= len(self): raise IndexError data = {} for name, var in self.trajectory.file.variables.items(): if 'step_number' not in var.dimensions: continue if 'atom_number' in var.dimensions: if 'xyz' in var.dimensions: array = ParticleProperties.ParticleVector(self.universe, self.trajectory.readParticleVector(name, item)) else: array = ParticleProperties.ParticleScalar(self.universe, self.trajectory.readParticleScalar(name, item)) else: bs = self.block_size if bs == 1: array = var[item] else: if len(var.shape) == 2: array = var[item/bs, item%bs] else: array = var[item/bs, ..., item%bs] data[name] = 0.+array if data.has_key('configuration'): box = data.get('box_size', None) if box is not None: box = box.astype(N.Float) conf = data['configuration'] data['configuration'] = \ ParticleProperties.Configuration(conf.universe, conf.array, box) return data def __getslice__(self, first, last): return self[(slice(first, last),)] def __getattr__(self, name): try: var = self.trajectory.file.variables[name] except KeyError: raise AttributeError("no variable named " + name) if 'atom_number' in var.dimensions: return TrajectoryVariable(self.universe, self, name) elif 'box_size_length' in var.dimensions: if 'minor_step_number' in var.dimensions: bs = N.transpose(var[:], [0, 2, 1]) bs = N.reshape(bs, (bs.shape[0]*bs.shape[1], bs.shape[2])) return bs[:len(self)] else: return var[:] else: return N.ravel(N.array(var))[:len(self)] def defaultStep(self): try: step = int(self.trajectory.file.last_step[0]) except AttributeError: step = 0 return step def readParticleTrajectory(self, atom, first=0, last=None, skip=1, variable = "configuration"): """ Read trajectory information for a single atom but for multiple time steps. :param atom: the atom whose trajectory is requested :type atom: :class:`~MMTK.ChemicalObjects.Atom` :param first: the number of the first step to be read :type first: int :param last: the number of the first step not to be read. A value of None indicates that the whole trajectory should be read. :type last: int :param skip: the number of steps to skip between two steps read :type skip: int :param variable: the name of the trajectory variable to be read. If the variable is "configuration", the resulting trajectory is made continuous by eliminating all jumps caused by periodic boundary conditions. The pseudo-variable "box_coordinates" can be read to obtain the values of the variable "configuration" scaled to box coordinates. For non-periodic universes there is no difference between box coordinates and real coordinates. :type variable: str :returns: the trajectory for a single atom :rtype: :class:`~MMTK.Trajectory.ParticleTrajectory` """ return ParticleTrajectory(self, atom, first, last, skip, variable) def readRigidBodyTrajectory(self, object, first=0, last=None, skip=1, reference = None): """ Read the positions for an object at multiple time steps and extract the rigid-body motion (center-of-mass position plus orientation as a quaternion) by an optimal-transformation fit. :param object: the object whose rigid-body trajectory is requested :type object: :class:`~MMTK.Collections.GroupOfAtoms` :param first: the number of the first step to be read :type first: int :param last: the number of the first step not to be read. A value of None indicates that the whole trajectory should be read. :type last: int :param skip: the number of steps to skip between two steps read :type skip: int :param reference: the reference configuration for the fit :type reference: :class:`~MMTK.ParticleProperties.Configuration` :returns: the trajectory for a single rigid body :rtype: :class:`~MMTK.Trajectory.RigidBodyTrajectory` """ return RigidBodyTrajectory(self, object, first, last, skip, reference) def variables(self): """ :returns: a list of the names of all variables that are stored in the trajectory :rtype: list of str """ vars = copy.copy(self.trajectory.file.variables.keys()) vars.remove('step') try: vars.remove('description') except ValueError: pass return vars def view(self, first=0, last=None, skip=1, object = None): """ Show an animation of the trajectory using an external visualization program. :param first: the number of the first step in the animation :type first: int :param last: the number of the first step not to include in the animation. A value of None indicates that the whole trajectory should be used. :type last: int :param skip: the number of steps to skip between two steps read :type skip: int :param object: the object to be animated, which must be in the universe stored in the trajectory. None stands for the whole universe. :type object: :class:`~MMTK.Collections.GroupOfAtoms` """ Visualization.viewTrajectory(self, first, last, skip, object) def _boxTransformation(self, pt_in, pt_out, to_box=0): from MMTK_trajectory import boxTransformation try: box_size = self.trajectory.recently_read_box_size except AttributeError: return boxTransformation(self.universe._spec, pt_in, pt_out, box_size, to_box) class SubTrajectory(object): """ Reference to a subset of a trajectory A SubTrajectory object is created by slicing a Trajectory object or another SubTrajectory object. It provides all the operations defined on Trajectory objects. """ def __init__(self, trajectory, indices): self.trajectory = trajectory self.indices = indices self.universe = trajectory.universe def __len__(self): return len(self.indices) def __getitem__(self, item): if isinstance(item, int): return self.trajectory[int(self.indices[item])] else: return SubTrajectory(self.trajectory, self.indices[item]) def __getslice__(self, first, last): return self[(slice(first, last),)] def __getattr__(self, name): return SubVariable(getattr(self.trajectory, name), self.indices) def readParticleTrajectory(self, atom, first=0, last=None, skip=1, variable = "configuration"): if last is None: last = len(self.indices) indices = self.indices[first:last:skip] first = indices[0] last = indices[-1]+1 if len(self.indices) > 1: skip = self.indices[1]-self.indices[0] else: skip = 1 return self.trajectory.readParticleTrajectory(atom, first, last, skip, variable) def readRigidBodyTrajectory(self, object, first=0, last=None, skip=1, reference = None): if last is None: last = len(self.indices) indices = self.indices[first:last:skip] first = indices[0] last = indices[-1]+1 if len(self.indices) > 1: skip = self.indices[1]-self.indices[0] else: skip = 1 return RigidBodyTrajectory(self.trajectory, object, first, last, skip, reference) def variables(self): return self.trajectory.variables() def view(self, first=0, last=None, step=1, subset = None): Visualization.viewTrajectory(self, first, last, step, subset) def close(self): del self.trajectory def _boxTransformation(self, pt_in, pt_out, to_box=0): Trajectory._boxTransformation(self.trajectory, pt_in, pt_out, to_box) # # Trajectory variables # class TrajectoryVariable(object): """ Variable in a trajectory A TrajectoryVariable object is created by extracting a variable from a Trajectory object if that variable contains data for each atom and is thus potentially large. No data is read from the trajectory file when a TrajectoryVariable object is created; the read operation takes place when the TrajectoryVariable is indexed with a specific step number. If t is a TrajectoryVariable object, then: * len(t) is the number of steps * t[i] is the data for step i, in the form of a ParticleScalar, a ParticleVector, or a Configuration object, depending on the variable * t[i:j] and t[i:j:n] return a SubVariable object that refers to a subset of the total number of steps """ def __init__(self, universe, trajectory, name): self.universe = universe self.trajectory = trajectory self.name = name self.var = self.trajectory.trajectory.file.variables[self.name] if self.name == 'configuration': try: self.box_size = \ self.trajectory.trajectory.file.variables['box_size'] except KeyError: self.box_size = None def __len__(self): return len(self.trajectory) def __getitem__(self, item): if not isinstance(item, int): return SubVariable(self, N.arange(len(self)))[item] item = int(item) # gets rid of numpy.intXX objects if item < 0: item = item + len(self.trajectory) if item >= len(self.trajectory): raise IndexError if self.name == 'configuration': if self.box_size is None: box = None elif len(self.box_size.shape) == 3: bs = self.trajectory.block_size box = self.box_size[item/bs, :, item%bs].astype(N.Float) else: box = self.box_size[item].astype(N.Float) array = ParticleProperties.Configuration(self.universe, self.trajectory.trajectory.readParticleVector(self.name, item), box) elif 'xyz' in self.var.dimensions: array = ParticleProperties.ParticleVector(self.universe, self.trajectory.trajectory.readParticleVector(self.name, item)) else: array = ParticleProperties.ParticleScalar(self.universe, self.trajectory.trajectory.readParticleScalar(self.name, item)) return array def __getslice__(self, first, last): return self[(slice(first, last),)] def average(self): sum = self[0] for value in self[1:]: sum = sum + value return sum/len(self) class SubVariable(TrajectoryVariable): """ Reference to a subset of a :class:`~MMTK.Trajectory.TrajectoryVariable` A SubVariable object is created by slicing a TrajectoryVariable object or another SubVariable object. It provides all the operations defined on TrajectoryVariable objects. """ def __init__(self, variable, indices): self.variable = variable self.indices = indices def __len__(self): return len(self.indices) def __getitem__(self, item): if isinstance(item, int): return self.variable[self.indices[item]] else: return SubVariable(self.variable, self.indices[item]) def __getslice__(self, first, last): return self[(slice(first, last),)] # # Trajectory consisting of multiple files # class TrajectorySet(object): """ Trajectory file set A TrajectorySet permits to treat a sequence of trajectory files like a single trajectory for reading data. It behaves exactly like a :class:`~MMTK.Trajectory.Trajectory` object. The trajectory files must all contain data for the same system. The variables stored in the individual files need not be the same, but only variables common to all files can be accessed. Note: depending on how the sequence of trajectories was constructed, the first configuration of each trajectory might be the same as the last one in the preceding trajectory. To avoid counting it twice, specify (filename, 1, None, 1) for all but the first trajectory in the set. """ def __init__(self, object, filenames): """ :param object: the object whose data is stored in the trajectory files. This can be (and usually is) None; in that case, a universe object is constructed from the description stored in the first trajectory file. This universe object can be accessed via the attribute universe of the trajectory set object. :param filenames: a list of trajectory file names or (filename, first_step, last_step, increment) tuples. """ first = filenames[0] if isinstance(first, tuple): first = Trajectory(object, first[0])[first[1]:first[2]:first[3]] else: first = Trajectory(object, first) self.universe = first.universe self.trajectories = [first] self.nsteps = [0, len(first)] self.cell_parameters = [] for file in filenames[1:]: if isinstance(file, tuple): t = Trajectory(self.universe, file[0])[file[1]:file[2]:file[3]] else: t = Trajectory(self.universe, file) self.trajectories.append(t) self.nsteps.append(self.nsteps[-1]+len(t)) try: self.cell_parameters.append(t[0]['box_size']) except KeyError: pass vars = {} for t in self.trajectories: for v in t.variables(): vars[v] = vars.get(v, 0) + 1 self.vars = [] for v, count in vars.items(): if count == len(self.trajectories): self.vars.append(v) def close(self): for t in self.trajectories: t.close() def __len__(self): return self.nsteps[-1] def __getitem__(self, item): if not isinstance(item, int): return SubTrajectory(self, N.arange(len(self)))[item] if item >= len(self): raise IndexError tindex = N.add.reduce(N.greater_equal(item, self.nsteps))-1 return self.trajectories[tindex][item-self.nsteps[tindex]] def __getslice__(self, first, last): return self[(slice(first, last),)] def __getattr__(self, name): if name not in self.vars+['step']: raise AttributeError("no variable named " + name) var = self.trajectories[0].trajectory.file.variables[name] if 'atom_number' in var.dimensions: return TrajectorySetVariable(self.universe, self, name) else: data = [] for t in self.trajectories: var = t.trajectory.file.variables[name] data.append(N.ravel(N.array(var))[:len(t)]) return N.concatenate(data) def readParticleTrajectory(self, atom, first=0, last=None, skip=1, variable = "configuration"): total = None self.steps_read = [] for i in range(len(self.trajectories)): if self.nsteps[i+1] <= first: self.steps_read.append(0) continue if last is not None and self.nsteps[i] >= last: break n = max(0, (self.nsteps[i]-first+skip-1)/skip) start = first+skip*n-self.nsteps[i] n = (self.nsteps[i+1]-first+skip-1)/skip stop = first+skip*n if last is not None: stop = min(stop, last) stop = stop-self.nsteps[i] if start >= 0 and start < self.nsteps[i+1]-self.nsteps[i]: t = self.trajectories[i] pt = t.readParticleTrajectory(atom, start, stop, skip, variable) self.steps_read.append((stop-start)/skip) if total is None: total = pt else: if variable == "configuration" \ and self.cell_parameters[0] is not None: jump = pt.array[0]-total.array[-1] mult = -(jump/self.cell_parameters[i-1]).astype('i') if len(N.nonzero(mult)) > 0: t._boxTransformation(pt.array, pt.array, 1) N.add(pt.array, mult[N.NewAxis, : ], pt.array) t._boxTransformation(pt.array, pt.array, 0) jump = pt.array[0] - total.array[-1] mask = N.less(jump, -0.5*self.cell_parameters[i-1])- \ N.greater(jump, 0.5*self.cell_parameters[i-1]) if len(N.nonzero(mask)) > 0: t._boxTransformation(pt.array, pt.array, 1) N.add(pt.array, mask[N.NewAxis, :], pt.array) t._boxTransformation(pt.array, pt.array, 0) elif variable == "box_coordinates" \ and self.cell_parameters[0] is not None: jump = pt.array[0]-total.array[-1] mult = -jump.astype('i') if len(N.nonzero(mult)) > 0: N.add(pt.array, mult[N.NewAxis, : ], pt.array) jump = pt.array[0] - total.array[-1] mask = N.less(jump, -0.5)- \ N.greater(jump, 0.5) if len(N.nonzero(mask)) > 0: N.add(pt.array, mask[N.NewAxis, :], pt.array) total.array = N.concatenate((total.array, pt.array)) else: self.steps_read.append(0) return total def readRigidBodyTrajectory(self, object, first=0, last=None, skip=1, reference = None): return RigidBodyTrajectory(self, object, first, last, skip, reference) def _boxTransformation(self, pt_in, pt_out, to_box=0): n = 0 for i in range(len(self.steps_read)): t = self.trajectories[i] steps = self.steps_read[i] if steps > 0: t._boxTransformation(pt_in[n:n+steps], pt_out[n:n+steps], to_box) n = n + steps def variables(self): return self.vars def view(self, first=0, last=None, step=1, object = None): Visualization.viewTrajectory(self, first, last, step, object) class TrajectorySetVariable(TrajectoryVariable): """ Variable in a trajectory set A TrajectorySetVariable object is created by extracting a variable from a TrajectorySet object if that variable contains data for each atom and is thus potentially large. It behaves exactly like a TrajectoryVariable object. """ def __init__(self, universe, trajectory_set, name): self.universe = universe self.trajectory_set = trajectory_set self.name = name def __len__(self): return len(self.trajectory_set) def __getitem__(self, item): if not isinstance(item, int): return SubVariable(self, N.arange(len(self)))[item] if item >= len(self.trajectory_set): raise IndexError tindex = N.add.reduce(N.greater_equal(item, self.trajectory_set.nsteps))-1 step = item-self.trajectory_set.nsteps[tindex] t = self.trajectory_set.trajectories[tindex] return getattr(t, self.name)[step] # # Cache for atom trajectories # class ParticleTrajectoryReader(object): def __init__(self, trajectory): self.trajectory = trajectory self.natoms = self.trajectory.universe.numberOfAtoms() self._trajectory = trajectory.trajectory self.cache = {} self.cache_lifetime = 2 def __call__(self, atom, variable, first, last, skip, correct, box): if isinstance(atom, int): index = atom else: index = atom.index if atom.universe() is not self.trajectory.universe: raise ValueError("objects not in the same universe") key = (index, variable, first, last, skip, correct, box) data, count = self.cache.get(key, (None, 0)) if data is not None: self.cache[key] = (data, self.cache_lifetime) return data delete = [] for k, value in self.cache.items(): data, count = value count -= 1 if count == 0: delete.append(k) else: self.cache[k] = (data, count) for k in delete: del self.cache[k] cache_size = min(10, max(1, 100000/max(1, len(self.trajectory)))) natoms = min(cache_size, self.natoms-index) data = self._trajectory.readParticleTrajectories(index, natoms, variable, first, last, skip, correct, box) for i in range(natoms): key = (index+i, variable, first, last, skip, correct, box) self.cache[key] = (data[i], self.cache_lifetime) return data[0] # # Single-atom trajectory # class ParticleTrajectory(object): """ Trajectory data for a single particle A ParticleTrajectory object is created by calling the method :func:`~MMTK.Trajectory.Trajectory.readParticleTrajectory` on a :class:`~MMTK.Trajectory.Trajectory` object. If pt is a ParticleTrajectory object, then * len(pt) is the number of steps stored in it * pt[i] is the value at step i (a vector) """ def __init__(self, trajectory, atom, first=0, last=None, skip=1, variable = "configuration"): if last is None: last = len(trajectory) if variable == "box_coordinates": variable = "configuration" box = 1 else: box = 0 reader = trajectory.particle_trajectory_reader self.array = reader(atom, variable, first, last, skip, variable == "configuration", box) def __len__(self): return self.array.shape[0] def __getitem__(self, index): return Vector(self.array[index]) def translateBy(self, vector): """ Adds a vector to the values at all steps. This does B{not} change the data in the trajectory file. :param vector: the vector to be added :type vector: Scientific.Geometry.Vector """ N.add(self.array, vector.array[N.NewAxis, :], self.array) # # Rigid-body trajectory # class RigidBodyTrajectory(object): """ Rigid-body trajectory data A RigidBodyTrajectory object is created by calling the method :func:`~MMTK.Trajectory.Trajectory.readRigidBodyTrajectory` on a :class:`~MMTK.Trajectory.Trajectory` object. If rbt is a RigidBodyTrajectory object, then * len(rbt) is the number of steps stored in it * rbt[i] is the value at step i (a vector for the center of mass and a quaternion for the orientation) """ def __init__(self, trajectory, object, first=0, last=None, skip=1, reference = None): self.trajectory = trajectory universe = trajectory.universe if last is None: last = len(trajectory) first_conf = trajectory.configuration[first] offset = universe.contiguousObjectOffset([object], first_conf, True) if reference is None: reference = first_conf reference = universe.contiguousObjectConfiguration([object], reference) steps = (last-first+skip-1)/skip mass = object.mass() ref_cms = object.centerOfMass(reference) atoms = object.atomList() possq = N.zeros((steps,), N.Float) cross = N.zeros((steps, 3, 3), N.Float) rcms = N.zeros((steps, 3), N.Float) # cms of the CONTIGUOUS object made of CONTINUOUS atom trajectories for a in atoms: r = trajectory.readParticleTrajectory(a, first, last, skip, "box_coordinates").array w = a._mass/mass N.add(rcms, w*r, rcms) if offset is not None: N.add(rcms, w*offset[a].array, rcms) # relative coords of the CONTIGUOUS reference r_ref = N.zeros((len(atoms), 3), N.Float) for a in range(len(atoms)): r_ref[a] = atoms[a].position(reference).array - ref_cms.array # main loop: storing data needed to fill M matrix for a in range(len(atoms)): r = trajectory.readParticleTrajectory(atoms[a], first, last, skip, "box_coordinates").array r = r - rcms # (a-b)**2 != a**2 - b**2 if offset is not None: N.add(r, offset[atoms[a]].array,r) trajectory._boxTransformation(r, r) w = atoms[a]._mass/mass N.add(possq, w*N.add.reduce(r*r, -1), possq) N.add(possq, w*N.add.reduce(r_ref[a]*r_ref[a],-1), possq) N.add(cross, w*r[:,:,N.NewAxis]*r_ref[N.NewAxis, a,:],cross) self.trajectory._boxTransformation(rcms, rcms) # filling matrix M (formula no 40) k = N.zeros((steps, 4, 4), N.Float) k[:, 0, 0] = -cross[:, 0, 0]-cross[:, 1, 1]-cross[:, 2, 2] k[:, 0, 1] = cross[:, 1, 2]-cross[:, 2, 1] k[:, 0, 2] = cross[:, 2, 0]-cross[:, 0, 2] k[:, 0, 3] = cross[:, 0, 1]-cross[:, 1, 0] k[:, 1, 1] = -cross[:, 0, 0]+cross[:, 1, 1]+cross[:, 2, 2] k[:, 1, 2] = -cross[:, 0, 1]-cross[:, 1, 0] k[:, 1, 3] = -cross[:, 0, 2]-cross[:, 2, 0] k[:, 2, 2] = cross[:, 0, 0]-cross[:, 1, 1]+cross[:, 2, 2] k[:, 2, 3] = -cross[:, 1, 2]-cross[:, 2, 1] k[:, 3, 3] = cross[:, 0, 0]+cross[:, 1, 1]-cross[:, 2, 2] del cross for i in range(1, 4): for j in range(i): k[:, i, j] = k[:, j, i] N.multiply(k, 2., k) for i in range(4): N.add(k[:,i,i], possq, k[:,i,i]) del possq quaternions = N.zeros((steps, 4), N.Float) fit = N.zeros((steps,), N.Float) from Scientific.LA import eigenvectors for i in range(steps): e, v = eigenvectors(k[i]) j = N.argmin(e) if e[j] < 0.: fit[i] = 0. else: fit[i] = N.sqrt(e[j]) if v[j,0] < 0.: quaternions[i] = -v[j] # eliminate jumps else: quaternions[i] = v[j] self.fit = fit self.cms = rcms self.quaternions = quaternions def __len__(self): return self.cms.shape[0] def __getitem__(self, index): from Scientific.Geometry.Quaternion import Quaternion return Vector(self.cms[index]), Quaternion(self.quaternions[index]) # # Type check for trajectory objects # def isTrajectory(object): """ :param object: any Python object :returns: True if object is a trajectory """ import MMTK_trajectory return isinstance(object, (Trajectory, MMTK_trajectory.trajectory_type)) # # Base class for all objects that generate trajectories # class TrajectoryGenerator(object): """ Trajectory generator base class This base class implements the common aspects of everything that generates trajectories: integrators, minimizers, etc. """ def __init__(self, universe, options): self.universe = universe self.options = options def setCallOptions(self, options): self.call_options = options def getActions(self): try: self.actions = self.getOption('actions') except ValueError: self.actions = [] try: if self.getOption('background'): import MMTK_state_accessor self.state_accessor = MMTK_state_accessor.StateAccessor() self.actions.append(self.state_accessor) except ValueError: pass try: steps = self.getOption('steps') except ValueError: steps = None return map(lambda a, t=self, s=steps: a.getSpecificationList(t, s), self.actions) def cleanupActions(self): for a in self.actions: a.cleanup() def getOption(self, option): try: value = self.call_options[option] except KeyError: try: value = self.options[option] except KeyError: try: value = self.default_options[option] except KeyError: raise ValueError('undefined option: ' + option) return value def optionString(self, options): s = '' for o in options: s = s + o + '=' + `self.getOption(o)` + ', ' return s[:-2] def run(self, function, args): if self.getOption('background'): import ThreadManager return ThreadManager.TrajectoryGeneratorThread(self.universe, function, args, self.state_accessor) else: apply(function, args) # # Trajectory action base class # class TrajectoryAction(object): """ Trajectory action base class Subclasses of this base class implement the actions that can be inserted into trajectory generation at regular intervals. """ def __init__(self, first, last, skip): self.first = first self.last = last self.skip = skip spec_type = 'function' def _getSpecificationList(self, trajectory_generator, steps): first = self.first last = self.last if first < 0: first = first + steps if last is None: import MMTK_trajectory last = MMTK_trajectory.maxint elif last < 0: last = last + steps+1 return (self.spec_type, first, last, self.skip) def getSpecificationList(self, trajectory_generator, steps): return self._getSpecificationList(trajectory_generator, steps) \ + (self.Cfunction, self.parameters) def cleanup(self): pass class TrajectoryOutput(TrajectoryAction): """ Trajectory output action A TrajectoryOutput object can be used in the action list of any trajectory-generating operation. It writes any of the available data to a trajectory file. It is possible to use several TrajectoryOutput objects at the same time in order to produce multiple trajectories from a single run. """ def __init__(self, trajectory, data = None, first=0, last=None, skip=1): """ :param trajectory: a trajectory object or a string, which is interpreted as the name of a file that is opened as a trajectory in append mode :param data: a list of data categories. All variables provided by the trajectory generator that fall in any of the listed categories are written to the trajectory file. See the descriptions of the trajectory generators for a list of variables and categories. By default (data = None) the categories "configuration", "energy", "thermodynamic", and "time" are written. :param first: the number of the first step at which the action is run :type first: int :param last: the number of the step at which the action is suspended. A value of None indicates that the action should be applied indefinitely. :type last: int :param skip: the number of steps to skip between two action runs :type skip: int """ TrajectoryAction.__init__(self, first, last, skip) self.destination = trajectory self.categories = data self.must_be_closed = None spec_type = 'trajectory' def getSpecificationList(self, trajectory_generator, steps): if type(self.destination) == type(''): destination = self._setupDestination(self.destination, trajectory_generator.universe) else: destination = self.destination if self.categories is None: categories = self._defaultCategories(trajectory_generator) else: if self.categories == 'all' or self.categories == ['all']: categories = trajectory_generator.available_data else: categories = self.categories for item in categories: if item not in trajectory_generator.available_data: raise ValueError('data item %s is not available' % item) return self._getSpecificationList(trajectory_generator, steps) \ + (destination, categories) def _setupDestination(self, destination, universe): self.must_be_closed = Trajectory(universe, destination, 'a') return self.must_be_closed def cleanup(self): if self.must_be_closed is not None: self.must_be_closed.close() def _defaultCategories(self, trajectory_generator): available = trajectory_generator.available_data return tuple(filter(lambda x, a=available: x in a, self.default_data)) default_data = ['configuration', 'energy', 'thermodynamic', 'time'] class RestartTrajectoryOutput(TrajectoryOutput): """ Restart trajectory output action A RestartTrajectoryOutput object is used in the action list of any trajectory-generating operation. It writes those variables to a trajectory that the trajectory generator declares as necessary for restarting. """ def __init__(self, trajectory, skip=100, length=3): """ :param trajectory: a trajectory object or a string, which is interpreted as the name of a file that is opened as a trajectory in append mode with a cycle length of length and double-precision variables :param skip: the number of steps between two write operations to the restart trajectory :type skip: int :param length: the number of steps stored in the restart trajectory; used only if trajectory is a string """ TrajectoryAction.__init__(self, 0, None, skip) self.destination = trajectory self.categories = None self.length = length def _setupDestination(self, destination, universe): self.must_be_closed = Trajectory(universe, destination, 'a', 'Restart trajectory', 1, self.length) return self.must_be_closed def _defaultCategories(self, trajectory_generator): if trajectory_generator.restart_data is None: raise ValueError("Trajectory generator does not permit restart") return trajectory_generator.restart_data class LogOutput(TrajectoryOutput): """ Protocol file output action A LogOutput object can be used in the action list of any trajectory-generating operation. It writes any of the available data to a text file. """ def __init__(self, file, data = None, first=0, last=None, skip=1): """ :param file: a file object or a string, which is interpreted as the name of a file that is opened in write mode :param data: a list of data categories. All variables provided by the trajectory generator that fall in any of the listed categories are written to the trajectory file. See the descriptions of the trajectory generators for a list of variables and categories. By default (data = None) the categories "configuration", "energy", "thermodynamic", and "time" are written. :param first: the number of the first step at which the action is run :type first: int :param last: the number of the step at which the action is suspended. A value of None indicates that the action should be applied indefinitely. :type last: int :param skip: the number of steps to skip between two action runs :type skip: int """ TrajectoryOutput.__init__(self, file, data, first, last, skip) def _setupDestination(self, destination, universe): self.must_be_closed = open(destination, 'w') return self.must_be_closed spec_type = 'print' default_data = ['energy', 'time'] class StandardLogOutput(LogOutput): """ Standard protocol output action A StandardLogOutput object can be used in the action list of any trajectory-generating operation. It is a specialization of LogOutput to the most common case and writes data in the categories "time" and "energy" to the standard output stream. :param skip: the number of steps to skip between two action runs :type skip: int """ def __init__(self, skip=50): LogOutput.__init__(self, sys.stdout, None, 0, None, skip) # # Snapshot generator # class SnapshotGenerator(TrajectoryGenerator): """ Trajectory generator for single steps A SnapshotGenerator is used for manual assembly of trajectory files. At each call it writes one step to the trajectory, using the current state of the universe (configuration, velocities, etc.) and data provided explicitly with the call. Each call to the SnapshotGenerator object produces one step. All the keyword options can be specified either when creating the generator or when calling it. """ def __init__(self, universe, **options): """ :param universe: the universe on which the generator acts :keyword data: a dictionary that supplies values for variables that are not part of the universe state (e.g. potential energy) :keyword actions: a list of actions to be executed periodically (default is none) """ TrajectoryGenerator.__init__(self, universe, options) self.available_data = [] try: e, g = self.universe.energyAndGradients() except: pass else: self.available_data.append('energy') self.available_data.append('gradients') try: self.universe.configuration() self.available_data.append('configuration') except: pass if self.universe.cellVolume() is not None: self.available_data.append('thermodynamic') if self.universe.velocities() is not None: self.available_data.append('velocities') self.available_data.append('energy') self.available_data.append('thermodynamic') default_options = {'steps': 0, 'actions': []} def __call__(self, **options): self.setCallOptions(options) from MMTK_trajectory import snapshot data = copy.copy(options.get('data', {})) energy_terms = 0 for name in data.keys(): if name == 'time' and 'time' not in self.available_data: self.available_data.append('time') if name[-7:] == '_energy': energy_terms = energy_terms + 1 if 'energy' not in self.available_data: self.available_data.append('energy') if (name == 'temperature' or name == 'pressure') \ and 'thermodynamic' not in self.available_data: self.available_data.append('thermodynamic') if name == 'gradients' and 'gradients' not in self.available_data: self.available_data.append('gradients') actions = self.getActions() for action in actions: categories = action[-1] for c in categories: if c == 'energy' and not data.has_key('kinetic_energy'): v = self.universe.velocities() if v is not None: m = self.universe.masses() e = (v*v*m*0.5).sumOverParticles() data['kinetic_energy'] = e df = self.universe.degreesOfFreedom() data['temperature'] = 2.*e/df/Units.k_B/Units.K if c == 'configuration': if data.has_key('configuration'): data['configuration'] = data['configuration'].array else: data['configuration'] = \ self.universe.configuration().array if c == 'velocities': if data.has_key('velocities'): data['velocities'] = data['velocities'].array else: data['velocities'] = self.universe.velocities().array if c == 'gradients': if data.has_key('gradients'): data['gradients'] = data['gradients'].array p = self.universe.cellParameters() if p is not None: data['box_size'] = p volume = self.universe.cellVolume() if volume is not None: data['volume'] = volume try: m = self.universe.masses() data['masses'] = m.array except: pass snapshot(self.universe, data, actions, energy_terms) # # Trajectory reader (not yet functional...) # if False: class TrajectoryReader(TrajectoryGenerator): def __init__(self, trajectory, options): TrajectoryGenerator.__init__(self, trajectory.universe, options) self.input = trajectory self.available_data = trajectory.variables() default_options = {'trajectory': None, 'log': None, 'options': []} def __call__(self, **options): self.setCallOptions(options) from MMTK_trajectory import readTrajectory readTrajectory(self.universe, self.input.trajectory, [self.getOption('trajectory'), self.getOption('log')] + self.getOption('options')) # # Print information about trajectory file # def trajectoryInfo(filename): """ :param filename: the name of a trajectory file :type filename: str :returns: a string with summarial information about the trajectory """ from Scientific.IO import NetCDF file = NetCDF.NetCDFFile(filename, 'r') nsteps = file.variables['step'].shape[0] if 'minor_step_number' in file.dimensions.keys(): nsteps = nsteps*file.variables['step'].shape[1] s = 'Information about trajectory file ' + filename + ':\n' try: s += file.comment + '\n' except AttributeError: pass s += `file.dimensions['atom_number']` + ' atoms\n' s += `nsteps` + ' steps\n' s += file.history file.close() return s MMTK-2.7.9/MMTK/Units.py0000644000076600000240000000736511662461640015156 0ustar hinsenstaff00000000000000# Define unit conversion factors and physical constants # # Written by Konrad Hinsen # """ Units and physical constants This module defines constants and prefactors that convert from various units to MMTK's internal unit system. There are also some common physical constants. MMTK's unit system is defined by: * nm for length * ps for time * amu (g/mol) for mass * the charge of a proton for charge * rad for angles All formulas that do not contain electrical quantities (charge, electric/magnetic field, ...) have the same form in this unit system as in the SI system. The energy unit that results from the choices given above is kJ/mol. The constants defined in this module are: * SI Prefixes: ato, femto, pico, nano, micro, milli, centi, deci, deca, hecto, kilo, mega, giga, tera, peta * Length units: m, cm, mm, nm, pm, fm, Ang, Bohr * Angle units: rad, deg * Volume units: l * Time units: s, ns, ps, fs * Frequency units: Hz, invcm (wavenumbers) * Mass units: amu, g, kg * Quantity-of-matter units: mol * Energy units: J, kJ, cal (thermochemical), kcal, Hartree, Bohr * Temperature units: K * Force units: N, dyn * Pressure units: Pa, MPa, GPa, bar, kbar, atm * Electrostatic units: C, A, V, D, eV, e * Physical constants: * c (speed of light) * Nav (Avogadro number) * h (Planck constant) * hbar (Planck constant divided by 2*Pi) * k_B (Boltzmann constant) * eps0 (permittivity of vacuum) * me (electron mass) * Other: * akma_time (the time unit in the DCD trajectory format) * electrostatic_energy (the prefactor in Coulomb's law) """ from Scientific import N as Numeric # Prefixes ato = 1.0e-18 femto = 1.0e-15 pico = 1.0e-12 nano = 1.0e-9 micro = 1.0e-6 milli = 1.0e-3 centi = 1.0e-2 deci = 1.0e-1 deca = 1.0e1 hecto = 1.0e2 kilo = 1.0e3 mega = 1.0e6 giga = 1.0e9 tera = 1.0e12 peta = 1.0e15 # Angles # internal unit: radian rad = 1. deg = (Numeric.pi/180.)*rad # Length units # internal unit: nm m = 1./nano # Meter (SI) cm = centi*m mm = milli*m nm = nano*m pm = pico*m fm = femto*m Ang = 1.e-10*m # Angstrom # Volume units l = (deci*m)**3 # liter # Time units # internal unit: ps s = 1./pico # Second (SI) ns = nano*s ps = pico*s fs = femto*s # Speed of light c = 299792458.*m/s # Frequency units # internal unit: THz (1./ps) Hz = 1./s invcm = c/cm # Avogadro number Nav = 6.0221367e23 # Mass units # internal unit: amu (= g/mol) amu = 1 # Atomic mass unit g = Nav*amu kg = kilo*g # Kilogram (SI) # Quantity of matter # internal unit: number mol = Nav # Energy units # internal unit: kJ/mol J = mol/kilo # Joule (SI) kJ = kilo*J cal = 4.184*J # Thermochemical calorie kcal = kilo*cal # Force units # internal unit: kJ/mol/nm N = J/m dyn = 1.e-5*N # Electrostatic units # internal unit: charge in e e = 1. C = 6.24150636309e18*e # Coulomb (SI) A = C/s # Ampere (SI) D = 1.e-15*C/c # Debye V = J/C # Volt eV = e*V # electron volt # Temperature units # internal unit: K K = 1 # Kelvin # Pressure units # internal unit: kJ/mol/nm**3 Pa = J/m**3 # Pascal MPa = mega*Pa GPa = giga*Pa bar = 1.e5*Pa # bar kbar = kilo*bar atm = 101325*Pa # atmosphere # Constants h = 6.626176e-34*J*s hbar = h/(2.*Numeric.pi) k_B = 1.3806513e-23*J/K eps0 = 1./(4.e-7*Numeric.pi)*A**2*m/J/c**2 me = 0.51099906*mega*eV/c**2 electrostatic_energy = 1/(4.*Numeric.pi*eps0) # CHARMM time unit akma_time = Numeric.sqrt(Ang**2/(kcal/mol)) # "Atomic" units Bohr = 4.*Numeric.pi*eps0*hbar**2/(me*e**2) Hartree = hbar**2/(me*Bohr**2) # Remove symbol "Numeric" del Numeric MMTK-2.7.9/MMTK/Universe.py0000644000076600000240000017456312117632051015651 0ustar hinsenstaff00000000000000# This module implements the various types of universes # (infinite, periodic etc.). A universe defines the # geometry of space, the force field, and external interactions # (boundary conditions, external fields, etc.) # # Written by Konrad Hinsen # """ Universes """ __docformat__ = 'restructuredtext' from MMTK import Bonds, ChemicalObjects, Collections, Environment, \ Random, Utility, ParticleProperties, Visualization from Scientific.Geometry import Transformation from Scientific.Geometry import Vector, isVector from Scientific import N import copy try: import threading if not hasattr(threading, 'Thread'): threading = None except ImportError: threading = None # # The base class for all universes. # class Universe(Collections.GroupOfAtoms, Visualization.Viewable): """ Universe A universe represents a complete model of a chemical system, i.e. the molecules, their environment (topology, boundary conditions, thermostats, etc.), and optionally a force field. The class Universe is an abstract base class that defines properties common to all kinds of universes. To create universe objects, use one of its subclasses. In addition to the methods listed below, universe objects support the following operations (u is any universe object, o is any chemical object): * len(u) yields the number of chemical objects in the universe * u[i] returns object number i * u.name = o adds o to the universe and also makes it accessible as an attribute * del u.name removes the object that was assigned to u.name from the universe """ def __init__(self, forcefield, properties): self._forcefield = forcefield self._evaluator = {} self.name = '' if properties.has_key('name'): self.name = properties['name'] del properties['name'] self._objects = Collections.Collection() self._environment = [] self._configuration = None self._masses = None self._atom_properties = {} self._atoms = None self._bond_database = None self._bond_pairs = None self._version = 0 self._np = None is_universe = True is_periodic = False is_orthogonal = False def __getstate__(self): state = copy.copy(self.__dict__) state['_evaluator'] = {} state['_configuration'] = None del state['_masses'] del state['_bond_database'] del state['_bond_pairs'] del state['_np'] del state['_spec'] return state def __setstate__(self, state): state['_np'] = None state['_atoms'] = None state['_bond_database'] = None state['_bond_pairs'] = None self.__dict__['_environment'] = [] if state.has_key('atom_properties'): self.__dict__['_atom_properties'] = state['atom_properties'] del state['atom_properties'] for attr, value in state.items(): self.__dict__[attr] = value self._evaluator = {} self._masses = None self._createSpec() def __len__(self): return len(self._objects) def __getitem__(self, item): return self._objects[item] def __setattr__(self, attr, value): if attr[0] != '_' and self.__dict__.has_key(attr): try: self.removeObject(self.__dict__[attr]) except ValueError: pass self.__dict__[attr] = value if attr[0] != '_' and (ChemicalObjects.isChemicalObject(value) or Environment.isEnvironmentObject(value)): self.addObject(value) def __delattr__(self, attr): try: self.removeObject(self.__dict__[attr]) except ValueError: pass del self.__dict__[attr] def __repr__(self): return self.__class__.__name__ + ' ' + self.name + ' containing ' + \ `len(self._objects)` + ' objects.' __str__ = __repr__ def __copy__(self): return copy.deepcopy(self) def objectList(self, klass = None): """ :param klass: an optional class argument :type klass: class :returns: a list of all chemical objects in the universe. If klass is given, only objects that are instances of klass are returned. :rtype: list """ return self._objects.objectList(klass) def environmentObjectList(self, klass = None): """ :param klass: an optional class argument :type klass: class :returns: a list of all environment objects in the universe. If klass is given, only objects that are instances of klass are returned. :rtype: list """ if klass is None: return self._environment else: return filter(lambda o, k=klass: o.__class__ is k, self._environment) def atomList(self): """ :returns: a list of all atoms in the universe :rtype: list """ if self._atoms is None: self._atoms = self._objects.atomList() return self._atoms def atomIterator(self): return self._objects.atomIterator() def bondedUnits(self): return self._objects.bondedUnits() def universe(self): """ :returns: the universe itself """ return self def addObject(self, object, steal = False): """ Adds object to the universe. If object is a Collection, all elements of the Collection are added to the universe. :param object: the object (chemical or environment) to be added :param steal: if True, permit stealing the object from another universe, otherwise the object must not yet be attached to any universe. :type steal: bool """ if ChemicalObjects.isChemicalObject(object): if (not steal) and object.parent is not None: if isUniverse(object.parent): raise ValueError(`object` + ' is already in another universe') else: raise ValueError(`object` + ' is part of another object') object.parent = self self._objects.addObject(object) self._changed(True) elif Environment.isEnvironmentObject(object): for o in self._environment: o.checkCompatibilityWith(object) self._environment.append(object) self._changed(False) elif Collections.isCollection(object) \ or Utility.isSequenceObject(object): for o in object: self.addObject(o, steal) else: raise TypeError(repr(object) + ' cannot be added to a universe') def removeObject(self, object): """ Removes object from the universe. If object is a Collection, each of its elements is removed. The object to be removed must be in the universe. :param object: the object (chemical or environment) to be removed """ if ChemicalObjects.isChemicalObject(object): if object.parent != self: raise ValueError(`object` + ' is not in this universe.') object.parent = None self._objects.removeObject(object) self._changed(True) elif Collections.isCollection(object) \ or (Utility.isSequenceObject(object) # Strings are nasty because their elements are strings # as well. This creates infinite recursion without # this special-case handling. and not isinstance(object, basestring)): for o in object: self.removeObject(o) elif Environment.isEnvironmentObject(object): self._environment.remove(object) self._changed(False) else: raise ValueError(`object` + ' is not in this universe.') def selectShell(self, point, r1, r2=0.): """ :param point: a point in space :type point: Scientific.Geometry.Vector :param r1: one of the radii of a spherical shell :type r1: float :param r2: the other of the two radii of a spherical shell :type r2: float :returns: a Collection of all objects in the universe whose distance from point lies between r1 and r2. """ return self._objects.selectShell(point, r1, r2) def selectBox(self, p1, p2): """ :param p1: one corner of a box in space :type p1: Scientific.Geometry.Vector :param p2: the other corner of a box in space :type p2: Scientific.Geometry.Vector :returns: a Collection of all objects in the universe that lie within the box whose diagonally opposite corners are given by p1 and p2. """ return self._objects.selectBox(p1, p2) def _changed(self, system_size_changed): self._evaluator = {} self._bond_database = None self._version += 1 if system_size_changed: if self._configuration is not None: for a in self.atomList(): a.unsetArray() self._configuration = None self._masses = None self._atom_properties = {} self._atoms = None self._np = None self._bond_pairs = None else: if self._configuration is not None: self._configuration.version = self._version if self._masses is not None: self._masses.version = self._version def acquireReadStateLock(self): """ Acquire the universe read state lock. Any application that uses threading must acquire this lock prior to accessing the current state of the universe, in particular its configuration (particle positions). This guarantees the consistency of the data; while any thread holds the read state lock, no other thread can obtain the write state lock that permits modifying the state. The read state lock should be released as soon as possible. The read state lock can be acquired only if no thread holds the write state lock. If the read state lock cannot be acquired immediately, the thread will be blocked until it becomes available. Any number of threads can acquire the read state lock simultaneously. """ return self._spec.stateLock(1) def acquireWriteStateLock(self): """ Acquire the universe write state lock. Any application that uses threading must acquire this lock prior to modifying the current state of the universe, in particular its configuration (particle positions). This guarantees the consistency of the data; while any thread holds the write state lock, no other thread can obtain the read state lock that permits accessing the state. The write state lock should be released as soon as possible. The write state lock can be acquired only if no other thread holds either the read state lock or the write state lock. If the write state lock cannot be acquired immediately, the thread will be blocked until it becomes available. """ return self._spec.stateLock(-1) def releaseReadStateLock(self, write=False): """ Release the universe read state lock. """ return self._spec.stateLock(2) def releaseWriteStateLock(self, write=False): """ Release the universe write state lock. """ return self._spec.stateLock(-2) def acquireConfigurationChangeLock(self, waitflag=True): """ Acquire the configuration change lock. This lock should be acquired before starting an algorithm that changes the configuration continuously, e.g. minimization or molecular dynamics algorithms. This guarantees the proper order of execution when several such operations are started in succession. For example, when a minimization should be followed by a dynamics run, the use of this flag permits both operations to be started as background tasks which will be executed one after the other, permitting other threads to run in parallel. The configuration change lock should not be confused with the universe state lock. The former guarantees the proper sequence of long-running algorithms, whereas the latter guarantees the consistency of the data. A dynamics algorithm, for example, keeps the configuration change lock from the beginning to the end, but acquires the universe state lock only immediately before modifying configuration and velocities, and releases it immediately afterwards. :param waitflag: if true, the method waits until the lock becomes available; this is the most common mode. If false, the method returns immediately even if another thread holds the lock. :type waitflag: bool :returns: a flag indicating if the lock was successfully acquired (1) or not (0). :rtype: int """ if waitflag: return self._spec.configurationChangeLock(1) else: return self._spec.configurationChangeLock(0) def releaseConfigurationChangeLock(self): """ Releases the configuration change lock. """ self._spec.configurationChangeLock(2) def setForceField(self, forcefield): """ :param forcefield: the new forcefield for this universe :type forcefield: :class:`~MMTK.ForceFields.ForceField.ForceField` """ self._forcefield = forcefield self._evaluator = {} self._bond_database = None def position(self, object, conf): if ChemicalObjects.isChemicalObject(object) \ or Collections.isCollection(object): return object.position(conf) elif isVector(object): return object else: return Vector(object) def numberOfAtoms(self): return self._objects.numberOfAtoms() def numberOfPoints(self): if self._np is None: self._np = Collections.GroupOfAtoms.numberOfPoints(self) return self._np numberOfCartesianCoordinates = numberOfPoints def configuration(self): """ :returns: the configuration object describing the current configuration of the universe. Note that this is not a copy of the current state, but a reference: the positions in the configuration object will change when coordinate changes are applied to the universe in whatever way. :rtype: :class:`~MMTK.ParticleProperties.Configuration` """ if self._configuration is None: np = self.numberOfAtoms() coordinates = N.zeros((np, 3), N.Float) index_map = {} redef = [] for a in self.atomList(): if a.index is None or a.index >= np: redef.append(a) else: if index_map.get(a.index, None) is None: index_map[a.index] = a else: redef.append(a) free_indices = [i for i in xrange(np) if index_map.get(i, None) is None] assert len(free_indices) == len(redef) for a, i in zip(redef, free_indices): a.index = i # At this point a.index runs from 0 to np-1 in the universe. for a in self.atomList(): if a.array is None: try: coordinates[a.index, :] = a.pos.array del a.pos except AttributeError: coordinates[a.index, :] = Utility.undefined else: coordinates[a.index, :] = a.array[a.index, :] a.array = coordinates # Define configuration object. self._configuration = 1 # a hack to prevent endless recursion self._configuration = \ ParticleProperties.Configuration(self, coordinates) return self._configuration def copyConfiguration(self): """ This operation is thread-safe; it won't return inconsistent data even when another thread is modifying the configuration. :returns: a copy of the current configuration :rtype: :class:`~MMTK.ParticleProperties.Configuration` """ self.acquireReadStateLock() try: conf = copy.copy(self.configuration()) finally: self.releaseReadStateLock() return conf def atomNames(self): self.configuration() names = self.numberOfAtoms()*[None] for a in self.atomList(): names[a.index] = a.fullName() return names def setConfiguration(self, configuration, block=True): """ Update the current configuration of the universe by copying the given input configuration. :param configuration: the new configuration :type configuration: :class:`~MMTK.ParticleProperties.Configuration` :param block: if True, the operation blocks other threads from accessing the configuration before the update is completed. If False, it is assumed that the caller takes care of locking. :type block: bool """ if not ParticleProperties.isConfiguration(configuration): raise TypeError('not a universe configuration') conf = self.configuration() if block: self.acquireWriteStateLock() try: conf.assign(configuration) self.setCellParameters(configuration.cell_parameters) finally: if block: self.releaseWriteStateLock() def addToConfiguration(self, displacement, block=True): """ Update the current configuration of the universe by adding the given displacement vector. :param displacement: the displacement vector for each atom :type displacement: :class:`~MMTK.ParticleProperties.ParticleVector` :param block: if True, the operation blocks other threads from accessing the configuration before the update is completed. If False, it is assumed that the caller takes care of locking. :type block: bool """ conf = self.configuration() if block: self.acquireWriteStateLock() try: conf.assign(conf+displacement) finally: if block: self.releaseWriteStateLock() def getParticleScalar(self, name, datatype = N.Float): """ :param name: the name of an atom attribute :type name: str :param datatype: the datatype of the array allocated to hold the data :returns: the values of the attribute 'name' for each atom in the universe. :rtype: :class:`~MMTK.ParticleProperties.ParticleScalar` """ conf = self.configuration() array = N.zeros((len(conf),), datatype) for a in self.atomList(): array[a.index] = getattr(a, name) return ParticleProperties.ParticleScalar(self, array) getAtomScalarArray = getParticleScalar def getParticleBoolean(self, name): """ :param name: the name of an atom attribute :type name: str :returns: the values of the boolean attribute 'name' for each atom in the universe, or False for atoms that do not have the attribute. :rtype: :class:`~MMTK.ParticleProperties.ParticleScalar` """ conf = self.configuration() array = N.zeros((len(conf),), N.Int) for a in self.atomList(): try: array[a.index] = getattr(a, name) except AttributeError: pass return ParticleProperties.ParticleScalar(self, array) getAtomBooleanArray = getParticleBoolean def masses(self): """ :returns: the masses of all atoms in the universe :rtype: :class:`~MMTK.ParticleProperties.ParticleScalar` """ if self._masses is None: self._masses = self.getParticleScalar('_mass') return self._masses def charges(self): """ Return the atomic charges defined by the universe's force field. :returns: the charges of all atoms in the universe :rtype: :class:`~MMTK.ParticleProperties.ParticleScalar` """ ff = self._forcefield if ff is None: raise ValueError("no force field defined") return ff.charges(self) def velocities(self): """ :returns: the current velocities of all atoms, or None if no velocities are defined. Note that this is not a copy of the current state but a reference to it; its data will change whenever any changes are made to the current velocities. :rtype: :class:`~MMTK.ParticleProperties.ParticleVector` """ try: return self._atom_properties['velocity'] except KeyError: return None def setVelocities(self, velocities, block=True): """ Update the current velocities of the universe by copying the given input velocities. :param velocities: the new velocities, or None to remove the velocity definition from the universe :type velocities: :class:`~MMTK.ParticleProperties.ParticleVector` :param block: if True, the operation blocks other threads from accessing the configuration before the update is completed. If False, it is assumed that the caller takes care of locking. :type block: bool """ if velocities is None: try: del self._atom_properties['velocity'] except KeyError: pass else: try: v = self._atom_properties['velocity'] except KeyError: v = ParticleProperties.ParticleVector(self) self._atom_properties['velocity'] = v if block: self.acquireWriteStateLock() try: v.assign(velocities) finally: if block: self.releaseWriteStateLock() def initializeVelocitiesToTemperature(self, temperature): """ Generate random velocities for all atoms from a Boltzmann distribution. :param temperature: the reference temperature for the Boltzmann distribution :type temperature: float """ self.configuration() masses = self.masses() if self._atom_properties.has_key('velocity'): del self._atom_properties['velocity'] fixed = self.getParticleBoolean('fixed') np = self.numberOfPoints() velocities = N.zeros((np, 3), N.Float) for i in xrange(np): m = masses[i] if m > 0. and not fixed[i]: velocities[i] = Random.randomVelocity(temperature, m).array self._atom_properties['velocity'] = \ ParticleProperties.ParticleVector(self, velocities) self.adjustVelocitiesToConstraints() def scaleVelocitiesToTemperature(self, temperature, block=True): """ Scale all velocities by a common factor in order to obtain the specified temperature. :param temperature: the reference temperature :type temperature: float :param block: if True, the operation blocks other threads from accessing the configuration before the update is completed. If False, it is assumed that the caller takes care of locking. :type block: bool """ velocities = self.velocities() factor = N.sqrt(temperature/self.temperature()) if block: self.acquireWriteStateLock() try: velocities.scaleBy(factor) finally: if block: self.releaseWriteStateLock() def degreesOfFreedom(self): return GroupOfAtoms.degreesOfFreedom(self) \ - self.numberOfDistanceConstraints() def distanceConstraintList(self): """ :returns: the list of distance constraints :rtype: list """ return self._objects.distanceConstraintList() def numberOfDistanceConstraints(self): """ :returns: the number of distance constraints :rtype: int """ return self._objects.numberOfDistanceConstraints() def setBondConstraints(self): """ Sets distance constraints for all bonds. """ self.configuration() self._objects.setBondConstraints(self) self.enforceConstraints() def removeDistanceConstraints(self): """ Removes all distance constraints. """ self._objects.removeDistanceConstraints(self) def enforceConstraints(self, configuration=None, velocities=None): """ Enforces the previously defined distance constraints by modifying the configuration and velocities. :param configuration: the configuration in which the constraints are enforced (None for current configuration) :type configuration: :class:`~MMTK.ParticleProperties.Configuration` :param velocities: the velocities in which the constraints are enforced (None for current velocities) :type velocities: :class:`~MMTK.ParticleProperties.ParticleVector` """ from MMTK import Dynamics Dynamics.enforceConstraints(self, configuration) self.adjustVelocitiesToConstraints(velocities) def adjustVelocitiesToConstraints(self, velocities=None, block=True): """ Modifies the velocities to be compatible with the distance constraints, i.e. projects out the velocity components along the constrained distances. :param velocities: the velocities in which the constraints are enforced (None for current velocities) :type velocities: :class:`~MMTK.ParticleProperties.ParticleVector` :param block: if True, the operation blocks other threads from accessing the configuration before the update is completed. If False, it is assumed that the caller takes care of locking. :type block: bool """ from MMTK import Dynamics if velocities is None: velocities = self.velocities() if velocities is not None: if block: self.acquireWriteStateLock() try: Dynamics.projectVelocities(self, velocities) finally: if block: self.releaseWriteStateLock() def bondLengthDatabase(self): if self._bond_database is None: self._bond_database = None if self._bond_database is None: ff = self._forcefield try: self._bond_database = ff.bondLengthDatabase(self) except AttributeError: pass if self._bond_database is None: self._bond_database = Bonds.DummyBondLengthDatabase(self) return self._bond_database def forcefield(self): """ :returns: the force field :rtype: :class:`~MMTK.ForceFields.ForceField.ForceField` """ return self._forcefield def energyEvaluatorParameters(self, subset1 = None, subset2 = None): self.configuration() from MMTK.ForceFields import ForceField ffdata = ForceField.ForceFieldData() return self._forcefield.evaluatorParameters(self, subset1, subset2, ffdata) def energyEvaluator(self, subset1 = None, subset2 = None, threads=None, mpi_communicator=None): if self._forcefield is None: raise ValueError("no force field defined") try: eval = self._evaluator[(subset1, subset2, threads)] except KeyError: from MMTK.ForceFields import ForceField eval = ForceField.EnergyEvaluator(self, self._forcefield, subset1, subset2, threads, mpi_communicator) self._evaluator[(subset1, subset2, threads)] = eval return eval def energy(self, subset1 = None, subset2 = None, small_change=False): """ :param subset1: a subset of a universe, or None :type subset1: :class:`~MMTK.ChemicalObjects.ChemicalObject` :param subset2: a subset of a universe, or None :type subset2: :class:`~MMTK.ChemicalObjects.ChemicalObject` :param small_change: if True, algorithms optimized for small configurational changes relative to the last evaluation may be used. :type small_change: bool :returns: the potential energy of interaction between the atoms in subset1 and the atoms in subset2. If subset2 is None, the interactions within subset1 are calculated. It both subsets are None, the potential energy of the whole universe is returned. :rtype: float """ eval = self.energyEvaluator(subset1, subset2) return eval(0, 0, small_change) def energyAndGradients(self, subset1 = None, subset2 = None, small_change=False): """ :returns: the energy and the energy gradients :rtype: (float, :class:`~MMTK.ParticleProperties.ParticleVector`) """ eval = self.energyEvaluator(subset1, subset2) return eval(1, 0, small_change) def energyAndForceConstants(self, subset1 = None, subset2 = None, small_change=False): """ :returns: the energy and the force constants :rtype: (float, :class:`~MMTK.ParticleProperties.SymmetricPairTensor`) """ eval = self.energyEvaluator(subset1, subset2) e, g, fc = eval(0, 1, small_change) return e, fc def energyGradientsAndForceConstants(self, subset1 = None, subset2 = None, small_change=False): """ :returns: the energy, its gradients, and the force constants :rtype: (float, :class:`~MMTK.ParticleProperties.ParticleVector`, :class:`~MMTK.ParticleProperties.SymmetricPairTensor`) """ eval = self.energyEvaluator(subset1, subset2) return eval(1, 1, small_change) def energyTerms(self, subset1 = None, subset2 = None, small_change=False): """ :returns: a dictionary containing the energy values for each energy term separately. The energy terms are defined by the force field. :rtype: dict """ eval = self.energyEvaluator(subset1, subset2) eval(0, 0, small_change) return eval.lastEnergyTerms() def configurationDifference(self, conf1, conf2): """ :param conf1: a configuration :type conf1: :class:`~MMTK.ParticleProperties.Configuration` :param conf2: a configuration :type conf2: :class:`~MMTK.ParticleProperties.Configuration` :returns: the difference vector between the two configurations for each atom, taking into account the universe topology (e.g. minimum-image convention). :rtype: :class:`~MMTK.ParticleProperties.ParticleVector` """ d = conf2-conf1 cell = conf1.cell_parameters if cell is not None: self._spec.foldCoordinatesIntoBox(d.array) return d def distanceVector(self, p1, p2, conf=None): """ :param p1: a vector or a chemical object whose position is taken :param p2: a vector or a chemical object whose position is taken :param conf: a configuration (None for the current configuration) :returns: the distance vector between p1 and p2 (i.e. the vector from p1 to p2) in the configuration conf, taking into account the universe's topology. """ p1 = self.position(p1, conf) p2 = self.position(p2, conf) if conf is None: return Vector(self._spec.distanceVector(p1.array, p2.array)) else: cell = self._fixCellParameters(conf.cell_parameters) if cell is None: return Vector(self._spec.distanceVector(p1.array, p2.array)) else: return Vector(self._spec.distanceVector(p1.array, p2.array, cell)) def distance(self, p1, p2, conf = None): """ :param p1: a vector or a chemical object whose position is taken :param p2: a vector or a chemical object whose position is taken :param conf: a configuration (None for the current configuration) :returns: the distance between p1 and p2, i.e. the length of the distance vector :rtype: float """ return self.distanceVector(p1, p2, conf).length() def angle(self, p1, p2, p3, conf = None): """ :param p1: a vector or a chemical object whose position is taken :param p2: a vector or a chemical object whose position is taken :param p3: a vector or a chemical object whose position is taken :param conf: a configuration (None for the current configuration) :returns: the angle between the distance vectors p1-p2 and p3-p2 :rtype: float """ v1 = self.distanceVector(p2, p1, conf) v2 = self.distanceVector(p2, p3, conf) return v1.angle(v2) def dihedral(self, p1, p2, p3, p4, conf = None): """ :param p1: a vector or a chemical object whose position is taken :param p2: a vector or a chemical object whose position is taken :param p3: a vector or a chemical object whose position is taken :param p4: a vector or a chemical object whose position is taken :param conf: a configuration (None for the current configuration) :returns: the dihedral angle between the plane containing the distance vectors p1-p2 and p3-p2 and the plane containing the distance vectors p2-p3 and p4-p3 :rtype: float """ v1 = self.distanceVector(p2, p1, conf) v2 = self.distanceVector(p3, p2, conf) v3 = self.distanceVector(p3, p4, conf) a = v1.cross(v2).normal() b = v3.cross(v2).normal() cos = a*b sin = b.cross(a)*v2/v2.length() return Transformation.angleFromSineAndCosine(sin, cos) def _deleteAtom(self, atom): pass def basisVectors(self): """ :returns: the basis vectors of the elementary cell of a periodic universe, or None for a non-periodic universe :rtype: NoneType or list """ return None def reciprocalBasisVectors(self): """ :returns: the reciprocal basis vectors of the elementary cell of a periodic universe, or None for a non-periodic universe :rtype: NoneType or list """ return None def cellParameters(self): return None def setCellParameters(self, parameters): if parameters is not None: raise ValueError('incompatible cell parameters') def _fixCellParameters(self, cell_parameters): return cell_parameters def cellVolume(self): """ :returns: the volume of the elementary cell of a periodic universe, None for a non-periodic universe :rtype: NoneType or float """ return None def largestDistance(self): """ :returns: the largest possible distance between any two points that can be represented independent of orientation, i.e. the radius of the largest sphere that fits into the simulation cell. Returns None if no such upper limit exists. :rtype: NoneType or float """ return None def contiguousObjectOffset(self, objects = None, conf = None, box_coordinates = False): """ :param objects: a list of chemical objects, or None for all objects in the universe :type objects: list :param conf: a configuration (None for the current configuration) :param box_coordinates: use box coordinates rather than real ones :type box_coordinates: bool :returns: a set of displacement vectors relative to the conf which, when added to the configuration, create a configuration in which none of the objects is split across the edge of the elementary cell. For nonperiodic universes the return value is None. :rtype: :class:`~MMTK.ParticleProperties.ParticleVector` """ return None def contiguousObjectConfiguration(self, objects = None, conf = None): """ :param objects: a list of chemical objects, or None for all objects in the universe :type objects: list :param conf: a configuration (None for the current configuration) :returns: configuration conf (default: current configuration) corrected by the contiguous object offsets for that configuration. :rtype: :class:`~MMTK.ParticleProperties.Configuration` """ if conf is None: conf = self.configuration() offset = self.contiguousObjectOffset(objects, conf) if offset is not None: return conf + offset else: return copy.copy(conf) def realToBoxCoordinates(self, vector): """ Box coordinates are defined only for periodic universes; their components have values between -0.5 and 0.5; these extreme values correspond to the walls of the simulation box. :param vector: a point in the universe :returns: the box coordinate equivalent of vector, or the original vector if no box coordinate system exists :rtype: Scientific.Geometry.Vector """ return vector def boxToRealCoordinates(self, vector): """ :param vector: a point in the universe expressed in box coordinates :returns: the real-space equivalent of vector :rtype: Scientific.Geometry.Vector """ return vector def _realToBoxPointArray(self, array, parameters=None): return array def _boxToRealPointArray(self, array, parameters=None): return array def cartesianToFractional(self, vector): """ Fractional coordinates are defined only for periodic universes; their components have values between 0. and 1. :param vector: a point in the universe :type vector: Scientific.Geometry.Vector :returns: the fractional coordinate equivalent of vector :rtype: Scientific.N.array_type """ raise ValueError("Universe is not periodic") def cartesianToFractionalMatrix(self): raise ValueError("Universe is not periodic") def fractionalToCartesian(self, array): """ Fractional coordinates are defined only for periodic universes; their components have values between 0. and 1. :param array: an array of fractional coordinates :type array: Scientific.N.array_type :returns: the real-space equivalent of vector :rtype: Scientific.Geometry.Vector """ raise ValueError("Universe is not periodic") def fractionalToCartesianMatrix(self): raise ValueError("Universe is not periodic") def foldCoordinatesIntoBox(self): return def randomPoint(self): """ :returns: a random point from a uniform distribution within the universe. This operation is defined only for finite-volume universes, e.g. periodic universes. :rtype: Scientific.Geometry.Vector """ raise TypeError("undefined operation") def map(self, function): """ Apply a function to all objects in the universe and return the list of the results. If the results are chemical objects, a Collection object is returned instead of a list. :param function: the function to be applied :type function: callable :returns: the list or collection of the results """ return self._objects.map(function) def description(self, objects = None, index_map = None): if objects is None: objects = self attributes = {} for attr in dir(self): if attr[0] != '_': object = getattr(self, attr) if ChemicalObjects.isChemicalObject(object) \ or Environment.isEnvironmentObject(object): attributes[object] = attr items = [] for o in objects.objectList(): attr = attributes.get(o, None) if attr is not None: items.append(repr(attr)) items.append(o.description(index_map)) for o in self._environment: attr = attributes.get(o, None) if attr is not None: items.append(repr(attr)) items.append(o.description()) try: classname = self.classname_for_trajectories except AttributeError: classname = self.__class__.__name__ s = 'c(%s,[%s])' % \ (`classname + self._descriptionArguments()`, ','.join(items)) return s def _graphics(self, conf, distance_fn, model, module, options): return self._objects._graphics(conf, distance_fn, model, module, options) def setFromTrajectory(self, trajectory, step = None): """ Set the state of the universe to the one stored in a trajectory. This operation is thread-safe; it blocks other threads that want to access the configuration or velocities while the data is being updated. :param trajectory: a trajectory object for this universe :type trajectory: :class:`~MMTK.Trajectory.Trajectory` :param step: a step number, or None for the default step (0 for a standard trajectory, the last written step for a restart trajectory) :type step: int """ if step is None: step = trajectory.defaultStep() self.acquireWriteStateLock() try: self.setConfiguration(trajectory.configuration[step], False) vel = self.velocities() try: vel_tr = trajectory.velocities[step] except AttributeError: if vel is not None: Utility.warning("velocities were not modified because " + "the trajectory does not contain " + "velocity data.") return if vel is None: self._atom_properties['velocity'] = vel_tr else: vel.assign(vel_tr) finally: self.releaseWriteStateLock() # # More efficient reimplementations of methods in Collections.GroupOfAtoms # def numberOfFixedAtoms(self): return self.getParticleBoolean('fixed').sumOverParticles() def degreesOfFreedom(self): return 3*(self.numberOfAtoms()-self.numberOfFixedAtoms()) \ - self.numberOfDistanceConstraints() def mass(self): return self.masses().sumOverParticles() def centerOfMass(self, conf = None): m = self.masses() if conf is None: conf = self.configuration() return (m*conf).sumOverParticles()/m.sumOverParticles() def kineticEnergy(self, velocities = None): if velocities is None: velocities = self.velocities() return 0.5*velocities.massWeightedDotProduct(velocities) def momentum(self, velocities = None): if velocities is None: velocities = self.velocities() return (self.masses()*velocities).sumOverParticles() def translateBy(self, vector): conf = self.configuration().array N.add(conf, vector.array[N.NewAxis, :], conf) def applyTransformation(self, t): conf = self.configuration().array rot = t.rotation().tensor.array conf[:] = N.dot(conf, N.transpose(rot)) N.add(conf, t.translation().vector.array[N.NewAxis, :], conf) def writeXML(self, file): file.write('\n\n') file.write('\n\n') file.write('\n\n') memo = {'counter': 1} instances = [] atoms = [] for object in self._objects.objectList(): instances = instances + object.writeXML(file, memo, 1) atoms = atoms + object.getXMLAtomOrder() file.write('\n\n\n') file.write('\n' % self.XMLSpec()) for instance in instances: file.write(' ') file.write(instance) file.write('\n') conf = self.configuration() if conf.hasValidPositions(): file.write(' \n') file.write(' \n') file.write(' \n') file.write('\n\n') file.write('\n') # # Infinite universes # class InfiniteUniverse(Universe): """ Infinite (unbounded and nonperiodic) universe. """ def __init__(self, forcefield=None, **properties): """ :param forcefield: a force field, or None for no force field :type forcefield: :class:`~MMTK.ForceFields.ForceField.ForceField` """ Universe.__init__(self, forcefield, properties) self._createSpec() def CdistanceFunction(self): from MMTK_universe import infinite_universe_distance_function return infinite_universe_distance_function, N.array([0.]) def CcorrectionFunction(self): from MMTK_universe import infinite_universe_correction_function return infinite_universe_correction_function, N.array([0.]) def CvolumeFunction(self): from MMTK_universe import infinite_universe_volume_function return infinite_universe_volume_function, N.array([0.]) def CboxTransformationFunction(self): return None, N.array([0.]) def _createSpec(self): from MMTK_universe import InfiniteUniverseSpec self._spec = InfiniteUniverseSpec() def _descriptionArguments(self): if self._forcefield is None: return '()' else: return '(%s)' % self._forcefield.description() def XMLSpec(self): return 'topology="infinite"' # # 3D periodic universe base class # class Periodic3DUniverse(Universe): is_periodic = True def setVolume(self, volume): """ Multiplies all edge lengths by the same factor such that the cell volume becomes equal to the specified value. :param volume: the desired volume :type volume: float """ factor = (volume/self.cellVolume())**(1./3.) self.scaleSize(factor) def foldCoordinatesIntoBox(self): self._spec.foldCoordinatesIntoBox(self.configuration().array) def basisVectors(self): return [self.boxToRealCoordinates(Vector(1., 0., 0.)), self.boxToRealCoordinates(Vector(0., 1., 0.)), self.boxToRealCoordinates(Vector(0., 0., 1.))] def cartesianToFractional(self, vector): r1, r2, r3 = self.reciprocalBasisVectors() return N.array([r1*vector, r2*vector, r3*vector]) def cartesianToFractionalMatrix(self): return N.array(self.reciprocalBasisVectors()) def fractionalToCartesian(self, array): e1, e2, e3 = self.basisVectors() return array[0]*e1 + array[1]*e2 + array[2]*e3 def fractionalToCartesianMatrix(self): return N.transpose(self.basisVectors()) def randomPoint(self): return self.boxToRealCoordinates(Random.randomPointInBox(1., 1., 1.)) def contiguousObjectOffset(self, objects = None, conf = None, box_coordinates = 0): from MMTK_universe import contiguous_object_offset if objects is None or objects == self or objects == [self]: default = True objects = self._objects.objectList() pairs = self._bond_pairs else: default = False pairs = None if conf is None: conf = self.configuration() cell = self._fixCellParameters(conf.cell_parameters) offset = ParticleProperties.ParticleVector(self) if pairs is None: pairs = [] for o in objects: new_object = True if ChemicalObjects.isChemicalObject(o): units = o.bondedUnits() elif Collections.isCollection(o) or isUniverse(o): units = set([u for element in o for u in element.topLevelChemicalObject() .bondedUnits()]) else: raise ValueError(str(o) + " not a chemical object") for bu in units: atoms = [a.index for a in bu.atomsWithDefinedPositions()] mpairs = bu.traverseBondTree(lambda a: a.index) mpairs = [(a1, a2) for (a1, a2) in mpairs if a1 in atoms and a2 in atoms] if len(mpairs) == 0: mpairs = Utility.pairs(atoms) new_object = False pairs.extend(mpairs) pairs = N.array(pairs) if default: self._bond_pairs = pairs if cell is None: contiguous_object_offset(self._spec, pairs, conf.array, offset.array, box_coordinates) else: contiguous_object_offset(self._spec, pairs, conf.array, offset.array, box_coordinates, cell) return offset def _graphics(self, conf, distance_fn, model, module, options): objects = self._objects._graphics(conf, distance_fn, model, module, options) v1, v2, v3 = self.basisVectors() p = -0.5*(v1+v2+v3) color = options.get('color', 'white') material = module.EmissiveMaterial(color) objects.append(module.Line(p, p+v1, material=material)) objects.append(module.Line(p, p+v2, material=material)) objects.append(module.Line(p+v1, p+v1+v2, material=material)) objects.append(module.Line(p+v2, p+v1+v2, material=material)) objects.append(module.Line(p, p+v3, material=material)) objects.append(module.Line(p+v1, p+v1+v3, material=material)) objects.append(module.Line(p+v2, p+v2+v3, material=material)) objects.append(module.Line(p+v1+v2, p+v1+v2+v3, material=material)) objects.append(module.Line(p+v3, p+v1+v3, material=material)) objects.append(module.Line(p+v3, p+v2+v3, material=material)) objects.append(module.Line(p+v1+v3, p+v1+v2+v3, material=material)) objects.append(module.Line(p+v2+v3, p+v1+v2+v3, material=material)) return objects # # Orthorhombic universe with periodic boundary conditions # class OrthorhombicPeriodicUniverse(Periodic3DUniverse): """ Periodic universe with orthorhombic elementary cell. """ def __init__(self, size = None, forcefield = None, **properties): """ :param size: a sequence of length three specifying the edge lengths along the x, y, and z directions :param forcefield: a force field, or None for no force field :type forcefield: :class:`~MMTK.ForceFields.ForceField.ForceField` """ Universe.__init__(self, forcefield, properties) self.data = N.zeros((3,), N.Float) if size is not None: self.setSize(size) self._createSpec() is_orthogonal = True def __setstate__(self, state): Universe.__setstate__(self, state) if len(self.data.shape) == 2: self.data = self.data[0] def setSize(self, size): self.data[:] = size def scaleSize(self, factor): """ Multiplies all edge lengths by a factor. :param factor: the scale factor :type factor: float """ self.data[:] = factor*self.data self._spec.foldCoordinatesIntoBox(self.configuration().array) def setCellParameters(self, parameters): if parameters is not None: self.data[:] = parameters def realToBoxCoordinates(self, vector): x, y, z = vector return Vector(x/self.data[0], y/self.data[1], z/self.data[2]) def boxToRealCoordinates(self, vector): x, y, z = vector return Vector(x*self.data[0], y*self.data[1], z*self.data[2]) def _realToBoxPointArray(self, array, parameters=None): if parameters is None: parameters = self.data if parameters.shape == (3,): parameters = parameters[N.NewAxis, :] return array/parameters def _boxToRealPointArray(self, array, parameters=None): if parameters is None: parameters = self.data if parameters.shape == (3,): parameters = parameters[N.NewAxis, :] return array*parameters def CdistanceFunction(self): from MMTK_universe import orthorhombic_universe_distance_function return orthorhombic_universe_distance_function, self.data def CcorrectionFunction(self): from MMTK_universe import orthorhombic_universe_correction_function return orthorhombic_universe_correction_function, self.data def CvolumeFunction(self): from MMTK_universe import orthorhombic_universe_volume_function return orthorhombic_universe_volume_function, self.data def CboxTransformationFunction(self): from MMTK_universe import orthorhombic_universe_box_transformation return orthorhombic_universe_box_transformation, self.data def cellParameters(self): return self.data def reciprocalBasisVectors(self): return [Vector(1., 0., 0.)/self.data[0], Vector(0., 1., 0.)/self.data[1], Vector(0., 0., 1.)/self.data[2]] def cellVolume(self): return N.multiply.reduce(self.data) def largestDistance(self): return 0.5*N.minimum.reduce(self.data) def _createSpec(self): from MMTK_universe import OrthorhombicPeriodicUniverseSpec self._spec = OrthorhombicPeriodicUniverseSpec(self.data) def _descriptionArguments(self): if self._forcefield is None: return '((0.,0.,0.),)' else: return '((0.,0.,0.),%s)' % self._forcefield.description() def XMLSpec(self): return 'topology="periodic3d" ' + \ 'cellshape="orthorhombic" ' + \ ('cellsize="%f %f %f" ' % tuple(self.data)) + \ 'units="units:nm"' # # Cubic universe with periodic boundary conditions # class CubicPeriodicUniverse(OrthorhombicPeriodicUniverse): """ Periodic universe with cubic elementary cell. """ def setSize(self, size): """ Set the edge length to a given value. :param size: the new size :type size: float """ OrthorhombicPeriodicUniverse.setSize(self, 3*(size,)) def _descriptionArguments(self): if self._forcefield is None: return '(0.)' else: return '(0.,%s)' % self._forcefield.description() # # Parallelepipedic universe with periodic boundary conditions # class ParallelepipedicPeriodicUniverse(Periodic3DUniverse): """ Periodic universe with parallelepipedic elementary cell. """ def __init__(self, shape = None, forcefield = None, **properties): """ :param shape: the basis vectors :type shape: sequence of Scientific.Geometry.Vector :param forcefield: a force field, or None for no force field :type forcefield: :class:`~MMTK.ForceFields.ForceField.ForceField` """ Universe.__init__(self, forcefield, properties) self.data = N.zeros((19,), N.Float) if shape is not None: self.setShape(shape) self._createSpec() is_periodic = True def setShape(self, shape): self.data[:9] = N.ravel(N.transpose([list(s) for s in shape])) from MMTK_universe import parallelepiped_invert parallelepiped_invert(self.data) def scaleSize(self, factor): """ Multiplies all edge lengths by a factor. :param factor: the scale factor :type factor: float """ self.data[:9] = factor*self.data[:9] from MMTK_universe import parallelepiped_invert parallelepiped_invert(self.data) self._spec.foldCoordinatesIntoBox(self.configuration().array) def setCellParameters(self, parameters): if parameters is not None: self.data[:9] = parameters from MMTK_universe import parallelepiped_invert parallelepiped_invert(self.data) def _fixCellParameters(self, cell_parameters): full_parameters = 0.*self.data full_parameters[:9] = cell_parameters from MMTK_universe import parallelepiped_invert parallelepiped_invert(full_parameters) return full_parameters def realToBoxCoordinates(self, vector): x, y, z = vector return Vector(self.data[0+9]*x + self.data[1+9]*y + self.data[2+9]*z, self.data[3+9]*x + self.data[4+9]*y + self.data[5+9]*z, self.data[6+9]*x + self.data[7+9]*y + self.data[8+9]*z) def boxToRealCoordinates(self, vector): x, y, z = vector return Vector(self.data[0]*x + self.data[1]*y + self.data[2]*z, self.data[3]*x + self.data[4]*y + self.data[5]*z, self.data[6]*x + self.data[7]*y + self.data[8]*z) def _realToBoxPointArray(self, array, parameters=None): if parameters is None: matrix = N.reshape(self.data[9:18], (1, 3, 3)) else: parameters = N.concatenate([parameters, N.zeros((10,), N.Float)]) from MMTK_universe import parallelepiped_invert parallelepiped_invert(parameters) matrix = N.reshape(parameters[9:18], (1, 3, 3)) return N.add.reduce(matrix*array[:, N.NewAxis, :], axis=-1) def _boxToRealPointArray(self, array, parameters=None): if parameters is None: parameters = self.data[:9] matrix = N.reshape(parameters, (1, 3, 3)) return N.add.reduce(matrix*array[:, N.NewAxis, :], axis=-1) def CdistanceFunction(self): from MMTK_universe import parallelepipedic_universe_distance_function return parallelepipedic_universe_distance_function, self.data def CcorrectionFunction(self): from MMTK_universe import parallelepipedic_universe_correction_function return parallelepipedic_universe_correction_function, self.data def CvolumeFunction(self): from MMTK_universe import parallelepipedic_universe_volume_function return parallelepipedic_universe_volume_function, self.data def CboxTransformationFunction(self): from MMTK_universe import parallelepipedic_universe_box_transformation return parallelepipedic_universe_box_transformation, self.data def cellParameters(self): return self.data[:9] def reciprocalBasisVectors(self): return [Vector(self.data[9:12]), Vector(self.data[12:15]), Vector(self.data[15:18])] def cellVolume(self): return abs(self.data[18]) def largestDistance(self): return min([0.5/v.length() for v in self.reciprocalBasisVectors()]) def _createSpec(self): from MMTK_universe import ParallelepipedicPeriodicUniverseSpec self._spec = ParallelepipedicPeriodicUniverseSpec(self.data) def _descriptionArguments(self): if self._forcefield is None: return '((Vector(0.,0.,0.),Vector(0.,0.,0.),Vector(0.,0.,0.)))' else: return '((Vector(0.,0.,0.),Vector(0.,0.,0.),Vector(0.,0.,0.)),%s)'\ % self._forcefield.description() def XMLSpec(self): return 'topology="periodic3d" ' + \ 'cellshape="parallelepipedic" ' + \ ('cellshape="%f %f %f %f %f %f %f %f %f" ' % tuple(self.data[:9])) + \ 'units="units:nm"' # # Recognition functions # def isUniverse(object): """ :param object: any Python object :returns: True if object is a universe. """ return isinstance(object, Universe) MMTK-2.7.9/MMTK/Utility.py0000644000076600000240000001774611662461640015523 0ustar hinsenstaff00000000000000# This module contains useful functions that are needed in various # places. # # Written by Konrad Hinsen # import os, sys from Scientific import N # Constants undefined_limit = 1.e30 undefined = 10.*undefined_limit # Error class MMTKError(Exception): pass # # Unique ID store. # Certain objects must have a unique ID to allow unique ordering, # e.g. of atoms in a bond. Usually the id() function is sufficient, # except when running in a parallel environment, where the IDs # must be identical across all processors as long as the code is # identical. # class StandardUniqueIDGenerator: def __call__(self, object): return id(object) def registerObject(self, object): pass class DeterministicUniqueIDGenerator: def __init__(self): self.number = 0 self.id = {} def __call__(self, object): return self.id[object] def registerObject(self, object): self.id[object] = self.number self.number += self.number parallel = True try: from Scientific.MPI import world if world is None: parallel = False elif world.size == 1: parallel = False del world except ImportError: parallel = False if parallel: uniqueID = DeterministicUniqueIDGenerator() else: uniqueID = StandardUniqueIDGenerator() del parallel # # This function substitutes all references to one object by references to # another object in a given object recursively. The substitution is # specified by a dictionary. # Attention: For some object types, substitution is destructive! # def substitute(obj, *exchange): if len(exchange) == 1: exchange = exchange[0] else: exchange = dict(zip(exchange[0], exchange[1])) if isinstance(obj, list): return [substitute(e, exchange) for e in obj] if isinstance(obj, tuple): return tuple([substitute(e, exchange) for e in obj]) if isinstance(obj, dict): newdict = {} for key, value in obj.items(): newdict[substitute(key, exchange)] = substitute(value, exchange) return newdict if hasattr(obj, '_substitute'): for attr in vars(obj).keys(): setattr(obj, attr, substitute(getattr(obj, attr), exchange)) return obj try: return exchange[obj] except KeyError: return obj def _put(dict, key, value): dict[key] = value # # Return a unique attribute name # _unique_attributes = 0 def uniqueAttribute(): global _unique_attributes _unique_attributes = (_unique_attributes + 1) % 10000 return '_' + `_unique_attributes` + '__' # # Ensure that items in a pair are ordered # def normalizePair(pair): i, j = pair if i > j: return j, i else: return i, j # # Return an iterator over all pairs of objects in a given sequence # def pairs(seq): n = len(seq) for i in range(n): a = seq[i] for j in range(i+1, n): b = seq[j] yield (a, b) # # Return an iterator over all ordered pairs of objects in a given sequence # def orderedPairs(seq): n = len(seq) for i in range(n): a = seq[i] for j in range(i+1, n): b = seq[j] if a > b: yield (b, a) else: yield (a, b) # # Type check for sequence objects # def isSequenceObject(obj): try: it = iter(obj) return True except: return False # # Check if an object represents a well-defined position # def isDefinedPosition(p): if p is None: return False if N.add.reduce(N.greater(p.array, undefined_limit)) > 0: return False return True # # Print a warning with reasonable line breaks. # def warning(text): words = text.split() text = 'Warning:' l = len(text) while words: lw = len(words[0]) if l + lw + 1 < 60: text = text + ' ' + words[0] l = l + lw else: text = text + '\n' + 9*' ' + words[0] l = lw + 9 words = words[1:] sys.stderr.write(text+"\n") # # Pickler and unpickler taking care of non-pickled objects # try: array_package = N.package except AttributeError: array_package = 'Numeric' if array_package == 'Numeric': BasePickler = N.Pickler BaseUnpickler = N.Unpickler else: from pickle import Pickler as BasePickler from pickle import Unpickler as BaseUnpickler del array_package class Pickler(BasePickler): def persistent_id(self, obj): if hasattr(obj, 'is_chemical_object_type'): id = obj._restoreId() return id else: return None class _EmptyClass: pass class Unpickler(BaseUnpickler): def __init__(self, *args): BaseUnpickler.__init__(self, *args) self.dispatch['i'] = Unpickler.load_inst def persistent_load(self, id): from MMTK import Database return eval(id) def find_class(self, module, name): env = {} exec 'from %s import %s' % (module, name) in env return env[name] # Modified load_inst removes argument lists for classes that used # to have __getinitargs__ but don't have it any more. # This makes it possible to read pickle files from older MMTK versions. def load_inst(self): import types k = self.marker() args = tuple(self.stack[k+1:]) del self.stack[k:] module = self.readline()[:-1] name = self.readline()[:-1] klass = self.find_class(module, name) instantiated = False if ((not args or hasattr(klass, "__had_initargs__")) and type(klass) is types.ClassType and not hasattr(klass, "__getinitargs__")): try: value = _EmptyClass() value.__class__ = klass instantiated = True except RuntimeError: # In restricted execution, assignment to inst.__class__ is # prohibited pass if not instantiated: try: if not hasattr(klass, '__safe_for_unpickling__'): raise UnpicklingError('%s is not safe for unpickling' % klass) value = apply(klass, args) except TypeError, err: raise TypeError, "in constructor for %s: %s" % ( klass.__name__, str(err)), sys.exc_info()[2] self.append(value) # # General routines for writing objects to files and reading them back # def save(obj, filename): """Writes |obj| to a newly created file with the name |filename|, for later retrieval by 'load()'.""" import ChemicalObjects filename = os.path.expanduser(filename) file = open(filename, 'wb') if ChemicalObjects.isChemicalObject(obj): parent = obj.parent obj.parent = None Pickler(file).dump(obj) obj.parent = parent else: Pickler(file).dump(obj) file.close() def load(filename): """Loads the file indicated by |filename|, which must have been produced by 'save()', and returns the object stored in that file.""" filename = os.path.expanduser(filename) file = open(filename, 'rb') obj = Unpickler(file).load() file.close() return obj # # URL related functions # def isURL(filename): return filename.find(':/') > 1 def joinURL(url, filename): if url[-1] == '/': return url+filename else: return url+'/'+filename def checkURL(filename): if isURL(filename): import urllib try: urllib.urlopen(filename) return True except IOError: return False else: return os.path.exists(filename) def readURL(filename): try: if isURL(filename): import urllib file = urllib.urlopen(filename) else: file = open(filename) except IOError, details: if details[0] == 2: print "File " + filename + " not found." raise IOError(details) data = file.read() file.close() return data MMTK-2.7.9/MMTK/Visualization.py0000644000076600000240000005143012117632051016675 0ustar hinsenstaff00000000000000# This module contains interfaces to external visualization programs # and a visualization base class # # Written by Konrad Hinsen # """ Visualization of chemical objects, including animation This module provides visualization of chemical objects and animated visualization of normal modes and sequences of configurations, including trajectories. Visualization depends on external visualization programs. On Unix systems, these programs are defined by environment variables. Under Windows NT, the system definitions for files with extension "pdb" and "wrl" are used. A viewer for PDB files can be defined by the environment variable 'PDBVIEWER'. For showing a PDB file, MMTK will execute a command consisting of the value of this variable followed by a space and the name of the PDB file. A viewer for VRML files can be defined by the environment variable 'VRMLVIEWER'. For showing a VRML file, MMTK will execute a command consisting of the value of this variable followed by a space and the name of the VRML file. Since there is no standard for launching viewers for animation, MMTK supports only two programs: VMD and XMol. MMTK detects these programs by inspecting the value of the environment variable 'PDBVIEWER'. This value must be the file name of the executable, and must give "vmd" or "xmol" after stripping off an optional directory specification. """ __docformat__ = 'restructuredtext' from MMTK import Units, Utility from Scientific import N import subprocess, sys, tempfile, os # # If you want temporary files in a non-standard directory, make # its name the value of this variable: # tempdir = None # # Identify OS # running_on_windows = sys.platform == 'win32' running_on_macosx = sys.platform.startswith('darwin') running_on_linux = sys.platform.startswith('linux') # # Get visualization program names # viewer = {} try: pdbviewer = os.environ['PDBVIEWER'] prog = os.path.split(pdbviewer)[1].lower().split('.')