pax_global_header00006660000000000000000000000064147042226700014516gustar00rootroot0000000000000052 comment=04d78d11da8ac3851876895fdc341344d47d1430 pyobjcryst-2024.2.1/000077500000000000000000000000001470422267000141565ustar00rootroot00000000000000pyobjcryst-2024.2.1/.coveragerc000066400000000000000000000011071470422267000162760ustar00rootroot00000000000000# Configuration of the coverage.py tool for reporting test coverage. [report] # RE patterns for lines to be excluded from consideration. exclude_lines = ## Have to re-enable the standard pragma pragma: no cover ## Don't complain if tests don't hit defensive assertion code: raise AssertionError raise NotImplementedError ^[ ]*assert False ## Don't complain if non-runnable code isn't run: ^[ ]*@unittest.skip\b ^[ ]{4}unittest.main() if __name__ == .__main__.: [run] omit = ## exclude debug.py from codecov report */tests/debug.py pyobjcryst-2024.2.1/.gitarchive.cfg000066400000000000000000000002161470422267000170410ustar00rootroot00000000000000[DEFAULT] commit = 04d78d11da8ac3851876895fdc341344d47d1430 date = 2024-10-17 16:56:56 +0200 timestamp = 1729177016 refnames = tag: v2024.2.1 pyobjcryst-2024.2.1/.readthedocs.yaml000066400000000000000000000010361470422267000174050ustar00rootroot00000000000000# .readthedocs.yaml # Read the Docs configuration file # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details # Required version: 2 # Set the OS, Python version and other tools you might need build: os: "ubuntu-22.04" tools: python: "mambaforge-latest" # Build documentation in the docs/ directory with Sphinx sphinx: configuration: doc/manual/source/conf.py # If using Sphinx, optionally build your docs in additional formats such as PDF # formats: # - pdf conda: environment: doc/environment.yml pyobjcryst-2024.2.1/AUTHORS.txt000066400000000000000000000002371470422267000160460ustar00rootroot00000000000000Authors: Christopher L. Farrow Pavol Juhas Simon J.L. Billinge Vincent Favre-Nicolin Contributors: https://github.com/diffpy/pyobjcryst/graphs/contributors pyobjcryst-2024.2.1/CHANGELOG.md000066400000000000000000000110211470422267000157620ustar00rootroot00000000000000# Release notes ## Version 2024.2.1 ### Changes - PowderPattern: - fix re-using a matplotlib figure when plotting - add 'figure' property ## Version 2024.2 ### Changes - DiffractionDataSingleCrystal: add SetHklIobs, SetIobs, SetSigma, GetSigma, GetChi2, FitScaleFactorForRw and FitScaleFactorForR (https://github.com/diffpy/pyobjcryst/issues/42) - Add a single crystal data notebook example - Online documentation notebooks now include the plots (https://pyobjcryst.readthedocs.io/en/latest/examples) ### Fixes - From libobjcryst: update the ScatteringComponentList when a Scatterer is removed from a Crystal (https://github.com/diffpy/pyobjcryst/issues/41) ## Version 2024.1 ### Changes - Add python access to MolZAtom, for Molecule.AsZMatrix() ## Version 2.2.6 ### Changes - Support for windows and python>=3.8 - Added a zoom limit for 3D crystal views ### Fixes - Correct error preventing pyobjcryst import for windows and python>=3.8 (https://github.com/diffpy/pyobjcryst/issues/33) - Fix for matplotlib >=3.7.0 when removing hkl labels ## Version 2.2.5 ### Changes - Raise an exception if alpha, beta or gamma are not within ]0;pi[ when changing lattice angles - Add UnitCell.ChangeSpaceGroup() ### Fixes - Avoid duplication of plots when using ipympl (aka %matplotlib widget) - Correct powder pattern tests to avoid warnings ### Deprecated - loadCrystal - use create_crystal_from_cif() instead ## Version 2.2.4 ### Changes - the list of HKL reflections will now be automatically be re-generated for a PowderPatternDiffraction when the Crystal's spacegroup changes, or the lattice parameters are modified by more than 0.5% ### Fixes - Fixed the powder pattern indexing test ## Version 2.2.3 ### Added - Support for windows install (works with python 3.7, and also -only with pypy- 3.8 and 3.9) - Native support for Apple arm64 (M1, M2) processors - Fourier maps calculation - Add gDiffractionDataSingleCrystalRegistry to globals ## Version 2.2.2 ### Changes - Add correct wrapping for C++-instantiated objects available through global registries, e.g. when loading an XML file. The objects are decorated with the python functions when accessed through the global registries GetObj() - Moved global object registries to pyobjcryst.globals - Update documentation ### Fixed - Fix access to PRISM_TETRAGONAL_DICAP, PRISM_TRIGONAL, ICOSAHEDRON and TRIANGLE_PLANE. - Fix powder pattern plot issues (NaN and update of hkl text with recent matplotlib versions) ## Version 2.2.1 -- 2021-11-28 - Add quantitative phase analysis with PowderPattern.qpa(), including an example notebook using the QPA Round-Robin data. - Correct import of urllib.request.urllopen() when loading CIF or z-matrix files from http urls. - Fix blank line javascript output when updating the Crystal 3D view - Add RefinableObj.xml() to directly get the XMLOutput() as a string - Add example notebooks to the sphinx-generated html documentation - Fix issue when using Crystal.XMLInput() for a non-empty structure. Existing scattering power will be re-used when possible, and otherwise not deleted anymore (which could lead to crashes). ## Version 2.2.0 -- 2021-06-08 Notable differences from version 2.1.0. - Add access to Radiation class & functions to change RadiationType, wavelength in PowderPattern and ScatteringData (and hence DiffractionDataSingleCrystal) classes. - Fix the custodian_ward when creating a PowderPatternDiffraction: PowderPatternDiffraction must persist while PowderPattern exists, and Crystal must persist while PowderPatternDiffraction exists. - Add 3D Crystal viewer `pyobjcryst.crystal.Crystal.widget_3d`. ## Version 2.1.0 -- 2019-03-11 Notable differences from version 2.0.1. ### Added - Support for Python 3.7. - Validation of compiler options from `python-config`. - Make scons scripts compatible with Python 3 and Python 2. - Support np.array arguments for `SetPowderPatternX`, `SetPowderPatternObs`. - Declare compatible version requirements for client Anaconda packages. - Facility for silencing spurious console output from libobjcryst. ### Changed - Build Anaconda package with Anaconda C++ compiler. - Update to libobjcryst 2017.2.x. ### Deprecated - Variable `__gitsha__` in the `version` module which was renamed to `__git_commit__`. ### Removed - Support for Python 3.4. ### Fixed - Ambiguous use of boost::python classes and functions. - Name suffix resolution of `boost_python` shared library. - `SetPowderPatternX` crash for zero-length argument. - Incorrectly doubled return value from `GetInversionCenter`. pyobjcryst-2024.2.1/LICENSE.txt000066400000000000000000000130471470422267000160060ustar00rootroot00000000000000OPEN SOURCE LICENSE AGREEMENT ============================= Copyright (c) 2009-2011, University of Tennessee Copyright (c) 1989, 1991 Free Software Foundation, Inc. Copyright (c) 2006, The Regents of the University of California through Lawrence Berkeley National Laboratory Copyright (c) 2014, Australian Synchrotron Research Program Inc., ("ASRP") Copyright (c) 2006-2007, Board of Trustees of Michigan State University Copyright (c) 2008-2012, The Trustees of Columbia University in the City of New York Copyright (c) 2014-2019, Brookhaven Science Associates, Brookhaven National Laboratory The "DiffPy-CMI" is distributed subject to the following license conditions: SOFTWARE LICENSE AGREEMENT Software: DiffPy-CMI (1) The "Software", below, refers to the aforementioned DiffPy-CMI (in either source code, or binary form and accompanying documentation). Part of the software was derived from the DANSE, ObjCryst++ (with permission), PyCifRW, Python periodictable, CCTBX, and SasView open source projects, of which the original Copyrights are contained in each individual file. Each licensee is addressed as "you" or "Licensee." (2) The copyright holders shown above and their third-party Licensors hereby grant licensee a royalty-free nonexclusive license, subject to the limitations stated herein and U.S. Government license rights. (3) You may modify and make a copy or copies of the software for use within your organization, if you meet the following conditions: (a) Copies in source code must include the copyright notice and this software license agreement. (b) Copies in binary form must include the copyright notice and this Software License Agreement in the documentation and/or other materials provided with the copy. (4) You may modify a copy or copies of the Software or any portion of it, thus forming a work based on the Software, and distribute copies of such work outside your organization, if you meet all of the following conditions: (a) Copies in source code must include the copyright notice and this Software License Agreement; (b) Copies in binary form must include the copyright notice and this Software License Agreement in the documentation and/or other materials provided with the copy; (c) Modified copies and works based on the Software must carry prominent notices stating that you changed specified portions of the Software. (d) Neither the name of Brookhaven Science Associates or Brookhaven National Laboratory nor the names of its contributors may be used to endorse or promote products derived from this software without specific written permission. (5) Portions of the Software resulted from work developed under a U.S. Government contract and are subject to the following license: The Government is granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable worldwide license in this computer software to reproduce, prepare derivative works, and perform publicly and display publicly. (6) WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS" WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDERS, THEIR THIRD PARTY LICENSORS, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4) DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL BE CORRECTED. (7) LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT HOLDERS, THEIR THIRD PARTY LICENSORS, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT, INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT (INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE, EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE POSSIBILITY OF SUCH LOSS OR DAMAGES. Brookhaven National Laboratory Notice ===================================== Acknowledgment of sponsorship ----------------------------- This software was produced by the Brookhaven National Laboratory, under Contract DE-AC02-98CH10886 with the Department of Energy. Government disclaimer of liability ---------------------------------- Neither the United States nor the United States Department of Energy, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any data, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Brookhaven disclaimer of liability ---------------------------------- Brookhaven National Laboratory makes no representations or warranties, express or implied, nor assumes any liability for the use of this software. Maintenance of notice --------------------- In the interest of clarity regarding the origin and status of this software, Brookhaven National Laboratory requests that any recipient of it maintain this notice affixed to any distribution by the recipient that contains a copy or derivative of this software. END OF LICENSE pyobjcryst-2024.2.1/LICENSE_DANSE.txt000066400000000000000000000035261470422267000167210ustar00rootroot00000000000000This program is part of the DiffPy and DANSE open-source projects at Columbia University and is available subject to the conditions and terms laid out below. Copyright (c) 2009-2012, The Trustees of Columbia University in the City of New York. All rights reserved. For more information please visit the diffpy web-page at http://diffpy.org or email Prof. Simon Billinge at sb2896@columbia.edu. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the names of COLUMBIA UNIVERSITY, MICHIGAN STATE UNIVERSITY nor the names of their contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. pyobjcryst-2024.2.1/MANIFEST.in000066400000000000000000000006631470422267000157210ustar00rootroot00000000000000recursive-include src * include SConstruct include AUTHORS.txt LICENSE*.txt README.rst recursive-exclude src *.pyc *.so global-exclude .gitattributes .gitignore .gitarchive.cfg global-exclude .DS_Store # Avoid user content in setup.cfg to make distribution reproducible. exclude setup.cfg # Exclude git-tracked files spuriously added by setuptools_scm exclude .coveragerc exclude .travis* prune conda-recipe prune devutils prune doc pyobjcryst-2024.2.1/README.rst000066400000000000000000000172001470422267000156450ustar00rootroot00000000000000.. image:: https://travis-ci.org/diffpy/pyobjcryst.svg?branch=master :target: https://travis-ci.org/diffpy/pyobjcryst .. image:: https://codecov.io/gh/diffpy/pyobjcryst/branch/master/graph/badge.svg :target: https://codecov.io/gh/diffpy/pyobjcryst pyobjcryst ========== Python bindings to ObjCryst++, the Object-Oriented Crystallographic Library. The documentation for this release of pyobjcryst can be found on-line at https://pyobjcryst.readthedocs.io/ INSTALLATION ------------ pyobjcryst is available for Python 3.7 (deprecated), and 3.8 to 3.11. Using conda (recommended) ^^^^^^^^^^^^^^^^^^^^^^^^^ We recommend to use **conda** as it allows to install all software dependencies together with pyobjcryst, without too much compiling hastle. Two distributions can be used: * `Anaconda Python `_, the historical main conda-based distribution * `Mamba-forge `_ , which has the advantage off providing **mamba** in addition to conda, and is *much faster* when resolving dependencies during installation. It also uses by default the conda-forge repository, which is what almost all users would want. Using conda, we you can install pyobjcryst using the "conda-forge" channel :: conda install -c conda-forge pyobjcryst Alternatively using mamba :: mamba install pyobjcryst You can also install from the "diffpy" channel - especially if you use pyobjcryst allong with the other diffpy tools for PDF calculations, but it is not updated as often as conda-forge. pyobjcryst is also included in the "diffpy-cmi" collection of packages for structure analysis :: conda install -c diffpy diffpy-cmi Complete new conda environment with optional GUI and jupyter dependencies ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Assuming you have installed `Mamba-forge `_, you can directly create a new conda environment named `pyobjcryst` with all useful dependencies (including jupyter-lab, python 3.11..) using :: mamba create -n pyobjcryst python=3.11 pyobjcryst matplotlib py3dmol jupyterlab ipympl multiprocess Then you can activate the environment and launch jupyter-lab using :: conda activate pyobjcryst jupyter-lab From source ^^^^^^^^^^^ The requirements are: * ``libobjcryst`` - Object-Oriented Crystallographic Library for C++, https://github.com/diffpy/libobjcryst * ``setuptools`` - tools for installing Python packages * ``NumPy`` - library for scientific computing with Python * ``python-dev`` - header files for interfacing Python with C * ``libboost-all-dev`` - Boost C++ libraries and development files * ``scons`` - software construction tool (optional) The above requirements are easily installed through conda using e.g.:: conda install numpy compilers boost scons libobjcryst Alternatively, on Ubuntu Linux the required software can be installed using:: sudo apt-get install \ python-setuptools python-numpy scons \ build-essential python-dev libboost-all-dev The libobjcryst library can also be installed as per the instructions at https://github.com/diffpy/libobjcryst. Make sure other required software are also in place and then run from the pyobjcryst directory:: pip install . You may need to use ``sudo`` with system Python so the process is allowed to copy files to system directories, unless you are installing into a conda environment. If administrator (root) access is not available, see the usage information from ``python setup.py install --help`` for options to install to a user-writable location. The installation integrity can be verified by executing the included tests with :: python -m pyobjcryst.tests.run An alternative way of installing pyobjcryst is to use the SCons tool, which can speed up the process by compiling C++ files in several parallel jobs (-j4):: scons -j4 install See ``scons -h`` for description of build targets and options. Optional graphical dependencies for jupyter notebooks ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Some of the classes can produce graphical outputs, which can be displayed in a jupyter notebook: * a Crystal structure can be displayed in 3D: this requires the ``py3dmol`` and ``ipywidgets`` modules. See the notebook ``examples/cystal_3d_widget.ipynb`` * a PowderPattern can be displayed (and live-updated) if ``matplotlib`` and ``ipympl`` are installed. See the notebook ``examples/cimetidine-structure-solution-powder.ipynb`` In short, ``conda install jupyter matplotlib ipympl py3dmol`` will give you all the required dependencies. You can also use this in jupyterlab. These packages can also be installed using ``pip`` if you do not use conda. DEVELOPMENT ----------- pyobjcryst is an open-source software originally developed as a part of the DiffPy-CMI complex modeling initiative at the Brookhaven National Laboratory, and is also further developed at ESRF. The pyobjcryst sources are hosted at https://github.com/diffpy/pyobjcryst. Feel free to fork the project and contribute. To install pyobjcryst in a development mode, where its sources are directly used by Python rather than copied to a system directory, use :: python setup.py develop --user When developing it is preferable to compile the C++ files with SCons using the ``build=develop`` option, which compiles the extension module with debug information and C-assertions checks :: scons -j4 build=debug develop The build script checks for a presence of ``sconsvars.py`` file, which can be used to permanently set the ``build`` variable. The SCons construction environment can be further customized in a ``sconscript.local`` script. The package integrity can be verified by executing unit tests with ``scons -j4 test``. When developing with Anaconda Python it is essential to specify header path, library path and runtime library path for the active Anaconda environment. This can be achieved by setting the ``CPATH``, ``LIBRARY_PATH`` and ``LDFLAGS`` environment variables as follows:: # resolve the prefix directory P of the active Anaconda environment P=$CONDA_PREFIX export CPATH=$P/include export LIBRARY_PATH=$P/lib export LDFLAGS=-Wl,-rpath,$P/lib # compile and re-install pyobjcryst scons -j4 build=debug develop Note the Anaconda package for the required libobjcryst library is built with a C++ compiler provided by Anaconda. This may cause incompatibility with system C++. In such case please use Anaconda C++ to build pyobjcryst. Quick conda environment from libobjcryst and pyobjcryst sources ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ If ``conda`` is available, you can create a pyobjcryst environment from the git repositories (downloaded in the current directory) using:: conda create --yes --name pyobjcryst numpy matplotlib ipywidgets jupyter conda install --yes -n pyobjcryst -c conda-forge boost scons py3dmol conda activate pyobjcryst git clone https://github.com/diffpy/libobjcryst.git cd libobjcryst scons -j4 install prefix=$CONDA_PREFIX cd .. git clone https://github.com/diffpy/pyobjcryst.git cd pyobjcryst export CPATH=$CONDA_PREFIX/include export LIBRARY_PATH=$CONDA_PREFIX/lib export LDFLAGS=-Wl,-rpath,$CONDA_PREFIX/lib scons -j4 install prefix=$CONDA_PREFIX CONTACTS -------- For more information on pyobjcryst please visit the project web-page http://www.diffpy.org or email Prof. Simon Billinge at sb2896@columbia.edu. You can also contact Vincent Favre-Nicolin (favre@esrf.fr) if you are using pyobjcryst outside diffpy, e.g. to display structures in a notebook, refine powder patterns or solve structures using the global optimisation algorithms, etc.. pyobjcryst-2024.2.1/SConstruct000066400000000000000000000220531470422267000162120ustar00rootroot00000000000000# This SConstruct is for faster parallel builds. # Use "setup.py" for normal installation. MY_SCONS_HELP = """\ SCons rules for compiling and installing pyobjcryst. SCons build is much faster when run with parallel jobs (-j4). Usage: scons [target] [var=value] Targets: module build Python extension module _pyobjcryst.so [default] install install to default Python package location develop copy extension module to src/pyobjcryst/ directory test execute unit tests Build configuration variables: %s Variables can be also assigned in a user script sconsvars.py. SCons construction environment can be customized in sconscript.local script. """ import os from os.path import join as pjoin import re import subprocess import platform def subdictionary(d, keyset): return dict(kv for kv in d.items() if kv[0] in keyset) def getsyspaths(*names): pall = sum((os.environ.get(n, '').split(os.pathsep) for n in names), []) rv = [p for p in pall if os.path.exists(p)] return rv def pyoutput(cmd): proc = subprocess.Popen([env['python'], '-c', cmd], stdout=subprocess.PIPE, universal_newlines=True) out = proc.communicate()[0] return out.rstrip() def pyconfigvar(name): cmd = ('from distutils.sysconfig import get_config_var\n' 'print(get_config_var(%r))\n') % name return pyoutput(cmd) # copy system environment variables related to compilation DefaultEnvironment(ENV=subdictionary(os.environ, ''' PATH PYTHONPATH GIT_DIR HOMEPATH HOMEDRIVE CPATH CPLUS_INCLUDE_PATH LIBRARY_PATH LD_RUN_PATH LD_LIBRARY_PATH DYLD_LIBRARY_PATH DYLD_FALLBACK_LIBRARY_PATH MACOSX_DEPLOYMENT_TARGET LANG _PYTHON_SYSCONFIGDATA_NAME _CONDA_PYTHON_SYSCONFIGDATA_NAME '''.split()) ) # Create construction environment env = DefaultEnvironment().Clone() # Variables definitions below work only with 0.98 or later. env.EnsureSConsVersion(0, 98) # Customizable compile variables vars = Variables('sconsvars.py') # Set PREFIX for installation and linking # TODO: also amend paths when VIRTUAL_ENV variable exists ? if 'PREFIX' in os.environ: # building with a set prefix vars.Add(PathVariable( 'prefix', 'installation prefix directory', os.environ['PREFIX'])) elif 'CONDA_PREFIX' in os.environ: # building for a conda environment vars.Add(PathVariable( 'prefix', 'installation prefix directory', os.environ['CONDA_PREFIX'])) else: vars.Add(PathVariable('prefix', 'installation prefix directory', None)) vars.Update(env) vars.Add(EnumVariable('build', 'compiler settings', 'fast', allowed_values=('debug', 'fast'))) vars.Add(EnumVariable('tool', 'C++ compiler toolkit to be used', 'default', allowed_values=('default', 'intelc', 'clang', 'clangxx'))) vars.Add(BoolVariable('profile', 'build with profiling information', False)) vars.Add('python', 'Python executable to use for installation.', 'python') vars.Update(env) env.Help(MY_SCONS_HELP % vars.GenerateHelpText(env)) # Use Intel C++ compiler if requested by the user. icpc = None if env['tool'] == 'intelc': icpc = env.WhereIs('icpc') if not icpc: print("Cannot find the Intel C/C++ compiler 'icpc'.") Exit(1) env.Tool('intelc', topdir=icpc[:icpc.rfind('/bin')]) # Figure out compilation switches, filter away C-related items. good_python_flag = lambda n: ( not isinstance(n, str) or not re.match(r'(-g|-Wstrict-prototypes|-O\d|-fPIC)$', n)) # Determine python-config script name. if 'PY_VER' in os.environ: pyversion = os.environ['PY_VER'] else: pyversion = pyoutput('import sys; print("%i.%i" % sys.version_info[:2])') if 'CONDA_BUILD' in os.environ and 'PY_VER' in os.environ: # Messy: if CONDA_BUILD and PY_VER are in the path, we are building a conda package # using several environment. Make sure python3.X-config points to the destination # (host) environment pythonconfig = pjoin(os.environ['PREFIX'], 'bin', 'python%s-config' % os.environ['PY_VER']) print("Using $PREFIX and $PY_VER to determine python-config pth: %s" % pythonconfig) xpython = pjoin(os.environ['PREFIX'], 'bin', 'python') pyversion = os.environ['PY_VER'] else: pycfgname = 'python%s-config' % (pyversion if pyversion[0] == '3' else '') # realpath gets the real path if exec is a link (e.g. in a python environment) xpython = os.path.realpath(env.WhereIs(env['python'])) pybindir = os.path.dirname(xpython) pythonconfig = pjoin(pybindir, pycfgname) # for k in sorted(os.environ.keys()): # print(" ", k, os.environ[k]) if platform.system().lower() == "windows": # See https://scons.org/faq.html#Linking_on_Windows_gives_me_an_error env['ENV']['TMP'] = os.environ['TMP'] # the CPPPATH directories are checked by scons dependency scanner cpppath = getsyspaths('CPLUS_INCLUDE_PATH', 'CPATH') env.AppendUnique(CPPPATH=cpppath) # Insert LIBRARY_PATH explicitly because some compilers # ignore it in the system environment. env.PrependUnique(LIBPATH=getsyspaths('LIBRARY_PATH')) if env['prefix'] is not None: env.Append(CPPPATH=[pjoin(env['prefix'], 'include')]) env.Append(CPPPATH=[pjoin(env['prefix'], 'Library', 'include')]) # Windows conda library paths are a MESS ('lib', 'libs', 'Library\lib'...) env.Append(LIBPATH=[pjoin(env['prefix'], 'Library', 'lib')]) env.Append(LIBPATH=[pjoin(env['prefix'], 'libs')]) # This disable automated versioned named e.g. libboost_date_time-vc142-mt-s-x64-1_73.lib # so we can use conda-installed libraries env.AppendUnique(CPPDEFINES='BOOST_ALL_NO_LIB') # Prevent the generation of an import lib (.lib) in addition to the dll # env.AppendUnique(no_import_lib=1) env.PrependUnique(CCFLAGS=['/Ox', '/EHsc', '/MD', '/DREAL=double']) env.AppendUnique(CPPDEFINES={'NDEBUG': None}) else: if 'CONDA_BUILD' not in os.environ: # Verify python-config comes from the same path as the target python. xpythonconfig = env.WhereIs(pythonconfig) if os.path.dirname(xpython) != os.path.dirname(xpythonconfig): print("Inconsistent paths of %r and %r" % (xpython, xpythonconfig)) Exit(1) # Process the python-config flags here. env.ParseConfig(pythonconfig + " --cflags") env.Replace(CCFLAGS=[f for f in env['CCFLAGS'] if good_python_flag(f)]) env.Replace(CPPDEFINES='BOOST_ERROR_CODE_HEADER_ONLY') # the CPPPATH directories are checked by scons dependency scanner cpppath = getsyspaths('CPLUS_INCLUDE_PATH', 'CPATH') env.AppendUnique(CPPPATH=cpppath) # Insert LIBRARY_PATH explicitly because some compilers # ignore it in the system environment. env.PrependUnique(LIBPATH=getsyspaths('LIBRARY_PATH')) # Add shared libraries. # Note: ObjCryst and boost_python are added from SConscript.configure. fast_linkflags = ['-s'] fast_shlinkflags = pyconfigvar('LDSHARED').split()[1:] # Specify minimum C++ standard. Allow later standard from sconscript.local. # In case of multiple `-std` options the last option holds. env.PrependUnique(CXXFLAGS='-std=c++11', delete_existing=1) # Need this to avoid missing symbol with boost<1.66 env.PrependUnique(CXXFLAGS=['-DBOOST_ERROR_CODE_HEADER_ONLY']) # Use double precision for objcryst's REAL env.PrependUnique(CCFLAGS=['-DREAL=double']) # Platform specific intricacies. if env['PLATFORM'] == 'darwin': darwin_shlinkflags = [n for n in env['SHLINKFLAGS'] if n != '-dynamiclib'] env.Replace(SHLINKFLAGS=darwin_shlinkflags) env.AppendUnique(SHLINKFLAGS=['-bundle']) env.AppendUnique(SHLINKFLAGS=['-undefined', 'dynamic_lookup']) fast_linkflags[:] = [] # Compiler specific options if icpc: # options for Intel C++ compiler on hpc dev-intel07 env.AppendUnique(CCFLAGS=['-w1', '-fp-model', 'precise']) env.PrependUnique(LIBS=['imf']) fast_optimflags = ['-fast', '-no-ipo'] else: # g++ options env.AppendUnique(CCFLAGS=['-Wall', '-fno-strict-aliasing']) fast_optimflags = ['-ffast-math'] # Configure build variants if env['build'] == 'debug': env.AppendUnique(CCFLAGS='-g') elif env['build'] == 'fast': env.AppendUnique(CCFLAGS=['-O3'] + fast_optimflags) env.AppendUnique(CPPDEFINES='NDEBUG') env.AppendUnique(LINKFLAGS=fast_linkflags) env.AppendUnique(SHLINKFLAGS=fast_shlinkflags) if env['profile']: env.AppendUnique(CCFLAGS='-pg') env.AppendUnique(LINKFLAGS='-pg') env.Append(CPPPATH=[pjoin(env['prefix'], 'include')]) env.Append(LIBPATH=[pjoin(env['prefix'], 'lib')]) builddir = env.Dir('build/%s-%s' % (env['build'], platform.machine())) Export('env', 'pyconfigvar', 'pyoutput', 'pyversion') if os.path.isfile('sconscript.local'): env.SConscript('sconscript.local') env.SConscript('src/extensions/SConscript', variant_dir=builddir) # vim: ft=python pyobjcryst-2024.2.1/examples/000077500000000000000000000000001470422267000157745ustar00rootroot00000000000000pyobjcryst-2024.2.1/examples/QPA-Quantitative phase analysis.ipynb000066400000000000000000005732251470422267000250370ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "5536331c", "metadata": {}, "source": [ "## Quantitative phase analysis (QPA)\n", "Fox/ObjCryst++ was not designed with QPA in mind, but it\n", "is still possible to do it when the phases are known\n", "and the profiles not too complicated.\n", "\n", "Here we just try the 'simple' cases of the QPA Round Robin of\n", "1999 (https://www.iucr.org/__data/iucr/powder/QARR/index.html)" ] }, { "cell_type": "code", "execution_count": 1, "id": "6738497f", "metadata": {}, "outputs": [], "source": [ "# 'widget' allows live update and works in both classical notebooks and jupyter-lab.\n", "# Otherwise 'notebook', 'ipympl', 'inline' can be used\n", "%matplotlib widget\n", "\n", "import os\n", "import pyobjcryst\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from pyobjcryst.crystal import *\n", "from pyobjcryst.powderpattern import *\n", "from pyobjcryst.indexing import *\n", "from pyobjcryst.molecule import *\n", "from pyobjcryst.globaloptim import MonteCarlo\n", "from pyobjcryst.io import xml_cryst_file_save_global\n", "from pyobjcryst.lsq import LSQ\n", "from pyobjcryst.refinableobj import refpartype_scattdata_scale" ] }, { "cell_type": "markdown", "id": "c3a87c32", "metadata": {}, "source": [ "### Data and CIF sources" ] }, { "cell_type": "code", "execution_count": 2, "id": "bc37d7f1", "metadata": {}, "outputs": [], "source": [ "# Data from QPA round-robin\n", "# https://www.iucr.org/__data/iucr/powder/QARR/samples.htm\n", "# & Crystal structures from the Crystallography Open Database\n", "# Try samples 1a to 1h\n", "data_file = \"cpd-1h.prn\"\n", "cod_phases = [1000032, 9008877, 5000222] # Al2O3, ZnO, CaF2 - needs checking" ] }, { "cell_type": "markdown", "id": "284ffc63", "metadata": {}, "source": [ "### Create Powder pattern" ] }, { "cell_type": "code", "execution_count": 3, "id": "ab29d515", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.5418366591135662\n", "Imported powder pattern: 7251 points, 2theta= 5.000 -> 150.000, step= 0.020\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "eb5a16017cbd44b8861a27bff409366b", "version_major": 2, "version_minor": 0 }, "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4QAAAGQCAYAAAD2lq6fAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABfPElEQVR4nO3deXxU5d3//3fIngAjIYYQCYtCAUUUQSFolQoiVOT2py1VbNS7VMUFjIBYSlW0FRS/ClZaF2rFCjTVKt7WJQLaYhFZBKOAFFGRPQQ1ZCEh6/X7g57DTDJZZjKT2V7Px2Meysw1Z65zZTI57/lc5zpRxhgjAAAAAEDEaRfoDgAAAAAAAoNACAAAAAARikAIAAAAABGKQAgAAAAAEYpACAAAAAARikAIAAAAABGKQAgAAAAAEYpACAAAAAARikAIAAAAABGKQAgAAAAAEYpACAAAAAARikAIAAAAABGKQAgAAAAAEYpACAAAAAARikAIAAAAABGKQAgAAAAAEYpACAAAAAARikAIAAAAABGKQAgAAAAAEYpACAAAAAARikAIAAAAABGKQAgAAAAAEYpACAAAAAARikAIAAAAABGKQAgAAAAAEYpACAAAAAARikAIAAAAABGKQAgAAAAAEYpACAAAAAARikAIAAAAABGKQAgAAAAAEYpACAAAAAARikAIAAAAABGKQAgAAAAAEYpACAAAAAARikAIAAAAABGKQAgAAAAAEYpACAAAAAARikAIAAAAABGKQAgAAAAAEYpACAAAAAARikAIAAAAABGKQAgAAAAAEYpACAAAAAARikAIAAAAABGKQAgAAAAAESom0B1A69TV1engwYPq0KGDoqKiAt0dAAAAoNWMMSotLVVGRobataOG5U8EwhB38OBBZWZmBrobAAAAgM/t27dP3bp1C3Q3whqBMMR16NBB0olflo4dOwa4NwAAAEDrlZSUKDMz0z7Whf8QCEOcNU20Y8eOBEIAAACEFU6J8j8m5AIAAABAhCIQAgAAAECEIhACAAAAQITiHEIAAADAR2pra1VdXR3obgS92NhYRUdHB7obEIEQAAAAaDVjjAoKCnT06NFAdyVknHLKKUpPT2fhmAAjEAIAAACtZIXBtLQ0JSUlEXKaYIxReXm5CgsLJUldu3YNcI8iG4EQAAAAaIXa2lo7DHbu3DnQ3QkJiYmJkqTCwkKlpaUxfTSAWFQGAAAAaAXrnMGkpKQA9yS0WOPFOZeBRSAEAAAAfIBpop5hvIIDgRAAAAAAIhSBEIDfvPbaa1qxYkWguwEAALzwr3/9S1FRUaycGuZYVAaA31xzzTWSTqwmBgAAgOBDhRAAAAAAIhSBEAAAAIhQlZWVmjp1qtLS0pSQkKCLLrpImzZtcmnz4Ycf6pxzzlFCQoKGDh2qrVu32o/t2bNHV155pTp16qTk5GSdddZZevvtt9t6N9AKBEIAAAAgQs2cOVOvvvqqXnzxRW3ZskW9e/fW5Zdfru+//95uc8899+j//b//p02bNiktLU3jx4+3LxVxxx13qLKyUh988IG2bt2qRx99VO3btw/U7sALnEMIAAAA+EF5ebn+85//tPnr9uvXr0XXRDx27JiefvppLVmyRGPHjpUkLV68WKtWrdLzzz+v888/X5L0wAMP6LLLLpMkvfjii+rWrZtWrFihCRMmaO/evbrmmmt09tlnS5JOP/10P+0V/IVACAAAAPjBf/7zHw0ePLjNX3fz5s0677zzmm331Vdfqbq6WhdeeKF9X2xsrC644ALt2LHDDoRZWVn24ykpKerbt6927NghSZo6dapuu+02rVy5UqNGjdI111yjgQMH+niP4E8EQgAAAMAP+vXrp82bNwfkdVvCWgW8/gXijTHNXjTeevyXv/ylLr/8cr311ltauXKl5s2bp8cff1xTpkzxoucIBAIhAAAA4AdJSUktqtQFSu/evRUXF6e1a9dq4sSJkqTq6mp9/PHHysnJsdutX79e3bt3lyQVFRXpiy++cAmdmZmZmjx5siZPnqxZs2Zp8eLFBMIQQiAEAAAAIlBycrJuu+023XPPPUpJSVH37t01f/58lZeXa9KkSfr0008lSQ899JA6d+6sLl26aPbs2UpNTdVVV10lScrJydHYsWP1gx/8QEVFRXr//ffVv3//AO4VPEUgBAAAACLUI488orq6OmVnZ6u0tFRDhgzRu+++q06dOrm0ueuuu7Rr1y6dc845euONNxQXFydJqq2t1R133KH9+/erY8eOGjNmjBYsWBCo3YEXoow1eRghqaSkRA6HQ8XFxerYsWOguwO4sM4v4GMGABDOjh8/rt27d6tXr15KSEgIdHdCRlPjxjFu2+E6hAAAAAAQoQiEAAAAABChCIQAAAAAEKEIhAAAAAAQoQiEAAAAABChCIQAAAAAEKEIhAAAAAAQoQiEAAAAABChCIQAAAAAPDZnzhyde+65ge4GWolA6MaBAwf085//XJ07d1ZSUpLOPfdcbd682X7cGKM5c+YoIyNDiYmJGjFihLZv3+6yjcrKSk2ZMkWpqalKTk7W+PHjtX//fpc2RUVFys7OlsPhkMPhUHZ2to4ePdoWuwgAAAAABML6ioqKdOGFFyo2NlbvvPOOPv/8cz3++OM65ZRT7Dbz58/XE088oUWLFmnTpk1KT0/XZZddptLSUrtNTk6OVqxYodzcXK1du1ZlZWUaN26camtr7TYTJ05Ufn6+8vLylJeXp/z8fGVnZ7fl7gIAAACIYATCeh599FFlZmbqhRde0AUXXKCePXtq5MiROuOMMySdqA4uXLhQs2fP1tVXX60BAwboxRdfVHl5uZYvXy5JKi4u1vPPP6/HH39co0aN0qBBg7R06VJt3bpVq1evliTt2LFDeXl5+tOf/qSsrCxlZWVp8eLFevPNN7Vz586A7T8AAAAiR11dnR599FH17t1b8fHx6t69ux5++GFJ0r333qsf/OAHSkpK0umnn6777rtP1dXVTW7vz3/+s8466yzFx8era9euuvPOO9tiN9AKBMJ63njjDQ0ZMkQ//elPlZaWpkGDBmnx4sX247t371ZBQYFGjx5t3xcfH69LLrlE69atkyRt3rxZ1dXVLm0yMjI0YMAAu81HH30kh8OhoUOH2m2GDRsmh8NhtwEAAAD8adasWXr00Ud133336fPPP9fy5cvVpUsXSVKHDh20ZMkSff7553ryySe1ePFiLViwoNFtPf3007rjjjt0yy23aOvWrXrjjTfUu3fvttoVeCkm0B0INl9//bWefvppTZs2Tb/+9a+1ceNGTZ06VfHx8brhhhtUUFAgSfYviqVLly7as2ePJKmgoEBxcXHq1KlTgzbW8wsKCpSWltbg9dPS0uw27lRWVqqystL+d0lJiXc7CgAAgIhWWlqqJ598UosWLdKNN94oSTrjjDN00UUXSZJ+85vf2G179uyp6dOn629/+5tmzpzpdnu/+93vNH36dN111132feeff74f9wC+QCCsp66uTkOGDNHcuXMlSYMGDdL27dv19NNP64YbbrDbRUVFuTzPGNPgvvrqt3HXvrntzJs3Tw8++GCL9gUAAAABVF4u/ec/bf+6/fpJSUnNNtuxY4cqKys1cuRIt4///e9/18KFC/Xll1+qrKxMNTU16tixo9u2hYWFOnjwYKPbQvAiENbTtWtXnXnmmS739e/fX6+++qokKT09XdKJCl/Xrl3tNoWFhXbVMD09XVVVVSoqKnKpEhYWFmr48OF2m8OHDzd4/SNHjjSoPjqbNWuWpk2bZv+7pKREmZmZnu4mAAAA/O0//5EGD2771928WTrvvGabJSYmNvrY+vXrde211+rBBx/U5ZdfLofDodzcXD3++OMebwvBjUBYz4UXXthgUZcvvvhCPXr0kCT16tVL6enpWrVqlQYNGiRJqqqq0po1a/Too49KkgYPHqzY2FitWrVKEyZMkCQdOnRI27Zt0/z58yVJWVlZKi4u1saNG3XBBRdIkjZs2KDi4mI7NLoTHx+v+Ph43+40AAAAfK9fvxPhLBCv2wJ9+vRRYmKi3nvvPf3yl790eezDDz9Ujx49NHv2bPs+6/Qodzp06KCePXvqvffe049+9CPv+o2AIBDWc/fdd2v48OGaO3euJkyYoI0bN+q5557Tc889J+nENM+cnBzNnTtXffr0UZ8+fTR37lwlJSVp4sSJkiSHw6FJkyZp+vTp6ty5s1JSUjRjxgydffbZGjVqlKQTVccxY8bo5ptv1rPPPitJuuWWWzRu3Dj17ds3MDsPAAAA30lKalGlLlASEhJ07733aubMmYqLi9OFF16oI0eOaPv27erdu7f27t2r3NxcnX/++Xrrrbe0YsWKJrc3Z84cTZ48WWlpaRo7dqxKS0v14YcfasqUKW20R/AGgbCe888/XytWrNCsWbP00EMPqVevXlq4cKGuv/56u83MmTNVUVGh22+/XUVFRRo6dKhWrlypDh062G0WLFigmJgYTZgwQRUVFRo5cqSWLFmi6Ohou82yZcs0depUezXS8ePHa9GiRW23swAAAIho9913n2JiYnT//ffr4MGD6tq1qyZPnqxJkybp7rvv1p133qnKykpdccUVuu+++zRnzpxGt3XjjTfq+PHjWrBggWbMmKHU1FT95Cc/abudgVeijDEm0J2A90pKSuRwOFRcXNzoSb5AoFgLJPExAwAIZ8ePH9fu3bvVq1cvJSQkBLo7IaOpceMYt+1wHUIAAAAAiFAEQiDMHDt2TN9//32guwEAAIAQQCAEwsygQYPUuXPnQHcDAAAAIYBACISZXbt2BboLAAAACBEEQgAAAACIUARCAAAAwAdYVdszjFdwIBACAAAArRAbGytJKi8vD3BPQos1Xtb4ITC4MD0AAADQCtHR0TrllFNUWFgoSUpKSrKvxYuGjDEqLy9XYWGhTjnlFEVHRwe6SxGNQAgAAAC0Unp6uiTZoRDNO+WUU+xxQ+AQCAEAAIBWioqKUteuXZWWlqbq6upAdyfoxcbGUhkMEgRCAAAAwEeio6MJOggpLCoDAAAAABGKQAgAAAAAEYpACABuvP3228rNzQ10NwAAAPwqynBFyJBWUlIih8Oh4uJidezYMdDdQRCwlrkOhl/tYOqLp0K57wAAhDqOcdsOFUIAAAAAiFAEQgAAAACIUARCAAAAAIhQBEIAAAAAiFAEQgAAAACIUARCAAAAAIhQBEIAAAAAiFAEQgAAAACIUARCAAAAAIhQBEIAAAAAiFAEQgAAAACIUARCAAAAAIhQBEIAAAAAiFAEQgAAAACIUARCAAAAAIhQBEIAAAAAiFAEQgAAAACIUATCeubMmaOoqCiXW3p6uv24MUZz5sxRRkaGEhMTNWLECG3fvt1lG5WVlZoyZYpSU1OVnJys8ePHa//+/S5tioqKlJ2dLYfDIYfDoezsbB09erQtdhEAAAAAJBEI3TrrrLN06NAh+7Z161b7sfnz5+uJJ57QokWLtGnTJqWnp+uyyy5TaWmp3SYnJ0crVqxQbm6u1q5dq7KyMo0bN061tbV2m4kTJyo/P195eXnKy8tTfn6+srOz23Q/AQAAAES2mEB3IBjFxMS4VAUtxhgtXLhQs2fP1tVXXy1JevHFF9WlSxctX75ct956q4qLi/X888/rpZde0qhRoyRJS5cuVWZmplavXq3LL79cO3bsUF5entavX6+hQ4dKkhYvXqysrCzt3LlTffv2bbudBQAAABCxqBC6sWvXLmVkZKhXr1669tpr9fXXX0uSdu/erYKCAo0ePdpuGx8fr0suuUTr1q2TJG3evFnV1dUubTIyMjRgwAC7zUcffSSHw2GHQUkaNmyYHA6H3aYxlZWVKikpcbkBAAAAgDcIhPUMHTpUf/nLX/Tuu+9q8eLFKigo0PDhw/Xdd9+poKBAktSlSxeX53Tp0sV+rKCgQHFxcerUqVOTbdLS0hq8dlpamt2mMfPmzbPPO3Q4HMrMzPR6XwEAAABENgJhPWPHjtU111yjs88+W6NGjdJbb70l6cTUUEtUVJTLc4wxDe6rr34bd+1bsp1Zs2apuLjYvu3bt6/ZfQIAAAAAdwiEzUhOTtbZZ5+tXbt22ecV1q/iFRYW2lXD9PR0VVVVqaioqMk2hw8fbvBaR44caVB9rC8+Pl4dO3Z0uQEAAACANwiEzaisrNSOHTvUtWtX9erVS+np6Vq1apX9eFVVldasWaPhw4dLkgYPHqzY2FiXNocOHdK2bdvsNllZWSouLtbGjRvtNhs2bFBxcbHdBgAAAAD8jVVG65kxY4auvPJKde/eXYWFhfrd736nkpIS3XjjjYqKilJOTo7mzp2rPn36qE+fPpo7d66SkpI0ceJESZLD4dCkSZM0ffp0de7cWSkpKZoxY4Y9BVWS+vfvrzFjxujmm2/Ws88+K0m65ZZbNG7cOFYYBQAAANBmCIT17N+/X9ddd52+/fZbnXrqqRo2bJjWr1+vHj16SJJmzpypiooK3X777SoqKtLQoUO1cuVKdejQwd7GggULFBMTowkTJqiiokIjR47UkiVLFB0dbbdZtmyZpk6daq9GOn78eC1atKhtdxYAAABARIsyxphAdwLeKykpkcPhUHFxMecTQtLJBYuC4Vc7mPriqVDuOwAAoY5j3LbDOYQAAAAAEKEIhAAAAAAQoQiEAAAAABChCIQAAAAAEKEIhAAAAAAQoQiEAAAAABChCIRAmOJyCQAAAGgOgRAIU8EUCIOpLwAAADiJQAgAAAAAEYpACISpYKrKBVNfAAAAcBKBEAhTwRTCgqkvAAAAOIlACISpYAphwdQXAAAAnEQgBAAAAIAIRSAEwlQwVeWCqS8AAAA4iUAIhKlgCmHB1BcAAACcRCAEwlQwhbBg6gsAAABOIhACAAAAQIQiEAJhKpiqcsHUFwAAAJxEIATCVDCFsGDqCwAAAE4iEAJhihAGAACA5hAIAfgd4RQAACA4EQiBMBVMISyY+gIAAICTCIRAmAqmEBZMfQEAAMBJBEIgTBHCAAAA0BwCIQC/I5wCAAAEJwIhEKaCKYQFU18AAABwEoEQgN8RCAEAAIITgRAIU4QwAAAANIdACISpYAqEwdQXAAAAnEQgBOB3BEIAAIDgRCAEwlQwhbBg6gsAAABOIhA2Y968eYqKilJOTo59nzFGc+bMUUZGhhITEzVixAht377d5XmVlZWaMmWKUlNTlZycrPHjx2v//v0ubYqKipSdnS2HwyGHw6Hs7GwdPXq0DfYKAAAAAAiETdq0aZOee+45DRw40OX++fPn64knntCiRYu0adMmpaen67LLLlNpaandJicnRytWrFBubq7Wrl2rsrIyjRs3TrW1tXabiRMnKj8/X3l5ecrLy1N+fr6ys7PbbP8Q3oKpKhdMfQEAAMBJBMJGlJWV6frrr9fixYvVqVMn+35jjBYuXKjZs2fr6quv1oABA/Tiiy+qvLxcy5cvlyQVFxfr+eef1+OPP65Ro0Zp0KBBWrp0qbZu3arVq1dLknbs2KG8vDz96U9/UlZWlrKysrR48WK9+eab2rlzZ0D2GeElmEJYMPUFAAAAJxEIG3HHHXfoiiuu0KhRo1zu3717twoKCjR69Gj7vvj4eF1yySVat26dJGnz5s2qrq52aZORkaEBAwbYbT766CM5HA4NHTrUbjNs2DA5HA67DRAuCIQAAADBiUDoRm5urrZs2aJ58+Y1eKygoECS1KVLF5f7u3TpYj9WUFCguLg4l8qiuzZpaWkNtp+Wlma3caeyslIlJSUuN8Adb0LYoUOHNGnSJNXU1PihRwAAAAg2BMJ69u3bp7vuuktLly5VQkJCo+2ioqJc/m2MaXBfffXbuGvf3HbmzZtnL0LjcDiUmZnZ5GsicnkTCB966CH9+c9/1meffRbwvgAAAMD/CIT1bN68WYWFhRo8eLBiYmIUExOjNWvW6Pe//71iYmLsymD9Kl5hYaH9WHp6uqqqqlRUVNRkm8OHDzd4/SNHjjSoPjqbNWuWiouL7du+fftatb+As3btTnwk+DrAEQgBAACCE4GwnpEjR2rr1q3Kz8+3b0OGDNH111+v/Px8nX766UpPT9eqVavs51RVVWnNmjUaPny4JGnw4MGKjY11aXPo0CFt27bNbpOVlaXi4mJt3LjRbrNhwwYVFxfbbdyJj49Xx44dXW6AO96EMCsQ1tXVBbwvAAAA8L+YQHcg2HTo0EEDBgxwuS85OVmdO3e278/JydHcuXPVp08f9enTR3PnzlVSUpImTpwoSXI4HJo0aZKmT5+uzp07KyUlRTNmzNDZZ59tL1LTv39/jRkzRjfffLOeffZZSdItt9yicePGqW/fvm24x8BJ1nRlAhwAAEBkIBB6YebMmaqoqNDtt9+uoqIiDR06VCtXrlSHDh3sNgsWLFBMTIwmTJigiooKjRw5UkuWLFF0dLTdZtmyZZo6daq9Gun48eO1aNGiNt8fhCdvQp0VCKkQAgAARIYow5FaSCspKZHD4VBxcTHTRyHpZKg7ePCgunbt6tFz7777bi1cuFDr1q1TVlaWz/py4MABZWRktHp7bYlqKQAAgcMxbtvhHEIANn+FIEIVAABAcCIQAmEqmKaMAgAAIDgRCAHYuOxEQ6HcdwAAgOYQCIEw1ZoKIYHwpFDuOwAAQHMIhECYIhACAACgOQRCADZ/XZg+lBFmAQBAOCMQAmGKCqFvhHLfAQAAmkMgBGAjEDYUyn0HAABoDoEQCFNUCH0jlPsOAADQHAIhEKa8CTKcQ9gQgRAAAIQzAiEAGxXChkK57wAAAM0hEAJhiimjvhHKfQcAAGgOgRAIU8E0ZTSUQ1Uo9x0AAKA5BEIANn9VCEMZYwEAAMIZgRAIU60JMlYw9BVCFQAAQHAiEAJogHMITwrlvgMAADSHQAiEqdYsKuNroRyqQrnvAAAAzSEQAmGKIOMbjCMAAAhnBEIAfhfKoSqU+w4AANAcAiEQploTZDiH8KRQ7jsAAEBzCIQAGiAQnhTKfQcAAGgOgRAIU8FUIQxljAUAAAhnBEIgTLVmldG6urqA9yVYhHLfAQAAmkMgBNAAU0ZPCuW+AwAANIdACIQppoz6BmMBAADCGYEQCFPBFAgJVQAAAMGJQAigAQLhSaHcdwAAgOYQCIEwRYXQN2prawPdBQAAAL8hEAKw+WuV0VBWXV0d6C4AAAD4DYEQCFNUCH2DQAgAAMIZgRAIU625DiGB8KSqqqpAdwEAAMBvCIQAGiAQnkSFEAAAhDMCYT1PP/20Bg4cqI4dO6pjx47KysrSO++8Yz9ujNGcOXOUkZGhxMREjRgxQtu3b3fZRmVlpaZMmaLU1FQlJydr/Pjx2r9/v0uboqIiZWdny+FwyOFwKDs7W0ePHm2LXUSECKYpo6GMsQAAAOGMQFhPt27d9Mgjj+jjjz/Wxx9/rEsvvVT/8z//Y4e++fPn64knntCiRYu0adMmpaen67LLLlNpaam9jZycHK1YsUK5ublau3atysrKNG7cOJfVCidOnKj8/Hzl5eUpLy9P+fn5ys7ObvP9BdyhQngSC+wAAIBwFhPoDgSbK6+80uXfDz/8sJ5++mmtX79eZ555phYuXKjZs2fr6quvliS9+OKL6tKli5YvX65bb71VxcXFev755/XSSy9p1KhRkqSlS5cqMzNTq1ev1uWXX64dO3YoLy9P69ev19ChQyVJixcvVlZWlnbu3Km+ffu27U4jLLXmHEJfh6BQDoSh3HcAAIDmUCFsQm1trXJzc3Xs2DFlZWVp9+7dKigo0OjRo+028fHxuuSSS7Ru3TpJ0ubNm1VdXe3SJiMjQwMGDLDbfPTRR3I4HHYYlKRhw4bJ4XDYbRpTWVmpkpISlxvgTjBNGQ3lUEWFEAAAhDMCoRtbt25V+/btFR8fr8mTJ2vFihU688wzVVBQIEnq0qWLS/suXbrYjxUUFCguLk6dOnVqsk1aWlqD101LS7PbNGbevHn2eYcOh0OZmZle7yfQmFAOcL7GWAAAgHBGIHSjb9++ys/P1/r163Xbbbfpxhtv1Oeff24/bk2rsxhjGtxXX/027tq3ZDuzZs1ScXGxfdu3b19LdgkRiAqhb4Ry3wEAAJpDIHQjLi5OvXv31pAhQzRv3jydc845evLJJ5Weni5JDap4hYWFdtUwPT1dVVVVKioqarLN4cOHG7zukSNHGlQf64uPj7dXQLVugK8RCE9iyigAAAhnBMIWMMaosrJSvXr1Unp6ulatWmU/VlVVpTVr1mj48OGSpMGDBys2NtalzaFDh7Rt2za7TVZWloqLi7Vx40a7zYYNG1RcXGy3AVor0BVC522EciAM5b4DAAA0h1VG6/n1r3+tsWPHKjMzU6WlpcrNzdW//vUv5eXlKSoqSjk5OZo7d6769OmjPn36aO7cuUpKStLEiRMlSQ6HQ5MmTdL06dPVuXNnpaSkaMaMGTr77LPtVUf79++vMWPG6Oabb9azzz4rSbrllls0btw4VhiFz3gTZKznUBU7ibEAAADhjEBYz+HDh5Wdna1Dhw7J4XBo4MCBysvL02WXXSZJmjlzpioqKnT77berqKhIQ4cO1cqVK9WhQwd7GwsWLFBMTIwmTJigiooKjRw5UkuWLFF0dLTdZtmyZZo6daq9Gun48eO1aNGitt1ZoB4rEFIhPCmU+w4AANAcAmE9zz//fJOPR0VFac6cOZozZ06jbRISEvTUU0/pqaeearRNSkqKli5d6m03gWa1pkLY0uceP35cxhglJib6vC/BggohAAAIZ5xDCISptjiHsHfv3kpKSmp2G6EcCEO57wAAAM0hEAKweVohPHDggD+7ExSoEAIAgHBGIATCVFtMGW3p64dylS2U+w4AANAcAiEAm79WGQ3lUBXKfQcAAGgOgRAIU1yH0DeYMgoAAMIZgRAIU4GeMhouGAsAABDOCIQAbJxD2BAVQgAAEM4IhECYCqYKYSgHwlDuOwAAQHMIhABsVAgbokIIAADCGYEQCFOtCWGRHoLCJcwCAAA0h0AIhKlATxkNl1AV6eEYAACENwIhABvnEJ4QLmEWAACgOQRCIExRIfSNUO47AABAcwiEABqI9BDkvP9MGQUAAOGMQAiEKSqE3gvlvgMAAHiCQAiEqUAHwtb2JVhQIQQAAOGMQAjAZgU3X4SgUK6yhXLfAQAAPEEgBMJUa4IMIegkKoQAACCcEQiBMBXoKaOhXGUL5b4DAAB4gkAIwMY5hA1RIQQAAOGMQAiEKSqE3gvlvgMAAHiCQAjA5q8KYShjLAAAQDgjEAJhqjVBhlVGQ7fvAAAAniAQAmGKKaPeC+W+AwAAeIJACMBGIGwolPsOAADQHAIhEKaCqUIYapz7ziqjAAAgnBEIATRAhfCkUO47AABAcwiEQJgKpgphqIWqUO47AACAJwiEQJhqTSD09TTJUA5Vodx3AACA5hAIAdioEJ4Qyn0HAADwBIEQCFNMGfVeKPX9wIED2rNnT6C7AQAAQhSBsJ558+bp/PPPV4cOHZSWlqarrrpKO3fudGljjNGcOXOUkZGhxMREjRgxQtu3b3dpU1lZqSlTpig1NVXJyckaP3689u/f79KmqKhI2dnZcjgccjgcys7O1tGjR/29i4gQrQkykR4InQV737t166aePXsGuhsAACBEEQjrWbNmje644w6tX79eq1atUk1NjUaPHq1jx47ZbebPn68nnnhCixYt0qZNm5Senq7LLrtMpaWldpucnBytWLFCubm5Wrt2rcrKyjRu3DjV1tbabSZOnKj8/Hzl5eUpLy9P+fn5ys7ObtP9BZxRITyBy04AAIBIERPoDgSbvLw8l3+/8MILSktL0+bNm3XxxRfLGKOFCxdq9uzZuvrqqyVJL774orp06aLly5fr1ltvVXFxsZ5//nm99NJLGjVqlCRp6dKlyszM1OrVq3X55Zdrx44dysvL0/r16zV06FBJ0uLFi5WVlaWdO3eqb9++bbvjCDtMGfWNUO47AABAc6gQNqO4uFiSlJKSIknavXu3CgoKNHr0aLtNfHy8LrnkEq1bt06StHnzZlVXV7u0ycjI0IABA+w2H330kRwOhx0GJWnYsGFyOBx2G6CtebvKqLvQFMqBMJT7DgAA4AkqhE0wxmjatGm66KKLNGDAAElSQUGBJKlLly4ubbt06WIv7FBQUKC4uDh16tSpQRvr+QUFBUpLS2vwmmlpaXYbdyorK1VZWWn/u6SkxIs9QyRoy3MI6+rqFB0d3eg2QjlUhXLfAQAAmkOFsAl33nmnPvvsM/31r39t8FhUVJTLv40xDe6rr34bd+2b2868efPsRWgcDocyMzOb2w1EqLacMkqFEAAAIDQRCBsxZcoUvfHGG/rnP/+pbt262fenp6dLUoMqXmFhoV01TE9PV1VVlYqKippsc/jw4Qave+TIkQbVR2ezZs1ScXGxfdu3b593O4iw1Nrw4m0gdDfFNFxCVSj3HQAAoDkEwnqMMbrzzjv12muv6f3331evXr1cHu/Vq5fS09O1atUq+76qqiqtWbNGw4cPlyQNHjxYsbGxLm0OHTqkbdu22W2ysrJUXFysjRs32m02bNig4uJiu4078fHx6tixo8sNsLQ2hBEITwjlvgMAAHiCcwjrueOOO7R8+XL93//9nzp06GBXAh0OhxITExUVFaWcnBzNnTtXffr0UZ8+fTR37lwlJSVp4sSJdttJkyZp+vTp6ty5s1JSUjRjxgydffbZ9qqj/fv315gxY3TzzTfr2WeflSTdcsstGjduHCuMwmu+Ci9MGQ3dvgMAAHiCQFjP008/LUkaMWKEy/0vvPCCbrrpJknSzJkzVVFRodtvv11FRUUaOnSoVq5cqQ4dOtjtFyxYoJiYGE2YMEEVFRUaOXKklixZ4rLwxrJlyzR16lR7NdLx48dr0aJF/t1BhDVfVQg9XWU03CqEzrgOIQAACGcEwnpacuAaFRWlOXPmaM6cOY22SUhI0FNPPaWnnnqq0TYpKSlaunSpN90E3ArUlNHm2odaIAyXMAsAANAcziEEwkiwLioTysJlPwAAANwhEAJhJJgqhKFcZQvlvgMAAHiCQAiggUhfZdRZKPfdH44fPx7oLgAAAB8iEAJhhAqhb4Ry3/3p73//uxITE3Xo0KFAdwUAAPgIgRAII6wy6huh3Hd/+uc//ylJBEIAAMIIgRAII4FaVCbcKoTOuOwEAAAIZwRCIIz4KoRF+jmEodx3f4qKigp0FwAAgI8RCIEwEqhzCAmEbeeNN97QwIEDA90NAAAQJrgwPRBGmDLqG8Hc9xkzZmjXrl2B7gYAAAgTVAiBMEKF0DeCue/t2vGxDQAAfIcjCyCM+KpC6OlCKsYYvfbaa7rpppvc9iUQoaq2tlbV1dVePTfQfW+KFQgDudhNsI0JAADwHoEQCCOBXFTmmmuu0YsvvujzvnjriiuuUFxcnFfPdQ5bwRZ+rEBYW1vb5q/tz0Vl3nzzTVVWVvpt+wAAwD0CIRBGuDD9Se+++67Xzw1035tiBcKampoA98R39u3bpyuvvFL33XdfoLsCAEDEIRACYaS14cWqjPniHEJnwRaqmuPc32C7DqEVCL2dDhuMrDHeu3dvgHsCAEDkIRACYSSQFcLo6Gif9iWQgrnvwVAh9PWYxMbGShJTRgEACAACIRBGAnXZibq6ugarXwZzqGpOKJxDGIhAaJ1D6OuqqbeLGQEAgNYjEAJhpLUhrDVTRpuqEIYa575/9dVXuvDCC1VRURHAHp0UDBVCXy9oQxAEACBwuDA9EEZ8NWXUm8tOWIHQGKOoqKiQrhA69/fll1+WJG3btk3nn39+oLpkC4ZA6OsARyAEACBwqBACYSSQU0atQGgFlXAJhJZgCS3NLSqTl5enzz//3K99oEIIAED4oEIIhJFATRl1rhBWV1crNjaWQOgn1nl8jYWysWPHSvLPmPvrHEJrX0LtfQIAQDigQgiEkUCtMuq8qEw4VAjdBZ5gCYSWQFyY3l+vHWxjCwBAJCEQAmEkUFNGrfMGJffVnlALhKFcIWwL/jqH8B//+AfXIgQAoI0RCIEwEshVRgmEbSMYAqE/K4S33HKLT7cNAACaRiAEwoivKoTerDJqTRklEPpXIAOhv84hdN5e/etZAgAA/+IvLxBG2vIcQuc2wVwh9Ca8BHMgtITTlFHnfSEQAgDQtvjLC4SRtpwyGiqB0JvgFMyLyoT7lFECIQAAbYu/vEAYactFZeoHvqaCSqgFwmCuEAZDIPTnlFFr/wAAQNsgEAJhigrhCdZlMDzhrr+BDGDOGguEbTnGVAgBAAgf/OUFwkhrKzeeVAidXyuYLzvhq0BYXV3ti+74TP1Q1haBtS0WlaFCCABA2yIQAmGkfkjzlCerjIZKhdBX5xAGWyCs38e2nNLq6/DpvD0CIQAAbYtACIQR5wNrf08Zba5C2Npw6iutqRA6T1/0Zjv+0NiU0bYMhFQIAQAIHwRCIIy05ZTR5iqEwRIIW7OoTHR0tH1fsFQIGwuEbXmOoz/PISQQAgDQtgiEbnzwwQe68sorlZGRoaioKL3++usujxtjNGfOHGVkZCgxMVEjRozQ9u3bXdpUVlZqypQpSk1NVXJyssaPH6/9+/e7tCkqKlJ2drYcDoccDoeys7N19OhRP+8dwpmvpoz6ukIYSOFWIbTUD2UffPCB31+TcwgBAAg/BEI3jh07pnPOOUeLFi1y+/j8+fP1xBNPaNGiRdq0aZPS09N12WWXqbS01G6Tk5OjFStWKDc3V2vXrlVZWZnGjRvnchA3ceJE5efnKy8vT3l5ecrPz1d2drbf9w/hq7WVm9asMmqFp2CrEPoqEAZ7hfCtt97y+2tb40KFEACA8BET6A4Eo7Fjx2rs2LFuHzPGaOHChZo9e7auvvpqSdKLL76oLl26aPny5br11ltVXFys559/Xi+99JJGjRolSVq6dKkyMzO1evVqXX755dqxY4fy8vK0fv16DR06VJK0ePFiZWVlaefOnerbt2/b7CyC3s6dO3X77bcrLy9PsbGxTbZtywphOE8ZtfoeSoGwrKzM76/tyaJDnmBRGQAAAocKoYd2796tgoICjR492r4vPj5el1xyidatWydJ2rx5s6qrq13aZGRkaMCAAXabjz76SA6Hww6DkjRs2DA5HA67jTuVlZUqKSlxuSG8/e53v9P777+vPXv2NNu2tYvKeHLA39iUUasiFyyBsDUVQudzCIN9ymhbTDe3fqZUCAEACB8EQg8VFBRIkrp06eJyf5cuXezHCgoKFBcXp06dOjXZJi0trcH209LS7DbuzJs3zz7n0OFwKDMzs1X7g+BnHSB7el6fN3x5YfpgCYStWVQm2CuEzuPaFhVC62fKOYQAAIQPAqGX6h+0OFdIGlO/jbv2zW1n1qxZKi4utm/79u3zsOcIVd4s9OLta/hyUZmoqCgqhD7kPM7OP4OzzjrL769tvd5rr73ml+1KriEcAAD4H395PZSeni5JDap4hYWFdtUwPT1dVVVVKioqarLN4cOHG2z/yJEjDaqPzuLj49WxY0eXG8KbJxUTX00Z9WWFMDo6OmQDYTBWCC31A2G3bt3a5DUl6b333vPpdqkQAgAQOARCD/Xq1Uvp6elatWqVfV9VVZXWrFmj4cOHS5IGDx6s2NhYlzaHDh3Stm3b7DZZWVkqLi7Wxo0b7TYbNmxQcXGx3QaQPFvqvy2njLa0QhjoQNiaRWWCuUJYXFzsMq5t0T9/XUokWC5RAgBAJGKVUTfKysr05Zdf2v/evXu38vPzlZKSou7duysnJ0dz585Vnz591KdPH82dO1dJSUmaOHGiJMnhcGjSpEmaPn26OnfurJSUFM2YMUNnn322vepo//79NWbMGN1888169tlnJUm33HKLxo0bxwqjcGEFgJZUqKgQNhSuFcKpU6fq5ptvtv8dyoGQVUYBAAgcAqEbH3/8sX70ox/Z/542bZok6cYbb9SSJUs0c+ZMVVRU6Pbbb1dRUZGGDh2qlStXqkOHDvZzFixYoJiYGE2YMEEVFRUaOXKklixZ4lJxWLZsmaZOnWqvRjp+/PhGr30ItCSQ+KpC2JLtOIe8cKsQBnMgdP7ZOP9/KAdCpowCABA4BEI3RowY0eQBbFRUlObMmaM5c+Y02iYhIUFPPfWUnnrqqUbbpKSkaOnSpa3pKiKIp4HQm4N3bxeVCeYKoTdBzl0gDJYpo439jH19KQh3/PUaBEIAAAKHcwiBIOftlFF/B8L6r2WFp3AIhNY+xMSc/M4sWCqEVt+6d+/eoELo73FuiwphWwTbSLBv3z5FRUUpPz8/0F0BAAQ5AiEQIlpSoWrtgbUni8rUP18xWCuElZWVHj/H2ofY2Fj7vmCrEPbp06fBojL+XpylLc4hJBD6xpYtWyRJ77zzToB7AgAIdgRCIES05EC5LaeM1q8QhmMgDMYKoTWuVVVVDSqEzqH1m2++8dtr+3O7zu+rJUuWqLCw0C+vGe48+V0GAEQ2AiEQ5KwDupZUqFpbabFew5sKYf37Ax0IrQWcwm3KqDWu9a9DWFtb6/Ieee6553z+2v6q3rl731ZXV+t///d/9Ytf/MIvrwkAAE4gEAJBzgpTbVEhtEKPp9c8dFfhCXQgrF+x9IQVrIIxEFr7U3+KaE1Njd+nXrZFhdAae6uyW1ZW5pfXDHfW+59FegAAzSEQAiGiJQf4VAhPsg6EvTn3z905hMESCK1xdRcInfd10KBBfnttf27XGvvjx49LksuletByTBUFALQUgRAIcp5MGbUOrGNiYlpVIfTmHELnqYzOfQn0Ab03wbgtpoyWlZXplVde8fh5zuNcf1EZfy/I4vye8mXgcPdFRkFBgSTJ4XD47HUAAEBDBEIgRHgyZTQ2NrbNK4TWc+sHwqioqIBUK5wraZ5qi0A4efJkTZgwQcXFxV71rbkKoT8qmq2tQJeVlWnhwoUN3g/uKoTWlNHExERvuoogsnbtWmVnZwe6GwCARhAIgSDnSbBxnuroTYXQ20BYV1dn/9v5v+3atWs0EH7xxRf2tEB/qN8fb57rzymjBw8elCSPx8D5/VA/oPk7EDq/p7zZ/ty5c3X33Xfbl0SwOO9HTU2NHnjgAW3cuNH7jiJoLpMiSRMnTtTSpUsD3Q0AQCMIhECQqz8VsyVtY2JivApC3k4ZdVchrK6uVmxsrNtAWFtbq759+2ratGke97El6k+l9JT1HH8GQuscR08vi+H8frDGul27dg0CYjAGQmtfKyoqGmw3MTFR119/vWpra/XQQw/p9ttvb11nI1xVVVWgu2Cz3ivBFFIBACcRCIEg50mlyxcVwpaef9hchbC6ulpxcXFuA2FJSYkk6ZNPPvG4jy3R2HXtWso5aFl8HbCsbXsaCJ2njFoH2PHx8W02ZdSaRuvLg/va2lpFR0crOjpax44dc3mMxVG8E0yB0OLPGQEAAO8RCIEg5xwAmuOLcwhjY2O9mjJav0JYVVWl2NhYtWvXrkHAtA5W/XWw7+4yBp6wAorzkv3BEgjdTRm1AqG/K4S1tbVKSEjwevuN/bzr6urUrl07RUdHq7y8vFV9xAmeXEKmrdSvDMM/ysvLG3yxAgBNIRACQa5+0GqKcyD0dpXRuLg4r6aMuqsQxsbGKjY2tkF4sAJhW17o3NPnx8TEtEkg9PYcQucpo/Hx8W1yDqFzIPQmaFt9r9+3uro6RUdHKyYmhkDoI9bvWDBUCq3PEwJh2zjttNOUmpoa6G4ACCEEQiDIeXL+jfPqmJ4Gobq6OhljWlwhrH9h+samjDYVCNvyQueeqKmpsQNKa7bTFF9MGa1fIfR3IKypqWlVhdD6udQPKdYCRNHR0frmm29a3c/WmDlzpk499dSA9sEXrDEOlutnSkwZbStHjx5lrAF4JKb5JgACyZNqWnV1tR1kPA1b1oGjN1NGm1pUpqlA2BZTRlszdTYuLs7lPl9qzZTR6OjoBhXCtp4y2poKYf1AaE0Z/fDDD1vfyVZ67LHHAt0Fn7B+/sFQIbRQIQSA4ESFEAhyngZC67w9T4OQdYDvzZRRdxVC6xxCd4HQ+re/poy2tkJo9d05EAbTKqNxcXEuAdD6t/O++iMI1NTU2NcFbM05hPX32Tpn8/vvv299JyHp5M+/uffXJ598ooyMDHuhJ3+iagUAwYlACAQ568CuJcGmqqpKcXFxio6O9rhC2JpA2FiFMFBTRlt7DqE1jsFYIaytrW0QCK1zCIO9Quj8ZYEzq0LofM4mWsca40WLFunjjz9utN2f/vQnHTp0SDt37vR7n6gQAkBwIhACQa6tKoRWgIiLi/PZZSeamzIarOcQOodZ5/t8qTVTRuPi4txOGbX2NSYmJijPIdy1a5ck94EwOjra7XO47IR3nMd4+vTpjbazzpNti3MNCYQAEJwIhECQ86RCaIWw1lQIfXUOYVNTRoP9HMK2qBBaAeh3v/udR89zN2W0/jmEiYmJQXnZiffff19S04vK1Ld27Vovegrnn09Tv2eNTeP1B6aM+p+/puH7y6ZNm/Tb3/420N0AIh6BEAhybX0OoTeB0DmMeDJltC0uOxGs5xBatm3b5lF7a8poUxXChIQEv1cIWxOQ6wfCqqoqxcfHa9++fQ3a7t+/Xy+//LLXr+ULDzzwQJtMqfQl5zFuye+zrwJhdXW11q9f7/YxKoT+V1ZWZv9/KITDyy+/XPfffz8zAYAAIxACQc6bQOhNhdAKENb5aM2x2kRHR7scTLZkyqi/L5rtfDDszUGRFWb9WSG0xuCUU07x6HlWhVA6uZ/1F5XxZ4UwPj5ekucB2fmAz10gjIuL0/333+/2uYFYKdN63xhj9NBDD+m8885r8z60hvPvZFsGwt/+9rfKyspSQUFBg8eC8RqTN954o+bMmRPobviM8+JAbVH1ba3S0lJJwfneACIJgRAIcp4uKtPaCmGHDh1adCDhPD3R+Y95MJxD6C6geqJ+hTAuLs7nAcva3qWXXurR89wFwvqLyiQkJPhtlVFvK4TO/an//rIC4QMPPOD2udbKpm2p/oXdQ+2A1bka15ZTRr/88ktJrpUq671+8803a9myZT55HV/5y1/+ogcffDDQ3XDrzDPP1PLlyz16jnMgDIWKrPU50har3AJoHIEQCHKeVgitVUa9XVSmffv2LTqQcA6Ex44da7Ad6yA/EOcQOh/celPZO378uBISEuzg1aFDB59XCK0x8PS8KmvKqPNzExIS2qxC6O05hM4/k8YqhI0JxLlnhw4dkhQaVRZ3nANsWwZC6zxQ588E58+TlStXerS9devW6dtvv/VJ30LJ999/rx07duj666/36HlWxU3yz+9Nbm6urrnmGp9v17nfANoegRAIct6eQ+jtojLt27dv0YFEeXm52rVrpw4dOqioqKhBf50rhPXDlL/PIbQObhMTE716jYqKCiUmJtohpX379qqpqfFpgLUClaff4jtXCK1v1Tt27OhyHqe/ziGsrq72ukLo/J6qP0WvuUAYiErHGWec0eC1Q+GcLItzIGxsBVfpZJXeV+HBWj23uLhY0onA6bztpKQkj7Z34YUXauzYsT7pWygpLCyUdOLLKE84V9q6devm80u5XHfddXrttdd8uk0p8BXCgwcPasOGDU22qampUU5Ojv2zAcIJgRAIct6uMtqaKaO1tbXNvl5ZWZnat2+v+Ph4lwuKuwuE7ipCzv/1NSsQJicne1XZq6ioUEJCgn3wak1Z9GUg8CYQWgfX7du3lyQdPXpU0slA6O8KYXl5uX3OY2sqhNa2LMFYIbQ4H6ha50+GAufxtUKaO9bv4C233NLk9Qpbynota9yqqqpcvkhxvpRLc6znbd++vdX98sYrr7yiv/3tbwF5bevnl5qa6tHzdu/e7Y/u+F2gA+GkSZM0bNiwJtu8+uqrevLJJ/X444+3Ua+AtkMgBIKct4vKeDtl1PpGurmDcHeBsH379i5TIePj4+VwOOxqQf3Xcp5W5kvOgbA1FUJrLP7zn/9I8u1Ko9a+exIIKysrVVtbq44dO0o6UYWJi4tTUlKSamtr7f1OSkryeSA0xujYsWN2IPQ0aFt9+8UvfiHJdYqYdc6mOw6HI2gCYShVCCsqKpSVlSWp6SmjzmPr6SVQ3LGqkda41f/ZNVWtrM/6HfHX1PLmfkcmTJiga6+91mevV1dX1+LPPKudp+eu/uY3v/G4X97w9Zd5JSUl2r59e8BWG83Ly5PU9N+krVu3SpL+7//+r036BLQlAiEQxJwXCmnJAbgVwjp06ODxORnOU0al5oOKFQjj4uLsQJiSkmIfKHz33XdKSUlRampqg3OArDalpaV+WVjGOghtbYXQqlqlp6dL8u1Ko9aiG56EHetgpVOnTpJOnGfUvn17xcTEqKamRqWlpYqOjlbHjh19HggrKipkjPG6Qmi9n3r06CHJtWJYXFwsh8MhSTrnnHNcnpeQkNBmU0bdHYzWr1yEyvL45eXluuCCC3TLLbc0OX7O7z9PwlpjrN/nzz//XJWVlQ1e25NzFZ2nnbaWMUY333yzy+VD2vq8tdmzZ6t9+/Yt2h9vA+FPfvKTBvf54z3ri7Fz/jx9//33NWDAAE2YMKHV2/WG9YXUwYMHG21jfe42V0n0B67hCX8jEKJVrINE+IfzQXdLDsC/++47de7cWZ06dXI5r68lnKeMSt5VCFNSUux+Wn1JTU1VeXl5gymCFn+s3uhcKfOmqmMFlB//+Me6/vrr9fDDD7ts1xesQOhJ2LEOEtPS0iSdOM+offv2io6OVk1Njb7//nt17tzZ7TRdX/XX20BoHUCeeuqpklzH8ttvv7Wnxv3jH//Q9u3btXz5cj355JNKTExss0DovDKmxQqEkydPdvl3sCsvL1dSUlKDVYDrcx5bTwJhdXW1XTFxZr1HH374YU2ePLnB54gnswI8CYSHDx9W586d7VVO6/v222/1pz/9SXfeead9n3OoGTRoUKOL1/hqJsMLL7wgSQ1mTLhjfaaWlpYqKiqqxdN5jx49aleGLf6YibFy5UqdccYZrfrddB7/Xbt2STpZqZOkBQsW6KWXXvK+kx6wAuHhw4cbbWP9TNr6nObNmzcrMTFRb7/9dpu+LiILgRBee+mll5SUlKThw4drzZo1mj9/fsDO9QhXngan7777TqmpqUpJSXE5r68lrIMUqxrW3DfAViA8evSo/a2qVSGsq6vTkSNH1LlzZzsAHDlyxO1++eObT+cpo95Uyr799ludeuqpSk5O1tKlS9W9e3dJvq0olJWV6dRTT/Xo4MIKLFYgLCgoUHJysh0ACwsLdeqpp7bqMhlHjhxRVFSUNm7c6HK/dVBpVfI8rZZaQcoKfo0FwszMTJ155pm67rrrNHXqVCUnJ3t1QPvCCy/o008/9eg53333ncu/6+rq7N+LcePGSZLHX7QESnl5uRITE5WUlNTke8w54DZ1rmF9Dz/8sAYOHNggRDn/jqxZs6bBa3vyBZA19nV1dfaqr4354IMP9P333+vVV191+7j1/KioKPuzyLmv+fn56tevn9vn+moRkZiYGJe+NMX581KSLr744ha9Rm5urj766COX+/yxCMpdd92lr7/+utEA3hLO7739+/dLksaMGWPfN23aNN1www3ed9ID1uI7zuP+3XffKSoqSrm5uTLGaO7cuZJO/Pzq6ur0hz/8oU3CofXzvOKKK/z+WohcBEJ47c9//rMkaf369RoxYoTuvfdeDRgwQHfccYfmzp2rPXv2BLiHoc/5oNld9aI+68A6KSlJu3bt8qh6u2fPHkVHR+v888+XJH399ddNtrcCoXOVoGvXrioqKtK0adMknTi4t8KL8x9a5z+i/giExcXFateunU499VSPw0RFRUWDgzGrauqrQGidS3Tqqad6tP/W6n7WSp9r1qzR4cOHlZycrPLych0+fFhpaWlKTk5u0fvFnc8++0ySGhxYW2Gpffv2io2N9bhaah381a8QGmN05MiRRhfP8DYQ/uIXv/D4YvLWPt50002STrzHS0pKFB8fry5dukg6uZCPtyoqKvTb3/7WbwsqOb9OUlKSkpOTm3zfOu+PJ4HQCttWZcfifJAfHR1tv79feuklnXPOOR69L61t1dTUKCMjo8nnWq/TWJXT+tJq1apVSktLkzGmwbg4ny/o/IXHGWec4ZPqjNW3N998Uy+//HKTbet/Bo0ePbrFr3PNNddo3rx5uu+++yQ1XfXyhPP0fqt/rVnExgqB0okpxpL04YcfSmr7y71Y+3bkyBHNnz9f0dHRdhi97rrr9M0339htDxw4oHXr1unOO+9sMMXdUltb2+ALJm9Z78VBgwY12a68vFz//ve/ffKaiDwEQnjt73//u9v7//jHP2r27Nnq2bOnoqKi7NvPfvYzHT58WNu2bdOTTz7Zom9JI917771n/39zB1LGGDsQvvXWW5JOHty3xI4dO3TmmWeqW7duSkxMbPabXysQOhswYIC2b9+uL774QpJ0wQUXKDMzU5K0ZcsWu53zwY4/AuGBAweUnp6uzp07t2h6lrP169dLkvr06WPfZ1XFfFUdsgJOamqqR98wp6SkSHI9OPz++++VmJgoY4xefvllJScnKy0trcEBZUtZAcGqZliys7MlnajuujsvtDnWwXfnzp0lnfy5l5SUqLq6utFA2L59e48DofVFiKfnp1oHcNY5QqWlpSopKVHHjh3tqbLeBMKdO3fqkksukTFGzzzzjO6//36XqXH+YE0ZTU1N1ffff+8yFuXl5Ro9erT+85//uOxP/c+YZ555ptGQaP1e/eMf/3AJt86BsLKy0g4jw4YN0+DBgz36HTpw4IDLv5uqdFltN27cqH379jV4vP7fm3379jUIhNbPWHINUcYYnyy4Y43lzJkz9bOf/azJtvV/v1ryvrNC1MUXX6xf/epXuuOOOyS5jtvWrVs9+rvgzN3foNYEwq+++krSib8T1vvTeg3n7b799tvq2bOnR19wVlVVtXgWw/Hjx+3P4SNHjujee+9VXV2dy5cAVrjOysrS119/rR/+8IeSGn4hYnnssceUmprqkzBuhVFrvAoLC3XllVc2+FJp9OjRuvjii1tVtZWkTZs2uRx7cFpQZCAQwmvWwfaRI0f0yCOPNNv+5ZdfVnp6us4++2zl5OQoIyNDUVFRuvXWWxUVFaXJkyfb3xLiBOugfOTIkc0GwgceeECVlZX2uTKStHfv3ha9zmeffaann35aW7duVbt27XTGGWc0+ofOsnHjRiUnJ+svf/mLfV9ycrKOHz+ud955R4mJiUpLS7MP9G+99Va73Zdffqlu3bpJ8k8gfOihh3Tw4EEVFBRoy5YtHp1H+Ne//lWSdNVVV9n39erVS5JrqG0N62eZnp6umpqaFofCTz75RB07drQDqnRiWtUHH3xg//vNN99UWlqavv32W6/On7QWpZg7d67L862VVvv06aP09HQVFBR4tN19+/YpNTXV/rlb781bbrlF0smgWF/Hjh09DuLuzk1qCSsQWj/vwsJCOxBaY26dS+iJfv366YMPPtC2bds0Y8YMSScW0Wjpgdb+/fubrShJJ8a4srJS+/btU01NjYqLi5WWlqa6ujqXKeQbNmzQqlWr1L9/f+3fv9+ejlh/Vsdtt90mY4zuuuuuBq9ljdW8efM0a9Ys+37nGQPt2rXTjh07lJSUpDPOOMPjqeyTJk1y+5ruWO+nV155xZ7i7az+YiFff/11g+05v2/qf3bW/3LBWnXXE/UDqPVZ486zzz7r8m/nCpU7X3zxhV555RVJslchtn6nNm3aZLcbOHBgo1Wt5rgLpZ5+sTt16lTNnz9fknTjjTdKcv3yrbS0VMeOHbN/T6QTUyX37Nljf9Hp7LPPPlNUVJR27Njhcn98fLw9zbs5zj/rNWvWuG2zbNkySdL999/fom0uX75c0skvzlszs+DJJ5+UdOLLls2bN6tLly568803NX78eJd2VnXV+vLCk1VtLcYYXXDBBRo1apTKyso0d+5c9e7d2+u+I4QYBNwf/vAH07NnTxMfH2/OO+8888EHH7T4ucXFxUaSKS4u9mMPW662ttY8//zz5sEHHzSS/HIbOHCgGT9+vLnnnntMdHS0y2MffPCBKSsrM5s3bzYrV640n376qTlw4ID56quvzFdffWXWrVtn93XDhg2mrq4ugKPVvOuuu85IMrfddpvp27dvk22tMfjiiy9MXV2dkWSGDRvW7D5abSWZc8891xhjzA9/+EMjybz++uvmmmuuMc8995zLdn77298aSWbChAn28y+++GKzYMECe1tjx45t0LcxY8aY48ePG0lm9OjRRpJ58803WzFCDVm/E863nTt3Nrn/xhhz8803uzynPuv+ysrKVvfxhRdeMJLMww8/bCS5vC8bY42bJFNdXe2yb/v27bP/PX78eLNy5Uojydx+++2msLDQLFu2zFRVVdnbqqqqMsOGDTNr1651+bk6vxckmU8//dR+rH379va4NDZGTZFkhg4daowxJjMz05xxxhkmNzfX3tbx48fdPm/atGkmIyPDo9fatWuXvd3Bgwe3+HljxowxksyRI0eMJPPyyy+b6667zgwePNhUVlba2ywqKjKzZs0ykkxtbW2z283IyDCSTO/evU3fvn3t7axevbpF/Tr//PONJJOXl9dom0svvdTeblJSkv37++GHHxpJZu7cuXbbZ5991uXnPH36dPPAAw+Yrl27GmOMqampMevXr3dp8/rrr9vPr62tdXns6quvNsYYs3nzZvu+SZMmNfh9+p//+R8jyaxfv77Zfa6pqWnwe9zUZ8XFF1/s0vbYsWMuj/fo0cPl8auuusrMnj3bxMbG2vddccUVdvsbbrihwes7++lPf2okmU2bNjW7L8YYU1RU5Pbvmbv3z8GDBxu0i46ONqtWrXK7b6tXr3Zp++9//9t+zLrv7bffNv/4xz/sfw8ZMsT079+/RX2vv8/Ot0GDBplPP/202b8ztbW1Lp8v5513nv3/zzzzjMs2rfdv/Zv198Zy7Ngxc8oppxhJJiEhwbzzzjvm1VdfdXnvVlRUmDvuuMO8++67jfbt17/+tf1Z4e51nW/1PyOd/7ZZvvrqK5fHXnnlFY8/M8vLy016enqL+lNcXGw++OADl/vnzJlj//+9997botdsbN8k3/zd80awHeOGMwJhgOXm5prY2FizePFi8/nnn5u77rrLJCcnmz179rTo+aHyy/LKK6+YBx54wLz22mvNfsCFwu1HP/qR6devn/1vK0D97ne/M7/4xS9MVlaWuf32240kc/rpp5vbbrvNPhi69NJLzZIlS8zVV19tNmzYYP7yl7+YRx55xEyfPt189dVXZtWqVWb27Nnm1ltvtbdvBUNJ5vrrrzfz5s0zP/vZzxr0y10Is24jR4403bp1M7fddptZvHix2/06ePCgMcaYs846q0XjYLX//PPPTUlJiTl06JD92NGjR+2+rFixosFz7777bvv/f/nLX5rt27ebJUuWmD/96U9m2bJl5rHHHjNvvfWW+eijj8z9999vNm7caLZs2WIee+wx8+Mf/9js3LnTXHvttebcc881ycnJDbY/bdo0c/3119v/7tatm+nRo4eZMmWKkdTgANL59uMf/7jBe7ixtv379zeSzN///nfzxhtvmEmTJpmVK1ea3Nxc85Of/MSsXLnSPPPMM+ayyy4ziYmJLj/XTz75xP7/U0891fzv//6vSUtLMz//+c/NPffcY7p3725OO+00l9fr2bOn29+xGTNmmISEBGPMiYMJd33NyckxHTp0aHB/79697dAhyVxzzTVun//YY48ZY4zLQbR1e+GFF8yf//xn+8D7nnvuMdOmTWvQzhhjevbs6fZ+dx5//PFGx/6ee+4xZWVl5tNPPzWHDx82N954o/n222/N5Zdf7tJu1apVZsuWLWbPnj3m6NGjZtiwYUaSufzyy81dd91lnnvuOTNhwgS7ff0DowkTJri8B37+8583GL+ysjJTV1dnqqurjTEnDq4OHDhg3n333Ub7P27cOPPFF1+YkpISU1NTY44ePWoOHz5sHn30UfP888+bRYsWuRzESzL33XefKSkpMQ8++KB5+eWXzWuvvWZ+8pOfuN1+ZWWl+e677+x/jxo1yu1B75tvvmmWL19uJNfPmvq3JUuWmPvvv9/cdNNNDR5zDlwzZswwo0aNavDzfeihh+x/T5061ezatctUVFSYoqIi89e//tVs2rTJfPPNN2bmzJmN9iE/P98cP37cPPLII+bAgQPm1Vdfdfkiyvkz5dixY+bJJ59s8PNyvp155pnm+uuvN6effnqjbZxvN910k1myZEmD+5988knz6KOPmq1bt5rKykpz/PhxU1NT4/I5583t73//e5PvIXc355Dp7u9E/Vtubq656aabzPr1682iRYvMQw89ZH7/+9+7fHnhfHMXDM844wyzbt06c+2115qHH37YLFu2zPTs2dNcffXVTb72yJEjTV1dnfnggw/M7t27W7R/Y8aMsf+eenLr1KlTk4//9a9/tf//nnvuMZJMaWmpfd8jjzxijDFm2bJl5qmnnjLvvPOOVz/TzMxMM3nyZDNw4ECX+xsbb+u1W/M+sm69evUyQ4YMMXfeeafp3r17i54zYsSIRj+f/S1UjnHDAYEwwC644AIzefJkl/v69etnfvWrX7Xo+eHwy7J//35TWFhoDh48aJ555hmzfPly895775m9e/eaefPmmenTp5uFCxeaU0891ScfiKF2GzFihPnss89a1LawsNAeV+vb5JbefvjDH9rPLSkpadFzGuPu2+L6BwY1NTXmoosu8tu4ffvtt01+49nYbdiwYW6/DS0oKPB5H41pPGg2dqupqWnR79XQoUO97teXX37p9v6ysjJjzIlv5r3ZrlVNqf9+/sMf/tDofhQWFvrtPeLuZlUxne977rnn7P60ZV88vf3mN7+xD3pfeumlJvs8fPhwU1paarepqKhw2+7jjz9u9PWsymf9W21trcv2rMqmc5W1pbfvv/++xeO+fv16t18+Wbfp06cbY4xJSUmx73v77beNMcaliuN8KysrM/v37/fZz+jAgQOmoqLCbN26tdm21ue5J59jy5Yta/A75Mv32FVXXWVvt34V2NPbNddc02Rf33nnHVNQUGAuvvhiM2/ePJ/uR/1bx44djTEnZiQsX77cHnfr866xWSZNbdPdFxXe3Lp27Wrq6upc3gd79+512/brr792+Xf96mtLbg8//LDJysoy0okvtAMpHI5xQ0WUMZwtGihVVVVKSkrSK6+8ov/v//v/7Pvvuusu5efnu53LXllZ6bL6VklJiTIzM1VcXGyfN9Bm/vY36b/z6oNNVXW1yo8dk+OUU3S0qEidOnVSaVmZykpLlZCQoOPHjys2Lk6lJSVq36GDYmJiTqx4ZowOHTqklM6dXc45iYmOVrvoaJ0zcKB27typkja6oPG5556r7v9dlKWkpERr1qxR/V/YhPh4tWvXTpeOHKl2/10622Ik7du7V9u3b1d1vRPso3RiEYU+P/iB0tLSGjzXeRvfHjmirdu2KS0tTac4HEpPT2+w6EhLGEnHysqUkJiomP+uuHfg4EFt3rzZ421JUruoKNXV+wj7QZ8+LsvHH6+s1MqVK+1/JyUlKSkpSZ1OOUXfFxWppKRE5w0apLS0NHvp8ab6v2PHDn355ZdK6dRJ33u5yExMdLQuvvhitW/fXrV1ddq0aVOTC2Z06tRJR48e1dixY+1xa47RifOmampqFB8f3+AyEhdeeKEOFxToy/8uVOB8f+eUFNXV1Wn16tU6XlmpXj176syzzlK00wIjtXV12r9vn/bs3dvk+THtoqLUoWNHnT9kiJKSkhr0UcY0O+51xmjb1q365r/nuLVr167ZBWMGnXuuMk47ze15R9KJ97+7P35XXnmloiRVHD+uwwUFioqKUvcePWT18Kuvv7YvrzNgwABt27atyX5IUof27XXB0KEnzo8qK9Ppp5+uwsJCbaj3M2nKyEsv1d59+5o8J9Lqe33l5eVa7bRIRLt27XTFFVc0aHv48GG7T7169lS3zEx1OuUUHT9+XCtXrXJpm5qaqqysLBlj9Oabb0qSuqan67zBg13eJ/VVV1frnRYsqBMdHa0Lzj/fXpXWep3GDlh69uihgQMHSjqxIMnWej+X3r17q3+/fvZ77Vh5uWJjYxX33+vPGZ04L9p5EZCxY8a4XJ/u8x07XM4DvOiii/TN7t3aX2/xm8ZcdOGF9sJQ0onfoWPHjulf//pXg7bDhg61V2iWpOqaGn333XdqFxWl9Rs26Kwzz9T2/55z369fP51x+ulNXkdyz969qq6q0hm9e6uoqEgOh0P79+3Tpy1cYCZK0nmDB+u0jAz7PqMT56lVVlbq0/x8HXGz0FR8fLzi4uLUt29f7d+/X2eddZaMMUpISHD7WVZVXa3SkhIlJiUpKTHR5bFPP/1Ue+qd22mNkzFGdcaoprpa3xcVnbivrk5FRUXqnJqqPd98o//s3ClTV6cap3OjU1NTlZ6erl7/XQTPW+UVFVq9erXaJycrvWtX9endWzGxsSoqKtLx48d1amqqotq1065du3TgwIEGl185/fTTVVpaqvbJyaozRj/o00cJCQlN9qm2tlYfffSRvi8q0g/69FHffv3s3+k6Y3T06FGldOrk8plVX0J8vKJjYtT3Bz9QdHS0oqKi7EtP2dq1k15/3euxaY2SkhI5HI7AHONGGAJhAB08eFCnnXaaPvzwQw0fPty+f+7cuXrxxRe1c+fOBs+ZM2eOHnzwwQb3EwiB4GQktwfpaHvV1dWqratTQny8z7dtjFFpWZni4+MVHxfX8uep8fdHnTGqqamxQws8w+8e4AMEwojg+Vf88Ln63wCZJr4xnzVrln2NN+lkhTAgfvazEzcAjeKANHjE/vfmD1GSvDlcaer90U5Sy6Ml6uN3DwBahkAYQKmpqYqOjm6wfHthYaF9EeT64uPjFe+Hb7cBAAAARB6uQxhAcXFxGjx4sFbVOzdj1apVLlNIAQAAAMAfqBAG2LRp05Sdna0hQ4YoKytLzz33nPbu3evVxY8BAAAAwBMEwgD72c9+pu+++04PPfSQDh06pAEDBujtt99Wjx49At01AAAAAGGOVUZDHCswAQAAINxwjNt2OIcQAAAAACIUgRAAAAAAIhSBEAAAAAAiFIEQAAAAACIUgRAAAAAAIhSBEAAAAAAiFNchDHHWVUNKSkoC3BMAAADAN6xjW66Q538EwhBXWloqScrMzAxwTwAAAADfKi0tlcPhCHQ3whoXpg9xdXV1OnjwoDp06KCoqCi3bUpKSpSZmal9+/ZxYU8/YHz9h7H1L8bXfxhb/2J8/Yex9S/Gt+WMMSotLVVGRobateMsN3+iQhji2rVrp27durWobceOHfnw8SPG138YW/9ifP2HsfUvxtd/GFv/Ynxbhspg2yBuAwAAAECEIhACAAAAQIQiEEaA+Ph4PfDAA4qPjw90V8IS4+s/jK1/Mb7+w9j6F+PrP4ytfzG+CEYsKgMAAAAAEYoKIQAAAABEKAIhAAAAAEQoAiEAAAAARCgCIQAAAABEKAJhBPjjH/+oXr16KSEhQYMHD9a///3vQHcp5MybN0/nn3++OnTooLS0NF111VXauXOnSxtjjObMmaOMjAwlJiZqxIgR2r59e4B6HLrmzZunqKgo5eTk2Pcxtq1z4MAB/fznP1fnzp2VlJSkc889V5s3b7YfZ3y9U1NTo9/85jfq1auXEhMTdfrpp+uhhx5SXV2d3YaxbbkPPvhAV155pTIyMhQVFaXXX3/d5fGWjGVlZaWmTJmi1NRUJScna/z48dq/f38b7kVwampsq6urde+99+rss89WcnKyMjIydMMNN+jgwYMu22BsG9fce9fZrbfeqqioKC1cuNDlfsYXgUQgDHN/+9vflJOTo9mzZ+uTTz7RD3/4Q40dO1Z79+4NdNdCypo1a3THHXdo/fr1WrVqlWpqajR69GgdO3bMbjN//nw98cQTWrRokTZt2qT09HRddtllKi0tDWDPQ8umTZv03HPPaeDAgS73M7beKyoq0oUXXqjY2Fi98847+vzzz/X444/rlFNOsdswvt559NFH9cwzz2jRokXasWOH5s+fr8cee0xPPfWU3Yaxbbljx47pnHPO0aJFi9w+3pKxzMnJ0YoVK5Sbm6u1a9eqrKxM48aNU21tbVvtRlBqamzLy8u1ZcsW3XfffdqyZYtee+01ffHFFxo/frxLO8a2cc29dy2vv/66NmzYoIyMjAaPMb4IKIOwdsEFF5jJkye73NevXz/zq1/9KkA9Cg+FhYVGklmzZo0xxpi6ujqTnp5uHnnkEbvN8ePHjcPhMM8880yguhlSSktLTZ8+fcyqVavMJZdcYu666y5jDGPbWvfee6+56KKLGn2c8fXeFVdcYX7xi1+43Hf11Vebn//858YYxrY1JJkVK1bY/27JWB49etTExsaa3Nxcu82BAwdMu3btTF5eXpv1PdjVH1t3Nm7caCSZPXv2GGMYW080Nr779+83p512mtm2bZvp0aOHWbBggf0Y44tAo0IYxqqqqrR582aNHj3a5f7Ro0dr3bp1AepVeCguLpYkpaSkSJJ2796tgoICl7GOj4/XJZdcwli30B133KErrrhCo0aNcrmfsW2dN954Q0OGDNFPf/pTpaWladCgQVq8eLH9OOPrvYsuukjvvfeevvjiC0nSp59+qrVr1+rHP/6xJMbWl1oylps3b1Z1dbVLm4yMDA0YMIDx9lBxcbGioqLsmQSMbevU1dUpOztb99xzj84666wGjzO+CLSYQHcA/vPtt9+qtrZWXbp0cbm/S5cuKigoCFCvQp8xRtOmTdNFF12kAQMGSJI9nu7Ges+ePW3ex1CTm5urLVu2aNOmTQ0eY2xb5+uvv9bTTz+tadOm6de//rU2btyoqVOnKj4+XjfccAPj2wr33nuviouL1a9fP0VHR6u2tlYPP/ywrrvuOkm8d32pJWNZUFCguLg4derUqUEb/ua13PHjx/WrX/1KEydOVMeOHSUxtq316KOPKiYmRlOnTnX7OOOLQCMQRoCoqCiXfxtjGtyHlrvzzjv12Wefae3atQ0eY6w9t2/fPt11111auXKlEhISGm3H2Hqnrq5OQ4YM0dy5cyVJgwYN0vbt2/X000/rhhtusNsxvp7729/+pqVLl2r58uU666yzlJ+fr5ycHGVkZOjGG2+02zG2vuPNWDLeLVddXa1rr71WdXV1+uMf/9hse8a2eZs3b9aTTz6pLVu2eDxWjC/aClNGw1hqaqqio6MbfLtUWFjY4FtWtMyUKVP0xhtv6J///Ke6detm35+eni5JjLUXNm/erMLCQg0ePFgxMTGKiYnRmjVr9Pvf/14xMTH2+DG23unatavOPPNMl/v69+9vLyzFe9d799xzj371q1/p2muv1dlnn63s7GzdfffdmjdvniTG1pdaMpbp6emqqqpSUVFRo23QuOrqak2YMEG7d+/WqlWr7OqgxNi2xr///W8VFhaqe/fu9t+4PXv2aPr06erZs6ckxheBRyAMY3FxcRo8eLBWrVrlcv+qVas0fPjwAPUqNBljdOedd+q1117T+++/r169erk83qtXL6Wnp7uMdVVVldasWcNYN2PkyJHaunWr8vPz7duQIUN0/fXXKz8/X6effjpj2woXXnhhg0ukfPHFF+rRo4ck3rutUV5ernbtXP+MRkdH25edYGx9pyVjOXjwYMXGxrq0OXTokLZt28Z4N8MKg7t27dLq1avVuXNnl8cZW+9lZ2frs88+c/kbl5GRoXvuuUfvvvuuJMYXQSBAi9mgjeTm5prY2Fjz/PPPm88//9zk5OSY5ORk88033wS6ayHltttuMw6Hw/zrX/8yhw4dsm/l5eV2m0ceecQ4HA7z2muvma1bt5rrrrvOdO3a1ZSUlASw56HJeZVRYxjb1ti4caOJiYkxDz/8sNm1a5dZtmyZSUpKMkuXLrXbML7eufHGG81pp51m3nzzTbN7927z2muvmdTUVDNz5ky7DWPbcqWlpeaTTz4xn3zyiZFknnjiCfPJJ5/YK122ZCwnT55sunXrZlavXm22bNliLr30UnPOOeeYmpqaQO1WUGhqbKurq8348eNNt27dTH5+vsvfuMrKSnsbjG3jmnvv1ld/lVFjGF8EFoEwAvzhD38wPXr0MHFxcea8886zL5WAlpPk9vbCCy/Yberq6swDDzxg0tPTTXx8vLn44ovN1q1bA9fpEFY/EDK2rfOPf/zDDBgwwMTHx5t+/fqZ5557zuVxxtc7JSUl5q677jLdu3c3CQkJ5vTTTzezZ892OYhmbFvun//8p9vP2RtvvNEY07KxrKioMHfeeadJSUkxiYmJZty4cWbv3r0B2Jvg0tTY7t69u9G/cf/85z/tbTC2jWvuvVufu0DI+CKQoowxpi0qkQAAAACA4MI5hAAAAAAQoQiEAAAAABChCIQAAAAAEKEIhAAAAAAQoQiEAAAAABChCIQAAAAAEKEIhAAAAAAQoQiEAAAAABChCIQAAAAAEKEIhAAAAAAQoQiEAAAAABChCIQAAAAAEKEIhAAAAAAQoQiEAAAAABChCIQAAAAAEKEIhAAAAAAQoQiEAAAAABChCIQAAAAAEKEIhAAAAAAQoQiEAAAAABChCIQAAAAAEKEIhAAAAAAQof5/Zv7XiPGxUUQAAAAASUVORK5CYII=", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", " \n", "
\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p = PowderPattern()\n", "if not os.path.exists(data_file):\n", " os.system(\"curl -O https://www.iucr.org/__data/iucr/powder/QARR/col/%s\" % data_file)\n", "\n", "p.ImportPowderPattern2ThetaObs(data_file)\n", "# Copper K-alpha1+alpha2. Use \"Cua1\" for Cu-alpha1 only\n", "p.SetWavelength(\"Cu\")\n", "print(p.GetWavelength())\n", "\n", "p.plot(hkl=True)" ] }, { "cell_type": "markdown", "id": "324669c6", "metadata": {}, "source": [ "### Add crystalline phases\n", "We assume all structures are known.\n", "\n", "This will update the above plot, though the scales will be incorrect." ] }, { "cell_type": "code", "execution_count": 4, "id": "13e6ea08", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "========================== WARNING =========================\n", " In ScatteringPowerAtom::GetTemperatureFactor():\n", " Anisotropic Displacement Parameters are not currently properly handled\n", " for Debye-Waller calculations (no symmetry handling for ADPs).\n", " =>The Debye-Waller calculations will instead use only isotropic DPs\n", "\n" ] } ], "source": [ "for cod_id in cod_phases:\n", " c = CreateCrystalFromCIF(\"http://crystallography.net/cod/%d.cif\" % cod_id)\n", " #print(c,\"\\n\")\n", " p.AddPowderPatternDiffraction(c)\n", "\n", "p.FitScaleFactorForRw()\n", "p.UpdateDisplay()" ] }, { "cell_type": "markdown", "id": "69693f9b", "metadata": {}, "source": [ "### Add an automatic background\n", "This uses a Bayesian estimation of the background, which should be\n", "good enough if there is a good separation of the peaks" ] }, { "cell_type": "code", "execution_count": 5, "id": "e5f73733", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No background, adding one automatically\n" ] } ], "source": [ "# Add background if necessary\n", "need_background = True\n", "for i in range(p.GetNbPowderPatternComponent()):\n", " if isinstance(p.GetPowderPatternComponent(i), PowderPatternBackground):\n", " need_background = False\n", " break\n", "if need_background:\n", " print(\"No background, adding one automatically\")\n", " x = p.GetPowderPatternX()\n", " bx = np.linspace(x.min(), x.max(), 30) # Number of interpolation points\n", " by = np.zeros(bx.shape)\n", " b = p.AddPowderPatternBackground()\n", " b.SetInterpPoints(bx, by)\n", " # b.Print()\n", " b.UnFixAllPar()\n", " b.OptimizeBayesianBackground()\n", "p.UpdateDisplay()" ] }, { "cell_type": "markdown", "id": "e7533c3b", "metadata": {}, "source": [ "### Fit profile, step 1\n", "Conservative fit, starting with a fixed width (W=1e-5) \n", "\n", "Note that as we know the crystalline structures, we don't need to perform a Le Bail fit" ] }, { "cell_type": "code", "execution_count": 6, "id": "4e9ef3ab", "metadata": {}, "outputs": [], "source": [ "# Multiple phases, so can't use quick_fit_profile\n", "#\n", "# NOTE: we don't use this function as the phases are known, but that's the way\n", "# to do it with multiple crystalline phases\n", "def do_lebail(init=False):\n", " \"\"\"\n", " This performs a Le Bail fit by looping over all phases,\n", " one at a time. Le Bail is disabled on output\n", " \"\"\"\n", " for i in range(20):\n", " for i in range(p.GetNbPowderPatternComponent()):\n", " pdiff = p.GetPowderPatternComponent(i)\n", " if not isinstance(pdiff, PowderPatternDiffraction):\n", " continue\n", " if i==0 or init:\n", " pdiff.SetExtractionMode(True, True)\n", " else:\n", " pdiff.SetExtractionMode(True, False)\n", " pdiff.ExtractLeBail(1)\n", " pdiff.SetExtractionMode(False, False)\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "d8d8d7ef", "metadata": {}, "outputs": [], "source": [ "for i in range(p.GetNbPowderPatternComponent()):\n", " pdiff = p.GetPowderPatternComponent(i)\n", " if not isinstance(pdiff, PowderPatternDiffraction):\n", " continue\n", " pdiff.SetReflectionProfilePar(ReflectionProfileType.PROFILE_PSEUDO_VOIGT, 0.00001)\n", "\n", "p.UpdateDisplay()\n" ] }, { "cell_type": "markdown", "id": "651ee954", "metadata": {}, "source": [ "### Fit profile, step 2\n", "Refine only constant width, zero, Eta Gaussian/Voigt mix, and a, b, c parameters " ] }, { "cell_type": "code", "execution_count": 8, "id": "d6155988", "metadata": {}, "outputs": [], "source": [ "lsq = LSQ()\n", "lsq.SetRefinedObj(p, 0, True, True)\n", "lsq.PrepareRefParList(True)\n", "# lsq.GetCompiledRefinedObj().Print()\n", "lsqr = lsq.GetCompiledRefinedObj()\n", "\n", "lsqr.FixAllPar()\n", "# lsqr.Print()\n", "# print(lsq.ChiSquare())\n", "lsq.SetParIsFixed(refpartype_scattdata_scale, False)\n", "for par in [\"W\", \"Zero\", \"Eta0\", \"a\", \"b\", \"c\"]:\n", " for i in range(p.GetNbPowderPatternComponent()):\n", " # This is a KLUDGE - we need this because parameter names are\n", " # unique, and thus \"U\" gets renamed to \"U~\", \"U~~\" in case of 2,3 phases.. \n", " lsq.SetParIsFixed(par + \"~\"*i, False)\n", "lsq.SafeRefine(nbCycle=10, useLevenbergMarquardt=True, silent=True)\n", "\n", "p.UpdateDisplay()\n" ] }, { "cell_type": "markdown", "id": "2312897c", "metadata": {}, "source": [ "### Fit profile, final\n", "Refine more parameters, and fit the scale factor" ] }, { "cell_type": "code", "execution_count": 9, "id": "001df7f2", "metadata": {}, "outputs": [], "source": [ "lsqr.FixAllPar()\n", "# lsqr.Print()\n", "# print(lsq.ChiSquare())\n", "lsq.SetParIsFixed(refpartype_scattdata_scale, False)\n", "for par in [\"U\", \"V\", \"W\", \"Zero\", \"Eta0\", \"Eta1\", \"a\", \"b\", \"c\", \"2ThetaDispl\", \"2ThetaTransp\"]:\n", " for i in range(p.GetNbPowderPatternComponent()):\n", " # This is a KLUDGE - we need this because parameter names are\n", " # unique, and thus \"U\" gets renamed to \"U~\", \"U~~\" in case of 2,3 phases.. \n", " lsq.SetParIsFixed(par + \"~\"*i, False)\n", "lsq.SafeRefine(nbCycle=10, useLevenbergMarquardt=True, silent=True)\n", "\n", "p.FitScaleFactorForRw()\n", "p.UpdateDisplay()\n" ] }, { "cell_type": "markdown", "id": "ab8f9a18", "metadata": {}, "source": [ "### Compute weight percentages\n", "This uses the formula: \n", "$w_i = \\frac{S_iZ_iM_iV_i}{\\Sigma_iS_iZ_iM_iV_i}$\n", "\n", "where:\n", "* $w_i$ is the weight fraction of crystalline phase i\n", "* $S_i$ its scale factor in the Rietveld refinement\n", "* $Z_i$ the multiplicity of the formula in the unit cell\n", "* $M_i$ the crystal formula's molecular weight\n", "* $V_i$ the unit cell volume for the phase\n", "\n", "This assumes that the structure is known (and thus that the\n", "CIF files are correct), and that we know all present phases.\n", "\n", "The obtained numbers can be compared to:\n", "https://www.iucr.org/__data/iucr/powder/QARR/results.htm" ] }, { "cell_type": "code", "execution_count": 10, "id": "288ab0c8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Weight percentages:\n", "Aluminium oxide - $-alpha: 37.42%\n", " Zincite: 28.35%\n", " Calcium fluoride: 34.23%\n" ] } ], "source": [ "# TODO: check if the method is correctly applied, notably\n", "# is there a shortcut in ObjCryst++ so that the calculated\n", "# structure factor sometimes skips a factor 2 (centrosymmetry\n", "# or other centering factors which allow to avoid a direct sum)\n", "\n", "w = p.qpa(verbose=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "9a5cb5f8", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "6f5d27a997294cae96dbaaf9c8551bba": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "a569e911321f439aa32b738cb4738001": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "e5c2ee7c228b408582fbacd5d1eeda80": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "ToolbarModel", "state": { "_model_module_version": "^0.11", "_view_module_version": "^0.11", "collapsed": true, "layout": "IPY_MODEL_a569e911321f439aa32b738cb4738001", "orientation": "vertical", "toolitems": [ [ "Home", "Reset original view", "home", "home" ], [ "Back", "Back to previous view", "arrow-left", "back" ], [ "Forward", "Forward to next view", "arrow-right", "forward" ], [ "Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan" ], [ "Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom" ], [ "Download", "Download plot", "floppy-o", "save_figure" ] ] } }, "eb5a16017cbd44b8861a27bff409366b": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "MPLCanvasModel", "state": { "_cursor": "default", "_data_url": "data:image/png;base64,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", "_figure_label": "Figure 1", "_image_mode": "diff", "_model_module_version": "^0.11", "_size": [ 900, 400 ], "_view_module_version": "^0.11", "layout": "IPY_MODEL_6f5d27a997294cae96dbaaf9c8551bba", "toolbar": "IPY_MODEL_e5c2ee7c228b408582fbacd5d1eeda80", "toolbar_position": "left" } } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 5 } pyobjcryst-2024.2.1/examples/crystal_3d_widget.ipynb000066400000000000000000053054621470422267000224700ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "0eba2823", "metadata": {}, "source": [ "## Creating a crystal from a CIF & display it in 3D\n", "In this short example you can open a CIF file directly from a filename or an URL, and display it in 3D.\n", "\n", "You can also play with the spacegroup and see how that changes the crystal structure.\n", "\n", "*Note: this requires installing `ipywidgets` and `py3Dmol`*" ] }, { "cell_type": "code", "execution_count": 1, "id": "f42880b5", "metadata": { "tags": [] }, "outputs": [], "source": [ "from pyobjcryst.crystal import *\n", "from pyobjcryst.atom import Atom\n", "from pyobjcryst.scatteringpower import ScatteringPowerAtom\n", "from math import pi" ] }, { "cell_type": "markdown", "id": "9c9f9528", "metadata": {}, "source": [ "### From IUCr journals" ] }, { "cell_type": "code", "execution_count": 2, "id": "1bb9a6fd", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "C15 Cl H14 N O S\n" ] }, { "data": { "application/3dmoljs_load.v0": "
\n

3Dmol.js failed to load for some reason. Please check your browser console for error messages.

\n
\n", "text/html": [ "
\n", "

3Dmol.js failed to load for some reason. Please check your browser console for error messages.

\n", "
\n", "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "495d94566f4341749dda289bdb120e05", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Box(children=(VBox(children=(HBox(children=(VBox(children=(FloatRangeSlider(value=(0.0, 1.0), description='Xra…" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c = create_crystal_from_cif(\"http://scripts.iucr.org/cgi-bin/sendcif?dt3034sup1\",\n", " oneScatteringPowerPerElement=True,\n", " connectAtoms=True)\n", "print(c.GetFormula())\n", "c.widget_3d()" ] }, { "cell_type": "markdown", "id": "a3ce9a83", "metadata": {}, "source": [ "### From the Crystallography Open Database" ] }, { "cell_type": "code", "execution_count": 3, "id": "88943599", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "C29 H32 N4 O5\n" ] }, { "data": { "application/3dmoljs_load.v0": "
\n

3Dmol.js failed to load for some reason. Please check your browser console for error messages.

\n
\n", "text/html": [ "
\n", "

3Dmol.js failed to load for some reason. Please check your browser console for error messages.

\n", "
\n", "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5067998b22c8498d98bb6b5129e7f3d0", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Box(children=(VBox(children=(HBox(children=(VBox(children=(FloatRangeSlider(value=(0.0, 1.0), description='Xra…" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c1 = create_crystal_from_cif(\"http://crystallography.net/cod/4506702.cif\",\n", " oneScatteringPowerPerElement=True,\n", " connectAtoms=True)\n", "print(c1.GetFormula())\n", "c1.widget_3d()" ] }, { "cell_type": "markdown", "id": "e13439ec", "metadata": {}, "source": [ "### Create a Crystal structure and change the spacegroup" ] }, { "cell_type": "code", "execution_count": 4, "id": "c3826ab4", "metadata": {}, "outputs": [ { "data": { "application/3dmoljs_load.v0": "
\n

3Dmol.js failed to load for some reason. Please check your browser console for error messages.

\n
\n", "text/html": [ "
\n", "

3Dmol.js failed to load for some reason. Please check your browser console for error messages.

\n", "
\n", "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4516edbf801540df842bd8149d4ab321", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Box(children=(VBox(children=(HBox(children=(VBox(children=(FloatRangeSlider(value=(0.0, 1.0), description='Xra…" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c = Crystal(5, 5, 5, pi/2, pi/2, pi/2, \"P1\")\n", "cu = ScatteringPowerAtom(\"Cu\", \"Cu\")\n", "c.AddScatteringPower(cu)\n", "c.AddScatterer(Atom(0,0,0, \"Cu\", cu))\n", "c.widget_3d(extra_opacity=0.1, extra_dist=2)" ] }, { "cell_type": "code", "execution_count": 5, "id": "f77c7597", "metadata": {}, "outputs": [], "source": [ "# Change the spacegroup and update the display\n", "c.ChangeSpaceGroup(\"Fm-3m\")\n", "c.UpdateDisplay()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "0921e449007545b2b81dda95ac2ecf54": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_6f57ecaab35f4723bcc96373be76feee", "outputs": [ { "data": { "application/3dmoljs_load.v0": "", "text/html": "" }, "metadata": {}, "output_type": "display_data" } ] } }, "0996b2b4073143ebb5b16d7489742b46": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "0c32ae6902f74739996c3852feae170b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "0fcae9423c724342b19093dfd44c82cd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "fullMol opac", "layout": "IPY_MODEL_7db99a4120ba4ae0a317143da0aea915", "max": 1, "readout_format": ".01f", "step": 0.1, "style": "IPY_MODEL_761773dafaaf4cf8b5475d261a532407", "tooltip": "Opacity to display fully molecules\nwhich have at least one atom inside the limits", "value": 0.5 } }, "10100ea5595c4b0da85ad3afee38b370": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Xrange", "layout": "IPY_MODEL_35422ae44de44937b9e5c53335c1dfff", "max": 1.5, "min": -0.5, "step": 0.05263157894736842, "style": "IPY_MODEL_dea9ad9825f24ae0b1671409af0cb939", "value": [ 0.02631578947368418, 1.026315789473684 ] } }, "14070e12d15748b7b31ab796fa8253b9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "extra dist", "layout": "IPY_MODEL_9044d195564e43d8bd246fe976fafd5b", "max": 10, "readout_format": ".1f", "step": 0.5, "style": "IPY_MODEL_9f73d955e78d408f9c85e8b17242331a", "tooltip": "Extra distance (A) with semi-transparent display", "value": 2 } }, "14714c2ef5d64defa7d7bb33b67fce70": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "14ad80730dcb4abaa818d491d62b5011": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "16853aa07399419988e9a6a2ab28f24e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "178bc599d6484c4cbfc8cfc7f6f78a31": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "17fd2f77126040fe8b16ce322910427c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Zrange", "layout": "IPY_MODEL_14ad80730dcb4abaa818d491d62b5011", "max": 1.5, "min": -0.5, "step": 0.045454545454545456, "style": "IPY_MODEL_3061fd45f58a458fa9a352c9b4de6c05", "value": [ 1.1102230246251565e-16, 1 ] } }, "1d1afc02139344ed9e5148dfaa7fbcf0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "1d4fc1381fb44dd3832481d92b693535": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "210be537b08c442d94eb15a2e1024c82": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "21ec5323df2f403caebc6eb8409293b4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Zrange", "layout": "IPY_MODEL_939bd79955a34756ac39b880169158c3", "max": 1.5, "min": -0.5, "step": 0.1, "style": "IPY_MODEL_963737802eef4d2ab0869d7fa59ecd68", "value": [ 0, 1 ] } }, "2403588c42004841ba40868ae459ee64": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_10100ea5595c4b0da85ad3afee38b370", "IPY_MODEL_a0ea3331d1534110a1387b896e1f078f", "IPY_MODEL_510998bf9bfc417a8ac402ef5ee87bf1" ], "layout": "IPY_MODEL_1d1afc02139344ed9e5148dfaa7fbcf0" } }, "24d32f5ecd234d07b9f1bd5f6f8d96ae": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "29b6ec09b2824961a1e5dfc0232e3f63": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "3061fd45f58a458fa9a352c9b4de6c05": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "35422ae44de44937b9e5c53335c1dfff": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "3727818b1b904f3b904b61c9ed6e7cd9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "extra opac.", "layout": "IPY_MODEL_4a76ab79eebc4f148f1910b6d479b91a", "max": 1, "readout_format": ".01f", "step": 0.1, "style": "IPY_MODEL_61e0531754254ffeb4cad570d6419944", "tooltip": "Opacity for extra distance display", "value": 0.1 } }, "3ab29658d38f4c24b3b0ac5237dbb835": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "3b45c857b7e74909b47f3fedc5da4ddc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "3bd40a1d13eb47cbaacb913212bf5388": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "40f8eee4198543c992f233329c799556": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "extra dist", "layout": "IPY_MODEL_4abf9de7d4cd4ebaa4837db4338af3bf", "max": 10, "readout_format": ".1f", "step": 0.5, "style": "IPY_MODEL_f8a67573c53442eca21df95904ff7a5f", "tooltip": "Extra distance (A) with semi-transparent display", "value": 2 } }, "4516edbf801540df842bd8149d4ab321": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "BoxModel", "state": { "children": [ "IPY_MODEL_6d5aa6a7ac724254a162954fb8f23743" ], "layout": "IPY_MODEL_e6ef85cd7ae44c07bb9872dd1c5c6307" } }, "49494b595c504b408268f8566a618da0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "495d94566f4341749dda289bdb120e05": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "BoxModel", "state": { "children": [ "IPY_MODEL_a27698a7cf014b6380ed28ddb0379265" ], "layout": "IPY_MODEL_14714c2ef5d64defa7d7bb33b67fce70" } }, "4a76ab79eebc4f148f1910b6d479b91a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "4abf9de7d4cd4ebaa4837db4338af3bf": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "4e7ea6f148eb43258a753657cd405cf7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "4f245a0ee86a47fcaf06a49dd41a0d77": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "5067998b22c8498d98bb6b5129e7f3d0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "BoxModel", "state": { "children": [ "IPY_MODEL_987bc0d34a424be29a3829ddb7c8d45f" ], "layout": "IPY_MODEL_0c32ae6902f74739996c3852feae170b" } }, "510998bf9bfc417a8ac402ef5ee87bf1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Zrange", "layout": "IPY_MODEL_fd74b6035f094e3f9e931635a9740ccc", "max": 1.5, "min": -0.5, "step": 0.034482758620689655, "style": "IPY_MODEL_dae07a806e54464dac1cf105d691f759", "value": [ 0.01724137931034475, 1.0172413793103448 ] } }, "52859705a56a4508a3fe2f6bcb47dc88": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_fd65c6a4ca3b43248e929e7f47c7857c", "IPY_MODEL_b66372fa3d84415bb6145a6a8ed8708e", "IPY_MODEL_21ec5323df2f403caebc6eb8409293b4" ], "layout": "IPY_MODEL_f3ac3e8c4e7b43a1ad1e481e6f9de83e" } }, "61e0531754254ffeb4cad570d6419944": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "6253ddcc8aba4de5a8e8964bd75aeaf1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "extra dist", "layout": "IPY_MODEL_fc79d024adb1496ea29fcb76579feb58", "max": 10, "readout_format": ".1f", "step": 0.5, "style": "IPY_MODEL_3ab29658d38f4c24b3b0ac5237dbb835", "tooltip": "Extra distance (A) with semi-transparent display", "value": 2 } }, "62b95578393d4072bb039068c05fb231": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "6411c614d81d4cfd970cd917afdac37b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_6253ddcc8aba4de5a8e8964bd75aeaf1", "IPY_MODEL_3727818b1b904f3b904b61c9ed6e7cd9", "IPY_MODEL_7e6bcc83da5c427f93d9f465875812e6" ], "layout": "IPY_MODEL_8e3e21a86f924c97b660d1857de586e7" } }, "69cfa453144143d5837ba9ab8182d9c6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "6d5aa6a7ac724254a162954fb8f23743": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_ef835f70ed374772bf7927218c6d914b", "IPY_MODEL_0921e449007545b2b81dda95ac2ecf54" ], "layout": "IPY_MODEL_a2a19745dad64ec5b97073e69f111a67" } }, "6e2b9fc849214d66a98cd6722e599897": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "6f57ecaab35f4723bcc96373be76feee": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "6fa1b9f83e0e44008283d59bf430d7aa": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "761773dafaaf4cf8b5475d261a532407": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "7db99a4120ba4ae0a317143da0aea915": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "7df078a47e15423396cecb2887e12340": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_bd377757e2334a75a2e273ecc2025fb4", "IPY_MODEL_edaf85216c2d488c8dd44543bee9f6be", "IPY_MODEL_17fd2f77126040fe8b16ce322910427c" ], "layout": "IPY_MODEL_df3dd82b133147a38622ed0955220a5f" } }, "7e6bcc83da5c427f93d9f465875812e6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "fullMol opac", "layout": "IPY_MODEL_890a7e1c724d474a9b5b900cbb183dcc", "max": 1, "readout_format": ".01f", "step": 0.1, "style": "IPY_MODEL_210be537b08c442d94eb15a2e1024c82", "tooltip": "Opacity to display fully molecules\nwhich have at least one atom inside the limits", "value": 0.5 } }, "81ef18f49e5140fba36f9761bcab52ea": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_178bc599d6484c4cbfc8cfc7f6f78a31", "outputs": [ { "data": { "application/3dmoljs_load.v0": "", "text/html": "" }, "metadata": {}, "output_type": "display_data" } ] } }, "826995a7222c47309862149d9f9ea575": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_40f8eee4198543c992f233329c799556", "IPY_MODEL_abe719db6cf84175a482329974e5688b", "IPY_MODEL_d422de768be5451eb58c3cb2c883979f" ], "layout": "IPY_MODEL_985208e5749349fd8227ec644aeb1d9c" } }, "890a7e1c724d474a9b5b900cbb183dcc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "8c2288c939be4b0ba86569b8e69d9cb9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "extra opac.", "layout": "IPY_MODEL_8f8bfff060b74d178b744446a2fa6a74", "max": 1, "readout_format": ".01f", "step": 0.1, "style": "IPY_MODEL_f5efd6cdf7724c75a8cfab2fe71c4437", "tooltip": "Opacity for extra distance display", "value": 0.5 } }, "8e3e21a86f924c97b660d1857de586e7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "8f3aa573c4e449799d140b7f5cc367bd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "8f8bfff060b74d178b744446a2fa6a74": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "9044d195564e43d8bd246fe976fafd5b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "91548cc738aa4a758a1a92fa25d72991": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "939bd79955a34756ac39b880169158c3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "963737802eef4d2ab0869d7fa59ecd68": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "985208e5749349fd8227ec644aeb1d9c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "987bc0d34a424be29a3829ddb7c8d45f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_cda5ee20257342d9af7a0f04a01b9361", "IPY_MODEL_aa1e934c102b442da86ae5fa8e37df68" ], "layout": "IPY_MODEL_49494b595c504b408268f8566a618da0" } }, "9c2be96f42a34fdca56d08fed2ed1cfb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", "state": { "children": [ "IPY_MODEL_7df078a47e15423396cecb2887e12340", "IPY_MODEL_826995a7222c47309862149d9f9ea575" ], "layout": "IPY_MODEL_24d32f5ecd234d07b9f1bd5f6f8d96ae" } }, "9f73d955e78d408f9c85e8b17242331a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "a0ea3331d1534110a1387b896e1f078f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Yrange", "layout": "IPY_MODEL_f465a109647e458a8b770a2ce7eda5f0", "max": 1.5, "min": -0.5, "step": 0.045454545454545456, "style": "IPY_MODEL_0996b2b4073143ebb5b16d7489742b46", "value": [ 1.1102230246251565e-16, 1 ] } }, "a27698a7cf014b6380ed28ddb0379265": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_9c2be96f42a34fdca56d08fed2ed1cfb", "IPY_MODEL_81ef18f49e5140fba36f9761bcab52ea" ], "layout": "IPY_MODEL_6fa1b9f83e0e44008283d59bf430d7aa" } }, "a2a19745dad64ec5b97073e69f111a67": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "aa1e934c102b442da86ae5fa8e37df68": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_3b45c857b7e74909b47f3fedc5da4ddc", "outputs": [ { "data": { "application/3dmoljs_load.v0": "", "text/html": "" }, "metadata": {}, "output_type": "display_data" } ] } }, "abe719db6cf84175a482329974e5688b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "extra opac.", "layout": "IPY_MODEL_4e7ea6f148eb43258a753657cd405cf7", "max": 1, "readout_format": ".01f", "step": 0.1, "style": "IPY_MODEL_c623c65608784c7a8b660d334458525a", "tooltip": "Opacity for extra distance display", "value": 0.5 } }, "b66372fa3d84415bb6145a6a8ed8708e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Yrange", "layout": "IPY_MODEL_69cfa453144143d5837ba9ab8182d9c6", "max": 1.5, "min": -0.5, "step": 0.1, "style": "IPY_MODEL_62b95578393d4072bb039068c05fb231", "value": [ 0, 1 ] } }, "bd377757e2334a75a2e273ecc2025fb4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Xrange", "layout": "IPY_MODEL_91548cc738aa4a758a1a92fa25d72991", "max": 1.5, "min": -0.5, "step": 0.06666666666666667, "style": "IPY_MODEL_f523abc4f4754d9582ac6b87b5345be8", "value": [ 0.033333333333333326, 1.0333333333333334 ] } }, "c623c65608784c7a8b660d334458525a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "cda5ee20257342d9af7a0f04a01b9361": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", "state": { "children": [ "IPY_MODEL_2403588c42004841ba40868ae459ee64", "IPY_MODEL_dfdfc9ba850f410d8c23c6f473919846" ], "layout": "IPY_MODEL_4f245a0ee86a47fcaf06a49dd41a0d77" } }, "d422de768be5451eb58c3cb2c883979f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "fullMol opac", "layout": "IPY_MODEL_1d4fc1381fb44dd3832481d92b693535", "max": 1, "readout_format": ".01f", "step": 0.1, "style": "IPY_MODEL_3bd40a1d13eb47cbaacb913212bf5388", "tooltip": "Opacity to display fully molecules\nwhich have at least one atom inside the limits", "value": 0.5 } }, "dae07a806e54464dac1cf105d691f759": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "dea9ad9825f24ae0b1671409af0cb939": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "df3dd82b133147a38622ed0955220a5f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "dfdfc9ba850f410d8c23c6f473919846": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_14070e12d15748b7b31ab796fa8253b9", "IPY_MODEL_8c2288c939be4b0ba86569b8e69d9cb9", "IPY_MODEL_0fcae9423c724342b19093dfd44c82cd" ], "layout": "IPY_MODEL_6e2b9fc849214d66a98cd6722e599897" } }, "e6ef85cd7ae44c07bb9872dd1c5c6307": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "edaf85216c2d488c8dd44543bee9f6be": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Yrange", "layout": "IPY_MODEL_16853aa07399419988e9a6a2ab28f24e", "max": 1.5, "min": -0.5, "step": 0.02857142857142857, "style": "IPY_MODEL_f7eecb350c914446bf867b35d6bc4ee0", "value": [ 0.014285714285714346, 1.0142857142857145 ] } }, "ef835f70ed374772bf7927218c6d914b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", "state": { "children": [ "IPY_MODEL_52859705a56a4508a3fe2f6bcb47dc88", "IPY_MODEL_6411c614d81d4cfd970cd917afdac37b" ], "layout": "IPY_MODEL_29b6ec09b2824961a1e5dfc0232e3f63" } }, "f3ac3e8c4e7b43a1ad1e481e6f9de83e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "f465a109647e458a8b770a2ce7eda5f0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "f523abc4f4754d9582ac6b87b5345be8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "f5efd6cdf7724c75a8cfab2fe71c4437": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "f7eecb350c914446bf867b35d6bc4ee0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "f8a67573c53442eca21df95904ff7a5f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "f9874ad38f914564835d989492660370": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "fc79d024adb1496ea29fcb76579feb58": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "fd65c6a4ca3b43248e929e7f47c7857c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Xrange", "layout": "IPY_MODEL_f9874ad38f914564835d989492660370", "max": 1.5, "min": -0.5, "step": 0.1, "style": "IPY_MODEL_8f3aa573c4e449799d140b7f5cc367bd", "value": [ 0, 1 ] } }, "fd74b6035f094e3f9e931635a9740ccc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 5 } pyobjcryst-2024.2.1/examples/single-crystal-data.ipynb000066400000000000000000003714161470422267000227220ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 5, "id": "99020362-2694-4b9b-9b87-8c19649b8cd8", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from pyobjcryst.crystal import *\n", "from pyobjcryst.diffractiondatasinglecrystal import *\n", "\n", "# Example structure and data from http://crystallography.net/cod/1550649.html\n", "c = create_crystal_from_cif(\"http://crystallography.net/cod/2016712.cif\")\n", "d = create_singlecrystaldata_from_cif(\"http://crystallography.net/cod/2016712.hkl\", c)" ] }, { "cell_type": "code", "execution_count": 9, "id": "0b30a339-5511-4de5-86d6-83a5342b7681", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "s = d.GetSinThetaOverLambda()\n", "obs = d.GetIobs()\n", "calc = d.GetIcalc()\n", "\n", "plt.figure(figsize=(12,6))\n", "plt.plot(s, obs, 'k', label='Iobs')\n", "plt.plot(s, calc, 'r', label='Icalc')\n", "plt.plot(s, calc - obs - 0.05 * obs.max(), 'g', label='diff')\n", "plt.legend()\n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": null, "id": "3c985455-b457-4543-bfb7-b4fce860f056", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 5 } pyobjcryst-2024.2.1/examples/structure-solution-multiprocessing.ipynb000066400000000000000000023246731470422267000262170ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Advanced structure solution: parallel process & testing multiple configurations\n", "You should first try the cimetidine structure solution notebook before this one.\n", "\n", "In this notebook, you can try solving a structure while:\n", "* testing different spacegroups and adapting the crystal contents (number of independent molecules)\n", "* using multiple parallel process to go faster\n", "\n", "This is an example of meta-structure solution, 'meta' meaning that instead if having a single description for the contents of you crystal (spacegroup, number of molecules, atoms or polyhedra), you can try any different combinations using python. It requires a little programming but can be very powerful when several choices for the configuration of your structure are possible.\n", "\n", "For this to work you need to install the following packages:\n", "* `multiprocess`\n", "* `ipywidgets` and `py3dmol` (optional)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of processors or cores available: 8\n" ] } ], "source": [ "%matplotlib widget\n", "\n", "import os\n", "import sys\n", "import io\n", "import timeit\n", "\n", "# Note: we need 'multiprocess' instead of standard multiprocessing because of the\n", "# following issue (specific to ipython or notebooks):\n", "# https://bugs.python.org/issue25053\n", "# https://stackoverflow.com/questions/41385708/multiprocessing-example-giving-attributeerror\n", "#\n", "# In a python script (not in ipython or a notebook) multiprocessing could be used instead\n", "try:\n", " from multiprocess import Pool, current_process\n", "except ImportError:\n", " print(\"Please install `multiprocess` using 'pip', 'conda' or 'mamba' to run this notebook\")\n", " print()\n", " sys.exit()\n", "\n", "import pyobjcryst\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from pyobjcryst.crystal import *\n", "from pyobjcryst.powderpattern import *\n", "from pyobjcryst.indexing import *\n", "from pyobjcryst.molecule import *\n", "from pyobjcryst.globaloptim import MonteCarlo\n", "from pyobjcryst.io import xml_cryst_file_save_global\n", "try:\n", " import ipywidgets as widgets\n", "except ImportError:\n", " widgets = None\n", "\n", "try:\n", " # Get the real number of processor cores available - requires psutil\n", " # os.sched_getaffinity is only available on some *nix platforms\n", " import psutil\n", " nproc = len(os.sched_getaffinity(0)) * psutil.cpu_count(logical=False) // psutil.cpu_count(logical=True)\n", "except:\n", " nproc = os.cpu_count()\n", "\n", "print(\"Number of processors or cores available: \", nproc)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create powder pattern object, index & fit profile\n", "Same as the cimetidine structure solution notebook, so read that for details" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Imported powder pattern: 7699 points, 2theta= 8.010 -> 84.990, step= 0.010\n", "Indexed unit cell:\n", "( 6.83 18.82 10.39 90.0 106.4 90.0 V=1281 MONOCLINIC P, 130.0296630859375)\n", "No background, adding one automatically\n", "Selected PowderPatternDiffraction: with Crystal: \n", "Profile fitting finished.\n", "Remember to use SetExtractionMode(False) on the PowderPatternDiffraction object\n", "to disable profile fitting and optimise the structure.\n", "Fit result: Rw= 5.51% Chi2= 34095.69 GoF= 4.43 LLK= 6397.991\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a662bb9be73e421f941df676d5562d10", "version_major": 2, "version_minor": 0 }, "image/png": "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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", " \n", "
\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p = PowderPattern()\n", "if not os.path.exists(\"cime.dat\"):\n", " os.system(\"curl -O https://raw.githubusercontent.com/vincefn/objcryst/master/Fox/example/tutorial-cimetidine/cime.dat\")\n", "p.ImportPowderPatternFullprof(\"cime.dat\")\n", "p.SetWavelength(1.52904)\n", "\n", "# Index\n", "pl = p.FindPeaks(1.5, -1, 1000, verbose=False)\n", "if len(pl) > 20:\n", " pl.resize(20) # Only keep 20 peaks\n", "\n", "ex = quick_index(pl, verbose=False)\n", "\n", "print(\"Indexed unit cell:\")\n", "for s in ex.GetSolutions():\n", " print(s)\n", "\n", "# Use solution to create a crystal\n", "uc = ex.GetSolutions()[0][0].DirectUnitCell()\n", "c = Crystal(uc[0], uc[1], uc[2], uc[3], uc[4], uc[5], \"P1\")\n", "pdiff = p.AddPowderPatternDiffraction(c)\n", "\n", "# Fit profile\n", "p.SetMaxSinThetaOvLambda(0.3)\n", "p.quick_fit_profile(auto_background=True,plot=False, init_profile=True,verbose=True)\n", "p.quick_fit_profile(plot=False, init_profile=False, asym=True, displ_transl=True, verbose=False)\n", "\n", "# Plot\n", "p.plot(diff=True, fig=None, hkl=True)\n", "print(\"Fit result: Rw=%6.2f%% Chi2=%10.2f GoF=%8.2f LLK=%10.3f\" %\n", " (p.rw * 100, p.chi2, p.chi2/p.GetNbPointUsed(), p.llk))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Find the spacegroup\n", "We'll use part of the list of possible spacegroups as options to test" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Beginning spacegroup exploration... 37 to go...\n", " (# 1) P 1 : Rwp= 6.80% GoF= 14.98 nGoF= 3.24 (186 reflections, 0 extinct)\n", " (# 2) P -1 : Rwp= 6.80% GoF= 14.98 nGoF= 3.24 (186 reflections, 0 extinct) [same extinctions as:P 1]\n", " (# 3) P 1 2 1 : Rwp= 6.69% GoF= 14.46 nGoF= 1.84 (105 reflections, 0 extinct)\n", " (# 4) P 1 21 1 : Rwp= 6.61% GoF= 14.11 nGoF= 1.65 (101 reflections, 2 extinct)\n", " (# 5) C 1 2 1 : Rwp= 62.70% GoF= 1246.13 nGoF= 311.68 ( 52 reflections, 84 extinct)\n", " (# 5) A 1 2 1 : Rwp= 62.83% GoF= 1253.74 nGoF= 313.87 ( 53 reflections, 85 extinct)\n", " (# 5) I 1 2 1 : Rwp= 60.91% GoF= 1196.43 nGoF= 246.94 ( 52 reflections, 87 extinct)\n", " (# 6) P 1 m 1 : Rwp= 6.69% GoF= 14.46 nGoF= 1.84 (105 reflections, 0 extinct) [same extinctions as:P 1 2 1]\n", " (# 7) P 1 c 1 : Rwp= 6.58% GoF= 13.92 nGoF= 1.66 ( 96 reflections, 15 extinP 1 21/c 1 nGoF= 1.4866 GoF= 13.575 Rw= 6.50 [ 92 reflections, extinct446= 17]\n", "P 1 21 1 nGoF= 1.6488 GoF= 14.108 Rw= 6.61 [101 reflections, extinct446= 2]\n", "P 1 21/m 1 nGoF= 1.6488 GoF= 14.108 Rw= 6.61 [101 reflections, extinct446= 2]\n", "P 1 c 1 nGoF= 1.6649 GoF= 13.922 Rw= 6.58 [ 96 reflections, extinct446= 15]\n", "P 1 2/c 1 nGoF= 1.6649 GoF= 13.922 Rw= 6.58 [ 96 reflections, extinct446= 15]\n", "P 1 2 1 nGoF= 1.8392 GoF= 14.458 Rw= 6.69 [105 reflections, extinct446= 0]\n", "P 1 m 1 nGoF= 1.8392 GoF= 14.458 Rw= 6.69 [105 reflections, extinct446= 0]\n", "P 1 2/m 1 nGoF= 1.8392 GoF= 14.458 Rw= 6.69 [105 reflections, extinct446= 0]\n", "P 1 nGoF= 3.2428 GoF= 14.982 Rw= 6.80 [186 reflections, extinct446= 0]\n", "P -1 nGoF= 3.2428 GoF= 14.982 Rw= 6.80 [186 reflections, extinct446= 0]\n", "P 1 21/n 1 nGoF= 5.4155 GoF= 26.697 Rw= 9.11 [ 92 reflections, extinct446= 19]\n", "P 1 2/n 1 nGoF= 5.7672 GoF= 27.063 Rw= 9.17 [ 96 reflections, extinct446= 17]\n", "P 1 n 1 nGoF= 5.7672 GoF= 27.063 Rw= 9.17 [ 96 reflections, extinct446= 17]\n", "Chosen spacegroup (smallest nGoF): P 1 21/c 1\n", "ct)\n", " (# 7) P 1 n 1 : Rwp= 9.17% GoF= 27.06 nGoF= 5.77 ( 96 reflections, 17 extinct)\n", " (# 7) P 1 a 1 : Rwp= 9.26% GoF= 27.58 nGoF= 5.97 ( 97 reflections, 14 extinct)\n", " (# 8) C 1 m 1 : Rwp= 62.70% GoF= 1246.13 nGoF= 311.68 ( 52 reflections, 84 extinct) [same extinctions as:C 1 2 1]\n", " (# 8) A 1 m 1 : Rwp= 62.83% GoF= 1253.74 nGoF= 313.87 ( 53 reflections, 85 extinct) [same extinctions as:A 1 2 1]\n", " (# 8) I 1 m 1 : Rwp= 60.91% GoF= 1196.43 nGoF= 246.94 ( 52 reflections, 87 extinct) [same extinctions as:I 1 2 1]\n", " (# 9) C 1 c 1 : Rwp= 62.51% GoF= 1236.15 nGoF= 280.76 ( 47 reflections, 93 extinct)\n", " (# 9) A 1 n 1 : Rwp= 62.99% GoF= 1258.62 nGoF= 291.60 ( 49 reflections, 93 extinct)\n", " (# 9) I 1 a 1 : Rwp= 59.00% GoF= 1120.91 nGoF= 221.05 ( 48 reflections, 93 extinct)\n", " (# 9) A 1 a 1 : Rwp= 62.99% GoF= 1258.62 nGoF= 291.60 ( 49 reflections, 93 extinct) [same extinctions as:A 1 n 1]\n", " (# 9) C 1 n 1 : Rwp= 62.51% GoF= 1236.15 nGoF= 280.76 ( 47 reflections, 93 extinct) [same extinctions as:C 1 c 1]\n", " (# 9) I 1 c 1 : Rwp= 59.00% GoF= 1120.91 nGoF= 221.05 ( 48 reflections, 93 extinct) [same extinctions as:I 1 a 1]\n", " (# 10) P 1 2/m 1 : Rwp= 6.69% GoF= 14.46 nGoF= 1.84 (105 reflections, 0 extinct) [same extinctions as:P 1 2 1]\n", " (# 11) P 1 21/m 1 : Rwp= 6.61% GoF= 14.11 nGoF= 1.65 (101 reflections, 2 extinct) [same extinctions as:P 1 21 1]\n", " (# 12) C 1 2/m 1 : Rwp= 62.70% GoF= 1246.13 nGoF= 311.68 ( 52 reflections, 84 extinct) [same extinctions as:C 1 2 1]\n", " (# 12) A 1 2/m 1 : Rwp= 62.83% GoF= 1253.74 nGoF= 313.87 ( 53 reflections, 85 extinct) [same extinctions as:A 1 2 1]\n", " (# 12) I 1 2/m 1 : Rwp= 60.91% GoF= 1196.43 nGoF= 246.94 ( 52 reflections, 87 extinct) [same extinctions as:I 1 2 1]\n", " (# 13) P 1 2/c 1 : Rwp= 6.58% GoF= 13.92 nGoF= 1.66 ( 96 reflections, 15 extinct) [same extinctions as:P 1 c 1]\n", " (# 13) P 1 2/n 1 : Rwp= 9.17% GoF= 27.06 nGoF= 5.77 ( 96 reflections, 17 extinct) [same extinctions as:P 1 n 1]\n", " (# 13) P 1 2/a 1 : Rwp= 9.26% GoF= 27.58 nGoF= 5.97 ( 97 reflections, 14 extinct) [same extinctions as:P 1 a 1]\n", " (# 14) P 1 21/c 1 : Rwp= 6.50% GoF= 13.58 nGoF= 1.49 ( 92 reflections, 17 extinct)\n", " (# 14) P 1 21/n 1 : Rwp= 9.11% GoF= 26.70 nGoF= 5.42 ( 92 reflections, 19 extinct)\n", " (# 14) P 1 21/a 1 : Rwp= 9.20% GoF= 27.22 nGoF= 5.61 ( 93 reflections, 16 extinct)\n", " (# 15) C 1 2/c 1 : Rwp= 62.51% GoF= 1236.15 nGoF= 280.76 ( 47 reflections, 93 extinct) [same extinctions as:C 1 c 1]\n", " (# 15) A 1 2/n 1 : Rwp= 62.99% GoF= 1258.62 nGoF= 291.60 ( 49 reflections, 93 extinct) [same extinctions as:A 1 n 1]\n", " (# 15) I 1 2/a 1 : Rwp= 59.00% GoF= 1120.91 nGoF= 221.05 ( 48 reflections, 93 extinct) [same extinctions as:I 1 a 1]\n", " (# 15) A 1 2/a 1 : Rwp= 62.99% GoF= 1258.62 nGoF= 291.60 ( 49 reflections, 93 extinct) [same extinctions as:A 1 n 1]\n", " (# 15) C 1 2/n 1 : Rwp= 62.51% GoF= 1236.15 nGoF= 280.76 ( 47 reflections, 93 extinct) [same extinctions as:C 1 c 1]\n", " (# 15) I 1 2/c 1 : Rwp= 59.00% GoF= 1120.91 nGoF= 221.05 ( 48 reflections, 93 extinct) [same extinctions as:I 1 a 1]\n", "Restoring best spacegroup: P 1 21/c 1\n" ] } ], "source": [ "p.SetMaxSinThetaOvLambda(0.2) # Important for stability of profile fit. And faster !\n", "spgex = SpaceGroupExplorer(pdiff)\n", "\n", "# NB:verbose C++ output does not appear in a notebook\n", "spgex.RunAll(keep_best=True, update_display=False, fitprofile_p1=False)\n", "\n", "for sol in spgex.GetScores():\n", " #if sol.nGoF > 4 * spgex.GetScores()[0].nGoF:\n", " if sol.GoF <= 2 * spgex.GetScores()[0].GoF:\n", " print(sol)\n", "\n", "print(\"Chosen spacegroup (smallest nGoF): \", c.GetSpaceGroup())\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setup the 'meta'-structure solution\n", "In the spacegroup search above the first solution `P 1 21/c 1` actually is the correct one, and with its multiplicity (4) only requires a single independent cimetidine molecule in the asymmetric unit cell. But let's proceed as if we did not actually know that.\n", "\n", "What we do is:\n", "* download the cimetidine z-matrix\n", "* create a list of the possible spacegroups that we want to test\n", "* create a function which, given a spacegroup, will :\n", " * apply the spacegroup to the crystal\n", " * determine the appropriate number of independent molecules (as a function of the multiplicity)\n", " * optimise for 5 runs and 2 million trials\n", " * return the solutions using the XML string of the Crystal\n", "* use `multiprocessing` or `multiprocess` to test the different spacegroups in parallel with multiple processor cores\n", "\n", "Note: *this is a generic approach - it would be possible also to completely change the contents of the crystal, e.g. if the number of independent units (atoms, molecule, polyhedra) was unknown, as is often the case for inorganic or metallic structures due to special positions*" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [] }, "outputs": [], "source": [ "# Get the cimetidine z-matrix\n", "if not os.path.exists(\"cime.fhz\"):\n", " os.system(\"curl -O https://raw.githubusercontent.com/vincefn/objcryst/master/Fox/example/tutorial-cimetidine/cime.fhz\")\n", "\n", "# Disable dynamical occupancy correction (no special position expected, usual in organic structures)\n", "c.GetOption(1).SetChoice(0)\n", "\n", "# Create the Monte-Carlo object with the crystal and the powder pattern\n", "mc = MonteCarlo()\n", "mc.AddRefinableObj(c)\n", "mc.AddRefinableObj(p)\n", "mc.GetOption(\"Automatic Least Squares Refinement\").SetChoice(2)\n", "\n", "nb_step = 1e6 # Increase this for more complex problems - it's ok if the spacegroup is correct (1 independent molecule)\n", "\n", "# Disable profile fitting for the powder pattern (or nothing gets optimised !)\n", "pdiff.SetExtractionMode(False)\n", "\n", "\n", "def solve_for_spacegroup(spgname):\n", " # Set spacegroup\n", " spg = c.GetSpaceGroup()\n", " spg.ChangeSpaceGroup(spgname)\n", " \n", " # Multiplicity\n", " mult = spg.GetNbSymmetrics()\n", " \n", " # Empty Crystal of existing scatterers (should not be necessary here)\n", " for i in range(c.GetNbScatterer()):\n", " c.RemoveScatterer(c.GetScatterer(0))\n", " \n", " # Add 4/mult independent cimetidine molecules\n", " nb_mol = 4//mult\n", " for i in range(nb_mol):\n", " m = ImportFenskeHallZMatrix(c,\"cime.fhz\")\n", " \n", " # Disable all display update if not in the Main process-or strange things happen !\n", " if current_process().name != 'MainProcess':\n", " # we must do that for the crystal, monte-carlo and powder pattern object\n", " for o in (c, mc, p):\n", " o.disable_display_update()\n", " \n", " # Run the parallel tempering optimisation\n", " # We could run multiple times the optimisation (using nb_run), but it is more \n", " # efficient to distribute that using the multiprocessing pool\n", " nb_run = 1\n", " t0 = timeit.default_timer()\n", " for i in range(nb_run):\n", " mc.MultiRunOptimize(nb_run=1, nb_step=nb_step)\n", " dt = timeit.default_timer() - t0\n", " print(\"Spacegroup: %12s LLK: %12.2f dt=%3.0fs\" % # /run (%3.0fs remaining)\n", " (spgname,mc.GetLogLikelihood(), dt,)) # dt/(i+1) * (nb_run-i-1)\n", " \n", " # Extract all solutions as the XML output of the crystal\n", " vsol = []\n", " for i in range(mc.GetNbParamSet()-nb_run, mc.GetNbParamSet()):\n", " mc.RestoreParamSet(i, update_display=False)\n", " s = c.xml()\n", " llk = mc.GetLogLikelihood()\n", " vsol.append({'xml': s, 'llk': llk, 'spg': spgname, 'nb_mol': nb_mol})\n", "\n", " return vsol\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the tests: **this will take a little while (5 to 20 minutes for each spacegroup, in parallel processes), and is longer for spacegroups with lower multiplicity and not centrosymmetric (more independant atoms)**." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Solving structures in // - this will take a little while, be patient !\n", "Spacegroup: P 1 21/c 1 LLK: 18500.13 dt= 26s\n", "Spacegroup: P 1 21/c 1 LLK: 49206.74 dt= 27s\n", "Spacegroup: P 1 21/m 1 LLK: 260046.12 dt= 28s\n", "Spacegroup: P 1 21/m 1 LLK: 267669.81 dt= 28s\n", "Spacegroup: P 1 2/c 1 LLK: 461123.68 dt= 27s\n", "Spacegroup: P 1 2/c 1 LLK: 306248.10 dt= 27s\n", "Spacegroup: P 1 c 1 LLK: 87454.66 dt= 75s\n", "Spacegroup: P 1 c 1 LLK: 91092.14 dt= 76s\n", "Spacegroup: P 1 21/c 1 LLK: 56249.86 dt= 27s\n", "Spacegroup: P 1 2 1 LLK: 232080.59 dt= 81s\n", "Spacegroup: P 1 2 1 LLK: 239530.07 dt= 82s\n", "Spacegroup: P -1 LLK: 101645.03 dt= 61s\n", "Spacegroup: P -1 LLK: 115241.95 dt= 64s\n", "Spacegroup: P 1 2/c 1 LLK: 670519.05 dt= 27s\n", "Spacegroup: P 1 21/m 1 LLK: 259924.47 dt= 28s\n", "Spacegroup: P 1 c 1 LLK: 86015.56 dt= 74s\n", "Spacegroup: P 1 21/c 1 LLK: 18596.19 dt= 27s\n", "Spacegroup: P 1 21 1 LLK: 82685.43 dt= 72s\n", "Spacegroup: P 1 21 1 LLK: 62145.94 dt= 74s\n", "Spacegroup: P 1 m 1 LLK: 260089.71 dt= 78s\n", "Spacegroup: P 1 2/c 1 LLK: 262462.19 dt= 27s\n", "Spacegroup: P 1 m 1 LLK: 261603.69 dt= 80s\n", "Spacegroup: P 1 2 1 LLK: 188882.56 dt= 82s\n", "Spacegroup: P -1 LLK: 123163.25 dt= 62s\n", "Spacegroup: P 1 21/m 1 LLK: 275339.56 dt= 28s\n", "Spacegroup: P 1 21/c 1 LLK: 49213.79 dt= 27s\n", "Spacegroup: P 1 21 1 LLK: 69958.12 dt= 73s\n", "Spacegroup: P 1 2/c 1 LLK: 372234.25 dt= 30s\n", "Spacegroup: P 1 c 1 LLK: 100616.81 dt= 72s\n", "Spacegroup: P 1 21/m 1 LLK: 242776.10 dt= 31s\n", "Spacegroup: P 1 2 1 LLK: 217691.94 dt= 84s\n", "Spacegroup: P 1 c 1 LLK: 109668.98 dt= 75s\n", "Spacegroup: P -1 LLK: 127211.68 dt= 63s\n", "Spacegroup: P 1 m 1 LLK: 343660.20 dt= 79s\n", "Spacegroup: P 1 2 1 LLK: 211306.76 dt= 80s\n", "Spacegroup: P -1 LLK: 155139.61 dt= 55s\n", "Spacegroup: P 1 21 1 LLK: 87547.20 dt= 68s\n", "Spacegroup: P 1 21 1 LLK: 101361.24 dt= 66s\n", "Spacegroup: P 1 m 1 LLK: 215645.85 dt= 71s\n", "Spacegroup: P 1 m 1 LLK: 274219.33 dt= 64s\n" ] } ], "source": [ "# List of spacegroups to test (this can be larger than the number \n", "# of availabe processor cores, process will loop over possible spacegroups)\n", "v_spacegroup = [\"P 1 21/c 1\", \"P 1 2/c 1\", \"P 1 c 1\", \"P 1 21 1\",\n", " \"P 1 2 1\", \"P 1 m 1\", \"P 1 21/m 1\", \"P -1\"] * 5\n", "\n", "# Use a multiprocess pool to solve in parallel - this should use all available processor cores\n", "print(\"Solving structures in // - this will take a little while, be patient !\")\n", "with Pool(nproc) as pool:\n", " res = pool.map(solve_for_spacegroup, v_spacegroup)\n", "\n", "# Merge all the results\n", "vsol = []\n", "for v in res:\n", " vsol += v\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Look at solutions\n", "If you have ipywidgets, just use the dropdown menu to select available solutions" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "XML: Loading Crystal:\n", "XML: Loading Crystal:(spg:P 1 21/c 1)\n", "Input ScatteringPowerAtom:C(C)\n", "Input ScatteringPowerAtom:N(N)\n", "Input ScatteringPowerAtom:S(S)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "80b4c4a7d1b4400b8c56129aa2a6d244", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(Dropdown(description='solution', options=('# 0 P 1 21/c 1 : 1 mol LLK= 18500.13', …" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/3dmoljs_load.v0": "
\n

3Dmol.js failed to load for some reason. Please check your browser console for error messages.

\n
\n", "text/html": [ "
\n", "

3Dmol.js failed to load for some reason. Please check your browser console for error messages.

\n", "
\n", "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "63b312a80e604e2ea00d166acd3bc97a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Box(children=(VBox(children=(HBox(children=(VBox(children=(FloatRangeSlider(value=(0.0, 1.0), description='Xra…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ed737ff1ac4e477baadcef40072a155e", "version_major": 2, "version_minor": 0 }, "image/png": "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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", " \n", "
\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# restore the best solution (first in list)\n", "c.XMLInput(vsol[0]['xml'])\n", "\n", "# sort solutions as a function of the log-likelihood\n", "vsol = sorted(vsol, key=lambda sol: sol['llk'])\n", "\n", "if widgets is not None:\n", " v = []\n", " for i in range(len(vsol)):\n", " sol = vsol[i]\n", " v.append(\"#%2d %12s : %d mol LLK=%12.2f\"%(i, sol['spg'], sol['nb_mol'], sol['llk']))\n", " w = widgets.Dropdown(options=v, description='Solutions:', disabled=False,)\n", " def show_solution(solution):\n", " i = v.index(solution)\n", " # Crystal display is automatically updated when loaded\n", " c.XMLInput(vsol[i]['xml'])\n", " # Update powder pattern display manually\n", " p.FitScaleFactorForIntegratedRw()\n", " p.UpdateDisplay()\n", " widgets.interact(show_solution, solution=v)\n", "else:\n", " # print solutions\n", " for i in range(len(vsol)):\n", " sol = vsol[i]\n", " print(\"#%2d %12s : %d mol LLK=%12.2f\"%(i, sol['spg'], sol['nb_mol'], sol['llk']))\n", "\n", "# displays - requires ipywidgets for crystal 3D display\n", "display(c.widget_3d())\n", "p.plot(fig=None,diff=True,hkl=True)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Try some other solutions\n", "If ipywidgets is not shown in the previous cell, load solutions manually" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "XML: Loading Crystal:\n", "XML: Loading Crystal:(spg:P 1 21/c 1)\n", "Input ScatteringPowerAtom:C(C)\n", "Input ScatteringPowerAtom:N(N)\n", "Input ScatteringPowerAtom:S(S)\n" ] } ], "source": [ "# Crystal display is automatically updated when loaded\n", "c.XMLInput(vsol[2]['xml'])\n", "# Update powder pattern display manually\n", "p.UpdateDisplay()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Save the selected result to CIF and Fox (.xmlgz) formats\n", "All the solutions (or just the best ones) could be saved automatically that way by looping over the results" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "tags": [] }, "outputs": [], "source": [ "# Save result so it can be opened by Fox\n", "xml_cryst_file_save_global('result.xmlgz')\n", "# Also export to the CIF format\n", "c.CIFOutput(\"result.cif\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "00b5443bf8f44a05b031982d370c43fd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "fullMol opac", "layout": "IPY_MODEL_4c15e7db02eb4a66bd13831ecaa4567d", "max": 1, "readout_format": ".01f", "step": 0.1, "style": "IPY_MODEL_63c400754e654442bc150a3bf95c0518", "tooltip": "Opacity to display fully molecules\nwhich have at least one atom inside the limits", "value": 0.5 } }, "0447ac470bb345b599d6f258ae1b48de": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "04e61f021c074f559434b63b34d75522": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Yrange", "layout": "IPY_MODEL_443a6abb5c054a36b5862439e3d0def3", "max": 1.5, "min": -0.5, "step": 0.02631578947368421, "style": "IPY_MODEL_ff8827d44d654c298db2de63a4940c5f", "value": [ -5.551115123125783e-17, 0.9999999999999998 ] } }, "057ee3da241449c68e0976a26ae40c2a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "135874284cbf450e9ab0830d972a356a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "DropdownModel", "state": { "_options_labels": [ "# 0 P 1 21/c 1 : 1 mol LLK= 18500.13", "# 1 P 1 21/c 1 : 1 mol LLK= 18596.19", "# 2 P 1 21/c 1 : 1 mol LLK= 49206.74", "# 3 P 1 21/c 1 : 1 mol LLK= 49213.79", "# 4 P 1 21/c 1 : 1 mol LLK= 56249.86", "# 5 P 1 21 1 : 2 mol LLK= 62145.94", "# 6 P 1 21 1 : 2 mol LLK= 69958.12", "# 7 P 1 21 1 : 2 mol LLK= 82685.43", "# 8 P 1 c 1 : 2 mol LLK= 86015.56", "# 9 P 1 c 1 : 2 mol LLK= 87454.66", "#10 P 1 21 1 : 2 mol LLK= 87547.20", "#11 P 1 c 1 : 2 mol LLK= 91092.14", "#12 P 1 c 1 : 2 mol LLK= 100616.81", "#13 P 1 21 1 : 2 mol LLK= 101361.24", "#14 P -1 : 2 mol LLK= 101645.03", "#15 P 1 c 1 : 2 mol LLK= 109668.98", "#16 P -1 : 2 mol LLK= 115241.95", "#17 P -1 : 2 mol LLK= 123163.25", "#18 P -1 : 2 mol LLK= 127211.68", "#19 P -1 : 2 mol LLK= 155139.61", "#20 P 1 2 1 : 2 mol LLK= 188882.56", "#21 P 1 2 1 : 2 mol LLK= 211306.76", "#22 P 1 m 1 : 2 mol LLK= 215645.85", "#23 P 1 2 1 : 2 mol LLK= 217691.94", "#24 P 1 2 1 : 2 mol LLK= 232080.59", "#25 P 1 2 1 : 2 mol LLK= 239530.07", "#26 P 1 21/m 1 : 1 mol LLK= 242776.10", "#27 P 1 21/m 1 : 1 mol LLK= 259924.47", "#28 P 1 21/m 1 : 1 mol LLK= 260046.12", "#29 P 1 m 1 : 2 mol LLK= 260089.71", "#30 P 1 m 1 : 2 mol LLK= 261603.69", "#31 P 1 2/c 1 : 1 mol LLK= 262462.19", "#32 P 1 21/m 1 : 1 mol LLK= 267669.81", "#33 P 1 m 1 : 2 mol LLK= 274219.33", "#34 P 1 21/m 1 : 1 mol LLK= 275339.56", "#35 P 1 2/c 1 : 1 mol LLK= 306248.10", "#36 P 1 m 1 : 2 mol LLK= 343660.20", "#37 P 1 2/c 1 : 1 mol LLK= 372234.25", "#38 P 1 2/c 1 : 1 mol LLK= 461123.68", "#39 P 1 2/c 1 : 1 mol LLK= 670519.05" ], "description": "Solutions:", "index": 0, "layout": "IPY_MODEL_8dd929d319574430988c9a3c5b38dbaa", "style": "IPY_MODEL_6686e236ced74289ba9f38771df23bde" } }, "15ebdb6b1ce94d499186cee7343cf828": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_2ccba0fa274643bea4615033c6bdddf7", "outputs": [ { "data": { "application/3dmoljs_load.v0": "", "text/html": "" }, "metadata": {}, "output_type": "display_data" } ] } }, "1b240d7dab9e4b5385cb091ec20f2ab9": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_cb6eada368fd458faec9a1849e8654a2", "outputs": [ { "name": "stdout", "output_type": "stream", "text": "XML: Loading Crystal:\nXML: Loading Crystal:(spg:P 1 21/c 1)\nInput ScatteringPowerAtom:C(C)\nInput ScatteringPowerAtom:N(N)\nInput ScatteringPowerAtom:S(S)\n" } ] } }, "1e59b464625a48d595ec1f60f5b69222": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "extra dist", "layout": "IPY_MODEL_3aa42e4d60ec4e2ea45541dccf675ff4", "max": 10, "readout_format": ".1f", "step": 0.5, "style": "IPY_MODEL_3b721c73a0b44186aafb0f3f4e1709d3", "tooltip": "Extra distance (A) with semi-transparent display", "value": 2 } }, "28089224bf4a426c9e36d0497c89e66c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "2ccba0fa274643bea4615033c6bdddf7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "2ef668dcdd7e4885968d2100cd2105ad": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_7fd1607d62214374ba19a30315055b9a", "IPY_MODEL_04e61f021c074f559434b63b34d75522", "IPY_MODEL_8e6094d6f13f444f808fb64d99f75738" ], "layout": "IPY_MODEL_ffe42167326340e8bbd49511adf9ac1f" } }, "31b4b5e9d32e4653ba578d4caae14831": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "35652a678f864aedb06c8d16a1374191": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "3aa42e4d60ec4e2ea45541dccf675ff4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "3b721c73a0b44186aafb0f3f4e1709d3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "3c1116b547234f2b97e22db310a953c2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "DescriptionStyleModel", "state": { "description_width": "" } }, "430ff3580cf946aea74a7c7d82a31cb2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "443a6abb5c054a36b5862439e3d0def3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "4c15e7db02eb4a66bd13831ecaa4567d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "4da865c6de3348f1b9e574778800d005": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_1e59b464625a48d595ec1f60f5b69222", "IPY_MODEL_8ab052e209784f5e9e9655d92f39b2b9", "IPY_MODEL_00b5443bf8f44a05b031982d370c43fd" ], "layout": "IPY_MODEL_feabaa8d66914513a3ef4538b18a03cd" } }, "558e8d323a3e4263bb3f1754319dee94": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "63b312a80e604e2ea00d166acd3bc97a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "BoxModel", "state": { "children": [ "IPY_MODEL_fedaff6bdc6c4459b41e654d04c13d91" ], "layout": "IPY_MODEL_9a73b1a1f18443a3ab2be158d16b01db" } }, "63c400754e654442bc150a3bf95c0518": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "6686e236ced74289ba9f38771df23bde": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "DescriptionStyleModel", "state": { "description_width": "" } }, "6875cd83f2a64d9bb4c859f97afb563b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "745d2005f8264321843115d31627eb66": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "7d0e4c75c10245b5b743cf7238af0177": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", "state": { "children": [ "IPY_MODEL_2ef668dcdd7e4885968d2100cd2105ad", "IPY_MODEL_4da865c6de3348f1b9e574778800d005" ], "layout": "IPY_MODEL_35652a678f864aedb06c8d16a1374191" } }, "7fd1607d62214374ba19a30315055b9a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Xrange", "layout": "IPY_MODEL_c09b14582f4048efb50a75392d700ba1", "max": 1.5, "min": -0.5, "step": 0.07142857142857142, "style": "IPY_MODEL_0447ac470bb345b599d6f258ae1b48de", "value": [ 0, 1 ] } }, "80b4c4a7d1b4400b8c56129aa2a6d244": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "_dom_classes": [ "widget-interact" ], "children": [ "IPY_MODEL_bd033c36be3d4db3803d2c759d8d4e6f", "IPY_MODEL_1b240d7dab9e4b5385cb091ec20f2ab9" ], "layout": "IPY_MODEL_430ff3580cf946aea74a7c7d82a31cb2" } }, "810ebc4df77a43a9b5b2c1e4e71c2d7f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "8ab052e209784f5e9e9655d92f39b2b9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "extra opac.", "layout": "IPY_MODEL_ffdec305128247aba21dc5789a9aff1d", "max": 1, "readout_format": ".01f", "step": 0.1, "style": "IPY_MODEL_810ebc4df77a43a9b5b2c1e4e71c2d7f", "tooltip": "Opacity for extra distance display", "value": 0.5 } }, "8dd929d319574430988c9a3c5b38dbaa": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "8e6094d6f13f444f808fb64d99f75738": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Zrange", "layout": "IPY_MODEL_6875cd83f2a64d9bb4c859f97afb563b", "max": 1.5, "min": -0.5, "step": 0.047619047619047616, "style": "IPY_MODEL_28089224bf4a426c9e36d0497c89e66c", "value": [ 0.023809523809523836, 1.0238095238095237 ] } }, "9a73b1a1f18443a3ab2be158d16b01db": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "b77e4f516b1644cf98b2bb54b4d2ffb1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "bd033c36be3d4db3803d2c759d8d4e6f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "DropdownModel", "state": { "_options_labels": [ "# 0 P 1 21/c 1 : 1 mol LLK= 18500.13", "# 1 P 1 21/c 1 : 1 mol LLK= 18596.19", "# 2 P 1 21/c 1 : 1 mol LLK= 49206.74", "# 3 P 1 21/c 1 : 1 mol LLK= 49213.79", "# 4 P 1 21/c 1 : 1 mol LLK= 56249.86", "# 5 P 1 21 1 : 2 mol LLK= 62145.94", "# 6 P 1 21 1 : 2 mol LLK= 69958.12", "# 7 P 1 21 1 : 2 mol LLK= 82685.43", "# 8 P 1 c 1 : 2 mol LLK= 86015.56", "# 9 P 1 c 1 : 2 mol LLK= 87454.66", "#10 P 1 21 1 : 2 mol LLK= 87547.20", "#11 P 1 c 1 : 2 mol LLK= 91092.14", "#12 P 1 c 1 : 2 mol LLK= 100616.81", "#13 P 1 21 1 : 2 mol LLK= 101361.24", "#14 P -1 : 2 mol LLK= 101645.03", "#15 P 1 c 1 : 2 mol LLK= 109668.98", "#16 P -1 : 2 mol LLK= 115241.95", "#17 P -1 : 2 mol LLK= 123163.25", "#18 P -1 : 2 mol LLK= 127211.68", "#19 P -1 : 2 mol LLK= 155139.61", "#20 P 1 2 1 : 2 mol LLK= 188882.56", "#21 P 1 2 1 : 2 mol LLK= 211306.76", "#22 P 1 m 1 : 2 mol LLK= 215645.85", "#23 P 1 2 1 : 2 mol LLK= 217691.94", "#24 P 1 2 1 : 2 mol LLK= 232080.59", "#25 P 1 2 1 : 2 mol LLK= 239530.07", "#26 P 1 21/m 1 : 1 mol LLK= 242776.10", "#27 P 1 21/m 1 : 1 mol LLK= 259924.47", "#28 P 1 21/m 1 : 1 mol LLK= 260046.12", "#29 P 1 m 1 : 2 mol LLK= 260089.71", "#30 P 1 m 1 : 2 mol LLK= 261603.69", "#31 P 1 2/c 1 : 1 mol LLK= 262462.19", "#32 P 1 21/m 1 : 1 mol LLK= 267669.81", "#33 P 1 m 1 : 2 mol LLK= 274219.33", "#34 P 1 21/m 1 : 1 mol LLK= 275339.56", "#35 P 1 2/c 1 : 1 mol LLK= 306248.10", "#36 P 1 m 1 : 2 mol LLK= 343660.20", "#37 P 1 2/c 1 : 1 mol LLK= 372234.25", "#38 P 1 2/c 1 : 1 mol LLK= 461123.68", "#39 P 1 2/c 1 : 1 mol LLK= 670519.05" ], "description": "solution", "index": 0, "layout": "IPY_MODEL_31b4b5e9d32e4653ba578d4caae14831", "style": "IPY_MODEL_3c1116b547234f2b97e22db310a953c2" } }, "bf0bbda1a483459a8ae709f67037d7e4": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "ToolbarModel", "state": { "_model_module_version": "^0.11", "_view_module_version": "^0.11", "collapsed": true, "layout": "IPY_MODEL_057ee3da241449c68e0976a26ae40c2a", "orientation": "vertical", "toolitems": [ [ "Home", "Reset original view", "home", "home" ], [ "Back", "Back to previous view", "arrow-left", "back" ], [ "Forward", "Forward to next view", "arrow-right", "forward" ], [ "Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan" ], [ "Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom" ], [ "Download", "Download plot", "floppy-o", "save_figure" ] ] } }, "c09b14582f4048efb50a75392d700ba1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "cb6eada368fd458faec9a1849e8654a2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "e394c6a5c23c4d56868f65d07be70d28": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "f172c22d8dde4f0989fab8518c4a1fb2": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "ToolbarModel", "state": { "_model_module_version": "^0.11", "_view_module_version": "^0.11", "collapsed": true, "layout": "IPY_MODEL_b77e4f516b1644cf98b2bb54b4d2ffb1", "orientation": "vertical", "toolitems": [ [ "Home", "Reset original view", "home", "home" ], [ "Back", "Back to previous view", "arrow-left", "back" ], [ "Forward", "Forward to next view", "arrow-right", "forward" ], [ "Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan" ], [ "Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom" ], [ "Download", "Download plot", "floppy-o", "save_figure" ] ] } }, "feabaa8d66914513a3ef4538b18a03cd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "fedaff6bdc6c4459b41e654d04c13d91": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_7d0e4c75c10245b5b743cf7238af0177", "IPY_MODEL_15ebdb6b1ce94d499186cee7343cf828" ], "layout": "IPY_MODEL_e394c6a5c23c4d56868f65d07be70d28" } }, "ff8827d44d654c298db2de63a4940c5f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "ffdec305128247aba21dc5789a9aff1d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "ffe42167326340e8bbd49511adf9ac1f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 } pyobjcryst-2024.2.1/examples/structure-solution-powder-cimetidine.ipynb000066400000000000000000071550131470422267000263730ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Cimetidine tutorial: indexing, spacegroup determination & structure solution\n", "In this notebook, you can:\n", "* Load the powder diffraction data and create the PowderPattern object\n", "* Find the diffraction peaks and index them (determine the unit cell)\n", "* Perform a profile fit to optimise the background and reflection profiles\n", "* Determine the spacegroup\n", "* Add a molecule to describe the contents of the Crystal structure\n", "* Solve the Crystal structure using a Monte-Carlo/Parallel tempering algorithm\n", "* Save the best result to a CIF file and to Fox .xmlgz format\n", "\n", "Notes:\n", "* This is an *ideal* case for structure solution from powder diffraction - a clean powder diffraction data easily indexed, an unambiguous spacegroup, and a relatively simple structure.\n", "* It is important to follow the steps relatively linearly and avoid going back to previous cells until you know better. For example to avoid adding multiple times Scatterer/Molecule objects in the crystal structure, or multiple crystalline phases to the powder pattern with the same crystal, etc..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# 'widget' allows live update and works in both classical notebooks and jupyter-lab.\n", "# Otherwise 'notebook', 'ipympl', 'inline' can be used\n", "%matplotlib widget\n", "\n", "import os\n", "import pyobjcryst\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from pyobjcryst.crystal import *\n", "from pyobjcryst.powderpattern import *\n", "from pyobjcryst.indexing import *\n", "from pyobjcryst.molecule import *\n", "from pyobjcryst.globaloptim import MonteCarlo\n", "from pyobjcryst.io import xml_cryst_file_save_global" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create powder pattern object, download data if necessary" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Imported powder pattern: 7699 points, 2theta= 8.010 -> 84.990, step= 0.010\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bd48c8151b264b11a214f6b3f326fbfc", "version_major": 2, "version_minor": 0 }, "image/png": "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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", " \n", "
\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p = PowderPattern()\n", "if not os.path.exists(\"cime.dat\"):\n", " os.system(\"curl -O https://raw.githubusercontent.com/vincefn/objcryst/master/Fox/example/tutorial-cimetidine/cime.dat\")\n", "p.ImportPowderPatternFullprof(\"cime.dat\")\n", "p.SetWavelength(1.52904)\n", "\n", "p.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Find peaks & index the reflections\n", "In this case the peaks are automatically found without any parasitic phase.\n", "\n", "And the unit cell is also indexed without any ambiguity. This uses the dichotomy in volume approach (Louër & Boultif).\n", "\n", "... It is not always that easy !" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Peak dobs=0.10632+/-0.00011 iobs=2.146855e+04 (? ? ?))\n", "Peak dobs=0.11354+/-0.00011 iobs=4.111350e+03 (? ? ?))\n", "Peak dobs=0.14620+/-0.00011 iobs=9.429778e+04 (? ? ?))\n", "Peak dobs=0.15277+/-0.00011 iobs=1.388049e+03 (? ? ?))\n", "Peak dobs=0.16177+/-0.00011 iobs=1.420839e+03 (? ? ?))\n", "Peak dobs=0.16602+/-0.00014 iobs=1.141690e+05 (? ? ?))\n", "Peak dobs=0.18616+/-0.00011 iobs=1.609675e+05 (? ? ?))\n", "Peak dobs=0.18839+/-0.00011 iobs=4.474511e+04 (? ? ?))\n", "Peak dobs=0.18984+/-0.00011 iobs=1.839251e+05 (? ? ?))\n", "Peak dobs=0.20064+/-0.00011 iobs=1.290410e+05 (? ? ?))\n", "Peak dobs=0.20760+/-0.00011 iobs=1.182234e+05 (? ? ?))\n", "Peak dobs=0.21186+/-0.00006 iobs=2.198665e+03 (? ? ?))\n", "Peak dobs=0.21262+/-0.00011 iobs=8.717511e+03 (? ? ?))\n", "Peak dobs=0.21507+/-0.00011 iobs=1.818877e+04 (? ? ?))\n", "Peak dobs=0.22072+/-0.00008 iobs=2.098754e+04 (? ? ?))\n", "Peak dobs=0.22153+/-0.00011 iobs=6.288388e+04 (? ? ?))\n", "Peak dobs=0.22394+/-0.00011 iobs=1.562582e+05 (? ? ?))\n", "Peak dobs=0.22705+/-0.00014 iobs=3.681909e+03 (? ? ?))\n", "Peak dobs=0.23104+/-0.00011 iobs=2.493547e+04 (? ? ?))\n", "Peak dobs=0.23505+/-0.00011 iobs=1.279133e+03 (? ? ?))\n", "Predicting volumes from 20 peaks between d=94.058 and d= 4.254\n", "\n", "Starting indexing using 20 peaks\n", " CUBIC P : V= 4699 -> 52534 A^3, max length=112.36A\n", " -> 0 sols in 0.00s, best score= 0.0\n", "\n", " TETRAGONAL P : V= 1744 -> 12585 A^3, max length= 69.78A\n", " -> 0 sols in 0.01s, best score= 0.0\n", "\n", "RHOMBOEDRAL P : V= 1932 -> 13204 A^3, max length= 70.91A\n", " -> 0 sols in 0.00s, best score= 0.0\n", "\n", " HEXAGONAL P : V= 2382 -> 17415 A^3, max length= 77.76A\n", " -> 0 sols in 0.02s, best score= 0.0\n", "\n", "ORTHOROMBIC P : V= 1014 -> 6526 A^3, max length= 56.06A\n", " -> 1 sols in 0.03s, best score= 7.3\n", "\n", " MONOCLINIC P : V= 756 -> 4210 A^3, max length= 48.44A\n", " -> 1 sols in 0.01s, best score= 130.0\n", "\n", "Solutions:\n", "( 6.83 18.82 10.39 90.0 106.4 90.0 V=1281 MONOCLINIC P, 130.0296630859375)\n" ] } ], "source": [ "# Index\n", "pl = p.FindPeaks(1.5, -1, 1000)\n", "if len(pl) > 20:\n", " pl.resize(20) # Only keep 20 peaks\n", "for peak in pl:\n", " print(peak)\n", "\n", "ex = quick_index(pl)\n", "\n", "print(\"Solutions:\")\n", "for s in ex.GetSolutions():\n", " print(s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a crystal phase using the indexed unit cell" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bb14d41c8eb247b58211bd24e5aa93e1", "version_major": 2, "version_minor": 0 }, "image/png": "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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", " \n", "
\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "uc = ex.GetSolutions()[0][0].DirectUnitCell()\n", "c = pyobjcryst.crystal.Crystal(uc[0], uc[1], uc[2], uc[3], uc[4], uc[5], \"P1\")\n", "pdiff = p.AddPowderPatternDiffraction(c)\n", "\n", "# Plot with indexing in new figure\n", "p.plot(diff=False,fig=None,hkl=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fit the profile and background\n", "We use a maximum sin(theta)/lambda because we don't really need high angle/high resolution data.\n", "\n", "This will go faster and is more reliable for spacegroup indexing and structure solution." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No background, adding one automatically\n", "Selected PowderPatternDiffraction: with Crystal: \n", "Profile fitting finished.\n", "Remember to use SetExtractionMode(False) on the PowderPatternDiffraction object\n", "to disable profile fitting and optimise the structure.\n", "Fit result: Rw= 5.45% Chi2= 33309.27 GoF= 4.33 LLK= 6248.657\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ea1a1633984a452693bb538cd640b799", "version_major": 2, "version_minor": 0 }, "image/png": "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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", " \n", "
\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p.SetMaxSinThetaOvLambda(0.3)\n", "p.quick_fit_profile(auto_background=True,plot=False, init_profile=True,verbose=True)\n", "p.quick_fit_profile(plot=False, init_profile=False, asym=True, displ_transl=True, verbose=False)\n", "\n", "# Plot in new figure\n", "p.plot(diff=True, fig=None, hkl=True)\n", "print(\"Fit result: Rw=%6.2f%% Chi2=%10.2f GoF=%8.2f LLK=%10.3f\" %\n", " (p.rw * 100, p.chi2, p.chi2/p.GetNbPointUsed(), p.llk))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Find the spacegroup\n", "The SpaceGroupExplorer can be used to find the optimal spacegroup. \n", "\n", "What `RunAll()` does is try all spacegroups and settings which are compatible with the unit cell (in this case all monoclinic and triclinic), and perform a profile fit (Le Bail only, we don't refine profile parameters or background since these parameters should be OK).\n", "\n", "From this several values are extracted for each spacegroup setting:\n", "* **Rw** - the standard full-profile weighted R factor $R_{wp}$\n", "* **GoF**: the chi2 (full profile $\\chi^2=\\Sigma\\frac{(obs-calc)^2}{\\sigma^2}$) divided by the number of points used\n", "* **nGoF**: this is the Goodness-of-Fit, but computed on integration intervals defined by P1 reflections, and then multipled by the number of reflections used divided by the number of reflections for the P1 spacegroup. This is more discriminating and allows to put forward spacegroups which yield a good fit with more extinctions.\n", "* **reflections** is the number of reflections actually taken into account for this spacegroup up to the maximum sin(theta)/lambda\n", "* **extinct446** gives the number of extinct reflections for 0<=H<=4 0<=K<=4 0<=L<=6 (which is used internally as a unique fingerprint for the extinctions)\n", "\n", "Some C++ verbose output does not appear here but will be in the jupyter server log if you see it.\n", "\n", "The results are sorting by ascending **nGOF**\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Beginning spacegroup exploration... 37 to go...\n", " (# 1) P 1 : Rwp= 6.72% GoF= 14.60 nGoF= 3.32 (186 reflections, 0 extinct)\n", " (# 2) P -1 : Rwp= 6.72% GoF= 14.60 nGoF= 3.32 (186 reflections, 0 extinct) [same extinctions as:P 1]\n", " (# 3) P 1 2 1 : Rwp= 6.69% GoF= 14.47 nGoF= 1.90 (105 reflections, 0 extinct)\n", " (# 4) P 1 21 1 : Rwp= 6.62% GoF= 14.12 nGoF= 1.70 (101 reflections, 2 extinct)\n", " (# 5) C 1 2 1 : Rwp= 62.70% GoF= 1245.88 nGoF= 311.62 ( 52 reflections, 84 extinct)\n", " (# 5) A 1 2 1 : Rwp= 62.84% GoF= 1254.25 nGoF= 314.24 ( 53 reflections, 85 extinct)\n", " (# 5) I 1 2 1 : Rwp= 60.90% GoF= 1196.31 nGoF= 246.95 ( 52 reflections, 87 extinct)\n", " (# 6) P 1 m 1 : Rwp= 6.69% GoF= 14.47 nGoF= 1.90 (105 reflections, 0 extinct) [same extinctions as:P 1 2 1]\n", " (# 7) P 1 c 1 : Rwp= 6.58% GoF= 13.94 nGoF= 1.66 ( 96 reflections, 15 extinP 1 21/c 1 nGoF= 1.4785 GoF= 13.598 Rw= 6.50 [ 92 reflections, extinct446= 17]\n", "P 1 c 1 nGoF= 1.6645 GoF= 13.940 Rw= 6.58 [ 96 reflections, extinct446= 15]\n", "P 1 2/c 1 nGoF= 1.6645 GoF= 13.940 Rw= 6.58 [ 96 reflections, extinct446= 15]\n", "P 1 21 1 nGoF= 1.6973 GoF= 14.123 Rw= 6.62 [101 reflections, extinct446= 2]\n", "P 1 21/m 1 nGoF= 1.6973 GoF= 14.123 Rw= 6.62 [101 reflections, extinct446= 2]\n", "P 1 2 1 nGoF= 1.8984 GoF= 14.468 Rw= 6.69 [105 reflections, extinct446= 0]\n", "P 1 m 1 nGoF= 1.8984 GoF= 14.468 Rw= 6.69 [105 reflections, extinct446= 0]\n", "P 1 2/m 1 nGoF= 1.8984 GoF= 14.468 Rw= 6.69 [105 reflections, extinct446= 0]\n", "P 1 nGoF= 3.3186 GoF= 14.598 Rw= 6.72 [186 reflections, extinct446= 0]\n", "P -1 nGoF= 3.3186 GoF= 14.598 Rw= 6.72 [186 reflections, extinct446= 0]\n", "P 1 21/n 1 nGoF= 5.4006 GoF= 26.711 Rw= 9.12 [ 92 reflections, extinct446= 19]\n", "P 1 2/n 1 nGoF= 5.7594 GoF= 27.072 Rw= 9.17 [ 96 reflections, extinct446= 17]\n", "P 1 n 1 nGoF= 5.7594 GoF= 27.072 Rw= 9.17 [ 96 reflections, extinct446= 17]\n", "Chosen spacegroup (smallest nGoF): P 1 21/c 1\n", "ct)\n", " (# 7) P 1 n 1 : Rwp= 9.17% GoF= 27.07 nGoF= 5.76 ( 96 reflections, 17 extinct)\n", " (# 7) P 1 a 1 : Rwp= 9.26% GoF= 27.59 nGoF= 6.02 ( 97 reflections, 14 extinct)\n", " (# 8) C 1 m 1 : Rwp= 62.70% GoF= 1245.88 nGoF= 311.62 ( 52 reflections, 84 extinct) [same extinctions as:C 1 2 1]\n", " (# 8) A 1 m 1 : Rwp= 62.84% GoF= 1254.25 nGoF= 314.24 ( 53 reflections, 85 extinct) [same extinctions as:A 1 2 1]\n", " (# 8) I 1 m 1 : Rwp= 60.90% GoF= 1196.31 nGoF= 246.95 ( 52 reflections, 87 extinct) [same extinctions as:I 1 2 1]\n", " (# 9) C 1 c 1 : Rwp= 62.50% GoF= 1235.90 nGoF= 280.65 ( 47 reflections, 93 extinct)\n", " (# 9) A 1 n 1 : Rwp= 63.01% GoF= 1259.14 nGoF= 291.94 ( 49 reflections, 93 extinct)\n", " (# 9) I 1 a 1 : Rwp= 59.00% GoF= 1121.25 nGoF= 221.14 ( 48 reflections, 93 extinct)\n", " (# 9) A 1 a 1 : Rwp= 63.01% GoF= 1259.14 nGoF= 291.94 ( 49 reflections, 93 extinct) [same extinctions as:A 1 n 1]\n", " (# 9) C 1 n 1 : Rwp= 62.50% GoF= 1235.90 nGoF= 280.65 ( 47 reflections, 93 extinct) [same extinctions as:C 1 c 1]\n", " (# 9) I 1 c 1 : Rwp= 59.00% GoF= 1121.25 nGoF= 221.14 ( 48 reflections, 93 extinct) [same extinctions as:I 1 a 1]\n", " (# 10) P 1 2/m 1 : Rwp= 6.69% GoF= 14.47 nGoF= 1.90 (105 reflections, 0 extinct) [same extinctions as:P 1 2 1]\n", " (# 11) P 1 21/m 1 : Rwp= 6.62% GoF= 14.12 nGoF= 1.70 (101 reflections, 2 extinct) [same extinctions as:P 1 21 1]\n", " (# 12) C 1 2/m 1 : Rwp= 62.70% GoF= 1245.88 nGoF= 311.62 ( 52 reflections, 84 extinct) [same extinctions as:C 1 2 1]\n", " (# 12) A 1 2/m 1 : Rwp= 62.84% GoF= 1254.25 nGoF= 314.24 ( 53 reflections, 85 extinct) [same extinctions as:A 1 2 1]\n", " (# 12) I 1 2/m 1 : Rwp= 60.90% GoF= 1196.31 nGoF= 246.95 ( 52 reflections, 87 extinct) [same extinctions as:I 1 2 1]\n", " (# 13) P 1 2/c 1 : Rwp= 6.58% GoF= 13.94 nGoF= 1.66 ( 96 reflections, 15 extinct) [same extinctions as:P 1 c 1]\n", " (# 13) P 1 2/n 1 : Rwp= 9.17% GoF= 27.07 nGoF= 5.76 ( 96 reflections, 17 extinct) [same extinctions as:P 1 n 1]\n", " (# 13) P 1 2/a 1 : Rwp= 9.26% GoF= 27.59 nGoF= 6.02 ( 97 reflections, 14 extinct) [same extinctions as:P 1 a 1]\n", " (# 14) P 1 21/c 1 : Rwp= 6.50% GoF= 13.60 nGoF= 1.48 ( 92 reflections, 17 extinct)\n", " (# 14) P 1 21/n 1 : Rwp= 9.12% GoF= 26.71 nGoF= 5.40 ( 92 reflections, 19 extinct)\n", " (# 14) P 1 21/a 1 : Rwp= 9.20% GoF= 27.23 nGoF= 5.65 ( 93 reflections, 16 extinct)\n", " (# 15) C 1 2/c 1 : Rwp= 62.50% GoF= 1235.90 nGoF= 280.65 ( 47 reflections, 93 extinct) [same extinctions as:C 1 c 1]\n", " (# 15) A 1 2/n 1 : Rwp= 63.01% GoF= 1259.14 nGoF= 291.94 ( 49 reflections, 93 extinct) [same extinctions as:A 1 n 1]\n", " (# 15) I 1 2/a 1 : Rwp= 59.00% GoF= 1121.25 nGoF= 221.14 ( 48 reflections, 93 extinct) [same extinctions as:I 1 a 1]\n", " (# 15) A 1 2/a 1 : Rwp= 63.01% GoF= 1259.14 nGoF= 291.94 ( 49 reflections, 93 extinct) [same extinctions as:A 1 n 1]\n", " (# 15) C 1 2/n 1 : Rwp= 62.50% GoF= 1235.90 nGoF= 280.65 ( 47 reflections, 93 extinct) [same extinctions as:C 1 c 1]\n", " (# 15) I 1 2/c 1 : Rwp= 59.00% GoF= 1121.25 nGoF= 221.14 ( 48 reflections, 93 extinct) [same extinctions as:I 1 a 1]\n", "Restoring best spacegroup: P 1 21/c 1\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4b7730f297f840d39edcc3fbd3208be7", "version_major": 2, "version_minor": 0 }, "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4QAAAGQCAYAAAD2lq6fAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAADR60lEQVR4nOzdeVxU5f4H8M8MOwojaoikaTcLcSlNUdFyFzVJ0wq9IMrNuJUpecElU0vrqpVbaWnZz6tpmKU3t/SaW2JuiYoGqWmlqSlpyiowzHJ+fzDnNDPMwKwMw3zerxcvnTMPZ54Z5syc7/k+z/eRCYIggIiIiIiIiDyO3NUdICIiIiIiItdgQEhEREREROShGBASERERERF5KAaEREREREREHooBIRERERERkYdiQEhEREREROShGBASERERERF5KAaEREREREREHooBIRERERERkYdiQEhEREREROShGBASERERERF5KAaEREREREREHooBIRERERERkYdiQEhEREREROShGBASERERERF5KAaEREREREREHooBIRERERERkYdiQEhEREREROShGBASERERERF5KAaEREREREREHooBIRERERERkYdiQEhEREREROShGBASERERERF5KAaEREREREREHooBIRERERERkYdiQEhEREREROShGBASERERERF5KAaEREREREREHooBIRERERERkYdiQEhEREREROShGBASERERERF5KAaEREREREREHooBIRERERERkYdiQEhEREREROShGBASERERERF5KAaEREREREREHooBIRERUR2QkJDg6i4QEZEb8nZ1B4iIiMhyU6dOrbRNEAQcOXLEBb0hIiJ3x4CQiIjIjaSnp2P9+vWVtn/77bcu6A0REbk7BoRERERu5LnnnkNkZCRCQ0MNto8ZM8ZFPSIiIncmEwRBcHUniIiIiIiIqOaxqAwREREREZGHYkBIRERERETkoRgQEhERubmsrCxXd4GIiNwUi8oQERG5keXLlxvcFgQBK1aswPjx4zF+/HgX9YqIiNwVM4RERERu5MMPP8TXX3+NwMBA1KtXD/Xr14dcLkf9+vVd3TUiInJDrDLqxgRBQGFhIYqKihAUFASZTObqLhERkZMJgoBNmzZhy5YtGD58OEaMGIG4uDhs2rTJ1V0jInIYQRBQVFSE8PBwyOXMYTkTA0I3VlhYCIVC4epuEBERERE5xdWrV9GsWTNXd6NO4xxCNxYUFISrV6+iefPmuHr1KoKDg13dJSIiqmFarRZ//PEHmjZt6uquEBE5TGFhIZo3b46goCBXd6XuE8itFRQUCACEgoICQRAEYcmSJYKXl5cwduxYQRAEQaFQCMHBwUJgYKCQkJBg8Lu///67UK9ePQGA8NVXX0nbBwwYIHTq1EnYsWOHEBwcLAQHBwvjx48Xvv32WyEtLU3o37+/oFAohO3btxvsLyEhQcjPzzfY1qpVK6FXr15Ct27dhNTUVGHGjBlC06ZNhbS0NKnNokWLhHvuuUd48cUXhdWrVwsBAQHCxo0bBUEQhE6dOln1erzxxhtCu3bthF69egk9e/YUfv/9d0EQBGHFihXC6tWrDdq+8sorQklJibB69WohKipKuPfee616rKqUlpYKvXr1Mnt/enq60KJFi0qvIRERERFVPscl5+GA3DqoQ4cOWLNmjXT7p59+wtWrV7FhwwaUlJRI2xs2bIjMzEw88MADBr+/e/duAEC7du3QoUMH/P7778jLy8P58+cBAGvXrsWkSZMMfker1aK4uLjSEFaFQoEDBw7g6NGjOH36NJ5++mmkp6cbtElNTcUTTzyBl19+GQBw7733YtCgQTY///nz5+PAgQN4/vnnsXr1arPt3nvvPQQEBAAAxowZg7CwMJsf01rx8fFISkqqsccjIiIiIjKFQ0Y9RMOGDREQEIBff/0V7dq1AwD4+/sjMjLS7O/cd999UqEaHx8feHl5AYDBsKRdu3Zh7ty5yM/PR5s2bQAAc+fOxY4dO+Dj44PS0lIAgEajgUqlwn333YdLly7hyy+/xPHjxxEaGoovvvgCxcXF+Ne//oWEhASTfenduzceeeQRfPfddygrK0NRURFKS0sxZcoUTJs2DT/88ANeeuklXLlyBb/88gtiY2Px22+/4T//+Q8OHz6MX3/9FUBFufYuXbogOzsbZ86cwTfffGPy8dasWYMtW7ZApVIhPz8fL774IjZs2IDi4mLs3r0bXl5eGDNmDH7//XfUq1cPn332GRo2bIiXX34ZOTk56Natm7SvEydOYMqUKVCr1Rg2bBgmT55s0d+MiMgaubm5NXphi4iI6gZmCD3ETz/9hLKyMrRu3drq383KysKff/6JBx980GC7VqvFm2++iX379mH48OH4+eefcerUKZw4cQKHDx9GRkYGlEolevfujTZt2qBFixZo1KgRgoKC8Mwzz+DgwYO47777sH//fov6ERcXhz59+uD27dvYvn07pk2bJq3H9dprr+H//u//8Nxzz2HLli3o1KkT3n77bcydOxc7d+7EI488gldffRXTpk3Dzp07kZGRgTZt2uDf//632ce75557sGPHDvTt2xenTp3Cjh078OijjyIjIwObN2/Gfffdh4yMDPz973/HsmXLcOLECeTl5SEjIwP9+/eX9jNt2jR89dVX+O6773D48GH88ccfVv8NiIiqM2bMGFd3gYiI3BAzhB4gIiICMpkMr776Kry9rfuTK5VKTJ06Fdu2bcOPP/5ocF9hYSEuXryImJgY5OTkIDQ0FJmZmejRo4eUWWzQoAEOHDgAQRAQFxeH7777DkVFRdi2bRtOnjyJGzdu4JFHHrGoLw8//DDmzp2L0tJSTJo0CaWlpRB0RXL/+OMPREZGQiaTYeDAgXjppZcwZcoU7NixA3//+99RVFSEefPmwcvLC7m5uejduzfOnj1b5WM//PDDACqGsPr5+Un/z8vLw6VLlxAVFQUAiIqKwu7du/Hzzz+jU6dOAIAuXbpI+8nOzsbw4cMBAHl5ebh69SqaNGli0XMmIjKm//kiEgQBFy9edEFviGovrVaL8vJyV3eDzNAffUauVacCwoMHD2LBggVSoLF582Y89dRT0v3m1ul79913MWXKFAAVQxMzMjIM7h85ciQ2bNgg3c7Ly0NKSgq2bdsGABg6dCiWLVuGBg0aSG2uXLmCl19+Gfv370dAQADi4+OxcOFC+Pr6Sm2ys7MxYcIEHD9+HA0bNsQLL7yAWbNmOXw9wZ9++smmYUTFxcU4d+4cdu/ejXvuuafS/cHBwYiMjMSGDRswY8YMfPTRRzh79izmzJkjtREDNplMBoVCgTt37mDPnj1o2bIl9u7di9TUVKlNdWQyGVq3bo3Lly/jwIEDACAFYE2aNMG5c+cgCAJ++eUXPPDAA2jZsiUuX76M27dv4/jx41iwYAGCgoIwceJEHDhwAL1798bmzZuxdetWs49n6v+CIKBVq1bIzMzE008/jczMTDz44INo1aoVvv76awAVw0RFjzzyCDZt2gSFQgGNRsO1dIjILkVFRcjOzq50gW/AgAEu6hFR7VNeXo5Lly5Bq9W6uitUhQYNGiAsLIxrabtYnQoI7969i0ceeQT/+Mc/8PTTT1e6/8aNGwa3//e//2HcuHGV2iYnJ+PNN9+UbouFR0Tx8fG4du0adu3aBQD45z//icTERGzfvh1AxXy5IUOG4J577sGhQ4dw+/ZtjB07FoIgYNmyZQAqsmsDBgxAnz59kJmZiQsXLiApKQn16tVDWlqa/S+GhUJDQ3H79m2MGTMGI0eOxP/93/9J9y1btgylpaVIS0uDl5cXnnrqKWzcuBHr169HXl4eQkND0a9fP/Tp0wcymQwJCQn48ssv0bFjR0RHRyMgIAB//vknevfuDY1GgyZNmuDnn39GRkYGfv75ZzRr1gyPP/64lIkzpbCwEP3798eZM2fw/PPP48MPP8SaNWvQt29fAJCK5MydOxfPP/88rly5ApVKhaSkJBQWFkKj0SA+Ph5+fn5455138NRTTyEgIAC9evXCmTNn8N577+H++++3+nV76qmn8NVXX6Fnz56oV68e0tPT0bBhQwQHB6Nnz57o2rWr1Pbtt9/GiBEjoNVq4evriy1btlR6TxERWWrevHkoKipCSEiIwfaZM2e6qEdEtYsgCLhx4wa8vLzQvHlzXoithQRBQElJCW7evAkAXDbH1VxV3tTZAAibN2+uss2wYcOEvn37Gmzr1auX8Morr5j9nbNnzwoAhGPHjknbjh49KgAQzp8/LwiCIOzcuVOQy+XSkgeCIAiff/654OfnJ5XOXb58uaBQKISysjKpzfz584Xw8HBBq9Va+jQrleT95JNPBH9/f2nZCVsMGDBAePLJJwVBEISRI0cKPXv2FNRqtdn2u3btEu7cuWPz4y1atEho27at8NNPPwkbN24UHn30UeHrr7+2eX+2SExMFKKiomrs8dLT04VHHnlE2LdvX409JhHVTefOnXN1F4hqlfLycuHs2bOVlsKi2ufPP/8Uzp49a/I8k8tO1ByZIFg4Xs/NyGSySkNG9f3xxx9o1qwZPv30U8THx0vbe/fujR9//BGCIKBJkyYYPHgw3njjDWlRzP/85z9ITU1Ffn6+wf4aNGiAJUuW4B//+Adef/11bN26FWfOnJHuz8vLQ8OGDbF//3706dMHY8aMQUFBgcFwxaysLDz66KP49ddfzWatlEollEqldFtctLOgoIAL0xMReaC+fftaXJyLyBOUlZXh0qVLaNmyJUfk1HKlpaW4fPky7r//fvj7+xvcV1hYCIVCwXPcGlCnhoxa49NPP0VQUBBGjBhhsD0hIQH3338/wsLCkJOTg+nTp+PMmTPYs2cPgIqy3qGhoZX2FxoaitzcXKmNcdGQkJAQ+Pr6GrRp2bKlQRvxd3Jzc80GhPPnzzeYo0dERJ7Nx8fH1V0gqpU4L63249+odvDYgPA///kPEhISKl2NSE5Olv7frl07PPjgg+jcuTNOnTqFRx99FIDpN68gCGaLkFjaRtArwGLO9OnTkZqaKt0WM4REROSZzK2nSkREZAmPnGX73Xff4aeffsLzzz9fbdtHH30UPj4+UjnvsLAwk+vI3bp1S8rwhYWFSZlAUV5eHlQqVZVtxIm1VS1J4Ofnh+DgYIMfIiLyYFotMHUqoPsOIaK66cCBA5DJZJWmLRHZyyMDwlWrVqFTp04WrX/3448/QqVSSdWPoqOjUVBQgOPHj0ttvv/+exQUFKB79+5Sm5ycHIOqprt374afn5+0TEJ0dDQOHjxosD7O7t27ER4eXmkoKRERkVlnzwILFgDTp7u6J0RE5IbqVEBYXFyM06dP4/Tp0wCAS5cu4fTp07hy5YrUprCwEBs3bjSZHfzll1/w5ptv4sSJE7h8+TJ27tyJZ599Fh07dkSPHj0AAJGRkRg0aBCSk5Nx7NgxHDt2DMnJyYiNjUVERAQAICYmBm3atEFiYiKysrKwb98+TJ48GcnJyVJGT1wKISkpCTk5Odi8eTPmzZuH1NRUjqcmIiLLibXh+N1BREQ2qFMB4YkTJ9CxY0d07NgRAJCamoqOHTvi9ddfl9ps2LABgiDg73//e6Xf9/X1xb59+zBw4EBEREQgJSUFMTEx2Lt3L7y8vKR26enpaN++PWJiYhATE4OHH34Y69atk+738vLCjh074O/vjx49eiAuLg5PPfUUFi5cKLVRKBTYs2cPrl27hs6dO2P8+PFITU01mB9IRERULQaERHWGUqlESkoKQkND4e/vj8ceewyZmZkGbQ4fPoxHHnkE/v7+6Nq1K7Kzs6X7fvvtNzz55JMICQlBvXr10LZtW+zcubOmnwa5mTq77ISnYEleIiIPd+YM0KEDkJwMrFzp6t4QuZy47ISppQxqu1deeQWbNm3C//3f/6FFixZ49913sW3bNvz888/44Ycf0KdPH0RGRuL9999HWFgYXnvtNeTk5ODChQvw8fFBbGwsysvLsWjRItSrVw9nz55FcHAwevbs6eqnZlJVfyue49Ycj60ySkREVKcwQ0hkVklJCc6fP1/jj9u6dWsEBgZa1Pbu3btYsWIF1qxZg8GDBwMAPvnkE+zZswerVq1CVFQUAOCNN97AgAEDAFQso9asWTNs3rwZcXFxuHLlCp5++mm0b98eAPC3v/3NCc+K6hoGhERERO6MA32IqnX+/HmpsF9NOnnypLRsWXV++eUXqFQqqW4FULHOaJcuXXDu3DkpIIyOjpbub9iwISIiInDu3DkAQEpKCl566SXs3r0b/fv3x9NPP42HH37Ygc+I6iIGhERERO6McwiJqtW6dWucPHnSJY9rKXPrURuvY22KeP/zzz+PgQMHYseOHdi9ezfmz5+PRYsWYeLEiVb2nDwJA0IiIiJ3xoCQqFqBgYEWZ+pcpVWrVvD19cWhQ4cQHx8PAFCpVDhx4gQmTZoktTt27Bjuu+8+ABXrXF+4cMEg8GzevDlefPFFvPjii5g+fTo++eQTBoRUJQaEREREbqyoqAhBAK7+/juau7ozRGSzevXq4aWXXsKUKVPQsGFD3HfffXj33XdRUlKCcePG4cyZMwCAN998E40aNUKTJk0wY8YMNG7cGE899RQAYNKkSRg8eDAeeugh5OXlYf/+/YiMjHThsyJ3wICQiIjIjeXm5iIIwMWff2ZASOTm3n77bWi1WiQmJqKoqAidO3fGN998g5CQEIM2r7zyCi5evIhHHnkE27Ztg6+vLwBAo9Hg5ZdfxrVr1xAcHIxBgwZhyZIlrno65CYYEBIREbkxQaMBUHneERG5H39/fyxduhRLly6tdF/v3r2leYaxsbEmf3/ZsmVO7R/VTXVqYXoiIiJPY64QBRERkSUYEBIREbkxQaut+JcBIRER2YABIRERkRsTA0JmCImIyBYMCImIiNyYlnMIiYjIDgwIiYiI3BnXISQiIjswICQiInJjYoaQASEREdmCASEREZEbE+cQMiAkIiJbMCAkIiJyY1JAKOdXOhERWY/fHkRERG5MXJhezgwhkUebPXs2OnTo4OpukBtiQEhEROTGBM4hJCIiOzAgJCIicmMMCImIyB4MCImIiNyZLiAUOIeQyO1ptVq88847aNWqFfz8/HDfffdh7ty5AIBp06bhoYceQmBgIP72t79h1qxZUKlUVe7vP//5D9q2bQs/Pz80bdoUEyZMqImnQW7G29UdICIiItsJXJieqM6YPn06PvnkEyxZsgSPPfYYbty4gfPnzwMAgoKCsGbNGoSHhyM7OxvJyckICgrC1KlTTe5rxYoVSE1Nxdtvv43BgwejoKAAhw8frsmnQ26CASEREZEb06rVABgQErm7oqIivP/++/jggw8wduxYAMADDzyAxx57DAAwc+ZMqW3Lli2RlpaGL774wmxA+O9//xtpaWl45ZVXpG1RUVFOfAbkrhgQEhERuTHOISSyQEkJoMu01ajWrYHAQIuanjt3DkqlEv369TN5/6ZNm/Dee+/h559/RnFxMdRqNYKDg022vXnzJq5fv252X0T6GBASERG5MSlDyDmEROadPw906lTzj3vyJPDooxY1DQgIMHvfsWPHMGrUKMyZMwcDBw6EQqHAhg0bsGjRIqv3RWSMASEREZEbY4aQyAKtW1cEZ654XAs9+OCDCAgIwL59+/D8888b3Hf48GG0aNECM2bMkLb99ttvZvcVFBSEli1bYt++fejTp4/1/SaPwoCQiIjIjWl0GUIGhERVCAy0OFPnKv7+/pg2bRqmTp0KX19f9OjRA7du3cKPP/6IVq1a4cqVK9iwYQOioqKwY8cObN68ucr9zZ49Gy+++CJCQ0MxePBgFBUV4fDhw5g4cWINPSNyFwwIiYiI3JlWW/EvA0Iitzdr1ix4e3vj9ddfx/Xr19G0aVO8+OKLGDduHP71r39hwoQJUCqVGDJkCGbNmoXZs2eb3dfYsWNRVlaGJUuWYPLkyWjcuDGeeeaZmnsy5Dbq1ISDgwcP4sknn0R4eDhkMhm2bNlicH9SUhJkMpnBT7du3QzaKJVKTJw4EY0bN0a9evUwdOhQXLt2zaBNXl4eEhMToVAooFAokJiYiPz8fIM2V65cwZNPPol69eqhcePGSElJQXl5uUGb7Oxs9OrVCwEBAbj33nvx5ptvQhAEh70eRETkAcSAkIjcnlwux4wZM3D58mWUl5fjt99+w/Tp0wEA7777Lv78808UFRVhw4YNmDRpksH55+zZs3H69GmD/b3wwgs4f/48ysvLcf36dSxdurQGnw25izoVEN69exePPPIIPvjgA7NtBg0ahBs3bkg/O3fuNLh/0qRJ2Lx5MzZs2IBDhw6huLgYsbGx0IhzNADEx8fj9OnT2LVrF3bt2oXTp08jMTFRul+j0WDIkCG4e/cuDh06hA0bNuC///0v0tLSpDaFhYUYMGAAwsPDkZmZiWXLlmHhwoVYvHixA18RIiKq83QBIS8nEhGRLerUkNHBgwdj8ODBVbbx8/NDWFiYyfsKCgqwatUqrFu3Dv379wcAfPbZZ2jevDn27t2LgQMH4ty5c9i1axeOHTuGrl27AgA++eQTREdH46effkJERAR2796Ns2fP4urVqwgPDwcALFq0CElJSZg7dy6Cg4ORnp6OsrIyrFmzBn5+fmjXrh0uXLiAxYsXIzU1letJERGRRWTMEBIRkR3qVIbQEgcOHEBoaCgeeughJCcn4+bNm9J9J0+ehEqlQkxMjLQtPDwc7dq1w5EjRwAAR48ehUKhkIJBAOjWrRsUCoVBm3bt2knBIAAMHDgQSqUSJ3UVro4ePYpevXrBz8/PoM3169dx+fJlpzx3IiKqewQGhEREZAePCggHDx6M9PR07N+/H4sWLUJmZib69u0LpVIJAMjNzYWvry9CQkIMfq9JkybIzc2V2oSGhlbad2hoqEGbJk2aGNwfEhICX1/fKtuIt8U2piiVShQWFhr8EBERgXPQiYjIBnVqyGh1Ro4cKf2/Xbt26Ny5M1q0aIEdO3ZgxIgRZn9PEASDIZymhnM6oo1YUKaq4aLz58/HnDlzzN5PRESehRlCIiKyh0dlCI01bdoULVq0wMWLFwEAYWFhKC8vR15enkG7mzdvStm7sLAw/PHHH5X2devWLYM2xlm+vLw8qFSqKtuIw1eNM4f6pk+fjoKCAunn6tWr1jxlIiKqa3QXE1mlmoiIbOHRAeHt27dx9epVNG3aFADQqVMn+Pj4YM+ePVKbGzduICcnB927dwcAREdHo6CgAMePH5fafP/99ygoKDBok5OTgxs3bkhtdu/eDT8/P3Tq1Elqc/DgQYOlKHbv3o3w8HC0bNnSbJ/9/PwQHBxs8ENERB5MHF3i4m4QEZF7qlMBYXFxMU6fPi2twXLp0iWcPn0aV65cQXFxMSZPnoyjR4/i8uXLOHDgAJ588kk0btwYw4cPBwAoFAqMGzcOaWlp2LdvH7KysjB69Gi0b99eqjoaGRmJQYMGITk5GceOHcOxY8eQnJyM2NhYREREAABiYmLQpk0bJCYmIisrC/v27cPkyZORnJwsBXDx8fHw8/NDUlIScnJysHnzZsybN48VRomIyCrMDBIRkT3q1BzCEydOoE+fPtLt1NRUAMDYsWOxYsUKZGdnY+3atcjPz0fTpk3Rp08ffPHFFwgKCpJ+Z8mSJfD29kZcXBxKS0vRr18/rFmzBl5eXlKb9PR0pKSkSNVIhw4darD2oZeXF3bs2IHx48ejR48eCAgIQHx8PBYuXCi1USgU2LNnD15++WV07twZISEhSE1NlfpMRERkEXEdQgaGRERkA5nAbxC3VlhYCIVCgYKCAg4fJSLyQN89/zweX7UK3z3+OB4/eNDV3SFyubKyMly6dAn3338//P39Xd2dGjN79mxs2bJFGinnDGvWrMGkSZOQn5/vkP1V9bfiOW7NqVNDRomIiDyNVGWU13eJiMgGDAiJiIjcGQNBIiKyAwNCIiIid8aAkKjO0Gq1eOedd9CqVSv4+fnhvvvuw9y5cwEA06ZNw0MPPYTAwED87W9/w6xZs6BSqarc33/+8x+0bdsWfn5+aNq0KSZMmFBl+7y8PIwZMwYhISEIDAzE4MGDpeXZ9G3ZsgUPPfQQ/P39MWDAAINl0M6cOYM+ffogKCgIwcHB6NSpE06cOGHDq0E1hQEhERGRO+M6hER1xvTp0/HOO+9g1qxZOHv2LNavXy+tTx0UFIQ1a9bg7NmzeP/99/HJJ59gyZIlZve1YsUKvPzyy/jnP/+J7OxsbNu2Da1atary8ZOSknDixAls27YNR48ehSAIeOKJJwwCz5KSEsydOxeffvopDh8+jMLCQowaNUq6PyEhAc2aNUNmZiZOnjyJV199FT4+Pna+MuRMdarKKBERkaeRAkEGhERuraioCO+//z4++OADjB07FgDwwAMP4LHHHgMAzJw5U2rbsmVLpKWl4YsvvsDUqVNN7u/f//430tLS8Morr0jboqKizD7+xYsXsW3bNhw+fFhaWzs9PR3NmzfHli1b8OyzzwIAVCoVPvjgA3Tt2hUA8OmnnyIyMhLHjx9Hly5dcOXKFUyZMgWtW7cGADz44IO2viRUQ5ghJCIicmcMCInqhHPnzkGpVKJfv34m79+0aRMee+wxhIWFoX79+pg1axauXLlisu3Nmzdx/fp1s/t68cUXUb9+felHfHxvb28p0AOARo0aISIiAufOnZO2eXt7o3PnztLt1q1bo0GDBlKb1NRUPP/88+jfvz/efvtt/PLLL9a9EFTjmCEkIiJyZxwySmSRl75+Cb8X/V5jj3dv0L1YEbvC4vYBAQFm7zt27BhGjRqFOXPmYODAgVAoFNiwYQMWLVpk9b4A4M0338TkyZMNtpn7DBEEATKZzGCb8W39bbNnz0Z8fDx27NiB//3vf3jjjTewYcMGDB8+vMo+keswICQiInJjHDJKZBlrgjNXePDBBxEQEIB9+/bh+eefN7jv8OHDaNGiBWbMmCFt++2338zuKygoCC1btsS+ffvQp0+fSveHhoYiNDTUYFubNm2gVqvx/fffS0NGb9++jQsXLiAyMlJqp1arceLECXTp0gUA8NNPPyE/P18aIgoADz30EB566CH861//wt///nesXr2aAWEtxoCQiIjInXEdQqI6wd/fH9OmTcPUqVPh6+uLHj164NatW/jxxx/RqlUrXLlyBRs2bEBUVBR27NiBzZs3V7m/2bNn48UXX0RoaCgGDx6MoqIiHD58GBMnTjTZ/sEHH8SwYcOQnJyMjz/+GEFBQXj11Vdx7733YtiwYVI7Hx8fTJw4EUuXLoWPjw8mTJiAbt26oUuXLigtLcWUKVPwzDPP4P7778e1a9eQmZmJp59+2qGvFTkW5xASERG5MWnglhgYEpHbmjVrFtLS0vD6668jMjISI0eOxM2bNzFs2DD861//woQJE9ChQwccOXIEs2bNqnJfY8eOxXvvvYfly5ejbdu2iI2NNbmEhL7Vq1ejU6dOiI2NRXR0NARBwM6dOw2qhAYGBmLatGmIj49HdHQ0AgICsGHDBgCAl5cXbt++jTFjxuChhx5CXFwcBg8ejDlz5tj/4pDTyAROOnBrhYWFUCgUKCgoQHBwsKu7Q0RENezAqFHo/cUXyOjUCb241hcRysrKcOnSJdx///3w9/d3dXeoClX9rXiOW3OYISQiInJnHDJKRER2YEBIRETkzsRAkENGiYjIBgwIiYiI3JkuIKxcBJ6IiKh6DAiJiIjcmbgOoYu7QURE7okBIRERkRvjOoRERGQPBoRERETujAEhkUkspF/78W9UOzAgJCIicmfiHEKeWBEBqFgLDwDKy8td3BOqTklJCQAYrHNINc/b1R0gIiIiOzBDSGTA29sbgYGBuHXrFnx8fCCXM/9R2wiCgJKSEty8eRMNGjSQgnhyDQaERERE7owBIZEBmUyGpk2b4tKlS/jtt99c3R2qQoMGDRAWFubqbng8BoRERETujAEhUSW+vr548MEHOWy0FvPx8WFmsJZgQEhEROTOGAgSmSSXy+Hv7+/qbhDVehxUTURE5M4YEBIRkR0YEBIREbkzDhklIiI7MCAkIiJyZ1x2goiI7MCAkIiIyJ0xQ0hERHaoUwHhwYMH8eSTTyI8PBwymQxbtmyR7lOpVJg2bRrat2+PevXqITw8HGPGjMH169cN9tG7d2/IZDKDn1GjRhm0ycvLQ2JiIhQKBRQKBRITE5Gfn2/Q5sqVK3jyySdRr149NG7cGCkpKZUqXWVnZ6NXr14ICAjAvffeizfffBMCv9CJiMgaDAiJiMgOdSogvHv3Lh555BF88MEHle4rKSnBqVOnMGvWLJw6dQpfffUVLly4gKFDh1Zqm5ycjBs3bkg/H3/8scH98fHxOH36NHbt2oVdu3bh9OnTSExMlO7XaDQYMmQI7t69i0OHDmHDhg3473//i7S0NKlNYWEhBgwYgPDwcGRmZmLZsmVYuHAhFi9e7MBXhIiI6jpeSCQiInvUqWUnBg8ejMGDB5u8T6FQYM+ePQbbli1bhi5duuDKlSu47777pO2BgYFmF8k8d+4cdu3ahWPHjqFr164AgE8++QTR0dH46aefEBERgd27d+Ps2bO4evUqwsPDAQCLFi1CUlIS5s6di+DgYKSnp6OsrAxr1qyBn58f2rVrhwsXLmDx4sVITU2FTCZzxEtCRER1HTOERERkhzqVIbRWQUEBZDIZGjRoYLA9PT0djRs3Rtu2bTF58mQUFRVJ9x09ehQKhUIKBgGgW7duUCgUOHLkiNSmXbt2UjAIAAMHDoRSqcTJkyelNr169YKfn59Bm+vXr+Py5ctOeLZERFQniUVlXNwNIiJyT3UqQ2iNsrIyvPrqq4iPj0dwcLC0PSEhAffffz/CwsKQk5OD6dOn48yZM1J2MTc3F6GhoZX2FxoaitzcXKlNkyZNDO4PCQmBr6+vQZuWLVsatBF/Jzc3F/fff7/JfiuVSiiVSul2YWGhlc+ciIjqEikQZIaQiIhs4JEBoUqlwqhRo6DVarF8+XKD+5KTk6X/t2vXDg8++CA6d+6MU6dO4dFHHwUAk8M5BUEw2G5LG3EeSFXDRefPn485c+ZU9fSIiMiTcMgoERHZweOGjKpUKsTFxeHSpUvYs2ePQXbQlEcffRQ+Pj64ePEiACAsLAx//PFHpXa3bt2SMnxhYWFSJlCUl5cHlUpVZZubN28CQKXsor7p06ejoKBA+rl69Wo1z5iIiOo0BoJERGQHjwoIxWDw4sWL2Lt3Lxo1alTt7/z4449QqVRo2rQpACA6OhoFBQU4fvy41Ob7779HQUEBunfvLrXJycnBjRs3pDa7d++Gn58fOnXqJLU5ePCgwVIUu3fvRnh4eKWhpPr8/PwQHBxs8ENERB6MC9MTEZEd6lRAWFxcjNOnT+P06dMAgEuXLuH06dO4cuUK1Go1nnnmGZw4cQLp6enQaDTIzc1Fbm6uFJT98ssvePPNN3HixAlcvnwZO3fuxLPPPouOHTuiR48eAIDIyEgMGjQIycnJOHbsGI4dO4bk5GTExsYiIiICABATE4M2bdogMTERWVlZ2LdvHyZPnozk5GQpgIuPj4efnx+SkpKQk5ODzZs3Y968eawwSkRE1uGQUSIiskOdCghPnDiBjh07omPHjgCA1NRUdOzYEa+//jquXbuGbdu24dq1a+jQoQOaNm0q/YjVQX19fbFv3z4MHDgQERERSElJQUxMDPbu3QsvLy/pcdLT09G+fXvExMQgJiYGDz/8MNatWyfd7+XlhR07dsDf3x89evRAXFwcnnrqKSxcuFBqIy6Dce3aNXTu3Bnjx49HamoqUlNTa+jVIiKiOoEBIRER2UEmcEVbt1ZYWAiFQoGCggIOHyUi8kDf9uyJPt99h8MtW6LHpUuu7g4RkUPwHLfm1KkMIRERkcfhdV0iIrIDA0IiIiI3JrCoDBER2YEBIRERkTvjHEIiIrIDA0IiIiIiIiIPxYCQiIjIjck4ZJSIiOzAgJCIiMidccgoERHZgQEhERGRO2MgSEREdmBASERE5M6YISQiIjswICQiInJn4hxCF3eDiIjcEwNCIiIid8YMIRER2YEBIRERkTtjIEhERHZgQEhERFQHcNkJIiKyBQNCIiIid8Yho0REZAcGhERERO6MgSAREdmBASEREZEbY3VRIiKyBwNCIiIid8Yho0REZAcGhERERO6M6xASEZEdGBASERG5MzEgZIaQiIhswICQiIjInXHIKBER2YEBIRERERERkYdiQEhEROTOOGSUiIjswICQiIjIjYmBIMNBIiKyBQNCIiIid8YMIRER2YEBIRERkRtjGEhERPZgQEhEROTOuA4hERHZgQEhERGRG5NxyCgREdmBASEREREREZGHqlMB4cGDB/Hkk08iPDwcMpkMW7ZsMbhfEATMnj0b4eHhCAgIQO/evfHjjz8atFEqlZg4cSIaN26MevXqYejQobh27ZpBm7y8PCQmJkKhUEChUCAxMRH5+fkGba5cuYInn3wS9erVQ+PGjZGSkoLy8nKDNtnZ2ejVqxcCAgJw77334s0334TAK7xERGQNLkxPRER2qFMB4d27d/HII4/ggw8+MHn/u+++i8WLF+ODDz5AZmYmwsLCMGDAABQVFUltJk2ahM2bN2PDhg04dOgQiouLERsbC41GI7WJj4/H6dOnsWvXLuzatQunT59GYmKidL9Go8GQIUNw9+5dHDp0CBs2bMB///tfpKWlSW0KCwsxYMAAhIeHIzMzE8uWLcPChQuxePFiJ7wyRERUV3HIKBER2UWoowAImzdvlm5rtVohLCxMePvtt6VtZWVlgkKhED766CNBEAQhPz9f8PHxETZs2CC1+f333wW5XC7s2rVLEARBOHv2rABAOHbsmNTm6NGjAgDh/PnzgiAIws6dOwW5XC78/vvvUpvPP/9c8PPzEwoKCgRBEITly5cLCoVCKCsrk9rMnz9fCA8PF7RarcXPs6CgQAAg7ZeIiDxLxoMPCgIgnGrQwNVdISJyGJ7j1pw6lSGsyqVLl5Cbm4uYmBhpm5+fH3r16oUjR44AAE6ePAmVSmXQJjw8HO3atZPaHD16FAqFAl27dpXadOvWDQqFwqBNu3btEB4eLrUZOHAglEolTp48KbXp1asX/Pz8DNpcv34dly9fNvs8lEolCgsLDX6IiIiIiIhs4TEBYW5uLgCgSZMmBtubNGki3ZebmwtfX1+EhIRU2SY0NLTS/kNDQw3aGD9OSEgIfH19q2wj3hbbmDJ//nxp7qJCoUDz5s2rfuJERFS3ccgoERHZwWMCQpFMZrhSkyAIlbYZM25jqr0j2gjil3oV/Zk+fToKCgqkn6tXr1bZdyIiqtsYCBIRkT08JiAMCwsDUDn7dvPmTSkzFxYWhvLycuTl5VXZ5o8//qi0/1u3bhm0MX6cvLw8qFSqKtvcvHkTQOUspj4/Pz8EBwcb/BARkQdjhpCIiOzgMQHh/fffj7CwMOzZs0faVl5ejoyMDHTv3h0A0KlTJ/j4+Bi0uXHjBnJycqQ20dHRKCgowPHjx6U233//PQoKCgza5OTk4MaNG1Kb3bt3w8/PD506dZLaHDx40GApit27dyM8PBwtW7Z0/AtARERERERkpE4FhMXFxTh9+jROnz4NoKKQzOnTp3HlyhXIZDJMmjQJ8+bNw+bNm5GTk4OkpCQEBgYiPj4eAKBQKDBu3DikpaVh3759yMrKwujRo9G+fXv0798fABAZGYlBgwYhOTkZx44dw7Fjx5CcnIzY2FhEREQAAGJiYtCmTRskJiYiKysL+/btw+TJk5GcnCxl9OLj4+Hn54ekpCTk5ORg8+bNmDdvHlJTU6sdwkpERGSM3xxERGQLb1d3wJFOnDiBPn36SLdTU1MBAGPHjsWaNWswdepUlJaWYvz48cjLy0PXrl2xe/duBAUFSb+zZMkSeHt7Iy4uDqWlpejXrx/WrFkDLy8vqU16ejpSUlKkaqRDhw41WPvQy8sLO3bswPjx49GjRw8EBAQgPj4eCxculNooFArs2bMHL7/8Mjp37oyQkBCkpqZKfSYiIrIIF6YnIiI7yASB3yDurLCwEAqFAgUFBZxPSETkgQ7dfz8eu3wZPwQF4WEuRUREdQTPcWtOnRoySkRE5HFYVIaIiOzAgJCIiIiIiMhDMSAkIiJyYzJmCImIyA4MCImIiNyZGBC6uBtEROSeGBASEZHTJCQkuLoLREREVIU6tewEERG5xtSpUyttEwQBR44ccUFvPAuHihIRkT0YEBIRkd3S09Oxfv36Stu//fZbF/TGw3AOIRER2YEBIRER2e25555DZGQkQkNDDbaPGTPGRT0iIiIiS3BhejfHRTuJiDzbkWbN0P3333EuIACRJSWu7g4RkUPwHLfmsKgMERGRG+NQUSIisgcDQiIiInfGZSeIiMgODAiJiIjqAGYKiYjIFgwIiYjIbl9++SU6duyI0aNH49NPP0VERASioqKwbds2V3etzmMgSERE9mCVUSIistuiRYtw+PBhFBUVoUOHDjh//jz8/f3Rv39/DB061NXdq9sYEBIRkR0YEBIRkd0CAgIQGBiIwMBA9OvXDwqFAgDg7c2vmZrCOYRERGQLDhklIiK7dejQARqNBgDw2WefAQDKy8vRqFEjV3bLI8i4MD0REdmBl26JiMhu7733XqVtvr6+2LRpU813hoiIiCzGDCEREZEbk3HZCSIisgMDQiIiojqAQ0aJiMgWDAiJiMhpcnNzXd0FIiIiqgIDQiIicpoxY8a4ugt1HzODRERkBxaVISIiu3Xp0qXSNkEQcPHiRRf0xrNwDmHdlpWVhVmzZsHHxwdTpkxB9+7dAQDjxo3DqlWrXNw7IqoLGBASEZHdioqKkJ2dXWndwQEDBrioR56HcwjrppSUFKxevRre3t6YMmUKjh07htTUVPz666+u7hoR1REcMkpERHabN28eioqKKm2fOXOmC3rjWRgI1m0ymQytWrVCy5YtsXHjRhQWFiI5ORlqtdrVXSOiOoIBIRGRhRISElzdhVpr+PDhCAkJkW6fP38eANCrVy9XdcnjcMho3RQaGorLly9Lt2fPno1evXohMzPTdZ0iojpFJgi8tOjOCgsLoVAoUFBQgODgYFd3h6hOmDp1aqVtgiBg06ZNuHTpkgt65H769u2L/fv3u7obHuFkw4bolJeH37y90UKlcnV3iIgcgue4NcfjMoQtW7aETCar9PPyyy8DAJKSkird161bN4N9KJVKTJw4EY0bN0a9evUwdOhQXLt2zaBNXl4eEhMToVAooFAokJiYiPz8fIM2V65cwZNPPol69eqhcePGSElJQXl5uVOfPxFVLz09HUOGDDH4iY2NRaNGjVzdNbfh4+Pj6i4QERGRBTyuqExmZiY0Go10OycnBwMGDMCzzz4rbRs0aBBWr14t3fb19TXYx6RJk7B9+3Zs2LABjRo1QlpaGmJjY3Hy5El4eXkBAOLj43Ht2jXs2rULAPDPf/4TiYmJ2L59OwBAo9FgyJAhuOeee3Do0CHcvn0bY8eOhSAIWLZsmdOePxFV77nnnkNkZCRCQ0MNtnMJBct98803ru6Cx2CVUSIisofHDxmdNGkSvv76a1y8eBEymQxJSUnIz8/Hli1bTLYvKCjAPffcg3Xr1mHkyJEAgOvXr6N58+bYuXMnBg4ciHPnzqFNmzY4duwYunbtCgA4duwYoqOjcf78eUREROB///sfYmNjcfXqVYSHhwMANmzYgKSkJNy8edPi1DjT6UREnu1USAgezc/HFW9v3Mcho0RUR/Act+Z43JBRfeXl5fjss8/w3HPPQSb769rqgQMHEBoaioceegjJycm4efOmdN/JkyehUqkQExMjbQsPD0e7du1w5MgRAMDRo0ehUCikYBAAunXrBoVCYdCmXbt2UjAIAAMHDoRSqcTJkyfN9lmpVKKwsNDgh4iIPBczhJ5J/zyEiMgeHjdkVN+WLVuQn5+PpKQkadvgwYPx7LPPokWLFrh06RJmzZqFvn374uTJk/Dz80Nubi58fX0NqukBQJMmTZCbmwsAyM3NrTTUDKioFKbfpkmTJgb3h4SEwNfXV2pjyvz58zFnzhxbnzIREdVRXH6iboqLi6u0TRAEZGdnu6A3RFQXeXRAuGrVKgwePNggSycOAwWAdu3aoXPnzmjRogV27NiBESNGmN2XIAgGWUb9/9vTxtj06dORmpoq3S4sLETz5s3NticiIiL3lZmZif3790Mu/2tQlyAISExMdGGviKgu8dgho7/99hv27t2L559/vsp2TZs2RYsWLXDx4kUAQFhYGMrLy5GXl2fQ7ubNm1LGLywsDH/88Uelfd26dcugjXEmMC8vDyqVqlLmUJ+fnx+Cg4MNfojIsfbs2YOuXbuie/fu2LBhg7R9yJAhLuwVkWkcMlq3TZkyBUFBQWjRooX007JlS0yYMMHVXSOiOsJjA8LVq1cjNDS02hO827dv4+rVq2jatCkAoFOnTvDx8cGePXukNjdu3EBOTg66d+8OAIiOjkZBQQGOHz8utfn+++9RUFBg0CYnJwc3btyQ2uzevRt+fn7o1KmTw54nEVnvjTfewDfffIO9e/fi+PHjmDhxIrRaLUpKSlzdNZfLyspCbGwshg8fLs2JBoBx48a5sFcEMCCsq8aPH4/GjRtX2q4/oomIyB4eOWRUq9Vi9erVGDt2LLy9/3oJiouLMXv2bDz99NNo2rQpLl++jNdeew2NGzfG8OHDAQAKhQLjxo1DWloaGjVqhIYNG2Ly5Mlo3749+vfvDwCIjIzEoEGDkJycjI8//hhAxbITsbGxiIiIAFAxGbxNmzZITEzEggULcOfOHUyePBnJycnM+hG5mJeXFxo0aAAAWLx4MdasWYNhw4ahuLjYtR2rBVJSUrB69Wp4e3tjypQpOHbsGFJTU/Hrr7+6umseS8oQcg4hERHZwCMzhHv37sWVK1fw3HPPGWz38vJCdnY2hg0bhoceeghjx47FQw89hKNHjyIoKEhqt2TJEjz11FOIi4tDjx49EBgYiO3bt0trEAIVC1u3b98eMTExiImJwcMPP4x169YZPNaOHTvg7++PHj16IC4uDk899RQWLlzo/BeAiCRqtbrStg4dOuDy5cvS7aSkJKSmpqKoqKgGe1Y7yWQytGrVCi1btsTGjRtRWFiI5ORkk68jERER1X4evw6hu+MaLUTWy87OxrRp05Cfnw9vb2+oVCo0btwY8+bNQ/v27V3dvVrtmWeewcKFC9GyZUtp22effYbnn38eZWVlruuYB/shKAgPFxfjhlyOphqNq7tDNaSoqMjgYjVRXcNz3JrDgNDN8WAhst7jjz+O9evXG1TovXLlChISEvDdd9+5sGdE1hMDwly5HGEMCOuctWvXYsGCBfDx8UFcXBxeffVVAEDfvn2xf/9+F/eOyHl4jltzPHLIKBF5No1GA4VCYbAtODgYGp5MkxtildG6bcWKFTh16hROnTqFwMBAPPPMMygpKQGv5xORo3hkURki8mxvv/02YmNj4evri+DgYBQUFECtVuOdd96p8vdyc3MRFhZWQ70ksozM6F+qW+RyOXx8fABUFHWKjIzE4MGDcefOHRf3jIjqCgaERORxevbsiYMHD6K0tBT5+fkICQmBv79/tb83ZswY7N69uwZ6SGQ9T6gympWVhVmzZsHHxwdTpkyRlnIaN24cVq1a5eLeOceQIUNw+fJlad7ugAEDEB4ejrS0NNd2jIjqDAaEROSxAgICEBAQUGl7ly5dKm0TBAEXL16siW65pZiYGAbLLuIJgaDIE5c9ee211wxunz9/Hm3btsWuXbtc1CMiqmsYEBIRGSkqKkJ2drbBOqVAxZV5TxcXF1dpmyAIyM7OdkFvSJ8nDBkVlz0BgI0bN2L27Nket+zJ+PHjWUyGiByKASERkZF58+ahqKgIISEhBttnzpzpoh7VHpmZmdi/fz/k8r9qkgmCgMTERBf2ijxFaGiowfDJ2bNn47PPPjNY57euE+cTEhE5CpedcHMsyUtkvT179mDmzJnw8vJCSkoKRo0aBaBirs6OHTsqtT9//jxat25d092slZYvX464uDg0btzYYPsXX3yBkSNHuqhXnu1sYCDalJbitkyGRlqtq7tDROQQPMetOVx2gog8zhtvvIFvvvkGe/fuxfHjxzFx4kRotVqUlJSYbD9+/Pga7mHtNX78+ErBIAAGg7WAJwwZJSIix2NASEQex8vLCw0aNEBgYCAWL16MTp06YdiwYSguLjbZnkO0qDbjshNERGQPBoRE5HE6dOiAy5cvS7eTkpKQmpqKoqIik+2/+eabGuoZkfU8fWH6rKwsV3eBiMitcQ6hm+P4aqqtEhISkJ6e7upuOI5MhoIxY6D49FNX96RWys3NRVhYmKu74ZHOBwSgdVkZCgAo6vhX+vLlyw1uC4KAFStWYPz48RzaTVTH8By35rDKKBHZZerUqZW2CYKAI0eOuKA3zqVYuxZgQGjSmDFjuA6hi3hSZvDDDz9EixYtEBcXB5ms4pnL5XLUr1/fxT0jInJfDAiJyC7p6elYv359pe3ffvutC3pDztalS5dK2wRBwMWLF13QGwI8a2H6nJwcfP7559i4cSPi4uIwcuRIfPHFFxgzZoyru0ZE5LYYEBKRXZ577jlERkYiNDTUYLs7nqBx2GP1ioqKkJ2dDW9vw6+PAQMGuKhHnkGtVld6zY15QqZQJpMhPj4eo0aNQnp6OoYOHYo///zT1d0iInJrLCpDRHZ56623KgWDAJCSkuKC3tinJoPYrKwsxMbGYvjw4QbDa8eNG1djfbDFvHnzTBbfmTlzpgt6U7dlZ2fjiSeeQPfu3dG3b19ER0fjySefRHZ2tkE7T6wyKpfLkZiYiG3btmHLli2u7o5N3PUzgIjqHmYIicjj1IZhjykpKVi9ejW8vb0xZcoUHDt2DKmpqfj1119rrA+2GD58uMHt8+fPo3Xr1ujVq5eLelR3jR8/HuvXr0fz5s2lbVeuXEFCQgK+++67Su09KSAUyeVyhIeHu7obNnHXzwAiqnuYISQij1NUVIQjR47g+PHj0k9mZiaioqJqrA8ymQytWrVCy5YtsXHjRhQWFiI5ORlqtbrG+uAIrOzoPBqNBgqFwmBbcHAwNBqNwTZPX3bCXdWVzwAicn/MEBKRxxGHPYaEhBhsr8lhj6Ghobh8+TJatmwJAJg9ezY+++wzrFu3rsb64Ag+Pj6u7kKd9fbbbyM2Nha+vr4IDg5GQUEB1Go13nnnnUptNfCcgFCpVOLHH3/EnTt30LBhQ7Rr1w6+vr6u7pbV6spnABG5P65D6Oa4RgvVVjExMe61DMGtW8A995i/X1fiHvzIpBpWWlqK/Px8hISEwN/fv9L9v/j54b7ycqgBBNTx9+e6deuwYsUKREVFITg4GPn5+Th16hReeukljB492tXdIyIH4jluzWGGkIjsEhcXV2mbIAiVCl/UasePA127AgcOAJwLR7VMQEAAAgICzDcQBAjwjAzhxx9/jEOHDkEu/2vGi1qtRu/evRkQEhHZiAEhEdklMzMT+/fvNzhBEwQBiYmJLuyVle7cqfh3zx4GhOR2ZPCcIaMBAQHIyMhAnz59AFR81mRkZJjMnBIRkWVYVIaI7DJlyhQEBQWhRYsW0k/Lli0xYcIEV3fNckFBFf/evFnjD33nzh38/PPPuCMGpRZguXrPlZWVVWmbXBA8JiBct24dtm7dih49eqBbt27o2bMnvv76a3z22Wc11gdHF32x5jOABWeIyBmYISQiu5irMjly5Mga7ontfvzhB7QFcPPWLVReURFOmTe4b98+vP7662jcuLE0FyovLw9z5sxBv379qvxddyhXv3fvXgwYMAB5eXlo0KCBq7vjlpYvX25wWxAErFixAuPHjzc47mQAtPCMgDAsLAzvvfdejT9udnY2pk2bhvz8fHh7e0OlUqFx48aYN28e2rdvb9M+Lf0McMZjExHpY0BIRB7v2m+/oS2AW2YCQq1a7fDhFK+//jp27dqFIDE7iYoJ9IMHD642IBTL1QPAxo0bMXv27FpXrn79+vUAgOvXrzMgtNGHH36IFi1aIC4uDjJdUSO5XI769esbtJMBEGQyafkJj7NiBdCzJ9C2rdMewto1IS1h6WeAMx6biEgfh4wSkccL0JWsV5kJqDTl5Q5/TLlcjlu3bhlsu3XrlsFcTHPEcvWi2bNno1evXsjMzHR0N23mVVqK8QC8LHg+ZFpOTg5Gjx6NrVu3wtfXF4mJiWjWrBnGjBlj0E7mQUNGTRo/HqoePZz6EJauCWkNSz8DnPHYVHdkZGSgf//+SE1Nxe7du9GxY0f07t0bR48edXXXyI14VIZw9uzZmDNnjsG2Jk2aIDc3F0DFcJw5c+Zg5cqVyMvLQ9euXfHhhx+ird5VR6VSicmTJ+Pzzz9HaWkp+vXrh+XLl6NZs2ZSm7y8PKSkpGDbtm0AgKFDh2LZsmUGV8mvXLmCl19+Gfv370dAQADi4+OxcOFCt1xLiciU3NxchIWFubobFrEkIHT0ansrV65EWloacnNzIQgCZDIZmjZtipUrV1b7u5s2baq0bfTo0bWqyuLwrCw8AeC3S5eA1q1d3R23JJPJEB8fj1GjRiE9PR1Dhw7Fn3/+WbkdPGfIqDl3CwrQwIn7t2ZNSEtZ+hngjMemumPatGnYuHEj8vPzERMTg8zMTAQGBmLEiBE4cOCAq7tHbsKjAkIAaNu2Lfbu3Svd9vLykv7/7rvvYvHixVizZg0eeugh/Pvf/8aAAQPw008/SUM6Jk2ahO3bt2PDhg1o1KgR0tLSEBsbi5MnT0r7io+Px7Vr17Br1y4AwD//+U8kJiZi+/btACqu9g0ZMgT33HMPDh06hNu3b2Ps2LEQBAHLli2rqZeCyKnGjBnjNusQ+uiuyKvMXHF3RoYwMjISmzdvdvh+aws/8TWrRcNY3ZVcLkdiYiISEhKkC5gG9wPQevKQUVQ8f2fq2bMnDh48WO2akNaw9DPAGY9NdYe/vz+aN2+O5s2bIzIyUkpQ6J/fElXH4wJCb29vk1kLQRDw3nvvYcaMGRgxYgQA4NNPP0WTJk2wfv16vPDCCygoKMCqVauwbt069O/fHwDw2WefoXnz5ti7dy8GDhyIc+fOYdeuXTh27Bi6du0KAPjkk08QHR2Nn376CREREdi9ezfOnj2Lq1evIjw8HACwaNEiJCUlYe7cuVx8k9xKly5dKm0TBAEXL150QW9sI+iCFm0NBoR1nVYXnHBYm+PI5XLpO8OAIEBb892pFdRKJbzh/IBQVO2akHX0san2CgsLg0ajgZeXF/bv3w+gohqtVuupnwpkC4+b3HHx4kWEh4fj/vvvx6hRo6SqfJcuXUJubi5iYmKktn5+fujVq5dU1v3kyZNQqVQGbcLDw9GuXTupzdGjR6FQKKRgEAC6desGhUJh0KZdu3YGX+wDBw6EUqnEyZMnnffkiZygqKgIR44cwfHjx6WfzMxMREVFubprFhMDQsHMSaVWparJ7tQJAgPCGiNHxXvX477QAZTm5wOAxwbERBs2bKiUDfT29saePXtc1CNyRx71/dG1a1esXbsW33zzDT755BPk5uaie/fuuH37tjQMp0mTJga/oz/HMDc3F76+vggJCamyTWho5TqFoaGhBm2MHyckJAS+vr4mhwPpUyqVKCwsNPghcqV58+ahqKio0vaZM2e6oDe2EaoZ1uisDKFSqcSpU6ewd+9enDp1CuV2Po7+xSpXkzKEHDLqcMbfE+IcQuCvQNxTSAGhjRlCawtyGK8Z6IzKvqY+T0XXr1/HgQMHDIpKEZni7e1xgwDJDh71bhk8eLD0//bt2yM6OhoPPPAAPv30U3Tr1g0ApNLeInGid1WM25hqb0sbU+bPn1+pMA6RKw0fPtzg9vnz59G6dWv06tXLRT2ynqDLAJrLEFYXMNpi3bp1WLFiBaKioqQ1yE6dOoWXXnqp2uIwcXFxlfsoCMjOznZ4P20lDldihtDxjOfnygUBWrEypSAg6/RpzJo1Cz4+PpgyZQq6d+8OABg3bhxWrVrlii47TVlBAQDbA0JLC3Lorxmo0Whw/PhxlJWVoWXLlqhXr55N6wKuXbsWCxYsgI+PD+Li4vDqq68CAIYNGyYN/QOAESNG4KuvvsLKlSuxfv16dO/eHWfOnEH37t0xY8YMm543OVZGRgbeeustPPzwwxg0aBCmTZsGhUKB+fPnIzo62tXdI6qWRwWExurVq4f27dvj4sWLeOqppwBUXHlt2rSp1ObmzZtSNi8sLAzl5eXIy8szyBLevHlT+sINCwvDH3/8Uemxbt26ZbCf77//3uD+vLw8qFSqSplDY9OnT0dqaqp0u7Cw0GBtIiJXGz9+vMHJjDtwRYbw448/xqFDhwxKzKvVavTu3bvagDAzMxP79+83+F1BEJCYmOjwftpKyhByuK3NrJmfK+YFBa0WKSkpWL16Nby9vTFlyhQcO3YMqamp0hSJukSpGyVja0BoaUEO/TUDH3/8cZw8eRIKhQKDBw/G4cOHbVoXcMWKFTh16hR8fHywdOlSPPPMM1i7dm2lLK84Eujzzz83OO4ff/xxBoS1BCt9krvzqCGjxpRKJc6dO4emTZvi/vvvR1hYmMGY6/LycmRkZEjBXqdOneDj42PQ5saNG8jJyZHaREdHo6CgAMePH5fafP/99ygoKDBok5OTgxs3bkhtdu/eDT8/P3Tq1KnKPvv5+SE4ONjghzxDQkKCq7tgER8fRy/Q4HxaF8whDAgIQEZGhnRbEARkZGRYVD1wypQpCAoKQosWLaSfli1bYsKECQ7vp704/9J2ls7PlQPQ6N67glYLmUyGVq1aoWXLlti4cSMKCwuRnJzslOGNrqYpKan418aAUCzIAaDKghz6awaK6wLqrxloy7qAcrlc+rxMSUnBCy+8gMGDB1daWqRVq1b4+uuvERUVhe3bt6OoqAgHDx40WNCeXEu8sNC+fXvpwkLDhg1rpNLnnj170LVrV3Tv3h3z589HbGwshg8fLp1zAhWjA4iq4lEZwsmTJ+PJJ5/Efffdh5s3b+Lf//43CgsLMXbsWMhkMkyaNAnz5s3Dgw8+iAcffBDz5s1DYGAg4uPjAQAKhQLjxo1DWloaGjVqhIYNG2Ly5Mlo3769VHU0MjISgwYNQnJyMj7++GMAFctOxMbGIiIiAkDFPJ82bdogMTERCxYswJ07dzB58mQkJyczwCNMnTq10jZBEKSiRLXdN9984+ouWK+aE2VnBDXr1q3D22+/jZkzZ0Kj0cDHxwedO3fGZ599Vu3vjh8/3uT2kSNHOrqbttOdKGtZodVm4vxc43nrxvNzZfjrYoag1SI0NBSXL19Gy5YtAVSswfvZZ59h3bp1NdHtGiUolQBsLyqzYcOGSttMFeTQXzOwoKAAzZo1g7+/Px555BH069fPpnUBhwwZYvB3GjBgAMLDw5GWlmbQbunSpVi2bBmysrKwbds2hISEoEePHlizZo1Vj0fO48pKn2+88Qa++eYb+Pr64sEHH0Tfvn0xZ84cdOvWDYsXL66zowPIwQQPMnLkSKFp06aCj4+PEB4eLowYMUL48ccfpfu1Wq3wxhtvCGFhYYKfn5/Qs2dPITs722AfpaWlwoQJE4SGDRsKAQEBQmxsrHDlyhWDNrdv3xYSEhKEoKAgISgoSEhISBDy8vIM2vz222/CkCFDhICAAKFhw4bChAkThLKyMqufU0FBgQBAKCgosPp3qXYKDw8XDhw4UOmnU6dOru6a5RYuFIScHFf3wmJHn39eEADhQIcOJu//bd8+QQAqfsgiXzdvLgiAkLlkiau7UrfcvVtpU55MJlz08xMEQCgvKXFBp1znx/XrBQEQfvX1rfHHLikpEa5fvy6UlpY6ZH/nzp1zyH6o9lCpVE5/jMcee0z6/+OPPy6sXr1aiI2NFTp37iy88cYbwvPPP2/Qxp3wHLfm8OzGzfFgqXtmzpwp/PHHH5W2v//++y7ojQ00morAKSLC1T2x2JGkpCoDwkvffOP8gDA1VRB693be/muYGBAef+cdV3el7rh4seI9uGuXweY8QLigCwiVxcUu6pxrZH/6qSAAwiUfH1d3xWrx8fEGt/v06eOUxzl16pQwZMgQ4amnnhIOHz4sbX/uueec8nju1h93N2HCBOHSpUuCIAjC008/LVy6dEnYv3+/EKH7Dl63bp3g5+fnwh7ajue4NcejhowSuYO33nrL5PaUlJQa7omNdPNfNPXrw/mzJxxD0M39MTcLqUbmwS1ebFVzpVKJH3/8EXfu3EHDhg3Rrl07+Pr6Wvz7WVlZzq1EqRvCyCGjDnTtWsW/334LDBwobZbjr6IqgoctRu0Oc1QtnQbgrPnXta3IUG3rjzvLyMjAuXPnsHTpUgwaNAi//PILkpKSMH/+fJw/fx4AMHr06GoLlRExICQihyq5dAmBAM7duoV2ru6MpXRzCOVmTqZr20mnPUtWiJx9UiZjQOhwSh8f+AEovXULAXrbjecQehKxQrBtJWXMi4mJMVjaw5yioiLUr1+/yiWj0tPTsX79+krbv/32W4PbVc2/tucCkFhkCAA2btyI2bNnu7TIUG3rj7NkZWWhY8eOTn0MVjclR2FASEQOdfWXXxABoERX7MEdSMtOmFnUu7YFhPYsWSGqqZMyoZa9du7s5OnT6A7gpxMn0EFvu36GEIKAL7/8EvPnz0fbtm0xYMAAzJs3D8HBwZg1axaGDh1a8x13IvHYlJk5dqtj6Zqe+msGNm3aFDt27MAPP/yAxx9/HG3atEF5eTkWLVqE3r17V9rfc889h8jISISGhhpsHzNmjEV9NHkB6Pvv8VJKikXHe20rMlTb+uMIy5cvN7gtCAJWrFiB8ePHmy0C5giWLptijtNHipDbYEBIRA4lBU82loF3BSkgNFM2vrYFhOKSFX369AFg3ZIVImeflAnMEDpcgG5Ioby01GC78ZDRRYsW4fDhwygqKkKHDh1w/vx5+Pv7o3///nUuIKxuDdHqWLqmp/6agQ899BCeeeYZ5Ofno3Xr1jh69Chu3ryJ4cOH4/Dhw5Uew95pAMYXgM69+SYezMzEYwsWWBQQbtq0qdI2Vw4jrG39cYQPP/wQLVq0QFxcnJQtlsvlqF+/vlMft6rqpkVFRQgKCqoyU+mokSLXr19HeHh4te0yMjLw1ltv4eGHH8agQYMwbdo0KBQKzJ8/H9HR0VY9JjmWR69DSFQb6a8ppF8SfciQIS7sleU0bpQZlIiBoBsNGd26dSt69OiBbt26oWfPnvj6668tWrJCtGnTJikYFI0ePRplZWWO7aydJ+zusv5mTfD3rriGqzF6nxoPGQ0ICEBgYCCaNGmCfv36QaFQwM/PD97ede8asL1DRi1d01N/zcCQkBAMHjwYmZmZKNGtgxgaGmoQVDqS8Zql17ZuRQYAmTt+1tZROTk5GD16NLZu3QpfX18kJiaiWbNmFmeBbbVhwwakp6ejffv2ePTRRzFs2DCsXLkSI0aMQOfOnfHhhx8iMTGxUgZTZMuapTt37jT42bFjB4YNG4adO3dW299p06Zh9erV+Mc//oGxY8di+/bt+OqrrzB9+nSbXwNyjLr37UDk5vTXFJo5cyYOHz6M999/XzrxqO1qW/BkEbGojJmA0N4shKOFhYXhvffec3U3qiSeoFv62rn7+ps1QaZ7n6qNMtnGGcIOHTpIWQPxIkF5eTkaNWpUo/2tCeLnjW0DRi1f01N/zcA2bdrg0KFD6Nu3LwoLCwFUzCVs2LChjb2o2ksvvYSxY8dCrVYjODgYv124gEYAXtcrLESuJZPJEB8fj1GjRiE9PR1Dhw7Fn7oCa86mn71u2rQpvv/+e8yZM0fKUFaVqbRlpEhSUhI6d+6Mbt26QdAN1c7Pz8eJEyfwxBNPVNlXe4e4kvMwICSqZby8vNCgQQMAwOLFi7FmzRoMGzYMxcXFru2YhdxxzphQTYawtgWEpmRt3YqOw4a5uhuVWPraWVp4w5OJw2+NA0KDDKEgmLxY4Ovra3KonrurqWPztddek/6/evXqSvcHBQVh69atTnnsd999F4cPH5YKhyx/8EEMu3ABg3ftwj+d8ojWSUhIQHp6us2/LwhClUV53IlcLkdiYiISEhKQm5tbY48pZq+vX7+O1157DTNmzEBAQAASExPxxRdfmM1UWjp8V39I6KVLl7B06VJkZ2dj4sSJiI6Oxvfff4/XX3+92r5WNcSVXItDRolqmQ4dOuDy5cvS7aSkJKSmpqKoqMh1nbKCeNJq6xV7lxBPKmvDkFEz8xir8v2yZej41FM4NmeO1b97584d/Pzzz7hz547Vv1sVqciHhc9HLLzRq1cvgx9nD7lyB+KwWXPHlhyQ5ux6apVRR8vKykJsbCyGDx9ukKUeN26c1fuydxqAmFVp3749IiMj0QBAQ1g+TPbLL79Ex44dMXr0aHz66aeIiIhAVFQUtm3bZtXzmDp1aqWfKVOmWJ3FX7RoEQDghx9+QHR0NLp3745OnTq5vCqmI4eny+Vyi+bUOYKYvQYqMpXz58/Ht99+i5CQEJsyldUNCa1Xrx6mT5+Ojz/+GPv27UNcXBzy8/Mt2veGDRsqZQO9vb2xZ88eq/pIjscMIVEts2zZskrb+vTpI60pVNvZO4TLJWrTkFGlEggMtOpXSm7dAgB4f/st8MYbFv3Ovn378Prrr6Nx48ZS5cK8vDzMmTMH/fr1s7rbxsS/v2BhQOj26286QHXDZsWA0DgQ4LITjvfiiy8iPT3dIcuy2DsNwDir8t+HHoIagNbC5+6oIkOOyuLv2LEDaWlpmDJlCtasWYOIiIgqi/LYoqrqmaaGTjtyeLq9GVNr6WevAeD8+fNIS0vDDz/8AK1Wa3Wm0tIhoUFBQZg5cyYKCwvtPj+pi/Ob3Q3/AkTkUG5ZVbI2FZWx4YTeLzgYAOClm89kjlqtlr54X3/9dezatQtBQUHS/YWFhRg8eLBDAkIpuHaD4ba1RXUn3NLFFqPhdXIAWl1Bk6oCwtzcXISFhTmot7WDvQGhuZP3CxcuWL0si7nX195pAPpZRQCQyeXwBvDx3/9u0e+LRYYCAwOlIkOA9Sfhli6fUV1AdPfuXZw9exZFRUWIiIgA4PiiPFVVz9y1a5dDAltL5j07+5gztWzKgQMHpCycNZnK69ev48KFCzhw4AC2bt1q8ZDQ4OBgdOnSxfYnQbUCA0Iicii3DgjNrUNYk0GNDUNGA3QndioT/czOzsa0adOQn58Pb29vqFQqNG7cGKWlpbh165ZBQHjr1i2HnZRJAaENz8dTVXfCbfLY0r1nBb11CM0ZM2aMRYutuxMxIKxuRIK5k/dNmzbh4sWLlbYXFxdbXWzD3OsrTgMQ95WUlIQWLVrgpZdeqqbXpknLGlhYEdhRRYaMs/j6r6n4f0sybW3atMGCBQvQunVr5OXlISQkxOFFeapaZ9XedSFFlmRMnX3MmVo2JTExUcrsVWfEiBH46quvsHLlSqxfvx7du3fHmTNn0L17d3z88cd4//33sWTJEouHhJL7YkBIVAsplUr8+OOPuHPnDho2bIh27drB19fX1d2ySI0GT45izZBRQXDqGouCWm11CX2xkI9xsRGgoori+vXr0bx5c2nblStXMHz4cKSlpSE3N1cq6tC0aVOsXLnSosfUzzaaIte9lpYOGaXqh80KJjKEgkYDGQwXpjd1tV4QhEqBT11gb9GiDz74AEeOHKn0Xh4wYIDJZVlGjx5t9evr6GkA4l9fa2FA6KwiQ7YOIa2JojxVVc/87rvvTP6OtcPT9QNL/ffErVu30KVLlxo55sRlUxo3bixtO3jwIL744guDduKahMbEKrmff/65QWD5+OOPY8aMGQ4bEir68ssvMX/+fLRt2xYDBgzAvHnzEBwcjFmzZtW5NVLdDQNColpm3bp1WLFiBaKioqS5XadOncJLL73kFgv3Cm6cIbQkIBQ0GsicON9BVVYGa0P/qgr5aDQaaYiYKDg4GH5+fti8ebNVj2Mu2zhv3jy0b9/eoK3MyoBwz549mDlzJry8vJCSkoJRo0YBqCiYsGPHDqv6WVeZyhBqNRp4AdJFCq1Gg6KiImRnZ5sMcuoaSwNCc1mh+Ph4FBUVISQkxGD7zJkzze7L1a+vWJJD6+J1CB2VaXMGS6tn2kP/Ao6r3hPGy6asXbsWCxYsgI+PDy5duoRXX30VADBs2DCpqqe+Vq1a4euvv0ZUVBS2b9+Ovn37IisryyB4tHRIaExMTLXZUEfNZyXHY0BIVMt8/PHHOHTokMEQELVajd69e7tFQAgLh3DVKmIgaEFAqFWr4eXEgFCtVFofEOpODI3nlgHA22+/jdjYWPj6+iI4OBgFBQVQq9V45513rO6buWxjQkJCpavuYoZQZmFA6O7rb9YEaUkXowwhAAi6OUOCVot58+ZZHeS4K0sDQnPZ11WrVhncPn/+PFq3bo1evXqZ3VdNv77GF0vEyzsp//sfHFOCxTYsBPWX2nLM6a9JuHTpUjzzzDNYu3at2SGkS5cuxbJly5CVlYVt27YhJCQEPXr0wJo1a8w+hql5i4IgIDs7u9r+OWo+Kzkel50gqmUCAgKQkZEh3RYEARkZGfD393dhryzn1lVGzXxp6p90apxcYEZjQ4bVVKAg6tmzJw4ePIjt27fjww8/xI4dO5CRkYHHH3/c+r6ZyTZqTAR9cnFum4Un7GLhjcDAQCxevBidOnVyq/U3a4Jg4tjS6l57saiMVq3G8OHDDU5MxeFeVQU5NcGRSzlIHDwk2dxC9QCQkZGB/v3747vvvkNmZiY6duyI3r174+jRowBMv75VFaGxdCkh8WLJ3r17cfz4cXyUmwstgDI7P4usrT7pqOUrHNUfR7h+/ToOHDhgsNSTLWrLMae/JmFKSgpeeOEFDB482OzSE76+vkhLS8OePXtw/vx5HD16FAsXLkRoaGilJYnE43fHjh2Ii4vDggULsGDBAshkMixYsECas1kVcT4rALvms5LjMSQnqmXWrVuHt99+GzNnzoRGo4GPjw86d+4sfXjWdu64ML3MiqIyzp4jaUvAaUkhn4CAAAQEBFTbrqqqeGazjV27AocOAY89JrWVW1lUxtGFN9xRdfNrTB1bUoZQLyA0Nn78eJPDxWpaVZUfbeXoZSfEk2lTQ5inTZuGwMBA/OMf/0BMTAwyMzMRGBiIESNGGKyhZzy0+vr167h58ybuvfdejB07ttphfMaMq5ROSU/HMACldn7WWlvwxNnD/Wqq6FFVhVRmzJjhkMdw1TEnrkno7++PsLAwDBgwAOHh4UhLS7N4H+aWJCooKMDmzZvx+eefY+3atbhy5QpSU1Nx8+ZNtGzZEhMmTKh2386az0r2Y0BIVMuEhYWZ/NB0F+JJq7lsW20ks2IOobMDQluqtJoqNmKrqk7KxGxjaWkp8vPzERISUpG5lsmA994zCKjl1QzDNebu6286QnUn3OLfWX9oj7jMhBgQmnq9xSDH1aqq/Ggz8dh1RAcBfPPNNwBMD2H29/eHIAjSAvHNmjUDgEoLbRsPrY6OjsY333yDpKQkBAYGVjuMz5jxxZJB9erhCQBJFgaE9hYZEtf0++WXX3D69Gl0794d/fr1Q2pqKlatWiUN9xOLUzm7P5Ywd3ElISEBBw8exPDhw/Hbb7/hxIkTkMvlGDduHHbt2mVVQFjVvGfjYy4jIwNvvfUWHn74YQwaNAjTpk2DQqHA/PnzER0d7bDnLa5JKM7nO3/+PNq2bYtdu3ZZvA9zSxI1b94crVq1wqxZswCg0vE7cuRIhz0PqnkMCIlqu+Ji4KOPgIkTAT8/V/emWmLwZOncsVpBnO9mLnipyYDQjgyhpSF4VlYWXnjhhUrbLT0psyTbaG2GkKqfXyMGhF56gYT4fqwqQygGOa5WVeVHW0mfNzZegDJXCMPU2oFfffUVmjZtCgBS9ketVkNr9LlhPLRaLpejUaNG0Gg0SElJQWRkJAYPHiwNxauO8cUSmUaDPgC+sHDYt70FT8TM7rx587B48WIcO3YMn332GVq1aoXy8nJ4e3tLQU15eTkWLVqE3r17O60/ljB3cSU0NBRPPPEE+vfvj3fffRfPP/883n//fZw6dUr621qqqnnPxpVWp02bho0bNyI/P7/K7LK1qguubclUyuVynD17Fl27dpW23bp1Cz4+Pg4/fqn2YEBIVMvdnT0b9RYtAqKiABfPAbKEdIJmwwLrriKzYg6h04eM2pIhFANCE1fnly9fbthWELBixQr8+eefuHbtmtNOysQ5hAwILVfdenFS9l3v2BIzhFqxqIyp13vzZiAkBKjiJL0mOKXyo+75elUTEFpbCMPSIcze3t7Ys2ePwTbjodW5ubkYOHAgFi5cCAA2DePTJ11ss/DYsrfgiZjZ/c9//gPgr8zQn3/+CV9fX8jlcqxZswYRERG4efMmhg8fjsOHzZe7qYkCLOYurshkMnz66adYtmwZ7rvvPmzbtg07duyAj4+P1RdOTF00MDfv2d/fH82bN0fz5s2rzC5bq7rg2pbRAStXrkSvXr3wwAMPGCxJ9N1335ldisVe5pbFoJrDgJColtu3ZQuGAigvKbG6+qRLiMPa3CkgFDOEJk4qExISMF4vG+aMgFAQBGnImy1zCKvKknz44Ydo0aIF4uLi/lrQWi5HXFycU0/KHBUQVjWnsa6pdn6N+N7TDwiNqoyazDCPGKFr7D7DuC0lvverq5BnagFvQRCQmJhosr01Q5iNT8bNDq3WsWUYnz7pApaFnxXDhw83ud3SgifmMrurVq3C2bNnUVRUhIiICKmt/mtsSX8sqexqLXMXV+rXr4/r168jLS1NCsg/++wzPP/885WWz7DkMSyd9xwWFib1p6rssrWqC66rC3LNZRjLy8ulYkmWsuSzWn9ZjLi4OKvn05LzMCAkqu10X/53bt6EW5wWVzMfr1bSaDAVwB+lpWgydaq0WRAEHDlyBC/pnag4IyDUaDTSh7EtQ0YhZhVNBF85OTn4/PPPsXHjRsTFxWHkyJH44osvsHTpUoN2lpyUmS16AsC4pISXg4aM1lShCXdgKvtuPGTU0nUf6wzd85VXE+yaWsAbgEWFMGxlbmi1LcP4lEolfvzxR9y5cweXlUp0BwyGsjuTuczuvn37sGDBArRu3Rp5eXkICQlBUVERGjZsaNX+nVGAxdzFlWvXrlXabmuWy5qLBhs2bKi0zVR22Vr2BvuOHL5ryWe1tctiUM1hQEhUy4knFHdu3XKLgNDeOT2uINNqkQ5gjpcXHhwyxOC+b7/91ulDRtVqtV0BodQ/E8GATCZDfHw8Ro0ahfT0dAwdOtRkCXJLTsrMFj3x968UEEon6BZeGKiJQhPuzuRwbHF5D90JnbOHNDvCnTt3cOfOHTRs2NDq4KEScchoNc3MLSfhikIY1g7jW7duHVasWIGoqCgEBwfjbGkpVgMYcOMGulb7286zevVqAIbD/YKCgrB161ar9lNbih65gqvX37Nl+K49n9XGy2JYO5+WnIcBIVEt56Ub5qcuK3NxTyzkpkNGnwPwN5ms0pXVMWPGQNAbOuOMDIz+On42BYTi3LIq+iaXy5GYmIiEhAST631ZclJmtuiJqcezMiCsiUIT7koaimXi2JICwCqqjNYW5srZz5kzB/369bNtpxbOIXQUU8PizBWmMcfauWoff/wxDh06JA3FPPzhh+haVoZOubmYbdWe7OOs4X61peiRJw1PF1kzfHf27NmYPXs2/vzzT4SGhkImkxkUEbLks1pcFkMcZmvvfFpyHC5MT1TLiZk2jVLp4p5YRgxK3C0gfAtAYxP3paSkGGTenJUhlPZvy5BR8XcseM3lcjnCw8MrbbfkpMzkosKlpTC1pLB4gm5ptVnxSrUxRxaacFdjxoyp+I+JDKFxUZnanCEUy9lv3boV69atw/bt27Fz5068/vrrtu9U/LxxUB+rEhcXhw4dOiAuLk76efbZZ80WpjEWExNj0+MGBAQgIyNDui3TaJABoKZrTovD/U6ePCktn1FSUuJWw/1MfcZkZWUB0DvOnPAYpoiPW5uYy6QDkKYZBAcHY+nSpTh69Cj+97//SUt1WPJZ/dprrxkUprF3Pi05DjOERLWcGBDasj6dS7jpkFHAwiqjdi4GbYp+IRl7AkJnL/Vhal6ODwBTSwpbmyG0dy5MXVDdUCxT1XDFjLWsqiqjtYRcLsetW7cMqgneunXLZBESce07Hx8fTJkyBd27dwcAjBs3DqtWrfqroZ0Bobnqhqb+FmfOnIGfnx8WLFggbTNVmMbaiqbVWbduHd5++23MnDkTGo0GZXfvog+AObp1Dp1t0aJFSEtLQ1lZGXr27AmgokDL3//+d5PD/WpDps1cNrNz585QGl1cvXnzJu655x4UFBQ45DFMZUzNVXseP358lUGYORYfH1by8fEx+zlUWFiIs2fPIjAwUGoTGhqKMt3oJVs+q50xf5Rsw4CQqJaTAkInBCJOIVb9c6eAUMxmmeuzkzOE+tlfm/7O1az9qF+QomHDhmjXrh18fR1Ts1ZdWgpTg02tzRCSBcNmTRWVMa4yWoszhCtXrkRaWhpyc3MNytmvXLmyUltx7Ttvb29MmTIFx44dQ2pqKn799VfDhhbOIbR2uKOpv8Xy5cvxxRdfoEWLFgZtjQvTWFvRtDphYWEGF2My69dH1N27OGHBIvCOsGPHDqSlpaG4uBgLFy5Ev379pOUlxL+pvtpQCMpc8ZJr165BJpPh/fffl/62ixYtwuTJk61eT8+aAinmqj3Xr1/fpudn8fFhpW+++QaRkZEmP4fCw8NNFhG6cuWKzY/nyfNHaxsGhES1nLtmCL3cbMgoYFlA6IwMjP78ULsCQhOvuXFBivz8fJw6dQovvfSSQ9aPUpWUmAwIrc0QkgUFHsSLLfqvqRgQiovX1+IAPDIyEps3b7aorbj2HQBs3LhRWvtObRzwWhgQWlvd0NTfYvz48Wjbtm2ltsaFaZxd0VT8+9fUxZa7d+/i7NmzuOeee6S5nqGhoTh9+jT+8Y9/APgro1pbCkGZK15y//33Y8CAAfjqq68wevRoqerymDFjKgX61j5GmzZtzBZIMVft2dZhqhYfHzYw9zn0+eefV8oCBgUF4dFHH7X5sWrL/FHysDmE8+fPR1RUFIKCghAaGoqnnnoKP/30k0GbpKQkyGQyg59u3boZtFEqlZg4cSIaN26MevXqYejQoZVKGefl5SExMREKhQIKhQKJiYnIz883aHPlyhU8+eSTqFevHho3boyUlBSUu8tJP9UcNwsIxaDErTKEWi2+BPBMeTlGjx6NTz/9FBEREYiKisK2bdsMTrKdkYHR/9sKtuzfxPp0IrEgxfvvv4+33noLy5YtQ0ZGBj766CNbu2v40KWlqFyiRi9DyIDQYsOHD690EgboDcUyMWRUvIAgBoR1JQAX174TzZ49G7169UJmZqZBO3EOZXUBofHJ+wsvvIDBgwebrLgLWPC3qML48eMrBYOA4yqayms4+96mTRuDzBBQkUFVKpU4cuQIjh8/Lv1kZmYiKiqqRvpVFbF4iWjAgAFYvnw5mjVrhiVLlmD79u1Qq9UYOnQorl69CsD6IY/6j5GQkID+/ftj+fLluPfeeyu1Fas9//e//5Ue19x7zxKWHh+2MH7vi8tomHt9GNTVDR6VIczIyMDLL7+MqKgoqNVqzJgxAzExMTh79izq1asntRs0aJBUThlApaFVkyZNwvbt27FhwwY0atQIaWlpiI2NxcmTJ+GlG7YTHx+Pa9euSRNl//nPfyIxMRHbt28HUFFVcMiQIbjnnntw6NAh3L59G2PHjoUgCCbXtiHPJXfTIaPuNIdQrtViEYD1Xl4IX7TIcEmF/v3xlt6i0k6pMqo3ZNSWhellVWQIxYIUffr0AVBxBT8jI8NgoWx7qEtKMAaA8QAx6culFmes3I3M1HBs4wxhFRcUBK0WsmoWDa8tzK19Z5zVlullCKt6fnWpuqF4sUVeQ8eW/vmQKCgoCO3atbN6yYKa8tprrxncNi5eol91+fHHH7d4v1P11qkFKoYRi+vVAqi2QEp11Z4tZenx4Qic5+cZPCogND5IV69ejdDQUJw8eVKaKA0Afn5+ZidEFxQUYNWqVVi3bh369+8PoKLaXvPmzbF3714MHDgQ586dw65du3Ds2DF07VqxStAnn3yC6Oho/PTTT4iIiMDu3btx9uxZXL16Var4t2jRIiQlJWHu3LkIDg52xktAbkgceim4SUAonqB5u1FAKBMEBAAIlMnQpEkTwyUVvL0NghpbArbqOCpDaOoE0bgghY+PDzp37ixVCbWGqWIDquJiXDLRVs4MocOZquCrEd87uguXVWWwVWVl8A0MdF4HbWRXERKjY9Pbz3TtzeoCBEs5cz6upbxqeMioOadPnza5vTYWgjIX1Fg7jy89PR3r16+vtP3bb7+1qj/mqj3XRtXN86sNRYTIfh4VEBoTK0oZL4x74MABhIaGokGDBujVqxfmzp2L0NBQAMDJkyehUqkMykeHh4ejXbt2OHLkCAYOHIijR49CoVBIwSAAdOvWDQqFAkeOHEFERASOHj2Kdu3aGXwgDBw4EEqlEidPnpSu5htTKpUGFbIKCwvtfyGoVhOzAu4yZFSq+udGAaFcq0UH/DX8TFpSobwcjRo1Am7dktrWxjmEUvVJE8GXcUEKe5gqtPH7/v1I0s0rEguFABwy6hRVDBkVA8JK70+9tqrS0loZENpVhES/wI5KBZgJCI3ZkvWwdz6uuYqm1pLXcIawLqgqqLFmyONzzz2HyMhI6ZxQ5KglK2qj6l6f2lBEiOznsQGhIAhITU3FY489hnbt2knbBw8ejGeffRYtWrTApUuXMGvWLPTt2xcnT56En58fcnNz4evrW2mIRJMmTaT0f25ubqUPC6BizLd+myZNmhjcHxISAl9f3yqHEcyfPx9z5syx+Xk7klqtrlSFihxP+tKvxdUD9Yn9dY+BaRVkgoD3APxstN3X1xebNm3Cgccek7Y5IyB0VIbQ2cN0TRUb0JSVQRwgplIq4asbiip+MjAgdByZiYst4nBjme6E1/j9qVUqpWNRVVrq/E5WobplNWyhnyXTWHHRzJbqhsYLxAMV34O9e/c2CAidtYC7qKaHjAL2Z0YzMjLw1ltv4eGHH8agQYMwbdo0KBQKzJ8/H9HR0U7seYXqghpLs1xvvfWWye0pKSnV/q6zssvXr1/HhQsX0LJlS4M1/hzNGccv1R4eezY/YcIE/PDDDzh06JDBdv1J3+3atUPnzp3RokUL7NixAyNGjDC7P/0r4wAM/m9PG2PTp09HamqqdLuwsBDNa2gtIgDIzs7GtGnTkJ+fD29vb6hUKjRu3Bjz5s1D+/bta6wfnkSuO9l3tyGjXm6WIQTMB7EGZf6dEJgLeieytuy/qgyhI5laK1BTWgpxgJiqtFQKCKW/PwNChzEVEEoZQl1mzDggVN29Ky1grnZxQFjtshoWiImJMcxG6A+fteIz0pZCGJbOx7W2oqm1ajogdESl4mnTpmHjxo3Iz89HTEwMMjMzERgYiBEjRuDAgQPOfQIWcHaWy9HVnkeMGIGvvvoKK1euxPr169G9e3ecOXMG3bt3lxaKdzRHHL9Ue3lkQDhx4kRs27YNBw8eRLNmzaps27RpU7Ro0UK6AhIWFoby8nJpDRbRzZs3pYVBw8LC8Mcff1Ta161bt6SsYFhYGL7//nuD+/Py8qBSqSplDvX5+fnBz8IhMc4wfvx4rF+/3iAIvXLlChISEvDdd9+5rF91mfilX5vXFzNgYdW/2sSadQidXVTGlsDf1NyymqBduRL3v/CCdFut9zzEvz8zhI5TVUAoNzNktFw/INQbmuwK1S6rocfSxd1tzRDawtL5uNYsR2ALKSCsoWPL0sxoVfz9/dG8eXM0b94ckZGR0rmXWIivprgqy+WI11CfOF3o888/N1jz8vHHH3dYQJiQkID09HTptjXHL7kfjwoIBUHAxIkTsXnzZhw4cAD3339/tb9z+/ZtXL16FU2bNgUAdOrUCT4+PtizZ4/0hXXjxg3k5OTg3XffBQBER0ejoKAAx48flz58vv/+exQUFEhBY3R0NObOnYsbN25I+969ezf8/PzQqVMnhz93R9FoNFKxDVFwcDA0nMvgNNJVYDfJEIr99QKg1WoNvgBrK/EEW63V4tSpU5WH9OideDl7yKg9GcKanrd54b330Frvtn7AwSGjjmdqSRfx/SIzExCqS0r++r+LA0JTGWbAdBESSxd3lxnPIXQiS+fjGlc07d+/P5o2bVptRVNNZibKp0xBwP79QBWfm941HBA6olJxWFgYNBoNvLy8pGGzarUa2hr+fHBGlisrKwsdO3asso2jqz23atUKX3/9NaKiorB9+3b07dsXWVlZNs1RNa6cKvZPrJwqsub4JffjUQHhyy+/jPXr12Pr1q0ICgqS5uopFAoEBASguLgYs2fPxtNPP42mTZvi8uXLeO2119C4cWPpQFAoFBg3bhzS0tLQqFEjNGzYEJMnT0b79u2lqqORkZEYNGgQkpOT8fHHHwOoWHYiNjYWERERACqGvbRp0waJiYlYsGAB7ty5g8mTJyM5OblWVxh9++23ERsbC19fXwQHB6OgoABqtRrvvPOOq7tWZ8ndrcqorr/eqLiA4C4B4ToA7wPo8emnlYb0NK/l6xDKa2jIqLFf7t41CAj156gxQ+h4chOBv/i5INedWFYaMlqLAkJrWLy4uwUBoXGmw1Hu3r2L4uLiSqN6jCuaAuaXI9CfW9d+/Xos/eMPBD/+ON5euNDs3Drx2KqpgNARlYo3bNhQaZu3tzf27NnjyK5Wy94s1/Llyw1uC4KAFStWYPz48Rg/frzZ33NktWcAWLp0KZYtW4asrCxs27YNISEh6NGjB9asWWP1vhxVOZXcm0cFhCtWrAAA9O7d22D76tWrkZSUBC8vL2RnZ2Pt2rXIz89H06ZN0adPH3zxxRcGV12WLFkCb29vxMXFobS0FP369cOaNWsMhj6kp6cjJSVFqkY6dOhQfPDBB9L9Xl5e2LFjB8aPH48ePXogICAA8fHxWLhwoRNfAfv17NkTBw8eRGlpKfLz8xESEuKw9czINC83LSrjhYorwLYUb6hpckHAxwA2A2j+/vvSdnFIz9wazBDaEhC6KkPoZVQQwWSG0I3mktZ2JjOEuiDIXIbQXQNCcyfXxou76w8ZfeOddxBoFECaynQ4SlhoKBQhIbh27ZrN+9CfW9f7vfdwBoBq7lz8Y/p0k3PrBEGQji2vGhqZ48hKxcZqujCdvVmuDz/8EC1atEBcXJxU78GSpSsc/Rr6+voiLS3NIetoemLlVKrMowLC6iZ0BwQEWDTR3N/fH8uWLatyAfmGDRtWe+Xnvvvuw9dff13t49VGAQEBCAgIcHU3PIK0DqGbBIQyvTmEajfps5duHcJjAMTZsfpDemQFBVCj4gPTKUVl9DMbdmQIazog9DUK9sWAQxAEiPcwQ+g4+hdbRNIcQjNFZTR6WVtXF5VxBv3313+//hobNm6s1MZZmY4dJSUo1Au4baE/t66ZXI5mGg1uFBebnVun0WikEzd3WtqnrsjJycHnn3+OjRs3Ii4uDiNHjsQXX3zh1sGTPZVTqe7wqICQyB2JAaHbZAj1AkKlmwxzFYeMvg7gvR49Kg3pudCvH1So+MCsjXMI5SYyRzVBblQRWQwItWo1h4w6gakMoXihSAwIUcUcQv3iRXWF/vtrZGxsjWY6ejpgH/pz62YHBADFxSi+ds3s3Dq1Wg1vAErUfBEpqqgOHx8fj1GjRiE9PR1Dhw7Fn3/+6epuEdmt9k/uIfJw7hYQ6s8hdJcMoVwQEApgLoDDhw/j2LFj+O6777BkyRKEhYVBptFADNmckanVzxDaM4fQq4ZPEI2Hg4oBh37gwSGjjmMq8Bcra5qbQ6jRGyZaFwJC43V69QPCtOeeM7kGsKWZjqysLMTGxmL48OEGw0zHjRtnY2+rt2HDBikbKM63VhcXm51bp1Kp4A2gDIC3GwWEX375JTp27IjRo0fj008/RUREBKKiorBt2zZXd80mcrkciYmJ2LZtG7Zs2eKSPiiVSpw6dQp79+7FqVOnUO7kKrtUtzEgJKvs2bMHXbt2Rffu3Q0miQ8ZMsSFvarbxIpyxlf+ayu5mwaEKlR8IOoPLb9+/TqAipNOtS4bVivnELoqQ2hBQMgshuPoZ99FlYrKGL3etanKqCMYZ/tkWq3DLtakpKTgvffew5IlS7BkyRIsXrwYAPDrr7/atV9LiQGhsqDA7Nw6MUNYJpO51ZDRRYsW4fDhw1i0aBFeffVVHD9+HIcOHcKCBQtqrA/O+D6Sy+UIDw93+H6rs27dOvTp0weffvopMjIysHr1avTp08fkVKWEhIQq95Wfn++kXpI7YUBIVnnjjTfwzTffYO/evTh+/DgmTpwIrVaLEjvnUVT3geXJpKvAbhYQAoDaTa5YegkCVDIZvABpCZVzmZk4fe+92LNyJWQajVMDQv2F6W35O0uZIwvbO+p48zY6wRLXIRSzVlq4LkPoimyPs0kBoYkho15icS/jIaN68wbdKUPYpUuXSj9RUVE4fvy4QTu5RgMxzLV32QmZTIZWrVqhZcuW2LhxIwoLC5GcnGxXIGHNsSYev9oqvk/VajV8AChlMqdnCB15DAUEBCAwMBBNmjRBv379oFAo4Ofn5/SiMtnZ2XjiiSdwzz33oG/fvoiOjsaTTz5ZaT3L2qa695y4ruH777+Pt956C8uWLUN0dDReffVVTJ06VfqZMmVKtUWVwsPD0b9/f3z00Ue4efOmI59GlTIyMhAWFobU1FTs3r0bHTt2RO/evXH06NEa6wP9hXMIySpeXl5o0KABAGDx4sVYs2YNhg0bhuLiYot+39L1bugv4smfzMqTErVa7dAvW0v356XVQgnAD+6TkZADUMtk8BKEiqIN3t64e/IkngBw+r33UKLRQOXMgNDOIaNeZjKEzj7e5EYBvxhwaHX/KlEREGZlZWHWrFnw8fHBlClTpPVYx40bh1WrVjmkL8ZSUlKwevVqeHt7Y8qUKTh27BhSU1NrLNvjDOLfVz/wF4MgLzNDRrX6Q0bd5HgELF8vTq7RQCmTAYJgMiC0ZI04UWhoqMH6gbNnz8Znn32GdevWVWqrVamkv4MgCJg2bVqlNtYea+LFE20VxX9U4rEllyPYyRdbLD2G9JfOGDRoEKZNmwaFQoH58+dLS2d06NBBmispZrHKy8vRqFEjh/bZ+DNv/fr1GDJkCM6dO4eDBw8CAK5cuYKEhAR89913DnvcoqKiSmsAWvK66MvOzsa0adOQn58Pb29vqFQqNG7cGPPmzUP79u0N2ppa13DNmjVo0aJFpRFb1RVV6tq1K9asWYNNmzbhmWeegY+PD5555hk8/fTTJodg28LUd9Fnn30GHx8f/OMf/0BMTAwyMzMRGBiIESNGmKywS87FgJCs0qFDB4MvzKSkJLRo0QIvvfSSRb/P9W6sZ82QUWu+UCxhy/7kggCVXA4/rVbKGNV2ckFAuZcXgrRaKUMovu6y0lLINRqo5HJAo3GrgNDZx5uX0Qm4NGRUF3iUoSKr9bILgjMx2wMAGzduxOzZs+3O9tjL3sDY5JBRMUMoVn02en76QaDWTTL2gOXrxcm1WpTrAsJ1W7finsuXpfssXSNOtGnTpkrbRo8ejdGjR1farszPh1hnW61W23ys7dmzBzNnzoSXlxd6l5fjCQCakhIMGTIEO3bsqNRezPiq5PJKGXqDdg64IGjpMaS/dIa5E3tTSy74+vqafM3tYfx32LlzJ4YNG4aTJ09K24KDg6XPeWutXbsWCxYsgI+PD+Li4vDqq68CAIYNG4b9+/cbtLXkddE3fvx4rF+/Hs2bN5e2mQteTa1r+MADD2DVqlVo164dbt26hcDAQNSrV6/aokoymQzNmjXDpEmTMGnSJPz+++/YtGkTnn32WWRkZNj0OhkzdXzs3r0b3t7eaN++PSIjI9GsWTMAMFthl5yLASFZxdRSG3369MH58+ct+n2ud2M9HysyhNZ8oVjClv15abVSNk3jJiegXgDK5PKKuTm6EwVVQUHFv4JQERB6eQEqlfOXnbBlyKjuPeJtFBA6+3gzFxCKJ61KmQwyQXB6cGZq4XFrsj01xd6spZcgQAnTAaG8Xr2KDcZ/k1pcVKaqBeMtXS/OS6NBuVwOaLX4bOdOPHj1qtVrxNlCPyB8Z+JEPFCvnk3HmjgNw9fXF0kNGmAigL+XlpqdhiFdbPH2rnT8OfqCoKXHkL+/P1599VWkp6c79MTelqDW+DNv+fLlmDlzJoqKijBixAgUFBRArVbjnXfesalPK1aswKlTp+Dj44OlS5fimWeewdq1a00ua6a/pIglr4tGo4FCoTDYZi54rbSuYXl5xcWgwEAAwOrQUJSHh2Pm779XW1TJeB3Ne++9F6+88gpeeeUVaZu12U5jpr6LWrdujW7dugGAFEyr1WqzFXbJuRgQUo3iejfWkw5SCz4krflCsYQt+5Ppsm3QaNxnyKguq+kD4K7uBFutCwgFubxiDqHuS9zeeUqmiAFhOezMEBptd/bx5m30WogZKDEgLJfLKyq4Oig4s2YIrDXZnppib2As1w3HDtTbJmUIdUGPzOhvIugFga7KEDpi6PLVWbMQ9NFHaHDrlsF2uVZbcbFGrcb2d9/FKUGokTXiynWfDwAw8+OPMRMAbKhwqj8NI9XHB+dVKkzNyIBS7yKcPnFdyXJv70oXgBx9QdDUMfTDDz8gJSXF4G+am5uLy7rMrL0n9vYGtcafeT179sTBgwdRWlqK/Px8hISEwF+cb2sDuVwOH936qykpKYiMjMTgwYNx586dSm31lxSx5HV5++23ERsbC19fXwQHB1sVvJZ16wb/rCxA956YCgC6omgic8ehyoLvNGuzncZMfRfpFyYUeXt7m62wS87FgJColvPV/SuzIKiz5wvFUfvzEgQpeHKnDKHKqM+awsKKO2WyiiyE7kq1M06q9QNCWzKE4jzTmh5o4228xIFRhrBcJoNcq3VYcObuQ87tDYy9BAFKmQzBggBBq4VMLpcKEokBYVUZQq2LMoSO+LsF//vfUAAVJ7x66196abUVw7lRMX8yfvRoh64RZ24Ookr8fLCT/jQMX40GSQCKmjfHh0VFJtuLAaHKxwc+gMHr4egLgqaY+lsOGTIEaWlpBttMndgrlUr8+OOPuHPnDho2bIh27drB19fXoI2jg1pRQEAAAsRh1XYYMmSIwTE8YMAAhIeHV3r+gPUBjz3Bq39WlvR/QRAgM9HGnuPQ2mynPZxdaIhM46tOVrPkQ50cQ9BqpYNUbsGXuqOvhtqyP3cMCL2hFxDqTqDFgFCALguhuyrslIBQLBQhk9kWEGq1UKMiINRqtVL5emfzMZ6vJhaV0b2GSi+vSlkMe7j7kHN7A2MxIIQgQKNUwjsgADLd+9EnKKjigoLR+1NbCzKEjvi7lQJQAFDdugUfvf14abUoM8rei2vEJSQkVFq3sCrLly83uF3VHERHBYT60zB8dcd+p3r1cD4z02R7KSAUT5o1GkD3f0dfEDTFmr+l/on9unXrsGLFCkRFRSE4OBj5+fk4deoUXnrpJYP3f00EtfZ47bXXDG6fP38ebdu2xa5duyzeR3UBj13Bq0aDkpISFAC4AKClXvBqz3FobbaT3A8DQrKKpR/q5BiasjLpIJVZ8cHrqKuhtuzPSxCg1J2guUVRGUGAH4Byo4BPK1bOVangpdGgVPf8BScFhCpUVDq1pWiNGCh4CwLUanWNXaDx0WpxCsAWAG/ir9dODKrVXl7wceAJg6cPOfcSBBR7eQFaLcqLi+EdEACUlaEcQGD9+iiHYVVRoHYMGXXE3018VgUXL6KxUUCo0r3fjYdzW7tG3IcffogWLVpYNAdRbaKytnjCDOiyNDJTeRrz/MRjpYqh9uLfV+3np+uIWgoIHX1B0BRb/5biMgn6F6vUajV69+5tcO7gzKD2+vXruHDhAlq2bCkFSfYaP358pWIyrjICwIY7d7Dsgw+wC0B3AGdefhndu3fHjBkzqvzbVXeh39HDO00dH7YcM+Q4XIeQrGJq7ZuMjAx89NFHdu03JibGQT2sW1R6hQWsCQhdyUsQoHHi8EpHE4MXle4ES7wt6E74vMrLIddqodF9OQpOmBcpKJUoB6ABbB4yWi6TwRuWLb7sqOPNV6PBlUceQYLupEA8IReHjKq9vFy2ePaePXvQtWtXdO/e3eBkxrgkuzvxFufnAlDdvQug4v1YhoosSjkqDwutDQGhI4iniUVGGT8vrRZq3eeNvQvT5+TkYPTo0diang7f27eRmJiIZs2amcygqI2GdC4CcLewED/88AOio6PRvXt3dOrUyary+X5iAbEq/k5ihlAjBnomnnNAQACaNm1aZTA4vF8/fLR0aZX9ceQxJC6TIBIEARkZGZX6KAa127dvx4cffogdO3YgIyMDjz/+uNWPCQAjRowAALRp0wbx8fHYvXs3Xn75ZcydO9em/RkT5xM6S5beUNDqFAIovnkTa9etw34A8wB8vXVrtdlLaxa5N2bp8M5FixYBgMHx0axZMxw4cMCuY4YchxlCsoqptW9MfaibExcXV2mbIAi1fpFYVym/e1eqZGfJkFHRnTt3pCt9DRs2dE7nzPASBGh1XxLuMGS0vLgYAfjrirt00qwLxr3U6oqTTvGKvBOKykCphEomg9bGIaPegoBSb28EabUo0TtBdPbx5icIqN+4MVpFRlbs22gdQpW3N+RKpUFp/ZSUFIwaNQoAzJbWdwT96o0zZ87E4cOH8f7775ut3ugOfAUBpT4+FVlB3QULbWkpygAE+fujEJUvWAjl5dCi4uqvOweE4kwlZV6e4XZBgFrMEJq5WJObm4uwsLBqH0MmkyE+Ph6jEhKQvncvhu7fb3YOonFAuAPA32/exJQpU7BmzRpERETg5s2bGD58OA4fPlztYwOAv15AaK7P4nMUxO9cGz6PNBoNNu/fj6379wNVZPYsPYa+/PJLzJ8/H23btsWAAQMwb948BAcHY9asWRg6dCgA08skdO7c2WzQYesoF+PPvMOHD+PZZ5/FL7/8gpycHClD+fjjj2PGjBlW79/YN998Y/a+AwcOoFevXpWyXjExMdi9e3el9tYMWTalFYCv//tfxF66hO0A+gL4fufOSusjGrM0e2uPHTt2IC0tzeD4eOyxxzBjxgzUr1/f5mOGHIcBIVnF2g91Y5mZmdi/f7/BB48gCEhMTHRWl92amGkR13Orzr59+/D666+jcePG0pDevLw8zJkzB/369XNYv8x9oQEVFTu1uhM0dwgIVbqAUCMGhLpgRq476fFWqeCl1ULw8YEWzhsyWm5HQOgFoNzLC94qlcEwXWcfb75aLWQBAfDSlToXgxFpyKiPD7wEwanBmbmiH/rVGxcvXow1a9Zg2LBhKDYx1K+mWHLiXBU/QYDSzw8oKoJa99oJJSUol8kgk8mgkskMMoJAxXurBEB9OOe9awtbhoaJJyvl+fmG2/Uu1phb0H3MmDFmP69MkQNIBJCwbZvZOYhaXYZWdBfAmePHUVRUhIiICAAVRYQsnc+rVquli3+y8nKzfZYCQjPrTloi98oV3AtgWDXtqjuGxCGY8+bNw5EjR1BUVIQOHTrg/Pnz8Pf3R//+/aX3daVlEpzE+DPvtddeQ9++fXH06FFs374dffv2RVZWVrVBkjV+T01FSEICAjt1krad3rYNy4cNw8yICIQ//LC0vaoLctYMWTZlKYC3Tp1CFoBtAEIAdPrqK3zyySdV/p69F/otcffuXZw9e9bg+FCpVFCr1RAEwaZjhhyLASFZxd4P9SlTpiAoKAiNGzc22D5hwgQ7e1Y3SQGhXG5RQPj6669j165dBl92hYWFGDx4sE0BoS0ZJm8AWicWYHE0ceidVneCJfZZpnvtvTUaeAsCBG9vk0U7HKK8vCJDCNicIVTqsrJqvZNiZx9v/gBkAQGAmD01Wpi+3NcXPoLgsODMmivo+tUbASApKQktWrTASy+9ZN2TdKBFixbh8OHDVZ44m6Wb66rSvU+lIaOlpRULswMVAaHx+7O8HGUyGeoLgnOy2xZYtGgR0tLS8MMPP+CFF17QdascixYtQu/evS3ah5ghVOkt9wBUvPfFYzfxww9Rb+tWg/sFQcDFixct76zeEOeq5iAaB4RtAHy8ejVat26NvLw8hISEoKioqNoRGl26dKnYn1YLGSqGxhb/9huu37xpsr20lqTuIoz+39TSTHyJbt3LMlQcw+aYOoZWrVqFoqIirFy5EuvXr0f37t1x9epVLFmyBDNmzEC/fv2kojBVDScUBAE/NWiA0ogIdDx+vIpeWMf4M2/16tVYtmwZGjZsiGnTpiEkJAQ9evTAmjVrHPJ4gkaDe5csgXrJEoP3jvbiRWQCmO/jg+gFC/5qX8UFuZycHHz++ec2L5viC+Af998P/ZmCv44ahbHNm2PgpElo0a2byQtSEyZMwNatW6u80G/vKI82bdpgwYIFBsfHgw8+iIMHD6J///5WHTPkHAwIyS6CVotzAwag5T//icCRI6ttb27Yw0gLftcTiXMISy0sziGXy3Hr1i2DgPDWrVs2X3GzJcPkJwhQ664sukOGUBp6Z1Q0xksX1PjohowK3t5QwTlZFpkuICyXyyutI2cJfwClYlZWL+vmzONNUKngDUBWr54UEApGVUbV/v7w1WptCs7Onz+Pjz76CEuWLJGulltzBV2/eqOoT58+OH/+vM3P2V4BAQEIDAxEYGCgxSfOIqGsDDIAal0gIGUIlcq/5hWaCghVKqhkMpQJghSw1zRTw8WsHRomvkJqo+qe3oIAQfea3FUqcebIkUqv54ABAyzuq7KgAH4WtDMOCFcDOB4fjy7PPy9tCwoKwlajANVYUVERsrOzUXjnDho2aQIAOHnPPXhVNwy70uPqLvjIdZ/xar3CY5Zm4sv++ANA9cvUmDqG/Pz8cP78efTp00f6bigpKcH//vc/zJgxQwoiysvL0ahRI7P7LispQevCQsComqq9WXTjzzxfX1+kpaWZXBbCEUpu3EA9VD6ZLi8sxBQAQWVlaNGihcF95i7ISUOWrVw2RVtWJhUEKTO6+HHrzBnsFQQcWboU/zp82OwFqeqW9LB3lMfq1asrbTM1ssySY4acgwEh2eXa8eNos38/rmdmWhQQknXEAgKl3t7wsSBztHLlSqSlpSE3N1caltW0aVOsXLnSpse3OsMkCAgApBM0jYsyEtZQi5kWXZ/FDKG37uTZR6uFr27IqFomc06WRaWCWi6HysuryoISpggqFXwAlItBuJlhc46mKiyEL1AxXNTLC2oAMt1rJgYl2sBA+AqCTcHZqqFD8d7Fi7gzYwYa3nMPAPuvoLtahw4dpEqUlp44i1TFxfAFINSrB+CvgFBWVga17oKNSi6X/gYS3fxUpVxu9XvLUUwNF7NmaJggCBBLd2iM5u75CQI0uuDohagoFBUVISQkxKDNzJkzLe5r0e+//xUQ6tb4MzWfzzggBADl7dsWP45o3rx5KCoqglJvKKyXSmW2zyrdBSwf3XNUFRdLJ3KWZuKVt24BsK2qYKtWrfD1118jKipKGoI5YsQIXLhwwaCdr6+vyWVWRAXXrsHUDEG7suguUHD5MuqZ2F6em4vxAA75Vb68UN0FOWuXTbmbmwvxErBw9SoAYNsbb2DonDko0Y3maavVWndB6vhxICwMuO8+ALVzCD45Fgfqkl3yzp4FACjd4MTfHYknfUofH/hakCGMjIzE5s2bcfToURw7dgxHjx7FV199hUgzV5urM378+ErBIGD+C02lO1mT6U7QasucpaqIQ++gO9EW5xD66Prup9UiQKuFOiAAKsApAaGsvBwqubyiKqeVWZxy3RC6cjEIr6GiKUrd48p1j6vEX9URBV0f1EFB8LWxyuiI69cBALf0MgjiFfT//ve/UKvVDll4vCa99957lRZyru7EWSS+3oKYGdK9xj4lJbirG6Jd4uUFH6O/v6ysDEovLyhlMqvfW45iPFwMgFVDw8rLyiDmgDX6J6CCUHEyrjtRfbxp00rBIAD06tXL4r6W6J2Al+uykaYuOGhNHGflRgVvqnToEHDnDoYPH46QkBDpOM5ERSErc30WA2JvXYCqvx6imIkXJSUlITU1FUVGQXS57pix5chcunQpfvrpJ2RlZWHatGmIiYnBtm3brB6CWawLXEQvvPACjh49KgUtTZo0kYIWPz+/WrtY+d1r10xu1+iG/Ap2fF9YumxK8Y0b0v99//gDSgCh7doBALS//IJcAGr8dUEKQPUXpLp2RfkDD0g3LX1vkfuqnUcYuY27v/wCoOLKtKW4sL3llHfuAABKAgPR2A2uxJXduQMfADLdlUd3GDIqZghlumGHYhDrrfsi99VqESAIkNWrB7VMZtOQzurIVSpo5PKKqpxWvmZleXnwA6DW9d9cpUV9pjIewt27uP3OO2g8ezZgwfGsLChAEABv3eOWy2R/DUkU52UqFBVzlHSZFmuU6k4Ai86cAZ54wuA+WxceByyvOFkr/PknkJ0N9OkDZWEhgqB3bOkywX7FxfhTlx0u9vFBY6MMsbykBEpvb5QLgtXvLUcxNVzMmqFhJbduSVk7QS8zJ5SWQgbAu0GDivm9Jt77Pxw8iNAWLRBmNGzPlC5duqDs9m1pXp36scfg5e+PixcvQqNWY29CArrOnIkG7dtDe/cu/gSgf7lMbVTwBjDzfrtzB3j8caiffRbeX34JoOI4BoBXAKyp4jNGDAjluuGlar05lZZm4jW67xVb+Pr64p///KfdQzBLdBd8gIpzghErV2LFF1+gQ1KSzVn0qhQVFRlMpXDU50CJmYBQ0GWLvWvgAl2pbggwAAQVFOAOgGa6i8D+v/+OMQDWyWQm6z+YuiClLS2FHICvXsEiZw3BN/67AG72GV2HMENIdin77TcAgMbCkz171rvxRGJAqAwKktaoqs3Ekxqv4GAA9l0drSniAtNy3bBE8QTLT9f3+oIAX1TM2XHWkFGv8nKovLyg8faGl5X7F4eaaXWBmSVDRk1lPE4kJ6PxW2/h2qefWva44t9a97gqvWBZU1iIEgB+uswNVCoolUqcOnUKe/fuxalTp1BeTXDipXu/l+ud7BizduFxwPRzr620jz4K9O0LCALKxUBA95qKyx743b0Lte54Kw8IgI/RUEa5UgmVtzeUcjm83OACjSn6J7z6AaH0eRMUBCUqL7lRVFiIh3v1woXu3S16nKKiIvzfP/+J4wCOA9jy3nvIzMxEVFQUft62DQO//BI/PvlkRT9KSlBkdOFEbVTwBjD9frumqx567ejRv56LLmun9fJCUBV/J6GoCBoAMl2AZFxkxxIa3esmB6Cu4vNm7dq1aN++PR599FG8/fbb0vZhw6qrT1qhqos1ZXpZravnz2MggDUFBXZl0a3ps6M+B5R6z/HmqVM488ILgCBApvtc9jFxkcLarFp17Ut12cguAJ4sK8NAAMPHjkUXAC/8/jusLdlzxwnzrK15L7nTZ3Rdwgwh2UWju8rna+GJRk2sd1OXqHRfKurgYJuH3gGOv+Jmbn/iCZpMF1zBxDyb2kap+zKtp5vbVK4Lwv00GuQDaKBr5xUcjDK5HF5OGHbnXVaGMj8/aH18EGBjQCjoggL90vtiBUN95qouXv75Z0QBuHXqFJr94x/VPm6Z7nXz0Q0pLtebo6YpLMRdAH66Pq1ZtQor161DVFSUtBzKqVOn8NJLL5k97v10Q5vUNg4Jtea51yRrRkjIxWF1f/4Jle596X///QD+OglUlJZCrRt6qapfH/5G1Sm9lUqU+/pCpVZD7gYXaExR6r8H9DIupbdvIwCAd3BwxbItRsfm9ePHEQEgWi8bVZV58+bh9r590u27upP9mTNnovDgQQBAfd0QTaG0tKKYj24ofxcA+f/7H+bove/Mvd9u5uSgGYBiveGe4nOc07QpGlTRX+HuXZTIZPDRXRhQmchKVkfQ/Y4cQOHNm2hw770m261YsQKnTp2Cj48Pli5dimeeeQZr166FYOa7qLSkBCt690bsRx/hoUcfrXK5j3K99+nV/fvRCo7JUBj3uWHDhvjb3/6GCxcuSJ8JjvwcUOldrDg1ciQG/fwzrvbpA+/CQqwF8NadOwh69FHExcXh1VdfBVARBO3fv7/SvtauXYsFCxbAx8fHovYi8b1TBCAbQJafHzpnZqJMLkeRICABQJBWa9FyL3dOnMDNpCQp811eVgZfByw/Yeq9dPnyZYO/C1A7PqM9FQNCsotMPJm2cC2kmljvpi4RhyAJISEWVb4zx9p1uGzdnxicyHXBopcbLAJeriuw0Ei3VpR04q3R4JavLxqIQ0gbNMBdb294OyHI9S0rQ2m9epDJZFJ1U0tJc4h0c6f05zaJFQyN5988Eh6Oox9/jGjdEgAAUF8XSJbosv7VEbM29XUZunIvL+lkXSguRqlMBrmucuvK//wHh77/3qoLQQ3EJSyMCnVYGlCZe+7WVJx0tHXr1mHFihVWBcYAUHj+PEp0GZWQDh0AAMo//sDd27fRRK2Gr+5ixt3/b+++4+Mo7sf/v3b3unQ6SZYsyZYty7jb2GBwbJqBBByIf2AgPcSQRgkkQEiF8KGEb2ihJhBCQgkECIEk4FAMGHDDxrjj3mXLRbL63enq3u78/riCbFzBWLb0fj4e95Bur83d7MzOe2Z2trSU8s2b00FK5rd2JJPEXC7MePygR58Ppc9yqkAysw9YpM+JzIpltrsKC4lp2ifqm/BBjnRceOGFvPfKK7n7q5ctY+jFF3P66acz+8kn0xszwZAei5EwjNyMgSBw98iRXJgJHLP2tL9lZ9Z0nJKXrXdiZWV4t21Ll6XspSU6ikSIaxp5mU63+AFM//zEtLwOgWi0vn6vAaGu6zgz56dec801DB06lHPPPZempiYWL16cy8srrrgCTdOItbbi3bCBx8aOpeC44/bZsE9l6l2AUGal2f1dBuNA7J7me++9F5/PR1VVFfM7XN7iUNUDHeunQKaM7lywAHd7O48CUxwOhi1efEAB9cEG4FnZ8vF/pIPCmNeLpmlENI2eSnET4AMizc3k7WFNgI77x0ff/S5nrl2be6xh7VoqR43a62fnOocbG8Hjgb1c33FP+9KkSZPoU1zMK7170+s//8nVWZ1ZR3dnEhCKz8SVGREKKEUymdzvAf6zXti+u7Gy04GKiw/oXKxDPSpysO8Xz/SoOzKXFzgaAkKzqYkUUJYJCFNtbSilCNg2tYWFkOn0cJSUEHe7PzEl71BwJxJYJSVoto3zIC80nV0gIrsaXMcRtewKhh0X2gi2tfHHujpOuvJK6BAQ5mUaFfZern+2u+wIYTYgjLrdGNmpTZEIMcPINQ7chnHQHUE9sr9DhxGQgwmo9vTd4eBWnDzUDmqGRIdZF/WLFhHLNKADAwYQBbatXMmfTjiB/wJDL7gAALN/fzwffghbt0LmnDl3MkmooIBENIqzkxaV+bSBcJaZ+e47NQ29wwh4ItMp4SguJuxw5KbpZcVqagByK5QeCLNDA/+OF17gorvvBsDOjNbmZS9HE4kQcbmoi8fJA240DKK7XRID9ry/pTIjgD065EcqcyxN9e0Lixalzx/NlOmOVChE3OnE37Mn8PGMBjjwESatw/nosX2U94kTJ+5yuZizzz6bhQsXcs899/D000/n8nLlypU8+uijDNywgZP/3/9jSV4exy9YsM+Gvd3hd9ZWrkx/970+e8/nmh1Imh966CHKysr43e9+t8vzDlU9oHX4/cuyq4Jv2EBh5lIQgcxMh44Bdctegvjdg6Zhw4bt8/lZqaYmbOAkp5Mi0ySZmcYf1XWwLMZlnvf4I4/w+L//vc/9Y/fjW9OaNfsMCLOdw2ZVFTidOPcyhXlP+9Kdd97Jf6+7jl7bt5OsrcWVeawz6+juTAJC8ZnkZRqAXmDHtm306t9/n8//rBe2726sxkaCAMXFGKQP/q59nFx/qEdFDvb9Ypme78CIEZgcHQGh3dJCGCjKzycCWG1thOrqCACxvn1zAaF30CBCXi/5n8N38pgmdl4eVipF3kGe5xWrrQWgx7j0Yb/jOXcXXnjhJ56/9YMP2NP6hYFMY1Y/wKXzsyOrgcpKAOJeL65MQ9PT2kqry5WbOvzHq67iif1c+HiX945Gye7leocGxsEEVLt/9zVr1jBkyJCDWnHyUDuYGRKRdetyy9m3rliBnRklKK6upt3tZsvixQx1uUg5HPTKLLrjGz0a/vlPoosW4csEhIFYjO2FhUTjcQKdNIX7s54qkNi+HYAGr3eXEfRoJuDzDxxIm8eDsVtAluow2m22teWmWe5TczOtuk6RbaN3uNSPnhn9yQZxrvZ2Qj4fF/Xrx8Zly1jicrGoQ4C3r/0tO7Omh2WlF2Jyuz++ZEVmSrBqbETbQ0DoaG0l5PVSUVREM2B3KO8HOsLkDIUIA34+7tjZkxtvvHGX+2vWrOH111+nubk5l5cNDQ2cfvrp3HvvvdyRqQuszDTafTbsOwQ5RZmFWTzA3596ivvuv/+gp03uLc3ZeuDNN9/cZfuhqgf0YJAU6cZ0/+xK4LW1+BMJJgLNlkV2/PXss8+mV69ee12QZ/eg6ayzzqKiomK/C/hYDQ20aRpBrxdMEyuznyccDrAsFnk8nByP8/fnn2fxihX73D/8uy1eF9ywAdh357CybZyxGMRie+203tO+dO2113LtddcBUDdnDrF4vNPr6O5MFpURn5pSisJIhE2Z84SCmUtQHBTbZtOxx1L7y18e4tR1ETt20ORy4e7TB9j/yd7ZUZHdfdoet4N9v+S2bZhA8THHENK0z2U07VAzamupy1wrqt7hQNuyhcbMvqydcELueZVjxhD3+/Ef6oBQKUpNE7OkBKukhKKDDQi3biUFHHP66aT4OFDbk3X//jcjOq7Y2aEhUJrp3XYd4CIVZm0tLZqGJ7ugid+PJ/PbeFpaCBcUEBgwIH2/tZUHH3yQOXPmMG/ePGbPns0DDzyw1/Nagxs25A5Ozg4NlGxA9XHyD3zK+e4XrO4M//jHP5gyZQqnnHIK48aNY/z48bz22mt7DIwblyzJ/R/bsIHkunXU6zqevDy8xx3HueXl3NynD45Jk3JTC0eefz47gO3ZVT2Votg00SoqSAUC+Dqpg+az5BtAsq6OBBAuKcHboT6KbNyIDZQNH07c58Oze13VYeGS1qVLD+iznC0tNHq9JIG7Tzopt92TCWCKlMIMh/FEo6T8fmbMn8/GUIi4y7XLJT/2tb85OwRD0UyD29q+PV1nZs8R3bRpj6/1BYNECwooKChgJ6A6BHS7jzBdeeWVuSmeHXlDIbZlpnMn9lFf7O6qq67aJS/XLVvG38vKWD5tGvn5+WiZAL05U+/vq2HvaG5me6be7Zcp4w7gkT/+kcWLF7N48WJ8Ph9f+9rXiEaj+502uTevPvQQb/zqV7n7iXicZ8aOZf4zz3yq99udo72dzbvtx86GBgpTKS52OBgOqEzHwpo1axg+fPgngtOsG2+8MRcMZu3r+TlNTbQ5HMQyl05KVlQAYGVmbC3LdNQr09zv/tEzHGaZy8X7kycDEMl0qoTDYebOncv8+fNzt+yCS/WLFuVeHzrAUw6uuuoq7A6zYZoXLjwi6ujuTALCLuK9X/2Kt6+7jg9/9zsW3HUXix94gBl33MGse+7hrd//nukPPMCiv/+dzVOnsn3GDDa9+y6hFSsIrVlDcts2Ytu3E62rI7hjR/o6ZqnULo3FjizLIh6Ps+i11+hv2zRlDprRjz466HSvvO8++q9YQd9770UdwHX2uhtvXR1tBQXkZyr0UIe5/XuSvabV7j5tj9vBvp++Zg1bHA4cTifbXC48mdGrI5Vt2/SoqaExM5LVWFJC0erVrM0shz/qssv4D3CPy0VJWRntffrQOxqFQ3jtpejGjeQphXvIEByVlRQohdrD1LO9focNG6jTdfyBAE2Ato8FKep363hpy3QwWLEY5Znyl3eAQbyqqaHB48ktUqBXV9OrvZ26LVuobG0lUllJydChJID4bp1FW9eu5d3rr99rmQ9mppBtMQxcHRrZBxNQ7S7bEOpM2RkSBxIYN2YaWRudTho/+ghz/nwaMuf/5F10ESfU1+PevBl+/evcawYMGsSM0lJ6vPEGN15xBf++7z58gGvIEKwePShOJPZar3+e9pZvEydOPKDOKlVXR5OmkSopoaDDlFG1dCmbDAN/URHh8nIq2tp2+X7u+nqyNVB4xYoDSqsnGCTs89FqGNhr1zKnXz82PP44/nCYzZnVLxuXLaM0FsMsLcXtduP3+wnl5RHo0Hmxr/2toK2NxZlgqDWTLr2ujmaPhx4jRxICwh1WIM2aN28epcEgqk8fHA4HzW73xwsP8fEIU9ZZZ53Fn//8Z3rvdo5gfjRKc3YxqA5B8/44nc5d8vL8CRN4FVjz3HM8++yzeDPvdYdtk9jP9GRvMEhdpjOpD5DNVTuROKCg9kCdet11fOUPfyDe1kbDzp0sfeIJLpk/n0hmZArgXw89xIPnnov9KdogeeEw9R3Oy2vWdXo2NlIIbC8vxwBa160DDr5TKtbQwIbycjb88Y/7fJ6juZmwxwOZANAzfDgAM086iWeAVGYlz5P79t3n/pEMhaiwLFq+9S1OfeopbCCxbRskEtx4/PGEdrvERjIe56eXX872d97Jbds8Y8YBfTen00ldh3M6YytXHhF1dLemRKd75JFHVL9+/ZTb7VajR49Ws2bNOuDXBoNBBahg+jB4yG8mqBiodlBBTVMtuq52gqoHZYEK6bqKbNigtjgcql3T1LqiIvWh368+LClRc8vL1azKSjXnmGPU+wMGqJn9+6vZgwap2UOGqDnDhqmZQ4aozQ5H7rPeHDBATf/yl9XUCRPUtAsvVO984xvqvYsvVrN++EM176c/VbOuukrNvOYa9cbll6tFt96q3rruOjXjppvUzFtuUXPvuksteOgh9f5996l5jzyilvz972rViy+qD59+Wq145RW1efp0VTtnjmpctky1rl2rwlu2qHhDg0oFg0rF40qZplKWpWzLUkopZdt27nY4JZNJtX79evXqlCnq8YsvVu2g3v3Sl1TT9u2qHdSSU05RKpU64PdrqK9XyxcuPGTpq1+2TK164oldtsViMfXBBx+ov1x9tWrWNPXWwIFKKaVeGzBAtRqGMrduPWSff6isX79e3Xzjjeq2Hj2UArXolluUUkotuO02pUDVgWp0u5WybbVu3Tq1du1apZRS/3vkEZUEteP731fqM+4bpmmqJUuWqFe+8AVlgVo3c6aa88QTSoHaeNtt+319KpVSi+bOVevcbjW7Vy+llFKz8/PV+tLSPabNtiy12TCUAvXW8ccrBWrFY48ppZTa/NprSoFaUlCgduj6Pj+3ra1N/fPpp9V6XVfzhg/Pbd/xwgvp98zUDTMeeUSlUik11+lUG3v1UipTtpRSama/fkqB+jDzu+9uwc9/rhSoGX37qg0u135/iwNldUjDkcCORlVs48Y9Pjb1C19QTbquNp5wgoqCSoGqu/XW9IPJpFJ/+5tS8+Z94nXL3npLtei6WgdqXqbubly3Ts35zW+UArVlypTP8ysdsGg0qn4H6s+ggsHgPp+7sKpKzfP71XsXXKCioF5+4QV17/XXq0ZNU+9l9sGp116rFKjaZ57JvW5Vfr6a2rOnMkGt+MlPdnnPWHOzijQ2fuKz1jqd6v2RI9WyQCB3bFoWCKigpqmZffooBWrWFVcoBerdq6/OvW7GsGFqvcez3+9tW5Zq0TT12vDhSoH66IYblG3b6n2vV31UVaXq6urULFBbTj7549fYtnryySfVsZqmFKiGP/9ZKaXUv/r2Vc1ud/oYdoDsVEqFQb199tkqCGrW+ecf8Gt399awYUqBWu90KqWUWuvxqGZdVxNA1eznuLPJ7VbvjRih6jNtgA9KS5UCdfWECaqmpmaX565YsUJ9+ctfPuj0NaxZk8vDt845RzWBWpifrxSo1YaRe97qzO/64QsvHPRn7DQM9fYpp+Q+Z35VVe7/dyZNUgrUuuefV0opNWHChIN67w+uuUYpUBv8/n0+b5nPp2YPHKhe/v3v1Qug1q9YoZRSatOmTeqGG25QrY2NKgFq1je/uc/32fS//ykFauH99yullGpxONR/jj9eLfzBD9LHyfPO2+X571RVqRZQUzt8/2lXXnnA32/h3XcrBapR09ScTLthd7k27n7qCPHZaUodBRc368L+9a9/MXnyZP785z9zyimn8Nhjj/H444+zatUq+u7h/IHdhUIhAoEANQsWUFFSQltTE6lEAiuRQJkmyjRp2rkTMx7HpWm0NjXhdTox43HMaJSmnTvxOBy4DYNUIoFT12mqqyOQl4eZSGBGo/hcLlLxOAbg1HUKvF7yfT7MwkKO+clPKD7uONa8+Sbrb7oJbds2XKZJgcuFw7YhkUC3bZRlYSYSeNxutEzVoZG+cLXrgQdouOkmBm7ejMu28QBuwMjcDjcLsDvcLEBpGrZS2Jr28f3s38z/6Do2kFIKG9Cdzk+8j515TOk6KdvGtm1028awLAzbxiB9PmZv0r/BusJCei5ZQmG/fvzjuOOY/NFHtAG1bjem1wv5+USVwnA6cbjdqEyaTMtCKUX5pk30tW3mV1ZCaSm604mm65iWRSyZxOt2YxgGiXgcXdNwu1zoShGLxXA7nTgMg0QigUPXMeNxhq9ZQ4lSzCoupt3pJBkO44/F6KsUA4Ha/HyYMYO+J5zAwilT6HPBBfg1jXWBAC0FBfh69sQoLMQ2DHA40LJ/HQ7Q9fSUR8tCNwwchgFKoes6uq6jKUUkGiXg96NrGpqmYZomzkyvvaHrpEwTpRQp06Q9HEYphbJtwsEgydZWWjZvpsC2qTBNRgA9gJrzz6f6lVfS5z0oRc1vf4uaNo3y22/Hd845u+wbqVSKv1ZWctXOndS63WypqMAqK0OVlOAIBMgLBLAz38Pp8eAyDOKRCGYigUqlCLW2EmxpIdHaSnTtWkaaJqcAH44fz9iZM7Esi7cLCvhiNMqiykrCVVW4ysrSK3a6XMRCIRLt7TRv2YJRW8s5tk05sOHJJxnx/e/z3OTJXPzss6woL6f5+ONp9nio6N8fzTAIz5/P2TNmsOjOOxl62WVoJSUsHTAAc+JE9Ndf5wsbNjDvkksY/8wzzJ48GWPIEKLRKG6Hg+Zt22jetIm2LVtwbt3KBNOkv6YRffNNCidMyP0+kVtuofUf/2DbpEmMe+ABAP76jW/wo5deYk1hIRuHDMFZVsaZU6bgBpYGArgffRR3jx5YySThpiaiDQ24//AHSlpa2PyNb3DaCy9Q+7e/UXrKKTjy8nDm52MYBslolPffeIPa997ji9dcQ9Xxx6cTkTmk2baNBmiaRiqV4orLLuP9d97h5WnTGLaPRRJy9nBoVLst225ZVnrf3O15tm2jZfbRZDKJ0+kkFAoRj0ZxWRbB+nrCW7ei/ehHDAoGmfWzn9FcUkKkpobEmjXE1q3jGw0NtAwZwsiHHsL6+teJf/nL5D33HBxIL/rq1UR++UuiNTVYv/415ZdcQrS1lW0lJRQAq8aMQQ0diqdfP0oGDEAVFmIUFxMoLSXf7ycSiVBUXIxuGNhKoRsGmq6j6Xq6nGgaCtL3IbcN207flPr4/z1sS8bj/Pnyy7nujTcAmHXllZx4003YySTxUIhYMMim1asJbdyIuXAh506fznsnnsixt91G34kTWUl6VMnyetEWLaJw6FBikQhri4upTqVYOGwYlJZyxvTpTLvwQoa88gpN/frR95ln8JeV0bplC/GJEykwTZbffTclo0YRra+nYepUvvLCCyy5+mrMRYs4ad68XX7WubfdxvBbbyWgFG2AuX49pZlp0a995zv8f//8J++efz5l55/PzgcfpHjzZryPPUbzn/7E8PnzWXrFFWDbnPHYYyy4/XaG3HwzH/p8LInFuN622fiznzHo/vt5uqyMCxobeXviRBrcblYtWEB+bS2/8vkoqqxE/+gj8Hh4/uab+cbtt7Nm0CCMa6+l4qyzKOzfHzLnfIe2bWPphRdiVVdz2nPP0drYyPqHH+bkO+/k/VtuwX/33eglJRy7adMB7Vfbtmxh8/vvc/K3voVSihqfj5JUikKlqHvwQUqvu47Zxx7LmcuXs+immzjh9ts/8R7JZJL6uXPpfeaZzLr4YtxTp3JySwszLr+cMX/9Kx9NmMDJb721/338AMz86U85/eGH9/iYDTSuWEH78uUc8+1vA/DKuHFcsIeR2d0ppQiHw6x66SXG/ehHTP/JT2h/5x0qNmxAXX89Y+65B4AtH35I77FjWXTxxYz9FAvnvd+vH6du2UIMcIbDODKLxQAEg0FWr17Nsmef5XuPPMKSSy7hC3//Oy0tLfTYwzoDa7xe2goL+cLWrejZ/WPNGhKNjZSedhpKKaZOmsQ5r75KaMsWCvv2ZVVpKW2miS+Z5LhYjLUeD4OiUTRNY+uTT9Lnhz/Mvf86v5+SaJQFo0fz5Q4jf3tjx+N8OHo0o1avZmllJYG2NsqnTsU++2zqLrmEkY89lk5jpo0bDAYpyIwoi8+HBISdbOzYsYwePZpHH300t23o0KFccMEF3Hnnnft9fVcrLLs3uJRtE49EiASDmLEYPpcLp67TmAla45EIbsOgva2N9mCQSDCIlUhQ4PNhmyZN9fW4dB2VSmEohUqlsBMJbNPETiZRmb92KoWVSqFSKZRl4XO7MRMJsCyS8ThOwyDa3o6haeR5vdimSbCtjTyPB10pkvE4ZiKB1+1GWRZ2KoWuFDrpoFaHdBBj29imidPhwKnrGIaB5nKhuVzoLhe6242vqAh3dTWlZ52F68wzIRPwKKWY+6c/Efz3vwk0NmKHwxixGE7LQlcqPR9fKRyahpFpnIXKy2kpKmLAqlU4M40yLfPXqevYto3StPQiAJoGuo4CUpaVDmoNIx20Ohzouk5jZSVGeTklq1djWhbuvDzo2ZP8wYPpcd55OC+6KL30dMb8V19l+x13ULp5M0WxGLS347Ws9IXeM7+Ng4+D/+z9XP5nbh3/39/93R/LNsxjDgcxpxM7Px9X//5UnHEGrq99DUaPPqh9dOfOncy94w4Cb71F0datFCWT9Eil8LH/DoxsZ0PCMAgHAuhDhlD04x/juvji3In4NatXs2jyZPqvXEnfZJL8TCdJVpL0Nf9CRUWoMWPw//a3FJx6KgCmafLyd79L/1dfZWAsRmC3z59bXs64TINgxplncvKMGbhIT9f64MwzGffii6yvrmbUbgsLZKU0jXAggPuMM/DddBN0OMdybxKJBM9deinHvvUWA4NBCpVijcvFpokT+fLLL+/xN0sA73372xz/298SGzWK6g6Le3QlEdIdQLufuxF3OmkeMoSy//wHx8CBh+zzPnrtNZp+/GOG79iRmyLcmRr79aOmrY0v7ONaeilgcZ8+9J85k5LqasJPP40+dSqeIUMwrrwSOky3XTFzJvVXXsmgDRsoTKVYmp/PkI8+4sPJk5k4d+4uv3MMsDSN/N2aQCtLSxm8YQOxlhYWXHUV1b/4BQ2XXko8EODUpUvZ8fTTRF58keKf/5yeHTpDWhsbmTN8OOc2NmJk3t8AXKTLfDCzUA3AioICBtfVsemaa6h66imShkHtGWcw4o03wOFg+XvvYV14Icd1mDqecjpxXHgh3HsvZM4pV0rxzwsv5Iz//Y9eHb5HCEDT8ChFdt3vKB93tH7kdjNg507e/N73+OorrxDXNJqdTmK6jp25WZljgUMpHEqhWxZFsRhFwPbMd/EBH9x8M3m3387ITJDcNHMm9oQJDEok2GkYtDqdpDKdwB7LwmdZ9CKzYuzq1dQsWsTGRx/l/NdeY+6oUZy9ZQvbPB7aPR6SbjfK6Ux3QGRu6DrJVAqXy5XubM0ct0mlcOg6euY4r1kWfVtbafT72Vlezvj165lTXc2omhqmT5rEWVOm0OZwkGdZbHU4SFZVUb1hA7V9+pBwONLHjw7zpZRtE4/F0GMxHPE4BbbNEKDOMOixYwfOoiIMwyAeDDJ/3DjMUaM4+8UXmd2zJyc3NrLJ5yPi9abbBw4HCY+HwsZGPMCOykrito1hGOimiUok0ONxTmprY051NeNraljj8dDi82EoRTIcxplK0ReoBHYEAvTcsAHHHi4pkTVr8mTGP/ssTZrGdocD0zAYHo/jBRYYBl7LYhCwvLKSEzLTkD+49FJOypxrueS44zh+6VLmut2YLhcnhsMs9HjICwQ4cedO5n/969hLlzJ8/XpW9OmDlZdHUtdJZa7VaSWTGLaNQ9NQ8TgD6+vpm0rx3ujROI4/nvFPPEEQCAAmMLNfP0y/n1aluHjFii7Txj2SSUDYiZLJJD6fj5deemmXFfGuvfZali5dustJ+FmJRGKXufmhUIg+ffpIYRFHDcuyMIyPwwClFHZmtFTTNCzLIhQK4fP58Hg8WJaFZVmkUinC4TC6rpNKpdA0LXfOQfZ12RGZ/Px8CjusKLi/i/EeCq2trSTi8XSgrxSx9nbCkQj+wkLyAwEcLhfePV1XbD9s20azbTTLSp8jcqDfRSmsaJTmbdtw6jqu0lLydltlsW7zZvy6jiMQwBMI5D6vad06Ylu3UlhUhK1p5JeU4CwtBa/3wD9/L2lKJZM4Mt+jbvFi2lasINnSgu52k1dUhL9nT/zHH48nc+5qsLGR1f/+N4naWkgkULEYqVQKS9cpq65m0PjxfPjcc1ihEJZtE4/HQdPIz88nnkjgdrvRdJ3qY46hrE8f5r/3HmYigWVZqMzFmiG9j3i93tzqibZS6UZhphGvlCIYDGLbNh6PB4/Hg57p1DFNEzPTSFWkO3/sTCPS5XKhlMLlcuFwubBdLnw9epDXowc9zzkHf0UFzW++ScDrTf/GAwfu9Vpeh1KiuZnG1atpralBD4WwmpqIh8NEIxES8TiGruMwDDTATCZJmSa2UhiaRigYJFBQQCIex7Ys9Ez5swHd4UDpevoyRB4PmmHgdLvRDINILJa+73Ix6gtfoN+ll9La0sKyBx9EN01wOjE8Hlx+P5XV1RQecwyewYMhL29/X+cTOnYuKqVY+sYbRJYsId7Whtvtpt83vkFpRQUrH38cAygoLydv1CjKRo/+TPv4thUraPjwQ6pOPhkjL4/Nzz1H8Qkn0OfMM9k5ZQqWz0evCRPSsyL2p6UlffP5oGfP3Mjf7hKRCOtffZW2BQtoq6nBk0hgJhLk9+hB7yuvxFyxgu3TplFQVoY1YADH/vjH+AoKUErx7n33kXj7bRzNzdjt7Tg1DWWakErhzASGlq5jGwb5/fqhKiqIL1+O3rMnZeeey4if/pSls2dT88QTDL74YoadfTabFi1i41/+AitW4I5EcGoamq5j+3w4AgGs6mqG/OxnFGVGV7NioRBv//CHWAsX4rdtXPF4bj0DLXOzUim8Lhcp00x3shpGeqaJrpPMXH9TGQa600mipITjH3+cgmHDqJszhz5f+hKtzc0U9ejB3D/8gbbHHsNXXk6fe+/FZRh89M1vkt/aSl5mdkFu5DszIm4YBo7CQozCQpzFxWjHHkvpVVelR2X3IrhzJwuuuw5t0SLc0Siaw4GeSOBOJIiXlGBqGkU7duDMXCLCcjhQLhfK7aatupoxr77KuvvuI/6Pf+A2TazM9V0LKyrQq6qonDgR4/zz03Xzfiz7y18I//e/uJub0eJxYtXVkJ+Pf80atEAA58iRDLnjDrRs/aMUHz38MF6fj0E/+AFLfvADrOnTcdg2yUGDGPDkkzgdDprffJN+3/se4dpaVnzrW7jWrMFtmriVwpHpdFaGQYp0pygOB61lZajrruOkyy8H22bqeedhbN1K9R13sOPXv6Zy61Y8to0/HqcYpI17GEhA2Il27NhB7969mTNnDieffHJu+x133MHTTz/N2j0sIHLrrbdy2223fWK7FBYhhBBCCNFVhM49l8Cbb0ob9zCQVUaPALuPXuw+bbKjG264gWAwmLtt7bDCmBBCCCGEEF3CCy90dgq6DbkwfScqKSnBMAzq6+t32d7Q0EBZWdkeX+N2u3FnlqsWQgghhBCiSzoMp3uINBkh7EQul4sTTjiBadOm7bJ92rRpu0whFUIIIfZlZ/vOzk6CEEKIo5SMEHay66+/nsmTJ3PiiSdy0kkn8de//pXa2lquvPLKzk6aEEKIo0T5feWoW2RJACGEEAdPAsJO9s1vfpPm5mZ+97vfUVdXx4gRI3jjjTeoqqrq7KQJIYQ4ili2haF3xtVbhRBCHM1kldGjXFe7DqEQnWX2ltmMrRyLy3Dt/8lCHGG02zTab2gnz3Xwl2gQQogjkbRxDx85h1AIIYBHFjzS6edhdfbni6NbLBXr7CSIw2TKmimc/8/zOzsZQoguQgJCIY4CMpD/+WuONXdqgzqSjPDD//2w0z5fHP1+N/N3Uld0Ex9s+4Cl9Us7OxlCiC5CAkIhjgL676Soft6ao83EzM4LCBsiDYST4U77fHF0+MOcP9AQadhlW1u8DYA/zf8TzbHmTkiVONxq2mo4pviYzk6GEKKLkFamEEeJo6nn/5QnT6E2WNvZyTgozbFm4qn4Lts6/ub/Xf1fbp1x6+f2+Q2RBsKJTx8Qnvn0mWwLbTuEKTowKxtWMr1mOq+te22X7dtD2w97WrqijvugrWx+9c6vqGmtyW2rC9dRdHdR7v6apjWHNX3i0Hq/9n0mvzx5v8+LmTEqCyppjkoHgNi7o6ndIDqXBITiM1vfvJ5QItTZyeiyklYS4BPBypGqMdLI3K1zj6p9ImbG0iOEu00ZPfbRY5m5eSanPXUav5/9e26beRsfbP3gkH++rWx2hHewNbSV9c3rD/r1W9q2MGPzDJbtXPaZGgCWbR3U81c2rGTEoyP44jNf5N1N7+a2L61fSuUDlaxrXkfVg1XYyv7UaerO5m+fT58H+uTuV95fCUBLrCW37Z1N7+zymoc+fGiXfEzZKd5Y/wbPLnv2c07t58u0zD2WvV9N+xXPL3+ecCJ8yBq/y3cuZ0ndkn0+Z2n9Ui7732VsD21nSd2Sg/7svy36W25kt6PTnjqN55Y9x7rmdft8vUIxonQEi+oWHdTniq5pRcMK1jevp6a1huU7l2NaJnXhOgJ3BVjduPpz//x4Kv6pZ9jUt9cf4tSIT0NWGT3KZVdgun7K9fQu7U0wHsw95jScQPrcJE3TcOpO2uJtuAwXDt1BxIxg2RYKhVN34jJcuB1utoe3k+/Mx1IWiVSCHr4eJFIJbGXjNJz08vfCbbjZEd7BpCGTuP+D+5m1ZRanVZ2GaZmU+kopyy9D13QiyQgKhVKKYCJIz7yeaGjYykahKHAXMLt2Nm3xNk7tcyqxVIxSXymapgHphnI8FSeRSmApiwJ3AV6Hl5ZYC+X55cRSMXxOH4lUgqgZJZgIkufMw+f0YSubYCKI3+UnYkbwOX25hpKmaWhouc/J/h9KhLCVTYG7gJSdwmW4aIu34XP6iJpRdE2n2FuMrWy2BLdQkV+R+56mbZLnzMNpOEnZKUzLRNM0DM1A13QM3cDQDDRNI5FKoGkaHoeHgDtAoacQp5HOgwJ3AU7dScSMsGD7ApbUL+H19a8D8JWBX2Fwj8H4XX4SVoL2ZDsuw4WhGUTMCEopXIYr/ZtYCQLuAJqmEUlGgHRwadomtrIp8hQRTobRNZ2oGcWpO3MrFKbsFCk7RYG7gMZIIwXuAjwOT24/ynPloaFRVVhFWV4ZHoeHpJXk+eXP8/CChwE4tuexFHoKGVA8gCJPEXmuPHaEd9CebMfv8u9xeXwNDUtZtCfbc3ltKQtb2aTsFBEzgqEZmLaJQ3fg0l0oVO51uqYTSoTwOX14HB4K3AW4DTcBTwBd0+nl74VTd3JM8TEMLx1OMBHk4fkPc9vM23JpuPfsexlXOQ7TNjnz6TMZ02sMQ0uH8sxHzwDwnWO/w9bgVk6oOAGFwm240TWd5lgzuqbjMlzomk48Fac92U6hpxCf04fX4aU0r5QiTxFuh5shJUPId+WzqnEVD8x7gGU7l9EQaeC48uMozy+nf2F/Snwlu5SbIm8RfpefPFcexd5inLqTqRumcvus23PpP7PfmVQWVNKnoA8O3UE4GcbQDCxloZQiaSWxlY3P6SOcDNPD24OIGaGmrYZ52+ZxychLsJVNviufsvwyNDRK80op9ZVSmldKRX4FfrefObVz+OIzX8RluPj1Kb+mMdLINWOvoT3Zzs/f/jkO3cEloy7hXyv/ha1syvPLqS6sxrItHLqDlJ0imAhS5CkiYSXIc+Zh2iahRIitoa0AnDfoPGZumYlDc5DvysdWNpaycuW4xFdC74LeNEYa2R7eTjwVx7RNvA4vBe4CIsl0uQc4q/9ZnNjrRP4w9w/EzBimbaKUygWruqZT6ClE07TcdkXmr1IoFJFkhGgqitfhJc+Zh0N34NAdxFIx4qk4ec48bGXj0B1YyiJlp/C7/LnfvsBdgKZp6JqO23DTFG0imAgyrnIcFfkVJK0kXqeXHt4eWMriN+/8hmAiyPvff5+athpGPjqSsZVjaY42s+rqVQBc8vIlNEWbmLphKmN7j+WasdfwyppXePqCp2mJtfC1l77GvG3zADi5z8n0L+pPeV45boc7l36H7kDXdNqT7bQn23Pf2aE7cBpOklaSeCpOyk7h1J20xlsp8ZUQNaMkrSSappG0kjh0B0qp3P6f58rD0Azchhu3w01jtDFdRnQXZ/U/i3MGnMOyncv454p/5uqhYm8xbsNNZUElXqeXmBljztY5PLf8OQBuO+M2rhpzFQXuAv666K/cPuv23BTaM/udSWleKVWBKhKpBDvadzCkxxAAVjetptBTiKEZ6TrWcOJz+oin4vhdfpyGk2A8SO+C3tw+63aUUtxw6g1sbN1Ie7IdDY3GaCM+p48vVX+Jp5Y+RdSMcmzZsbxf+z5fGfAV5m2fx/DS4eQ583AZrtwtYSXQNZ1SXykAjdFG7nz/Tm449Qa+VP2l3DF4XOU4AncFePmbL3P/vPu5fPTl9MzryZjeY/A6vLm63Of0cf4L5/PE+U/wrX9/i7L8MsrzynN5lbSSmJZJhb+ClJ0iz5lHyk7hc/rYEd5BTVsN14y9hnc2vcPyhuUUuAvwOXy5/UDX9Nx+mruPlquz25PtuePA6IrRPDz/YfJd+RR6Cpk0eBKza2cTNaO5Ojjb5nDoDrwOL82xZhoiDQwtGcrr618nz5XHVSdexZS1U6gN1qJQ9Pb3RiN9fM62IRSKtngb5fnltMZb0+0c3YlTd+bq3uxx1lY2boebUl8p9e31uWN+U7SJY4qPYWtwKw3RBuKpOIOKBzG6YjRvbnwz935Ow5mrc/KceXgcHgDyXHn4XX58Th8bWjbwbs27zN8+n1P6nsKYXmN4ZMEjXHzsxbgNN/0K+/H2prcpcBcQcAdw6A4Mzdil3Bl6+n62XCmlMHQDDQ2FYm3zWtyGm+GlwwHS5cORLh9O3YnX6WVJ3RIeXfgoS+qXcM6Ac2iONnPFCVfwo1d/xGWjL6OXvxcO3ZFrS3mdXjwOD7XBWnRNZ1zlOHRNpzXWyqrGVby67lVKfCWM6TWGsvwyAu4Ab296m1P6nILf5UfXdNY2r6WuvY6RPUdS7C3mpVUv4dAdnDfoPHaEd+S+l8/pw224MW2TRCqRazu0xltZUr+EIT2G8Mf5f+TbI75NwB2gPlJPRX4Fvf29cRpO3Ck3151xnawyehhIQHiUywaE7656F+VWFHmLcpVoNrhRKBKpBPmufAKeAIZmEEqEKHAXEEqEKPYWEzWjtCfbASjyFuUqwmzBzVY8Sim2BLcQT8Vx6A7er30/dxAoyy8jZafYEd5Byk4BpBvtmWX8s8FdNvjKBlLHFB9DXbgOAJfhojXeistwoZRC07RcYyJpJUnZKerb66kurKY2WIvP6csFB9mGeTwVR9d0nEY6APY6vOS58nLBUseDC7DL//mu/Nzvo2s6sVSMPGde7jVOw0l7sh1d0/G7/MRTcWxlo2s6HocH0zZzQXChpzDXsMw2YrP/x8wYAU+ASDJCe7KdYCKIaZkkrAShRIhgPIjTcDK291gApm6YypCSIVw09CJWNa7C0AychpNCTyHhRDqo87v9KKVI2Snak+0YukFTtAmPw4PLcOFxeHAb7lwDtjXWSpG3CF3TSdmpXEMOIJgI4nF4coEDpHsAswFFe7KdeCrO9vB2mqJNNEWbMDSDi0dezLDSYVz9+tX8Y9k/iN8UZ3toO8FEkPZkO739vclz5REzY7nfvCPLtoilYuS78nOflw2ms/nkcXhyQXbUjOby1NAMklYy18hrjjXnvle2LHy4/UPchpudkZ0s27kMl+HiyhOvZNGORdw0/SYAXv/O6yypW5JL858n/pmP6j/iuMeOA8D8PxNd01nVuCp30E7ZKfJd+bgNNyk7haUsPA4PHoeHtngbkWSElJ2iOdZMU7SJmBljwY4FGJpB30BfJo+aTO/7ezOybCRLr1iKrWxWNKwgYSVyDShDMwgmgoQSITa2bCSWimFoBl8Z+BWKvcUMeWQIVYEqNl+3me2hdL40RhupLEiPKmV/s5ZYC9tC26guqqaHtwet8Vb8Lj8tsRbq2+vxu/2U5ZURSoRyDfjGSCON0UYaI43UhmqJJCMcV34cl42+jHgqjqEbVD9UzY9P/DEaGr8d/1vu/+B+dkZ2ctnoyxhfNZ6NLRupa6/LNYiiZpSy/LJcAB81ozh0BwF3gMqCSl5a9RKTX57MvB/Oo6qwKlfush0CAOFEmG2hbfTM65kLIHRNJ5FKlyOPw0PCSqCU4u45d7OobhEPfvlBqouqcRkuNNL1kCK9X4cSody2jo3i7Lbs/pXtUEtaSTS0XF5ny51pmbkGX1u8DbfDnUuvrWwSVrqDze/yk+/KZ/72+bTEWtA0jZSdoinahK7pfHXoV6lrr+OX035Jz7yePHn+kxR6Crns1cu4+fSb6VPQh4v/ezEDigfw0IcPcVb/s5g2eRqvr3ud37z7G3r5e3HbGbcx8fmJtMRaaPplE4ZuUN9en6tPs51Xpm2mAwOnj3AiTJG3KFefaJqGz+nDqTsxbROPw0NTtCkXSDl1Z66OdzvSZcBtuHOdVNmA0u/249SdRM0ozy1/jv+t/R9+t5+/TPwLmqbhMlyEEiESqQTbQtuImlHcDjfHlR/HmqY1VAWqWNe8jhdXvUhduI6vD/s64yrH5cqmuiUdMOwI78C0TAb2GMiYv43hgsEX8IuTf5HrEGtPtmNaJs2xZjwOD5FkJFfXNUbTHWCmZWIpi17+XrlOwFWNqyj1lbKkfgkTB07k2WXPctvM23AaTi4ddSn3nH0PW9q25Do8skF0to5qjDRiKYsSXwkeh4fFdYtpjDTm6qoPtn3AiRUnct+X76Mp2sR/V/+X1lgr0zdPJ2JGuGjIRfxl0V/47rHfZf6O+bz67VfT9XU8SGO0MfdZ2U7CuvY6XIaLcCKMQ3fkOtqqi6o565mzGF81nnvOvod4Kk7UjObqS1vZu3SIZG+mZRJMBOnt742lLJqjzTy++HHuPvtu8px5NEQaeHbZs5za91R6F6QDulAihGmb6XrRtmiLt9Ezryc9fD34zTu/4YIhF3Bq31MZ+/hYbh5/M18b9jW8Tm9uFDzbnskGdD6nj6ZoEz18PdDQcnkaM2NYysptcxpO4qk4y3Yuo9RXSpG3iGA8yNDSobyx/g2GlgxlTO8xuA030zdPZ2XDSi4YcgFuhztXHqJmFMu2Pu4kyXQKhZNhwokw1UXVDC8dzuK6xVT4K6hprWFwyWBqg7UUegp5Z9M7XDv2WkzbJJwI58pb9pbtNErZKZJWMhc0ZttP2cA4nAxT316PUorWeCuNkUZCiRBRM4ppmwzuMZjzB5/Psp3LOLHXifx+9u95ceWLPHH+E3yh9xcY98Q4evt789vTfkuBu4BYKkbMjNHD14P2ZDsbWjaQslMUe4vp4e3BMcXHEIwHeWXNK5xWdRrhRJhjy45l2c5luY7u/kX9KfQUsqpxFTvbdzK4ZDCmZdISa6GqMH0d7ZSdIpKM5DpEfE5f7rcscBfkynN5fnmuU6kqUMXmts3UtdehofHAzAd4+dKXJSA8DCQgPMrJNVrEkejvS//O96d8H3XL0VG9JFIJNrZuZGPLRs4bfN4nHq8L19Hr/l4UeYpo+XXLHt7hs9Nu02j+VTPF3uJP9foHPniAhkgDd5515yFO2f7FU3G8v/di32znRt0f+OAB7ppzF9uv345Ddxz2NO0uG5hkg7OjSbZzLGtz22a++uJXGd93PKf3O53FdYu5fdbtXDDkAl7+5st7fI9fvP0L7p1w7+FK8gHJjvTuabbAgYokI1Q9WEVzrPmw1zcbWjZw5tNn8s7kd+hf1D83K+fTGvnoSH558i+ZPOqT5xDGzBgvrXqJU/qcwndf/i4Digfwjwv/8Zk+70iSslNHRD3RFYQSIWZvmc3EQRM7Oymf2ex1sxk/eLy0cQ8DKX1CiEOuh7dHZyfhoLgdboaVDmNY6bA9Pl6al57qNbhk8OeWhvqf13/qYBDgZyf97BCm5uB4HB5af926S9DSu6A3I3qOOGIaeZqmHZXBILDL7wrQr7Afz174LG9tfItJgyflFpLxu/x7fY8jLRiEzxYIZuW58mj6VVOnnGM9oHgAW3+29ZC936LLF+01qPQ6vVwy6hIg3eAPuAOH7HOPBEdKPdEVFLgLukQwCDCqfFRnJ6HbkBIohDjkzh14LquuWtXZyThkHLqDi4ZexDnHnPO5fUZZftnn9t6HQ6GncJf7A4oHcHrV6Z2TmG5gaOlQhpYOBdLTgWHfAWFXlz3H62h2oCOMlm3lplIKIcShIAGhEOKQc+iOXGO1q/jPN/7T2Uk4qoyuGM3x5cd3djK6hez5uH539w0Iu5N/f+PfR90sDCHEkU0CQiGEEJ+L3ac6is9HdimA7jxC2J2M6Dmis5MghOhi5DqEQgghxFFMoQi4A/x07E87OylCCCGOQhIQCiGEEEex7CV1CtyyCp8QQoiDJwGhEEIIcRRTqEOyYqcQQojuSQJCIYQQ4ih2zdhrmP+j+Z2dDCGEEEcpWVRGCCGEOIrlu/LJd+V3djKEEEIcpWSEUAghhBBCCCG6KQkIhRBCCCGEEKKbkoBQCCGEEEIIIbopCQiFEEIIIYQQopuSgFAIIYQQQgghuikJCIUQQgghhBCim5KAUAghhBBCCCG6KQkIhRBCCCGEEKKbkoBQCCGEEEIIIbopCQiFEEIIIYQQopuSgFAIIYQQQgghuilHZydAfHpKKUKhEEDurxBCCCGEEEe7bNtWKdXJKen6NCW/8lErFAoRCAQ6OxlCCCGEEEJ8LrZu3UplZWVnJ6NLk4DwKJYdIQyHw/j9fjRN6+wkfWqhUIg+ffqwdetWCgoKOjs54jCRfO+eJN+7L8n77knyvXv6rPmulCIcDtOrVy90Xc5y+zzJlNGjmKZpBAKBLjVKWFBQIAeLbkjyvXuSfO++JO+7J8n37umz5HtXauMeySTcFkIIIYQQQohuSgJCIYQQQgghhOimJCAURwS3280tt9yC2+3u7KSIw0jyvXuSfO++JO+7J8n37kny/eghi8oIIYQQQgghRDclI4RCCCGEEEII0U1JQCiEEEIIIYQQ3ZQEhEIIIYQQQgjRTUlAKIQQQgghhBDdlASE4rCZNWsW5513Hr169ULTNF555ZVdHldKceutt9KrVy+8Xi9nnHEGK1eu7JzEikNqf3n/ve99D03TdrmNGzeucxIrDpk777yTMWPG4Pf76dmzJxdccAFr167d5TlS7rueA8l3KfNdz6OPPsrIkSNzFyE/6aSTmDp1au5xKetd1/7yXsr7kU8CQnHYRCIRRo0axcMPP7zHx++55x7uv/9+Hn74YRYsWEB5eTlnn3024XD4MKdUHGr7y3uAc845h7q6utztjTfeOIwpFJ+HmTNncvXVVzNv3jymTZtGKpViwoQJRCKR3HOk3Hc9B5LvIGW+q6msrOSuu+5i4cKFLFy4kC9+8YtMmjQpF/RJWe+69pf3IOX9iKeE6ASAevnll3P3bdtW5eXl6q677spti8fjKhAIqL/85S+dkELxedk975VS6tJLL1WTJk3qlPSIw6ehoUEBaubMmUopKffdxe75rpSU+e6iqKhIPf7441LWu6Fs3isl5f1oICOE4ohQU1NDfX09EyZMyG1zu92cfvrpzJ07txNTJg6XGTNm0LNnTwYNGsRll11GQ0NDZydJHGLBYBCA4uJiQMp9d7F7vmdJme+6LMvihRdeIBKJcNJJJ0lZ70Z2z/ssKe9HNkdnJ0AIgPr6egDKysp22V5WVsaWLVs6I0niMDr33HP5+te/TlVVFTU1Nfzf//0fX/ziF1m0aBFut7uzkycOAaUU119/PaeeeiojRowApNx3B3vKd5Ay31UtX76ck046iXg8Tn5+Pi+//DLDhg3LBX1S1ruuveU9SHk/GkhAKI4omqbtcl8p9Yltouv55je/mft/xIgRnHjiiVRVVfH6669z0UUXdWLKxKHyk5/8hGXLlvH+++9/4jEp913X3vJdynzXNHjwYJYuXUpbWxv/+c9/uPTSS5k5c2bucSnrXdfe8n7YsGFS3o8CMmVUHBHKy8uBj0cMshoaGj7Royi6voqKCqqqqli/fn1nJ0UcAj/96U/53//+x/Tp06msrMxtl3Lfte0t3/dEynzX4HK5GDBgACeeeCJ33nkno0aN4qGHHpKy3g3sLe/3RMr7kUcCQnFEqK6upry8nGnTpuW2JZNJZs6cycknn9yJKROdobm5ma1bt1JRUdHZSRGfgVKKn/zkJ/z3v//lvffeo7q6epfHpdx3TfvL9z2RMt81KaVIJBJS1ruhbN7viZT3I49MGRWHTXt7Oxs2bMjdr6mpYenSpRQXF9O3b1+uu+467rjjDgYOHMjAgQO544478Pl8fOc73+nEVItDYV95X1xczK233spXv/pVKioq2Lx5MzfeeCMlJSVceOGFnZhq8VldffXVPP/880yZMgW/358bHQgEAni9XjRNk3LfBe0v39vb26XMd0E33ngj5557Ln369CEcDvPCCy8wY8YM3nzzTSnrXdy+8l7K+1Gi09Y3Fd3O9OnTFfCJ26WXXqqUSi9Bf8stt6jy8nLldrvV+PHj1fLlyzs30eKQ2FfeR6NRNWHCBFVaWqqcTqfq27evuvTSS1VtbW1nJ1t8RnvKc0A99dRTuedIue969pfvUua7ph/84AeqqqpKuVwuVVpaqr70pS+pt99+O/e4lPWua195L+X96KAppdThDECFEEIIIYQQQhwZ5BxCIYQQQgghhOimJCAUQgghhBBCiG5KAkIhhBBCCCGE6KYkIBRCCCGEEEKIbkoCQiGEEEIIIYTopiQgFEIIIYQQQohuSgJCIYQQQgghhOimJCAUQgghhBBCiG5KAkIhhBBCCCGE6KYkIBRCCCGEEEKIbkoCQiGEEEIIIYTopiQgFEIIIYQQQohuSgJCIYQQQgghhOimJCAUQgghhBBCiG5KAkIhhBBCCCGE6KYkIBRCCCGEEEKIbkoCQiGEEEIIIYTopiQgFEIIIYQQQohuSgJCIYQQQgghhOimJCAUQgghhBBCiG5KAkIhhBBCCCGE6KYkIBRCCCGEEEKIbur/B7gAbJGbwxYBAAAAAElFTkSuQmCC", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", " \n", "
\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p.SetMaxSinThetaOvLambda(0.2) # Important for stability of profile fit. And faster !\n", "spgex = SpaceGroupExplorer(pdiff)\n", "\n", "# NB:verbose C++ output does not appear in a notebook\n", "spgex.RunAll(keep_best=True, update_display=False, fitprofile_p1=False)\n", "\n", "for sol in spgex.GetScores():\n", " #if sol.nGoF > 4 * spgex.GetScores()[0].nGoF:\n", " if sol.GoF <= 2 * spgex.GetScores()[0].GoF:\n", " print(sol)\n", "\n", "print(\"Chosen spacegroup (smallest nGoF): \", c.GetSpaceGroup())\n", "\n", "# Updated plot with optimal spacegroup\n", "p.plot(diff=True, fig=None, hkl=True, reset=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add Cimetidine molecule to the crystal structure\n", "This is imported from a Fenske-Hall z-matrix, downloaded as needed.\n", "\n", "We can also look at options, and disable the Dynamical Occupancy Correction, because we don't expect the molecule atoms to be overlapping with each other." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Crystal Formula: C8.9 N6 S\n", "\n", "Option #0: Constrain Lattice to SpaceGroup Symmetry = Yes (Default)\n", " Choice 0: Yes (Default)\n", " Choice 1: No (Allow Crystallographic Pseudo-Symmetry)\n", "Option #1: Use Dynamical Occupancy Correction = Yes\n", " Choice 0: No\n", " Choice 1: Yes\n", "Option #2: Display Enantiomer = No\n", " Choice 0: No\n", " Choice 1: Yes\n" ] } ], "source": [ "if not os.path.exists(\"cime.fhz\"):\n", " os.system(\"curl -O https://raw.githubusercontent.com/vincefn/objcryst/master/Fox/example/tutorial-cimetidine/cime.fhz\")\n", "\n", "# This will automatically add the Molecule to the Crystal object\n", "m = ImportFenskeHallZMatrix(c,\"cime.fhz\")\n", "print(\"Crystal Formula:\", c.GetFormula())\n", "print()\n", "\n", "# List the options to see which are available\n", "for i in range(c.GetNbOption()):\n", " o = c.GetOption(i)\n", " print(\"Option #%d: %s = %s\"% (i, o.GetName(), o.GetChoiceName(o.GetChoice())))\n", " for j in range (o.GetNbChoice()):\n", " print(\" Choice %d: %s\" %(j, o.GetChoiceName(j)))\n", "\n", "\n", "c.GetOption(1).SetChoice(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a MonteCarlo object and add objects (crystal, powder pattern) for optimisation" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "mc = MonteCarlo()\n", "mc.AddRefinableObj(c)\n", "mc.AddRefinableObj(p)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Disable profile fitting before Monte-Carlo\n", "..or else the crystal structure will not be optimised\n", "\n", "Note that the following display will be live-updated during the optimisation done below (the last plot is alwasy updated)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "77f6fca9951e478dbd016ed5226eb856", "version_major": 2, "version_minor": 0 }, "image/png": "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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", " \n", "
\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pdiff.SetExtractionMode(False)\n", "p.FitScaleFactorForRw() # for a better initial display\n", "p.plot(fig=None,diff=False,hkl=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Display the 3D crystal structure\n", "*Note: this requires installing `ipywidgets` and `py3Dmol` (as of 2021/05 the conda-forge version is obsolete, so just install using pip). Otherwise You will just get a warning message*\n", "\n", "This will be updated live during the optimisation, and also when using `RestoreParamSet()` to restore some specific solutions (and generally everytime the underlying Crystal's `UpdateDisplay()` function is called). Just scroll back to see what is being done in the widget.\n", "\n", "The `display()` is only really necessary to make sure the widget appears in the notebook. In fact if `c.widget_3d()` is the *last* command in the notebook cell, the display is done automatically. See the ipywidgets documentation if you want to understand this in more details.\n", "\n", "Note that bonds may disappear during optimisation, because they are automatically assigned by the javascript viewer, which is quite strict about allowed distances. In the final solution some bonds in the middle of the chain are often missing, though you can see the atoms are reasonably close. But rest assured that any bond defined in the object still exists as defined in pyobjcryst !" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/3dmoljs_load.v0": "
\n

3Dmol.js failed to load for some reason. Please check your browser console for error messages.

\n
\n", "text/html": [ "
\n", "

3Dmol.js failed to load for some reason. Please check your browser console for error messages.

\n", "
\n", "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fce5512877b245a09d4f92a1c77f9716", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Box(children=(VBox(children=(HBox(children=(VBox(children=(FloatRangeSlider(value=(0.0, 1.0), description='Xra…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(c.widget_3d())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run multiple optimisations\n", "We also enable the automatic least squares every 150k trials, which allows a better convergence\n", "\n", "We perform 3 runs, each of 1 million trials using parallel tempering, with default parameters (which should be adequate for all problems). Normally for this structure it would be better to use 2 millions trials so that the correct solution is found during almost every run.\n", "\n", "Each run starts from a randomised configuration." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LSQ option: Every 150000 trials, and at the end of each run\n" ] }, { "data": { "application/3dmoljs_load.v0": "
\n

3Dmol.js failed to load for some reason. Please check your browser console for error messages.

\n
\n", "text/html": [ "
\n", "

3Dmol.js failed to load for some reason. Please check your browser console for error messages.