[0] viewer['pdb'] = (prog, pdbviewer) except KeyError: pass try: vrmlviewer = os.environ['VRMLVIEWER'] prog = os.path.split(vrmlviewer)[1].lower().split('.')[0] viewer['vrml'] = (prog, vrmlviewer) except KeyError: pass def definePDBViewer(progname, exec_path): """ Define the program used to view PDB files. :param progname: the canonical name of the PDB viewer. If it is a known one (one of "vmd", "xmol", "imol"), special features such as animation may be available. :type progname: str :param exec_path: the path to the executable program :type exec_path: str """ viewer['pdb'] = (progname.lower(), exec_path) def defineVRMLiewer(progname, exec_path): """ Define the program used to view VRML files. :param progname: the canonical name of the VRML viewer :type progname: str :param exec_path: the path to the executable program :type exec_path: str """ viewer['vrml'] = (progname.lower(), exec_path) # # Visualization base class. Defines methods for general visualization # tasks. # class Viewable(object): """ Any viewable chemical object This is a mix-in class that defines a general visualization method for all viewable objects, i.e. chemical objects (atoms, molecules, etc.), collections, and universes. """ def graphicsObjects(self, **options): """ :keyword configuration: the configuration in which the objects are drawn (default: the current configuration) :type configuration: :class:`~MMTK.ParticleProperties.Configuration` :keyword model: the graphical representation to be used (one of "wireframe", "tube", "ball_and_stick", "vdw" and "vdw_and_stick"). The vdw models use balls with the radii taken from the atom objects. Default is "wireframe". :type model: str :keyword ball_radius: the radius of the balls representing the atoms in a ball_and_stick model, default: 0.03 This is also used in vdw and vdw_and_stick when an atom does not supply a radius. :type ball_radius: float :keyword stick_radius: the radius of the sticks representing the bonds in a ball_and_stick, vdw_and_stick or tube model. Default: 0.02 for the tube model, 0.01 for the ball_and_stick and vdw_and_stick models :type stick_radius: float :keyword graphics_module: the module in which the elementary graphics objects are defined (default: Scientific.Visualization.VRML) :type graphics_module: module :keyword color_values: a color value for each atom which defines the color via the color scale object specified by the option color_scale. If no value is given, the atoms' colors are taken from the attribute 'color' of each atom object (default values for each chemical element are provided in the chemical database). :type color_values: :class:`~MMTK.ParticleProperties.ParticleScalar` :keyword color_scale: an object that returns a color object (as defined in the module Scientific.Visualization.Color) when called with a number argument. Suitable objects are defined by Scientific.Visualization.Color.ColorScale and Scientific.Visualization.Color.SymmetricColorScale. The object is used only when the option color_values is specified as well. The default is a blue-to-red color scale that covers the range of the values given in color_values. :type color_scale: callable :keyword color: a color name predefined in the module Scientific.Visualization.Color. The corresponding color is applied to all graphics objects that are returned. :returns: a list of graphics objects that represent the object for which the method is called. :rtype: list """ conf = options.get('configuration', None) model = options.get('model', 'wireframe') if model == 'tube': model = 'ball_and_stick' radius = options.get('stick_radius', 0.02) options['stick_radius'] = radius options['ball_radius'] = radius try: module = options['graphics_module'] except KeyError: from Scientific.Visualization import VRML module = VRML color = options.get('color', None) if color is None: color_values = options.get('color_values', None) if color_values is not None: lower = N.minimum.reduce(color_values.array) upper = N.maximum.reduce(color_values.array) options['color_scale'] = module.ColorScale((lower, upper)) try: distance_fn = self.universe().distanceVector except AttributeError: from MMTK import Universe distance_fn = Universe.InfiniteUniverse().distanceVector return self._graphics(conf, distance_fn, model, module, options) def _atomColor(self, atom, options): color = options.get('color', None) if color is not None: return color color_values = options.get('color_values', None) if color_values is None: return atom.color else: scale = options['color_scale'] return scale(color_values[atom]) # # View anything viewable. # def view(object, *parameters): "Equivalent to object.view(parameters)." object.view(*parameters) # # Display an object or a collection of objects using an external # viewing program. # def genericViewConfiguration(object, configuration = None, format = 'pdb', label = None): format = format.lower() viewer_format = format.split('.')[0] tempfile.tempdir = tempdir filename = tempfile.mktemp() tempfile.tempdir = None if viewer_format == 'pdb': filename = filename + '.pdb' elif viewer_format == 'vrml': filename = filename + '.wrl' if running_on_windows: object.writeToFile(filename, configuration, format) try: os.startfile(filename) except win32api.error, error_number: #Looking for error 31, SE_ERR_NOASSOC, in particular file_type = os.path.splitext(filename)[1] if error_number[0]==31: print ('There is no program associated with .%s files,' + \ ' please install a suitable viewer') % file_type else: print 'Unexpected error attempting to open .%s file' % file_type print sys.exc_value elif viewer.has_key(viewer_format): # On Unix-like systems, give priority to a user-specified viewer. object.writeToFile(filename, configuration, format) if os.fork() == 0: pipe = os.popen(viewer[format][1] + ' ' + filename + \ ' 1> /dev/null 2>&1', 'w') pipe.close() os.unlink(filename) os._exit(0) elif running_on_macosx: object.writeToFile(filename, configuration, format) subprocess.call(["/usr/bin/open", filename]) elif running_on_linux: object.writeToFile(filename, configuration, format) subprocess.call(["xdg-open", filename]) else: Utility.warning('No viewer for %s defined.' % viewer_format) return def viewConfiguration(*args, **kwargs): pdbviewer, exec_path = viewer.get('pdb', (None, None)) function = {'vmd': viewConfigurationVMD, 'xmol': viewConfigurationXMol, 'imol': viewConfigurationIMol} \ .get(pdbviewer,genericViewConfiguration) function(*args, **kwargs) # # Normal mode and trajectory animation # def viewSequence(object, conf_list, periodic = False, label = None): """ Launches an animation using an external viewer. :param object: the object for which the animation is displayed. :type object: :class:`~MMTK.Collections.GroupOfAtoms` :param conf_list: a sequence of configurations that define the animation :type conf_list: sequence :param periodic: if True, turn animation into a loop :param label: an optional text string that some interfaces use to pass a description of the object to the visualization system. :type label: str """ pdbviewer, exec_path = viewer.get('pdb', (None, None)) function = {'vmd': viewSequenceVMD, 'xmol': viewSequenceXMol, 'imol': viewSequenceIMol, None: None}[pdbviewer] if function is None: Utility.warning('No viewer with animation feature defined.') else: function(object, conf_list, periodic, label) def viewTrajectory(trajectory, first=0, last=None, skip=1, subset = None, label = None): """ Launches an animation based on a trajectory using an external viewer. :param trajectory: the trajectory :type trajectory: :class:`~MMTK.Trajectory.Trajectory` :param first: the first trajectory step to be used :type first: int :param last: the first trajectory step NOT to be used :type last: int :param skip: the distance between two consecutive steps shown :type skip: int :param subset: the subset of the universe that is shown (default: the whole universe) :type subset: :class:`~MMTK.Collections.GroupOfAtoms` :param label: an optional text string that some interfaces use to pass a description of the object to the visualization system. :type label: str """ if type(trajectory) == type(''): from MMTK.Trajectory import Trajectory trajectory = Trajectory(None, trajectory, 'r') if last is None: last = len(trajectory) elif last < 0: last = len(trajectory) + last universe = trajectory.universe if subset is None: subset = universe viewSequence(subset, trajectory.configuration[first:last:skip], label) def viewMode(mode, factor=1., subset=None, label=None): universe = mode.universe if subset is None: subset = universe conf = universe.configuration() viewSequence(subset, [conf, conf+factor*mode, conf, conf-factor*mode], 1, label) # # XMol support # # # Animation with XMol. # viewConfigurationXMol = viewConfiguration def viewSequenceXMol(object, conf_list, periodic = 0, label = None): tempfile.tempdir = tempdir file_list = [] for conf in conf_list: file = tempfile.mktemp() file_list.append(file) object.writeToFile(file, conf, 'pdb') bigfile = tempfile.mktemp() tempfile.tempdir = None subprocess.call(['cat'] + file_list + ['>', bigfile]) for file in file_list: os.unlink(file) if os.fork() == 0: pipe = os.popen('xmol -readFormat pdb ' + bigfile + \ ' 1> /dev/null 2>&1', 'w') pipe.close() os.unlink(bigfile) os._exit(0) # # VMD support # # # View configuration # def isCalpha(object): from MMTK.Proteins import isProtein, isPeptideChain from MMTK.Universe import isUniverse from MMTK.Collections import isCollection if isProtein(object): chain_list = list(object) elif isPeptideChain(object): chain_list = [object] elif isUniverse(object) or isCollection(object): chain_list = [] for element in object: if isProtein(element): chain_list = chain_list + list(element) elif isPeptideChain(element): chain_list.append(element) else: return False else: return False for chain in chain_list: try: if chain.model != 'calpha': return False except AttributeError: return False return True def viewConfigurationVMD(object, configuration = None, format = 'pdb', label = None): from MMTK import Universe format = format.lower() if format != 'pdb': return genericViewConfiguration(object, configuration, format) tempfile.tempdir = tempdir filename = tempfile.mktemp() filename_tcl = filename.replace('\\', '\\\\') script = tempfile.mktemp() script_tcl = script.replace('\\', '\\\\') tempfile.tempdir = None object.writeToFile(filename, configuration, format) file = open(script, 'w') file.write('mol load pdb ' + filename_tcl + '\n') if isCalpha(object): file.write('mol modstyle 0 all trace\n') file.write('color Name 1 white\n') file.write('color Name 2 white\n') file.write('color Name 3 white\n') if Universe.isUniverse(object): # add a box around periodic universes basis = object.basisVectors() if basis is not None: v1, v2, v3 = basis p = -0.5*(v1+v2+v3) for p1, p2 in [(p, p+v1), (p, p+v2), (p+v1, p+v1+v2), (p+v2, p+v1+v2), (p, p+v3), (p+v1, p+v1+v3), (p+v2, p+v2+v3), (p+v1+v2, p+v1+v2+v3), (p+v3, p+v1+v3), (p+v3, p+v2+v3), (p+v1+v3, p+v1+v2+v3), (p+v2+v3, p+v1+v2+v3)]: file.write('graphics 0 line {%f %f %f} {%f %f %f}\n' % (tuple(p1/Units.Ang) + tuple(p2/Units.Ang))) file.write('file delete ' + filename_tcl + '\n') if sys.platform != 'win32': # Under Windows, it seems to be impossible to delete # the script file while it is still in use. For the moment # we just don't delete it at all. file.write('file delete ' + script_tcl + '\n') file.close() subprocess.Popen([viewer['pdb'][1], '-nt', '-e', script]) # # Animate sequence # def viewSequenceVMD(object, conf_list, periodic = 0, label=None): tempfile.tempdir = tempdir script = tempfile.mktemp() script_tcl = script.replace('\\', '\\\\') np = object.numberOfPoints() universe = object.universe() if np == universe.numberOfPoints() \ and len(conf_list) > 2: from MMTK import DCD pdbfile = tempfile.mktemp() pdbfile_tcl = pdbfile.replace('\\', '\\\\') dcdfile = tempfile.mktemp() dcdfile_tcl = dcdfile.replace('\\', '\\\\') tempfile.tempdir = None sequence = DCD.writePDB(universe, conf_list[0], pdbfile) indices = map(lambda a: a.index, sequence) DCD.writeDCD(conf_list[1:], dcdfile, 1./Units.Ang, indices) file = open(script, 'w') file.write('mol load pdb ' + pdbfile_tcl + '\n') if isCalpha(object): file.write('mol modstyle 0 all trace\n') file.write('animate read dcd ' + dcdfile_tcl + '\n') if periodic: file.write('animate style loop\n') else: file.write('animate style once\n') file.write('animate forward\n') file.write('file delete ' + pdbfile_tcl + '\n') file.write('file delete ' + dcdfile_tcl + '\n') if sys.platform != 'win32': # Under Windows, it seems to be impossible to delete # the script file while it is still in use. For the moment # we just don't delete it at all. file.write('file delete ' + script_tcl + '\n') file.close() else: file_list = [] for conf in conf_list: file = tempfile.mktemp() file_list.append(file) object.writeToFile(file, conf, 'pdb') tempfile.tempdir = None file = open(script, 'w') file.write('mol load pdb ' + file_list[0] + '\n') for conf in file_list[1:]: file.write('animate read pdb ' + conf.replace('\\', '\\\\') + '\n') if periodic: file.write('animate style loop\n') else: file.write('animate style once\n') file.write('animate forward\n') for conf in file_list: file.write('file delete ' + conf.replace('\\', '\\\\') + '\n') if sys.platform != 'win32': # Under Windows, it seems to be impossible to delete # the script file while it is still in use. For the moment # we just don't delete it at all. file.write('file delete ' + script_tcl + '\n') file.close() subprocess.Popen([viewer['pdb'][1], '-nt', '-e', script]) # # iMol support # # # View configuration # def viewConfigurationIMol(object, configuration = None, format = 'pdb', label = None): format = format.lower() if format != 'pdb': return genericViewConfiguration(object, configuration, format) tempfile.tempdir = tempdir filename = tempfile.mktemp() + '.pdb' tempfile.tempdir = None object.writeToFile(filename, configuration, format) subprocess.call(['open', '-a', prog, filename]) # # Animate sequence # def viewSequenceIMol(object, conf_list, periodic = 0, label=None): from MMTK import PDB tempfile.tempdir = tempdir filename = tempfile.mktemp() + '.pdb' file = PDB.PDBOutputFile(filename) for conf in conf_list: file.nextModel() file.write(object, conf) file.close() tempfile.tempdir = None subprocess.call(['open', '-a', prog, filename]) # # PyMOL support # from MMTK import PyMOL if PyMOL.in_pymol: _representation = None def viewConfiguration(object, configuration = None, format = 'pdb', label = None): global _representation if label is None: label = "MMTK Object" if _representation is not None: _representation.remove() _representation = PyMOL.Representation(object, label, configuration) _representation.show() def viewSequence(object, conf_list, periodic = 0, label = None): global _representation if label is None: label = "MMTK Object" if _representation is not None: _representation.remove() _representation = PyMOL.Representation(object, label, conf_list[0]) _representation.movie(conf_list) genericViewMode = viewMode def viewMode(mode, factor=1., subset=None, label=None): from pymol import cmd cmd.set("movie_delay","200",log=1) genericViewMode(mode, factor, subset, label) MMTK-2.7.9/MMTK/XML.py0000644000076600000240000001015111662461640014477 0ustar hinsenstaff00000000000000# XML I/O # # Written by Konrad Hinsen # """ XML format for describing molecular systems Note: this format is not used by any other program at the moment. It should be considered experimental and subject to change. """ __docformat__ = 'restructuredtext' import MMTK from MMTK.MoleculeFactory import MoleculeFactory from xml.etree.ElementTree import iterparse from Scientific import N class XMLMoleculeFactory(MoleculeFactory): """ XML molecule factory An XML molecule factory reads an XML specification of a molecular system and builds the molecule objects and universe described therein. The universe can be obtained through the attribute *universe*. :param file: the name of an XML file, or a file object """ def __init__(self, file): MoleculeFactory.__init__(self) for event, element in iterparse(file): tag = element.tag ob_id = element.attrib.get('id', None) if tag == 'molecule' and ob_id is not None: self.makeGroup(element) element.clear() elif tag == 'templates': element.clear() elif tag == 'universe': self.makeUniverse(element) def makeGroup(self, element): group = element.attrib['id'] self.createGroup(group) for molecule_element in element.findall('molecule'): self.addSubgroup(group, molecule_element.attrib.get('title', ''), molecule_element.attrib['ref']) atom_array = element.find('atomArray') if atom_array is not None: for atom_element in atom_array: self.addAtom(group, atom_element.attrib['title'], atom_element.attrib['elementType']) bond_array = element.find('bondArray') if bond_array is not None: for bond_element in bond_array: atom1, atom2 = bond_element.attrib['atomRefs2'].split() atom1 = '.'.join(atom1.split(':')) atom2 = '.'.join(atom2.split(':')) self.addBond(group, atom1, atom2) def makeUniverse(self, element): topology = element.attrib.get('topology', 'infinite') if topology == 'infinite': universe = MMTK.InfiniteUniverse() elif topology == 'periodic3d': cellshape = element.attrib['cellshape'] if cellshape == 'orthorhombic': cellsize = element.attrib['cellsize'].split() units = element.attrib.get('units', 'units:nm') factor = getattr(MMTK.Units, units.split(':')[1]) universe = MMTK.OrthorhombicPeriodicUniverse(tuple( [factor*float(size) for size in cellsize])) else: raise ValueError("cell shape %s not implemented" % cellshape) else: raise ValueError("topology %s not implemented" % topology) atom_index = 0 for subelement in element: if subelement.tag == 'molecule': molecule = self.retrieveMolecule(subelement.attrib['ref']) for atom in molecule.atomList(): atom.index = atom_index atom_index += 1 universe.addObject(molecule) elif subelement.tag == 'atom': atom = MMTK.Atom(subelement.attrib['elementType']) atom.index = atom_index atom_index += 1 universe.addObject(atom) configuration = element.find('configuration') if configuration is not None: array = configuration.find('atomArray') x = map(float, array.attrib['x3'].split()) y = map(float, array.attrib['y3'].split()) z = map(float, array.attrib['z3'].split()) units = array.attrib.get('units', 'units:nm') factor = getattr(MMTK.Units, units.split(':')[1]) array = universe.configuration().array array[:, 0] = x array[:, 1] = y array[:, 2] = z N.multiply(array, factor, array) self.universe = universe MMTK-2.7.9/README0000644000076600000240000001755712117632051013606 0ustar hinsenstaff00000000000000The Molecular Modelling Toolkit =============================== This is release 2.7 of MMTK, the Molecular Modelling Toolkit. For more information about MMTK, look at the manual (in the directories Doc/HTML and Doc/PDF) and at the examples in the directory Examples. Please remember that MMTK, like Python, comes with neither a price tag nor any warranty (see the file LICENSE for details). I use it for my own work, and I consider it reasonably stable, but your mileage may vary. If you find any problems with MMTK, please let me know, but understand that I cannot promise extensive support (I am a scientist, not a programmer, and much less a software distributor!). I also gratefully accept additional example applications and other code donations. Installation ============ Prerequisites: Python 2.5 or higher, ScientificPython 2.6 or higher, NumPy 1.x. It should still be possible to use Numerical Python 22.x or 23.x with this release of MMTK, but migration to NumPy is strongly encouraged. MMTK is NOT compatible with Numerical Python 24.x. Note: MMTK is developed and tested using Python 2.6/2.7and NumPy 1.6. However, it should work with Python 2.5 as well. On most Unix systems, installation requires two commands: python setup.py build python setup.py install The second command often requires root priviledges, as MMTK will be installed in the Python library tree. There are many options for building and installing, type python setup.py build --help or python setup.py install --help for details. If you want MMTK's MPI support enabled, you must first install ScientificPython with MPI support and then use mpipython instead of python for running the compilation/installation procedure via setup.py. Note that a version of MMTK compiled with MPI support works only with the mpipython executable, not with an ordinary python interpreter. However, there is no overhead for using mpipython on a single processor. Numeric vs NumPy ================ There are three nearly compatible implementations of numeric arrays for Python: the original one, Numerical Python (module name "Numeric"), a later rewrite called numarray, and an evolution of Numeric that integrates features from numarray, called NumPy. In the long run, only NumPy will be maintained and further developed. MMTK was originally written for Numeric, but has supported NumPy as well for quite a while. When MMTK is installed, it checks whether ScientificPython was compiled for Numeric or for NumPy, and uses the same package. MMTK development is done with NumPy, and Numeric support will be removed in a future version. Unless you have a good reason to use Numeric, please use NumPy! MMTK does not support numarray at all. Since numarray development has stopped, this is not likely to change in the future. Platform-specific notes ======================= This section is based on information I got from MMTK users in the past. There is a risk that it is not up to date. Any platform-specific information is welcome! Windows: -------- MMTK can be compiled under Windows using the free MinGW compiler (available from www.mingw.org). You will need to have MinGW setup properly to compile python extensions, see http://www.python.org/doc/2.2.1/inst/non-ms-compilers.html for details. The option "--compiler=mingw32" must be added when running setup.py. MMTK can be compiled using Microsoft Visual Studio 6.0 as well. For more information, check the Wiki pages at: http://dirac.cnrs-orleans.fr/mmtk_wiki For using visualization under Windows, you need Mark Hammond's Win32 extensions, available at http://starship.python.net/crew/mhammond/win32/Downloads.html Mac OS X: --------- The most convenient way to use MMTK with MacOS X is through the MacPorts project, which is located at http://www.macports.org/ MacPorts provides an install-and-build system for Unix software on the Mac. You will find many other goodies there, including Python and many Python packages. Of course, MacOS X being a variant of Unix, it is also possible and quite straightforward to install MMTK and all the prerequisites from source code. Just make sure to have Xcode (Apple's developer kit) installed. Using VMD for visualization under MacOS X ----------------------------------------- The VMD release for MacOS presents itself as a standard Mac application. Internally, it has the standard Unix command line interface that MMTK expects, but using the two together requires some particular steps: 1) Create a script called "vmd" in a directory on your shell's search path (e.g. /usr/bin) containing the following two lines: #!/bin/bash xterm -e '/Applications/VMD 1.8.2.app/Contents/Resources/VMD.app/Contents/MacOS/VMD' $* Don't forget to make this file executable (chmod 755 vmd). 2) Set the environment variable PDBVIEWER to "vmd". The most general method to do this is by editing the file ~/.MacOS/environment.plist. The most convenient tool to edit (or create) this file is the Property List Editor (in Applications/Utilities), but if you feel comfortable editing XML files, any text editor will do. Note that changes to this file become active only after the next login. 3) When using VMD from MMTK, make sure to have X11 running (because the script uses xterm). One way not to forget this is to put X11 (in /Applications/Utilities) into your startup item list with the "hide" option selected. External programs ================= MMTK uses external programs for visualization and animation. The names of these programs must be supplied via environment variables. If no names are provided, the visualization functions will do nothing. The environment variables are - PDBVIEWER to specify a program that can show the contents of a file in the PDB format. The program will be called with the name of a PDB file as a command line argument. - VRMLVIEWER to specify a program that can show a file in the VRML format. The program will be called with the name of a PDB file as a command line argument. Animation is implemented only for the programs VMD and XMol. It is sufficient to specify one of these programs as PDBVIEWER in order to have access to animation functions. Documentation ============= The MMTK manual is included in HTML (Doc/HTML) format. The source code for the Spinx documentation tool is also included. A list of changes along MMTK development versions can be found in Doc/CHANGELOG. Useful tools ============ The directory Tools/TrajectoryViewer contains a small program for viewing trajectories. It allows that quick inspection of energies, thermodynamic quantities, etc., as well as animation via an external viewer. Web site ======== The MMTK home page can be found at http://dirac.cnrs-orleans.fr/MMTK/ Mailing list ============ To be informed about updates to MMTK, and to communicate with other MMTK users, you should subscribe to the MMTK mailing list via the Web page http://starship.python.net/mailman/listinfo/mmtk This is a low-traffic list; there is little reason not to subscribe. License ======= MMTK is licensed under the CeCILL-C license. See the file LICENSE for the text of this license. Acknowledgements ================ The DCD reader was contributed by Lutz Ehrlich, and uses a C library for reading DCD files (files Src/ReadDCD.c and Src/ReadDCD.h) which was written by Mark Nelson at the University of Illinois. The MolecularSurface module was contributed by Peter McCluskey. Patches to make MMTK work under Windows were provided by Jon Michelsen. Peter Cook contributed Windows code, examples, code improvements, and good suggestions. Chris Ing translated the documentation to Sphinx, wrote test cases, and made lots of good suggestions. Routines for matrix diagonalization were taken from LAPACK, and for the erfc function from the CEPHES library. Konrad Hinsen Centre de Biophysique Moleculaire (CNRS) Rue Charles Sadron 45071 Orleans Cedex 2 France E-Mail: hinsen@cnrs-orleans.fr MMTK-2.7.9/setup.cfg0000644000076600000240000000026011662461640014536 0ustar hinsenstaff00000000000000[bdist_rpm] release = 1 packager = Konrad Hinsen group = Libraries/Python doc-files = README Doc/HTML Doc/PDF Doc/XML Examples use-rpm-opt-flags = 0 MMTK-2.7.9/setup.py0000644000076600000240000003676612117632051014443 0ustar hinsenstaff00000000000000#!/usr/bin/env python package_name = "MMTK" from distutils.core import setup, Command, Extension from distutils.command.build import build from distutils.command.sdist import sdist from distutils.command.install_data import install_data from distutils import dir_util from distutils.filelist import FileList, translate_pattern import distutils.sysconfig sysconfig = distutils.sysconfig.get_config_vars() import os, sys, types import ctypes, ctypes.util from glob import glob class Dummy: pass pkginfo = Dummy() execfile('MMTK/__pkginfo__.py', pkginfo.__dict__) # Check for Cython and use it if the environment variable # MMTK_USE_CYTHON is set to a non-zero value. use_cython = int(os.environ.get('MMTK_USE_CYTHON', '0')) != 0 if use_cython: try: from Cython.Distutils import build_ext use_cython = True except ImportError: use_cython = False if not use_cython: from distutils.command.build_ext import build_ext src_ext = 'pyx' if use_cython else 'c' # Check that we have Scientific 2.6 or higher try: from Scientific import __version__ as scientific_version if scientific_version[-2:] == 'hg': scientific_version = scientific_version[:-2] scientific_version = scientific_version.split('.') scientific_ok = int(scientific_version[0]) >= 2 and \ int(scientific_version[1]) >= 6 except ImportError: scientific_ok = False if not scientific_ok: print "MMTK needs ScientificPython 2.6 or higher" raise SystemExit compile_args = [] include_dirs = ['Include'] if (int(scientific_version[1]) >= 8 or \ (int(scientific_version[1]) == 7 and int(scientific_version[2]) >= 8)): netcdf_h = os.path.join(sys.prefix, 'include', 'python%d.%d' % sys.version_info[:2], 'Scientific', 'netcdf.h') if os.path.exists(netcdf_h): compile_args.append("-DUSE_NETCDF_H_FROM_SCIENTIFIC=1") else: # Take care of the common problem that netcdf is in /usr/local but # /usr/local/include is not on $CPATH. if os.path.exists('/usr/local/include/netcdf.h'): include_dirs.append('/usr/local/include') from Scientific import N try: num_package = N.package except AttributeError: num_package = "Numeric" if num_package == "NumPy": compile_args.append("-DNUMPY=1") import numpy.distutils.misc_util include_dirs.extend(numpy.distutils.misc_util.get_numpy_include_dirs()) headers = glob(os.path.join ("Include", "MMTK", "*.h")) paths = [os.path.join('MMTK', 'ForceFields'), os.path.join('MMTK', 'ForceFields', 'Amber'), os.path.join('MMTK', 'Database', 'Atoms'), os.path.join('MMTK', 'Database', 'Groups'), os.path.join('MMTK', 'Database', 'Molecules'), os.path.join('MMTK', 'Database', 'Complexes'), os.path.join('MMTK', 'Database', 'Proteins'), os.path.join('MMTK', 'Database', 'PDB'), os.path.join('MMTK', 'Tools', 'TrajectoryViewer')] data_files = [] for dir in paths: files = [] for f in glob(os.path.join(dir, '*')): if f[-3:] != '.py' and f[-4:-1] != '.py' and os.path.isfile(f): files.append(f) data_files.append((dir, files)) class ModifiedFileList(FileList): def findall(self, dir=os.curdir): from stat import ST_MODE, S_ISREG, S_ISDIR, S_ISLNK list = [] stack = [dir] pop = stack.pop push = stack.append while stack: dir = pop() names = os.listdir(dir) for name in names: if dir != os.curdir: fullname = os.path.join(dir, name) else: fullname = name stat = os.stat(fullname) mode = stat[ST_MODE] if S_ISREG(mode): list.append(fullname) elif S_ISDIR(mode) and not S_ISLNK(mode): list.append(fullname) push(fullname) self.allfiles = list class modified_build(build): def has_sphinx(self): if sphinx is None: return False setup_dir = os.path.dirname(os.path.abspath(__file__)) return os.path.isdir(os.path.join(setup_dir, 'Doc')) sub_commands = build.sub_commands + [('build_sphinx', has_sphinx)] class modified_sdist(sdist): def run (self): self.filelist = ModifiedFileList() self.check_metadata() self.get_file_list() if self.manifest_only: return self.make_distribution() def make_release_tree (self, base_dir, files): self.mkpath(base_dir) dir_util.create_tree(base_dir, files, verbose=self.verbose, dry_run=self.dry_run) if hasattr(os, 'link'): # can make hard links on this system link = 'hard' msg = "making hard links in %s..." % base_dir else: # nope, have to copy link = None msg = "copying files to %s..." % base_dir if not files: self.warn("no files to distribute -- empty manifest?") else: self.announce(msg) for file in files: if os.path.isfile(file): dest = os.path.join(base_dir, file) self.copy_file(file, dest, link=link) elif os.path.isdir(file): dir_util.mkpath(os.path.join(base_dir, file)) else: self.warn("'%s' not a regular file or directory -- skipping" % file) class modified_install_data(install_data): def run(self): install_cmd = self.get_finalized_command('install') self.install_dir = getattr(install_cmd, 'install_lib') return install_data.run(self) class test(Command): user_options = [] def initialize_options(self): self.build_lib = None def finalize_options(self): self.set_undefined_options('build', ('build_lib', 'build_lib')) def run(self): import sys, subprocess self.run_command('build_py') self.run_command('build_ext') ff = sum((fns for dir, fns in data_files if 'ForceFields' in dir), []) for fn in ff: self.copy_file(fn, os.path.join(self.build_lib, fn), preserve_mode=False) subprocess.call([sys.executable, 'Tests/all_tests.py'], env={'PYTHONPATH': self.build_lib, 'MMTKDATABASE': 'MMTK/Database'}) cmdclass = { 'build' : modified_build, 'sdist': modified_sdist, 'install_data': modified_install_data, 'build_ext': build_ext, 'test': test } # Build the sphinx documentation if Sphinx is available try: import sphinx except ImportError: sphinx = None if sphinx: from sphinx.setup_command import BuildDoc as _BuildDoc class BuildDoc(_BuildDoc): def run(self): # make sure the python path is pointing to the newly built # code so that the documentation is built on this and not a # previously installed version build = self.get_finalized_command('build') sys.path.insert(0, os.path.abspath(build.build_lib)) ff = sum((fns for dir, fns in data_files if 'ForceFields' in dir), []) for fn in ff: self.copy_file(fn, os.path.join(build.build_lib, fn), preserve_mode=False) try: sphinx.setup_command.BuildDoc.run(self) except UnicodeDecodeError: print >>sys.stderr, "ERROR: unable to build documentation because Sphinx do not handle source path with non-ASCII characters. Please try to move the source package to another location (path with *only* ASCII characters)." sys.path.pop(0) cmdclass['build_sphinx'] = BuildDoc ################################################################# # Check various compiler/library properties libraries = [] if sysconfig['LIBM'] != '': libraries.append('m') macros = [] try: from Scientific.MPI import world except ImportError: world = None if world is not None: if type(world) == types.InstanceType: world = None if world is not None: macros.append(('WITH_MPI', None)) if hasattr(ctypes.CDLL(ctypes.util.find_library('m')), 'erfc'): macros.append(('LIBM_HAS_ERFC', None)) if sys.platform != 'win32': if ctypes.sizeof(ctypes.c_long) == 8: macros.append(('_LONG64_', None)) if sys.version_info[0] == 2 and sys.version_info[1] >= 2: macros.append(('EXTENDED_TYPES', None)) ################################################################# # System-specific optimization options low_opt = [] if sys.platform != 'win32' and 'gcc' in sysconfig['CC']: low_opt = ['-O0'] low_opt.append('-g') high_opt = [] if sys.platform[:5] == 'linux' and 'gcc' in sysconfig['CC']: high_opt = ['-O3', '-ffast-math', '-fomit-frame-pointer', '-fkeep-inline-functions'] if sys.platform == 'darwin' and 'gcc' in sysconfig['CC']: high_opt = ['-O3', '-ffast-math', '-fomit-frame-pointer', '-fkeep-inline-functions', '-falign-loops=16'] if sys.platform == 'aix4': high_opt = ['-O4'] if sys.platform == 'odf1V4': high_opt = ['-O2', '-fp_reorder', '-ansi_alias', '-ansi_args'] high_opt.append('-g') ################################################################# setup (name = package_name, version = pkginfo.__version__, description = "Molecular Modelling Toolkit", long_description= """ The Molecular Modelling Toolkit (MMTK) is an Open Source program library for molecular simulation applications. It provides the most common methods in molecular simulations (molecular dynamics, energy minimization, normal mode analysis) and several force fields used for biomolecules (Amber 94, Amber 99, several elastic network models). MMTK also serves as a code basis that can be easily extended and modified to deal with non-standard situations in molecular simulations. """, author = "Konrad Hinsen", author_email = "hinsen@cnrs-orleans.fr", url = "http://dirac.cnrs-orleans.fr/MMTK/", license = "CeCILL-C", packages = ['MMTK', 'MMTK.ForceFields', 'MMTK.ForceFields.Amber', 'MMTK.NormalModes', 'MMTK.Tk', 'MMTK.Tools', 'MMTK.Tools.TrajectoryViewer'], headers = headers, ext_package = 'MMTK.'+sys.platform, ext_modules = [Extension('MMTK_DCD', ['Src/MMTK_DCD.c', 'Src/ReadDCD.c'], extra_compile_args = compile_args, include_dirs=include_dirs, libraries=libraries, define_macros=macros), Extension('MMTK_deformation', ['Src/MMTK_deformation.c'], extra_compile_args = compile_args + high_opt, include_dirs=include_dirs, libraries=libraries, define_macros=macros), Extension('MMTK_dynamics', ['Src/MMTK_dynamics.c'], extra_compile_args = compile_args, include_dirs=include_dirs, libraries=libraries, define_macros=macros), Extension('MMTK_minimization', ['Src/MMTK_minimization.c'], extra_compile_args = compile_args, include_dirs=include_dirs, libraries=libraries, define_macros=macros), Extension('MMTK_surface', ['Src/MMTK_surface.c'], extra_compile_args = compile_args, include_dirs=include_dirs, libraries=libraries, define_macros=macros), Extension('MMTK_trajectory', ['Src/MMTK_trajectory.c'], extra_compile_args = compile_args, include_dirs=include_dirs, libraries=libraries, define_macros=macros), Extension('MMTK_universe', ['Src/MMTK_universe.c'], extra_compile_args = compile_args, include_dirs=include_dirs, libraries=libraries, define_macros=macros), Extension('MMTK_forcefield', ['Src/MMTK_forcefield.c', 'Src/bonded.c', 'Src/nonbonded.c', 'Src/ewald.c', 'Src/sparsefc.c'], extra_compile_args = compile_args + high_opt, include_dirs=include_dirs + ['Src'], define_macros = [('SERIAL', None), ('VIRIAL', None), ('MACROSCOPIC', None)] + macros, libraries=libraries), Extension('MMTK_energy_term', ['Src/MMTK_energy_term.%s' % src_ext], extra_compile_args = compile_args, include_dirs=include_dirs, libraries=libraries, define_macros=macros), Extension('MMTK_restraints', ['Src/MMTK_restraints.%s' % src_ext], extra_compile_args = compile_args, include_dirs=include_dirs, libraries=libraries, define_macros=macros), Extension('MMTK_trajectory_action', ['Src/MMTK_trajectory_action.%s' % src_ext], extra_compile_args = compile_args, include_dirs=include_dirs, libraries=libraries, define_macros=macros), Extension('MMTK_trajectory_generator', ['Src/MMTK_trajectory_generator.%s' % src_ext], extra_compile_args = compile_args, include_dirs=include_dirs, libraries=libraries, define_macros=macros), Extension('MMTK_state_accessor', ['Src/MMTK_state_accessor.%s' % src_ext], extra_compile_args = compile_args, include_dirs=include_dirs, libraries=libraries, define_macros=macros), ], data_files = data_files, scripts = ['tviewer'], cmdclass = cmdclass, command_options = { 'build_sphinx': { 'source_dir' : ('setup.py', 'Doc')} }, ) MMTK-2.7.9/Src/0000755000076600000240000000000012156626572013454 5ustar hinsenstaff00000000000000MMTK-2.7.9/Src/bonded.c0000644000076600000240000005513112117632051015042 0ustar hinsenstaff00000000000000/* Low-level force field calculations: bonded interactions * * Written by Konrad Hinsen */ #define NO_IMPORT #define _FORCEFIELD_MODULE #define PY_ARRAY_UNIQUE_SYMBOL PyArray_MMTKFF_API #include "MMTK/forcefield.h" #include "MMTK/forcefield_private.h" /* Harmonic bond potential */ void harmonic_bond_evaluator(PyFFEnergyTermObject *self, PyFFEvaluatorObject *eval, energy_spec *input, energy_data *energy) { vector3 *x = (vector3 *)input->coordinates->data; long *index = (long *)((PyArrayObject *)self->data[0])->data; double *param = (double *)((PyArrayObject *)self->data[1])->data; int nterms = (self->n+input->nslices-1)/(input->nslices); int term = input->slice_id*nterms; int last_term = (input->slice_id+1)*nterms; double e = 0., v = 0.; if (last_term > self->n) last_term = self->n; index += 2*term; param += 2*term; /* Loop over bond terms */ while (term++ < last_term) { /* * E = k (r-r0)^2 * r = | R_i-R_j | * * index[0], index[1]: particle indices i, j * param[0]: r0 * param[1]: k */ int i = index[0]; int j = index[1]; vector3 rij; double lrij, dr; self->universe_spec->distance_function(rij, x[j], x[i], self->universe_spec->geometry_data); lrij = vector_length(rij); dr = lrij-param[0]; e += param[1]*sqr(dr); v += -2.*param[1]*dr*lrij; if (energy->gradients != NULL) { double deriv = (lrij == 0.) ? 0. : 2.*param[1]*dr/lrij; vector3 grad; grad[0] = deriv*rij[0]; grad[1] = deriv*rij[1]; grad[2] = deriv*rij[2]; #ifdef GRADIENTFN if (energy->gradient_fn != NULL) { (*energy->gradient_fn)(energy, i, grad); vector_changesign(grad); (*energy->gradient_fn)(energy, j, grad); } else #endif { vector3 *f = (vector3 *)((PyArrayObject *)energy->gradients)->data; f[i][0] += grad[0]; f[i][1] += grad[1]; f[i][2] += grad[2]; f[j][0] -= grad[0]; f[j][1] -= grad[1]; f[j][2] -= grad[2]; } } if (energy->force_constants != NULL) { double f1 = 2.*param[1]*dr/lrij; double f2 = 2.*param[1]; add_pair_fc(energy, i, j, rij, sqr(lrij), f1, f2); } index += 2; param += 2; } energy->energy_terms[self->index] = e; energy->energy_terms[self->virial_index] += v; } /* Harmonic angle potential */ void harmonic_angle_evaluator(PyFFEnergyTermObject *self, PyFFEvaluatorObject *eval, energy_spec *input, energy_data *energy) { vector3 *x = (vector3 *)input->coordinates->data; long *index = (long *)((PyArrayObject *)self->data[0])->data; double *param = (double *)((PyArrayObject *)self->data[1])->data; int nterms = (self->n+input->nslices-1)/(input->nslices); int term = input->slice_id*nterms; int last_term = (input->slice_id+1)*nterms; double e = 0.; if (last_term > self->n) last_term = self->n; index += 3*term; param += 2*term; /* Loop over bond angle terms */ while (term++ < last_term) { /* * E = k (theta-theta0)^2 * cos theta = (R_i-R_j)*(R_k-R_j) * * index[1]: index of central particle j * index[0], index[2]: indices of outer particles i, k * param[0]: theta0 (radians) * param[1]: k */ int i = index[0]; int j = index[1]; int k = index[2]; vector3 rij, rkj; double lrij, lrkj; double cos_theta, sin_theta, theta, dtheta; self->universe_spec->distance_function(rij, x[j], x[i], self->universe_spec->geometry_data); lrij = vector_length(rij); vector_scale(rij, 1./lrij); self->universe_spec->distance_function(rkj, x[j], x[k], self->universe_spec->geometry_data); lrkj = vector_length(rkj); vector_scale(rkj, 1./lrkj); cos_theta = dot(rij, rkj); if (cos_theta > 1.) cos_theta = 1.; if (cos_theta < -1.) cos_theta = -1.; sin_theta = sqrt(1.-sqr(cos_theta)); theta = acos(cos_theta); dtheta = (theta-param[0]); e += param[1]*sqr(dtheta); if (energy->gradients != NULL || energy->force_constants != NULL) { /* First derivative of angle potential */ double deriv = -2.*param[1]*dtheta/sin_theta; vector3 di, dk, dj; di[0] = (rkj[0]-cos_theta*rij[0])/lrij; di[1] = (rkj[1]-cos_theta*rij[1])/lrij; di[2] = (rkj[2]-cos_theta*rij[2])/lrij; dk[0] = (rij[0]-cos_theta*rkj[0])/lrkj; dk[1] = (rij[1]-cos_theta*rkj[1])/lrkj; dk[2] = (rij[2]-cos_theta*rkj[2])/lrkj; dj[0] = -di[0]-dk[0]; dj[1] = -di[1]-dk[1]; dj[2] = -di[2]-dk[2]; if (energy->gradients != NULL) { #ifdef GRADIENTFN if (energy->gradient_fn != NULL) { vector3 grad; vector_copy(grad, di); vector_scale(grad, deriv); (*energy->gradient_fn)(energy, i, grad); vector_copy(grad, dj); vector_scale(grad, deriv); (*energy->gradient_fn)(energy, j, grad); vector_copy(grad, dk); vector_scale(grad, deriv); (*energy->gradient_fn)(energy, k, grad); } else #endif { vector3 *f = (vector3 *)((PyArrayObject *)energy->gradients)->data; f[i][0] += deriv*di[0]; f[i][1] += deriv*di[1]; f[i][2] += deriv*di[2]; f[j][0] += deriv*dj[0]; f[j][1] += deriv*dj[1]; f[j][2] += deriv*dj[2]; f[k][0] += deriv*dk[0]; f[k][1] += deriv*dk[1]; f[k][2] += deriv*dk[2]; } } if (energy->force_constants != NULL) { /* Second derivative of angle potential */ double deriv2 = 2.*param[1] * (1.-(cos_theta/sin_theta)*dtheta)/sqr(sin_theta); double min_r = (lrij < lrkj) ? sqr(lrij) : sqr(lrkj); int swapij = (i > j); int swapik = (i > k); int swapjk = (j > k); if (energy->fc_fn != NULL) { int l, m; tensor3 fcii, fcjj, fckk, fcij, fcik, fcjk; for (l = 0; l < 3; l++) for (m = 0; m < 3; m++) { double a, b, ab, ba; a = 3.*cos_theta*rij[l]*rij[m]-rij[l]*rkj[m]-rkj[l]*rij[m]; b = 3.*cos_theta*rkj[l]*rkj[m]-rkj[l]*rij[m]-rij[l]*rkj[m]; ab = rij[l]*rkj[m]*cos_theta-rij[l]*rij[m]-rkj[l]*rkj[m]; ba = rkj[l]*rij[m]*cos_theta-rkj[l]*rkj[m]-rij[l]*rij[m]; if (l == m) { a -= cos_theta; b -= cos_theta; ab += 1.; ba += 1.; } a *= deriv/sqr(lrij); b *= deriv/sqr(lrkj); ab *= deriv/(lrij*lrkj); ba *= deriv/(lrij*lrkj); fcii[l][m] = deriv2*di[l]*di[m] + a; fcjj[l][m] = deriv2*dj[l]*dj[m] + a + b + ab + ba; fckk[l][m] = deriv2*dk[l]*dk[m] + b; fcij[l][m] = deriv2*di[l]*dj[m] - a - ab; fcik[l][m] = deriv2*di[l]*dk[m] + ab; fcjk[l][m] = deriv2*dj[l]*dk[m] - ab - b; } (*energy->fc_fn)(energy, i, i, fcii, min_r); (*energy->fc_fn)(energy, j, j, fcjj, min_r); (*energy->fc_fn)(energy, k, k, fckk, min_r); if (swapij) { tensor_transpose(fcij); (*energy->fc_fn)(energy, j, i, fcij, min_r); } else (*energy->fc_fn)(energy, i, j, fcij, min_r); if (swapik) { tensor_transpose(fcik); (*energy->fc_fn)(energy, k, i, fcik, min_r); } else (*energy->fc_fn)(energy, i, k, fcik, min_r); if (swapjk) { tensor_transpose(fcjk); (*energy->fc_fn)(energy, k, j, fcjk, min_r); } else (*energy->fc_fn)(energy, j, k, fcjk, min_r); } else { double *fc_data = (double *)((PyArrayObject *)energy->force_constants)->data; double *fcii = fc_data + 9*input->natoms*i+3*i; double *fcjj = fc_data + 9*input->natoms*j+3*j; double *fckk = fc_data + 9*input->natoms*k+3*k; double *fcij, *fcik, *fcjk; int l, m; if (swapij) fcij = fc_data + 9*input->natoms*j+3*i; else fcij = fc_data + 9*input->natoms*i+3*j; if (swapik) fcik = fc_data + 9*input->natoms*k+3*i; else fcik = fc_data + 9*input->natoms*i+3*k; if (swapjk) fcjk = fc_data + 9*input->natoms*k+3*j; else fcjk = fc_data + 9*input->natoms*j+3*k; for (l = 0; l < 3; l++) for (m = 0; m < 3; m++) { int o = 3*input->natoms*l + m; double a, b, ab, ba; fcii[o] += deriv2*di[l]*di[m]; fcjj[o] += deriv2*dj[l]*dj[m]; fckk[o] += deriv2*dk[l]*dk[m]; if (swapij) fcij[o] += deriv2*dj[l]*di[m]; else fcij[o] += deriv2*di[l]*dj[m]; if (swapik) fcik[o] += deriv2*dk[l]*di[m]; else fcik[o] += deriv2*di[l]*dk[m]; if (swapjk) fcjk[o] += deriv2*dk[l]*dj[m]; else fcjk[o] += deriv2*dj[l]*dk[m]; a = 3.*cos_theta*rij[l]*rij[m]-rij[l]*rkj[m]-rkj[l]*rij[m]; b = 3.*cos_theta*rkj[l]*rkj[m]-rkj[l]*rij[m]-rij[l]*rkj[m]; ab = rij[l]*rkj[m]*cos_theta-rij[l]*rij[m]-rkj[l]*rkj[m]; ba = rkj[l]*rij[m]*cos_theta-rkj[l]*rkj[m]-rij[l]*rij[m]; if (l == m) { a -= cos_theta; b -= cos_theta; ab += 1.; ba += 1.; } a *= deriv/sqr(lrij); b *= deriv/sqr(lrkj); ab *= deriv/(lrij*lrkj); ba *= deriv/(lrij*lrkj); fcii[o] += a; fcjj[o] += a + b + ab + ba; fckk[o] += b; if (swapij) fcij[o] -= a + ba; else fcij[o] -= a + ab; if (swapik) fcik[o] += ba; else fcik[o] += ab; if (swapjk) fcjk[o] -= ba + b; else fcjk[o] -= ab + b; } } } } index += 3; param += 2; } energy->energy_terms[self->index] = e; } /* Cosine dihedral potential */ static void add_fc_tensor(double *fc, int n, int swap, tensor3 t, double f) { int i, j; if (swap) { for (i = 0; i < 3; i++) for (j = 0; j < 3; j++) { int o = 3*n*i+j; fc[o] += f*t[j][i]; } } else { for (i = 0; i < 3; i++) for (j = 0; j < 3; j++) { int o = 3*n*i+j; fc[o] += f*t[i][j]; } } } void cosine_dihedral_evaluator(PyFFEnergyTermObject *self, PyFFEvaluatorObject *eval, energy_spec *input, energy_data *energy) { vector3 *x = (vector3 *)input->coordinates->data; long *index = (long *)((PyArrayObject *)self->data[0])->data; double *param = (double *)((PyArrayObject *)self->data[1])->data; int nterms = (self->n+input->nslices-1)/(input->nslices); int term = input->slice_id*nterms; int last_term = (input->slice_id+1)*nterms; double e = 0.; if (last_term > self->n) last_term = self->n; index += 4*term; param += 4*term; /* Loop over dihedral angle terms */ while (term++ < last_term) { /* * E = V [1 + cos(n phi-gamma)] * phi = angle between plane1 and plane2 using the IUPAC sign convention * plane1 defined by R_i, R_j, R_k * plane2 defined by R_j, R_k, R_l * * index[1], index[1]: indices of the axis particles j, k * index[0], index[3]: indices of outer particles i, l * param[0]: n = int(param[0]) * param[1]: cos gamma * param[2]: sin gamma * param[3]: V */ int i = index[0]; int j = index[1]; int k = index[2]; int l = index[3]; int n; vector3 rij, rkj, rlk, rkj_cross_rkl, rij_cross_rkj, r, s; double lrij, lrkj, lrlk, lm, ln, lr, ls; double dot_rij_rkj, dot_rlk_rkj; double cos_phi, sqr_cos_phi, cos_n_phi; double sin_phi, sin_n_phi_ratio; double phi, dphi, sign_phi, cos_phase, sin_phase; self->universe_spec->distance_function(rij, x[j], x[i], self->universe_spec->geometry_data); lrij = vector_length(rij); self->universe_spec->distance_function(rkj, x[j], x[k], self->universe_spec->geometry_data); lrkj = vector_length(rkj); self->universe_spec->distance_function(rlk, x[k], x[l], self->universe_spec->geometry_data); lrlk = vector_length(rlk); cross(rkj_cross_rkl, rlk, rkj); ln = vector_length(rkj_cross_rkl); cross(rij_cross_rkj, rij, rkj); lm = vector_length(rij_cross_rkj); sign_phi = 1.; if (dot(rij, rkj_cross_rkl) < 0.) sign_phi = -1.; vector_scale(rkj, 1./lrkj); dot_rij_rkj = dot(rij, rkj); r[0] = rij[0]-dot_rij_rkj*rkj[0]; r[1] = rij[1]-dot_rij_rkj*rkj[1]; r[2] = rij[2]-dot_rij_rkj*rkj[2]; lr = vector_length(r); vector_scale(r, 1./lr); dot_rlk_rkj = dot(rlk, rkj); s[0] = rlk[0]-dot_rlk_rkj*rkj[0]; s[1] = rlk[1]-dot_rlk_rkj*rkj[1]; s[2] = rlk[2]-dot_rlk_rkj*rkj[2]; ls = vector_length(s); vector_scale(s, 1./ls); cos_phi = dot(r, s); if (cos_phi > 1.) cos_phi = 1.; if (cos_phi < -1.) cos_phi = -1.; n = (int)param[0]; if (n == 0) { phi = acos(cos_phi)*sign_phi; dphi = phi-param[1]; dphi = fmod(dphi+2.*M_PI, 2.*M_PI); if (dphi > M_PI) dphi -= 2*M_PI; e += param[3]*sqr(dphi); /* initialize some variables used only for n > 0, to make gc happy */ sin_phase = cos_phase = sin_n_phi_ratio = sin_phi = cos_n_phi = sqr_cos_phi = 0.; } else { cos_phase = param[1]; sin_phase = param[2]; sqr_cos_phi = sqr(cos_phi); sin_phi = 2.; switch (n) { case 1: cos_n_phi = cos_phi; sin_n_phi_ratio = 1.; break; case 2: cos_n_phi = 2.*sqr_cos_phi-1.; sin_n_phi_ratio = 2.*cos_phi; break; case 3: cos_n_phi = (4.*sqr_cos_phi-3.)*cos_phi; sin_n_phi_ratio = 4.*sqr_cos_phi - 1.; break; case 4: cos_n_phi = 8.*(sqr_cos_phi-1.)*sqr_cos_phi+1.; sin_n_phi_ratio = 4.*(2.*sqr_cos_phi-1.)*cos_phi; break; #if 0 /* n=5 and n=6 don't occur in the Amber force field and have therefore not been tested. Use this section at your own risk. */ case 5: cos_n_phi = ((16.*sqr_cos_phi-20.)*sqr_cos_phi+5.)*cos_phi; sin_n_phi_ratio =4.*(4.*sqr_cos_phi-3.)*sqr_cos_phi+1.; break; case 6: cos_n_phi = ((32.*sqr_cos_phi-48.)*sqr_cos_phi+18.)*sqr_cos_phi-1.; sin_n_phi_ratio = (32.*(sqr_cos_phi-1.)*sqr_cos_phi+6.)*cos_phi; break; #endif default: phi = acos(cos_phi)*sign_phi; sin_phi = sqrt(1.-sqr_cos_phi)*sign_phi; cos_n_phi = cos(n*phi); if (fabs(sin_phi) > 1.e-4) sin_n_phi_ratio = sin(n*phi)/sin_phi; else sin_n_phi_ratio = n; } if (fabs(sin_phase) < 1.e-8) e += param[3]*(1.+cos_n_phi*cos_phase); else { if (sin_phi == 2.) sin_phi = sqrt(1.-sqr_cos_phi)*sign_phi; e += param[3]*(1.+cos_n_phi*cos_phase + sin_n_phi_ratio*sin_phi*sin_phase); } dphi = 0.; /* initialize to make gcc happy */ } if (energy->gradients != NULL || energy->force_constants != NULL) { double deriv; vector3 di, dj, dk, dl, ds; if (n == 0) deriv = 2.*param[3]*dphi; else { if (sin_phi == 2.) sin_phi = sqrt(1.-sqr_cos_phi)*sign_phi; deriv = n*param[3]*(-sin_n_phi_ratio*sin_phi*cos_phase +cos_n_phi*sin_phase); } vector_copy(di, rij_cross_rkj); vector_scale(di, lrkj/sqr(lm)); vector_copy(dl, rkj_cross_rkl); vector_scale(dl, -lrkj/sqr(ln)); ds[0] = (dot_rij_rkj*di[0]+dot_rlk_rkj*dl[0])/lrkj; ds[1] = (dot_rij_rkj*di[1]+dot_rlk_rkj*dl[1])/lrkj; ds[2] = (dot_rij_rkj*di[2]+dot_rlk_rkj*dl[2])/lrkj; dj[0] = ds[0]-di[0]; dj[1] = ds[1]-di[1]; dj[2] = ds[2]-di[2]; dk[0] = -ds[0]-dl[0]; dk[1] = -ds[1]-dl[1]; dk[2] = -ds[2]-dl[2]; if (energy->gradients != NULL) { #ifdef GRADIENTFN if (energy->gradient_fn != NULL) { vector3 grad; vector_copy(grad, di); vector_scale(grad, deriv); (*energy->gradient_fn)(energy, i, grad); vector_copy(grad, dj); vector_scale(grad, deriv); (*energy->gradient_fn)(energy, j, grad); vector_copy(grad, dk); vector_scale(grad, deriv); (*energy->gradient_fn)(energy, k, grad); vector_copy(grad, dl); vector_scale(grad, deriv); (*energy->gradient_fn)(energy, l, grad); } else #endif { vector3 *f = (vector3 *)((PyArrayObject *)energy->gradients)->data; f[i][0] += deriv*di[0]; f[i][1] += deriv*di[1]; f[i][2] += deriv*di[2]; f[j][0] += deriv*dj[0]; f[j][1] += deriv*dj[1]; f[j][2] += deriv*dj[2]; f[k][0] += deriv*dk[0]; f[k][1] += deriv*dk[1]; f[k][2] += deriv*dk[2]; f[l][0] += deriv*dl[0]; f[l][1] += deriv*dl[1]; f[l][2] += deriv*dl[2]; } } if (energy->force_constants != NULL) { double deriv2; int swapij = (i > j); int swapik = (i > k); int swapil = (i > l); int swapjk = (j > k); int swapjl = (j > l); int swapkl = (k > l); vector3 ga, fa, gb, hb; tensor3 aga, afa, bgb, bhb, gg, fg, hg, temp; double ff, gg1, gg2, gg3, gg4, hh; int i1, i2; if (n == 0) deriv2 = 2.*param[3]; else deriv2 = -n*n*param[3]*(cos_n_phi*cos_phase + sin_n_phi_ratio*sin_phi*sin_phase); /************/ cross(ga, rkj, rij_cross_rkj); cross(fa, rij_cross_rkj, rij); cross(gb, rkj, rkj_cross_rkl); cross(hb, rkj_cross_rkl, rlk); symmetric_tensor_product(aga, rij_cross_rkj, ga, -lrkj); symmetric_tensor_product(afa, rij_cross_rkj, fa, -1.); symmetric_tensor_product(bgb, rkj_cross_rkl, gb, -lrkj); symmetric_tensor_product(bhb, rkj_cross_rkl, hb, -1.); /************/ ff = lrkj/sqr(sqr(lm)); gg1 = 0.5/(cube(lrkj)*sqr(lm)); gg2 = -dot_rij_rkj/sqr(sqr(lm)); gg3 = -0.5/(cube(lrkj)*sqr(ln)); gg4 = dot_rlk_rkj/sqr(sqr(ln)); hh = -lrkj/sqr(sqr(ln)); tensor_copy(gg, aga); tensor_scale(gg, gg1); tensor_add(gg, afa, gg2); tensor_add(gg, bgb, gg3); tensor_add(gg, bhb, gg4); /************/ tensor_product(fg, fa, rij_cross_rkj, lrkj); tensor_product(temp, rij_cross_rkj, ga, -dot_rij_rkj*lrkj); tensor_add(fg, temp, 1.); tensor_scale(fg, 1./sqr(sqr(lm))); tensor_product(hg, hb, rkj_cross_rkl, lrkj); tensor_product(temp, rkj_cross_rkl, gb, -dot_rlk_rkj*lrkj); tensor_add(hg, temp, 1.); tensor_scale(hg, -1./sqr(sqr(ln))); /************/ if (energy->fc_fn != NULL) { tensor3 fcii, fcjj, fckk, fcll, fcij, fcik, fcil, fcjk, fcjl, fckl; double min_r = lrij; if (lrkj < min_r) min_r = lrkj; if (lrlk < min_r) min_r = lrlk; min_r = sqr(min_r); for (i1 = 0; i1 < 3; i1++) for (i2 = 0; i2 < 3; i2++) { fcii[i1][i2] = deriv2*di[i1]*di[i2]; fcjj[i1][i2] = deriv2*dj[i1]*dj[i2]; fckk[i1][i2] = deriv2*dk[i1]*dk[i2]; fcll[i1][i2] = deriv2*dl[i1]*dl[i2]; fcij[i1][i2] = deriv2*di[i1]*dj[i2]; fcik[i1][i2] = deriv2*di[i1]*dk[i2]; fcil[i1][i2] = deriv2*di[i1]*dl[i2]; fcjk[i1][i2] = deriv2*dj[i1]*dk[i2]; fcjl[i1][i2] = deriv2*dj[i1]*dl[i2]; fckl[i1][i2] = deriv2*dk[i1]*dl[i2]; } /************/ tensor_add(fcii, aga, ff*deriv); tensor_add(fcjj, aga, ff*deriv); tensor_add(fcjj, gg, deriv); tensor_add(fcjj, fg, -deriv); tensor_add(fckk, bgb, hh*deriv); tensor_add(fckk, gg, deriv); tensor_add(fckk, hg, deriv); tensor_add(fcll, bgb, hh*deriv); tensor_add(fcij, aga, -ff*deriv); tensor_add(fcij, fg, deriv); tensor_add(fcik, fg, -deriv); tensor_add(fcjk, gg, -deriv); tensor_add(fcjk, fg, deriv); tensor_add(fckl, bgb, -hh*deriv); /************/ tensor_transpose(fg); tensor_transpose(hg); /************/ tensor_add(fcjj, fg, -deriv); tensor_add(fckk, hg, deriv); tensor_add(fcjk, hg, -deriv); tensor_add(fcjl, hg, deriv); tensor_add(fckl, hg, -deriv); /************/ (*energy->fc_fn)(energy, i, i, fcii, min_r); (*energy->fc_fn)(energy, j, j, fcjj, min_r); (*energy->fc_fn)(energy, k, k, fckk, min_r); (*energy->fc_fn)(energy, l, l, fcll, min_r); if (swapij) { tensor_transpose(fcij); (*energy->fc_fn)(energy, j, i, fcij, min_r); } else (*energy->fc_fn)(energy, i, j, fcij, min_r); if (swapik) { tensor_transpose(fcik); (*energy->fc_fn)(energy, k, i, fcik, min_r); } else (*energy->fc_fn)(energy, i, k, fcik, min_r); if (swapil) { tensor_transpose(fcil); (*energy->fc_fn)(energy, l, i, fcil, min_r); } else (*energy->fc_fn)(energy, i, l, fcil, min_r); if (swapjk) { tensor_transpose(fcjk); (*energy->fc_fn)(energy, k, j, fcjk, min_r); } else (*energy->fc_fn)(energy, j, k, fcjk, min_r); if (swapjl) { tensor_transpose(fcjl); (*energy->fc_fn)(energy, l, j, fcjl, min_r); } else (*energy->fc_fn)(energy, j, l, fcjl, min_r); if (swapkl) { tensor_transpose(fckl); (*energy->fc_fn)(energy, l, k, fckl, min_r); } else (*energy->fc_fn)(energy, k, l, fckl, min_r); } else { double *fc_data = (double *)((PyArrayObject *)energy->force_constants)->data; double *fcii = fc_data + 9*input->natoms*i+3*i; double *fcjj = fc_data + 9*input->natoms*j+3*j; double *fckk = fc_data + 9*input->natoms*k+3*k; double *fcll = fc_data + 9*input->natoms*l+3*l; double *fcij, *fcik, *fcil, *fcjk, *fcjl, *fckl; if (swapij) fcij = fc_data + 9*input->natoms*j+3*i; else fcij = fc_data + 9*input->natoms*i+3*j; if (swapik) fcik = fc_data + 9*input->natoms*k+3*i; else fcik = fc_data + 9*input->natoms*i+3*k; if (swapil) fcil = fc_data + 9*input->natoms*l+3*i; else fcil = fc_data + 9*input->natoms*i+3*l; if (swapjk) fcjk = fc_data + 9*input->natoms*k+3*j; else fcjk = fc_data + 9*input->natoms*j+3*k; if (swapjl) fcjl = fc_data + 9*input->natoms*l+3*j; else fcjl = fc_data + 9*input->natoms*j+3*l; if (swapkl) fckl = fc_data + 9*input->natoms*l+3*k; else fckl = fc_data + 9*input->natoms*k+3*l; for (i1 = 0; i1 < 3; i1++) for (i2 = 0; i2 < 3; i2++) { int o = 3*input->natoms*i1 + i2; fcii[o] += deriv2*di[i1]*di[i2]; fcjj[o] += deriv2*dj[i1]*dj[i2]; fckk[o] += deriv2*dk[i1]*dk[i2]; fcll[o] += deriv2*dl[i1]*dl[i2]; if (swapij) fcij[o] += deriv2*dj[i1]*di[i2]; else fcij[o] += deriv2*di[i1]*dj[i2]; if (swapik) fcik[o] += deriv2*dk[i1]*di[i2]; else fcik[o] += deriv2*di[i1]*dk[i2]; if (swapil) fcil[o] += deriv2*dl[i1]*di[i2]; else fcil[o] += deriv2*di[i1]*dl[i2]; if (swapjk) fcjk[o] += deriv2*dk[i1]*dj[i2]; else fcjk[o] += deriv2*dj[i1]*dk[i2]; if (swapjl) fcjl[o] += deriv2*dl[i1]*dj[i2]; else fcjl[o] += deriv2*dj[i1]*dl[i2]; if (swapkl) fckl[o] += deriv2*dl[i1]*dk[i2]; else fckl[o] += deriv2*dk[i1]*dl[i2]; } add_fc_tensor(fcii, input->natoms, 0, aga, ff*deriv); add_fc_tensor(fcjj, input->natoms, 0, aga, ff*deriv); add_fc_tensor(fcij, input->natoms, swapij, aga, -ff*deriv); add_fc_tensor(fcjj, input->natoms, 0, gg, deriv); add_fc_tensor(fckk, input->natoms, 0, gg, deriv); add_fc_tensor(fcjk, input->natoms, swapjk, gg, -deriv); add_fc_tensor(fckk, input->natoms, 0, bgb, hh*deriv); add_fc_tensor(fcll, input->natoms, 0, bgb, hh*deriv); add_fc_tensor(fckl, input->natoms, swapkl, bgb, -hh*deriv); add_fc_tensor(fcij, input->natoms, swapij, fg, deriv); add_fc_tensor(fcjj, input->natoms, 0, fg, -deriv); add_fc_tensor(fcjj, input->natoms, 1, fg, -deriv); add_fc_tensor(fcik, input->natoms, swapik, fg, -deriv); add_fc_tensor(fcjk, input->natoms, swapjk, fg, deriv); add_fc_tensor(fcjk, input->natoms, !swapjk, hg, -deriv); add_fc_tensor(fckk, input->natoms, 1, hg, deriv); add_fc_tensor(fckk, input->natoms, 0, hg, deriv); add_fc_tensor(fcjl, input->natoms, !swapjl, hg, deriv); add_fc_tensor(fckl, input->natoms, !swapkl, hg, -deriv); } } } index += 4; param += 4; } energy->energy_terms[self->index] = e; } MMTK-2.7.9/Src/ewald.c0000644000076600000240000002326712117632051014710 0ustar hinsenstaff00000000000000/* Ewald method for electrostatic interactions * * Written by Konrad Hinsen */ #define NO_IMPORT #define _FORCEFIELD_MODULE #define PY_ARRAY_UNIQUE_SYMBOL PyArray_MMTKFF_API #include "MMTK/forcefield.h" #include "MMTK/forcefield_private.h" #define DEBUG 0 /* * Include erfc code, unless user wants to use an erfc from libm. */ #ifndef LIBM_HAS_ERFC #include "polevl.c" #include "ndtr.c" #endif #ifndef M_PI #define M_PI 3.14159265358979323846264338327950288 #endif /* * Plain Ewald sum, for orthorhombic systems only. */ typedef struct { double real; double imag; } complex; static complex c_1 = {1., 0.}; #define c_mult_real(a, b) ((a).real*(b).real - (a).imag*(b).imag) #define c_mult_imag(a, b) ((a).real*(b).imag + (a).imag*(b).real) #if 0 static inline complex c_mult(complex a, complex b) { complex r; r.real = a.real*b.real - a.imag*b.imag; r.imag = a.real*b.imag + a.imag*b.real; return r; } #endif static double c_abssq(complex z) { return z.real*z.real + z.imag*z.imag; } static double reciprocal_sum(energy_spec *input, energy_data *energy, double volume, double *charge, double beta, long *kmax, double cutoff_sq, box_fn *box_transformation_fn, double *universe_data, void *scratch, PyFFEvaluatorObject *eval, PyFFEnergyTermObject *term) { vector3 *x = (vector3 *)input->coordinates->data; vector3 *xb = (vector3 *)scratch; complex *eikx = (complex *)(xb + input->natoms); complex *eiky = eikx + input->natoms*(kmax[0]+1); complex *eikz = eiky + input->natoms*(2*kmax[1]+1); complex *eikr = eikz + input->natoms*(2*kmax[2]+1); int *nkvect = (int *)(eikr+input->natoms); int *kx = nkvect + 1; int *ky = kx + *nkvect; int *kz = ky + *nkvect; vector3 r1, r2, r3; int nx, ny, nz, negy, negz, xk, yk, zk; int atoms_per_slice, first_atom, last_atom; int vectors_per_slice, first_vector, last_vector; double e; int i, nk; if (input->thread_id == 0) { (*box_transformation_fn)(x, xb, input->natoms, universe_data, 1); for (i = 0; i < input->natoms; i++) { xb[i][0] *= 2.*M_PI; xb[i][1] *= 2.*M_PI; xb[i][2] *= 2.*M_PI; } } else { eikr = (complex *)malloc(2*input->natoms*sizeof(double)); if (eikr == NULL) { energy->error = 1; return 0.; } } #ifdef WITH_THREAD barrier(eval->binfo+term->barrier_index, input->thread_id, input->nthreads); #endif r1[0] = 2.*M_PI; r1[1] = r1[2] = 0.; r2[1] = 2.*M_PI; r2[2] = r2[0] = 0.; r3[2] = 2.*M_PI; r3[0] = r3[1] = 0.; (*box_transformation_fn)(&r1, &r1, 1, universe_data, 3); (*box_transformation_fn)(&r2, &r2, 1, universe_data, 3); (*box_transformation_fn)(&r3, &r3, 1, universe_data, 3); nx = kmax[0]+1; ny = 2*kmax[1]+1; nz = 2*kmax[2]+1; negy = kmax[1]; negz = kmax[2]; atoms_per_slice = (input->natoms+input->nslices-1)/input->nslices; first_atom = input->slice_id*atoms_per_slice; last_atom = (input->slice_id+1)*atoms_per_slice; if (last_atom > input->natoms) last_atom = input->natoms; for (i = first_atom; i < last_atom; i++) { eikx[nx*i] = c_1; eiky[ny*i] = c_1; eikz[nz*i] = c_1; eikx[nx*i+1].real = cos(xb[i][0]); eikx[nx*i+1].imag = sin(xb[i][0]); eiky[ny*i+1].real = cos(xb[i][1]); eiky[ny*i+1].imag = sin(xb[i][1]); eikz[nz*i+1].real = cos(xb[i][2]); eikz[nz*i+1].imag = sin(xb[i][2]); eiky[ny*i+negy+1].real = eiky[ny*i+1].real; eiky[ny*i+negy+1].imag = -eiky[ny*i+1].imag; eikz[nz*i+negz+1].real = eikz[nz*i+1].real; eikz[nz*i+negz+1].imag = -eikz[nz*i+1].imag; for (xk = 2; xk <= kmax[0]; xk++) { eikx[nx*i+xk].real = c_mult_real(eikx[nx*i+xk-1], eikx[nx*i+1]); eikx[nx*i+xk].imag = c_mult_imag(eikx[nx*i+xk-1], eikx[nx*i+1]); } for (yk = 2; yk <= kmax[1]; yk++) { eiky[ny*i+yk].real = c_mult_real(eiky[ny*i+yk-1], eiky[ny*i+1]); eiky[ny*i+yk].imag = c_mult_imag(eiky[ny*i+yk-1], eiky[ny*i+1]); eiky[ny*i+negy+yk].real = eiky[ny*i+yk].real; eiky[ny*i+negy+yk].imag = -eiky[ny*i+yk].imag; } for (zk = 2; zk <= kmax[2]; zk++) { eikz[nz*i+zk].real = c_mult_real(eikz[nz*i+zk-1], eikz[nz*i+1]); eikz[nz*i+zk].imag = c_mult_imag(eikz[nz*i+zk-1], eikz[nz*i+1]); eikz[nz*i+negz+zk].real = eikz[nz*i+zk].real; eikz[nz*i+negz+zk].imag = -eikz[nz*i+zk].imag; } } #ifdef WITH_THREAD barrier(eval->binfo+term->barrier_index+1, input->thread_id, input->nthreads); #endif vectors_per_slice = (*nkvect+input->nslices-1)/input->nslices; first_vector = input->slice_id*vectors_per_slice; last_vector = (input->slice_id+1)*vectors_per_slice; if (last_vector > *nkvect) last_vector = *nkvect; e = 0.; for (nk = first_vector; nk < last_vector; nk++) { int xk = kx[nk]; int yk = ky[nk]; int zk = kz[nk]; int iyk = (yk < 0) ? negy-yk : yk; int izk = (zk < 0) ? negz-zk : zk; vector3 k = {0., 0., 0.}; double ksq, expfactor; complex sum; vector_add(k, r1, xk); vector_add(k, r2, yk); vector_add(k, r3, zk); ksq = vector_length_sq(k); expfactor = exp(-ksq/(4.*beta*beta))/ksq; if (xk != 0) expfactor *= 2; sum.real = sum.imag = 0.; for (i = 0; i < input->natoms; i++) { complex temp; temp.real = c_mult_real(eikx[nx*i+xk], eiky[ny*i+iyk]); temp.imag = c_mult_imag(eikx[nx*i+xk], eiky[ny*i+iyk]); eikr[i].real = c_mult_real(temp, eikz[nz*i+izk]); eikr[i].imag = c_mult_imag(temp, eikz[nz*i+izk]); sum.real += charge[i]*eikr[i].real; sum.imag += charge[i]*eikr[i].imag; } e += c_abssq(sum)*expfactor; if (energy->gradients != NULL) { for (i = 0; i < input->natoms; i++) { vector3 grad; double a = 4.*M_PI*expfactor*electrostatic_energy_factor * charge[i]*(sum.imag*eikr[i].real-sum.real*eikr[i].imag)/volume; grad[0] = a*k[0]; grad[1] = a*k[1]; grad[2] = a*k[2]; #ifdef GRADIENTFN if (energy->gradient_fn != NULL) (*energy->gradient_fn)(energy, i, grad); else #endif { vector3 *f = (vector3 *)((PyArrayObject *)energy->gradients)->data; f[i][0] += grad[0]; f[i][1] += grad[1]; f[i][2] += grad[2]; } } } if (energy->force_constants != NULL) { #if 0 tensor3 fij; int j; tensor_product(fij, k, k, 4.*M_PI*expfactor*electrostatic_energy_factor/volume); for (i = 0; i < input->natoms; i++) for (j = i; j < input->natoms; j++) { double f = charge[i]*charge[j]; } if (energy->fc_fn != NULL) { } else { } #endif PyErr_SetString(PyExc_ValueError, "not yet implemented"); energy->error = 1; } } if (input->thread_id != 0) free(eikr); return 2.*M_PI*e*electrostatic_energy_factor/volume; } /* * Count k vectors for memory allocation and initialize scratch area. */ int init_kvectors(box_fn *box_transformation_fn, double *universe_data, int natoms, long *kmax, double cutoff_sq, void *scratch, int nvect) { vector3 *xb = (vector3 *)scratch; /* Size natoms */ complex *eikx = (complex *)(xb + natoms); /* Size (kmax[0]+1)*natoms */ complex *eiky = eikx + natoms*(kmax[0]+1); /* Size (2*kmax[1]+1)*natoms */ complex *eikz = eiky + natoms*(2*kmax[1]+1); /* Size (2*kmax[2]+1)*natoms */ complex *eikr = eikz + natoms*(2*kmax[2]+1); /* Size natoms */ int *nkvect = (int *)(eikr+natoms); /* Size 1 */ int *kx = nkvect + 1; /* Size nvect */ int *ky = kx + nvect; /* Size nvect */ int *kz = ky + nvect; /* Size nvect */ vector3 r1, r2, r3; int x, y, z; int nk = 0; r1[0] = 2.*M_PI; r1[1] = r1[2] = 0.; r2[1] = 2.*M_PI; r2[2] = r2[0] = 0.; r3[2] = 2.*M_PI; r3[0] = r3[1] = 0.; (*box_transformation_fn)(&r1, &r1, 1, universe_data, 3); (*box_transformation_fn)(&r2, &r2, 1, universe_data, 3); (*box_transformation_fn)(&r3, &r3, 1, universe_data, 3); if (scratch != NULL) *nkvect = nvect; for (x = 0; x <= kmax[0]; x++) { for (y = -kmax[1]; y <= kmax[1]; y++) { for (z = -kmax[2]; z <= kmax[2]; z++) { vector3 k = {0., 0., 0.}; double ksq; vector_add(k, r1, x); vector_add(k, r2, y); vector_add(k, r3, z); ksq = vector_length_sq(k); if (ksq < cutoff_sq && ksq > 0) { if (scratch != NULL) { kx[nk] = x; ky[nk] = y; kz[nk] = z; } nk++; } } } } #if 0 printf("%d k vectors\n", nk); #endif return nk; } /* The Ewald evaluator does not calculate the real-space sum, which is evaluated in nonbonded_evaluator! */ void es_ewald_evaluator(PyFFEnergyTermObject *self, PyFFEvaluatorObject *eval, energy_spec *input, energy_data *energy) { box_fn *box_transformation_fn = self->universe_spec->box_function; volume_fn *v_fn = self->universe_spec->volume_function; double *universe_data = self->universe_spec->geometry_data; double volume = (*v_fn)(1., universe_data); double *charge = (double *)((PyArrayObject *)self->data[1])->data; long *kmax = (long *)((PyArrayObject *)self->data[2])->data; double inv_cutoff = (self->param[0] == 0.) ? 0. : 1./self->param[0]; double beta = self->param[2]; double reciprocal_cutoff_sq = self->param[3]; double charge_sum; double e = 0.; int k; if (reciprocal_cutoff_sq > 0.) inv_cutoff = 0.; if (input->slice_id == 0) { charge_sum = 0.; for (k = 0; k < input->natoms; k++) charge_sum += charge[k]*charge[k]; e -= charge_sum*electrostatic_energy_factor * (beta/sqrt(M_PI)+0.5*inv_cutoff*erfc(beta*self->param[0])); } energy->energy_terms[self->index] = e; if (reciprocal_cutoff_sq > 0.) energy->energy_terms[self->index+1] = reciprocal_sum(input, energy, volume, charge, beta, kmax, reciprocal_cutoff_sq, box_transformation_fn, universe_data, self->scratch, eval, self); energy->energy_terms[self->virial_index] += energy->energy_terms[self->index] + energy->energy_terms[self->index+1]; } MMTK-2.7.9/Src/ewald1.i0000644000076600000240000000143111662461640014774 0ustar hinsenstaff00000000000000for (k = 0; k < nblist->npairs; k++, pair++) { if (cutoff_sq == 0. || pair->r_sq <= cutoff_sq) { int i = pair->i1; int j = pair->i2; double r_sq = pair->r_sq; double r = sqrt(r_sq); double f1 = erfc(beta*r); double f2 = erfc_cutoff; vector3 rij; if (pair->one_four) f1 += one_four_factor-1.; #ifdef NBLIST_WITH_VECTORS vector_copy(rij, pair->d); #else distance_vector_function(rij, x[j], x[i], universe_data); #endif # include "ewald2.i" } } for (k = 0; k < n_ex; k += 2) { int i = excluded[k]; int j = excluded[k+1]; double r_sq, r, f1, f2; vector3 rij; distance_vector_function(rij, x[j], x[i], universe_data); r_sq = vector_length_sq(rij); r = sqrt(r_sq); f1 = erfc(beta*r)-1.; f2 = erfc_cutoff; # include "ewald2.i" } MMTK-2.7.9/Src/ewald2.i0000644000076600000240000000247311662461640015004 0ustar hinsenstaff00000000000000{ double qiqj = charge[i]*charge[j]*electrostatic_energy_factor; double ef = 2./sqrt(M_PI); self->energy += qiqj*(f1/r-f2*inv_cutoff); if (gradients != NULL || force_constants != NULL) { double deriv = -qiqj*(f1/r_sq-f2*inv_cutoff +ef*beta*(exp(-beta*beta*r_sq)/r -inv_cutoff*exp(-beta*beta*cutoff_sq)))/r; if (gradients != NULL) { vector3 grad; grad[0] = deriv*rij[0]; grad[1] = deriv*rij[1]; grad[2] = deriv*rij[2]; if (gradient_fn != NULL) { (*gradient_fn)(self, gradients, i, grad); vector_changesign(grad); (*gradient_fn)(self, gradients, j, grad); } else { vector3 *f = (vector3 *)((PyArrayObject *)gradients)->data; f[i][0] += grad[0]; f[i][1] += grad[1]; f[i][2] += grad[2]; f[j][0] -= grad[0]; f[j][1] -= grad[1]; f[j][2] -= grad[2]; } } if (force_constants != NULL) { double deriv2 = 2.*qiqj*(f1/(r*r_sq) - f2*cube(inv_cutoff) + ef*beta*exp(-beta*beta*r_sq) * (beta*beta+1./r_sq)); if (inv_cutoff > 0.) deriv2 -= 2.*qiqj*ef*beta*exp(-beta*beta*cutoff_sq) * (beta*beta+sqr(inv_cutoff)); add_pair_fc(self, force_constants, fc_fn, i, j, rij, r_sq, deriv, deriv2); } } } MMTK-2.7.9/Src/linuxopt0000644000076600000240000000031112041514037015235 0ustar hinsenstaff00000000000000#!/bin/csh rm -f nonbonded.o ewald.o bonded.o MMTK_forcefieldmodule.so dsyev.o lapack_dsyevmodule.so make OPT='-O3 -ffast-math -fomit-frame-pointer -fkeep-inline-functions' cp *.so ../MMTK/linux-i386 MMTK-2.7.9/Src/mconf.h0000644000076600000240000001132111662461640014717 0ustar hinsenstaff00000000000000/* mconf.h * * Common include file for math routines * * * * SYNOPSIS: * * #include "mconf.h" * * * * DESCRIPTION: * * This file contains definitions for error codes that are * passed to the common error handling routine mtherr() * (which see). * * The file also includes a conditional assembly definition * for the type of computer arithmetic (IEEE, DEC, Motorola * IEEE, or UNKnown). * * For Digital Equipment PDP-11 and VAX computers, certain * IBM systems, and others that use numbers with a 56-bit * significand, the symbol DEC should be defined. In this * mode, most floating point constants are given as arrays * of octal integers to eliminate decimal to binary conversion * errors that might be introduced by the compiler. * * For little-endian computers, such as IBM PC, that follow the * IEEE Standard for Binary Floating Point Arithmetic (ANSI/IEEE * Std 754-1985), the symbol IBMPC should be defined. These * numbers have 53-bit significands. In this mode, constants * are provided as arrays of hexadecimal 16 bit integers. * * Big-endian IEEE format is denoted MIEEE. On some RISC * systems such as Sun SPARC, double precision constants * must be stored on 8-byte address boundaries. Since integer * arrays may be aligned differently, the MIEEE configuration * may fail on such machines. * * To accommodate other types of computer arithmetic, all * constants are also provided in a normal decimal radix * which one can hope are correctly converted to a suitable * format by the available C language compiler. To invoke * this mode, define the symbol UNK. * * An important difference among these modes is a predefined * set of machine arithmetic constants for each. The numbers * MACHEP (the machine roundoff error), MAXNUM (largest number * represented), and several other parameters are preset by * the configuration symbol. Check the file const.c to * ensure that these values are correct for your computer. * * Configurations NANS, INFINITIES, MINUSZERO, and DENORMAL * may fail on many systems. Verify that they are supposed * to work on your computer. */ /* Cephes Math Library Release 2.3: June, 1995 Copyright 1984, 1987, 1989, 1995 by Stephen L. Moshier */ #ifndef _MCONF_H #define _MCONF_H /* Constant definitions for math error conditions */ #define DOMAIN 1 /* argument domain error */ #define SING 2 /* argument singularity */ #define OVERFLOW 3 /* overflow range error */ #define UNDERFLOW 4 /* underflow range error */ #define TLOSS 5 /* total loss of precision */ #define PLOSS 6 /* partial loss of precision */ #define EDOM 33 #define ERANGE 34 /* Complex numeral. */ typedef struct { double r; double i; } cmplx; /* Long double complex numeral. */ /* typedef struct { long double r; long double i; } cmplxl; */ /* Type of computer arithmetic */ /* PDP-11, Pro350, VAX: */ #undef DEC /* Intel IEEE, low order words come first: */ #undef IBMPC /* Motorola IEEE, high order words come first * (Sun 680x0 workstation): */ #undef MIEEE /* UNKnown arithmetic, invokes coefficients given in * normal decimal format. Beware of range boundary * problems (MACHEP, MAXLOG, etc. in const.c) and * roundoff problems in pow.c: * (Sun SPARCstation) */ #define UNK 1 /* If you define UNK, then be sure to set BIGENDIAN properly. */ #define BIGENDIAN 0 /* Define this `volatile' if your compiler thinks * that floating point arithmetic obeys the associative * and distributive laws. It will defeat some optimizations * (but probably not enough of them). * * #define VOLATILE volatile */ #define VOLATILE /* For 12-byte long doubles on an i386, pad a 16-bit short 0 * to the end of real constants initialized by integer arrays. * * #define XPD 0, * * Otherwise, the type is 10 bytes long and XPD should be * defined blank (e.g., Microsoft C). * * #define XPD */ #define XPD 0, /* Define to support tiny denormal numbers, else undefine. */ #define DENORMAL 1 /* Define to ask for infinity support, else undefine. */ #define INFINITIES 1 /* Define to ask for support of numbers that are Not-a-Number, else undefine. This may automatically define INFINITIES in some files. */ #define NANS 1 /* Define to distinguish between -0.0 and +0.0. */ #define MINUSZERO 1 /* Define 1 for ANSI C atan2() function See atan.c and clog.c. */ #define ANSIC 1 /* ANSI function prototypes */ #define ANSIPROT extern double polevl ( double x, double *P, int N ); extern double p1evl ( double x, double *P, int N ); extern double ndtr ( double a ); extern double erfc ( double a ); extern double erf ( double x ); /* No error reporting */ #define mtherr(function, type) /* Constants */ #define MAXLOG 8.8029691931113054295988E1 #define SQRTH 7.07106781186547524401E-1 #endif MMTK-2.7.9/Src/MMTK_DCD.c0000644000076600000240000002037112041514572015032 0ustar hinsenstaff00000000000000/* I/O in DCD format * * Written by Lutz Ehrlich * Adapted to MMTK conventions by Konrad Hinsen */ #include "MMTK/universe.h" #include "MMTK/trajectory.h" #include "MMTK/readdcd.h" /* Global variables */ double angstrom_factor; double akma_time_factor; /* Allocate and initialize Output variable descriptors */ static PyTrajectoryVariable * get_data_descriptors(PyArrayObject *configuration, double *time, double *box_size, int box_size_length) { static PyTrajectoryVariable vars[4]; if (vars != NULL) { vars[0].name = "time"; vars[0].text = "Time: %lf\n"; vars[0].unit = time_unit_name; vars[0].type = PyTrajectory_Scalar; vars[0].class = PyTrajectory_Time; vars[0].value.dp = time; vars[1].name = "configuration"; vars[1].text = "Configuration:\n"; vars[1].unit = length_unit_name; vars[1].type = PyTrajectory_ParticleVector; vars[1].class = PyTrajectory_Configuration; vars[1].value.array = configuration; if (box_size_length > 0) { vars[2].name = "box_size"; vars[2].text = "Box size:"; vars[2].unit = length_unit_name; vars[2].type = PyTrajectory_BoxSize; vars[2].class = PyTrajectory_Configuration; vars[2].value.dp = box_size; vars[2].length = box_size_length; vars[3].name = NULL; } else vars[2].name = NULL; } return vars; } static PyObject * readDCD(PyObject *dummy, PyObject *args) { PyObject *universe; PyUniverseSpecObject *universe_spec; PyArrayObject *configuration; PyListObject *spec_list; PyTrajectoryOutputSpec *output; vector3 *x; int atoms; char buffer[100]; PyTrajectoryVariable *data_descriptors; int j; /* DCD transformation variables */ char * dcdFileName; FILE * dcdFile; int * dcdFreeatoms; int dcdAtoms; int dcdNFrames=0; int dcdFrameStart=0; int dcdFrameSkip=0; float dcdTimeStep; int dcdNamnf; float *dcdX = NULL; float *dcdY = NULL; float *dcdZ = NULL; int dcdErrcode; int currFrames=0; double time; dcdFreeatoms = NULL; /* Parse and check arguments */ if (!PyArg_ParseTuple(args, "OO!O!s", &universe, &PyArray_Type, &configuration, &PyList_Type, &spec_list, &dcdFileName)) return NULL; universe_spec = (PyUniverseSpecObject *) PyObject_GetAttrString(universe, "_spec"); if (universe_spec == NULL) return NULL; atoms = configuration->dimensions[0]; x = (vector3 *)configuration->data; /* Prepare output data descriptors */ data_descriptors = get_data_descriptors(configuration, &time, universe_spec->geometry_data, universe_spec->geometry_data_length); /* Initialize output */ output = PyTrajectory_OutputSpecification(universe, spec_list, dcdFileName, data_descriptors); if (output == NULL) return NULL; /* open DCD file */ dcdFile = open_dcd_read( dcdFileName ); if ( dcdFile == NULL ){ PyErr_SetString(PyExc_IOError, "Cannot open file"); goto error; } /* read header */ dcdErrcode = read_dcdheader(dcdFile, &dcdAtoms, &dcdNFrames, &dcdFrameStart, &dcdFrameSkip, &dcdTimeStep, &dcdNamnf, &dcdFreeatoms); if ( dcdErrcode == DCD_BADFORMAT ) { PyErr_SetString(PyExc_IOError, "Not a DCD file"); goto error; } else if ( dcdErrcode != 0 ) { PyErr_SetString(PyExc_IOError, "DCD reading error"); goto error; } if( atoms != dcdAtoms ){ snprintf(buffer, sizeof(buffer), "number of atoms in DCD file (%d) doesn't " "match universe (%d)", dcdAtoms, atoms); PyErr_SetString(PyExc_ValueError, buffer); goto error; } if ( dcdNamnf != 0 ){ PyErr_SetString(PyExc_ValueError, "Can't read DCD files with free atoms"); goto error; } /* allocate the dcd{X,Y,Z} arrays */ dcdX = ( float*) malloc(dcdAtoms * sizeof(float) ); dcdY = ( float*) malloc(dcdAtoms * sizeof(float) ); dcdZ = ( float*) malloc(dcdAtoms * sizeof(float) ); if( (dcdX==NULL) || (dcdY==NULL) || (dcdZ==NULL) ){ PyErr_NoMemory(); goto error; } /* read in the frames one after the other */ currFrames = 0; time = 0.; while ( 1 ){ int err_code = read_dcdstep(dcdFile, dcdAtoms, dcdX, dcdY, dcdZ, dcdNamnf, (currFrames == 0), dcdFreeatoms); if (err_code == -1) break; if (err_code < 0) { PyErr_SetString(PyExc_IOError, "DCD read error"); goto error; } for (j = 0; j < dcdAtoms; j++) { x[j][0] = angstrom_factor * dcdX[j]; x[j][1] = angstrom_factor * dcdY[j]; x[j][2] = angstrom_factor * dcdZ[j]; } if (PyTrajectory_Output(output, currFrames, data_descriptors, NULL) == -1) goto error; currFrames++; time += dcdFrameSkip*dcdTimeStep*akma_time_factor; } close_dcd_read(dcdFile,0,dcdFreeatoms); /* Clean up and return None */ if( dcdX != NULL ) free(dcdX); if( dcdY != NULL ) free(dcdY); if( dcdZ != NULL ) free(dcdZ); PyTrajectory_OutputFinish(output, currFrames-1, 0, 1, data_descriptors); Py_INCREF(Py_None); return Py_None; /* Clean up and return error */ error: if( dcdX != NULL ) free(dcdX); if( dcdY != NULL ) free(dcdY); if( dcdZ != NULL ) free(dcdZ); close_dcd_read(dcdFile,0,dcdFreeatoms); PyTrajectory_OutputFinish(output, currFrames, 1, 1, data_descriptors); return NULL; } static PyObject * writeOpenDCD(PyObject *dummy, PyObject *args) { /* DCD transformation variables */ char * dcdFileName; int dcdAtoms; int dcdNFrames=0; int dcdFrameStart=0; int dcdNSavc; double time; FILE * fd; /* Parse and check arguments */ if (!PyArg_ParseTuple(args, "siiiid", &dcdFileName,&dcdAtoms, &dcdNFrames, &dcdFrameStart, &dcdNSavc, &time)) return NULL; /* open DCD file */ fd = open_dcd_write( dcdFileName ); if ( fd == NULL ){ PyErr_SetString(PyExc_IOError, "Cannot open file"); return NULL; } /* write header */ write_dcdheader(fd , dcdFileName, dcdAtoms, dcdNFrames, dcdFrameStart, dcdNSavc, time/akma_time_factor); return (PyObject*)PyCObject_FromVoidPtr((void *) fd, NULL); } static PyObject * writeDCDStep(PyObject *dummy, PyObject *args) { int dcdAtoms; float *dcdX = NULL; float *dcdY = NULL; float *dcdZ = NULL; int err; FILE * fd; PyObject *fd_cobj; PyArrayObject *xconfig, *yconfig, *zconfig; /* Parse and check arguments */ if (!PyArg_ParseTuple(args, "O!O!O!O!", &PyCObject_Type, &fd_cobj, &PyArray_Type, &xconfig, &PyArray_Type, &yconfig, &PyArray_Type, &zconfig)) return NULL; fd = PyCObject_AsVoidPtr(fd_cobj); dcdAtoms = xconfig->dimensions[0]; dcdX = (float *)xconfig->data; dcdY = (float *)yconfig->data; dcdZ = (float *)zconfig->data; err = write_dcdstep( fd, dcdAtoms, dcdX, dcdY, dcdZ); if (err != 1) { PyErr_SetString(PyExc_IOError, "Couldn't write DCD step"); return NULL; } Py_INCREF(Py_None); return Py_None; } static PyObject * writeCloseDCD(PyObject *dummy, PyObject *args) { PyObject *fd_cobj; FILE * fd; /* Parse and check arguments */ if (!PyArg_ParseTuple(args, "O!", &PyCObject_Type, &fd_cobj)) return NULL; fd = PyCObject_AsVoidPtr(fd_cobj); close_dcd_read(fd, 0, NULL); Py_INCREF(Py_None); return Py_None; } static PyMethodDef DCD_methods[] = { {"readDCD", readDCD, 1}, {"writeOpenDCD", writeOpenDCD, 1}, {"writeDCDStep", writeDCDStep, 1}, {"writeCloseDCD", writeCloseDCD, 1}, {NULL, NULL} /* sentinel */ }; /* Initialization function for the module */ DL_EXPORT(void) initMMTK_DCD(void) { PyObject *units; /* Create the module and add the functions */ Py_InitModule("MMTK_DCD", DCD_methods); /* Import the array module */ #ifdef import_array import_array(); #endif /* Import MMTK modules */ import_MMTK_trajectory(); /* Get the length and time unit conversion factor from Units */ units = PyImport_ImportModule("MMTK.Units"); if (units != NULL) { PyObject *module_dict = PyModule_GetDict(units); PyObject *factor = PyDict_GetItemString(module_dict, "Ang"); angstrom_factor = PyFloat_AsDouble(factor); factor = PyDict_GetItemString(module_dict, "akma_time"); akma_time_factor = PyFloat_AsDouble(factor); } /* Check for errors */ if (PyErr_Occurred()) Py_FatalError("can't initialize module MMTK_DCD"); } MMTK-2.7.9/Src/MMTK_deformation.c0000644000076600000240000004764211662461640016766 0ustar hinsenstaff00000000000000/* Deformation energy calculation. * * Written by Konrad Hinsen */ #include "MMTK/universe.h" #include "MMTK/forcefield.h" #include "MMTK/forcefield_private.h" /* String copy with memory allocation */ char * allocstring(char *string) { char *memory = (char *)malloc(strlen(string)+1); if (memory != NULL) strcpy(memory, string); return memory; } /* * Deformation evaluation */ static double deformation(vector3 *x, vector3 *v, vector3 *g, double *la, int natoms, PyNonbondedListObject *nblist, double cutoff, double fc_length, double factor, int normalize, int version) { int n; double cutoff_sq = sqr(cutoff); double norm, def; struct nblist_iterator iterator; if (normalize) { norm = 0.; for (n = 0; n < natoms; n++) norm += v[n][0]*v[n][0] + v[n][1]*v[n][1] + v[n][2]*v[n][2]; norm = sqrt(norm/natoms); } else norm = 1.; if (g != NULL) for (n = 0; n < natoms; n++) g[n][0] = g[n][1] = g[n][2] = 0.; if (la != NULL) for (n = 0; n < natoms; n++) la[n]= 0.; def = 0.; iterator.state = nblist_start; while (PyNonbondedListIterate(nblist, &iterator)) { int i = iterator.a1; int j = iterator.a2; vector3 rij; double r_sq; nblist->universe_spec->distance_function(rij, x[iterator.a2], x[iterator.a1], nblist->universe_spec->geometry_data); r_sq = vector_length_sq(rij); if (r_sq <= cutoff_sq) { vector3 vij; double k, l, l2; vij[0] = v[i][0]-v[j][0]; vij[1] = v[i][1]-v[j][1]; vij[2] = v[i][2]-v[j][2]; if (version == 0) /* exponential */ k = factor * exp((0.01-r_sq)/sqr(fc_length)); else if (version == 1) { /* derived from Amber94 force constant matrix */ if (r_sq < 0.16) { double r = sqrt(r_sq); k = (860000.*r-239000.)*factor; } else k = 128.*factor/(r_sq*r_sq*r_sq); } else k = 0.; l = dot(rij, vij)/norm; l2 = k*l*l/r_sq; if (g != NULL) { g[i][0] += 2*k*l*rij[0]/(norm*natoms*r_sq); g[i][1] += 2*k*l*rij[1]/(norm*natoms*r_sq); g[i][2] += 2*k*l*rij[2]/(norm*natoms*r_sq); g[j][0] -= 2*k*l*rij[0]/(norm*natoms*r_sq); g[j][1] -= 2*k*l*rij[1]/(norm*natoms*r_sq); g[j][2] -= 2*k*l*rij[2]/(norm*natoms*r_sq); } if (la != NULL) { la[i] += 0.5*l2; la[j] += 0.5*l2; } def += l2; } } if (g != NULL) { if (normalize) for (n = 0; n < natoms; n++) { g[n][0] = g[n][0] - 2.*def*v[n][0]/(sqr(natoms)*sqr(norm)); g[n][1] = g[n][1] - 2.*def*v[n][1]/(sqr(natoms)*sqr(norm)); g[n][2] = g[n][2] - 2.*def*v[n][2]/(sqr(natoms)*sqr(norm)); } } return def/natoms; } static double finite_deformation(vector3 *x, vector3 *v, vector3 *g, double *la, int natoms, PyNonbondedListObject *nblist, double cutoff, double fc_length, double factor, int version) { int n; double cutoff_sq = sqr(cutoff); double def; struct nblist_iterator iterator; if (g != NULL) for (n = 0; n < natoms; n++) g[n][0] = g[n][1] = g[n][2] = 0.; if (la != NULL) for (n = 0; n < natoms; n++) la[n]= 0.; def = 0.; iterator.state = nblist_start; while (PyNonbondedListIterate(nblist, &iterator)) { int i = iterator.a1; int j = iterator.a2; vector3 rij; double rij2; nblist->universe_spec->distance_function(rij, x[iterator.a2], x[iterator.a1], nblist->universe_spec->geometry_data); rij2 = vector_length_sq(rij); if (rij2 <= cutoff_sq) { vector3 rvij; double rvij2, rrvij2, k, l, l2; rvij[0] = rij[0] + v[i][0]-v[j][0]; rvij[1] = rij[1] + v[i][1]-v[j][1]; rvij[2] = rij[2] + v[i][2]-v[j][2]; if (version == 0) /* exponential */ k = factor * exp((0.01-rij2)/sqr(fc_length)); else if (version == 1) { /* derived from Amber94 force constant matrix */ if (rij2 < 0.16) { double r = sqrt(rij2); k = (860000.*r-239000.)*factor; } else { k = 128.*factor/(rij2*rij2*rij2); } } else k = 0.; rvij2 = vector_length_sq(rvij); rrvij2 = sqrt(rvij2); l = rrvij2-sqrt(rij2); l2 = k*l*l; if (g != NULL) { double f = 2.*k*l/(natoms*rrvij2); g[i][0] += f*rvij[0]; g[i][1] += f*rvij[1]; g[i][2] += f*rvij[2]; g[j][0] -= f*rvij[0]; g[j][1] -= f*rvij[1]; g[j][2] -= f*rvij[2]; } if (la != NULL) { la[i] += 0.5*l2; la[j] += 0.5*l2; } def += l2; } } return def/natoms; } static PyObject * deformation_py(PyObject *dummy, PyObject *args) { PyArrayObject *configuration; PyArrayObject *displacement; PyArrayObject *gradient = NULL; PyArrayObject *l_atom = NULL; PyNonbondedListObject *nblist; int natoms; vector3 *x, *v, *g; double *la; double cutoff, fc_length, factor; int normalize = 0, finite = 0, version = 0; if (!PyArg_ParseTuple(args, "O!O!O!OOdddi|ii", &PyArray_Type, &configuration, &PyArray_Type, &displacement, &PyNonbondedList_Type, &nblist, &gradient, &l_atom, &cutoff, &fc_length, &factor, &normalize, &finite, &version)) return NULL; natoms = configuration->dimensions[0]; x = (vector3 *)configuration->data; v = (vector3 *)displacement->data; if ((PyObject *)gradient == Py_None) g = NULL; else { if (PyArray_Check(gradient)) g = (vector3 *)gradient->data; else { PyErr_SetString(PyExc_TypeError, "not an array"); return NULL; } } if ((PyObject *)l_atom == Py_None) la = NULL; else { if (PyArray_Check(l_atom)) la = (double *)l_atom->data; else { PyErr_SetString(PyExc_TypeError, "not an array"); return NULL; } } if (finite) return PyFloat_FromDouble(finite_deformation(x, v, g, la, natoms, nblist, cutoff, fc_length, factor, version)); else return PyFloat_FromDouble(deformation(x, v, g, la, natoms, nblist, cutoff, fc_length, factor, normalize, version)); } /* Deformation reduction */ static void reduce_deformation(vector3 *x, vector3 *v, vector3 *g, int natoms, int niter, PyNonbondedListObject *nblist, double cutoff, double fc_length, double factor, int version) { struct nblist_iterator iterator; double f, min_dist_sq, max_k; int i, j; min_dist_sq = 1.e30; iterator.state = nblist_start; while (PyNonbondedListIterate(nblist, &iterator)) { vector3 rij; double r_sq; nblist->universe_spec->distance_function(rij, x[iterator.a2], x[iterator.a1], nblist->universe_spec->geometry_data); r_sq = vector_length_sq(rij); if (r_sq < min_dist_sq) min_dist_sq = r_sq; } if (version == 0) max_k = factor * exp((0.01-min_dist_sq)/sqr(fc_length)); else /* version == 1 */ { if (min_dist_sq < 0.16) { double r = sqrt(min_dist_sq); max_k = (860000.*r-239000.)*factor; } else max_k = 128.*factor/(min_dist_sq*min_dist_sq*min_dist_sq); } f = 0.9/max_k; for (i = 0; i < niter; i++) { deformation(x, v, g, NULL, natoms, nblist, cutoff, fc_length, factor, 0, version); for (j = 0; j < natoms; j++) { v[j][0] -= f*g[j][0]; v[j][1] -= f*g[j][1]; v[j][2] -= f*g[j][2]; } } } static void reduce_finite_deformation(vector3 *x, vector3 *v, vector3 *g, int natoms, double rms_reduction, PyNonbondedListObject *nblist, double cutoff, double fc_length, double factor, int version) { double rms_sq, rms_limit, rms_sq_last, rms_grad, scale, step; int i; rms_sq = 0.; for (i = 0; i < natoms; i++) rms_sq += v[i][0]*v[i][0] + v[i][1]*v[i][1] + v[i][2]*v[i][2]; rms_limit = sqrt(rms_sq/natoms)-rms_reduction; if (rms_limit < 0.) rms_limit = 0.; rms_limit = natoms*rms_limit*rms_limit; step = 0.01; while (1) { if (rms_sq <= rms_limit) break; finite_deformation(x, v, g, NULL, natoms, nblist, cutoff, fc_length, factor, version); rms_grad = 0.; for (i = 0; i < natoms; i++) rms_grad += g[i][0]*g[i][0] + g[i][1]*g[i][1] + g[i][2]*g[i][2]; rms_sq_last = rms_sq; while (1) { scale = step/sqrt(rms_grad); for (i = 0; i < natoms; i++) { v[i][0] -= scale*g[i][0]; v[i][1] -= scale*g[i][1]; v[i][2] -= scale*g[i][2]; } rms_sq = 0.; for (i = 0; i < natoms; i++) rms_sq += v[i][0]*v[i][0] + v[i][1]*v[i][1] + v[i][2]*v[i][2]; if (rms_sq > rms_sq_last) { for (i = 0; i < natoms; i++) { v[i][0] += scale*g[i][0]; v[i][1] += scale*g[i][1]; v[i][2] += scale*g[i][2]; } step /= 2; } else break; } if (fabs(rms_sq - rms_sq_last) < 1.e-14) break; } } static PyObject * reduce_deformation_py(PyObject *dummy, PyObject *args) { PyArrayObject *configuration; PyArrayObject *displacement; PyNonbondedListObject *nblist; double cutoff, fc_length, factor; int niter; int version = 0; int natoms; vector3 *x, *v; vector3 *g; if (!PyArg_ParseTuple(args, "O!O!O!dddi|i", &PyArray_Type, &configuration, &PyArray_Type, &displacement, &PyNonbondedList_Type, &nblist, &cutoff, &fc_length, &factor, &niter, &version)) return NULL; natoms = configuration->dimensions[0]; x = (vector3 *)configuration->data; v = (vector3 *)displacement->data; g = (vector3 *)malloc(natoms*sizeof(vector3)); if (g == NULL) { PyErr_NoMemory(); return NULL; } reduce_deformation(x, v, g, natoms, niter, nblist, cutoff, fc_length, factor, version); free(g); Py_INCREF(Py_None); return Py_None; } static PyObject * reduce_finite_deformation_py(PyObject *dummy, PyObject *args) { PyArrayObject *configuration; PyArrayObject *displacement; PyNonbondedListObject *nblist; double cutoff, fc_length, factor; double rms_reduction; int version = 0; int natoms; vector3 *x, *v; vector3 *g; if (!PyArg_ParseTuple(args, "O!O!O!dddd|i", &PyArray_Type, &configuration, &PyArray_Type, &displacement, &PyNonbondedList_Type, &nblist, &cutoff, &fc_length, &factor, &rms_reduction, &version)) return NULL; natoms = configuration->dimensions[0]; x = (vector3 *)configuration->data; v = (vector3 *)displacement->data; g = (vector3 *)malloc(natoms*sizeof(vector3)); if (g == NULL) { PyErr_NoMemory(); return NULL; } reduce_finite_deformation(x, v, g, natoms, rms_reduction, nblist, cutoff, fc_length, factor, version); free(g); Py_INCREF(Py_None); return Py_None; } /* Forcefield evaluator for deformation energy */ static void pair_term(energy_data *energy, int i, int j, vector3 dr, double r_sq, double f2) { if (energy->fc_fn != NULL) { if ((*energy->fc_fn)(energy, i, j, NULL, r_sq)) { tensor3 fij; int k, l; for (k = 0; k < 3; k++) { for (l = 0; l < 3; l++) fij[k][l] = f2*dr[k]*dr[l]/r_sq; } (*energy->fc_fn)(energy, i, i, fij, r_sq); (*energy->fc_fn)(energy, j, j, fij, r_sq); tensor_changesign(fij); if (i > j) (*energy->fc_fn)(energy, j, i, fij, r_sq); else (*energy->fc_fn)(energy, i, j, fij, r_sq); } } else { double *data = (double *)((PyArrayObject *)energy->force_constants)->data; int n = ((PyArrayObject *)energy->force_constants)->dimensions[0]; double *fcii = data + 9*n*i+3*i; double *fcjj = data + 9*n*j+3*j; double *fcij; int k, l; if (i > j) { int temp = i; i = j; j = temp; } fcij = data + 9*n*i+3*j; for (k = 0; k < 3; k++) { for (l = 0; l < 3; l++) { double f = f2*dr[k]*dr[l]/r_sq; fcii[3*n*k+l] += f; fcjj[3*n*k+l] += f; fcij[3*n*k+l] -= f; } } } } void deformation_evaluator(PyFFEnergyTermObject *self, PyFFEvaluatorObject *eval, energy_spec *input, energy_data *energy) { vector3 *x = (vector3 *)input->coordinates->data; distance_fn *d_fn = self->universe_spec->distance_function; double *distance_data = self->universe_spec->geometry_data; PyNonbondedListObject *nblist = (PyNonbondedListObject *)self->data[0]; struct nblist_iterator iterator; double fc_length = self->param[0]; double cutoff_sq = sqr(self->param[1]); double scale_factor = self->param[2]; double one_four_factor = self->param[3]; int k; int states[3] = {nblist_start, nblist_start_excluded, nblist_start_14}; double factors[3] = {1., -1., -0.5}; factors[2] = one_four_factor-1.; if (energy->force_constants == NULL) return; for (k = 0; k < 3; k++) { iterator.state = states[k]; while (PyNonbondedListIterate(nblist, &iterator)) { double r_sq; vector3 rij; (*d_fn)(rij, x[iterator.a2], x[iterator.a1], distance_data); r_sq = vector_length_sq(rij); if (cutoff_sq == 0. || r_sq <= cutoff_sq) { double deriv2 = factors[k]*scale_factor * exp((0.01-r_sq)/sqr(fc_length)); pair_term(energy, iterator.a1, iterator.a2, rij, r_sq, deriv2); } } } } static PyObject * DeformationTerm(PyObject *dummy, PyObject *args) { PyFFEnergyTermObject *self = PyFFEnergyTerm_New(); if (self == NULL) return NULL; if (!PyArg_ParseTuple(args, "O!Odddd", &PyUniverseSpec_Type, &self->universe_spec, &self->data[0], &self->param[0], &self->param[1], &self->param[2], &self->param[3])) return NULL; Py_INCREF(self->universe_spec); Py_INCREF(self->data[0]); self->eval_func = deformation_evaluator; self->evaluator_name = "deformation"; self->term_names[0] = allocstring("deformation"); if (self->term_names[0] == NULL) return PyErr_NoMemory(); self->nterms = 1; return (PyObject *)self; } void calpha_evaluator(PyFFEnergyTermObject *self, PyFFEvaluatorObject *eval, energy_spec *input, energy_data *energy) { vector3 *x = (vector3 *)input->coordinates->data; distance_fn *d_fn = self->universe_spec->distance_function; double *distance_data = self->universe_spec->geometry_data; PyNonbondedListObject *nblist = (PyNonbondedListObject *)self->data[0]; struct nblist_iterator iterator; double cutoff_sq = sqr(self->param[0]); int version = (int)self->param[2]; int k; int states[2] = {nblist_start, nblist_start_excluded}; double factors[2] = {1., -1.