\n", "
\n", "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "42440b516d0e4b71a747cd0a376846fc", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Box(children=(VBox(children=(HBox(children=(VBox(children=(FloatRangeSlider(value=(0.0, 1.0), description='Xra…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d056fc2fab4948999db31f797217e2d0", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Box(children=(HBox(children=(Label(value='MonteCarlo:', layout=Layout(max_width='25%', width='11em')), Text(va…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Final LLK: 18608.95\n" ] } ], "source": [ "mc.GetOption(\"Automatic Least Squares Refinement\").SetChoice(2)\n", "print(\"LSQ option: \", mc.GetOption(\"Automatic Least Squares Refinement\").GetChoiceName(2))\n", "\n", "# 3D structure view which will be live-updated with the best\n", "# configuration of the current run\n", "display(c.widget_3d())\n", "\n", "# Small widget to see the progress of the optimisation, with the current run\n", "# best log-likelihood, the run number and remaining number of trials.\n", "display(mc.widget())\n", "\n", "# The powder pattern plot a few cells above should also be updated for each run best solution\n", "mc.MultiRunOptimize(nb_run=3, nb_step=1e6)\n", "print(\"Final LLK: %.2f\" % mc.GetLogLikelihood())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### List solutions\n", "All solutions are stored in a \"Parameter Set\" which can be restored (assuming that the objects - crystal structure and powder pattern are not altered e.g. by changing the list of atoms, the profile, or the fixed parameters etc...).\n", "\n", "This will only record changes of parameters such as atom coordinates, but will not record other changes such as a different spacegroup, or a change of the Scatterers (number of atoms or molecules) inside a Crystal. It can only be used to browse results obtained at the end of `MultiRunOptimize()`.\n", "\n", "At the end of the optimisation the best solution is automatically restored." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0: LLK= 21130.00, name=Best Configuration\n", " 1: LLK= 21135.00, name=Run #3\n", " 2: LLK= 21130.00, name=Run #2\n", " 3: LLK= 21155.00, name=Run #1\n" ] } ], "source": [ "for i in range(mc.GetNbParamSet()):\n", " idx = mc.GetParamSetIndex(i)\n", " cost = mc.GetParamSetCost(i)\n", " name = mc.GetFullRefinableObj().GetParamSetName(idx)\n", " print(\"%3d: LLK=%10.2f, name=%s\"%(idx, cost, name))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Restore a chosen solution (set of parameters)\n", "Restoring a solution will also update the 3D crystal view above." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a5767929d16a4a418579b71574cf1d2c", "version_major": 2, "version_minor": 0 }, "image/png": "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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", " \n", "
\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p.plot(fig=None, diff=True)\n", "mc.RestoreParamSet(3, update_display=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Save results to CIF and Fox (.xmlgz) formats" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Save result so it can be opened by Fox\n", "xml_cryst_file_save_global('result.xmlgz')\n", "# Also export to the CIF format\n", "c.CIFOutput(\"result.cif\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "033033acc6c74c3daef886add86efd13": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "04f420e67f7a42979e353000c07eb348": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Yrange", "layout": "IPY_MODEL_8bf9ae6abf444ae08df94efb582c5ffd", "max": 1.5, "min": -0.5, "step": 0.02631578947368421, "style": "IPY_MODEL_692f4a896dde4a909e75b3ed9b1ac0a3", "value": [ -5.551115123125783e-17, 0.9999999999999998 ] } }, "063c720771e5455fb5d1ffd0c27ad6f7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_a9e855977db64785bcc8b4f2e0803fb8", "IPY_MODEL_04f420e67f7a42979e353000c07eb348", "IPY_MODEL_14341ebe2bae445389d5046be8750dab" ], "layout": "IPY_MODEL_f386dc3ff5b04e3189ba60b62d49ab07" } }, "07e57849dcd842cd8665047a4abb8a04": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "extra dist", "layout": "IPY_MODEL_4a4568a59aa84c15afbdcbf800787fca", "max": 10, "readout_format": ".1f", "step": 0.5, "style": "IPY_MODEL_a474a39f1b7040f5b8e31bcae6884fe2", "tooltip": "Extra distance (A) with semi-transparent display", "value": 2 } }, "0d96107a068d4ce08fc8601de79e6ca9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "0e1f70ffb8d140abb0c6598cc779bf96": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "14341ebe2bae445389d5046be8750dab": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Zrange", "layout": "IPY_MODEL_43962757a342404cab79c3302fadef3f", "max": 1.5, "min": -0.5, "step": 0.047619047619047616, "style": "IPY_MODEL_885742531923446e9bc635bd3250a95d", "value": [ 0.023809523809523836, 1.0238095238095237 ] } }, "1460d182a1ff4472bcc2ce497114909a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "LabelModel", "state": { "layout": "IPY_MODEL_f443b3682ae945d3a83acf637668d8bc", "style": "IPY_MODEL_c04c3145800248a7bdd17a11e7c03cf9", "value": "MonteCarlo:" } }, "158d3575ad3344d9a5eef47bed7cd54c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "16d499f663fc47b49799b1bbd59260eb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "251d7558b7a74c7bbe7b6ec9ac170760": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_dfeb9f48eaf54bfca3cb61951d344ff5", "IPY_MODEL_5f125f9947c24b8890be63be330e6398" ], "layout": "IPY_MODEL_8f4b38dea47045c2bd33aa3884f2767b" } }, "25ae317dc2e34d888314185867a0da2c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "28749649ff664779b102bb5134f03a8d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "29c30c7055f6440c9756d62047dd7913": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "2b2629219dc940fbb60b7d4f865da6b0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "2e07987270a3424885b9240ba747c3d8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "3070a2fefae345c5abab322ef7c9a10e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "33cd6a561aae4867bd27837bc6ed082d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "37a448aac66f4ed782973ae253db53c8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "37efa633a61c4835be911037898323ce": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "3b65a4ece7b54989b9018526ffdc1d40": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_47bceb18d5df428cac704bb60babc52c", "IPY_MODEL_b463e11ceb97432485b4d1d458ab460d", "IPY_MODEL_5e181deaf97748278d56bd28622d3aac" ], "layout": "IPY_MODEL_b82e01cf3881477b86207dd7b856d281" } }, "3fa4d9033bb94736a381a1c6d9f1ac21": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "42440b516d0e4b71a747cd0a376846fc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "BoxModel", "state": { "children": [ "IPY_MODEL_251d7558b7a74c7bbe7b6ec9ac170760" ], "layout": "IPY_MODEL_33cd6a561aae4867bd27837bc6ed082d" } }, "43402ab0214f43c09714b65a30d43d14": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "43962757a342404cab79c3302fadef3f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "47bceb18d5df428cac704bb60babc52c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Xrange", "layout": "IPY_MODEL_803acabdb80c46f9a6de317791d95549", "max": 1.5, "min": -0.5, "step": 0.07142857142857142, "style": "IPY_MODEL_ef0730344db64535880c58c7ab569d4a", "value": [ 0, 1 ] } }, "4a052a1c296e478686e683d539f4866f": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "ToolbarModel", "state": { "_model_module_version": "^0.11", "_view_module_version": "^0.11", "collapsed": true, "layout": "IPY_MODEL_2b2629219dc940fbb60b7d4f865da6b0", "orientation": "vertical", "toolitems": [ [ "Home", "Reset original view", "home", "home" ], [ "Back", "Back to previous view", "arrow-left", "back" ], [ "Forward", "Forward to next view", "arrow-right", "forward" ], [ "Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan" ], [ "Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom" ], [ "Download", "Download plot", "floppy-o", "save_figure" ] ] } }, "4a4568a59aa84c15afbdcbf800787fca": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "4b7730f297f840d39edcc3fbd3208be7": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "MPLCanvasModel", "state": { "_cursor": "default", "_data_url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA94AAAG4CAYAAACzXz9FAAAAAXNSR0IArs4c6QAAIABJREFUeF7snQd4lUUW909CaKH3KqFYQIo0pYWqFJVd11VcEWzA59rABVGRLiKIgA1QWXHXwiquyAICgiA19KaAgDQpKy2G3lu+78x673cTEu5N3rw3c+/9zfPkgdy8c+a8v/NOMv93Zs5EJScnJwsFAhCAAAQgAAEIQAACEIAABCAAAVcIRCG8XeGKUQhAAAIQgAAEIAABCEAAAhCAgCGA8OZBgAAEIAABCEAAAhCAAAQgAAEIuEgA4e0iXExDAAIQgAAEIAABCEAAAhCAAAQQ3jwDEIAABCAAAQhAAAIQgAAEIAABFwkgvF2Ei2kIQAACEIAABCAAAQhAAAIQgADCm2cAAhCAAAQgAAEIQAACEIAABCDgIgGEt4twMQ0BCEAAAhCAAAQgAAEIQAACEEB48wxAAAIQgAAEIAABCEAAAhCAAARcJIDwdhEupiEAAQhAAAIQgAAEIAABCEAAAghvngEIQAACEIAABCAAAQhAAAIQgICLBBDeLsLFNAQgAAEIQAACEIAABCAAAQhAAOHNMwABCEAAAhCAAAQgAAEIQAACEHCRAMLbRbiYhgAEIAABCEAAAhCAAAQgAAEIILx5BiAAAQhAAAIQgAAEIAABCEAAAi4SQHi7CBfTEIAABCAAAQhAAAIQgAAEIAABhDfPAAQgAAEIQAACEIAABCAAAQhAwEUCCG8X4WIaAhCAAAQgAAEIQAACEIAABCCA8OYZgAAEIAABCEAAAhCAAAQgAAEIuEgA4e0iXExDAAIQgAAEIAABCEAAAhCAAAQQ3jwDEIAABCAAAQhAAAIQgAAEIAABFwkgvF2Ei2kIQAACEIAABCAAAQhAAAIQgADCm2cAAhCAAAQgAAEIQAACEIAABCDgIgGEt4twMQ0BCEAAAhCAAAQgAAEIQAACEEB48wxAAAIQgAAEIAABCEAAAhCAAARcJIDwdhEupiEAAQhAAAIQgAAEIAABCEAAAghvngEIQAACEIAABCAAAQhAAAIQgICLBBDeLsLFNAQgAAEIQAACEIAABCAAAQhAAOHNMwABCEAAAhCAAAQgAAEIQAACEHCRAMLbRbiYhgAEIAABCEAAAhCAAAQgAAEIILx5BiAAAQhAAAIQgAAEIAABCEAAAi4SQHi7CBfTEIAABCAAAQhAAAIQgAAEIAABhDfPAAQgAAEIQAACEIAABCAAAQhAwEUCCG8X4WIaAhCAAAQgAAEIQAACEIAABCCA8OYZgAAEIAABCEAAAhCAAAQgAAEIuEgA4e0iXExDAAIQgAAEIAABCEAAAhCAAAQQ3jwDEIAABCAAAQhAAAIQgAAEIAABFwkgvF2Ei2kIQAACEIAABCAAAQhAAAIQgADCm2cAAhCAAAQgAAEIQAACEIAABCDgIgGEt4twMQ0BCEAAAhCAAAQgAAEIQAACEEB48wxAAAIQgAAEIGANgTFjxkj37t2t8QdHIAABCEAAAllBAOGdFRSxAQEIQAACEIBAhgjs3bs3zes7d+4sixcvzpAtLoYABCAAAQjYTgDhbXuE8A8CEIAABCAQhgRiY2OlYcOGkpycnOLuNmzYIElJSWF4x9wSBCAAAQhEMgGEdyRHn3uHAAQgAAEIZBOBWrVqyYoVK0QFuG9p3bq1zJ07N5u8olkIQAACEICAOwQQ3u5wxSoEIAABCEAAAtcgoMvJa9euLQULFkxx1datW6Vq1aqwgwAEIAABCIQVAYR3WIWTm4EABCAAAQhAAAIQgAAEIAAB2wggvG2LCP5AAAIQgAAEIAABCEAAAhCAQFgRQHiHVTi5GQhAAAIQgAAEIAABCEAAAhCwjQDC27aI4A8EIAABCEAgQgns3LlTqlSpEqF3z21DAAIQgEA4E0B4h3N0uTcIQAACEICApQTSOqv7xRdflJEjR0rTpk0t9Rq3IAABCEAAApkjgPDOHDdqQQACEIAABCDggECuXLmkZcuWUq5cOe9Z3nqMWJs2beQf//iHA8tUhQAEIAABCNhHAOFtX0zwCAIQgAAEIBD2BH7++Wd55ZVXpFKlSvLSSy+ZY8XuvPNO+fbbb8P+3rlBCEAAAhCIPAII7wiK+blz52Tjxo1SokQJiYmJiaA751YhAAEIQMBWAitXrpRx48ZJfHy8LFy4UD7//HNbXcUvCEAAAiFF4NKlS5KYmCg1a9aUPHnyhJTv4egswjsco5rOPa1evVpuu+22CLpjbhUCEIAABCAAAQhAAAKRTWDVqlVy6623RjYEC+4e4W1BEILlwu7du82SPu18ZcqUCVaztAMBCEAAAhCAAAQgAAEIBJnAgQMHzKTbL7/8IhUrVgxy6zSXmgDCO4Keif/+979y3XXXyb59+6R8+fLmzt9++215/vnnpVGjRpKQkCAtWrSQ5cuXm+UoV65ckU8//VTuvffeFJRUvO/du9eI+B07dpifnTp1Stq3by8q7v/1r3+ZOmfOnJGbbrpJ/v3vf8vtt98us2fPloceeki2bdsmM2bMMG15ypo1a0y9t956y/vZ4MGDZdKkSVK6dGljv3v37uaXxrPPPivbt2+XrVu3en+JjB07VgYMGCA9e/aURx55RCpXrixdunSRCRMmyMcff2z8UnsZKcqgYcOGJumP/l99KVKkiDz22GPmy9f/gwcPyuuvv254arvz5s2TVq1ambazsuh9aNu6HDO90q1bN5k8ebL88MMP/JLNSvjYggAEIAABCEAAAiFEIK2xfwi5H3auIrzDLqTp31B6wltFq0dAq5i8/vrrjWB96qmn5Ouvv5bDhw+nMKpL1pcuXSq+9TwXqDDWn2miHBWAuq9cxfuyZcvkp59+kgsXLhhxnFq4qmhWce4rZlUoqz299vTp03LzzTfLjz/+aPanq8hXUev79s5zvdqoWrWqaVtLZoW32lahq2XIkCFSuHBh6dGjR5rC2xeQ+qFtqxjPDuGtviiD1Hwi6FHnViEAAQhAAAIQgEDEE0B42/UIILztioer3mRUeM+aNcvMXJ8/f/4qv3RmNz3h7ZmVVcGsSR0++eQTWbJkiVfEemaMN23aJF9++aW5JikpSbZs2WJmtlWwq9jXWfW//vWv0qdPHzl06JA5dmbz5s2imXAbNGhgZtPz5s3rnRWvXbu2tGvXTp588sk0hbfexJ49e+To0aOmLRXSO3fuNPen99O1a1fzAuKJJ56Qy5cvi75gUB+KFy9uXgroz3R/zNq1a6VQoUISHR0tVapUMQJb/daXAzqbn57w1lUGHTp0MC8m7rjjDsmXL5/o0Tk5cuQQZZ07d2557bXXzGoAbf+ZZ56RRx991Nx7x44dzQuH6tWry/r1682Mt65c0JcjurJA66rQLlWqlIkVwtvVroRxCEDARQLHjx83v2MpEIAABCDgjADC2xm/rK6N8M5qohbby6jwVpGoy861XuoSiPB+4IEHzFExeh7r3XffnUJ4q4j+4osvzLExKn4bN24sKsRHjx5tlsHrsnIVsHqNLjXXn6ktXWZ+9uxZI7BV0M+ZM0d+/fVXMyMdiPD+7bffzAsDXUKu/1cRr3ZefvllI3D1RYO2rcvEc+bMadrWAaAubVcOetyNil9dEaDLztVvtaN7aOrUqWOue++999Kc8dYza/WlhO6v15cGw4YNk/vvv9+8aNB29b7/9re/yffff29WBtSrV08WLVokQ4cONbZVhCtLXUGgwvvBBx80PDQT8PTp02X+/PnmBQLC2+JOiGsQgIBfAnqO93fffef3Oi6AAAQgAIFrE0B42/WEhL3w1lnKUaNGyYoVK4x40yXI+q9viYqKSjcq+/fv9yYi06XHOmOauqgQ9E3Rf/LkSendu7fZZ6uzqSrixowZI3FxcSmq6uyoLl3W2WCd/dRZTd0nrLO4vkVnQ/v162dmaVWc9erVS55++ukMP0mBCm8V2+pDbGysWWqu+78zKrwffvhhI2p1Cfkf/vAHsyTcs2xbZ7z1e89SaJ25vXjxovznP/8x16v4rlatmhHenqXmJ06ckFtuucWwUt7qk842K18Vpf/85z8DEt4ag8cff9zY1eXg+jzojLvuh9ZZa21XZ5Q1nuXKlTNtDho0yMyi6yz7Bx98YNrR/e9aT/eZezLF61Ftixcvlm+++SZN4a1te54fnZFWn3WfvN6n/qttatv6DGj5y1/+Yvbfa/sqqFWsa3vqiwpvfQGgR8NpUV/UxmeffYbwznDPoAIEIJAdBDQfRuqiOTV0JVBaL3yzw0fahEA4EdD+pZMFOv7RlXWU0CWgqyV13KirMq+lYxDedsU47IX3tGnTzKygiiYVuiqYUgtvFeWpiyboUjGsQshTVKzpUmMVQ75Fbfs+9Lr/eN26dUZA6gzpwIEDRYXjhg0bvKL62LFjUqNGDSPGVWzq0moV1DqTO3HiRK95FcHNmjUzCcM6d+5slimrEBs/fryZKc1ICVR4e/Z4X8v2tWa89WWHJjfTRGoeEZlaeDdv3twIbY3PnXfeaZacKyvdG65MdJm1CtIKFSqYZGX6B0KF59SpU+XDDz80syE6W64vJTRLu4r4QGa8PUJehW/+/PnNsm4tHlHsO+Otv8x0lll90tlnjf37779vEtRp27pUvWnTpmbvusZfr1VBrDPzae3x9mXguxTc84JB/df976lnvF999VXTvj4Dep/6pe3oioIXXnjBezyEzpLrrLoWlppnpGdwLQQgkB0E9OWmrmpSMeBb9MW1bvWhQAACWUdA+5muENTJIR0rqHCjhC4BHRfruK9AgQJmoig98Y3wtivGYS+8VWjrXlwtOtOq2bNTC+/UIfEcu/XGG28YYeMrvFVU61Ll9MrKlSvNMuaZM2fKXXfdZS7TfcI6O6uz3jpbqWXEiBFmebTOgKrA0/L5559Lp06dzD5mnXnVoqL0yJEjonY9Rfcgq2DUzuS5t0Aeq6wS3jrbrEnOdLZZl2HrnmedbdWi4rJ///5mRl6Toak41X9VIKu41Vlh3QutPFTA6l4+XWpev359I6p1v7LutdY3sjoTrr9IVHzriwtdfqjiVl9A6Ay1tq2cbrjhBr/CW1lr0Zl8fbmhL0v0BYrW1aIvRbRNfTmj+8r1udEs7xpL3VutMdQ3iyr+9WVIsWLFjA0VybqvWv+AaV1dcj58+PBMCW99PtVPZaW/UJWhzs6rTX2JoX54Esx59njrCwqdqdeibPRLC8I7kB7BNRCAQHYS0JVR+rewbNmyKdzo27ev2YpDgQAEso5AYmKiGeeULFnSjGEooU9A8yPpxJ3qCM8KyNR3hfC2K85hL7x9cQcqvPUPvopHFVueY7c8otKf8NbZ6HfffdeIZd+3T7qnWWdYdRmyFp3xVVGqM76e4hGymmBLRaF+r7PAOmusM6Geovt+VVjpSwSdCQ20pNX5dK+yrghQ4atCM7PFc5yYLnlWOzpjof/qMn/dg5xeUbGqfwyaNGmS2aZNPX0ZoveicVOxrMnLtF39LNjFc//avt5/sIuuhFAf9Egz3+c32H7QHgQgAAEIQAACdhDQyQydIdWJD0r4ENDJK13BoJNZaRWEt12xRninEY+aNWuat4Gpz0rW2VydodVZWU28pUvA9W29Xu8puvxXBXvq5es6M6mJwDzHdukbR11C7VmK7amv+3Z1RlkFo8586/e6rFlnaT1FharW1/28OvsbaKHzBUqK6yAAAQhAINgEdIXZyJEjg90s7UEgIgh48uz4HsMaETce5jfpL66M/e16ABDeqeKhS441oZbuodYl3b5FE6Hp8mJd+rxr1y5z9JNmwtZ94LqnWUvr1q3NsuPZs2enqKszsZrtWmfCtahw1727elSWb9FZUhXVU6ZMMfu59Xtd2qyzuJ6is8pa/5133jHJ2dIrujxbvzxFlyxrIjB968lMqF0dEW8gAAEIRDoBXRm2YMGCSMfA/UPAFQL+BJorjWLUdQL+4orwdj0EGWoA4Z0KlybSeuutt8zZzEWLFr0mTBWymhVb92WrqPYIb92Lq7PUvkWzkmtGbN2P4RHeekyUtudbdMm1HmGl2cQ9wltnz1XwpxbeuqRdj75Kr2jSLk30lbogvDPUR7gYAhCAAASCQED/ZukXBQIQyHoC/gRa1reIxWAQ8BdXhHcwohB4GwhvH1aa8VGX4Gh2ad+919fCqedTa7IKT/Izm5aaM+MdeEfgSghAAAIQgAAEIBCuBPwJtHC973C/L39xRXjb9QQgvH3ioWdE677tSZMmmTOUAymauVxnsT3CO9SSqwVyj1wDAQhAAAIQcJ3AlSuaKVNEz/fOn9/15mgAApFEwJ9ACzcWmqdJt6/o0YSaQDhci7+4IrztijzC2yceTz31lDlDW/dtx8bG+o3U/v37zXFWeiSK54gxz3FivgnRdGm37gFPfZyY7vHW48Q8xzqo4O/YseNVx4npmd+6z9tT9Eiy6dOnZ8lxYn5vkgsgAAEIQAACwSDw5ZciDz4oovlVxo8PRou0AYGIIeBPoIUbCIT3/yKK8LbryQ574a0ZyPVcZC3jxo0zZ0a/+eab5ns90stz7p0mLCtTpozJHq7ZwlOXL774wpzNredq65mjmlxNz2vWZGm+51hrPT1yTBOujR492hwHNnDgQJMNXRO36TnSWlRM16hRwyxtHzBggDmHr1evXtK2bVsj/j1FBbfOwutRaLqXXPd9qz1N/qbHRmWk0PkyQotrIQABCEAgqAT0+Mf/839E7rtPZPLkoDZNYxAIdwII7/CMsL+4Mva3K+5hL7z1gaxUqVKa1DV7qp6HrUVFtQpmFekqrlMXTXCmGch/+uknI5r1DO5WrVrJkCFD5Kabbkpxue6t1nOsJ0+ebM5M1Ot0tjsuLi7FdXqGtSZH0zOXdYZdZ7v1eDKPOPdcrD717dtXtmzZYrKRq0DX48kyWuh8GSXG9RCAAAQgEDQCH374v9luhHfQkNNQ5BDwJ9BCkcTUqVNNEmEdH+u4/L777jPj6Pz585sjgXWpuY7v9YhePdJXJ8N03K1jak/RsbGOqxctWmQmyXQS7k9/+pNJtBwKxV9cGfvbFcWwF9524c5eb+h82cuf1iEAAQhA4BoE/v53kb/+VeT++0W++gpUEIBAFhLwJ9CysKmgmNItlyqQO3ToYFaF6krUl19+2RybO2/ePK/wLleunJnYuuOOO8zno0aNkvfff19026YWnRzTraMq4EuVKiV79+6VNWvWiJ4cFArFX1wZ+9sVRYS3XfFw1Rs6n6t4MQ4BCEAAAk4I6L5uHQx36CDy7387sURdCEAgFYH0BJrmN9q4cWO28KpZs6YRwZkpdevWlZw5c3qTG6sN3Rb60EMPia5o1aIz3pqH6dNPP/U2od/rbLjmWIqOjjaz47p19FrH82bGv2DVQXgHi3TWtIPwzhqOIWEF4R0SYcJJCEAAApFJwCO8H3hARBOtUSAAgSwjEE7C+9SpU2bZ+MiRI+X555/3MtJ8TbpdU3MhNW3a1AjvKVOmyL333uu95j//+Y/8+c9/NsK7QoUKJo+S/l+3k7Zu3Vquv/76LGMeDEMI72BQzro2EN5Zx9J6Swhv60OEgxCAAAQil8AHH4g89ZQIwjtynwHu3DUC/gSaaw27YNgzntVkxJp42LeULl3aLC2/5557jPDWPEpNmjTxXqJJiuPj40VzNzVo0EAOHDgg/fr1E90vfvToUZO3adiwYUach0LxF1fG/nZFEeFtVzxc9YbO5ypejEMAAhCAgBMCuuT06af/d6TYF184sURdCEAgFQF/Ai2UgHlmvHW/tiZG85TMzHh76l65csWcUjR06FCTaPnnn382RwHbXvzFlbG/XRFEeNsVD1e9ofO5ihfjEIAABCDghMB774noiR0dO4p8/rkTS9SFAATCWHjrreke71y5cpmZa0/58ssv5cEHH/S7x3v+/Pmyb98+s8c7ddH97rVq1TJZ0Nu0aWP9c4Twtj5EKRxEeIdWvBx5i/B2hI/KEIAABCDgIoGkoUOl2IABcvD226X0vHkutoRpCEQeAX8CLdSIeLKa/+Uvf5FHH33Um9X81ltvTTOrue7fnjt3rslqPm7cOHn66afN8WFt27Y1Cdh0ifnFixdNNnMV89u3b5fixYtbj8VfXBn72xVChLdd8XDVGzqfq3gxDgEIQAACDggs69RJGn/+uSy57jppunevA0tUhQAEUhPwJ9BCkZgmShsyZIhs3rzZnON9//33X3WO94wZM2T8+PHy/fffS4ECBeTZZ5+V/v37m9s9f/68+X7JkiXmGDFNzFa/fn2z3FwFfCgUf3Fl7G9XFBHedsXDVW/ofK7ixTgEIAABCDggsLRjR2kyaZIsqVBBmu7Z48ASVSEAgUgQ3kRZBOEdWk8Bwju04uXIW4S3I3xUhgAEIAABFwkkPPigxH/5pSyJi5Omu3e72BKmIRB5BPwJtMgjEh537C+ujP3tijPC2654uOoNnc9VvBiHAAQgAAEHBBIeeEDiv/pKFlesKM1++cWBJapCAALMeEfGM4DwDq04I7xDK16OvEV4O8JHZQhAAAIQcJFAQocOEj95siyuVEma7drlYkuYhkDkEfAn0CKPSHjcsb+4Mva3K84Ib7vi4ao3dD5X8WIcAhCAAAQcEFj85z9Ls//8R5ZUrixNd+50YImqEIAAM96R8QwgvEMrzgjv0IqXI28R3o7wURkCEIAABFwksPjee6XZ1KmypEoVabpjh4stYRoCkUfAn0CLPCLhccf+4srY3644I7ztioer3tD5XMWLcQhAAAIQcEBg0Z/+JM2nTZMl118vTbdvd2CJqhCAADPekfEMILxDK84I79CKlyNvEd6O8FEZAhCAAARcJLDwnnukxfTpsuSGG6Tptm0utoRpCEQeAX8CLfKIhMcd+4srY3+74ozwtisernpD53MVL8YhAAEIQMABgYV/+IO0mDFDEm64QeIR3g5IUhUCVxPwJ9BgFpoE/MWVsb9dcUV42xUPV72h87mKF+MQgAAEIOCAwIK775aWs2ZJwo03SvzPPzuwRFUIQCA1AX8CDWKhScBfXBn72xVXhLdd8XDVGzqfq3gxDgEIQAACDgh4hXfVqhK/ZYsDS1SFAAQQ3v6fgccee0zWrFkjmzZt8n+xpVcgvC0NTDpuIbxDK16OvEV4O8JHZQhAAAIQcJHA/DvvlFazZ0tCtWoSv3mziy1hGgKRR8CfQIs8IiII70iMevbeM8I7e/kHtXWEd1Bx0xgEIAABCGSAwPft2sntc+bI0ptvliY//ZSBmlwKAQj4I4DwvpoQwtvfU8PPs5oAwjuriVpsD+FtcXBwDQIQgECEE/i+TRu5fe5cWVq9ujQJ4aWfER5Gbt9SAuEovJcvXy6DBg2SFStWSHJystx8880ydOhQad26tfTp00dmzpwpv/zyixQqVEiaNWsmb775ppQpU8YbobSE96+//iovv/yyzJkzR06cOCFxcXHy1FNPyXPPPWdlZP3FlbG/XWFDeNsVD1e9ofO5ihfjEIAABCDggMC81q3ljnnzZGmNGtJk40YHlqgKAQikJuBPoIUasaVLl0qrVq2kYcOG8swzz0jhwoXNfu1SpUpJ165dpUuXLkaAly1bVhITE2X06NGSlJQkmzdvlpiYGHO7qYW3/rxOnTrmZwMHDpTKlSvL9u3bZefOnfLGG29YichfXBn72xU2hLdd8XDVGzqfq3gxDgEIQAACDgjMu+MOueP772VpzZrSZMMGB5aoCgEIhLvwbtKkiRw7dkw2bNggOXLkuGbAL1++LAcPHpTy5cubmew2bdqkKbz79etnBPrWrVulYsWKIfEQIbxDIkxeJxHeoRUvR94ivB3hozIEIAABCLhIYG6rVtJ6wQJZesst0uSHH1xsCdMQiDwC6Qq0p54Sya4VJjVrirz/foaDcebMGSlQoIAMHz5cXnzxxTTrf/vtt/Lqq6/KTz/9ZJaMe8qYMWPk2WefTVN46+x50aJFZdasWRn2KbsqILyzi3zm2kV4Z45bSNZCeIdk2HAaAhCAQEQQmNuypbReuFCW3XKLNEZ4R0TMucngEQgn4a37sHX2+rPPPpPOnTtfBXH16tXSuHFjueeee+SRRx6RkiVLSlRUlFmWPnLkSOndu3eawvuGG26Q5s2by4QJE4IXGIctIbwdAgxydYR3kIFnZ3MI7+ykT9sQgAAEIHAtAnNatJC2ixbJsjp1pPG6dcCCAASykIA/gZaFTblu6vTp01KwYMF0Z7z79u0rf//73+Xw4cMSHR1t/NmzZ49ZPn4t4c2Mt+uhi/gGEN4R9AggvCMo2NwqBCAAgRAjMKd5c2m7eLEsq1tXGq9dG2Le4y4E7CYQTsJbScfHx5s93j/++ONVe7x79uwpkyZNkv3795uZbi3Dhg0T3cN9LeHdv39/GTVqlGzbtk0qVKhgd0B/985fXBn72xVGhLdd8XDVGzqfq3gxDgEIQAACDgjMadpU2iYkyPJ69aTRmjUOLFEVAhBITcCfQAs1YgkJCSaruSZZe/rpp6VIkSKybt06KV68uJQuXVruvvtuk+383nvvFT12TJelq6C+lvD2ZDXXWfIBAwaYrOa7du0y9UaMGGElIn9xZexvV9jCXnjv2LHDvL3SM/42bdokVatWNf/6Fj1O4JNPPrkqMpqYoV27dik+V1tjx4412RFr1qxpOnCLFi1SXHPy5Emzf2Ty5Mly/vx584tBkznoWYC+RTtyjx49ZMmSJZIvXz7p2LGjvP7665I3b94U12mSB31Lt2XLFrOnpVevXuaXTEYLnS+jxLgeAhCAAASCReC7pk2lTUKCLKtXTxojvIOFnXYihIA/gRaKGJYtWyY6S71y5Uoz6129enVzjvftt99ujv/SsfeRI0eMOB83bpzceOON1xTeymDfvn3mHG/VAJrETZen65i7e/fuViLyF1fG/naFLeyF97Rp00z2wgYNGpg3VleuXElTeKv4/de//pUiOtWqVZNChQp5P1PRrftGdLkaTxQdAAAgAElEQVRK3bp15cMPP5SpU6fKqlWrjAj3lPbt25u3bnokge5B0bMANaOiHnngEdW6PKZGjRpGjOtbNd2HooJahf7EiRO9tvQtXbNmzUxyCE0goecWDho0SMaPHy/dunXL0NNE58sQLi6GAAQgAIEgEvguPl7aLF2K8A4ic5qKHAL+BFrkkAivO/UXV8b+dsU77IW3Cm1PYgWd2V6zZk2awjutz31DpTPXpUqVkieeeMK8RdOi5wKq4K5Vq5bZS6JF37ppcoaZM2fKXXfdZT7bu3evVKlSxbx5e/LJJ81numRlyJAhJtmDLovR8vnnn0unTp1k8+bNoqJfy5133mne1qldT1EfZsyYIdqZPPcWyGNF5wuEEtdAAAIQgEB2EJjbtKm01hnv+vWl8erV2eECbUIgbAn4E2hhe+NhfmP+4srY364HIOyFty9uJ8J7wYIFZsm4zmTXqVPHa/aVV14xM9vHjx83CRx0Nvrdd981YtmT0EEvbtmypeTPn1+++eYbU1ePKyhcuLDojLynqLjXGfbXXntNnn/+ebNMXWfMdfm5JorwlEWLFpnl7fqyoF69egE/UXS+gFFxIQQgAAEIBJmAR3gvrVdPmrDUPMj0aS7cCfgTaOF+/+F6f/7iytjfrsgjvEVEBfmXX34puXPnNvs5dBZbl3//6U9/8kbrvffeM0ka9Oe+e7C/+uoreeCBB8yeEN1/rf/XGW7dU+5btO6cOXNE95xr0TMFu3TpYkS1b9H9KY0aNTJnCOrMt36feq95YmKiqZ/e+YXpPWJ0Prs6H95AAAIQgMD/J4Dw5mmAgHsE/Ak091rGspsE/MWVsb+b9DNuG+EtIu+8847ExMQYkat7r99//3357rvvREX1/fffb6jqLPSrr74q586dS0F53rx50rp1a3OcgS451/9rgofZs2enuE6TP6h415lwLTlz5jT2+vTpk+I6PR5BRfWUKVPMfm79Xvd56/J1T7l06ZKpr35rcrb0iu4r1y9POXDggNx2223elwQZf1yoAQEIQAACEHCHAMLbHa5YhYAS8CfQoBSaBPzFFeFtV1wR3mnEQ/eFN27c2IhWnXX2CG/NlHj27NkUNebOnStt2rQxidN0plyFt4p4naX2LZqV/IMPPhA9qsAjvNXeSy+9lOI6zbyoxyB8/fXXXuGts+eaHC618NYl7dfKsjh48GDRpfCpi2d23q5HEW8gAAEIQCCSCSC8Izn63LvbBPwJNLfbx747BPzFFeHtDvfMWkV4p0NOjwl78cUXvUvLPUvNVXjnyZPHW8vmpebMeGe2W1APAhCAAASCTWBus2bSeskSSahXT+LZ4x1s/LQX5gT8CbQwv/2wvT1/cUV42xV6hHc68dDM5Tob7dnTTXI1ux5cvIEABCAAgfAiMK9ZM7kD4R1eQeVurCHgT6BZ4yiOZIiAv7givDOE0/WLEd5pINal5rqnWkX3pk2bzBWe48T0ODBPQjQ9Tkz3desS89THifkmRNOl3ZUrV77qODHd463HiRUrVsy0oTY6dux41XFiuu9c93l7ivowffp0jhNzvXvQAAQgAAEIBIvA982aye1LlsjSunWlydq1wWqWdiAQEQT8CbSIgBCGN+kvrghvu4Ie9sJbxfOsWbMM9XHjxsnOnTvlzTffNN/rkV76c81qroJXz9o+evSoSa6mM9y6z/ree+/1RmzUqFHSt29fGT58uNStW9dkHtckaKtWrTLi21Pat28v69evN8eM6XFgAwcONMeN6T5wT0Z0FdM1atSQihUrmgzqhw8fll69eknbtm1l4sSJXlsquJs1a2Z81DO+NeGa2hs/frx069YtQ08TnS9DuLgYAhCAAASCSGB+s2bSSme869aVeIR3EMnTVCQQ8CfQIoFBON6jv7gy9rcr6mEvvPWBrFSpUprUVVzrjPXjjz8ua9euFT2mK1euXFK/fn2TbVxFsG9JTk4WFd9jx46VQ4cOGbGtS9L1jG7fonure/fuLZMnT5YLFy6Y87/HjBkjcXFxKa7btm2bSY6WkJAgsbGxRvyPGDEixXFlWkFfHKjg37JlizmyTAW6Hk+W0ULnyygxrocABCAAgWARWNCsmbRU4V2njsSvWxesZmkHAhFBwJ9AiwgIYXiT/uLK2N+uoIe98LYLd/Z6Q+fLXv60DgEIQAAC6ROY37SptEpIkITatSV+/XpQQQACWUjAn0DLwqZCxpSuJl2zZo13W6nbjrdo0ULy588vM2bMyLKm/MWVsX+Woc4SQwjvLMEYGkbofKERJ7yEAAQgEIkE5sfHS6ulS2Vp7drSBOEdiY8A9+wiAX8CzcWmrTWN8LY2NGHrGMI7bEN79Y0hvCMo2NwqBCAAgRAjML9JE2m1bJkk3HKLxP/wQ4h5j7sQsJsAwvvq+CC87X5mw9E7hHc4RjWde0J4R1CwuVUIQAACIUYA4R1iAcPdkCIQjsJbExAPGjRIVqxYIZqH6eabb5ahQ4dK69atTa6mmTNnyi+//CKFChUyiYo1uXKZMmW8cUtLeP/666/y8ssvy5w5c0RzNml+pqeeekqee+65a8b7yJEj8uKLL8q0adPk1KlTJg+U+tKmTRtvPc9S8wceeEBeeeUV2b9/vzRo0MAkTL7pppu81/3jH/8wCZp37dplckBVq1ZN3nrrLbn11luv8sFfXBn729VNEd52xcNVb+h8ruLFOAQgAAEIOCAwv3FjabV8OTPeDhhSFQLpEfAn0EKNnJ7yo8mL9fhfTThcuHBhs1+7VKlS0rVrV+nSpYsR4GXLljXJk1XIJiUlmSN7Y2JizO2mFt768zp16pif6QlCehTw9u3bzYlImkw5vaLHCzdu3Fh27NhhTj7SRMh6QpIeLTx37lxvEmYV3ppYWV8EDBkyxJjr37+/ObL4559/lty5c8vixYvNqUuapPmuu+4ypy/p6Unx8fHmflIXf3Fl7G/Xk43wtisernpD53MVL8YhAAEIQMABgfmNGkmrFSskoVYtif/xRweWqAoBCGRUoIUasSZNmogezatH9ebIkeOa7qswPnjwoBHEOpPtmYVOLbz79etnBPrWrVvNcb+BlunTp8s999xjZthVLGu5cuWKOTa4ZMmSsnDhQvOZCu8lS5YY+zfccIP5TIV91apVjVB/4oknzOlJKt71JUAgBeEdCCV7rkF42xML1z1BeLuOmAYgAAEIQCCTBOY3bCitVq6UJTVrStMNGzJphWoQgEBaBK4l0P785Z/l8OnDQQdXMl9JmfKXKRluV2eBCxQoYASqLu9Oq+hs86uvvio//fSTWTLuKXq877PPPmu+TS28dfa8aNGi5hjftIqKaf3ylOjoaNGvF154wSwX921Hrxk8eLAMGzZMzp49a14OqPA+evSo/JjqxeItt9wi9erVE11iPn/+fLn99tvl0UcflU6dOom+YNDl5ukVhHeGH59srYDwzlb8wW0c4R1c3rQGAQhAAAKBE5jfoIG0WrUK4R04Mq6EQMAE/Am0gA1ZcKHuw9bZ688++0w6d+58lUerV682S791FvqRRx4xs85RUVFmWfrIkSPNMu60hLfOQusy7wkTJqR5lyqkdW+2p+j+cv2sW7dusmjRIjN77Vs++OADsz9cZ+Z1ebkK75w5c5rl575Fl5Dr8nd9WaBl4sSJ8s4778jatWslT548cv/998vbb79tXgqkLv7iytjfggfWxwWEt13xcNUbOp+reDEOAQhAAAIOCCy47TZpuXq1LKleXZpu2uTAElUhAIGMCrRQInb69GkpWLBgujPeffv2lb///e9y+PBhMyOtZc+ePWb5+LWEt78Zb02Gpl+eovvH9Utn3VVkZ8WMt28cfvvtN5OsrWfPntKhQwf56KOPEN6h9KCm4SvCO8QDmBH3Ed4ZocW1EIAABCAQTAILbr1VWq5ZI0tuvlma/vRTMJumLQiEPQF/M6OhBkCTjelMsi7bTr3HW4XqpEmTjEjWmW4tuuRb93BfS3hrojPdY60J0CpUqBAwEs8eb52xbteunamnS9I1s3mJEiUC2uP93nvvyV//+tc027zvvvvkwIEDsmzZMoR3wFGx80KEt51xccUrhLcrWDEKAQhAAAJZQGBB/frScu1aWVKtmjTdvDkLLGICAhDwEAg34Z2QkGCymuse6KefflqKFCki69atk+LFi0vp0qXl7rvvNtnO7733XtFjx3RZugrqawlvT1ZznSUfMGCAyWquR3ppvREjRqT7MHmymuu1KvB1GbzOgGuytXnz5pkl5lp8s5rr/nM9Ak3b0T3r2oYuK9fl6+qHXqtL5Ddu3Ghm1Hv16mX2rKcu/uLK2N+u3wEIb7vi4ao3dD5X8WIcAhCAAAQcEPAI74SqVSV+yxYHlqgKAQhkVKCFIjGdAdZZ6pUrV5pZ7+rVq5uzszU5mR7/pYnU9HxtFefjxo2TG2+88ZrCWxns27fPnOOts9cqiHV5ugr77t27XxORtqNJ1jzneNeqVcsI5bZt23rrec7x1hlsPU5MZ7H1HG8V6XpWt5YZM2aY/dyarV2XrquI133sep+eY9B8HUF4h9aTi/AOrXg58hbh7QgflSEAAQhAwEUCC+rVk5br1knCTTdJ/NatLraEaQhEHgF/Ai3yiITHHfuLK2N/u+KM8LYrHq56Q+dzFS/GIQABCEDAAYEFdetKy/XrJeHGGyX+558dWKIqBCCQmoA/gQax0CTgL66M/e2KK8Lbrni46g2dz1W8GIcABCAAAQcEFtSpIy1/+EESbrhB4rdtc2CJqhCAAMI7Mp4BhHdoxRnhHVrxcuQtwtsRPipDAAIQgICLBLzC+/rrJT7VebguNotpCEQEAX8CLSIghOFN+osrY3+7go7wtisernpD53MVL8YhAAEIQMABgQW1a0vLH3+UhCpVJH7HDgeWqAoBCDDjHRnPAMI7tOKM8A6teDnyFuHtCB+VIQABCEDARQILbrlFWm7YgPB2kTGmI5eAP4EWuWRC+879xZWxv13xRXjbFQ9XvaHzuYoX4xCAAAQg4IDAwlq1pMXGjbK0cmVpsnOnA0tUhQAEUhPQY7IuXLggVapUAU4YEdi5c6fkypVLrrvuujTvirG/XcFGeNsVD1e9ofO5ihfjEIAABCDggADC2wE8qkLAD4HExET57bffpGTJklKsWDF4hQGBpKQkOXz4sBQvXlxKlCiB8A6BmCK8QyBIWeUiwjurSGIHAhCAAASymsDCmjWlxaZNsrRSJWmya1dWm8ceBCKaQHJysvz6669y8uRJM0OaI0eOiOYR6jd/+fJls4KhQIECUq5cOYmKikJ4h0BQEd4hEKSschHhnVUksQMBCEAAAllNYFGNGtL8p59kacWK0uSXX7LaPPYgEPEEVHzrrPe5c+dEhRsldAnoi5M8efKY2e70RLfeHWN/u2KM8LYrHq56Q+dzFS/GIQABCEDAAYFF1atL882bEd4OGFIVAhCAgC8Bxv52PQ8Ib7vi4ao3dD5X8WIcAhCAAAQcEFhYvbq02LxZlsXFSePdux1YoioEIAABCCgBxv52PQcIb7vi4ao3dD5X8WIcAhCAAAQcEFh4883SYssWWVahgjTes8eBJapCAAIQgADC275nAOFtX0xc8wjh7RpaDEMAAhCAgEMCi6pVk+ZbtyK8HXKkOgQgAAEPAcb+dj0LCG+74uGqN3Q+V/FiHAIQgAAEHBBYVLWqNP/5Z1l+3XXSaO9eB5aoCgEIQAACSoCxv13PAcLbrni46g2dz1W8GIcABCAAAQcEFt10kzTftg3h7YAhVSEAAQj4EmDsb9fzgPC2Kx6uekPncxUvxiEAAQhAwAEBr/AuX14a7dvnwBJVIQABCEBACTD2t+s5CHvhvWPHDhk1apSsWLFCNm3aJFWrVjX/eoqeYzh69GiZOXOmbN68WS5duiQ1a9aUQYMGye23354iWhUrVpQ9aSR8OXv2rDlLz1NOnjwpvXv3lsmTJ8v58+elVatWMmbMGImLi0thb9u2bdKjRw9ZsmSJ5MuXTzp27Civv/665M2bN8V1s2bNkn79+smWLVukfPny0qtXL3n66acz/CTR+TKMjAoQgAAEIBAkAotvvFGabd8uyxHeQSJOMxCAQLgTYOxvV4TDXnhPmzZNnn32WWnQoIGo0L1y5UoK4X3q1CkjZh999FFp3bq15MyZUz7++GP58ssvZfr06dK+fXtvxFR433rrrfL888+niKLa9j28XuusW7fOCPqCBQvKwIED5cSJE7JhwwavqD527JjUqFHDiPEBAwbI4cOHjaBu166dTJw40Wt/+fLl0qxZM3nkkUekc+fOsnTpUvNSYPz48dKtW7cMPU10vgzh4mIIQAACEAgiAY/wXlGunDT873+D2DJNQQACEAhPAoz97Ypr2AtvFdrR0dGG+mOPPSZr1qy5asZbRXGRIkW8kUlOTpb69esb0bxgwYIUwltF9dixY9ON4sqVK6Vhw4ZmBv2uu+4y1+3du1eqVKliZr2ffPJJ89mIESNkyJAhZga9ePHi5rPPP/9cOnXqZGbeq1WrZj6788475ciRI6J2PeWJJ56QGTNmmOUjnnsL5LGi8wVCiWsgAAEIQCA7CCy+4QZptmOHrChbVhr++mt2uECbEIAABMKKAGN/u8IZ9sLbF3dawju9cHTt2lUSEhLk559/zpDw1tnod99914hl31nwli1bSv78+eWbb74x9po3by6FCxcWnZH3FF2WXqhQIXnttdfMrLp+r+Jfl5/37NnTe92iRYukRYsW5iVCvXr1An6i6HwBo+JCCEAAAhAIMoEl118vTXfuRHgHmTvNQQAC4UuAsb9dsUV4pxEPnSWvXr263HTTTTJ16tQUwvv48eNy5swZsyRdl4DrzLXuCfeUBx54wMxw655y3/LMM8/InDlzRPecaylZsqR06dLFiGrfou02atRIJkyYYGa+9ftvv/3WLEH3lMTERFP/s88+M8vPAy10vkBJcR0EIAABCASbwJIqVaTprl2yskwZabB/f7Cbpz0IQAACYUeAsb9dIUV4pxGPd955x8wwL1y40IhrT9FEaLqfu0KFCrJr1y4zM33o0CFZv369VK5c2Vym+8Rz5Mghs2fPTmG5f//+8t5775mZcC0q3F999VXp06dPiuvi4+ONqJ4yZYrZz63f6z5vXb7uKZoATuurn+pTekWX0OuXpxw4cEBuu+022bdvn9nXToEABCAAAQjYQgDhbUsk8AMCEAgXAghvuyKJ8E4VD13G3aZNG3nuuefkjTfeuGa0VMhqlnTdl62i2iO8Y2JizCy1b9Gs5B988IEkJSV5hffQoUPlpZdeSnFdkyZNpHTp0vL11197hbfOnqvgTy28dUl79+7d0/Vx8ODB8sorr1z1c4S3XZ0QbyAAAQhAQGRJ5crS9JdfZGXp0tLgwAGQQAACEICAQwIIb4cAs7g6wtsHqGYd1xnutm3byqRJk1Ls0U6P+9133y2//fabN/mZTUvNmfHO4t6COQhAAAIQcI3AkkqVpOnu3Qhv1whjGAIQiDQCCG+7Io7w/j0eO3fuNMu6NZu4LhPPlStXQJHSzOU6i+3JOk5ytYCwcREEIAABCEAgBQGP8F5VqpTcdvAgdCAAAQhAwCEBhLdDgFlcHeEtIgcPHhRd4q0ZxHWpuf4bSNm/f78R6g8//LD3iDHPcWK+CdF0abfuAU99nJju8dbjxIoVK2aa01n2jh07XnWcmJ75rfu8PUWPJNMzxjlOLJAocQ0EIAABCIQCgYSKFSV+zx5BeIdCtPARAhAIBQIIb7uiFPbCWzOQz5o1y1AfN26c6Mz2m2++ab7XI730iC/NIq6fT5w4UUqVKpUiQp6kZl988YU5m1vP1S5btqxJrjZ8+HCTLG3t2rVSqVIlbz0961sTro0ePdqI+IEDB4pmQ9el7Hnz5jXXqZiuUaOGVKxYUQYMGCCHDx+WXr16mWXu6oenqODW5e96FJruJdeEa2pv/Pjx0q1btww9TXS+DOHiYghAAAIQCCKBhLg4id+7V1aXLCm3HjoUxJZpCgIQgEB4EmDsb1dcw1547969O4Uo9sW/YMECI3x9RXPq8CQnJ5uPNMGZZiD/6aefjGjWM7hbtWolQ4YMMceO+RbdW927d2+ZPHmyXLhwwVyns91xcXEprtu2bZtJjqbnhcfGxprZbj2ezCPOPRfri4O+ffvKli1bTDZyFeh6PFlGC50vo8S4HgIQgAAEgkVgaVycNFHhXaKE3Hr4cLCapR0IQAACYUuAsb9doQ174W0X7uz1hs6XvfxpHQIQgAAE0iewtEIFabJvH8KbhwQCEIBAFhFg7J9FILPIDMI7i0CGghk6XyhECR8hAAEIRCYBhHdkxp27hgAE3CPA2N89tpmxjPDODLUQrUPnC9HA4TYEIACBCCCw7LrrpPF//ytriheX+omJEXDH3CIEIAABdwkw9neXb0atI7wzSiyEr6fzhXDwcB0CEIBAmBNYVr68NP71V4R3mMeZ24MABIJHgLF/8FgH0hLCOxBKYXINnS9MAsltQAACEAhDAh7hvbZYMan3229heIfcEgQgAIHgEmDsH1ze/lpDePsjFEY/p/OFUTC5FQhAAAJhRmBZuXLSeP9+QXiHWWC5HQhAINsIMPbPNvRpNozwtisernpD53MVL8YhAAEIQMABAY/wXle0qNRNSnJgiaoQgAAEIKAEGPvb9RwgvO2Kh6ve0PlcxYtxCEAAAhBwQGB52bLS6MABWVu0qNRDeDsgSVUIQAAC/yPA2N+uJwHhbVc8XPWGzucqXoxDAAIQgIADAsvLlJFGBw8KM94OIFIVAhCAgA8Bxv52PQ4Ib7vi4ao3dD5X8WIcAhCAAAQcEPAK7yJFpO6RIw4sURUCEIAABJQAY3+7ngOEt13xcNUbOp+reDEOAQhAAAIOCKwoXVoaHjok6wsXljpHjzqwRFUIQAACEEB42/cMILzti4lrHiG8XUOLYQhAAAIQcEgA4e0QINUhAAEIpCLA2N+uRwLhbVc8XPWGzucqXoxDAAIQgIADAitLlZIGhw/LD4UKSe1jxxxYoioEIAABCCgBxv52PQcIb7vi4ao3dD5X8WIcAhCAAAQcEEB4O4BHVQhAAAJpEGDsb9djgfC2Kx6uekPncxUvxiEAAQhAwAGBVSVLym2Jicx4O2BIVQhAAAK+BBj72/U8ILztioer3tD5XMWLcQhAAAIQcEDAI7x/LFhQbjl+3IElqkIAAhCAgBJg7G/Xc4DwtisernpD53MVL8YhAAEIQMABgVUlSshtv/0mCG8HEKkKAQhAwIcAY3+7HgeEt13xcNUbOp+reDEOAQhAAAIOCHiE94aCBaUWM94OSFIVAhCAwP8IMPa360lAeNsVD1e9ofO5ihfjEIAABCDggMDq4sXl1qQk2VCggNQ6ccKBJapCAAIQgADC275nAOFtX0xc8wjh7RpaDEMAAhCAgEMCHuG9sUABqYnwdkiT6hCAAASY8bbtGUB42xYRF/1BeLsIF9MQgAAEIOCIwJpixaT+kSOyKX9+qXHypCNbVIYABCAAAYS3bc8Awtu2iLjoD8LbRbiYhgAEIAABRwQ8wntj/vxSE+HtiCWVIQABCCgBxv52PQcIb7vi4ao3dD5X8WIcAhCAAAQcEFhbtKjUO3pUNuXLJzVOnXJgiaoQgAAEIIDwtu8ZQHjbFxPXPEJ4u4YWwxCAAAQg4JAAwtshQKpDAAIQSEWAsb9djwTC2654uOoNnc9VvBiHAAQgAAEHBNYVKSJ1jx2Tn/Llk+rMeDsgSVUIQAAC/yPA2N+uJwHhbVc8XPWGzucqXoxDAAIQgIADAh7hvTk2Vm4+fdqBJapCAAIQgADC275nAOFtX0xc8wjh7RpaDEMAAhCAgEMC6wsXljrHjwvC2yFIqkMAAhD4nQBjf7seBYS3XfFw1Rs6n6t4MQ4BCEAAAg4IrCtcWOoePy5bYmOlGjPeDkhSFQIQgMD/CDD2t+tJQHjbFQ9XvaHzuYoX4xCAAAQg4IDA+kKFpM6JE7Ilb16pduaMA0tUhQAEIAABhLd9z0DYC+8dO3bIqFGjZMWKFbJp0yapWrWq+Td1mTVrlvTr10+2bNki5cuXl169esnTTz991XVqa+zYsXLw4EGpWbOmjBw5Ulq0aJHiupMnT0rv3r1l8uTJcv78eWnVqpWMGTNG4uLiUly3bds26dGjhyxZskTy5csnHTt2lNdff13y5s2b4rpAffP3eCG8/RHi5xCAAAQgkF0EEN7ZRZ52IQCBcCXA2N+uyIa98J42bZo8++yz0qBBA1Ghe+XKlauE9/Lly6VZs2byyCOPSOfOnWXp0qUyaNAgGT9+vHTr1s0bMRXdffv2lWHDhkndunXlww8/lKlTp8qqVauMCPeU9u3by7p162T06NFSsGBBGThwoJw4cUI2bNjgFdXHjh2TGjVqGDE+YMAAOXz4sBH77dq1k4kTJ3ptBepbII8VnS8QSlwDAQhAAALZQeCHQoWk9okTsjVvXqnKjHd2hIA2IQCBMCPA2N+ugIa98FahHR0dbag/9thjsmbNmquE95133ilHjhyRlStXeqPzxBNPyIwZM8zeCK2vM9elSpUS/fyNN94w112+fNkI7lq1asmkSZPMZ2qjYcOGMnPmTLnrrrvMZ3v37pUqVaqYWe8nn3zSfDZixAgZMmSI7NmzR4oXL24++/zzz6VTp06yefNmqVatmvksEN8CfaTofIGS4joIQAACEAg2gR8LFpRbTp6Un/PkkZvOng1287QHAQhAIOwIMPa3K6RhL7x9caclvFVQ66y0LvHu2bOn9/JFixaZJeQq1OvVqycLFiwwS8Z1JrtOnTre61555RUzs338+HGJiooyM+XvvvuuEfL6vae0bNlS8ufPL9988435qHnz5lL4/2Vw1Rl5T1FfChUqJK+99po8//zzRuwH4lugjxSdL1BSXAcBCDKQR4EAACAASURBVEAAAsEmgPAONnHagwAEwp0AY3+7Ihzxwltnl6tXry7ffvutWebtKYmJiVKyZEn57LPPzPLz9957T5555hk5c+ZMij3YX331lTzwwAOyb98+szdc/68z3Lqn3Ldo3Tlz5ojuOdeitrt06WIEv29RXxo1aiQTJkwwM9+B+BboI0XnC5QU10EAAhCAQLAJbChQQGqdOiXb8uSRG5nxDjZ+2oMABMKQAGN/u4Ia8cJb93PHx8eL7qXWJeKecunSJcmZM6e88847JgGazkK/+uqrcu7cuRQRnDdvnrRu3Vp+/PFHs+Rc/58jRw6ZPXt2iuv69+9vxLvOhGtR22qvT58+Ka5TX1SUT5kyxew1D8S39B4p3VeuX55y4MABue2227wvCex6FPEGAhCAAAQimYBHeG/PnVtuSPW3NpK5cO8QgAAEMksA4Z1Zcu7UQ3j/Lm51hloTsKUW3rpsvHv37kZ4Dx06VM6megs/d+5cadOmjUmcpvu9VXjHxMSYGXTfohnTP/jgA0lKSvIKb7X30ksvpbiuSZMmUrp0afn666+9wtufb+k9GoMHDxZdCp+6eGbn3XmksAoBCEAAAhDIOIGN+fNLzdOnBeGdcXbUgAAEIJAWAYS3Xc9FxAvvQJdze5aaq/DOkyePN4o2LzVnxtuuzoY3EIAABCCQPgGEN08HBCAAgawlgPDOWp5OrUW88A40gRnJ1Zw+atSHAAQgAAEIpE9gU/78UuP0admRO7dcz1JzHhUIQAACjgkgvB0jzFIDES+8laYe2aXnaus+b0/RY7+mT59+1XFi+rknIZoeJ6b7unWJeerjxHyTtenS7sqVK191nJju8dbjxIoVK2aaVRsdO3a86jgxf74F+kTQ+QIlxXUQgAAEIBBsAj/lyyfVz5yRnblySZXz54PdPO1BAAIQCDsCjP3tCmnYC2/NQj5r1ixDfdy4cbJz50558803zfd6pFeJEiWM4G7WrJk551vP0dakZgMHDpTx48dLt27dvBEbNWqU9O3bV4YPHy5169Y1mcc1CdqqVauM+PaU9u3by/r1680xY3ocmNrS48Z0H3jevHnNZSqma9SoIRUrVpQBAwbI4cOHpVevXtK2bVuZOHGi11agvgXyWNH5AqHENRCAAAQgkB0ENuXLJzUQ3tmBnjYhAIEwJcDY367Ahr3w3r17t1SqVClN6rp8XM/q1qLiXEX1li1bzLFgKoL1CDDfkpycLCq+x44dK4cOHTJi+4033hA9o9u36N7q3r17y+TJk+XChQvm/O8xY8ZIXFxciuu2bdtmErclJCRIbGysme0eMWJEiuPKAvUtkMeKzhcIJa6BAAQgAIHsIOCZ8d6VK5dUZsY7O0JAmxCAQJgRYOxvV0DDXnjbhTt7vaHzZS9/WocABCAAgfQJbI6NlZvPnpVfcuaUShcugAoCEIAABBwSYOzvEGAWV0d4ZzFQm83R+WyODr5BAAIQiGwCCO/Ijj93DwEIZD0Bxv5Zz9SJRYS3E3ohVpfOF2IBw10IQAACEURgS2ysVGPGO4Iizq1CAAJuE2Ds7zbhjNlHeGeMV0hfTecL6fDhPAQgAIGwJrA1b16peu6c7M6ZUyqy1DysY83NQQACwSHA2D84nANtBeEdKKkwuI7OFwZB5BYgAAEIhCkBj/DeExMjcRcvhuldclsQgAAEgkeAsX/wWAfSEsI7EEphcg2dL0wCyW1AAAJZSkBPndATJijZS+DnvHnlpnPnBOGdvXGgdQhAIHwIMPa3K5YIb7vi4ao3dD5X8WIcAhCwnMDevXvT9LBz586yePFiy70Pf/e25ckjN54/L3tjYqQCM97hH3DuEAIQcJ0AY3/XEWeoAYR3hnCF9sV0vtCOH95DAALOCMTGxkrDhg0lOTk5haENGzZIUlKSM+PUdkzAI7z3xcTIdQhvxzwxAAEIQICxv13PAMLbrni46g2dz1W8GIcABCwnUKtWLVmxYoWoAPctrVu3lrlz51ruffi7tz1PHrnh/Hn5b44cUv7SpfC/Ye4QAhCAgMsEGPu7DDiD5hHeGQQWypfT+UI5evgOAQg4JaDLyWvXri0FCxZMYWrr1q1StWpVp+ap75DA9ty55YYLFxDeDjlSHQIQgICHAGN/u54FhLdd8XDVGzqfq3gxDgEIQAACDgggvB3AoyoEIACBNAgw9rfrsUB42xUPV72h87mKF+MQgAAEIOCAwI7cueX6Cxfk1xw5pBxLzR2QpCoEIACB/xFg7G/Xk4DwtisernpD53MVL8YhAAEIQMABgZ25c0uVCxdkf44cUhbh7YAkVSEAAQggvG18BhDeNkbFJZ8Q3i6BxSwEIAABCDgmsCtXLql88aIciI6WMpcvO7aHAQhAAAKRToCxv11PAMLbrni46g2dz1W8GIcABCwn8OOPP8orr7widevWlXbt2skzzzwj+fLlk7fffls04zklewl4hPfB6GgpjfDO3mDQOgQgEBYEGPvbFUaEt13xcNUbOp+reDEOAQhYTiA+Pl6GDBkix48fl+eee07+/e9/mwzn3bt3l++//95y78PfvV9y5ZJKFy8Kwjv8Y80dQgACwSHA2D84nANtBeEdKKkwuI7OFwZB5BYgAIFME2jRooUsXLjQ1G/UqJEsX77c/L9Vq1Yyf/78TNulYtYQ2J0zp1S8dAnhnTU4sQIBCECA5GqWPQMIb8sC4qY7CG836WIbAhCwnUCTJk1k6dKlxk39V7/X4ivIbb+HcPbPI7wPRUdLKZaah3OouTcIQCBIBBj7Bwl0gM0gvAMEFQ6X0fnCIYrcAwQgkFkC69evN3u5c+TI4TVx/vx5mTp1qvzlL3/JrFnqZRGBPTlzStylS3I4OlpKIryziCpmIACBSCbA2N+u6CO87YqHq97Q+VzFi3EIQAACEHBAYG/OnFLh0iVJjIqSEleuOLBEVQhAAAIQUAKM/e16DhDedsXDVW/ofK7ixTgEIAABCDggsDcmRipcvozwdsCQqhCAAAR8CTD2t+t5QHjbFQ9XvaHzuYoX4xCAAAQg4ICAR3j/FhUlxZnxdkCSqhCAAAT+R4Cxv11PAsLbrni46g2dz1W8GIcABEKUgB4vVqhQoRD1Pnzc3hcTI9ddvixJUVFSDOEdPoHlTiAAgWwjwNg/29Cn2TDC2654uOoNnc9VvBiHAARClECbNm3ku+++C1Hvw8ft/8bESHmEd/gElDuBAASynQBj/2wPQQoHEN52xcNVb+h8ruLFOAQgYDmBLl26XOVhcnKyzJ071yzHo2QvgV9jYqTc5ctyJCpKijLjnb3BsKD1ixcvyieffGJOIXjooYckd+7cxqsJEyZIt27dLPAQFyBgPwHG/nbFCOFtVzxc9YbO5ypejEMAApYTKFeunHzxxReiYtu39O7dW1avXm259+Hv3v4cOaTslStyNCpKiiC8wz/gfu6wU6dOUqFCBYmJiZE5c+bIV199JXFxcdKqVSuZP39+xPMBAAQCIcDYPxBKwbsG4R081tneEp0v20OAAxCAQDYSePjhh2XEiBFStmzZFF707dtXhg0blo2e0bQS8AjvY1FRUhjhHfEPRYsWLWThwoWGw6ZNm6Rr167y0UcfSY8ePRDeEf90ACBQAoz9AyUVnOsQ3sHhbEUrdD4rwoATEHBMYMyYMdK9e3fHdjAAAZsIHMiRQ8pcuSLHRaRQqlUJNvmJL8Eh0KBBA1m5cqW3sd27d0uHDh0kMTFR9P8UCEDAPwHG/v4ZBfMKhHcwaWdzW3S+bA4AzUMggwT27t2bZo3OnTvL4sWLM2iNy1MTeOGFF2TkyJGAsYTAwRw5pDTC25JoZL8b77//vrRt21YqV67sdWb//v3y0ksvyWeffZb9DuIBBEKAAGN/u4KE8P49HrqkadGiRWlGR/cEPvjgg/LYY4+ZRB+py7fffivt2rVL8fGoUaNk7NixcvDgQalZs6YZ3GkbvuXkyZOiewsnT54s58+fN/uWdCZL9zD5lm3btpmlVUuWLJF8+fJJx44d5fXXX5e8efNm6Gmi82UIFxdDINsJxMbGSsOGDa/ak7xhwwZJSkrKdv9C3YGWLVvKggULQv02wsb/QzlySKkrV+SEiBRkxjts4sqNQAAC2UeAsX/2sU+rZYT371Q2b94sJ07on/v/X95++235+uuv5cCBA1K8eHEjvFX8/utf/0pxXbVq1VKcAaui27NnsG7duvLhhx/K1KlTZdWqVUaEe0r79u1l3bp1Mnr0aClYsKAMHDjQ+KCDao+oPnbsmNSoUcOI8QEDBsjhw4elV69eRuhPnDgxQ08TnS9DuLgYAtlOoFatWrJixQpRAe5bWrdubTJxU5wRGDx4sOgXxQ4Ch6OjpWRyMsLbjnDgBQQgEAYEGPvbFUSE9zXiocubVFTPnDnTXKXCe82aNSbJR3pFZ65LlSolTzzxhLzxxhvmssuXLxvBrYPoSZMmmc9035LOZKntu+66y3ymy0qrVKliZr2ffPJJ85kmAhoyZIjs2bPHiH8tn3/+uWi2T31ZoP4FWuh8gZLiOgjYQUCXk9euXdu8mPMtW7dulapVq9rhJF5AIIsIeIT3SREpwIx3FlHFDAQgEMkEGPvbFX2EdzrxWLZsmTRp0sTMbuv5kVoCEd66bFGXjOtMdp06dbzWX3nlFTOzffz4cYmKipJBgwbJu+++K0eOHDHfe4oufcyfP79888035qPmzZtL4cKFZdq0ad5rVNwXKlRIXnvtNXn++ecDfqLofAGj4kIIQAACEAgyAY/wPiUi+RHeQaZPcxCAQDgSYOxvV1QR3unE49lnn5V//vOfZmm37qv2CO8vv/xScufOLWfOnDGz2Lr8+09/+pPXynvvvSfPPPOM+bnvHmw9f/KBBx6Qffv2Sfny5c3/dYZbl5H6Fq2r51Xu2LHDfFyyZEnp0qWL2dPtW6pXry6NGjWSCRMmBPxE0fkCRsWFEIAABCAQZAKJ0dFSIjlZTotIPoR3kOmHTnPdunXL0NgndO4MTyGQ9QQY+2c9UycWEd5p0Lt06ZKUK1dObr/9drOs21PeeecdiYmJERW9uvdaM25+9913oqL6/vvvN5fpLPSrr74q586dS2F53rx5ovsyf/zxR7PkXP+fI0cOmT17dorr+vfvLyredSZcS86cOY29Pn36pLguPj7eiPIpU6akG3/dL+67b133qt92221e8e/kwaEuBCAAAQhAICsJ/BYdLcWTk+WMiMQivLMSbUja+vTTT6/yOzk5WTSPzsaNG0PynnAaAsEmgPAONvFrt4fwToOPZinXfde63FsToKVXrly5Io0bNzbiVvdbe4T30KFD5ezZsymqaSKkNm3amMRpOlOuwltFvLblW/r16ycffPCBN2OxCm+1p8dn+BZdBl+6dGmT/C29okmDdIl76uKZdbfrUcQbCEAAAhCIZAJJ0dFSLDlZ9K9nXoR3JD8K5t6LFCkiPXv2vOpUBz1KzLMqMOIhAQACfgggvO16RBDeacTj4YcfNoJYZ4hV+F6r6DFhL774ondpuWepuQrvPHnyeKtmx1JzZrzt6mx4A4GMEvjll19EV8HoSzpd9eJJpqhbYfS4QgoEwonAkehoKZqcLLpeLA/CO5xCm6l70e1006dPlxIlSqSof/fdd3uT3mbKMJUgEEEEEN52BRvhnSoeKpg1K7lmDdel5P6KZi7X2WjPnm6Sq/kjxs8hAIFACejKmN69exvhrath9MhBXY3D+dOBEuS6UCJwNDpaiiC8Qylkrvp69OhRc6KDbsujQAACmSOA8M4cN7dqIbxTkdXkaQ8++KDoMT5Nmza9Jnddaq5Hgqno9hwx5jlOTI8D8yRE0+PEdF+3LjFPfZyYzqzrmdxadAm4HmGW+jgx3eOtx4kVK1bMXKc2OnbsyHFibvUK7ELAEgK+AvvkyZMmKaOesqCJH+fPn2+Jl/a5cfHiRfnkk0/MgF15aUJMLZqMUhMzUewkcCwqSgqLyHkRyc2Mt51BwisIQCCkCCC87QoXwjtVPO655x754YcfZPfu3SmO+VLhq8eJqeDVs7b1TazOiOsMt+6zvvfee72WNPFH3759Zfjw4VK3bl0z2NMkaKtWrTLi21N0//j69evNMWP6Vldns/S4Md0H7smIrkncatSoIRUrVjQZ1DXLeq9evaRt27YyceLEDD1NdL4M4eJiCGQ7Ac0hoQLbs23lwoULRnwvXLjQJHikpE1AVyxVqFDBrBTQUyJ0q09cXJw56pEXFvY+NR7hfUFEciG87Q0UnkEAAiFDgLG/XaFCePvEQ8W0Jiz729/+JiNGjEgRKc0y/vjjj8vatWslMTFRcuXKJfXr1zf7LlUE+xZP1k3dg3no0CEjtnVJus5e+Rbdg63LSCdPniw6oNZBoc526wDRt2zbtk26d+8uCQkJEhsba8S/+ud7XFkgjxWdLxBKXAOB4BD4/vvvzYu7pKQks5qlRYsWcscdd6RofNmyZVKpUiUpU6aM93NdQfPxxx9L165dg+NoCLaiLPXlhBZdjaSsPvroI+nRowfC2+J4Ho+KkkIiclFP9EB4WxwpXIMABEKFAGN/uyKF8LYrHq56Q+dzFS/GIRAwAX3hpi/ldIVN4cKFzey1JhFSAf7WW28FbIcL0ybQoEEDWblypfeHuoKpQ4cO5qWp/p9iJ4ETUVFSUEQuiUgMwtvOIFnglR75qqtZKBCAgH8CjP39MwrmFQjvYNLO5rbofNkcAJqHwO8EmjdvLosWLbqKR3qfAy5jBHQbkK5E0pwZnrJ//36TCFOPIqLYSeBkVJQUEJHLIpID4W1nkILo1ezZs81qv+joaHn55ZfNdj8tbBkJYhBoKuQJMPa3K4QIb7vi4ao3dD5X8WIcAgET0GzlmgfCd/uJLo0eOnSozJs3L2A7XAiBcCLgEd5XRCQa4R1Ooc3UvTRp0kS++eYbkxxRBbhuBRw2bBinOmSKJpUilQBjf7sij/C2Kx6uekPncxUvxiEQMAHtizr7unr1atHTETT7tuaM0ISMmhTMX9EkjIUK6W5YCgTCh8CpqCjJLyII7/CJqZM7adasmTlhxlM0wezBgwdF896ktWLISVvUhUC4EmDsb1dkEd52xcNVb+h8ruLFOASCRqBNmzby3XffBa09GoJAMAicjoqSfJ6GInzGe8eOHWaJte92iTVr1pgXdJFSbr/9dnN6i29ySU0s269fP9F93hQIQMA/Acb+/hkF8wqEdzBpZ3NbdL5sDgDNQyCDBLp06XJVDT01Ye7cuaL9mQKBcCJwJipKYhHe5hSUadOmmZUwKrw//PBDyZkzZ8TtbdaTZnSZuZ7m4lv0pcT1118fTo8+9wIB1wgw9ncNbaYMI7wzhS00K9H5QjNueB25BMqVKydffPGFqNj2LZoVXZepUzJGoFu3bjJhwoSMVeLqoBE4GxUleX9vLfnyZYmKjg5a2zY11LhxY9GjBLXo0YH6O0CPHf3jH/9ojiCM1PLCCy/IyJEjI/X2uW8IZIoAY/9MYXOtEsLbNbT2Gabz2RcTPILAtQg8/PDDoksry5Ytm+IyTcymSYYoaRP49NNPr/qBvrwYNWqUbNy4EWyWEjgXFSV5fvftysWLEh2hR0b5Cm/FMWvWLNPfT506JT/88IOl0XPfLU1GGckvHtwnTAvhSICxv11RRXjbFQ9XvaHzuYoX4xAImMDFixflk08+MUtJH3roIbOcUovOxuqsLMUZgSJFikjPnj2vWimgR4npMlWKnQTOR0XJ/3qCyOXz5yVHrlx2OuqyV/o7oE+fPimWU+upB/oibt++fS63bq/5wYMHi35RIACBwAkw9g+cVTCuRHgHg7IlbdD5LAkEbkQ8gU6dOpns5TExMTJnzhz56quvJC4uzu8eTpZaBvboNGrUSKZPny4lSpRIUeHuu++WmTNnBmaEq4JO4EJUlHik9qWzZyUmj2f+O+iu0CAEIACBsCDA2N+uMCK87YqHq97Q+VzFi3EIBEygRYsWojNYWjZt2iRdu3aVjz76SHr06CHz589P1w5LLQNDrEmZChYsaFYUUEKHwMWoKMn5u7sXT5+WnKmSaoXOneApBCAAATsIMPa3Iw4eLxDedsXDVW/ofK7ixTgEAibQoEEDWblypff63bt3S4cOHSQxMVH0/+kVlloGjJgLQ5DApagoifnd7wunTkmufN7DxULwbnAZAhCAQPYTYOyf/THw9QDhbVc8XPWGzucqXoxDIGAC77//vrRt2zbFGb379++Xl156SXQfMgUCkUjgclSUeNYoXDh5UnLlzx+JGLhnCEAAAllGgLF/lqHMEkMI7yzBGBpG6HyhESe8zByBMWPGSPfu3TNXOVRqbd4scuWKSI0aoeIxfkIgYAK+wvv88eOSu2DBgOuG+4X33XeffP311+F+m9wfBCCQxQQY+2cxUIfmEN4OAYZSdTpfKEULX9MjsHfv3jR/1LlzZ1m8eHF4g4uK+t/9pTrXO7xvOuvu7vjx41KoUKGsM4ilLCVwJSpKPCd3nzt6VPIULpyl9kPFWHR0tEm2GBUV5c3Mf+jQISldurTs2rUrVG4DPyEAAQsIMPa3IAg+LiC87YqHq97Q+VzFi/EgEYiNjZWGDRtedVTUhg0bJCkpKUheZFMzCG9H4Nu0aSPfffedIxtUdoeAnrMeFe2R3SJnk5Ikb9Gi7jRmuVVNtDhlyhR57rnnRJ9ZLXfeead8++23lnuOexCAgG0EGPvbFRGEt13xcNUbOp+reDEeJAK1atWSFStWiApw39K6dWuZO3dukLzIpmYQ3gGB79Kly1XXqbDT50N/D1LsI3DlyhWJ9slCfyYxUWKLF7fP0SB5dPbsWRk1apSsW7dO+vfvLwMGDJBZs2YFqXWagQAEwoUAY3+7IonwtisernpD53MVL8aDRECXk9euXdscF+Vbtm7dKlWrVg2SF+40061bN5kwYUL6xl0U3jt27BBd4lq5cmVv+2vWrJH69eu7c7MuWi1Xrpx88cUXV62K6N27t6xevdrFljGdFoHvv/9eFixYYFakFCtWTPQ4vTvuuCPFpZcvXZIcOT2HiYmcPnRI8pUsGfFADx8+LIMGDZKdO3eGxWqNixcvyieffGKO+nvooYckd+7cJsb6e09//1EgAIGsJcDYP2t5OrWG8HZKMITq0/lCKFi4GtYEPv3006vuT2dkdYZr48aNQRfeb7zxhkybNs0MhlV4f/jhh5IzZ05p1arVNc8VtzVIDz/8sIwYMULKli2bwsW+ffvKsGHDbHU7LP3Slx26P/mee+6RwoULy7Fjx2T69OlGgL/11lvee7504YLE/C7C9MNTBw9K/lKlwpJJJN9Up06dpEKFChITEyNz5syRr776yuxnD9XfNZEcS+49NAgw9rcrTghvu+Lhqjd0PlfxYhwCARMoUqSI9OzZ86oZWT1KTGee0y0uzXg3btxYli1bZpr9+OOPzWzx5MmT5Y9//KOZqaRAILMEmjdvLosWLbqqeurPL547Jznz5vVed+rAAclfunRmm6WepQR0tcPChQuNd5s2bZKuXbuK7mnv0aNHSL7ksxQzbkHAS4Cxv10PA8Lbrni46g2dz1W8GIdAwAQaNWpkZv1KlCiRos7dd98tM2fOzFbhrY3rXlKdGT516pT88MMPAd+XrRe+8MILMnLkSFvdC2u/NPeCrjRo2bKl9z5VeA0dOlTmzZvn/ezCmTOSK18+uSJiMpuf/PVXKZBqxUJYg4qQm2vQoIGsXLnSe7e7d++WDh06SGJiouj/KRCAQNYSYOyftTydWkN4OyUYQvXpfCEULFwNawJHjx41e9R1aXeGiksz3rq3sk+fPnL99denEEe6ZHvfvn0ZctHGi1X0MXOfPZHRvzsvvfSS2VuvCdT0mde8AcOHDzdLjj3l/KlTkrtAAbkkIjEicmLfPilYvnz2OG1Jq7of+siRI1K0aFGz9SMcyvvvvy9t27ZNkUti//795hnRFT8UCEAgawkw9s9ank6tIbydEgyh+nS+EAoWrmaYgN/EZBm2aGEFj/C+dEkko6LdwtsJlkuDBw8W/aLYS+DciROSp1AhuSAiuUTk+J49UshHmNvredZ7pkuwn376adEXdHruvJ4/r9tTxo4dK3qqAwUCEIBAoAQY+wdKKjjXIbyDw9mKVuh8VoQBJxwSyHRiMoftul599GiROnVEWrVKvymP8D51SiRfPtddogEIBIvA2WPHJG+RInJeRDTP9fHdu6VQXFywmreqnWbNmpl9zzfccIPXr+3bt5v90HqqAwUCEIBAoAQY+wdKKjjXIbyDw9mKVuh8VoQBJxwSyHRiMoftul49kGXkv19z5cgRiS5SxHWXaAACwSJw5sgRiS1WTM6ISKyIHNu1SwpXqhSs5q1qJz4+XhISEq7yKb3PrXIeZyAAAasIMPa3KhyC8LYrHq56Q+dzFS/Gg0Qg04nJguRfppvJgPA+f+CA5LYw43M4nQWe6ThSMVMETiUmSv6SJeWUiOQXkaM7d0oRnzPlM2U0RCvpsYKaZLF9+/ZmibkuOdfv77zzTtFEgeFQwnH/ejjEhXsIPwKM/e2KKcLbrni46g2dz1W8GA8SgUwnJguSf5luJgPC++Qvv0iBihUz3VTqiidOnBA9y1sTkGkyJz1jWY/90UG+7jENpITiWeA6+NfM7SpuKMEjcN9998nXX3+dosGTBw9KgTJl5ISIFBSRI9u3S1GfZH/B886OlnRpufbHpKQkb3+88cYbg+rc999/f5UPd9xxhyMfMrp/3Q0fHN0AlSEQYgQY+9sVMIS3XfFw1Rs6n6t4MQ4BZwQyILyPb98uhbJQlNx7771mdu2ee+6RwoULy7Fjx8xxZ/o1derUgO4rFM8Cv//++40ATE5ODugeuSjjBKKjoyUuLk6ioqK8nA8dOiSlS5eWXbt2eQ0e//VXKVS+vByPipJCyclyZNs2KeqzxznjLVPDCYHe7X1zRgAAIABJREFUvXuLxin17wR9KffWW29l2nRG9q+75UOmnaciBEKQAGN/u4KG8LYrHq56Q+dzFS/GIeCMQAaE99EtW6RI1arO2vOprYPhtJI2NW/eXBYtWhRQO77CWyuEwlngKga1XL58WVQgUrKegCYJmzJlijz33HPSpk0b04Aumf72229TNHZs714pHBcnR6KipGhysiRt3SrFbrop6x0KRYvTpon89ptI165B8z69vp+R3wlpOZuR/etu+RA0iDRkJQFd4fXJJ59IzZo1zdfIkSMlNjZW/va3v5ljPsOtMPa3K6IIbxH5+OOP5fHHH78qMnqu5Ouvv+79XAeS/fr1ky1btkj58uWlV69e5siP1EX3Z+mxHwcPHvR2al226VtOnjwp+jZ38uTJcv78eWnVqpWMGTPGzAz4lm3btkmPHj1kyZIlki9fPunYsaPxKW/evBl+kuh8GUZGBQgEj0AGhHfSxo1SrEaNLPPt0UcfNb97dHbLs6f0m2++MTOSaWWRT6vhUDwLvEJUlNQTkS/Pn5dcufQQK4obBM6ePSv6d3HdunXSv39/GTBggHkx41uO7tkjRSpWlN+ioqR4crL8tnmzFK9WzQ13Qs9mIL8bsviuWrduLX379pWWLVt6LS9cuFCGDh0q8+bNy3RrGdm/7pYPmXaeimFB4A9/+IPUqVNHTp8+LcuWLZM//vGPZqWXbmvQMXm4Fcb+dkUU4e0jvGfPnp1iP2O5cuXkuuuuMxFbvny56KzQI488Ip07d5alS5fKoEGDZPz48aIDTk/RPyr6x2rYsGFSt25d+fDDD81SzVWrVhkR7im6rFMHIaNHjzZv2AYOHCj6Fm7Dhg1eUa3LPWvUqGEGxDpQOXz4sBH77dq1k4kTJ2b4SaLzZRgZFUKIgJ51G+h+ZOtuS5c6e2Zcr7Xs+fcB+G8//CDFb7kly27jwoULZgbAs6e0ePHiorNNjz32WFgL0sNRUVJSRM6cOiWxHM+WZc9Teob0b5j+3dy5c6d89913KS47snOn2dN9ODpaSl65IombNkmJ6tVd9ykkGsgG4a3jBZ18WL16tVy5ckVy5Mgh9evXl+HDh0sFh+erB7p/3U0fQiLuOOkKAd+VFDou37hxo2lHXzLp38BwK4z97YoowttHeCcmJooOONMqujROkw6tXLnS++MnnnhCZsyYIfpQ6zJFnbkuVaqU6OeaaEiLLmHUjl2rVi2ZNGmS+UxtNGzY0GQpveuuu8xne/fulSpVqphZ7//L3nWAR1F93xM6Eor03gkQAoQQSEgPvUlTQUBQUQQs2BFQQEFEFBRRRBEFFUX+KAiI9BLSISGhQ+i9qii9/z2Pmfwmy5aZ3dkW5n6fnyF55b77ZnbffffecwYPHix+N3HiRIwdOxaHDx/O1uunn35C3759sXPnTtTXGA0wXj7PevkMbfS1ANNYTQ/z+s7gxNFu3gTy5787gQrH+0xaGso2ZazWEIcsIDk0//75J4qVLOnQUEZnxyzw5969KOXnh1N58qA8He9t21BGx6wOx7Rzc283ON5uXrExvWEBp1kgMjJSZJFSpk2bhueff178bDjeTjO5MbDCAobjrcLxpkPNqDRTvF955ZVs87H2kSnkaWlpaNq0qbgpY8o4I9lMY5Hl3XffFZFtRuRYU8gb/6lTpwpHXq4xlF96X19fMMWTwls5pr8sYn2XJNSFUb3x48fjtdde0/QwG463JnMZjT3UAgMGDLhHM4JjrVq1SlyCeaVcuwYUKnRXdRWO96mUFJQPCfHKpXqU0pJD89fx4yhZsaJHqXa/KXNu926RWn4yb15UuHULZ7ZuRVlFltj9Zo8c6zUc7/t6+43F62uBmTNnimyufPny5Thb82yuLC/Vd1b3jWac/d1ne3MzG463wvEuW7Yszp07J1K7Bw4ciGHDhon0KkaXGzRoIMBgmOYtCyPk7PPDDz+I9PMvvvhC3Jxdvnw5Rw32/Pnz0bNnTxw9elTUhvNnRrhTUlJy7An7rlixAuTCpXBsOhmmHwTUhVzG/PDQIsbLp8VaRltPtQBLQObOnXsPEjUxE5gW6Y1y59Il+PiSvVid430iIQEVw8O9camepbPk0Jw9cABlatTwLN3uM23O7NiBsgEBOJ43LyrduoXTmZkop2M5hVeb03C8vXr7DOUNC7jTAsbZ353Wv3duw/EGhLPL9O+QkBARgSaFzvTp0zFkyBABksZ6biJxss6bKeKy3Lx5E/nz58enn34qANAYhR43bhyuXr2aw9IEIiFIyJYtW0TKOX+mQ8+acqUQdIbOOyPhFI7N8YYPH56jHXWhU06kWGvCmnH+J8vJkyfRvHnz7AsAz3oUDW0MC6izQL9+/UQZRkWTCKWMraBuFM9qdfPvv5FPTnVWEfE+HheHSlFRui+CvNb8/ClZsqT4/NFDWD7z4osv6jGU/mNIDs2pXbtQXkeUeP0Vzf0jntm2DWUbNcLRfPlQ5eZNnNq8GeUVmWO53wJWVqiT4+3JaM48TykjkPf1fhuLNyygowUMx1tHY+owlOF4WzDiG2+8IbgqGaUmsi+dXUao6ZzLIjveTBvnwZKONxE/ieCqFKbAsv6UwGms96bjzS8YUzoVIqZ/+eWX+PPPP0V3Hnw5HgFOlBIeHi44UMk/a03eeecdMM3dVOTIuw7PjzGEYQHDAjpY4Nrp0yhYvvzdkVQ43kfXrEGVli11mPnuENu3bxcMDX///bcoZWFZDNHNefHIy0I1wiwec8JsIHNUZWrGdHobyaE5npmJSkZ01enmVk5AUFJl1tapzEzhaB/Jlw9V6Xinp6NUw4YC9I8X1X369EHBggXFEOynBDV1qeLumEwnx1sLmjOddGLVsISOZwaW8/DCn+V07dq1Q+vWre2yBAMOPC8RF2fEiBEi5ZfCcdeuXZtjTGYLPvroo2DQgKV1PEP5+fkJvWrXrm3X/EYnwwL3mwUMx9uzdtxwvC3sB1NWGR0m5QlTz7WkmtPxLiTXawJwV6q5EfH2rJfN0MY5FuAlGXk4vVkuHz2KB2SkYAuO951bt+Aj1aQdWbkSVdu00W3JZGwg33KdOnWyxyTy8NNPP63aaSYPKjOCeEBXCg/L8mWibgrrNZDk0BxJTUXV5s31GtUYR2EBc3R0fEbIACKjCbP5yfR0VAgOxsH8+VHjxg2c3LQJr3/yiUDQ5kU1M9P4XcrvY3NOWq42uk6OtxY05+7du0NmXyHDSqtWrUTZHS/R6tWrh1KlSonghFZh4IA4NrxEoQPOIAJZYMwBW3FOUjyxPK9Xr17o1KmTyE5kUCE3ok9rtaW3tffkjAtvs6UWfQ3HW4u1nN/WcLwt2Jj0X4xu0/Hml7wBrub8h9GYwX0W4GGKzpc3Sm5AIr144AB8a9W6a34Ljvetq1eRt3Bh0eTQ0qWoLjEi6LFnzOhJSEi4ZyhLvzc3JyPjzAqiA64UZvgw68cjRXJoDm7YgBqRkR6porcrxcwJgpKaXsgQG0XGM+EaT2zahIrNm+NAgQKoef06Tm7ciN5vvAFyR1OYlcGLIF4QsbTLNDrq7Xayqr9OjrcWNGd+H/B7QemsU0f536a/V2t/eVy5PalST506haysLBCwVimMqsulesrPkPvu4kWtcT28nZaMC2cu5eDBg2BpJy/0WMrJ7Alm1hA7hmf+3JZZYzjeznyatI9tON4WbMa0JtZu84HljSzpxHjryzpvWUj7xXpwUzox/l4GRCOdGA+kTDE3pRNTgrUxlatmzZr30Imxxpt0YrxdpnCM3r17G3Ri2p91o4cVC3gzFRejH/zPm+Wf3btRXKYHtOB4X//3XxQoXlws88CiRajZpYtuS2b0kfSGjHDRUWLKOf/Nzz1mFKgRHtIDAwPFJaVSdu/eLSJkHimSQ7Nv1SrUtjN11iPX5UFKEQiU35NlypTJoRWjl3zGZDmekoJKLVpgX8GCqH3tGk6kpKD70KE5KDwPHTokUo8JbMqf7xvRyfHWgub8xBNPiOwCZhrwooPvMCPVLL0j6CvL4OgUaxVGsefMmYMKFSpkdyVmB0vtWL6nlClTpuD48eOCR5z4E/x84uUez0S8gDHEuyygJePCmSvjZTDBWOl487krXLiwyNai481yityWWWM43s58mrSPbTjegKhX4pdBgMQZykPCjBkz8NJLL2WnUtHh5k0t65HIo03AtdGjR+Orr77KUWvGAyxBniZMmICgoCBRi0YQNEbQ6XzLwi+QjIwMQTPGgyrHYl0l0zL5IUCho0+dqlevDt4KnzlzBq+++qrQl19cWsV4+bRaLPe15+WOqTASxWfr0qVLuW/BXrKiv7dtw4NyLfXt24B80Fbof+XcORSWnJf9CxagVvfuuq6OqeVM32RaOC/6SJXIespcLZKds5YuhZ+OGQS52mYaF8dLHH7HsU7bmhxLSkLl8HDsLVgQda5dw/HkZCzOyBDfd8rPrRMnTgjcE0bM7weh05lHtp01/AedjXH9+nURBeTlCNO7+f3AEjpSnDZr1kyccVgGoFX4PDCiaJoZw+wHc3XbK1euxLx584QDTtBHntX69++vG/ijVv2N9vZbQEvGhf2z2O6pzJK7cOGCuGBioG3WrFmC6je3ZdYYZ3/bz4QrWxiONyAcbEaf+XDyS46HTQK3sP5IybPNFBQ61bt27RK0YHSCSQGmFLl2jaBEp0+fFs42gUD4oiuFtS68cfvll1/ALzimThH9lx8ASmH6FfVgGii/qBjt5u2w7JxreViMl0+LtXJn27p164psCdNDsEenA9vaCjqq770HPP00UKmSrdYe+fezaWko06zZXd0Y9THjpFw8cQK+0vr2zZ+P2o884pFr8SqlJMd714IFqK/zRYZX2cEDlD2WkIDKkZHYU6gQ6l69imOJiagcFuYBmrlXhRvXryO/BCp3+8YN5FFwD7tXM31mzw0YHfpYIvePoiXjwpnWCAsLE6UqMhYT8ZzIksKyFga8cltmjXH2d+bTpH1sw/HWbjOv7WG8fF67dbopzltdXt4wnUopjCgQvMYrhfXDbdsCnToBv//ulUs4nZyMcrKTcf06KQ3uWcc/hw+jePXq4vd7f/4ZdZy5X3T+u3YFyKjgpbX/qh4EyfHeMXcuGjz2mKouRiMdLMDMDTKEKN7XI3FxqBoTg92FC6PelSs4Gh+PKhEROkzm3UNcZaTZ11cs4tqFCygo/eytqzKlF3QVRgcj6kRSV2ZPpKWlITg42O2mZBq9gd7vum1ISkpCjRo1sssdSB9MPAGWSzHaTclNmTXG2d91z5aamQzHW42Vckkb4+XLJRtpLCOnBWbPBp566q6j+NtvXmmdkxs2oEJ09F3dr14FpAiXcjF/79uHByXU8d1z5qBe377OW+uBAwDB3sgtLtEbqp1MDy5wlx1EJcd76+zZaPTEE2qXaLRz1AJmapYPr12LakTOfuAB+F++jGPx8ahsON649PffKML3EMCls2dRpHRpR63vkv5q6QVdgdHBrMNFixaJTC863l9//bVIVfcUkDaWLxro/S55LMUkZFNg1irxTEhvx2xSbwWXVWM14+yvxkqua2M43q6ztdtnMl4+t2+BoYATLPD3iBF48IMPcLhdO1RbvtwJMzh/yONr1qCSBO51++JF5ClS5J5Jz+3cidINGojf75o9G/Wd6SiSk7taNdwpVgw+//yjygB6cIHLE7nsICo5gJkzZiBw4EBV6zQa6WABM473odWrUb1NG+woUgQNLl3C0bg4VMnN2RYqzfjv6dMoVr68aH3+6FGUqFxZZU/1zUw51dX3tNxST3pBRy/zmFrMKCdl9uzZAkSLZX5dunTxCFoy4mkY6P16PHW2x2BpAzGXfH19UalSJRHZ5s+8kCGtXW4U4+zvWbtqON6etR9O1cZ4+ZxqXmNwN1kgrlUrRK9di5X166Ptzp1u0sKxaY8sW4aqErjXjb/+Qv4HH7xnwDNbtqBsYKD4/c5vvoH/gAGOTWql99nNm1GmaVNcyZsXhU2Qhi1104MLXB7bZQdRyQHMmDYNTZ57zmn2NAY2sYAZx/vgihWo0b49tvn6ouHFiziybh3+fvBBvPvuuwKotH379gJTpUiRIiDaNdlC7gc5f+wYSlSpIpb65759KCXTDtqxeLWc6hxaply6du0ayK5Sv3594bR+/PHHGDRokPiPqNCWRA96Qb0u85SON/UlXg+drIsXLyIzM9MOS+rbhdS1BLCTJbfVGFuz1v79+1HLgWda607Itia2EuelrZkJoYU602UZWVoXZ6G9cfbXyZA6DWM43joZ0huGMV4+b9glQ0etFlgTHY1WGzYgPigIkenpWrt7RPtDixejOlPlmWl++jQKmdTg8/cnN21ChebNRZsdM2aggRMjtPvXrEGt1q1xFUAhlUjKenCBy5vhsoOo5ACmf/IJmr78skPPAusDc3O6okPGMe1szvFetgw1OnbElqJF0fjCBRxeswZ9R4/G2LFjBeMHQVD/7//+TyCkE3B0zZo1uqrkqYP9dfAgSkpsFKe2bUN5iX3FHn3VcqpzbJlyiTzHRCEn8CtBXTdv3iyQ5sl+8O2331pUQw96QXOXecs7d8Y7Bw8iZccO1SZgRF/ma5Y7McLcr18/kMrV3cIa4/sBvZ/PhKkMGzYMH330EYh47gpRXuoOHDhQlB1QWHZABH35MsnahYDLMrLMGITAzMQDIHAz/6PtmF3y8ssv30PlKXc3zv6ueLLUz2E43upt5fUtjZfP67fQ4QV4202tmgXnBsf7wC+/oOajj4rlXjp2DEXMoLMfT0pCpfBw0Wb79OkIGDxYjXnsarN32TLU6dgRNwDkV+l468EFLivrsoOo5ABumjgRzYYNs8tWcqe2bduKg5shKixgxvHe//vvqPXQQ9hSrBga//svmHr+5Lhx2Sm45AMnrad8SCYq8f0gZ/fsQZl69cRST6Sno2JQkN3LVsupzglk0DM6JKyPpuPNTANGvWl7JTWU3QrZ6Gh6mXfz2jXkK1QIhNxLUPm55CzdjHG1W6BAgQLiuWKKNxmAKKtWrQI/O61d4mifyXKPIUOGCD54OqoE2xsxYoSo9yafN89HfMYp1i4E9M7Ievjhh/Hrr7+qWuZDDz2EJk2aCHo/lk+wXIIUf7yIZPmEOTHO/qpM67JGhuPtMlO7fyLj5XP/HrhbA3fe1Dpr7aujotA6Pt6rI9775s5F7T59hIn+PXgQxST0cqXNRM1rTIz41dbPPkOjF15wlkmxe+FC1OvRA7cB5NFwwPU6LnDJAdw4bhyav/22KnsqUZHlDjxEnjlzRhyGDFFhAXOO9+LFqNW1KzKLF0fgP//g0KpV6DtmDBITE8WA/H+4dPGkPPiqmM2rm5zevh3lGjYUaziWnIzKoaF2r0ctpzonkCmXmGL+1FNPYc6cOSLdPC4uTlAuhYaGIiUlxW5d1HSUL/OaNm0qHP/zZ87gl/feA0nmpmv4XFIzl9HG+RbYs2ePKB0hovibb74pIrQdOnQQdL6uFH6OLFmyRGRyMHuGEWSimm/dulWUtFCsXQg4kpFFZ5/PMqmK5csHUg+XL18eBwhqakOio6PFO0hhxJtAcRRr7ADG2d+WVV37d8Pxdq293Tqb8fK51fweMbneN7WesCjZ8U4ICkKEl6aaZ33/PfwksLTzWVkoIaGXK+17ZM0aVJUA2LZMmYLGL73kNPNvmTMHjfv1uzt+bj7gSg5g6jvvIGTMGFX2rFu3Lnbu3CnqApXC1Fwe1gxRYQEzjve+335D7e7dkVGiBJqcPw/WfJ8vU0bUcittzZrj3377zXvpD1WYR9nkxObNqNi0qfjVkQ0bUNVFKbky5ZIyi4PRtoULF+KRRx4RKa7vvfeextVob840caYo02EpVrgwimRmohyAmiNGeBwYlilVmvbV3h894uPjMWHCBOHs0ul29ecmSxiUae/PPfecyFZi3TdxBWxdCDiSkfXNN99gwYIFonSGkX6KlssHZabJtGnTBO6F4Xh713tjON7etV8OaWs43g6ZL1d0duSm1lMNkBsc793ffIN6zzwjTPznjh0o5e9/j7kPSjWw/EPm5MkIfPVVp23Jxq++QnM5ld0Bx3vthAkIHjgQxTyVAklyAFPefhuh48apsuenn36K3r17o6xJHf68efPuG2dQlaFMGuWogTfjeO/99VfUeeQRpJcsiaZ//YUDy5ahphR9sme+3NLnWGpqdpT74OrVqNGqlVOW5qk810owrKqVKuH4uXPglVdEeDgSEhKcYgtbg6qlSrM1jrf83VkXCj///LMAt/vggw9caopWrVqJDI4KFSpkzztx4kSMHDlS1No7+0LgypUrYDYH8RLefvttjBo1SgD+qZGZM2fiySefzAFsyMvIMWPGWLSjcfZXY1nXtTEcb9fZ2u0zGS+f27fA7Qo4clPrduUtKLAmKgqt4uOR0LQpItLSPFVNq3rt+uor1Jcc3bNbtqCMGcTm/VIqLgfK+I+XtskbbzhtrQmffIII2bG30/Heu2YN6rRujfUVKyLm+HG7dHWURsjmpJIDmDx8OFpMmGCzudHAfgtk18DzecqT5+5Aimcra/58+PXsibRSpRD855848McfqNmhg/0T5pKeRxISsqPcB5YvR8127XRfmTN4rmVUdIJVEdiMaeqUF154AZ9//rnqNSiztLrFxuK39etF35axsaLWXI3YA0hlbVw9qdI4z++//y7qjs+dO4fXXntNOGT16tXD5MmTBb+3nmIN50WOwJrO9/jjj+eIEDuqj7uBKFlywTRz7qNSePlUu3Zt6HkhYK1+m+VJdJgJ5OZMfBDj7O/oE6tvf8Px1teeHj2a8fJ59PYYytlpgTUREWiVmOjVjvf2zz5DwNChwgKn0tJQXkotVZpEjgjyd5vffx9BI0bYaTHb3daOH4+Wcs2znY73nj/+QN1OnTTXiVM7vWiEbK5UcryTXn8dYR99ZLO50cC2BWzWwN+6Bcg0VPxZcsL3/Pwz6vbujU2lS6PZuXM4sHQpakoUe7Znzb0tDq1bh+otW4oF7luyBLX/Q/XWKrYilspMKL14rmVUdDreBK4aPXo0OnbsaLUW1dy6CIZF/TlOwvTpiHjuOVz7L+unW/v2qmuD7QGksmZjtVRpah1MAtjxEqF///6CGYFYLKtXrwZTiZcvX651u622t4bzwpp91u7LtcfyQKx9Joq9XuJpQJTk9uYFB+u9HRFH67cdmdtaX+Ps7yzL2jeu4XjbZzev7GW8fF65bYbSNiywLiwMscnJXu14b/vkEzSUIswnkpNR0QyAUta8efB77DFhjfRx49BUJRiYPQ/QylGj0Fau37TT8RapwrLjZGUMorGuW7dOHOxKlSoFRrhIIcVauDqKWncCtz399NO6Rl4gOd6JL7+M8E8+scdURh8TC9isgb92DShU6G6v69eB/PnFj7t/+gn1+vbFxjJl0PzsWexfsgS1LDiZpBcrXrz4fWH7AytXZke5sxYuhF+3bhbXbW8KNFGRCZgmix4810qwpwsXLqBnz57o06cPZs2apTpSbbrQhI8/RsRrr939tYbPJXsAqaw9XGqp0tQ6mLLjrdST81sDzLL34beG88KIO51v00iwvfgVNi/h7F2EA/3MvSO9evVC/vz57f5uod1Kly4tvrMcqd92YFlWuxpnf2dZ1r5xDcfbPrt5ZS/j5fPKbTOUtmGBdaGhiE1NhTeDq2358EM0fvNNsdJj8fGoHEHCnJyye84c1JMAz9LeeQfBKsHA7HmAVowYgXZy3Z2GA65yrn2LFqG27CRYGOP1118HEV27du0qKFF4+F+8eLGgRjluJj3dEle4PWsUfSTHO2HoUER8+qndwxgd/2cBWzXwty9cQJ5ixe52uHIl2wmXn+/UcuUQcvo09i1ejNoPPWTWtGodmtywL/uWLs2Ocu/+v/9DPYl20NzabKVADxgw4J5ujG6SH33Lli0izVYWWzzXBD1jvaklkVHRC0mXLASuovPNcZVOvpY9in/vPUSOGnW3i4bPJXsAqbTo5aiDSX51UkTt2LFDIFsXKVJETE8093SdAUOt4bx8//33CAwMvIcPevfu3SL1XavYvISTBnTlRZq5d4RlEbwcUhvVZwbH+PHjBb0hEf/53fXXX38JKjJelthbv63VvmrbG2d/tZZyTTvD8XaNnT1iFuPl84ht8AglnF4768JVrg8ORkx6OhIaN0ZEZqYLZ9Zvqsz330fgW2+JAQ+vXYtqsbH3DL5z9mz4P/WU+P2mUaPQbOxY/RQwGWnFa6+h3ccfaz7gKofZ83//h7q9elkdwzTCI/fnQZaUK6x75KGUNXlLly4V6K9MC7Qm5iLoBMsxK7Lj/dxziJg2TZU9rdVI0hkxxLoFbpw7h/xlyohGt//9F3mKFhU/7/zuO/g/+SRSy5dHyKlT4MXN+7/9ds9gdBSJgszvs/tBsn77DX7du9+10Y8/wl+iHTS3dlsp0ORPnjt37j2pxLwA27Rpk1lz0hkzFe4BnQuZyshcRxkVXQlgdevWLcEDzswVeyR+1ChE2pGJYw8glRb91DqYlsY8fPhw9p8qVqwooq+sS2cmEC8l9RRX4rzYuoST1+XKizRb74jS1jdv3swBYib/jeBs/J6h3ny2mKbOyyR+P9EZpzirfpsXZKRkCwoKEtRnRDXnRQ2dfq7NnBhnfz3fIMfHMhxvx23oNSMYL5/XbJXTFHVZ7azTVnDvwOubNEFMZiYSAwIQLnFaunB6XabKGDsWTaQI9sGVK1GjTZt7xt3+9dcIePZZ8fuNI0ei+fjxusxtbpDlQ4ei/Wef3f2ThsiScqydP/wA//79rY7BFEYiyTJKIAsjYqQp4gHRNAXdz8/P6potRdCZwv6JSSo5nQcfqb44/tlnEfnVV6rsaa1GUi3Yk6qJcmmjK8eOoXCVKmJ1wgkvVUr8vHPWLPgPGIDkihXR4sQJ7F24EDHPP69ENZOAAAAgAElEQVTZUcxtZts9fz7q9ewplrXju+/QQH6nzCzUVgp0v379QPRmOndK4Tv4/vvvmzUdL75eeeWVe5z1H374AQSjcqVsGD4cURMnOvS55Ax91TqYzpjbm8a0lHHhyos0c+8I6+gHDRoEZmeMGDFCoIZT5BIAUxvze4sXwd26dcOiRYvERQnFlKZMy97YyiCRx2LWF0uxmCVASjJmq5D+jPXpvAwwJ8bZX8tOOL+t4Xg738YeM4Px8nnMVrhNEX4xuKR21oUrXN+oEWK2bUOivz/Cd+xw4cz6TZU+ahSaSpGc/UuXopYZUKmtX3yBRhJn58bhw9HciSjcy4YMQYcvv3TogLt95kwEDBxodQx+Jr355psi2nb79m3B1xwcHCw4Xu1B87UUQTf3exHNkA5M8U8/jUgrabPKnbZWI2k43rbfiYv798NXSmm+cvw4CktO4I5vvkGDZ55BUuXKCDt2DFm//IJxv/2m2VG0rYFzWjiLjmvX3LmoL0W5t82ciYZ2RovtXXWLFi1E+UcZKUtBHqdTp07C+XC2KDNMKmVkoK10ITjz669hZJg42/r6jm9PxoW+GpgfLTw8HEuWLBFI53Rgy5cvLy6iLNXY80KYTAB8J/bs2QNGwNPS0sB3gs6wNbE3g0QeU/n9w3dTjrBbuiRgP+Ps74qnSP0chuOt3lZe39J4+bx+Cx1egKUaWd1rZx3WVP0AcQ0aIHrnTiTWrYvw3bvVd/SglmkjRiBYqqneu2gR6nTpco92W6ZORWPpSz3l9dcR6kQU7j+eeQYdv/nGIcdbeVFgb9Rc6xZZi6ATJVgp5D4tKNWfbnjiCUTNnq1qOms1kocOHVI1xv3c6PzOnSjRoIEwwcWDB+Fbvbr4efuMGQgYNAiJVasi/MgRiFIFK/XMnmRDZ9Bxyevb8f33aPDEE+KfW7/8Eo0GDdJt6Szb+MjK5whTyX19fcUlGKOSrI/l5a0rRZlhMn/GDKw4cwbVNNKJ6a2vPem+euvgivFk0DC95rIn40Kvua2NYxqpJq/2qVOnkJWVhbi4OLNdWdPNiydikZQsWVJEx5UYCaad5Is51u3LGSQnTpzIzj5Rm0HCS4LExEQxPP/Pf1OUDrnp3MbZ3xVPkfo5DMdbva28vqXx8nn9Fjq8ANbl8cvCntpZhyd30gBx9eohes8eJNeujRZ79zppFucOu/GNN9B80iQxyZ5ff0XdHj3umTBj8mQ0ef118fvkV19Fi8mTnabUH08+iY7ffXd3fDtTzTOnTEHgK684NIbWBWqJoF++fBkPSCBGcY8/jugfflA1nStrJFUp5GGNbHEm/5mRgVJBQULrf7OyUExCrt/+1VcIGDwYCdWrI+LQIeyeOxf1JBR/eYm2HEV3mYJAYqxppuhFxyWvZds336ChhB2Q+fnnCJSyXvRYqxzRM4dbQFsz0k1ngpFKOgl0wom/YC4tXS22gqW6WUvrUToU3/TsiRnz54NXgkM18HjrYSvlGPak+9qjgytBx6ifNdAwUsHlVmHEes6cOVDiEbAkg/bg86pVTDF0lBdzBKrLzMwUDrcySq02gyQjI0PUcjM7TBZeIv/2228gOrs5Mc7+WnfQue0Nx9u59vWo0Y2Xz6O2w23KkJZJa+2s25RVMfGGOnUQtW8fkmvWRIv9+1X08Lwmqa+8gpApU4Riu+bNQ32pplOp6eaJExE0fLj4VfJLL6GF1N4Zq1naty86/fTT3aHtdLyV+to7huna7D6ITp0KEKiLNXASoNrFixfhKwF7xfXujWh5vc4w6H00pi3O5DMpKSjbooWwyN/bt+NBKfq97Ysv0PD55xFfqxYi9+/Hzjlz4N+3bw7LOYNeSY+tUTreHE8POi5Zr61ffYVGgweLf2ZMmYImNlJZtaznnXfeAf8zh1tABHKCNrH+tVatWmA2Bw/75rKjzGEr8IJq8+bNKFu2rKq6WUt6KzNM1jz9NGp9+y0eBXC2WjWhkyOitq5WnkOOWrJWmVgUFDpB5LmmWEv3tUdPZ4COWYvWDx48WDig/L8MGsazAjEA5JRmLeuwBURp6SKNmRaVK1cWwJp8/pydaUHwTqaZm9Kocb+tRbFNbWEJQ4eXkXS2KeRmZ134r7/+ii5duoizmLPFOPs728Laxjccb2328urWxsvn1dtnKG/BAhtq1ULUgQNIqVYNoQ4exNxl5JQXX0To55+L6XfMmYMGJg4Hf58+fnw2d3fSCy8gTAY/c4LSS3v1Qqf/+7+7I9vpeG8aOxbNZMozO8cwXZrdB1HJ2caePYAE0Cac+BIlxBTrH30UMfJ6nWDP+2lIW5zJJ+PiUCEmRphERL8DA8XPWz//HI1efBHiIm3vXuyYPTs7xVq2n+woepo96cANHz5cEx2X2jWIKPeLL4rmmydNQpDMY612gP9qPG05mOZwC4i5ULRoUcG5PXDgQHz99ddiRnPOpTkMBabAygj0aupmLS1HmWGyun9/tP7hB5z4j4nuzccfB9Nz1YijdbWcwzRqyRRjgmoRXZqZAXQUaUcCc5ECjPRbBOzKly+fTRVdCTpmKVpPJ5Cp5QEBAeJCg2nWdES534z6EpRMq9gCojR3kUZnfOPGjSK7Qk2mhVadLLVXviP2ZtZYwtBhejmdb1n0vJhTs37j7K/GSq5rYzjerrO122cyXj63b4FnKsBaXqIMt23rmfrZ0Eqkph4+jNQqVRBy5IhXriFlyBCESmBm22bPRkOpplO5mE3vvINm774rfpX03HMIU0l/ZY9Blj78MDotWHC3q51O88ZRo9DcDuqfhx9+GMWLF79HbYdopGTHm5Gphg3F2KzRKykhaq/v0QMxv/5qj6mMPiYWsMWZfGzlSlRu1070OpOairLNm4uft06dikYvvQRROrJ7N3Z8+y0aSPR597ORRZRbKtlImzABwVLWizmb2OtgmsMtaN68OQoXLgwl1RVTWonkvGzZshzTm8NWaNy4sQCfkrEV1NTN2trnVb17o83PP2v+XNIDmV2Z1UA6J9bXylFLHx8fYZPHHntMfHYx5ZcXFuSF/vbbb20tSziYWmnebA5qoYElcC7qzXRlXjDQSWT0lhcu1H/MmDE2QcPMTWcLiNLcRZr8LJL33VamhT02UPOO2JtZowQ7U+pWrlw58bwoo+fMmGDN+9GjR+1ZhqY+xtlfk7mc3thwvJ1uYs+ZwHj5PGcvPEaTy5eBIkVwJ18++Ny44TFqaVEkoWpVRBw9io2VKqG5l3L7Jj3zDMIkMLMtM2eisRnkYsHdLTmyCYMGIUJGHddiLJVtl3bpgk5LluA2gDx2Ot4pb76J0A8/tHpIzpMnj+Dr5sGVjjXl9OnT4KGLB1f5d7La1viGrS5NcrzPLF+OspLTJ4CDJKTm9V27IsYMZ7RKcxnNFBawxZl8eMkSVJPAA0/Gx6NCRITovWXKFDR+5RWs9/dHzM6dd8HWZFR8eXw+I8Q24MWUCcp2bt0EEeWWuOs3jRuHZm+/bXGp9jqYjuIWmMNWYEYJkaKbNWuWra8jdbMcZOWjj6LtL79Y/UwxZxw9kNmtlRMQXIufV4x4EoyLn2cU5SWUtefTlaBjlsC56Hhzz2TQMNbsJyQkiKj+rl277Hq97AGiVDrrtjIt7FFKzTtib2YN6S55ieIODB1r2AnG2d+eJ8V5fQzH23m29biRjZfP47bE7QpdOnYMRSROXXsjm+5eRFKlSgg7cQKbKlRAsxNMQvQ+SXrySYRJYGYZX36JJibIxUzzK7RsGZpLyOcJzzyDCCn10xmrXdqxIzotWwbCyuSz0/FOfuWV/9WhWxiD1HYLFiwQ0RSmkVM6dOiA0qVL20UjZRHYa8wYFAOw76efULt3bzHP6ZMnUU6islrXuTNilyxxyJS20nkdGjwXdT7466+o8cgjYkXH165FJYnDPXPyZAS+/jrWNWyI2G3bsG36dDSUapuzl5+WBjRrhttRUchjAW04F5lKLEVEuUeOFD9vHDMGzf+ry7YkejiYetlPr7pZpT4ru3VD20WL7v5Kw+cSdSHXsRKQSus6rZUTMJ38qaeewo8//giCZ8mOd2hoKFJSUrRO5dT2lsC5+Lk7Y8YMXaOy9lzoDBkyBJ999lmOFH1LmRb2GEqPd8RaaQBrtu3B0FGLX8Kad5Zu8NJaDec4bWSc/e15UpzXx3C8nWdbjxvZePk8bkvcrtD5PXtQol49zQcZtyuuUCCpQgWEnTqFtHLlEHzqlCepplqXxMcfR/iPP4r2mz//HEEmyMV0Ssc2boxQCfk8/qmnEKkihVG1AiYNl7Zti06rVoE5EPk1HHCVwyQ+/zzCv/jC5rN15coVEG2fQExvv/02mJLKGjh7xCKw13PPgXGyPbNmoe6TT4qhTxw6hIo1aoif13XogFiVc6pJVbRH9/ulj7j8kDAMjq5YgSrShUvGRx+hybBhWNekCWIzMu7WfJu8B4eWLEH1Ll1w/oEHUOLSpfvCZCLKPXq0WGvqW28hRC7fMLN6PRxMZxnV3rpZWR8Cdf3asSMeXr0a+fnL27ezgRKdpbPacb+TGSAAkYovR49J1faelf2yNL6jtlKr9/3YTo93xBmlAWrxS7RyjnOPjbO/Zz3phuPtWfvhVG2Ml8+p5vXKwc9mZKCMRO2jJYLgSYtNLlcOLc6cweYyZRB05ownqaZal8TevRH+88+oCeBaqVIoWIzx2bvCdOszZ85g9bPPZkeQ4/v3R6TisKd6IpUNl7ZsiU7/odleB1DATsc74dln/xeVVzEG18hawv3792PlypU5NFV7ELUI7OXjA2LH7vzqK/g/+6wY+1hWFirXrSt+XteuHWKXL1dlHTWpiqoGuk8bZc2eDT+pdvvw77+jWqdOwhIZEyeiyfDhWNu0KVqmp2PLp5+i8dChOay0e948QTHmyHPpDLMz04K1sYx0MVW3VKlSAmiLz605vAItOogo99ixokvK8OEInTBBS3ePaWtv3awSKfr2sWPIc/48HgTw+ebNaNSkicesj4popUqzpLy9tvIoY+RiZRwpDXAUSM8eznHj7O9ZD6PheHvWfjhVG+Plc6p5vXLwk4mJ2TWW3up4p5Ypg5Bz55BRqhSanDvnlfuQ8OijiPjlF9ANnP3hh2gh1XTKiyF40dh69dBCQj6Pf/xxRKpE9LXHIEujotApPh7XABRU4TSbm0NE5WfPvvsnO8eQx1V7ELUI7CU53tumTkVDCSH6yI4dqBoQcNfxbtUKsatXqzKVHqmKqibKpY12ffUV6ksp5AcWLEDN7t3FSjdPmICgkSOxJiQErVJTkfnxx//jgZdsse/nn7NLBRx9pvQ0b/fu3UVdZ9euXVGiRAmcP39eIF3zPwJWOSIiyv3++2KIpNdfR9hHHzkynMN9mQbMVFetYm/drBIpekWrVmi3di32Ang6PBwbEhK0qqFLe3vSfbVMbK+ttMyhpa3aNGgtY95PbXn2Juo9hRe3/D6rWrWqoBOTyx/U4pfYwzlunP0962kzHG/P2g+namO8fE41r1cOfnT1alRp0+au7g46R+4ywMZSpdD8r7+Q+eCDCPzrL3ep4dC8CT16IGLhQnwKwG/UKHSQIlzyoPPmzUPl9esRLgGqbejTB1FSarpDE1vo/Ed4ODomJTnmePNyQNbRwWdL7UHUIrBXoUL4gABekyahsUTHdCgjA9WlbI91MTGIVcmnqkeqojP2zNPHlGvgd3z2GRpIkex98+ahtsRZv3n8eAS9/TbWtGiBVsnJyJw0CYEm1Fm7v/0W9WTgQQefKT3tZRqFksc2R7OldV4lSGHiyy8j/JNPtA5hV/sjR45g2LBhIpKvlMcff1w1tZS9TrpyPiVv+IroaLSTaK0iQkORkJxs19oc7WRPuq+jczq7v7Vovdo0aGfr6Mj4Mgd7zZrMK7sraWlpCA4OdmRY0ddWRpb8+fDWW2/h559/BlHxyaeuRL0nV/r70gWbNYXswU4wzv4Ob7GuAxiOt67m9OzBjJfPs/fHHdodXLIENSSEYW91vNMefBDB589ja/HiaHT+vDvM6PCc8V26IFIC90p+7z20eOute8ZUpm7H9eqFaJlWx+HZ7x3gj5AQdNy4EVcBFLLTwdnwH6VOlINc4LotTUIZFs6dBFS1PzUVtUJDxRTro6IQc5+AdelmUwsD2aqB3zZ5Mhq+/rronTVnDvykeu/0cePQdPRorImMRKv4+Lup58OG5ZhlxxdfoIFc923nc+mM9T/xxBMCnZ8Rb0a0eDgmoveBAwdgzh7UQa0jkPzaa2jx8cdC7cQXXkD4Z59pXoKtFGhzOhJRmojWSlRyTrx161bhMCiFTro50eKkW1oU8R+WLl0qMgqOT5+OSvv3YylBGN99F29Ite+aDWJHB2XUsm7dugIMUubqpiN16tQpwX1NVHNPFmvR+urVq+dQnUjtjM6uWrVK1AlrlYMHDwrcDoLPkee+fv36YogXXngBn0vZW2rGJMUkqdvsFSUHOx1vctLz2TbHSW/PHLYysoh6zxIUe1Hvrelky+lnX+Psb8+uOq+P4Xg7z7YeN7Lx8nnclrhdoX3/93+o3avXXT086CCrxTDpxYujKXlHixVDwD//aOnqMW3jO3VCpATulfTuuwgzc6BMGDAAEbNmCZ3jHnkE0fPnO03/ZcHB6JCejisACtv5XMT16IHohQtVPVsEThK82iVLigOR7iI53mljxiBYQoXel5CA2pGRYqp14eGIdVPaqu5rdfOAtmrgt3zwARqPGCG03D17NupJnPXp776Lpu+8gzUxMWi1fj2UlyTykrZOnoxGr7/uEM2dM8xD+jsCbMloxkTlZ7T7ySefRIECBe6ZUosjkPTSSwibOlWMkTB4MCKmT7e4BHtToM3t2RdffIEiRYqIywOlsOyFjphSHnjgARDB25T+z5yTbo/99+7dK2ybPG4cWhw7hhhmBhHPw4WUcrKTxqglua15IZCZmZkdtTRHleaJTAeWovVEYmfaM7nXZZE/k5kmvWnTJs1bx2eFKdR0vGm30aNHo2PHjsKZ536aE0sUk+XLl7/nWVSrkJIKbvbs2YIz/ZdffhGp3pb0UDs228kZWZbqt3/44QexdnOo9yyPIgifvWLL6ee4xtnfXus6p5/heAOYP3++eCHS09PF4a9WrVogpcGgQYMEZD+FX6BK5Ep5O5YtW4b27dvn2B1+IPM2jzegDRs2FC8VgVaUcuHCBfGBxJefVAn8UGdaFm/NlcIb1KFDhyI+Pl58Cfbu3RsffPABChcurPmJMF4+zSbL9R12f/cd6kkoz97qeGcWLYrAixexw9cXDS5c8Mo929C+PaJWrBC6J4wahQiTVHP+XgCqSXXdwql1IAJgy0jLmzRB+8xMEDe6iL2O90MPIfr33+9OZWEMJXCSjARMJ4Cfn40aNbKlpvq/S473xhEj0FxK58tauxZ+rVqJMda1aIHYpCT14xktLVrAVg18xtixaDJmjOi/c8YM+Etc3eJSZOxYrGndGq1Wr0b62LFoOmpUjnkyxo9Hk/+Q728ByGvnc+kJW6fFEVCyA8QPHIjIGTMsLsHeFGhze0YKQyJymwIdki6LkV6l8F0lbRYdcKWYc9Idsf/K4GC0TU+/+5Fy/Dh8JDpAR8ZU21d2vBm1ZN1+oUKFxHrpODE7gHL69GmUK1dO+si7I5zzbdu2qZ3CJe0sgXMxUn/p0iWhM8+ZFFKM8YyrNg3adAFKp5Bn3p49e6JPnz6YNWuW4D03J5YoJqmHvWKNg52XJ3qJJbRz+g90zimmqPeJiYkOOf9qyrCMs79eO6zPOIbjDYibWjq8BEjhhyZvwCZMmICXX345+yaKLw6dXzroSmHqjBK1lB9a8odUUFCQSGnhh/TGjRuFEy4L06ZInzN58mTBL8nbMCKj8oZYdqoJ0BIQECB0I8UOUX9fffVV4ejPmTNH8xNgvHyaTZbrOwiUZ5kr10sPslt8fdH40iXsfuAB1PNSiqENrVsjas0a8bxtGDkSUePH3/PsbejbF1E//SR+H9et2/+iyU54Slc0bIh227c7FPFe36EDYmSkcAvPlhI4SV4Go1tPP/206jpSVcuXHO/k119HCym6sHvFCtSTLk3XN2+OmNRUVUMZjaxbwFYN/OZRoxAkUSxtnzYNAc89JwZMe+stBL//PlZ36IDWy5ZBmZ0gz5g+ahSavveeQ/zynrB/WhwBEeX+6qu7nw1PPYUoKzSC9iAec1xbe2bLZnTSAwMDxVlGKeacdFtjWfv7ysBAtN2yRTS5uX8/8inqdR0ZV03fGjVqWOTqpvPGc59pxJ+RTpYUeJJYA+eic6wXtSPXzOecDjYvKSiffPKJSMVfv369ACC0JHpSTHIOaxzsR48e1W177EE7V+M4O6qgcfZ31IL69jccbwBnz57NkV5DE9PBnT59uvhwKFiwoIh4E4iBERpLwsg1Hfdnn302G5Dk1q1bwuHmjTBBFSipqanC2WfdEtNuKKyRYqSdUe/BkiPE1KWxY8fi8OHDYOoa5aeffkLfvn2xc+fO7HoZtY+E8fKptdT9027bp5+i4csv312wlzre2x54AA2vXMGewoVR9/Jlr9y8DbGxiFq/Hv8CGNWqFbq//XZ2tgyjKrwEzBw4MLtmen2XLohZtMhpa13p74+2u3Y5RNu0vnVrxKxZYzUtWAmcpFyMpd/bu+BnfHwwkzWyQ4ci/FNC2AE7lyyBv4RvsL5pU8Skpdk7vNFPgwXS3nwTwRJgl/j8kYDW0oYPR/DEiVj10ENos2QJNo0ahWYmmR+bhg1Ds48+cohfXoOqTmuqxRFIeOYZRHzzjdBlQ//+iLJCI2gP4rHTFumEgVc1bIg20hns2s6dKCjVDDthqnuGtMbVzXMbHWxlmjYH6NSpkzjneZKoAeeSqR0ZZT506JBm9eWaf56Zq1Spkp0FwJp/BraY7s3LVVtijWLSVl/j7/+zgHH296ynwXC8LewHbyr79++PEydOoEKFCqocb36gMB2JkewmCn5JAm8wsk1KBtbRkKt26tSpIq2d/5aFaTm+vr4ClIXCGjFSkyxSHLDp3DPCPn78eLxmgvhq69EyXj5bFrr//p45cSIChw+/u3Avdbx3FC6MBlevYm+hQqhzhVXJ3icboqIQFR+Ph0g3EhKCMuHhSEpKEjVo/AxYs2YNSOAjp5ev79QJMXIatxOWu6puXbTJynIopXd9TIwALLOWFqwETpJBqXhQZYojQWO0ikVgryefBBM+lTWyOxYsQIOHHxZTxDVpgujNm7VOZ7S3wwKbXn0VzSRkbiXKfNobbyB40iSs7N4dbRcuxKaRI9HMJPMjdehQhHz2mUNo+3ao7NYu8U8+iUjJ2RZZL1ay3dQ4VW5djIOTr/b3R+tdu8QolzdvxgMewuP95ptvCkRqmRrKwWW6tLs1cC419cPmlHV2zb9LDeTAZGqAzxwYXnVX4+yv2lQuaWg43hbMzKg1URR548YPU0a8SenD6Pfly5dFNIrp36zXkIVgJM8//7z4u7IGmzXkrG1hSgtBKvgzbwRZE6UU9l2xYkV2alLZsmVBsAbWdCulQYMGYE0WqXO0iPHyabHW/dF287vvIkiqPfJWx3tXoUKof+0aDhQsiJpXicPtfbIhPBxRSUmI/k/1d156CbFTpojPGLk+kAegd4oXR7R0CbeuQwfESmBszljt6jp10FpOkbx9G1BcEKqdb314OGKSkkRacN7bt3NcMirHkIGTiJRcqlQpgYfh5+endpoc7SwCe73zDpjwuWHAAERJ0cNt8+ah4WOPif5xjRsjWsdaP7uUv086pb74IkIkROPMDz5A4JtvipVveuUVNJsyBat69UKbefOwafhwNJswIYdVkgcNQosZMxxC23elmfUA14rv1w+RkrOtlc1Aj4O/04EPNWzImrp10SorS/S4kJyMohIrgYYhjKYmFrDmXNubBu2qmn9P30x7Ly70Xpdx9tfboo6NZzjeZuzH9Bg6toxMkwqB8umnnwpkRjq9TD9nGjpBR+hUP/LII6INo9Djxo3DVZPD/+rVq0GQEfL28QOJP9OZJwKpUjgXnXdGwilE9+V4pGFQCtMw6ZQvWLDA6u6zZpz/yXLy5Ek0b948+wLAsUfH6J0bLJA2ciSC5cOtl0a8swoUgN+NGzhUoACqX7vmlduyITQUUampIMb2u88/j5aff45p06aJizyKcLx9fbPByta1a4dYk88PPRe+pmZNtDp48O6QN28CefNqHn59aKiom2bE+86NG+Lz09liEdjLx0dQEMX164fo778XamydMweN+vUTP8c1bIjorVudrZ4xPoCUQYMQKgGEbR43TnB3Uza++CKaf/451vTvj1bff4+Nb7yB5iYc0klPPomw775zCHvAGZtgi0LNkTk39O6NKKlMTSubgSMHf5cBH2owztratdFy/37R4/y6dShhAlqrYSiPaMqLVQZjeGHI8yAjxazTd6XY61xb09FVNf+utJM9c9myrR4Xc2r0MhxvNVZyXRvD8TaxNZHIQ0JCxIchASAsUdvcvn1bAEfQsWW9NYWON1FACQyhFFJvkPORwGmMYtHx5iHUFKWRdAtffvllNkcm5+Z4TGNSCpFLSa1gi9eQLz3T3E1Fjry77jH730xMmZUpV+ToVuvWrd2hijEn8QZeew0hEkest0a89+XPj9o3b+JI/vyoev26V+7rhmbNEJWWJuqQqz77LNpKYEpcDMtLeAnYYetWREvIrutat0asCaWPngtfV60aYmVuXl5mmKFEsjVfXHAwoiUE4quXL6OQHUwMtuYw/btFkCgpYr/+sccQM3eu6LZl1iw0HjBA/LzB3x9RO3Zonc5ob4cFkgcMQAuJFi99zBhBIUZJHTIEIV9+ibXPPIOWM2ci9dVXETJ5co4ZEnr3RsTPP4NIDg940EWhLQo1O8yU3WVDz56IkqgD47p3R7SNC3flXLYO/tb00gJ8qBdfsxPFkyIAACAASURBVC07ratRA7FSzfGfy5ejVLt2trro/ne9MgCYjUDQXZYXEg2bZY38mTzTTFu/30UvO9OODJA9+uijYPCJJZo8izOrirR+tWvXdrqpnXkxp0Z5w/FWYyXXtTEcb4WtWYPNNEdGrBMSEkTaozUhTdiwYcOyU8vlVHM63jKKo/zSuyPV3NMi3qRPI91G165dRd0qMwcWL14s7Ey0S0Ncb4GU559H6Bdf3J3Ygw6yWixxMF8+1Lh1C8fy5UPlGze0dPWYthuaNkWUVGO8euBAtDZDGRTXrh2iV64UOq9r2RKxEgq6MxYRV7kyoo8fv/tYXLoEHxOaIDVzirppKX374vnz8C1eXE03/dvwuZZoIdc+/DBa/vKLmCNjxgw0GTRI/Bxfrx4ipdpR/RUwRlRaIKl/f4RJtHjKOu6UgQMROnMm1j3/PGKnTUPqSy8hZMqUHMZLePhhRCxYgIsAfD3o88oWhZraJ4AgXS++SDSH/4mgDly4UPzC2aCKynm1AB/aw9es1ibKduurVkWMhEJ9dtEilJHAEe0ZS2sfvTMAGOAh0C454AmsSxAzZkLqDSypdZ2m7V0VlZXn1dvOHJeggwz68Bzeq1cvAXpH2/NiSg8eb1s2dubFnK25+XfD8VZjJde1MRxvydZ0ttu1awfWGyYnJ9/Dp21uS3hbxmi0XNNtgKtZf3AJFkcqCVOx9HvXvQb370xJzzyDMKnm9c7Nm/CxI6XY3dY7nC8fqt26hZN586IC06K9UOIDAxEp0eSsGjAAbaQ9yXEAb90a0ZKzvS4mBrHr1jltpRsqVkTUyZNi/FvnzyOvHU7zhoCA7Cjy+VOnUELit3Wa0uYGvngRYEaHxBu9pksXtJLq5DdPm4agF14QveL9/BC5Z49LVbtfJ0vq3RthUur0xmHD0HziRGGK5KeeQovZs7H+1VcR8/HHSHnhBYR+9lkOM8U/9BAif/8dFwAU9SDHWysdl4z6bPoMEPWZabo53vuuXRG9eLH4lbNBFZXzagE+tIev2Z7nX3kheHr+fJSTyvzsGUtrHy0ZAGrGZpCHWZUU8oCTepYi84WrGUPPNu6Oyspr0dvOHJdZlXLJJzNQZXGWrfkOK0sG9LqYs3e/DcfbXss5p5/heIsSxpvo0aOH+MLjf6zDtiVMNSclGJ1umWJMphMjHZgMiEY6MY7HFHNTOjGmmpOTm8L0b6YYmdKJscabdGJy9J1j9O7d2yvpxHgrTq5LfknLwi8eptPzQ9EQ11sgsX9/hEvRp+sXL6JAkSKuV8LBGY/lzYvKt2/jdJ48KHeLFcXeJ/ENGyJSoslZ2b8/2pqhDIqLjUW0dFBbFxmJWJMDup6rTihfHhGnT4shr50+jYJly2oePr5+fUTu3i36nSMlYtWqmsdwtMO1119HQUW6suCIlkDp0qdMQdNXXhFTxNeujci9ex2dzuivwgKJjzyC8F9/FS1TXn0VodL+JPfrhxZz5iBu+HBEf/ABUp57DqHTpuUYcUPbtohatUrQ7hXzIMdbxbJzNNGC+hzXuTOiJUqqde3bI1YqN9E6pz3t1QIfmvI1M4rL6KItvuZsnYhrU6JEdmaKJV3jK1RA5KlT4s8nfvgBFR9/3J5l2dVHSwaAmgmGDBkizntK7AueIQnYa1qGqGY8R9s4Oyq7f/9+Edm3JXrbmfNNmTIFx48fB8/tTGHv3LmzADfm2fobM5fctnS09XeWlhIDShatF3O2xtf6d8Px1mox57Y3HG8AgwYNwowZM0S9R2Qk4Y3+J/7+/uBLQ1RzOrz84OC/Ca7GCDfrrLt3757dgbfEdC4nTJiAoKAggTxOEDTW8tD5loUvfkZGhqAZK1asGEaPHi3oxlh7IiOiMxU7ICAA1atXFwjqRFgnvzgj83OsUIpYemTc/fJxfmYIbNq0SXwAMq0qODhY2KqqGw7lzn21vGP0hMceQ8S8eULZS2fPoojEF+8d2t/V8kSePKh45w7O5cmD0t7qeDdogEgJK2JF375oOWuWAFksWbJkNs7E+qgoxMTHizWvCw9HbEKC07YpqUwZhJ07J8a/fPQoHqhcWfNcCX5+iJCc2dN796KcC2rpTJVcFRKCNhs3Zv96dZs2aC0diNImTUKwRFmWWLMmwiXQJs0LNTposkBCt26IkLIOkocORQuJV12OhMePGYPId99FyuDBCJ0+PcfYMt/9PwCKe7HjrQX1Oa5DB0RLQIrWsB1Mo2yaNsXBxqQ+rFGjhqBelYVBB0t8zSyDIy82z0QBpUtjUsOGyBcejmF//CHOQ5Ykvlw5RJ45I/587JtvUFnCaHBQfVXdtWQAqBrQwxrpGZU1zdrgUlmWyfJM0zO2qRmcZWc6wmQmogPO71Wmn5My2BKOk5rtYbDMVO7cuSPO6pcuXVIzhEvauPvs75JFetEkhuMNCMeWN1/mhM41vySfeuoppKen4+zZsyhQoIBwGIk2TidYKXzp+MHx+eefi3pmfrHQoVdGedmeXzysef7ll19EjQ9TXnj7Wa1atRzjZWVliZov1pzzlpzO/8SJE3PQlal93oyXT62l7p928T16IFKqHzx/9ChK2OFgudtaItJ95w7+9vHBg6S+8kJJqFcPEXv2YDuA3mXKAOXKoXjx4uIyjpEIfp78NWSIoOeirAsLQ2xiotNWmlKqFEIldoWLBw/Ct3p1zXMl1qqF8AMHRL/j27ejUoMGmsdwtMPC6Gh0V2QGrIqJQRspRX/ThAloNnKkmCKxenWEyyjujk5q9LdqgYTOnREhRXCTn3sOLaSodlLPngibPx+J77+P8JEjkTxwoKAOU4pMu3f+vyhWCS92vLWgPse1bYtoKT12fcuWiLGA7WAaZdP7MeSZZenSpXjssccsUgOqnfOhhx5CkyZNhHOyetEiPLZ/P3zy50daly7iTGRJEsuUQbh0IXjkiy9QdcgQtVPq0k5tBoAuk7l4ED2jsjwj88xL0DieiSkyyPC3335rc2XeYue6deuK7FNT/nZmdypT2m0u2MkNjLO/kw2scXjD8dZoMG9ubrx83rx7ztF9Q+fOiJIOwef27kVpN0QlHV3ZOR8flOZllhennyb6+SF8716QSKZ/+/Z4RpFOykPI008/jbE3biAmJUWYa11oKGKTkx01ncX+Gx98EM3P070B/tmzB8Xt4NVOql4dYdKF5pHNm1G1SROn6Wtp4KXt2qGTIuVvVUQE2khZAxvHjkVzqfY7uWpVtLBw+epypXP5hPHt2yNyxQqxyqRnn0WYhOCf2KMHwhcuRMrHHyP0v8yu5KefRouZxPn/n2wICUHUxo1e/a5r3d64Vq0QvXat6LY+OhoDZLYBxUCuiLLFRkYiLSEBKdu3C1pVR0SJ61KrUiXsP3ECZ/PnR8/wcKtgV8klS6LF33+LqQ99+imqDx3qiBpGXydZYM+ePYJRh1kQzHJkFkOHDh3ckkLvpCWKYUkzzGAY6X2Vwsg6Qdw8RYyzv6fsxF09DMfbs/bDqdoYL59TzeuVg8s1k1T+1LZtKB8Q4HXrEJFuwOOQjrUYMqlWLYQdOIAIACN79EBHqQZWHoN1b+OvX0fkpk3iV3HNmiFWkUKtZS41bdNKlEDwP0zoBf7atg0l7XgukqtUQYtjx8QYB5OTUSM0VM3UurZZ27EjWiouMVaGhKCtdHmROno0QsaNE/OlVK6MUAktWVcFjMHusUD8fwjDkZIjmThgAMKlGsuErl0RsXgxNk2fjmZDhkBwdku0Y/IgG4KCEJWRIejECt++7XDk1Ru2Jy4mBtESKOn6iAgMOnPGLVG2qfnyYeitW0hbuRLBbdo4ZDqmG8dLF2AvPvQQPiNgXr586BIRYdXxTi1RAiHS59LBDz9EDalUxCFljM5OswD3mKWEBDdj3bonRYGdtmgPHNg4+3vWphiOt2fth1O1MV4+p5rXKweXayap/LFNm1A5ONjr1nHBxwdFAVwFUMhL00+Ta9RAi0OHMAnA96VL44nhw0WKOdP/mN7JaEHzn39G2ObN8CEYWNOmiE1Lc9pebS5WDEEXiB0NnE1PR5mgIM1zpVasiBAJGX1fXBxqRzGe71qJb9sWkQoU2xXBwWgnXV6kjBiB0A8+EAoJXSX6NNdqeP/NFh8djUgp/T+hf39ESECCCZ06IeKPP5A+axaaPvUUBO2YCcigDEIoSAOvX3eoPtNbLB8XGYloCc+BJSZbe/Z0T5TNh588QPpXX6Hps886ZD5i3xA3h8Bii199FV0++QT8HB8/bFg2MK25CTYVK4bGFy6gAID9772HWm+95ZAe7uysrHNnSSLrn1lO+PLLL1utc3enzvbOTVDgzMxMq3tr79ha+unJDa5lXne3Nc7+7t6BnPMbjrdn7YdTtXH3y8cPPQKqsB6mT58+KFiwoFgvv4TJFWmI6y2wISICUVKt8KGEBFQPD3e9Eg7MSJC+a3nzojAAHsbze6vjXa0aQo4cQR4AM2JigN698eeffwo2A9LO+Pn5gbzYIZmZok1iUBBi09MdsJz1rlt8fdFYAoc5nZKCciEhmufaVK4cmklASHtWrULd1q01j+Foh4ToaEQoarxXBgaibUaGGDb5jTfQYhKvOoDUChUQcuKEo9MZ/VVYID48HJESVkF8nz6I/PFH0SuhXTtErFyJzLlzEdi7N5L69kWYCYhoQv36iJCQ8i9duIAivr4qZvTuJnFhYYiWykrWh4Rkl5s4uirN38eS45368ccIkdgAHNWB/RcNHoyuX32F63nyoIANcMz0okVR++JFFAewd/Ro1Hn3XT1UcMsYyjp3gtN16dIFJUqUEHzT1urc3aKsl0/qDG5wbzKJu8/+3mQrV+hqON6usLKHzOHul69v374CvZy33CtWrMD8+fMFmJxeXIruRHX1kC3WrIZcM8mO+1avRu1WrTSP4c4OPDyiQAHkB0BYtTxe6ninVKmCJseOgVdRv3fogM4S5ZXSthsaNULTbduQl6nRgYGIkRxIZ9h/e5EiCLjMhF7gxIYNqGjC9qBmzrQyZRAsASHtXLoU/h072uym9zucEBKCCEVK/sqAALTdtk3okfTKKwibMkX8vLFcOTSXaIpsKumkBpodISfp4exh40NDEZmaKqbZ0KsXoiRO74T/uHYj1qzBtoUL0bB7dwiU859+yqFOUu3aCJPQ58+fO4cSpUo5W12Xjs8LaF5EKyUuNBTRqakgUWJ8cDBipIwNRxXT/H0sOd6J48Yh/O23LU5PkFgCwqqVhf37o/sPP+BqnjwoZMPxzihSBBUvX0Y5AFnDh8NvwgS103hcO2WdOyPe26TPJSUnurOVppNPAGHlJS/TwnObaOUG1/t7yJn2VJM54e6zvzPX741jG463N+6anTq7++Vj5I68nhTeQBIwihyKQ4cOxVqp5s/OpYluzkZ1dUQ3T+0r10xSvz1Ll6KuCufIk9Zy5fJlFFZwj9+5eRM+eemaepekVqqEBidPwvfOHfzeti06S+BTKatWYXa3bhi7fz92t2qFwJ07kY+OYqNGiNmyxWmL3Fm4MPyvMnkfOLZmDSq3bKl5rs2lSiFIQkYXzlS3bjbH0PsdTmzcGOFbt2bPu7J+fbSVaNuSX3wRLT7/XPxNXBJI0XmbSjqpgWZHyEl6OHvYhOBgREjZGhsefhhREop1QmwsItavx85ly+DfoQMSe/VCuOSUyzolV6+eDYJ39uhRlPFCFgau5fvvv7/HzDIjiuyAyQ02BAcjVLJXUlAQYnTKdNH8fSw53nFvvil41o+YAXmjzo8//jjM0UlZeq4WPvoouv/yCy7nyYMHbDjeWwoXRvGrV0GOhT2vvYa6UsaKM59ZZ12IKevcp02bhueff14sw1WON1l1SF/77LPPikg76WsXL14ssqw++eQTZ5pUt7HVXhxo5QZX+z30xhtvCBA1shy5S0g53KNHD8EQYClzwt1nf3fZxlPnNRxvT90ZJ+jl7pcvJCQEqVKkg8s7dOgQHn30UUHRxp/VirdwJ6pdjzvbxTdqhEjppn3Hr7+iQY8e7lRH89wX/voLRRVRr1tXriBvoUKax3F3h40VKsDvzBmUuH0bv7dqhc6rVwuVfvLzQ5+9e5E4cCDubNgA/6wsFLhzB2kBAYiR9s0Zuu8pVAh1r10TQx9ZvhxVTWgT1cyZWaIEAiUgpMyff0agAuXVVe9wUv36CJNSk6nzqjp10CYrS6ifNHiwQNTmKreXLo2mZ89i3759yJMnD5T6paWlueRgpdkRUrMJHtgmITAQEdKl0YZu3RAl0RkmREUhIj4eWevXw+8/2rfERx5B+Pz5OVaQWrlydi3+yawsVKhTxwNXaFsl4je88sor2VRLco8ffvhBPINKiQ8MRNCWLaLEZGPjxojOzLQ9gYoWmr+PJcd77YsvouXUqaIeOTQ09J410JljBFWtLOzcGd2XLsXlvHnxwM2bVrttL1gQBW7ehN/t29j94ouoN3Wq2mnsbqf1QkxNBJLKKOvcZeWuXbuGMWPGuKQWmhF3lvuR31opyki83Uaz0PHmzZsi49FU1NpM2Y8XB6Ts7dq1q82LA0vc4Fu2bBG0nUrRwhBAKuJatWoJPfic8NKpSpUqeptNjGfpoovZEqQdpVjKnHD32d8pBvHiQQ3H24s3T6vq7n75pk+fLnjPlYfaEydOCLoJHjjUirdwJ6pdjzvbKWsmt86di0aPPaZKHbU3zaoGUzTSOu75kydRomLFu2nmAK6dP4+CJl+kWnVwR3vWQ1f/80+UuXULv8fEoLPENf1rQAAe3rEDW2JjcfHIEdQ5cEAAyG3290fMjh1OU3VfwYKoff26GP/g4sWo8dBDmufaVqwYGkoAbenff4+m/fplj+GqdzilVi2ESlzinHxVzZpoI6UqJz39NMK+/RYkTTtYsiRWvfkmFi1aJDAo+Bn19ddfC/AuvUphbBlQsyNka0An/d3Ry4nEhg0Rvp2M9UBc586IXrJE/JwQHo6IpCQcSElBzdBQJHbvjvAFC3KsghdUcknAkS1bULVRIyet0rnDtmjRQkQXy5Qpk2OiTp06CTBFpSQEBCBgxw6R6bK5YUNEKTI45HYPP/wwfjVhQrC1As3fx5LjvfqZZ9D666/RqFEjpKSkCAdcKWo4jA8ePIi3335bOGHBe/bgxdRUXMybF8MHD8bnUhaKOf13FiiA23fuIODmTewaPBj1p0+3tUyH/671QswTa7fNXXSePHlSXJpclTKbaChmJL733ntYLV382mu85cuXi3IDXmKOGDFCAOlRLH2W2mMzSxcEln5vjhuc8zrCwy1nJxw9ehQ//vij+I/vdP/+/bPXbK8NTftZuuhKSEiAKLkDYClzwt1nf71skFvGMRzv3LKTKtaRW14+b+FOVLElbm+SWKcOwqUIy+ZZsxAkfUFaU0zLTbOWBdoz7p8HD6JUzZqCXojHv8unT+MBE05NLTq4q216mTIo/88/qHTjBpZGRqKTBAj2e1AQOmdkYGNEBG4eO4bqR46gyJ07yKhXDzFSyrQzdD5YoABqSF/m+3/9FbXsyITY4euLBhJA26ZvvkGzAQOyVXXVO7yxShU0lyjNOPnqatXQWsquSezfH+E//IAzPj44XqIEnq9XT6TqUWbPno25c+cKkCOCHrEOUi+xVD+o2RHSSyEN43z44YcOX04k+vsjZNcu4Uiu79ABMRKeQUJoKEJSU3GSDnXjxkjq0gVhixbl0C69bFmRmUA5kJqKms2ba9DetU2t1YmSrYDcxrzksSUJ9eqhXlaWAI7c0qCBeO+JjeLj45MdbWbErXz58jiguGSyNa7mv0uO98o+fVBh+HCcOXMGzZo1uweBe/fu3ahXr57V4emc8/Oejvdz3brhk4sXEZk3L7r8h+Bu7V3Lyp8fF/LmRdNr17BzwAD4S1R0ysm0Xt7asoPWC7HAwECB4E1xV+226ZrMXXTyPMj9K1q0KAhSymeRKdOk/yIWjyMSHh6OJUuWiIg6HXA+m++//77FNHp76t35DI0cOVKMKYvWiwPl99D3kyahRkAAItu3hxoebr7fzE4wfV6ZITVnzhxMkfBDqBvLRypXriyYSngpQSeadedaxNJFl7+/vygZUGYSmGZO5JazvxZ7eXJbw/H25N3RWTfj5dPZoLlguKQaNRAmOSKbvvwSzQYNsrkqrTfNNgeUGtgz7umdO1GuQQP85eODknfu4N8jR1DMSaleatdhTzvWQz946RJqXLuGP1q0QEfJAVzetCnab96MlIgI4PBhVDxxAkVv38aWOnUQs2ePPVOp6nMkXz5Uleot986bhzo9e6rqp2y0q0gR1JcA2lKmT0fo4MGax3C0Q3r58mh6+nT2MGsqV0Yria874bHHEDFvHo7myYO/ihbFEH//bMebHf744w9xWLx48WL2QdpRfdhfbf2gHnPpPUZYWJjDlxNJdeuicVYWitDxbtsWMRKeQWKzZmialoa/srJQ0c8PyR07ooVJ9DejVCk0kXADsuLi4Kfx8Kq3PayNp3mfeaFAlPbC5Gj4nxBQrubBgyLTZWvdutj7+utYsGABXnrpJfEsUUg3SJ5kp4rkeE+vXx+Fdu3CnTfewIAPP7RrSmUd88+BgfhuyxZ08/HBvJgYq3gvB/LmxenChdHi0iXs6N8fDUzo5uy5vLW1AK0XYjINJMfVq3bb0csEV110yrakU6ms8x81ahROnTqFrKwsxEmc9Eq721PvzvMssyU3bdpk38UBS1z4+SGXqvn44DCAaioBWvnu8fPwnXfesfoIsQ5848aN8PX1RaVKlcAsT/7MLAR+v6gV2pOXOrywU4qaiy7j7K/Wyq5pZzjerrGzR8xivHwesQ0epURy1apoITkiqVOnIkQFGq0eN83mjGDPuCcyMlAxKAin8+RBudu3cX7/fpSoWdOjbKxGmYySJVHk6lX4XbmCZc2bo4OEhbCqcWO02boVSaGhyHf4MEoTyfnmTWyrVQvRe/eqGdquNify5UOpW7cEyvqeOXNQt29fzeNkFS4MPymNMWnqVISpeLY0T2Kjw5ZSpdBYctTYdG2FCmgp0YYl9OiBiIULsT9fPlx64AFMffRRDB8+HLVr184elRGUfv36gamEWsVVdexa9XKkvdLx5jj2XE4k166Nevv340E63i1bImbNGqESKfICMzJw4cgRlK9aFalt2yJEcsplnbcWL45G//4r/rlr5UrUb9PGkeXo0le3ffbxwckqVVDBBLQsuUYNVD1yRDjeO+vUQeSePbhy5QpYt7p582aRsk3HhnvhVJEcb+aEhAHIqFEDTeyMsPM5IqBqoUKFsCQgAO3+K6fpzmegeHEB8mVJeEl2oHhxRJ8/jx2PPYYGc+fmaGrP5a29NrO073SsCHRlLQKpnNMWt7QzLhPsXbPafq1atRJR3woVKmR3mThxIt566y2wzttUXF3vfufUKfhUqIArgYEonJGBW7duIa9ce27ieDv6fssZE9evXxf14MQzYnaBJcA3tTbW0s44+2uxlvPbGo63823sMTN4ystn64vGYwx2HyiSWrEiQk6eFCtNmjQJYa+9ZnPVDt80W5jBnnGPpaSgcosWOJovH6rcvIlzO3agtL+/zTV4WoMtJUog782bCLh0CcuDgtBeQi5e5++P2F27kNSkCQqcOIFif/8tHO/dVasi6uBBpy3jNLnRb98G79Z3zZ6N+k88oXmu/YUKoZYE0JYwaRIiVDxbmiex0WFH8eJoIDlqrFhPLFsWsVIEPL5zZ0QuXYrdBQrgZsGCCJDa6aWDq+rY9dJXzTiku3L0ciKlRg3UOHwY5e7cwfroaMRITBdJjRujwdatuHbqFMqWL49NLVuimeSUy7rtKFoUDS5eFP/ctmgRGnbpokZtp7bRY59v3riBfAUK3NXT5OCfUrUqKpw8KRC/d9eqhUjFhRvTvZnuun///ntAsuxZNMehc2BWJMf7HIDSrMlv0AARUq2+1rlY0lGjRg3hmP3h54eOe/eC8FC/zJwp2E4syWkfH2wtUwZtzp7F9ocfRoCEiC+3t+fyVqvucntH910tt7QrLxPstYVpP5ZSMM3ctP6f+BDKi0295tM6zon4eFSMihIUfXnv3MG5Y8dQWs6UM3n/HN1nJUbAwIEDBXYIxVXYIZzLU87+Wvcpt7Y3HO/curNm1uXul0/tF819tCVuXypBvZpJNErx48cjcuRIt+ukRYHD69ejWmwsBBjYtWs4k5mJso0baxnCI9puK14ct+7cQaMLF8AodzupRnBDzZrCwU7290eBM2fwwMWLKHbzJg5WqIAIC3Q+eizorzx5wLhE2Tt3sGPGDDQYOFDzsIcKFBA16+RY3zBhAqKGD9c8hqMdsooUgZ+U7v6vjw82lyyJGIlbPL5dO0SuXImthQsjX5488JccOkfnlPu7Or1TL72dPU4qHckTJ0Qpw/qwMMQkJoopU/39UW3XLuQ/dw6+pUtjW2QkgiWsA1mn3UWKoJ60nxnz5qGJHSUQeq9Pj30+sX8/KsqZFiYH/42VKqHU2bMocusW9levjnAJHNDRdZij/Bo2bBg++ugjMPU3hzBKmZ9vMnAJEGUCcQEBiNaBWWF59epof/gweDFGxgZrwpIiXhZ3OHEC27t0QYAJBoA9l7f22tHRfVfLLe3KywR7bWGrH9Ot+Vx5ihxetAjVZHrLO3ewLz4eteWyldu3AemSifo6us9DhgwBue1NMyC6devm/PIQyeDuPvt7yr57ih6G4+0pO+ECPdz98qn9orHXFPxwI5CHIeotkE4aJYn6Je4/oJBoG/VK6kd2TcsDK1agZvv22F24MOpduYKTGzeiQrNmrplcx1l2+fricv78aHj+vDjQtpEOtEmVKiHsxAmk1K6NQn/+iXzXrqHojRs4XqYMwo4f11GDnENd8PHB33nzCudo27RpaPjcc5rnOpI/v0hXJxjc+rFjETNqlM0x9H6HDxQqhPzXrmEhgIcKFMDRIkUQArYjSwAAIABJREFUJaWeJ7RsiYh165Du64sHbt9GfQkIzqaSTmqgRHpmVLl+/fpiphdeeMEq0rOT1HHasBsrV0apM2dQ7cYNkNM7ZtMmMVe6nx/K7NuHksw8KFoUu0JC0CwlJYceewsXRh2pfGHTd9+hWf/+TtPTlQNvXb0ajeS0eRPnM618eZHp4nvrFg5WqYJwnTJdChQoIICpWHdKdGvKqlWrRN34t99+m2P5d/79Fz4mbBHvV66MkUeP4ty5c3jttddE2jtB1SZPnqwJnGtVpUpoI5V/gLgSechPca9Qx0t58iCuRg10OngQ2zp0QENnp9c78SFQyy3trMuE+fPnCzpXoptz/wjQ5efnBwIo6h2VdhU3udrt2jNzJuoOHAiSFT565w5WTpuG2S+8gK0AanfujEmffKLJBu7M4vz999/RuXNnq++hu8/+avflfmlnON73y057QLqJ2i8aW1tiic+QHIrmbvFtjXc//z3zwQcRKNXUrRsxArEawD48wW77Fi9G7a5dsc3XFw0vXsSxhARUDg/3BNU06UCH4i9fXwScO4fk+vXRWkIs31S6NJr9+Sc2Vq2KQv/8gzu3bqHo9es48x86auipU5rm0NL4mo8PjhYoICjFtn76KRoNHaqlu2jLOnHCRD146xbWjRqF2LFjs8dw1Tt8PH9+7CpeHBe+/hqB/frhWL58iJSe94TISEQkJGBjiRIofu0a6kqRVM0L1amDEumZtZCjR49Gx44dLSIB6zSty4dJq1ABxf76C5WvX8cmBS81a4Z9jxxB9atXcaFAAexv0gTNNm/Ood/BggVRQ6K5S5kxA6F2ZGK4fMEqJkz98UeEPP743ZYmjvfmMmVQ6OJFceF2lBdxhwkB5bjs2bMH744ZgxqlSuHNCRMEaJMlkLZrR4+ioAnSdZNChZBx5YqgTuKlOnmMSUNFQDEiN6uVdSz/kJDqwXfQBFxOHoc1sihYEGvq1UOr3buxu1UrNHKA9orRfZ5JyFpgTZx1IWaJW5p7wAixs4V12Iy6xsfHo1evXiCVXep/2CIEC9OTxYHr4Ji2QMj0WK9aWr0tEyag8ciRaAVgzZ07CK1TB2/s24dOZL6YMweTZ85UZQNXZnFacrCPHz8uAC+tvYeG463H06XfGIbjrZ8tPX4kd798en3RWOIz5I3tn1L01uM3w0MUVIIVrX71VbSePNlDNFOnRtb8+fDr2RObS5RA0PnzOLJ2Laoq6EXUjeL+VnQoTj34IBqcOYO0OnXQUkIszyheHE3+/RdE5y506RLoEPtev47zvr5oLh9WdVb/zu3b8MmbFzsLF4b/lSvInDQJgXbUZ7NO/HbevKhw4wbWDhuGlhMnZmvqqnf4LNdRtSqiDx7EvmLFcOa/6EaYxC1O+qrQ1FSklyqFkpcuofq//+K7774TwDd9+vQRNYoUAv+wttnZoowKXbhwAT179hR6zJo1yyrSs7P10nv8zDJlkO/iRVS5ehVb/P0RJfHRb6lUCfnOnIH/9esCLPFowP+zdx1wUR3P/3uUox5NelEQFBSQLojSFJEYu2lqNGr8aRJL1BRjoqaoiTGJacZomho1MUWNXVEUpIgiKAoIqCAovfd+/Jn1Hf+TUO4QFM0tHz7H8fbtzszuvrczO/MdO7i1cmUmL4q+HDhT5LffwnPRou4m75G0F7F5M4aLvLVaKd5xOjqQq62FRl0dsg0M4CGWHu9BiW2aPRvhO3fik9Gj4R8YyFxf6dS7dSlPSIDAzu6+fzvz+YitrUXrGGRpTzfDtbUxgjOGFaelQdvcvE22KisqoCYQ4ISjI7yvXMENHx84cPgA0sqBwOnyVVWxA8CaTtzbpTWIxcXF4cMPP4QzYXUEBmLhwoVQU1Nj6aUoJZR4aSu3NJ06d3dpy9BJzxbKs03POfExf5ixx8QnnRYrcmEMrfmm5y49f9sqRHtX0+pFv/UW3D7/HP4A9sTEYLqLC+5BPAKZkZGY+d57Ej1ze9qLU5xv0bi0VrApRzrteztah49679/d8/lxb0+meD/uIygF/b1h8XXHi6a9fIb0gmxr0yCFiP5zVSnX8oDKShCsT9DixQj45ptOZVBWVsbc0cgqXlRUhD59+oAARMhKr9nKHbHTxiSo0JH78fU9ezDoxRfBToYLCpB24gQsxoyRoNXeVeWOoiIyDA0xKCsLVy0s4MvlVk9UVb2n/PbpA+WaGlQoKkK1rg7VSkpwEUPr7k5u6isroaiujjiBAA7l5bi8YQOcVqyQuosCOTmUKyvDoroawa+/jlFieU0f1houlZND7IAB8EtORpK2Nsrq6jCUcyknN2eXmBjE6elBr6wMa6ZOZS6yFIt38uRJkCsmbewe1kZUHOmZhE0nfKR8E7J6R0jPUg/MA9xAa5+ME5SfmH4pbpOMKEuXLv1Xmpv2uonX1kZDXR1MqquRZGUFr5QUVvWavj4ay8rgWFODOzwecq2t4ZqUdF8z2QoK0BQKodrUhLDPP4dXFwxCD8B+j90a8uGH8BWF+bSKMb2mpcVQl8ngVqStjaHteLp0pKS0SzgXy1p97RoOxseztHkbNmz4V/Wi8+eh05w6SbwIeDy4+foiISGB5Q8n5ZKKi4sLYjhwSEkEdkEggDuHr+A/YgROh4W1eVtJfj609PVxysMDQ6OikDZsGBy5tIuS9CNeJz4yEnYiz6hOFG9pDWJ0iv7RRx+htLSUpXz7888/2dqgMDhKC/YoSluGTsrUQKelr732GlN+yV05KioK6enp+LmN/OgPRLdozhoa3tfMoW++QcHrr6Nw3ToYtEoDSqEFdFhDObDbKkRjV9PqnZ87F8O2b8dXAC65usLg0iXUN/89DsDR6dNRoawskQy6y4tTEtmK3kOtFWwyWhAmQ0frsDfs/SXh8b9SR6Z4/1dGuhe4mneXqB8kn2F30fCktJOkqgoTihsWCnFiwQIEbt3aKWuTJ09mL+mJEydCS0uLKQWHDh1iv//880+n97dXoSvuxwk//wzbefNw3sgIw7KzcevwYViOo9fn41VyFBRww8wM1hkZuM6d0BIHt5SUYFlXh3hNTSjRxltVFaq1tWiUl28JEehuTqsIxM3AAJe0teFaXIyYtWvhsmqV1N2UyMkhV10d1uXlCH71VYzasqWljYe1hmt4PETY22PU1atI0NVFXWUlnKqrGR0RDg6wv3oV1wwNYVZUhFnDhjEllwq5EBK6Mm3ulixZItHphyQC6sgVUhzpWdQWKVw7duzoEOlZkn67q8748ePh5OTE0iURveSmS88AUij+boUw3V6f1zU0UMnjwbiyEql9+2IEl5LqurY2Kuvq4FpZiTTKrW5hAZdWQGLkwSDP40GHgNnWr4dvLwCDpE2tqakpY5fGipROinVesGDBfYBKHY1B8LJlLYapppoa8DhvC7onQUODebqo1NejSlUVCZs2/aupzpSUdvvmFO+7+/fDdDIl9Gq75Jw8CcPAwPsu/qOqCicuJMbY2JidWpJhhgyy9G7oqMydO7flcuGvvzIsCIoyD9LXRyaXdaD1/XlpadBvBps84+cHh7NnkeHqCicOH0BUlxRISbxWon/4AW4LFty7rRPFuz2DGBn5aR20jpEmZZZctqkMGzYM58+fZ393ZsBL3L8flYcOwW0HncN3b2nP0Emn8rSeiWYdHR2Q+zmdqLZ3At1Vqig0Sr+kBE319eCJ0nYBCDMxgVdWFgR8Pt56770WrAFRP7t27QIhobdXuppWL2LqVAzfv581+6W/P+JPnwYhpugAsJ4zBzpOTuwQgWTRkbHxh+Z5dPToUbYfEuVvp+/i4QKSzsnOZEvt01i1VrDtmj2DqE8q7a1DmeLdmXQf7nWZ4v1w5f1Ie+t1i49eeGTNbOV+9UiF9B/r/IayMjQbG6Hf0IATc+YgsBWoTlviIPeqtmLp20t7IqlIu+J+HL9lC+wWLkQ4beAzMpDy998YOHWqpF32mnqFcnJIsLKCVWoqbtFmhIvjpJhkU0ojpKYGfkMDcjU1oVpTAzmhEPacy3R3M1GalgbN/v1x3sAAw3JzcWnNGrh++KHU3ZBylaqtDfviYgTPnYtR3X2K0hlFdHIoL49gNzeMungRcYaGkC8uhh2X4izS1hbWiYmINzXFwJwcTHJ2btkwU9OUb5XAh/Lz89nfHRWqQwqo+Ib1QVwhO2PtUV0XX+N04i06jZLGvfimmhoKlJVhVFaGTCMjeHLo/CkCAQhN36O0FCmUW93YGE6tkPvJmEMghMZ1dTizejVGiuEGPCqZiBQqissnBYbiZSlHNbl/tgYpa4/GU3PmYDSncFVlZkLV2LilapK6OvN0Uaqvh1BBAb48HpYtWya1ktJm35zinbhpEwYvW9auCO/89RfMWiHIX1ZWbjFiSSt7AnT7/fffGQ+p/v7oz4UPLLWxweXr19tsLishAcZ2dggdOxbWx44hx8EBjlz2B9ENFGcuiddK5Nq18Fyz5t5tnSje7RnESOG5fv0680oRj5EmYxSddlOJiIjAcO5kXTytVEdjUZWfD1VdSth2r3SHl8nDMnS2Ow+4eVackgLtAQNaqp2ztIR3airstLVxNjkZenp69zVBcecipbKjOSZtWr0wf394cd4HYWPHwksMpC/myy9xadkyUAby8UIhMy52ZGzszItT0jnZ2RoiTwRRkdbQ1ev2/p0x+4RflyneT/gAi7PX2xZfHFmuQ0LQePky5B0d/0Mj0XtYTePzIScvj341NTgxbRoCf/utU+Jeeukl5oJLpxoiK+/hw4eZu+Gvv/7a6f3tVeiK+3Hcxo1wWLECIQMHwjclBcl798L6+ee7TMOjupGluho8GJYpKcgwMMBwLo6TFPI+TU2gnNiKjY24Qy7ntbVQqa3F4B5C4S6Ij4euvT3O9esH7/R0XHzrLQzduFE60TQ1QSgnhxgjI7hlZyP4xRcxatcu6dp4wNrCykrIqavj9IgR8A8LQ6yJCdTy8mBdT06FwHlra/S/cQOJ5uYYkp6OPzdvxpgxY9C/f/+WnrOysrBixQrQyUt7paamBiNUVOAxcyY2i83/B3GFfEDWe+x2cmkkMCYqBKJF8atUpFG8bysrI1NTE4YlJcjv0wceHKJ1qqoqslVUMLywEIl8Pup0deEoQrvmOKrg8ZCnqor+VVU48+abGNkLUhSJFG8ySIaGhoLHKRnisupsQIKmTkUAdwJXdO0adMTiqW+qqKCQDG/19eALhZhnZ8e8i7qqpNxHC0dr9PLlcOsA3yNt+3ZYiJ1SUxvXFRQwiFtLnfHX+vrMmTPx6aefshO6FEVFDOQU78XPP49v9+5ts7nbFy7A3MMDkc8+i75//YWCwYPhyOEDiG4QV2478loJXboUPl9/fe+2ThTv9njz9/dnYHKtQ9zc3NyYyzZhRYhKbW0t8wYjBb3dwo1F5oULMBk6tKVad3iZSDs+3V6f4y09OBj9Ro5saT7E2pq9t4/4+uKp06fvk1m30yDWYJi7O7wuXmT/CXd3x4gLF3BnxgyY7dmDyDVr4MkZ9CpLSjB2wgS2rql0xdgo6ZzsSX57296/J3l9HNqWKd6Pwyh1E429bfEV8Hggu27Kt99i4BMCktNNQ/XQmiGwomoVFeYOfGLKFATu29dp3xR7Su585FJIpzq6uroM2INAPihFTVdLV6zyMatXw2XdOgQ7OWHU5ctI/PVXDJ45s6skPLL7yCX6vKMjLBISkK2ri2GcwlFNLqYAaJyUhEIkGRtDtaoKGpWVsOZcprub6OyoKBgNG4YzgwdjZGIizr/2GoZ9951U3QirqyGnqooIS0uWdzj4uecw6o8/pGrjQStXZ2VBxcQEp/394X/qFKLNzaF79y4suE3+BUtLmNy+jWQrKwy9cYOFW3SlJF2+DBtnZ4Q3I+KOaLWJ76orZFfoeBj3ENARrfPWOWnff//9NmOD26KJkOZT9fWhX1SEMoEAbnl5rFqmkhJu6ujAJzsbV8jQJBDAVhxAsKkJjXJyIJd0O/KiWLgQozZvfhhsd9iHhYUF5syZgz179iApKalF8fbw8GAKmCTlVEAARnOgZpkhITDx8Wm57Y6SEtJ0dBi2g6C6GvqZmSxmWFyxk6SPNutwClHErFkYvnMnq0Knta2xOlK+/hoDly69r4k0Hg8WXVwz4g2ly8vDRCiEAoB0wlV45pk2SU06eRI2gYGInj0b2jt2oNLKCg43btxX193dXSKvlbMvvQQ/kZGsi4o3gaWRh4NQKOyeGGmR98Eff2CwmHdBd3iZdHl+dNeNHG8Ju3fDdsaMllbPNYcOeKelIcTDA76cS353ddlRO+G2thjBhUlctLCAZVoaSvbsgeWMGTi1YAFGb9vGbs+Ki8Pzzc8ZSY2NDSkpKFu2DNo//gge57Ui6ZzsSb57296/J3l9HNqWKd6Pwyh1E429bfHRKZ8G5W9dsQIubQC6dMb2o8yd2Bltj8v1HHl55OnoYEhBAU4+/TTGHDnyuJDO6LywbBncv/oKwc0ncaPCwhD/00+we/nlx4oHduIiJ4ezbm4wI2R+LS245+SgobYWCsrKjBcaJ8o1TZsEQUUFdEtLYcnlM+5uZjOCg9GXTnPc3OAfHY3IOXPgKUEIgjgdVVlZUDUxQeiQIfC5ehVnJk3CyAOUTfvhlZLr16E1eDBOT5gA/4MHccHKCqZpaTChXMEAYs3MIMjORvqgQfCKj4dSFzfgZ7dvh5/oNLCdNqR1hRSXUluK0MOTYvf3RHHaCX37Qi8vDzXKynDhMlGUy8sjom9fBKalIVxdHcby8ujPuewSFQ2VlVBQV0e0qSnc7t7F6Zdfhn87iMfdT3X7LZIRUlQoPRMprTRmBDy3bt06iUg57ekJf07xuLV/PyzF4q3zFBSQYGLCQky6dd1zzx0i8NyECfA+eJDRSnm8g4KC7qM7Yd062K5ejQaAKchUcpu9HvQaGxkytkTzlQDU6CSR4vK1tFpuyeXxsBYAmVBubt8Oq9mz25TZ1b17MWTaNMQsXgylzZvBMzODbavUat9//71EXivBEydi1KFDrB8C+lNoB1W7s8EjOf3xxx8PHCNNLvc8To7Rn3+OC0pKDIyN8FMoflhSxa8zeh/F9fqyMihyoKuXPv8crmKAiOcNDVk40zlbW3jHxz808qIsLODBhQ8laGlBpaICykePwnjMGBwZPRrjOCNY8qFDCMvNldjYGDtlCpwPHMDVceMw5PBhxo+kc7K7mRd/b/S2vX938/q4tSdTvB+3EXsAenvb4iPAGErYEzF7NoZv3y4xZw8zd6LERD2mFUt5PCSbm2Po7dsI8vdHQBupZHoza5Hz5sHz559xZuxYjDx2DFe//x5DXnmlN5P8L9oaa2ogr6KCs8OHwygmBhXq6nDNz0dZVhY0TExY/SIeD4KmJpy1tYV6URGMCwpgzuUz7m5mUw8fRv8JE3Bq1CiMDg5G+PPPY0Q77p/t9V2UkMDcZc96esIvMvLe+HAAMB3R2x4yc2V4OEq3bYMxnVJxpyed8Z0XFQV9Orl//nmM3LsXkYMGwSo5GfrcKR2BqjUVFyPXzg6jKV90KzTpztoXXT/2/vsYK4o17qLy3lFfbSlCktLWK+pdvgxQ3O706YwcitO+bGWFPjk5LBzBkVJJkcsyn48jQ4ZgXFwcTurowKG6GoZiXh3l6ekQmJsjwsYGw5OSEDxjBkbt3t0rWHxQIs46OMDv6lXWzPVffsGgOXNamiyTk0P0gAFMOTDNz0ffNtb9yS1bYO7sDGsPD4lJmT1zJuQ4+eUYGMBw7FgWc02gYVGHDiHxpZcY0Je2iwvi3n4bDp99hhw5ORhy66eyORWXsKwMAoHgvj7bm691y5aB/9VXaNy4EfJcnmo6LSaDyxgeD1FNTUjetAnW7cSaX9q6Fa6vvoor772H+g0boKGnB+vsbIn5Fa8Y7OeHURyQYkl2NrRaoW1L0mhDQ4PE4HmdtVdRXAx1HYL2AkKXLcOxnTvxaVERLp04gSt37kis+HXWjyTXu4SO30HDhVevoo+DA6sRTrnTxdJKXtHQgGN5OSIsLDCcA1mUhMYHrXPJ0BCuHIgfeeAUKijALCwM2q6uOGZtjf3JyaAkZle2boWjCIRPgk7Dhg6FV3Q0zrq6wq8V8J8Et3epSnvzUHwd9ra9f5cYfYJukineT9BgdsZKb1p84hZeAkvxkWBTLuLvYeZO7Eymj/N1ytdM6NhR9vYYce0aTnt7w5+LZXpc+AqfNo0phaHTpsHn999x+auv4PT6648L+YzOqpwcqBoZIXjkSPS5eJG5kNqXlCD36lUYcBsWikpWbEbJPenmBrWcHJhnZ8O0i/GVnQnnxm+/YQC53E2ditH79iFswgR4cadhnd0rup4dEQGjESNwJjAQI0+cwFl/f/iJGXXawgLoCJk5R0UFhjU1KI2Ohqarq0Rk3Dl+HGbNz5azc+bA75dfEG5vD7v4eGhxyjGlFyuvq0PxkCEIIJdgUvKUlZnbKKXJI5RfSdB9D8+bh/Ei4LgHULzFkZ5FDIoUIXp2P7ZFZCjhDBtVpGTZ2kIrM5MBhtlWVKAhOxsKxsY46ueHp8+cwRETE3jn5kKDCwsg3vNjY6Hn4oIQd3f4XriA4KlTMUpCJPXeLruwAQPgxaE3x23aBAcx5bOex0OIgwNUiothmZkJIzGZEF834+NhZW+PK+rqTImRtBgZGOCPvDyGJh5Hbtuc98Cbb76Jj83MMPrAAYQ5OMDryhXEvPYaXL7/HklKSrDhwAkJl7zqueegyqURo347mq+XbGzgmpyMuBdfhAOHmUC5uZUFAsxXUsLPtbVIfvddWK9f3yYL5zdswLCVKxG/cSOKV62CsUAAy4ICSdm9r94ZV1eM5FKeZcbGwsTJqd12Tpw4wU6f6WR/5cqVTAmm0hlKeesGO/Jcybp6Fcbcs/7U9Okw278fNjU1CFu2DF5toNhLy3RaWhpWrVrFDAXvvPMOBg0axPBY6JeQu8WfNx2l8JK2X6p/+8gRmI8fz249M2cOBASO6OYGk3nzcENJCQPq6nDR0BBD2zCidGTcaIsn6mPRokXY3EkISpy2NhxKSkCINBRgRPgS/dauheIbb4BSeB6tqgIlMYtaswYeUgCLnh84EMNu3ECYtTW8WqVC7IrsxO9pbx4aGRkxFHXx0nod9qa9/4PK4Um4X6Z4PwmjKCEPvWnxleTlQcvA4N7D2N0dIyWMhaP6DzN3ooSifSyr1ZaWQklLC6EjRsAnPBzBHh4Y9RDjrLpDaOcmToT3oUM4/8YbGPbFF7j4wQcY+v773dH0Q2sjLyYG+q6uODNlCtRDQqBVXY2BVVVIP3uWAdEUA9DmqDnh6wvl9HTYpadDl3OZ7m5C47/9FnZLluDs4sXw+/ZbhAUEwOvkSam6yTh+HH1J6X3hBfjt3YsQHx/4cidM1BCB8kmDzJxF6afoNPDjjzFo5UqJaLn566+wao7lDF2+HD5ffIFzzs5wu3wZKpxyfEtdHbkKCqhwckJASAiuhIVhybvvori4uMVdmOikTRwB/7VXDo8fj/GiEI0HULzFkZ7F+yJFKPohnZ5IJFjQAbWExgkxd2ZQLLe2NqCoiCAXF2hmZUGvoAD96+qQf/Ys9EaOxMlp0zDmt99wcNAgjE9KghwZl7j0Q3dOn4bZ6NE4GxgIvxMncGbcOIzk3Dklpbu31jtvZoZhnHHl0vvvw5XL6d1UV8dSiwW5u0O5oAC2aWks9ZZ4Of/llxi2fPm9f0kx/yaNHIktZ8+ydRU+YABGcPnU3333XfiHh2NkWBgiBwyAZ0oKLs6ahaG7duGaujrsKypQC4AgCDf++CNMxVCqiYT25iuFEQy/cwcXPDzgzr1nctPSYNC/P46Zm2Ps7du4Pm8eBv34Y5vDFLZyJbw2bEDyDz/g7tKlGKCggL5ioQjSjG2ojQ18kpPZLTeOHcOAVoqLeFuESk7goUqc+7ehoSE+/vjjDgEFSfHZNG4c3F5+Gd5TprDmOvJcSTl6FAO5NJgnRo+GYXg4HKurEfLMM/D96y9pWGuzLgHA0biQ4k3o+2vWrAGhbVMmBpEhQXRjZym8pCUmYetW2L76KrstKCAAAaIwhqYm5MrLw0AoxGY1NXxtZCSVcaMtnsaOHSsR0CNlCbGpqmLvVcLyT9PQgDnhDH38MfKbMyucEApBSczOzZ8Pby7euzO+kxYuhPovv8C0pgZRBgbwEOUu7+xGCa+3Nw9pXpKXCs259t4bvWnvLyG7T3Q1meL9RA/v/cz1psV3MyQEVn5+jMAz1tYYKYV1kCyyneVO/A8Na5dZLb55k6X2CHnqKQw/fhzhzs7w404ButJod7uoiWjoqN1Qf3/4BAfjyjffwHHJEkQuXQrPL7/sCvmP7J60w4dhMWECzs2bB/mjR2FWUMDcSZP37YP1M88ghc/HQM69NGjcODSlpsI3MZGBrUnqdi0Nc7Fr1sB57VpEffYZPN56C2He3vCS0hPi5t69sJo2DaGvvgqf77/HOU9PeEdEtJBB+W3bQmb2cnPDyqefxlhO8RDdQHGgZKa7MGcO3CWMN7+6cSOGrFiBCxs2wH3FCpwbPhzekZGoq64GX1kZd5WUkKqpiRpnZwScPInhbm7YsWcPBogpEpQqhvJ5t5U+T0Tb0WHD8DRnOGysq4N8q3hRSZVUcaRn8fEiRYg2+r2hSBvmIywshByXGqkqIgIKFhbgGxvjxMiR0Lh7F5Y3brCNd8wHH8Dlww8RuWEDPFeswO8+Pph27hxA4FlWVoz1m3/+Cavnn0fIjBnw3bMHZ0ePhl+rWORHKSNJx7ktGmP09ODCnd5GLVsGD+6UszYvD0rNWQ5OjRwJ+ZwcuCcmQq3VBjtkyRL4fvvtvWalULwv//wznObNY7edNzFpUfzpOzuxS0lBNGUlyMpC1NSp8Ni/H9cEApbGkJDpV9fUYNZvv2H0tGn3sdTefL1CKPWFhQzk0C3uW980AAAgAElEQVQtjd0jQirf7+GBKVFRSJw4EYP/+afNYQxZuBC+W7Yg9c8/cWP+fDjU1NwXiiDN2Ef27QvPO3fYLdd27ID9Sy+1e3vr9JmrV69GTk4OUlJSGNp163Enz5XqsjKo7NuHagAqc+Z06AlAHcf9+CMc5s9nNBxzdka/hATY1tbibDNSvp+Uz962GBHPOlBeXs5SoNGzjYwI4eEEC/n/RdIUXpLKO2bNGrispSh+4GzfvvDjUgQ2NTaiTl6ehRs6y8nhdH6+VMaNtniaPn06tm/fztL5dVTS+Xzo1tfDv7lvivSPGzQI/gkJqJeTAw8AZaGnzNhnJ0+GH5dtoKP2WCYCMePsVYEAQ8rKJBXRvXXbSQhVe/Pw4IEDCBo9Go7ff38fdoL4OuxNe3/JhfLk1pQp3k/u2P6Ls960+GK2bYMLF4sbYmwM38xMqUais9yJUjX2H62cef48TJpjcEOffRbOf/2FWHt7BoTVWZHWTbiz9kTXu9JumIcHPC9cQOqRIxgwbhzCZ8/GCCnwAiSlrSfrJf74IwbPn4/z77yD+j//xKDbtxlo0eVvv4XTkiW4oKcHdw7dOWjGDAhv3kTghQtoqqwET1W120m7sHgx3DdvxrXff4f1tGm4QHFrFy5I1c/177/HoNdeQ/i778Lh449xzcEBnmI5d+lUuS1k5jweD/p0olpdDUUOWI46FgExhgcGYsTx4xLREv3WW3D7/HNc3b4dQ2bPRti4cfA6ehR5FOc9cCDyFRRwzdgYDa6uCDhwAB6OjoiieORWpT0PG1G109bW8OdOC4tTU6FtYcEuSaukSsTUI64kbZhPfkgI9DgDa+o330Dd1RX6np44/dxzUElLg0t0NOKjo3F4xAh8WFuLsqQkaDSneft1zhzMIuTqP/6AGofwnLhtGwa/8grOLVsG7y+/RKiXF3xIOX/EpTvGOVFDA3oVFdBrakL4yy9jBOf2XUKhEfb2ODV+PJry8hBA676mhp2Ct8y/SZMYeCCVtgw/7YknYt06DF+9ml2O0dHB3rlzGSAclUt6enAtKECiujoGl5fjgr8/3IODccjAABNyc3FNTw/2+fmI2rMHHlzsfmfDQGnRrGpqcF1LC4OKyY8HSDpyBDbjx+PoxInwOHgQeSNGYBCXrq51e0HTpyPg99+RFxqKS889B4/CQui0CreR1P04Wk8PbpyhI+bzz+EiBvjVut9Ro0Zh9+7dIJdeUaFUaKTY0Clkaw8ZQrb/dP589Be5zHOePh15rlxYuxbuXF7xE5aWGJSWhn5CIUJtbeHTDaBjns2GT1JGlblnKmUmISBAyjMuyjne2fh19fr5BQsw7Icf7j0T+XzYcUbk3KgoGHCYBEN5PFwUQ8hvbdxoq++2eCKDQkhICAOl66gUyMujUk6OhbIQwO9pDw+MOX8eBPyoJ0bHWV9fmP/yy7/c9KltcZf2+C1bYMelVqRrqYqKzJOno9JyoFBTgwY9PQiXLgWfM1C0dV978/C9d95hoIfpa9agXztu8b1p79/VefQk3SdTvJ+k0eyEl960+M69/Ta8uZd8pKYmPDt5UP6HhumhsZqybx8GPvMMwufPx4Aff8QtKyvmVthZkdZNuLP2RNe70m7kwIGwvHkTwpgYGDk749zkyfCWwEItKU0Po17s+vVwXrUKsZs2ofSXX+AeHw/VpiZEvPkmhn/xBUKcneFL4F8AThAoUVwcAoOCUJ2aChVOyetOOiNnzIDnb78hLSQEmr6+SLGzg8c1iniTvBAAkuPHH+PC5s0wWbwYWRYWGHrrVocNiOM+3DpyBJZPP83qlxcWQk1XF4SdHObsDC8JvTIi586F5/btuHn8OKwCAxE5ezY8d+7EzZMnYeXvj3p5eZy2sYHQwwNP79iBD+bORWhqKkMRFuWnJ88aip97iwODaouBCCMjDOfcCll4gK8vqyatktq6bepTpAhJLvmerSltmE/St9/CZskSRtSl//0PWs1KjNULL+DcokUQZGTA6dAhli7vN8oFrq4OLS5Gef/mzZiyeDHuLlsGU+70N27NGjisXYsL33yDwUuW4KqDA4aLGXN6lvP2W3/QcaaWM5SUmHHJrrYW5557Dt5c6r27Bw/CdNIknKKT6cxMjD5+HOUpKRCIeWWwECHOMJZ/+TL0HB0lEsWZRYsw8rvvUEPu1mpqWOLmxlJEUrmhooIBNTXIkpeHcUMDLrq4YGBsLNa9+ioav/8etm5umBcdjZBvvoHv4sXsns7mq0ipyVZUhBGnlMT+/DOc581D6CuvwHDrVsjZ2mJAO4rm8VGj8NSZM6jLzMQxLy88nZoKRXK7F0NVl9T9+JqGBjMoUKbtqJUr4dGBRwkp1uTOq9rKyEn5un8jPIxWHjK0RrZNmYJJW7cyudRWV0NJWZkp6u15roQtWgQvLmXjaV1deBUUsJPgSFNTGJ0716ni19mAR0ZGgtLeiRsPGhsbsWPHDubR05MlfMoUjOAyWoiwSqi/6NWr4cYpmt4A/sjK+pdxg9ziKc67rdJVnuikvVFBAbG6uhjKGV9Ojh2LMUePIlVJiSnMyXJyzPAR5eiI9bq6/3LTb+3SHjl/PjzFQiSKeTxocwp8ZwcKeUFB0B8z5h6LHXistDcP9woEeKGiAtGBgXA7frzNddib9v49Odcel7ZlivfjMlLdQGdvWnxBzz2HgL/+Qr6iInJ4PNhzgC1dYbPq1Cko8vlQFMt92pV2/mv3XFq/Hq6k8H36KVRXr0aVtjacJYhLas9N+EFd1LrSboyBAdRLSmCQmgotU1Oc8/eH92OGzH5+7lwM274dyQcPImPjRoyOiEBdQQHCFy7EyD/+QOiLL8KHQx++sGsXSo8eRcDevcgPDYWeN21ZurdEBgbC8+RJ5N64gTIbG1QYG8OJcw+UtKeoWbPgsWsXUk6eROP48ajR1oZTB3NLWF+P6KVL4b5lC+vi4urVGMohhaedPQuLkSPZ/yPNzeHJual2Rsu5sWPhffw48pOSoGdtjegVK+C2cSPifvoJ9k89BTkTExwYMQJqXl4I+OQTpH33HRoohpjLT9+nTx/4+vpi4MCBHXYVp6EBB05hTNy9G4O5PLXSKqmtOxF3peyM14d1Xdown+jFi+HGAR2F+flB3c8PTmvW4OKnn2KQoiIEy5djjLo6jlFaqueeA6/ZAEIl7soVBnhV4uQEK87oFDVzJjx270bKqVNQHjMG2WZmcOdSAj0s/tvq50HHmdokpPerhobwzs5GyKhR8D19mnVFQGJ2K1Yg4uOPUZucjJE7d+LuyZMwDQhoISXM0hJeHCL0rV9/heXMmRKJg4En7t/PgKUaGxqwpxk74QMuxKNYTg7aTU0gRUmhoQExAwdCl+KxKyvxxRdf4BkFBdisXImgDz5AAIep0dF8pZN4KCkxRZelJCNlSl4eIatXw3fdOlzftAmFy5fDzNQU/TgX8NZMBA0ZAr9r15iy/buLC6aR0YUUpz59WqpK6n5MrsYUKd+/2d04fMECjOCUZEkEJzIwiI97eGgodn/2GTb9+SeL5f5IS6sli0PahQuwGDq0w6aZS/M//6BEXh7khO8kSnmopYUVrq6dKn6S0N26DuUgJwBJFRUyfd0raUlJEDY0wNLOritNtnnPOR8feJ87h2wFhfuAAUNGjoTvmTPI5/FQ39QErcrKfxk3bt68CSsu1KS7CCpLT4eGuTmCXVwwijPinnj5ZQT+9BPiBQLYVVTgb3V1jKiqwk1zc6zu27fFICVy02/t0n7O2xvenKfGZVVVOFVVob6yEoqqqp3imUQuXgxPERicFKEionmYpKoKm+pqXLK2hmtSUpsx7r1p799d4/g4tyNTvB+D0aNYoiVLlrBcjmpqapg2bRo2bNhw3wNTEjZ60+I7Til8EhJwpX9/WBBgjJh7jyS8iOrUFRWBz714GwoLocCl5JCmjf9q3XNz5sB7xw6kHT2KgunTIWhshI0EqLjtuQk/qBy70u4tVVUUKinBmXIC8/mId3SERxvuwg9KW0/eHzFsGNyjolBbVISYlSsZmEvS7t1I++orPHXpElKCgzFw1ChGQmZ6Oq5u2YKnPv0U6V9/jX7caWJ30nfR1hYWiYnQrq/HFS0tqNMmW0qPlPBRozDizBkUZWQgbfBgqPF4sOkg5u3srFnw45COiZfQyZPhw3kuxH37LRw4PmN1dODM5X3ujGfKDetMMbGNjSxHbvK2bbB+5RWcXbYMtj4+0J80CcdeegnaI0Zg2P/+h/g33oDd55931uy/rmfw+czlVZ1cdjduhAt3Oi6tktq6YVKCRIqQ1ET14A3ShPmEBwRgxKlTTIGLtLJCjbk5xpw+jbwrV6BPyperKxoNDVn8MihO3t2dUU7eD38JBBhP8fgFBZDX1kakjw88z51DSU4O7jZviBtVVeEg4VzoQXGgvXF2cXHBxo0bweskdrOpvh48Ph+nnJ3hGRuLGHt7eHMhP5fmzIHrjh24duAASmJi4LVuHZK++go2YpkbYnR1W3Khx7/7LuzaQQVvLYPTXl4MRO2SmRlM7t6FRn4+quk0fdAgKGlrg9KFqZFRIDkZd4YPR1N5OYbU0Pk4kPnnnzB5/nmcfPVVjOGMZR3N19wrV2Dg5IQMAH3JJf7OHcibmuLkjBkMTC8vOBjnnnoKo+XkoCmWQk6c5jMGBrAtKoJBfT1+GzMG04OC0BAXBwWx2FpJ3Y/LeTwkciE84h4GkswTkXIvPu5XNmyAY0EBdvXtixmLFmHor7+2uIi/GBCA3Z2AU4Z4esL3/Hmk6OqiH3faTbQQ6vf8YcM6VfwkoVu8Tk1VFcIEAlRpaGAi5/ZfX1eHm+R5IScHV8KqkCczCUDKL+Wm19PTk7YbVj/cwQEOV68iXUMDdmLvgIt6ehian48YLS24lJSgpqAAytxerjPvibYIaczNRfnJk9ASQ2lvq15GcDD6NmPDnJ0+HX6/ka8NEPbVV/B6/XWcGDQIgc14Qx84OWFGYiJKNDXxuqXlv9z0W7u0n7ewQL+MDMinp4NA1nwOHULe1avQt7dHZwcKId7e8OWU9rLMTGgYE9xh54Xm4ZngYFTKy7N3T6KWFgY3j2Vb67A37f075+zJryFTvHv5GFOsip2dHfr16weKe8nLy8Py5csRGBjI4o6kKaLF94+SEvT5fDTJy4OnoMByqdLfNQ0NaJKTQ11TE+oaGqCsrg5VDQ3IKSmhjk4k+HxmpS6rqkIfAwPUCYXMek3XaONgaGICvqoqs15SvdrGRvZdSU2NtV9QUsLchrKvXcNwcmnT0MBtsjqeOYPkjz6C6dNPo0ZODo1Em7Iy1LW1oaymxsCKqmtqWuKTxDcyYdOmwYvLMXx59Wo4ifLpSiOY/2jdUHt7eJBbX3ExIocMgVNmJrToZIJ74fZ2sVRnZIDfrx9OOzlhTGwsrikpQUlVFQO5jURvp59AeeIuXYKOjw8qFBQwpKoKGWfOoO+oUTjaty+0s7PRj1w9q6qwztkZJRoa+CI0FNFBQXAcMwa3R43CAO5krDt5vSEQoLyxEc5VVThtYgK3nBxoSjkvLpmaQj87G2YNDQgixObs7HupodpRQkKNjOAjdiIe6ugIH86AErVgATx++AG5cnIo5vOZdb+zQorbJU1NphBbcvXrcnLANzLCdj09mBgbIyAuDuF79kB38GD0d3JCso8P7MWQ12urqhBkbg4VGxv4txNL3NTQgHpFRSSoq8OpouJf4H7SKKmd8fQ4XqfTWJu0NJSrqCClqgrKAAbT+0c0hoGBAAGkvfkmwIUeifjcuWABXvrhB9AZ+EZzc+y4fRuaCgoMaDDM0BADi4pg2EMp9aSVdetxppOxrW++iTe2bMErHKJze22WxsZC08UFJ8aPx6Djx0E5td3v3mXGhwv9+mHgnTtQrqhARnQ0bPz8EDN+PFwOESTUvXKHz8ddHg/DmuN2rzz/PBy592FnPIQMHAiXmzcR5+kJz4gIxGhrw624GHHr1sGBPKEEAjiXl+Pm339D44UXcE1HB6O43McFMTHQdXVF8KRJGMW5EXfU37Vt22D/yis4racH//x85B89Cr2xY3HI3R0TLl5EQ14eNjd7OCwlrBdKKyWWV5vikSmd1/IffkCDqSls79zB/uXLMeXLL5H73XcweO21lq4lcT+uLSyEUrM7d7CbG0ZFR+Ncc9pD0WllZzKj6+KKjWjcry1cCHvaM9nYYNb167ikrw9XDpfD0dSU5eLuqEQMGADrW7eQZWmJIVxaudsKCsxoONHDo1PFTxK6xevE7dgBBy5XfGEz1sLhxYsxMCAAnpyBMykkBDY+PigvLcV+bW3UaGhggZTGV1F/sUZGUC8sRJGuLjyys5GsqgrrqioU8njo09R0L5tKVBRY1gLOwNwVb59bOjqwLC5G1tGjMB47tl2RXP7sMzi9/fa9DCich8eNy5cxwNER27/8EnuWL8eo9euZcVu+qQnlJ0506qafKBCglseDU1kZwpvn+Yht20ChfDry8iifORPqv/4KvUmTWmiqr6nBqWeewcClS5E7axaGc6nU4vbsgYOEmAk0D1+dNIkZtKiwUI523PJlire0K6Rn68sU756V7wO3TiAeH330EdIpfRCHDktxRZQKIjExkeVjlLSIFt8JPh/mioqQa2qCnFAIeTptbmyEAsD+R9/J1knf6Vf0N+UR7q5CdvN8QmIdMgRqPj7Q6aRhyrVIrmGiX/pOSj+lg0iivJD19YzOuwoKLH6nhjb4nFFBZFigWLAmulZfz4wCBN6kqKKC8upqKJC7FSmcZIzg81FZU8Ou8xQVmfGgqKyM3WNgbMwMEQrkNkeGCEp1Q/dwRgz6pP/RCZucoiKrI0c0AQxJuayyEgp8PjMoUF2+igqaeDymMDaQu5WODjNkEK10r6q6OoQ8HjOEUBtqGhrsf3SNoSfLy6OUTqnl5VFVU8Ms0xAKUVlWBl5TE/JyclBfW4uG2loU5ucjKzGRnTZpxcVhVn4+LuvrwzM3FwemTsXk/fsRO2IEmubOhYWPD1QMDKAilqO1rSGizeE/q1axPLxTKUaPZNINhVwfg6dNg2pmJosDVe3TB6mpqUi/fRuXgoNRevYsJkRHw62xEVHffQfP117DXy4ueDY2FrdeeQVGq1dDVULLcTeQK1ETtIHMyMhAbm4uzhw5gvidOzE7OxuUgfPaypWw5+IMYx0c4MydeKVOnYr+rXIV19TU4IiWFibU1SFrzRoYvf46O6HqaqExJNTuCxERUNu5EwtTUnDCzw+BZ87gn2efxaS//0b64sXo99VX98VTivdHbZDHwrnQUGTt2oV5Bw4gon9/+N26hb/HjsUzx48j97PPYEAKVqtSV1GBaoEAd9XVUffFF9BYvBhFmppwo/RTdGIydCg8oqNx0dwcg2/fhqAN5HCqRzQUFhbir7/+QvKPP2Lj5cuIb0ZPd46MbOnxrp0dTBMS2Pez2trwystjKWzOqqnBo74eSleuQIFzs7y0ciVcN2xgdQuvX0cfG5t/0V5w/jx0mxWXoOHDERARgdCAAPhImXqtvXETCoVswz106FAGRNcbS0FkJHJ++QW2mzeDJwaGV11djdiYGOj5+rIwAxNnZ/ThEMiL334b2p9+eo8devfQqXUbp2nE/9WAADgGB7ewfn7qVAz7+28cf+opPHXiBG5+9hms2phTj1pWX9naYmliIr6xtMQSTpFqj6aEDz+E7Qcf4NyHH0KJMBHy8/Hxc89B7cwZfFpQgIghQ+AVF8eM1rGqqiyNl/r161C2tERdfj4U9PVxePBgjExMxC0PDziKpYQkd9dLixbBdMYMmPkThvP/l6uqqpBXUEA5AZWJ8tDTO1RJCaa1tQjy9ERAZCT+7t8fz6Sm4oSPDwI5wxShdstpauKKszPcOXfd8qIi5N28CcuhQ9EkFOL2nj0wDAhg7xEGerlzJ45PnoynDhxAzKpVEMycibv29iCnZv2aGnwWEIC3Tp9Gzb59UJ4yha3nK1eusJhVpeBghjJd+cEHUHv/fUQFB2OIvz9yhgxBf3I578SrQJzvW7//Dsvp0xHyyiuw3rYNGebmcOdc9bsyb0qysqBiYsJism+oqmJAZSXIC4Y8sAZWVmKmlRV2ETp/B4UUN/IKqXB1xXBOxmccHTHyyhWcPnAAtu7u3RqfHTxmDEZx6zHc1BQjuFR2IhJDm9NJ+nz9NcI++ABeHGBXVlwcjDtIq9gee+nKyshSV0e5lRUDBzzfvz+sCEBUKGT4AjErVmD4p5/i2uefw54DuZPW26eurAx82vuQZ82YMfA8caJdaZ8aNw6jjx5FVlgYTh48iOz4eKw8dox5pojmHKWPJA+dwenp0GloYPu5dp/T9fWo5PMRY2UF3xs3EPPJJ3B5911c/PBDKGzbBuesLMSYm8NFLEQqas4ceOzYgUQFBSg0NcFMKGRpLoMWLkRAJznIxem4/PXXcFq6FNeVlTGopoZ5zfF5PDRmZNznCSJTvLuysnvuHpni3XOy7ZaWfXx8WK7FgxxqKTVaW1vLFKz169fjjQ7QOFsTIM3io00PbUhpA0UPI0LDLCV3oKoq1FVVobK0FHxSZMnC1tCAu7dvQ5XPB6+xEaWFhUwJVuTxUFtZyerR3+qk6JLiq67OUompcyihqTExuPjNNxBmZkKxoQG6ampQqK9HVVkZFOghUlvLLK+KhEKppsaQWykmlIwF6ubmGPj11wxASfjDD6jLy4OaggKUyKhAp/SccYEMDPQ35UXly8vfMzBwv9QH/c2MDPQ/or1bRq93N3LdxAR6x49D194ed9PTEW1ri8mV5GD4/6WCi79q5PGYAYA8F8hAQN9p00wWeVGEHaV8KqPNt7w8GoVCMOMIfTY1QUlREcLGRlCmSTIIUD5RKgR0QvOLvtNmjcBFKJ2HUm0t+nLhBwW0IeHGhPqiUzMq5GIV9/LL8OTybKYlJCDb0RGenNWXYsfoJFmoooI66p9ShXCGEmaMafVLxg4CwqEXMNHDV1RkqWKIrtqaGqiqqDD3O3k5uXv/p7nFvawbyfBDxhemSwjZGiX50MaW0lfJ1dZCvrqauYTpcr/EQ4OiIgpffx0G4qd9DQ0o+fFHNhcFBKrUhjHjwNatMH/1VZCtm7BTsxQVUaSighoCACI61dSYBZ7NczL6KCqiobERDXRy3dTENvHVlZVsfSpXV0O7oQFkwhMAuGVmhr4US6mpicyMDNzq3x/ejY2g8c1QU0M9WffJo4XkBKCmogKN1dVQq6kBRUOTK2m2khKawsNh7OqKm4mJqLa3h71QiBQ1NeTq6KCeDE8qKuDz+QzdmkIFRCBHdEo6NDUV0Q4OzODklJiI22pqqJg8GV67diGyf3+UGRujtqEBijQfKytRlZvL4uIF9fWwB0AJqErU1aFOrqj9SVXhChnQPvsM6Xl56PvJJ1DmQlN+eeMNTNm0iY3PZSUllJDbXkEBTLhYy/NGRqj192dyFdbVobq8HPWELRAdjaElJYjZuRP6c+agUVERxfPmQcHAgCHOy5NBj+YxPSOvX0fJoUNQpg3d3LnMQEegSzRHaF2UVVSw52xDfT2bQ/v37cOBf/7BYBsbvP7669Dt04fNMQJFUiC+yYBYW8vicwkMjnkC0VqSl2fPbSU+n40zuZBWUryhoiKUlZTYXKa5S5/0P5qzFOdZU119b92SkY/mSkMDW7PUPn1nf4t951Fe6Z07oSsUIsTAALfs7VFFaPVZWZDLyYFNUxPcyFV18WJYUaqk5cvR5OUF3nvvtWvA+dcTk7ynjhxBQ3Aw5NzcIEenQTwe7iQno2bwYAwQCnFNVRV5hoao19KCwMwMQhojMnoqK0NTVxdNfD4znOqbmDCjqZCe/woK7P1G8iBZ0dquorXf1AS+khJ7T9GaoXEgOauTUaiuDirNIFn0SeuenmskK5Il1SP5kFHs5KFDGHfkCEwBkOnozqpVUNLSYhvi8uJituZy09NRd/cuFLKzmdJD61R4+zbyv/sOdmLPgnwTE2jExkJJn7D+gX1vvIGxmzax+UJeFqqNjbCtrkboBx9Ad906aNHamzsX8vr6rE7Dzp0YeucOMuXkEDtjBkNDF9LJZVwcJty4gShfX1ht3AgtLgb5tpwcrIRClAKoTkiAsr09tIRCBvxWFBoKcy+vliE6o6GB4eXlOGdjA2VbWxgePsyAqSjzgOLt2/BMSsItPh9JY8ei//HjMKBnT1wcS7tEuTMId5oQKpInTYL1gQM4e/AgXCZNYvHuYSYmSC8uBq+iAs48HkbToQBhLVA+e4GAzcPtpqaYl5ODa0ZGqPD1haqjI8NyoGeLmkCAktxc5Cxdin5377I5qOXqitLkZCj/8gs87txBwuHDKJ45E7a0ln//HdrDhkHRwOCeJ2A7ihbtdzIjIxH/2WcwnjYN/YcNw8W338bIv//GJRUVuFZX4+aMGbDaswenvL3hHBaGm8rKMNq3D6YjR7K5J1pDFYWFyGhW+mojIjD0449xzs4OchMnwm/9eiQoKqJg0SL4fPklYl9+Gc7kzt9NRm2az3E6OjCvrGQnzuKFfIlo7xNlbw/3iAik9OsHW86DLPzFFzFCLByovd0NrQfyzEyJj0dxszF/wpYtCPLygtGyZcibPRum27ah5I034J6VxbySdIOCYO7ujig7OyYvvhbN4vtLwa1b0DE1Zeu3rRK1ahU8uBCLBA0NhppP65sKxbKTuzzJOuPIETwbFMTCp4wpbKIDg03oM8/AZ98+hM2fD5t330UfMzO290zduBEFubkwWb6cHXrc+vJLTPn1V4S99BK8duxA6a1b0LSyQriODlyLith+hRKL1WVkQNfMDLnh4VD18YFALLzy0rBhcD1/Hsfs7DBWQiDTvJAQ5D/3HGzz8xEyejR8T51C4tdfQ+nDD2FZVISkb76BDQd8KM3ev3fvWp8M6mSKdy8fR319fVBeSIrpFpVZEvAAACAASURBVC+2trYsduQnLu2IJGzIFl/nUqKXBv1WkjJBG67mfKX0sKX0RvnZ2RCoqKAwL499NtTUsE04bb7IEEBKpPgn3VdSVMQMBqR4VldU3DNcVFdDl062q6tRlJ8PbcolXFmJutpatinTFAjAI2twVRUqSkshIEMEj8dOsZUJFKaujvVFhgfqm9qXpxcIbcgVFdkJOinJdLIu0NRkp+N0uq6sqoo+FhbQGTQIPEK+FUuPQpJhrs9//IHcw4ehWlKCxqIigE426AVBhovGRpD7rbKiIlO4mBcCnbw7OKBBQwMKJ04w4BuiiehhCjZXjyGT8nhM+WMKKl2Xk7unGJD3AYUXcJsAops8Eaq9vaFmagr5I0dQW1cHNU1NKBkbow/FDDs7Q5EAhlqd9OZmZSF640YIIyKgSTmEy8vBq6yEUkMDkyHjg/PoEBlZRJ+i2UF0kbJN/FGhrYloeyL+STzQ5lt86yJ+XaRQ0ZiQO3IjzRkVFSgZGsLAzQ06np7A6NH3gQN1PkP/v8blS5eQtnkztK9ehXxGBvQoHKO2lgEX0cuefslLgvhozQPxyDaXlFaFjCW6ulAbMgQ606aBN2XKfeEGmWlpiHj1VehdvgyjigrwyQhBxjja2NMYysmxDWWdqirkLC2hN3kyVMj9kzuBIIoTKX5x0SL0TUmBMRkghEJmHKNSRCf+VlbwS0xkXhy3jh9H1aRJbBNPW6dUmmO//IK+Y8bgCsVt5+W1GF9E0iDjQ52SEurV1dFAQFATJ4L3v/8BEmI+0Nw79PXXUP3pJ5jevg2tmhrU83hIW74cjb/9hpGtToRE/dJGNcjWFhOuXcOZ117D0K1bmfGivUJ0do9PiDQzpefqkvv/XYEALqWkqt0rNN8qKeSIYrenTIHmJ5/c8wzq5nLjwgXcWLgQ/ZKSYFpVBU0pgIm6mZR/NVcnJ4fs+fOht3UrOkr4R6ectwwMUP3BB3Ci9JrEA6XLI0A58rAYP56BkokKzdOzmzdDYfNm6FGcdEMDrtnYYMKlSzizeDELybh37if2nNDWZuE3FK8tPkZXjY1hEx0NZWNjXN+1i3l3kaGokIz506fD46OPUB4ZiZL9+2G4YAEUxdC7qZ3LJ0+i7NlnmfJNo0uGzsamJhhynSSrqTHljqivojX+6qsMPDFt4UIIdu9mOZwL/fxgT67x5L1FOZWXLMGQHTtgLoY1UmtqCqWpU4FVq9hzSlRI+aFc7r7x8SwNmzTlUP/+GH/zJk6sXo0R69f/a83SuJBHHRmY6RlEp5LMI7CdTjIVFJB54AC0xo9nxkdKlpYbFITMNWuYG7WoiNolmYi3VcDjofT4cfQdORJ/z58P+1mzYGpjgwIzM1hxxr8qHg9VFApIz20y+Ip+Re9iapPWGXfYQO9f2kfQp8gAS5/8xkaWQuvCmDFounoVbtnZiHz6aRgdO4b4gADoxsZiRH4+W8dE48HAQNifPo2+DQ3I4/NRLS9/751CMucM62SIol9eQwNUhEL2fqDnIBl98yl88OJFGLq4tMjhZlAQ7rz3Hozefx8248bhrJER/HJy2PuqlPYJJG8ej4UW0Z6C+ibX9Gw6eKE+yEtQKASffhsa2Nov4vFwjUIXL11iRmKK41dsamK/ZFAV+QwR/SXffw8jej90UCg1ZImNDSy4cJZyTh6idUTfaRzpeV5ARtTUVGb4oxJlaQmP1FTU0rvvhRfgtHcve88VAqAa9N6IpLz0333HZFwaEoLcZ5+FQ34+i7OnE/QGeqfS2IqNI40n7V+Ib1vu/Rg6eDBs//gD6vb2961xou8qyUtJCRlCIWaWlODOnTswNSWToKw8SgnIFO9HKX0J+qYTibVr1+Kdd965rzYhapJSvr+D1EllZWWgX1HJzs5mbouyxSeB4GVV/pMSIOMDrTkyCNAvnUDSBoNOuej/9EmFTrgovYzIUEPWdfqb7qF6j7KI00R0iFywBQIBS4vTmwqdxsqRF0ErXAGimcaCTsRbFzpZpBNuVQoFobAO4qmH+SpMTkb13buoKSlhYSkaffpAoKcHxX797jNS5NLpYlgYcwEWVlWxXwpboU28nokJHGfPxq2wMGQFBzPPCTp1JoMbndyrq6mhqqqK8UzGGnIvtx8yBLdv30ba7dv3TqBpE0ahJyIPCzK6ET6HKCsEd43mJuFiMA8NBQV24k1GLzq5VVZRYfNXoKHBZFxKYSnkkSQQtNSne+he0djQd/ICafk/yV1ZGYajRkFNTw9NaWng0buGjFvW1j0+Hm3NYTKEZl2/jgYyGNbVoaa8HEW5uQxduKywEBoqKuw0T54MiJy86FNVTY29J+lvkgGdgIsMhCQflq6KFBY+HxWkSNJc4wyHNIYsrIg+SfmQl0c/CwsMfvZZ8AwNcfviRdwlHAahEFX19dA1MGCYJ2ZWVtAgtGYCk+pGo8Td1FTkUWqxsjJmnFUwNsaQiRNRmZHBvMJoTmkaGUHL0ZEB1nVHKcjMRElSEvoNHcqU99sHD4Kvo4O+o0dD2IxJU3bjBlMKFKQJlyC5k/eVunqn8qH1czcsDHkXL6Lw5k2okNGsro4ZmvWffhqWvr6I/ugjKJDCYmQEVQ8PDBw9ugX0Lu70adzZuZN5aTQVFDDvPDKok+InR+8BUlxJ4WxoYGOnrKsLjbFjkXP0KPNKUh04EIMWLoSOjQ3iIiNx+6+/MGTaNIZkTrRFbN4MYWwsmm7ehDJ519H7g9YPGdQ1NCBvbQ3r116DehuhURR/fXrxYsinpECdlMiaGkYTzScyDpNSzQzd5FVIp+m1tUwhZ2F1nIJMyqwiGSVJUebxwFdXh8qIEXD7/ntWpzQ1FdoDBrCTeFrzFC5wYd488IuLoUzgiBs2IOHAAaQ3pzdUIw8pAgMkPmhdkNGVvKroWUbPJCUlyGlosNAnNRMTqLm5QXfOHPANDDqcanQocYFC1sLDoVBaCmUFBbYGFYuL0UBeawMGQDEjA8p0kk1hdXRQQCF8ZNAmo72aGgxWroTt1Km4OHMmeFeuMIWcjMJkTNIxM4NCv34wHD4cChT/LYaE3xFh1fn5iF27Fk1kFC4oYAbsWhcXNoaa168zDyg5is9ftQryYuNHY3Jt61aYDB0K3aFDkbBqFar+/hv8+noWeqOzZg0GTJiAgsuXmbeNqo0NylNTET9nDnjx8VCh/QZ5CnLPe3aYwnnosQMMBQUUksfiZ5/Bhgshid66FdnffgvBqFEwINyYJUugXVbGDBcK1dVM4Zft/bvjiffgbcgU7weXYY+2QJv4devWYcWKFff1M3z4cBgaGmLfvn3t9k9xMh9y8TnilWSLr0eHTNa4TAIyCcgkIJOATAIyCcgkIJOATAKPXAJ3bW1hlpgoU7wf+UjcI0CmePeSgWiPjAdxNZedePfywZWRJ5OATAIyCcgkIJOATAIyCcgkIJNAD0ng7o0bMBs4UKZ495B8pW1WpnhLK7GHXP9Rgas9ZDZl3ckkIJOATAIyCcgkIJOATAIyCcgkIJNAN0pAhu/UjcLshqZkinc3CLEnm6B0YhTjTenE+nBxKXv37sW0adO6nE5M5mrekyMma1smAZkEZBKQSUAmAZkEZBKQSUAmgUcvAZni/ejHQJwCmeLdu8bjX9SUlJTAzs4O5ubmWL16NUvTsHz5cowZMwa7d++WinrZ4pNKXLLKMgnIJCCTgEwCD1kCep/pIf+t/Ifcq6w7mQRkEpBJ4MmUgGzv37vGVaZ4967xaJOalJQULF68GOGU/09VlZ1200k45V6VpsgWnzTSktWVSUAmAZkEZBJ42BLgfciDcI2wBXX6Yfcv608mAZkEZBJ4kiQg2/v3rtGUKd69azx6lBrZ4utR8coal0lAJgGZBGQSeEAJkOJdu6oWfPknKeP5AwpFdrtMAjIJyCTQRQnI9v5dFFwP3SZTvHtIsL2xWdni642jIqNJJoF7EriScwVXc69ilsMsmUhkEvjPSoAU75IVJdBU1vzPykDGeMcSyCzLhImGiUxMMgnIJCCBBGR7fwmE9BCryBTvhyjsR92VbPE96hGQ9S+TQPsS+CP+D1zOuYwN/ht6hZhuFt2Eopwi+mn16xX0yIj4b0iAFO/sN7JhqG7432BYxqVUEqiqr4Lax2ooXlEMLWUtqe6VVZZJ4L8oAdnev3eNukzx7l3j0aPUyBZfj4r3sW28QdjAaFeQU3hseXgSCN8SvQXJBcn4+qmvewU7GyM2QldVF3Od5vYKemRE/DckQIq3g4EDzr50Ftoq2v8NpmVcSiyB2OxYuPzggoi5EfA085T4PllFmQT+qxKQ7f1718jLFO/eNR49So1s8fWoeB/bxmfsn8FONndM2vHY8vAkEL42dC3ulN3BD+N/6BXsvBn0Jvpq9sUS9yW9gh4ZEU+OBEh5Op16Gm8Pf7uFKWGTECU1JeizsQ/7X9TLUXA3dX9ymJZx0i0S+DPhT6wPW4+Fbgsx32V+t7Qpa0QmgSdZArK9f+8aXZni3bvGo0epkS2+HhXvY9u40zYn6KnqIWhm0GPLA23WyT31cQZkWnpiKQqrC7Fr8q52x4FiG5/7+zl22tPTZdaBWbDuY433vN/rclfLTy6Hk6ETZjrM7HIbD3pjekk69NT08N3F7/DW8LdammsUNoLcVgVKggftQna/lBKYuHciLmVdQubyzJY7j904hqd/e7rl++9Tf8cLdi9I2bKs+uMogfzKfGwI38C8a2z1bTtkYfPFzUgtTgV5an3z1DePI7symmUSeKgSkO39H6q4O+1Mpnh3KqInp0JvXHwFVQXMnVVWHp0EBn03CAN0BuDQtEOPjogH6JlOyuQ/kme5fx/nuTTzwExU11fj7+f+vk8apptMEbsgFp9Hfg5lBWWsPbf2oZwGBu4OhLmWOb4b+x3k5eSlHiHaGGt/qo2ZQ2Ziy9NbpL6/O26oqKuA4BMB+qj0YUaNpvebWppddWYVfo//HX8+8ydWBq98rA1P3SGrnmrjxM0T+CrqK5x48QTr4q+Ev5jx6Cmrp3BsxrGWbsnw9PWF/w+z8DX3RfCsYMjx5O4jbceVHQi0CnziYsBrGmpA87X1M4w8AygMaJDuIBioG3TrMJHxiQAdnYycOmy3sq4S7wa/iy8Dv8T1/OudKsftNXYm7QxGWoz812XrzdZIKUzBPKd5+HHCjx3S8kHIB3A0dMQ3F77BmZfOdKs8ZI39dyRAxhsLLQtklmdCwBcwMEd6Z43dMxavu7+Opwf+vxGwt0ulqampw/SLvXHv39tl2pP0yRTvnpRuL2tbtPh+OvsTBlkOAg881AvrIc+TR1F1EXRUdEBKTGNTI0prSll8XWFVIeOij2ofkJIsigemenS6qCSvBCUFJWSX3ztt5PF4oJc5XacHWW1DLfubrtGGgjZRZbVlsNKxYt+1PtXCp/6fQk1RDXWNddBX02enU7TRoHp0Hykj1EZlfSVTBOg70Uj0K8orIiIjAqklqZhmN43xQae3IlqIdrqXHkz0mVORg/7a/VlfRAu5NlpoW7BP4oU2P7WNtaxvcr8mHug+FQUVpJemM5pJJqL/Ew1UiG/2w+O+g4cmNIE2LPQ/erBXN1QzOuiUjfgkHogeI3UjtuEi5YYs/wQqRLRRPbpHna/O+qe/SS7EP7Ut4klDSYONFbVB4yOSC9WltuhTxIOxwJjRS/Wu5V1DRmkGJv8xmf1vss1ktqEl2lQVVVn7ND/ok36o3dzKXCYD+ptopLYJ4IbkTW7SNJeoL7qPZKyppMnupXEhPkjONNZUn35J1tQWKZTEH/GmoqjCvlNbNL6kNFEhFFvqSxSLTvPxl8u/YMXpFez6XMe57BqNJ80vNgc4musb66HGV2PzkeQuXkRjSP8jmqkfmv/ldeWMfiZTbnxF40T30Hqh+UI0iuY83UNjRWuCrtP4GwmM2NyiTTONB8mH+BOVa7nXMOufWQzVnAq5UK4fuZ7NMbpf/RN1uJu4M9kTfVnlWTDVMIWltiUbr6EmQ9m8oTlAbdPfNDYkR/pOtOZV5rGTXZI99U1/E28kTxoX+iQeidegW0Hs9In6o82wi5ELU5LsDexhoGbAFFiac3Qf8UVyJbnT2qS+aBxpTQbsDmjh0aefDyZaT4SZphm0le/F7YpkSX3SHCPeaA0QLTS2xAPVpTVSWluKIylHmNs7yZzWkwh8i+RMc5JkLl5EIEz0vymDpiC3Ihfhc8NZFTptXXRsEZt/NG8+OvcRPEw9MMZyDDvlJ55oYyYaQ2qbxpX4pF8ac9Hcis6MxnT76WwDd6f0DpMv1Sc6SUbEJ40DPZeIf/JaIBnSXKd1IlpH1Bc9d6gfkoOJwARuJm64VXQL5JpNdWme0TVqm+Y33UvrhPoQrVGSI40v8U/XqB71T+NC40Vrm77TfKH1QHOW7iG5U3vS/NJ6prlFsqPxp76IBxHoFc3f0btG40bRDeS9mcdkT4a+5MJkJr+UxSlsrVIZ9vMwxl90VjT6afbDm55vIjwjnIXAkEyTCpIw9c+pSMxPhJ2+Hfwt/GGta83WAdFMMidaaHxoPtDzm77T/BLNLdH4Ed8035nHA1+A4priFhmQLOl5Qdfpk+RNbRAN9FwQye++yQYw+VOfpBTSuFE5f+c8e8ZSob7o3UZ1aP3S3KBxvFV8C9tituFc+jn2/cqCK4wvKoT5YP+9PRt7Ks5GzvDu680+6XlCz1Wa12SkoPGleUJjTLSTTKgvmle0Xun9Sc9S4ofuo/lF8p13eB7Sl6azuZtWksbuo7EhekleYweMZc+DTVGbmJGKjCapS1LZfaP7j4aZhhl71lBfNP70N91P8qDv9D4hmUbdjcKCIwuQsigFljqWjB/il+Rpt8WOgTjSu3XliJV4asBTrD3R+1Rc1rRuX3Z6GRsjN7Kxozhv4kX0/KI5ROua2qX5T4X4J3poHGk8aA3SmiUwS5IZPWdozYjWlGgNiN5zTCacTOl/xCPNLZI13UPPIhrPj8M/hq2eLZsrzwx+BuRtk5CfwMaAaKFnumgOitYmPTdoPAb2GcjWOq0Net7TmG6/sp2NA/VPPIqK6P1P32lcSVbEL7VD4ysaCxEf9DyjNUA00pokedB7Lq04jTVJ9xRXF7N3Fck08k4kkyGNz7ODn2X3hqaHsvcH1SW+RWNO71N6ttEzhWilPmieUx83Cm8gLCOM9UN7SNrrEJ20xv3M/Vh/JKvjN46zv2kcqF3R/odkLTL6MgM7T56tc9G6JFmS/Ol9Qc9TWkv0bKD3FLVBzwGqQ+tNNJeIRjL+zT44m61V+k5jRnxO3z+dyZ5ksylgE+Jy41ifovcpPZtpPIk3qke85Ffls3dFWHoYq0fXaQ7TM4XGnUBTxw0cx+YX1ae9Mhkjh5kNY/ySXGgPRu99737e7DrJQDR/ReuI5i49W2l8iBd6z8dkx7D3P3kKvTHsDQwxGMLGl3ga0GcAW3tUaotq4T3EG3fu3IGpqWnrR5fs+0OWgEzxfsgCf5TdiRTv/+36H9T11NnLkR5itKDpgUIPbXq4iZRvenjTA/T/2jsXGK2KLI+fAXzFBFdMi6CLzUCM4KAGEEV5re7y2MwQEMNIZAUVbZFHlDFghAZXGSFCmEHGdWiHGJcxhAioILjgJI0IKM1joqCtCLGdFdrIisiow9Ns/tUWfDTdfC9uf9X3/m5Cuvn6VtU5v3Pq3DpVdetT4FEQUbDxyaoeNH8//Hc3aFMQ1CBOQUZBTh1fgUOBQfUrWCs4K2HwA5AvDn5hVQeqrGebnqZkUP9UVg9+yaVAr8GtD/A+oVYdCuw+EZfcGrhpcK57FMTVlsr7BC31IapgpgRRMktGBWY97PxAUff67cpu4NqkqatTgV0PdPGQPu4h0KTpiaRUevkE1f+un9LBP0Skix6Sqk/y6X49iMVRsmgw6B+Qql/ldK9/oEkeJUvSQZdPBhX49dDR5ZMvn/AreZLculefVX9X7YKy2lYiVXxRsa39fK0bgK0dsdZ2fLXDPUQ8Y8kl//AMxUuDbffAP/K9+1x16qeCfGrC7B8UYq2HqgZVqs/Ztsk5NXUc/f7EgMD7o/xND33VL8ZirzKa+BAjPXx0qS49rCTr6JWjnVxrhq9xAyIN0vXAEjsNWFSP6pBtz3SInAaILtn9KRmV3/stybKVZPOTBOIoPXxSI7/wgwLPXQ9bDYxV765vdjlfkw+Jv/qQZJI9e7TpYb9Z8xun15AOQ9z96iPqX6pXK4OSo/28miRl8R2L3cqRklENztX3JI8G6Ppdcul3PwDWA151ydfUpgZUSmbFVp+r/8g/ZTP1Sf38ry01q9R7J+x1q2LyEZ/UKQFRe/Ir6Soba8AoP1CcUKKtwYwmFHTtHr/bDbw14NX9sqPeH1c7sofqUf9V3/TJi+qUreU7Kz9d6fqqEgef/Cu+qKxspD4g2aSv9135w6RbJrkERew0qFId4q1+NLf/XLtv+X1uADWk4xCX7K3YucL5juTTIEbxSPZWnFH9kldc9VN2l8/r99LyUqdn2S/L3H3yX/GUXCovncRb+muglZqs+Mkt6S/ZVF51b/xio4tLWmHUpIX3P7UnP5OMXibVKfYqp7bkP6pL9/qJRPm+PpPt5X+6R/WonHj4CQL/DDjTT8VY/V06yp/lG5pM8DFKuvo4qEGokkrZo+rbKucbk3tOttv++zZ3iJoOE5SOU9dOdYPODf+7wQ2et4/ebn/a9id78u0nrWNRR+envyj6hZVtqzkD4S//8Rc3WSX7O7v8FGfEREmE9JTt5RMqK9tL5tR4Jh7q7+qnKuf7o09c/P3yKXHUT8VaxfXal2e67m/rTJMxiq9qSxMEfvJZskpO9e2DRw66vi25NIGmMxXu73y/vfrxq66ck+3o9y4B6FzW2TW3/M7lTgZNPqjP6h5NNA5bOswN4jWJnfpckj3UVxWj1ZZsoD6jcrKXPr+1+FbHXBNO8jf9U7+THvJvPQ8GXjXQtaNdIkrw1fem9Z7mYpj6rPqH/FhsxNA/n2QD9QG1rTY1SbLs42XOdxWnFcP7XNnHtlRvsfX3rHfxSO9vK+GRP6q/y19KupbYrA2zXB1KbGb92ywXFxRTxEL9yj9vZQfVq/L+EgNdsoN8QvIuqVzi5C3tVeps62Ojm6j/6dmROmmf+rnYK5nT80D6Sl4x1OsR8mHprMkU+fH1La93NpHd5YeKRbKv/h04fMB+/k8/dz6g+/VNEtodVLa1zDbt2eRkE2PxE5u6Jvj9xK9YKw6oftlC7Ukvyecn7KSvGMoPFFPFVvFJsUd9TDbX82pct3EuNqjc7zf93o3P5Md+YcUvUOin9PBjAbdQIv/4YZ/rg1pR7lDUwU3mamxyVYurnP38JIKeBXpuTLx5opNBfiefFWs/weUmj3/2M8da7envktlPjKmPKPHX/zV5ockubysxUxt+fCS7yvZdW3d1kzfytQUDF7hJYskqVrP7zra+C/s6m5Z0KXHPGzFWHWpfLFSvkny1rT7S4vyayV/pqIkG6aEJa913yz/f4nxD9jjy4xE3+aBnrHxXdchGQzsOdeXUvp6L6h+eg/xWTBXH1DfVpi71d01W6XmlMbDGV4rLYivb6XP1U9lkxZYVtrt0N4n3aVG7MB+QeBeGe0FaZbtJQbA3ikb1IG7MX82iQevv3vudHZ966mp2qPA1MPATURrA6SG7YNsCG/8/4+3riV+75NkPFDX40YPfb5vW52f7RF/Jo0tJmK4/bvmjm8wQT/9ZLiyXVS5zq5SpW7xzqSebMhqAKLmo/T3QOi1b2+Z1IJMGcRqoaTVW92sl3E9oZdOWv3fTF5vc6kntVfdc6vJlZPcFf13gVusb8ysU3o9f+/g1N6jUoFeXBqI9XuxhfxjwB6enViD1GoW2Vne/orttvG+ju09JjlbMtKquxGXlzpVuEqUhfSpbO2oA/eJfX7SR14/MyScUD6S3kgGXbP1nzU6q+r5mTbHB7yLKVtZM71di2nxmc5v/y/nOHjP+dUamRc9436+X/NperXzVjpTWJOz+UkzSBIVWB/Vet5JErfCJhyZdlFzneik5VzKonQXaqXE2rnTbfdO1ockBxS1Nviv51aRSv/b90hXj72eBgGLTvIp5buJekyTir4l+rSbH4Sp9rdSmD55O4h2IMUm8AzFEQ4hB4t0QlGmjEATGvznerRIcmnKoEM03WJt+AK6toUpioroq9lSYTg/W7H8+l1ZCtJ1TK/qFvsasHGNTe0895T3ZEa+NcAnO6uGrCy1eItvXqsydS+50q4crhq2wXy36lVuJ69uu7xlt4nevJAWaOCn51GFi+UyE5ctLyaq2CNe14p9r3dqxU/l/lTbo6kFpq9A2c03S/GNyzQ4vLghAID0Bxv7pGTXkHSTeDUm7wG3R+QpsAJqPjIBLEjfOtor7KyJrI4SKfeJ9ZErNqxhc+RHQgVHawvfb236bX0WUzpmAJmfky1qxHbhooNvur1X+pUOX5lwnBeNJwO/ECXm3QzzJo1VjJsDYPyzrkXiHZY9IpaHzRYqXygtMIN+tfgUWP6Pmtf1QieJ1l12X0f3cdGYC5Z+Vu3fSu7TuAqoACGjFW+cWjLhuhDtUjQsCqQT8V86ReOMXEMicAGP/zFk1xJ0k3g1BOZA26HyBGAIxIAABCEDgNAI6nVfJ1dgbxtq8f58HIQicQkCTqzq4qvb5DWCCAATqJ8DYPyzvIPEOyx6RSkPnixQvlUMAAhCAQB4EBrw8wB2mpdOcn/yXJ/OoiaIQgAAEICACjP3D8gMS77DsEak0dL5I8VI5BCAAAQjkQaD/n/u7r7iqGFWR02ngeTRNUQhAAAKxJMDYPyyzkniHZY9IpaHzRYqXyiEAAQhAIA8C/f7cz3337LaSbXnUQlEIQAACEPAEGPuH5Qsk3mHZI1Jp6HyR4qVyCEAAAhDIg0DfhX3t6398bVsf2JpHLRSFAAQgAAES7zB9gMQ7TLtE1pdf3AAADRNJREFUIhWJdyRYqRQCEIAABM4CgQ1/22CHjx+2W9veehZqowoIQAACEGDsH5YPkHiHZY9IpaHzRYqXyiEAAQhAAAIQgAAEIBAMAcb+wZjCCULiHZY9IpWGzhcpXiqHAAQgAAEIQAACEIBAMAQY+wdjChLvsEwRvTR0vugZ0wIEIAABCEAAAhCAAARCIMDYPwQrnJSBFe+w7BGpNHS+SPFSOQQgAAEIQAACEIAABIIhwNg/GFOw4h2WKaKXhs4XPWNagAAEIAABCEAAAhCAQAgEGPuHYAVWvMOyQgNJQ+drINA0AwEIQAACEIAABCAAgQITYOxfYAPUap6t5mHZI1Jp6HyR4qVyCEAAAhCAAAQgAAEIBEOAsX8wpnCCkHiHZY9IpaHzRYqXyiEAAQhAAAIQgAAEIBAMAcb+wZiCxDssU0QvDZ0vesa0AAEIQAACEIAABCAAgRAIMPYPwQonZWDFOyx7RCoNnS9SvFQOAQhAAAIQgAAEIACBYAgw9g/GFKx4h2WK6KWh80XPmBYgAAEIQAACEIAABCAQAgHG/iFYgRXvsKzQQNLQ+RoINM1AAAIQgAAEIAABCECgwAQY+xfYALWaZ6t5WPaIVBo6X6R4qRwCEIAABCAAAQhAAALBEGDsH4wpnCAk3mHZI1Jp6HyR4qVyCEAAAhCAAAQgAAEIBEOAsX8wpiDxDssU0UtTVVVlbdu2tYqKCmvVqlX0DdICBCAAAQhAAAIQgAAEIFAQAtXV1datWzf77LPPrLi4uCAy0OhJAqx4J8gbNm/e7DofFwQgAAEIQAACEIAABCCQDAJadLvhhhuSoWzAWpJ4B2ycsy3aoUOHbPv27VZUVGTNmjU729UXtD4/o8dqfkHN0Cgax1cahZmCEBJfCcIMjUIIfKVRmCkIIfGVIMzQKIQ4G75y7Ngx27dvn3Xq1MnOP//8RqF3nIUk8Y6zdROkG++wJMjYeaqKr+QJMEHF8ZUEGTtPVfGVPAEmqDi+kiBj56kqvpInwACLk3gHaBREyp4AwSl7Zkktga8k1fLZ642vZM8sqSXwlaRaPnu98ZXsmSW1BL4SP8uTeMfPponUiOCUSLPnpDS+khO2RBbCVxJp9pyUxldywpbIQvhKIs2ek9L4Sk7Ygi5E4h20eRAuUwIHDx60OXPm2IQJE6x58+aZFuO+BBLAVxJo9BxVxldyBJfAYvhKAo2eo8r4So7gElgMX4mf0Um842dTNIIABCAAAQhAAAIQgAAEIACBgAiQeAdkDESBAAQgAAEIQAACEIAABCAAgfgRIPGOn03RCAIQgAAEIAABCEAAAhCAAAQCIkDiHZAxEAUCEIAABCAAAQhAAAIQgAAE4keAxDt+No2tRrt27bLZs2fbe++9Zzt27LCrr77a/ax9rVq1yiZPnmyVlZV2xRVXuAPXHnroodhyQbHTCWTiKyNHjrSXXnrptMJvvvmm9e/fH6wJIPDKK6/Yyy+/bFu3brX9+/dbu3btbPTo0VZSUmJNmjQ5QYCYkgBnSKNiJr5CTMFPRGD16tX29NNP20cffWQ6HOvyyy+3QYMG2bRp0+yiiy4iruAmJwhk4ivElXg5DIl3vOwZa21ef/11Gzt2rN144422c+dO+/HHH09LvN99913r1auX3X333TZ8+HDbsGGDe9jNnz/fRo0aFWs+KHeSQCa+oofZO++84xKv1KtDhw6nDI7gGl8CN910k1155ZU2ePBga9mypZWXl9uMGTPs4YcftlmzZjnFiSnxtX82mmXiK8SUbIjG995FixbZBx98YN26dbOLL77YjVOeeOIJ69y5s61Zs4a4El/TZ61ZJr5CXMkaa9AFSLyDNg/CpRJQou1XoRSItmzZclriPWDAALdytWnTphNFH3jgAXvjjTdM34eYuooF3fgSyMRX6vOh+FJBs9oE9u3bZ0VFRad8rB0yzz//vB04cMDOO+88I6bgNyKQia8QU/CV+gi88MILprHInj17rHXr1sQVXKVeArV9hbgSL2ch8Y6XPROjTV2B6PDhw+47vGfOnGmPPPLICRZvv/229enTxyXqXbp0SQwjFK0hUN9Di4cZHlIXgYULF7odM3v37rUWLVoQU3CTegmk+kqrVq3qjTUghMCyZctsyJAhVlVVZZdddhlxBZeol0Cqr2hHFmOVeDkLiXe87JkYbeoKRHqf6pprrrHa7+hqpeLSSy81DZK0/ZwrWQTOlHgvXrzYrWr+8MMP1qlTJystLXXv4nEll4BWpZYuXWpfffWVffLJJ8SU5LpCWs1TfaVp06ZugExMSYstMTccP37cjh496t71vvfee61Nmza2fPly93/GKolxg4wUrc9X/OIBcSUjjI3iJhLvRmEmhKxNoK5kSu9z9+jRw72Tqffx/HXs2DE755xzbO7cuTZ+/HhgJoxAfYm3/KFZs2ZuAKRtxdperPfvdIjSHXfckTBKqCsC2hXTvXt3dy7ElClT3BkRxBR8oy4CtX1F9xBT8JVUAjrcVVvLdenAziVLltiFF15IXMFNTiNQn68QV+LnLCTe8bNpIjQ6U+KtU891AFvtxPvZZ5+1cePGJYIPSp4kkOk2Lb0XfvPNN7tTaLUiwZUsAl9++aWLGxoArV271k3W+cSbmJIsX0inbV2+UlcZYko6kvH+uw5Y++677+zDDz+0p556ytq3b29vvfWW+2YWTegRV+Jt/2y0q89XtJOm9kVcyYZsePeSeIdnEyTKgABbzTOAxC2OQKaJt+7VSdYTJ050W88vuOACCCaEwLfffuvOgTh06JCtX7/eLrnkEqc5W0IT4gBZqFmfr9RXBTElC7gxvlVfWdi1a1e3o6pjx45sNY+xrfNVLdVX6tt9R1zJl3LhypN4F449LedBgMPV8oCXsKLZJN7PPPOMTZo0icQ7QT6iZLtfv3726aefutdUdJiNvziwMUGOkIGqZ/KV+ooTUzIAm4Bb9A6vzhOZPn26O/yVg2ATYPQcVUz1lccee6zOWogrOcINoBiJdwBGQITsCZzp68T0vq4G0P568MEH3YEmfJ1Y9pzjUCLTxFvbt3Q2gFa79b2rXPEnoPMfbr/9dlu3bp37d+21156mtL5OjJgSf19Ip2EmvlK7DmJKOqrJ+bt20vTs2dMdvjd06FD3dWLEleTYPxtNa/sKcSUbeuHfS+Idvo2Q8CcCSohWrVrl/vfcc8/Z7t27bc6cOe7/vXv3dt/Hq4S7V69ebnvxXXfd5d7RnDp1qs2fP99GjRoFy4QQSOcr+rt8ZNiwYdauXTv75ptv3OFq5eXl7kTrwYMHJ4RUstUsKSmxsrIy0+qBBsWpl7aDalWKmJJsH/Hap/MVxRBiCr4iAprM07ZyTeTplaX333/fxZiWLVva5s2b7dxzzyWu4CqOQDpfqa6uJq7EzFdIvGNm0Diro++/bNu2bZ0qKmHSO5q6lJw//vjjVllZ6Q5KmjBhgo0ZMybOaNCtFoF0vqIB0T333GN6l0pfN6eBkAZK2talbcdcySBQXFxsn3/+OTElGebOS8t0vkJMyQtvrArPnDnTrWxrcUC7HuQ7SrAeffRRN5nnL8YqsTJ7Tsqk85X9+/czVsmJbLiFSLzDtQ2SQQACEIAABCAAAQhAAAIQgEAMCJB4x8CIqAABCEAAAhCAAAQgAAEIQAAC4RIg8Q7XNkgGAQhAAAIQgAAEIAABCEAAAjEgQOIdAyOiAgQgAAEIQAACEIAABCAAAQiES4DEO1zbIBkEIAABCEAAAhCAAAQgAAEIxIAAiXcMjIgKEIAABCAAAQhAAAIQgAAEIBAuARLvcG2DZBCAAAQgAAEIQAACEIAABCAQAwIk3jEwIipAAAIQgAAEIAABCEAAAhCAQLgESLzDtQ2SQQACEIAABCAAAQhAAAIQgEAMCJB4x8CIqAABCEAAAhCAAAQgAAEIQAAC4RIg8Q7XNkgGAQhAAAIQgAAEIAABCEAAAjEgQOIdAyOiAgQgAAEIQAACEIAABCAAAQiES4DEO1zbIBkEIAABCEAAAhCAAAQgAAEIxIAAiXcMjIgKEIAABCAAAQhAAAIQgAAEIBAuARLvcG2DZBCAAAQgAAEIQAACEIAABCAQAwIk3jEwIipAAAIQgAAEIAABCEAAAhCAQLgESLzDtQ2SQQACEIAABCAAAQhAAAIQgEAMCJB4x8CIqAABCEAAAhCAAAQgAAEIQAAC4RIg8Q7XNkgGAQhAAAIQgAAEIAABCEAAAjEgQOIdAyOiAgQgAAEIQAACEIAABCAAAQiES4DEO1zbIBkEIAABCEAAAhCAAAQgAAEIxIAAiXcMjIgKEIAABCAAAQhAAAIQgAAEIBAuARLvcG2DZBCAAAQgAAEIQAACEIAABCAQAwIk3jEwIipAAAIQgAAEIAABCEAAAhCAQLgESLzDtQ2SQQACEIAABCAAAQhAAAIQgEAMCJB4x8CIqAABCEAAAhCAAAQgAAEIQAAC4RL4f0M7bt+uMYqNAAAAAElFTkSuQmCC", "_figure_label": "Figure 4", "_image_mode": "diff", "_model_module_version": "^0.11", "_size": [ 900, 400 ], "_view_module_version": "^0.11", "layout": "IPY_MODEL_e586890e3f434a0dacb8bb200fc4b534", "toolbar": "IPY_MODEL_fdc61c966cb145379d5f2eb7c7b0ab2c", "toolbar_position": "left" } }, "4e6d91d411954df8b8afa2326de84023": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_f88b4168a4674454bf3a719070e3d4b5" } }, "518e216c806341f7b881de45166a654a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_e3eb11ea7e674c91a68e328cb138076c", "IPY_MODEL_fbc49008a6ac4d1f9a187d31997c7eba", "IPY_MODEL_73f686f827b74f6ba7f203d2b1186c5c" ], "layout": "IPY_MODEL_b4208c41ae8d41389eeaee5b296c4103" } }, "51e645aa573242a19f50ca09ff06fb52": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "520bd0b61ed14bb989f9a7aa5f29a644": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "5402ac42a28945c2958ab09ac7378075": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "56bb4150895440fd865b0485322e0a63": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "5d3f36397d8d4525bea4003f2ee990a6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", "state": { "children": [ "IPY_MODEL_1460d182a1ff4472bcc2ce497114909a", "IPY_MODEL_7f17c993d05a442d845970254290ec15" ], "layout": "IPY_MODEL_f63426b10f1c41098c3972c570056a3f" } }, "5e181deaf97748278d56bd28622d3aac": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Zrange", "layout": "IPY_MODEL_ee202cb6d45c47b991d635f7af13f43e", "max": 1.5, "min": -0.5, "step": 0.047619047619047616, "style": "IPY_MODEL_29c30c7055f6440c9756d62047dd7913", "value": [ 0.023809523809523836, 1.0238095238095237 ] } }, "5f125f9947c24b8890be63be330e6398": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_c72974fe24104a288f13f77712bbd047", "outputs": [ { "data": { "application/3dmoljs_load.v0": "", "text/html": "" }, "metadata": {}, "output_type": "display_data" } ] } }, "692f4a896dde4a909e75b3ed9b1ac0a3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "6bf5f947234b4e66b143a70d0a3f544b": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "ToolbarModel", "state": { "_model_module_version": "^0.11", "_view_module_version": "^0.11", "collapsed": true, "layout": "IPY_MODEL_520bd0b61ed14bb989f9a7aa5f29a644", "orientation": "vertical", "toolitems": [ [ "Home", "Reset original view", "home", "home" ], [ "Back", "Back to previous view", "arrow-left", "back" ], [ "Forward", "Forward to next view", "arrow-right", "forward" ], [ "Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan" ], [ "Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom" ], [ "Download", "Download plot", "floppy-o", "save_figure" ] ] } }, "73f686f827b74f6ba7f203d2b1186c5c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "fullMol opac", "layout": "IPY_MODEL_7c61a7a2348b4ba58cdad2537a85225b", "max": 1, "readout_format": ".01f", "step": 0.1, "style": "IPY_MODEL_c46357387bb643b7a7740174241f32fa", "tooltip": "Opacity to display fully molecules\nwhich have at least one atom inside the limits", "value": 0.5 } }, "7426e5f971394d249d5f4e099477dae2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "77f6fca9951e478dbd016ed5226eb856": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "MPLCanvasModel", "state": { "_cursor": "default", "_data_url": "data:image/png;base64,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", "_figure_label": "Figure 5", "_image_mode": "diff", "_model_module_version": "^0.11", "_size": [ 900, 400 ], "_view_module_version": "^0.11", "layout": "IPY_MODEL_16d499f663fc47b49799b1bbd59260eb", "toolbar": "IPY_MODEL_6bf5f947234b4e66b143a70d0a3f544b", "toolbar_position": "left" } }, "7c0bfe2f4f2348c98baeb1db2d521c00": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "ToolbarModel", "state": { "_model_module_version": "^0.11", "_view_module_version": "^0.11", "collapsed": true, "layout": "IPY_MODEL_fb20ebc822d148e690ab89453aec3bed", "orientation": "vertical", "toolitems": [ [ "Home", "Reset original view", "home", "home" ], [ "Back", "Back to previous view", "arrow-left", "back" ], [ "Forward", "Forward to next view", "arrow-right", "forward" ], [ "Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan" ], [ "Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom" ], [ "Download", "Download plot", "floppy-o", "save_figure" ] ] } }, "7c61a7a2348b4ba58cdad2537a85225b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "7f17c993d05a442d845970254290ec15": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TextModel", "state": { "disabled": true, "layout": "IPY_MODEL_8d9d2ffde3994085b21a854dd34593c0", "style": "IPY_MODEL_c9d71f937d1e4d569f05c0d1d874a595", "value": "LLK= 18612.26 " } }, "803acabdb80c46f9a6de317791d95549": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "885742531923446e9bc635bd3250a95d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "8b12d904b277450c8ee9b4a6a7749c60": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_07e57849dcd842cd8665047a4abb8a04", "IPY_MODEL_eb563daee4fd48b88cb155fc153a43e4", "IPY_MODEL_e8b7df83ee4346bc9dd2b32d20222683" ], "layout": "IPY_MODEL_bb978d1baf324b599a8747447e56dbb0" } }, "8bf9ae6abf444ae08df94efb582c5ffd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "8d9d2ffde3994085b21a854dd34593c0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "max_width": "50%", "width": "40em" } }, "8eacf287c05749d887254f7f4b1b0d07": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "ToolbarModel", "state": { "_model_module_version": "^0.11", "_view_module_version": "^0.11", "collapsed": true, "layout": "IPY_MODEL_dd94b2509958449e8752fcf027643eca", "orientation": "vertical", "toolitems": [ [ "Home", "Reset original view", "home", "home" ], [ "Back", "Back to previous view", "arrow-left", "back" ], [ "Forward", "Forward to next view", "arrow-right", "forward" ], [ "Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan" ], [ "Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom" ], [ "Download", "Download plot", "floppy-o", "save_figure" ] ] } }, "8f1150436e1544ec8236bff78a7e27fb": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "ToolbarModel", "state": { "_model_module_version": "^0.11", "_view_module_version": "^0.11", "collapsed": true, "layout": "IPY_MODEL_d35a9babe4d14f45ae7cf67845afa6a3", "orientation": "vertical", "toolitems": [ [ "Home", "Reset original view", "home", "home" ], [ "Back", "Back to previous view", "arrow-left", "back" ], [ "Forward", "Forward to next view", "arrow-right", "forward" ], [ "Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan" ], [ "Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom" ], [ "Download", "Download plot", "floppy-o", "save_figure" ] ] } }, "8f4b38dea47045c2bd33aa3884f2767b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "953995b09f404c7db76aa00c42eec519": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "a2729d7502c4449695df8f9440c273f9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", "state": { "children": [ "IPY_MODEL_3b65a4ece7b54989b9018526ffdc1d40", "IPY_MODEL_8b12d904b277450c8ee9b4a6a7749c60" ], "layout": "IPY_MODEL_cb372953b8e5456681e333899f6a4bf2" } }, "a474a39f1b7040f5b8e31bcae6884fe2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "a5767929d16a4a418579b71574cf1d2c": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "MPLCanvasModel", "state": { "_cursor": "default", "_data_url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA94AAAG4CAYAAACzXz9FAAAAAXNSR0IArs4c6QAAIABJREFUeF7svQeYlEXWv31mGHLOCMgQDCBBksQhKkHl1XWNCIYF/q4JXBAFySCCKJgAlRV3DazhFVlQggSJQ0aQICACElbC4JBzmu87tXa/PcMM3TNPPz3V3XddF5dMU8+pU/d5aqxfV9WpmJSUlBShQAACEIAABCAAAQhAAAIQgAAEIOAKgRiEtytcMQoBCEAAAhCAAAQgAAEIQAACEDAEEN68CBCAAAQgAAEIQAACEIAABCAAARcJILxdhItpCEAAAhCAAAQgAAEIQAACEIAAwpt3AAIQgAAEIAABCEAAAhCAAAQg4CIBhLeLcDENAQhAAAIQgAAEIAABCEAAAhBAePMOQAACEIAABCAAAQhAAAIQgAAEXCSA8HYRLqYhAAEIQAACEIAABCAAAQhAAAIIb94BCEAAAhCAAAQgAAEIQAACEICAiwQQ3i7CxTQEIAABCEAAAhCAAAQgAAEIQADhzTsAAQhAAAIQgAAEIAABCEAAAhBwkQDC20W4mIYABCAAAQhAAAIQgAAEIAABCCC8eQcgAAEIQAACEIAABCAAAQhAAAIuEkB4uwgX0xCAAAQgAAEIQAACEIAABCAAAYQ37wAEIAABCEAAAhCAAAQgAAEIQMBFAghvF+FiGgIQgAAEIAABCEAAAhCAAAQggPDmHYAABCAAAQhAAAIQgAAEIAABCLhIAOHtIlxMQwACEIAABCAAAQhAAAIQgAAEEN68AxCAAAQgAAEIQAACEIAABCAAARcJILxdhItpCEAAAhCAAAQgAAEIQAACEIAAwpt3AAIQgAAEIAABCEAAAhCAAAQg4CIBhLeLcDENAQhAAAIQgAAEIAABCEAAAhBAePMOQAACEIAABCAAAQhAAAIQgAAEXCSA8HYRLqYhAAEIQAACEIAABCAAAQhAAAIIb94BCEAAAhCAAAQgAAEIQAACEICAiwQQ3i7CxTQEIAABCEAAAhCAAAQgAAEIQADhzTsAAQhAAAIQgAAEIAABCEAAAhBwkQDC20W4mIYABCAAAQhAAAIQgAAEIAABCCC8eQcgAAEIQAACEIAABCAAAQhAAAIuEkB4uwgX0xCAAAQgAAEIQAACEIAABCAAAYQ37wAEIAABCEAAAhCAAAQgAAEIQMBFAghvF+FiGgIQgAAEIAABCEAAAhCAAAQggPDmHYAABCAAAQhAAAIQgAAEIAABCLhIAOHtIlxMQwACEIAABCAAAQhAAAIQgAAEEN68AxCAAAQgAAEIQAACEIAABCAAARcJILxdhItpCEAAAhCAAAQgAAEIQAACEIAAwpt3AAIQgAAEIAABCEAAAhCAAAQg4CIBhLeLcDENAQhAAAIQgAAEIAABCEAAAhBAePMOQAACEIAABCAAAQhAAAIQgAAEXCSA8HYRLqYhAAEIQAACEIAABCAAAQhAAAIIb94BCEAAAhCAAASsITB27Fjp3r27Nf7gCAQgAAEIQCAYBBDewaCIDQhAAAIQgAAEMkVgz5496dbv3LmzLF68OFO2qAwBCEAAAhCwnQDC2/YI4R8EIAABCEAgAgnky5dPGjVqJCkpKal6t2HDBklOTo7AHtMlCEAAAhCIZgII72iOPn2HAAQgAAEIZBOBWrVqyYoVK0QFuG9p06aNzJ07N5u8olkIQAACEICAOwQQ3u5wxSoEIAABCEAAAlchoNvJa9euLYUKFUpVa+vWrVK1alXYQQACEIAABCKKAMI7osJJZyAAAQhAAAIQgAAEIAABCEDANgIIb9sigj8QgAAEIAABCEAAAhCAAAQgEFEEEN4RFU46AwEIQAACEIAABCAAAQhAAAK2EUB42xYR/IEABCAAAQhEKYEdO3ZIlSpVorT3dBsCEIAABCKZAMI7kqNL3yAAAQhAAAKWEkjvru4XX3xRXn/9dWnWrJmlXuMWBCAAAQhAIGsEEN5Z48ZTEIAABCAAAQg4IJArVy5p1aqVlCtXznuXt14j1rZtW/nHP/7hwDKPQgACEIAABOwjgPC2LyZ4BAEIQAACEIh4Aj///LMMHTpUKlWqJH369DHXit1+++0ya9asiO87HYQABCAAgegjgPCOopifPXtWNm7cKCVLlpS4uLgo6jldhQAEIAABWwmsXLlSxo8fLwkJCbJw4UL57LPPbHUVvyAAAQiEFYGLFy/KoUOHpGbNmpInT56w8j0SnUV4R2JUM+jT6tWrpUGDBlHUY7oKAQhAAAIQgAAEIACB6CawatUqueWWW6IbggW9R3hbEIRQubBr1y6zpU8H3zXXXBOqZmkHAhCAAAQgAAEIQAACEAgxgf3795tFt19//VUqVqwY4tZpLi0BhHcUvRP/+c9/5Nprr5W9e/dK+fLlvT2fPHmyPPnkk3LhwgWT4KZ+/foyf/58ueeee2TGjBly+fJl0a0qnqL1u3TpYn7UzLNax7ckJiaaz3v37m2y02rRre0eGz169JBJkyaZtjSxzpQpU1I9/+6775rzfjly5DDPDB48WF544QWJiYmRUqVKycGDB+Wtt96S559/XgoWLCjnzp2Tp556St54441UdjLy31NJr63Rvl66dMn4om0MGjTI/HPRokXl2LFj0rx5c7P10VN0u75+aXHkyJGrvjlq++abb5ZTp05J165dZeLEiaa+9k391mRC27dvj6K3j65CAAIQgAAEIAABCISSQEZz/1D6QFv/RwDhHUVvQ3qDTz/Tb8A+/fRT6dixo6HxyiuvSP/+/WXDhg1yww03SIECBVIJb02A89VXX0mbNm2kWLFi8uWXX0q7du28JFV4t27dWjRj7fHjxyU2NtYrvKdPny5PP/207Ny5M8Nz5trezJkzjeg9f/68HDhwQCpUqJBKvKvwHjdunBGv2p7WVVGsbXpKRv57/j0pKUn27NljxPcvv/wiN910k/nyQYueOfzggw+MfV/hrV8k6BcNvp+l9wqdPn3a9PHZZ5+V6667ziu8ta6v71H0+tFVCEAAAhCAAAQgAIEQEkB4hxB2AE0hvAOAFClV0ht8zzzzjBG5ugUlo+K7Wq1CWledVeRqufPOO83qsK5ge4oK4Q4dOhix3L59e3nttde8olkFuQppXbXWFW29Mkbr+ha1p0JeV7DLli3r/SdfP9KKV7WlQrt69epXdMP3uYz6qCJcdwGo0PeUbt26XSG8a9WqJS+99JL5kqJp06amTbX/+OOPy5tvvnmF+ZYtWyK8I2UA0Q8IQCAkBHS3UeHChUPSFo1AAAIQiGQCCG+7oovwtisernqT3uDTq1uOHj0qy5cvD0h4r127VlRMqgDX8uijjxoRPXv27CuEt25X15Vorasr0bptvFq1aqa93bt3y+effy7du3f32vIYWLp0qajo1e3auvo9depUYycj4a1b3x988EGzWq2r62lLIML7+uuvNxl1//nPf15VeOtkUCeFw4YNk7///e9mxVzbVMHuu9ruMYLwdvWVxjgEIBCBBPQe7zlz5kRgz+gSBCAAgdASQHiHlre/1hDe/ghF0L9ntOKtW6c18VpGJasr3iqwdYVYV7119VqFd+PGjU2CN891MTlz5vRu706vfT17/t133xn/0gpvzxlvFb56Nvtvf/tbul3wJ7xVcKto1+3lviXtive//vUvs3q/fv16ueOOO8yKjH55cLWC8I6gAURXIACBoBLw5ArxNar5NubOnSv6/ysKBCAQXAI6vn7//Xeza1Hz21DCl4Du9NTrwUqUKGFyIGVUEN52xTjihbee0R09erSsWLFCNm3aJFWrVjX/9S1Xe2H37dvnzQCuZ6F1pTZtOXPmTKq78U6cOGESi+lKrCb+0u3VY8eOlfj4+FSPbtu2TTTR2JIlSyR//vxm+/Krr74qefPmTVVPt4LrmestW7aY7dC9evUy56QzWwI94z1y5EizndpT0gpXPeP973//2yRG0zPeKj515dxTPFvNVXivWbPGrJDrL3kV3ipc9ey0nqnWc9K6VV0TkPkW7a8KWy3qy/vvv2+4X22r+dVYXE14q9+6sq6xSFvSCu8WLVrIXXfdZZKjDR061JzbVr9Y8c7sm0h9CEAAAmKSTOr/P1QM+Bb9/6def0mBAASCR0DH2W+//SY6R9UdeircKOFLQL840d2WmmRYf5dmpGUQ3nbFOOKF97Rp00yCq4YNGxpxpRm60wpvFeVpi26hVjG8bt067z+p8NY78FR4+Ra17fvC65ll3ZI9ZswYUZGqq7G63VrPA3tEtYrSGjVqGDE+cOBA0TPGKqh1ddj3vLRuAddt1upP586dRbdha5bvCRMmmO3YmSmZzWruyT6uGbz13LVnVVknSn/961/NZElXi2fNmpXKDV/hrf+gq96bN282wlv5N2rUyMRCf2nouei0/ahZs6Y5c66r4Vp0dVwFcmaFd0b+e5zVVRXd0qgx8sRPGen2dk0qp9vI1Uftu8ZH/6vXMug3jFq0H/ouqZ/pnfHWs/DKTv/npufdPeKe5GqZeWupCwEIRCqBRx55REaNGpUql4f2tV+/fjJixIhI7Tb9gkC2EDh06JBZ7da5SfHixbPFBxoNLoHk5GQzP9VV75IlS6ZrHOEdXOZOrUW88Fah5zn3q+JIV2DTCu+0ED33XevqrF4x5SkqvFVUazbtjIpuV1ZBptu3Pau2KuCqVKliVr1167QWnWzoOWFdMdUB4xGYnTp1MiJVz0JrUcF5+PDhVNugn3jiCdHs4DqY0jvTnJFv4T74VODqCrueKQ91OXnypLz44ovmOjAnRZ9XO7rzQt9FCgQgAAEIQAACEHCbgF4lqyukOh+lRA4B3bWpOxj0uuD0SrjP/SMnUv/tScQLb9+ABSq89Zv2AQMGmBVP3/uuAxHeuhr9zjvvGLHsuwqu27J1JfXbb781Lum25SJFioiuyHuKbkvXc8N6nZeuquvPuhqr28979uzprbdo0SKzfVuFW7169QJ+Jxl8AaOiIgQgAAEIhJiAftGtVzZSIACB4BPw5PLRuSwlcgj4iytzf7tijfBOJx661Vm34aS9q1l/WWlGa72jWVdfdQu4rlxrfU954IEHjGBPu31dr+3SzN965lyLbvXRxDIqqn2LXoelCcj0/LCufOvPupVbt6B7im4X0uf17m3dfh5oYfAFSop6EIAABCAQagL6BfWCBQtC3SztQSAqCPgTaFEBIQI76S+uzP3tCjrCO0089Bz2zTffbM5Q65Zu36JnhvU8t57X3blzp1mZ1m3Peg68cuXKpmqbNm3MmV7NxO1bdAVdtxnrSrgWFe4vv/yy9O3bN1U9PTOtonrKlCnmPLf+rOe8dfu6p+hZaX3+7bffNsnZMip6rtxz7ZfW0fPJDRo0EN1u5LuSb9criTcQgAAEIBCNBIYMGSL6hwIBCASfgD+BFvwWsRgKAv7iivAORRQCbwPhnYZVnz59TMKvAwcOmPPEVysqZPWsrp7L9pz9VeGtScDSJhzTrOSanVsTIXiE9/Dhw0Xb8y1NmzaVMmXKyNdff+0V3rp6roI/rfDWLe16D3ZGRScwmn07bUF4Bz5AqAkBCEAAAhCAAATCnYA/gRbu/YtW//3FFeFt15uB8PaJh2bp1u3ktWvXTnX2+moh0+uwNEuk5w5om7aas+Jt12DDGwhAAAIQuAqBy5dFNHlply4iBQqACgIQCCIBfwItiE1ZYUqPi+rxFb2asH79+lb45IYT/uKK8HaDetZtIrx92Ol92npu+4svvpAHH3wwIKqauVxXsT3Cm+RqAWGjEgQgAAEIQCA1gS+/FHnoIRE95jVhAnQgAIEgEvAn0ILYlBWmEN7/DQPC24rX0esEwtsnHk899ZS5Q1vPbefLl89vpPbt22eu/dK7SD1XjHmuE/NNiKZbu/UMeNrrxPSMt14n5rlPUQV/x44dr7hOTO/81nPenqJXkn3zzTdRd52Y34BQAQIQgAAEwpfAxIki/+//idx7r8jkyeHbDzyHgIUEEN4WBiUILvmLK8I7CJCDaCLihbdmIJ85c6ZBNn78eNH77t544w3zs17p5blwXhOWXXPNNSZ7uGYLT1s+//xzcze33qtdtmxZk1xt5MiRJlnaDz/8IJUqVfI+ond9a8K1MWPGmOvABg0aZLKha+K2vHnzmnoqpmvUqGG2tg8cOFCSkpKkV69e0q5dOyP+PUUFt67C61VoepZcE66pPU3+1q1bt0y9Cgy+TOGiMgQgAAEIhJLABx/8d7Ub4R1K6rQVJQT8CbRwxDB16lSTy2jLli3mit57773X3Dak1/d6Vrx17q43BenNQjon19xI/fr183ZX58Y6/9arenWurlrgT3/6k8n3FA7FX1yZ+9sVxYgX3vpC+opiX/x6bYneh61FB6YKZhXpKq7TFk1wphnIf/rpJyOadYC3bt1ahg0bJjfeeGOq6nq2unfv3jJ58mQ5f/68qaer3fHx8anqbdu2zfwCSExMNCvsutqtvzA84txTWX3SXxL6i0WzkesvCL2eLLOFwZdZYtSHAAQgAIGQEfj730X++leR++4T+eqrkDVLQxCIBgL+BFq4MdCdnyqQ77//frM4pQtiL730krm9Z968eV7hXa5cOTO/vu2228zno0ePlvfee09096gWnaPrDlYV8KVLlzZXAq9Zs0Y0gXE4FH9xZe5vVxQjXnjbhTt7vWHwZS9/WocABCAAgasQ0HPdOhm+/36R//1fUEEAAkEkkJFA02OWGzduDGJLgZuqWbOmEcFZKXXr1jVX63pyLKkN3Z368MMPiy6sadHkanoc9JNPPvE2oT/rarge9YyNjTWr47qD9Wq3BGXFv1A9g/AOFengtIPwDg7HsLCC8A6LMOEkBCAAgegk4BHeDzwgoonWKBCAQNAIRJLwPnnypNk2/vrrr8vzzz/vZaTHRnXXqB7JbNasmRHeU6ZMkXvuucdb59///rf8+c9/NsK7QoUK5jin/l13teqVwNddd13QmIfCEMI7FJSD1wbCO3gsrbeE8LY+RDgIAQhAIHoJvP++yFNPiSC8o/cdoOeuEfAn0Fxr2AXDnvms5kTS/Ee+pUyZMmZr+d13322Etx7nbNq0qbeK5kpKSEgQPULasGFD2b9/v/Tv31/0vPiRI0fM8dERI0YYcR4OxV9cmfvbFUWEt13xcNUbBp+reDEOAQhAAAJOCOiW06ef/u+VYp9/7sQSz0IAAmkI+BNo4QTMs+Kt57U175GnZGXF2/Ps5cuXTbLk4cOHm3xPP//8s7mRyPbiL67M/e2KIMLbrni46g2Dz1W8GIcABCAAAScE3n1XRBOHduwo8tlnTizxLAQgEMHCW7umZ7xz5cplVq495csvv5SHHnrI7xnv+fPni171q2e80xY9716rVi2TBb1t27bWv0cIb+tDlMpBhHd4xcuRtwhvR/h4GAIQgAAE3CTgEd4PPyzyr3+52RK2IRB1BPwJtHAD4slq/uCDD8pjjz3mzWp+yy23pJvVXM9vz50712Q11+uFn376aXN9mF7jqwnXdIv5hQsXTDZzFfO//PKLlChRwnos/uLK3N+uECK87YqHq94w+FzFi3EIQAACEHBA4PjIkVKoXz9Jbt9eis+a5cASj0IAAmkJ+BNo4UhME6Xptb6bN2821/zed999V9zjPX36dJkwYYJ8//33UrBgQXn22WdlwIABprvnzp0zPy9ZssRcI6aJ2erXr2+2m6uAD4fiL67M/e2KIsLbrni46g2Dz1W8GIcABCAAAQcElnXqJE0++0wWX3utNN+zx4ElHoUABKJBeBNlEYR3eL0FCO/wipcjbxHejvDxMAQgAAEIuEhgWceO0uSLL2SxXvGze7eLLWEaAtFHwJ9Aiz4ikdFjf3Fl7m9XnBHedsXDVW8YfK7ixTgEIAABCDggkPjQQ5Lw5ZeyOD5emu/a5cASj0IAAqx4R8c7gPAOrzgjvMMrXo68RXg7wsfDEIAABCDgIoHEBx6QhK++ksUVK0rzX391sSVMQyD6CPgTaNFHJDJ67C+uzP3tijPC2654uOoNg89VvBiHAAQgAAEHBBLvv18SJk+WxZUqSfOdOx1Y4lEIQIAV7+h4BxDe4RVnhHd4xcuRtwhvR/h4GAIQgAAEXCSw5M9/lmb//rcsqVxZmu3Y4WJLmIZA9BHwJ9Cij0hk9NhfXJn72xVnhLdd8XDVGwafq3gxDgEIQAACDggsvuceaT51qiypUkWabd/uwBKPQgACrHhHxzuA8A6vOCO8wytejrxFeDvCx8MQgAAEIOAigUV/+pO0mDZNllx3nTT75RcXW8I0BKKPgD+BFn1EIqPH/uLK3N+uOCO87YqHq94w+FzFi3EIQAACEHBAYNFdd0mLb7+VJddfL822bXNgiUchAAFWvKPjHUB4h1ecEd7hFS9H3iK8HeHjYQhAAAIQcJHAov/5H2kxfbosueEGafbzzy62hGkIRB8BfwIt+ohERo/9xZW5v11xRnjbFQ9XvWHwuYoX4xCAAAQg4IDAwg4dpOWMGbLkxhul2datDizxKAQgwIq3/3fg8ccflzVr1simTZv8V7a0BsLb0sBk4BbCO7zi5chbhLcjfDwMAQhAAAIuElhwxx3SatYsSaxaVRK2bHGxJUxDIPoI+BNo0UdEBOEdjVHP3j4jvLOXf0hbR3iHFDeNQQACEIBAJgjMv/12af3dd5JYrZokbN6ciSepCgEI+COA8L6SEMLb31vDvwebAMI72EQttofwtjg4uAYBCEAgygnMb99eWs+eLYk33SQJP/0U5TToPgSCSyAShffy5ctl8ODBsmLFCklJSZGbbrpJhg8fLm3atJG+ffvKjBkz5Ndff5XChQtL8+bN5Y033pBrrrnGCzY94f3bb7/JSy+9JLNnz5bjx49LfHy8PPXUU/Lcc88FNyBBsuYvrsz9gwQ6SGYQ3kECGQ5mGHzhECV8hAAEIBCdBL5v00ZunTdPEqtXl4QwPnMZndGj17YT8CfQbPc/rX9Lly6V1q1bS6NGjeSZZ56RIkWKmPPapUuXlq5du0qXLl2MAC9btqwcOnRIxowZI8nJybJ582aJi4sz5tIKb/33OnXqmH8bNGiQVK5cWX755RfZsWOHvPbaa1Yi8hdX5v52hQ3hbVc8XPWGwecqXoxDAAIQgIADAvNuu01u+/57SaxZUxI2bHBgiUchAIG0BPwJtHAj1rRpUzl69Khs2LBBcuTIcVX3L126JAcOHJDy5cubley2bdumK7z79+9vBPrWrVulYsWKYYHEX1yZ+9sVRoS3XfFw1RsGn6t4MQ4BCEAAAg4IzLv1Vrlt/nxJrFVLEtavd2CJRyEAgYCF91NPiWzcmD3AatYUee+9TLd9+vRpKViwoIwcOVJefPHFdJ+fNWuWvPzyy/LTTz+ZLeOeMnbsWHn22WfTFd66el6sWDGZOXNmpn3KrgcQ3tlFPmvtIryzxi0sn0J4h2XYcBoCEIBAVBCY17q13LZggSy9+WZp+uOPUdFnOgmBUBHIUKCFofDWc9i6ev3pp59K586dr0C4evVqadKkidx9993y6KOPSqlSpSQmJsZsS3/99deld+/e6Qrv66+/Xlq0aCETJ04MVVgct4PwdowwpAYQ3iHFnb2NIbyzlz+tQwACEIBAxgTmtmwpbRYtkmW1a0uTdetABQEIBJGAP4EWxKZcN3Xq1CkpVKhQhive/fr1k7///e+SlJQksbGxxp/du3eb7eNXE96seLseuqhvAOEdRa8AwjuKgk1XIQABCIQZgTktWkjbxYtlWZ060mTt2jDzHnchYDeBSBLeSjohIcGc8V6/fv0VZ7x79uwpX3zxhezbt8+sdGsZMWKE6BnuqwnvAQMGyOjRo2Xbtm1SoUIFuwP6h3f+4src364wIrztioer3jD4XMWLcQhAAAIQcEBgTvPm0nbJEllWt640+eEHB5Z4FAIQSEvAn0ALN2KJiYkmq7kmWXv66aelaNGisnbtWilRooSUKVNG7rzzTpPt/J577hG9dky3paugvprw9mQ111XygQMHmqzmO3fuNM+NGjXKSkT+4src366wIbztioer3jD4XMWLcQhAAAIQcEBgTrNm0jYxUZbVqydN1qxxYIlHIQCBSBfe2r9ly5aJrlKvXLnSrHpXr17d3ON96623muu/NJHa4cOHjTgfP3683HDDDVcV3mpz79695h5vTc6mSdx0e7oK++7du1v5UiG8rQxLhk5FvPDevn272TayYsUK2bRpk1StWtX817foPX4ff/zxFZB00LVv3z7V52pr3Lhx5lqCmjVrmgHcsmXLVHVOnDhhEjdMnjxZzp07Z76R08EfHx+fqp5+g9ajRw9ZsmSJ5M+fXzp27Civvvqq5M2bN1U9za6o22O2bNlikkn06tXL/BLIbEF4Z5YY9SEAAQhAIFQE5iQkSNulSxHeoQJOO1FFwJ9AiyoYEdRZf3Fl7m9XsCNeeE+bNs1cG9CwYUOzVeTy5cvpCm8Vv//6179SRadatWpSuHBh72cqujVhg54TqVu3rnzwwQcydepUWbVqlRHhntKhQwez3UXvAtTkD4MGDTJXGehdgx5RredSatSoYcS4bmfRBBAqqFXoT5o0yWtLt8c0b97cZGXUzI1Lly6VwYMHy4QJE6Rbt26ZepsYfJnCRWUIQAACEAghAa/wrl9fmqxeHcKWaQoCkU/An0CLfAKR2UN/cWXub1fcI154q9D2ZDTUle01a9akK7zT+9w3VLpyXbp0aXniiSfM9hUtly5dMoK7Vq1aJomDFt3uolkRZ8yYIXfccYf5bM+ePVKlShWz6v3kk0+az/SsyLBhw0yWRT2PouWzzz6TTp06yebNm0VFv5bbb7/dbJNRu56iPkyfPl10MHn6FshrxeALhBJ1IAABCEAgOwggvLODOm1GCwF/Ai1aOERaP/3Flbm/XRGPeOHti9uJ8F6wYIHZMq4r2XXq1PGaHTp0qFnZPnbsmMmcqKvR77zzjhHLnkyKWrlVq1ZSoEAB+fZjxa4xAAAgAElEQVTbb82zek9gkSJFRFfkPUXFva6wv/LKK/L888+bbeq6Yq7bzzVDo6csWrTIbG/XLwvq1asX8BvF4AsYFRUhAAEIQCDEBOY2ayZtEhNlaf360pQV7xDTp7lIJ+BPoEV6/yO1f/7iytzfrsgjvEVEBfmXX34puXPnNokUdBVbt3//6U9/8kbr3XffNdkR9d99z2B/9dVX8sADD5hkDHr+Wv+uK9x6pty36LOzZ88WPXOupVSpUtKlSxcjqn2LJoZo3LixTJw40ax8689pz5ofOnTIPK8ZGnX7eaCFwRcoKepBAAIQgECoCXiFd7160pTkaqHGT3sRTsCfQIvw7kds9/zFlbm/XaFHeIvI22+/LXFxcUbk6tnr9957T+bMmSMqqu+77z4TMV2Ffvnll+Xs2bOpIjhv3jxp06aNuUdQt5zr3zWz4nfffZeqnmZdVPGuK+FacubMaez17ds3VT29l1BF9ZQpU8x5bv1Zz3nr9nVPuXjxonle/dbkbBkVPVeufzxl//790qBBA++XBHa9ingDAQhAAALRTADhHc3Rp+9uE/An0NxuH/vuEPAXV4S3O9yzahXhnQ45PRfepEkTI1p11dkjvPWKgjNnzqR6Yu7cudK2bVuTOE1XylV4q4jXVWrfolnJ33//fdE7Aj3CW+316dMnVT298kDvH/z666+9wltXzzU5XFrhrVvar3a9wZAhQ0S3wqctntX5rL40PAcBCEAAAhAINoG5zZtLmyVLZCkr3sFGiz0IiD+BBqLwJOAvrghvu+KK8M4gHnpN2IsvvujdWu7Zaq7CO0+ePN6nbN5qzoq3XYMNbyAAAQhAIGMC85o3l9uWLJHEevUkga3mvCoQCCoBfwItqI1hLGQE/MUV4R2yUATUEMI7A0yauVxXoz1nukmuFtD7RCUIQAACEIBAlgjMa9ZMbtPkanXrStMffsiSDR6CAATSJ+BPoMEtPAn4iyvC2664IrzTiYduNdcz1Sq6N23aZGp4rhPT68A8CdH0OjE9161bzNNeJ+abEE23dleuXPmK68T0jLdeJ1a8eHHThtro2LHjFdeJ6blzPeftKerDN998w3Vido0lvIEABCAAAQcEvm/WTG5FeDsgyKMQyJiAP4EGu/Ak4C+uCG+74hrxwlvF88yZMw318ePHy44dO+SNN94wP+uVXvrvmtVcBa/etX3kyBGTXE1XuPWc9T333OON2OjRo6Vfv34ycuRIqVu3rsk8rknQVq1aZcS3p3To0EHWrVtnrhnT68AGDRpkrhvTc+CejOgqpmvUqCEVK1Y0GdSTkpKkV69e0q5dO5k0aZLXlgru5s2bGx/1jm9NuKb2JkyYIN26dcvU28TgyxQuKkMAAhCAQAgJzG/eXFrrGe86daTp2rUhbJmmIBD5BPwJtMgnEJk99BdX5v52xT3ihbe+kJUqVUqXuoprXbH+y1/+Ij/88IPoNV25cuWS+vXrm2zjKoJ9S0pKiqj4HjdunBw8eNCIbd2Srnd0+xY9W927d2+ZPHmynD9/3tz/PXbsWImPj09Vb9u2bSY5WmJiouTLl8+I/1GjRqW6rkwf0C8OVPBv2bLFXFmmAl2vJ8tsYfBllhj1IQABCEAgVAQWNG8urfSMd506koDwDhV22okSAv4EWpRgSNVNXdRas2aNd3er2wxatmwpBQoUkOnTpwetKX9xZe4fNNRBMRTxwjsolCLECIMvQgJJNyAAAQhEIIEFzZpJq8RESaxdWxLWrYvAHtIlCGQfAX8CLfs8y76WEd7Zxz5aW0Z4R1HkEd5RFGy6CgEIQCDMCCxISJBWS5fK0tq1pSnCO8yih7u2E0B4XxkhhLftb23k+YfwjryYZtgjhHcUBZuuQgACEAgzAguaNpVWy5ZJ4s03S8KPP4aZ97gLAbsJRKLw1jxIgwcPlhUrVogeB73ppptk+PDh0qZNG3NkdMaMGfLrr79K4cKFTb4kzfF0zTXXeAOVnvD+7bff5KWXXpLZs2eLHh3VY6JPPfWUPPfcc1cN8OHDh801xNOmTZOTJ0+a46jqS9u2bb3PebaaP/DAAzJ06FDZt2+fNGzY0ORtuvHGG731/vGPf5g8UTt37jRHUatVqyZvvvmm3HLLLVf44C+uzP3tGpcIb7vi4ao3DD5X8WIcAhCAAAQcEFjQpIm0Wr4c4e2AIY9CICMC/gRauJHTZMOaQ0lvIdK8R0WKFDHntUuXLi1du3aVLl26GAFetmxZk8NJhWxycrK5OSguLs50N63w1n+vU6eO+TdNZKw3Ev3yyy8mMbPmdMqo6C1HTZo0ke3bt5sEzJqPSRM16w1Hc+fO9eaCUuGt+Z30i4Bhw4YZcwMGDDA3J/3888+SO3duWbx4sUn+rLmi7rjjDpMEWpM4JyQkmP6kLf7iytzfrjcb4W1XPFz1hsHnKl6MQwACEICAAwILGjeWVitWILwdMORRCESL8G7atKnoDUF6Y1COHDmuGngVxgcOHDCCWFeyPavQaYV3//79jUDfunWruXUo0KJX/N59991mhV3Fsha9mlhvLypVqpQsXLjQfKbCe8mSJcb+9ddfbz5TYV+1alUj1J944gmTxFnFu34JEEhBeAdCyZ46CG97YuG6Jwhv1xHTAAQgAAEIZJHAgkaNpNXKlZJYq5YkrF+fRSs8BgEIpEfgagLtz1/+WZJOJYUcXKn8pWTKg1My3a6uAhcsWNAIVN3enV7R1eaXX35ZfvrpJ7Nl3FP0lqFnn33W/JhWeOvqebFixbzXEKe1q2Ja/3hKbGys6J8XXnjBbBf3bUfrDBkyREaMGCFnzpwxXw6o8NZri9en+f128803S7169US3mM+fP19uvfVWeeyxx8w1wvoFg243z6ggvDP9+mTrAwjvbMUf2sYR3qHlTWsQgAAEIBA4gQUNG0qrVasksWZNSdiwIfAHqQkBCPgl4E+g+TVgUQU9h62r159++ql07tz5Cs9Wr15ttn7rKvSjjz5qVp1jYmLMtvTXX3/dbONOT3jrKrRu8544cWK6vVUhrWezPUXPl+tn3bp1k0WLFpnVa9/y/vvvm/PhujKv28tVeOfMmdNsP/ctuoVct7/rlwVaJk2aJG+//ba56jhPnjxy3333yVtvvWW+FEhb/MWVub9FL66IILztioer3jD4XMWLcQhAAAIQcEBgQYMG0mr1akmsUUMSNm50YIlHIQCBzAq0cCJ26tQpKVSoUIYr3v369ZO///3vkpSUZFaktezevdtsH7+a8Pa34q3J0PSPp+j5cf2jq+4qsoOx4u0bh99//90ka+vZs6fcf//98uGHHyK8w+lFTcdXhHeYBzAz7iO8M0OLuhCAAAQgEEoCC265RVqtWSOJ1atLwqZNoWyatiAQ8QT8rYyGGwBNNqYrybptO+0ZbxWqX3zxhRHJutKtRbd86xnuqwlvTXSmZ6w1AVqFChUCRuI5460r1u3btzfP6ZZ0zWxesmTJgM54v/vuu/LXv/413Tbvvfde2b9/vyxbtgzhHXBU7KyI8LYzLq54hfB2BStGIQABCEAgCAS8wvummyThp5+CYBETEICAh0CkCe/ExEST1VzPQD/99NNStGhRWbt2rZQoUULKlCkjd955p8l2fs8994heO6bb0lVQX014e7Ka6yr5wIEDTVZzvdJLnxs1alSGL5Mnq7nWVYGv2+B1BVyTrc2bN89sMdfim9Vcz5/rFWjajp5Z1zZ0W7luX1c/tK5ukd+4caNZUe/Vq5c5s562+Isrc3+7fgcgvO2Kh6veMPhcxYtxCEAAAhBwQGBB/frS6ocfZEm1atJs82YHlngUAhDIrEALR2K6Aqyr1CtXrjSr3tWrVzd3Z2tyMr3+SxOp6f3aKs7Hjx8vN9xww1WFtzLYu3evucdbV69VEOv2dBX23bt3vyoibUeTrHnu8a5Vq5YRyu3atfM+57nHW1ew9ToxXcXWe7xVpOtd3VqmT59uznNrtnbduq4iXs+xaz8916D5OoLwDq83F+EdXvFy5C3C2xE+HoYABCAAARcJeIR3YtWqkrBli4stYRoC0UfAn0CLPiKR0WN/cWXub1ecEd52xcNVbxh8ruLFOAQgAAEIOCCwoF49abV2rSTeeKMkbN3qwBKPQgACaQn4E2gQC08C/uLK3N+uuCK87YqHq94w+FzFi3EIQAACEHBAYGHdutJy3TpJvOEGSfj5ZweWeBQCEEB4R8c7gPAOrzgjvMMrXo68RXg7wsfDEIAABCDgIoGFdepIyx9/lKXXXy9Nt21zsSVMQyD6CPgTaNFHJDJ67C+uzP3tijPC2654uOoNg89VvBiHAAQgAAEHBBbWri0t16+XxOuuk4RffnFgiUchAAFWvKPjHUB4h1ecEd7hFS9H3iK8HeHjYQhAAAIQcJHAwptvlpYbNsjSKlWk6fbtLraEaQhEHwF/Ai36iERGj/3Flbm/XXFGeNsVD1e9YfC5ihfjEIAABCDggMDCWrWk5caNsrRyZWm6Y4cDSzwKAQikJaDXZJ0/f16qVKkCnAgisGPHDsmVK5dce+216faKub9dwUZ42xUPV71h8LmKF+MQgAAEIOCAAMLbATwehYAfAocOHZLff/9dSpUqJcWLF4dXBBBITk6WpKQkKVGihJQsWRLhHQYxRXiHQZCC5SLCO1gksQMBCEAAAsEmsKhmTWmxaRMr3sEGiz0IiEhKSor89ttvcuLECbNCmiNHDriEMYFLly6ZHQwFCxaUcuXKSUxMDMI7DOKJ8A6DIAXLRYR3sEhiBwIQgAAEgk1gYc2a0lKFd6VK0nTnzmCbxx4Eop6Aim9d9T579qyocKOELwH94iRPnjxmtTsj0a29Y+5vV4wR3nbFw1VvGHyu4sU4BCAAAQg4ILCoRg1p8dNPsqxiRWny668OLPEoBCAAAQggvO17BxDe9sXENY8Q3q6hxTAEIAABCDgksKh6dWmxebMsi4+XJrt2ObTG4xCAAAQgwNzfrncA4W1XPFz1hsHnKl6MQwACEICAAwKLbrpJWmzZgvB2wJBHIQABCPgSYO5v1/uA8LYrHq56w+BzFS/GIQABCEDAAYFF1apJi61bZXmFCtJ4924HlngUAhCAAASUAHN/u94DhLdd8XDVGwafq3gxDgEIQAACDgh4hPeyChWkCcLbAUkehQAEIPBfAsz97XoTEN52xcNVbxh8ruLFOAQgAAEIOCCwqGpVafHzz7L82mul8Z49DizxKAQgAAEIILztewcQ3vbFxDWPEN6uocUwBCAAAQg4JLD4xhul+bZtCG+HHHkcAhCAgIcAc3+73gWEt13xcNUbBp+reDEOAQhAAAIOCCy+4QZp/ssvsqJ8eWm0d68DSzwKAQhAAAJKgLm/Xe9BxAvv7du3y+jRo2XFihWyadMmqVq1qvmvp1y6dEnGjBkjM2bMkM2bN8vFixelZs2aMnjwYLn11ltTRatixYqyO51zZ2fOnDGX2HvKiRMnpHfv3jJ58mQ5d+6ctG7dWsaOHSvx8fGp7G3btk169OghS5Yskfz580vHjh3l1Vdflbx586aqN3PmTOnfv79s2bJFypcvL7169ZKnn346028Sgy/TyHgAAhCAAARCRMArvMuVk0b/+U+IWqUZCEAAApFLgLm/XbGNeOE9bdo0efbZZ6Vhw4aiQvfy5cuphPfJkyeNmH3sscekTZs2kjNnTvnoo4/kyy+/lG+++UY6dOjgjZgK71tuuUWef/75VFFU2zExMd7P9Jm1a9caQV+oUCEZNGiQHD9+XDZs2OAV1UePHpUaNWoYMT5w4EBJSkoygrp9+/YyadIkr63ly5dL8+bN5dFHH5XOnTvL0qVLzZcCEyZMkG7dumXqbWLwZQoXlSEAAQhAIIQEllx/vTTbvl1WILxDSJ2mIACBSCbA3N+u6Ea88FahHRsba6g//vjjsmbNmitWvFUUFy1a1BuZlJQUqV+/vhHNCxYsSCW8VVSPGzcuwyiuXLlSGjVqZFbQ77jjDlNvz549UqVKFbPq/eSTT5rPRo0aJcOGDTMr6CVKlDCfffbZZ9KpUyez8l6tWjXz2e233y6HDx8WtespTzzxhEyfPt1sH/H0LZDXisEXCCXqQAACEIBAdhBYfP310nz7dllZtqw0/O237HCBNiEAAQhEFAHm/naFM+KFty/u9IR3RuHo2rWrJCYmys8//5wp4a2r0e+8844Ry76r4K1atZICBQrIt99+a+y1aNFCihQpIroi7ym6Lb1w4cLyyiuvmFV1/VnFv24/79mzp7feokWLpGXLluZLhHr16gX8RjH4AkZFRQhAAAIQCDGBJdddJ8127JAVZctKI4R3iOnTHAQgEIkEmPvbFVWEdzrx0FXy6tWry4033ihTp05NJbyPHTsmp0+fNlvSdQu4rlzrmXBPeeCBB8wKt54p9y3PPPOMzJ49W/TMuZZSpUpJly5djKj2Ldpu48aNZeLEiWblW3+eNWuW2YLuKYcOHTLPf/rpp2b7eaCFwRcoKepBAAIQgECoCSypUkWa7dwpK6+5Rhru2xfq5mkPAhCAQMQRYO5vV0gR3unE4+233zYrzAsXLjTi2lM0EZqe565QoYLs3LnTrEwfPHhQ1q1bJ5UrVzbV9Jx4jhw55LvvvktlecCAAfLuu++alXAtKtxffvll6du3b6p6CQkJRlRPmTLFnOfWn/Wct25f9xRNAKfPq5/qU0ZFt9DrH0/Zv3+/NGjQQPbu3WvOtVMgAAEIQAACthBAeNsSCfyAAAQihQDC265IIrzTxEO3cbdt21aee+45ee21164aLRWymiVdz2WrqPYI77i4OLNK7Vs0K/n7778vycnJXuE9fPhw6dOnT6p6TZs2lTJlysjXX3/tFd66eq6CP63w1i3t3bt3z9DHIUOGyNChQ6/4d4S3XYMQbyAAAQhAQGRJ5crS7NdfZVWZMtJg/36QQAACEICAQwIIb4cAg/w4wtsHqGYd1xXudu3ayRdffJHqjHZG3O+88075/fffvcnPbNpqzop3kEcL5iAAAQhAwDUCiZUqScKuXQhv1whjGAIQiDYCCG+7Io7w/iMeO3bsMNu6NZu4bhPPlStXQJHSzOW6iu3JOk5ytYCwUQkCEIAABCCQikBixYqSsHu3rCpdWhocOAAdCEAAAhBwSADh7RBgkB9HeIvIgQMHRLd4awZx3Wqu/w2k7Nu3zwj1Rx55xHvFmOc6Md+EaLq1W8+Ap71OTM9463VixYsXN83pKnvHjh2vuE5M7/zWc96eoleS6R3jXCcWSJSoAwEIQAAC4UDAI7xXlyoltxw8GA4u4yMEIAABqwkgvO0KT8QLb81APnPmTEN9/Pjxoivbb7zxhvlZr/TSK740i7h+PmnSJCldunSqCHmSmn3++efmbm69V7ts2bImudrIkSNNsrQffvhBKlWq5H1O7/rWhGtjxowxIn7QoEGi2dB1K3vevHlNPRXTNWrUkIoVK8rAgQMlKSlJevXqZba5qx+eooJbt7/rVWh6llwTrqm9CRMmSLdu3TL1NjH4MoWLyhCAAAQgEEICS+PjpemePYLwDiF0moIABCKaAHN/u8Ib8cJ7165dqUSxL/4FCxYY4esrmtOGJyUlxXykCc40A/lPP/1kRLPewd26dWsZNmyYuXbMt+jZ6t69e8vkyZPl/Pnzpp6udsfHx6eqt23bNpMcTe8Lz5cvn1nt1uvJPOLcU1m/OOjXr59s2bLFZCNXga7Xk2W2MPgyS4z6EIAABCAQKgJLK1SQpnv3yuqSJeWWpKRQNUs7EIAABCKWAHN/u0Ib8cLbLtzZ6w2DL3v50zoEIAABCGRMYFmFCtIE4c0rAgEIQCBoBJj7Bw1lUAwhvIOCMTyMMPjCI054CQEIQCAaCXhWvNeULCn1WfGOxleAPkMAAkEmwNw/yEAdmkN4OwQYTo8z+MIpWvgKAQhAILoILLv2Wmnyn//ImhIlpP6hQ9HVeXoLAQhAwAUCzP1dgOrAJMLbAbxwe5TBF24Rw18IQAAC0UNgWfny0uS33+SH4sWl3u+/R0/H6SkEIAABlwgw93cJbBbNIryzCC4cH2PwhWPU8BkCEIBAdBBAeEdHnOklBCAQOgLM/UPHOpCWEN6BUIqQOgy+CAkk3YAABCAQgQSWlysnjfftY8U7AmNLlyAAgewhwNw/e7hn1CrC2654uOoNg89VvBiHAAQgAAEHBJaXLSuN9++XtcWKSd3kZAeWeBQCEIAABJQAc3+73gOEt13xcNUbBp+reDEOAQhAAAIOCCC8HcDjUQhAAALpEGDub9drgfC2Kx6uesPgcxUvxiEAAQhAwAGBFddcI40OHJC1RYtK3cOHHVjiUQhAAAIQUALM/e16DxDedsXDVW8YfK7ixTgEIAABCDggsKJMGWl08KCsK1pU6iC8HZDkUQhAAAL/JcDc3643AeFtVzxc9YbB5ypejEMAAhCAgAMCK8uUkYYHD8qPRYpI7SNHHFjiUQhAAAIQQHjb9w4gvO2LiWseIbxdQ4thCEAAAhBwSADh7RAgj0MAAhBIQ4C5v12vBMLbrni46g2Dz1W8GIcABCAAAQcEVpYuLQ2TkuTHwoWl9tGjDizxKAQgAAEIKAHm/na9Bwhvu+LhqjcMPlfxYhwCEIAABBwQQHg7gMejEIAABNIhwNzfrtcC4W1XPFz1hsHnKl6MQwACEICAAwKrSpWSBocOyfpCheTmY8ccWOJRCEAAAhBQAsz97XoPEN52xcNVbxh8ruLFOAQgAAEIOCCA8HYAj0chAAEIpEOAub9drwXC2654uOoNg89VvBiHAAQgAAEHBFaVLCkNfv9dNhQqJLVY8XZAkkchAAEI/JcAc3+73gSEt13xcNUbBp+reDEOAQhAAAIOCKwuUUJuSU6WjQULSs3jxx1Y4lEIQAACEEB42/cOILzti4lrHiG8XUOLYQhAAAIQcEgA4e0QII9DAAIQSEOAub9drwTC2654uOoNg89VvBiHAAQgAAEHBNYULy71Dx9mxdsBQx6FAAQg4EuAub9d7wPC2654uOoNg89VvBiHAAQgAAEHBDzCe1OBAlLjxAkHlngUAhCAAASUAHN/u94DhLdd8XDVGwafq3gxDgEIQAACDgj8ULy41Dt8WBDeDiDyKAQgAAEfAsz97XodEN52xcNVbxh8ruLFOAQgAAEIOCDwQ7FiUu/IEfkpf36pfvKkA0s8CgEIQAACSoC5v13vAcLbrni46g2Dz1W8GIcABCAAAQcE1hYtKnWPHkV4O2DIoxCAAAR8CTD3t+t9QHjbFQ9XvWHwuYoX4xCAAAQg4ICAR3hvzp9fbmLF2wFJHoUABCDwXwLM/e16ExDedsXDVW8YfK7ixTgEIAABCDgg4BHeW/Llk2qnTjmwxKMQgAAEIIDwtu8dQHjbFxPXPEJ4u4YWwxCAAAQg4JDAuiJFpM6xY7I5Xz65CeHtkCaPQwACEGDF27Z3AOFtW0Rc9Afh7SJcTEMAAhCAgCMCHuHNircjjDwMAQhAwEuAub9dLwPC2654uOoNg89VvBiHAAQgAAEHBH4sXFhqHz8uW/PmlaqnTzuwxKMQgAAEIKAEmPvb9R5EvPDevn27jB49WlasWCGbNm2SqlWrmv+mLTNnzpT+/fvLli1bpHz58tKrVy95+umnr6intsaNGycHDhyQmjVryuuvvy4tW7ZMVe/EiRPSu3dvmTx5spw7d05at24tY8eOlfj4+FT1tm3bJj169JAlS5ZI/vz5pWPHjvLqq69K3rx5U9UL1Dd/rxaDzx8h/h0CEIAABLKLAMI7u8jTLgQgEKkEmPvbFdmIF97Tpk2TZ599Vho2bCgqdC9fvnyF8F6+fLk0b95cHn30UencubMsXbpUBg8eLBMmTJBu3bp5I6aiu1+/fjJixAipW7eufPDBBzJ16lRZtWqVEeGe0qFDB1m7dq2MGTNGChUqJIMGDZLjx4/Lhg0bvKL66NGjUqNGDSPGBw4cKElJSUbst2/fXiZNmuS1FahvgbxWDL5AKFEHAhCAAASyg8D6QoXk5hMn5Oc8eeTGM2eywwXahAAEIBBRBJj72xXOiBfeKrRjY2MN9ccff1zWrFlzhfC+/fbb5fDhw7Jy5UpvdJ544gmZPn262aKhz+vKdenSpUU/f+2110y9S5cuGcFdq1Yt+eKLL8xnaqNRo0YyY8YMueOOO8xne/bskSpVqphV7yeffNJ8NmrUKBk2bJjs3r1bSpQoYT777LPPpFOnTrJ582apVq2a+SwQ3wJ9pRh8gZKiHgQgAAEIhJoAwjvUxGkPAhCIdALM/e2KcMQLb1/c6QlvFdS6Kq1bvHv27OmtvmjRIrOFXIV6vXr1ZMGCBWbLuK5k16lTx1tv6NChZmX72LFjEhMTY1bK33nnHSPk9WdPadWqlRQoUEC+/fZb81GLFi2kyP+fwVVX5D1FfSlcuLC88sor8vzzzxuxH4hvgb5SDL5ASVEPAhCAAARCTWBDoUJS68QJ2ZYnj9zAineo8dMeBCAQgQSY+9sV1KgX3rq6XL16dZk1a5bZ5u0phw4dklKlSsmnn35qtp+/++678swzz8jp06dTncH+6quv5IEHHpC9e/eas+H6d13h1jPlvkWfnT17tuiZcy1qu0uXLkbw+xb1pXHjxjJx4kSz8h2Ib4G+Ugy+QElRDwIQgAAEQk1gY8GCUvPkSYR3qMHTHgQgELEEmPvbFdqoF956njshIUH0LLVuEfeUixcvSs6cOeXtt982CdB0Ffrll1+Ws2fPporgvHnzpE2bNrJ+/Xqz5Vz/niNHDvnuu+9S1RswYIAR77oSrkVtq72+ffumqqe+qCifMmWKOWseiG8ZvVJ6rlz/eMr+/fulQYMG3i8J7HoV8QYCEIAABKKZgEd4/5I7t1yf5v+10cyFvkMAAhDIKgGEd1bJufMcwvsPcasr1JqALa3w1m3j3bt3N8J7+PDhcibN9re5cyExHs4AACAASURBVOdK27ZtTeI0Pe+twjsuLs6soPsWzZj+/vvvS3Jysld4q70+ffqkqte0aVMpU6aMfP31117h7c+3jF6NIUOGiG6FT1s8q/PuvFJYhQAEIAABCGSewMYCBaTmqVOC8M48O56AAAQgkB4BhLdd70XUC+9At3N7tpqr8M6TJ483ijZvNWfF267BhjcQgAAEIJAxAY/w3p47t1zHijevCgQgAAHHBBDejhEG1UDUC+9AE5iRXC2o7x3GIAABCEAAAqkIbCpQQGqcOiU7cuWSKufOQQcCEIAABBwSQHg7BBjkx6NeeCtPvbJL79XWc96eotd+ffPNN1dcJ6afexKi6XVieq5bt5invU7MN1mbbu2uXLnyFdeJ6RlvvU6sePHiplm10bFjxyuuE/PnW6DvBIMvUFLUgwAEIACBUBP4KX9+qX76NMI71OBpDwIQiFgCzP3tCm3EC2/NQj5z5kxDffz48bJjxw554403zM96pVfJkiWN4G7evLm551vv0dakZoMGDZIJEyZIt27dvBEbPXq09OvXT0aOHCl169Y1mcc1CdqqVauM+PaUDh06yLp168w1Y3odmNrS68b0HHjevHlNNRXTNWrUkIoVK8rAgQMlKSlJevXqJe3atZNJkyZ5bQXqWyCvFYMvEErUgQAEIACB7CDgEd47c+WSyqx4Z0cIaBMCEIgwAsz97QpoxAvvXbt2SaVKldKlrtvH9a5uLSrOVVRv2bLFXAumIlivAPMtKSkpouJ73LhxcvDgQSO2X3vtNdE7un2Lnq3u3bu3TJ48Wc6fP2/u/x47dqzEx8enqrdt2zaTuC0xMVHy5ctnVrtHjRqV6rqyQH0L5LVi8AVCiToQgAAEIJAdBDbnyyc3nTkjCO/soE+bEIBAJBJg7m9XVCNeeNuFO3u9YfBlL39ahwAEIACBjAl4hPevOXNKpfPnQQUBCEAAAg4JMPd3CDDIjyO8gwzUZnMMPpujg28QgAAEopvAlnz5pNqZM4Lwju73gN5DAALBI8DcP3gsg2EJ4R0MimFig8EXJoHCTQhAAAJRSGBr3rxS9exZ2ZUzp1RkxTsK3wC6DAEIBJsAc/9gE3VmD+HtjF9YPc3gC6tw4SwEIACBqCLgEd67c+aUeIR3VMWezkIAAu4QYO7vDtesWkV4Z5VcGD7H4AvDoOEyBCAAgSgh8HPevHLj2bOyJy5OKly4ECW9ppsQgAAE3CPA3N89tlmxjPDOCrUwfYbBF6aBw20IQMBVAnrrhN4wQcleAgjv7OVP6xCAQOQRYO5vV0wR3nbFw1VvGHyu4sU4BCBgOYE9e/ak62Hnzp1l8eLFlnsf+e5ty5NHbjh3jhXvyA81PYQABEJEgLl/iEAH2AzCO0BQkVCNwRcJUaQPEIBAVgnky5dPGjVqJCkpKalMbNiwQZKTk7NqlueCRMAjvPfGxcm1bDUPElXMQAAC0UyAub9d0Ud42xUPV71h8LmKF+MQgIDlBGrVqiUrVqwQFeC+pU2bNjJ37lzLvY98937Jk0euP3dO/pMjh5S/eDHyO0wPIQABCLhMgLm/y4AzaR7hnUlg4VydwRfO0cN3CEDAKQHdTl67dm0pVKhQKlNbt26VqlWrOjXP8w4JbM+dW647fx7h7ZAjj0MAAhDwEGDub9e7gPC2Kx6uesPgcxUvxiEAAQhAwAEBj/D+LUcOKceKtwOSPAoBCEDgvwSY+9v1JiC87YqHq94w+FzFi3EIQAACEHBAYEfu3FLl/HnZlyOHlEV4OyDJoxCAAAQQ3ja+AwhvG6Pikk8Ib5fAYhYCEIAABBwT2Jkrl1S+cEH258gh1yC8HfPEAAQgAAHm/na9Awhvu+LhqjcMPlfxYhwCEIAABBwQ8Arv2Fi55tIlB5Z4FAIQgAAElABzf7veA4S3XfFw1RsGn6t4MQ4BCFhOYP369TJ06FCpW7eutG/fXp555hnJnz+/vPXWW6IZzynZS+DXXLmkkq54I7yzNxC0DgEIRAwB5v52hRLhbVc8XPWGwecqXoxDAAKWE0hISJBhw4bJsWPH5LnnnpP//d//NRnOu3fvLt9//73l3ke+e7ty5ZKKFy7IgdhYKcOKd+QHnB5CAAKuE2Du7zriTDWA8M4UrvCuzOAL7/jhPQQg4IxAy5YtZeHChcZI48aNZfny5ebvrVu3lvnz5zszztOOCezKmVMqXrwoB2NjpTTC2zFPDEAAAhBg7m/XO4DwtisernrD4HMVL8YhAAHLCTRt2lSWLl1qvNT/6s9afAW55V2IaPc8wjspNlZKIbwjOtZ0DgIQCA0B5v6h4RxoKwjvQElFQD0GXwQEkS5AAAJZJrBu3TpzljtHjhxeG+fOnZOpU6fKgw8+mGW7PBgcArtz5pT4ixflUGyslER4BwcqViAAgagmwNzfrvAjvO2Kh6veMPhcxYtxCEAAAhBwQGBPzpxS4eJF+T0mRkpcvuzAEo9CAAIQgIASYO5v13uA8LYrHq56w+BzFS/GIQABCEDAAYG9cXFy7aVLcigmRkoivB2QjIxHL1y4IB9//LHZofLwww9L7ty5TccmTpwo3bp1i4xO0gsIuEyAub/LgDNpHuGdSWDhXJ3BF87Rw3cIQAACkU3AI7xZ8Y7sOAfau06dOkmFChUkLi5OZs+eLV999ZXEx8eTDDFQgNSDACve1r0DCG/rQuKeQwhv99hiGQIQCF8Cer1Y4cKFw7cDEeL5f+LipPylS5IcEyPFWfGOkKhmvRu+SQ83bdokXbt2lQ8//FB69OjBLQRZx8qTUUaAub9dAUd42xUPV71h8LmKF+MQgECYEmjbtq3MmTMnTL2PHLd/i4uTcpcuyeGYGCmG8I6cwGaxJw0bNpSVK1d6n961a5fcf//9cujQIdG/UyAAAf8EmPv7ZxTKGgjvUNLO5rYYfNkcAJqHQJAIjB07Vrp37x4ka9FjpkuXLld0NiUlRebOnWsS0FCyl8C+uDgpe+mSHImJkaII7+wNhgWtv/fee9KuXTupXLmy15t9+/ZJnz595NNPP7XAQ1yAgP0EmPvbFSOEt13xcNUbBp+reDEOgaAT2LNnT7o2O3fuLIsXLw56e5FusFy5cvL555+Lim3f0rt3b1m9enWkd9/6/u3PkUOuuXxZjsbESBGEt/XxwkEIQMB+Asz97YoRwtuueLjqDYPPVbwYh0DQCeTLl08aNWp0hVDcsGGDJCcnB729SDf4yCOPyKhRo6Rs2bKputqvXz8ZMWJEpHff+v55hPcxESmc5ssR653HQQhAAAIWEmDub1dQEN52xcNVbxh8ruLFOASCTqBWrVqyYsUKUQHuW9q0aWO2R1MgEEkEEN6RFE36AgEI2ECAub8NUfg/HxDedsXDVW8YfK7ixTgEgk5At5PXrl1bChUqlMr21q1bpWrVqkFvL9oMvvDCC/L6669HW7et7e+BHDmkzOXLwoq3tSHCMQhAIMwIMPe3K2AI7z/ioddWLFq0KN3o6JnAhx56SB5//HH5+OOPr6gza9Ysad++farPR48eLePGjZMDBw5IzZo1zeRO2/AtJ06cED1bOHnyZDl37py5m1KTJuk9lb5l27Zt5vqMJUuWSP78+aVjx47y6quvSt68eTP1NjH4MoWLyhCAQIQTaNWqlSxYsCDCexk+3TuYI4eUvnxZjotIIbaah0/gQuxpt27dZOLEiSFuleYgEJ4EmPvbFTeE9x/x2Lx5sxw/rv+7/7/y1ltvyddffy379++XEiVKGOGt4vdf//pXqnrVqlVLdQesim7PmcG6devKBx98IFOnTpVVq1YZEe4pHTp0kLVr18qYMWPMitagQYOMD3p+0yOqjx49KjVq1DBifODAgZKUlCS9evUyQn/SpEmZepsYfJnCRWUIQCDCCQwZMkT0D8UOAkmxsVIqJUVOiEhBhLcdQclGLz755JMrWtfEiDrH2rhxYzZ6RtMQCB8CzP3tihXC+yrx0CssVFTPmDHD1FLhvWbNGtm0aVOGT+nKdenSpeWJJ56Q1157zdS7dOmSEdx6XvOLL74wn+ndlJo0SW3fcccd5jPNYFylShWz6v3kk0+azzQR0LBhw2T37t1G/Gv57LPPpFOnTqJfFqh/gRYGX6CkqAcBCEAAAqEm4BHeJ0WkAMI71Pita69o0aLSs2fPK5JL6lVi27dvt85fHIKAjQSY+9sVFYR3BvFYtmyZNG3a1KxuP/zwwwELb922qFvGdSW7Tp06XutDhw41K9vHjh2TmJgYGTx4sLzzzjty+PBh87On6NbHAgUKyLfffms+atGihRQpUkSmTZvmraPivnDhwvLKK6/I888/H/AbxeALGBUVIQABCEAgxAQOxcZKyZQUOSUi+RHeIaZvX3ONGzeWb775RkqWLJnKuTvvvNO7IGKf13gEAbsIMPe3Kx4I7wzi8eyzz8o///lPs7Vbz1Vr0RXvL7/8UnLnzi2nT582q9i6/ftPf/qT18q7774rzzzzjPl33zPYX331lTzwwAOyd+9eKV++vPm7rnBrxmLfos/Onj3b+21uqVKlpEuXLuZMt2+pXr266P+UMnPOicFn1+DDGwj4I/Drr7/KgAEDJC4uTvr27evd4aK/nzSHBAUCkUTg99hYKZGSIqdFJB/CO5JCm6W+HDlyxBzDy5EjR5ae5yEIQECEub9dbwHCO514XLx4UcqVKye33nqr2dbtKW+//baZAKvo1bPX7733nsyZM0dUVN93332mmq5Cv/zyy3L27NlUlufNmyd6BdD69evNlnP9u/7P5LvvvktVTyfZKt51JVxLzpw5jT2ddPuWhIQEUVE+ZcqUDN8oPS/ue25dz6o3aNDAK/7tehXxBgIQSEtAf09oAkb9vdO/f3+TB0KPppAUjHclEgkkx8ZKcYR3JIaWPkEAAtlEAOGdTeAzaBbhnQ4YzVKuk1vd7q0J0DIqly9fliZNmhhxq+etPcJ7+PDhcubMmVSP6Z27bdu2NYnTdKVcJ9Q6mda2fItOrt9//31JTk72Cm+116dPn1T1dBt8mTJlTPK3jIomDdIt7mmLZ9XdrlcRbyAAgbQEfAW23oKgO2X06Ivuxpk/fz7AIBBRBA7HxkqxlBTR/3vmZcU7omJLZyAAgewhgPDOHu4ZtYrwTofMI488YgSxrhDrivPVil4T9uKLL3q3lnu2mqvwzpMnj/fR7Nhqzoq3XYMNbyCQWQL6xZ4KbM/vkvPnzxvxvXDhQrPrhgKBSCJwJDZWiqakiO4Xy4PwjqTQ0hcIQCCbCCC8swl8Bs0ivNOAUcGsWck1a7huJfdXNHO5rkZ7znSTXM0fMf4dAhAIlIAmeaxUqZJcc8013kf0loSPPvpIunbtGqgZ6kEgLAgcjYmRIiJyTkRyI7zDImbZ4aQeB9QdgxQIQMA/AYS3f0ahrIHwTkNbk6c99NBDsnjxYmnWrNlVY6FbzfVKMBXdnivGPNeJ6XVgnoRoOlHWc926xTztdWK6sq53cmvRLeB6hVna68T0jLdeJ1a8eHFTT2107NiR68RCOVJoCwIQCBsCFy5ckI8//tjk0dCt+ZoQU4smo+zWrVvY9CPaHPUI7/MikgvhHW3hv6K/mgOne/fuEhsbKy+99JJJcKtFb47hqE3Uvx4ACJAAwjtAUCGqhvBOA/ruu++WH3/8UXbt2pXqmi8VvvpLXwWv3rWt2TZ1RVxXuPWc9T333OO1NHr0aOnXr5+MHDlS6tatayZ7mgRt1apVRnx7ip4fX7dunblmTDN3auIkvW5Mz4F7MqLrdtIaNWpIxYoVTQZ1zbLeq1cvadeunUyaNClTrwmDL1O4qAwBCIQpAd2xVKFCBbMqprdE6FGf+Ph4JuyWx9MjvC9oYlGEt+XRct89zWWjuXb0izMV4JrXZsSIESSXdB89LUQQAeb+dgUT4e0TDxXT+ov9b3/7m4waNSpVpDTL+F/+8hf54Ycf5NChQ5IrVy6pX7++yTauIti3pKSkiIpvve7n4MGDRmzrlnRNlORb9Ay2ZiyePHmy6NlN/RZXV7t1guhbtm3bZv6nk5iYKPny5TPiX/3zva4skNeKwRcIJepAAALhTqBly5bmHLwW3Y2k2/I//PBD6dGjBytlFgf3WEyMFBaRiyISh/C2OFKhca158+Zm96Gn6OLDgQMHROdEixYtCo0TtAKBMCfA3N+uACK87YqHq94w+FzFi3EIZIrA999/b3bM6A0GeoxExeJtt90WkA3dGVO4sEoUSnoEGjZsKCtXrvT+k+5guv/++82Xpvp3ip0EjsfESCGEt53ByQav9EpX3dnnm+NCFx309hc9502BAAT8E2Du759RKGsgvENJO5vbYvBlcwBoHgJ/ENCdLrobRo+2FClSxGQo/+abb4wAf/PNN/1y0qsJ58yZ47detFbQY0C6E0lzZnjKvn37TCLMTz/9NFqxWN/vEzExUlBELolIDla8rY+X2w7qLkTdZq47/XzL9u3b5brrrnO7eexDICIIMPe3K4wIb7vi4ao3DD5X8WIcAgETaNGiRbpbJdN+3qVLlyts6lGWuXPnio5nCgQiicDJmBgpICKXRSQW4R1JoQ1KX1544QXRK1wpEIBA4ASY+wfOKhQ1Ed6hoGxJGww+SwKBG1FPoE2bNiYBo2/eBz2TPHz4cJk3b56XT7ly5eTzzz8XFdu+RVfMV69eHfUcARBZBE7FxEh+T5cQ3pEV3CD0Rn9f6vEcCgQgEDgB5v6BswpFTYR3KChb0gaDz5JA4EbUE9CxqNueVTzrtYR67ZUma9SbEDQbt6c88sgjJpFi2bJlUzFT0a7ZfSkQiCQCp2NixLupOMqFt26n1mu0fI9LrFmzxvyeiNYyZMgQ0T8UCEAgcALM/QNnFYqaCO9QULakDQafJYHADQhAAAIQuILAmZgYyev59PJlkZiYqKSkt6BMmzbNfCGnwvuDDz6QnDlzch1eVL4NdBoCzggw93fGL9hPI7yDTdRieww+i4ODaxAIgABnHAOAdJUq3bp1k4kTJzozwtOuETgbEyN5/rCecvGixOTI4VpbNhtu0qSJLFu2zLj40UcfmeMmeu3oXXfdxVZrmwOHbxCwkABzf7uCgvC2Kx6uesPgcxUvxiHgOgHOOAaG+JNPPrmiop6THz16tGzcuDEwI9QKOYFzMTGS+49WL1+4ILFxcSH3wYYGfYW3+jNz5kxztOTkyZPy448/2uAiPkAAAmFCgLm/XYFCeNsVD1e9YfC5ihfjEHCdAGccA0NctGhR6dmz5xVJ6fQqMT07S7GTgK/wvnTunOTIlctOR132Sndm9O3bN9WVWZp8UXM+7N271+XWMQ8BCEQSAeb+dkUT4W1XPFz1hsHnKl6MQyBgAhcuXJCPP/7YnOF8+OGHzV21WnQbtE66Kc4ING7c2NyLXrJkyVSG7rzzTpkxY4Yz4zztGoHzMTHikdoXz5yRuDyejeeuNYlhCEAAAhFNgLm/XeFFeNsVD1e9YfC5ihfjEAiYQKdOnUz28ri4OJk9e7Z89dVXEh8f7z950tixIufOifTuHXBb0VjxyJEjUqhQIfPFBiV8CFyIiZGcf7h74dQpyZnPm+M8fDqBpxCAAAQsIsDc36JgiAjC2654uOoNg89VvBiHQMAEWrZsKbp1VMumTZuka9eu8uGHH0qPHj1k/vz5GdvxZHmO8quWAgZNxbAicDEmRjynus+fOCG5ChQIK/9xFgIQgIBtBJj72xURhLdd8XDVGwafq3gxns0Exo4dK927d89mLwJrvmHDhrJy5Upv5V27dsn9998vhw4dEv17hgXhHRhgaoUlgUsxMeLZo3Du2DHJXahQWPYDpyEAAQjYQoC5vy2R+K8fCG+74uGqNww+V/FiPEQE9uzZk25LnTt3lsWLF4fIC2fNvPfee9KuXTtzR6+n7Nu3T/r06SOaAAzh7YwvT4cngcsxMRL7h+vnjh6V3IULh2dHXPD63nvvla+//toFy5iEAAQimQBzf7uii/C2Kx6uesPgcxUvxkNEIF++fNKoUaMrMlZv2LBBkpOTQ+RFNjXDircj8MeOHZPCiDlHDF192PN+i8jZI0ckT5EirjZnq/HY2FiT8yEmJsb7e+7gwYNSpkwZ2blzp61uB+QXiSUDwkQlCASNAHP/oKEMiiGEd1AwhocRBl94xAkvr06gVq1asmLFClEB7lvatGkjc+fOjWx8LgpvvWZLJ/y+q/Br1qyR+vXrRwzTtm3bypw5cyKmP5HUEb1nPSbWs94tciY5WfIWKxZJXQy4L5rvYcqUKfLcc8+JvrNabr/9dpk1a1bANmytmOXEkrZ2CL8gYDkB5v52BQjhbVc8XPWGwecqXoyHiIBuJ69du7bJWu1btm7dKlWrVg2RF9nUjEvC+7XXXpNp06aZLOAqvD/44APJmTOn/yzr2YTBX7NdunS5oooKO/1iRn8PUkJL4Pvvv5cFCxaYHSnFixcXTS542223pXLi8qVLEhvnSa0mcvrQIclXokRoHbWotTNnzsjo0aNl7dq1MmDAABk4cKDMnDnTIg+z5kqWE0tmrTmegkDUE2Dub9crgPC2Kx6uesPgcxUvxiHgPgGXhHeTJk1k2bJlxv+PPvpIPv/8c5k8ebLcddddRjCFWylXrpzpg4pt39K7d29ZvXp1uHUnrP1V5rpN+u6775YiRYrI0aNHzR3rKsDffPNNb98uXbggOXJ5bvEWOXXwoOQvVSqs+x4M55OSkmTw4MGyY8eOiNitkeXEksGAiQ0IRCEB5v52BR3hbVc8XPWGwecqXoxDwDGBbt26ycSJEzO2EwLhrY3rytqIESPk5MmT8uOPPzruV6gNPPLIIzJq1CgpW7Zsqqb79etn+kUJHYEWLVrIokWLrmgw7ecXz52TuDx5vPVO7t8vBcqUCZ2jtBQSAllOLBkS72gEApFHgLm/XTFFeNsVD1e9YfC5ihfjEAiYwCeffHJFXV2d1a2lGzdu9C+8L1wQ8dmWG3DDGVRUwd+3b1+57rrrvDX0nnEVsHv37nVqnuejmIDmXtAvPFq1apXq3Ro+fLjMmzfP+9mFs2clZ9683p9P/PabFEzzxUkUY6TrEIAABLJEgLl/lrC59hDC2zW09hlm8NkXEzyKTgJFixaVnj17XrEVWq8S0yRnGRbPivfJkyL580cnvCz0+oUXXpDXX389C0/yiFMC+v8dvSZPt/hfvnzZ5BHQhH0jR46UChUqeM2fP31acuXPL5dEzF3ex/fulULlyzttnuchAAEIRDUB5v52hR/hbVc8XPWGwecqXoxnMwG/27Sz2T/f5hs3bmzOuZYsWTKVV3feeafMmDHDv/A+elSEa7ECjqiutobjWfWAOxgBFc+dPCm5CxaUiyKiKdYQ3iJ69dbhw4elWLFiJtkhBQIQgEBmCTD3zywxd+sjvN3la5V1Bp9V4cCZLBLI8jbtLLbnxmNHjhwxWdl19S9TxbPiffiwSNGimXo0misPGTJE9A/FXgJnjx+XPIULy3kR0RRrx3bvlsI+K+L2eh58zzZt2iRPP/206O8JvXde75/XXTLjxo0TvU4xEgpfKkRCFOlDOBBg7m9XlBDedsXDVW8YfK7ixXiICGR5m3aI/HO1GY/w/v13keLFXW0K4xAIJYEzR49K3qJF5ZyI5FbhvWuXFI6PD6UL1rTVvHlz0bu8r7/+eq9Pv/zyi3Tt2lX0OsVwLpn9UiGQq+jCmQe+Q8BtAsz93SacOfsI78zxCuvaDL6wDh/O/0Egy9u0bSe4ebOIXp90tbuL/xDel/bvlxxBzPh8/Phx0bu8dTu2bm313LWsZ6N1xY0CAbcJnEpOlvwlSshpEcknIkd37pQilSq53ayV9hMSEiQxMfEK3zL63MpOZOBUZr5UCPQqunDqP75CINQEmPuHmvjV20N42xUPV71h8LmKF+MhIpDlbdoh8i/LzaioLlhQ5PjxjE38IbzP7toleYK4GnjPPfdIhw4drrhrWc+hT506NeAuaWK42NhYqVy5sveZNWvWmGRaFAh4CNx7773y9ddfpwJyMilJCpQuLSdEpKCIHNm+XYpWqRKV0PR2A831oGNSd/jo7zz9+fbbbxf9MiycS2a+VAj0Krpw5oHvoSegXzR//PHHUrNmTfNHE2/my5dP/va3v5kjYJFWmPvbFVGEt13xcNUbBp+reDEOAWcEArmj+486J37+WQrecIOz9nye1lWo9LawZjTxTa9hXTGfNm2aObeuwvuDDz4wCaFat24t8+fPD5qvwTSkq/v6ZUGDBg2CaRZbPgT0i5j4+HiJiYnxZvE/ePCglClTRnbu3OmteWL/fnN92DER0T0Wh7dtk2I+W62jDapuLdcdKMnJyd4dKDcEccwHwtONbd6Z+VIh0KvoAukLdSDgIfA///M/UqdOHTl16pQsW7ZM7rrrLilSpIjo+z558uSIA8Xc366QIrztioer3jD4XMWLcQg4I5AJ4X3sp5+k8E03OWvP5+nHHnvMiKO7777bu8L27bffGmGUXjK79Bpu0qSJmcRo+eijj+Tzzz83kxid1NiaUbx69eqyefNmc82VCkNK8AnoWeUpU6bIc889J23btjUN6MrtrFmzUjV27D//kcLXXitHYmKkaEqKJG/dKsVvvDH4DmExIAJubvMO9EuFQK+iC6hDVILAHwR8v1DWFe+NGzeaf4nU2y+Y+9v16iO87YqHq94w+FzFi3EIOCOQCeF9dMMGKVKzprP2fJ4+f/682XrnWWErUaKE6OTk8ccfl1y5NMe0/+IrvLX2zJkzZcSIEXLy5En58ccf/RvIhhoesX3x4sXMZ5jPBn/DtckzZ86IrnSuXbtWBgwYIAMHDjTvh285unu3FKlYUZJjYqQ4wjt1qBctEtErBO++O2SvANu8Q4aahkJMoFmzZrJkyRLT6vjx4+WZZ55BeIc4BtHcHML7j9WZv/zlL1e8B3369JFXX33V+7lOFPr37y9btmyRcb+JeQAAIABJREFU8uXLS69evcyVH2mLTjD02o8DBw54z4+0bNkyVbUTJ06IfqOsK0Lnzp0z2zHHjh1rVp18y7Zt26RHjx7ml0T+/PmlY8eOxqe8efNm+r1FeGcaGQ+EEQG9ciesE4FlQngn//CDFK9b16ro6D3qffv2leuuu87r18KFC+WRRx6RvXv3WuWrx5laMTFyq4i8evas5M6tubQpbhJISkqSwYMHy44dO2TOnDmpmjry669StHJl+T0mRkqkpMjvmzdLiWrV3HQnfGwH8rshyL1hm3eQgWLOGgITJ040XyrHxcV5fdJ5uP5u8p3zW+OwQ0eY+zsEGOTHEd4+wvu7775LNXEvV66cXHvttQb58uXLRc9BPvroo9K5c2dZunSpGaQTJkwQnXB6iorufv36mZWeunXrmnOOmpxo1apVRoR7iiZN0W//x4wZY5I5DBo0SDThw4YNG7yi+ujRo1KjRg0jxnWFQCctKvbbt28vkyZNyvSrwODLNDIeCCMCuo017WQ+jNwXCWRy/UedQytXSknOJTsP7x88T504IfkLFHBuDwtZJnB4+3ZzpvtgbKyUvnxZDm3aJCWrV8+yvYh6MJDfDUHuMNu8gwwUcxDIJgLM/bMJfAbNIrx9hPehQ4dEt1imV/RMmibiWblypfefn3jiCZk+fbroS60JZPQbs9KlS4t+romGtFy6dMkI7lq1askXX3xhPlMbjRo1MllK77jjDvPZnj17pEqVKmbV+8knnzSfjRo1SoYNGya7d+/2+vXZZ59Jp06dzLnEaplcDWDw2TX48CZrBLp06XLFgykpKTJ37lwzFsOypKSIxMb+13X9e0bljwl40rJlUqpx47DsqlVO/8HzeHKyFCpWzCrXos2Z5G3bzJnuA7GxUkaF98aNUuTGG80RCE3Y9/DDD3t3JeiKle8X3hHPKhuEd6iZ6nEP3xXIULdPexCIVALM/e2KLMI7AOGtglpXpXULSs+ePb0RXLRokegWcr0up169euZ8pG4Z15VszZjoKUOHDjUr27oVVs8U6kr5O++8Y4S8b0IfTexQoEAB0aRGWvSMlWZa1EzBnqK+6HbaV155RZ5//vlMvU0MvkzhorKlBHQniibuUrHtW/ToxurVqy312o9bly6JeLa9BSC8DyxeLGWaNQt6Xy9cuGB+LxUrVsxkJI/48oegOfzbb1KsbNmI767NHTy0ebNZ4f4tx//H3nWAR1U93xN6773X0AkkhIR0ehMQARGliIhIUUEQAQUEKQLyAwQVURGliQIConRIDwkpEEKN9N5BaiDA33N5L//Nspt9b1t2w5vv4yPJ3jJ3Xtk7d2bOyY7yjx/j8r59GDFjBipVqiQcsi1btuD3338XGWCOjJRvExtnIcebmYXvvfeeCFaMHTtWpPxSDF1TXu8ePXrgwoULYr/DjEAiuzOwoVvSYhOba4NqFsgiFtD2/o51ITXHW8fxLlWqFK5evSq+2AcOHIjRo0eLk3ZGl4l+SxRWpnnLwgg5+yxdulSkn3/zzTcCpOHevXvparD55fHqq6+KOkfWhvNnRrh3796d7m5gX24uSG9D4diM7unXnFCXZs2agaf+akR7+NRYS2vrqBZgzTCzQcrpOUpyiYej6p2RXk9TUuCSJ8+zJsYcb52o+IVdu1BWDzfCknUnJSUJvAryBfNgj4eE5A8mVgWzdbKsyKn7x4+jZNWqWXaZzrCwy0lJKNWgAc5mz44Kjx/jUkICeg4fDuIEUHiPDhgwAERJJ+6Jo1LU2cTWWcjx9vX1FcEFYirQASetHEvzDCFKt2zZUlA8cc/Us2dPdOzYUWQMfvbZZw7LlGCT668NqlnAAgtoe38LjGeDrprjDQhnly9zLy8vEYHesGEDvv32WwwePFhsPFnP7efnJ+q8mSIuC1OjGBWaN2+e2AgwCv3555/jwYMH6S7V9u3bQaCSffv2iU0sf6ZDz5NfXSHaK513RpwoHJvjEbBIV6gLnXJStGQkrBnnP1l4aky+WvkAwAb3kzakZgHNAmZYIPX2beQoVOhZT2OOd2oqXwqiyblt21C+VSszZjLchfgVdGhq6vAmk/KHjo4hfm81E7N8hhtshxTJoblw4ADKWpGezSHX6mBKMVVc9/D4cmIiSrm54VSOHKicmoqLcXHoMnhwuvKukydPiggoD7358wsjVnK8uR9g6j7L3/hv1qxZyJcvH4YPHy6y+uwhfNfovlOIX0MgWgLJMotQV1q1agV5/8RSIlleuIwHe1yYF2iOEydOCHYFZtJwfy2XbQ4bNkzs+bOaaI63Y11RzfE2cj0++ugjzJkzRzip5LKls8sINZ1zWWTHm2nj3FjS8Z4yZQpInaIr/MIg8BPTpPhlR8ebD7w+jykR0xcuXIhr166J7nS8OR7R1XWFJ8Y8JV6zZk2GdxNPhZnmri+a4+1YD6GmjWUW4LPKDaQzS8q1a8gt40sYc7xTUgApKn5m0yZU1Mm+sXTtfL+Fh4c/N4yxvxuaj1k8hoTZQJY675auz2h/yaE5Gx+PCjrlQTab7wUc2BAPPMtECEQq8+fSLBcTElDG3R0nc+ZElUePcCE2FutiYtC2bVtUq1YtzXLnz58X34nMNHthxEqOd6dOnUQZ3N27dxEZGYnOnTuLcjZGlcmwoit00pnSzRI67hl4zXjgT6eX14ROsTnCKDbBYcuWLZvWnRlM3P9wT6Urc+fOxblz5/DkyROwDIagtNyHEfeGB4WaOJcFHOHghxbjHpyladyH874juDHxljQeb+e6n5xVW83xNnLlWCvK6DApxJh6ribVnI53HjltFBB1aZmRaq5FvJ31sdT0VmOBrPBleffcOeSvUOHZso043k/v3YNL/vyiyemNG1GpY0c1ZsqwLZ0ggj1yY8sUc6ac83eCSvJgQ4kwcsaMIP3aex44yoeJSsaxaxvJoTkVFYXKOtlMdtUhi0/G+4nYKPr3BR1nuayKJqCjXdbTEydy5kRVOt579qBskyZZ3DoKl2clx1uXm5tBAPngw9A7tGvXruJ9QMwaMqzQYWbZHQ/RateujeLFi4vghFrhu4Vp5nxf6ArvBUN122SqWLVqlXDAiT1BPcgu80JgUKg1roO3V3PwY8ul6N7vpPbl/pzgjT/99FOWLGHRIt62vJvUj6053kZsRvovRrfpePOEVwNXU39zaT00C9jDAszs4D9nltsnT6KgXGNsxPFOvXkTOYoWFcs8uX49qnTubNUlM7Wc0S06ydxUEziSQEZKhWU0jEbpb6gZXdBNE1U6nl3aSQ7NiZAQVA0IsMuUL9okxCNh+VbJkiXTLZ31ujzckeV8TAzKeXnhWK5cqP7wIS7ExAhHXBMooxpUYCh/f3+EhYWJll9//bXApKEYcrzllHBdZ51t5d/1/65geoNNskLGkrlrf9H6qTn4saVtfHx8hIMtB8iYAcLsriNHjohDZx4MUbIKe4LmeNvyblI/tuZ4G7EZETRZu80blmndjPzw1Jd13rKQ9osbCn06Mf5dBkQjnRg3pDxd1qcT0wVrYyoX0+n06cRY4820Km6EKRyjV69eGp2Y+ntd65GVLXDhAtEIgezZnXKVN48eFdRJQow43imXLyN36dKiyYk1a1D1lVccaq2MhDVq1Oi5WtHDhw+LCJlDiuR4/7N1K2q0bu2QKjq7UtzI8uCauCYZybmoKJT38UFy7tyomZKC87t3C0f8RRdmCrgooRpUYCg6EkQR16XtIlMKmVb0QVz79esnsv2IgUMMGz7DBEVj6R1BX1kGx/prSyUrZCxZaoMXpb+agx9b2oRlFlWrVk0rdyBFL4GPmdFx6dKlLMeeoDnetryb1I+tOd6AqFdi+lL9+vWFBelML1q0CB988EFaKhUdbp4A80uLDykB11gX8t1336XjE2XKJtGVp0+fDnd3d3FiRhA0RtDpfMvCFK6EhARBM8ZNCccikjDTMvPmzSua0dGnTlWqVAEBSC5fvowPP/xQ6MsaKbWiPXxqLfbitKfTxPvbKSU5GWBkdsoU4JNPnHIJ15KSUFx+PxhxvO+dPYt8FSuK9R1btQrVX33VdmulDmPHAv36AXXq2G6ezB5ZcryP/Pknar30UmZr80LPfzYiAhX8/HA0Tx64PniAc5GRKK9x1YOH99klqsGnT56koyC15Q3z8OFDAcTGrASCzzIqyAgha8I9PT3FHodUb5ZKVshYstQGL0p/NQc/9rQJs7uyMnuCtve3591kei7N8QaEg83oM29OgngwvZKIqwRM0+XZZto5nepDhw6J0zE6wXKqlmxqGTSGyIg8OaOzTYASnurqCuuvCe5AQBN+wTGdndFunjDrCpE+qQeBj5jCyWg3gUhk59z0Jf7/FtrDp8ZaL1Zbgv+xls4p5bffgJ49AYL96CDfOtNariQkoKS7+zOVnzz5/9RSnUXcOXECBSSQqX9WrECNXr1st0QiRjP1vUwZgNkEdhYCKXHTzygpa+9slvonOd4HV69G3W7d7LzKF3g6Avkxi0vnUOdMaCgqBgbicN68qH3/Ps6Gh+NagQICIJSH2KTy5Pdt/vz5QdCtLE1zp3NrPExJQS4JM+bxo0dpTriz3j3msBzwfUC2F9Z4m1vbzRpycofrAvXFxsaiiQPgCNjtfeesN42N9WZZKQ+XZMlq7Ana3t/GN5DK4TXHW6XBnLm59vA589Wzju66mw55RB4WMZuCEQ2nlPnzgfffB954AzAjE8QR1nwpJgal5bTax48BObVUR7lbR46gsJSynbx0KWr27m071YlQzkNAUgzdumW7eYyMzKwiRtOYEstUVwJU8lDS6jRCkuOdtGIF6tvyIMPuFnTwCQ2AhZ0ODkal5s1xKG9e1KHjHRaG18aMweTJk0U2GA/If/vtN5EhxsNoInG/CHL/zh3kLVhQLPXB7dvIU6CAUyzbGiwH5G4fMmSIqLstXLiwuA+K5siBBfPmoaGKDC0GP9avXy8O8vgd+P333wsH3urvEzOvjN3ed2bql9W7kT44K7MnaHt/x7qDNcfbsa6HTbXRHj6bmtcpBq9Vq5aoY9Kvt3RoACxTlv38c2DCBDwZMgTZvv7aVGuH/PxceDjK+/sL3Z6mpMAlV67n9LyelIRiUjr6kZ9+Qq0337TdWs6eBZjWThT1O3dUzWON6JTdUv8kB3DfTz/BzZb2VGXBF6CxAcf71I4dqNyqFQ7my4e69+6BEfA+48enpYASpE3GWHEUh8keV+rujRvIX6yYmOrOlSsoINMO2mNyC+awBssBy59IG1azZs1nmjx5guTs2TEgXz6EqjgoJpgW63opS5YswcqVK0W2IenUCCiZ2WK3911mL9RB5ieaP7NWybiwefNmkU3Ke410drr4Bw6irsVqaHt/i01o1QE0x9uq5nTswbSHz7Gvjz20I2AgyxXIx6orpGvpyXRtJ5SzgwahwqJFSPD3R+PQUCdcAXBm505UbNlS6P747l1k16Pa4d+v7d2L4hLX9OEffkDtAQNst1Yp1fxp7txwefBA0TwGo1NFi4JlN2rTgu2W+ic5gHsXLUKjgQMVrVNrZAULGHC8T27bhipt2uBA/vyod/cuGAHvNW6cwFOh8H9fX1/xs66jYgVtHHqIfy9dQiGWfPxXFnfj9GkUlXAerKk0S+tYf2tNsQbLAZGmWWaXJrdviywcPwDhRrAwDK1B1/Hm5ywbnDZtGu7cuYO9e/dac9lmjWW3951Z2tm207Fjx1C9enXbTqIzOlH06WwTJJBZD8ykqlOnjsiEICsH0c6zmmh7f8e6oprj7VjXw6baaA+fTc2rDZ5JFtgZFIQWISHY1aABmicmZpIWlk17assWVG7XTgzy8OZN5Cpc+LkBr+zZg5JNm4q/H/ruO9R55x3LJs2g97mQEJQPCsIjADkVbnCfi04BIEXZgAEDBPevGrFb6p/kAMYvWAB3iVpJjZ5aWzMtYMDxPrF5M6q2b4/9BQqgwZ07OE1qu8KFxaGNboYOUbjXrVvntAeFai1248wZFJVAzK4mJ6NEjRpqh0hr/8svvzzXV8alkTm95QYnTpzAp59+Ctqb7Cp0Thgt/t///odBgwaJfxlFB63BckCwWoK7EYyW0ckbJ0/ir88/R3sAHyl8L3E9PFgYM2ZMOp5wgmn16dMHZJTJbLHb+y6TF2roe2D06NGYNWsWiHhuD+EhB+/bNWvWwMPDA8S3KVu2rPiOoiOuJAPC2Wrytb2/Pe4s5XNojrdyWzl9S+3hc/pLqC3AgAV2BAaiZWgowho3hn98vFPa6MSff6KqxMt9//Jl5NXjPOaiLkZEoIwfYz3Aga+/Rr0hQ2y21uN//41qHTviCYBsCje4z0WnJO2M/d1myqsZWHIA9/y3wfccOVJNz+faOjUzgEUrN6OzAcdbvucSCxZEw9u3IVLPW7QwY/Cs1eXa8eMoLkUEL+7fjzIS+4o5q6TzOmLECNDZ1pWlS5eC4GO6wvIjAsDSYSW4ISODBHWNj48X9bDXrl3D4sWLzVFDVR8e3tEZ4nzFU1MRNGECauTIgWyPeCyoiTNZIFeuXAJouHz58mn34LZt24Tza497ibZitgxBlPm+HjhwoKj3J2uQXNIQEhIiTJpRJN7aNfndunUTBwFKhMDMBB4lcDP/8dCCqfLDhw9/jspTHk/b+yuxrP3aaI63/Wyd6TNpD1+mXwJNARtYIM3xdneHf1ycDWaw/ZDH1q5FdQlV+865cyhQrtxzk54PDkY5iR0h6auvUP+992ymWPIff6CmzBOu0PF+Ljp144aIVrVv3x5M73NIkRzAmOnT0XTMGItUdGpmAItWbkZnA473sT//RPXOnbG3UCE0+vdfnNq+HZWl8gszZsgyXS4fPoxSEvr7udhYlPfwMHttrJMnXWpJvYO9jh07imdVV2R+bdbTE5iMjjfR5Bn1ZjquLiez2Qqp7PgwKgq5fHxwG0BBhe8llVNY1NwcxHaLJnSyzkeOHBEsBeTQ/vjjj4WjyO8HsgrZSwYPHgzqsXz58jQeb/LR9+/fH+fPn0+LeGcUibekJp/I+nyWyJgkH4CRAalMmTIi/d2UdOrUCY0bNxZguMQtIE4BKf4INkncAkOi7f1NWdW+n2uOt33tnamzaQ9fpppfm9xGFtgeEIBWYWEIc2LHO3nVKtR87TVhoVsnT6KwHq0g/3522zZUaNNGtNk/Zw4aDB9uI4sCh3/9FbVllG8VG9x00anixUV0gfSMDiuy4/3552j66aeK1MySzACKVm5Zo3QZAYYc7w0bUL1LFyQULozGt27h5NatqNK6tWWTZoHeIsrdsKFYyZnISFS0gNuc6OB0dvTBNQ2ZiXXRdLCZYk6nZNmyZeJnRgRv3rwJb29vURNrT7mxbh2Kdu2KKwBKqngvWVtHayC2W1snZxovLCxM8MC3atVKON2MettT+Bwwi4ORYoociS9YsCD4j5JRJN6SmnxG1teuXStYGnhYS1Fz+BAYGCieQQoj3nKJiHxQZsiO2t7fnneX6bk0x9u0jbJMC+3hyzKX0uyFOFttkpKFpjneHh7wj41V0sXh2hxZtgy1+vQRet1ITkZRA3WcpzdtQqUOHUSbxC+/REMLU6MzMsK+H3+E29tvP2uSiRtcayCkZ3ixJQcweuJEeH32maL7IksyAyhauWWN0mUEGHC8k9euRc1u3RBXtCg8btyAqPlu29bgpKSVIr3UiyDn4+NRTopynwoJQWUVNFqW2IfRNEYmt27dmjYMo21//PEHunfvLlJcp0yZYskUqvueW7wY5QcMwEUAZTLxvWQNxHbVi8+CHX799VcBbvfFF19k6uqIQ3L//n3FkXhLa/I5FzPEWLZBHAWmuhPwT4noZpp8/fXXGCphk2iOtxLrOUYbzfF2jOtgFy00x9suZnboSaxdm+QIi93h74+W4eEId3eHn5Ommh/66SfUeestYc6rBw+ihJRaqmvfkxs2oEqXLuJP+2bMgNvo0TYzf8xXX6HpBx9Y5Hizjo4p5twYGIoSZ6S8NRHSlTjeuz/5BN4KnYisyAxgzRtJUUaAIcd7zRrU7N4dccWKweP6dRzftAnVJMBBff1epLT+s9HRqODtLUxwYvt2VLVR+j1rvJkGq3v9YmNj0aRJE7NuDxmcjUBWrBNntJwybNgwwXRgjiTPnYuaI0bgsosLSj0hAoUyMacuNqORrYHYrjv+xo0bBYDc1atXMXLkSOGQ1a5dG7Nnz0YlCVhP2Upt0yqrp9DLTqstIvEZ1W9fvnwZEydOFPXkugdcGV1Fsg+8+eab6YANCYDIcYwdYGh7f9s8F+aOqjne5lrOCftpD58TXjQrq2xJbZKVVbHacLv8/NA8IgLhHh7wc9KI94FFi1Bv0CBhk8uJiSgl8XXrGun4mjWo1r27+NPeadPQaOxYq9lQf6CIGTPgK9c8mxlZiouLw9QmTXCuTh1EHzyoSldrIqRnOLHkAEaNGYNm06er0lFrbNgCJjMC6DBlz/6sM3+WrsGRVatQ67XXEFu8OJpcuwaCrU35/ffnJmFdJNNA+X32Isip8HBUlhCfj23ahOpGDiMsscXMmTNFHTdT0Ol4E3CKCM+W8KXL4Gx0vD/55BNMmDABHTp0EOBaSpCjDa0n8fPP0XDCBFxxcUFJFY63OXWxGdlTKWK7UsBF2c59+/YVfNI8IGfdMSOapL6ypmSU9SanPuvP17t3b9XMFKbsx3U6inz2X7YT/8libiTe0vptW9lD2/vbyrLmjas53ubZzSl7aQ+fU142qyptSW2SVRWx4mDBPj4Iiopyasc76ZtvUF9KGbsQF4ey7u7PWeifX39FDanuOuHzz9FYYU2yOabeNXEimk+e/KyrmY730c2b4dq+PUJy5UJgSooqNeyGkC45fRGjRsF31ixVOmqNDVvAZEYA0ahz5XrWmT/nyCF+PPLrr6jVqxf2lCwJzytXcGzjRgS88w5Wrlz5HAo30bb37NnzQlyCEzt3pkW5kzdsQM1OnVSv21TEUvd7geBptDmBmgjcZK6TrOtg3759G6+++ipef/11/PTTT2ZzJcd/+incp07FNRcXFFfheJtTF6vayAY6KM3MkB1vXT05nCWHFMb0zyjrjTX7rN3XR71PTEwUqPLWEqV2sdZ8SsYx9YwoGcPS+m0lc5jTRtv7m2M12/XRHG/b2dbhRtYePoe7JHZXyNLaJLsrrGDCEG9vBEZHI8LdHb5OmmqeOG8eGkpgaed270Z5L6/nVn506VK49u0r/h7/2WdwnzhRgXXMa7Jj7Fi0lOvuzHS8j23Z8v/RuQzGIBprGl2QBMjGur90/L22QkiXHe/hw+E7Z455xtJ6qbPA/fuABGqEBw+A3LlF/8PLl6N2796IKVUKTS9fBlHOP1u1CjNmzEA5PZT/cePGYdq0aermddLWus/RkbVrUatrV6MrMRf0i6jIBEyThfWmtO+dO3dEDa45IoOz5cmTR3R/+PChcL7Jn607l5KxCSBVoUIFHPviC1ydORMkEuuk4r1kTl2sEr3kNorKKzIYkDRvRKo+cOCAQLbOnz+/aE2eaWYOWVMyynpjqjudbxl0TJ6X2QvmAKAptYs9MRvMfUYyuga0W4kSJUQTS+q3rXmddcfS9v62sqx542qOt3l2c8pe2sPnlJdNU9qEBUI9PREQG4vIRo3gk5DglPba++WXaCRRbp0JD0dFX9/n1nF48WLUHjBA/D1u/Hh4yBFpG6x428iRaP2//z0bWcUGV1eVo3/+CVeJm9zYGIxckkqlS5cughKFG3LSHRUvXhxDhgx5ziG3OkK65HiHDxsGv/nzbWBJbUh9Czz5919kk4DRnt65AxfJyTi0dCnq9O2L6NKl4XXpEv5Zvx415PvnBTZj8saNaVHuw7/9hto9ehi1hinQr7ckHAndARjd/O2337Bv3z7U0AF1pIPcp08fnDlzxuB8b7/9NlhvakxkcLayZcumNXn8+LGgIyOYlVIhTkRMTAwKFCiAXKdP41FSEpgvUXvsWMWHL+bUxSrVj+1MlleYGOzUqVNpLXjIxDR/1qXzQJLvRmtKRllvv/zyCxo1avQcH/Thw4dFzblaUWoXe0bATT0jStbI0ompU6ciKipKIP7zu+v69euCbo/lFBRz6reVzG1OG23vb47VbNdHc7xtZ1uHG1l7+BzukmgKWcECoe7uCEhIQFSDBmiWmGiFEe0/RML06Wg8bpyY+FRwMCoHBj6nxMHvvkPdd98Vf48dNw5Npk61maLbPvgArb/66tn4Zjreh1ev/n8nwcgY+qmV8oKM/V3Jgg1F0ElbY1Akxzvs3Xfh/+23SoZHVmQGULRwKzV6ePkycpUuLUZ7fOMGshcpIn4+9PPPqPPmm4gqWxbNLlzAP+vWoYae00EnjGjaL5IcXbcOrlKU++Dy5aj7+utGl28K9Kt8+fKqU/fpjOkLnXWiMstURra8HrKjyIh5xeLFcf7OHcHj/ZKvL8LDw205teKxTZZXKB7J9g3tmfWmbxf54IfAe0TMf/b1Yl/MBlPPiO4VSE1NTQdiJn/WsmVLwZvNAwMe6hAAj4fGpAWjM26OmDrIksfkARm50N3d3dGuXTsBXsoMCTr9XJsh0fb+5lwR2/XRHG/b2dbhRtYePoe7JJpCVrBAqJsbAhITEVW3LpodOGCFEe0/RNzkyfCQUsdPbNuGqgYcxQPffIN6Uh34no8/hqcNKVi2DBmCtrIjaqbjnbR8Oer37v3MmEbGYAoj04ZZyygLI22kKSK4kFrJKII+Ry+VnKjr2SSQr7CBA+G/aJGi6bIiM4CihVup0b3Tp5FP4ql/eOkScpUqJUY++NNPqPvWW4gqVw7Nzp/H0TVr4PrKK+lmtUXNq5WWZbNhDv/+O2q/+qoYP2nJEtTv18/oXKZAvxjBVpu6zzToESNGPFf3u3TpUhAJ3daimxrdpU4drD98GEyKf6V5c7NrxS3V2Rznx9I5s0J/cw7ah6PWAAAgAElEQVR+rL1uQ88IAewGDRoElkWMHTtWoIZTjIEL8nuLpVAvv/yyACVkhgKFgHEcPyOx9CCL+CeTJ08G0/PJBc5slUKFCuG9994ThwGGRNv7W/susmw8zfG2zH5O1Vt7+JzqctlUWZvzI9tU+/SDh9WvD/8DBxBVuzaaHTpkx5mtN1XshAlo8vnnYsB//v4bNdq3f27w/fPmoYFUBx4zahSa2jDyt2XgQLSV00jNdLwTFy9GQzml1MgYfCd9/PHHAiiLjjBRlUlfNH36dLNodNRE0PkM5JRAvkLfegsBP/6o6IJmRWYARQtX2MgUddPt5GQUdHUVo90/cwZ5K1QQPx/44QfUGzgQkRUqwOfsWRxdvRqu3bqlm1UffVihSnZpZm06LlnpgytWoO4bb4hf9//wAxqoSNO2xsKbNWsmyj9KliyZbriOHTsK58PWMnjwYBD4iujouwcMgPfixbj8H7Vav3btsGnTJltPb3B8c5yfTFHUwSY15+DHHkvw9fXFn3/+idy5cwsHtkyZMqKMwdhBH0sAyATAZ+LIkSNgBJzUe3wm6AwbEvn9wLp9+SDr/PnzafgVSg+ydL9/+GzKEfaMGAi0vb897iLlc2iOt3JbOX1L7eFz+kto8QLsxo9ssabKBwirUwf+hw9jd82a8D56VHlHB2q5Z9w4eEp0Vkc3bICrAeTixNmz0XDUKKF19Icfwmv2bJutYPObb6Ldzz8/G99Mx3vvt9+i0ZAhFo2hv0BTIDxqIugPHjxAnrx5xRQhffsiUF6vCatmRWYAa95Ipqibbuzfj6JSSuSdY8dQoFo1MX3SokWoP2gQwitXht+pUxD0YlKkN51+BAFjjbjMBW5N5c0cyxZ0XLIqST//jPpSBC5x4UI0lGgHzVQ1XTdTqftMJWdtNVNpCa7F+lhjNFBKSzyMpe8qWU9U375otnQpbgEobOZ7Sck8ptqY4/yYGtPQ56bed+aMqaaPLmiYmn7O1lY/Uj1+/HhcvHgRR48eRUhIiMHlsKabB0/nzp1DsWLFRHRcxkjQD2zovh9YL0/AQtby6zrLSg+yeEgQEREhdOL//J2ie0/qK6zt/R3rjtQcb8e6HjbVRnv4bGpepxjcbvzIdrRGuKsr/JKTEV2tGryOHbPjzNabKvqjj+D15ZdiwMNr1qC2Xoot/75vxgy4Sdza0R98AK+5c62ngN5Im3v3Rrvly5/91cwNbvy8eXCXIvTmjqG/QFMgPGoi6Pfu3UM+Cdgr5I03ELhsmSJ72rNGUpFCDtbIFHXT1dhYlPD0FFrfOnQIhSXQpv3ffosGQ4YgrGpV+J84gcMrVqC2RJ+XtkQCfVWqBPCQSuaZd4D1E8GbYGIUa9Fxycva/+OPaPD22+LXvfPno9GwYVZbsRzRM4RbQKeckW46E0wRZnSOTjiRqvUR5Q2VePA5iY+PR6lSpRSl7ypZVOTrr8Nn5UqrOd5K62r1dTPH+VGyPrXvO3PGzKgPAS3laymDhl24cAG8ljJomDXnNHXwY825MhqLEetly5ZBFwiQJRkEUeNBkVIxFthgFpDMDkBudqanr1mzxiy6voSEBFHLzewwWVJSUrBu3Tr07NnToKra3l/pFbRPO83xto+dHWIW7eFziMuQqUrYjR/ZjqsMr14dfsePI6ZKFTQ9ccKOM1tvqt0jRsBbcqQPrlqFugYifQlTp6Zxd+8eNgzeNkTh3vzaa2i3atWzBZrpeO+ZOROeH39s1hjG0JcZdeN7TLUwQsBsiP7907qSW7hgoULi95DXXkPgypWqh9U6PG8BU9RNlyIiUNrPT3QU0e/69cXP+7/5Bg2GDkVo9eoIOHYMAuVcxgiQp4mNBei0V68O2KG+WOn11XW82ccadFzy3PsWLoTb4MHi1/g5c/7/MEupchm0k1P3DeEWkPqLoE2sf61evTpOnjwpNvuGvkMMlXjQOZWBs5Sk7ypZTkSPHvBdvVq1421pXa2sm5wuzEi07PwwxZipyUw7pvNDOilSgBEFnHXDTJE3JVZ/32UwobH6dDc3N8GxznVxnWFhYeK683mm82kOaJgpIEpjqdwyfRzxBXj/ZZRpYcq2Sj6/ceOGSDPXp1GjHXSR/k2NZSywwfRyOt+yWPP9YEonfq7t/ZVYyX5tNMfbfrbO9Jm0hy/TL0GmK0AkWrvwI9txpRFVqsD31CnEVKqEpjq0LHZUweKpot57D80WLBDjCFAyA8jFgrt70iTRJmrwYDT75huL5zU2wKZu3dB+7dpnH5vpeMdMmYKm48ebNYa1QXieliwJl6tXgXPnAIkTmii0RYoWFfoFd++OoN9/t5k9X6SBTVE3nd+5E+VathQmuRoXhxLu7uLnxPnz0fD99xHq6oqAo0dxcMkS1NUHEktKAho0AHjdrl93GLMycjpmzBhVdFxKld+7YAEavfeeaB43axY8pHITpf3ZzlRk1xBuATEXChYsKADMBg4ciO+//15MaaiW1FCJBx05OqMySKKS9F1Ta4ro2hW+69apdrytARCXUTkB9aadGCFl6jEjj/z92rVrWLx4salliYyClStXPgdgx+gz8S+sKcbq01kv3LRpU7E/IM5G6dKlsXbtWhGVJQ2cKdAwQzqaAqI0hNmgSx9nKtPCmnbRfUbMjcQbC2zQlkwLV0PXZ821aXt/a1rT8rE0x9tyGzrNCNrD5zSXyqaKJicn254f2aYrSD94ZKVK8DlzBnsqVICnEc5ZO6pj1lSRgwfDZ+HCZw7Izz+jYd++z40juLunTBF/j3znHfh8951ZcynptKlLF7TfsAFPAGQz0/HePWECvCXAODXOe7du3UTkQS36cobrkuqBn+zbh2xSfTFr9IoVLy66Bb/yCoLWrFFiGq2NhRY4s2kTKspct7t3o5SXlxhx39y5cBsxAsF16iDo0CEk/fAD6usDiSUkAJKjruaeslDlTO2eMHcuGo8YIXTYM20aPMeONaqPuZFdQ7gFdMLy5s0LXY5pprQSyVkf1MxQiQejwgSs8pTKCqi0Oem7ulHTyr//jhabNgnH+/fvvxcHCkrEGgBxGZUTuLi4CEebEU/WBPN3im72R0Z62hN0zFh9euHChUXEXgYNi46OBstxeEBJlgljoGEZrcscIEpd+jhTmRZKrr1+GyXPiLnsCbQTsQ5eeukl8LCHkXQeZJBmjM58Zom2988syxueV3O8Het62FQb7eGzqXmdcnCmAn46YgR6vPYaGnl7O+UaoipUQLNz5xBXtiw8zp93yjVEvPMOfKWI0t4ffkAjPYeD0YZ8mzahiUQhFvn22/CR2ttiwZs6dkT7v/8Gq9tymOl4R40Zg2YzZjxTz8gY2bJlQ+XKlcVGlfci5dKlSyJ18/jx49ZbmrQRPrVuHSpL3NBXLl9GSYlPeleXLmi+bp315tNGMmqBU+vXo/LLL4vPL4SGoqy/v/h535w5cPvwQ+yqXx/Nk5Kw/7vv0OCdd9KPExMDSI76i+J4iyj36NHCDns+/xyen35q1LbmRnZtgVtgrfRd3ajp6vnzsfnWLRRWSSdGXUi5pFsXq/YRzaicgIcMrIlevnw5CJ4lO97e3t7YvXu32qls2t5YfTrrm5leTqAwGTTsypUrWLFiBVjnbY6YA0Sp66ybyrQwRyclz4i57AnEIGENN1HPme1QvHhxAXrmKrE4ZKSvpUB6GYEWant/c+4U2/XRHG/b2dbhRtYePoe7JJmu0K1r11C4RAls+K9+sLOZDlZmL2J32bLwvngR8aVLw/3ixcxWx6z5I/r3h++SJaJv/MKFcNdDLuYX+hQ3NzSVANhEewUpjGYpA2BT27Zov3UrHgHIaeZ9ETFyJHz/979nKhgZ48cffxTpjIymcI0URgf0I2pKU/+MUllNnAhWcyevWIGaEmDXxXPnUEaistrVsSOab9xorrlEP1PpvBYNnoU6H//tN1STQIDObt+OClLa+b7Zs+E2ahR2urmhxb59SPz6azSUUfGl9T8ND4eL5Ki/KI537PTpaDJunLBAzMSJaPrZZ0bvBmtEdm11qyl9hvXn13XEfvb2xjfR0ZgD4FM783hnVE7ASCeFKdnM2GH0mI7UrFmzRLRYrZhrKyXzmAPOpWRcQ23MOdDRpY+TxzSWaWGOXtZ4RmxRk28KOFReK2veiZnAQ2slnOPsp+39zblTbNdHc7xtZ1uHG1l7+BzukmS6QtdPnEAxic7HWTey0aVLw+vyZSSULInGl8nw6nwS3qcP/JYtA4mVHhYvjlwS6BdXwkjw5cuXsfOdd9KQzCP69Utz1G2x2k0tW6L9zp14CCCXmY53+HvvwU+qW8/o3iIYEbEHiID86aefgrWgBJ/RFaWpf0aprIYMwWoAh5YsQR2pbvjc8eMoT5AuALvat0dzvTmN2VVJqqItrklWGTN52TLU7NNHLOf0pk2o1K6d+Dlh5kw0/vhj7HB3R8v4+Gc133oI3ke++w613n33mSnMvC+dzY4xkyej6cSJQu3d48bBe+pUo0uwRmTXVvZR+gzrz68bNQ0LCkLFkBC8AuB65coC8C0zxBznR42e5tpKzRwvaltrPCOW1ORb6rSr5Rznddb2/o51t2uOt2NdD5tqoz18NjWvUw5+KSkJpQlW5MQb2ZiSJdH06lXsK14cbgTQckIJ69UL/r/+iloAfv7f/+At1XTKSyF40ee1a8NbcmTDe/eG39KlNlvp5sBAtAsNtcjxDhs0CP6LFim+t3i4MHHiRBw7dgxbt25NtzalqX9GqaxcXLCLwHULFwquaLEZOXoUFWrR4sCuNm3QfMsWRfZUkqqoaKAXrJGcEXBk8WLUkkopTm7YgCoSZ33CF1+g8dix2OnpiRZ79jyr+f7gg3RWOrFoEaoOGmQR9oAtzM5MC9bGMsWU2AFyiikjl4x+WiLREybAS8JKiBo9+v/LNywZNBP6Kn2G9VXTjZqG+fvDPzwch/47EJzWuzeW2vAdmJGJzHF+1JjcXFupmUNra74F1Nbkc+9dQcqu4vcHD1YqVaokgOvk8gelQHrmcI5re3/zr7UtemqOty2s6qBjag+fg16YTFTr3J49KN+0qWLnKBNVNTp1bPHiaHL9OhKLFkVDB0I6VmOrsFdfhf/vv2MeANeJE9FeL5101apVqBwcDG8JgC3s9dfhL/Nsq5lIYdvNvr5oFxmJFBcX5H5CiDX1EvrWWwj46Se73ltGqawkxzvxq6/QUEKIPrV/PypLQGu7WrRA8x07FC3SGqmKiiZy0kamMgIOLVyIOhI91rHVq1G9Wzex0vhp0+D+ySfY4e2Nlrt3Y+/s2Wj04YfprHB8wQJUe+89PAaQ3YEi3l27dhWASl26dEGRIkUEIBU5sPmPgFWGRKamIi+2LKSmIqK0rogoN3nLCao4ahR8Zs3K1Dtj/vz5ItVViahpq2S8sGbN4L97N0jMVCgTr785zo+S9TlqG0vrjx11XfbSS75fiHr/66+/YtKkSSCtmy7q/bhx4zBt2jSTKpnDOa7t/U2a1a4NNMfbrubO3Mm0hy9z7e+Is58KCUHloCC7OkfWtkNc0aLwuHkT+4sUQYMbN6w9vF3GC33lFQT88YeYK2rGDDSTwJR0JxdI5hKgWljPniJCbivZ4u2NttHReODigjxmOt4hffogcNkyu95bRqms8uTBFwD2fvklGo0cKXQ6EReHqpKTsysoCM13MSZuWqyRqmh6FudtYSoj4MBXX6GeFMlO/vVX1JTqveOnTIH7+PHY4euLlhERz1LP9ZCA/5k9GzVGjbII9M8WltV3xOQ5DPFb87OMqKmIjq0ruz/+GN4zZ4o/RQwfDt85rHC2vZw+fRqjR48WuupK7969n6OWYltDYqitJZqHeXrCPzYWdwHkz0TH2xznx5J126OvIXAu1quvWbNG4G/oZyHZQydnmcNUTT4B65gJYy7qva4dzAEt1Pb+jnUnaY63Y10Pm2qjPXw2Na9TDn5s82ZUb9/+me6ZuJGxxHgJhQuj8b//IqlQIdS/RaIZ55PQLl0QsIEQd0DklCnw+eST5xYR+dZb8JEiyGE9esD/t99sttAtnp5oGxuLey4uyGem4x382msIWrXKMe4tCdU8bupUeEhAVcejo1FNQvLf5e+P5qGhNrPnizSwqYyA/V9+iQaSQ3106VK49u4tzBM3eTI8Jk7EjsBAtAwJQfz06XAfMyad6Y5MnYpan35qEeifLa5Fv379BDo/I94yjRCptIjMbygDICNqKqar60rkyJHwkUAKI4YNg+/8+aqXkBHiMQczpCMRpXPmzJmODoxtExMTRaROV0j/RwRvmZlA/sxQW9XK63QIc3eHf0ICUgDktvP3lW66MJGr9+/fjwYNGmDQoEHIkSOH0JJZDLpczY4IuGisPp1I7AUKFEh3eYi/QUo5lktw/WrlxIkTAreD9iHPfZ06dcQQw4YNwwIZ/0PBoPIBgIKmRpsozTAxZw65Jt9Y/TZLIiZMmGAQ9Z5ZWgThM1dMOf0cV9v7m2td2/TTHG/b2NUhR9UePoe8LJmq1JG1a1FLSvV0Vsd7b6FCaHT7Ng4VKIA6t29nqj3NnTykY0cESuBe4ZMmwW/ChOeGiujbF75STWNot24IWE24MNvI1saN0WbvXosc712vvILmUhTf1L1Frl7Bq12smNjsW10kx3vPxInwlNL4k0NDUTMwUEy1y8cHzSMirD7tizigqYyAfdOmwU06WDq8eDFq9+8vzBT32WfwmDQJO1q0QMudOxE3ZQo89A6gDo4fj7pTpliEPWCLa/Lw4UP8/PPPaTRCJUqUAKPdb775JnLlyvXclBlRU+3duzdd+4gPPoDvV1+Jv4W/+y78vv3W6BLMBf0ylKXwzTffIH/+/M/R+hFvYtu2bel0aNiwoaDNogOuK4bammt/viP+dnNDh0OHQJZsc2kOzZ2/RYsWgqub6cLnzp1Dz549xe88hLh48SJee+21dEPzEIKgkXTQHUmM1afXrl1bgNVNnTo1rdxBznhQWn+sv05ef/al40270fns0KGDqHHWP2CS+9qCYlJNhok510quyTcGusb3ANtQXn755XSo9xEREUZtoUQXJUB82t5fiSXt10ZzvAH8/vvv4iQqLi5ObP6qV68OUhrwJJMvAQofHH6x6gtpb9pJqKzyZ3zZ8jSPL2OeiPI0i5QYunL79m3xQlq9ejVIlcCXOuuheGquK0ePHsX7778v+BX5JdirVy988cUX4hRSrWgPn1qLZf32B5cvR10p4mTKOXJUayQWKICGd+/icP78qH3njqOqmaFeIe3aIVAC9wqfMAF+kyY91z78jTfgt2KF+Hvoyy+npabbYsHbGjZE6/378QBAHjMjS7s6d0bzP/98pp6RMZKSkjBkyBDQWZMpeOgE8P3JzbzVRHK8d48Zk1Yve2T7dtRq3VpMEeztjaCoKKtNpw1k3AIJn32GxtL9ffC771BX4uqOHT8eTaZMwfbWrdFq2zbETZoED70DqKTRo1F/1qxMiXha85pmRE115syZdFNFDB0K32++EX8Le/tt+EvlJob0MRf0y1CWQmhoqKDC0k8xJk81nTRdYdtGjRoJrmxdMdRWrR113xEux47h6f37KArgq/h4NGrcWO1wZreXHW9D6cI8dCAbg37En5FORlodSTKqT6fjyH/UWZdhQmn9sf46dZ1C7nmJUE8KrJ9++kkcWhgSpRSTamyqJsNEzbj6bdWCrrG/pUB6Svpre39Lrqr1+2qONyBSpOjwEiCldOnS4vRp+vTpGD58eFoKCB1vOr900HWFqTO6qKV0uuWXlLu7O77//nsBrhITEyOccFkIxEL6nNmzZ4svK54EEhmVqVmyU02Alvr16wvd+FIn6u+HH34oHP1lcu2kintCe/hUGOsFabr/xx/R4O23n63WTAcrs02VlD8/6t+7h6P58sH1Lqv/nE9CWrdG4PbtAjRofOvW6DpuXNqhHaNIfBclDhwIPym9PKRzZwSuX2+zhW6vVw+tDh60KKVXUHRt3pzhvcVNIDdaNWvWTFtLcnIyBgwY8FwdqUWLlRzvqJEj0UziQj+0aRPqdOgghg329ERQTIxFU2idlVkg/pNP4C6BCCUtWID6Q4eKjnGffAKPadOwrUMHtP77b8RNnAgPPZDBxPffR8P58y06EFKmpeO0Ch80CH4SO0BY//7wX7zYqHLmgn6ZylLITGvoviPC69SB3+HDSP6Piu6t/7AAwsLD7aZa1apV0b9/f4PpwoyAcz9XsmTJdPp07NgRf/31l910VDKRkvr0jBgmlMwh1/xzT71y5UrkyZNHdHv99dfBbJDg4GABQGhMlFBMKtFDbqMmw0TNuM7SVtv7O9aV0hxvAFeuXHnuhUkHlzQWfDnkzp1bRLyJOMrTV2PCyDUd93feeScNkOTx48diA83oDdEMKdHR0cLZ5wuZaTcUvqgYaWfU+12Jp3TGjBmYPHkyTp06JV5WlBUrVuCNN97AwYMH0+pllN5S2sOn1FIvTru9CxagkYxQ66SO98G8eVH3wQP8kycPaty/75QXL+S/1LvA4GB0+i+SU6xZM5Ro1gyRkZGCboQoyTt27ACJlfzXrBHrE6npGzfabK07atdGyyNHLKJt2tW6NZpv3/5MRyP3lp+fH8INbJ6N/d3cBb/t4oIfCE713ntpabsHN2xA3S5dxJDBHh4Iio01d3itnwoLxH70EZpIhx+Jc+ag4fDhonfsxx+jycyZ2NqlC9qsX4/YTz9FE4lGSx5+76BBaLRoEe79B1CWz0nfVypMJZqGDRiQ5myH9umDgF9+MTqEEqdK7fyZ3V73XRDp6gqfZLrdgI+3NyLtmKWim/Gony48cuRIEUCRqaEy22YZza8GnEtJ/bChueSaf+6f6XRzD01hYIlO/ZIlS8Thqimx9ABAHl9NhokpndR8bq791MyhpK2291diJfu10RxvI7ZmilDfvn1x/vx5lC1bVpHjzUg505F48tlYJwWK1AGMbJOSgQAW5Kr96quvRFo7f5eFaTkEtyAoC4U1Ytx0r9eJbNG5Z4SddTh82asRR3j46EDQTqyLkrlOW7VqpWYZWlsrWiB+9my4jxr1bEQn3cgezpMHtVNScDx3blR7wORo55OQgAAEhoWBFceTRo5EEAGoGjRIqw/ku2Hyfw64v0RNFNK+fVpNuC1Wu7NmTbSQUyQJrqbznlI6386gILQICcnQeWeGEA8gmQEkg1Lx9/bt24ObFrVilMrqzTfBSkvBLS5RsiWtWYP63buLKUIaN0ZgfLza6bT2Zlhgz/Dh8JxH4jxg36xZcJPeP7GjRqHJ7NnY2r072qxejT1jx8JTj14noX9/NF6yJNNRrc1Yttldwt58E/5SmVvI668jMAMaQTVOldkK2bmj7jvi+hdfoNjVq2AMucWECfjUQEmOndVz+ukycg6V1A8bMoA9av6dwfDm2s/aa3OEvb+11+TM42mOt5Grx6g1aRR44sZTTEa8yaXLk7t79+6JTTHTv3nyKQvBSIYOHSo+163BZg35q6++CtZuVahQQfzMCDfrgnSFfbds2ZJWE1SqVCkQJZE13bpSr149sCaL1DlqJLMfPta0X7p06TmuUzrgc+xEkaLGXi9C2z1TpsBz/PhnS3VSx/to7txwffgQJ3PlQpUU4t06n4T4+iIwMhL+dLw/+AAt5s4FkXP5TqAIx7tgQfhLh3IhbdsiUE7jtsFyd1WrhuYnTjwbOTUVyJ5d9Sy7/PwEYFkqgGyPH6fhZegPxNRy/cM4V1dX1fOxg1Eqq88+AystQ956C4E//ijG3v/rr2jQq5f4OaRhQwTu22fWnFondRaIHjoUXlLNcsK0aWg8dqwYQHbIt/XqhdYrVyJm9Gg0nTEj3eBxr78Oj5UrQSSHAk7wvrIGqnVo794IkJzt4J49EaSCRtAaETebAx8quH3kd0TCxx+j8c2bIGJOqdOnUaRiRQW9tSYZWSAj51BJ/bChsW1Z8+9MV9OU/azxflBij8ze+yvR8UVqozneBq42U8rp2DIyTYAJyrx58wQyI51eps8wDZ2gI3Squ0tRE0ahP//8czzQi7pt374dRHfct2+fSDnnz3TmiUCqK5yLzjsj4RSi+3I80jDoClOv6JSvXbs2w3uVNeP8J8uFCxfQtGnTtAMAe9/oxjhNjf3d3vq9iPNFf/opvKZOfbZ0J9jIGrpG/+TKhRqPHuF0zpyo9PChU17GEG9vBEZHi3ToSoMHo43kmHAxzHLhu6jj/v3wl5DPg1u1QpAesrA1Fx5cpQqCTp16NiQPMwwgM5uab5e3N5pHRwvH++nDh7ZBK9dTwiiVlYuLiJLt6tMHzaVU3cSlS9Gwb18xQkj9+gh0MPRhU/Z11s93v/MOvCWAsPjJkwV3NyVm6FA0/eYbbH/zTbRasgTRo0bBS49mJ7Z7dzRZs0ZgIRRyoPeV0UwLlajWLDV7Ty79kS5waK9eCJCc7ZDu3RH4+++KL70lETc1wIfWoo0ytbDdlSrBWwKfu5acjOI1apjq4tCfE/GcwRgeGHI/yBRt1rTbU0w5h/bUJavOZa33g7n20Rxvcy1nm36a461nVyKRE3mRL0MCQBijtnny5AkI2EDHlvXWFDreRAElMISukHqjTZs2or6FkXI63nTiiYiuK6RbWLhwYRpHJufmeB9//HG6dkQuLVOmjIjIZyR8oTLNXV/kyLttbinjo3LdBJ7jZkAW2phr5OGEJva3QNRHH6WBTTmr430iZ05UTU3F2Rw5UOHRI/sb0Qozhnh6IlCqMd7+7rtoZYAyKLRdOwRIyOfBLVogaMcOK8xseIjQihURIPO23rsHmMGisKtJEzSPixMT3NfLArKV4kZBoqRU+Z09e6KF5MTs/fFHNJKABUPr1kXAgQO2UksbV8cCUf37o9mSJeIvAsl88mTxc/SgQfBatAg73nkHLRctQvSHH8Jr9ux0ttvTuTM8//wTtwAUdk0VGU4AACAASURBVCDH22imhRFUaxl8Sv/G6N2793OggiE9eiBQog4MfvllBMkUfQruKkucKjXAh+bQRqW/sHsAshhItcDGlhZTvjyanj8vPr6UlITS9eopsIJ1m1grA4DZCATdZXkhkcRZ1sifq1Wrhml6JRbWXYG60ewVldXXylp2Vrda27RW+36wthaa421ti1o2nuZ469iPNdik/WLEmoA/TIHOSEgTRp5DObVcTjWn4y2jOLJ/ZqWaO1rEmw8/DxH27NkDHlww6t+kSROBIF+pUiXL7mStt1kWEGBTCxY86+tAG1k1izmVIwcqP36MC9mzoyzTop1QQjw80mqMtw0ciNYSirHuUkJbtUKA5GwHBwUhaNcum600vHx5+Ekb3Kf//guXggVVzxXcuDGCJE7i2zdvomDhwqrHsEoH3tcSLeSObt3QUnJiEr77Do0lIMuwWrXgf/iwVabTBsnYApG9e8NHSp3WrePePWAAvBcvxq733kPz+fMR/cEH8Jo7N91gMe3aoemWLSAechEHel8ZzbQwgmotg0/p00/xcJ74J7oS8sorCJSc7eBOnRC0YYNdbjE1wIf6tFEspyOCdUa0UWmLIJYEWQ3eeguQykCMLXBPmTLwvHRJfHwhPh5l7UgnpiYDQMkFYoCHQLvkgCewLjm0uSeyNrCkEl3YJrOjsrKe1razvAfv0aMHmPVJbCQ+ZyxnIr93DTtkTah9Pyi9ZkrbaY63UkvZp53meEt2prPdtm1bsJYoKirqOT5tQ5eDDy0dSdnx1sDV7HPTarNYzwK6VDWPU1OdApVVf/Vns2dHhSdPcDlbNpR6/Nh6xrHjSKGNGiFAqjHe2r8/2higDApt3hwBwcFCq+CAAASFhNhMw4gyZeArbXBTr15FDhOHkIYUCW7QAEESC8SNS5dQtFQpm+lrdOArV/D0o4/gIoFTbe/cGa0ksMq4+fPh8f77omtYzZrwP3rU/vq9gDNGvPYafFetEiuP+egjNJ05U/wc1a8fmv3yC4JHjRLggruHDYP3/PnpLBTTsiWa7tyJG6zndyDHWy0dlxrwqeAuXdKc7eAOHRBkJ3oqNcCHzP4jL7MccKAzSefbFG2UuLjM9PH0BKpXB0xwXseWKoUmV66Ibmd370YFLy+7PUFqMgCUKMUgD+1DGThwoKCepch84UrGsGYbW0dljx07Jg4YTIm17cz5iPZPYF/ekz179gRp3njowYwQ7tutLaxx1y0ZUPt+sLY+muNtbYtaNp7meAvsoFS88sorIsWL//ilaEoYsSUlGJ1umWJMphMjHZgMiEY6MY7HFHN9OjGmmpOTm8L0b6YY6dOJscabdGJy9J1j9OrVS6MTM3WBtM8VWSC0f38ESGmfKffuIbcZKcWKJrJhowvZsqHs06e4mi0bSjir492wIQKkGuMtffuixQ8/CKyHYsWKpZW7hAYGIiA0VFgy2M8PQWFhNrNqVKlSaCZtcFPOn0fusmVVzxVapw4CpCjylVOnUDITslqu9uuHEjr0S9vatxcc0ZS4OXPg8eGH4ufw6tXhZ2LTr9oAWgeDFojo1g2+Ej5J9IgR8Prf/0S7qN690Wz5coR+8gkCpk7F7sGD4a2DdcA20QEB8AoLc7gab7WXWg34VMhLLyFQcrZ3tW2L5jYEVdRfh1LgQ1IfkueaDDCycO+jiDaKWTGMXBMo7fTpDE0ZX6IE3KWMgFOhoajsTzhK+4iaDAAlGg0ePFjs91h2KAv3kATs1S9DVDKepW2sGZXl/a0vzA5llqi/iWtmbTtTDzLnyFhLLP2UxVaHHCwtJQaUo4jmeDvKlXimh+Z4/5eyNGjQICxatEiknei/FOrWrQueVhHVnA4vT+z4O8HVeFLGOuuuXbumXVWeErOOmenT7u7uAnmcIGis5aHzLQvpcxISEgTNGPkfJ0yYIOjGmAIjI6ITxK1+/fqoUqWKQFAnwjr5xRmZX7Zsmeo7SXv4VJssy3cI6dULgVLN653r11GgaFGnW/OlbNlQ+ulTXHdxQTFSXzmhhNarh4CDB5EEoFfJkkDp0oI2kO8ERiIWLFiAm0OGICAiQqwu2McHQdLPtlhudIkS8JI2uHdPnED+KlVUTxPm6gp/iXP3YnIyytghpU9fyT89PNBJhyZsW6tWaC1tvPbMnAlPCT8jompV+B4/rnqNWgf1Fgjv0gV+Urp01LBhaCZFtSNfew0+q1YhfPJk+E2YgKh33kGz775LN0G0tze8oqNfKB7v4PbtESQ527tatUJzI6CK+lE29Vcmc3qkhIUhd0AAHhUvjpxXr2aoREKxYmh8g/kOwPFt21DNjlSkajIAMseSls1qzahsrly5BJYPa9flcgoZ62ixgWwuXc1tYee5c+fi3LlzosSRtePcf5NViEGtH02UN2RkVQbL9IXr5V797t27ll0QK/bW9v5WNKYVhtIcb0A4tnwADQmda0as+/fvj7i4OFy5cgV8qbA2mWjjdIJ1hQ8dXxzcKJM6i842HXpdQDG2Z/016bVWr14tanx48sbTz8qVK6cb7+jRowLllDXnrAuj8z9jxox0dGVK7wPt4VNqqRenXXD37giSQPpunDuHouXKOd3ir7q4oASfKRcXFHJSxzusdm34HzkC4tn27dgRb2/cmHYdGHUaMGAApjx6hACJgjDY2xtBUVE2u1Z7ihWDp7TBvZ2cjIJmOM0iiiw5s2eTklAhE4CQ1jVvjpeldE4aa2tgINpIv8dMnYqmEmtFZOXK8Dl50mb21Ab+fwuEd+gAPwlYNPLdd+EjAQlG9ugBn9WrETlzJnxGj0bU22+jmZR+K/eObtIEXnFxIHdBLgdKNbfl9Q1u3RpBEvjorubN0XznToPT2TrKxsw+BgqIZaNLl2rp2uPnzoX7iBG4lT07CpvA6NhXpAjcbhFaD0j+6y/U7NDB0ulV9VeaAaBq0CzY+MiRIwLYl1kQLMdkcKl9+/aKI/m2sDMj0KQEpgPOTDKmn/ft29cito1atWqJ7FPW5usKwQZ1I+uZfYm1vX9mX4H082uOt2NdD5tqk9kPH08af/75Z/GSIvAKOdEpzAogcqYm9rdAcOfOCJK4oa8cO4aSBk5w7a+VuhlvuLiAcXpn4fY1tLpwV1f4JSfDD8C47t3RQY8yiOl30x4+hO+ePXD5rw4wtGlTBP1Xo2YriStSBB7SBvfmgQMoUreu6qkiKleGr5Q6eio+HpXtCIQkK7u5XTu0k5Dg+betvr5oEx4uPo6ZNAlNP/tM/BxVsSKamUhzVW0ArYNBC4S3aQM/KWobMXAgfCUgwciuXeGzbh12z5sH7w8+QGT//vDRi47FuLmhaWIimNeS7UVxvMlgINWh7goIwACJTkvXuPaIspFJhcEEBiCYzWeJMPDAvQADEw83b8bOGTOQ08UFH928KZw0Y7K/UCE0uH1bfHzkjz9Q6+WXLVFD62tjC4T9l83A7E+mejN93pGcUWssnTTDDIaR3ldX6OCzltxRJLP3/o5iB0fRQ3O8HeVK2EGPzH743njjDYFezpqmLVu2CLR3RvhtVWdjB5M6/RS72rVDc8kxOZ+UhHKZEJW01Igi0k3KKgB5nXQzHlG9ukh1/pLosiVKoN+YMSLFnOl/f/31l4gWeK1aBe+4OGRjTXKTJgjas8dS0xntn1CoEBpLG9zre/eimJub6rkiK1aEj0RJdjwqCtW8vVWPYWmHXR06oLkObeMWLy+0lbIGosePh9eUKc8c7woV0MyAQ2Pp/Fr/5y0Q3qIF/CRHMqJ/f/hKznVEp07w3bgRe777Dp6DBiHyzTfh89NP6QaIqVsXTQ8dEn978ugRsunUx2ZVWwcHBiJIqpnd5euLd69cyZQoG6PcOR88wJbISLAe2BLp1KkTGjduLNJxN69Ygd4XLyJvtmwI79pVZAEakwMFCqCelMJ7cNUq1H31VUvU0PrayQLEJtq7d28a9pGdptWmkSyQ2Xt/7UKkt4DmeL9Ad0RmP3y6KJ5MW2P6LOtr3n//fYGIqon9LbCrRQs0lzbBp2NjUcnDw/5KWDjjPRcX5PsvUuzM6aeRVaumpTovatkSePVVQStEUEU+N6Q+CWvcGE327gWT2iI9PBAk8X5baD6D3fcVLAi3O8whAK7ExKAkUYdVyu5y5eB94YLolRwSgpoBTKTPWKxdpxrWujX8pTRdzrzFwwNtJbvtHjMG3jNmCIWErufOmVJP+9wKFggPCICfBAwY3qcP/CTwOzkFPf7nn+Herx8i+/SBjw4wHqeOdXVFEwk3IOXff5HbDJo7KyzBZkMY4kwWQIoytkOzZtjXs6dVomxqM9BG5cyJL1NTEbl8OXxef90iGwQGBiJEYmWoWqwYTty4gds5cqCzn1+GKNMH8+dH3Xv3xNxJS5eifu/eFumRmZ11o/6M/BN4jOWEw4cPzzDqn5k6a3M7pwUye+/vnFazndaa42072zrcyJn98Mm8lbJhyFtJbkXWzfNnTexvgV3+/mgupd4eDw9HNV9f+ythwYwES0nNnh25GAFz4vTTqMqV01KdN3bsiJd0arxl84S6ucE9MRHEwN3dqBGCEhIssFzGXZPy50d9aYN7MTwcZcy4L2JKl0bTy5fFRIf+q6+r07q1SX2tXaca5u8Pf+n+5uRb3NzQVuIWjxo5Es0kRO2YMmXQVDokMKmk1sAiC4T7+MBPwicI69UL/itWiPHkFPR9q1bBrWdPRL7xBnz0QETjqlWDx4kTov2dS5dQIDMo6ixa/bPOajiTQ3x8ECjZK9iKJSZqM9BuurigCA/9vvwSPiNHWmQFgtgyDZkytEkTfB0Xh39z5EAXE473kbx5UevBA9Fv348/wo3c304qulF/osJ37twZRYoUEbRXGUX9rblcax90WlM3W4zFwyZ9thBbzONoY2b23t/R7JHZ+miOd2ZfATvOn9kPH5HgCUaniwR5/vx5Ab6xdOlSiy3xon2JWGwwALu8vdFcqhU+un07XBltVSDcHBB4UDcqyzoue8vDlBTkypPn/6cluJoLq6CdS1hj7HH2rDhA+LN9e3SSKK+eHjqElPffR57ffkO4vz8aHDiAnKxPdnNDkORA2mKlB/PlQ937TN4Hzu/ahXJBQaqn2VOyJDwllOKkjRtRv2PHtDHshQYb7ukJP53MgC316qGtxC0eNXw4ms2bJ3TaU6oUPCXectULtVIHtRFIK01r92HCmzaFn1QmEdqjBwJ++03oEN6yJfx27kTS+vWo36ULBMr5ypXp9IuvVAnuUknAjVOnUDQTKOqsYTA1nMkhXl7wj4kRB4vhVsx0UZuBdtvFBQWJLzFpEgImTDBqBoLEEhA2IyGuC5liWHa2uWtXtFu3Dpf+A1ebM2pUhunIyXnyoGZKihh678KFaDRokDUuR4Zj2Oq51I36M+K9X6KTJBCvLbilDS3Sw8NDlDFl9ve4uRdR6T6EGZZDhgwRpVv6bCFK6IPN1c8e/ZRkTmT23t8ednCmOTTH25muloW6ZvWHz9rRMgvN7RTdg7mRk+iWDv31F+ooQIklGj9Bdrp06SJO6El7t2HDBpEWPWfOHLuu+96//yJf4cJpcz59+BAuOemaOpfsLl8eDS9cQL6nT/FnmzboJNXdJwYEoGFYGC6++y6OBQej9uHDICRhXIMGCExMtNkij+bJA1dpg3t22zZUMONQJa54cXhcvy50TFy7Fg11aBfthQYb0bAhfKUNLfXYWrs22kg1wpFDh8Lnm2/wGEBCyZJocvky/vnnH2TLli3d4WBsbKxgsbC1qI1A2lofW41P59FPeueEvvIKAiRWhfCgIPiFhDzLjmjTBhE9esBXcsplXRLKl0fj8+fFr1eOHEFJV1dbqWnTcdVwJod6eMBLsldU48Zp72tLFVSbgSY73rs++gjNZ87EaSNghL1794YhHmdj+soAiNdz5kSxhywYMi4ncuVCxUePRNZP3FdfwcOEg2+pjdhf7XOpxBHiuLpR/6+//hpDhw4V6trC8TZ00ElnmzX2rL+21/d4ampqOt5y+footZnu9VSzDwkICBBljTVr1kwbQmYLSbtXWe5YoQLg6iruX/YxJR999JEAUbPH94MxXUg5/Morr4hraSxzIqvv/U1dJ0f7XHO8He2K2FCfrPLw2StaZsNL4TBDBzdsiCDJMUn64w/UV4ASq3tSr7sQY39Xs1ilJ9hpX9iXL6NQ6dJpU6Tevo0cBQqomdIh2kaXLYs6XMuTJ/izZUt0kuqSf61dG68dOYIDrVvj1smTqP7PPwJALqFePQRKkVtbLOCf3LlRQ9oEn/77b1Rq3171NHuLFEEjCRk94ddf0VgH5dVeaLCRtWrB5+jRNN231qyJNtLvkYMGwWfRIgHKd6h4cWwfPRrr168XrAt8x3z//feCasZe4I9qI5CqL4iDdAhv1Ah++/YJbUK6dEHgunXi5wh/f/iGhyM5LAw1/f0R0a0bfPWAtvaVLg03qXzhwr59KNuwoYOsSp0aajiTw9zc0DgxUWA7MNMl0EqZLmoz0GQQyx1DhqDl11+LemRvb+80nmbZAomJiSKCqlQ2BwWhXUgIruXMieImHO/TdM5TU8E3/J7Zs+H54YdKpzG7ndrnUmkKuW7UX1YuJSUFEydOtDoImaGDTn5fkxpXH2ncGt/jmzdvFlkPPMQcO3asyG6gGHuXKrWZkv2GIf3JCkJKXn1J93c5U+7pUygN4pCKuHr16iIQwQMaHjpVrFjR7Hsto47GDrqYLXFL+p41ljmRVfb+NjFsJgyqOd6ZYPTMmjKrPHz2ipZl1nWy57whdesiUIoA7l25Eo1ee83k9OSoHDduXDpu+uDgYEyZMgXbdYCsTA6k10DNCbbc9cbp0yhaufIzRHMAD65eRZ7ixdVOnent95QujarXrqHE48f4MygInSTAuz8aNkTX/fsR37w5Hp48iYqnTqHAkyfYy+t24IDN9GZkqeqjR2L8E+vWoWqXLqrnSixUCA0lZPTYX35Bkz59VI9haYfoqlXhpYMfsa1qVbSWuMUj33pLoGbf4BqLFcOwWrVExICyZMkSrFy5UtRasvbSHqmfaiOQltrG3P6WZgVE1K8PX+neDenYEYESnkGEjw+8o6JwKjoa1by8IOjF1q5Np+b+kiXRQCpfOBMdjYpNm5q7DJv3Uxo1S1OETif5gPU4gcPr1UO9gwdFiYmxTJdu3bphjZQ5YKuF3XJxAXOLtvXrh9ZLloApurt37xYOuK6o5TDe0qyZYBq4nDMnSplwvC/kyIGcT5+ixJMniJ4+HV5jxjy3XLWHt6bspfa5bNSokUDwpmRWCrn+mgwddPI6MfNi8uTJac2t8T3OwXx9ffHnn38Kylg64KSimzZtmtFovjlp92r2IV9++aVgB3nppZfSsYXs27dPpJ4/+6J7hh3xtEoVXL58WUSQTYmcnXDmzBksX75c/CtZsqTgB5cPG0yNofRzYwddPFBgOQTFWOZEVtn7K7WVo7fTHG9Hv0JW1M9RHj5LAS7sFS2zoukddqgwV1f4SyjBcUuWwKNfP5O68j5iXf6ePXtAcDNGCJlqRb5O0sWZK+ZE0q8mJ6OEqyvEpvDpU9w9fx75y5Y1V4VM6xdbsiTK3rqF8o8eYaOfH16SgIc2ubujfUICovz9ke3UKZQ5d05ExRNr1Uo7MLGF0owsVUpNFUMf+/13VO/eXfU0SQUKoL60eYn54Qc0HTBA9RiWdthTvjw8pdRkjrW9UiW0OnVKDBvRuzd8ly/HRRcXXCxcGEPq1ElzvPn533//LTaLd+7cSdtIW6oP+xtzyNRGIK2hi9oxZs6caXFWQGSdOmhy+LDAMwhu1w5BEt1bhJcXPGNicCkpCRXr10dUp05otmFDOhUPFCuGejd4VAIcDw5GtcBAtUuwW3ulUbM0hRhx69wZWL8+nY4RtWrBNTkZuZ4+xb66dRF06JCg4XRxcUmLNjPiRufmuHSoZItFyuBqm7t1w34vL9SoUQMtW7Z8DoH78OHDqF27tmIVtrq7o01CAi7lzInSJhzvK9mz41G2bCiXmoqoyZPRbPz4dPOYc3hrSlG1z6VMA8lxbZ1Cbkr3jD631fc452Satm65wfjx43Hx4kUcPXo0Dc1eVzdz0u7V6s/Ucn1cGkbaDx48KLJJIFMTPn0KJYdHXB+zE/QPZVmatGzZMsydOzdtiazfr1ChgnD6mQ1AJ1pJKruujYwddNWtWxfMMiFegiz6mROOsve35H7NSn01xzsrXU0Ta8nshy8rA1w4620UVq0a/KWT3pjvvkPTd97JtKWoOcGWlby0fz9KN2yIy9myodSTJyIdu3Dlypm2BnMnji9eHMXu3kWVlBT81awZOkqR1+2NGqHVvn3Y7euL7CdOoNjlyyjy+DEO1KiBAJ0UanPnNdbvXI4cIvrOevLkFStQs1cv1VMcypcPdSSAtqhvv0Wzd99VPYalHeJLlYL7lStpw2wvXx6tJG7x8B494Ld6NU5ny4YbBQtifvfuGDNmjHAoZGEEqE+fPmBEw1qi2iGz1sRWGMfHx8firIBIV1c0SE4WQF3BrVohaNs2oVlEkyZwj4vDjWPHUK56dexu1w7eOhzsbHOocGHU+fdf0f7o5s1wbdvWCquybAirlT7ppLrqahRZvTqqnjyJPE+fYn+tWkgeNQpr167FBx98IFJiKQTI2qRnK8tW9Xxv2fGeXKIEQq9ehfuQIZj59ddmTXPixAl8+umnwlkIiIjAgGPHcC17dkx8910sWLDA6Jg3smXDrZw5UeXhQ0SOHw8fnWgtO5lzeGvWAv5Dpjd23QkYy2hpRo6Q2jmtHcVXO7/a9jyQofNZVucQfMaMGfjkk0/AOm99sWfave7caUEcArTKke/UVKxavVrUbmckfPb4Pvzss88ybMc68JiYGBQoUADly5cH7w/+zPuHB7tKhY4+sykKFSqUrouSg67M3vsrXeOL0k5zvF+UKw0gsx8+RQAXL9D1cISlRlaqBB/JqYj66is0swNYjbF1qz3B5jjn9+xBuaZNcTZ7dlR4/Bg3jhxBUScEXEooWhT5U1Lgev8+/vb0RIeYGGGmENZyHzyIaA8PZGe0+/p1FE5NxdEqVeB/7JjNbqGL2bMj35Mn4Ff8kZ9/Rq2+fVXPpUv9EzFvHnzff1/1GJZ22FesGNykCCnH2lG2LFpKEfDwl1+G3/r1+CdHDtzPlw8NpDo5S+eU+1vNIbOWQlYYR9fx5nDmZAVEVa8O1+PHwYKQ4ObNEURQIzrejRvDbe9e3D13DqXLl0dMq1ZoKjnlsupHCxaEq8Qvf3DdOtQ1owTCCmZIN4RVSp8Y6c3NYy7muj5NN35UlSqoePYs8jx5gkM1asD/6FHcv38fTJ+Nj48XDiwjirwWlsqxY8dEzaohkR1v5iB0/i+qubNRI7Qwk9KQh6yMTtM5fa9jR3yZkgI/Fxd0CQzMsKyDAG8X8+ZFzfv3ET5mDPymT0+nqjmHt+bazCrXHRBpwhlRXNkiim/umpX2I4YB08z1yxBYpqJ7sKl0PGu3e/DgAWqVKIGpX3+N3v36IeX4ceSW73uWR+ngxFj6HpdLFR4+fCieLVLnMkvQWN25tdfK8TJ772+LNTnzmJrj7cxXT6Xumf3wKQK4ULkmrbllFogqVw7NJP7i8Fmz4DdqlGUD2rn3mfBwVPT3x/GcOVHt0SNc3b8fJerXt7MWlk9HILKcqamod/cuNru7o11cnBg0skYN+Bw7hpj69ZHj0iXk+fdf4XifLF8evlLKtOWzPz/C1WzZwO1/yadPcejHH1HHDL7cY7lzo7qUOhr25ZfwV8D9q4SKSM16DxYqhLpSnTnZfyNLlUILiTYsrGNH+P/9Nw7Q4cmZE/WkdmrGz6ittTbm1tLHGuO8/fbbFmcFRNORPHMG5Z48QbC/P4JCQ4VqUQ0awDUpCU8uX0axUqUQHxgIz+DgdGofy5cP1aUsiv2rVqHBq69aY1kWjWGN0qf7Z88irwzKpOd4R1esiNIXL4qDsKNVq8Lvn3/S9GUtKtNd6TBv3bpV1ToMIY+PHj0as2bNEojb+nLDxQVFATDfgAdy0bVqwevwYVVzyo11kbv/rlgR88+eBYtZljdvjp3SQYyhgVNcXHCsYEHxTIeNHAn/L79M18ycw1uzFgDA0uuuNAPQnlF8c21hqh+jvryvHEVOJSaispsb5mfPjvdSU3EpNBSl5bIVfj+UKpWmqqXvcV1wvoEDBwrQToq9QDs5V2bv/R3lujuKHprj7ShXwg56ZPbDZwzggmlyfDFrYn8LxJQujaYSSnDo1KkIGDfO/kpYMOOpHTtQuVUrHM6TB7UfPMCluDiUdne3YMTM6bq/UCHB09vg9m1sc3NDWwmch5tur7NnsadmTeS6dg3Z799HoUePcK50aTSTUqZtoTERjP/Nlg0VnjzBgYULUc8MvlwCtFV49EiAQoVMm4bAsWPTVLUWFZGptSfnyyeiYxSmqe4rWhRBEjhXWOvW8N++Hfvy5UNOFxfUlSKppsZU+rmlG3Ol8zhbu5iKFVHy4kVUTU1FsLc3gqKixBJi6tRBhcOHkff6deQpVgwHfH3RRA+J+GSePKIcg7L3l1/QKBMA+2xhb/kAUYyt53jHlCuH4levIt/jxzheuTJ8rVTHTURrOsBMf30qzUmEa6bQLl68+LllyhFv+QORjWMmswIzJ+hg58mTB2HlysHrwgX04HuicGFBT2lMnri4YD+zWK5fR8j77yNw3jxbXA67jKk0A9CWUXxrH3QaM5wtKNIsuUjJ27ahZps2ICQZwfqSV61CTQlY9umxY3CpVi1teEvf44MHDwbtrF968PLLL9u8PEReRGbv/S25Vlmxr+Z4Z8WramRNjvDwGQK4cLVSarC9vkSy0i0TW6IEmkjUL7smTEDzSZNMLo+cmwRZIqgIU+TI381TXR6epCGEmhzFOg2O//03qnXsiKT8+QWQ1/moKJTzPeYLOgAAIABJREFU9rbO4HYc5VD+/LiXOzca3LiB4Hr10Eba0MaWLi34pWOrVEHumzfx5NEjFHz0CFeKFRObVVvJfRcXnM+RA9VTU7F//nw0GDZM9VQEaCv++DHyP32KXZMmofmECWljWIuKyJRSJ3Pnxs2HD0E3YkSePDidNy8CJW7xsKAg+IeEIK5gQeHU1FGAYmtqPks+1617Za15nTp1xHDDhg3LsO7Vkjkzoy8B74peuYJKjx4h0sMDQbGxQo34GjVQ9PhxlL5zB6n58+OIp6cAW9OVM7lzo6KURRH3/ffwePvtzFiC1efcv3IlGrz++rNx9RxvvgMK3rqFgqmpOFGhAnx1UPotUeTIkSOYNGkSqlatKsAyWTtqrFacIJop2bML5ghZplaogE/OnMHVq1cxcuRIkfZOULXZs2ebBNkkewDnZQ0ws1B8rlzBY7IJLFqEAQMHGlwW333ZcuVCQunSaHzpEna9+y6af/ut2SZ4cvUqsjGlmPW9GYitnkulGYDWiOIbOugkyjeZG1atWiWuHwG6uBfjd7u108FZB22qFtrsC6nTUSm6/75ly+AmH9qRnnPuXDQeMUKM9CA2Fnk8PFSpY6pcQNVgKhtv3LhRoLVn9Bw6wt5f5bKydHPN8c7Slzf94rLKw2evaNmLcGvEFy0KdynCsGPsWLRUAPbRtWtX8aLv0qULihQpIiIUGzZsEP/WSZy89rJd8tq1qNmtG/YWKoRG//6LsyEhqBAQYK/prTbP0bx5cZMo4FevIrJ2bbSSKN4YoXW7eRPx5cohz+3bYLp0gYcPcatQIXhKmQpWU0JnoMcuLjiaOzfqpKQgcc4cNBw+XPU0dNy5US/6+DF2fPIJWk6ZkjaGtaiITCl1PmdOJJcujXp79+J2lSoCC8BfquUO8/WFf2QkoosWRZEHD1Dt1i38/PPPov7u9ddfFzWKFAL/MMXa1qJb90oQogkTJqBDhw5GKXhsrY+txo8vUwb5btxAhYcP09FjJVSujHznzqFGSgpu5MiBk25uaKLHWc3rWTw1VYD+RS9YAK+hQ22lpl3HjV2wAE1kfA09xzuhRAnkvntXZLqcYWnQ6dPW0+3KFYQtXIjpUVFo1aqViMDp8zpzsvv37iFv/vzp5m2UJw/23r8vqJMYvSWPMekkieRN5GalElO0KJrKUW5mpxhxhFNu3ULuIkUQW6kSmpw+je1vvYVWP/6odJrn27m44EC5cqh37lyGY6h9LklRxQMNd5YMtWuHoUOHIn/+/ALlmu89WeyZAWjooJOUZ+TZJhAagcQ6duyI6Oho4SDbhD6R97UMIKjwqv0fe1cCF1X1/b8zw7Dv+yKbCiiIgMgiIOKCuGe5lKXmVmq/NCvLytSysmxf1MpKzS3TtMx9l01BRUUF2RRR9n1fZmD4c65v+I/EMoNAaHP9zMdh3n333nPuve/ds30PPXfp+dtcobG3F93/4ldfsRzwewBMqa/HYmdn5N+8iWsNyh87Pz98/euvcikf5A0XkJPcVqu1JGBnZGQwwMvW9uHjcvbvCD52hzaUgnd3mIUuGkO323xRUQAdnI4dA4yN5eZCV1nL5B7QI1wxVk8PbhxK8InXX0dwk5i55khrmipEWqelWLSHZU9rngxJu3bBcdo0XDI0xMDCQqSdOMFczx+1kqqmhhxDQzjn5DC38uGJiYyEeG1tOFdUINbEBGqVlShXUYGWSIQqDQ0M4DwVOprWerEYPFVVXNPURP/KSlz99FO4tyMUJEcggERFBRYiEU4uXYoRMjF+D4PQqgi9+QIBbtjZIejWLdzS1UVOfT38uFjucB8f+F64ACmi/HtPPcUsdeQSeOzYMezZs4cd7LoqFk/WHbOsrAxTp05lCoDNmze3GveqCD+6Q91YY2PwKypgVVODG05OCOSUTNcsLMArKICrSIRM8rhwdsbAJrnqc1VUoCaRsNSB57/8EoM4K1V3oOthxsBSY61adb+JujqAz29sjpRvfJEI2mIxckxM4NuGoKjIOGo9PKBCyo2cHOw6fZqlzfvkk0/+0URxdjb0m6Rp9CDrc03NP5DEFXUrvqKrCw9uT9YWFEDF0LBZEsoyM6FjZcWej17JyTg+fTpGbtumCLmNdQuSkmDk5HT/7yaKjqYNKrovyZJNubFLSkoY8vzu3buZNwHlsyZ0ctnSmR6Asv00p+gkRQulpKMiq2zplOfdrFnA5cvANRJt/79Q2ivKHEHp8JoWCn8g5QSl4mqu/PLLL+1G949cvhz+a9ZgOICvYmMxzs0NXwEYC+DQihVYFx4ul/JB3nCBdi3SJjdJ56WpgE35wgsKClrdh93u7N8RDHmE21AK3o/w5Ck69G63+QhMhuJUT54EhtMjUL7SVdYy+UbzaNeiXMt9KPUJgGOLFiHk22/bJOj5559nAglZvKU5Sw8cOMByyG7durXN+1uq0B5Phpu//oq+s2YhytQUvrm5SD18GPajR7d7DP/WjfeEQtwzN0efzEzE2ttjKAeglKKuziyAcfr6UK2pQaGGBjRralAnFMJdBq27I8ctKi6GqoEBYvT04FlSgitr1sBDJj5b3r4IoK1MXR32VVU4uXgxRvwL8ZhlfD4uOTlh6M2bSDA0RFlDujYvzqU8wtMTAy5fRqypKcxKSjCnId6YDoFUyJIxd+5c0OFu8eLFXSL4ysa90hgIBZeEbxpTa3Gv8s5HR9SjMBPyCnB1dWUfAkwiReiSJUv+keampf5oLdfU1cGqshLJPXsiIDmZVY0zNkZNRQUGVFUhjc9Hfu/e8GySMq9IIICIx4NZXR0i1qxBQDvWZUfwQbYNeq9Sjl4qW7ZsQUxMDHO5nj9//gNxna31G/H66wj48sv7VQhrQMa6fENPD7USCTTFYpTo6cGrGSGFbmvNOthi35zgVXT+PAxaCdHJTUyEaZPc3Do8HryCghAXF8ee/WTVpeLp6cl4IG+5oamJfhwOQ0VqKrTs7Jq9tSglBQYODoh2c4NPbCyOTJmC0bt3P1CXXH7l8VqJ+/VXuJAwSKUNwbulfUnCKqUNy8rKesBVmyyQZDmmMmjQIJznMAw6RaCVk8nNKTrJAk9CLXmtEd/Iiy0qKgppaWnsudehRWrpJs8GadouAFIwP0qvRaFqUqwBad/btm0DIaG3VNqL7n92wQIE/fgjSEU/f/duLJk6FVK/h9T16zH3jz/keubLGy7QEbyUrp+mBg6hUMjAEFvbh93u7N8RDHmE21AK3o/w5Ck69G63+YyMAIq3pDQoCghLXWUtU5S/j2L9eE1N2NbUQEsiwZH58zH6hx/aJIMEAjrckDsaaVqNjY2ZtpU0rwTY097SHk+GuI0bGfBXJKF8Z2Tg1v796DWBkt08WoXSdyXb2sLp7l3ctLbGEC63erpQiB61tUjU1mao5zna2kzw5vN4HZ7+Ssqx8vR0aFtbI8rYGL75+Yh5/314ysRny8vZEj4f2dracCorw8n58zFCjrUlb9vy1iMhLdzNDcOvXsUNExPUlpXBvZoc9oFz/fvD+fp13LCwgG1+PiZ7eDQemOk6pX2ZMmUK8vLy2PeOKK3FIMrGvUr7qqurY8IcKQG6Qxk/fjw8PDyYwEHjnTBhAju4kyXvjz/+kGuIiTo6KBEIYFlejjRra/hzaz1BXx9ltbXwKi/HLYEAJTY2GMBdkzZMipQCLo9z2LvvIvCDD+TqszMrSQ/EFB5AQhe57RJwGD0bmwMpa24sYfPmIZATduqysyEwM2usFq+jgyqKrxaJUK2hgRtfkW3uwdKWdbBF+jmBKHXHDthLY8ybqZx+8SJ6eHs/cGWfpiY84+PZb5aWliABgBQz9F4gpay8JVFNDU5c3H7auXOwHTSo2Vvzrl+HSf/+OO/nh0HnzuHIuHEYfeDAA3XJ3V0er5XLn36KAcuW3b+3DcG7pX3Zr18/3Lx5kynHZF21aU+QtZtKZGQk/P392XdZdOvmCNy0bh1uHjmCTw8ebLREy8vD9tYjJHyK8aZ1a2hoyNzOyaJKc9mhhVtntbdvQ8XevrHpmWPHosfhw/jd3h5R0dEwMTF5oFtyf6dY9LaKouj+p55+GsN378bXlBIxOBj5J04gAMC4BsXRgWHDcCYhAeMdHfHxmTNsTbekbNy4cSMbHyktpEYI+lsWMFheZVBbNFL79OxtKmDTOpTyqKV92O3O/m0R+5hfVwrej/kEy5LX3TZfvY4OeKTd//13oBukhfkPLYVGUpPU1WFYVwfj2locmTULozdv/tfY0B5PhhvffIN+S5bgrL09glJTkbR7NxynED7uo1UK+HzEOTjA4dYtpFhaYjAXx0mu0sYSCVI0NCCsq0O6gQE0qquhXlvb4SjcUo6xXOh9+iDMygqBGRm49M47GPjRRwoztJJS/xgawrWwECdnz8aIZpCSFW5UkRvIZVdFBSd9fDAiKgqxFhZQKSiAC3fIP+fsDMebNxFnbQ2nzEz8+d13CAkJgWze1szMTAY8RZaXFkt9PRKeegoG8+bBbCw5K94vDxODqAiZXVlX1tpCFm+pG6gi7sWUEixXUxMWpaXIJk8VDp0/WVsb+Q1u/oOKi5FIudUtLOB+794D5BHoX5qmJvpUViJ06VIM6QYpiqSCN7mdhoaGNgpNZIUKDw+Xa3rOTpmCIE5xUR4fD20OWI9uTtTSQqmaGtTEYuaCPoTPx6uvvqqwdbDZgXAC0bU1a9C/Fe+B1FOnYN8khCdGTQ2enBJLLiJbqMRSQdbWsqtB3t44y1mLm1bPjIqCJVmQR47EoOPHcTQ4GKOapFCTFW5b81qJWrYMvp9+er+LNgTvlmgjV22KaacYcFlXbS8vL2Y5JqwIaSGXasI/IQG9pRLF44FgQYtzc6EvI4B2hJfJw8xPh9zLrbP80FAYy2CwhDo4YEhKCn57+mlM3bHjAZ51SL8tNHJy1CiMoBBHAHNmz0bs5s0gkZ+CHJxCQvDOsWMsG4ekthZPTJzYqrKxrXABeZVBbdFLngjSoqiiq7ud/dui9XG/rhS8H/cZlqGvu20+kUAAVYkEVd99B412oCb/h6au00i9raoKoUAA6+pqHJ42DWN27uy0vtpquD2eDFc/+gju776Lky4uGBEXh4QdO9CnFctNW2P4t66X8Xi47OKCnomJuGtmBn9OGCnn8aANII3miQRwMzNoVlVBp6oKTpWVnTLc3MuXYerpidO9e2NYSgouLFkC72asbK12TodZcvO2tMTAzEycfPZZjNixo1PG21KjtSUlUNHXx8nAQIwIDcXlHj2gnZMDRxJgKG+0oyPsUlJws2dPeKSmMhC49pTi1FTo9+zJ0sHxZQ7xDxOD2J5xdMU9ssIkgWgRcBQVRQTvu2pquGtgAPOiIhQaGsKbQ+e/o6GBdG1tBOTlIU5VFbVGRnCTRe6XSACBADEGBvAk9P+XX0bQd991Bdmt9kHo3LNnz8aOHTuQkJDQKHj7+voyAUyecnbUKARxgkBeeDhMAsj+dr/c1tBgni4keJPCba6rKwOybK918IHxcAJR9P/+B59169glstY2zU6R9NdfcHzyyQdujRMI4MIJzPLQKFtnzpw5jX9Wbt4MTZJ/ARw1NkZWXl6zzaUePcrCiKKeeQa+u3bhSGAgRoeGPlDXx8dHLq+ViBdeQIAUtKudgje5apOlmBDfO8RVW+p9EBUFex+fRro6wstE0fnp0PrcvqU27+zaBTsZ5UNEr14IuH0bJ0aORDC3/ju07xYaO+nnhxFcCMAbS5fC8fPPIcXSv/zCCxjA5douTk/HE88+yxRqVNpUNtJaIrwCXcp0f7/IqwzqTLq729m/M2l9FNpWCt6Pwix10Bi72+Yj5GTSCd9atAi95Igt7iA2KJuR4UCaUAiRhgYcyspw5MknMXrfvkeKPzFvvQXPtWtxwtsbwRcuIH7LFjg///wjRQMNtobHw3l3d9jGxyPb0BCDsrJQL5GgXiAAwSxlcaBSl62toV1ZCZPSUvTqAGtTc4zKiIiA1eDBLJ94cGwsol58Eb4//qgQT2vLy6Gio4NzDg7wS07GycmTMWIPYci2XlqMU92w4T4QYyuox01bLk9Lg7adHU6FhGD40aO4aGcHk3v3YMcJ2Bfs7WF29y5SHB3hm5jIwi3aU67v3QvXyZPv39rkEN/eGMT2jKMr7iGEYQopaZqTdtWqVc2CcrExEb9p3ih1E61lQpo3N4dpfj7KtbUxkBO0coRCxJuaYmhGBi5qaECHLNsyAIL1FRXgaWsjwtISAZmZODtvHoK4A3JX0N5SH+SGKi2Um5eEVhJeKf79Qxkk/9bGGDp4MIZwOcvTDxxAj3Hk9Hq/ZKiqIsXYGJoiEfQqKmCSmcni6WUtqu2mnxP2IqZNQwCndKU83uSCLFtubNmCfrNnP/DbbQA9FRFaaX/RM+Cpp2BlZ8dSWZGLfHFQEMt+QEj1i3r3xjUu5r8pTYm7dsFp2jREv/QSfDZswFFvb4xqYh3//vvv5fJaCZsyBYHS0AhFaGgyqA511ebm4upvv6H3uHHMm4RixDvCy6Td66MjbiS3e3191lL8N9/AefHixlZP9uqFEbdv45i/P0K49d8RXbbVxmlXVwzjUnbOmToVm3bvRp2mJgSVlYgYPx4BXAjD3YgIPPfWW42eK20pG0u2bYPezJmo+v13aHBenPIqg9oa88Nc725n/4eh5XG4Vyl4Pw6zKCcN3W7zcS+auGnT4NIOS+u/mTtRTpZ3+2pZAgEKjIzQLy8PR8eMwSg54qm6E1HMUrNhA04OG4YRp0/jxk8/oV8XpH7qSB6QgM0TCHDG2xvWsbEo1NODd04OaoqLoWZgwLoiV3QSDM86OkK3tBSWhYWwq6npyGE0tpV27BhsR426fxiKjMS56dPhpyB6cNndu9BpQAQPc3dH4NWrOPXEExguk2quORC+VuNUdXTug04Rwm2/fnLRXXjtGgzd3HBq4kQM//NPRDk4wObWLVhyAvbVHj2gnpODe87OCLp2DcJ2HsBD16zBkOXL74+phTYUjUGUJbA5C6RcDOgulSjGlYQpLv0dQ5q3tYVxbi7Eqqrw4PKqV/P5ON2rF8YkJ+Osri4T6mw4tGsiRZSVBVVLS5xxcMBQqjN9OoIUXJfdhSVNxxE2YAACr1xhP9/atg29pk9vrEL8um5jA42qKpgWFaFnB+17stLztmxh/WRbWcF85EgmCJPbNJ0V6LsU9Vqa57gCgDSpGOFQG4pE/4gHbnG9Enjc668DP/yAGRERWLt2LYsNJ6+eKj4fJhIJ5k+YgB/37292mq7/+CNcFyzAhXfegfeaNTjm4YEQQspuRwkdORJDTpxgd7I0ZTIWSnmbq62tlRs8r602CctBoEIQp0Dkp59iXUNO9F27doEARymzgTRkoS3Br6V+OisXeVt00fXqxESoc8B8Me+8A0+ZsKUTlpYIzsrC8f79MTI2Vp7mOqROaM+ejTgqq/v0wcqEBFRMngytP/5AqLs7hnBpDG/u2oXIsjK5lY1RQUHwDQ1FpL8//DlFgrzKoA4hTKYR2X3Y7c7+HU3sI9aeUvB+xCbsYYbbrTYfWUA0ycEMuDpyJNwVcDPqytyJD8PvR+HeYh4PKfb2GJiaimPDhyOEEObbWdqFqtvOvqS3nZs1C36//orTEydi2F9/4dr69ej/0ksP2WrX3i6uqIBQWxtnAgJgfukSKrW04Jmfj+Jbt6DfuzcbjPTAe8TdHTp5ebDLyUEPzmW6o0d7a98+9Jo0CcfGjEHI4cOInDQJ/nICZ0nHkn/tGozd3HBm8GAMDQ/H6dGjMYxAFLlCQDGKxKmKzMygmpuL+r17wXvqKblIzgoPh0VgIM5Mm4ahO3fiXN++cExIgDEnHN8wNUVtaSnyXF0RfOkSQG6zAgFzGy0sLGRgQ/KADJ383/8wgizyVNopvLdGUHMWSLkY0F0qSRGNyUNDTQ0EkHbR0RFGWVnMm8OVLGLc++DgwIEYd/EijhgbY2BZGUxkhMySGzeg5+qKE56eCI6JwdnJkxEkhxdFd2FDa+OI7NMH/tIUgt9+C2dpTm/a+3w+opycoFFWBuucHFg32fcktNGzl3JGtxZD3LR/SwsL7MrOZi7eV11c4L5+PauydOlSlnOacsiTRZcsdhfeew/e77+PdIEAPTiPEXomiQoLGaiUbGlpvZaOHw/dgwdRumoVdN97j91SKxKBr6aGVFVV9BKJcGPjRvR7Qer0++CIpYBol9euRd9lyxDh7IzgJunm5J3rULIkc2EABQkJ/59arJkGKCc5pQIjzIa3336bCWFUFEUpb02BVpSfDwMurvvkK6/gk337EHDvHgJPnMDtO3fkFvxaol+RXOQd/R7PPXECpiNHsqGde/FF+Ml4T0Xo6SGgtBSnevbE8Fu3/jH81pQbD6NMOGdhAb/sbNbfd/r6WFRcjNKrV6Hr7o5oc3P4cNdiPv8cnqQskrNEDhgA/ytXEObhgcB2KoXk7KqxWks8kt2H3ersryiBj2F9peD9GE5qSyR1p81Xl5YGAZc25JK3Nwa2AKjSHC1dmTvxcV4ekro6lmf5gpsb/GJjcSIwEMFNYuaao19ha2UnMjFiyhQEkJZ6zhwM2bQJ7HDWjpzTnTjENpuWWodPBwfDICqKgbr0Ky1FVnQ0LHx9QZHc91VUwGE/P2hlZKBPejrM2hlf2daAkjZtguPcuTg2YwZCtm1DxJgxCFDQEyIzPByWJPSOG4ehBw/izIgRGMpZmKh/cqFUJE41h8+HWX09Cj75BEZSNOI2CEnbvx+2EyfizIsvYmhD6piI/v3R//p16HLCcaK+Pkpqa1Hs7o6RkZG4Hh2N/y1diqKiokZ3YRIq1q1bBwL+a6kcnzwZI/fuvX/5IQRv2bhXaV+yFsi25q1bXqc1KkVIJnAgGxuWDizc1RV6GRnQrqpiQGlVycnQcHTE4VGjMObIEfxtZ4cRd+9CU8b9PycsDGZDhuDYsGEIOX0aZydMQFAL1tFuyYtWBhVtawsfDlAx9sMP4SbjQSHh83HKwwPqRUVwvHuXpVKTLRcuXMCXPj7I0NFBeGmp3KRPGjMG3x05AssGITLM1RWBXI7ld955hwE68tauRd7TT2PNrl049+qr8Pv6a9zU0EBfLvUXRWmXT5kCSgUlz3oNt7VloJGxo0bB7cgRdktJZib0rKwwS1UVW0QiXP3yS7i3kJs9evly+KxZg+vffw+bl17Cxd69MaJJujl5iaf1N5hzNb4XGgprGcCvpm0QKjmly1RTU2MCuLm5OdasWdMmrkFTgag1BVra5cuw9fRkXR95+mnoHzqEQeXlOLxsGcY0k1ddXjql9ZrLRU7o72RJf+uttx6Yv9ZyZyvaL9W/vXkzenIx/eTi/7O6OsuGMHnyZFxVV4d7Q1aVzwwMsNHISCHlhiLKhKbjvmRggIGU2gzAngZg0ykSCSg8SqStjTR1dXxRXY2fCQfkjTcwSArCJwfxF+zs4J2WhnP29vC7TcEYHVdaUgBZWFgwFHXZ0vS90Z3O/h3HkUe3JaXg/ejOncIj706bL//0aRhzubsv9OkD75s35aanK3Mnyj2oR7BiRUEBtIyNERYYiMCwMJz09W0EHGmNHEWtlQ/LmtY08OFjx2Lw4cOIWrkSvqtX33dDbAcC98OO8WHuzzp/HhZ+fjg7dSo0T52CQUUFHKqqkHrkCOzHjMFdPh82nAByJDgYgjt34JeSAu12xiS3Ndbrn34K12XLcPbttxH08ccIHzYMg0+dauu2B66nHjgA+wbBiLkDb9+Os4GBCJJR6pBwq0icahGPB7KrJc6dCycpKFIbI0r86Sc4vfgiIt5+GwFr1iBs4ED4xMQwQEdyoU3V0kKmmhoqPDww8vRpDPb1xaatW+Hg4NDYMiHWUiovAv5rqZwYMgTB0uskFPHJjqt4sbKyaox7lb2bLJAXL15UvMFOvENurwCyHFlYsJHUk4Wxf3/wNDVx1McH+pmZsMzMhE1tLdL/+AM9pkzB0blzMernn7HPzQ1PkSBIruacYHd3/37YTJyI41OmYOSePQgNCcGQo0c7kUrFmpabJ800e9ncHAO4/NyX3nwTA9euvc+zqirGrxP+/szjwzUlBYZN9v3RX3/FqFmzUNBgvTNSQPFzc98+9J00ifUTQfnUZSyOf44YgSdPncLpvn0xLD4eEXPnImDTJsRpa8OlvByELt+7vh6f/PADbJrk925pvV62tMSArCzEuLjAkxN6s2JjYeHuDk81NcTU1ODC8uXwbiEuPvKVV+D/7bdI/O03GE6fjmtWVhgug/SsyGyd69WrUTBqKxMGKfpl9/+KFSuQnZ2NpKQkBrrVdN5JgUZK7fytW1Fhbs4A4dpSoMX//TecuRRsB4YOhVVUFMtnf2zyZIR0gFdHc7nISWEiteLL8q6t3NmK8Jnqxq1ZAxdOkXR48GCM5ZD+iSepAgHsJRL0FwhwNjdXIeVGc8oEcsvfvHlzmzm4r2tpwbWyElsJuBSAKaXr27QJZc8/zzxACNHkOimkZs5EoAyGQ0u003wf9fOD38WL0K+vxyUjIwzMz1eUVa3Wb0kBRAohCg9pmgNddh92p7N/hzLlEW1MKXg/ohPXnmF3p82X8vPP6M25lF3s0QNeTdLGtEYfaWTbyp3YHv781+7JS0iASd++CB07FoMOHULEgAEYFhPTJhsUtVa22SBXoT2W9LCgIASGhuLapk3oP2cOzi1cCD+p26+8Hf/L9VL27kXvyZMRvmAB+AcOwIZzJ03Yvh19ZszAFU1NeHAI5seefBJ1qakYQzFonGt0Rw8/ZtkyeH76KS5s2ADvl15ChL8/AhQEvkncsQNO06cjbNEiBHz3HSJ9fTGYQ5FtbbwUD0eHWYpvbCz19aD4X3UAsaNHw03GZb21tq5++CHcV6zAxa++gteSJQgfMgSDw8JQUVQELX19ZKqqIsnICKIBAzDy8GEMGjAA55tZ/y3ucElmAAAgAElEQVQp+qR9ywL11OTkQM3U9IFhySuQzZgxozHuVbYBskCSha07FEXDfEQXL0KVy/9c/OuvUPX3h2bv3jg2ahS0795F3/h4GNbXI3zhQgz+4QeW1sdt1ixsGz0aM44eRf2lS+BxlsDkLVvgMHs2Ti5YwHLChw0ZgsCzZ/91tijKk+YGHNdg8bMsLmbKpagFC+D7/fesWmV6OjQbcp2fCA4GPysLATduQI0Eb6n7PoBDr76KsV9TRmLFPC4urV+PgVw2kShzc+ydPp0BwlHZ7+KCJ+LjccbKCkPT0xE2dSoC9+xpFLzvamhgeVUVpm/dipAZMx4gqaX1mqCnhz6lpbhhZoZ+nCuvNE3ZbDMzbM7JQeSCBfDnaG/Kp7DZsxG4ZQtSDx+G6pNPIllXF0EcboC0rrzuxxctLODFjaGt8CTKbb19+3aQZVFaKD6d6CRhqKmHDCHbr1+5EqbScAFujbamQIv5/nt4ciFSB9zcYJuQgP41NTg6eDBGtaL0k3fxN5eLnFD3n3nmGSxZsuSBZuTNnS1v35dfeQUDOPDc32xtMTAtDZT9faxYjAqhEHoNubM9+XzEyHhyNFVuNNdXc8oEyql+9uxZFHPW7JbGeEtVFbZiMUshRnkZKjU0oLdsGUrff595RFHyyJQGRPKzY8ci6ODBNkklEDabwYMb6zHPEEWyjmzdSqkhAGvrFvtqSQH05++/I6aqCj3CwiAYNKjxftl92J3O/m0y8z9QQSl4/wcmWUpid9p8V1esgDun2b5saIgBMui18kxJW7kT5Wnjv17nbng4bAIDET5tGtx++w1X+vXDEAKvaqMoaq1sqz3p9fZY0iM9POB+9SryzpyB3dChiHj2WQR0cdoqeelrqd719evh+vLLiF6xAtW7dqFfSgqMJBLErFkDz+XLccbWFkM5y85hUlYlJGBMeDgk+fngGxk9bPf/uD/6xRfh89NPiP/rL9hPnIjLnp7wpxhoBQpDr12yBJGrVsH5/feR0K8fBrW2tlJTgalT4XzpEsgBMKmsrNGFVVJSAj6HinvZywsDLlyQayTRr7wCn2+/RdzOnXCZNg0RTz6JgL/+QsaVK7Byd0ehQIArNjYQN+TdHbVnD1a//DLO3LiBcePGsbhVWuek4CM3vjdaCV+ItLWFP+cmnHfpEkw4QbEjBDK5CO3CSoqG+WT9+issuJjY5DfegGZICKxGjMDpmTOhnpICn3PnsHrFCmh88AHeILmxuBgqenrY9uqrmPH11yhavx4GnECS8Nln6PPmmzjzwQfwXbECV728MEjOtdCZLFKUJ82N5Y6mJqpEIvStq0PkjBnwp4M4gSpevAgjb2+cIMt0g2AaHBGBupISCGTAwA489RTG//knq19dVQV1dVJRtV1CV6zAEOk7WF8frzeASp05c4bdeMrMDMNzcxGlowPf0lKEjRqFwGPH8LOtLealpeGymRmz0Ef++iv8Z85suzMOnd1KLGZ52G0rKEIcuPn77+j7zDMIGzMGgYcPI3TqVAz5/fdm2zszdiyGHj6Mwps3keftzZ4TPk1c6+V1P441MIAbJ5zFfPABPN99t0Ua6DlAVkVNDpNGWpHyde/cufO+hwxZNxsQsJOdnREweDC2LVyIke+/f78q54XQmgLt3KpV8Fu9mlU/3ID47pSWhl4EdPcQcextTUpnvceb9hv97LPw+e039vMhoZAJ3Gzur15FX3d39t2Px8PejIx/KDeWL18OctlvrjSnTCC8gy1btjAvpdZKNnklCQR4srYWf9M+69kTfW7dAgnkvcRijKWxkuA9aBBsd+zAu+++y4D0yC2/b9++rOmXX36ZhSFRifnsM3i++WZjlwwLoY1QMKknX31WFniWlhAbGEDIAU02N/aWFEDL33oLxKG4efPg0kKWh+509m9rXf4XrisF7//CLHM0dqfNd272bPht2YI7AgGqVVVZnJ+ydC0H4nbsgMv06Tj/8suw27ABd+zsMKgZgJM2R0UWGPpwqKxt1m+hQnss6RRTZX7vHtSuX4eZiwvCx4/H4L/pVfrolEsrV2LgBx/g6nffoejnn1m8PVm1IhYuRMCPP+Jkg0JhBHcgPvbhh5BER2P0gQMou34dOnIifCvCjXNTpsDvjz9wNzoaAh8fZDk5YWBCgiJNIOb11+H55Ze4smkTDOfNQ661Nbzu3Gm5jXfeAT7+uPH6xfPn4eXry/4ujI6GIff9Su/e8Ggh3VDTxiOmT2dKmLQzZ2AbFISo+fPhu3EjEvfvh1NICKCujgOuruAPGoSxGzci+48/UNa/PxM+CgoKYGRkxHKwOjo6tkr7FSOjRmTuVFJWcC6jDyuQkbAvtUAqxPxOrKxomM/N5cvRl7PWX544EVoTJsBpzhxELF3KUM37bN0KSjR0QkUFjtbW0OPiIg/u3Ilxzz2HO88/DzsOefvqokVwX7cOF3fsgNVzzyHD0RFeHCBZJ5LcZtOK8qS5BvNVVHBTSwuDS0sRMWECArjY9dvbt6PnjBk4vXgxJHfvYsRff6Hw6lWG1i8th319MYbDSLl37RqsXV3bHDNVODlvHkb88guK+Hxkqalhd4Pg8B4HenZZQwMDqquRLBDAobYW4V5e8Ll0CTs3bcIPc+ZgybhxeObgQZz64gsMf+011l9b67WCx2OI6ASup8NZNy999RUGvvYaol9+meURPztyZGM+86ZEnPb2RhCFXIhEuNajB4QlJXBpklJRXvfjFE1NaFdXw7y+HlFLlsD3q6/k4pksnbLzXv/qq+CR18HBgwj4+GN84uTEXPOplOTkQK+JF0zTzs7On4+gjRvZz0f19TGkuJilWAu1soJNeHibgp/cg5dWJGUAWVitrIBOVlSfCwmB3/HjKODxUFtfDzNuDAfXrsU4Dq8jgceDTXn5P5QbKSkp6M0BjCpMYws31NXWok4oRKKJCXrk5YEybl/39oZ7dDTiNTTgXF0NAp3l1dcj1tkZHzQg75O3AgnepAhYuXIlAx6UXWth06cjUIaPDJeFU7i05cmX/OOPcFiw4P5oWwkVaUkBtFdHB5PKy3HJ3x8DIyKa3Yfd6ezfUfP4KLejFLwf5dlTcOzdafNRXHFAeDgizc3RJzcXJk0AYxQhjeKvSMvvzbk0KnLvf7nu+XffxaCPPsKNdevAX7oUNdra8OBy6irEF8phTAdgismUcYFUqA2AWRgViful9mMJkKWqCvbp6dA2MkLEkCEI6Abup4rQHjl1Kvz37MGdsDCkfPABRpw4gZKkJMS89hqGETDZsmUYysV8Xj5+HHm7diFk0ybk7N8PswkTFOlKrrrRgYHwDA9HSXY2Msn1zdAQrpxbplwNUEocKU0NB4GK4cNRq6MDt1bWVtVzz0FDJqXg0bVrMYqzIMiC81wzNUV/Lha2rbGEBgVhSGgoyrKzoWNmhpiVK+H5wQeI+e479AsOhlqfPtg/ciS0G7w+hr/7LpI/+wwOS5c+2CwdhNpY08mamiwmn8qNDRvQb+FC9v1hBTLZg11btHbVdUXDfC4//TQG7N7Nhnd+wADwAwPh8/XXuLpxI1wpF/Uzz+A7FxcsInTqFSsAzuqXmZmJQisr6JLgkZ7O7o9+4gn4/P03Uq9eRbWXF2qMjOCeldVVpLfYj6I8+UdD9fUQEYCaoyNGJiXh/KBBCDh3jlWLXbUKbqtXI/qbb1AVF8eEszt//gm7iRMbm2GI0OQxQnmSt26FcxPX75YGfmLcOAQfOoQEXV2oVlWhp0jEqlKsaKZAAKv6epQD0BCLEe3khJ537sC8rg7kyaF79ixsFi3C0eXLMYqzmre2XmsKCqBmbMwscyxpFhe7H9rgZTJk/XokUwq1GTMQ5uWFoBa8GEJ790a/27eZN1C0vT0s796FdZNzg7zux3kCAdI1NOBRUYGIWbMQsHmz3OtISqfsvPM/+YR5IP1lbY0JixbBu+G5PYRzEZ87aRJ+aSMrxKlx4zD80CEUCoVIE4vhwY3mop4e3vHyalPwk3vw0oqk4OrV6/5fMrgU27//HrzaWjwng6qvcNtNbjjn48O8lFJlgPmoyq7gYDxz4gRK+XwIJBIGpChNX9eWEqfZMZElnehycmp1yAW3bsGod2+c8/JiMdlUYp54Ap5//YUoY2P4FhQgvgFgTUcsRrqlJd7p1avRE6SsrAzkzt40lvwspxS6qKuLKmNjBN6+jcq8PGgaGzPvqdYyeJDH4WAutKq+tpalFpWnSHl0T1WVZTq4am0N97t3mwX9605nf3loe9zrKAXvR2CGCcRj8eLFDIFSS0sL06ZNwyeffAINDdKJyl+kmy/yyBH0sLGBqqYmtHR1wVdVhbqGBqqqqthLl1yq6AFIwBudVaLMzGBVUIAEd3cMj4kBr7ISPAXpobHl5ORgurk5S0uzq5nUJp01/seh3dNTp2LYnj3IDA1F+sSJMKiuhoOingd0gJK6PcbHA5wbVofwR6r9bUnwqa9HnlCIm8bGCMjMBOUkz3ZwgGc7kW47ZMztaCTS2RnuN29CraYGl1atgu8nn+DS2rUo+OknBKakoCIjA2VWVrjWYAEZWlKCcxs3YtQbb+D222+jZyfE/sbY28MoLQ22dXWIMjCAuVgMe841VF7ywgcNgl9UFGqKinDDzg6GYjF6t9JGhrk5rGQE6r+nTsUEzuX0+ltvwZUQlsn6ra0NJ5nczq2NJ7xXLzinprKDOpX0P/9Ej6eewpEZM+Di5webhQtx9JVXYOTnB6+nn8a1F19Ef5lUN2RZq+nfH/y+fSHkXHmb669YIGBjc6DwgFWr4MlZDR9WICPro9QCKS/fu6KeImE+0QMHol9MDHLJqmpqiixdXUxKSkJFVha06KBMmS1ofsiNlw7NZlJ7GPCLtTXmpqfj9FdfwW3GDKQFBsIuPh4aFRW4bm4Og/p6OMi5FjqbL015Qm7IR44cwQsvvAATLk1US2OQFBSAb2yMIwEBGBAZidsNMfCDuGdY9IQJ8DlwAMkREcg7fx5+b7yB6ytXwlXqxkwKCUND+BQVseYvL1uGAXKiYJ/y8cHwCxcQ5egIh6QkVM2fD4Pdu1F37hy0+/Zl71Qq2fHxyAgMhHZZGZw4C3P233/D/IkncGzuXIRwYIetrde0o0dhO3o0ooVC+IjFqI6NhXr//jgxfjyCDx5E0bVrqPLwwB1LS/hxYRuy/KqursYVfX0YqqjAqbwc53x9WSYUMYfXIK0rl/sxIVgLBAiztcWwtDSEjxuHwQcOyL1EZOmUznvqwoUMJKyHqyvGXLsGWfA2z169EJNCEcMtl9NeXhhy6RJSzM3hkJ3dyPtEVVUs8PNrU/CTe/BcxdItW6A7e/b9v2jf2dujtLiYuTtTki278nJ21qSyYcMGEAI6heC0p1xycIDZrVvIMTTEQJmQwsPGxhiTn484IyO4FBTcR7jn4ujbo3QsGzcOOocOoT4tDTwbmxaHmnLkCHqPGYNzM2fCjwvpuLlsGfp+8gnC+vVDYFwcTtnZwTInB5Wamljk6MjA2qQhHCKRiAnfsrHkUVZWsMjNZXHjoU8/jSG7dyM9IgI91NX/P4PHqFEPjGns8OE4dOIEwgYMQCCXwzzr8mVYeEjVLq1zm3h0/OBB8LW1QaL6LU1N9Gp4xza3D5WCd3tWbufdoxS8O4+3HdIygUT069cPtra2IMCJ3NxcvPbaayxnJwF+KFIaNx+AHk1upAQlpI2W/k/f6VPP54PSmYjr69n/dIStrquDiro6cxuiOtJrGtra4KmosN+orqi+HnwVFSbY099lhNBaVwd+Xh6GiEQ47+qKQm9vjP3lFyTa2aHC3R055eXQMDaGSCCAUFMTGjo60NDSQnF5OXSNjFAlFrO/eUIhay9uzx4suHKFUbN5wgT4zZ8PVR0d1PL50NbTg0BNDUINDfZRpf/V1VFMMXLUvlAIVVVVVFRUQEdHp1HbqghPH9W6GenpuO3uDtfCQujV1OCkiwsCkpOhXlwMnh7BnchZCOhqLEVEASBXuRZysMrZ2oPVZs5E/YUL4JEbZZMxkeaZrIuD3noLh4KDMfb4cUTq6sKxuhomFPcnZ5xju8bVQTddvnwZxzdtwovr1yPJzAy+2dkoTUuD0M4OSSoqMKitZVY9h/x8/LBhA3gNwsv8BQuQcOMGdMid1NYWVnRo6kAFWWFmJuptbHCdgIsKC7Hf0RFjk5PBy8mBoA13SSlbSHl3Q08PqjU1cKqpwSEHB4xISYEaCd5N4iTZPTU1EGtoII0ULXw+ekskOOzmdh9ADsClceMw8NAhnCWhu7oaFnLkL6c83NlmZqgnFGZOKCGEaJGmJiLI5VVDA/0rK3H3wgXomJtD3cYGeU5O6CPjUl+0Zw8Mpk69TxbFgzazL2oLCqBibIwTFhYIzsoCyysvYz1TREjtoGXVrZq5YmwMrdJSSPT1YZKXx/LRSywsYJeZeX+cBKRFCpZVq+67vsqU0N274fb000ypQY7AHwG4ZmCAIYWFOOjggJEpKRAq+rzqIu58+uabqPrsM/AWLMDKFsDCpEPJ+ftvmD3xBA6RW/2uXagjxOX8fMRevgzh4MFQF4lgLxIhKzkZJn36IN7TE+4ymAt3VVSQq66OgRUVuDRpEga2YV2V9htmbY0+WVlIGj4cAcePN3ImZuRIeB4/jjh9fbgUFyP+t9+gP306Us3M4J+RweoV3bwJA2dnnB4zBsNaSjVIngqWlmxPX1mzBh7Ll+NPBwc8mZyM9G3b0GP6dBx1csLQpCT2rLhmYgLV2lr0baKgoz00ZuhQXM7IQKq3N/pHR+PcvHnwo3PD9u1weu45hWa15Pp16PXvj6MhISxu/YqHB/wfIudyfWEheBzWRpKxMRzz8nBdXx99SkpYasgZ7u7Yxp1RWhoo4UT0TE9HrpMT3LgML7fV1aFfXY1xgwa1KfgpxADC0HjmGThyis3ENWvw7ddfw2v8eMz65Zf7z9w//sDASZOQlpaGWXZ2oCR1F+ns1o53TYKBAcpFIpSZmWFoairy1NRgUlMD8mOhc2hkUBD8z55FysGD6M2dJRRWOnLvDhr7rTfeQK9WUoAR7oj/6tW48fnncF66lCk5CkJDYRQYiCtE/0svofzLL6GxYgV0q6uRf/Ik7O3tH4g/bxpLnqShgVJNTaZYaEy9t20bDDZtgtqZM6gaORKWx441TlNhZCQMAwJwa/BgFF6/Di8ObyD666/h88orck0n8WiWnx/sQkLYmbuG3mvkvdCMoUIpeMvF0i6rpBS8u4zV7euI0DNXr17NHoDGxsasEQL0eO655xAfH98I9CBP69LNt8XbG72NjJgQLCH3sro6VJWVQZXPR51IhLqaGlSUlMBARweiykqQ+4uGUAhxdTX4dXWoqayEmkDA6tM1IcXu1NRAjc9n3+vr6tgLR4XHg6C+nmnj6G8h1ScrNVmu7OzgExoKnr4+O5gPzc6GoTxENFOnSEsLOhUV913Y5ChNFQwPKB1o/DweaiSSRqUDgXBUisUQSyRQ09SERCBg7kD0P1NGEF08Hvub/qeDBv1GghLVqxcIUMcpIapFIvabipoaq0tKCeKJiqoqU2TQvdJrdI86pdLh8VjfJWVlMKTDibo6a4OUHFS/oqoKdTweRGIxNHV0GAdojihGqby4mM0xzVNFaSnKSZuenQ1PsRiUNTTUxwdDoqJw4M03Mfazz3BbUxNxDg7QdXODqrExDK2tUSEWQ6iqyvrVNzAA0UBjqi4vh9HmzTC8cAH8+noUeXmh7sMPUU9CL0dXVXU1xLW1sLSyYuPX0NRk9NL9xJ/aujqIamuhp68PUXk5cxsXFRWh5ORJOHIIuzdGj0bu4MFIv3MHlRkZyI+NhXlmJp6RSFgslvjiRdh7emLX88/jma1bccvSEnmjRzOLiqaVFXq6uUGFrPLEL3Ljoo/s93YcJlgsltQiL/u/zHfSjAvpUJyTg8KMDFw8cwbZycnQqqrC3chImDUcJJ8nV04eDxXHjsEkOJjNHcXF9vr4Y/DogLtxI0znzXtgVZNg+03Pnlhy5w6S7O1RMHYsTIYMgV6/ftCzs2Pz1FahNkpLS3Hv3j3k37uHs3v2oDQiAhNSUhBUX49IQuz96CMc/uADjFq5Erfp8PXKKzAZOpRZdSSkdCO3OB6PpdbJyszElTNncOfMGTidP4/Z5eU4NXQohp8+jb0vvYRJ33+PeC8vmKxbByNyOZbBA5AKuNvGjsW0v/5CprY2MjU04EsCMwnxPXpALzMT8X5+CDl3DhW3b0PL3r6RRKKFFJLXr19HasMh/cLWrXCJigJh9SbOmwcnGcCZ/AZAHOP169m9hKwdwuUT3mFpieeysnDjueegMm8eszqkjxkDF87lN+3zz2H7+uv/YGvapk2wnTsXB557DqN27MDFgQMb3RebVqZxkocOPccpXrCtQnXff/99zJw5E4RA3B0L5WLft28fi0Unyy4dSLNIAREZiVvHjuHlzZtxvU8feE6eDLUPP2SWRt6xYxBwqSTboilr505ov/ACdCorUaSmBgJrc25wX//7rbcwYe1aXHFwgPGPP8I6KKjNkIC2+uqo6+V37iDB0REDxWKc1NbGiFas8iXFxYibNAl+p0/j3KZNEK1dC9/ERCxXVUWgSIQnSAiaNw8Df/qJeaMdMjPDyLw8xC1aBNOFCyEoL4e5tzeODRoEn/PnkeLmhoGcworooXMDectNmjSJrSNpqa6sRLauLopIYJw7Fx4y+ArSOucbLNqD9u/HAYEA4+vqEP7EExj811/ssrimBqXq6siwtkZ/An7k8XB39myoHz8Ow/BwiCIioEEpzkaOhPHRowgLDkbgyZONKQp3Dh+OFFtbzNy0CdXGxujToIw/Q+8issBv2ACnqVORmJiI/fv3Y9cPP+DDykrMqK1F/fHj4AUH486FCzDz8UGKpSX6xMdD2IKyuIpcwA8cgBaNm/M8uLl2Lfq+9RbCV6yA6ccfQ0ShNHKGrzDiyXpNYREEuDhgALJ++w0Wzz6LQnqPU/x6VRVK1NURb24Ov6wsnPHzw9DIyFaXVwoZBLS0UO3tjQAuRR4LlTl7Foe2bsWAESNaFfwUXbs3SJC8cwcEzVnI5/8jRd3J6dMxYts2HFywAON+/BElZBiPiICHv79CXdHZo1xdHdFWVqj18GDYJFednWGfkAA9iYQp4hI/+ggDli8HwzqR8eRQpKMHcECcneFBoSvNFNpD+7y8MCkmBqVxcUg/cgTi69fhxuFIyN5ypn9/BggqqKhgRqCWSmVBAYTGxoh0d0fQlSuI++UXuDTgJ0S89BIcfv4ZZiIRe59ZyngTRnl5wZdTnlEawEptbViXl+Po009jlGxGjzaYEPX228xDLtrICD7kNUDeRHS+OHMGPosXNxqTlIK3Iqup8+sqBe/O5/FD9TBkyBDo6+uzF5C01NTUQE9PDx999BFeb+Yw2FKHXbX56OFGH6l2lL5LY3daehjeSU1F1q1bEFRXw1RLC/yaGuTn5DBBngS8wrw8pigwNTRkQqW4qooJlL379YPV1KkovnMHdzZtwl16oKuqsvuYUoFe1vQRi9mnrLgYOuTSTr/V1TElg5aaGvufjsL0P93HFBAiEavLI1fI2lqIKiqgqa7OrpOSgYRN+jDlAvchYZf9TrIl0S2RsO+khGDXAKaIkP2fvneeU/8/OV4tECDf2BjlEyfCad06JsCTkPjbhAkYRAKvgvH2lIBGu0Ghcj+yteMKwXlRjOHAZpokj4j0/v2htW4dzPz8WI3Kykps9/bGk3FxLE2IvIW8OEj5QgoBEnap0P+y3+m3jp6jOlI8DB4M1S+/BG/AgAeHKz2sc4qUprTcjI/HgcGDMZ88FppcrAYgon1Dii9u3HxSKHHKAkYXrU0u3lL2SFGlqoqsRYvQ8/PPWavk4rnJ3R3zEhOhyvVD/CLwmBrufto3pFiTXqdqNwYORN/wcAjU1dm8/G1vj2e41D8UDV1OChOK7ePxYFxbyywq6WFhcG1InyM9lOSSV4pEwlyKD/fpA4P58zHo1VfZvfeEQlTTc4aUfWIxy2lOIF3EC+k85QYHw5RcSNXUHuQQoauTckImX3fEkSOoHT8eQdzaJx6S+mKfUIjhYjHj5z0DA4jJe4f2cXU1hCIRHOk5RAbx2Fhk+ftjQHk5EiwsUKWjAwnVVVW9z2uJBEX5+ajOyABfSwumrq5s7CpCIWhuqFTRM0hFheUAprlKTEpCfkEBVIVCuLq6QkdXlymr6JqA1o5EAjEp1errmVuodF75AgETjEjpQ20RmBDNAY2DcvfW0bOQFCdiMVRIEKZ5FArv/04KR4kEEu4j/bu5/0nxkk3YCrR/OCUiWS4J89eKwIVIQOPxUPz33/eVSvv2AQQK5uws79a8X4/cmwlQj2JSuQMwhUXtcXTETC7+m9ZPvkDAPBoqhUIm4JMiVKilBdpnZSIRtPT0IFFRYX/T+4h4STwihSX9T/ygQnNAHlHEGykf1NTVGR+pHikX6H7pe072f0l1NXoQ+FltLXLV1NCjpgaXbW1Ro6PD9gi9tyiEobqkBFoVFSycg5zrI7W0mLWsKDISGuPGQa+qCmKhEJULF0KPQLu4NXLx0CGoTpgAN5lc3vTsuvnbbxDNnctSGN2wskINR3daZibyS0vZc8CGPDtor1ZXw66khOXhvvbyy3B67z1kWVoy0DNy9R5/+zbSVVRgVlCAfBsbWJSUIF1fHwY3bkCLwLi4sq9PHzyVmIh7PB7KtbXRl3tm3VNXh4FIxPYklataWizMJE1DAw4ZGag0MYE+t88oqjz7559hM3cu7p0/Dy0/P7aHkygMHGDp1ey45wsIxO2LLxr7PxAYiPHh4Uz5SpZmkakpBIQOTfOqro76/Hz4cQLvTUND3KV847m5cElNhXZdHSqTknApJAQTUlNxzd8fYgcH5tVDSndNPT1U19YyxTe9u8kAoS4UoryoCGvxxVgAACAASURBVHa//gojUqZqayPpiSegcewYXPLz8ZubG2bExiLJ3ByO2dk4Nm0abPbuhbVIhFxHR5QMHYoKei7Q3qqvh7i0FGVpadBJS0NwcjJOjxgBg9mz0fe55xBpYwPdxYvhtXQpi2fXmz0bfENDqBoaQkVDgynq6UMKeNrvpKSvqa2FgaFho1K0troaNRUV988zNTXsDENnqILkZDh8+CHOcd5UPWV2I4HdGtbV4Y6+Pupmz4b9N99An5vHo/37w534zz1Pad/QWbSGzkMUrsjjoSgrC1VFRchOS0NJYiLMYmMRkp2NU/PmwW3lSsTOmoWB33yDW5MmYUBSEqJtbeEUHg5NGxvE6+qiZuFCaNnZobyqihl3jFxcUCqRgLzDevbsyRDkSWFM4ZC07+gsWHbvHso//xwBxHvKZc/n4/batag3MGA0305MRG5aGqrJOyw+Hi+KRCgxNoZtG3g2UW+8Ad/PP0dkjx7MYi20tmZenBlXrzI6Lb29ISoshGZoKCZlZuLyqlUY8N57EJeXs/1O7wxKlUhjInjOwzNnQsPdHRUJCQyrgZ6b0hI9fTpctm/HDV1dmJKy0doaQq4NMk6QYUv6oXOppKwM1Rs2sNSa9B6++tprCPjySxxycoLLrVuwq63FXyNGwGXNGuYpejcjA96BgUzR3qNHU39XxR7HytoPzwGl4P3wPOzUFkxNTTFnzhwW0y1bXFxcWOzIz1x8lTyD6CrBW56x/BfrSBUQ9LKi9CR0gCNAMQLfKCN3zNpapmwwNjRkcY/lJSUoIcAxLS0ISVjOzYWWujrIWiERi1n9WpEIqnTApIMiHc7oJUkCFnkkaGjA1MKCxfLzhcL7ll46uJJ1oBXAqLtxcai9cwfF6emoyMkBn1BASRig8RYUQJsUI+QySsoNCws4TJ7MlAqEyq1WXIx68j7gDu5kAaBDe1VlJRMWRNXVzL2ffqdDLB2g6TdtTU0WHkCHTVUDAwaq1OPZZ2FgaorsXbtQUVgICxsbGPXqBRWK36IDYFOBiltUuRkZSD58GLzbt1GemYlyAmCqrGQeF/Tykh5ASBhiHzpIk8BBwkhdHbS0tVFTXc0O5HS4IaUEHbQFKiqorKpiIQpUl3ARyJpPCqY6iYQd1mvEYkYf8YsEJapH1nyBlhbUTUygaWYGSxcXaJPwQYIEeTS0s1DbqYmJKD59GrlRUdDIz4c4Lw81lJKpthYkbrJQkQbE4YrKSiZQ0IcOaiRIaNKYSGAzNoZF//4w8PICSInRRNinfi4dPYqCvXuhkZGBqsxM5hJKBy0K5aCwD1UtLWjZ2MCK8jSTNVPGIk3kET/O/fgjqg4dglZ2NnjkTsrxuFJHB4IXXkAIp0QkUJrQJ5+EemoqBJqaKLKxwfDdu6FlaIhjixdD9e+/mYeLBqf8qiXvCl1d6NvaQocOrJ6eUCG35TbQyJuyPSc7G4QiLT59GpTiheL7+mzYgORNm2CwdSu0ad9Sihgej6Hi1qmqIs/QEKJZsxg4W8KJE7gzYwZsCwoYarM6rRFOoUNzQB8RzXdVFTvcyipypEoe6Zg6QslD/ZEySfqhttv7N9EsVUyRp47Ua4U8doT6+qik8B2iUVUVxVZW0HJygrGXF4SUBktGwdHOpd7sbbSmordtQ9W+fVBJSoJaWRkEdCgnAZmUE8Rj+i6jGJIqiNj4m5Smv7WnDgnBKUIh8hpCYLzmz0e8iwucS8heCJBYX0veYST4q6kxKxcJi/WDB6Pne+9BRxrKQc+oe/fuu2k3Y2kjz6X4L75AZVgYJJWV4I8diyErVzK0/sw5c2BbUgIdiaRRGaYuEDBvqmpS1PD5qFFRYdZu3sSJ8CBMA1KC1dSw55e4shJxH34I62eegQkpA8vLAYo379//H1krykpKcGbGDOhHRjJFQVbPniiwsYHvqVMoEQqR/vrrMNmxA2ZZWSgwMYHZTz/BJjgYVZcuIefrr6Gqpwed55+Hjgwo6p2TJ1FGbr63b0NbIGDvAXXKPEDriFOwSqeNnkvh778P/pYtsMrKgpFIxBCqZct5dXXc690bE27cYHuRFIbJamrI+N//MOaLL3AjIgJZo0ZhSEXFA4rD1tYpKeW+BTCfU/TRnB8aOBC++/Yhpm9feFVU4KqhITzj4xH7008Qr1gBl4ZILMsWGiXlw1UrK/S9eBE6FhZIuHABti4u7D0SRq74HHBeR+4dUmqk/PILsq9cge7u3bD44gvcWb0amgsWoGTfPoRwCot7xK/162G4fDnc28iN3dz4yLuQMAS8rlxh3oLSIsrJwZ1169DjpZegaWGB0xMnMiR/WeWtovSGGRpCsHgxvN97j639lkpm794w3rsXqrSmWykk4BIAm++1a622R00kODjAMS7u/jmLFGkLF8Ju40ZkGRnBfO9eqAUFwURGWVYgEKB43z7kvvgitPl8OF6/jpipU5nniyLlPJ1B3ngD3q+/jtumpnCuqWEKK+K0LERbGqfAUgreinC38+oqBe/O422HtEyH/A8++IDlD5QthJhLQjm5+bVUSDNIH2khF0BC/lZuvg6ZGmUjSg4oOaDkQKscIOGABERStknBiug3wpUgQYcs0aR8okM2eTHR85pAM+lvuk4fqk/1yPuArGWk6KF8zdLrVJcUd0yZRtY58tTR0Wnsg94hVMrLyxlAEFm9qR1S+NG4qE+xWAxDsqZx1t72xHJ256UgtUozSzd5g3AgYcR74lU+5WEG6Zx0UFJSwnjIFGmcQEr8It7RNZofKfgo8Ynq0f/S78TXph5e1D/xmOZKWTqHAzRHWaSso9A5mlc9PRgTeB+l9MrPZ8KTJrnWN1E6k+dGOilpb91CSWoq85arKS+/HzYnFjOlBSmxK6qroWdoCEMvL9i6uyPuwgWolZfD2s0NGpwbO81zXl4eC7uQrgH6LfvePWSHhUGjro55+dBaUSewuN69odqjBwStKGAvnzqFbLIKSySoo9Ax8sojZTgpASUSprgm7ww18pAoL2ft05rkkQKYlIQU+9uAji4iJbOKCvSsrNBn6lQYNFGOSmeFxntlzx6IMjPh8uyzTCFUVVqK8NWrUU8u+aSIJg8nekaR0rmujn0vqamBpqEhS51mam0Ns4ZUXAIXF4ADaWtr1kvv3cOtv/5CdXY2MzBQCFrl7dtQrauDja0t8vPyUJCfz5TGIs47SED7SU8PfBsb9H/5ZRDOUNb588g5fhyCykrm3aJtYICezs7gk7Le1pZl6VCkVObkID8qioU4kZKeQq3U9fRQcP06tCwsoO7sDJWesn4D/2y9MjMTt7ZsAZ88J8zNWYrA5rB07uzZg5Q//wSf5pGU59zznkLjKDyL5o8U3WR0UBk4EK4LFjDPHSpFOTm4unkz+o4dC11LS8RSbvqMDLZWehw7BjuJRHn2V2TiO7GuUvDuROZ2RNN0aPrwww+xjMt3KG3T398f5ubm2Lt3b4vdEPgCxQg2LUrBuyNmRtmGkgNKDig5oOSAkgNKDig5oOSAkgPdlwPp8+fDeuNGpeDdTaZIKXh3k4loaRgP42qutHh388lVDk/JASUHlBxQckDJASUHlBxQckDJgU7igDLMtJMY285mlYJ3OxnXVbc9iuBqXcUbZT9KDig5oOSAkgOPFwfultyFjV7LeXgfL2qV1Cg5oOSAkgOdywGl4N25/FW0daXgrSjHurg+pROjGG9KC2LE5YrctWsXpk2b1u50YkpX8y6eRGV3Sg4oOaDkgJIDcnGA934DYviq5qDN5LpdWUnJASUHlBxQckCGA0rBu3stB6Xg3b3m4x+jKS4uRr9+/WBnZ4cVK1awfLWvvfYaQkJCsH37doVGr9x8CrFLWVnJASUHlBxQcqCLOUCCd93KOvB5HYHt3sWDV3an5ICSA0oOdDMOKM/+3WtClIJ395qPZkeTlJSERYsWISIiguUvJGs3WcIJXVWRotx8inBLWVfJASUHlBxQcqCrOUCCd+U7ldAQKvZ+6+pxKvtTckDJASUHHgUOKM/+3WuWlIJ395qPTh2NcvN1KnuVjSs58FhxoKS6BAK+ANqq7c81/lgxRElMl3CABO+CNwtgqKFY2p8uGZyyEyUHlBxQcuAR44Dy7N+9JkwpeHev+ejU0Sg3X6eyV9m4kgNdwgFJvaRL3HC/Ov8VjDSNMNNtZpfQpexEyQHiAAneGa9lwFLHslmG5FbkwkTz//MkK7mm5ICSA0oOKDnQMgeUZ//utTqUgnf3mo9OHY1y83Uqe5WNKznwUBy4VXgLKYUpCOkd0mI7cblx6Pd9vy4Bn3r75Nuw1rPGS14vPRRdypuVHFCEAyR4R8+LhreVd7O30fVTM09hmP0wRZpV1lVyQMkBJQf+kxxQnv2717QrBe/uNR+dOprutvnqJHWw+tIK2UuzO5VuZeOPPwc+CvsIbw9+u0sswZ3FzT/i/0BMZgw+HvFxi13surEL0/ZO6xLBe+7+uehj3Adv+L/RWSQr2/2PcqBWUovi6mIYaxr/gwMkWFNJfDkRjkaOzV7f9uQ2TO8//T/KvceP7CPJR9hc9zLs1SZxGaUZsNCxeKSf9W0SqazQ5RwoqirCneI78LDw6PK+O7vD7nb272x6u3v7SsG7u89QB46vu22+q9lX4fGjB8QrxFDhq3QgpcqmupIDB5MOMkvtEt8lXdntA33RYb14WTH01PX+tTE8bMc/XPoBN/Nu4pvR3zzQVFZZFsy1zcHj8bDh4gb87/D/ukTwHv/beHhaeOK9oPceljTl/f9hDqSXpoOe9eMcxzVy4d3T7+Knyz8hZ2lO42/kQr7z+k68euxV9tvpmacx1H7oA5wjZa3KByr4bvR3eNn75f8wVx8v0un5/e2ob7HIZ1GbhFHdc3POYZD1oDbrKisoOSAvB/bE7cGfCX9i56Sd8t7yyNTrbmf/R4ZxnTRQpeDdSYztjs12t833ddTXWHZyGa4tuAYnY6fuyLL/xJg2XdkEHniY7TG7XfQGbwtGUkES0paktev+h72pXFQOnY91kP5qOqx0rR62uX/t/g/DPsTdkrvYOH7jP5QKyYuSkV+Zjx3XdmDdxXWQrJQwQbwzi8/PPgi0CcRnIz9rdzeiOhEEPAEDaWutVImrMO/APGyduLXZuqSQcP3elSnpFKV73819COkVgri8uAfcl++V3GP8pgM88dZUy7TddCpvbJkDrxx5Bd9e+LZRWUTWbuf1zsxqGTortPHGHy/9iAWHFjT+/cXIL/DaoNceaDi5IBl+m/yw2HsxVgxZ8Vix/fcbv+N20W3mufO4FrIo2unb/YM8EqY/C/4MS/2Wtkp6fX09+Kv5OD79OIJ7BXcrNpXWlEJXTbdbjam1wdB46fncnNdJRxNBz1nqr59pv45uusPaWxO+BtuvbUf8/+I7rM3u0lB3O/t3F778W+NQCt7/Fuf/hX672+Yji1pvg97w7eGLp/s9/S9wRNklccD7J2+Wuufs82cVFmoyyzLx/F/Ps5f3+0HvN+sa2l4u70/Yj4T8BCwLWNZiE3QQG7tzLI6kHEHYrDAmRD2q3hNLji5hAuD2p7Y/QC8dSr8c+SVeO/7/QoiqQBU3Ft6Ag5FDe9nb4n1kVSRh9Y0TbyCtJK3d1qXE/ETM+XsOnIyc8GXIl9BX12+xTwoV2BK7hVkyR/Ue9UA9ApN7du+zIM8Kcj9WRLly/NZxhGz//5j5+lX1jW0/sesJkIvrrsm7MGn3JBS+WQgDDYPG67S2Tt4+yVDdW7OukaWW3KaFfCHsDew7fD4e5QZJyB70y33L5IV5F9jzZeafM7Ht2jYMtRuK08+fbiRvyp4piLgbgezybGgKNVmYw65JuxrXeHVtNey+tsPwnsOZZbwlV/RHkV+/XP6FKZ5o7R957sgDJJDigpQU0/pNY8/pjlQQkTC0NmItPhr+EWi9F1UXtYomL64TY92FdXh10Kug70KB8IGxUhs0x/T/pcxLcDVzhbqKOqtDWRL01+qj9K1S6KjpNN5H+1uwWoA3/d7E2uC1rU4f7TWzz82w86mdmOY6rc2prhRXIqc85x/7Unac1IgiyjxSEpKARv1LMz7Q85KeIY/KmqS589zoyfbZ+bnnFaK/TaY3qVAhqsCMP2fgUPIhliawLSWsou1L61M/5FnjZ+3XLnpm75+NU7dPIe6luAfWp6LjIQ812q9USLGx8sxKvOn/Zqv76u/Ev5l3mSLvNtlxkdJugtOEFlMwdrezv6I8fdzqKwXvx21GW6FHuvkm/DgBtta2zMpJDwYSVAqrC2Gobgh6CdJhif4m5NiCqgLWIl2j71S/rr6OWbHoPjoI0P8kgGmoaLAHHh3cqY6emh5q6mpYm3QoNdIwYm3lVeaxFxYdsH6e8DOG/TqMaYr5PD574Nno2UCFp4IyURl7sdOBi9qgB6u9vj2qaqtQj3pWh+rSC5BcnQ8lHUJJTQl70csKX3QvvWjp/6zyLPQy6MXooP6Ka4pZm3RwVhOosb6q66pB8T4k3FBKG7qP2iSLGdUl4YiECPpd+tImXjb9TmOkFz9d01HVaRw3WWjNtcwZj4gH5EZcIa5gPCU+EqAVja2spozxj+6l/uk+NZX7YyR66B+NgXhnoG7ArtPc0Ryo8lXZC47aJl5QfZqrHro92N90eCChluruuL6DWVqItt6GvZnwoS3UhgQSdriifmhOqS4dYqQ8IR6mFqcyoZDqvH78dTibOLOx04foofmjesQzoqOwqpC97OlD/CX6iD9EH80rHQTpO801jZNcx+lvYw1jxhdak2baZuxabHYsdt7YCTMtM0Z3WFoYbPVt2dqy0rFivKK2cypy2HUtoRbrj2iRLbKHLpovWz1bto7oO7VF9xIN0nERbVQI8Zv4SHRQHeIBzRn1S3VpvdO8WmhbsL/pwEy/UX8uJi5wMXVBTW0N1l9cj+8ufMd4Q/NJ/b8V8Bbj89bYrexemrdA20B2uKPy0sCXcLf0Lije0d3cne1NWgN0P+0v4j3NtxQBndYZrRPiAbVHc0G8pT1KNNK80/io3jfR3+By1mXWDpXRvUez9mkt0bqlZ4Ouqi7bq7RntFS12P4m3pMChvh24tYJbLi0oZHNNEbqmw4k+mr6jFc011SoPvFsddBqrDq7ilm+yPpA/VMfZOUk2slaQmMjOqgfWhPUN80H8Zi8Zuga7W9q73TqaZAwR4WEFrK6kAKA1h55DqSXpbN1P8FxAnt+3C6+zebx/9o7G2CtqnIBL5Qyxzs02iCCpCB5r8gFFBAoFaghQ23yj0v+kD/EjxpZMY4aIJZ6jTGjYjIvObeBzGtU+EtHkbkXFFF+tERUrn+JGlFQaqZcDdQ7z8J1PB7O8XznfNtz9rf3s2fOnO+cb++13/d53/Wu9a619trUAxit+/O6aKcNf9kQ2SILemJvfqhTDEyQYCRWo3qNqo89+B4+gk9g8wO6HBD4fvWm1WHT3zfFuoVfbX9re/TJdC7y4d8f7/LxcNmoy8KChxeEu39/dzwH+fiOGIAMlIut8bOUTFAm9oUrMYDzuBe8sTnl4xfoRH1APq6h3lN2/OnUOdbd+r/T/xv8hgV2HdZzWFj9h9WxnsGfGIYPc++Vz6+Mtn7mpWfCzKNnxnuM+fmYcHLfk8O37/l2WDRuUfyMbsxkIxc8Gay55Yu3hPG3jA/XfPaaGFcuXHphOOagY+IgLck8dqf+DdxvYLQLNiPOIDNxgt9btm2JZad2LumT4g6MsCvxH6YpjnI+bOHNkdoA7gcXykQvDrg3PJCL7+afMD+wqot6ysG1XffqGmVlx3bqHeXSrrAc/5zDzgn3v3B/bEcvGHZBrG88WsIgBedw4BP4Cb+Je7SR6DD32Lmx/fvFY7+IuiM/9ZkyLh1xaVj1h1VxGS33xC+pz8TIVZtWhbqn6sL0o6aHZRuXxcR7QLcBYcC+A8L0o6fHuETMQq5tO7aFCYdNCCPmjwjrz1sfPr3g03H2mvtR32BPG3TQ3geFra9tjTq+8MoLYWiPodEO2JX799+3f6wHHCQp8CR2MxuOnQ/e5+A46EKcH7b/sOiv+NLjWx+PbQzMPnvQZ8PyjctjnKa+Yyt8HBbEPeIqNqDOoy/Pj1MO3+Of6Mn/U1+G7+BK2ekntbGp7xBb3Ld3xi3uz2Av5dJuwXvG0TPCRUsvqh94GNR9UIwvxAj6C9QNWBIH0Rk7I2uKBScecmL839X3Xx3G9h0bTv3XU8OkOyZFefHblMw1lgedqOOURRuS2l38LLXFxEn8LzGAy5TBU6J8+BYDPr/b/LvInNjKgCM6cR5tEXKz0go5KJO4T33jHjDEB2Ifbftr0caUwW8GX/GrsYeOje3ZHU/eEepOr4uxgsGmIT2GxIG0qXVTYyzChtQ54jllUSafif30NZAXltgv9UGRifjLxqN9u/aNdZm4zfepvuGn+Ch88VH8hgSdDU3xrdEHjQ6j5o8KX+z3xXB+3fmxTwPXB/7wQNQFPWEHR+yHLdEbDvg9NsRO9C/x88mDJodX/vFKbJ9JqO99/t5w5MePjO0tceYv//eXKC/3PqzbYeH2J2+PMZNJKMpKq8Tox8ER2WHDtXxGBxikgQYGgogJ1F1iLG0sPJEPOXf7+27h2tOuDS+88ELo2bPne+KVf7Q/ARPv9mfeYXdMiffitYtD1+5dYwAlYNOJIIgStFMyF5PI0Ck23gQHviNo8JmOGo0UgYkgR3AkMBOMCIIETAIcAYkgQaBKSS8BhYDO34yGE1RoSAgOBFw6/jSaBA2CXUpiuD+yEMgIfpxLA0gDf/m9l0dd5n1+XpQxJaBpND41PujDtTQmBDEaKoIqwTfNxiUmSQ+uSZ0K9OAzMsMsjdw2bKBjR+ydBprPyECZMbnYfY9dOgYwgydBPXb89tyZQMMHGbAPn7kebtwbttE+nZCuU2x0aFQ5uD4NUvA9PFPSTDJLB4/OGnagkaIzc+mynUs2X/3mq9F28EGelKxzfUykd9s9NrBwRzZsQEOdEtGUYFNWGnzhntiWTh6dPRimTi3n8X3Si3JSEodt0BNdHt3yaGwon33p2Sg7HTTsR7I44fAJ8ZoZ/z0jXHXfVXE5K3KxjDh1oBhFpmyugWfDQZnUeU6VEhswM0OZJC/cLw1GpYElfnMerPBRvufvNJiQfB0GdOoZrMCmJHewxUZ8JrFDrkmDJsVO9DE/PyaKseDEBZE9yQodVGYG0ywSyyw5Hp7ycEw48Au44L98Jomjg4OO6Juu457YDTngnfTEJvzvkT8/Eusy157ef+fsMn6RZoiR54m/PhHrJmwoC9ulwRjK5HEDOgPUweP/+fjIMelEOfgoMYFOF7IxaMYBg+RHdH7gSvJN8sVnfAd/SAf3+uu2v0a/jzq/uT12yOjkU5foSGIDdsX+8uFfjv6HLfpe2zd2TBggGNNnTHy0YuT8kbHzPG34tBiPKJey4EiHifrOQZyBGf/n/vzAhOQiLdWk40VcTAf3pL4RW6k7fE+Hlk7k4fsdHmNDSuRhABt8KA1WMnhx5Yorw+jeo8PMETNj3ceu6BMHrXa8UT8Qit1iwvf227FcbIqfJnmxVRqkSAN3sKUcYkGMQ//4e33HlzrU+IeyGv+PDQEZ4KAekuQRpxkkeu7l56KcfD6j/xnRJgwaoR/+jW3Wblob9bvt1NviCgs2Flzx/Ir4Q4d87aS1kd/4m8fH3zA4+7Cz6/miK6yo68kmyMD/kAM++BUHfoGdUl1ObQJ+BRvYJlapHmObNOgIO7hhy4YzvTECv/PIB/LAh7JJdFlRwUDP8QcfH+8La+oH19O5pn5gK5JMBnsoh/NgQZJMu0RdnDx4ctjz33f6P49l4afM/BLbkRFbs3qMJGPlhJXxb+TEZ0l6FqxbEP+eNWJWZIMcxGHqPgPMJIjUXZIP9GRAllcJrt+yPs7ADew2MIw8cGRsZ4fvPzwOAqHDWQPPChMHTYyywIwYRNnYinLwSRJrEs84aPdP+8Xv73jijlgXqL8k2cR2Emy+J64QZ9JjIMufWx7r7rh+4wIbS+I7XEcC/vmDPx99IibXnTrVt5fEBOSJm7e+8+gROnHAgTqLHPgffk87iO3SoA11NdmV39gztbXpPvwv9VeoZ+ja0A/gzGoaBiGIOXyHbrDCL7kWvYm5xB/k6ffjftEuy85atnMfj/B2mHrE1PiWi2SvhnIlmdJAEP5J/EFvfLnxwB6xg3qCz1Ne2g8FDos2LAon/MsJYenvl0a/uvzTl8f+CnIdcf0RMQ7ePO7mnf2C8HasC+jBb+o5dSgN8qU4j2600eiPP3DMXT039uEY8PnSgC9F32SQh5h8St9Ton1giw+lyRjqCoMCrO6iPOo2vpYGTrA3voNd0ZHHkoht6J/aG+o/sjJIgu7cY8b/zIic8O81k9bExPv+L98f2xQen6P+/PLffhnLJEbyf8qlztDWEE9gQL2jTOodA5H4Nj+0z/BDLuIh1yTfpI1k4B1ZOZd4SD0hJqRHMfAR/Ihy4yBqp90jf/5PvwI+9Inpw6UJKexADMG3sB8xHf+85JZLwl3n32XiXd96dOwHE++O5d+udy/qchMCakqY2xVoAW5GEJ9w24Q4+nvukHefr6w11Rom3rUmO/LS2en+vZ3L07ZcuCU25k0ddOaO+6/jwmvTX4uN8Ad1IA+dgsabW7XmfnR8PvOzz8QOw/Kzl1d0KSP3JL6zRs6q6PzWnMSSfTruDd9LTsLC/Vi235rlpq25bzXnYgM65h/U8sxqZGs4wNBW+XhMhRlLBiRY8k8sWvLMkviqMF4ZVssHneisNntMO703fhwi8Rn9s9FxZpLkufHBwAgDGa15/pgk8YZ1N4RJgyfF5IGDGPvTh38aN0H7/qrvh/sm3NcuO4uT6BAXaKMG9xgcBwkH/sfAJjffq2V/YXaYpJgBSRLN+Q/P32WjzY7Qj/hIYpkGsaqRgUGB4f85PK5+OeGQE+JKh4m3Q3wtLgAAD8hJREFUT4zLuxs/tlDNfSq5lvaJQXxWdbDx4/ce+F5YMn5JJZfW3DkL718YTj3yVBPvnFjOxDsnhmgPMYqaeLcHO++RbwIsW5u8eHJ4c9Z7l5HnW+r3Ssfo/K3/e2ucRXq/g6VsdKRr4WDmghk8ls529MHr0XiGtOFmQucuPjfOQi4cu7CjxSvl/ZmdYW8DVgEws3vsjceGu56+K86+3XrqraVk0pTSzL6xaoCZ544aIGKm+roHrwt1Z9R1qF2YRe8xp0dcoZVmUjtUIG9e0wQY4OBRCgaYfnTcj2pal+aEt++fL7OaeOfLHh+oNFa+DxSvhXcgAZYKsrzq/Tbw6kDxvHVOCVxxzxVxeaPvKs+HgXj+mxlv3tHNu7o98kOAGMvqsoaPfXSEdMjBIESed8juCC7es20E1mxaE75w0xfCN4/6Zvja8K+1rZCcX2XfP18GMvHOlz0+UGmsfB8oXguXgARqjADPh/IMXHNL+2tMnZoXNyXe5w05L/z4+Hc356t5xVRAAhLIJQGe/+4yu0v4zem/CccdfFwuZaxWKPv+1RLM9noT72x55ro0K1+uzaNwEpCABEpNgFe/8Qq4i4+8OMwePbvULFReAhJoHwIH/uDAsOKcFfUbfrbPXdvvLvb92491JXcy8a6EUkHOsfIVxJCqIQEJSKCABI654Zi44zc7O2f5vuoColIlCUhAAhURsO9fEaZ2O8nEu91Qd/yNrHwdbwMlkIAEJCCBpgnwGh9e6fbbKb8VkQQkIAEJZEDAvn8GEDMswsQ7Q5h5L8rKl3cLKZ8EJCCB8hIg8eY9vQ9Nfqi8ENRcAhKQQIYE7PtnCDODoky8M4BYK0VY+WrFUsopAQlIoHwEVj6/Mrzx5hvxPd4eEpCABCRQPQH7/tUzzLIEE+8saea8LCtfzg2keBKQgAQkIAEJSEACEsiIgH3/jEBmVIyJd0Yga6EYK18tWEkZJSABCUhAAhKQgAQkUD0B+/7VM8yyBBPvLGnmvCwrX84NpHgSkIAEJCABCUhAAhLIiIB9/4xAZlSMiXdGIGuhGCtfLVhJGSUgAQlIQAISkIAEJFA9Afv+1TPMsgQT7yxp5rwsK1/ODaR4EpCABCQgAQlIQAISyIiAff+MQGZUjIl3RiBroRgrXy1YSRklIAEJSEACEpCABCRQPQH7/tUzzLIEE+8saea8LCtfzg2keBKQgAQkIAEJSEACEsiIgH3/jEBmVIyJd0Yga6EYK18tWEkZJSABCUhAAhKQgAQkUD0B+/7VM8yyBBPvLGnmvCwrX84NpHgSkIAEJCABCUhAAhLIiIB9/4xAZlSMiXdGIGuhGCtfLVhJGSUgAQlIQAISkIAEJFA9Afv+1TPMsgQT7yxp5rwsK1/ODaR4EpCABCQgAQlIQAISyIiAff+MQGZUjIl3RiBroRgrXy1YSRklIAEJSEACEpCABCRQPQH7/tUzzLIEE+8saea8LCtfzg2keBKQgAQkIAEJSEACEsiIgH3/jEBmVIyJd0Yga6EYK18tWEkZJSABCUhAAhKQgAQkUD0B+/7VM8yyBBPvLGnmvKyNGzeG3r17hzVr1oTu3bvnXFrFk4AEJCABCUhAAhKQgATaSmDz5s1h6NCh4dlnnw29evVqazFelxEBE++MQNZCMWvXro2Vz0MCEpCABCQgAQlIQAISKAcBJt2OOOKIciibYy1NvHNsnKxFe/3118P69etD165dQ+fOnbMuvkPLSyN6zuZ3qBlq4ub6Sk2YKRdC6iu5MENNCKGv1ISZciGkvpILM9SEEFn4yo4dO8LWrVtD//79w0c+8pGa0LvIQpp4F9m6JdLNZ1hKZOwqVdVXqgRYosv1lRIZu0pV9ZUqAZbocn2lRMauUlV9pUqAObzcxDuHRlGk1hMwOLWeWVmv0FfKavnW662vtJ5ZWa/QV8pq+dbrra+0nllZr9BXimd5E+/i2bSUGhmcSmn2Nimtr7QJWykv0ldKafY2Ka2vtAlbKS/SV0pp9jYpra+0CVuuLzLxzrV5FK5SAq+88kqYM2dOmDZtWujSpUull3leCQnoKyU0ehtV1lfaCK6El+krJTR6G1XWV9oIroSX6SvFM7qJd/FsqkYSkIAEJCABCUhAAhKQgAQkkCMCJt45MoaiSEACEpCABCQgAQlIQAISkEDxCJh4F8+maiQBCUhAAhKQgAQkIAEJSEACOSJg4p0jYyiKBCQgAQlIQAISkIAEJCABCRSPgIl38WxaWI2efvrpcM0114RVq1aFRx99NBxyyCHxd+Ojrq4uzJgxI2zYsCH07Nkzbrh2/vnnF5aLiu1KoBJfOfvss8OCBQt2ufjOO+8MY8aMEWsJCPzqV78KN954Y3jooYfCiy++GPr06RPOO++8MGXKlLDbbrvVEzCmlMAZWlCxEl8xpugnEFiyZEm46qqrwuOPPx7YHGv//fcPJ554YrjsssvCRz/6UeOKblJPoBJfMa4Uy2FMvItlz0Jrc9ttt4WpU6eGYcOGhSeffDK89dZbuyTeDzzwQBgxYkQ488wzw/jx48PKlStjYzdv3rwwceLEQvNRuXcJVOIrNGYrVqyIiVfDo2/fvu/pHMm1uASGDx8eDjzwwHDSSSeFbt26hWXLloXvfOc74etf/3r47ne/GxU3phTX/q3RrBJfMaa0hmhxz73pppvCI488EoYOHRr23nvv2E/51re+FQYNGhTuvvtu40pxTd9qzSrxFeNKq7Hm+gIT71ybR+EaEiDRTrNQBKIHH3xwl8T72GOPjTNXq1evrr908uTJYfHixYH3ITacxZJucQlU4ivN+VBxqahZYwJbt24NXbt2fc+/WSFz3XXXhZdffjnssccewZii30CgEl8xpugrzRG4/vrrA32RTZs2hR49ehhXdJVmCTT2FeNKsZzFxLtY9iyNNk0FojfeeCO+w3v27NnhG9/4Rj2Le+65J4waNSom6oMHDy4NIxXdSaC5RsvGTA9pisANN9wQV8z88Y9/DPvss48xRTdplkBDX+nevXuzsUaEErj55pvDKaecEjZu3Bj2228/44ou0SyBhr7Ciiz7KsVyFhPvYtmzNNo0FYh4nqpfv36h8TO6zFTsu+++gU4Sy889ykXg/RLvhQsXxlnNbdu2hf79+4dLL700PovnUV4CzEotWrQobNmyJTzxxBPGlPK6QouaN/SV3XffPXaQjSktYivNCW+++WbYvn17fNZ7woQJ4YADDgi33357/Nu+SmncoCJFm/OVNHlgXKkIY02cZOJdE2ZSyMYEmkqmeJ77qKOOis9k8jxeOnbs2BE+9KEPhR/+8IfhggsuEGbJCDSXeOMPnTt3jh0glhWzvJjn79hEaezYsSWjpLoQYFXMJz/5ybgvxMyZM+MeEcYUfaMpAo19hXOMKfpKQwJs7srScg427Pz1r38d9tprL+OKbrILgeZ8xbhSPGcx8S6eTUuh0fsl3ux6zgZsjRPvuXPnhq9+9aul4KOS7xKodJkWz4V/6lOfirvQMiPhUS4Cf/rTn2LcoAO0fPnyOFiXEm9jSrl8oSVtm/KVpq4xprREstjfs8Haq6++Gh577LFwxRVXhE984hNh6dKl8c0sDOgZV4pt/9Zo15yvsJKm8WFcaQ3Z/J1r4p0/myhRBQRcal4BJE+JBCpNvDmXnawvuuiiuPR8zz33lGBJCPztb3+L+0C8/vrr4b777gsf+9jHouYuCS2JA7RCzeZ8pbkijCmtgFvgU3ll4ZAhQ+KKqkMPPdSl5gW2dbWqNfSV5lbfGVeqpdxx15t4dxx771wFATdXqwJeyS5tTeJ99dVXh4svvtjEu0Q+QrL9uc99Ljz11FPxMRU2s0mHGzaWyBEqUPX9fKW5y40pFYAtwSk8w8t+IldeeWXc/NWNYEtg9Daq2NBXLrnkkiZLMa60EW4OLjPxzoERFKH1BN7vdWI8r0sHOh3nnntu3NDE14m1nnMRrqg08Wb5FnsDMNvNe1c9ik+A/R9OPvnkcO+998afAQMG7KI0rxMzphTfF1rSsBJfaVyGMaUlquX5npU0Rx99dNx8b9y4cfF1YsaV8ti/NZo29hXjSmvo5f9cE+/820gJ3yFAQlRXVxf/uvbaa8MzzzwT5syZE/8eOXJkfB8vCfeIESPi8uIzzjgjPqM5a9asMG/evDBx4kRZloRAS77C9/jIaaedFvr06RNeeumluLnasmXL4o7WJ510UklIlVvNKVOmhJ/85CeB2QM6xQ0PloMyK2VMKbePJO1b8hViiDFFX4EAg3ksK2cgj0eW1q1bF2NMt27dwtq1a8OHP/xh44quEgm05CubN282rhTMV0y8C2bQIqvD+y979+7dpIokTDyjyUFyPn369LBhw4a4UdK0adPCV77ylSKjUbdGBFryFTpE55xzTuBZKl43R0eIjhLLulh27FEOAr169QrPPfecMaUc5q5Ky5Z8xZhSFd5CXTx79uw4s83kAKse8B0SrAsvvDAO5qXDvkqhzN4mZVrylRdffNG+SpvI5vciE+/82kbJJCABCUhAAhKQgAQkIAEJSKAABEy8C2BEVZCABCQgAQlIQAISkIAEJCCB/BIw8c6vbZRMAhKQgAQkIAEJSEACEpCABApAwMS7AEZUBQlIQAISkIAEJCABCUhAAhLILwET7/zaRskkIAEJSEACEpCABCQgAQlIoAAETLwLYERVkIAEJCABCUhAAhKQgAQkIIH8EjDxzq9tlEwCEpCABCQgAQlIQAISkIAECkDAxLsARlQFCUhAAhKQgAQkIAEJSEACEsgvARPv/NpGySQgAQlIQAISkIAEJCABCUigAARMvAtgRFWQgAQkIAEJSEACEpCABCQggfwSMPHOr22UTAISkIAEJCABCUhAAhKQgAQKQMDEuwBGVAUJSEACEpCABCQgAQlIQAISyC8BE+/82kbJJCABCUhAAhKQgAQkIAEJSKAABEy8C2BEVZCABCQgAQlIQAISkIAEJCCB/BIw8c6vbZRMAhKQgAQkIAEJSEACEpCABApAwMS7AEZUBQlIQAISkIAEJCABCUhAAhLILwET7/zaRskkIAEJSEACEpCABCQgAQlIoAAETLwLYERVkIAEJCABCUhAAhKQgAQkIIH8EjDxzq9tlEwCEpCABCQgAQlIQAISkIAECkDAxLsARlQFCUhAAhKQgAQkIAEJSEACEsgvARPv/NpGySQgAQlIQAISkIAEJCABCUigAARMvAtgRFWQgAQkIAEJSEACEpCABCQggfwSMPHOr22UTAISkIAEJCABCUhAAhKQgAQKQMDEuwBGVAUJSEACEpCABCQgAQlIQAISyC8BE+/82kbJJCABCUhAAhKQgAQkIAEJSKAABEy8C2BEVZCABCQgAQlIQAISkIAEJCCB/BL4f7w3H+6w4453AAAAAElFTkSuQmCC", "_figure_label": "Figure 6", "_image_mode": "diff", "_model_module_version": "^0.11", "_size": [ 900, 400 ], "_view_module_version": "^0.11", "layout": "IPY_MODEL_953995b09f404c7db76aa00c42eec519", "toolbar": "IPY_MODEL_8f1150436e1544ec8236bff78a7e27fb", "toolbar_position": "left" } }, "a9e855977db64785bcc8b4f2e0803fb8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Xrange", "layout": "IPY_MODEL_158d3575ad3344d9a5eef47bed7cd54c", "max": 1.5, "min": -0.5, "step": 0.07142857142857142, "style": "IPY_MODEL_033033acc6c74c3daef886add86efd13", "value": [ 0, 1 ] } }, "a9f875e8ac5d49359c2914757810071c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "b4208c41ae8d41389eeaee5b296c4103": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "b463e11ceb97432485b4d1d458ab460d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Yrange", "layout": "IPY_MODEL_d6ab9bd45fb64b7b9785bed9d60872bb", "max": 1.5, "min": -0.5, "step": 0.02631578947368421, "style": "IPY_MODEL_a9f875e8ac5d49359c2914757810071c", "value": [ -5.551115123125783e-17, 0.9999999999999998 ] } }, "b6db3472924a4760b2e21af077e8ddcf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_a2729d7502c4449695df8f9440c273f9", "IPY_MODEL_4e6d91d411954df8b8afa2326de84023" ], "layout": "IPY_MODEL_cf7312d87d364721b24398b437361f91" } }, "b82e01cf3881477b86207dd7b856d281": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "bb14d41c8eb247b58211bd24e5aa93e1": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "MPLCanvasModel", "state": { "_cursor": "default", "_data_url": "data:image/png;base64,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", "_figure_label": "Figure 2", "_image_mode": "diff", "_model_module_version": "^0.11", "_size": [ 900, 400 ], "_view_module_version": "^0.11", "layout": "IPY_MODEL_5402ac42a28945c2958ab09ac7378075", "toolbar": "IPY_MODEL_8eacf287c05749d887254f7f4b1b0d07", "toolbar_position": "left" } }, "bb978d1baf324b599a8747447e56dbb0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "bd48c8151b264b11a214f6b3f326fbfc": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "MPLCanvasModel", "state": { "_cursor": "default", "_data_url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA94AAAG4CAYAAACzXz9FAAAAAXNSR0IArs4c6QAAIABJREFUeF7s3QeYFEX+//EvUZKAgoCRZAABlSCIIMkjiNwp6nlyGBARQYJHUJCwZAkCHkEExcyhnohI5vQOEZAgQRFBEZDwkyhKUJKE//Ot//Xc7LLLTM9O7dTMvPt57jldu6trX9X1PP3Zqq7Kdvbs2bPCgQACCCCAAAIIIIAAAggggAACVgSyEbytuFIoAggggAACCCCAAAIIIIAAAkaA4M2DgAACCCCAAAIIIIAAAggggIBFAYK3RVyKRgABBBBAAAEEEEAAAQQQQIDgzTOAAAIIIIAAAggggAACCCCAgEUBgrdFXIpGAAEEEEAAAQQQQAABBBBAgODNM4AAAggggAACCCCAAAIIIICARQGCt0VcikYAAQQQQAABBBBAAAEEEECA4M0zgAACCCCAAAIIIIAAAggggIBFAYK3RVyKRgABBBBAAAEEEEAAAQQQQIDgzTOAAAIIIIAAAggggAACCCCAgEUBgrdFXIpGAAEEEEAAAQQQQAABBBBAgODNM4AAAggggAACCCCAAAIIIICARQGCt0VcikYAAQQQQAABBBBAAAEEEECA4M0zgAACCCCAAAIIIIAAAggggIBFAYK3RVyKRgABBBBAAAEEEEAAAQQQQIDgzTOAAAIIIIAAAggggAACCCCAgEUBgrdFXIpGAAEEEEAAAQQQQAABBBBAgODNM4AAAggggAACCCCAAAIIIICARQGCt0VcikYAAQQQQAABBBBAAAEEEECA4M0zgAACCCCAAAIIIIAAAggggIBFAYK3RVyKRgABBBBAAAEEEEAAAQQQQIDgzTOAAAIIIIAAAggggAACCCCAgEUBgrdFXIpGAAEEEEAAAQQQQAABBBBAgODNM4AAAggggAACCCCAAAIIIICARQGCt0VcikYAAQQQQAABBBBAAAEEEECA4M0zgAACCCCAAAIIIIAAAggggIBFAYK3RVyKRgABBBBAAAEEEEAAAQQQQIDgzTOAAAIIIIAAAggggAACCCCAgEUBgrdFXIpGAAEEEEAAAQQQQAABBBBAgODNM4AAAggggAACCCCAAAIIIICARQGCt0VcikYAAQQQQAABBBBAAAEEEECA4M0zgAACCCCAAAIIIIAAAggggIBFAYK3RVyKRgABBBBAAAEEEEAAAQQQQIDgzTOAAAIIIIAAAggggAACCCCAgEUBgrdFXIpGAAEEEEAAAQQQQAABBBBAgODNM4AAAggggAACCCCAAAIIIICARQGCt0VcikYAAQQQQAABBBBAAAEEEECA4M0zgAACCCCAAAIIIIAAAggggIBFAYK3RVyKRgABBBBAAAEEEEAAAQQQQIDgzTOAAAIIIIAAAggggAACCCCAgEUBgrdFXIpGAAEEEEAAAQQQQAABBBBAgODNM4AAAggggAACCCCAAAIIIICARQGCt0VcikYAAQQQQAABBBBAAAEEEECA4M0zgAACCCCAAAIIIIAAAggggIBFAYK3RVyKRgABBBBAAAEEEEAAAQQQQIDgzTOAAAIIIIAAAggggAACCCCAgEUBgrdFXIpGAAEEEEAAAQQQQAABBBBAIOGD9+bNm2XkyJGyfPlyWb9+vZQrV878f/CRLVu2DJ+EXbt2yaWXXmr+e6lSpWT79u3nnHvs2DHJkydP4OdHjhyR7t27y7Rp0+TEiRPSoEEDGTdunJQsWTLVtZs2bZLOnTvL4sWLJX/+/NKiRQsZNmyY5M2bN9V5c+fOld69e8vGjRvliiuukK5du8qTTz7J04sAAggggAACCCCAAAIIIBAHAgkfvD/66CPp2LGj1KhRQzTonjlz5pzgraE87fHwww+bMLx27drAf9LgffPNN0u3bt1Sna5lB4f3Zs2ayZo1a2TUqFFSsGBBSUlJkcOHD8u6desCofrgwYNSsWJFE8b79u0r+/btM4G6SZMmMmXKlED5y5Ytkzp16ojW58EHH5SlS5dKv379ZNKkSdKmTZs4eMSoIgIIIIAAAggggAACCCCQ3AIJH7w1aGfPnt20cqtWrWTVqlXnBO+0j8C2bdukdOnSMmLECHn66adTBW8N1ePHj8/wqVmxYoXccsstMmfOHGnatKk5b8eOHVK2bFkz6t2uXTvzs+HDh8vAgQPNCHrRokXNz6ZOnSotW7aUDRs2SPny5c3P7rjjDvn5559Fy/WOtm3byuzZs+X//u//Ar9bcj/G/PYIIIAAAggggAACCCCAgLsCCR+8g+nDDd7PPfec9OnTxwRmndrtHTriHSp462j02LFjTVgOHgWvX7++FChQQGbNmmWKq1u3rhQuXFh0RN47dFp6oUKFZMiQIWZUXf9dR8x1+nmXLl0C5y1atEjq1atn/ohQtWpVd58uaoYAAggggAACCCCAAAIIICAE73QegkqVKkmRIkXk008/TfVfNXgfOnRIjh49Krly5TJTwHXkWs/3jvvvv98E9rTT1zt06CALFiwQ/eZcj2LFiknr1q1NqA4+KlSoIDVr1pTJkyebkW/993nz5pkp6N6xf/9+c/3bb79tpp+Hexw/fly+/vprueSSSyRnzpzhXsZ5CCCAAAIIIIAAAgggEGcCp06dEs0NmlWC16OKs18jYapL8E7TlPod9o033mi+odYp3cGHLoSm33NfddVVsnXrVjMyvXfvXvMdeJkyZcypDRs2lBw5csj8+fNTXasj6BMmTDAj4XpocB80aJD07Nkz1Xm1a9c2oXr69Onme279d/3OW6eve4d2Ir1+zJgxZnG2jA79rlz/5x1ffvml/PGPf0yYh5dfBAEEEEAAAQQQQAABBM4vsHLlSrNOFUdsBQjeafx79OghL7zwguzZs0cuvvji87bO7t27zSrp+l22hmoveOtoso5SBx+6KvnEiRPlwIEDgeA9ePBg0fsFH7Vq1ZISJUrIBx98EAjeOnqugT9t8NYp7Z06dcqwjv3795cBAwac89+183krtcf28ePuCCCAAAIIIIAAAgggYENAs0r16tXlhx9+MLszccRWgOAd5H/27FnzUN50002pvr0+XxPdeeed8tNPPwUWP3NpqnnaEW+v8+3cuTPVt+uxfQS5OwIIIIAAAggggAACCERbQBdivvLKK4V3/2jLRlYewTvITffT1u+23333XfnLX/4SlqiuXK6j2N6q4y4vrkbnC6tJOQkBBBBAAAEEEEAAgbgX4N3frSYkeAe1R/v27c0e2vrddr58+UK21K5du8y2Xw899FBgizFvO7HgBdH0r0z6DXja7cT0G2/dTkwXctNDA3+LFi3O2U5M9/zW77y9Q7ckmzlzpu/txOh8IZuUExBAAAEEEEAAAQQQSAgB3v3dasaED966AvncuXON+osvvihbtmyR0aNHm3/XLb10hW89dMEy/e5ZVw/X1cLTHu+8847Zm1v31b7sssvM4mpDhw41i6WtXr3a7PvtHbrlmC64NmrUKLMdWEpKilkNXRduy5s3rzlNw3TFihXN1Pa+ffvKvn37pGvXrtK4cWMT/r1DA7eOwutWaPotuS64puXp4m9t2rTx9TTR+XxxcTICCCCAAAIIIIAAAnErwLu/W02X8MF727ZtqUJxMP/ChQvNfth6aKjWwKwhXcN12kMXONMVyL/55hsTmnUP7gYNGsjAgQPluuuuS3W6flvdvXt3mTZtmpw8edKcp6PdJUuWTHXepk2bzOJoS5YsMSPsOtqt25N54dw7WevUq1cv2bhxo/k2WwO6bk/m96Dz+RXjfAQQQAABBBBAAIFoCeh6Sro2km5xe/r06WgVm3Tl6A5Kuj1Y0aJFJVu2bBn+/rz7u/VoJHzwdos7trWh88XWn7sjgAACCCCAAALJKqCh+8cff5QjR45I7ty5zfa7HJEJ6B8tdHDvwgsvlMsvvzzD8M27f2S+tq4ieNuSdbBcOp+DjUKVEEAAAQQQQACBJBDYv3+/Ge0uVqxYYH2jJPi1rf2Kurizfqqqo97ep7Npb8a7vzX+iAomeEfEFp8X0fnis92oNQIIIIAAAgggEO8CutiwjtKWLVs23n8VZ+qva1fp7AHdMiy9g3d/Z5rKVITg7VZ7WK0Nnc8qL4UjgAACCCCAAAIIZCCg6y7poQsLc0RHIJQp7/7RcY5WKQTvaEnGQTl0vjhoJKqIAAIIIIAAAggkoECokJiAv7L1XymUKe/+1pvA1w0I3r644vtkOl98tx+1RwABBBBAAAEE4lUgVEiM198rlvUOZcq7fyxb59x7E7zdag+rtaHzWeWlcAQQQAABBBBAAIEMBEKFROD8C4Qy5d3fv6nNKwjeNnUdK5vO51iDUJ2QAr/99pu8+uqr0rFjR8mePXvI8zkBAQQQQAABBNwUCBUS3ax1ZLX69NNPpX79+vLFF19ItWrVIiskjKtCmfLuHwZiFp5C8M5C7Fjfis4X6xbg/n4FOnToIBMmTJCpU6dKixYt/F7O+QgggAACCCDgiECokOhINaNSDYJ3VBgTrhCCd8I1aca/EME7iRo7QX7Vv/zlL/LPf/5TXn75ZXn88ccT5Lfi10AAAQQQQCD5BAje0W/zUKa8+0ffPDMlErwzoxdn19L54qzBqK7cf//98v777xO8eRYQQAABBBCIc4FQITHefr0ZM2bIgAEDZOPGjVK4cGG59957Zfjw4VKgQAHxRrznzJkjkydPlgULFkjBggWlU6dO0qtXr8Cvqu/mXbt2lUWLFsmhQ4fk0ksvlbvvvlteeOGFsDhCmfLuHxZjlp1E8M4y6tjfiM4X+zagBv4ECN7+vDgbAQQQQAABVwVChURX651evWbOnGkC8p///Gdp1aqVbN26VZ599lmpXr26fPLJJ4Hgffnll5tP5f7whz+Yn48cOVJeeukladeunSm2QYMGsmvXLhPgixcvLjt27JBVq1bJ2LFjw+IIZcq7f1iMWXYSwTvLqGN/Izpf7NuAGvgTYKq5Py/ORgABBBBAwFWBjEJi+/bt5euvv45JtStVqmSCsN+jSpUqkitXLlmxYkXg0nfeeUf++te/ysKFC83PdHG1hx56SN56663AOfrvOhq+fft2s2isjo4PHTrUjIRHchC8I1GL3TUE79jZZ/mdCd5ZTs4NMynAiHcmAbkcAQQQQAABRwQSJXj/+uuvZtr4888/L926dQvonjp1SvLmzSspKSly2223meA9ffp0ad68eeCcDz/8UO655x4TvK+66iqpU6eO+eeePXtKw4YN5eqrr/bVWgRvX1wxP5ngHfMmyLoKELyzzpo7RUeA4B0dR0pBAAEEEEAg1gKhQmKs6xfu/b336SlTpkjLli1TXVaiRAkztfyuu+4ywXvJkiVSq1atwDlLly6V2rVry/Lly6VGjRqye/du6d27t+j34r/88otcd9118txzz5lwHs4RypR3/3AUs+4cgnfWWcf8TnS+mDcBFfApwFRzn2CcjgACCCCAgKMCoUKio9U+p1reiLd+r60Lo3lHJCPe3rVnzpyR1atXy+DBg2Xu3Lny3XffSZkyZUKShDLl3T8kYZaeQPDOUu7Y3ozOF1t/7u5fgBFv/2ZcgQACCCCAgIsCoUKii3XOqE76jXfu3LnNyLV3vPfee/LAAw+E/Mb7P//5j+zcudN845320G/db7jhBrMKeqNGjUKShDLl3T8kYZaeQPDOUu7Y3ozOF1t/7u5fwAver7zyirRp08Z/AVyBAAIIIIAAAk4IhAqJTlQyzEp4q5rrzLxHHnkksKr5zTffnO6q5vr99scff2xWNX/xxRflySefNNuHNW7c2CzAplPMf//9d7OauYb577//XooWLRqyNqFMefcPSZilJxC8s5Q7tjej88XWn7v7F2DE278ZVyCAAAIIIOCiQKiQ6GKdz1cnXSht4MCBsmHDBrOP93333XfOPt6zZ8+WSZMmyb///W+58MILpWPHjtKnTx9T7IkTJ8y/L1682GwjpguzVatWzUw31wAfzhHKlHf/cBSz7hyCd9ZZx/xOdL6YNwEV8ClA8PYJxukIIIAAAgg4KhAqJDpabaerFcqUd3+3mo/g7VZ7WK0Nnc8qL4VbEGCquQVUikQAAQQQQCAGAqFCYgyqFPe3DGXKu79bTUzwdqs9rNaGzmeVl8ItCDDibQGVIhFAAAEEEIiBQKiQGIMqxf0tQ5ny7u9WExO83WoPq7Wh81nlpXALAgRvC6gUiQACCCCAQAwEQoXEGFQp7m8ZypR3f7eamODtVntYrQ2dzyovhVsQYKq5BVSKRAABBBBAIAYCoUJiDKoU97cMZcq7v1tNTPB2qz2s1obOZ5WXwi0IELwtoFIkAggggAACMRAIFRJjUKW4v2UoU9793Wpigrdb7WG1NnQ+q7wUbkGA4G0BlSIRQAABBBCIgUCokBiDKsX9LUOZ8u7vVhMTvN1qD6u1ofNZ5aVwCwIEbwuoFIkAAggggEAMBEKFxBhUKe5vGcqUd3+3mpjg7VZ7WK0Nnc8qL4VbECB4W0ClSAQQQAABBGIgECokxqBKcX/LUKa8+7vVxARvt9rDam3ofFZ5KdyCAMHbAipFIoAAAgggEAOBUCExBlWK+1uGMuXd360mJni71R5Wa0Pns8pL4RYECN4WUCkSAQQQQACBGAiECokxqFJMb9mqVStZtWqVrF+/PuJ6hDLl3T9iWisXErytsLpZKJ3PzXahVhkL/PnPf5Zp06bJK6+8Im3atIEKAQQQQAABBOJUIFRIjNNfK+JqE7wjpovbCwnecdt0/itO8PZvxhWxFSB4x9afuyOAAAIIIBAtAYJ3akmCd7SerPgph+AdP22V6ZoSvDNNSAFZLMBU8ywG53YIIIAAAghYEki04L1s2TLp16+fLF++XM6ePSvXX3+9DB48WBo2bCg9e/aUOXPmyA8//CCFChWSOnXqyOjRo+XSSy8N6KYXvH/88Ud59tlnZcGCBXL48GEpWbKktG/fXp566ql0WyWUKe/+lh7mCIsleEcIF4+X0fnisdWSu86MeCd3+/PbI4AAAggkjkCokBhPv+nSpUulQYMGcsstt0iHDh2kcOHC5nvt4sWLy2OPPSatW7c2Afyyyy6T/fv3y6hRo+TAgQOyYcMGyZkzp/lV0wZv/e+VK1c2/y0lJUXKlCkj33//vWzZskVGjBhB8I6nBySDuiZ88N68ebOMHDnS/DVKFy8oV67cOYsY6IP/5ptvnkM0b948adKkSaqfa1njx4+XPXv2SKVKleT555+XevXqpTrnyJEj0r17d/Nt6okTJ0zHHDdunPmrVfCxadMm6dy5syxevFjy588vLVq0kGHDhknevHlTnTd37lzp3bu3bNy4Ua644grp2rWrPPnkk74fP4K3bzIuiLEAwTvGDcDtEUAAAQQQiJJAIgXvWrVqycGDB2XdunWSI0eO8wqdPn3a5AZ9h9eR7EaNGqUbvPVdXwP6t99+K6VKlQpLPZQp7/5hMWbZSQkfvD/66CPp2LGj1KhRQzTonjlzJt3greH3H//4Ryr48uXLm+kh3qGhu1evXvLcc89JlSpVzIJPM2bMkJUrV5oQ7h3NmjWTNWvWmM5TsGBB81crnS6indML1dpZK1asaMJ43759Zd++fSZQa9CfMmVKoCydxqLTUx5++GF58MEHRf/CptNaJk2a5HuxKTpflvUrbhQlAaaaRwmSYhBAAAEEEIixQIYhsX17ka+/jk3t9P39pZd83fvo0aNy4YUXytChQ+WZZ55J91odvBs0aJB88803JgN4hw7EaS7RI+2It46eX3zxxaIDbuEeBO9wpdw4L+GDtwbt7Nmzp/uAe00QzuIGOnKt00fatm0bmO6hf8HSwH3DDTfIu+++a4pbsWKFmXai33U0bdrU/GzHjh1StmxZM+rdrl0787Phw4fLwIEDZfv27VK0aFHzs6lTp0rLli3NNBQN/Xrccccd8vPPP5tyvUPrMHv2bNEg7f1u4TxOBO9wlDjHJQFGvF1qDeqCAAIIIIBA5AKJErz1O2wdvX777bfNoFja44svvpBbb71V7rrrLjNwVqxYMcmWLZvJBzpTVmfF6pE2f1xzzTVSt25dmTx5ctjIBO+wqZw4MeGDd7ByRgE7nOC9cOFCM2VcR7K97y+07AEDBpiR7UOHDplOpaPRY8eONWFZ/9076tevLwUKFJBZs2aZH2nH0u9BdETeOzTc6wj7kCFDpFu3bmaauo6Y6/TzLl26BM5btGiRmd6u35JUrVo17AeJ4B02FSc6IkDwdqQhqAYCCCCAAAKZFAgVEjNZfJZd/ttvv5n384xGvHV27Msvv2xms3oDZDrQptPHzxe8GfHOsiaM2Y0I3v/9i9N7770nF1xwgej0ER3F1unfd999d6BhJkyYYBZP0P8e/A32+++/LzoddufOneavX/rPOsKt35QHH3qtfteh35zroX/90oUXNFQHHxUqVJCaNWuav3bpyLf+e9pvzXWRBr0+o7+0ZfQ0Ebxj1s+4cYQCBO8I4bgMAQQQQAABxwQSJXgra+3atc033l999dU533jrYJnOhN21a1dgEE4/U9VvuM8XvPv06WPWpdJPY6+66qqwWi+UKe/+YTFm2UkEbxEZM2aMWWFQQ652opdeekn+9a9/iYbq++67zzSGjkLrtxrHjx9P1TiffPKJWbVQO55OOdd/1kUW5s+fn+o87Uwa3nUkXI9cuXKZ8nS7geBDO7KG6unTp5vvufXf9Ttv/SuYd5w6dcpcr/XWxdkyOvSbkuDvSnbv3i3Vq1cP/JEgy54yboRAhAJe8NY/ROkqoRwIIIAAAgggEJ8CoUJiPP1WS5YsMTNhdZE1XfD4oosuMrNi9fPREiVKyJ133mkG7Jo3b27e43WwTAP1+YK3t6q5jpLrAKCuar5161ZznX6imt4RypTg7dZTRfBOpz30u3D9NkNDq446e8Fb9+Y7duxYqis+/vhjszqhLpymI+UavDXE6yh18KF/5Zo4caLZSsAL3lpejx49Up2nHVg77AcffBAI3jp6rovDpQ3eOqW9U6dOGT5R/fv3N1Ph0x7e6LxbjyK1QeBcAUa8eSoQQAABBBBIDIFQITHefsvPP/9cdGBN12HSQTcdwNN3+9tvv92sB6VrO+mAm77bv/jii3LttdeeN3jr76/v6LqPt+YInWWr09M12Gf0vh/KlODt1lNF8M6gPfQvUrpSoTe13JtqrsE7T548gatcnmrOiLdbnY3a+BcgePs34woEEEAAAQRcFAgVEl2ss+t1CmVK8HarBQneGbSH/qVKR6O94M3iam49uNQmOQSYap4c7cxviQACCCCQ+AKhQmLiC0T/NwxlSvCOvnlmSiR4p6OnU831m2oN3evXrzdneNuJ6XZg3oJoup2YftetU8zTbicWvCCaThvR7zTSbiem33jrKodFihQx99AyWrRocc52YvrduX4f4h1ah5kzZ7KdWGaefK6NCwGCd1w0E5VEAAEEEEAgpECokBiyAE44RyCUKcHbrYcm4YO3hmdvI3r9vmLLli0yevRo0wq6pZf+d91OTAOv7rX9yy+/mMXVdIRbv7PWRRG8Q1ca1C0CdPuAKlWqmJXHdRG0lStXmvDtHc2aNZO1a9eabcZ0u4GUlBSz3Zh+B+6tiK5humLFiubbDV1AQbcc6Nq1qzRu3FimTJkSKEsDd506dUwddY9vXXBNy5s0aZK0adPG19NE5/PFxckOCBC8HWgEqoAAAggggEAUBEKFxCjcIumKCGXKu79bj0TCB299IEuXLp2uuoZrHbF+9NFHZfXq1aLbdOXOnVuqVatmVhvXEBx8nD171izzP378eNm7d68J2zolXffoDj702+ru3bvLtGnT5OTJk2bVQx3tLlmyZKrzdJVCXSxBV0bMly+fCf+6amHwdmV6gf7hQAP/xo0bzZZlGtB1pUS/B53Prxjnx1qA4B3rFuD+CCCAAAIIREcgVEiMzl2Sq5RQprz7u/U8JHzwdos7trWh88XWn7v7FyB4+zfjCgQQQAABBFwUCBUSXayz63UKZcq7v1stSPB2qz2s1obOZ5WXwi0IELwtoFIkAggggAACMRDQNY90Jqh+2skRHQH9hFZn61555ZXpFsi7f3Sco1UKwTtaknFQDp0vDhqJKqYSIHjzQCCAAAIIIJAYAvpJ508//STFihULLCycGL9ZbH6LAwcOmDWiihYtKpdccgnBOzbN4OuuBG9fXPF9MsE7vtsvGWt/3333mUUOdSHDxx57LBkJ+J0RQAABBBBICAFdK+nHH3+UI0eOmFHaHDlyJMTvFYtfQndW0tkDF154oVx++eWSLVs2gncsGsLnPQnePsHi+XSCdzy3XnLWneCdnO3Ob40AAgggkJgCGr511Pv48eOi4ZEjMgH9o0WePHnMaHdGoVtL5t0/Ml9bVxG8bck6WC6dz8FGoUrnFWCqOQ8IAggggAACCCAQmQDv/pG52bqK4G1L1sFy6XwONgpVOq8AI948IAgggAACCCCAQGQCvPtH5mbrKoK3LVkHy6XzOdgoVCms4P3qq69K69at0UIAAQQQQAABBBAIU4B3/zChsug0gncWQbtwGzqfC61AHfwIMOLtR4tzEUAAAQQQQACB/wnw7u/W00Dwdqs9rNaGzmeVl8ItCBC8LaBSJAIIIIAAAggkhQDv/m41M8HbrfawWhs6n1VeCrcg4AVvpppbwKVIBBBAAAEEEEhoAd793Wpegrdb7WG1NnQ+q7wUbkGAEW8LqBSJAAIIIIAAAkkhwLu/W81M8HarPazWhs5nlZfCLQgQvC2gUiQCCCCAAAIIJIUA7/5uNTPB2632sFobOp9VXgq3IMBUcwuoFIkAAggggAACSSHAu79bzUzwdqs9rNaGzmeVl8ItCDDibQGVIhFAAAEEEEAgKQR493ermQnebrWH1drQ+azyUrgFAYK3BVSKRAABBBBAAIGkEODd361mJni71R5Wa0Pns8pL4RYEmGpuAZUiEUAAAQQQQCApBHj3d6uZCd5utYdnLwj9AAAgAElEQVTV2tD5rPJSuAUBgrcFVIpEAAEEEEAAgaQQ4N3frWYmeLvVHlZrQ+ezykvhFgQI3hZQKRIBBBBAAAEEkkKAd3+3mpng7VZ7WK0Nnc8qL4VbECB4W0ClSAQQQAABBBBICgHe/d1qZoK3W+1htTZ0Pqu8FG5B4N5775Xp06fLq6++Kq1bt7ZwB4pEAAEEEEAAAQQSU4B3f7faleDtVntYrQ2dzyovhVsQIHhbQKVIBBBAAAEEEEgKAd793Wpmgrdb7WG1NnQ+q7wUbkGAqeYWUCkSAQQQQAABBJJCgHd/t5qZ4O1We1itDZ3PKi+FWxBgxNsCKkUigAACCCCAQFII8O7vVjMTvN1qD6u1ofNZ5aVwCwIEbwuoFIkAAggggAACSSHAu79bzUzwdqs9rNaGzmeVl8ItCDDV3AIqRSKAAAIIIIBAUgjw7u9WMxO83WoPq7Wh81nlpXALAox4W0ClSAQQQAABBBBICgHe/d1qZoK3W+1htTZ0Pqu8FG5BgOBtAZUiEUAAAQQQQCApBHj3d6uZCd5utYfV2tD5rPJSuAUBgrcFVIpEAAEEEEAAgaQQ4N3frWYmeLvVHlZrQ+ezykvhFgS84P3aa6/Jo48+auEOFIkAAggggAACCCSmAO/+brUrwdut9rBaGzqfVV4KtyBA8LaASpEIIIAAAgggkBQCvPu71cwEb7faw2pt6HxWeSncggDB2wIqRSKAAAIIIIBAUgjw7u9WMxO83WoPq7Wh81nlpXALAgRvC6gUiQACCCCAAAJJIcC7v1vNTPB2qz2s1obOZ5WXwi0IELwtoFIkAggggAACCCSFAO/+bjVzwgfvzZs3y8iRI2X58uWyfv16KVeunPl/7zh9+rSMGjVK5syZIxs2bJBTp05JpUqVpF+/fnL77benaq1SpUrJ9u3bz2nBY8eOSZ48eQI/P3LkiHTv3l2mTZsmJ06ckAYNGsi4ceOkZMmSqa7dtGmTdO7cWRYvXiz58+eXFi1ayLBhwyRv3rypzps7d6707t1bNm7cKFdccYV07dpVnnzySd9PEp3PNxkXxFiA4B3jBuD2CCCAAAIIIBC3Arz7u9V0CR+8P/roI+nYsaPUqFFDNOieOXMmVfD+9ddfTZh95JFHpGHDhpIrVy5544035L333pOZM2dKs2bNAi2mwfvmm2+Wbt26pWpFLTtbtmyBn+k1a9asMYG+YMGCkpKSIocPH5Z169YFQvXBgwelYsWKJoz37dtX9u3bZwJ1kyZNZMqUKYGyli1bJnXq1JGHH35YHnzwQVm6dKn5o8CkSZOkTZs2vp4mOp8vLk52QIDg7UAjUAUEEEAAAQQQiEsB3v3daraED94atLNnz27UW7VqJatWrTpnxFtD8UUXXRRombNnz0q1atVMaF64cGGq4K2hevz48Rm24ooVK+SWW24xI+hNmzY15+3YsUPKli1rRr3btWtnfjZ8+HAZOHCgGUEvWrSo+dnUqVOlZcuWZuS9fPny5md33HGH/Pzzz6Llekfbtm1l9uzZop3J+93CeazofOEocY5LAgRvl1qDuiCAAAIIIIBAPAnw7u9WayV88A7mTi94Z9Qcjz32mCxZskS+++47X8FbR6PHjh1rwnLwKHj9+vWlQIECMmvWLFNe3bp1pXDhwqIj8t6h09ILFSokQ4YMMaPq+u8a/nX6eZcuXQLnLVq0SOrVq2f+iFC1atWwnyg6X9hUnOiIAMHbkYagGggggAACCCAQdwK8+7vVZATvdNpDR8krVKgg1113ncyYMSNV8D506JAcPXrUTEnXKeA6cq3fhHvH/fffb0a49Zvy4KNDhw6yYMEC0W/O9ShWrJi0bt3ahOrgQ+9bs2ZNmTx5shn51n+fN2+emYLuHfv37zfXv/3222b6ebgHnS9cKc5zRYDg7UpLUA8EEEAAAQQQiDcB3v3dajGCdzrtMWbMGDPC/Omnn5pw7R26EJp+z33VVVfJ1q1bzcj03r17Ze3atVKmTBlzmn4nniNHDpk/f36qkvv06SMTJkwwI+F6aHAfNGiQ9OzZM9V5tWvXNqF6+vTp5ntu/Xf9zlunr3uHLgCn12s9tU4ZHTqFXv/nHbt375bq1avLzp07zXftHAi4LkDwdr2FqB8CCCCAAAIIuCpA8HarZQjeadpDp3E3atRInnrqKRkxYsR5W0uDrK6Srt9la6j2gnfOnDnNKHXwoauST5w4UQ4cOBAI3oMHD5YePXqkOq9WrVpSokQJ+eCDDwLBW0fPNfCnDd46pb1Tp04Z1rF///4yYMCAc/47wdutTkhtMhYgePN0IIAAAggggAACkQkQvCNzs3UVwTtIVlcd1xHuxo0by7vvvpvqG+2MGuDOO++Un376KbD4mUtTzRnxttVtKDerBAjeWSXNfRBAAAEEEEAg0QQI3m61KMH7v+2xZcsWM61bVxPXaeK5c+cOq6V05XIdxfZWHWdxtbDYOAmBsATuuece+fDDD+W1116TRx99NKxrOAkBBBBAAAEEEEBAzA5IV155JZ+ZOvIwELxFZM+ePaJTvHUFcZ1qrv8fzrFr1y4T1B966KHAFmPedmLBC6Lp1G79BjztdmL6jbduJ1akSBFzOx1lb9GixTnbieme3/qdt3folmS6xzjbiYXTSpwTzwJe8H799dfNdoAcCCCAAAIIIIAAAuEJELzDc8qqsxI+eOsK5HPnzjWeL774oujI9ujRo82/65ZeusWXriKuP58yZYoUL148lb23qNk777xj9ubWfbUvu+wys7ja0KFDzWJpq1evltKlSweu072+dcG1UaNGmRCfkpIiuhq6TmXPmzevOU/DdMWKFaVUqVLSt29f2bdvn3Tt2tVMc9d6eIcGbp3+rqFDvyXXBde0vEmTJkmbNm18PSd0Pl9cnOyAACPeDjQCVUAAAQQQQACBuBTg3d+tZkv44L1t27ZUoTiYf+HChSb4BofmtM1z9uxZ8yNd4ExXIP/mm29MaNY9uBs0aCADBw40244FH/ptdffu3WXatGly8uRJc56OdpcsWTLVeZs2bTKLo+l+4fny5TOj3bo9mRfOvZP1Dwe9evWSjRs3mtXINaDr9mR+DzqfXzHOj7UAwTvWLcD9EUAAAQQQQCBeBXj3d6vlEj54u8Ud29rQ+WLrz939CzDV3L8ZVyCAAAIIIIAAAirAu79bzwHB2632sFobOp9VXgq3IMCItwVUikQAAQQQQACBpBDg3d+tZiZ4u9UeVmtD57PKS+EWBAjeFlApEgEEEEAAAQSSQoB3f7eameDtVntYrQ2dzyovhVsQYKq5BVSKRAABBBBAAIGkEODd361mJni71R5Wa0Pns8pL4RYEGPG2gEqRCCCAAAIIIJAUArz7u9XMBG+32sNqbeh8Vnkp3IIAwdsCKkUigAACCCCAQFII8O7vVjMTvN1qD6u1ofNZ5aVwCwJMNbeASpEIIIAAAgggkBQCvPu71cwEb7faw2pt6HxWeSncggDB2wIqRSKAAAIIIIBAUgjw7u9WMxO83WoPq7Wh81nlpXALAgRvC6gUiQACCCCAAAJJIcC7v1vNTPB2qz2s1obOZ5WXwi0IELwtoFIkAggggAACCCSFAO/+bjUzwdut9rBaGzqfVV4KtyDQvHlzmTFjhrz++uvSqlUrC3egSAQQQAABBBBAIDEFePd3q10J3m61h9Xa0Pms8lK4BQGCtwVUikQAAQQQQACBpBDg3d+tZiZ4u9UeVmtD57PKS+EWBJhqbgGVIhFAAAEEEEAgKQR493ermQnebrWH1drQ+azyUrgFAUa8LaBSJAIIIIAAAggkhQDv/m41M8HbrfawWhs6n1VeCrcgQPC2gEqRCCCAAAIIIJAUArz7u9XMBG+32sNqbeh8Vnkp3IIAU80toFIkAggggAACCCSFAO/+bjUzwdut9rBaGzqfVV4KtyDAiLcFVIpEAAEEEEAAgaQQ4N3frWYmeLvVHlZrQ+ezykvhFgQI3hZQKRIBBBBAAAEEkkKAd3+3mpng7VZ7WK0Nnc8qL4VbEGCquQVUikQAAQQQQACBpBDg3d+tZiZ4u9UeVmtD57PKS+EWBBjxtoBKkQgggAACCCCQFAK8+7vVzARvt9rDam3ofFZ5KdyCgBe833jjDXnkkUcs3IEiEUAAAQQQQACBxBTg3d+tdiV4u9UeVmtD57PKS+EWBAjeFlApEgEEEEAAAQSSQoB3f7eameDtVntYrQ2dzyovhVsQIHhbQKVIBBBAAAEEEEgKAd793Wpmgrdb7WG1NnQ+q7wUbkGA4G0BlSIRQAABBBBAICkEePd3q5kJ3m61h9Xa0Pms8lK4BQGCtwVUikQAAQQQQACBpBDg3d+tZiZ4u9UeVmtD57PKS+EWBAjeFlApEgEEEEAAAQSSQoB3f7eameDtVntYrQ2dzyovhVsQIHhbQKVIBBBAAAEEEEgKAd793Wpmgrdb7WG1NnQ+q7wUbkGA4G0BlSIRQAABBBBAICkEePd3q5kJ3m61h9Xa0Pms8lK4BQGCtwVUikQAAQQQQACBpBDg3d+tZiZ4u9UeVmtD57PKS+EWBAjeFlApEgEEEEAAAQSSQoB3f7eameDtVntYrQ2dzyovhVsQIHhbQKVIBBBAAAEEEEgKAd793Wpmgrdb7WG1NnQ+q7wUbkGA4G0BlSIRQAABBBBAICkEePd3q5kJ3m61h9Xa0Pms8lK4BQGCtwVUikQAAQQQQACBpBDg3d+tZiZ4u9UeVmtD57PKS+EWBO6++2756KOP5I033pBHHnnEwh0oEgEEEEAAAQQQSEwB3v3dateED96bN2+WkSNHyvLly2X9+vVSrlw58/9pj7lz50rv3r1l48aNcsUVV0jXrl3lySefPOc8LWv8+PGyZ88eqVSpkjz//PNSr169VOcdOXJEunfvLtOmTZMTJ05IgwYNZNy4cVKyZMlU523atEk6d+4sixcvlvz580uLFi1k2LBhkjdv3lTnhVu3UI8WnS+UEP/dNQGCt2stQn0QQAABBBBAIF4EePd3q6USPnjraFnHjh2lRo0aokH3zJkz5wTvZcuWSZ06deThhx+WBx98UJYuXSr9+vWTSZMmSZs2bQItpqG7V69e8txzz0mVKlXklVdekRkzZsjKlStNCPeOZs2ayZo1a2TUqFFSsGBBSUlJkcOHD8u6desCofrgwYNSsWJFE8b79u0r+/btM2G/SZMmMmXKlEBZ4dYtnMeKzheOEue4JEDwdqk1qAsCCCCAAAIIxJMA7/5utVbCB28N2tmzZzfqrVq1klWrVp0TvO+44w75+eefZcWKFYHWadu2rcyePVv0gdXrdeS6ePHioj8fMWKEOe/06dMmcN9www3y7rvvmp9pGbfccovMmTNHmjZtan62Y8cOKVu2rBn1bteunfnZ8OHDZeDAgbJ9+3YpWrSo+dnUqVOlZcuWsmHDBilfvrz5WTh1C/eRovOFK8V5rgh4wfvNN980fxjjQAABBBBAAAEEEAhPgHf/8Jyy6qyED97BkOkFbw3UOiqtU7y7dOkSOH3RokVmCrkG9apVq8rChQvNlHEdya5cuXLgvAEDBpiR7UOHDkm2bNnMSPnYsWNNkNd/94769etLgQIFZNasWeZHdevWlcKFC5vvV71D61KoUCEZMmSIdOvWzYT9cOoW7sNC5wtXivNcEWDE25WWoB4IIIAAAgggEG8CvPu71WJJH7x1dLlChQoyb948M83bO/bv3y/FihWTt99+20w/nzBhgnTo0EGOHj2a6hvs999/X+6//37ZuXOn+TZc/1lHuPWb8uBDr12wYIHoN+d6aNmtW7c2gT/40LrUrFlTJk+ebEa+w6lbuI8UnS9cKc5zRYDg7UpLUA8EEEAAAQQQiDcB3v3darGkD976PXft2rVFv6XWKeLecerUKcmVK5eMGTPGLICmo9CDBg2S48ePp2rBTz75RBo2bChfffWVmXKu/5wjRw6ZP39+qvP69OljwruOhOuhZWt5PXv2THWe1kVD+fTp08235uHULaNHSr8r1/95x+7du6V69eqBPxK49ShSGwTOFWCqOU8FAggggAACCCAQmQDBOzI3W1cRvP8bbnWEWhdgSxu8ddp4p06dTPAePHiwHDt2LFVbfPzxx9KoUSOzcJp+763BO2fOnGYEPfjQFdMnTpwoBw4cCARvLa9Hjx6pzqtVq5aUKFFCPvjgg0DwDlW3jB6O/v37i06FT3t4o/O2HirKRSBaAox4R0uSchBAAAEEEEAg2QQI3m61eNIH73Cnc3tTzTV458mTJ9CKLk81Z8Tbrc5GbfwLELz9m3EFAggggAACCCCgAgRvt56DpA/e4S5gxuJqbj241CY5BJhqnhztzG+JAAIIIIAAAtEXIHhH3zQzJSZ98FY83bJL99XW77y9Q7f9mjlz5jnbienPvQXRdDsx/a5bp5in3U4seLE2ndpdpkyZc7YT02+8dTuxIkWKmNtqGS1atDhnO7FQdQv3AaDzhSvFea4IELxdaQnqgQACCCCAAALxJsC7v1stlvDBW1chnzt3rlF/8cUXZcuWLTJ69Gjz77ql1yWXXGICd506dcw+37qPti5qlpKSIpMmTZI2bdoEWmzkyJHSq1cvGTp0qFSpUsWsPK6LoK1cudKEb+9o1qyZrF271mwzptuBaVm63Zh+B543b15zmobpihUrSqlSpaRv376yb98+6dq1qzRu3FimTJkSKCvcuoXzWNH5wlHiHJcECN4utQZ1QQABBBBAAIF4EuDd363WSvjgvW3bNildunS66jp9XPfq1kPDuYbqjRs3mm3BNATrFmDBx9mzZ0XD9/jx42Xv3r0mbI8YMUJ0j+7gQ7+t7t69u0ybNk1Onjxp9v8eN26clCxZMtV5mzZtMgu3LVmyRPLly2dGu4cPH55qu7Jw6xbOY0XnC0eJc1wSIHi71BrUBQEEEEAAAQTiSYB3f7daK+GDt1vcsa0NnS+2/tzdv8Bdd91lPvl488035eGHH/ZfAFcggAACCCCAAAJJKsC7v1sNT/B2qz2s1obOZ5WXwi0IELwtoFIkAggggAACCCSFAO/+bjUzwdut9rBaGzqfVV4KtyDAVHMLqBSJAAIIIIAAAkkhwLu/W81M8HarPazWhs5nlZfCLQgw4m0BlSIRQAABBBBAICkEePd3q5kJ3m61h9Xa0Pms8lK4BQGCtwVUikQAAQQQQACBpBDg3d+tZiZ4u9UeVmtD57PKS+EWBJhqbgGVIhFAAAEEEEAgKQR493ermQnebrWH1drQ+azyUrgFAUa8LaBSJAIIIIAAAggkhQDv/m41M8HbrfawWhs6n1VeCrcgQPC2gEqRCCCAAAIIIJAUArz7u9XMBG+32sNqbeh8Vnkp3IIAU80toFIkAggggAACCCSFAO/+bjUzwdut9rBaGzqfVV4KtyDAiLcFVIpEAAEEEEAAgaQQ4N3frWYmeLvVHlZrQ+ezykvhFgS84P3WW2/JQw89ZOEOFIkAAggggAACCCSmAO/+brUrwdut9rBaGzqfVV4KtyDAiLcFVIpEAAEEEEAAgaQQ4N3frWYmeLvVHlZrQ+ezykvhFgQI3hZQKRIBBBBAAAEEkkKAd3+3mpng7VZ7WK0Nnc8qL4VbECB4W0ClSAQQQAABBBBICgHe/d1qZoK3W+1htTZ0Pqu8FG5BwAveb7zxhjzyyCMW7kCRCCCAAAIIIIBAYgrw7u9WuxK83WoPq7Wh81nlpXALAgRvC6gUiQACCCCAAAJJIcC7v1vNTPB2qz2s1obOZ5WXwi0IELwtoFIkAggggAACCCSFAO/+bjUzwdut9rBaGzqfVV4KtyDgBe/XX39dWrVqZeEOFIkAAggggAACCCSmAO/+brUrwdut9rBaGzqfVV4KtyBA8LaASpEIIIAAAgggkBQCvPu71cwEb7faw2pt6HxWeSncggDB2wIqRSKAAAIIIIBAUgjw7u9WMxO83WoPq7Wh81nlpXALAn/6059k1qxZwlRzC7gUiQACCCCAAAIJLcC7v1vNS/B2qz2s1obOZ5WXwi0IeMH7tddek0cffdTCHSgSAQQQQAABBBBITAHe/d1qV4K3W+1htTZ0Pqu8FG5BwJtqTvC2gEuRCCCAAAIIIJDQArz7u9W8BG+32sNqbeh8Vnkp3IIAI94WUCkSAQQQQAABBJJCgHd/t5qZ4O1We1itDZ3PKi+FWxDwgverr74qrVu3tnAHikQAAQQQQAABBBJTgHd/t9qV4O1We1itDZ3PKi+FWxAgeFtApUgEEEAAAQQQSAoB3v3damaCt1vtYbU2dD6rvBRuQYDgbQGVIhFAAAEEEEAgKQR493ermQnebrWH1drQ+azyUrgFAS94T548WR577DELd6BIBBBAAAEEEEAgMQV493erXQnebrWH1drQ+azyUrgFAYK3BVSKRAABBBBAAIGkEODd361mJni71R5Wa0Pns8pL4RYEvOD9yiuvSJs2bSzcgSIRQAABBBBAAIHEFODd3612JXi71R5Wa0Pns8pL4RYEvH28Cd4WcCkSAQQQQAABBBJagHd/t5qX4O1We1itDZ3PKi+FWxBgxNsCKkUigAACCCCAQFII8O7vVjMTvN1qD6u1ofNZ5aVwCwJe8H755Zfl8ccft3AHikQAAQQQQAABBBJTgHd/t9qV4O1We1itDZ3PKi+FWxDwppoTvC3gUiQCCCCAAAIIJLQA7/5uNS/B+7/tUa9ePVm0aFG6rfPOO+/IAw88IK1atZI333zznHPmzZsnTZo0SfXzkSNHyvjx42XPnj1SqVIlef7550XvEXwcOXJEunfvLtOmTZMTJ05IgwYNZNy4cVKyZMlU523atEk6d+4sixcvlvz580uLFi1k2LBhkjdvXl9PE53PFxcnOyDAiLcDjUAVEEAAAQQQQCAuBXj3d6vZCN7/bY8NGzbI4cOHU7XO3//+d/nggw9k9+7dUrRoURO8Nfz+4x//SHVe+fLlpVChQoGfaeju1auXPPfcc1KlShXRhaFmzJghK1euNCHcO5o1ayZr1qyRUaNGScGCBSUlJcXUYd26dYFQffDgQalYsaIJ43379pV9+/ZJ165dTdCfMmWKr6eJzueLi5MdEPCC96RJk6Rt27YO1IgqIIAAAggggAAC8SHAu79b7UTwPk97lClTRjRUz5kzx5ylwXvVqlWyfv36DK/SkevixYubkDBixAhz3unTp03gvuGGG+Tdd981P1uxYoXccsstpuymTZuan+3YsUPKli1rRr3btWtnfjZ8+HAZOHCgbN++3YR/PaZOnSotW7YU/WOB1i/cg84XrhTnuSLgTTUneLvSItQDAQQQQAABBOJFgHd/t1qK4J1Be3z++edSq1YtM7r917/+NezgvXDhQjNlXEeyK1euHCh9wIABZmT70KFDki1bNunXr5+MHTtWfv75Z/Pv3lG/fn0pUKCAzJo1y/yobt26UrhwYfnoo48C52i41xH2IUOGSLdu3cJ+ouh8YVNxoiMCjHg70hBUAwEEEEAAAQTiToB3f7eajOCdQXt07NhRXn/9dTO1W7+r1kNHvN977z254IIL5OjRo2YUW6d/33333YFSJkyYIB06dDD/Pfgb7Pfff1/uv/9+2blzp1xxxRXmn3WEe/ny5alqoNcuWLBANm/ebH5erFgxad26tfmmO/ioUKGC1KxZUyZPnhz2E0XnC5uKEx0R8IL3xIkT5YknnnCkVlQDAQQQQAABBBBwX4B3f7faiOCdTnucOnVKLr/8crn99tvNtG7vGDNmjOTMmVM09Oq31y+99JL861//Eg3V9913nzlNR6EHDRokx48fT1XyJ598Ig0bNpSvvvrKTDnXf86RI4fMnz8/1Xl9+vQRDe86Eq5Hrly5THk9e/ZMdV7t2rVNKJ8+fXqGT5R+Lx783bp+q169evVA+HfrUaQ2CJwrQPDmqUAAAQQQQAABBCITIHhH5mbrKoJ3OrK6Srl+d63TvXUBtIyOM2fOyK233mrCrX5v7QXvwYMHy7Fjx1Jd9vHHH0ujRo3Mwmk6Uq7BW0O83iv46N27t+jo3oEDBwLBW8vr0aNHqvN0GnyJEiXM4m8ZHf379xed4p728EbdbT1UlItAtAQI3tGSpBwEEEAAAQQQSDYBgrdbLU7wTqc9HnroIROIdYRYR5zPd+g2Yc8880xgark31VyDd548eQKXxmKqOSPebnU2auNfwAveOrvEW3DQfylcgQACCCCAAAIIJJ8AwdutNid4p2kPDcy6KrmuGq4v+6EOXblcR6O9b7pZXC2UGP8dgfAFCN7hW3EmAggggAACCCAQLEDwdut5IHinaQ9dPO2BBx6Qzz77TG677bbztpZONdctwTR0e1uMeduJ6eictyCabiem33XrFPO024npyLruya2HTgHXLczSbiem33jrdmJFihQx52kZLVq0YDsxt/oStbEg4AVvnUnSvn17C3egSAQQQAABBBBAIDEFCN5utSvBO0176L7BX375pWzbti3VNl8afHVVcw28utf2L7/8YkbEdYRbv7Nu3rx5oKSRI0dKr169ZOjQoVKlShWz8rgugrZy5UoTvr1Dvx9fu3at2WasYMGCkpKSYrYb0+/AvRXRdRG3ihUrSqlSpcwK6rrKeteuXaVx48YyZcoUX08Tnc8XFyc7IODt403wdqAxqAICCCCAAAIIxJUA7/5uNRfBO6g9NEzrgmV/+9vfZPjw4alaSlcZf/TRR2X16tWyf/9+yZ07t1SrVs2sNq4hOPg4e/asaPgeP3687N2714RtnZKue3QHH/oNdvfu3WXatGly8uRJs/+3jnaXLFky1XmbNm2STp06yZIlSyRfvnwm/Gv9grcrC+exovOFo8Q5Lgkw4u1Sa1AXBBBAAAEEEIgnAd793Wotgrdb7WG1NnQ+q7wUbkHAC94vvviiPPnkkxbuQJEIIIAAAggggEBiCvDu7ynwJqEAACAASURBVFa7Erzdag+rtaHzWeWlcAsC3lRzgrcFXIpEAAEEEEAAgYQW4N3freYleLvVHlZrQ+ezykvhFgQY8baASpEIIIAAAgggkBQCvPu71cwEb7faw2pt6HxWeSncgoAXvHW9hA4dOli4A0UigAACCCCAAAKJKcC7v1vtSvB2qz2s1obOZ5WXwi0IeFPNCd4WcCkSAQQQQAABBBJagHd/t5qX4O1We1itDZ3PKi+FWxBgxNsCKkUigAACCCCAQFII8O7vVjMTvN1qD6u1ofNZ5aVwCwJe8NZt9jp27GjhDhSJAAIIIIAAAggkpgDv/m61K8HbrfawWhs6n1VeCrcgQPC2gEqRCCCAAAIIIJAUArz7u9XMBG+32sNqbeh8Vnkp3IKAF7zHjh0rnTp1snAHikQAAQQQQAABBBJTgHd/t9qV4O1We1itDZ3PKi+FWxDwFlcjeFvApUgEEEAAAQQQSGgB3v3dal6Ct1vtYbU2dD6rvBRuQYARbwuoFIkAAggggAACSSHAu79bzUzwdqs9rNaGzmeVl8ItCHjBe8yYMdK5c2cLd6BIBBBAAAEEEEAgMQV493erXQnebrWH1drQ+azyUrgFAW+qOcHbAi5FIoAAAggggEBCC/Du71bzErzdag+rtaHzWeWlcAsCjHhbQKVIBBBAAAEEEEgKAd793Wpmgrdb7WG1NnQ+q7wUbkHAC95///vf5amnnrJwB4pEAAEEEEAAAQQSU4B3f7faleDtVntYrQ2dzyovhVsQ8KaaE7wt4FIkAggggAACCCS0AO/+bjUvwdut9rBaGzqfVd4sKXzPnj2ybNkyad68eZbcL9Y3YcQ71i3A/RFAAAEEEEAgXgV493er5QjebrWH1drQ+azyZknh5cuXl2+//VYOHTokBQsWzJJ7xvImXvB+4YUX5G9/+1ssq8K9EUAAAQQQQACBuBLg3d+t5iJ4u9UeVmtD57PKmyWF58yZU06fPi0//fSTFClSJEvuGcubELxjqc+9EUAAAQQQQCCeBXj3d6v1CN5utYfV2tD5rPJmSeG5cuWSU6dOyf79+6Vo0aJZcs9Y3oTgHUt97o0AAggggAAC8SzAu79brUfwdqs9rNaGzmeVN0sKT9bgPXr0aOnSpUuWGHMTBBBAAAEEEEAgEQR493erFQnebrWH1drQ+azyZknhuXPnlt9//1327dsnl1xySZbcM5Y38Ua8Cd6xbAXujQACCCCAAALxKMC7v1utRvB2qz2s1obOZ5U3Swq/4IIL5OTJk7J3714pVqxYltwzljfxgveoUaOka9eusawK90YAAQQQQAABBOJKgHd/t5qL4O1We1itDZ3PKm+WFJ4nTx45ceKE6LZixYsXz5J7xvIm3j7eBO9YtgL3RgABBBBAAIF4FODd361WI3i71R5Wa0Pns8qbJYV7wXv37t1SokSJLLlnLG/CiHcs9bk3AggggAACCMSzAO/+brUewdut9rBaGzqfVd4sKTxfvnxy7Ngx2bVrl1x66aVZcs9Y3sQL3iNHjpRu3brFsircGwEEEEAAAQQQiCsB3v3dai6Ct1vtYbU2dD6rvFlSeP78+eXo0aPy448/ymWXXZYl94zlTbyp5gTvWLYC90YAAQQQQACBeBTg3d+tViN4u9UeVmtD57PKmyWFFyhQQH777TfRtrz88suz5J6xvAkj3rHU594IIIAAAgggEM8CvPu71XoEb7faw2pt6HxWebOkcC9479ixQ6688sosuWcsb+IF7+eff166d+8ey6pwbwQQQAABBBBAIK4EePd3q7kI3m61h9Xa0Pms8mZJ4RdeeKH8+uuvsm3bNilZsmSW3DOWN/GmmhO8Y9kK3BsBBBBAAAEE4lGAd3+3Wo3g7VZ7WK0Nnc8qr7XC9+3bJ8uXLxcd/S1YsKAcOXJEtm7dKqVLl7Z2T6/gLVu2mG/J8+bNa/1e6d2AEe+YsHNTBBBAAAEEEEgAAd793WpEgrdb7WG1NnQ+q7zWCq9SpYqsXbtWfvrpJxO2NXhrIC5Tpoy1e2rB+i25Tm3XUecZM2ZYvVdGhXvBe8SIEfL000/HpA7cFAEEEEAAAQQQiEcB3v3dajWCt1vtYbU2dD6rvNYKD15QrXz58iZ4b968WcqWLWvtnlrwgQMHpGjRopIzZ075/fffrd6L4B0TXm6KAAIIIIAAAgkswLu/W41L8HarPazWhs5nldda4YUKFZLDhw/L9u3bpUKFCuYb76wM3jly5JBTp05Z+/3OV7A34j18+HB55plnolqHb7/9Vn755RepWbNmVMulMAQQQAABBBBAwAUB3v1daIX/1YHgLSJvvPGGPProo+e0TI8ePWTYsGGBn8+dO1d69+4tGzdulCuuuEK6du0qTz755DnX6Z7D48ePlz179kilSpVEF4aqV69eqvN01FJXaZ42bZqcOHFCGjRoIOPGjTtnwaxNmzZJ586dZfHixaJ7OLdo0cLUKZJvbul8bnW+cGtz8cUXm4Co08tvvPFGE7y///57ufrqq8MtIqLzvBHvWAZvb3E1G8E7W7ZsxuXs2bMR+XARAggggAACCCDgsgDv/m61DsE7KHjPnz9fdHTRO3SfZG/LpmXLlkmdOnXk4YcflgcffFCWLl0q/fr1k0mTJkmbNm0C12jo7tWrlzz33HOi3+a+8sor5vvYlStXmhDuHc2aNZM1a9bIqFGjzIJZKSkpZlRz3bp1gVB98OBBqVixognjffv2FV1kS8N+kyZNZMqUKb6fJDqfbzInLihWrJjs379f9I8w+kxp8NZ/vuaaa6zWz6Wp5gRvq01N4QgggAACCCCQgAK8+7vVqATvoOCt4Ua/aU3vuOOOO+Tnn3+WFStWBP5z27ZtZfbs2aIPdfbs2c3IdfHixUV/rotB6XH69GkTuG+44QZ59913zc+0jFtuuUXmzJkjTZs2NT/TfZn1m10d9W7Xrp35mYaNgQMHminGXr2mTp0qLVu2lA0bNoh+7+vnoPP50XLn3EsvvdTMntCZFjfffLMJ3t99951ce+21VivpUvDWWR46AyWaByPe0dSkLAQQQAABBBBwTYB3f7dahOAdRvDWQK2j0vry36VLl0ALLlq0yEwhX7VqlVStWlUWLlxopozrSHblypUD5w0YMMCMbB86dEj0ZV9HyseOHWuCvPfyryfXr1/frCI9a9Ysc23dunWlcOHC8tFHHwXK0rroqPyQIUOkW7duvp4mOp8vLmdO1pkXu3btMn9sqV69elIFb2+qOcHbmceRiiCAAAIIIIBAnAjw7u9WQxG8g4K3TunVLZt0avfjjz9uFnPS71s18OiiVvPmzTPTvL1DR8j1mrfffttMP58wYYJ06NBBjh49muob7Pfff1/uv/9+2blzp/k2XP9ZR7h1b+bgQ69dsGCBWThLDy27devWqb4z159rXXRBqMmTJ/t6muh8vricOTm94K0Lg1133XVW6+jCN97e4moEb6tNTeEIIIAAAgggkIACvPu71agEbxETdnX6d40aNcwI9MyZM+Wll16S9u3bm0XS9Hvu2rVri37nrVPEvUNXes6VK5eMGTPGLICmo9CDBg2S48ePp2rlTz75RBo2bChfffWVmXKu/6yBXr8pDz769OljwruOhOuhZWt5PXv2THWe1kVD+fTp08/7NOk34/o/79i9e7cZMfX+AODWo0htMhLwgvc333xjnlGdaq7TzsuVK2cVzaXgPXTo0HP6QWZ/eaaaZ1aQ6xFAAAEEEEDAZQGCt1utQ/DOoD2efvppeeGFF0xI3bp1qwneOkKtwSdt8NZp4506dTLBe/DgwXLs2LFUpX788cfSqFEjs3Cafu+twVv3RtYR9OBDV0yfOHGi2T/ZC95aXtpvW2vVqiUlSpSQDz744LxPU//+/UWnuac9CN5udcJQtUkveEfyjX+o+6T97zr745JLLjF/JIrVdmLeVHOCt9/W43wEEEAAAQQQSHYBgrdbTwDBO4P2+OKLL8zosG4hplPP/Uw11+CdJ0+eQMmxmmrOiLdbnS3S2sQqeHufUsQyeHtTzQnekT49XIcAAggggAACySpA8Har5QneGbSHbv+lo9savHXBNBZXc+vBTaba6LoAP/74o+hUc/1j0G+//Wb++frrr7fKsHfvXjOzwoXgrdvzPfvss1H9fZlqHlVOCkMAAQQQQAABxwQI3m41CME7g/bQFcP12219YDV86HZiuq+2fuftHbrtl34PnnY7Mf25Lgalh24npt916xTztNuJBS/WptO/y5Qpc852YvqNt24nVqRIEVOeltGiRQu2E3OrH1mtTXrBe/369WYWhs1DtzDTrcwI3jaVKRsBBBBAAAEEELAjQPC24xppqQRvEWncuLHcfvvtUrFiReOoYfrll1+Wp556ynznrYcG7jp16kirVq3MPtq64FpKSopMmjRJ2rRpE/AfOXKk9OrVS3RqbJUqVczK47oImo6ga/j2jmbNmsnatWvNNmM6mq5l6XZj+h143rx5zWka9LVOpUqVkr59+8q+ffuka9eupr5Tpkzx3eZ0Pt9kTlzgBW8N2zoLQ0e8v/7668DzaquSuhjfZZddRvC2BUy5CCCAAAIIIICARQHe/S3iRlA0wVvEBGwdfdaH88yZM3LttdeaMK0LpgXvs63TzjVU64rSGoY0BOsWYMHH2bNnRcO3roauU3U1bI8YMcLs0R186PfX3bt3l2nTpsnJkyfNdPZx48aZ78mDj02bNpl6LFmyRPLly2dGu4cPH55qu7Jw253OF66UW+d5wVvDtq6qn1XBW/cO1+/LXRjx1oULte9F82CqeTQ1KQsBBBBAAAEEXBPg3d+tFiF4u9UeVmtD57PKa61wL3jrbAgN3rpPvLdCvrWbipjvyvXeBG+bypSNAAIIIIAAAgjYEeDd345rpKUSvCOVi8Pr6Hxx2GgiJvxqCNZ94GvWrGmCt7cnvM3fyHteXAjeuq2ebrcXzYMR72hqUhYCCCCAAAIIuCbAu79bLULwdqs9rNaGzmeVN8PCdYG948ePS/78+SOqgBe8v/zyS9E93HWquf7zjTfeGFF54V60Y8cO8+lDLIO3t483wTvcVuM8BBBAAAEEEEDg/wvw7u/Wk0Dwdqs9rNaGzmeVN8PCH3nkEXnrrbfMCvfZs2f3XQkveOtifLVr1zbBW//5pptuSrcsXRcgV65cUrp0ad/3Cr5AV9PXhf1iEbx1D3H9HXWthFmzZgnBO1NNycUIIIAAAgggkIQCvPu71egEb7faw2pt6HxWeTMs3JvSfOrUKRNi/R5e8F6zZo0J3jrV/HzBO1pTqLdt22bCeyyCt67mr3uV624D//73v0W31evTp49fuvOeHy2nqFaKwhBAAAEEEEAAgSgJ8O4fJcgoFUPwjhJkPBRD54tNK3kB7/fff5ecOXP6roQXvFevXi233XabCd4awitXrpxuWdEKlD/88IPZWz4Wwdv7HXQLv88++yyugvf3338vRYsWlYsuush3W3MBAggggAACCCAQLQHe/aMlGZ1yCN7RcYyLUuh8sWkmL0TqtnE6Bdzv4QXvVatWmb3kNXhrCNd94tM7ohW8t27dKmXLljXT43WafFYe+gcKvad+07506dK4Ct7qf+WVV4p+I8+BAAIIIIAAAgjESoB3/1jJp39fgrdb7WG1NnQ+q7wZFu4FYV1g7YILLvBdCS94r1y5UurVq2eCt4bwqlWrWg3eW7ZskauvvjomwTt37tyiMwR0+7Tly5fLwIEDpW/fvr7tzndBtP5AkfYetsqN6i9PYQgggAACCCCQ8AK8+7vVxARvt9rDam3ofFZ5QwbvY8eOSZ48eXxXQkdPte1WrFgh9evXz7LgvXnzZrnmmmtiErzz5s1rVoK/+eab5YsvviB4+35quAABBBBAAAEEkl2Ad3+3ngCCt1vtYbU2dD6rvCGDt45Ua6D0e3jBW0d+NXhrgNcwWq1atXSLitaIq66Oft1118XkG+8CBQqY1dt1Or1+z86It9+nhvMRQAABBBBAINkFePd36wkgeLvVHlZrQ+ezyhsyeGuQzJcvn+9KeMF72bJl0qBBAxO8ddq5jgand6QXvHV7roceeshsa1asWLGw6vDdd99JuXLlYjLiXbBgQTly5IjZq/yrr76SAQMGSEpKSlj1DvekaP2BIu39bJUb7u/FeQgggAACCCCAgArw7u/Wc0Dwdqs9rNaGzmeVN2Tw1iCpI7l+j6uuukp27twpn3/+udleS4O3TjuvXr162MG7Z8+eMnz4cNH/Hzp0aFhV+Pbbb6V8+fIxCd6FChWSw4cPyw033CDr1q0jeIfVYpyEAAIIIIAAAgj8T4B3f7eeBoK3W+1htTZ0Pqu8IYO3BskLL7zQdyW84K2re//hD3+IKHjrHthDhgyRp59+WkaMGBFWHTZu3CjXX399TIK3N+JdqVIl+frrr6V///7Sr1+/sOod7km2RqZtlRvu78V5CCCAAAIIIICACvDu79ZzQPB2qz2s1obOZ5U3ZPA+dOiQaKD0e3jBe8mSJdKwYUMTvPV77xo1aqRbVHrBT0OrfifdpUsXGT16dFhV2LBhg1SoUCGmwbtixYqyfv16gndYLcZJCCCAAAIIIIDA/wR493fraSB4u9UeVmtD57PKGzJ4Hzx4UHQKtd/DC96LFy+WRo0ameCt33vrVlvpHekF70GDBplvpDt16iRjx44NqwrffPONaPCNxT7e3oi3Bn+th80R7zNnzohnFhZMiJMY8Y6GImUggAACCCCAQGYFePfPrGB0ryd4R9fT6dLofLFpHi+I/fLLL1K4cGHflfCC92effSaNGzc2wVu/965Zs2bYwVu/6+7Vq5e0b99eJkyYEFYddKRZp3rHMnjrVHcdedcRew3f0Ty8djl9+rT5HaN1ELyjJUk5CCCAAAIIIJAZAd79M6MX/WsJ3tE3dbZEOl9smsYLYgcOHJCLL77YdyW84L1o0SJp0qRJRMF72LBh8uyzz0q7du3kpZdeCqsOuqiZrioey+Cti7vpt+Y2g/fvv/8uOXPmDMsknJMI3uEocQ4CCCCAAAII2Bbg3d+2sL/yCd7+vOL6bDpfbJrPC2I//fSTFClSxHclvOD96aefyh133GGCty60duutt6ZbVnrBzwvefka8dRuvm266KabBW7cz09XVbQbvkydPSq5cuXy3S0YXELyjRklBCCCAAAIIIJAJAd79M4Fn4VKCtwVUV4uk88WmZbwgpntpFy1a1HclvOC9cOFCE7yPHz8uutBarVq1fAdvPyPeX375pVSuXDkmwVtXf//111/luuuuE91PXL9P1728o3l47aKeF1xwQVSKPnv2bGDauv4zBwIIIIAAAgggECsB3v1jJZ/+fQnebrWH1drQ+azyZli4F/D27dsnl1xyie9KeMH7P//5jzRt2tQEb11orXbt2mEHb28Pbz/Be+3atVKlSpWYBu9rr71WNm3aZDV46wyCPHny+G6X9C7Q78W9aesE76iQUggCCCCAAAIIRCjAu3+EcJYuI3hbgnWxWDpfbFrFC9579uyR4sWL+66EF7z//e9/y5133pmp4P3EE0/IxIkTw6rDmjVrpGrVqmbFb135OysPb8T76quvls2bN1sN3r/99pvky5cvKr/eqVOnAtPWCd5RIaUQBBBAAAEEEIhQgHf/COEsXUbwtgTrYrF0vti0ihe8d+/eLSVKlPBdCS94f/LJJ9KsWTMTvHWF89tuuy3dstL7xnjEiBHSo0cP8RO8V69eLdWqVYtp8C5btqxs2bJF+vbta/Yhj+bhOemU9vz580elaP1e3Ju2TvCOCimFIIAAAggggECEArz7Rwhn6TKCtyVYF4ul88WmVbyAt2vXLrn00kt9V8IL3h9//LEJ3idOnBBd4bxOnTpWg/cXX3wh1atXj0nw9vbxLlOmjGzdutVq8D58+LDoCHs0Dm0bb9o6wTsaopSBAAIIIIAAApEK8O4fqZyd6wjedlydLJXOF5tm8YL3jz/+KJdddpnvSnjBe8GCBfKnP/3JBG9d4bxu3bpWgrdOK9f9wjV0P/fcczH5xtsL3qVLl5YffvjBavA+dOiQ6P2icej34t60dYJ3NEQpAwEEEEAAAQQiFeDdP1I5O9cRvO24OlkqnS+yZtF9nnUrsEhGq/WOXvBW/8svv9x3JUqWLCk7duyQ+fPny1133WWCt65wXq9ePd/Bu23btjJp0qTz1kG/eS5QoEDgnFh+412qVCnZtm2b9OnTRwYNGuTb7nwXeO3yyy+/SOHChaNS9tGjRwPT1gneUSGlEAQQQAABBBCIUIB3/wjhLF1G8LYE62KxdL7IWkVXAtew+vPPP8tFF13kuxAv4Gl4vvLKK31fr+Fz+/btMm/ePLn77ruTKnjrHx30d7cZvCNt1/QaUr8X96atE7x9P+pcgAACCCCAAAJRFODdP4qYUSiK4B0FxHgpgs4XWUvpSuS6FZhua3XNNdf4LsQL3hogddq438ML3nPnzpXmzZub4K1bi9WvXz/dotJbXO3555+XZ555RsIZ8T5y5Mg5U6+zOkR6q5qrl/7Bonfv3qJTwnWhNXWIxuE56WyGIkWKRKNICbbLarOo/AIUggACCCCAAAIJI8C7v1tNSfB2qz2s1obOFxmvjlKr3fr166VChQq+C/ECnk6Z1hFcv4cXvOfMmSP33HOPCd66tViDBg18B+/HH39cXn755fNWQRcbK1SoUOCcWE41V/udO3ea4D1kyBBTp2gFWq9d9u/fL0WLFvXbLOmer38c8KatR6ueUakYhSCAAAIIIIBA0gnw7u9WkxO83WoPq7Wh80XGqytr6wJfuq915cqVfRfiBTwtQ0O038ML3rNnz5Z7773XBG/dWuz222+3ErwPHjyYakp9LIP3FVdcYf7oYTN47927V4oVK+a3WdI9P9iO4B0VUgpBAAEEEEAAgQgFePePEM7SZQRvS7AuFkvni6xVdC9p3dJK97WuUqWK70K84K3TpDXE+z284D1r1iy57777rAdvXWzs4osvTlXNrA6R3lRzXYxOV4Pv1auXWWFdj2jVxWuXPXv2iH5OEI1Dvxf3pq1Hq57RqBdlIIAAAggggEDyCfDu71abE7zdag+rtaHzRcZ79dVXm2+LMxu8N2/eLBri/R5e8J45c6b8+c9/NsFb9/T+wx/+kG5R6X3jPXLkSHn66aclnKnmBw4cOGfqdVaHSF1VXVdX1+3XdP9zm8E70v3V08MPtstqM7/PFecjgAACCCCAQGIL8O7vVvsSvN1qD6u1ofNFxusF71WrVknVqlV9F+IF4e+//160LL+HF7w/+ugjE7xPnjwp//rXv6Rhw4a+g3ebNm3klVdeOW8VdLGxSy65JNU5WR0iveBdokQJ0RFpm8E70v3V00PU78W9aetZbeb3ueJ8BBBAAAEEEEhsAd793Wpfgrdb7WG1NnS+yHijFbwjXRXdC94zZsyQ+++/3wTvBQsWSKNGjcIO3qNGjZLu3btLOMFbV3BPO/U6q0OkN9Vc66HfYD/77LMydOhQ8/tGqy7eH0R08Tb9ljwaR7BdtOoZjXpRBgIIIIAAAggknwDv/m61OcHbrfawWhs6X2S80Qre3333nVx77bW+K+EF7w8//FD+8pe/RBS8vanm4QRvDbo60hx8ZHWI9IK3jh5rmLUZvCPdXz29htTR+UsvvTSqfyDw/cBwAQIIIIAAAgggIGIWqPV2iInWIAOwkQsQvEXk/fffl3/84x/mG15dHEm/w23fvr088cQTkj17dqPbqlUrefPNN8+RnjdvnjRp0iTVzzXkjB8/3kyRrVSpkugeyvXq1Ut1ju73qyOQ06ZNM9/s6tZQ48aNO2e7KR0l7dy5syxevFjy588vLVq0kGHDhknevHl9tzqdzzeZuUD37tbvszM71Xzjxo1Srlw535Xwgvf06dPlgQceMMF7/vz50rhx43TLOt833uEE7927d5tvq10I3jrlXadv2wzekW7zlh5+sF1W/7HC94PFBQgggAACCCCQ0AK8+7vVvARvEbnllltM4G3evLmZYrtw4UIzrfVvf/ubCc1e8NbwqwE9+ChfvnyqPY81dHvfo+oK2Po9rU4RXrlypQnh3tGsWTOzPZVOAS5YsKCkpKSI7p+8bt26QKjWrYkqVqxo6ta3b18z8te1a1cT9KdMmeL7SaLz+SZLFby/+OILqVatmu9CvCC8YcMG0efF7+EF7w8++MAE799//13S+4OPV256wdvPVHNdbExXE3cheOv+2vrNec+ePc0fnPSIVqDN7DZv6bWjfi/u/UU5WvX0+7xwPgIIIIAAAgggoAK8+7v1HBC8RcyIWtrFpDTgvvTSS6Lh94ILLjAj3jriuX79+gxbUEeuNbi3bdtWRowYYc47ffq0Cdw33HCDvPvuu+ZnK1asMGF/zpw50rRpU/Mzne6qI+066t2uXTvzs+HDh8vAgQNl+/btgVWmp06dKi1btpRIQhydL7LO54146x9Pbr75Zt+FeAHvm2++keuvv9739V7w1tkROuNBg/fcuXPljjvuSLes8wXvxx57TCZPnnzeOnjPSSyDt7e4mm7NpSuF2wzekW7zlh6ifi9+1VVXRfUPBL4fGC5AAAEEEEAAAQQI3s49AwTvDJrk7bfflocffthsZaTfbIYTvHWkXKeM60h25cqVAyUPGDDAjGwfOnRINBT169dPxo4da6a1eyFJT65fv75o4ND9mvWoW7euFC5cWHQ1a+/QcF+oUCEZMmSIdOvWzdcDRfD2xRU42Qve+geT6tWr+y7Ea2P9o02FChV8X+8Fb/0k4q9//WtEwXv06NHmeQkneAeHR6+yWT166wXviy66SHRf8R49epg/ROkRrbp47RLpNm/pNaT+AU1nqESznr4fGC5AAAEEEEAAAQQI3s49AwTvDJpER611aq9O786RI4cJ3u+9954Z/T569KgZxdbpYmPj2QAAIABJREFU33fffXeghAkTJkiHDh3Mfw/+BlsDk65G7a2erP+sL+jLly9PdXe9Vler1iCghy4s1bp168AUW+9kDW81a9YMOXKZ9lcjeEfW/3RBNN0KbNmyZWamgt/DC3j6GUHw5wbhlqNBTp+Xf/7zn2a2g454B8+WSFtOeiPeXvDW5+nVV1897611hoWG/eAjWmE33N/ZC976hyeddWIzeEe62nx6v4t+L166dGmCd7gNzXkIIIAAAgggYE2Ad39rtBEVTPBOh02nlGuw1ZHpPn36mDPGjBkjOXPmNCOWGgR0Grrupayh+r777jPn6Cj0oEGD5Pjx46lK/eSTT8yey1999ZWZcq7/rGFeF8gKPvReGt51JFyPXLlymfJ0mm3wUbt2bRPKdbGt8x36zbj+zzt04ScdsY3m9kkRPXVxdpEXvJcuXSq33nqr79p7Qdhrf78FeMFbP1V46KGHTPCePXu23HnnnekWldngHRwevRvEKnjr+gf6DNsM3pGuNp8e/g8//CBlypQx/ymrzfw+V5yPAAIIIIAAAoktQPB2q30J3mnaQ1cir1Gjhlkg6dNPPzXhN73jzJkzJoRpKNDvrfXQ4D148GA5duxYqks+/vhjs+eyN+KpwVtDvC6QFXz07t1bJk6caL5p1UPvreVp6Ag+atWqZbZ70hH58x39+/cXneae9iB4++uE1113neioqC6up3/08Ht4QfjLL7+UG2+80e/lZvRZR6HfeecdE7xPnTplPkfQBfrSO84XvMOZar5161az3kDwkdUh0hvx1v//9ddf5ZlnngmsmxCtunhOka42n569fi+u28/pEa16+n5guAABBBBAAAEEEGCquXPPAME7qEn0G2zd9ktHrJcsWSK6sNP5Dl3xXAOBN7Xcm2quwTtPnjyBS2M11ZwR7+j0Ny94L1q0SOrUqeO7UC/grV27Vm666Sbf13vBWxfW03UHzhe8Nex5W+AFBz8/U82Dw6NX2awOkV7w1k82tD8FB2/9o1fw2gi+Qf97QWZXm0/vvvqZiK4JQPCOtFW4DgEEEEAAAQSiJcCId7Qko1MOwfu/jhq2dV9k71teb4Gk8zHryuU6Gu0FbxZXi85D6VopXvDWGRC64J3fwwt4aRfdC7ccL3jrVnaPPPKICd4zZ86UP/7xj+cUoaFUP2NIG/xeeOEFsxVdON94ax/Q6fXBR6yCt66poAsKBgdv3SnA++NCuIbpnZfZRe/SK1NnRujzQvDOTMtwLQIIIIAAAghEQ4DgHQ3F6JVB8BYxQeaee+6Rzz77zPxPv8MOdWjA0YW2NHR7W4x524npdmDensMaErQ8XVQr7XZiwXsx6/Rv/TY07XZi+o23TjP2Rt+1DN1Siu3EQrVQ9P67F7z/85//mJXn/R5ewNO1A6pWrer3crNKti6upnu36yJ/+rzqSvd/+tOfzilLnzf9jCGj4P3oo4/Ka6+9dt466LZp+rmFC8FbP7fQb9qffvpp0Rkmeujv7/1xwTdm0AVeu3z99ddSsWLFzBQVuFa/Fy9XrhzBOyqaFIIAAggggAACmREgeGdGL/rXErxF5IknnpCXX37ZfEN62223pVLWfZd1OyMNPBp49dtX/XddXE1HuPU76+bNmweuGTlypPTq1UuGDh0qVapUMSuP6yJoGmaCV7TW73N16rFuM6YLSKWkpJjtxvQ7cG9FdF3ETQOBjnjqCuq6wrqOWurIvIYwvwedz6/Y/z9fg5QGKl0k7/bbb/ddiBfwvvjiC6lWrZrv673grVvc6XOo4XrGjBly1113nVOWhtTcuXOfE/z+/ve/S5cuXSSc4J3eNO5YjXhrwNbfNzh4nzx5MsO1F/zgZnbRO+9e+kc4/WOAuuv34t5e7Vlt5ud351wEEEAAAQQQSHwB3v3damOCt0hg8ar0mkbDtY5Ya2BZvXq17N+/37xga4DS1cY1BAcf+rKt4Xv8+PGyd+9eE7Y10KcdKdXvr7t37y7Tpk0TDRK6/7eOdqed4q5TVzt16mS+Oc+XL58J/7qfcfB2ZeE+UnS+cKVSn+cFb13FXhfG83t4AU//+HLzzTf7vTww4v3WW2+Z51CD6IcffphqKzuvUH2WdHq2HsHBL96Cd/78+c1sErXT3yM4eOtnId7v6Bsz6ILMLnrnFaV9UmeiaD11Joq3VzvBOzOtw7UIIIAAAgggkFkB3v0zKxjd6wne0fV0ujQ6X2TN4wVv3WNdV6f3e3gBb8WKFWY7N7+HN+L95ptvmm+0zxe89XMHb2G/RAjenpX+kUr/oKWHt6aCX8e05/v59l5nxVx88cVmJktG5ai3fnbizWwheGe2hbgeAQQQQAABBDIjwLt/ZvSify3BO/qmzpZI54usabzgHfxNvp+SvIC3bNkysy6A3+Oqq64ye6+/8cYbotuBafDWzxeCP3HwytTRYG82RHrBW6eqv/766+etggtTzXV2R/C2fMHBW7cX0xHxzB7e76kzWfSzkPMd6W3R5p0f/N/0e3FvjQiCd2ZbiOsRQAABBBBAIDMCvPtnRi/61xK8o2/qbIl0vsiapnz58vLtt9/KnDlzpGnTpr4L8YLZ559/LjVr1vR9vRe8NTC3adPGBG9dW0AXBEx76GiwF0qDg5+3qnkiBG/9TOPCCy/07Zj2Aj/f3ocTvPVbb12jwdsyjuCd6SaiAAQQQAABBBDIhADv/pnAs3ApwdsCqqtF0vkiaxkveM+aNUt0UTy/hxfali5dKrfeeqvfy8UL3roauQZvDXi6NsC99957Tlm//fab6B7YeqS3j3e8BG8dtdfRe+/o1q2bWYhQD12EUBckzOzh59v7cIK3LrCmI96VK/+/9s4DWopia9tlACQoIFlAMPspmMWAgpj1mgNijog5i/mas2LOy6VerzlizjlgzqKixKtIkCyCoP7/U9c9t06fmTPTE3uGt9ZiAed0V1c/Vd1d7967dq1Zj3+hbdX5IiACIiACIiACIhCXgOb+cYmV9ngJ79LyTVTtevjy6w4T3pm28MpWq4k2EuT17t072+H1fm/C+/bbb3cDBw5sUHgThm3e4FB4I1oJ12YfcELWGypJCDVvSHizq0CrVq1ic4yeYPc5bNiwetunZTo2nRfb6mF9PWu8bcs4ebwL7iJVIAIiIAIiIAIiUAABzf0LgFeCUyW8SwA1qVXq4cuvZ0x4Z9rCK1utJszefPNNt9FGG2U7vN7vu3bt6ug7tqY79NBDvfB+6KGH3G677VbvWMKwW7Zs6X8eCj/2wB48eLDfjqwa1nhHhTfb6A0ZMsTf1y+//OITnRVa4qy9z8XjTZg/Wc1tyzgJ70J7SOeLgAiIgAiIgAgUQkBz/0LoFf9cCe/iM01sjXr48usa9mVmf+ZMCc2y1Wqi7fXXX3d9+vTJdnhG4Y3QJsSc8uCDD7rdd9+93rGEYZs3OBR+bEHH9ne1ILzZ0q9t27axOUZPiLMEIBfhTbQBwtsy10t4F9xFqkAEREAEREAERKAAApr7FwCvBKdKeJcAalKr1MOXX8+Y8M60rjpbrSbaXnvtNde3b99sh2cU3uEvHnjgAde/f/96x06bNs21bt3a/zwUfpdccok77bTTqkZ4syUaodtWQo/3hAkTXPv27WNzzCS8c1kCkIvwxuhBEr711luvHv+CG6sKREAEREAEREAERCAmAc39YwIr8eES3iUGnKTq9fDl1xsmvDN5mbPVaqLt1VdfdZtsskm2wwsS3qx/tjDsUHhfdNFF7owzznAHHnigI0lbQyUJa7yjwvv44493ZGanjB8/3nXs2DE2x0zC+4033nAbb7xxTkwaWuM9ZcoU9+2336Yy18vjXXAXqQIREAEREAEREIECCGjuXwC8Epwq4V0CqEmtUg9ffj1jwjuTlzlbrSZkX3nlFdevX79sh+ckvO+//363xx571DuW9c8Whh0KvwsuuMCdddZZVSO8mzRp4n7//ffU/YXC+6effnKdOnWKzTGT8M5lCUAuHu/Jkye77777LpW5XsK74C5SBSIgAiIgAiIgAgUQ0Ny/AHglOFXCuwRQk1qlHr78embVVVf1a3fvu+8+N2DAgNiVmGh76aWX3GabbRb7fEuuFp6YqS2Iv3bt2vlDQ+F33nnnubPPPtsddNBBjuzoDZUkeLwbEt6M486dO8fmmEl45xKJYEzGjBnjt3cLi/2OEPgRI0akEuhVq/Cm3QsvvLA755xz/JhREQEREAEREAERqE4Cmvsnq98kvJPVHyVtjR6+/PCa8L7nnnvcXnvtFbsSE2Yvvvii23zzzWOfn05433vvvW7PPfesVxeJx2z9cyj8EFHnnntu1Qjvxo0bu3nz5qXu77jjjnNXX321///YsWMdTAotcSIR7NiePXu6zz//PK3wxhP/ww8/pMLWq1V4wx3+UeNNobx1vgiIgAiIgAiIQHkJaO5fXt7ZribhnY1QDf1eD19+nWmh5nfffbfbZ599Yldiou2FF15wW2yxRezz4wjviRMnug4dOtQTTXgu8XpXi8e7IeE9evRo161bt9gcOYHwdbzpJJq7+OKLfR25RCJYH9IXCP+w2O94vhDelkCvGoX3999/7wYNGuRYFiHhndcQ00kiIAIiIAIikBgCmvsnpit8QyS8k9UfJW2NHr788No+3v/617/cvvvuG7sSE2bPP/+823LLLWOfn054Z/K+//zzz6n1z6HwY30367wPPvhgvx94QyUJoeaNGjVy8+fPTzUz9HiPHDnSLbPMMrE5cgIJ0Nq0aeNDqdkPnZJLJIIx6d69uxs1alRa4U0YOm2zdfzVKLy33nprxzi1Uo33kNfA0EkiIAIiIAIiUIMENPdPVqdKeCerP0raGj18+eE14X3nnXe6/fffP3YlJtqee+45t9VWW8U+v0uXLu7HH3+sc96///1vt/fee9eri4zfSy21lP85wtKuTUZzMptXq/A+5phj3LXXXuvvC6/scsstF5sjJ1jyuVB452IQMY7LLrus92qHxX6H6MYbv+mmm/pfV6No3W677dzTTz8t4Z3X6NJJIiACIiACIpAsApr7J6s/JLyT1R8lbY0evvzwmvBmGy6244pbTJg988wzbptttol7uksnvDOFvbPO2BKP/fHHH96zSyG0mr28q0V4L7rooo72WznyyCPdDTfc4P9LArPll18+NkdOsDXw9IkJ41wMItaHXJfrpxPeGAQQ3raOvxqF90477eSGDh0q4Z3X6NJJIiACIiACIpAsApr7J6s/JLyT1R8lbY0evvzwrrzyyn5/ZkK0Ea5xi4k2PInbbrtt3NPTCu9MYe94xhHqFEK1F1lkEf/vU0891V166aU5rfFeY4013GeffVanneUWkVHhfdhhh7mbb77Zt4m+WHHFFWNz5AQzTITCO5tBxLJ8c/5KK63kvvnmm7TCm3ax/tvW8UeZvfzyy27NNddM7bOe1w2U+KSdd97ZPf744xLeJeas6kVABERABESgHAQ09y8H5dyvIeGdO6uqP1IPX35diNhif+Zbb73VDRw4MHYlJryfeuop949//CP2+ek83pmE97hx41LbXZFIjLXSlMGDB7vLL788p328N954Y/fWW28lSnjD/bbbbvNtGj58uMMYkk9BGJOYLRTe2QwieN4xBFBItPfVV1+lFd78nGfMlhOEwnv69OmuVatWrlevXu69997Lp+llOUce77Jg1kVEQAREQAREoCwENPcvC+acLyLhnTOq6j9QD19+fWjC+5ZbbnGHHnpo7EpMeD/55JOONbRxC6HjeGrDctddd7n99tuvXlUmLPnF3LlzU9tCnXTSSe7KK690BxxwgLvjjjsabMKGG27o3n333UQJb7KxE+pPQeAigPMprMOOrg9P1y+slX/wwQcda8vZXotM6BS2lvvyyy/TCm+2GaOfSFBGCYW3hbhjCMEgUu5C9AOh+kQO2L2ka4OEd7l7RtcTAREQAREQgdIR0Ny/dGzzqVnCOx9qVXqOHr78Os6E90033eSFS9xiwpu1szvssEPc0/2a7ajwzpTojczaZN6mzJkzJyWyTjjhBHfVVVd5sY5ob6ist9567v33369zSLlDzQmRt6zjNASDAfdM+eKLL1yPHj1ic+QEwsGj3vInnnjCbb/99nXq6927t3vnnXfcp59+6sPamzVr5n/Pdbl+WKx/P/74Y0dWeVtOEDKzbd7wnIf7k+d1EzFOog2sSWd7sMMPP9ydeeaZ7vzzz89Yg4R3DLhpDoV3ul0BCqtVZ4uACIiACIhAfgQ098+PW6nOkvAuFdkE1quHL79OMeGNx/CII46IXYlNxFk7u+OOO8Y+P53wxmuNGI0Wtroi8zZl9uzZrmnTpv7fxx9/vLv66qv9dmiEqTdU1llnHffRRx/VOaTSwpt2k1COwvrz1VZbLTZHTsBbHhXt6frF+pywcLzriy++uL9eQ8L7gw8+cAhsW04QMrO19xgUwm3SMt0Efcc9szzA+jOfG2bbOfae32STTdxrr73mIzaI3MhUGJ8YIqxgrGErN5XsBDBwYKTJZXu67LXpCBEQAREQAREonIDm/oUzLGYNEt7FpJnwuvTw5ddBTKaZVF933XXuqKOOil2JCe/HHnvM4VGMW+II7zCU+tdff015ao899li/HRdbkLEVWUOFBGB4esMSikj+Tbh1z549495KzseTjT285l577eXuvfdefz5tW3311XOuKzyQc7m/sKTrF0uoR8g9We1Zn01pSHhz7OTJk1Pe87D9ZDtn7/FchTf3Sl/hPQ+394p702ZwadmypWOdOVn5LWQ/XV1EZBB6n6nv415/QTqe54vnjC0HLTpjQbp/3asIiIAIiEDyCGjun6w+kfBOVn+UtDXV+vDh4STTNh5FvLHlLiussILfO5qJ9dFHHx378ia8H330UUfW6LiFfblZcxyWTFubsce0bbU1c+ZM16JFC38a7b7++usdAhYvaEMFb3I0nDoUkUOGDHEnnniie/XVV70ntRQlKrz32GMP98ADD/hLEdIdFc+5tuHDDz906667bp3DH3nkEbfLLrvU+RlebpK4vf322z40vU2bNv73Da3xfvPNN/0+4WZcCZmZN5T7CrdJy9Ru81RLeOfas5U/zoR3Lss5Kt9atUAEREAERGBBIFCtc/9a7RsJ71rt2TT3Va0P34UXXujXphLmbXs5l7PbTHgTqo1HK24x4Z1O4OVSVzrhffvtt/utwaIFAwHtpcyYMSMVIs0+2DfeeKMbMGCAu++++xq8LOLy66+/rnNMKCJ333139/DDD/ss44ccckgutxD7mOg62V133dXBj4J4XnvttWPXyQnDhg1zG2ywQZ1zuRfqDwuebcLSEdNEPHTo0MH/uiHhjSEC4b3bbrv5Y0Nm8ORc7itcu57pJohKINScZHxRD3ScG7e1/UsssYQfD4V6vGfNmpUy5sRpx4JwLBExJOPLZTnHgsBD9ygCIiACIlB5AtU69688udK0QMK7NFwTWWu1PnzsP80+1CQ2I8FZuYsJbzy9hO7GLSYiH3rooZQoi1NHOuGdaU9xtj1jfTKF0GIEFwWjBezwHN9///0NXt7CrMODQhGJsEQE57uveS73HhXehEDb2mMSv0W91rnUyTFsk8Z2aWEheznGhLAQRk84/UsvveRDzQn3pzQkvFnbi/DGuEEJmVnURq7Cm/XslrW+kPslMoFxS+QDohljDUabTIUkc2x7l67vzWiRbfu1XPui1o4jooTIEtbUWz6CWrtH3Y8IiIAIiEB1EajWuX91Uc69tRLeubOq+iOr9eG77LLL3CmnnOIGDRrkbr755rL3A6HbhHBfccUVPsQ6bjERmU7g5VJXHOEdZu2eOnVqam0yRguSaiEwaUdDxe43nfjiZ3iHCZsvp/DeZptt3LPPPuubhAAk83o+hQRj/fr1q3MqIeyEdDdv3jyVkZo15GwPBitCw/fcc09/DkYJQtApCGsSpTVu3Nj//7nnnvNrvBFe9nu7UBjinkuiulB4c+/PPPNMPrfrbBs57o01/4UI77POOstdcMEFDi86W9Op1CVANA45IHLJo5ArO8vgr0zpuRLTcSIgAiIgAiGBap3712ovSnjXas+mua9qffjI7Dx48OCsGZlL1ZXs+0zSMgwAJ598cuzL2KQZgde/f//Y56cT3pnCvL/55hvvoaVMmTLFtW7d2v+bbNacg7caz7sVS/qFIO3bt6//MUnA+HlYQrHIemgSkmVaZx77BtOcEBUam2++ufc+U9jmKxounus1qWOLLbaoc/gll1ziIyrCNfyWYK5Tp0511tcT/UBUAYWw7TCJFp5ihLdlmyek3O6DxGvsj07JRXiTeZ4kXRT2BTejQ673accxXjEYkd3+t99+yyq8CW2PJnOz9v7zn//0W5FJeKfvhWILb4wtZMgv5ZKOuONJx4uACIiACFQXgWqd+1cX5dxbK+GdO6uqP7JaHz6EAwKC9cRMQstdTHgT8o4BIG4x8UWIN6HecQvij/2hw3Lrrbe6gQMH1qvK1hLzC0SgJQWDHSHGeKtZ02wFLzje8DAEfemll3bjxo2rU3c64Z1pS7O49xcejweZdbLRJQUYBV5//XV/KAnPTMTGvRZeaTzIYbE9wvGi402nsIacJG7RwtZeRD9QosYBtiWDua17x1NOMjXKG2+8kTJsZBLe9HHHjh398ey1bgJ+q6228t70fArjFcOVlYMPPthHKmQqCL2odz0qvFluQfh6UgrLHjBO4NWvZCGHArkUcklgmEs7ySkB6wU9SzrvOSJQ8tlRIhfOOkYEREAEaplAtc79a7VPJLxrtWfT3Fe1PnyEtRIym000lKorTXhffPHF3jMat5hAY4soC1mOU0e7du28oAsLghkvdrQQHm1bbU2aNMm1bdvWH0KIMUKZrOqEiVtB4LL+m3bZdl3pti8LxSJ1IDJLIbw/+eQTt9Zaa9W7L4Q2nm4KCc822mijOAhTx+KVZh1zWBA2CN1evXo59u2mpNvLnJ9369YtFQ0QFd4YNOgnDBmUefPmuUUXXdT/++WXX3Z47SnphDdGBTLEW1QEnnQ86pRChDdLNIjUsJLNeEXIfdS7bu0lpJ57SpLH2yIJkiBObY13+CzlNUid80sYmjRp4hPxLejJ2uw5yyVSJF/eOk8EREAEapVAtc79a7U/JLxrtWfT3Fe1PnwmvLNlZC5VV+LlHDVqlCO7+umnnx77MjZxZIsovGFxy5JLLulYrx0WJvl416IFL61l/J44caJDtFMsLJqtrggTJwnYpptu6j3HrJsP16XidZ0wYUKq6mhCMOoYOnSoD7O2cOi495Tp+LD94TEkU2M7OQoitU+fPnldEoNBdEs3kpgR2o3Ytmsgwu3f4YW6dOmSigaICm9EM8YO88zNnTu3zvpv87SnExDXXHONO+6441Iii4gGchokSXjb/ZLngCiUTIWxwfKIfBPgxelYjAQYC1h6YIaZOOcX61ieKZ5Htv3LZeeAbNcNcwIs6MnaJLyzjRb9XgREQAQyE6jWuX+t9qmEd632bJr7qtaHj4ROJHbK5q0rVVfammfWt7KtWdxiE0e2iELgxi1kJmdP7rBgjMDzGC1kwLbEY4Qu2zZYCGTEJdnBEUaszyaCwEroVYt62BdZZBHvgbNCHWxxVYo13oSRp/Nmk9SM9euUQvYPxysdzWBuW5Xhaf/oo4/8NdZff/2U9ztkTNj/Tz/95H8UFd4YVhDeCGgKa6oXW2wx/294wY2STnhHM2Lb1lQcv+WWW7rnn38+7rDxx0dDzbM9QxgHomHt1t50whtPPuveWZ5gpZxCCS6EmYfRCnmBKvCkcCyk2zkAIxXLPqLRFpkuG0d40z+1kHyNUP3evXunInZgQ4SFJS+spMfbEik2atSowJGi00VABESgvASqde5fXkrlu5qEd/lYV/xK1frw4WUmzJu1fngCy11szfN5553nDQBxCut8LdyYTNWW8TpOHaxdnT17dp1TLrroInfaaafVqwavH5NXCt43WzNM+CtrzJn4sy1XVHjj9SXcmhL1sDPxxXtrZccdd/R1EKZuYdVx7qehY9MlP0PYkSgOLz0FsYe3Pp/CHubRqAOEG2JzjTXWcIS6UzIJb8bCmDFj/DFRsQM/hDfLIihkEW/WrJn/N+H9tld4OgFhiblsfbBFeXBuIcLb9vE2VtmeIVhERX5UeGNYuOqqq1IMooaZcgpv83jjXcfolGshhwEGJvIetG/f3kdB2HOaax3hcdGxEBpdwrHSkHgcO3asI6KCvADhtncNebx5ronM+P777/NpdlnP4fklfJ6t7aKFkHrGESVkxJaIrVq1qvfzsjbcOUcCRt634RaN5W6DricCIiAC+RCo1rl/PvdaDedIeFdDLxWpjdX68DHRJxSXZFOsKy53MQ/wOeec484+++xYl58zZ47PKE3B44xnOW5hsvr777/XOY120J5oCZN4kZmcNckUy0RO8izWOVumeDs/XCO7+OKL+z2frSAeEZFWLNSc5E/HHnts3Ntp8HgEPcLeSvfu3f3a5zB7OPtl23rpuBcPt+mycwlbh1uPHj0c2zdRiBpIJ+TYA5xjKVGxhYhjjTfrqikwtIRfrJ+3aIeo+CJEnf5kKzhbH2xRHtRDFvYXXngh7a1ikEEwYmSxvg4PJPwZT6KVbMKb9eTRa0WFt23rx5hkbFLCe4oKb+59s802S0VfxO2zho4nsRqZ+sNlAuHxjHWeP65vhegNPJeMM6I/KEQmsJQkGkVCtn/eO+zD3rJly4xNSbfenwzxGK34XTZjBEtZWNKCYQ8DH31AX1BCo1i0AenqpS9gQkQLYfhhIdoDUc94KXehrWGOBPqBdxjvRCJJjG84loguIedEdIyVu+22/IZxsNpqq5X78rqeCIiACORNoFrn/nnfcMJPlPBOeAcVs3nV+vAhFsjEHF07aV7CUOAUk5fVhYcG4cl2Sueee26sS4Qem3yyVtN5AAAgAElEQVSSkTEJxRMUFWskeSMKIFrCCftXX33lVlllFX+IZatmIs52UVGhYJN7roOQwwNlhQnxtGnTUv834Z1vlveGAOKVtwR0JH1DgCJEEPlWuMfolmC5dgp9QKK5sCDaCO0N9+jOlFwtXEscZUjCO9bGM04oYVZ5RDlh3tH18hyXLkyZJQ0IQUom4Y2Xk2gAKyxHiHoTLZu9HZOv8A5Dfm2s4N3HW0yx8RlGePAzGGC4YhwyHotdbNu1cJmAhfWPGDEixSd8fugjiwQJ28OSDp7XsNhaf56ZqIgNj4uOBYwNRC8QuUBmcstun8njbR5utgJkZwIMAjxnlIYSx9l1w3wCeJYtqWJ4Pf5t7cBLzhgvZ4kaCUhkSGQJER08H127dq0zlhDm7GLBeyAcY5na/OWXX/px2LNnz6y3hYBeccUVU0bRbCfYzhKFLHPJdg14ELlhfZTteP1eBERABHIhUK1z/1zurRqPkfCugl5j32C2WCKbMx40hAmhb+ZJzfUWkvLwMaHC88EaZIQAk0CSauGF5eesSyZBGPfHZI1/EwIczchta58RTZZQLFcWcY5D+CJEEUOs845TfvzxRx8+SsnHQxyKm/C6mbZ0CkOa8dhagiu8xiQlswzZUaFga7wJkcXDjafOvOx4wAnpRRzgzTXhnU/ofTZ2oTC2/Yst8Zidy3ZX0S3BMtVrghEBf8YZZ/iJfDQbPKIQsbP88ss7xBqFyTsT+WgJ1xJHGeJZ/vTTT1PLIfBi4rGnmJEIowZtCktYjxmXSGBmW3bh3cfLHy3m7bWfk4sAjyYGESuIZLz8VqIJCvGYEz5P+1ibjGf4lVdeqXMpxExoQELkcgye/+i69WhoMF58DBqUUARiLGOM2frdbOPCfh9dz2xZ+fGK8n6jmJGJ0H9L/sc7h2eGpRE8y+nEGd57khhaX+EFhz1LGw4//PA6kQPR9kbHAkYxWwoStiOT8LaQeYu6IB+ERcc0lNXcrst7hoR27EXPchwzctj1hg8fnjLChSxz5Z7uOMYOiQTxWodr/NMdmy46IoxWIKcBRq1wnESXSYS7BKS7RraoAjvHDC9hQslsHEx4P/jgg95AQFv5Zq255prZTvW/J6kj37ZMx7ONIXVaxENOleogERABEciBQFLm/jk0dYE4RMI74d2Mp5HJGB9tPspkqmZCwlpMJmdxSlIePrwNrKctpLDFl+2nTD14G/FKI3aYgBK2yZ/WrVv7v/NNPhRm2SZslyRYVmyym65u1pCSTAmPHJN2CsIPARinIKLCLczo86OPPtqHyaYLu7e9f7lG6J1B/CCCbC1stM22ttj2Abd1z9ZWJtasQUWYIN6YSLL23ryyce6poWMRm4hOCsnUVlppJZ9FPJzY4x2Leq0z1Tly5EjHWKEgQkI+dg6GHibjiGTGDwURHo4vfoYXkXZYArYoQ9bds/YZYwklDEu1vegRdyw/CEtYj4WahyHitjwgeo8PPfSQ69+/f71bD8UdYhqxYCW6Xti2n2Os4p02Dy/iAsMY4nPGjBk+oZyF/FIXzy9GBit2TcaIhbwjcPHiRQUV58CBsYgxJ9fCOn/6iGcQAwOiGkOkLUOwNtg6dbZRI7kchfskRBgvJ++Kfv36pb0sohv2lr0ecYb3GsMVz1O6EnqS7fe2N3z0eJjQ30Rt4A3HO4/xwYQ2WwHC1cQ/5xM2zvNLNIMtY7B6beww1vCURg0KxsQiLsL22O9gA1vu0QrPF88OofbRwvjG+EUkjO0QgGHD1minY2SGBX5nDOif6P3we9rFH8YiRlUrvHtY742hjOgDy6zP8hDqtG9KQ+voqcsSOPJt4L5zKSa8Ma7xjsBIHBpjGI8YpBiPGM8sEoS+hBXvfru3dNfjGeVZJQKALfLiFJ5T2NsOFnHOzfVYDGpEVXBfPONE2+SbZyOXaxK9w3wHY7dFbzDGYEsERCmvnUv77BjGD8tWME6riEBSCSRl7p9UPuVul4R3uYnHvB7CC88iCZ3sA2TrRRFJhCbmWpL08OFZZZ0hHy4m59wnAitTYTLMZLQUhckck1YmL4gcJvJ4fZkERb1/eM3YgouJBx4t1k4jBpjsMZFjUoKoiLaVjzMTBzw7TB7w1iNo+NuKhXgzwcRbTVsoTDLw+jLxh0Pfvn29kDABgjjC+8Q64XCbJwQl98JaU0KArSDAEbRhYQKN4GUySYQF3l48pYjfULgxycXwgwfWtpVCoMIAfog36uI+czWuwA7PIuuqSQ6HZ54JP/dphZ+TeI17hAf/zsWYQjsJZbUEYHi78XpTEH5wtMRxiE2ENUKRLdbCde4cz8+JvEC00ZfpvNAch2gheR3GCTzChCnTfryg9BVjPyzhfVhUh+27znGZhLd5e6PPRCg86HuubyUaNUJSOaIHeAYYdwgM+o3tsTD4cA0MBXBqqD9DEWf7yCNSGBcWos0x5B3gPWYT1YZEEu8GJuAm5G2vc8QNbKPLDXgGiJLh54yPsHC8hW7T/0TaRAtjhGfajGPUR/9Rlxll8NzyLId9xnMcXf8dNUyEzx7vb55dQsJtnFsyPSJ3ePYx4lgiR/rf+jDkZWvVqRtPN1FR0UL+AN4bGJIs6Z8dg1GXbwKGXQoRDOwowPNg0VT0va3jt/PsGYKhPUuIdML3o1FYtJd+tG8XdfB/3pXkCkiXLBMRzZiJZtfn+0BbTNRy/xgUbNs9a1+Y1DDKg/c5hjMMMiSRtISNMMJgS52cj+i3wj3wOwTuySef7A1yRBZRiCTgPUtdVogqIokj3xQMOOF7JDrezUjHu9YMjtTD+9O2JbR6+U7SNt5/vM+JNOB8DEkYlTC+pCvwtNwfjFvaAFuScPIchmMZA6TthGF1kXeD7xzXpC7efzyLOAHoy+i3hPN4dvgT9rvVxzPE+5V6QkMR3yW+Lxia+L0ZrBlbRPOYwd7eVeG90icYHsLvaToW3AN181zb0oa00IIf2rIrWwLAuxCetI9vNobdTMkNMY6uuuqq9aJNotfEsIlhLdof2dqWhN+TR4Mxw7eTvqEveL4Yl6GxlrYy9jCm8X4MdwjgPZ9E4wXtpW3huGKcssQrXBLCO5N5Okah6D0noY+SNPdPAo9Kt0HCu9I9kOX6iA9eZpYEiMN5yJno4W0MP9bZbqVWHj4+3ExAeBki9JiQ8IHHi4QXGrEYx5OWiRsfE7zliCCYRydC2Xjb7/GwMME2r0eu59lx4VphfsaHjolQNGTZjmeCguCKGjJYJ8+a32yFybAJk9CDGT0PAWRJ15i8I+D42CJEKHj9EJpMiJikmacRYcYHjQ80HzU+ZExqrWRKHMfvEQbw5CPPOOA8fgYLxgETYCbNfNiZ+CFIrFhIOX3KR5LJF5MntgHLVhBC1JcpYoGJIZ4wPsiEjfJ/JlJ8nC0E2q7Bx5kJJ5O2dAIQr7xtSWbnMCFFGOId5X2A0YqIAzym0UKUBmOE62MkQaiYx4p24WlkoseEkDBqyxXARJQxY2v3TQzCNJtnkIknk1raEwo8PPiIEAoGHDz0MLKQfp4r/jDpoi30LYITMUDSO5bXMD4Yh0zAoyIreu94Iml3OJ44hvcDz1G2Ygw4DuMJz6wlIkO0IXr4Q5g0Y5/24qm0gmAi4WCYI6Gha3Kf0fXjlnOA82zMWh1MWuk3uPCOYxzHLRi5MCYgfjEkWk4CngX6GbFuhT7g28M45nkmMoJIm9CQx7F8pxBjRHzwvBNZQf0Yc6KF8Y1Q5F1En0YLy3nS7R7Bs4Ln3J4n2p3u/mk/fcQY4Bo8A9wH+TkQXWHBGINIwJgaFvqU9xh/eJ9GjXDZmDPeEBbRJHYYnug3lgJgOGcHgWgEjNWNyKfNPB/2vPI7jGcYAqLGXfqJPkAY8t7FsMIyBYx+LHfA+MK7NUzwiSGDdxsGhzD6gGvwzDGWWa5ihWc7NO7yc95JOAa4XyI3eM65JgUDHh5/DDR8n+HMcg+L1iNKgGea8WCRMfw+7CcMsrSZMW9jEx6MSb4j3C9RLhitMGhhMOcZIlrFts/E8MZxlquE3/MzjAq8j7kvODCuMcxgeGecEWlCm/ne8F3kPWqJGjFAmeGIdyrfeNrOexqDkBln4MCzxT1jCKcNLK+jXqLHGOtholQMGYxb3t+8+/hO0C6eWd5tcIY5The+b/yb37FrBn8Y+4wtuPFv3rm8b+lP3vXMYzAIYUyEASyJHuKbgdEd4zL3zbuBbylRBxiYMCjxPsc4wzuA8ZZtDkqfcB/0A+8CrkEUCO3i+swZ+HbTvxhc4MicAPYw49vFfWIkwiDJvfMO4lvMdw6DGWOMvzGEk1CS7yPX5Rnhu4kBmHcRnGHJ2KJejrPvAs8F32+SpzKGYAJX+HKftBWDKffBd4S+5x4wNjLW+RZb4X3C3ISxRX9TZ6VLrcz9K82xWNeX8C4WyRLVwwsC4cea7rAw+eJDhZjKtejhq0/KQiSZKPNx44XJC5ePZzR0ko88DJnQ8RHhxc5HmgkQf9NXCBsEKR9xfh9aP/kw86HjZY0QtS2ZrFW8zPmg8SFCnPABwHuXzrPLBI6JBd5A2sLEkI8XgpyPCR93wtExThCazkeC+2EygADm/4wfs8gjWplkIviYpIYFIcnEiQ8fH13ahGeUSVBUVOYyFs3rEh7LxAsBzSTP9iFPVxceKj6STFz58JrIb+i6fHTpZ/5mYsJk0ryosOajyQed/rBJMH3EJARxxYfeJtFMlmCPuGCigEilv21Swc/4PxMhhALHWrFw1WyhsHY8Y4FJUGg8SHefFj2RiQEikbHA/TGZb6ggrrgeQhgRzgSVsUohFBl+jCcrjKmoCMtlDESP4XrRverzqacU5zA5zrabAks1mIQzYSeiJYwSybdNiAnGmwnjfOsJz2NcI2CYVDMJLrTYkoRMRsCw/qgRgd/xLkEQ8V5JVxizPHsNGT2zjf9C79HOR8za+wGDNxP4cLeHYlwHocOkP5/3ajGun6Q62PoynWExSW1UW0QgJBA18PJu4nm23U0qRUtz/0qRT39dCe9k9Ue91jDpwAMQrvPlIEQKQi+0qkZPNou9/RzrOx92JtSW8Ksit88a2L8zNlfk+rpo0QhYsiv+9gJsoYXcIgsvnPL4/fHnn+6vv9e1YtTgA/QXmbz/bkEu4eINNdZELIYTJv92PUuIh0enkP2ZCwH1519/ublz5rjGTZp448nCCy3kfp0923sf/vzjDy/oMQZYG/HgzPr1V88RL8Gifyf1I+zfwto4H76/z5vnz+3erZsP7538yy+eKc/4jJkz3aeffOLXnPK8hyFxGI0QC3gMEBF4sxFjvCt410RDQ7nOz+wH36mTa9yokccB82++/dZ17dLF9yftw/Cz+BJLuMWaNPHeISJPMP5MnDTJrbTiij68nmvQZtrIdfk/9cPFxs78efPcb3PmuB//8x9vtBo1erRr3qyZbyPGDv4gbjF0IeQwjvTDuzl+vL8nv39206beMNSsaVN/T0w6MNgQwUI/YFiD77Tp0/2/W7dq5cfr6DFj3Izp01237t19G/8zbpxPhsU1vZFn5kzPGANBk8UWc+N/+sm3df311nMWXm/jxUK0uR599sf8+X4cTpg40XPk9126dnWTJk50U6dNc9OnTXNrrrWWN27R9m5LL+37FdZffvWVP4d24CHGA0r7qHv9DTZwbdu08f9nPHEteDVv0cLN/vVX76GzcEnuq+lii6UMeUwIMargzeE+vh8xIhUeTV1Tpk71opj6YEh7lu7W7b/1N2/uORBmy9+MK4tAatS4sVti8cV9RAsGPuphzNLn9Ofwr792nbt0cW2WXDL1HYI/zwsCfOqUKa5N27auU8eOqceP+8PYhtcWbmPHjPGGRIyb9DHjEQ/Wb7Nn+7GBSOb7CHPbco/rL9GypZvz229+bPEc4K2i/USQUXfbdu18n/CzefPnewMUfLknxgnFjLWMXY4d95//uJ49engGS7Zp436ZPNm3jeeOcU2/zf39d/9zIgp4Lr4bMcIbsjq0b+/HAiHjPK+23MiL7x9/TJ3DmF59tdX87xnjXHv55ZZLhefCBkaMHe6Tfzdt1sx71OGIMZV7bd+hgx8ni7do4XnTPsYxz/3yK6zgJk+a5I3PXAOvKffRo2dP31eMO1jyzHAdnl+egx/+/3pvQq+5T8Zdi+bN/XsMJhh0x44b5w3Cw9591/00frzr0rmzX/ZB3TxXjGPGfctWrdwG66/vn0tyMfD80kcYH2HEe7TzUkt5z+1nn3/u+2LZZZbx75gd8ahOm+amTZ3q205UE+9Z7mH+H394tmusvrp75dVX3Vprrul+njDhv3U2auQ6LbWUj5IbPWqUf0cwbsb//LOvCwN46yWXdL/OmuX7k/vn97wPGaeMo5GjRrlRI0f6YxnfvCN47jgOI/JXX3/NSzP17mFs8of+nTVzpj+uQ8eOflz6besmTvxvP8yb59s4buxY/05dqnNn/16ECd852rJUp07++8H45R3MOX5rvr93aHjv/fc9bxjxbt2wd283ZvRoP7ZpA98Yii0Z4H4WWnhhf813hw3zfPk/33D6nTHD/fH80waeCfodzjgdeF5431gkE22dPmOGbzfPFn1qeVLsW0P/812grdRHuxi7vKt4b9M2/k/9POu8SzF0Y0DEMIwHmv7lGSXyAgcHY573Gu2BL98j2jRn7lw/fvh2wZN3Ecdx31yX55534sxZs/yY98/K4ou74d98478RjD+eoQk//+z7mLHFOMFxYOHotJf75lmd36KF63r77YVMI4pyroR3UTAWrRIJ76KhLE1FvGgIb40moSH0hpc03qxMhfCldNtfVVx4//CDc3+vKywNNdUqAiIgAiIgAiIgAiIgAhUi0Lmzc99/X6GL/++yEt4V74I6DZDwTlZ/1GtNIaHmifV4J5y5micCIiACIiACIiACIiAC1U5AwjtZPSjhnaz+qNcaJVdLeAepeSIgAiIgAiIgAiIgAiKQQAIS3snqFAnvZPVHvdaQvIk13qxdYb0J5f777/fZJat5O7GEY1fzREAEREAEREAEREAERKCqCUh4J6v7JLyT1R/1WkMiFrZqIgEE2yGQ+IHtUdjawbbkyPUW9PDlSkrHiYAIiIAIiIAIiIAIiEB1E9DcP1n9J+GdrP5I2xoy97KFD/t/ktEXbzeecLLMxil6+OLQ0rEiIAIiIAIiIAIiIAIiUL0ENPdPVt9JeCerP0raGj18JcWrykVABERABERABERABEQgMQQ0909MV/iGSHgnqz9K2ho9fCXFq8pFQAREQAREQAREQAREIDEENPdPTFdIeCerK0rfGj18pWesK4iACIiACIiACIiACIhAEgho7p+EXvhfG+TxTlZ/lLQ1evhKileVi4AIiIAIiIAIiIAIiEBiCGjun5iukMc7WV1R+tbo4Ss9Y11BBERABERABERABERABJJAQHP/JPSCPN7J6oUytUYPX5lA6zIiIAIiIAIiIAIiIAIiUGECmvtXuAMil1eoebL6o6St0cNXUryqXAREQAREQAREQAREQAQSQ0Bz/8R0hW+IhHey+qOkrdHDV1K8qlwEREAEREAEREAEREAEEkNAc//EdIWEd7K6ovSt0cNXesa6ggiIgAiIgAiIgAiIgAgkgYDm/knohf+1QR7vZPVHSVszevRot8wyy7j333/fderUqaTXUuUiIAIiIAIiIAIiIAIiIAKVIzB+/HjXq1cvN2rUKNe9e/fKNURX9gQkvBeggfDBBx/4h09FBERABERABERABERABERgwSCA023dddddMG42wXcp4Z3gzil20+bMmeO++OIL165dO7fooosWu/qK1GeWPHnxS4tfnEvLN6xdrMvDWpzLw5mriLVYl49Aea6kMV0eznp/FM55/vz5btKkSa5nz55uscUWK7xC1VAQAQnvgvDp5EoT0NqV8vSAOJeHM1cR6/KwFufycNaYLh9nsS4fa70/xLp8BHSlWiIg4V1LvbkA3os+fuXpdHEuD2dNnMW5fATKdyW9P8S6fATKcyWN6fJw1jexfJx1pfIQkPAuD2ddpUQE9PErEdhIteJcHs6aZIhz+QiU70p6f4h1+QiU50oa0+XhrG9i+TjrSuUhIOFdHs66SokIzJgxww0ZMsSdcMIJbokllijRVVStOJdvDIh1eViLc3k4cxWxFuvyESjPlTSmy8NZ74/ycdaVykNAwrs8nHUVERABERABERABERABERABERCBBZSAhPcC2vG6bREQAREQAREQAREQAREQAREQgfIQkPAuD2ddRQREQAREQAREQAREQAREQAREYAElIOG9gHa8blsEREAEREAEREAEREAEREAERKA8BCS8y8NZVymAwPfff++uuOIKN2zYMPfll1+6lVde2f8dLc8884w744wz3PDhw12XLl18wrUjjjiigCsvWKc+9NBD7p577nEfffSRmzJliltuueXc4Ycf7gYNGuQWXnjhFAxxLmxcPP/88+6iiy5yX3/9tU861blzZ7fTTju5s88+27Vs2VKcC8Ob8exZs2b5d8ePP/7oPvjgA7fOOuuIdZFY33nnne7AAw+sV9spp5ziLrnkEnEuEuewmttvv91de+217ttvv/WJRddff333xBNPiHURWW+yySbu9ddfT1vjfffd5wYMGOB/p29i4dAff/xxd/HFF/v5W9OmTV3v3r39/1daaaU6lYt14axVQ2UJSHhXlr+ungOBoUOHuqOOOsqtt9567rvvvnN//vlnPeH97rvvuj59+rj99tvP7bPPPu7tt9/2QuaWW25xhxxySA5X0SFM3Lp16+Z23nln16FDB/fqq6/6D99xxx3nLr/8cg9InAsfJ0zYPv/8c9erVy/XunVrP5bPOecct9Zaa7kXXnhBnAtHnLYGROBdd93lJkyYUEd4a0wXDtyE93PPPVfHeIRRqWvXrhrThSOuUwPvi6uuusobmvkuYiiFPd87vaeLB9uMo2GNV199tXvkkUfc+PHjXdu2bfVNLALul156yW255ZZ+7rbvvvu6adOm+W8ihumvvvoqtWON3tVFgK0qKk5AwrviXaAGZCOA0DaP6wEHHOA+/PDDesJ7m2228ZOP9957L1XdoYce6p566inHfpuhxzbb9RbU30+aNMm1a9euzu0TNXDTTTf5D2GTJk2cOJdmdNx2222O8Yo3dqmllhLnImP+5ptvvIf7yiuvdIcddlgd4a0xXThsE968QxAj6Yo4F86ZGvAI9uzZ03tZEStiXRyuuday7LLLuv/7v/9zTz/9tD9F4zpXcpmPwzny8ssvu5EjR7qFFlrIH/j+++97oxLjHMZiXThn1ZAMAhLeyegHtSJHAumE99y5c71FlJDG448/PlUTIWKEiiHU11577RyvoMNCAnfffbePIvjpp5/ckksuKc4lGh6PPvqo23XXXd3o0aNdx44dxbnInBEoq622mttuu+1cv379UsJb747igM4mvMW5OJyphciNxx57zEd/pStiXTzW0ZreeecdHwLNkqy99trLiXVxWO+///7uk08+8ZFgVhjfhJlj4Nh2223FujioVUsCCEh4J6AT1ITcCaQT3oSDrbrqqu7ZZ591W2+9daoyvC/t27d3iEdCmFTiE8ALS1jdxIkT/VpCcY7PMNMZf/zxh5s3b55f633QQQe5pZde2q/R1HguHmNqevjhh92RRx7pRowY4T7++OM6wlusi8PahDfv28mTJ/slKwMHDnSDBw92iyyyiMZ0cTD7Wvr27eujCtZYYw133XXX+WikDTbYwF1zzTX+ZxrTRYQdqYolb3fccYf/HjZv3lysi4T6jTfecJtttpkbMmRIKtT8mGOOceT3QZATbadxXSTYqqbiBCS8K94FakAcAumEN+u5N9poI7/WinXKVubPn+8aNWrkJyS8xFXiESBSgAkda+XPPPNMv25enOMxbOhoEgASWk7BYIRAZDInzsVjPHv2bJ9QjfWCGDdee+21OsJbrIvDmoSBLPMhNJRQUQxILFEhOeP111+vMV0czL4WvIBEILF+niSNjRs3dueee66PlsG4xJpYvaeLCPzvqphPwByBeO+99/qf6v1RPM4sCySKYObMmb7SVVZZxfFe4Tsp1sXjrJoqT0DCu/J9oBbEINCQ8CbrORO/qPAm8+vRRx8d4yo69Oeff/Ys+eghVjBg2CRDnIszPgirI9M2E+Xzzz/fLb/88u7FF1/02fuZOItz4ZxPP/10zxRRSJ6HTMJbrAtnHa3h5JNP9gnAxo0b59duakwXh/EKK6zgPYEkZSQCiUKir2WWWcadd955PhRarIvDOqyFiDpCnp988km/ZCUUg3p/FMabEH7YMr/bYYcd3PTp071Rac6cOX7ewVJCzT8KY6yzk0NAwjs5faGW5EBAoeY5QCrwED56rI3no/fWW2+5Nm3a+BoV6lUg2AZOZws3kn+xpRuWfoX0F856zJgxbsUVV/TrYTfccENfIeN5++239xn74T127FixLhx12hrYso3M/SRHIvRcY7o4oDGIMrYxjoaFMPPVV1/drwEX6+KwDmsh2zbiGyMHhmh9E4vHmHcxS63IdWKFpYIY/tlZhSSvmn8Uj7dqqiwBCe/K8tfVYxJQcrWYwGIejtjeaqutfMgioftMmK0okUxMmDEOZ70369guuOACnyBQyQJjwMtwqHm3M9WEgCEBo1gXzjpdDWFW4k033VSci4SZbyBbh0WFN6KbLQlvvvlmsS4Sa6vmt99+81ts7r333n4Jhb6JxQXcrFkzd+qpp7p//vOfdSomigNP+A033KDkasVFrtoqSEDCu4Lwden4BBraTowkM4hFK2wbxFpDbSeWG2fWsO2yyy6ORCf8IQt0tLCthzjnxjPOUXhiN954Y/fAAw+4/v37++1TxDkOwfrHwu/TTz+t8wv+j2EDcbLuuut6oSLWhXHOdPaJJ57o82vw/iVTvzgXhzO5IHbffQcb1xYAAASoSURBVHf3xRdfuB49evhKyRXBNleXXnqpO+6448S6OKhTtfBeHjBggP8u8p4Oi8Z14bDZno3opKFDh6Yqw7CEF/zCCy90LFuhiHXhrFVD5QlIeFe+D9SCLARIkES4IgXL5w8//OCzX1LI8Mre0wjuPn36+DVCWKVZD4T19JZbbnHsEamSncCgQYPcrbfe6i677LJ6kwvCn/EMinN2jtmOwLhBaB2GjaZNm7rPPvvMM8ejQnguyZLEORvF/H4fXeNNLWKdH8vwLKJkSDplQhCDJ++SY4891q/zFufCGVsNRMcQwk8SKiJkeF+wttt2niBBo8Z08XhT04477uiNeCSws32m7QpiXThrEjCSh4fdJ2CN0ZQ13iypIAdKp06d9A4pHLNqSAgBCe+EdISakZkAHztCjtIV1mqyHpmCOCeZ0vDhw/3aINYF8SJXyY1A9+7d/YdOnHPjle9R7DePBwUD0p9//ungjhg/6aSTvHHDisZzvoQzn5dOeOvdUThnBDbrX/FuM6bxXmHwZDIdChWN6cJZUwMim8gN9jhmS0IM0Bg4yHiu90dxGFstU6dO9REbRBIQUZCuaFwXxvyvv/5yt912m7vxxht94sAWLVp44xLe7p49e9apXKwLY62zK09AwrvyfaAWiIAIiIAIiIAIiIAIiIAIiIAI1DABCe8a7lzdmgiIgAiIgAiIgAiIgAiIgAiIQOUJSHhXvg/UAhEQAREQAREQAREQAREQAREQgRomIOFdw52rWxMBERABERABERABERABERABEag8AQnvyveBWiACIiACIiACIiACIiACIiACIlDDBCS8a7hzdWsiIAIiIAIiIAIiIAIiIAIiIAKVJyDhXfk+UAtEQAREQAREQAREQAREQAREQARqmICEdw13rm5NBERABERABERABERABERABESg8gQkvCvfB2qBCIiACIiACIiACIiACIiACIhADROQ8K7hztWtiYAIiIAIiIAIiIAIiIAIiIAIVJ6AhHfl+0AtEAEREAEREAEREAEREAEREAERqGECEt413Lm6NREQAREQAREQAREQAREQAREQgcoTkPCufB+oBSIgAiIgAiIgAiIgAiIgAiIgAjVMQMK7hjtXtyYCIiACIiACIiACIiACIiACIlB5AhLele8DtUAEREAEREAEREAEREAEREAERKCGCUh413Dn6tZEQAREQAREQAREQAREQAREQAQqT0DCu/J9oBaIgAiIgAiIgAiIgAiIgAiIgAjUMAEJ7xruXN2aCIiACIiACIiACIiACIiACIhA5QlIeFe+D9QCERABERABERABERABERABERCBGiYg4V3DnatbEwEREAEREAEREAEREAEREAERqDwBCe/K94FaIAIiIAIiIAIiIAIiIAIiIAIiUMMEJLxruHN1ayIgAiIgAiIgAiIgAiIgAiIgApUnIOFd+T5QC0RABERABERABERABERABERABGqYgIR3DXeubk0EREAEREAEREAEREAEREAERKDyBCS8K98HaoEIiIAIiIAIiIAIiIAIiIAIiEANE5DwruHO1a2JgAiIgAiIgAiIgAiIgAiIgAhUnsD/A24yxEx9tRNfAAAAAElFTkSuQmCC", "_figure_label": "Figure 1", "_image_mode": "diff", "_model_module_version": "^0.11", "_size": [ 900, 400 ], "_view_module_version": "^0.11", "layout": "IPY_MODEL_3070a2fefae345c5abab322ef7c9a10e", "toolbar": "IPY_MODEL_7c0bfe2f4f2348c98baeb1db2d521c00", "toolbar_position": "left" } }, "c04c3145800248a7bdd17a11e7c03cf9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "LabelStyleModel", "state": { "description_width": "", "font_family": null, "font_size": null, "font_style": null, "font_variant": null, "font_weight": null, "text_color": null, "text_decoration": null } }, "c46357387bb643b7a7740174241f32fa": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "c72974fe24104a288f13f77712bbd047": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "c9d71f937d1e4d569f05c0d1d874a595": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TextStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "cb372953b8e5456681e333899f6a4bf2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "cf7312d87d364721b24398b437361f91": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "d056fc2fab4948999db31f797217e2d0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "BoxModel", "state": { "children": [ "IPY_MODEL_5d3f36397d8d4525bea4003f2ee990a6" ], "layout": "IPY_MODEL_dc17af8032034860a500a1eb9240ee72" } }, "d35a9babe4d14f45ae7cf67845afa6a3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "d6ab9bd45fb64b7b9785bed9d60872bb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "dc17af8032034860a500a1eb9240ee72": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "dd94b2509958449e8752fcf027643eca": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "dfeb9f48eaf54bfca3cb61951d344ff5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", "state": { "children": [ "IPY_MODEL_063c720771e5455fb5d1ffd0c27ad6f7", "IPY_MODEL_518e216c806341f7b881de45166a654a" ], "layout": "IPY_MODEL_7426e5f971394d249d5f4e099477dae2" } }, "e3eb11ea7e674c91a68e328cb138076c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "extra dist", "layout": "IPY_MODEL_25ae317dc2e34d888314185867a0da2c", "max": 10, "readout_format": ".1f", "step": 0.5, "style": "IPY_MODEL_3fa4d9033bb94736a381a1c6d9f1ac21", "tooltip": "Extra distance (A) with semi-transparent display", "value": 2 } }, "e586890e3f434a0dacb8bb200fc4b534": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "e8b7df83ee4346bc9dd2b32d20222683": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "fullMol opac", "layout": "IPY_MODEL_37efa633a61c4835be911037898323ce", "max": 1, "readout_format": ".01f", "step": 0.1, "style": "IPY_MODEL_2e07987270a3424885b9240ba747c3d8", "tooltip": "Opacity to display fully molecules\nwhich have at least one atom inside the limits", "value": 0.5 } }, "ea1a1633984a452693bb538cd640b799": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "MPLCanvasModel", "state": { "_cursor": "default", "_data_url": "data:image/png;base64,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", "_figure_label": "Figure 3", "_image_mode": "diff", "_model_module_version": "^0.11", "_size": [ 900, 400 ], "_view_module_version": "^0.11", "layout": "IPY_MODEL_28749649ff664779b102bb5134f03a8d", "toolbar": "IPY_MODEL_4a052a1c296e478686e683d539f4866f", "toolbar_position": "left" } }, "eb563daee4fd48b88cb155fc153a43e4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "extra opac.", "layout": "IPY_MODEL_0e1f70ffb8d140abb0c6598cc779bf96", "max": 1, "readout_format": ".01f", "step": 0.1, "style": "IPY_MODEL_0d96107a068d4ce08fc8601de79e6ca9", "tooltip": "Opacity for extra distance display", "value": 0.5 } }, "ee202cb6d45c47b991d635f7af13f43e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "ef0730344db64535880c58c7ab569d4a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "f386dc3ff5b04e3189ba60b62d49ab07": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "f443b3682ae945d3a83acf637668d8bc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "max_width": "25%", "width": "11em" } }, "f63426b10f1c41098c3972c570056a3f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "f88b4168a4674454bf3a719070e3d4b5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "fb20ebc822d148e690ab89453aec3bed": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "fbc49008a6ac4d1f9a187d31997c7eba": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "extra opac.", "layout": "IPY_MODEL_37a448aac66f4ed782973ae253db53c8", "max": 1, "readout_format": ".01f", "step": 0.1, "style": "IPY_MODEL_56bb4150895440fd865b0485322e0a63", "tooltip": "Opacity for extra distance display", "value": 0.5 } }, "fce5512877b245a09d4f92a1c77f9716": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "BoxModel", "state": { "children": [ "IPY_MODEL_b6db3472924a4760b2e21af077e8ddcf" ], "layout": "IPY_MODEL_43402ab0214f43c09714b65a30d43d14" } }, "fdc61c966cb145379d5f2eb7c7b0ab2c": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "ToolbarModel", "state": { "_model_module_version": "^0.11", "_view_module_version": "^0.11", "collapsed": true, "layout": "IPY_MODEL_51e645aa573242a19f50ca09ff06fb52", "orientation": "vertical", "toolitems": [ [ "Home", "Reset original view", "home", "home" ], [ "Back", "Back to previous view", "arrow-left", "back" ], [ "Forward", "Forward to next view", "arrow-right", "forward" ], [ "Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan" ], [ "Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom" ], [ "Download", "Download plot", "floppy-o", "save_figure" ] ] } } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 } pyobjcryst-2024.2.1/examples/structure-solution-powder-pbso4.ipynb000066400000000000000000123151071470422267000253060ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## PbSO4 tutorial: indexing, spacegroup determination & structure solution\n", "In this notebook, you can:\n", "* Load the powder diffraction data and create the PowderPattern object, for two diffrection patterns (X-ray and neutron)\n", "* Find the diffraction peaks and index them (determine the unit cell)\n", "* Perform a profile fit to optimise the background and reflection profiles\n", "* Determine the spacegroup\n", "* Add a molecule to describe the contents of the Crystal structure\n", "* Solve the Crystal structure using a Monte-Carlo/Parallel tempering algorithm, using both X-ray and neutron diffraction patterns\n", "* Save the best result to a CIF file and to Fox .xmlgz format\n", "\n", "Notes:\n", "* This is a simple case, which illustrates the possibility of joint X-ray/neutron optimisation.\n", "* It is important to follow the steps relatively linearly and avoid going back to previous cells until you know better. For example to avoid adding multiple times Scatterer/Molecule objects in the crystal structure, or multiple crystalline phases to the powder pattern with the same crystal, etc..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# 'widget' allows live update and works in both classical notebooks and jupyter-lab.\n", "# Otherwise 'notebook', 'ipympl', 'inline' can be used\n", "%matplotlib widget\n", "\n", "import os\n", "import pyobjcryst\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from pyobjcryst.crystal import *\n", "from pyobjcryst.scatteringpower import ScatteringPowerAtom\n", "from pyobjcryst.atom import Atom\n", "from pyobjcryst.polyhedron import MakeTetrahedron\n", "from pyobjcryst.powderpattern import *\n", "from pyobjcryst.radiation import RadiationType\n", "from pyobjcryst.indexing import *\n", "from pyobjcryst.molecule import *\n", "from pyobjcryst.globaloptim import MonteCarlo\n", "from pyobjcryst.io import xml_cryst_file_save_global" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create powder pattern object, download data if necessary\n", "We start with the X-ray data which will be used to determine the unit cell and spacegroup." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", " 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Radiation: X-Ray,Wavelength= tube: Cu, Alpha1/Alpha2= 0.5Imported powder pattern: 6001 points, 2theta= 10.000 -> 160.000, step= 0.025\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100 48688 100 48688 0 0 100k 0 --:--:-- --:--:-- --:--:-- 100k\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f7376c77ccf8409ca67e032eccf72af9", "version_major": 2, "version_minor": 0 }, "image/png": "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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", " \n", "
\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "px = PowderPattern()\n", "if not os.path.exists(\"pbso4-x.dat\"):\n", " os.system(\"curl -o pbso4-x.dat https://raw.githubusercontent.com/vincefn/objcryst/master/Fox/example/tutorial-pbso4/xray.dat\")\n", "px.ImportPowderPatternFullprof(\"pbso4-x.dat\")\n", "px.SetWavelength(\"Cu\") # Valid strings for X-ray tubes are \"Cu\", \"CuA1\",...\n", "print(px.GetRadiation()) # Better check the string was understood\n", "px.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Find peaks & index the reflections\n", "In this case the peaks are automatically found without any parasitic phase.\n", "\n", "And the unit cell is also indexed without any ambiguity. This uses the dichotomy in volume approach (Louër & Boultif).\n", "\n", "... It is not always that easy !" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Peak dobs=0.23410+/-0.00049 iobs=5.631088e+04 (? ? ?))\n", "Peak dobs=0.26180+/-0.00049 iobs=3.661422e+04 (? ? ?))\n", "Peak dobs=0.27573+/-0.00048 iobs=1.369719e+04 (? ? ?))\n", "Peak dobs=0.28690+/-0.00041 iobs=2.120413e+04 (? ? ?))\n", "Peak dobs=0.29939+/-0.00048 iobs=5.965503e+04 (? ? ?))\n", "Peak dobs=0.31016+/-0.00048 iobs=4.475041e+04 (? ? ?))\n", "Peak dobs=0.33210+/-0.00048 iobs=6.886522e+04 (? ? ?))\n", "Peak dobs=0.36110+/-0.00048 iobs=2.483898e+04 (? ? ?))\n", "Peak dobs=0.36997+/-0.00047 iobs=3.424055e+04 (? ? ?))\n", "Peak dobs=0.38119+/-0.00047 iobs=6.008678e+03 (? ? ?))\n", "Peak dobs=0.41488+/-0.00047 iobs=1.116247e+04 (? ? ?))\n", "Peak dobs=0.43867+/-0.00047 iobs=1.291596e+04 (? ? ?))\n", "Peak dobs=0.44637+/-0.00047 iobs=3.054905e+03 (? ? ?))\n", "Peak dobs=0.45514+/-0.00046 iobs=3.965780e+03 (? ? ?))\n", "Peak dobs=0.46161+/-0.00046 iobs=1.755345e+04 (? ? ?))\n", "Peak dobs=0.46832+/-0.00046 iobs=3.017849e+03 (? ? ?))\n", "Peak dobs=0.48313+/-0.00053 iobs=5.114404e+04 (? ? ?))\n", "Peak dobs=0.49213+/-0.00052 iobs=3.115425e+04 (? ? ?))\n", "Peak dobs=0.50613+/-0.00046 iobs=1.278484e+04 (? ? ?))\n", "Peak dobs=0.53141+/-0.00045 iobs=3.895334e+03 (? ? ?))\n", "Predicting volumes from 20 peaks between d=42.716 and d= 1.882\n", "\n", "Starting indexing using 20 peaks\n", " CUBIC P : V= 407 -> 4545 A^3, max length= 49.70A\n", " -> 0 sols in 0.00s, best score= 0.0\n", "\n", " TETRAGONAL P : V= 151 -> 1089 A^3, max length= 30.86A\n", " -> 0 sols in 0.01s, best score= 0.0\n", "\n", "RHOMBOEDRAL P : V= 167 -> 1143 A^3, max length= 31.36A\n", " -> 0 sols in 0.00s, best score= 0.0\n", "\n", " HEXAGONAL P : V= 206 -> 1507 A^3, max length= 34.39A\n", " -> 0 sols in 0.01s, best score= 0.0\n", "\n", "ORTHOROMBIC P : V= 88 -> 565 A^3, max length= 25.00A\n", " -> 1 sols in 0.01s, best score= 57.5\n", "\n", " MONOCLINIC P : V= 65 -> 364 A^3, max length= 25.00A\n", " -> 3 sols in 0.06s, best score= 56.5\n", "\n", "Solutions:\n", "( 5.40 6.97 8.49 90.0 90.0 90.0 V= 320 ORTHOROMBIC P, 63.80767059326172)\n", "( 5.40 8.49 6.97 90.0 90.0 90.0 V= 320 MONOCLINIC P, 61.222660064697266)\n", "( 6.97 5.40 8.49 90.0 90.0 90.0 V= 320 MONOCLINIC P, 57.77278137207031)\n" ] } ], "source": [ "# Index\n", "pl = px.FindPeaks(1.5, -1, 1000)\n", "if len(pl) > 20:\n", " pl.resize(20) # Only keep 20 peaks\n", "for peak in pl:\n", " print(peak)\n", "\n", "ex = quick_index(pl)\n", "\n", "print(\"Solutions:\")\n", "for s in ex.GetSolutions():\n", " print(s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a crystal phase using the indexed unit cell" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b6316e4843fb46de8b20b4e4b8f30e3c", "version_major": 2, "version_minor": 0 }, "image/png": "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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", " \n", "
\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "uc = ex.GetSolutions()[0][0].DirectUnitCell()\n", "c = pyobjcryst.crystal.Crystal(uc[0], uc[1], uc[2], uc[3], uc[4], uc[5], \"P1\")\n", "pdiff = px.AddPowderPatternDiffraction(c)\n", "\n", "# Plot with indexing in new figure\n", "px.plot(diff=False,fig=None,hkl=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fit the profile and background\n", "We use a maximum sin(theta)/lambda because we don't really need high angle/high resolution data.\n", "\n", "This will go faster and is more reliable for spacegroup indexing and structure solution." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No background, adding one automatically\n", "Selected PowderPatternDiffraction: with Crystal: \n", "Profile fitting finished.\n", "Remember to use SetExtractionMode(False) on the PowderPatternDiffraction object\n", "to disable profile fitting and optimise the structure.\n", "Fit result: Rw= 8.06% Chi2= 7704.09 GoF= 1.28 LLK= 1194.686\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "286640c0bf39426a80f443c1333499e6", "version_major": 2, "version_minor": 0 }, "image/png": "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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", " \n", "
\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "px.SetMaxSinThetaOvLambda(0.3)\n", "px.quick_fit_profile(auto_background=True,plot=False, init_profile=True,verbose=True)\n", "px.quick_fit_profile(plot=False, init_profile=False, asym=True, displ_transl=True, verbose=False)\n", "\n", "# Plot in new figure\n", "px.plot(diff=True, fig=None, hkl=True)\n", "print(\"Fit result: Rw=%6.2f%% Chi2=%10.2f GoF=%8.2f LLK=%10.3f\" %\n", " (px.rw * 100, px.chi2, px.chi2/px.GetNbPointUsed(), px.llk))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Find the spacegroup\n", "The SpaceGroupExplorer can be used to find the optimal spacegroup. \n", "\n", "What `RunAll()` does is try all spacegroups and settings which are compatible with the unit cell (in this case all monoclinic and triclinic), and perform a profile fit (Le Bail only, we don't refine profile parameters or background since these parameters should be OK).\n", "\n", "From this several values are extracted for each spacegroup setting:\n", "* **Rw** - the standard full-profile weighted R factor $R_{wp}$\n", "* **GoF**: the chi2 (full profile $\\chi^2=\\Sigma\\frac{(obs-calc)^2}{\\sigma^2}$) divided by the number of points used\n", "* **nGoF**: this is the Goodness-of-Fit, but computed on integration intervals defined by P1 reflections, and then multipled by the number of reflections used divided by the number of reflections for the P1 spacegroup. This is more discriminating and allows to put forward spacegroups which yield a good fit with more extinctions.\n", "* **reflections** is the number of reflections actually taken into account for this spacegroup up to the maximum sin(theta)/lambda\n", "* **extinct446** gives the number of extinct reflections for 0<=H<=4 0<=K<=4 0<=L<=6 (which is used internally as a unique fingerprint for the extinctions)\n", "\n", "Some C++ verbose output does not appear here but will be in the jupyter server log if you see it.\n", "\n", "The results are sorting by ascending **nGOF**\n", "\n", "Unfortunately in this case the correct spacegroup (Pcmn, or Pnma if the axis were exchanged) is only one among possible choices (P21cn has the same extinctions), so we'll select it but in a real case, the different possible spacegroups would need to be tested." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Beginning spacegroup exploration... 348 to go...\n", " (# 1) P 1 : Rwp= 9.99% GoF= 6.74 nGoF= 1.81 (158 reflections, 0 extinct)\n", " (# 2) P -1 : Rwp= 9.99% GoF= 6.74 nGoF= 1.81 (158 reflections, 0 extinct) [same extinctions as:P 1]\n", " (# 3) P 1 2 1 : Rwp= 10.36% GoF= 7.14 nGoF= 1.18 ( 95 reflections, 0 extinct)\n", " (# 3) P 1 1 2 : Rwp= 10.21% GoF= 6.93 nGoF= 1.11 ( 93 reflections, 0 extinct)\n", " (# 3) P 2 1 1 : Rwp= 10.49% GoF= 7.30 nGoF= 1.26 ( 98 reflections, 0 extinct)\n", " (# 4) P 1 21 1 : Rwp= 10.56% GoF= 7.40 nGoF= 1.16 ( 93 reflections, 2 extinct)\n", " (# 4) P 1 1 21 : Rwp= 10.26% GoF= 6.99 nGoF= 1.18 ( 90 reflections, 3 extinct)\n", " (# 4) P 21 1 1 : Rwp= 10.44% GoF= 7.22 nGoF= 1.21 ( 96 reflections, 2 extinct)\n", " (# 5) C 1 2 1 : Rwp= 67.10% GoF= 302.47 nGoF= 75.71 ( 47 reflections, 84 extinct)\n", " (# 5) A 1 2 1 : P 21 c n nGoF= 0.6932 GoF= 6.600 Rw=10.06 [ 48 reflections, extinct446= 27]\n", "P m c n nGoF= 0.6932 GoF= 6.600 Rw=10.06 [ 48 reflections, extinct446= 27]\n", "P 21 m n nGoF= 0.7580 GoF= 7.658 Rw=10.82 [ 56 reflections, extinct446= 12]\n", "P m 21 n nGoF= 0.7580 GoF= 7.658 Rw=10.82 [ 56 reflections, extinct446= 12]\n", "P m m n :1 nGoF= 0.7580 GoF= 7.658 Rw=10.82 [ 56 reflections, extinct446= 12]\n", "P m m n :2 nGoF= 0.7580 GoF= 7.658 Rw=10.82 [ 56 reflections, extinct446= 12]\n", "P 21 21 2 nGoF= 0.7761 GoF= 7.636 Rw=10.79 [ 60 reflections, extinct446= 4]\n", "P 21 21 21 nGoF= 0.7936 GoF= 6.667 Rw=10.09 [ 57 reflections, extinct446= 7]\n", "P 2 c m nGoF= 0.7952 GoF= 7.402 Rw=10.64 [ 56 reflections, extinct446= 15]\n", "P m c m nGoF= 0.7952 GoF= 7.402 Rw=10.64 [ 56 reflections, extinct446= 15]\n", "P m c 21 nGoF= 0.7952 GoF= 7.402 Rw=10.64 [ 56 reflections, extinct446= 15]\n", "P 21 2 2 nGoF= 0.7972 GoF= 7.379 Rw=10.60 [ 62 reflections, extinct446= 2]\n", "P 2 21 2 nGoF= 0.8149 GoF= 7.689 Rw=10.82 [ 62 reflections, extinct446= 2]\n", "P 21 2 21 nGoF= 0.8332 GoF= 7.464 Rw=10.67 [ 59 reflections, extinct446= 5]\n", "P 2 21 21 nGoF= 0.8337 GoF= 6.719 Rw=10.12 [ 59 reflections, extinct446= 5]\n", "P 2 2 2 nGoF= 0.8359 GoF= 7.424 Rw=10.63 [ 64 reflections, extinct446= 0]\n", "P m m 2 nGoF= 0.8359 GoF= 7.424 Rw=10.63 [ 64 reflections, extinct446= 0]\n", "P 2 m m nGoF= 0.8359 GoF= 7.424 Rw=10.63 [ 64 reflections, extinct446= 0]\n", "P m 2 m nGoF= 0.8359 GoF= 7.424 Rw=10.63 [ 64 reflections, extinct446= 0]\n", "P m m m nGoF= 0.8359 GoF= 7.424 Rw=10.63 [ 64 reflections, extinct446= 0]\n", "P 2 2 21 nGoF= 0.8737 GoF= 7.506 Rw=10.70 [ 61 reflections, extinct446= 3]\n", "P 1 1 n nGoF= 1.0025 GoF= 7.164 Rw=10.41 [ 81 reflections, extinct446= 12]\n", "P 1 1 2/n nGoF= 1.0025 GoF= 7.164 Rw=10.41 [ 81 reflections, extinct446= 12]\n", "P 1 1 21/n nGoF= 1.0394 GoF= 6.164 Rw= 9.66 [ 78 reflections, extinct446= 15]\n", "P 1 21/c 1 nGoF= 1.0680 GoF= 6.298 Rw= 9.78 [ 80 reflections, extinct446= 17]\n", "P 1 1 2 nGoF= 1.1058 GoF= 6.932 Rw=10.21 [ 93 reflections, extinct446= 0]\n", "P 1 1 m nGoF= 1.1058 GoF= 6.932 Rw=10.21 [ 93 reflections, extinct446= 0]\n", "P 1 1 2/m nGoF= 1.1058 GoF= 6.932 Rw=10.21 [ 93 reflections, extinct446= 0]\n", "P 1 c 1 nGoF= 1.1097 GoF= 7.088 Rw=10.36 [ 82 reflections, extinct446= 15]\n", "P 1 2/c 1 nGoF= 1.1097 GoF= 7.088 Rw=10.36 [ 82 reflections, extinct446= 15]\n", "P 1 21 1 nGoF= 1.1626 GoF= 7.403 Rw=10.56 [ 93 reflections, extinct446= 2]\n", "P 1 21/m 1 nGoF= 1.1626 GoF= 7.403 Rw=10.56 [ 93 reflections, extinct446= 2]\n", "P 1 1 21 nGoF= 1.1787 GoF= 6.993 Rw=10.26 [ 90 reflections, extinct446= 3]\n", "P 1 1 21/m nGoF= 1.1787 GoF= 6.993 Rw=10.26 [ 90 reflections, extinct446= 3]\n", "P 1 2 1 nGoF= 1.1799 GoF= 7.136 Rw=10.36 [ 95 reflections, extinct446= 0]\n", "P 1 m 1 nGoF= 1.1799 GoF= 7.136 Rw=10.36 [ 95 reflections, extinct446= 0]\n", "P 1 2/m 1 nGoF= 1.1799 GoF= 7.136 Rw=10.36 [ 95 reflections, extinct446= 0]\n", "P 21 1 1 nGoF= 1.2087 GoF= 7.218 Rw=10.44 [ 96 reflections, extinct446= 2]\n", "P 21/m 1 1 nGoF= 1.2087 GoF= 7.218 Rw=10.44 [ 96 reflections, extinct446= 2]\n", "P 2 1 1 nGoF= 1.2603 GoF= 7.300 Rw=10.49 [ 98 reflections, extinct446= 0]\n", "P m 1 1 nGoF= 1.2603 GoF= 7.300 Rw=10.49 [ 98 reflections, extinct446= 0]\n", "P 2/m 1 1 nGoF= 1.2603 GoF= 7.300 Rw=10.49 [ 98 reflections, extinct446= 0]\n", "P 1 nGoF= 1.8070 GoF= 6.738 Rw= 9.99 [158 reflections, extinct446= 0]\n", "P -1 nGoF= 1.8070 GoF= 6.738 Rw= 9.99 [158 reflections, extinct446= 0]\n", "Chosen spacegroup (smallest nGoF): P m c n\n", "Rwp= 84.49% GoF= 473.87 nGoF= 135.82 ( 49 reflections, 85 extinct)\n", " (# 5) I 1 2 1 : Rwp= 72.81% GoF= 344.19 nGoF= 96.53 ( 46 reflections, 87 extinct)\n", " (# 5) A 1 1 2 : Rwp= 84.33% GoF= 472.07 nGoF= 129.57 ( 47 reflections, 85 extinct)\n", " (# 5) B 1 1 2 : Rwp= 71.98% GoF= 336.58 nGoF= 90.94 ( 45 reflections, 85 extinct)\n", " (# 5) I 1 1 2 : Rwp= 72.52% GoF= 341.81 nGoF= 94.15 ( 45 reflections, 87 extinct)\n", " (# 5) B 2 1 1 : Rwp= 71.94% GoF= 335.24 nGoF= 95.03 ( 47 reflections, 85 extinct)\n", " (# 5) C 2 1 1 : Rwp= 67.25% GoF= 303.93 nGoF= 77.53 ( 48 reflections, 84 extinct)\n", " (# 5) I 2 1 1 : Rwp= 72.77% GoF= 343.85 nGoF= 100.61 ( 48 reflections, 87 extinct)\n", " (# 6) P 1 m 1 : Rwp= 10.36% GoF= 7.14 nGoF= 1.18 ( 95 reflections, 0 extinct) [same extinctions as:P 1 2 1]\n", " (# 6) P 1 1 m : Rwp= 10.21% GoF= 6.93 nGoF= 1.11 ( 93 reflections, 0 extinct) [same extinctions as:P 1 1 2]\n", " (# 6) P m 1 1 : Rwp= 10.49% GoF= 7.30 nGoF= 1.26 ( 98 reflections, 0 extinct) [same extinctions as:P 2 1 1]\n", " (# 7) P 1 c 1 : Rwp= 10.36% GoF= 7.09 nGoF= 1.11 ( 82 reflections, 15 extinct)\n", " (# 7) P 1 n 1 : Rwp= 38.30% GoF= 96.80 nGoF= 45.75 ( 80 reflections, 17 extinct)\n", " (# 7) P 1 a 1 : Rwp= 43.27% GoF= 123.51 nGoF= 51.72 ( 81 reflections, 14 extinct)\n", " (# 7) P 1 1 a : Rwp= 33.41% GoF= 73.81 nGoF= 27.88 ( 81 reflections, 10 extinct)\n", " (# 7) P 1 1 n : Rwp= 10.41% GoF= 7.16 nGoF= 1.00 ( 81 reflections, 12 extinct)\n", " (# 7) P 1 1 b : Rwp= 33.47% GoF= 74.07 nGoF= 27.89 ( 81 reflections, 10 extinct)\n", " (# 7) P b 1 1 : Rwp= 33.79% GoF= 75.16 nGoF= 25.79 ( 80 reflections, 14 extinct)\n", " (# 7) P n 1 1 : Rwp= 33.65% GoF= 74.54 nGoF= 32.91 ( 81 reflections, 17 extinct)\n", " (# 7) P c 1 1 : Rwp= 38.57% GoF= 97.83 nGoF= 39.17 ( 79 reflections, 15 extinct)\n", " (# 8) C 1 m 1 : Rwp= 67.10% GoF= 302.47 nGoF= 75.71 ( 47 reflections, 84 extinct) [same extinctions as:C 1 2 1]\n", " (# 8) A 1 m 1 : Rwp= 84.49% GoF= 473.87 nGoF= 135.82 ( 49 reflections, 85 extinct) [same extinctions as:A 1 2 1]\n", " (# 8) I 1 m 1 : Rwp= 72.81% GoF= 344.19 nGoF= 96.53 ( 46 reflections, 87 extinct) [same extinctions as:I 1 2 1]\n", " (# 8) A 1 1 m : Rwp= 84.33% GoF= 472.07 nGoF= 129.57 ( 47 reflections, 85 extinct) [same extinctions as:A 1 1 2]\n", " (# 8) B 1 1 m : Rwp= 71.98% GoF= 336.58 nGoF= 90.94 ( 45 reflections, 85 extinct) [same extinctions as:B 1 1 2]\n", " (# 8) I 1 1 m : Rwp= 72.52% GoF= 341.81 nGoF= 94.15 ( 45 reflections, 87 extinct) [same extinctions as:I 1 1 2]\n", " (# 8) B m 1 1 : Rwp= 71.94% GoF= 335.24 nGoF= 95.03 ( 47 reflections, 85 extinct) [same extinctions as:B 2 1 1]\n", " (# 8) C m 1 1 : Rwp= 67.25% GoF= 303.93 nGoF= 77.53 ( 48 reflections, 84 extinct) [same extinctions as:C 2 1 1]\n", " (# 8) I m 1 1 : Rwp= 72.77% GoF= 343.85 nGoF= 100.61 ( 48 reflections, 87 extinct) [same extinctions as:I 2 1 1]\n", " (# 9) C 1 c 1 : Rwp= 64.35% GoF= 277.20 nGoF= 62.41 ( 40 reflections, 93 extinct)\n", " (# 9) A 1 n 1 : Rwp= 85.15% GoF= 479.72 nGoF= 117.17 ( 41 reflections, 93 extinct)\n", " (# 9) I 1 a 1 : Rwp= 72.80% GoF= 342.99 nGoF= 83.92 ( 40 reflections, 93 extinct)\n", " (# 9) A 1 a 1 : Rwp= 85.15% GoF= 479.72 nGoF= 117.17 ( 41 reflections, 93 extinct) [same extinctions as:A 1 n 1]\n", " (# 9) C 1 n 1 : Rwp= 64.35% GoF= 277.20 nGoF= 62.41 ( 40 reflections, 93 extinct) [same extinctions as:C 1 c 1]\n", " (# 9) I 1 c 1 : Rwp= 72.80% GoF= 342.99 nGoF= 83.92 ( 40 reflections, 93 extinct) [same extinctions as:I 1 a 1]\n", " (# 9) A 1 1 a : Rwp= 62.57% GoF= 259.13 nGoF= 63.23 ( 41 reflections, 91 extinct)\n", " (# 9) B 1 1 n : Rwp= 71.99% GoF= 335.75 nGoF= 78.85 ( 39 reflections, 91 extinct)\n", " (# 9) I 1 1 b : Rwp= 73.52% GoF= 350.18 nGoF= 83.75 ( 39 reflections, 91 extinct)\n", " (# 9) B 1 1 b : Rwp= 71.99% GoF= 335.75 nGoF= 78.85 ( 39 reflections, 91 extinct) [same extinctions as:B 1 1 n]\n", " (# 9) A 1 1 n : Rwp= 62.57% GoF= 259.13 nGoF= 63.23 ( 41 reflections, 91 extinct) [same extinctions as:A 1 1 a]\n", " (# 9) I 1 1 a : Rwp= 73.52% GoF= 350.18 nGoF= 83.75 ( 39 reflections, 91 extinct) [same extinctions as:I 1 1 b]\n", " (# 9) B b 1 1 : Rwp= 73.59% GoF= 349.69 nGoF= 83.02 ( 39 reflections, 93 extinct)\n", " (# 9) C n 1 1 : Rwp= 72.53% GoF= 352.00 nGoF= 78.33 ( 39 reflections, 93 extinct)\n", " (# 9) I c 1 1 : Rwp= 74.34% GoF= 357.22 nGoF= 82.40 ( 38 reflections, 93 extinct)\n", " (# 9) C c 1 1 : Rwp= 72.53% GoF= 352.00 nGoF= 78.33 ( 39 reflections, 93 extinct) [same extinctions as:C n 1 1]\n", " (# 9) B n 1 1 : Rwp= 73.59% GoF= 349.69 nGoF= 83.02 ( 39 reflections, 93 extinct) [same extinctions as:B b 1 1]\n", " (# 9) I b 1 1 : Rwp= 74.34% GoF= 357.22 nGoF= 82.40 ( 38 reflections, 93 extinct) [same extinctions as:I c 1 1]\n", " (# 10) P 1 2/m 1 : Rwp= 10.36% GoF= 7.14 nGoF= 1.18 ( 95 reflections, 0 extinct) [same extinctions as:P 1 2 1]\n", " (# 10) P 1 1 2/m : Rwp= 10.21% GoF= 6.93 nGoF= 1.11 ( 93 reflections, 0 extinct) [same extinctions as:P 1 1 2]\n", " (# 10) P 2/m 1 1 : Rwp= 10.49% GoF= 7.30 nGoF= 1.26 ( 98 reflections, 0 extinct) [same extinctions as:P 2 1 1]\n", " (# 11) P 1 21/m 1 : Rwp= 10.56% GoF= 7.40 nGoF= 1.16 ( 93 reflections, 2 extinct) [same extinctions as:P 1 21 1]\n", " (# 11) P 1 1 21/m : Rwp= 10.26% GoF= 6.99 nGoF= 1.18 ( 90 reflections, 3 extinct) [same extinctions as:P 1 1 21]\n", " (# 11) P 21/m 1 1 : Rwp= 10.44% GoF= 7.22 nGoF= 1.21 ( 96 reflections, 2 extinct) [same extinctions as:P 21 1 1]\n", " (# 12) C 1 2/m 1 : Rwp= 67.10% GoF= 302.47 nGoF= 75.71 ( 47 reflections, 84 extinct) [same extinctions as:C 1 2 1]\n", " (# 12) A 1 2/m 1 : Rwp= 84.49% GoF= 473.87 nGoF= 135.82 ( 49 reflections, 85 extinct) [same extinctions as:A 1 2 1]\n", " (# 12) I 1 2/m 1 : Rwp= 72.81% GoF= 344.19 nGoF= 96.53 ( 46 reflections, 87 extinct) [same extinctions as:I 1 2 1]\n", " (# 12) A 1 1 2/m : Rwp= 84.33% GoF= 472.07 nGoF= 129.57 ( 47 reflections, 85 extinct) [same extinctions as:A 1 1 2]\n", " (# 12) B 1 1 2/m : Rwp= 71.98% GoF= 336.58 nGoF= 90.94 ( 45 reflections, 85 extinct) [same extinctions as:B 1 1 2]\n", " (# 12) I 1 1 2/m : Rwp= 72.52% GoF= 341.81 nGoF= 94.15 ( 45 reflections, 87 extinct) [same extinctions as:I 1 1 2]\n", " (# 12) B 2/m 1 1 : Rwp= 71.94% GoF= 335.24 nGoF= 95.03 ( 47 reflections, 85 extinct) [same extinctions as:B 2 1 1]\n", " (# 12) C 2/m 1 1 : Rwp= 67.25% GoF= 303.93 nGoF= 77.53 ( 48 reflections, 84 extinct) [same extinctions as:C 2 1 1]\n", " (# 12) I 2/m 1 1 : Rwp= 72.77% GoF= 343.85 nGoF= 100.61 ( 48 reflections, 87 extinct) [same extinctions as:I 2 1 1]\n", " (# 13) P 1 2/c 1 : Rwp= 10.36% GoF= 7.09 nGoF= 1.11 ( 82 reflections, 15 extinct) [same extinctions as:P 1 c 1]\n", " (# 13) P 1 2/n 1 : Rwp= 38.30% GoF= 96.80 nGoF= 45.75 ( 80 reflections, 17 extinct) [same extinctions as:P 1 n 1]\n", " (# 13) P 1 2/a 1 : Rwp= 43.27% GoF= 123.51 nGoF= 51.72 ( 81 reflections, 14 extinct) [same extinctions as:P 1 a 1]\n", " (# 13) P 1 1 2/a : Rwp= 33.41% GoF= 73.81 nGoF= 27.88 ( 81 reflections, 10 extinct) [same extinctions as:P 1 1 a]\n", " (# 13) P 1 1 2/n : Rwp= 10.41% GoF= 7.16 nGoF= 1.00 ( 81 reflections, 12 extinct) [same extinctions as:P 1 1 n]\n", " (# 13) P 1 1 2/b : Rwp= 33.47% GoF= 74.07 nGoF= 27.89 ( 81 reflections, 10 extinct) [same extinctions as:P 1 1 b]\n", " (# 13) P 2/b 1 1 : Rwp= 33.79% GoF= 75.16 nGoF= 25.79 ( 80 reflections, 14 extinct) [same extinctions as:P b 1 1]\n", " (# 13) P 2/n 1 1 : Rwp= 33.65% GoF= 74.54 nGoF= 32.91 ( 81 reflections, 17 extinct) [same extinctions as:P n 1 1]\n", " (# 13) P 2/c 1 1 : Rwp= 38.57% GoF= 97.83 nGoF= 39.17 ( 79 reflections, 15 extinct) [same extinctions as:P c 1 1]\n", " (# 14) P 1 21/c 1 : Rwp= 9.78% GoF= 6.30 nGoF= 1.07 ( 80 reflections, 17 extinct)\n", " (# 14) P 1 21/n 1 : Rwp= 38.20% GoF= 96.17 nGoF= 44.60 ( 78 reflections, 19 extinct)\n", " (# 14) P 1 21/a 1 : Rwp= 43.29% GoF= 123.52 nGoF= 50.45 ( 79 reflections, 16 extinct)\n", " (# 14) P 1 1 21/a : Rwp= 33.43% GoF= 73.76 nGoF= 26.93 ( 78 reflections, 13 extinct)\n", " (# 14) P 1 1 21/n : Rwp= 9.66% GoF= 6.16 nGoF= 1.04 ( 78 reflections, 15 extinct)\n", " (# 14) P 1 1 21/b : Rwp= 33.31% GoF= 73.23 nGoF= 26.90 ( 78 reflections, 13 extinct)\n", " (# 14) P 21/b 1 1 : Rwp= 40.44% GoF= 107.55 nGoF= 32.31 ( 78 reflections, 16 extinct)\n", " (# 14) P 21/n 1 1 : Rwp= 33.63% GoF= 74.37 nGoF= 32.07 ( 79 reflections, 19 extinct)\n", " (# 14) P 21/c 1 1 : Rwp= 44.04% GoF= 127.39 nGoF= 44.51 ( 77 reflections, 17 extinct)\n", " (# 15) C 1 2/c 1 : Rwp= 64.35% GoF= 277.20 nGoF= 62.41 ( 40 reflections, 93 extinct) [same extinctions as:C 1 c 1]\n", " (# 15) A 1 2/n 1 : Rwp= 85.15% GoF= 479.72 nGoF= 117.17 ( 41 reflections, 93 extinct) [same extinctions as:A 1 n 1]\n", " (# 15) I 1 2/a 1 : Rwp= 72.80% GoF= 342.99 nGoF= 83.92 ( 40 reflections, 93 extinct) [same extinctions as:I 1 a 1]\n", " (# 15) A 1 2/a 1 : Rwp= 85.15% GoF= 479.72 nGoF= 117.17 ( 41 reflections, 93 extinct) [same extinctions as:A 1 n 1]\n", " (# 15) C 1 2/n 1 : Rwp= 64.35% GoF= 277.20 nGoF= 62.41 ( 40 reflections, 93 extinct) [same extinctions as:C 1 c 1]\n", " (# 15) I 1 2/c 1 : Rwp= 72.80% GoF= 342.99 nGoF= 83.92 ( 40 reflections, 93 extinct) [same extinctions as:I 1 a 1]\n", " (# 15) A 1 1 2/a : Rwp= 62.57% GoF= 259.13 nGoF= 63.23 ( 41 reflections, 91 extinct) [same extinctions as:A 1 1 a]\n", " (# 15) B 1 1 2/n : Rwp= 71.99% GoF= 335.75 nGoF= 78.85 ( 39 reflections, 91 extinct) [same extinctions as:B 1 1 n]\n", " (# 15) I 1 1 2/b : Rwp= 73.52% GoF= 350.18 nGoF= 83.75 ( 39 reflections, 91 extinct) [same extinctions as:I 1 1 b]\n", " (# 15) B 1 1 2/b : Rwp= 71.99% GoF= 335.75 nGoF= 78.85 ( 39 reflections, 91 extinct) [same extinctions as:B 1 1 n]\n", " (# 15) A 1 1 2/n : Rwp= 62.57% GoF= 259.13 nGoF= 63.23 ( 41 reflections, 91 extinct) [same extinctions as:A 1 1 a]\n", " (# 15) I 1 1 2/a : Rwp= 73.52% GoF= 350.18 nGoF= 83.75 ( 39 reflections, 91 extinct) [same extinctions as:I 1 1 b]\n", " (# 15) B 2/b 1 1 : Rwp= 73.59% GoF= 349.69 nGoF= 83.02 ( 39 reflections, 93 extinct) [same extinctions as:B b 1 1]\n", " (# 15) C 2/n 1 1 : Rwp= 72.53% GoF= 352.00 nGoF= 78.33 ( 39 reflections, 93 extinct) [same extinctions as:C n 1 1]\n", " (# 15) I 2/c 1 1 : Rwp= 74.34% GoF= 357.22 nGoF= 82.40 ( 38 reflections, 93 extinct) [same extinctions as:I c 1 1]\n", " (# 15) C 2/c 1 1 : Rwp= 72.53% GoF= 352.00 nGoF= 78.33 ( 39 reflections, 93 extinct) [same extinctions as:C n 1 1]\n", " (# 15) B 2/n 1 1 : Rwp= 73.59% GoF= 349.69 nGoF= 83.02 ( 39 reflections, 93 extinct) [same extinctions as:B b 1 1]\n", " (# 15) I 2/b 1 1 : Rwp= 74.34% GoF= 357.22 nGoF= 82.40 ( 38 reflections, 93 extinct) [same extinctions as:I c 1 1]\n", " (# 16) P 2 2 2 : Rwp= 10.63% GoF= 7.42 nGoF= 0.84 ( 64 reflections, 0 extinct)\n", " (# 17) P 2 2 21 : Rwp= 10.70% GoF= 7.51 nGoF= 0.87 ( 61 reflections, 3 extinct)\n", " (# 17) P 21 2 2 : Rwp= 10.60% GoF= 7.38 nGoF= 0.80 ( 62 reflections, 2 extinct)\n", " (# 17) P 2 21 2 : Rwp= 10.82% GoF= 7.69 nGoF= 0.81 ( 62 reflections, 2 extinct)\n", " (# 18) P 21 21 2 : Rwp= 10.79% GoF= 7.64 nGoF= 0.78 ( 60 reflections, 4 extinct)\n", " (# 18) P 2 21 21 : Rwp= 10.12% GoF= 6.72 nGoF= 0.83 ( 59 reflections, 5 extinct)\n", " (# 18) P 21 2 21 : Rwp= 10.67% GoF= 7.46 nGoF= 0.83 ( 59 reflections, 5 extinct)\n", " (# 19) P 21 21 21 : Rwp= 10.09% GoF= 6.67 nGoF= 0.79 ( 57 reflections, 7 extinct)\n", " (# 20) C 2 2 21 : Rwp= 67.30% GoF= 302.54 nGoF= 46.86 ( 29 reflections, 87 extinct)\n", " (# 20) A 21 2 2 : Rwp= 84.49% GoF= 469.69 nGoF= 85.94 ( 31 reflections, 87 extinct)\n", " (# 20) B 2 21 2 : Rwp= 72.02% GoF= 334.54 nGoF= 58.65 ( 29 reflections, 87 extinct)\n", " (# 21) C 2 2 2 : Rwp= 67.34% GoF= 303.40 nGoF= 51.69 ( 32 reflections, 84 extinct)\n", " (# 21) A 2 2 2 : Rwp= 84.49% GoF= 470.23 nGoF= 91.49 ( 33 reflections, 85 extinct)\n", " (# 21) B 2 2 2 : Rwp= 72.02% GoF= 334.95 nGoF= 62.69 ( 31 reflections, 85 extinct)\n", " (# 22) F 2 2 2 : Rwp= 76.91% GoF= 392.21 nGoF= 37.80 ( 16 reflections, 127 extinct)\n", " (# 23) I 2 2 2 : Rwp= 72.85% GoF= 342.91 nGoF= 65.06 ( 31 reflections, 87 extinct)\n", " (# 24) I 21 21 21 : Rwp= 72.85% GoF= 342.91 nGoF= 65.06 ( 31 reflections, 87 extinct) [same extinctions as:I 2 2 2]\n", " (# 25) P m m 2 : Rwp= 10.63% GoF= 7.42 nGoF= 0.84 ( 64 reflections, 0 extinct) [same extinctions as:P 2 2 2]\n", " (# 25) P 2 m m : Rwp= 10.63% GoF= 7.42 nGoF= 0.84 ( 64 reflections, 0 extinct) [same extinctions as:P 2 2 2]\n", " (# 25) P m 2 m : Rwp= 10.63% GoF= 7.42 nGoF= 0.84 ( 64 reflections, 0 extinct) [same extinctions as:P 2 2 2]\n", " (# 26) P m c 21 : Rwp= 10.64% GoF= 7.40 nGoF= 0.80 ( 56 reflections, 15 extinct)\n", " (# 26) P c m 21 : Rwp= 38.62% GoF= 97.41 nGoF= 26.29 ( 53 reflections, 15 extinct)\n", " (# 26) P 21 m a : Rwp= 33.50% GoF= 73.51 nGoF= 19.69 ( 57 reflections, 10 extinct)\n", " (# 26) P 21 a m : Rwp= 43.46% GoF= 123.66 nGoF= 35.71 ( 56 reflections, 14 extinct)\n", " (# 26) P b 21 m : Rwp= 34.03% GoF= 75.69 nGoF= 17.62 ( 54 reflections, 14 extinct)\n", " (# 26) P m 21 b : Rwp= 33.56% GoF= 73.73 nGoF= 19.70 ( 57 reflections, 10 extinct)\n", " (# 27) P c c 2 : Rwp= 38.60% GoF= 97.05 nGoF= 23.82 ( 48 reflections, 27 extinct)\n", " (# 27) P 2 a a : Rwp= 50.20% GoF= 164.52 nGoF= 44.37 ( 51 reflections, 22 extinct)\n", " (# 27) P b 2 b : Rwp= 43.99% GoF= 126.10 nGoF= 29.74 ( 49 reflections, 22 extinct)\n", " (# 28) P m a 2 : Rwp= 43.46% GoF= 123.66 nGoF= 35.71 ( 56 reflections, 14 extinct) [same extinctions as:P 21 a m]\n", " (# 28) P b m 2 : Rwp= 34.03% GoF= 75.69 nGoF= 17.62 ( 54 reflections, 14 extinct) [same extinctions as:P b 21 m]\n", " (# 28) P 2 m b : Rwp= 33.56% GoF= 73.73 nGoF= 19.70 ( 57 reflections, 10 extinct) [same extinctions as:P m 21 b]\n", " (# 28) P 2 c m : Rwp= 10.64% GoF= 7.40 nGoF= 0.80 ( 56 reflections, 15 extinct) [same extinctions as:P m c 21]\n", " (# 28) P c 2 m : Rwp= 38.62% GoF= 97.41 nGoF= 26.29 ( 53 reflections, 15 extinct) [same extinctions as:P c m 21]\n", " (# 28) P m 2 a : Rwp= 33.50% GoF= 73.51 nGoF= 19.69 ( 57 reflections, 10 extinct) [same extinctions as:P 21 m a]\n", " (# 29) P c a 21 : Rwp= 62.23% GoF= 251.95 nGoF= 63.79 ( 45 reflections, 29 extinct)\n", " (# 29) P b c 21 : Rwp= 33.86% GoF= 74.62 nGoF= 15.07 ( 46 reflections, 29 extinct)\n", " (# 29) P 21 a b : Rwp= 50.22% GoF= 164.48 nGoF= 42.63 ( 49 reflections, 24 extinct)\n", " (# 29) P 21 c a : Rwp= 33.54% GoF= 73.37 nGoF= 17.03 ( 49 reflections, 25 extinct)\n", " (# 29) P c 21 b : Rwp= 46.81% GoF= 142.56 nGoF= 34.27 ( 46 reflections, 25 extinct)\n", " (# 29) P b 21 a : Rwp= 48.70% GoF= 154.51 nGoF= 32.54 ( 47 reflections, 24 extinct)\n", " (# 30) P n c 2 : Rwp= 33.90% GoF= 74.86 nGoF= 19.64 ( 48 reflections, 29 extinct)\n", " (# 30) P c n 2 : Rwp= 60.42% GoF= 237.67 nGoF= 62.88 ( 46 reflections, 29 extinct)\n", " (# 30) P 2 n a : Rwp= 46.62% GoF= 141.76 nGoF= 39.52 ( 49 reflections, 25 extinct)\n", " (# 30) P 2 a n : Rwp= 43.48% GoF= 123.34 nGoF= 31.89 ( 50 reflections, 24 extinct)\n", " (# 30) P b 2 n : Rwp= 41.11% GoF= 110.18 nGoF= 20.01 ( 48 reflections, 24 extinct)\n", " (# 30) P n 2 b : Rwp= 43.81% GoF= 125.04 nGoF= 32.12 ( 48 reflections, 25 extinct)\n", " (# 31) P m n 21 : Rwp= 38.36% GoF= 96.24 nGoF= 30.94 ( 54 reflections, 17 extinct)\n", " (# 31) P n m 21 : Rwp= 33.93% GoF= 75.18 nGoF= 21.71 ( 53 reflections, 17 extinct)\n", " (# 31) P 21 m n : Rwp= 10.82% GoF= 7.66 nGoF= 0.76 ( 56 reflections, 12 extinct)\n", " (# 31) P 21 n m : Rwp= 38.36% GoF= 96.24 nGoF= 30.94 ( 54 reflections, 17 extinct) [same extinctions as:P m n 21]\n", " (# 31) P n 21 m : Rwp= 33.93% GoF= 75.18 nGoF= 21.71 ( 53 reflections, 17 extinct) [same extinctions as:P n m 21]\n", " (# 31) P m 21 n : Rwp= 10.82% GoF= 7.66 nGoF= 0.76 ( 56 reflections, 12 extinct) [same extinctions as:P 21 m n]\n", " (# 32) P b a 2 : Rwp= 54.80% GoF= 195.52 nGoF= 44.29 ( 46 reflections, 28 extinct)\n", " (# 32) P 2 c b : Rwp= 33.43% GoF= 72.83 nGoF= 17.02 ( 49 reflections, 25 extinct)\n", " (# 32) P c 2 a : Rwp= 51.01% GoF= 169.41 nGoF= 37.84 ( 46 reflections, 25 extinct)\n", " (# 33) P n a 21 : Rwp= 61.57% GoF= 246.67 nGoF= 65.58 ( 45 reflections, 31 extinct)\n", " (# 33) P b n 21 : Rwp= 51.86% GoF= 174.89 nGoF= 39.36 ( 44 reflections, 31 extinct)\n", " (# 33) P 21 n b : Rwp= 46.56% GoF= 141.24 nGoF= 37.89 ( 47 reflections, 27 extinct)\n", " (# 33) P 21 c n : Rwp= 10.06% GoF= 6.60 nGoF= 0.69 ( 48 reflections, 27 extinct)\n", " (# 33) P c 21 n : Rwp= 44.41% GoF= 128.33 nGoF= 25.93 ( 45 reflections, 27 extinct)\n", " (# 33) P n 21 a : Rwp= 43.80% GoF= 124.89 nGoF= 30.78 ( 46 reflections, 27 extinct)\n", " (# 34) P n n 2 : Rwp= 59.76% GoF= 232.47 nGoF= 65.08 ( 46 reflections, 31 extinct)\n", " (# 34) P 2 n n : Rwp= 38.25% GoF= 95.37 nGoF= 27.50 ( 48 reflections, 27 extinct)\n", " (# 34) P n 2 n : Rwp= 33.91% GoF= 74.93 nGoF= 19.25 ( 47 reflections, 27 extinct)\n", " (# 35) C m m 2 : Rwp= 67.34% GoF= 303.40 nGoF= 51.69 ( 32 reflections, 84 extinct) [same extinctions as:C 2 2 2]\n", " (# 35) A 2 m m : Rwp= 84.49% GoF= 470.23 nGoF= 91.49 ( 33 reflections, 85 extinct) [same extinctions as:A 2 2 2]\n", " (# 35) B m 2 m : Rwp= 72.02% GoF= 334.95 nGoF= 62.69 ( 31 reflections, 85 extinct) [same extinctions as:B 2 2 2]\n", " (# 36) C m c 21 : Rwp= 64.42% GoF= 276.89 nGoF= 42.22 ( 27 reflections, 93 extinct)\n", " (# 36) C c m 21 : Rwp= 72.59% GoF= 351.39 nGoF= 52.23 ( 26 reflections, 93 extinct)\n", " (# 36) A 21 m a : Rwp= 62.74% GoF= 258.86 nGoF= 44.91 ( 29 reflections, 91 extinct)\n", " (# 36) A 21 a m : Rwp= 85.15% GoF= 476.52 nGoF= 80.02 ( 28 reflections, 93 extinct)\n", " (# 36) B b 21 m : Rwp= 73.68% GoF= 349.64 nGoF= 55.38 ( 26 reflections, 93 extinct)\n", " (# 36) B m 21 b : Rwp= 72.03% GoF= 334.31 nGoF= 54.62 ( 27 reflections, 91 extinct)\n", " (# 37) C c c 2 : Rwp= 70.89% GoF= 334.73 nGoF= 47.38 ( 24 reflections, 99 extinct)\n", " (# 37) A 2 a a : Rwp= 73.84% GoF= 357.94 nGoF= 57.40 ( 26 reflections, 97 extinct)\n", " (# 37) B b 2 b : Rwp= 73.70% GoF= 349.37 nGoF= 51.13 ( 24 reflections, 97 extinct)\n", " (# 38) A m m 2 : Rwp= 84.49% GoF= 470.23 nGoF= 91.49 ( 33 reflections, 85 extinct) [same extinctions as:A 2 2 2]\n", " (# 38) B m m 2 : Rwp= 72.02% GoF= 334.95 nGoF= 62.69 ( 31 reflections, 85 extinct) [same extinctions as:B 2 2 2]\n", " (# 38) B 2 m m : Rwp= 72.02% GoF= 334.95 nGoF= 62.69 ( 31 reflections, 85 extinct) [same extinctions as:B 2 2 2]\n", " (# 38) C 2 m m : Rwp= 67.34% GoF= 303.40 nGoF= 51.69 ( 32 reflections, 84 extinct) [same extinctions as:C 2 2 2]\n", " (# 38) C m 2 m : Rwp= 67.34% GoF= 303.40 nGoF= 51.69 ( 32 reflections, 84 extinct) [same extinctions as:C 2 2 2]\n", " (# 38) A m 2 m : Rwp= 84.49% GoF= 470.23 nGoF= 91.49 ( 33 reflections, 85 extinct) [same extinctions as:A 2 2 2]\n", " (# 39) A b m 2 : Rwp= 84.55% GoF= 472.67 nGoF= 77.79 ( 28 reflections, 91 extinct)\n", " (# 39) B m a 2 : Rwp= 72.05% GoF= 334.62 nGoF= 56.66 ( 28 reflections, 91 extinct)\n", " (# 39) B 2 c m : Rwp= 72.05% GoF= 334.62 nGoF= 56.66 ( 28 reflections, 91 extinct) [same extinctions as:B m a 2]\n", " (# 39) C 2 m b : Rwp= 69.01% GoF= 318.13 nGoF= 49.97 ( 29 reflections, 88 extinct)\n", " (# 39) C m 2 a : Rwp= 69.01% GoF= 318.13 nGoF= 49.97 ( 29 reflections, 88 extinct) [same extinctions as:C 2 m b]\n", " (# 39) A c 2 m : Rwp= 84.55% GoF= 472.67 nGoF= 77.79 ( 28 reflections, 91 extinct) [same extinctions as:A b m 2]\n", " (# 40) A m a 2 : Rwp= 85.15% GoF= 476.52 nGoF= 80.02 ( 28 reflections, 93 extinct) [same extinctions as:A 21 a m]\n", " (# 40) B b m 2 : Rwp= 73.68% GoF= 349.64 nGoF= 55.38 ( 26 reflections, 93 extinct) [same extinctions as:B b 21 m]\n", " (# 40) B 2 m b : Rwp= 72.03% GoF= 334.31 nGoF= 54.62 ( 27 reflections, 91 extinct) [same extinctions as:B m 21 b]\n", " (# 40) C 2 c m : Rwp= 64.42% GoF= 276.89 nGoF= 42.22 ( 27 reflections, 93 extinct) [same extinctions as:C m c 21]\n", " (# 40) C c 2 m : Rwp= 72.59% GoF= 351.39 nGoF= 52.23 ( 26 reflections, 93 extinct) [same extinctions as:C c m 21]\n", " (# 40) A m 2 a : Rwp= 62.74% GoF= 258.86 nGoF= 44.91 ( 29 reflections, 91 extinct) [same extinctions as:A 21 m a]\n", " (# 41) A b a 2 : Rwp= 85.30% GoF= 480.04 nGoF= 66.08 ( 23 reflections, 99 extinct)\n", " (# 41) B b a 2 : Rwp= 73.70% GoF= 349.24 nGoF= 49.01 ( 23 reflections, 99 extinct)\n", " (# 41) B 2 c b : Rwp= 72.10% GoF= 334.37 nGoF= 48.62 ( 24 reflections, 97 extinct)\n", " (# 41) C 2 c b : Rwp= 66.51% GoF= 294.64 nGoF= 40.16 ( 24 reflections, 97 extinct)\n", " (# 41) C c 2 a : Rwp= 73.55% GoF= 360.14 nGoF= 47.63 ( 23 reflections, 97 extinct)\n", " (# 41) A c 2 a : Rwp= 67.38% GoF= 299.66 nGoF= 41.31 ( 24 reflections, 97 extinct)\n", " (# 42) F m m 2 : Rwp= 76.91% GoF= 392.21 nGoF= 37.80 ( 16 reflections, 127 extinct) [same extinctions as:F 2 2 2]\n", " (# 42) F 2 m m : Rwp= 76.91% GoF= 392.21 nGoF= 37.80 ( 16 reflections, 127 extinct) [same extinctions as:F 2 2 2]\n", " (# 42) F m 2 m : Rwp= 76.91% GoF= 392.21 nGoF= 37.80 ( 16 reflections, 127 extinct) [same extinctions as:F 2 2 2]\n", " (# 43) F d d 2 : Rwp= 80.89% GoF= 444.51 nGoF= 29.02 ( 11 reflections, 137 extinct)\n", " (# 43) F 2 d d : Rwp= 81.61% GoF= 452.81 nGoF= 32.05 ( 12 reflections, 136 extinct)\n", " (# 43) F d 2 d : Rwp= 80.89% GoF= 432.90 nGoF= 31.66 ( 12 reflections, 136 extinct)\n", " (# 44) I m m 2 : Rwp= 72.85% GoF= 342.91 nGoF= 65.06 ( 31 reflections, 87 extinct) [same extinctions as:I 2 2 2]\n", " (# 44) I 2 m m : Rwp= 72.85% GoF= 342.91 nGoF= 65.06 ( 31 reflections, 87 extinct) [same extinctions as:I 2 2 2]\n", " (# 44) I m 2 m : Rwp= 72.85% GoF= 342.91 nGoF= 65.06 ( 31 reflections, 87 extinct) [same extinctions as:I 2 2 2]\n", " (# 45) I b a 2 : Rwp= 74.45% GoF= 356.58 nGoF= 49.93 ( 23 reflections, 99 extinct)\n", " (# 45) I 2 c b : Rwp= 73.79% GoF= 350.67 nGoF= 53.79 ( 25 reflections, 97 extinct)\n", " (# 45) I c 2 a : Rwp= 75.26% GoF= 364.34 nGoF= 51.07 ( 23 reflections, 97 extinct)\n", " (# 46) I m a 2 : Rwp= 72.84% GoF= 342.28 nGoF= 58.75 ( 28 reflections, 93 extinct)\n", " (# 46) I b m 2 : Rwp= 74.44% GoF= 357.02 nGoF= 56.43 ( 26 reflections, 93 extinct)\n", " (# 46) I 2 m b : Rwp= 73.80% GoF= 351.28 nGoF= 60.25 ( 28 reflections, 91 extinct)\n", " (# 46) I 2 c m : Rwp= 72.84% GoF= 342.28 nGoF= 58.75 ( 28 reflections, 93 extinct) [same extinctions as:I m a 2]\n", " (# 46) I c 2 m : Rwp= 74.44% GoF= 357.02 nGoF= 56.43 ( 26 reflections, 93 extinct) [same extinctions as:I b m 2]\n", " (# 46) I m 2 a : Rwp= 73.80% GoF= 351.28 nGoF= 60.25 ( 28 reflections, 91 extinct) [same extinctions as:I 2 m b]\n", " (# 47) P m m m : Rwp= 10.63% GoF= 7.42 nGoF= 0.84 ( 64 reflections, 0 extinct) [same extinctions as:P 2 2 2]\n", " (# 48) P n n n :1 : Rwp= 59.75% GoF= 231.94 nGoF= 59.42 ( 42 reflections, 39 extinct)\n", " (# 48) P n n n :2 : Rwp= 59.75% GoF= 231.94 nGoF= 59.42 ( 42 reflections, 39 extinct) [same extinctions as:P n n n :1]\n", " (# 49) P c c m : Rwp= 38.60% GoF= 97.05 nGoF= 23.82 ( 48 reflections, 27 extinct) [same extinctions as:P c c 2]\n", " (# 49) P m a a : Rwp= 50.20% GoF= 164.52 nGoF= 44.37 ( 51 reflections, 22 extinct) [same extinctions as:P 2 a a]\n", " (# 49) P b m b : Rwp= 43.99% GoF= 126.10 nGoF= 29.74 ( 49 reflections, 22 extinct) [same extinctions as:P b 2 b]\n", " (# 50) P b a n :1 : Rwp= 54.90% GoF= 195.82 nGoF= 40.57 ( 42 reflections, 36 extinct)\n", " (# 50) P b a n :2 : Rwp= 54.90% GoF= 195.82 nGoF= 40.57 ( 42 reflections, 36 extinct) [same extinctions as:P b a n :1]\n", " (# 50) P n c b :1 : Rwp= 43.82% GoF= 124.72 nGoF= 28.80 ( 43 reflections, 37 extinct)\n", " (# 50) P n c b :2 : Rwp= 43.82% GoF= 124.72 nGoF= 28.80 ( 43 reflections, 37 extinct) [same extinctions as:P n c b :1]\n", " (# 50) P c n a :1 : Rwp= 63.12% GoF= 258.66 nGoF= 61.32 ( 41 reflections, 37 extinct)\n", " (# 50) P c n a :2 : Rwp= 63.12% GoF= 258.66 nGoF= 61.32 ( 41 reflections, 37 extinct) [same extinctions as:P c n a :1]\n", " (# 51) P m m a : Rwp= 33.50% GoF= 73.51 nGoF= 19.69 ( 57 reflections, 10 extinct) [same extinctions as:P 21 m a]\n", " (# 51) P m m b : Rwp= 33.56% GoF= 73.73 nGoF= 19.70 ( 57 reflections, 10 extinct) [same extinctions as:P m 21 b]\n", " (# 51) P b m m : Rwp= 34.03% GoF= 75.69 nGoF= 17.62 ( 54 reflections, 14 extinct) [same extinctions as:P b 21 m]\n", " (# 51) P c m m : Rwp= 38.62% GoF= 97.41 nGoF= 26.29 ( 53 reflections, 15 extinct) [same extinctions as:P c m 21]\n", " (# 51) P m c m : Rwp= 10.64% GoF= 7.40 nGoF= 0.80 ( 56 reflections, 15 extinct) [same extinctions as:P m c 21]\n", " (# 51) P m a m : Rwp= 43.46% GoF= 123.66 nGoF= 35.71 ( 56 reflections, 14 extinct) [same extinctions as:P 21 a m]\n", " (# 52) P n n a : Rwp= 62.46% GoF= 253.26 nGoF= 62.77 ( 41 reflections, 39 extinct)\n", " (# 52) P n n b : Rwp= 62.46% GoF= 253.28 nGoF= 62.77 ( 41 reflections, 39 extinct)\n", " (# 52) P b n n : Rwp= 51.89% GoF= 174.71 nGoF= 35.81 ( 40 reflections, 39 extinct)\n", " (# 52) P c n n : Rwp= 60.41% GoF= 236.81 nGoF= 54.69 ( 40 reflections, 39 extinct)\n", " (# 52) P n c n : Rwp= 33.89% GoF= 74.60 nGoF= 17.18 ( 42 reflections, 39 extinct)\n", " (# 52) P n a n : Rwp= 61.56% GoF= 246.08 nGoF= 59.74 ( 41 reflections, 39 extinct)\n", " (# 53) P m n a : Rwp= 46.62% GoF= 141.76 nGoF= 39.52 ( 49 reflections, 25 extinct) [same extinctions as:P 2 n a]\n", " (# 53) P n m b : Rwp= 43.81% GoF= 125.04 nGoF= 32.12 ( 48 reflections, 25 extinct) [same extinctions as:P n 2 b]\n", " (# 53) P b m n : Rwp= 41.11% GoF= 110.18 nGoF= 20.01 ( 48 reflections, 24 extinct) [same extinctions as:P b 2 n]\n", " (# 53) P c n m : Rwp= 60.42% GoF= 237.67 nGoF= 62.88 ( 46 reflections, 29 extinct) [same extinctions as:P c n 2]\n", " (# 53) P n c m : Rwp= 33.90% GoF= 74.86 nGoF= 19.64 ( 48 reflections, 29 extinct) [same extinctions as:P n c 2]\n", " (# 53) P m a n : Rwp= 43.48% GoF= 123.34 nGoF= 31.89 ( 50 reflections, 24 extinct) [same extinctions as:P 2 a n]\n", " (# 54) P c c a : Rwp= 51.06% GoF= 169.27 nGoF= 33.83 ( 41 reflections, 37 extinct)\n", " (# 54) P c c b : Rwp= 46.82% GoF= 142.28 nGoF= 30.61 ( 41 reflections, 37 extinct)\n", " (# 54) P b a a : Rwp= 58.73% GoF= 223.98 nGoF= 47.33 ( 41 reflections, 36 extinct)\n", " (# 54) P c a a : Rwp= 64.66% GoF= 271.27 nGoF= 61.76 ( 40 reflections, 37 extinct)\n", " (# 54) P b c b : Rwp= 43.93% GoF= 125.21 nGoF= 24.98 ( 41 reflections, 37 extinct)\n", " (# 54) P b a b : Rwp= 58.86% GoF= 224.90 nGoF= 47.49 ( 41 reflections, 36 extinct)\n", " (# 55) P b a m : Rwp= 54.80% GoF= 195.52 nGoF= 44.29 ( 46 reflections, 28 extinct) [same extinctions as:P b a 2]\n", " (# 55) P m c b : Rwp= 33.43% GoF= 72.83 nGoF= 17.02 ( 49 reflections, 25 extinct) [same extinctions as:P 2 c b]\n", " (# 55) P c m a : Rwp= 51.01% GoF= 169.41 nGoF= 37.84 ( 46 reflections, 25 extinct) [same extinctions as:P c 2 a]\n", " (# 56) P c c n : Rwp= 44.59% GoF= 129.01 nGoF= 23.23 ( 40 reflections, 39 extinct)\n", " (# 56) P n a a : Rwp= 64.00% GoF= 265.79 nGoF= 62.90 ( 40 reflections, 39 extinct)\n", " (# 56) P b n b : Rwp= 56.43% GoF= 206.51 nGoF= 42.55 ( 39 reflections, 39 extinct)\n", " (# 57) P b c m : Rwp= 33.86% GoF= 74.62 nGoF= 15.07 ( 46 reflections, 29 extinct) [same extinctions as:P b c 21]\n", " (# 57) P c a m : Rwp= 62.23% GoF= 251.95 nGoF= 63.79 ( 45 reflections, 29 extinct) [same extinctions as:P c a 21]\n", " (# 57) P m c a : Rwp= 33.54% GoF= 73.37 nGoF= 17.03 ( 49 reflections, 25 extinct) [same extinctions as:P 21 c a]\n", " (# 57) P m a b : Rwp= 50.22% GoF= 164.48 nGoF= 42.63 ( 49 reflections, 24 extinct) [same extinctions as:P 21 a b]\n", " (# 57) P b m a : Rwp= 48.70% GoF= 154.51 nGoF= 32.54 ( 47 reflections, 24 extinct) [same extinctions as:P b 21 a]\n", " (# 57) P c m b : Rwp= 46.81% GoF= 142.56 nGoF= 34.27 ( 46 reflections, 25 extinct) [same extinctions as:P c 21 b]\n", " (# 58) P n n m : Rwp= 59.76% GoF= 232.47 nGoF= 65.08 ( 46 reflections, 31 extinct) [same extinctions as:P n n 2]\n", " (# 58) P m n n : Rwp= 38.25% GoF= 95.37 nGoF= 27.50 ( 48 reflections, 27 extinct) [same extinctions as:P 2 n n]\n", " (# 58) P n m n : Rwp= 33.91% GoF= 74.93 nGoF= 19.25 ( 47 reflections, 27 extinct) [same extinctions as:P n 2 n]\n", " (# 59) P m m n :1 : Rwp= 10.82% GoF= 7.66 nGoF= 0.76 ( 56 reflections, 12 extinct) [same extinctions as:P 21 m n]\n", " (# 59) P m m n :2 : Rwp= 10.82% GoF= 7.66 nGoF= 0.76 ( 56 reflections, 12 extinct) [same extinctions as:P 21 m n]\n", " (# 59) P n m m :1 : Rwp= 33.93% GoF= 75.18 nGoF= 21.71 ( 53 reflections, 17 extinct) [same extinctions as:P n m 21]\n", " (# 59) P n m m :2 : Rwp= 33.93% GoF= 75.18 nGoF= 21.71 ( 53 reflections, 17 extinct) [same extinctions as:P n m 21]\n", " (# 59) P m n m :1 : Rwp= 38.36% GoF= 96.24 nGoF= 30.94 ( 54 reflections, 17 extinct) [same extinctions as:P m n 21]\n", " (# 59) P m n m :2 : Rwp= 38.36% GoF= 96.24 nGoF= 30.94 ( 54 reflections, 17 extinct) [same extinctions as:P m n 21]\n", " (# 60) P b c n : Rwp= 41.23% GoF= 110.30 nGoF= 16.90 ( 40 reflections, 39 extinct)\n", " (# 60) P c a n : Rwp= 62.26% GoF= 251.41 nGoF= 55.35 ( 39 reflections, 39 extinct)\n", " (# 60) P n c a : Rwp= 43.80% GoF= 124.58 nGoF= 27.46 ( 41 reflections, 39 extinct)\n", " (# 60) P n a b : Rwp= 64.00% GoF= 265.79 nGoF= 62.90 ( 40 reflections, 39 extinct)\n", " (# 60) P b n a : Rwp= 56.39% GoF= 206.20 nGoF= 42.51 ( 39 reflections, 39 extinct)\n", " (# 60) P c n b : Rwp= 63.12% GoF= 258.37 nGoF= 58.35 ( 39 reflections, 39 extinct)\n", " (# 61) P b c a : Rwp= 48.69% GoF= 153.78 nGoF= 27.12 ( 39 reflections, 39 extinct)\n", " (# 61) P c a b : Rwp= 64.71% GoF= 271.41 nGoF= 58.75 ( 38 reflections, 39 extinct)\n", " (# 62) P n m a : Rwp= 43.80% GoF= 124.89 nGoF= 30.78 ( 46 reflections, 27 extinct) [same extinctions as:P n 21 a]\n", " (# 62) P m n b : Rwp= 46.56% GoF= 141.24 nGoF= 37.89 ( 47 reflections, 27 extinct) [same extinctions as:P 21 n b]\n", " (# 62) P b n m : Rwp= 51.86% GoF= 174.89 nGoF= 39.36 ( 44 reflections, 31 extinct) [same extinctions as:P b n 21]\n", " (# 62) P c m n : Rwp= 44.41% GoF= 128.33 nGoF= 25.93 ( 45 reflections, 27 extinct) [same extinctions as:P c 21 n]\n", " (# 62) P m c n : Rwp= 10.06% GoF= 6.60 nGoF= 0.69 ( 48 reflections, 27 extinct) [same extinctions as:P 21 c n]\n", " (# 62) P n a m : Rwp= 61.57% GoF= 246.67 nGoF= 65.58 ( 45 reflections, 31 extinct) [same extinctions as:P n a 21]\n", " (# 63) C m c m : Rwp= 64.42% GoF= 276.89 nGoF= 42.22 ( 27 reflections, 93 extinct) [same extinctions as:C m c 21]\n", " (# 63) C c m m : Rwp= 72.59% GoF= 351.39 nGoF= 52.23 ( 26 reflections, 93 extinct) [same extinctions as:C c m 21]\n", " (# 63) A m m a : Rwp= 62.74% GoF= 258.86 nGoF= 44.91 ( 29 reflections, 91 extinct) [same extinctions as:A 21 m a]\n", " (# 63) A m a m : Rwp= 85.15% GoF= 476.52 nGoF= 80.02 ( 28 reflections, 93 extinct) [same extinctions as:A 21 a m]\n", " (# 63) B b m m : Rwp= 73.68% GoF= 349.64 nGoF= 55.38 ( 26 reflections, 93 extinct) [same extinctions as:B b 21 m]\n", " (# 63) B m m b : Rwp= 72.03% GoF= 334.31 nGoF= 54.62 ( 27 reflections, 91 extinct) [same extinctions as:B m 21 b]\n", " (# 64) C m c a : Rwp= 66.51% GoF= 294.64 nGoF= 40.16 ( 24 reflections, 97 extinct) [same extinctions as:C 2 c b]\n", " (# 64) C c m b : Rwp= 73.55% GoF= 360.14 nGoF= 47.63 ( 23 reflections, 97 extinct) [same extinctions as:C c 2 a]\n", " (# 64) A b m a : Rwp= 67.38% GoF= 299.66 nGoF= 41.31 ( 24 reflections, 97 extinct) [same extinctions as:A c 2 a]\n", " (# 64) A c a m : Rwp= 85.30% GoF= 480.04 nGoF= 66.08 ( 23 reflections, 99 extinct) [same extinctions as:A b a 2]\n", " (# 64) B b c m : Rwp= 73.70% GoF= 349.24 nGoF= 49.01 ( 23 reflections, 99 extinct) [same extinctions as:B b a 2]\n", " (# 64) B m a b : Rwp= 72.10% GoF= 334.37 nGoF= 48.62 ( 24 reflections, 97 extinct) [same extinctions as:B 2 c b]\n", " (# 65) C m m m : Rwp= 67.34% GoF= 303.40 nGoF= 51.69 ( 32 reflections, 84 extinct) [same extinctions as:C 2 2 2]\n", " (# 65) A m m m : Rwp= 84.49% GoF= 470.23 nGoF= 91.49 ( 33 reflections, 85 extinct) [same extinctions as:A 2 2 2]\n", " (# 65) B m m m : Rwp= 72.02% GoF= 334.95 nGoF= 62.69 ( 31 reflections, 85 extinct) [same extinctions as:B 2 2 2]\n", " (# 66) C c c m : Rwp= 70.89% GoF= 334.73 nGoF= 47.38 ( 24 reflections, 99 extinct) [same extinctions as:C c c 2]\n", " (# 66) A m a a : Rwp= 73.84% GoF= 357.94 nGoF= 57.40 ( 26 reflections, 97 extinct) [same extinctions as:A 2 a a]\n", " (# 66) B b m b : Rwp= 73.70% GoF= 349.37 nGoF= 51.13 ( 24 reflections, 97 extinct) [same extinctions as:B b 2 b]\n", " (# 67) C m m a : Rwp= 69.01% GoF= 318.13 nGoF= 49.97 ( 29 reflections, 88 extinct) [same extinctions as:C 2 m b]\n", " (# 67) C m m b : Rwp= 69.01% GoF= 318.13 nGoF= 49.97 ( 29 reflections, 88 extinct) [same extinctions as:C 2 m b]\n", " (# 67) A b m m : Rwp= 84.55% GoF= 472.67 nGoF= 77.79 ( 28 reflections, 91 extinct) [same extinctions as:A b m 2]\n", " (# 67) A c m m : Rwp= 84.55% GoF= 472.67 nGoF= 77.79 ( 28 reflections, 91 extinct) [same extinctions as:A b m 2]\n", " (# 67) B m c m : Rwp= 72.05% GoF= 334.62 nGoF= 56.66 ( 28 reflections, 91 extinct) [same extinctions as:B m a 2]\n", " (# 67) B m a m : Rwp= 72.05% GoF= 334.62 nGoF= 56.66 ( 28 reflections, 91 extinct) [same extinctions as:B m a 2]\n", " (# 68) C c c a :1 : Rwp= 72.03% GoF= 344.97 nGoF= 42.78 ( 21 reflections, 103 extinct)\n", " (# 68) C c c a :2 : Rwp= 72.03% GoF= 344.97 nGoF= 42.78 ( 21 reflections, 103 extinct) [same extinctions as:C c c a :1]\n", " (# 68) C c c b :1 : Rwp= 72.03% GoF= 344.97 nGoF= 42.78 ( 21 reflections, 103 extinct) [same extinctions as:C c c a :1]\n", " (# 68) C c c b :2 : Rwp= 72.03% GoF= 344.97 nGoF= 42.78 ( 21 reflections, 103 extinct) [same extinctions as:C c c a :1]\n", " (# 68) A b a a :1 : Rwp= 75.75% GoF= 381.58 nGoF= 48.10 ( 21 reflections, 103 extinct)\n", " (# 68) A b a a :2 : Rwp= 75.75% GoF= 381.58 nGoF= 48.10 ( 21 reflections, 103 extinct) [same extinctions as:A b a a :1]\n", " (# 68) A c a a :1 : Rwp= 75.75% GoF= 381.58 nGoF= 48.10 ( 21 reflections, 103 extinct) [same extinctions as:A b a a :1]\n", " (# 68) A c a a :2 : Rwp= 75.75% GoF= 381.58 nGoF= 48.10 ( 21 reflections, 103 extinct) [same extinctions as:A b a a :1]\n", " (# 68) B b c b :1 : Rwp= 73.75% GoF= 349.28 nGoF= 44.79 ( 21 reflections, 103 extinct)\n", " (# 68) B b c b :2 : Rwp= 73.75% GoF= 349.28 nGoF= 44.79 ( 21 reflections, 103 extinct) [same extinctions as:B b c b :1]\n", " (# 68) B b a b :1 : Rwp= 73.75% GoF= 349.28 nGoF= 44.79 ( 21 reflections, 103 extinct) [same extinctions as:B b c b :1]\n", " (# 68) B b a b :2 : Rwp= 73.75% GoF= 349.28 nGoF= 44.79 ( 21 reflections, 103 extinct) [same extinctions as:B b c b :1]\n", " (# 69) F m m m : Rwp= 76.91% GoF= 392.21 nGoF= 37.80 ( 16 reflections, 127 extinct) [same extinctions as:F 2 2 2]\n", " (# 70) F d d d :1 : Rwp= 80.89% GoF= 444.51 nGoF= 29.02 ( 11 reflections, 139 extinct)\n", " (# 70) F d d d :2 : Rwp= 80.89% GoF= 444.51 nGoF= 29.02 ( 11 reflections, 139 extinct) [same extinctions as:F d d d :1]\n", " (# 71) I m m m : Rwp= 72.85% GoF= 342.91 nGoF= 65.06 ( 31 reflections, 87 extinct) [same extinctions as:I 2 2 2]\n", " (# 72) I b a m : Rwp= 74.45% GoF= 356.58 nGoF= 49.93 ( 23 reflections, 99 extinct) [same extinctions as:I b a 2]\n", " (# 72) I m c b : Rwp= 73.79% GoF= 350.67 nGoF= 53.79 ( 25 reflections, 97 extinct) [same extinctions as:I 2 c b]\n", " (# 72) I c m a : Rwp= 75.26% GoF= 364.34 nGoF= 51.07 ( 23 reflections, 97 extinct) [same extinctions as:I c 2 a]\n", " (# 73) I b c a : Rwp= 75.28% GoF= 363.96 nGoF= 44.43 ( 20 reflections, 103 extinct)\n", " (# 73) I c a b : Rwp= 75.28% GoF= 363.96 nGoF= 44.43 ( 20 reflections, 103 extinct) [same extinctions as:I b c a]\n", " (# 74) I m m a : Rwp= 73.80% GoF= 351.28 nGoF= 60.25 ( 28 reflections, 91 extinct) [same extinctions as:I 2 m b]\n", " (# 74) I m m b : Rwp= 73.80% GoF= 351.28 nGoF= 60.25 ( 28 reflections, 91 extinct) [same extinctions as:I 2 m b]\n", " (# 74) I b m m : Rwp= 74.44% GoF= 357.02 nGoF= 56.43 ( 26 reflections, 93 extinct) [same extinctions as:I b m 2]\n", " (# 74) I c m m : Rwp= 74.44% GoF= 357.02 nGoF= 56.43 ( 26 reflections, 93 extinct) [same extinctions as:I b m 2]\n", " (# 74) I m c m : Rwp= 72.84% GoF= 342.28 nGoF= 58.75 ( 28 reflections, 93 extinct) [same extinctions as:I m a 2]\n", " (# 74) I m a m : Rwp= 72.84% GoF= 342.28 nGoF= 58.75 ( 28 reflections, 93 extinct) [same extinctions as:I m a 2]\n", "Restoring best spacegroup: P 21 c n\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "41b2183e23d24bef8a796e9c46a28b2d", "version_major": 2, "version_minor": 0 }, "image/png": "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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", " \n", "
\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#px.SetMaxSinThetaOvLambda(0.3) # This can be used to change the number of used reflections\n", "spgex = SpaceGroupExplorer(pdiff)\n", "\n", "# NB:verbose C++ output does not appear in a notebook\n", "spgex.RunAll(keep_best=True, update_display=False, fitprofile_p1=False)\n", "\n", "for sol in spgex.GetScores():\n", " #if sol.nGoF > 4 * spgex.GetScores()[0].nGoF:\n", " if sol.GoF <= 2 * spgex.GetScores()[0].GoF:\n", " print(sol)\n", "\n", "c.GetSpaceGroup().ChangeSpaceGroup(\"Pmcn\")\n", "print(\"Chosen spacegroup (smallest nGoF): \", c.GetSpaceGroup())\n", "\n", "# Updated plot with optimal spacegroup\n", "px.plot(diff=True, fig=None, hkl=True, reset=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add SO4 and Pb to the crystal structure\n", "First create the atomic scattering powers, then " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "UnitCell : (P m c n)\n", " Cell dimensions : 5.39801 6.95849 8.47876 90.00000 90.00000 90.00000\n", "List of scattering components (atoms): 6\n", "Pb at : 0.2500 0.2500 0.2500, Occup=1.0000 * 0.5000, ScattPow:Pb , Biso= 1.0000\n", "S at : -0.0000 0.0000-0.0000, Occup=1.0000 * 0.5000, ScattPow:S , Biso= 1.5000\n", "O1 at : -0.0000 0.0000 0.1769, Occup=1.0000 * 1.0000, ScattPow:O , Biso= 2.0000\n", "O2 at : -0.0000 0.2033-0.0589, Occup=1.0000 * 1.0000, ScattPow:O , Biso= 2.0000\n", "O3 at : 0.2270-0.1017-0.0589, Occup=1.0000 * 0.5000, ScattPow:O , Biso= 2.0000\n", "O4 at : -0.2270-0.1017-0.0589, Occup=1.0000 * 0.5000, ScattPow:O , Biso= 2.0000\n", "\n", "Occupancy = occ * dyn, where:\n", " - occ is the 'real' occupancy\n", " - dyn is the dynamical occupancy correction, indicating either\n", " an atom on a special position, or several identical atoms \n", " overlapping (dyn=0.5 -> atom on a symetry plane / 2fold axis..\n", " -> OR 2 atoms strictly overlapping)\n", "\n", " Total number of components (atoms) in one unit cell : 32\n", " Chemical formula: O3 Pb0.5 S0.5\n", " Weight: 167.635 g/mol\n" ] } ], "source": [ "pb = ScatteringPowerAtom(\"Pb\", \"Pb\", 1.0)\n", "s = ScatteringPowerAtom(\"S\", \"S\", 1.5)\n", "o = ScatteringPowerAtom(\"O\", \"O\", 2.0)\n", "\n", "# When manually creating atomic scattering power, they must be added\n", "# to the Crystal. This is done automatically when importing a Molecule.\n", "c.AddScatteringPower(pb)\n", "c.AddScatteringPower(s)\n", "c.AddScatteringPower(o)\n", "\n", "c.AddScatterer(Atom(0.25,0.25,0.25,\"Pb\", pb))\n", "c.AddScatterer(MakeTetrahedron(c,\"SO4\",s,o,1.5))\n", "\n", "# Let's see what is the resulting crystal contents\n", "print(c)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create the neutron powder diffraction data and fit the profile\n", "The same steps as for the X-ray data are performed.\n", "\n", "However the peak width is larger so that must be fixed to start" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", "100 17902 100 17902 0 0 45463 0 --:--:-- --:--:-- --:--:-- 45552\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Imported powder pattern: 2920 points, 2theta= 10.000 -> 155.950, step= 0.050\n", "No background, adding one automatically\n", "Selected PowderPatternDiffraction: with Crystal: \n", "Profile fitting finished.\n", "Remember to use SetExtractionMode(False) on the PowderPatternDiffraction object\n", "to disable profile fitting and optimise the structure.\n", "Fit result: Rw= 4.51% Chi2= 874.61 GoF= 0.30 LLK= 219.850\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b294cb148fbd42c991da279f33ad318f", "version_major": 2, "version_minor": 0 }, "image/png": "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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", " \n", "
\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pn = PowderPattern()\n", "if not os.path.exists(\"pbso4-n.dat\"):\n", " os.system(\"curl -o pbso4-n.dat https://raw.githubusercontent.com/vincefn/objcryst/master/Fox/example/tutorial-pbso4/neutron.dat\")\n", "pn.ImportPowderPatternFullprof(\"pbso4-n.dat\")\n", "pn.GetRadiation().SetRadiationType(RadiationType.RAD_NEUTRON)\n", "pn.SetWavelength(1.909)\n", "pn.SetMaxSinThetaOvLambda(0.3)\n", "pdiffn = pn.AddPowderPatternDiffraction(c)\n", "\n", "#\n", "pdiffn.GetProfile().GetPar(\"W\").SetValue(0.001)\n", "\n", "\n", "pn.FitScaleFactorForIntegratedRw()\n", "\n", "# Plot\n", "pn.plot(diff=True, fig=None, hkl=True)\n", "\n", "# Fit profile - we keep the unit cell fixed as it was already refined\n", "pn.quick_fit_profile(auto_background=True,plot=True, init_profile=True, cell=False, verbose=True)\n", "pn.quick_fit_profile(plot=False, init_profile=False, cell=False, asym=True, displ_transl=True,\n", " backgd=False, verbose=False)\n", "\n", "print(\"Fit result: Rw=%6.2f%% Chi2=%10.2f GoF=%8.2f LLK=%10.3f\" %\n", " (pn.rw * 100, pn.chi2, pn.chi2/pn.GetNbPointUsed(), pn.llk))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a MonteCarlo object and add objects (crystal, powder patterns) for optimisation" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "mc = MonteCarlo()\n", "mc.AddRefinableObj(c)\n", "mc.AddRefinableObj(px)\n", "mc.AddRefinableObj(pn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Disable profile fitting before Monte-Carlo\n", "..or else the crystal structure will not be optimised\n", "\n", "Note that the following display will be live-updated during the optimisation done below (the last plot is always updated)." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "52ff63992bd74a9e9ac8c11c7a0f0f56", "version_major": 2, "version_minor": 0 }, "image/png": "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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", " \n", "
\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6fe905883a134654b5f6d8fdbc6c3bff", "version_major": 2, "version_minor": 0 }, "image/png": "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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", " \n", "
\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pdiff.SetExtractionMode(False)\n", "pdiffn.SetExtractionMode(False)\n", "\n", "px.FitScaleFactorForRw()\n", "pn.FitScaleFactorForRw()\n", "\n", "pn.plot(fig=None,diff=False,hkl=True)\n", "px.plot(fig=None,diff=False,hkl=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Display the 3D crystal structure\n", "*Note: this requires installing `ipywidgets` and `py3Dmol` (as of 2021/05 the conda-forge version is obsolete, so just install using pip). Otherwise You will just get a warning message*\n", "\n", "This will be updated live during the optimisation, and also when using `RestoreParamSet()` to restore some specific solutions (and generally everytime the underlying Crystal's `UpdateDisplay()` function is called). Just scroll back to see what is being done in the widget.\n", "\n", "The `display()` is only really necessary to make sure the widget appears in the notebook. In fact if `c.widget_3d()` is the *last* command in the notebook cell, the display is done automatically. See the ipywidgets documentation if you want to understand this in more details.\n", "\n", "Note that bonds may disappear during optimisation, because they are automatically assigned by the javascript viewer, which is quite strict about allowed distances. In the final solution some bonds in the middle of the chain are often missing, though you can see the atoms are reasonably close. But rest assured that any bond defined in the object still exists as defined in pyobjcryst !" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "application/3dmoljs_load.v0": "
\n