}; if (energy->force_constants == NULL) return; for (k = 0; k < 2; k++) { iterator.state = states[k]; while (PyNonbondedListIterate(nblist, &iterator)) { double r_sq; vector3 rij; (*d_fn)(rij, x[iterator.a2], x[iterator.a1], distance_data); r_sq = vector_length_sq(rij); if (cutoff_sq == 0. || r_sq <= cutoff_sq) { double deriv2 = 0.; switch (version) { case 0: /* fitted from CPC trajectory (CHARMM) */ if (r_sq < 0.16) { double r = sqrt(r_sq); deriv2 = (2280600.*r-750400.)*self->param[1]; } else { deriv2 = 651.*self->param[1]/(r_sq*r_sq*r_sq); } break; case 1: /* derived from Amber94 force constant matrix */ if (r_sq < 0.16) { double r = sqrt(r_sq); deriv2 = (860000.*r-239000.)*self->param[1]; } else { deriv2 = 128.*self->param[1]/(r_sq*r_sq*r_sq); } break; case 2: /* same as 1 but with a fix for very short neighbour distances */ if (r_sq < 0.16) { double r = sqrt(r_sq); if (r < 0.37) r = 0.37; deriv2 = (860000.*r-239000.)*self->param[1]; } else { deriv2 = 128.*self->param[1]/(r_sq*r_sq*r_sq); } break; } pair_term(energy, iterator.a1, iterator.a2, rij, r_sq, factors[k]*deriv2); } } } } static PyObject * CalphaTerm(PyObject *dummy, PyObject *args) { PyFFEnergyTermObject *self = PyFFEnergyTerm_New(); if (self == NULL) return NULL; if (!PyArg_ParseTuple(args, "O!Oddd", &PyUniverseSpec_Type, &self->universe_spec, &self->data[0], &self->param[0], &self->param[1], &self->param[2])) return NULL; Py_INCREF(self->universe_spec); Py_INCREF(self->data[0]); self->eval_func = calpha_evaluator; self->evaluator_name = "calpha_deformation"; self->term_names[0] = allocstring("calpha_deformation"); if (self->term_names[0] == NULL) return PyErr_NoMemory(); self->nterms = 1; return (PyObject *)self; } void an_evaluator(PyFFEnergyTermObject *self, PyFFEvaluatorObject *eval, energy_spec *input, energy_data *energy) { vector3 *x = (vector3 *)input->coordinates->data; distance_fn *d_fn = self->universe_spec->distance_function; double *distance_data = self->universe_spec->geometry_data; PyNonbondedListObject *nblist = (PyNonbondedListObject *)self->data[0]; struct nblist_iterator iterator; double cutoff_sq = sqr(self->param[0]); int k; int states[2] = {nblist_start, nblist_start_excluded}; double factors[2] = {1., -1.}; if (energy->force_constants == NULL) return; for (k = 0; k < 2; k++) { iterator.state = states[k]; while (PyNonbondedListIterate(nblist, &iterator)) { double r_sq; vector3 rij; (*d_fn)(rij, x[iterator.a2], x[iterator.a1], distance_data); r_sq = vector_length_sq(rij); if (cutoff_sq == 0. || r_sq <= cutoff_sq) { pair_term(energy, iterator.a1, iterator.a2, rij, r_sq, factors[k]*self->param[1]); } } } } static PyObject * ANTerm(PyObject *dummy, PyObject *args) { PyFFEnergyTermObject *self = PyFFEnergyTerm_New(); if (self == NULL) return NULL; if (!PyArg_ParseTuple(args, "O!Odd", &PyUniverseSpec_Type, &self->universe_spec, &self->data[0], &self->param[0], &self->param[1])) return NULL; Py_INCREF(self->universe_spec); Py_INCREF(self->data[0]); self->eval_func = an_evaluator; self->evaluator_name = "anistropic_network"; self->term_names[0] = allocstring("anisotropic_network"); if (self->term_names[0] == NULL) return PyErr_NoMemory(); self->nterms = 1; return (PyObject *)self; } /* * List of functions defined in the module */ static PyMethodDef deformation_methods[] = { {"deformation", deformation_py, 1}, {"reduceDeformation", reduce_deformation_py, 1}, {"reduceFiniteDeformation", reduce_finite_deformation_py, 1}, {"DeformationTerm", DeformationTerm, 1}, {"CalphaTerm", CalphaTerm, 1}, {"ANTerm", ANTerm, 1}, {NULL, NULL} /* sentinel */ }; /* Initialization function for the module */ DL_EXPORT(void) initMMTK_deformation(void) { /* Create the module and add the functions */ Py_InitModule("MMTK_deformation", deformation_methods); /* Import the array module */ #ifdef import_array import_array(); #endif /* Import MMTK modules */ import_MMTK_universe(); import_MMTK_forcefield(); /* Check for errors */ if (PyErr_Occurred()) Py_FatalError("can't initialize module MMTK_deformation"); } MMTK-2.7.9/Src/MMTK_dynamics.c0000644000076600000240000013335311662461640016261 0ustar hinsenstaff00000000000000/* Low-level dynamics integrators * * Written by Konrad Hinsen */ #include "MMTK/universe.h" #include "MMTK/forcefield.h" #include "MMTK/trajectory.h" #define DEBUG 0 /* Global variables */ double kB, temperature_factor; /* Allocate and initialize Output variable descriptors */ static PyTrajectoryVariable * get_data_descriptors(int n, PyUniverseSpecObject *universe_spec, PyArrayObject *configuration, PyArrayObject *velocities, PyArrayObject *gradients, PyArrayObject *masses, int *ndf, double *time, double *p_energy, double *k_energy, double *n_energy, double *a_energy, double *temperature, double *xi, double *pressure, double *volume, double *alpha, double *box_size) { PyTrajectoryVariable *vars = (PyTrajectoryVariable *) malloc((n+1)*sizeof(PyTrajectoryVariable)); int i = 0; if (vars == NULL) return (PyTrajectoryVariable *)PyErr_NoMemory(); if (time != NULL && i < n) { vars[i].name = "time"; vars[i].text = "Time: %lf\n"; vars[i].unit = time_unit_name; vars[i].type = PyTrajectory_Scalar; vars[i].class = PyTrajectory_Time; vars[i].value.dp = time; i++; } if (p_energy != NULL && i < n) { vars[i].name = "potential_energy"; vars[i].text = "Potential energy: %lf, "; vars[i].unit = energy_unit_name; vars[i].type = PyTrajectory_Scalar; vars[i].class = PyTrajectory_Energy; vars[i].value.dp = p_energy; i++; } if (k_energy != NULL && i < n) { vars[i].name = "kinetic_energy"; vars[i].text = "Kinetic energy: %lf\n"; vars[i].unit = energy_unit_name; vars[i].type = PyTrajectory_Scalar; vars[i].class = PyTrajectory_Energy; vars[i].value.dp = k_energy; i++; } if (n_energy != NULL && i < n) { vars[i].name = "nose_energy"; vars[i].text = "Nose energy: %lf\n"; vars[i].unit = energy_unit_name; vars[i].type = PyTrajectory_Scalar; vars[i].class = PyTrajectory_Energy; vars[i].value.dp = n_energy; i++; } if (a_energy != NULL && i < n) { vars[i].name = "andersen_energy"; vars[i].text = "Andersen energy: %lf\n"; vars[i].unit = energy_unit_name; vars[i].type = PyTrajectory_Scalar; vars[i].class = PyTrajectory_Energy; vars[i].value.dp = a_energy; i++; } if (temperature != NULL && i < n) { vars[i].name = "temperature"; vars[i].text = "Temperature: %lf\n"; vars[i].unit = temperature_unit_name; vars[i].type = PyTrajectory_Scalar; vars[i].class = PyTrajectory_Thermodynamic; vars[i].value.dp = temperature; i++; } if (xi != NULL && i < n) { vars[i].name = "thermostat_coordinate"; vars[i].text = "Thermostat coordinate: %lf\n"; vars[i].unit = frequency_unit_name; vars[i].type = PyTrajectory_Scalar; vars[i].class = PyTrajectory_Auxiliary; vars[i].value.dp = xi; i++; } if (pressure != NULL && i < n) { vars[i].name = "pressure"; vars[i].text = "Pressure: %lf\n"; vars[i].unit = pressure_unit_name; vars[i].type = PyTrajectory_Scalar; vars[i].class = PyTrajectory_Thermodynamic; vars[i].value.dp = pressure; i++; } if (alpha != NULL && i < n) { vars[i].name = "barostat_coordinate"; vars[i].text = "Barostat coordinate: %lf\n"; vars[i].unit = frequency_unit_name; vars[i].type = PyTrajectory_Scalar; vars[i].class = PyTrajectory_Auxiliary; vars[i].value.dp = alpha; i++; } if (volume != NULL && i < n) { vars[i].name = "volume"; vars[i].text = "Volume: %lf\n"; vars[i].unit = volume_unit_name; vars[i].type = PyTrajectory_Scalar; vars[i].class = PyTrajectory_Thermodynamic; vars[i].value.dp = volume; i++; } if (configuration != NULL && i < n) { vars[i].name = "configuration"; vars[i].text = "Configuration:\n"; vars[i].unit = length_unit_name; vars[i].type = PyTrajectory_ParticleVector; vars[i].class = PyTrajectory_Configuration; vars[i].value.array = configuration; i++; } if (box_size != NULL && i < n) { vars[i].name = "box_size"; vars[i].text = "Box size:"; vars[i].unit = length_unit_name; vars[i].type = PyTrajectory_BoxSize; vars[i].class = PyTrajectory_Configuration; vars[i].value.dp = box_size; vars[i].length = universe_spec->geometry_data_length; i++; } if (velocities != NULL && i < n) { vars[i].name = "velocities"; vars[i].text = "Velocities:\n"; vars[i].unit = velocity_unit_name; vars[i].type = PyTrajectory_ParticleVector; vars[i].class = PyTrajectory_Velocities; vars[i].value.array = velocities; i++; } if (gradients != NULL && i < n) { vars[i].name = "gradients"; vars[i].text = "Energy gradients:\n"; vars[i].unit = energy_gradient_unit_name; vars[i].type = PyTrajectory_ParticleVector; vars[i].class = PyTrajectory_Gradients; vars[i].value.array = gradients; i++; } if (masses != NULL && i < n) { vars[i].name = "masses"; vars[i].text = "Masses:\n"; vars[i].unit = mass_unit_name; vars[i].type = PyTrajectory_ParticleScalar; vars[i].class = PyTrajectory_Internal; vars[i].value.array = masses; i++; } if (ndf != NULL && i < n) { vars[i].name = "degrees_of_freedom"; vars[i].text = "Degrees of freedom: %d\n"; vars[i].unit = ""; vars[i].type = PyTrajectory_IntScalar; vars[i].class = PyTrajectory_Internal; vars[i].value.ip = ndf; i++; } vars[i].name = NULL; return vars; } /* Constraints: SHAKE */ static void shake(long *const_pairs, int from, int to, vector3 *x, double *m, vector3 *const_vect, double *const_dist, distance_fn *d_fn, double *d_data) { const int max_iter = 500; const double tolerance = 1.e-8; int i, j; for (i = 0; i < max_iter; i++) { double max_dev = 0.; for (j = from; j < to; j++) { vector3 d; double s, dev, l; int i1, i2; i1 = const_pairs[2*j]; i2 = const_pairs[2*j+1]; (*d_fn)(d, x[i1], x[i2], d_data); s = 0.5*(vector_length_sq(d)-const_dist[j]); dev = fabs(s)/const_dist[j]; if (dev > max_dev) max_dev = dev; if (dev > tolerance) { l = -s*m[i1]*m[i2]/((m[i1]+m[i2])*dot(d, const_vect[j])); x[i1][0] -= l*const_vect[j][0]/m[i1]; x[i1][1] -= l*const_vect[j][1]/m[i1]; x[i1][2] -= l*const_vect[j][2]/m[i1]; x[i2][0] += l*const_vect[j][0]/m[i2]; x[i2][1] += l*const_vect[j][1]/m[i2]; x[i2][2] += l*const_vect[j][2]/m[i2]; } } if (max_dev < tolerance) { break; } } #if 0 if (i == max_iter) printf("No convergence in SHAKE\n"); else printf("%d SHAKE iterations\n", i); #endif } /* Constraints: Lagrange multipliers */ typedef struct { double multiplier[4]; /* mu1, mu2, g, gdot */ double diag; double scratch; } projection_data; enum multipliers {MU1=0, MU2=1, G=2, GDOT=3}; static void project(int n_const, long *const_pairs, double *const_dist, vector3* const_vect, projection_data *p, int mindex, double *mass, vector3 *in, vector3 *out, int natoms) { const int max_iter = 1000; const double tolerance = 1.e-8; int converged, niter; int i; for (i = 0; i < natoms; i++) out[i][0] = out[i][1] = out[i][2] = 0.; for (i = 0; i < n_const; i++) { int a1 = const_pairs[2*i]; int a2 = const_pairs[2*i+1]; double m = p[i].multiplier[mindex]; out[a2][0] += m*const_vect[i][0]/mass[a2]; out[a2][1] += m*const_vect[i][1]/mass[a2]; out[a2][2] += m*const_vect[i][2]/mass[a2]; out[a1][0] -= m*const_vect[i][0]/mass[a1]; out[a1][1] -= m*const_vect[i][1]/mass[a1]; out[a1][2] -= m*const_vect[i][2]/mass[a1]; } niter = 0; while (1) { converged = 0; for (i = 0; i < n_const; i++) { int a1 = const_pairs[2*i]; int a2 = const_pairs[2*i+1]; double dm; if (mindex == G) dm = (- const_dist[i] - const_vect[i][0]*(out[a2][0]-out[a1][0]) - const_vect[i][1]*(out[a2][1]-out[a1][1]) - const_vect[i][2]*(out[a2][2]-out[a1][2]))/p[i].diag; else dm = (const_vect[i][0]*(in[a2][0]-in[a1][0]-out[a2][0]+out[a1][0]) + const_vect[i][1]*(in[a2][1]-in[a1][1]-out[a2][1]+out[a1][1]) + const_vect[i][2]*(in[a2][2]-in[a1][2]-out[a2][2]+out[a1][2])) / p[i].diag; if (fabs(dm) < tolerance*fabs(p[i].multiplier[mindex])) converged++; p[i].multiplier[mindex] += dm; out[a2][0] += dm*const_vect[i][0]/mass[a2]; out[a2][1] += dm*const_vect[i][1]/mass[a2]; out[a2][2] += dm*const_vect[i][2]/mass[a2]; out[a1][0] -= dm*const_vect[i][0]/mass[a1]; out[a1][1] -= dm*const_vect[i][1]/mass[a1]; out[a1][2] -= dm*const_vect[i][2]/mass[a1]; } niter++; if (converged == n_const) { #if DEBUG printf("%d projection iterations for %d\n", niter, mindex); #endif break; } if (niter > max_iter) { #if DEBUG printf("No convergence in projection for %d\n", mindex); #endif break; } } } static void project2(int n_const, long *const_pairs, double *const_dist, vector3* const_vect, projection_data *p, int mindex, double *mass, double *in, vector3 *out, int natoms) { const int max_iter = 1000; const double tolerance = 1.e-8; int converged, niter; int i; for (i = 0; i < natoms; i++) out[i][0] = out[i][1] = out[i][2] = 0.; for (i = 0; i < n_const; i++) { int a1 = const_pairs[2*i]; int a2 = const_pairs[2*i+1]; double m = p[i].multiplier[mindex]; out[a2][0] += m*const_vect[i][0]/mass[a2]; out[a2][1] += m*const_vect[i][1]/mass[a2]; out[a2][2] += m*const_vect[i][2]/mass[a2]; out[a1][0] -= m*const_vect[i][0]/mass[a1]; out[a1][1] -= m*const_vect[i][1]/mass[a1]; out[a1][2] -= m*const_vect[i][2]/mass[a1]; } niter = 0; while (1) { converged = 0; for (i = 0; i < n_const; i++) { int a1 = const_pairs[2*i]; int a2 = const_pairs[2*i+1]; double dm; dm = ((const_vect[i][0]*(out[a2][0]-out[a1][0]) + const_vect[i][1]*(out[a2][1]-out[a1][1]) + const_vect[i][2]*(out[a2][2]-out[a1][2])) + in[i]) / p[i].diag; if (fabs(dm) < tolerance*fabs(p[i].multiplier[mindex])) converged++; p[i].multiplier[mindex] -= dm; out[a2][0] -= dm*const_vect[i][0]/mass[a2]; out[a2][1] -= dm*const_vect[i][1]/mass[a2]; out[a2][2] -= dm*const_vect[i][2]/mass[a2]; out[a1][0] += dm*const_vect[i][0]/mass[a1]; out[a1][1] += dm*const_vect[i][1]/mass[a1]; out[a1][2] += dm*const_vect[i][2]/mass[a1]; } niter++; if (converged == n_const) { #if DEBUG printf("%d projection iterations for %d\n", niter, mindex); #endif break; } if (niter > max_iter) { #if DEBUG printf("No convergence in projection for %d\n", mindex); #endif break; } } } static void mult_by_h_plus_one(vector3 *in, vector3 *out, int natoms, double *mass, long *const_pairs, projection_data *p, int n_const) { int i; for (i = 0; i < natoms; i++) vector_copy(out[i], in[i]); for (i = 0; i < n_const; i++) { int a1 = const_pairs[2*i]; int a2 = const_pairs[2*i+1]; double g = p[i].multiplier[0]; out[a1][0] += g*(in[a1][0]-in[a2][0])/mass[a1]; out[a1][1] += g*(in[a1][1]-in[a2][1])/mass[a1]; out[a1][2] += g*(in[a1][2]-in[a2][2])/mass[a1]; out[a2][0] += g*(in[a2][0]-in[a1][0])/mass[a2]; out[a2][1] += g*(in[a2][1]-in[a1][1])/mass[a2]; out[a2][2] += g*(in[a2][2]-in[a1][2])/mass[a2]; } } /* Velocity-Verlet integrator */ static PyObject * integrateVV(PyObject *dummy, PyObject *args) { PyObject *universe; PyUniverseSpecObject *universe_spec; PyArrayObject *configuration; PyArrayObject *velocities; PyArrayObject *masses; PyArrayObject *fixed; PyArrayObject *gradients; PyArrayObject *constraints, *constraint_distances_squared, *c_blocks; PyArrayObject *t_parameters, *t_coordinates; PyArrayObject *b_parameters, *b_coordinates; PyListObject *spec_list; PyFFEvaluatorObject *evaluator; PyTrajectoryOutputSpec *output; PyTrajectoryVariable *data_descriptors = NULL; double delta_t, dth; int first_step, last_step; char *description; vector3 *x, *v, *f; double *m, *const_dist; long *fix, *const_pairs, *const_blocks; vector3 *const_vect; vector3 *scratch; vector3 *xp, *xph, *vh, *v1, *v2, *xold; double *temp; projection_data *pdata; double time; energy_data p_energy; double k_energy, n_energy, a_energy; double temperature, volume, const_virial1, const_virial2, pressure; double t_temp, t_tau, t_mass, t_energy, *t_xi, *t_lns; double b_press, b_tau, b_mass, *b_alpha; double dxi, dalpha, factor1, factor2; int atoms, n_const, n_const_blocks, df; int pressure_available, thermostat, barostat; int i, j; /* Parse and check arguments */ if (!PyArg_ParseTuple(args, "OO!O!O!O!O!O!O!O!O!O!O!O!diiO!s", &universe, &PyArray_Type, &configuration, &PyArray_Type, &velocities, &PyArray_Type, &masses, &PyArray_Type, &fixed, &PyFFEvaluator_Type, &evaluator, &PyArray_Type, &constraints, &PyArray_Type, &constraint_distances_squared, &PyArray_Type, &c_blocks, &PyArray_Type, &t_parameters, &PyArray_Type, &t_coordinates, &PyArray_Type, &b_parameters, &PyArray_Type, &b_coordinates, &delta_t, &first_step, &last_step, &PyList_Type, &spec_list, &description)) return NULL; universe_spec = (PyUniverseSpecObject *) PyObject_GetAttrString(universe, "_spec"); if (universe_spec == NULL) return NULL; /* Create gradient array */ #if defined(NUMPY) gradients = (PyArrayObject *)PyArray_Copy(configuration); #else gradients = (PyArrayObject *)PyArray_FromDims(configuration->nd, configuration->dimensions, PyArray_DOUBLE); #endif if (gradients == NULL) return NULL; /* Set some convenient variables */ atoms = configuration->dimensions[0]; n_const = constraints->dimensions[0]; n_const_blocks = c_blocks->dimensions[0]-1; thermostat = (t_parameters->dimensions[0] > 0); barostat = (b_parameters->dimensions[0] > 0); dth = 0.5*delta_t; x = (vector3 *)configuration->data; v = (vector3 *)velocities->data; f = (vector3 *)gradients->data; m = (double *)masses->data; fix = (long *)fixed->data; const_pairs = (long *)constraints->data; const_dist = (double *)constraint_distances_squared->data; const_blocks = (long *)c_blocks->data; /* Calculate number of degrees of freedom */ df = 3*atoms; for (j = 0; j < atoms; j++) { if (fix[j]) df -= 3; } for (j = 0; j < n_const; j++) { if (fix[const_pairs[2*j]] || fix[const_pairs[2*j+1]]) { PyErr_SetString(PyExc_ValueError, "distance constraint on a fixed atom"); return NULL; } df--; } /* Thermostat and barostat information. Note: the coordinates are guaranteed to be zero when there is no thermostat and/or barostat. */ if (thermostat) { t_temp = *(double *)t_parameters->data; t_tau = *(((double *)t_parameters->data)+1); t_energy = df*t_temp*kB; t_mass = t_energy*t_tau*t_tau; } else { t_mass = 0.; t_energy = 0.; t_temp = 0.; /* unused, initialize just to make gcc happy */ t_mass = 0; /* unused, initialize just to make gcc happy */ } t_xi = (double *)t_coordinates->data; t_lns = t_xi + 1; if (barostat) { b_press = *(double *)b_parameters->data; b_tau = *(((double *)b_parameters->data)+1); } else { b_mass = 0.; b_press = 0.; b_tau = 0.; /* unused, initialize just to make gcc happy */ } b_alpha = (double *)b_coordinates->data; /* Allocate arrays for temporary data */ pdata = NULL; if (n_const > 0) { scratch = (vector3 *)malloc((5*atoms+n_const)*sizeof(vector3)); if (scratch == NULL) { PyErr_NoMemory(); goto error2; } v1 = scratch; v2 = v1 + atoms; xp = v2 + atoms; xph = xp + atoms; vh = xph + atoms; const_vect = vh + atoms; xold = v1; temp = (double *)v2; pdata = (projection_data *)malloc(n_const*sizeof(projection_data)); if (pdata == NULL) { PyErr_NoMemory(); goto error2; } } else { scratch = NULL; v1 = v2 = NULL; xp = xph = x; vh = v; xold = NULL; temp = NULL; const_vect = NULL; /* unused, initialize just to make gcc happy */ } /* Enforce constraints and initialize constraint data */ Py_BEGIN_ALLOW_THREADS; PyUniverseSpec_StateLock(universe_spec, -1); universe_spec->correction_function(x, atoms, universe_spec->geometry_data); if (n_const > 0) { for (j = 0; j < n_const; j++) { universe_spec->distance_function(const_vect[j], x[const_pairs[2*j]], x[const_pairs[2*j+1]], universe_spec->geometry_data); pdata[j].multiplier[MU1] = 0.; pdata[j].multiplier[MU2] = 0.; pdata[j].multiplier[G] = 0.; pdata[j].multiplier[GDOT] = 0.; pdata[j].diag = const_dist[j] * (1./m[const_pairs[2*j]] + 1./m[const_pairs[2*j+1]]); } for (j = 0; j < n_const_blocks; j++) shake(const_pairs, const_blocks[j], const_blocks[j+1], x, m, const_vect, const_dist, universe_spec->distance_function, universe_spec->geometry_data); for (j = 0; j < n_const; j++) universe_spec->distance_function(const_vect[j], x[const_pairs[2*j]], x[const_pairs[2*j+1]], universe_spec->geometry_data); if (barostat) { project(n_const, const_pairs, const_dist, const_vect, pdata, G, m, NULL, xp, atoms); mult_by_h_plus_one(v, vh, atoms, m, const_pairs, pdata, n_const); project(n_const, const_pairs, const_dist, const_vect, pdata, MU2, m, vh, v2, atoms); for (j = 0; j < n_const; j++) { int a1 = const_pairs[2*j]; int a2 = const_pairs[2*j+1]; vector3 diff1, diff2; diff1[0] = (*b_alpha)*(const_vect[j][0] + xp[a2][0]-xp[a1][0]) + v[a2][0] - v[a1][0]; diff1[1] = (*b_alpha)*(const_vect[j][1] + xp[a2][1]-xp[a1][1]) + v[a2][1] - v[a1][1]; diff1[2] = (*b_alpha)*(const_vect[j][2] + xp[a2][2]-xp[a1][2]) + v[a2][2] - v[a1][2]; diff2[0] = v[a2][0] - v[a1][0]; diff2[1] = v[a2][1] - v[a1][1]; diff2[2] = v[a2][2] - v[a1][2]; temp[j] = dot(diff1, diff2); diff1[0] = f[a1][0]/m[a1] - f[a2][0]/m[a1]; diff1[1] = f[a1][1]/m[a1] - f[a2][1]/m[a1]; diff1[2] = f[a1][2]/m[a1] - f[a2][2]/m[a1]; temp[j] += dot(const_vect[j], diff1); temp[j] *= dth/(dth*(*t_xi)-1.); } project2(n_const, const_pairs, const_dist, const_vect, pdata, MU1, m, temp, v1, atoms); } const_virial1 = const_virial2 = 0.; for (j = 0; j < n_const; j++) { const_virial1 += pdata[j].multiplier[MU1]*const_dist[j]; const_virial2 += pdata[j].multiplier[MU2]*const_dist[j]; } } else const_virial1 = const_virial2 = 0.; PyUniverseSpec_StateLock(universe_spec, -2); Py_END_ALLOW_THREADS; /* Initial force calculation */ p_energy.gradients = (PyObject *)gradients; p_energy.gradient_fn = NULL; p_energy.force_constants = NULL; p_energy.fc_fn = NULL; #ifdef WITH_THREAD evaluator->tstate_save = PyEval_SaveThread(); #endif PyUniverseSpec_StateLock(universe_spec, 1); (*evaluator->eval_func)(evaluator, &p_energy, configuration, 0); PyUniverseSpec_StateLock(universe_spec, 2); #ifdef WITH_THREAD PyEval_RestoreThread(evaluator->tstate_save); #endif if (p_energy.error) goto error2; /* Check if pressure can be calculated */ Py_BEGIN_ALLOW_THREADS; PyUniverseSpec_StateLock(universe_spec, 1); volume = universe_spec->volume_function(1., universe_spec->geometry_data); PyUniverseSpec_StateLock(universe_spec, 2); Py_END_ALLOW_THREADS; pressure_available = volume > 0. && p_energy.virial_available; if (thermostat && barostat) b_mass = df*t_temp*kB*b_tau*b_tau/(volume*volume); /* Initialize output */ data_descriptors = get_data_descriptors(9 + pressure_available + 2*thermostat + 3*barostat + (universe_spec->geometry_data_length > 0), universe_spec, configuration, velocities, gradients, masses, &df, &time, &p_energy.energy, &k_energy, thermostat ? &n_energy : NULL, barostat ? &a_energy : NULL, &temperature, thermostat ? t_xi : NULL, pressure_available ? &pressure:NULL, barostat ? &volume : NULL, barostat ? b_alpha : NULL, (universe_spec->geometry_data_length > 0) ? universe_spec->geometry_data : NULL); if (data_descriptors == NULL) goto error2; output = PyTrajectory_OutputSpecification(universe, spec_list, description, data_descriptors); if (output == NULL) goto error2; evaluator->tstate_save = PyEval_SaveThread(); /* Get write access for the integration, switching to read access only during energy evaluation */ PyUniverseSpec_StateLock(universe_spec, -1); /** Main integration loop **/ time = first_step*delta_t; for (i = first_step; i < last_step; i++) { /* Calculation of thermodynamic properties */ k_energy = 0.; for (j = 0; j < atoms; j++) if (!fix[j]) k_energy += m[j]*dot(v[j], v[j]); k_energy *= 0.5; n_energy = 0.5*(*t_xi)*(*t_xi)*t_mass*exp(2*(*t_lns)) + t_energy*(*t_lns); a_energy = 4.5*b_mass*volume*volume*(*b_alpha)*(*b_alpha) + volume*b_press; temperature = 2.*k_energy*temperature_factor/df; pressure = (2.*k_energy+p_energy.virial + ((*t_xi)-1./dth)*const_virial1 + (*b_alpha)*const_virial2)/(3.*volume); if (barostat && !thermostat) b_mass = 0.5*k_energy*b_tau*b_tau/(volume*volume); /* Trajectory and log output */ if (PyTrajectory_Output(output, i, data_descriptors, &evaluator->tstate_save) == -1) { PyUniverseSpec_StateLock(universe_spec, -2); PyEval_RestoreThread(evaluator->tstate_save); goto error; } /* First part of integration step */ if (barostat) { if (n_const > 0) { project(n_const, const_pairs, const_dist, const_vect, pdata, G, m, NULL, xp, atoms); for (j = 0; j < atoms; j++) v1[j][0] = v1[j][1] = v1[j][2] = 0.; for (j = 0; j < n_const; j++) { int a1 = const_pairs[2*j]; int a2 = const_pairs[2*j+1]; vector3 diff1, diff2; diff1[0] = (*b_alpha)*(const_vect[j][0] + xp[a2][0]-xp[a1][0]) + v[a2][0] - v[a1][0]; diff1[1] = (*b_alpha)*(const_vect[j][1] + xp[a2][1]-xp[a1][1]) + v[a2][1] - v[a1][1]; diff1[2] = (*b_alpha)*(const_vect[j][2] + xp[a2][2]-xp[a1][2]) + v[a2][2] - v[a1][2]; diff2[0] = xp[a2][0] - xp[a1][0]; diff2[1] = xp[a2][1] - xp[a1][1]; diff2[2] = xp[a2][2] - xp[a1][2]; temp[j] = dot(diff1, diff2); v1[a2][0] += diff1[0]*pdata[j].multiplier[G]/m[a2]; v1[a2][1] += diff1[1]*pdata[j].multiplier[G]/m[a2]; v1[a2][2] += diff1[2]*pdata[j].multiplier[G]/m[a2]; v1[a1][0] -= diff1[0]*pdata[j].multiplier[G]/m[a1]; v1[a1][1] -= diff1[1]*pdata[j].multiplier[G]/m[a1]; v1[a1][2] -= diff1[2]*pdata[j].multiplier[G]/m[a1]; } for (j = 0; j < n_const; j++) { int a1 = const_pairs[2*j]; int a2 = const_pairs[2*j+1]; vector3 diff; diff[0] = v1[a2][0] - v1[a1][0]; diff[1] = v1[a2][1] - v1[a1][1]; diff[2] = v1[a2][2] - v1[a1][2]; temp[j] += dot(const_vect[j], diff); } project2(n_const, const_pairs, const_dist, const_vect, pdata, GDOT, m, temp, v1, atoms); for (j = 0; j < atoms; j++) { xp[j][0] += x[j][0]; xp[j][1] += x[j][1]; xp[j][2] += x[j][2]; } mult_by_h_plus_one(xp, xph, atoms, m, const_pairs, pdata, n_const); } dalpha = (pressure-b_press)/(3.*volume*b_mass) -(*b_alpha)*((*t_xi)+3.*(*b_alpha)); factor1 = (*b_alpha) + dth*dalpha; factor2 = dth*(*b_alpha)*(*b_alpha); } else { factor1 = factor2 = 0.; dalpha = 0.; /* unused, initialize just to make gcc happy */ } for (j = 0; j < atoms; j++) if (!fix[j]) { vector3 dv; dv[0] = -dth*(f[j][0]/m[j]+(*t_xi)*v[j][0]); dv[1] = -dth*(f[j][1]/m[j]+(*t_xi)*v[j][1]); dv[2] = -dth*(f[j][2]/m[j]+(*t_xi)*v[j][2]); x[j][0] += delta_t*(v[j][0] + dv[0] + factor1*xp[j][0] + factor2*xph[j][0]); x[j][1] += delta_t*(v[j][1] + dv[1] + factor1*xp[j][1] + factor2*xph[j][1]); x[j][2] += delta_t*(v[j][2] + dv[2] + factor1*xp[j][2] + factor2*xph[j][2]); if (barostat && n_const > 0) { x[j][0] += delta_t*dth*v1[j][0]; x[j][1] += delta_t*dth*v1[j][1]; x[j][2] += delta_t*dth*v1[j][2]; } v[j][0] += dv[0]-dth*(*b_alpha)*vh[j][0]; v[j][1] += dv[1]-dth*(*b_alpha)*vh[j][1]; v[j][2] += dv[2]-dth*(*b_alpha)*vh[j][2]; } if (barostat) { volume = universe_spec->volume_function(1.+delta_t*(factor1+factor2), universe_spec->geometry_data); (*b_alpha) += dth*dalpha; } if (thermostat) { dxi = (2.*k_energy - t_energy + 9.*b_mass*volume*volume*(*b_alpha)*(*b_alpha))/t_mass; (*t_xi) += dth*dxi; (*t_lns) += delta_t*(*t_xi); } /* Constraints: SHAKE */ if (n_const > 0) { memcpy(xold, x, atoms*sizeof(vector3)); for (j = 0; j < n_const_blocks; j++) shake(const_pairs, const_blocks[j], const_blocks[j+1], x, m, const_vect, const_dist, universe_spec->distance_function, universe_spec->geometry_data); for (j = 0; j < atoms; j++) if (!fix[j]) { v[j][0] += (x[j][0]-xold[j][0])/delta_t; v[j][1] += (x[j][1]-xold[j][1])/delta_t; v[j][2] += (x[j][2]-xold[j][2])/delta_t; } for (j = 0; j < n_const; j++) universe_spec->distance_function(const_vect[j], x[const_pairs[2*j]], x[const_pairs[2*j+1]], universe_spec->geometry_data); } /* Coordinate correction (for periodic universes etc.) */ universe_spec->correction_function(x, atoms, universe_spec->geometry_data); /* Mid-step energy evaluation */ PyUniverseSpec_StateLock(universe_spec, -2); PyUniverseSpec_StateLock(universe_spec, 1); (*evaluator->eval_func)(evaluator, &p_energy, configuration, 1); PyUniverseSpec_StateLock(universe_spec, 2); if (p_energy.error) { PyEval_RestoreThread(evaluator->tstate_save); goto error; } PyUniverseSpec_StateLock(universe_spec, -1); /* Second part of integration step */ for (j = 0; j < atoms; j++) if (!fix[j]) { double factor = dth/m[j]; v[j][0] -= factor*f[j][0]; v[j][1] -= factor*f[j][1]; v[j][2] -= factor*f[j][2]; } /* Constraints: constraint forces */ if (n_const > 0) { project(n_const, const_pairs, const_dist, const_vect, pdata, MU1, m, v, v1, atoms); if (barostat) { mult_by_h_plus_one(v, vh, atoms, m, const_pairs, pdata, n_const); project(n_const, const_pairs, const_dist, const_vect, pdata, MU2, m, vh, v2, atoms); } const_virial1 = const_virial2 = 0.; for (j = 0; j < n_const; j++) { const_virial1 += pdata[j].multiplier[MU1]*const_dist[j]; const_virial2 += pdata[j].multiplier[MU2]*const_dist[j]; } } /* Iterative determination of xi and alpha */ if (thermostat || barostat) { double tol = 1.e-15; int niter = 1000; double alpha, xi; double ke1, ke2, ke3; ke1 = ke2 = ke3 = 0.; if (barostat && n_const > 0) for (j = 0; j < atoms; j++) { vector3 va, vb; va[0] = v[j][0]-v1[j][0]; va[1] = v[j][1]-v1[j][1]; va[2] = v[j][2]-v1[j][2]; vb[0] = vh[j][0]-v2[j][0]; vb[1] = vh[j][1]-v2[j][1]; vb[2] = vh[j][2]-v2[j][2]; ke1 += m[j]*dot(va, va); ke2 += m[j]*dot(vb, vb); ke3 += m[j]*dot(va, vb); } else { for (j = 0; j < atoms; j++) ke1 += m[j]*dot(v[j], v[j]); ke2 = ke3 = ke1; } if (barostat) (*b_alpha) += dth*(p_energy.virial/(3.*volume)-b_press) / (3.*volume*b_mass); if (thermostat) (*t_xi) -= dth*t_energy/t_mass; alpha = (*b_alpha); xi = (*t_xi); while (1) { double ke = (1.-dth*xi)*(1.-dth*xi)*ke1 + dth*dth*alpha*alpha*ke2 - 2*(1.-dth*xi)*dth*alpha*ke3; double kea = delta_t*(dth*alpha*ke2-(1.-dth*xi)*ke3); double kex = delta_t*((dth*xi-1.)*ke1+dth*alpha*ke3); double cv = (xi-1./dth)*const_virial1 + alpha*const_virial2; double vf = 9.*volume*volume*b_mass; double h1 = alpha - (*b_alpha) - dth*((ke+cv)/vf - (xi+3.*alpha)*alpha); double h1a = -1+dth*((kea+const_virial2)/vf-xi-6.*alpha); double h1x = dth*((kex+const_virial1)/vf-alpha); double h2 = xi - (*t_xi) - dth*(ke+vf*alpha*alpha)/t_mass; double h2a = dth*(kea+2.*vf*alpha)/t_mass; double h2x = -1+dth*kex/t_mass; double da = 0.; double dx = 0.; double converged; if (thermostat && barostat) { da = (h2x*h1-h1x*h2)/(h2x*h1a-h1x*h2a); dx = (h2-h2a*da)/h2x; } else if (thermostat) dx = h2/h2x; else da = h1/h1a; xi += dx; alpha += da; converged = 1; if (thermostat) converged = converged && fabs(dx) < tol*fabs(xi); if (barostat) converged = converged && fabs(da) < tol*fabs(alpha); if (converged) { #if DEBUG printf("%d iterations, %lf/%lf\n", 1000-niter, h1, h2); #endif break; } if (niter-- == 0) { #if DEBUG printf("No convergence\n"); #endif break; } } if (thermostat) (*t_xi) = xi; if (barostat) (*b_alpha) = alpha; } /* Final velocity calculation */ for (j = 0; j < atoms; j++) if (!fix[j]) { if (n_const > 0) { v[j][0] -= v1[j][0]; v[j][1] -= v1[j][1]; v[j][2] -= v1[j][2]; if (barostat) { vh[j][0] -= v2[j][0]; vh[j][1] -= v2[j][1]; vh[j][2] -= v2[j][2]; } } v[j][0] = (1.-dth*(*t_xi))*v[j][0] - dth*(*b_alpha)*vh[j][0]; v[j][1] = (1.-dth*(*t_xi))*v[j][1] - dth*(*b_alpha)*vh[j][1]; v[j][2] = (1.-dth*(*t_xi))*v[j][2] - dth*(*b_alpha)*vh[j][2]; } /* The End - next time step! */ time += delta_t; } /** End of main integration loop **/ /* Final thermodynamic property evaluation */ k_energy = 0.; for (j = 0; j < atoms; j++) if (!fix[j]) k_energy += m[j]*dot(v[j], v[j]); k_energy *= 0.5; n_energy = 0.5*(*t_xi)*(*t_xi)*t_mass*exp(2*(*t_lns)) + t_energy*(*t_lns); a_energy = 4.5*b_mass*volume*volume*(*b_alpha)*(*b_alpha) + volume*b_press; temperature = 2.*k_energy*temperature_factor/df; pressure = (2.*k_energy+p_energy.virial + ((*t_xi)-1./dth)*const_virial1 + (*b_alpha)*const_virial2)/(3.*volume); /* Final trajectory and log output */ if (PyTrajectory_Output(output, i, data_descriptors, &evaluator->tstate_save) == -1) { PyUniverseSpec_StateLock(universe_spec, -2); PyEval_RestoreThread(evaluator->tstate_save); goto error; } /* Cleanup */ PyUniverseSpec_StateLock(universe_spec, -2); PyEval_RestoreThread(evaluator->tstate_save); PyTrajectory_OutputFinish(output, i, 0, 1, data_descriptors); free(scratch); free(pdata); free(data_descriptors); Py_DECREF(gradients); Py_INCREF(Py_None); return Py_None; /* Error return */ error: PyTrajectory_OutputFinish(output, i, 1, 1, data_descriptors); error2: free(scratch); free(pdata); free(data_descriptors); Py_DECREF(gradients); return NULL; } /* Trajectory functions */ static int getMassesAndVelocities(PyTrajectoryVariable *dynamic_data, PyArrayObject **masses, PyArrayObject **velocities) { PyTrajectoryVariable *var = dynamic_data; int found = 0; while (var->name != NULL) { if (strcmp(var->name, "masses") == 0) { *masses = var->value.array; found++; } if (strcmp(var->name, "velocities") == 0) { *velocities = var->value.array; found++; } var++; } if (found != 2) { PyErr_SetString(PyExc_ValueError, "trajectory function needs masses and velocities"); return 0; } return 1; } static int getDegreesOfFreedom(PyTrajectoryVariable *dynamic_data) { int natoms = -1, ndf = -1; PyTrajectoryVariable *var = dynamic_data; while (var->name != NULL) { if (strcmp(var->name, "degrees_of_freedom") == 0) ndf = *var->value.ip; if (strcmp(var->name, "configuration") == 0) natoms = var->value.array->dimensions[0]; var++; } if (ndf >= 0) return ndf; else return 3*natoms; } static double * getScalar(PyTrajectoryVariable *dynamic_data, char *name) { PyTrajectoryVariable *var = dynamic_data; double *variable = NULL; while (var->name != NULL) { if (strcmp(var->name, name) == 0) { variable = var->value.dp; } var++; } return variable; } static PyArrayObject * getConfiguration(PyTrajectoryVariable *dynamic_data) { PyTrajectoryVariable *var = dynamic_data; PyArrayObject *configuration = NULL; while (var->name != NULL) { if (strcmp(var->name, "configuration") == 0) { configuration = var->value.array; } var++; } return configuration; } /* Temperature scaling option */ static int scaleVelocities(PyTrajectoryVariable *dynamic_data, PyObject *parameters, int step, void **scratch, PyObject *universe) { PyArrayObject *p_array = (PyArrayObject *)parameters; double temperature = ((double *)p_array->data)[0]; double window = ((double *)p_array->data)[1]; typedef struct {PyArrayObject *masses; PyArrayObject *velocities; double *thermostat; int df;} workspace; workspace *ws = (workspace *)*scratch; if (step == -1) { ws = (workspace *)malloc(sizeof(workspace)); *scratch = (void *)ws; if (ws == NULL) { PyErr_NoMemory(); return 0; } if (!getMassesAndVelocities(dynamic_data, &ws->masses, &ws->velocities)) return 0; ws->thermostat = getScalar(dynamic_data, "thermostat_coordinate"); ws->df = getDegreesOfFreedom(dynamic_data); } else if (step == -2) { free(ws); } else { /* Scale velocities */ vector3 *v = (vector3 *)ws->velocities->data; int atoms = ws->velocities->dimensions[0]; double *m = (double *)ws->masses->data; double k_energy = 0.; double t; int j; for (j = 0; j < atoms; j++) k_energy += m[j]*(v[j][0]*v[j][0]+v[j][1]*v[j][1]+v[j][2]*v[j][2]); t = k_energy*temperature_factor/ws->df; if (t > 0. && fabs(t-temperature) > window) { double f = sqrt(temperature/t); for (j = 0; j < atoms; j++) { v[j][0] *= f; v[j][1] *= f; v[j][2] *= f; } } k_energy = 0.; for (j = 0; j < atoms; j++) k_energy += m[j]*(v[j][0]*v[j][0]+v[j][1]*v[j][1]+v[j][2]*v[j][2]); t = k_energy*temperature_factor/ws->df; if (ws->thermostat != NULL) { ws->thermostat[0] = 0.; ws->thermostat[1] = 0.; } } return 1; } static int heat(PyTrajectoryVariable *dynamic_data, PyObject *parameters, int step, void **scratch, PyObject *universe) { PyArrayObject *p_array = (PyArrayObject *)parameters; double temp1 = ((double *)p_array->data)[0]; double temp2 = ((double *)p_array->data)[1]; double gradient = ((double *)p_array->data)[2]; typedef struct {PyArrayObject *masses; PyArrayObject *velocities; double *thermostat; double *time; int df;} workspace; workspace *ws = (workspace *)*scratch; if (step == -1) { /* Initialization */ ws = (workspace *)malloc(sizeof(workspace)); *scratch = (void *)ws; if (ws == NULL) { PyErr_NoMemory(); return 0; } if (!getMassesAndVelocities(dynamic_data, &ws->masses, &ws->velocities)) return 0; ws->thermostat = getScalar(dynamic_data, "thermostat_coordinate"); if (ws->thermostat != NULL) { PyErr_SetString(PyExc_ValueError, "heating not allowed with thermostat"); return 0; } ws->df = getDegreesOfFreedom(dynamic_data); ws->time = getScalar(dynamic_data, "time"); } else if (step == -2) { /* Clean up */ free(ws); } else { /* Scale velocities */ vector3 *v = (vector3 *)ws->velocities->data; int atoms = ws->velocities->dimensions[0]; double *m = (double *)ws->masses->data; double temperature = temp1 + (*ws->time) * gradient; double k_energy = 0.; double t, f; int j; if ((gradient > 0. && temperature > temp2) || (gradient < 0. && temperature < temp2)) temperature = temp2; for (j = 0; j < atoms; j++) k_energy += m[j]*(v[j][0]*v[j][0]+v[j][1]*v[j][1]+v[j][2]*v[j][2]); t = k_energy*temperature_factor/ws->df; if (t > 0.){ f = sqrt(temperature/t); for (j = 0; j < atoms; j++) { v[j][0] *= f; v[j][1] *= f; v[j][2] *= f; } } } return 1; } /* Resetting the barostat */ static int resetBarostat(PyTrajectoryVariable *dynamic_data, PyObject *parameters, int step, void **scratch, PyObject *universe) { typedef struct {double *barostat;} workspace; workspace *ws = (workspace *)*scratch; if (step == -1) { ws = (workspace *)malloc(sizeof(workspace)); *scratch = (void *)ws; if (ws == NULL) { PyErr_NoMemory(); return 0; } ws->barostat = getScalar(dynamic_data, "barostat_coordinate"); if (ws->barostat == NULL) { PyErr_SetString(PyExc_ValueError, "no barostat to reset"); return 0; } } else if (step == -2) { free(ws); } else { /* Reset barostat */ *ws->barostat = 0.; } return 1; } /* Elimination of global translation */ static int removeTranslation(PyTrajectoryVariable *dynamic_data, PyObject *parameters, int step, void **scratch, PyObject *universe) { typedef struct {PyArrayObject *masses; PyArrayObject *velocities;} workspace; workspace *ws = (workspace *)*scratch; if (step == -1) { ws = (workspace *)malloc(sizeof(workspace)); *scratch = (void *)ws; if (ws == NULL) { PyErr_NoMemory(); return 0; } if (!getMassesAndVelocities(dynamic_data, &ws->masses, &ws->velocities)) return 0; } else if (step == -2) { free(ws); } else { /* Remove translation */ vector3 *v = (vector3 *)ws->velocities->data; int atoms = ws->velocities->dimensions[0]; double *m = (double *)ws->masses->data; double total_mass, momentum; int i, j; total_mass = 0.; for (j = 0; j < atoms; j++) total_mass += m[j]; for (i = 0; i < 3; i++) { momentum = 0.; for (j = 0; j < atoms; j++) momentum += m[j]*v[j][i]; momentum /= total_mass; for (j = 0; j < atoms; j++) v[j][i] -= momentum; } } return 1; } /* Eliminitation of global rotation */ static void solve_3x3(tensor3 A, vector3 B, vector3 X) { double a = A[0][0]; double b = A[1][1]; double c = A[2][2]; double d = A[0][1]; double e = A[0][2]; double f = A[1][2]; double o = B[0]; double p = B[1]; double q = B[2]; double af_de = a*f-d*e; double aq_eo = a*q-e*o; double ab_dd = a*b-d*d; double ac_ee = a*c-e*e; double z = (af_de*(a*p-d*o)-ab_dd*aq_eo) / (af_de*af_de-ab_dd*ac_ee); double y = (aq_eo - z*ac_ee)/af_de; double x = (o - d*y - e*z)/a; X[0] = x; X[1] = y; X[2] = z; } static int removeRotation(PyTrajectoryVariable *dynamic_data, PyObject *parameters, int step, void **scratch, PyObject *universe) { typedef struct {PyArrayObject *configuration; PyArrayObject *masses; PyArrayObject *velocities;} workspace; workspace *ws = (workspace *)*scratch; if (step == -1) { /* Initialization */ ws = (workspace *)malloc(sizeof(workspace)); *scratch = (void *)ws; if (ws == NULL) { PyErr_NoMemory(); return 0; } if (!getMassesAndVelocities(dynamic_data, &ws->masses, &ws->velocities)) return 0; ws->configuration = getConfiguration(dynamic_data); if (ws->configuration == NULL) { PyErr_SetString(PyExc_ValueError, "rotation remover needs configuration"); return 0; } } else if (step == -2) { /* Clean up */ free(ws); } else { /* Remove rotation */ vector3 *v = (vector3 *)ws->velocities->data; vector3 *x = (vector3 *)ws->configuration->data; double *m = (double *)ws->masses->data; int atoms = ws->masses->dimensions[0]; double total_mass, trace; vector3 cm, l, o; tensor3 inertia; int i, j, k; total_mass = 0.; cm[0] = cm[1] = cm[2] = 0.; for (i = 0; i < atoms; i++) { total_mass += m[i]; cm[0] += m[i]*x[i][0]; cm[1] += m[i]*x[i][1]; cm[2] += m[i]*x[i][2]; } cm[0] /= total_mass; cm[1] /= total_mass; cm[2] /= total_mass; for (j = 0; j < 3; j++) for (k = 0; k < 3; k++) inertia[j][k] = 0.; l[0] = l[1] = l[2] = 0.; for (i = 0; i < atoms; i++) { vector3 temp, l1; tensor3 i1; temp[0] = x[i][0]-cm[0]; temp[1] = x[i][1]-cm[1]; temp[2] = x[i][2]-cm[2]; cross(l1, temp, v[i]); vector_scale(l1, m[i]); l[0] += l1[0]; l[1] += l1[1]; l[2] += l1[2]; tensor_product(i1, temp, temp, m[i]); for (j = 0; j < 3; j++) for (k = 0; k < 3; k++) inertia[j][k] -= i1[j][k]; } trace = inertia[0][0] + inertia[1][1] + inertia[2][2]; inertia[0][0] -= trace; inertia[1][1] -= trace; inertia[2][2] -= trace; solve_3x3(inertia, l, o); for (i = 0; i < atoms; i++) { vector3 temp, v1; temp[0] = x[i][0]-cm[0]; temp[1] = x[i][1]-cm[1]; temp[2] = x[i][2]-cm[2]; cross(v1, o, temp); v[i][0] -= v1[0]; v[i][1] -= v1[1]; v[i][2] -= v1[2]; } } return 1; } /* * Enforce distance constraints by calling SHAKE */ static PyObject * enforceConstraints(PyObject *dummy, PyObject *args) { PyUniverseSpecObject *universe_spec; PyArrayObject *configuration; PyArrayObject *masses; PyArrayObject *constraints, *constraint_distances_squared, *c_blocks; vector3 *x; double *m; int n_const, n_const_blocks; long *const_pairs, *const_blocks; double *const_dist; vector3 *const_vect = NULL; int i; if (!PyArg_ParseTuple(args, "O!O!O!O!O!O!", &PyUniverseSpec_Type, &universe_spec, &PyArray_Type, &configuration, &PyArray_Type, &masses, &PyArray_Type, &constraints, &PyArray_Type, &constraint_distances_squared, &PyArray_Type, &c_blocks)) return NULL; n_const = constraints->dimensions[0]; n_const_blocks = c_blocks->dimensions[0]-1; x = (vector3 *)configuration->data; m = (double *)masses->data; const_pairs = (long *)constraints->data; const_dist = (double *)constraint_distances_squared->data; const_blocks = (long *)c_blocks->data; const_vect = (vector3 *)malloc(n_const*sizeof(vector3)); if (const_vect == NULL) { PyErr_NoMemory(); return NULL; } for (i = 0; i < n_const; i++) universe_spec->distance_function(const_vect[i], x[const_pairs[2*i]], x[const_pairs[2*i+1]], universe_spec->geometry_data); for (i = 0; i < n_const_blocks; i++) shake(const_pairs, const_blocks[i], const_blocks[i+1], x, m, const_vect, const_dist, universe_spec->distance_function, universe_spec->geometry_data); free(const_vect); Py_INCREF(Py_None); return Py_None; } /* * Project velocities onto constraint surface */ static PyObject * projectVelocities(PyObject *dummy, PyObject *args) { PyUniverseSpecObject *universe_spec; PyArrayObject *configuration; PyArrayObject *velocities; PyArrayObject *masses; PyArrayObject *constraints, *constraint_distances_squared, *c_blocks; int atoms; vector3 *x, *v; double *m; int n_const; long *const_pairs; double *const_dist; vector3 *const_vect = NULL; vector3 *vc = NULL; projection_data *pdata = NULL; int i; if (!PyArg_ParseTuple(args, "O!O!O!O!O!O!O!", &PyUniverseSpec_Type, &universe_spec, &PyArray_Type, &configuration, &PyArray_Type, &velocities, &PyArray_Type, &masses, &PyArray_Type, &constraints, &PyArray_Type, &constraint_distances_squared, &PyArray_Type, &c_blocks)) return NULL; atoms = configuration->dimensions[0]; n_const = constraints->dimensions[0]; x = (vector3 *)configuration->data; v = (vector3 *)velocities->data; m = (double *)masses->data; const_pairs = (long *)constraints->data; const_dist = (double *)constraint_distances_squared->data; pdata = (projection_data *)malloc(n_const*sizeof(projection_data)); const_vect = (vector3 *)malloc(n_const*sizeof(vector3)); vc = (vector3 *)malloc(atoms*sizeof(vector3)); if (pdata == NULL || const_vect == NULL || vc == NULL) { free(pdata); free(const_vect); free(vc); PyErr_NoMemory(); return NULL; } for (i = 0; i < n_const; i++) { universe_spec->distance_function(const_vect[i], x[const_pairs[2*i]], x[const_pairs[2*i+1]], universe_spec->geometry_data); pdata[i].multiplier[MU1] = 0.; pdata[i].diag = const_dist[i] * (1./m[const_pairs[2*i]] + 1./m[const_pairs[2*i+1]]); } project(n_const, const_pairs, const_dist, const_vect, pdata, MU1, m, v, vc, atoms); for (i = 0; i < atoms; i++) { v[i][0] -= vc[i][0]; v[i][1] -= vc[i][1]; v[i][2] -= vc[i][2]; } free(pdata); free(const_vect); free(vc); Py_INCREF(Py_None); return Py_None; } /* * List of functions defined in the module */ static PyMethodDef dynamics_methods[] = { {"integrateVV", integrateVV, 1}, {"enforceConstraints", enforceConstraints, 1}, {"projectVelocities", projectVelocities, 1}, {NULL, NULL} /* sentinel */ }; /* Initialization function for the module */ DL_EXPORT(void) initMMTK_dynamics(void) { PyObject *m, *dict; PyObject *units; /* Create the module and add the functions */ m = Py_InitModule("MMTK_dynamics", dynamics_methods); dict = PyModule_GetDict(m); /* Import the array module */ #ifdef import_array import_array(); #endif /* Import MMTK modules */ import_MMTK_universe(); import_MMTK_forcefield(); import_MMTK_trajectory(); /* Get the temperature conversion factor from Units */ units = PyImport_ImportModule("MMTK.Units"); if (units != NULL) { PyObject *module_dict = PyModule_GetDict(units); PyObject *factor = PyDict_GetItemString(module_dict, "k_B"); kB = PyFloat_AsDouble(factor); temperature_factor = 1./kB; } /* Add addresses of C functions to module dictionary */ PyDict_SetItemString(dict, "scaleVelocities", PyCObject_FromVoidPtr((void *)scaleVelocities, NULL)); PyDict_SetItemString(dict, "heat", PyCObject_FromVoidPtr((void *)heat, NULL)); PyDict_SetItemString(dict, "resetBarostat", PyCObject_FromVoidPtr((void *)resetBarostat, NULL)); PyDict_SetItemString(dict, "removeTranslation", PyCObject_FromVoidPtr((void *)removeTranslation, NULL)); PyDict_SetItemString(dict, "removeRotation", PyCObject_FromVoidPtr((void *)removeRotation, NULL)); 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"" : "s", num_found); } static void __Pyx_RaiseDoubleKeywordsError( const char* func_name, PyObject* kw_name) { PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION >= 3 "%s() got multiple values for keyword argument '%U'", func_name, kw_name); #else "%s() got multiple values for keyword argument '%s'", func_name, PyString_AS_STRING(kw_name)); #endif } static int __Pyx_ParseOptionalKeywords( PyObject *kwds, PyObject **argnames[], PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, const char* function_name) { PyObject *key = 0, *value = 0; Py_ssize_t pos = 0; PyObject*** name; PyObject*** first_kw_arg = argnames + num_pos_args; while (PyDict_Next(kwds, &pos, &key, &value)) { name = first_kw_arg; while (*name && (**name != key)) name++; if (*name) { values[name-argnames] = value; } else { #if PY_MAJOR_VERSION < 3 if (unlikely(!PyString_CheckExact(key)) && unlikely(!PyString_Check(key))) { #else if (unlikely(!PyUnicode_CheckExact(key)) && unlikely(!PyUnicode_Check(key))) { #endif goto invalid_keyword_type; } else { for (name = first_kw_arg; *name; name++) { #if PY_MAJOR_VERSION >= 3 if (PyUnicode_GET_SIZE(**name) == PyUnicode_GET_SIZE(key) && PyUnicode_Compare(**name, key) == 0) break; #else if (PyString_GET_SIZE(**name) == PyString_GET_SIZE(key) && _PyString_Eq(**name, key)) break; #endif } if (*name) { values[name-argnames] = value; } else { /* unexpected keyword found */ for (name=argnames; name != first_kw_arg; name++) { if (**name == key) goto arg_passed_twice; #if PY_MAJOR_VERSION >= 3 if (PyUnicode_GET_SIZE(**name) == PyUnicode_GET_SIZE(key) && PyUnicode_Compare(**name, key) == 0) goto arg_passed_twice; #else if (PyString_GET_SIZE(**name) == PyString_GET_SIZE(key) && _PyString_Eq(**name, key)) goto arg_passed_twice; #endif } if (kwds2) { if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; } else { goto invalid_keyword; } } } } } return 0; arg_passed_twice: __Pyx_RaiseDoubleKeywordsError(function_name, **name); goto bad; invalid_keyword_type: PyErr_Format(PyExc_TypeError, "%s() keywords must be strings", function_name); goto bad; invalid_keyword: PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION < 3 "%s() got an unexpected keyword argument '%s'", function_name, PyString_AsString(key)); #else "%s() got an unexpected keyword argument '%U'", function_name, key); #endif bad: return -1; } static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; PyThreadState *tstate = PyThreadState_GET(); tmp_type = tstate->curexc_type; tmp_value = tstate->curexc_value; tmp_tb = tstate->curexc_traceback; tstate->curexc_type = type; tstate->curexc_value = value; tstate->curexc_traceback = tb; Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); } static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { PyThreadState *tstate = PyThreadState_GET(); *type = tstate->curexc_type; *value = tstate->curexc_value; *tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; } #if PY_MAJOR_VERSION < 3 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { /* cause is unused */ Py_XINCREF(type); Py_XINCREF(value); Py_XINCREF(tb); /* First, check the traceback argument, replacing None with NULL. */ if (tb == Py_None) { Py_DECREF(tb); tb = 0; } else if (tb != NULL && !PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto raise_error; } /* Next, replace a missing value with None */ if (value == NULL) { value = Py_None; Py_INCREF(value); } #if PY_VERSION_HEX < 0x02050000 if (!PyClass_Check(type)) #else if (!PyType_Check(type)) #endif { /* Raising an instance. The value should be a dummy. */ if (value != Py_None) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto raise_error; } /* Normalize to raise , */ Py_DECREF(value); value = type; #if PY_VERSION_HEX < 0x02050000 if (PyInstance_Check(type)) { type = (PyObject*) ((PyInstanceObject*)type)->in_class; Py_INCREF(type); } else { type = 0; PyErr_SetString(PyExc_TypeError, "raise: exception must be an old-style class or instance"); goto raise_error; } #else type = (PyObject*) Py_TYPE(type); Py_INCREF(type); if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto raise_error; } #endif } __Pyx_ErrRestore(type, value, tb); return; raise_error: Py_XDECREF(value); Py_XDECREF(type); Py_XDECREF(tb); return; } #else /* Python 3+ */ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { if (tb == Py_None) { tb = 0; } else if (tb && !PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto bad; } if (value == Py_None) value = 0; if (PyExceptionInstance_Check(type)) { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto bad; } value = type; type = (PyObject*) Py_TYPE(value); } else if (!PyExceptionClass_Check(type)) { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto bad; } if (cause) { PyObject *fixed_cause; if (PyExceptionClass_Check(cause)) { fixed_cause = PyObject_CallObject(cause, NULL); if (fixed_cause == NULL) goto bad; } else if (PyExceptionInstance_Check(cause)) { fixed_cause = cause; Py_INCREF(fixed_cause); } else { PyErr_SetString(PyExc_TypeError, "exception causes must derive from " "BaseException"); goto bad; } if (!value) { value = PyObject_CallObject(type, NULL); } PyException_SetCause(value, fixed_cause); } PyErr_SetObject(type, value); if (tb) { PyThreadState *tstate = PyThreadState_GET(); PyObject* tmp_tb = tstate->curexc_traceback; if (tb != tmp_tb) { Py_INCREF(tb); tstate->curexc_traceback = tb; Py_XDECREF(tmp_tb); } } bad: return; } #endif static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) { PyObject *local_type, *local_value, *local_tb; PyObject *tmp_type, *tmp_value, *tmp_tb; PyThreadState *tstate = PyThreadState_GET(); local_type = tstate->curexc_type; local_value = tstate->curexc_value; local_tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; PyErr_NormalizeException(&local_type, &local_value, &local_tb); if (unlikely(tstate->curexc_type)) goto bad; #if PY_MAJOR_VERSION >= 3 if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) goto bad; #endif *type = local_type; *value = local_value; *tb = local_tb; Py_INCREF(local_type); Py_INCREF(local_value); Py_INCREF(local_tb); tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = local_type; tstate->exc_value = local_value; tstate->exc_traceback = local_tb; /* Make sure tstate is in a consistent state when we XDECREF these objects (XDECREF may run arbitrary code). */ Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); return 0; bad: *type = 0; *value = 0; *tb = 0; Py_XDECREF(local_type); Py_XDECREF(local_value); Py_XDECREF(local_tb); return -1; } static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb) { PyThreadState *tstate = PyThreadState_GET(); *type = tstate->exc_type; *value = tstate->exc_value; *tb = tstate->exc_traceback; Py_XINCREF(*type); Py_XINCREF(*value); Py_XINCREF(*tb); } static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; PyThreadState *tstate = PyThreadState_GET(); tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = type; tstate->exc_value = value; tstate->exc_traceback = tb; Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); } static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, long level) { PyObject *py_import = 0; PyObject *empty_list = 0; PyObject *module = 0; PyObject *global_dict = 0; PyObject *empty_dict = 0; PyObject *list; py_import = __Pyx_GetAttrString(__pyx_b, "__import__"); if (!py_import) goto bad; if (from_list) list = from_list; else { empty_list = PyList_New(0); if (!empty_list) goto bad; list = empty_list; } global_dict = PyModule_GetDict(__pyx_m); if (!global_dict) goto bad; empty_dict = PyDict_New(); if (!empty_dict) goto bad; #if PY_VERSION_HEX >= 0x02050000 { PyObject *py_level = PyInt_FromLong(level); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, NULL); Py_DECREF(py_level); } #else if (level>0) { PyErr_SetString(PyExc_RuntimeError, "Relative import is not supported for Python <=2.4."); goto bad; } module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, NULL); #endif bad: Py_XDECREF(empty_list); Py_XDECREF(py_import); Py_XDECREF(empty_dict); return module; } static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { if (s1 == s2) { /* as done by PyObject_RichCompareBool(); also catches the (interned) empty string */ return (equals == Py_EQ); } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) { if (PyBytes_GET_SIZE(s1) != PyBytes_GET_SIZE(s2)) { return (equals == Py_NE); } else if (PyBytes_GET_SIZE(s1) == 1) { if (equals == Py_EQ) return (PyBytes_AS_STRING(s1)[0] == PyBytes_AS_STRING(s2)[0]); else return (PyBytes_AS_STRING(s1)[0] != PyBytes_AS_STRING(s2)[0]); } else { int result = memcmp(PyBytes_AS_STRING(s1), PyBytes_AS_STRING(s2), (size_t)PyBytes_GET_SIZE(s1)); return (equals == Py_EQ) ? (result == 0) : (result != 0); } } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { return (equals == Py_NE); } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { return (equals == Py_NE); } else { int result; PyObject* py_result = PyObject_RichCompare(s1, s2, equals); if (!py_result) return -1; result = __Pyx_PyObject_IsTrue(py_result); Py_DECREF(py_result); return result; } } static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { if (s1 == s2) { /* as done by PyObject_RichCompareBool(); also catches the (interned) empty string */ return (equals == Py_EQ); } else if (PyUnicode_CheckExact(s1) & PyUnicode_CheckExact(s2)) { if (PyUnicode_GET_SIZE(s1) != PyUnicode_GET_SIZE(s2)) { return (equals == Py_NE); } else if (PyUnicode_GET_SIZE(s1) == 1) { if (equals == Py_EQ) return (PyUnicode_AS_UNICODE(s1)[0] == PyUnicode_AS_UNICODE(s2)[0]); else return (PyUnicode_AS_UNICODE(s1)[0] != PyUnicode_AS_UNICODE(s2)[0]); } else { int result = PyUnicode_Compare(s1, s2); if ((result == -1) && unlikely(PyErr_Occurred())) return -1; return (equals == Py_EQ) ? (result == 0) : (result != 0); } } else if ((s1 == Py_None) & PyUnicode_CheckExact(s2)) { return (equals == Py_NE); } else if ((s2 == Py_None) & PyUnicode_CheckExact(s1)) { return (equals == Py_NE); } else { int result; PyObject* py_result = PyObject_RichCompare(s1, s2, equals); if (!py_result) return -1; result = __Pyx_PyObject_IsTrue(py_result); Py_DECREF(py_result); return result; } } static CYTHON_INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject* x) { const unsigned char neg_one = (unsigned char)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(unsigned char) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(unsigned char)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to unsigned char" : "value too large to convert to unsigned char"); } return (unsigned char)-1; } return (unsigned char)val; } return (unsigned char)__Pyx_PyInt_AsUnsignedLong(x); } static CYTHON_INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject* x) { const unsigned short neg_one = (unsigned short)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(unsigned short) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(unsigned short)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to unsigned short" : "value too large to convert to unsigned short"); } return (unsigned short)-1; } return (unsigned short)val; } return (unsigned short)__Pyx_PyInt_AsUnsignedLong(x); } static CYTHON_INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject* x) { const unsigned int neg_one = (unsigned int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(unsigned int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(unsigned int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to unsigned int" : "value too large to convert to unsigned int"); } return (unsigned int)-1; } return (unsigned int)val; } return (unsigned int)__Pyx_PyInt_AsUnsignedLong(x); } static CYTHON_INLINE char __Pyx_PyInt_AsChar(PyObject* x) { const char neg_one = (char)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(char) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(char)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to char" : "value too large to convert to char"); } return (char)-1; } return (char)val; } return (char)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE short __Pyx_PyInt_AsShort(PyObject* x) { const short neg_one = (short)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(short) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(short)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to short" : "value too large to convert to short"); } return (short)-1; } return (short)val; } return (short)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE int __Pyx_PyInt_AsInt(PyObject* x) { const int neg_one = (int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to int" : "value too large to convert to int"); } return (int)-1; } return (int)val; } return (int)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE signed char __Pyx_PyInt_AsSignedChar(PyObject* x) { const signed char neg_one = (signed char)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(signed char) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(signed char)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to signed char" : "value too large to convert to signed char"); } return (signed char)-1; } return (signed char)val; } return (signed char)__Pyx_PyInt_AsSignedLong(x); } static CYTHON_INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject* x) { const signed short neg_one = (signed short)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(signed short) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(signed short)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to signed short" : "value too large to convert to signed short"); } return (signed short)-1; } return (signed short)val; } return (signed short)__Pyx_PyInt_AsSignedLong(x); } static CYTHON_INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject* x) { const signed int neg_one = (signed int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(signed int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(signed int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to signed int" : "value too large to convert to signed int"); } return (signed int)-1; } return (signed int)val; } return (signed int)__Pyx_PyInt_AsSignedLong(x); } static CYTHON_INLINE int __Pyx_PyInt_AsLongDouble(PyObject* x) { const int neg_one = (int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to int" : "value too large to convert to int"); } return (int)-1; } return (int)val; } return (int)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject* x) { const unsigned long neg_one = (unsigned long)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned long"); return (unsigned long)-1; } return (unsigned long)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned long"); return (unsigned long)-1; } return (unsigned long)PyLong_AsUnsignedLong(x); } else { return (unsigned long)PyLong_AsLong(x); } } else { unsigned long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (unsigned long)-1; val = __Pyx_PyInt_AsUnsignedLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject* x) { const unsigned PY_LONG_LONG neg_one = (unsigned PY_LONG_LONG)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned PY_LONG_LONG"); return (unsigned PY_LONG_LONG)-1; } return (unsigned PY_LONG_LONG)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned PY_LONG_LONG"); return (unsigned PY_LONG_LONG)-1; } return (unsigned PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { return (unsigned PY_LONG_LONG)PyLong_AsLongLong(x); } } else { unsigned PY_LONG_LONG val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (unsigned PY_LONG_LONG)-1; val = __Pyx_PyInt_AsUnsignedLongLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE long __Pyx_PyInt_AsLong(PyObject* x) { const long neg_one = (long)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long)-1; } return (long)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long)-1; } return (long)PyLong_AsUnsignedLong(x); } else { return (long)PyLong_AsLong(x); } } else { long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (long)-1; val = __Pyx_PyInt_AsLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject* x) { const PY_LONG_LONG neg_one = (PY_LONG_LONG)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to PY_LONG_LONG"); return (PY_LONG_LONG)-1; } return (PY_LONG_LONG)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to PY_LONG_LONG"); return (PY_LONG_LONG)-1; } return (PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { return (PY_LONG_LONG)PyLong_AsLongLong(x); } } else { PY_LONG_LONG val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (PY_LONG_LONG)-1; val = __Pyx_PyInt_AsLongLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject* x) { const signed long neg_one = (signed long)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed long"); return (signed long)-1; } return (signed long)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed long"); return (signed long)-1; } return (signed long)PyLong_AsUnsignedLong(x); } else { return (signed long)PyLong_AsLong(x); } } else { signed long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (signed long)-1; val = __Pyx_PyInt_AsSignedLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject* x) { const signed PY_LONG_LONG neg_one = (signed PY_LONG_LONG)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed PY_LONG_LONG"); return (signed PY_LONG_LONG)-1; } return (signed PY_LONG_LONG)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed PY_LONG_LONG"); return (signed PY_LONG_LONG)-1; } return (signed PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { return (signed PY_LONG_LONG)PyLong_AsLongLong(x); } } else { signed PY_LONG_LONG val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (signed PY_LONG_LONG)-1; val = __Pyx_PyInt_AsSignedLongLong(tmp); Py_DECREF(tmp); return val; } } static int __Pyx_check_binary_version(void) { char ctversion[4], rtversion[4]; PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { char message[200]; PyOS_snprintf(message, sizeof(message), "compiletime version %s of module '%.100s' " "does not match runtime version %s", ctversion, __Pyx_MODULE_NAME, rtversion); #if PY_VERSION_HEX < 0x02050000 return PyErr_Warn(NULL, message); #else return PyErr_WarnEx(NULL, message, 1); #endif } return 0; } #ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict) { PyObject *py_module = 0; PyObject *result = 0; PyObject *py_name = 0; char warning[200]; py_module = __Pyx_ImportModule(module_name); if (!py_module) goto bad; #if PY_MAJOR_VERSION < 3 py_name = PyString_FromString(class_name); #else py_name = PyUnicode_FromString(class_name); #endif if (!py_name) goto bad; result = PyObject_GetAttr(py_module, py_name); Py_DECREF(py_name); py_name = 0; Py_DECREF(py_module); py_module = 0; if (!result) goto bad; if (!PyType_Check(result)) { PyErr_Format(PyExc_TypeError, "%s.%s is not a type object", module_name, class_name); goto bad; } if (!strict && ((PyTypeObject *)result)->tp_basicsize > (Py_ssize_t)size) { PyOS_snprintf(warning, sizeof(warning), "%s.%s size changed, may indicate binary incompatibility", module_name, class_name); #if PY_VERSION_HEX < 0x02050000 if (PyErr_Warn(NULL, warning) < 0) goto bad; #else if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; #endif } else if (((PyTypeObject *)result)->tp_basicsize != (Py_ssize_t)size) { PyErr_Format(PyExc_ValueError, "%s.%s has the wrong size, try recompiling", module_name, class_name); goto bad; } return (PyTypeObject *)result; bad: Py_XDECREF(py_module); Py_XDECREF(result); return NULL; } #endif #ifndef __PYX_HAVE_RT_ImportModule #define __PYX_HAVE_RT_ImportModule static PyObject *__Pyx_ImportModule(const char *name) { PyObject *py_name = 0; PyObject *py_module = 0; #if PY_MAJOR_VERSION < 3 py_name = PyString_FromString(name); #else py_name = PyUnicode_FromString(name); #endif if (!py_name) goto bad; py_module = PyImport_Import(py_name); Py_DECREF(py_name); return py_module; bad: Py_XDECREF(py_name); return 0; } #endif static int __Pyx_SetVtable(PyObject *dict, void *vtable) { #if PY_VERSION_HEX >= 0x02070000 && !(PY_MAJOR_VERSION==3&&PY_MINOR_VERSION==0) PyObject *ob = PyCapsule_New(vtable, 0, 0); #else PyObject *ob = PyCObject_FromVoidPtr(vtable, 0); #endif if (!ob) goto bad; if (PyDict_SetItemString(dict, "__pyx_vtable__", ob) < 0) goto bad; Py_DECREF(ob); return 0; bad: Py_XDECREF(ob); return -1; } #include "compile.h" #include "frameobject.h" #include "traceback.h" static void __Pyx_AddTraceback(const char *funcname, int __pyx_clineno, int __pyx_lineno, const char *__pyx_filename) { PyObject *py_srcfile = 0; PyObject *py_funcname = 0; PyObject *py_globals = 0; PyCodeObject *py_code = 0; PyFrameObject *py_frame = 0; #if PY_MAJOR_VERSION < 3 py_srcfile = PyString_FromString(__pyx_filename); #else py_srcfile = PyUnicode_FromString(__pyx_filename); #endif if (!py_srcfile) goto bad; if (__pyx_clineno) { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno); #else py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno); #endif } else { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromString(funcname); #else py_funcname = PyUnicode_FromString(funcname); #endif } if (!py_funcname) goto bad; py_globals = PyModule_GetDict(__pyx_m); if (!py_globals) goto bad; py_code = PyCode_New( 0, /*int argcount,*/ #if PY_MAJOR_VERSION >= 3 0, /*int kwonlyargcount,*/ #endif 0, /*int nlocals,*/ 0, /*int stacksize,*/ 0, /*int flags,*/ __pyx_empty_bytes, /*PyObject *code,*/ __pyx_empty_tuple, /*PyObject *consts,*/ __pyx_empty_tuple, /*PyObject *names,*/ __pyx_empty_tuple, /*PyObject *varnames,*/ __pyx_empty_tuple, /*PyObject *freevars,*/ __pyx_empty_tuple, /*PyObject *cellvars,*/ py_srcfile, /*PyObject *filename,*/ py_funcname, /*PyObject *name,*/ __pyx_lineno, /*int firstlineno,*/ __pyx_empty_bytes /*PyObject *lnotab*/ ); if (!py_code) goto bad; py_frame = PyFrame_New( PyThreadState_GET(), /*PyThreadState *tstate,*/ py_code, /*PyCodeObject *code,*/ py_globals, /*PyObject *globals,*/ 0 /*PyObject *locals*/ ); if (!py_frame) goto bad; py_frame->f_lineno = __pyx_lineno; PyTraceBack_Here(py_frame); bad: Py_XDECREF(py_srcfile); Py_XDECREF(py_funcname); Py_XDECREF(py_code); Py_XDECREF(py_frame); } static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { while (t->p) { #if PY_MAJOR_VERSION < 3 if (t->is_unicode) { *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); } else if (t->intern) { *t->p = PyString_InternFromString(t->s); } else { *t->p = PyString_FromStringAndSize(t->s, t->n - 1); } #else /* Python 3+ has unicode identifiers */ if (t->is_unicode | t->is_str) { if (t->intern) { *t->p = PyUnicode_InternFromString(t->s); } else if (t->encoding) { *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); } else { *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); } } else { *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); } #endif if (!*t->p) return -1; ++t; } return 0; } /* Type Conversion Functions */ static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { int is_true = x == Py_True; if (is_true | (x == Py_False) | (x == Py_None)) return is_true; else return PyObject_IsTrue(x); } static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { PyNumberMethods *m; const char *name = NULL; PyObject *res = NULL; #if PY_VERSION_HEX < 0x03000000 if (PyInt_Check(x) || PyLong_Check(x)) #else if (PyLong_Check(x)) #endif return Py_INCREF(x), x; m = Py_TYPE(x)->tp_as_number; #if PY_VERSION_HEX < 0x03000000 if (m && m->nb_int) { name = "int"; res = PyNumber_Int(x); } else if (m && m->nb_long) { name = "long"; res = PyNumber_Long(x); } #else if (m && m->nb_int) { name = "int"; res = PyNumber_Long(x); } #endif if (res) { #if PY_VERSION_HEX < 0x03000000 if (!PyInt_Check(res) && !PyLong_Check(res)) { #else if (!PyLong_Check(res)) { #endif PyErr_Format(PyExc_TypeError, "__%s__ returned non-%s (type %.200s)", name, name, Py_TYPE(res)->tp_name); Py_DECREF(res); return NULL; } } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_TypeError, "an integer is required"); } return res; } static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { Py_ssize_t ival; PyObject* x = PyNumber_Index(b); if (!x) return -1; ival = PyInt_AsSsize_t(x); Py_DECREF(x); return ival; } static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { #if PY_VERSION_HEX < 0x02050000 if (ival <= LONG_MAX) return PyInt_FromLong((long)ival); else { unsigned char *bytes = (unsigned char *) &ival; int one = 1; int little = (int)*(unsigned char*)&one; return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0); } #else return PyInt_FromSize_t(ival); #endif } static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject* x) { unsigned PY_LONG_LONG val = __Pyx_PyInt_AsUnsignedLongLong(x); if (unlikely(val == (unsigned PY_LONG_LONG)-1 && PyErr_Occurred())) { return (size_t)-1; } else if (unlikely(val != (unsigned PY_LONG_LONG)(size_t)val)) { PyErr_SetString(PyExc_OverflowError, "value too large to convert to size_t"); return (size_t)-1; } return (size_t)val; } #endif /* Py_PYTHON_H */ MMTK-2.7.9/Src/MMTK_energy_term.pyx0000644000076600000240000000471211662461640017364 0ustar hinsenstaff00000000000000# Energy term interface for Python code # # Written by Konrad Hinsen # include "MMTK/python.pxi" include "MMTK/numeric.pxi" include "MMTK/core.pxi" include "MMTK/universe.pxi" include 'MMTK/forcefield.pxi' import MMTK from Scientific import N cdef class PyEnergyTerm(EnergyTerm): cdef public object universe cdef object configuration def __init__(self, name, universe): EnergyTerm.__init__(self, universe, name, (name,)) self.eval_func = PyEnergyTerm.c_evaluate self.universe = universe self.configuration = None def __getattr__(self, name): if name == "name": return self.evaluator_name elif name == "term_names": names = [] for i in range(self.nterms): names.append(self.term_names[i]) return tuple(names) else: raise AttributeError, name cdef void c_evaluate(self, PyFFEvaluatorObject *eval, energy_spec *input, energy_data *energy) except *: cdef int do_gradients, do_fc do_gradients = energy.gradients != NULL do_fc = energy.force_constants != NULL if self.configuration is None: self.configuration = MMTK.Configuration(self.universe, input.coordinates) else: self.configuration.array = input.coordinates try: energy_dict = self.evaluate(self.configuration, do_gradients, do_fc) except: energy.error = 1 raise energy.energy_terms[self.index] = energy_dict['energy'] try: energy.energy_terms[self.virial_index] = \ energy.energy_terms[self.virial_index] + energy_dict['virial'] except KeyError: energy.virial_available = 0 if do_gradients: try: N.add(energy.gradients, energy_dict['gradients'].array, energy.gradients) except (KeyError, AttributeError): energy.error = 1 raise if do_fc: try: N.add(energy.force_constants, energy_dict['force_constants'].array, energy.force_constants) except (KeyError, AttributeError): energy.error = 1 raise MMTK-2.7.9/Src/MMTK_forcefield.c0000644000076600000240000014731512147633006016553 0ustar hinsenstaff00000000000000/* Low-level force field calculations * * Written by Konrad Hinsen */ #define _FORCEFIELD_MODULE #define PY_ARRAY_UNIQUE_SYMBOL PyArray_MMTKFF_API #ifdef WITH_MPI #define PyMPI_API PyMPI_API_MMTKFF #endif #include "MMTK/forcefield.h" #include "MMTK/forcefield_private.h" #define DEBUG 0 #define THREAD_DEBUG 0 #define MPI_DEBUG 0 /* Windows header that defines Sleep() */ #ifdef WITH_THREAD #ifdef MS_WINDOWS #include "Windows.h" #endif #endif /* Global variables */ double electrostatic_energy_factor; distance_fn *distance_vector_pointer; distance_fn *orthorhombic_distance_vector_pointer; distance_fn *parallelepipedic_distance_vector_pointer; #ifdef WITH_MPI static PyObject *PyExc_MPIError; #endif staticforward ff_eval_function evaluator; /* * Threading support */ #ifdef WITH_THREAD #ifdef _POSIX_THREADS static int allocate_barrier(barrierinfo *binfo) { if (pthread_mutex_init(&binfo->lock, NULL) != 0) return 0; if (pthread_cond_init(&binfo->cond, NULL) != 0) return 0; binfo->n = 0; return 1; } static void deallocate_barrier(barrierinfo *binfo) { pthread_mutex_destroy(&binfo->lock); pthread_cond_destroy(&binfo->cond); free(binfo); } void barrier(barrierinfo *binfo, int thread_id, int nthreads) { if (nthreads > 1) { #if THREAD_DEBUG printf("Thread %d arrived at barrier\n", thread_id); #endif pthread_mutex_lock(&binfo->lock); if (binfo->n == nthreads) binfo->n = 1; else binfo->n++; pthread_cond_broadcast(&binfo->cond); #if THREAD_DEBUG printf("Thread %d: barrier count is %d\n", thread_id, binfo->n); #endif while (binfo->n != nthreads) { pthread_cond_wait(&binfo->cond, &binfo->lock); } pthread_mutex_unlock(&binfo->lock); #if THREAD_DEBUG printf("Thread %d continuing after barrier\n", thread_id); #endif } } #else static int allocate_barrier(barrierinfo *binfo) { binfo->lock = PyThread_allocate_lock(); binfo->n = 0; return (binfo->lock != NULL); } static void deallocate_barrier(barrierinfo *binfo) { PyThread_free_lock(binfo->lock); free(binfo); } void barrier(barrierinfo *binfo, int thread_id, int nthreads) { int done = 0; if (nthreads > 1) { PyThread_acquire_lock(binfo->lock, 1); if (binfo->n == nthreads) binfo->n = 1; else binfo->n++; PyThread_release_lock(binfo->lock); while (!done) { PyThread_acquire_lock(binfo->lock, 1); done = (binfo->n == nthreads); PyThread_release_lock(binfo->lock); } } } #endif #endif /* String copy with memory allocation */ char * allocstring(char *string) { char *memory = (char *)malloc(strlen(string)+1); if (memory != NULL) strcpy(memory, string); return memory; } /* Type "energy term" * * Objects of this type represent the individual force field terms. * They are called by an energy evaluator object (see below). * * The structure declaration is in mmtk_forcefield.h. */ /* Allocation and deallocation */ #ifdef EXTENDED_TYPES static PyObject * PyFFEnergyTerm_new(PyTypeObject *type, PyObject *args, PyObject *kw) { PyFFEnergyTermObject *self; int i; self = (PyFFEnergyTermObject *)type->tp_alloc(type, 0); if (self != NULL) { self->user_info = NULL; self->universe_spec = NULL; self->scratch = NULL; for (i = 0; i < MMTK_MAX_DATA; i++) self->data[i] = NULL; self->evaluator_name = NULL; for (i = 0; i < MMTK_MAX_TERMS; i++) self->term_names[i] = NULL; self->threaded = 0; self->thread_safe = 0; self->parallelized = 0; self->n = self->nterms = 0; } return (PyObject *)self; } static int PyFFEnergyTerm_init(PyObject *self_po, PyObject *args, PyObject *kw) { PyFFEnergyTermObject *self = (PyFFEnergyTermObject *)self_po; char *name; PyObject *universe; PyObject *term_names; int thread_safe = 0; int i; if (! PyArg_ParseTuple(args, "OsO!|i", &universe, &name, &PyTuple_Type, &term_names, &thread_safe)) return -1; self->nterms = (int)PyTuple_Size(term_names); if (self->nterms == 0) { PyErr_SetString(PyExc_ValueError, "at least one term name required"); return -1; } if (self->nterms > MMTK_MAX_TERMS) { PyErr_SetString(PyExc_ValueError, "too many terms"); return -1; } self->universe_spec = (PyUniverseSpecObject *) PyObject_GetAttrString(universe, "_spec"); if (self->universe_spec == NULL) return -1; Py_INCREF(self->universe_spec); self->evaluator_name = allocstring(name); if (self->evaluator_name == NULL) { PyErr_NoMemory(); return -1; } for (i = 0; i < self->nterms; i++) { PyObject *name = PyTuple_GetItem(term_names, (Py_ssize_t)i); if (!PyString_Check(name)) { PyErr_SetString(PyExc_TypeError, "term names must be strings"); return -1; } self->term_names[i] = allocstring(PyString_AsString(name)); if (self->term_names[i] == NULL) { PyErr_NoMemory(); return -1; } } self->thread_safe = thread_safe; return 0; } #endif PyFFEnergyTermObject * PyFFEnergyTerm_New(void) { int i; PyFFEnergyTermObject *self; self = PyObject_NEW(PyFFEnergyTermObject, &PyFFEnergyTerm_Type); if (self == NULL) { PyErr_NoMemory(); return NULL; } self->user_info = NULL; self->universe_spec = NULL; self->scratch = NULL; for (i = 0; i < MMTK_MAX_DATA; i++) self->data[i] = NULL; self->evaluator_name = NULL; for (i = 0; i < MMTK_MAX_TERMS; i++) self->term_names[i] = NULL; self->threaded = 0; self->parallelized = 0; self->n = self->nterms = 0; return self; } static void energyterm_dealloc(PyFFEnergyTermObject *self) { int i; for (i = 0; i < self->nterms; i++) free(self->term_names[i]); Py_XDECREF(self->user_info); Py_XDECREF(self->universe_spec); for (i = 0; i < MMTK_MAX_DATA; i++) Py_XDECREF(self->data[i]); if (self->scratch != NULL) free(self->scratch); self->ob_type->tp_free((PyObject *)self); } /* Add nonbonded term to NonbondedListTerm */ static PyObject * add_term(PyObject *self, PyObject *args) { PyFFEnergyTermObject *ev = (PyFFEnergyTermObject *)self; PyFFEnergyTermObject *ev_term; int ev_type; if (!PyArg_ParseTuple(args, "O!i", &PyFFEnergyTerm_Type, &ev_term, &ev_type)) return NULL; if (strcmp(ev->evaluator_name, "nonbonded list summation") != 0) { PyErr_SetString(PyExc_ValueError, "not a NonbondedListTerm"); return NULL; } Py_INCREF(ev_term); ev->data[ev_type+1] = (PyObject *)ev_term; Py_INCREF(Py_None); return Py_None; } /* Documentation string */ static char PyFFEnergyTerm_Type__doc__[] = "energy term in force field"; /* Method table */ static struct PyMethodDef energyterm_methods[] = { {"addTerm", add_term, 1}, {NULL, NULL} /* sentinel */ }; /* Attribute and element access. */ static PyObject * energyterm_getattr(PyFFEnergyTermObject *self, char *name) { if (strcmp(name, "name") == 0) { return PyString_FromString(self->evaluator_name); } else if (strcmp(name, "term_names") == 0) { PyObject *ret = PyTuple_New((Py_ssize_t)self->nterms); int i; for (i = 0; i < self->nterms; i++) PyTuple_SetItem(ret, (Py_ssize_t)i, PyString_FromString(self->term_names[i])); return ret; } else if (strcmp(name, "info") == 0) { if (self->user_info == NULL) { PyErr_SetString(PyExc_AttributeError, "attribute not defined"); return NULL; } Py_INCREF(self->user_info); return self->user_info; } else return Py_FindMethod(energyterm_methods, (PyObject *)self, name); } static int energyterm_setattr(PyFFEnergyTermObject *self, char *name, PyObject *value) { if (strcmp(name, "info") == 0) { Py_XDECREF(self->user_info); Py_INCREF(value); self->user_info = value; return 0; } else { PyErr_SetString(PyExc_AttributeError, "attribute not defined"); return -1; } } /* The garbage collector routines do nothing, but they need to be present because Pyrex derived types call them. */ static int energyterm_traverse(PyFFEnergyTermObject *self, visitproc visit, void *arg) { return 0; } static Py_ssize_t energyterm_clear(PyFFEnergyTermObject *self) { return 0; } /* Type object */ PyTypeObject PyFFEnergyTerm_Type = { PyObject_HEAD_INIT(NULL) 0, /*ob_size*/ "FFEnergyTerm", /*tp_name*/ sizeof(PyFFEnergyTermObject), /*tp_basicsize*/ 0, /*tp_itemsize*/ /* methods */ (destructor)energyterm_dealloc, /*tp_dealloc*/ 0, /*tp_print*/ (getattrfunc)energyterm_getattr,/*tp_getattr*/ (setattrfunc)energyterm_setattr,/*tp_setattr*/ 0, /*tp_compare*/ 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ /* Functions to access object as input/output buffer */ /* PyBufferProcs *tp_as_buffer */ 0, /* long tp_flags */ Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /* Documentation string */ PyFFEnergyTerm_Type__doc__ #ifdef EXTENDED_TYPES , /* Assigned meaning in release 2.0 */ /* call function for all accessible objects */ /* traverseproc tp_traverse */ (traverseproc)energyterm_traverse, /* delete references to contained objects */ /* inquiry tp_clear */ (inquiry)energyterm_clear, /* Assigned meaning in release 2.1 */ /* rich comparisons */ /* richcmpfunc tp_richcompare */ 0, /* weak reference enabler */ /* long tp_weaklistoffset */ 0, /* Added in release 2.2 */ /* Iterators */ /* getiterfunc tp_iter */ 0, /* iternextfunc tp_iternext */ 0, /* Attribute descriptor and subclassing stuff */ /* struct PyMethodDef *tp_methods */ 0, /* struct PyMemberDef *tp_members */ 0, /* struct PyGetSetDef *tp_getset */ 0, /* struct _typeobject *tp_base */ 0, /* PyObject *tp_dict */ 0, /* descrgetfunc tp_descr_get */ 0, /* descrsetfunc tp_descr_set */ 0, /* long tp_dictoffset */ 0, /* initproc tp_init */ PyFFEnergyTerm_init, /* allocfunc tp_alloc */ 0, /* newfunc tp_new */ PyFFEnergyTerm_new, /* freefunc tp_free */ 0, /* inquiry tp_is_gc */ 0, /* PyObject *tp_bases */ 0, /* PyObject *tp_mro */ 0, /* PyObject *tp_cache */ 0, /* PyObject *tp_subclasses */ 0, /* PyObject *tp_weaklist */ 0, /* destructor tp_del */ 0 #endif }; /* Type "evaluator" * * Objects of this type are callable, expecting a coordinate array, * a gradient array (None if no gradients are to be calculated), * and a force constant array (None if no force constants are to be * calculated). They return a float object containing the energy. * For efficient access from C code, these objects have an attribute * cfunc, which is a pointer to an equivalent C function. * * The structure declaration is in mmtk_forcefield.h. */ /* Allocation and deallocation */ PyFFEvaluatorObject * PyFFEvaluator_New(void) { PyFFEvaluatorObject *self; self = PyObject_NEW(PyFFEvaluatorObject, &PyFFEvaluator_Type); if (self == NULL) { PyErr_NoMemory(); return NULL; } self->universe_spec = NULL; self->terms = NULL; self->energy_terms = NULL; self->nterms = self->ntermobjects = 0; self->scratch = NULL; self->nthreads = 0; #ifdef WITH_THREAD self->global_lock = NULL; self->binfo = NULL; #endif return self; } static void evaluator_dealloc(PyFFEvaluatorObject *self) { int i; #ifdef WITH_THREAD if (self->eval_func == evaluator) { threadinfo *tinfo = (threadinfo *)self->scratch; if (self->global_lock != NULL) PyThread_free_lock(self->global_lock); if (self->binfo != NULL) deallocate_barrier(self->binfo); for (i = 1; i < self->nthreads; i++) { int j = 50; tinfo->exit = 1; #if THREAD_DEBUG printf("Releasing thread %d\n", tinfo->input.thread_id); #endif PyThread_release_lock(tinfo->lock); while (!tinfo->stop && j--) { #if THREAD_DEBUG printf("evaluator_dealloc: Waiting for thread %d to stop (tinfo->stop is %d)\n", tinfo->input.thread_id, tinfo->stop); #endif #ifdef MS_WINDOWS Sleep(10); /* 10 ms */ #else { struct timeval tv; tv.tv_sec = 0; tv.tv_usec = 10000; /* 10 ms */ select(0, NULL, NULL, NULL, &tv); } #endif } Py_XDECREF(tinfo->energy.gradients); free(tinfo->energy.energy_terms); PyThread_free_lock(tinfo->lock); tinfo++; } } #endif #ifdef WITH_MPI if (self->energy_parts) free(self->energy_parts); if (self->gradient_parts) free(self->gradient_parts); #endif Py_XDECREF(self->universe_spec); Py_XDECREF(self->terms); Py_XDECREF(self->energy_terms_array); if (self->scratch != NULL) free(self->scratch); PyObject_Del(self); } /* Call */ #if 0 int test_gradient_function(struct ffeval *evaluator, PyObject *data, int i, vector3 gradient) { PyArrayObject *array = (PyArrayObject *)data; vector3 *f = (vector3 *)array->data; int natoms = array->dimensions[0]; if (i < 0) { int i; #if 0 printf("Zeroing gradient array\n"); #endif for (i = 0; i < natoms; i++) { f[i][0] = 0.; f[i][1] = 0.; f[i][2] = 0.; } } else { #if 0 printf("Gradient for atom %d from %s\n", i, evaluator->name); printf(" %lf, %lf, %lf\n", gradient[0], gradient[1], gradient[2]); #endif f[i][0] += gradient[0]; f[i][1] += gradient[1]; f[i][2] += gradient[2]; } return 1; } int test_fc_function(struct ffeval *evaluator, PyObject *data, int i, int j, tensor3 fc, double r_sq) { PyArrayObject *array = (PyArrayObject *)data; double *sd = (double *)array->data; int natoms = array->dimensions[0]; if (i < 0) { long i; #if 0 printf("Zeroing force constant array\n"); #endif for (i = 0; i < 9L*(long)natoms*(long)natoms; i++) sd[i] = 0.; } else if (fc != NULL) { double *fcij = sd + 9L*(long)natoms*(long)i + 3L*(long)j; int l, m; #if 0 printf("Force constants for atom pair (%d, %d) with distance %lf from %s\n", i, j, sqrt(r_sq), evaluator->name); #endif if (i > j) printf("Illegal index pair: %d > %d\n", i, j); for (l = 0; l < 3; l++) { for (m = 0; m < 3; m++) fcij[m] += fc[l][m]; fcij += 3*natoms; } } return 1; } #endif static PyObject * evaluator_call(PyFFEvaluatorObject *self, PyObject *args) { PyArrayObject *coordinates = NULL; PyObject *gradients = NULL; PyObject *force_constants = NULL; int small_change = 0; gradient_function *gf = NULL; fc_function *fcf = NULL; energy_data energy; if (!PyArg_ParseTuple(args, "O!|OOi", &PyArray_Type, &coordinates, &gradients, &force_constants, &small_change)) return NULL; if (gradients == Py_None) gradients = NULL; if (force_constants == Py_None) force_constants = NULL; if (gradients != NULL && !PyArray_Check(gradients)) { PyObject *fnptr = PyObject_CallMethod(gradients, "accessFunction", NULL); if (fnptr == NULL) return NULL; gf = (gradient_function *)PyCObject_AsVoidPtr(fnptr); } if (force_constants != NULL && !PyArray_Check(force_constants)) { PyObject *fnptr = PyObject_CallMethod(force_constants, "accessFunction", NULL); if (fnptr == NULL) return NULL; fcf = (fc_function *)PyCObject_AsVoidPtr(fnptr); } energy.gradients = gradients; energy.gradient_fn = gf; energy.force_constants = force_constants; energy.fc_fn = fcf; #ifdef WITH_THREAD self->tstate_save = PyEval_SaveThread(); #endif (*self->eval_func)(self, &energy, coordinates, small_change); #ifdef WITH_THREAD PyEval_RestoreThread(self->tstate_save); #endif if (energy.error) return NULL; else return PyFloat_FromDouble(energy.energy); } static void evaluator(PyFFEvaluatorObject *self, energy_data *energy, PyArrayObject *coordinates, int small_change) { int natoms = coordinates->dimensions[0]; PyFFEnergyTermObject *term; energy_spec input; int i; input.coordinates = coordinates; input.natoms = natoms; input.small_change = small_change; input.nthreads = self->nthreads; input.nprocs = self->nprocs; input.nslices = self->nslices; input.proc_id = self->proc_id; input.thread_id = 0; input.slice_id = self->nthreads*self->proc_id; energy->energy_terms = self->energy_terms; for (i = 0; i < self->nterms+1; i++) energy->energy_terms[i] = 0.; energy->virial_available = 1; energy->error = 0; if (energy->force_constants != NULL) { input.nthreads = 1; input.nprocs = 1; input.nslices = 1; if (energy->fc_fn != NULL) (*energy->fc_fn)(energy, -1, -1, NULL, 0.); else { double *data = (double *) ((PyArrayObject *)energy->force_constants)->data; long nelements = 9L*(long)natoms*(long)natoms; long j; for (j = 0; j < nelements; j++) data[j] = 0.; } } if (energy->gradients != NULL) { if (energy->gradient_fn != NULL) { #ifdef GRADIENTFN (*energy->gradient_fn)(energy, -1, NULL); #else PyErr_SetString(PyExc_EnvironmentError, "gradient function support not available"); energy->error = 1; return; #endif } else { double *data = (double *)((PyArrayObject *)energy->gradients)->data; for (i = 0; i < 3*natoms; i++) data[i] = 0.; #ifdef WITH_MPI if (self->communicator != NULL && input.nprocs > 1 && self->gradient_parts == NULL) { self->gradient_parts = malloc(3*natoms*self->nprocs*sizeof(double)); if (self->gradient_parts == NULL) { energy->error = 1; return; } } #endif } } #ifdef WITH_THREAD { threadinfo *tinfo = (threadinfo *)self->scratch; #if THREAD_DEBUG printf("%d threads to be released\n", input.nthreads-1); #endif for (i = 1; i < input.nthreads; i++) { tinfo->input.coordinates = coordinates; tinfo->input.natoms = natoms; tinfo->input.small_change = small_change; tinfo->with_gradients = (energy->gradients != NULL); if (tinfo->with_gradients && tinfo->energy.gradients == NULL) { #if defined(NUMPY) npy_intp dims[2]; dims[0] = 3; dims[1] = natoms; tinfo->energy.gradients = PyArray_SimpleNew(2, dims, PyArray_DOUBLE); #else int dims[2]; dims[0] = 3; dims[1] = natoms; tinfo->energy.gradients = PyArray_FromDims(2, dims, PyArray_DOUBLE); #endif if (tinfo->energy.gradients == NULL) { energy->error = 1; return; } } if (tinfo->with_gradients) { double *data = (double *) ((PyArrayObject *)tinfo->energy.gradients)->data; int j; for (j = 0; j < 3*natoms; j++) data[j] = 0.; } #if THREAD_DEBUG printf("Releasing thread %d\n", tinfo->input.thread_id); #endif PyThread_release_lock(tinfo->lock); tinfo++; } } #endif for (i = 0; i < self->ntermobjects; i++) { term = ((PyFFEnergyTermObject **)self->terms->data)[i]; #ifdef WITH_THREAD if (term->thread_safe) (*term->eval_func)(term, self, &input, energy); else { PyEval_RestoreThread(self->tstate_save); (*term->eval_func)(term, self, &input, energy); self->tstate_save = PyEval_SaveThread(); } #else (*term->eval_func)(term, self, &input, energy); #endif #if THREAD_DEBUG { int j; printf("Thread 0: %s: ", term->evaluator_name); for (j = term->index; j < term->index+term->nterms; j++) printf("%lf ", self->energy_terms[j]); printf("\n"); } #endif } #ifdef WITH_THREAD if (input.nthreads > 1) { int done = 1; #if THREAD_DEBUG printf("Collecting data from other threads...\n"); #endif while (done < self->nthreads) { threadinfo *tinfo = (threadinfo *)self->scratch; PyThread_acquire_lock(self->global_lock, 1); for (i = 1; i < self->nthreads; i++) { if (tinfo->done) { int j; for (j = 0; j < self->nterms+1; j++) energy->energy_terms[j] += tinfo->energy.energy_terms[j]; energy->virial_available &= tinfo->energy.virial_available; energy->error |= tinfo->energy.error; if (energy->gradients) { double *data = (double*)((PyArrayObject *)energy->gradients)->data; double *tdata = (double*)((PyArrayObject *) tinfo->energy.gradients)->data; for (i = 0; i < 3*natoms; i++) data[i] += tdata[i]; } tinfo->done = 0; done++; } tinfo++; } PyThread_release_lock(self->global_lock); #if THREAD_DEBUG if (done > 1) printf("%d threads finished\n", done); #endif } } #endif #if MPI_DEBUG { int j; printf("Proc %d before: ", self->proc_id); for (j = 0; j < self->nterms+1; j++) printf("%lf ", energy->energy_terms[j]); printf("\n"); fflush(stdout); } #endif #ifdef WITH_MPI if (self->communicator != NULL && input.nprocs > 1) { int i, j; if (PyMPI_Barrier(self->communicator) != MPI_SUCCESS) { PyErr_SetString(PyExc_MPIError, "Error in MPI_Barrier"); energy->error = 1; } if (PyMPI_Share(self->communicator, energy->energy_terms, self->energy_parts, PyArray_DOUBLE, self->nterms+1) != MPI_SUCCESS) { PyErr_SetString(PyExc_MPIError, "Error in MPI_Share (energy)"); energy->error = 1; } for (j = 0; j < self->nterms+1; j++) { energy->energy_terms[j] = 0.; for (i = 0; i < self->nprocs; i++) energy->energy_terms[j] += self->energy_parts[i*(self->nterms+1)+j]; } if (energy->gradients != NULL) { double *gdata = (double *)((PyArrayObject *)energy->gradients)->data; if (PyMPI_Share(self->communicator, gdata, self->gradient_parts, PyArray_DOUBLE, 3*natoms) != MPI_SUCCESS) { PyErr_SetString(PyExc_MPIError, "Error in MPI_Share (gradients)"); energy->error = 1; } for (j = 0; j < 3*natoms; j++) gdata[j] = 0.; for (i = 0; i < self->nprocs; i++) for (j = 0; j < 3*natoms; j++) gdata[j] += self->gradient_parts[3*i*natoms+j]; } } #endif #if MPI_DEBUG { int j; printf("Proc %d after: ", self->proc_id); for (j = 0; j < self->nterms+1; j++) printf("%lf ", energy->energy_terms[j]); printf("\n"); fflush(stdout); } #endif energy->energy = 0.; for (i = 0; i < self->nterms; i++) energy->energy += energy->energy_terms[i]; energy->virial = energy->energy_terms[self->nterms]; } /* Evaluator loop for parallel threads */ #ifdef WITH_THREAD static void evaluator_thread(void *arg) { threadinfo *info = (threadinfo *)arg; PyFFEnergyTermObject *term; int i; while (1) { #if THREAD_DEBUG printf("Thread %d waiting for lock...\n", info->input.thread_id); #endif PyThread_acquire_lock(info->lock, 1); #if THREAD_DEBUG printf("Thread %d running\n", info->input.thread_id); #endif if (info->exit) { info->stop = 1; break; } for (i = 0; i < info->evaluator->nterms+1; i++) info->energy.energy_terms[i] = 0.; info->energy.energy = 0.; info->energy.virial_available = 1; info->energy.error = 0; if (info->with_gradients && info->energy.gradients != NULL) { double *data = (double *)((PyArrayObject *)info->energy.gradients)->data; for (i = 0; i < 3*info->input.natoms; i++) data[i] = 0.; } PyThread_acquire_lock(info->evaluator->global_lock, 1); info->done = 0; PyThread_release_lock(info->evaluator->global_lock); for (i = 0; i < info->evaluator->ntermobjects; i++) { term = ((PyFFEnergyTermObject **)info->evaluator->terms->data)[i]; if (term->threaded) { (*term->eval_func)(term, info->evaluator, &info->input, &info->energy); #if THREAD_DEBUG { int j; printf("Thread %d: %s: ", info->input.thread_id, term->evaluator_name); for (j = term->index; j < term->index+term->nterms; j++) printf("%lf ", info->energy.energy_terms[j]); printf("\n"); } #endif } } PyThread_acquire_lock(info->evaluator->global_lock, 1); info->done = 1; PyThread_release_lock(info->evaluator->global_lock); } } #endif /* Return self */ static PyObject * C_evaluator(PyObject *self, PyObject *args) { if (!PyArg_ParseTuple(args, "")) return NULL; Py_INCREF(self); return self; } /* Documentation string */ static char PyFFEvaluator_Type__doc__[] = "force field evaluator"; /* Method table */ static struct PyMethodDef evaluator_methods[] = { {"CEvaluator", C_evaluator, 1}, {NULL, NULL} /* sentinel */ }; /* Attribute and element access. */ static PyObject * evaluator_getattr(PyObject *self, char *name) { if (strcmp(name, "last_energy_values") == 0) { PyFFEvaluatorObject *ev = (PyFFEvaluatorObject *)self; Py_INCREF(ev->energy_terms_array); return (PyObject *)ev->energy_terms_array; } return Py_FindMethod(evaluator_methods, self, name); } static Py_ssize_t evaluator_length(PyFFEvaluatorObject *self) { return self->ntermobjects; } static PyObject * evaluator_item(PyFFEvaluatorObject *self, Py_ssize_t i) { if (i < 0) i = self->ntermobjects + i; if (i < 0 || i >= self->ntermobjects) { PyErr_SetString(PyExc_IndexError, "index out of bounds"); return NULL; } Py_INCREF(((PyObject **)self->terms->data)[i]); return ((PyObject **)self->terms->data)[i]; } /* Type object */ static PySequenceMethods PyFFEvaluatorObject_as_sequence = { (lenfunc)evaluator_length, /*sq_length*/ (binaryfunc)NULL, /*nb_add*/ (ssizeargfunc)NULL, /*nb_multiply*/ (ssizeargfunc)evaluator_item, /*sq_item*/ (ssizessizeargfunc)NULL, /*sq_slice*/ (ssizeobjargproc)NULL, /*sq_ass_item*/ (ssizessizeobjargproc)NULL, /*sq_ass_slice*/ }; PyTypeObject PyFFEvaluator_Type = { PyObject_HEAD_INIT(NULL) 0, /*ob_size*/ "FFEvaluator", /*tp_name*/ sizeof(PyFFEvaluatorObject), /*tp_basicsize*/ 0, /*tp_itemsize*/ /* methods */ (destructor)evaluator_dealloc, /*tp_dealloc*/ 0, /*tp_print*/ (getattrfunc)evaluator_getattr, /*tp_getattr*/ 0, /*tp_setattr*/ 0, /*tp_compare*/ 0, /*tp_repr*/ 0, /*tp_as_number*/ &PyFFEvaluatorObject_as_sequence, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ (ternaryfunc)evaluator_call, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ /* Space for future expansion */ 0L,0L, /* Documentation string */ PyFFEvaluator_Type__doc__ }; /* Type "non-bonded list" * * Objects of this type provide a list of atoms pairs whose distance * is below a certain cutoff and which do not appear in the excluded * pair list. Pairs that are in the 1-4 list are marked. * * The structure declaration is in forcefield.h. */ /* Allocation and deallocation */ static PyNonbondedListObject * nblist_new(void) { PyNonbondedListObject *self; self = PyObject_NEW(PyNonbondedListObject, &PyNonbondedList_Type); if (self == NULL) { PyErr_NoMemory(); return NULL; } self->iterator.state = nblist_start; self->iterator.n = -1; self->excluded_pairs = NULL; self->one_four_pairs = NULL; self->atom_subset = NULL; self->universe_spec = NULL; self->box_number = NULL; self->box_atoms = NULL; self->boxes = NULL; self->nboxes = 0; self->allocated_boxes = 0; return self; } static void nblist_dealloc(PyNonbondedListObject *self) { Py_XDECREF(self->excluded_pairs); Py_XDECREF(self->one_four_pairs); Py_XDECREF(self->atom_subset); Py_XDECREF(self->universe_spec); free(self->box_number); free(self->boxes); PyObject_Del(self); } /* Sequence protocol */ static Py_ssize_t nblist_length(PyNonbondedListObject *self) { struct nblist_iterator iterator; iterator.state = nblist_start; while (nblist_iterate(self, &iterator)) ; return iterator.n+1; } static PyObject * nblist_item(PyNonbondedListObject *self, Py_ssize_t i) { if (i < 0) { PyErr_SetString(PyExc_IndexError, "index must be positive"); return NULL; } if (i < self->iterator.n) { self->iterator.state = nblist_start; self->iterator.n = -1; } while (i > self->iterator.n) { if (!nblist_iterate(self, &self->iterator)) { PyErr_SetString(PyExc_IndexError, "index too large"); return NULL; } } return Py_BuildValue("ii", self->iterator.a1, self->iterator.a2); } /* Methods */ static PyObject * nblist_update_py(PyObject *self, PyObject *args) { PyNonbondedListObject *nblist = (PyNonbondedListObject *)self; PyObject *conf, *geometry; PyArrayObject *array; double *geometry_data; geometry = NULL; if (!PyArg_ParseTuple(args, "O|O", &conf, &geometry)) return NULL; if (!PyArray_Check(conf)) { geometry = PyObject_GetAttrString(conf, "cell_parameters"); if (geometry == NULL) return NULL; conf = PyObject_GetAttrString(conf, "array"); if (conf == NULL) return NULL; } if (geometry != NULL && !PyArray_Check(geometry)) { if (geometry == Py_None) geometry = NULL; else { PyErr_SetString(PyExc_ValueError, "geometry data not an array"); return NULL; } } if (geometry == NULL) geometry_data = nblist->universe_spec->geometry_data; else geometry_data = (double *)((PyArrayObject *)geometry)->data; array = (PyArrayObject *)conf; nblist_update(nblist, array->dimensions[0], (double *)array->data, geometry_data); Py_INCREF(Py_None); return Py_None; } static PyObject * nblist_set_cutoff(PyObject *self, PyObject *args) { PyObject *cutoff_ob; if (!PyArg_ParseTuple(args, "O", &cutoff_ob)) return NULL; if (cutoff_ob == Py_None) ((PyNonbondedListObject *)self)->cutoff = 0.; else if (PyNumber_Check(cutoff_ob)) { cutoff_ob = PyNumber_Float(cutoff_ob); ((PyNonbondedListObject *)self)->cutoff = PyFloat_AsDouble(cutoff_ob); } else { PyErr_SetString(PyExc_TypeError, "cutoff must be a number or None"); return NULL; } Py_INCREF(Py_None); return Py_None; } static PyObject * nblist_pair_distances(PyObject *self, PyObject *args) { PyNonbondedListObject *nblist = (PyNonbondedListObject *)self; struct nblist_iterator iterator; PyArrayObject *array; vector3 dv; double *d; int i; #if defined(NUMPY) npy_intp n; #else int n; #endif if (!PyArg_ParseTuple(args, "")) return NULL; n = nblist_length(nblist); #if defined(NUMPY) array = (PyArrayObject *)PyArray_SimpleNew(1, &n, PyArray_DOUBLE); #else array = (PyArrayObject *)PyArray_FromDims(1, &n, PyArray_DOUBLE); #endif if (array == NULL) return NULL; d = (double *)array->data; iterator.state = nblist_start; i = 0; while (nblist_iterate(nblist, &iterator)) { nblist->universe_spec->distance_function(dv, nblist->lastx[iterator.a1], nblist->lastx[iterator.a2], nblist->universe_spec->geometry_data); d[i++] = vector_length(dv); } return (PyObject *)array; } static PyObject * nblist_pair_indices(PyObject *self, PyObject *args) { PyNonbondedListObject *nblist = (PyNonbondedListObject *)self; struct nblist_iterator iterator; PyArrayObject *array; long *index; int i; #if defined(NUMPY) npy_intp n[2]; #else int n[2]; #endif if (!PyArg_ParseTuple(args, "")) return NULL; n[0] = nblist_length(nblist); n[1] = 2; #if defined(NUMPY) array = (PyArrayObject *)PyArray_SimpleNew(2, n, PyArray_LONG); #else array = (PyArrayObject *)PyArray_FromDims(2, n, PyArray_LONG); #endif if (array == NULL) return NULL; index = (long *)array->data; iterator.state = nblist_start; i = 0; while (nblist_iterate(nblist, &iterator)) { index[i++] = iterator.a1; index[i++] = iterator.a2; } return (PyObject *)array; } /* Documentation string */ static char PyNonbondedList_Type__doc__[] = "nonbonded list"; /* Method table */ static struct PyMethodDef nblist_methods[] = { {"update", nblist_update_py, 1}, {"setCutoff", nblist_set_cutoff, 1}, {"pairDistances", nblist_pair_distances, 1}, {"pairIndices", nblist_pair_indices, 1}, {NULL, NULL} /* sentinel */ }; /* Attribute access. */ static PyObject * nblist_getattr(PyNonbondedListObject *self, char *name) { return Py_FindMethod(nblist_methods, (PyObject *)self, name); } /* Type object */ static PySequenceMethods nblist_as_sequence = { (lenfunc)nblist_length, /*sq_length*/ 0, /*nb_add, concat is numeric add*/ 0, /*nb_multiply, repeat is numeric multiply*/ (ssizeargfunc)nblist_item, /* sq_item*/ 0, /*sq_slice*/ 0, /*sq_ass_item*/ 0, /*sq_ass_slice*/ }; PyTypeObject PyNonbondedList_Type = { PyObject_HEAD_INIT(NULL) 0, /*ob_size*/ "NonbondedList", /*tp_name*/ sizeof(PyNonbondedListObject), /*tp_basicsize*/ 0, /*tp_itemsize*/ /* methods */ (destructor)nblist_dealloc, /*tp_dealloc*/ 0, /*tp_print*/ (getattrfunc)nblist_getattr, /*tp_getattr*/ 0, /*tp_setattr*/ 0, /*tp_compare*/ 0, /*tp_repr*/ 0, /*tp_as_number*/ &nblist_as_sequence, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ /* Space for future expansion */ 0L,0L, /* Documentation string */ PyNonbondedList_Type__doc__ }; /* * Force field evaluation function for combined force fields */ #if 0 /* * Force field evaluation function for Python force fields */ static void python_evaluator(PyFFEnergyTermObject *self, PyFFEvaluatorObject *eval, energy_spec *input, energy_data *energy) { PyObject *py_eval = self->data[0]; PyObject *args, *result; PyObject *gradients, *force_constants; gradients = energy->gradients; if (gradients == NULL) gradients = Py_None; force_constants = energy->force_constants; if (force_constants == NULL) force_constants = Py_None; args = PyTuple_New((Py_ssize_t)3); Py_INCREF(input->coordinates); Py_INCREF(gradients); Py_INCREF(force_constants); PyTuple_SetItem(args, (Py_ssize_t)0, (PyObject *)input->coordinates); PyTuple_SetItem(args, (Py_ssize_t)1, gradients); PyTuple_SetItem(args, (Py_ssize_t)2, force_constants); result = PyObject_CallObject(py_eval, args); Py_DECREF(args); if (result == NULL) { energy->error = 1; } else { energy->energy_terms[self->index] = PyFloat_AsDouble(result); energy->virial_available = 0; } } #endif /* * Support functions for pair force fields */ void add_pair_fc(energy_data *energy, int i, int j, vector3 dr, double r_sq, double f1, double f2) { if (energy->fc_fn != NULL) { if ((*energy->fc_fn)(energy, i, j, NULL, r_sq)) { tensor3 fij; int k, l; for (k = 0; k < 3; k++) { for (l = 0; l < 3; l++) fij[k][l] = (f2-f1)*dr[k]*dr[l]/r_sq; fij[k][k] += f1; } (*energy->fc_fn)(energy, i, i, fij, r_sq); (*energy->fc_fn)(energy, j, j, fij, r_sq); tensor_changesign(fij); if (i > j) (*energy->fc_fn)(energy, j, i, fij, r_sq); else (*energy->fc_fn)(energy, i, j, fij, r_sq); } } else { double *data = (double *)((PyArrayObject *)energy->force_constants)->data; int n = ((PyArrayObject *)energy->force_constants)->dimensions[0]; double *fcii = data + 9*n*i+3*i; double *fcjj = data + 9*n*j+3*j; double *fcij; int k, l; if (i > j) { int temp = i; i = j; j = temp; } fcij = data + 9*n*i+3*j; for (k = 0; k < 3; k++) { for (l = 0; l < 3; l++) { double f = (f2-f1)*dr[k]*dr[l]/r_sq; fcii[3*n*k+l] += f; fcjj[3*n*k+l] += f; fcij[3*n*k+l] -= f; } fcii[k*(3*n+1)] += f1; fcjj[k*(3*n+1)] += f1; fcij[k*(3*n+1)] -= f1; } } } /* * Constructor functions for the various evaluators */ static PyObject * ListOfNParticleTerms(PyObject *args, ff_eterm_function f, char *eval_name, char *default_term_name) { PyFFEnergyTermObject *self; char *name = default_term_name; self = PyFFEnergyTerm_New(); if (self == NULL) return NULL; if (!PyArg_ParseTuple(args, "O!OO|s", &PyUniverseSpec_Type, &self->universe_spec, &self->data[0], &self->data[1], &name)) return NULL; self->evaluator_name = eval_name; self->term_names[0] = allocstring(name); if (self->term_names[0] == NULL) return PyErr_NoMemory(); Py_INCREF(self->universe_spec); Py_INCREF(self->data[0]); /* indices */ Py_INCREF(self->data[1]); /* parameters */ self->n = ((PyArrayObject *)self->data[0])->dimensions[0]; self->nterms = 1; self->eval_func = f; self->threaded = 1; self->thread_safe = 1; self->nbarriers = 0; self->parallelized = 1; return (PyObject *)self; } static PyObject * HarmonicDistanceTerm(PyObject *dummy, PyObject *args) { char *eval_name = "harmonic distance"; char *default_name = "harmonic bond"; return ListOfNParticleTerms(args, harmonic_bond_evaluator, eval_name, default_name); } static PyObject * HarmonicAngleTerm(PyObject *dummy, PyObject *args) { char *eval_name = "harmonic angle"; char *default_name = "harmonic bond angle"; return ListOfNParticleTerms(args, harmonic_angle_evaluator, eval_name,default_name); } static PyObject * CosineDihedralTerm(PyObject *dummy, PyObject *args) { char *eval_name = "cosine dihedral angle"; char *default_name = "cosine dihedral angle"; return ListOfNParticleTerms(args, cosine_dihedral_evaluator, eval_name, default_name); } static PyObject * LennardJonesTerm(PyObject *dummy, PyObject *args) { PyFFEnergyTermObject *self = PyFFEnergyTerm_New(); if (self == NULL) return NULL; if (!PyArg_ParseTuple(args, "O!O!O!O!dd", &PyUniverseSpec_Type, &self->universe_spec, &PyNonbondedList_Type, &self->data[0], &PyArray_Type, &self->data[1], &PyArray_Type, &self->data[2], &self->param[0], &self->param[1])) return NULL; Py_INCREF(self->universe_spec); Py_INCREF(self->data[0]); Py_INCREF(self->data[1]); Py_INCREF(self->data[2]); self->eval_func = lennard_jones_evaluator; self->evaluator_name = "Lennard-Jones"; self->nterms = 0; self->thread_safe = 1; return (PyObject *)self; } static PyObject * ElectrostaticTerm(PyObject *dummy, PyObject *args) { PyFFEnergyTermObject *self = PyFFEnergyTerm_New(); if (self == NULL) return NULL; if (!PyArg_ParseTuple(args, "O!O!O!dd", &PyUniverseSpec_Type, &self->universe_spec, &PyNonbondedList_Type, &self->data[0], &PyArray_Type, &self->data[1], &self->param[0], &self->param[1])) return NULL; Py_INCREF(self->universe_spec); Py_INCREF(self->data[0]); Py_INCREF(self->data[1]); self->eval_func = electrostatic_evaluator; self->evaluator_name = "electrostatic"; self->term_names[0] = allocstring("electrostatic/neutralization"); if (self->term_names[0] == NULL) return PyErr_NoMemory(); self->nterms = 1; self->thread_safe = 1; return (PyObject *)self; } static PyObject * EsEwaldTerm(PyObject *dummy, PyObject *args) { PyFFEnergyTermObject *self = PyFFEnergyTerm_New(); PyArrayObject *box_shape; long *kmax; int natoms, nkvect; size_t scratch_size; if (self == NULL) return NULL; if (!PyArg_ParseTuple(args, "O!O!O!O!ddO!dd", &PyUniverseSpec_Type, &self->universe_spec, &PyArray_Type, &box_shape, &PyNonbondedList_Type, &self->data[0], &PyArray_Type, &self->data[1], &self->param[0], &self->param[3], &PyArray_Type, &self->data[2], &self->param[1], &self->param[2])) return NULL; natoms = ((PyArrayObject *)self->data[1])->dimensions[0]; kmax = (long *)((PyArrayObject *)self->data[2])->data; nkvect = init_kvectors(self->universe_spec->box_function, self->universe_spec->geometry_data, natoms, (long *)((PyArrayObject *)self->data[2])->data, self->param[3], NULL, 0); scratch_size = natoms*sizeof(vector3) + (2*natoms*(kmax[0]+2*kmax[1]+2*kmax[2]+4))*sizeof(double) + (3*nkvect+1)*sizeof(int); self->scratch = malloc(scratch_size); if (self->scratch == NULL) { PyErr_NoMemory(); return NULL; } nkvect = init_kvectors(self->universe_spec->box_function, self->universe_spec->geometry_data, natoms, (long *)((PyArrayObject *)self->data[2])->data, self->param[3], self->scratch, nkvect); Py_INCREF(self->universe_spec); Py_INCREF(self->data[0]); Py_INCREF(self->data[1]); Py_INCREF(self->data[2]); self->eval_func = es_ewald_evaluator; self->thread_safe = 1; self->threaded = 1; self->nbarriers = 2; self->parallelized = 1; self->evaluator_name = "electrostatic ewald"; self->term_names[0] = allocstring("electrostatic/ewald self term"); if (self->term_names[0] == NULL) return PyErr_NoMemory(); self->term_names[1] = allocstring("electrostatic/ewald reciprocal sum"); if (self->term_names[1] == NULL) return PyErr_NoMemory(); self->nterms = 2; return (PyObject *)self; } static PyObject * NonbondedListTerm(PyObject *dummy, PyObject *args) { PyFFEnergyTermObject *self = PyFFEnergyTerm_New(); if (self == NULL) return NULL; if (!PyArg_ParseTuple(args, "O!", &PyNonbondedList_Type, &self->data[0])) return NULL; Py_INCREF(self->data[0]); self->eval_func = nonbonded_evaluator; self->thread_safe = 1; self->threaded = 1; self->nbarriers = 1; self->parallelized = 1; self->n = 0; self->evaluator_name = "nonbonded list summation"; self->term_names[0] = allocstring("Lennard-Jones"); if (self->term_names[0] == NULL) return PyErr_NoMemory(); self->term_names[1] = allocstring("electrostatic/pair sum"); if (self->term_names[1] == NULL) return PyErr_NoMemory(); self->term_names[2] = allocstring("electrostatic/ewald direct sum"); if (self->term_names[2] == NULL) return PyErr_NoMemory(); self->nterms = 3; return (PyObject *)self; } /* Energy evaluator with thread support */ static PyObject * Evaluator(PyObject *dummy, PyObject *args) { PyFFEvaluatorObject *self = PyFFEvaluator_New(); #ifdef WITH_MPI PyMPICommunicatorObject *communicator; #else PyObject *communicator; #endif int nthreads = 1, nbarriers = 0; int error = 0; int i; if (self == NULL) return NULL; if (!PyArg_ParseTuple(args, "O!|iO", &PyArray_Type, &self->terms, &nthreads, &communicator)) return NULL; Py_INCREF(self->terms); self->eval_func = evaluator; self->nthreads = nthreads; #ifdef WITH_MPI if ((PyObject *)communicator == Py_None) { self->nprocs = 1; self->proc_id = 0; self->communicator = NULL; self->gradient_parts = NULL; } else { self->nprocs = communicator->size; self->proc_id = communicator->rank; self->communicator = communicator; self->gradient_parts = NULL; PyMPI_Barrier(communicator); } #else self->nprocs = 1; self->proc_id = 0; #endif self->nslices = self->nprocs*self->nthreads; self->ntermobjects = self->terms->dimensions[0]; self->nterms = 0; for (i = 0; i < self->ntermobjects; i++) { PyFFEnergyTermObject *term = ((PyFFEnergyTermObject **) self->terms->data)[i]; term->index = self->nterms; self->nterms += term->nterms; if (term->threaded) { term->barrier_index = nbarriers; nbarriers += term->nbarriers; } } for (i = 0; i < self->ntermobjects; i++) { PyFFEnergyTermObject *term = ((PyFFEnergyTermObject **) self->terms->data)[i]; term->virial_index = self->nterms; } self->nterms++; #if defined(NUMPY) { npy_intp dims = self->nterms; self->energy_terms_array = (PyArrayObject *) PyArray_SimpleNew(1, &dims, PyArray_DOUBLE); } #else self->energy_terms_array = (PyArrayObject *) PyArray_FromDims(1, &self->nterms, PyArray_DOUBLE); #endif self->nterms--; if (self->energy_terms_array == NULL) { nthreads = 1; error = 1; } else self->energy_terms = (double *)self->energy_terms_array->data; #ifdef WITH_MPI self->energy_parts = malloc((self->nterms+1)*self->nprocs*sizeof(double)); if (self->energy_parts == NULL) { self->nprocs = 1; error = 1; } #endif if (nthreads > 1) { #ifdef WITH_THREAD threadinfo *tinfo; int i; self->global_lock = PyThread_allocate_lock(); if (self->global_lock == NULL) { PyErr_SetString(PyExc_OSError, "couldn't allocate lock"); return NULL; } if (nbarriers > 0) { self->binfo = malloc(nbarriers*sizeof(barrierinfo)); if (self->binfo == NULL) return PyErr_NoMemory(); for (i = 0; i < nbarriers; i++) { if (!allocate_barrier(self->binfo+i)) { PyErr_SetString(PyExc_OSError, "couldn't allocate barrier"); return NULL; } } } self->scratch = malloc((nthreads-1)*sizeof(threadinfo)); if (self->scratch == NULL) return PyErr_NoMemory(); tinfo = (threadinfo *)self->scratch; for (i = 1; i < nthreads; i++) { tinfo->evaluator = self; tinfo->done = 0; tinfo->exit = 0; tinfo->stop = 0; tinfo->energy.gradients = NULL; tinfo->energy.gradient_fn = NULL; tinfo->energy.force_constants = NULL; tinfo->energy.fc_fn = NULL; tinfo->energy.energy_terms = (double *)malloc((self->nterms+1)*sizeof(double)); if (tinfo->energy.energy_terms == NULL) { PyErr_NoMemory(); error = 1; } tinfo->lock = NULL; tinfo++; } tinfo = (threadinfo *)self->scratch; for (i = 1; i < nthreads; i++) { tinfo->lock = PyThread_allocate_lock(); if (tinfo->lock == NULL) { PyErr_SetString(PyExc_OSError, "couldn't allocate lock"); error = 1; break; } PyThread_acquire_lock(tinfo->lock, 1); tinfo++; } tinfo = (threadinfo *)self->scratch; if (!error) for (i = 1; i < nthreads; i++) { tinfo->input.nthreads = nthreads; tinfo->input.thread_id = i; tinfo->input.nprocs = self->nprocs; tinfo->input.proc_id = self->proc_id; tinfo->input.nslices = self->nthreads*self->nprocs; tinfo->input.slice_id = self->nthreads*self->proc_id+i; if (!PyThread_start_new_thread(evaluator_thread, (void *)tinfo)) { PyErr_SetString(PyExc_OSError, "couldn't start thread"); error = 1; break; } #if THREAD_DEBUG printf("Thread %d started\n", i); #endif PyThread_acquire_lock(self->global_lock, 1); PyThread_release_lock(self->global_lock); tinfo++; } #else PyErr_SetString(PyExc_OSError, "no thread support"); error = 1; #endif } if (error) { evaluator_dealloc(self); self = NULL; } return (PyObject *)self; } /* * Constructor function for non-bonded lists */ static PyObject * NonbondedList(PyObject *dummy, PyObject *args) { PyNonbondedListObject *self = nblist_new(); PyObject *cutoff_ob = NULL; if (self == NULL) return NULL; if (!PyArg_ParseTuple(args, "O!O!O!O!O", &PyArray_Type, &self->excluded_pairs, &PyArray_Type, &self->one_four_pairs, &PyArray_Type, &self->atom_subset, &PyUniverseSpec_Type, &self->universe_spec, &cutoff_ob)) { nblist_dealloc(self); return NULL; } if (cutoff_ob == Py_None) self->cutoff = 0.; else if (PyNumber_Check(cutoff_ob)) { cutoff_ob = PyNumber_Float(cutoff_ob); self->cutoff = PyFloat_AsDouble(cutoff_ob); } else { PyErr_SetString(PyExc_TypeError, "cutoff must be a number or None"); nblist_dealloc(self); return NULL; } Py_INCREF(self->excluded_pairs); Py_INCREF(self->one_four_pairs); Py_INCREF(self->atom_subset); Py_INCREF(self->universe_spec); return (PyObject *)self; } /* C-API access functions for nonbonded lists */ int PyNonbondedListUpdate(PyNonbondedListObject *nblist, int natoms, double *coordinates, double *geometry_data) { return nblist_update(nblist, natoms, coordinates, geometry_data); } int PyNonbondedListIterate(PyNonbondedListObject *nblist, struct nblist_iterator *iterator) { return nblist_iterate(nblist, iterator); } /* * List of functions defined in the module */ static PyMethodDef forcefield_methods[] = { {"HarmonicDistanceTerm", HarmonicDistanceTerm, 1}, {"HarmonicAngleTerm", HarmonicAngleTerm, 1}, {"CosineDihedralTerm", CosineDihedralTerm, 1}, {"LennardJonesTerm", LennardJonesTerm, 1}, {"ElectrostaticTerm", ElectrostaticTerm, 1}, {"EsEwaldTerm", EsEwaldTerm, 1}, {"NonbondedListTerm", NonbondedListTerm, 1}, {"Evaluator", Evaluator, 1}, {"NonbondedList", NonbondedList, 1}, {"SparseForceConstants", SparseForceConstants, 1}, {NULL, NULL} /* sentinel */ }; /* Initialization function for the module */ #ifndef PyMODINIT_FUNC /* declarations for DLL import/export */ #define PyMODINIT_FUNC void #endif PyMODINIT_FUNC initMMTK_forcefield(void) { PyObject *m, *d, *module; #ifdef WITH_MPI PyObject *mpi_module; #endif static void *PyFF_API[PyFF_API_pointers]; /* Create the module and add the functions */ m = Py_InitModule("MMTK_forcefield", forcefield_methods); /* Import the array and MPI modules */ #ifdef import_array import_array(); #endif #ifdef WITH_MPI import_mpi(); mpi_module = PyImport_ImportModule("Scientific.MPI"); if (mpi_module != NULL) { PyObject *module_dict = PyModule_GetDict(mpi_module); PyExc_MPIError = PyDict_GetItemString(module_dict, "MPIError"); } #endif /* Add C API pointer array */ PyFF_API[PyFFEnergyTerm_Type_NUM] = (void *)&PyFFEnergyTerm_Type; PyFF_API[PyFFEvaluator_Type_NUM] = (void *)&PyFFEvaluator_Type; PyFF_API[PyNonbondedList_Type_NUM] = (void *)&PyNonbondedList_Type; PyFF_API[PySparseFC_New_NUM] = (void *)&PySparseFC_New; PyFF_API[PySparseFC_Type_NUM] = (void *)&PySparseFC_Type; PyFF_API[PySparseFC_Zero_NUM] = (void *)&PySparseFC_Zero; PyFF_API[PySparseFC_Find_NUM] = (void *)&PySparseFC_Find; PyFF_API[PySparseFC_AddTerm_NUM] = (void *)&PySparseFC_AddTerm; PyFF_API[PySparseFC_CopyToArray_NUM] = (void *)&PySparseFC_CopyToArray; PyFF_API[PySparseFC_AsArray_NUM] = (void *)&PySparseFC_AsArray; PyFF_API[PySparseFC_VectorMultiply_NUM] = (void *)&PySparseFC_VectorMultiply; PyFF_API[PySparseFC_Scale_NUM] = (void *)&PySparseFC_Scale; PyFF_API[PyFFEnergyTerm_New_NUM] = (void *)&PyFFEnergyTerm_New; PyFF_API[PyFFEvaluator_New_NUM] = (void *)&PyFFEvaluator_New; PyFF_API[PyNonbondedListUpdate_NUM] = (void *)&PyNonbondedListUpdate; PyFF_API[PyNonbondedListIterate_NUM] = (void *)&PyNonbondedListIterate; #ifdef EXTENDED_TYPES if (PyType_Ready(&PyFFEnergyTerm_Type) < 0) return; if (PyType_Ready(&PyFFEvaluator_Type) < 0) return; if (PyType_Ready(&PyNonbondedList_Type) < 0) return; if (PyType_Ready(&PySparseFC_Type) < 0) return; #else PyFFEnergyTerm_Type.ob_type = &PyType_Type; PyFFEvaluator_Type.ob_type = &PyType_Type; PyNonbondedList_Type.ob_type = &PyType_Type; PySparseFC_Type.ob_type = &PyType_Type; #endif d = PyModule_GetDict(m); PyDict_SetItemString(d, "_C_API", PyCObject_FromVoidPtr((void *)PyFF_API, NULL)); PyDict_SetItemString(d, "EnergyTerm", (PyObject *)&PyFFEnergyTerm_Type); PyDict_SetItemString(d, "EnergyEvaluator", (PyObject *)&PyFFEvaluator_Type); /* Get the energy conversion factor from Units */ module = PyImport_ImportModule("MMTK.Units"); if (module != NULL) { PyObject *module_dict = PyModule_GetDict(module); PyObject *factor = PyDict_GetItemString(module_dict, "electrostatic_energy"); electrostatic_energy_factor = PyFloat_AsDouble(factor); } /* Get function pointers from _universe */ module = PyImport_ImportModule("MMTK_universe"); if (module != NULL) { PyObject *module_dict = PyModule_GetDict(module); PyObject *c_api_object = PyDict_GetItemString(module_dict, "_C_API"); PyObject *fn; if (PyCObject_Check(c_api_object)) PyUniverse_API = (void **)PyCObject_AsVoidPtr(c_api_object); fn = PyDict_GetItemString(module_dict, "infinite_universe_distance_function"); distance_vector_pointer = (distance_fn *)PyCObject_AsVoidPtr(fn); fn = PyDict_GetItemString(module_dict, "orthorhombic_universe_distance_function"); orthorhombic_distance_vector_pointer = (distance_fn *)PyCObject_AsVoidPtr(fn); fn = PyDict_GetItemString(module_dict, "parallelepipedic_universe_distance_function"); parallelepipedic_distance_vector_pointer = (distance_fn *)PyCObject_AsVoidPtr(fn); } /* Check for errors */ if (PyErr_Occurred()) Py_FatalError("can't initialize module MMTK_forcefield"); } MMTK-2.7.9/Src/MMTK_minimization.c0000644000076600000240000003735611662461640017167 0ustar hinsenstaff00000000000000/* Low-level minimization * * Written by Konrad Hinsen */ #include "MMTK/universe.h" #include "MMTK/forcefield.h" #include "MMTK/trajectory.h" /* Utility functions */ #define max(a, b) ((a) > (b) ? (a) : (b)) #define min(a, b) ((a) < (b) ? (a) : (b)) /* Operations on vector arrays */ static void copy_vectors(vector3 *s, vector3 *d, int n) { double *src = (double *)s; double *dest = (double *)d; n *= 3; while (n--) *dest++ = *src++; } static void add_vectors(vector3 *s, vector3 *d, int n) { double *src = (double *)s; double *dest = (double *)d; n *= 3; while (n--) *dest++ += *src++; } static void scale_vectors(vector3 *v, double f, int n) { double *vec = (double *)v; n *= 3; while (n--) *vec++ *= f; } /* Allocate and initialize Output variable descriptors */ static PyTrajectoryVariable * get_data_descriptors(PyArrayObject *configuration, PyArrayObject *gradients, double *p_energy, double *norm, double *box_size, int box_size_length) { static PyTrajectoryVariable vars[6]; if (vars != NULL) { vars[0].name = "potential_energy"; vars[0].text = "Potential energy: %lf, "; vars[0].unit = energy_unit_name; vars[0].type = PyTrajectory_Scalar; vars[0].class = PyTrajectory_Energy; vars[0].value.dp = p_energy; vars[1].name = "gradient_norm"; vars[1].text = "Gradient norm: %lf\n"; vars[1].unit = energy_gradient_unit_name; vars[1].type = PyTrajectory_Scalar; vars[1].class = PyTrajectory_Energy; vars[1].value.dp = norm; vars[2].name = "configuration"; vars[2].text = "Configuration:\n"; vars[2].unit = length_unit_name; vars[2].type = PyTrajectory_ParticleVector; vars[2].class = PyTrajectory_Configuration; vars[2].value.array = configuration; vars[3].name = "gradients"; vars[3].text = "Energy gradients:\n"; vars[3].unit = energy_gradient_unit_name; vars[3].type = PyTrajectory_ParticleVector; vars[3].class = PyTrajectory_Gradients; vars[3].value.array = gradients; vars[4].name = NULL; if (box_size != NULL) { vars[4].name = "box_size"; vars[4].text = "Box size:"; vars[4].unit = length_unit_name; vars[4].type = PyTrajectory_BoxSize; vars[4].class = PyTrajectory_Configuration; vars[4].value.dp = box_size; vars[4].length = box_size_length; vars[5].name = NULL; } } return vars; } /* Steepest descent minimizer */ static PyObject * steepestDescent(PyObject *dummy, PyObject *args) { PyObject *universe; PyUniverseSpecObject *universe_spec; PyArrayObject *configuration; PyArrayObject *fixed; PyListObject *spec_list; PyFFEvaluatorObject *evaluator; PyTrajectoryOutputSpec *output; vector3 *x, *f; long *fix; int atoms, moving_atoms; int steps; double step_size, gradient_convergence; char *description; PyArrayObject *gradients; PyTrajectoryVariable *data_descriptors; energy_data p_energy; double norm, factor; double min_energy, min_norm; vector3 *min_configuration = NULL, *min_gradients = NULL; int i, j; /* Parse and check arguments */ if (!PyArg_ParseTuple(args, "OO!O!O!iddO!s", &universe, &PyArray_Type, &configuration, &PyArray_Type, &fixed, &PyFFEvaluator_Type, &evaluator, &steps, &step_size, &gradient_convergence, &PyList_Type, &spec_list, &description)) return NULL; universe_spec = (PyUniverseSpecObject *) PyObject_GetAttrString(universe, "_spec"); if (universe_spec == NULL) return NULL; /* Create gradient array */ #if defined(NUMPY) gradients = (PyArrayObject *)PyArray_Copy(configuration); #else gradients = (PyArrayObject *)PyArray_FromDims(configuration->nd, configuration->dimensions, PyArray_DOUBLE); #endif if (gradients == NULL) return NULL; /* Set some convenient variables */ atoms = configuration->dimensions[0]; x = (vector3 *)configuration->data; f = (vector3 *)gradients->data; fix = (long *)fixed->data; moving_atoms = atoms; for (j = 0; j < atoms; j++) if (fix[j]) moving_atoms--; /* Prepare output data descriptors */ data_descriptors = get_data_descriptors(configuration, gradients, &p_energy.energy, &norm, universe_spec->geometry_data, universe_spec->geometry_data_length); /* Allocate arrays to keep track of current best point */ min_configuration = (vector3 *)malloc(atoms*sizeof(vector3)); min_gradients = (vector3 *)malloc(atoms*sizeof(vector3)); if (min_configuration == NULL || min_gradients == NULL) { PyErr_SetString(PyExc_MemoryError, ""); goto error2; } /* Initialize output */ output = PyTrajectory_OutputSpecification(universe, spec_list, description, data_descriptors); if (output == NULL) goto error2; /* Get write access for the minimization, switching to read access only during energy evaluation */ #ifdef WITH_THREAD evaluator->tstate_save = PyEval_SaveThread(); #endif PyUniverseSpec_StateLock(universe_spec, -1); /* Minimization main loop */ p_energy.gradients = (PyObject *)gradients; p_energy.gradient_fn = NULL; p_energy.force_constants = NULL; p_energy.fc_fn = NULL; min_energy = 0.; /* initialize to make gcc happy */ min_norm = 0.; /* initialize to make gcc happy */ for (i = 0; i < steps; i++) { PyUniverseSpec_StateLock(universe_spec, -2); PyUniverseSpec_StateLock(universe_spec, 1); (*evaluator->eval_func)(evaluator, &p_energy, configuration, i > 0); PyUniverseSpec_StateLock(universe_spec, 2); if (p_energy.error) { #ifdef WITH_THREAD PyEval_RestoreThread(evaluator->tstate_save); #endif goto error; } PyUniverseSpec_StateLock(universe_spec, -1); norm = 0.; for (j = 0; j < atoms; j++) if (!fix[j]) norm += f[j][0]*f[j][0] + f[j][1]*f[j][1] + f[j][2]*f[j][2]; norm = sqrt(norm/moving_atoms); if (i == 0 || p_energy.energy < min_energy) { min_energy = p_energy.energy; min_norm = norm; copy_vectors(x, min_configuration, atoms); copy_vectors(f, min_gradients, atoms); step_size *= 1.1; } else { p_energy.energy = min_energy; norm = min_norm; copy_vectors(min_configuration, x, atoms); copy_vectors(min_gradients, f, atoms); step_size *= 0.5; } if (norm < gradient_convergence) break; if (PyTrajectory_Output(output, i, data_descriptors, &evaluator->tstate_save) == -1) { PyUniverseSpec_StateLock(universe_spec, -2); #ifdef WITH_THREAD PyEval_RestoreThread(evaluator->tstate_save); #endif goto error; } factor = step_size/norm; for (j = 0; j < atoms; j++) if (!fix[j]) { x[j][0] -= factor*f[j][0]; x[j][1] -= factor*f[j][1]; x[j][2] -= factor*f[j][2]; } universe_spec->correction_function(x, atoms, universe_spec->geometry_data); } /* Restore minimum and do output */ p_energy.energy = min_energy; norm = min_norm; copy_vectors(min_configuration, x, atoms); copy_vectors(min_gradients, f, atoms); if (PyTrajectory_Output(output, i, data_descriptors, &evaluator->tstate_save) == -1) { PyUniverseSpec_StateLock(universe_spec, -2); #ifdef WITH_THREAD PyEval_RestoreThread(evaluator->tstate_save); #endif goto error; } /* Clean up and return None */ PyUniverseSpec_StateLock(universe_spec, -2); #ifdef WITH_THREAD PyEval_RestoreThread(evaluator->tstate_save); #endif PyTrajectory_OutputFinish(output, i, 0, 1, data_descriptors); free(min_configuration); free(min_gradients); Py_DECREF(gradients); Py_INCREF(Py_None); return Py_None; /* Clean up and return error */ error: PyTrajectory_OutputFinish(output, i, 1, 1, data_descriptors); error2: if (min_configuration != NULL) free(min_configuration); if (min_gradients != NULL) free(min_gradients); Py_DECREF(gradients); return NULL; } /* Conjugate gradient minimizer */ static PyObject * conjugateGradient(PyObject *dummy, PyObject *args) { PyObject *universe; PyUniverseSpecObject *universe_spec; PyArrayObject *configuration; PyArrayObject *fixed; PyListObject *spec_list; PyFFEvaluatorObject *evaluator; PyTrajectoryOutputSpec *output; vector3 *x, *f1, *f2, *h; long *fix; int atoms, moving_atoms; int steps; double step_size, gradient_convergence; char *description; PyArrayObject *gradients1, *gradients2, *direction; PyTrajectoryVariable *data_descriptors; energy_data p_energy; double norm_sq, last_norm_sq, dot, norm; double norm_h, line_convergence, sign, step; double last, a, b, ea, eb, da, db; int i, j, reset_count, niter; /* Parse and check arguments */ if (!PyArg_ParseTuple(args, "OO!O!O!iddO!s", &universe, &PyArray_Type, &configuration, &PyArray_Type, &fixed, &PyFFEvaluator_Type, &evaluator, &steps, &step_size, &gradient_convergence, &PyList_Type, &spec_list, &description)) return NULL; universe_spec = (PyUniverseSpecObject *) PyObject_GetAttrString(universe, "_spec"); if (universe_spec == NULL) return NULL; /* Create gradient and direction arrays */ #if defined(NUMPY) gradients1 = (PyArrayObject *)PyArray_Copy(configuration); #else gradients1 = (PyArrayObject *)PyArray_FromDims(configuration->nd, configuration->dimensions, PyArray_DOUBLE); #endif if (gradients1 == NULL) return NULL; #if defined(NUMPY) gradients2 = (PyArrayObject *)PyArray_Copy(configuration); #else gradients2 = (PyArrayObject *)PyArray_FromDims(configuration->nd, configuration->dimensions, PyArray_DOUBLE); #endif if (gradients2 == NULL) { Py_DECREF(gradients1); return NULL; } #if defined(NUMPY) direction = (PyArrayObject *)PyArray_Copy(configuration); #else direction = (PyArrayObject *)PyArray_FromDims(configuration->nd, configuration->dimensions, PyArray_DOUBLE); #endif if (direction == NULL) { Py_DECREF(gradients1); Py_DECREF(gradients2); return NULL; } /* Set some convenient variables */ atoms = configuration->dimensions[0]; x = (vector3 *)configuration->data; f1 = (vector3 *)gradients1->data; f2 = (vector3 *)gradients2->data; h = (vector3 *)direction->data; fix = (long *)fixed->data; moving_atoms = atoms; for (j = 0; j < atoms; j++) if (fix[j]) moving_atoms--; /* Prepare output data descriptors */ data_descriptors = get_data_descriptors(configuration, gradients1, &p_energy.energy, &norm, universe_spec->geometry_data, universe_spec->geometry_data_length); /* Initialize output */ output = PyTrajectory_OutputSpecification(universe, spec_list, description, data_descriptors); if (output == NULL) goto error2; /* Minimization main loop */ #ifdef WITH_THREAD evaluator->tstate_save = PyEval_SaveThread(); #endif reset_count = 0; p_energy.gradients = (PyObject *)gradients1; p_energy.gradient_fn = NULL; p_energy.force_constants = NULL; p_energy.fc_fn = NULL; PyUniverseSpec_StateLock(universe_spec, 1); (*evaluator->eval_func)(evaluator, &p_energy, configuration, 0); PyUniverseSpec_StateLock(universe_spec, 2); if (p_energy.error) { #ifdef WITH_THREAD PyEval_RestoreThread(evaluator->tstate_save); #endif i = 0; goto error; } /* Get write access for the minimization, switching to read access only during energy evaluation */ PyUniverseSpec_StateLock(universe_spec, -1); norm_sq = 0.; for (i = 0; i < steps; i++) { last_norm_sq = norm_sq; norm_sq = 0.; for (j = 0; j < atoms; j++) if (!fix[j]) norm_sq += f1[j][0]*f1[j][0] + f1[j][1]*f1[j][1] + f1[j][2]*f1[j][2]; norm = sqrt(norm_sq/moving_atoms); if (norm < gradient_convergence) break; if (norm > 50.*gradient_convergence) reset_count++; if (PyTrajectory_Output(output, i, data_descriptors, &evaluator->tstate_save) == -1) { PyUniverseSpec_StateLock(universe_spec, -2); #ifdef WITH_THREAD PyEval_RestoreThread(evaluator->tstate_save); #endif goto error; } if (i == 0) copy_vectors(f1, h, atoms); else { dot = 0.; for (j = 0; j < atoms; j++) if (!fix[j]) { dot += f1[j][0]*f2[j][0] + f1[j][1]*f2[j][1] + f1[j][2]*f2[j][2]; f2[j][0] = f1[j][0]; f2[j][1] = f1[j][1]; f2[j][2] = f1[j][2]; } if (reset_count == 5*atoms) { for (j = 0; j < atoms; j++) { h[j][0] = 0.; h[j][1] = 0.; h[j][2] = 0.; } reset_count = 0; } else scale_vectors(h, (norm_sq-dot)/last_norm_sq, atoms); add_vectors(f1, h, atoms); } line_convergence = 1.e-3*norm; if (line_convergence < gradient_convergence) line_convergence = gradient_convergence; /* Line minimization */ #define eval(p) \ { \ double d = (p)-last; \ int j; \ for (j = 0; j < atoms; j++) \ if (!fix[j]) { \ x[j][0] += sign*d*h[j][0]; \ x[j][1] += sign*d*h[j][1]; \ x[j][2] += sign*d*h[j][2]; \ } \ PyUniverseSpec_StateLock(universe_spec, -2); \ PyUniverseSpec_StateLock(universe_spec, 1); \ (*evaluator->eval_func)(evaluator, &p_energy, configuration, 0); \ PyUniverseSpec_StateLock(universe_spec, 2); \ if (p_energy.error) { \ PyEval_RestoreThread(evaluator->tstate_save); \ goto error; \ } \ PyUniverseSpec_StateLock(universe_spec, -1); \ dot = 0.; \ for (j = 0; j < atoms; j++) \ if (!fix[j]) \ dot += f1[j][0]*h[j][0] + f1[j][1]*h[j][1] + f1[j][2]*h[j][2]; \ dot /= (sign*norm_h); \ last = (p); \ } norm_h = 0.; for (j = 0; j < atoms; j++) if (!fix[j]) norm_h += h[j][0]*h[j][0] + h[j][1]*h[j][1] + h[j][2]*h[j][2]; norm_h = sqrt(norm_h); dot = 0.; for (j = 0; j < atoms; j++) if (!fix[j]) dot += f1[j][0]*h[j][0] + f1[j][1]*h[j][1] + f1[j][2]*h[j][2]; sign = (dot > 0.) ? -1. : 1.; dot /= (sign*norm_h); step = step_size/norm_h; last = 0.; a = b = 0.; ea = eb = p_energy.energy; da = db = dot; niter = 0; while (1) { a = b; ea = eb; da = db; b += step; eval(b); eb = p_energy.energy; db = dot; if (db > 0) break; if (++niter == 100) break; } niter = 0; while (1) { double new = ((eb-ea)/norm_h+a*da-b*db)/(da-db); if (new < a || new > b) new = 0.5*(a+b); eval(new); if (dot < 0) { a = new; ea = p_energy.energy; da = dot; } else { b = new; eb = p_energy.energy; db = dot; } if (db < 0.01*line_convergence) break; if (++niter == 100) break; } #undef eval universe_spec->correction_function(x, atoms, universe_spec->geometry_data); } /* Final output */ if (PyTrajectory_Output(output, i, data_descriptors, &evaluator->tstate_save) == -1) goto error; /* Clean up and return None */ PyUniverseSpec_StateLock(universe_spec, -2); #ifdef WITH_THREAD PyEval_RestoreThread(evaluator->tstate_save); #endif PyTrajectory_OutputFinish(output, i, 0, 1, data_descriptors); Py_DECREF(gradients1); Py_DECREF(gradients2); Py_INCREF(Py_None); return Py_None; /* Clean up and return error */ error: PyTrajectory_OutputFinish(output, i, 1, 1, data_descriptors); error2: Py_DECREF(gradients1); Py_DECREF(gradients2); Py_DECREF(direction); return NULL; } /* * List of functions defined in the module */ static PyMethodDef minimization_methods[] = { {"steepestDescent", steepestDescent, 1}, {"conjugateGradient", conjugateGradient, 1}, {NULL, NULL} /* sentinel */ }; /* Initialization function for the module */ DL_EXPORT(void) initMMTK_minimization(void) { /* Create the module and add the functions */ Py_InitModule("MMTK_minimization", minimization_methods); /* Import the array module */ #ifdef import_array import_array(); #endif /* Import MMTK modules */ import_MMTK_universe(); import_MMTK_forcefield(); import_MMTK_trajectory(); /* Check for errors */ if (PyErr_Occurred()) Py_FatalError("can't initialize module MMTK_minimization"); } MMTK-2.7.9/Src/MMTK_restraints.c0000644000076600000240000127235312041515235016645 0ustar hinsenstaff00000000000000/* Generated by Cython 0.16 on Tue Oct 23 15:24:45 2012 */ #define PY_SSIZE_T_CLEAN #include "Python.h" #ifndef Py_PYTHON_H #error Python headers needed to compile C extensions, please install development version of Python. #elif PY_VERSION_HEX < 0x02040000 #error Cython requires Python 2.4+. #else #include /* For offsetof */ #ifndef offsetof #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) #endif #if !defined(WIN32) && !defined(MS_WINDOWS) #ifndef __stdcall #define __stdcall #endif #ifndef __cdecl #define __cdecl #endif #ifndef __fastcall #define __fastcall #endif #endif #ifndef DL_IMPORT #define DL_IMPORT(t) t #endif #ifndef DL_EXPORT #define DL_EXPORT(t) t #endif #ifndef PY_LONG_LONG #define PY_LONG_LONG LONG_LONG #endif #ifndef Py_HUGE_VAL #define Py_HUGE_VAL HUGE_VAL #endif #ifdef PYPY_VERSION #define CYTHON_COMPILING_IN_PYPY 1 #define CYTHON_COMPILING_IN_CPYTHON 0 #else #define CYTHON_COMPILING_IN_PYPY 0 #define CYTHON_COMPILING_IN_CPYTHON 1 #endif #if CYTHON_COMPILING_IN_PYPY #define __Pyx_PyCFunction_Call PyObject_Call #else #define __Pyx_PyCFunction_Call PyCFunction_Call #endif #if PY_VERSION_HEX < 0x02050000 typedef int Py_ssize_t; 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} if (!result) { PyErr_SetObject(PyExc_NameError, name); } } return result; } static void __Pyx_RaiseArgtupleInvalid( const char* func_name, int exact, Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found) { Py_ssize_t num_expected; const char *more_or_less; if (num_found < num_min) { num_expected = num_min; more_or_less = "at least"; } else { num_expected = num_max; more_or_less = "at most"; } if (exact) { more_or_less = "exactly"; } PyErr_Format(PyExc_TypeError, "%s() takes %s %"PY_FORMAT_SIZE_T"d positional argument%s (%"PY_FORMAT_SIZE_T"d given)", func_name, more_or_less, num_expected, (num_expected == 1) ? 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'C' : 'R'); case 'O': return 'O'; case 'P': return 'P'; default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { if (ctx->head == NULL || ctx->head->field == &ctx->root) { const char* expected; const char* quote; if (ctx->head == NULL) { expected = "end"; quote = ""; } else { expected = ctx->head->field->type->name; quote = "'"; } PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected %s%s%s but got %s", quote, expected, quote, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); } else { __Pyx_StructField* field = ctx->head->field; __Pyx_StructField* parent = (ctx->head - 1)->field; PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), parent->type->name, field->name); } } static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { char group; size_t size, offset, arraysize = 1; if (ctx->enc_type == 0) return 0; if (ctx->head->field->type->arraysize[0]) { int i, ndim = 0; if (ctx->enc_type == 's' || ctx->enc_type == 'p') { ctx->is_valid_array = ctx->head->field->type->ndim == 1; ndim = 1; if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %zu", ctx->head->field->type->arraysize[0], ctx->enc_count); return -1; } } if (!ctx->is_valid_array) { PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", ctx->head->field->type->ndim, ndim); return -1; } for (i = 0; i < ctx->head->field->type->ndim; i++) { arraysize *= ctx->head->field->type->arraysize[i]; } ctx->is_valid_array = 0; ctx->enc_count = 1; } group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); do { __Pyx_StructField* field = ctx->head->field; __Pyx_TypeInfo* type = field->type; if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); } else { size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); } if (ctx->enc_packmode == '@') { size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); size_t align_mod_offset; if (align_at == 0) return -1; align_mod_offset = ctx->fmt_offset % align_at; if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; if (ctx->struct_alignment == 0) ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, ctx->is_complex); } if (type->size != size || type->typegroup != group) { if (type->typegroup == 'C' && type->fields != NULL) { size_t parent_offset = ctx->head->parent_offset + field->offset; ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = parent_offset; continue; } __Pyx_BufFmt_RaiseExpected(ctx); return -1; } offset = ctx->head->parent_offset + field->offset; if (ctx->fmt_offset != offset) { PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch; next field is at offset %"PY_FORMAT_SIZE_T"d but %"PY_FORMAT_SIZE_T"d expected", (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); return -1; } ctx->fmt_offset += size; if (arraysize) ctx->fmt_offset += (arraysize - 1) * size; --ctx->enc_count; /* Consume from buffer string */ while (1) { if (field == &ctx->root) { ctx->head = NULL; if (ctx->enc_count != 0) { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } break; /* breaks both loops as ctx->enc_count == 0 */ } ctx->head->field = ++field; if (field->type == NULL) { --ctx->head; field = ctx->head->field; continue; } else if (field->type->typegroup == 'S') { size_t parent_offset = ctx->head->parent_offset + field->offset; if (field->type->fields->type == NULL) continue; /* empty struct */ field = field->type->fields; ++ctx->head; ctx->head->field = field; ctx->head->parent_offset = parent_offset; break; } else { break; } } } while (ctx->enc_count); ctx->enc_type = 0; ctx->is_complex = 0; return 0; } static CYTHON_INLINE PyObject * __pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) { const char *ts = *tsp; int i = 0, number; int ndim = ctx->head->field->type->ndim; ; ++ts; if (ctx->new_count != 1) { PyErr_SetString(PyExc_ValueError, "Cannot handle repeated arrays in format string"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; while (*ts && *ts != ')') { if (isspace(*ts)) continue; number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) return PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %d", ctx->head->field->type->arraysize[i], number); if (*ts != ',' && *ts != ')') return PyErr_Format(PyExc_ValueError, "Expected a comma in format string, got '%c'", *ts); if (*ts == ',') ts++; i++; } if (i != ndim) return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", ctx->head->field->type->ndim, i); if (!*ts) { PyErr_SetString(PyExc_ValueError, "Unexpected end of format string, expected ')'"); return NULL; } ctx->is_valid_array = 1; ctx->new_count = 1; *tsp = ++ts; return Py_None; } static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { int got_Z = 0; while (1) { switch(*ts) { case 0: if (ctx->enc_type != 0 && ctx->head == NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; if (ctx->head != NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } return ts; case ' ': case 10: case 13: ++ts; break; case '<': if (!__Pyx_IsLittleEndian()) { PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '>': case '!': if (__Pyx_IsLittleEndian()) { PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '=': case '@': case '^': ctx->new_packmode = *ts++; break; case 'T': /* substruct */ { const char* ts_after_sub; size_t i, struct_count = ctx->new_count; size_t struct_alignment = ctx->struct_alignment; ctx->new_count = 1; ++ts; if (*ts != '{') { PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; /* Erase processed last struct element */ ctx->enc_count = 0; ctx->struct_alignment = 0; ++ts; ts_after_sub = ts; for (i = 0; i != struct_count; ++i) { ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); if (!ts_after_sub) return NULL; } ts = ts_after_sub; if (struct_alignment) ctx->struct_alignment = struct_alignment; } break; case '}': /* end of substruct; either repeat or move on */ { size_t alignment = ctx->struct_alignment; ++ts; if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; /* Erase processed last struct element */ if (alignment && ctx->fmt_offset % alignment) { ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); } } return ts; case 'x': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->fmt_offset += ctx->new_count; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->enc_packmode = ctx->new_packmode; ++ts; break; case 'Z': got_Z = 1; ++ts; if (*ts != 'f' && *ts != 'd' && *ts != 'g') { __Pyx_BufFmt_RaiseUnexpectedChar('Z'); return NULL; } /* fall through */ case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': case 'l': case 'L': case 'q': case 'Q': case 'f': case 'd': case 'g': case 'O': case 's': case 'p': if (ctx->enc_type == *ts && got_Z == ctx->is_complex && ctx->enc_packmode == ctx->new_packmode) { ctx->enc_count += ctx->new_count; } else { if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_count = ctx->new_count; ctx->enc_packmode = ctx->new_packmode; ctx->enc_type = *ts; ctx->is_complex = got_Z; } ++ts; ctx->new_count = 1; got_Z = 0; break; case ':': ++ts; while(*ts != ':') ++ts; ++ts; break; case '(': if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; break; default: { int number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; ctx->new_count = (size_t)number; } } } } static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { buf->buf = NULL; buf->obj = NULL; buf->strides = __Pyx_zeros; buf->shape = __Pyx_zeros; buf->suboffsets = __Pyx_minusones; } static CYTHON_INLINE int __Pyx_GetBufferAndValidate( Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack) { if (obj == Py_None || obj == NULL) { __Pyx_ZeroBuffer(buf); return 0; } buf->buf = NULL; if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; if (buf->ndim != nd) { PyErr_Format(PyExc_ValueError, "Buffer has wrong number of dimensions (expected %d, got %d)", nd, buf->ndim); goto fail; } if (!cast) { __Pyx_BufFmt_Context ctx; __Pyx_BufFmt_Init(&ctx, stack, dtype); if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; } if ((unsigned)buf->itemsize != dtype->size) { PyErr_Format(PyExc_ValueError, "Item size of buffer (%"PY_FORMAT_SIZE_T"d byte%s) does not match size of '%s' (%"PY_FORMAT_SIZE_T"d byte%s)", buf->itemsize, (buf->itemsize > 1) ? "s" : "", dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); goto fail; } if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; return 0; fail:; __Pyx_ZeroBuffer(buf); return -1; } static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { if (info->buf == NULL) return; if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; __Pyx_ReleaseBuffer(info); } static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { if (unlikely(!type)) { PyErr_Format(PyExc_SystemError, "Missing type object"); return 0; } if (likely(PyObject_TypeCheck(obj, type))) return 1; PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", Py_TYPE(obj)->tp_name, type->tp_name); return 0; } static void __Pyx_RaiseBufferFallbackError(void) { PyErr_Format(PyExc_ValueError, "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); } static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { #if CYTHON_COMPILING_IN_CPYTHON PyObject *tmp_type, *tmp_value, *tmp_tb; PyThreadState *tstate = PyThreadState_GET(); tmp_type = tstate->curexc_type; tmp_value = tstate->curexc_value; tmp_tb = tstate->curexc_traceback; tstate->curexc_type = type; tstate->curexc_value = value; tstate->curexc_traceback = tb; Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); #else PyErr_Restore(type, value, tb); #endif } static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { #if CYTHON_COMPILING_IN_CPYTHON PyThreadState *tstate = PyThreadState_GET(); *type = tstate->curexc_type; *value = tstate->curexc_value; *tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; #else PyErr_Fetch(type, value, tb); #endif } static void __Pyx_RaiseBufferIndexError(int axis) { PyErr_Format(PyExc_IndexError, "Out of bounds on buffer access (axis %d)", axis); } #if PY_MAJOR_VERSION < 3 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, CYTHON_UNUSED PyObject *cause) { Py_XINCREF(type); Py_XINCREF(value); Py_XINCREF(tb); if (tb == Py_None) { Py_DECREF(tb); tb = 0; } else if (tb != NULL && !PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto raise_error; } if (value == NULL) { value = Py_None; Py_INCREF(value); } #if PY_VERSION_HEX < 0x02050000 if (!PyClass_Check(type)) #else if (!PyType_Check(type)) #endif { if (value != Py_None) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto raise_error; } Py_DECREF(value); value = type; #if PY_VERSION_HEX < 0x02050000 if (PyInstance_Check(type)) { type = (PyObject*) ((PyInstanceObject*)type)->in_class; Py_INCREF(type); } else { type = 0; PyErr_SetString(PyExc_TypeError, "raise: exception must be an old-style class or instance"); goto raise_error; } #else type = (PyObject*) Py_TYPE(type); Py_INCREF(type); if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto raise_error; } #endif } __Pyx_ErrRestore(type, value, tb); return; raise_error: Py_XDECREF(value); Py_XDECREF(type); Py_XDECREF(tb); return; } #else /* Python 3+ */ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { if (tb == Py_None) { tb = 0; } else if (tb && !PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto bad; } if (value == Py_None) value = 0; if (PyExceptionInstance_Check(type)) { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto bad; } value = type; type = (PyObject*) Py_TYPE(value); } else if (!PyExceptionClass_Check(type)) { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto bad; } if (cause) { PyObject *fixed_cause; if (PyExceptionClass_Check(cause)) { fixed_cause = PyObject_CallObject(cause, NULL); if (fixed_cause == NULL) goto bad; } else if (PyExceptionInstance_Check(cause)) { fixed_cause = cause; Py_INCREF(fixed_cause); } else { PyErr_SetString(PyExc_TypeError, "exception causes must derive from " "BaseException"); goto bad; } if (!value) { value = PyObject_CallObject(type, NULL); } PyException_SetCause(value, fixed_cause); } PyErr_SetObject(type, value); if (tb) { PyThreadState *tstate = PyThreadState_GET(); PyObject* tmp_tb = tstate->curexc_traceback; if (tb != tmp_tb) { Py_INCREF(tb); tstate->curexc_traceback = tb; Py_XDECREF(tmp_tb); } } bad: return; } #endif static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { PyErr_Format(PyExc_ValueError, "need more than %"PY_FORMAT_SIZE_T"d value%s to unpack", index, (index == 1) ? "" : "s"); } static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { PyErr_Format(PyExc_ValueError, "too many values to unpack (expected %"PY_FORMAT_SIZE_T"d)", expected); } static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); } static void __Pyx_UnpackTupleError(PyObject *t, Py_ssize_t index) { if (t == Py_None) { __Pyx_RaiseNoneNotIterableError(); } else if (PyTuple_GET_SIZE(t) < index) { __Pyx_RaiseNeedMoreValuesError(PyTuple_GET_SIZE(t)); } else { __Pyx_RaiseTooManyValuesError(index); } } #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { PyObject *getbuffer_cobj; #if PY_VERSION_HEX >= 0x02060000 if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); #endif if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); #if PY_VERSION_HEX < 0x02060000 if (obj->ob_type->tp_dict && (getbuffer_cobj = PyMapping_GetItemString(obj->ob_type->tp_dict, "__pyx_getbuffer"))) { getbufferproc func; #if PY_VERSION_HEX >= 0x02070000 && !(PY_MAJOR_VERSION == 3 && PY_MINOR_VERSION == 0) func = (getbufferproc) PyCapsule_GetPointer(getbuffer_cobj, "getbuffer(obj, view, flags)"); #else func = (getbufferproc) PyCObject_AsVoidPtr(getbuffer_cobj); #endif Py_DECREF(getbuffer_cobj); if (!func) goto fail; return func(obj, view, flags); } else { PyErr_Clear(); } #endif PyErr_Format(PyExc_TypeError, "'%100s' does not have the buffer interface", Py_TYPE(obj)->tp_name); #if PY_VERSION_HEX < 0x02060000 fail: #endif return -1; } static void __Pyx_ReleaseBuffer(Py_buffer *view) { PyObject *obj = view->obj; PyObject *releasebuffer_cobj; if (!obj) return; #if PY_VERSION_HEX >= 0x02060000 if (PyObject_CheckBuffer(obj)) { PyBuffer_Release(view); return; } #endif if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } #if PY_VERSION_HEX < 0x02060000 if (obj->ob_type->tp_dict && (releasebuffer_cobj = PyMapping_GetItemString(obj->ob_type->tp_dict, "__pyx_releasebuffer"))) { releasebufferproc func; #if PY_VERSION_HEX >= 0x02070000 && !(PY_MAJOR_VERSION == 3 && PY_MINOR_VERSION == 0) func = (releasebufferproc) PyCapsule_GetPointer(releasebuffer_cobj, "releasebuffer(obj, view)"); #else func = (releasebufferproc) PyCObject_AsVoidPtr(releasebuffer_cobj); #endif Py_DECREF(releasebuffer_cobj); if (!func) goto fail; func(obj, view); return; } else { PyErr_Clear(); } #endif goto nofail; #if PY_VERSION_HEX < 0x02060000 fail: #endif PyErr_WriteUnraisable(obj); nofail: Py_DECREF(obj); view->obj = NULL; } #endif /* PY_MAJOR_VERSION < 3 */ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, long level) { PyObject *py_import = 0; PyObject *empty_list = 0; PyObject *module = 0; PyObject *global_dict = 0; PyObject *empty_dict = 0; PyObject *list; py_import = __Pyx_GetAttrString(__pyx_b, "__import__"); if (!py_import) goto bad; if (from_list) list = from_list; else { empty_list = PyList_New(0); if (!empty_list) goto bad; list = empty_list; } global_dict = PyModule_GetDict(__pyx_m); if (!global_dict) goto bad; empty_dict = PyDict_New(); if (!empty_dict) goto bad; #if PY_VERSION_HEX >= 0x02050000 { #if PY_MAJOR_VERSION >= 3 if (level == -1) { if (strchr(__Pyx_MODULE_NAME, '.')) { /* try package relative import first */ PyObject *py_level = PyInt_FromLong(1); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, NULL); Py_DECREF(py_level); if (!module) { if (!PyErr_ExceptionMatches(PyExc_ImportError)) goto bad; PyErr_Clear(); } } level = 0; /* try absolute import on failure */ } #endif if (!module) { PyObject *py_level = PyInt_FromLong(level); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, NULL); Py_DECREF(py_level); } } #else if (level>0) { PyErr_SetString(PyExc_RuntimeError, "Relative import is not supported for Python <=2.4."); goto bad; } module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, NULL); #endif bad: Py_XDECREF(empty_list); Py_XDECREF(py_import); Py_XDECREF(empty_dict); return module; } #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return ::std::complex< float >(x, y); } #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return x + y*(__pyx_t_float_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { __pyx_t_float_complex z; z.real = x; z.imag = y; return z; } #endif #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float denom = b.real * b.real + b.imag * b.imag; z.real = (a.real * b.real + a.imag * b.imag) / denom; z.imag = (a.imag * b.real - a.real * b.imag) / denom; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrtf(z.real*z.real + z.imag*z.imag); #else return hypotf(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { float denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(a, a); case 3: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(z, a); case 4: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } r = a.real; theta = 0; } else { r = __Pyx_c_absf(a); theta = atan2f(a.imag, a.real); } lnr = logf(r); z_r = expf(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cosf(z_theta); z.imag = z_r * sinf(z_theta); return z; } #endif #endif #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return ::std::complex< double >(x, y); } #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return x + y*(__pyx_t_double_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { __pyx_t_double_complex z; z.real = x; z.imag = y; return z; } #endif #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double denom = b.real * b.real + b.imag * b.imag; z.real = (a.real * b.real + a.imag * b.imag) / denom; z.imag = (a.imag * b.real - a.real * b.imag) / denom; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrt(z.real*z.real + z.imag*z.imag); #else return hypot(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { double denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(a, a); case 3: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(z, a); case 4: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } r = a.real; theta = 0; } else { r = __Pyx_c_abs(a); theta = atan2(a.imag, a.real); } lnr = log(r); z_r = exp(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cos(z_theta); z.imag = z_r * sin(z_theta); return z; } #endif #endif static CYTHON_INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject* x) { const unsigned char neg_one = (unsigned char)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(unsigned char) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(unsigned char)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to unsigned char" : "value too large to convert to unsigned char"); } return (unsigned char)-1; } return (unsigned char)val; } return (unsigned char)__Pyx_PyInt_AsUnsignedLong(x); } static CYTHON_INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject* x) { const unsigned short neg_one = (unsigned short)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(unsigned short) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(unsigned short)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to unsigned short" : "value too large to convert to unsigned short"); } return (unsigned short)-1; } return (unsigned short)val; } return (unsigned short)__Pyx_PyInt_AsUnsignedLong(x); } static CYTHON_INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject* x) { const unsigned int neg_one = (unsigned int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(unsigned int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(unsigned int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to unsigned int" : "value too large to convert to unsigned int"); } return (unsigned int)-1; } return (unsigned int)val; } return (unsigned int)__Pyx_PyInt_AsUnsignedLong(x); } static CYTHON_INLINE char __Pyx_PyInt_AsChar(PyObject* x) { const char neg_one = (char)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(char) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(char)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to char" : "value too large to convert to char"); } return (char)-1; } return (char)val; } return (char)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE short __Pyx_PyInt_AsShort(PyObject* x) { const short neg_one = (short)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(short) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(short)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to short" : "value too large to convert to short"); } return (short)-1; } return (short)val; } return (short)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE int __Pyx_PyInt_AsInt(PyObject* x) { const int neg_one = (int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to int" : "value too large to convert to int"); } return (int)-1; } return (int)val; } return (int)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE signed char __Pyx_PyInt_AsSignedChar(PyObject* x) { const signed char neg_one = (signed char)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(signed char) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(signed char)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to signed char" : "value too large to convert to signed char"); } return (signed char)-1; } return (signed char)val; } return (signed char)__Pyx_PyInt_AsSignedLong(x); } static CYTHON_INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject* x) { const signed short neg_one = (signed short)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(signed short) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(signed short)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to signed short" : "value too large to convert to signed short"); } return (signed short)-1; } return (signed short)val; } return (signed short)__Pyx_PyInt_AsSignedLong(x); } static CYTHON_INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject* x) { const signed int neg_one = (signed int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(signed int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(signed int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to signed int" : "value too large to convert to signed int"); } return (signed int)-1; } return (signed int)val; } return (signed int)__Pyx_PyInt_AsSignedLong(x); } static CYTHON_INLINE int __Pyx_PyInt_AsLongDouble(PyObject* x) { const int neg_one = (int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to int" : "value too large to convert to int"); } return (int)-1; } return (int)val; } return (int)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject* x) { const unsigned long neg_one = (unsigned long)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned long"); return (unsigned long)-1; } return (unsigned long)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned long"); return (unsigned long)-1; } return (unsigned long)PyLong_AsUnsignedLong(x); } else { return (unsigned long)PyLong_AsLong(x); } } else { unsigned long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (unsigned long)-1; val = __Pyx_PyInt_AsUnsignedLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject* x) { const unsigned PY_LONG_LONG neg_one = (unsigned PY_LONG_LONG)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned PY_LONG_LONG"); return (unsigned PY_LONG_LONG)-1; } return (unsigned PY_LONG_LONG)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned PY_LONG_LONG"); return (unsigned PY_LONG_LONG)-1; } return (unsigned PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { return (unsigned PY_LONG_LONG)PyLong_AsLongLong(x); } } else { unsigned PY_LONG_LONG val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (unsigned PY_LONG_LONG)-1; val = __Pyx_PyInt_AsUnsignedLongLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE long __Pyx_PyInt_AsLong(PyObject* x) { const long neg_one = (long)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long)-1; } return (long)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long)-1; } return (long)PyLong_AsUnsignedLong(x); } else { return (long)PyLong_AsLong(x); } } else { long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (long)-1; val = __Pyx_PyInt_AsLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject* x) { const PY_LONG_LONG neg_one = (PY_LONG_LONG)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to PY_LONG_LONG"); return (PY_LONG_LONG)-1; } return (PY_LONG_LONG)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to PY_LONG_LONG"); return (PY_LONG_LONG)-1; } return (PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { return (PY_LONG_LONG)PyLong_AsLongLong(x); } } else { PY_LONG_LONG val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (PY_LONG_LONG)-1; val = __Pyx_PyInt_AsLongLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject* x) { const signed long neg_one = (signed long)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed long"); return (signed long)-1; } return (signed long)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed long"); return (signed long)-1; } return (signed long)PyLong_AsUnsignedLong(x); } else { return (signed long)PyLong_AsLong(x); } } else { signed long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (signed long)-1; val = __Pyx_PyInt_AsSignedLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject* x) { const signed PY_LONG_LONG neg_one = (signed PY_LONG_LONG)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed PY_LONG_LONG"); return (signed PY_LONG_LONG)-1; } return (signed PY_LONG_LONG)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed PY_LONG_LONG"); return (signed PY_LONG_LONG)-1; } return (signed PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { return (signed PY_LONG_LONG)PyLong_AsLongLong(x); } } else { signed PY_LONG_LONG val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (signed PY_LONG_LONG)-1; val = __Pyx_PyInt_AsSignedLongLong(tmp); Py_DECREF(tmp); return val; } } static void __Pyx_WriteUnraisable(const char *name, int clineno, int lineno, const char *filename) { PyObject *old_exc, *old_val, *old_tb; PyObject *ctx; __Pyx_ErrFetch(&old_exc, &old_val, &old_tb); #if PY_MAJOR_VERSION < 3 ctx = PyString_FromString(name); #else ctx = PyUnicode_FromString(name); #endif __Pyx_ErrRestore(old_exc, old_val, old_tb); if (!ctx) { PyErr_WriteUnraisable(Py_None); } else { PyErr_WriteUnraisable(ctx); Py_DECREF(ctx); } } static int __Pyx_check_binary_version(void) { char ctversion[4], rtversion[4]; PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { char message[200]; PyOS_snprintf(message, sizeof(message), "compiletime version %s of module '%.100s' " "does not match runtime version %s", ctversion, __Pyx_MODULE_NAME, rtversion); #if PY_VERSION_HEX < 0x02050000 return PyErr_Warn(NULL, message); #else return PyErr_WarnEx(NULL, message, 1); #endif } return 0; } #ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict) { PyObject *py_module = 0; PyObject *result = 0; PyObject *py_name = 0; char warning[200]; py_module = __Pyx_ImportModule(module_name); if (!py_module) goto bad; py_name = __Pyx_PyIdentifier_FromString(class_name); if (!py_name) goto bad; result = PyObject_GetAttr(py_module, py_name); Py_DECREF(py_name); py_name = 0; Py_DECREF(py_module); py_module = 0; if (!result) goto bad; if (!PyType_Check(result)) { PyErr_Format(PyExc_TypeError, "%s.%s is not a type object", module_name, class_name); goto bad; } if (!strict && (size_t)((PyTypeObject *)result)->tp_basicsize > size) { PyOS_snprintf(warning, sizeof(warning), "%s.%s size changed, may indicate binary incompatibility", module_name, class_name); #if PY_VERSION_HEX < 0x02050000 if (PyErr_Warn(NULL, warning) < 0) goto bad; #else if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; #endif } else if ((size_t)((PyTypeObject *)result)->tp_basicsize != size) { PyErr_Format(PyExc_ValueError, "%s.%s has the wrong size, try recompiling", module_name, class_name); goto bad; } return (PyTypeObject *)result; bad: Py_XDECREF(py_module); Py_XDECREF(result); return NULL; } #endif #ifndef __PYX_HAVE_RT_ImportModule #define __PYX_HAVE_RT_ImportModule static PyObject *__Pyx_ImportModule(const char *name) { PyObject *py_name = 0; PyObject *py_module = 0; py_name = __Pyx_PyIdentifier_FromString(name); if (!py_name) goto bad; py_module = PyImport_Import(py_name); Py_DECREF(py_name); return py_module; bad: Py_XDECREF(py_name); return 0; } #endif static int __Pyx_SetVtable(PyObject *dict, void *vtable) { #if PY_VERSION_HEX >= 0x02070000 && !(PY_MAJOR_VERSION==3&&PY_MINOR_VERSION==0) PyObject *ob = PyCapsule_New(vtable, 0, 0); #else PyObject *ob = PyCObject_FromVoidPtr(vtable, 0); #endif if (!ob) goto bad; if (PyDict_SetItemString(dict, "__pyx_vtable__", ob) < 0) goto bad; Py_DECREF(ob); return 0; bad: Py_XDECREF(ob); return -1; } static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { int start = 0, mid = 0, end = count - 1; if (end >= 0 && code_line > entries[end].code_line) { return count; } while (start < end) { mid = (start + end) / 2; if (code_line < entries[mid].code_line) { end = mid; } else if (code_line > entries[mid].code_line) { start = mid + 1; } else { return mid; } } if (code_line <= entries[mid].code_line) { return mid; } else { return mid + 1; } } static PyCodeObject *__pyx_find_code_object(int code_line) { PyCodeObject* code_object; int pos; if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { return NULL; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { return NULL; } code_object = __pyx_code_cache.entries[pos].code_object; Py_INCREF(code_object); return code_object; } static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { int pos, i; __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; if (unlikely(!code_line)) { return; } if (unlikely(!entries)) { entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); if (likely(entries)) { __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = 64; __pyx_code_cache.count = 1; entries[0].code_line = code_line; entries[0].code_object = code_object; Py_INCREF(code_object); } return; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { PyCodeObject* tmp = entries[pos].code_object; entries[pos].code_object = code_object; Py_DECREF(tmp); return; } if (__pyx_code_cache.count == __pyx_code_cache.max_count) { int new_max = __pyx_code_cache.max_count + 64; entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( __pyx_code_cache.entries, new_max*sizeof(__Pyx_CodeObjectCacheEntry)); if (unlikely(!entries)) { return; } __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = new_max; } for (i=__pyx_code_cache.count; i>pos; i--) { entries[i] = entries[i-1]; } entries[pos].code_line = code_line; entries[pos].code_object = code_object; __pyx_code_cache.count++; Py_INCREF(code_object); } #include "compile.h" #include "frameobject.h" #include "traceback.h" static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyObject *py_srcfile = 0; PyObject *py_funcname = 0; #if PY_MAJOR_VERSION < 3 py_srcfile = PyString_FromString(filename); #else py_srcfile = PyUnicode_FromString(filename); #endif if (!py_srcfile) goto bad; if (c_line) { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #else py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #endif } else { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromString(funcname); #else py_funcname = PyUnicode_FromString(funcname); #endif } if (!py_funcname) goto bad; py_code = __Pyx_PyCode_New( 0, /*int argcount,*/ 0, /*int kwonlyargcount,*/ 0, /*int nlocals,*/ 0, /*int stacksize,*/ 0, /*int flags,*/ __pyx_empty_bytes, /*PyObject *code,*/ __pyx_empty_tuple, /*PyObject *consts,*/ __pyx_empty_tuple, /*PyObject *names,*/ __pyx_empty_tuple, /*PyObject *varnames,*/ __pyx_empty_tuple, /*PyObject *freevars,*/ __pyx_empty_tuple, /*PyObject *cellvars,*/ py_srcfile, /*PyObject *filename,*/ py_funcname, /*PyObject *name,*/ py_line, /*int firstlineno,*/ __pyx_empty_bytes /*PyObject *lnotab*/ ); Py_DECREF(py_srcfile); Py_DECREF(py_funcname); return py_code; bad: Py_XDECREF(py_srcfile); Py_XDECREF(py_funcname); return NULL; } static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyObject *py_globals = 0; PyFrameObject *py_frame = 0; py_code = __pyx_find_code_object(c_line ? c_line : py_line); if (!py_code) { py_code = __Pyx_CreateCodeObjectForTraceback( funcname, c_line, py_line, filename); if (!py_code) goto bad; __pyx_insert_code_object(c_line ? c_line : py_line, py_code); } py_globals = PyModule_GetDict(__pyx_m); if (!py_globals) goto bad; py_frame = PyFrame_New( PyThreadState_GET(), /*PyThreadState *tstate,*/ py_code, /*PyCodeObject *code,*/ py_globals, /*PyObject *globals,*/ 0 /*PyObject *locals*/ ); if (!py_frame) goto bad; py_frame->f_lineno = py_line; PyTraceBack_Here(py_frame); bad: Py_XDECREF(py_code); Py_XDECREF(py_frame); } static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { while (t->p) { #if PY_MAJOR_VERSION < 3 if (t->is_unicode) { *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); } else if (t->intern) { *t->p = PyString_InternFromString(t->s); } else { *t->p = PyString_FromStringAndSize(t->s, t->n - 1); } #else /* Python 3+ has unicode identifiers */ if (t->is_unicode | t->is_str) { if (t->intern) { *t->p = PyUnicode_InternFromString(t->s); } else if (t->encoding) { *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); } else { *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); } } else { *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); } #endif if (!*t->p) return -1; ++t; } return 0; } /* Type Conversion Functions */ static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { int is_true = x == Py_True; if (is_true | (x == Py_False) | (x == Py_None)) return is_true; else return PyObject_IsTrue(x); } static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { PyNumberMethods *m; const char *name = NULL; PyObject *res = NULL; #if PY_VERSION_HEX < 0x03000000 if (PyInt_Check(x) || PyLong_Check(x)) #else if (PyLong_Check(x)) #endif return Py_INCREF(x), x; m = Py_TYPE(x)->tp_as_number; #if PY_VERSION_HEX < 0x03000000 if (m && m->nb_int) { name = "int"; res = PyNumber_Int(x); } else if (m && m->nb_long) { name = "long"; res = PyNumber_Long(x); } #else if (m && m->nb_int) { name = "int"; res = PyNumber_Long(x); } #endif if (res) { #if PY_VERSION_HEX < 0x03000000 if (!PyInt_Check(res) && !PyLong_Check(res)) { #else if (!PyLong_Check(res)) { #endif PyErr_Format(PyExc_TypeError, "__%s__ returned non-%s (type %.200s)", name, name, Py_TYPE(res)->tp_name); Py_DECREF(res); return NULL; } } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_TypeError, "an integer is required"); } return res; } static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { Py_ssize_t ival; PyObject* x = PyNumber_Index(b); if (!x) return -1; ival = PyInt_AsSsize_t(x); Py_DECREF(x); return ival; } static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { #if PY_VERSION_HEX < 0x02050000 if (ival <= LONG_MAX) return PyInt_FromLong((long)ival); else { unsigned char *bytes = (unsigned char *) &ival; int one = 1; int little = (int)*(unsigned char*)&one; return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0); } #else return PyInt_FromSize_t(ival); #endif } static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject* x) { unsigned PY_LONG_LONG val = __Pyx_PyInt_AsUnsignedLongLong(x); if (unlikely(val == (unsigned PY_LONG_LONG)-1 && PyErr_Occurred())) { return (size_t)-1; } else if (unlikely(val != (unsigned PY_LONG_LONG)(size_t)val)) { PyErr_SetString(PyExc_OverflowError, "value too large to convert to size_t"); return (size_t)-1; } return (size_t)val; } #endif /* Py_PYTHON_H */ MMTK-2.7.9/Src/MMTK_restraints.pyx0000644000076600000240000001550712041514037017235 0ustar hinsenstaff00000000000000# Center-of-mass restraints # # Written by Konrad Hinsen include "MMTK/python.pxi" include "MMTK/numeric.pxi" include "MMTK/core.pxi" include "MMTK/universe.pxi" include 'MMTK/forcefield.pxi' cdef extern from "math.h": double sqrt(double) import numpy as N cimport numpy as N # # Trap potential (harmonic restraint to a fixed point in space) # cdef class HarmonicCMTrapTerm(EnergyTerm): cdef N.ndarray atom_indices, masses cdef vector3 ref cdef double k def __init__(self, universe, N.ndarray[N.int_t] atom_indices, N.ndarray[N.float64_t] masses, reference, force_constant): cdef N.ndarray[N.float64_t] ref_array EnergyTerm.__init__(self, universe, "harmonic_cm_trap", ("harmonic_cm_trap",)) self.eval_func = HarmonicCMTrapTerm.evaluate self.atom_indices = atom_indices self.masses = masses ref_array = reference.array for i in range(3): self.ref[i] = (ref_array.data)[i] self.k = force_constant cdef void evaluate(self, PyFFEvaluatorObject *eval, energy_spec *input, energy_data *energy): cdef vector3 *coordinates, *gradients cdef N.ndarray[N.int_t] atom_indices cdef N.ndarray[N.float_t] masses cdef N.ndarray[N.float_t, ndim=4] fc cdef vector3 cm, d cdef double m, lsq, kw cdef int i, j, n, offset coordinates = input.coordinates.data atom_indices = self.atom_indices masses = self.masses # This code will never be run for a periodic universe, so # it's safe to ignore periodic boundary conditions. m = 0. cm[0] = cm[1] = cm[2] = 0. for i in atom_indices: m += masses[i] for j in range(3): cm[j] += masses[i]*coordinates[i][j] for j in range(3): cm[j] /= m for j in range(3): d[j] = cm[j]-self.ref[j] lsq = d[0]*d[0]+d[1]*d[1]+d[2]*d[2] energy.energy_terms[self.index] = self.k*lsq energy.energy_terms[self.virial_index] -= 2.*self.k*lsq if energy.gradients != NULL: gradients = ( energy.gradients).data kw = 2.*self.k/m for i in atom_indices: for j in range(3): gradients[i][j] += kw*d[j]*masses[i] if energy.force_constants != NULL: fc = energy.force_constants kw = 2.*self.k/(m*m) for i in atom_indices: for j in atom_indices: for n in range(3): fc [i, n, j, n] += kw*masses[i]*masses[j] # # Harmonic potential between two centers of mass # cdef class HarmonicCMDistanceTerm(EnergyTerm): cdef N.ndarray atom_indices_1, atom_indices_2, masses cdef double k, l0 def __init__(self, universe, N.ndarray[N.int_t] atom_indices_1, N.ndarray[N.int_t] atom_indices_2, N.ndarray[N.float64_t] masses, distance, force_constant): EnergyTerm.__init__(self, universe, "harmonic_cm_distance", ("harmonic_cm_distance",)) self.eval_func = HarmonicCMDistanceTerm.evaluate self.atom_indices_1 = atom_indices_1 self.atom_indices_2 = atom_indices_2 self.masses = masses self.l0 = distance self.k = force_constant cdef void evaluate(self, PyFFEvaluatorObject *eval, energy_spec *input, energy_data *energy): cdef vector3 *coordinates, *gradients cdef N.ndarray[N.int_t] atom_indices_1 cdef N.ndarray[N.int_t] atom_indices_2 cdef N.ndarray[N.float_t] masses cdef N.ndarray[N.float_t, ndim=4] fc cdef tensor3 fcij cdef vector3 cm1, cm2, d cdef double m1, m2, lsq, l, dl, deriv, w cdef int i, j, n, offset coordinates = input.coordinates.data atom_indices_1 = self.atom_indices_1 atom_indices_2 = self.atom_indices_2 masses = self.masses # This code will never be run for a periodic universe, so # it's safe to ignore periodic boundary conditions. m1 = 0. cm1[0] = cm1[1] = cm1[2] = 0. for i in atom_indices_1: m1 += masses[i] for j in range(3): cm1[j] += masses[i]*coordinates[i][j] for j in range(3): cm1[j] /= m1 m2 = 0. cm2[0] = cm2[1] = cm2[2] = 0. for i in atom_indices_2: m2 += masses[i] for j in range(3): cm2[j] += masses[i]*coordinates[i][j] for j in range(3): cm2[j] /= m2 for j in range(3): d[j] = cm2[j]-cm1[j] lsq = d[0]*d[0]+d[1]*d[1]+d[2]*d[2] l = sqrt(lsq) dl = l - self.l0 energy.energy_terms[self.index] = self.k*dl*dl energy.energy_terms[self.virial_index] -= 2.*self.k*dl*dl if energy.gradients != NULL or energy.force_constants != NULL: deriv = 0. if l == 0. else 2.*self.k*dl/l if energy.gradients != NULL: gradients = ( energy.gradients).data deriv = 0. if l == 0. else 2.*self.k*dl/l f1 = self.k*deriv/m1 f2 = self.k*deriv/m2 for i in atom_indices_1: for j in range(3): gradients[i][j] -= f1*d[j]*masses[i] for i in atom_indices_2: for j in range(3): gradients[i][j] += f2*d[j]*masses[i] if energy.force_constants != NULL: fc = energy.force_constants for m in range(3): for n in range(3): fcij[m][n] = (2.*self.k-deriv)*d[m]*d[n]/lsq fcij[m][m] += deriv for i in atom_indices_1: for j in atom_indices_1: w = masses[i]*masses[j]/(m1*m1) for m in range(3): for n in range(3): fc [i, m, j, n] += w*fcij[m][n] for i in atom_indices_2: for j in atom_indices_2: w = masses[i]*masses[j]/(m2*m2) for m in range(3): for n in range(3): fc [i, m, j, n] += w*fcij[m][n] for i in atom_indices_1: for j in atom_indices_2: w = masses[i]*masses[j]/(m1*m2) for m in range(3): for n in range(3): if i < j: fc [i, m, j, n] -= w*fcij[m][n] else: fc [j, m, i, n] -= w*fcij[m][n] MMTK-2.7.9/Src/MMTK_state_accessor.c0000644000076600000240000031530211662664650017456 0ustar hinsenstaff00000000000000/* Generated by Cython 0.15 on Tue Nov 22 10:21:44 2011 */ #define PY_SSIZE_T_CLEAN #include "Python.h" #ifndef Py_PYTHON_H #error Python headers needed to compile C extensions, please install development version of Python. #else #include /* For offsetof */ #ifndef offsetof #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) #endif #if !defined(WIN32) && !defined(MS_WINDOWS) #ifndef __stdcall #define __stdcall #endif #ifndef __cdecl #define __cdecl #endif #ifndef __fastcall #define __fastcall #endif #endif #ifndef DL_IMPORT #define DL_IMPORT(t) t #endif #ifndef DL_EXPORT #define DL_EXPORT(t) t #endif #ifndef PY_LONG_LONG #define PY_LONG_LONG LONG_LONG #endif #if PY_VERSION_HEX < 0x02040000 #define METH_COEXIST 0 #define PyDict_CheckExact(op) (Py_TYPE(op) == &PyDict_Type) #define PyDict_Contains(d,o) PySequence_Contains(d,o) #endif #if PY_VERSION_HEX < 0x02050000 typedef int Py_ssize_t; #define PY_SSIZE_T_MAX INT_MAX #define PY_SSIZE_T_MIN INT_MIN #define PY_FORMAT_SIZE_T "" #define PyInt_FromSsize_t(z) PyInt_FromLong(z) #define PyInt_AsSsize_t(o) __Pyx_PyInt_AsInt(o) #define PyNumber_Index(o) PyNumber_Int(o) #define PyIndex_Check(o) PyNumber_Check(o) #define PyErr_WarnEx(category, message, stacklevel) PyErr_Warn(category, message) #endif #if PY_VERSION_HEX < 0x02060000 #define Py_REFCNT(ob) (((PyObject*)(ob))->ob_refcnt) #define Py_TYPE(ob) (((PyObject*)(ob))->ob_type) #define Py_SIZE(ob) (((PyVarObject*)(ob))->ob_size) #define PyVarObject_HEAD_INIT(type, size) \ PyObject_HEAD_INIT(type) size, #define PyType_Modified(t) typedef struct { void *buf; PyObject *obj; Py_ssize_t len; Py_ssize_t itemsize; int readonly; int ndim; char *format; Py_ssize_t *shape; Py_ssize_t *strides; Py_ssize_t *suboffsets; void *internal; } Py_buffer; #define PyBUF_SIMPLE 0 #define PyBUF_WRITABLE 0x0001 #define PyBUF_FORMAT 0x0004 #define PyBUF_ND 0x0008 #define PyBUF_STRIDES (0x0010 | PyBUF_ND) #define PyBUF_C_CONTIGUOUS (0x0020 | PyBUF_STRIDES) #define PyBUF_F_CONTIGUOUS (0x0040 | PyBUF_STRIDES) #define PyBUF_ANY_CONTIGUOUS (0x0080 | PyBUF_STRIDES) #define PyBUF_INDIRECT (0x0100 | PyBUF_STRIDES) #endif #if PY_MAJOR_VERSION < 3 #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" #else #define __Pyx_BUILTIN_MODULE_NAME "builtins" #endif #if PY_MAJOR_VERSION >= 3 #define Py_TPFLAGS_CHECKTYPES 0 #define Py_TPFLAGS_HAVE_INDEX 0 #endif #if (PY_VERSION_HEX < 0x02060000) || (PY_MAJOR_VERSION >= 3) #define Py_TPFLAGS_HAVE_NEWBUFFER 0 #endif #if PY_MAJOR_VERSION >= 3 #define PyBaseString_Type PyUnicode_Type #define PyStringObject PyUnicodeObject #define PyString_Type PyUnicode_Type #define PyString_Check PyUnicode_Check #define PyString_CheckExact PyUnicode_CheckExact #endif #if PY_VERSION_HEX < 0x02060000 #define PyBytesObject PyStringObject #define PyBytes_Type PyString_Type #define PyBytes_Check PyString_Check #define PyBytes_CheckExact PyString_CheckExact #define PyBytes_FromString PyString_FromString #define PyBytes_FromStringAndSize PyString_FromStringAndSize #define PyBytes_FromFormat PyString_FromFormat #define PyBytes_DecodeEscape PyString_DecodeEscape #define PyBytes_AsString PyString_AsString #define PyBytes_AsStringAndSize PyString_AsStringAndSize #define PyBytes_Size PyString_Size #define PyBytes_AS_STRING PyString_AS_STRING #define PyBytes_GET_SIZE PyString_GET_SIZE #define PyBytes_Repr PyString_Repr #define PyBytes_Concat PyString_Concat #define PyBytes_ConcatAndDel PyString_ConcatAndDel #endif #if PY_VERSION_HEX < 0x02060000 #define PySet_Check(obj) PyObject_TypeCheck(obj, &PySet_Type) #define PyFrozenSet_Check(obj) PyObject_TypeCheck(obj, &PyFrozenSet_Type) #endif #ifndef PySet_CheckExact #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) #endif #define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) #if PY_MAJOR_VERSION >= 3 #define PyIntObject PyLongObject #define PyInt_Type PyLong_Type #define PyInt_Check(op) PyLong_Check(op) #define PyInt_CheckExact(op) PyLong_CheckExact(op) #define PyInt_FromString PyLong_FromString #define PyInt_FromUnicode PyLong_FromUnicode #define PyInt_FromLong PyLong_FromLong #define PyInt_FromSize_t PyLong_FromSize_t #define PyInt_FromSsize_t PyLong_FromSsize_t #define PyInt_AsLong PyLong_AsLong #define PyInt_AS_LONG PyLong_AS_LONG #define PyInt_AsSsize_t PyLong_AsSsize_t #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask #endif #if PY_MAJOR_VERSION >= 3 #define PyBoolObject PyLongObject #endif #if PY_VERSION_HEX < 0x03020000 typedef long Py_hash_t; #define __Pyx_PyInt_FromHash_t PyInt_FromLong #define __Pyx_PyInt_AsHash_t PyInt_AsLong #else #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t #endif #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) #else #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) #endif #if (PY_MAJOR_VERSION < 3) || (PY_VERSION_HEX >= 0x03010300) #define __Pyx_PySequence_GetSlice(obj, a, b) PySequence_GetSlice(obj, a, b) #define __Pyx_PySequence_SetSlice(obj, a, b, value) PySequence_SetSlice(obj, a, b, value) #define __Pyx_PySequence_DelSlice(obj, a, b) PySequence_DelSlice(obj, a, b) #else #define __Pyx_PySequence_GetSlice(obj, a, b) (unlikely(!(obj)) ? \ (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), (PyObject*)0) : \ (likely((obj)->ob_type->tp_as_mapping) ? 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__pyx_vtable_19MMTK_state_accessor_StateAccessor.__pyx_base.finalize = (int (*)(struct __pyx_obj_22MMTK_trajectory_action_TrajectoryAction *, PyObject *, long, PyTrajectoryVariable *))__pyx_f_19MMTK_state_accessor_13StateAccessor_finalize; __pyx_type_19MMTK_state_accessor_StateAccessor.tp_base = __pyx_ptype_22MMTK_trajectory_action_TrajectoryAction; if (PyType_Ready(&__pyx_type_19MMTK_state_accessor_StateAccessor) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 18; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (__Pyx_SetVtable(__pyx_type_19MMTK_state_accessor_StateAccessor.tp_dict, __pyx_vtabptr_19MMTK_state_accessor_StateAccessor) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 18; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (__Pyx_SetAttrString(__pyx_m, "StateAccessor", (PyObject *)&__pyx_type_19MMTK_state_accessor_StateAccessor) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 18; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_19MMTK_state_accessor_StateAccessor = &__pyx_type_19MMTK_state_accessor_StateAccessor; /*--- Type import code ---*/ __pyx_ptype_22MMTK_trajectory_action_ArrayType = __Pyx_ImportType("Scientific.N", "ArrayType", sizeof(PyArrayObject), 0); if (unlikely(!__pyx_ptype_22MMTK_trajectory_action_ArrayType)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 27; __pyx_clineno = __LINE__; goto __pyx_L1_error;} /*--- Variable import code ---*/ /*--- Function import code ---*/ /*--- Execution code ---*/ /* "Include/MMTK/numeric.pxi":37 * object PyArray_FromDims(int n_dimensions, int dimensions[], int item_type) * * import_array() # <<<<<<<<<<<<<< */ import_array(); /* "Include/MMTK/universe.pxi":32 * cdef void **PyUniverse_API * * import_MMTK_universe() # <<<<<<<<<<<<<< */ import_MMTK_universe(); /* "Include/MMTK/trajectory.pxi":66 * cdef void **PyTrajectory_API * * import_MMTK_trajectory() # <<<<<<<<<<<<<< * */ import_MMTK_trajectory(); /* "MMTK_state_accessor.pyx":15 * cimport MMTK_trajectory_action * * import MMTK # <<<<<<<<<<<<<< * import MMTK_trajectory * */ __pyx_t_1 = __Pyx_Import(((PyObject *)__pyx_n_s__MMTK), 0, -1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 15; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_1); if (PyObject_SetAttr(__pyx_m, __pyx_n_s__MMTK, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 15; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "MMTK_state_accessor.pyx":16 * * import MMTK * import MMTK_trajectory # <<<<<<<<<<<<<< * * cdef class StateAccessor(MMTK_trajectory_action.TrajectoryAction): */ __pyx_t_1 = __Pyx_Import(((PyObject *)__pyx_n_s__MMTK_trajectory), 0, -1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 16; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_1); if (PyObject_SetAttr(__pyx_m, __pyx_n_s__MMTK_trajectory, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 16; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "MMTK_state_accessor.pyx":1 * # A special trajectory action that provides access to the state of # <<<<<<<<<<<<<< * # any trajectory generator * # */ __pyx_t_1 = PyDict_New(); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_1)); if (PyObject_SetAttr(__pyx_m, __pyx_n_s____test__, ((PyObject *)__pyx_t_1)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(((PyObject *)__pyx_t_1)); __pyx_t_1 = 0; goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); if (__pyx_m) { __Pyx_AddTraceback("init MMTK_state_accessor", __pyx_clineno, __pyx_lineno, __pyx_filename); Py_DECREF(__pyx_m); __pyx_m = 0; } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_ImportError, "init MMTK_state_accessor"); } __pyx_L0:; __Pyx_RefNannyFinishContext(); #if PY_MAJOR_VERSION < 3 return; #else return __pyx_m; #endif } /* Runtime support code */ #if CYTHON_REFNANNY static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) { PyObject *m = NULL, *p = NULL; void *r = NULL; m = PyImport_ImportModule((char *)modname); 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"" : "s", num_found); } static CYTHON_INLINE int __Pyx_CheckKeywordStrings( PyObject *kwdict, const char* function_name, int kw_allowed) { PyObject* key = 0; Py_ssize_t pos = 0; while (PyDict_Next(kwdict, &pos, &key, 0)) { #if PY_MAJOR_VERSION < 3 if (unlikely(!PyString_CheckExact(key)) && unlikely(!PyString_Check(key))) #else if (unlikely(!PyUnicode_CheckExact(key)) && unlikely(!PyUnicode_Check(key))) #endif goto invalid_keyword_type; } if ((!kw_allowed) && unlikely(key)) goto invalid_keyword; return 1; invalid_keyword_type: PyErr_Format(PyExc_TypeError, "%s() keywords must be strings", function_name); return 0; invalid_keyword: PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION < 3 "%s() got an unexpected keyword argument '%s'", function_name, PyString_AsString(key)); #else "%s() got an unexpected keyword argument '%U'", function_name, key); #endif return 0; } static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name) { PyObject *result; result = PyObject_GetAttr(dict, name); if (!result) { if (dict != __pyx_b) { PyErr_Clear(); result = PyObject_GetAttr(__pyx_b, name); } if (!result) { PyErr_SetObject(PyExc_NameError, name); } } return result; } static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, long level) { PyObject *py_import = 0; PyObject *empty_list = 0; PyObject *module = 0; PyObject *global_dict = 0; PyObject *empty_dict = 0; PyObject *list; py_import = __Pyx_GetAttrString(__pyx_b, "__import__"); if (!py_import) goto bad; if (from_list) list = from_list; else { empty_list = PyList_New(0); if (!empty_list) goto bad; list = empty_list; } global_dict = PyModule_GetDict(__pyx_m); if (!global_dict) goto bad; empty_dict = PyDict_New(); if (!empty_dict) goto bad; #if PY_VERSION_HEX >= 0x02050000 { PyObject *py_level = PyInt_FromLong(level); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, NULL); Py_DECREF(py_level); } #else if (level>0) { PyErr_SetString(PyExc_RuntimeError, "Relative import is not supported for Python <=2.4."); goto bad; } module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, NULL); #endif bad: Py_XDECREF(empty_list); Py_XDECREF(py_import); Py_XDECREF(empty_dict); return module; } static CYTHON_INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject* x) { const unsigned char neg_one = (unsigned char)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(unsigned char) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(unsigned char)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to unsigned char" : "value too large to convert to unsigned char"); } return (unsigned char)-1; } return (unsigned char)val; } return (unsigned char)__Pyx_PyInt_AsUnsignedLong(x); } static CYTHON_INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject* x) { const unsigned short neg_one = (unsigned short)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(unsigned short) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(unsigned short)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to unsigned short" : "value too large to convert to unsigned short"); } return (unsigned short)-1; } return (unsigned short)val; } return (unsigned short)__Pyx_PyInt_AsUnsignedLong(x); } static CYTHON_INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject* x) { const unsigned int neg_one = (unsigned int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(unsigned int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(unsigned int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to unsigned int" : "value too large to convert to unsigned int"); } return (unsigned int)-1; } return (unsigned int)val; } return (unsigned int)__Pyx_PyInt_AsUnsignedLong(x); } static CYTHON_INLINE char __Pyx_PyInt_AsChar(PyObject* x) { const char neg_one = (char)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(char) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(char)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? 