3Dmol.js failed to load for some reason. Please check your browser console for error messages.

\n
\n", "text/html": [ "
\n", "

3Dmol.js failed to load for some reason. Please check your browser console for error messages.

\n", "
\n", "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "79259c7636504fe7baccfa6ee4d2f0f5", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Box(children=(VBox(children=(HBox(children=(VBox(children=(FloatRangeSlider(value=(0.0, 1.0), description='Xra…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(c.widget_3d())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run multiple optimisations\n", "We also enable the automatic least squares every 150k trials, which allows a better convergence\n", "\n", "We perform 3 runs, each of 1 million trials using parallel tempering, with default parameters (which should be adequate for all problems). Normally for this structure it would be better to use 2 millions trials so that the correct solution is found during almost every run.\n", "\n", "Each run starts from a randomised configuration." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LSQ option: Every 150000 trials, and at the end of each run\n" ] }, { "data": { "application/3dmoljs_load.v0": "
\n

3Dmol.js failed to load for some reason. Please check your browser console for error messages.

\n
\n", "text/html": [ "
\n", "

3Dmol.js failed to load for some reason. Please check your browser console for error messages.

\n", "
\n", "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8d18d8c1d9d94a51b12a8d1b9d843bea", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Box(children=(VBox(children=(HBox(children=(VBox(children=(FloatRangeSlider(value=(0.0, 1.0), description='Xra…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "79965d9b2f58431580723d7e13cca396", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Box(children=(HBox(children=(Label(value='MonteCarlo:', layout=Layout(max_width='25%', width='11em')), Text(va…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Final LLK: 2968.07\n" ] } ], "source": [ "mc.GetOption(\"Automatic Least Squares Refinement\").SetChoice(2)\n", "print(\"LSQ option: \", mc.GetOption(\"Automatic Least Squares Refinement\").GetChoiceName(2))\n", "\n", "# 3D structure view which will be live-updated with the best\n", "# configuration of the current run\n", "display(c.widget_3d())\n", "\n", "# Small widget to see the progress of the optimisation, with the current run\n", "# best log-likelihood, the run number and remaining number of trials.\n", "display(mc.widget())\n", "\n", "# The powder pattern plot a few cells above should also be updated for each run best solution\n", "mc.MultiRunOptimize(nb_run=3, nb_step=1e5)\n", "print(\"Final LLK: %.2f\" % mc.GetLogLikelihood())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### List solutions\n", "All solutions are stored in a \"Parameter Set\" which can be restored (assuming that the objects - crystal structure and powder pattern are not altered e.g. by changing the list of atoms, the profile, or the fixed parameters etc...).\n", "\n", "This will only record changes of parameters such as atom coordinates, but will not record other changes such as a different spacegroup, or a change of the Scatterers (number of atoms or molecules) inside a Crystal. It can only be used to browse results obtained at the end of `MultiRunOptimize()`.\n", "\n", "At the end of the optimisation the best solution is automatically restored." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0: LLK= 3778.00, name=Best Configuration\n", " 1: LLK= 3839.00, name=Run #3\n", " 2: LLK= 3827.00, name=Run #2\n", " 3: LLK= 3776.00, name=Run #1\n" ] } ], "source": [ "for i in range(mc.GetNbParamSet()):\n", " idx = mc.GetParamSetIndex(i)\n", " cost = mc.GetParamSetCost(i)\n", " name = mc.GetFullRefinableObj().GetParamSetName(idx)\n", " print(\"%3d: LLK=%10.2f, name=%s\"%(idx, cost, name))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Restore a chosen solution (set of parameters)\n", "Restoring a solution will also update the 3D crystal view above." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "26fc02afd8ef4a268848164426b18468", "version_major": 2, "version_minor": 0 }, "image/png": "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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", " \n", "
\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "75ce021dae3d45469c8ed911c3e2f017", "version_major": 2, "version_minor": 0 }, "image/png": "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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", " \n", "
\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pn.plot(fig=None, diff=True)\n", "px.plot(fig=None, diff=True)\n", "mc.RestoreParamSet(3, update_display=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Save results to CIF and Fox (.xmlgz) formats" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "tags": [] }, "outputs": [], "source": [ "# Save result so it can be opened by Fox\n", "xml_cryst_file_save_global('result-pbso4.xmlgz')\n", "# Also export to the CIF format\n", "c.CIFOutput(\"result-pbso4.cif\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "00eaf61720014df896bc641802b04946": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "0495e49e6e0e4a8b8559285baac68113": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "LabelStyleModel", "state": { "description_width": "", "font_family": null, "font_size": null, "font_style": null, "font_variant": null, "font_weight": null, "text_color": null, "text_decoration": null } }, "052c9fcf0466424f89e70ab56b1ba04e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "0b565d28f35d41829424bc135c3ada98": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "ToolbarModel", "state": { "_model_module_version": "^0.11", "_view_module_version": "^0.11", "collapsed": true, "layout": "IPY_MODEL_0cfb1f371f2249a6aabefd7234527296", "orientation": "vertical", "toolitems": [ [ "Home", "Reset original view", "home", "home" ], [ "Back", "Back to previous view", "arrow-left", "back" ], [ "Forward", "Forward to next view", "arrow-right", "forward" ], [ "Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan" ], [ "Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom" ], [ "Download", "Download plot", "floppy-o", "save_figure" ] ] } }, "0b6b7c6da8374d51858014a5007ed74c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "0cfb1f371f2249a6aabefd7234527296": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "10689d3795154357b394411e725adca9": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_28188eda6206475bae8e071d8a3da906", "outputs": [ { "data": { "application/3dmoljs_load.v0": "", "text/html": "" }, "metadata": {}, "output_type": "display_data" } ] } }, "10b85b79060f44dda7efc39a17d81acf": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "12b76e5952bf470fb5313325228333c7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "1e36803b619a49e8a0cb07e14932afd0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "1eb7beebb72c48b5a3a8a23bad63e31c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "extra dist", "layout": "IPY_MODEL_e8e4671905f846749ac9eca5483872f7", "max": 10, "readout_format": ".1f", "step": 0.5, "style": "IPY_MODEL_e40113cd66ef463c8c9d6e436835bdfd", "tooltip": "Extra distance (A) with semi-transparent display", "value": 2 } }, "21318bc0690c43349106e866f635ef68": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "220b1d52d1754ddc8d6209607d62d637": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "fullMol opac", "layout": "IPY_MODEL_a93a12ad870a4e4aa0277ac6bc9424c0", "max": 1, "readout_format": ".01f", "step": 0.1, "style": "IPY_MODEL_12b76e5952bf470fb5313325228333c7", "tooltip": "Opacity to display fully molecules\nwhich have at least one atom inside the limits", "value": 0.5 } }, "222afec0ff0d4378acecef72118b93d7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "max_width": "25%", "width": "11em" } }, "24d01c655ef34f899fb164756e6fb72c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Xrange", "layout": "IPY_MODEL_00eaf61720014df896bc641802b04946", "max": 1.5, "min": -0.5, "step": 0.09090909090909091, "style": "IPY_MODEL_3666a876565b490484e4b71af85f7edc", "value": [ -0.045454545454545414, 1.0454545454545456 ] } }, "26fc02afd8ef4a268848164426b18468": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "MPLCanvasModel", "state": { "_cursor": "default", "_data_url": "data:image/png;base64,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", "_figure_label": "Figure 8", "_image_mode": "diff", "_model_module_version": "^0.11", "_size": [ 900, 400 ], "_view_module_version": "^0.11", "layout": "IPY_MODEL_aa1a86eca1494a07bd5f8e6596a5c35a", "toolbar": "IPY_MODEL_c6a82d9176b340ee8ea6f29f4519045f", "toolbar_position": "left" } }, "28188eda6206475bae8e071d8a3da906": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "284f4e9448354ad3939b2e9fd74df854": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Yrange", "layout": "IPY_MODEL_c5b6632c164c4d68bbc325efde6a9c99", "max": 1.5, "min": -0.5, "step": 0.07142857142857142, "style": "IPY_MODEL_8e7950cd11584233b3c2f03ff6871d4b", "value": [ 0, 1 ] } }, "286640c0bf39426a80f443c1333499e6": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "MPLCanvasModel", "state": { "_cursor": "default", "_data_url": "data:image/png;base64,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", "_figure_label": "Figure 3", "_image_mode": "diff", "_model_module_version": "^0.11", "_size": [ 900, 400 ], "_view_module_version": "^0.11", "layout": "IPY_MODEL_ff3a688ce673446bb68592d211035390", "toolbar": "IPY_MODEL_ce6fafe5ce1240c5a05d408c83b616cb", "toolbar_position": "left" } }, "2b523dcf54c8416ba7728c0d4be21d96": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Zrange", "layout": "IPY_MODEL_10b85b79060f44dda7efc39a17d81acf", "max": 1.5, "min": -0.5, "step": 0.058823529411764705, "style": "IPY_MODEL_481817bad0fc4419ab12f53e471c841c", "value": [ 0.02941176470588236, 1.0294117647058822 ] } }, "2c1cf7cee2f746a18afb9803d93d739b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "2f6c7a475345472b8cadc25454ad2ec0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "3115968ff7c74370abf416b2716cf6d7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "31afd8393d17463e8dbd2f498574f003": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "ToolbarModel", "state": { "_model_module_version": "^0.11", "_view_module_version": "^0.11", "collapsed": true, "layout": "IPY_MODEL_7005a3e2a9884e1aa8d0ad144653d488", "orientation": "vertical", "toolitems": [ [ "Home", "Reset original view", "home", "home" ], [ "Back", "Back to previous view", "arrow-left", "back" ], [ "Forward", "Forward to next view", "arrow-right", "forward" ], [ "Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan" ], [ "Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom" ], [ "Download", "Download plot", "floppy-o", "save_figure" ] ] } }, "3376c0fb8be1491db70b0b916cca8047": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "3666a876565b490484e4b71af85f7edc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "36d2fc7bf4c141eeb4178ab1efaa27f9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "38e638f5224f434880b8a9d76af0ae67": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "41b2183e23d24bef8a796e9c46a28b2d": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "MPLCanvasModel", "state": { "_cursor": "default", "_data_url": "data:image/png;base64,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", "_figure_label": "Figure 4", "_image_mode": "diff", "_model_module_version": "^0.11", "_size": [ 900, 400 ], "_view_module_version": "^0.11", "layout": "IPY_MODEL_b196b30410c84a5eb65c8849163f8f4c", "toolbar": "IPY_MODEL_63ea1550325d47698266026a72267e76", "toolbar_position": "left" } }, "45a7f5a2c1344edbadc4a86871656c36": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "481817bad0fc4419ab12f53e471c841c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "49c1ee4d797b43e5b70c748ea9793f00": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "4c1578ba0c5d4662a8ae44aeed1def67": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "4dab9349f99849f2822256d59f06f777": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", "state": { "children": [ "IPY_MODEL_512f51d09ba9491593a247c46ae27dda", "IPY_MODEL_603cd15746f84b9e984e9bdf5e10d2ff" ], "layout": "IPY_MODEL_66d4d118953c4675b90d4984b8faa6bb" } }, "4f519d6521644854a4ac515d05555ac2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "50ec4c68bf28420cb5542356f51974ff": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "512f51d09ba9491593a247c46ae27dda": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "LabelModel", "state": { "layout": "IPY_MODEL_222afec0ff0d4378acecef72118b93d7", "style": "IPY_MODEL_0495e49e6e0e4a8b8559285baac68113", "value": "MonteCarlo:" } }, "51ac207626824a568f4b7ebb47df1fa0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "52ff63992bd74a9e9ac8c11c7a0f0f56": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "MPLCanvasModel", "state": { "_cursor": "default", "_data_url": "data:image/png;base64,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", "_figure_label": "Figure 6", "_image_mode": "diff", "_model_module_version": "^0.11", "_size": [ 900, 400 ], "_view_module_version": "^0.11", "layout": "IPY_MODEL_93538cfcf70d424f881d54f4ff88ab74", "toolbar": "IPY_MODEL_bab241dee1224efd9fe00fa0f4bc2b94", "toolbar_position": "left" } }, "56e04f0c6af14ba5a5125dac01b3d6a7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "603cd15746f84b9e984e9bdf5e10d2ff": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TextModel", "state": { "disabled": true, "layout": "IPY_MODEL_b98200d1ee444845ad5c0839d12980cf", "style": "IPY_MODEL_790ec6b844d640268e95787db04b8f68", "value": "LLK= 2968.07 " } }, "635916476a354fe2a32e0891bc70cfc0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "63ea1550325d47698266026a72267e76": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "ToolbarModel", "state": { "_model_module_version": "^0.11", "_view_module_version": "^0.11", "collapsed": true, "layout": "IPY_MODEL_97754a0f682940f3a20eba45c0a995b4", "orientation": "vertical", "toolitems": [ [ "Home", "Reset original view", "home", "home" ], [ "Back", "Back to previous view", "arrow-left", "back" ], [ "Forward", "Forward to next view", "arrow-right", "forward" ], [ "Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan" ], [ "Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom" ], [ "Download", "Download plot", "floppy-o", "save_figure" ] ] } }, "66d4d118953c4675b90d4984b8faa6bb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "6fe905883a134654b5f6d8fdbc6c3bff": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "MPLCanvasModel", "state": { "_cursor": "default", "_data_url": "data:image/png;base64,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", "_figure_label": "Figure 7", "_image_mode": "diff", "_model_module_version": "^0.11", "_size": [ 900, 400 ], "_view_module_version": "^0.11", "layout": "IPY_MODEL_49c1ee4d797b43e5b70c748ea9793f00", "toolbar": "IPY_MODEL_31afd8393d17463e8dbd2f498574f003", "toolbar_position": "left" } }, "7005a3e2a9884e1aa8d0ad144653d488": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "752c8e13ba1e446491d3a5abee4328ef": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_e12de330dfdc4f97a3d0e6ad5752fbb2", "IPY_MODEL_d8080aed98834c4a8629085cbd9ca248" ], "layout": "IPY_MODEL_f18a443125594637a95f35331695a4b5" } }, "75ce021dae3d45469c8ed911c3e2f017": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "MPLCanvasModel", "state": { "_cursor": "default", "_data_url": "data:image/png;base64,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", "_figure_label": "Figure 9", "_image_mode": "diff", "_model_module_version": "^0.11", "_size": [ 900, 400 ], "_view_module_version": "^0.11", "layout": "IPY_MODEL_3115968ff7c74370abf416b2716cf6d7", "toolbar": "IPY_MODEL_a523347e6aa3465e9798a8c436fc240e", "toolbar_position": "left" } }, "787040eb6b8e4ebeaeb69f0917088853": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_e13fd36aea0c4ed4be8280202e13ddf8", "IPY_MODEL_10689d3795154357b394411e725adca9" ], "layout": "IPY_MODEL_a275e9cc903a4cde873fb45b89859fb7" } }, "790ec6b844d640268e95787db04b8f68": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TextStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "79259c7636504fe7baccfa6ee4d2f0f5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "BoxModel", "state": { "children": [ "IPY_MODEL_787040eb6b8e4ebeaeb69f0917088853" ], "layout": "IPY_MODEL_bf9bec96616c45b9a72403718b4d9645" } }, "79965d9b2f58431580723d7e13cca396": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "BoxModel", "state": { "children": [ "IPY_MODEL_4dab9349f99849f2822256d59f06f777" ], "layout": "IPY_MODEL_9f5c33f717bb4ec1b1584f0b8f343d3f" } }, "82100fdd54b845cbb2b74ddc15119179": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "8d18d8c1d9d94a51b12a8d1b9d843bea": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "BoxModel", "state": { "children": [ "IPY_MODEL_752c8e13ba1e446491d3a5abee4328ef" ], "layout": "IPY_MODEL_21318bc0690c43349106e866f635ef68" } }, "8e7950cd11584233b3c2f03ff6871d4b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "8f9db8afa75e4d27b9bfbd99bf903a39": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "91f6361f684f4a7a87e39dbeee5eb805": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "ToolbarModel", "state": { "_model_module_version": "^0.11", "_view_module_version": "^0.11", "collapsed": true, "layout": "IPY_MODEL_a1f532a83ee549daa387864c1f587224", "orientation": "vertical", "toolitems": [ [ "Home", "Reset original view", "home", "home" ], [ "Back", "Back to previous view", "arrow-left", "back" ], [ "Forward", "Forward to next view", "arrow-right", "forward" ], [ "Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan" ], [ "Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom" ], [ "Download", "Download plot", "floppy-o", "save_figure" ] ] } }, "93538cfcf70d424f881d54f4ff88ab74": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "93ebe40b69054fe0b529511c4c53dc9d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Zrange", "layout": "IPY_MODEL_50ec4c68bf28420cb5542356f51974ff", "max": 1.5, "min": -0.5, "step": 0.058823529411764705, "style": "IPY_MODEL_51ac207626824a568f4b7ebb47df1fa0", "value": [ 0.02941176470588236, 1.0294117647058822 ] } }, "97754a0f682940f3a20eba45c0a995b4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "9889904b8d3d420583cbd39730f77213": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_d4fd79f612e241e28b76464d6a5ce976", "IPY_MODEL_284f4e9448354ad3939b2e9fd74df854", "IPY_MODEL_93ebe40b69054fe0b529511c4c53dc9d" ], "layout": "IPY_MODEL_36d2fc7bf4c141eeb4178ab1efaa27f9" } }, "988f855b8ee047b68016b83e8064829b": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "ToolbarModel", "state": { "_model_module_version": "^0.11", "_view_module_version": "^0.11", "collapsed": true, "layout": "IPY_MODEL_2f6c7a475345472b8cadc25454ad2ec0", "orientation": "vertical", "toolitems": [ [ "Home", "Reset original view", "home", "home" ], [ "Back", "Back to previous view", "arrow-left", "back" ], [ "Forward", "Forward to next view", "arrow-right", "forward" ], [ "Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan" ], [ "Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom" ], [ "Download", "Download plot", "floppy-o", "save_figure" ] ] } }, "9891994771884d8aaff0f83c43e05ffc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_24d01c655ef34f899fb164756e6fb72c", "IPY_MODEL_e45a4441a0ef462399b08d685c98c1bc", "IPY_MODEL_2b523dcf54c8416ba7728c0d4be21d96" ], "layout": "IPY_MODEL_4f519d6521644854a4ac515d05555ac2" } }, "9f5c33f717bb4ec1b1584f0b8f343d3f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "a1f532a83ee549daa387864c1f587224": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "a275e9cc903a4cde873fb45b89859fb7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "a30e04847ce24f578378a88e7f067ff1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "extra dist", "layout": "IPY_MODEL_45a7f5a2c1344edbadc4a86871656c36", "max": 10, "readout_format": ".1f", "step": 0.5, "style": "IPY_MODEL_b84bcf6d75264fd2b40b439c7bbbe1e3", "tooltip": "Extra distance (A) with semi-transparent display", "value": 2 } }, "a4509bc59ef74c63b49811c2ccab661b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "a523347e6aa3465e9798a8c436fc240e": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "ToolbarModel", "state": { "_model_module_version": "^0.11", "_view_module_version": "^0.11", "collapsed": true, "layout": "IPY_MODEL_e013364b181b422fbd352c603b6e21c0", "orientation": "vertical", "toolitems": [ [ "Home", "Reset original view", "home", "home" ], [ "Back", "Back to previous view", "arrow-left", "back" ], [ "Forward", "Forward to next view", "arrow-right", "forward" ], [ "Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan" ], [ "Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom" ], [ "Download", "Download plot", "floppy-o", "save_figure" ] ] } }, "a880651c85ea49fc82b3d7f4bb9ff812": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "fullMol opac", "layout": "IPY_MODEL_a4509bc59ef74c63b49811c2ccab661b", "max": 1, "readout_format": ".01f", "step": 0.1, "style": "IPY_MODEL_bc27902e09b54162b4d179e781e37797", "tooltip": "Opacity to display fully molecules\nwhich have at least one atom inside the limits", "value": 0.5 } }, "a93a12ad870a4e4aa0277ac6bc9424c0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "aa1a86eca1494a07bd5f8e6596a5c35a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "ad60336fcd1546e282bd8d16021ef0fc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "b0aa788d822e4eff81235b3c472361c4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "b196b30410c84a5eb65c8849163f8f4c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "b294cb148fbd42c991da279f33ad318f": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "MPLCanvasModel", "state": { "_cursor": "default", "_data_url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA94AAAG4CAYAAACzXz9FAAAAAXNSR0IArs4c6QAAIABJREFUeF7snQV4VMf3/t+QBCvuVtytSCkOBb4EihQoVkpx90Jxd4cWt1C0BQqlQLHgbsUluBcLEhxCQvL/nwmb3yYkbHZzd/fe5J3nyQOEOzNnPmcnmfeeMzMuQUFBQWAhARIgARIgARIgARIgARIgARIgARKwCwEXCm+7cGWjJEACJEACJEACJEACJEACJEACJKAIUHjzg0ACJEACJEACJEACJEACJEACJEACdiRA4W1HuGyaBEiABEiABEiABEiABEiABEiABCi8+RkgARIgARIgARIgARIgARIgARIgATsSoPC2I1w2TQIkQAIkQAIkQAIkQAIkQAIkQAIU3vwMkAAJkAAJkAAJkAAJkAAJkAAJkIAdCVB42xEumyYBEiABEiABEiABEiABEiABEiABCm9+BkiABEiABEiABEiABEiABEiABEjAjgQovO0Il02TAAmQAAmQAAmQAAmQAAmQAAmQAIU3PwMkQAIkQAIkQAIkQAIkQAIkQAIkYEcCFN52hMumSYAESIAESIAESIAESIAESIAESIDCm58BEiABEiABEiABEiCBaE1gz549KFeuXLQeIwdHAiSgbwIU3vr2D60jARIgARIgARIgARKIIgEPDw9s2bIliq2wOgmQAAnYToDC23Z2rEkCJEACJEACJEACJKAjAlmzZv3ImqCgIPj4+ODVq1c6spSmkAAJxDQCFN4xzeMcLwmQAAmQAAmQAAlEUwK5cuWCt7c3XF1dQ42wcuXK2Lp1azQdNYdFAiRgBAIU3kbwEm0kARIgARIgARIgARKwSGDKlClo1KgRUqVKFerZFStWoGHDhhbr8wESIAESsBcBCm97kWW7JEACJEACJEACJEACJEACJEACJACAwpsfAxIgARIgARIgARIgARIgARIgARKwIwEKbzvCZdMkQAIkQAIkQAIkQAIkQAIkQAIkQOHNzwAJkAAJkAAJkAAJkAAJkAAJkAAJ2JEAhbcd4bJpEiABEiABEiABEiABxxF4/vw5Fi1ahAIFCqivCRMmIH78+Pjpp5+QKFEixxnCnkiABEggDAEKb34kSIAESIAESIAESIAEogWBmjVronDhwurO7gMHDuDbb79FkiRJsH37dqxatSpajJGDIAESMCYBCm9j+o1WkwAJkAAJkAAJkAAJhCFQvnx57N69W31XIt5nzpxRf69QoQJ27txJXiRAAiTgNAIU3k5Dz45JgARIgARIgARIgAS0JFC2bFns3btXNTljxgx06tSJwltLwGyLBEjAZgIU3jajY0USIAESIAESIAESIAE9EfD09ETz5s3h5uYWYpafnx+GDBmCsWPH6slU2kICJBDDCFB4xzCHc7gkQAIkQAIkQAIkQAIkQAIkQAKOJUDh7Vje7I0ESIAESIAESIAESIAESIAESCCGEaDwjmEO53BJgARIgARIgARIgARIgARIgAQcS4DC27G82RsJkAAJkAAJkAAJkAAJkAAJkEAMI0DhHcMczuGSAAmQAAmQAAmQQEwj0Lp1a8jBaywkQAIk4CwCFN7OIs9+SYAESIAESIAESIAENCWwePHij9oLCgrCxIkTQ+701rRDNkYCJEACkSRA4R1JUHyMBEiABEiABEiABEhA3wSSJk2K7t27Q8S2eVmyZAmuXLmib+NpHQmQQLQmQOEdrd3LwZEACZAACZAACZBAzCFQsmRJrFu3DilTpgw16OrVq2PDhg0xBwRHSgIkoDsCFN66cwkNIgESIAESIAESIAESsIWAr68vEiVKBFdXV1uqsw4JkAAJ2I0Ahbfd0LJhEiABEiABEiABEiABEiABEiABEgAovPkpIAESIAESIAESIAESIAESIAESIAE7EqDwtiNcNk0CJEACJEACJEACJEACJEACJEACFN78DJAACZAACZAACZAACZAACZAACZCAHQlQeNsRLpsmARIgARIgARIgARIgARIgARIgAQpvfgZIgARIgARIgARIgARIgARIgARIwI4EKLztCJdNkwAJkAAJkAAJkAAJkAAJkAAJkACFNz8DJEACJEACJEACJEACJEACJEACJGBHAhTedoTLpkmABEiABEiABEiABJxPYM+ePShXrpzzDaEFJEACMZYAhXeMdT0HTgIkQAIkQAIkQAIxg4CHhwe2bNkSMwbLUZIACeiSAIW3Lt1Co0iABEiABEiABEiABKwlkDVr1o+qBAUFwcfHB69evbK2OT5PAiRAApoRoPDWDCUbIgESIAESIAESIAEScCaBXLlywdvbG66urqHMqFy5MrZu3epM09g3CZBADCdA4R3DPwAcPgmQAAmQAAmQAAlEFwJTpkxBo0aNkCpVqlBDWrFiBRo2bBhdhslxkAAJGJAAhbcBnUaTSYAESIAESIAESIAESIAESIAEjEOAwts4vqKlJEACJEACJEACJEACJEACJEACBiRA4W1Ap9FkEiABEiABEiABEiABEiABEiAB4xCg8DaOr2gpCZAACZAACZAACZAACZAACZCAAQlQeFvhNC8vL4wePVqdlvn8+XOkT58etWvXxpAhQ5A4ceKQljZu3IgBAwbg/PnzyJAhA3r06IGOHTt+1NPEiRMxffp03L9/HwUKFMCECRPw9ddfh3ruxYsX6NmzJ1atWgU/Pz9UrFgR06ZNQ6ZMmaywPPjRt2/f4syZM0iZMiXc3Nysrs8KJEACJEACJEACJKBnAv7+/mrNFCtWLLVGixMnjjJ32bJl6tA1FhKISQQCAgLw8OFDpTPixo0bk4auy7FSeFvhFvmhffr0aXz11VdImjQpzp49i6FDh6JIkSLYsmWLaungwYMoV64cmjZtih9//BH79+9XwnzOnDlo3bp1SG8iuvv376+EvNSfN28e1qxZgyNHjqjJYSo1atTA8ePHMWnSJCRKlAiDBw9Wol/siBcvnhXWA//++6+ynYUESIAESIAESIAESIAESCBmEBB9UaxYsZgxWB2PksI7is4Rwdy2bVvcuXMH6dKlwzfffIMnT57g8OHDIS3L/69fvx7//fefegMrkevUqVOreuPHj1fPvX//XgnuggULYvny5ep70kaJEiWwYcMGVKtWTX3v1q1byJYtm4p6t2/f3irrb9y4gSxZsihxnzZtWqvq8mESIAESIAESIAES0DuB+vXrY+XKlcrMCxcuoFevXiqjUIIgcqUYCwnEJAL37t1TQbfr168jc+bMMWnouhwrhXcU3bJ69WrUrVsXImrTpEmjotJjx45F9+7dQ1revXu3SiE/evQoihYtip07d6qUcYlkFy5cOOS5YcOGqcj2s2fP4OLion5JTJ06VQl5+bepVKhQAQkSJMA///xjlfUi/D///HPcvn1bpcCbyp9//okOHToo8R8YGIh27dqpX1JSRKC/efMGkrol/coLBPOyb98+lC1bVqXaywuF6tWrqxQvGW/27Nnh6ekZ6vmbN2+qlwmvX79GihQpcOLECcXMvCRJkgSSGiPp8GKnZBaYF/k/eUlhepEhfcsLCbFd3uZduXIFrq6u2LRpE4oXLx6qrtglWQmmbAFpQ1hKX/Kn/JKW+uGNySrYGj0sL2h8fHwQFBSkmIrtnTt3Vp8T4ST+KFOmTKR7k6yLhQsXqrGKT2WcmzdvjrC+PC8/qAcOHKgYCXt7lIg+L6a+tm/fjlq1aikbnj59ag8T2CYJkAAJkEA0ICC/982DH7I+EzEu6bbydxYSiEkEIlr7xyQGehorhbcN3hCBKqJF9nq3bNkSGTNmxLp169S/8+XLpwRf1apVQ1qWH/apUqXCkiVLVPr5zJkz0alTJyU+zdPF5Q1tgwYNQoSx/F0E5aFDh0JZKXVlv7kIRGtKeJNPvifC6vfff0fDhg1x9epVNYZZs2ahRYsWePnypRJpYqsIXOnTfH+5CG9JhxcxJLZKW/JC4aeffgpXeJcqVQo5cuTAokWLlGDMmjUrFi9e/JHw/pSglBcUUkdslSJ/ShbAmDFjMHfuXPVWTzIJhHPYX7IRCTxzURnRmAoVKmQNbs2eNdkW1nZbhbf4cNeuXXj37p3yrfxbPsOWijOFt9hm7hdLtvL/SYAESIAEYiYBWb9UqVJFrS9M5e7du+jTp49ah7GQQEwiQOGtL29TeNvgD4nCSmq5FBHYEuH97LPP1H5uEZMSlZSorqlIlNDd3R1TpkxB165dMWrUKIwYMUIddmZetm3bhsqVK+PUqVMq5Vz+LpHbsBFJiT6KqJRI+KeK7AWXL1MxpZuYR7xFxEsqu7lAlZcDx44dU4fDmYpMXIkySxvmhzOEFUMi5CQNXoS19COHw4l4l8i9RE8TJkyoUt3z5MmjnpGoflhxLPvn5eWGpOX37dtXfZkXaV++J3vuRfTLc1JKliypviZPnqyi32KniEvzIuJV3oSLPyTrQPbVS4lIeMv/mcYkLyJMRaL58tJEotGvXr1C79691UF5wvuXX35RGQTmRXw/aNAgZau8hLl06VKo/5f+5WyAkydPquwGyQyQfs1ti4zw/vnnn1WWgUTI5UXBnj17QvUjPjAJb7FdMiDksyx+krMGpJ6wlxcx3377bajMhfCEt7QnL4Gknrxg6tevn+r/8ePHanvEX3/9pT5HYrtkRMjY/v77b/VvWQTJ50Je6EjbMpekbkRjoPC24YcVq5AACZAACZAACcRYAhTe+nI9hbcN/pCDzURMnjt3TgloEWFbt25VkWkR3vKneYqzSXiLyOjSpYsS3iNHjlQp3OZF2vDw8FAHp4nIFeEtgkQi6OZFTkyfPXu2EjefKnLwm0SHwxZz4S170n19fUNF1cU2sVWEmRRJdxZRJcJQIqXmxVwMSaT7yy+/xMWLF9GmTRuVCi4iT8YlJ4uKQI0dO7YSpyKK5ftywuijR49CtSn1c+XKpQSypEKHF5EVESyiWfwggk3S7kXMy/dFBEsRcS2ZCebl8uXLKjouEXrxm7AUoReR8DYfk0TqTUXq5s6dW6V6/+9//1OnxT948EC9EJED82TspmJ6CXDgwAHFR14GCAfzIv3LeQFiv7QtLz/Ef1IiG/GWSH/+/PmVHSLa5dR9eQkhmQymYp5qbhLbcuieZDTI51TS/uXz+dtvv6lMAnOxH5HwFvamscvnW17OyPjk9HzhL4L+iy++wIIFC9Q2DHlxIi8W5MWF+FbslG0H9erVU/6IaAwU3jb8sGIVEiABEiABEiCBGEuAwltfrqfwjqI/JDIsYkrSxPPmzWtVqrkIb/Posdap5lpGvEWESxq5HPwmkVBTMd/jLd9r1aqV2n8sgk0ElUROzUVwZCLe5i6RiKy8CGjWrFm4npL0MRGqEkUVoWop4m3eyPfff68ivRLxDyu8TXu8zcdkXlf6lCvi5Ko4EbMi6GUvv/CoWbOmeplhKvKCRl7EyEuCiIr0L4JcIuLCTkS+CHFrhLdkEMjWB2EsRSLMctaAvAgwF96miLfYIy9VJBpfunRp9dJHPjMS8ZaXFjKGyAhv87Gbth2Y2y0CXGyTCLhkaci5ATJvxFfCX4pkiIjgFu4RjYHCO4o/rFidBEiABEggRhOQ7DQJdkjGpfyuZzEuAcmIFQ0hgQvzc6DCjojCW18+pvCOoj/kB5fcESniUESO0Q5Xk33Zsg8q7B5vSZuWK9FM0VqJhkoKuIhUiXybSkRiSASbiGJJqd6xY4cSoxLxFrElkWKJforIkv3iS5cuDWlP+pN6ks4vf8op7JIBIBFwU5EotKRryzPyvOx1lpNK5fumSK0cDjdjxoyP0tgl0ip9Sj3Z1ywRZsla+FSqeXgfERHecsiZ7GU3T98Oj4eJoaTYS/p3RBFv08FlYVPKIxvxFgEs0WWJPsePH19FsOXL/OWOua0yLvGpRKmFsfhHshAkS0LS5SUrIDLC2yTkw47dZHd4EW+Jzsv2DImqix+TJ0+uDimU9POIxkDhHcUfVqxOAiRAAiQQYwmI6DZtLZOsNBFuLMYlIPpD1pMSbJFAV0Tim8JbXz6m8I6iP0wRXxF+chiapG6LYJF93qYi137J4WthrxOT70vqrRSZQLKvW1LMw14nZn5Ym6SJi1C25TqxiCaf7JWWvd4iEMUOiThKFFREqqQIm+yT1Hc5xd28fEp4i6iXqKp8iZCT09JFcIn4lmi/iC3Z0ywvKyRdXESwCHR5eyeiTeyR78lYzYuI9m7duqkfMmKvRJPl1Gt5XkSb9CG/UCQFWiK58lJE9pHL/mERzCJM5ReQpKZLmrUUewpvaV9SvmU/u9gV0R5vW4S3jFlslyJXpkhEWQ6Yk8i5fMn+a8nIMBXzVHPpT6LPkiYvz0kGgKSoy4sJEdNaCW+J+IuAl7Rz8ZmciVCpUiVlr7zgEf/LGGTuiI/k++GNgcI7ij+sWJ0ESIAEYgABWQOYbuKQ82Dkd70UeVkuv3NiapEtgxLtljWIrL9YjE9AtpxKRqqsmyW7MLxC4a0vP1N4W+GP7777TokYEciyP1UOQZPTsyVdVwScvEEUwS0R4ebNm6Nx48bqwLXBgwdjzpw5KjJqKhMnTkT//v3VSdwiFkVwiKiVqKiIb1OR1F25ckvSt0WgSFty3ZhEKM1PRI/MMDj5IqYkbEWoynVici2cXop8tkRMi2itU6cOtmzZou5vl89DTCryYkXGL9kN8kKHhQRIgARIgATCIyBBgp49e6oXunJuiKybZKuTXIkq17nG1CKBG4mQyjk3LNGHgGQOiv6QrZkU3vr3K4W3FT6S6LREtuVDLpFG2fMsYlx+wJvfRb1x40YlquU0Z0mjlX3AElE2LxJxFfEtb18lAitiW0S8/GIwL7LnVtqXKKH8wJSTuCUCbH6lV2SHQOEdWVJ8jgRIgARIgARIwIgEzAW2nCMi2Yg//PCD2uImW99iajHdICNrV5boQ8CSX7n215evKbz15Q+7WsPJZ1e8bJwESIAESIAESMDJBEqVKqUEtul8EwlaiPiWW1lkC1VMLZYEWkzlYvRxW/Ir1/768jCFt778YVdrOPnsipeNkwAJkAAJkAAJOJmAXN0pB7PKGSamIufBLFy4UN28ElOLJYEWU7kYfdyW/Mq1v748TOGtL3/Y1RpOPrviZeMkQAIkQAIkQAIkoEsClgSaLo2mURYJWPIr1/4WETr0AQpvh+J2bmecfM7lz95JgARIgARIgAScQGDBAqBSJSBjRid0ro8uLQk0fVipnRWytUD2+8vhx+a3u2jXgz5asuRXrv314SeTFRTe+vKHXa3h5LMrXjZOAiRAAiRAAiSgNwJ37wLp0wNyPerJk3qzzmH2WBJoDjPEQR1ReAeD5trfQR+4SHZD4R1JUNHhMU6+6OBFjoEESIAESIAESCDSBA4fBkqUADJkAG7fjnS16PYghXd082jweCz5lWt/ffmdwltf/rCrNZx8dsXLxkmABEiABEiABPRGYMUK4PvvGfG+cUN5JjpdJ7ZmzRoMGzZMXd+bJEkS1K1bF+PGjUOCBAnUKfaSar5hwwZ4enrCy8tLXf3bpUsXdeWvqcjaWK793b17N549e6YO5atduzZ++eUXvX2Sw7WHwtsQbgoxksLbWP6KkrUU3lHCx8okQAIkQAIkQAJGIzBuHNC3L1CxIrB9u9Gs18xeSwJNs44c1NC6deuUQK5fvz6aN2+Oa9euoV+/fvjqq6+wbdu2EOGdPn16NGrUCP/73//U9ydOnIhZs2ahffv2ytKKFSvi7t27SsCnTp0at27dwtGjRzF16lQHjSRq3VjyK9f+UeOrdW0Kb62J6rg9Tj4dO4emkQAJkAAJkAAJaE9ABNacOUDVqsCmTdq3b5AWIxJoHTp0wJkzZ5wyigIFCigRbEspUqQI3N3dcVi2Enwoy5Ytww8//ICdO3eq70jEu0mTJli8eHHIM/JviYbfvHkTsWLFUtHxMWPGqEi4EQuFt7G8RuFtLH9FyVoK7yjhY2USIAESIAESIAGjEahdG1i7FihdGti3z2jWa2ZvdBLeL1++VGnjEyZMwM8//xzCKCAgAPHixcPgwYNRtmxZJbxXr16NOnXqhDzz999/47vvvlPCO2PGjChXrpz6e9++fVG5cmVkz55dM+aOaIjC2xGUteuDwls7lrpvicJb9y6igSRAAiRAAiRAAloSkEi3lxdQsCBw6pSWLRuqLUsCzUiDMa1nly5disaNG4cyPU2aNCq1vFatWkp479u3D6XlpcuHsn//fpQpUwaHDh1C8eLFce/ePQwYMACyX9zX1xe5cuXC6NGjlTg3QrHkV6799eVFCm99+cOu1nDy2RUvGycBEiABEiABEtAbga+/BnbvBrJkAa5d05t1DrPHkkBzmCEadGSKeMt+bTkYzVRsiXib6gYGBuLYsWMYOXIkNm7ciIsXLyJr1qwaWGvfJiz5lWt/+/K3tnUKb2uJGfh5Tj4DO4+mkwAJkAAJkAAJWE9ArhKTfcDJkwOPHllfP5rUsCTQjDZM2eMdO3ZsFbk2lRUrVuD777+3uMd7x44duH37ttrjHbbIfveCBQuqU9A9PDx0j8WSX7n215cLKbz15Q+7WsPJZ1e8bJwESIAESIAESEBvBAoXBk6eBGLHBvz89Gadw+yxJNAcZohGHZlONW/YsCGaNWsWcqp5sWLFwj3VXPZvb926VZ1qPmPGDHTs2FFdH1alShV1AJukmPv7+6vTzEXMX758GSlSpNDIWvs1Y8mvXPvbj70tLVN420LNoHU4+QzqOJpNAiRAAiRAAiRgG4E8eYALF4Lrvn0LxIljWzsGr2VJoBlxeHJQ2vDhw+Ht7a3u8a5Xr95H93ivX78ec+bMwfbt25EwYUJ07twZAwcOVMP18/NT/967d6+6RkwOZvvyyy9VurkIeCMUS37l2l9fXqTw1pc/7GoNJ59d8bJxEiABEiABEiABvRGQvd03bgRb9fAhYIAopj0QWhJo9uiTbdqfgCW/cu1vfx9Y0wOFtzW0DP4sJ5/BHUjzSYAESIAESIAErCOQLh1w715wnevXgcyZrasfTZ62JNCiyTBj3DAs+ZVrf319JCi89eUPu1rDyWdXvGycBEiABEiABEhAbwSSJQNevAACAoJTznPl0puFDrHHkkBziBHsRHMClvzKtb/myKPUIIV3lPAZqzInn7H8RWtJgARIgARIgASiSCB+fCBePODJE+D0aaBAgSg2aMzqlgSaMUdFqy35lWt/fX1GKLz15Q+7WsPJZ1e8bJwESIAESIAESEBPBIKCADc34PPPgZs3gaNHgaJF9WShw2yxJNAcZgg70pSAJb9y7a8p7ig3RuEdZYTGaYCTzzi+oqUkQAIkQAIkQAJRJODvH3yNmES5z5wBDhwASpaMYqPGrG5JoBlzVLTakl+59tfXZ4TCW1/+sKs1nHx2xcvGSYAESIAESIAE9ETg5UsgYUKgdGlg/35g1y6gfHk9WegwWywJNIcZwo40JWDJr1z7a4o7yo1ReEcZoXEa4OQzjq9oKQmQAAmQAAmQQBQJPHoEpEwJVK0KbN4MbNkCVK4cxUaNWd2SQDPmqGi1Jb9y7a+vzwiFt778YVdrOPnsipeNkwAJkAAJkAAJ6InAf/8F7+9u0AD4809g/XqgenWrLBw2bBiGDBliVR09PmxJoOnRZtpkmYAlv3Ltb5mhI5+g8HYkbSf3xcnnZAewexIgARIgARIgAccRuHoVyJ4daN0a8PQEVq8G6tSJsP+KFSuG+r+goCCcPn0aX3zxBXbs2OE4u+3QkyWBZocu2aQDCFjyK9f+DnCCFV1QeFsBy+iPcvIZ3YO0nwRIgARIgARIINIEzp0D8ucHevQAJk8Gli8HGjaMsHqTJk3w8uVLDBw4EClSpIAI7+bNm2PRokXIlClTpLvV44OWBJoebba3TeLbo0eP4uzZs/buym7tW/Ir1/52Q29TwxTeNmEzZiVOPmP6jVaTAAmQAAmQAAnYQODYMeDLL4GhQ4O/Fi8GmjT5ZEMnTpzA8OHDUbRoUfz888+oW7cuNm7caEPn+qpiSaDpy1rHWEPh7RjO7OX/CFB4x6BPA4V3DHI2h0oCJEACJEACMZ2AXB8mJ5pLtFui3pJu3qpVpKhs2LABkydPxtOnT3FMBLzBC4X3xw6k8Db4h9qA5lN4G9BptppM4W0rOdYjARIgARIgARIwHAHZl12pEjBvHtCmDTBzJtChQ6SHERgYiPv37yNdunSRrqPXB6Oj8D548KA6+O7QoUNqW0DevHkxcuRIVK5cGX379oW8PLl+/ToSJ06McuXKqRcpadOmDXFReML7zp076NevH7y8vPD8+XO1xaBDhw7o1q2bLl1rya9c++vLbRTe+vKHXa3h5LMrXjZOAiRAAiRAAiSgJwKSIi6nmK9YEby3+9dfAZ0KKHtjsyTQ7N2/1u3v378fchheiRIl0KlTJyRJkkTt106dOjVatWqFli1bKgEuL00ePnyISZMm4fHjx/D29oabm5syJ6zwlv8vXLiw+r/Bgwcja9asuHz5Mq5evYrx48drPQRN2rPkV679NcGsWSMU3pqh1H9DnHz69xEtJAESIAESIAES0IjA338D330HiACvVg0Q8dSrl0aNG6sZSwLNWKORHQSl1TYAOXXe1dX1k+a/f/9eZS5kyJBBRbI9PDzCFd4DBgxQAv3ChQvInDmzIZBY8ivX/vpyI4W3vvxhV2s4+eyKl42TAAmQAAmQAAnoiYBEur//HtizByhXDhg5EhgwQE8WOsyWCAWapN6fOeMwO0J1VKAAMGuW1X2/fv0aCRMmxJgxY9C7d+9w62/atAkjRozAuXPnVMq4qUybNg2dO3cOV3hL9DxZsmSGOkyPwtvqj49TK1B4OxW/Yzun8HYsb/ZGAiRAAiRAAiTgRAK//w78+CNw9Gjw6eaDBwPDhlltkIi1Ll26WF1PTxWik/CWfdgSvV6yZAl+FP+GKf/++y9KlSqFWrVqoWnTpkiVKhVcXFxUWvqECRPQs2fPcIV3jhw5UL4u2g5BAAAgAElEQVR8eXjKIXwGKRTeBnHUBzMpvI3lryhZS+EdJXysTAIkQAIkQAIkYCQCCxcCLVoA3t5A3rxA377AmDERjuDWrVvh/p+Iuz0SNTdwsSTQjDS0V69eIVGiRBFGvPv374+5c+fCx8cHsWLFUkO7efOmSh//lPBmxNtInwJj2krhbUy/2WQ1hbdN2FiJBEiABEiABEjAiAQkcimnmV++HCy8JWo9aVKEI4kfP76KisoJ2eZF9hHLwVtGLtFJeIsfypQpo/Z4nzp16qM93t27d8fy5ctx9+5dFemWMnr0aMge7k8J74EDB2LixIm4dOkSMmbMaAh3W/Ir1/76ciOFtxX+WLlyJX7//Xd1n+OTJ0+QLVs2dcVAu3btQt6oyQmJixYt+qhV2WtStWrVUN+XyT19+nR14EOBAgXUD4Ovv/461DMvXrxQKTGrVq2Cn5+fOsFRUp7kegNrCyeftcT4PAmQAAmQAAmQgGEJyP7hjh2BGzeAfPmCo9/TpkU4nIIFC6qrqUSAmxc5HXvr1q2GxSCGWxJoRhvcvn371JpYDlnr2LEjkiZNiuPHjyNFihRIkyYNqlevrk47r1OnDuTaMUlLF0H9KeFtOtVcouSDBg1Sp5pfu3ZN1Rs3bpwuEVnyK9f++nIbhbcV/pC3oCJ4ZRLLdQU7d+5UaS4//fSTmshSRHjv3btXCXTzkidPHnWPoKmI6JZUGHkDV6RIEcybNw9r1qzBkSNHlAg3lRo1aqgfJHLKoqTVyPUGckiEvH2NFy+eFdYDnHxW4eLDJEACJEACJEACRiYgIrtrV1kAAbK2ql8fmDMnwhFJOnmhQoXUesu8yCnXuXPnNjKJaCe8xRkHDhyARKkPHz6sot758uVT93hXqlRJXf8lgSoJlIk4nzFjBnLmzPlJ4S1t3r59W93jLQEzOcRN0tNF2Ot1jz+Ft7GmJYW3Ff6SewBTpkwZqkaPHj0wa9Ysle4SJ06cj+4EDK95iVyLcG/btm3IvYBy1YEIbnnbKukxUuQHiYj9DRs2oJpcgwFA9h9JpF1+mLRv394K6ym8rYLFh0mABEiABEiABIxNYPJk4OefgQcPgIIFgW++ARYsMPaYbLTekkCzsVlWczIBS35l0M3JDgrTPYV3FP0hqStyYqLsI0mbNm2khLdEyiU9RiLZhQsXDrFg2LBhKrL97NkztSdlyJAhmDp1qnpbZ9qjIg9XqFABCRIkwD///GOV9Zx8VuHiwyRAAiRAAiRAAkYmIPd29+kDyP7sQoWAsmWBMBmJRh6eNbZbEmjWtMVn9UPAkl+59tePr8QSCu8o+kOi1n/99Zc6OVHSXCTVfMWKFSr6LSkqEsWWfSK1a9cO6WnmzJlq34n8v3m6uOwhb9CggUpzkWsS5O8S4Zb9RuZF6np5eeHKlStWWc/JZxUuPkwCJEACJEACJGBkAqNGAQMHAnKPswQ65GvlSiOPyGbbLQk0mxtmRacSsORXrv2d6p6POqfwjoI/jh49ipIlS6rItOwxkTJlyhS4ubmpfSaSfi5p6Fu2bIGI6nr16qlnRo0ahREjRuDt27ehet+2bRvkAA85oVFSzuXvIuY3b94c6jnpS8S7RMI/VWQvuHyZyr179/DVV1+FCPsoDJ1VSYAESIAESIAESEDfBOTO7qFDgdevgaJFgezZgXXrLNrs7++v1ljJkiWDu7u7xeeN8IAlgWaEMdDGjwlY8iuFt74+NRTeNvpDTiIvXry4ikzv2rUrwh/MgYGBKFWqlBLA3nKP5AfhLYc/vHnzJlTvcmKmh4eHOjhNIuUivEXEywEP5kWuQ5g9e7bFqy2GDh0KSV8PW0wRdRuHzmokQAIkQAIkQAIkoH8CEhSRqLe/P/Dll0CaNECYYIb5IM6ePasO0vL19VUH4srWPzktW26gkYCIkYslgWbkscVk2y35lcJbX58OCm8b/CE/iOXaL4lYy3UGyZMn/2QrcuJ57969Q1LLTanmIrzjxo0bUlfrVHNGvG1wLquQAAmQAAmQAAlEDwJ9+wJyDVRgIFC8OJAgAbBjR4RjK1euHObPn48cOXKEPHP58mW0atUKcuK5kYslgWbkscVk2y35lcJbX58OCm8r/SFiu0qVKpAfxHIvYGTu05YrDfr06RMivHm4mpXQ+TgJkAAJkAAJkAAJWEtATjSfOjU44l2mTHDtffsibKVMmTIqoBK2RPR9a81x5vOWBJozbWPfthOw5FcKb9vZ2qMmhbcVVAMCAvDdd9+pt57yFZm0I0k1lyvB5CA1SWGSYrpOTK4DGzt2rPqeXCcm7UmKedjrxCTVvGrVquo5SRPPmjUrrxOzwm98lARIgARIgARIwP4E5NDXWLFiqXWKqch5OF9KmrczSrduwLx5+MPTE3WnT0ec9+/lrtYILZk4caK6wrVGjRoqxVxSzuXf33zzDXr16uWMEWjWpyWBpllHbMihBCz5lcLboe6w2BmFt0VE//dAu3btMHfuXHX3dlm5ksKs5M2bV/2AllPNGzVqpO7aln/L4WoS4ZaTz+vUqRNSQ3649+/fH2PGjEGRIkXg6emJ1atX48iRI0p8m4r88D9x4oS6ZixRokQYPHiw2nMk+8DNT0SPzDA4+SJDic+QAAmQAAmQAAlYS0DWRmvXrlWHworwnjdvnjr/Rq5P3fGJ9G5r+7Hq+Y4dEbhkCVxfvsSeOHFQNl8+4NixTzYhGY2ybnv8+LHaSihbC3PmzGlVt3p82JJA06PNtMkyAUt+5drfMkNHPkHhbQXtzJkz4+bNm+HWkB/SErFu0aIFjh07hocPHyJ27NjqLW/fvn1Verp5CQoKgohvObDjwYMHSmzLLy25o9u8yD7tnj17YtWqVXj37p36BTZt2rRIpbiHNZSTzwpn81ESIAESIAESIIFIE5CDZA8cOKCeX7hwIZYtW6bWLt9++60Ssk4pbdsi4M8/4f7sGTYA+KZAAbicPu0UU5zdqSWB5mz72L9tBCz5lWt/27jaqxaFt73I6rBdTj4dOoUmkQAJkAAJkEA0IGAuvGU4GzduxOjRo/Hy5UucPHnSOSNs0QJ+f/+NuM+eYa0I7xw54H7pknNscXKvlgSak81j9zYSsORXrv1tBGunahTedgKrx2Y5+fToFdpEAiRAAiRAAsYn0Lp1a5Xhl13uyv5Q5LrVJk2aqPNpnFKaNMHL9euR8OlTrAJQI2NGxIkgc/HMmTPqiljZ271582bEjx8fcsp5dCmWBFp0Gac145DtoXIGgekMJmvq2vKsbFtIkCAB1q9fb0v1cOtY8ivX/pqh1qQhCm9NMBqjEU4+Y/iJVpIACZAACZAACWhA4Pvv8WTTJiR//hzLAdROlw5x7tz5qGE5OE3O2BFRlD59ety9e1f9XfaqS9Q+OhRLAi06jNHaMVB4W0uMz0eVAIV3VAkaqD6Ft4GcRVNJgARIgARIgASiRqBePdz38kLaly+xBED9lCkRx8fnozaLFy+Ow4cPq7N05HBcEalySFx0uEbMNFgK748/ShTeUZterG09AQpv65kZtgaFt2FdR8NJgARIgARIgASsJVCrFm5v346Mr15hAYBGSZMizpMnH7UiKcCSFi+lTZs26kR2KU49kd3asVp4PjoK74MHD2LIkCE4dOgQ5NBiuWFo5MiRqFy5str2IFfBXb9+HYkTJ1bbBiZPnoy0adOGkApPeN+5cwf9+vWDl5cX5IDjTJkyoUOHDugmV9N9ojx58gS9e/dWJ/vLuQZyaLLY4uHhEVLLlGreoEEDDBs2TGVWyEufOXPmIFeuXCHP/fbbb+o2o2vXrqktD3ny5MEvv/yCYsWKfWSBJb9y7a/xRIpicxTeUQRopOqcfEbyFm0lARIgARIgARKIEoHq1XF1925kf/UKcwE0S5gQsZ8//6hJEVZyY4ybm1vI//n5+aF27drYtGlTlEzQS2VLAk0vdkbWjv3796sXIyVKlECnTp2QJEkStV87derUaNWqFVq2bKkEeLp06dRNQyJk5Yo4b2/vED+HFd7y/4ULF1YmyPW9stVArpe7evWqunkoovL+/XvI4YJyj71cEyxnBch1wvLZ2bp1a8iNRSK8L126pF4EDB8+XDU3cOBAyGft4sWLiBMnDvbs2YPy5curG42qVauG169fq20Qkn0h4wlbLPmVa//IfqIc8xyFt2M466IXTj5duIFGkAAJkAAJkECMISCCtkuXLs4Zr4cHLuzfjxLu7hjz7BlaxYuH2K9fO8cWJ/dqSaA52Tyruy9dujSePn2K06dPq20BnyoijO/fv68EsUSyTVHosMJ7wIABSqBfuHABcoVwZMu6detQq1YtFWEXsSwlMDAQ+fPnR6pUqUKyKUR47927V7WfI0cO9ZwI+9y5cyuh3rZtW3XVsIh3eQkQmWLJr1z7R4ai456h8HYca6f3xMnndBfQABIgARIgARKIlgRu3boV7rh+/PFHFcVzSqlYEacPHUKdtGnR7do1dIwdG25+fk4xxdmdfkqgfbfiO/i8+njvu71tTvVZKqxuuNrqbiQKnDBhQiVQJb07vCLR5hEjRuDcuXMqZdxU5EVQ586d1T/DCm+JnidLlkxdhRdeETEtX6YSK1YsyJcczifp4ub9yDNDhw5Vh/O9efNGvRwQ4e3r64tTp06Fav6LL75A0aJFISnmO3bsQKVKldCsWTM0btwY8oJB0s0jKhTeVn98nFqBwtup+B3bOYW3Y3mzNxIgARIgARKIKQREHIhwkb225kUikpGN3mnOqmxZHDt6FO3z50ejo0fRzdUVrgEBmndjhAYtCTQjjMFko+zDluj1kiVLIC92wpZ///1XpX5LFLpp06Yq6uzi4qI+nxMmTFBp3OEJb4lCS5q3p6dnuDhESMvebFOR/eXyPblKb/fu3Sp6bV5mz56t9odLZF7Sy0V4u7u7q/Rz8yIp5LLNwbStYenSpZgyZQqOHTuGuHHjol69evj111/VS4GwxZJfufbX1yebwltf/rCrNZx8dsXLxkmABEiABEggxhIoWLCgOuQqbHROREVYoeEwSCVL4sDx4xheoQIqeHmht4sLXMwilg6zQwcdWRJoOjAx0ia8evUKiRIlijDi3b9/f8ydOxc+Pj4qIi3l5s2bKn38U8LbUsRbDkOTL1OR/ePyJVF3EdlaRLzNITx69Egd1ta9e3fUr18f8+fPp/CO9KdEnw9SeOvTL3axisLbLljZKAmQAAmQAAnEeAKSTl6oUCEliMyL7GeVPaxOKV9+iV0nT2Jew4bI+8cfGCBGSMQ7zJ5gf39/LFq0SKUD//DDD+qQKykS+ZRoZnQo0Ul4iz/ksDGJJEvadtg93iJUly9frkSyRLqlSMq37OH+lPCWg85kj7UcgJYxY8ZIu920x1si1lWrVlX1JCVdTjZPmTJlpPZ4z5w5E+3atQu3z7p16+LevXs4cOAAhXekvaLPBym89ekXu1hF4W0XrGyUBEiABEiABEhAjwQKFcLm06exsXNnJJs2DUPFxrdvgQ/C2mSy7KUVoSXpvnL41sqVK9U1UrxOTI9ODbZp3759yj+yB7pjx45ImjQpjh8/jhQpUiBNmjSoXr26Ou28Tp06kGvHJC1dBPWnhLfpVHOJkg8aNEidai5Xekm9cePGRQjDdKq5PCsCX9LgJQIuh61t27ZNpZhLMT/VXPafy7YM6Uf2rEsfklYu6etihzwrKfJnzpxREfUePXqoPethi6UXKlz76+szTOGtL3/Y1RpOPrviZeMkQAIkQAIkQAI6IhCULx/We3vjUP/+cB07FsMlzfzFCyBBglBWmt/jffbsWXUdlaT1du3aVR12FR2KJYFmxDFKBFii1IcPH1ZR73z58qm7s+VwMrn+Sw5Sk/u1RZzPmDEDOXPm/KTwFga3b99W93hL9FoEsaSni7C3dDK/9COHrJnu8ZatFyKUq1SpEoLWdI+3RLDlOjGJYss93iLS5a5uKevXr1f7ueVsBEldFxEv+9hlnObX3ZkateRXrv319cmm8NaXP+xqDSefXfGycRIgARIgARIgAR0RCMyRA2uuXMH5kSPhN3w4hr97B/j6AkmShLJSxI+IN3MxI3tq5f5nk7DR0bBsMsWSQLOpUVZyOgFLfuXa3+kuCmUAhbe+/GFXazj57IqXjZMACZAACZAACeiIQECmTFh96xbuTJ6Mp4MHY9jLl4CPD5AyZSgr5Q5liUxKarGpyP7gPn36qBTl6FAsCbToMMaYOAZLfuXaX1+fCgpvffnDrtZw8tkVLxsnARIgARIggRhL4Pr16yHpsH379g1JnZU7k6dPn+4ULgHp0mHFvXt4OXs27vXrh6ES7ZZTqdOmdYo9zuzUkkBzpm3s23YClvzKtb/tbO1Rk8LbHlR12iYnn04dQ7NIgARIgARIwOAE5NowuR9Z9qHK6dGDBw9GtWrVUKFCBezcudMpo/NPmRJ/PHoEl0WLcKN3bwx+8EDulQKsOLHaKYbboVNLAs0OXbJJBxCw5Feu/R3gBCu6oPC2ApbRH+XkM7oHaT8JkAAJkAAJ6JOAucB+8eIFGjRooK7mWrBggdMOKPNPmhSLnj5FkpUrcb5HDwy6fRu4ehUwSynXJ03trbIk0LTvkS06goAlv3Lt7wgvRL4PCu/IszL8k5x8hnchB0ACJEACJEACuiRQqlQpJbDlSiQp7969U+J7165d6r5lZ5SAhAnh+fIlMm7YgBNdu2KAiO4LF4BcuZxhjlP7tCTQnGocO7eZgCW/cu1vM1q7VKTwtgtWfTbKyadPv9AqEiABEiABEjA6AbnaKUuWLEhrtn9a7jdeuHChup7LGSUgXjzMevsWBXbuxMFOndDP2xs4exbIly9S5rRu3Rqenp6RelbvD8k1WfIyJFu2bHo3lfZZQeDq1auIHTs2Pv/883Brce1vBUwHPErh7QDIeumCk08vnqAdJEACJEACJEAC9ibw3t0dUwMCUOrQIexs1w59T50CTpwAChUK1fXixYs/MiUoKAgTJ07EmTNn7G2mQ9qXq9EePXqEVKlSIXny5A7pk53Yl8Djx4/h4+ODFClSIGWYk/pNPXPtb18fWNs6hbe1xAz8PCefgZ1H00mABEiABEjAaAQWLABKlwZy5nSK5YGxYmFiUBA8TpyAV9u26PPvv4B8ffllKHuSJk2K7t27Q8S2eZGrxK5cueIU27XuVMZ2584dyP57iZC6urpq3QXbcyABySaRDIaECRMiffr0cHFxCbd3rv0d6JRIdEXhHQlI0eURTr7o4kmOgwRIgARIgAR0TuDtWyBePCBBAuDFC8cbGxgIuLpiNIA63t5Y36YNeu3fDxw4AJQsGcqekiVLYt26dR9FDatXr44NGzY43nY79SjiW6Leb9++hQg3FuMSkBcncp6CRLsjEt0yOq799eVjCm99+cOu1nDy2RUvGycBEiABEiABEjARuHULyJQp+F9hIskOgfTuHRAnDoYB+PHKFfzdpg16yrVme/YAZcuGMsHX1xeJEiX6OAr88mXwywNGhx3iMnaiPQGu/bVnGpUWKbyjQs9gdTn5DOYwmksCJEACJEACRiVw9ChQrJjzhPerVyraPghA21u3sLJNG/Tw8gK2bwcqVrRM9dkzIEkS4OefgYkTLT/PJ0hAhwS49teXUyi89eUPu1rDyWdXvGycBEiABEiABEjARGDjRqB6decJ7w/CuS+A7vfvY1nbtvhp3TpAxLeHh2U/jR8P9OkDyGnREr1nIQEDEuDaX19Oo/DWlz/sag0nn13xsnESIAESIAESIAETgYULgRYtgv/1+nVwyrYjy6NHQMqU6AlgwJMnWNquHbqsXAmsX/9/LwQ+ZU+zZoCcdl6pErBtmyMtZ18koBkBrv01Q6lJQxTemmA0RiOcfMbwE60kARIgARIgAcMTGDcO6CvxZgCXLwPZszt2SPfvA2nTohuA0S9fYmH79ui0dCmC/v4bLrVrW7alVi1AIuQFCwJyDRkLCRiQANf++nIahbe+/GFXazj57IqXjZMACZAACZAACZgIyN7oyZOD/yX7vYsWdSyb27eBjBnRCcCv795hfocOaD9/PvyXLYP7999btqV8+eCD2NKlA+7csfw8nyABHRLg2l9fTqHw1pc/7GoNJ59d8bJxEiABEiABEiABE4GmTYElS4L/tXcvUKaMY9lcvw5kzYq2AOYEBsKzSxe0mTEDb377DfFMKfAfLJK7umPFioWsWbOG2Hg0Rw58KXd4x44NyNVoEdyT7NhBsTcSsI4A1/7W8bL30xTe9iaso/Y5+XTkDJpCAiRAAiRAAtGZQL16wF9/BY8wsgeaacnj0iUgVy60c3PDHH9/LOjZEy0mTcKLGTOQsGPHkJ7Gjx+PtWvXqqvERHjPmzcP7u7uqBg3Lnb4+QU/Jwe1JUqkpXVsiwQcQoBrf4dgjnQnFN6RRmX8Bzn5jO9DjoAESIAESIAEDEFATjSXk82lrFkDyJ5pR5Zz54D8+dE2blzMffMGiwcORNNRo+A7eTKSdu8eYkmpUqVw4MAB9e+FCxdi2bJlWLVqFb5NmhQ75Zvv3wNXr6roOQsJGI0A1/768hiFt778YVdrOPnsipeNkwAJkAAJkAAJmAjIXdn79gH+/sCyZUBk9lVrSU8ORCtUCG0TJMDcFy/wx8iR+GHQIDwaNQop+vcPV3jLNzdu3IjRo0bh5YEDOJkzJyCR88OHga++0tI6tkUCDiHAtb9DMEe6EwrvSKMy/oOcfMb3IUdAAiRAAiRAAoYgULIkcOMGIKeLz58PtGzpWLPlQLdixdAmSRLM8/XFn5MmoUHPnrg/aBDSDB8eYkvr1q3Rt29fZDc7dX3XunVoUqsWbstVYtu3Azt2ABUqONZ+9kYCGhDg2l8DiBo2QeGtIUy9N8XJp3cP0T4SIAESIAESiCYEChUKjnZ7ewPTpwOd5HxxB5ZDh4CSJdE2ZUrM9fHB6hkz8F3nzrjTuzfSy1VnnyrXrgHZsgFNmgQfEBfZu78dODx2RQKRIcC1f2QoOe4ZCm/HsXZ6T5x8TncBDSABEiABEiCBmEEgd24gZcrgdPPx44FevRw7bjlJvVw5tE2XDnPv3ME/CxagZsuWuNWtGzL++uunbTl+PPj6sz59ABHpf/4J1K/vWPvZGwloQIBrfw0gatgEhbcVMFeuXInff/8dx44dw5MnT5AtWzZ06NAB7dq1U9dQmIrsDxowYADOnz+PDBkyoEePHuhodoKm6bmJEydi+vTpuH//PgoUKIAJEybg66+/DmXRixcv0LNnT3XQh5+fHypWrIhp06YhU6ZMVlge/Cgnn9XIWIEESIAESIAESMAWArJOkaj3unXAsGHA4MG2tGJ7nZ07gYoV0TZTJsy9cQObly9H1UaNcKNdO2SePfvT7e7eDch6bNIkQO4jX7gQaNbMdltYkwScRIBrfyeBj6BbCm8r/FGiRAkleOvUqYPUqVNj586dGDNmDH766SclmqUcPHgQ5cqVQ9OmTfHjjz9i//79GDJkCObMmQPZR2Quuvv374/Ro0ejSJEi6vqKNWvW4MiRI0qEm0qNGjVw/PhxTJo0CYkSJcLgwYPx/PlznD59GvHixbPCegpvq2DxYRIgARIgARIgAdsJpEoFVKsGLFoE9O0LjBlje1u21NyyBahSBe2yZ8ecy5exfd06VKpVC9datEDW3377dIubNv2f7SK4Z84EOnSwxQrWIQGnEqDwdir+jzqn8LbCHw8fPkRKSZsyKxLNnjVrFp4+fYo4ceLgm2++UdHww3IC5ofStm1brF+/XkWcJTIukWsR7vJ9uT9Syvv375XgLliwIJYvX66+J22I2N+wYQOqyS8vALdu3VKRdol6t2/f3grrKbytgsWHSYAESIAESIAEbCeQMGHwHukFC4C2bYEpU2xvy5aacpVZ9eponycPZnt7Y8/WrSjn4YHLP/yAHL//HtKiBDMWLVqk1mCm7MP4N27gpxUrkPCff+BSsyYwcWJw5JuFBAxGgMJbXw6j8I6iP5YsWaKi23fv3kWyZMlUVHrs2LHobnZH5O7du1UK+dGjR1G0aFEVKZeUcYlkFy5cOMSCYcOGqcj2s2fP4OLioiLlU6dOVUJe/m0qFSpUQIIECfDPP/9YZT0nn1W4+DAJkAAJkAAJkICtBNzdga5dg4V3vXrA3Lm2tmRbvbVrgdq10bFQIcw8cQIH9+9HyTJlcKlePeRcuTKkzZo1a6q12KtXr9R93t9++y2SXLqE7QsXIn7OnFh86RKChg2Di6NT5W0bNWuRQCgCXPvr6wNB4R1Ff0jU+q+//oKPjw8uXryIfPnyYdOmTahatWpIyxIpT5UqFUSkS/r5zJkz0alTJ7x+/TpUurjsIW/QoAFu376t9obL3yXCfUhO5jQrUtfLywtXrlyxynpOPqtw8WESIAESIAESIAFbCAQEACK8BwwI3h8t+6WXLrWlJdvr/PWXEvydihXDjCNHVLCjUNGiuFSzJnLLvvMPpXz58pAAiRSJeJ85cwbw9ESFNm3wH4DLAB60aIHUltLTbbeUNUnAbgS49rcbWpsapvC2CVtwJYlglyxZUkWmBw4cqPZzlylTRu3zlhRxUwkICIC7uzumTJmCrl27YtSoURgxYgTevn0bqvdt27ahcuXKOHXqlEo5l7+7urpi8+bNoZ6TvkS8SyT8U0XSp+TLVO7du4evvvoqRNhHYeisSgIkQAIkQAIkQALhE3jxAkiUCBg5Mlh4FywIiBB2ZJFte40aoUuZMpi2dy/OnTuH7Pnz42qVKshrtq4qW7Ys9soJ6ABmzJihAiOYOhUVunXDpQ/C+3GdOvh89WpHWs++SEATAhTemmDUrBEKbxtRyknkxYsXV5HpXbt2KWFtEt4SoZb/Cyu8JW28S5cuSniPHDkSb968CdX71q1b4eHhoQ5Ok7euIrzd3NxUBN28yInps2fPxuPHjz9p/dChQyHp62GLKaJu4/jDdUwAACAASURBVNBZjQRIgARIgARIgAQiJuDjA6ROHbw3evFiIH16QPZcO7JIhL1JE3SrWBFTtm/H5cuXkS5nTtyoWBH5tm8PscTT0xPNmzdX6y1T8Rs1CkMGDsScD8L7lYcHMnl5OdJ69kUCmhCg8NYEo2aNUHjbgFL2YMuebYlY79u3D8mTJ1eteHt7W5VqLsI7bty4IRZonWrOiLcNzmUVEiABEiABEiCBqBG4eRPInFlCyMHCW9Y6u3ZFrU1ra8ve8pYt8VPVqvh10ya1dS9Rpkz4r0wZ5P8Q4Y6wyaFD1RVoskKTqHdQ6dLIJPeRs5CAwQhQeOvLYRTeVvpDxHaVKlXUm1NJKTe/T1tOK+fhalYC5eMkQAIkQAIkQALRi8DFi0Du3IDsixbh/fq1XNXi2DHOm6dOU+9RqxYmr1kDyVR0S5sW90uUQP6DBz9tS+/eCJwwAa4ALgCIW6QIMh075lj72RsJaECAwlsDiBo2QeFtBUzZq/3dd99hz5496kv2YYctcp2YXC0motxU5NqvdevWfXSdmHxfTkCXIteJSXuSYh72OjHzw9okTTxr1qy8TswKv/FREiABEiABEiABBxI4eRKQW1uWLQsW3v/9B5w+7UADEHz3dqdO6NWgASasWKHOxfFLnhyPixZF/qNHP21Lly7wmz0bcQMCcBxA8jx5kNHb27H2szcS0IAAhbcGEDVsgsLbCpjt2rXD3Llz1d3bchiHecmbN6+KdovgLleunNov1LhxY7Xve/DgwZgzZw5at24dUmXixIno378/xowZgyJFikD2GK1evRpHjhxR4ttUatSogRMnTqhrxqR9aUtS3WUfeLx48aywnvd4WwWLD5MACZAACZAACdhGQIIPpUoBa9YEXycmEfDz521ry9ZaU6cC3bqhT5MmGLd4MV68eIGniRLhWcGCyH/q1Eet+vv7K3EuV8O6d+iA50uWIGO8eNjw7BkyZcmCDNeu2WoJ65GA0whQeDsNfbgdU3hb4Y/MmTPjpuxbCqfI3dyy71vKxo0blag+f/68OnytR48ewadkmpWgoCCI+J4+fToePHigxLYIermj27zIPu2ePXti1apVePfunbr/e9q0aaFS3CM7BE6+yJLicyRAAiRAAiRAAjYT2LEDqFQJkNPD588HJE376lWbm7Op4qRJQM+eGNCmDUbNnQvZDngnbly8zpcP+c+eDWny7Nmz6NixI3x9fZE4cWIV3Ej64AGGPX2KLjlz4pdz55AvbVqku3vXJjNYiQScSYBrf2fS/7hvCm99+cOu1nDy2RUvGycBEiABEiABEhACchtLtWrBB6rJXmu5J/v2bceyGTcO6NsXg7t0wfCpUxEYGIgrrq7wz5kT+SQC/6FIluL8+fORI0eOkO9drlwZP27fjkSVKqHztm0omSwZUlm4Scaxg2NvJBA5Alz7R46To56i8HYUaR30w8mnAyfQBBIgARIgARKI7gTWrgVq1wYk5XzuXGDDBuDBA4eOOnD4cMQaMgTDe/XC4PHjVd/nXFyArFmRzyz6XqZMGXVDTahStSqKbNmCHPXro95ff6Fi/PhI/vy5Q+1nZySgBQGu/bWgqF0bFN7asdR9S5x8uncRDSQBEiABEiAB4xNYuRJo0CA4xVyE94oVgK+vQ8flP2AA3EePxqhBgzBg+HDV9+lYseD2+efIa7ZtULb9bdiwAXKmTtKkSVXK+YYRI5Dv+XP4t22LCgsWoIqbG5K8euVQ+9kZCWhBgGt/LShq1waFt3Ysdd8SJ5/uXUQDSYAESIAESMD4BJYuBZo0Ac6cCRbess/bwcL1bc+eiDtpEsaNGoU+/fsrpifc3BA3TRrkkVPWzYpcEStn9Tx+/BjJkydH+WnTcOfsWWzt2xcFp01DDX9/JPTzM75fOIIYR4Brf325nMJbX/6wqzWcfHbFy8ZJgARIgARIgASEgNzf3aoVcgJYVLo0Ssod3v7+DmXzqksXxJs+Hb9OmqQOuZVy1N0dCVKkQO579z5pS0CePNh84QLOjx+PdKNH47sXLxAvIMCh9rMzEtCCANf+WlDUrg0Kb+1Y6r4lTj7du4gGkgAJkAAJkIDxCcyeDXTogMwA2gJQ8eb374FYsRw2thdt2iCOpyfmTpuGzp07q34Px4mDJIkTI5ePzyft8M+YEWtu38azefMQp39/NHr0CG6BgQ6znR2RgFYEuPbXiqQ27VB4a8PREK1w8hnCTTSSBEiABEiABIxN4MMd2ukAdIkdG/3evQPevgXixHHYuJ41awa3xYvx+5w5aNtW5D9wMF48pIgfHzksnFD+LnVq/Onjg3irVuF1t25ocueOw18cOAwUO4rWBLj215d7Kbz15Q+7WsPJZ1e8bJwESIAESIAESEAITJwI9OqF5AB6xI+PAa9fA3IqeMKEDuPj+/33iLViBdYsXIhmzZqpfvd99hnSxo6NbBYOenuXJAmWPHuGz728cL99ezS9fh148waIG9dh9rMjEtCCANf+WlDUrg0Kb+1Y6r4lTj7du4gGkgAJkAAJkIDxCYweDQwYAJHZ7USHy4gePQKSixR3THlcty6wejW2/PEHGjVqpDrdmzAhMri4IIuFq8H848fHvDdvUHDvXlxr2xZNz58Hnj4FEid2jPHshQQ0IsC1v0YgNWqGwlsjkEZohpPPCF6ijSRAAiRAAiRgcAJDhwLDhkESy0V4T5Xh3L0LpE3rsIE9qlEDARs2YP+qVagrIhzAriRJkCUgAJlevvykHe/d3TE1IADljx2Dd6tW+PHkyeB7yFOlcpj97IgEtCDAtb8WFLVrg8JbO5a6b4mTT/cuooEkQAIkQAIkYHwCcn3XmDGQo9Rkd/VsGdGNG0CmTA4b28MqVfB2yxacXLcONWvWVP3uTJYMOd6+RQZJfY+oBAWpQ+DGAqh9/jyOtWiBxocOAbdvAxkyOMx+dkQCWhDg2l8Litq1QeGtHUvdt8TJp3sX0UASIAESIAESMD6Bnj0RMHky3IOC0EJuF5MRXboE5MjhsLH5VKiAV7t24dLmzahSpYrqd3vKlMjz4gXSyUFvERW5rztuXAz//+8Kmt+8if3Nm6PRzp3AlStAtmwOs58dkYAWBLj214Kidm1QeGvHUvctcfLp3kU0kARIgARIgASMT6BrV7yZMQMZkyVDlUePsFRGdPYskC+fw8b2oEwZPN+/H//t2IEKFSqofrelTYsCjx8jtZyyHlF58QJIlAgDAHR/+BDbmjbF95s2IejsWbg40H6HgWJH0ZoA1/76ci+Ft778YVdrOPnsipeNkwAJkAAJkAAJCIF27fDM0xOl8+RB3nPn8Kd87/hxoHBhh/G5X7w4nhw5At99+1C6dGnV75YMGVDkwQOk8PeP2A45BC5lSvQEMPzVK2xs2hT1/voLfocOIU7x4g6znx2RgBYEuPbXgqJ2bVB4a8dS9y1x8uneRTSQBEiABEiABIxPoEULPFy8GN9//TUS7tiBNTIi2SftQOF6v0gR+Jw4Ab8jR1CsWLFg4Z0pE766cwdJAgIiZix3dmfIgC4Aprx/j7XNm6POkiV45uWFxB4exvcNRxCjCHDtry93U3jryx92tYaTz6542TgJkAAJkAAJkIAQaNwYd5YvR6+GDfF02TJslO/t3g2UK+cwPvcKFsTdM2fgdvIkvvjiC9WvV7ZsKHXjBhK+fx+xHdeuqb3cnd3dMf3dO/zToQNqzp4Nn5UrkapePYfZz45IQAsCXPtrQVG7Nii8tWOp+5Y4+XTvIhpIAiRAAiRAAsYnUL8+rv31F37p1AmXZs6EV2AgsHUr8L//OWxs9/Lkwa0LF5D4/Hnkzp1b9bs5Vy6Uv3wZ8cSeiIrc2Z03Lzp99hlmvHyJTT174ptJk3BnwQKkb97cYfazIxLQggDX/lpQ1K4NCm/tWOq+JU4+3buIBpIACZAACZCA4QkE1aqF8+vWYWGvXjg3axY2yL3ZGzYA1ao5bGz3c+TAlStXkO7qVWTNmjVYeOfLh4re3ogtV4ZFVOTO7sKF0TlZMkx//Bjbhw5FpWHDcG3qVGTtIgnoLCRgHAJc++vLVxTe+vKHXa3h5LMrXjZOAiRAAoYjsGfPHpRzYPqv4QDRYJsIvPfwwJmtW7FmyBCcmjMHf9+/D/z9N1C7tk3t2VLpfpYsuHDjBrLfvo0MH+7f3vzFF6h6+jQgEW8Xl/CbPXJE7UXvnDYtpt+9i90TJqB87964OHo0cvXrZ4sprEMCTiPAtb/T0IfbMYW3vvxhV2s4+eyKl42TAAmQgOEIeHh4YMuWLYazmwbrm4B/uXI4sXcvdo4di+Oenlghd2CvWAE0aOAwwx98/jnO/PcfCj54gFSpUql+vb78ElWOHQPkcDVX1/Bt2btX7UXvkiULpl27hoOzZ6Nkhw44278/8o8a5TD72REJaEGAa38tKGrXBoW3dix13xInn+5dRANJgARIwC4ETKm25o0HBQXBx8cHr169skufbDTmEvArXhz/HjmCE1On4shvv2GJpG8vWQL8+KPDoDxIlw7H791DSV9fJEmSRPW7uUQJVD18GHjzBogbN3xbtm0DKldGt7x5MeXcORz/4w8UadwYJ7p1Q+Fff3WY/eyIBLQgwLW/FhS1a4PCWzuWum+Jk0/3LqKBJEACJGAXArly5YK3tzdcw0T5KleujK1y6BULCVggcObMGZWynTRpUmzevBnx48ePcJvC2y++wIHTp3Ft3jwcXLQI8/ftA+bPB1q2dBhnn1SpcPjhQ1R69UrZKsWrbFlU2bcPQc+fwyVhwvBtkb3oNWqge9Gi+OXoUZz75x/k+/Zb/Nu6NYrNm+cw+9kRCWhBgGt/LShq1waFt3Ysdd8SJ5/uXUQDSYAESMAuBKZMmYJGjRqFpNyaOlmxYgUaNmxolz7ZaPQh0KtXLxw5cgQJEiRA+vTpcffuXfV3yaQYPXr0RwN9kzs3dl+8iMdLl2L3okWYKy93Zs0C2rd3GJSHyZNj75Mn+NbfH25ubqrfLZUqwWPHDgT4+MAtZcrwbVm9GqhbFz3LlsXEPXtwdc8eZCtfHocaN0aJpUsdZj87IgEtCHDtrwVF7dqg8NaOpe5b4uTTvYtoIAmQAAmQAAnojkDx4sVx+PBhvHv3DtmyZcONGzdU9kSZMmWwT6LZYcrrrFmx7fp1vF+9GlsXLcLMtWuBKVOArl0dNrbHSZJg+7NnqB8YCJcPB6ltrVoVlb288PrGDcTPlCl8W5YtA374AX08PDDOywt3Tp1C+kKFcKBOHZQSUc5CAgYiwLW/vpxF4a0vf9jVGk4+u+Jl4yRAAiRAAiQQLQl8/fXX2LVrlxpbmzZtMO9DynXFihWxY8eOj4V3+vTYKFHxTZuwcfFiTBUxO2EC0LOnw/j4JkwIr9ev8f379yF9bq1ZE5XXr8czb28kzpMnfFsWLQKaN0e/WrUwZs0aPLlxA8myZMHeqlVRdtMmh9nPjkhACwJc+2tBUbs2KLy1Y6n7ljj5dO8iGkgCJEACJEACuiPQoUMHTJs2LSRlWwz08/ND7dq1sSkcMfomVSr8/fAh0u/ahXVLl2KSpycgJ4L37++wsT377DNs8PfHD+/ehfS5vW5dVFq9Go+OHUOKIkXCt2XuXKBdOwxs1Agj//gDr319ET9ZMuwuXx7lP7x8cNgg2BEJRJEA1/5RBKhxdQpvjYHquTlOPj17h7aRAAkYkcCVK1cQK1YstdfVVI4ePYovv/zSiMOhzSSgCYG3SZNi+dOnyHfkCFYtXYpxU6cCQ4YAQ4dq0n5kGnkRLx7WBgXhx7dvQx7f0agRKi5fjvv79iFN6dLhNzNtmkqJH9yyJYbPn4/A9+8Ry80Nu0uUQPmDByPTNZ8hAd0Q4NpfN65QhlB468sfdrWGk8+ueNk4CZBADCMwfvx4rF27Vu11FeEt6bfu7u6IKP3WmXj8/f2xaNEiZesPP/yAOHHiKHM8PT3RunVrZ5pmqL75oiVy7vJLkACLXr1CqTNnsOKPPzBizBgE9umDWGPHRq4BDZ56FTs2Vru7o4nZdXm7mjXD14sX479t25ChUqVwewmaMAEuvXtjWOfOGCIiXKL7Li44VKQIyssd4CwkYCACXPvry1kU3vryh12t4eSzK142TgIkEMMIlCpVCgcOHFCjXrhwIZYtW4ZVq1bh22+/xc6dO3VFo3HjxsiYMaNKFfby8sLKlSuRKVMmXb4k0BU4M2OM9KLF2Qz948bFbD8/VLtyRc2LvoMGIbBLF8SWyLeDylt3d/wZLx6aPn8e0uPutm1Rft48XF+3Dllq1gzXkoDhw+E2ZAhG9u6NgePGqWdeuLjgeP78KH/mjIOsZzckoA0Brv214ahVKxTeWpE0QDucfAZwEk0kARIwDAFz4S1Gb9y4UV2t9PLlS5w8eVJX4zA/HOvs2bNo1aoV5s+fj65du4Z7OJaujNeJMUZ60eJsZO/d3PDr+/f44e5d/PHHH+ggh6q1bo34DrwH+52rK/5IlAjNfX1DcOzr3BllZszAlZUrkb1evXAx+fXpgzjjx2PssGHoO3iweuZxrFjwzpEDZS9edDZa9k8CVhHg2t8qXHZ/mMLb7oj10wEnn358QUtIgASMT0BStPv27Yvs2bOHDEZOfm7SpAlu376tqwGaroMyGSXXQdWvXx8PHz5UV0OxWCZgpBctlkdj3RNWbVUICgJixcIYAB18ffH777+jcefOcGvcGAkceA/2excXLE6RAi0ePgwZ7IGff0apyZNxYfFi5G7SJFwIr7t2Rfxp0zB54kT0+Pln9cw9V1dczZQJZa5dsw4cnyYBJxPg2t/JDgjTPYW3vvxhV2s4+eyKl42TAAmQgG4JzJo1C1WqVAl1CNzdu3fRp08fLFmyRLd268kwI71o0ZqbVVsV5BTxOHEwDEDft2+xdOlS1GzdGnHq1kXiVau0Ni389gIDAVdX/JY6NVrevx/yzKF+/VBi7FicnTsX+du0Cbfui9atEXv+fHhOn45OnTqpZ265u+NOmjQoqbMXao6ByV6MTIBrf315j8JbX/6wqzWcfHbFy8ZJgARIgARIIFoSsGqrwsuXQMKEGOjighHv36s93uUaN8Zn1aoh6YYNjuHzQfz/lj49Wv73X0if/w4bhmJDh+LU1Kn4okuXcG152rgxYv3xB1Z6eqotGVKuxYkDn2TJUOLePcfYz15IQCMCXPtrBFKjZii8NQJphGY4+YzgJdpIAiRAAiRAAvoiYNVWhSdPgOTJMcDdHaPevcPq1atRSKLdlSoh+bZtjhnY69fAZ59hfubMaHX9ekifx8eORZF+/XB8wgQUkX3n4RTf775DwN9/w2vJEvz444/qiYvx4uH5/78XvNijR46xn72QgEYEuPbXCKRGzVB4awTSCM1w8hnBS7SRBEiABEiABPRFwKqtChIVTpcOAz77DKNevsSmTZuQpVo1pChdGin27XPMwJ49A5IkwW/Zs6Pl5cshfZ769Vd80b07/h0xAsUGDgzXlsfffIM3mzfj0MqVqPfhALZzCRLAz90dRcwOanPMQNgLCUSNANf+UeOndW0Kb62J6rg9Tj4dO4emkQAJRBsC06ZNQ5cI0lijzSA5EBKIiMDNm0DmzBiQNClGPXmirtZLXrEi0hYrhpRHjjiG2+PHQIoU+C13brQ8fz6kz7OzZiF/x444NHAgSowYEa4tjypUwLNdu+C9bh1qfrhy7HTixEBgIAq+eOEY+9kLCWhEgGt/jUBq1AyFt5Ugr1y5gokTJ+LQoUOQK1ly586t/jQvzZs3x6JFiz5qWd76Vq1aNdT3pa3p06fj/v37KFCgACZMmADZS2VeXrx4gZ49e6r7Yf38/NS9q7KwkztYrSmcfNbQ4rMkQAIk8GkCt27dCvcBSU/ds2ePIfDJgWGenp6GsNXZRlp1srezjXVm/xJhzpkTA1Knxqj799V6ya1kSWQsWBCpTp1yjGUPHgBp0uC3AgXQ8vTpkD4vLFyI3C1aYH/Pnig9YUK4tjwsVQqPDh7Ef1u2oHLlyuqZE8mTI/bbt8j36pVj7GcvJKARAa79NQKpUTMU3laCXLt2LTp37gzZ73Tp0iUEBgaGK7z37t2rrtAwL3ny5EFieWv6oYjo7t+/v7r3tUiRIpg3bx7WrFmDI0eOKBFuKjVq1MDx48cxadIkJEqUCIMHD8bz589x+vRpxIsXL9Ij4OSLNCo+SAIkQAIWCcSPHx8lSpRAkFyfZFbkZ/NjibjpqCxevPgja8Ru+T105swZHVmqX1OsOtlbv8Owv2XnzgH582NQpkwYceOGutP+deHCyJ47N1KZRZ/taogcqPb551hQpAhaHDsW0tWV5cuRvVEj7O3aFWWnTAnXhIdFi+LO8eN4sWcPypYtq545mioVEj5/jlxv39rVbDZOAloT4Npfa6JRa4/C20p+IrRjxYqlaklk++jRo+EK7/C+b96VRK5Tp06Ntm3bYvz48eq/3r9/rwR3wYIFsXz5cvW9w4cPq4Xdhg0bUK1aNfU9ibJky5ZNRb3bt28f6RFw8kUaFR8kARIgAYsE5Ge1RPNEgJsXiZJt3brVYn1HPpA0aVJ07979o5cEcpWYZHKxWCZg1cnelpsz5BMS9X/y5AmSJUsGd3f38Mdw4gRQpAgG5cyJERcv4uLFi7iXOzfyZs2KVFevOmbccjd9lixYULw4Whw6FNLnjbVrkbl2bexu1w7lZ88O15aH+fPj2rlzcDl8GF999VXwWix9eqR8+BBZ5bR0FhIwEAGu/fXlLArvKPgjKsJb9jxJyrhEsgsXLhxixbBhw1Rk+9mzZ3BxccGQIUMwdepU9YtO/m0qFSpUQIIECfDPP/9EegScfJFGxQdJgARIwCIBSScvVKiQykQyLxcuXFDbkPRUSpYsiXXr1iFlypShzKpevbp6sctimYBVJ3tbbs5QT8iWuo4dO8LX11dl7skaRV7myFY5eQEVqhw+DJQogcEFC2L4qVO4efMmLmbOjMKff46UEWzP0BzGh3T3hWXKoPnevSHN3968GZ9/8w12tmiBCr/9Fm63D3PmxPnLl5Hk1KmQsR3KlAnp7txBxoAAzU1lgyRgTwJc+9uTrvVtU3hbzyykxqeE94oVKxAnThy8fv1aRbEHDRqE2rVrh9SdOXMmOnXqpP7fPF185cqVaNCgAW7fvo0MGTKov0uEW6Iq5kXqenl5WRWp4OSLgrNZlQRIgAQMTEAEk7wgcHV1NfAonGu6VSd7O9dUzXsvV64c5s+fjxw5coS0ffnyZXXP9UfnGYjQLVcOw776CkMOH4aPjw+OpE6NEmnSIIWj7sGWlPa8ebGoYkU02749xOb7e/YgTfny2N64MSotXRoup0eZM+PEzZvIeOECcuXKpZ45kD07sl6/jjTv32vOlg2SgD0JcO1vT7rWt03hbT0zi8J7ypQpcHNzQ758+fD06VPIL+stW7ZARLXpaopRo0ZhxIgReBtmv9C2bdvUYR6nPrxplb/LQmnz5s2hLB04cCBEvEskPKIi+8Dly1Tu3bun0qZMoj4KQ2dVEiABEiABEiCBGEKgTJky2BfOVWDhfl+E7v/+h5Hly2Pgrl1qHbItcWKUT/7/2LsKsCi3JvzSLY0ICKKCoAiIKIidgImBitiN3YHdhd2J3XFtuXZeRRQTE8UGFdGLBQj8dz6W/ZdlVZYNduXM8/hc7n4n5rxnPzhzZuYdU5jKqw42Eaq5uWGTnx86HD3K36X3kZEw8/LCicBA1Nu5U+TufbCywqU3b1A+Lo5PYnuhbFmUvXcPJkJ8DoVk+9kylRgBZngr1uYxw1uC/fiZx1t4SMoL9/Hx4f74xMTEcI/J8J46dSq+ffuWoznlBTZo0IAjTiNPORneZMQTI7qgjBkzBitWrPglgc/EiRNBoevCwgxvCTaddWUIMAQYAn8CAlSP2NYW2LoV4JVM+hOWxdYgGwSIhI9SEojslULMKYKC/t/f3x/Dhw/POSmdVxo2xHRfX4QeO4bU1FTs1dKCr6EhjD9+lI2CQqNmXrsGFU9PbG7aFO337+c//Xj7NoxcXfF3s2Zo8NdfInVJsrDA6XfvUDU+nuPiITnv7o4KN29Cnxnectk/Non0EGCGt/SwlMZIzPCWAMW8Gt40BZUJGzFiBD+0PDvUnAxvbW1tvhbSDDVnHm8JNpd1ZQgwBBgCfzICJ04AVCqpbFmAWKiZMAR+gwCFlhM/DTH2m5qacqVPHR0dc/ciQzcgALObN8eIvXs5Qr/NqqpoqqcHw8+f5YLzj4sXoV6tGra2aoV2u3bx5/waGwvd0qUR4e8P3yNHROryydgYRz5+hH9SEoyMjLIM70qV4BUVBfX0dD7BrlwWwiZhCEiIADO8JQRQyt2Z4S0BoOIY3sRcPnLkSL7hzcjVJACedWUIMAQYAgqMgFLUxg4PB7p2ZYa3An+PlFY1MnRbt8a84GAM4eVRh6urI1BDA/pCUX6yWuP3kyehXa8etrdrh7YCpV3T3ryBhpUVjtWrB7+fVB74rK+P3V++oI0AB8/5atVQ/eJFfP/yBdpCVQxktQY2LkNAGggww1saKEpvDGZ4S4BlXg1vCjWnkmBEpEbMoCTZ5cSoHNjMmTO5z6icGLGDUoi5cDkxCjX38/Pj2lGoeMmSJVk5MQn2jnVlCDAEGAKSIqDUtbFHjwbobw+RR92/LykUhbY/EYvR92DNmjWFCwPK1X77NuviRkh+rF8P9S5dsKhHDwxYtYp7ulZbG+0yMqAjp3JcyYcPw6BxY+zs0gWtBdjLM5OSoGJigqM1a8L/zBmRe/ZNWxsbU1LQMyODX03mfN26qH7qFD7Fx8OQF35euDacrVZZEWCGt2LtHDO8xdwPMp6P8MKTli5ditjYWMybN48bpWbNmpxxTQZ5UFAQV2ub8qCIXI083Hv27EHz5s35M1LOVGhoKGbMmAEPDw/uD/fevXsRGRnJGd/ZQjlV0dHRXJkxatj1xgAAIABJREFUYqUdP348V8qD8sAFGdF/txT28v0OIfacIcAQYAjkHQGlro3dqhWwZw9A3rvkZEBVNe8LL6QtRV20UDQblfq8fft24UKFjE8yvEXkPH9buhQ6/fphxeDB6M07H63W10enb9+gKSdW8KRdu2DcujX29O6NlsuX/39vvnwB9PVxxMcHDS9eFLlnqRoaWJ2Zib4CpcPON2qE6keO4N2jRzAvXbpw7TVbrVIjwM7+irV9zPAWcz/i4uJgb28vshcZ1+Sx7tKlC65du4Z3795BU1MTnp6eGDVqFHx9fXP0o7wnMr6pDmZCQgJnbNMfcarRLSiUqz1s2DDs3r2bIymh+t+LFy/ms23mdQns5csrUqwdQ4AhwBD4PQJKXRvb2xugesskiYmAicnvF1yIW1CUGdWjFq7Z/uXLF9ja2opV2lPpYSRjO/uihsjSDA1zLOnTrFkwHDUKa8eORbcpU7hnq4yN0f3jR6hmZAAqKjKH4N2mTTDv2BH7Bg5E8wUL/j9fSgqgrY0jnp5oePWqSD0yVFWxREMDA6gtTy62aIGq+/bhVXQ0rN3dZa4/m4AhIC0E2NlfWkhKZxxmeEsHR6UYhb18SrFNTEmGAENASRBQ6trYFCJMtY5J4uIAOzslQb1g1KR6zhThcPDgQZibm/OVoMojdMFODN+FRoiMz8Ula7nk6c/+mQdA4oQJMJ08GRtnzEDHUaO4T1daWKDXu3dAWhqgri5zqN6sWoVivXrhwIgRaDpr1v/nI8NfTQ1H3N3RMDo6tx7k5dbQwAJdXQwi7zhPLrVrB59t2/D03DnYV68uc/3ZBAwBaSHAzv7SQlI64zDDWzo4KsUo7OVTim1iSjIEGAIMAdkjULw48PJl1jxU81ggvUn2kyvfDAsXLuRKZ1EKmZqaGn8BO3bsQJs2bZRvQZJoTJcMjRtnjUA/N2yYY7SE4cNRNCwMO5YuRZs+fbhnK2xs0PvVK4CMWTmQk71cvBg2Awbg8PjxaCRUVjVDRQXHXFzQUFR6wNevgJ4e5hoaYqhA6bPL3brBe906PDxyBI7+/pKgx/oyBOSKADv7yxXu307GDO/fQvTnNGAv35+zl2wlDAGGgAIiQLmkp04B9F8zMwVUUEAlCg8uUiTL+KZcVx8fxdaXaac4CBBTeYcOWfrQd7137xy6vezbFzbLlmHfhg1o3rFjluFtb4/eFFlB9eN5JbpOnjyZqzxZvXr1pLLOuNmzUWLkSERMnw5fIhIUkBQVFZwsUwYNRZEKkn4mJggzM8Mw8tDzJLJvX1Retgx3d+1COeJHYMIQUBIE2NlfsTaKGd6KtR8y1Ya9fDKFlw3OEGAIFHYEypUDYmKAadOA0FDFRYPCbSnct3LlrDzvo0cBXtUMxVWaaaYwCCxZAvTvn6UOhZLPmJFDtWddusBu/Xoc+Y8stiGPUHaFkxN6P3gAxMcDRYtyvDXEbdOsWTOuVvbHjx9x4MABrj74/PnzJV5q7JQpKDV+PE7Mm4d6gwfnGO+rqirOliwJ/8ePc89D+hUrhtnFimHE69f851HDhsFz7lzcCA+He+fOEuvHBmAIyAsBdvaXF9J5m4cZ3nnD6Y9oxV6+P2Ib2SIYAgwBRUWAcl0p/7VfP2DxYkXVMovFnLzdLVoAe/cCO3ZwdZeZMATyhAARpo0fz+VCo0sXYOXKHN2etG2Lkjt24NTx46jD82CvdHVFLwrtfvECsLHhqsCcPXs213Q/+zxPegk0ejh2LBynTcOZZctQKyQkR/d/1dRw0dYW/k+f5h722TOgRAnMsrPDSPLQ8yR6/HhUmDIFUUuXwpMXPi+uTqw9Q6AgEGBn/4JA/edzMsNbsfZDptqwl0+m8LLBGQIMgcKOgIUFQOGpQUHA1q2KiwZ58qytgQEDgEWLgNWrge7dFVdfppliITB0aNb3xsoKqFo113f9UUAASuzfj6hLl0DM/ySrKlVCz6goIDYWKFkSREpH5VQFq7icOXMGU6dOxYkTJyRe7/3hw+EUFoYL69ahGl0OCEiSujoiixWDL10CCMvDh1xt+1kODhhJP/Pk1owZcA0NxeXZs+E9fLjE+rEBGALyQoCd/eWFdN7mYYZ33nD6I1qxl++P2Ea2CIYAQ0AREaD6xOQBpFJL9esDf/+tiFpm6US5rc7OwOzZwIgRwNy5wJAhiquvAmtGpT37Z4ddK7CeUlWta1fg4EHA0jKLDf/QoRzDP/Dzg01EBB7fuAE3Nzfu2epq1dCDuAQoFcPZGXQeGTlyJK5evYqMjAyOsI5Kr86YMYMrzyap3B0wAOUWL8blbdvg3bZtjuHea2jgmrk5fAVCyfkNyCvv6opZLi4YKUC+FrNoEcoOHIgLkyahGnn7mTAElAQBdvZXrI1ihrdi7YdMtWEvn0zhZYMzBBgChRkB8nSTx5ukQgXg+nXFRSMyEvDyArZsAYKDgQkTgIkTFVdfBdDs+fPnIrVo3749zp07pwAaylEFytumlAoqq0YM70Lrv1+rFoqePYt3Dx7A0dGRU2xNvXrofvIkcOMGwDPGZanxrV694LpqFaL27YNnQECOqRK0tHDL0BD1377NrQJ55StVwixPT4wUqPP9cM0aOPbogXMjR6LGzJmyVJ2NzRCQKgLs7C9VOCUejBneEkOoPAOwl0959oppyhBgCCgZAoK1jcljR7miiioUypvtlW/UKCsnfd48RdVWIfTS1dWFt7c3MimiQUBu3bqFxMREhdBRbkrUrp1VFszEJIssjYxpAbnn7Q2TK1eQ+vw5ilPZOgDrmjRBV/KM06VPpUoyV/VGly5wX78eN44dg7uvb475XuvoIEZXF/VE7Rt55atVw+yqVTHiwgV+v6fbt8M+KAinBwxA7YULZa4/m4AhIC0E2NlfWkhKZxxmeEsHR6UYhb18SrFNTEmGAENAGRE4fRqoUwcoXRqgEFYyTBRV9u3LIlYjRnOqSUw/U543k58i4OrqisuXL4MMcEGhXOXjx48XLuTc3TlmclBJOvIQP3mSY/0xFSrA4MYN6L5/z7GUk2wIDESn3buRce4cVKtXlzle0e3aocK2bbh79izK1aiRY76Xenp4qKGBOgJ1uvkNeO/xnLp1MVwg1/zFgQMo3qwZTvbogbqrVslcfzYBQ0BaCLCzv7SQlM44zPCWDo5KMQp7+ZRim5iSDAGGgDIisGtXFjN448ZZOa9fvwI6Ooq5kg0bACqJdO9eluFNZcWI2ZzJTxGgcHJ3d3cUITZ4Abl//z6cnJwKF3IlSmSlKhgYAH/9Bbx/n9PwLlcOWjExKPblC/+iYnPHjmi/aRO+Hz4M7YYNZY7XtVatUHHPHjyKioJDxYo55ntmYIAnKiqo/e+/ufU4dox7J+Y2boyhlMfOk/hTp2BZty6Ot2+P+ps2yVx/NgFDQFoIsLO/tJCUzjjM8JYOjkoxCnv5lGKbmJIMAYaAMiIQHg4Q6RQRbVEpMV7ZJIVcytKlWeHllLdMRhCFAx85opCqMqUUEAEzM4DypukSgr5LKSk5DW8HB+DxYzilp0NVVZV7tq1XLwStWoXknTthEBiIf//9Fxs2bED58uW5f3PmzOGM9EGDBuW63MgPAlFNm8Lz4EHE3b2LEmXL5hjiqaEhXqSno8bnz7mH3r+fW9v8wEAM3rmT//xDZCRMvLwQ0aoVfOmSjQlDQEkQYGd/xdooZngr1n7IVBv28skUXjY4Q4AhUJgRoPJKAwcCM2YAo0fz2ZsVEpKwMIBKIpGnkgxvCp+mEFsmDIG8IEDfFyo/Z2wMTJ4MfP8OaGnxe8aUKIG058/hlpHB/2zXkCEInD8fievWwbRLFzRp0gQVKlTAly9fcOnSJTRt2hRGRkY4efIkdu/enRctftkm0t8flY8dw6snT2Btb5+jbayJCd58+4Zq377lGiNzxw6otG2LhR07YiBFhvDk87170C9bFscaN4afgCdcYkXZAAwBGSPAzv4yBljM4ZnhLSZgytycvXzKvHtMd4YAQ0ChEZg+HRgzBiDPN9UNlhOJVL4wIWOJmMwpD53I1SgsnvK9mTAEfocAkcuRF3vkyCxW82HDAGIHp595cs/aGl8TElDxxw/+Z/vGjkXzadMQv3gxLPv1Q82aNXH27FnuOXm8b/NKd1Fd79NSuAS6Uq8evE6exLv4eJhTPrqAPDYzw7vkZFQR8tRTkx/h4VDv2hWLe/VC/xUr+L1SX7yApq0tjtavD39FLhX4u/1jzwsdAuzsr1hbzgxvxdoPmWrDXj6ZwssGZwgwBAozAqGhWd7uw4ezjNlTpwBif1ZEIY88lUSi2uNNmmSFnAvULBal8uPHj7mw4ZIlS/IfR0VFcbWXmRQiBMhLTB7vSZOAYsWAnj25sHKUKsUH4YGFBT59+oTKAobtoRkz0Dg0FC9mzEDxUaNQvXp1nD9/nuuzdOlS9O3bl/tZaoZ3jRrwPH8enz9+hCGRwAnIQ0tLJCUmwistLdfGfVu8GDoDBmD5kCEIofr2PMlMSoKKiQmO1qgBf96FgaS7PmnSJEygCzAmDAEZIsDO/jIENx9DM8M7H6Apaxf28inrzjG9GQIMAYVHYMAAYO1agLxh1aoBBw5kGbWKKIMGAStXAmREtWqVVQ6KjKefyOzZs7F//36oqalxhvfq1auhQazQdergFF0wFAJ5+vQpxo4dC3V1dYwaNQrOzs7cqvv164clS5YUAgR4S6QSXJTjPWcOYGMDBAVl1ayn2vU8eWhigsTv31GFIil4cmzhQvgNGoSn48bBfvJkrFmzBp07d+bwzJaUlBTOEJ0phTrZl6tUgfvly8j8+hU6QiSHD6ytkZyQAE8Bj3y2DskzZ8Jg9GisGjsWPadM+f++0lr09HDU2xv+//wj9n7TuyIoVJaOStG5ubkVmndIbNBYB6kgwM7+UoFRaoMww1tqUCr+QOzlU/w9YhoyBBgCSooAEauRt5sMbyq3tG0b0LatYi6GvJR79gBkRHXsCFDZJCqB9hPx8fHh8nBJ1q9fj23btnF5uJSXK42wYMUEKadWVDZs2LBhnKE4ZswYjB8/Hg0bNpSah1YZMOB0JNJAW1vc6tULVxMS0I1YzclzTZdNPIktUgTxmZmompzM/+zUunWo060bHg8bhtJktMtYLnt6wuXaNej8+MFdGAnKfVtbfHv5EhUEctCznyeNGwfjqVMRPnMmulA4fbZQdIi6Oo5WqAB/umgQUzp06IDPnz9zlzdmZmZcPXi6eCCCOTs7OzFHY80ZAnlHgJ39846VPFoyw1seKCvIHOzlU5CNYGowBBgCfx4CVErs2rUsw5tqeVNdbCKgUkTp0CGLTO3lS6B3b2D7dkBUTWOe7oKGN3105MgRTJ8+nTMkbpC3vBCIYAh0cnIyWrdujXbt2iE8PLxweSwfPACcnNAFwDMAXLwDleDy9eV/C57q6eGFpiZqJCXxPzu/Yweqt22L+336wImY0GUs/7i7w+nmTRhTTrqQ3LO3x4+4OJQX8ezd0KEwnzcPmxYvRgdi/heQHyoqOF6uHPzv3MmX9tHR0Zg8eTIqVqyIoUOHomXLlty7xIQhIEsE2NlfluiKPzYzvMXHTGl7sJdPabeOKc4Q+GMR+FNyh7/WqoW3N27A4OJFmLq4APPnAxTSrYjSsiVw6xbw6BEwZAhAodKpqT/VtHv37lx4dWm6UODJmTNnQF68F+QBLQRClw8UVq+trc2tNjU1lTO+CYePv7i0+OOgiY4GPDzQmmd4c5R8FD3RogV/qS+0tfHEwAA1373jf3bl8GF4NW6Mu126oNy6dTKH5XK5cigVEwNzEcZ1DK/cmXNGBlRUVHLokhASgqIrVmB7eDjaUq17AfmmqoqzDg7wo8sHCeTw4cOYN28e9725Rpd1TBgCMkSAnf1lCG4+hmaGdz5AU9Yu7OVT1p1jejME/kwE/qTc4ThbW7x88QLbO3XCEipDRPmhY8cq5sY1bIhP9+7B6ft3XKpTB/ZbtwJENCWQb6uYihecVhRqb29vj2JEKMaT9PR0LvS+W7duBaeYvGe+eJELK28MIMXSEsfj44FNm4D27fmavNHUxAMzM9QSSF+4cf483GvUwO22bVGe0jBkLJfLlIHdo0coJiKcPMbZGRr378MuJQWampo5NHnTuTOKbdiAvbt2oQXxHwjIJzU1XLazg++TJxJrn5GRgfj4eFhZWUk8FhuAIfArBNjZX7G+H8zwVqz9kKk27OWTKbxscIYAQ0BMBP6k3OHnZmaISUxEWwMDfKTcVsoPlQJJlJiQ5q157dq4FRkJt69fcdjHBw0pf/vffwEDg7z1Z62yalcTed7gwVm10AuLHD8ONGiAugBK1auHVcQPQGW3evXiI/BOXR13rK1R+xkFo2fJvRs34FyhAm4GBMBt376fokXRFUS8JqlcLlUKNnFxsKHcbCG5W748dO/cgXlyMvT19XM8fdmmDYru3Injhw9zOfyCkqiujuvFiqF+IYnykHQPWH/FQICd/RVjH7K1YIa3Yu2HTLVhL59M4WWDMwQYAmIi8CflDr/Q0UFUWhpapKcjRVMTmkRgtnixmIjIqbmXFy5GR6NaWhpWODmh1/37AHkuheody0kb5Zxm40agUyfAzS2LFb4ARa7pGvv3AwEBaGRiAvfmzTFt7VpkhoVBZehQPgJJqqq4UaoUalMqA0+exsbCvnRpRPv6osKxY9hI+AkJEY6FhYXxa3pLAukVOztYvnoFOxHM5Xc9PGAYHQ3dxESYmJjkmOZF06YwPngQkSdPcqz9ghKvqYm7JiaoS+8KE4aAkiDAzv6KtVHM8Fas/ZCpNuzlkym8bHCGAENATAT+pNzheFVVxJYqhXovXyJBRQVF2rQBwsPFREQ+zTPKl8fRO3e4cOFRxsaYQSRYFD5rby8fBf6EWSinmTy39eoB5AUuIJF7ugaFibdrh7bOzvBs3RrDJk3Ct9BQ6Eybxkfgs4oKIsuWRZ27d/mfvXnzBmZWVrhdqxY8Tp+GsbExBg8ezLF7C8qmTZtAFwmSSqSNDczevkVJEdwFd728YBoZCZX4eBQVumx61qAB9I8fx4OLF0EXg4LyUkcHj/X0UOv9e0nVY/0ZAnJDgJ395QZ1niZihneeYPozGrGX78/YR7YKhgBDQLEQoFzfrxRe6+GBNu/e4eqHDyjq7w/s2qVYivK0SStRAvufPUOImRn8378H53skI6lsWZH6pqWlcWWPqCwTMXlraWlx7SgkmC5PCqWQUUb1nImo6+HDAoNA7ukaVKu+e3f0qlMHVYKD0blbN7zv3h1mxOLPk1QVFVysUAG1BcpuEZGYurExHlSpgoqXLqFKlSo4cOAAzM3Nc2DXqFEjEPmYpHLV0hJGSUlwSEnJNVRM1aooeukSvj5/juLFi+d4HlezJjTPnUP8tWvw8PDI+YzY2jU0UD0fZHr//vsv6JKEyu99+PABpqamqFWrFoYPHw5DQ0NJl8v6MwR+igA7+yvWl4MZ3oq1HzLVhr18MoWXDc4QYAgUUgQ+ffwIA2NjXKlRA/0+f8bOhw9RiuoaHz2qkIikmJtj5/v32OrnB71jx7CbtLx6FfD0FKlvcHAwbG1tuRrWERER2LVrF1d7mEJxiem7UAoxvMfGAhoawLdvgFCtaHlhIvd0DUqfGDAAQ9u2Re3gYNRt0gQfWraE9W7uW4SM9HSoqqvjjLc3atHFBE++f/+Orzo6eOLhAc9r15CUlIQiRYrkqrEtLdyizM2h//kznGhvhCSmVi1Ynz2L948fo1SpUjmNay8vZEZG4tvduygrdBFF9ckTMjPhI1CfPK/6Nm/eHI0bN0azZs1gZGTEMZrTxQP9+4tqoTNhCMgIAXb2lxGw+RyWGd75BE4Zu7GXTxl3jenMEGAIKDoCr2JjYV26NK785+WemJmJGefOwZ28ZefPK6TqKfr6WP/lC56OHIlbs2aBqyR89ixQo4ZIfckzR2WzSO7cucOxeK9duxYDBgwovIZ3kSJAtgFGJGK2tgWy13JP15g1Cxg1CmNCQuAXFATnGjXwrX59FKf69QC+JCVBz8QEZ2vWRE3ed4Y+p5DyBFVVvHRxgeft2zLH6rqJCbRTUlD2y5dcc92rXx92J07gWUwMnJ2dcxre7u74fvMmNEQY5Q9MTPDp+3dU/vpVbP1r1KiBc+fO5epXs2ZNnKV3jwlDQEYIsLO/jIDN57DM8M4ncMrYjb18yrhrTGeGAENA0RF4ePkyHKtUwdXWrbFYSwvdduxATarlraA1en9oaGDJjx8w3bgRazt2BGdSk3fez08k1F5eXrhyhavYzElcXBwCAwPx7t077udCJ8RorqMDODpmhZlTSHWFCoUChh9jxkB9+nRMDg1Fs9atYeTuDlSpAjtixgfwPi4OZvb2OO/nh+pCER8vVFTw1tERFSWsg50XoKONjKCRng4XEd7p+40aoeSRI7h/8yZcXV1zDPesbFl8vHcPZi9fwtraOseze+bm+JqcjIq0/2JKp06duCgR8nhTfjt5/A8ePIgnT56IJJoTc3jWnCHwUwTY2V+xvhzM8Fas/ZCpNuzlkym8bHCGAENATAT+lLzHm4cPw+2/MNLobt2w2dAQNefPR5MyZaBy756YiMihOdU1VlPDdAB1L1/GAG9vcCb13r1A8+YiFVi+fDl8fX1RsmRJ/vPXr19j5MiRIDKsQidUToo83I0bA4cOARRuX7t2oYDha9++0Fq2DAvmzEHLVq3wzd4eOuXLo8StW9z6X92+DWtXV1wKCICPUNmwWDU1fLSzQ0Up1MH+Hdi3qDSemhpcReRjPwgIQJn9+xEVGQnPSpVyGt6lSyMhNhal3r/n8rAF5Y6VFTLevYMr1bwXU1JTUzmeBMrxTkxMhJmZGcjb3blz51y1xMUcmjVnCPwSAXb2V6wvCDO8FWs/ZKoNe/lkCi8bnCHAEBATgT8l7/Hyxo3w7tQJt4cMwWFzc9iNHo3WNjZQU8R6vxQmq6eHKZqa6PToERrZ2YEL/N28GQgOFnMHC2lzimSgfPghQ4B584A9ewBiOS8E8qlDB6hu3oxtK1eiVatWeGJqCvNSpWDHYyJ/fP48Steogctt28KbGNAF5IGGBr5YWsJDDu/FbT09ZGhpwe3Dh1y78rB1azju2oVL587Bp3r1HM9f2Noi7sULeHz+DD09vRzPbtvaQu3VK5QVURu8EGw9W6KSIsDO/oq1cczwVqz9kKk27OWTKbxscIYAQ0BMBP6UvMczCxag1uDBeDBpEs5bWQE9eqCLsTHURBz6xYRI+s0TEwEzM0wzMsLAFy/gZmCAWJpl1SpObyZ5QODYMYBY61esAHr3Bojpu2vXPHRU/iaJzZrhx4EDOL1tG1q2bIlLmpqwt7aG7cuX3OLuHjmCco0a4WqXLqi0bl2OBd/V0kKasTHc5VAH+66ODlL19VHh3btcoD9u3x6lt2zBuYgI1GjQIMfzV8WK4V58PGqlpXFkgoJys1Qp6D59CgeKGmHCEMgjApTbT3/rCkrY2b+gkBc9LzO8FWs/ZKoNe/lkCi8bnCHAEBATgT8l7/HviRPRYNIkPF24EJFFiyKhbVv009aGqghGZTEhkn5zXpj0DBsbjHr+HCW0tPCMQmcXLAAGDpT+fH/iiLxa1iBCMTLcwsKAoUMLZKXyTtd4V6cOPp8+jZhDh0ClvyLU1OBibAxrXm3r6zt2wKNtW9zo1w/uxIAuILcoL15PD65yqIN9X0sLX42N4SHCyI/t1g2l1q3Dqf37Uadp0xw6vjEzQ/SHD2gowriOdnKC8YMHKCFUe7xANp5NqjQINGjQAH/zyAcLQml29i8I1H8+JzO8FWs/ZKoNe/lkCi8bnCHAEBATgT8l7/HQoEFovHAh3mzahBumprjZsCFGERYUkqqqKiYqMm5OZGBlymCOszOGx8TAydIS9xMSgBkzOLZqYTl58iQ/L5XKIFFt44CAAGhQGa3CKlSzumdPgHL4iRV77FhgypQCQUPe6RoJlSvj/dWr+HDuHKpXr45D2tqopKGBojwSs4urVqFqr164O3Ikys2cmQOT6wYG0CRDPR91sMUF95GGBj5ZWMDz1atcXZ+GhMB+xQpEbN8O3zZtcjx/a2SEy58/o+mPH7n6XXd1RbHbt2Euwhsurn6s/Z+HgCAHRvbqiM3/7du3+CKCXV9eCLCzv7yQzts8zPDOG05/RCv28v0R28gWwRBgCCgYAn917YqA8HAkHT6MGENDRFSrhsmkIx22dHUVS9sbNzgG7oXe3hj4zz9wL1sWN8iAHDcOmMxpzZdhw4YhISEBFSpUwPr167naw1SPOSMjg6txvGTJklys0Iq1WBlpQ3nd5OGmCwt7e6BLF2DJEhlN9uth5Z2uEe/ighd370IjOhru7u74i8gEU1JgzGP6PjV3LuoMG4ZHU6bAgS4kBCTKyAh66elwzkcdbHHBfaKujg/W1vCkUm9C8mzgQNgtWoTD4eFo1LlzjqeJ+vo4m5aGFikpufpd9/SE3bVr0BaR/y2uftntqRzcmjVr8tud9VMgBMqUKYOYmJhctenr16+P48ePF5im7OxfYNCLnJgZ3oq1HzLVhr18MoWXDc4QYAgUUgR2t2qFVnv2IOXKFTzQ1sZGNzeEERaUX2pmplio/PMP4OOD5X5+CDl6FD5VquDc5ctQJ0OSQqYFJLvGMBl3VLfbwcGBe0qfk7FA9bxF1SZWrAXLQBu6oJgwAZmfP0OlTBmgVq0scroCEHmnaySULIn7T5+ieGwsx3K/28IC/h8+QI/nIT46YQL8J0/GswULYCeUuhBpZgbjL1/gwEvBEIymIAZxqhdfr149qaD4TE0Nb+3tUYlH+iY46MsRI2AzZw7+WroUAX365Jjvo44OjquqIlCEh/K6jw8c//kHqYmJMDExEUvPjRs35mpP3tCwsDDclkNdc7GUZY3zhcDChQsRFBQECwuLHP137NiBNkKRFfmaIJ+fwzHlAAAgAElEQVSd2Nk/n8DJqBszvGUErCIOy14+RdwVphNDgCGg7Ajs8PVFm7//RuaDB3imqYnZ9vZYRosibxuVnVIgSYuIgIafH8KDgtBl61auTNieEyegTyRhS5fm0JQ8NaGhoRg3bhwuXLjAPTtz5gymTp2KEydOoFq1avzPFWiJsldl+HBkzpsHCrZ/Z2UFY6oFffiw7OcVMYO80zXeWlnh2ps38Hz7Fubm5thlZ4cWz59DjXKiVVRwYMgQNJ0/Hwnr16Nop045NL5saQmLpCSUTElBdjQF1bWmFAaKpjhw4ABXwmv+/PkSY/lKRQVvypSB5/37ucaKnzABlpMnY1dYGAKFcvM/a2rikI4O2n76lKvf9dq14XLmDBJfv0axYsXE0pFqdw8ePBhkbAsKleN7LOJyQKzBWWOGwC8QYGd/xfp6MMNbzP2gX5B0Q3n58mXcuXMHTk5O3H+F5ciRIxgzZgzu3bsHGxsbDBkyBH2EblapD41F4Xrx8fEoX7485syZw936CkpycjL3R2r37t1ISUlBnTp1sHjxYtjZ2YmlPXv5xIKLNWYIMAQYAnlCYFvVqgi6dAl4+RKJ2toYYmaGDdSTQridnPI0hrwaJW3aBOOOHbGjXz+0WbwYgYGBWL5vH8w6dADCw3OoQX8zqFY3EQN9/vwZ+vr6KFWqFOeVvHTpEvz9/TF8+HB5qa4484SE4Pv69dD5/h3X9PTg4eYGXLyoOPrJUJPE/zzTZz58QOPv36GlpYWdzs5oTcYtebG1tbGnRw+0XLMGH/ftg1FAQA5NLhUvDpv4eNimpXFRE2fPns2l6c8+F2dJZNy+U1XFs/LlUYlXX1ywf+LMmTAdPRpbJk1C8PjxOYb+/l/t771GRmhH7P9Cct3PDx4REYiLjUUJgZr2edGtSpUq3MUCXVYIChHUHS6gS5u86M3aKD8C7OyvWHvIDG8x92P//v3o168fvLy88PDhQy7XTdjw/ueff7jSAR07dkT79u1x8eJFTJgwAStXrgTl82QLGd3kTZg+fTpHWLN69Wr89ddfiIyM5IzwbGncuDGuX7+OuXPnokiRIhg/fjyIyfTWrVvQIZbQPAp7+fIIFGvGEGAIFCgCypb3uNXdHe1u3gQ+fECqnh6CtbSwixCMigIqVixQLIUnfzF/PooPGYJDkyah8fjx6Nq1KyZs2AC7Vq2AHTt+quujR4/4JGvZYcGOjo4KtTa5KdO+Pd7t3g2LlBRcMTVFZSohJ8LAk5s+cpzoo74+Dn/7hmBeLeudnp5oTXXNyVA1McH2oCC03b4d30+fhraQE+FiyZKwf/YMVunpyI6mqF27Nl97wWgKSZZEUQBftbTwyMMDlUg3Ifm0cCEMBw3C+lGj0JlIBQUkXUUF24oWRXsRbOjRzZqhwoEDeHDjBsrQZYsYkpSUxJ3f1NTUxOjFmjIEJEeAnf0lx1CaIzDDW0w0ydBW5bHUdu7cGVFRUbkMb/ICfPjwAVeuXOGP3rNnTxw6dAj0AlB/8lwXLVoU9Pns2bO5dunp6ZzB7erqiu3bt3Of0Rje3t7cjWjDhg25z54/f855Hcjr3ZvCA/Mo7OXLI1CsGUOAISAXBP6UvMctDg4IpnBRIpjS0kJzTU3soxJd584B1avLBcu8TvJgzBiUmT4dZ5YtQ62QEAwaNAg9Fy1C2UaNgIMH8zpM4W4XEIDYo0dROjUVh/T00IhyOp88KRSYfNHSwm4VFXTikantrl0brc6coYMJULw4tjRtiuCDB5F5/TpUKlTIgckFJyc4PXwIs4wM7ixE0RRXr17lHBhkkHp6emLGjBmwlTA9gxwT6oaGuFulCipRJIqQfFu9Gjo9e2L1gAHosXDh/59SnrqGBjba2KAjld0Tkptt2sBt507cvnAB5atWLRT7zRap/Aiws79i7SEzvCXYD1GGNxnUdKs5c+ZMLp8nWyikikLIyVCvWLEi5zmgkHHyZBNjbLZMmjSJ82x/+vQJKioqnKd80aJFnCFP/58tdEtMYX8HxTgosZdPgs1mXRkCDAGpI/Cn5D1usbZG29ev+XmuzY2MsI9yRI8dA3x9pY6bJAPeDAmB24oVuLZnDyq2aMHlbzedOhUV69SB6smTkgxdaPpm1q2L66dPwzMzE+tUVNDZ1BQqRKRXCCRNTQ2b9PXRlZcDva9pUzQ/eBCpN29C09UVG+rUQafTp0WmWVx0dYXL7dswlHEd7Hfv3sHYwgI3ataEJ10KCMmPLVug3r49lvXogT6rVv3/6devXJ3x9fb26CziIuVW585w3bAB148dg4ek7zWVVHvwAPDyKgTfmsKxRLrw2bBhA+dAy04d1dXV5S43yS4oKGFn/4JCXvS8zPCWYD9EGd5USqBcuXI4evQo/Pz8+KPTHwJiOiQiDQo/X7ZsGfr27YuvX7/mCBfftWsXWrdujRcvXnC54fQzebgpp1xQqG9ERIRYpBzs5ZNgs1lXhgBDQOoI/Cl5j1tNTRHw6RN0eczOLW1ssIfqB+/dCzRvLnXcJBnwStu28NqxA48vXkRpHx+OV8RrxAhUqVQJGpGRkgxdaPqmeXjgfHQ0utjaYvDz5xiopQUVngdY3iA8ffoUY8eOhbq6OkaNGgVnqisOcClxxB8jVeF5hFdbWKAHlVIDcLBDBzTZvBlJJ07AuG5dhFepgi50XiHDlUqtCcgFT09UvHYNWunp/MhBqerHG+xFXByK29vjWoMGqBgRkWuKzN27oRIYiIXBwRgoyEaflMSFy29wdkanmJhc/e727o1yK1fiyp498GrRQjLVidSNytJt2QK0ayfZWKy3QiDQpEkTzpFGNbuJA6Np06YccSCx9xNHU0EJO/sXFPKi52WGtwT7IcrwpnxuYnqlPG8KEc+WHz9+QENDA1RuYMCAAZg2bRqmTJnC1UQVFGKKpdynmzdvciHn9DOFYB0jz4mA0B9aMt7JE/4zods3+pctb968QeXKlflGvQRLZ10ZAgwBhoDECPwpeY879PTgl54OQ97v80AnJ+wibxYd6oODJcZJmgOc9/dH9WPHkPj0KUxLlMCKFStQIiQEdZydoSlkbNDfD0qFoggt+luTndtNhGqGhobSVEupxvpmb4/jcXHY2qYNXHbsAFetOjWVC1OWt9AZgchXyfAmQlfigKG0NIqKo32TqlD97SJFsMrODj3j4rihj/XvD78lS/By61bYBAVhnZsbulK+++vXgBDz98WqVeF96RJShRwOUtURQOzduyjl4oLrTZrA48CB3MPTZ82aYW7LlhgqaBBRXnexYthYoQI6Xr+eq9+9wYPhvGABLm7ciKpERiiJkGOGLgUoxePQIUlGYn0VBAFBYkDyeGeXiZPJuyjGmpnhLQZYcmjKDG8JQP6V4U0eaiJgy5Zsw5vCxvv3788Z3lSS5RuvnmV2u+PHj6NBgwYccRq9uPRHlf6gkgddUOgPLB2YEkUwb2a3mzhxIih0XViyvekSLJ11ZQgwBBgCskGAcqMLwICRZDF7NDRQU0cHZryLzqDKlbHt6lWAwlh79JBkaKn3PVW1KupcuoT079+hpqWFrVu3Qjs4GI3s7KDFM6ayJ23evDmI3FO45BOxMxMRaGGVr2Zm2JeYiIS5c5EwdChmERB0CW5sLHdIBA/1VAGFouTatWuH8PBwnDp1Srr6kJfb0hKrnJzQkxj7/6vpfnr8eNSeMgWPFy9G6X79sNbREd0ePRKJx6W6deFz6hQ+JCTARKjWsTQVjbl8GWWrVEF0YCAq7NyZe+gjRziDd1bDhhgpyChO5f9KlMBmHx+0F8FS/yA0FGVmzMDpZctQOyREIpUzypWDakwMPtjawoTmZaL0CFSvXh3nz5/n1rF06VIuqpWEGd5Kv7VSXQAzvCWAUxqh5mR4a2tr87WQZqg583hLsLmsK0OAISB/BPr1y6olvWsXQCzbSiBEDHVITQ1epqYo+v49p3GH+vWx6cQJYMECYOBAhVrF366uqHX7NjR5ebbEE/KpaVMEWlhAixc+nK0wVec4RwRxQiKNkk8KBYqYynzX1cWGb99Q7vx5bK1ePatmO49cTMyhJG7u4+PDGdjZ5whi9CbjmxjCqTa2VOXpU6BkSayuWBE9iLEfwD8LF6LKoEG4M3EiXCZMwFo7O3QjLChfWqjqyuWGDeF99CjePH4MM1tbLh+WIvroooBKk5GsWbMmR/WX/Oh/4+RJuNerh1sdOsB148bcQxw/DjRogOl16yKU3tNsefgQKFMGW+vWRTvBz3nPY6dORalx43AiLAz1hOp/i6VnRgbStLWhkZaGj2pqMOKlqIg1RiFvTN5kSscknhCKCKVcavp9VZBC312yC8hZli3E+0RcTcT7VFDCPN4FhbzoeZnhLcF+MHI1CcBjXRkCDAGGgDACVJ6KvGXDhgFz5igFPkSEedXICGVtbGDFY0LuFhiItRTCOm0aEBqqUOuIKFkSPnFxMMjI4PQi4s+HtWqhk4EBNAVSk+hZp06dYGdnx3m86YBLqQFkqD958gSiGOkVaqGyUiYzE+lqalikqoquiYnob2QEzrS7excoW1ZWs/50XMoltbe3RzGBsG6qkLJ+/Xp069ZNuvrQGl1csK5mTXTlkZZFb96MCv/leV8bOBAVFyzA2qJF0e3tWyrTAvAqwGQrEdmyJSrv3Yun169jbFgYx15ORgrx1ZDTgb5rRDorqac+cv9+VA4IwJ0ePeAiSJ6WrQiF4NepgylVq2LchQt8jDJu3oSquzt2NGmCNiJC1OPmzUOJoUNxbPJk+I0bl39sif/BxgZJALgYiS9fAF3d/I9XyHpSqguV3SWCYWtra7x+/Zr7uWTJklx5XiY5EWCGt2J9I5jhLcF+/KqcGN00U553tlDZLwrPEy4nRp9n34TRH0vK66YQc+FyYoJkbRQqTr9gWDkxCTaPdWUIMAQUCwHywOrrZ3nKyNtNXm8lECK/fGVnB1sHB1iTxwxAnx49sGzNGmDMGGDqVIVaxd9WVvB49w5mFNIPcJU1zlesiL4aGlCnPGUBIe8peSUpV5jSmszMzEDebvrbp6mpqVDrkpsylMevo4NFJibo//49grS1sZ1wo/KhlSvLTY1fTkSM42Lm4FNkw+88hpmRkVDx8sL6xo3RmVdR5WFEBBz9/HC5c2d4h4djnZEROiQnQ4NX51tQz6jgYHhu3Yp7p08jZOJEzitPcufOHe6SYO3atRwHjqSG98WtW1E1OBj3BgyAs2C5sGxlyNj+r8zfRE9PTKSUEJ58PXcOujVrYnebNmjFK+kqqP/LFStgExKCw6NGoZFQ/W+xNp7yxytWBNG+cTUPiA+CLh2Z5AkBSuOkUrv0+4lK68bFxXGRE8SvdEHgIiVPgxWCRszwVqxNZoa3mPtBLORHKD+Il8MRGxuLecRM+V+uEx1IzM3NOYOb/oDR4SQ4OBhEuEaEJytXrswRQhUWFobQ0FCubqWHhwcXYrV3717uJo+M72yhHLvo6GiuzBiVJKCxyMtCeeA6QqFcv1oOe/nE3GzWnCHAEJAfAhSmbW6eNV/FigAvlFV+CuRvJgp5zHB1hZmbG6xv3OAGIY/MtLAwaAwaBJX58/M3sIx6nTA1RdmvX2HF4xd5/Pgxdjk4YDTNJ8JLKSM1lHdYKhtmYYGVDg7o9fAhOlhaYhOF6FNoct26Bb8uIvPbuhWgi38BgtffKUbcMn///fcvm6X8/Te0fH2xJTgYwTw28FfR0bD28MCFgABU27cP67S1EZSRAR2hSxwaOLp7d1RYuxY3Dx5EzylTOOMpW8h4CgwMBFWAoZ8lkTOrV6NWz554MGIEysziMvBzCrGuV6mCcS4umHL7Nv9Z0qFDMG7SBH917YqAtWtzdYvftAmWHTviwMCBaEppJPkVyr2vWxdzAQwFkHb0KDQEquDkd9jC0o9K82Zf2vTo0QOrV6/mli6NaIk/EUN29lesXWWGt5j7QX8QKKxLlJBXgH4hkJBxTkb1vXv3uDyUIUOG8IkWsvtmZmaCjG8q+ZGQkMAZ28QgS0QMgkK52sRaSuUI6IaPfrmQt5vCssQR9vKJgxZryxBgCMgVATK0K1XKCrkk3otfEEfKVa/fTEYelqLVq0PPxwdWPEImqljRf/x46HXtCg0RB/iC1P+0gQFKAbAlhmqA+9uzxNISU+h/WMjr77eGl+e8sWpVdLxwAd3KlcNaYoPftw8ICPh9f1m2oKiR7PBu8vQOGJBrNoqWExY6i7x9+5Yrg/Qr+bhtG4zatcPOPn3QmrgYAPz75g2KWFnhbN26qP7339igpoZAXV3oixjr1oABcF28GFe3bUNUUhJ8fX256L1soZDhkSNHcmVXJZHj/zGP1x88GLETJ6LUhAm5h+L9rhnj6Ihp5G3myZstW1CsfXsc6t8fjRctytUvce9emLZsiX09eqC5qBD2vCpN35UWLUD0bMv/K/32fskSmPGIuPI6hDjt6HJNVVU1B9ZRUVHw/M/jr4wSEhLCnYGFc6kDAgJyEREr4/qkrTM7+0sbUcnGY4a3ZPgpVW/28inVdjFlGQKFCwHKiQ4MBBo2pJtLgMJlixRReAwOHz4M98aNoe7ri6K8so9UNjJw0CAYtWoFXQULmb+grY1i+vooxSOCI4LPcf8ZSmGENHlzzcwUHvOCVDA9OhpqHh7Y1qQJgg4cQEj16lhO4a1kLLZvX5Cq0S0KxzrOCREVLl6cS58yZcogJiaGC80VFKqgQlVVfiVvlixBsf798VdoKAKIvwBAZno6VNTVcZJqdB8/jiPGxmhkaAhDEcRuMaNGoeysWbi4ejWqdu8uM6yOzZgBv9BQPJs9G3bDh+eeh7zcrq4ItbXFdAFG8WcrVsAuJAQRoaHw5a1PsPOXkyehV68edgUHI1Cw/reYK8lYtw6q3bqha7FiWPfmDZ4MH46Ss2eLOUrempMzZ//+/dx+0yUHeYeptC3zDucNv+xWVIs7O+Umu6xivXr1cgzy9OlTUKlfuhAYNWoUnJ2dea9iP87BVlDCzv4FhbzoeZnhrVj7IVNt2MsnU3jZ4AwBhoAkCJAHjYwFIiMjgpzHj4FS5JtVbNm8eTMad+iA9NatYbpjB6fsunXrUL1bN1j6+sKAZ4wryioi1dVhVLQoHIngiQynzEz0V1PDEvKWkhFia6soqiqkHskRETDw88OuDh0QuHEjBjRrhkVExLVsGSBhiSmJF8wLoebG8fUFRHz36FIoKCgIFkLlvHbs2IE2bdr8UoW4KVNQYvx4HJ0zB/5EgMiTbyoqOF+mDEofOYLoUqVQ18ICRkIM+dT0weTJKDNhAs7Mn49agwZJvNyfDXBw7Fg0mTYNr5YsgbUoTzKPvXyMpSWmvXnDH+bhnDlwHDECp6dPR+3RXPJFDkmLjISGlxe2N2+Otnv35lv/z9OmQX/sWAyoXx+Ljh/H3eBglJPAkP+VIsR6TwR8JES4t23bNi56smnTptKv855vRBS7I0WcUmSQcFlFMsDnC6QS0eUVtSXDm0r+Ulpow4YNf1lOTB7s7Ozsr1jfL2Z4K9Z+yFQb9vLJFF42OEOAISAJAkRCRkzB4eFAly5AZGRW6LmCC3kyevbvj5QePWDACz+lg61DYCBK+PjAUEQ94IJaEpU+u62mBq2SJeEUG8tXo6+eHpYSqR3VZnZy4n+uqB6cgsKP5k3YuBFFO3XCXwMHImDBAgzr2hVh9J0lj6Uo76o8laXcbsrxLlECUFEBnjyR6uwPhw2D49y5OLNuHWrRO8qTD+rqiLK0hOn+/Yj39ERVW1sYiahN/SQsDCWHD8fxqVNRn4gHZSR/DR2KgHnzkBAejqKdO+eehXLI7e0x1sQEUwVSWu6OH49yU6bg0uLF8KFLQGHhsbpv8fVFsAQXagl9+6LosmWYNno0Rs+YgTu+vnCVYLxfwShoeFM7SoMk5u/Pnz/jBo+TQkbb8McM+7PyicKfC9brTk5O5sr6Uam88PBwkYSB8mJnZ2d/xfoqMsNbsfZDptqwl0+m8LLBGQIMAUkQoLq4lJdK4a516gBHjwJKQDg0beJEjJk0CakjRkCTR+RE5ZGK+PnB2dUVRjdvSoKKVPvSYTC+SBH8KFcOznfu8MfuY2aGZWSAENtyhQr8z/PjwZGqwgo42LO5c2E3bBiOTp4M/3HjMG7kSEyZPRsZY8ZAtaAZ7ClShAxaMjbJCCcGdjLApSR3uneHy9q1uLJ/P7yaNuWPGq+jg1s6OlDZsQOqDRqgkqMjigjkTmc3fLFiBYqHhODI6NFoKMOyT3v69EHL5cvxYccOmLRunXv15OW2ssJYAwNMFSihd2PIELjPn4+o9evh2alT7n5U6tDREZtq1kQHHiN7fqB92aYNiu7ciV2bN8OvfXs8r1QJ7nTRKAPp3r07F/ZcunRp/uhETNahQwdQhRwmv0eAfg8SZ5Mg/xJhOHXqVJwQqPdOlxzEyK9NHCUAx8lExje1pUpHwiIvdnZ29v/9HsuzBTO85Yl2Ac/FXr4C3gA2PUOAIfBzBLp2BahE0cmTgJsbQKGX5L1TcAnt1w/Tly5F5rRpUOHV7KbKFl99fOBRujSM6bCuIPKKVz84uVIlOAkc9PvZ2WHJ8+cA5SpXrcrXVlwPjoIsU6ZqPAwNheOMGTi9fDlq9+6NWbNmYdCoUcgMCYE2hZsXpBCZGjE8jx2b9Y+MSgMDqWl0o00buO/ciTuXLsGlShX+uC8NDXE/LQ2J4eGwadsWbu7u0I+OzjXv2y1bYNG+Pf7q3x9ugwfLLB92Z5cuaL1+PZIPHYJBo0a51//hA2BqivFaWphMlxM8ierVC56rVuHOvn1wEUWUR++InR02VK6MTgKM7OIC/KxePeidPImHFy+iaNWq+OLgAFdeKUJxxyqM7YlwmMocEiEx/ZszZw50dXUxaNAgrvKPtIXOzkT6d/XqVVDUEOXLEzEdVSSiWvTZQiH9RL5crFgx/mdUJphC/KlcnrDIi52dnf2l/Y2QbDxmeEuGn1L1Zi+fUm0XU5YhULgQaN4coFDO06cBG5ss77cIVmZFA2Vw27aYT7ndAvrevXsXT1xcUN3aGkYvXyqMyvfv30dRZ2e8rVULZQhnnvQvVw6LiZmbyknVr8//XFwPjsIsVIaK3O3dG+VWrsSVPXvg1aIFVya0Ve/e0GzTBgYiaj/LUJXcQwcH41tEBKIbNYLPxo0ApROIYDHPr07X/P1R8dgxPH34EPYODvxhnllYIC4xEfeWLkWlkBCUrVoVOiLqKX86cACGzZphV5cuWPXihdj5sHnVe3tQENpu347vZ85Au2bN3N2IcV1fHxP/I5ib+OMH//nlDh3gvXkzHp08CQeKuhEWHnndeldXdJYgkuWppyfSr11D+v37+O7iAg0zM5QVyDXP6zoLa7smTZqgQoUKHAs/GbuUr25kZAQiQKM0H2URQXb2c+fOcWWIU1JSIG12dnb2V6xvBDO8FWs/ZKoNe/lkCi8bnCHAEJAEgVq18Ck+Hv8QI3GLFsD48cCkSZKMKJe+IQ0aYDmFx5OnkcfU/Pz5c/xjZwd/U1MU4bGHy0WZ30wSGRkJNy8vvGjSBKWJEIwng729MZ88eH/9BTRrxv9cXA+OIqxR1jpke31vX7iA8lWrgkjJvP67fDFs0ADGERGynv6X42f6+eFmRARXGm4PtaQ9rVw5Rx9JvIVRNWqg/Pnz+Pz+PYhYKlviSpRA/LNnODRmDIKmTYOjry80ROQsp5w5A63atbG1TRusTkjgk3vlJR9WHGC3NG+O4L/+QsbVq1AVVTKLjG0NDUwFMPrHDz7D+8UWLVB13z68vH4dNgIpF/y5KYLA0BDrHR3RWUQofV51fOboiPePHsH27Vs8treHhZoaSlEVByZ5QkAwt5o83kRQRiIYoZOngRSoUYMGDfA3XXzKQNjZXwagSjAkM7wlAE/ZurKXT9l2jOnLECg8CKSXL48zDx6gZ/HiiCXvD4XmiSiHpGiIdK1UCeuoLvCWLUC7dpx6lM+3z9gYrfX1ocerl60Iep+MiEBdPz88bdcO9qQvT4bVr48wylXctg1o21YRVFVYHa77+cEjIgJxjx+jRKlSOHbsGGz8/VHM2xum//xToHqnuLriwu3boOuqc6TJ4cNZ5fkERBJvYWTFinC8fh26KSnQ1NTkj/qsXDn8GxODkf7+WHbsGOwCA7l8b2HJvHYNKp6e2Ni4MVYkJoqVDysOsJv8/NCBLkHIIHNxEdk1Q1UVczMz0ffLFy5MmeSCnx+qRUTg/dOnMCOCOmFJTQW0tLDBzg6diKAtn/LSygoP37xB9dRUXLC2htOnTyiWkpLP0Qpft+rVq+P8+fPcwpcuXYq+POZ6ZTC8BevWZ+8cVZZ4+/Yt58GXhbCzvyxQzf+YzPDOP3ZK15O9fEq3ZUxhhkChQSDZxASHkpJApmu6tTVUa9TIIohScOlAZEuUx71vH8DLC/3x4wdWaGigh6YmtBToQH1gyxY0bd8eL0JCUFwgHzm0VStM37MHWLsWoFz7XwmVz6GwdPLwF0KJ8vGB8z//IPXDBxgbG+Py5cvIrFIFDmXLwoxSJQpQvhQtisNv32IigBjSY/16QIgkTBJv4VVnZ1jfvw8rKj0nIK+rVcP3ixfhbmCAuK9fYUJz0ndJWIjQr3x5rKtTB05TpoiVDysOrOtr1kTnc+cA8ko7OorsmqahgeU/fiBYwHt/vkYNVD9/Ht+Sk6HzXyh6LsnMBBnsW4sVQ/vXr8VRKUfbBCMjRH75giZpafi7ZEl4P3uGIunp+R7vVx0liXCQiUJSGHTNmjXo3LkzV7YrWyhEe8KECZg5c6YUZpDdEGXKlEFMTAw/yiJ7JiJwO06RUzIQdvaXAagSDMkMbwnAU7au7OVTth1j+jIECgcCdOP/RU0NW1RU0DsjA58dHKBnbw8UcOhuXtAPsrTENsr9FMqPnq+hgYE/fkA1I0OqzNJ50elnbbYtWICgwdnrfzkAACAASURBVIPxdvRoWAiwSk/o1g2T1q3LijAQVUYpe0AyDrIPu1LOH5ZkXfLsG+XmhuK3bsGMF6JMefPPnZ3h8R9BnZkEXlBprCFFSwtrU1Oxq2xZnKbLkbAwgKoFCIgk3sKoEiVg+uIF7IWMxI8BAUjZvx+WAL6rqkKLvkPEeSAsPFbwNd7e6C7D6IB1Xl7oSuSBT59mlVYTISm6ugj/9g1NXr6EtbU11+J8pUrwioqCRkYGVH7CBv9dXR17jIw4gz2/8lFHByfU1NDq82dEuLmh/q1bMvs9IUmEQ37XVxj6tWzZEnvoslJMWbhwIYKCgmBhYZGjJ6WstGnTRszR8tacnf3zhpO8WjHDW15IK8A87OVTgE1gKjAEGAK5EIh/8QKWtrY45OqKJrdu4VWZMrAyMgIuX1Z4tNro6WEH1cAWYgQP09fHMAod/PYN4JWXKejFrB07Ft2mTcO/c+agyLBhfHVmDB+O0WFh+DFtGtR5zOwidb106f+s53PmAAJjFPTa5DX/9dKlYfjkCUrRhQqAN2/e4B8rK9Q2N4fxf97mAhNeGHSYjg5ig4OxdM0aqI4aBcyYkUMlSbyF1y0toZuUBCehKI4fvXrh26pVMAbAUZXRd2jatNxQUPkqW1uscXNDd+Ea0j9+IGP0aCQ1bw5THx+JYFzj7o7uRH5GLP5WViLH+lakCLYnJ6P6o0f8UlvnXVzgdvcuigh59AUHSNbSwmEtLbQVKEMmrrKpamrYbWqKdm/f4u8aNdDg/Hmkf/wINUNDcYf6bXtJIhx+O3ghaaCqqgo7OzvuMoYuiUkSEhJgaWmJJ0+e5BuFtLQ0jp2dWNKp3reWlhY3Fr2jVAZOWsLO/tJCUjrjMMNbOjgqxSjs5VOKbWJKMgQKHQJRR4/Cs2FDXA8KQsVt23C3TBmUpQOOBARG8gCRSsUEqatjJ00mVAN7tpkZRlBtbPpnYiIPdX47x9I+fdB3+XKkrlsHzS5d+O3nTZ+OIWPG4OuIEdDl1SIXOdiaNUCPHlmP6GBYCMPNb1hZQeP9e5QjQxd0r/INO3V10UJfHwYFmc/Pq009x8oK33r1Qr8JE6DfuTM0w8N/+73Ia4ObJiZQSUmBq3Au6vDhyAgLA33LuWrFFE0xenTuYclLbG6ONY6O6C78bp86BdSty/VX+fgRhhIYoaudnNCDxqeLEHNzkcv7YmaG/YmJcL19Gy68PPALDg4oExsLc96liqiOH3R1cRJAIF225Ufo0kJbG5tKlECHp09xomlT1Dt4EEm3b8P4J/no+Zkmu48kEQ6SzPsn9V27di327t2LgQMHgkjQSPz9/XH06FGJlhkcHMyVI6OQ+YiICOzatYsz8OvUqcPxH0hL2NlfWkhKZxxmeEsHR6UYhb18SrFNTEmGgFIgQKVbTp8+jcTERI7hmGqS1qtXL1+6H54/H42GDMGz8eNRYfFiHDAzQzVi+aUQbgWWpKQkDDYxwXrS8f59oEwZvrazbW0xgjx89I/KoymAzG3bFkOJ9IpCJIk5nicrV6xAj5AQ/NuzJ4xWrvy5psQ0P2VK1jppTUTIVsjkjrEx0lJTUYFnfJIHbKW6OjqrqUGbZ4wXCCSUX+7igvnu7jDo2xd1evSAuZ8fDCQ0DgTXEqOvDwq19vjImdf/l4kTuQoEpQE8pk8XLQL6988NA6+M15rixdGdamILSGq/ftBcupT75MbBg3Bv3DjfMK6yt0dPCvsnPX9iwH8uVgwR8fGwjYxEpUqVuLku2drC9vVr2AiUGBNW4p2hIS5+/YqAtLT86UeXAUWLYoOrKzrdvIkznTuj1oYNeBERgeI8oy5/A4vuJUmEgzT1kMVY5DH+8OEDTExMoKGhIYsp+GPSBVtYWBiuX7/O1Z8fN24cjhw5ItGcgnW879y5w9X6JiN/wIABzPCWCFnF7swMb8XeH6lqxwxvqcLJBvsDEXj8+DEorEyQeTQqKgqeokrS/IHrz+uShg0bxoXaNWvWjKufSizeBw4c4Azw+US+JaZs7NMHHZcvR/KGDfCZMweTk5PRnDx4378rTH60qCVRmGFYqVJYRg/JkChenN9stpMTRpDX7RcET2LCJHHzGX5+GE1582SM+fnxx9u6dSuaUQ3otm1hRszmP5POnQE6bFarBty6BTzmzKxCJQ90dfFJRweVKZKBJ4v19NCfPKCU+6yqWiB4ZF64AJXq1bG8YUPY9e0Ly0aNUMLTEyZXr0pNn0fa2kgyNERl4QsxSjsYMQKfjhyBIbGoE1+AQEQFXwHyJKupYa2FBboJjXHfyIhj9yY5OXo06gpwEIi7gFXW1uhJ5Ge0Jzo6Irsn29nh7PPnMDx3DuQVJrlsaQnzpCSU+gUh4hszM1z/8AGNfuEV/6W+vDz3jTVqoOPZs7g4fDiqhoXh3vr1cBYiwhN33dJoT6W5bGxsOOJAYuwnxneqL61IQkZqnz59QBefFBnx6dMnTt8lS5bA1dVVpqoS+ziRuMXGxkpc/svLywtXqOQfT+Li4hAYGIh3796BfpaWsLO/tJCUzjjM8JYOjkoxCnv5lGKbmJIFhMDs2bOxf/9+Lt+KDO/Vq1dzt+jSDvsqoOVJdVrBvEHBgX/2+e8mX9ywIfofPYrMU6fgP2sWmkZHow95hn5xcP7dmPJ4Tt6PbRUrYg5NJhRSHubpiWHXrgHR0YC7uzzU+e0cU3x8MI5Irc6eBQQO04cOHYJXkyZIb9QIlocO/XScjNq18T42Fp/c3OBABjzlr6up/XbeP6lBnIYG4osWhffLl/xlzTczw2Dafwo1F8WGLQcA/t22DUXatcPmnj1RrndvJHl4wK1kSZgSCZ4IyY+38Lm6Ol5bWcFbyFsNYsinkk4USdGyJbBzJxAYKHpeNTVsLlIEXZKScjxPUFHBfQA1ARz194e/BN7EVebm6Elh7eSVFmC+Fpww2cEBlx8/RmZEBD98OMrEBPopKXD6RVmnV8WKISY+HrXT0nKwaud1i9OvXIGatze2BAQgeN8+XJs9GxVHjsT1mTPhMXJkXoeRSbvhw4cjMjIS+vr6HOHc69evuZ/p7+F0CS5CpK0sXQSQZ9jBwYE/9KNHjziP8Tlis1cSWb58OXx9fTl8Fy9ejP79+3OYjxw5Eps2bZLaKtjZX2pQSmUgZnhLBUblGIS9fMqxT0zLgkHAx8cHl4g8iqvCsx7btm3D7t270bRpUy6kmsn/EaDSJ6GhoaC6qdly5swZTJ06FSfyEX48p0IFDCeypRs30HXhQlju2IHpZHST16pYMYWFnsLtz9Wrx9VNFiZRm1e7NoacOQNcvAhISBYlLQAmuLhgEoUkkxdUIIrj7NmzKFGrFjRq1oQV6fwTofDcv+PjQS0WURsFCqOXFka/GydeVRVPHBzgI5CjPNfWFkMJiwL8vr6YORPFR4/GwWnTULlbN1ywtEQdMzMYv3uXY0mSeAsTVFURK7R2bnAq+xccnMVkPnBgVlSEv79IKL9oamKvhgY6CBi36cnJUCtSBMe9vFD1yhVcdHBA/YcPf7cVP32+2tAQXZKTof4Lr3Syiwuu372LT/v3c7/jSW4UKQI1FRWU53neRU3wokQJPHv2DG7//gsDAwOxdfy0bx8MW7TAjm7d0GbNGsRs2ICynTvj0pAh8Jk7V+zxpNkh2wObmpqKUqVKcV5XuoiuVq0aLhB5pILIz/RRND1FwfVc+NKK16h9+/YyuzRgZ38F+eLy1GCGt2Lth0y1YS+fTOFlgys5AoKGNy2F8rfolv/z58+4IczAq+RrlVR9+l1Ct/JXr15FRkYGdzijcPwZM2ZwZDHiyixbW4wkw+XZM4xbvRrPp07FBhqESiI5O4s7nNzaUzmZx61aYYSKClQozFigBNHCpk0x8ODBXGXG5KaciInGlSiBKc+eAWR8ly3LbxEdHQ0dDw/oe3rC5mehyZmZSNPU5Gofnwewi3oLGfAFuTZ5zE312b9raOB2xYqoEhXFn3JuuXIYSt9VMhYFvHDy0Cl7jkeDB8NhwQKcWr8etTp0wHp1dbTW1oa+EAlYfr2FlMuerKqKO25u8BH+fXjsWJahTaXLyHg8fz4rHUGEfNLVxZEfPxAkkA8ff/o0LOvUwfGgINjv349EdXV4ffqESZMmcWG94kq4nh7apqZC5xd52Mmenrh77Rqebd/OL+N0V0cHaXp6cP9FqbBnZcrg3cOHKB4fj6JFi4qrGl4vWQKr/v2xPzQUzaZNw/Pjx2HboAHOduyImhu433oFJoI5xz169OCivkgULeqLcq0PHz6Mxo0bcyHmFHJO/0+EZ+S1V2Sh0H1vb28+O3q2rrdu3eL4UmQh7OwvC1TzPyYzvPOPndL1ZC+f0m0ZU1iOCFD5jlGjRvFLy9DU5MXt0KEDXpBRyERmCMwxNMRwKs/z779YvnkzjvXpg/00G0UgVKkis3klHZiIi7716IG+urpQFQpPXdahA/ps3oyMvXuh2ry5pFNJpf8YMzNMo8OdUH1j4jb418EBFmXLwoaMclHCI8YaR/WO6d2gNr/wbEpFYQUb5P3btzArWhRXateGlwDr8DxvbwyhXE1KLfDwKBCtY9q3R9ktW3D1xAlUqlsXK/T00P3bN6gLXQjl11v45csXaOnr46qPD6pQFIeg0AVM5cpZ4eW7dv0yvSLR0BCnkpMRKOCNvhsWhv7Dh+OTvT3UXr2CSmoqdGvVAhkjbm5uYhNNbdLURICaGgwoFeIn8rlaNTy6eBE3w8PRmbgLADzU1ESyqSkqEr/ET+SZqyuSb9+G3pMnsLe3F3uvn4wdi5LTpuHvRYvQoH9/JN27B+OyZXGycWPUpYs6OQn9vaPfX4ISEhLChTwTy3a2pKSkICAgQGIGb2kvi0LLhck9HR0dpT1NjvHyk54hrBDloF++fJnLnRcUiiI7fvy4TPRnZ3+ZwJrvQZnhnW/olK8je/mUb8+YxgyBPx0B8qQtUFfHgMxMqKWn48DBg5jbrBnO0sIV3LAjz4vx8OHoYm4OVaEazmv690f3JUvwbe1a6HTtqhDbOEZPD9PIA0rEVhYWfJ2IMOjhf0zL9iVKwJqMclFCnvISJTDRwgK7377FHWqzcSPQoYNCrE0eSjyKjoaDhweuNW+Oinv38qdc6OeHgZTzLpQ7Lw+dsue45e+PcseOIfbePTg6OWGppSX60j5//gzo6fFVya+3MP7FC1ja2uJygwb4H3tXARbl1nUX3SlIKGKggogoIoqFCtgoiqBiN9jd3V33t7sDL1dFvXYXeu3uLuxWFPhdLwPfMM4MMzDUvbOf53u+ey/nPWef88acffbaa5XjXMWNdeROTkDZsgAPIEggxn+XYjHW1oh+9QrVv31L1i2ObtsW/7dsGZ6VK4e2796hGBUCzp1Dz549BZ1jSiwpY+s0NVHLyAjmcuTdvvj54T5LRebNQ1hYmND9A21txOTNizJyiK0eeHnhx+nTiL1yBcXEUCOK+netUye4LFyIo5s2oWJQEJJh9hUrwp9IARXbSr6jEsZvLp8DEqn9a4wHt6amGTKd9JRniDtEBAfLs0qWLAlTCV+vX78OZ2fnDPFfvffPkGVNc6fqwDvNS5fzLlS/fDnvnqk9Vq/Av30FKAezOVcuNDEyguGnTzjzC/7ZxtMTFzlx1o42bZptl2DIkCFwGz8ejfPnh4ZEwLpy2DC0HDsWb6dMgUXfvlk+B262h2tpYQz10SVIwCiVc9TQEK52drBnnbI0YzbX0xNzq1TBuidPcITBFWHFvXtn+dwyy4F/tm2DZ0AALrZtixJLliQPO7dpU3Revx4JUVHQSIcMVnrmcaFcOThERyP+5UtYWVnhj6JF0Y3Qdyl1+GnJFt4+cwZOnp443bAhypBETdxIlEatevIxMFvM/9naSp3O87x5cfnJE7jHxMBapLF9yM8PPgyCd+7E6DZtUOXZM1TevRsTZ8xQWrKJWcmturrwsbSElRzo7reAADzZtg1bp09Hr169BF+faWriYeHCKCupMS42kwc+PtA+fBjPTp9Ok9rFpaAguEVG4mJ0NEoQJZCQgJ+amjhQogT8L1xIzyMg9VpCsTk/vv/iRvIuIl3+FTZ2LDBsGEBiNRFDvSrnlZbyDMLzxY3rL4nguHfvniBNRoQB0XYuorKqrl27CgztqjL13l9VK6maftSBt2rWMUf0on75csRtUjupXoH/1ApcvXoV111d4WtjA7Pnz/Hs2TOUtbeHoPJLtuTw8Gy7Hl26dEGdBQtQm7rWEhDtiJkzEdyrF54PHgzbceOyfA7kKphlYoIh9IQ6xWJs5NwURmlpoayFBWxkBCufIyNhFBSEla1b46KZGabOmoXvvXtDL4sJoTJzYQ8tWQKf9u1xvW9fOFNCS2SLu3ZF+zlz8HXpUhhIk9HKBCcvubjA6Pp15BOxbc/x9kaXkycBZjWLF0+3Bxd27YJ7zZo417o1Si1blrI/wsapo5wEH5fIsos3fu7kJEgx2d6+LRB40fa7uqLs1aswSkjAo8GDcXHCBAwqXBg6JibCQZwy9uHDBxwxM0MZGxvkfv5c5qWxISGIiYjAqvHjMWjQICEwfaepieslSsBbTgD8sFYtGO3ciSuHDqVJZut8tWpwOXAAMQ8fwkEkP/hOSwtnHB3he/euMlNVqK23t7cg9Zh0yJF0UZ06dYS66BxvPFAgNJ7P3pAhAINwFVtayjNYosZvLgNrHoTx+WJJgziCg/BySnMy8OYh7vDhw1G7dm0hK65KQlf13l/FD0Q6u1MH3ulcwJx0ufrly0l3S+2regWy7wowq8QNBEnVQkNDkyGjrBlk7aAytn//fmj4+qJU0aIwv34dcXFxsNLVxVtupBiwDh6sTHeZ2pZzD4+MRCU3t0SiMTGLWrUKAS1b4lGXLnBQYfYirRPk9z/CwQHdtLWhLYV06k9dXVTR00MuGfDcJNbsrWPH4quTE/yaNEF8YCCs//orrS7luOt2TpyImoMG4d6ECSgwcGCy/6tHjkTzUaPweuJE5JIhCZXR+shXHBzw8+lTuLOmG8DCevXQMSoKCYcPC/resixu7FgkXLoE7dWrE4NnGXZi9Wp4t2iByz16oPjMmb+3srJKlNQzMwPevZPZz/MSJfD00iVonD2LUqVKCe0O29qi0Js3yBMbi6/r1sEgNBQLW7dG3XHjYG9vr9Rz8vz5c1y1s0OxfPlgy/IIGRbXqhVer1yJ/xs2DKNHjwZrmRP09XGubFl488BChj0OCoJFZCSO/P03atasqZRvbHzewwP2587B6NMnGIlKAJ7o6eGWhQWqyDkoUHog0QUkHiOsmd/qf6WxxCeJ5M7PD8iAOum0lmeQtJLPVunSpdGnTx8EBQWlQHCIB9gfP35ESEiI8Hu6bNkypXkN5N1b9d4/ez356sA7e92PDPVG/fJl6PKqO8/hK8BMCbW8edJM+HOuXLlAlleypJpxM6m25BVo1qyZwF7Ok/pdu3YhIiJCqMNMC/stZducQ0NRyNsbpiI5N4c8efDg2TNoEqI9eXK2XXlmJ8YfOoSSlOZifa+Y7fv7b/jWro27zZqhIIOaLDbWKR5zc0NrIyPoMSMpYRuMjVHr50+Yfvsm1dMr1IdetAgn/vwT8TY2sKpYEaZeXrBjTe9/xLb07Yv606bh2eLFsGvXLnnWmxcsQGBYGB737o28UhAAmaGPfCtXLrz68gXeIkKxFR06oNXixfiyfj0MGzeWfoeYLdTUFP72Y8YM6PTsKfNOHpg9G1V79MDNESNQZOTI39uR2IrlBzyEuigUiki1F+XK4XV0NF4ePAgfH6p2A6cMDWGmp4eihKyfOiXUis+sUAE90yBhRfjus4IFUcDJCXb0R4YlhIXhw4IFGNevn/Ddf/PqFSytrXGiShV4y5GPfNaiBXKvXo0tmzahITXLlbQLhQvD8PZtOMXHQ0OkgnDH2BgvtLRQXo6MmZLDqLR5ksa0SjtVVWeEl/M5MjdPVJV480ZVPafoJy3lGUkdEFkwffp0vHv3LgWCg0oqPHjW19cXmlLGjcE3SV3ZVlWm3vuraiVV04868FbNOuaIXtQvX464TWons2gFGjRoIMiT1K9fH+bm5sIPHyF6/N/mzZuzyKvsOay47AwDunbt2mHJkiXo3r270if1M2bMQIPevWETEACDrVuFCVNPds/ZszAlbHfhwuy5CCDhujdWXboEp8qVE4ngxOxUdDQ8ypXDvXr1UHiLwNGepXbs2DHcrVgRjXLlgoEUuaR1uXIh8P17GBCGLsX+qVULnjt34t7169DQ08PjAgVQqGBB2JFY6z9iEW3bInjZMrzfuhVmAQHJs96/bRuqBQTgTqtWKLR8+W+rkRn6yI+MjHBLRwfVRBv2iJEjETxqFF5MmQIbGRwD306dgj4J0QDcKFsWReVkev8eORK1Ro3Cgxkz4CgtQKf6AK+vUwfYtk3mE/HK1xcf9+/H5a1bERAQIEBwr2tp4aejI9zIk8Ca9Hz5MDVfPvSVk7GWNcCVK1fwvXhx2BYvDnt55GE9e+L7rFno27WrwOR978oVFCheHP/UrQtPOeziMeHhyD1/PtYtX46mrVop/eRfsbPDz1ev4C6GOrlqZYVvnz7BQ8ahl9KDpPGCrNCYTqOr/7uMvw+dOgGhoYmcICRZS4O+err9SKUDym4SjfHy5UtBJs/DwwN2dnZCLTfr8GfOnAkynhPxtXz5cuE3VVWm3vuraiVV04868FbNOuaIXtQvX464TWons2gFSKBymKfnEsaszCGJbGYWuZhthk0KJJIcun//PoKDg4VNBf9ZGSOpzMBJk2DaoQM0RUF2w4YNMTMqCvkow7VxozLdZWpbkuHse/QI9oScbtqUYuxr164hT7FieFqtGpz37ctUv6QNxqzLt7p1UStvXhhKkcdbkycPmj59Ck1C/MX0yJP6OlmqFIqdPw+D2FghWNqup4eKlpawziDt2SxfMCkOrAsKQtPISPyIjoYOibFEdu7sWZQoXRo3AwLgIjo8Er88M/SR3+jo4JS1NWqKyPH+XrwYtX5lve/16oUC06dLXc7Lffqg+PTpiAHwLVcu5JOjX72lZ0/UnzULL1asgE3Llr/317UrMGcOwL/J0aN+W78+vm/din2rV4PImRcvXkDL1hZPvbxQgugJBqS6upihp4deaQhEKdVk6u0NizJlYMfsuSxjCcuECejQti0WLVmCC/v3w93XFxdCQ+G+Zo3My9706wfLqVOxfOZMtO7RQ+nH9Ja5OV7/+IFyYvKDFx0coPPsGVxkHHopPUgaL8gKjek0uvq/ywYMQMKUKRj1C24+klD9a9eADGIHT7evoLx9RQF+/v79e/To0QMbN24USgG6deuGfRn0O6He+6vizqmuD3Xgrbq1zPY9qV++bH+L1A5m4Qq0atVKgEsz480TaNbGRUVF4e7du5AmyZKFrmb50PPmzUONGjVQsGDBZF+ePn2KAQMGgGy5ylibVq2wZOVKaLI2duJE4VKyurafOxfufn7Q2L1bme4yta2trS2uffgAi0aNEqW1xOzJkyfQypsXb7284JIN4Nhr166FRbNmqFK0KAwo1yRhq52c0JzZa0KVRdBH8SYn8+WD3dOncBQFB6sNDBAYHw/j798zdc2zcrDVvr5oTv1usl6LaQY/ePAA5vnz41GlSigu5fAuw/WRExIQq6mJ3UWKoK6IkfvE3r3w9vfH9dBQOMsIJI/UqIFKu3djg6EhGnz7Bl3eSzENZ/G1jmjdGsErVuDDtm0wZVZb0jhG8+ZA/fqAHITQx+bNEb9mDdbOnQuuy6njx+FVoQJO16mDLSVLCqU+r06cEA536vXpg2HDhilV6kMtZKfq1WHg4wPbg4LavHQTMWG3aNAAqyIjcXTtWlRs1gzXunaFyx9/yLzsw5gxMB0+HItGj0YHMmkraY8MDHDHyAhVxA45zrm4INf163AQg58r2a1KmmeFxnS6HW/TBl82bULgp08QfilY481a72xq4odwREydOHFC8DQtZVqKTlG991d0pTKnnTrwzpx1zhajqF++bHEb1E5k0xVgfRUJw7jxe/36tcBEymw3mUh1dXWzqdc5361gPz9E8KSfQbeImGrChAkoO3gwKnt4QFtJVuPMXBHW5r2Lj4d+hw6J2T4xI2fAKzMzAfbqkg30cufOnQvXLl1QzsMDelLWdHWJEmhOP5nBpjSUhJ0xM4NBfDyKicjXltraoi11omNj5ZJyZeb9yOixVnl5oQVJ9J48AcRIv8he/N7EBG/c3eF2/nxGu/F7/9RmNzJClKcnAkQkfzdv3EABZ2dc9/ODmwzCqf3OzvC6cQPrPDzQ4exZufrbawID0WzLFsRFR0NLLNuf7Ayz04T88h2Wo2/9rXNnaM6bhxkTJwoHdVELFyKgUyfUKFQIIYMGCQefCR4eOPjoES4PH44LFy4oVeoTGRkJ76AgaNeuDWt5rN1EAfTpg+DKlRFx6BB2z5yJ6r164c6IESgkrYZdNNGvf/wBg+7dMa9PH4RPnar0vX6trY2z9vbwfyjoNgh21ssLBU+fhub797/pOys9QDouIOIrszWm0+Fu4qW1a+NpdDT83rzBVQAJy5ZBo3XrdHebUR1UqFABLPuh8f/57zTxgFzVY6v3/qpe0fT1pw6807d+Oepq9cuXo26X2ln1CvwnVqBOsWLYTnjg/PmJG3cQqboCJq1bo46jI/SUhK5n1qJR+9rI0BDxHFAsW580Pmv6rmhpQadgQThngzpoHmb4DR6MUtQhlpIJXOvtjVDW6D5+DOTJ89syXtPRwXdra5QUQZkXu7qi/dWrAOGdSazCmbX4WTTOymLF0JLPKkmwTE2TvWB29qamJmILFoRbFtzrHw8eQCd/fmzz90ddEUKEiJ0flpZ4Vro03P/5R+qKHbGwQL7v33EwIACtNm7E9507oVejhtS2y6tWRWs+N7znIr3htNyG+EGDoDlxIoYOGICxNvb+OwAAIABJREFUEydiRe/eaDVjBso7OeG4iAztbfnyeHLiBG5FRgq1r8qU+vDbUY+BV3AwLOSVqfB7Ex6Oem5u2HrxIrYMGYL648fj2R9/wI6weRn2c+VKaLdqhf/r0AFd08A/8V1DAwddXFCD6yiyc/7+cN+7F4/u3oVjgQJpWdb/7jW/DpvO3L6Nqu/f4wOAjwMGwESEnMrKRZGl/EHJsBEjRqRgmSejPnlkGssiQUznRNR7/3QuoIovVwfeKl7Q7Nyd+uXLzndH7Zt6Bf6bK1DD2hq7CLvcsAEICREWYe/evXjo74/mZmbQVSG7qypXmHrjRezt8ZGdErZKDVkJO6mlBSsbGziJglVVjq9sX8wutpw8GcXq1oWGFPKo9b9gyU327gVu3gQKF07RPdEgr/T08NzVFR6XLwt/W+brizaEXatIJ1rZ+WRF+9X58qE56+MldNDpywUdHWhbWcH12bNMd+3NkSOwrFwZO5o2RW0STDHzl5CAW5qa+ObkhBJS2L359wva2tCzscGN8HAEDh+Ox+PHI++gQVL9X1y6NNozK86DMEfHtM9xwgRBIrBf+/aYsmgR/q9+fXTduhWt/Pzg6O0tZLz1R47Ei23bMKl6ddjY2ChV6kOyqnbduuFn27YwWbJEtp8sDWnVCjUdHLDz4UNsCAtD4wUL8G79epjLC4BIlBgYiD+aNkU30VoruhgJX79Cw9AQO8uWRU0xIruLISEoERGBi0ePooQoA6pon//5dg4O+DMmBu0MDPDo/Xt8btAAtpGRWb4sqlT+SO9k1Hv/9K6gaq9XB96qXc9s3Zv65cvWt0ftnHoFctwK8FSf0muWlpbQkaMBLGtizArX0NHBHhJ67doFVK8uNCUz8c7ixdFTSwtaJFuSQvaV1Yt19epV+Lm64ikdmTUL6N79N5cO6OujoKEhHDNI4kaZNejUqRMGL1oERx5urF//26URQUEIjoxE7KlT0C1TJsXf7965g7xOTrjm6ytk5mhrW7VC6MqV+BwVBaO6dZVxJce2XWdtjQZv3kBfpJUtPpFTRkYw09VNlMTKZHu4bh3yhYZib/fu8OOzKLJzurrQtbSEqxR96FevXuGntTVelSqFT+PGoVzt2rgSGgpXGfXgC4sWRUceyrC8IHfutM+QJRldu6JHgwaYFRmJqb/G73v+PGLPnMGKM2cSS32io2F19y5e8DBhzx6lSn0mjB+PQUOG4FuPHtCXpjee5DnJEIOD4Wdmhr3v3mFVSAhaRETg58GD0BbJnEmdJJ9/f3/MCghADylEevIW5svduzAsVAi7atRAjZ07k5teDQ9HsfnzcXzjRpQPDk772v7XrkxIELTXZ8fG4lhwMMZHRAjfrnzySPUyaY1UqfyRXpfVe//0rqBqr1cH3qpdz2zdm/rly9a3R+1cNlmB9AaT2WQaGeoGJcQ6d+4sENBR45wMrSSkY7aJBD2KGlnQw3Lnxp+8gJslUcBH2ZU5dnYYw/9OzWkjI0W7zLR2DBDCqlXDDY64dClA6TMJ22lqCve4ONiJMRhnmoMSA5F1fl5kJKwogUR/Jeyv1q3RYMUKvNuxA+a1aqX46+Ft21A5IAA3WrZEURFj9dYRI1Bv9Gg8nj4deXv1yqppZeq4ESYm8P/xA+ZS2LaPWFoi7/fvKKDEvVaVPvK1qVPh0q8fjowbh0pk6xbZCRMTWGtowIkSSxJ2+vhxlK5QATd+ZW9zzZ8PQ1tb3CpXDqVEZE+S7RflyYMORG7IIN9T+EaIMs0dypfHomPHMCF3bgx6+TJlycK8eUDnzujk748FSpIrjhgwAKMmT0b8sGHQHD1atluU/6tTB1U1NbH/508sq1EDbVkLzxp9d3fZ13F9ypfHzKpV0ZOIDyXs2cGDsKtaFftCQ+ErdsBxZ/hwFBozBntnz4Zft25K9Pgfb8qSD3Nz8Il3nD8fbmFhyOPoCMdsUJ6kSuWP9N5l9d4/vSuo2uvVgbdq1zNb96Z++bL17VE7l8UroKpgMounkSnDU3qNut2FxSDJt27dErRHpUmyyXLq0qVLmFmiBARAqBjEmdDmPnp6ELiFZdQcy5sofaCPGWnr1q3D1NBQnOEgEREAmc0lbKu1NSq/fw9zEpBlsZE1N+rQIRiFhf1GBEfXtvfsiTqzZuHZypWwa9Eihbd/TpmCoP798XTECNiLiKcOr16Nyi1a4Hq3bnCePTuLZ5c5w/+lo4OKhoaw5oZfwg7Y2cH59WvYSbnXGa2PfK5vX5SaNg1nli1DaTFiqYO2tij89i3ySGGe3zpnDup17YqH/frBYdIk3NTWxrc8eeAuRvolPsVlFhZo8f49tJntTw8ChYznDRqgSf78WHP7Nqbr6qJPfDw0iWxJYlQXwbk7uLhgkVgttCJ3eUCnTpjE2mtC2gcOlH0J69WrVkVNvr4fPmCdnx868vCPsHwnJ9nXXbwoBOazypZFDzm659I6uLVmDQo3b47DPXui8owZyU2ezpkD+65dETV4MALGjVNkmgq3uX37NjQ1NVMoUPzzzz/w9PT8rY979+5h6NCh0P71LFDmkXKJNKpM8FA12xl/M4oWRXsAHU6exOtKlVDKwAB2Ut7PzPZdlcof6fVdvfdP7wqq9np14K3a9czWvalfvmx9e7LMucwIUrJsckoMrKpgUokhc2xTapEePXr0N/9l/XdZE6X0z67q1SFwA8fEANbWyU076OtjEQMG1hS7uiq1VtWrV8duJTNlSg0AYPr06djapw8EwaK//wao5S1hkQ4OqPPkCfQIpc9iK+XujjMXL6aQbRN3afeYMag+fDjuTZ+OAhIZ7MUdO6L9okX4umoVDCgZBeDamTNw8fTEucBAlPrrryyeXcYPTyTMHl1dlMqdG3aEW0vYnoIFUYayYlJg6BmtjxzdqhXKrlyJG3v3oqivb7Jn+woXRuk7d2Au5flbER6OVvPn49PKlTBu0QLRpqawSEhAERFrveT8VhkYoIEq5OOoYODnh3qECF+7hv0FCqCpsTEMxMdlAFy2LDpZWmKBkjrxPRo3xiySqjGw7dlT9oMhGqMhK0UePsR2f3+EUYqNNfq2trKvu31b4ECY5eaGHgzClbALU6fCvV8/nBo/Hl5itfRvN22CRXAwNnXogEZpIGyT5cLkyZOxZcsWgciL0o+LFi0SSoJkSVf5+/ujb9++QuA9ZMgQkAisdu3aqFq1qlACkO2Mv0GVKiGAnBMvX+JIsWLwffsWpjzEUaFRXztJ7SRXrlwCA7mfiiTLePicN29eATG2c+dO8Fuh6kNj9d5fhQ+DCrpSB94qWMSc0oX65cspdypz/cyMICVzZ5S20VQVTKZt9Jx11dSpU7F9+3bUrVs3WfOc/16rVi3069dP4cmsXr0a91u0wFBeISFL1S53biwhBJWbKxmEQ+I64kmDkjQqJiYGn5WA/CrssFjD/v3748bUqdiSkCDTxwhnZwRzM58NJLec8+TBdUKFmVETgyMnTenQ/PnwCQ/HjUGDUHT8+BRLMqt6dfQgDPfQIUCEJHj37h10LCxw1dsbZY4fT8sS5qhrWP5w084OTvnzw/7evd9831W8OKpduQIdBrkSGeGM1kc+Xrs2yv/9N57dvAk7MRTKPk9PVD1zBho/f0JDSyuFz/MZaLJemVnbsmVx0MEBxZ49Q26RTrt4Y75TG7W04G9iAsv0ZhNFAW8QgKYREdAPDoZPvnwwefBAGFIo9bl8GZYeHuinoYFpP36kYIBO7aEJr1UL81g/LaaSIPUaHui5uYHHSP0vXMCh6tXRjQcqPAAwNpY9DANze3vMLlgQ3ZVksD/Zrx/KTZ2Ky8uWobgYMuHnyZPQ9vbGuoYN0fRPofBGJVa+fHkcF72by5cvB1E6mzZtQr169YRActSoUQLDdpKJB9gfP35ESEgIQkNDsWzZMuxXElavkgmk1glJ1IKCUFlXF4e+fcNfxYujIRESPLBVkQQoDyJevHghkP6Zm5uD372tW7eCAfgMMdRCaq5K+zt/K0+dOgVjY2PkyZMHRN3ly5dPOCQZL/ENTkv/Sdeo9/7pWT3VX6sOvFW/ptm2R/XLl21vTaY4lpVBSqZMMJ2DqCqYTKcbOeZyQsslswBFihRRyn+uuX6/fuhiaAgNiUC5XZEiWELY57ZtQi2mNCtatChIcsaMjrgxc8NsekZaixYtYBIVhbkMRC5cAKTUtm8oXRqNyQQtQxs7I/2T7NvJwAC3WZtMWLiUOtIzW7eidP36uNCuHdwXL05x+eRixdCfMlpXriRrNDMYe6SpiZfMqhLy+S83bopj3dxg6+YGeymZzp3lyqFmdLTUGuiM1kc+7OWF8tTvjo2FthjJ4YHq1VF1zx68v38fZhJM5PNdXRHGIIUs7XnzYo+HB3zPnYNGbCw0JIgSeYh1yNgYnrlzI7eUbL9St/76dUGOjErLnxs1Qr9Nm1CyQgXcnD//f7wRpqZ4f/w4PgFYvHcvfMWy+KmN1b5CBSxmsLlsGSBPz/nuXaBQIXT8VeUSeuAALtSqhR58P4hY0NSUPQzr5c3MMNvWFt2VZLA/3KoVKq9cifu7dyO/v///xhBBpldXrYrmKgxwxQNvZrlfv36NBw8eIC4uToCaX7x4Ee7u7slBNdszwNbX1xd8Y8kPg++DBw8KAWe2MxEXQKU8eXDk8WNE+fsjYO9efL97F3oqkmXz8fGRKmcn67//tkY8yCKvQdWqgIRPSXXgXOdChQrB2dlZ+N1SFjmW2n1R7/1TW6HM/bs68M7c9c7S0dQvX5Yuf5YPnpVBSpZPXkEHVBFMKjjUv68ZM79K1n7yxJ+1qU3z5IEGAwAx6+DtjUXMxq1eDTRrJnW9Zs2ahaZNmyK3BMvyhg0bMkwTNckRBvc+169jKGvQuYmXstHbUKUKGjNLnF4JpnQ+LdQcL2VoiOvsZ/lyQUZJ0u5fuoT8JUrgVL168GKNrZhNtLbGQEq+SZQDXNbTQ5y5OdzTG4ylc36ZcTmDjzxVq8KkfHnYHjv225C7/P1RY+9exD55Al17+8xwKXmMg5QMu3sXlhKQ8qPNm6PimjW4f+AA8lepksKn5TY2aBETk6gaoK2NvXXrwm/7dry7cgXmxYqlaMsa9QeOjnAqUAB2fNbTY0+eCIE+KcRYNfxYRwd5GjZE5adPU/BGfDU3x+z377GhVCmc5eGVgta2RAkspcSdDN6F5G7I9G5nB4LRi7OMokMHdNLRgW5qfAwMzLW18YepKbopmf0/EBCAqtu24c21a7B0dv7fjES+rPLwQIszAmuESqx9+/ZCrbaTkxN4UPjp0yfU/FUSM3LkSJw4cQKtW7cGdc8dRYcyzI4X4D22s0sen0E6s+Xk78h2NmoUfk0G1by8sD86Gvs6dIDv4sW4/9dfyB8YqBJ3+Z0fPHiwALdPMn4Lxo4dK8hepmpjxgC/tLvJvq7BZ9/SMrnenpKUSWtNolGqfPCQS1YpQKpjyWig3vundeUy5jp14J0B68qPVBspDLfUUZ04cWLyiDt27BDqaK5duybUePTu3Vs48ZU0ZoVIbEGom5ubG6ZMmSLUmChr6pdP2RX7d7XPyiDl37WSOX82yhDuKDTb8PDEbCizNUkESQpcSK3Ttn/+CV/CY7lZFrPOAQGYy2w3SX26dFGgt8xtUpxyZz9/oj2h5ITEW1n95sDGevUQEhWFuPPnoSWPKTmDXX/y5Aka5M2LUxyHUNaGrGxNaZ8/foSBqSmOV6iAihL1+zP19NA9NhaaEvrV0RYWsIiNRZEMhvVn8PIo1D0hupWCg6FVpw6s+FxK2O6gIFSPjMTbM2dg4eGhUJ+qanQod24U/PABDhJs66f79EGZ6dNxftkylJTI/v5pYIBqGhqw+PJFcONw166oPGcObq1fj8ISOtanT5+GrpcXbNzdYUvW7/QYodympqDqPQsaYrW1oRMejopnz6bgjfhcpAiO37qF7s7Owh5JUWtToACW8aBLBu9Ccj+izDUZsV+0bYuKS5eisYkJDKUwwEuO/UNbG4u0tdFZCru9PD/3VagA3+PHEff5M7QMDf/XlPfAyAirnZzQXIrmuqJzT63duXPnMHr0aJQuXRp9+vRBUFAQuA/NsdalCz7Pm4cWgYGIjIzE6UmTUGbgQJyfNAkl+/dXybS4b+bene8AA2Oiq4gWmDBhggALl2txcfhhZYW4d+8gYAgmTUL7mzdx5MgRAalF0joqG7CmnsYgf9u2bQgMDMTffH5VZOq9v4oWUkXdqANvFS2keDdJgTeJEii1k2Ss4XBwcBD+laeNJFBo2bIlmjdvjmPHjgm1NgsWLABPKZOMQTdP21jv4eHhIZBjbN68WagLYRCujKlfPmVWS91WvQL/zhVQlnAn1VUgzLpkycRmhCgrkRnhAeLsf/5BCQYqhw+nGKpX+/aYsWQJMHYsMITb9OxlVlZWWOLggPoMRLgB19P7zcE/W7ZE0KpV+LhrF0xEGuVZMQtCSnu7u0PIzxCCL4MY6L2GBi64uqIy619F9u3bN6w1MEBjQ0MYSQTYR/LlQ6GnT2EvpS44K+aZkWPOnz8frcPD8b1NG5hJkWPb164dfJcuxeMdO5BXQo4tI/0S9hNGRrDS0UFhCTjw1enTUaxPHxz9JVVVcajApCAYs5iHtbXhZGsLBxFc+uysWfDo2RNnRoxAaRFzfVL7qKgouNarB9MqVWCVXpItZuW1tPCPry8GfPiAfYTIjxmDqfr6KXgjXk6YgMjbt2Fdp44QjChqrSwtsYJa6nK4IYS+mOnX1cUUXV0sLVgQo69fR21bWxgpAB//bGCAtd++oX18PDSUQPnsc3NDhcuXoU90kLglJOCnlhY22toilDwMGWzk4yA5JOHjZ1SYYc9gt3/vvlEj3IuMxJSwMMydOxfXIiLgEhKCk126oFwWs7CvJLycHACjR2MRgJpaWsibLx+mGhkJe/zMRGqp9/6Z/mTKHVAdeGfA/UgKvAkd4eZMmpGE6M2bN4hmTZjIOnbsKPzA8CWh/MP3799hY2MD/ndulpN+MBlwk6xl/fr1SnmvfvmUWi51Y/UK/CtXIDXCHaUnPXUqkESo1qQJsG6dwl1Qjmz/ixdwIIJn69YU1w0bOhQjSATWsye000lio7BDCjZkTZ6enh6OenqiAut9pcg1sastPXqg/uzZeLlqFaxFbOAKDqHSZqzb/MPXFwL3OH9zvLyk9v9MWxs37e3hIyYpRZjxBUdHVLC1haVEUHKwVCmUO38eenFx0JBXF6vS2WRNZxNHjsTAUaPwbdAg6EshPjrcty8qT5uGW8uXo7AUKH9Gen1OSwv6dnZwYdmDmD3ctAn5goOxJzwc/nPnJv+FENeP9vbQKl4chURIk3t796KAvz+OtGiBSgwaxIwH/oEdO0K3USOYEcKdXjMxAShZ16dPonTXggVAx44QL/Wx3L4dpY4dw/LwcCGoUsTIO9BSRwerCAdPTY+bHerqYoWpKVq/fg3mfasULgwDBfgKPpiZIerDB9T78AEmnIuCdjB/frg+fgxrKQdVH3R1sdPQECGZVEvN7C1RlPaZXBah4FIp1CyuQgWcOn4ce0aNEhjYn54/D/tSpXAkMBCVslhpgSzlvUqXRsK+fYJqR1kAFQGsLlAAt9NbrqHQ6vyvkXrvr+SCZXBzdeCdAQucWuDNgNrU1FSAnfcSk205dOiQACGnxiKhQCQuYq0H65tKlSqV7CmZKKdNm4b3798rddqqfvky4Garu1SvQA5bAfHAm64TakhEDev/zqcFRspgm3XMpUsDhIQqyPTLTTKlU55rasIsKCiRgEbMWF7TpFs36DduDGMlDxkz+pY8evRIgBle9fKCC9mYWaMpxbaPGoU6I0fi0bRpcOjdO6Pdktk/2Yx3hoZiBVuQUEukzyt5wT1DQzzR10fFN2+S/0SI5U8vLzi5uMBaQlP5cPXqqLxnD94+fAgLEZoryyaZwQMPadcO45YuRcL06dCQkFvj0CfGj4f3kCG4PHUqijOgVMCY+VosQWSnwGUpmpB9OsbUFN9dXVFMDKnARh9On4aplxe2NWyIumJs2WfPnEERT0888/dHYZHs3ueXL2GUOzcO+vigCjWuxYz1rP2GDYNGeDh0FQyC5c4jT55EsimWkJQvD1Dbu379lJeQbXv0aNTz88NWBYkSWR/by9gYC9kTv0MFC8pfTnNz7DA1RZ1Hj3CKEOLy5aEhgbyR1sFbGxsciomB56NHQpmgonbU2hoOHz/CUQpE/aWJCY7/+IH6SsLXFR3739gutlAh7Lx7F88XLBASVN+/fIGOkRGO/iKJqyyFhyEz18Db2xuRuXIhYft2jA0PR/yePZh/+zbqlC6N7f/8k5muCMk8om35u6XM85qpTv6HBlMH3hlws5MCbxL+vHr1SiCu6NChAyg/w/oQ1na4uroKNRwkukgyZsh5zapVqwT4OU95u3Tpgi9fvsDAwCC5XUREhMA0qexLpH75MuBmq7tUr0AOWwFxwp0k10kWQ/IdflOUtYSCBfH0l96uXUAANAlRlVHvLNkvET+UZPmhpQXtrl2BmTNTNOF3zj0kBNY+PrCQCASSGlJ6iORA/K5S9oZZaBqDGfGSHWXnlFp7BqNeXl54Uro07Am/llGDumf+fPiHh+POgAEoJMbvkVr/qv77zJkzcbtXL4HMCsyKMvCRYjcsLfHu2zeUFdX9sglhqUXr1oVZ5cqw5gGLmB1r2RIVVq3C3X37ULBaNVW7na3661GjBmYxSJXBln12wQJ4hIXh7KBB8JDIiAuwUwnjwRNLyajjmx67ceMGrJyd8apSJRSVCBoTnj+Hhp0dIitWRMMjR5KH2b52Leo0a4b7HTogv5huNEsNzjs7w0fiee4RHo5Z8+cjYcgQaLD0I71GBQBmWjt1SuQbIAu5t3fKXikHFh6Oqo6OOMCabQWM368Z+fJhOtuS8E+CdPG3LvLlwzVLSxS7cAH39fTgyBIBBTKlrwoWxIV792B7+bKwl1PUoo2MYKGjgyJSstpPrK1x5fVr+BM9ogR8Xd7YWfV9VHQ90tvuh6kpVnz8iDw7dghSlrSXmpq4WbgwKpB7Iwvt7du30HRzw7EnTxAfFYXjO3di7Jw5iOvfHzqTJmWqZ+q9f6Yud6qDqQPvVJdI+Qa7du0SIOSUCuAHlJp/8+bNQ3h4uECSxnpuygWwzrtcuXLJA/z8+RM6OjogCVb37t0xbtw4jBkzBqyxEzcyKZKE4cKFCwLkXJZ9+PAB/F+SEV7GzaKyAbvyK6C+IietAMk9ukmRF8pJc/gv+kqJIvJEZKmJSIG4FfcMC0NNbpbFtJ7l+caAo0KJEhC+UFLquDk/LR8fuBQrBksSt0kxkrMx80xyGn53GazzoFPVrLCSQ/ObTl3Xd+7uMCNJkgwd68ObN6Nygwa42bYtirBePYuMzMZakyZhHMfnb4IMeOxVBwf8ePoU7oTqiowavg3btkV848awkEAe/DNwIDwnTcL5X9wjJcW4SbJomhk6bCd3dyxgWQEDMymMydeiouBSrx5OtmuHchJZbAF22qsXGGyLGw/ZSXSYHjuwfz8q+/riQaNGKCgJA2cJhL4+Njk7o5FYML2sXz+0mToV72bNgnn37snD39XTQ4y5OcpJsNS3DgjActZZM2BQBWlVjRqJKJG2bYVSkiRJsxTrwNKT+vVRRUsL+75/V0jLm4idv0qVwi+ua4AHYuIEZtIW2d0d8ba26O/mhgmLF0OHhwBS6vclL31ZsiQeXLiA2GPHQASRonZZSwsJ9vZwk3LA+SB/fjx58AAlP38WkECqsKz6PqrC91T7ENXok6CvwdWrcBGheG7q6eGDhQU8ZaCQUu1XwQap7pu+fEGCsTEmJiSg87t32L17N9xCQmDj5QULUYkp9+c8OGb5aBJxMu99z549BVSsqkwdeKtqJVXTjzrwVs06ptoLZXNmzJghBL13794VAu+TJ08KwXmSJQXes2fPFgIhBt6EeFEKRtyo81e9enVBg1EewRolIwhLlzR14J3q7fpXNmCtpjQjuoJBjtpy1grwG8Af86y0b6dPQ9/LCy0BMAu0g7VrzKB16JCqWwxee9WvjztsKarxFL+ImbzrzL7Z2sJcBuERS3OYradRZ5mSN0uWLBEOLlnXnFG2cOFCdOrUCd+KFIEea1S3b5c61JlTp1C6bFlcq18fLoTTZpFRZaPUhg3ozt8SOTrFV1xcoHf9OvJ+/Zqs5TtFBDP+2qcPDFjPL2ZX5s6Fa5cuODZ0KCpQNudfbG3s7LCMm3mSi0lRFXl54wasnZ1xqFYt+EgwRRN2yufd2to6xQrVqVNHQBSkxzYsWoTGHTviedeusP3jj9+6+qytjT1WVggUC0Rm1qmDnjt2IGHXLmiIkf6dz5ULOl+/wlUM8cAOm3h6Yj1JuBR8t1OdDxnWuUbkPaDPTC5oaaW8jGUvpUqhOc/l7t1D/vz5U+2W7/wZX1/01dSEBuuoU8sc8z7ynaBsIbXLeQgg8YxLG/Sljw/eHz6MW2KZ1tSc4z7ulaEh3hQvDncpKId7xYvjC6XcHj8GiXhVYYp8H1MNIFXhSDr74D6W5MMpjL8J9vaCHNy4T59gZGQk/PmMuTkM4uJQjOz5KrA075t4SOfuji4mJpjz4QPYz2FHRwQZGsLg0yfh2QwICBDKSFkiQaRs3759YW5ujn379oEqCqoydeCtqpVUTT/qwFs165hqL0nQRNZTMiOjDNScH2x9fUGMQDBFoebqjHeqt+U/1YAnqURYSGZdeIDz+vXr/9RaSE6WP3TkVOA6EP7MDYufDObnzF6oglLqFHkPY2JihB/srLQFNWqg0+7d6FW2LP4vOhqxWlrQ6NEDmDYtVbfIqruxTx+cZMvISKBBgxTXkHF3k4UFmhkYwEAiEEhqyINLcYLK+/fvIzg4GCzb4T9nlCUdasbZ20OTtarUGpdi3GyZODriaYUKcJWQ6MqLvW+lAAAgAElEQVQo36T1Sxhmq1On0IQaxXI2pFfLl0fuEyfw+f79ZG3f/s2bY/KaNUiYMQMaDEzE7OHffyNf7do40KYNqiqQKczMOatyLIG0S08Pq5hlO3fufyz+YoPEM9DT0cGB0qXhK1HDSdgpM1gsiVC1LRg4EJ0mTcKnKVNg3Lfvb90/NzbG+YQE1BT7VkxyccGA69cTJQDFNLtPODkh/927sJPQA2/k4IBNLFGQIUWn9JwGDQJYehEQwBMzQBrZlEh2jOGWz759AoolNePe6FVICDoaGUGLwU1qxm8O14D3iwo0CioovA4MRPyWLdi7dq3ATq2I3bt3D7kLFsRdHx+4SSmduVehAvSOH8ebS5dAqUJVmPj3kd8iJl2IvGSZDzW7adnt4F3yPvPd4x7F3d095WGqSE2jk7ExFoh9047mzYv8L14gL99VFVia901ExjRsiI7Fi2PhpUvCvmusqSmGffoETQ0N4ftKcjtbW1vBywcPHgiHS0zMUTOc+xFVmTrwVtVKqqYfdeCtmnVMtRfKf/EjyMCbHxY1uVqqS6ZuoOIVYFkCURaSMDaWLRBF8V81njK/ePFCgA7ztJkBH7NTDMCJUslqK1q0qMALIblpz+r7xoA3slw5sFot5soV2Li6IiZXLlizfEYB+Z/OnTsjZskSbGIwyPrTiuR8/Z9xozJFWxv9GQRwEyVFH5wlPDVq1ID44cTTp08F3VXCeDPKyNnBjMRb+t6mTaLWuBTjoekzQ0N8dXGBqwQxWUb5Jq1fSlFOffoU1XR1ARnIF153IzAQTlu24Mzx4/AS1dy28fDAMgaba9YAoaEpuv94+zZMftVT7qlVC/45WQ84lZtBItP+5uZYwHYMEgsUkHrFWy0tnHV0hG8mshZPb9IEvTdsQEJEBDQaNfrNr3u2tnjy8iUq/PyZXDs82cwM/VlywFpjMcnTY+XLo9yJE4ilpruxcXJfDQ0MEMmstIxsv9LP8uzZAA/oGHQ4Oyf2K8V+5sqFtW/eIHbRIoU4GyjHahQWhiY2NtBWBGpMqHtUVGLgzYw6iePCw1OdzseOHaG/aBHmz5qFbmJQfXkXnjhwAN7VquF6SAicN2z4/T7Vqwf7qCicOnQIlVRUQiT+fUwKIFm6yOAuCZqd3Q7eyTVCos+hQ4cKqkD8HWjdurUAyWawmmx791L4Gl0KF8YcMSb6gyVKwOvSJSHrrQqlhTTvm6ZMEcoyujRqhDmiEpAx3t4YdvIkFvXqhc03bgjBNpFatGLFigm/8zRVB97UbOdBtxrtmuqrnSkN1IF3piwzVTP6CLXbPHniCRczENzgs847ycLCwoQNv6ScGP87GdBp1N/kh4AQc7WcWCbdvH/JMISTlyxZ8rfaoevXr8OZm5//qPn4+ICKApIm679n9jLxu5EZmp/KQg5J2GXcqxfamphA8/17OBUujDWfPqEsN+wK1K3y4KD89esYxUwas29Fi/62tGPMzTHs/fvEelAbm8xeepnj8fv97MEDnGfdLPWR5cCsKfVkYGcHZwmpp8ycDGUp92ppwY1kU3KY6++Fh6PA/PnYuXo1ajZrJrgYZGaGPxmksazB3z+F2wmxsdDQ08NuT09Upx6zDGM9P9l0Weu8c+dO4fAvy/kJlLgB165dw/JixYRDJpDx3cJC6tWPDAxw19AQPqkgiHgQT2US/q4XkBHEK+rehIoVMYgMzjK4FW45O+PnjRvI8/698O0nWetyIyO009GBHmvAxeDYJ0JC4B0RgXvHj6OA6OCFqJqOxsZYQ4cIn3VzU9Q12e0YiISEJP6d0mvLl0ttG1+uHI5HR2P7wIGYMGFCquOSE8dt+HDUK1wYmgrIgoEIAZI68tn18EiUQqRKQyr2c/hwaI8ZgzEDB2KYAn6xu+2LF6NOhw540LMnHKUc6D5s3x75lizB9vXrUadx49RcUPrvaQ4glR4p/RecO3cOo0ePFtR9uHcOCgoSklYpbO1aoFkz9KxSBTPFDm6SlBZe37+PXOKBehrdSuu+KbZdO3xfuhTThg/HSFHJ54bJk9F4wABcCwtD/unThd91HixQCm3YsGHCHKl6RFh90p5fWbelIQbIfcB4Qx14K7uaGdNeHXhnwLoyA+Pr65sMF2IwzZrAHj16JGfQGHBz48GTPBJgkHCNLx9PbMXZeMl6OnjwYOFHh1kLsvVGRkaCP9zy6rulTUsNN8mAm63uMsevAANAvmM8ZU4y1g2TX4FEhv82S3PNmsRCMDMRvn49vEuWhMbp02jUqBF8d+9GOCGthIaLGMZlrR8DjjHGxmjOE38GKpaWvzUdUaAARhEyzjZKsAdn9D3jJrZYrlxYT8goYfVypMKOGhrC7hdMudDbtxntltT+KTfFgOuJnR3sSUC0b59MP56PHw/bIUMQMXYsgocMEVQ5ullbQ1BmlwGxfqepiTMFC8JXxmEL+U34e2VsbCzUrhKRwH8mSoEydjnBSNx3rmZNDNDQgAbRFzIg4zdz5cKbz59RTo4kFJmm7ezsoPv6NRzLlUtx+J6WtRhXuDCGcO1lyMTdqlQJFkeP4oWIgZuHIHdLlEAVW1uYSXAnnBswAKUmT8Y/CxbAs2NHwR1mR2cWKoTZ/Bc5jPhK+X7qFJDEb0O0CGXFpFnz5ohZtw7dGjXCBilZYslLiKIJWbgQVUqWTMxip2bjxiUenG3ZkihntmsXIFbzLvPyWbOEevABzZtjkoLImrVDhiB0/Hi8nj4duaTI0T0bOhR248bhz2nTEJQB0oNpDSBTW8KM/Dv5D5ipZdB4hhwD4pZ0D1q0wCQx1YDo9u1RdskSXI2KQrG6dTPSPbl9fyxXDrejo3FFpFLExl+/fMFPIyOcK1oUlXnYTLRYTIwQaN+5c0clnC3SEAMM8Il2VAfeWfY4pBhYHXhnwH1ggE2pMAa68fHxKFKkiBBMkzBNXCaCp1vc8PM0ndmA3r17C/Jh4pYkOUI2dMJhGWxPnjw5RZCg6BTUgbeiK6Vu919aAb4XhCaTh4HvKyHdnp6ewmEXGbP/bZbmmjWJhSBPxeE7d5CLTMBr1woHFXeHDcNStpOoHZVcQ57q04+DpUuj0tmzACHbmpq/LfXwMmUwmhtoEqWJHYxk9T2xtLRE52rVMJY1r6xtJtxchu2zsoLLp0+wzyJ9XmaPeGj70doaxiST2rhRpq+f1q6FcbNmWNGxI1otWCBkp7fXqgWBsotMzFI0ix/o6+OBiQkqU0ZOiiXVmcbGxqJQoUJC7T3fMRKMHs3CundlniEenGt16oTW5ubQknOAcrVAAcTdvw/XuDhoSnmeOSbRNVurVAFZEEYD6PjsWXKdpzI+JbWdYGmJQfRJhozfo9BQ5Fm3DtsiI1GvQQPh4N4hKAhFPDxgJhHM3F66FE7t2uFAnz6oKiIZYy3w7goVMJID8kBNTNo0Lf4K17B8JOnwgocGhQpJ70qk5V2xZEkc5cFPKtbgV832yL//hjuz9YrUyBJazj3X9OmJh2c8EChTJrVhgBUrgNat0d3PD7MVLNNa2KIFOq5ejR+RkdCR4LPggG/nzYNF585Y27cvQglTVpvoUYkXaqHtKT8nZj8oyzVlCsYMG4Zho/kmJdqVcePgOnQojk6ZgopSOA8ya1k/29jg75gY5Dl+HCRXTLJLxsYCBL64mOKQqn2SRAyQxJE14+rAW9Urnbb+1IF32tYtR16lDrxz5G1TO61eAZWugCogh1RgsDYwwFsSSv2C0mHUKAEdMMLfH8fobSokTCxvYI3hrbJl4cSa46dPpc5x5K8s1EjKChFWKIfEiFlEEgYxIKYkY0Yaobpk0F3cqhXacQPOGlE5mZVdBQrA++FDmIpJdGWkf5J9b9y4EY0bN8YPIyNok0Wakm8yLOHoUWhUqoQ5NWqgy86dQp2l/rhxGMr2PDiQgmK4amWFr58+obSMgwVxZmXWxi9atEgYPaMl31S5xkOGDEHZ8eNRt2hRaIoyVdL6v8pg9tw56Lx4gdwyNKSH9+6NQTNmwAAA6Z82z5uH4LCwNLnLg/np2trolZAATb6LUoL9D8OHw3TMGMwdMQKdR47EpEmT0HLgQFg0agR9Cfmxt6dOwaJsWfzdsCFq8R0WeA8j8SQoCJ319KClysMj6qGzfIHwblnGTGarVqhgaopjLDlJxXjIs+nSJTgwa62IigDHJm8Ba715gMZvkYNDasMk9t2gATqVKIEFJPlSwGb7+qI7DxBZFiBFgix2+3bo1q2LZaGhaEM+BbXJXYGPTZrgx4YNiJg/X1CYSLKnmzbBPjgYf4eFoda8eVmzit+/I97AAJMTEtAuJiaFmsFBJyd43L2b+HuQGut+Or1PQgyQbJRIF3Xgnc4FVdHl6sBbRQuZE7pRB9454S5lvI+ZGaRk/GzUIyi7AqqAHJKht0HBgjjPwQm1bN5cqB0tZG6OZ4TiEkJM5mIZtm3bNkFK5a27O8xJ+MVMkxQb2707hv7xB+ImToTWgAG/tSAxDeGlZI02MzMDSbBYR0yEEA8YMsJu3boloJj2dOwIP8orsT7U01PmUDs8PFCb2ToZgWtG+CjeJ+HcI4YMEYI8DB4MEF4ryxhUurhgsrMz+l+7JrD797x4EYHMTsrIaJ8vUgRmt24h38+fUlm7yaJM/gBqrScZEQ+BgYECMiwnGMvBekVEwJPEgXKkF2/UrAnHXbtw7cwZlGLNsBQb5uyMMTduIG7ECGiNGoXl3t5oLUMHPrW1YSlAlLU1QoyMYCSDxTth1SpotGyJMY0aYVhEBNo1a4Yla9ciYfBgaEg8CwkfP0LD1BQ7ypZFbUpsAUJ5XO7evdHYzg7aMg7IUvMzzX9nkFqxIgIBLHvzRni35RkRSldfvIBxy5aA6IBH7gUM/KkpThbxGzcSpcUUYZ5nNr1aNbTPmxeLpWhySxtzhpsberFkhrwQ0jhViPwpXRpzK1dGZymcI2lZQ36neXjGd2/gwIHJhGpdu3YVvpE52d78WqeYI0dwY/NmgRg1+dty9Sr0XF3xV+3aaJBOqb40rw+fJWdndNPTw+yvX1MgXXfVqoUaO3fiw+XLMM2E8imi+M6ePYsyZcqoA+8031DVXqgOvFW7ntm6N3Xgna1vT4Y7lxVBSoZP6j88gLJkaKpcKuqHL6lRAwI3b3Q04OUldF+pUiVEnTgBcxJzMRssw0jM1qtXL/xwdIR2qVIApVek2Mzp09G5Tx/Etm8PYykbafJkULe7cOHCyVczMKaed0Zp07P+n3wAl8PC4MrscSpZMkK16+zciW83bkC/SBFV3gaF+qKG9/HNm3GDDNaED/fpI/u6V68Aa2uMJet1TIxwmHGWUHkrq8Qabyl2zscHxQ4fxsuHD5FXkWyhQl5nr0aExa//5x/krVMnEc0hw+516IACixdj57p1qCmFpItw+3UGBggh4d6nT3hnYoJDv5jQ633+nGJzrujsyUj90N0dFfPkEfSfpRp5CKpWxWA3N4y/eBG1CxfGDsK7ZWhyf9LSwmEHB9QWyfGR3DVo8WL4ubpCQ8HsrqL+p9qOpIp2duATG/rPPwLZlixjgGGkp4evzPwPHAgoQnpG9vkkmLuTE3DrVqouCQ34Lnh4oD2RL4rIlgH4w84O3TgfCSb55AEfPBCY1WcWLoyeihDDKeAp+Uuo2sHAm6gN8gjVrl1b5azZCrii8iavnZ1x9cYN6J48KSgGJRsh3GZmiChZEsEKlCek5tiFCxdAHXGW69SsWVMoByXiib9hMg93SQRXpw46FSmCBQzCxezg0KGoMm4cbsyYgYJdukhFarE0lXxOqjL13l9VK6maftSBt2rWMUf0on75csRtyjAnsyJIybDJ/Ic6VhUZmiqXbM6cOYjp2hWj2CkhoKamQveE/LVesgTlPD2hIcqYSRuXm5dlS5eCKuQa7dsDfwhVxL/ZunXr4B0aCjN/f1gwOyVhsuqEM7J+ePXq1SCBzfMOHWDDw4BUMtl7wsLgv2ABHm3aJNTWZrZxLfJ+/Ij1ZKQmvLd1a9kuxMUhXlsbrDD13LtX0LJ/b2cHU2b0CfmXYldatoTrqlWI3r4dZWvXzuzpZcp4hI0/ePcOBu3aAXLgqzGTJiH3r8zihhEj0HikUBWdwljTblWpEkzd3GB/8SLulCgB7UuX8O36dVA2UFkjYsC2dm04li4NS1lkYqLgsoeODkY8f46mVlbYlZAglaWe4z82NcX12Fj4iWDlRD0sPHUKRShxtXOnsi6mr31CAuKMjbHkyxdYbNyI4OBgmf0RTuuaOzdi2CK1A6akXgj3NTICyO5Oxn4p3xipAz55IvAddAcw6csXGChQ977Y0BAtY2OhS0SQNIgxSSmNjTHDwgK9yJyvAhOXpSLJYkhICEJDQ7Fs2bJkTWxVB3npdZuIPEqHkQeCvuqJylsYiIoTD7+3tsbeX4iP0vfuCfrXyZaQgO+/eFq22dsjSAVKEvx+kmGdaCryN7F0h2SV5GzaJ4Oo8tukSdAfOBBjOnTAMB5widmtw4fx3ccHLXLlwkt9fYHrJKl8qWXLlgLfE0mVCQ1Xlan3/qpaSdX0ow68VbOOOaIX9cuXI25ThjmZFUFKhk0mEzrOyoyy+PRURYamyiUjfLjawoVoZGsLDW5CRUaJJPO+fdHG1BSazOzIqGGj8sPnR49wlLBLZqaYoZJizC7rVK0qQCQtpehgc4PCOra6desKMFRCzvnvlPsim3ZGGGtkCdv82rw59AllTGWTfGLqVHj364fL48ahOKHemWyUr+xcogSGkwSKwXNAgFwPvlpaIvLtW4QZGwt6unGGhtCk5BOJqKTY/TFjkH/4cGyfMAF1ZNzHTJ6ySod7/fo17K2s8J29DhsGiBE5SQ70fetW6NWvj+XNm6O1FLbrIeHhGDd/Pr707QvDKVPwondv2MyYgeVjx6L1kCFK+02d5kadO0OvYUOYysrEM6jU1wf541/27IlPM2dCqLJnJk4KAuO2szN+3LgBCxHpG5+fW2/fwqRFC0CFWThFJ/vDywtnT5/GgQkThPdOlhFO26J0aVxhA6JtCDdXxCg5RdRKhw6JKABFjMGzrq6wpoFXrggazPKMSIfNenrwMzODJb+LMuybnh5W/PyJDj9+yCTnU8S9pDbly5cXAmx9fX2sXLkS5OYgxJwcG/PnzxekrFQd5Cnjn7S2LOtgyQCz9FQTiIiIEPS7JTkhvurpYeWPH2j7/ftvvB4vDQ0RraWFuh8/ptcdodyGv0M0kqQlSQDL46h4UK8eckdFYf/Wragj8b2N+/kTFXV00DN/foS9eycgv3gf+J3ZsmUL2rZti1W/mNBvKyDJqejk1Ht/RVcqc9qpA+/MWedsMYr65csWtyHLnMiKICXLJqvEwNkxoyzuvirI0KQtR3pq/akHH3H7Ngqz5lVMci0qKgqH69UTMqbytLcpJVW/QAHMINmQqEZcmo9ksz1iZ4fqFhYwkxHgElpOxlZuXHLlyiVslFiDnVHGTAezL1/8/aGRJOMkZ7Br27bBJSAA0W3aoCwJnDLRPnz4IMDFNzZqhOBNmxRibU4oVw5Xb95E8bdvUcTaGjdY2y2nZv9DZCRMg4Kwvk0bNMnk+WXGUjJL3aRSJQhA7tmzgW7dZA9LVIG7OxZUrIhOR4781q6tnR2WEm7M2vaaNZFw+DA0fHwwuUwZ9JfBcyBvjt07dsTsRYsQP3gwNOXU7sfb2GD9q1doFh+Pqfr66MNsNuuZ9fV/6/5hgwbIvXkz9m3dCq9y5eCYOze+sBUZxqVk8TP6HiSEh+Pr/Pno1a4dFsgJ/Ddv3owZDRrgEB3igZii6IvAwEQ5MX7HfH0Vns4Pc3OsfP8eNlFRwsGfPGOgG+PigqIFC8Lmzh2ZTd/Y2OBITAy8nj4VJOfSa2Skp2wj++LBJIO8uLg4UNeZ0GmaqoO89PosHuiyPI5lQywn6t69e3KWXkAZGRhgjqUlulCKUsIe2tjgzqtXqPLzZ5pKOMS7q1ChgiD3S+P/899p4n5Kjn+vaFF8unkT1jIUC0ppa2ONnh7alSgByg1bW1sLXSQlR8hCzgPktBi/+VQ+4m8iCUf5m8h7zUSCmlwtLSuq+mvUgbfq1zTb9qgOvLPtrck0xzI7SMm0iaVjoOyYURafjirI0MT7S2+tPyGLFmZm+KqlBR2yyYqR9Ny8eRN9ixaFAEpmlsDH57c7w+wPoZlL69dHK9Z2k6yqUiWpd5CZgP8zNETn79+hxcxdBjOWK/IYNWzYUIAB3iLRE2GqqcgWvXrwAFb58+Oonx8qKig9pIgfirRhFpB1sf80b47Sq1cDRCdIyPL81k9ICGKPHYPe06eYRcmk5csBXsu6fSmWcPUqNFxdsdLHBy1FmSFFfMsuiJLUfCUL+8KOHXGaDaklHRIi+xJRjfx0W1v0ltDI5sHQHCsrDOfVPETi8/P1q6Dru0hfH2FpqPNu7OmJDZQEI9eAGLPzbw6WKYMLt2+j5Lt3uOrsDBcGLvfuSZ3H5wkTYDR4MGb27o2ifn7oXrs2hMpnllWwLCSzbcECICwMbcqWxTI55SuzZs3C4Z49IVTgi/FOpOou79OLFwC1v5WwH0WLYvfNm7g9c6YAQZZnzGS6BAbCslIlWMkh54txc8ODy5cRL1m3rIRfspoyW8sgjwE4n2kiGSi/lp4gTwVu/dZFkvxg0h8oP8gSA5YS8J8F4/8XKIB5xYsjXAok+84vcsgvN27AVoJRPC3+UpqLh9+EvicZySF50EO1CGn2ytQUx798QcCPH1ID/y52drj8/DmqDB8OBwcHAcauKqQW7ykPgkg4Z25uLmigL1++XECAqQPvtDwBqr9GHXirfk2zbY/qwDvb3hq1Y1m4AhmVUc7CKckdOr21/mQk7xYQAGHbztrsrl2Tx2M2xcPICBcYJHPD3LHjb76QEMrd3R3RQUHwIjyWm14Z0ku8eErhwujHzLIMaGzSAHv27BGyIlOmTEk1A5Wee8NsPaHv28lmXquWXBI5jsPDg4+amrjk6ooKZDXOREuSEnsaGgo7Bo28L6mxNlP7duZM3L12DflOnoQ2IbusH5ZFbMXMqaEh1hUsiKZSsnnZHVGS2u0gb8GnFSuwhmvHwE+cyEny4oQE/NTWxv/Fx6PDp08CCVOSUW4v3t8f5X/VcBqLMWE/y5cP9x89Qt6HD4VNuKLG56quiQm2szZ42zaBzEmmtWiBuO3bMblvXwxcuhQahEbLqNkXssV162Jo+fLQq1kT+4cPxwF2LMrSK+qfytqJiMy6m5hg1vv3MjOYffr0wdeZMzGXGuGsay9QQGUuSOsowc8PZ/fvx8pu3cCgX57xmxTWvz+0WraEoRzSyde1auHbzp04tmGDUI+tSmMZDrXlmzZtil1//414QIAzF5Kloa7KwZXoi+UTLEXidzbJnj59igEDBgjZedrP/fuh7euLxYGBaC+FmPNelSowPHQID6Kj4SUi/lTCBYWaHjlyRAjIiShKYd+/I05fHyusrNBWhhLEyZYtkWvVKqxhmUjevCpFavH3XZJYVL33V+iWZlojdeCdaUud9QOpX76svwfZygOSYjGDQe1XZrUUIIjJVv6ryBlVZ5RV5FaGdZPeWn/Wdz9ZvBhbyR4sBZ5ZqVw5HDh1Ctq9egHTpv02D8LguIl6HxwMU5IZvX0rV890ekAAem/bhoStW6Ehoz55x44dIDGN9uvXMNHTw/aLFzMEbv7gwQOByGfG6NHoKdIvF3TMU7Eb+vr4bGwMD2ZEM9HGUYN76FB8b9gQuoQyKyJ9xCCiZ8/EuldmOceMAVgraWws0/M3+vo4rqeHulK0lrM7oiS128Gyig4fP6ILgznKaaUCAf5kY4PdMTHIfeSIAB1NsvFjx6LLsGHQadwYhuvXJ//3x8HByL1pE3asXYtAOVr1kn4SvTS6SBEIocj58wLEXaaRbIycByKZI0HqTxY0XUTGNtjYGDsKFULN588xkYdjzCxSdiuz7edPxBoZYXlsLKrdugUnso9LMWZFK+/Zg258BnkYYWiYsZ42a4bnGzeiQ82aYImNPOvWogX+WL0aCaNHQ4M8ATLsc9eu0J8zBzMnTUKf/v1V7n/L5s1RZc0atNbQwFYNDSz188OWnTvTDcdWuaOpdPh82jTY9u2LTcOGoZEUzoUXrVrBauVKbFy1Ck2bN1ete9++4WnVqvh08iT+rFsXgyTvPXlLihXD/5Usia4yWNXvLl+Ogm3aYE+XLvAXl3W7fRvzhw7F3MuXsX7jxlS5A6RNrFWrVkJNPDPeSbwna9asEWQB1Rlv1T4Kae1NHXindeVy4HXqwDsH3rSMdJk/7FOEatzE2j3W8KntX78C6a3158a3n5YWOlHyhqyxefKkWDNqxHaZO1eAqWpKsART8od6op8/f8Z1ZrkJeU2ltnXekCEIHz8en0aPhrGUTSuh86xhq587N9a9eoW479/RNXduTL99GyYmJiq9n6w1JLPupVWrUJxkU3Ig2OIDn8iTB3liYpCPpEyZaJQSI7z0NfViY2MTM7apmUgKB7t2JQbehO0yCJdjd2xtBShoWSk1lTkZUUJyOWa0DpUqhYoMPJnd50GlHPvq44Pbhw9j7/TpQk1tkjVyccEm6qRLIEG+rl4NgxYtsKBZM3Ti86SgMfv3sGVLCJRslLQSy67/1gUPyMjazW/+5MnyIfMJCYg1NsaaL1/QFsAFPz+UYAkBxxAxTCvoosqaffDywpPTp3Fy6VLwmZZm/C4t/vwZVYjoUAGbdarO9+2Ln9Ono2TRorjMYEuOtS9VCot5OLJuHSBFZi7p0oTp06HRpw+GtmmDsSrmS/j69SsGm5tjBr8DRG1ER2MigMKbNiEoC9QWUl1fOQ2utWoFl5UrcXTrVlSUchj7bdw46A8diin9+qEfn3cVWsKkSdAQkfzd1tREwS9foCn2XnyPiIBeSAjmNm6MzmIHbOIu/HzxAtq2tojy8kIAv6+06IHYLHcAACAASURBVGjEVa4MrV+lWJMA7KhcGYfSoOfOUi6ywifxnlhZWQmZeRITqgNvFT4I6ehKHXinY/Fy2qXqwDun3bEM9PfLl8TMTbVqiXJQZIxmNiwb1NBm4KzVXYtWIK21/iQ7I1HPhZIlUYK6syS2kWAuX7p0KbTatUNzExNokcFXLFDZsGEDmjRpgnlz5yKMLM7cNMmBXtLdPyMiUD0kBF/q1YMNSZAkbOzYsRg2bBi+lisH/QsX8N3AAK/evMHqESMwQMVkUCTUYZ3h3SlToEko6PHjpLpN9bk67O2NCidP4vvbtzA0N0+1vaoakAiITMbRhDxSL12OBnXymDExgI0NMHYswM0/Za4YjMuxG56eMD1zBl+kQFdzMqKEG1/e8ydeXrDns64A03BCt274/n//h3ZNmmANAy0RpHdi4cIQlHlJwObm9r/VZI2xvT2WFCqEdgr0n3QhJfn8589HfQcHaCTVvsq6R6wp5z0lSkVbG+A9Zo25DIuvWhU3T5zAyMBArI2JgSaRGvQ7iyxu3DhoDR2KIE9P/MkSDwljQEEm7Kd588KO0GkluAbSPCXKynXujGK6urj89atcFvKOZmZYSH1p+k5pPlm2eTPQoAEGV6yI8VLI+dLsK/nmtmxBicBAGBUpAstr1xDXpAniIiIQ5OGBKPIE5CC7UL48Cp44gY+PH8Ne4uBXmMaaNUDz5hhcqxbGp/LtUmracXH4ljs3Tr95g11OThh7+za2h4WhjpjE4K2wMBResAA7Fi9GbcoPyrCHv3gdnuvrw4u/kQkJiPf0xKsLF/BQWxsesbEolJCAP8+cSSbBU8pPicbqvX96Vk/116oDb9WvabbtUf3yZdtbk/mOsS6qYcPE2kBmMngKn1U1fJk/+3/FiCrVX2VQQWhmKuUGf/75Jxo1aoTPDg4wZJ2oFF1fktEs9PDAPK4yM0HOzsnrzbpFytu8OHIEWrx+zhxh8yrP2N97Dw+458sHCwb7Ekat2rz37mEV/zZxIlCihMBoPMrBASNSydQq8yAkMYQTJj+RdX2UBmMAI2KkldfXyXbtUG7pUlz96y8UI4tyJhhJ8Mho27V9e0xnlpW125OYS1HAKLFEtMCVKwppIj9q2xYOy5bhr+XL0YDSY/8SS5KOiy1WDDq2toAM3d4U0yV0tFs3+Dg64pAoICaZlX7HjgjlYRRLKyTq7GNMTHDu2zdUj41VGPZLCau/HzyAI/W1+e1OzfiNZ50/a8H53ZdnAwciYcoUaBBizveXXAZS5NFSG1JlfxfVeXcC0Pn8eYEjQtxWr16NFi1a4IexMbR5ILZkSYq/P/v4DPff3Ye3Q+qHZAr7TFLFatXAyvqFjx8jj7QAEBDIraZYWGAcO04i1ZM1iAimPMLeHqPEZBoV9klOw7l+fui8bx9iV62CLuHXRF+4uAiSaO2eP4cND2aykclT3bicLx+0Hj+Gc1yc9PeFyB5vb/QrUABTWDqhIovdvRu6NWqgT+7cGHz2LPQcHbFLUxO+MTECkRntdOnScDl7Ft9fvkQuKyuZIx8uVgylr12D/pcv0CLpZv36CAdQf+5c1OjaFdPi4/Fx+HCMGjUq3d6r9/7pXkKVdqAOvFW6nNm7M/XLl73vT6Z6R5gss4fMhDELQthvcHBirbfastUKUH9V0lSlvzp79mx8Hj8eA0UBpAYDaWZGZRiJprasWIHnJJpKqnGWaEuom7eREc78P3tXAR7V0XZPPCGQ4B4CBCe4OxQLWqC4S6FQtECB4m5FinxIixZ3d3eHBncNRYIkSEggIX/PwM2/2exmd5PdzU2Y6ZOHNHt35p0zd2Zef/le0RLQubN4gpZXlk2pWbMmllWvDlBAo6Xla1kbfWMy4+ucpEnxq40NbBlrrOFSu3LlSpEs6IanJ3LwMwo6rq54njIlnrx+LbI3O5rJPZb1W1kXlwnLGtGSyYRjRgr21+bORe7OnXG8Tx+UZrytFdr69euFC+nRWbNQhsoN7m1jhWJaahRXVyaEo6t6NC146VI4t2qFWS1a4GcT3KWtAEOshmAG+3/OnMFd7g9mDWc5MUONTHS1akIgW/rqlYizbNG8OSavXIk0devChlZNrXa9RAmkPn0aL69fR3Z6GBhovMuzeXjgva0t7JhRe8oUQ1/54tHEknJ8B5Inj/75/6zdKF0aaN/+y3tgKGu64dFj98TnzwjNnBnHHj3Cxl69RLyqZuvYsSP2LV+Ou/Tk0lH6bv75+fh5+8/w+8UPqVy/lG6KdWO8f4YM6A2g9r59os60rnb69GlcLVECzVxd4UQld3Tt40eRmOsPAN0+fICTmc4uJr085uKCfLa2SEbLu6OjoCKgTBmEHT+O9X/8gY4GMrPHGi8jOzCm6sZjZ2fccHHBd1Ri6Wpfqwv8ZmODgYGBZgs58mvQAKk3bMC2hQtRv21bPK9dG4m3bcPC8ePRtX9/kUjzEhXYdnbIb2Ct93bujCpz5+LO+PHwWrYMz27dQrnUqXH93j3Y1qqFF/v2oaa3N06fP28kcvofk7x/rCE0awdS8DYrnOruTG4+da+P1ahjjBcFbdY5Xb78y7C0hlDoooXDTJe91eZjoYHMalGOBY1K/VVe6pottvVXKdBPbNMGvgDoaFjA3V1kWAUv+q+MmeZ4jM/OmDEjumXIgIEUOhkDXK2azpmVK1sWq0+eROqqVWH31RrHesjlypUDrVMt6P5HqxsZJCPCG7qmT4//0SWX7yiF9q+NgnB6Pz+spVDBJDtfY8BPt2qF4kuX4ub8+chB4cEMjbW7yeBfuXIFeUgD4yQpyBjRXj54gKSZM+NM+fIoGYO4PSOGiPII398VK1bg9ezZcKSwxVj6YsWM64oKBc6PJcSMURR8Tdo1IWtW9I+mTrFxg6vjKe43WjGbentjCoVp1pCOxnU0gurAQIQnS4bx4eEouns3smfPjoY5cuAs4/u1qgAo37k7aBCyjh2LXSNHono0ybeU51keaFG7djjIP/AdNHeMLjODM4kZS47xPmDtcSuGSOh8AwYPFgnhCiZKhL0s0ffVmsh1IsbtEifGIF9fnWUMB+4biA3XN2B1w9XIl0bDzT82rxrdg93csOj9e1z55RdM1pFIkt3TQ6b1xInIWqYMXI4eNTjim7RpcfDZM3jqsOwb/LKeB84uX46iLVrA9/vvUUBD8RO+Zw9sqlXD+MyZMUBPebmYjhnT7xmquvH26VMkSZcOe0qUQFV9OSvCw4X3w8KgIGTetQvV9NxTJtH4+TMC3d1x9N07lHzxQngTKfiNzZsXAy9fxtkTJ+BdujRulC+PAgbO+acPHyLc0xNKtXamgEvTu/eX94h8WYsW4E23OiAgauZ0kwhnygM/UTFBxnibCJyFHpeCt4WAVWO3cvOpcVXigCYleZImw/Y1tkxYwevWjQOi4m5IS1qUzTErpf4qrcWaLTb1V5kFnN9nLl4fFxd4OznhOzs7zHr5EqEjRsBeR6buffv2oUqVKrhevjxyMlaRbpPOzjqnyJqkD2vXRicHB9j9+y9CkyYFrYcsRfb8/n2kpAWVoQ4G4ruVzqeMHIkuw4YhuFEjJFu9WvyZsdZZsmTB9WzZkJMWSbqafxUOrp8+jQwlSuBJoULIYQaLAcdjoqz//e9/eH/lChxy5Pjitm1k5mFhCbG3h1OaNMhJS5mFmyI0Fi1aFJsZT0wrIC1dpiSbo/BlIJFYxDTCwvDJ2RnzwsLQ/PVrnYxidK6jFoYjRt0rccNbmzdHLTLChuJzNUb5lD8/zly6hC0DBoh3vtX16/iVbrFUEOlwSQ5+8ACOmTPjQNGiqKwjhll7Ai1atECB9evRj8kJ6bUUjUtrjCbPL1FR0707QEto8+Yx7sZsX3z0CJ+zZsXS0FA8mzhR1CVmU8JfLpUuDW8mMOO5pKU8brymMZzsndCxcEeU9yxvNpKoePz36FEUcXcH82Yk1sr8Ty+fXGnS4DZpouKAFQIMtNdVqiB43z5smDkTP3ftauhxoz4/WLQoyp07hze+vkjGUBylhYfDn4kRnz+HzZUryM3wnzhuhqpuHJs2DWV69cKFX39FoWgSp4UWLIgjvr7YP3gwRhmBu6FpB+/fD+fKlTEpd270ZU4ctrAwvEmSBIeDg5Hr5k2sGTQIv61ejbfTpyMJ946BNr5CBbQ4fBhrAIxPmRJHjh5FTnq8vH+P0JQpsSI4GCm2bRNeYsa2e/fuiUoW9vb2IqEaS19K3t9Y9KzznBS8rYOzKkaRm08VyxD3RNCKxRIYtGIoJVfIwDHGq3btL4lJvqFmKYuyuSBk/VU3NzfYGaq/bMKAJUqUQOYHD7CKHg7Dh2ORp6fIFszIz+qOjrC/dQvIlClSj4ztPrh7N/ydnGBTsuSXd0hPo2tjtZQpsY+JY0aMQI//LAQzZswQJU420oW1f3+dpcj09Xf37l2c9/JCHWdnOD1+jFA3ty/MyMGD2E1LopbbOwXP5SlSoMnr17DXkXndBKjEo+zP29tbMNanGJJBpp/MV+7cRne1MlUqNHj5Eo5kwi1sPbx27ZooRUNFwc/czyyvRKHEgu11vnzwu3wZdzduFOusNGNcRy1IVoy7VhIBPqlVC2kZz8s8CHoUTVEGGTAAnydMACtJP2dob7JkcGFcMvvR0865uyPD+/dI+uoVnN3c9D7HUI6M6dPjQkgIMjD+2ghBPcYgqO2LzBL/X4351nZ2GHz1qhAuWNGgUOrU2P/4MWx4JqyhGBO5lVlQBnVz1EXuVLlRN6cZFctfy7R5AWg2aBCY6FGzMZ/FjMqVsYF/pMKbsfIGWti0abDr1QtFEyfGPj+/WFs7wz98wOvEiXEjWTKU0lHO8NWECUg+YADmN26MDswBEMfNUNWNxeXKoc3Rowg6cQKJeA/pa23aIHDZMtQtXRqHDh+O9awuVqiAXIcP4/i6dahIpfHXFtiqFZyXLgVre3S0tcU4Kix5f+ope6dJCBUzDF1iEkeGfWmGFnxs3hyfVqxAs0qVsGnfPqNzP1StWhV9+/YVe2PQoEEYOnSoyGouLd6xfgXM1oEUvM0Gpfo7koK3+tfI4hQyDpYCdpMmwMKFkYdr3Rpg0jVaD7+hmt6WsChbfB1jMcCuXbtQw8cHjz08kI6x2syknCQJ6EpeM0cObLpzB6E+PnDVSNj077//iqzB87/7Dm3odsv3xECSMNYTbbZsGarZ2aHWx49I16oV5vn4wJblgJgQiqXGtDKiRzeteh4e2Ojnh/tlymBdnTqYMmAAriVPDje+q0wUpGVtWti/P9pNnIg7TZrAS09ZF2NhVOK7Z0ydim6MW6dFzcQsz0MqVsSoQ4fwee5c2HbqZOzQMXpu4cKFaN++PXyPHkX+ihWBHj101lSPUed6vvRp0CA4MF9Au3YYr1EKyZDrqDlpMGdftCpv37oVrxIlgg3d7nXEZusdj3sqe3bQNyNlrlz4ju+ngeSV5wYNQpGxY3GkeXOUi0b5uXz5cmxp0QIiX7qx7u/mBCYu+woKwqPs2eHx77849F8iqgsA7AH8lCYNHBjvyyRsOqy2peeXRruC7eBo54g2Bc2Y/I8CVs6c2JI5M1q+fCms3ixhOGzYMNSvX1/822vvXvgkSwZberoYE8bFnAr58qEXgBxUnBlIPmloObbVq4damzZhc6dOqMski9otKAiBSZPiho0Nirx/DztmvY/jpq/qxs2bN+Hr7Q0fGxskYTx/dMror0kOmXX+LKtJxKKu+6cPHxCQODGuJUmCcq9fRxaCmaekaFH8RuVLokTInTEjHBh6E9v2NVdECwBdjx0T+UWMaUw2ylJibEywyYSmPj4+6NWrl3Q1NwZAKzwjBW8rgKyWIaTgrZaViEM6mJm2dWv8kj8/CvbuDQpHEW3bti8Wb0vEDMbhlA0NbQmLsqExY/P5ixcvcOb0afCCdTZRQULLLV3Pmrx8iRG0fvz5J9CxYwQ5Fy9exNbChTEwLEzUcA5p1UpYqhmX/e/Fi3hMBpdliCh0GmDQ1qxZg56NG+OYvT2yMNGa0uj6zLrCzDNgQhs+fDgcRowQdYsfA0hmawsXWt8pwJctG6Un1q09lDw5Kn78CGcyyFmzmjDa/z9KzFhS6tKlS3jcsSNc6N5IQZ7KKxPaogULULJDB3ikSwdXZto11npqwhjKo0yCxzV78/vvsKO76v79QKVKMejJhK8cOybWof9/ibuG+/nB5eu7ach11IQRrPbokSNHRFjFxBIl0JNlnZjgkAkpTWn0ilDi43nOUtEZjaIpPDQU5xInRu6QEAROnYr0TOam7G8qyJ4/R9CVK/izcWN0fvcOTgULwoZJ0IwR5kyhW+XPBr94gSc9e8J51y6kePkS4XZ2cKLlc9w4oFy5KNSHhIbAZ5kPuhbrCr83fuhVkiKtGVvbtiJkZrqtLfaFh+NxeDjeMJkkgIYARBXp3377Eu5hTAsPR3jhwnh49Spqenjgn2vX4GBEHgxdXW9esgSFW7dGkKsrUj98iKR6kuqdbdMGRf/+G/OrVUO95ctF/LKaGi24zGvwKSgIt4OD4VClCpLy3I+ufc1sziCJyvPmoYMx+Rn09Lezb1/4TJ6MUz16oMS0aVGfYmI9xZuF7+HXOt+xwjAsDJ8zZMCe58+x5eefMZOKBCMaBXR6Wjh/vV/oIVOnTh3s3r1bCt5G4GeNR6TgbQ2UVTKGMYI3a67mzZsX48aNA+uEMoZStgSCAJNzlSiBp76+yPDxIz7/V1+2ZcuW6Ny5s9Cm2tBll7W9ixc3rjxNAoElPkyDmmsKsttXrkS1PXtAEeCTvT0e1aiBfcWKoYcRCZk4TyY4a1uuHK46O8OR9WSZAEYrjrdzhw5oumABGAl5omhRjDl7FoVSpEB/Bwe40d2WQpwOQVcbR6X8VhIAs0qXRkt+h0I3XbVjICy8e/cOdWrXRrpDh9AYQP7y5ZGVyao0Yxa1iPije3f8OHMm7HPmhDPdDU0U9tndxg0bMKxBA6wpUAA5mLyJ9NMl0wRrPfuhIqB9qlRYQbdvegswvj0al+KYvpd07S5evDjqVKyIVbS00tJz6ZLJ9Jo8fng43v2XjOz6kydY8+uvmPA1/tKQ66jJ41j4C0pYQZpXr7D782fYMy6eZZ5MFX543lI4oBLLx8eoePmZ/fuj8sSJEAEM3JcUvOm6+uFDpFn7FymCVJs3i/rf33J79eqVsDBHJ5hS2O6zuw86Fe6Eww8OY0Sl2JdnioQ5La9M4KjPTZsKL7qZm6JoW79eJMyjaLnK1RXVf/oJjSk48l3kD/c038dozqDHly7hVrFiKB8Sgk979sCpShW9r0rohw/wS5kSjkFB+MHDA1vOn49IXheX79ezZ8/QunVrITSyTcqZE31oTTbGOBASgvDUqbE1PBx90qYVSTFjosAg33wua1ZUDQuD06tXsGMpSe3G/CJUrtGbkMYNHclJY4Rj374ImzwZJZIlw4lnz4yi//jx44JvT0dejqFet2/jQapUyJw5sxS8Y7QI5v+SFLzNj6lqe1QE7w0bNuDcuXPCDaVnz57o0qULRJ1RZ2ek1mBMaalgOZomTZqIQzgoKEgw7iyb4evri5I64muYkGbOnDnCxcrRXIePahFVN2F0HeaaM8aHF9iAnDlRYdo0MOVH8qFDhTsSLTtsXPtNmzah2vHjIiYX/LsRwpW6EYgBdffuIbxPH1EqSriXUnttRKxWDEYy+isUBOi26L9pExh9z8jro5kyIfDhQ9T5zw3NH8Dr7t2Rg9lQoxEOXvj7o1u+fPj9+XNkSJIEtkyepKN0Ect39frxR1ReuxbMtBrRMmf+YiGvWtVo2kX25UWLQNdncynxlLOLVmhD7eHDh+iTPTuWffoEW8bJt2jxJT6ddarJQFGw0RRumOuALqt0C2UirIsX4bd+PTKSiXN2hg0TTTFRj6lC2FdC+/TpA6epU0VN38/p08OOtbVZd9hMAhRj6xnPl/LxY+z28oITXW/psli5siGozPM567J364bu9vYY7OcXURtYn+uoeQY1Qy/0yGBW5+vXcXn1ajxauhTVbW1hSyGHtbuLFDHDIIa74F4fxhCKiRNR38MDeTJlgne+fPj3wwc4ZsyIOdu2Yd39+7jg7y9iOGUzjMCFJxcw/8J84Wq+2HcxptcwoiSc4W6jPkGl5KVL+PT0KcIDAiAKdvF8rVDBKKWLdofhU6Yg7LffYM8qJDraZxubL2dSokSwpSCunGWJEiH0zRuEXr0Kx/Bw3OrRAzl1WWm1+gyl90TFivj340fsKVcOHVauxM03b0R5O3pZaSf3jAlE0X2H7z55FuYyoZV24/r12DNoEIrdv49qnp7IxDuLCkTuxVOnjMOUtd3XrkWK4GBUqltXJOLjvmFstTH75/z585jUtCmW3rqFV23aIKW1y60+eICw7Nmx69MnvFm2DE2NTXLIO+yPPwCu+5s38DtzBh5Fi0rB29wvbQz7k4J3DIGLj19TBG9dtPNQVWJddX3u5eWFp0+f4v3798iRIwcYa3P9+nXBTPOg5M/vv/+Ofl+z/LLUEa2phhotrQ8ePECBAgUwcuRIMAsvx2ByHmZkrFChgkEtHw9sGy3N740bN8Dsjoxt0Wy0Bj1//lxvzU1D9Or7XBcNMe2LeNAK3aNHD9QwIhmLMg4vE15YjGUKDg4WyaeUWB8Ka6cooP1nwSsI4NiZM/D09BTrRcGIje/Aqj//RPnOnWFHSxxdtQzVe43pJFX0PV72jx8/RvoTJxDapg1CQ0Jw0NYW1ezt4UCr05gxX2JkzZjczBADsmXLFtBdmHHVfRs0wIsBA/CTjQ0+p00L++XLEVq2rHiHq7i6osb+/Sj28SOeurnBrWNHJGLCJVpXPn7EpaNH8eLGDXy8dg1er18j239ejyEpU8KJYQX0bNDT+C7RVTn0wQM0KVQISah8IBNpJQzM+XpQqTi7UyfQzlWTZxUtkEa2d66uOPL+PZzq1MF3VDqkTWvkN3U/RgGU5ydVBlNtbFBQKRFHqz0ZSnoEEGdaK/jDTPbRYU5rKBn+p0/x8tQpbB01CtkfPoSIBmTM+6xZprtIx2aGoaF4W6IEEp0/j5WJE+N6sWJoOHo0MuTIIZjdNGnSiLM6ICBAZPvmWcVs96Y2vect8aQFMjDw/3+YzV3j/z88eYL3d+7A9vlzBN+/D5fAQLh9+AA7Yvm1PXZwQJqOHWFPl1EPD1PJi9XzVHzxXrz6NXMy71YqVJRGt1vetbIZh8Cu27tw6vEptMzfEsMODsOS+kuM+6IanmJs8cGDaF+zJug5lNLREY684wG4fP1J6eqKzGnS4M3Tp7APDYVTWBjeh4XhFoBic+agGEMWjG3Hj8O/Zk2k4n756i7/CkAQAEd3d4S7uMApWTJ84D5zdUWooyMev3yJ/MWL431oKJKnS4eg8HDc//dfuCZPDhsXF2TOmRMh/1UsvX7/PoLCwlC6UiU8ePYMd/z8kD5LFmTLmxfX7t3DjDlzcP3sWXSpXx8BW7eixrNn4r4KY2UMJiZkiBOrYdC7y1i+hIaEMmXwT5EiKHruHJIkTYps2bIJ6zdLQzJhaNKkSUXizH/++UcYlrJmzYrkyZMLHuwzQ69sbZEmSRI4M2+DJaoHGFibT+PGwWHgQOygwj1HDvw4YgT27NkDVhnJmysXGtepA6/06WEbHAy7Fy8QtH8/HHfsgH1ICJ44OYk8LoMaN8ZYKhQfPRIlQWWLWwSk4B23+Ft1dG3Bm1ZsajJj2mhxOnv2LKpXry6s4kzioDS6uVCYZv1EluG5ffu2EHhpZeehxlqFFBJpfWejdZz/z76Y/ElpZcqUERkfaZklvbly5QJLIdGVkoIi/968eXNMnDgxQtDXnCdLRc2dO1d8b9asWRFZI+n2yUzJdIcdP368sAozO6q4bL5qQ0kbmURmmqQnQJEiRbBy5Uoh0JMuJg2i0DZixAj8+eefwqpHQT8kJEQc7LT6c1z2zwy5/D6Zbn6HuJMJ1W4UlNkHseDzxFdpd+7cEUwqsTt48KAop8KsxaSHiYCIA7/HQ7lbt26gy1FiAPt//BF56AZHV113dxwICBBzVLJkEwcKnpwPW6NkybAyMBC2OXMifPJkhFauDAcTvBeIKa3ndG0iA8nEVLNnz0bv3r3FO6GpaSYDzR9bPWWLOJ9Tp04J/KnMIdPJuVPhw77YyKgy67e28oXzX7BggcCaNZgZt0ZlEct1UAFDF0W6wR5ZuRJ9X71CawDM+zw6Xz74fvgAh8ePsT1DBmS+fRuPMmVCKMvBVKyIfYcOicRVHI/vImmsW7eueAf4/lJYjpgjGZTQULx8/Bh7t25FzUqVcPboUSSytUUu1nU+cAAVChWCw4cPCH31CttWrMA9X184hoSA7H5OJtgh88GJtmsHO1q1yYBotH8uXMDmtm1R7+JFaBSKifTMYxsbBKZPj/C6dZF3/HiLuDjH9Byx9Pe43/juTZs2TTCsK0aNgtPTpyjg5YXj/5XtKZAjB1KmSQPXlCkREByMD05OGDRzJrb7+uIluOQVhZDo6upqFlLpRUSm6U1gIHjiTK9eHaWDghB+8SJsvjK8mgOF2Nris4sLPjk4wMXNDUEBAXCgEiY4GM4fP8Jeq777ZQDZ+/WDE63pWiXozDIBA52EBwZi6X97rWlYGBy+Pvvoa3bvF3wPbW3xLixMMOP8KV+lCtKkS4ebdIv//BnPnz5F8qRJ4ejgABsAJYoWhf+TJ7jl64t3/v5IbGuLRDY2yErFRFAQXD5/xqe3b5HEzg42797BTgsPXeRSAfkEwNOv//L32wCuUQFZtiwmL1kCT3p4xFHjmcb7kvcN7w7er3STpXKc57T2WRdHZMaLYZdeXIq3IW/R1LspWm9sjS3N9FdjUOuEaHklD1W5cmVMmDABzZo1E/cXyzaS92HySypiefeQX6FCi15S5XTEvBuaY+DLlxhStSqyvXiBj48egdHeFPIVUwnHFAAAIABJREFUYV/zX/7OH6evP8LKb6b2wssLSfv1g33TprG7r+ilNH06Pri64qGjIwI+fEBQcLAIteMPzxi7r0n66EOi/CQH4Mln3Nxgy1CBMmXMNDMTu2HOgMGD4fT773BiOKCBRpUJuepZAJhtn0kYWSSOZ5sUvA2hZ53PpeBtHZxVMYoikJ61s0OqsDCkTpMGT589Q/jXA4j/8sfewQEfP32K8nf3pEmFJVU5tJTnNf91dHYW33/z9q34Pn8oYD559kwccsrfGJv0mUIXgLLly4OxWhcvX474XJMmRycnBNPV8+v3lX4orIV+/hzx95SpUsHG1hbP/f0j/V15nmWjXr5+LZ4vVKgQzl24EEETBbfGTZti7969uPvggbC0792/PxI9mTw9ce/Bg4i/UahkH0EfPkT0w9rIp06fxjN//wj8kqdIgRdMAsMDME8ePHn6FC9evcIPDRvi6rVr4Pxq162L7Tt2wPfiRdja2SF9xoy4dv060qZLJzS0b969wz8XLyI0LCwKRrzsKGArP0m/uiOXSJsWKVnjldaSAgUQPn8+gvPkEcoGXUL/xo0bxWXNxqInKxIlgntQEJ7a2MC9alU4584Nm6xZsf/iRTz58AFZCxdG2hw5sPfIEWTMmhU58+XDtTt30KRNG9A5TtcVQQULs2u2atUKixcvxvr164UChQwDk1dR6VCwYEHBZDDUgZZfKhcUq082Ly/cvXNHXJRDBw3C3du3sXHdOrRs3hzTpkzBTJbksLdHWFAQpo4fD1aZ5k+96tUR+u4dzh47JiwHVHnQdkkH0gpfL98V6dLh8/DhaNWxoxifjAz3zI+MLfvPQsnILs7rIfeLszM+keH/+FHQkiRRIoQEBYlLm0xKqv8y24ZTA/3pk/jc1BZMLb+HBwJTp8ahjx9RcsoUZKDLYjSNCqZfO3dGJW9v5PPywppNm/DO1halatTAH3PmfPOabrqdZ8+eXShKdDVaPijwUBHExuR1VCAydIZMrrka61nz7KISiPGLZKrJSPNdOrlunWCW+G56OjjA/dMnUNznDxlcCrLcV8pPiJMT7tOyQQWUmxvqDBmCrAULisRgcdmoHHty6RJqOjri1IIFSOrvj5Q2NrB7+RL2b9/C3ckJDnQtDQ4WTLvt1ztIYYaVM1v8a2OD0PBwYXULc3TEm9BQvPv8GYx65t/4w9/58/Y/hjN1tmx4EBCA2y9egEwof5jsSvmd/7qnSiUUMVxjKuF4JlLBQuWKZkmfuMQwoYx98dlFZEmaBUmczLeHTMFmyokp8HT3RP3c9VFxUUUcbhf70lKmjG/pZxkCSA+S9GYKV9Gkd+fOnULhTQU2lUD0kuP5ycSJzLlBfpBGBib+4h2eyNkZT+7fxx8TJoh71/f0aSycMweZ06ZFz86dcefqVaxduhTF8ufHD7VrY9bUqeK51k2aIEuGDHDPmBE3X7xApvr1kcSEUo3RYszzfPXqL+UvHz4EyNt+/ozgoCC8DQjA2/fv4c+yk05OCKcb+ufPcHJ1RdZ8+eBerRpsmDwvlp5OZnkH3r3Dnc2bMernn8U9xdrkKVKmRP/hw3GX8d/u7rj3X5hYQJIk6Pzzz8JgtbZVK2To2hWr+/dHkwkTpOBtloWIfSdS8I49hvGmB0XwvlqliohDypc3Lz7QLe+r1fGJn59IAMS/h4WGit8/hYTg2Nc4YAo9adOkwc0bN4RFggIQtYXKT6ECBZA8WTJxoJ0+fVr8HsDSC1+fSZE0KZwcHQVjw8/4d/bv6uIimF3S8pqMmb093r55g5DgYLglTiz+TZI4MZydnODPusNfGTVXxjZRKHRzE9Yjfp8MHPultcTF2RlB79/DjaWSQkPF75r08ncnxmqGh4v5an6m9KP9t/iw2O9sbPDY1hbZqlSBnbc3UL06QEbciGRQdHOnCxbjaINevRIxvnTWzweA9h9TNdq01AoxRkPRoktho/034k+Blf9q/26uNSBT/zZjRrj/8APQpUuUeGcyFRTWyNjcPH8eGzp0QAlaoJ2dkcTRETahoXBLlgyBb98i+ONHOCVKhERubrh2/z4+2dkJdz/XFCnwmvvI3h6JkicX7nbBdAEsWxYOSZLgydu3OH75Mvz/i2krWK4cfBo3RkNe9Ho8AAzNfcV/FnNmqucec3d3x9q1a3XmYjDUT0L9nBp/4kPPElqFmOGdSSSZ2Z7Z2/39/YVHCvNX0GvDnAK3LkzJvDLum94SVErRO6hevXrCa2XHjh1CScV/yVQzTwN/Z3khKuPoTUNFAufC5EOcBz1q4lOjC/XtW7fwj6+vyDPB+vL0iqIgTEb+zJkzwpOJFl+6V7OOOhvDkai44Fox7InrR2yaNm0qlHpK+AiFBbqTMvSIihWuKUOm2Bc9qGSzPAJ1V9SFu7N7nLl4/7b3N9TIXgPlPcuj7IKyONr+qOUnLUeIQIBKAe5tJcs2z1h6n1H5yDONdxX3Zlw2KkNjkngtLmgmX04MeT5qtwsXLohzjTW7RWMJw5o14bd6NTz+q9MuLd5xsWJRx5SCtzrWwSpUGJPVXBchT548EQnYyBxqJl+jkEZXbzJ+tBDyX8VlmMwTmSR+PnbsWMFMMmbG2EZXaDJI2vEodBumto8Mq+JqzD7JwB07dkwwbhSYeJgrB70yJi0b/IwWVro+05W7UaNGQuhnFncKmyw5wYOL/TBGmowaBS+FAacgPnXKFNy/d09YYnt2747JkybhY0gIFsybB7r9NqhfH9WYgOqrQE8XSiotZkyfjjFjxqBjhw7o0b07Gv3wA1o0b447t29jx/btWLxoEYox0zRjDamlDQ/H+3fv4PvPPyhYoAAWLliANwEBQgnR9eefcfXKFXhlzQpnJdMpmdJEifCRcZZv38aqJAjjCwcPHizc5PkvM2pTAE5HC3G+fKhXsSIObtiAoKdP0bd7dwSw1E1AAD69f49cXl7ImiGDiDF+8/q1UKxQQUJm+dmTJ0Khs3/vXuGGXqRQIRw/dgyXL11ClsyZxXzs7eyQJn16bN+5E/4vXyJX3rxwTpQI2XPnxtp161CmQgXkyp0bwZ8+wSVxYuENMXLsWOF1UL5iRTi6uODWo0fo1L27EG4Zj7Zw+XKUqVwZ+YoVQ5CNDRJ7eX2J1zIhQRGTVvG9ZGLB6C7pdu3aibj5qVOnCsFJiUflvBkeQKt+bZZt02jmzBFAnPnuK6EExu65b+U5xaLNs4AYKUkgzbkGpmDJcRn7zXOJXjmyRUYgrtZFroN5EGANbbbjHY6bp0MTe+m0pRN6luiJvKnzSsHbROzk4/EcAZY4o+fe0qXwaNlSCt4qWU4peKtkIaxBRkwFb2vQprYxqDCgYE8XdH2NscuMCzfWNZGaXyaQGzJkSBQGOz5oXCk4UgmhWeOTwou2gsOYtaSFSlPI0JVllLHydGszxur4xx9/iLhHZum3VKMCioKapvJJ11i01tEaxzwEskkEJAISgW8VgdDPoai0uBJsYIP9bfbD3tb6mdibrm2KSdUmIaNbRil4f6sv4rc676/J5fzmz4dHhw5S8FbJeyAFb5UshDXIkIK3NVCWY0gEJAISAYmAREAicPvVbYw4NAIUwMdXHg/PpExXZd1Wc1lNrGy4Em5Obqj8d2Vsa74NzvaMLJZNIpDAETh3DihaFH6zZ8OjSxcpeKtkuaXgrZKFsAYZUvC2BspyDImAREAiIBGQCEgEdtzagbP/nsW7j+9QO0dtlPMsZ3VQyi8sj4NtD8LWxhYNVzfEzJozkTZx7MoCWn0SckCJQEwQuHIF8PaG37Rp8OjZUwreMcHQAt+RgrcFQFVrl1LwVuvKSLokAhIBiYD1Eei5oyd+yPODSDwlm0TA3AhMOzlNCLkvP7yEu5M7WuRvYe4hDPanmVCt4+aO6F2qN3Knym3we/IBiUC8R4C1x7Nnh9+ECfDo318K3ipZUCl4q2QhrEGGFLytgbIcQyIgEZAIxA8ECs0thGTOyUT8rWwSAXMj0HVbV/xY+Ec8fvsYl55dwm/lfjP3EAb70xS8f939qygrVtrjS8I32SQCCRqBR4+ATJngN2oUPIYMkYK3ShZbCt4qWQhrkCEFb2ugLMeQCEgEJALqR+DVh1dotaEVXn94LUos0RVXNomAORGotqQa1jVeh3sB9zDrzCzMqT3HnN0b1Zem4D32yFgUSFMAtXLUMuq78iGJQLxGwN8fSJ0afkOGwGPUKCl4q2QxpeCtkoWwBhlS8LYGynIMiYBEQCKgfgSOPTyGjdc34nnQcwwsOxA5U+ZUP9GSwniFAEuJsYxYYHAgmq1rhu0ttluVfpaiK7ewXETt7tlnZiOJUxK0zN/SqnTIwSQCcYLAmzeAuzv8+vWDx8SJUvCOk0WIOqgUvFWyENYgQwre1kBZjiERkAhIBNSPwPzz8xGOcJH4Kl3idGji3UT9REsK4w0CFHrLLiyLY+2PCZrLLCgT8bu1JvH+43s0WN0Au1ruEkOuvLwSL4JeoFvxbtYiQY4jEYg7BD5+BJyc4NerFzz++EMK3nG3EpFGloK3ShbCGmRIwdsaKMsxJAISAYmA+hEYcXAESmYsKQg94XcCwysOVz/RksJ4gwCF3vqr6mN3q90RgvfRdkdhY2NjtTn8+/Zf9NrZC6sbrRZj7rq9C6cfn8aQCkOMoiE4NBgBwQEyC7pRaMmHVIdAeDhgawu/Ll3gMXu2FLxVskBS8FbJQliDDCl4WwNlOYZEQCIgEVA/Ap22dEKPEj1EfeNf9/yKVQ1XqZ9oSaFqEHj27hnuvr6LUh6ldNL0MPAhBuwdgOU/LBefs542f0/qnNRqc7j+4jomHZ+EeXXniTFP+Z3CqiurMKX6FIM00GJfb1U9kXxwUb1FBp+XD0gEVImAszP8WrSAx4IFUvBWyQJJwVslC2ENMqTgbQ2U5RgSAYnAt4rA03dPUX1pdXQr1g0di3RUNQy1l9fGkvpLhCCk6RKsaqIlcapBYPjB4bgfcF+vUHr+yXksvLAQM2rOEDQzkd/wCsPhldzLanOgdZvu5YqgffPlTYw7Og4Lv19okAbmP9h9ZzcuP7+Mw+0OG3xePiARUCUCSZPCr04deCxdKgVvlSyQFLxVshDRkXHz5k306NEDR44cgaurK5o1a4bx48fDxcXFJOql4G0SXPJhiYBEIA4QoKXp7ce3whIb39qqy6vw6M0jrLm6BkfaHYGjnaNqp8Bsz6SRrr+Mv7W2G7BqgZGEGYVAyXklkcghkd5SdBRaTzw6gWEVh4n+euzogTYF2qBI+iJG9W+Oh/be3YujD49GhFH4v/dHxy0dsbHpxmi7/xT2SSijtjffjjor6ogEcbJJBOIlAmnSwK9CBXisWSMFb5UsoBS8VbIQ+sgICAiAt7c3PD09MWTIEDx//hy9e/eGj48Pli5dahL1UvA2CS75sERAIhAHCGy/tR0jD43EyR9PxsHosRuSWZOpMLj24pqoFVwze83YdWjBb2uWWfp+5feYV2ceUrmmsuCIsuuEggBjn2ssqwEKqCxFp6stv7RclKrrWryr+HjYgWEo51kOVbJWsRoM66+tx4OAB/il1C9iTNJbdUlVHGx7MFoajjw4An53qs9U0DNkcb3FSJEohdXolgNJBMyGAOt4Fy4Mj02bpOBtNlBj15EUvGOHn8W/PWHCBIwcORIPHjxAypQpxXjLly9HixYtcPXqVeTOndtoGqTgbTRU8kGJgEQgjhDwWeoD/yB/7G21F8lcksURFTEblnWCC6YtCCc7Jxy4fwCjvxsds44s/C1tAaTb9m5oV7CdVa2RFp6i7N6CCFx6dgkzT8/EjZc3RPI0XZ4d009NRxrXNBHZ8v84+QcyJMmARnkbWZCyyF3T1f1z+Gd0KNwh4gNNhZM+QiYemwivZF74Ic8PIjlb83zNUTxDcavRLQeSCJgNgRw54OflBY+dO6XgbTZQY9eRFLxjh5/Fv12hQgUkTZoUmzZtihgrJCQE7u7uGDNmDPr06WM0DVLwNhoq+aBEQCIQRwiw9m+t7LXgndob3+f6Po6oiNmwfXf3RcM8DZEteTa03dgWW5tvjVlHFv4WY9G7bu+KdY3XiZEoaORIkQP1ctWz8MgJp3sm7uq+o7tIvqVkzU44s4t+JmuurBEhFf88/QdDKwwV77t2o4W7vGd5VM5aWXy0+J/FCAkLQacinawG07ST05A+SfpIwr4xgneDVQ0ws+ZM8d3xR8cjb6q8qJOzjtXolgNJBMyGQL588EuVCh4HDkjB22ygxq4jKXjHDj+Lfzt16tRo3769iOnWbHnz5kWpUqUwb96XbJ3GNCl4G4OSfEYiIBGIKwSev3+OHzf/iA6FOgh37QFlB8QVKTEat/2m9uhfpj9ypsyJUvNL4USHEzHqx9Jfuup/FbRA/lnnTzHUiksrRH3j7iW6W3roBNM/y7EVSlcIk09Mxs4WO+HiYFrOlfgMxKhDo4R3BLOEl81UFlW9qkaZTtdtXUWCQXqAsG2+sRnX/K+hf9n+Vps66aSlunq26hFjGiN4a9Yc12U1t9oE5EASgdgiUKwY/Jyc4HHsmBS8Y4ulmb4vBW8zAWmpbhwcHDBq1CgMGBCZAS1btiwolK9fv17v0G/evAF/lPbkyRMUL15cbj5LLZbsVyIgEYgVAgfuHcCeu3vQMn9LTDkxJaIMUKw6teKXWbf4z9p/ilhpxoayDFHKRF9ChNTUGMPKWPpxVcYJspiAioLRxKoT1USmqmlhrPDaRmsx+vBoYQ2ldfdbaS3Xt8TISiNx+MFhETetK4N/k7VNMLnaZGR0yyhg4Tu37dY2jK8S2YhgScx+3f0rGuRuEKnkWcVFFbGn1R442DnoHJrJHTWz/G+7uQ2+z3wxsNxAS5Iq+5YIWAaB8uXhd+QIPADJ+1sGYZN7lYK3yZBZ9wsUvEePHo3+/SNricuUKYO0adNi3bovroK62vDhwzFixIgoHz169AgZM365DGX7thEg4zT0wFCsb7IeyV2Sf9tgyNnHOQIzTs0QSYzILLPu7/42++OcJlMI0GTqB+8fjEqZK0W42prSj6WfZamk269uo2/pvmIoJqAasG8AVvywwtJDJ4j+KZyVXlBaeDRsur4JV/yvfFOCGa3Gh9oeEgqbXXd2YWzlsVHWtfLflbG12dYITwCW5eL+nltnrtXeAdaq71WyF/KkyhMxZr2V9fBXnb/0JhIMDA5Es3XNsL3FdvGdM4/PYNmlZfjD5w+r0S0HkgiYDYGDB+G3ahU85syRgrfZQI1dR1Lwjh1+Fv92bFzNpcXb4ssTrwe4+/ouaJXIlzofqntVj0iCE68nJYmP1wj8tOUnkQU5f5r8YKx3fCvjo+nGuvbqWiHQ9iltfB4Oay3e/PPzYWtji3aF2okhabWssqSKEKZkM4zAm5A34uzc0WKHcNFvvaF1hKBm+Nvx+wkqHeiKzb35MPAh+u/tr1Nho+muzRk/fvMYv+z6xarx8E3XNsWkapMirO6ko92mdvit7G8ip4Gupq0giG6O8XslJfXfCgIyzFRdKy0Fb3WtRxRqZHI1lS9QPCbvz3N/wgY2ouzRuKPjsLSBaeXp4vHUJekqRYAW410td8HJ3gmtNrRCv9L9kC9NPpVSG5UsTcH71stbGHV4FP6u/7fq6GcytVwpc6FuzroRtGkLSqojWkUEMbHa78d+x/zv5wuqGM/POuh2tnYqotIypDwKfITeu3tjTaM1CPschkqLK+Fwu8NRBtOOpQ76FASWraObt7UavWZWNlwpSvwprfeu3mjq3VRvlvIdt3bg3JNzGFx+sPgKS6fFR+8ba2Ec1+PQiyJt4rRWzZYf13M2dXwpeJuKmGWfl4K3ZfGNde8sJ8YYb5YTS5HiSx3JlStXolmzZrKcWKzR/bY7aLSmESZWmYjMSTOjwqIKwt1cjfGo3/YqfTuz17Skcdb77u4TcciTq0+ONyBoChssY8T/V6PVvv+e/kLoLpOpTAS25RaWw8E2B40SHn/Z+Qt6l+oND3dGDn57bf+9/WA+glHfjRKTp5JoeIXh8EruFedgMGM9M3H/XOxnvVbd2BC59eZWkc1cEUx1KWy4l/k+adb41vW32NBhzHfLLywvanbTu0NpIw+NRIkMJSIlXNPsiwpplgNsU7BNxJ+9Z3ljSf0lIpmebOpBgIqfkvNLIl3idMKLoZRHKfUQpyJKpOCtosUAIAVvda1HFGoCAgLg7e2NzJkzY8iQIXj+/Dl69+6N6tWrY+lS0yyUcvOpfLGtSB4vLMYonvrxlBiVcYpbbm6Jd8msrAiZHMoAAnPOzgGZ8piW0Hob8haN1zYW7rtsoZ9DheB68seT8QJ7WsZqLa+Ffa33RdCracFX0ySYOb5PqT7InSp3BFlUxP1R/Q9kcMsQLamvPrxChikZRJwsk+B9i23pxaXg+9qlWBcx/XFHxiFv6ryRPAjiChe6V1fOUhnrr6+P2EvmpIWCa9H0RVEze03RLZPMbWyyEa6OrhHDvPv4Dj+s/kF4r2g2a3tVUPg/0u5IJBpoIWXyQ1q9dbUh+4egYuaKkXIzUAnIxHBTqk8xJ5Syr1gi4PvUV9STZ731E49OYESlqDmNYjlEgvi65P3VtYxS8FbXeuik5ubNm+jevTuOHj2KRIkSCWs3LeEuLqaVL5GbLx4stpVIPPvvWcw7Pw9zas+JGLHh6oYYVG6Q1OpbaQ0S0jCH7h/CzDMzhWXp19K/Csbc1Hbn1R2MPDwSi+stjiQMTqgyAVmTZTW1O6s//+TtE1HXeW3jtRFjs6TSj4V/VN2eYp1i7v3UrqkjaO2zq49gYBl6El2bfWY2Ljy9AHtbe8yqNcvqOKthQLrq50yRM6LOPBWXaih/9+zdM7Tc0FK4c1MJNLf23EjxzebAjsnJ+O7QvZet4+aOIoEZFQ9Kux9wH4P2D8KyBssiDWltwVtX6TBtpYk2Jm03thWJ8jRjwBmf3mtXL+Feb67GOzgm56S5xk8I/dCzg1nz6+Sog4ZrGlo1jCE+4Sd5f3WtlhS81bUeFqVGbj6LwhuvOueFlT15dsFoK+3cv+cw4/QMUQJJNomAKQj03d1XWPuYGb/jlo4iNrt+7vqmdCEsFuuvrcfv1X6P+B6z7rPeNMMg1N60a2OT3r99/0ZAcAB6lOihKvJpid/beq8QnpXGesUfwz7ip6I/RUsrv0sBhIIdPWZsbGxUNTdrENNjRw+0LtA6QnBiPD+VRnRHjsvGhH73Xt/Dr2V+FYpVeo10LtrZrCRpC89jj4wVyRBr56gdMQ6FyiW+SzCtxrRIY1MQZjy4puu3WYnT6kyX4G2oPFiVv6tgc7PNSOSQKKI3eohVXFwxivU8prRzbXrt7IVLXS4hS7IsMe3mm/7eh08fUH5ReRxrfwyOdo5gFv3lDZYjTeI0qsCFoUYMr1BD3gfJ+6vilYggQgre6loPi1IjN59F4Y03nfNC8FnqI5LOaJYQ4yVRYl4J4Z7Ikk6ySQSMRYAunXtb7RVJ0d5/fI/aK2qL/AHFMhQztgsR7nDz5U0hNGg21sOeX3e+ahgqfRNiaaUtN7ZgQtUJEY88f/8ctKAppYmMBsPCD+pywb3w5AL+Ov9XtFbsXbd3Yd21dfizzp/4edvPaJGvRaQ4cQuTrZrumVBsW/NtEcIZBdzvFn+nM8mYNYmm1wIVXmUzlYXfGz+xRhQizdm0Be8Vl1aIzO7dS3SPGIbvyenHpzGkwpBIQzNJGUvWuTu76ySJd5C5FDm6Qj846PFHx8Fyevpq1uuzypvTWs93xSebD5I6J0WnIp3MuTzfTF/Ms7D7zu6IuvBUOlHhY8068dGBzcoRk09MxoE2B+L87pK8v7q2hRS81bUeFqVGbj6LwhsvOmdCIJZ0oXuWrlhcMm5/X/wbd3vcRRKnJPFiTpLI2CPAjMOM3eV7YWqji3if3X2wsenGiK8y9o6Z8gunK4xSGUuhnGc5g93+de4vIbjTkqjZpp+ajmOPjmGaz7QI91aDnUXzwJ47e0DlExNEVfOqFuEGvubKGjE+LdRMrtTEu4lJw1DoptW7f9n+kb7HBE87W+6MENKIMwUMRblFWg7eP4gi6YroFUhMIsSIh3UJ3iGhIai2tFq0JcUYPzz6u9HIljwbjjw4IoTwb62+MYVsrql20jxLlL+jpfXRm0fwdPc0KJAqyQmZY8DF4UsYmrlpopWxzoo6wltCaSf9ToJ7RzMJ4rKLy8CSa0oMvPJsi/UtMOa7MSKhp67GuPB5deYhmUsyI97i6B/RV76Me3Tqian4q+5fUToghmUXlhVWVO3GBKRULjrYOcSKNtLVaWsnEQbA0mb/q/k/iyTBixWR8eDLww4MQ8mMJVEjew1BLc9RrtHqhquRLkm6OJ8BQ/eYB4HKTIZL0ROFCqcqWasgsWPiKPStvrIa9XLVE9Z7czfJ+5sb0dj1JwXv2OEXr75tqc3Hy4ouimRaE0oj05DEMUm0zA4ZVVPnTJfE7CmyxxlMA/cNFGv1S8lf9CZRmnJiirCW3Hh5A2sbrYWzvbPq15dzovuiputsbECm8EVLJWPwmC2V7mLUsDvYOhgUImnlpPa9S9EuUd6fwOBAUdrGHFYdzpn0aPbFGM8OmzuI2F13J3dh1TGGURx9eLRgEObUmiOsZcRSM1lSdFgOPTBU4KRZmorPd9jUQWR5JhYnOpwwuFfGHB6DIumLCEuQZmOWZiavevz2MSp4VhCus8bMSbMPWt9YPqvZumaiZvWl55eEtXbH7R1iPTjfPCnzIDgsGNzXT949ERZ7JhwcVWmUUe6Ci/9ZjJCwkCgWrOEHh6NY+mIC1113doGuuTlT5hSJzCi0TDg2AQXSFMD5p+cxvvJ4kdSJ1jqeFd6pvcU0lDX2f+8vKg9ovz/0MjB2vXhe00VTO+kUx6GgRqFD3/tZcl5JsZb8nEIhy2ipxd1cl7WUa635rlBZcMX/Sqzcr1nnmYKbUkpWSmCzAAAgAElEQVRMec/oRbS60epIpatiegZRQco4epa1Yu4Avp+0KlMgpaCWJ1WeKF3zXWJG7gXfL4j4jN4iDB0yVK2Cijd6PGhmuddF+93XdzHs4LBILvU8c7ps6xIpHGTayWlC+Gmct3Gkbrpt74YOhTrozHnAfec+3l0I9dwrsW0Xn10E8xHMrj07Ule6cjEoD/Ds7rSlUyQlovIZlQYjK46MdeZ6JnfjXm1fqD0YskCBi3vOUEZ87jfeQxQwNV31qfhkeTsKoJZw4dceL7brYo7vK0omJu/TNBAw0RrPc23lrTnGZB88T95/eo9jD4+hVo5aerude3YuFvkuEmcl9yUTECZzTiZKY6Z1TRvl7OC7yr1KoXxe3XkxXkcmfCS/FhYehglHJ4g7ecGFBeifvz88PDzw6NEjZMxounLdXPjJfr4gIAXvb+hNMEbw1sW88G+sv7mp6aZIDBkvqb139wprDbOYLv9heQSa/CxVolTYd28f5p6bi9b5W6NOzjomof0y6GUUl2cKxGQSyGhqX1TUKFKTHo5wUZ9am3kk48K58GBefmm5uNwVIZhZS+kqysuQSWNy/y+3sLDpOlyv+V8TQgRdz3a33B1RZ5iabFonmHRHl8aeQlfayWlxq/stYVnkBUy3QNJFd70xlcdEKzjefnVbCFPEgHPX55bHy4EYabvzKdp80hwdk87De9qpaaJ/Wud4kDNDLpk3Xv6MV22erznoTsiEQkysw2asmyCf23Rjk7gUyCiQXjKz2qVaHgY+BH80mTBiTwFKe20H7B0gcGTSHwotXAsKN0ojw8h3Qx9jQppYuoqMLi/KZ++fCcsML0Rm6mbCr67buwprKEvkKHRT0KewRSFYUdSQKV91ZZUQRvuX6Y87r++I51nCpsCcAvguy3eYVG0SKERxHoxh5ppS4KQGX9vqTAubtkKB9H7393eoka0G+pbuK/CjNarbjm5CoKML44rLK8TacS/8Vu43vXMn7XyXmQiJyobXwa9hZ2Mn3rGuxboKq3B0rfrS6sLKoM99lIJnwbQFhTY/utZzR09RwodWcu1G5m/S8UlCGH336Z2wDnPe0TW6lNKqzb1OHPgOcH+zDBKFBSonDj04JCzymgo0YsvY8jYb26B+rvq4+eomNjfdbFD45jpy7RrmaRiJrPNPzmPEoREo61EWJx+fFAL96COjccrvFCplroRB5QchfZL0Yq/9tPUnsE4yhUWuAffZ78d/R5UsVQTDRwUChRm+L8QpX+p8mHJyiji7SCuVPTyLoyt7pJ09XpNYWjMXfb9IZ6gJMWP8vqbrMpUrdDHm+mo27hvuXc24X31rxbOcwjBjanl2fvr8SexhnnVM0tV8fXM0924uxlH2L/c615eNTDDPM8bTV81aVeQIYL4K7mNiwwzffA+4xlwf7iUqQmhFpgB75t8z4nme2by3GK/MUlz6LE90U2ZIRL8y/SJNiYn06MUwstJIk+455WG+dzx3Vl1eJfYuM8ancEmBZvmaiXeC9xLnzDwI2rHT7IOMOy1rmknO+u3pJxJP6fM44dlCpRszdnOP0doc3bujK5xCV5kwZgavlKWSOOs0m76/8xmuF9+/0ZVGG8wzoPSp62xUPqPygvwHPTQ0mz4XdD7Dfc97QJe7MgUZJlwzNXeF9svAWGTmSFDCvOiBs/P2zmjLJrJm/IH7B8Rajj82XiSw5HrxbLn16pY4w8iH8R3ne0uLKs8DnndsPDf4zhur8KWSmf3wbmCSPLpLUyGfO2VuBIYEir1D7MlnkJ/6dc+vQtjlHUQFopIgkzwca777ePmI9/nlh5eg8p9nN/ceE93xbNY+Mw1tIGJGpej0GtMjPUrlEatrzK0z11AXEZ/z/eV5qu/+Uh7k3BhCxTOJPAuVbMUzFI9yr5LP81nmI0ozKvcKla3kk1zsXUQ4H71SqDDguvA9XXN1jeBd6DVFPHhf62qklfPjeTmjxgyxnvx/0k8lPPkOnqM8h3h+8pw88vAIfJv7SsHb6DfC8g9KwdvyGKtmBEXwvnDjApbcWyIOesbE8OB8/eG1OMD/ffuvsNiQ6fN95ivqI846M0swMA96PRCCBzPiDqswDIt9F4sDgNaatx/f4u96fwtmncJZlmlZhLWN7qa0LJFZ4KVFxupD6AdhcaBAQEGVwgbbpWeXBJPMuFAyjzysWuVvJQ4llv7IkCQD5l2YJ4QqMkjtCrYThx5LSNAdlcIcDx8ysGRYy2Uqh1GHR4lLgBYsCk7UqpOxypI0C049PoWOhTsKZqznzp4ik+mYI2MEM0ulwXG/48iVIpeYGy8Z/tswd0NMPz0dPFx5kTwIfCAYBQo6uf6XCx5uHl8STBXuKDTQzPb8277fxOVPhnfOuTni0qLAtPCfheLiIqNZK3stIfR9n/N7MWce2BTGKVBtvrkZFT0rCuGXDDrXivGFDVY3EEw8GRxeirxomf2ZzDExXtd4XcS7RyaYGvsyHmWMjoHiIU9BgJc7E+LceHFDXJZVllQRVhjW8OV8eUHTOs41U6zp1NxS0GWCHVpRKOSQuSDjpqwjmeThFYeDibn4dzK+xIHrRzqZ6I1CbaG0hXAv4J6IDcwxI4dgbMlEkFFjWRi+V7S4VMtaTTAnxI59rGq4SgjpV55fEVjx9x8L/Yj5F+aLUkpkYitkriAuOjK7vKQa5G4gBGaup2Ip4yX/vzP/E8w958t14vqT8aEGm9YGVwdXgQOZEq4PS2DR/ZIWVb5rfm/9QMsE30sqq/g+ZnLPJKxMSmZ5Mv9ca+7Hxnkai8R3vIiHHxou6KUwQNzJTHI/kc49d/cIQYLadNLDy1gpB0XmgCWiuC4U2jhPCv/ET1Oo4N7humkKsnwvORaZJu6z6IRmWj2p2dfXKCxzD2hm+9b1bOM1jTG1+tRoy1nRE4PPUQk0ouIInUICzweuH626PxX5SZQG4jtC/A1ZlXTRNXj/YHHGNcrbKNqznJZ/WuRpsdZuZNrJMD/s9VAwXNxbPE+41voa140WegqOTIRIxRsTnzGjNhUPfH+pFKByh/TxPOaZyTNA0xVYu399Gaf5HBM+NfNuhhIZS0Qha/ONzeKMpqJAafwbk1VN9ZkaKRkV3SypcOJ5xn3PvUQaySBy//BOYPZ0vnvcM96pvMXfud8pGPEcV4Tshd8vxIbrG8Q7zzOFZw332w+5fxD9UahkfoBxVcaB9Cz6Z5HY6xRaOxTuIPYj9ynvNFoMiePKyyvFnqHbMfcPhVvuAZ77ZOh57v5d/2+dS0NrLoVg7VAE0td7V29hvTRUkk1Xx4zHppKPZxLvj/Ke5aM8xruVjDstfZp5OLgveL9ROa7ZDCXMG3FwhJj/L6V+EXcq37f9rfd/UdjpSJrHM42eJ4qyVRlLO/65y9Yuws2cd61m4z3F+08zqSc/55ozqRnfPYYvMKeDvqRjPE+YYZx3AO/8AWUG6AwLoacN7z3Wm9duupKu8Rl6DPBsbFuwbZTv8B6gYK7Ubtd+gO8iFTJ8v3hP897SDtfimdp+c/tIZd7IWxC/kx1ORsGc76FIVHdxCRrkaiCGpJBKOsm38A5g2IcSWsBzhfwEaaEwzDOvuld1cedRoTKk/JAoSjL2eebxGcErkc/i2UklIoVs8ii8sxhLzbOU/Br3FnkBCnjcT3zvmfyS/ArXjHfj6qurxR7l3MgrUiDk/uY6M/EmeSiOQ6Ub9xLfPfKBfOd5hvG85llKJTA9JDQNGZxjzeU1MavmrCjvCPdH0b+KipAAfXlq+H3eldz7fEeoDOC+41nM95XnDteQPBvnSt6Fijsq91mhgjwC7xgqiXhGH2p7CJ5JPSNeB77jVMJ3LNJR5/nx297fBF9AwwD5aSpkacyg4E/6GerA/a15N5CHoWKFCk2+n28+vkH+1PmFoo57n9gJZXyW74ThiX9Tyv3x3VpVbZUUvKO9va37oRS8rYt3nI6mCN4zds/A8AvD8azvM3jP9hZaOCW+kxcuBa2qXlWF5pcuyTyQ07imEbF8vAR4KNFSxM9oRaK2lQwRD3oeJhR4qcnjIUHNbKsCrUQ8IMegYM9LiVYcXvhk8OmqSMspDzIylIr2nVpbWoRoiaMAT0aKTBYvAQp2FGTJRPbf218wKkzkwkOfAhJjGHlwdSvWTQjTnBe1zJwLBbbzP50XTB+FLV4yjE/locUDi7TyAqKwQOGFbjteybzE5U7XMAq1ZNAYo8XspHQpooKCFkvSQfp5GZFB4cFMayBjJ8kQ002QghetNLRaEGteYLxAiRkvAs6b45ORapmvpdCwk8kmxnn+lwe/V/1dzImXHbXHbLQsERdeGrwoyYCSiSHDyQtUYcj5mbFab+Vl1XY1I+48/IkLGWS6RFKY5rrywi/yZxGBPZkOXlpcIzJJZJ7JiFPgpOKFFiriRHfavKnyisuX68RLhFYoChxkxPle9CrRC1tvbRWWLzIhxIiMgH+Qv/CooPKDOPH9pEKH7xatv3wfyFBTCcELltadoRWGiu+QJjJSbBRsxlYeaxAbvsPEVmHMyIgSi+9zfS80z5wfhUJtV2heihQYeJHTcsh58qLVxpYXMXEgLnwXiCGT3VHY5LtKJoWWTq43rYxkgKj8IjOgrylCHgUPMlK02LHcEN8D/s53g27HusImSAvd5Gpnry3eK01rGsfTZQXVRQdj79Y3Xh9t0j7uWVoJjMkCy/0z8fhEoYzRzD7MPUAGifuOa0RlWmwbrTY1ltUQzFB0sacUnKi01Lb+KuOTWYuJQGYK/VxrCk+kQ9vaqPTDM4fCqba1iJ/TVZOKQ661dhu0b5BQ8vHsVRoZaSrOqNihNY77kgoz9kElEBlaKtO4HmQS+d7zrKDLPc/JHMlzRAjyZDr5TvJO4Vmiua4cj2PR8ksmlwpUS+agaLm+pfAe0OV6TeGaXgeKwlgTJ1aL4B6noorrzZrXjPnXLIdHBRtp5/1ILw4KK7zneC9QWWjofKYyl+EJVL4qz1K5QQ+KYRWHRVo20tB0XVMhHGh7+/DuZYZ6KgmVzxguwRwgFDqo6NNuk49PFvhrWyiZ4X5p/aUR+4PnFfkFrqNm05WIjZ8zERWFrG7Fuwklaia3TDprMvMdoWKZfALp4P0z9OBQYZHVfl8onDJXA71otJs+wTu6teX5Sy8Y4q7daA3lHc97jMImeScKllT0DSw7EKU8SomvMI8Fz3ztygGtN7QWCjTlfOW7TmGrx84e4uylkp+KblMbBX3eJdzPVF7TwECBmGEAVIBwPyqWcirxiSXvIMWDifcd9zKVV7GNu1cEXuV85zhcM96f5LN4r3MPkEcirdwnfC9prKDHD99J4Yl2eIzgL6ho09WIPRVxmlZvjk1+jvc9sSUPRwML32N+xvuT+5FnEu9v8h7kH3jOkAfhulKBo4T+cNwN1zYIbH7e/rNQrvcs0VOcf+TbqHxTlCG6aOSYHItKYe14b/KlXBee4XyOOPEOpvcZDQ08T8kv00jFM4h8Z3QhdlLwNnXXWP55KXhbHmPVjKAI3skHJke6DOmEsEehmwcb3ar5e7+9/YRmVWhYczcQ1ma60zTN21T8jZcPXcqbr2uOez3vCasND09ufB4EZDyo9WWZDGNimZlchII8mYC/6vwlhArGdNKqPqX6FCEQGcqwzQuKllhNhoXJXUgHreG8fHhQUyCnZZVaZM0SKxQedJWg4AWp7bLOC4KHtqY2kphQGKOrleYBSAGfFlJTmG0yFj9u+RHUrk+uNjmKJp8WULrsKzTzUKYgzEt5x60d4qKqnq260CQz1pdMNgV2WpXIfPFAt3TjZUHBmRc7caXgTbd6Wrd5yWgzloZc1B8EPBDvANeUGmfG55KhU2oQ6/s+caGih5p2XYKEpXGIbf9UApEJ4JrxveNlq21BiskY/ff0F/uYmNAKS0WOPmGR/ZN5J+a0/DI7t2btZyXURNudU5suMkNUcM2sOVNvIiFTswbTm4JnF5UIPIO6b+8u3BzJGFPJQIbO1BwM+vCkYENFkWY4jfazdCHd0mxLFAEgJmsUm+9QUVB/VX3h2UDGTrvxbKEiT1tI43MMH6HXiC5X22pLqgnlpbZLJs9JelaQ+eRepycChU9jFCixmaclv0tLYZO1TTC71uwomfmZAGxmjZk6EzjRRZeMM8uKtdrQSnghzTo7Syj5eI9yT9NKq3h/0ZuE9x+9WOhxEp0HhOZ8GZZBxQoVfmz6BGJ+Rvdu3sWaca8UDil8ckxtIZrCFsNHBpcbHNG/MjYFRFpNte92xm7TrVkJE2HWbioMtfef4irPUBvNRms33y0Kd7y7KMgfbHsw4hFauVmnmXTTG4jKU+UeYYw0PRj4fc13jnkhqHjWFe6gT/Bm1nUq+vUJmcxxQO8vTY8hCmcUtuhNpFg5qQCkMkV46KxtjOk+04XwzXdqXOVxkRQxnCSVrMIzqNxvghfKOyuvUCrQc4WKanM1hjdRAc0znetMoZLCZHTnv7nGjmk/5InIV9Kjh7TSgh9dyUPyA7xLGBLD+5N8IEOoKLAq3jrkMZU69DGlS/kelfs0PpAHpIcF+dbYrBmNF7xLGPZBHo4GDuYZMSZsR9dc+K6vrLZSWrxju9Bm/L4UvM0Iptq7UgTvzac3473LeyGAckMzmQjLw/Ai4yZlbCtdcxmHQi06Lb1kCKgppAWDLn60zipaXM1589KgVZHWQ2MaLwIKi9rCdXSxW8b0G9+foaBCpllbi2/KvHgB8T9aRWlxMZcQYgoN8ln1IcD3gozegn8WCAHCWPdrCmz07iBjqjTGs1OxpJ1ESdes6ZLM2NWlDZZG+ZhnC5ne/W32mwQYk9gM2DcAjfI0ElZXTdpM6siIh2nFG1RuEAqkLRDlaWJaekHpaF3ujRjCbI/QJZyKDu0EYByACgRa6HQxr1TwKK6OtNgpjcI1mdnoQgrMRrxKOqLFjdZ7JifTbNHVoiZOjMe97H9ZKEd3t9otLJe0+NNKTeGbwrhm/omYTJeCA935lZrYVGDThTd3qtxRuqMCkvkgqHhV7hPe4xSC9d3TZP6ZwZ6JJTXjw5l8T8lxoTkQBX8qeBUXcn1KNCpC6TWmGXaiS9BmvPqsWrMiBBiGatClmooGXdY9Kt3oZaAZ5kHlBL3taGDQbvSu0eUFYCgLPKt+UPGtqdCihwlDo2iR1OWtQIsyvQHJVzFe+vSPp6M8xzXi+UG3Y97ZFIQpKMbEyh2T9yk+fEc7UWJ0NDNumphzr9FTjuEqVAbGl8bzm0kMqZgj76btaWbKPKTgbQpa1nlWCt7WwVkVoxiTXI2uuozFY3Kt7sW7S2FNFSsniZAIqAMBWvHIDChuzGT46SJpLFPDeHDGkGq74dEqQZd3bSHH0Kwp6CjMPLPBatalN/RdUz8nA02BRdtax37oHk2PgOgs4qaOF5vniYuScVy7H7pqMt5TiQHU/pyeJcxboBnLynhIukVrZsyODX3x5bu0cDIMQwkdoYcRvQno2qyvEXsmNaOF2RweKvrGYdJCCvRUijNm9Gi7o3q9DOjizLARWvxooWWeDrqGa8amao/DRJYD9w/EhiYbxEdUqtMSrWvudGfmHlDyROgTvIkNS7FReFcavUkYOqL5vtGNl95ayt+oZGD+B32CKD3ZqEzULG1HzwS6LeuybFKwp1eCpmWbyijOLzrlHz18qICcUHWCIF/kxNnSSWdpTk08+R3GNTMkSpfijs8yOSzdnWeemSm8BcxVoSO+7DVz00mjDvMTMWSCLtrfapOCt/pWXgre6lsTi1FkjOBtscFlxxIBiUC8R4CMJplghTlljCiZaGOZRMa8MrZOOzMwGVMyzwwNUWujVYpCl67EZRQe6AVEF1i1NFosGd6hrRTpvLWzsM7RDVdXo2tnyfklIyxztDQxbp7u0dG5eKpl3uakgzG/tKQqic7oLUBXfiYji+vGdWHSLLo90wWX1nR9jR5klRZXEsmbaFFlPCrziBhqVKwx9IxWXoYyHXt0LEqWcPbBJJGkhaEkzKlBV3V9ygltqzJzB9CCzLwySiO9VBwxWRxzrrAKwMXOF/XGvyvvrKZVPzrPBCoQGRKhGXtPoZeCmpLsUhc2VFoQR6WGOz1uiKdm6JohTOXnEgFrIiAFb2uibdxYUvA2DqcE8ZQUvBPEMspJSATiFAHWxl16aamIEaRr5F91/zKaHlpSaSFisjtNKwQZXmaCpWuomhuFgePtj0cRAGhRpPKhXaF2qiGfrv1UZmgnIaJVjy75mq7k2kQzJpLJM2mdoyWRPxRIovuOaiZuRkLojs06uPSmYKu7oq6wosZXF2Dmy6D1XjvpmT7IqGSot6oeDrc9LBJzNcnbRGfCOXoCsGoG8yswAR7dfFc2XKmzWyYqXN5geYS1mfHgjMfVTjJFJQerSdA9fkq1KdGWOeNAjK1nnholzjq6nBF0d29ToA2KpC8SQSMrW7DahHbGdu1J1FtZT+TE4X6nIs5Q0kUzvo6yK4mAyQhIwdtkyCz+BSl4Wxxi9QwgBW/1rIWkRCIQ3xGgEM1qB6Zmu2XGWcaXa5Y++mXnLyKXhK5M0WrCiVmNaS3Wti4OOzBMxMJqZvyOa7qVMkU7W+yMtEYUSOiWHF32bLoOU0FCl1dWgqCgoa+8U1zP05LjixI9f38nSmWK2sir6kdK+mXJsdXSN70dmIeFpQsZ46/Pu0URdOn5wezlLDGnq/26+1fUzVlX7BfF7Z3u/LoaLdnM/8DEqIYac5mMPTpWlA/lu88EVbSA62rcr/Ri0IwJZ5mnal7VROb+6Bozv+++u1vkTmFlE03h3RCN8nOJgLURoOC9ouoKZMqUCY8ePULGjBmtTYIcTwsBKXh/Q6+EFLy/ocWWU5UIqBgBuqtPrDoxwtWTMZdkmA1VMIjrKTEOs+2mtqLsmabg2n5Te/Qr08/oWHdrzUOXK6yhBFIKbXS9peWb7riGarBbaz5xMQ5LEDGrMpOHsVQma35/S43KhyEHhohEZyxxpK8pCh1md2bpRO4HXY2WbMamM/aWHhlU8jATdGybZgJAWt377ukryrPpaqyywYzXmmupbYnXRw9LazFJGhPTqv28ii2m8vvxHwEqoOaWn4vsWbJLwVslyykFb5UshDXIkIK3NVCWY0gEJAKGEKD7Lq1dXYp1EY/ShTu+ZMxm6SRmy9W0lrHOKpNQabvLGsLB0p8zo/bkE5MjkqLRdZiKA5Y9M6adeXxGlASMTXkcY8ZR+zOsd88EgEwepl0PW+20W4s+lgBlJnJixVrR+sJG/nn6DyYemygSEbLkF3MQaOd8iCnNFJ7pncBSnr7PfDG4/GCdXTF5253XdyKSwVG5wDOINc1lkwgkJARYLWRSiUnImz2vFLxVsrBS8FbJQliDDCl4WwNlOYZEQCJgCAHWt6UAu7HpRlHrlgm/tjbfauhrqvicpWpYp1czdtpYK7K1J0BXXZZOUpJBsTwkax7rcwO2Nn1yvISDAEttsSY4Ld4+2XxQIXMFnZOjkMswBoZrMCHbnlZ7kMQpiVmAYP4J1vJmPWUmbNMX+sHqLUt8l2BajWliXGZkH7hvoN64dLMQJzuRCMQBAgyPGVZoGArlLCQF7zjAX9eQUvBWyUJYgwwpeFsDZTmGREAiYAwCFFYPtj2IKSemiJI/bQu2NeZrcf4Mk8q1XN8S21tsF7QwizPd+fTFqcY1wYoVkMnsWCosMCRQuPnKJhEwJwIzT89EMudkWH99PWbWmIl0SdLp7Z7eLq03tkbpjKXRs2RPs5HBkmlMgBcWHoa9rfbC3dldZ9+sg951e1eR5JFt0T+LRLk0uS/MthSyI5UgwOoWvfP0Rom8JaTgrZI1kYK3ShbCGmRIwdsaKMsxJAISAWMQ6L+nP3bd2YWcKXNi4fcLRfbi+NI0XePpzj315NSIzNdqm8PUE1OR2jU1WuRvAdZ+Lu1RWiSRkk0iYE4EmFSN8dusv32s/bFok/eZc1ztvljaj4L997m+1zuMdj1xfaX3LEmn7FsiYA0EmKejg1cHlM9fXgre1gDciDGk4G0ESAnlESl4J5SVlPOQCMR/BOgGzQRl8TFmts6KOkJZwARNq6+sxuM3j1VR21nXW3Hn1R00WdsEqxutRsctHbHyh5WRSrnF/zdJzkANCChZ9B1sHXC0/VE1kBQtDUp4CENdWK+cLu+ySQQSGgI/bfkJjTwaoWqhqlLwVsniSsFbJQthDTKk4G0NlOUYEgGJQEJHYPD+waiUuZJIsDb0wFCR7bqqV1XVTpsZyllnOU+qPHozPauWeElYvEGA2d8/hX3CkApDVE8zk04tbbBUJHorlbFUtBZy1U9GEigR0IMAa9b7pPJBrWK1pOCtkrdECt4qWQhrkCEFb2ugLMeQCEgEEjoCa66swaM3j9C7VG/8sPoHzKgxA+mTpFf1tJ+9ewY3Jze4OLiomk5JnETAGghQefb47WORiG13y90iKZtsEoGEhkC/Pf1Q2r006peoLwVvlSyuFLxVshDWIEMK3tZAWY4hEZAIJHQEWFpq1OFRWFJ/iShDdLz98TiLaU3oWMv5SQQsgQBLiv28/Wfc73kfTvZOlhhC9ikRiHMEhuwfAm9nbzQt01QK3nG+Gl8IkIK3ShbCGmRIwdsaKMsxJAISgYSOAEsilZhXAvta70PdlXVxoM2BhD5lOT+JQIJCIDA4EEwIp+YQkQQFuJxMnCAw+vBoeNp4onX51lLwjpMViDqoFLxVshDWIEMK3tZAWY4hEZAIfAsIkKFZcXkFOhXuZNaSSN8CdnKOEgGJgERAImB5BH4/9juSfUqGjpU6SsHb8nAbNYIUvI2CKWE8JAXvhLGOchYSAYlA3CNAqzfrBdvb2sc9MZICiYBEQCIgEZAIaCEw49QM2L61Rbeq3aTgrZK3QwreKlkIa5AhBW9roCzHkAhIBCQCEgGJgERAIiARkAjELQJ/nPwDv6z+BZgKKXjH7VJEjC4Fb5UshNPo9msAABGkSURBVDXIkIK3NVCWY0gEJAISAYmAREAiIBGQCEgE4haBK8+v4NS1U+hQsYMUvON2KaTgrRL8rUqGFLytCrccTCIgEZAISAQkAhIBiYBEQCIQZwhI3j/OoNc5sLR4q2s9LEqN3HwWhVd2LhGQCEgEJAISAYmAREAiIBFQDQKS91fNUghCpOCtrvWwKDVy81kUXtm5REAiIBGQCEgEJAISAYmAREA1CEjeXzVLIQVvdS2F5amRm8/yGMsRJAISAYmAREAiIBGQCEgEJAJqQEDy/mpYhf+nQVq81bUeFqVGbj6Lwis7lwhIBCQCEgGJgERAIiARkAioBgHJ+6tmKaTFW11LYXlq5OazPMZyBImAREAiIBGQCEgEJAISAYmAGhCQvL8aVkFavNW1ClaiRm4+KwEth5EISAQkAhIBiYBEQCIgEZAIxDECkveP4wXQGl66mqtrPSxKjdx8FoVXdi4RkAhIBCQCEgGJgERAIiARUA0CkvdXzVIIQqTgbYH1aNu2LRYvXhyl5x07dsDHxyfS3ydNmoSZM2fi6dOnyJcvH37//XdUrFgx0jNv375F3759sXbtWoSEhOC7777DjBkz4OnpaRL1cvOZBJd8WCIgEZAISAQkAhIBiYBEQCIQbxGQvL+6lk4K3hZYDwreR44cwbJlyyL1njt3bri7u0f8jUL3wIEDMXbsWBQuXBh//fUXNm7ciNOnTwshXGm1a9fG+fPnMXnyZLi5uWHo0KF48+YNLl68CBcXF6NnIDef0VDJByUCEgGJgERAIiARkAhIBCQC8RoByfura/mk4G2B9aDgffbsWVy+fFlv77Rcp0mTBp06dcLEiRPFc2FhYULgzp8/P1auXCn+durUKZQsWRLbtm1DzZo1xd8ePnwILy8vYfXu3Lmz0TOQm89oqOSDEgGJgERAIiARkAhIBCQCEoF4jYDk/dW1fFLwtsB6GCN4HzhwQLiM05JdqFChCCpGjBghLNuBgYGwsbHBsGHDMH36dLx69Ur8v9IqVaqExIkTY8uWLUbPQG4+o6GSD0oEJAISAYmAREAiIBGQCEgE4jUCkvdX1/JJwdsC60HBe9WqVXByckJQUJCwYg8ZMgT16tWLGG3WrFno2rWr+FzTXXzNmjVo3LgxHj16hIwZM4rfaeE+efJkJEr53V27duH27dtGz0BuPqOhkg9KBCQCEgGJgERAIiARkAhIBOI1ApL3V9fyScHbAusxbdo02NvbI2/evAgICMDs2bOxe/duUKhu2LChGHHMmDEYNWoUgoODI1Gwd+9eVK1aFb6+vsLlnL/b2dlh586dkZ4bPHgwKLzTEq6vMQ6cP0p78uQJihcvHiHUW2DqskuJgERAIiARkAhIBCQCEgGJgERABQhIwVsFi6BBghS8jVgPun1TaDXUsmTJIqzc2u3z588oXbq0EIKvXr0aIXiPHj0aHz58iPT4nj17UK1aNZE4jZZyCt4U4pkRXbMNGjQIc+bMwcuXL/WSNXz4cNB1XbsxeVu6dOkMTUd+LhGQCEgEJAISAYmAREAiIBGQCMRTBBSj271795A5c+Z4OouEQ7YUvI1Yy0WLFqFdu3YGn7xw4QIKFiyo8zmWCevXr1+Ea7niak7B29nZOeI75nQ117Z4Hz9+HE2aNDE4D/mAREAiIBGQCEgEJAISAYmAREAikDAQoNGtWLFiCWMy8XgWUvC20uIxc3n//v0jBO+4SK7GePDs2bODAriHh4eVZi6HUQsCitZTejyoZUWsS4dcf+virbbR5PqrbUWsS49cf+virbbR5PqrbUWsR09oaCj8/f2FF62moc96FMiRNBGQgrcV3ge6mrMkGBOpKSXGlHJiLAc2fvx4QQXLiTGum5tDu5wYXc19fHzEc0y8ljVrVllOzAprl5CGkHE+CWk1TZ+LXH/TMUtI35Drn5BW0/S5yPU3HbOE9A25/glpNeVc4jMCUvA28+o9ePAAzGrerFkzUWv79evXIrkaLdzr1q1D/fr1I0acNGkSBg4ciHHjxqFw4cKYN28e1q9fD1okKXwrrXbt2qAbO8uMubm5YejQoaLcGOPANTOiG5qKPHgNIZSwP5frn7DX19Ds5PobQihhfy7XP2Gvr6HZyfU3hFDC/lyuf8JeXzm7+IOAFLzNvFbMMs548HPnzgnXDkdHRxQtWhQDBgxA9erVI40WHh4OCt8zZ87Es2fPhLBNl3TW6NZsjNXu27cv1q5di48fP4r63zNmzICnp6dJ1MuD1yS4EtzDcv0T3JKaNCG5/ibBleAeluuf4JbUpAnJ9TcJrgT3sFz/BLekckLxFAEpeMfThYsJ2RTgp0yZgt7/196dhtjYxnEc/yMkL2Qd2UK27JGdsZWlKBFvSNIwdmNshexpELLFkCJJskV2bxBZkyXGniVL8oJIhOnpd9WZZ2aeg2aemTnXuc73rueFp5lzX9fnd597zv8+15Ke7r4550gsAfJPrLzz95b8yZ/7f+JeA7z/Ezd79Zz8Ezt/eu+PAIW3P1nQEgQQQAABBBBAAAEEEEAAgQAFKLwDDJUuIYAAAggggAACCCCAAAII+CNA4e1PFrQEAQQQQAABBBBAAAEEEEAgQAEK7wBDpUsIIIAAAggggAACCCCAAAL+CFB4+5NFkbTkyZMnbqX0K1euuD3DmzVrlrN3eO4TnDhxwubPn29ZWVlWp04dt+DapEmTiqQNvEhsBPbv32979uxxK+prdX1tZzdx4kRLTU210qVL5zSK7GOTT3Gf9fTp07ZixQq7f/++W0indu3aNmTIEFu0aJFVqlSJ/Is7AM9e/8uXL+7+//r1a7t+/brbXSNycA/wLKwias7OnTvdrir5j7lz51pGRgb5F5Gz7y+zY8cO27Bhgz18+NAtpNu5c2c7evQo+fseHO1LCAEK78BiPnLkiE2ZMsU6depkjx49suzs7P8U3pcvX7bk5GQbPXq0jRo1yi5duuQ+nGdmZlpKSkpgIonTHf1x1RZz2is+KSnJ7R2vPeLT0tJs9erVDoLsw70e9u7da3fu3LGOHTta5cqV3ft+8eLF1q5dOztz5gz5hxt91J6p2Nq1a5fbqjJ34c09INwLIVJ4nzp1Ks/DNj2Eq1u3LveAcKPP6Znu+evWrXNfrOhzoB7C63rQ5zs+AyTABUAXvReg8PY+ooI1UIV25NvNMWPG2I0bN/5TeA8cONDdjK9evZrz4uPHj7djx46Z9nrM/e1owc7OT8dSQPvGV69ePU8TNJJhy5Yt9vHjRytfvryRfSwTKvlzb9++3fTe1reetWrVIv+SjyAmZ3zw4IH7hnvNmjU2YcKEPIU394CYRFIiJ40U3vpbUK1atajnJP8SiSImJ9EIxlatWplGtPTr14/8Y5ICJ0XgzwIU3gFfIdEK7+/fv7uhRxp2NmPGjJzenz9/3nr16uUK9fbt2wesklhd2717txvZ8ObNG6tSpQrZJ1b8dujQIRs2bJg9f/7catasSf4Jkr8+dLdu3doGDRpkvXv3zim8uf+HfQH8rfAm/7Dz1yiXw4cPu9GO0Q7yDzt/ehcfAhTe8ZFToVoZrfDW/M8WLVrYyZMnbcCAATmvqyfkNWrUMBVqGn7OEYaAvu08ePCgvX//3s33Ivswcv1TL379+mU/fvxwc73Hjh1r9erVc/P7eO+Hn716eODAAZs8ebI9fvzYbt68mafw5hoI+xqIFN76W/7hwwc39WjcuHE2Z84cK1OmDPeAsOO3nj17upEObdu2tY0bN7qRbl26dLH169e7/8f7P/ALgO7FhQCFd1zEVLhGRiu8NZ+7e/fubq6v5gRHjp8/f1rZsmXdDXratGmFOyG/5ZWARi/oj67m7y9YsMDN5Sd7ryIqlsZosUQNLdehh2sqxCpWrEj+xaLt14t+/frVLaimeZ566HLu3Lk8hTf3AL/yKurWaIFFTSHT3N5SpUq5B26aaqRFNjdt2sQ9oKjBPXu9pk2butFtmtOvhTbLlStnS5YscSOe9CDu3r17fAbwLDOak3gCFN4BZ/6nwlurnuuPc/7CWythTp06NWCVxOjau3fvXL4qwvThWw9VIh+6yT7sa0ALrGlFa33IWrZsmTVq1MjOnj3rdjrQgxfyDzf/efPmuaxVfGmtjt8V3lwD4V4D+Xs2e/Zst9jWq1ev7NmzZ9wDAo6+cePGpp1ttLCmRrfpePv2rTVo0MCWLl1q3bp1I/+A86dr8SFA4R0fORWqlQw1LxRb3P/Sp0+f3Hz9b9++2cWLF61q1aquTwwzi/toC9wBbS2nRba01Vzz5s2ZalBgwfj5hRcvXliTJk3cHM+uXbu6huv9P3jwYLfDga6Dly9fcg3ET6RF0lKtaK+dDrTgloaeM92oSFi9fBE9bNd9QA/ecx8aZt6mTRvTHHDy9zI6GpVAAhTeAYfN4moBh/ubrqnY7t+/vxtWpukE+qAVOVhYJfGuB8331mr2y5cvd4spsrBiuNdA5Nvt3/VQH8q1iCbXQLjXQLSeXbt2zY1+UuHdp08f8g84fn3m09Zh+QtvFd3aVnLr1q3kH3D+dC0+BCi84yOnQrXyT9uJadENFWaRQ1vOaD4Y24kVitqLX9I8/aFDh9qFCxfcf1rVOP+hrWTI3ou4SqQR+sazR48etm/fPhsxYoTbToz8S4S+xE+iXG/dupXnvPq3HrjoA3eHDh3ch2+ugRKPJqYnnDlzplu7RX/btbMB+cc0jmI9udbzGD58uN29e9datmzpzqX1Pho2bGgrV660tLQ08i/WBHhxBP4uQOH9d6O4+gktrqMn2zo2b95sT58+tbVr17p/a8VL7fOsgjs5OdlUmI8cOdLN/V24cKFlZmZaSkpKXPWXxv4rkJqaatu2bbNVq1a5Yiv3oWHG+qaL7MO9YvTQRcOJ9cClQoUKdvv2bXctJCUlue2ktNAO+Yebf7Se5Z/jrZ/hGgj3GtBop759++YUXXqYrr8J06dPd/O8yT/c7NUzjXDStILPnz+7UU6652tud2RXEy2yyfs/7GuA3vkvQOHtf0YFaqFWr9RCGtEOzfPT3F8dKs61EE9WVpZbgCs9Pd1tQcMRvwL169d387vIPn4z/D8tz8jIcN9s62Fbdna26XpQMT5r1iz30CVy8N7/P8rx9bvRCm/u//GVYUFaqwJbW4Xq223dAzTnXw/TtWCqVjnnHlAQzfj8WRXZGuVy/Phxt62kvnDRQxeteE7+8ZkprQ5LgMI7rDzpDQIIIIAAAggggAACCCCAgGcCFN6eBUJzEEAAAQQQQAABBBBAAAEEwhKg8A4rT3qDAAIIIIAAAggggAACCCDgmQCFt2eB0BwEEEAAAQQQQAABBBBAAIGwBCi8w8qT3iCAAAIIIIAAAggggAACCHgmQOHtWSA0BwEEEEAAAQQQQAABBBBAICwBCu+w8qQ3CCCAAAIIIIAAAggggAACnglQeHsWCM1BAAEEEEAAAQQQQAABBBAIS4DCO6w86Q0CCCCAAAIIIIAAAggggIBnAhTengVCcxBAAAEEEEAAAQQQQAABBMISoPAOK096gwACCCCAAAIIIIAAAggg4JkAhbdngdAcBBBAAAEEEEAAAQQQQACBsAQovMPKk94ggAACCCCAAAIIIIAAAgh4JkDh7VkgNAcBBBBAAAEEEEAAAQQQQCAsAQrvsPKkNwgggAACCCCAAAIIIIAAAp4JUHh7FgjNQQABBBBAAAEEEEAAAQQQCEuAwjusPOkNAggggAACCCCAAAIIIICAZwIU3p4FQnMQQAABBBBAAAEEEEAAAQTCEqDwDitPeoMAAggggAACCCCAAAIIIOCZAIW3Z4HQHAQQQAABBBBAAAEEEEAAgbAEKLzDypPeIIAAAggggAACCCCAAAIIeCZA4e1ZIDQHAQQQQAABBBBAAAEEEEAgLAEK77DypDcIIIAAAggggAACCCCAAAKeCVB4exYIzUEAAQQQQAABBBBAAAEEEAhLgMI7rDzpDQIIIIAAAggggAACCCCAgGcCFN6eBUJzEEAAAQQQQAABBBBAAAEEwhL4Bya1X6Kyf1+QAAAAAElFTkSuQmCC", "_figure_label": "Figure 5", "_image_mode": "diff", "_model_module_version": "^0.11", "_size": [ 900, 400 ], "_view_module_version": "^0.11", "layout": "IPY_MODEL_d242510866ac48f3bca6790320e4e690", "toolbar": "IPY_MODEL_91f6361f684f4a7a87e39dbeee5eb805", "toolbar_position": "left" } }, "b6316e4843fb46de8b20b4e4b8f30e3c": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "MPLCanvasModel", "state": { "_cursor": "default", "_data_url": "data:image/png;base64,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", "_figure_label": "Figure 2", "_image_mode": "diff", "_model_module_version": "^0.11", "_size": [ 900, 400 ], "_view_module_version": "^0.11", "layout": "IPY_MODEL_f6f397e97c544246995e00ec745f84a4", "toolbar": "IPY_MODEL_0b565d28f35d41829424bc135c3ada98", "toolbar_position": "left" } }, "b84bcf6d75264fd2b40b439c7bbbe1e3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "b98200d1ee444845ad5c0839d12980cf": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "max_width": "50%", "width": "40em" } }, "bab241dee1224efd9fe00fa0f4bc2b94": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "ToolbarModel", "state": { "_model_module_version": "^0.11", "_view_module_version": "^0.11", "collapsed": true, "layout": "IPY_MODEL_c772c45ecfb04831bf88aa7cf637d44c", "orientation": "vertical", "toolitems": [ [ "Home", "Reset original view", "home", "home" ], [ "Back", "Back to previous view", "arrow-left", "back" ], [ "Forward", "Forward to next view", "arrow-right", "forward" ], [ "Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan" ], [ "Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom" ], [ "Download", "Download plot", "floppy-o", "save_figure" ] ] } }, "bc27902e09b54162b4d179e781e37797": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "bf9bec96616c45b9a72403718b4d9645": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "c5b6632c164c4d68bbc325efde6a9c99": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "c6a82d9176b340ee8ea6f29f4519045f": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "ToolbarModel", "state": { "_model_module_version": "^0.11", "_view_module_version": "^0.11", "collapsed": true, "layout": "IPY_MODEL_8f9db8afa75e4d27b9bfbd99bf903a39", "orientation": "vertical", "toolitems": [ [ "Home", "Reset original view", "home", "home" ], [ "Back", "Back to previous view", "arrow-left", "back" ], [ "Forward", "Forward to next view", "arrow-right", "forward" ], [ "Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan" ], [ "Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom" ], [ "Download", "Download plot", "floppy-o", "save_figure" ] ] } }, "c772c45ecfb04831bf88aa7cf637d44c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "ce6fafe5ce1240c5a05d408c83b616cb": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "ToolbarModel", "state": { "_model_module_version": "^0.11", "_view_module_version": "^0.11", "collapsed": true, "layout": "IPY_MODEL_56e04f0c6af14ba5a5125dac01b3d6a7", "orientation": "vertical", "toolitems": [ [ "Home", "Reset original view", "home", "home" ], [ "Back", "Back to previous view", "arrow-left", "back" ], [ "Forward", "Forward to next view", "arrow-right", "forward" ], [ "Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan" ], [ "Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom" ], [ "Download", "Download plot", "floppy-o", "save_figure" ] ] } }, "cf4154132eda430299d4553e4ab22dcd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "extra opac.", "layout": "IPY_MODEL_0b6b7c6da8374d51858014a5007ed74c", "max": 1, "readout_format": ".01f", "step": 0.1, "style": "IPY_MODEL_ad60336fcd1546e282bd8d16021ef0fc", "tooltip": "Opacity for extra distance display", "value": 0.5 } }, "d242510866ac48f3bca6790320e4e690": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "d4fd79f612e241e28b76464d6a5ce976": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Xrange", "layout": "IPY_MODEL_052c9fcf0466424f89e70ab56b1ba04e", "max": 1.5, "min": -0.5, "step": 0.09090909090909091, "style": "IPY_MODEL_fd96c963ced042ba8a3ef66acfba134a", "value": [ -0.045454545454545414, 1.0454545454545456 ] } }, "d8080aed98834c4a8629085cbd9ca248": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_3376c0fb8be1491db70b0b916cca8047", "outputs": [ { "data": { "application/3dmoljs_load.v0": "", "text/html": "" }, "metadata": {}, "output_type": "display_data" } ] } }, "dbbba91d199d47f79d8ad33d2fe200d7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatSliderModel", "state": { "behavior": "drag-tap", "description": "extra opac.", "layout": "IPY_MODEL_b0aa788d822e4eff81235b3c472361c4", "max": 1, "readout_format": ".01f", "step": 0.1, "style": "IPY_MODEL_2c1cf7cee2f746a18afb9803d93d739b", "tooltip": "Opacity for extra distance display", "value": 0.5 } }, "de50c16f3c2141349fbdf896c271d3ef": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "e013364b181b422fbd352c603b6e21c0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "e12de330dfdc4f97a3d0e6ad5752fbb2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", "state": { "children": [ "IPY_MODEL_9889904b8d3d420583cbd39730f77213", "IPY_MODEL_e6a470a592fa42d0ab3da35ba887172e" ], "layout": "IPY_MODEL_f78e21a3234c4c90866200ae39b2543d" } }, "e13fd36aea0c4ed4be8280202e13ddf8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", "state": { "children": [ "IPY_MODEL_9891994771884d8aaff0f83c43e05ffc", "IPY_MODEL_eca133ff47774924bf0c6b3ce24c2960" ], "layout": "IPY_MODEL_38e638f5224f434880b8a9d76af0ae67" } }, "e40113cd66ef463c8c9d6e436835bdfd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "e45a4441a0ef462399b08d685c98c1bc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatRangeSliderModel", "state": { "_model_name": "FloatRangeSliderModel", "_view_name": "FloatRangeSliderView", "behavior": "drag-tap", "description": "Yrange", "layout": "IPY_MODEL_4c1578ba0c5d4662a8ae44aeed1def67", "max": 1.5, "min": -0.5, "step": 0.07142857142857142, "style": "IPY_MODEL_635916476a354fe2a32e0891bc70cfc0", "value": [ 0, 1 ] } }, "e6a470a592fa42d0ab3da35ba887172e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_1eb7beebb72c48b5a3a8a23bad63e31c", "IPY_MODEL_dbbba91d199d47f79d8ad33d2fe200d7", "IPY_MODEL_a880651c85ea49fc82b3d7f4bb9ff812" ], "layout": "IPY_MODEL_de50c16f3c2141349fbdf896c271d3ef" } }, "e8e4671905f846749ac9eca5483872f7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "eca133ff47774924bf0c6b3ce24c2960": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_a30e04847ce24f578378a88e7f067ff1", "IPY_MODEL_cf4154132eda430299d4553e4ab22dcd", "IPY_MODEL_220b1d52d1754ddc8d6209607d62d637" ], "layout": "IPY_MODEL_1e36803b619a49e8a0cb07e14932afd0" } }, "f18a443125594637a95f35331695a4b5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "f6f397e97c544246995e00ec745f84a4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "f7376c77ccf8409ca67e032eccf72af9": { "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "model_name": "MPLCanvasModel", "state": { "_cursor": "default", "_data_url": "data:image/png;base64,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", "_figure_label": "Figure 1", "_image_mode": "diff", "_model_module_version": "^0.11", "_size": [ 900, 400 ], "_view_module_version": "^0.11", "layout": "IPY_MODEL_82100fdd54b845cbb2b74ddc15119179", "toolbar": "IPY_MODEL_988f855b8ee047b68016b83e8064829b", "toolbar_position": "left" } }, "f78e21a3234c4c90866200ae39b2543d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "fd96c963ced042ba8a3ef66acfba134a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "SliderStyleModel", "state": { "description_width": "" } }, "ff3a688ce673446bb68592d211035390": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 } pyobjcryst-2024.2.1/pyproject.toml000066400000000000000000000001531470422267000170710ustar00rootroot00000000000000# pyproject.toml [build-system] requires = ["setuptools", "numpy"] build-backend = "setuptools.build_meta" pyobjcryst-2024.2.1/setup.py000066400000000000000000000165511470422267000157000ustar00rootroot00000000000000#!/usr/bin/env python # Installation script for pyobjcryst """pyobjcryst - Python bindings to ObjCryst++ Object-Oriented Crystallographic Library Packages: pyobjcryst """ import os from os.path import join as pjoin import re import sys import glob import platform from setuptools import setup from setuptools import Extension import numpy as np # Use this version when git data are not available as in a git zip archive. # Update when tagging a new release. FALLBACK_VERSION = '2024.2.1' # define extension arguments here ext_kws = { 'libraries': ['ObjCryst'], 'extra_compile_args': ['-std=c++11', '-DBOOST_ERROR_CODE_HEADER_ONLY', '-DREAL=double'], 'extra_link_args': [], 'include_dirs': [np.get_include()], 'library_dirs': [] } if platform.system() == 'Windows': ext_kws['extra_compile_args'] = ['-DBOOST_ERROR_CODE_HEADER_ONLY', '-DREAL=double'] if 'CONDA_PREFIX' in os.environ: ext_kws['include_dirs'] += [pjoin(os.environ['CONDA_PREFIX'], 'include'), pjoin(os.environ['CONDA_PREFIX'], 'Library', 'include')] ext_kws['library_dirs'] += [pjoin(os.environ['CONDA_PREFIX'], 'Library', 'lib'), pjoin(os.environ['CONDA_PREFIX'], 'Library', 'bin'), pjoin(os.environ['CONDA_PREFIX'], 'libs')] ext_kws['libraries'] = ['libObjCryst'] elif platform.system() == 'Darwin': ext_kws['extra_compile_args'] += ['-fno-strict-aliasing'] # determine if we run with Python 3. PY3 = (sys.version_info[0] == 3) # Figure out the tagged name of boost_python library. def get_boost_libraries(): """Check for installed boost_python shared library. Returns list of required boost_python shared libraries that are installed on the system. If required libraries are not found, an Exception will be thrown. """ baselib = "boost_python" major, minor = (str(x) for x in sys.version_info[:2]) pytags = [major + minor, major, ''] mttags = ['', '-mt'] boostlibtags = [(pt + mt) for mt in mttags for pt in pytags] + [''] from ctypes.util import find_library for tag in boostlibtags: lib = baselib + tag found = find_library(lib) if found: break # Show warning when library was not detected. if not found: import platform import warnings ldevname = 'LIBRARY_PATH' if platform.system() == 'Darwin': ldevname = 'DYLD_FALLBACK_LIBRARY_PATH' wmsg = ("Cannot detect name suffix for the %r library. " "Consider setting %s.") % (baselib, ldevname) warnings.warn(wmsg) libs = [lib] return libs def create_extensions(): "Initialize Extension objects for the setup function." blibs = [n for n in get_boost_libraries() if not n in ext_kws['libraries']] ext_kws['libraries'] += blibs ext = Extension('pyobjcryst._pyobjcryst', glob.glob("src/extensions/*.cpp"), **ext_kws) return [ext] # versioncfgfile holds version data for git commit hash and date. # It must reside in the same directory as version.py. MYDIR = os.path.dirname(os.path.abspath(__file__)) versioncfgfile = os.path.join(MYDIR, 'src/pyobjcryst/version.cfg') gitarchivecfgfile = os.path.join(MYDIR, '.gitarchive.cfg') def gitinfo(): from subprocess import Popen, PIPE kw = dict(stdout=PIPE, cwd=MYDIR, universal_newlines=True) proc = Popen(['git', 'describe', '--match=v[[:digit:]]*', '--tags'], **kw) desc = proc.stdout.read() proc = Popen(['git', 'log', '-1', '--format=%H %ct %ci'], **kw) glog = proc.stdout.read() rv = {} rv['version'] = '.post'.join(desc.strip().split('-')[:2]).lstrip('v') rv['commit'], rv['timestamp'], rv['date'] = glog.strip().split(None, 2) return rv def getversioncfg(): if PY3: from configparser import RawConfigParser else: from ConfigParser import RawConfigParser vd0 = dict(version=FALLBACK_VERSION, commit='', date='', timestamp=0) # first fetch data from gitarchivecfgfile, ignore if it is unexpanded g = vd0.copy() cp0 = RawConfigParser(vd0) cp0.read(gitarchivecfgfile) if len(cp0.get('DEFAULT', 'commit')) > 20: g = cp0.defaults() mx = re.search(r'\btag: v(\d[^,]*)', g.pop('refnames')) if mx: g['version'] = mx.group(1) # then try to obtain version data from git. gitdir = os.path.join(MYDIR, '.git') if os.path.exists(gitdir) or 'GIT_DIR' in os.environ: try: g = gitinfo() except OSError: pass # finally, check and update the active version file cp = RawConfigParser() cp.read(versioncfgfile) d = cp.defaults() rewrite = not d or (g['commit'] and ( g['version'] != d.get('version') or g['commit'] != d.get('commit'))) if rewrite: cp.set('DEFAULT', 'version', g['version']) cp.set('DEFAULT', 'commit', g['commit']) cp.set('DEFAULT', 'date', g['date']) cp.set('DEFAULT', 'timestamp', g['timestamp']) with open(versioncfgfile, 'w') as fp: cp.write(fp) return cp versiondata = getversioncfg() with open(os.path.join(MYDIR, 'README.rst')) as fp: long_description = fp.read() # define distribution setup_args = dict( name="pyobjcryst", version=versiondata.get('DEFAULT', 'version'), author="Simon J.L. Billinge", author_email="sb2896@columbia.edu", maintainer='Vincent-Favre-Nicolin', maintainer_email='favre@esrf.fr', description="Python bindings to the ObjCryst++ library.", long_description=long_description, long_description_content_type='text/x-rst', license="BSD-style license", url="https://github.com/diffpy/pyobjcryst", # Required python packages install_requires=['numpy', 'packaging'], extras_require={'gui': ['ipywidgets', 'jupyter', 'matplotlib', 'ipympl', 'py3dmol>=2.0.1'], 'doc': ['sphinx', 'm2r2', 'sphinx_py3doc_enhanced_theme', 'nbsphinx', 'nbsphinx-link']}, # What we're installing packages=['pyobjcryst', 'pyobjcryst.tests'], package_dir={'': 'src'}, test_suite='pyobjcryst.tests', include_package_data=True, zip_safe=False, keywords="objcryst atom structure crystallography powder diffraction", classifiers=[ # List of possible values at # http://pypi.python.org/pypi?:action=list_classifiers 'Development Status :: 5 - Production/Stable', 'Environment :: Console', 'Intended Audience :: Developers', 'Intended Audience :: Education', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: BSD License', 'Operating System :: MacOS :: MacOS X', 'Operating System :: POSIX', 'Operating System :: Unix', 'Programming Language :: C++', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', 'Programming Language :: Python :: 3.10', 'Programming Language :: Python :: 3.11', 'Programming Language :: Python :: 3.12', 'Programming Language :: Python :: 3.13', 'Topic :: Scientific/Engineering :: Chemistry', 'Topic :: Scientific/Engineering :: Physics', 'Topic :: Software Development :: Libraries', ], ) if __name__ == '__main__': setup_args['ext_modules'] = create_extensions() setup(**setup_args) pyobjcryst-2024.2.1/src/000077500000000000000000000000001470422267000147455ustar00rootroot00000000000000pyobjcryst-2024.2.1/src/extensions/000077500000000000000000000000001470422267000171445ustar00rootroot00000000000000pyobjcryst-2024.2.1/src/extensions/SConscript000066400000000000000000000036161470422267000211640ustar00rootroot00000000000000import numpy as np Import('env', 'pyoutput') # make sure numpy headers are available env.AppendUnique(CPPPATH=[np.get_include()]) # configure the boost_python library, which may have different extensions if not (GetOption('clean') or env.GetOption('help')): SConscript('SConscript.configure') # python extension module module = env.SharedLibrary('_pyobjcryst', Glob('*.cpp'), SHLIBPREFIX='', SHLIBSUFFIX='.so') Alias('module', module) # update egg info when package version changes. basedir = Dir('#').abspath version = pyoutput( 'import sys\n' 'sys.path.insert(0, %r)\n' 'from setup import versiondata\n' 'print(versiondata.get("DEFAULT", "version"))\n' % basedir) egginfo = env.Command(NoCache('#/src/pyobjcryst.egg-info/PKG-INFO'), env.Value(version), '$python -Wignore setup.py egg_info') # install extension module in a development mode. develop = Alias('develop', [egginfo, Install('#/src/pyobjcryst', module)]) test = env.Alias('test', develop, '$python -m pyobjcryst.tests.run') AlwaysBuild(test) def resolve_distutils_target(target, source, env): tgt = pyoutput('\n'.join([ "from setuptools import Distribution, Extension", "ext = Extension('pyobjcryst._pyobjcryst', [])", "attrs = dict(ext_modules=[ext])", "dist = Distribution(attrs)", "bcmd = dist.get_command_obj('build_ext')", "bcmd.finalize_options()", "print(bcmd.get_ext_fullpath(ext.name))", ])) env['distsofile'] = env.File(tgt) return 0 cmd_install = '$python setup.py install' if 'prefix' in env: cmd_install += ' --prefix=$prefix' install = env.Alias('install', module, [ resolve_distutils_target, Mkdir('$distsofile.dir'), Copy('$distsofile', '$SOURCE'), Touch('$distsofile'), cmd_install, ]) AlwaysBuild(install) # default targets: Default(module) # vim: ft=python pyobjcryst-2024.2.1/src/extensions/SConscript.configure000066400000000000000000000064111470422267000231400ustar00rootroot00000000000000import platform from os.path import join as pjoin Import('env', 'pyconfigvar', 'pyversion') # Helper functions ----------------------------------------------------------- env.Append(LIBPATH=pjoin(env['prefix'], 'Library', 'bin')) env.Append(LIBPATH=pjoin(env['prefix'], 'Library', 'lib')) env.Append(CPPPATH=[pjoin(env['prefix'], 'include')]) env.Append(CPPPATH=[pjoin(env['prefix'], 'Library', 'include')]) def CheckOptimizerFlag(context, flag): ccflags_save = context.env['CCFLAGS'] context.Message('Checking if compiler allows {!r}... '.format(flag)) context.env.Replace(CCFLAGS=[flag]) result = context.TryCompile('int a;\n', '.cpp') context.Result(result) if not result: ccflags_save.remove(flag) context.env.Replace(CCFLAGS=ccflags_save) return result def configure_boost_library(libname): '''Detect name extension of a boost library and add it to the environment. libname -- boost library name without any extension Note: CheckLib function automatically adds library to the environment. ''' mttags = ['', '-mt'] boostlibtags = mttags # check more tags for boost_python if libname == 'boost_python': major, minor = pyversion.split('.') pytags = [major + minor, major, ''] boostlibtags = [(pt + mt) for mt in mttags for pt in pytags] # using global conf defined below for t in boostlibtags: libnamefull = libname + t if conf.CheckLib(libnamefull, language='C++'): return # library not found here print('This program requires the %r library.' % libname) Exit(1) # Start configuration -------------------------------------------------------- # Anaconda Python is compiled with super fancy gcc optimizer flags. # Remove any flags that are not supported by the current compiler. custom_tests = {'CheckOptimizerFlag' : CheckOptimizerFlag} conf = Configure(env, custom_tests=custom_tests) optflags = [o for o in env['CCFLAGS'] if o[:2] in ('-f', '-m')] for o in optflags: conf.CheckOptimizerFlag(o) conf.Finish() if platform.system().lower() == "windows": ecfg = env.Clone() # ecfg.MergeFlags(pyconfigvar('BLDLIBRARY')) ecfg.Append(LIBS=['libObjCryst']) else: # Create configuration environment that links with Python shared_library, so # that the boost_python check does not fail due to unresolved Python symbols. ecfg = env.Clone() ecfg.Append(LIBS=[]) ecfg.MergeFlags(pyconfigvar('BLDLIBRARY')) # make sure there are no implicit dependency nodes in added LIBS ecfg.Replace(LIBS=[str(n) for n in ecfg['LIBS']]) newlibsindex = len(ecfg['LIBS']) conf = Configure(ecfg) if platform.system().lower() == "windows": # Why libObjCryst and not ObjCryst on windows ? if not conf.CheckLib('libObjCryst', language='C++'): print("This program requires the 'libObjCryst' library.") Exit(1) else: if not conf.CheckLib('ObjCryst', language='C++'): print("This program requires the 'libObjCryst' library.") Exit(1) configure_boost_library('boost_python') conf.Finish() if platform.system().lower() != "windows": # Use libraries that were found in the configuration. env.AppendUnique(LIBS=ecfg['LIBS'][newlibsindex:]) else: env.AppendUnique(LIBS=ecfg['LIBS']) # vim: ft=python pyobjcryst-2024.2.1/src/extensions/asymmetricunit_ext.cpp000066400000000000000000000027531470422267000236140ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::AsymmetricUnit. * *****************************************************************************/ #include #include #include namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; void wrap_asymmetricunit() { class_("AsymmetricUnit") // Constructors .def(init(bp::arg("spg"))) // Methods .def("SetSpaceGroup", &AsymmetricUnit::SetSpaceGroup, bp::arg("spg")) .def("IsInAsymmetricUnit", &AsymmetricUnit::IsInAsymmetricUnit, (bp::arg("x"), bp::arg("y"), bp::arg("z"))) .def("Xmin", &AsymmetricUnit::Xmin) .def("Xmax", &AsymmetricUnit::Xmax) .def("Ymin", &AsymmetricUnit::Ymin) .def("Ymax", &AsymmetricUnit::Ymax) .def("Zmin", &AsymmetricUnit::Zmin) .def("Zmax", &AsymmetricUnit::Zmax) ; } pyobjcryst-2024.2.1/src/extensions/atom_ext.cpp000066400000000000000000000047211470422267000214740ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::Atom. * * Changes from ObjCryst::Atom * - The default constructor has been disabled. When not followed-up by Init, it * will cause segmentation faults, even if it is printed. * *****************************************************************************/ #include #include #include #include #include namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; namespace { // Helper function - avoid dereferencing NULL when Atom is dummy. const ScatteringPower* getscatteringpowerpointer(const Atom& a) { const ScatteringPower* rv = a.IsDummy() ? NULL : &(a.GetScatteringPower()); return rv; } } // namespace void wrap_atom() { class_ >("Atom", init(bp::arg("old"))) // Constructors .def(init >( (bp::arg("x"), bp::arg("y"), bp::arg("z"), bp::arg("name"), bp::arg("pow"), bp::arg("popu"))) [with_custodian_and_ward<1,6>()]) // Methods .def("Init", &Atom::Init, (bp::arg("x"), bp::arg("y"), bp::arg("z"), bp::arg("name"), bp::arg("pow"), bp::arg("popu")=1.0), with_custodian_and_ward<1,6>()) .def("GetMass", &Atom::GetMass) .def("GetRadius", &Atom::GetRadius) .def("IsDummy", &Atom::IsDummy) // FIXME - this should be returned as a constant reference. However, I // can't get this to work. This returns it as an internal reference, // which is probably a bad idea. .def("GetScatteringPower", &getscatteringpowerpointer, return_internal_reference<>()) //return_value_policy()) ; } pyobjcryst-2024.2.1/src/extensions/crystal_ext.cpp000066400000000000000000000346321470422267000222210ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::Crystal. * * Changes from ObjCryst::Crystal * - CIFOutput accepts a python file-like object * - CIFOutput has default mindist = 0, rather than 0.5 * - CalcDynPopCorr is not enabled, as the API states that this is for internal * use only. * - GetScatteringComponentList returns an actual list. * * Other Changes * - CreateCrystalFromCIF is placed here instead of in a seperate CIF module. This * method accepts a python file rather than a CIF object. * *****************************************************************************/ #include #include #include #include #include #include #include #undef B0 #include #include #include #include #include #include "python_streambuf.hpp" #include "helpers.hpp" namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; namespace { // Overloaded so that AddScatterer does not add NULL void _AddScatterer(Crystal& crystal, Scatterer* scatt) { if(NULL == scatt) { PyErr_SetString(PyExc_ValueError, "Cannot add nonexistant Scatterer"); throw_error_already_set(); } // Make sure the associated ScatteringPower exists in the Crystal if(scatt->GetClassName()=="Atom") { Atom *pat=dynamic_cast(scatt); if(!(pat->IsDummy())) { const ScatteringPower *psp = &pat->GetScatteringPower(); if(crystal.GetScatteringPowerRegistry().Find(psp)<0) throw ObjCryst::ObjCrystException("The Atom's scattering power must be added to the Crystal first."); } } else if(scatt->GetClassName()=="Molecule") { Molecule *pm=dynamic_cast(scatt); for(int i=0; iGetNbComponent(); i++) { if(!(pm->GetAtom(i).IsDummy())) { if(crystal.GetScatteringPowerRegistry().Find(&(pm->GetAtom(i).GetScatteringPower()))<0) throw ObjCryst::ObjCrystException("The Molecule or Polyhedra scattering powers must be added to the Crystal first."); } } } crystal.AddScatterer(scatt); } // Overloaded so that RemoveScatterer cannot delete the passed scatterer void _RemoveScatterer(Crystal& crystal, Scatterer* scatt) { if(NULL == scatt) { PyErr_SetString(PyExc_ValueError, "Cannot remove nonexistant Scatterer"); throw_error_already_set(); } crystal.RemoveScatterer(scatt, false); } // Overload to handle invalid index values Scatterer& _GetScattByIndex(Crystal& crystal, int idx) { int i = check_index(idx, crystal.GetNbScatterer(), ALLOW_NEGATIVE); return crystal.GetScatt(i); } // Overload to handle invalid names Scatterer* _GetScattByName(Crystal& crystal, const std::string& name) { Scatterer* rv = NULL; try { CaptureStdOut gag; rv = &(crystal.GetScatt(name)); } catch (ObjCrystException e) { rv = NULL; } if (!rv) { bp::object emsg = ("Invalid atom name %r" % bp::make_tuple(name)); PyErr_SetObject(PyExc_ValueError, emsg.ptr()); throw_error_already_set(); } return rv; } // Overloaded so that AddScatteringPower does not add NULL void _AddScatteringPower(Crystal& crystal, ScatteringPower* scattpow) { if(NULL == scattpow) { PyErr_SetString(PyExc_ValueError, "Cannot add nonexistant ScatteringPower"); throw_error_already_set(); } crystal.AddScatteringPower(scattpow); } // Overloaded so that RemoveScatteringPower cannot delete the passed // scatteringpower void _RemoveScatteringPower(Crystal& crystal, ScatteringPower* scattpow) { if(NULL == scattpow) { PyErr_SetString(PyExc_ValueError, "Cannot remove nonexistant ScatteringPower"); throw_error_already_set(); } crystal.RemoveScatteringPower(scattpow, false); } void _PrintMinDistanceTable(const Crystal& crystal, const double minDistance = 0.1) { crystal.PrintMinDistanceTable(minDistance); } // We want to turn a ScatteringComponentList into an actual list bp::list _GetScatteringComponentList(Crystal& c) { const ScatteringComponentList& scl = c.GetScatteringComponentList(); bp::list l; for(int i = 0; i < scl.GetNbComponent(); ++i) { l.append(scl(i)); } return l; } void _CIFOutput(Crystal& c, bp::object output, double mindist) { boost_adaptbx::python::ostream os(output); c.CIFOutput(os, mindist); os.flush(); } std::string _CIF(const Crystal &c, double mindist) { std::stringstream s; c.CIFOutput(s, mindist); return s.str(); } void _ImportCrystalFromCIF(Crystal &cryst, bp::object input, const bool oneScatteringPowerPerElement=false, const bool connectAtoms=false) { // Reading a cif file creates some output via fpObjCrystInformUser. // Mute the output and restore it on return or exception. // Also mute any hardcoded output to cout. MuteObjCrystUserInfo muzzle; CaptureStdOut gag; boost_adaptbx::python::streambuf sbuf(input); boost_adaptbx::python::streambuf::istream in(sbuf); ObjCryst::CIF cif(in); const bool verbose = false; const bool checkSymAsXYZ = true; ObjCryst::CreateCrystalFromCIF(cif, verbose, checkSymAsXYZ, oneScatteringPowerPerElement, connectAtoms, &cryst); gag.release(); muzzle.release(); } // wrap the virtual functions that need it class CrystalWrap : public Crystal, public wrapper { public: CrystalWrap() : Crystal() { SetDeleteSubObjInDestructor(false); SetDeleteRefParInDestructor(false); } CrystalWrap(const Crystal& c) : Crystal(c) { SetDeleteSubObjInDestructor(false); SetDeleteRefParInDestructor(false); } CrystalWrap(const double a, const double b, const double c , const std::string& sg) : Crystal(a, b, c, sg) { SetDeleteSubObjInDestructor(false); SetDeleteRefParInDestructor(false); } CrystalWrap(const double a, const double b, const double c , const double alpha, const double beta, const double gamma, const std::string& sg) : Crystal(a, b, c, alpha, beta, gamma, sg) { SetDeleteSubObjInDestructor(false); SetDeleteRefParInDestructor(false); } const ScatteringComponentList& default_GetScatteringComponentList() const { return this->Crystal::GetScatteringComponentList(); } const ScatteringComponentList& GetScatteringComponentList() const { override f = this->get_override("GetScatteringComponentList"); if (f) return f(); return default_GetScatteringComponentList(); } void default_UpdateDisplay() const { this->Crystal::UpdateDisplay();} virtual void UpdateDisplay() const { override f = this->get_override("UpdateDisplay"); if (f) f(); else default_UpdateDisplay(); } }; // Easier than exposing all the CIF classes // Also allow oneScatteringPowerPerElement and connectAtoms Crystal* _CreateCrystalFromCIF(bp::object input, const bool oneScatteringPowerPerElement=false, const bool connectAtoms=false) { // Reading a cif file creates some output via fpObjCrystInformUser. // Mute the output and restore it on return or exception. // Also mute any hardcoded output to cout. MuteObjCrystUserInfo muzzle; CaptureStdOut gag; boost_adaptbx::python::streambuf sbuf(input); boost_adaptbx::python::streambuf::istream in(sbuf); ObjCryst::CIF cif(in); int idx0 = gCrystalRegistry.GetNb(); const bool verbose = false; const bool checkSymAsXYZ = true; ObjCryst::CreateCrystalFromCIF(cif, verbose, checkSymAsXYZ, oneScatteringPowerPerElement, connectAtoms); gag.release(); muzzle.release(); int idx = gCrystalRegistry.GetNb(); if(idx == idx0) { throw ObjCryst::ObjCrystException("Cannot create crystal from CIF"); } idx--; ObjCryst::Crystal* c = &gCrystalRegistry.GetObj( idx ); c->SetDeleteSubObjInDestructor(false); c->SetDeleteRefParInDestructor(false); return c; } } // namespace void wrap_crystal() { scope().attr("refpartype_crystal") = object(ptr(gpRefParTypeCrystal)); // Global object registry scope().attr("gCrystalRegistry") = boost::cref(gCrystalRegistry); class_, boost::noncopyable>("Crystal") /* Constructors */ .def(init( (bp::arg("a"), bp::arg("b"), bp::arg("c"), bp::arg("SpaceGroupId")))) .def(init( (bp::arg("a"), bp::arg("b"), bp::arg("c"), bp::arg("alpha"), bp::arg("beta"), bp::arg("gamma"), bp::arg("SpaceGroupId")))) .def(init(bp::arg("oldCryst"))) /* Methods */ .def("AddScatterer", &_AddScatterer, with_custodian_and_ward<1,2,with_custodian_and_ward<2,1> >()) .def("RemoveScatterer", &_RemoveScatterer) .def("GetNbScatterer", &Crystal::GetNbScatterer) .def("GetScatt", _GetScattByName, return_internal_reference<>()) .def("GetScatt", _GetScattByIndex, return_internal_reference<>()) .def("GetScatterer", _GetScattByName, return_internal_reference<>()) .def("GetScatterer", _GetScattByIndex, return_internal_reference<>()) .def("GetScattererRegistry", ( ObjRegistry& (Crystal::*) ()) &Crystal::GetScattererRegistry, return_internal_reference<>()) .def("GetScatteringPowerRegistry", ( ObjRegistry& (Crystal::*) ()) &Crystal::GetScatteringPowerRegistry, return_internal_reference<>()) .def("AddScatteringPower", &_AddScatteringPower, with_custodian_and_ward<1,2,with_custodian_and_ward<2,1> >()) .def("RemoveScatteringPower", &_RemoveScatteringPower) .def("GetScatteringPower", (ScatteringPower& (Crystal::*)(const std::string&)) &Crystal::GetScatteringPower, return_internal_reference<>()) .def("GetMasterClockScatteringPower", &Crystal::GetMasterClockScatteringPower, return_value_policy()) .def("GetScatteringComponentList", &_GetScatteringComponentList) .def("GetClockScattCompList", &Crystal::GetClockScattCompList, return_value_policy()) .def("GetMinDistanceTable", &Crystal::GetMinDistanceTable, (bp::arg("minDistance")=1.0)) .def("PrintMinDistanceTable", &_PrintMinDistanceTable, (bp::arg("minDistance")=1.0)) //.def("CalcDynPopCorr", &Crystal::CalcDynPopCorr, // (bp::arg("overlapDist")=1.0, // bp::arg("mergeDist")=0.0)) .def("ResetDynPopCorr", &Crystal::ResetDynPopCorr) .def("SetUseDynPopCorr", &Crystal::SetUseDynPopCorr) .def("GetUseDynPopCorr", &Crystal::GetUseDynPopCorr) .def("GetBumpMergeCost", &Crystal::GetBumpMergeCost) .def("SetBumpMergeDistance", (void (Crystal::*)(const ScatteringPower&, const ScatteringPower&, const double)) &Crystal::SetBumpMergeDistance, (bp::arg("scatt1"), bp::arg("scatt2"), bp::arg("dist")=1.5)) .def("SetBumpMergeDistance", (void (Crystal::*) (const ScatteringPower&, const ScatteringPower&, const double, const bool)) &Crystal::SetBumpMergeDistance, (bp::arg("scatt1"), bp::arg("scatt2"), bp::arg("dist"), bp::arg("allowMerge"))) .def("RemoveBumpMergeDistance", &Crystal::RemoveBumpMergeDistance) .def("GetBumpMergeParList", (Crystal::VBumpMergePar& (Crystal::*)()) &Crystal::GetBumpMergeParList, return_internal_reference<>()) .def("GetClockScattererList", &Crystal::GetClockScattererList, return_value_policy()) .def("CIFOutput", &_CIFOutput, (bp::arg("file"), bp::arg("mindist")=0)) .def("CIF", &_CIF, (bp::arg("mindist")=0)) .def("AddBondValenceRo", &Crystal::AddBondValenceRo) .def("RemoveBondValenceRo", &Crystal::AddBondValenceRo) .def("GetBondValenceCost", &Crystal::GetBondValenceCost) .def("GetBondValenceRoList", (std::map< pair< const ScatteringPower *, const ScatteringPower * >, double > & (Crystal::*)()) &Crystal::GetBondValenceRoList, return_internal_reference<>()) .def("ConnectAtoms", &Crystal::ConnectAtoms, (bp::arg("min_relat_dist")=0.4, bp::arg("max_relat_dist")=1.3, bp::arg("warnuser_fail")=false)) .def("GetFormula", &Crystal::GetFormula) .def("GetWeight", &Crystal::GetWeight) .def("ImportCrystalFromCIF", &_ImportCrystalFromCIF, (bp::arg("input"), bp::arg("oneScatteringPowerPerElement")=false, bp::arg("connectAtoms")=false)) .def("UpdateDisplay", &Crystal::UpdateDisplay, &CrystalWrap::default_UpdateDisplay) ; class_("BumpMergePar", no_init) .def_readwrite("mDist2", &Crystal::BumpMergePar::mDist2) .def_readwrite("mCanOverlap", &Crystal::BumpMergePar::mCanOverlap) ; def("CreateCrystalFromCIF", &_CreateCrystalFromCIF, (bp::arg("file"), bp::arg("oneScatteringPowerPerElement")=false, bp::arg("connectAtoms")=false), return_value_policy()); } pyobjcryst-2024.2.1/src/extensions/diffractiondatasinglecrystal_ext.cpp000066400000000000000000000212101470422267000264520ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst Complex Modeling Initiative * (c) 2015 Brookhaven Science Associates * Brookhaven National Laboratory. * All rights reserved. * * File coded by: Vincent Favre-Nicolin, Kevin Knox * * See AUTHORS.txt for a list of people who contributed. * See LICENSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::DiffractionDataSingleCrystal. * Changes from ObjCryst::DiffractionDataSingleCrystal * - SetHklIobs takes float(64) H, K, L arrays rather than long integers - easier * because passing numpy int array seesm complicated, and more practical anyway * since GetH() GetK() GetL() functions from ScatteringData natively use floats. *****************************************************************************/ #include #include #include #include #include #undef B0 #include #include #include #include #include #include "python_streambuf.hpp" #include "helpers.hpp" namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; namespace { DiffractionDataSingleCrystal* _CreateSingleCrystalDataFromCIF(bp::object input, Crystal &cryst) { // Reading a cif file creates some output via fpObjCrystInformUser. // Mute the output and restore it on return or exception. // Also mute any hardcoded output to cout. MuteObjCrystUserInfo muzzle; CaptureStdOut gag; boost_adaptbx::python::streambuf sbuf(input); boost_adaptbx::python::streambuf::istream in(sbuf); ObjCryst::CIF cif(in); int idx0 = gDiffractionDataSingleCrystalRegistry.GetNb(); ObjCryst::DiffractionDataSingleCrystal* d = ObjCryst::CreateSingleCrystalDataFromCIF(cif, &cryst); gag.release(); muzzle.release(); int idx = gDiffractionDataSingleCrystalRegistry.GetNb(); if(idx == idx0) { throw ObjCryst::ObjCrystException("Cannot create single crystal diffraction data from CIF"); } return d; } void setdiffractiondatasinglecrystal_iobs(DiffractionDataSingleCrystal& diff, bp::object iobs) { CrystVector_REAL iiobs; assignCrystVector(iiobs, iobs); if(iiobs.size() != diff.GetIobs().size()) throw ObjCryst::ObjCrystException("DiffractionDataSingleCrystal::SetIobs(): " "number of elements does not match the previous Iobs list. " "Use SetHklIobs if you want to change the number of reflections."); MuteObjCrystUserInfo muzzle; diff.SetIobs(iiobs); } void setdiffractiondatasinglecrystal_sigma(DiffractionDataSingleCrystal& diff, bp::object sigma) { CrystVector_REAL ssigma; assignCrystVector(ssigma, sigma); if(ssigma.size() != diff.GetIobs().size()) throw ObjCryst::ObjCrystException("DiffractionDataSingleCrystal::SetSigma(): " "number of elements does not match the Iobs list. " "Use SetHklIobs if you want to change the number of reflections."); MuteObjCrystUserInfo muzzle; diff.SetSigma(ssigma); } // TODO: For SetHklIobs we should pass directly an integer array but that seems difficult-passed numpy arrays // are always interpreted as doubles (?). It's more practical this way. void assignCrystVector(CrystVector& cv, bp::object obj) { bp::stl_input_iterator begin(obj), end; std::list values(begin, end); cv.resize(values.size()); std::list::const_iterator vv = values.begin(); long* dst = cv.data(); for (; vv != values.end(); ++vv, ++dst) *dst = lround(*vv); } void setdiffractiondatasinglecrystal_hkliobs(DiffractionDataSingleCrystal& diff, bp::object h,bp::object k, bp::object l, bp::object iobs, bp::object sigma) { CrystVector hh; assignCrystVector(hh, h); CrystVector kk; assignCrystVector(kk, k); CrystVector ll; assignCrystVector(ll, l); CrystVector_REAL iiobs; assignCrystVector(iiobs, iobs); CrystVector_REAL ssigma; assignCrystVector(ssigma, sigma); if(hh.size() != kk.size()) throw ObjCryst::ObjCrystException("DiffractionDataSingleCrystal::SetHklIobs(): h and k array sizes differ"); if(hh.size() != ll.size()) throw ObjCryst::ObjCrystException("DiffractionDataSingleCrystal::SetHklIobs(): h and l array sizes differ"); if(hh.size() != iiobs.size()) throw ObjCryst::ObjCrystException("DiffractionDataSingleCrystal::SetHklIobs(): h and iobs array sizes differ"); if(hh.size() != ssigma.size()) throw ObjCryst::ObjCrystException("DiffractionDataSingleCrystal::SetHklIobs(): h and sigma array sizes differ"); MuteObjCrystUserInfo muzzle; diff.SetHklIobs(hh, kk, ll, iiobs, ssigma); } } // namespace void wrap_diffractiondatasinglecrystal() { // Global object registry scope().attr("gDiffractionDataSingleCrystalRegistry") = object(boost::cref(gDiffractionDataSingleCrystalRegistry)); class_ >( "DiffractionDataSingleCrystal", init((bp::arg("cryst"), bp::arg("regist")=true)) [with_custodian_and_ward<1,2>()]) // FIXME ... add crystal-less constructor .def("GetIcalc", &DiffractionDataSingleCrystal::GetIcalc, return_value_policy()) .def("GetIobs", &DiffractionDataSingleCrystal::GetIobs, return_value_policy()) .def("GetSigma", &DiffractionDataSingleCrystal::GetSigma, return_value_policy()) .def("SetIobs", &setdiffractiondatasinglecrystal_iobs, bp::arg("iobs")) .def("SetSigma", &setdiffractiondatasinglecrystal_sigma, bp::arg("sigma")) .def("SetHklIobs", &setdiffractiondatasinglecrystal_hkliobs, (bp::arg("h"),bp::arg("k"),bp::arg("l"), bp::arg("iobs"), bp::arg("sigma"))) .def("SetIobsToIcalc", &DiffractionDataSingleCrystal::SetIobsToIcalc) .def("GetRw", &DiffractionDataSingleCrystal::GetRw) .def("GetR", &DiffractionDataSingleCrystal::GetR) .def("GetChi2", &DiffractionDataSingleCrystal::GetChi2) .def("FitScaleFactorForRw", &DiffractionDataSingleCrystal::FitScaleFactorForRw) .def("FitScaleFactorForR", &DiffractionDataSingleCrystal::FitScaleFactorForR) // TODO: These functions should print a limited number of reflections - problems otherwise .def("PrintObsData", &DiffractionDataSingleCrystal::PrintObsData) .def("PrintObsCalcData", &DiffractionDataSingleCrystal::PrintObsCalcData) .def("SetUseOnlyLowAngleData", &DiffractionDataSingleCrystal::SetUseOnlyLowAngleData) .def("SaveHKLIobsIcalc", &DiffractionDataSingleCrystal::SaveHKLIobsIcalc) .def("GetLogLikelihood", &DiffractionDataSingleCrystal::GetLogLikelihood) .def("ImportHklIobs", &DiffractionDataSingleCrystal::ImportHklIobs, (bp::arg("fileName"), bp::arg("nbRefl"), bp::arg("skipLines")=0)) .def("ImportHklIobsSigma", &DiffractionDataSingleCrystal::ImportHklIobsSigma, (bp::arg("fileName"), bp::arg("nbRefl"), bp::arg("skipLines")=0)) .def("ImportShelxHKLF4", &DiffractionDataSingleCrystal::ImportShelxHKLF4) .def("ImportCIF", &DiffractionDataSingleCrystal::ImportCIF) .def("SetWavelength", (void(DiffractionDataSingleCrystal::*)(const REAL)) &DiffractionDataSingleCrystal::SetWavelength, bp::arg("wavelength")) .def("SetWavelength", (void (DiffractionDataSingleCrystal::*)(const string&, const REAL)) &DiffractionDataSingleCrystal::SetWavelength, (bp::arg("XRayTubeElementName"), bp::arg("alpha2Alpha2ratio")=0.5)) .def("SetEnergy", &DiffractionDataSingleCrystal::SetEnergy, bp::arg("nrj_kev")) ; def("CreateSingleCrystalDataFromCIF", &_CreateSingleCrystalDataFromCIF, (bp::arg("file"), bp::arg("crystal")), with_custodian_and_ward_postcall<0,2, return_value_policy >()); } pyobjcryst-2024.2.1/src/extensions/general_ext.cpp000066400000000000000000000047071470422267000221550ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to general structures and objects defined in * ObjCryst/ObjCryst/General.h * *****************************************************************************/ #include #include #include #include #include #if LIBOBJCRYST_VERSION < 2017002002000LL #error pyobjcryst requires libobjcryst 2017.2.2 or later. #endif using namespace boost::python; using namespace ObjCryst; // wrappers ------------------------------------------------------------------ namespace { const char* doc__get_libobjcryst_version_info_dict = "\ Return dictionary with version data for the loaded libobjcryst library.