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"can't convert negative value to short" : "value too large to convert to short"); } return (short)-1; } return (short)val; } return (short)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE int __Pyx_PyInt_AsInt(PyObject* x) { const int neg_one = (int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? 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val = __Pyx_PyInt_AsUnsignedLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject* x) { const unsigned PY_LONG_LONG neg_one = (unsigned PY_LONG_LONG)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned PY_LONG_LONG"); return (unsigned PY_LONG_LONG)-1; } return (unsigned PY_LONG_LONG)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned PY_LONG_LONG"); return (unsigned PY_LONG_LONG)-1; } return (unsigned PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { return (unsigned PY_LONG_LONG)PyLong_AsLongLong(x); } } else { unsigned PY_LONG_LONG val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (unsigned PY_LONG_LONG)-1; val = __Pyx_PyInt_AsUnsignedLongLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE long __Pyx_PyInt_AsLong(PyObject* x) { const long neg_one = (long)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long)-1; } return (long)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long)-1; } return (long)PyLong_AsUnsignedLong(x); } else { return (long)PyLong_AsLong(x); } } else { long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (long)-1; val = __Pyx_PyInt_AsLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject* x) { const PY_LONG_LONG neg_one = (PY_LONG_LONG)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to PY_LONG_LONG"); return (PY_LONG_LONG)-1; } return (PY_LONG_LONG)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to PY_LONG_LONG"); return (PY_LONG_LONG)-1; } return (PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { return (PY_LONG_LONG)PyLong_AsLongLong(x); } } else { PY_LONG_LONG val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (PY_LONG_LONG)-1; val = __Pyx_PyInt_AsLongLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject* x) { const signed long neg_one = (signed long)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed long"); return (signed long)-1; } return (signed long)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed long"); return (signed long)-1; } return (signed long)PyLong_AsUnsignedLong(x); } else { return (signed long)PyLong_AsLong(x); } } else { signed long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (signed long)-1; val = __Pyx_PyInt_AsSignedLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject* x) { const signed PY_LONG_LONG neg_one = (signed PY_LONG_LONG)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed PY_LONG_LONG"); return (signed PY_LONG_LONG)-1; } return (signed PY_LONG_LONG)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed PY_LONG_LONG"); return (signed PY_LONG_LONG)-1; } return (signed PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { return (signed PY_LONG_LONG)PyLong_AsLongLong(x); } } else { signed PY_LONG_LONG val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (signed PY_LONG_LONG)-1; val = __Pyx_PyInt_AsSignedLongLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; PyThreadState *tstate = PyThreadState_GET(); tmp_type = tstate->curexc_type; tmp_value = tstate->curexc_value; tmp_tb = tstate->curexc_traceback; tstate->curexc_type = type; tstate->curexc_value = value; tstate->curexc_traceback = tb; Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); } static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { PyThreadState *tstate = PyThreadState_GET(); *type = tstate->curexc_type; *value = tstate->curexc_value; *tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; } static void __Pyx_WriteUnraisable(const char *name, int clineno, int lineno, const char *filename) { PyObject *old_exc, *old_val, *old_tb; PyObject *ctx; __Pyx_ErrFetch(&old_exc, &old_val, &old_tb); #if PY_MAJOR_VERSION < 3 ctx = PyString_FromString(name); #else ctx = PyUnicode_FromString(name); #endif __Pyx_ErrRestore(old_exc, old_val, old_tb); if (!ctx) { PyErr_WriteUnraisable(Py_None); } else { PyErr_WriteUnraisable(ctx); Py_DECREF(ctx); } } static int __Pyx_check_binary_version(void) { char ctversion[4], rtversion[4]; PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { char message[200]; PyOS_snprintf(message, sizeof(message), "compiletime version %s of module '%.100s' " "does not match runtime version %s", ctversion, __Pyx_MODULE_NAME, rtversion); #if PY_VERSION_HEX < 0x02050000 return PyErr_Warn(NULL, message); #else return PyErr_WarnEx(NULL, message, 1); #endif } return 0; } #ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict) { PyObject *py_module = 0; PyObject *result = 0; PyObject *py_name = 0; char warning[200]; py_module = __Pyx_ImportModule(module_name); if (!py_module) goto bad; #if PY_MAJOR_VERSION < 3 py_name = PyString_FromString(class_name); #else py_name = PyUnicode_FromString(class_name); #endif if (!py_name) goto bad; result = PyObject_GetAttr(py_module, py_name); Py_DECREF(py_name); py_name = 0; Py_DECREF(py_module); py_module = 0; if (!result) goto bad; if (!PyType_Check(result)) { PyErr_Format(PyExc_TypeError, "%s.%s is not a type object", module_name, class_name); goto bad; } if (!strict && ((PyTypeObject *)result)->tp_basicsize > (Py_ssize_t)size) { PyOS_snprintf(warning, sizeof(warning), "%s.%s size changed, may indicate binary incompatibility", module_name, class_name); #if PY_VERSION_HEX < 0x02050000 if (PyErr_Warn(NULL, warning) < 0) goto bad; #else if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; #endif } else if (((PyTypeObject *)result)->tp_basicsize != (Py_ssize_t)size) { PyErr_Format(PyExc_ValueError, "%s.%s has the wrong size, try recompiling", module_name, class_name); goto bad; } return (PyTypeObject *)result; bad: Py_XDECREF(py_module); Py_XDECREF(result); return NULL; } #endif #ifndef __PYX_HAVE_RT_ImportModule #define __PYX_HAVE_RT_ImportModule static PyObject *__Pyx_ImportModule(const char *name) { PyObject *py_name = 0; PyObject *py_module = 0; #if PY_MAJOR_VERSION < 3 py_name = PyString_FromString(name); #else py_name = PyUnicode_FromString(name); #endif if (!py_name) goto bad; py_module = PyImport_Import(py_name); Py_DECREF(py_name); return py_module; bad: Py_XDECREF(py_name); return 0; } #endif static void* __Pyx_GetVtable(PyObject *dict) { void* ptr; PyObject *ob = PyMapping_GetItemString(dict, (char *)"__pyx_vtable__"); if (!ob) goto bad; #if PY_VERSION_HEX >= 0x02070000 && !(PY_MAJOR_VERSION==3&&PY_MINOR_VERSION==0) ptr = PyCapsule_GetPointer(ob, 0); #else ptr = PyCObject_AsVoidPtr(ob); #endif if (!ptr && !PyErr_Occurred()) PyErr_SetString(PyExc_RuntimeError, "invalid vtable found for imported type"); Py_DECREF(ob); return ptr; bad: Py_XDECREF(ob); return NULL; } static int __Pyx_SetVtable(PyObject *dict, void *vtable) { #if PY_VERSION_HEX >= 0x02070000 && !(PY_MAJOR_VERSION==3&&PY_MINOR_VERSION==0) PyObject *ob = PyCapsule_New(vtable, 0, 0); #else PyObject *ob = PyCObject_FromVoidPtr(vtable, 0); #endif if (!ob) goto bad; if (PyDict_SetItemString(dict, "__pyx_vtable__", ob) < 0) goto bad; Py_DECREF(ob); return 0; bad: Py_XDECREF(ob); return -1; } #include "compile.h" #include "frameobject.h" #include "traceback.h" static void __Pyx_AddTraceback(const char *funcname, int __pyx_clineno, int __pyx_lineno, const char *__pyx_filename) { PyObject *py_srcfile = 0; PyObject *py_funcname = 0; PyObject *py_globals = 0; PyCodeObject *py_code = 0; PyFrameObject *py_frame = 0; #if PY_MAJOR_VERSION < 3 py_srcfile = PyString_FromString(__pyx_filename); #else py_srcfile = PyUnicode_FromString(__pyx_filename); #endif if (!py_srcfile) goto bad; if (__pyx_clineno) { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno); #else py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno); #endif } else { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromString(funcname); #else py_funcname = PyUnicode_FromString(funcname); #endif } if (!py_funcname) goto bad; py_globals = PyModule_GetDict(__pyx_m); if (!py_globals) goto bad; py_code = PyCode_New( 0, /*int argcount,*/ #if PY_MAJOR_VERSION >= 3 0, /*int kwonlyargcount,*/ #endif 0, /*int nlocals,*/ 0, /*int stacksize,*/ 0, /*int flags,*/ __pyx_empty_bytes, /*PyObject *code,*/ __pyx_empty_tuple, /*PyObject *consts,*/ __pyx_empty_tuple, /*PyObject *names,*/ __pyx_empty_tuple, /*PyObject *varnames,*/ __pyx_empty_tuple, /*PyObject *freevars,*/ __pyx_empty_tuple, /*PyObject *cellvars,*/ py_srcfile, /*PyObject *filename,*/ py_funcname, /*PyObject *name,*/ __pyx_lineno, /*int firstlineno,*/ __pyx_empty_bytes /*PyObject *lnotab*/ ); if (!py_code) goto bad; py_frame = PyFrame_New( PyThreadState_GET(), /*PyThreadState *tstate,*/ py_code, /*PyCodeObject *code,*/ py_globals, /*PyObject *globals,*/ 0 /*PyObject *locals*/ ); if (!py_frame) goto bad; py_frame->f_lineno = __pyx_lineno; PyTraceBack_Here(py_frame); bad: Py_XDECREF(py_srcfile); Py_XDECREF(py_funcname); Py_XDECREF(py_code); Py_XDECREF(py_frame); } static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { while (t->p) { #if PY_MAJOR_VERSION < 3 if (t->is_unicode) { *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); } else if (t->intern) { *t->p = PyString_InternFromString(t->s); } else { *t->p = PyString_FromStringAndSize(t->s, t->n - 1); } #else /* Python 3+ has unicode identifiers */ if (t->is_unicode | t->is_str) { if (t->intern) { *t->p = PyUnicode_InternFromString(t->s); } else if (t->encoding) { *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); } else { *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); } } else { *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); } #endif if (!*t->p) return -1; ++t; } return 0; } /* Type Conversion Functions */ static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { int is_true = x == Py_True; if (is_true | (x == Py_False) | (x == Py_None)) return is_true; else return PyObject_IsTrue(x); } static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { PyNumberMethods *m; const char *name = NULL; PyObject *res = NULL; #if PY_VERSION_HEX < 0x03000000 if (PyInt_Check(x) || PyLong_Check(x)) #else if (PyLong_Check(x)) #endif return Py_INCREF(x), x; m = Py_TYPE(x)->tp_as_number; #if PY_VERSION_HEX < 0x03000000 if (m && m->nb_int) { name = "int"; res = PyNumber_Int(x); } else if (m && m->nb_long) { name = "long"; res = PyNumber_Long(x); } #else if (m && m->nb_int) { name = "int"; res = PyNumber_Long(x); } #endif if (res) { #if PY_VERSION_HEX < 0x03000000 if (!PyInt_Check(res) && !PyLong_Check(res)) { #else if (!PyLong_Check(res)) { #endif PyErr_Format(PyExc_TypeError, "__%s__ returned non-%s (type %.200s)", name, name, Py_TYPE(res)->tp_name); Py_DECREF(res); return NULL; } } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_TypeError, "an integer is required"); } return res; } static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { Py_ssize_t ival; PyObject* x = PyNumber_Index(b); if (!x) return -1; ival = PyInt_AsSsize_t(x); Py_DECREF(x); return ival; } static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { #if PY_VERSION_HEX < 0x02050000 if (ival <= LONG_MAX) return PyInt_FromLong((long)ival); else { unsigned char *bytes = (unsigned char *) &ival; int one = 1; int little = (int)*(unsigned char*)&one; return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0); } #else return PyInt_FromSize_t(ival); #endif } static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject* x) { unsigned PY_LONG_LONG val = __Pyx_PyInt_AsUnsignedLongLong(x); if (unlikely(val == (unsigned PY_LONG_LONG)-1 && PyErr_Occurred())) { return (size_t)-1; } else if (unlikely(val != (unsigned PY_LONG_LONG)(size_t)val)) { PyErr_SetString(PyExc_OverflowError, "value too large to convert to size_t"); return (size_t)-1; } return (size_t)val; } #endif /* Py_PYTHON_H */ MMTK-2.7.9/Src/MMTK_state_accessor.pyx0000644000076600000240000000742111662461640020046 0ustar hinsenstaff00000000000000# A special trajectory action that provides access to the state of # any trajectory generator # # Written by Konrad Hinsen # include "MMTK/python.pxi" include "MMTK/numeric.pxi" include "MMTK/core.pxi" include "MMTK/universe.pxi" include 'MMTK/trajectory.pxi' cimport MMTK_trajectory_action import MMTK import MMTK_trajectory cdef class StateAccessor(MMTK_trajectory_action.TrajectoryAction): cdef long generator_id cdef PyTrajectoryVariable *dynamic_data cdef object universe cdef bint accessing_state def __init__(self): MMTK_trajectory_action.TrajectoryAction.__init__(self, 0, None, MMTK_trajectory.maxint) def __cinit__(self): # Cython zeros all of these automatically, but... # ... explicit is better than implicit, says The Zen of Python. self.generator_id = 0 self.dynamic_data = NULL self.universe = None self.accessing_state = False def copyState(self): """ Makes a copy of the current state of a trajectory generator in a thread-safe way. @returns: a copy of the current state, or C{None} if no trajectory generator is currently active @rtype: C{dict} """ cdef PyTrajectoryVariable *dynamic_data = self.dynamic_data # Make sure the trajectory generator has already started. if self.generator_id == 0: return None self.universe.acquireReadStateLock() state = {} # Timing can be tricky. The trajectory generator runs in another # thread and may well terminate while copyState is executed. # copyState sets self.accessing_state while it reads data, which # prevents finalize from returning. However, it is still possible # that finalize sets self.generator_id to 0 and returns just before # self.accesing_state is set to 1. Therefore the test for # termination must be where it is (and not before). self.accessing_state = True if self.generator_id == 0: self.accessing_state = False self.universe.releaseReadStateLock() return None while dynamic_data.name != NULL: name = dynamic_data.name dtype = dynamic_data.type if name == b'configuration': state[name] = MMTK.copy(self.universe.configuration()) elif name != b'box_size': if dtype == PyTrajectory_Scalar: state[name] = dynamic_data.value.dp[0] elif dtype == PyTrajectory_IntScalar: state[name] = dynamic_data.value.ip[0] else: array = dynamic_data.value.array array = array.copy() if dtype == PyTrajectory_ParticleScalar: state[name] = MMTK.ParticleScalar(self.universe, array) elif dtype == PyTrajectory_ParticleVector: state[name] = MMTK.ParticleVector(self.universe, array) dynamic_data += 1 self.accessing_state = False self.universe.releaseReadStateLock() return state cdef int initialize(self, object universe, long generator_id, PyTrajectoryVariable *dynamic_data): self.universe = universe self.dynamic_data = dynamic_data self.generator_id = generator_id return 1 cdef int finalize(self, object universe, long generator_id, PyTrajectoryVariable *dynamic_data): assert self.generator_id == generator_id assert self.universe == universe assert self.dynamic_data == dynamic_data while self.accessing_state: continue self.generator_id = 0 return 1 MMTK-2.7.9/Src/MMTK_surface.c0000644000076600000240000004721211662461640016100 0ustar hinsenstaff00000000000000/* Molecular surface code * * Copyright 2000 by Peter McCluskey (pcm@rahul.net). * You may do anything you want with it, provided this notice is kept intact. */ #include "Python.h" #include #include #include "MMTK/core.h" typedef double TPoint[3]; typedef struct { int index; double dist2; } NeighborData; static void normalize(TPoint p) { double length = sqrt(p[0]*p[0] + p[1]*p[1] + p[2]*p[2]); p[0] /= length; p[1] /= length; p[2] /= length; } static int add_point(const TPoint p, TPoint *r, int r_index, PyObject *pt_dict) { PyObject *p_orig; char key[512]; snprintf(key, sizeof(key), "%.6f,%.6f,%.6f", p[0], p[1], p[2]); p_orig = PyDict_GetItemString(pt_dict, key); if(p_orig) Py_DECREF(p_orig); else { PyObject *py_one = PyInt_FromLong(1); PyDict_SetItemString(pt_dict, key, py_one); r[r_index][0] = p[0]; r[r_index][1] = p[1]; r[r_index][2] = p[2]; ++r_index; } return r_index; } static int tess_triangle(const TPoint v0, const TPoint v1, const TPoint v2, int npts, TPoint *r, int r_index, PyObject *pt_dict) { int n4 = npts/4; TPoint new_pt0; TPoint new_pt1; TPoint new_pt2; new_pt0[0] = v0[0] + v1[0]; new_pt0[1] = v0[1] + v1[1]; new_pt0[2] = v0[2] + v1[2]; new_pt1[0] = v1[0] + v2[0]; new_pt1[1] = v1[1] + v2[1]; new_pt1[2] = v1[2] + v2[2]; new_pt2[0] = v2[0] + v0[0]; new_pt2[1] = v2[1] + v0[1]; new_pt2[2] = v2[2] + v0[2]; normalize(new_pt0); normalize(new_pt1); normalize(new_pt2); if(n4 <= 3) { /* other 2 points in pts will be created as pts[0] of */ /* another triangle, except for top level points */ r_index = add_point(v0, r, r_index, pt_dict); r_index = add_point(new_pt0, r, r_index, pt_dict); r_index = add_point(new_pt1, r, r_index, pt_dict); r_index = add_point(new_pt2, r, r_index, pt_dict); return r_index; } else { int i = tess_triangle(v0, new_pt0, new_pt2, n4, r, r_index, pt_dict); i = tess_triangle(new_pt0, v1, new_pt1, n4, r, i, pt_dict); i = tess_triangle(new_pt0, new_pt1, new_pt2, n4, r, i, pt_dict); return tess_triangle(new_pt1, v2, new_pt2, n4, r, i, pt_dict); } } static TPoint * tesselate(int num_points) { static const TPoint north = {0.0, 0.0, 1.0}; static const TPoint south = {0.0, 0.0, -1.0}; static const TPoint noon = {1.0, 0.0, 0.0}; static const TPoint night = {-1.0, 0.0, 0.0}; static const TPoint dawn = {0.0, 1.0, 0.0}; static const TPoint dusk = {0.0, -1.0, 0.0}; int npts = (num_points - 2)/4; int i = 0; PyObject *pt_dict = PyDict_New(); TPoint *r = (TPoint *)malloc(num_points * sizeof(TPoint)); i = add_point(north, r, i, pt_dict); i = tess_triangle(north, dawn, noon, npts, r, i, pt_dict); i = add_point(noon, r, i, pt_dict); i = tess_triangle(north, noon, dusk, npts, r, i, pt_dict); i = add_point(dusk, r, i, pt_dict); i = tess_triangle(north, dusk, night, npts, r, i, pt_dict); i = add_point(night, r, i, pt_dict); i = tess_triangle(north, night, dawn, npts, r, i, pt_dict); i = add_point(dawn, r, i, pt_dict); i = add_point(south, r, i, pt_dict); i = tess_triangle(south, dawn, night, npts, r, i, pt_dict); i = tess_triangle(south, night, dusk, npts, r, i, pt_dict); i = tess_triangle(south, dusk, noon, npts, r, i, pt_dict); i = tess_triangle(south, noon, dawn, npts, r, i, pt_dict); Py_DECREF(pt_dict); if(i != num_points) { free(r); return NULL; } return r; } static int nbor_data_1_atom(PyObject *nbors, int i, PyObject *atom_data, NeighborData *nbor_data) { double max_dist_2; PyObject *boxes; PyObject *bsize; int j; double box_size; int n_nbors1 = 0; boxes = PyObject_GetAttrString(nbors, "boxes"); bsize = PyObject_GetAttrString(nbors, "box_size"); box_size = PyFloat_AsDouble(bsize); Py_DECREF(bsize); max_dist_2 = box_size*box_size; { PyObject *pos1 = PyList_GetItem(atom_data, (Py_ssize_t)i); double posx = PyFloat_AsDouble(PyTuple_GetItem(pos1, (Py_ssize_t)0)); double posy = PyFloat_AsDouble(PyTuple_GetItem(pos1, (Py_ssize_t)1)); double posz = PyFloat_AsDouble(PyTuple_GetItem(pos1, (Py_ssize_t)2)); int boxn; static const int nbor_boxes[27][3] = { { -1, -1, -1}, { -1, -1, 0}, { -1, -1, 1}, { -1, 0, -1}, { -1, 0, 0}, { -1, 0, 1}, { -1, 1, -1}, { -1, 1, 0}, { -1, 1, 1}, { 0, -1, -1}, { 0, -1, 0}, { 0, -1, 1}, { 0, 0, -1}, { 0, 0, 0}, { 0, 0, 1}, { 0, 1, -1}, { 0, 1, 0}, { 0, 1, 1}, { 1, -1, -1}, { 1, -1, 0}, { 1, -1, 1}, { 1, 0, -1}, { 1, 0, 0}, { 1, 0, 1}, { 1, 1, -1}, { 1, 1, 0}, { 1, 1, 1}, }; int key0 = (int)floor(posx/box_size); int key1 = (int)floor(posy/box_size); int key2 = (int)floor(posz/box_size); for(boxn = 0; boxn < 27; ++boxn) { char key[128]; PyObject *alist; snprintf(key, sizeof(key), "%d %d %d", key0+nbor_boxes[boxn][0], key1+nbor_boxes[boxn][1], key2+nbor_boxes[boxn][2]); alist = PyDict_GetItemString(boxes, key); if(!alist && i == -1) printf("none in list at %s, atom %d\n", key, i); if(alist) { int n2 = (int)PyObject_Length(alist); if(i == -1) printf("%3d in list at %s\n", n2, key); for(j = 0; j < n2; ++j) { PyObject * py_i2 = PyList_GetItem(alist, (Py_ssize_t)j); int i2 = PyInt_AsLong(py_i2); if(i2 != i) { PyObject *apos = PyList_GetItem(atom_data, (Py_ssize_t)i2); double vaax = PyFloat_AsDouble(PyTuple_GetItem(apos, (Py_ssize_t)0)) - posx; double vaay = PyFloat_AsDouble(PyTuple_GetItem(apos, (Py_ssize_t)1)) - posy; double vaaz = PyFloat_AsDouble(PyTuple_GetItem(apos, (Py_ssize_t)2)) - posz; double d2 = vaax*vaax + vaay*vaay + vaaz*vaaz; if(d2 <= max_dist_2) { nbor_data[n_nbors1].index = i2; nbor_data[n_nbors1++].dist2 = d2; } } } } } } Py_DECREF(boxes); return n_nbors1; } /* * returns a tuple of length 2. The first member of the tuple is a list of * positions of surface points. The second member is None or a list of * unit vectors. */ static PyObject * surface1atom(PyObject *dummy, PyObject *args) { PyObject *nbors; NeighborData *nbor_data; PyObject *atom_data; PyObject *ai_pos; PyObject *points; double radius, rad1sq, rad1_2; int point_density; int return_unit_pts; /* should I return a list of unit points? */ PyObject *unit_points = NULL; PyObject *ret_tup; int n_tess, n_nbors; Py_ssize_t n_atoms; int i, j, aindex; double *vx, *vy, *vz, *thresh; double aposx, aposy, aposz; int last_index = 0; static TPoint *tesselations = NULL; static int last_point_density = -1; if (!PyArg_ParseTuple(args, "OiOOdii", &nbors, &aindex, &atom_data, &ai_pos, &radius, &point_density, &return_unit_pts)) return NULL; if(PyObject_Length(ai_pos) < 3) { PyErr_SetString(PyExc_TypeError, "3rd argument must be a tuple of 3 floats"); return NULL; } if(point_density != last_point_density) { if(tesselations) free(tesselations); last_point_density = point_density; tesselations = tesselate(point_density); if(!tesselations) { PyErr_SetString(PyExc_ValueError, "point_density invalid, must be 2**(2*N) + 2, where N > 1"); return NULL; } } ret_tup = PyTuple_New((Py_ssize_t)2); if(return_unit_pts) unit_points = PyList_New((Py_ssize_t)0); n_atoms = PyObject_Length(atom_data); nbor_data = (NeighborData*)malloc(n_atoms*sizeof(nbor_data[0])); n_nbors = nbor_data_1_atom(nbors, aindex, atom_data, nbor_data); aposx = PyFloat_AsDouble(PyTuple_GetItem(ai_pos, (Py_ssize_t)0)); aposy = PyFloat_AsDouble(PyTuple_GetItem(ai_pos, (Py_ssize_t)1)); aposz = PyFloat_AsDouble(PyTuple_GetItem(ai_pos, (Py_ssize_t)2)); points = PyList_New((Py_ssize_t)0); n_tess = point_density; vx = (double *)malloc(n_nbors*sizeof(*vx)); vy = (double *)malloc(n_nbors*sizeof(*vy)); vz = (double *)malloc(n_nbors*sizeof(*vz)); thresh = (double *)malloc(n_nbors*sizeof(*thresh)); rad1sq = radius*radius; rad1_2 = 2*radius; for(i = 0; i < n_nbors; ++i) { double r2; PyObject *apos; apos = PyList_GetItem(atom_data, (Py_ssize_t)nbor_data[i].index); if(!apos) return NULL; vx[i] = PyFloat_AsDouble(PyTuple_GetItem(apos, (Py_ssize_t)0)) - aposx; vy[i] = PyFloat_AsDouble(PyTuple_GetItem(apos, (Py_ssize_t)1)) - aposy; vz[i] = PyFloat_AsDouble(PyTuple_GetItem(apos, (Py_ssize_t)2)) - aposz; r2 = PyFloat_AsDouble(PyTuple_GetItem(apos, (Py_ssize_t)3)); thresh[i] = (nbor_data[i].dist2 + rad1sq - r2*r2) / rad1_2; } for(i = 0; i < n_tess; ++i) { int buried = 0; double ptx = tesselations[i][0]; double pty = tesselations[i][1]; double ptz = tesselations[i][2]; for(j = last_index; j < n_nbors; ++j) { if(ptx*vx[j] + pty*vy[j] + ptz*vz[j] > thresh[j]) { buried = 1; last_index = j; break; } #if 0 else { double l2 = sqrt(ptx*ptx + pty*pty + ptz*ptz) *sqrt(vx[j]*vx[j] + vy[j]*vy[j] + vz[j]*vz[j]); double cosa = (ptx*vx[j] + pty*vy[j] + ptz*vz[j])/l2; double dist41 = sqrt((radius*ptx+0.0933)*(radius*ptx+0.0933) + (radius*pty-0.2487)*(radius*pty-0.2487) + (radius*ptz+0.0903)*(radius*ptz+0.0903)); double dist28 = sqrt(radius*ptx*radius*ptx + radius*pty*radius*pty + radius*ptz*radius*ptz); if(cosa > 0.75 && fabs(ptx) < 0.02 && pty > 0.93 && fabs(ptz - 0.195) < 0.05 && ++cnt0 < 1000) printf("%2d %2d %5.2f*%5.2f + %5.2f*%5.2f + %5.2f*%5.2f > %4.2f (%4.2f) %5.2f %6.2f %6.2f\n", i,j,ptx*10, vx[j]*10, pty*10, vy[j]*10, ptz*10, vz[j]*10, thresh[j]*10, 10*(ptx*vx[j] + pty*vy[j] + ptz*vz[j]), cosa, dist28, dist41); } #endif } if(!buried) { for(j = 0; j < last_index; ++j) { if(ptx*vx[j] + pty*vy[j] + ptz*vz[j] > thresh[j]) { buried = 1; last_index = j; break; } #if 0 else { double l2 = sqrt(ptx*ptx + pty*pty + ptz*ptz) *sqrt(vx[j]*vx[j] + vy[j]*vy[j] + vz[j]*vz[j]); double cosa = (ptx*vx[j] + pty*vy[j] + ptz*vz[j])/l2; if(cosa > 0.75 && fabs(ptx) < 0.02 && pty > 0.93 && fabs(ptz - 0.195) < 0.05 && ++cnt0 < 1000) printf("%4d %3d %5.2f*%6.2f + %5.2f*%6.2f + %5.2f*%6.2f > %5.2f (%5.2f) %6.2f\n", i,j,ptx*10, vx[j]*10, pty*10, vy[j]*10, ptz*10, vz[j]*10, thresh[j]*10, 10*(ptx*vx[j] + pty*vy[j] + ptz*vz[j]), cosa); } #endif } } if(!buried) { PyObject *new_pt = PyTuple_New((Py_ssize_t)3); PyTuple_SetItem(new_pt, (Py_ssize_t)0, PyFloat_FromDouble(radius*ptx + aposx)); PyTuple_SetItem(new_pt, (Py_ssize_t)1, PyFloat_FromDouble(radius*pty + aposy)); PyTuple_SetItem(new_pt, (Py_ssize_t)2, PyFloat_FromDouble(radius*ptz + aposz)); PyList_Append(points, new_pt); Py_DECREF(new_pt); if(return_unit_pts) { PyObject *new_pt = PyTuple_New((Py_ssize_t)3); PyTuple_SetItem(new_pt, (Py_ssize_t)0, PyFloat_FromDouble(ptx)); PyTuple_SetItem(new_pt, (Py_ssize_t)1, PyFloat_FromDouble(pty)); PyTuple_SetItem(new_pt, (Py_ssize_t)2, PyFloat_FromDouble(ptz)); PyList_Append(unit_points, new_pt); Py_DECREF(new_pt); } } } free(vx); free(vy); free(vz); free(thresh); free(nbor_data); /* surface_(&total_area, areas, dareas, doublereal *radius, doublereal *weight, doublereal *probe); */ PyTuple_SetItem(ret_tup, (Py_ssize_t)0, points); if(return_unit_pts) PyTuple_SetItem(ret_tup, (Py_ssize_t)1, unit_points); else { Py_INCREF(Py_None); PyTuple_SetItem(ret_tup, (Py_ssize_t)1, Py_None); } return ret_tup; } /* not sure whether this is still usefull; may be obsoleted by the next function, which uses less memory due to lazy evaluation. */ static PyObject* FindNeighbors(PyObject *dummy, PyObject *args) { PyObject *atoms; double radius; double max_rad; PyObject *atom_data; double max_dist_2; PyObject *nbors, *boxes; Py_ssize_t n_atoms; int i, j; double box_size; PyObject **nlist3, *nlist; if (!PyArg_ParseTuple(args, "OddOd", &atoms, &radius, &max_rad, &atom_data, &max_dist_2)) return NULL; n_atoms = PyObject_Length(atoms); nbors = PyTuple_New(n_atoms); nlist3 = (PyObject **)malloc(n_atoms*sizeof(PyObject*)); boxes = PyDict_New(); box_size = 2*(max_rad + radius); printf("box_size %.2f %.2f %.2f\n", box_size*10, max_rad*10, radius*10); for(i = 0; i < n_atoms; ++i) { PyObject *v, *tmp; char key[128]; PyObject *pos = PyList_GetItem(atom_data, (Py_ssize_t)i); snprintf(key, sizeof(key), "%d %d %d", (int)floor(PyFloat_AsDouble(PyTuple_GetItem(pos, (Py_ssize_t)0))/box_size), (int)floor(PyFloat_AsDouble(PyTuple_GetItem(pos, (Py_ssize_t)1))/box_size), (int)floor(PyFloat_AsDouble(PyTuple_GetItem(pos, (Py_ssize_t)2))/box_size)); v = PyDict_GetItemString(boxes, key); if(!v) PyDict_SetItemString(boxes, key, v = PyList_New((Py_ssize_t)0)); PyList_Append(v, tmp = PyInt_FromLong(i)); Py_DECREF(tmp); if(0) printf("key %12.12s %3d %6.2f %6.2f %6.2f\n", key, (int)PyObject_Length(v), PyFloat_AsDouble(PyTuple_GetItem(pos, (Py_ssize_t)0)), PyFloat_AsDouble(PyTuple_GetItem(pos, (Py_ssize_t)1)), PyFloat_AsDouble(PyTuple_GetItem(pos, (Py_ssize_t)2))); } for(i = 0; i < n_atoms; ++i) { PyObject *pos1 = PyList_GetItem(atom_data, (Py_ssize_t)i); double posx = PyFloat_AsDouble(PyTuple_GetItem(pos1, (Py_ssize_t)0)); double posy = PyFloat_AsDouble(PyTuple_GetItem(pos1, (Py_ssize_t)1)); double posz = PyFloat_AsDouble(PyTuple_GetItem(pos1, (Py_ssize_t)2)); int n_nbors1 = 0; int boxn; static const int nbor_boxes[27][3] = { { -1, -1, -1}, { -1, -1, 0}, { -1, -1, 1}, { -1, 0, -1}, { -1, 0, 0}, { -1, 0, 1}, { -1, 1, -1}, { -1, 1, 0}, { -1, 1, 1}, { 0, -1, -1}, { 0, -1, 0}, { 0, -1, 1}, { 0, 0, -1}, { 0, 0, 0}, { 0, 0, 1}, { 0, 1, -1}, { 0, 1, 0}, { 0, 1, 1}, { 1, -1, -1}, { 1, -1, 0}, { 1, -1, 1}, { 1, 0, -1}, { 1, 0, 0}, { 1, 0, 1}, { 1, 1, -1}, { 1, 1, 0}, { 1, 1, 1}, }; int key0 = (int)floor(posx/box_size); int key1 = (int)floor(posy/box_size); int key2 = (int)floor(posz/box_size); for(boxn = 0; boxn < 27; ++boxn) { char key[128]; PyObject *alist; snprintf(key, sizeof(key), "%d %d %d", key0+nbor_boxes[boxn][0], key1+nbor_boxes[boxn][1], key2+nbor_boxes[boxn][2]); alist = PyDict_GetItemString(boxes, key); if(!alist && i == -1) printf("none in list at %s\n", key); if(alist) { int n2 = (int)PyObject_Length(alist); if(i == -1) printf("%3d in list at %s\n", n2, key); for(j = 0; j < n2; ++j) { PyObject * py_i2 = PyList_GetItem(alist, (Py_ssize_t)j); int i2 = PyInt_AsLong(py_i2); if(i2 != i) { PyObject *apos = PyList_GetItem(atom_data, (Py_ssize_t)i2); double vaax = PyFloat_AsDouble(PyTuple_GetItem(apos, (Py_ssize_t)0)) - posx; double vaay = PyFloat_AsDouble(PyTuple_GetItem(apos, (Py_ssize_t)1)) - posy; double vaaz = PyFloat_AsDouble(PyTuple_GetItem(apos, (Py_ssize_t)2)) - posz; double d2 = vaax*vaax + vaay*vaay + vaaz*vaaz; if(d2 <= max_dist_2) { PyObject *tup1 = PyTuple_New((Py_ssize_t)2); Py_INCREF(py_i2); PyTuple_SetItem(tup1, (Py_ssize_t)0, py_i2); PyTuple_SetItem(tup1, (Py_ssize_t)1, PyFloat_FromDouble(d2)); nlist3[n_nbors1++] = tup1; } } } } } nlist = PyTuple_New((Py_ssize_t)n_nbors1); for(j = 0; j < n_nbors1; ++j) { PyTuple_SetItem(nlist, (Py_ssize_t)j, nlist3[j]); } PyTuple_SetItem(nbors, (Py_ssize_t)i, nlist); } free(nlist3); Py_DECREF(boxes); return nbors; } static PyObject* FindNeighborsOfAtom(PyObject *dummy, PyObject *args) { PyObject *atoms; PyObject *atom_data; double max_dist_2; PyObject *boxes; Py_ssize_t n_atoms; int i, j; double box_size; PyObject **nlist3, *nlist; if (!PyArg_ParseTuple(args, "OiOdO", &atoms, &i, &boxes, &box_size, &atom_data)) return NULL; n_atoms = PyObject_Length(atoms); nlist3 = (PyObject **)malloc(n_atoms*sizeof(PyObject*)); max_dist_2 = box_size*box_size; { PyObject *pos1 = PyList_GetItem(atom_data, (Py_ssize_t)i); double posx = PyFloat_AsDouble(PyTuple_GetItem(pos1, (Py_ssize_t)0)); double posy = PyFloat_AsDouble(PyTuple_GetItem(pos1, (Py_ssize_t)1)); double posz = PyFloat_AsDouble(PyTuple_GetItem(pos1, (Py_ssize_t)2)); int n_nbors1 = 0; int boxn; static const int nbor_boxes[27][3] = { { -1, -1, -1}, { -1, -1, 0}, { -1, -1, 1}, { -1, 0, -1}, { -1, 0, 0}, { -1, 0, 1}, { -1, 1, -1}, { -1, 1, 0}, { -1, 1, 1}, { 0, -1, -1}, { 0, -1, 0}, { 0, -1, 1}, { 0, 0, -1}, { 0, 0, 0}, { 0, 0, 1}, { 0, 1, -1}, { 0, 1, 0}, { 0, 1, 1}, { 1, -1, -1}, { 1, -1, 0}, { 1, -1, 1}, { 1, 0, -1}, { 1, 0, 0}, { 1, 0, 1}, { 1, 1, -1}, { 1, 1, 0}, { 1, 1, 1}, }; int key0 = (int)floor(posx/box_size); int key1 = (int)floor(posy/box_size); int key2 = (int)floor(posz/box_size); for(boxn = 0; boxn < 27; ++boxn) { char key[128]; PyObject *alist; snprintf(key, sizeof(key), "%d %d %d", key0+nbor_boxes[boxn][0], key1+nbor_boxes[boxn][1], key2+nbor_boxes[boxn][2]); alist = PyDict_GetItemString(boxes, key); if(!alist && i == -1) printf("none in list at %s\n", key); if(alist) { int n2 = (int)PyObject_Length(alist); if(i == -1) printf("%3d in list at %s\n", n2, key); for(j = 0; j < n2; ++j) { PyObject * py_i2 = PyList_GetItem(alist, (Py_ssize_t)j); int i2 = PyInt_AsLong(py_i2); if(i2 != i) { PyObject *apos = PyList_GetItem(atom_data, (Py_ssize_t)i2); double vaax = PyFloat_AsDouble(PyTuple_GetItem(apos, (Py_ssize_t)0)) - posx; double vaay = PyFloat_AsDouble(PyTuple_GetItem(apos, (Py_ssize_t)1)) - posy; double vaaz = PyFloat_AsDouble(PyTuple_GetItem(apos, (Py_ssize_t)2)) - posz; double d2 = vaax*vaax + vaay*vaay + vaaz*vaaz; if(d2 <= max_dist_2) { PyObject *tup1 = PyTuple_New((Py_ssize_t)2); Py_INCREF(py_i2); PyTuple_SetItem(tup1, (Py_ssize_t)0, py_i2); PyTuple_SetItem(tup1, (Py_ssize_t)1, PyFloat_FromDouble(d2)); nlist3[n_nbors1++] = tup1; } } } } } nlist = PyTuple_New((Py_ssize_t)n_nbors1); for(j = 0; j < n_nbors1; ++j) { PyTuple_SetItem(nlist, (Py_ssize_t)j, nlist3[j]); } } free(nlist3); return nlist; } /* * List of functions defined in the module */ static PyMethodDef surface_methods[] = { {"surface1atom", surface1atom, METH_VARARGS}, {"FindNeighbors", FindNeighbors, METH_VARARGS}, {"FindNeighborsOfAtom", FindNeighborsOfAtom, METH_VARARGS}, {NULL, NULL} /* sentinel */ }; /* Initialization function for the module */ DL_EXPORT(void) #ifdef IBMPC __declspec(dllexport) #endif initMMTK_surface(void) { /* Create the module and add the functions */ Py_InitModule("MMTK_surface", surface_methods); /* Check for errors */ if (PyErr_Occurred()) Py_FatalError("can't initialize module MMTK_surface"); } MMTK-2.7.9/Src/MMTK_trajectory.c0000644000076600000240000015271112117632051016627 0ustar hinsenstaff00000000000000/* * Trajectory objects using netCDF files. * * Written by Konrad Hinsen */ #define _TRAJECTORY_MODULE #include "MMTK/trajectory.h" #include "MMTK/universe.h" #include #include /* Names of standard dimensions */ char *step_number = "step_number"; char *minor_step_number = "minor_step_number"; char *atom_number = "atom_number"; char *xyz = "xyz"; char *box_size_length = "box_size_length"; /* Names of data classes */ typedef struct { char *name; int number; } PyTrajectory_DataClassName; static PyTrajectory_DataClassName class_names[] = { {"configuration", PyTrajectory_Configuration}, {"position", PyTrajectory_Configuration}, {"positions", PyTrajectory_Configuration}, {"velocity", PyTrajectory_Velocities}, {"velocities", PyTrajectory_Velocities}, {"gradient", PyTrajectory_Gradients}, {"gradients", PyTrajectory_Gradients}, {"energy", PyTrajectory_Energy}, {"energies", PyTrajectory_Energy}, {"thermodynamic", PyTrajectory_Thermodynamic}, {"temperature", PyTrajectory_Thermodynamic}, {"time", PyTrajectory_Time}, {"auxiliary", PyTrajectory_Auxiliary}, {NULL, 0} }; /* Destroy trajectory object */ static void trajectory_dealloc(PyTrajectoryObject *self) { if (self->file != NULL) PyNetCDFFile_Close(self->file); Py_XDECREF(self->universe); Py_XDECREF(self->index_map); Py_XDECREF(self->file); Py_XDECREF(self->var_step); Py_XDECREF(self->sbuffer); Py_XDECREF(self->vbuffer); Py_XDECREF(self->box_buffer); PyObject_Del(self); } /* Close trajectory file */ static int PyTrajectory_Close(PyTrajectoryObject *trajectory) { int ret = PyNetCDFFile_Close(trajectory->file); Py_DECREF(trajectory->file); trajectory->file = NULL; return ret; } static PyObject * trajectory_close(PyTrajectoryObject *self, PyObject *args) { if (!PyArg_ParseTuple(args, "")) return NULL; if (PyTrajectory_Close(self) == 0) { Py_INCREF(Py_None); return Py_None; } else return NULL; } /* Write remaining data to file */ static int PyTrajectory_Flush(PyTrajectoryObject *trajectory) { return PyNetCDFFile_Sync(trajectory->file); } static PyObject * trajectory_flush(PyTrajectoryObject *self, PyObject *args) { if (!PyArg_ParseTuple(args, "")) return NULL; if (PyTrajectory_Flush(self) == 0) { Py_INCREF(Py_None); return Py_None; } else return NULL; } /* Retrieve variable, or create it if it doesn't exist */ static PyObject * PyTrajectory_GetVariable(PyTrajectoryObject *trajectory, char *name, int rank, int integer_flag, char *units, int trajectory_flag) { PyNetCDFVariableObject *var; char *dimensions[4]; int nd = 0; if (trajectory_flag) dimensions[nd++] = step_number; if (rank == PyTrajectory_BoxSize) dimensions[nd++] = box_size_length; else { if (rank != PyTrajectory_Scalar) dimensions[nd++] = atom_number; if (rank == PyTrajectory_ParticleVector) dimensions[nd++] = xyz; } if (trajectory_flag && trajectory->block_size > 1) dimensions[nd++] = minor_step_number; var = PyNetCDFFile_GetVariable(trajectory->file, name); if (var == NULL) { char type; if (integer_flag) type = 'l'; else if (trajectory->floattype == PyArray_FLOAT) type = 'f'; else type = 'd'; var = PyNetCDFFile_CreateVariable(trajectory->file, name, type, dimensions, nd); if (var != NULL && units != NULL) PyNetCDFVariable_SetAttribute(var, "units", PyString_FromString(units)); } return (PyObject *)var; } /* Read data */ static PyArrayObject * PyTrajectory_ReadParticleVector(PyTrajectoryObject *trajectory, PyObject *variable, int step) { PyNetCDFVariableObject *v = (PyNetCDFVariableObject *)variable; PyNetCDFIndex *indices; PyArrayObject *data, *ret; #if defined(NUMPY) npy_intp dim[2]; #else int dim[2]; #endif int i; indices = PyNetCDFVariable_Indices(v); if (indices == NULL) return NULL; if (trajectory->block_size > 1) { int minor = step % trajectory->block_size; int major = step / trajectory->block_size; indices[0].start = major; indices[0].stop = major + 1; indices[0].item = 1; indices[v->nd-1].start = minor; indices[v->nd-1].stop = minor + 1; indices[v->nd-1].item = 1; } else { indices[0].start = step; indices[0].stop = step + 1; indices[0].item = 1; } data = PyNetCDFVariable_ReadAsArray(v, indices); if (data == NULL) return NULL; if (trajectory->natoms == trajectory->trajectory_atoms && data->descr->type_num == PyArray_DOUBLE) return data; dim[0] = trajectory->natoms; dim[1] = 3; #if defined(NUMPY) ret = (PyArrayObject *)PyArray_SimpleNew(2, dim, PyArray_DOUBLE); #else ret = (PyArrayObject *)PyArray_FromDims(2, dim, PyArray_DOUBLE); #endif if (ret == NULL) { Py_DECREF(data); return NULL; } if (data->descr->type_num == PyArray_DOUBLE) { double *s = (double *)data->data; double *d = (double *)ret->data; for (i = 0; i < 3*trajectory->trajectory_atoms; i++) *d++ = *s++; for (; i < 3*trajectory->natoms; i++) *d++ = undefined; } else { float *s = (float *)data->data; double *d = (double *)ret->data; for (i = 0; i < 3*trajectory->trajectory_atoms; i++) *d++ = (double)*s++; for (; i < 3*trajectory->natoms; i++) *d++ = undefined; } Py_DECREF(data); return ret; } static PyArrayObject * trajectory_read_particle_vector(PyTrajectoryObject *self, PyObject *args) { PyObject *variable; char *var_name; int step; if (!PyArg_ParseTuple(args, "si", &var_name, &step)) return NULL; variable = PyDict_GetItemString(self->file->variables, var_name); if (variable == NULL) return NULL; return PyTrajectory_ReadParticleVector(self, variable, step); } static PyArrayObject * PyTrajectory_ReadParticleTrajectories(PyTrajectoryObject *trajectory, int atom, int natoms, char *variable, int first, int last, int skip, int correct, int box_coordinates) { PyNetCDFVariableObject *var; PyNetCDFVariableObject *box_var; PyNetCDFIndex *indices, *box_indices; PyArrayObject *data, *box; PyUniverseSpecObject *universe_spec; int bs = trajectory->block_size; int steps = (last-first+skip-1)/skip; int i, j, k; universe_spec = (PyUniverseSpecObject *) PyObject_GetAttrString(trajectory->universe, "_spec"); if (universe_spec == NULL) return NULL; var = (PyNetCDFVariableObject *) PyDict_GetItemString(trajectory->file->variables, variable); if (var == NULL) { PyErr_SetString(PyExc_ValueError, "variable not in trajectory"); return NULL; } box_var = NULL; box = NULL; if (universe_spec->is_periodic && (correct || box_coordinates)) { box_var = (PyNetCDFVariableObject *) PyDict_GetItemString(trajectory->file->variables, "box_size"); if (box_var != NULL) { if (trajectory->box_buffer != NULL) { if (trajectory->box_buffer_first == first && trajectory->box_buffer_last == last && trajectory->box_buffer_skip == skip) { box = trajectory->box_buffer; Py_INCREF(box); } else { Py_DECREF(trajectory->box_buffer); trajectory->box_buffer = NULL; } } } else PyErr_Clear(); } if (bs > 1) { PyArrayObject *read; double *data_data, *box_data; int bfirst, bsteps, rsteps, istep; #if defined(NUMPY) npy_intp dim[3]; #else int dim[3]; #endif dim[0] = natoms; dim[1] = steps; dim[2] = 3; #if defined(NUMPY) data = (PyArrayObject *)PyArray_SimpleNew(3, dim, PyArray_DOUBLE); #else data = (PyArrayObject *)PyArray_FromDims(3, dim, PyArray_DOUBLE); #endif if (data == NULL) return NULL; data_data = (double *)data->data; if (box_var != NULL && box == NULL) { dim[0] = steps; dim[1] = universe_spec->geometry_data_length; #if defined(NUMPY) box = (PyArrayObject *)PyArray_SimpleNew(2, dim, PyArray_DOUBLE); #else box = (PyArrayObject *)PyArray_FromDims(2, dim, PyArray_DOUBLE); #endif if (box == NULL) { Py_DECREF(data); return NULL; } box_data = (double *)box->data; } else box_data = NULL; istep = first; rsteps = steps; while (istep < last) { bfirst = istep % bs; bsteps = (bs-bfirst+skip-1)/skip; if (bsteps > rsteps) bsteps = rsteps; indices = PyNetCDFVariable_Indices(var); if (indices == NULL) return NULL; indices[0].start = istep/bs; indices[0].stop = istep/bs+1; indices[0].item = 1; indices[1].start = atom; indices[1].stop = atom+natoms; indices[3].start = bfirst; indices[3].stop = bfirst+skip*bsteps; indices[3].stride = skip; read = PyNetCDFVariable_ReadAsArray(var, indices); if (read == NULL) { Py_DECREF(data); return NULL; } if (read->descr->type_num != PyArray_DOUBLE) { float *read_data = (float *)read->data; for (k = 0; k < natoms; k++) for (j = 0; j < 3; j++) for (i = 0; i < bsteps; i++) data_data[(k*steps+i)*3+j] = (double)read_data[(k*3+j)*bsteps+i]; } else { double *read_data = (double *)read->data; for (k = 0; k < natoms; k++) for (i = 0; i < bsteps; i++) for (j = 0; j < 3; j++) data_data[(k*steps+i)*3+j] = read_data[(k*3+j)*bsteps+i]; } Py_DECREF(read); data_data += 3*bsteps; if (box_data != NULL) { int dl = universe_spec->geometry_data_length; box_indices = PyNetCDFVariable_Indices(box_var); if (box_indices == NULL) return NULL; box_indices[0].start = istep/bs; box_indices[0].stop = istep/bs+1; box_indices[0].item = 1; box_indices[2].start = bfirst; box_indices[2].stop = bfirst+skip*bsteps; box_indices[2].stride = skip; read = PyNetCDFVariable_ReadAsArray(box_var, box_indices); if (read == NULL) { Py_DECREF(data); return NULL; } if (read->descr->type_num != PyArray_DOUBLE) { float *read_data = (float *)read->data; for (i = 0; i < bsteps; i++) for (j = 0; j < dl; j++) box_data[dl*i+j] = (double)read_data[bsteps*j+i]; } else { double *read_data = (double *)read->data; for (i = 0; i < bsteps; i++) for (j = 0; j < dl; j++) box_data[dl*i+j] = read_data[bsteps*j+i]; } Py_DECREF(read); box_data += dl*bsteps; } rsteps -= bsteps; istep += bsteps*skip; } } else { indices = PyNetCDFVariable_Indices(var); if (indices == NULL) return NULL; indices[0].start = first; indices[0].stop = last; indices[0].stride = skip; indices[1].start = atom; indices[1].stop = atom+natoms; data = PyNetCDFVariable_ReadAsArray(var, indices); if (data == NULL) return NULL; if (natoms == 1) { data->dimensions[0] = 1; data->dimensions[1] = steps; data->dimensions[2] = 3; if (data->descr->type_num != PyArray_DOUBLE) { PyArrayObject *discard = data; data = (PyArrayObject *) PyArray_ContiguousFromObject((PyObject *)data, PyArray_DOUBLE, 3, 3); Py_DECREF(discard); if (data == NULL) return NULL; } } else { #if defined(NUMPY) npy_intp dim[3]; #else int dim[3]; #endif double *d1; PyArrayObject *discard = data; dim[0] = natoms; dim[1] = steps; dim[2] = 3; #if defined(NUMPY) data = (PyArrayObject *)PyArray_SimpleNew(3, dim, PyArray_DOUBLE); #else data = (PyArrayObject *)PyArray_FromDims(3, dim, PyArray_DOUBLE); #endif if (data == NULL) { Py_DECREF(discard); return NULL; } d1 = (double *)data->data; if (discard->descr->type_num == PyArray_DOUBLE) { double *d2 = (double *)discard->data; for (k = 0; k < natoms; k++) for (j = 0; j < steps; j++) for (i = 0; i < 3; i++) d1[3*(steps*k+j)+i] = d2[3*(natoms*j+k)+i]; } else { float *d2 = (float *)discard->data; for (k = 0; k < natoms; k++) for (j = 0; j < steps; j++) for (i = 0; i < 3; i++) d1[3*(steps*k+j)+i] = (double)d2[3*(natoms*j+k)+i]; } Py_DECREF(discard); } if (box_var != NULL && box == NULL) { box_indices = PyNetCDFVariable_Indices(box_var); if (box_indices == NULL) return NULL; box_indices[0].start = first; box_indices[0].stop = last; box_indices[0].stride = skip; box = PyNetCDFVariable_ReadAsArray(box_var, box_indices); if (box == NULL) { Py_DECREF(data); return NULL; } if (box->descr->type_num != PyArray_DOUBLE) { PyArrayObject *discard = box; box = (PyArrayObject *) PyArray_ContiguousFromObject((PyObject *)box, PyArray_DOUBLE, 2, 2); Py_DECREF(discard); if (box == NULL) { Py_DECREF(data); return NULL; } } } } if (box_var != NULL && trajectory->box_buffer == NULL) { trajectory->box_buffer = box; Py_INCREF(box); trajectory->box_buffer_first = first; trajectory->box_buffer_last = last; trajectory->box_buffer_skip = skip; } if (universe_spec->is_periodic && (correct || box_coordinates)) { vector3 *t = (vector3 *)data->data; if (box_var == NULL) universe_spec->box_function(t, t, steps*natoms, universe_spec->geometry_data, 1); else { for (i = 0; i < natoms; i++) universe_spec->trajectory_function(t+i*steps, t+i*steps, steps, (double *)box->data, 1); } if (correct) for (i = 0; i < natoms; i++) { for (j = 1; j < steps; j++) { int ai = i*steps+j; double dx = t[ai][0]-t[ai-1][0]; double dy = t[ai][1]-t[ai-1][1]; double dz = t[ai][2]-t[ai-1][2]; while (dx > 0.5) { t[ai][0] -= 1.; dx -= 1.; } while (dx < -0.5) { t[ai][0] += 1.; dx += 1.; } while (dy > 0.5) { t[ai][1] -= 1.; dy -= 1.; } while (dy < -0.5) { t[ai][1] += 1.; dy += 1.; } while (dz > 0.5) { t[ai][2] -= 1.; dz -= 1.; } while (dz < -0.5) { t[ai][2] += 1.; dz += 1.; } } } if (!box_coordinates) { if (box_var == NULL) { universe_spec->box_function(t, t, steps*natoms, universe_spec->geometry_data, 0); } else { for (i = 0; i < natoms; i++) universe_spec->trajectory_function(t+i*steps, t+i*steps, steps, (double *)box->data, 0); } } if (box_var != NULL) { Py_DECREF(box); } } return data; } static PyArrayObject * trajectory_read_particle_trajectories(PyTrajectoryObject *self, PyObject *args) { int atom, natoms, first, last, skip, correct=0, box=0; char *variable; if (!PyArg_ParseTuple(args, "iisiii|ii", &atom, &natoms, &variable, &first, &last, &skip, &correct, &box)) return NULL; return PyTrajectory_ReadParticleTrajectories(self, atom, natoms, variable, first, last, skip, correct, box); } static PyArrayObject * trajectory_read_particle_scalar(PyTrajectoryObject *self, PyObject *args) { PyObject *variable; char *var_name; int step; if (!PyArg_ParseTuple(args, "si", &var_name, &step)) return NULL; variable = PyDict_GetItemString(self->file->variables, var_name); if (variable == NULL) return NULL; PyErr_SetString(PyExc_SystemError, "not yet implemented"); return NULL; } /* Write data */ static int PyTrajectory_WriteArray(PyTrajectoryObject *trajectory, PyObject *variable, PyArrayObject *value) { PyNetCDFVariableObject *v = (PyNetCDFVariableObject *)variable; PyNetCDFIndex *indices; if (trajectory->write) { indices = PyNetCDFVariable_Indices(v); if (indices == NULL) return 0; if (trajectory->block_size > 1) { int step = trajectory->steps-1; int minor = step % trajectory->block_size; int major = step / trajectory->block_size; indices[0].start = major; indices[0].stop = major + 1; indices[0].item = 1; indices[v->nd-1].start = minor; indices[v->nd-1].stop = minor + 1; indices[v->nd-1].item = 1; } else { indices[0].start = trajectory->steps-1; indices[0].stop = indices[0].start + 1; indices[0].item = 1; } return PyNetCDFVariable_WriteArray(v, indices, (PyObject *)value); ; } else return 0; } static int PyTrajectory_WriteFloats(PyTrajectoryObject *trajectory, PyObject *variable, double *values, int n) { static PyArrayObject *a[2] = {NULL, NULL}; static int last_n[2] = {0, 0}; int type = (trajectory->floattype == PyArray_DOUBLE) ? 1 : 0; if (last_n[type] != n) { Py_XDECREF(a[type]); a[type] = NULL; } if (a[type] == NULL) { #if defined(NUMPY) { npy_intp dim = n; a[type] = (PyArrayObject *)PyArray_SimpleNew((n == 1) ? 0 : 1, &dim, trajectory->floattype); } #else a[type] = (PyArrayObject *)PyArray_FromDims((n == 1) ? 0 : 1, &n, trajectory->floattype); #endif if (a[type] == NULL) return -1; last_n[type] = n; } if (trajectory->floattype == PyArray_DOUBLE) { double *data = (double *)(a[type]->data); int i; for (i = 0; i < n; i++) data[i] = values[i]; } else { float *data = (float *)(a[type]->data); int i; for (i = 0; i < n; i++) data[i] = (float)values[i]; } return PyTrajectory_WriteArray(trajectory, variable, a[type]); } static int PyTrajectory_WriteInteger(PyTrajectoryObject *trajectory, PyObject *variable, long value) { static PyArrayObject *a = NULL; if (a == NULL) { #if defined(NUMPY) npy_intp n = 1; a = (PyArrayObject *)PyArray_SimpleNew(0, &n, PyArray_LONG); #else int n = 1; a = (PyArrayObject *)PyArray_FromDims(0, &n, PyArray_LONG); #endif if (a == NULL) return -1; } *(long *)(a->data) = value; return PyTrajectory_WriteArray(trajectory, variable, a); } /* Set step number */ static int PyTrajectory_Step(PyTrajectoryObject *trajectory, int step) { if (trajectory->cycle > 0) { trajectory->write = 1; trajectory->steps = (trajectory->steps % trajectory->cycle) + 1; return PyTrajectory_WriteInteger(trajectory, (PyObject *)trajectory->var_step, step); } else if (step >= trajectory->first_step) { trajectory->first_step = 1; trajectory->write = 1; trajectory->steps++; return PyTrajectory_WriteInteger(trajectory, (PyObject *)trajectory->var_step, step); } else { trajectory->write = 0; return 0; } } /* Method table */ static PyMethodDef trajectory_methods[] = { {"readParticleVector", (PyCFunction)trajectory_read_particle_vector, 1}, {"readParticleScalar", (PyCFunction)trajectory_read_particle_scalar, 1}, {"readParticleTrajectories", (PyCFunction)trajectory_read_particle_trajectories, 1}, {"close", (PyCFunction)trajectory_close, 1}, {"flush", (PyCFunction)trajectory_flush, 1}, {NULL, NULL} /* sentinel */ }; /* Attribute access */ static PyObject * getattr(PyTrajectoryObject *self, char *name) { if (self->file == NULL) { PyErr_SetString(PyExc_ValueError, "access to closed trajectory"); return NULL; } if (strcmp(name, "file") == 0) { Py_INCREF(self->file); return (PyObject *)self->file; } else if (strcmp(name, "nsteps") == 0) { return PyInt_FromLong((long)self->steps); } else if (strcmp(name, "recently_read_box_size") == 0) { if (self->box_buffer == NULL) { PyErr_SetString(PyExc_AttributeError, "no box size information"); return NULL; } Py_INCREF(self->box_buffer); return (PyObject *)self->box_buffer; } else return Py_FindMethod(trajectory_methods, (PyObject *)self, name); } static int PyTrajectory_SetAttribute(PyTrajectoryObject *self, char *name, PyObject *value) { return PyNetCDFFile_SetAttribute(self->file, name, value); } /* Type definition */ PyTypeObject PyTrajectory_Type = { PyObject_HEAD_INIT(NULL) 0, /*ob_size*/ "Trajectory", /*tp_name*/ sizeof(PyTrajectoryObject), /*tp_basicsize*/ 0, /*tp_itemsize*/ /* methods */ (destructor)trajectory_dealloc, /*tp_dealloc*/ 0, /*tp_print*/ (getattrfunc)getattr, /*tp_getattr*/ (setattrfunc)PyTrajectory_SetAttribute, /*tp_setattr*/ 0, /*tp_compare*/ 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ }; /* Add a time stamp */ static int PyTrajectory_TimeStamp(PyTrajectoryObject *self, char *text) { time_t now = time(NULL); static char time_stamp[200]; snprintf(time_stamp, sizeof(time_stamp), text, ctime(&now)); time_stamp[strlen(time_stamp)-1] = '\0'; return PyNetCDFFile_AddHistoryLine(self->file, time_stamp); } static char * skip_token(char *p) { if (*p == '\'' || *p == '"') { char delimiter = *p++; while (*p && *p != delimiter) { if (*p == '\\') p += 2; else p++; } if (*p) p++; } else p++; return p; } /* Compare two universe description strings */ static char * skip_object(char *p) { int parens = 0; if (*p == '\'' || *p == '"') p = skip_token(p); else { while (*p && *p != '(') p = skip_token(p); while (*p) { if (*p == '(') parens++; else if (*p == ')') { parens--; if (parens == 0) break; } p = skip_token(p); } while (*p && *p != ',') p = skip_token(p); } while (*p && (*p == ',' || *p == ' ')) p = skip_token(p); return p; } static int verify_description(char *d1, char *d2) { char *p1 = d1; char *p2 = d2; while (*p1 && *p1 != '[') p1++; while (*p1 && (*p1 == '[' || *p1 == ' ')) p1++; while (*p2 && *p2 != '[') p2++; while (*p2 && (*p2 == '[' || *p2 == ' ')) p2++; while (*p1 && *p2) { char *e1, *e2; int n1, n2; while (*p1 && ((*p1 == 'o' && *(p1+1) == '(') || *p1 == '\'' || *p1 == '"')) p1 = skip_object(p1); while (*p2 && ((*p2 == 'o' && *(p2+1) == '(') || *p2 == '\'' || *p2 == '"')) p2 = skip_object(p2); if (!*p1 || !*p2) break; if (*p1 == ']' || *p2 == ']') return *p1 == ']' && *p2 == ']'; e1 = skip_object(p1); n1 = e1-p1; e2 = skip_object(p2); n2 = e2-p2; if (n1 != n2) return 0; if (strncmp(p1, p2, n1) != 0) return 0; p1 = e1; p2 = e2; } return 1; } /* Create a trajectory object */ static PyTrajectoryObject * PyTrajectory_Open(PyObject *universe, PyObject *description, PyArrayObject *index_map, char *filename, char *mode, int floattype, int cycle, int block_size) { PyTrajectoryObject *self; PyUniverseSpecObject *universe_spec; PyObject *n_ob; PyNetCDFVariableObject *description_var; if (!PyObject_HasAttrString(universe, "is_universe")) { PyErr_SetString(PyExc_TypeError, "not a universe object"); return NULL; } universe_spec = (PyUniverseSpecObject *) PyObject_GetAttrString(universe, "_spec"); if (universe_spec == NULL) return NULL; if (!PyString_Check(description)) { PyErr_SetString(PyExc_TypeError, "system description not a string"); return NULL; } n_ob = PyObject_CallMethod((PyObject *)universe, "numberOfAtoms", NULL); if (n_ob == NULL) return NULL; self = PyObject_NEW(PyTrajectoryObject, &PyTrajectory_Type); if (self == NULL) return NULL; self->natoms = PyInt_AsLong(n_ob); Py_DECREF(n_ob); self->universe = universe; Py_INCREF(self->universe); self->index_map = index_map; Py_XINCREF(self->index_map); self->var_step = NULL; self->sbuffer = NULL; self->vbuffer = NULL; self->box_buffer = NULL; self->floattype = floattype; self->trajectory_atoms = (index_map == NULL) ? self->natoms : index_map->dimensions[0]; self->cycle = cycle; if (cycle > 0) block_size = 1; self->block_size = block_size; self->file = PyNetCDFFile_Open(filename, mode); if (self->file == NULL) { goto error; } Py_INCREF(self->file); if ((self->index_map != NULL || floattype != PyArray_DOUBLE) && mode[0] != 'r') { #if defined(NUMPY) npy_intp dim[2]; #else int dim[2]; #endif if (self->index_map != NULL) dim[0] = self->index_map->dimensions[0]; else dim[0] = self->natoms; dim[1] = 3; #if defined(NUMPY) self->vbuffer = (PyArrayObject *)PyArray_SimpleNew(2, dim, floattype); #else self->vbuffer = (PyArrayObject *)PyArray_FromDims(2, dim, floattype); #endif if (self->vbuffer == NULL) goto error; #if defined(NUMPY) self->sbuffer = (PyArrayObject *)PyArray_SimpleNewFromData(1, dim, floattype, self->vbuffer->data); #else self->sbuffer = (PyArrayObject *)PyArray_FromDimsAndData(1, dim, floattype, self->vbuffer->data); #endif if (self->sbuffer == NULL) goto error; } self->first_step = 0; self->var_step = PyNetCDFFile_GetVariable(self->file, "step"); if (self->var_step == NULL) { if (mode[0] == 'r') { PyErr_SetString(PyExc_TypeError, "not a trajectory file"); goto error; } else { char *description_length = "description_length"; Py_ssize_t len; int ret; PyNetCDFFile_SetAttributeString(self->file, "Conventions", "MMTK/Trajectory"); if (block_size == 1) PyNetCDFFile_SetAttribute(self->file, "trajectory_type", PyInt_FromLong(0)); else PyNetCDFFile_SetAttribute(self->file, "trajectory_type", PyInt_FromLong(1)); PyTrajectory_TimeStamp(self, "Created %s"); if (PyNetCDFFile_CreateDimension(self->file, step_number, cycle) == -1) goto error; if (block_size > 1 && PyNetCDFFile_CreateDimension(self->file, minor_step_number, block_size) == -1) goto error; if (PyNetCDFFile_CreateDimension(self->file, atom_number, self->trajectory_atoms) == -1) goto error; if (PyNetCDFFile_CreateDimension(self->file, xyz, 3) == -1) goto error; if (universe_spec->geometry_data_length > 0 && PyNetCDFFile_CreateDimension(self->file, box_size_length, universe_spec->geometry_data_length) == -1) goto error; len = PyString_Size(description); if (len > INT_MAX) { PyErr_SetString(PyExc_ValueError, "description string too long"); goto error; } if (PyNetCDFFile_CreateDimension(self->file, description_length, (int)len) == -1) goto error; description_var = PyNetCDFFile_CreateVariable(self->file, "description", 'c', &description_length, 1); if (description_var == NULL) goto error; ret = PyNetCDFVariable_WriteString(description_var, (PyStringObject *)description); Py_DECREF(description_var); if (ret == -1) goto error; if (self->block_size > 1) { char *dim_names[2]; dim_names[0] = step_number; dim_names[1] = minor_step_number; self->var_step = PyNetCDFFile_CreateVariable(self->file, "step", 'l', dim_names, 2); } else self->var_step = PyNetCDFFile_CreateVariable(self->file, "step", 'l', &step_number, 1); if (self->var_step == NULL) goto error; if (self->cycle > 0 && PyTrajectory_SetAttribute(self, "last_step", PyInt_FromLong(-1)) == -1) goto error; self->steps = 0; } } else { size_t *step_shape = PyNetCDFVariable_GetShape(self->var_step); Py_INCREF(self->var_step); if (PyNetCDFVariable_GetRank(self->var_step) == 2) { PyArrayObject *step_array; self->block_size = step_shape[1]; if (step_shape[0] == 0) self->steps = 0; else { PyNetCDFIndex *indices; int i; self->steps = (step_shape[0]-1)*step_shape[1]; indices = PyNetCDFVariable_Indices(self->var_step); indices[0].start = step_shape[0]-1; indices[0].stop = step_shape[0]; indices[0].item = 1; step_array = PyNetCDFVariable_ReadAsArray(self->var_step, indices); if (step_array == NULL) return NULL; for (i = 0; i < step_shape[1]; i++) { #ifdef _LONG64_ if (((int *)step_array->data)[i] == NC_FILL_INT) #else if (((long *)step_array->data)[i] == NC_FILL_INT) #endif break; self->steps++; } Py_DECREF(step_array); } } else { self->block_size = 1; self->steps = step_shape[0]; } if (mode[0] == 'a') { PyObject *name; PyNetCDFVariableObject *variable; Py_ssize_t pos = 0; while (PyDict_Next(self->file->variables, &pos, &name, (PyObject **)&variable)) if (variable->type == PyArray_FLOAT || variable->type == PyArray_DOUBLE) { self->floattype = variable->type; break; } self->first_step = 1; if (self->cycle > 0) { PyObject *last = PyNetCDFFile_GetAttribute(self->file, "last_step"); if (last != NULL) { int step = *(int *)((PyArrayObject *)last)->data; if (step >= 0) self->steps = (step+1)%self->cycle; } } } n_ob = PyDict_GetItemString(self->file->dimensions, atom_number); if (n_ob == NULL) goto error; self->trajectory_atoms = PyInt_AsLong(n_ob); description_var = PyNetCDFFile_GetVariable(self->file, "description"); if (description_var != NULL) { PyStringObject *traj_description = PyNetCDFVariable_ReadAsString(description_var); if (traj_description == NULL) goto error; if (!verify_description(PyString_AsString(description), PyString_AsString((PyObject *) traj_description))) { PyErr_SetString(PyExc_ValueError, "trajectory file not compatible with universe"); goto error; } Py_DECREF(traj_description); } else PyErr_Clear(); } self->last_flush = clock(); return self; error: trajectory_dealloc(self); return NULL; } static PyObject * Trajectory(PyObject *self, PyObject *args) { PyObject *universe, *description, *index_map; char *filename; char *mode = "r"; int dpflag = 0; int cycle = 0; int block_size = 1; if (!PyArg_ParseTuple(args, "OO!Os|siii:Trajectory", &universe, &PyString_Type, &description, &index_map, &filename, &mode, &dpflag, &cycle, &block_size)) return NULL; if (index_map == Py_None) index_map = NULL; else if (!PyArray_Check(index_map)) { PyErr_SetString(PyExc_TypeError, "index map must be an array"); return NULL; } return (PyObject *)PyTrajectory_Open(universe, description, (PyArrayObject *)index_map, filename, mode, dpflag?PyArray_DOUBLE:PyArray_FLOAT, cycle, block_size); } /* * Handle regular output during trajectory generation. */ enum PySpec_TYPE {PySpec_None, PySpec_Trajectory, PySpec_Print, PySpec_Function}; /* Preprocess an output specification */ static int get_spec(PyObject *universe, PyObject *spec, PyTrajectoryOutputSpec *output, int type, char *description, PyTrajectoryVariable *data, int nvar) { static char text[200]; int i; output->first = PyInt_AsLong(PyTuple_GetItem(spec, (Py_ssize_t)1)); output->last = PyInt_AsLong(PyTuple_GetItem(spec, (Py_ssize_t)2)); output->frequency = PyInt_AsLong(PyTuple_GetItem(spec, (Py_ssize_t)3)); output->close = 0; output->type = type; output->universe = universe; Py_INCREF(universe); output->destination = NULL; output->parameters = NULL; output->scratch = NULL; if (type != PySpec_Function) { PyObject *what = PyTuple_GetItem(spec, (Py_ssize_t)5); Py_ssize_t n = PyObject_Length(what); output->destination = PyTuple_GetItem(spec, (Py_ssize_t)4); if (output->destination == Py_None) return 0; output->what = 0; while (n-- > 0) { PyObject *item = PyObject_GetItem(what, PyInt_FromSsize_t(n)); PyTrajectory_DataClassName *class = class_names; char *s; if (!PyString_Check(item)) { PyErr_SetString(PyExc_TypeError, "output item not a string"); Py_DECREF(item); return -1; } s = PyString_AsString(item); while (class->name != NULL) { if (strcmp(s, class->name) == 0) output->what |= class->number; class++; } Py_DECREF(item); } } if (type == PySpec_Trajectory) { PyTrajectoryObject *trajectory; output->variables = (PyObject **)malloc(nvar*sizeof(PyObject *)); if (output->variables == NULL) return -1; if (PyTrajectory_Check(output->destination)) { trajectory = (PyTrajectoryObject *)output->destination; Py_INCREF(output->destination); } else { while (!PyTrajectory_Check(output->destination)) { output->destination = PyObject_GetAttrString(output->destination, "trajectory"); trajectory = (PyTrajectoryObject *)output->destination; if (output->destination == NULL) { PyErr_SetString(PyExc_TypeError, "not a trajectory"); return -1; } } } if (!PyTrajectory_Check(output->destination)) { PyErr_SetString(PyExc_TypeError, "not a trajectory"); Py_DECREF(output->destination); return -1; } if (description != NULL) { if (strlen(description) < 150) strcpy(text, description); else strcpy(text, "Trajectory"); strcat(text, " started %s"); if (PyTrajectory_TimeStamp(trajectory, text) == -1) return -1; } for (i = 0; i < nvar; i++) { if (output->what & data[i].class) { output->variables[i] = PyTrajectory_GetVariable(trajectory, data[i].name, data[i].type, 0, data[i].unit, 1); if (output->variables[i] == NULL) { Py_DECREF(output->destination); free(output->variables); return -1; } } } } if (type == PySpec_Print) { if (PyString_Check(output->destination)) { PyObject *file = PyFile_FromString(PyString_AsString(output->destination), "a"); if (file == NULL) return -1; output->destination = file; output->close = 1; } else Py_INCREF(output->destination); if (!PyObject_HasAttrString(output->destination, "write")) { PyErr_SetString(PyExc_TypeError, "not a file"); Py_DECREF(output->destination); return -1; } } if (type == PySpec_Function) { output->function = (trajectory_fn *) PyCObject_AsVoidPtr(PyTuple_GetItem(spec, (Py_ssize_t)4)); output->parameters = PyTuple_GetItem(spec, (Py_ssize_t)5); Py_INCREF(output->parameters); if (!output->function(data, output->parameters, -1, &output->scratch, output->universe)) return -1; } return 1; } static PyTrajectoryOutputSpec * PyTrajectory_OutputSpecification(PyObject *universe, PyListObject *spec_list, char *description, PyTrajectoryVariable *data) { PyTrajectoryOutputSpec *output; PyTrajectoryVariable *v; Py_ssize_t nspecs = PyList_Size((PyObject *)spec_list); int nvar = 0; for (v = data; v->name != NULL; v++) nvar++; output = (PyTrajectoryOutputSpec *) malloc((nspecs+1)*sizeof(PyTrajectoryOutputSpec)); if (output != NULL) { int n = 0; int ret; int i; for (i = 0; i < nspecs; i++) { PyObject *spec = PyList_GetItem((PyObject *)spec_list, (Py_ssize_t)i); PyObject *which; char *which_str; int type; if (!PyTuple_Check(spec)) { PyErr_SetString(PyExc_TypeError, "must be a tuple"); free(output); return NULL; } which = PyTuple_GetItem(spec, (Py_ssize_t)0); if (!PyString_Check(which)) { PyErr_SetString(PyExc_TypeError, "must be a string"); free(output); return NULL; } which_str = PyString_AsString(which); if (strcmp(which_str, "print") == 0) type = PySpec_Print; else if (strcmp(which_str, "trajectory") == 0) type = PySpec_Trajectory; else if (strcmp(which_str, "function") == 0) type = PySpec_Function; else { PyErr_SetString(PyExc_TypeError, "illegal specification id"); free(output); return NULL; } ret = get_spec(universe, spec, output+n, type, description, data, nvar); if (ret == -1) return NULL; if (ret == 1) n++; } output[n].type = PySpec_None; } return output; } /* Clean up */ static void PyTrajectory_OutputFinish(PyTrajectoryOutputSpec *spec, int step, int error_flag, int time_stamp_flag, PyTrajectoryVariable *data) { PyTrajectoryOutputSpec *s = spec; PyTrajectory_Output(spec, -step, data, NULL); while (spec->type != PySpec_None) { if (spec->type == PySpec_Trajectory) { char *text; PyTrajectory_Flush((PyTrajectoryObject *)spec->destination); if (error_flag) { if (PyErr_CheckSignals()) text = "Trajectory interrupted %s"; else text = "Trajectory terminated by error %s"; } else text = "Trajectory finished %s"; if (time_stamp_flag || error_flag) PyTrajectory_TimeStamp((PyTrajectoryObject *)spec->destination, text); PyTrajectory_Flush((PyTrajectoryObject *)spec->destination); free(spec->variables); } if (spec->type == PySpec_Function) { spec->function(data, spec->parameters, -2, &spec->scratch, spec->universe); } if (spec->close) { if (spec->type == PySpec_Trajectory) PyTrajectory_Close((PyTrajectoryObject *)spec->destination); else PyObject_CallMethod(spec->destination, "close", NULL); } Py_XDECREF(spec->destination); Py_XDECREF(spec->parameters); Py_XDECREF(spec->universe); spec++; } free(s); } /* Do output for one step */ static int PyTrajectory_Output(PyTrajectoryOutputSpec *spec, int step, PyTrajectoryVariable *data, PyThreadState **thread) { PyTrajectoryVariable *var; double *array_data; char buffer[100]; int interrupt = 0; int i; for (var = data; var->name != NULL; var++) var->modified = 0; while (spec->type != PySpec_None) { if ((step >= spec->first && step < spec->last && (step-spec->first) % spec->frequency == 0) || step < 0) { if (spec->type == PySpec_Print && step >= 0) { if (thread != NULL) PyEval_RestoreThread(*thread); if (step > 0) PyFile_WriteString("\n", spec->destination); snprintf(buffer, sizeof(buffer), "Step %d\n", step); PyFile_WriteString(buffer, spec->destination); for (var = data; var->name != NULL; var++) if (spec->what & var->class) { switch (var->type) { case PyTrajectory_Scalar: snprintf(buffer, sizeof(buffer), var->text, *var->value.dp); PyFile_WriteString(buffer, spec->destination); break; case PyTrajectory_ParticleScalar: strcpy(buffer, var->text); PyFile_WriteString(buffer, spec->destination); array_data = (double *)var->value.array->data; for (i = 0; i < var->value.array->dimensions[0]; i++) { snprintf(buffer, sizeof(buffer), " %5d: %g\n", i, *array_data++); PyFile_WriteString(buffer, spec->destination); } break; case PyTrajectory_ParticleVector: strcpy(buffer, var->text); PyFile_WriteString(buffer, spec->destination); array_data = (double *)var->value.array->data; for (i = 0; i < var->value.array->dimensions[0]; i++) { snprintf(buffer, sizeof(buffer)," %5d: %g, %g, %g\n", i, array_data[0], array_data[1], array_data[2]); PyFile_WriteString(buffer, spec->destination); array_data += 3; } break; case PyTrajectory_BoxSize: strcpy(buffer, var->text); PyFile_WriteString(buffer, spec->destination); array_data = var->value.dp; for (i = 0; i < var->length; i++) { snprintf(buffer, sizeof(buffer), " %g", *array_data++); PyFile_WriteString(buffer, spec->destination); if (i == var->length-1) snprintf(buffer, sizeof(buffer), "\n"); else snprintf(buffer, sizeof(buffer), ","); PyFile_WriteString(buffer, spec->destination); } break; } } if (PyObject_HasAttrString(spec->destination, "flush")) PyObject_CallMethod(spec->destination, "flush", NULL); if (PyErr_CheckSignals()) interrupt = -1; if (thread != NULL) *thread = PyEval_SaveThread(); } if (spec->type == PySpec_Trajectory) { PyTrajectoryObject *trajectory = (PyTrajectoryObject *)spec->destination; PyObject **tvar; clock_t cpu_time; if (trajectory->cycle > 0 && step < 0) step = -step; if (step >= 0) { if (thread != NULL) PyEval_RestoreThread(*thread); if (PyTrajectory_Step(trajectory, step) == -1) { if (thread != NULL) *thread = PyEval_SaveThread(); return -1; } for (var = data, tvar = spec->variables; var->name != NULL; var++, tvar++) if (spec->what & var->class) { PyArrayObject *array; switch (var->type) { case PyTrajectory_Scalar: if (PyTrajectory_WriteFloats(trajectory, *tvar, var->value.dp, 1) == -1) { if (thread != NULL) *thread = PyEval_SaveThread(); return -1; } break; case PyTrajectory_ParticleScalar: if (trajectory->index_map != NULL) { int i; long *indices = (long *)trajectory->index_map->data; double *source = (double *)var->value.array->data; if (trajectory->floattype == PyArray_DOUBLE) { double *dest = (double *)trajectory->sbuffer->data; for (i = 0; i < trajectory->index_map->dimensions[0]; i++) dest[i] = source[indices[i]]; } else { float *dest = (float *)trajectory->sbuffer->data; for (i = 0; i < trajectory->index_map->dimensions[0]; i++) dest[i] = (float)source[indices[i]]; } array = trajectory->sbuffer; } else { if (trajectory->floattype == PyArray_DOUBLE) array = var->value.array; else { double *source = (double *)var->value.array->data; float *dest = (float *)trajectory->sbuffer->data; int natoms = var->value.array->dimensions[0]; int i; for (i = 0; i < natoms; i++) dest[i] = (float)source[i]; array = trajectory->sbuffer; } } if (PyTrajectory_WriteArray(trajectory, *tvar, array) == -1) { if (thread != NULL) *thread = PyEval_SaveThread(); return -1; } break; case PyTrajectory_ParticleVector: if (trajectory->index_map != NULL) { int i; long *indices = (long *)trajectory->index_map->data; vector3 *source = (vector3 *)var->value.array->data; if (trajectory->floattype == PyArray_DOUBLE) { vector3 *dest = (vector3 *)trajectory->vbuffer->data; for (i = 0; i < trajectory->index_map->dimensions[0]; i++){ dest[i][0] = source[indices[i]][0]; dest[i][1] = source[indices[i]][1]; dest[i][2] = source[indices[i]][2]; } } else { float *dest = (float *)trajectory->vbuffer->data; for (i = 0; i < trajectory->index_map->dimensions[0]; i++){ dest[3*i] = (float)source[indices[i]][0]; dest[3*i+1] = (float)source[indices[i]][1]; dest[3*i+2] = (float)source[indices[i]][2]; } } array = trajectory->vbuffer; } else { if (trajectory->floattype == PyArray_DOUBLE) array = var->value.array; else { vector3 *source = (vector3 *)var->value.array->data; int natoms = var->value.array->dimensions[0]; float *dest = (float *)trajectory->vbuffer->data; int i; for (i = 0; i < natoms; i++) { dest[3*i] = (float)source[i][0]; dest[3*i+1] = (float)source[i][1]; dest[3*i+2] = (float)source[i][2]; } array = trajectory->vbuffer; } } if (PyTrajectory_WriteArray(trajectory, *tvar, array) == -1) { if (thread != NULL) *thread = PyEval_SaveThread(); return -1; } break; case PyTrajectory_BoxSize: if (PyTrajectory_WriteFloats(trajectory, *tvar, var->value.dp, var->length) == -1) { if (thread != NULL) *thread = PyEval_SaveThread(); return -1; } break; } } cpu_time = clock(); if (trajectory->cycle > 0) { if (PyTrajectory_SetAttribute(trajectory, "last_step", PyInt_FromLong(trajectory->steps-1)) == -1) { if (thread != NULL) *thread = PyEval_SaveThread(); return -1; } if (PyTrajectory_Flush(trajectory) == -1) { if (thread != NULL) *thread = PyEval_SaveThread(); return -1; } } else if (cpu_time-trajectory->last_flush > 900*CLOCKS_PER_SEC) { if (PyTrajectory_Flush(trajectory) == -1) { if (thread != NULL) *thread = PyEval_SaveThread(); return -1; } trajectory->last_flush = cpu_time; } if (PyErr_CheckSignals()) interrupt = -1; if (thread != NULL) *thread = PyEval_SaveThread(); } } if (spec->type == PySpec_Function && step >= 0) { if (thread != NULL) PyEval_RestoreThread(*thread); if (!spec->function(data, spec->parameters, step, &spec->scratch, spec->universe)) interrupt = -1; if (thread != NULL) *thread = PyEval_SaveThread(); } } spec++; } return interrupt; } /* Transformation from/to box coordinates */ static PyObject * boxTransformation(PyObject *dummy, PyObject *args) { PyUniverseSpecObject *universe_spec; PyArrayObject *pt_in, *pt_out, *box_size; int to_box; vector3 *in, *out; double *box; if (!PyArg_ParseTuple(args, "O!O!O!O!i", &PyUniverseSpec_Type, &universe_spec, &PyArray_Type, &pt_in, &PyArray_Type, &pt_out, &PyArray_Type, &box_size, &to_box)) return NULL; in = (vector3 *)pt_in->data; out = (vector3 *)pt_out->data; box = (double *)box_size->data; universe_spec->trajectory_function(in, out, pt_in->dimensions[0], box, to_box); Py_INCREF(Py_None); return Py_None; } /* Snapshot generator: process the current situation as one step of a trajectory */ static PyObject * snapshot(PyObject *dummy, PyObject *args) { PyObject *universe; PyListObject *spec_list; PyObject *data_dict, *data_key, *data_value; PyTrajectoryVariable *vars, *v, *vtemp; PyTrajectoryOutputSpec *output; char string_buffer[80]; char *name; int energy_terms; Py_ssize_t dict_pos; /* Parse and check arguments */ if (!PyArg_ParseTuple(args, "OO!O!i", &universe, &PyDict_Type, &data_dict, &PyList_Type, &spec_list, &energy_terms)) return NULL; vars = (PyTrajectoryVariable *)malloc((9+energy_terms) * sizeof(PyTrajectoryVariable)); if (vars == NULL) { PyErr_NoMemory(); return NULL; } vars[0].name = "temperature"; vars[0].text = "Temperature: %lf\n"; vars[0].unit = temperature_unit_name; vars[0].type = PyTrajectory_Scalar; vars[0].class = PyTrajectory_Thermodynamic; vars[0].value.dp = NULL; vars[1].name = "pressure"; vars[1].text = "Pressure: %lf\n"; vars[1].unit = pressure_unit_name; vars[1].type = PyTrajectory_Scalar; vars[1].class = PyTrajectory_Thermodynamic; vars[1].value.dp = NULL; vars[2].name = "configuration"; vars[2].text = "Configuration:\n"; vars[2].unit = length_unit_name; vars[2].type = PyTrajectory_ParticleVector; vars[2].class = PyTrajectory_Configuration; vars[2].value.array = NULL; vars[3].name = "velocities"; vars[3].text = "Velocities:\n"; vars[3].unit = velocity_unit_name; vars[3].type = PyTrajectory_ParticleVector; vars[3].class = PyTrajectory_Velocities; vars[3].value.array = NULL; vars[4].name = "gradients"; vars[4].text = "Energy gradients:\n"; vars[4].unit = energy_gradient_unit_name; vars[4].type = PyTrajectory_ParticleVector; vars[4].class = PyTrajectory_Gradients; vars[4].value.array = NULL; vars[5].name = "gradient_norm"; vars[5].text = "Gradient norm: %lf\n"; vars[5].unit = energy_gradient_unit_name; vars[5].type = PyTrajectory_Scalar; vars[5].class = PyTrajectory_Energy; vars[5].value.dp = NULL; vars[6].name = "box_size"; vars[6].text = "Box size:"; vars[6].unit = length_unit_name; vars[6].type = PyTrajectory_BoxSize; vars[6].class = PyTrajectory_Configuration; vars[6].value.dp = NULL; vars[7].name = "time"; vars[7].text = "Time: %lf\n"; vars[7].unit = time_unit_name; vars[7].type = PyTrajectory_Scalar; vars[7].class = PyTrajectory_Time; vars[7].value.dp = NULL; vars[8].name = NULL; for (v = vars; v->name != NULL; ) { PyObject *value = PyDict_GetItemString(data_dict, v->name); if (value == NULL) { for (vtemp = v; vtemp->name != NULL; vtemp++) *vtemp = *(vtemp+1); } else { if (v->type == PyTrajectory_Scalar) { v->value.dp = (double *)malloc(sizeof(double)); if (v->value.dp == NULL) { PyErr_NoMemory(); goto error2; } *(v->value.dp) = PyFloat_AsDouble(value); } else if (v->type == PyTrajectory_BoxSize) { v->value.dp = (double *)((PyArrayObject *)value)->data; v->length = ((PyArrayObject *)value)->dimensions[0]; } else v->value.array = (PyArrayObject *)value; v++; } } dict_pos = 0; while (PyDict_Next(data_dict, &dict_pos, &data_key, &data_value)) { name = PyString_AsString(data_key); if (strcmp(name+strlen(name)-7, "_energy") == 0) { char *s; strcpy(string_buffer, name); for (s = string_buffer; *s; s++) if (*s == '_') *s = ' '; strcpy(string_buffer + strlen(string_buffer), ": %lf\n"); v->name = name; v->text = string_buffer; v->unit = energy_unit_name; v->type = PyTrajectory_Scalar; v->class = PyTrajectory_Energy; v->value.dp = (double *)malloc(sizeof(double)); if (v->value.dp == NULL) { PyErr_NoMemory(); goto error2; } *(v->value.dp) = PyFloat_AsDouble(data_value); v++; } } v->name = NULL; output = PyTrajectory_OutputSpecification(universe, spec_list, NULL, vars); if (output == NULL) goto error2; if (PyTrajectory_Output(output, 1, vars, NULL) == -1) goto error; PyTrajectory_OutputFinish(output, 1, 0, 0, vars); for (v = vars; v->name != NULL; v++) if (v->type == PyTrajectory_Scalar) free(v->value.dp); free(vars); Py_INCREF(Py_None); return Py_None; error: PyTrajectory_OutputFinish(output, 1, 1, 0, vars); error2: for (v = vars; v->name != NULL; v++) if (v->type == PyTrajectory_Scalar) free(v->value.dp); free(vars); return NULL; } /* Trajectory reader */ static PyObject * readTrajectory(PyObject *dummy, PyObject *args) { PyObject *universe; PyTrajectoryObject *input; PyListObject *spec_list; PyObject *input_vars; Py_ssize_t ninvars; PyTrajectoryVariable *vars; PyObject *name; PyNetCDFVariableObject *var; Py_ssize_t i; int j; /* Parse and check arguments */ if (!PyArg_ParseTuple(args, "OO!O!", &universe, &PyTrajectory_Type, &input, &PyList_Type, &spec_list)) return NULL; /* Create temporary arrays for all variables */ input_vars = input->file->variables; ninvars = PyDict_Size(input_vars); vars = (PyTrajectoryVariable *) malloc((ninvars+1)*sizeof(PyTrajectoryVariable)); if (vars == NULL) { PyErr_NoMemory(); return NULL; } i = 0; j = 0; while (PyDict_Next(input_vars, &i, &name, (PyObject **)&var)) { char *n = PyString_AsString(name); if (var->unlimited && strcmp(n, "step") != 0) { if (var->nd == 3) { /* particle array */ #if defined(NUMPY) npy_intp shape[2]; #else int shape[2]; #endif vars[j].type = PyTrajectory_ParticleVector; shape[0] = input->natoms; shape[1] = 3; #if defined(NUMPY) vars[j].value.array = (PyArrayObject *) PyArray_SimpleNew(2, shape,input->floattype); #else vars[j].value.array = (PyArrayObject *) PyArray_FromDims(2, shape,input->floattype); #endif if (vars[j].value.array == NULL) { PyErr_NoMemory(); goto error; } } else if (var->nd == 2) { /* particle scalar */ /* not implemented */ continue; } else { /* scalar */ vars[j].type = PyTrajectory_Scalar; vars[j].value.dp = (double *)malloc(sizeof(double)); if (vars[j].value.dp == NULL) { PyErr_NoMemory(); goto error; } } vars[j].name = n; vars[j].unit = PyString_AsString(PyNetCDFVariable_GetAttribute(var, "units")); vars[j].text = ""; vars[j].class = 0; j += 1; } } vars[j].name = NULL; /* Loop over all steps */ /* Error exit */ error: return NULL; } /* Table of functions defined in the module */ static PyMethodDef module_methods[] = { {"Trajectory", Trajectory, 1}, {"boxTransformation", boxTransformation, 1}, {"snapshot", snapshot, 1}, {"readTrajectory", readTrajectory, 1}, {NULL, NULL} /* sentinel */ }; /* Module initialization */ DL_EXPORT(void) initMMTK_trajectory(void) { PyObject *module, *dict; PyObject *netcdf; static void *PyTrajectory_API[PyTrajectory_API_pointers]; /* Patch object type */ #ifdef EXTENDED_TYPES if (PyType_Ready(&PyTrajectory_Type) < 0) return; #else PyTrajectory_Type.ob_type = &PyType_Type; #endif /* Create the module and add the type object */ module = Py_InitModule("MMTK_trajectory", module_methods); dict = PyModule_GetDict(module); PyDict_SetItemString(dict, "trajectory_type",(PyObject *)&PyTrajectory_Type); /* Import the array module */ #ifdef import_array import_array(); #endif /* Import the universe module */ module = PyImport_ImportModule("MMTK_universe"); if (module != NULL) { PyObject *module_dict = PyModule_GetDict(module); PyObject *c_api_object = PyDict_GetItemString(module_dict, "_C_API"); if (PyCObject_Check(c_api_object)) PyUniverse_API = (void **)PyCObject_AsVoidPtr(c_api_object); } /* Initialize C API pointer array and store in module */ PyTrajectory_API[PyTrajectory_Type_NUM] = (void *)&PyTrajectory_Type; PyTrajectory_API[PyTrajectory_Open_NUM] = (void *)&PyTrajectory_Open; PyTrajectory_API[PyTrajectory_Close_NUM] = (void *)&PyTrajectory_Close; PyTrajectory_API[PyTrajectory_OutputSpecification_NUM] = (void *)&PyTrajectory_OutputSpecification; PyTrajectory_API[PyTrajectory_OutputFinish_NUM] = (void *)&PyTrajectory_OutputFinish; PyTrajectory_API[PyTrajectory_Output_NUM] = (void *)&PyTrajectory_Output; PyDict_SetItemString(dict, "_C_API", PyCObject_FromVoidPtr((void *)PyTrajectory_API, NULL)); /* Define maxint */ PyDict_SetItemString(dict, "maxint", PyInt_FromLong(INT_MAX)); /* Check for errors */ if (PyErr_Occurred()) Py_FatalError("can't initialize module MMTK_trajectory"); /* Import netcdf and retrieve its C API address array */ netcdf = PyImport_ImportModule("Scientific.IO.NetCDF"); if (netcdf != NULL) { PyObject *module_dict = PyModule_GetDict(netcdf); PyObject *c_api_object = PyDict_GetItemString(module_dict, "_C_API"); fflush(stdout); if (PyCObject_Check(c_api_object)) { PyNetCDF_API = (void **)PyCObject_AsVoidPtr(c_api_object); 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"" : "s", num_found); } static void __Pyx_RaiseDoubleKeywordsError( const char* func_name, PyObject* kw_name) { PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION >= 3 "%s() got multiple values for keyword argument '%U'", func_name, kw_name); #else "%s() got multiple values for keyword argument '%s'", func_name, PyString_AS_STRING(kw_name)); #endif } static int __Pyx_ParseOptionalKeywords( PyObject *kwds, PyObject **argnames[], PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, const char* function_name) { PyObject *key = 0, *value = 0; Py_ssize_t pos = 0; PyObject*** name; PyObject*** first_kw_arg = argnames + num_pos_args; while (PyDict_Next(kwds, &pos, &key, &value)) { name = first_kw_arg; while (*name && (**name != key)) name++; if (*name) { values[name-argnames] = value; } else { #if PY_MAJOR_VERSION < 3 if (unlikely(!PyString_CheckExact(key)) && unlikely(!PyString_Check(key))) { #else if (unlikely(!PyUnicode_CheckExact(key)) && unlikely(!PyUnicode_Check(key))) { #endif goto invalid_keyword_type; } else { for (name = first_kw_arg; *name; name++) { #if PY_MAJOR_VERSION >= 3 if (PyUnicode_GET_SIZE(**name) == PyUnicode_GET_SIZE(key) && PyUnicode_Compare(**name, key) == 0) break; #else if (PyString_GET_SIZE(**name) == PyString_GET_SIZE(key) && _PyString_Eq(**name, key)) break; #endif } if (*name) { values[name-argnames] = value; } else { /* unexpected keyword found */ for (name=argnames; name != first_kw_arg; name++) { if (**name == key) goto arg_passed_twice; #if PY_MAJOR_VERSION >= 3 if (PyUnicode_GET_SIZE(**name) == PyUnicode_GET_SIZE(key) && PyUnicode_Compare(**name, key) == 0) goto arg_passed_twice; #else if (PyString_GET_SIZE(**name) == PyString_GET_SIZE(key) && _PyString_Eq(**name, key)) goto arg_passed_twice; #endif } if (kwds2) { if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; } else { goto invalid_keyword; } } } } } return 0; arg_passed_twice: __Pyx_RaiseDoubleKeywordsError(function_name, **name); goto bad; invalid_keyword_type: PyErr_Format(PyExc_TypeError, "%s() keywords must be strings", function_name); goto bad; invalid_keyword: PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION < 3 "%s() got an unexpected keyword argument '%s'", function_name, PyString_AsString(key)); #else "%s() got an unexpected keyword argument '%U'", function_name, key); #endif bad: return -1; } static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name) { PyObject *result; result = PyObject_GetAttr(dict, name); if (!result) { if (dict != __pyx_b) { PyErr_Clear(); result = PyObject_GetAttr(__pyx_b, name); } if (!result) { PyErr_SetObject(PyExc_NameError, name); } } return result; } static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, long level) { PyObject *py_import = 0; PyObject *empty_list = 0; PyObject *module = 0; PyObject *global_dict = 0; PyObject *empty_dict = 0; PyObject *list; py_import = __Pyx_GetAttrString(__pyx_b, "__import__"); if (!py_import) goto bad; if (from_list) list = from_list; else { empty_list = PyList_New(0); if (!empty_list) goto bad; list = empty_list; } global_dict = PyModule_GetDict(__pyx_m); if (!global_dict) goto bad; empty_dict = PyDict_New(); if (!empty_dict) goto bad; #if PY_VERSION_HEX >= 0x02050000 { PyObject *py_level = PyInt_FromLong(level); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, NULL); Py_DECREF(py_level); } #else if (level>0) { PyErr_SetString(PyExc_RuntimeError, "Relative import is not supported for Python <=2.4."); goto bad; } module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, NULL); #endif bad: Py_XDECREF(empty_list); Py_XDECREF(py_import); Py_XDECREF(empty_dict); return module; } static CYTHON_INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject* x) { const unsigned char neg_one = (unsigned char)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(unsigned char) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(unsigned char)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to unsigned char" : "value too large to convert to unsigned char"); } return (unsigned char)-1; } return (unsigned char)val; } return (unsigned char)__Pyx_PyInt_AsUnsignedLong(x); } static CYTHON_INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject* x) { const unsigned short neg_one = (unsigned short)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(unsigned short) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(unsigned short)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to unsigned short" : "value too large to convert to unsigned short"); } return (unsigned short)-1; } return (unsigned short)val; } return (unsigned short)__Pyx_PyInt_AsUnsignedLong(x); } static CYTHON_INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject* x) { const unsigned int neg_one = (unsigned int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(unsigned int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(unsigned int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to unsigned int" : "value too large to convert to unsigned int"); } return (unsigned int)-1; } return (unsigned int)val; } return (unsigned int)__Pyx_PyInt_AsUnsignedLong(x); } static CYTHON_INLINE char __Pyx_PyInt_AsChar(PyObject* x) { const char neg_one = (char)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(char) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(char)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to char" : "value too large to convert to char"); } return (char)-1; } return (char)val; } return (char)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE short __Pyx_PyInt_AsShort(PyObject* x) { const short neg_one = (short)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(short) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(short)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to short" : "value too large to convert to short"); } return (short)-1; } return (short)val; } return (short)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE int __Pyx_PyInt_AsInt(PyObject* x) { const int neg_one = (int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to int" : "value too large to convert to int"); } return (int)-1; } return (int)val; } return (int)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE signed char __Pyx_PyInt_AsSignedChar(PyObject* x) { const signed char neg_one = (signed char)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(signed char) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(signed char)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to signed char" : "value too large to convert to signed char"); } return (signed char)-1; } return (signed char)val; } return (signed char)__Pyx_PyInt_AsSignedLong(x); } static CYTHON_INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject* x) { const signed short neg_one = (signed short)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(signed short) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(signed short)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to signed short" : "value too large to convert to signed short"); } return (signed short)-1; } return (signed short)val; } return (signed short)__Pyx_PyInt_AsSignedLong(x); } static CYTHON_INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject* x) { const signed int neg_one = (signed int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(signed int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(signed int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to signed int" : "value too large to convert to signed int"); } return (signed int)-1; } return (signed int)val; } return (signed int)__Pyx_PyInt_AsSignedLong(x); } static CYTHON_INLINE int __Pyx_PyInt_AsLongDouble(PyObject* x) { const int neg_one = (int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to int" : "value too large to convert to int"); } return (int)-1; } return (int)val; } return (int)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject* x) { const unsigned long neg_one = (unsigned long)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned long"); return (unsigned long)-1; } return (unsigned long)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned long"); return (unsigned long)-1; } return (unsigned long)PyLong_AsUnsignedLong(x); } else { return (unsigned long)PyLong_AsLong(x); } } else { unsigned long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (unsigned long)-1; val = __Pyx_PyInt_AsUnsignedLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject* x) { const unsigned PY_LONG_LONG neg_one = (unsigned PY_LONG_LONG)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned PY_LONG_LONG"); return (unsigned PY_LONG_LONG)-1; } return (unsigned PY_LONG_LONG)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned PY_LONG_LONG"); return (unsigned PY_LONG_LONG)-1; } return (unsigned PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { return (unsigned PY_LONG_LONG)PyLong_AsLongLong(x); } } else { unsigned PY_LONG_LONG val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (unsigned PY_LONG_LONG)-1; val = __Pyx_PyInt_AsUnsignedLongLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE long __Pyx_PyInt_AsLong(PyObject* x) { const long neg_one = (long)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long)-1; } return (long)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long)-1; } return (long)PyLong_AsUnsignedLong(x); } else { return (long)PyLong_AsLong(x); } } else { long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (long)-1; val = __Pyx_PyInt_AsLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject* x) { const PY_LONG_LONG neg_one = (PY_LONG_LONG)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to PY_LONG_LONG"); return (PY_LONG_LONG)-1; } return (PY_LONG_LONG)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to PY_LONG_LONG"); return (PY_LONG_LONG)-1; } return (PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { return (PY_LONG_LONG)PyLong_AsLongLong(x); } } else { PY_LONG_LONG val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (PY_LONG_LONG)-1; val = __Pyx_PyInt_AsLongLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject* x) { const signed long neg_one = (signed long)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed long"); return (signed long)-1; } return (signed long)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed long"); return (signed long)-1; } return (signed long)PyLong_AsUnsignedLong(x); } else { return (signed long)PyLong_AsLong(x); } } else { signed long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (signed long)-1; val = __Pyx_PyInt_AsSignedLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject* x) { const signed PY_LONG_LONG neg_one = (signed PY_LONG_LONG)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed PY_LONG_LONG"); return (signed PY_LONG_LONG)-1; } return (signed PY_LONG_LONG)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed PY_LONG_LONG"); return (signed PY_LONG_LONG)-1; } return (signed PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { return (signed PY_LONG_LONG)PyLong_AsLongLong(x); } } else { signed PY_LONG_LONG val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (signed PY_LONG_LONG)-1; val = __Pyx_PyInt_AsSignedLongLong(tmp); Py_DECREF(tmp); return val; } } static int __Pyx_check_binary_version(void) { char ctversion[4], rtversion[4]; PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { char message[200]; PyOS_snprintf(message, sizeof(message), "compiletime version %s of module '%.100s' " "does not match runtime version %s", ctversion, __Pyx_MODULE_NAME, rtversion); #if PY_VERSION_HEX < 0x02050000 return PyErr_Warn(NULL, message); #else return PyErr_WarnEx(NULL, message, 1); #endif } return 0; } #ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict) { PyObject *py_module = 0; PyObject *result = 0; PyObject *py_name = 0; char warning[200]; py_module = __Pyx_ImportModule(module_name); if (!py_module) goto bad; #if PY_MAJOR_VERSION < 3 py_name = PyString_FromString(class_name); #else py_name = PyUnicode_FromString(class_name); #endif if (!py_name) goto bad; result = PyObject_GetAttr(py_module, py_name); Py_DECREF(py_name); py_name = 0; Py_DECREF(py_module); py_module = 0; if (!result) goto bad; if (!PyType_Check(result)) { PyErr_Format(PyExc_TypeError, "%s.%s is not a type object", module_name, class_name); goto bad; } if (!strict && ((PyTypeObject *)result)->tp_basicsize > (Py_ssize_t)size) { PyOS_snprintf(warning, sizeof(warning), "%s.%s size changed, may indicate binary incompatibility", module_name, class_name); #if PY_VERSION_HEX < 0x02050000 if (PyErr_Warn(NULL, warning) < 0) goto bad; #else if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; #endif } else if (((PyTypeObject *)result)->tp_basicsize != (Py_ssize_t)size) { PyErr_Format(PyExc_ValueError, "%s.%s has the wrong size, try recompiling", module_name, class_name); goto bad; } return (PyTypeObject *)result; bad: Py_XDECREF(py_module); Py_XDECREF(result); return NULL; } #endif #ifndef __PYX_HAVE_RT_ImportModule #define __PYX_HAVE_RT_ImportModule static PyObject *__Pyx_ImportModule(const char *name) { PyObject *py_name = 0; PyObject *py_module = 0; #if PY_MAJOR_VERSION < 3 py_name = PyString_FromString(name); #else py_name = PyUnicode_FromString(name); #endif if (!py_name) goto bad; py_module = PyImport_Import(py_name); Py_DECREF(py_name); return py_module; bad: Py_XDECREF(py_name); return 0; } #endif static int __Pyx_SetVtable(PyObject *dict, void *vtable) { #if PY_VERSION_HEX >= 0x02070000 && !