\n\ "; dict get_libobjcryst_version_info_dict() { dict rv; rv["version"] = libobjcryst_version_info::version; rv["version_str"] = libobjcryst_version_info::version_str; rv["major"] = libobjcryst_version_info::major; rv["minor"] = libobjcryst_version_info::minor; rv["micro"] = libobjcryst_version_info::micro; rv["date"] = libobjcryst_version_info::date; rv["git_commit"] = libobjcryst_version_info::git_commit; rv["patch"] = libobjcryst_version_info::patch; return rv; } } // namespace // --------------------------------------------------------------------------- void wrap_general() { enum_("RadiationType") .value("RAD_NEUTRON", RAD_NEUTRON) .value("RAD_XRAY", RAD_XRAY) .value("RAD_ELECTRON", RAD_ELECTRON) ; // Only import wavelength types actually used enum_("WavelengthType") .value("WAVELENGTH_MONOCHROMATIC", WAVELENGTH_MONOCHROMATIC) .value("WAVELENGTH_ALPHA12", WAVELENGTH_ALPHA12) .value("WAVELENGTH_TOF", WAVELENGTH_TOF) ; def("_get_libobjcryst_version_info_dict", get_libobjcryst_version_info_dict, doc__get_libobjcryst_version_info_dict); } pyobjcryst-2024.2.1/src/extensions/globaloptim_ext.cpp000066400000000000000000000216061470422267000230460ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst * * File coded by: Vincent Favre-Nicolin * * See AUTHORS.txt for a list of people who contributed. * See LICENSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::GlobalOptimObj and ObjCryst::MonteCarloObj. * * Changes from ObjCryst::MonteCarloObj: * - add access to mAutoLSQ option * * Other Changes * *****************************************************************************/ #include #include #include #include #include #include "helpers.hpp" namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; namespace { // Overload MonteCarlo class so that it does not auto-save to XML class MonteCarloObjWrap: public MonteCarloObj, public wrapper { public: MonteCarloObjWrap(): MonteCarloObj() { this->GetOption("Save Best Config Regularly").SetChoice(0); } MonteCarloObjWrap(const string name): MonteCarloObj(name) { this->GetOption("Save Best Config Regularly").SetChoice(0); } MonteCarloObjWrap(const MonteCarloObj &old): MonteCarloObj(old) {} void default_UpdateDisplay() const { this->MonteCarloObj::UpdateDisplay();} virtual void UpdateDisplay() const { override f = this->get_override("UpdateDisplay"); if (f) f(); // Always call the default UpdateDisplay, which updates all // other objects display default_UpdateDisplay(); } }; void run_optimize(MonteCarloObj& obj, long nbSteps, const bool silent, const double finalcost, const double maxTime) { CaptureStdOut gag; obj.Optimize(nbSteps, silent, finalcost, maxTime); } void multirun_optimize(MonteCarloObj& obj, long nbCycle, long nbSteps, const bool silent, const double finalcost, const double maxTime) { CaptureStdOut gag; obj.MultiRunOptimize(nbCycle, nbSteps, silent, finalcost, maxTime); } void mc_sa(MonteCarloObj& obj, long nbSteps, const bool silent, const double finalcost, const double maxTime) { CaptureStdOut gag; obj.RunSimulatedAnnealing(nbSteps, silent, finalcost, maxTime); } void mc_pt(MonteCarloObj& obj, long nbSteps, const bool silent, const double finalcost, const double maxTime) { CaptureStdOut gag; obj.RunParallelTempering(nbSteps, silent, finalcost, maxTime); } /* void mc_random_lsq(MonteCarloObjWrap& obj, long nbCycle) { obj.RunRandomLSQMethod(nbCycle); } */ } // namespace void wrap_globaloptim() { // Global object registry scope().attr("gOptimizationObjRegistry") = boost::cref(gOptimizationObjRegistry); enum_("AnnealingSchedule") .value("CONSTANT", ANNEALING_CONSTANT) .value("BOLTZMANN", ANNEALING_BOLTZMANN) .value("CAUCHY", ANNEALING_CAUCHY) .value("EXPONENTIAL", ANNEALING_EXPONENTIAL) .value("SMART", ANNEALING_SMART) .value("GAMMA", ANNEALING_GAMMA) ; enum_("GlobalOptimType") .value("SIMULATED_ANNEALING", GLOBAL_OPTIM_SIMULATED_ANNEALING) .value("PARALLEL_TEMPERING", GLOBAL_OPTIM_PARALLEL_TEMPERING) .value("RANDOM_LSQ", GLOBAL_OPTIM_RANDOM_LSQ) .value("SIMULATED_ANNEALING_MULTI", GLOBAL_OPTIM_SIMULATED_ANNEALING_MULTI) .value("PARALLEL_TEMPERING_MULTI", GLOBAL_OPTIM_PARALLEL_TEMPERING_MULTI) ; class_("MonteCarlo") .def(init<>()) .def(init(bp::arg("old"))) .def(init(bp::arg("name"))) //////////////// OptimizationObj methods .def("RandomizeStartingConfig", &MonteCarloObj::RandomizeStartingConfig) .def("Optimize", &run_optimize, (bp::arg("nbSteps"), bp::arg("silent")=false, bp::arg("finalcost")=0.0, bp::arg("maxTime")=-1)) .def("MultiRunOptimize", &multirun_optimize, (bp::arg("nbCycle"), bp::arg("nbSteps"), bp::arg("silent")=false, bp::arg("finalcost")=0.0, bp::arg("maxTime")=-1)) .def("FixAllPar", &MonteCarloObj::FixAllPar) .def("SetParIsFixed", (void (MonteCarloObj::*)(const std::string&, const bool)) &MonteCarloObj::SetParIsFixed) .def("SetParIsFixed", (void (MonteCarloObj::*)(const RefParType*, const bool)) &MonteCarloObj::SetParIsFixed) .def("UnFixAllPar", &MonteCarloObj::UnFixAllPar) .def("SetParIsUsed", (void (MonteCarloObj::*)(const std::string&, const bool)) &MonteCarloObj::SetParIsUsed) .def("SetParIsUsed", (void (MonteCarloObj::*)(const RefParType*, const bool)) &MonteCarloObj::SetParIsUsed) .def("SetLimitsRelative", ( void (MonteCarloObj::*) (const std::string&, const double, const double) ) &MonteCarloObj::SetLimitsRelative) .def("SetLimitsRelative", ( void (MonteCarloObj::*) (const RefParType*, const double, const double) ) &MonteCarloObj::SetLimitsRelative) .def("SetLimitsAbsolute", ( void (MonteCarloObj::*) (const std::string&, const double, const double) ) &MonteCarloObj::SetLimitsAbsolute) .def("SetLimitsAbsolute", ( void (MonteCarloObj::*) (const RefParType*, const double, const double) ) &MonteCarloObj::SetLimitsAbsolute) .def("GetLogLikelihood", &MonteCarloObj::GetLogLikelihood) .def("StopAfterCycle", &MonteCarloObj::StopAfterCycle) .def("AddRefinableObj", &MonteCarloObj::AddRefinableObj, bp::arg("obj"), with_custodian_and_ward<1,2>()) .def("GetFullRefinableObj", &MonteCarloObj::GetFullRefinableObj, bp::arg("rebuild")=true, with_custodian_and_ward_postcall<1,0, return_internal_reference<> >()) .def("GetName", &MonteCarloObj::GetName, return_value_policy()) .def("SetName", &MonteCarloObj::SetName) .def("GetClassName", &MonteCarloObj::GetClassName) .def("Print", &MonteCarloObj::Print, &MonteCarloObj::Print) .def("RestoreBestConfiguration", &MonteCarloObj::RestoreBestConfiguration) .def("GetNbParamSet", &MonteCarloObj::GetNbParamSet) .def("GetParamSetIndex", &MonteCarloObj::GetParamSetIndex) .def("GetParamSetCost", &MonteCarloObj::GetParamSetCost) .def("RestoreParamSet", &MonteCarloObj::RestoreParamSet, (bp::arg("idx"), bp::arg("update_display")=true)) .def("IsOptimizing", &MonteCarloObj::IsOptimizing) .def("GetLastOptimElapsedTime", &MonteCarloObj::GetLastOptimElapsedTime) //.def("GetMainTracker", &MonteCarloObj::GetMainTracker) //.add_property("name", &MonteCarloObj::GetName, &MonteCarloObj::SetName) .def("GetNbOption", &MonteCarloObj::GetNbOption) .def("GetOption", (RefObjOpt& (MonteCarloObj::*)(const unsigned int)) &MonteCarloObj::GetOption, return_internal_reference<>()) .def("GetOption", (RefObjOpt& (MonteCarloObj::*)(const string&)) &MonteCarloObj::GetOption, with_custodian_and_ward_postcall<1,0,return_internal_reference<> >()) .add_property("trial", &MonteCarloObj::GetTrial) .add_property("run", &MonteCarloObj::GetRun) .add_property("llk", &MonteCarloObj::GetLogLikelihood) //////////////// MonteCarlo methods .def("SetAlgorithmParallTempering", &MonteCarloObj::SetAlgorithmParallTempering, (bp::arg("scheduleTemp"), bp::arg("tMax"), bp::arg("tMin"), bp::arg("scheduleMutation")=ANNEALING_SMART, bp::arg("mutMax")=16, bp::arg("mutMin")=.125)) .def("SetAlgorithmSimulAnnealing", &MonteCarloObj::SetAlgorithmSimulAnnealing, (bp::arg("scheduleTemp"), bp::arg("tMax"), bp::arg("tMin"), bp::arg("scheduleMutation")=ANNEALING_SMART, bp::arg("mutMax")=16, bp::arg("mutMin")=.125, bp::arg("nbTrialRetry")=0, bp::arg("minCostRetry")=0.)) .def("RunSimulatedAnnealing", &mc_sa, (bp::arg("nbSteps"), bp::arg("silent")=false, bp::arg("finalcost")=0.0, bp::arg("maxTime")=-1)) .def("RunParallelTempering", &mc_pt, (bp::arg("nbSteps"), bp::arg("silent")=false, bp::arg("finalcost")=0.0, bp::arg("maxTime")=-1)) // TODO: seems unstable //.def("RunRandomLSQ", &mc_random_lsq, // bp::arg("nbCycle")) .def("GetLSQObj", (LSQNumObj& (MonteCarloObj::*)()) &MonteCarloObj::GetLSQObj, with_custodian_and_ward_postcall<1,0, return_internal_reference<> >()) .def("InitLSQ", &MonteCarloObj::InitLSQ, bp::arg("useFullPowderPatternProfile")=true) ; } pyobjcryst-2024.2.1/src/extensions/globalscatteringpower_ext.cpp000066400000000000000000000024711470422267000251350ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::GlobalScatteringPower from * ObjCryst/ObjCryst/ZScatterer.h * *****************************************************************************/ #include #include #include using namespace boost::python; using namespace ObjCryst; void wrap_globalscatteringpower() { typedef void (GlobalScatteringPower::*GSPInitType)(const ZScatterer&); GSPInitType theinit = &GlobalScatteringPower::Init; class_ >("GlobalScatteringPower") .def(init()) .def(init()) .def("Init", theinit) .def("GetRadius", &GlobalScatteringPower::GetRadius) ; } pyobjcryst-2024.2.1/src/extensions/helpers.cpp000066400000000000000000000070411470422267000213140ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * Implementation of non-template binding-specific helper functions. * *****************************************************************************/ #include "helpers.hpp" #include #include #include #include // Use numpy here, but initialize it later in the extension module. #include "pyobjcryst_numpy_setup.hpp" #define NO_IMPORT_ARRAY #include namespace bp = boost::python; namespace { void black_hole_info(const std::string& s) { } } // namespace // class MuteObjCrystUserInfo ------------------------------------------------ MuteObjCrystUserInfo::MuteObjCrystUserInfo() : msave_info_func(ObjCryst::fpObjCrystInformUser) { ObjCryst::fpObjCrystInformUser = black_hole_info; } MuteObjCrystUserInfo::~MuteObjCrystUserInfo() { this->release(); } void MuteObjCrystUserInfo::release() { using ObjCryst::fpObjCrystInformUser; if (msave_info_func) fpObjCrystInformUser = msave_info_func; msave_info_func = NULL; } // class CaptureStdOut ------------------------------------------------------- CaptureStdOut::CaptureStdOut() : msave_cout_buffer(std::cout.rdbuf()) { std::cout.rdbuf(moutput.rdbuf()); } CaptureStdOut::~CaptureStdOut() { this->release(); } std::string CaptureStdOut::str() const { return moutput.str(); } void CaptureStdOut::release() { if (msave_cout_buffer) std::cout.rdbuf(msave_cout_buffer); msave_cout_buffer = NULL; } // free functions ------------------------------------------------------------ void assignCrystVector(CrystVector& cv, bp::object obj) { // copy data directly if it is a numpy array of doubles PyArrayObject* a = PyArray_Check(obj.ptr()) ? reinterpret_cast(obj.ptr()) : NULL; bool isdoublenumpyarray = a && (1 == PyArray_NDIM(a)) && (NPY_DOUBLE == PyArray_TYPE(a)); if (isdoublenumpyarray) { const double* src = static_cast(PyArray_DATA(a)); npy_intp stride = PyArray_STRIDE(a, 0) / PyArray_ITEMSIZE(a); cv.resize(PyArray_SIZE(a)); double* dst = cv.data(); const double* last = dst + cv.size(); for (; dst != last; ++dst, src += stride) *dst = *src; } // otherwise copy elementwise converting each element to a double else { bp::stl_input_iterator begin(obj), end; // use intermediate list to preserve cv when conversion fails. std::list values(begin, end); cv.resize(values.size()); std::list::const_iterator vv = values.begin(); double* dst = cv.data(); for (; vv != values.end(); ++vv, ++dst) *dst = *vv; } } int check_index(int idx, int size, NegativeIndexFlag nflag) { const int lo = (nflag == ALLOW_NEGATIVE) ? -size : 0; if (idx < lo || idx >= size) { PyErr_SetString(PyExc_IndexError, "Index out of range."); bp::throw_error_already_set(); } int rv = (idx < 0) ? size + idx : idx; return rv; } pyobjcryst-2024.2.1/src/extensions/helpers.hpp000066400000000000000000000066161470422267000213300ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * This is home to converters and utility functions that are explicitly applied * within the extensions, rather than registered in registerconverters.cpp. * *****************************************************************************/ #ifndef HELPERS_HPP #define HELPERS_HPP #include #include #include #include #include #include #include #include #include #include namespace bp = boost::python; typedef std::numeric_limits doublelim; class MuteObjCrystUserInfo { public: MuteObjCrystUserInfo(); ~MuteObjCrystUserInfo(); void release(); private: // pointer to the previous info function void (*msave_info_func)(const std::string &); }; class CaptureStdOut { public: CaptureStdOut(); ~CaptureStdOut(); std::string str() const; void release(); private: std::ostringstream moutput; std::streambuf* msave_cout_buffer; }; template std::string __str__(const T& obj) { CaptureStdOut outbuf; obj.Print(); outbuf.release(); std::string outstr = outbuf.str(); // Remove the trailing newline size_t idx = outstr.find_last_not_of("\n"); if (idx != std::string::npos) outstr.erase(idx+1); return outstr; } template std::set pyIterableToSet(const bp::object& l) { std::set cl; T typeobj; for(int i=0; i < len(l); ++i) { typeobj = bp::extract(l[i]); cl.insert(typeobj); } return cl; } // For turning vector-like containers into lists // It is assumed that T contains non-pointers template bp::list containerToPyList(T& v) { bp::list l; for(typename T::const_iterator it = v.begin(); it != v.end(); ++it) { l.append(*it); } return l; } // For turning vector-like containers into lists // It is assumed that T contains pointers template bp::list ptrcontainerToPyList(T& v) { bp::list l; for(typename T::const_iterator it = v.begin(); it != v.end(); ++it) { l.append(bp::ptr(*it)); } return l; } template bp::list setToPyList(std::set& v) { return containerToPyList< typename std::set >(v); } template bp::list vectorToPyList(std::vector& v) { return containerToPyList< typename std::vector >(v); } template bp::list listToPyList(std::list& v) { return containerToPyList< typename std::list >(v); } // Extract CrystVector from a Python object template class CrystVector; void assignCrystVector(CrystVector& cv, bp::object obj); // check index bounds and convert negative index to equivalent value enum NegativeIndexFlag {POSITIVE, ALLOW_NEGATIVE}; int check_index(int idx, int size, NegativeIndexFlag nflag); #endif pyobjcryst-2024.2.1/src/extensions/indexing_ext.cpp000066400000000000000000000402321470422267000223360ustar00rootroot00000000000000/***************************************************************************** * * * File coded by: Vincent Favre-Nicolin * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::Indexing. * * Changes from Indexing: * *****************************************************************************/ #include #include #include #include #include #include #include #include #include #include #include #undef B0 #include #include #include #include "python_streambuf.hpp" #include "helpers.hpp" namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; #ifndef M_PI #define M_PI 3.14159265358979323846264338327950288 #endif #define RAD2DEG (180./M_PI) namespace { bp::tuple _direct_unit_cell(const RecUnitCell &r, const bool equiv, const bool degrees=false) { const std::vector &v = r.DirectUnitCell(equiv); if(degrees) return bp::make_tuple(v[0], v[1], v[2], v[3]*RAD2DEG, v[4]*RAD2DEG, v[5]*RAD2DEG, v[6]); return bp::make_tuple(v[0], v[1], v[2], v[3], v[4], v[5], v[6]); } bp::list _vDicVolHKL(const PeakList::hkl& h) { return listToPyList< PeakList::hkl0 > (h.vDicVolHKL); } std::string __str__RecUnitCell(RecUnitCell& ruc) { std::string sys; switch(ruc.mlattice) { case TRICLINIC:sys="TRICLINIC"; break; case MONOCLINIC:sys="MONOCLINIC"; break; case ORTHOROMBIC:sys="ORTHOROMBIC"; break; case HEXAGONAL:sys="HEXAGONAL"; break; case RHOMBOEDRAL:sys="RHOMBOEDRAL"; break; case TETRAGONAL:sys="TETRAGONAL"; break; case CUBIC:sys="CUBIC"; break; } char centc; switch(ruc.mCentering) { case LATTICE_P:centc='P'; break; case LATTICE_I:centc='I'; break; case LATTICE_A:centc='A'; break; case LATTICE_B:centc='B'; break; case LATTICE_C:centc='C'; break; case LATTICE_F:centc='F'; break; } std::vector d = ruc.DirectUnitCell(); if(ruc.mNbSpurious>0) return (boost::format("%5.2f %5.2f %5.2f %5.1f %5.1f %5.1f V=%4.0f %s %c (%d SPURIOUS)") % d[0] % d[1] % d[2] % (d[3]*RAD2DEG) % (d[4]*RAD2DEG) % (d[5]*RAD2DEG) % d[6] % sys % centc % ruc.mNbSpurious).str(); return (boost::format("%5.2f %5.2f %5.2f %5.1f %5.1f %5.1f V=%4.0f %s %c") % d[0] % d[1] % d[2] % (d[3]*RAD2DEG) % (d[4]*RAD2DEG) % (d[5]*RAD2DEG) % d[6] % sys % centc).str(); } std::string __str__hkl0(PeakList::hkl0& hkl) { return (boost::format("(%2d %2d %2d)") % hkl.h % hkl.k % hkl.l).str(); } std::string __str__hkl(PeakList::hkl& hkl) { if(hkl.isIndexed) return (boost::format("Peak dobs=%7.5f+/-%7.5f iobs=%6e (%2d %2d %2d))") % hkl.dobs % hkl.dobssigma % hkl.iobs % hkl.h % hkl.k % hkl.l).str(); return (boost::format("Peak dobs=%7.5f+/-%7.5f iobs=%6e (? ? ?))") % hkl.dobs % hkl.dobssigma % hkl.iobs).str(); } class PeakListWrap : public PeakList, public wrapper { public: PeakListWrap():PeakList() {} PeakListWrap(const PeakList &p):PeakList(p) {} void ImportDhklDSigmaIntensity(bp::object input, const float defaultsigma) { CaptureStdOut gag; const std::string cname = bp::extract (input.attr("__class__").attr("__name__")); if(cname.compare("str")==0) { // Filename std::string p = bp::extract(input); std::ifstream is(p); this->PeakList::ImportDhklDSigmaIntensity(is, defaultsigma); } else { // Python file object boost_adaptbx::python::streambuf sbuf(input); boost_adaptbx::python::streambuf::istream is(sbuf); this->PeakList::ImportDhklDSigmaIntensity(is, defaultsigma); } } void ImportDhklIntensity(bp::object input) { CaptureStdOut gag; const std::string cname = bp::extract (input.attr("__class__").attr("__name__")); if(cname.compare("str")==0) { // Filename std::string p = bp::extract(input); std::ifstream is(p); this->PeakList::ImportDhklIntensity(is); } else {// Python file object boost_adaptbx::python::streambuf sbuf(input); boost_adaptbx::python::streambuf::istream is(sbuf); this->PeakList::ImportDhklIntensity(is); } } void default_ImportDhkl(bp::object input) { CaptureStdOut gag; const std::string cname = bp::extract (input.attr("__class__").attr("__name__")); if(cname.compare("str")==0) { // Filename std::string p = bp::extract(input); std::ifstream is(p); this->PeakList::ImportDhkl(is); } else {// Python file object boost_adaptbx::python::streambuf sbuf(input); boost_adaptbx::python::streambuf::istream is(sbuf); this->PeakList::ImportDhkl(is); } } void Import2ThetaIntensity(bp::object input, const float wavelength) { CaptureStdOut gag; const std::string cname = bp::extract (input.attr("__class__").attr("__name__")); if(cname.compare("str")==0) { // Filename std::string p = bp::extract(input); std::ifstream is(p); this->PeakList::Import2ThetaIntensity(is, wavelength); } else {// Python file object boost_adaptbx::python::streambuf sbuf(input); boost_adaptbx::python::streambuf::istream is(sbuf); this->PeakList::Import2ThetaIntensity(is, wavelength); } } void ExportDhklDSigmaIntensity(bp::object output) { CaptureStdOut gag; const std::string cname = bp::extract (output.attr("__class__").attr("__name__")); if(cname.compare("str")==0) { // Filename std::string sout =bp::extract(output); std::ofstream out(sout); this->PeakList::ExportDhklDSigmaIntensity(out); } else {// Python file object boost_adaptbx::python::streambuf sbuf(output); boost_adaptbx::python::streambuf::ostream out(sbuf); this->PeakList::ExportDhklDSigmaIntensity(out); } } void set_dobs_list(bp::list &l) { this->GetPeakList().clear(); bp::ssize_t n = bp::len(l); for(bp::ssize_t i=0;iAddPeak(bp::extract(l[i])); } unsigned int Length() const {return this->GetPeakList().size();} std::vector::const_iterator GetPeakIterBegin() {return this->GetPeakList().begin();} std::vector::const_iterator GetPeakIterEnd() {return this->GetPeakList().end();} void resize(const unsigned int nb) {this->GetPeakList().resize(nb);} void clear() {this->GetPeakList().clear();} bp::list _getPeakList() { return vectorToPyList< PeakList::hkl > (this->GetPeakList()); } }; std::list >::const_iterator _GetSolutionsIterBegin(const CellExplorer& c) {return c.GetSolutions().begin();} std::list >::const_iterator _GetSolutionsIterEnd(const CellExplorer& c) {return c.GetSolutions().end();} void _DicVolGag(CellExplorer &ex, const float minScore,const unsigned int minDepth, const float stopOnScore,const unsigned int stopOnDepth, const bool verbose=true) { CaptureStdOut gag; if(verbose) gag.release(); ex.DicVol(minScore, minDepth, stopOnScore, stopOnDepth); } // Custom converter struct pair_ruc_float_to_tuple { static PyObject* convert(std::pair const &p) { bp::object tpl = bp::make_tuple(bp::ptr(&p.first), p.second); PyObject* rv = tpl.ptr(); return bp::incref(rv); } static PyTypeObject const* get_pytype() { return &PyTuple_Type; } }; bp::list _GetSolutions(CellExplorer &ex) { // See containerToPyList bp::list l; for(std::list >::const_iterator pos = ex.GetSolutions().begin(); pos != ex.GetSolutions().end(); ++pos) l.append(*pos); return l; } } // namespace void wrap_indexing() { enum_("CrystalSystem") .value("TRICLINIC", TRICLINIC) .value("MONOCLINIC", MONOCLINIC) .value("ORTHOROMBIC", ORTHOROMBIC) .value("HEXAGONAL", HEXAGONAL) .value("RHOMBOEDRAL", RHOMBOEDRAL) .value("TETRAGONAL", TETRAGONAL) .value("CUBIC", CUBIC) ; enum_("CrystalCentering") .value("LATTICE_P", LATTICE_P) .value("LATTICE_I", LATTICE_I) .value("LATTICE_A", LATTICE_A) .value("LATTICE_B", LATTICE_B) .value("LATTICE_C", LATTICE_C) .value("LATTICE_F", LATTICE_F) ; def("EstimateCellVolume", &EstimateCellVolume, (bp::arg("dmin"), bp::arg("dmax"), bp::arg("nbrefl"), bp::arg("system"), bp::arg("centering"), bp::arg("kappa")=1.)); class_ ("RecUnitCell") .def(init( (bp::arg("zero")=0, bp::arg("par0")=0, bp::arg("par1")=0, bp::arg("par2")=0, bp::arg("par3")=0, bp::arg("par4")=0, bp::arg("par5")=0, bp::arg("lattice")=CUBIC, bp::arg("cent")=LATTICE_P, bp::arg("nbspurious")=0))) .def(init(bp::arg("old"))) .def("hkl2d", &RecUnitCell::hkl2d, (bp::arg("h"), bp::arg("k"), bp::arg("l"), bp::arg("derivpar")=NULL, bp::arg("derivhkl")=0)) .def("DirectUnitCell", &_direct_unit_cell, (bp::arg("equiv")=false, bp::arg("degrees")=false)) //.def_readonly("par", &RecUnitCell::par) .def_readonly("mlattice", &RecUnitCell::mlattice) .def_readonly("lattice", &RecUnitCell::mlattice) .def_readonly("mCentering", &RecUnitCell::mCentering) .def_readonly("centering", &RecUnitCell::mCentering) .def_readonly("nb_spurious", &RecUnitCell::mNbSpurious) .def_readonly("mNbSpurious", &RecUnitCell::mNbSpurious) .def("__str__", &__str__RecUnitCell) .def("__repr__", &__str__RecUnitCell) ; // Avoid the name hkl which may conflict somewhere else class_ ("PeakList_hkl0") .def(init( (bp::arg("h")=0, bp::arg("k")=0, bp::arg("l")=0))) .def_readonly("h", &PeakList::hkl0::h) .def_readonly("k", &PeakList::hkl0::k) .def_readonly("l", &PeakList::hkl0::l) .def("__str__", &__str__hkl0) .def("__repr__", &__str__hkl0) ; // Avoid the name hkl which may conflict somewhere else class_ ("PeakList_hkl") .def(init( (bp::arg("dobs")=1.0, bp::arg("iobs")=0, bp::arg("dobssigma")=0, bp::arg("iobssigma")=0, bp::arg("h")=0, bp::arg("k")=0, bp::arg("l")=0, bp::arg("d2calc")=0))) .def_readonly("dobs", &PeakList::hkl::dobs) .def_readonly("dobssigma", &PeakList::hkl::dobssigma) .def_readonly("d2obs", &PeakList::hkl::d2obs) .def_readonly("d2obsmin", &PeakList::hkl::d2obsmin) .def_readonly("d2obsmax", &PeakList::hkl::d2obsmax) .def_readonly("iobs", &PeakList::hkl::iobs) .def_readonly("iobssigma", &PeakList::hkl::iobssigma) .def_readonly("h", &PeakList::hkl::h) .def_readonly("k", &PeakList::hkl::k) .def_readonly("l", &PeakList::hkl::l) .def_readonly("isIndexed", &PeakList::hkl::isIndexed) .add_property("vDicVolHKL", &_vDicVolHKL) .def_readonly("isSpurious", &PeakList::hkl::isSpurious) .def_readonly("stats", &PeakList::hkl::stats) .def_readonly("d2calc", &PeakList::hkl::d2calc) .def_readonly("d2diff", &PeakList::hkl::d2diff) .def("__str__", &__str__hkl) .def("__repr__", &__str__hkl) ; class_ ("PeakList") .def("ImportDhklDSigmaIntensity", &PeakListWrap::ImportDhklDSigmaIntensity, (bp::arg("file"), bp::arg("defaultsigma")=0.001)) .def("ImportDhklIntensity", &PeakListWrap::ImportDhklIntensity, bp::arg("file")) .def("ImportDhkl", &PeakListWrap::ImportDhkl, bp::arg("file")) .def("Import2ThetaIntensity", &PeakListWrap::Import2ThetaIntensity, (bp::arg("file"), bp::arg("wavelength"))) .def("ExportDhklDSigmaIntensity", &PeakListWrap::ExportDhklDSigmaIntensity, bp::arg("file")) .def("Simulate", &PeakList::Simulate, (bp::arg("zero"), bp::arg("a"), bp::arg("b"), bp::arg("c"), bp::arg("alpha"), bp::arg("beta"), bp::arg("gamma"), bp::arg("deg"), bp::arg("nb")=20, bp::arg("nbspurious")=0, bp::arg("sigma")=0, bp::arg("percentMissing")=0, bp::arg("verbose")=false)) .def("GetPeakList", &PeakListWrap::_getPeakList, with_custodian_and_ward_postcall<1,0>()) // Python only .def("resize", &PeakListWrap::resize, bp::arg("nb")=20) .def("clear", &PeakListWrap::clear) .def("set_dobs_list", &PeakListWrap::set_dobs_list, (bp::arg("dobs"))) .def("__len__", &PeakListWrap::Length) .def("__iter__", range > (&PeakListWrap::GetPeakIterBegin, &PeakListWrap::GetPeakIterEnd)) ; // Register converter boost::python::to_python_converter, pair_ruc_float_to_tuple>(); class_, boost::noncopyable>("CellExplorer", init( (bp::arg("dhkl"), bp::arg("lattice"), bp::arg("nbSpurious")=0)) [with_custodian_and_ward<1,2>()]) .def("SetLengthMinMax", &CellExplorer::SetLengthMinMax) .def("SetAngleMinMax", &CellExplorer::SetAngleMinMax) .def("SetVolumeMinMax", &CellExplorer::SetVolumeMinMax) .def("SetNbSpurious", &CellExplorer::SetNbSpurious) .def("SetD2Error", &CellExplorer::SetD2Error) .def("SetMinMaxZeroShift", &CellExplorer::SetMinMaxZeroShift) .def("SetCrystalSystem", &CellExplorer::SetCrystalSystem) .def("SetCrystalCentering", &CellExplorer::SetCrystalCentering) .def("Print", &CellExplorer::Print) .def("DicVol", &_DicVolGag, (bp::arg("minScore")=10, bp::arg("minScore")=10, bp::arg("stopOnScore")=50, bp::arg("stopOnDepth")=6, bp::arg("verbose")=true)) .def("ReduceSolutions", &CellExplorer::ReduceSolutions, bp::arg("updateReportThreshold")=false) .def("GetBestScore", &CellExplorer::GetBestScore) .def("GetSolutions", &_GetSolutions) // Python only .def("__iter__", range > (&_GetSolutionsIterBegin, &_GetSolutionsIterEnd)) ; } pyobjcryst-2024.2.1/src/extensions/io_ext.cpp000066400000000000000000000062321470422267000211420ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst/RefinableObj/IO.h. * * Changes from ObjCryst::XMLCrystTag * - The istream constructor of XMLCrystTag is not wrapped. * *****************************************************************************/ #include #include #include #include #include #include #include "helpers.hpp" #include "python_streambuf.hpp" namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; void _XMLCrystFileLoadAllObject(bp::object input, const bool verbose=false) { // Mute all output MuteObjCrystUserInfo muzzle; CaptureStdOut gag; if(verbose) { gag.release(); muzzle.release(); } boost_adaptbx::python::streambuf sbuf(input); boost_adaptbx::python::streambuf::istream in(sbuf); XMLCrystFileLoadAllObject(in); } void _XMLCrystFileSaveGlobal(bp::object output) { boost_adaptbx::python::streambuf sbuf(output); boost_adaptbx::python::streambuf::ostream out(sbuf); XMLCrystFileSaveGlobal(out); } void wrap_io() { class_ ("XMLCrystTag", init ((bp::arg("tagName"), bp::arg("isEndTag")=false, bp::arg("isEmptyTag")=false))) .def("GetName", &XMLCrystTag::GetName, return_value_policy()) .def("GetClassName", &XMLCrystTag::GetClassName, return_value_policy()) .def("GetNbAttribute", &XMLCrystTag::GetNbAttribute) .def("AddAttribute", &XMLCrystTag::AddAttribute, (bp::arg("attName"), bp::arg("attValue"))) .def("GetAttribute", &XMLCrystTag::GetAttribute, (bp::arg("attNum"), bp::arg("attName"), bp::arg("attValue"))) .def("GetAttributeName", &XMLCrystTag::GetAttributeName, return_value_policy()) .def("GetAttributeValue", &XMLCrystTag::GetAttributeValue, return_value_policy()) .def("SetIsEndTag", &XMLCrystTag::SetIsEndTag) .def("IsEndTag", &XMLCrystTag::IsEndTag) .def("SetIsEmptyTag", &XMLCrystTag::SetIsEmptyTag) .def("IsEmptyTag", &XMLCrystTag::IsEmptyTag) .def("Print", &XMLCrystTag::Print) .def("__str__", &__str__) // python-only ; def("XMLCrystFileLoadAllObject", &_XMLCrystFileLoadAllObject, (bp::arg("file"), bp::arg("verbose")=false)); def("XMLCrystFileSaveGlobal", &_XMLCrystFileSaveGlobal, bp::arg("file")); } pyobjcryst-2024.2.1/src/extensions/lsq_ext.cpp000066400000000000000000000104461470422267000213340ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst * * File coded by: Vincent Favre-Nicolin * * See AUTHORS.txt for a list of people who contributed. * See LICENSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::LSQNumObj. * * Changes from ObjCryst::LSQNumObj * * Other Changes * *****************************************************************************/ #include #include #include #include #include #include "helpers.hpp" namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; namespace { bool _SafeRefine(LSQNumObj & lsq, REAL maxChi2factor, int nbCycle, bool useLevenbergMarquardt, const bool silent, const bool callBeginEndOptimization,const float minChi2var) { CaptureStdOut gag; if(!silent) gag.release(); std::list vnewpar; std::list vnewpartype; return lsq.SafeRefine(vnewpar, vnewpartype, nbCycle, useLevenbergMarquardt, silent, callBeginEndOptimization, minChi2var); } } // namespace void wrap_lsq() { class_("LSQ") /// LSQNumObj::PrepareRefParList() must be called first! .def("SetParIsFixed", (void (LSQNumObj::*)(const std::string&, const bool)) &LSQNumObj::SetParIsFixed, (bp::arg("parName"), bp::arg("fix"))) .def("SetParIsFixed", (void (LSQNumObj::*)(const RefParType *, const bool)) &LSQNumObj::SetParIsFixed, (bp::arg("type"), bp::arg("fix"))) .def("SetParIsFixed", (void (LSQNumObj::*)(RefinablePar &, const bool)) &LSQNumObj::SetParIsFixed, (bp::arg("par"), bp::arg("fix"))) //void SetParIsFixed(RefinableObj &obj, const bool fix); .def("UnFixAllPar", &LSQNumObj::UnFixAllPar) //void SetParIsUsed(const std::string& parName, const bool use); //void SetParIsUsed(const RefParType *type, const bool use); .def("Refine", &LSQNumObj::Refine, (bp::arg("nbCycle")=1, bp::arg("useLevenbergMarquardt")=false, bp::arg("silent")=false, bp::arg("callBeginEndOptimization")=true, bp::arg("minChi2var")=0.01)) .def("SafeRefine", &_SafeRefine, (bp::arg("maxChi2factor")=1.01,bp::arg("nbCycle")=1, bp::arg("useLevenbergMarquardt")=false, bp::arg("silent")=false, bp::arg("callBeginEndOptimization")=true, bp::arg("minChi2var")=0.01)) .def("Rfactor", &LSQNumObj::Rfactor) .def("RwFactor", &LSQNumObj::RwFactor) .def("ChiSquare", &LSQNumObj::ChiSquare) .def("SetRefinedObj", &LSQNumObj::SetRefinedObj, (bp::arg("obj"), bp::arg("LSQFuncIndex")=0, bp::arg("init")=true, bp::arg("recursive")=false), with_custodian_and_ward<1,2>()) .def("GetCompiledRefinedObj", (RefinableObj& (LSQNumObj::*)()) &LSQNumObj::GetCompiledRefinedObj, with_custodian_and_ward_postcall<0,1,return_internal_reference<> >()) .def("PrintRefResults", &LSQNumObj::PrintRefResults) .def("PrepareRefParList", &LSQNumObj::PrepareRefParList, (bp::arg("copy_param")=false)) .def("GetLSQCalc", &LSQNumObj::GetLSQCalc, return_value_policy()) .def("GetLSQObs", &LSQNumObj::GetLSQObs, return_value_policy()) .def("GetLSQWeight", &LSQNumObj::GetLSQWeight, return_value_policy()) .def("GetLSQDeriv", &LSQNumObj::GetLSQDeriv, bp::arg("par"), return_value_policy()) .def("BeginOptimization", &LSQNumObj::BeginOptimization, (bp::arg("allowApproximations")=false, bp::arg("enableRestraints")=false)) .def("EndOptimization", &LSQNumObj::EndOptimization) ; } pyobjcryst-2024.2.1/src/extensions/molatom_ext.cpp000066400000000000000000000061301470422267000222000ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::MolAtom. * * Changes from ObjCryst::MolAtom * - Wrapped as a to-python converter only. * - File IO is disabled * - X, Y and Z are wrapped as properties rather than methods. * *****************************************************************************/ #include #include #include #include #include namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; namespace { // Helper function - avoid dereferencing NULL when MolAtom is dummy. const ScatteringPower* getscatteringpowerpointer(const MolAtom& a) { const ScatteringPower* rv = a.IsDummy() ? NULL : &(a.GetScatteringPower()); return rv; } std::string __str__(MolAtom& a) { std::stringstream s; s << a.GetName() << " " << a.GetX() << " " << a.GetY() << " " << a.GetZ(); return s.str(); } } // namespace void wrap_molatom() { class_ ("MolAtom", init()) .def("GetName", (const std::string& (MolAtom::*)() const) &MolAtom::GetName, return_value_policy()) .def("SetName", &MolAtom::SetName) .def("GetMolecule", (Molecule& (MolAtom::*)()) &MolAtom::GetMolecule, return_internal_reference<>()) .def("GetX", &MolAtom::GetX) .def("GetY", &MolAtom::GetY) .def("GetZ", &MolAtom::GetZ) .def("GetOccupancy", &MolAtom::GetOccupancy) .def("SetX", &MolAtom::SetX) .def("SetY", &MolAtom::SetY) .def("SetZ", &MolAtom::SetZ) .def("SetOccupancy", &MolAtom::SetOccupancy) .def("IsDummy", &MolAtom::IsDummy) // FIXME - this should be returned as a constant reference. However, I // can't get this to work. This returns it as an internal reference, // which is probably a bad idea. .def("GetScatteringPower", getscatteringpowerpointer, return_internal_reference<>()) //return_value_policy()) .def("SetIsInRing", &MolAtom::SetIsInRing) .def("IsInRing", &MolAtom::IsInRing) // Python-only .add_property("X", &MolAtom::GetX, &MolAtom::SetX) .add_property("Y", &MolAtom::GetY, &MolAtom::SetY) .add_property("Z", &MolAtom::GetZ, &MolAtom::SetZ) .add_property("Occupancy", &MolAtom::GetOccupancy, &MolAtom::SetOccupancy) .def("__str__", &__str__) .def("int_ptr", &MolAtom::int_ptr) ; } pyobjcryst-2024.2.1/src/extensions/molbond_ext.cpp000066400000000000000000000072301470422267000221640ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::MolBond. * * Changes from ObjCryst::MolBond * - Added __getitem__ access for MolAtoms. * - File IO is disabled * - GetDeriv and CalcGradient are not wrapped. * - Length0, LengthDelta, LengthSigma and BondOrder are wrapped as properties * rather than methods. * *****************************************************************************/ #include #include namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; namespace { MolAtom* _GetAtom(MolBond& mb, size_t i) { MolAtom* rv = NULL; switch(i) { case 0: rv = &(mb.GetAtom1()); break; case 1: rv = &(mb.GetAtom2()); break; default: PyErr_SetString(PyExc_IndexError, "Index out of range"); throw_error_already_set(); } // Do this to avoid ugly compiler warnings return rv; } } // namespace void wrap_molbond() { class_ > ("MolBond", no_init) //init()) .def("GetMolecule", (Molecule& (MolBond::*)()) &MolBond::GetMolecule, return_internal_reference<>()) .def("GetLogLikelihood", (double (MolBond::*)() const) &MolBond::GetLogLikelihood) .def("GetLogLikelihood", (double (MolBond::*)(const bool, const bool) const) &MolBond::GetLogLikelihood) .def("GetName", &MolBond::GetName) .def("GetAtom1", (MolAtom& (MolBond::*)()) &MolBond::GetAtom1, return_internal_reference<>()) .def("GetAtom2", (MolAtom& (MolBond::*)()) &MolBond::GetAtom2, return_internal_reference<>()) .def("SetAtom1", &MolBond::SetAtom1, with_custodian_and_ward<1,2>()) .def("SetAtom2", &MolBond::SetAtom2, with_custodian_and_ward<1,2>()) .def("GetLength", &MolBond::GetLength) .def("GetLength0", &MolBond::GetLength0) .def("GetLengthDelta", &MolBond::GetLengthDelta) .def("GetLengthSigma", &MolBond::GetLengthSigma) .def("GetBondOrder", &MolBond::GetBondOrder) .def("SetLength0", &MolBond::SetLength0) .def("SetLengthDelta", &MolBond::SetLengthDelta) .def("SetLengthSigma", &MolBond::SetLengthSigma) .def("SetBondOrder", &MolBond::SetBondOrder) .def("IsFreeTorsion", &MolBond::IsFreeTorsion) .def("SetFreeTorsion", &MolBond::SetFreeTorsion) // Python-only .add_property("Length", &MolBond::GetLength) .add_property("Length0", &MolBond::GetLength0, &MolBond::SetLength0) .add_property("LengthDelta", &MolBond::GetLengthDelta, &MolBond::SetLengthDelta) .add_property("LengthSigma", &MolBond::GetLengthSigma, &MolBond::SetLengthSigma) .add_property("BondOrder", &MolBond::GetBondOrder, &MolBond::SetBondOrder) .def("__getitem__", &_GetAtom, return_internal_reference<>()) .def("int_ptr", &MolBond::int_ptr) ; } pyobjcryst-2024.2.1/src/extensions/molbondangle_ext.cpp000066400000000000000000000101401470422267000231650ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::MolBondAngle. * * Changes from ObjCryst::MolBondAngle * - Wrapped as a to-python converter only (no constructor) * - Added __getitem__ access for MolAtoms. * - File IO is disabled * - GetDeriv and CalcGradient are not wrapped. * - Angle0, AngleDelta and AngleSigma are wrapped as properties rather than * methods. * - IsFlexible and SetFlexible are not wrapped, as they are not implemented in * the library. * *****************************************************************************/ #include #include #include namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; namespace { MolAtom* _GetAtom(MolBondAngle& mb, size_t i) { MolAtom* rv = NULL; switch(i) { case 0: rv = &(mb.GetAtom1()); break; case 1: rv = &(mb.GetAtom2()); break; case 2: rv = &(mb.GetAtom3()); break; default: PyErr_SetString(PyExc_IndexError, "Index out of range"); throw_error_already_set(); } return rv; } } // namespace void wrap_molbondangle() { class_ > ("MolBondAngle", no_init) .def("GetMolecule", (Molecule& (MolBondAngle::*)()) &MolBondAngle::GetMolecule, return_internal_reference<>()) .def("GetName", &MolBondAngle::GetName) .def("GetLogLikelihood", (double (MolBondAngle::*)() const) &MolBondAngle::GetLogLikelihood) .def("GetLogLikelihood", (double (MolBondAngle::*)(const bool, const bool) const) &MolBondAngle::GetLogLikelihood) .def("GetAngle", &MolBondAngle::GetAngle) .def("GetAngle0", &MolBondAngle::GetAngle0) .def("GetAngleDelta", &MolBondAngle::GetAngleDelta) .def("GetAngleSigma", &MolBondAngle::GetAngleSigma) .def("SetAngle0", &MolBondAngle::SetAngle0) .def("SetAngleDelta", &MolBondAngle::SetAngleDelta) .def("SetAngleSigma", &MolBondAngle::SetAngleSigma) .def("GetAtom1", (MolAtom& (MolBondAngle::*)()) &MolBondAngle::GetAtom1, return_internal_reference<>()) .def("GetAtom2", (MolAtom& (MolBondAngle::*)()) &MolBondAngle::GetAtom2, return_internal_reference<>()) .def("GetAtom3", (MolAtom& (MolBondAngle::*)()) &MolBondAngle::GetAtom3, return_internal_reference<>()) .def("SetAtom1", &MolBondAngle::SetAtom1, with_custodian_and_ward<1,2>()) .def("SetAtom2", &MolBondAngle::SetAtom2, with_custodian_and_ward<1,2>()) .def("SetAtom3", &MolBondAngle::SetAtom3, with_custodian_and_ward<1,2>()) //.def("IsFlexible", &MolBondAngle::IsFlexible) //.def("SetFlexible", &MolBondAngle::SetFlexible) // Python-only .add_property("Angle", &MolBondAngle::GetAngle) .add_property("Angle0", &MolBondAngle::GetAngle0, &MolBondAngle::SetAngle0) .add_property("AngleDelta", &MolBondAngle::GetAngleDelta, &MolBondAngle::SetAngleDelta) .add_property("AngleSigma", &MolBondAngle::GetAngleSigma, &MolBondAngle::SetAngleSigma) .def("__getitem__", &_GetAtom, return_internal_reference<>()) // Iterate other the atoms involved .def("__iter__", range > (&MolBondAngle::begin, &MolBondAngle::end)) .def("int_ptr", &MolBondAngle::int_ptr) ; } pyobjcryst-2024.2.1/src/extensions/moldihedralangle_ext.cpp000066400000000000000000000103461470422267000240270ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::MolDihedralAngle. * * Changes from ObjCryst::MolDihedralAngle * - Wrapped as a to-python converter only (no constructor) * - Added __getitem__ access for MolAtoms. * *****************************************************************************/ #include #include #include namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; namespace { MolAtom* _GetAtom(MolDihedralAngle& mb, size_t i) { MolAtom* rv = NULL; switch(i) { case 0: rv = &(mb.GetAtom1()); break; case 1: rv = &(mb.GetAtom2()); break; case 2: rv = &(mb.GetAtom3()); break; case 3: rv = &(mb.GetAtom4()); break; default: PyErr_SetString(PyExc_IndexError, "Index out of range"); throw_error_already_set(); } return rv; } } // namespace void wrap_moldihedralangle() { class_ > ("MolDihedralAngle", no_init) .def("GetMolecule", (Molecule& (MolDihedralAngle::*)()) &MolDihedralAngle::GetMolecule, return_internal_reference<>()) .def("GetName", &MolDihedralAngle::GetName) .def("GetLogLikelihood", (double (MolDihedralAngle::*)() const) &MolDihedralAngle::GetLogLikelihood) .def("GetLogLikelihood", (double (MolDihedralAngle::*)(const bool, const bool) const) &MolDihedralAngle::GetLogLikelihood) .def("GetAngle", &MolDihedralAngle::GetAngle) .def("GetAngle0", &MolDihedralAngle::GetAngle0) .def("GetAngleDelta", &MolDihedralAngle::GetAngleDelta) .def("GetAngleSigma", &MolDihedralAngle::GetAngleSigma) .def("SetAngle0", &MolDihedralAngle::SetAngle0) .def("SetAngleDelta", &MolDihedralAngle::SetAngleDelta) .def("SetAngleSigma", &MolDihedralAngle::SetAngleSigma) .def("GetAtom1", (MolAtom& (MolDihedralAngle::*)()) &MolDihedralAngle::GetAtom1, return_internal_reference<>()) .def("GetAtom2", (MolAtom& (MolDihedralAngle::*)()) &MolDihedralAngle::GetAtom2, return_internal_reference<>()) .def("GetAtom3", (MolAtom& (MolDihedralAngle::*)()) &MolDihedralAngle::GetAtom3, return_internal_reference<>()) .def("GetAtom4", (MolAtom& (MolDihedralAngle::*)()) &MolDihedralAngle::GetAtom4, return_internal_reference<>()) .def("SetAtom1", &MolDihedralAngle::SetAtom1, with_custodian_and_ward<1,2>()) .def("SetAtom2", &MolDihedralAngle::SetAtom2, with_custodian_and_ward<1,2>()) .def("SetAtom3", &MolDihedralAngle::SetAtom3, with_custodian_and_ward<1,2>()) .def("SetAtom4", &MolDihedralAngle::SetAtom4, with_custodian_and_ward<1,2>()) // Python-only .add_property("Angle", &MolDihedralAngle::GetAngle) .add_property("Angle0", &MolDihedralAngle::GetAngle0, &MolDihedralAngle::SetAngle0) .add_property("AngleDelta", &MolDihedralAngle::GetAngleDelta, &MolDihedralAngle::SetAngleDelta) .add_property("AngleSigma", &MolDihedralAngle::GetAngleSigma, &MolDihedralAngle::SetAngleSigma) .def("__getitem__", &_GetAtom, return_internal_reference<>()) // Iterate other the atoms involved .def("__iter__", range > (&MolDihedralAngle::begin, &MolDihedralAngle::end)) .def("int_ptr", &MolDihedralAngle::int_ptr) ; } pyobjcryst-2024.2.1/src/extensions/molecule_ext.cpp000066400000000000000000000672751470422267000223560ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::Molecule. * * Note that all indices are zero-based. * * Changes from ObjCryst::Molecule * - The public data are not wrapped. * - Added __getitem__ access for MolAtoms. * - AddAtom returns the added MolAtom * - AddBond returns the added MolBond * - AddBondAngle returns the added MolBondAngle * - AddDihedralAngle returns the added MolDihedralAngle * - RemoveAtom returns None, has an indexed version * - RemoveBond returns None, has an indexed version * - RemoveBondAngle returns None, has an indexed version * - RemoveDihedralAngle returns None, has an indexed version * - RemoveRigidGroup returns None * - Added GetNbAtoms * - Added GetNbBonds * - Added GetNbBondAngles * - Added GetNbDihedralAngles * - Added GetNbRigidGroups * - Added GetBond * - Added GetBondAngle * - Added GetDihedralAngle * - Added GetRigidGroup * - FindBond returns the bond if found, None otherwise * - FindBondAngle returns the bond angle if found, None otherwise * - FindDihedralAngle returns the dihedral angle if found, None otherwise * - FindAtom returns the Atom if found or None otherwise * - FlipAtomGroup is not wrapped. * - FlipGroup, RotorGroup and StretchModeGroup are not wrapped. * - StretchMode getters are not wrapped. * - Quaternion ordinates Q0, Q1, Q2 and Q3 wrapped as properties. * *****************************************************************************/ #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include "helpers.hpp" namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; namespace { // Overloaded to return added object and manage the lifetime of the object. MolAtom& _AddAtom(Molecule& m, const double x, const double y, const double z, const ScatteringPower* pow, const std::string& name, const bool updateDisplay=true) { m.AddAtom(x, y, z, pow, name, updateDisplay); m.SetDeleteSubObjInDestructor(false); std::vector& v = m.GetAtomList(); return *v.back(); } MolBond& _AddBond(Molecule& m, MolAtom& atom1, MolAtom& atom2, const double length, const double sigma, const double delta, const double bondOrder = 1., const bool updateDisplay = true) { m.AddBond(atom1, atom2, length, sigma, delta, bondOrder, updateDisplay); m.SetDeleteSubObjInDestructor(false); std::vector& v = m.GetBondList(); return *v.back(); } MolBondAngle& _AddBondAngle(Molecule& m, MolAtom& atom1, MolAtom& atom2, MolAtom& atom3, const double angle, const double sigma, const double delta, const bool updateDisplay = true) { m.AddBondAngle(atom1, atom2, atom3, angle, sigma, delta, updateDisplay); m.SetDeleteSubObjInDestructor(false); std::vector& v = m.GetBondAngleList(); return *v.back(); } MolDihedralAngle& _AddDihedralAngle(Molecule& m, MolAtom& atom1, MolAtom& atom2, MolAtom& atom3, MolAtom& atom4, const double angle, const double sigma, const double delta, const bool updateDisplay = true) { m.AddDihedralAngle(atom1, atom2, atom3, atom4, angle, sigma, delta, updateDisplay); m.SetDeleteSubObjInDestructor(false); std::vector& v = m.GetDihedralAngleList(); return *v.back(); } // New Functions size_t _GetNbAtoms(Molecule& m) { std::vector& v = m.GetAtomList(); return v.size(); } size_t _GetNbBonds(Molecule& m) { std::vector& v = m.GetBondList(); return v.size(); } size_t _GetNbBondAngles(Molecule& m) { std::vector& v = m.GetBondAngleList(); return v.size(); } size_t _GetNbDihedralAngles(Molecule& m) { std::vector& v = m.GetDihedralAngleList(); return v.size(); } size_t _GetNbRigidGroups(Molecule& m) { std::vector& v = m.GetRigidGroupList(); return v.size(); } // Overloaded for safety MolAtom& _GetAtomIdx(Molecule& m, int idx) { int i = check_index(idx, m.GetNbComponent(), ALLOW_NEGATIVE); return m.GetAtom(i); } // Overloaded for safety MolAtom* _FindAtom(Molecule& m, const std::string& name) { MolAtom* rv = NULL; auto ii = m.FindAtom(name); if (ii != m.mvpAtom.rend()) rv = *ii; return rv; } // Overloaded for safety MolAtom* _GetAtomByName(Molecule& m, const std::string& name) { MolAtom* rv = _FindAtom(m, name); if (!rv) { bp::object emsg = ("Invalid atom name %r" % bp::make_tuple(name)); PyErr_SetObject(PyExc_ValueError, emsg.ptr()); throw_error_already_set(); } return rv; } MolBond& _GetBondIdx(Molecule& m, int idx) { std::vector& v = m.GetBondList(); if(idx < 0) idx += v.size(); if(0 == v.size() || idx < 0 || idx >= (int) v.size()) { PyErr_SetString(PyExc_IndexError, "Index out of range"); throw_error_already_set(); } return *v[idx]; } MolBondAngle& _GetBondAngleIdx(Molecule& m, int idx) { std::vector& v = m.GetBondAngleList(); if(idx < 0) idx += v.size(); if(0 == v.size() || idx < 0 || idx >= (int) v.size()) { PyErr_SetString(PyExc_IndexError, "Index out of range"); throw_error_already_set(); } return *v[idx]; } MolDihedralAngle& _GetDihedralAngleIdx(Molecule& m, int idx) { std::vector& v = m.GetDihedralAngleList(); if(idx < 0) idx += v.size(); if(0 == v.size() || idx < 0 || idx >= (int) v.size()) { PyErr_SetString(PyExc_IndexError, "Index out of range"); throw_error_already_set(); } return *v[idx]; } // Overloaded for void return type and index access. void _RemoveAtom(Molecule& m, MolAtom& ma) { m.RemoveAtom(ma, false); } void _RemoveAtomIdx(Molecule& m, int idx) { m.RemoveAtom(_GetAtomIdx(m, idx), false); } void _RemoveBond(Molecule& m, const MolBond& mb) { m.RemoveBond(mb, false); } void _RemoveBondIdx(Molecule& m, int idx) { m.RemoveBond(_GetBondIdx(m, idx), false); } void _RemoveBondAngle(Molecule& m, MolBondAngle& mba) { m.RemoveBondAngle(mba, false); } void _RemoveBondAngleIdx(Molecule& m, int idx) { m.RemoveBondAngle(_GetBondAngleIdx(m, idx), false); } void _RemoveDihedralAngle(Molecule& m, MolDihedralAngle& mda) { m.RemoveDihedralAngle(mda, false); } void _RemoveDihedralAngleIdx(Molecule& m, int idx) { m.RemoveDihedralAngle(_GetDihedralAngleIdx(m, idx), false); } // Overloaded to return new rigid group object RigidGroup& _AddRigidGroup(Molecule& m, const RigidGroup& r, const bool ud=true) { m.AddRigidGroup(r, ud); // Get the new rigid group and return it std::vector& v = m.GetRigidGroupList(); return *v.back(); } // Overloaded to accept python iterable and to return the rigid group object RigidGroup& _AddRigidGroupIterable(Molecule& m, bp::object& l, const bool ud=true) { // convert l to a rigid group RigidGroup* r = new RigidGroup(); for(int i=0; i < len(l); ++i) { MolAtom* a = extract(l[i]); r->insert(a); } // Add this rigid group and delete it, since AddRigidGroup makes a copy. m.AddRigidGroup(*r, ud); delete r; // Get the new rigid group and return it std::vector& v = m.GetRigidGroupList(); return *v.back(); } // Overloaded for void return type void _RemoveRigidGroup(Molecule& m, RigidGroup& mda, const bool ud=true) { m.RemoveRigidGroup(mda, ud, false); } PyObject* _FindBond(const Molecule& m, const MolAtom& ma1, const MolAtom& ma2) { std::vector::const_iterator mbi; mbi = m.FindBond(ma1, ma2); const std::vector& bondlist = m.GetBondList(); PyObject* retval; if(bondlist.end() == mbi) { // return None retval = Py_None; } else { reference_existing_object::apply::type converter; retval = converter(*mbi); } bp::incref(retval); return retval; } PyObject* _FindBondAngle(const Molecule& m, const MolAtom& ma1, const MolAtom& ma2, const MolAtom& ma3) { std::vector::const_iterator mbai; mbai = m.FindBondAngle(ma1, ma2, ma3); const std::vector& bondanglelist = m.GetBondAngleList(); PyObject* retval; if(bondanglelist.end() == mbai) { // return None retval = Py_None; } else { reference_existing_object::apply::type converter; retval = converter(*mbai); } bp::incref(retval); return retval; } PyObject* _FindDihedralAngle(const Molecule& m, const MolAtom& ma1, const MolAtom& ma2, const MolAtom& ma3, const MolAtom& ma4) { std::vector::const_iterator mdai; mdai = m.FindDihedralAngle(ma1, ma2, ma3, ma4); const std::vector& dihedralanglelist = m.GetDihedralAngleList(); PyObject* retval; if(dihedralanglelist.end() == mdai) { // return None retval = Py_None; } else { reference_existing_object::apply::type converter; retval = converter(*mdai); } bp::incref(retval); return retval; } // This could usually be done with an indexing suite, but it doesn't work with // vectors of pointers. bp::list _GetAtomList(const Molecule& m) { bp::list l; const std::vector& v = m.GetAtomList(); l = ptrcontainerToPyList< const std::vector >(v); return l; } bp::list _GetBondList(const Molecule& m) { bp::list l; const std::vector& v = m.GetBondList(); l = ptrcontainerToPyList< const std::vector >(v); return l; } bp::list _GetBondAngleList(const Molecule& m) { bp::list l; const std::vector& v = m.GetBondAngleList(); l = ptrcontainerToPyList< const std::vector >(v); return l; } bp::list _GetDihedralAngleList(const Molecule& m) { bp::list l; const std::vector& v = m.GetDihedralAngleList(); l = ptrcontainerToPyList< const std::vector >(v); return l; } bp::list _GetStretchModeBondLengthList(const Molecule& m) { bp::list l; const std::list& v = m.GetStretchModeBondLengthList(); l = containerToPyList< const std::list >(v); return l; } bp::list _GetStretchModeBondAngleList(const Molecule& m) { bp::list l; const std::list& v = m.GetStretchModeBondAngleList(); l = containerToPyList< const std::list >(v); return l; } bp::list _GetStretchModeTorsionList(const Molecule& m) { bp::list l; const std::list& v = m.GetStretchModeTorsionList(); l = containerToPyList< const std::list >(v); return l; } bp::list _GetRigidGroupList(const Molecule& m) { bp::list l; const std::vector& v = m.GetRigidGroupList(); l = ptrcontainerToPyList< const std::vector >(v); return l; } // Access using standard iterators std::vector::const_iterator _GetAtomIterBegin(const Molecule& m) {return m.GetAtomList().begin();} std::vector::const_iterator _GetAtomIterEnd(const Molecule& m) {return m.GetAtomList().end();} std::vector::const_iterator _GetBondIterBegin(const Molecule& m) {return m.GetBondList().begin();} std::vector::const_iterator _GetBondIterEnd(const Molecule& m) {return m.GetBondList().end();} std::vector::const_iterator _GetBondAngleIterBegin(const Molecule& m) {return m.GetBondAngleList().begin();} std::vector::const_iterator _GetBondAngleIterEnd(const Molecule& m) {return m.GetBondAngleList().end();} std::vector::const_iterator _GetDihedralAngleIterBegin(const Molecule& m) {return m.GetDihedralAngleList().begin();} std::vector::const_iterator _GetDihedralAngleIterEnd(const Molecule& m) {return m.GetDihedralAngleList().end();} // Get atoms by slice bp::object getAtomSlice(Molecule& m, bp::slice& s) { bp::list l = _GetAtomList(m); return l[s]; } // Overloaded to accept a python iterable instead of a std::set. Again, could be // done with converters, but there are issues with pointers. Perhaps another // day... void _RotateAtomGroup(Molecule& m, const MolAtom& at1, const MolAtom& at2, const bp::object& atoms, const double angle, const bool keepCenter=true) { std::set catoms = pyIterableToSet(atoms); m.RotateAtomGroup(at1, at2, catoms, angle, keepCenter); } void _RotateAtomGroupVec(Molecule& m, const MolAtom& at1, const double vx, const double vy, const double vz, const bp::object& atoms, const double angle, const bool keepCenter=true) { std::set catoms = pyIterableToSet(atoms); m.RotateAtomGroup(at1, vx, vy, vz, catoms, angle, keepCenter); } // A new method for three-tuples void _RotateAtomGroup2Vec(Molecule& m, bp::object& v1, bp::object& v2, const bp::object& atoms, const double angle, const bool keepCenter=true) { double x, y, z; x = extract(v1[0]); y = extract(v1[1]); z = extract(v1[2]); MolAtom& a = _AddAtom(m, x, y, z, 0, "_rag2vectemp", false); x = extract(v2[0]); y = extract(v2[1]); z = extract(v2[2]); _RotateAtomGroupVec(m, a, x, y, z, atoms, angle, keepCenter); m.RemoveAtom(a, true); return; } void _TranslateAtomGroup(Molecule& m, const bp::object& atoms, const double dx, const double dy, const double dz, const bool keepCenter=true) { std::set catoms = pyIterableToSet(atoms); m.TranslateAtomGroup(catoms, dx, dy, dz, keepCenter); } bp::dict _GetConnectivityTable(Molecule& m) { const std::map >& ct = m.GetConnectivityTable(); std::map >::const_iterator miter; bp::dict d; for(miter = ct.begin(); miter != ct.end(); ++miter) { bp::object key(bp::ptr(miter->first)); d[key] = ptrcontainerToPyList< const std::set >(miter->second); } return d; } bp::list _AsZMatrix(const Molecule& m, const bool keeporder) { bp::list l; const std::vector& v = m.AsZMatrix(keeporder); l = containerToPyList< const std::vector >(v); return l; } std::string quatparname(const Molecule& m, int idx) { using namespace std; static bool didseparator = false; static bool prefixmolname = false; static string separator; if (!didseparator) { map qnames; for (long i = 0; i < m.GetNbPar(); ++i) { const string& pname = m.GetPar(i).GetName(); size_t n = pname.size(); if (n < 2) continue; if (pname[n - 2] != 'Q') continue; if (pname.find_last_of("0123", n - 1) == string::npos) continue; qnames[pname.substr(0, n - 2)] += 1; } map::iterator qni; const string& mname = m.GetName(); for (qni = qnames.begin(); qni != qnames.end(); ++qni) { if (qni->second == 4) { const string& qnm = qni->first; prefixmolname = (qnm.size() >= mname.size() && qnm.substr(0, mname.size()) == mname); size_t p0 = prefixmolname ? mname.size() : 0; separator = qnm.substr(p0); didseparator = true; } } } ostringstream rv; rv << (prefixmolname ? m.GetName() : "") << separator << 'Q' << idx; return rv.str(); } // Setters and getters for position void _setQ0(Molecule& m, double val) { m.GetPar(quatparname(m, 0)).SetValue(val); } double _getQ0(Molecule& m) { return m.GetPar(quatparname(m, 0)).GetValue(); } void _setQ1(Molecule& m, double val) { m.GetPar(quatparname(m, 1)).SetValue(val); } double _getQ1(Molecule& m) { return m.GetPar(quatparname(m, 1)).GetValue(); } void _setQ2(Molecule& m, double val) { m.GetPar(quatparname(m, 2)).SetValue(val); } double _getQ2(Molecule& m) { return m.GetPar(quatparname(m, 2)).GetValue(); } void _setQ3(Molecule& m, double val) { m.GetPar(quatparname(m, 3)).SetValue(val); } double _getQ3(Molecule& m) { return m.GetPar(quatparname(m, 3)).GetValue(); } const ScatteringPower* getscatteringpowerpointer(const MolZAtom& a) { const ScatteringPower* rv = a.mpPow==0 ? NULL : a.mpPow; return rv; } } // namespace void wrap_molecule() { class_ > ("Molecule", init(bp::arg("oldMolecule"))) /* Constructors */ .def(init( (bp::arg("cryst"), bp::arg("name")=""))) /* Methods */ .def("GetFormula", &Molecule::GetFormula) .def("AddAtom", &_AddAtom, (bp::arg("x"), bp::arg("y"), bp::arg("z"), bp::arg("pPow"), bp::arg("name"), bp::arg("updateDisplay")=true), with_custodian_and_ward<1,5, return_internal_reference<> >()) .def("RemoveAtom", &_RemoveAtom) .def("RemoveAtom", &_RemoveAtomIdx) .def("AddBond", &_AddBond, (bp::arg("atom1"), bp::arg("atom2"), bp::arg("length"), bp::arg("sigma"), bp::arg("delta"), bp::arg("bondOrder")=1, bp::arg("updateDisplay")=true), with_custodian_and_ward<1,2, with_custodian_and_ward<1,3, return_internal_reference<> > >()) .def("RemoveBond", &_RemoveBond) .def("RemoveBond", &_RemoveBondIdx) .def("GetBond", &_GetBondIdx, return_internal_reference<>()) .def("FindBond", &_FindBond, with_custodian_and_ward_postcall<1,0>()) .def("AddBondAngle", &_AddBondAngle, (bp::arg("atom1"), bp::arg("atom2"), bp::arg("atom3"), bp::arg("angle"), bp::arg("sigma"), bp::arg("delta"), bp::arg("updateDisplay")=true), with_custodian_and_ward<1,2, with_custodian_and_ward<1,3, with_custodian_and_ward<1,4, return_internal_reference<> > > >()) .def("RemoveBondAngle", &_RemoveBondAngle) .def("RemoveBondAngle", &_RemoveBondAngleIdx) .def("GetBondAngle", &_GetBondAngleIdx, return_internal_reference<>()) .def("FindBondAngle", &_FindBondAngle, with_custodian_and_ward_postcall<1,0>()) .def("AddDihedralAngle", &_AddDihedralAngle, (bp::arg("atom1"), bp::arg("atom2"), bp::arg("atom3"), bp::arg("atom4"), bp::arg("angle"), bp::arg("sigma"), bp::arg("delta"), bp::arg("updateDisplay")=true), with_custodian_and_ward<1,2, with_custodian_and_ward<1,3, with_custodian_and_ward<1,4, with_custodian_and_ward<1,5, return_internal_reference<> > > > >()) .def("RemoveDihedralAngle", &_RemoveDihedralAngle) .def("RemoveDihedralAngle", &_RemoveDihedralAngleIdx) .def("GetDihedralAngle", &_GetDihedralAngleIdx, return_internal_reference<>()) .def("FindDihedralAngle", &_FindDihedralAngle, with_custodian_and_ward_postcall<1,0>()) .def("AddRigidGroup", &_AddRigidGroup, (bp::arg("group"), bp::arg("updateDisplay") = true), return_internal_reference<>()) .def("AddRigidGroup", &_AddRigidGroupIterable, (bp::arg("group"), bp::arg("updateDisplay") = true), return_internal_reference<>()) .def("RemoveRigidGroup", &_RemoveRigidGroup, (bp::arg("group"), bp::arg("updateDisplay") = true)) .def("GetAtom", &_GetAtomIdx, return_internal_reference<>()) .def("GetAtom", &_GetAtomByName, return_internal_reference<>()) .def("FindAtom", &_FindAtom, return_internal_reference<>()) .def("OptimizeConformation", &Molecule::OptimizeConformation, (bp::arg("nbTrial")=10000, bp::arg("stopCost")=0)) .def("OptimizeConformationSteepestDescent", &Molecule::OptimizeConformationSteepestDescent, (bp::arg("maxStep")=0.1, bp::arg("nbSteps")=1)) .def("GetNbAtoms", &_GetNbAtoms) .def("GetNbBonds", &_GetNbBonds) .def("GetNbBondAngles", &_GetNbBondAngles) .def("GetNbDihedralAngles", &_GetNbDihedralAngles) .def("GetNbRigidGroups", &_GetNbRigidGroups) .def("GetAtomList", &_GetAtomList, with_custodian_and_ward_postcall<1,0>()) .def("GetBondList", &_GetBondList, with_custodian_and_ward_postcall<1,0>()) .def("GetBondAngleList", &_GetBondAngleList, with_custodian_and_ward_postcall<1,0>()) .def("GetDihedralAngleList", &_GetDihedralAngleList, with_custodian_and_ward_postcall<1,0>()) .def("GetRigidGroupList", &_GetRigidGroupList, with_custodian_and_ward_postcall<1,0>()) .def("GetStretchModeBondLengthList", &_GetStretchModeBondLengthList, with_custodian_and_ward_postcall<1,0>()) .def("GetStretchModeBondAngleList", &_GetStretchModeBondAngleList, with_custodian_and_ward_postcall<1,0>()) .def("GetStretchModeTorsionList", &_GetStretchModeTorsionList, with_custodian_and_ward_postcall<1,0>()) .def("RotateAtomGroup", &_RotateAtomGroup, (bp::arg("at1"), bp::arg("at2"), bp::arg("atoms"), bp::arg("angle"), bp::arg("keepCenter")=true ) ) .def("RotateAtomGroup", &_RotateAtomGroupVec, (bp::arg("at1"), bp::arg("vx"), bp::arg("vy"), bp::arg("vz"), bp::arg("atoms"), bp::arg("angle"), bp::arg("keepCenter")=true ) ) .def("RotateAtomGroup", &_RotateAtomGroup2Vec, (bp::arg("v1"), bp::arg("v2"), bp::arg("atoms"), bp::arg("angle"), bp::arg("keepCenter")=true ) ) .def("TranslateAtomGroup", &_TranslateAtomGroup, (bp::arg("atoms"), bp::arg("dx"), bp::arg("dy"), bp::arg("dz"), bp::arg("keepCenter")=true ) ) .def("GetConnectivityTable", &_GetConnectivityTable, with_custodian_and_ward_postcall<1,0>()) .def("GetBondListClock", (RefinableObjClock& (Molecule::*)()) &Molecule::GetBondListClock, return_internal_reference<>()) .def("GetAtomPositionClock", (RefinableObjClock& (Molecule::*)()) &Molecule::GetAtomPositionClock, return_internal_reference<>()) .def("GetRigidGroupClock", (RefinableObjClock& (Molecule::*)()) &Molecule::GetRigidGroupClock, return_internal_reference<>()) .def("RigidifyWithDihedralAngles", &Molecule::RigidifyWithDihedralAngles) .def("BondLengthRandomChange", &Molecule::BondLengthRandomChange, (bp::arg("mode"), bp::arg("amplitude"), bp::arg("respectRestraint")=true) ) .def("BondAngleRandomChange", &Molecule::BondAngleRandomChange, (bp::arg("mode"), bp::arg("amplitude"), bp::arg("respectRestraint")=true) ) .def("DihedralAngleRandomChange", &Molecule::DihedralAngleRandomChange, (bp::arg("mode"), bp::arg("amplitude"), bp::arg("respectRestraint")=true) ) .def("GetCenterAtom", &Molecule::GetCenterAtom, return_internal_reference<>()) // Memory management shouldn't be necessary here, but there is the // possibility that a MolAtom that was created in another Molecule is // passed to this one. This could lead to memory corruption if the // original Molecule was to be deleted before this one, hence the // with_custodian_and_ward. .def("SetCenterAtom", &Molecule::SetCenterAtom, with_custodian_and_ward<1,2>()) .def("AsZMatrix", &_AsZMatrix, with_custodian_and_ward_postcall<1,0>()) .def("BuildRingList", &Molecule::BuildRingList) .def("BuildConnectivityTable", &Molecule::BuildConnectivityTable) .def("BuildRotorGroup", &Molecule::BuildRotorGroup) .def("TuneGlobalOptimRotationAmplitude", &Molecule::TuneGlobalOptimRotationAmplitude) .def("BuildFlipGroup", &Molecule::BuildFlipGroup) .def("BuildStretchModeBondLength", &Molecule::BuildStretchModeBondLength) .def("BuildStretchModeBondAngle", &Molecule::BuildStretchModeBondAngle) .def("BuildStretchModeTorsion", &Molecule::BuildStretchModeTorsion) .def("BuildStretchModeTwist", &Molecule::BuildStretchModeTwist) .def("BuildStretchModeGroups", &Molecule::BuildStretchModeGroups) .def("UpdateScattCompList", &Molecule::UpdateScattCompList) .def("InitOptions", &Molecule::InitOptions) // Python only functions .def("__getitem__", &getAtomSlice, with_custodian_and_ward_postcall<1,0>()) .def("__getitem__", &_GetAtomIdx, return_internal_reference<>()) .def("__len__", &_GetNbAtoms) .def("__iter__", range > (&_GetAtomIterBegin, &_GetAtomIterEnd)) .def("IterAtom", range > (&_GetAtomIterBegin, &_GetAtomIterEnd)) .def("IterBond", range > (&_GetBondIterBegin, &_GetBondIterEnd)) .def("IterBondAngle", range > (&_GetBondAngleIterBegin, &_GetBondAngleIterEnd)) .def("IterDihedralAngle", range > (&_GetDihedralAngleIterBegin, &_GetDihedralAngleIterEnd)) // Properties for molecule position .add_property("Q0", &_getQ0, &_setQ0) .add_property("Q1", &_getQ1, &_setQ1) .add_property("Q2", &_getQ2, &_setQ2) .add_property("Q3", &_getQ3, &_setQ3) ; // Wrap some functions def("GetBondLength", &GetBondLength); def("GetBondAngle", &GetBondAngle); def("GetDihedralAngle", &GetDihedralAngle); def("ZScatterer2Molecule", (Molecule* (*)(ZScatterer*)) &ZScatterer2Molecule, bp::arg("zscatt"), return_value_policy()); // Wrap class_ ("MolZAtom", init()) .def("GetScatteringPower", getscatteringpowerpointer, return_internal_reference<>()) //return_value_policy()) .add_property("bond_atom", &MolZAtom::mBondAtom) .add_property("bond_angle_atom", &MolZAtom::mBondAngleAtom) .add_property("dihdral_angle_atom", &MolZAtom::mDihedralAtom) .add_property("bond_length", &MolZAtom::mBondLength) .add_property("bond_angle", &MolZAtom::mBondAngle) .add_property("dihdral_angle", &MolZAtom::mDihedralAngle) ; } pyobjcryst-2024.2.1/src/extensions/objregistry_ext.cpp000066400000000000000000000165431470422267000231040ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::ObjRegistry template class. * * Changes from ObjCryst::ObjRegistry * - DeleteAll not wrapped * - C++ methods that can return const or non-const objects return non-const * objects in python. * *****************************************************************************/ #include #include #include #include #undef B0 #include #include #include #include #include #include #include #include #include "helpers.hpp" using namespace ObjCryst; using namespace boost::python; namespace { // Get objects by slice template bp::object getObjSlice(ObjRegistry& o, bp::slice& s) { bp::list l; for(typename std::vector::const_iterator it = o.begin(); it != o.end(); ++it) { l.append(bp::ptr(*it)); } return l[s]; } // Get a MonteCarloObj by dynamic casting. This assumes that the caller (from python) // first checked the correct type e.g. using GetClassName() template MonteCarloObj& getObjCastMonteCarlo(ObjRegistry& reg, const unsigned int i) { T* obj = ®.GetObj(i); MonteCarloObj *p = dynamic_cast(obj); return *p; } // Explicit specialization for ZAtom which is not polymorphic template <> MonteCarloObj& getObjCastMonteCarlo(ObjRegistry& reg, const unsigned int i) { throw ObjCryst::ObjCrystException("Cannot cast a ZAtom to a MonteCarloObj"); } /* Wrap all the class methods for the template class */ template void wrapClass(class_ > & c) { /* Functions */ c.def("Register", &ObjRegistry::Register) .def("DeRegister", (void (ObjRegistry::*)(T&)) &ObjRegistry::DeRegister) .def("DeRegister", (void (ObjRegistry::*)(const string&)) &ObjRegistry::DeRegister) .def("DeRegisterAll", &ObjRegistry::DeRegisterAll) // Dangerous and not wrapped //.def("DeleteAll", &ObjRegistry::DeleteAll) .def("GetObj", (T& (ObjRegistry::*)(const unsigned int)) &ObjRegistry::GetObj, return_internal_reference<>()) .def("GetObj", (T& (ObjRegistry::*)(const string&)) &ObjRegistry::GetObj, return_internal_reference<>()) .def("GetObj", (T& (ObjRegistry::*)(const string&, const string&)) &ObjRegistry::GetObj, return_internal_reference<>()) .def("GetNb", &ObjRegistry::GetNb) .def("Print", &ObjRegistry::Print) .def("SetName", &ObjRegistry::SetName) .def("GetName", &ObjRegistry::GetName, return_value_policy()) .def("Find", (long (ObjRegistry::*)(const string&) const) &ObjRegistry::Find) .def("Find", (long (ObjRegistry::*) (const string&, const string&, const bool) const) &ObjRegistry::Find) .def("Find", (long (ObjRegistry::*)(const T&) const) &ObjRegistry::Find) .def("GetRegistryClock", &ObjRegistry::GetRegistryClock, return_value_policy()) // Python-only methods .def("__str__", &__str__< ObjRegistry >) .def("__len__", &ObjRegistry::GetNb) .def("__getitem__", &getObjSlice, with_custodian_and_ward_postcall<1,0>()) .def("__getitem__", (T& (ObjRegistry::*)(const unsigned int)) &ObjRegistry::GetObj, return_internal_reference<>()) // Note to indexing robots: It took me a while to understand how this worked, // so that the object would be returned instead of the pointer ! // The definition of NextPolicies use in boost.python range is quite obscure // // Unrelated note: this can be dangerous, as the registry is susceptible to // change while being iterated. Example // c = pyobjcryst.crystal.Crystal(...) // for c in pyobjcryst.crystal.gCrystalRegistry: print(c.GetName()) // => this will erase the first crystal 'c' when looping other the registry, // which will effectively invalidate the iterator... .def("__iter__", range > (&ObjRegistry::list_begin, &ObjRegistry::list_end)) // Special casting functions so base class object registries can still // provide access to the correct python object .def("_getObjCastMonteCarlo", &getObjCastMonteCarlo, return_internal_reference<>()) ; } } // end namespace void wrap_objregistry() { // Template instantiation // ObjRegistry class_< ObjRegistry > CrystalRegistry("CrystalRegistry", init()); wrapClass(CrystalRegistry); // ObjRegistry class_< ObjRegistry > PowderPatternRegistry("PowderPatternRegistry", init()); wrapClass(PowderPatternRegistry); // ObjRegistry class_< ObjRegistry > DiffractionDataSingleCrystalRegistry("DiffractionDataSingleCrystalRegistry", init()); wrapClass(DiffractionDataSingleCrystalRegistry); // ObjRegistry class_< ObjRegistry > OptimizationObjRegistry("OptimizationObjRegistry", init()); wrapClass(OptimizationObjRegistry); // ObjRegistry class_< ObjRegistry > RefinableObjRegistry("RefinableObjRegistry", init()); wrapClass(RefinableObjRegistry); // ObjRegistry class_< ObjRegistry > RefObjOptRegistry("RefObjOpt", init()); wrapClass(RefObjOptRegistry); // ObjRegistry class_< ObjRegistry > ScattererRegistry("ScattererRegistry", init()); wrapClass(ScattererRegistry); // ObjRegistry class_< ObjRegistry > ScatteringPowerRegistry("ScatteringPowerRegistry", init()); wrapClass(ScatteringPowerRegistry); // ObjRegistry class_< ObjRegistry > ScatteringPowerAtomRegistry("ScatteringPowerAtomRegistry", init()); wrapClass(ScatteringPowerAtomRegistry); // ObjRegistry class_< ObjRegistry > ZAtomRegistry("ZAtomRegistry", init()); wrapClass(ZAtomRegistry); } pyobjcryst-2024.2.1/src/extensions/polyhedron_ext.cpp000066400000000000000000000070411470422267000227150ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::Polyhedron module. * *****************************************************************************/ #include #include #include #include #include namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; void wrap_polyhedron() { def("MakeTetrahedron", &MakeTetrahedron, (bp::arg("cryst"), bp::arg("name"), bp::arg("centralAtom"), bp::arg("peripheralAtom"), bp::arg("dist")), with_custodian_and_ward_postcall<0,3, with_custodian_and_ward_postcall<0,4, return_value_policy > >()); def("MakeOctahedron", &MakeOctahedron, (bp::arg("cryst"), bp::arg("name"), bp::arg("centralAtom"), bp::arg("peripheralAtom"), bp::arg("dist")), with_custodian_and_ward_postcall<0,3, with_custodian_and_ward_postcall<0,4, return_value_policy > >()); def("MakeSquarePlane", &MakeSquarePlane, (bp::arg("cryst"), bp::arg("name"), bp::arg("centralAtom"), bp::arg("peripheralAtom"), bp::arg("dist")), with_custodian_and_ward_postcall<0,3, with_custodian_and_ward_postcall<0,4, return_value_policy > >()); def("MakeCube", &MakeCube, (bp::arg("cryst"), bp::arg("name"), bp::arg("centralAtom"), bp::arg("peripheralAtom"), bp::arg("dist")), with_custodian_and_ward_postcall<0,3, with_custodian_and_ward_postcall<0,4, return_value_policy > >()); def("MakeAntiPrismTetragonal", &MakeAntiPrismTetragonal, (bp::arg("cryst"), bp::arg("name"), bp::arg("centralAtom"), bp::arg("peripheralAtom"), bp::arg("dist")), with_custodian_and_ward_postcall<0,3, with_custodian_and_ward_postcall<0,4, return_value_policy > >()); def("MakePrismTrigonal", &MakePrismTrigonal, (bp::arg("cryst"), bp::arg("name"), bp::arg("centralAtom"), bp::arg("peripheralAtom"), bp::arg("dist")), with_custodian_and_ward_postcall<0,3, with_custodian_and_ward_postcall<0,4, return_value_policy > >()); def("MakeIcosahedron", &MakeIcosahedron, (bp::arg("cryst"), bp::arg("name"), bp::arg("centralAtom"), bp::arg("peripheralAtom"), bp::arg("dist")), with_custodian_and_ward_postcall<0,3, with_custodian_and_ward_postcall<0,4, return_value_policy > >()); def("MakeTriangle", &MakeTriangle, (bp::arg("cryst"), bp::arg("name"), bp::arg("centralAtom"), bp::arg("peripheralAtom"), bp::arg("dist")), with_custodian_and_ward_postcall<0,3, with_custodian_and_ward_postcall<0,4, return_value_policy > >()); } pyobjcryst-2024.2.1/src/extensions/powderpattern_ext.cpp000066400000000000000000000342371470422267000234370ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst * * File coded by: Vincent Favre-Nicolin * * See AUTHORS.txt for a list of people who contributed. * See LICENSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::PowderPattern. * * Changes from ObjCryst::PowderPattern * * Other Changes * *****************************************************************************/ #include #include #include #include #include #undef B0 #include #include #include #include #include "python_streambuf.hpp" #include "helpers.hpp" namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; namespace { // This creates a C++ PowderPattern object PowderPattern* _CreatePowderPatternFromCIF(bp::object input) { // Reading a cif file creates some output via fpObjCrystInformUser. // Mute the output and restore it on return or exception. // Also mute any hardcoded output to cout. MuteObjCrystUserInfo muzzle; CaptureStdOut gag; boost_adaptbx::python::streambuf sbuf(input); boost_adaptbx::python::streambuf::istream in(sbuf); ObjCryst::CIF cif(in); int idx0 = gPowderPatternRegistry.GetNb(); ObjCryst::PowderPattern* p = ObjCryst::CreatePowderPatternFromCIF(cif); gag.release(); muzzle.release(); int idx = gPowderPatternRegistry.GetNb(); if(idx == idx0) { throw ObjCryst::ObjCrystException("Cannot create powder pattern from CIF"); } return p; } // This reads the CIF into an existing PowderPattern object PowderPattern* _CreatePowderPatternFromCIF(bp::object input, PowderPattern &p) { // Reading a cif file creates some output via fpObjCrystInformUser. // Mute the output and restore it on return or exception. // Also mute any hardcoded output to cout. MuteObjCrystUserInfo muzzle; CaptureStdOut gag; boost_adaptbx::python::streambuf sbuf(input); boost_adaptbx::python::streambuf::istream in(sbuf); ObjCryst::CIF cif(in); bool import_ok = false; for(map::iterator pos=cif.mvData.begin();pos!=cif.mvData.end();++pos) { if(pos->second.mPowderPatternObs.size()>10) { p.ImportPowderPatternCIF(cif); (*fpObjCrystInformUser)((boost::format("CIF: Imported POWDER PATTERN, with %d points") % p.GetNbPoint()).str()); import_ok = true; break; // only import one powder pattern } } gag.release(); muzzle.release(); if(!import_ok) { throw ObjCryst::ObjCrystException("Cannot create powder pattern from CIF"); } return &p; } PowderPatternBackground& addppbackground(PowderPattern& pp) { PowderPatternBackground* ppc = new PowderPatternBackground(); pp.AddPowderPatternComponent(*ppc); return *ppc; } PowderPatternDiffraction& addppdiffraction(PowderPattern& pp, Crystal& crst) { PowderPatternDiffraction* ppc = new PowderPatternDiffraction(); ppc->SetCrystal(crst); pp.AddPowderPatternComponent(*ppc); pp.Prepare(); return *ppc; } void setpowderpatternx (PowderPattern& pp, bp::object x) { CrystVector_REAL cvx; assignCrystVector(cvx, x); pp.SetPowderPatternX(cvx); } void setpowderpatternobs (PowderPattern& pp, bp::object x) { CrystVector_REAL cvx; assignCrystVector(cvx, x); MuteObjCrystUserInfo muzzle; pp.SetPowderPatternObs(cvx); } // Allow override (since we can't benefit from override in RefinableObjWrap) class PowderPatternWrap : public PowderPattern, public wrapper { public: PowderPatternWrap() : PowderPattern() {} PowderPatternWrap(const PowderPattern& p) : PowderPattern(p){} PeakList _FindPeaks(const float dmin=2.0,const float maxratio=0.01, const unsigned int maxpeak=100, const bool verbose=true) { CaptureStdOut gag; if(verbose) gag.release(); return this->FindPeaks(dmin, maxratio, maxpeak); } void default_UpdateDisplay() const { this->PowderPattern::UpdateDisplay();} virtual void UpdateDisplay() const { override f = this->get_override("UpdateDisplay"); if (f) f(); else default_UpdateDisplay(); } }; std::string __str__SPGScore(SPGScore& s) { if(s.ngof>0.0001) return (boost::format("%-13s nGoF=%9.4f GoF=%8.3f Rw=%5.2f [%3d reflections, extinct446=%3d]") % s.hm % s.ngof % s.gof % s.rw %s.nbreflused %s.nbextinct446).str(); return (boost::format("%-13s GoF=%8.3f Rw=%4.2f [extinct446=%3d]") % s.hm % s.gof % s.rw %s.nbreflused %s.nbextinct446).str(); } bp::list _GetScores(const SpaceGroupExplorer &spgex) { return containerToPyList(spgex.GetScores()); } } // namespace void wrap_powderpattern() { // Global object registry scope().attr("gPowderPatternRegistry") = boost::cref(gPowderPatternRegistry); class_ >("PowderPattern") .def("AddPowderPatternBackground", &addppbackground, return_internal_reference<>()) .def("AddPowderPatternDiffraction", &addppdiffraction, with_custodian_and_ward_postcall<1,0, with_custodian_and_ward_postcall<0,2,return_internal_reference<> > > ()) .def("GetNbPowderPatternComponent", &PowderPattern::GetNbPowderPatternComponent) .def("GetPowderPatternComponent", (PowderPatternComponent& (PowderPattern::*) (const int)) &PowderPattern::GetPowderPatternComponent, return_internal_reference<>()) .def("FindPeaks", &PowderPatternWrap::_FindPeaks, (bp::arg("dmin")=2.0, bp::arg("maxratio")=0.01, bp::arg("maxpeak")=100, bp::arg("verbose")=false)) .def("GetScaleFactor", (REAL (PowderPattern::*) (const int) const) &PowderPattern::GetScaleFactor) .def("GetScaleFactor", (REAL (PowderPattern::*) (const PowderPatternComponent&) const) &PowderPattern::GetScaleFactor) .def("SetScaleFactor", (void (PowderPattern::*) (const int, REAL)) &PowderPattern::SetScaleFactor) .def("SetPowderPatternPar", &PowderPattern::SetPowderPatternPar, (bp::arg("xmin"), bp::arg("xstep"), bp::arg("nbpoints"))) .def("SetPowderPatternX", &setpowderpatternx, bp::arg("x")) .def("GetPowderPatternCalc", &PowderPattern::GetPowderPatternCalc, return_value_policy()) .def("GetPowderPatternObs", &PowderPattern::GetPowderPatternObs, return_value_policy()) .def("GetPowderPatternX", &PowderPattern::GetPowderPatternX, return_value_policy()) .def("GetNbPoint", &PowderPattern::GetNbPoint) .def("GetNbPointUsed", &PowderPattern::GetNbPoint) .def("GetRadiation", (Radiation& (PowderPattern::*)()) &PowderPattern::GetRadiation, return_internal_reference<>()) .def("GetRadiationType", &PowderPattern::GetRadiationType) .def("SetRadiationType", &PowderPattern::SetRadiationType) .def("GetWavelength", &PowderPattern::GetWavelength) .def("SetWavelength", (void (PowderPattern::*) (const REAL)) &PowderPattern::SetWavelength, bp::arg("wavelength")) .def("SetWavelength", (void (PowderPattern::*) (const string&, const REAL)) &PowderPattern::SetWavelength, (bp::arg("XRayTubeElementName"), bp::arg("alpha2Alpha2ratio")=0.5)) .def("SetEnergy", &DiffractionDataSingleCrystal::SetEnergy, bp::arg("nrj_kev")) .def("ImportPowderPatternFullprof", &PowderPattern::ImportPowderPatternFullprof, bp::arg("filename")) .def("ImportPowderPatternPSI_DMC", &PowderPattern::ImportPowderPatternPSI_DMC, bp::arg("filename")) .def("ImportPowderPatternILL_D1A5", &PowderPattern::ImportPowderPatternILL_D1A5, bp::arg("filename")) .def("ImportPowderPatternXdd", &PowderPattern::ImportPowderPatternXdd, bp::arg("filename")) .def("ImportPowderPatternSietronicsCPI", &PowderPattern::ImportPowderPatternSietronicsCPI, bp::arg("filename")) .def("ImportPowderPattern2ThetaObsSigma", &PowderPattern::ImportPowderPattern2ThetaObsSigma, (bp::arg("filename"), bp::arg("nbSkip")=0)) .def("ImportPowderPatternFullprof4", &PowderPattern::ImportPowderPatternFullprof4, bp::arg("filename")) .def("ImportPowderPatternMultiDetectorLLBG42", &PowderPattern::ImportPowderPatternMultiDetectorLLBG42, bp::arg("filename")) .def("ImportPowderPattern2ThetaObs", &PowderPattern::ImportPowderPattern2ThetaObs, (bp::arg("filename"), bp::arg("nbSkip")=0)) .def("ImportPowderPatternTOF_ISIS_XYSigma", &PowderPattern::ImportPowderPatternTOF_ISIS_XYSigma, bp::arg("filename")) .def("ImportPowderPatternGSAS", &PowderPattern::ImportPowderPatternGSAS, bp::arg("filename")) .def("SetPowderPatternObs", &setpowderpatternobs, bp::arg("obs")) .def("FitScaleFactorForR", &PowderPattern::FitScaleFactorForR) .def("FitScaleFactorForIntegratedR", &PowderPattern::FitScaleFactorForIntegratedR) .def("FitScaleFactorForRw", &PowderPattern::FitScaleFactorForRw) .def("FitScaleFactorForIntegratedRw", &PowderPattern::FitScaleFactorForIntegratedRw) .def("SetMaxSinThetaOvLambda", &PowderPattern::SetMaxSinThetaOvLambda, bp::arg("max")) .def("GetMaxSinThetaOvLambda", &PowderPattern::GetMaxSinThetaOvLambda) .def("X2XCorr", &PowderPattern::X2XCorr) .def("X2PixelCorr", &PowderPattern::X2PixelCorr) .def("X2Pixel", &PowderPattern::X2Pixel) .def("STOL2X", &PowderPattern::STOL2X) .def("X2STOL", &PowderPattern::X2STOL) .def("X2XCorr", &PowderPattern::X2XCorr) .def("STOL2Pixel", &PowderPattern::STOL2Pixel) .def("UpdateDisplay", &PowderPattern::UpdateDisplay, &PowderPatternWrap::default_UpdateDisplay) .def("GetR", &PowderPattern::GetR) .def("GetIntegratedR", &PowderPattern::GetIntegratedR) .def("GetRw", &PowderPattern::GetRw) .def("GetIntegratedRw", &PowderPattern::GetIntegratedRw) .def("GetChi2", &PowderPattern::GetChi2) .def("GetIntegratedChi2", &PowderPattern::GetIntegratedChi2) .def("GetLogLikelihood", &PowderPattern::GetLogLikelihood) .def("Prepare", &PowderPattern::Prepare) .add_property("mur", &PowderPattern::GetMuR, &PowderPattern::SetMuR) .add_property("r", &PowderPattern::GetR) .add_property("r_integrated", &PowderPattern::GetIntegratedR) .add_property("rw", &PowderPattern::GetRw) .add_property("rw_integrated", &PowderPattern::GetIntegratedRw) .add_property("chi2", &PowderPattern::GetChi2) .add_property("chi2_integrated", &PowderPattern::GetIntegratedChi2) .add_property("llk", &PowderPattern::GetLogLikelihood) .add_property("wavelength", &PowderPattern::GetWavelength, (void (PowderPattern::*)(double)) &PowderPattern::SetWavelength) ; class_("SPGScore", init > ((bp::arg("hermann_mauguin"), bp::arg("rw"), bp::arg("gof"), bp::arg("nbextinct"), bp::arg("ngof")=0))) .def_readonly("hermann_mauguin", &SPGScore::hm) .def_readonly("Rw", &SPGScore::rw) .def_readonly("GoF", &SPGScore::gof) .def_readonly("nGoF", &SPGScore::ngof) .def("__str__", &__str__SPGScore) .def("__repr__", &__str__SPGScore) ; //,init(bp::arg("powdiff")),with_custodian_and_ward_postcall<1,2>() class_("SpaceGroupExplorer", init ((bp::arg("powdiff"))) [with_custodian_and_ward<1,2>()]) .def("Run", (SPGScore (SpaceGroupExplorer::*)(const string&, const bool, const bool, const bool, const bool, const double, const double)) &SpaceGroupExplorer::Run, (bp::arg("spg"), bp::arg("fitprofile")=false, bp::arg("verbose")=false, bp::arg("restore_orig")=false, bp::arg("update_display")=false, bp::arg("relative_length_tolerance")=0.01, bp::arg("absolute_angle_tolerance_degree")=1)) .def("RunAll", &SpaceGroupExplorer::RunAll, (bp::arg("fitprofile_all")=false, bp::arg("verbose")=true, bp::arg("keep_best")=true, bp::arg("update_display")=true, bp::arg("fitprofile_p1")=true, bp::arg("relative_length_tolerance")=0.01, bp::arg("absolute_angle_tolerance_degree")=1)) .def("GetScores", &_GetScores) ; // This will only return a C++ PowderPattern object def("CreatePowderPatternFromCIF", (PowderPattern* (*)(bp::object input)) &_CreatePowderPatternFromCIF, bp::arg("file"), return_value_policy()); // This can update a PowderPattern object with python methods def("CreatePowderPatternFromCIF", (PowderPattern* (*)(bp::object input, PowderPattern &)) &_CreatePowderPatternFromCIF, (bp::arg("file"), bp::arg("powpat")), return_value_policy()); } pyobjcryst-2024.2.1/src/extensions/powderpatternbackground_ext.cpp000066400000000000000000000055551470422267000255000ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst * * File coded by: Vincent Favre-Nicolin * * See AUTHORS.txt for a list of people who contributed. * See LICENSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::PowderPatternBackground. * * Changes from ObjCryst::PowderPatternBackground * * Other Changes * *****************************************************************************/ #include #include #include #include #undef B0 #include #include "helpers.hpp" namespace bp = boost::python; using namespace ObjCryst; using namespace boost::python; namespace { void _SetInterpPoints(PowderPatternBackground& b, bp::object tth, bp::object backgd) { CrystVector_REAL cvtth, cvbackg; assignCrystVector(cvtth, tth); assignCrystVector(cvbackg, backgd); b.SetInterpPoints(cvtth, cvbackg); } void _OptimizeBayesianBackground(PowderPatternBackground *pbackgd, const bool verbose=false) { CaptureStdOut gag; if(verbose) gag.release(); pbackgd->OptimizeBayesianBackground(); } const CrystVector_REAL& _GetInterpPointsX(PowderPatternBackground *pbackgd) { return *(pbackgd->GetInterpPoints().first); } const CrystVector_REAL& _GetInterpPointsY(PowderPatternBackground *pbackgd) { return *(pbackgd->GetInterpPoints().second); } } // namespace void wrap_powderpatternbackground() { scope().attr("refpartype_scattdata_background") = object(ptr(gpRefParTypeScattDataBackground)); class_, boost::noncopyable>( "PowderPatternBackground", no_init) //.def("SetParentPowderPattern", &PowderPatternBackground::SetParentPowderPattern) .def("GetPowderPatternCalc", &PowderPatternBackground::GetPowderPatternCalc, return_value_policy()) .def("ImportUserBackground", &PowderPatternBackground::ImportUserBackground, bp::arg("filename")) .def("SetInterpPoints", _SetInterpPoints, (bp::arg("tth"), bp::arg("backgd"))) .def("GetInterpPointsX", &_GetInterpPointsX, return_value_policy()) .def("GetInterpPointsY", &_GetInterpPointsY, return_value_policy()) .def("OptimizeBayesianBackground", &_OptimizeBayesianBackground, (bp::arg("verbose")=false)) .def("FixParametersBeyondMaxresolution", &PowderPatternBackground::FixParametersBeyondMaxresolution, bp::arg("obj")) ; } pyobjcryst-2024.2.1/src/extensions/powderpatterncomponent_ext.cpp000066400000000000000000000021111470422267000253440ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst * * File coded by: Vincent Favre-Nicolin * * See AUTHORS.txt for a list of people who contributed. * See LICENSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::PowderPatternComponent. * * Changes from ObjCryst::PowderPatternComponent * * Other Changes * *****************************************************************************/ #include #undef B0 #include namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; void wrap_powderpatterncomponent() { class_, boost::noncopyable> ("PowderPatternComponent", no_init) .def("GetParentPowderPattern", (PowderPattern& (PowderPatternComponent::*)()) &PowderPatternComponent::GetParentPowderPattern, return_internal_reference<>()) ; } pyobjcryst-2024.2.1/src/extensions/powderpatterndiffraction_ext.cpp000066400000000000000000000057121470422267000256440ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst * * File coded by: Vincent Favre-Nicolin * * See AUTHORS.txt for a list of people who contributed. * See LICENSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::PowderPatternDiffraction. * * Changes from ObjCryst::PowderPatternDiffraction * * Other Changes * *****************************************************************************/ #include #include #include #include #undef B0 #include #include namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; void wrap_powderpatterndiffraction() { enum_("ReflectionProfileType") .value("PROFILE_GAUSSIAN", PROFILE_GAUSSIAN) .value("PROFILE_LORENTZIAN", PROFILE_LORENTZIAN) .value("PROFILE_PSEUDO_VOIGT", PROFILE_PSEUDO_VOIGT) .value("PROFILE_PSEUDO_VOIGT_FINGER_COX_JEPHCOAT", PROFILE_PSEUDO_VOIGT_FINGER_COX_JEPHCOAT) .value("PROFILE_PEARSON_VII", PROFILE_PEARSON_VII) ; class_ >( "PowderPatternDiffraction", no_init) .def("GetPowderPatternCalc", &PowderPatternDiffraction::GetPowderPatternCalc, return_value_policy()) .def("SetReflectionProfilePar", &PowderPatternDiffraction::SetReflectionProfilePar, (bp::arg("type")=PROFILE_PSEUDO_VOIGT, bp::arg("fwhmCagliotiW")=1e-6, bp::arg("fwhmCagliotiU")=0, bp::arg("fwhmCagliotiV")=0, bp::arg("eta0")=0.5, bp::arg("eta1")=0)) .def("GetProfile", (ReflectionProfile& (PowderPatternDiffraction::*)()) &PowderPatternDiffraction::GetProfile, return_internal_reference<>()) .def("SetExtractionMode", &PowderPatternDiffraction::SetExtractionMode, (bp::arg("extract")=true, bp::arg("init")=false)) .def("GetExtractionMode", &PowderPatternDiffraction::GetExtractionMode) .def("ExtractLeBail", &PowderPatternDiffraction::ExtractLeBail, (bp::arg("nbcycle")=1)) .def("SetCrystal", &PowderPatternDiffraction::SetCrystal, bp::arg("crystal"), with_custodian_and_ward<1, 2>()) .def("GetNbReflBelowMaxSinThetaOvLambda", &PowderPatternDiffraction::GetNbReflBelowMaxSinThetaOvLambda) .def("GetFhklObsSq", &PowderPatternDiffraction::GetFhklObsSq, return_value_policy()) ; } pyobjcryst-2024.2.1/src/extensions/pyobjcryst.cpp000066400000000000000000000070131470422267000220610ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * definition of the _pyobjcryst Python extension module. * *****************************************************************************/ #include // Initialize numpy here. #include "pyobjcryst_numpy_setup.hpp" #include void wrap_asymmetricunit(); void wrap_atom(); void wrap_crystal(); void wrap_diffractiondatasinglecrystal(); void wrap_general(); void wrap_globaloptim(); void wrap_globalscatteringpower(); void wrap_indexing(); void wrap_io(); void wrap_lsq(); void wrap_molatom(); void wrap_molbond(); void wrap_molbondangle(); void wrap_moldihedralangle(); void wrap_molecule(); void wrap_objregistry(); void wrap_polyhedron(); void wrap_powderpattern(); void wrap_powderpatternbackground(); void wrap_powderpatterncomponent(); void wrap_powderpatterndiffraction(); void wrap_quaternion(); void wrap_radiation(); void wrap_refinableobj(); void wrap_refinableobjclock(); void wrap_refinablepar(); void wrap_reflectionprofile(); void wrap_refobjopt(); void wrap_refpartype(); void wrap_registerconverters(); void wrap_restraint(); void wrap_rigidgroup(); void wrap_scatterer(); void wrap_scatteringcomponent(); void wrap_scatteringcomponentlist(); void wrap_scatteringdata(); void wrap_scatteringpower(); void wrap_scatteringpoweratom(); void wrap_scatteringpowersphere(); void wrap_spacegroup(); void wrap_stretchmode(); void wrap_unitcell(); void wrap_zatom(); void wrap_zpolyhedron(); void wrap_zscatterer(); namespace { #if PY_MAJOR_VERSION >= 3 void* initialize_numpy() { import_array(); return NULL; } #else void initialize_numpy() { import_array(); } #endif } // namespace // Wrappers must be called according to inheritance hierarchy BOOST_PYTHON_MODULE(_pyobjcryst) { // initialize numpy module initialize_numpy(); // General stuff wrap_general(); wrap_io(); wrap_objregistry(); wrap_quaternion(); wrap_refinableobjclock(); wrap_refobjopt(); wrap_refpartype(); wrap_registerconverters(); // Core objects wrap_restraint(); wrap_refinablepar(); wrap_refinableobj(); // Other base classes wrap_scatteringdata(); wrap_scatterer(); wrap_scatteringpower(); wrap_zscatterer(); wrap_unitcell(); wrap_powderpatterncomponent(); wrap_reflectionprofile(); // Other stuff in no particular order. wrap_asymmetricunit(); wrap_atom(); wrap_crystal(); wrap_diffractiondatasinglecrystal(); wrap_globaloptim(); wrap_globalscatteringpower(); wrap_indexing(); wrap_lsq(); wrap_molatom(); wrap_molbond(); wrap_molbondangle(); wrap_moldihedralangle(); wrap_molecule(); wrap_polyhedron(); wrap_powderpattern(); wrap_powderpatternbackground(); wrap_powderpatterndiffraction(); wrap_radiation(); wrap_rigidgroup(); wrap_scatteringcomponent(); wrap_scatteringcomponentlist(); wrap_scatteringpoweratom(); wrap_scatteringpowersphere(); wrap_spacegroup(); wrap_stretchmode(); wrap_zatom(); wrap_zpolyhedron(); } pyobjcryst-2024.2.1/src/extensions/pyobjcryst_numpy_setup.hpp000066400000000000000000000021031470422267000245310ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst Complex Modeling Initiative * (c) 2017 Brookhaven Science Associates * Brookhaven National Laboratory. * All rights reserved. * * File coded by: Pavol Juhas * * See AUTHORS.txt for a list of people who contributed. * See LICENSE.txt for license information. * ****************************************************************************** * * Define PY_ARRAY_UNIQUE_SYMBOL for the pyobjcryst extension module. * *****************************************************************************/ #ifndef PYOBJCRYST_NUMPY_SETUP_HPP_INCLUDED #define PYOBJCRYST_NUMPY_SETUP_HPP_INCLUDED // Specify the version of NumPy API that will be used. #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION // This macro is required for extension modules that are in several files. // It must be defined before inclusion of numpy/arrayobject.h #define PY_ARRAY_UNIQUE_SYMBOL PYOBJCRYST_NUMPY_ARRAY_SYMBOL #endif // PYOBJCRYST_NUMPY_SETUP_HPP_INCLUDED pyobjcryst-2024.2.1/src/extensions/python_streambuf.cpp000066400000000000000000000053651470422267000232520ustar00rootroot00000000000000/****************************************************************************** *** License agreement *** cctbx Copyright (c) 2006, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: (1) Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. (2) Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. (3) Neither the name of the University of California, Lawrence Berkeley National Laboratory, U.S. Dept. of Energy nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. You are under no obligation whatsoever to provide any bug fixes, patches, or upgrades to the features, functionality or performance of the source code ("Enhancements") to anyone; however, if you choose to make your Enhancements available either publicly, or directly to Lawrence Berkeley National Laboratory, without imposing a separate written license agreement for such Enhancements, then you hereby grant the following license: a non-exclusive, royalty-free perpetual license to install, use, modify, prepare derivative works, incorporate into other computer software, distribute, and sublicense such enhancements or derivative works thereof, in binary and source code form. ******************************************************************************/ #include "python_streambuf.hpp" namespace boost_adaptbx { namespace python { // Initialize the static class variable. std::size_t streambuf::default_buffer_size = 1024; } // python } // namespace boost_adaptbx pyobjcryst-2024.2.1/src/extensions/python_streambuf.hpp000066400000000000000000000443321470422267000232540ustar00rootroot00000000000000/****************************************************************************** *** License agreement *** cctbx Copyright (c) 2006, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: (1) Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. (2) Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. (3) Neither the name of the University of California, Lawrence Berkeley National Laboratory, U.S. Dept. of Energy nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. You are under no obligation whatsoever to provide any bug fixes, patches, or upgrades to the features, functionality or performance of the source code ("Enhancements") to anyone; however, if you choose to make your Enhancements available either publicly, or directly to Lawrence Berkeley National Laboratory, without imposing a separate written license agreement for such Enhancements, then you hereby grant the following license: a non-exclusive, royalty-free perpetual license to install, use, modify, prepare derivative works, incorporate into other computer software, distribute, and sublicense such enhancements or derivative works thereof, in binary and source code form. ******************************************************************************/ /****************************************************************************** CHANGELOG: 2009-07-07 - CLF Added license notice 2009-07-07 - CLF Changed file extension to hpp 2009-07-07 - Removed buffer_size, to be defined within the wrapper. 2015-09-24 - PJ sync with r22950 (last change in r11117). ******************************************************************************/ #ifndef BOOST_ADAPTBX_PYTHON_STREAMBUF_H #define BOOST_ADAPTBX_PYTHON_STREAMBUF_H #include #include #include #include #include #include #include // Substitute macros otherwise defined in tbxx/error_utils.hpp --------------- #include #define TBXX_ASSERT(a) assert(a) #define TBXX_UNREACHABLE_ERROR() \ std::logic_error( \ "Control flow passes through branch that should be unreachable.") // --------------------------------------------------------------------------- namespace boost_adaptbx { namespace python { namespace bp = boost::python; /// A stream buffer getting data from and putting data into a Python file object /** The aims are as follow: - Given a C++ function acting on a standard stream, e.g. \code void read_inputs(std::istream& input) { ... input >> something >> something_else; } \endcode and given a piece of Python code which creates a file-like object, to be able to pass this file object to that C++ function, e.g. \code import gzip gzip_file_obj = gzip.GzipFile(...) read_inputs(gzip_file_obj) \endcode and have the standard stream pull data from and put data into the Python file object. - When Python \c read_inputs() returns, the Python object is able to continue reading or writing where the C++ code left off. - Operations in C++ on mere files should be competitively fast compared to the direct use of \c std::fstream. \b Motivation - the standard Python library offer of file-like objects (files, compressed files and archives, network, ...) is far superior to the offer of streams in the C++ standard library and Boost C++ libraries. - i/o code involves a fair amount of text processing which is more efficiently prototyped in Python but then one may need to rewrite a time-critical part in C++, in as seamless a manner as possible. \b Usage This is 2-step: - a trivial wrapper function \code using boost_adaptbx::python::streambuf; void read_inputs_wrapper(streambuf& input) { streambuf::istream is(input); read_inputs(is); } def("read_inputs", read_inputs_wrapper); \endcode which has to be written every time one wants a Python binding for such a C++ function. - the Python side \code from boost.python import streambuf read_inputs(streambuf(python_file_obj=obj, buffer_size=1024)) \endcode \c buffer_size is optional. See also: \c default_buffer_size Note: references are to the C++ standard (the numbers between parentheses at the end of references are margin markers). */ class streambuf : public std::basic_streambuf { private: typedef std::basic_streambuf base_t; public: /* The syntax using base_t::char_type; would be nicer but Visual Studio C++ 8 chokes on it */ typedef base_t::char_type char_type; typedef base_t::int_type int_type; typedef base_t::pos_type pos_type; typedef base_t::off_type off_type; typedef base_t::traits_type traits_type; // work around Visual C++ 7.1 problem inline static int traits_type_eof() { return traits_type::eof(); } /// The default size of the read and write buffer. /** They are respectively used to buffer data read from and data written to the Python file object. It can be modified from Python. */ static std::size_t default_buffer_size; /// Construct from a Python file object /** if buffer_size is 0 the current default_buffer_size is used. */ streambuf( bp::object& python_file_obj, std::size_t buffer_size_=0) : py_read (getattr(python_file_obj, "read", bp::object())), py_write(getattr(python_file_obj, "write", bp::object())), py_seek (getattr(python_file_obj, "seek", bp::object())), py_tell (getattr(python_file_obj, "tell", bp::object())), buffer_size(buffer_size_ != 0 ? buffer_size_ : default_buffer_size), write_buffer(0), pos_of_read_buffer_end_in_py_file(0), pos_of_write_buffer_end_in_py_file(buffer_size), farthest_pptr(0) { TBXX_ASSERT(buffer_size != 0); /* Some Python file objects (e.g. sys.stdout and sys.stdin) have non-functional seek and tell. If so, assign None to py_tell and py_seek. */ if (py_tell != bp::object()) { try { py_tell(); } catch (bp::error_already_set&) { py_tell = bp::object(); py_seek = bp::object(); /* Boost.Python does not do any Python exception handling whatsoever So we need to catch it by hand like so. */ PyErr_Clear(); } } if (py_write != bp::object()) { // C-like string to make debugging easier write_buffer = new char[buffer_size + 1]; write_buffer[buffer_size] = '\0'; setp(write_buffer, write_buffer + buffer_size); // 27.5.2.4.5 (5) farthest_pptr = pptr(); } else { // The first attempt at output will result in a call to overflow setp(0, 0); } if (py_tell != bp::object()) { off_type py_pos = bp::extract(py_tell()); pos_of_read_buffer_end_in_py_file = py_pos; pos_of_write_buffer_end_in_py_file = py_pos; } } /// Mundane destructor freeing the allocated resources virtual ~streambuf() { if (write_buffer) delete[] write_buffer; } /// C.f. C++ standard section 27.5.2.4.3 /** It is essential to override this virtual function for the stream member function readsome to work correctly (c.f. 27.6.1.3, alinea 30) */ virtual std::streamsize showmanyc() { int_type const failure = traits_type::eof(); int_type status = underflow(); if (status == failure) return -1; return egptr() - gptr(); } /// C.f. C++ standard section 27.5.2.4.3 virtual int_type underflow() { int_type const failure = traits_type::eof(); if (py_read == bp::object()) { throw std::invalid_argument( "That Python file object has no 'read' attribute"); } read_buffer = py_read(buffer_size); char *read_buffer_data; bp::ssize_t py_n_read; if (PyBytes_AsStringAndSize(read_buffer.ptr(), &read_buffer_data, &py_n_read) == -1) { setg(0, 0, 0); throw std::invalid_argument( "The method 'read' of the Python file object " "did not return a string."); } off_type n_read = (off_type)py_n_read; pos_of_read_buffer_end_in_py_file += n_read; setg(read_buffer_data, read_buffer_data, read_buffer_data + n_read); // ^^^27.5.2.3.1 (4) if (n_read == 0) return failure; return traits_type::to_int_type(read_buffer_data[0]); } /// C.f. C++ standard section 27.5.2.4.5 virtual int_type overflow(int_type c=traits_type_eof()) { if (py_write == bp::object()) { throw std::invalid_argument( "That Python file object has no 'write' attribute"); } farthest_pptr = std::max(farthest_pptr, pptr()); off_type n_written = (off_type)(farthest_pptr - pbase()); bp::str chunk(pbase(), farthest_pptr); py_write(chunk); if (!traits_type::eq_int_type(c, traits_type::eof())) { py_write(traits_type::to_char_type(c)); n_written++; } if (n_written) { pos_of_write_buffer_end_in_py_file += n_written; setp(pbase(), epptr()); // ^^^ 27.5.2.4.5 (5) farthest_pptr = pptr(); } return traits_type::eq_int_type( c, traits_type::eof()) ? traits_type::not_eof(c) : c; } /// Update the python file to reflect the state of this stream buffer /** Empty the write buffer into the Python file object and set the seek position of the latter accordingly (C++ standard section 27.5.2.4.2). If there is no write buffer or it is empty, but there is a non-empty read buffer, set the Python file object seek position to the seek position in that read buffer. */ virtual int sync() { int result = 0; farthest_pptr = std::max(farthest_pptr, pptr()); if (farthest_pptr && farthest_pptr > pbase()) { off_type delta = pptr() - farthest_pptr; int_type status = overflow(); if (traits_type::eq_int_type(status, traits_type::eof())) result = -1; if (py_seek != bp::object()) py_seek(delta, 1); } else if (gptr() && gptr() < egptr()) { if (py_seek != bp::object()) py_seek(gptr() - egptr(), 1); } return result; } /// C.f. C++ standard section 27.5.2.4.2 /** This implementation is optimised to look whether the position is within the buffers, so as to avoid calling Python seek or tell. It is important for many applications that the overhead of calling into Python is avoided as much as possible (e.g. parsers which may do a lot of backtracking) */ virtual pos_type seekoff(off_type off, std::ios_base::seekdir way, std::ios_base::openmode which= std::ios_base::in | std::ios_base::out) { /* In practice, "which" is either std::ios_base::in or out since we end up here because either seekp or seekg was called on the stream using this buffer. That simplifies the code in a few places. */ int const failure = off_type(-1); if (py_seek == bp::object()) { throw std::invalid_argument( "That Python file object has no 'seek' attribute"); } // we need the read buffer to contain something! if (which == std::ios_base::in && !gptr()) { if (traits_type::eq_int_type(underflow(), traits_type::eof())) { return failure; } } // compute the whence parameter for Python seek int whence; switch (way) { case std::ios_base::beg: whence = 0; break; case std::ios_base::cur: whence = 1; break; case std::ios_base::end: whence = 2; break; default: return failure; } // Let's have a go boost::optional result = seekoff_without_calling_python( off, way, which); if (!result) { // we need to call Python if (which == std::ios_base::out) overflow(); if (way == std::ios_base::cur) { if (which == std::ios_base::in) off -= egptr() - gptr(); else if (which == std::ios_base::out) off += pptr() - pbase(); } py_seek(off, whence); result = off_type(bp::extract(py_tell())); if (which == std::ios_base::in) underflow(); } return *result; } /// C.f. C++ standard section 27.5.2.4.2 virtual pos_type seekpos(pos_type sp, std::ios_base::openmode which= std::ios_base::in | std::ios_base::out) { return streambuf::seekoff(sp, std::ios_base::beg, which); } private: bp::object py_read, py_write, py_seek, py_tell; std::size_t buffer_size; /* This is actually a Python string and the actual read buffer is its internal data, i.e. an array of characters. We use a Boost.Python object so as to hold on it: as a result, the actual buffer can't go away. */ bp::object read_buffer; /* A mere array of char's allocated on the heap at construction time and de-allocated only at destruction time. */ char *write_buffer; off_type pos_of_read_buffer_end_in_py_file, pos_of_write_buffer_end_in_py_file; // the farthest place the buffer has been written into char *farthest_pptr; boost::optional seekoff_without_calling_python( off_type off, std::ios_base::seekdir way, std::ios_base::openmode which) { boost::optional const failure; // Buffer range and current position off_type buf_begin, buf_end, buf_cur, upper_bound; off_type pos_of_buffer_end_in_py_file; if (which == std::ios_base::in) { pos_of_buffer_end_in_py_file = pos_of_read_buffer_end_in_py_file; buf_begin = reinterpret_cast(eback()); buf_cur = reinterpret_cast(gptr()); buf_end = reinterpret_cast(egptr()); upper_bound = buf_end; } else if (which == std::ios_base::out) { pos_of_buffer_end_in_py_file = pos_of_write_buffer_end_in_py_file; buf_begin = reinterpret_cast(pbase()); buf_cur = reinterpret_cast(pptr()); buf_end = reinterpret_cast(epptr()); farthest_pptr = std::max(farthest_pptr, pptr()); upper_bound = reinterpret_cast(farthest_pptr) + 1; } else { throw TBXX_UNREACHABLE_ERROR(); } // Sought position in "buffer coordinate" off_type buf_sought; if (way == std::ios_base::cur) { buf_sought = buf_cur + off; } else if (way == std::ios_base::beg) { buf_sought = buf_end + (off - pos_of_buffer_end_in_py_file); } else if (way == std::ios_base::end) { return failure; } else { throw TBXX_UNREACHABLE_ERROR(); } // if the sought position is not in the buffer, give up if (buf_sought < buf_begin || buf_sought >= upper_bound) return failure; // we are in wonderland if (which == std::ios_base::in) gbump(buf_sought - buf_cur); else if (which == std::ios_base::out) pbump(buf_sought - buf_cur); return pos_of_buffer_end_in_py_file + (buf_sought - buf_end); } public: class istream : public std::istream { public: istream(streambuf& buf) : std::istream(&buf) { exceptions(std::ios_base::badbit); } ~istream() { if (this->good()) this->sync(); } }; class ostream : public std::ostream { public: ostream(streambuf& buf) : std::ostream(&buf) { exceptions(std::ios_base::badbit); } ~ostream() { if (this->good()) this->flush(); } }; }; struct streambuf_capsule { streambuf python_streambuf; streambuf_capsule( bp::object& python_file_obj, std::size_t buffer_size=0) : python_streambuf(python_file_obj, buffer_size) {} }; struct ostream : private streambuf_capsule, streambuf::ostream { ostream( bp::object& python_file_obj, std::size_t buffer_size=0) : streambuf_capsule(python_file_obj, buffer_size), streambuf::ostream(python_streambuf) {} ~ostream() { try { if (this->good()) this->flush(); } catch (bp::error_already_set&) { PyErr_Clear(); throw std::runtime_error( "Problem closing python ostream.\n" " Known limitation: the error is unrecoverable. Sorry.\n" " Suggestion for programmer: add ostream.flush() before" " returning."); } } }; }} // boost_adaptbx::python #endif // GUARD pyobjcryst-2024.2.1/src/extensions/quaternion_ext.cpp000066400000000000000000000055331470422267000227230ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::Quaternion. * * Changes from ObjCryst::Quaternion * - IO is not wrapped * - Q0, Q1, Q2 and Q3 are wrapped as properties, rather than functions. * - RotateVector overloaded to return tuple of the mutated arguments. * *****************************************************************************/ #include #include #include #include #include namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; namespace { double getQ0(const Quaternion& q) { return q.Q0(); } double getQ1(const Quaternion& q) { return q.Q1(); } double getQ2(const Quaternion& q) { return q.Q2(); } double getQ3(const Quaternion& q) { return q.Q3(); } void setQ0(Quaternion& q, double val) { q.Q0() = val; } void setQ1(Quaternion& q, double val) { q.Q1() = val; } void setQ2(Quaternion& q, double val) { q.Q2() = val; } void setQ3(Quaternion& q, double val) { q.Q3() = val; } // Overloaded to return a tuple bp::tuple _RotateVector( const Quaternion& q, double v1, double v2, double v3 ) { q.RotateVector(v1, v2, v3); return bp::make_tuple(v1, v2, v3); } } // namespace void wrap_quaternion() { class_("Quaternion") .def(init( (bp::arg("q0"), bp::arg("q1"), bp::arg("q2"), bp::arg("q3"), bp::arg("unit")=true )) ) .def("GetConjugate", &Quaternion::GetConjugate) //.def("RotateVector", &Quaternion::RotateVector, // (bp::arg("v1"), bp::arg("v2"), bp::arg("v3"))) .def("RotateVector", &_RotateVector, (bp::arg("v1"), bp::arg("v2"), bp::arg("v3"))) .def("Normalize", &Quaternion::Normalize) .def("GetNorm", &Quaternion::GetNorm) .def("RotationQuaternion", &Quaternion::RotationQuaternion, (bp::arg("ang"), bp::arg("v1"), bp::arg("v2"), bp::arg("v3"))) .staticmethod("RotationQuaternion") .add_property("Q0", &getQ0, &setQ0) .add_property("Q1", &getQ1, &setQ1) .add_property("Q2", &getQ2, &setQ2) .add_property("Q3", &getQ3, &setQ3) .def(self * self) .def(self *= self) ; } pyobjcryst-2024.2.1/src/extensions/radiation_ext.cpp000066400000000000000000000036151470422267000225070ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst * * File coded by: Vincent Favre-Nicolin * * See AUTHORS.txt for a list of people who contributed. * See LICENSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::Radiation. * * Changes from ObjCryst::Radiation * - GetWavelength returns a scalar instead of a vector * *****************************************************************************/ #include #include #include #include namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; namespace{ double _GetWavelength(Radiation& r) { return r.GetWavelength()(0); } } // anonymous namespace void wrap_radiation() { class_, boost::noncopyable>("Radiation") .def("SetRadiationType", &Radiation::SetRadiationType) .def("GetRadiationType", &Radiation::GetRadiationType) .def("SetWavelengthType", &Radiation::SetWavelengthType) .def("GetWavelengthType", &Radiation::GetWavelengthType) // Overloaded to return a single wavelength instead of a vector // .def("GetWavelength", &Radiation::GetWavelength, return_value_policy()) .def("GetWavelength", &_GetWavelength) .def("SetWavelength", (void (Radiation::*)(const REAL)) &Radiation::SetWavelength) .def("SetWavelength", (void (Radiation::*)(const std::string &,const REAL)) &Radiation::SetWavelength, (bp::arg("XRayTubeElementName"), bp::arg("alpha2Alpha2ratio")=0.5)) .def("GetXRayTubeDeltaLambda", & Radiation::GetXRayTubeDeltaLambda) .def("GetXRayTubeAlpha2Alpha1Ratio", & Radiation::GetXRayTubeAlpha2Alpha1Ratio) ; } pyobjcryst-2024.2.1/src/extensions/refinableobj_ext.cpp000066400000000000000000000515231470422267000231600ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::RefinableObj. This is a virtual class that * can be derived from in python. These bindings are used by ObjCryst objects * that inherit from RefinableObj (see for example unitcell_ext.cpp). * RefinableObj derivatives can be created in python and will work in c++ * functions that are also bound into python. * * Changes from ObjCryst::RefinableObj * - XMLOutput and XMLInput accept python file-like objects. * - GetPar that takes a const double* is not exposed, as it is designed for * internal use. * - GetParamSet returns a copy of the internal data so that no indirect * manipulation can take place from python. * - SetDeleteRefParInDestructor(false) is called in the constructors of the * python class and the parameter accessors. * - SetDeleteRefParInDestructor is not exposed. * - RemovePar is overloaded to return None. * - GetClientRegistry() override is deactivated for windows *****************************************************************************/ #include #include #include #include #include #include #include #include #include #include "helpers.hpp" #include "python_streambuf.hpp" namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; namespace { // Workaround to SetDeleteRefParInDestructor(0) when a parameter is added void _AddPar(RefinableObj& obj, RefinablePar* p) { obj.AddPar(p); obj.SetDeleteRefParInDestructor(0); } void _AddParObj(RefinableObj& obj, RefinableObj& o, const bool copyParam = false) { obj.AddPar(o, copyParam); obj.SetDeleteRefParInDestructor(0); } RefinablePar& _GetParLong(RefinableObj& obj, const long i) { obj.SetDeleteRefParInDestructor(0); return obj.GetPar(i); } RefinablePar& _GetParString(RefinableObj& obj, const string& s) { obj.SetDeleteRefParInDestructor(0); return obj.GetPar(s); } RefinablePar& _GetParNotFixedLong(RefinableObj& obj, const long i) { obj.SetDeleteRefParInDestructor(0); return obj.GetParNotFixed(i); } class RefinableObjWrap : public RefinableObj, public wrapper { public: /* alias the constructors from RefinableObj */ RefinableObjWrap() : RefinableObj() { RefinableObj::SetDeleteRefParInDestructor(false); } RefinableObjWrap(const bool internal) : RefinableObj(internal) { RefinableObj::SetDeleteRefParInDestructor(false); } // Fix for const void issue void EraseAllParamSet() { this->RefinableObj::EraseAllParamSet(); } const std::string& default_GetClassName() const { return this->RefinableObj::GetClassName(); } const std::string& GetClassName() const { override f = this->get_override("GetClassName"); if (f) return f(); return default_GetClassName(); } const std::string& default_GetName() const { return this->RefinableObj::GetName(); } const std::string& GetName() const { override f = this->get_override("GetName"); if (f) return f(); return default_GetName(); } void default_SetName(const std::string& name) { this->RefinableObj::SetName(name); } void SetName(const std::string& name) { override f = this->get_override("SetName"); if (f) f(name); else default_SetName(name); } void default_Print() const { this->RefinableObj::Print();} void Print() const { override f = this->get_override("Print"); if (f) f(); else default_Print(); } void default_RegisterClient(RefinableObj& client) const { this->RefinableObj::RegisterClient(client); } void RegisterClient(RefinableObj& client) const { override f = this->get_override("RegisterClient"); if (f) f(client); else default_RegisterClient(client); } void default_DeRegisterClient(RefinableObj& client) const { this->RefinableObj::DeRegisterClient(client); } void DeRegisterClient(RefinableObj& client) const { override f = this->get_override("DeRegisterClient"); if (f) f(client); else default_DeRegisterClient(client); } ObjRegistry< RefinableObj >& default_GetClientRegistry() { return this->RefinableObj::GetClientRegistry(); } ObjRegistry< RefinableObj >& GetClientRegistry() { override f = this->get_override("GetClientRegistry"); if (f) { #ifdef _MSC_VER return call&>(f.ptr()); #else return f(); #endif } return default_GetClientRegistry(); } void default_BeginOptimization(const bool allowApproximations, const bool enableRestraints) { this->RefinableObj::BeginOptimization(allowApproximations, enableRestraints); } void BeginOptimization(const bool allowApproximations, const bool enableRestraints) { override f = this->get_override("BeginOptimization"); if (f) f(allowApproximations, enableRestraints); else default_BeginOptimization(allowApproximations, enableRestraints); } void default_EndOptimization() { this->RefinableObj::EndOptimization();} void EndOptimization() { override f = this->get_override("EndOptimization"); if (f) f(); else default_EndOptimization(); } void default_RandomizeConfiguration() { this->RefinableObj::RandomizeConfiguration();} void RandomizeConfiguration() { override f = this->get_override("RandomizeConfiguration"); if (f) f(); else default_RandomizeConfiguration(); } void default_GlobalOptRandomMove(const double mutationAmplitude, const RefParType* type) { this->RefinableObj::GlobalOptRandomMove(mutationAmplitude, type);} void GlobalOptRandomMove(const double mutationAmplitude, const RefParType* type) { override f = this->get_override("GlobalOptRandomMove"); if (f) f(mutationAmplitude, type); else default_GlobalOptRandomMove(mutationAmplitude, type); } double default_GetLogLikelihood() const { return this->RefinableObj::GetLogLikelihood(); } double GetLogLikelihood() const { override f = this->get_override("GetLogLikelihood"); if (f) return f(); return default_GetLogLikelihood(); } unsigned int default_GetNbLSQFunction() const { return this->RefinableObj::GetNbLSQFunction(); } unsigned int GetNbLSQFunction() const { override f = this->get_override("GetNbLSQFunction"); if (f) return f(); return default_GetNbLSQFunction(); } const CrystVector& default_GetLSQCalc(const unsigned int i) const { return this->RefinableObj::GetLSQCalc(i); } const CrystVector& GetLSQCalc(const unsigned int i) const { override f = this->get_override("GetLSQCalc"); if (f) return f(i); return default_GetLSQCalc(i); } const CrystVector& default_GetLSQObs(const unsigned int i) const { return this->RefinableObj::GetLSQObs(i); } const CrystVector& GetLSQObs(const unsigned int i) const { override f = this->get_override("GetLSQObs"); if (f) return f(i); return default_GetLSQObs(i); } const CrystVector& default_GetLSQWeight(const unsigned int i) const { return this->RefinableObj::GetLSQWeight(i); } const CrystVector& GetLSQWeight(const unsigned int i) const { override f = this->get_override("GetLSQWeight"); if (f) return f(i); return default_GetLSQWeight(i); } const CrystVector& default_GetLSQDeriv(const unsigned int i, RefinablePar& rp) { return this->RefinableObj::GetLSQDeriv(i, rp); } const CrystVector& GetLSQDeriv(const unsigned int i, RefinablePar& rp) { override f = this->get_override("GetLSQDeriv"); if (f) return f(i, rp); return default_GetLSQDeriv(i, rp); } void default_XMLOutput(std::ostream& os, int indent) const { this->RefinableObj::XMLOutput(os, indent); } void XMLOutput(std::ostream& os, int indent) const { override f = this->get_override("XMLOutput"); if (f) f(os, indent); else default_XMLOutput(os, indent); } void default_XMLInput(std::istream& is, const ObjCryst::XMLCrystTag& tag) { this->RefinableObj::XMLInput(is, tag); } void XMLInput(std::istream& is, const ObjCryst::XMLCrystTag& tag) { override f = this->get_override("XMLInput"); if (f) f(is, tag); else default_XMLInput(is, tag); } void default_GetGeneGroup(const ObjCryst::RefinableObj& obj, CrystVector& groupIndex, unsigned int& firstGroup) const { this->RefinableObj::GetGeneGroup(obj, groupIndex, firstGroup);} void GetGeneGroup(const ObjCryst::RefinableObj& obj, CrystVector& groupIndex, unsigned int& firstGroup) const { override f = this->get_override("GetGeneGroup"); if (f) f(obj, groupIndex, firstGroup); else default_GetGeneGroup(obj, groupIndex, firstGroup); } void default_UpdateDisplay() const { this->RefinableObj::UpdateDisplay();} void UpdateDisplay() const { override f = this->get_override("UpdateDisplay"); if (f) f(); else default_UpdateDisplay(); } double default_GetRestraintCost() const { return this->RefinableObj::GetRestraintCost();} double GetRestraintCost() const { override f = this->get_override("GetRestraintCost"); if (f) return f(); return default_GetRestraintCost(); } void default_TagNewBestConfig() const { this->RefinableObj::TagNewBestConfig();} void TagNewBestConfig() const { override f = this->get_override("TagNewBestConfig"); if (f) f(); else default_TagNewBestConfig(); } // Protected methods void default_Prepare() { RefinableObj::Prepare();} void Prepare() { override f = this->get_override("Prepare"); if (f) f(); else default_Prepare(); } long FindPar(const std::string& name) const { return RefinableObj::FindPar(name); } void AddOption(RefObjOpt* opt) { RefinableObj::AddOption(opt); } std::map, std::string> >::iterator FindParamSet(unsigned long id) const { return RefinableObj::FindParamSet(id); } }; void _RemovePar(RefinableObj& obj, RefinablePar* refpar) { obj.RemovePar(refpar); return; } void _XMLOutput( const RefinableObj& r, bp::object output, int indent = 0) { boost_adaptbx::python::ostream os(output); os.precision(doublelim::digits10); r.XMLOutput(os, indent); os.flush(); } std::string _xml(const RefinableObj &r) { std::stringstream s; int indent=0; r.XMLOutput(s, indent); return s.str(); } void _XMLInput( RefinableObj& r, bp::object input, XMLCrystTag& tag) { boost_adaptbx::python::streambuf sbuf(input); boost_adaptbx::python::streambuf::istream in(sbuf); r.XMLInput(in, tag); in.sync(); } void _XMLInputString(RefinableObj& r, const string& s) { stringstream ss(s); ss.imbue(std::locale::classic()); XMLCrystTag tag; ss>>tag; r.XMLInput(ss, tag); } } // anonymous namespace void wrap_refinableobj() { scope().attr("refpartype_objcryst") = object(ptr(gpRefParTypeObjCryst)); // Global object registry scope().attr("gRefinableObjRegistry") = boost::cref(gRefinableObjRegistry); // Global top object registry, exposed at the pyobjcryst module level scope().attr("gTopRefinableObjRegistry") = boost::cref(gTopRefinableObjRegistry); class_("RefinableObj") .def(init()) // Defined not implemented //.def(init()) /* Methods */ .def("PrepareForRefinement", &RefinableObj::PrepareForRefinement) .def("FixAllPar", &RefinableObj::FixAllPar) .def("UnFixAllPar", &RefinableObj::UnFixAllPar) .def("SetParIsFixed", (void (RefinableObj::*)(const long, const bool)) &RefinableObj::SetParIsFixed) .def("SetParIsFixed", (void (RefinableObj::*)(const std::string&, const bool)) &RefinableObj::SetParIsFixed) .def("SetParIsFixed", (void (RefinableObj::*)(const RefParType*, const bool)) &RefinableObj::SetParIsFixed) .def("SetParIsUsed", (void (RefinableObj::*)(const std::string&, const bool)) &RefinableObj::SetParIsUsed) .def("SetParIsUsed", (void (RefinableObj::*)(const RefParType*, const bool)) &RefinableObj::SetParIsUsed) .def("GetNbPar", &RefinableObj::GetNbPar) .def("GetNbParNotFixed", &RefinableObj::GetNbParNotFixed) .def("GetPar", &_GetParLong, return_internal_reference<>()) .def("GetPar", &_GetParString, return_internal_reference<>()) .def("GetParNotFixed", &_GetParNotFixedLong, return_internal_reference<>()) .def("AddPar", &_AddPar, bp::arg("par"), with_custodian_and_ward<1,2>()) .def("AddPar", &_AddParObj, (bp::arg("newRefParList"), bp::arg("copyParam")=false), with_custodian_and_ward<1,2>()) .def("RemovePar", &_RemovePar) .def("CreateParamSet", &RefinableObj::CreateParamSet, (bp::arg("name")="")) .def("ClearParamSet", &RefinableObj::ClearParamSet) .def("SaveParamSet", &RefinableObj::SaveParamSet) .def("RestoreParamSet", &RefinableObj::RestoreParamSet) .def("GetParamSet", (const CrystVector& (RefinableObj::*) (const unsigned long) const) &RefinableObj::GetParamSet, return_value_policy()) .def("GetParamSet_ParNotFixedHumanValue", &RefinableObj::GetParamSet_ParNotFixedHumanValue) .def("EraseAllParamSet", &RefinableObjWrap::EraseAllParamSet) .def("GetParamSetName", &RefinableObj::GetParamSetName, return_value_policy()) .def("SetLimitsAbsolute", ( void (RefinableObj::*) (const std::string&, const double, const double) ) &RefinableObj::SetLimitsAbsolute) .def("SetLimitsAbsolute", ( void (RefinableObj::*) (const RefParType*, const double, const double) ) &RefinableObj::SetLimitsAbsolute) .def("SetLimitsRelative", ( void (RefinableObj::*) (const std::string&, const double, const double) ) &RefinableObj::SetLimitsRelative) .def("SetLimitsRelative", ( void (RefinableObj::*) (const RefParType*, const double, const double) ) &RefinableObj::SetLimitsRelative) .def("SetLimitsProportional", ( void (RefinableObj::*) (const std::string&, const double, const double) ) &RefinableObj::SetLimitsProportional) .def("SetLimitsProportional", ( void (RefinableObj::*) (const RefParType*, const double, const double) ) &RefinableObj::SetLimitsProportional) .def("SetGlobalOptimStep", &RefinableObj::SetGlobalOptimStep) .def("GetSubObjRegistry", ( ObjRegistry& (RefinableObj::*) ()) &RefinableObj::GetSubObjRegistry, return_internal_reference<>()) .def("IsBeingRefined", &RefinableObj::IsBeingRefined) .def("BeginGlobalOptRandomMove", &RefinableObj::BeginGlobalOptRandomMove) .def("ResetParList", &RefinableObj::ResetParList) .def("GetOptionList", &RefinableObj::GetOptionList, return_value_policy()) .def("GetNbOption", &RefinableObj::GetNbOption) .def("GetOption", (RefObjOpt& (RefinableObj::*)(const unsigned int)) &RefinableObj::GetOption, return_internal_reference<>()) // Not exposed, as we want python to manage the objects //.def("SetDeleteRefParInDestructor", // &RefinableObj::SetDeleteRefParInDestructor) .def("GetRefParListClock", &RefinableObj::GetRefParListClock, return_value_policy()) .def("AddRestraint", &RefinableObj::AddRestraint, with_custodian_and_ward<1,2>()) .def("RemoveRestraint", &RefinableObj::RemoveRestraint) .def("GetClockMaster", &RefinableObj::GetClockMaster, return_value_policy()) // Virtual .def("GetClassName", &RefinableObj::GetClassName, &RefinableObjWrap::default_GetClassName, return_value_policy()) .def("GetName", &RefinableObj::GetName, &RefinableObjWrap::default_GetName, return_value_policy()) .def("SetName", &RefinableObj::SetName, &RefinableObjWrap::default_SetName) .def("Print", &RefinableObj::Print, &RefinableObjWrap::default_Print) .def("RegisterClient", &RefinableObj::RegisterClient, &RefinableObjWrap::default_RegisterClient, with_custodian_and_ward<1,2>()) .def("DeRegisterClient", &RefinableObj::DeRegisterClient, &RefinableObjWrap::default_DeRegisterClient) .def("GetClientRegistry", ( ObjRegistry& (RefinableObj::*) ()) &RefinableObj::GetClientRegistry, &RefinableObjWrap::default_GetClientRegistry, return_internal_reference<>()) .def("BeginOptimization", &RefinableObj::BeginOptimization, &RefinableObjWrap::default_BeginOptimization, (bp::arg("allowApproximations")=false, bp::arg("enableRestraints")=false)) .def("EndOptimization", &RefinableObj::EndOptimization, &RefinableObjWrap::default_EndOptimization) .def("RandomizeConfiguration", &RefinableObj::RandomizeConfiguration, &RefinableObjWrap::default_RandomizeConfiguration) .def("GlobalOptRandomMove", &RefinableObj::GlobalOptRandomMove, &RefinableObjWrap::default_GlobalOptRandomMove, (bp::arg("mutationAmplitude"), bp::arg("type")=gpRefParTypeObjCryst)) .def("GetLogLikelihood", &RefinableObj::GetLogLikelihood, &RefinableObjWrap::default_GetLogLikelihood) .def("GetNbLSQFunction", &RefinableObj::GetNbLSQFunction, &RefinableObjWrap::default_GetNbLSQFunction) .def("GetLSQCalc", &RefinableObj::GetLSQCalc, &RefinableObjWrap::default_GetLSQCalc, return_value_policy()) .def("GetLSQObs", &RefinableObj::GetLSQObs, &RefinableObjWrap::default_GetLSQObs, return_value_policy()) .def("GetLSQWeight", &RefinableObj::GetLSQWeight, &RefinableObjWrap::default_GetLSQWeight, return_value_policy()) .def("GetLSQDeriv", &RefinableObj::GetLSQDeriv, &RefinableObjWrap::default_GetLSQDeriv, return_value_policy()) .def("XMLOutput", &_XMLOutput, (bp::arg("file"), bp::arg("indent")=0)) .def("XMLOutput", &RefinableObj::XMLOutput, &RefinableObjWrap::default_XMLOutput) .def("xml", &_xml) .def("XMLInput", &_XMLInput, (bp::arg("file"), bp::arg("tag"))) .def("XMLInput", &RefinableObj::XMLInput, &RefinableObjWrap::default_XMLInput) .def("XMLInput", &_XMLInputString, (bp::arg("xml"))) .def("GetGeneGroup", &RefinableObj::GetGeneGroup, &RefinableObjWrap::default_GetGeneGroup) .def("UpdateDisplay", &RefinableObj::UpdateDisplay, &RefinableObjWrap::default_UpdateDisplay) .def("GetRestraintCost", &RefinableObj::GetRestraintCost, &RefinableObjWrap::default_GetRestraintCost) .def("TagNewBestConfig", &RefinableObj::TagNewBestConfig, &RefinableObjWrap::default_TagNewBestConfig) .def("int_ptr", &RefinableObj::int_ptr) // Additional methods for python only .def("__str__", &__str__) ; } pyobjcryst-2024.2.1/src/extensions/refinableobjclock_ext.cpp000066400000000000000000000066711470422267000242000ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::RefinableObjClock. * * Changes from ObjCryst::RefinableObjClock * - operator= is wrapped as the SetEqual method * a.SetEqual(b) -> a = b * *****************************************************************************/ #include #include #include #include "helpers.hpp" using namespace boost::python; using ObjCryst::RefinableObjClock; namespace { const char* classdoc = "We need to record exactly when refinable objects\n\ have been modified for the last time (to avoid re-computation),\n\ and to do that we need a precise time. Since the clock() function is not\n\ precise enough (and is architecture-dependant), we use a custom time,\n\ which records the number of events in the program which uses the library.\n\ This is purely internal, so don't worry about it...\n\ \n\ The clock values have nothing to do with 'time' as any normal person undertands it."; const char* addchilddoc = "Add a 'child' clock. Whenever a child clock is clicked, it will also click its parent.\n\ This function takes care of adding itself to the list of parent in the children clock."; const char* addparentdoc = "Add a 'parent' clock. Whenever a clock is clicked, all parent clocks\n\ also are."; const char* clickdoc = "Record an event for this clock (generally, the 'time'\n\ an object has been modified, or some computation has been made)"; const char * printdoc = "Print clock value. Only for debugging purposes."; const char * printstaticdoc = "Print current general clock value. Only for debugging purposes."; const char * removechilddoc = "remove a child clock. This also tells the child clock to remove the parent."; const char * removeparentdoc = "remove a parent clock"; const char * resetdoc = "Reset a Clock to 0, to force an update"; // set clock1 equal to clock2 (operator=) void SetEqual(RefinableObjClock& c1, const RefinableObjClock& c2) { c1 = c2; } } // anonymous namespace void wrap_refinableobjclock() { class_ ("RefinableObjClock", classdoc) .def("AddChild", &RefinableObjClock::AddChild, addchilddoc, with_custodian_and_ward<1,2>()) .def("AddParent", &RefinableObjClock::AddParent, addparentdoc, with_custodian_and_ward<1,2>()) .def("Click", &RefinableObjClock::Click, clickdoc) .def("Print", &RefinableObjClock::Print, printdoc) .def("PrintStatic", &RefinableObjClock::PrintStatic, printstaticdoc) .def("RemoveChild", &RefinableObjClock::RemoveChild, removechilddoc) .def("RemoveParent", &RefinableObjClock::RemoveParent, removeparentdoc) .def("Reset", &RefinableObjClock::Reset, resetdoc) .def("SetEqual", &SetEqual) .def(self < self) .def(self <= self) .def(self > self) .def(self >= self) .def("__str__", &__str__) ; } pyobjcryst-2024.2.1/src/extensions/refinablepar_ext.cpp000066400000000000000000000145171470422267000231720ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::RefinablePar and * ObjCryst::RefParDerivStepModel. * * Changes from ObjCryst::RefinablePar * - The constructor has been changed to accept a double, * rather than a pointer to a double. * - The copy and default constructors and Init are not wrapped in order to avoid * memory corruption. Since boost cannot implicitly handle double* object, a * wrapper class had to be created. However, this wrapper class cannot be used * to convert RefinablePar objected created in c++. Thus, * ObjCryst::RefinablePar objects created in c++ are passed into python as * instances of _RefinablePar, which is a python wrapper around * ObjCryst::RefinablePar. The RefinablePar python class is a wrapper around * the C++ class PyRefinablePar, which manages its own double*. These python * classes are interchangable once instantiated, so users should not notice. * - XML input/output are not exposed. * *****************************************************************************/ #include #include #include #include #include #include #include "helpers.hpp" namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; namespace { /* A little wrapper around the initializers since python doubles and double* don't * get along. */ class PyRefinablePar : public RefinablePar { public: PyRefinablePar() : RefinablePar(), pval(0) {}; PyRefinablePar(const string& name, double value, const double min, const double max, const RefParType* type, RefParDerivStepModel derivMode=REFPAR_DERIV_STEP_RELATIVE, const bool hasLimits=true, const bool isFixed=false, const bool isUsed=true, const bool isPeriodic=false, const double humanScale=1., double period=1.) : RefinablePar() { pval = new double(value); RefinablePar::Init(name, pval, min, max, type, derivMode, hasLimits, isFixed, isUsed, isPeriodic, humanScale, period); } ~PyRefinablePar() { if( pval != NULL ) { delete pval; } } private: double* pval; }; } // anonymous namespace void wrap_refinablepar() { enum_("RefParDerivStepModel") .value("REFPAR_DERIV_STEP_ABSOLUTE", REFPAR_DERIV_STEP_ABSOLUTE) .value("REFPAR_DERIV_STEP_RELATIVE", REFPAR_DERIV_STEP_RELATIVE) ; // Class for holding RefinablePar instances created in c++. This should not // be exported to the pyobjcryst namespace class_ > ("_RefinablePar", no_init) .def("GetValue", &RefinablePar::GetValue) .def("SetValue", &RefinablePar::SetValue) .def("GetHumanValue", &RefinablePar::GetHumanValue, return_value_policy()) .def("SetHumanValue", &RefinablePar::SetHumanValue) .def("Mutate", &RefinablePar::Mutate) .def("MutateTo", &RefinablePar::MutateTo) .def("GetSigma", &RefinablePar::GetSigma) .def("GetHumanSigma", &RefinablePar::GetHumanSigma) .def("SetSigma", &RefinablePar::SetSigma) .def("GetName", &RefinablePar::GetName) .def("SetName", &RefinablePar::SetName) .def("Print", &RefinablePar::Print) .def("IsFixed", &RefinablePar::IsFixed) .def("SetIsFixed", &RefinablePar::SetIsFixed) .def("IsLimited", &RefinablePar::IsLimited) .def("SetIsLimited", &RefinablePar::SetIsLimited) .def("IsUsed", &RefinablePar::IsUsed) .def("SetIsUsed", &RefinablePar::SetIsUsed) .def("IsPeriodic", &RefinablePar::IsPeriodic) .def("SetIsPeriodic", &RefinablePar::SetIsPeriodic) .def("GetHumanScale", &RefinablePar::GetHumanScale) .def("SetHumanScale", &RefinablePar::SetHumanScale) .def("GetMin", &RefinablePar::GetMin) .def("SetMin", &RefinablePar::SetMin) .def("GetHumanMin", &RefinablePar::GetHumanMin) .def("SetHumanMin", &RefinablePar::SetHumanMin) .def("GetMax", &RefinablePar::GetMax) .def("SetMax", &RefinablePar::SetMax) .def("GetHumanMax", &RefinablePar::GetHumanMax) .def("SetHumanMax", &RefinablePar::SetHumanMax) .def("GetPeriod", &RefinablePar::GetPeriod) .def("SetPeriod", &RefinablePar::SetPeriod) .def("GetDerivStep", &RefinablePar::GetDerivStep) .def("SetDerivStep", &RefinablePar::SetDerivStep) .def("GetGlobalOptimStep", &RefinablePar::GetGlobalOptimStep) .def("SetGlobalOptimStep", &RefinablePar::SetGlobalOptimStep) .def("AssignClock", &RefinablePar::AssignClock) .def("SetLimitsAbsolute", &RefinablePar::SetLimitsAbsolute) .def("SetLimitsRelative", &RefinablePar::SetLimitsRelative) .def("SetLimitsProportional", &RefinablePar::SetLimitsProportional) // Python-only attributes .def("__str__", &__str__) .add_property("value", &RefinablePar::GetValue, &RefinablePar::SetValue) ; // Class for creating new RefinablePar instances class_ > ("RefinablePar", //... init(( bp::arg("name"), bp::arg("value"), bp::arg("min"), bp::arg("max"), bp::arg("type"), bp::arg("derivMode")=REFPAR_DERIV_STEP_RELATIVE, bp::arg("hasLimits")=true, bp::arg("isFixed")=false, bp::arg("isUsed")=true, bp::arg("isPeriodic")=false, bp::arg("humanScale")=1., bp::arg("period")=1.)) [with_custodian_and_ward<1,6>()]) ; } pyobjcryst-2024.2.1/src/extensions/reflectionprofile_ext.cpp000066400000000000000000000045661470422267000242560ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst * * File coded by: Vincent Favre-Nicolin * * See AUTHORS.txt for a list of people who contributed. * See LICENSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::ReflectionProfile. * * Changes from ObjCryst::ReflectionProfile * * Other Changes * *****************************************************************************/ #include #include #include #undef B0 #include #include namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; namespace { class ReflectionProfileWrap : public ReflectionProfile, public wrapper { public: // Pure virtual functions ReflectionProfile* CreateCopy() const { return this->get_override("CreateCopy")(); } CrystVector_REAL GetProfile( const CrystVector_REAL& x, const REAL xcenter, const REAL h, const REAL k, const REAL l) const { bp::override f = this->get_override("GetProfile"); return f(x, xcenter, h, k, l); } REAL GetFullProfileWidth( const REAL relativeIntensity, const REAL xcenter, const REAL h, const REAL k, const REAL l) { bp::override f = this->get_override("GetFullProfileWidth"); return f(relativeIntensity, xcenter, h, k, l); } void XMLOutput(ostream& os, int indent) const { bp::override f = this->get_override("XMLOutput"); f(os, indent); } void XMLInput(istream& is, const XMLCrystTag& tag) { bp::override f = this->get_override("GetProfile"); f(is, tag); } }; } // namespace void wrap_reflectionprofile() { class_, boost::noncopyable>( "ReflectionProfile") // TODO add pure_virtual bindings to the remaining public methods .def("CreateCopy", pure_virtual(&ReflectionProfile::CreateCopy), return_value_policy()) ; } pyobjcryst-2024.2.1/src/extensions/refobjopt_ext.cpp000066400000000000000000000043571470422267000225330ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::RefObjOpt. * *****************************************************************************/ #include #include #include #include using namespace boost::python; using namespace ObjCryst; namespace { class RefObjOptWrap : public RefObjOpt, public wrapper { public: void default_SetChoice(const int choice) { RefObjOpt::SetChoice(choice); } void SetChoice(const int choice) { override f = this->get_override("SetChoice"); if (f) f(choice); else default_SetChoice(choice); } }; // AtomWrap } // anonymous namespace void wrap_refobjopt() { class_("RefObjOpt") .def("Init", &RefObjOpt::Init) .def("GetNbChoice", &RefObjOpt::GetNbChoice) .def("GetChoice", &RefObjOpt::GetChoice) .def("SetChoice", (void (RefObjOpt::*)(const int)) &RefObjOpt::SetChoice, &RefObjOptWrap::default_SetChoice) .def("SetChoice", (void (RefObjOpt::*)(const std::string&)) &RefObjOpt::SetChoice) .def("GetName", &RefObjOpt::GetName, return_value_policy()) .def("GetClassName", &RefObjOpt::GetClassName, return_value_policy()) .def("GetChoiceName", &RefObjOpt::GetChoiceName, return_value_policy()) .def("GetClock", &RefObjOpt::GetClock, return_value_policy()) .def("XMLOutput", &RefObjOpt::XMLOutput) .def("XMLInput", &RefObjOpt::XMLInput) ; } pyobjcryst-2024.2.1/src/extensions/refpartype_ext.cpp000066400000000000000000000026331470422267000227150ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::RefParType. * *****************************************************************************/ #include #include #include #include using namespace ObjCryst; using namespace boost::python; namespace { bool __eq__(const RefParType* rpt1, const RefParType* rpt2) { return rpt1 == rpt2; } } // anonymous namespace void wrap_refpartype() { class_("RefParType", init()) .def(init() [with_custodian_and_ward<1,2>()]) /* Functions */ .def("IsDescendantFromOrSameAs", &RefParType::IsDescendantFromOrSameAs) .def("__eq__", &__eq__) .def("GetName", &RefParType::GetName, return_value_policy()) ; } pyobjcryst-2024.2.1/src/extensions/registerconverters.cpp000066400000000000000000000172641470422267000236210ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings for various conversions used in pyobjcryst. * *****************************************************************************/ #include #include #include #include #include #include #include #include #include #include // Use numpy here, but initialize it later in the extension module. #include "pyobjcryst_numpy_setup.hpp" #define NO_IMPORT_ARRAY #include using namespace boost::python; using namespace ObjCryst; namespace { namespace bp = boost::python; typedef std::pair< ScatteringPower const*, ScatteringPower const* > sppair; typedef std::map< sppair, double > mapsppairtodouble; typedef std::map< sppair, Crystal::BumpMergePar > mapsppairtobmp; typedef std::set MolAtomSet; typedef std::vector MolAtomVec; // Make an array out of a data pointer and a dimension vector PyObject* makeNdArray(const double* data, std::vector& dims) { PyObject* pyarray = PyArray_SimpleNew(dims.size(), &dims[0], NPY_DOUBLE); PyArrayObject* a = reinterpret_cast(pyarray); double* adata = static_cast(PyArray_DATA(a)); std::copy(data, data + PyArray_SIZE(a), adata); return bp::incref(pyarray); } // CrystVector to ndarray struct CrystVector_REAL_to_ndarray { static PyObject* convert(const CrystVector& cv) { std::vector dims(1); dims[0] = cv.numElements(); return makeNdArray((double *) cv.data(), dims); } static PyTypeObject const* get_pytype() { return &PyDoubleArrType_Type; } }; // CrystMatrix to ndarray struct CrystMatrix_REAL_to_ndarray { static PyObject* convert(const CrystMatrix& cm) { std::vector dims(2); dims[0] = cm.rows(); dims[1] = cm.cols(); return makeNdArray((double *) cm.data(), dims); } static PyTypeObject const* get_pytype() { return &PyDoubleArrType_Type; } }; // std::pair to tuple template struct std_pair_to_tuple { static PyObject* convert(std::pair const& p) { bp::object tpl = bp::make_tuple(p.first, p.second); PyObject* rv = tpl.ptr(); return bp::incref(rv); } static PyTypeObject const* get_pytype() { return &PyTuple_Type; } }; // Helper for convenience. template struct std_pair_to_python_converter { std_pair_to_python_converter() { bp::to_python_converter< std::pair, std_pair_to_tuple >(); } }; /* For MolAtomSet (std::set) */ void _addMAS(MolAtomSet& mas, MolAtom* a) { mas.insert(a); } void _updateMAS(MolAtomSet& mas, const MolAtomSet& other) { mas.insert(other.begin(), other.end()); } bool _containsMAS(const MolAtomSet& mas, MolAtom* a) { return mas.find(a) != mas.end(); } MolAtom* _getItemMAS(const MolAtomSet& mas, size_t i) { // Look for size violation if (i >= mas.size()) { PyErr_SetString(PyExc_IndexError, "index out of range"); throw_error_already_set(); } MolAtomSet::const_iterator p = mas.begin(); while (i > 0) { p++; i--; } return *p; } void _discardMAS(MolAtomSet& mas, MolAtom* a) { if( _containsMAS(mas, a) ) { mas.erase(a); } } void _removeMAS(MolAtomSet& mas, MolAtom* a) { if( _containsMAS(mas, a) ) { mas.erase(a); } else { PyErr_SetString(PyExc_KeyError, "KeyError"); throw_error_already_set(); } } /* For MolAtomVec */ void _appendMAV(MolAtomVec& mav, MolAtom* a) { mav.push_back(a); } void _extendMAV(MolAtomVec& mav, const MolAtomVec& other) { for(MolAtomVec::iterator p; p < other.end(); ++p) { mav.push_back(*p); } } bool _containsMAV(const MolAtomVec& mav, MolAtom* a) { return std::find(mav.begin(), mav.end(), a) != mav.end(); } MolAtom* _getItemMAV(const MolAtomVec& mav, size_t i) { return mav[i]; } void _setItemMAV(MolAtomVec& mav, size_t i, MolAtom* a) { mav[i] = a; } void _deleteMAV(MolAtomVec& mav, size_t i) { mav.erase(mav.begin()+i); } /* Exception translation */ PyObject* pyobjcryst_ObjCrystException; void translateException(const ObjCrystException& e) { PyErr_SetString(pyobjcryst_ObjCrystException, e.message.c_str()); } // // For testing // CrystVector getTestVector() { /* Should produce * 0 1 2 */ CrystVector tv(3); for(int i=0;i<3;i++) { tv(i) = i; } return tv; } CrystMatrix getTestMatrix() { /* Should produce * 0 1 * 2 3 * 4 5 */ CrystMatrix tm(3,2); int counter = 0; for(int i=0;i<3;i++) { for(int j=0;j<2;j++) { tm(i,j) = counter; counter++; } } return tm; } } // namespace void wrap_registerconverters() { /* Exceptions */ pyobjcryst_ObjCrystException = PyErr_NewException((char*)"pyobjcryst.ObjCrystException", 0, 0); register_exception_translator(translateException); // We want silent exceptions ObjCrystException::verbose = false; // Put ObjCrystException in module namespace scope().attr("ObjCrystException") = object(handle<>(pyobjcryst_ObjCrystException)); /* Data type converters */ to_python_converter< CrystVector, CrystVector_REAL_to_ndarray >(); to_python_converter< CrystMatrix, CrystMatrix_REAL_to_ndarray >(); // From boost sources std_pair_to_python_converter (); // Semi-converter for mapsppairtodouble class_("mapsppairtodouble", no_init) .def(map_indexing_suite()); // Semi-converter for mapsppairtobmp class_("mapsppairtobmp", no_init) .def(map_indexing_suite()); class_< MolAtomSet >("MolAtomSet", no_init) .def("add", &_addMAS, with_custodian_and_ward<1,2>()) .def("clear", &std::set::clear) .def("discard", &_discardMAS) .def("remove", &_removeMAS) .def("update", &_updateMAS, with_custodian_and_ward<1,2>()) .def("__contains__", &_containsMAS) .def("__getitem__", &_getItemMAS, return_internal_reference<>()) .def("__len__", &MolAtomSet::size) ; class_< MolAtomVec >("MolAtomVec", no_init) .def("append", &_appendMAV, with_custodian_and_ward<1,2>()) .def("extend", &_extendMAV, with_custodian_and_ward<1,2>()) .def("contains", &_containsMAV) .def("delete", &_deleteMAV) .def("__getitem__", &_getItemMAV, return_internal_reference<>()) .def("__setitem__", &_setItemMAV, with_custodian_and_ward<1,2>()) .def("__len__", &MolAtomVec::size) ; // some tests def("getTestVector", &getTestVector); def("getTestMatrix", &getTestMatrix); } pyobjcryst-2024.2.1/src/extensions/restraint_ext.cpp000066400000000000000000000054301470422267000225450ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * Changes from ObjCryst::Restraint * - The default and copy constructors are not wrapped, nor is Init. * - GetType returns a non-const reference to the RefParType. This should be a * no-no, but RefParType has no mutating methods, so this should no lead to * trouble. * - XML input/output are not exposed. * *****************************************************************************/ #include #include #include namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; namespace { class RestraintWrap : public Restraint, public wrapper { public: RestraintWrap() : Restraint() {}; RestraintWrap(const RefParType* type) : Restraint(type) {}; const RefParType* default_GetType() const { return this->Restraint::GetType(); } const RefParType* GetType() const { override f = this->get_override("GetType"); if(f) return f(); return this->default_GetType(); } void default_SetType(const RefParType* type) { this->Restraint::SetType(type); return; } void SetType(const RefParType* type) { override f = this->get_override("SetType"); if(f) { f(type); return; } return this->default_SetType(type); } double default_GetLogLikelihood() const { return this->Restraint::GetLogLikelihood(); } double GetLogLikelihood() const { override f = this->get_override("GetLogLikelihood"); if(f) return f(); return this->default_GetLogLikelihood(); } }; } // anonymous namespace void wrap_restraint() { class_("Restraint") .def(init((bp::arg("type")))[ with_custodian_and_ward<1,2>()]) .def("GetType", &Restraint::GetType, &RestraintWrap::default_GetType, return_internal_reference<>()) .def("SetType", &Restraint::SetType, &RestraintWrap::default_SetType, with_custodian_and_ward<1,2>()) .def("GetLogLikelihood", &Restraint::GetLogLikelihood, &RestraintWrap::default_GetLogLikelihood) ; } pyobjcryst-2024.2.1/src/extensions/rigidgroup_ext.cpp000066400000000000000000000023551470422267000227100ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::RigidGroup. * * Changes from ObjCryst::RigidGroup * - RigidGroup is wrapped to have python-set methods rather than stl::set * methods. * *****************************************************************************/ #include #include #include namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; typedef std::set MolAtomSet; namespace { } // namespace void wrap_rigidgroup() { class_ >("RigidGroup") .def(init()) .def("GetName", &RigidGroup::GetName) .def("int_ptr", &RigidGroup::int_ptr) ; } pyobjcryst-2024.2.1/src/extensions/scatterer_ext.cpp000066400000000000000000000224331470422267000225300ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::Scatterer. This is a virtual class that * can be derived from in python. These bindings are used by ObjCryst objects * that inherit from Scatterer (see for example atom_ext.cpp). * * Changes from ObjCryst::Scatterer * - C++ methods that can return const or non-const objects return non-const * objects in python. * - Operator string() is not exposed. * - Internal use only methods have not been exposed. * - InitRefParList is not exposed, as it is not used inside of Scatterer. * - GetClockScattCompList is exposed using a workaround, because it is not * implemented in the library. * - Methods related to visualization are not exposed. * *****************************************************************************/ #include #include #include #include #include #include #include #include #include #include #include "helpers.hpp" using namespace ObjCryst; using namespace boost::python; namespace { class ScattererWrap : public Scatterer, public wrapper { public: ScattererWrap() : Scatterer() {} ScattererWrap(const ScattererWrap& S) : Scatterer(S) {} void default_SetX(const double x) { this->Scatterer::SetX(x);} void SetX(const double x) { override f = this->get_override("SetX"); if (f) f(x); else default_SetX(x); } void default_SetY(const double y) { this->Scatterer::SetY(y);} void SetY(const double y) { override f = this->get_override("SetY"); if (f) f(y); else default_SetY(y); } void default_SetZ(const double z) { this->Scatterer::SetZ(z);} void SetZ(const double z) { override f = this->get_override("SetZ"); if (f) f(z); else default_SetZ(z); } void default_SetOccupancy(const double occ) { this->Scatterer::SetOccupancy(occ);} void SetOccupancy(const double occ) { override f = this->get_override("SetOccupancy"); if (f) f(occ); else default_SetOccupancy(occ); } // Pure virtual Scatterer* CreateCopy() const { return this->get_override("CreateCopy")(); } int GetNbComponent() const { return this->get_override("GetNbComponent")(); } const ScatteringComponentList& GetScatteringComponentList() const { return this->get_override("GetScatteringComponentList")(); } std::string GetComponentName(const int i) const { return this->get_override("GetComponentName")(i); } void Print() const { this->get_override("Print")(); } //Need to implement the pure virtual function POVRayDescription std::ostream& default_POVRayDescription(std::ostream& os, const CrystalPOVRayOptions& options) const { if(this->GetClassName()=="Molecule") { const Molecule *p = dynamic_cast(this); return p->POVRayDescription(os, options); } if(this->GetClassName()=="ZScatterer") { const ZScatterer *p = dynamic_cast(this); return p->POVRayDescription(os, options); } const Atom *p = dynamic_cast(this); return p->POVRayDescription(os, options); } std::ostream& POVRayDescription(std::ostream& os, const CrystalPOVRayOptions& options) const { #ifdef _MSC_VER override f = this->get_override("POVRayDescription"); if(f) return call(f.ptr(), os, options); else return default_POVRayDescription(os, options); #else return this->get_override("POVRayDescription")(os, options); #endif } // interface prior to Fox version 2016.2 void GLInitDisplayList(const bool noSymmetrics, const REAL xMin, const REAL xMax, const REAL yMin, const REAL yMax, const REAL zMin, const REAL zMax, const bool displayEnantiomer, const bool displayNames, const bool hideHydrogens) const { // no operation } void GLInitDisplayList(const bool noSymmetrics, const REAL xMin, const REAL xMax, const REAL yMin, const REAL yMax, const REAL zMin, const REAL zMax, const bool displayEnantiomer, const bool displayNames, const bool hideHydrogens, const REAL fadeDistance=0) const { // no operation } const RefinableObjClock& _GetClockScattCompList() const { return mClockScattCompList; } protected: // Needed for compilation void InitRefParList() {}; }; // ScattererWrap // We want to turn a ScatteringComponentList into an actual list bp::list _GetScatteringComponentList(Scatterer& s) { const ScatteringComponentList& scl = s.GetScatteringComponentList(); bp::list l; for(int i = 0; i < scl.GetNbComponent(); ++i) { l.append(scl(i)); } return l; } } // anonymous namespace void wrap_scatterer() { scope().attr("refpartype_scatt") = object(gpRefParTypeScatt); scope().attr("refpartype_scatt_transl") = object(ptr(gpRefParTypeScattTransl)); scope().attr("refpartype_scatt_transl_x") = object(ptr(gpRefParTypeScattTranslX)); scope().attr("refpartype_scatt_transl_y") = object(ptr(gpRefParTypeScattTranslY)); scope().attr("refpartype_scatt_transl_z") = object(ptr(gpRefParTypeScattTranslZ)); scope().attr("refpartype_scatt_orient") = object(ptr(gpRefParTypeScattOrient)); scope().attr("refpartype_scatt_conform") = object(ptr(gpRefParTypeScattConform)); scope().attr("refpartype_scatt_conform_bondlength") = object(ptr(gpRefParTypeScattConformBondLength)); scope().attr("refpartype_scatt_conform_bondangle") = object(ptr(gpRefParTypeScattConformBondAngle)); scope().attr("refpartype_scatt_conform_dihedangle") = object(ptr(gpRefParTypeScattConformDihedAngle)); scope().attr("refpartype_scatt_conform_x") = object(ptr(gpRefParTypeScattConformX)); scope().attr("refpartype_scatt_conform_y") = object(ptr(gpRefParTypeScattConformY)); scope().attr("refpartype_scatt_conform_z") = object(ptr(gpRefParTypeScattConformZ)); scope().attr("refpartype_scatt_occup") = object(ptr(gpRefParTypeScattOccup)); // Global object registry scope().attr("gScattererRegistry") = boost::cref(gScattererRegistry); class_ > ("Scatterer") /* Constructors */ .def(init()) /* Methods */ .def("GetX", &Scatterer::GetX) .def("GetY", &Scatterer::GetY) .def("GetZ", &Scatterer::GetZ) .def("GetOccupancy", &Scatterer::GetOccupancy) // virtual methods .def("SetX", &Scatterer::SetX, &ScattererWrap::default_SetX) .def("SetY", &Scatterer::SetY, &ScattererWrap::default_SetY) .def("SetZ", &Scatterer::SetZ, &ScattererWrap::default_SetZ) .def("SetOccupancy", &ObjCryst::Scatterer::SetOccupancy, &ScattererWrap::default_SetOccupancy) .def("GetClockScatterer", (RefinableObjClock & (Scatterer::*)()) &Scatterer::GetClockScatterer, return_internal_reference<>()) .def("SetCrystal", &Scatterer::SetCrystal, with_custodian_and_ward<1,2>()) .def("GetCrystal", (Crystal &(Scatterer::*)()) &Scatterer::GetCrystal, return_internal_reference<>()) // pure virtual methods .def("GetNbComponent", pure_virtual(&Scatterer::GetNbComponent)) .def("GetComponentName", pure_virtual(&Scatterer::GetComponentName)) //.def("GetScatteringComponentList", // pure_virtual(&Scatterer::GetScatteringComponentList), // return_value_policy()) .def("GetScatteringComponentList", &_GetScatteringComponentList, with_custodian_and_ward_postcall<1,0>()) .def("Print", pure_virtual(&Scatterer::Print)) .def("__str__", &__str__) // protected methods .def("GetClockScattCompList", &ScattererWrap::_GetClockScattCompList, return_value_policy()) // Properties - to be compatible with MolAtom .add_property("X", &Scatterer::GetX, &Scatterer::SetX) .add_property("Y", &Scatterer::GetY, &Scatterer::SetY) .add_property("Z", &Scatterer::GetZ, &Scatterer::SetZ) .add_property("Occupancy", &Scatterer::GetOccupancy, &Scatterer::SetOccupancy) ; } pyobjcryst-2024.2.1/src/extensions/scatteringcomponent_ext.cpp000066400000000000000000000040021470422267000246120ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::ScatteringComponent. * * Changes from ObjCryst::ScatteringComponent * - Added attributes X, Y, Z, Occupancy to conform to MolAtom. * *****************************************************************************/ #include #include #include "helpers.hpp" using namespace boost::python; using namespace ObjCryst; namespace { const ScatteringPower* _getScatteringPower(ScatteringComponent& s) { return s.mpScattPow; } } void wrap_scatteringcomponent() { class_("ScatteringComponent") .def("Print", &ScatteringComponent::Print) .def_readwrite("mX", &ScatteringComponent::mX) .def_readwrite("X", &ScatteringComponent::mX) .def_readwrite("mY", &ScatteringComponent::mY) .def_readwrite("Y", &ScatteringComponent::mY) .def_readwrite("mZ", &ScatteringComponent::mZ) .def_readwrite("Z", &ScatteringComponent::mZ) .def_readwrite("mOccupancy", &ScatteringComponent::mOccupancy) .def_readwrite("Occupancy", &ScatteringComponent::mOccupancy) .def_readonly("mDynPopCorr", &ScatteringComponent::mDynPopCorr) // Workaround to give attribute access. Again, returning the object, // where it should be read-only. .add_property("mpScattPow", make_function( &_getScatteringPower, return_internal_reference<>())) .def("__str__", &__str__) ; } pyobjcryst-2024.2.1/src/extensions/scatteringcomponentlist_ext.cpp000066400000000000000000000051101470422267000255070ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::ScatteringComponentList. * * Changes from ObjCryst::ScatteringComponentList * - Wrapped as a to-python converter only (no constructor) * - Added python list-like access * *****************************************************************************/ #include #include #include #include #include "helpers.hpp" using namespace boost::python; using namespace ObjCryst; namespace { const ScatteringComponent& getItem(const ScatteringComponentList& scl, long idx) { long n = scl.GetNbComponent(); if(idx < 0) idx += n; if(idx < 0 || idx >= n) { PyErr_SetString(PyExc_IndexError, "index out of range"); throw_error_already_set(); } return scl(idx); } bool contains(const ScatteringComponentList& scl, const ScatteringComponent& sc) { for(long i=0; i < scl.GetNbComponent(); ++i) { if( scl(i) == sc ) return true; } return false; } // Get slices directly from the boost python object bp::object getSCSlice(bp::object & scl, bp::slice& s) { bp::list l(scl); return l[s]; } } void wrap_scatteringcomponentlist() { class_ ("ScatteringComponentList", no_init) //("ScatteringComponentList", init()) //.def(init()) .def("Reset", &ScatteringComponentList::Reset) .def("GetNbComponent", &ScatteringComponentList::GetNbComponent) .def("Print", &ScatteringComponentList::Print) .def(self == self) .def(self += self) .def(self += ScatteringComponent()) .def("__str__", &__str__) // Container-type things .def("__len__", &ScatteringComponentList::GetNbComponent) .def("__getitem__", &getItem, return_internal_reference<>()) .def("__getitem__", &getSCSlice, with_custodian_and_ward_postcall<1,0>()) .def("__contains__", &contains) ; } pyobjcryst-2024.2.1/src/extensions/scatteringdata_ext.cpp000066400000000000000000000166311470422267000235340ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst Complex Modeling Initiative * (c) 2015 Brookhaven Science Associates * Brookhaven National Laboratory. * All rights reserved. * * File coded by: Kevin Knox * * See AUTHORS.txt for a list of people who contributed. * See LICENSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::ScatteringData. These bindings are * used by ObjCryst objects that inherit from ScatteringData (see, for example, * diffractiondatasinglecrystal_ext.cpp). * * Changes from ObjCryst::ScatteringData * - GetWavelength returns a scalar instead of a vector * *****************************************************************************/ #include #include #include #include #include #include namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; namespace { void _PrintFhklCalc(const ScatteringData& sd) { sd.PrintFhklCalc(); } void _PrintFhklCalcDetail(const ScatteringData& sd) { sd.PrintFhklCalcDetail(); } double _GetWavelength(ScatteringData& s) { return s.GetWavelength()(0); } bp::dict _GetScatteringFactor(ScatteringData& data) { const std::map& vsf = data.GetScatteringFactor(); std::map::const_iterator pos; bp::dict d; for(pos = vsf.begin(); pos != vsf.end(); ++pos) { bp::object key(bp::ptr(pos->first)); d[key] = pos->second; } return d; } } // anonymous namespace void wrap_scatteringdata() { scope().attr("refpartype_scattdata") = object(ptr(gpRefParTypeScattData)); scope().attr("refpartype_scattdata_scale") = object(ptr(gpRefParTypeScattDataScale)); scope().attr("refpartype_scattdata_profile") = object(ptr(gpRefParTypeScattDataProfile)); scope().attr("refpartype_scattdata_profile_type") = object(ptr(gpRefParTypeScattDataProfileType)); scope().attr("refpartype_scattdata_profile_width") = object(ptr(gpRefParTypeScattDataProfileWidth)); scope().attr("refpartype_scattdata_profile_asym") = object(ptr(gpRefParTypeScattDataProfileAsym)); scope().attr("refpartype_scattdata_corr") = object(ptr(gpRefParTypeScattDataCorr)); scope().attr("refpartype_scattdata_corr_pos") = object(ptr(gpRefParTypeScattDataCorrPos)); scope().attr("refpartype_scattdata_radiation") = object(ptr(gpRefParTypeRadiation)); scope().attr("refpartype_scattdata_radiation_wavelength") = object(ptr(gpRefParTypeRadiationWavelength)); class_, boost::noncopyable>( "ScatteringData", no_init) /* Methods */ // Have to convert from Python array to C++ array //.def("SetHKL", &ScatteringData::SetHKL) .def("GenHKLFullSpace2", (void (ScatteringData::*)(const REAL,const bool)) &ScatteringData::GenHKLFullSpace2, (bp::arg("maxsithsl"), bp::arg("unique")=false)) .def("GenHKLFullSpace", (void (ScatteringData::*)(const REAL,const bool)) &ScatteringData::GenHKLFullSpace, (bp::arg("maxtheta"), bp::arg("unique")=false)) // have to figure this out //.def("GetRadiationType", &ScatteringData::GetRadiationType, // return_value_policy()) .def("SetCrystal", &ScatteringData::SetCrystal) .def("GetCrystal", (Crystal& (ScatteringData::*)()) &ScatteringData::GetCrystal, return_internal_reference<>()) .def("HasCrystal", &ScatteringData::HasCrystal) .def("GetNbRefl", &ScatteringData::GetNbRefl) .def("GetH", &ScatteringData::GetH, return_value_policy()) .def("GetK", &ScatteringData::GetK, return_value_policy()) .def("GetL", &ScatteringData::GetL, return_value_policy()) .def("GetH2Pi", &ScatteringData::GetH2Pi, return_value_policy()) .def("GetK2Pi", &ScatteringData::GetK2Pi, return_value_policy()) .def("GetL2Pi", &ScatteringData::GetL2Pi, return_value_policy()) .def("GetReflX", &ScatteringData::GetReflX, return_value_policy()) .def("GetReflY", &ScatteringData::GetReflY, return_value_policy()) .def("GetReflZ", &ScatteringData::GetReflZ, return_value_policy()) .def("GetSinThetaOverLambda", &ScatteringData::GetSinThetaOverLambda, return_value_policy()) .def("GetTheta", &ScatteringData::GetTheta, return_value_policy()) .def("GetClockTheta", &ScatteringData::GetClockTheta, return_value_policy()) .def("GetFhklCalcSq", &ScatteringData::GetFhklCalcSq, return_value_policy()) //.def("GetFhklCalcSq_FullDeriv") .def("GetFhklCalcReal", &ScatteringData::GetFhklCalcReal, return_value_policy()) .def("GetFhklCalcImag", &ScatteringData::GetFhklCalcImag, return_value_policy()) .def("GetFhklObsSq", &ScatteringData::GetFhklObsSq, return_value_policy()) //.def("GetScatteringFactor", // (const std::map< const ScatteringPower *, CrystVector_REAL > & // (ScatteringData::*)()) &ScatteringData::GetScatteringFactor, // return_internal_reference<>()) .def("GetRadiation", (Radiation& (ScatteringData::*)()) &ScatteringData::GetRadiation, return_internal_reference<>()) .def("GetRadiationType", &ScatteringData::GetRadiationType) // Overloaded to return a single wavelength instead of a vector .def("GetWavelength", &_GetWavelength) .def("SetIsIgnoringImagScattFact", &ScatteringData::SetIsIgnoringImagScattFact) .def("IsIgnoringImagScattFact", &ScatteringData::IsIgnoringImagScattFact) .def("PrintFhklCalc", &_PrintFhklCalc) .def("PrintFhklCalcDetail", &_PrintFhklCalcDetail) // These don't seem necessary as I doubt we'll use ObjCryst for optimizations //.def("BeginOptimization") //.def("EndOptimization") //.def("SetApproximationFlag") .def("SetMaxSinThetaOvLambda", &ScatteringData::SetMaxSinThetaOvLambda) .def("GetMaxSinThetaOvLambda", &ScatteringData::GetMaxSinThetaOvLambda) .def("GetNbReflBelowMaxSinThetaOvLambda", &ScatteringData::GetNbReflBelowMaxSinThetaOvLambda) .def("GetClockNbReflBelowMaxSinThetaOvLambda", &ScatteringData::GetClockNbReflBelowMaxSinThetaOvLambda, return_value_policy()) .def("GetScatteringFactor", &_GetScatteringFactor, with_custodian_and_ward_postcall<1,0>()) ; } pyobjcryst-2024.2.1/src/extensions/scatteringpower_ext.cpp000066400000000000000000000253231470422267000237550ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::ScatteringPower. * *****************************************************************************/ #include #include #include #include #include #include #include #include using namespace boost::python; using namespace ObjCryst; namespace { class ScatteringPowerWrap : public ScatteringPower, public wrapper { public: ScatteringPowerWrap() : ScatteringPower() {} ScatteringPowerWrap(const ScatteringPower& S) : ScatteringPower(S) {} ScatteringPowerWrap(const ScatteringPowerWrap& S) : ScatteringPower(S) {} // Pure Virtual functions CrystVector GetScatteringFactor( const ScatteringData& data, const int spgSymPosIndex) const { return this->get_override("GetScatteringFactor")(data, spgSymPosIndex); } double GetForwardScatteringFactor(const RadiationType type) const { return this->get_override("GetForwardScatteringFactor")(type); } CrystVector GetTemperatureFactor( const ScatteringData& data, const int spgSymPosIndex) const { return this->get_override("GetTemperatureFactor")(data, spgSymPosIndex); } CrystMatrix GetResonantScattFactReal( const ScatteringData& data, const int spgSymPosIndex) const { return this->get_override("GetResonantScattFactReal")(data, spgSymPosIndex); } CrystMatrix GetResonantScattFactImag( const ScatteringData& data, const int spgSymPosIndex) const { return this->get_override("GetResonantScattFactImag")(data, spgSymPosIndex); } double GetRadius() const { return this->get_override("GetRadius")(); } // Just plain virtual functions // bool default_IsScatteringFactorAnisotropic() const { return ScatteringPower::IsScatteringFactorAnisotropic(); } bool IsScatteringFactorAnisotropic() const { override f = this->get_override("IsScatteringFactorAnisotropic"); if (f) return f(); return default_IsScatteringFactorAnisotropic(); } bool default_IsTemperatureFactorAnisotropic() const { return ScatteringPower::IsTemperatureFactorAnisotropic(); } bool IsTemperatureFactorAnisotropic() const { override f = this->get_override("IsTemperatureFactorAnisotropic"); if (f) return f(); return default_IsTemperatureFactorAnisotropic(); } bool default_IsResonantScatteringAnisotropic() const { return ScatteringPower::IsResonantScatteringAnisotropic(); } bool IsResonantScatteringAnisotropic() const { override f = this->get_override("IsResonantScatteringAnisotropic"); if (f) return f(); return default_IsResonantScatteringAnisotropic(); } const std::string& default_GetSymbol() const { return ScatteringPower::GetSymbol(); } const std::string& GetSymbol() const { override f = this->get_override("GetSymbol"); if (f) return f(); return default_GetSymbol(); } void default_SetBiso(const double newB) { ScatteringPower::SetBiso(newB); } void SetBiso(const double newB) { override f = this->get_override("SetBiso"); if (f) f(newB); else default_SetBiso(newB); } void default_SetBij(const size_t& i, const size_t& j, const double newB) { ScatteringPower::SetBij(i, j, newB); } void SetBij(const size_t& i, const size_t& j, const double newB) { override f = this->get_override("SetBij"); if (f) f(i, j, newB); else default_SetBij(i, j, newB); } double default_GetFormalCharge() const { return ScatteringPower::GetFormalCharge(); } double GetFormalCharge() const { override f = this->get_override("GetFormalCharge"); if (f) return f(); return default_GetFormalCharge(); } void default_SetFormalCharge(const double charge) { ScatteringPower::SetFormalCharge(charge); } void SetFormalCharge(const double charge) { override f = this->get_override("SetFormalCharge"); if (f) f(charge); else default_SetFormalCharge(charge); } protected: void InitRefParList() { this->get_override("InitRefParList")(); } }; // ScatteringPowerWrap // Accessors for Bij parameters template double _GetBij(ScatteringPower& sp) { return sp.GetBij(i, j); } template void _SetBij(ScatteringPower& sp, const double newB) { return sp.SetBij(i, j, newB); } boost::python::tuple _GetColourRGB(ScatteringPower& sp) { return make_tuple(sp.GetColourRGB()[0], sp.GetColourRGB()[1], sp.GetColourRGB()[2]); } } // anonymous namespace void wrap_scatteringpower() { scope().attr("refpartype_scattpow") = object(ptr(gpRefParTypeScattPow)); // scope().attr("refpartype_scattpow_resonant") = object(ptr(gpRefParTypeScattPowResonant)); scope().attr("refpartype_scattpow_temperature") = object(ptr(gpRefParTypeScattPowTemperature)); // scope().attr("refpartype_scattpow_temperature_iso") = object(ptr(gpRefParTypeScattPowTemperatureIso)); // scope().attr("refpartype_scattpow_temperature_aniso") = object(ptr(gpRefParTypeScattPowTemperatureAniso)); // Global object registry scope().attr("gScatteringPowerRegistry") = boost::cref(gScatteringPowerRegistry); // By making this non-copyable ScatteringPower can be passed from c++ when // copy_const_reference is uses, but they are turned into RefinableObj // instances. class_ > ("ScatteringPower") .def(init()) .def("GetScatteringFactor", pure_virtual(&ScatteringPower::GetScatteringFactor), (boost::python::arg("data"), boost::python::arg("spgSymPosIndex")=-1)) .def("GetForwardScatteringFactor", pure_virtual(&ScatteringPower::GetForwardScatteringFactor)) .def("GetTemperatureFactor", pure_virtual(&ScatteringPower::GetTemperatureFactor), (boost::python::arg("data"), boost::python::arg("spgSymPosIndex")=-1)) .def("GetResonantScattFactReal", pure_virtual(&ScatteringPower::GetResonantScattFactReal), (boost::python::arg("data"), boost::python::arg("spgSymPosIndex")=-1)) .def("GetResonantScattFactImag", pure_virtual(&ScatteringPower::GetResonantScattFactImag), (boost::python::arg("data"), boost::python::arg("spgSymPosIndex")=-1)) .def("IsScatteringFactorAnisotropic", &ScatteringPower::IsScatteringFactorAnisotropic, &ScatteringPowerWrap::default_IsScatteringFactorAnisotropic) .def("IsTemperatureFactorAnisotropic", &ScatteringPower::IsTemperatureFactorAnisotropic, &ScatteringPowerWrap::default_IsTemperatureFactorAnisotropic) .def("IsResonantScatteringAnisotropic", &ScatteringPower::IsResonantScatteringAnisotropic, &ScatteringPowerWrap::default_IsResonantScatteringAnisotropic) .def("GetSymbol", &ScatteringPower::GetSymbol, &ScatteringPowerWrap::default_GetSymbol, return_value_policy()) .def("GetBiso", (double (ScatteringPower::*)()const) &ScatteringPower::GetBiso) .def("SetBiso", &ScatteringPower::SetBiso, &ScatteringPowerWrap::default_SetBiso) .def("GetBij", (double (ScatteringPower::*)(const size_t&, const size_t&) const) &ScatteringPower::GetBij) .def("SetBij", (void (ScatteringPower::*) (const size_t&, const size_t&, const double)) &ScatteringPower::SetBij, &ScatteringPowerWrap::default_SetBij) .def("IsIsotropic", &ScatteringPower::IsIsotropic) .def("GetDynPopCorrIndex", &ScatteringPower::GetDynPopCorrIndex) .def("GetNbScatteringPower", &ScatteringPower::GetNbScatteringPower) .def("GetLastChangeClock", &ScatteringPower::GetLastChangeClock, return_value_policy()) .def("GetRadius", pure_virtual(&ScatteringPower::GetRadius)) .def("GetMaximumLikelihoodPositionError", pure_virtual(&ScatteringPower::GetMaximumLikelihoodPositionError)) .def("SetMaximumLikelihoodPositionError", pure_virtual(&ScatteringPower::SetMaximumLikelihoodPositionError)) .def("GetMaximumLikelihoodNbGhostAtom", pure_virtual(&ScatteringPower::GetMaximumLikelihoodNbGhostAtom)) .def("SetMaximumLikelihoodNbGhostAtom", pure_virtual(&ScatteringPower::SetMaximumLikelihoodNbGhostAtom)) .def("GetMaximumLikelihoodParClock", &ScatteringPower::GetMaximumLikelihoodParClock, return_value_policy()) .def("GetFormalCharge", &ScatteringPower::GetFormalCharge, &ScatteringPowerWrap::default_GetFormalCharge) .def("SetFormalCharge", &ScatteringPower::SetFormalCharge, &ScatteringPowerWrap::default_SetFormalCharge) .def("GetColourRGB", &_GetColourRGB) .def("GetColour", &_GetColourRGB) .def("SetColour", (void (ScatteringPower::*)(const float,const float,const float)) &ScatteringPower::SetColour, (boost::python::arg("r"), boost::python::arg("g"), boost::python::arg("b"))) .add_property("Biso", (double (ScatteringPower::*)()const) &ScatteringPower::GetBiso, &ScatteringPower::SetBiso) .add_property("B11", &_GetBij<1,1>, &_SetBij<1,1>) .add_property("B22", &_GetBij<2,2>, &_SetBij<2,2>) .add_property("B33", &_GetBij<3,3>, &_SetBij<3,3>) .add_property("B12", &_GetBij<1,2>, &_SetBij<1,2>) .add_property("B13", &_GetBij<1,3>, &_SetBij<1,3>) .add_property("B23", &_GetBij<2,3>, &_SetBij<2,3>) ; } pyobjcryst-2024.2.1/src/extensions/scatteringpoweratom_ext.cpp000066400000000000000000000034461470422267000246400ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::ScatteringPowerAtom. * *****************************************************************************/ #include #include #include #include namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; void wrap_scatteringpoweratom() { typedef void (ScatteringPowerAtom::*SPAInitType)( const string&, const string&, const double); SPAInitType theinit = &ScatteringPowerAtom::Init; class_ > ("ScatteringPowerAtom", init()) .def( init > ((bp::arg("name"), bp::arg("symbol"), bp::arg("bIso")=1.0))) .def("Init", theinit, (bp::arg("name"), bp::arg("symbol"), bp::arg("biso")=1.0 )) .def("SetSymbol", &ScatteringPowerAtom::SetSymbol) .def("GetElementName", &ScatteringPowerAtom::GetElementName) .def("GetAtomicNumber", &ScatteringPowerAtom::GetAtomicNumber) .def("GetAtomicWeight", &ScatteringPowerAtom::GetAtomicWeight) ; } pyobjcryst-2024.2.1/src/extensions/scatteringpowersphere_ext.cpp000066400000000000000000000030301470422267000251530ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::ScatteringPowerSphere. * *****************************************************************************/ #include #include #include #include namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; void wrap_scatteringpowersphere() { typedef void (ScatteringPowerSphere::*SPSInitType)( const string&, const double, const double); SPSInitType theinit = &ScatteringPowerSphere::Init; class_ > ("ScatteringPowerSphere") .def(init >()) .def("Init", theinit, (boost::python::arg("name"), boost::python::arg("radius"), boost::python::arg("biso")=1.0 )) .def("GetRadius", &ScatteringPowerSphere::GetRadius) ; } pyobjcryst-2024.2.1/src/extensions/spacegroup_ext.cpp000066400000000000000000000127421470422267000227060ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::SpaceGroup. * *****************************************************************************/ #include #include #include #include #include #include #include "helpers.hpp" namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; namespace { // This returns a list of translation operations bp::list GetTranslationVectors(const SpaceGroup& sg) { const std::vector& tv = sg.GetTranslationVectors(); bp::list outlist; std::vector::const_iterator vec; for(vec = tv.begin(); vec != tv.end(); ++vec) { CrystVector translation(3); for(int idx = 0; idx < 3; ++idx) { translation(idx) = vec->tr[idx]; } outlist.append(translation); } return outlist; } // Returns a list of (translation vector, rotation) tuples bp::list GetSymmetryOperations(const SpaceGroup& sg) { const std::vector& sv = sg.GetSymmetryOperations(); bp::list outlist; int r, c; std::vector::const_iterator tup; for(tup = sv.begin(); tup != sv.end(); ++tup) { CrystVector translation(3); for(int idx = 0; idx < 3; ++idx) { translation(idx) = tup->tr[idx]; } CrystMatrix rotation(3,3); for(int idx = 0; idx < 9; ++idx) { r = idx/3; c = idx%3; rotation(r,c) = tup->mx[idx]; } outlist.append(bp::make_tuple(translation, rotation)); } return outlist; } SpaceGroup* CreateSpaceGroup(const std::string& sgid) { MuteObjCrystUserInfo muzzle; // this may throw invalid_argument which is translated to ValueError SpaceGroup* rv = new SpaceGroup(sgid); return rv; } void SafeChangeSpaceGroup(SpaceGroup& sg, const std::string& sgid) { MuteObjCrystUserInfo muzzle; // this may throw invalid_argument which is translated to ValueError sg.ChangeSpaceGroup(sgid); } } // namespace void wrap_spacegroup() { class_("SpaceGroup") // Constructors .def("__init__", make_constructor(CreateSpaceGroup)) // Methods .def("ChangeSpaceGroup", &SafeChangeSpaceGroup) .def("GetName", &SpaceGroup::GetName, return_value_policy()) .def("IsInAsymmetricUnit", &SpaceGroup::IsInAsymmetricUnit) .def("ChangeToAsymmetricUnit", &SpaceGroup::ChangeToAsymmetricUnit) .def("IsInAsymmetricUnit", &SpaceGroup::IsInAsymmetricUnit) .def("GetAsymUnit", &SpaceGroup::GetAsymUnit, return_internal_reference<>()) .def("GetSpaceGroupNumber", &SpaceGroup::GetSpaceGroupNumber) .def("IsCentrosymmetric", &SpaceGroup::IsCentrosymmetric) .def("GetNbTranslationVectors", &SpaceGroup::GetNbTranslationVectors) .def("GetTranslationVectors", &GetTranslationVectors) .def("GetSymmetryOperations", &GetSymmetryOperations) .def("GetAllSymmetrics", &SpaceGroup::GetAllSymmetrics, (bp::arg("x"), bp::arg("y"), bp::arg("z"), bp::arg("noCenter")=false, bp::arg("noTransl")=false, bp::arg("noIdentical")=false)) .def("GetNbSymmetrics", &SpaceGroup::GetNbSymmetrics, (bp::arg("noCenter")=false, bp::arg("noTransl")=false)) .def("GetInversionCenter", &SpaceGroup::GetInversionCenter) .def("Print", &SpaceGroup::Print) .def("HasInversionCenter", &SpaceGroup::HasInversionCenter) .def("IsInversionCenterAtOrigin", &SpaceGroup::IsInversionCenterAtOrigin) // Requires cctbx? Forward declaration doesn't work //.def("GetCCTbxSpg", &SpaceGroup::GetCCTbxSpg, // return_value_policy()) .def("GetClockSpaceGroup", &SpaceGroup::GetClockSpaceGroup, return_value_policy()) .def("GetUniqueAxis", &SpaceGroup::GetUniqueAxis) .def("GetExtension", &SpaceGroup::GetExtension) .def("GetAllEquivRefl", &SpaceGroup::GetAllEquivRefl, (bp::arg("h"), bp::arg("k"), bp::arg("l"), bp::arg("excludeFriedelMate")=false, bp::arg("forceFriedelLaw")=false)) .def("IsReflSystematicAbsent", &SpaceGroup::IsReflSystematicAbsent) .def("IsReflCentric", &SpaceGroup::IsReflCentric) .def("GetExpectedIntensityFactor", &SpaceGroup::GetExpectedIntensityFactor) .def("__str__", &SpaceGroup::GetName, return_value_policy()) .def("__repr__", &SpaceGroup::GetName, return_value_policy()) ; } pyobjcryst-2024.2.1/src/extensions/stretchmode_ext.cpp000066400000000000000000000176551470422267000230670ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::StretchMode and its derivatives. * * Note that all indices are zero-based. * *****************************************************************************/ #include #include #include #include #include #include #include "helpers.hpp" namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; namespace { typedef std::set MolAtomSet; class StretchModeWrap : public StretchMode, public wrapper { public: StretchModeWrap() : StretchMode() {} StretchModeWrap(const StretchModeWrap& sm) : StretchMode(sm) {} // Pure virtual void CalcDeriv(const bool derivllk=true) const { this->get_override("CalcDeriv")(derivllk); } void Stretch(const double change, const bool keepCenter) { this->get_override("Stretch")(change, keepCenter); } void RandomStretch(const double change, const bool keepCenter) { this->get_override("RandomStretch")(change); } }; // These gives us a way to add an atom to a stretch mode void _AddAtomSMBL(StretchModeBondLength& mode, MolAtom& a) { mode.mvTranslatedAtomList.insert(&a); } void _AddAtomsSMBL(StretchModeBondLength& mode, bp::object& l) { for(int i=0; i < len(l); ++i) { MolAtom* a = extract(l[i]); mode.mvTranslatedAtomList.insert(a); } } void _AddAtomsSetSMBL(StretchModeBondLength& mode, MolAtomSet& l) { MolAtomSet::const_iterator p; for(p = l.begin(); p != l.end(); ++p) { mode.mvTranslatedAtomList.insert(*p); } } bp::list _GetAtomsSMBL(StretchModeBondLength& mode) { bp::list l; MolAtomSet& v = mode.mvTranslatedAtomList; l = ptrcontainerToPyList< MolAtomSet >(v); return l; } // This one is for the angle modes template void _AddAtom(T& mode, MolAtom& a) { mode.mvRotatedAtomList.insert(&a); } template void _AddAtoms(T& mode, bp::object& l) { for(int i=0; i < len(l); ++i) { MolAtom* a = extract(l[i]); mode.mvRotatedAtomList.insert(a); } } template void _AddAtomsSet(T& mode, MolAtomSet& l) { MolAtomSet::const_iterator p; for(p = l.begin(); p != l.end(); ++p) { mode.mvRotatedAtomList.insert(*p); } } template bp::list _GetAtoms(T& mode) { bp::list l; MolAtomSet& v = mode.mvRotatedAtomList; l = ptrcontainerToPyList< MolAtomSet >(v); return l; } // These are accessors for the atoms. template MolAtom* _GetAtom0(T& mode) { return mode.mpAtom0; } template MolAtom* _GetAtom1(T& mode) { return mode.mpAtom1; } template MolAtom* _GetAtom2(T& mode) { return mode.mpAtom2; } } // namespace void wrap_stretchmode() { class_ ("StretchMode", no_init ) .def("CalcDeriv", pure_virtual(&StretchMode::CalcDeriv), (bp::arg("derivllk")=true)) .def("Stretch", pure_virtual(&StretchMode::Stretch), (bp::arg("amplitude"), bp::arg("keepCenter")=true)) .def("RandomStretch", pure_virtual(&StretchMode::RandomStretch), bp::arg("amplitude")) ; class_ > ("StretchModeBondLength", init ((bp::arg("at0"), bp::arg("at1"), bp::arg("pBond")=0)) [with_custodian_and_ward<1,2, with_custodian_and_ward<1,3, with_custodian_and_ward<1,4> > >()]) .def("AddAtom", &_AddAtomSMBL, with_custodian_and_ward<1,2>()) .def("AddAtoms", &_AddAtomsSMBL, with_custodian_and_ward<1,2>()) .def("AddAtoms", &_AddAtomsSetSMBL, with_custodian_and_ward<1,2>()) .def("GetAtoms", &_GetAtomsSMBL, with_custodian_and_ward_postcall<1,0>()) .add_property("mpAtom0", make_function( &_GetAtom0, return_internal_reference<>())) .add_property("mpAtom1", make_function( &_GetAtom1, return_internal_reference<>())) ; class_ > ("StretchModeBondAngle", init ((bp::arg("at0"), bp::arg("at1"), bp::arg("at2"), bp::arg("pBondAngle")=0)) [with_custodian_and_ward<1,2, with_custodian_and_ward<1,3, with_custodian_and_ward<1,4, with_custodian_and_ward<1,5> > > >()]) .def("AddAtom", &_AddAtom, with_custodian_and_ward<1,2>()) .def("AddAtoms", &_AddAtoms, with_custodian_and_ward<1,2>()) .def("AddAtoms", &_AddAtomsSet, with_custodian_and_ward<1,2>()) .def("GetAtoms", &_GetAtoms, with_custodian_and_ward_postcall<1,0>()) .add_property("mpAtom0", make_function( &_GetAtom0, return_internal_reference<>())) .add_property("mpAtom1", make_function( &_GetAtom1, return_internal_reference<>())) .add_property("mpAtom2", make_function( &_GetAtom2, return_internal_reference<>())) ; class_ > ("StretchModeTorsion", init ((bp::arg("at0"), bp::arg("at1"), bp::arg("pDihedralAngle")=0)) [with_custodian_and_ward<1,2, with_custodian_and_ward<1,3, with_custodian_and_ward<1,4> > >()]) .def("AddAtom", &_AddAtom, with_custodian_and_ward<1,2>()) .def("AddAtoms", &_AddAtoms, with_custodian_and_ward<1,2>()) .def("AddAtoms", &_AddAtomsSet, with_custodian_and_ward<1,2>()) .def("GetAtoms", &_GetAtoms, with_custodian_and_ward_postcall<1,0>()) .add_property("mpAtom1", make_function( &_GetAtom1, return_internal_reference<>())) .add_property("mpAtom2", make_function( &_GetAtom2, return_internal_reference<>())) ; class_ > ("StretchModeTwist", init ((bp::arg("at0"), bp::arg("at1"))) [with_custodian_and_ward<1,2, with_custodian_and_ward<1,3> >()]) .def("AddAtom", &_AddAtom, with_custodian_and_ward<1,2>()) .def("AddAtoms", &_AddAtoms, with_custodian_and_ward<1,2>()) .def("AddAtoms", &_AddAtomsSet, with_custodian_and_ward<1,2>()) .def("GetAtoms", &_GetAtoms, with_custodian_and_ward_postcall<1,0>()) .add_property("mpAtom1", make_function( &_GetAtom1, return_internal_reference<>())) .add_property("mpAtom2", make_function( &_GetAtom2, return_internal_reference<>())) ; } pyobjcryst-2024.2.1/src/extensions/unitcell_ext.cpp000066400000000000000000000141351470422267000223530ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::UnitCell. * *****************************************************************************/ #include #include #include #include #include #include #include #include "helpers.hpp" using namespace boost::python; using namespace ObjCryst; namespace bp = boost::python; namespace { bp::tuple FractionalToOrthonormalCoords(const UnitCell& uc, double x, double y, double z) { uc.FractionalToOrthonormalCoords(x,y,z); return bp::make_tuple(x,y,z); } bp::tuple OrthonormalToFractionalCoords(const UnitCell& uc, double x, double y, double z) { uc.OrthonormalToFractionalCoords(x,y,z); return bp::make_tuple(x,y,z); } bp::tuple MillerToOrthonormalCoords(const UnitCell& uc, double x, double y, double z) { uc.MillerToOrthonormalCoords(x,y,z); return bp::make_tuple(x,y,z); } bp::tuple OrthonormalToMillerCoords(const UnitCell& uc, double x, double y, double z) { uc.OrthonormalToMillerCoords(x,y,z); return bp::make_tuple(x,y,z); } // Setter for the lattice parameters. void _seta(UnitCell& u, double val) { u.GetPar("a").SetValue(val); } double _geta(UnitCell& u) { return u.GetLatticePar(0); } void _setb(UnitCell& u, double val) { u.GetPar("b").SetValue(val); } double _getb(UnitCell& u) { return u.GetLatticePar(1); } void _setc(UnitCell& u, double val) { u.GetPar("c").SetValue(val); } double _getc(UnitCell& u) { return u.GetLatticePar(2); } void _setalpha(UnitCell& u, double val) { if((val<=0)||(val>=M_PI)) throw ObjCrystException("alpha must be within ]0;pi["); RefinablePar &p = u.GetPar("alpha"); if(p.IsUsed()) p.SetValue(val); // Throwing an exception here would be risky - a warning would be more adequate // else throw ObjCrystException("alpha is fixed and cannot be changed"); } double _getalpha(UnitCell& u) { return u.GetLatticePar(3); } void _setbeta(UnitCell& u, double val) { if((val<=0)||(val>=M_PI)) throw ObjCrystException("beta must be within ]0;pi["); RefinablePar &p = u.GetPar("beta"); if(p.IsUsed()) p.SetValue(val); // Throwing an exception here would be risky - a warning would be more adequate // else throw ObjCrystException("beta is fixed and cannot be changed"); } double _getbeta(UnitCell& u) { return u.GetLatticePar(4); } void _setgamma(UnitCell& u, double val) { if((val<=0)||(val>=M_PI)) throw ObjCrystException("gamma must be within ]0;pi["); RefinablePar &p = u.GetPar("gamma"); if(p.IsUsed()) p.SetValue(val); // Throwing an exception here would be risky - a warning would be more adequate // else throw ObjCrystException("gamma is fixed and cannot be changed"); } double _getgamma(UnitCell& u) { return u.GetLatticePar(5); } void SafeChangeSpaceGroup(UnitCell& u, const std::string& sgid) { MuteObjCrystUserInfo muzzle; // this may throw invalid_argument which is translated to ValueError u.ChangeSpaceGroup(sgid); } } void wrap_unitcell() { scope().attr("refpartype_unitcell") = object(ptr(gpRefParTypeUnitCell)); scope().attr("refpartype_unitcell_length") = object(ptr(gpRefParTypeUnitCellLength)); scope().attr("refpartype_unitcell_angle") = object(ptr(gpRefParTypeUnitCellAngle)); class_ > ("UnitCell") // Constructors .def(init()) .def(init()) .def(init()) .def("GetLatticePar", (CrystVector (UnitCell::*)() const) &UnitCell::GetLatticePar) .def("GetLatticePar", (double (UnitCell::*)(const int) const) &UnitCell::GetLatticePar) .def("GetClockLatticePar", &UnitCell::GetClockLatticePar, return_value_policy()) .def("GetBMatrix", &UnitCell::GetBMatrix, return_value_policy()) .def("GetOrthMatrix", &UnitCell::GetOrthMatrix, return_value_policy()) .def("GetClockMetricMatrix", &UnitCell::GetClockMetricMatrix, return_value_policy()) .def("GetOrthonormalCoords", &UnitCell::GetOrthonormalCoords) // Modified to return a tuple .def("OrthonormalToFractionalCoords", &OrthonormalToFractionalCoords) // Modified to return a tuple .def("FractionalToOrthonormalCoords", &FractionalToOrthonormalCoords) // Modified to return a tuple .def("MillerToOrthonormalCoords", &MillerToOrthonormalCoords) // Modified to return a tuple .def("OrthonormalToMillerCoords", &OrthonormalToMillerCoords) .def("GetSpaceGroup", (SpaceGroup& (UnitCell::*)()) &UnitCell::GetSpaceGroup, return_internal_reference<>()) .def("ChangeSpaceGroup", &SafeChangeSpaceGroup) .def("GetVolume", &UnitCell::GetVolume) .def("__str__", &__str__) // python-only .add_property("a", &_geta, &_seta) .add_property("b", &_getb, &_setb) .add_property("c", &_getc, &_setc) .add_property("alpha", &_getalpha, &_setalpha) .add_property("beta", &_getbeta, &_setbeta) .add_property("gamma", &_getgamma, &_setgamma) ; } pyobjcryst-2024.2.1/src/extensions/zatom_ext.cpp000066400000000000000000000060771470422267000216740ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::ZAtom. * * Changes from ObjCryst::ZAtom * - XMLOutput and Input are not wrapped. * *****************************************************************************/ #include #include #include #include namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; namespace { } void wrap_zatom() { /* This class is created internally by a ZScatterer, so it does not have an * init function */ class_("ZAtom", no_init) //init( // (bp::arg("scatt"), bp::arg("pow"), bp::arg("atomBond")=0, // bp::arg("bondLength")=1, bp::arg("atomAngle")=0, // bp::arg("bondAngle")=0, bp::arg("atomDihedral")=0, // bp::arg("popu")=1, bp::arg("name")="") // ) //[with_custodian_and_ward<2,1,with_custodian_and_ward<1,3> >()] //) // Methods .def("GetClassName", &ZAtom::GetClassName, return_value_policy()) .def("GetName", &ZAtom::GetName, return_value_policy()) .def("SetName", &ZAtom::SetName) .def("GetZScatterer", (ZScatterer& (ZAtom::*)()) &ZAtom::GetZScatterer, return_internal_reference<>()) .def("GetZBondAtom", &ZAtom::GetZBondAtom) .def("GetZAngleAtom", &ZAtom::GetZAngleAtom) .def("GetZDihedralAngleAtom", &ZAtom::GetZDihedralAngleAtom) .def("GetZBondLength", &ZAtom::GetZBondLength, return_value_policy()) .def("GetZAngle", &ZAtom::GetZAngle, return_value_policy()) .def("GetZDihedralAngle", &ZAtom::GetZDihedralAngle, return_value_policy()) .def("GetOccupancy", &ZAtom::GetOccupancy, return_value_policy()) .def("GetScatteringPower", &ZAtom::GetScatteringPower, return_internal_reference<>()) .def("SetZBondLength", &ZAtom::SetZBondLength) .def("SetZAngle", &ZAtom::SetZAngle) .def("SetZDihedralAngle", &ZAtom::SetZDihedralAngle) .def("SetOccupancy", &ZAtom::SetOccupancy) .def("SetScatteringPower", &ZAtom::SetScatteringPower, with_custodian_and_ward<1,2>()) ; } pyobjcryst-2024.2.1/src/extensions/zpolyhedron_ext.cpp000066400000000000000000000045551470422267000231160ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::ZPolyhedron. * *****************************************************************************/ #include #include #include #include #include namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; void wrap_zpolyhedron() { class_ > ("ZPolyhedron", init()) /* Constructors */ .def(init( (bp::arg("type"), bp::arg("cryst"), bp::arg("x"), bp::arg("y"), bp::arg("z"), bp::arg("name"), bp::arg("centralAtomPow"), bp::arg("periphAtomPow"), bp::arg("centralPeriphDist"), bp::arg("ligandPopu")=1, bp::arg("phi")=0, bp::arg("chi")=0, bp::arg("psi")=0) ) [with_custodian_and_ward<3,1, with_custodian_and_ward<1,8, with_custodian_and_ward<1,9> > >() ] ) ; enum_("RegularPolyhedraType") .value("TETRAHEDRON", TETRAHEDRON) .value("OCTAHEDRON", OCTAHEDRON) .value("SQUARE_PLANE", SQUARE_PLANE) .value("CUBE", CUBE) .value("ANTIPRISM_TETRAGONAL", ANTIPRISM_TETRAGONAL) .value("PRISM_TETRAGONAL_MONOCAP", PRISM_TETRAGONAL_MONOCAP) .value("PRISM_TETRAGONAL_DICAP", PRISM_TETRAGONAL_DICAP) .value("PRISM_TRIGONAL", PRISM_TRIGONAL) .value("PRISM_TRIGONAL_TRICAPPED", PRISM_TRIGONAL_TRICAPPED) .value("ICOSAHEDRON", ICOSAHEDRON) .value("TRIANGLE_PLANE", TRIANGLE_PLANE) ; } pyobjcryst-2024.2.1/src/extensions/zscatterer_ext.cpp000066400000000000000000000077701470422267000227310ustar00rootroot00000000000000/***************************************************************************** * * pyobjcryst by DANSE Diffraction group * Simon J. L. Billinge * (c) 2009 The Trustees of Columbia University * in the City of New York. All rights reserved. * * File coded by: Chris Farrow * * See AUTHORS.txt for a list of people who contributed. * See LICENSE_DANSE.txt for license information. * ****************************************************************************** * * boost::python bindings to ObjCryst::ZScatterer. * * Changes from ObjCryst++ * - XMLOutput and Input are not wrapped. * * - Import and output is not implemented yet. * *****************************************************************************/ #include #include #include #include #include #include "python_streambuf.hpp" #include "helpers.hpp" namespace bp = boost::python; using namespace boost::python; using namespace ObjCryst; void _ImportFenskeHallZMatrix(ZScatterer &scatt, bp::object input , bool named) { // Mute output MuteObjCrystUserInfo muzzle; CaptureStdOut gag; boost_adaptbx::python::streambuf sbuf(input); boost_adaptbx::python::streambuf::istream in(sbuf); scatt.ImportFenskeHallZMatrix(in, named); } void wrap_zscatterer() { class_ > ("ZScatterer", init(bp::arg("old"))) /* Constructors */ .def(init ((bp::arg("name"), bp::arg("cryst"), bp::arg("x")=0, bp::arg("y")=0, bp::arg("z")=0, bp::arg("phi")=0, bp::arg("chi")=0, bp::arg("psi")=0) ) [with_custodian_and_ward<1,6>()]) /* Methods */ .def("GetClassName", &ZScatterer::GetClassName, return_value_policy()) .def("AddAtom", &ZScatterer::AddAtom, (bp::arg("name"), bp::arg("pow"), bp::arg("atomBond"), bp::arg("bondLength"), bp::arg("atomAngle"), bp::arg("bondAngle"), bp::arg("atomDihedral"), bp::arg("dihedralAngle"), bp::arg("popu")=1.0 ), with_custodian_and_ward<1,3>() ) .def("GetPhi", &ZScatterer::GetPhi) .def("GetChi", &ZScatterer::GetChi) .def("GetPsi", &ZScatterer::GetPsi) .def("SetPhi", &ZScatterer::SetPhi) .def("SetChi", &ZScatterer::SetChi) .def("SetPsi", &ZScatterer::SetPsi) .def("GetZAtomX", &ZScatterer::GetZAtomX) .def("GetZAtomY", &ZScatterer::GetZAtomY) .def("GetZAtomZ", &ZScatterer::GetZAtomZ) .def("GetZBondAtom", &ZScatterer::GetZBondAtom) .def("GetZAngleAtom", &ZScatterer::GetZAngleAtom) .def("GetZDihedralAngleAtom", &ZScatterer::GetZDihedralAngleAtom) .def("GetZBondLength", &ZScatterer::GetZBondLength) .def("GetZAngle", &ZScatterer::GetZAngle) .def("GetZDihedralAngle", &ZScatterer::GetZDihedralAngle) .def("SetZBondLength", &ZScatterer::SetZBondLength) .def("SetZAngle", &ZScatterer::SetZAngle) .def("SetZDihedralAngle", &ZScatterer::SetZDihedralAngle) .def("GetZAtomRegistry", &ZScatterer::GetZAtomRegistry, return_value_policy()) .def("GetXCoord", &ZScatterer::GetXCoord, return_value_policy()) .def("GetYCoord", &ZScatterer::GetYCoord, return_value_policy()) .def("GetZCoord", &ZScatterer::GetZCoord, return_value_policy()) .def("SetCenterAtomIndex", &ZScatterer::SetCenterAtomIndex) .def("GetCenterAtomIndex", &ZScatterer::GetCenterAtomIndex) .def("ImportFenskeHallZMatrix", &_ImportFenskeHallZMatrix) // Python-only methods .def("__str__", &__str__) ; } pyobjcryst-2024.2.1/src/pyobjcryst/000077500000000000000000000000001470422267000171555ustar00rootroot00000000000000pyobjcryst-2024.2.1/src/pyobjcryst/__init__.py000066400000000000000000000071471470422267000212770ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Python wrapping of ObjCryst++. Objects are wrapped according to their header file in the ObjCryst source. See the online ObjCryst++ documentation (https://objcryst.readthedocs.io). Modules atom -- Wrapping of Atom.h crystal -- Wrapping of Crystal.h general -- Wrapping of General.h globaloptim -- Wrapping of GlobalOptimObj.h io -- Wrapping of IO.h lsq -- Wrapping of LSQNumObj.h molecule -- Wrapping of Molecule.h polyhedron -- Wrapping of Polyhedron.h refinableobj -- Wrapping of RefinableObj.h scatterer -- Wrapping of Scatterer.h scatteringpower -- Wrapping of ScatteringPower.h scatteringpowersphere -- Wrapping of ScatteringPowerSphere.h spacegroup -- Wrapping of SpaceGroup.h unitcell -- Wrapping of UnitCell.h zscatterer -- Wrapping of ZScatterer.h General Changes - C++ methods that can return const or non-const objects return non-const objects in python. - Classes with a Print() method have the output of this method exposed in the __str__ python method. Thus, obj.Print() == print obj. - CrystVector and CrystMatrix are converted to numpy arrays. - Indexing methods raise IndexError when index is out of bounds. See the modules' documentation for specific changes. """ import warnings # Let's put this on the package level from pyobjcryst.general import ObjCrystException # version data from pyobjcryst.version import __version__ # import submodules that only import from _pyobjcryst import pyobjcryst.atom import pyobjcryst.crystal import pyobjcryst.diffractiondatasinglecrystal import pyobjcryst.general import pyobjcryst.globaloptim import pyobjcryst.indexing import pyobjcryst.io import pyobjcryst.lsq import pyobjcryst.molecule import pyobjcryst.polyhedron import pyobjcryst.powderpattern import pyobjcryst.radiation import pyobjcryst.refinableobj import pyobjcryst.reflectionprofile import pyobjcryst.scatterer import pyobjcryst.scatteringdata import pyobjcryst.scatteringpower import pyobjcryst.scatteringpowersphere import pyobjcryst.spacegroup import pyobjcryst.unitcell import pyobjcryst.zscatterer from pyobjcryst._pyobjcryst import gTopRefinableObjRegistry def loadCrystal(filename): """Load pyobjcryst Crystal object from a CIF file. :param filename: CIF file to be loaded. :return: a new Crystal object. ..deprecated:: 2.2.4 Use pyobjcryst.crystal.create_crystal_from_cif() instead, which has more options when importing a CIF, including using an URL instead of a file. """ warnings.warn("loadCrystal is deprecated. Please use " "pyobjcryst.crystal.create_crystal_from_cif() instead", DeprecationWarning) from pyobjcryst.crystal import CreateCrystalFromCIF with open(filename, 'rb') as fp: rv = CreateCrystalFromCIF(fp) return rv # silence the pyflakes syntax checker assert ObjCrystException is not None assert __version__ or True assert pyobjcryst.zscatterer pyobjcryst-2024.2.1/src/pyobjcryst/atom.py000066400000000000000000000015621470422267000204730ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Python wrapping of Atom.h See the online ObjCryst++ documentation (https://objcryst.readthedocs.io). Changes from ObjCryst::Atom - The default constructor has been disabled. When not followed-up by Init, it will cause segmentation faults, even if it is printed. """ __all__ = ["Atom"] from pyobjcryst._pyobjcryst import Atom pyobjcryst-2024.2.1/src/pyobjcryst/crystal.py000066400000000000000000001004151470422267000212110ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow, Vincent Favre-Nicolin # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Python wrapping of Crystal.h. See the online ObjCryst++ documentation (https://objcryst.readthedocs.io). Changes from ObjCryst::Crystal - CIFOutput accepts a python file-like object - CalcDynPopCorr is not enabled, as the API states that this is for internal use only. Other Changes - CreateCrystalFromCIF is placed here instead of in a seperate CIF module. This method accepts a python file or a filename rather than a CIF object. """ __all__ = ["Crystal", "BumpMergePar", "CreateCrystalFromCIF", "create_crystal_from_cif"] import warnings from types import MethodType from urllib.request import urlopen from multiprocessing import current_process import numpy as np from pyobjcryst._pyobjcryst import Crystal as Crystal_orig from pyobjcryst._pyobjcryst import BumpMergePar from pyobjcryst._pyobjcryst import CreateCrystalFromCIF as CreateCrystalFromCIF_orig from .refinableobj import wrap_boost_refinableobjregistry try: import py3Dmol except ImportError: py3Dmol = None try: import ipywidgets as widgets except ImportError: widgets = None class Crystal(Crystal_orig): def CIFOutput(self, file, mindist=0): """ Save the crystal structure to a CIF file. :param file: either a filename, or a python file object opened in write mode """ if isinstance(file, str): super().CIFOutput(open(file, "w"), mindist) else: super().CIFOutput(file, mindist) def UpdateDisplay(self): try: if self._display_update_disabled: return except: pass # test for _3d_widget is a bit ugly, but to correctly implement this we'd need an # __init__ function which overrides the 3 different Crystal constructors which # could be messy as well. try: if self._3d_widget is not None: self._widget_update() except AttributeError: # self._3d_widget does not exist pass def disable_display_update(self): """ Disable display (useful for multiprocessing)""" self._display_update_disabled = True def enable_display_update(self): """ Enable display""" self._display_update_disabled = False def _display_cif(self, xmin=0, xmax=1, ymin=0, ymax=1, zmin=0, zmax=1, enantiomer=False, full_molecule=True, only_independent_atoms=False): """ Create a CIF with the full list of atoms, including those deduced by symmetry or translation up to neighbouring unit cells :param xmin, xmax, ymin, ymax, zmin, zmax: the view limits in fractional coordinates. :param enantiomer: if True, will mirror the structure along the x axis :param full_molecule: if True, a Molecule (or Scatterer) which has at least one atom inside the view limits is entirely shown. :param only_independent_atoms: if True, only show the independent atoms, no symmetry or translation is applied :return : the CIF as a string """ cif = "data_crystal_for3d\n\n" cif += "_computing_structure_solution 'FOX http://objcryst.sourceforge.net'\n\n"; cif += "_cell_length_a %8.3f\n" % self.a cif += "_cell_length_b %8.3f\n" % self.b cif += "_cell_length_c %8.3f\n" % self.c cif += "_cell_length_alpha %8.3f\n" % np.rad2deg(self.alpha) cif += "_cell_length_beta %8.3f\n" % np.rad2deg(self.beta) cif += "_cell_length_gamma %8.3f\n" % np.rad2deg(self.gamma) cif += "loop_\n" cif += " _atom_site_label\n" cif += " _atom_site_type_symbol\n" cif += " _atom_site_fract_x\n" cif += " _atom_site_fract_y\n" cif += " _atom_site_fract_z\n" cif += " _atom_site_occupancy\n" spg = self.GetSpaceGroup() for i in range(self.GetNbScatterer()): scatt = self.GetScatterer(i) v = scatt.GetScatteringComponentList() nat = len(v) if only_independent_atoms: for j in range(len(v)): s = v[j] symbol = s.mpScattPow.GetSymbol() name = scatt.GetComponentName(j) # 3dmol.js does not like ' in names, # despite https://www.iucr.org/resources/cif/spec/version1.1/cifsyntax#bnf name = name.replace("'", "_") occ = s.Occupancy x, y, z = s.X % 1, s.Y % 1, s.Z % 1 if enantiomer: x = -x % 1 cif += " %12s %4s %8.4f %8.4f %8.4f %6.4f\n" % (name, symbol, x, y, z, occ) else: # Generate all symmetrics to enable full molecule display nsym = spg.GetNbSymmetrics() # print(nsym) vxyz = np.empty((nsym, nat, 3), dtype=np.float32) for j in range(nat): s = v[j] x, y, z = s.X, s.Y, s.Z if enantiomer: x = -x xyzsym = spg.GetAllSymmetrics(x, y, z) for k in range(nsym): vxyz[k, j, :] = xyzsym[k] for k in range(nsym): xc, yc, zc = vxyz[k].mean(axis=0) vxyz[k, :, 0] -= (xc - xc % 1) vxyz[k, :, 1] -= (yc - yc % 1) vxyz[k, :, 2] -= (zc - zc % 1) # print(vxyz, vxyz.shape) for j in range(nat): s = v[j] symbol = s.mpScattPow.GetSymbol() name = scatt.GetComponentName(j) # 3dmol.js does not like ' in names, # despite https://www.iucr.org/resources/cif/spec/version1.1/cifsyntax#bnf name = name.replace("'", "_") occ = s.Occupancy for k in range(nsym): for dx in (-1, 0, 1): for dy in (-1, 0, 1): for dz in (-1, 0, 1): x, y, z = vxyz[k, j] + np.array((dx, dy, dz)) # print(" %12s %4s %8.4f %8.4f %8.4f %6.4f" % \ # (name, symbol, x, y, z, occ)) if full_molecule: # If any atom is within limits, display all vx, vy, vz = vxyz[k, :, 0] + dx, vxyz[k, :, 1] + dy, vxyz[k, :, 2] + dz tmp = (vx >= xmin) * (vx <= xmax) * (vy >= ymin) * \ (vy <= ymax) * (vz >= zmin) * (vz <= zmax) if tmp.sum(): cif += " %12s %4s %8.4f %8.4f %8.4f %6.4f\n" % \ (name, symbol, x, y, z, occ) else: if xmin <= x <= xmax and ymin <= y <= ymax and zmin <= z <= zmax: cif += " %12s %4s %8.4f %8.4f %8.4f %6.4f\n" % \ (name, symbol, x, y, z, occ) return cif def _display_list(self, xmin=0, xmax=1, ymin=0, ymax=1, zmin=0, zmax=1, enantiomer=False, full_molecule=True, only_independent_atoms=False): """ Create a list of atoms to be displayed, so it can be supplied to py3Dmol :param xmin, xmax, ymin, ymax, zmin, zmax: the view limits in fractional coordinates. :param enantiomer: if True, will mirror the structure along the x axis :param full_molecule: if True, a Molecule (or Scatterer) which has at least one atom inside the view limits is entirely shown. :param only_independent_atoms: if True, only show the independent atoms, no symmetry or translation is applied :return : the list of atoms and bonds to be displayed for 3dmol """ spg = self.GetSpaceGroup() vv = [] idx = 0 for i in range(self.GetNbScatterer()): scatt = self.GetScatterer(i) v = scatt.GetScatteringComponentList() nat = len(v) if scatt.GetClassName() == "Molecule": # We need to generate all atomic positions and the associated bonds atoms = {} for j in range(nat): s = v[j] a = scatt.GetAtom(j) if a.IsDummy(): continue name = scatt.GetComponentName(j) name = name.replace("'", "_") symbol = s.mpScattPow.GetSymbol() occ = s.Occupancy x, y, z = s.X, s.Y, s.Z if enantiomer: x = -x atoms[a.int_ptr()] = {'x': x, 'y': y, 'z': z, 'name': name, 'j': j, 'symbol': symbol, 'bonds': [], 'bondOrder': []} for bond in scatt.IterBond(): o = bond.BondOrder if o < 1: o = 1 i1 = bond.GetAtom1().int_ptr() i2 = bond.GetAtom2().int_ptr() atoms[i1]['bonds'].append(i2) atoms[i2]['bonds'].append(i1) atoms[i1]['bondOrder'].append(o) atoms[i2]['bondOrder'].append(o) if only_independent_atoms: # Generate the index for the atoms for a in atoms.values(): a['idx'] = idx idx += 1 for a in atoms.values(): vb = [atoms[int_ptr]['idx'] for int_ptr in a['bonds']] x, y, z = self.FractionalToOrthonormalCoords(a['x'], a['y'], a['z']) vv.append({'elem': a['symbol'], 'x': x, 'y': y, 'z': z, 'bonds': vb, 'bondOrder': a['bondOrder']}) else: # Generate all symmetrics to enable full molecule display nsym = spg.GetNbSymmetrics() # print(nsym) vxyz = np.empty((nsym, nat, 3), dtype=np.float32) for j in range(nat): s = v[j] x, y, z = s.X, s.Y, s.Z if enantiomer: x = -x xyzsym = spg.GetAllSymmetrics(x, y, z) vxyz[:, j, :] = xyzsym for k in range(nsym): xc, yc, zc = vxyz[k].mean(axis=0) vxyz[k, :, 0] -= (xc - xc % 1) vxyz[k, :, 1] -= (yc - yc % 1) vxyz[k, :, 2] -= (zc - zc % 1) if full_molecule: for k in range(nsym): for dx in (-1, 0, 1): for dy in (-1, 0, 1): for dz in (-1, 0, 1): vx, vy, vz = vxyz[k, :, 0] + dx, vxyz[k, :, 1] + dy, vxyz[k, :, 2] + dz # Is at least one atom inside the limits ? tmp = (vx >= xmin) * (vx <= xmax) * (vy >= ymin) * (vy <= ymax) * ( vz >= zmin) * (vz <= zmax) if tmp.sum(): for a in atoms.values(): a['idx'] = idx idx += 1 for a in atoms.values(): j = a['j'] vb = [atoms[int_ptr]['idx'] for int_ptr in a['bonds']] x, y, z = vxyz[k, j] + np.array((dx, dy, dz)) x, y, z = self.FractionalToOrthonormalCoords(x, y, z) vv.append({'elem': a['symbol'], 'x': x, 'y': y, 'z': z, 'bonds': vb, 'bondOrder': a['bondOrder']}) else: # TODO add 'visible' value in dictionnary to determine which atoms are shown, # then update the bond and bondOrder lists for k in range(nsym): for dx in (-1, 0, 1): for dy in (-1, 0, 1): for dz in (-1, 0, 1): vx, vy, vz = vxyz[k, :, 0] + dx, vxyz[k, :, 1] + dy, vxyz[k, :, 2] + dz for a in atoms.values(): j = a['j'] x, y, z = vx[j], vy[j], vz[j] if xmin <= x <= xmax and ymin <= y <= ymax and zmin <= z <= zmax: a['idx'] = idx a['visible'] = True idx += 1 else: a['visible'] = False for a in atoms.values(): if not a['visible']: continue j = a['j'] vb = [] vo = [] for l in range(len(a['bonds'])): int_ptr = a['bonds'][l] if atoms[int_ptr]['visible']: vb.append(atoms[int_ptr]['idx']) vo.append(a['bondOrder'][l]) x, y, z = vxyz[k, j] + np.array((dx, dy, dz)) x, y, z = self.FractionalToOrthonormalCoords(x, y, z) vv.append({'elem': a['symbol'], 'x': x, 'y': y, 'z': z, 'bonds': vb, 'bondOrder': vo}) else: if only_independent_atoms: for j in range(len(v)): s = v[j] symbol = s.mpScattPow.GetSymbol() name = scatt.GetComponentName(j) # 3dmol.js does not like ' in names, # despite https://www.iucr.org/resources/cif/spec/version1.1/cifsyntax#bnf name = name.replace("'", "_") occ = s.Occupancy x, y, z = s.X, s.Y, s.Z if enantiomer: x = -x x, y, z = self.FractionalToOrthonormalCoords(x, y, z) vv.append({'elem': symbol, 'x': x, 'y': y, 'z': z}) else: # Generate all symmetrics to enable full molecule display nsym = spg.GetNbSymmetrics() # print(nsym) vxyz = np.empty((nsym, nat, 3), dtype=np.float32) for j in range(nat): s = v[j] x, y, z = s.X, s.Y, s.Z if enantiomer: x = -x xyzsym = spg.GetAllSymmetrics(x, y, z) for k in range(nsym): vxyz[k, j, :] = xyzsym[k] for k in range(nsym): xc, yc, zc = vxyz[k].mean(axis=0) vxyz[k, :, 0] -= (xc - xc % 1) vxyz[k, :, 1] -= (yc - yc % 1) vxyz[k, :, 2] -= (zc - zc % 1) # print(vxyz, vxyz.shape) for j in range(nat): s = v[j] symbol = s.mpScattPow.GetSymbol() name = scatt.GetComponentName(j) # 3dmol.js does not like ' in names, # despite https://www.iucr.org/resources/cif/spec/version1.1/cifsyntax#bnf name = name.replace("'", "_") occ = s.Occupancy for k in range(nsym): for dx in (-1, 0, 1): for dy in (-1, 0, 1): for dz in (-1, 0, 1): x, y, z = vxyz[k, j] + np.array((dx, dy, dz)) if xmin <= x <= xmax and ymin <= y <= ymax and zmin <= z <= zmax: x, y, z = self.FractionalToOrthonormalCoords(x, y, z) vv.append({'elem': symbol, 'x': x, 'y': y, 'z': z}) return vv def display_3d(self, xmin=0, xmax=1, ymin=0, ymax=1, zmin=0, zmax=1, enantiomer=False, full_molecule_opacity=0.5, extra_dist=2, extra_opacity=0.5): """ This will return a 3D view of the Crystal structure which can be displayed in a notebook. This cannot be automatically updated, but will remain in the notebook as a static javascript object, so it can still be useful. :param xmin, xmax, ymin, ymax, zmin, zmax: the view limits in fractional coordinates. :param enantiomer: if True, will mirror the structure along the x axis :param full_molecule_opacity: if >0, a Molecule (or Scatterer) which has at least one atom inside the view limits is entirely shown, with the given opacity (0-1) :param extra_dist: extra distance (in Angstroms) beyond the view limits, where atoms & bonds are still displayed semi-transparently :param extra_opacity: the opacity (0-1) to display the atoms within the extra distance. """ if py3Dmol is None: warnings.warn("Yout need to install py3Dmol>=0.9 to use Crystal.display_3d()") return v = py3Dmol.view() if full_molecule_opacity > 0: v.addModel() m = v.getModel() atoms = self._display_list(xmin, xmax, ymin, ymax, zmin, zmax, full_molecule=True, only_independent_atoms=False, enantiomer=enantiomer) m.addAtoms(atoms) m.setStyle({'stick': {'radius': 0.2, 'opacity': full_molecule_opacity}, 'sphere': {'scale': 0.3, 'colorscheme': 'jmol', 'opacity': full_molecule_opacity}}) if extra_opacity > 0 and extra_dist > 0: dx, dy, dz = extra_dist / self.a, extra_dist / self.b, extra_dist / self.c v.addModel() m = v.getModel() atoms = self._display_list(xmin - dx, xmax + dx, ymin - dy, ymax + dy, zmin - dz, zmax + dz, full_molecule=False, only_independent_atoms=False, enantiomer=enantiomer) m.addAtoms(atoms) m.setStyle({'stick': {'radius': 0.2, 'opacity': extra_opacity}, 'sphere': {'scale': 0.3, 'colorscheme': 'jmol', 'opacity': extra_opacity}}) v.addModel() m = v.getModel() m.setCrystData(self.a, self.b, self.c, np.rad2deg(self.alpha), np.rad2deg(self.beta), np.rad2deg(self.gamma)) v.addUnitCell({'box': {'color': 'purple'}, 'alabel': 'X', 'blabel': 'Y', 'clabel': 'Z', 'alabelstyle': {'fontColor': 'black', 'backgroundColor': 'white', 'inFront': True, 'fontSize': 40}, 'astyle': {'color': 'darkred', 'radius': 5, 'midpos': -10}}) atoms = self._display_list(xmin, xmax, ymin, ymax, zmin, zmax, full_molecule=False, only_independent_atoms=False, enantiomer=enantiomer) m.addAtoms(atoms) m.setStyle({'stick': {'radius': 0.2, 'opacity': 1}, 'sphere': {'scale': 0.3, 'colorscheme': 'jmol', 'opacity': 1}}) v.zoomTo() return v def widget_3d(self, xmin=0, xmax=1, ymin=0, ymax=1, zmin=0, zmax=1, enantiomer=False, full_molecule_opacity=0.5, extra_dist=2, extra_opacity=0.5, width=640, height=480): """ This will return a 3D view of the Crystal structure which can be displayed in a notebook, along with controls for the display. This can be live-updated. :param xmin, xmax, ymin, ymax, zmin, zmax: the view limits in fractional coordinates. :param enantiomer: if True, will mirror the structure along the x axis :param full_molecule_opacity: if >0, a Molecule (or Scatterer) which has at least one atom inside the view limits is entirely shown, with the given opacity (0-1) :param extra_dist: extra distance (in Angstroms) beyond the view limits, where atoms & bonds are still displayed semi-transparently :param extra_opacity: the opacity (0-1) to display the atoms within the extra distance. :param width, height: the width and height of the 3D view """ if widgets is None or py3Dmol is None: warnings.warn("You need to install py3Dmol>=0.9 and ipywidgets to use Crystal.widget_3d()") return self._3d_widget = widgets.Box() # TODO: toggle for labels, toggle for stick (bonds), radius for atoms, enantiomer # Use a step of ~0.5 Angstroem xstep = 0.5 / self.a # Adapt step so we can keep orginal values as integral number steps xstep = (xmax - xmin) / np.ceil((xmax - xmin) / xstep) self.xrange = widgets.FloatRangeSlider(value=[xmin, xmax], min=xmin - 0.5, max=xmax + 0.5, step=xstep, description='Xrange', disabled=False, continuous_update=True, orientation='horizontal', readout=True) ystep = 0.5 / self.b ystep = (ymax - ymin) / np.ceil((ymax - ymin) / ystep) self.yrange = widgets.FloatRangeSlider(value=[ymin, ymax], min=ymin - 0.5, max=ymax + 0.5, step=ystep, description='Yrange', disabled=False, continuous_update=True, orientation='horizontal', readout=True) zstep = 0.5 / self.c zstep = (zmax - zmin) / np.ceil((zmax - zmin) / zstep) self.zrange = widgets.FloatRangeSlider(value=[zmin, zmax], min=zmin - 0.5, max=zmax + 0.5, step=zstep, description='Zrange', disabled=False, continuous_update=True, orientation='horizontal', readout=True) self.vbox_range = widgets.VBox([self.xrange, self.yrange, self.zrange]) self.extra_dist = widgets.FloatSlider(value=extra_dist, min=0, max=10, step=0.5, description='extra dist', tooltip='Extra distance (A) with semi-transparent display', disabled=False, continuous_update=True, orientation='horizontal', readout=True, readout_format='.1f') self.extra_opacity = widgets.FloatSlider(value=extra_opacity, min=0, max=1, step=0.1, description='extra opac.', tooltip='Opacity for extra distance display', disabled=False, continuous_update=True, orientation='horizontal', readout=True, readout_format='.01f') self.full_molecule_opacity = widgets.FloatSlider(value=full_molecule_opacity, min=0, max=1, step=0.1, description='fullMol opac', tooltip='Opacity to display fully molecules\n' 'which have at least one atom inside the limits', disabled=False, continuous_update=True, orientation='horizontal', readout=True, readout_format='.01f') self.vbox_options = widgets.VBox([self.extra_dist, self.extra_opacity, self.full_molecule_opacity]) self.hbox_options = widgets.HBox([self.vbox_range, self.vbox_options]) # catch the py3dmol display in widgets.Output ? self.output_view = widgets.Output() with self.output_view: self.py3dmol_view = py3Dmol.view(width=width, height=height) self.vbox = widgets.VBox([self.hbox_options, self.output_view]) self._3d_widget.children = [self.vbox] self._widget_update(show=True, zoom=True) self.xrange.observe(self._widget_on_change_parameter) self.yrange.observe(self._widget_on_change_parameter) self.zrange.observe(self._widget_on_change_parameter) self.extra_dist.observe(self._widget_on_change_parameter) self.extra_opacity.observe(self._widget_on_change_parameter) self.full_molecule_opacity.observe(self._widget_on_change_parameter) return self._3d_widget def _widget_update(self, show=False, zoom=False): xmin, xmax = self.xrange.value ymin, ymax = self.yrange.value zmin, zmax = self.zrange.value extra_dist = self.extra_dist.value extra_opacity = self.extra_opacity.value full_molecule_opacity = self.full_molecule_opacity.value v = self.py3dmol_view v.removeAllModels() if full_molecule_opacity > 0: v.addModel() m = v.getModel() atoms = self._display_list(xmin, xmax, ymin, ymax, zmin, zmax, full_molecule=True, only_independent_atoms=False) m.addAtoms(atoms) m.setStyle({'stick': {'radius': 0.2, 'opacity': full_molecule_opacity}, 'sphere': {'scale': 0.3, 'colorscheme': 'jmol', 'opacity': full_molecule_opacity}}) if extra_opacity > 0 and extra_dist > 0: dx, dy, dz = extra_dist / self.a, extra_dist / self.b, extra_dist / self.c v.addModel() m = v.getModel() atoms = self._display_list(xmin - dx, xmax + dx, ymin - dy, ymax + dy, zmin - dz, zmax + dz, full_molecule=False, only_independent_atoms=False) m.addAtoms(atoms) m.setStyle({'stick': {'radius': 0.2, 'opacity': extra_opacity}, 'sphere': {'scale': 0.3, 'colorscheme': 'jmol', 'opacity': extra_opacity}}) v.addModel() m = v.getModel() m.setCrystData(self.a, self.b, self.c, np.rad2deg(self.alpha), np.rad2deg(self.beta), np.rad2deg(self.gamma)) v.addUnitCell({'box': {'color': 'purple'}, 'alabel': 'X', 'blabel': 'Y', 'clabel': 'Z', 'alabelstyle': {'fontColor': 'black', 'backgroundColor': 'white', 'inFront': True, 'fontSize': 40}, 'astyle': {'color': 'darkred', 'radius': 5, 'midpos': -10}}) atoms = self._display_list(xmin, xmax, ymin, ymax, zmin, zmax, full_molecule=False, only_independent_atoms=False) m.addAtoms(atoms) m.setStyle({'stick': {'radius': 0.2, 'opacity': 1}, 'sphere': {'scale': 0.3, 'colorscheme': 'jmol', 'opacity': 1}}) if zoom: v.zoomTo() v.setZoomLimits(0.001, 1000) if show: v.show() else: # This avoids adding extra lines in the javascript output everytime # the model is update. Only a flicker (line removed/added) remains. self.output_view.clear_output() with self.output_view: v.update() def _widget_on_change_parameter(self, v): if v is not None: if v['name'] != 'value': return self._widget_update(zoom=True) def create_crystal_from_cif(file, oneScatteringPowerPerElement=False, connectAtoms=False, multiple=False): """ Create a crystal object from a CIF file or URL Example: create_crystal_from_cif('http://www.crystallography.net/cod/2201530.cif') :param file: the filename/URL or python file object (need to open with mode='rb') If the string begins by 'http' it is assumed that it is an URL instead, e.g. from the crystallography open database :param oneScatteringPowerPerElement: if False (the default), then there will be as many ScatteringPowerAtom created as there are different elements. If True, only one will be created per element. :param connectAtoms: if True, call Crystal::ConnectAtoms to try to create as many Molecules as possible from the list of imported atoms. :param multiple: if True, all structures from the CIF will be imported, but the returned Crystal object and those created in the globa registry will not have been created in python, and so will miss the derived functions for display & widget. :return: the imported Crystal structure :raises: ObjCrystException - if no Crystal object can be imported """ if multiple: if isinstance(file, str): if len(file) > 4: if file[:4].lower() == 'http': return CreateCrystalFromCIF_orig(urlopen(file), oneScatteringPowerPerElement, connectAtoms) with open(file, 'rb') as cif: # Make sure file object is closed afterwards c = CreateCrystalFromCIF_orig(cif, oneScatteringPowerPerElement, connectAtoms) else: c = CreateCrystalFromCIF_orig(file, oneScatteringPowerPerElement, connectAtoms) else: c = Crystal() if isinstance(file, str): if len(file) > 4: if file[:4].lower() == 'http': c.ImportCrystalFromCIF(urlopen(file), oneScatteringPowerPerElement, connectAtoms) return c with open(file, 'rb') as cif: # Make sure file object is closed afterwards c.ImportCrystalFromCIF(cif, oneScatteringPowerPerElement, connectAtoms) else: c.ImportCrystalFromCIF(file, oneScatteringPowerPerElement, connectAtoms) return c def wrap_boost_crystal(c: Crystal): """ This function is used to wrap a C++ Object by adding the python methods to it. :param c: the C++ created object to which the python function must be added. """ if '_display_cif' not in dir(c): for func in ['CIFOutput', 'UpdateDisplay', 'disable_display_update', 'enable_display_update', '_display_cif', '_display_list', 'display_3d', 'widget_3d', '_widget_update', '_widget_on_change_parameter']: exec("c.