(PY_MAJOR_VERSION==3&&PY_MINOR_VERSION==0) PyObject *ob = PyCapsule_New(vtable, 0, 0); #else PyObject *ob = PyCObject_FromVoidPtr(vtable, 0); #endif if (!ob) goto bad; if (PyDict_SetItemString(dict, "__pyx_vtable__", ob) < 0) goto bad; Py_DECREF(ob); return 0; bad: Py_XDECREF(ob); return -1; } #include "compile.h" #include "frameobject.h" #include "traceback.h" static void __Pyx_AddTraceback(const char *funcname, int __pyx_clineno, int __pyx_lineno, const char *__pyx_filename) { PyObject *py_srcfile = 0; PyObject *py_funcname = 0; PyObject *py_globals = 0; PyCodeObject *py_code = 0; PyFrameObject *py_frame = 0; #if PY_MAJOR_VERSION < 3 py_srcfile = PyString_FromString(__pyx_filename); #else py_srcfile = PyUnicode_FromString(__pyx_filename); #endif if (!py_srcfile) goto bad; if (__pyx_clineno) { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno); #else py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno); #endif } else { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromString(funcname); #else py_funcname = PyUnicode_FromString(funcname); #endif } if (!py_funcname) goto bad; py_globals = PyModule_GetDict(__pyx_m); if (!py_globals) goto bad; py_code = PyCode_New( 0, /*int argcount,*/ #if PY_MAJOR_VERSION >= 3 0, /*int kwonlyargcount,*/ #endif 0, /*int nlocals,*/ 0, /*int stacksize,*/ 0, /*int flags,*/ __pyx_empty_bytes, /*PyObject *code,*/ __pyx_empty_tuple, /*PyObject *consts,*/ __pyx_empty_tuple, /*PyObject *names,*/ __pyx_empty_tuple, /*PyObject *varnames,*/ __pyx_empty_tuple, /*PyObject *freevars,*/ __pyx_empty_tuple, /*PyObject *cellvars,*/ py_srcfile, /*PyObject *filename,*/ py_funcname, /*PyObject *name,*/ __pyx_lineno, /*int firstlineno,*/ __pyx_empty_bytes /*PyObject *lnotab*/ ); if (!py_code) goto bad; py_frame = PyFrame_New( PyThreadState_GET(), /*PyThreadState *tstate,*/ py_code, /*PyCodeObject *code,*/ py_globals, /*PyObject *globals,*/ 0 /*PyObject *locals*/ ); if (!py_frame) goto bad; py_frame->f_lineno = __pyx_lineno; PyTraceBack_Here(py_frame); bad: Py_XDECREF(py_srcfile); Py_XDECREF(py_funcname); Py_XDECREF(py_code); Py_XDECREF(py_frame); } static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { while (t->p) { #if PY_MAJOR_VERSION < 3 if (t->is_unicode) { *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); } else if (t->intern) { *t->p = PyString_InternFromString(t->s); } else { *t->p = PyString_FromStringAndSize(t->s, t->n - 1); } #else /* Python 3+ has unicode identifiers */ if (t->is_unicode | t->is_str) { if (t->intern) { *t->p = PyUnicode_InternFromString(t->s); } else if (t->encoding) { *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); } else { *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); } } else { *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); } #endif if (!*t->p) return -1; ++t; } return 0; } /* Type Conversion Functions */ static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { int is_true = x == Py_True; if (is_true | (x == Py_False) | (x == Py_None)) return is_true; else return PyObject_IsTrue(x); } static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { PyNumberMethods *m; const char *name = NULL; PyObject *res = NULL; #if PY_VERSION_HEX < 0x03000000 if (PyInt_Check(x) || PyLong_Check(x)) #else if (PyLong_Check(x)) #endif return Py_INCREF(x), x; m = Py_TYPE(x)->tp_as_number; #if PY_VERSION_HEX < 0x03000000 if (m && m->nb_int) { name = "int"; res = PyNumber_Int(x); } else if (m && m->nb_long) { name = "long"; res = PyNumber_Long(x); } #else if (m && m->nb_int) { name = "int"; res = PyNumber_Long(x); } #endif if (res) { #if PY_VERSION_HEX < 0x03000000 if (!PyInt_Check(res) && !PyLong_Check(res)) { #else if (!PyLong_Check(res)) { #endif PyErr_Format(PyExc_TypeError, "__%s__ returned non-%s (type %.200s)", name, name, Py_TYPE(res)->tp_name); Py_DECREF(res); return NULL; } } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_TypeError, "an integer is required"); } return res; } static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { Py_ssize_t ival; PyObject* x = PyNumber_Index(b); if (!x) return -1; ival = PyInt_AsSsize_t(x); Py_DECREF(x); return ival; } static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { #if PY_VERSION_HEX < 0x02050000 if (ival <= LONG_MAX) return PyInt_FromLong((long)ival); else { unsigned char *bytes = (unsigned char *) &ival; int one = 1; int little = (int)*(unsigned char*)&one; return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0); } #else return PyInt_FromSize_t(ival); #endif } static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject* x) { unsigned PY_LONG_LONG val = __Pyx_PyInt_AsUnsignedLongLong(x); if (unlikely(val == (unsigned PY_LONG_LONG)-1 && PyErr_Occurred())) { return (size_t)-1; } else if (unlikely(val != (unsigned PY_LONG_LONG)(size_t)val)) { PyErr_SetString(PyExc_OverflowError, "value too large to convert to size_t"); return (size_t)-1; } return (size_t)val; } #endif /* Py_PYTHON_H */ MMTK-2.7.9/Src/MMTK_trajectory_action.pyx0000644000076600000240000000423211662461640020564 0ustar hinsenstaff00000000000000# Trajectory actions in Cython # # Written by Konrad Hinsen # import MMTK_trajectory cdef extern from "cobject.h": object PyCObject_FromVoidPtr(void *pointer, void (*destruct)(void*)) void *PyCObject_AsVoidPtr(object) # The function run_action implements the C interface for trajectory action # functions, mapping it to the nicer OO interface of the Cython class. cdef run_action(PyTrajectoryVariable *dynamic_data, TrajectoryAction action, int step, void **scratch, object universe): # Every trajectory generator has its own scratch pointer, # therefore this can be used as an ID for the generator. if step == -1: return action.initialize(universe, scratch, dynamic_data) elif step == -2: return action.finalize(universe, scratch, dynamic_data) else: return action.apply(universe, scratch, step, dynamic_data) # The base class for trajectoy actions cdef class TrajectoryAction: def __init__(self, long first, object last, long skip): self.first = first if last is None: self.last = MMTK_trajectory.maxint else: self.last = last self.skip = skip def getSpecificationList(self, object trajectory_generator, long steps): cdef long first cdef long last first = self.first if first < 0: first += steps last = self.last if last < 0: last += steps+1 return ('function', first, last, self.skip, PyCObject_FromVoidPtr(run_action, NULL), self) def cleanup(self): pass # These three methods need to be overridden by subclasses to # do any useful work. cdef int initialize(self, object universe, long generator_id, PyTrajectoryVariable *dynamic_data): return 1 cdef int finalize(self, object universe, long generator_id, PyTrajectoryVariable *dynamic_data): return 1 cdef int apply(self, object universe, long generator_id, int step, PyTrajectoryVariable *dynamic_data): return 1 MMTK-2.7.9/Src/MMTK_trajectory_generator.c0000644000076600000240000157714212147436245020721 0ustar hinsenstaff00000000000000/* Generated by Cython 0.17.3 on Thu May 23 17:58:18 2013 */ #define PY_SSIZE_T_CLEAN #include "Python.h" #ifndef Py_PYTHON_H #error Python headers needed to compile C extensions, please install development version of Python. #elif PY_VERSION_HEX < 0x02040000 #error Cython requires Python 2.4+. #else #include /* For offsetof */ #ifndef offsetof #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) #endif #if !defined(WIN32) && !defined(MS_WINDOWS) #ifndef __stdcall #define __stdcall #endif #ifndef __cdecl #define __cdecl #endif #ifndef __fastcall #define __fastcall #endif #endif #ifndef DL_IMPORT #define DL_IMPORT(t) t #endif #ifndef DL_EXPORT #define DL_EXPORT(t) t #endif #ifndef PY_LONG_LONG #define PY_LONG_LONG LONG_LONG #endif #ifndef Py_HUGE_VAL #define Py_HUGE_VAL HUGE_VAL #endif #ifdef PYPY_VERSION #define CYTHON_COMPILING_IN_PYPY 1 #define CYTHON_COMPILING_IN_CPYTHON 0 #else #define CYTHON_COMPILING_IN_PYPY 0 #define CYTHON_COMPILING_IN_CPYTHON 1 #endif #if PY_VERSION_HEX < 0x02050000 typedef int Py_ssize_t; #define PY_SSIZE_T_MAX INT_MAX #define PY_SSIZE_T_MIN INT_MIN #define PY_FORMAT_SIZE_T "" #define CYTHON_FORMAT_SSIZE_T "" #define PyInt_FromSsize_t(z) PyInt_FromLong(z) #define PyInt_AsSsize_t(o) __Pyx_PyInt_AsInt(o) #define PyNumber_Index(o) ((PyNumber_Check(o) && !PyFloat_Check(o)) ? PyNumber_Int(o) : \ (PyErr_Format(PyExc_TypeError, \ "expected index value, got %.200s", Py_TYPE(o)->tp_name), \ (PyObject*)0)) #define __Pyx_PyIndex_Check(o) (PyNumber_Check(o) && !PyFloat_Check(o) && \ !PyComplex_Check(o)) #define PyIndex_Check __Pyx_PyIndex_Check #define PyErr_WarnEx(category, message, stacklevel) PyErr_Warn(category, message) #define __PYX_BUILD_PY_SSIZE_T "i" #else #define __PYX_BUILD_PY_SSIZE_T "n" #define CYTHON_FORMAT_SSIZE_T "z" #define __Pyx_PyIndex_Check PyIndex_Check #endif #if PY_VERSION_HEX < 0x02060000 #define Py_REFCNT(ob) (((PyObject*)(ob))->ob_refcnt) #define Py_TYPE(ob) (((PyObject*)(ob))->ob_type) #define Py_SIZE(ob) (((PyVarObject*)(ob))->ob_size) #define PyVarObject_HEAD_INIT(type, size) \ PyObject_HEAD_INIT(type) size, #define PyType_Modified(t) typedef struct { void *buf; PyObject *obj; Py_ssize_t len; Py_ssize_t itemsize; int readonly; int ndim; char *format; Py_ssize_t *shape; Py_ssize_t *strides; Py_ssize_t *suboffsets; void *internal; } Py_buffer; #define PyBUF_SIMPLE 0 #define PyBUF_WRITABLE 0x0001 #define PyBUF_FORMAT 0x0004 #define PyBUF_ND 0x0008 #define PyBUF_STRIDES (0x0010 | PyBUF_ND) #define PyBUF_C_CONTIGUOUS (0x0020 | PyBUF_STRIDES) #define PyBUF_F_CONTIGUOUS (0x0040 | PyBUF_STRIDES) #define PyBUF_ANY_CONTIGUOUS (0x0080 | PyBUF_STRIDES) #define PyBUF_INDIRECT (0x0100 | PyBUF_STRIDES) #define PyBUF_RECORDS (PyBUF_STRIDES | PyBUF_FORMAT | PyBUF_WRITABLE) #define PyBUF_FULL (PyBUF_INDIRECT | PyBUF_FORMAT | PyBUF_WRITABLE) typedef int (*getbufferproc)(PyObject *, Py_buffer *, int); typedef void (*releasebufferproc)(PyObject *, Py_buffer *); #endif #if PY_MAJOR_VERSION < 3 #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \ PyCode_New(a, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #else #define __Pyx_BUILTIN_MODULE_NAME "builtins" #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \ PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) #endif #if PY_MAJOR_VERSION < 3 && PY_MINOR_VERSION < 6 #define PyUnicode_FromString(s) PyUnicode_Decode(s, strlen(s), "UTF-8", "strict") #endif #if PY_MAJOR_VERSION >= 3 #define Py_TPFLAGS_CHECKTYPES 0 #define Py_TPFLAGS_HAVE_INDEX 0 #endif #if (PY_VERSION_HEX < 0x02060000) || (PY_MAJOR_VERSION >= 3) #define Py_TPFLAGS_HAVE_NEWBUFFER 0 #endif #if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) #define CYTHON_PEP393_ENABLED 1 #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ? \ 0 : _PyUnicode_Ready((PyObject *)(op))) #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) #else #define CYTHON_PEP393_ENABLED 0 #define __Pyx_PyUnicode_READY(op) (0) #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) #define __Pyx_PyUnicode_READ(k, d, i) ((k=k), (Py_UCS4)(((Py_UNICODE*)d)[i])) #endif #if PY_MAJOR_VERSION >= 3 #define PyBaseString_Type PyUnicode_Type #define PyStringObject PyUnicodeObject #define PyString_Type PyUnicode_Type #define PyString_Check PyUnicode_Check #define PyString_CheckExact PyUnicode_CheckExact #endif #if PY_VERSION_HEX < 0x02060000 #define PyBytesObject PyStringObject #define PyBytes_Type PyString_Type #define PyBytes_Check PyString_Check #define PyBytes_CheckExact PyString_CheckExact #define PyBytes_FromString PyString_FromString #define PyBytes_FromStringAndSize PyString_FromStringAndSize #define PyBytes_FromFormat PyString_FromFormat #define PyBytes_DecodeEscape PyString_DecodeEscape #define PyBytes_AsString PyString_AsString #define PyBytes_AsStringAndSize PyString_AsStringAndSize #define PyBytes_Size PyString_Size #define PyBytes_AS_STRING PyString_AS_STRING #define PyBytes_GET_SIZE PyString_GET_SIZE #define PyBytes_Repr PyString_Repr #define PyBytes_Concat PyString_Concat #define PyBytes_ConcatAndDel PyString_ConcatAndDel #endif #if PY_VERSION_HEX < 0x02060000 #define PySet_Check(obj) PyObject_TypeCheck(obj, &PySet_Type) #define PyFrozenSet_Check(obj) PyObject_TypeCheck(obj, &PyFrozenSet_Type) #endif #ifndef PySet_CheckExact #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) #endif #define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) #if PY_MAJOR_VERSION >= 3 #define PyIntObject PyLongObject #define PyInt_Type PyLong_Type #define PyInt_Check(op) PyLong_Check(op) #define PyInt_CheckExact(op) PyLong_CheckExact(op) #define PyInt_FromString PyLong_FromString #define PyInt_FromUnicode PyLong_FromUnicode #define PyInt_FromLong PyLong_FromLong #define PyInt_FromSize_t PyLong_FromSize_t #define PyInt_FromSsize_t PyLong_FromSsize_t #define PyInt_AsLong PyLong_AsLong #define PyInt_AS_LONG PyLong_AS_LONG #define PyInt_AsSsize_t PyLong_AsSsize_t #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask #endif #if PY_MAJOR_VERSION >= 3 #define PyBoolObject PyLongObject #endif #if PY_VERSION_HEX < 0x03020000 typedef long Py_hash_t; #define __Pyx_PyInt_FromHash_t PyInt_FromLong #define __Pyx_PyInt_AsHash_t PyInt_AsLong #else #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t #endif #if (PY_MAJOR_VERSION < 3) || (PY_VERSION_HEX >= 0x03010300) #define __Pyx_PySequence_GetSlice(obj, a, b) PySequence_GetSlice(obj, a, b) #define __Pyx_PySequence_SetSlice(obj, a, b, value) PySequence_SetSlice(obj, a, b, value) #define __Pyx_PySequence_DelSlice(obj, a, b) PySequence_DelSlice(obj, a, b) #else #define __Pyx_PySequence_GetSlice(obj, a, b) (unlikely(!(obj)) ? \ (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), (PyObject*)0) : \ (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_GetSlice(obj, a, b)) : \ (PyErr_Format(PyExc_TypeError, "'%.200s' object is unsliceable", (obj)->ob_type->tp_name), (PyObject*)0))) #define __Pyx_PySequence_SetSlice(obj, a, b, value) (unlikely(!(obj)) ? \ (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \ (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_SetSlice(obj, a, b, value)) : \ (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice assignment", (obj)->ob_type->tp_name), -1))) #define __Pyx_PySequence_DelSlice(obj, a, b) (unlikely(!(obj)) ? \ (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \ (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_DelSlice(obj, a, b)) : \ (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice deletion", (obj)->ob_type->tp_name), -1))) #endif #if PY_MAJOR_VERSION >= 3 #define PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func)) #endif #if PY_VERSION_HEX < 0x02050000 #define __Pyx_GetAttrString(o,n) PyObject_GetAttrString((o),((char *)(n))) #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),((char *)(n)),(a)) #define __Pyx_DelAttrString(o,n) PyObject_DelAttrString((o),((char *)(n))) #else #define __Pyx_GetAttrString(o,n) PyObject_GetAttrString((o),(n)) #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),(n),(a)) #define __Pyx_DelAttrString(o,n) PyObject_DelAttrString((o),(n)) #endif #if PY_VERSION_HEX < 0x02050000 #define __Pyx_NAMESTR(n) ((char *)(n)) #define __Pyx_DOCSTR(n) ((char *)(n)) #else #define __Pyx_NAMESTR(n) (n) #define __Pyx_DOCSTR(n) (n) #endif #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) #else #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) #endif #ifndef __PYX_EXTERN_C #ifdef __cplusplus #define __PYX_EXTERN_C extern "C" #else #define __PYX_EXTERN_C extern #endif #endif #if defined(WIN32) || defined(MS_WINDOWS) #define _USE_MATH_DEFINES #endif #include #define __PYX_HAVE__MMTK_trajectory_generator #define __PYX_HAVE_API__MMTK_trajectory_generator #include "stdio.h" #include "stdlib.h" #include "numpy/arrayobject.h" #include "numpy/ufuncobject.h" #include "MMTK/arrayobject.h" #include "MMTK/core.h" #include "MMTK/universe.h" #include "MMTK/trajectory.h" #include "MMTK/forcefield.h" #ifdef _OPENMP #include #endif /* _OPENMP */ #ifdef PYREX_WITHOUT_ASSERTIONS #define CYTHON_WITHOUT_ASSERTIONS #endif /* inline attribute */ #ifndef CYTHON_INLINE #if defined(__GNUC__) #define CYTHON_INLINE __inline__ #elif defined(_MSC_VER) #define CYTHON_INLINE __inline #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L #define CYTHON_INLINE inline #else #define CYTHON_INLINE #endif #endif /* unused attribute */ #ifndef CYTHON_UNUSED # if defined(__GNUC__) # if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) # define CYTHON_UNUSED __attribute__ ((__unused__)) # else # define CYTHON_UNUSED # endif # elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) # define CYTHON_UNUSED __attribute__ ((__unused__)) # else # define CYTHON_UNUSED # endif #endif typedef struct {PyObject **p; char *s; const long n; const char* encoding; const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; /*proto*/ /* Type Conversion Predeclarations */ #define __Pyx_PyBytes_FromUString(s) PyBytes_FromString((char*)s) #define __Pyx_PyBytes_AsUString(s) ((unsigned char*) PyBytes_AsString(s)) #define __Pyx_Owned_Py_None(b) (Py_INCREF(Py_None), Py_None) #define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False)) static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x); static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject*); #if CYTHON_COMPILING_IN_CPYTHON #define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) #else #define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) #endif #define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) #ifdef __GNUC__ /* Test for GCC > 2.95 */ #if __GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)) #define likely(x) __builtin_expect(!!(x), 1) #define unlikely(x) __builtin_expect(!!(x), 0) #else /* __GNUC__ > 2 ... */ #define likely(x) (x) #define unlikely(x) (x) #endif /* __GNUC__ > 2 ... */ #else /* __GNUC__ */ #define likely(x) (x) #define unlikely(x) (x) #endif /* __GNUC__ */ static PyObject *__pyx_m; static PyObject *__pyx_b; static PyObject *__pyx_empty_tuple; static PyObject *__pyx_empty_bytes; static int __pyx_lineno; static int __pyx_clineno = 0; static const char * __pyx_cfilenm= __FILE__; static const char *__pyx_filename; #if !defined(CYTHON_CCOMPLEX) #if defined(__cplusplus) #define CYTHON_CCOMPLEX 1 #elif defined(_Complex_I) #define CYTHON_CCOMPLEX 1 #else #define CYTHON_CCOMPLEX 0 #endif #endif #if CYTHON_CCOMPLEX #ifdef __cplusplus #include #else #include #endif #endif #if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) #undef _Complex_I #define _Complex_I 1.0fj #endif static const char *__pyx_f[] = { "MMTK_trajectory_generator.pyx", "numpy.pxd", "numeric.pxi", "forcefield.pxi", "type.pxd", }; #define IS_UNSIGNED(type) (((type) -1) > 0) struct __Pyx_StructField_; #define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) typedef struct { const char* name; /* for error messages only */ struct __Pyx_StructField_* fields; size_t size; /* sizeof(type) */ size_t arraysize[8]; /* length of array in each dimension */ int ndim; char typegroup; /* _R_eal, _C_omplex, Signed _I_nt, _U_nsigned int, _S_truct, _P_ointer, _O_bject, c_H_ar */ char is_unsigned; int flags; } __Pyx_TypeInfo; typedef struct __Pyx_StructField_ { __Pyx_TypeInfo* type; const char* name; size_t offset; } __Pyx_StructField; typedef struct { __Pyx_StructField* field; size_t parent_offset; } __Pyx_BufFmt_StackElem; typedef struct { __Pyx_StructField root; __Pyx_BufFmt_StackElem* head; size_t fmt_offset; size_t new_count, enc_count; size_t struct_alignment; int is_complex; char enc_type; char new_packmode; char enc_packmode; char is_valid_array; } __Pyx_BufFmt_Context; /* "numpy.pxd":723 * # in Cython to enable them only on the right systems. * * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t */ typedef npy_int8 __pyx_t_5numpy_int8_t; /* "numpy.pxd":724 * * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t */ typedef npy_int16 __pyx_t_5numpy_int16_t; /* "numpy.pxd":725 * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< * ctypedef npy_int64 int64_t * #ctypedef npy_int96 int96_t */ typedef npy_int32 __pyx_t_5numpy_int32_t; /* "numpy.pxd":726 * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< * #ctypedef npy_int96 int96_t * #ctypedef npy_int128 int128_t */ typedef npy_int64 __pyx_t_5numpy_int64_t; /* "numpy.pxd":730 * #ctypedef npy_int128 int128_t * * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t */ typedef npy_uint8 __pyx_t_5numpy_uint8_t; /* "numpy.pxd":731 * * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t */ typedef npy_uint16 __pyx_t_5numpy_uint16_t; /* "numpy.pxd":732 * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< * ctypedef npy_uint64 uint64_t * #ctypedef npy_uint96 uint96_t */ typedef npy_uint32 __pyx_t_5numpy_uint32_t; /* "numpy.pxd":733 * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< * #ctypedef npy_uint96 uint96_t * #ctypedef npy_uint128 uint128_t */ typedef npy_uint64 __pyx_t_5numpy_uint64_t; /* "numpy.pxd":737 * #ctypedef npy_uint128 uint128_t * * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< * ctypedef npy_float64 float64_t * #ctypedef npy_float80 float80_t */ typedef npy_float32 __pyx_t_5numpy_float32_t; /* "numpy.pxd":738 * * ctypedef npy_float32 float32_t * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< * #ctypedef npy_float80 float80_t * #ctypedef npy_float128 float128_t */ typedef npy_float64 __pyx_t_5numpy_float64_t; /* "numpy.pxd":747 * # The int types are mapped a bit surprising -- * # numpy.int corresponds to 'l' and numpy.long to 'q' * ctypedef npy_long int_t # <<<<<<<<<<<<<< * ctypedef npy_longlong long_t * ctypedef npy_longlong longlong_t */ typedef npy_long __pyx_t_5numpy_int_t; /* "numpy.pxd":748 * # numpy.int corresponds to 'l' and numpy.long to 'q' * ctypedef npy_long int_t * ctypedef npy_longlong long_t # <<<<<<<<<<<<<< * ctypedef npy_longlong longlong_t * */ typedef npy_longlong __pyx_t_5numpy_long_t; /* "numpy.pxd":749 * ctypedef npy_long int_t * ctypedef npy_longlong long_t * ctypedef npy_longlong longlong_t # <<<<<<<<<<<<<< * * ctypedef npy_ulong uint_t */ typedef npy_longlong __pyx_t_5numpy_longlong_t; /* "numpy.pxd":751 * ctypedef npy_longlong longlong_t * * ctypedef npy_ulong uint_t # <<<<<<<<<<<<<< * ctypedef npy_ulonglong ulong_t * ctypedef npy_ulonglong ulonglong_t */ typedef npy_ulong __pyx_t_5numpy_uint_t; /* "numpy.pxd":752 * * ctypedef npy_ulong uint_t * ctypedef npy_ulonglong ulong_t # <<<<<<<<<<<<<< * ctypedef npy_ulonglong ulonglong_t * */ typedef npy_ulonglong __pyx_t_5numpy_ulong_t; /* "numpy.pxd":753 * ctypedef npy_ulong uint_t * ctypedef npy_ulonglong ulong_t * ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<< * * ctypedef npy_intp intp_t */ typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; /* "numpy.pxd":755 * ctypedef npy_ulonglong ulonglong_t * * ctypedef npy_intp intp_t # <<<<<<<<<<<<<< * ctypedef npy_uintp uintp_t * */ typedef npy_intp __pyx_t_5numpy_intp_t; /* "numpy.pxd":756 * * ctypedef npy_intp intp_t * ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<< * * ctypedef npy_double float_t */ typedef npy_uintp __pyx_t_5numpy_uintp_t; /* "numpy.pxd":758 * ctypedef npy_uintp uintp_t * * ctypedef npy_double float_t # <<<<<<<<<<<<<< * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t */ typedef npy_double __pyx_t_5numpy_float_t; /* "numpy.pxd":759 * * ctypedef npy_double float_t * ctypedef npy_double double_t # <<<<<<<<<<<<<< * ctypedef npy_longdouble longdouble_t * */ typedef npy_double __pyx_t_5numpy_double_t; /* "numpy.pxd":760 * ctypedef npy_double float_t * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< * * ctypedef npy_cfloat cfloat_t */ typedef npy_longdouble __pyx_t_5numpy_longdouble_t; #if CYTHON_CCOMPLEX #ifdef __cplusplus typedef ::std::complex< float > __pyx_t_float_complex; #else typedef float _Complex __pyx_t_float_complex; #endif #else typedef struct { float real, imag; } __pyx_t_float_complex; #endif #if CYTHON_CCOMPLEX #ifdef __cplusplus typedef ::std::complex< double > __pyx_t_double_complex; #else typedef double _Complex __pyx_t_double_complex; #endif #else typedef struct { double real, imag; } __pyx_t_double_complex; #endif /*--- Type declarations ---*/ struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator; struct __pyx_obj_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator; struct __pyx_obj_25MMTK_trajectory_generator___pyx_scope_struct__optionString; struct __pyx_obj_25MMTK_trajectory_generator___pyx_scope_struct_1_genexpr; /* "numpy.pxd":762 * ctypedef npy_longdouble longdouble_t * * ctypedef npy_cfloat cfloat_t # <<<<<<<<<<<<<< * ctypedef npy_cdouble cdouble_t * ctypedef npy_clongdouble clongdouble_t */ typedef npy_cfloat __pyx_t_5numpy_cfloat_t; /* "numpy.pxd":763 * * ctypedef npy_cfloat cfloat_t * ctypedef npy_cdouble cdouble_t # <<<<<<<<<<<<<< * ctypedef npy_clongdouble clongdouble_t * */ typedef npy_cdouble __pyx_t_5numpy_cdouble_t; /* "numpy.pxd":764 * ctypedef npy_cfloat cfloat_t * ctypedef npy_cdouble cdouble_t * ctypedef npy_clongdouble clongdouble_t # <<<<<<<<<<<<<< * * ctypedef npy_cdouble complex_t */ typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t; /* "numpy.pxd":766 * ctypedef npy_clongdouble clongdouble_t * * ctypedef npy_cdouble complex_t # <<<<<<<<<<<<<< * * cdef inline object PyArray_MultiIterNew1(a): */ typedef npy_cdouble __pyx_t_5numpy_complex_t; struct __pyx_opt_args_25MMTK_trajectory_generator_19TrajectoryGenerator_finalizeTrajectoryActions; struct __pyx_opt_args_25MMTK_trajectory_generator_30EnergyBasedTrajectoryGenerator_calculateEnergies; struct __pyx_opt_args_25MMTK_trajectory_generator_30EnergyBasedTrajectoryGenerator_finalizeTrajectoryActions; /* "MMTK_trajectory_generator.pxd":48 * * cdef void initializeTrajectoryActions(self) except * * cdef void finalizeTrajectoryActions(self, int last_step, int error=?) \ # <<<<<<<<<<<<<< * except * * cdef int trajectoryActions(self, int step) except -1 */ struct __pyx_opt_args_25MMTK_trajectory_generator_19TrajectoryGenerator_finalizeTrajectoryActions { int __pyx_n; int error; }; /* "MMTK_trajectory_generator.pxd":67 * cdef PyFFEvaluatorObject *evaluator * * cdef void calculateEnergies(self, N.ndarray[double, ndim=2] conf_array, # <<<<<<<<<<<<<< * energy_data *energy, int small_change=?) \ * except * */ struct __pyx_opt_args_25MMTK_trajectory_generator_30EnergyBasedTrajectoryGenerator_calculateEnergies { int __pyx_n; int small_change; }; /* "MMTK_trajectory_generator.pyx":269 * self.evaluator = self.c_evaluator_object * * cdef void finalizeTrajectoryActions(self, int last_step, # <<<<<<<<<<<<<< * int error=False) except *: * TrajectoryGenerator.finalizeTrajectoryActions(self, last_step, error) */ struct __pyx_opt_args_25MMTK_trajectory_generator_30EnergyBasedTrajectoryGenerator_finalizeTrajectoryActions { int __pyx_n; int error; }; /* "MMTK_trajectory_generator.pxd":21 * cdef void free(void *ptr) * * cdef class TrajectoryGenerator(object): # <<<<<<<<<<<<<< * * cdef readonly universe, options, name */ struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator { PyObject_HEAD struct __pyx_vtabstruct_25MMTK_trajectory_generator_TrajectoryGenerator *__pyx_vtab; PyObject *universe; PyObject *options; PyObject *name; PyObject *call_options; PyObject *features; PyObject *actions; PyObject *state_accessor; PyTrajectoryVariable *tvars; PyTrajectoryOutputSpec *tspec; PyUniverseSpecObject *universe_spec; int natoms; int df; PyArrayObject *conf_array; int lock_state; }; /* "MMTK_trajectory_generator.pxd":61 * cdef start(self) * * cdef class EnergyBasedTrajectoryGenerator(TrajectoryGenerator): # <<<<<<<<<<<<<< * * cdef evaluator_object */ struct __pyx_obj_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator { struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator __pyx_base; PyObject *evaluator_object; PyObject *c_evaluator_object; PyFFEvaluatorObject *evaluator; }; /* "MMTK_trajectory_generator.pyx":69 * return value * * def optionString(self, options): # <<<<<<<<<<<<<< * return sum((o + '=' + `self.getOption(o)` + ', ' for o in options), * '')[:-2] */ struct __pyx_obj_25MMTK_trajectory_generator___pyx_scope_struct__optionString { PyObject_HEAD PyObject *__pyx_v_options; struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *__pyx_v_self; }; /* "MMTK_trajectory_generator.pyx":70 * * def optionString(self, options): * return sum((o + '=' + `self.getOption(o)` + ', ' for o in options), # <<<<<<<<<<<<<< * '')[:-2] * */ struct __pyx_obj_25MMTK_trajectory_generator___pyx_scope_struct_1_genexpr { PyObject_HEAD struct __pyx_obj_25MMTK_trajectory_generator___pyx_scope_struct__optionString *__pyx_outer_scope; PyObject *__pyx_v_o; PyObject *__pyx_t_0; Py_ssize_t __pyx_t_1; PyObject *(*__pyx_t_2)(PyObject *); }; /* "MMTK_trajectory_generator.pyx":24 * # Base class for trajectory generators * # * cdef class TrajectoryGenerator(object): # <<<<<<<<<<<<<< * * """ */ struct __pyx_vtabstruct_25MMTK_trajectory_generator_TrajectoryGenerator { void (*declareTrajectoryVariable_double)(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *, double *, char *, char *, char *, int); void (*declareTrajectoryVariable_int)(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *, int *, char *, char *, char *, int); void (*declareTrajectoryVariable_array)(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *, PyArrayObject *, char *, char *, char *, int); void (*declareTrajectoryVariable_box)(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *, double *, int); void (*_addTrajectoryVariable)(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *, PyTrajectoryVariable); void (*initializeTrajectoryActions)(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *); void (*finalizeTrajectoryActions)(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *, int, struct __pyx_opt_args_25MMTK_trajectory_generator_19TrajectoryGenerator_finalizeTrajectoryActions *__pyx_optional_args); int (*trajectoryActions)(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *, int); void (*foldCoordinatesIntoBox)(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *); void (*acquireReadLock)(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *); void (*releaseReadLock)(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *); void (*acquireWriteLock)(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *); void (*releaseWriteLock)(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *); PyObject *(*start)(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *); }; static struct __pyx_vtabstruct_25MMTK_trajectory_generator_TrajectoryGenerator *__pyx_vtabptr_25MMTK_trajectory_generator_TrajectoryGenerator; /* "MMTK_trajectory_generator.pyx":252 * # generators thread-safe. * # * cdef class EnergyBasedTrajectoryGenerator(TrajectoryGenerator): # <<<<<<<<<<<<<< * * def __init__(self, universe, options, name): */ struct __pyx_vtabstruct_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator { struct __pyx_vtabstruct_25MMTK_trajectory_generator_TrajectoryGenerator __pyx_base; void (*calculateEnergies)(struct __pyx_obj_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator *, PyArrayObject *, energy_data *, struct __pyx_opt_args_25MMTK_trajectory_generator_30EnergyBasedTrajectoryGenerator_calculateEnergies *__pyx_optional_args); 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static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex, __pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex); static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex); #if 1 static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex); static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex, __pyx_t_float_complex); #endif #endif static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); #if CYTHON_CCOMPLEX #define __Pyx_c_eq(a, b) ((a)==(b)) #define __Pyx_c_sum(a, b) ((a)+(b)) #define __Pyx_c_diff(a, b) ((a)-(b)) #define __Pyx_c_prod(a, b) ((a)*(b)) #define __Pyx_c_quot(a, b) ((a)/(b)) #define __Pyx_c_neg(a) (-(a)) #ifdef __cplusplus #define __Pyx_c_is_zero(z) ((z)==(double)0) #define __Pyx_c_conj(z) (::std::conj(z)) #if 1 #define __Pyx_c_abs(z) (::std::abs(z)) #define __Pyx_c_pow(a, b) (::std::pow(a, b)) #endif #else #define __Pyx_c_is_zero(z) ((z)==0) #define __Pyx_c_conj(z) (conj(z)) #if 1 #define __Pyx_c_abs(z) (cabs(z)) #define __Pyx_c_pow(a, b) (cpow(a, b)) #endif #endif #else static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex, __pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex); static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex); #if 1 static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex); static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex, __pyx_t_double_complex); #endif #endif static CYTHON_INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject *); static CYTHON_INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject *); static CYTHON_INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject *); static CYTHON_INLINE char __Pyx_PyInt_AsChar(PyObject *); static CYTHON_INLINE short __Pyx_PyInt_AsShort(PyObject *); static CYTHON_INLINE int __Pyx_PyInt_AsInt(PyObject *); static CYTHON_INLINE signed char __Pyx_PyInt_AsSignedChar(PyObject *); static CYTHON_INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject *); static CYTHON_INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject *); static CYTHON_INLINE int __Pyx_PyInt_AsLongDouble(PyObject *); static CYTHON_INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject *); static CYTHON_INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject *); static CYTHON_INLINE long __Pyx_PyInt_AsLong(PyObject *); static CYTHON_INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject *); static CYTHON_INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject *); static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject *); static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); /*proto*/ #define __Pyx_Generator_USED #include #include typedef PyObject *(*__pyx_generator_body_t)(PyObject *, PyObject *); typedef struct { PyObject_HEAD __pyx_generator_body_t body; PyObject *closure; PyObject *exc_type; PyObject *exc_value; PyObject *exc_traceback; PyObject *gi_weakreflist; PyObject *classobj; PyObject *yieldfrom; int resume_label; char is_running; // using T_BOOL for property below requires char value } __pyx_GeneratorObject; static __pyx_GeneratorObject *__Pyx_Generator_New(__pyx_generator_body_t body, PyObject *closure); static int __pyx_Generator_init(void); static int __Pyx_Generator_clear(PyObject* self); #if 1 || PY_VERSION_HEX < 0x030300B0 static int __Pyx_PyGen_FetchStopIterationValue(PyObject **pvalue); #else #define __Pyx_PyGen_FetchStopIterationValue(pvalue) PyGen_FetchStopIterationValue(pvalue) #endif static int __Pyx_check_binary_version(void); #if !defined(__Pyx_PyIdentifier_FromString) #if PY_MAJOR_VERSION < 3 #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s) #else #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s) #endif #endif static PyObject *__Pyx_ImportModule(const char *name); /*proto*/ static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); /*proto*/ static int __Pyx_SetVtable(PyObject *dict, void *vtable); /*proto*/ typedef struct { int code_line; PyCodeObject* code_object; } __Pyx_CodeObjectCacheEntry; struct __Pyx_CodeObjectCache { int count; int max_count; __Pyx_CodeObjectCacheEntry* entries; }; static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); static PyCodeObject *__pyx_find_code_object(int code_line); static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename); /*proto*/ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/ /* Module declarations from 'cpython.buffer' */ /* Module declarations from 'cpython.ref' */ /* Module declarations from 'libc.stdio' */ /* Module declarations from 'cpython.object' */ /* Module declarations from '__builtin__' */ /* Module declarations from 'cpython.type' */ static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; /* Module declarations from 'libc.stdlib' */ /* Module declarations from 'numpy' */ /* Module declarations from 'numpy' */ static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/ /* Module declarations from 'Scientific.N' */ /* Module declarations from 'MMTK_forcefield' */ /* Module declarations from 'cython' */ /* Module declarations from 'MMTK_trajectory_generator' */ static PyTypeObject *__pyx_ptype_25MMTK_trajectory_generator_ArrayType = 0; static PyTypeObject *__pyx_ptype_25MMTK_trajectory_generator_EnergyTerm = 0; static PyTypeObject *__pyx_ptype_25MMTK_trajectory_generator_TrajectoryGenerator = 0; static PyTypeObject *__pyx_ptype_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator = 0; static PyTypeObject *__pyx_ptype_25MMTK_trajectory_generator___pyx_scope_struct__optionString = 0; static PyTypeObject *__pyx_ptype_25MMTK_trajectory_generator___pyx_scope_struct_1_genexpr = 0; static __Pyx_TypeInfo __Pyx_TypeInfo_double = { "double", NULL, sizeof(double), { 0 }, 0, 'R', 0, 0 }; #define __Pyx_MODULE_NAME "MMTK_trajectory_generator" int __pyx_module_is_main_MMTK_trajectory_generator = 0; /* Implementation of 'MMTK_trajectory_generator' */ static PyObject *__pyx_builtin_ValueError; static PyObject *__pyx_builtin_KeyError; static PyObject *__pyx_builtin_sum; static PyObject *__pyx_builtin_MemoryError; static PyObject *__pyx_builtin_range; static PyObject *__pyx_builtin_RuntimeError; static int __pyx_pf_25MMTK_trajectory_generator_19TrajectoryGenerator___init__(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *__pyx_v_self, PyObject *__pyx_v_universe, PyObject *__pyx_v_options, PyObject *__pyx_v_name); /* proto */ static PyObject *__pyx_pf_25MMTK_trajectory_generator_19TrajectoryGenerator_2setCallOptions(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *__pyx_v_self, PyObject *__pyx_v_options); /* proto */ static PyObject *__pyx_pf_25MMTK_trajectory_generator_19TrajectoryGenerator_4getActions(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_25MMTK_trajectory_generator_19TrajectoryGenerator_6cleanupActions(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_25MMTK_trajectory_generator_19TrajectoryGenerator_8getOption(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *__pyx_v_self, PyObject *__pyx_v_option); /* proto */ static PyObject *__pyx_pf_25MMTK_trajectory_generator_19TrajectoryGenerator_12optionString_genexpr(PyObject *__pyx_self); /* proto */ static PyObject *__pyx_pf_25MMTK_trajectory_generator_19TrajectoryGenerator_10optionString(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *__pyx_v_self, PyObject *__pyx_v_options); /* proto */ static PyObject *__pyx_pf_25MMTK_trajectory_generator_19TrajectoryGenerator_12__call__(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *__pyx_v_self, PyObject *__pyx_v_options); /* proto */ static PyObject *__pyx_pf_25MMTK_trajectory_generator_19TrajectoryGenerator_14start_py(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_25MMTK_trajectory_generator_19TrajectoryGenerator_8universe___get__(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_25MMTK_trajectory_generator_19TrajectoryGenerator_7options___get__(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_25MMTK_trajectory_generator_19TrajectoryGenerator_4name___get__(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_25MMTK_trajectory_generator_19TrajectoryGenerator_12call_options___get__(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_25MMTK_trajectory_generator_19TrajectoryGenerator_8features___get__(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_25MMTK_trajectory_generator_19TrajectoryGenerator_7actions___get__(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *__pyx_v_self); /* proto */ static PyObject *__pyx_pf_25MMTK_trajectory_generator_19TrajectoryGenerator_14state_accessor___get__(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *__pyx_v_self); /* proto */ static int __pyx_pf_25MMTK_trajectory_generator_19TrajectoryGenerator_14state_accessor_2__set__(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *__pyx_v_self, PyObject *__pyx_v_value); /* proto */ static int __pyx_pf_25MMTK_trajectory_generator_19TrajectoryGenerator_14state_accessor_4__del__(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *__pyx_v_self); /* proto */ static int __pyx_pf_25MMTK_trajectory_generator_30EnergyBasedTrajectoryGenerator___init__(struct __pyx_obj_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator *__pyx_v_self, PyObject *__pyx_v_universe, PyObject *__pyx_v_options, PyObject *__pyx_v_name); /* proto */ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ static void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info); /* proto */ static char __pyx_k_2[] = "getSpecificationList"; static char __pyx_k_3[] = "undefined option: "; static char __pyx_k_4[] = "="; static char __pyx_k_5[] = ", "; static char __pyx_k_6[] = ""; static char __pyx_k_9[] = "Configuration:\n"; static char __pyx_k_10[] = "Masses:\n"; static char __pyx_k_11[] = "Degrees of freedom: %d\n"; static char __pyx_k_13[] = "TrajectoryGeneratorThread"; static char __pyx_k_14[] = "array must be 1D or 2D"; static char __pyx_k_16[] = "Box size:"; static char __pyx_k_18[] = "ndarray is not C contiguous"; static char __pyx_k_20[] = "ndarray is not Fortran contiguous"; static char __pyx_k_22[] = "Non-native byte order not supported"; static char __pyx_k_24[] = "unknown dtype code in numpy.pxd (%d)"; static char __pyx_k_25[] = "Format string allocated too short, see comment in numpy.pxd"; static char __pyx_k_28[] = "Format string allocated too short."; static char __pyx_k__B[] = "B"; static char __pyx_k__H[] = "H"; static char __pyx_k__I[] = "I"; static char __pyx_k__L[] = "L"; static char __pyx_k__N[] = "N"; static char __pyx_k__O[] = "O"; static char __pyx_k__Q[] = "Q"; static char __pyx_k__b[] = "b"; static char __pyx_k__d[] = "d"; static char __pyx_k__f[] = "f"; static char __pyx_k__g[] = "g"; static char __pyx_k__h[] = "h"; static char __pyx_k__i[] = "i"; static char __pyx_k__l[] = "l"; static char __pyx_k__q[] = "q"; static char __pyx_k__Zd[] = "Zd"; static char __pyx_k__Zf[] = "Zf"; static char __pyx_k__Zg[] = "Zg"; static char __pyx_k__sum[] = "sum"; static char __pyx_k__MMTK[] = "MMTK"; static char __pyx_k__name[] = "name"; static char __pyx_k___spec[] = "_spec"; static char __pyx_k__array[] = "array"; static char __pyx_k__numpy[] = "numpy"; static char __pyx_k__range[] = "range"; static char __pyx_k__steps[] = "steps"; static char __pyx_k__masses[] = "masses"; static char __pyx_k__actions[] = "actions"; static char __pyx_k__cleanup[] = "cleanup"; static char __pyx_k__options[] = "options"; static char __pyx_k__threads[] = "threads"; static char __pyx_k__Features[] = "Features"; static char __pyx_k__KeyError[] = "KeyError"; static char __pyx_k____init__[] = "__init__"; static char __pyx_k____main__[] = "__main__"; static char __pyx_k____test__[] = "__test__"; static char __pyx_k__box_size[] = "box_size"; static char __pyx_k__start_py[] = "start_py"; static char __pyx_k__universe[] = "universe"; static char __pyx_k__getOption[] = "getOption"; static char __pyx_k__CEvaluator[] = "CEvaluator"; static char __pyx_k__ValueError[] = "ValueError"; static char __pyx_k__background[] = "background"; static char __pyx_k__getActions[] = "getActions"; static char __pyx_k__MemoryError[] = "MemoryError"; static char __pyx_k__RuntimeError[] = "RuntimeError"; static char __pyx_k__StateAccessor[] = "StateAccessor"; static char __pyx_k__ThreadManager[] = "ThreadManager"; static char __pyx_k__checkFeatures[] = "checkFeatures"; static char __pyx_k__configuration[] = "configuration"; static char __pyx_k__numberOfAtoms[] = "numberOfAtoms"; static char __pyx_k__setCallOptions[] = "setCallOptions"; static char __pyx_k__default_options[] = "default_options"; static char __pyx_k__energyEvaluator[] = "energyEvaluator"; static char __pyx_k__degreesOfFreedom[] = "degreesOfFreedom"; static char __pyx_k__degrees_of_freedom[] = "degrees_of_freedom"; static char __pyx_k__MMTK_state_accessor[] = "MMTK_state_accessor"; static PyObject *__pyx_n_s_13; static PyObject *__pyx_kp_s_14; static PyObject *__pyx_kp_u_18; static PyObject *__pyx_n_s_2; static PyObject *__pyx_kp_u_20; static PyObject *__pyx_kp_u_22; static PyObject *__pyx_kp_u_24; static PyObject *__pyx_kp_u_25; static PyObject *__pyx_kp_u_28; static PyObject *__pyx_kp_s_3; static PyObject *__pyx_kp_s_4; static PyObject *__pyx_kp_s_5; static PyObject *__pyx_kp_s_6; static PyObject *__pyx_n_s__CEvaluator; static PyObject *__pyx_n_s__Features; static PyObject *__pyx_n_s__KeyError; static PyObject *__pyx_n_s__MMTK; static PyObject *__pyx_n_s__MMTK_state_accessor; static PyObject *__pyx_n_s__MemoryError; static PyObject *__pyx_n_s__N; static PyObject *__pyx_n_s__RuntimeError; static PyObject *__pyx_n_s__StateAccessor; static PyObject *__pyx_n_s__ThreadManager; static PyObject *__pyx_n_s__ValueError; static PyObject *__pyx_n_s____init__; static PyObject *__pyx_n_s____main__; static PyObject *__pyx_n_s____test__; static PyObject *__pyx_n_s___spec; static PyObject *__pyx_n_s__actions; static PyObject *__pyx_n_s__array; static PyObject *__pyx_n_s__background; static PyObject *__pyx_n_s__checkFeatures; static PyObject *__pyx_n_s__cleanup; static PyObject *__pyx_n_s__configuration; static PyObject *__pyx_n_s__default_options; static PyObject *__pyx_n_s__degreesOfFreedom; static PyObject *__pyx_n_s__energyEvaluator; static PyObject *__pyx_n_s__getActions; static PyObject *__pyx_n_s__getOption; 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} static int __pyx_setprop_25MMTK_trajectory_generator_19TrajectoryGenerator_state_accessor(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) { if (v) { return __pyx_pw_25MMTK_trajectory_generator_19TrajectoryGenerator_14state_accessor_3__set__(o, v); } else { return __pyx_pw_25MMTK_trajectory_generator_19TrajectoryGenerator_14state_accessor_5__del__(o); } } static PyMethodDef __pyx_methods_25MMTK_trajectory_generator_TrajectoryGenerator[] = { {__Pyx_NAMESTR("setCallOptions"), (PyCFunction)__pyx_pw_25MMTK_trajectory_generator_19TrajectoryGenerator_3setCallOptions, METH_O, __Pyx_DOCSTR(0)}, {__Pyx_NAMESTR("getActions"), (PyCFunction)__pyx_pw_25MMTK_trajectory_generator_19TrajectoryGenerator_5getActions, METH_NOARGS, __Pyx_DOCSTR(0)}, {__Pyx_NAMESTR("cleanupActions"), (PyCFunction)__pyx_pw_25MMTK_trajectory_generator_19TrajectoryGenerator_7cleanupActions, METH_NOARGS, __Pyx_DOCSTR(0)}, {__Pyx_NAMESTR("getOption"), (PyCFunction)__pyx_pw_25MMTK_trajectory_generator_19TrajectoryGenerator_9getOption, METH_O, __Pyx_DOCSTR(0)}, {__Pyx_NAMESTR("optionString"), (PyCFunction)__pyx_pw_25MMTK_trajectory_generator_19TrajectoryGenerator_11optionString, METH_O, __Pyx_DOCSTR(0)}, {__Pyx_NAMESTR("start_py"), (PyCFunction)__pyx_pw_25MMTK_trajectory_generator_19TrajectoryGenerator_15start_py, METH_NOARGS, __Pyx_DOCSTR(0)}, {0, 0, 0, 0} }; static struct PyGetSetDef __pyx_getsets_25MMTK_trajectory_generator_TrajectoryGenerator[] = { {(char *)"universe", __pyx_getprop_25MMTK_trajectory_generator_19TrajectoryGenerator_universe, 0, 0, 0}, {(char *)"options", __pyx_getprop_25MMTK_trajectory_generator_19TrajectoryGenerator_options, 0, 0, 0}, {(char *)"name", __pyx_getprop_25MMTK_trajectory_generator_19TrajectoryGenerator_name, 0, 0, 0}, {(char *)"call_options", __pyx_getprop_25MMTK_trajectory_generator_19TrajectoryGenerator_call_options, 0, 0, 0}, {(char *)"features", __pyx_getprop_25MMTK_trajectory_generator_19TrajectoryGenerator_features, 0, 0, 0}, {(char *)"actions", __pyx_getprop_25MMTK_trajectory_generator_19TrajectoryGenerator_actions, 0, 0, 0}, {(char *)"state_accessor", __pyx_getprop_25MMTK_trajectory_generator_19TrajectoryGenerator_state_accessor, __pyx_setprop_25MMTK_trajectory_generator_19TrajectoryGenerator_state_accessor, 0, 0}, {0, 0, 0, 0, 0} }; static PyNumberMethods __pyx_tp_as_number_TrajectoryGenerator = { 0, /*nb_add*/ 0, /*nb_subtract*/ 0, /*nb_multiply*/ #if PY_MAJOR_VERSION < 3 0, /*nb_divide*/ #endif 0, /*nb_remainder*/ 0, /*nb_divmod*/ 0, /*nb_power*/ 0, /*nb_negative*/ 0, /*nb_positive*/ 0, /*nb_absolute*/ 0, /*nb_nonzero*/ 0, /*nb_invert*/ 0, /*nb_lshift*/ 0, /*nb_rshift*/ 0, /*nb_and*/ 0, /*nb_xor*/ 0, /*nb_or*/ #if PY_MAJOR_VERSION < 3 0, /*nb_coerce*/ #endif 0, /*nb_int*/ #if PY_MAJOR_VERSION < 3 0, /*nb_long*/ #else 0, /*reserved*/ #endif 0, /*nb_float*/ #if PY_MAJOR_VERSION < 3 0, /*nb_oct*/ #endif #if PY_MAJOR_VERSION < 3 0, /*nb_hex*/ #endif 0, /*nb_inplace_add*/ 0, /*nb_inplace_subtract*/ 0, /*nb_inplace_multiply*/ #if PY_MAJOR_VERSION < 3 0, /*nb_inplace_divide*/ #endif 0, /*nb_inplace_remainder*/ 0, /*nb_inplace_power*/ 0, /*nb_inplace_lshift*/ 0, /*nb_inplace_rshift*/ 0, /*nb_inplace_and*/ 0, /*nb_inplace_xor*/ 0, /*nb_inplace_or*/ 0, /*nb_floor_divide*/ 0, /*nb_true_divide*/ 0, /*nb_inplace_floor_divide*/ 0, /*nb_inplace_true_divide*/ #if PY_VERSION_HEX >= 0x02050000 0, /*nb_index*/ #endif }; static PySequenceMethods __pyx_tp_as_sequence_TrajectoryGenerator = { 0, /*sq_length*/ 0, /*sq_concat*/ 0, /*sq_repeat*/ 0, /*sq_item*/ 0, /*sq_slice*/ 0, /*sq_ass_item*/ 0, /*sq_ass_slice*/ 0, /*sq_contains*/ 0, /*sq_inplace_concat*/ 0, /*sq_inplace_repeat*/ }; static PyMappingMethods __pyx_tp_as_mapping_TrajectoryGenerator = { 0, /*mp_length*/ 0, /*mp_subscript*/ 0, /*mp_ass_subscript*/ }; static PyBufferProcs __pyx_tp_as_buffer_TrajectoryGenerator = { #if PY_MAJOR_VERSION < 3 0, /*bf_getreadbuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getwritebuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getsegcount*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getcharbuffer*/ #endif #if PY_VERSION_HEX >= 0x02060000 0, /*bf_getbuffer*/ #endif #if PY_VERSION_HEX >= 0x02060000 0, /*bf_releasebuffer*/ #endif }; static PyTypeObject __pyx_type_25MMTK_trajectory_generator_TrajectoryGenerator = { PyVarObject_HEAD_INIT(0, 0) __Pyx_NAMESTR("MMTK_trajectory_generator.TrajectoryGenerator"), /*tp_name*/ sizeof(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_25MMTK_trajectory_generator_TrajectoryGenerator, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ &__pyx_tp_as_number_TrajectoryGenerator, /*tp_as_number*/ &__pyx_tp_as_sequence_TrajectoryGenerator, /*tp_as_sequence*/ &__pyx_tp_as_mapping_TrajectoryGenerator, /*tp_as_mapping*/ 0, /*tp_hash*/ __pyx_pw_25MMTK_trajectory_generator_19TrajectoryGenerator_13__call__, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ &__pyx_tp_as_buffer_TrajectoryGenerator, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ __Pyx_DOCSTR("\n Trajectory generator base class\n\n This base class implements the common aspects of everything that\n generates trajectories: integrators, minimizers, etc.\n "), /*tp_doc*/ __pyx_tp_traverse_25MMTK_trajectory_generator_TrajectoryGenerator, /*tp_traverse*/ __pyx_tp_clear_25MMTK_trajectory_generator_TrajectoryGenerator, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_25MMTK_trajectory_generator_TrajectoryGenerator, /*tp_methods*/ 0, /*tp_members*/ __pyx_getsets_25MMTK_trajectory_generator_TrajectoryGenerator, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ __pyx_pw_25MMTK_trajectory_generator_19TrajectoryGenerator_1__init__, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_25MMTK_trajectory_generator_TrajectoryGenerator, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ #if PY_VERSION_HEX >= 0x02060000 0, /*tp_version_tag*/ #endif }; static struct __pyx_vtabstruct_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator __pyx_vtable_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator; static PyObject *__pyx_tp_new_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator(PyTypeObject *t, PyObject *a, PyObject *k) { struct __pyx_obj_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator *p; PyObject *o = __pyx_tp_new_25MMTK_trajectory_generator_TrajectoryGenerator(t, a, k); if (!o) return 0; p = ((struct __pyx_obj_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator *)o); p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_25MMTK_trajectory_generator_TrajectoryGenerator*)__pyx_vtabptr_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator; p->evaluator_object = Py_None; Py_INCREF(Py_None); p->c_evaluator_object = Py_None; Py_INCREF(Py_None); return o; } static void __pyx_tp_dealloc_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator(PyObject *o) { struct __pyx_obj_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator *p = (struct __pyx_obj_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator *)o; Py_CLEAR(p->evaluator_object); Py_CLEAR(p->c_evaluator_object); __pyx_tp_dealloc_25MMTK_trajectory_generator_TrajectoryGenerator(o); } static int __pyx_tp_traverse_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator(PyObject *o, visitproc v, void *a) { int e; struct __pyx_obj_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator *p = (struct __pyx_obj_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator *)o; e = __pyx_tp_traverse_25MMTK_trajectory_generator_TrajectoryGenerator(o, v, a); if (e) return e; if (p->evaluator_object) { e = (*v)(p->evaluator_object, a); if (e) return e; } if (p->c_evaluator_object) { e = (*v)(p->c_evaluator_object, a); if (e) return e; } return 0; } static int __pyx_tp_clear_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator(PyObject *o) { struct __pyx_obj_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator *p = (struct __pyx_obj_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator *)o; PyObject* tmp; __pyx_tp_clear_25MMTK_trajectory_generator_TrajectoryGenerator(o); tmp = ((PyObject*)p->evaluator_object); p->evaluator_object = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); tmp = ((PyObject*)p->c_evaluator_object); p->c_evaluator_object = Py_None; Py_INCREF(Py_None); Py_XDECREF(tmp); return 0; } static PyMethodDef __pyx_methods_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator[] = { {0, 0, 0, 0} }; static PyNumberMethods __pyx_tp_as_number_EnergyBasedTrajectoryGenerator = { 0, /*nb_add*/ 0, /*nb_subtract*/ 0, /*nb_multiply*/ #if PY_MAJOR_VERSION < 3 0, /*nb_divide*/ #endif 0, /*nb_remainder*/ 0, /*nb_divmod*/ 0, /*nb_power*/ 0, /*nb_negative*/ 0, /*nb_positive*/ 0, /*nb_absolute*/ 0, /*nb_nonzero*/ 0, /*nb_invert*/ 0, /*nb_lshift*/ 0, /*nb_rshift*/ 0, /*nb_and*/ 0, /*nb_xor*/ 0, /*nb_or*/ #if PY_MAJOR_VERSION < 3 0, /*nb_coerce*/ #endif 0, /*nb_int*/ #if PY_MAJOR_VERSION < 3 0, /*nb_long*/ #else 0, /*reserved*/ #endif 0, /*nb_float*/ #if PY_MAJOR_VERSION < 3 0, /*nb_oct*/ #endif #if PY_MAJOR_VERSION < 3 0, /*nb_hex*/ #endif 0, /*nb_inplace_add*/ 0, /*nb_inplace_subtract*/ 0, /*nb_inplace_multiply*/ #if PY_MAJOR_VERSION < 3 0, /*nb_inplace_divide*/ #endif 0, /*nb_inplace_remainder*/ 0, /*nb_inplace_power*/ 0, /*nb_inplace_lshift*/ 0, /*nb_inplace_rshift*/ 0, /*nb_inplace_and*/ 0, /*nb_inplace_xor*/ 0, /*nb_inplace_or*/ 0, /*nb_floor_divide*/ 0, /*nb_true_divide*/ 0, /*nb_inplace_floor_divide*/ 0, /*nb_inplace_true_divide*/ #if PY_VERSION_HEX >= 0x02050000 0, /*nb_index*/ #endif }; static PySequenceMethods __pyx_tp_as_sequence_EnergyBasedTrajectoryGenerator = { 0, /*sq_length*/ 0, /*sq_concat*/ 0, /*sq_repeat*/ 0, /*sq_item*/ 0, /*sq_slice*/ 0, /*sq_ass_item*/ 0, /*sq_ass_slice*/ 0, /*sq_contains*/ 0, /*sq_inplace_concat*/ 0, /*sq_inplace_repeat*/ }; static PyMappingMethods __pyx_tp_as_mapping_EnergyBasedTrajectoryGenerator = { 0, /*mp_length*/ 0, /*mp_subscript*/ 0, /*mp_ass_subscript*/ }; static PyBufferProcs __pyx_tp_as_buffer_EnergyBasedTrajectoryGenerator = { #if PY_MAJOR_VERSION < 3 0, /*bf_getreadbuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getwritebuffer*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getsegcount*/ #endif #if PY_MAJOR_VERSION < 3 0, /*bf_getcharbuffer*/ #endif #if PY_VERSION_HEX >= 0x02060000 0, /*bf_getbuffer*/ #endif #if PY_VERSION_HEX >= 0x02060000 0, /*bf_releasebuffer*/ #endif }; static PyTypeObject __pyx_type_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator = { PyVarObject_HEAD_INIT(0, 0) __Pyx_NAMESTR("MMTK_trajectory_generator.EnergyBasedTrajectoryGenerator"), /*tp_name*/ sizeof(struct __pyx_obj_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator), /*tp_basicsize*/ 0, /*tp_itemsize*/ __pyx_tp_dealloc_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator, /*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ &__pyx_tp_as_number_EnergyBasedTrajectoryGenerator, /*tp_as_number*/ &__pyx_tp_as_sequence_EnergyBasedTrajectoryGenerator, /*tp_as_sequence*/ &__pyx_tp_as_mapping_EnergyBasedTrajectoryGenerator, /*tp_as_mapping*/ 0, /*tp_hash*/ #if CYTHON_COMPILING_IN_PYPY __pyx_pw_25MMTK_trajectory_generator_19TrajectoryGenerator_13__call__, /*tp_call*/ #else 0, /*tp_call*/ #endif 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ &__pyx_tp_as_buffer_EnergyBasedTrajectoryGenerator, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ 0, /*tp_doc*/ __pyx_tp_traverse_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator, /*tp_traverse*/ __pyx_tp_clear_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator, /*tp_clear*/ 0, /*tp_richcompare*/ 0, /*tp_weaklistoffset*/ 0, /*tp_iter*/ 0, /*tp_iternext*/ __pyx_methods_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator, /*tp_methods*/ 0, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ __pyx_pw_25MMTK_trajectory_generator_30EnergyBasedTrajectoryGenerator_1__init__, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ 0, /*tp_del*/ #if PY_VERSION_HEX >= 0x02060000 0, /*tp_version_tag*/ #endif }; static PyObject *__pyx_tp_new_25MMTK_trajectory_generator___pyx_scope_struct__optionString(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { struct __pyx_obj_25MMTK_trajectory_generator___pyx_scope_struct__optionString *p; PyObject *o = (*t->tp_alloc)(t, 0); if (!o) return 0; p = ((struct __pyx_obj_25MMTK_trajectory_generator___pyx_scope_struct__optionString *)o); p->__pyx_v_options = 0; p->__pyx_v_self = 0; return o; } static void __pyx_tp_dealloc_25MMTK_trajectory_generator___pyx_scope_struct__optionString(PyObject *o) { struct __pyx_obj_25MMTK_trajectory_generator___pyx_scope_struct__optionString *p = (struct __pyx_obj_25MMTK_trajectory_generator___pyx_scope_struct__optionString *)o; Py_CLEAR(p->__pyx_v_options); Py_CLEAR(p->__pyx_v_self); (*Py_TYPE(o)->tp_free)(o); } static int __pyx_tp_traverse_25MMTK_trajectory_generator___pyx_scope_struct__optionString(PyObject *o, visitproc v, void *a) { int e; struct __pyx_obj_25MMTK_trajectory_generator___pyx_scope_struct__optionString *p = (struct __pyx_obj_25MMTK_trajectory_generator___pyx_scope_struct__optionString *)o; 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__pyx_vtable_25MMTK_trajectory_generator_TrajectoryGenerator.finalizeTrajectoryActions = (void (*)(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *, int, struct __pyx_opt_args_25MMTK_trajectory_generator_19TrajectoryGenerator_finalizeTrajectoryActions *__pyx_optional_args))__pyx_f_25MMTK_trajectory_generator_19TrajectoryGenerator_finalizeTrajectoryActions; __pyx_vtable_25MMTK_trajectory_generator_TrajectoryGenerator.trajectoryActions = (int (*)(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *, int))__pyx_f_25MMTK_trajectory_generator_19TrajectoryGenerator_trajectoryActions; __pyx_vtable_25MMTK_trajectory_generator_TrajectoryGenerator.foldCoordinatesIntoBox = (void (*)(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *))__pyx_f_25MMTK_trajectory_generator_19TrajectoryGenerator_foldCoordinatesIntoBox; __pyx_vtable_25MMTK_trajectory_generator_TrajectoryGenerator.acquireReadLock = (void (*)(struct __pyx_obj_25MMTK_trajectory_generator_TrajectoryGenerator *))__pyx_f_25MMTK_trajectory_generator_19TrajectoryGenerator_acquireReadLock; 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__pyx_type_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator.tp_base = __pyx_ptype_25MMTK_trajectory_generator_TrajectoryGenerator; if (PyType_Ready(&__pyx_type_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 252; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (__Pyx_SetVtable(__pyx_type_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator.tp_dict, __pyx_vtabptr_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 252; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (__Pyx_SetAttrString(__pyx_m, "EnergyBasedTrajectoryGenerator", (PyObject *)&__pyx_type_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 252; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator = &__pyx_type_25MMTK_trajectory_generator_EnergyBasedTrajectoryGenerator; if (PyType_Ready(&__pyx_type_25MMTK_trajectory_generator___pyx_scope_struct__optionString) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 69; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_25MMTK_trajectory_generator___pyx_scope_struct__optionString = &__pyx_type_25MMTK_trajectory_generator___pyx_scope_struct__optionString; if (PyType_Ready(&__pyx_type_25MMTK_trajectory_generator___pyx_scope_struct_1_genexpr) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 70; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_25MMTK_trajectory_generator___pyx_scope_struct_1_genexpr = &__pyx_type_25MMTK_trajectory_generator___pyx_scope_struct_1_genexpr; /*--- Type import code ---*/ __pyx_ptype_7cpython_4type_type = __Pyx_ImportType(__Pyx_BUILTIN_MODULE_NAME, "type", #if CYTHON_COMPILING_IN_PYPY sizeof(PyTypeObject), #else sizeof(PyHeapTypeObject), #endif 0); if (unlikely(!__pyx_ptype_7cpython_4type_type)) {__pyx_filename = __pyx_f[4]; __pyx_lineno = 9; __pyx_clineno = __LINE__; 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if (unlikely(!__pyx_ptype_5numpy_ufunc)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 861; __pyx_clineno = __LINE__; goto __pyx_L1_error;} /*--- Variable import code ---*/ /*--- Function import code ---*/ /*--- Execution code ---*/ /* "MMTK_trajectory_generator.pyx":6 * # * * import_MMTK_universe() # <<<<<<<<<<<<<< * import_MMTK_trajectory() * import_MMTK_forcefield() */ import_MMTK_universe(); /* "MMTK_trajectory_generator.pyx":7 * * import_MMTK_universe() * import_MMTK_trajectory() # <<<<<<<<<<<<<< * import_MMTK_forcefield() * */ import_MMTK_trajectory(); /* "MMTK_trajectory_generator.pyx":8 * import_MMTK_universe() * import_MMTK_trajectory() * import_MMTK_forcefield() # <<<<<<<<<<<<<< * * from MMTK import Features */ import_MMTK_forcefield(); /* "MMTK_trajectory_generator.pyx":10 * import_MMTK_forcefield() * * from MMTK import Features # <<<<<<<<<<<<<< * import numpy as N * cimport numpy as N */ __pyx_t_1 = PyList_New(1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 10; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(((PyObject *)__pyx_n_s__Features)); PyList_SET_ITEM(__pyx_t_1, 0, ((PyObject *)__pyx_n_s__Features)); __Pyx_GIVEREF(((PyObject *)__pyx_n_s__Features)); __pyx_t_2 = __Pyx_Import(((PyObject *)__pyx_n_s__MMTK), ((PyObject *)__pyx_t_1), -1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 10; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(((PyObject *)__pyx_t_1)); __pyx_t_1 = 0; __pyx_t_1 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__Features); if (__pyx_t_1 == NULL) { if (PyErr_ExceptionMatches(PyExc_AttributeError)) __Pyx_RaiseImportError(__pyx_n_s__Features); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 10; __pyx_clineno = __LINE__; goto __pyx_L1_error;} } __Pyx_GOTREF(__pyx_t_1); if (PyObject_SetAttr(__pyx_m, __pyx_n_s__Features, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 10; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; /* "MMTK_trajectory_generator.pyx":11 * * from MMTK import Features * import numpy as N # <<<<<<<<<<<<<< * cimport numpy as N * import cython */ __pyx_t_2 = __Pyx_Import(((PyObject *)__pyx_n_s__numpy), 0, -1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 11; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); if (PyObject_SetAttr(__pyx_m, __pyx_n_s__N, __pyx_t_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 11; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; /* "MMTK_trajectory_generator.pyx":1 * # Trajectory generators in Cython # <<<<<<<<<<<<<< * # * # Written by Konrad Hinsen */ __pyx_t_2 = PyDict_New(); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_2)); if (PyObject_SetAttr(__pyx_m, __pyx_n_s____test__, ((PyObject *)__pyx_t_2)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(((PyObject *)__pyx_t_2)); __pyx_t_2 = 0; /* "numpy.pxd":975 * arr.base = baseptr * * cdef inline object get_array_base(ndarray arr): # <<<<<<<<<<<<<< * if arr.base is NULL: * return None */ goto __pyx_L0; __pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_2); if (__pyx_m) { __Pyx_AddTraceback("init MMTK_trajectory_generator", __pyx_clineno, __pyx_lineno, __pyx_filename); Py_DECREF(__pyx_m); __pyx_m = 0; } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_ImportError, "init MMTK_trajectory_generator"); } __pyx_L0:; __Pyx_RefNannyFinishContext(); #if PY_MAJOR_VERSION < 3 return; #else return __pyx_m; #endif } /* Runtime support code */ #if CYTHON_REFNANNY static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) { PyObject *m = NULL, *p = NULL; void *r = NULL; m = PyImport_ImportModule((char *)modname); if (!m) goto end; p = PyObject_GetAttrString(m, (char *)"RefNannyAPI"); if (!p) goto end; r = PyLong_AsVoidPtr(p); end: Py_XDECREF(p); Py_XDECREF(m); return (__Pyx_RefNannyAPIStruct *)r; } #endif /* CYTHON_REFNANNY */ static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name) { PyObject *result; result = PyObject_GetAttr(dict, name); if (!result) { if (dict != __pyx_b) { PyErr_Clear(); result = PyObject_GetAttr(__pyx_b, name); } if (!result) { PyErr_SetObject(PyExc_NameError, name); } } return result; } static void __Pyx_RaiseArgtupleInvalid( const char* func_name, int exact, Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found) { Py_ssize_t num_expected; const char *more_or_less; if (num_found < num_min) { num_expected = num_min; more_or_less = "at least"; } else { num_expected = num_max; more_or_less = "at most"; } if (exact) { more_or_less = "exactly"; } PyErr_Format(PyExc_TypeError, "%s() takes %s %" CYTHON_FORMAT_SSIZE_T "d positional argument%s (%" CYTHON_FORMAT_SSIZE_T "d given)", func_name, more_or_less, num_expected, (num_expected == 1) ? "" : "s", num_found); } static void __Pyx_RaiseDoubleKeywordsError( const char* func_name, PyObject* kw_name) { PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION >= 3 "%s() got multiple values for keyword argument '%U'", func_name, kw_name); #else "%s() got multiple values for keyword argument '%s'", func_name, PyString_AsString(kw_name)); #endif } static int __Pyx_ParseOptionalKeywords( PyObject *kwds, PyObject **argnames[], PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, const char* function_name) { PyObject *key = 0, *value = 0; Py_ssize_t pos = 0; PyObject*** name; PyObject*** first_kw_arg = argnames + num_pos_args; while (PyDict_Next(kwds, &pos, &key, &value)) { name = first_kw_arg; while (*name && (**name != key)) name++; if (*name) { values[name-argnames] = value; continue; } name = first_kw_arg; #if PY_MAJOR_VERSION < 3 if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { while (*name) { if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) && _PyString_Eq(**name, key)) { values[name-argnames] = value; break; } name++; } if (*name) continue; else { PyObject*** argname = argnames; while (argname != first_kw_arg) { if ((**argname == key) || ( (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) && _PyString_Eq(**argname, key))) { goto arg_passed_twice; } argname++; } } } else #endif if (likely(PyUnicode_Check(key))) { while (*name) { int cmp = (**name == key) ? 0 : #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : #endif PyUnicode_Compare(**name, key); if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; if (cmp == 0) { values[name-argnames] = value; break; } name++; } if (*name) continue; else { PyObject*** argname = argnames; while (argname != first_kw_arg) { int cmp = (**argname == key) ? 0 : #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : #endif PyUnicode_Compare(**argname, key); if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; if (cmp == 0) goto arg_passed_twice; argname++; } } } else goto invalid_keyword_type; if (kwds2) { if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; } else { goto invalid_keyword; } } return 0; arg_passed_twice: __Pyx_RaiseDoubleKeywordsError(function_name, key); goto bad; invalid_keyword_type: PyErr_Format(PyExc_TypeError, "%s() keywords must be strings", function_name); goto bad; invalid_keyword: PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION < 3 "%s() got an unexpected keyword argument '%s'", function_name, PyString_AsString(key)); #else "%s() got an unexpected keyword argument '%U'", function_name, key); #endif bad: return -1; } static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) { PyObject *local_type, *local_value, *local_tb; #if CYTHON_COMPILING_IN_CPYTHON PyObject *tmp_type, *tmp_value, *tmp_tb; PyThreadState *tstate = PyThreadState_GET(); local_type = tstate->curexc_type; local_value = tstate->curexc_value; local_tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; #else PyErr_Fetch(&local_type, &local_value, &local_tb); #endif PyErr_NormalizeException(&local_type, &local_value, &local_tb); #if CYTHON_COMPILING_IN_CPYTHON if (unlikely(tstate->curexc_type)) #else if (unlikely(PyErr_Occurred())) #endif goto bad; #if PY_MAJOR_VERSION >= 3 if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) goto bad; #endif Py_INCREF(local_type); Py_INCREF(local_value); Py_INCREF(local_tb); *type = local_type; *value = local_value; *tb = local_tb; #if CYTHON_COMPILING_IN_CPYTHON tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = local_type; tstate->exc_value = local_value; tstate->exc_traceback = local_tb; /* Make sure tstate is in a consistent state when we XDECREF these objects (DECREF may run arbitrary code). */ Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); #else PyErr_SetExcInfo(local_type, local_value, local_tb); #endif return 0; bad: *type = 0; *value = 0; *tb = 0; Py_XDECREF(local_type); Py_XDECREF(local_value); Py_XDECREF(local_tb); return -1; } static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { #if CYTHON_COMPILING_IN_CPYTHON PyObject *tmp_type, *tmp_value, *tmp_tb; PyThreadState *tstate = PyThreadState_GET(); tmp_type = tstate->curexc_type; tmp_value = tstate->curexc_value; tmp_tb = tstate->curexc_traceback; tstate->curexc_type = type; tstate->curexc_value = value; tstate->curexc_traceback = tb; Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); #else PyErr_Restore(type, value, tb); #endif } static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { #if CYTHON_COMPILING_IN_CPYTHON PyThreadState *tstate = PyThreadState_GET(); *type = tstate->curexc_type; *value = tstate->curexc_value; *tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; #else PyErr_Fetch(type, value, tb); #endif } #if PY_MAJOR_VERSION < 3 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, CYTHON_UNUSED PyObject *cause) { Py_XINCREF(type); if (!value || value == Py_None) value = NULL; else Py_INCREF(value); if (!tb || tb == Py_None) tb = NULL; else { Py_INCREF(tb); if (!PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto raise_error; } } #if PY_VERSION_HEX < 0x02050000 if (PyClass_Check(type)) { #else if (PyType_Check(type)) { #endif #if CYTHON_COMPILING_IN_PYPY if (!value) { Py_INCREF(Py_None); value = Py_None; } #endif PyErr_NormalizeException(&type, &value, &tb); } else { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto raise_error; } value = type; #if PY_VERSION_HEX < 0x02050000 if (PyInstance_Check(type)) { type = (PyObject*) ((PyInstanceObject*)type)->in_class; Py_INCREF(type); } else { type = 0; PyErr_SetString(PyExc_TypeError, "raise: exception must be an old-style class or instance"); goto raise_error; } #else type = (PyObject*) Py_TYPE(type); Py_INCREF(type); if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto raise_error; } #endif } __Pyx_ErrRestore(type, value, tb); return; raise_error: Py_XDECREF(value); Py_XDECREF(type); Py_XDECREF(tb); return; } #else /* Python 3+ */ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { PyObject* owned_instance = NULL; if (tb == Py_None) { tb = 0; } else if (tb && !PyTraceBack_Check(tb)) { PyErr_SetString(PyExc_TypeError, "raise: arg 3 must be a traceback or None"); goto bad; } if (value == Py_None) value = 0; if (PyExceptionInstance_Check(type)) { if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto bad; } value = type; type = (PyObject*) Py_TYPE(value); } else if (PyExceptionClass_Check(type)) { PyObject *args; if (!value) args = PyTuple_New(0); else if (PyTuple_Check(value)) { Py_INCREF(value); args = value; } else args = PyTuple_Pack(1, value); if (!args) goto bad; owned_instance = PyEval_CallObject(type, args); Py_DECREF(args); if (!owned_instance) goto bad; value = owned_instance; if (!PyExceptionInstance_Check(value)) { PyErr_Format(PyExc_TypeError, "calling %R should have returned an instance of " "BaseException, not %R", type, Py_TYPE(value)); goto bad; } } else { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto bad; } if (cause && cause != Py_None) { PyObject *fixed_cause; if (PyExceptionClass_Check(cause)) { fixed_cause = PyObject_CallObject(cause, NULL); if (fixed_cause == NULL) goto bad; } else if (PyExceptionInstance_Check(cause)) { fixed_cause = cause; Py_INCREF(fixed_cause); } else { PyErr_SetString(PyExc_TypeError, "exception causes must derive from " "BaseException"); goto bad; } PyException_SetCause(value, fixed_cause); } PyErr_SetObject(type, value); if (tb) { PyThreadState *tstate = PyThreadState_GET(); PyObject* tmp_tb = tstate->curexc_traceback; if (tb != tmp_tb) { Py_INCREF(tb); tstate->curexc_traceback = tb; Py_XDECREF(tmp_tb); } } bad: Py_XDECREF(owned_instance); return; } #endif static CYTHON_INLINE void __Pyx_RaiseClosureNameError(const char *varname) { PyErr_Format(PyExc_NameError, "free variable '%s' referenced before assignment in enclosing scope", varname); } static CYTHON_INLINE int __Pyx_CheckKeywordStrings( PyObject *kwdict, const char* function_name, int kw_allowed) { PyObject* key = 0; Py_ssize_t pos = 0; #if CPYTHON_COMPILING_IN_PYPY if (!kw_allowed && PyDict_Next(kwdict, &pos, &key, 0)) goto invalid_keyword; return 1; #else while (PyDict_Next(kwdict, &pos, &key, 0)) { #if PY_MAJOR_VERSION < 3 if (unlikely(!PyString_CheckExact(key)) && unlikely(!PyString_Check(key))) #endif if (unlikely(!PyUnicode_Check(key))) goto invalid_keyword_type; } if ((!kw_allowed) && unlikely(key)) goto invalid_keyword; return 1; invalid_keyword_type: PyErr_Format(PyExc_TypeError, "%s() keywords must be strings", function_name); return 0; #endif invalid_keyword: PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION < 3 "%s() got an unexpected keyword argument '%s'", function_name, PyString_AsString(key)); #else "%s() got an unexpected keyword argument '%U'", function_name, key); #endif return 0; } static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { if (unlikely(!type)) { PyErr_Format(PyExc_SystemError, "Missing type object"); return 0; } if (likely(PyObject_TypeCheck(obj, type))) return 1; PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", Py_TYPE(obj)->tp_name, type->tp_name); return 0; } static CYTHON_INLINE int __Pyx_IsLittleEndian(void) { unsigned int n = 1; return *(unsigned char*)(&n) != 0; } static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, __Pyx_BufFmt_StackElem* stack, __Pyx_TypeInfo* type) { stack[0].field = &ctx->root; stack[0].parent_offset = 0; ctx->root.type = type; ctx->root.name = "buffer dtype"; ctx->root.offset = 0; ctx->head = stack; ctx->head->field = &ctx->root; ctx->fmt_offset = 0; ctx->head->parent_offset = 0; ctx->new_packmode = '@'; ctx->enc_packmode = '@'; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->is_complex = 0; ctx->is_valid_array = 0; ctx->struct_alignment = 0; while (type->typegroup == 'S') { ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = 0; type = type->fields->type; } } static int __Pyx_BufFmt_ParseNumber(const char** ts) { int count; const char* t = *ts; if (*t < '0' || *t > '9') { return -1; } else { count = *t++ - '0'; while (*t >= '0' && *t < '9') { count *= 10; count += *t++ - '0'; } } *ts = t; return count; } static int __Pyx_BufFmt_ExpectNumber(const char **ts) { int number = __Pyx_BufFmt_ParseNumber(ts); if (number == -1) /* First char was not a digit */ PyErr_Format(PyExc_ValueError,\ "Does not understand character buffer dtype format string ('%c')", **ts); return number; } static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { PyErr_Format(PyExc_ValueError, "Unexpected format string character: '%c'", ch); } static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { switch (ch) { case 'c': return "'char'"; case 'b': return "'signed char'"; case 'B': return "'unsigned char'"; case 'h': return "'short'"; case 'H': return "'unsigned short'"; case 'i': return "'int'"; case 'I': return "'unsigned int'"; case 'l': return "'long'"; case 'L': return "'unsigned long'"; case 'q': return "'long long'"; case 'Q': return "'unsigned long long'"; case 'f': return (is_complex ? "'complex float'" : "'float'"); case 'd': return (is_complex ? "'complex double'" : "'double'"); case 'g': return (is_complex ? "'complex long double'" : "'long double'"); case 'T': return "a struct"; case 'O': return "Python object"; case 'P': return "a pointer"; case 's': case 'p': return "a string"; case 0: return "end"; default: return "unparseable format string"; } } static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return 2; case 'i': case 'I': case 'l': case 'L': return 4; case 'q': case 'Q': return 8; case 'f': return (is_complex ? 8 : 4); case 'd': return (is_complex ? 16 : 8); case 'g': { PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); return 0; } case 'O': case 'P': return sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { switch (ch) { case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(short); case 'i': case 'I': return sizeof(int); case 'l': case 'L': return sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(float) * (is_complex ? 2 : 1); case 'd': return sizeof(double) * (is_complex ? 2 : 1); case 'g': return sizeof(long double) * (is_complex ? 2 : 1); case 'O': case 'P': return sizeof(void*); default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } typedef struct { char c; short x; } __Pyx_st_short; typedef struct { char c; int x; } __Pyx_st_int; typedef struct { char c; long x; } __Pyx_st_long; typedef struct { char c; float x; } __Pyx_st_float; typedef struct { char c; double x; } __Pyx_st_double; typedef struct { char c; long double x; } __Pyx_st_longdouble; typedef struct { char c; void *x; } __Pyx_st_void_p; #ifdef HAVE_LONG_LONG typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_st_float) - sizeof(float); case 'd': return sizeof(__Pyx_st_double) - sizeof(double); case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } /* These are for computing the padding at the end of the struct to align on the first member of the struct. This will probably the same as above, but we don't have any guarantees. */ typedef struct { short x; char c; } __Pyx_pad_short; typedef struct { int x; char c; } __Pyx_pad_int; typedef struct { long x; char c; } __Pyx_pad_long; typedef struct { float x; char c; } __Pyx_pad_float; typedef struct { double x; char c; } __Pyx_pad_double; typedef struct { long double x; char c; } __Pyx_pad_longdouble; typedef struct { void *x; char c; } __Pyx_pad_void_p; #ifdef HAVE_LONG_LONG typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; #endif static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { switch (ch) { case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); #ifdef HAVE_LONG_LONG case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); #endif case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); default: __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { switch (ch) { case 'c': return 'H'; case 'b': case 'h': case 'i': case 'l': case 'q': case 's': case 'p': return 'I'; case 'B': case 'H': case 'I': case 'L': case 'Q': return 'U'; case 'f': case 'd': case 'g': return (is_complex ? 'C' : 'R'); case 'O': return 'O'; case 'P': return 'P'; default: { __Pyx_BufFmt_RaiseUnexpectedChar(ch); return 0; } } } static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { if (ctx->head == NULL || ctx->head->field == &ctx->root) { const char* expected; const char* quote; if (ctx->head == NULL) { expected = "end"; quote = ""; } else { expected = ctx->head->field->type->name; quote = "'"; } PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected %s%s%s but got %s", quote, expected, quote, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); } else { __Pyx_StructField* field = ctx->head->field; __Pyx_StructField* parent = (ctx->head - 1)->field; PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), parent->type->name, field->name); } } static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { char group; size_t size, offset, arraysize = 1; if (ctx->enc_type == 0) return 0; if (ctx->head->field->type->arraysize[0]) { int i, ndim = 0; if (ctx->enc_type == 's' || ctx->enc_type == 'p') { ctx->is_valid_array = ctx->head->field->type->ndim == 1; ndim = 1; if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %zu", ctx->head->field->type->arraysize[0], ctx->enc_count); return -1; } } if (!ctx->is_valid_array) { PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", ctx->head->field->type->ndim, ndim); return -1; } for (i = 0; i < ctx->head->field->type->ndim; i++) { arraysize *= ctx->head->field->type->arraysize[i]; } ctx->is_valid_array = 0; ctx->enc_count = 1; } group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); do { __Pyx_StructField* field = ctx->head->field; __Pyx_TypeInfo* type = field->type; if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); } else { size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); } if (ctx->enc_packmode == '@') { size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); size_t align_mod_offset; if (align_at == 0) return -1; align_mod_offset = ctx->fmt_offset % align_at; if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; if (ctx->struct_alignment == 0) ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, ctx->is_complex); } if (type->size != size || type->typegroup != group) { if (type->typegroup == 'C' && type->fields != NULL) { size_t parent_offset = ctx->head->parent_offset + field->offset; ++ctx->head; ctx->head->field = type->fields; ctx->head->parent_offset = parent_offset; continue; } if ((type->typegroup == 'H' || group == 'H') && type->size == size) { } else { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } } offset = ctx->head->parent_offset + field->offset; if (ctx->fmt_offset != offset) { PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); return -1; } ctx->fmt_offset += size; if (arraysize) ctx->fmt_offset += (arraysize - 1) * size; --ctx->enc_count; /* Consume from buffer string */ while (1) { if (field == &ctx->root) { ctx->head = NULL; if (ctx->enc_count != 0) { __Pyx_BufFmt_RaiseExpected(ctx); return -1; } break; /* breaks both loops as ctx->enc_count == 0 */ } ctx->head->field = ++field; if (field->type == NULL) { --ctx->head; field = ctx->head->field; continue; } else if (field->type->typegroup == 'S') { size_t parent_offset = ctx->head->parent_offset + field->offset; if (field->type->fields->type == NULL) continue; /* empty struct */ field = field->type->fields; ++ctx->head; ctx->head->field = field; ctx->head->parent_offset = parent_offset; break; } else { break; } } } while (ctx->enc_count); ctx->enc_type = 0; ctx->is_complex = 0; return 0; } static CYTHON_INLINE PyObject * __pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) { const char *ts = *tsp; int i = 0, number; int ndim = ctx->head->field->type->ndim; ; ++ts; if (ctx->new_count != 1) { PyErr_SetString(PyExc_ValueError, "Cannot handle repeated arrays in format string"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; while (*ts && *ts != ')') { if (isspace(*ts)) continue; number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) return PyErr_Format(PyExc_ValueError, "Expected a dimension of size %zu, got %d", ctx->head->field->type->arraysize[i], number); if (*ts != ',' && *ts != ')') return PyErr_Format(PyExc_ValueError, "Expected a comma in format string, got '%c'", *ts); if (*ts == ',') ts++; i++; } if (i != ndim) return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", ctx->head->field->type->ndim, i); if (!*ts) { PyErr_SetString(PyExc_ValueError, "Unexpected end of format string, expected ')'"); return NULL; } ctx->is_valid_array = 1; ctx->new_count = 1; *tsp = ++ts; return Py_None; } static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { int got_Z = 0; while (1) { switch(*ts) { case 0: if (ctx->enc_type != 0 && ctx->head == NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; if (ctx->head != NULL) { __Pyx_BufFmt_RaiseExpected(ctx); return NULL; } return ts; case ' ': case 10: case 13: ++ts; break; case '<': if (!__Pyx_IsLittleEndian()) { PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '>': case '!': if (__Pyx_IsLittleEndian()) { PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); return NULL; } ctx->new_packmode = '='; ++ts; break; case '=': case '@': case '^': ctx->new_packmode = *ts++; break; case 'T': /* substruct */ { const char* ts_after_sub; size_t i, struct_count = ctx->new_count; size_t struct_alignment = ctx->struct_alignment; ctx->new_count = 1; ++ts; if (*ts != '{') { PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); return NULL; } if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; /* Erase processed last struct element */ ctx->enc_count = 0; ctx->struct_alignment = 0; ++ts; ts_after_sub = ts; for (i = 0; i != struct_count; ++i) { ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); if (!ts_after_sub) return NULL; } ts = ts_after_sub; if (struct_alignment) ctx->struct_alignment = struct_alignment; } break; case '}': /* end of substruct; either repeat or move on */ { size_t alignment = ctx->struct_alignment; ++ts; if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_type = 0; /* Erase processed last struct element */ if (alignment && ctx->fmt_offset % alignment) { ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); } } return ts; case 'x': if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->fmt_offset += ctx->new_count; ctx->new_count = 1; ctx->enc_count = 0; ctx->enc_type = 0; ctx->enc_packmode = ctx->new_packmode; ++ts; break; case 'Z': got_Z = 1; ++ts; if (*ts != 'f' && *ts != 'd' && *ts != 'g') { __Pyx_BufFmt_RaiseUnexpectedChar('Z'); return NULL; } /* fall through */ case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': case 'l': case 'L': case 'q': case 'Q': case 'f': case 'd': case 'g': case 'O': case 's': case 'p': if (ctx->enc_type == *ts && got_Z == ctx->is_complex && ctx->enc_packmode == ctx->new_packmode) { ctx->enc_count += ctx->new_count; } else { if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; ctx->enc_count = ctx->new_count; ctx->enc_packmode = ctx->new_packmode; ctx->enc_type = *ts; ctx->is_complex = got_Z; } ++ts; ctx->new_count = 1; got_Z = 0; break; case ':': ++ts; while(*ts != ':') ++ts; ++ts; break; case '(': if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; break; default: { int number = __Pyx_BufFmt_ExpectNumber(&ts); if (number == -1) return NULL; ctx->new_count = (size_t)number; } } } } static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { buf->buf = NULL; buf->obj = NULL; buf->strides = __Pyx_zeros; buf->shape = __Pyx_zeros; buf->suboffsets = __Pyx_minusones; } static CYTHON_INLINE int __Pyx_GetBufferAndValidate( Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack) { if (obj == Py_None || obj == NULL) { __Pyx_ZeroBuffer(buf); return 0; } buf->buf = NULL; if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; if (buf->ndim != nd) { PyErr_Format(PyExc_ValueError, "Buffer has wrong number of dimensions (expected %d, got %d)", nd, buf->ndim); goto fail; } if (!cast) { __Pyx_BufFmt_Context ctx; __Pyx_BufFmt_Init(&ctx, stack, dtype); if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; } if ((unsigned)buf->itemsize != dtype->size) { PyErr_Format(PyExc_ValueError, "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", buf->itemsize, (buf->itemsize > 1) ? "s" : "", dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); goto fail; } if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; return 0; fail:; __Pyx_ZeroBuffer(buf); return -1; } static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { if (info->buf == NULL) return; if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; __Pyx_ReleaseBuffer(info); } static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { PyErr_Format(PyExc_ValueError, "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); } static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { PyErr_Format(PyExc_ValueError, "need more than %" CYTHON_FORMAT_SSIZE_T "d value%s to unpack", index, (index == 1) ? "" : "s"); } static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); } static CYTHON_INLINE int __Pyx_IterFinish(void) { #if CYTHON_COMPILING_IN_CPYTHON PyThreadState *tstate = PyThreadState_GET(); PyObject* exc_type = tstate->curexc_type; if (unlikely(exc_type)) { if (likely(exc_type == PyExc_StopIteration) || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration)) { PyObject *exc_value, *exc_tb; exc_value = tstate->curexc_value; exc_tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; Py_DECREF(exc_type); Py_XDECREF(exc_value); Py_XDECREF(exc_tb); return 0; } else { return -1; } } return 0; #else if (unlikely(PyErr_Occurred())) { if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) { PyErr_Clear(); return 0; } else { return -1; } } return 0; #endif } static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) { if (unlikely(retval)) { Py_DECREF(retval); __Pyx_RaiseTooManyValuesError(expected); return -1; } else { return __Pyx_IterFinish(); } return 0; } static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb) { #if CYTHON_COMPILING_IN_CPYTHON PyThreadState *tstate = PyThreadState_GET(); *type = tstate->exc_type; *value = tstate->exc_value; *tb = tstate->exc_traceback; Py_XINCREF(*type); Py_XINCREF(*value); Py_XINCREF(*tb); #else PyErr_GetExcInfo(type, value, tb); #endif } static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb) { #if CYTHON_COMPILING_IN_CPYTHON PyObject *tmp_type, *tmp_value, *tmp_tb; PyThreadState *tstate = PyThreadState_GET(); tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = type; tstate->exc_value = value; tstate->exc_traceback = tb; Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); #else PyErr_SetExcInfo(type, value, tb); #endif } #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { CYTHON_UNUSED PyObject *getbuffer_cobj; #if PY_VERSION_HEX >= 0x02060000 if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); #endif if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); #if PY_VERSION_HEX < 0x02060000 if (obj->ob_type->tp_dict && (getbuffer_cobj = PyMapping_GetItemString(obj->ob_type->tp_dict, "__pyx_getbuffer"))) { getbufferproc func; #if PY_VERSION_HEX >= 0x02070000 && !(PY_MAJOR_VERSION == 3 && PY_MINOR_VERSION == 0) func = (getbufferproc) PyCapsule_GetPointer(getbuffer_cobj, "getbuffer(obj, view, flags)"); #else func = (getbufferproc) PyCObject_AsVoidPtr(getbuffer_cobj); #endif Py_DECREF(getbuffer_cobj); if (!func) goto fail; return func(obj, view, flags); } else { PyErr_Clear(); } #endif PyErr_Format(PyExc_TypeError, "'%100s' does not have the buffer interface", Py_TYPE(obj)->tp_name); #if PY_VERSION_HEX < 0x02060000 fail: #endif return -1; } static void __Pyx_ReleaseBuffer(Py_buffer *view) { PyObject *obj = view->obj; CYTHON_UNUSED PyObject *releasebuffer_cobj; if (!obj) return; #if PY_VERSION_HEX >= 0x02060000 if (PyObject_CheckBuffer(obj)) { PyBuffer_Release(view); return; } #endif if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } #if PY_VERSION_HEX < 0x02060000 if (obj->ob_type->tp_dict && (releasebuffer_cobj = PyMapping_GetItemString(obj->ob_type->tp_dict, "__pyx_releasebuffer"))) { releasebufferproc func; #if PY_VERSION_HEX >= 0x02070000 && !(PY_MAJOR_VERSION == 3 && PY_MINOR_VERSION == 0) func = (releasebufferproc) PyCapsule_GetPointer(releasebuffer_cobj, "releasebuffer(obj, view)"); #else func = (releasebufferproc) PyCObject_AsVoidPtr(releasebuffer_cobj); #endif Py_DECREF(releasebuffer_cobj); if (!func) goto fail; func(obj, view); return; } else { PyErr_Clear(); } #endif goto nofail; #if PY_VERSION_HEX < 0x02060000 fail: #endif PyErr_WriteUnraisable(obj); nofail: Py_DECREF(obj); view->obj = NULL; } #endif /* PY_MAJOR_VERSION < 3 */ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, long level) { PyObject *py_import = 0; PyObject *empty_list = 0; PyObject *module = 0; PyObject *global_dict = 0; PyObject *empty_dict = 0; PyObject *list; py_import = __Pyx_GetAttrString(__pyx_b, "__import__"); if (!py_import) goto bad; if (from_list) list = from_list; else { empty_list = PyList_New(0); if (!empty_list) goto bad; list = empty_list; } global_dict = PyModule_GetDict(__pyx_m); if (!global_dict) goto bad; empty_dict = PyDict_New(); if (!empty_dict) goto bad; #if PY_VERSION_HEX >= 0x02050000 { #if PY_MAJOR_VERSION >= 3 if (level == -1) { if (strchr(__Pyx_MODULE_NAME, '.')) { /* try package relative import first */ PyObject *py_level = PyInt_FromLong(1); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, NULL); Py_DECREF(py_level); if (!module) { if (!PyErr_ExceptionMatches(PyExc_ImportError)) goto bad; PyErr_Clear(); } } level = 0; /* try absolute import on failure */ } #endif if (!module) { PyObject *py_level = PyInt_FromLong(level); if (!py_level) goto bad; module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, py_level, NULL); Py_DECREF(py_level); } } #else if (level>0) { PyErr_SetString(PyExc_RuntimeError, "Relative import is not supported for Python <=2.4."); goto bad; } module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, NULL); #endif bad: Py_XDECREF(empty_list); Py_XDECREF(py_import); Py_XDECREF(empty_dict); return module; } static CYTHON_INLINE void __Pyx_RaiseImportError(PyObject *name) { #if PY_MAJOR_VERSION < 3 PyErr_Format(PyExc_ImportError, "cannot import name %.230s", PyString_AsString(name)); #else PyErr_Format(PyExc_ImportError, "cannot import name %S", name); #endif } #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return ::std::complex< float >(x, y); } #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return x + y*(__pyx_t_float_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { __pyx_t_float_complex z; z.real = x; z.imag = y; return z; } #endif #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float denom = b.real * b.real + b.imag * b.imag; z.real = (a.real * b.real + a.imag * b.imag) / denom; z.imag = (a.imag * b.real - a.real * b.imag) / denom; return z; } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrtf(z.real*z.real + z.imag*z.imag); #else return hypotf(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { float denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(a, a); case 3: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(z, a); case 4: z = __Pyx_c_prodf(a, a); return __Pyx_c_prodf(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } r = a.real; theta = 0; } else { r = __Pyx_c_absf(a); theta = atan2f(a.imag, a.real); } lnr = logf(r); z_r = expf(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cosf(z_theta); z.imag = z_r * sinf(z_theta); return z; } #endif #endif #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return ::std::complex< double >(x, y); } #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return x + y*(__pyx_t_double_complex)_Complex_I; } #endif #else static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { __pyx_t_double_complex z; z.real = x; z.imag = y; return z; } #endif #if CYTHON_CCOMPLEX #else static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) { return (a.real == b.real) && (a.imag == b.imag); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double denom = b.real * b.real + b.imag * b.imag; z.real = (a.real * b.real + a.imag * b.imag) / denom; z.imag = (a.imag * b.real - a.real * b.imag) / denom; return z; } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = -a.real; z.imag = -a.imag; return z; } static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) { return (a.real == 0) && (a.imag == 0); } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrt(z.real*z.real + z.imag*z.imag); #else return hypot(z.real, z.imag); #endif } static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { if (b.real < 0) { double denom = a.real * a.real + a.imag * a.imag; a.real = a.real / denom; a.imag = -a.imag / denom; b.real = -b.real; } switch ((int)b.real) { case 0: z.real = 1; z.imag = 0; return z; case 1: return a; case 2: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(a, a); case 3: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(z, a); case 4: z = __Pyx_c_prod(a, a); return __Pyx_c_prod(z, z); } } if (a.imag == 0) { if (a.real == 0) { return a; } r = a.real; theta = 0; } else { r = __Pyx_c_abs(a); theta = atan2(a.imag, a.real); } lnr = log(r); z_r = exp(lnr * b.real - theta * b.imag); z_theta = theta * b.real + lnr * b.imag; z.real = z_r * cos(z_theta); z.imag = z_r * sin(z_theta); return z; } #endif #endif static CYTHON_INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject* x) { const unsigned char neg_one = (unsigned char)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(unsigned char) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(unsigned char)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to unsigned char" : "value too large to convert to unsigned char"); } return (unsigned char)-1; } return (unsigned char)val; } return (unsigned char)__Pyx_PyInt_AsUnsignedLong(x); } static CYTHON_INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject* x) { const unsigned short neg_one = (unsigned short)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(unsigned short) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(unsigned short)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to unsigned short" : "value too large to convert to unsigned short"); } return (unsigned short)-1; } return (unsigned short)val; } return (unsigned short)__Pyx_PyInt_AsUnsignedLong(x); } static CYTHON_INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject* x) { const unsigned int neg_one = (unsigned int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(unsigned int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(unsigned int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to unsigned int" : "value too large to convert to unsigned int"); } return (unsigned int)-1; } return (unsigned int)val; } return (unsigned int)__Pyx_PyInt_AsUnsignedLong(x); } static CYTHON_INLINE char __Pyx_PyInt_AsChar(PyObject* x) { const char neg_one = (char)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(char) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(char)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to char" : "value too large to convert to char"); } return (char)-1; } return (char)val; } return (char)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE short __Pyx_PyInt_AsShort(PyObject* x) { const short neg_one = (short)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(short) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(short)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to short" : "value too large to convert to short"); } return (short)-1; } return (short)val; } return (short)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE int __Pyx_PyInt_AsInt(PyObject* x) { const int neg_one = (int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to int" : "value too large to convert to int"); } return (int)-1; } return (int)val; } return (int)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE signed char __Pyx_PyInt_AsSignedChar(PyObject* x) { const signed char neg_one = (signed char)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(signed char) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(signed char)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to signed char" : "value too large to convert to signed char"); } return (signed char)-1; } return (signed char)val; } return (signed char)__Pyx_PyInt_AsSignedLong(x); } static CYTHON_INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject* x) { const signed short neg_one = (signed short)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(signed short) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(signed short)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to signed short" : "value too large to convert to signed short"); } return (signed short)-1; } return (signed short)val; } return (signed short)__Pyx_PyInt_AsSignedLong(x); } static CYTHON_INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject* x) { const signed int neg_one = (signed int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(signed int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(signed int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to signed int" : "value too large to convert to signed int"); } return (signed int)-1; } return (signed int)val; } return (signed int)__Pyx_PyInt_AsSignedLong(x); } static CYTHON_INLINE int __Pyx_PyInt_AsLongDouble(PyObject* x) { const int neg_one = (int)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; if (sizeof(int) < sizeof(long)) { long val = __Pyx_PyInt_AsLong(x); if (unlikely(val != (long)(int)val)) { if (!unlikely(val == -1 && PyErr_Occurred())) { PyErr_SetString(PyExc_OverflowError, (is_unsigned && unlikely(val < 0)) ? "can't convert negative value to int" : "value too large to convert to int"); } return (int)-1; } return (int)val; } return (int)__Pyx_PyInt_AsLong(x); } static CYTHON_INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject* x) { const unsigned long neg_one = (unsigned long)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned long"); return (unsigned long)-1; } return (unsigned long)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned long"); return (unsigned long)-1; } return (unsigned long)PyLong_AsUnsignedLong(x); } else { return (unsigned long)PyLong_AsLong(x); } } else { unsigned long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (unsigned long)-1; val = __Pyx_PyInt_AsUnsignedLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject* x) { const unsigned PY_LONG_LONG neg_one = (unsigned PY_LONG_LONG)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned PY_LONG_LONG"); return (unsigned PY_LONG_LONG)-1; } return (unsigned PY_LONG_LONG)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to unsigned PY_LONG_LONG"); return (unsigned PY_LONG_LONG)-1; } return (unsigned PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { return (unsigned PY_LONG_LONG)PyLong_AsLongLong(x); } } else { unsigned PY_LONG_LONG val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (unsigned PY_LONG_LONG)-1; val = __Pyx_PyInt_AsUnsignedLongLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE long __Pyx_PyInt_AsLong(PyObject* x) { const long neg_one = (long)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long)-1; } return (long)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to long"); return (long)-1; } return (long)PyLong_AsUnsignedLong(x); } else { return (long)PyLong_AsLong(x); } } else { long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (long)-1; val = __Pyx_PyInt_AsLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject* x) { const PY_LONG_LONG neg_one = (PY_LONG_LONG)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to PY_LONG_LONG"); return (PY_LONG_LONG)-1; } return (PY_LONG_LONG)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to PY_LONG_LONG"); return (PY_LONG_LONG)-1; } return (PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { return (PY_LONG_LONG)PyLong_AsLongLong(x); } } else { PY_LONG_LONG val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (PY_LONG_LONG)-1; val = __Pyx_PyInt_AsLongLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject* x) { const signed long neg_one = (signed long)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed long"); return (signed long)-1; } return (signed long)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed long"); return (signed long)-1; } return (signed long)PyLong_AsUnsignedLong(x); } else { return (signed long)PyLong_AsLong(x); } } else { signed long val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (signed long)-1; val = __Pyx_PyInt_AsSignedLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject* x) { const signed PY_LONG_LONG neg_one = (signed PY_LONG_LONG)-1, const_zero = 0; const int is_unsigned = neg_one > const_zero; #if PY_VERSION_HEX < 0x03000000 if (likely(PyInt_Check(x))) { long val = PyInt_AS_LONG(x); if (is_unsigned && unlikely(val < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed PY_LONG_LONG"); return (signed PY_LONG_LONG)-1; } return (signed PY_LONG_LONG)val; } else #endif if (likely(PyLong_Check(x))) { if (is_unsigned) { if (unlikely(Py_SIZE(x) < 0)) { PyErr_SetString(PyExc_OverflowError, "can't convert negative value to signed PY_LONG_LONG"); return (signed PY_LONG_LONG)-1; } return (signed PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { return (signed PY_LONG_LONG)PyLong_AsLongLong(x); } } else { signed PY_LONG_LONG val; PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (signed PY_LONG_LONG)-1; val = __Pyx_PyInt_AsSignedLongLong(tmp); Py_DECREF(tmp); return val; } } static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; #if CYTHON_COMPILING_IN_CPYTHON PyThreadState *tstate = PyThreadState_GET(); tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; tstate->exc_type = *type; tstate->exc_value = *value; tstate->exc_traceback = *tb; #else PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); PyErr_SetExcInfo(*type, *value, *tb); #endif *type = tmp_type; *value = tmp_value; *tb = tmp_tb; } static PyObject *__Pyx_Generator_Next(PyObject *self); static PyObject *__Pyx_Generator_Send(PyObject *self, PyObject *value); static PyObject *__Pyx_Generator_Close(PyObject *self); static PyObject *__Pyx_Generator_Throw(PyObject *gen, PyObject *args); static PyTypeObject *__pyx_GeneratorType = 0; #define __Pyx_Generator_CheckExact(obj) (Py_TYPE(obj) == __pyx_GeneratorType) #define __Pyx_Generator_Undelegate(gen) Py_CLEAR((gen)->yieldfrom) #if 1 || PY_VERSION_HEX < 0x030300B0 static int __Pyx_PyGen_FetchStopIterationValue(PyObject **pvalue) { PyObject *et, *ev, *tb; PyObject *value = NULL; __Pyx_ErrFetch(&et, &ev, &tb); if (!et) { Py_XDECREF(tb); Py_XDECREF(ev); Py_INCREF(Py_None); *pvalue = Py_None; return 0; } if (unlikely(et != PyExc_StopIteration) && unlikely(!PyErr_GivenExceptionMatches(et, PyExc_StopIteration))) { __Pyx_ErrRestore(et, ev, tb); return -1; } if (likely(et == PyExc_StopIteration)) { if (likely(!ev) || !PyObject_IsInstance(ev, PyExc_StopIteration)) { if (!ev) { Py_INCREF(Py_None); ev = Py_None; } Py_XDECREF(tb); Py_DECREF(et); *pvalue = ev; return 0; } } PyErr_NormalizeException(&et, &ev, &tb); if (unlikely(!PyObject_IsInstance(ev, PyExc_StopIteration))) { __Pyx_ErrRestore(et, ev, tb); return -1; } Py_XDECREF(tb); Py_DECREF(et); #if PY_VERSION_HEX >= 0x030300A0 value = ((PyStopIterationObject *)ev)->value; Py_INCREF(value); Py_DECREF(ev); #else { PyObject* args = PyObject_GetAttrString(ev, "args"); Py_DECREF(ev); if (likely(args)) { value = PyObject_GetItem(args, 0); Py_DECREF(args); } if (unlikely(!value)) { __Pyx_ErrRestore(NULL, NULL, NULL); Py_INCREF(Py_None); value = Py_None; } } #endif *pvalue = value; return 0; } #endif static CYTHON_INLINE void __Pyx_Generator_ExceptionClear(__pyx_GeneratorObject *self) { PyObject *exc_type = self->exc_type; PyObject *exc_value = self->exc_value; PyObject *exc_traceback = self->exc_traceback; self->exc_type = NULL; self->exc_value = NULL; self->exc_traceback = NULL; Py_XDECREF(exc_type); Py_XDECREF(exc_value); Py_XDECREF(exc_traceback); } static CYTHON_INLINE int __Pyx_Generator_CheckRunning(__pyx_GeneratorObject *gen) { if (unlikely(gen->is_running)) { PyErr_SetString(PyExc_ValueError, "generator already executing"); return 1; } return 0; } static CYTHON_INLINE PyObject *__Pyx_Generator_SendEx(__pyx_GeneratorObject *self, PyObject *value) { PyObject *retval; assert(!self->is_running); if (unlikely(self->resume_label == 0)) { if (unlikely(value && value != Py_None)) { PyErr_SetString(PyExc_TypeError, "can't send non-None value to a " "just-started generator"); return NULL; } } if (unlikely(self->resume_label == -1)) { PyErr_SetNone(PyExc_StopIteration); return NULL; } if (value) { #if CYTHON_COMPILING_IN_PYPY #else /* Generators always return to their most recent caller, not * necessarily their creator. */ if (self->exc_traceback) { PyThreadState *tstate = PyThreadState_GET(); PyTracebackObject *tb = (PyTracebackObject *) self->exc_traceback; PyFrameObject *f = tb->tb_frame; Py_XINCREF(tstate->frame); assert(f->f_back == NULL); f->f_back = tstate->frame; } #endif __Pyx_ExceptionSwap(&self->exc_type, &self->exc_value, &self->exc_traceback); } else { __Pyx_Generator_ExceptionClear(self); } self->is_running = 1; retval = self->body((PyObject *) self, value); self->is_running = 0; if (retval) { __Pyx_ExceptionSwap(&self->exc_type, &self->exc_value, &self->exc_traceback); #if CYTHON_COMPILING_IN_PYPY #else /* Don't keep the reference to f_back any longer than necessary. It * may keep a chain of frames alive or it could create a reference * cycle. */ if (self->exc_traceback) { PyTracebackObject *tb = (PyTracebackObject *) self->exc_traceback; PyFrameObject *f = tb->tb_frame; Py_CLEAR(f->f_back); } #endif } else { __Pyx_Generator_ExceptionClear(self); } return retval; } static CYTHON_INLINE PyObject *__Pyx_Generator_FinishDelegation(__pyx_GeneratorObject *gen) { PyObject *ret; PyObject *val = NULL; __Pyx_Generator_Undelegate(gen); __Pyx_PyGen_FetchStopIterationValue(&val); ret = __Pyx_Generator_SendEx(gen, val); Py_XDECREF(val); return ret; } static PyObject *__Pyx_Generator_Next(PyObject *self) { __pyx_GeneratorObject *gen = (__pyx_GeneratorObject*) self; PyObject *yf = gen->yieldfrom; if (unlikely(__Pyx_Generator_CheckRunning(gen))) return NULL; if (yf) { PyObject *ret; gen->is_running = 1; ret = Py_TYPE(yf)->tp_iternext(yf); gen->is_running = 0; if (likely(ret)) { return ret; } return __Pyx_Generator_FinishDelegation(gen); } return __Pyx_Generator_SendEx(gen, Py_None); } static PyObject *__Pyx_Generator_Send(PyObject *self, PyObject *value) { __pyx_GeneratorObject *gen = (__pyx_GeneratorObject*) self; PyObject *yf = gen->yieldfrom; if (unlikely(__Pyx_Generator_CheckRunning(gen))) return NULL; if (yf) { PyObject *ret; gen->is_running = 1; if (__Pyx_Generator_CheckExact(yf)) { ret = __Pyx_Generator_Send(yf, value); } else { if (value == Py_None) ret = PyIter_Next(yf); else ret = PyObject_CallMethod(yf, (char*)"send", (char*)"O", value); } gen->is_running = 0; if (likely(ret)) { return ret; } return __Pyx_Generator_FinishDelegation(gen); } return __Pyx_Generator_SendEx(gen, value); } static int __Pyx_Generator_CloseIter(__pyx_GeneratorObject *gen, PyObject *yf) { PyObject *retval = NULL; int err = 0; if (__Pyx_Generator_CheckExact(yf)) { retval = __Pyx_Generator_Close(yf); if (!retval) return -1; } else { PyObject *meth; gen->is_running = 1; meth = PyObject_GetAttrString(yf, "close"); if (unlikely(!meth)) { if (!PyErr_ExceptionMatches(PyExc_AttributeError)) { PyErr_WriteUnraisable(yf); } PyErr_Clear(); } else { retval = PyObject_CallFunction(meth, NULL); Py_DECREF(meth); if (!retval) err = -1; } gen->is_running = 0; } Py_XDECREF(retval); return err; } static PyObject *__Pyx_Generator_Close(PyObject *self) { __pyx_GeneratorObject *gen = (__pyx_GeneratorObject *) self; PyObject *retval, *raised_exception; PyObject *yf = gen->yieldfrom; int err = 0; if (unlikely(__Pyx_Generator_CheckRunning(gen))) return NULL; if (yf) { Py_INCREF(yf); err = __Pyx_Generator_CloseIter(gen, yf); __Pyx_Generator_Undelegate(gen); Py_DECREF(yf); } if (err == 0) #if PY_VERSION_HEX < 0x02050000 PyErr_SetNone(PyExc_StopIteration); #else PyErr_SetNone(PyExc_GeneratorExit); #endif retval = __Pyx_Generator_SendEx(gen, NULL); if (retval) { Py_DECREF(retval); PyErr_SetString(PyExc_RuntimeError, "generator ignored GeneratorExit"); return NULL; } raised_exception = PyErr_Occurred(); if (!raised_exception || raised_exception == PyExc_StopIteration #if PY_VERSION_HEX >= 0x02050000 || raised_exception == PyExc_GeneratorExit || PyErr_GivenExceptionMatches(raised_exception, PyExc_GeneratorExit) #endif || PyErr_GivenExceptionMatches(raised_exception, PyExc_StopIteration)) { if (raised_exception) PyErr_Clear(); /* ignore these errors */ Py_INCREF(Py_None); return Py_None; } return NULL; } static PyObject *__Pyx_Generator_Throw(PyObject *self, PyObject *args) { __pyx_GeneratorObject *gen = (__pyx_GeneratorObject *) self; PyObject *typ; PyObject *tb = NULL; PyObject *val = NULL; PyObject *yf = gen->yieldfrom; if (!PyArg_UnpackTuple(args, (char *)"throw", 1, 3, &typ, &val, &tb)) return NULL; if (unlikely(__Pyx_Generator_CheckRunning(gen))) return NULL; if (yf) { PyObject *ret; Py_INCREF(yf); #if PY_VERSION_HEX >= 0x02050000 if (PyErr_GivenExceptionMatches(typ, PyExc_GeneratorExit)) { int err = __Pyx_Generator_CloseIter(gen, yf); Py_DECREF(yf); __Pyx_Generator_Undelegate(gen); if (err < 0) return __Pyx_Generator_SendEx(gen, NULL); goto throw_here; } #endif gen->is_running = 1; if (__Pyx_Generator_CheckExact(yf)) { ret = __Pyx_Generator_Throw(yf, args); } else { PyObject *meth = PyObject_GetAttrString(yf, "throw"); if (unlikely(!meth)) { Py_DECREF(yf); if (!PyErr_ExceptionMatches(PyExc_AttributeError)) { gen->is_running = 0; return NULL; } PyErr_Clear(); __Pyx_Generator_Undelegate(gen); gen->is_running = 0; goto throw_here; } ret = PyObject_CallObject(meth, args); Py_DECREF(meth); } gen->is_running = 0; Py_DECREF(yf); if (!ret) { ret = __Pyx_Generator_FinishDelegation(gen); } return ret; } throw_here: __Pyx_Raise(typ, val, tb, NULL); return __Pyx_Generator_SendEx(gen, NULL); } static int __Pyx_Generator_traverse(PyObject *self, visitproc visit, void *arg) { __pyx_GeneratorObject *gen = (__pyx_GeneratorObject *) self; Py_VISIT(gen->closure); Py_VISIT(gen->classobj); Py_VISIT(gen->yieldfrom); Py_VISIT(gen->exc_type); Py_VISIT(gen->exc_value); Py_VISIT(gen->exc_traceback); return 0; } static int __Pyx_Generator_clear(PyObject *self) { __pyx_GeneratorObject *gen = (__pyx_GeneratorObject *) self; Py_CLEAR(gen->closure); Py_CLEAR(gen->classobj); Py_CLEAR(gen->yieldfrom); Py_CLEAR(gen->exc_type); Py_CLEAR(gen->exc_value); Py_CLEAR(gen->exc_traceback); return 0; } static void __Pyx_Generator_dealloc(PyObject *self) { __pyx_GeneratorObject *gen = (__pyx_GeneratorObject *) self; PyObject_GC_UnTrack(gen); if (gen->gi_weakreflist != NULL) PyObject_ClearWeakRefs(self); PyObject_GC_Track(self); if (gen->resume_label > 0) { Py_TYPE(gen)->tp_del(self); if (self->ob_refcnt > 0) return; /* resurrected. :( */ } PyObject_GC_UnTrack(self); __Pyx_Generator_clear(self); PyObject_GC_Del(gen); } static void __Pyx_Generator_del(PyObject *self) { PyObject *res; PyObject *error_type, *error_value, *error_traceback; __pyx_GeneratorObject *gen = (__pyx_GeneratorObject *) self; if (gen->resume_label <= 0) return ; assert(self->ob_refcnt == 0); self->ob_refcnt = 1; __Pyx_ErrFetch(&error_type, &error_value, &error_traceback); res = __Pyx_Generator_Close(self); if (res == NULL) PyErr_WriteUnraisable(self); else Py_DECREF(res); __Pyx_ErrRestore(error_type, error_value, error_traceback); /* Undo the temporary resurrection; can't use DECREF here, it would * cause a recursive call. */ assert(self->ob_refcnt > 0); if (--self->ob_refcnt == 0) return; /* this is the normal path out */ /* close() resurrected it! Make it look like the original Py_DECREF * never happened. */ { Py_ssize_t refcnt = self->ob_refcnt; _Py_NewReference(self); self->ob_refcnt = refcnt; } #if CYTHON_COMPILING_FOR_CPYTHON assert(PyType_IS_GC(self->ob_type) && _Py_AS_GC(self)->gc.gc_refs != _PyGC_REFS_UNTRACKED); /* If Py_REF_DEBUG, _Py_NewReference bumped _Py_RefTotal, so * we need to undo that. */ _Py_DEC_REFTOTAL; #endif /* If Py_TRACE_REFS, _Py_NewReference re-added self to the object * chain, so no more to do there. * If COUNT_ALLOCS, the original decref bumped tp_frees, and * _Py_NewReference bumped tp_allocs: both of those need to be * undone. */ #ifdef COUNT_ALLOCS --Py_TYPE(self)->tp_frees; --Py_TYPE(self)->tp_allocs; #endif } static PyMemberDef __pyx_Generator_memberlist[] = { {(char *) "gi_running", #if PY_VERSION_HEX >= 0x02060000 T_BOOL, #else T_BYTE, #endif offsetof(__pyx_GeneratorObject, is_running), READONLY, NULL}, {0, 0, 0, 0, 0} }; static PyMethodDef __pyx_Generator_methods[] = { {__Pyx_NAMESTR("send"), (PyCFunction) __Pyx_Generator_Send, METH_O, 0}, {__Pyx_NAMESTR("throw"), (PyCFunction) __Pyx_Generator_Throw, METH_VARARGS, 0}, {__Pyx_NAMESTR("close"), (PyCFunction) __Pyx_Generator_Close, METH_NOARGS, 0}, {0, 0, 0, 0} }; static PyTypeObject __pyx_GeneratorType_type = { PyVarObject_HEAD_INIT(0, 0) __Pyx_NAMESTR("generator"), /*tp_name*/ sizeof(__pyx_GeneratorObject), /*tp_basicsize*/ 0, /*tp_itemsize*/ (destructor) __Pyx_Generator_dealloc,/*tp_dealloc*/ 0, /*tp_print*/ 0, /*tp_getattr*/ 0, /*tp_setattr*/ #if PY_MAJOR_VERSION < 3 0, /*tp_compare*/ #else 0, /*reserved*/ #endif 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ 0, /*tp_as_buffer*/ Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC, /* tp_flags*/ 0, /*tp_doc*/ (traverseproc) __Pyx_Generator_traverse, /*tp_traverse*/ 0, /*tp_clear*/ 0, /*tp_richcompare*/ offsetof(__pyx_GeneratorObject, gi_weakreflist), /* tp_weaklistoffse */ 0, /*tp_iter*/ (iternextfunc) __Pyx_Generator_Next, /*tp_iternext*/ __pyx_Generator_methods, /*tp_methods*/ __pyx_Generator_memberlist, /*tp_members*/ 0, /*tp_getset*/ 0, /*tp_base*/ 0, /*tp_dict*/ 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ 0, /*tp_init*/ 0, /*tp_alloc*/ 0, /*tp_new*/ 0, /*tp_free*/ 0, /*tp_is_gc*/ 0, /*tp_bases*/ 0, /*tp_mro*/ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ __Pyx_Generator_del, /*tp_del*/ #if PY_VERSION_HEX >= 0x02060000 0, /*tp_version_tag*/ #endif }; static __pyx_GeneratorObject *__Pyx_Generator_New(__pyx_generator_body_t body, PyObject *closure) { __pyx_GeneratorObject *gen = PyObject_GC_New(__pyx_GeneratorObject, &__pyx_GeneratorType_type); if (gen == NULL) return NULL; gen->body = body; gen->closure = closure; Py_XINCREF(closure); gen->is_running = 0; gen->resume_label = 0; gen->classobj = NULL; gen->yieldfrom = NULL; gen->exc_type = NULL; gen->exc_value = NULL; gen->exc_traceback = NULL; gen->gi_weakreflist = NULL; PyObject_GC_Track(gen); return gen; } static int __pyx_Generator_init(void) { __pyx_GeneratorType_type.tp_getattro = PyObject_GenericGetAttr; __pyx_GeneratorType_type.tp_iter = PyObject_SelfIter; if (PyType_Ready(&__pyx_GeneratorType_type)) { return -1; } __pyx_GeneratorType = &__pyx_GeneratorType_type; return 0; } static int __Pyx_check_binary_version(void) { char ctversion[4], rtversion[4]; PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { char message[200]; PyOS_snprintf(message, sizeof(message), "compiletime version %s of module '%.100s' " "does not match runtime version %s", ctversion, __Pyx_MODULE_NAME, rtversion); #if PY_VERSION_HEX < 0x02050000 return PyErr_Warn(NULL, message); #else return PyErr_WarnEx(NULL, message, 1); #endif } return 0; } #ifndef __PYX_HAVE_RT_ImportModule #define __PYX_HAVE_RT_ImportModule static PyObject *__Pyx_ImportModule(const char *name) { PyObject *py_name = 0; PyObject *py_module = 0; py_name = __Pyx_PyIdentifier_FromString(name); if (!py_name) goto bad; py_module = PyImport_Import(py_name); Py_DECREF(py_name); return py_module; bad: Py_XDECREF(py_name); return 0; } #endif #ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict) { PyObject *py_module = 0; PyObject *result = 0; PyObject *py_name = 0; char warning[200]; py_module = __Pyx_ImportModule(module_name); if (!py_module) goto bad; py_name = __Pyx_PyIdentifier_FromString(class_name); if (!py_name) goto bad; result = PyObject_GetAttr(py_module, py_name); Py_DECREF(py_name); py_name = 0; Py_DECREF(py_module); py_module = 0; if (!result) goto bad; if (!PyType_Check(result)) { PyErr_Format(PyExc_TypeError, "%s.%s is not a type object", module_name, class_name); goto bad; } if (!strict && (size_t)((PyTypeObject *)result)->tp_basicsize > size) { PyOS_snprintf(warning, sizeof(warning), "%s.%s size changed, may indicate binary incompatibility", module_name, class_name); #if PY_VERSION_HEX < 0x02050000 if (PyErr_Warn(NULL, warning) < 0) goto bad; #else if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; #endif } else if ((size_t)((PyTypeObject *)result)->tp_basicsize != size) { PyErr_Format(PyExc_ValueError, "%s.%s has the wrong size, try recompiling", module_name, class_name); goto bad; } return (PyTypeObject *)result; bad: Py_XDECREF(py_module); Py_XDECREF(result); return NULL; } #endif static int __Pyx_SetVtable(PyObject *dict, void *vtable) { #if PY_VERSION_HEX >= 0x02070000 && !(PY_MAJOR_VERSION==3&&PY_MINOR_VERSION==0) PyObject *ob = PyCapsule_New(vtable, 0, 0); #else PyObject *ob = PyCObject_FromVoidPtr(vtable, 0); #endif if (!ob) goto bad; if (PyDict_SetItemString(dict, "__pyx_vtable__", ob) < 0) goto bad; Py_DECREF(ob); return 0; bad: Py_XDECREF(ob); return -1; } static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { int start = 0, mid = 0, end = count - 1; if (end >= 0 && code_line > entries[end].code_line) { return count; } while (start < end) { mid = (start + end) / 2; if (code_line < entries[mid].code_line) { end = mid; } else if (code_line > entries[mid].code_line) { start = mid + 1; } else { return mid; } } if (code_line <= entries[mid].code_line) { return mid; } else { return mid + 1; } } static PyCodeObject *__pyx_find_code_object(int code_line) { PyCodeObject* code_object; int pos; if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { return NULL; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { return NULL; } code_object = __pyx_code_cache.entries[pos].code_object; Py_INCREF(code_object); return code_object; } static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { int pos, i; __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; if (unlikely(!code_line)) { return; } if (unlikely(!entries)) { entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); if (likely(entries)) { __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = 64; __pyx_code_cache.count = 1; entries[0].code_line = code_line; entries[0].code_object = code_object; Py_INCREF(code_object); } return; } pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { PyCodeObject* tmp = entries[pos].code_object; entries[pos].code_object = code_object; Py_DECREF(tmp); return; } if (__pyx_code_cache.count == __pyx_code_cache.max_count) { int new_max = __pyx_code_cache.max_count + 64; entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( __pyx_code_cache.entries, new_max*sizeof(__Pyx_CodeObjectCacheEntry)); if (unlikely(!entries)) { return; } __pyx_code_cache.entries = entries; __pyx_code_cache.max_count = new_max; } for (i=__pyx_code_cache.count; i>pos; i--) { entries[i] = entries[i-1]; } entries[pos].code_line = code_line; entries[pos].code_object = code_object; __pyx_code_cache.count++; Py_INCREF(code_object); } #include "compile.h" #include "frameobject.h" #include "traceback.h" static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyObject *py_srcfile = 0; PyObject *py_funcname = 0; #if PY_MAJOR_VERSION < 3 py_srcfile = PyString_FromString(filename); #else py_srcfile = PyUnicode_FromString(filename); #endif if (!py_srcfile) goto bad; if (c_line) { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #else py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); #endif } else { #if PY_MAJOR_VERSION < 3 py_funcname = PyString_FromString(funcname); #else py_funcname = PyUnicode_FromString(funcname); #endif } if (!py_funcname) goto bad; py_code = __Pyx_PyCode_New( 0, /*int argcount,*/ 0, /*int kwonlyargcount,*/ 0, /*int nlocals,*/ 0, /*int stacksize,*/ 0, /*int flags,*/ __pyx_empty_bytes, /*PyObject *code,*/ __pyx_empty_tuple, /*PyObject *consts,*/ __pyx_empty_tuple, /*PyObject *names,*/ __pyx_empty_tuple, /*PyObject *varnames,*/ __pyx_empty_tuple, /*PyObject *freevars,*/ __pyx_empty_tuple, /*PyObject *cellvars,*/ py_srcfile, /*PyObject *filename,*/ py_funcname, /*PyObject *name,*/ py_line, /*int firstlineno,*/ __pyx_empty_bytes /*PyObject *lnotab*/ ); Py_DECREF(py_srcfile); Py_DECREF(py_funcname); return py_code; bad: Py_XDECREF(py_srcfile); Py_XDECREF(py_funcname); return NULL; } static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename) { PyCodeObject *py_code = 0; PyObject *py_globals = 0; PyFrameObject *py_frame = 0; py_code = __pyx_find_code_object(c_line ? c_line : py_line); if (!py_code) { py_code = __Pyx_CreateCodeObjectForTraceback( funcname, c_line, py_line, filename); if (!py_code) goto bad; __pyx_insert_code_object(c_line ? c_line : py_line, py_code); } py_globals = PyModule_GetDict(__pyx_m); if (!py_globals) goto bad; py_frame = PyFrame_New( PyThreadState_GET(), /*PyThreadState *tstate,*/ py_code, /*PyCodeObject *code,*/ py_globals, /*PyObject *globals,*/ 0 /*PyObject *locals*/ ); if (!py_frame) goto bad; py_frame->f_lineno = py_line; PyTraceBack_Here(py_frame); bad: Py_XDECREF(py_code); Py_XDECREF(py_frame); } static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { while (t->p) { #if PY_MAJOR_VERSION < 3 if (t->is_unicode) { *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); } else if (t->intern) { *t->p = PyString_InternFromString(t->s); } else { *t->p = PyString_FromStringAndSize(t->s, t->n - 1); } #else /* Python 3+ has unicode identifiers */ if (t->is_unicode | t->is_str) { if (t->intern) { *t->p = PyUnicode_InternFromString(t->s); } else if (t->encoding) { *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); } else { *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); } } else { *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); } #endif if (!*t->p) return -1; ++t; } return 0; } /* Type Conversion Functions */ static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { int is_true = x == Py_True; if (is_true | (x == Py_False) | (x == Py_None)) return is_true; else return PyObject_IsTrue(x); } static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { PyNumberMethods *m; const char *name = NULL; PyObject *res = NULL; #if PY_VERSION_HEX < 0x03000000 if (PyInt_Check(x) || PyLong_Check(x)) #else if (PyLong_Check(x)) #endif return Py_INCREF(x), x; m = Py_TYPE(x)->tp_as_number; #if PY_VERSION_HEX < 0x03000000 if (m && m->nb_int) { name = "int"; res = PyNumber_Int(x); } else if (m && m->nb_long) { name = "long"; res = PyNumber_Long(x); } #else if (m && m->nb_int) { name = "int"; res = PyNumber_Long(x); } #endif if (res) { #if PY_VERSION_HEX < 0x03000000 if (!PyInt_Check(res) && !PyLong_Check(res)) { #else if (!PyLong_Check(res)) { #endif PyErr_Format(PyExc_TypeError, "__%s__ returned non-%s (type %.200s)", name, name, Py_TYPE(res)->tp_name); Py_DECREF(res); return NULL; } } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_TypeError, "an integer is required"); } return res; } static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { Py_ssize_t ival; PyObject* x = PyNumber_Index(b); if (!x) return -1; ival = PyInt_AsSsize_t(x); Py_DECREF(x); return ival; } static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { #if PY_VERSION_HEX < 0x02050000 if (ival <= LONG_MAX) return PyInt_FromLong((long)ival); else { unsigned char *bytes = (unsigned char *) &ival; int one = 1; int little = (int)*(unsigned char*)&one; return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0); } #else return PyInt_FromSize_t(ival); #endif } static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject* x) { unsigned PY_LONG_LONG val = __Pyx_PyInt_AsUnsignedLongLong(x); if (unlikely(val == (unsigned PY_LONG_LONG)-1 && PyErr_Occurred())) { return (size_t)-1; } else if (unlikely(val != (unsigned PY_LONG_LONG)(size_t)val)) { PyErr_SetString(PyExc_OverflowError, "value too large to convert to size_t"); return (size_t)-1; } return (size_t)val; } #endif /* Py_PYTHON_H */ MMTK-2.7.9/Src/MMTK_trajectory_generator.pyx0000644000076600000240000002615112117632051021271 0ustar hinsenstaff00000000000000# Trajectory generators in Cython # # Written by Konrad Hinsen # import_MMTK_universe() import_MMTK_trajectory() import_MMTK_forcefield() from MMTK import Features import numpy as N cimport numpy as N import cython cdef extern from "stdlib.h": ctypedef long size_t cdef void *malloc(size_t size) cdef void free(void *ptr) # # Base class for trajectory generators # cdef class TrajectoryGenerator(object): """ Trajectory generator base class This base class implements the common aspects of everything that generates trajectories: integrators, minimizers, etc. """ def __init__(self, universe, options, name): self.universe = universe self.options = options self.name = name self.call_options = {} self.features = [] self.tvars = NULL self.tspec = NULL def setCallOptions(self, options): self.call_options = options def getActions(self): try: steps = self.getOption('steps') except ValueError: steps = None return [a.getSpecificationList(self, steps) for a in self.actions] def cleanupActions(self): for a in self.actions: a.cleanup() def getOption(self, option): try: value = self.call_options[option] except KeyError: try: value = self.options[option] except KeyError: try: value = self.default_options[option] except KeyError: raise ValueError('undefined option: ' + option) return value def optionString(self, options): return sum((o + '=' + `self.getOption(o)` + ', ' for o in options), '')[:-2] def __call__(self, **options): self.setCallOptions(options) try: self.actions = self.getOption('actions') except ValueError: self.actions = [] try: if self.getOption('background'): import MMTK_state_accessor self.state_accessor = MMTK_state_accessor.StateAccessor() self.actions.append(self.state_accessor) except ValueError: pass Features.checkFeatures(self, self.universe) if self.tvars != NULL: free(self.tvars) self.tvars = NULL configuration = self.universe.configuration() self.conf_array = configuration.array self.declareTrajectoryVariable_array(self.conf_array, "configuration", "Configuration:\n", length_unit_name, PyTrajectory_Configuration) self.universe_spec = self.universe._spec if self.universe_spec.geometry_data_length > 0: self.declareTrajectoryVariable_box( self.universe_spec.geometry_data, self.universe_spec.geometry_data_length) masses = self.universe.masses() self.declareTrajectoryVariable_array(masses.array, "masses", "Masses:\n", mass_unit_name, PyTrajectory_Internal) self.natoms = self.universe.numberOfAtoms() self.df = self.universe.degreesOfFreedom() self.declareTrajectoryVariable_int(&self.df, "degrees_of_freedom", "Degrees of freedom: %d\n", "", PyTrajectory_Internal) if self.getOption('background'): from MMTK import ThreadManager return ThreadManager.TrajectoryGeneratorThread( self.universe, self.start_py, (), self.state_accessor) else: self.start() return None def start_py(self): self.start() cdef start(self): pass cdef void _addTrajectoryVariable(self, PyTrajectoryVariable v) except *: cdef PyTrajectoryVariable *tv cdef int i, n if self.tvars == NULL: self.tvars = \ malloc(2*sizeof(PyTrajectoryVariable)) if self.tvars == NULL: raise MemoryError self.tvars[0] = v self.tvars[1].name = NULL else: n = 0 tv = self.tvars while tv.name != NULL: n += 1 tv += 1 tv = \ malloc((n+2)*sizeof(PyTrajectoryVariable)) if tv == NULL: raise MemoryError for i in range(n): tv[i] = self.tvars[i] tv[n] = v tv[n+1].name = NULL free(self.tvars) self.tvars = tv cdef void declareTrajectoryVariable_double(self, double * var, char *name, char *text, char*unit, int data_class) except *: cdef PyTrajectoryVariable v v.name = name v.text = text v.unit = unit v.type = PyTrajectory_Scalar v.data_class = data_class v.value.dp = var self._addTrajectoryVariable(v) cdef void declareTrajectoryVariable_int(self, int * var, char *name, char *text, char*unit, int data_class) except *: cdef PyTrajectoryVariable v v.name = name v.text = text v.unit = unit v.type = PyTrajectory_IntScalar v.data_class = data_class v.value.ip = var self._addTrajectoryVariable(v) cdef void declareTrajectoryVariable_array(self, N.ndarray array, char*name, char *text, char*unit, int data_class) except *: cdef PyTrajectoryVariable v v.name = name v.text = text v.unit = unit v.data_class = data_class v.value.array = array if array.ndim == 1: v.type = PyTrajectory_ParticleScalar elif array.ndim == 2: v.type = PyTrajectory_ParticleVector else: v.type = 0 # suppress warning from gcc assert ValueError("array must be 1D or 2D") self._addTrajectoryVariable(v) cdef void declareTrajectoryVariable_box(self, double * var, int l) except *: cdef PyTrajectoryVariable v v.name = "box_size" v.text = "Box size:" v.unit = length_unit_name v.type = PyTrajectory_BoxSize v.data_class = PyTrajectory_Configuration v.value.dp = var v.length = l self._addTrajectoryVariable(v) cdef void initializeTrajectoryActions(self) except *: actions = self.getActions() self.tspec = PyTrajectory_OutputSpecification(self.universe, actions, self.name, self.tvars); if self.tspec == NULL: raise MemoryError cdef void finalizeTrajectoryActions(self, int last_step, int error=False) except *: if error: PyTrajectory_OutputFinish(self.tspec, last_step, 1, 1, self.tvars) else: PyTrajectory_OutputFinish(self.tspec, last_step, 0, 1, self.tvars) cdef int trajectoryActions(self, int step) except -1: return PyTrajectory_Output(self.tspec, step, self.tvars, NULL) cdef void foldCoordinatesIntoBox(self) nogil: self.universe_spec.correction_function(self.conf_array.data, self.natoms, self.universe_spec.geometry_data) cdef void acquireReadLock(self) nogil: PyUniverseSpec_StateLock(self.universe_spec, 1) self.lock_state = 1 cdef void releaseReadLock(self) nogil: PyUniverseSpec_StateLock(self.universe_spec, 2) self.lock_state = 0 cdef void acquireWriteLock(self) nogil: PyUniverseSpec_StateLock(self.universe_spec, -1) self.lock_state = -1 cdef void releaseWriteLock(self) nogil: PyUniverseSpec_StateLock(self.universe_spec, -2) self.lock_state = 0 # # Base class for trajectory generators that call the C-level # energy evaluators. It implements a mechanism that makes such # generators thread-safe. # cdef class EnergyBasedTrajectoryGenerator(TrajectoryGenerator): def __init__(self, universe, options, name): TrajectoryGenerator.__init__(self, universe, options, name) self.evaluator_object = None self.c_evaluator_object = None self.evaluator = NULL cdef void initializeTrajectoryActions(self) except *: TrajectoryGenerator.initializeTrajectoryActions(self) # Construct a C evaluator object for the force field, using # the specified number of threads or the default value nt = self.getOption('threads') self.evaluator_object = self.universe.energyEvaluator(threads=nt) self.c_evaluator_object = self.evaluator_object.CEvaluator() self.evaluator = self.c_evaluator_object cdef void finalizeTrajectoryActions(self, int last_step, int error=False) except *: TrajectoryGenerator.finalizeTrajectoryActions(self, last_step, error) self.evaluator = NULL self.c_evaluator_object = None self.evaluator_object = None cdef int trajectoryActions(self, int step) except -1: cdef int ret_code self.evaluator.tstate_save = PyEval_SaveThread() if self.lock_state != 0: PyUniverseSpec_StateLock(self.universe_spec, 2*self.lock_state) ret_code = PyTrajectory_Output(self.tspec, step, self.tvars, &self.evaluator.tstate_save) if self.lock_state != 0: PyUniverseSpec_StateLock(self.universe_spec, self.lock_state) PyEval_RestoreThread(self.evaluator.tstate_save) return ret_code cdef void calculateEnergies(self, N.ndarray[double, ndim=2] conf_array, energy_data *energy, int small_change=0) \ except *: self.evaluator.tstate_save = PyEval_SaveThread() if self.lock_state == -1: PyUniverseSpec_StateLock(self.universe_spec, -2) if self.lock_state != 1: PyUniverseSpec_StateLock(self.universe_spec, 1) self.evaluator.eval_func(self.evaluator, energy, conf_array, small_change) if self.lock_state != 1: PyUniverseSpec_StateLock(self.universe_spec, 2) if self.lock_state == -1: PyUniverseSpec_StateLock(self.universe_spec, -1) PyEval_RestoreThread(self.evaluator.tstate_save) MMTK-2.7.9/Src/MMTK_universe.c0000644000076600000240000006630211662461640016311 0ustar hinsenstaff00000000000000/* Low-level functions for universes * * Written by Konrad Hinsen */ #define _UNIVERSE_MODULE #include "MMTK/core.h" #include "MMTK/universe.h" #define DEBUG 0 #define THREAD_DEBUG 0 /* * Utility functions */ /* Calculate determinant and inverse of the coordinate transformation * matrix for parallelepipedic universes. */ static void parallelepiped_invert(double *data) { int i; data[9+0] = data[4]*data[8]-data[7]*data[5]; data[9+3] = data[6]*data[5]-data[3]*data[8]; data[9+6] = data[3]*data[7]-data[6]*data[4]; data[9+1] = data[7]*data[2]-data[1]*data[8]; data[9+4] = data[0]*data[8]-data[6]*data[2]; data[9+7] = data[6]*data[1]-data[0]*data[7]; data[9+2] = data[1]*data[5]-data[4]*data[2]; data[9+5] = data[3]*data[2]-data[0]*data[5]; data[9+8] = data[0]*data[4]-data[3]*data[1]; data[18] = data[0]*data[9+0]+data[1]*data[9+3]+data[2]*data[9+6]; if (fabs(data[18]) > 0.) { double r = 1./data[18]; for (i = 0; i < 9; i++) data[i+9] *= r; } else { for (i = 0; i < 9; i++) data[i+9] = 0.; } } static PyObject * parallelepiped_invert_py(PyObject *dummy, PyObject *args) { PyArrayObject *geometry; if (!PyArg_ParseTuple(args, "O!", &PyArray_Type, &geometry)) return NULL; if (geometry->nd != 1 || geometry->dimensions[0] != 19) { PyErr_SetString(PyExc_ValueError, "Bad universe data shape"); return NULL; } parallelepiped_invert((double *)geometry->data); Py_INCREF(Py_None); return Py_None; } /* * Distance vector functions */ static void distance_vector(vector3 d, vector3 r1, vector3 r2, double *data) { distance_vector_1(d, r1, r2, data); } static void orthorhombic_distance_vector(vector3 d, vector3 r1, vector3 r2, double *data) { distance_vector_2(d, r1, r2, data); } static void parallelepipedic_distance_vector(vector3 d, vector3 r1, vector3 r2, double *data) { distance_vector_3(d, r1, r2, data); } /* * Position correction functions (to fold coordinates into the central box) */ static void no_correction(vector3 *x, int natoms, double *data) { } static void orthorhombic_correction(vector3 *x, int natoms, double *data) { double a = data[0]; double b = data[1]; double c = data[2]; double ah = 0.5*a, bh = 0.5*b, ch = 0.5*c; int i; #if DEBUG if (a > 0. && b > 0. && c > 0.) for (i = 0; i < natoms; i++) { if (x[i][0] > ah) x[i][0] -= a; while (x[i][0] > ah) { printf("x[%d]=%lf, a=%lf\n", i, x[i][0], a); x[i][0] -= a; } if (x[i][0] < -ah) x[i][0] += a; while (x[i][0] < -ah) { printf("x[%d]=%lf, -a=%lf\n", i, x[i][0], -a); x[i][0] += a; } if (x[i][1] > bh) x[i][1] -= b; while (x[i][1] > bh) { printf("x[%d]=%lf, b=%lf\n", i, x[i][1], b); x[i][1] -= b; } if (x[i][1] < -bh) x[i][1] += b; while (x[i][1] < -bh) { printf("x[%d]=%lf, -b=%lf\n", i, x[i][1], -b); x[i][1] += b; } if (x[i][2] > ch) x[i][2] -= c; while (x[i][2] > ch) { printf("x[%d]=%lf, c=%lf\n", i, x[i][2], c); x[i][2] -= c; } if (x[i][2] < -ch) x[i][2] += c; while (x[i][2] < -ch) { printf("x[%d]=%lf, -c=%lf\n", i, x[i][2], -c); x[i][2] += c; } } #else if (a > 0. && b > 0. && c > 0.) for (i = 0; i < natoms; i++) { while (x[i][0] >= ah) x[i][0] -= a; while (x[i][0] < -ah) x[i][0] += a; while (x[i][1] >= bh) x[i][1] -= b; while (x[i][1] < -bh) x[i][1] += b; while (x[i][2] >= ch) x[i][2] -= c; while (x[i][2] < -ch) x[i][2] += c; } #endif } static void parallelepipedic_correction(vector3 *x, int natoms, double *data) { double xf, yf, zf; int i; for (i = 0; i < natoms; i++) { xf = data[0+9]*x[i][0] + data[1+9]*x[i][1] + data[2+9]*x[i][2]; yf = data[3+9]*x[i][0] + data[4+9]*x[i][1] + data[5+9]*x[i][2]; zf = data[6+9]*x[i][0] + data[7+9]*x[i][1] + data[8+9]*x[i][2]; while (xf >= 0.5) xf -= 1.; while (xf < -0.5) xf += 1.; while (yf >= 0.5) yf -= 1.; while (yf < -0.5) yf += 1.; while (zf >= 0.5) zf -= 1.; while (zf < -0.5) zf += 1.; x[i][0] = data[0]*xf + data[1]*yf + data[2]*zf; x[i][1] = data[3]*xf + data[4]*yf + data[5]*zf; x[i][2] = data[6]*xf + data[7]*yf + data[8]*zf; } } /* * Volume scaling functions */ static double no_volume(double scale_factor, double *data) { return -1.; } static double orthorhombic_volume(double scale_factor, double *data) { data[0] *= scale_factor; data[1] *= scale_factor; data[2] *= scale_factor; return data[0]*data[1]*data[2]; } static double parallelepipedic_volume(double scale_factor, double *data) { int i; for (i = 0; i < 9; i++) data[i] *= scale_factor; for (i = 9; i < 18; i++) data[i] /= scale_factor; data[18] *= scale_factor*scale_factor*scale_factor; return fabs(data[18]); } /* * Box coordinate transformation functions */ static void no_box(vector3 *x, vector3 *b, int n, double *data, int mode) { } static void orthorhombic_box(vector3 *x, vector3 *b, int n, double *data, int mode) { int i; if (mode == 1 || mode == 3) /* real-to-box or reciprocal box-to-real */ for (i = 0; i < n; i++) { b[i][0] = x[i][0]/data[0]; b[i][1] = x[i][1]/data[1]; b[i][2] = x[i][2]/data[2]; } else if (mode == 0 || mode == 2) /* box-to-real or reciprocal real-to-box */ for (i = 0; i < n; i++) { x[i][0] = b[i][0]*data[0]; x[i][1] = b[i][1]*data[1]; x[i][2] = b[i][2]*data[2]; } } static void parallelepipedic_box(vector3 *x, vector3 *b, int n, double *data, int mode) { int i; vector3 r; if (mode == 1) /* real-to-box */ for (i = 0; i < n; i++) { r[0] = data[0+9]*x[i][0] + data[1+9]*x[i][1] + data[2+9]*x[i][2]; r[1] = data[3+9]*x[i][0] + data[4+9]*x[i][1] + data[5+9]*x[i][2]; r[2] = data[6+9]*x[i][0] + data[7+9]*x[i][1] + data[8+9]*x[i][2]; vector_copy(b[i], r); } else if (mode == 3) /* reciprocal box-to-real */ for (i = 0; i < n; i++) { r[0] = data[0+9]*b[i][0] + data[3+9]*b[i][1] + data[6+9]*b[i][2]; r[1] = data[1+9]*b[i][0] + data[4+9]*b[i][1] + data[7+9]*b[i][2]; r[2] = data[2+9]*b[i][0] + data[5+9]*b[i][1] + data[8+9]*b[i][2]; vector_copy(x[i], r); } else if (mode == 0) /* box-to-real */ for (i = 0; i < n; i++) { r[0] = data[0]*b[i][0] + data[1]*b[i][1] + data[2]*b[i][2]; r[1] = data[3]*b[i][0] + data[4]*b[i][1] + data[5]*b[i][2]; r[2] = data[6]*b[i][0] + data[7]*b[i][1] + data[8]*b[i][2]; vector_copy(x[i], r); } else if (mode == 2) /* reciprocal real-to-box */ for (i = 0; i < n; i++) { r[0] = data[0]*x[i][0] + data[3]*x[i][1] + data[6]*x[i][2]; r[1] = data[1]*x[i][0] + data[4]*x[i][1] + data[7]*x[i][2]; r[2] = data[2]*x[i][0] + data[5]*x[i][1] + data[8]*x[i][2]; vector_copy(b[i], r); } } /* * Trajectory transformation functions */ static void no_trajectory(vector3 *x, vector3 *b, int nsteps, double *data, int to_box) { } static void orthorhombic_trajectory(vector3 *x, vector3 *b, int nsteps, double *data, int to_box) { int i; if (to_box) for (i = 0; i < nsteps; i++) { b[i][0] = x[i][0]/data[3*i]; b[i][1] = x[i][1]/data[3*i+1]; b[i][2] = x[i][2]/data[3*i+2]; } else for (i = 0; i < nsteps; i++) { x[i][0] = b[i][0]*data[3*i]; x[i][1] = b[i][1]*data[3*i+1]; x[i][2] = b[i][2]*data[3*i+2]; } } static void parallelepipedic_trajectory(vector3 *x, vector3 *b, int nsteps, double *data, int to_box) { double ud[19]; int i, j; if (to_box) for (i = 0; i < nsteps; i++) { for (j = 0; j < 9; j++) ud[j] = data[9*i+j]; parallelepiped_invert(ud); b[i][0] = ud[0+9]*x[i][0] + ud[1+9]*x[i][1] + ud[2+9]*x[i][2]; b[i][1] = ud[3+9]*x[i][0] + ud[4+9]*x[i][1] + ud[5+9]*x[i][2]; b[i][2] = ud[6+9]*x[i][0] + ud[7+9]*x[i][1] + ud[8+9]*x[i][2]; } else for (i = 0; i < nsteps; i++) { x[i][0] = data[9*i+0]*b[i][0] + data[9*i+1]*b[i][1] + data[9*i+2]*b[i][2]; x[i][1] = data[9*i+3]*b[i][0] + data[9*i+4]*b[i][1] + data[9*i+5]*b[i][2]; x[i][2] = data[9*i+6]*b[i][0] + data[9*i+7]*b[i][1] + data[9*i+8]*b[i][2]; } } /* * Bounding box functions */ static void infinite_bounding_box(vector3 *box1, vector3 *box2, vector3 *x, int n, double *data) { int i; vector_copy(*box1, x[0]); vector_copy(*box2, x[0]); for (i = 1; i < n; i++) { if (x[i][0] < (*box1)[0]) (*box1)[0] = x[i][0]; if (x[i][1] < (*box1)[1]) (*box1)[1] = x[i][1]; if (x[i][2] < (*box1)[2]) (*box1)[2] = x[i][2]; if (x[i][0] > (*box2)[0]) (*box2)[0] = x[i][0]; if (x[i][1] > (*box2)[1]) (*box2)[1] = x[i][1]; if (x[i][2] > (*box2)[2]) (*box2)[2] = x[i][2]; } } static void orthorhombic_bounding_box(vector3 *box1, vector3 *box2, vector3 *x, int n, double *data) { (*box2)[0] = 0.5*data[0]; (*box2)[1] = 0.5*data[1]; (*box2)[2] = 0.5*data[2]; (*box1)[0] = -(*box2)[0]; (*box1)[1] = -(*box2)[1]; (*box1)[2] = -(*box2)[2]; } static double max3(double a1, double a2, double a3) { double max = fabs(a1); if (fabs(a2) > max) max = fabs(a2); if (fabs(a3) > max) max = fabs(a3); if (fabs(a1+a2) > max) max = fabs(a1+a2); if (fabs(a1+a3) > max) max = fabs(a1+a3); if (fabs(a2+a3) > max) max = fabs(a2+a3); if (fabs(a1+a2+a3) > max) max = fabs(a1+a2+a3); return max; } static void parallelepipedic_bounding_box(vector3 *box1, vector3 *box2, vector3 *x, int n, double *data) { (*box2)[0] = 0.5*max3(data[0], data[3], data[6]); (*box2)[1] = 0.5*max3(data[1], data[4], data[7]); (*box2)[2] = 0.5*max3(data[2], data[5], data[8]); (*box1)[0] = -(*box2)[0]; (*box1)[1] = -(*box2)[1]; (*box1)[2] = -(*box2)[2]; } /* * Type "universe specification" * * Objects of this type provide a low-level description of universes, * in particular their geometry. * * The structure declaration is in mmtk_universe.h. */ /* Allocation and deallocation */ static PyUniverseSpecObject * universe_new(void) { PyUniverseSpecObject *self; int i, error; self = PyObject_NEW(PyUniverseSpecObject, &PyUniverseSpec_Type); if (self == NULL) { PyErr_NoMemory(); return NULL; } self->geometry = NULL; self->geometry_data = NULL; self->distance_function = NULL; self->correction_function = NULL; self->volume_function = NULL; self->box_function = NULL; self->trajectory_function = NULL; self->bounding_box_function = NULL; self->is_periodic = 0; self->is_orthogonal = 0; #ifdef WITH_THREAD error = 0; self->main_state_lock = PyThread_allocate_lock(); if (self->main_state_lock == NULL) error = 1; if (!error) { self->configuration_change_lock = PyThread_allocate_lock(); if (self->configuration_change_lock == NULL) error = 1; } for (i = 0; i < MMTK_MAX_THREADS && !error; i++) { self->state_wait_lock[i] = PyThread_allocate_lock(); if (self->state_wait_lock[i] == NULL) error = 1; else PyThread_acquire_lock(self->state_wait_lock[i], 1); self->state_access_type[i] = 0; } if (error) { PyErr_SetString(PyExc_OSError, "couldn't allocate lock"); PyObject_Del(self); return NULL; } self->state_access = 0; self->waiting_threads = 0; #endif return self; } static void universe_dealloc(PyUniverseSpecObject *self) { Py_XDECREF(self->geometry); PyObject_Del(self); } /* Methods */ static PyObject* call_correction_function_py(PyObject *self, PyObject *args) { PyUniverseSpecObject *universe = (PyUniverseSpecObject *)self; PyArrayObject *configuration; vector3 *x; int natoms; double *data; if (!PyArg_ParseTuple(args, "O!", &PyArray_Type, &configuration)) return NULL; x = (vector3 *)configuration->data; natoms = configuration->dimensions[0]; data = (double *)universe->geometry->data; universe->correction_function(x, natoms, data); Py_INCREF(Py_None); return Py_None; } static PyObject* distance_vector_py(PyObject *self, PyObject *args) { PyUniverseSpecObject *universe = (PyUniverseSpecObject *)self; PyArrayObject *r1, *r2; PyArrayObject *geometry_data = NULL; PyArrayObject *d; #if defined(NUMPY) npy_intp three = 3; #else int three = 3; #endif if (!PyArg_ParseTuple(args, "O!O!|O!", &PyArray_Type, &r1, &PyArray_Type, &r2, &PyArray_Type, &geometry_data)) return NULL; #if defined(NUMPY) d = (PyArrayObject *)PyArray_SimpleNew(1, &three, PyArray_DOUBLE); #else d = (PyArrayObject *)PyArray_FromDims(1, &three, PyArray_DOUBLE); #endif if (d == NULL) return NULL; if (geometry_data != NULL) universe->distance_function(*(vector3 *)d->data, *(vector3 *)r1->data, *(vector3 *)r2->data, (double *)geometry_data->data); else universe->distance_function(*(vector3 *)d->data, *(vector3 *)r1->data, *(vector3 *)r2->data, (double *)universe->geometry->data); return (PyObject *)d; } /* State lock management */ int PyUniverseSpec_StateLock(PyUniverseSpecObject *universe, int action) { /* action = 1: acquire read access action = 2: release read access action = -1: acquire write access action = -2: release write access */ #ifdef WITH_THREAD int error; int i; #if THREAD_DEBUG int id = PyThread_get_thread_ident(); #endif PyThread_acquire_lock(universe->main_state_lock, 1); #if THREAD_DEBUG printf("thread %d entering; state is %d, %d threads waiting\n", id, universe->state_access, universe->waiting_threads); for (i = 0; i < MMTK_MAX_THREADS; i++) { if (universe->state_access_type[i] == 1) printf(" lock %d waiting for read access\n", i); else if (universe->state_access_type[i] == -1) printf(" lock %d waiting for write access\n", i); } #endif error = 0; switch(action) { case 1: while (universe->state_access < 0) { if (universe->waiting_threads == MMTK_MAX_THREADS) { PyErr_SetString(PyExc_OSError, "too many threads"); error = 1; } for (i = 0; i < MMTK_MAX_THREADS; i++) { if (universe->state_access_type[i] == 0) break; } #if THREAD_DEBUG printf("thread %d waiting for read access using lock %d\n", id, i); #endif universe->state_access_type[i] = 1; universe->waiting_threads++; PyThread_release_lock(universe->main_state_lock); PyThread_acquire_lock(universe->state_wait_lock[i], 1); PyThread_acquire_lock(universe->main_state_lock, 1); universe->waiting_threads--; universe->state_access_type[i] = 0; } #if THREAD_DEBUG printf("thread %d has read access\n", id); #endif universe->state_access++; break; case 2: #if THREAD_DEBUG if (universe->state_access <= 0) printf("thread %d found state < 0 after read access\n", id); printf("thread %d gives up read access; %d threads waiting\n", id, universe->waiting_threads); #endif universe->state_access--; if (universe->state_access == 0 && universe->waiting_threads > 0) { for (i = 0; i < MMTK_MAX_THREADS; i++) { if (universe->state_access_type[i] == -1) { #if THREAD_DEBUG printf("thread %d releases lock %d\n", id, i); #endif PyThread_release_lock(universe->main_state_lock); PyThread_release_lock(universe->state_wait_lock[i]); PyThread_acquire_lock(universe->main_state_lock, 1); break; } } #if THREAD_DEBUG if (i == MMTK_MAX_THREADS) printf("Error: no thread waiting for write access!\n"); #endif } break; case -1: while (universe->state_access != 0) { if (universe->waiting_threads == MMTK_MAX_THREADS) { PyErr_SetString(PyExc_OSError, "too many threads"); error = 1; } for (i = 0; i < MMTK_MAX_THREADS; i++) { if (universe->state_access_type[i] == 0) break; } #if THREAD_DEBUG printf("thread %d waiting for write access using lock %d\n", id, i); #endif universe->state_access_type[i] = -1; universe->waiting_threads++; PyThread_release_lock(universe->main_state_lock); PyThread_acquire_lock(universe->state_wait_lock[i], 1); PyThread_acquire_lock(universe->main_state_lock, 1); universe->waiting_threads--; universe->state_access_type[i] = 0; } #if THREAD_DEBUG printf("thread %d has write access\n", id); #endif universe->state_access = -1; break; case -2: #if THREAD_DEBUG printf("thread %d gives up write access; %d threads waiting\n", id, universe->waiting_threads); #endif universe->state_access = 0; if (universe->waiting_threads > 0) { for (i = 0; i < MMTK_MAX_THREADS; i++) { if (universe->state_access_type[i] == -1) { #if THREAD_DEBUG printf("thread %d releases lock %d\n", id, i); #endif PyThread_release_lock(universe->main_state_lock); PyThread_release_lock(universe->state_wait_lock[i]); PyThread_acquire_lock(universe->main_state_lock, 1); break; } } if (i == MMTK_MAX_THREADS) { for (i = 0; i < MMTK_MAX_THREADS; i++) { if (universe->state_access_type[i] == 1) { #if THREAD_DEBUG printf("thread %d releases lock %d\n", id, i); #endif PyThread_release_lock(universe->main_state_lock); PyThread_release_lock(universe->state_wait_lock[i]); PyThread_acquire_lock(universe->main_state_lock, 1); } } #if THREAD_DEBUG if (i == MMTK_MAX_THREADS) printf("Error: no thread waiting for read access!\n"); #endif } } break; } PyThread_release_lock(universe->main_state_lock); return !error; #else return 1; #endif } static PyObject* state_lock_py(PyObject *object, PyObject *args) { PyUniverseSpecObject *self = (PyUniverseSpecObject *)object; int action; int ok; if (!PyArg_ParseTuple(args, "i", &action)) return NULL; Py_BEGIN_ALLOW_THREADS; ok = PyUniverseSpec_StateLock(self, action); Py_END_ALLOW_THREADS; if (ok) { Py_INCREF(Py_None); return Py_None; } else return NULL; } static PyObject* configuration_change_lock_py(PyObject *object, PyObject *args) { PyUniverseSpecObject *self = (PyUniverseSpecObject *)object; int action; int success; if (!PyArg_ParseTuple(args, "i", &action)) return NULL; /* action = 0: acquire lock non-blocking action = 1: acquire lock blocking action = 2: release lock */ #ifdef WITH_THREAD Py_BEGIN_ALLOW_THREADS; success = 0; /* initialize to make gcc happy */ switch(action) { case 0: success = PyThread_acquire_lock(self->configuration_change_lock, 0); break; case 1: success = PyThread_acquire_lock(self->configuration_change_lock, 1); break; case 2: PyThread_release_lock(self->configuration_change_lock); success = 1; break; } Py_END_ALLOW_THREADS; return PyInt_FromLong((long)success); #else return PyInt_FromLong(1L); #endif } /* Documentation string */ static char PyUniverseSpec_Type__doc__[] = "low-level universe specification"; /* Method table */ static struct PyMethodDef universe_methods[] = { {"distanceVector", distance_vector_py, 1}, {"foldCoordinatesIntoBox", call_correction_function_py, 1}, {"stateLock", state_lock_py, 1}, {"configurationChangeLock", configuration_change_lock_py, 1}, {NULL, NULL} /* sentinel */ }; /* Attribute access. */ static PyObject * universe_getattr(PyUniverseSpecObject *self, char *name) { return Py_FindMethod(universe_methods, (PyObject *)self, name); } /* Type object */ PyTypeObject PyUniverseSpec_Type = { PyObject_HEAD_INIT(NULL) 0, /*ob_size*/ "UniverseSpec", /*tp_name*/ sizeof(PyUniverseSpecObject), /*tp_basicsize*/ 0, /*tp_itemsize*/ /* methods */ (destructor)universe_dealloc, /*tp_dealloc*/ 0, /*tp_print*/ (getattrfunc)universe_getattr, /*tp_getattr*/ 0, /*tp_setattr*/ 0, /*tp_compare*/ 0, /*tp_repr*/ 0, /*tp_as_number*/ 0, /*tp_as_sequence*/ 0, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ /* Space for future expansion */ 0L,0L, /* Documentation string */ PyUniverseSpec_Type__doc__ }; /* * Creators for infinite and periodic universes */ static PyObject * InfiniteUniverseSpec(PyObject *dummy, PyObject *args) { PyUniverseSpecObject *new; if (!PyArg_ParseTuple(args, "")) return NULL; new = universe_new(); if (new == NULL) return NULL; #if defined(NUMPY) new->geometry = (PyArrayObject *)PyArray_SimpleNew(0, NULL, PyArray_DOUBLE); #else new->geometry = (PyArrayObject *)PyArray_FromDims(0, NULL, PyArray_DOUBLE); #endif new->geometry_data = NULL; new->geometry_data_length = 0; new->distance_function = distance_vector; new->correction_function = no_correction; new->volume_function = no_volume; new->box_function = no_box; new->trajectory_function = no_trajectory; new->bounding_box_function = infinite_bounding_box; new->is_periodic = 0; new->is_orthogonal = 0; return (PyObject *)new; } static PyObject * OrthorhombicPeriodicUniverseSpec(PyObject *dummy, PyObject *args) { PyUniverseSpecObject *new; PyArrayObject *geometry; if (!PyArg_ParseTuple(args, "O!", &PyArray_Type, &geometry)) return NULL; new = universe_new(); if (new == NULL) return NULL; new->geometry = geometry; Py_INCREF(geometry); new->geometry_data = (double *)new->geometry->data; new->geometry_data_length = 3; new->distance_function = orthorhombic_distance_vector; new->correction_function = orthorhombic_correction; new->volume_function = orthorhombic_volume; new->box_function = orthorhombic_box; new->trajectory_function = orthorhombic_trajectory; new->bounding_box_function = orthorhombic_bounding_box; new->is_periodic = 1; new->is_orthogonal = 1; return (PyObject *)new; } static PyObject * ParallelepipedicPeriodicUniverseSpec(PyObject *dummy, PyObject *args) { PyUniverseSpecObject *new; PyArrayObject *geometry; if (!PyArg_ParseTuple(args, "O!", &PyArray_Type, &geometry)) return NULL; if (geometry->nd != 1 || geometry->dimensions[0] != 19) { PyErr_SetString(PyExc_ValueError, "Bad universe data shape"); return NULL; } new = universe_new(); if (new == NULL) return NULL; new->geometry = geometry; Py_INCREF(geometry); new->geometry_data = (double *)new->geometry->data; new->geometry_data_length = 9; parallelepiped_invert(new->geometry_data); new->distance_function = parallelepipedic_distance_vector; new->correction_function = parallelepipedic_correction; new->volume_function = parallelepipedic_volume; new->box_function = parallelepipedic_box; new->trajectory_function = parallelepipedic_trajectory; new->bounding_box_function = parallelepipedic_bounding_box; new->is_periodic = 1; new->is_orthogonal = 0; return (PyObject *)new; } /* * Find offset to be added to each atom position to make all atoms * contiguous in spite of periodic boundary conditions. */ static PyObject * contiguous_object_offset(PyObject *dummy, PyObject *args) { PyUniverseSpecObject *spec; PyArrayObject *pairs, *conf, *offsets; PyArrayObject *geometry = NULL; double *geometry_data; long *p; vector3 *x, *o; int npairs, box_coor_flag; int i; if (!PyArg_ParseTuple(args, "O!O!O!O!i|O!", &PyUniverseSpec_Type, &spec, &PyArray_Type, &pairs, &PyArray_Type, &conf, &PyArray_Type, &offsets, &box_coor_flag, &PyArray_Type, &geometry)) return NULL; if (!PyArray_ISCONTIGUOUS(conf)) { PyErr_SetString(PyExc_ValueError, "configuration array not contiguous"); return NULL; } if (!PyArray_ISCONTIGUOUS(pairs)) { PyErr_SetString(PyExc_ValueError, "pair array not contiguous"); return NULL; } if (!PyArray_ISCONTIGUOUS(pairs)) { PyErr_SetString(PyExc_ValueError, "offset array not contiguous"); return NULL; } if (geometry == NULL) geometry_data = spec->geometry_data; else geometry_data = (double *)geometry->data; npairs = pairs->dimensions[0]; p = (long *)pairs->data; x = (vector3 *)conf->data; o = (vector3 *)offsets->data; for (i = 0; i < npairs; i++) { int a1 = p[2*i], a2=p[2*i+1]; vector3 pos1, d; vector_copy(pos1, x[a1]); vector_add(pos1, o[a1], 1.); spec->distance_function(d, pos1, x[a2], geometry_data); o[a2][0] = d[0] + pos1[0] - x[a2][0]; o[a2][1] = d[1] + pos1[1] - x[a2][1]; o[a2][2] = d[2] + pos1[2] - x[a2][2]; } if (box_coor_flag) spec->box_function(o, o, offsets->dimensions[0], geometry_data, 1); Py_INCREF(Py_None); return Py_None; } /* * Module method table */ static PyMethodDef universe_module_methods[] = { {"InfiniteUniverseSpec", InfiniteUniverseSpec, 1}, {"OrthorhombicPeriodicUniverseSpec", OrthorhombicPeriodicUniverseSpec, 1}, {"ParallelepipedicPeriodicUniverseSpec", ParallelepipedicPeriodicUniverseSpec, 1}, {"parallelepiped_invert", parallelepiped_invert_py, 1}, {"contiguous_object_offset", contiguous_object_offset, 1}, {NULL, NULL} /* sentinel */ }; /* * Initialization function for the module */ DL_EXPORT(void) initMMTK_universe(void) { PyObject *m, *d; static void *PyUniverse_API[PyUniverse_API_pointers]; /* Patch object type */ #ifdef EXTENDED_TYPES if (PyType_Ready(&PyUniverseSpec_Type) < 0) return; #else PyUniverseSpec_Type.ob_type = &PyType_Type; #endif /* Create the module */ m = Py_InitModule("MMTK_universe", universe_module_methods); d = PyModule_GetDict(m); /* Import the array module */ #ifdef import_array import_array(); #endif /* Add C API pointer array */ PyUniverse_API[PyUniverseSpec_Type_NUM] = (void *)&PyUniverseSpec_Type; PyUniverse_API[PyUniverseSpec_StateLock_NUM] = (void *)&PyUniverseSpec_StateLock; PyDict_SetItemString(d, "_C_API", PyCObject_FromVoidPtr((void *)PyUniverse_API, NULL)); /* Add function pointer objects */ PyDict_SetItemString(d, "infinite_universe_distance_function", PyCObject_FromVoidPtr((void *) distance_vector, NULL)); PyDict_SetItemString(d, "infinite_universe_correction_function", PyCObject_FromVoidPtr((void *) no_correction, NULL)); PyDict_SetItemString(d, "infinite_universe_volume_function", PyCObject_FromVoidPtr((void *) no_volume, NULL)); PyDict_SetItemString(d, "orthorhombic_universe_distance_function", PyCObject_FromVoidPtr((void *) orthorhombic_distance_vector, NULL)); PyDict_SetItemString(d, "orthorhombic_universe_correction_function", PyCObject_FromVoidPtr((void *) orthorhombic_correction, NULL)); PyDict_SetItemString(d, "orthorhombic_universe_volume_function", PyCObject_FromVoidPtr((void *) orthorhombic_volume, NULL)); PyDict_SetItemString(d, "orthorhombic_universe_box_transformation", PyCObject_FromVoidPtr((void *) orthorhombic_box, NULL)); PyDict_SetItemString(d, "parallelepipedic_universe_distance_function", PyCObject_FromVoidPtr((void *) parallelepipedic_distance_vector, NULL)); PyDict_SetItemString(d, "parallelepipedic_universe_correction_function", PyCObject_FromVoidPtr((void *) parallelepipedic_correction, NULL)); PyDict_SetItemString(d, "parallelepipedic_universe_volume_function", PyCObject_FromVoidPtr((void *) parallelepipedic_volume, NULL)); PyDict_SetItemString(d, "parallelepipedic_universe_box_transformation", PyCObject_FromVoidPtr((void *) parallelepipedic_box, NULL)); /* Check for errors */ if (PyErr_Occurred()) Py_FatalError("can't initialize module MMTK_universe"); } MMTK-2.7.9/Src/ndtr.c0000644000076600000240000002303111662461640014560 0ustar hinsenstaff00000000000000/* ndtr.c * * Normal distribution function * * * * SYNOPSIS: * * double x, y, ndtr(); * * y = ndtr( x ); * * * * DESCRIPTION: * * Returns the area under the Gaussian probability density * function, integrated from minus infinity to x: * * x * - * 1 | | 2 * ndtr(x) = --------- | exp( - t /2 ) dt * sqrt(2pi) | | * - * -inf. * * = ( 1 + erf(z) ) / 2 * = erfc(z) / 2 * * where z = x/sqrt(2). Computation is via the functions * erf and erfc. * * * ACCURACY: * * Relative error: * arithmetic domain # trials peak rms * DEC -13,0 8000 2.1e-15 4.8e-16 * IEEE -13,0 30000 3.4e-14 6.7e-15 * * * ERROR MESSAGES: * * message condition value returned * erfc underflow x > 37.519379347 0.0 * */ /* erf.c * * Error function * * * * SYNOPSIS: * * double x, y, erf(); * * y = erf( x ); * * * * DESCRIPTION: * * The integral is * * x * - * 2 | | 2 * erf(x) = -------- | exp( - t ) dt. * sqrt(pi) | | * - * 0 * * The magnitude of x is limited to 9.231948545 for DEC * arithmetic; 1 or -1 is returned outside this range. * * For 0 <= |x| < 1, erf(x) = x * P4(x**2)/Q5(x**2); otherwise * erf(x) = 1 - erfc(x). * * * * ACCURACY: * * Relative error: * arithmetic domain # trials peak rms * DEC 0,1 14000 4.7e-17 1.5e-17 * IEEE 0,1 30000 3.7e-16 1.0e-16 * */ /* erfc.c * * Complementary error function * * * * SYNOPSIS: * * double x, y, erfc(); * * y = erfc( x ); * * * * DESCRIPTION: * * * 1 - erf(x) = * * inf. * - * 2 | | 2 * erfc(x) = -------- | exp( - t ) dt * sqrt(pi) | | * - * x * * * For small x, erfc(x) = 1 - erf(x); otherwise rational * approximations are computed. * * * * ACCURACY: * * Relative error: * arithmetic domain # trials peak rms * DEC 0, 9.2319 12000 5.1e-16 1.2e-16 * IEEE 0,26.6417 30000 5.7e-14 1.5e-14 * * * ERROR MESSAGES: * * message condition value returned * erfc underflow x > 9.231948545 (DEC) 0.0 * * */ /* Cephes Math Library Release 2.2: June, 1992 Copyright 1984, 1987, 1988, 1992 by Stephen L. Moshier Direct inquiries to 30 Frost Street, Cambridge, MA 02140 */ #include "mconf.h" #ifdef UNK static double P[] = { 2.46196981473530512524E-10, 5.64189564831068821977E-1, 7.46321056442269912687E0, 4.86371970985681366614E1, 1.96520832956077098242E2, 5.26445194995477358631E2, 9.34528527171957607540E2, 1.02755188689515710272E3, 5.57535335369399327526E2 }; static double Q[] = { /* 1.00000000000000000000E0,*/ 1.32281951154744992508E1, 8.67072140885989742329E1, 3.54937778887819891062E2, 9.75708501743205489753E2, 1.82390916687909736289E3, 2.24633760818710981792E3, 1.65666309194161350182E3, 5.57535340817727675546E2 }; static double R[] = { 5.64189583547755073984E-1, 1.27536670759978104416E0, 5.01905042251180477414E0, 6.16021097993053585195E0, 7.40974269950448939160E0, 2.97886665372100240670E0 }; static double S[] = { /* 1.00000000000000000000E0,*/ 2.26052863220117276590E0, 9.39603524938001434673E0, 1.20489539808096656605E1, 1.70814450747565897222E1, 9.60896809063285878198E0, 3.36907645100081516050E0 }; static double T[] = { 9.60497373987051638749E0, 9.00260197203842689217E1, 2.23200534594684319226E3, 7.00332514112805075473E3, 5.55923013010394962768E4 }; static double U[] = { /* 1.00000000000000000000E0,*/ 3.35617141647503099647E1, 5.21357949780152679795E2, 4.59432382970980127987E3, 2.26290000613890934246E4, 4.92673942608635921086E4 }; #define UTHRESH 37.519379347 #endif #ifdef DEC static unsigned short P[] = { 0030207,0054445,0011173,0021706, 0040020,0067272,0030661,0122075, 0040756,0151236,0173053,0067042, 0041502,0106175,0062555,0151457, 0042104,0102525,0047401,0003667, 0042403,0116176,0011446,0075303, 0042551,0120723,0061641,0123275, 0042600,0070651,0007264,0134516, 0042413,0061102,0167507,0176625 }; static unsigned short Q[] = { /*0040200,0000000,0000000,0000000,*/ 0041123,0123257,0165741,0017142, 0041655,0065027,0173413,0115450, 0042261,0074011,0021573,0004150, 0042563,0166530,0013662,0007200, 0042743,0176427,0162443,0105214, 0043014,0062546,0153727,0123772, 0042717,0012470,0006227,0067424, 0042413,0061103,0003042,0013254 }; static unsigned short R[] = { 0040020,0067272,0101024,0155421, 0040243,0037467,0056706,0026462, 0040640,0116017,0120665,0034315, 0040705,0020162,0143350,0060137, 0040755,0016234,0134304,0130157, 0040476,0122700,0051070,0015473 }; static unsigned short S[] = { /*0040200,0000000,0000000,0000000,*/ 0040420,0126200,0044276,0070413, 0041026,0053051,0007302,0063746, 0041100,0144203,0174051,0061151, 0041210,0123314,0126343,0177646, 0041031,0137125,0051431,0033011, 0040527,0117362,0152661,0066201 }; static unsigned short T[] = { 0041031,0126770,0170672,0166101, 0041664,0006522,0072360,0031770, 0043013,0100025,0162641,0126671, 0043332,0155231,0161627,0076200, 0044131,0024115,0021020,0117343 }; static unsigned short U[] = { /*0040200,0000000,0000000,0000000,*/ 0041406,0037461,0177575,0032714, 0042402,0053350,0123061,0153557, 0043217,0111227,0032007,0164217, 0043660,0145000,0004013,0160114, 0044100,0071544,0167107,0125471 }; #define UTHRESH 14.0 #endif #ifdef IBMPC static unsigned short P[] = { 0x6479,0xa24f,0xeb24,0x3df0, 0x3488,0x4636,0x0dd7,0x3fe2, 0x6dc4,0xdec5,0xda53,0x401d, 0xba66,0xacad,0x518f,0x4048, 0x20f7,0xa9e0,0x90aa,0x4068, 0xcf58,0xc264,0x738f,0x4080, 0x34d8,0x6c74,0x343a,0x408d, 0x972a,0x21d6,0x0e35,0x4090, 0xffb3,0x5de8,0x6c48,0x4081 }; static unsigned short Q[] = { /*0x0000,0x0000,0x0000,0x3ff0,*/ 0x23cc,0xfd7c,0x74d5,0x402a, 0x7365,0xfee1,0xad42,0x4055, 0x610d,0x246f,0x2f01,0x4076, 0x41d0,0x02f6,0x7dab,0x408e, 0x7151,0xfca4,0x7fa2,0x409c, 0xf4ff,0xdafa,0x8cac,0x40a1, 0xede2,0x0192,0xe2a7,0x4099, 0x42d6,0x60c4,0x6c48,0x4081 }; static unsigned short R[] = { 0x9b62,0x5042,0x0dd7,0x3fe2, 0xc5a6,0xebb8,0x67e6,0x3ff4, 0xa71a,0xf436,0x1381,0x4014, 0x0c0c,0x58dd,0xa40e,0x4018, 0x960e,0x9718,0xa393,0x401d, 0x0367,0x0a47,0xd4b8,0x4007 }; static unsigned short S[] = { /*0x0000,0x0000,0x0000,0x3ff0,*/ 0xce21,0x0917,0x1590,0x4002, 0x4cfd,0x21d8,0xcac5,0x4022, 0x2c4d,0x7f05,0x1910,0x4028, 0x7ff5,0x959c,0x14d9,0x4031, 0x26c1,0xaa63,0x37ca,0x4023, 0x2d90,0x5ab6,0xf3de,0x400a }; static unsigned short T[] = { 0x5d88,0x1e37,0x35bf,0x4023, 0x067f,0x4e9e,0x81aa,0x4056, 0x35b7,0xbcb4,0x7002,0x40a1, 0xef90,0x3c72,0x5b53,0x40bb, 0x13dc,0xa442,0x2509,0x40eb }; static unsigned short U[] = { /*0x0000,0x0000,0x0000,0x3ff0,*/ 0xa6ba,0x3fef,0xc7e6,0x4040, 0x3aee,0x14c6,0x4add,0x4080, 0xfd12,0xe680,0xf252,0x40b1, 0x7c0a,0x0101,0x1940,0x40d6, 0xf567,0x9dc8,0x0e6c,0x40e8 }; #define UTHRESH 37.519379347 #endif #ifdef MIEEE static unsigned short P[] = { 0x3df0,0xeb24,0xa24f,0x6479, 0x3fe2,0x0dd7,0x4636,0x3488, 0x401d,0xda53,0xdec5,0x6dc4, 0x4048,0x518f,0xacad,0xba66, 0x4068,0x90aa,0xa9e0,0x20f7, 0x4080,0x738f,0xc264,0xcf58, 0x408d,0x343a,0x6c74,0x34d8, 0x4090,0x0e35,0x21d6,0x972a, 0x4081,0x6c48,0x5de8,0xffb3 }; static unsigned short Q[] = { 0x402a,0x74d5,0xfd7c,0x23cc, 0x4055,0xad42,0xfee1,0x7365, 0x4076,0x2f01,0x246f,0x610d, 0x408e,0x7dab,0x02f6,0x41d0, 0x409c,0x7fa2,0xfca4,0x7151, 0x40a1,0x8cac,0xdafa,0xf4ff, 0x4099,0xe2a7,0x0192,0xede2, 0x4081,0x6c48,0x60c4,0x42d6 }; static unsigned short R[] = { 0x3fe2,0x0dd7,0x5042,0x9b62, 0x3ff4,0x67e6,0xebb8,0xc5a6, 0x4014,0x1381,0xf436,0xa71a, 0x4018,0xa40e,0x58dd,0x0c0c, 0x401d,0xa393,0x9718,0x960e, 0x4007,0xd4b8,0x0a47,0x0367 }; static unsigned short S[] = { 0x4002,0x1590,0x0917,0xce21, 0x4022,0xcac5,0x21d8,0x4cfd, 0x4028,0x1910,0x7f05,0x2c4d, 0x4031,0x14d9,0x959c,0x7ff5, 0x4023,0x37ca,0xaa63,0x26c1, 0x400a,0xf3de,0x5ab6,0x2d90 }; static unsigned short T[] = { 0x4023,0x35bf,0x1e37,0x5d88, 0x4056,0x81aa,0x4e9e,0x067f, 0x40a1,0x7002,0xbcb4,0x35b7, 0x40bb,0x5b53,0x3c72,0xef90, 0x40eb,0x2509,0xa442,0x13dc }; static unsigned short U[] = { 0x4040,0xc7e6,0x3fef,0xa6ba, 0x4080,0x4add,0x14c6,0x3aee, 0x40b1,0xf252,0xe680,0xfd12, 0x40d6,0x1940,0x0101,0x7c0a, 0x40e8,0x0e6c,0x9dc8,0xf567 }; #define UTHRESH 37.519379347 #endif #ifndef ANSIPROT double polevl(), p1evl(), exp(), log(), fabs(); double erf(), erfc(); #endif double ndtr(a) double a; { double x, y, z; x = a * SQRTH; z = fabs(x); if( z < SQRTH ) y = 0.5 + 0.5 * erf(x); else { y = 0.5 * erfc(z); if( x > 0 ) y = 1.0 - y; } return(y); } double erfc(a) double a; { double p,q,x,y,z; if( a < 0.0 ) x = -a; else x = a; if( x < 1.0 ) return( 1.0 - erf(a) ); z = -a * a; if( z < -MAXLOG ) { under: mtherr( "erfc", UNDERFLOW ); if( a < 0 ) return( 2.0 ); else return( 0.0 ); } z = exp(z); if( x < 8.0 ) { p = polevl( x, P, 8 ); q = p1evl( x, Q, 8 ); } else { p = polevl( x, R, 5 ); q = p1evl( x, S, 6 ); } y = (z * p)/q; if( a < 0 ) y = 2.0 - y; if( y == 0.0 ) goto under; return(y); } double erf(x) double x; { double y, z; if( fabs(x) > 1.0 ) return( 1.0 - erfc(x) ); z = x * x; y = x * polevl( z, T, 4 ) / p1evl( z, U, 5 ); return( y ); } MMTK-2.7.9/Src/nonbonded.c0000644000076600000240000005717312117632051015565 0ustar hinsenstaff00000000000000/* Low-level force field calculations: non-bonded interactions * * Written by Konrad Hinsen */ #define NO_IMPORT #define _FORCEFIELD_MODULE #define PY_ARRAY_UNIQUE_SYMBOL PyArray_MMTKFF_API #include "MMTK/forcefield.h" #include "MMTK/forcefield_private.h" #define THREAD_DEBUG 0 /* Utility functions */ #define max(a, b) ((a) > (b) ? (a) : (b)) #define min(a, b) ((a) < (b) ? (a) : (b)) /* Nonbonded list update */ /* Sort all atoms into small subboxes such that atoms in any box * can interact only with particles in a small shell of neighbouring boxes */ int nblist_update(PyNonbondedListObject *nblist, int natoms, double *coordinates, double *geometry_data) { vector3 *x = (vector3 *)coordinates; long *subset = (long *)((PyArrayObject *)nblist->atom_subset)->data; int n_sub = ((PyArrayObject *)nblist->atom_subset)->dimensions[0]; vector3 box1, box2; double box_size[3]; int *p; int i, ix, iy, iz, minx, miny, minz, maxx, maxy, maxz, n; if (nblist->box_number == NULL) { nblist->box_number = (int *)malloc(2*natoms*sizeof(int)); if (nblist->box_number == NULL) { PyErr_NoMemory(); return 0; } nblist->box_atoms = nblist->box_number + natoms; } nblist->universe_spec->correction_function(x, natoms, geometry_data); nblist->universe_spec->bounding_box_function(&box1, &box2, x, natoms, geometry_data); nblist->lastx = x; #if 0 printf("box1: %lf, %lf, %lf\n", box1[0], box1[1], box1[2]); printf("box2: %lf, %lf, %lf\n", box2[0], box2[1], box2[2]); printf("cutoff: %lf\n", nblist->cutoff); #endif if (nblist->cutoff > 0. && (!nblist->universe_spec->is_periodic || nblist->universe_spec->is_orthogonal)) { int done = 0; double factor = 1.; while (!done) { int nboxes; nblist->box_count[0] = (int)(NBLIST_NEIGHBORS*(box2[0]-box1[0]) /(factor*nblist->cutoff)); nblist->box_count[1] = (int)(NBLIST_NEIGHBORS*(box2[1]-box1[1]) /(factor*nblist->cutoff)); nblist->box_count[2] = (int)(NBLIST_NEIGHBORS*(box2[2]-box1[2]) /(factor*nblist->cutoff)); if (nblist->box_count[0] == 0) nblist->box_count[0] = 1; if (nblist->box_count[1] == 0) nblist->box_count[1] = 1; if (nblist->box_count[2] == 0) nblist->box_count[2] = 1; nboxes = nblist->box_count[0]*nblist->box_count[1]*nblist->box_count[2]; if (nboxes > 2*natoms) factor *= 1.1; else done = 1; } } else nblist->box_count[0] = nblist->box_count[1] = nblist->box_count[2] = 1; box_size[0] = (box2[0]-box1[0])/nblist->box_count[0]; box_size[1] = (box2[1]-box1[1])/nblist->box_count[1]; box_size[2] = (box2[2]-box1[2])/nblist->box_count[2]; if (box_size[0] == 0.) box_size[0] = 1.; if (box_size[1] == 0.) box_size[1] = 1.; if (box_size[2] == 0.) box_size[2] = 1.; #if 0 printf("division: %d/%d/%d\n", nblist->box_count[0], nblist->box_count[1], nblist->box_count[2]); printf("cell size: %lf, %lf, %lf\n", box_size[0], box_size[1], box_size[2]); #endif nblist->neighbors[0][0] = 0; nblist->neighbors[0][1] = 0; nblist->neighbors[0][2] = 0; i = 1; minx = -(int)((nblist->cutoff+box_size[0])/box_size[0]); miny = -(int)((nblist->cutoff+box_size[1])/box_size[1]); minz = -(int)((nblist->cutoff+box_size[2])/box_size[2]); maxx = -minx+1; maxy = -miny+1; maxz = -minz+1; if (nblist->universe_spec->is_periodic) { maxx = min(maxx, (nblist->box_count[0]+1)/2); maxy = min(maxy, (nblist->box_count[1]+1)/2); maxz = min(maxz, (nblist->box_count[2]+1)/2); minx = max(minx, maxx-nblist->box_count[0]); miny = max(miny, maxy-nblist->box_count[1]); minz = max(minz, maxz-nblist->box_count[2]); } else { maxx = min(maxx, nblist->box_count[0]); maxy = min(maxy, nblist->box_count[1]); maxz = min(maxz, nblist->box_count[2]); minx = max(minx, 1-nblist->box_count[0]); miny = max(miny, 1-nblist->box_count[1]); minz = max(minz, 1-nblist->box_count[2]); } #if 0 printf("Box neighbor list:\n"); printf(" minx: %d\tmaxx: %d\n", minx, maxx); printf(" miny: %d\tmaxy: %d\n", miny, maxy); printf(" minz: %d\tmaxz: %d\n", minz, maxz); #endif for (ix = minx; ix < maxx; ix++) for (iy = miny; iy < maxy; iy++) for (iz = minz; iz < maxz; iz++) if (!(ix == 0 && iy == 0 && iz == 0)) { double dx = (abs(ix)-1.)*box_size[0]; double dy = (abs(iy)-1.)*box_size[1]; double dz = (abs(iz)-1.)*box_size[2]; if (dx < 0.) dx = 0.; if (dy < 0.) dy = 0.; if (dz < 0.) dz = 0.; if (dx*dx+dy*dy+dz*dz <= sqr(nblist->cutoff)) { nblist->neighbors[i][0] = ix; nblist->neighbors[i][1] = iy; nblist->neighbors[i][2] = iz; #if 0 printf(" %d: %d/%d/%d\n", i, ix, iy, iz); #endif i++; } #if 0 else { printf(" %d/%d/%d -> %f/%f/%f -> %f\n", ix, iy, iz, dx, dy, dz, sqrt(dx*dx+dy*dy+dz*dz)); } #endif } nblist->nneighbors = i; #if 0 printf("Box neighbors: %d\n", i); #endif nblist->nboxes = nblist->box_count[0]*nblist->box_count[1]*nblist->box_count[2]; if (nblist->nboxes > nblist->allocated_boxes) { free(nblist->boxes); nblist->boxes = malloc(nblist->nboxes*sizeof(nbbox)); if (nblist->boxes == NULL) { PyErr_NoMemory(); return 0; } nblist->allocated_boxes = nblist->nboxes; } i = 0; for (iz = 0; iz < nblist->box_count[2]; iz++) for (iy = 0; iy < nblist->box_count[1]; iy++) for (ix = 0; ix < nblist->box_count[0]; ix++) { nblist->boxes[i].ix = ix; nblist->boxes[i].iy = iy; nblist->boxes[i].iz = iz; nblist->boxes[i].n = 0; nblist->boxes[i].i = 0; i++; } n = (n_sub == 0) ? natoms : n_sub; for (i = 0; i < n; i++) { int box = 0; int ai, n; ai = (n_sub == 0) ? i : subset[i]; box = (int)((x[ai][0]-box1[0])/box_size[0]); if (box == nblist->box_count[0]) box--; n = (int)((x[ai][1]-box1[1])/box_size[1]); if (n == nblist->box_count[1]) n--; box += nblist->box_count[0]*n; n = (int)((x[ai][2]-box1[2])/box_size[2]); if (n == nblist->box_count[2]) n--; box += nblist->box_count[0]*nblist->box_count[1]*n; if (box < 0 || box > nblist->nboxes) { /* Prevent a crash due to an invalid box number. Unfortunately we can't raise a Python execption here because the code is called in a no-GIL setting, but silent failure is still better than a segment fault. */ return 0; } nblist->box_number[ai] = box; nblist->boxes[box].n++; #if 0 printf("Atom %d in box %d (%d/%d/%d)\n", ai, box, nblist->boxes[box].ix, nblist->boxes[box].iy, nblist->boxes[box].iz); #endif } p = nblist->box_atoms; for (i = 0; i < nblist->nboxes; i++) { nblist->boxes[i].atoms = p; nblist->boxes[i].i = 0; p += nblist->boxes[i].n; } for (i = 0; i < n; i++) { int ai = (n_sub == 0) ? i : subset[i]; nbbox *box = nblist->boxes + nblist->box_number[ai]; box->atoms[box->i++] = ai; } for (i = 0; i < nblist->nboxes; i++) nblist->boxes[i].i = 0; #if 0 ix = 0; for (i = 0; i < nblist->nboxes; i++) { int j; for (j = 0; j < nblist->boxes[i].n; j++) { int atom = nblist->boxes[i].atoms[j]; ix++; if (nblist->box_number[atom] != i) printf("Box number error: %d: %d - %d\n", atom, i, nblist->box_number[atom]); } } if (ix != ((n_sub == 0) ? natoms : n_sub)) printf("Atom number error: %d - %d\n", ix, natoms); #endif nblist->iterator.state = nblist_start; nblist->iterator.n = -1; return 1; } /* Iterator over nonbonded list */ int nblist_iterate(PyNonbondedListObject *nblist, struct nblist_iterator *iterator) { long *excluded, *one_four; int n_ex, n_14; int ix, iy, iz, use_box; switch (iterator->state) { case nblist_start: iterator->n = -1; iterator->ibox = -1; iterator->jbox = -1; iterator->ineighbor = nblist->nneighbors-1; iterator->box1 = nblist->boxes; iterator->box2 = nblist->boxes; iterator->i = iterator->box1->n-1; iterator->j = iterator->box2->n-1; iterator->state = nblist_continue; case nblist_continue: iterator->j++; if (iterator->j == iterator->box2->n) { int lasti = iterator->box1->n - ((iterator->box1==iterator->box2)?1:0); iterator->i++; if (iterator->i >= lasti) { do { iterator->ineighbor++; if (iterator->ineighbor == nblist->nneighbors) { do { iterator->ibox++; if (iterator->ibox == nblist->nboxes) { iterator->state = nblist_finished; return 0; } iterator->box1 = nblist->boxes+iterator->ibox; iterator->ineighbor = 0; } while (iterator->box1->n == 0); } ix = nblist->neighbors[iterator->ineighbor][0] + iterator->box1->ix; iy = nblist->neighbors[iterator->ineighbor][1] + iterator->box1->iy; iz = nblist->neighbors[iterator->ineighbor][2] + iterator->box1->iz; use_box = 1; if (nblist->universe_spec->is_periodic) { if (ix < 0) ix += nblist->box_count[0]; if (iy < 0) iy += nblist->box_count[1]; if (iz < 0) iz += nblist->box_count[2]; if (ix >= nblist->box_count[0]) ix -= nblist->box_count[0]; if (iy >= nblist->box_count[1]) iy -= nblist->box_count[1]; if (iz >= nblist->box_count[2]) iz -= nblist->box_count[2]; } else if (ix < 0 || iy < 0 || iz < 0 || ix >= nblist->box_count[0] || iy >= nblist->box_count[1] || iz >= nblist->box_count[2]) { use_box = 0; } iterator->jbox = ix + nblist->box_count[0] *(iy + nblist->box_count[1]*iz); if (iterator->jbox < iterator->ibox) use_box = 0; if (use_box) { if (nblist->boxes[iterator->jbox].n == 0) { use_box = 0; } if (iterator->ibox == iterator->jbox && nblist->boxes[iterator->jbox].n == 1) { use_box = 0; } } } while (!use_box); iterator->box2 = nblist->boxes+iterator->jbox; iterator->i = 0; } if (iterator->ibox == iterator->jbox) iterator->j = iterator->i + 1; else iterator->j = 0; } iterator->a1 = iterator->box1->atoms[iterator->i]; iterator->a2 = iterator->box2->atoms[iterator->j]; iterator->n++; return 1; break; case nblist_start_excluded: iterator->i = -2; iterator->state = nblist_continue_excluded; case nblist_continue_excluded: excluded = (long *)((PyArrayObject *)nblist->excluded_pairs)->data; n_ex = 2*((PyArrayObject *)nblist->excluded_pairs)->dimensions[0]; iterator->i += 2; if (iterator->i == n_ex) { iterator->state = nblist_finished; return 0; } iterator->a1 = excluded[iterator->i]; iterator->a2 = excluded[iterator->i+1]; return 1; break; case nblist_start_14: iterator->i = -2; iterator->state = nblist_continue_14; case nblist_continue_14: one_four = (long *)((PyArrayObject *)nblist->one_four_pairs)->data; n_14 = 2*((PyArrayObject *)nblist->one_four_pairs)->dimensions[0]; iterator->i += 2; if (iterator->i == n_14) { iterator->state = nblist_finished; return 0; } iterator->a1 = one_four[iterator->i]; iterator->a2 = one_four[iterator->i+1]; return 1; break; case nblist_finished: return 0; break; } /* we never get here, but a return makes gcc happy */ return 0; } /* Evaluator for all non-bonded interactions */ #ifdef GRADIENTFN #define pair_term(ljfactor, esfactor, ewaldfactor) \ { \ vector3 rij; \ double r_sq = 0., r = 0.; \ double deriv = 0., deriv2 = 0.; \ (*d_fn)(rij, x[a2], x[a1], distance_data); \ r_sq = vector_length_sq(rij); \ if (es_flag || ewald_flag) \ r = sqrt(r_sq); \ \ if (lj_flag && (lj_cutoff_sq == 0. || r_sq <= lj_cutoff_sq)) { \ int type1 = lj_type[a1]; \ int type2 = lj_type[a2]; \ if (type1 >= 0 && type2 >= 0) { \ double eps = ljfactor*eps_sigma[2*(ntypes*type1+type2)]; \ double sigma = eps_sigma[2*(ntypes*type1+type2)+1]; \ double sr2 = sqr(sigma)/r_sq; \ double sr6 = cube(sr2); \ double sr12 = sqr(sr6); \ double e = 4.*eps*(sr12-sr6); \ double v = 24.*eps*(2.*sr12-sr6); \ if (r_sq < 0.02) { \ lj_energy2 += e; \ lj_virial2 += v; \ } \ else { \ lj_energy1 += e; \ lj_virial1 += v; \ } \ deriv += -24.*eps*(2.*sr12-sr6)/r_sq; \ deriv2 += 24.*eps*(26.*sr12-7.*sr6)/r_sq; \ } \ } \ \ if (es_flag && (es_cutoff_sq == 0. || r_sq <= es_cutoff_sq)) { \ double qiqj = esfactor*charge[a1]*charge[a2]*electrostatic_energy_factor; \ double term = qiqj*(1./r-es_inv_cutoff); \ es_energy += term; \ deriv += -qiqj*(1./r_sq-sqr(es_inv_cutoff))/r; \ deriv2 += 2.*qiqj*(1./(r*r_sq)-es_inv_cutoff*sqr(es_inv_cutoff)); \ } \ \ if (ewald_flag && (ewald_cutoff_sq == 0. || r_sq<=ewald_cutoff_sq)) { \ double f1 = erfc(beta*r); \ double qiqj = ewaldfactor*charge[a1]*charge[a2] \ * electrostatic_energy_factor; \ double ef = 2./sqrt(M_PI); \ ewald_energy += qiqj*(f1/r-erfc_cutoff*ewald_inv_cutoff); \ if (energy->gradients != NULL) \ deriv += -qiqj*(f1/r_sq-erfc_cutoff*ewald_inv_cutoff \ +ef*beta*(exp(-beta*beta*r_sq)/r \ -ewald_inv_cutoff*exp(-beta*beta*ewald_cutoff_sq)))/r; \ if (energy->force_constants != NULL) { \ deriv2 += 2.*qiqj*(f1/(r*r_sq) - erfc_cutoff*cube(ewald_inv_cutoff) \ + ef*beta*exp(-beta*beta*r_sq) \ * (beta*beta+1./r_sq)); \ if (ewald_inv_cutoff > 0.) \ deriv2 -= 2.*qiqj*ef*beta*exp(-beta*beta*ewald_cutoff_sq) \ * (beta*beta+sqr(ewald_inv_cutoff)); \ } \ } \ \ if (energy->gradients != NULL) { \ vector3 grad; \ grad[0] = deriv*rij[0]; \ grad[1] = deriv*rij[1]; \ grad[2] = deriv*rij[2]; \ if (energy->gradient_fn != NULL) { \ (*energy->gradient_fn)(energy, a1, grad); \ vector_changesign(grad); \ (*energy->gradient_fn)(energy, a2, grad); \ } \ else { \ vector3 *f = (vector3 *)((PyArrayObject *)energy->gradients)->data; \ f[a1][0] += grad[0]; \ f[a1][1] += grad[1]; \ f[a1][2] += grad[2]; \ f[a2][0] -= grad[0]; \ f[a2][1] -= grad[1]; \ f[a2][2] -= grad[2]; \ } \ } \ if (energy->force_constants != NULL) { \ add_pair_fc(energy, a1, a2, rij, r_sq, deriv, deriv2); \ } \ } #else #define pair_term(ljfactor, esfactor, ewaldfactor) \ { \ vector3 rij; \ double r_sq = 0., r = 0.; \ double deriv = 0., deriv2 = 0.; \ (*d_fn)(rij, x[a2], x[a1], distance_data); \ r_sq = vector_length_sq(rij); \ if (es_flag || ewald_flag) \ r = sqrt(r_sq); \ \ if (lj_flag && (lj_cutoff_sq == 0. || r_sq <= lj_cutoff_sq)) { \ int type1 = lj_type[a1]; \ int type2 = lj_type[a2]; \ if (type1 >= 0 && type2 >= 0) { \ double eps = ljfactor*eps_sigma[2*(ntypes*type1+type2)]; \ double sigma = eps_sigma[2*(ntypes*type1+type2)+1]; \ double sr2 = sqr(sigma)/r_sq; \ double sr6 = cube(sr2); \ double sr12 = sqr(sr6); \ double e = 4.*eps*(sr12-sr6); \ double v = 24.*eps*(2.*sr12-sr6); \ if (r_sq < 0.02) { \ lj_energy2 += e; \ lj_virial2 += v; \ } \ else { \ lj_energy1 += e; \ lj_virial1 += v; \ } \ deriv += -24.*eps*(2.*sr12-sr6)/r_sq; \ deriv2 += 24.*eps*(26.*sr12-7.*sr6)/r_sq; \ } \ } \ \ if (es_flag && (es_cutoff_sq == 0. || r_sq <= es_cutoff_sq)) { \ double qiqj = esfactor*charge[a1]*charge[a2]*electrostatic_energy_factor; \ double term = qiqj*(1./r-es_inv_cutoff); \ es_energy += term; \ deriv += -qiqj*(1./r_sq-sqr(es_inv_cutoff))/r; \ deriv2 += 2.*qiqj*(1./(r*r_sq)-es_inv_cutoff*sqr(es_inv_cutoff)); \ } \ \ if (ewald_flag && (ewald_cutoff_sq == 0. || r_sq<=ewald_cutoff_sq)) { \ double f1 = erfc(beta*r); \ double qiqj = ewaldfactor*charge[a1]*charge[a2] \ * electrostatic_energy_factor; \ double ef = 2./sqrt(M_PI); \ ewald_energy += qiqj*(f1/r-erfc_cutoff*ewald_inv_cutoff); \ if (energy->gradients != NULL) \ deriv += -qiqj*(f1/r_sq-erfc_cutoff*ewald_inv_cutoff \ +ef*beta*(exp(-beta*beta*r_sq)/r \ -ewald_inv_cutoff*exp(-beta*beta*ewald_cutoff_sq)))/r; \ if (energy->force_constants != NULL) { \ deriv2 += 2.*qiqj*(f1/(r*r_sq) - erfc_cutoff*cube(ewald_inv_cutoff) \ + ef*beta*exp(-beta*beta*r_sq) \ * (beta*beta+1./r_sq)); \ if (ewald_inv_cutoff > 0.) \ deriv2 -= 2.*qiqj*ef*beta*exp(-beta*beta*ewald_cutoff_sq) \ * (beta*beta+sqr(ewald_inv_cutoff)); \ } \ } \ \ if (energy->gradients != NULL) { \ vector3 grad; \ grad[0] = deriv*rij[0]; \ grad[1] = deriv*rij[1]; \ grad[2] = deriv*rij[2]; \ { \ vector3 *f = (vector3 *)((PyArrayObject *)energy->gradients)->data; \ f[a1][0] += grad[0]; \ f[a1][1] += grad[1]; \ f[a1][2] += grad[2]; \ f[a2][0] -= grad[0]; \ f[a2][1] -= grad[1]; \ f[a2][2] -= grad[2]; \ } \ } \ if (energy->force_constants != NULL) { \ add_pair_fc(energy, a1, a2, rij, r_sq, deriv, deriv2); \ } \ } #endif void nonbonded_evaluator(PyFFEnergyTermObject *self, PyFFEvaluatorObject *eval, energy_spec *input, energy_data *energy) { PyNonbondedListObject *nblist = (PyNonbondedListObject *)self->data[0]; long *excluded = (long *)((PyArrayObject *)nblist->excluded_pairs)->data; int n_ex = 2*((PyArrayObject *)nblist->excluded_pairs)->dimensions[0]; long *one_four = (long *)((PyArrayObject *)nblist->one_four_pairs)->data; int n_14 = 2*((PyArrayObject *)nblist->one_four_pairs)->dimensions[0]; distance_fn *d_fn = nblist->universe_spec->distance_function; double *distance_data = nblist->universe_spec->geometry_data; vector3 *x = (vector3 *)input->coordinates->data; double *eps_sigma; int ntypes; long *lj_type; double lj_cutoff_sq, lj_one_four; double *charge; double es_cutoff_sq, es_inv_cutoff, es_one_four; double ewald_cutoff_sq, ewald_inv_cutoff, beta, erfc_cutoff, ewald_one_four; double lj_energy1, lj_energy2, lj_virial1, lj_virial2; double es_energy, ewald_energy; int lj_flag, es_flag, ewald_flag; int ibox, jbox, slicecounter, k; #if THREAD_DEBUG int paircount = 0; #endif PyFFEnergyTermObject *lj_ev, *es_ev, *ewald_ev; lj_ev = (PyFFEnergyTermObject *)self->data[1]; es_ev = (PyFFEnergyTermObject *)self->data[2]; ewald_ev = (PyFFEnergyTermObject *)self->data[3]; lj_flag = lj_ev != NULL; es_flag = es_ev != NULL; ewald_flag = ewald_ev != NULL; lj_energy1 = lj_energy2 = lj_virial1 = lj_virial2 = 0.; es_energy = 0.; ewald_energy = 0.; /* Initialize a couple of variables to make gcc happy. They will get their correct versions in the sections below. */ lj_cutoff_sq = es_cutoff_sq = erfc_cutoff = ewald_cutoff_sq = 0.; es_inv_cutoff = ewald_inv_cutoff = 0.; lj_one_four = es_one_four = ewald_one_four = 0.; beta = 0.; eps_sigma = charge = NULL; lj_type = NULL; ntypes = 0; if (lj_flag) { eps_sigma = (double *)((PyArrayObject *)lj_ev->data[1])->data; ntypes = ((PyArrayObject *)lj_ev->data[1])->dimensions[0]; lj_type = (long *)((PyArrayObject *)lj_ev->data[2])->data; lj_cutoff_sq = sqr(lj_ev->param[0]); lj_one_four = lj_ev->param[1]-1.; } if (es_flag) { charge = (double *)((PyArrayObject *)es_ev->data[1])->data; es_cutoff_sq = sqr(es_ev->param[0]); es_inv_cutoff = (es_ev->param[0] == 0.) ? 0. : 1./es_ev->param[0]; es_one_four = es_ev->param[1]-1.; } if (ewald_flag) { charge = (double *)((PyArrayObject *)ewald_ev->data[1])->data; ewald_cutoff_sq = sqr(ewald_ev->param[0]); ewald_inv_cutoff = (ewald_ev->param[0] == 0.) ? 0. : 1./ewald_ev->param[0]; if (ewald_ev->param[0] > 0. && ewald_ev->param[3] == 0.) ewald_inv_cutoff = 1./ewald_ev->param[0]; else ewald_inv_cutoff = 0.; beta = ewald_ev->param[2]; erfc_cutoff = erfc(beta*ewald_ev->param[0]); ewald_one_four = ewald_ev->param[1]-1.; } if (input->thread_id == 0) nblist_update(nblist, input->natoms, (double *)x, distance_data); #ifdef WITH_THREAD barrier(eval->binfo+self->barrier_index, input->thread_id, input->nthreads); #endif if (!(lj_flag || es_flag || ewald_flag)) return; slicecounter = input->nslices-input->slice_id; for (ibox = 0; ibox < nblist->nboxes; ibox++) { nbbox *box1 = &nblist->boxes[ibox]; int ineighbor; for (ineighbor = 0; ineighbor < nblist->nneighbors; ineighbor++) { int ix = nblist->neighbors[ineighbor][0]+box1->ix; int iy = nblist->neighbors[ineighbor][1]+box1->iy; int iz = nblist->neighbors[ineighbor][2]+box1->iz; nbbox *box2; int i, j; if (nblist->universe_spec->is_periodic) { if (ix < 0) ix += nblist->box_count[0]; if (iy < 0) iy += nblist->box_count[1]; if (iz < 0) iz += nblist->box_count[2]; if (ix >= nblist->box_count[0]) ix -= nblist->box_count[0]; if (iy >= nblist->box_count[1]) iy -= nblist->box_count[1]; if (iz >= nblist->box_count[2]) iz -= nblist->box_count[2]; } else if (ix < 0 || iy < 0 || iz < 0 || ix >= nblist->box_count[0] || iy >= nblist->box_count[1] || iz >= nblist->box_count[2]) continue; jbox = ix + nblist->box_count[0]*(iy + nblist->box_count[1]*iz); if (jbox < ibox) continue; box2 = &nblist->boxes[jbox]; if (ibox == jbox) { for (i = 0; i < box1->n; i++) { int a1 = box1->atoms[i]; for (j = i+1; j < box2->n; j++) { int a2 = box2->atoms[j]; if (--slicecounter == 0) { slicecounter = input->nslices; pair_term(1., 1., 1.); #if THREAD_DEBUG paircount++; #endif } } } } else { for (i = 0; i < box1->n; i++) { int a1 = box1->atoms[i]; for (j = 0; j < box2->n; j++) { int a2 = box2->atoms[j]; if (--slicecounter == 0) { slicecounter = input->nslices; pair_term(1., 1., 1.); #if THREAD_DEBUG paircount++; #endif } } } } } } #if THREAD_DEBUG printf("Slice %d: %d nonbonded pairs\n", input->slice_id, paircount); #endif es_inv_cutoff = 0.; ewald_inv_cutoff = 0.; erfc_cutoff = 0.; beta = 0.; for (k = 2*input->slice_id; k < n_ex; k += 2*input->nslices) { int a1 = excluded[k]; int a2 = excluded[k+1]; pair_term(-1., -1., -1.); } for (k = 2*input->slice_id; k < n_14; k += 2*input->nslices) { int a1 = one_four[k]; int a2 = one_four[k+1]; pair_term(lj_one_four, es_one_four, ewald_one_four); } energy->energy_terms[self->index] = lj_energy1 + lj_energy2; energy->energy_terms[self->index+1] = es_energy; energy->energy_terms[self->index+2] = ewald_energy; energy->energy_terms[self->virial_index] += lj_virial1 + lj_virial2 + es_energy + ewald_energy; } /* Lennard-Jones and electrostatic interactions * * The real work is done in nonbonded_evaluator, these two functions * just return the energy values under the proper name. */ void lennard_jones_evaluator(PyFFEnergyTermObject *self, PyFFEvaluatorObject *eval, energy_spec *input, energy_data *energy) { } void electrostatic_evaluator(PyFFEnergyTermObject *self, PyFFEvaluatorObject *eval, energy_spec *input, energy_data *energy) { PyNonbondedListObject *nblist = (PyNonbondedListObject *)self->data[0]; long *subset = (long *)((PyArrayObject *)nblist->atom_subset)->data; int n_sub = ((PyArrayObject *)nblist->atom_subset)->dimensions[0]; double *charge = (double *)((PyArrayObject *)self->data[1])->data; double cutoff_sq = sqr(self->param[0]); double inv_cutoff = (self->param[0] == 0.) ? 0. : 1./self->param[0]; double e = 0., v = 0.; int k; if (cutoff_sq > 0.) { int n = (n_sub == 0) ? input->natoms : n_sub; double sum = 0.; for (k = 0; k < n; k++) { int i = (n_sub == 0) ? k : subset[k]; sum += sqr(charge[i]); } e -= 0.5*inv_cutoff*sum*electrostatic_energy_factor; v -= 0.5*inv_cutoff*sum*electrostatic_energy_factor; } energy->energy_terms[self->index] = e; energy->energy_terms[self->virial_index] += v; } MMTK-2.7.9/Src/nonbonded1.i0000644000076600000240000000064311662461640015652 0ustar hinsenstaff00000000000000for (k = 0; k < n_ex; k += 2) { int i = excluded[k]; int j = excluded[k+1]; double qiqj = charge[i]*charge[j]*electrostatic_energy_factor; pair_es_energy(-qiqj, distance_vector_function); } for (k = 0; k < n_14; k += 2) { int i = one_four[k]; int j = one_four[k+1]; double qiqj = charge[i]*charge[j]*electrostatic_energy_factor; pair_es_energy(qiqj*(es_one_four_factor-1.), distance_vector_function); } MMTK-2.7.9/Src/polevl.c0000644000076600000240000000305511662461640015116 0ustar hinsenstaff00000000000000/* polevl.c * p1evl.c * * Evaluate polynomial * * * * SYNOPSIS: * * int N; * double x, y, coef[N+1], polevl[]; * * y = polevl( x, coef, N ); * * * * DESCRIPTION: * * Evaluates polynomial of degree N: * * 2 N * y = C + C x + C x +...+ C x * 0 1 2 N * * Coefficients are stored in reverse order: * * coef[0] = C , ..., coef[N] = C . * N 0 * * The function p1evl() assumes that coef[N] = 1.0 and is * omitted from the array. Its calling arguments are * otherwise the same as polevl(). * * * SPEED: * * In the interest of speed, there are no checks for out * of bounds arithmetic. This routine is used by most of * the functions in the library. Depending on available * equipment features, the user may wish to rewrite the * program in microcode or assembly language. * */ /* Cephes Math Library Release 2.1: December, 1988 Copyright 1984, 1987, 1988 by Stephen L. Moshier Direct inquiries to 30 Frost Street, Cambridge, MA 02140 */ double polevl( double x, double coef[], int N ) { double ans; int i; double *p; p = coef; ans = *p++; i = N; do ans = ans * x + *p++; while( --i ); return( ans ); } /* p1evl() */ /* N * Evaluate polynomial when coefficient of x is 1.0. * Otherwise same as polevl. */ double p1evl( double x, double coef[], int N ) { double ans; double *p; int i; p = coef; ans = x + *p++; i = N-1; do ans = ans * x + *p++; while( --i ); return( ans ); } MMTK-2.7.9/Src/ReadDCD.c0000644000076600000240000004352211662461640015006 0ustar hinsenstaff00000000000000/*************************************************************************** *cr *cr (C) Copyright 1995 The Board of Trustees of the *cr University of Illinois *cr All Rights Reserved *cr ***************************************************************************/ /*************************************************************************** * RCS INFORMATION: * * $RCSfile: ReadDCD.C,v $ * $Author: dalke $ $Locker: $ $State: Exp $ * $Revision: 1.6 $ $Date: 1997/03/13 17:38:56 $ * *************************************************************************** * * Modifications for use in MMTK: * * - Argument DELTA in read_dcdheader changed to FLOAT. * - Check for file existence in open_dcd_write removed. * - Include file name changed to mmtk_readdcd.h * - open/read/write/close replaced by fopen/fread/fwrite/fclose * (for portability to Windows) * *************************************************************************** * DESCRIPTION: * * C routines to read and write binary DCD files (which use the goofy * FORTRAN UNFORMATTED format). These routines are courtesy of * Mark Nelson (autographs available upon request and $1 tip). * ***************************************************************************/ #include #include "MMTK/readdcd.h" void pad(char *s, int len) { int curlen; int i; curlen=strlen(s); if (curlen>len) { s[len]=0; return; } for (i=curlen; ifirst time called */ /* indexes - indexes for free atoms */ /* */ /* OUTPUTS: */ /* read step populates the arrays X, Y, Z and returns a 0 if the */ /* read is successful. If the read fails, a negative error code is */ /* returned. */ /* */ /* read_step reads in the coordinates from one step. It places */ /* these coordinates into the arrays X, Y, and Z. */ /* */ /************************************************************************/ int read_dcdstep(FILE *fd, int N, float *X, float *Y, float *Z, int num_fixed, int first, int *indexes) { int ret_val; /* Return value from read */ int input_integer; /* Integer buffer space */ int i; /* Loop counter */ static float *tmpX; if (first && num_fixed) { tmpX = (float *) calloc(N-num_fixed, sizeof(float)); if (tmpX==NULL) { return(DCD_BADMALLOC); } } /* Get the first size from the file */ ret_val = fread(&input_integer, sizeof(int), 1, fd); /* See if we've reached the end of the file */ if (ret_val == 0) { free(tmpX); return(-1); } if ( (num_fixed==0) || first) { if (input_integer != 4*N) { return(DCD_BADFORMAT); } ret_val = fread(X, sizeof(float), N, fd); if (ret_val != N) return(DCD_BADREAD); ret_val = fread(&input_integer, sizeof(int), 1, fd); if (ret_val != 1) return(DCD_BADREAD); if (input_integer != 4*N) { return(DCD_BADFORMAT); } ret_val = fread(&input_integer, sizeof(int), 1, fd); if (ret_val != 1) return(DCD_BADREAD); if (input_integer != 4*N) { return(DCD_BADFORMAT); } ret_val = fread(Y, sizeof(float), N, fd); if (ret_val != N) return(DCD_BADREAD); ret_val = fread(&input_integer, sizeof(int), 1, fd); if (ret_val != 1) return(DCD_BADREAD); if (input_integer != 4*N) { return(DCD_BADFORMAT); } ret_val = fread(&input_integer, sizeof(int), 1, fd); if (ret_val != 1) return(DCD_BADREAD); if (input_integer != 4*N) { return(DCD_BADFORMAT); } ret_val = fread(Z, sizeof(float), N, fd); if (ret_val != N) return(DCD_BADREAD); ret_val = fread(&input_integer, sizeof(int), 1, fd); if (ret_val != 1) return(DCD_BADREAD); if (input_integer != 4*N) { return(DCD_BADFORMAT); } } else { if (input_integer != 4*(N-num_fixed)) { return(DCD_BADFORMAT); } ret_val = fread(tmpX, sizeof(float), N-num_fixed, fd); if (ret_val != N-num_fixed) return(DCD_BADREAD); for (i=0; inalloc; i++) { for (j = 0; j < 3; j++) for (k = 0; k < 3; k++) fc->data[i].fc[j][k] = 0.; } } /* Allocation and deallocation */ PySparseFCObject * PySparseFC_New(int natoms, int nalloc) { PySparseFCObject *self; int i; self = PyObject_NEW(PySparseFCObject, &PySparseFC_Type); if (self == NULL) { PyErr_NoMemory(); return NULL; } if (nalloc < natoms) nalloc = natoms; self->data = (struct pair_fc *)malloc(nalloc*sizeof(struct pair_fc)); self->index = (struct pair_descr_list *) \ malloc(2*natoms*sizeof(struct pair_descr_list)); if (self->data == NULL || self->index == NULL) { if (self->data != NULL) free(self->data); if (self->index != NULL) free(self->index); PyObject_Del(self); PyErr_NoMemory(); return NULL; } self->natoms = natoms; self->nalloc = nalloc; self->nused = natoms; for (i = 0; i < 2*natoms; i++) { self->index[i].nalloc = self->index[i].nused = 0; self->index[i].list = NULL; } for (i = 0; i < natoms; i++) { self->data[i].i = i; self->data[i].j = i; } PySparseFC_Zero(self); self->cutoff_sq = 0.; self->fc_fn = sparse_fc_function; return self; } static void sparsefc_dealloc(PySparseFCObject *self) { int i; for (i = 0; i < 2*self->natoms; i++) if (self->index[i].nalloc > 0) free(self->index[i].list); free(self->index); free(self->data); PyObject_Del(self); } /* Find an entry */ struct pair_descr * sparsefc_find(PySparseFCObject *fc, int i, int j) { int sum = i+j; int diff = j-i; struct pair_descr_list *pairs = fc->index + sum; struct pair_descr *entry = pairs->list; int k; for (k = 0; k < pairs->nused; k++) { if (entry->diffij == diff) { if (fc->data[entry->index].i != i || fc->data[entry->index].j != j) { printf("Index error\n"); } return entry; } entry++; } if (k < pairs->nalloc) return entry; else return NULL; } double * PySparseFC_Find(PySparseFCObject *fc, int i, int j) { struct pair_descr *pair; if (i == j) return (double *)fc->data[i].fc; pair = sparsefc_find(fc, i, j); if (pair == NULL) return NULL; if (pair->diffij < 0) return NULL; return (double *)fc->data[pair->index].fc; } /* Add a term */ int PySparseFC_AddTerm(PySparseFCObject *fc, int i, int j, double *term) { double *entry; int l; if (j < i) return 0; if (i == j) entry = (double *)fc->data[i].fc; else { struct pair_descr *pair = sparsefc_find(fc, i, j); if (pair == NULL) { struct pair_descr_list *list = fc->index + (i+j); int incr = (fc->nalloc/(2*fc->natoms)); void *new; if (incr < 1) incr = 1; #if 0 printf("Extending list %d from %d to %d\n", i+j, list->nalloc, list->nalloc+incr); #endif new = realloc(list->list, (list->nalloc+incr)*sizeof(struct pair_descr)); if (new == NULL) return 0; list->list = (struct pair_descr *)new; list->nalloc += incr; for (l = list->nused; l < list->nalloc; l++) list->list[l].diffij = -1; pair = list->list + list->nused; } if (pair->diffij < 0) { if (fc->nused == fc->nalloc) { int incr = fc->nalloc/10; void *new; if (incr < 1) incr = 1; #if 0 printf("Extending data space from %d to %d\n", fc->nalloc, fc->nalloc+incr); #endif new = realloc(fc->data, (fc->nalloc+incr)*sizeof(struct pair_fc)); if (new == NULL) return 0; fc->data = (struct pair_fc *)new; fc->nalloc += incr; for (l = fc->nused; l < fc->nalloc; l++) { double *data = (double *)fc->data[l].fc; int k; for (k = 0; k < 9; k++) *data++ = 0.; } } pair->index = fc->nused; fc->nused++; pair->diffij = j-i; fc->index[i+j].nused++; fc->data[pair->index].i = i; fc->data[pair->index].j = j; } #if 0 if (fc->data[pair->index].i != i || fc->data[pair->index].j != j) printf("Incorrect pair entry: %d/%d, %d/%d\n", fc->data[pair->index].i, i, fc->data[pair->index].j, j); #endif entry = (double *)fc->data[pair->index].fc; } for (l = 0; l < 9; l++) *entry++ += *term++; return 1; } /* Extract a subarray */ void PySparseFC_CopyToArray(PySparseFCObject *fc, double *data, int lastdim, int from1, int to1, int from2, int to2) { int i, j; double *temp; int pairs; temp = data; for (i = 0; i < 3*(to2-from2); i++) { for (j = 0; j < 3*(to1-from1); j++) temp[j] = 0.; temp += lastdim; } pairs = (to1-from1)*(to2-from2); for (i = 0; i < fc->nused; i++) { if (fc->data[i].i >= from1 && fc->data[i].i < to1 && fc->data[i].j >= from2 && fc->data[i].j < to2) { int offset = 3*lastdim*(fc->data[i].i-from1)+3*(fc->data[i].j-from2); int l, m; for (l = 0; l < 3; l++) { for (m = 0; m < 3; m++) data[offset+m] = fc->data[i].fc[l][m]; offset += lastdim; } pairs--; } if (fc->data[i].i != fc->data[i].j && fc->data[i].j >= from1 && fc->data[i].j < to1 && fc->data[i].i >= from2 && fc->data[i].i < to2) { int offset = 3*lastdim*(fc->data[i].j-from1)+3*(fc->data[i].i-from2); int l, m; for (l = 0; l < 3; l++) { for (m = 0; m < 3; m++) data[offset+m] = fc->data[i].fc[m][l]; offset += lastdim; } pairs--; } if (pairs == 0) break; } } PyObject * PySparseFC_AsArray(PySparseFCObject *fc, int from1, int to1, int from2, int to2) { PyArrayObject *array; #if defined(NUMPY) npy_intp dims[4]; #else int dims[4]; #endif dims[0] = to1-from1; if (dims[0] < 0) dims[0] = 0; dims[2] = to2-from2; if (dims[2] < 0) dims[2] = 0; dims[1] = dims[3] = 3; #if defined(NUMPY) array = (PyArrayObject *)PyArray_SimpleNew(4, dims, PyArray_DOUBLE); #else array = (PyArrayObject *)PyArray_FromDims(4, dims, PyArray_DOUBLE); #endif if (array == NULL) return NULL; PySparseFC_CopyToArray(fc, (double *)array->data, 3*dims[2], from1, to1, from2, to2); return (PyObject *)array; } /* Multiply with a vector */ void PySparseFC_VectorMultiply(PySparseFCObject *fc, double *result, double *vector, int from_i, int to_i, int from_j, int to_j) { struct pair_fc *entry = fc->data; int i; for (i = 3*(to_i-from_i)-1; i >= 0; i--) result[i] = 0.; for (i = 0; i < fc->nused; i++) { int l, m; if (entry->i >= from_i && entry->i < to_i && entry->j >= from_j && entry->j < to_j) { for (l = 0; l < 3; l++) for (m = 0; m < 3; m++) result[3*(entry->i-from_i)+l] += entry->fc[l][m]*vector[3*(entry->j-from_j)+m]; } if (entry->i != entry->j && entry->j >= from_i && entry->j < to_i && entry->i >= from_j && entry->i < to_j) { for (l = 0; l < 3; l++) for (m = 0; m < 3; m++) result[3*(entry->j-from_i)+m] += entry->fc[l][m]*vector[3*(entry->i-from_j)+l]; } entry++; } } /* Multiply with a vector */ static void solve_3x3(double *fc, double *vector, double *result) { double a = fc[0*3+0]; double b = fc[1*3+1]; double c = fc[2*3+2]; double d = fc[0*3+1]; double e = fc[0*3+2]; double f = fc[1*3+2]; double o = vector[0]; double p = vector[1]; double q = vector[2]; double af_de = a*f-d*e; double aq_eo = a*q-e*o; double ab_dd = a*b-d*d; double ac_ee = a*c-e*e; double z = (af_de*(a*p-d*o)-ab_dd*aq_eo) / (af_de*af_de-ab_dd*ac_ee); double y = (aq_eo - z*ac_ee)/af_de; double x = (o - d*y - e*z)/a; result[0] = x; result[1] = y; result[2] = z; } int PySparseFC_VectorSolve(PySparseFCObject *fc, double *result, double *vector, double tolerance, int max_iter) { int natoms = fc->natoms; int nc = 3*natoms; double *work, *r, *z, *p, *q; double rho, rho_prev, alpha, beta, residual; int i, iter; work = (double *)malloc(4*nc*sizeof(double)); if (work == NULL) return -1; r = work; z = r + nc; p = z + nc; q = p + nc; for (i = 0; i < nc; i++) { r[i] = vector[i]; result[i] = 0.; } iter = 0; rho = 0.; /* initialize to make gcc happy */ while (1) { for (i = 0; i < natoms; i++) { #if 1 /* Use 3x3 diagonal as preconditioner */ double *diag = PySparseFC_Find(fc, i, i); solve_3x3(diag, &r[3*i], &z[3*i]); #else /* No preconditioning */ z[3*i] = r[3*i]; z[3*i+1] = r[3*i+1]; z[3*i+2] = r[3*i+2]; #endif } rho_prev = rho; rho = 0.; for (i = 0; i < nc; i++) rho += r[i]*z[i]; if (iter == 0) { for (i = 0; i < nc; i++) p[i] = z[i]; } else { beta = rho/rho_prev; for (i = 0; i < nc; i++) p[i] = z[i] + beta*p[i]; } PySparseFC_VectorMultiply(fc, q, p, 0, natoms, 0, natoms); alpha = 0.; for (i = 0; i < nc; i++) alpha += p[i]*q[i]; alpha = rho/alpha; residual = 0.; for (i = 0; i < nc; i++) { result[i] += alpha*p[i]; r[i] -= alpha*q[i]; residual += r[i]*r[i]; } residual = sqrt(residual/natoms); #ifdef DEBUG printf("Residual: %lg\n", residual); #endif iter ++; if (iter > 2 && residual < tolerance) break; if (iter > max_iter) { free(work); return 0; } } #ifdef DEBUG printf("CG: %d iterations\n", iter); fflush(stdout); #endif free(work); return 1; } /* Scale by weight vector */ void PySparseFC_Scale(PySparseFCObject *fc, PyArrayObject *factors) { struct pair_fc *entry = fc->data; double *f = (double *)factors->data; int i; for (i = 0; i < fc->nused; i++) { int l, m; for (l = 0; l < 3; l++) for (m = 0; m < 3; m++) entry->fc[l][m] *= f[entry->i]*f[entry->j]; entry++; } } /* Methods */ static PyObject * setCutoff(PyObject *self, PyObject *args) { PySparseFCObject *fc = (PySparseFCObject *)self; double cutoff; if (!PyArg_ParseTuple(args, "d", &cutoff)) return NULL; fc->cutoff_sq = cutoff*cutoff; Py_INCREF(Py_None); return Py_None; } static PyObject * accessFunction(PyObject *self, PyObject *args) { PySparseFCObject *fc = (PySparseFCObject *)self; if (!PyArg_ParseTuple(args, "")) return NULL; return PyCObject_FromVoidPtr((void *)fc->fc_fn, NULL); } static PyObject * multiplyVector(PyObject *self, PyObject *args) { PySparseFCObject *fc = (PySparseFCObject *)self; PyArrayObject *vector = NULL; PyObject *result = NULL; PyArrayObject *result_array; int from_i = 0, to_i = fc->natoms; int from_j = 0, to_j = fc->natoms; #if defined(NUMPY) npy_intp dims[2]; #else int dims[2]; #endif if (!PyArg_ParseTuple(args, "O!|Oiiii", &PyArray_Type, &vector, &result, &from_i, &to_i, &from_j, &to_j)) return NULL; if (result == Py_None) result = NULL; if (result != NULL) { if (!PyArray_Check(result)) { PyErr_SetString(PyExc_TypeError, "result must be array"); return NULL; } result_array = (PyArrayObject *)result; if (result_array->nd != 2 || result_array->dimensions[0] != to_i-from_i || result_array->dimensions[1] != 3) { PyErr_SetString(PyExc_ValueError, "illegal array shape"); return NULL; } } if (vector->nd != 2 || vector->dimensions[0] != to_j-from_j || vector->dimensions[1] != 3) { PyErr_SetString(PyExc_ValueError, "illegal array shape"); return NULL; } if (from_i < 0 || to_i > fc->natoms || to_i < from_i || from_j < 0 || to_j > fc->natoms || to_j < from_j) { PyErr_SetString(PyExc_ValueError, "illegal subset"); return NULL; } if (result == NULL) { dims[0] = to_i-from_i; dims[1] = 3; #if defined(NUMPY) result = PyArray_SimpleNew(2, dims, PyArray_DOUBLE); #else result = PyArray_FromDims(2, dims, PyArray_DOUBLE); #endif if (result == NULL) return NULL; } else Py_INCREF(result); result_array = (PyArrayObject *)result; PySparseFC_VectorMultiply(fc, (double *)result_array->data, (double *)vector->data, from_i, to_i, from_j, to_j); return (PyObject *)result; } static PyObject * solveForVector(PyObject *self, PyObject *args) { PySparseFCObject *fc = (PySparseFCObject *)self; PyArrayObject *vector = NULL; PyObject *result = NULL; PyArrayObject *result_array; double tolerance = 1.e-8; #if defined(NUMPY) npy_intp dims[2]; #else int dims[2]; #endif int max_iter = 0; int ret; if (!PyArg_ParseTuple(args, "O!|Odi", &PyArray_Type, &vector, &result, &tolerance, &max_iter)) return NULL; if (result == Py_None) result = NULL; if (result != NULL) { if (!PyArray_Check(result)) { PyErr_SetString(PyExc_TypeError, "result must be array"); return NULL; } result_array = (PyArrayObject *)result; if (result_array->nd != 2 || result_array->dimensions[0] != fc->natoms || result_array->dimensions[1] != 3) { PyErr_SetString(PyExc_ValueError, "illegal array shape"); return NULL; } } if (vector->nd != 2 || vector->dimensions[0] != fc->natoms || vector->dimensions[1] != 3) { PyErr_SetString(PyExc_ValueError, "illegal array shape"); return NULL; } if (result == NULL) { dims[0] = fc->natoms; dims[1] = 3; #if defined(NUMPY) result = PyArray_SimpleNew(2, dims, PyArray_DOUBLE); #else result = PyArray_FromDims(2, dims, PyArray_DOUBLE); #endif if (result == NULL) return NULL; } else Py_INCREF(result); result_array = (PyArrayObject *)result; if (max_iter == 0) max_iter = 4*fc->natoms; ret = PySparseFC_VectorSolve(fc, (double *)result_array->data, (double *)vector->data, tolerance, max_iter); if (ret == -1) { PyErr_NoMemory(); Py_DECREF(result); return NULL; } if (ret == 0) { PyErr_SetString(PyExc_ValueError, "no convergence"); Py_DECREF(result); return NULL; } return (PyObject *)result; } static PyObject * asArray(PyObject *self, PyObject *args) { PySparseFCObject *fc = (PySparseFCObject *)self; return PySparseFC_AsArray(fc, 0, fc->natoms, 0, fc->natoms); } static PyObject * scale(PyObject *self, PyObject *args) { PySparseFCObject *fc = (PySparseFCObject *)self; PyArrayObject *factors; if (!PyArg_ParseTuple(args, "O!", &PyArray_Type, &factors)) return NULL; PySparseFC_Scale(fc, factors); Py_INCREF(Py_None); return Py_None; } /* Documentation string */ static char PySparseFC_Type__doc__[] = "sparse force constant matrix"; /* Method table */ static struct PyMethodDef sparsefc_methods[] = { {"setCutoff", setCutoff, 1}, {"accessFunction", accessFunction, 1}, {"multiplyVector", multiplyVector, 1}, {"solveForVector", solveForVector, 1}, {"asArray", asArray, 1}, {"scale", scale, 1}, {NULL, NULL} /* sentinel */ }; /* Attribute access. */ static PyObject * sparsefc_getattr(PySparseFCObject *self, char *name) { return Py_FindMethod(sparsefc_methods, (PyObject *)self, name); } /* Sequence protocol */ static Py_ssize_t sparsefc_length(PySparseFCObject *self) { return (Py_ssize_t)self->nused; } static PyObject * sparsefc_item(PySparseFCObject *self, Py_ssize_t i) { if (i < 0 || i >= self->nused) { PyErr_SetString(PyExc_IndexError,"index out of bounds"); return NULL; } else { PyArrayObject *array; PyObject *ret; #if defined(NUMPY) npy_intp dims[2]; #else int dims[2]; #endif dims[0] = 3; dims[1] = 3; #if defined(NUMPY) array = (PyArrayObject *)PyArray_SimpleNew(2, dims, PyArray_DOUBLE); #else array = (PyArrayObject *)PyArray_FromDims(2, dims, PyArray_DOUBLE); #endif if (array == NULL) return NULL; memcpy(array->data, self->data[i].fc, 9*sizeof(double)); ret = Py_BuildValue("iiO", self->data[i].i, self->data[i].j, (PyObject *)array); Py_DECREF(array); return ret; } } /* Mapping protocol */ static PyObject * sparsefc_subscript(PySparseFCObject *self, PyObject *index) { PyArrayObject *array; Py_ssize_t from[2], to[2], rank[2], stride; int i; if (PyInt_Check(index)) return sparsefc_item(self, PyInt_AsLong(index)); if (!PyTuple_Check(index) || PyTuple_Size(index) != 2) { PyErr_SetString(PyExc_TypeError, "index must be tuple of length 2"); return NULL; } for (i = 0; i < 2; i++) { PyObject *subscript = PyTuple_GetItem(index, (Py_ssize_t)i); if (PyInt_Check(subscript)) { from[i] = PyInt_AsLong(subscript); to[i] = from[i] + 1; rank[i] = 0; stride = 1; } else if (PySlice_Check(subscript)) { PySlice_GetIndices((PySliceObject *)subscript, self->natoms, &from[i], &to[i], &stride); rank[i] = 1; } else { PyErr_SetString(PyExc_TypeError, "illegal subscript type"); return NULL; } if (from[i] < 0 || to[i] > self->natoms || to[i] < from[i] || stride != 1) { PyErr_SetString(PyExc_IndexError, "illegal subscript"); return NULL; } } if (rank[0] != rank[1]) { PyErr_SetString(PyExc_IndexError, "illegal subscript"); return NULL; } array = (PyArrayObject *)PySparseFC_AsArray(self, from[0], to[0], from[1], to[1]); if (array == NULL) return NULL; if (rank[0] == 0) { PyObject *shape = PyTuple_New((Py_ssize_t)2); if (shape == NULL) return NULL; PyTuple_SetItem(shape, (Py_ssize_t)0, PyInt_FromLong(3)); PyTuple_SetItem(shape, (Py_ssize_t)1, PyInt_FromLong(3)); array = (PyArrayObject *)PyArray_Reshape(array, shape); Py_DECREF(shape); } return (PyObject *)array; } /* Type object */ static PySequenceMethods sparsefc_as_sequence = { (lenfunc)sparsefc_length, /*sq_length*/ 0, /*nb_add, concat is numeric add*/ 0, /*nb_multiply, repeat is numeric multiply*/ (ssizeargfunc)sparsefc_item,/* sq_item*/ 0, /*sq_slice*/ 0, /*sq_ass_item*/ 0, /*sq_ass_slice*/ }; static PyMappingMethods sparsefc_as_mapping = { (lenfunc)sparsefc_length, /*mp_length*/ (binaryfunc)sparsefc_subscript, /*mp_subscript*/ 0, /*mp_ass_subscript*/ }; PyTypeObject PySparseFC_Type = { PyObject_HEAD_INIT(NULL) 0, /*ob_size*/ "SparseForceConstants", /*tp_name*/ sizeof(PySparseFCObject), /*tp_basicsize*/ 0, /*tp_itemsize*/ /* methods */ (destructor)sparsefc_dealloc, /*tp_dealloc*/ 0, /*tp_print*/ (getattrfunc)sparsefc_getattr, /*tp_getattr*/ 0, /*tp_setattr*/ 0, /*tp_compare*/ 0, /*tp_repr*/ 0, /*tp_as_number*/ &sparsefc_as_sequence, /*tp_as_sequence*/ &sparsefc_as_mapping, /*tp_as_mapping*/ 0, /*tp_hash*/ 0, /*tp_call*/ 0, /*tp_str*/ 0, /*tp_getattro*/ 0, /*tp_setattro*/ /* Space for future expansion */ 0L,0L, /* Documentation string */ PySparseFC_Type__doc__ }; /* Interface to force field evaluators */ int sparse_fc_function(energy_data *energy, int i, int j, tensor3 term, double r_sq) { PySparseFCObject *fc = (PySparseFCObject *)energy->force_constants; if (i < 0) { PySparseFC_Zero(fc); return 1; } else if (term == NULL) { return r_sq < fc->cutoff_sq || fc->cutoff_sq == 0.; } else if (r_sq < fc->cutoff_sq || fc->cutoff_sq == 0.) { if (!PySparseFC_AddTerm(fc, i, j, (double *)term)) { energy->error = 1; PyErr_SetString(PyExc_IndexError, "couldn't access sparse array"); } return 1; } else return 0; } /* Constructor function */ PyObject * SparseForceConstants(PyObject *dummy, PyObject *args) { int natoms, nalloc; if (!PyArg_ParseTuple(args, "ii", &natoms, &nalloc)) { return NULL; } return (PyObject *)PySparseFC_New(natoms, nalloc); } MMTK-2.7.9/Tests/0000755000076600000240000000000012156626572014027 5ustar hinsenstaff00000000000000MMTK-2.7.9/Tests/all_tests.py0000644000076600000240000000166312117632051016363 0ustar hinsenstaff00000000000000# Run the complete test suite # # Written by Konrad Hinsen. # import unittest import basic_tests import universe_tests import pickle_tests import energy_tests import restraint_tests import trajectory_tests import normal_mode_tests import subspace_tests import enm_tests import internal_coordinate_tests def suite(): test_suite = unittest.TestSuite() test_suite.addTests(basic_tests.suite()) test_suite.addTests(universe_tests.suite()) test_suite.addTests(pickle_tests.suite()) test_suite.addTests(energy_tests.suite()) test_suite.addTests(restraint_tests.suite()) test_suite.addTests(normal_mode_tests.suite()) test_suite.addTests(subspace_tests.suite()) test_suite.addTests(trajectory_tests.suite()) test_suite.addTests(enm_tests.suite()) test_suite.addTests(internal_coordinate_tests.suite()) return test_suite if __name__ == '__main__': unittest.TextTestRunner(verbosity=2).run(suite()) MMTK-2.7.9/Tests/basic_tests.py0000644000076600000240000000323612117632051016672 0ustar hinsenstaff00000000000000# Basic tests # # Written by Konrad Hinsen # import unittest import MMTK from MMTK.Proteins import Protein from Scientific import N class GroupOfAtomTest(unittest.TestCase): """ Test the methods of Collections.GroupOfAtoms """ def setUp(self): self.molecule = MMTK.Molecule('water') self.results = {} self.results['numberOfAtoms'] = 3 def test_numberOfAtoms(self): self.assertEqual(self.molecule.numberOfAtoms(), self.results['numberOfAtoms']) class SuperpositionTest(unittest.TestCase): """ Test the findTransformation method """ def test_rotation_translation(self): for m in [MMTK.Molecule('water'), Protein('bala1')]: universe = MMTK.InfiniteUniverse() universe.addObject(m) ref_conf = universe.copyConfiguration() universe.translateBy(MMTK.Vector(0.1, -1.3, 1.2)) universe.rotateAroundOrigin(MMTK.Vector(0., 1., 1.), 0.7) tr, rms = universe.findTransformation(ref_conf) self.assert_(abs(rms) < 1.e-5) universe.applyTransformation(tr) self.assert_(universe.rmsDifference(ref_conf) < 1.e-5) axis, angle = tr.rotation().axisAndAngle() self.assert_(abs(angle - 0.7) < 1.e-5) self.assert_(abs(abs(N.cos(axis.angle(MMTK.Vector(0., 1., 1.))))-1) < 1.e-5) def suite(): loader = unittest.TestLoader() s = unittest.TestSuite() s.addTest(loader.loadTestsFromTestCase(GroupOfAtomTest)) s.addTest(loader.loadTestsFromTestCase(SuperpositionTest)) return s if __name__ == '__main__': unittest.main() MMTK-2.7.9/Tests/energy_tests.py0000644000076600000240000002114412117632051017100 0ustar hinsenstaff00000000000000# Energy tests # # Written by Konrad Hinsen # import unittest from subsets import SubsetTest from MMTK import * from MMTK.MoleculeFactory import MoleculeFactory from MMTK.ForceFields import Amber99ForceField, LennardJonesForceField from MMTK_forcefield import NonbondedList from MMTK.Random import randomPointInBox from MMTK.Geometry import SCLattice from MMTK.Utility import pairs from Scientific.Geometry import ex, ey, ez from Scientific import N from cStringIO import StringIO import itertools factory = MoleculeFactory() factory.createGroup('dihedral_test') factory.addAtom('dihedral_test', 'C1', 'C') factory.addAtom('dihedral_test', 'C2', 'C') factory.addAtom('dihedral_test', 'C3', 'C') factory.addAtom('dihedral_test', 'C4', 'C') factory.addBond('dihedral_test', 'C1', 'C2') factory.addBond('dihedral_test', 'C2', 'C3') factory.addBond('dihedral_test', 'C3', 'C4') factory.setAttribute('dihedral_test', 'C1.amber_atom_type', 'D1') factory.setAttribute('dihedral_test', 'C2.amber_atom_type', 'D2') factory.setAttribute('dihedral_test', 'C3.amber_atom_type', 'D3') factory.setAttribute('dihedral_test', 'C4.amber_atom_type', 'D4') factory.setAttribute('dihedral_test', 'C1.amber_charge', 0.) factory.setAttribute('dihedral_test', 'C2.amber_charge', 0.) factory.setAttribute('dihedral_test', 'C3.amber_charge', 0.) factory.setAttribute('dihedral_test', 'C4.amber_charge', 0.) factory.setPosition('dihedral_test', 'C1', Vector(1., 0., 0.)) factory.setPosition('dihedral_test', 'C2', Vector(0., 0., 0.)) factory.setPosition('dihedral_test', 'C3', Vector(0., 0., 1.)) factory.setPosition('dihedral_test', 'C4', Vector(1., 0., 1.)) def sorted_tuple(pair): if pair[0] < pair[1]: return (pair[0], pair[1]) else: return (pair[1], pair[0]) class DihedralTest(unittest.TestCase): def setUp(self): self.universe = InfiniteUniverse() self.universe.addObject(factory.retrieveMolecule('dihedral_test')) self.mod_template = """Amber parameters MASS D1 12.0 D2 12.0 D3 12.0 D4 12.0 BOND D1-D2 0.0 1.000 D2-D3 0.0 1.000 D3-D4 0.0 1.000 ANGL D1-D2-D3 0.0 90.0 D2-D3-D4 0.0 90.0 DIHEDRAL D1-D2-D3-D4 1%15.6f%15.6f%15.6f NONB D1 1.0000 0.0000 0.00000 D2 1.0000 0.0000 0.00000 D3 1.0000 0.0000 0.00000 D4 1.0000 0.0000 0.00000 """ def _gradientTest(self, delta = 0.0001): e0, grad = self.universe.energyAndGradients() atoms = self.universe.atomList() for a in atoms: num_grad = [] for v in [ex, ey, ez]: x = a.position() a.setPosition(x+delta*v) eplus = self.universe.energy() a.setPosition(x-delta*v) eminus = self.universe.energy() a.setPosition(x) num_grad.append(0.5*(eplus-eminus)/delta) self.assert_((Vector(num_grad)-grad[a]).length() < 1.e-2) def _forceConstantTest(self, delta = 0.0001): e0, grad0, fc = self.universe.energyGradientsAndForceConstants() atoms = self.universe.atomList() for a1, a2 in itertools.chain(itertools.izip(atoms, atoms), pairs(atoms)): num_fc = [] for v in [ex, ey, ez]: x = a1.position() a1.setPosition(x+delta*v) e_plus, grad_plus = self.universe.energyAndGradients() a1.setPosition(x-delta*v) e_minus, grad_minus = self.universe.energyAndGradients() a1.setPosition(x) num_fc.append(0.5*(grad_plus[a2]-grad_minus[a2])/delta) num_fc = N.array([a.array for a in num_fc]) diff = N.fabs(N.ravel(num_fc-fc[a1, a2].array)) error = N.maximum.reduce(diff) self.assert_(error < 5.e-2) def _dihedralTerm(self, n, phase, V): mod_file = self.mod_template % \ (V/(Units.kcal/Units.mol), phase/Units.deg, n) ff = Amber99ForceField(mod_files=[StringIO(mod_file)]) self.universe.setForceField(ff) param = self.universe.energyEvaluatorParameters() i1, i2, i3, i4, n_test, phase_test, V_test = \ param['cosine_dihedral_term'][0] self.assertEqual(n_test, n) # The accuracy is no better than five digits because the # parameters pass through a text representation. self.assertAlmostEqual(phase_test, phase, 5) self.assertAlmostEqual(V_test, V, 5) two_pi = 2.*N.pi m = self.universe[0] for angle in N.arange(0., two_pi, 0.1): m.C4.setPosition(Vector(N.cos(angle), N.sin(angle), 1.)) e = self.universe.energyTerms()['cosine dihedral angle'] da = self.universe.dihedral(m.C1, m.C2, m.C3, m.C4) e_ref = V*(1.+N.cos(n*angle-phase)) self.assertAlmostEqual(angle % two_pi, da % two_pi, 14) self.assertAlmostEqual(e, e_ref, 5) self._gradientTest() self._forceConstantTest() def test_dihedral(self): for n in [1, 2, 3, 4]: for phase in N.arange(0., 6., 0.5): for V in [-10., 2.]: self._dihedralTerm(n, phase, V) class NonbondedListTest: def test_nonbondedList(self): self.universe.configuration() atoms = self.universe.atomList() atom_indices = N.array([a.index for a in self.universe.atomList()]) empty = N.zeros((0, 2), N.Int) for cutoff in [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.]: nblist = NonbondedList(empty, empty, atom_indices, self.universe._spec, cutoff) nblist.update(self.universe.configuration().array) distances = nblist.pairDistances() pairs1 = nblist.pairIndices() pairs1 = [sorted_tuple(pairs1[i]) for i in range(len(pairs1)) if distances[i] < cutoff] pairs1.sort(lambda a, b: cmp(a[0], b[0]) or cmp(a[1], b[1])) pairs2 = [] for i in range(len(atoms)): for j in range(i+1, len(atoms)): d = self.universe.distance(atoms[i], atoms[j]) if d < cutoff: pairs2.append(sorted_tuple((atoms[i].index, atoms[j].index))) pairs2.sort(lambda a, b: cmp(a[0], b[0]) or cmp(a[1], b[1])) self.assertEqual(pairs1, pairs2) class InfiniteUniverseNonbondedListTest(unittest.TestCase, NonbondedListTest): def setUp(self): self.universe = InfiniteUniverse() for i in range(100): p = randomPointInBox(1.) self.universe.addObject(Atom('C', position = p)) class OrthorhombicUniverseNonbondedListTest(unittest.TestCase, NonbondedListTest): def setUp(self): self.universe = OrthorhombicPeriodicUniverse((1.5, 0.8, 1.1)) for i in range(100): p = self.universe.boxToRealCoordinates(randomPointInBox(1.)) self.universe.addObject(Atom('C', position = p)) class ParallelepipedicUniverseNonbondedListTest(unittest.TestCase, NonbondedListTest): def setUp(self): a = Vector(1.5, 0.3, 0.) b = Vector(-0.1, 0.8, 0.2) c = Vector(0., -0.2, 1.1) self.universe = ParallelepipedicPeriodicUniverse((a, b, c)) for i in range(100): p = self.universe.boxToRealCoordinates(randomPointInBox(1.)) self.universe.addObject(Atom('C', position = p)) class LennardJonesSubsetTest(unittest.TestCase, SubsetTest): def setUp(self): self.universe = OrthorhombicPeriodicUniverse((2., 2., 2.), LennardJonesForceField()) for point in SCLattice(0.5, 4): self.universe.addObject(Atom('Ar', position=point)) self.subset1 = Collection(self.universe.atomList()[:10]) self.subset2 = Collection(self.universe.atomList()[20:30]) def suite(): loader = unittest.TestLoader() s = unittest.TestSuite() s.addTest(loader.loadTestsFromTestCase(DihedralTest)) s.addTest(loader.loadTestsFromTestCase(InfiniteUniverseNonbondedListTest)) s.addTest(loader.loadTestsFromTestCase(OrthorhombicUniverseNonbondedListTest)) s.addTest(loader.loadTestsFromTestCase(ParallelepipedicUniverseNonbondedListTest)) s.addTest(loader.loadTestsFromTestCase(LennardJonesSubsetTest)) return s if __name__ == '__main__': unittest.main() MMTK-2.7.9/Tests/enm_tests.py0000644000076600000240000000316111662461640016375 0ustar hinsenstaff00000000000000# Elastic network model tests # # Written by Konrad Hinsen # import unittest from MMTK import * from MMTK.ForceFields import CalphaForceField, DeformationForceField, \ AnisotropicNetworkForceField from MMTK.Proteins import Protein from subsets import SubsetTest class CalphaFFSubsetTest(unittest.TestCase, SubsetTest): def setUp(self): self.universe = InfiniteUniverse(CalphaForceField()) protein = Protein('insulin_calpha') self.universe.addObject(protein) self.subset1 = protein[0] self.subset2 = protein[1] class DeformationFFSubsetTest(unittest.TestCase, SubsetTest): def setUp(self): self.universe = InfiniteUniverse(DeformationForceField(cutoff=5.)) protein = Protein('insulin_calpha') self.universe.addObject(protein) self.subset1 = protein[0] self.subset2 = protein[1] class ANMFFSubsetTest(unittest.TestCase, SubsetTest): def setUp(self): self.universe = InfiniteUniverse(AnisotropicNetworkForceField(cutoff=5.)) protein = Protein('insulin_calpha') self.universe.addObject(protein) self.subset1 = protein[0] self.subset2 = protein[1] def suite(): loader = unittest.TestLoader() s = unittest.TestSuite() s.addTest(loader.loadTestsFromTestCase(CalphaFFSubsetTest)) s.addTest(loader.loadTestsFromTestCase(DeformationFFSubsetTest)) s.addTest(loader.loadTestsFromTestCase(ANMFFSubsetTest)) return s if __name__ == '__main__': unittest.main() MMTK-2.7.9/Tests/internal_coordinate_tests.py0000644000076600000240000000445311662461640021646 0ustar hinsenstaff00000000000000# Internal coordinate tests # # Written by Konrad Hinsen # import unittest from MMTK.Proteins import Protein from MMTK.InternalCoordinates import BondLength, BondAngle, DihedralAngle from Scientific import N class ICTest(unittest.TestCase): def setUp(self): # Pick a proline residue because of its cycle self.protein = Protein("insulin.pdb") self.chain = self.protein[1] self.res = self.chain[27] def test_phipsi(self): phi, psi = self.res.phiPsi() phi_o = self.res.phiAngle() psi_o = self.res.psiAngle() self.assertAlmostEqual(phi, phi_o.getValue(), 10) self.assertAlmostEqual(psi, psi_o.getValue(), 10) def test_changephi(self): phi = self.res.phiAngle() current_phi = phi.getValue() for dphi in [-0.2, -0.1, 0.1, 0.2]: phi.setValue(current_phi+dphi) self.assertAlmostEqual(phi.getValue() % (2.*N.pi), (current_phi+dphi) % (2.*N.pi), 10) def test_changebond(self): b = BondLength(self.res.peptide.C_alpha, self.res.peptide.C) current_b = b.getValue() for db in [-0.02, -0.01, 0.01, 0.02]: b.setValue(current_b + db) self.assertAlmostEqual(b.getValue(), current_b + db) def test_changeangle(self): a = BondAngle(self.res.peptide.N, self.res.peptide.C_alpha, self.res.peptide.C) current_a = a.getValue() for da in [-0.2, -0.1, 0.1, 0.2]: a.setValue(current_a+da) self.assertAlmostEqual(a.getValue() % (2.*N.pi), (current_a+da) % (2.*N.pi), 10) def test_cyclic(self): self.assertRaises(ValueError, BondLength, self.res.peptide.N, self.res.peptide.C_alpha) self.assertRaises(ValueError, BondAngle, self.res.peptide.N, self.res.peptide.C_alpha, self.res.sidechain.C_beta) self.assertRaises(ValueError, DihedralAngle, self.res.peptide.N, self.res.peptide.C_alpha, self.res.sidechain.C_beta, self.res.sidechain.C_gamma) def suite(): return unittest.TestLoader().loadTestsFromTestCase(ICTest) if __name__ == '__main__': unittest.main() MMTK-2.7.9/Tests/normal_mode_tests.py0000644000076600000240000002452411662461640020120 0ustar hinsenstaff00000000000000# Normal mode tests # # Written by Konrad Hinsen # import unittest import MMTK from MMTK.Proteins import Protein from MMTK.ForceFields import Amber99ForceField from MMTK.Subspace import RigidMotionSubspace, PairDistanceSubspace from MMTK import NormalModes class WaterTest(unittest.TestCase): """ Test NormalModes.EnergeticModes and NormalModes.VibrationalModes """ def setUp(self): self.universe = MMTK.InfiniteUniverse(Amber99ForceField()) self.universe.water = MMTK.Molecule('water') def test_energeticModes(self): emodes = NormalModes.EnergeticModes(self.universe) fc = emodes.force_constants[6:] self.assertAlmostEqual(fc[0], 757849.3957485439, 5) self.assertAlmostEqual(fc[1], 1041551.2240706938, 5) self.assertAlmostEqual(fc[2], 1388251.2, 5) for i in range(len(emodes)): mi = emodes.rawMode(i) norm_sq = mi.dotProduct(mi) self.assertAlmostEqual(norm_sq, 1.) for j in range(i+1, len(emodes)): overlap = mi.dotProduct(emodes.rawMode(j)) self.assert_(overlap < 1.e-15) self.assertAlmostEqual(emodes[6].norm(), 0.0025656740328985168) self.assertAlmostEqual(emodes[7].norm(), 0.0021885627126988836) self.assertAlmostEqual(emodes[8].norm(), 0.0018956532696964624) f = emodes.fluctuations() self.assertAlmostEqual(f[self.universe.water.O], 3.359215370401356e-06) self.assertAlmostEqual(f[self.universe.water.H1], 2.061890142153454e-06) self.assertAlmostEqual(f[self.universe.water.H2], 2.061890142153454e-06) af = emodes.anisotropicFluctuations() self.assertAlmostEqual(f[self.universe.water.O], af[self.universe.water.O].trace()) self.assertAlmostEqual(f[self.universe.water.H1], af[self.universe.water.H1].trace()) self.assertAlmostEqual(f[self.universe.water.H2], af[self.universe.water.H2].trace()) def test_vibrationalModes(self): vmodes = NormalModes.VibrationalModes(self.universe) freq = vmodes.frequencies[6:] self.assertAlmostEqual(freq[0], 87.220841117866954) self.assertAlmostEqual(freq[1], 112.01238171677888) self.assertAlmostEqual(freq[2], 181.05242207954848) for i in range(len(vmodes)): mi = vmodes.rawMode(i) norm_sq = mi.dotProduct(mi) self.assertAlmostEqual(norm_sq, 1.) for j in range(i+1, len(vmodes)): overlap = mi.dotProduct(vmodes.rawMode(j)) self.assert_(overlap < 1.e-15) self.assertAlmostEqual(vmodes[6].norm(), 0.0038454773367577063) self.assertAlmostEqual(vmodes[7].norm(), 0.0030509249071616175) self.assertAlmostEqual(vmodes[8].norm(), 0.001953454891033823) f = vmodes.fluctuations() self.assertAlmostEqual(f[self.universe.water.O], 8.0141737611250569e-08) self.assertAlmostEqual(f[self.universe.water.H1], 6.9381391949153065e-06) self.assertAlmostEqual(f[self.universe.water.H2], 6.9381391949153023e-06) af = vmodes.anisotropicFluctuations() self.assertAlmostEqual(f[self.universe.water.O], af[self.universe.water.O].trace()) self.assertAlmostEqual(f[self.universe.water.H1], af[self.universe.water.H1].trace()) self.assertAlmostEqual(f[self.universe.water.H2], af[self.universe.water.H2].trace()) class PeptideTest(unittest.TestCase): """ Test VibrationalModes with a RigidMotionSubspace """ def setUp(self): self.universe = MMTK.InfiniteUniverse(Amber99ForceField()) self.universe.peptide = Protein('bala1') self.subspace = RigidMotionSubspace(self.universe, self.universe.peptide.residues()) def test_subspaceModes(self): vmodes = NormalModes.VibrationalModes(self.universe, subspace=self.subspace) freq = vmodes.frequencies[6:] self.assertAlmostEqual(freq[0], 3.29430105) self.assertAlmostEqual(freq[1], 3.5216842) self.assertAlmostEqual(freq[2], 6.36658335) self.assertAlmostEqual(freq[3], 6.81335036) self.assertAlmostEqual(freq[4], 8.96368475) self.assertAlmostEqual(freq[5], 9.80151378) self.assertAlmostEqual(freq[6], 12.26528168) self.assertAlmostEqual(freq[7], 12.64098176) self.assertAlmostEqual(freq[8], 16.13558466) self.assertAlmostEqual(freq[9], 17.15343073) self.assertAlmostEqual(freq[10], 24.86662786) self.assertAlmostEqual(freq[11], 29.0265789) for i in range(len(vmodes)): mi = vmodes.rawMode(i) norm_sq = mi.dotProduct(mi) self.assertAlmostEqual(norm_sq, 1.) for j in range(i+1, len(vmodes)): overlap = mi.dotProduct(vmodes.rawMode(j)) self.assert_(overlap < 1.e-13) self.assertAlmostEqual(vmodes[6].norm(), 0.0577716669202) self.assertAlmostEqual(vmodes[7].norm(), 0.0571769348227) self.assertAlmostEqual(vmodes[8].norm(), 0.0276758947516) self.assertAlmostEqual(vmodes[9].norm(), 0.0254182530029) self.assertAlmostEqual(vmodes[10].norm(), 0.0213488665278) self.assertAlmostEqual(vmodes[11].norm(), 0.0195579088896) self.assertAlmostEqual(vmodes[12].norm(), 0.0194026958464) self.assertAlmostEqual(vmodes[13].norm(), 0.0251256023118) self.assertAlmostEqual(vmodes[14].norm(), 0.0100970642246) self.assertAlmostEqual(vmodes[15].norm(), 0.00935723869926) self.assertAlmostEqual(vmodes[16].norm(), 0.00561663849164) self.assertAlmostEqual(vmodes[17].norm(), 0.00541615037839) def test_subspaceModesWithExclusion(self): excluded = PairDistanceSubspace(self.universe, [(self.universe.peptide[0][0].O, self.universe.peptide[0][-1].CH3)]) vmodes = NormalModes.VibrationalModes(self.universe, subspace=(excluded, self.subspace)) freq = vmodes.frequencies[6:] self.assertAlmostEqual(freq[0], 3.48436894) self.assertAlmostEqual(freq[1], 5.36671021) self.assertAlmostEqual(freq[2], 6.57184292) self.assertAlmostEqual(freq[3], 8.37798098) self.assertAlmostEqual(freq[4], 8.95620773) self.assertAlmostEqual(freq[5], 12.06606868) self.assertAlmostEqual(freq[6], 12.62157723) self.assertAlmostEqual(freq[7], 16.02985338) self.assertAlmostEqual(freq[8], 16.90566338) self.assertAlmostEqual(freq[9], 23.55890937) self.assertAlmostEqual(freq[10],28.92170134) self.assertAlmostEqual(freq[11],41.55110572) for i in range(len(vmodes)): mi = vmodes.rawMode(i) norm_sq = mi.dotProduct(mi) self.assertAlmostEqual(norm_sq, 1.) for j in range(i+1, len(vmodes)): overlap = mi.dotProduct(vmodes.rawMode(j)) self.assert_(overlap < 2.e-14) self.assertAlmostEqual(vmodes[6].norm(), 0.0578155246705) self.assertAlmostEqual(vmodes[7].norm(), 0.030148251724) self.assertAlmostEqual(vmodes[8].norm(), 0.0275287609872) self.assertAlmostEqual(vmodes[9].norm(), 0.0225332103616) self.assertAlmostEqual(vmodes[10].norm(), 0.0216566218055) self.assertAlmostEqual(vmodes[11].norm(), 0.0215173538578) self.assertAlmostEqual(vmodes[12].norm(), 0.0249366816925) self.assertAlmostEqual(vmodes[13].norm(), 0.0102016850103) self.assertAlmostEqual(vmodes[14].norm(), 0.00939822099626) self.assertAlmostEqual(vmodes[15].norm(), 0.00611049609175) self.assertAlmostEqual(vmodes[16].norm(), 0.0053756335944) self.assertAlmostEqual(vmodes[17].norm(), 0.00475443155674) def test_subspaceModesWithNumericalDifferentiation(self): vmodes = NormalModes.VibrationalModes(self.universe, subspace=self.subspace, delta=0.01) freq = vmodes.frequencies[6:] self.assertAlmostEqual(freq[0], 3.31849983, 5) self.assertAlmostEqual(freq[1], 3.54902184, 5) self.assertAlmostEqual(freq[2], 6.39993263, 5) self.assertAlmostEqual(freq[3], 6.82609026, 5) self.assertAlmostEqual(freq[4], 8.97880448, 5) self.assertAlmostEqual(freq[5], 9.85460764, 5) self.assertAlmostEqual(freq[6], 12.34554287, 5) self.assertAlmostEqual(freq[7], 12.89029636, 5) self.assertAlmostEqual(freq[8], 16.13726961, 5) self.assertAlmostEqual(freq[9], 17.15617066, 5) self.assertAlmostEqual(freq[10], 24.86718661, 5) self.assertAlmostEqual(freq[11], 29.02770494, 5) for i in range(len(vmodes)): mi = vmodes.rawMode(i) norm_sq = mi.dotProduct(mi) self.assertAlmostEqual(norm_sq, 1.) for j in range(i+1, len(vmodes)): overlap = mi.dotProduct(vmodes.rawMode(j)) self.assert_(overlap < 1.e-14) self.assertAlmostEqual(vmodes[6].norm(), 0.057265850728, 5) self.assertAlmostEqual(vmodes[7].norm(), 0.0568157841206, 5) self.assertAlmostEqual(vmodes[8].norm(), 0.0272242339256, 5) self.assertAlmostEqual(vmodes[9].norm(), 0.0253529888364, 5) self.assertAlmostEqual(vmodes[10].norm(), 0.0212445420919, 5) self.assertAlmostEqual(vmodes[11].norm(), 0.0188740025114, 5) self.assertAlmostEqual(vmodes[12].norm(), 0.0190325360167, 5) self.assertAlmostEqual(vmodes[13].norm(), 0.0252070482368, 5) self.assertAlmostEqual(vmodes[14].norm(), 0.010099061532, 5) self.assertAlmostEqual(vmodes[15].norm(), 0.00935799475998, 5) self.assertAlmostEqual(vmodes[16].norm(), 0.00561654885265, 5) self.assertAlmostEqual(vmodes[17].norm(), 0.00541642076462, 5) def suite(): loader = unittest.TestLoader() s = unittest.TestSuite() s.addTest(loader.loadTestsFromTestCase(WaterTest)) s.addTest(loader.loadTestsFromTestCase(PeptideTest)) return s if __name__ == '__main__': unittest.main() MMTK-2.7.9/Tests/pickle_tests.py0000644000076600000240000000614612117632051017063 0ustar hinsenstaff00000000000000# Pickle tests # # Written by Konrad Hinsen # import unittest import MMTK from MMTK.Proteins import Protein from MMTK.ForceFields import Amber99ForceField from MMTK import NormalModes from Scientific import N import os class PeptideTest(unittest.TestCase): """ Test pickling a small protein """ def setUp(self): self.universe = MMTK.InfiniteUniverse(Amber99ForceField()) self.universe.peptide = Protein('bala1') def test_energy(self): # Check that the energy terms after pickling and reloading # are the same. energy_terms = self.universe.energyTerms() MMTK.save(self.universe, 'test.pickle') restored_universe = MMTK.load('test.pickle') r_energy_terms = restored_universe.energyTerms() self.assertEqual(energy_terms.keys(), r_energy_terms.keys()) def test_normal_modes(self): emodes = NormalModes.EnergeticModes(self.universe) MMTK.save(emodes, 'test.pickle') restored_modes = MMTK.load('test.pickle') for i in range(len(emodes)): self.assertEqual(emodes[i].force_constant, restored_modes[i].force_constant) err = N.minimum.reduce(N.fabs(N.ravel(emodes[i].array -restored_modes[i].array))) self.assert_(err < 1.e-15) class WaterTest(unittest.TestCase): """ Test pickling a water molecule """ def setUp(self): self.universe = MMTK.InfiniteUniverse(Amber99ForceField()) self.universe.water1 = MMTK.Molecule('water', position=MMTK.Vector(0.,0.,0.)) self.universe.water2 = MMTK.Molecule('water', position=MMTK.Vector(0.5,0.,0.)) def tearDown(self): try: os.remove('test.pickle') except OSError: pass def test_energy(self): # Check that the energy terms after pickling and reloading # are the same. energy_terms = self.universe.energyTerms() MMTK.save(self.universe, 'test.pickle') restored_universe = MMTK.load('test.pickle') self.assertEqual(energy_terms, restored_universe.energyTerms()) def test_parent(self): # Check that pickling an object from a universe sets that # objects's parent to None, rather than pickling the whole universe. MMTK.save(self.universe.water1, 'test.pickle') restored_molecule = MMTK.load('test.pickle') self.assertEqual(restored_molecule.parent, None) def test_type(self): # Check that the MoleculeType object is not pickled, but # always taken from the database. MMTK.save(self.universe.water1, 'test.pickle') restored_molecule = MMTK.load('test.pickle') self.assertEqual(restored_molecule.type, self.universe.water1.type) def suite(): loader = unittest.TestLoader() s = unittest.TestSuite() s.addTest(loader.loadTestsFromTestCase(PeptideTest)) s.addTest(loader.loadTestsFromTestCase(WaterTest)) return s if __name__ == '__main__': unittest.main() MMTK-2.7.9/Tests/restraint_tests.py0000644000076600000240000002221212041515171017616 0ustar hinsenstaff00000000000000# Restraint tests # # Written by Konrad Hinsen # import unittest from subsets import SubsetTest from MMTK import * from MMTK.ForceFields import Restraints, SPCEForceField from Scientific import N class TrapTest(unittest.TestCase): def setUp(self): self.universe1 = InfiniteUniverse() atom = Atom('C', position=Vector(0.5, 0., 0.)) self.universe1.addObject(atom) ff = Restraints.HarmonicTrapForceField(atom, Vector(-0.5, 0., 0.), 1.) self.universe1.setForceField(ff) self.universe2 = InfiniteUniverse() atom1 = Atom('C', position=Vector(0.25, 0., 0.)) atom2 = Atom('C', position=Vector(0.75, 0., 0.)) cluster = Collection([atom1, atom2]) self.universe2.addObject(cluster) ff = Restraints.HarmonicTrapForceField(cluster, Vector(-0.5, 0., 0.), 1.) self.universe2.setForceField(ff) self.universe3 = InfiniteUniverse() water = Molecule('water', position=Vector(0.5, 0., 0.)) self.universe3.addObject(water) ff = Restraints.HarmonicTrapForceField(water, Vector(-0.5, 0., 0.), 1.) self.universe3.setForceField(ff) def test_energy_one_atom(self): e, g, fc = self.universe1.energyGradientsAndForceConstants() self.assertAlmostEqual(e, 1., 7) self.assert_((g[0]-Vector(2., 0., 0.)).length() < 1.e-5) fc = fc[0, 0] for i in range(3): for j in range(3): if i == j: self.assertAlmostEqual(fc[i, j], 2., 7) else: self.assertAlmostEqual(fc[i,j], 0., 7) def test_energy_two_atoms(self): e, g, fc = self.universe2.energyGradientsAndForceConstants() self.assertAlmostEqual(e, 1., 7) self.assert_((g[0]-Vector(1., 0., 0.)).length() < 1.e-5) self.assert_((g[1]-Vector(1., 0., 0.)).length() < 1.e-5) for a1 in [0, 1]: for a2 in [0, 1]: fc12 = fc[a1, a2] for i in range(3): for j in range(3): if i == j: self.assertAlmostEqual(fc12[i, j], 0.5, 7) else: self.assertAlmostEqual(fc12[i,j], 0., 7) def test_energy_water(self): H1 = self.universe3[0].H1 H2 = self.universe3[0].H2 O = self.universe3[0].O e, g, fc = self.universe3.energyGradientsAndForceConstants() self.assertAlmostEqual(e, 1., 7) self.assert_((g[H1]-g[H2]).length() < 1.e-5) self.assert_((g[H1]-g[O]*H1.mass()/O.mass()).length() < 1.e-5) m = 2*H1.mass() + O.mass() for a1 in [H1, H2, O]: for a2 in [H1, H2, O]: fc12 = fc[a1, a2] f = 2.*a1.mass()*a2.mass()/(m*m) for i in range(3): for j in range(3): if i == j: self.assertAlmostEqual(fc12[i, j], f, 7) else: self.assertAlmostEqual(fc12[i,j], 0., 7) def test_subsets(self): H1 = self.universe3[0].H1 H2 = self.universe3[0].H2 O = self.universe3[0].O subset = Collection([H1, H2, O]) e = self.universe3.energy(subset) self.assertAlmostEqual(e, 1., 7) subset = Collection() e = self.universe3.energy(subset) self.assertAlmostEqual(e, 0., 7) subset = Collection([H1, H2]) self.assertRaises(ValueError, self.universe3.energy, subset) class DistanceTest(unittest.TestCase): def setUp(self): self.universe1 = InfiniteUniverse() atom1 = Atom('C', position=Vector(0.5, 0., 0.)) atom2 = Atom('C', position=Vector(-0.5, 0., 0.)) self.universe1.addObject(atom1) self.universe1.addObject(atom2) ff = Restraints.HarmonicDistanceRestraint(atom1, atom2, 0.9, 1.) self.universe1.setForceField(ff) self.universe2 = InfiniteUniverse() atom1 = Atom('C', position=Vector(0.25, 0., 0.)) atom2 = Atom('C', position=Vector(0.75, 0., 0.)) cluster1 = Collection([atom1, atom2]) atom3 = Atom('C', position=Vector(-0.75, 0., 0.)) atom4 = Atom('C', position=Vector(-0.25, 0., 0.)) cluster2 = Collection([atom3, atom4]) self.universe2.addObject(cluster1) self.universe2.addObject(cluster2) ff = Restraints.HarmonicDistanceRestraint(cluster1, cluster2, 0.9, 1.) self.universe2.setForceField(ff) self.universe3 = InfiniteUniverse() water1 = Molecule('water', position=Vector(0.5, 0., 0.)) water2 = Molecule('water', position=Vector(-0.5, 0., 0.)) self.universe3.addObject(water1) self.universe3.addObject(water2) ff = Restraints.HarmonicDistanceRestraint(water1, water2, 0.9, 1.) self.universe3.setForceField(ff) def test_energy_single_atoms(self): e, g, fc = self.universe1.energyGradientsAndForceConstants() self.assertAlmostEqual(e, 0.01, 7) self.assert_((g[0]-Vector(0.2, 0., 0.)).length() < 1.e-5) fc = fc[0, 0] fc_ref = [2., 0.2, 0.2] for i in range(3): self.assertAlmostEqual(fc[i, i], fc_ref[i], 7) for j in range(3): if i != j: self.assertAlmostEqual(fc[i,j], 0., 7) def test_energy_atom_pairs(self): e, g, fc = self.universe2.energyGradientsAndForceConstants() self.assertAlmostEqual(e, 0.01, 7) self.assert_((g[0]-Vector(0.1, 0., 0.)).length() < 1.e-5) self.assert_((g[1]-Vector(0.1, 0., 0.)).length() < 1.e-5) self.assert_((g[2]-Vector(-0.1, 0., 0.)).length() < 1.e-5) self.assert_((g[3]-Vector(-0.1, 0., 0.)).length() < 1.e-5) p1 = [0, 1] p2 = [2, 3] fc_ref = [0.5, 0.05, 0.05] for a1 in range(4): for a2 in range(4): fcp = fc[a1, a2] for i in range(3): if (a1 in p1 and a2 in p1) or (a1 in p2 and a2 in p2): self.assertAlmostEqual(fcp[i, i], fc_ref[i], 7) else: self.assertAlmostEqual(fcp[i, i], -fc_ref[i], 7) for j in range(3): if i != j: self.assertAlmostEqual(fcp[i,j], 0., 7) def test_energy_water(self): e, g, fc = self.universe3.energyGradientsAndForceConstants() fc_ref = [2., 0.2, 0.2] self.assertAlmostEqual(e, 0.01, 7) for m, s in [(self.universe3[0], 1), (self.universe3[1], -1),]: self.assert_((g[m.H1]-g[m.H2]).length() < 1.e-5) self.assert_((g[m.H1]-g[m.O]*m.H1.mass()/m.O.mass()).length() < 1.e-5) mass = 2*m.H1.mass() + m.O.mass() for a1 in [m.H1, m.H2, m.O]: for a2 in [m.H1, m.H2, m.O]: fc12 = fc[a1, a2] f = a1.mass()*a2.mass()/(mass*mass) for i in range(3): for j in range(3): if i == j: self.assertAlmostEqual(fc12[i, j], f*fc_ref[i], 7) else: self.assertAlmostEqual(fc12[i,j], 0., 7) def test_subsets(self): m1 = self.universe3[0] m2 = self.universe3[1] subset = Collection([m1.H1, m1.H2, m1.O, m2.H1, m2.H2, m2.O]) e = self.universe3.energy(subset) self.assertAlmostEqual(e, 0.01, 7) subset = Collection() e = self.universe3.energy(subset) self.assertAlmostEqual(e, 0., 7) subset = Collection([m1.H1, m2.H2]) self.assertRaises(ValueError, self.universe3.energy, subset) class NonbondedExclusionTest(unittest.TestCase): def test_exclusion(self): universe = InfiniteUniverse() w1 = Molecule('water', position=(Vector(-0.2, 0., 0.))) w2 = Molecule('water', position=(Vector(+0.2, 0., 0.))) universe.addObject(w1) universe.addObject(w2) ff = SPCEForceField() + \ Restraints.HarmonicDistanceRestraint(w1.O, w2.O, 0.35, 1., False) universe.setForceField(ff) self.assert_(universe.energyTerms()['Lennard-Jones'] < -0.1) ff = SPCEForceField() + \ Restraints.HarmonicDistanceRestraint(w1.O, w2.O, 0.35, 1., True) universe.setForceField(ff) self.assertAlmostEqual(universe.energyTerms()['Lennard-Jones'], 0., 7) def suite(): loader = unittest.TestLoader() s = unittest.TestSuite() s.addTest(loader.loadTestsFromTestCase(TrapTest)) s.addTest(loader.loadTestsFromTestCase(DistanceTest)) s.addTest(loader.loadTestsFromTestCase(NonbondedExclusionTest)) return s if __name__ == '__main__': unittest.main() MMTK-2.7.9/Tests/subsets.py0000644000076600000240000000273411662461640016071 0ustar hinsenstaff00000000000000# Subset energy tests # # Written by Konrad Hinsen # from Scientific import N class SubsetTest(object): def test_singleSubset(self): outside = set(self.universe.atomList())-set(self.subset1.atomList()) e, fc = self.universe.energyAndForceConstants(self.subset1) for a1 in outside: for a2 in outside: self.assert_((N.fabs(fc[a1,a2].array) < 1.e-11).all()) for a1 in self.subset1.atomList(): for a2 in self.subset1.atomList(): self.assert_((N.fabs(fc[a1,a2].array) > 1.e-11).any()) def test_twoSubsets(self): inside = set(self.subset1.atomList()) | set(self.subset2.atomList()) outside = set(self.universe.atomList())-inside e, fc = self.universe.energyAndForceConstants(self.subset1, self.subset2) for a1 in self.subset1.atomList(): for a2 in self.subset1.atomList(): if a1 != a2: self.assert_((N.fabs(fc[a1,a2].array) < 1.e-11).all()) for a1 in self.subset2.atomList(): for a2 in self.subset2.atomList(): if a1 != a2: self.assert_((N.fabs(fc[a1,a2].array) < 1.e-11).all()) for a1 in self.subset1.atomList(): for a2 in self.subset2.atomList(): self.assert_((N.fabs(fc[a1,a2].array) > 1.e-11).any()) for a1 in outside: for a2 in outside: self.assert_((N.fabs(fc[a1,a2].array) < 1.e-11).all()) MMTK-2.7.9/Tests/subspace_tests.py0000644000076600000240000004745712117632051017433 0ustar hinsenstaff00000000000000# Subspace tests # # Written by Konrad Hinsen # import unittest import MMTK from MMTK.Proteins import Protein from MMTK.ForceFields import HarmonicForceField from MMTK.Subspace import RigidMotionSubspace, PairDistanceSubspace from MMTK import NormalModes from Scientific.Geometry.Transformation import Rotation, Translation class RigidBodyTest(unittest.TestCase): """ Test rigid-body motion subspace """ def test_projections(self): universe = MMTK.InfiniteUniverse() universe.m1 = MMTK.Molecule('water', position=MMTK.Vector(0., 0., 0.)) universe.m2 = MMTK.Molecule('water', position=MMTK.Vector(1., 0., 0.)) rbs = [universe.m1, MMTK.Collection([universe.m2.H1, universe.m2.H2])] s = RigidMotionSubspace(universe, rbs) self.checkOrthonormality(s) self.assertEqual(len(s), 11) basis = s.getBasis() self.assertEqual(len(basis), 11) complement = s.complement() complement_basis = complement.getBasis() self.assertEqual(len(complement_basis), universe.degreesOfFreedom()-11) for rb in rbs: for t in [Translation(MMTK.Vector(0.1, -0.2, 1.5)), Rotation(MMTK.Vector(0.3, 1.2, -2.3), 0.001)]: d = rb.displacementUnderTransformation(t) self.assert_((s.projectionOf(d)-d).norm() < 1.e-7) self.assert_(s.projectionComplementOf(d).norm() < 1.e-7) def test_lrb(self): universe = MMTK.InfiniteUniverse() for p in [MMTK.Vector(0., 0., 0.), MMTK.Vector(1., 0., 0.), MMTK.Vector(0., 1., 1.), MMTK.Vector(0., 1., 0.), MMTK.Vector(1., 0., 1.), MMTK.Vector(0., 0., 1.), MMTK.Vector(1., 1., 0.), MMTK.Vector(1., 1., 1.)]: universe.addObject(MMTK.Atom('C', position=p)) atoms = universe.atomList() # all atoms independent s = RigidMotionSubspace(universe, atoms) self.checkOrthonormality(s) self.assertEqual(len(s.getBasis()), 3*len(atoms)) # 1 rb, four free atoms rbs = [MMTK.Collection(atoms[:4])] + atoms[4:] s = RigidMotionSubspace(universe, rbs) self.checkOrthonormality(s) self.assertEqual(len(s.getBasis()), 6+4*3) # 2 independent rbs with > 2 atoms rbs = [MMTK.Collection(atoms[:4]), MMTK.Collection(atoms[4:])] s = RigidMotionSubspace(universe, rbs) self.checkOrthonormality(s) self.assertEqual(len(s.getBasis()), 12) # 2 independent rbs, one 2 atoms, one 6 atoms rbs = [MMTK.Collection(atoms[:6]), MMTK.Collection(atoms[6:])] s = RigidMotionSubspace(universe, rbs) self.checkOrthonormality(s) self.assertEqual(len(s.getBasis()), 11) # 2 rbs > 2 atoms each, one atom in common rbs = [MMTK.Collection(atoms[:5]), MMTK.Collection(atoms[4:])] s = RigidMotionSubspace(universe, rbs) self.checkOrthonormality(s) self.assertEqual(len(s.getBasis()), 9) # 2 rbs > 2 atoms each, two atoms in common rbs = [MMTK.Collection(atoms[:5]), MMTK.Collection(atoms[3:])] s = RigidMotionSubspace(universe, rbs) self.checkOrthonormality(s) self.assertEqual(len(s.getBasis()), 7) # 2 rbs > 2 atoms each, three atoms in common rbs = [MMTK.Collection(atoms[:5]), MMTK.Collection(atoms[2:])] s = RigidMotionSubspace(universe, rbs) self.checkOrthonormality(s) self.assertEqual(len(s.getBasis()), 6) # 2 rbs with 2 and 7 atoms, one atom in common rbs = [MMTK.Collection(atoms[:2]), MMTK.Collection(atoms[1:])] s = RigidMotionSubspace(universe, rbs) self.checkOrthonormality(s) self.assertEqual(len(s.getBasis()), 8) # 3 rbs > 2 atoms each, one atom in common between rb[n] and rb[n+1] rbs = [MMTK.Collection(atoms[:3]), MMTK.Collection(atoms[2:6]), MMTK.Collection(atoms[5:])] s = RigidMotionSubspace(universe, rbs) self.checkOrthonormality(s) self.assertEqual(len(s.getBasis()), 12) # 3 rbs > 2 atoms each, one atom in common for each pair # (chain of 3 rbs) rbs = [MMTK.Collection(atoms[:3]+atoms[7:]), MMTK.Collection(atoms[2:6]), MMTK.Collection(atoms[5:])] s = RigidMotionSubspace(universe, rbs) self.checkOrthonormality(s) self.assertEqual(len(s.getBasis()), 10) # 2 rbs > 2 atoms each, linked by a rigid bond rbs = [MMTK.Collection(atoms[:4]), MMTK.Collection(atoms[3:5]), MMTK.Collection(atoms[4:])] s = RigidMotionSubspace(universe, rbs) self.checkOrthonormality(s) self.assertEqual(len(s.getBasis()), 11) def checkOrthonormality(self, subspace): basis = subspace.getBasis() ndim = len(basis) for i in range(ndim): v = basis[i] self.assertAlmostEqual(v.dotProduct(v), 1., 10) for j in range(i+1, ndim): self.assert_(abs(v.dotProduct(basis[j])) < 1.e-10) class PeptideNormalModeTest(unittest.TestCase): """ Test mode projections on a subspace """ def setUp(self): self.universe = MMTK.InfiniteUniverse(HarmonicForceField()) self.universe.peptide = Protein('bala1') self.emodes = NormalModes.EnergeticModes(self.universe) self.rm = RigidMotionSubspace(self.universe, self.universe.peptide.residues()) self.pd = PairDistanceSubspace(self.universe, [(self.universe.peptide[0][0].O, self.universe.peptide[0][-1].CH3)]) def test_rmProjections(self): p = self.rm.projectionOf(self.emodes.rawMode(6)).norm() self.assertAlmostEqual(p, 0.932906130224) p = self.rm.projectionOf(self.emodes.rawMode(7)).norm() self.assertAlmostEqual(p, 0.946620894164) p = self.rm.projectionOf(self.emodes.rawMode(8)).norm() self.assertAlmostEqual(p, 0.918711763134) p = self.rm.projectionOf(self.emodes.rawMode(9)).norm() self.assertAlmostEqual(p, 0.914888590566) p = self.rm.projectionOf(self.emodes.rawMode(10)).norm() self.assertAlmostEqual(p, 0.954987614764) p = self.rm.projectionOf(self.emodes.rawMode(11)).norm() self.assertAlmostEqual(p, 0.490550926574) p = self.rm.projectionOf(self.emodes.rawMode(12)).norm() self.assertAlmostEqual(p, 0.974543149457) p = self.rm.projectionOf(self.emodes.rawMode(13)).norm() self.assertAlmostEqual(p, 0.902492405825) p = self.rm.projectionOf(self.emodes.rawMode(14)).norm() self.assertAlmostEqual(p, 0.873380594746) p = self.rm.projectionOf(self.emodes.rawMode(15)).norm() self.assertAlmostEqual(p, 0.772133237884) p = self.rm.projectionOf(self.emodes.rawMode(16)).norm() self.assertAlmostEqual(p, 0.883756751312) p = self.rm.projectionOf(self.emodes.rawMode(17)).norm() self.assertAlmostEqual(p, 0.577569822435) p = self.rm.projectionOf(self.emodes.rawMode(18)).norm() self.assertAlmostEqual(p, 0.703601140498) p = self.rm.projectionOf(self.emodes.rawMode(19)).norm() self.assertAlmostEqual(p, 0.341866735125) p = self.rm.projectionOf(self.emodes.rawMode(20)).norm() self.assertAlmostEqual(p, 0.590049489902) p = self.rm.projectionOf(self.emodes.rawMode(21)).norm() self.assertAlmostEqual(p, 0.432251617574) p = self.rm.projectionOf(self.emodes.rawMode(22)).norm() self.assertAlmostEqual(p, 0.245304052688) p = self.rm.projectionOf(self.emodes.rawMode(23)).norm() self.assertAlmostEqual(p, 0.211381043933) p = self.rm.projectionOf(self.emodes.rawMode(24)).norm() self.assertAlmostEqual(p, 0.401154809528) p = self.rm.projectionOf(self.emodes.rawMode(25)).norm() self.assertAlmostEqual(p, 0.161820108479) p = self.rm.projectionOf(self.emodes.rawMode(26)).norm() self.assertAlmostEqual(p, 0.208594682213) p = self.rm.projectionOf(self.emodes.rawMode(27)).norm() self.assertAlmostEqual(p, 0.329444853782) p = self.rm.projectionOf(self.emodes.rawMode(28)).norm() self.assertAlmostEqual(p, 0.335842626795) p = self.rm.projectionOf(self.emodes.rawMode(29)).norm() self.assertAlmostEqual(p, 0.246955423968) p = self.rm.projectionOf(self.emodes.rawMode(30)).norm() self.assertAlmostEqual(p, 0.102801852182) p = self.rm.projectionOf(self.emodes.rawMode(31)).norm() self.assertAlmostEqual(p, 0.0445276342964) p = self.rm.projectionOf(self.emodes.rawMode(32)).norm() self.assertAlmostEqual(p, 0.0810509154227) p = self.rm.projectionOf(self.emodes.rawMode(33)).norm() self.assertAlmostEqual(p, 0.166723733735) p = self.rm.projectionOf(self.emodes.rawMode(34)).norm() self.assertAlmostEqual(p, 0.254081370404) p = self.rm.projectionOf(self.emodes.rawMode(35)).norm() self.assertAlmostEqual(p, 0.221723386921) p = self.rm.projectionOf(self.emodes.rawMode(36)).norm() self.assertAlmostEqual(p, 0.133163531911) p = self.rm.projectionOf(self.emodes.rawMode(37)).norm() self.assertAlmostEqual(p, 0.0662977755488) p = self.rm.projectionOf(self.emodes.rawMode(38)).norm() self.assertAlmostEqual(p, 0.156961937663) p = self.rm.projectionOf(self.emodes.rawMode(39)).norm() self.assertAlmostEqual(p, 0.310640400206) p = self.rm.projectionOf(self.emodes.rawMode(40)).norm() self.assertAlmostEqual(p, 0.424586976986) p = self.rm.projectionOf(self.emodes.rawMode(41)).norm() self.assertAlmostEqual(p, 0.439565895727) p = self.rm.projectionOf(self.emodes.rawMode(42)).norm() self.assertAlmostEqual(p, 0.220029858675) p = self.rm.projectionOf(self.emodes.rawMode(43)).norm() self.assertAlmostEqual(p, 0.26737834485) p = self.rm.projectionOf(self.emodes.rawMode(44)).norm() self.assertAlmostEqual(p, 0.367708993752) p = self.rm.projectionOf(self.emodes.rawMode(45)).norm() self.assertAlmostEqual(p, 0.40324227143) p = self.rm.projectionOf(self.emodes.rawMode(46)).norm() self.assertAlmostEqual(p, 0.192735085639) p = self.rm.projectionOf(self.emodes.rawMode(47)).norm() self.assertAlmostEqual(p, 0.0840421758091) p = self.rm.projectionOf(self.emodes.rawMode(48)).norm() self.assertAlmostEqual(p, 0.15035662682) p = self.rm.projectionOf(self.emodes.rawMode(49)).norm() self.assertAlmostEqual(p, 0.154260578723) p = self.rm.projectionOf(self.emodes.rawMode(50)).norm() self.assertAlmostEqual(p, 0.125542420544) p = self.rm.projectionOf(self.emodes.rawMode(51)).norm() self.assertAlmostEqual(p, 0.0451196639208) p = self.rm.projectionOf(self.emodes.rawMode(52)).norm() self.assertAlmostEqual(p, 0.0458762025086) p = self.rm.projectionOf(self.emodes.rawMode(53)).norm() self.assertAlmostEqual(p, 0.00887400641221) p = self.rm.projectionOf(self.emodes.rawMode(54)).norm() self.assertAlmostEqual(p, 0.017086967116) p = self.rm.projectionOf(self.emodes.rawMode(55)).norm() self.assertAlmostEqual(p, 0.0609002158356) p = self.rm.projectionOf(self.emodes.rawMode(56)).norm() self.assertAlmostEqual(p, 0.0343388857234) p = self.rm.projectionOf(self.emodes.rawMode(57)).norm() self.assertAlmostEqual(p, 0.0559503540031) p = self.rm.projectionOf(self.emodes.rawMode(58)).norm() self.assertAlmostEqual(p, 0.0332620617319) p = self.rm.projectionOf(self.emodes.rawMode(59)).norm() self.assertAlmostEqual(p, 0.0583023511826) p = self.rm.projectionOf(self.emodes.rawMode(60)).norm() self.assertAlmostEqual(p, 0.0663342593349) p = self.rm.projectionOf(self.emodes.rawMode(61)).norm() self.assertAlmostEqual(p, 0.0568833304731) p = self.rm.projectionOf(self.emodes.rawMode(62)).norm() self.assertAlmostEqual(p, 0.161627264195) p = self.rm.projectionOf(self.emodes.rawMode(63)).norm() self.assertAlmostEqual(p, 0.1722951381) p = self.rm.projectionOf(self.emodes.rawMode(64)).norm() self.assertAlmostEqual(p, 0.239075225836) p = self.rm.projectionOf(self.emodes.rawMode(65)).norm() self.assertAlmostEqual(p, 0.241338995808) def test_pdProjections(self): p = self.pd.projectionOf(self.emodes.rawMode(6)).norm() self.assertAlmostEqual(p, 0.0283570029711) p = self.pd.projectionOf(self.emodes.rawMode(7)).norm() self.assertAlmostEqual(p, 0.00570349238657) p = self.pd.projectionOf(self.emodes.rawMode(8)).norm() self.assertAlmostEqual(p, 0.132182279933) p = self.pd.projectionOf(self.emodes.rawMode(9)).norm() self.assertAlmostEqual(p, 0.372076699964) p = self.pd.projectionOf(self.emodes.rawMode(10)).norm() self.assertAlmostEqual(p, 0.294163776112) p = self.pd.projectionOf(self.emodes.rawMode(11)).norm() self.assertAlmostEqual(p, 0.183147528471) p = self.pd.projectionOf(self.emodes.rawMode(12)).norm() self.assertAlmostEqual(p, 0.0271240228062) p = self.pd.projectionOf(self.emodes.rawMode(13)).norm() self.assertAlmostEqual(p, 0.178665480086) p = self.pd.projectionOf(self.emodes.rawMode(14)).norm() self.assertAlmostEqual(p, 0.128834250691) p = self.pd.projectionOf(self.emodes.rawMode(15)).norm() self.assertAlmostEqual(p, 0.0591397692555) p = self.pd.projectionOf(self.emodes.rawMode(16)).norm() self.assertAlmostEqual(p, 0.198830357382) p = self.pd.projectionOf(self.emodes.rawMode(17)).norm() self.assertAlmostEqual(p, 0.0421877341135) p = self.pd.projectionOf(self.emodes.rawMode(18)).norm() self.assertAlmostEqual(p, 0.00971216016261) p = self.pd.projectionOf(self.emodes.rawMode(19)).norm() self.assertAlmostEqual(p, 0.0736856356204) p = self.pd.projectionOf(self.emodes.rawMode(20)).norm() self.assertAlmostEqual(p, 0.114536706027) p = self.pd.projectionOf(self.emodes.rawMode(21)).norm() self.assertAlmostEqual(p, 0.274928559325) p = self.pd.projectionOf(self.emodes.rawMode(22)).norm() self.assertAlmostEqual(p, 0.0686378784385) p = self.pd.projectionOf(self.emodes.rawMode(23)).norm() self.assertAlmostEqual(p, 0.0130940520067) p = self.pd.projectionOf(self.emodes.rawMode(24)).norm() self.assertAlmostEqual(p, 0.0281201750134) p = self.pd.projectionOf(self.emodes.rawMode(25)).norm() self.assertAlmostEqual(p, 0.196135377387) p = self.pd.projectionOf(self.emodes.rawMode(26)).norm() self.assertAlmostEqual(p, 0.0194536676741) p = self.pd.projectionOf(self.emodes.rawMode(27)).norm() self.assertAlmostEqual(p, 0.0120311581935) p = self.pd.projectionOf(self.emodes.rawMode(28)).norm() self.assertAlmostEqual(p, 0.0479444837967) p = self.pd.projectionOf(self.emodes.rawMode(29)).norm() self.assertAlmostEqual(p, 0.113826710408) p = self.pd.projectionOf(self.emodes.rawMode(30)).norm() self.assertAlmostEqual(p, 0.0346732822148) p = self.pd.projectionOf(self.emodes.rawMode(31)).norm() self.assertAlmostEqual(p, 0.0693022722289) p = self.pd.projectionOf(self.emodes.rawMode(32)).norm() self.assertAlmostEqual(p, 0.0427314320652) p = self.pd.projectionOf(self.emodes.rawMode(33)).norm() self.assertAlmostEqual(p, 0.0145340306202) p = self.pd.projectionOf(self.emodes.rawMode(34)).norm() self.assertAlmostEqual(p, 0.0655196353298) p = self.pd.projectionOf(self.emodes.rawMode(35)).norm() self.assertAlmostEqual(p, 0.0446686455027) p = self.pd.projectionOf(self.emodes.rawMode(36)).norm() self.assertAlmostEqual(p, 0.0447414683339) p = self.pd.projectionOf(self.emodes.rawMode(37)).norm() self.assertAlmostEqual(p, 0.105694607933) p = self.pd.projectionOf(self.emodes.rawMode(38)).norm() self.assertAlmostEqual(p, 0.0369811368042) p = self.pd.projectionOf(self.emodes.rawMode(39)).norm() self.assertAlmostEqual(p, 0.099422515813) p = self.pd.projectionOf(self.emodes.rawMode(40)).norm() self.assertAlmostEqual(p, 0.214824447562) p = self.pd.projectionOf(self.emodes.rawMode(41)).norm() self.assertAlmostEqual(p, 0.0113247783174) p = self.pd.projectionOf(self.emodes.rawMode(42)).norm() self.assertAlmostEqual(p, 0.0428190611544) p = self.pd.projectionOf(self.emodes.rawMode(43)).norm() self.assertAlmostEqual(p, 0.0395942495882) p = self.pd.projectionOf(self.emodes.rawMode(44)).norm() self.assertAlmostEqual(p, 0.184574456157) p = self.pd.projectionOf(self.emodes.rawMode(45)).norm() self.assertAlmostEqual(p, 0.0389662995269) p = self.pd.projectionOf(self.emodes.rawMode(46)).norm() self.assertAlmostEqual(p, 0.0443754728854) p = self.pd.projectionOf(self.emodes.rawMode(47)).norm() self.assertAlmostEqual(p, 0.0189823224475) p = self.pd.projectionOf(self.emodes.rawMode(48)).norm() self.assertAlmostEqual(p, 0.106332137903) p = self.pd.projectionOf(self.emodes.rawMode(49)).norm() self.assertAlmostEqual(p, 0.182336029271) p = self.pd.projectionOf(self.emodes.rawMode(50)).norm() self.assertAlmostEqual(p, 0.222418516151) p = self.pd.projectionOf(self.emodes.rawMode(51)).norm() self.assertAlmostEqual(p, 0.208379764186) p = self.pd.projectionOf(self.emodes.rawMode(52)).norm() self.assertAlmostEqual(p, 0.161707260103) p = self.pd.projectionOf(self.emodes.rawMode(53)).norm() self.assertAlmostEqual(p, 0.29786008418) p = self.pd.projectionOf(self.emodes.rawMode(54)).norm() self.assertAlmostEqual(p, 0.00783831845487) p = self.pd.projectionOf(self.emodes.rawMode(55)).norm() self.assertAlmostEqual(p, 0.0265930190584) p = self.pd.projectionOf(self.emodes.rawMode(56)).norm() self.assertAlmostEqual(p, 0.00543966569196) p = self.pd.projectionOf(self.emodes.rawMode(57)).norm() self.assertAlmostEqual(p, 0.00405705977917) p = self.pd.projectionOf(self.emodes.rawMode(58)).norm() self.assertAlmostEqual(p, 0.0622064557348) p = self.pd.projectionOf(self.emodes.rawMode(59)).norm() self.assertAlmostEqual(p, 0.0489324788794) p = self.pd.projectionOf(self.emodes.rawMode(60)).norm() self.assertAlmostEqual(p, 0.199110460545) p = self.pd.projectionOf(self.emodes.rawMode(61)).norm() self.assertAlmostEqual(p, 0.148263694589) p = self.pd.projectionOf(self.emodes.rawMode(62)).norm() self.assertAlmostEqual(p, 0.14306616768) p = self.pd.projectionOf(self.emodes.rawMode(63)).norm() self.assertAlmostEqual(p, 0.0798824699709) p = self.pd.projectionOf(self.emodes.rawMode(64)).norm() self.assertAlmostEqual(p, 0.021465578636) p = self.pd.projectionOf(self.emodes.rawMode(65)).norm() self.assertAlmostEqual(p, 0.0166782530763) def suite(): loader = unittest.TestLoader() s = unittest.TestSuite() s.addTest(loader.loadTestsFromTestCase(RigidBodyTest)) s.addTest(loader.loadTestsFromTestCase(PeptideNormalModeTest)) return s if __name__ == '__main__': unittest.main() MMTK-2.7.9/Tests/trajectory_tests.py0000644000076600000240000001372012117632051017776 0ustar hinsenstaff00000000000000# Trajectory tests # # Written by Konrad Hinsen # import unittest from MMTK import * from MMTK.Trajectory import Trajectory, SnapshotGenerator, TrajectoryOutput from Scientific import N import os class InfiniteUniverseTest: def setUp(self): self.universe = InfiniteUniverse() self.universe.addObject(Molecule('water', position = Vector(-0.2, 0., 0.))) self.universe.addObject(Molecule('water', position = Vector(0.2, 0., 0.))) class OrthorhombicUniverseTest: def setUp(self): self.universe = OrthorhombicPeriodicUniverse((1., 1., 0.5)) self.universe.addObject(Molecule('water', position = Vector(-0.2, 0., 0.))) self.universe.addObject(Molecule('water', position = Vector(0.2, 0., 0.))) class ParallelepipedicUniverseTest: def setUp(self): self.universe = ParallelepipedicPeriodicUniverse((Vector(1., 0., 0.), Vector(0., 1., 0.), Vector(0.5, 0., 0.5))) self.universe.addObject(Molecule('water', position = Vector(-0.2, 0., 0.))) self.universe.addObject(Molecule('water', position = Vector(0.2, 0., 0.))) class SinglePrecisionTest: double_precision = False tolerance = 1.e-7 class DoublePrecisionTest: double_precision = True tolerance = 1.e-15 class TrajectoryTest: def tearDown(self): try: os.remove('test.nc') except OSError: pass def test_snapshot(self): initial = self.universe.copyConfiguration() transformation = Translation(Vector(0.,0.,0.01)) \ * Rotation(Vector(0.,0.,1.), 1.*Units.deg) trajectory = Trajectory(self.universe, "test.nc", "w", "trajectory test", double_precision = self.double_precision) snapshot = SnapshotGenerator(self.universe, actions = [TrajectoryOutput(trajectory, ["all"], 0, None, 1)]) snapshot() for i in range(100): self.universe.setConfiguration( self.universe.contiguousObjectConfiguration()) self.universe.applyTransformation(transformation) self.universe.foldCoordinatesIntoBox() snapshot() trajectory.close() self.universe.setConfiguration(initial) trajectory = Trajectory(None, "test.nc") t_universe = trajectory.universe for i in range(101): configuration = self.universe.configuration() t_configuration = trajectory[i]['configuration'] max_diff = N.maximum.reduce(N.ravel(N.fabs(configuration.array - t_configuration.array))) self.assert_(max_diff < self.tolerance) if configuration.cell_parameters is not None: max_diff = N.maximum.reduce(N.fabs(configuration.cell_parameters - t_configuration.cell_parameters)) self.assert_(max_diff < self.tolerance) self.universe.setConfiguration( self.universe.contiguousObjectConfiguration()) self.universe.applyTransformation(transformation) self.universe.foldCoordinatesIntoBox() trajectory.close() class InfiniteUniverseTestSP(unittest.TestCase, InfiniteUniverseTest, TrajectoryTest, SinglePrecisionTest): setUp = InfiniteUniverseTest.setUp class InfiniteUniverseTestDP(unittest.TestCase, InfiniteUniverseTest, TrajectoryTest, DoublePrecisionTest): setUp = InfiniteUniverseTest.setUp tearDown = TrajectoryTest.tearDown class OrthorhombicUniverseTestSP(unittest.TestCase, OrthorhombicUniverseTest, TrajectoryTest, SinglePrecisionTest): setUp = OrthorhombicUniverseTest.setUp tearDown = TrajectoryTest.tearDown class OrthorhombicUniverseTestDP(unittest.TestCase, OrthorhombicUniverseTest, TrajectoryTest, DoublePrecisionTest): setUp = OrthorhombicUniverseTest.setUp tearDown = TrajectoryTest.tearDown class ParallelepipedicUniverseTestSP(unittest.TestCase, ParallelepipedicUniverseTest, TrajectoryTest, SinglePrecisionTest): setUp = ParallelepipedicUniverseTest.setUp tearDown = TrajectoryTest.tearDown class ParallelepipedicUniverseTestDP(unittest.TestCase, ParallelepipedicUniverseTest, TrajectoryTest, DoublePrecisionTest): setUp = ParallelepipedicUniverseTest.setUp tearDown = TrajectoryTest.tearDown def suite(): loader = unittest.TestLoader() s = unittest.TestSuite() s.addTest(loader.loadTestsFromTestCase(InfiniteUniverseTestSP)) s.addTest(loader.loadTestsFromTestCase(InfiniteUniverseTestDP)) s.addTest(loader.loadTestsFromTestCase(OrthorhombicUniverseTestSP)) s.addTest(loader.loadTestsFromTestCase(OrthorhombicUniverseTestDP)) s.addTest(loader.loadTestsFromTestCase(ParallelepipedicUniverseTestSP)) s.addTest(loader.loadTestsFromTestCase(ParallelepipedicUniverseTestDP)) return s if __name__ == '__main__': unittest.main() MMTK-2.7.9/Tests/universe_tests.py0000644000076600000240000001035211662461640017456 0ustar hinsenstaff00000000000000# Universe tests # # Written by Konrad Hinsen # import unittest from MMTK import * from MMTK.Random import randomPointInBox from Scientific import N class PeriodicUniverseTest: def test_basisVectors(self): e1, e2, e3 = self.universe.basisVectors() self.assertEqual(e1, self.a) self.assertEqual(e2, self.b) self.assertEqual(e3, self.c) def test_reciprocalBasisVectors(self): r1, r2, r3 = self.universe.reciprocalBasisVectors() self.assertAlmostEqual(r1*self.a, 1., 14) self.assertAlmostEqual(r2*self.b, 1., 14) self.assertAlmostEqual(r3*self.c, 1., 14) self.assert_(abs(r1*self.b) < 1.e-15) self.assert_(abs(r1*self.c) < 1.e-15) self.assert_(abs(r2*self.a) < 1.e-15) self.assert_(abs(r2*self.c) < 1.e-15) self.assert_(abs(r3*self.a) < 1.e-15) self.assert_(abs(r3*self.b) < 1.e-15) def test_volume(self): volume = self.universe.cellVolume() self.assertAlmostEqual(volume, self.a*(self.b.cross(self.c)), 14) def test_boxCoordinates1(self): for i in range(500): p = randomPointInBox(5.) pt = self.universe.realToBoxCoordinates(p) self.assert_((self.universe.boxToRealCoordinates(pt)-p).length() < 1.e-14) pt = self.universe.boxToRealCoordinates(p) self.assert_((self.universe.realToBoxCoordinates(pt)-p).length() < 1.e-14) def test_boxCoordinates2(self): configuration = self.universe.copyConfiguration() configuration.convertToBoxCoordinates() for atom in self.universe.atomList(): rb = self.universe.realToBoxCoordinates(atom.position()) self.assert_((configuration[atom]-rb).length() < 1.e-15) configuration.convertFromBoxCoordinates() self.assert_(N.maximum.reduce(N.ravel(N.fabs( configuration.array-self.universe.configuration().array))) < 1.e-15) def test_coordinateFold(self): for i in range(500): self.universe.translateBy(randomPointInBox(0.5)) self.universe.foldCoordinatesIntoBox() for atom in self.universe.atomList(): rb = self.universe.realToBoxCoordinates(atom.position()) self.assert_(rb[0] >= -0.5) self.assert_(rb[1] >= -0.5) self.assert_(rb[2] >= -0.5) self.assert_(rb[0] <= 0.5) self.assert_(rb[1] <= 0.5) self.assert_(rb[2] <= 0.5) cconf = self.universe.contiguousObjectConfiguration( self.universe.objectList()) for o in self.universe: for bu in o.bondedUnits(): for bond in bu.bonds: l = (bond.a1.position(cconf) - bond.a2.position(cconf)).length() self.assert_(l < 0.16) class ParallelepipedicPeriodicUniverseTest(unittest.TestCase, PeriodicUniverseTest): def setUp(self): self.a = Vector(1.5, 0.3, 0.) self.b = Vector(-0.1, 0.8, 0.2) self.c = Vector(0., -0.2, 1.1) self.universe = ParallelepipedicPeriodicUniverse((self.a, self.b, self.c)) self.universe.addObject(Molecule('water')) class OrthorhombicPeriodicUniverseTest(unittest.TestCase, PeriodicUniverseTest): def setUp(self): self.a = Vector(0.7, 0., 0.) self.b = Vector(0., 1., 0.) self.c = Vector(0., 0., 0.8) self.universe = OrthorhombicPeriodicUniverse((self.a.length(), self.b.length(), self.c.length())) self.universe.addObject(Molecule('water')) def suite(): loader = unittest.TestLoader() s = unittest.TestSuite() s.addTest(loader.loadTestsFromTestCase(ParallelepipedicPeriodicUniverseTest)) s.addTest(loader.loadTestsFromTestCase(OrthorhombicPeriodicUniverseTest)) return s if __name__ == '__main__': unittest.main() MMTK-2.7.9/Tools/0000755000076600000240000000000012156626572014025 5ustar hinsenstaff00000000000000MMTK-2.7.9/Tools/add_amber12_atom_types.py0000644000076600000240000001152512107736605020703 0ustar hinsenstaff00000000000000import compiler import compiler.ast groups = ['ace_beginning_nt', 'adenine', 'ala_sidechain', 'alanine', 'alanine_ct', 'alanine_nt', 'arg_sidechain', 'arginine', 'arginine_ct', 'arginine_nt', 'asn_sidechain', 'asp_sidechain', 'asp_sidechain_neutral', 'asparagine', 'asparagine_ct', 'asparagine_nt', 'aspartic_acid', 'aspartic_acid_ct', 'aspartic_acid_neutral', 'aspartic_acid_nt', 'cym_sidechain', 'cys_sidechain', 'cysteine', 'cysteine_ct', 'cysteine_nt', 'cysteine_with_negative_charge', 'cystine_ss', 'cystine_ss_ct', 'cystine_ss_nt', 'cytosine', 'cyx_sidechain', 'd-adenosine', 'd-adenosine_3ter', 'd-adenosine_5ter', 'd-adenosine_5ter_3ter', 'd-cytosine', 'd-cytosine_3ter', 'd-cytosine_5ter', 'd-cytosine_5ter_3ter', 'd-guanosine', 'd-guanosine_3ter', 'd-guanosine_5ter', 'd-guanosine_5ter_3ter', 'd-thymine', 'd-thymine_3ter', 'd-thymine_5ter', 'd-thymine_5ter_3ter', 'desoxyribose', 'desoxyribose_3ter', 'desoxyribose_5ter', 'desoxyribose_5ter_3ter', 'gln_sidechain', 'glu_sidechain', 'glu_sidechain_neutral', 'glutamic_acid', 'glutamic_acid_ct', 'glutamic_acid_neutral', 'glutamic_acid_nt', 'glutamine', 'glutamine_ct', 'glutamine_nt', 'gly_nt_sidechain', 'gly_sidechain', 'glycine', 'glycine_ct', 'glycine_nt', 'guanine', 'hid_sidechain', 'hie_sidechain', 'hip_sidechain', 'histidine', 'histidine_ct', 'histidine_deltah', 'histidine_deltah_ct', 'histidine_deltah_nt', 'histidine_epsilonh', 'histidine_epsilonh_ct', 'histidine_epsilonh_nt', 'histidine_nt', 'histidine_plus', 'histidine_plus_ct', 'histidine_plus_nt', 'ile_sidechain', 'isoleucine', 'isoleucine_ct', 'isoleucine_nt', 'leu_sidechain', 'leucine', 'leucine_ct', 'leucine_nt', 'lys_sidechain', 'lys_sidechain_neutral', 'lysine', 'lysine_ct', 'lysine_neutral', 'lysine_neutral_ct', 'lysine_neutral_nt', 'lysine_nt', 'met_sidechain', 'methionine', 'methionine_ct', 'methionine_nt', 'na_phosphate', 'nmethyl_ct', 'peptide', 'peptide_ct', 'peptide_nt', 'peptide_nt_ct', 'peptide_proline', 'peptide_proline_ct', 'peptide_proline_nt', 'phe_sidechain', 'phenylalanine', 'phenylalanine_ct', 'phenylalanine_nt', 'pro_sidechain', 'pro_sidechain_nt', 'proline', 'proline_ct', 'proline_nt', 'r-adenosine', 'r-adenosine_3ter', 'r-adenosine_5ter', 'r-adenosine_5ter_3ter', 'r-cytosine', 'r-cytosine_3ter', 'r-cytosine_5ter', 'r-cytosine_5ter_3ter', 'r-guanosine', 'r-guanosine_3ter', 'r-guanosine_5ter', 'r-guanosine_5ter_3ter', 'r-uracil', 'r-uracil_3ter', 'r-uracil_5ter', 'r-uracil_5ter_3ter', 'ribose', 'ribose_3ter', 'ribose_5ter', 'ribose_5ter_3ter', 'ser_sidechain', 'serine', 'serine_ct', 'serine_nt', 'thr_sidechain', 'threonine', 'threonine_ct', 'threonine_nt', 'thymine', 'trp_sidechain', 'tryptophan', 'tryptophan_ct', 'tryptophan_nt', 'tyr_sidechain', 'tyrosine', 'tyrosine_ct', 'tyrosine_nt', 'uracil', 'val_sidechain', 'valine', 'valine_ct', 'valine_nt'] for filename in groups: ast = compiler.parse(file(filename).read()) stmts = ast.getChildren()[1].getChildren() vars = set() for stmt in stmts: if isinstance(stmt, compiler.ast.Assign): var_name = stmt.getChildren()[0].getChildren()[0] vars.add(var_name) if 'amber_atom_type' in vars \ and 'amber12_atom_type' not in vars: print filename with file(filename, 'a') as f: f.write("amber12_atom_type = amber_atom_type\n") MMTK-2.7.9/Tools/DatabaseEdit.py0000644000076600000240000002552311662461640016712 0ustar hinsenstaff00000000000000# Functions that help to create/modify the database # # Written by Konrad Hinsen # _undocumented = 1 import Database import copy, os, string class TypeClone(Database.ChemicalObjectType): def __init__(self, filename, module, instancevars): self._filename = os.path.split(filename)[-1] module.Atom = BlueprintAtom module.Group = BlueprintGroup module.Molecule = BlueprintMolecule module.Bond = BlueprintBond Database.ChemicalObjectType.__init__(self, filename, module, instancevars) self._written = 0 def attributes(self): dict = copy.copy(self.__dict__) del dict['_filename'] del dict['_written'] del dict['instance'] del dict['parent'] del dict['filename'] try: del dict['amber_atom_type'] except KeyError: pass try: del dict['amber_charge'] except KeyError: pass return dict def writeToFile(self, filename_converter): if not self._written: filename = filename_converter(self._filename, self.class_name) filename = Database.DatabasePath(filename, 'Output') file = open(filename, 'w') print '*** File ' + filename dict = self.attributes() try: atoms = copy.copy(dict['atoms']) del dict['atoms'] except KeyError: atoms = [] try: groups = copy.copy(dict['groups']) del dict['groups'] except KeyError: groups = [] try: molecules = copy.copy(dict['molecules']) del dict['molecules'] except KeyError: molecules = [] try: bonds = copy.copy(dict['bonds']) del dict['bonds'] except KeyError: bonds = [] values = [] for attr, value in dict.items(): if hasattr(value, 'is_blueprint'): file.write(attr + ' = ') self.writeValue(file, value, filename_converter) value.setString(attr) file.write('\n') eval(value.object_list).remove(value) del dict[attr] if atoms: file.write('atoms = ') self.writeValue(file, atoms, filename_converter) file.write('\n') if groups: file.write('groups = ') self.writeValue(file, groups, filename_converter) file.write('\n') if molecules: file.write('molecules = ') self.writeValue(file, molecules, filename_converter) file.write('\n') if bonds: file.write('bonds = ') self.writeValue(file, bonds, filename_converter) file.write('\n') for attr, value in dict.items(): file.write(attr + ' = ') self.writeValue(file, value, filename_converter) file.write('\n') del dict[attr] file.close() self._written = 1 def writeValue(self, file, value, filename_converter): if hasattr(value, 'is_blueprint'): value.writeToFile(self, file, filename_converter) elif type(value) == type([]): file.write('[') for element in value: self.writeValue(file, element, filename_converter) file.write(', ') file.write(']') elif type(value) == type(()): file.write('(') for element in value: self.writeValue(file, element, filename_converter) file.write(', ') file.write(')') elif type(value) == type({}): file.write('{') for k, v in value.items(): self.writeValue(file, k, filename_converter) file.write(': ') self.writeValue(file, v, filename_converter) file.write(', ') file.write('}') else: file.write(repr(value)) def parentName(self, parent): if parent != self: raise TypeError return '' def deleteAtoms(self, identifier): dict = self.attributes() try: del dict['atoms'] except KeyError: pass try: del dict['groups'] except KeyError: pass try: del dict['molecules'] except KeyError: pass try: del dict['bonds'] except KeyError: pass for attr, value in dict.items(): if hasattr(value, 'is_blueprint'): if value.__class__ == BlueprintAtom and identifier(value): delattr(self, attr) else: setattr(self, attr, _deleteAtoms(value, identifier)) self.atoms = filter(lambda a, i=identifier: not i(a), self.atoms) self.bonds = filter(lambda b, i=identifier: not (i(b.a1) or i(b.a2)), self.bonds) def _deleteAtoms(value, identifier): if type(value) == type([]): return map(_deleteAtoms, value, len(value)*[identifier]) if type(value) == type(()): return tuple(map(_deleteAtoms, value, len(value)*[identifier])) if type(value) == type({}): new = {} for k, v in value.items(): if not identifier(k) and not identifier(v): new[k] = _deleteAtoms(v, identifier) return new else: return value class AtomTypeClone(TypeClone): error = 'AtomTypeError' def __init__(self, filename): import AtomEnvironment TypeClone.__init__(self, filename, AtomEnvironment, ()) class_name = 'Atom' class GroupTypeClone(TypeClone): error = 'GroupTypeError' def __init__(self, filename): import GroupEnvironment TypeClone.__init__(self, filename, GroupEnvironment, ('atoms', 'groups', 'bonds')) class_name = 'Group' class MoleculeTypeClone(TypeClone): error = 'MoleculeTypeError' def __init__(self, filename): import MoleculeEnvironment TypeClone.__init__(self, filename, MoleculeEnvironment, ('atoms', 'groups', 'bonds')) class_name = 'Molecule' atom_types = Database.Database('Atoms', AtomTypeClone) group_types = Database.Database('Groups', GroupTypeClone) molecule_types = Database.Database('Molecules', MoleculeTypeClone) #crystal_types = Database.Database('Crystals', CrystalTypeClone) #complex_types = Database.Database('Complexes', ComplexTypeClone) class BlueprintObjectClone: def parentName(self, parent): if hasattr(self, 'parent') and self.parent != parent: return self.parent.parentName() + '.' + self._string else: return self._string def setString(self, string): self._string = string def writeToFile(self, parent, file, filename_converter): if hasattr(self, 'parent') and self.parent != parent: file.write(self.parent.parentName(parent) + '.') if self._string is not None: file.write(self._string) else: self.writeRepresentation(parent, file, filename_converter) class BlueprintObject(Database.BlueprintObject, BlueprintObjectClone): def __init__(self, original, database, memo): Database.BlueprintObject.__init__(self, original, database, memo) self._string = None def writeRepresentation(self, parent, file, filename_converter): self.type.writeToFile(filename_converter) if hasattr(self.parent, 'is_blueprint'): file.write(self.name) else: filename = filename_converter(self.type._filename, self.class_name) file.write(self.class_name + '(' + repr(filename) + ')') class BlueprintAtom(BlueprintObject): def __init__(self, type, memo = None): BlueprintObject.__init__(self, type, atom_types, memo) object_list = 'atoms' class_name = 'Atom' class BlueprintGroup(BlueprintObject): def __init__(self, type, memo = None): BlueprintObject.__init__(self, type, group_types, memo) object_list = 'groups' class_name = 'Group' class BlueprintMolecule(BlueprintObject): def __init__(self, type, memo = None): BlueprintObject.__init__(self, type, molecule_types, memo) object_list = 'molecules' class_name = 'Molecule' class BlueprintCrystal(BlueprintObject): def __init__(self, type, memo = None): BlueprintObject.__init__(self, type, crystal_types, memo) object_list = 'crystals' class BlueprintComplex(BlueprintObject): def __init__(self, type, memo = None): BlueprintObject.__init__(self, type, complex_types, memo) object_list = 'complexes' class BlueprintBond(BlueprintObjectClone): def __init__(self, a1, a2): self.a1 = a1 self.a2 = a2 self._string = None is_blueprint = 1 object_list = 'bonds' def writeRepresentation(self, parent, file, filename_converter): file.write('Bond(') self.a1.writeToFile(parent, file, filename_converter) file.write(', ') self.a2.writeToFile(parent, file, filename_converter) file.write(')') # # Delete hydrogens in all amino acids # residue_names = ['alanine', 'arginine', 'asparagine', 'aspartic_acid', 'cysteine', 'cystine_ss', 'glutamine', 'glutamic_acid', 'glycine', 'histidine', 'isoleucine', 'leucine', 'lysine', 'methionine', 'phenylalanine', 'proline', 'serine', 'threonine', 'tryptophan', 'tyrosine', 'valine', 'histidine_deltah', 'histidine_epsilonh', 'histidine_plus'] nt = map(lambda n: n + '_nt', residue_names) ct = map(lambda n: n + '_ct', residue_names) residue_names = residue_names + nt + ct def noh(x, class_name): if class_name == 'Atom': return x else: return x + '_noh' def uni(x, class_name): if class_name == 'Atom': return x else: return x + '_uni' def uni2(x, class_name): if class_name == 'Atom': return x else: return x + '_uni2' def hydrogenIdentifier(atom): return hasattr(atom, 'is_blueprint') and atom.__class__ == BlueprintAtom \ and atom.type.symbol == 'H' def amberToCHARMM19(atom): return hasattr(atom, 'is_blueprint') and atom.__class__ == BlueprintAtom \ and atom.type.symbol == 'LP' def markedIdentifier(atom): return hasattr(atom, 'is_blueprint') and atom.__class__ == BlueprintAtom \ and hasattr(atom, 'delete') def markNonpolar(group): for b in group.bonds: if b.a1.symbol == 'C' and b.a2.symbol == 'H': b.a2.delete = 1 print b.a2.name if b.a1.symbol == 'H' and b.a2.symbol == 'C': b.a1.delete = 1 print b.a1.name for name in residue_names: group_types.findType(name) group_types.findType(name+'_uni') group_types.findType(name+'_uni2') group_types.findType(name+'_noh') def numberedHydrogen(name): return name[0] == 'H' and name[-1] in string.digits def newname(name): return name[-1]+name[:-1] for t in group_types.types.values(): print t.name if hasattr(t, 'pdbmap'): try: pdbalt = t.pdb_alternative except AttributeError: pdbalt = {} for n1, n2 in pdbalt.items(): if numberedHydrogen(n2): pdbalt[n1] = newname(n2) pdbdict = t.pdbmap[0][1] for name, object in pdbdict.items(): print name if numberedHydrogen(name): new = newname(name) del pdbdict[name] pdbdict[new] = object pdbalt[name] = new if len(pdbalt) > 0: t.pdb_alternative = pdbalt for t in group_types.types.values(): t.writeToFile(lambda a, b: a) ## for t in group_types.types.values(): ## t.deleteAtoms(amberToCHARMM19) ## for name in residue_names: ## t = group_types.findType(name) ## t.writeToFile(uni2) ## for t in group_types.types.values(): ## t.deleteAtoms(hydrogenIdentifier) ## for name in residue_names: ## t = group_types.findType(name) ## t.writeToFile(noh) ## for name, t in group_types.types.items(): ## if string.find(name, 'peptide') != -1 or \ ## string.find(name, 'sidechain') != -1: ## print name ## markNonpolar(t) ## group_types.types['gly_sidechain'].H_alpha_3.delete = 1 ## group_types.types['gly_nt_sidechain'].H_alpha_3.delete = 1 ## for t in group_types.types.values(): ## t.deleteAtoms(markedIdentifier) ## for name in residue_names: ## t = group_types.findType(name) ## t.writeToFile(uni2) MMTK-2.7.9/tviewer0000755000076600000240000000037511662461640014337 0ustar hinsenstaff00000000000000#!python from MMTK.Tools.TrajectoryViewer.TrajectoryViewer import TrajectoryViewer import sys if len(sys.argv) < 2: sys.stderr.write('No trajectory specified\n') sys.exit() for trajectory in sys.argv[1:]: TrajectoryViewer(None, trajectory)