%s = MethodType(Crystal.%s, c)" % (func, func)) # PEP8, functions should be lowercase CreateCrystalFromCIF = create_crystal_from_cif pyobjcryst-2024.2.1/src/pyobjcryst/diffractiondatasinglecrystal.py000066400000000000000000000045751470422267000254700ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst Complex Modeling Initiative # (c) 2015 Brookhaven Science Associates # Brookhaven National Laboratory. # All rights reserved. # # File coded by: Kevin Knox # # See AUTHORS.txt for a list of people who contributed. # See LICENSE.txt for license information. # ############################################################################## """Python wrapping of DiffractionDataSingleCrystal.h See the online ObjCryst++ documentation (https://objcryst.readthedocs.io). Changes from ObjCryst::DiffractionDataSingleCrystal:: In development ! """ __all__ = ["DiffractionDataSingleCrystal", "gDiffractionDataSingleCrystalRegistry", "create_singlecrystaldata_from_cif"] from urllib.request import urlopen from pyobjcryst._pyobjcryst import DiffractionDataSingleCrystal from pyobjcryst._pyobjcryst import gDiffractionDataSingleCrystalRegistry from pyobjcryst._pyobjcryst import CreateSingleCrystalDataFromCIF as crcif def create_singlecrystaldata_from_cif(file, crystal): """ Create a DiffractionDataSingleCrystal object from a CIF file. Note that this will use the last created Crystal as a reference structure. Example using the COD to load both crystal and data: c=create_crystal_from_cif('http://www.crystallography.net/cod/2201530.cif') d=create_singlecrystaldata_from_cif('http://www.crystallography.net/cod/2201530.hkl', c) param file: the filename/URL or python file object (need to open with mode='rb'). If the string begins by 'http' it is assumed that it is an URL instead, e.g. from the crystallography open database param crystal: the Crystal object which will be used for this single crystal data. :return: the imported DiffractionDataSingleCrystal :raises: ObjCrystException - if no DiffractionDataSingleCrystal object can be created """ if isinstance(file, str): if len(file) > 4: if file[:4].lower() == 'http': return crcif(urlopen(file), crystal) with open(file, 'rb') as cif: # Make sure file object is closed c = crcif(cif, crystal) else: c = crcif(file, crystal) return c # PEP8 CreateSingleCrystalDataFromCIF = create_singlecrystaldata_from_cif pyobjcryst-2024.2.1/src/pyobjcryst/fourier.py000066400000000000000000000105461470422267000212100ustar00rootroot00000000000000############################################################################## # # Fourier maps calculations # # File coded by: Vincent Favre-Nicolin # # See AUTHORS.txt for a list of people who contributed. # See LICENSE.txt for license information. # ############################################################################## import numpy as np from .scatteringdata import ScatteringData def calc_fourier_map(data: ScatteringData, map_type="obs", sharpen=True, resolution=0.3): """ Compute a 3D Fourier map given a ScatteringData object :param data: a ScatteringData object with observed data, either q DiffractionDataSingleCrystal or PowderPatternDiffraction after extraction of intensities (profile fitting) :param map_type: either "obs" (the default), "diff" (difference) or "calc" :param sharpen: if True, normalise the structure factor Fourier coefficients by the average atomic scattering factor to sharpen the Fourier maps. :param resolution: approximate desired resolution for the map, in Angstroems :return: the 3D Fourier map, computed over one unit cell, with a resolution dictated by the largest HKL values. The map's origin is at the corner of the unit cell. """ if "calc" not in map_type: obs2 = data.GetFhklObsSq() nb = len(obs2) else: nb = data.GetNbReflBelowMaxSinThetaOvLambda() calc = (data.GetFhklCalcReal() + 1j * data.GetFhklCalcImag())[:nb] c = data.GetCrystal() if sharpen: # Dictionary of scattering factor for all elements vsf = data.GetScatteringFactor() # Make this a dictionary against int_ptr, so it can match # the Crystal's ScatteringComponentList vsf = {k.int_ptr(): v for k, v in vsf.items()} norm_sf = np.zeros(nb) norm0 = 0 for sc in c.GetScatteringComponentList(): norm_sf += sc.mOccupancy * sc.mDynPopCorr * vsf[sc.mpScattPow.int_ptr()][:nb] ** 2 norm0 += sc.mOccupancy * sc.mDynPopCorr * \ sc.mpScattPow.GetForwardScatteringFactor(data.GetRadiationType()) ** 2 norm_sf = np.sqrt(norm_sf / norm0) # Scale obs and calc if "calc" not in map_type: scale_fobs = np.sqrt((abs(calc) ** 2).sum() / obs2.sum()) # print(" Fourier map obs scale factor:", scale_fobs) vol = c.GetVolume() spg = c.GetSpaceGroup() h, k, l = data.GetH()[:nb], data.GetK()[:nb], data.GetL()[:nb] # Map size to achieve resolution nx = int(np.ceil(c.a / resolution)) ny = int(np.ceil(c.b / resolution)) nz = int(np.ceil(c.c / resolution)) # print("UnitCellMap::CalcFourierMap():",nx,",",ny,",",nz,",type:", map_type, ); rhof = np.zeros((nz, ny, nx), dtype=np.complex64) for i in range(nb): norm = 1 if sharpen: norm = 1 / norm_sf[i] if "calc" not in map_type: obs = scale_fobs * np.sqrt(obs2[i]) acalc = np.abs(calc[i]) for h0, k0, l0, fr, fi in spg.GetAllEquivRefl(h[i], k[i], l[i], False, data.IsIgnoringImagScattFact(), calc.real[i], calc.imag[i]): if abs(2 * h0) < nx and abs(2 * k0) < ny and abs(2 * l0) < nz: # Integer indices ih = int(np.round(h0)) ik = int(np.round(k0)) il = int(np.round(l0)) if "calc" in map_type.lower(): rhof[il, ik, ih] = (fr + 1j * fi) * norm / vol elif "obs" in map_type.lower(): rhof[il, ik, ih] = (fr + 1j * fi) * obs / max(acalc, 1e-6) * norm / vol else: rhof[il, ik, ih] = (fr + 1j * fi) * (obs - acalc) / max(acalc, 1e-6) * norm / vol # if (i<5): # print(int(h0)," ",int(k0)," ",int(l0),"(",spg.IsReflCentric(h0,k0,l0),"):" # ,fr+1j*fi," :",rhof[il, ik, ih]) if "obs" in map_type.lower() or "calc" in map_type.lower(): # F000 for obs and calc maps nbsym = spg.GetNbSymmetrics(False, False) for sc in c.GetScatteringComponentList(): sp = sc.mpScattPow rhof[0, 0, 0] += sp.GetForwardScatteringFactor(data.GetRadiationType()) * \ sc.mOccupancy * sc.mDynPopCorr * nbsym / vol # print("F000 =", rhof[0, 0, 0]) return np.fft.fftn(rhof) # , norm="backward" pyobjcryst-2024.2.1/src/pyobjcryst/general.py000066400000000000000000000015331470422267000211460ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Python wrapping of things from General.h. See the online ObjCryst++ documentation (https://objcryst.readthedocs.io). """ __all__ = ["RadiationType", "ObjCrystException", "WavelengthType"] from pyobjcryst._pyobjcryst import RadiationType, WavelengthType from pyobjcryst._pyobjcryst import ObjCrystException pyobjcryst-2024.2.1/src/pyobjcryst/globaloptim.py000066400000000000000000000147311470422267000220460ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst # # File coded by: Vincent Favre-Nicolin # # See AUTHORS.txt for a list of people who contributed. # See LICENSE.txt for license information. # ############################################################################## """Python wrapping of GlobalOptimObj.h See the online ObjCryst++ documentation (https://objcryst.readthedocs.io). Changes from ObjCryst::MonteCarloObj:: In development ! """ __all__ = ["MonteCarlo", "AnnealingSchedule", "GlobalOptimType"] import warnings from types import MethodType try: import ipywidgets as widgets except ImportError: widgets = None from pyobjcryst._pyobjcryst import MonteCarlo as MonteCarlo_orig, AnnealingSchedule, \ GlobalOptimType, OptimizationObjRegistry from .refinableobj import * class MonteCarlo(MonteCarlo_orig): def Optimize(self, nb_step: int, final_cost=0, max_time=-1): self._fix_parameters_for_global_optim() if type(self) == MonteCarlo_orig: self._Optimize(int(nb_step), True, final_cost, max_time) else: super().Optimize(int(nb_step), True, final_cost, max_time) def MultiRunOptimize(self, nb_run: int, nb_step: int, final_cost=0, max_time=-1): self._fix_parameters_for_global_optim() if type(self) == MonteCarlo_orig: self._MultiRunOptimize(int(nb_run), int(nb_step), True, final_cost, max_time) else: super().MultiRunOptimize(int(nb_run), int(nb_step), True, final_cost, max_time) def RunSimulatedAnnealing(self, nb_step: int, final_cost=0, max_time=-1): self._fix_parameters_for_global_optim() if type(self) == MonteCarlo_orig: self._RunSimulatedAnnealing(int(nb_step), True, final_cost, max_time) else: super().RunSimulatedAnnealing(int(nb_step), True, final_cost, max_time) def RunParallelTempering(self, nb_step: int, final_cost=0, max_time=-1): self._fix_parameters_for_global_optim() if type(self) == MonteCarlo_orig: self._RunParallelTempering(int(nb_step), True, final_cost, max_time) else: super().RunParallelTempering(int(nb_step), True, final_cost, max_time) def _fix_parameters_for_global_optim(self): # Fix parameters that should not be optimised in a MonterCarlo run self.SetParIsFixed(refpartype_unitcell, True) self.SetParIsFixed(refpartype_scattdata_scale, True) self.SetParIsFixed(refpartype_scattdata_profile, True) self.SetParIsFixed(refpartype_scattdata_corr, True) self.SetParIsFixed(refpartype_scattdata_background, True) self.SetParIsFixed(refpartype_scattdata_radiation, True) def widget(self): """ Display a simple widget for this MonteCarlo, which only updates the current cost (log-likelihood). Requires ipywidgets """ if widgets is None: warnings.warn("You need to install ipywidgets to use MonteCarlo.widget()") return self._widget = widgets.Box() # See https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20Styling.html self._widget_label = widgets.Label("", layout=widgets.Layout(max_width='25%', width='20em')) self._widget_llk = widgets.Text("", disabled=True, layout=widgets.Layout(max_width='50%', width='30em')) self._widget.children = [widgets.HBox([self._widget_label, self._widget_llk])] self._widget_update() return self._widget def UpdateDisplay(self): try: if self._display_update_disabled: return except: pass try: if self._widget is not None: self._widget_update() except AttributeError: # self._3d_widget does not exist pass def disable_display_update(self): """ Disable display (useful for multiprocessing)""" self._display_update_disabled = True def enable_display_update(self): """ Enable display""" self._display_update_disabled = False def _widget_update(self): self._widget_label.value = "MonteCarlo:%s" % self.GetName() self._widget_label.layout.width = '%dem' % len(self._widget_label.value) if self.IsOptimizing(): self._widget_llk.value = "LLK=%12.2f Run %2d Trial %8d" % (self.llk, self.run, self.trial) else: self._widget_llk.value = "LLK=%12.2f " % self.llk self._widget_llk.layout.width = '%dem' % len(self._widget_llk.value) def wrap_boost_montecarlo(c: MonteCarlo): """ This function is used to wrap a C++ Object by adding the python methods to it. :param c: the C++ created object to which the python function must be added. """ if 'widget' not in dir(c): for func in ['Optimize', 'MultiRunOptimize', 'RunSimulatedAnnealing', 'RunParallelTempering']: # We keep access to the original functions... Yes, it's a kludge... exec("c._%s = c.%s" % (func, func)) for func in ['Optimize', 'MultiRunOptimize', 'RunSimulatedAnnealing', 'RunParallelTempering', '_fix_parameters_for_global_optim', 'widget', 'UpdateDisplay', 'disable_display_update', 'enable_display_update', '_widget_update']: exec("c.%s = MethodType(MonteCarlo.%s, c)" % (func, func)) class OptimizationObjRegistryWrapper(OptimizationObjRegistry): """ Wrapper class with a GetObj() method which can correctly wrap C++ objects with the python methods. This is only needed when the objects have been created from C++, e.g. when loading an XML file. """ def GetObj(self, i): o = self._GetObj(i) # TODO print("Casting OptimizationObj to MonteCarlo and wrapping..") # We get the object as an OptimizationObj, which prevents access to some functions # So we use this function to cast to a MonteCarloObj o = self._getObjCastMonteCarlo(i) wrap_boost_montecarlo(o) return o def wrap_boost_optimizationobjregistry(o): """ This function is used to wrap a C++ Object by adding the python methods to it. :param c: the C++ created object to which the python function must be added. """ # TODO: moving the original function is not very pretty. Is there a better way ? if '_GetObj' not in dir(o): o._GetObj = o.GetObj o.GetObj = MethodType(OptimizationObjRegistryWrapper.GetObj, o) pyobjcryst-2024.2.1/src/pyobjcryst/globals.py000066400000000000000000000030201470422267000211450ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # globals # # File coded by: Vincent Favre-Nicolin # # See AUTHORS.txt for a list of people who contributed. # See LICENSE.txt for license information. # ############################################################################## """ Global objects are exposed here. These are the main objects registries, which are tweaked to wrap pure C++ objects with the python methods. """ __all__ = ["gCrystalRegistry","gPowderPatternRegistry", "gRefinableObjRegistry", "gScattererRegistry", "gOptimizationObjRegistry", "gTopRefinableObjRegistry", "gDiffractionDataSingleCrystalRegistry"] from .refinableobj import wrap_boost_refinableobjregistry from .globaloptim import wrap_boost_optimizationobjregistry from pyobjcryst._pyobjcryst import gCrystalRegistry from pyobjcryst._pyobjcryst import gOptimizationObjRegistry from pyobjcryst._pyobjcryst import gPowderPatternRegistry from pyobjcryst._pyobjcryst import gRefinableObjRegistry from pyobjcryst._pyobjcryst import gScattererRegistry from pyobjcryst._pyobjcryst import gTopRefinableObjRegistry from pyobjcryst._pyobjcryst import gDiffractionDataSingleCrystalRegistry # Wrap registries with python methods wrap_boost_refinableobjregistry(gCrystalRegistry) wrap_boost_refinableobjregistry(gPowderPatternRegistry) wrap_boost_refinableobjregistry(gRefinableObjRegistry) wrap_boost_refinableobjregistry(gTopRefinableObjRegistry) wrap_boost_optimizationobjregistry(gOptimizationObjRegistry) pyobjcryst-2024.2.1/src/pyobjcryst/indexing.py000066400000000000000000000110611470422267000213330ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Python wrapping of UnitCell.h See the online ObjCryst++ documentation (https://objcryst.readthedocs.io). """ __all__ = ["CrystalSystem", "CrystalCentering", "EstimateCellVolume", "RecUnitCell", "PeakList_hkl", "PeakList_hkl0", "PeakList", "CellExplorer", "quick_index"] import time from numpy import deg2rad from pyobjcryst._pyobjcryst import CrystalSystem, CrystalCentering, \ EstimateCellVolume, RecUnitCell, PeakList_hkl, PeakList_hkl0, PeakList,\ CellExplorer def quick_index(pl, min_obs_ratio=0.3, max_obs_ratio=1.5, nb_refl=20, try_centered_lattice=True, continue_on_sol=False, max_nb_spurious=0, verbose=True): if len(pl) > nb_refl: pl.resize(nb_refl) nb = len(pl) dmin = pl.GetPeakList()[nb - 1].dobs dmax = pl.GetPeakList()[0].dobs / 10 # assume there are no peaks at lower resolution if verbose: print("Predicting volumes from %2u peaks between d=%6.3f and d=%6.3f\n" % (nb, 1 / dmax, 1 / dmin)) print("Starting indexing using %2u peaks" % nb) ex = CellExplorer(pl, CrystalSystem.CUBIC, 0) ex.SetLengthMinMax(3, 25) ex.SetAngleMinMax(deg2rad(90), deg2rad(140)) ex.SetD2Error(0) stop_score = 50 report_score = 10 stop_depth = 6 + int(continue_on_sol) report_depth = 4 for nb_spurious in range(0, max_nb_spurious + 1): ex.SetNbSpurious(nb_spurious) for csys in [CrystalSystem.CUBIC, CrystalSystem.TETRAGONAL, CrystalSystem.RHOMBOEDRAL, CrystalSystem.HEXAGONAL, CrystalSystem.ORTHOROMBIC, CrystalSystem.MONOCLINIC]: if csys == CrystalSystem.CUBIC: vcen = [CrystalCentering.LATTICE_P, CrystalCentering.LATTICE_I, CrystalCentering.LATTICE_F] elif csys == CrystalSystem.TETRAGONAL: vcen = [CrystalCentering.LATTICE_P, CrystalCentering.LATTICE_I] elif csys == CrystalSystem.RHOMBOEDRAL: vcen = [CrystalCentering.LATTICE_P] elif csys == CrystalSystem.HEXAGONAL: vcen = [CrystalCentering.LATTICE_P] elif csys == CrystalSystem.ORTHOROMBIC: vcen = [CrystalCentering.LATTICE_P, CrystalCentering.LATTICE_A, CrystalCentering.LATTICE_B, CrystalCentering.LATTICE_C, CrystalCentering.LATTICE_I, CrystalCentering.LATTICE_F] elif csys == CrystalSystem.MONOCLINIC: vcen = [CrystalCentering.LATTICE_P, CrystalCentering.LATTICE_A,CrystalCentering.LATTICE_C, CrystalCentering.LATTICE_I] for cent in vcen: centc = 'P' if cent == CrystalCentering.LATTICE_I: centc = 'I' elif cent == CrystalCentering.LATTICE_A: centc = 'A' elif cent == CrystalCentering.LATTICE_B: centc = 'B' if cent == CrystalCentering.LATTICE_C: centc = 'C' elif cent == CrystalCentering.LATTICE_F: centc = 'F' minv = EstimateCellVolume(dmin, dmax, nb, csys, cent, max_obs_ratio) maxv = EstimateCellVolume(dmin, dmax, nb, csys, cent, min_obs_ratio) ex.SetVolumeMinMax(minv, maxv) lengthmax = 3 * maxv ** (1 / 3.) if lengthmax < 25: lengthmax = 25 ex.SetLengthMinMax(3, lengthmax) ex.SetCrystalSystem(csys) ex.SetCrystalCentering(cent) if verbose: print("%11s %c : V= %6.0f -> %6.0f A^3, max length=%6.2fA" % (csys.name, centc, minv, maxv, lengthmax)) t0 = time.time() ex.DicVol(report_score, report_depth, stop_score, stop_depth, verbose=False) if verbose: print(" -> %3u sols in %6.2fs, best score=%6.1f\n" % (len(ex.GetSolutions()), time.time() - t0, ex.GetBestScore())) if try_centered_lattice: break return ex pyobjcryst-2024.2.1/src/pyobjcryst/io.py000066400000000000000000000055151470422267000201440ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Python wrapping of IO.h See the online ObjCryst++ documentation (https://objcryst.readthedocs.io). Changes from ObjCryst::XMLCrystTag - The istream constructor of XMLCrystTag is not wrapped. Changes from IO functions: - use lower case for xml_cryst_file_load_all_object and xml_cryst_file_save_global - both functions accept either a file object or a filename, and can handle compressed files ('.xmlgz) """ __all__ = ["XMLCrystTag", "xml_cryst_file_load_all_object", "xml_cryst_file_save_global"] import gzip import os from pyobjcryst._pyobjcryst import XMLCrystTag, \ XMLCrystFileLoadAllObject as XMLCrystFileLoadAllObject_orig, \ XMLCrystFileSaveGlobal as XMLCrystFileSaveGlobal_orig from .globals import gTopRefinableObjRegistry def xml_cryst_file_load_all_object(file, verbose=False): """ Load all objects from an ObjCryst-formatted .xml or .xmlgz file :param file: the filename or python file object (need to open with mode='rb') :param verbose: if True, some information about the loaded objects is printed :return: a list of the imported top-level objects (Crystal, DiffractionDataSingleCrystal, PowderPattern) """ nb0 = len(gTopRefinableObjRegistry) if isinstance(file, str): if os.path.splitext(file)[-1] == '.xmlgz': o = gzip.open(file, mode='rb') XMLCrystFileLoadAllObject_orig(o, verbose=verbose) else: XMLCrystFileLoadAllObject_orig(open(file, 'rb'), verbose=verbose) else: XMLCrystFileLoadAllObject_orig(file, verbose=verbose) return gTopRefinableObjRegistry[nb0:] def xml_cryst_file_save_global(file): """ Save all top-level objects to an ObjCryst-formatted .xml or .xmlgz file :param file: the filename or python file object (need to open with mode='rb'). If a filename is given and the extension is 'xmlgz', the file will be compressed by gzip :return: nothing """ if isinstance(file, str): if os.path.splitext(file)[-1] == '.xmlgz': o = gzip.open(file, mode='wt', compresslevel=9, encoding=None, errors=None, newline=None) XMLCrystFileSaveGlobal_orig(o) else: XMLCrystFileSaveGlobal_orig(open(file, 'w')) else: XMLCrystFileSaveGlobal_orig(file) pyobjcryst-2024.2.1/src/pyobjcryst/lsq.py000066400000000000000000000010571470422267000203310ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst # # File coded by: Vincent Favre-Nicolin # # See AUTHORS.txt for a list of people who contributed. # See LICENSE.txt for license information. # ############################################################################## """Python wrapping of LSQNumObj.h See the online ObjCryst++ documentation (https://objcryst.readthedocs.io). Changes from ObjCryst::LSQNumObj:: In development ! """ from pyobjcryst._pyobjcryst import LSQ pyobjcryst-2024.2.1/src/pyobjcryst/molecule.py000066400000000000000000000121411470422267000213330ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Python wrapping of Molecule.h See the online ObjCryst++ documentation (https://objcryst.readthedocs.io). Changes from ObjCryst::Molecule - The public data are not wrapped. - Added __getitem__ access for MolAtoms. - AddAtom returns the added MolAtom - AddBond returns the added MolBond - AddBondAngle returns the added MolBondAngle - AddDihedralAngle returns the added MolDihedralAngle - RemoveAtom returns None, has an indexed version - RemoveBond returns None, has an indexed version - RemoveBondAngle returns None, has an indexed version - RemoveDihedralAngle returns None, has an indexed version - RemoveRigidGroup returns None - Added GetNbAtoms - Added GetNbBonds - Added GetNbBondAngles - Added GetNbDihedralAngles - Added GetNbRigidGroups - Added GetBond - Added GetBondAngle - Added GetDihedralAngle - Added GetRigidGroup - FindBond returns the bond if found, None otherwise - FindBondAngle returns the bond angle if found, None otherwise - FindDihedralAngle returns the dihedral angle if found, None otherwise - FindAtom is identical to GetAtom. - FlipAtomGroup is not wrapped. - FlipGroup, RotorGroup and StretchModeGroup are not wrapped. - StretchMode getters are not wrapped - Quaternion ordinates Q0, Q1, Q2 and Q3 wrapped as properties. Changes from ObjCryst::MolAtom - Wrapped as a to-python converter only (no constructor) - File IO is disabled - X, Y and Z are wrapped as properties rather than methods. Changes from ObjCryst::MolBondAngle - Wrapped as a to-python converter only (no constructor) - Added __getitem__ access for MolAtoms. - File IO is disabled - GetDeriv and CalcGradient are not wrapped. - Angle0, AngleDelta and AngleSigma are wrapped as properties rather than methods. - IsFlexible and SetFlexible are not wrapped, as they are not implemented in the library. Changes from ObjCryst::MolDihedralAngle - Wrapped as a to-python converter only (no constructor) - Added __getitem__ access for MolAtoms. Changes from ObjCryst::Quaternion - IO is not wrapped - Q0, Q1, Q2 and Q3 are wrapped as properties, rather than functions. - RotateVector overloaded to return tuple of the mutated arguments. Changes from ObjCryst::RigidGroup - RigidGroup is wrapped to have python-set methods rather than stl::set methods. """ __all__ = ["Molecule", "GetBondLength", "GetBondAngle", "GetDihedralAngle", "MolAtom", "MolBond", "MolBondAngle", "MolDihedralAngle", "Quaternion", "RigidGroup", "StretchMode", "StretchModeBondLength", "StretchModeBondAngle", "StretchModeTorsion", "StretchModeTwist", "ZScatterer2Molecule", "ImportFenskeHallZMatrix"] # TODO - MolRing from pyobjcryst._pyobjcryst import Molecule from pyobjcryst._pyobjcryst import GetBondLength from pyobjcryst._pyobjcryst import GetBondAngle from pyobjcryst._pyobjcryst import GetDihedralAngle from pyobjcryst._pyobjcryst import MolAtom from pyobjcryst._pyobjcryst import MolBond from pyobjcryst._pyobjcryst import MolBondAngle from pyobjcryst._pyobjcryst import MolDihedralAngle from pyobjcryst._pyobjcryst import Quaternion from pyobjcryst._pyobjcryst import RigidGroup from pyobjcryst._pyobjcryst import StretchMode from pyobjcryst._pyobjcryst import StretchModeBondLength from pyobjcryst._pyobjcryst import StretchModeBondAngle from pyobjcryst._pyobjcryst import StretchModeTorsion from pyobjcryst._pyobjcryst import StretchModeTwist from pyobjcryst._pyobjcryst import ZScatterer2Molecule from .zscatterer import ZScatterer def ImportFenskeHallZMatrix(cryst, src, named=False): """ Create a Molecule from a Fenske-Hall z-matrix. This is cleaner than importing the Z-matrix into a ZScatterer object and then using ZScatterer2Molecule, as it takes care of keeping only the created Molecule inside the Crystal. :param cryst: a Crystal object to which will belong the created Molecule :param src: either a python filed (opened in 'rb' mode), or a filename, or an url ("http://...") to a text file with the z-matrix :param named: if True, allows to read a named Z-matrix - the formatting is similar to a Fenske-Hall z-matrix but only relies on spaces between the different fields instead of a strict number of characters. """ z = ZScatterer("", cryst) z.ImportFenskeHallZMatrix(src,named) m = ZScatterer2Molecule(z) cryst.RemoveScatterer(z) cryst.AddScatterer(m) # TODO: this is a hack to keep a reference to the Crystal used for creation, # since with this function we can't use a custodian_and_ward. # It will just help avoiding deletion of the Crystal before the Molecule object. m._crystal = cryst return m pyobjcryst-2024.2.1/src/pyobjcryst/polyhedron.py000066400000000000000000000023471470422267000217200ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Python wrapping of Polyhedron.h See the online ObjCryst++ documentation (https://objcryst.readthedocs.io). """ __all__ = ["MakeTetrahedron", "MakeOctahedron", "MakeSquarePlane", "MakeCube", "MakeAntiPrismTetragonal", "MakePrismTrigonal", "MakeIcosahedron", "MakeTriangle"] from pyobjcryst._pyobjcryst import MakeTetrahedron from pyobjcryst._pyobjcryst import MakeOctahedron from pyobjcryst._pyobjcryst import MakeSquarePlane from pyobjcryst._pyobjcryst import MakeCube from pyobjcryst._pyobjcryst import MakeAntiPrismTetragonal from pyobjcryst._pyobjcryst import MakePrismTrigonal from pyobjcryst._pyobjcryst import MakeIcosahedron from pyobjcryst._pyobjcryst import MakeTriangle pyobjcryst-2024.2.1/src/pyobjcryst/powderpattern.py000066400000000000000000000540011470422267000224250ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst # # File coded by: Vincent Favre-Nicolin # # See AUTHORS.txt for a list of people who contributed. # See LICENSE.txt for license information. # ############################################################################## """Python wrapping of PowderPattern.h See the online ObjCryst++ documentation (https://objcryst.readthedocs.io/en/latest/). Changes from ObjCryst::PowderPattern:: Additional functions for plotting, basic QPA and profile fitting. """ from urllib.request import urlopen from packaging.version import parse as version_parse from multiprocessing import current_process import numpy as np __all__ = ["PowderPattern", "CreatePowderPatternFromCIF", "PowderPatternBackground", "PowderPatternComponent", "PowderPatternDiffraction", "ReflectionProfileType", "SpaceGroupExplorer"] from types import MethodType from pyobjcryst._pyobjcryst import PowderPattern as PowderPattern_objcryst from pyobjcryst._pyobjcryst import CreatePowderPatternFromCIF as CreatePowderPatternFromCIF_orig from pyobjcryst._pyobjcryst import PowderPatternBackground from pyobjcryst._pyobjcryst import PowderPatternComponent from pyobjcryst._pyobjcryst import PowderPatternDiffraction from pyobjcryst._pyobjcryst import ReflectionProfileType from pyobjcryst._pyobjcryst import LSQ from pyobjcryst.refinableobj import refpartype_scattdata_background from pyobjcryst._pyobjcryst import SpaceGroupExplorer from pyobjcryst import ObjCrystException class PowderPattern(PowderPattern_objcryst): def __init__(self): super(PowderPattern, self).__init__() self._plot_fig = None self._plot_xlim = None self._plot_ylim = None self._plot_diff = False self._plot_hkl = False self._plot_hkl_fontsize = 6 self._plot_phase_labels = None # xlim last time hkl were plotted self._last_hkl_plot_xlim = None self.evts = [] self._colour_phases = ["black", "blue", "green", "red", "brown", "olive", "cyan", "purple", "magenta", "salmon"] def UpdateDisplay(self): try: if self._display_update_disabled: return except: pass if self._plot_fig is not None: if self._plot_fig is not None: self.plot() def update_display(self, return_figure=False): """ Update the plotted figure (if it exists) :param return_figure: if True, returns the figure :return: the figure if return_figure is True """ self.UpdateDisplay() if return_figure: return self.figure def disable_display_update(self): """ Disable display (useful for multiprocessing)""" self._display_update_disabled = True def enable_display_update(self): """ Enable display""" self._display_update_disabled = False def plot(self, diff=None, hkl=None, figsize=(9, 4), fontsize_hkl=6, reset=False, **kwargs): """ Show the powder pattern in a plot using matplotlib :param diff: if True, also show the difference plot :param hkl: if True, print the hkl values :param figsize: the figure size passed to matplotlib :param fontsize_hkl: fontsize for hkl coordinates :param reset: if True, will reset the x and y limits, and remove the flags to plot the difference and hkl unless the options are set at the same time. :param kwargs: additional keyword arguments: fig=None will force creating a new figure fig=fig1 will plot in the given matplotlib figure """ import matplotlib.pyplot as plt obs = self.GetPowderPatternObs() try: calc = self.GetPowderPatternCalc() except ObjCrystException: # TODO: when importing objects from an XML file, the powder pattern does not compute # correctly, Prepare() needs to be called manually. Why ? self.Prepare() calc = self.GetPowderPatternCalc() if reset: self._plot_ylim = None self._plot_xlim = None self._plot_hkl = False self._plot_diff = False if 'fig' in kwargs: self._plot_fig = kwargs['fig'] if diff is not None: self._plot_diff = diff plot_diff = self._plot_diff if hkl is not None: self._plot_hkl = hkl plot_hkl = self._plot_hkl # TODO: handle other coordinates than angles (TOF) x = np.rad2deg(self.GetPowderPatternX()) if self._plot_fig is None or 'inline' in plt.get_backend(): self._plot_fig = plt.figure(figsize=figsize) else: self._plot_fig.clear() ax = self._plot_fig.axes[0] if len(self._plot_fig.axes) else self._plot_fig.subplots() ax.plot(x, obs, 'k', label='obs', linewidth=1) ax.plot(x, calc, 'r', label='calc', linewidth=1) m = min(1, self.GetMaxSinThetaOvLambda() * self.GetWavelength()) mtth = np.rad2deg(np.arcsin(m)) * 2 if plot_diff: diff = calc - obs - obs.max() / 20 # Mask difference above max sin(theta)/lambda diff = np.ma.masked_array(diff, x > mtth) ax.plot(x, diff, 'g', label='calc-obs', linewidth=0.5) ax.legend(loc='upper right') if self.GetName() != "": self._plot_fig.title("PowderPattern: %s" % self.GetName()) if self._plot_ylim is not None: ax.set_ylim(self._plot_ylim) if self._plot_xlim is not None: ax.set_xlim(self._plot_xlim) elif m < 1: ax.set_xlim(x.min(), mtth) if plot_hkl: self._do_plot_hkl(nb_max=100, fontsize_hkl=fontsize_hkl) # print PowderPatternDiffraction names self._plot_phase_labels = [] iphase = 0 for i in range(self.GetNbPowderPatternComponent()): s = "" comp = self.GetPowderPatternComponent(i) if comp.GetClassName() == "PowderPatternDiffraction": if comp.GetName() != "": s += "%s\n" % comp.GetName() else: c = comp.GetCrystal() if c.GetName() != "": s += c.GetName() else: s += c.GetFormula() s += "[%s]" % str(c.GetSpaceGroup()) if comp.GetExtractionMode(): s += "[Le Bail mode]" self._plot_phase_labels.append(s) ax.text(0.005, 0.995, "\n" * iphase + s, horizontalalignment="left", verticalalignment="top", transform=ax.transAxes, fontsize=6, color=self._colour_phases[iphase]) iphase += 1 if 'inline' not in plt.get_backend(): try: # Force immediate display. Not supported on all backends (e.g. nbagg) ax.draw() self._plot_fig.canvas.draw() if 'ipympl' not in plt.get_backend(): plt.pause(.001) except: pass # plt.gca().callbacks.connect('xlim_changed', self._on_xlims_change) # plt.gca().callbacks.connect('ylim_changed', self._on_ylims_change) self._plot_fig.canvas.mpl_connect('button_press_event', self._on_mouse_event) self._plot_fig.canvas.mpl_connect('draw_event', self._on_draw_event) def _do_plot_hkl(self, nb_max=100, fontsize_hkl=None): import matplotlib.pyplot as plt from matplotlib import __version__ as mpl_version if fontsize_hkl is None: fontsize_hkl = self._plot_hkl_fontsize else: self._plot_hkl_fontsize = fontsize_hkl ax = self._plot_fig.axes[0] # Plot up to nb_max hkl reflections obs = self.GetPowderPatternObs() calc = self.GetPowderPatternCalc() x = np.rad2deg(self.GetPowderPatternX()) # Clear previous text (assumes only hkl were printed) if version_parse(mpl_version) < version_parse("3.7.0"): # This will fail for matplotlib>=(3,7,0) ax.texts.clear() else: for t in ax.texts: t.remove() iphase = 0 for ic in range(self.GetNbPowderPatternComponent()): c = self.GetPowderPatternComponent(ic) if isinstance(c, PowderPatternDiffraction) is False: continue # print("HKL for:", c.GetName()) xmin, xmax = ax.get_xlim() vh = np.round(c.GetH()).astype(np.int16) vk = np.round(c.GetK()).astype(np.int16) vl = np.round(c.GetL()).astype(np.int16) stol = c.GetSinThetaOverLambda() if 'inline' not in plt.get_backend(): # 'inline' backend triggers a delayed exception (?) try: # need the renderer to avoid text overlap renderer = plt.gcf().canvas.renderer except: # Force immediate display. Not supported on all backends (e.g. nbagg) ax.draw() self._plot_fig.canvas.draw() if 'ipympl' not in plt.get_backend(): plt.pause(.001) try: renderer = self._plot_fig.canvas.renderer except: renderer = None else: renderer = None props = {'ha': 'center', 'va': 'bottom'} ct = 0 last_bbox = None tdi = ax.transData.inverted() for i in range(len(vh)): xhkl = np.rad2deg(self.X2XCorr(self.STOL2X(stol[i]))) idxhkl = int(round(self.X2PixelCorr(self.STOL2X(stol[i])))) # print(vh[i], vk[i], vl[i], xhkl, idxhkl) if xhkl < xmin or idxhkl < 0: continue if xhkl > xmax or idxhkl >= len(x): break ct += 1 if ct >= nb_max: break ihkl = max(calc[idxhkl], obs[idxhkl]) s = " %d %d %d" % (vh[i], vk[i], vl[i]) t = ax.text(xhkl, ihkl, s, props, rotation=90, fontsize=fontsize_hkl, fontweight='light', color=self._colour_phases[iphase]) if renderer is not None: # Check for overlap with previous bbox = t.get_window_extent(renderer) # print(s, bbox) if last_bbox is not None: if bbox.overlaps(last_bbox): b = bbox.transformed(tdi) t.set_y(ihkl + b.height * 1.2) last_bbox = t.get_window_extent(renderer) iphase += 1 self._last_hkl_plot_xlim = ax.get_xlim() if self._plot_phase_labels is not None: for iphase in range(len(self._plot_phase_labels)): s = self._plot_phase_labels[iphase] ax.text(0.005, 0.995, "\n" * iphase + s, horizontalalignment="left", verticalalignment="top", transform=ax.transAxes, fontsize=6, color=self._colour_phases[iphase]) @property def figure(self): """ return: the figure used for plotting, or None. Note that if you want to display it in a notebook using ipympl (aka 'matplotlib widget'), you should 'figure.canvas' to display also the toolbar (zoom, etc...). """ return self._plot_fig def quick_fit_profile(self, pdiff=None, auto_background=True, init_profile=True, plot=True, zero=True, constant_width=True, width=True, eta=True, backgd=True, cell=True, anisotropic=False, asym=False, displ_transl=False, verbose=True): if plot: self.plot() if auto_background: # Add background if necessary need_background = True for i in range(self.GetNbPowderPatternComponent()): if isinstance(self.GetPowderPatternComponent(i), PowderPatternBackground): need_background = False break if need_background: if verbose: print("No background, adding one automatically") x = self.GetPowderPatternX() bx = np.linspace(x.min(), x.max(), 20) by = np.zeros(bx.shape) b = self.AddPowderPatternBackground() b.SetInterpPoints(bx, by) # b.Print() b.UnFixAllPar() b.OptimizeBayesianBackground() if pdiff is None: # Probably just one diffraction phase, select it for i in range(self.GetNbPowderPatternComponent()): if isinstance(self.GetPowderPatternComponent(i), PowderPatternDiffraction): pdiff = self.GetPowderPatternComponent(i) break if verbose: print("Selected PowderPatternDiffraction: ", pdiff.GetName(), " with Crystal: ", pdiff.GetCrystal().GetName()) if init_profile: pdiff.SetReflectionProfilePar(ReflectionProfileType.PROFILE_PSEUDO_VOIGT, 0.0000001) pdiff.SetExtractionMode(True, True) pdiff.ExtractLeBail(10) if plot: self.UpdateDisplay() # LSQ lsq = LSQ() lsq.SetRefinedObj(self, 0, True, True) lsq.PrepareRefParList(True) # lsq.GetCompiledRefinedObj().Print() lsqr = lsq.GetCompiledRefinedObj() lsqr.FixAllPar() # lsqr.Print() # print(lsq.ChiSquare()) if zero: lsq.SetParIsFixed("Zero", False) if displ_transl: lsq.SetParIsFixed("2ThetaDispl", True) lsq.SetParIsFixed("2ThetaTransp", True) if lsqr.GetNbParNotFixed(): lsq.SafeRefine(nbCycle=10, useLevenbergMarquardt=True, silent=True) pdiff.ExtractLeBail(10) lsq.SafeRefine(nbCycle=10, useLevenbergMarquardt=True, silent=True) if plot: self.UpdateDisplay() if cell: lsq.SetParIsFixed("a", False) lsq.SetParIsFixed("b", False) lsq.SetParIsFixed("c", False) lsq.SetParIsFixed("alpha", False) lsq.SetParIsFixed("beta", False) lsq.SetParIsFixed("gamma", False) if constant_width: lsq.SetParIsFixed("W", False) if lsqr.GetNbParNotFixed(): lsq.SafeRefine(nbCycle=10, useLevenbergMarquardt=True, silent=True) pdiff.ExtractLeBail(10) lsq.SafeRefine(nbCycle=10, useLevenbergMarquardt=True, silent=True) if plot: self.UpdateDisplay() if width: lsq.SetParIsFixed("U", False) lsq.SetParIsFixed("V", False) if lsqr.GetNbParNotFixed(): lsq.SafeRefine(nbCycle=10, useLevenbergMarquardt=True, silent=True) pdiff.ExtractLeBail(10) lsq.SafeRefine(nbCycle=10, useLevenbergMarquardt=True, silent=True) if plot: self.UpdateDisplay() if eta: lsq.SetParIsFixed("Eta0", False) if lsqr.GetNbParNotFixed(): lsq.SafeRefine(nbCycle=10, useLevenbergMarquardt=True, silent=True) pdiff.ExtractLeBail(10) lsq.SafeRefine(nbCycle=10, useLevenbergMarquardt=True, silent=True) if plot: self.UpdateDisplay() if eta: lsq.SetParIsFixed("Eta1", False) if lsqr.GetNbParNotFixed(): lsq.SafeRefine(nbCycle=10, useLevenbergMarquardt=True, silent=True) pdiff.ExtractLeBail(10) lsq.SafeRefine(nbCycle=10, useLevenbergMarquardt=True, silent=True) if plot: self.UpdateDisplay() if asym: lsq.SetParIsFixed("Asym0", False) lsq.SetParIsFixed("Asym1", False) if lsqr.GetNbParNotFixed(): lsq.SafeRefine(nbCycle=10, useLevenbergMarquardt=True, silent=True) pdiff.ExtractLeBail(10) lsq.SafeRefine(nbCycle=10, useLevenbergMarquardt=True, silent=True) if plot: self.UpdateDisplay() if backgd: for i in range(self.GetNbPowderPatternComponent()): if isinstance(self.GetPowderPatternComponent(i), PowderPatternBackground): b = self.GetPowderPatternComponent(i) lsq.SetParIsFixed(refpartype_scattdata_background, False) b.FixParametersBeyondMaxresolution(lsqr) # lsqr.Print() lsq.SafeRefine(nbCycle=10, useLevenbergMarquardt=True, silent=True) break if verbose: print("Profile fitting finished.\n" "Remember to use SetExtractionMode(False) on the PowderPatternDiffraction object\n" "to disable profile fitting and optimise the structure.") def get_background(self): """ Access the background component. :return: the PowderPatternBackground for this powder pattern, or None """ for i in range(self.GetNbPowderPatternComponent()): if self.GetPowderPatternComponent(i).GetClassName() == "PowderPatternBackground": return self.GetPowderPatternComponent(i) def get_crystalline_components(self): """ Get the crystalline phase for this powder pattern :return: a list of the PowderPatternDiffraction components """ vc = [] for i in range(self.GetNbPowderPatternComponent()): if self.GetPowderPatternComponent(i).GetClassName() == "PowderPatternDiffraction": vc.append(self.GetPowderPatternComponent(i)) return vc def _on_mouse_event(self, event): if event.dblclick: # This does not work in a notebook self._plot_xlim = None self._plot_ylim = None self.plot() def _on_draw_event(self, event): if self._plot_hkl and self._last_hkl_plot_xlim is not None and len(self._plot_fig.axes): ax = self._plot_fig.axes[0] self._plot_xlim = ax.get_xlim() dx1 = abs(self._last_hkl_plot_xlim[0] - self._plot_xlim[0]) dx2 = abs(self._last_hkl_plot_xlim[1] - self._plot_xlim[1]) if max(dx1, dx2) > 0.1 * (self._last_hkl_plot_xlim[1] - self._last_hkl_plot_xlim[0]): # Need to update the hkl list self._do_plot_hkl() def qpa(self, verbose=False): """ Get the quantitative phase analysis for the current powder pattern, when multiple crystalline phases are present. :param verbose: if True, print the Crystal names and their weight percentage. :return: a dictionary with the PowderPatternDiffraction object as key, and the weight percentages as value. """ res = {} szmv_sum = 0 for pdiff in self.get_crystalline_components(): if not isinstance(pdiff, PowderPatternDiffraction): continue c = pdiff.GetCrystal() s = self.GetScaleFactor(pdiff) m = c.GetWeight() z = c.GetSpaceGroup().GetNbSymmetrics() v = c.GetVolume() # print("%25s: %12f, %10f, %3d, %10.2f" % (c.GetName(), s, m, z, v)) res[pdiff] = s * z * m * v szmv_sum += s * z * m * v if verbose: print("Weight percentages:") for k, v in res.items(): res[k] = v / szmv_sum if verbose: print("%25s: %6.2f%%" % (k.GetCrystal().GetName(), res[k] * 100)) return res def create_powderpattern_from_cif(file): """ Create a crystal object from a CIF file or URL Example from Acta Cryst. (2012). B68, 407-411 (https://doi.org/10.1107/S0108768112019994), data hosted at CCDC: c=create_crystal_from_cif('https://doi.org/10.1107/S0108768112019994/ps5016sup1.cif') p=create_powderpattern_from_cif('https://doi.org/10.1107/S0108768112019994/ps5016Isup3.rtv') :param file: the filename/URL or python file object (need to open with mode='rb') If the string begins by 'http' it is assumed that it is an URL instead, e.g. from the crystallography open database :return: the imported PowderPattern :raises: ObjCrystException - if no PowderPattern object can be imported """ p = PowderPattern() if isinstance(file, str): if len(file) > 4: if file[:4].lower() == 'http': return CreatePowderPatternFromCIF_orig(urlopen(file), p) with open(file, 'rb') as cif: # Make sure file object is closed return CreatePowderPatternFromCIF_orig(cif, p) return CreatePowderPatternFromCIF_orig(file, p) def wrap_boost_powderpattern(c: PowderPattern): """ This function is used to wrap a C++ Object by adding the python methods to it. :param c: the C++ created object to which the python function must be added. """ if '_plot_fig' not in dir(c): # Add attributes c._plot_fig = None c._plot_xlim = None c._plot_ylim = None c._plot_diff = False c._plot_hkl = False c._plot_hkl_fontsize = 6 c._plot_phase_labels = None c._last_hkl_plot_xlim = None c.evts = [] c._colour_phases = ["black", "blue", "green", "red", "brown", "olive", "cyan", "purple", "magenta", "salmon"] for func in ['UpdateDisplay', 'disable_display_update', 'enable_display_update', 'plot', '_do_plot_hkl', 'quick_fit_profile', 'get_background', 'get_crystalline_components', '_on_mouse_event', '_on_draw_event', 'qpa']: exec("c.%s = MethodType(PowderPattern.%s, c)" % (func, func)) # PEP8 CreatePowderPatternFromCIF = create_powderpattern_from_cif pyobjcryst-2024.2.1/src/pyobjcryst/radiation.py000066400000000000000000000012441470422267000215020ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst # # File coded by: Vincent Favre-Nicolin # # See AUTHORS.txt for a list of people who contributed. # See LICENSE.txt for license information. # ############################################################################## """Python wrapping of Radiation from ScatteringData.h See the online ObjCryst++ documentation (https://objcryst.readthedocs.io). Changes from ObjCryst::Radiation:: In development ! """ __all__ = ["Radiation", "RadiationType", "WavelengthType"] from pyobjcryst._pyobjcryst import Radiation, RadiationType, WavelengthType pyobjcryst-2024.2.1/src/pyobjcryst/refinableobj.py000066400000000000000000000167501470422267000221620ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Python wrapping of RefinableObj.h See the online ObjCryst++ documentation (https://objcryst.readthedocs.io). Changes from ObjCryst::RefinableObj - XMLOutput and XMLInput accept python file-like objects. - GetPar that takes a const double* is not exposed, as it is designed for internal use. - GetParamSet returns a copy of the internal data so that no indirect manipulation can take place from python. - SetDeleteRefParInDestructor(false) is called in the constructors of the python class and the parameter accessors. - SetDeleteRefParInDestructor is not exposed. - RemovePar is overloaded to return None. Changes from ObjCryst::RefinablePar - The constructor has been changed to accept a double, rather than a pointer to a double. - The copy and default constructors and Init are not wrapped in order to avoid memory corruption. Since boost cannot implicitly handle double* object, a wrapper class had to be created. However, this wrapper class cannot be used to convert RefinablePar objected created in c++. Thus, ObjCryst::RefinablePar objects created in c++ are passed into python as instances of _RefinablePar, which is a python wrapper around ObjCryst::RefinablePar. The RefinablePar python class is a wrapper around the C++ class PyRefinablePar, which manages its own double*. These python classes are interchangable once instantiated, so users should not notice. - XML input/output are not exposed. Changes from ObjCryst::RefinableObjClock - operator= is wrapped as the SetEqual method a.SetEqual(b) -> a = b Changes from ObjCryst::ObjRegistry - DeleteAll not wrapped Changes from ObjCryst::Restraint - The default and copy constructors are not wrapped, nor is Init. - GetType returns a non-const reference to the RefParType. This should be a no-no, but RefParType has no mutating methods, so this should no lead to trouble. - XML input/output are not exposed. """ __all__ = ["RefinableObjClock", "RefinableObj", "RefObjOpt", "RefinableObjRegistry", "RefParType", "RefParDerivStepModel", "RefinablePar", "Restraint", "ScattererRegistry", "ScatteringPowerRegistry", "ZAtomRegistry", "refpartype_crystal", "refpartype_objcryst", "refpartype_scatt", "refpartype_scatt_transl", "refpartype_scatt_transl_x", "refpartype_scatt_transl_y", "refpartype_scatt_transl_z", "refpartype_scatt_orient", "refpartype_scatt_conform", "refpartype_scatt_conform_bondlength", "refpartype_scatt_conform_bondangle", "refpartype_scatt_conform_dihedangle", "refpartype_scatt_conform_x", "refpartype_scatt_conform_y", "refpartype_scatt_conform_z", "refpartype_scatt_occup", "refpartype_scattdata", "refpartype_scattdata_background", "refpartype_scattdata_scale", "refpartype_scattdata_profile", "refpartype_scattdata_profile_type", "refpartype_scattdata_profile_width", "refpartype_scattdata_profile_asym", "refpartype_scattdata_corr", "refpartype_scattdata_corr_pos", "refpartype_scattdata_radiation", "refpartype_scattdata_radiation_wavelength", "refpartype_scattpow", "refpartype_scattpow_temperature", "refpartype_unitcell", "refpartype_unitcell_length", "refpartype_unitcell_angle"] from types import MethodType from pyobjcryst._pyobjcryst import RefinableObjClock from pyobjcryst._pyobjcryst import RefinableObj from pyobjcryst._pyobjcryst import RefObjOpt from pyobjcryst._pyobjcryst import RefinableObjRegistry from pyobjcryst._pyobjcryst import RefParType from pyobjcryst._pyobjcryst import RefParDerivStepModel from pyobjcryst._pyobjcryst import RefinablePar from pyobjcryst._pyobjcryst import Restraint from pyobjcryst._pyobjcryst import ScattererRegistry from pyobjcryst._pyobjcryst import ScatteringPowerRegistry from pyobjcryst._pyobjcryst import ZAtomRegistry from pyobjcryst._pyobjcryst import refpartype_crystal from pyobjcryst._pyobjcryst import refpartype_objcryst from pyobjcryst._pyobjcryst import refpartype_scatt from pyobjcryst._pyobjcryst import refpartype_scatt_transl from pyobjcryst._pyobjcryst import refpartype_scatt_transl_x from pyobjcryst._pyobjcryst import refpartype_scatt_transl_y from pyobjcryst._pyobjcryst import refpartype_scatt_transl_z from pyobjcryst._pyobjcryst import refpartype_scatt_orient from pyobjcryst._pyobjcryst import refpartype_scatt_conform from pyobjcryst._pyobjcryst import refpartype_scatt_conform_bondlength from pyobjcryst._pyobjcryst import refpartype_scatt_conform_bondangle from pyobjcryst._pyobjcryst import refpartype_scatt_conform_dihedangle from pyobjcryst._pyobjcryst import refpartype_scatt_conform_x from pyobjcryst._pyobjcryst import refpartype_scatt_conform_y from pyobjcryst._pyobjcryst import refpartype_scatt_conform_z from pyobjcryst._pyobjcryst import refpartype_scatt_occup from pyobjcryst._pyobjcryst import refpartype_scattdata from pyobjcryst._pyobjcryst import refpartype_scattdata_scale from pyobjcryst._pyobjcryst import refpartype_scattdata_profile from pyobjcryst._pyobjcryst import refpartype_scattdata_profile_type from pyobjcryst._pyobjcryst import refpartype_scattdata_profile_width from pyobjcryst._pyobjcryst import refpartype_scattdata_profile_asym from pyobjcryst._pyobjcryst import refpartype_scattdata_corr from pyobjcryst._pyobjcryst import refpartype_scattdata_corr_pos from pyobjcryst._pyobjcryst import refpartype_scattdata_radiation from pyobjcryst._pyobjcryst import refpartype_scattdata_radiation_wavelength from pyobjcryst._pyobjcryst import refpartype_scattdata_background from pyobjcryst._pyobjcryst import refpartype_scattpow from pyobjcryst._pyobjcryst import refpartype_scattpow_temperature from pyobjcryst._pyobjcryst import refpartype_unitcell from pyobjcryst._pyobjcryst import refpartype_unitcell_length from pyobjcryst._pyobjcryst import refpartype_unitcell_angle class ObjRegistryWrapper(RefinableObjRegistry): """ Wrapper class with a GetObj() method which can correctly wrap C++ objects with the python methods. This is only needed when the objects have been created from C++, e.g. when loading an XML file. """ def GetObj(self, i): o = self._GetObj(i) if o.GetClassName() == 'Crystal': from .crystal import wrap_boost_crystal wrap_boost_crystal(o) elif o.GetClassName() == 'PowderPattern': from .powderpattern import wrap_boost_powderpattern wrap_boost_powderpattern(o) elif o.GetClassName() == 'MonteCarloObj': from .globaloptim import wrap_boost_montecarlo wrap_boost_montecarlo(o) return o def wrap_boost_refinableobjregistry(o): """ This function is used to wrap a C++ Object by adding the python methods to it. :param c: the C++ created object to which the python function must be added. """ # TODO: moving the original function is not very pretty. Is there a better way ? if '_GetObj' not in dir(o): o._GetObj = o.GetObj o.GetObj = MethodType(ObjRegistryWrapper.GetObj, o) pyobjcryst-2024.2.1/src/pyobjcryst/reflectionprofile.py000066400000000000000000000012731470422267000232450ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst # # File coded by: Vincent Favre-Nicolin # # See AUTHORS.txt for a list of people who contributed. # See LICENSE.txt for license information. # ############################################################################## """Python wrapping of PowderPattern.h See the online ObjCryst++ documentation (https://objcryst.readthedocs.io). Changes from ObjCryst::ReflectionProfile:: In development ! """ __all__ = ["ReflectionProfile", "ReflectionProfileType"] from pyobjcryst._pyobjcryst import ReflectionProfile from pyobjcryst._pyobjcryst import ReflectionProfileType pyobjcryst-2024.2.1/src/pyobjcryst/scatterer.py000066400000000000000000000022301470422267000215200ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Python wrapping of Scatterer.h See the online ObjCryst++ documentation (https://objcryst.readthedocs.io). Changes from ObjCryst::Scatterer - C++ methods that can return const or non-const objects return non-const objects in python. - Operator string() is not exposed. - Internal use only methods have not been exposed. - InitRefParList is not exposed, as it is not used inside of Scatterer. - GetClockScattCompList is exposed using a workaround, because it is not implemented in the library. - Methods related to visualization are not exposed. """ __all__ = ["Scatterer"] from pyobjcryst._pyobjcryst import Scatterer pyobjcryst-2024.2.1/src/pyobjcryst/scatteringdata.py000066400000000000000000000013251470422267000225250ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst Complex Modeling Initiative # (c) 2015 Brookhaven Science Associates # Brookhaven National Laboratory. # All rights reserved. # # File coded by: Pavol Juhas # # See AUTHORS.txt for a list of people who contributed. # See LICENSE.txt for license information. # ############################################################################## """Python wrapping of ScatteringData class. See the online ObjCryst++ documentation (https://objcryst.readthedocs.io). """ __all__ = ["ScatteringData"] from pyobjcryst._pyobjcryst import ScatteringData pyobjcryst-2024.2.1/src/pyobjcryst/scatteringpower.py000066400000000000000000000024401470422267000227470ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Python wrapping of ScatteringPower.h See the online ObjCryst++ documentation (https://objcryst.readthedocs.io). Changes from ObjCryst::ScatteringComponent - Added attributes X, Y, Z, Occupancy to conform to MolAtom. Changes from ObjCryst::ScatteringComponentList - Wrapped as a to-python converter only (no constructor) """ __all__ = ["ScatteringPower", "ScatteringComponent", "ScatteringPowerAtom", "ScatteringComponentList", "gScatteringPowerRegistry"] from pyobjcryst._pyobjcryst import ScatteringPower from pyobjcryst._pyobjcryst import ScatteringComponent from pyobjcryst._pyobjcryst import ScatteringPowerAtom from pyobjcryst._pyobjcryst import ScatteringComponentList from pyobjcryst._pyobjcryst import gScatteringPowerRegistry pyobjcryst-2024.2.1/src/pyobjcryst/scatteringpowersphere.py000066400000000000000000000014001470422267000241510ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Python wrapping of ScatteringPowerSphere.h See the online ObjCryst++ documentation (https://objcryst.readthedocs.io). """ __all__ = ["ScatteringPowerSphere"] from pyobjcryst._pyobjcryst import ScatteringPowerSphere pyobjcryst-2024.2.1/src/pyobjcryst/spacegroup.py000066400000000000000000000014441470422267000217020ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Python wrapping of SpaceGroup.h. See the online ObjCryst++ documentation (https://objcryst.readthedocs.io). """ __all__ = ["SpaceGroup", "AsymmetricUnit"] from pyobjcryst._pyobjcryst import SpaceGroup from pyobjcryst._pyobjcryst import AsymmetricUnit pyobjcryst-2024.2.1/src/pyobjcryst/tests/000077500000000000000000000000001470422267000203175ustar00rootroot00000000000000pyobjcryst-2024.2.1/src/pyobjcryst/tests/__init__.py000066400000000000000000000045011470422267000224300ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst Complex Modeling Initiative # (c) 2013 Brookhaven Science Associates, # Brookhaven National Laboratory. # All rights reserved. # # File coded by: Pavol Juhas # # See AUTHORS.txt for a list of people who contributed. # See LICENSE.txt for license information. # ############################################################################## """Unit tests for pyobjcryst. """ import unittest def testsuite(pattern=''): '''Create a unit tests suite for the pyobjcryst package. Parameters ---------- pattern : str, optional Regular expression pattern for selecting test cases. Select all tests when empty. Ignore the pattern when any of unit test modules fails to import. Returns ------- suite : `unittest.TestSuite` The TestSuite object containing the matching tests. ''' import re from os.path import dirname from itertools import chain from pkg_resources import resource_filename loader = unittest.defaultTestLoader thisdir = resource_filename(__name__, '') depth = __name__.count('.') + 1 topdir = thisdir for i in range(depth): topdir = dirname(topdir) suite_all = loader.discover(thisdir, top_level_dir=topdir) # always filter the suite by pattern to test-cover the selection code. suite = unittest.TestSuite() rx = re.compile(pattern) tsuites = list(chain.from_iterable(suite_all)) tsok = all(isinstance(ts, unittest.TestSuite) for ts in tsuites) if not tsok: # pragma: no cover return suite_all tcases = chain.from_iterable(tsuites) for tc in tcases: tcwords = tc.id().split('.') shortname = '.'.join(tcwords[-3:]) if rx.search(shortname): suite.addTest(tc) # verify all tests are found for an empty pattern. assert pattern or suite_all.countTestCases() == suite.countTestCases() return suite def test(verbosity=1): '''Execute all unit tests for the pyobjcryst package. Returns ------- result : `unittest.TestResult` ''' suite = testsuite() runner = unittest.TextTestRunner(verbosity=verbosity) result = runner.run(suite) return result pyobjcryst-2024.2.1/src/pyobjcryst/tests/debug.py000066400000000000000000000015531470422267000217630ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst Complex Modeling Initiative # (c) 2016 Brookhaven Science Associates, # Brookhaven National Laboratory. # All rights reserved. # # File coded by: Pavol Juhas # # See AUTHORS.txt for a list of people who contributed. # See LICENSE.txt for license information. # ############################################################################## """ Convenience module for debugging the unit tests using python -m pyobjcryst.tests.debug Exceptions raised by failed tests or other errors are not caught. """ if __name__ == '__main__': import sys from pyobjcryst.tests import testsuite pattern = sys.argv[1] if len(sys.argv) > 1 else '' suite = testsuite(pattern) suite.debug() pyobjcryst-2024.2.1/src/pyobjcryst/tests/pyobjcrysttest.py000066400000000000000000000076701470422267000240130ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Small tests for pyobjcryst. To check for memory leaks, run valgrind --tool=memcheck --leak-check=full /usr/bin/python ./pyobjcrysttest.py """ from __future__ import print_function from pyobjcryst.atom import Atom from pyobjcryst.crystal import Crystal from pyobjcryst.refinableobj import RefParType, RefinablePar from pyobjcryst.scatteringpower import ScatteringPowerAtom from numpy import pi def makeScatterer(): sp = ScatteringPowerAtom("Ni", "Ni") sp.SetBiso(8*pi*pi*0.003) sp.B11 = 8*pi*pi*0.003 sp.SetBij(2, 2, 8*pi*pi*0.003) sp.SetBij(3, 3, 8*pi*pi*0.003) atom = Atom(0, 0, 0, "Ni", sp) print(sp.B11) return sp, atom def makeCrystal(sp, atom): c = Crystal(3.52, 3.52, 3.52, "225") c.AddScatterer(atom) c.AddScatteringPower(sp) return c def getScatterer(): """Make a crystal and return scatterer from GetScatt.""" sp, atom = makeScatterer() c = makeCrystal(sp, atom) sp2 = c.GetScatt(sp.GetName()) return sp2 def testCrystalScope(): """Test to see if the the crystal survives after it is out of scope.""" sp, atom = makeScatterer() makeCrystal(sp, atom) # The crystal is out of scope. Since the lifetime of the atom and scatterer # are linked, the crystal should stay alive in memory. print(sp) print(atom) print(repr(atom.GetCrystal())) return def testMultiAdd(): """Test exception for multi-crystal additions.""" sp, atom = makeScatterer() makeCrystal(sp, atom) # Force this exception try: makeCrystal(sp, atom) print(sp) print(atom) print(repr(atom.GetCrystal())) except AttributeError as e: print("Exception:", e) return def testScattererScope(): """Test when atoms go out of scope before crystal.""" c = makeCrystal(*makeScatterer()) print(c) sp2 = getScatterer() print(sp2) return def testRemoveFunctions(): """Test the RemoveScatterer and RemoveScatteringPower method.""" print("Making Crystal") sp, atom = makeScatterer() c = makeCrystal(sp, atom) print(atom) print(sp) print(c) # Try to add objects with same names print("Testing name duplication") sp2, atom2 = makeScatterer() try: c.AddScatterer(atom2) except AttributeError as e: print(e) try: c.AddScatteringPower(sp2) except AttributeError as e: print(e) # Remove the scatterers print("remove scatterers") c.RemoveScatterer(atom) c.RemoveScatteringPower(sp) print(atom) print(sp) print(c) # Try to remove scatterers that are not in the crystal try: c.RemoveScatterer(atom2) except AttributeError as e: print(e) try: c.RemoveScatteringPower(sp2) except AttributeError as e: print(e) return def parTest(): rpt = RefParType("default") testpar = RefinablePar("test", 3.0, 0, 10, rpt) print(testpar.__class__, testpar) sp, atom = makeScatterer() c = makeCrystal(sp, atom) par = c.GetPar(0) print(par.__class__, par) c.AddPar(testpar); testpar2 = c.GetPar("test") print(testpar2.__class__, testpar2) del sp, atom, c testpar2.SetValue(2.17) print(testpar.__class__, testpar) return def test1(): """Run some tests.""" testCrystalScope() testMultiAdd() testScattererScope() testRemoveFunctions() return if __name__ == "__main__": test1() pyobjcryst-2024.2.1/src/pyobjcryst/tests/pyobjcrysttestutils.py000066400000000000000000000116331470422267000250660ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Utilities for tests.""" from pyobjcryst.atom import Atom from pyobjcryst.molecule import Molecule from pyobjcryst.polyhedron import MakeOctahedron from pyobjcryst.crystal import Crystal from pyobjcryst.scatteringpower import ScatteringPowerAtom from numpy import pi def makeScatterer(): sp = ScatteringPowerAtom("Ni", "Ni") sp.SetBiso(8*pi*pi*0.003) atom = Atom(0, 0, 0, "Ni", sp) return sp, atom def makeScattererAnisotropic(): sp = ScatteringPowerAtom("Ni", "Ni") sp.B11 = sp.B22 = sp.B33 = 8*pi*pi*0.003 sp.B12 = sp.B13 = sp.B23 = 0 atom = Atom(0, 0, 0, "Ni", sp) return sp, atom def makeCrystal(sp, atom): c = Crystal(3.52, 3.52, 3.52, "225") c.AddScatteringPower(sp) c.AddScatterer(atom) return c def getScatterer(): """Make a crystal and return scatterer from GetScatt.""" sp, atom = makeScatterer() c = makeCrystal(sp, atom) sp2 = c.GetScatt(sp.GetName()) return sp2 c60xyz = \ """ 3.451266498 0.685000000 0.000000000 3.451266498 -0.685000000 0.000000000 -3.451266498 0.685000000 0.000000000 -3.451266498 -0.685000000 0.000000000 0.685000000 0.000000000 3.451266498 -0.685000000 0.000000000 3.451266498 0.685000000 0.000000000 -3.451266498 -0.685000000 0.000000000 -3.451266498 0.000000000 3.451266498 0.685000000 0.000000000 3.451266498 -0.685000000 0.000000000 -3.451266498 0.685000000 0.000000000 -3.451266498 -0.685000000 3.003809890 1.409000000 1.171456608 3.003809890 1.409000000 -1.171456608 3.003809890 -1.409000000 1.171456608 3.003809890 -1.409000000 -1.171456608 -3.003809890 1.409000000 1.171456608 -3.003809890 1.409000000 -1.171456608 -3.003809890 -1.409000000 1.171456608 -3.003809890 -1.409000000 -1.171456608 1.409000000 1.171456608 3.003809890 1.409000000 -1.171456608 3.003809890 -1.409000000 1.171456608 3.003809890 -1.409000000 -1.171456608 3.003809890 1.409000000 1.171456608 -3.003809890 1.409000000 -1.171456608 -3.003809890 -1.409000000 1.171456608 -3.003809890 -1.409000000 -1.171456608 -3.003809890 1.171456608 3.003809890 1.409000000 -1.171456608 3.003809890 1.409000000 1.171456608 3.003809890 -1.409000000 -1.171456608 3.003809890 -1.409000000 1.171456608 -3.003809890 1.409000000 -1.171456608 -3.003809890 1.409000000 1.171456608 -3.003809890 -1.409000000 -1.171456608 -3.003809890 -1.409000000 2.580456608 0.724000000 2.279809890 2.580456608 0.724000000 -2.279809890 2.580456608 -0.724000000 2.279809890 2.580456608 -0.724000000 -2.279809890 -2.580456608 0.724000000 2.279809890 -2.580456608 0.724000000 -2.279809890 -2.580456608 -0.724000000 2.279809890 -2.580456608 -0.724000000 -2.279809890 0.724000000 2.279809890 2.580456608 0.724000000 -2.279809890 2.580456608 -0.724000000 2.279809890 2.580456608 -0.724000000 -2.279809890 2.580456608 0.724000000 2.279809890 -2.580456608 0.724000000 -2.279809890 -2.580456608 -0.724000000 2.279809890 -2.580456608 -0.724000000 -2.279809890 -2.580456608 2.279809890 2.580456608 0.724000000 -2.279809890 2.580456608 0.724000000 2.279809890 2.580456608 -0.724000000 -2.279809890 2.580456608 -0.724000000 2.279809890 -2.580456608 0.724000000 -2.279809890 -2.580456608 0.724000000 2.279809890 -2.580456608 -0.724000000 -2.279809890 -2.580456608 -0.724000000 """ def makeC60(): c = Crystal(100, 100, 100, "P1") c.SetName("c60frame") m = Molecule(c, "c60") c.AddScatterer(m) sp = ScatteringPowerAtom("C", "C") sp.SetBiso(8*pi*pi*0.003) c.AddScatteringPower(sp) for i, l in enumerate(c60xyz.strip().splitlines()): x, y, z = map(float, l.split()) m.AddAtom(x, y, z, sp, "C%i"%i) return c def makeMnO6(): a = 5.6 crystal = Crystal(a, a, a, "P1") sp1 = ScatteringPowerAtom("Mn", "Mn") sp1.SetBiso(8*pi*pi*0.003) sp2 = ScatteringPowerAtom("O", "O") sp2.SetBiso(8*pi*pi*0.003) crystal.AddScatteringPower(sp1) crystal.AddScatteringPower(sp2) m = MakeOctahedron(crystal, "MnO6", sp1, sp2, 0.5*a) crystal.AddScatterer(m) return crystal def datafile(filename): from pkg_resources import resource_filename rv = resource_filename(__name__, "testdata/" + filename) return rv def loadcifdata(filename): from pyobjcryst import loadCrystal fullpath = datafile(filename) crst = loadCrystal(fullpath) return crst pyobjcryst-2024.2.1/src/pyobjcryst/tests/run.py000066400000000000000000000017461470422267000215050ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst Complex Modeling Initiative # (c) 2013 Brookhaven Science Associates, # Brookhaven National Laboratory. # All rights reserved. # # File coded by: Pavol Juhas # # See AUTHORS.txt for a list of people who contributed. # See LICENSE.txt for license information. # ############################################################################## """Convenience module for executing all unit tests with python -m pyobjcryst.tests.run """ if __name__ == '__main__': import sys # show warnings by default if not sys.warnoptions: import os, warnings warnings.simplefilter("default") # also affect subprocesses os.environ["PYTHONWARNINGS"] = "default" from pyobjcryst.tests import test # produce zero exit code for a successful test sys.exit(not test().wasSuccessful()) pyobjcryst-2024.2.1/src/pyobjcryst/tests/test_single_crystal_data.py000066400000000000000000000036441470422267000257520ustar00rootroot00000000000000#!/usr/bin/env python """Tests for diffractiondatasinglecrystal module.""" import unittest import gc import numpy as np from pyobjcryst.crystal import CreateCrystalFromCIF, Crystal from pyobjcryst.diffractiondatasinglecrystal import * from pyobjcryst.tests.pyobjcrysttestutils import loadcifdata, datafile class test_single_crystal_data(unittest.TestCase): def test_create(self): """Test creating a DiffractionDataSingleCrystal object""" c = Crystal(3.52, 3.52, 3.52, "225") d = DiffractionDataSingleCrystal(c) def test_create_set_hkliobs(self): """test SetHklIobs, SetIobs and SetSigma""" c = Crystal(3.1, 3.2, 3.3, "Pmmm") d = DiffractionDataSingleCrystal(c) n0 = 5 nb = n0 ** 3 r = np.arange(1, nb + 1, dtype=np.float64) h = r % n0 l = r // n0 ** 2 k = (r - l * n0 ** 2) // n0 iobs = np.random.uniform(0, 100, nb) sigma = np.sqrt(iobs) d.SetHklIobs(h, k, l, iobs, sigma) # SetHklIobs sorts reflecions by sin(theta)/lambda, so do the same for comparison s = np.sqrt(h ** 2 / 3.1 ** 2 + k ** 2 / 3.2 ** 2 + l ** 2 / 3.3 ** 2) / 2 idx = np.argsort(s) iobs = np.take(iobs, idx) sigma = np.take(sigma, idx) h = np.take(h, idx) k = np.take(k, idx) l = np.take(l, idx) self.assertTrue(np.all(iobs == d.GetIobs())) self.assertTrue(np.all(sigma == d.GetSigma())) self.assertTrue(np.all(h == d.GetH())) self.assertTrue(np.all(k == d.GetK())) self.assertTrue(np.all(l == d.GetL())) # Set Iobs and sigma individually iobs = np.random.uniform(0, 100, nb) d.SetIobs(iobs) self.assertTrue(np.all(iobs == d.GetIobs())) sigma = np.random.uniform(0, 10, nb) d.SetSigma(sigma) self.assertTrue(np.all(sigma == d.GetSigma())) if __name__ == "__main__": unittest.main() pyobjcryst-2024.2.1/src/pyobjcryst/tests/testcif.py000066400000000000000000000231551470422267000223400ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Tests for crystal module.""" import unittest import gc from pyobjcryst.crystal import CreateCrystalFromCIF from pyobjcryst.globals import gCrystalRegistry from pyobjcryst.diffractiondatasinglecrystal import create_singlecrystaldata_from_cif from numpy import pi from pyobjcryst.tests.pyobjcrysttestutils import loadcifdata, datafile class TestCif(unittest.TestCase): def test_Ag_silver_cif(self): '''Check loading of Ag_silver.cif ''' c = loadcifdata('Ag_silver.cif') self.assertTrue(c is not None) return def test_BaTiO3_cif(self): '''Check loading of BaTiO3.cif ''' c = loadcifdata('BaTiO3.cif') self.assertTrue(c is not None) return def test_C_graphite_hex_cif(self): '''Check loading of C_graphite_hex.cif ''' c = loadcifdata('C_graphite_hex.cif') self.assertTrue(c is not None) return def test_CaF2_fluorite_cif(self): '''Check loading of CaF2_fluorite.cif ''' c = loadcifdata('CaF2_fluorite.cif') self.assertTrue(c is not None) return def test_caffeine_cif(self): '''Check loading of caffeine.cif and the data inside. ''' c = loadcifdata('caffeine.cif') self.assertTrue(c is not None) self.assertEqual(24, c.GetNbScatterer()) self.assertAlmostEqual(14.9372, c.a, 6) self.assertAlmostEqual(14.9372, c.b, 6) self.assertAlmostEqual(6.8980, c.c, 6) self.assertAlmostEqual(pi/2, c.alpha, 6) self.assertAlmostEqual(pi/2, c.beta, 6) self.assertAlmostEqual(2*pi/3, c.gamma, 6) cifdata = """ C5 -0.06613 -0.06314 0.09562 0.00000 Uiso 1.00000 C C4 0.02779 -0.05534 0.10000 0.00000 Uiso 1.00000 C N3 0.11676 0.04116 0.08226 0.00000 Uiso 1.00000 N C2 0.10866 0.12960 0.06000 0.00000 Uiso 1.00000 C O11 0.18634 0.21385 0.04451 0.00000 Uiso 1.00000 O N1 0.01159 0.12154 0.05547 0.00000 Uiso 1.00000 N C6 -0.07738 0.02504 0.07321 0.00000 Uiso 1.00000 C O13 -0.16212 0.01800 0.06926 0.00000 Uiso 1.00000 O N7 -0.13793 -0.16353 0.11547 0.00000 Uiso 1.00000 N N9 0.01389 -0.15092 0.12255 0.00000 Uiso 1.00000 N C8 -0.08863 -0.21778 0.13210 0.00000 Uiso 1.00000 C C14 -0.25110 -0.20663 0.11847 0.00000 Uiso 1.00000 C C12 0.21968 0.04971 0.08706 0.00000 Uiso 1.00000 C C10 0.00300 0.21530 0.03186 0.00000 Uiso 1.00000 C H8 -0.12146 -0.29285 0.14834 0.00000 Uiso 1.00000 H H14a -0.27317 -0.19028 -0.00382 0.00000 Uiso 1.00000 H H14b -0.28567 -0.28182 0.13462 0.00000 Uiso 1.00000 H H14c -0.26951 -0.17639 0.22660 0.00000 Uiso 1.00000 H H12a 0.22513 0.00926 -0.01943 0.00000 Uiso 1.00000 H H12b 0.27338 0.12237 0.07281 0.00000 Uiso 1.00000 H H12c 0.22878 0.02315 0.21098 0.00000 Uiso 1.00000 H H10a 0.03488 0.24909 -0.09109 0.00000 Uiso 1.00000 H H10b -0.07008 0.19602 0.03170 0.00000 Uiso 1.00000 H H10c 0.03791 0.26293 0.13930 0.00000 Uiso 1.00000 H """ lines = filter(None, (line.strip() for line in cifdata.split('\n'))) for i, line in enumerate(lines): name, x, y, z, U, junk, occ, element = line.split() s = c.GetScatt(i) self.assertEqual(name, s.GetName()) self.assertAlmostEqual(float(x), s.X, 6) self.assertAlmostEqual(float(y), s.Y, 6) self.assertAlmostEqual(float(z), s.Z, 6) self.assertAlmostEqual(float(occ), s.Occupancy, 6) return def test_CaTiO3_cif(self): '''Check loading of CaTiO3.cif and its ADPs. ''' c = loadcifdata('CaTiO3.cif') self.assertTrue(c is not None) s = c.GetScatt(3) name = s.GetName() self.assertEqual(name, "O2") sp = c.GetScatteringPower(name) self.assertFalse(sp.IsIsotropic()) utob = 8 * pi**2 self.assertAlmostEqual(utob * 0.0065, sp.B11) self.assertAlmostEqual(utob * 0.0060, sp.B22) self.assertAlmostEqual(utob * 0.0095, sp.B33) self.assertAlmostEqual(utob * 0.0020, sp.B12) self.assertAlmostEqual(utob * -0.0008, sp.B13) self.assertAlmostEqual(utob * -0.0010, sp.B23) self.assertAlmostEqual(0.2897, s.X, 5) self.assertAlmostEqual(0.2888, s.Y, 5) self.assertAlmostEqual(0.0373, s.Z, 2) self.assertAlmostEqual(1, s.Occupancy) return def test_CdSe_cadmoselite_cif(self): '''Check loading of CdSe_cadmoselite.cif ''' c = loadcifdata('CdSe_cadmoselite.cif') self.assertTrue(c is not None) return def test_CeO2_cif(self): '''Check loading of CeO2.cif ''' c = loadcifdata('CeO2.cif') self.assertTrue(c is not None) return def test_lidocainementhol_cif(self): '''Check loading of lidocainementhol.cif ''' c = loadcifdata('lidocainementhol.cif') self.assertTrue(c is not None) return def test_NaCl_cif(self): '''Check loading of NaCl.cif ''' c = loadcifdata('NaCl.cif') self.assertTrue(c is not None) return def test_Ni_cif(self): '''Check loading of Ni.cif ''' c = loadcifdata('Ni.cif') self.assertTrue(c is not None) return def test_paracetamol_cif(self): '''Check loading of paracetamol.cif ''' c = loadcifdata('paracetamol.cif') self.assertTrue(c is not None) return def test_PbS_galena_cif(self): '''Check loading of PbS_galena.cif ''' c = loadcifdata('PbS_galena.cif') self.assertTrue(c is not None) return def test_PbTe_cif(self): '''Check loading of PbTe.cif ''' c = loadcifdata('PbTe.cif') self.assertTrue(c is not None) return def test_Si_cif(self): '''Check loading of Si.cif ''' c = loadcifdata('Si.cif') self.assertTrue(c is not None) return def test_Si_setting2_cif(self): '''Check loading of Si_setting2.cif ''' c = loadcifdata('Si_setting2.cif') self.assertTrue(c is not None) return def test_SrTiO3_tausonite_cif(self): '''Check loading of SrTiO3_tausonite.cif ''' c = loadcifdata('SrTiO3_tausonite.cif') self.assertTrue(c is not None) return def test_TiO2_anatase_cif(self): '''Check loading of TiO2_anatase.cif ''' c = loadcifdata('TiO2_anatase.cif') self.assertTrue(c is not None) return def test_TiO2_rutile_cif(self): '''Check loading of TiO2_rutile.cif and its ADP data ''' c = loadcifdata('TiO2_rutile.cif') self.assertTrue(c is not None) s = c.GetScatt(0) name = s.GetName() sp = c.GetScatteringPower(name) self.assertEqual(name, "Ti") self.assertTrue(sp.IsIsotropic()) utob = 8 * pi**2 self.assertAlmostEqual(utob * 0.00532, sp.Biso, 5) return def test_Zn_zinc_cif(self): '''Check loading of Zn_zinc.cif ''' c = loadcifdata('Zn_zinc.cif') self.assertTrue(c is not None) return def test_ZnS_sphalerite_cif(self): '''Check loading of ZnS_sphalerite.cif ''' c = loadcifdata('ZnS_sphalerite.cif') self.assertTrue(c is not None) return def test_ZnS_wurtzite_cif(self): '''Check loading of ZnS_wurtzite.cif ''' c = loadcifdata('ZnS_wurtzite.cif') self.assertTrue(c is not None) return def testBadCif(self): """Make sure we can read all cif files.""" from pyobjcryst import ObjCrystException fname = datafile('ni.stru') infile = open(fname, 'rb') self.assertRaises(ObjCrystException, CreateCrystalFromCIF, infile) infile.close() return def test_paracetamol_monomethanolate(self): """ Test loading crystal and diffraction data """ c = loadcifdata("paracetamol_monomethanolate.cif") d = create_singlecrystaldata_from_cif( datafile("paracetamol_monomethanolate_data_single_crystal.cif"), c) self.assertTrue(d is not None) def test_paracetamol_monomethanolate_ward(self): """ Test loading crystal and diffraction data, make sure custodian & ward works """ c = loadcifdata("paracetamol_monomethanolate.cif") d = create_singlecrystaldata_from_cif( datafile("paracetamol_monomethanolate_data_single_crystal.cif"), c) n = d.GetCrystal().GetName() # Replace c by another Crystal object c = loadcifdata('ZnS_sphalerite.cif') gc.collect() self.assertTrue(gCrystalRegistry.GetObj(n) is not None) # End of class TestCif if __name__ == "__main__": unittest.main() pyobjcryst-2024.2.1/src/pyobjcryst/tests/testclocks.py000066400000000000000000000031201470422267000230430ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2010 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Tests for crystal module.""" import unittest from pyobjcryst.tests.pyobjcrysttestutils import makeC60 from pyobjcryst.refinableobj import RefinableObjClock class TestClocks(unittest.TestCase): def testClockIncrement(self): """Make sure that clocks increment properly.""" c = makeC60() m = c.GetScatterer("c60") ref = RefinableObjClock() mclock = m.GetClockScatterer() self.assertTrue( mclock > ref ) ref.Click() self.assertFalse( mclock > ref ) m[0].X = 0.01 self.assertTrue( mclock > ref ) ref.Click() self.assertFalse( mclock > ref ) m[1].X = 0.01 self.assertTrue( mclock > ref ) ref.Click() self.assertFalse( mclock > ref ) m[1].Y = 0.01 self.assertTrue( mclock > ref ) ref.Click() self.assertFalse( mclock > ref ) m.Q0 = 1.001 self.assertTrue( mclock > ref ) ref.Click() self.assertFalse( mclock > ref ) if __name__ == "__main__": unittest.main() pyobjcryst-2024.2.1/src/pyobjcryst/tests/testconverters.py000066400000000000000000000022331470422267000237630ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Test the converters. This verifies results from tests built into the _registerconverters module. """ import unittest from pyobjcryst._pyobjcryst import getTestVector, getTestMatrix import numpy class TestConverters(unittest.TestCase): def testVector(self): tv = numpy.arange(3, dtype=float) v = getTestVector() self.assertTrue( numpy.array_equal(tv, v) ) return def testMatrix(self): tm = numpy.arange(6, dtype=float).reshape(3, 2) m = getTestMatrix() self.assertTrue( numpy.array_equal(tm, m) ) return if __name__ == "__main__": unittest.main() pyobjcryst-2024.2.1/src/pyobjcryst/tests/testcrystal.py000066400000000000000000000123031470422267000232510ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Tests for crystal module.""" import unittest from pyobjcryst.tests.pyobjcrysttestutils import ( makeScatterer, makeCrystal, getScatterer, makeScattererAnisotropic) from pyobjcryst.atom import Atom class TestCrystal(unittest.TestCase): def testCrystalScope(self): """Test to see if the the crystal survives after it is out of scope.""" sp, atom = makeScatterer() makeCrystal(sp, atom) # The crystal is out of scope. Since the lifetime of the atom and # scatterer are linked, the crystal should stay alive in memory. self.assertEqual("Ni", sp.GetName()) self.assertEqual("Ni", atom.GetName()) return def testScattererScope(self): """Test when atoms go out of scope before crystal.""" sp2 = getScatterer() self.assertEqual("Ni", sp2.GetName()) return def testScattererB(self): """Test Biso and Bij of scatterer.""" sp1, junk = makeScatterer() self.assertTrue(sp1.IsIsotropic()) sp2, junk = makeScattererAnisotropic() self.assertFalse(sp2.IsIsotropic()) return def testNullData(self): """Make sure we get an error when trying to add or remove Null.""" from pyobjcryst.crystal import Crystal c = Crystal() self.assertRaises(ValueError, c.AddScatterer, None) self.assertRaises(ValueError, c.RemoveScatterer, None) self.assertRaises(ValueError, c.AddScatteringPower, None) self.assertRaises(ValueError, c.RemoveScatteringPower, None) return def testRemoveFunctions(self): """Test the RemoveScatterer and RemoveScatteringPower method.""" sp, atom = makeScatterer() c = makeCrystal(sp, atom) # You can add scatterers with the same name. That should be a no-no. sp2, atom2 = makeScatterer() c.AddScatteringPower(sp2) c.AddScatterer(atom2) # These act according to the library. You can try to remove an object # that is not in the crystal, and it will gladly do nothing for you. # Remove the scatterers c.RemoveScatterer(atom) c.RemoveScatteringPower(sp) # Remove again c.RemoveScatterer(atom) c.RemoveScatteringPower(sp) # Try to remove scatterers that are not in the crystal c.RemoveScatterer(atom2) c.RemoveScatteringPower(sp2) return def testGetScatteringComponentList(self): """Test the RemoveScatterer and RemoveScatteringPower method.""" sp, atom = makeScatterer() c = makeCrystal(sp, atom) scl = c.GetScatteringComponentList() self.assertEqual(1, len(scl)) sclcopy = scl[:] self.assertEqual(1, len(scl)) del sclcopy[0] self.assertEqual(0, len(sclcopy)) self.assertEqual(1, len(scl)) del scl[0] self.assertEqual(0, len(scl)) return def testGetScatterer(self): """Test GetScatterer and GetScatt.""" sp, atom = makeScatterer() c = makeCrystal(sp, atom) for fget in (c.GetScatterer, c.GetScatt): for i in range(c.GetNbScatterer()): fget(i) a = fget(0) ani = fget("Ni") self.assertEqual([a.X, a.Y, a.Z], [ani.X, ani.Y, ani.Z]) a.X = 0.112 self.assertEqual(a.X, ani.X) aneg = fget(-1) self.assertEqual(a.X, aneg.X) self.assertRaises(ValueError, fget, 'invalid') self.assertRaises(IndexError, fget, 10) self.assertRaises(IndexError, fget, -2) return def testDummyAtom(self): """Test dummy atoms.""" c = makeCrystal(*makeScatterer()) c.AddScatterer(Atom(0, 0, 0, "dummy", None)) d = c.GetScatterer("dummy") self.assertTrue(d.GetScatteringPower() is None) return def testEmbedding(self): """Test integrity of mutually-embedded objects.""" c = makeCrystal(*makeScatterer()) class Level1(object): def __init__(self, c): self.c = c self.level2 = Level2(self) return class Level2(object): def __init__(self, level1): self.level1 = level1 return l1 = Level1(c) del l1 return def test_display_list(self): """Test the creation of a atoms list for display using 3dmol""" c = makeCrystal(*makeScatterer()) s = c._display_list() s = c._display_list(full_molecule=True) s = c._display_list(enantiomer=True) s = c._display_list(only_independent_atoms=True) if __name__ == "__main__": unittest.main() pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/000077500000000000000000000000001470422267000221305ustar00rootroot00000000000000pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/Ag_silver.cif000066400000000000000000000073601470422267000245340ustar00rootroot00000000000000#------------------------------------------------------------------------------ #$Date: 2008-03-10 08:25:41 +0000 (Mon, 10 Mar 2008) $ #$Revision: 255 $ #$URL: svn://cod.ibt.lt/cod/cif/9/9008459.cif $ #------------------------------------------------------------------------------ # # This file is available in the Crystallography Open Database (COD), # http://www.crystallography.net/ # # All data on this site have been placed in the public domain by the # contributors. # data_9008459 loop_ _publ_author_name 'Wyckoff R W G' _publ_section_title ; Second edition. Interscience Publishers, New York, New York Cubic closest packed, ccp, structure ; _journal_name_full 'Crystal Structures' _journal_page_first 7 _journal_page_last 83 _journal_volume 1 _journal_year 1963 _chemical_formula_sum Ag _chemical_name_mineral Silver _symmetry_space_group_name_H-M 'F m 3 m' _cell_angle_alpha 90 _cell_angle_beta 90 _cell_angle_gamma 90 _cell_length_a 4.0862 _cell_length_b 4.0862 _cell_length_c 4.0862 _cell_volume 68.227 loop_ _symmetry_equiv_pos_as_xyz x,y,z x,1/2+y,1/2+z 1/2+x,y,1/2+z 1/2+x,1/2+y,z z,-x,y z,1/2-x,1/2+y 1/2+z,-x,1/2+y 1/2+z,1/2-x,y -y,z,-x -y,1/2+z,1/2-x 1/2-y,z,1/2-x 1/2-y,1/2+z,-x x,-y,z x,1/2-y,1/2+z 1/2+x,-y,1/2+z 1/2+x,1/2-y,z -z,x,-y -z,1/2+x,1/2-y 1/2-z,x,1/2-y 1/2-z,1/2+x,-y y,-z,x y,1/2-z,1/2+x 1/2+y,-z,1/2+x 1/2+y,1/2-z,x -x,y,-z -x,1/2+y,1/2-z 1/2-x,y,1/2-z 1/2-x,1/2+y,-z x,-z,-y x,1/2-z,1/2-y 1/2+x,-z,1/2-y 1/2+x,1/2-z,-y -z,y,x -z,1/2+y,1/2+x 1/2-z,y,1/2+x 1/2-z,1/2+y,x y,-x,-z y,1/2-x,1/2-z 1/2+y,-x,1/2-z 1/2+y,1/2-x,-z -x,z,y -x,1/2+z,1/2+y 1/2-x,z,1/2+y 1/2-x,1/2+z,y z,-y,-x z,1/2-y,1/2-x 1/2+z,-y,1/2-x 1/2+z,1/2-y,-x -y,x,z -y,1/2+x,1/2+z 1/2-y,x,1/2+z 1/2-y,1/2+x,z x,z,y x,1/2+z,1/2+y 1/2+x,z,1/2+y 1/2+x,1/2+z,y -z,-y,-x -z,1/2-y,1/2-x 1/2-z,-y,1/2-x 1/2-z,1/2-y,-x y,x,z y,1/2+x,1/2+z 1/2+y,x,1/2+z 1/2+y,1/2+x,z -x,-z,-y -x,1/2-z,1/2-y 1/2-x,-z,1/2-y 1/2-x,1/2-z,-y z,y,x z,1/2+y,1/2+x 1/2+z,y,1/2+x 1/2+z,1/2+y,x -y,-x,-z -y,1/2-x,1/2-z 1/2-y,-x,1/2-z 1/2-y,1/2-x,-z z,x,-y z,1/2+x,1/2-y 1/2+z,x,1/2-y 1/2+z,1/2+x,-y -y,-z,x -y,1/2-z,1/2+x 1/2-y,-z,1/2+x 1/2-y,1/2-z,x x,y,-z x,1/2+y,1/2-z 1/2+x,y,1/2-z 1/2+x,1/2+y,-z -z,-x,y -z,1/2-x,1/2+y 1/2-z,-x,1/2+y 1/2-z,1/2-x,y y,z,-x y,1/2+z,1/2-x 1/2+y,z,1/2-x 1/2+y,1/2+z,-x -x,-y,z -x,1/2-y,1/2+z 1/2-x,-y,1/2+z 1/2-x,1/2-y,z -z,x,y -z,1/2+x,1/2+y 1/2-z,x,1/2+y 1/2-z,1/2+x,y y,-z,-x y,1/2-z,1/2-x 1/2+y,-z,1/2-x 1/2+y,1/2-z,-x -x,y,z -x,1/2+y,1/2+z 1/2-x,y,1/2+z 1/2-x,1/2+y,z z,-x,-y z,1/2-x,1/2-y 1/2+z,-x,1/2-y 1/2+z,1/2-x,-y -y,z,x -y,1/2+z,1/2+x 1/2-y,z,1/2+x 1/2-y,1/2+z,x x,-y,-z x,1/2-y,1/2-z 1/2+x,-y,1/2-z 1/2+x,1/2-y,-z -x,z,-y -x,1/2+z,1/2-y 1/2-x,z,1/2-y 1/2-x,1/2+z,-y z,-y,x z,1/2-y,1/2+x 1/2+z,-y,1/2+x 1/2+z,1/2-y,x -y,x,-z -y,1/2+x,1/2-z 1/2-y,x,1/2-z 1/2-y,1/2+x,-z x,-z,y x,1/2-z,1/2+y 1/2+x,-z,1/2+y 1/2+x,1/2-z,y -z,y,-x -z,1/2+y,1/2-x 1/2-z,y,1/2-x 1/2-z,1/2+y,-x y,-x,z y,1/2-x,1/2+z 1/2+y,-x,1/2+z 1/2+y,1/2-x,z -x,-z,y -x,1/2-z,1/2+y 1/2-x,-z,1/2+y 1/2-x,1/2-z,y z,y,-x z,1/2+y,1/2-x 1/2+z,y,1/2-x 1/2+z,1/2+y,-x -y,-x,z -y,1/2-x,1/2+z 1/2-y,-x,1/2+z 1/2-y,1/2-x,z x,z,-y x,1/2+z,1/2-y 1/2+x,z,1/2-y 1/2+x,1/2+z,-y -z,-y,x -z,1/2-y,1/2+x 1/2-z,-y,1/2+x 1/2-z,1/2-y,x y,x,-z y,1/2+x,1/2-z 1/2+y,x,1/2-z 1/2+y,1/2+x,-z -z,-x,-y -z,1/2-x,1/2-y 1/2-z,-x,1/2-y 1/2-z,1/2-x,-y y,z,x y,1/2+z,1/2+x 1/2+y,z,1/2+x 1/2+y,1/2+z,x -x,-y,-z -x,1/2-y,1/2-z 1/2-x,-y,1/2-z 1/2-x,1/2-y,-z z,x,y z,1/2+x,1/2+y 1/2+z,x,1/2+y 1/2+z,1/2+x,y -y,-z,-x -y,1/2-z,1/2-x 1/2-y,-z,1/2-x 1/2-y,1/2-z,-x loop_ _atom_site_label _atom_site_fract_x _atom_site_fract_y _atom_site_fract_z Ag 0.00000 0.00000 0.00000 pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/BaTiO3.cif000066400000000000000000000571311470422267000236430ustar00rootroot00000000000000#------------------------------------------------------------------------------ #$Date: 2014-01-29 16:16:25 +0000 (Wed, 29 Jan 2014) $ #$Revision: 97630 $ #$URL: file:///home/coder/svn-repositories/cod/cif/1/51/32/1513252.cif $ #------------------------------------------------------------------------------ # # This file is available in the Crystallography Open Database (COD), # http://www.crystallography.net/. The original data for this entry # were provided the Crystal Eye server at # http://wwmm.ch.cam.ac.uk/crystaleye/, created by Nick Day at the # Peter Murray-Rust laboratory. # # The file may be used within the scientific community so long as # proper attribution is given to the journal article from which the # data were obtained. # data_1513252 loop_ _publ_author_name 'Yasuda, Nobuhiro' 'Murayama, Haruno' 'Fukuyama, Yoshimitsu' 'Kim, Jungeun' 'Kimura, Shigeru' 'Toriumi, Koshiro' 'Tanaka, Yoshihito' 'Moritomo, Yutaka' 'Kuroiwa, Yoshihiro' 'Kato, Kenichi' 'Tanaka, Hitoshi' 'Takata, Masaki' _publ_section_title ; X-ray diffractometry for the structure determination of a submicrometre single powder grain ; _journal_coeditor_code IA5038 _journal_issue 3 _journal_name_full 'Journal of Synchrotron Radiation' _journal_page_first 352 _journal_page_last 357 _journal_paper_doi 10.1107/S090904950900675X _journal_volume 16 _journal_year 2009 _chemical_formula_moiety 'Ba O3 Ti' _chemical_formula_sum 'Ba O3 Ti' _chemical_formula_weight 233.24 _chemical_name_systematic ; ? ; _space_group_IT_number 99 _symmetry_cell_setting tetragonal _symmetry_space_group_name_Hall 'P 4 -2' _symmetry_space_group_name_H-M 'P 4 m m' _atom_sites_solution_primary direct _atom_sites_solution_secondary difmap _audit_creation_method SHELXL-97 _cell_angle_alpha 90.00 _cell_angle_beta 90.00 _cell_angle_gamma 90.00 _cell_formula_units_Z 1 _cell_length_a 3.9905(13) _cell_length_b 3.9905(13) _cell_length_c 4.0412(14) _cell_measurement_temperature 300(2) _cell_volume 64.35(4) _computing_cell_refinement 'Rapid Auto(Rigaku)' _computing_data_collection 'Rapid Auto(Rigaku)' _computing_data_reduction 'Rapid Auto(Rigaku)' _computing_structure_refinement 'SHELXL-97 (Sheldrick, 1997)' _computing_structure_solution 'SHELXS-97 (Sheldrick, 1990)' _diffrn_ambient_temperature 300(2) _diffrn_detector_area_resol_mean 29 _diffrn_measured_fraction_theta_full 1.000 _diffrn_measured_fraction_theta_max 1.000 _diffrn_measurement_device_type 'Rigaku Saturn 724' _diffrn_measurement_method 'Oscillation Photograph' _diffrn_radiation_monochromator 'Si(111) Channel Cut' _diffrn_radiation_source 'synchrotron radiation SPring-8 BL40XU' _diffrn_radiation_type Synchrotron _diffrn_radiation_wavelength 0.83351 _diffrn_reflns_av_R_equivalents 0.0940 _diffrn_reflns_av_sigmaI/netI 0.0823 _diffrn_reflns_limit_h_max 4 _diffrn_reflns_limit_h_min -2 _diffrn_reflns_limit_k_max 4 _diffrn_reflns_limit_k_min -4 _diffrn_reflns_limit_l_max 4 _diffrn_reflns_limit_l_min -4 _diffrn_reflns_number 468 _diffrn_reflns_theta_full 29.26 _diffrn_reflns_theta_max 29.26 _diffrn_reflns_theta_min 5.92 _exptl_absorpt_coefficient_mu 18.037 _exptl_absorpt_correction_type none _exptl_crystal_colour White _exptl_crystal_density_diffrn 6.018 _exptl_crystal_density_meas 'not measured' _exptl_crystal_density_method 'not measured' _exptl_crystal_F_000 102 _exptl_crystal_size_max 0.0006 _exptl_crystal_size_mid 0.0006 _exptl_crystal_size_min 0.0003 _refine_diff_density_max 2.079 _refine_diff_density_min -1.980 _refine_diff_density_rms 0.570 _refine_ls_abs_structure_details 'Flack H D (1983), Acta Cryst. A39, 876-881' _refine_ls_abs_structure_Flack 0.8(7) _refine_ls_extinction_coef none _refine_ls_extinction_method none _refine_ls_goodness_of_fit_ref 1.168 _refine_ls_matrix_type full _refine_ls_number_parameters 9 _refine_ls_number_reflns 88 _refine_ls_number_restraints 0 _refine_ls_restrained_S_all 1.168 _refine_ls_R_factor_all 0.0575 _refine_ls_R_factor_gt 0.0524 _refine_ls_shift/su_max <0.001 _refine_ls_shift/su_mean <0.001 _refine_ls_structure_factor_coef Fsqd _refine_ls_weighting_details 'calc w=1/[\s^2^(Fo^2^)+(0.0473P)^2^+0.0000P] where P=(Fo^2^+2Fc^2^)/3' _refine_ls_weighting_scheme calc _refine_ls_wR_factor_gt 0.0994 _refine_ls_wR_factor_ref 0.1024 _reflns_number_gt 85 _reflns_number_total 88 _reflns_threshold_expression >2\s(I) _[local]_cod_data_source_file ia5038sup1.cif _[local]_cod_data_source_block I _cod_depositor_comments ; The following automatic conversions were performed: '_symmetry_cell_setting' value 'Tetragonal' changed to 'tetragonal' according to /home/saulius/struct/CIF-dictionaries/cif_core.dic dictionary named 'cif_core.dic' version 2.4.2 from 2011-04-26. Automatic conversion script Id: cif_fix_values 1891 2012-01-12 08:04:46Z andrius ; _cod_database_code 1513252 loop_ _symmetry_equiv_pos_as_xyz 'x, y, z' '-y, x, z' '-x, -y, z' 'y, -x, z' '-x, y, z' 'x, -y, z' '-y, -x, z' 'y, x, z' loop_ _atom_site_label _atom_site_type_symbol _atom_site_fract_x _atom_site_fract_y _atom_site_fract_z _atom_site_U_iso_or_equiv _atom_site_adp_type _atom_site_occupancy _atom_site_symmetry_multiplicity _atom_site_calc_flag _atom_site_refinement_flags Ba1 Ba 0.0000 0.0000 0.0000 0.0073(10) Uani 1 8 d S Ti1 Ti 0.5000 0.5000 0.476(5) 0.005(2) Uiso 1 8 d S O1 O 0.5000 0.5000 0.043(13) 0.005(8) Uiso 1 8 d S O2 O 0.0000 0.5000 0.533(13) 0.007(6) Uiso 1 4 d S loop_ _atom_site_aniso_label _atom_site_aniso_U_11 _atom_site_aniso_U_22 _atom_site_aniso_U_33 _atom_site_aniso_U_23 _atom_site_aniso_U_13 _atom_site_aniso_U_12 Ba1 0.0055(10) 0.0055(10) 0.0108(16) 0.000 0.000 0.000 loop_ _atom_type_symbol _atom_type_description _atom_type_scat_dispersion_real _atom_type_scat_dispersion_imag _atom_type_scat_source Ba Ba -0.3244 2.2819 'International Tables Vol C Tables 4.2.6.8 and 6.1.1.4' Ti Ti 0.2776 0.4457 'International Tables Vol C Tables 4.2.6.8 and 6.1.1.4' O O 0.0106 0.0060 'International Tables Vol C Tables 4.2.6.8 and 6.1.1.4' loop_ _geom_angle_atom_site_label_1 _geom_angle_atom_site_label_2 _geom_angle_atom_site_label_3 _geom_angle _geom_angle_site_symmetry_1 _geom_angle_site_symmetry_3 O2 Ba1 O2 61.8(9) 2_554 1_544 O2 Ba1 O2 93.2(16) 2_554 2_654 O2 Ba1 O2 61.8(9) 1_544 2_654 O2 Ba1 O2 61.8(9) 2_554 1_554 O2 Ba1 O2 93.2(16) 1_544 1_554 O2 Ba1 O2 61.8(9) 2_654 1_554 O2 Ba1 O1 123.7(10) 2_554 . O2 Ba1 O1 123.7(10) 1_544 . O2 Ba1 O1 62.0(9) 2_654 . O2 Ba1 O1 62.0(9) 1_554 . O2 Ba1 O1 61.9(9) 2_554 1_445 O2 Ba1 O1 61.9(9) 1_544 1_445 O2 Ba1 O1 123.7(10) 2_654 1_445 O2 Ba1 O1 123.7(10) 1_554 1_445 O1 Ba1 O1 173(2) . 1_445 O2 Ba1 O1 62.0(9) 2_554 1_455 O2 Ba1 O1 123.7(10) 1_544 1_455 O2 Ba1 O1 123.7(10) 2_654 1_455 O2 Ba1 O1 62.0(9) 1_554 1_455 O1 Ba1 O1 89.78(13) . 1_455 O1 Ba1 O1 89.78(13) 1_445 1_455 O2 Ba1 O1 123.7(10) 2_554 1_545 O2 Ba1 O1 62.0(9) 1_544 1_545 O2 Ba1 O1 62.0(9) 2_654 1_545 O2 Ba1 O1 123.7(10) 1_554 1_545 O1 Ba1 O1 89.78(13) . 1_545 O1 Ba1 O1 89.78(13) 1_445 1_545 O1 Ba1 O1 173(2) 1_455 1_545 O2 Ba1 O2 176.2(15) 2_554 2_655 O2 Ba1 O2 120.27(12) 1_544 2_655 O2 Ba1 O2 90.60(11) 2_654 2_655 O2 Ba1 O2 120.27(12) 1_554 2_655 O1 Ba1 O2 58.3(9) . 2_655 O1 Ba1 O2 115.7(10) 1_445 2_655 O1 Ba1 O2 115.7(10) 1_455 2_655 O1 Ba1 O2 58.3(9) 1_545 2_655 O2 Ba1 O2 120.27(12) 2_554 . O2 Ba1 O2 176.2(15) 1_544 . O2 Ba1 O2 120.27(12) 2_654 . O2 Ba1 O2 90.60(11) 1_554 . O1 Ba1 O2 58.3(9) . . O1 Ba1 O2 115.7(10) 1_445 . O1 Ba1 O2 58.3(9) 1_455 . O1 Ba1 O2 115.7(10) 1_545 . O2 Ba1 O2 57.4(8) 2_655 . O2 Ba1 O2 90.60(11) 2_554 2 O2 Ba1 O2 120.27(12) 1_544 2 O2 Ba1 O2 176.2(15) 2_654 2 O2 Ba1 O2 120.27(12) 1_554 2 O1 Ba1 O2 115.7(10) . 2 O1 Ba1 O2 58.3(9) 1_445 2 O1 Ba1 O2 58.3(9) 1_455 2 O1 Ba1 O2 115.7(10) 1_545 2 O2 Ba1 O2 85.6(14) 2_655 2 O2 Ba1 O2 57.4(8) . 2 O2 Ba1 O2 120.27(12) 2_554 1_545 O2 Ba1 O2 90.60(11) 1_544 1_545 O2 Ba1 O2 120.27(12) 2_654 1_545 O2 Ba1 O2 176.2(15) 1_554 1_545 O1 Ba1 O2 115.7(10) . 1_545 O1 Ba1 O2 58.3(9) 1_445 1_545 O1 Ba1 O2 115.7(10) 1_455 1_545 O1 Ba1 O2 58.3(9) 1_545 1_545 O2 Ba1 O2 57.4(8) 2_655 1_545 O2 Ba1 O2 85.6(14) . 1_545 O2 Ba1 O2 57.4(8) 2 1_545 O1 Ti1 O2 96.6(15) . 2_665 O1 Ti1 O2 96.6(15) . 1_655 O2 Ti1 O2 89.2(3) 2_665 1_655 O1 Ti1 O2 96.6(15) . 2_655 O2 Ti1 O2 167(3) 2_665 2_655 O2 Ti1 O2 89.2(3) 1_655 2_655 O1 Ti1 O2 96.6(15) . . O2 Ti1 O2 89.2(3) 2_665 . O2 Ti1 O2 167(3) 1_655 . O2 Ti1 O2 89.2(4) 2_655 . O1 Ti1 O1 180.000(14) . 1_556 O2 Ti1 O1 83.4(15) 2_665 1_556 O2 Ti1 O1 83.4(15) 1_655 1_556 O2 Ti1 O1 83.4(15) 2_655 1_556 O2 Ti1 O1 83.4(15) . 1_556 O1 Ti1 Ba1 55.7(3) . . O2 Ti1 Ba1 130.2(10) 2_665 . O2 Ti1 Ba1 130.2(10) 1_655 . O2 Ti1 Ba1 59.0(11) 2_655 . O2 Ti1 Ba1 59.0(11) . . O1 Ti1 Ba1 124.3(3) 1_556 . O1 Ti1 Ba1 55.7(3) . 1_655 O2 Ti1 Ba1 130.2(10) 2_665 1_655 O2 Ti1 Ba1 59.0(11) 1_655 1_655 O2 Ti1 Ba1 59.0(11) 2_655 1_655 O2 Ti1 Ba1 130.2(10) . 1_655 O1 Ti1 Ba1 124.3(3) 1_556 1_655 Ba1 Ti1 Ba1 71.5(3) . 1_655 O1 Ti1 Ba1 55.7(3) . 1_565 O2 Ti1 Ba1 59.0(11) 2_665 1_565 O2 Ti1 Ba1 130.2(10) 1_655 1_565 O2 Ti1 Ba1 130.2(10) 2_655 1_565 O2 Ti1 Ba1 59.0(11) . 1_565 O1 Ti1 Ba1 124.3(3) 1_556 1_565 Ba1 Ti1 Ba1 71.5(3) . 1_565 Ba1 Ti1 Ba1 111.5(6) 1_655 1_565 O1 Ti1 Ba1 55.7(3) . 1_665 O2 Ti1 Ba1 59.0(11) 2_665 1_665 O2 Ti1 Ba1 59.0(11) 1_655 1_665 O2 Ti1 Ba1 130.2(10) 2_655 1_665 O2 Ti1 Ba1 130.2(10) . 1_665 O1 Ti1 Ba1 124.3(3) 1_556 1_665 Ba1 Ti1 Ba1 111.5(6) . 1_665 Ba1 Ti1 Ba1 71.5(3) 1_655 1_665 Ba1 Ti1 Ba1 71.5(3) 1_565 1_665 O1 Ti1 Ba1 126.9(3) . 1_666 O2 Ti1 Ba1 50.9(10) 2_665 1_666 O2 Ti1 Ba1 50.9(10) 1_655 1_666 O2 Ti1 Ba1 119.5(12) 2_655 1_666 O2 Ti1 Ba1 119.5(12) . 1_666 O1 Ti1 Ba1 53.1(3) 1_556 1_666 Ba1 Ti1 Ba1 177.4(6) . 1_666 Ba1 Ti1 Ba1 109.76(3) 1_655 1_666 Ba1 Ti1 Ba1 109.76(3) 1_565 1_666 Ba1 Ti1 Ba1 71.17(3) 1_665 1_666 O1 Ti1 Ba1 126.9(3) . 1_556 O2 Ti1 Ba1 119.5(12) 2_665 1_556 O2 Ti1 Ba1 119.5(12) 1_655 1_556 O2 Ti1 Ba1 50.9(10) 2_655 1_556 O2 Ti1 Ba1 50.9(10) . 1_556 O1 Ti1 Ba1 53.1(3) 1_556 1_556 Ba1 Ti1 Ba1 71.17(3) . 1_556 Ba1 Ti1 Ba1 109.76(3) 1_655 1_556 Ba1 Ti1 Ba1 109.76(3) 1_565 1_556 Ba1 Ti1 Ba1 177.4(6) 1_665 1_556 Ba1 Ti1 Ba1 106.2(6) 1_666 1_556 Ti1 O1 Ti1 180.000(5) . 1_554 Ti1 O1 Ba1 93.5(10) . . Ti1 O1 Ba1 86.5(10) 1_554 . Ti1 O1 Ba1 93.5(10) . 1_665 Ti1 O1 Ba1 86.5(10) 1_554 1_665 Ba1 O1 Ba1 173(2) . 1_665 Ti1 O1 Ba1 93.5(10) . 1_655 Ti1 O1 Ba1 86.5(10) 1_554 1_655 Ba1 O1 Ba1 89.78(13) . 1_655 Ba1 O1 Ba1 89.78(13) 1_665 1_655 Ti1 O1 Ba1 93.5(10) . 1_565 Ti1 O1 Ba1 86.5(10) 1_554 1_565 Ba1 O1 Ba1 89.78(13) . 1_565 Ba1 O1 Ba1 89.78(13) 1_665 1_565 Ba1 O1 Ba1 173(2) 1_655 1_565 Ti1 O2 Ti1 167(3) 1_455 . Ti1 O2 Ba1 94.5(10) 1_455 1_566 Ti1 O2 Ba1 94.5(10) . 1_566 Ti1 O2 Ba1 94.5(10) 1_455 1_556 Ti1 O2 Ba1 94.5(10) . 1_556 Ba1 O2 Ba1 93.2(16) 1_566 1_556 Ti1 O2 Ba1 85.2(11) 1_455 . Ti1 O2 Ba1 85.2(11) . . Ba1 O2 Ba1 176.2(15) 1_566 . Ba1 O2 Ba1 90.60(11) 1_556 . Ti1 O2 Ba1 85.2(11) 1_455 1_565 Ti1 O2 Ba1 85.2(11) . 1_565 Ba1 O2 Ba1 90.60(11) 1_566 1_565 Ba1 O2 Ba1 176.2(15) 1_556 1_565 Ba1 O2 Ba1 85.6(14) . 1_565 loop_ _geom_bond_atom_site_label_1 _geom_bond_atom_site_label_2 _geom_bond_distance _geom_bond_site_symmetry_2 Ba1 O2 2.75(4) 2_554 Ba1 O2 2.75(4) 1_544 Ba1 O2 2.75(4) 2_654 Ba1 O2 2.75(4) 1_554 Ba1 O1 2.827(3) . Ba1 O1 2.827(3) 1_445 Ba1 O1 2.827(3) 1_455 Ba1 O1 2.827(3) 1_545 Ba1 O2 2.94(4) 2_655 Ba1 O2 2.94(4) . Ba1 O2 2.94(4) 2 Ba1 O2 2.94(4) 1_545 Ti1 O1 1.75(5) . Ti1 O2 2.009(6) 2_665 Ti1 O2 2.009(6) 1_655 Ti1 O2 2.009(6) 2_655 Ti1 O2 2.009(6) . Ti1 O1 2.29(5) 1_556 Ti1 Ba1 3.414(12) 1_655 Ti1 Ba1 3.414(12) 1_565 Ti1 Ba1 3.414(12) 1_665 Ti1 Ba1 3.529(13) 1_666 Ti1 Ba1 3.529(13) 1_556 O1 Ti1 2.29(5) 1_554 O1 Ba1 2.827(3) 1_665 O1 Ba1 2.827(3) 1_655 O1 Ba1 2.827(3) 1_565 O2 Ti1 2.009(6) 1_455 O2 Ba1 2.75(4) 1_566 O2 Ba1 2.75(4) 1_556 O2 Ba1 2.94(4) 1_565 loop_ _geom_torsion_atom_site_label_1 _geom_torsion_atom_site_label_2 _geom_torsion_atom_site_label_3 _geom_torsion_atom_site_label_4 _geom_torsion _geom_torsion_site_symmetry_1 _geom_torsion_site_symmetry_4 O2 Ba1 Ti1 O1 -61.5(15) 2_554 . O2 Ba1 Ti1 O1 61.5(15) 1_544 . O2 Ba1 Ti1 O1 30.9(5) 2_654 . O2 Ba1 Ti1 O1 -30.9(5) 1_554 . O1 Ba1 Ti1 O1 180.000(3) 1_445 . O1 Ba1 Ti1 O1 -92.9(9) 1_455 . O1 Ba1 Ti1 O1 92.9(8) 1_545 . O2 Ba1 Ti1 O1 124.9(12) 2_655 . O2 Ba1 Ti1 O1 -124.9(12) . . O2 Ba1 Ti1 O1 -151.3(4) 2 . O2 Ba1 Ti1 O1 151.3(4) 1_545 . O2 Ba1 Ti1 O2 5.3(12) 2_554 2_665 O2 Ba1 Ti1 O2 128(3) 1_544 2_665 O2 Ba1 Ti1 O2 98(2) 2_654 2_665 O2 Ba1 Ti1 O2 35.9(12) 1_554 2_665 O1 Ba1 Ti1 O2 66.9(15) . 2_665 O1 Ba1 Ti1 O2 -113.1(15) 1_445 2_665 O1 Ba1 Ti1 O2 -26.1(17) 1_455 2_665 O1 Ba1 Ti1 O2 159.8(18) 1_545 2_665 O2 Ba1 Ti1 O2 -168(3) 2_655 2_665 O2 Ba1 Ti1 O2 -58.1(5) . 2_665 O2 Ba1 Ti1 O2 -84.4(12) 2 2_665 O2 Ba1 Ti1 O2 -141.9(19) 1_545 2_665 O2 Ba1 Ti1 O2 -128(3) 2_554 1_655 O2 Ba1 Ti1 O2 -5.3(12) 1_544 1_655 O2 Ba1 Ti1 O2 -35.9(11) 2_654 1_655 O2 Ba1 Ti1 O2 -98(2) 1_554 1_655 O1 Ba1 Ti1 O2 -66.9(15) . 1_655 O1 Ba1 Ti1 O2 113.1(15) 1_445 1_655 O1 Ba1 Ti1 O2 -159.8(18) 1_455 1_655 O1 Ba1 Ti1 O2 26.1(17) 1_545 1_655 O2 Ba1 Ti1 O2 58.1(5) 2_655 1_655 O2 Ba1 Ti1 O2 168(3) . 1_655 O2 Ba1 Ti1 O2 141.9(19) 2 1_655 O2 Ba1 Ti1 O2 84.4(12) 1_545 1_655 O2 Ba1 Ti1 O2 174(3) 2_554 2_655 O2 Ba1 Ti1 O2 -63.4(8) 1_544 2_655 O2 Ba1 Ti1 O2 -94.0(7) 2_654 2_655 O2 Ba1 Ti1 O2 -155.9(16) 1_554 2_655 O1 Ba1 Ti1 O2 -124.9(12) . 2_655 O1 Ba1 Ti1 O2 55.1(12) 1_445 2_655 O1 Ba1 Ti1 O2 142.1(15) 1_455 2_655 O1 Ba1 Ti1 O2 -32.0(14) 1_545 2_655 O2 Ba1 Ti1 O2 110(2) . 2_655 O2 Ba1 Ti1 O2 83.8(16) 2 2_655 O2 Ba1 Ti1 O2 26.3(8) 1_545 2_655 O2 Ba1 Ti1 O2 63.4(8) 2_554 . O2 Ba1 Ti1 O2 -174(3) 1_544 . O2 Ba1 Ti1 O2 155.9(16) 2_654 . O2 Ba1 Ti1 O2 94.0(7) 1_554 . O1 Ba1 Ti1 O2 124.9(12) . . O1 Ba1 Ti1 O2 -55.1(12) 1_445 . O1 Ba1 Ti1 O2 32.0(14) 1_455 . O1 Ba1 Ti1 O2 -142.1(15) 1_545 . O2 Ba1 Ti1 O2 -110(2) 2_655 . O2 Ba1 Ti1 O2 -26.3(8) 2 . O2 Ba1 Ti1 O2 -83.8(16) 1_545 . O2 Ba1 Ti1 O1 118.5(15) 2_554 1_556 O2 Ba1 Ti1 O1 -118.5(15) 1_544 1_556 O2 Ba1 Ti1 O1 -149.1(5) 2_654 1_556 O2 Ba1 Ti1 O1 149.1(5) 1_554 1_556 O1 Ba1 Ti1 O1 180.000(2) . 1_556 O1 Ba1 Ti1 O1 0.000(2) 1_445 1_556 O1 Ba1 Ti1 O1 87.1(8) 1_455 1_556 O1 Ba1 Ti1 O1 -87.1(9) 1_545 1_556 O2 Ba1 Ti1 O1 -55.1(12) 2_655 1_556 O2 Ba1 Ti1 O1 55.1(12) . 1_556 O2 Ba1 Ti1 O1 28.7(4) 2 1_556 O2 Ba1 Ti1 O1 -28.7(4) 1_545 1_556 O2 Ba1 Ti1 Ba1 -122.1(15) 2_554 1_655 O2 Ba1 Ti1 Ba1 0.9(16) 1_544 1_655 O2 Ba1 Ti1 Ba1 -29.7(6) 2_654 1_655 O2 Ba1 Ti1 Ba1 -91.6(5) 1_554 1_655 O1 Ba1 Ti1 Ba1 -60.62(19) . 1_655 O1 Ba1 Ti1 Ba1 119.38(19) 1_445 1_655 O1 Ba1 Ti1 Ba1 -153.5(9) 1_455 1_655 O1 Ba1 Ti1 Ba1 32.3(9) 1_545 1_655 O2 Ba1 Ti1 Ba1 64.3(12) 2_655 1_655 O2 Ba1 Ti1 Ba1 174.4(12) . 1_655 O2 Ba1 Ti1 Ba1 148.1(4) 2 1_655 O2 Ba1 Ti1 Ba1 90.7(5) 1_545 1_655 O2 Ba1 Ti1 Ba1 -0.9(16) 2_554 1_565 O2 Ba1 Ti1 Ba1 122.1(15) 1_544 1_565 O2 Ba1 Ti1 Ba1 91.6(5) 2_654 1_565 O2 Ba1 Ti1 Ba1 29.7(6) 1_554 1_565 O1 Ba1 Ti1 Ba1 60.62(19) . 1_565 O1 Ba1 Ti1 Ba1 -119.38(19) 1_445 1_565 O1 Ba1 Ti1 Ba1 -32.3(9) 1_455 1_565 O1 Ba1 Ti1 Ba1 153.5(9) 1_545 1_565 O2 Ba1 Ti1 Ba1 -174.4(12) 2_655 1_565 O2 Ba1 Ti1 Ba1 -64.3(12) . 1_565 O2 Ba1 Ti1 Ba1 -90.7(5) 2 1_565 O2 Ba1 Ti1 Ba1 -148.1(4) 1_545 1_565 O2 Ba1 Ti1 Ba1 -61.5(15) 2_554 1_665 O2 Ba1 Ti1 Ba1 61.5(15) 1_544 1_665 O2 Ba1 Ti1 Ba1 30.9(5) 2_654 1_665 O2 Ba1 Ti1 Ba1 -30.9(5) 1_554 1_665 O1 Ba1 Ti1 Ba1 0.000(1) . 1_665 O1 Ba1 Ti1 Ba1 180.0 1_445 1_665 O1 Ba1 Ti1 Ba1 -92.9(9) 1_455 1_665 O1 Ba1 Ti1 Ba1 92.9(9) 1_545 1_665 O2 Ba1 Ti1 Ba1 124.9(12) 2_655 1_665 O2 Ba1 Ti1 Ba1 -124.9(12) . 1_665 O2 Ba1 Ti1 Ba1 -151.3(4) 2 1_665 O2 Ba1 Ti1 Ba1 151.3(4) 1_545 1_665 O2 Ba1 Ti1 Ba1 118.5(15) 2_554 1_666 O2 Ba1 Ti1 Ba1 -118.5(15) 1_544 1_666 O2 Ba1 Ti1 Ba1 -149.1(5) 2_654 1_666 O2 Ba1 Ti1 Ba1 149.1(5) 1_554 1_666 O1 Ba1 Ti1 Ba1 180.0 . 1_666 O1 Ba1 Ti1 Ba1 0.0 1_445 1_666 O1 Ba1 Ti1 Ba1 87.1(9) 1_455 1_666 O1 Ba1 Ti1 Ba1 -87.1(9) 1_545 1_666 O2 Ba1 Ti1 Ba1 -55.1(12) 2_655 1_666 O2 Ba1 Ti1 Ba1 55.1(12) . 1_666 O2 Ba1 Ti1 Ba1 28.7(4) 2 1_666 O2 Ba1 Ti1 Ba1 -28.7(4) 1_545 1_666 O2 Ba1 Ti1 Ba1 118.5(15) 2_554 1_556 O2 Ba1 Ti1 Ba1 -118.5(15) 1_544 1_556 O2 Ba1 Ti1 Ba1 -149.1(5) 2_654 1_556 O2 Ba1 Ti1 Ba1 149.1(5) 1_554 1_556 O1 Ba1 Ti1 Ba1 180.0 . 1_556 O1 Ba1 Ti1 Ba1 0.0 1_445 1_556 O1 Ba1 Ti1 Ba1 87.1(9) 1_455 1_556 O1 Ba1 Ti1 Ba1 -87.1(9) 1_545 1_556 O2 Ba1 Ti1 Ba1 -55.1(12) 2_655 1_556 O2 Ba1 Ti1 Ba1 55.1(12) . 1_556 O2 Ba1 Ti1 Ba1 28.7(4) 2 1_556 O2 Ba1 Ti1 Ba1 -28.7(4) 1_545 1_556 O2 Ti1 O1 Ti1 -56(100) 2_665 1_554 O2 Ti1 O1 Ti1 -146(100) 1_655 1_554 O2 Ti1 O1 Ti1 124(100) 2_655 1_554 O2 Ti1 O1 Ti1 34(100) . 1_554 O1 Ti1 O1 Ti1 0.0 1_556 1_554 Ba1 Ti1 O1 Ti1 79(100) . 1_554 Ba1 Ti1 O1 Ti1 169(100) 1_655 1_554 Ba1 Ti1 O1 Ti1 -11(100) 1_565 1_554 Ba1 Ti1 O1 Ti1 -101(100) 1_665 1_554 Ba1 Ti1 O1 Ti1 -101(100) 1_666 1_554 Ba1 Ti1 O1 Ti1 79(100) 1_556 1_554 O2 Ti1 O1 Ba1 -135.000(4) 2_665 . O2 Ti1 O1 Ba1 135.000(1) 1_655 . O2 Ti1 O1 Ba1 45.000(3) 2_655 . O2 Ti1 O1 Ba1 -45.000(1) . . O1 Ti1 O1 Ba1 138(100) 1_556 . Ba1 Ti1 O1 Ba1 90.0 1_655 . Ba1 Ti1 O1 Ba1 -90.000(3) 1_565 . Ba1 Ti1 O1 Ba1 180.000(3) 1_665 . Ba1 Ti1 O1 Ba1 180.0 1_666 . Ba1 Ti1 O1 Ba1 0.000(3) 1_556 . O2 Ti1 O1 Ba1 45.000(4) 2_665 1_665 O2 Ti1 O1 Ba1 -45.000(1) 1_655 1_665 O2 Ti1 O1 Ba1 -135.000(3) 2_655 1_665 O2 Ti1 O1 Ba1 135.000(1) . 1_665 O1 Ti1 O1 Ba1 -42(100) 1_556 1_665 Ba1 Ti1 O1 Ba1 180.0 . 1_665 Ba1 Ti1 O1 Ba1 -90.0 1_655 1_665 Ba1 Ti1 O1 Ba1 90.000(3) 1_565 1_665 Ba1 Ti1 O1 Ba1 0.000(1) 1_666 1_665 Ba1 Ti1 O1 Ba1 180.000(2) 1_556 1_665 O2 Ti1 O1 Ba1 135.000(4) 2_665 1_655 O2 Ti1 O1 Ba1 45.000(1) 1_655 1_655 O2 Ti1 O1 Ba1 -45.000(3) 2_655 1_655 O2 Ti1 O1 Ba1 -135.000(2) . 1_655 O1 Ti1 O1 Ba1 48(100) 1_556 1_655 Ba1 Ti1 O1 Ba1 -90.0 . 1_655 Ba1 Ti1 O1 Ba1 180.000(3) 1_565 1_655 Ba1 Ti1 O1 Ba1 90.000(3) 1_665 1_655 Ba1 Ti1 O1 Ba1 90.0 1_666 1_655 Ba1 Ti1 O1 Ba1 -90.000(3) 1_556 1_655 O2 Ti1 O1 Ba1 -45.000(4) 2_665 1_565 O2 Ti1 O1 Ba1 -135.0 1_655 1_565 O2 Ti1 O1 Ba1 135.000(3) 2_655 1_565 O2 Ti1 O1 Ba1 45.000(1) . 1_565 O1 Ti1 O1 Ba1 -132(100) 1_556 1_565 Ba1 Ti1 O1 Ba1 90.0 . 1_565 Ba1 Ti1 O1 Ba1 180.0 1_655 1_565 Ba1 Ti1 O1 Ba1 -90.000(3) 1_665 1_565 Ba1 Ti1 O1 Ba1 -90.000(1) 1_666 1_565 Ba1 Ti1 O1 Ba1 90.000(2) 1_556 1_565 O2 Ba1 O1 Ti1 141.9(10) 2_554 . O2 Ba1 O1 Ti1 -141.9(10) 1_544 . O2 Ba1 O1 Ti1 -144.4(8) 2_654 . O2 Ba1 O1 Ti1 144.4(8) 1_554 . O1 Ba1 O1 Ti1 0.000(10) 1_445 . O1 Ba1 O1 Ti1 86.5(10) 1_455 . O1 Ba1 O1 Ti1 -86.5(10) 1_545 . O2 Ba1 O1 Ti1 -34.4(7) 2_655 . O2 Ba1 O1 Ti1 34.4(7) . . O2 Ba1 O1 Ti1 32.2(7) 2 . O2 Ba1 O1 Ti1 -32.2(7) 1_545 . O2 Ba1 O1 Ti1 -38.1(10) 2_554 1_554 O2 Ba1 O1 Ti1 38.1(10) 1_544 1_554 O2 Ba1 O1 Ti1 35.6(8) 2_654 1_554 O2 Ba1 O1 Ti1 -35.6(8) 1_554 1_554 O1 Ba1 O1 Ti1 180.000(10) 1_445 1_554 O1 Ba1 O1 Ti1 -93.5(10) 1_455 1_554 O1 Ba1 O1 Ti1 93.5(10) 1_545 1_554 O2 Ba1 O1 Ti1 145.6(7) 2_655 1_554 O2 Ba1 O1 Ti1 -145.6(7) . 1_554 O2 Ba1 O1 Ti1 -147.8(7) 2 1_554 O2 Ba1 O1 Ti1 147.8(7) 1_545 1_554 O2 Ba1 O1 Ba1 -38.1(10) 2_554 1_665 O2 Ba1 O1 Ba1 38.1(10) 1_544 1_665 O2 Ba1 O1 Ba1 35.6(8) 2_654 1_665 O2 Ba1 O1 Ba1 -35.6(8) 1_554 1_665 O1 Ba1 O1 Ba1 180.000(10) 1_445 1_665 O1 Ba1 O1 Ba1 -93.5(10) 1_455 1_665 O1 Ba1 O1 Ba1 93.5(10) 1_545 1_665 O2 Ba1 O1 Ba1 145.6(7) 2_655 1_665 O2 Ba1 O1 Ba1 -145.6(7) . 1_665 O2 Ba1 O1 Ba1 -147.8(7) 2 1_665 O2 Ba1 O1 Ba1 147.8(7) 1_545 1_665 O2 Ba1 O1 Ba1 -124.6(10) 2_554 1_655 O2 Ba1 O1 Ba1 -48.3(18) 1_544 1_655 O2 Ba1 O1 Ba1 -50.9(11) 2_654 1_655 O2 Ba1 O1 Ba1 -122.1(14) 1_554 1_655 O1 Ba1 O1 Ba1 93.5(10) 1_445 1_655 O1 Ba1 O1 Ba1 180.0 1_455 1_655 O1 Ba1 O1 Ba1 7(2) 1_545 1_655 O2 Ba1 O1 Ba1 59.2(10) 2_655 1_655 O2 Ba1 O1 Ba1 127.9(15) . 1_655 O2 Ba1 O1 Ba1 125.8(9) 2 1_655 O2 Ba1 O1 Ba1 61.3(15) 1_545 1_655 O2 Ba1 O1 Ba1 48.3(18) 2_554 1_565 O2 Ba1 O1 Ba1 124.6(10) 1_544 1_565 O2 Ba1 O1 Ba1 122.1(14) 2_654 1_565 O2 Ba1 O1 Ba1 50.9(11) 1_554 1_565 O1 Ba1 O1 Ba1 -93.5(10) 1_445 1_565 O1 Ba1 O1 Ba1 -7(2) 1_455 1_565 O1 Ba1 O1 Ba1 180.0 1_545 1_565 O2 Ba1 O1 Ba1 -127.9(15) 2_655 1_565 O2 Ba1 O1 Ba1 -59.2(10) . 1_565 O2 Ba1 O1 Ba1 -61.3(15) 2 1_565 O2 Ba1 O1 Ba1 -125.8(9) 1_545 1_565 O1 Ti1 O2 Ti1 0.000(16) . 1_455 O2 Ti1 O2 Ti1 96.6(14) 2_665 1_455 O2 Ti1 O2 Ti1 180.000(2) 1_655 1_455 O2 Ti1 O2 Ti1 -96.6(14) 2_655 1_455 O1 Ti1 O2 Ti1 180.000(14) 1_556 1_455 Ba1 Ti1 O2 Ti1 -43.0(6) . 1_455 Ba1 Ti1 O2 Ti1 -49.9(11) 1_655 1_455 Ba1 Ti1 O2 Ti1 43.0(6) 1_565 1_455 Ba1 Ti1 O2 Ti1 49.9(11) 1_665 1_455 Ba1 Ti1 O2 Ti1 139.5(6) 1_666 1_455 Ba1 Ti1 O2 Ti1 -133.2(9) 1_556 1_455 O1 Ti1 O2 Ba1 -133.2(9) . 1_566 O2 Ti1 O2 Ba1 -37(2) 2_665 1_566 O2 Ti1 O2 Ba1 46.8(9) 1_655 1_566 O2 Ti1 O2 Ba1 130.2(7) 2_655 1_566 O1 Ti1 O2 Ba1 46.8(9) 1_556 1_566 Ba1 Ti1 O2 Ba1 -176.2(15) . 1_566 Ba1 Ti1 O2 Ba1 176.9(7) 1_655 1_566 Ba1 Ti1 O2 Ba1 -90.2(3) 1_565 1_566 Ba1 Ti1 O2 Ba1 -83.3(18) 1_665 1_566 Ba1 Ti1 O2 Ba1 6.3(14) 1_666 1_566 Ba1 Ti1 O2 Ba1 93.6(18) 1_556 1_566 O1 Ti1 O2 Ba1 133.2(9) . 1_556 O2 Ti1 O2 Ba1 -130.2(7) 2_665 1_556 O2 Ti1 O2 Ba1 -46.8(9) 1_655 1_556 O2 Ti1 O2 Ba1 37(2) 2_655 1_556 O1 Ti1 O2 Ba1 -46.8(9) 1_556 1_556 Ba1 Ti1 O2 Ba1 90.2(3) . 1_556 Ba1 Ti1 O2 Ba1 83.3(18) 1_655 1_556 Ba1 Ti1 O2 Ba1 176.2(15) 1_565 1_556 Ba1 Ti1 O2 Ba1 -176.9(7) 1_665 1_556 Ba1 Ti1 O2 Ba1 -87.3(6) 1_666 1_556 O1 Ti1 O2 Ba1 43.0(6) . . O2 Ti1 O2 Ba1 139.6(9) 2_665 . O2 Ti1 O2 Ba1 -137.0(6) 1_655 . O2 Ti1 O2 Ba1 -54(2) 2_655 . O1 Ti1 O2 Ba1 -137.0(6) 1_556 . Ba1 Ti1 O2 Ba1 -6.9(16) 1_655 . Ba1 Ti1 O2 Ba1 86.0(13) 1_565 . Ba1 Ti1 O2 Ba1 92.9(8) 1_665 . Ba1 Ti1 O2 Ba1 -177.5(5) 1_666 . Ba1 Ti1 O2 Ba1 -90.2(3) 1_556 . O1 Ti1 O2 Ba1 -43.0(6) . 1_565 O2 Ti1 O2 Ba1 54(2) 2_665 1_565 O2 Ti1 O2 Ba1 137.0(6) 1_655 1_565 O2 Ti1 O2 Ba1 -139.6(9) 2_655 1_565 O1 Ti1 O2 Ba1 137.0(6) 1_556 1_565 Ba1 Ti1 O2 Ba1 -86.0(13) . 1_565 Ba1 Ti1 O2 Ba1 -92.9(8) 1_655 1_565 Ba1 Ti1 O2 Ba1 6.9(16) 1_665 1_565 Ba1 Ti1 O2 Ba1 96.5(12) 1_666 1_565 Ba1 Ti1 O2 Ba1 -176.2(15) 1_556 1_565 O2 Ba1 O2 Ti1 28(2) 2_554 1_455 O2 Ba1 O2 Ti1 -94.5(10) 1_544 1_455 O2 Ba1 O2 Ti1 142.8(5) 2_654 1_455 O2 Ba1 O2 Ti1 85.5(10) 1_554 1_455 O1 Ba1 O2 Ti1 141.5(14) . 1_455 O1 Ba1 O2 Ti1 -42.9(14) 1_445 1_455 O1 Ba1 O2 Ti1 29.5(10) 1_455 1_455 O1 Ba1 O2 Ti1 -146.1(9) 1_545 1_455 O2 Ba1 O2 Ti1 -148.2(7) 2_655 1_455 O2 Ba1 O2 Ti1 -40.8(13) 2 1_455 O2 Ba1 O2 Ti1 -94.5(10) 1_545 1_455 O2 Ba1 O2 Ti1 -142.8(5) 2_554 . O2 Ba1 O2 Ti1 94.5(10) 1_544 . O2 Ba1 O2 Ti1 -28(2) 2_654 . O2 Ba1 O2 Ti1 -85.5(10) 1_554 . O1 Ba1 O2 Ti1 -29.5(10) . . O1 Ba1 O2 Ti1 146.1(9) 1_445 . O1 Ba1 O2 Ti1 -141.5(14) 1_455 . O1 Ba1 O2 Ti1 42.9(14) 1_545 . O2 Ba1 O2 Ti1 40.8(13) 2_655 . O2 Ba1 O2 Ti1 148.2(7) 2 . O2 Ba1 O2 Ti1 94.5(10) 1_545 . O2 Ba1 O2 Ba1 -57.3(11) 2_554 1_566 O2 Ba1 O2 Ba1 180.00(6) 1_544 1_566 O2 Ba1 O2 Ba1 57.3(11) 2_654 1_566 O2 Ba1 O2 Ba1 0.0 1_554 1_566 O1 Ba1 O2 Ba1 56.0(7) . 1_566 O1 Ba1 O2 Ba1 -128.4(7) 1_445 1_566 O1 Ba1 O2 Ba1 -56.0(7) 1_455 1_566 O1 Ba1 O2 Ba1 128.4(7) 1_545 1_566 O2 Ba1 O2 Ba1 126.3(3) 2_655 1_566 O2 Ba1 O2 Ba1 -126.3(3) 2 1_566 O2 Ba1 O2 Ba1 180.0 1_545 1_566 O2 Ba1 O2 Ba1 122.7(11) 2_554 1_556 O2 Ba1 O2 Ba1 0.00(6) 1_544 1_556 O2 Ba1 O2 Ba1 -122.7(11) 2_654 1_556 O2 Ba1 O2 Ba1 180.0 1_554 1_556 O1 Ba1 O2 Ba1 -124.0(7) . 1_556 O1 Ba1 O2 Ba1 51.6(7) 1_445 1_556 O1 Ba1 O2 Ba1 124.0(7) 1_455 1_556 O1 Ba1 O2 Ba1 -51.6(7) 1_545 1_556 O2 Ba1 O2 Ba1 -53.7(3) 2_655 1_556 O2 Ba1 O2 Ba1 53.7(3) 2 1_556 O2 Ba1 O2 Ba1 0.0 1_545 1_556 O2 Ba1 O2 Ba1 -57.3(11) 2_554 1_565 O2 Ba1 O2 Ba1 180.00(6) 1_544 1_565 O2 Ba1 O2 Ba1 57.3(11) 2_654 1_565 O2 Ba1 O2 Ba1 0.0 1_554 1_565 O1 Ba1 O2 Ba1 56.0(7) . 1_565 O1 Ba1 O2 Ba1 -128.4(7) 1_445 1_565 O1 Ba1 O2 Ba1 -56.0(7) 1_455 1_565 O1 Ba1 O2 Ba1 128.4(7) 1_545 1_565 O2 Ba1 O2 Ba1 126.3(3) 2_655 1_565 O2 Ba1 O2 Ba1 -126.3(3) 2 1_565 O2 Ba1 O2 Ba1 180.0 1_545 1_565 pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/C_graphite_hex.cif000066400000000000000000000027211470422267000255260ustar00rootroot00000000000000#------------------------------------------------------------------------------ #$Date: 2008-03-10 08:25:41 +0000 (Mon, 10 Mar 2008) $ #$Revision: 255 $ #$URL: svn://cod.ibt.lt/cod/cif/9/9008569.cif $ #------------------------------------------------------------------------------ # # This file is available in the Crystallography Open Database (COD), # http://www.crystallography.net/ # # All data on this site have been placed in the public domain by the # contributors. # data_9008569 loop_ _publ_author_name 'Wyckoff R W G' _publ_section_title ; Second edition. Interscience Publishers, New York, New York ; _journal_name_full 'Crystal Structures' _journal_page_first 7 _journal_page_last 83 _journal_volume 1 _journal_year 1963 _chemical_formula_sum C _chemical_name_mineral Graphite _symmetry_space_group_name_H-M 'P 63 m c' _cell_angle_alpha 90 _cell_angle_beta 90 _cell_angle_gamma 120 _cell_length_a 2.456 _cell_length_b 2.456 _cell_length_c 6.696 _cell_volume 34.979 loop_ _symmetry_equiv_pos_as_xyz x,y,z -x,-x+y,1/2+z x-y,x,1/2+z -y,-x,z -y,x-y,z x-y,-y,1/2+z -x,-y,1/2+z x,x-y,z -x+y,-x,z y,x,1/2+z y,-x+y,1/2+z -x+y,y,z loop_ _atom_site_label _atom_site_fract_x _atom_site_fract_y _atom_site_fract_z C1 0.00000 0.00000 0.00000 C2 0.33333 0.66667 0.00000 pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/CaF2_fluorite.cif000066400000000000000000000106041470422267000252400ustar00rootroot00000000000000data_5000222 _chemical_name_systematic 'Calcium fluoride' _chemical_name_mineral 'Fluorite' loop_ _publ_author_name 'Batchelder, D N' 'Simmons, R O' _journal_name_full 'Journal of Chemical Physics' _journal_volume 41 _journal_year 1964 _journal_page_first 2324 _journal_page_last 2329 _cell_length_a 5.4625 _cell_length_b 5.4625 _cell_length_c 5.4625 _cell_angle_alpha 90 _cell_angle_beta 90 _cell_angle_gamma 90 _cell_volume 163.0 _cell_formula_units_Z 4 _symmetry_space_group_name_H-M 'F m -3 m' _symmetry_Int_Tables_number 225 _symmetry_cell_setting cubic loop_ _symmetry_equiv_pos_as_xyz 'x,y,z' 'y,z,x' 'z,x,y' 'x,z,y' 'y,x,z' 'z,y,x' 'x,-y,-z' 'y,-z,-x' 'z,-x,-y' 'x,-z,-y' 'y,-x,-z' 'z,-y,-x' '-x,y,-z' '-y,z,-x' '-z,x,-y' '-x,z,-y' '-y,x,-z' '-z,y,-x' '-x,-y,z' '-y,-z,x' '-z,-x,y' '-x,-z,y' '-y,-x,z' '-z,-y,x' '-x,-y,-z' '-y,-z,-x' '-z,-x,-y' '-x,-z,-y' '-y,-x,-z' '-z,-y,-x' '-x,y,z' '-y,z,x' '-z,x,y' '-x,z,y' '-y,x,z' '-z,y,x' 'x,-y,z' 'y,-z,x' 'z,-x,y' 'x,-z,y' 'y,-x,z' 'z,-y,x' 'x,y,-z' 'y,z,-x' 'z,x,-y' 'x,z,-y' 'y,x,-z' 'z,y,-x' 'x,1/2+y,1/2+z' '1/2+x,y,1/2+z' '1/2+x,1/2+y,z' 'y,1/2+z,1/2+x' '1/2+y,z,1/2+x' '1/2+y,1/2+z,x' 'z,1/2+x,1/2+y' '1/2+z,x,1/2+y' '1/2+z,1/2+x,y' 'x,1/2+z,1/2+y' '1/2+x,z,1/2+y' '1/2+x,1/2+z,y' 'y,1/2+x,1/2+z' '1/2+y,x,1/2+z' '1/2+y,1/2+x,z' 'z,1/2+y,1/2+x' '1/2+z,y,1/2+x' '1/2+z,1/2+y,x' 'x,1/2-y,1/2-z' '1/2+x,-y,1/2-z' '1/2+x,1/2-y,-z' 'y,1/2-z,1/2-x' '1/2+y,-z,1/2-x' '1/2+y,1/2-z,-x' 'z,1/2-x,1/2-y' '1/2+z,-x,1/2-y' '1/2+z,1/2-x,-y' 'x,1/2-z,1/2-y' '1/2+x,-z,1/2-y' '1/2+x,1/2-z,-y' 'y,1/2-x,1/2-z' '1/2+y,-x,1/2-z' '1/2+y,1/2-x,-z' 'z,1/2-y,1/2-x' '1/2+z,-y,1/2-x' '1/2+z,1/2-y,-x' '-x,1/2+y,1/2-z' '1/2-x,y,1/2-z' '1/2-x,1/2+y,-z' '-y,1/2+z,1/2-x' '1/2-y,z,1/2-x' '1/2-y,1/2+z,-x' '-z,1/2+x,1/2-y' '1/2-z,x,1/2-y' '1/2-z,1/2+x,-y' '-x,1/2+z,1/2-y' '1/2-x,z,1/2-y' '1/2-x,1/2+z,-y' '-y,1/2+x,1/2-z' '1/2-y,x,1/2-z' '1/2-y,1/2+x,-z' '-z,1/2+y,1/2-x' '1/2-z,y,1/2-x' '1/2-z,1/2+y,-x' '-x,1/2-y,1/2+z' '1/2-x,-y,1/2+z' '1/2-x,1/2-y,z' '-y,1/2-z,1/2+x' '1/2-y,-z,1/2+x' '1/2-y,1/2-z,x' '-z,1/2-x,1/2+y' '1/2-z,-x,1/2+y' '1/2-z,1/2-x,y' '-x,1/2-z,1/2+y' '1/2-x,-z,1/2+y' '1/2-x,1/2-z,y' '-y,1/2-x,1/2+z' '1/2-y,-x,1/2+z' '1/2-y,1/2-x,z' '-z,1/2-y,1/2+x' '1/2-z,-y,1/2+x' '1/2-z,1/2-y,x' '-x,1/2-y,1/2-z' '1/2-x,-y,1/2-z' '1/2-x,1/2-y,-z' '-y,1/2-z,1/2-x' '1/2-y,-z,1/2-x' '1/2-y,1/2-z,-x' '-z,1/2-x,1/2-y' '1/2-z,-x,1/2-y' '1/2-z,1/2-x,-y' '-x,1/2-z,1/2-y' '1/2-x,-z,1/2-y' '1/2-x,1/2-z,-y' '-y,1/2-x,1/2-z' '1/2-y,-x,1/2-z' '1/2-y,1/2-x,-z' '-z,1/2-y,1/2-x' '1/2-z,-y,1/2-x' '1/2-z,1/2-y,-x' '-x,1/2+y,1/2+z' '1/2-x,y,1/2+z' '1/2-x,1/2+y,z' '-y,1/2+z,1/2+x' '1/2-y,z,1/2+x' '1/2-y,1/2+z,x' '-z,1/2+x,1/2+y' '1/2-z,x,1/2+y' '1/2-z,1/2+x,y' '-x,1/2+z,1/2+y' '1/2-x,z,1/2+y' '1/2-x,1/2+z,y' '-y,1/2+x,1/2+z' '1/2-y,x,1/2+z' '1/2-y,1/2+x,z' '-z,1/2+y,1/2+x' '1/2-z,y,1/2+x' '1/2-z,1/2+y,x' 'x,1/2-y,1/2+z' '1/2+x,-y,1/2+z' '1/2+x,1/2-y,z' 'y,1/2-z,1/2+x' '1/2+y,-z,1/2+x' '1/2+y,1/2-z,x' 'z,1/2-x,1/2+y' '1/2+z,-x,1/2+y' '1/2+z,1/2-x,y' 'x,1/2-z,1/2+y' '1/2+x,-z,1/2+y' '1/2+x,1/2-z,y' 'y,1/2-x,1/2+z' '1/2+y,-x,1/2+z' '1/2+y,1/2-x,z' 'z,1/2-y,1/2+x' '1/2+z,-y,1/2+x' '1/2+z,1/2-y,x' 'x,1/2+y,1/2-z' '1/2+x,y,1/2-z' '1/2+x,1/2+y,-z' 'y,1/2+z,1/2-x' '1/2+y,z,1/2-x' '1/2+y,1/2+z,-x' 'z,1/2+x,1/2-y' '1/2+z,x,1/2-y' '1/2+z,1/2+x,-y' 'x,1/2+z,1/2-y' '1/2+x,z,1/2-y' '1/2+x,1/2+z,-y' 'y,1/2+x,1/2-z' '1/2+y,x,1/2-z' '1/2+y,1/2+x,-z' 'z,1/2+y,1/2-x' '1/2+z,y,1/2-x' '1/2+z,1/2+y,-x' loop_ _atom_type_symbol _atom_type_oxidation_number Ca2+ 2.000 F1- -1.000 loop_ _atom_site_label _atom_site_type_symbol _atom_site_symmetry_multiplicity _atom_site_Wyckoff_symbol _atom_site_fract_x _atom_site_fract_y _atom_site_fract_z _atom_site_occupancy _atom_site_attached_hydrogens _atom_site_calc_flag Ca1 Ca2+ 4 a 0. 0. 0. 1. 0 d F1 F1- 8 c 0.25 0.25 0.25 1. 0 d pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/CaTiO3.cif000066400000000000000000000054211470422267000236370ustar00rootroot00000000000000#------------------------------------------------------------------------------ #$Date: 2013-03-28 16:17:04 +0000 (Thu, 28 Mar 2013) $ #$Revision: 77586 $ #$URL: file:///home/coder/svn-repositories/cod/cif/1/00/00/1000022.cif $ #------------------------------------------------------------------------------ # # This file is available in the Crystallography Open Database (COD), # http://www.crystallography.net/ # # All data on this site have been placed in the public domain by the # contributors. # ##data_calcium titanate data_1000022 loop_ _publ_author_name 'Beran, A' 'Libowitzky, E' 'Armbruster, T' _publ_section_title ; A single-crystal infrared spectroscopic and X-ray diffraction study of untwinned San Benito perovskite containing O H groups ; _journal_coden_ASTM CAMIA6 _journal_name_full 'Canadian Mineralogist' _journal_page_first 803 _journal_page_last 809 _journal_volume 34 _journal_year 1996 _chemical_compound_source ; from Benitoite Gem mine, San Benito Co., California,USA ; _chemical_formula_structural 'Ca (Ti O3)' _chemical_formula_sum 'Ca O3 Ti' _chemical_name_mineral Perovskite _chemical_name_systematic 'Calcium titanate' _symmetry_cell_setting orthorhombic _symmetry_Int_Tables_number 62 _symmetry_space_group_name_H-M 'P b n m' _cell_angle_alpha 90 _cell_angle_beta 90 _cell_angle_gamma 90 _cell_formula_units_Z 4 _cell_length_a 5.380(1) _cell_length_b 5.440(1) _cell_length_c 7.639(1) _cell_volume 223.6 _exptl_crystal_density_meas 4.03 _refine_ls_R_factor_all 0.027 _cod_database_code 1000022 loop_ _symmetry_equiv_pos_as_xyz x,y,z 1/2-x,1/2+y,1/2-z -x,-y,1/2+z 1/2+x,1/2-y,-z -x,-y,-z 1/2+x,1/2-y,1/2+z x,y,1/2-z 1/2-x,1/2+y,z loop_ _atom_site_aniso_label _atom_site_aniso_U_11 _atom_site_aniso_U_12 _atom_site_aniso_U_13 _atom_site_aniso_U_22 _atom_site_aniso_U_23 _atom_site_aniso_U_33 Ti1 0.0059(2) .0000(1) .0000(1) 0.0052(2) 0.00025(9) 0.0045(2) Ca1 0.0082(2) 0.0016(2) 0. 0.0083(2) 0. 0.0079(2) O1 0.0082(6) 0.0002(5) 0. 0.0086(7) 0. 0.0045(5) O2 0.0065(4) 0.0020(4) -0.0008(3) 0.0060(4) -0.0010(3) 0.0095(4) loop_ _atom_site_label _atom_site_type_symbol _atom_site_symmetry_multiplicity _atom_site_Wyckoff_symbol _atom_site_fract_x _atom_site_fract_y _atom_site_fract_z _atom_site_occupancy _atom_site_attached_hydrogens _atom_site_calc_flag Ti1 Ti4+ 4 b 0. 0.5 0. 1. 0 d Ca1 Ca2+ 4 c 0.00648(8) 0.0356(1) 0.25 1. 0 d O1 O2- 4 c 0.5711(3) -0.0161(3) 0.25 1. 0 d O2 O2- 8 d 0.2897(2) 0.2888(2) 0.0373(2) 1. 0 d loop_ _atom_type_symbol _atom_type_oxidation_number Ti4+ 4.000 Ca2+ 2.000 O2- -2.000 pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/CdSe_cadmoselite.cif000066400000000000000000000027641470422267000260130ustar00rootroot00000000000000#------------------------------------------------------------------------------ #$Date: 2008-03-10 08:25:41 +0000 (Mon, 10 Mar 2008) $ #$Revision: 255 $ #$URL: svn://cod.ibt.lt/cod/cif/9/9008863.cif $ #------------------------------------------------------------------------------ # # This file is available in the Crystallography Open Database (COD), # http://www.crystallography.net/ # # All data on this site have been placed in the public domain by the # contributors. # data_9008863 loop_ _publ_author_name 'Wyckoff R W G' _publ_section_title ; Second edition. Interscience Publishers, New York, New York Note: wurtzite structure ; _journal_name_full 'Crystal Structures' _journal_page_first 85 _journal_page_last 237 _journal_volume 1 _journal_year 1963 _chemical_formula_sum 'Cd Se' _chemical_name_mineral Cadmoselite _symmetry_space_group_name_H-M 'P 63 m c' _cell_angle_alpha 90 _cell_angle_beta 90 _cell_angle_gamma 120 _cell_length_a 4.30 _cell_length_b 4.30 _cell_length_c 7.02 _cell_volume 112.410 loop_ _symmetry_equiv_pos_as_xyz x,y,z -x,-x+y,1/2+z x-y,x,1/2+z -y,-x,z -y,x-y,z x-y,-y,1/2+z -x,-y,1/2+z x,x-y,z -x+y,-x,z y,x,1/2+z y,-x+y,1/2+z -x+y,y,z loop_ _atom_site_label _atom_site_fract_x _atom_site_fract_y _atom_site_fract_z Cd 0.33333 0.66667 0.00000 Se 0.33333 0.66667 0.38500 pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/CeO2.cif000066400000000000000000000074111470422267000233460ustar00rootroot00000000000000#------------------------------------------------------------------------------ #$Date: 2008-03-10 08:25:41 +0000 (Mon, 10 Mar 2008) $ #$Revision: 255 $ #$URL: svn://cod.ibt.lt/cod/cif/9/9009008.cif $ #------------------------------------------------------------------------------ # # This file is available in the Crystallography Open Database (COD), # http://www.crystallography.net/ # # All data on this site have been placed in the public domain by the # contributors. # data_9009008 loop_ _publ_author_name 'Wyckoff R W G' _publ_section_title ; Second edition. Interscience Publishers, New York, New York Fluorite structure ; _journal_name_full 'Crystal Structures' _journal_page_first 239 _journal_page_last 444 _journal_volume 1 _journal_year 1963 _chemical_formula_sum 'Ce O2' _chemical_name_mineral Cerianite-(Ce) _symmetry_space_group_name_H-M 'F m 3 m' _cell_angle_alpha 90 _cell_angle_beta 90 _cell_angle_gamma 90 _cell_length_a 5.4110 _cell_length_b 5.4110 _cell_length_c 5.4110 _cell_volume 158.428 loop_ _symmetry_equiv_pos_as_xyz x,y,z x,1/2+y,1/2+z 1/2+x,y,1/2+z 1/2+x,1/2+y,z z,-x,y z,1/2-x,1/2+y 1/2+z,-x,1/2+y 1/2+z,1/2-x,y -y,z,-x -y,1/2+z,1/2-x 1/2-y,z,1/2-x 1/2-y,1/2+z,-x x,-y,z x,1/2-y,1/2+z 1/2+x,-y,1/2+z 1/2+x,1/2-y,z -z,x,-y -z,1/2+x,1/2-y 1/2-z,x,1/2-y 1/2-z,1/2+x,-y y,-z,x y,1/2-z,1/2+x 1/2+y,-z,1/2+x 1/2+y,1/2-z,x -x,y,-z -x,1/2+y,1/2-z 1/2-x,y,1/2-z 1/2-x,1/2+y,-z x,-z,-y x,1/2-z,1/2-y 1/2+x,-z,1/2-y 1/2+x,1/2-z,-y -z,y,x -z,1/2+y,1/2+x 1/2-z,y,1/2+x 1/2-z,1/2+y,x y,-x,-z y,1/2-x,1/2-z 1/2+y,-x,1/2-z 1/2+y,1/2-x,-z -x,z,y -x,1/2+z,1/2+y 1/2-x,z,1/2+y 1/2-x,1/2+z,y z,-y,-x z,1/2-y,1/2-x 1/2+z,-y,1/2-x 1/2+z,1/2-y,-x -y,x,z -y,1/2+x,1/2+z 1/2-y,x,1/2+z 1/2-y,1/2+x,z x,z,y x,1/2+z,1/2+y 1/2+x,z,1/2+y 1/2+x,1/2+z,y -z,-y,-x -z,1/2-y,1/2-x 1/2-z,-y,1/2-x 1/2-z,1/2-y,-x y,x,z y,1/2+x,1/2+z 1/2+y,x,1/2+z 1/2+y,1/2+x,z -x,-z,-y -x,1/2-z,1/2-y 1/2-x,-z,1/2-y 1/2-x,1/2-z,-y z,y,x z,1/2+y,1/2+x 1/2+z,y,1/2+x 1/2+z,1/2+y,x -y,-x,-z -y,1/2-x,1/2-z 1/2-y,-x,1/2-z 1/2-y,1/2-x,-z z,x,-y z,1/2+x,1/2-y 1/2+z,x,1/2-y 1/2+z,1/2+x,-y -y,-z,x -y,1/2-z,1/2+x 1/2-y,-z,1/2+x 1/2-y,1/2-z,x x,y,-z x,1/2+y,1/2-z 1/2+x,y,1/2-z 1/2+x,1/2+y,-z -z,-x,y -z,1/2-x,1/2+y 1/2-z,-x,1/2+y 1/2-z,1/2-x,y y,z,-x y,1/2+z,1/2-x 1/2+y,z,1/2-x 1/2+y,1/2+z,-x -x,-y,z -x,1/2-y,1/2+z 1/2-x,-y,1/2+z 1/2-x,1/2-y,z -z,x,y -z,1/2+x,1/2+y 1/2-z,x,1/2+y 1/2-z,1/2+x,y y,-z,-x y,1/2-z,1/2-x 1/2+y,-z,1/2-x 1/2+y,1/2-z,-x -x,y,z -x,1/2+y,1/2+z 1/2-x,y,1/2+z 1/2-x,1/2+y,z z,-x,-y z,1/2-x,1/2-y 1/2+z,-x,1/2-y 1/2+z,1/2-x,-y -y,z,x -y,1/2+z,1/2+x 1/2-y,z,1/2+x 1/2-y,1/2+z,x x,-y,-z x,1/2-y,1/2-z 1/2+x,-y,1/2-z 1/2+x,1/2-y,-z -x,z,-y -x,1/2+z,1/2-y 1/2-x,z,1/2-y 1/2-x,1/2+z,-y z,-y,x z,1/2-y,1/2+x 1/2+z,-y,1/2+x 1/2+z,1/2-y,x -y,x,-z -y,1/2+x,1/2-z 1/2-y,x,1/2-z 1/2-y,1/2+x,-z x,-z,y x,1/2-z,1/2+y 1/2+x,-z,1/2+y 1/2+x,1/2-z,y -z,y,-x -z,1/2+y,1/2-x 1/2-z,y,1/2-x 1/2-z,1/2+y,-x y,-x,z y,1/2-x,1/2+z 1/2+y,-x,1/2+z 1/2+y,1/2-x,z -x,-z,y -x,1/2-z,1/2+y 1/2-x,-z,1/2+y 1/2-x,1/2-z,y z,y,-x z,1/2+y,1/2-x 1/2+z,y,1/2-x 1/2+z,1/2+y,-x -y,-x,z -y,1/2-x,1/2+z 1/2-y,-x,1/2+z 1/2-y,1/2-x,z x,z,-y x,1/2+z,1/2-y 1/2+x,z,1/2-y 1/2+x,1/2+z,-y -z,-y,x -z,1/2-y,1/2+x 1/2-z,-y,1/2+x 1/2-z,1/2-y,x y,x,-z y,1/2+x,1/2-z 1/2+y,x,1/2-z 1/2+y,1/2+x,-z -z,-x,-y -z,1/2-x,1/2-y 1/2-z,-x,1/2-y 1/2-z,1/2-x,-y y,z,x y,1/2+z,1/2+x 1/2+y,z,1/2+x 1/2+y,1/2+z,x -x,-y,-z -x,1/2-y,1/2-z 1/2-x,-y,1/2-z 1/2-x,1/2-y,-z z,x,y z,1/2+x,1/2+y 1/2+z,x,1/2+y 1/2+z,1/2+x,y -y,-z,-x -y,1/2-z,1/2-x 1/2-y,-z,1/2-x 1/2-y,1/2-z,-x loop_ _atom_site_label _atom_site_fract_x _atom_site_fract_y _atom_site_fract_z Ce 0.00000 0.00000 0.00000 O 0.25000 0.25000 0.25000 pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/NaCl.cif000066400000000000000000000106241470422267000234330ustar00rootroot00000000000000#------------------------------------------------------------------------------ #$Date: 2008-01-26 13:05:32 +0000 (Sat, 26 Jan 2008) $ #$Revision: 19 $ #$URL: svn://cod.ibt.lt/cod/cif/4/4300180.cif $ #------------------------------------------------------------------------------ # # This file is available in the Crystallography Open Database (COD), # http://www.crystallography.net/ # # All data on this site have been placed in the public domain by the # contributors. # data_4300180 _journal_name_full 'Inorganic Chemistry' _journal_year 2000 _publ_section_title ; Trimerization of NaC2N3 to Na3C6N9 in the Solid: Ab Initio Crystal Structure Determination of two Polymorphs of NaC2N3 and of Na3C6N9 from X-ray Powder Diffractometry ; loop_ _publ_author_name 'Barbara Juergens' 'Elisabeth Irran' 'Julius Schneider' 'Wolfgang Schnick' _chemical_formula_sum 'Na Cl' _cell_length_a 5.6393(7) _cell_length_b 5.6393 _cell_length_c 5.6393 _cell_angle_alpha 90.0 _cell_angle_beta 90.0 _cell_angle_gamma 90.0 _symmetry_cell_setting cubic _symmetry_space_group_name_H-M "F m 3 m" _diffrn_ambient_temperature 295(2) _pd_proc_ls_prof_R_factor "0.053" _pd_proc_ls_prof_wR_factor "0.072" _pd_calc_method "Rietveld Refinement" loop_ _symmetry_equiv_pos_as_xyz +x,+y,+z +z,+x,+y +y,+z,+x +x,+y,-z -z,+x,+y +y,-z,+x -z,+x,-y -y,-z,+x +y,-z,-x -x,+y,-z -z,-x,+y +x,-y,-z +y,+x,+z +z,+y,+x +x,+z,+y +y,+x,-z -z,+y,+x +x,-z,+y -z,+y,-x -x,-z,+y +x,-z,-y -y,+x,-z -z,-y,+x +y,-x,-z -x,-y,-z -z,-x,-y -y,-z,-x -x,-y,+z +z,-x,-y -y,+z,-x +z,-x,+y +y,+z,-x -y,+z,+x +x,-y,+z +z,+x,-y -x,+y,+z -y,-x,-z -z,-y,-x -x,-z,-y -y,-x,+z +z,-y,-x -x,+z,-y +z,-y,+x +x,+z,-y -x,+z,+y +y,-x,+z +z,+y,-x -y,+x,+z +x,+y+1/2,+z+1/2 +z,+x+1/2,+y+1/2 +y,+z+1/2,+x+1/2 +x,+y+1/2,-z+1/2 -z,+x+1/2,+y+1/2 +y,-z+1/2,+x+1/2 -z,+x+1/2,-y+1/2 -y,-z+1/2,+x+1/2 +y,-z+1/2,-x+1/2 -x,+y+1/2,-z+1/2 -z,-x+1/2,+y+1/2 +x,-y+1/2,-z+1/2 +y,+x+1/2,+z+1/2 +z,+y+1/2,+x+1/2 +x,+z+1/2,+y+1/2 +y,+x+1/2,-z+1/2 -z,+y+1/2,+x+1/2 +x,-z+1/2,+y+1/2 -z,+y+1/2,-x+1/2 -x,-z+1/2,+y+1/2 +x,-z+1/2,-y+1/2 -y,+x+1/2,-z+1/2 -z,-y+1/2,+x+1/2 +y,-x+1/2,-z+1/2 -x,-y+1/2,-z+1/2 -z,-x+1/2,-y+1/2 -y,-z+1/2,-x+1/2 -x,-y+1/2,+z+1/2 +z,-x+1/2,-y+1/2 -y,+z+1/2,-x+1/2 +z,-x+1/2,+y+1/2 +y,+z+1/2,-x+1/2 -y,+z+1/2,+x+1/2 +x,-y+1/2,+z+1/2 +z,+x+1/2,-y+1/2 -x,+y+1/2,+z+1/2 -y,-x+1/2,-z+1/2 -z,-y+1/2,-x+1/2 -x,-z+1/2,-y+1/2 -y,-x+1/2,+z+1/2 +z,-y+1/2,-x+1/2 -x,+z+1/2,-y+1/2 +z,-y+1/2,+x+1/2 +x,+z+1/2,-y+1/2 -x,+z+1/2,+y+1/2 +y,-x+1/2,+z+1/2 +z,+y+1/2,-x+1/2 -y,+x+1/2,+z+1/2 +x+1/2,+y,+z+1/2 +z+1/2,+x,+y+1/2 +y+1/2,+z,+x+1/2 +x+1/2,+y,-z+1/2 -z+1/2,+x,+y+1/2 +y+1/2,-z,+x+1/2 -z+1/2,+x,-y+1/2 -y+1/2,-z,+x+1/2 +y+1/2,-z,-x+1/2 -x+1/2,+y,-z+1/2 -z+1/2,-x,+y+1/2 +x+1/2,-y,-z+1/2 +y+1/2,+x,+z+1/2 +z+1/2,+y,+x+1/2 +x+1/2,+z,+y+1/2 +y+1/2,+x,-z+1/2 -z+1/2,+y,+x+1/2 +x+1/2,-z,+y+1/2 -z+1/2,+y,-x+1/2 -x+1/2,-z,+y+1/2 +x+1/2,-z,-y+1/2 -y+1/2,+x,-z+1/2 -z+1/2,-y,+x+1/2 +y+1/2,-x,-z+1/2 -x+1/2,-y,-z+1/2 -z+1/2,-x,-y+1/2 -y+1/2,-z,-x+1/2 -x+1/2,-y,+z+1/2 +z+1/2,-x,-y+1/2 -y+1/2,+z,-x+1/2 +z+1/2,-x,+y+1/2 +y+1/2,+z,-x+1/2 -y+1/2,+z,+x+1/2 +x+1/2,-y,+z+1/2 +z+1/2,+x,-y+1/2 -x+1/2,+y,+z+1/2 -y+1/2,-x,-z+1/2 -z+1/2,-y,-x+1/2 -x+1/2,-z,-y+1/2 -y+1/2,-x,+z+1/2 +z+1/2,-y,-x+1/2 -x+1/2,+z,-y+1/2 +z+1/2,-y,+x+1/2 +x+1/2,+z,-y+1/2 -x+1/2,+z,+y+1/2 +y+1/2,-x,+z+1/2 +z+1/2,+y,-x+1/2 -y+1/2,+x,+z+1/2 +x+1/2,+y+1/2,+z +z+1/2,+x+1/2,+y +y+1/2,+z+1/2,+x +x+1/2,+y+1/2,-z -z+1/2,+x+1/2,+y +y+1/2,-z+1/2,+x -z+1/2,+x+1/2,-y -y+1/2,-z+1/2,+x +y+1/2,-z+1/2,-x -x+1/2,+y+1/2,-z -z+1/2,-x+1/2,+y +x+1/2,-y+1/2,-z +y+1/2,+x+1/2,+z +z+1/2,+y+1/2,+x +x+1/2,+z+1/2,+y +y+1/2,+x+1/2,-z -z+1/2,+y+1/2,+x +x+1/2,-z+1/2,+y -z+1/2,+y+1/2,-x -x+1/2,-z+1/2,+y +x+1/2,-z+1/2,-y -y+1/2,+x+1/2,-z -z+1/2,-y+1/2,+x +y+1/2,-x+1/2,-z -x+1/2,-y+1/2,-z -z+1/2,-x+1/2,-y -y+1/2,-z+1/2,-x -x+1/2,-y+1/2,+z +z+1/2,-x+1/2,-y -y+1/2,+z+1/2,-x +z+1/2,-x+1/2,+y +y+1/2,+z+1/2,-x -y+1/2,+z+1/2,+x +x+1/2,-y+1/2,+z +z+1/2,+x+1/2,-y -x+1/2,+y+1/2,+z -y+1/2,-x+1/2,-z -z+1/2,-y+1/2,-x -x+1/2,-z+1/2,-y -y+1/2,-x+1/2,+z +z+1/2,-y+1/2,-x -x+1/2,+z+1/2,-y +z+1/2,-y+1/2,+x +x+1/2,+z+1/2,-y -x+1/2,+z+1/2,+y +y+1/2,-x+1/2,+z +z+1/2,+y+1/2,-x -y+1/2,+x+1/2,+z loop_ _atom_site_label _atom_site_fract_x _atom_site_fract_y _atom_site_fract_z _atom_site_occupancy _atom_site_thermal_displace_type _atom_site_U_iso_or_equiv _atom_site_symmetry_multiplicity NA1 .0 .0 .0 1.0 Uiso 0 4 CL2 .0 .5 .0 1.0 Uiso 0 4 pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/Ni.cif000066400000000000000000000073631470422267000231720ustar00rootroot00000000000000#------------------------------------------------------------------------------ #$Date: 2008-03-10 08:25:41 +0000 (Mon, 10 Mar 2008) $ #$Revision: 255 $ #$URL: svn://cod.ibt.lt/cod/cif/9/9008476.cif $ #------------------------------------------------------------------------------ # # This file is available in the Crystallography Open Database (COD), # http://www.crystallography.net/ # # All data on this site have been placed in the public domain by the # contributors. # data_9008476 loop_ _publ_author_name 'Wyckoff R W G' _publ_section_title ; Second edition. Interscience Publishers, New York, New York Cubic closest packed, ccp, structure ; _journal_name_full 'Crystal Structures' _journal_page_first 7 _journal_page_last 83 _journal_volume 1 _journal_year 1963 _chemical_formula_sum Ni _chemical_name_mineral Nickel _symmetry_space_group_name_H-M 'F m 3 m' _cell_angle_alpha 90 _cell_angle_beta 90 _cell_angle_gamma 90 _cell_length_a 3.52387 _cell_length_b 3.52387 _cell_length_c 3.52387 _cell_volume 43.758 loop_ _symmetry_equiv_pos_as_xyz x,y,z x,1/2+y,1/2+z 1/2+x,y,1/2+z 1/2+x,1/2+y,z z,-x,y z,1/2-x,1/2+y 1/2+z,-x,1/2+y 1/2+z,1/2-x,y -y,z,-x -y,1/2+z,1/2-x 1/2-y,z,1/2-x 1/2-y,1/2+z,-x x,-y,z x,1/2-y,1/2+z 1/2+x,-y,1/2+z 1/2+x,1/2-y,z -z,x,-y -z,1/2+x,1/2-y 1/2-z,x,1/2-y 1/2-z,1/2+x,-y y,-z,x y,1/2-z,1/2+x 1/2+y,-z,1/2+x 1/2+y,1/2-z,x -x,y,-z -x,1/2+y,1/2-z 1/2-x,y,1/2-z 1/2-x,1/2+y,-z x,-z,-y x,1/2-z,1/2-y 1/2+x,-z,1/2-y 1/2+x,1/2-z,-y -z,y,x -z,1/2+y,1/2+x 1/2-z,y,1/2+x 1/2-z,1/2+y,x y,-x,-z y,1/2-x,1/2-z 1/2+y,-x,1/2-z 1/2+y,1/2-x,-z -x,z,y -x,1/2+z,1/2+y 1/2-x,z,1/2+y 1/2-x,1/2+z,y z,-y,-x z,1/2-y,1/2-x 1/2+z,-y,1/2-x 1/2+z,1/2-y,-x -y,x,z -y,1/2+x,1/2+z 1/2-y,x,1/2+z 1/2-y,1/2+x,z x,z,y x,1/2+z,1/2+y 1/2+x,z,1/2+y 1/2+x,1/2+z,y -z,-y,-x -z,1/2-y,1/2-x 1/2-z,-y,1/2-x 1/2-z,1/2-y,-x y,x,z y,1/2+x,1/2+z 1/2+y,x,1/2+z 1/2+y,1/2+x,z -x,-z,-y -x,1/2-z,1/2-y 1/2-x,-z,1/2-y 1/2-x,1/2-z,-y z,y,x z,1/2+y,1/2+x 1/2+z,y,1/2+x 1/2+z,1/2+y,x -y,-x,-z -y,1/2-x,1/2-z 1/2-y,-x,1/2-z 1/2-y,1/2-x,-z z,x,-y z,1/2+x,1/2-y 1/2+z,x,1/2-y 1/2+z,1/2+x,-y -y,-z,x -y,1/2-z,1/2+x 1/2-y,-z,1/2+x 1/2-y,1/2-z,x x,y,-z x,1/2+y,1/2-z 1/2+x,y,1/2-z 1/2+x,1/2+y,-z -z,-x,y -z,1/2-x,1/2+y 1/2-z,-x,1/2+y 1/2-z,1/2-x,y y,z,-x y,1/2+z,1/2-x 1/2+y,z,1/2-x 1/2+y,1/2+z,-x -x,-y,z -x,1/2-y,1/2+z 1/2-x,-y,1/2+z 1/2-x,1/2-y,z -z,x,y -z,1/2+x,1/2+y 1/2-z,x,1/2+y 1/2-z,1/2+x,y y,-z,-x y,1/2-z,1/2-x 1/2+y,-z,1/2-x 1/2+y,1/2-z,-x -x,y,z -x,1/2+y,1/2+z 1/2-x,y,1/2+z 1/2-x,1/2+y,z z,-x,-y z,1/2-x,1/2-y 1/2+z,-x,1/2-y 1/2+z,1/2-x,-y -y,z,x -y,1/2+z,1/2+x 1/2-y,z,1/2+x 1/2-y,1/2+z,x x,-y,-z x,1/2-y,1/2-z 1/2+x,-y,1/2-z 1/2+x,1/2-y,-z -x,z,-y -x,1/2+z,1/2-y 1/2-x,z,1/2-y 1/2-x,1/2+z,-y z,-y,x z,1/2-y,1/2+x 1/2+z,-y,1/2+x 1/2+z,1/2-y,x -y,x,-z -y,1/2+x,1/2-z 1/2-y,x,1/2-z 1/2-y,1/2+x,-z x,-z,y x,1/2-z,1/2+y 1/2+x,-z,1/2+y 1/2+x,1/2-z,y -z,y,-x -z,1/2+y,1/2-x 1/2-z,y,1/2-x 1/2-z,1/2+y,-x y,-x,z y,1/2-x,1/2+z 1/2+y,-x,1/2+z 1/2+y,1/2-x,z -x,-z,y -x,1/2-z,1/2+y 1/2-x,-z,1/2+y 1/2-x,1/2-z,y z,y,-x z,1/2+y,1/2-x 1/2+z,y,1/2-x 1/2+z,1/2+y,-x -y,-x,z -y,1/2-x,1/2+z 1/2-y,-x,1/2+z 1/2-y,1/2-x,z x,z,-y x,1/2+z,1/2-y 1/2+x,z,1/2-y 1/2+x,1/2+z,-y -z,-y,x -z,1/2-y,1/2+x 1/2-z,-y,1/2+x 1/2-z,1/2-y,x y,x,-z y,1/2+x,1/2-z 1/2+y,x,1/2-z 1/2+y,1/2+x,-z -z,-x,-y -z,1/2-x,1/2-y 1/2-z,-x,1/2-y 1/2-z,1/2-x,-y y,z,x y,1/2+z,1/2+x 1/2+y,z,1/2+x 1/2+y,1/2+z,x -x,-y,-z -x,1/2-y,1/2-z 1/2-x,-y,1/2-z 1/2-x,1/2-y,-z z,x,y z,1/2+x,1/2+y 1/2+z,x,1/2+y 1/2+z,1/2+x,y -y,-z,-x -y,1/2-z,1/2-x 1/2-y,-z,1/2-x 1/2-y,1/2-z,-x loop_ _atom_site_label _atom_site_fract_x _atom_site_fract_y _atom_site_fract_z Ni 0.00000 0.00000 0.00000 pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/PbS_galena.cif000066400000000000000000000073361470422267000246170ustar00rootroot00000000000000#------------------------------------------------------------------------------ #$Date: 2008-03-10 08:25:41 +0000 (Mon, 10 Mar 2008) $ #$Revision: 255 $ #$URL: svn://cod.ibt.lt/cod/cif/9/9000001.cif $ #------------------------------------------------------------------------------ # # This file is available in the Crystallography Open Database (COD), # http://www.crystallography.net/ # # All data on this site have been placed in the public domain by the # contributors. # data_9000001 loop_ _publ_author_name 'Ramsdell L S' _publ_section_title ; The crystal structures of some metallic sulfides ; _journal_name_full 'American Mineralogist' _journal_page_first 281 _journal_page_last 304 _journal_volume 10 _journal_year 1925 _chemical_formula_sum 'Pb S' _chemical_name_mineral Galena _symmetry_space_group_name_H-M 'F m 3 m' _cell_angle_alpha 90 _cell_angle_beta 90 _cell_angle_gamma 90 _cell_length_a 5.93 _cell_length_b 5.93 _cell_length_c 5.93 _cell_volume 208.528 loop_ _symmetry_equiv_pos_as_xyz x,y,z x,1/2+y,1/2+z 1/2+x,y,1/2+z 1/2+x,1/2+y,z z,-x,y z,1/2-x,1/2+y 1/2+z,-x,1/2+y 1/2+z,1/2-x,y -y,z,-x -y,1/2+z,1/2-x 1/2-y,z,1/2-x 1/2-y,1/2+z,-x x,-y,z x,1/2-y,1/2+z 1/2+x,-y,1/2+z 1/2+x,1/2-y,z -z,x,-y -z,1/2+x,1/2-y 1/2-z,x,1/2-y 1/2-z,1/2+x,-y y,-z,x y,1/2-z,1/2+x 1/2+y,-z,1/2+x 1/2+y,1/2-z,x -x,y,-z -x,1/2+y,1/2-z 1/2-x,y,1/2-z 1/2-x,1/2+y,-z x,-z,-y x,1/2-z,1/2-y 1/2+x,-z,1/2-y 1/2+x,1/2-z,-y -z,y,x -z,1/2+y,1/2+x 1/2-z,y,1/2+x 1/2-z,1/2+y,x y,-x,-z y,1/2-x,1/2-z 1/2+y,-x,1/2-z 1/2+y,1/2-x,-z -x,z,y -x,1/2+z,1/2+y 1/2-x,z,1/2+y 1/2-x,1/2+z,y z,-y,-x z,1/2-y,1/2-x 1/2+z,-y,1/2-x 1/2+z,1/2-y,-x -y,x,z -y,1/2+x,1/2+z 1/2-y,x,1/2+z 1/2-y,1/2+x,z x,z,y x,1/2+z,1/2+y 1/2+x,z,1/2+y 1/2+x,1/2+z,y -z,-y,-x -z,1/2-y,1/2-x 1/2-z,-y,1/2-x 1/2-z,1/2-y,-x y,x,z y,1/2+x,1/2+z 1/2+y,x,1/2+z 1/2+y,1/2+x,z -x,-z,-y -x,1/2-z,1/2-y 1/2-x,-z,1/2-y 1/2-x,1/2-z,-y z,y,x z,1/2+y,1/2+x 1/2+z,y,1/2+x 1/2+z,1/2+y,x -y,-x,-z -y,1/2-x,1/2-z 1/2-y,-x,1/2-z 1/2-y,1/2-x,-z z,x,-y z,1/2+x,1/2-y 1/2+z,x,1/2-y 1/2+z,1/2+x,-y -y,-z,x -y,1/2-z,1/2+x 1/2-y,-z,1/2+x 1/2-y,1/2-z,x x,y,-z x,1/2+y,1/2-z 1/2+x,y,1/2-z 1/2+x,1/2+y,-z -z,-x,y -z,1/2-x,1/2+y 1/2-z,-x,1/2+y 1/2-z,1/2-x,y y,z,-x y,1/2+z,1/2-x 1/2+y,z,1/2-x 1/2+y,1/2+z,-x -x,-y,z -x,1/2-y,1/2+z 1/2-x,-y,1/2+z 1/2-x,1/2-y,z -z,x,y -z,1/2+x,1/2+y 1/2-z,x,1/2+y 1/2-z,1/2+x,y y,-z,-x y,1/2-z,1/2-x 1/2+y,-z,1/2-x 1/2+y,1/2-z,-x -x,y,z -x,1/2+y,1/2+z 1/2-x,y,1/2+z 1/2-x,1/2+y,z z,-x,-y z,1/2-x,1/2-y 1/2+z,-x,1/2-y 1/2+z,1/2-x,-y -y,z,x -y,1/2+z,1/2+x 1/2-y,z,1/2+x 1/2-y,1/2+z,x x,-y,-z x,1/2-y,1/2-z 1/2+x,-y,1/2-z 1/2+x,1/2-y,-z -x,z,-y -x,1/2+z,1/2-y 1/2-x,z,1/2-y 1/2-x,1/2+z,-y z,-y,x z,1/2-y,1/2+x 1/2+z,-y,1/2+x 1/2+z,1/2-y,x -y,x,-z -y,1/2+x,1/2-z 1/2-y,x,1/2-z 1/2-y,1/2+x,-z x,-z,y x,1/2-z,1/2+y 1/2+x,-z,1/2+y 1/2+x,1/2-z,y -z,y,-x -z,1/2+y,1/2-x 1/2-z,y,1/2-x 1/2-z,1/2+y,-x y,-x,z y,1/2-x,1/2+z 1/2+y,-x,1/2+z 1/2+y,1/2-x,z -x,-z,y -x,1/2-z,1/2+y 1/2-x,-z,1/2+y 1/2-x,1/2-z,y z,y,-x z,1/2+y,1/2-x 1/2+z,y,1/2-x 1/2+z,1/2+y,-x -y,-x,z -y,1/2-x,1/2+z 1/2-y,-x,1/2+z 1/2-y,1/2-x,z x,z,-y x,1/2+z,1/2-y 1/2+x,z,1/2-y 1/2+x,1/2+z,-y -z,-y,x -z,1/2-y,1/2+x 1/2-z,-y,1/2+x 1/2-z,1/2-y,x y,x,-z y,1/2+x,1/2-z 1/2+y,x,1/2-z 1/2+y,1/2+x,-z -z,-x,-y -z,1/2-x,1/2-y 1/2-z,-x,1/2-y 1/2-z,1/2-x,-y y,z,x y,1/2+z,1/2+x 1/2+y,z,1/2+x 1/2+y,1/2+z,x -x,-y,-z -x,1/2-y,1/2-z 1/2-x,-y,1/2-z 1/2-x,1/2-y,-z z,x,y z,1/2+x,1/2+y 1/2+z,x,1/2+y 1/2+z,1/2+x,y -y,-z,-x -y,1/2-z,1/2-x 1/2-y,-z,1/2-x 1/2-y,1/2-z,-x loop_ _atom_site_label _atom_site_fract_x _atom_site_fract_y _atom_site_fract_z Pb 0.00000 0.00000 0.00000 S 0.50000 0.50000 0.50000 pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/PbTe.cif000066400000000000000000000073771470422267000234630ustar00rootroot00000000000000#------------------------------------------------------------------------------ #$Date: 2008-03-10 08:25:41 +0000 (Mon, 10 Mar 2008) $ #$Revision: 255 $ #$URL: svn://cod.ibt.lt/cod/cif/9/9008696.cif $ #------------------------------------------------------------------------------ # # This file is available in the Crystallography Open Database (COD), # http://www.crystallography.net/ # # All data on this site have been placed in the public domain by the # contributors. # data_9008696 loop_ _publ_author_name 'Wyckoff R W G' _publ_section_title ; Second edition. Interscience Publishers, New York, New York rocksalt structure ; _journal_name_full 'Crystal Structures' _journal_page_first 85 _journal_page_last 237 _journal_volume 1 _journal_year 1963 _chemical_formula_sum 'Pb Te' _chemical_name_mineral Altaite _symmetry_space_group_name_H-M 'F m 3 m' _cell_angle_alpha 90 _cell_angle_beta 90 _cell_angle_gamma 90 _cell_length_a 6.454 _cell_length_b 6.454 _cell_length_c 6.454 _cell_volume 268.836 loop_ _symmetry_equiv_pos_as_xyz x,y,z x,1/2+y,1/2+z 1/2+x,y,1/2+z 1/2+x,1/2+y,z z,-x,y z,1/2-x,1/2+y 1/2+z,-x,1/2+y 1/2+z,1/2-x,y -y,z,-x -y,1/2+z,1/2-x 1/2-y,z,1/2-x 1/2-y,1/2+z,-x x,-y,z x,1/2-y,1/2+z 1/2+x,-y,1/2+z 1/2+x,1/2-y,z -z,x,-y -z,1/2+x,1/2-y 1/2-z,x,1/2-y 1/2-z,1/2+x,-y y,-z,x y,1/2-z,1/2+x 1/2+y,-z,1/2+x 1/2+y,1/2-z,x -x,y,-z -x,1/2+y,1/2-z 1/2-x,y,1/2-z 1/2-x,1/2+y,-z x,-z,-y x,1/2-z,1/2-y 1/2+x,-z,1/2-y 1/2+x,1/2-z,-y -z,y,x -z,1/2+y,1/2+x 1/2-z,y,1/2+x 1/2-z,1/2+y,x y,-x,-z y,1/2-x,1/2-z 1/2+y,-x,1/2-z 1/2+y,1/2-x,-z -x,z,y -x,1/2+z,1/2+y 1/2-x,z,1/2+y 1/2-x,1/2+z,y z,-y,-x z,1/2-y,1/2-x 1/2+z,-y,1/2-x 1/2+z,1/2-y,-x -y,x,z -y,1/2+x,1/2+z 1/2-y,x,1/2+z 1/2-y,1/2+x,z x,z,y x,1/2+z,1/2+y 1/2+x,z,1/2+y 1/2+x,1/2+z,y -z,-y,-x -z,1/2-y,1/2-x 1/2-z,-y,1/2-x 1/2-z,1/2-y,-x y,x,z y,1/2+x,1/2+z 1/2+y,x,1/2+z 1/2+y,1/2+x,z -x,-z,-y -x,1/2-z,1/2-y 1/2-x,-z,1/2-y 1/2-x,1/2-z,-y z,y,x z,1/2+y,1/2+x 1/2+z,y,1/2+x 1/2+z,1/2+y,x -y,-x,-z -y,1/2-x,1/2-z 1/2-y,-x,1/2-z 1/2-y,1/2-x,-z z,x,-y z,1/2+x,1/2-y 1/2+z,x,1/2-y 1/2+z,1/2+x,-y -y,-z,x -y,1/2-z,1/2+x 1/2-y,-z,1/2+x 1/2-y,1/2-z,x x,y,-z x,1/2+y,1/2-z 1/2+x,y,1/2-z 1/2+x,1/2+y,-z -z,-x,y -z,1/2-x,1/2+y 1/2-z,-x,1/2+y 1/2-z,1/2-x,y y,z,-x y,1/2+z,1/2-x 1/2+y,z,1/2-x 1/2+y,1/2+z,-x -x,-y,z -x,1/2-y,1/2+z 1/2-x,-y,1/2+z 1/2-x,1/2-y,z -z,x,y -z,1/2+x,1/2+y 1/2-z,x,1/2+y 1/2-z,1/2+x,y y,-z,-x y,1/2-z,1/2-x 1/2+y,-z,1/2-x 1/2+y,1/2-z,-x -x,y,z -x,1/2+y,1/2+z 1/2-x,y,1/2+z 1/2-x,1/2+y,z z,-x,-y z,1/2-x,1/2-y 1/2+z,-x,1/2-y 1/2+z,1/2-x,-y -y,z,x -y,1/2+z,1/2+x 1/2-y,z,1/2+x 1/2-y,1/2+z,x x,-y,-z x,1/2-y,1/2-z 1/2+x,-y,1/2-z 1/2+x,1/2-y,-z -x,z,-y -x,1/2+z,1/2-y 1/2-x,z,1/2-y 1/2-x,1/2+z,-y z,-y,x z,1/2-y,1/2+x 1/2+z,-y,1/2+x 1/2+z,1/2-y,x -y,x,-z -y,1/2+x,1/2-z 1/2-y,x,1/2-z 1/2-y,1/2+x,-z x,-z,y x,1/2-z,1/2+y 1/2+x,-z,1/2+y 1/2+x,1/2-z,y -z,y,-x -z,1/2+y,1/2-x 1/2-z,y,1/2-x 1/2-z,1/2+y,-x y,-x,z y,1/2-x,1/2+z 1/2+y,-x,1/2+z 1/2+y,1/2-x,z -x,-z,y -x,1/2-z,1/2+y 1/2-x,-z,1/2+y 1/2-x,1/2-z,y z,y,-x z,1/2+y,1/2-x 1/2+z,y,1/2-x 1/2+z,1/2+y,-x -y,-x,z -y,1/2-x,1/2+z 1/2-y,-x,1/2+z 1/2-y,1/2-x,z x,z,-y x,1/2+z,1/2-y 1/2+x,z,1/2-y 1/2+x,1/2+z,-y -z,-y,x -z,1/2-y,1/2+x 1/2-z,-y,1/2+x 1/2-z,1/2-y,x y,x,-z y,1/2+x,1/2-z 1/2+y,x,1/2-z 1/2+y,1/2+x,-z -z,-x,-y -z,1/2-x,1/2-y 1/2-z,-x,1/2-y 1/2-z,1/2-x,-y y,z,x y,1/2+z,1/2+x 1/2+y,z,1/2+x 1/2+y,1/2+z,x -x,-y,-z -x,1/2-y,1/2-z 1/2-x,-y,1/2-z 1/2-x,1/2-y,-z z,x,y z,1/2+x,1/2+y 1/2+z,x,1/2+y 1/2+z,1/2+x,y -y,-z,-x -y,1/2-z,1/2-x 1/2-y,-z,1/2-x 1/2-y,1/2-z,-x loop_ _atom_site_label _atom_site_fract_x _atom_site_fract_y _atom_site_fract_z Pb 0.00000 0.00000 0.00000 Te 0.50000 0.50000 0.50000 pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/Si.cif000066400000000000000000000105131470422267000231660ustar00rootroot00000000000000data_9008565 _chemical_name 'Silicon' loop_ _publ_author_name 'Wyckoff R W G' _journal_name_full "Crystal Structures" _journal_volume 1 _journal_year 1963 _journal_page_first 7 _journal_page_last 83 _publ_section_title ; Second edition. Interscience Publishers, New York, New York Sample at T = 300 K ; _chemical_formula_sum 'Si' _cell_length_a 5.43070 _cell_length_b 5.43070 _cell_length_c 5.43070 _cell_angle_alpha 90 _cell_angle_beta 90 _cell_angle_gamma 90 _cell_volume 160.165 _symmetry_space_group_name_H-M 'F d 3 m' loop_ _symmetry_equiv_pos_as_xyz 'x,y,z' 'x,1/2+y,1/2+z' '1/2+x,y,1/2+z' '1/2+x,1/2+y,z' '3/4+z,3/4-x,1/4+y' '3/4+z,1/4-x,3/4+y' '1/4+z,3/4-x,3/4+y' '1/4+z,1/4-x,1/4+y' '-y,1/2+z,1/2-x' '-y,+z,-x' '1/2-y,1/2+z,-x' '1/2-y,+z,1/2-x' '3/4+x,3/4-y,1/4+z' '3/4+x,1/4-y,3/4+z' '1/4+x,3/4-y,3/4+z' '1/4+x,1/4-y,1/4+z' '-z,1/2+x,1/2-y' '-z,+x,-y' '1/2-z,1/2+x,-y' '1/2-z,+x,1/2-y' '3/4+y,3/4-z,1/4+x' '3/4+y,1/4-z,3/4+x' '1/4+y,3/4-z,3/4+x' '1/4+y,1/4-z,1/4+x' '-x,1/2+y,1/2-z' '-x,+y,-z' '1/2-x,1/2+y,-z' '1/2-x,+y,1/2-z' '1/2+x,-z,1/2-y' '1/2+x,1/2-z,-y' '+x,-z,-y' '+x,1/2-z,1/2-y' '3/4-z,3/4+y,1/4+x' '3/4-z,1/4+y,3/4+x' '1/4-z,3/4+y,3/4+x' '1/4-z,1/4+y,1/4+x' '1/2+y,-x,1/2-z' '1/2+y,1/2-x,-z' '+y,-x,-z' '+y,1/2-x,1/2-z' '3/4-x,3/4+z,1/4+y' '3/4-x,1/4+z,3/4+y' '1/4-x,3/4+z,3/4+y' '1/4-x,1/4+z,1/4+y' '1/2+z,-y,1/2-x' '1/2+z,1/2-y,-x' '+z,-y,-x' '+z,1/2-y,1/2-x' '3/4-y,3/4+x,1/4+z' '3/4-y,1/4+x,3/4+z' '1/4-y,3/4+x,3/4+z' '1/4-y,1/4+x,1/4+z' 'x,1/2+z,1/2+y' 'x,+z,+y' '1/2+x,1/2+z,+y' '1/2+x,+z,1/2+y' '1/4-z,3/4-y,3/4-x' '1/4-z,1/4-y,1/4-x' '3/4-z,3/4-y,1/4-x' '3/4-z,1/4-y,3/4-x' 'y,1/2+x,1/2+z' 'y,+x,+z' '1/2+y,1/2+x,+z' '1/2+y,+x,1/2+z' '1/4-x,3/4-z,3/4-y' '1/4-x,1/4-z,1/4-y' '3/4-x,3/4-z,1/4-y' '3/4-x,1/4-z,3/4-y' 'z,1/2+y,1/2+x' 'z,+y,+x' '1/2+z,1/2+y,+x' '1/2+z,+y,1/2+x' '1/4-y,3/4-x,3/4-z' '1/4-y,1/4-x,1/4-z' '3/4-y,3/4-x,1/4-z' '3/4-y,1/4-x,3/4-z' '3/4+z,1/4+x,3/4-y' '3/4+z,3/4+x,1/4-y' '1/4+z,1/4+x,1/4-y' '1/4+z,3/4+x,3/4-y' '-y,1/2-z,1/2+x' '-y,-z,+x' '1/2-y,1/2-z,+x' '1/2-y,-z,1/2+x' '3/4+x,1/4+y,3/4-z' '3/4+x,3/4+y,1/4-z' '1/4+x,1/4+y,1/4-z' '1/4+x,3/4+y,3/4-z' '-z,1/2-x,1/2+y' '-z,-x,+y' '1/2-z,1/2-x,+y' '1/2-z,-x,1/2+y' '3/4+y,1/4+z,3/4-x' '3/4+y,3/4+z,1/4-x' '1/4+y,1/4+z,1/4-x' '1/4+y,3/4+z,3/4-x' '-x,1/2-y,1/2+z' '-x,-y,+z' '1/2-x,1/2-y,+z' '1/2-x,-y,1/2+z' '1/4-z,3/4+x,3/4+y' '1/4-z,1/4+x,1/4+y' '3/4-z,3/4+x,1/4+y' '3/4-z,1/4+x,3/4+y' 'y,-z,-x' 'y,1/2-z,1/2-x' '1/2+y,-z,1/2-x' '1/2+y,1/2-z,-x' '1/4-x,3/4+y,3/4+z' '1/4-x,1/4+y,1/4+z' '3/4-x,3/4+y,1/4+z' '3/4-x,1/4+y,3/4+z' 'z,-x,-y' 'z,1/2-x,1/2-y' '1/2+z,-x,1/2-y' '1/2+z,1/2-x,-y' '1/4-y,3/4+z,3/4+x' '1/4-y,1/4+z,1/4+x' '3/4-y,3/4+z,1/4+x' '3/4-y,1/4+z,3/4+x' 'x,-y,-z' 'x,1/2-y,1/2-z' '1/2+x,-y,1/2-z' '1/2+x,1/2-y,-z' '1/2-x,1/2+z,-y' '1/2-x,+z,1/2-y' '-x,1/2+z,1/2-y' '-x,+z,-y' '1/4+z,3/4-y,3/4+x' '1/4+z,1/4-y,1/4+x' '3/4+z,3/4-y,1/4+x' '3/4+z,1/4-y,3/4+x' '1/2-y,1/2+x,-z' '1/2-y,+x,1/2-z' '-y,1/2+x,1/2-z' '-y,+x,-z' '1/4+x,3/4-z,3/4+y' '1/4+x,1/4-z,1/4+y' '3/4+x,3/4-z,1/4+y' '3/4+x,1/4-z,3/4+y' '1/2-z,1/2+y,-x' '1/2-z,+y,1/2-x' '-z,1/2+y,1/2-x' '-z,+y,-x' '1/4+y,3/4-x,3/4+z' '1/4+y,1/4-x,1/4+z' '3/4+y,3/4-x,1/4+z' '3/4+y,1/4-x,3/4+z' '-x,-z,y' '-x,1/2-z,1/2+y' '1/2-x,-z,1/2+y' '1/2-x,1/2-z,y' '3/4+z,3/4+y,1/4-x' '3/4+z,1/4+y,3/4-x' '1/4+z,3/4+y,3/4-x' '1/4+z,1/4+y,1/4-x' '-y,-x,z' '-y,1/2-x,1/2+z' '1/2-y,-x,1/2+z' '1/2-y,1/2-x,z' '3/4+x,3/4+z,1/4-y' '3/4+x,1/4+z,3/4-y' '1/4+x,3/4+z,3/4-y' '1/4+x,1/4+z,1/4-y' '-z,-y,x' '-z,1/2-y,1/2+x' '1/2-z,-y,1/2+x' '1/2-z,1/2-y,x' '3/4+y,3/4+x,1/4-z' '3/4+y,1/4+x,3/4-z' '1/4+y,3/4+x,3/4-z' '1/4+y,1/4+x,1/4-z' '1/4-z,1/4-x,1/4-y' '1/4-z,3/4-x,3/4-y' '3/4-z,1/4-x,3/4-y' '3/4-z,3/4-x,1/4-y' 'y,z,x' 'y,1/2+z,1/2+x' '1/2+y,z,1/2+x' '1/2+y,1/2+z,x' '1/4-x,1/4-y,1/4-z' '1/4-x,3/4-y,3/4-z' '3/4-x,1/4-y,3/4-z' '3/4-x,3/4-y,1/4-z' 'z,x,y' 'z,1/2+x,1/2+y' '1/2+z,x,1/2+y' '1/2+z,1/2+x,y' '1/4-y,1/4-z,1/4-x' '1/4-y,3/4-z,3/4-x' '3/4-y,1/4-z,3/4-x' '3/4-y,3/4-z,1/4-x' loop_ _atom_site_label _atom_site_fract_x _atom_site_fract_y _atom_site_fract_z Si 0.00000 0.00000 0.00000 pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/Si_setting2.cif000066400000000000000000000154061470422267000250130ustar00rootroot00000000000000#------------------------------------------------------------------------------ #$Date: 2008-04-05 08:13:02 +0000 (Sat, 05 Apr 2008) $ #$Revision: 340 $ #$URL: svn://cod.ibt.lt/cod/cif/2/2102763.cif $ #------------------------------------------------------------------------------ # # This file is available in the Crystallography Open Database (COD), # http://www.crystallography.net/. The original data for this entry # were provided by IUCr Journals, http://journals.iucr.org/. # # The file may be used within the scientific community so long as # proper attribution is given to the journal article from which the # data were obtained. # data_2102763 loop_ _publ_author_name 'Wu, Hui' 'Hartman, Michael R.' 'Udovic, Terrence J.' 'Rush, John J.' 'Zhou, Wei' 'Bowman Jr, Robert C.' 'Vajo, John J.' _publ_section_title ; Structure of the novel ternary hydrides Li~4~Tt~2~D (Tt = Si and Ge) ; _journal_issue 1 _journal_name_full 'Acta Crystallographica Section B' _journal_page_first 63 _journal_page_last 68 _journal_volume 63 _journal_year 2007 _chemical_formula_sum Si _chemical_formula_weight 28.09 _chemical_name_systematic ' ?' _symmetry_cell_setting cubic _symmetry_space_group_name_H-M 'F d -3 m' _cell_angle_alpha 90.0 _cell_angle_beta 90.0 _cell_angle_gamma 90.0 _cell_formula_units_Z 8 _cell_length_a 5.42712(9) _cell_length_b 5.42712 _cell_length_c 5.42712 _cell_volume 159.849(8) _[local]_cod_data_source_file ws5053.cif loop_ _symmetry_equiv_pos_site_id _symmetry_equiv_pos_as_xyz 1 +x,+y,+z 2 +z,+x,+y 3 +y,+z,+x 4 +x+1/4,+y+1/4,-z 5 -z,+x+1/4,+y+1/4 6 +y+1/4,-z,+x+1/4 7 -z+1/4,+x+1/2,-y+3/4 8 -y+3/4,-z+1/4,+x+1/2 9 +y+1/2,-z+1/4,-x+3/4 10 -x+3/4,+y+1/2,-z+1/4 11 -z+1/4,-x+3/4,+y+1/2 12 +x+1/2,-y+3/4,-z+1/4 13 +y,+x,+z 14 +z,+y,+x 15 +x,+z,+y 16 +y+1/4,+x+1/4,-z 17 -z,+y+1/4,+x+1/4 18 +x+1/4,-z,+y+1/4 19 -z+1/4,+y+1/2,-x+3/4 20 -x+3/4,-z+1/4,+y+1/2 21 +x+1/2,-z+1/4,-y+3/4 22 -y+3/4,+x+1/2,-z+1/4 23 -z+1/4,-y+3/4,+x+1/2 24 +y+1/2,-x+3/4,-z+1/4 -1 -x,-y,-z -2 -z,-x,-y -3 -y,-z,-x -4 -x+3/4,-y+3/4,+z -5 +z,-x+3/4,-y+3/4 -6 -y+3/4,+z,-x+3/4 -7 +z+3/4,-x+1/2,+y+1/4 -8 +y+1/4,+z+3/4,-x+1/2 -9 -y+1/2,+z+3/4,+x+1/4 -10 +x+1/4,-y+1/2,+z+3/4 -11 +z+3/4,+x+1/4,-y+1/2 -12 -x+1/2,+y+1/4,+z+3/4 -13 -y,-x,-z -14 -z,-y,-x -15 -x,-z,-y -16 -y+3/4,-x+3/4,+z -17 +z,-y+3/4,-x+3/4 -18 -x+3/4,+z,-y+3/4 -19 +z+3/4,-y+1/2,+x+1/4 -20 +x+1/4,+z+3/4,-y+1/2 -21 -x+1/2,+z+3/4,+y+1/4 -22 +y+1/4,-x+1/2,+z+3/4 -23 +z+3/4,+y+1/4,-x+1/2 -24 -y+1/2,+x+1/4,+z+3/4 101 +x,+y+1/2,+z+1/2 102 +z,+x+1/2,+y+1/2 103 +y,+z+1/2,+x+1/2 104 +x+1/4,+y+3/4,-z+1/2 105 -z,+x+3/4,+y+3/4 106 +y+1/4,-z+1/2,+x+3/4 107 -z+1/4,+x,-y+1/4 108 -y+3/4,-z+3/4,+x 109 +y+1/2,-z+3/4,-x+1/4 110 -x+3/4,+y,-z+3/4 111 -z+1/4,-x+1/4,+y 112 +x+1/2,-y+1/4,-z+3/4 113 +y,+x+1/2,+z+1/2 114 +z,+y+1/2,+x+1/2 115 +x,+z+1/2,+y+1/2 116 +y+1/4,+x+3/4,-z+1/2 117 -z,+y+3/4,+x+3/4 118 +x+1/4,-z+1/2,+y+3/4 119 -z+1/4,+y,-x+1/4 120 -x+3/4,-z+3/4,+y 121 +x+1/2,-z+3/4,-y+1/4 122 -y+3/4,+x,-z+3/4 123 -z+1/4,-y+1/4,+x 124 +y+1/2,-x+1/4,-z+3/4 -101 -x,-y+1/2,-z+1/2 -102 -z,-x+1/2,-y+1/2 -103 -y,-z+1/2,-x+1/2 -104 -x+3/4,-y+1/4,+z+1/2 -105 +z,-x+1/4,-y+1/4 -106 -y+3/4,+z+1/2,-x+1/4 -107 +z+3/4,-x,+y+3/4 -108 +y+1/4,+z+1/4,-x -109 -y+1/2,+z+1/4,+x+3/4 -110 +x+1/4,-y,+z+1/4 -111 +z+3/4,+x+3/4,-y -112 -x+1/2,+y+3/4,+z+1/4 -113 -y,-x+1/2,-z+1/2 -114 -z,-y+1/2,-x+1/2 -115 -x,-z+1/2,-y+1/2 -116 -y+3/4,-x+1/4,+z+1/2 -117 +z,-y+1/4,-x+1/4 -118 -x+3/4,+z+1/2,-y+1/4 -119 +z+3/4,-y,+x+3/4 -120 +x+1/4,+z+1/4,-y -121 -x+1/2,+z+1/4,+y+3/4 -122 +y+1/4,-x,+z+1/4 -123 +z+3/4,+y+3/4,-x -124 -y+1/2,+x+3/4,+z+1/4 201 +x+1/2,+y,+z+1/2 202 +z+1/2,+x,+y+1/2 203 +y+1/2,+z,+x+1/2 204 +x+3/4,+y+1/4,-z+1/2 205 -z+1/2,+x+1/4,+y+3/4 206 +y+3/4,-z,+x+3/4 207 -z+3/4,+x+1/2,-y+1/4 208 -y+1/4,-z+1/4,+x 209 +y,-z+1/4,-x+1/4 210 -x+1/4,+y+1/2,-z+3/4 211 -z+3/4,-x+3/4,+y 212 +x,-y+3/4,-z+3/4 213 +y+1/2,+x,+z+1/2 214 +z+1/2,+y,+x+1/2 215 +x+1/2,+z,+y+1/2 216 +y+3/4,+x+1/4,-z+1/2 217 -z+1/2,+y+1/4,+x+3/4 218 +x+3/4,-z,+y+3/4 219 -z+3/4,+y+1/2,-x+1/4 220 -x+1/4,-z+1/4,+y 221 +x,-z+1/4,-y+1/4 222 -y+1/4,+x+1/2,-z+3/4 223 -z+3/4,-y+3/4,+x 224 +y,-x+3/4,-z+3/4 -201 -x+1/2,-y,-z+1/2 -202 -z+1/2,-x,-y+1/2 -203 -y+1/2,-z,-x+1/2 -204 -x+1/4,-y+3/4,+z+1/2 -205 +z+1/2,-x+3/4,-y+1/4 -206 -y+1/4,+z,-x+1/4 -207 +z+1/4,-x+1/2,+y+3/4 -208 +y+3/4,+z+3/4,-x -209 -y,+z+3/4,+x+3/4 -210 +x+3/4,-y+1/2,+z+1/4 -211 +z+1/4,+x+1/4,-y -212 -x,+y+1/4,+z+1/4 -213 -y+1/2,-x,-z+1/2 -214 -z+1/2,-y,-x+1/2 -215 -x+1/2,-z,-y+1/2 -216 -y+1/4,-x+3/4,+z+1/2 -217 +z+1/2,-y+3/4,-x+1/4 -218 -x+1/4,+z,-y+1/4 -219 +z+1/4,-y+1/2,+x+3/4 -220 +x+3/4,+z+3/4,-y -221 -x,+z+3/4,+y+3/4 -222 +y+3/4,-x+1/2,+z+1/4 -223 +z+1/4,+y+1/4,-x -224 -y,+x+1/4,+z+1/4 301 +x+1/2,+y+1/2,+z 302 +z+1/2,+x+1/2,+y 303 +y+1/2,+z+1/2,+x 304 +x+3/4,+y+3/4,-z 305 -z+1/2,+x+3/4,+y+1/4 306 +y+3/4,-z+1/2,+x+1/4 307 -z+3/4,+x,-y+3/4 308 -y+1/4,-z+3/4,+x+1/2 309 +y,-z+3/4,-x+3/4 310 -x+1/4,+y,-z+1/4 311 -z+3/4,-x+1/4,+y+1/2 312 +x,-y+1/4,-z+1/4 313 +y+1/2,+x+1/2,+z 314 +z+1/2,+y+1/2,+x 315 +x+1/2,+z+1/2,+y 316 +y+3/4,+x+3/4,-z 317 -z+1/2,+y+3/4,+x+1/4 318 +x+3/4,-z+1/2,+y+1/4 319 -z+3/4,+y,-x+3/4 320 -x+1/4,-z+3/4,+y+1/2 321 +x,-z+3/4,-y+3/4 322 -y+1/4,+x,-z+1/4 323 -z+3/4,-y+1/4,+x+1/2 324 +y,-x+1/4,-z+1/4 -301 -x+1/2,-y+1/2,-z -302 -z+1/2,-x+1/2,-y -303 -y+1/2,-z+1/2,-x -304 -x+1/4,-y+1/4,+z -305 +z+1/2,-x+1/4,-y+3/4 -306 -y+1/4,+z+1/2,-x+3/4 -307 +z+1/4,-x,+y+1/4 -308 +y+3/4,+z+1/4,-x+1/2 -309 -y,+z+1/4,+x+1/4 -310 +x+3/4,-y,+z+3/4 -311 +z+1/4,+x+3/4,-y+1/2 -312 -x,+y+3/4,+z+3/4 -313 -y+1/2,-x+1/2,-z -314 -z+1/2,-y+1/2,-x -315 -x+1/2,-z+1/2,-y -316 -y+1/4,-x+1/4,+z -317 +z+1/2,-y+1/4,-x+3/4 -318 -x+1/4,+z+1/2,-y+3/4 -319 +z+1/4,-y,+x+1/4 -320 +x+3/4,+z+1/4,-y+1/2 -321 -x,+z+1/4,+y+1/4 -322 +y+3/4,-x,+z+3/4 -323 +z+1/4,+y+3/4,-x+1/2 -324 -y,+x+3/4,+z+3/4 loop_ _atom_type_symbol Si loop_ _atom_site_type_symbol _atom_site_label _atom_site_fract_x _atom_site_fract_y _atom_site_fract_z _atom_site_U_iso_or_equiv _atom_site_thermal_displace_type _atom_site_occupancy Si Si 0.125 0.125 0.125 0.0072(4) Uiso 1.0 loop_ _geom_bond_atom_site_label_1 _geom_bond_atom_site_label_2 _geom_bond_site_symmetry_2 _geom_bond_distance _geom_bond_publ_flag Si Si 4_555 2.35001(3) N Si Si 5_555 2.35001(3) N Si Si 6_555 2.35001(3) N Si Si -1_555 2.35001(3) N loop_ _geom_angle_atom_site_label_1 _geom_angle_atom_site_label_2 _geom_angle_atom_site_label_3 _geom_angle_site_symmetry_1 _geom_angle_site_symmetry_3 _geom_angle _geom_angle_publ_flag Si Si Si 4_555 5_555 109.4712(6) N Si Si Si 4_555 6_555 109.4712(6) N Si Si Si 4_555 -1_555 109.4712(12) N Si Si Si 5_555 6_555 109.4712(12) N Si Si Si 5_555 -1_555 109.4712(6) N Si Si Si 6_555 -1_555 109.4712(6) N pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/SrTiO3_tausonite.cif000066400000000000000000000025541470422267000257770ustar00rootroot00000000000000data_9006864 _chemical_name 'Tausonite' loop_ _publ_author_name 'Mitchell R H' 'Chakhmouradian A R' 'Woodward P M' _journal_name_full "Physics and Chemistry of Minerals" _journal_volume 27 _journal_year 2000 _journal_page_first 583 _journal_page_last 589 _publ_section_title ; Crystal chemistry of perovskite-type compounds in the tausonite-loparite series, (Sr1-2xNaxLax)TiO3 Sample: x = 0.00 ; _chemical_formula_sum 'Sr Ti O3' _cell_length_a 3.90528 _cell_length_b 3.90528 _cell_length_c 3.90528 _cell_angle_alpha 90 _cell_angle_beta 90 _cell_angle_gamma 90 _cell_volume 59.560 _symmetry_space_group_name_H-M 'P m -3 m' loop_ _symmetry_equiv_pos_as_xyz 'x,y,z' 'z,-x,y' '-y,z,-x' 'x,-y,z' '-z,x,-y' 'y,-z,x' '-x,y,-z' 'x,-z,-y' '-z,y,x' 'y,-x,-z' '-x,z,y' 'z,-y,-x' '-y,x,z' 'x,z,y' '-z,-y,-x' 'y,x,z' '-x,-z,-y' 'z,y,x' '-y,-x,-z' 'z,x,-y' '-y,-z,x' 'x,y,-z' '-z,-x,y' 'y,z,-x' '-x,-y,z' '-z,x,y' 'y,-z,-x' '-x,y,z' 'z,-x,-y' '-y,z,x' 'x,-y,-z' '-x,z,-y' 'z,-y,x' '-y,x,-z' 'x,-z,y' '-z,y,-x' 'y,-x,z' '-x,-z,y' 'z,y,-x' '-y,-x,z' 'x,z,-y' '-z,-y,x' 'y,x,-z' '-z,-x,-y' 'y,z,x' '-x,-y,-z' 'z,x,y' '-y,-z,-x' loop_ _atom_site_label _atom_site_fract_x _atom_site_fract_y _atom_site_fract_z Sr 0.50000 0.50000 0.50000 Ti 0.00000 0.00000 0.00000 O 0.50000 0.00000 0.00000 pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/TiO2_anatase.cif000066400000000000000000000034341470422267000250700ustar00rootroot00000000000000#------------------------------------------------------------------------------ #$Date: 2008-03-10 08:25:41 +0000 (Mon, 10 Mar 2008) $ #$Revision: 255 $ #$URL: svn://cod.ibt.lt/cod/cif/9/9009086.cif $ #------------------------------------------------------------------------------ # # This file is available in the Crystallography Open Database (COD), # http://www.crystallography.net/ # # All data on this site have been placed in the public domain by the # contributors. # data_9009086 loop_ _publ_author_name 'Wyckoff R W G' _publ_section_title ; Second edition. Interscience Publishers, New York, New York ; _journal_name_full 'Crystal Structures' _journal_page_first 239 _journal_page_last 444 _journal_volume 1 _journal_year 1963 _chemical_formula_sum 'Ti O2' _chemical_name_mineral Anatase _symmetry_space_group_name_H-M 'I 41/a m d' _cell_angle_alpha 90 _cell_angle_beta 90 _cell_angle_gamma 90 _cell_length_a 3.785 _cell_length_b 3.785 _cell_length_c 9.514 _cell_volume 136.300 loop_ _symmetry_equiv_pos_as_xyz x,y,z 1/2+x,1/2+y,1/2+z -y,1/2-x,1/4+z 1/2-y,-x,3/4+z 1/2+y,1/2+x,1/2-z +y,+x,-z 1/2+y,1/2-x,1/2-z +y,-x,-z -y,1/2+x,1/4+z 1/2-y,+x,3/4+z 1/2+x,1/2-y,1/2+z +x,-y,+z 1/2-x,y,3/4-z -x,1/2+y,1/4-z 1/2+x,y,3/4-z +x,1/2+y,1/4-z 1/2-x,1/2-y,1/2+z -x,-y,+z 1/2+y,x,3/4+z +y,1/2+x,1/4+z -y,-x,-z 1/2-y,1/2-x,1/2-z -y,x,-z 1/2-y,1/2+x,1/2-z 1/2+y,-x,3/4+z +y,1/2-x,1/4+z -x,y,z 1/2-x,1/2+y,1/2+z x,1/2-y,1/4-z 1/2+x,-y,3/4-z -x,1/2-y,1/4-z 1/2-x,-y,3/4-z loop_ _atom_site_label _atom_site_fract_x _atom_site_fract_y _atom_site_fract_z Ti 0.00000 0.00000 0.00000 O 0.00000 0.00000 0.20660 pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/TiO2_rutile.cif000066400000000000000000000021261470422267000247550ustar00rootroot00000000000000data_9004141 _chemical_name 'Rutile' loop_ _publ_author_name 'Meagher E P' 'Lager G A' _journal_name_full "The Canadian Mineralogist" _journal_volume 17 _journal_year 1979 _journal_page_first 77 _journal_page_last 85 _publ_section_title ; Polyhedral thermal expansion in the TiO2 polymorphs: Refinement of the crystal structure of rutile and brookite at high temperature Sample at 25 degrees C ; _chemical_formula_sum 'Ti O2' _cell_length_a 4.593 _cell_length_b 4.593 _cell_length_c 2.959 _cell_angle_alpha 90 _cell_angle_beta 90 _cell_angle_gamma 90 _cell_volume 62.422 _symmetry_space_group_name_H-M 'P 42/m n m' loop_ _symmetry_equiv_pos_as_xyz 'x,y,z' '-y,-x,z' 'y,x,-z' '1/2+y,1/2-x,1/2-z' '1/2-y,1/2+x,1/2+z' '1/2+x,1/2-y,1/2+z' '1/2-x,1/2+y,1/2-z' 'x,y,-z' '-x,-y,z' 'y,x,z' '-y,-x,-z' '1/2-y,1/2+x,1/2-z' '1/2+y,1/2-x,1/2+z' '1/2-x,1/2+y,1/2+z' '1/2+x,1/2-y,1/2-z' '-x,-y,-z' loop_ _atom_site_label _atom_site_fract_x _atom_site_fract_y _atom_site_fract_z _atom_site_U_iso_or_equiv Ti 0.00000 0.00000 0.00000 0.00532 O 0.30510 0.30510 0.00000 0.00760 pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/ZnS_sphalerite.cif000066400000000000000000000043651470422267000255550ustar00rootroot00000000000000data_9000107 _chemical_name 'Sphalerite' loop_ _publ_author_name 'Skinner B J' _journal_name_full "American Mineralogist" _journal_volume 46 _journal_year 1961 _journal_page_first 1399 _journal_page_last 1411 _publ_section_title ; Unit-cell edges of natural and synthetic sphalerites ; _chemical_formula_sum 'Zn S' _cell_length_a 5.4093 _cell_length_b 5.4093 _cell_length_c 5.4093 _cell_angle_alpha 90 _cell_angle_beta 90 _cell_angle_gamma 90 _cell_volume 158.279 _symmetry_space_group_name_H-M 'F -4 3 m' loop_ _symmetry_equiv_pos_as_xyz 'x,y,z' 'x,1/2+y,1/2+z' '1/2+x,y,1/2+z' '1/2+x,1/2+y,z' '-z,x,-y' '-z,1/2+x,1/2-y' '1/2-z,x,1/2-y' '1/2-z,1/2+x,-y' '-y,z,-x' '-y,1/2+z,1/2-x' '1/2-y,z,1/2-x' '1/2-y,1/2+z,-x' '-x,y,-z' '-x,1/2+y,1/2-z' '1/2-x,y,1/2-z' '1/2-x,1/2+y,-z' 'x,-z,-y' 'x,1/2-z,1/2-y' '1/2+x,-z,1/2-y' '1/2+x,1/2-z,-y' 'z,-y,-x' 'z,1/2-y,1/2-x' '1/2+z,-y,1/2-x' '1/2+z,1/2-y,-x' 'y,-x,-z' 'y,1/2-x,1/2-z' '1/2+y,-x,1/2-z' '1/2+y,1/2-x,-z' 'x,z,y' 'x,1/2+z,1/2+y' '1/2+x,z,1/2+y' '1/2+x,1/2+z,y' 'z,y,x' 'z,1/2+y,1/2+x' '1/2+z,y,1/2+x' '1/2+z,1/2+y,x' 'y,x,z' 'y,1/2+x,1/2+z' '1/2+y,x,1/2+z' '1/2+y,1/2+x,z' '-z,-x,y' '-z,1/2-x,1/2+y' '1/2-z,-x,1/2+y' '1/2-z,1/2-x,y' '-y,-z,x' '-y,1/2-z,1/2+x' '1/2-y,-z,1/2+x' '1/2-y,1/2-z,x' '-x,-y,z' '-x,1/2-y,1/2+z' '1/2-x,-y,1/2+z' '1/2-x,1/2-y,z' 'z,-x,-y' 'z,1/2-x,1/2-y' '1/2+z,-x,1/2-y' '1/2+z,1/2-x,-y' 'y,-z,-x' 'y,1/2-z,1/2-x' '1/2+y,-z,1/2-x' '1/2+y,1/2-z,-x' 'x,-y,-z' 'x,1/2-y,1/2-z' '1/2+x,-y,1/2-z' '1/2+x,1/2-y,-z' '-x,z,-y' '-x,1/2+z,1/2-y' '1/2-x,z,1/2-y' '1/2-x,1/2+z,-y' '-z,y,-x' '-z,1/2+y,1/2-x' '1/2-z,y,1/2-x' '1/2-z,1/2+y,-x' '-y,x,-z' '-y,1/2+x,1/2-z' '1/2-y,x,1/2-z' '1/2-y,1/2+x,-z' '-x,-z,y' '-x,1/2-z,1/2+y' '1/2-x,-z,1/2+y' '1/2-x,1/2-z,y' '-z,-y,x' '-z,1/2-y,1/2+x' '1/2-z,-y,1/2+x' '1/2-z,1/2-y,x' '-y,-x,z' '-y,1/2-x,1/2+z' '1/2-y,-x,1/2+z' '1/2-y,1/2-x,z' 'z,x,y' 'z,1/2+x,1/2+y' '1/2+z,x,1/2+y' '1/2+z,1/2+x,y' 'y,z,x' 'y,1/2+z,1/2+x' '1/2+y,z,1/2+x' '1/2+y,1/2+z,x' loop_ _atom_site_label _atom_site_fract_x _atom_site_fract_y _atom_site_fract_z Zn 0.00000 0.00000 0.00000 S 0.25000 0.25000 0.25000 pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/ZnS_wurtzite.cif000066400000000000000000000027641470422267000253130ustar00rootroot00000000000000#------------------------------------------------------------------------------ #$Date: 2008-03-10 08:25:41 +0000 (Mon, 10 Mar 2008) $ #$Revision: 255 $ #$URL: svn://cod.ibt.lt/cod/cif/9/9008878.cif $ #------------------------------------------------------------------------------ # # This file is available in the Crystallography Open Database (COD), # http://www.crystallography.net/ # # All data on this site have been placed in the public domain by the # contributors. # data_9008878 loop_ _publ_author_name 'Wyckoff R W G' _publ_section_title ; Second edition. Interscience Publishers, New York, New York Note: wurtzite structure ; _journal_name_full 'Crystal Structures' _journal_page_first 85 _journal_page_last 237 _journal_volume 1 _journal_year 1963 _chemical_formula_sum 'Zn S' _chemical_name_mineral Wurtzite-2H _symmetry_space_group_name_H-M 'P 63 m c' _cell_angle_alpha 90 _cell_angle_beta 90 _cell_angle_gamma 120 _cell_length_a 3.811 _cell_length_b 3.811 _cell_length_c 6.234 _cell_volume 78.411 loop_ _symmetry_equiv_pos_as_xyz x,y,z -x,-x+y,1/2+z x-y,x,1/2+z -y,-x,z -y,x-y,z x-y,-y,1/2+z -x,-y,1/2+z x,x-y,z -x+y,-x,z y,x,1/2+z y,-x+y,1/2+z -x+y,y,z loop_ _atom_site_label _atom_site_fract_x _atom_site_fract_y _atom_site_fract_z Zn 0.33333 0.66667 0.00000 S 0.33333 0.66667 0.38500 pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/Zn_zinc.cif000066400000000000000000000031441470422267000242270ustar00rootroot00000000000000#------------------------------------------------------------------------------ #$Date: 2008-03-10 08:25:41 +0000 (Mon, 10 Mar 2008) $ #$Revision: 255 $ #$URL: svn://cod.ibt.lt/cod/cif/9/9008522.cif $ #------------------------------------------------------------------------------ # # This file is available in the Crystallography Open Database (COD), # http://www.crystallography.net/ # # All data on this site have been placed in the public domain by the # contributors. # data_9008522 loop_ _publ_author_name 'Wyckoff R W G' _publ_section_title ; Second edition. Interscience Publishers, New York, New York Hexagonal closest packed, hcp, structure ; _journal_name_full 'Crystal Structures' _journal_page_first 7 _journal_page_last 83 _journal_volume 1 _journal_year 1963 _chemical_formula_sum Zn _chemical_name_mineral Zinc _symmetry_space_group_name_H-M 'P 63/m m c' _cell_angle_alpha 90 _cell_angle_beta 90 _cell_angle_gamma 120 _cell_length_a 2.6648 _cell_length_b 2.6648 _cell_length_c 4.9467 _cell_volume 30.421 loop_ _symmetry_equiv_pos_as_xyz x,y,z -x,-x+y,1/2+z x,x-y,1/2-z -x+y,-x,1/2-z x-y,x,1/2+z -y,-x,z y,x,-z y,-x+y,-z -y,x-y,z x-y,-y,1/2+z -x+y,y,1/2-z x,y,1/2-z -x,-y,1/2+z x,x-y,z -x,-x+y,-z x-y,x,-z -x+y,-x,z y,x,1/2+z -y,-x,1/2-z -y,x-y,1/2-z y,-x+y,1/2+z -x+y,y,z x-y,-y,-z -x,-y,-z loop_ _atom_site_label _atom_site_fract_x _atom_site_fract_y _atom_site_fract_z Zn 0.33333 0.66667 0.25000 pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/caffeine.cif000066400000000000000000000064231470422267000243600ustar00rootroot00000000000000#------------------------------------------------------------------------------ #$Date: 2008-01-26 13:05:32 +0000 (Sat, 26 Jan 2008) $ #$Revision: 19 $ #$URL: svn://cod.ibt.lt/cod/cif/2/2100202.cif $ #------------------------------------------------------------------------------ # # This file is available in the Crystallography Open Database (COD), # http://www.crystallography.net/ # # All data on this site have been placed in the public domain by the # contributors. # data_2100202 _journal_name_full 'Acta Crystallographica, Section B' _journal_year 2005 _journal_volume 61 _journal_page_first 329 _journal_page_last 334 _publ_section_title ; Ab initio structure determination of the high temperature phase of anhydrous caffeine by X-ray powder diffraction. ; loop_ _publ_author_name 'Derollez, Patrick' 'Correia, Nat\'alia T.' 'Dan\`ede, Florence' 'Capet, Fr\'ed\'eric' 'Affouard, Fr\'ed\'eric' 'Lefebvre, Jacques' 'Descamps, Marc' _chemical_name_common 'caffeine' _chemical_formula_moiety 'C8 H10 N4 O2' _chemical_formula_sum 'C8 H10 N4 O2' _chemical_formula_weight 194.20 _symmetry_cell_setting Trigonal _symmetry_space_group_name_H-M 'R 3 c' _symmetry_space_group_name_Hall 'R 3 -2"C' loop_ _symmetry_equiv_pos_as_xyz 'x,y,z' '-y,x-y,z' '-x+y,-x,z' '-y,-x,z+1/2' '-x+y,y,z+1/2' 'x,x-y,z+1/2' 'x+2/3,y+1/3,z+1/3' '-y+2/3,x-y+1/3,z+1/3' '-x+y+2/3,-x+1/3,z+1/3' '-y+2/3,-x+1/3,z+5/6' '-x+y+2/3,y+1/3,z+5/6' 'x+2/3,x-y+1/3,z+5/6' 'x+1/3,y+2/3,z+2/3' '-y+1/3,x-y+2/3,z+2/3' '-x+y+1/3,-x+2/3,z+2/3' '-y+1/3,-x+2/3,z+1/6' '-x+y+1/3,y+2/3,z+1/6' 'x+1/3,x-y+2/3,z+1/6' _cell_length_a 14.9372(5) _cell_length_b 14.9372(5) _cell_length_c 6.8980(2) _cell_angle_alpha 90.00000 _cell_angle_beta 90.00000 _cell_angle_gamma 120.00000 _cell_volume 1332.88(7) _cell_formula_units_Z 6 _cell_measurement_temperature 278 loop_ _atom_site_label _atom_site_fract_x _atom_site_fract_y _atom_site_fract_z _atom_site_U_iso_or_equiv _atom_site_adp_type _atom_site_occupancy _atom_site_type_symbol C5 -0.06613 -0.06314 0.09562 0.00000 Uiso 1.00000 C C4 0.02779 -0.05534 0.10000 0.00000 Uiso 1.00000 C N3 0.11676 0.04116 0.08226 0.00000 Uiso 1.00000 N C2 0.10866 0.12960 0.06000 0.00000 Uiso 1.00000 C O11 0.18634 0.21385 0.04451 0.00000 Uiso 1.00000 O N1 0.01159 0.12154 0.05547 0.00000 Uiso 1.00000 N C6 -0.07738 0.02504 0.07321 0.00000 Uiso 1.00000 C O13 -0.16212 0.01800 0.06926 0.00000 Uiso 1.00000 O N7 -0.13793 -0.16353 0.11547 0.00000 Uiso 1.00000 N N9 0.01389 -0.15092 0.12255 0.00000 Uiso 1.00000 N C8 -0.08863 -0.21778 0.13210 0.00000 Uiso 1.00000 C C14 -0.25110 -0.20663 0.11847 0.00000 Uiso 1.00000 C C12 0.21968 0.04971 0.08706 0.00000 Uiso 1.00000 C C10 0.00300 0.21530 0.03186 0.00000 Uiso 1.00000 C H8 -0.12146 -0.29285 0.14834 0.00000 Uiso 1.00000 H H14a -0.27317 -0.19028 -0.00382 0.00000 Uiso 1.00000 H H14b -0.28567 -0.28182 0.13462 0.00000 Uiso 1.00000 H H14c -0.26951 -0.17639 0.22660 0.00000 Uiso 1.00000 H H12a 0.22513 0.00926 -0.01943 0.00000 Uiso 1.00000 H H12b 0.27338 0.12237 0.07281 0.00000 Uiso 1.00000 H H12c 0.22878 0.02315 0.21098 0.00000 Uiso 1.00000 H H10a 0.03488 0.24909 -0.09109 0.00000 Uiso 1.00000 H H10b -0.07008 0.19602 0.03170 0.00000 Uiso 1.00000 H H10c 0.03791 0.26293 0.13930 0.00000 Uiso 1.00000 H pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/cime.fhz000066400000000000000000000007741470422267000235660ustar00rootroot00000000000000KEYWORDS GO HERE 17 C 1 N 1 1.465 C 2 1.366 1 119.987 N 3 1.321 2 120.030 1 6.0 C 4 1.355 3 119.982 2 6.8 N 5 1.136 4 180.000 3 46.3 N 3 1.366 2 120.022 1 186.0 C 7 1.466 3 119.988 2 354.9 C 8 1.529 7 109.482 3 185.0 S 9 1.814 8 109.456 7 180.0 C 10 1.815 9 99.984 8 180.0 C 11 1.507 10 109.466 9 180.0 N 12 1.341 11 126.014 10 90.0 C 13 1.305 12 109.239 11 180.0 N 14 1.349 13 108.723 12 360.0 C 15 1.344 14 107.593 13 359.1 C 16 1.515 15 127.952 14 179.1 pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/lidocainementhol.cif000066400000000000000000000337661470422267000261500ustar00rootroot00000000000000#------------------------------------------------------------------------------ #$Date: 2013-12-28 07:07:06 +0000 (Sat, 28 Dec 2013) $ #$Revision: 91932 $ #$URL: file:///home/coder/svn-repositories/cod/cif/1/50/26/1502677.cif $ #------------------------------------------------------------------------------ # # This file is available in the Crystallography Open Database (COD), # http://www.crystallography.net/ # # All data on this site have been placed in the public domain by the # contributors. # data_1502677 loop_ _publ_author_name 'Corvis, Yohann' 'N\'egrier, Philippe' 'Lazerges, Mathieu' 'Massip, St\'ephane' 'L\'eger, Jean-Michel' 'Espeau, Philippe' _publ_section_title ; Lidocaine/L-menthol binary system: cocrystallization versus solid-state immiscibility. ; _journal_issue 16 _journal_name_full 'The journal of physical chemistry. B' _journal_page_first 5420 _journal_page_last 5426 _journal_volume 114 _journal_year 2010 _chemical_absolute_configuration ad _chemical_formula_sum 'C24 H42 N2 O2' _chemical_formula_weight 390.60 _chemical_name_systematic ; ? ; _space_group_IT_number 19 _symmetry_cell_setting orthorhombic _symmetry_Int_Tables_number 19 _symmetry_space_group_name_Hall 'P 2ac 2ab' _symmetry_space_group_name_H-M 'P 21 21 21' _atom_sites_solution_hydrogens geom _atom_sites_solution_primary direct _atom_sites_solution_secondary difmap _audit_creation_method SHELXL-97 _cell_angle_alpha 90.00 _cell_angle_beta 90.00 _cell_angle_gamma 90.00 _cell_formula_units_Z 4 _cell_length_a 8.4016(5) _cell_length_b 13.4207(9) _cell_length_c 22.130(2) _cell_measurement_reflns_used 2942 _cell_measurement_temperature 233(2) _cell_measurement_theta_max 72.02 _cell_measurement_theta_min 6.53 _cell_volume 2495.3(3) _computing_cell_refinement CrystalClear _computing_data_collection CrystalClear _computing_data_reduction CrystalClear _computing_structure_refinement 'SHELXL-97 (Sheldrick, 1997)' _computing_structure_solution 'SHELXS-97 (Sheldrick, 1990)' _diffrn_ambient_temperature 233(2) _diffrn_measured_fraction_theta_full 0.949 _diffrn_measured_fraction_theta_max 0.949 _diffrn_measurement_device_type 'Rigaku RAXIS Rapid' _diffrn_measurement_method \w-scans _diffrn_radiation_monochromator confocal _diffrn_radiation_source 'micro-focus rotating anode' _diffrn_radiation_type CuK\a _diffrn_radiation_wavelength 1.54180 _diffrn_reflns_av_R_equivalents 0.0558 _diffrn_reflns_av_sigmaI/netI 0.0497 _diffrn_reflns_limit_h_max 10 _diffrn_reflns_limit_h_min -10 _diffrn_reflns_limit_k_max 16 _diffrn_reflns_limit_k_min -16 _diffrn_reflns_limit_l_max 24 _diffrn_reflns_limit_l_min -21 _diffrn_reflns_number 29398 _diffrn_reflns_theta_full 72.02 _diffrn_reflns_theta_max 72.02 _diffrn_reflns_theta_min 6.53 _exptl_absorpt_coefficient_mu 0.503 _exptl_absorpt_correction_T_max 0.9704 _exptl_absorpt_correction_T_min 0.9421 _exptl_absorpt_correction_type multi-scan _exptl_absorpt_process_details CrystalClear _exptl_crystal_colour colorless _exptl_crystal_density_diffrn 1.040 _exptl_crystal_density_method 'not measured' _exptl_crystal_description Prism _exptl_crystal_F_000 864 _exptl_crystal_size_max 0.12 _exptl_crystal_size_mid 0.10 _exptl_crystal_size_min 0.06 _refine_diff_density_max 0.239 _refine_diff_density_min -0.129 _refine_diff_density_rms 0.037 _refine_ls_abs_structure_details 'Flack H D (1983), Acta Cryst. A39, 876-881' _refine_ls_abs_structure_Flack 0.0(3) _refine_ls_extinction_method none _refine_ls_goodness_of_fit_ref 1.011 _refine_ls_hydrogen_treatment constr _refine_ls_matrix_type full _refine_ls_number_parameters 261 _refine_ls_number_reflns 4537 _refine_ls_number_restraints 0 _refine_ls_restrained_S_all 1.011 _refine_ls_R_factor_all 0.0705 _refine_ls_R_factor_gt 0.0485 _refine_ls_shift/su_max 0.000 _refine_ls_shift/su_mean 0.000 _refine_ls_structure_factor_coef Fsqd _refine_ls_weighting_details 'calc w=1/[\s^2^(Fo^2^)+(0.0650P)^2^+0.0000P] where P=(Fo^2^+2Fc^2^)/3' _refine_ls_weighting_scheme calc _refine_ls_wR_factor_gt 0.1151 _refine_ls_wR_factor_ref 0.1225 _reflns_number_gt 2942 _reflns_number_total 4537 _reflns_threshold_expression >2sigma(I) _[local]_cod_data_source_file jp101303j_si_001.cif _[local]_cod_data_source_block lidoment _[local]_cod_cif_authors_sg_H-M P212121 _cod_original_cell_volume 2495.3(4) _cod_database_code 1502677 loop_ _symmetry_equiv_pos_as_xyz 'x, y, z' 'x+1/2, -y+1/2, -z' '-x, y+1/2, -z+1/2' '-x+1/2, -y, z+1/2' loop_ _atom_site_label _atom_site_type_symbol _atom_site_fract_x _atom_site_fract_y _atom_site_fract_z _atom_site_U_iso_or_equiv _atom_site_adp_type _atom_site_occupancy _atom_site_symmetry_multiplicity _atom_site_calc_flag _atom_site_refinement_flags C1 C 0.1012(2) -0.07692(15) 0.38847(10) 0.0540(6) Uani 1 1 d . H1 H 0.1325 -0.1465 0.3797 0.065 Uiso 1 1 calc R C2 C -0.0481(2) -0.07904(16) 0.42647(12) 0.0662(6) Uani 1 1 d . H2A H -0.1318 -0.1145 0.4044 0.079 Uiso 1 1 calc R H2B H -0.0847 -0.0106 0.4331 0.079 Uiso 1 1 calc R C3 C -0.0228(3) -0.12918(16) 0.48712(12) 0.0717(7) Uani 1 1 d . H3 H 0.0063 -0.1995 0.4795 0.086 Uiso 1 1 calc R C4 C 0.1166(3) -0.0795(2) 0.51955(12) 0.0832(8) Uani 1 1 d . H4A H 0.0865 -0.0113 0.5305 0.100 Uiso 1 1 calc R H4B H 0.1390 -0.1159 0.5570 0.100 Uiso 1 1 calc R C5 C 0.2655(3) -0.07646(19) 0.48138(12) 0.0738(7) Uani 1 1 d . H5A H 0.3031 -0.1447 0.4747 0.089 Uiso 1 1 calc R H5B H 0.3488 -0.0405 0.5034 0.089 Uiso 1 1 calc R C6 C 0.2392(2) -0.02611(15) 0.42033(10) 0.0565(6) Uani 1 1 d . H6 H 0.2052 0.0431 0.4288 0.068 Uiso 1 1 calc R O7 O 0.07270(18) -0.02715(10) 0.33198(7) 0.0686(4) Uani 1 1 d . H7 H -0.0049 -0.0530 0.3149 0.103 Uiso 1 1 calc R C8 C -0.1747(4) -0.12841(19) 0.52495(15) 0.0957(10) Uani 1 1 d . H8A H -0.1587 -0.1678 0.5612 0.144 Uiso 1 1 calc R H8B H -0.2615 -0.1565 0.5016 0.144 Uiso 1 1 calc R H8C H -0.2006 -0.0604 0.5361 0.144 Uiso 1 1 calc R C9 C 0.3888(3) -0.01951(16) 0.38121(12) 0.0701(7) Uani 1 1 d . H9 H 0.3581 0.0150 0.3434 0.084 Uiso 1 1 calc R C10 C 0.5188(3) 0.04297(17) 0.41066(13) 0.0877(9) Uani 1 1 d . H10A H 0.6044 0.0537 0.3820 0.132 Uiso 1 1 calc R H10B H 0.5597 0.0083 0.4458 0.132 Uiso 1 1 calc R H10C H 0.4750 0.1067 0.4229 0.132 Uiso 1 1 calc R C11 C 0.4576(3) -0.1210(2) 0.36291(16) 0.1052(10) Uani 1 1 d . H11A H 0.5549 -0.1110 0.3401 0.158 Uiso 1 1 calc R H11B H 0.3809 -0.1564 0.3382 0.158 Uiso 1 1 calc R H11C H 0.4806 -0.1596 0.3989 0.158 Uiso 1 1 calc R C20 C 0.0416(3) 0.24893(14) 0.38801(11) 0.0599(6) Uani 1 1 d . C21 C 0.0901(4) 0.29535(16) 0.44111(13) 0.0789(7) Uani 1 1 d . H21 H 0.0169 0.3039 0.4728 0.095 Uiso 1 1 calc R C22 C 0.2444(4) 0.32898(17) 0.44785(14) 0.0870(9) Uani 1 1 d . H22 H 0.2755 0.3610 0.4837 0.104 Uiso 1 1 calc R C23 C 0.3524(3) 0.31547(17) 0.40199(15) 0.0790(8) Uani 1 1 d . H23 H 0.4569 0.3390 0.4069 0.095 Uiso 1 1 calc R C24 C 0.3105(2) 0.26759(15) 0.34839(11) 0.0605(6) Uani 1 1 d . C25 C 0.1551(2) 0.23489(13) 0.34298(10) 0.0513(5) Uani 1 1 d . C26 C -0.1268(3) 0.21493(18) 0.38020(13) 0.0818(8) Uani 1 1 d . H26A H -0.1716 0.2455 0.3443 0.123 Uiso 1 1 calc R H26B H -0.1887 0.2343 0.4153 0.123 Uiso 1 1 calc R H26C H -0.1290 0.1430 0.3760 0.123 Uiso 1 1 calc R C27 C 0.4290(3) 0.25376(17) 0.29894(14) 0.0887(8) Uani 1 1 d . H27A H 0.4332 0.1840 0.2877 0.133 Uiso 1 1 calc R H27B H 0.5331 0.2751 0.3128 0.133 Uiso 1 1 calc R H27C H 0.3978 0.2932 0.2642 0.133 Uiso 1 1 calc R N28 N 0.10683(18) 0.18584(11) 0.28874(8) 0.0525(5) Uani 1 1 d . H28 H 0.0957 0.1214 0.2895 0.063 Uiso 1 1 calc R C29 C 0.0780(3) 0.23293(14) 0.23719(11) 0.0617(6) Uani 1 1 d . O30 O 0.0949(2) 0.32390(10) 0.23126(8) 0.0856(6) Uani 1 1 d . C31 C 0.0273(4) 0.16955(15) 0.18464(12) 0.0881(9) Uani 1 1 d . H31A H -0.0719 0.1970 0.1683 0.106 Uiso 1 1 calc R H31B H 0.1083 0.1751 0.1530 0.106 Uiso 1 1 calc R N32 N 0.0027(2) 0.06571(12) 0.19730(9) 0.0656(5) Uani 1 1 d . C33 C 0.1186(4) -0.0023(2) 0.16463(15) 0.0989(10) Uani 1 1 d . H33A H 0.0863 -0.0717 0.1707 0.119 Uiso 1 1 calc R H33B H 0.1144 0.0117 0.1212 0.119 Uiso 1 1 calc R C34 C 0.2832(4) 0.0109(3) 0.18625(18) 0.1436(14) Uani 1 1 d . H34A H 0.3147 0.0799 0.1813 0.215 Uiso 1 1 calc R H34B H 0.3540 -0.0316 0.1631 0.215 Uiso 1 1 calc R H34C H 0.2893 -0.0071 0.2286 0.215 Uiso 1 1 calc R C35 C -0.1578(4) 0.0298(2) 0.18564(17) 0.1089(12) Uani 1 1 d . H35A H -0.1577 -0.0432 0.1865 0.131 Uiso 1 1 calc R H35B H -0.1905 0.0508 0.1451 0.131 Uiso 1 1 calc R C36 C -0.2747(4) 0.0672(3) 0.2301(2) 0.1500(16) Uani 1 1 d . H36A H -0.2530 0.0379 0.2694 0.225 Uiso 1 1 calc R H36B H -0.3813 0.0490 0.2173 0.225 Uiso 1 1 calc R H36C H -0.2666 0.1391 0.2329 0.225 Uiso 1 1 calc R loop_ _atom_site_aniso_label _atom_site_aniso_U_11 _atom_site_aniso_U_22 _atom_site_aniso_U_33 _atom_site_aniso_U_23 _atom_site_aniso_U_13 _atom_site_aniso_U_12 C1 0.0690(13) 0.0478(10) 0.0453(15) 0.0009(11) -0.0068(11) -0.0029(9) C2 0.0685(14) 0.0578(11) 0.0722(19) 0.0079(13) -0.0031(13) -0.0019(10) C3 0.0915(16) 0.0575(12) 0.0661(19) 0.0086(13) 0.0152(15) 0.0072(12) C4 0.121(2) 0.0802(15) 0.0482(18) 0.0012(14) -0.0051(16) 0.0019(16) C5 0.0855(16) 0.0780(15) 0.0578(19) 0.0037(15) -0.0175(14) -0.0040(13) C6 0.0709(13) 0.0517(11) 0.0468(15) 0.0023(11) -0.0132(12) -0.0044(9) O7 0.0846(10) 0.0670(8) 0.0542(11) 0.0124(8) -0.0237(8) -0.0196(8) C8 0.119(2) 0.0728(15) 0.095(3) 0.0158(16) 0.0372(19) 0.0087(15) C9 0.0758(15) 0.0669(13) 0.0674(19) 0.0027(13) -0.0049(13) -0.0131(11) C10 0.0719(15) 0.0809(16) 0.110(2) 0.0074(16) -0.0165(15) -0.0198(12) C11 0.0925(18) 0.099(2) 0.124(3) -0.0276(18) 0.0344(19) -0.0101(16) C20 0.0809(15) 0.0489(12) 0.0500(17) 0.0054(11) 0.0015(13) 0.0026(10) C21 0.118(2) 0.0632(14) 0.055(2) 0.0003(13) 0.0022(17) 0.0031(14) C22 0.139(3) 0.0592(14) 0.062(2) -0.0064(15) -0.032(2) -0.0046(16) C23 0.0880(18) 0.0606(14) 0.088(2) 0.0007(15) -0.0368(18) -0.0084(12) C24 0.0641(13) 0.0498(11) 0.067(2) 0.0060(13) -0.0114(13) 0.0015(10) C25 0.0668(12) 0.0396(9) 0.0477(17) 0.0051(10) -0.0070(11) 0.0009(9) C26 0.0734(15) 0.0809(15) 0.091(2) 0.0118(15) 0.0147(14) -0.0085(12) C27 0.0688(14) 0.0881(17) 0.109(3) 0.0108(16) 0.0011(16) 0.0028(13) N28 0.0661(10) 0.0452(8) 0.0462(13) 0.0047(9) -0.0077(9) -0.0006(7) C29 0.0824(14) 0.0476(11) 0.0552(17) 0.0044(12) -0.0166(13) 0.0029(10) O30 0.1342(14) 0.0454(8) 0.0770(13) 0.0074(8) -0.0354(11) 0.0016(8) C31 0.151(2) 0.0526(13) 0.0609(19) 0.0031(12) -0.0286(17) -0.0032(14) N32 0.0818(12) 0.0583(10) 0.0567(14) 0.0005(10) -0.0142(11) -0.0030(9) C33 0.156(3) 0.0833(18) 0.057(2) -0.0145(15) 0.015(2) -0.0115(19) C34 0.126(3) 0.195(4) 0.110(3) -0.007(3) 0.019(2) 0.045(3) C35 0.125(2) 0.0797(18) 0.122(3) 0.0031(19) -0.055(2) -0.0074(18) C36 0.082(2) 0.183(3) 0.185(4) -0.041(4) 0.001(2) 0.024(2) loop_ _atom_type_symbol _atom_type_description _atom_type_scat_dispersion_real _atom_type_scat_dispersion_imag _atom_type_scat_source C C 0.0181 0.0091 'International Tables Vol C Tables 4.2.6.8 and 6.1.1.4' H H 0.0000 0.0000 'International Tables Vol C Tables 4.2.6.8 and 6.1.1.4' N N 0.0311 0.0180 'International Tables Vol C Tables 4.2.6.8 and 6.1.1.4' O O 0.0492 0.0322 'International Tables Vol C Tables 4.2.6.8 and 6.1.1.4' loop_ _geom_angle_atom_site_label_1 _geom_angle_atom_site_label_2 _geom_angle_atom_site_label_3 _geom_angle O7 C1 C2 110.79(17) O7 C1 C6 108.79(16) C2 C1 C6 112.57(19) C1 C2 C3 112.65(18) C2 C3 C8 111.4(2) C2 C3 C4 109.2(2) C8 C3 C4 112.4(2) C5 C4 C3 112.6(2) C4 C5 C6 112.7(2) C1 C6 C5 108.83(18) C1 C6 C9 113.01(19) C5 C6 C9 114.04(19) C10 C9 C6 112.3(2) C10 C9 C11 109.4(2) C6 C9 C11 114.09(19) C21 C20 C25 117.7(2) C21 C20 C26 120.7(2) C25 C20 C26 121.6(2) C22 C21 C20 120.8(3) C23 C22 C21 119.8(3) C22 C23 C24 121.5(2) C25 C24 C23 117.3(2) C25 C24 C27 121.8(2) C23 C24 C27 120.9(2) C24 C25 C20 122.8(2) C24 C25 N28 119.2(2) C20 C25 N28 118.03(18) C29 N28 C25 123.73(16) O30 C29 N28 122.8(2) O30 C29 C31 120.6(2) N28 C29 C31 116.60(17) N32 C31 C29 116.0(2) C31 N32 C35 114.8(2) C31 N32 C33 113.5(2) C35 N32 C33 108.1(2) C34 C33 N32 112.0(3) N32 C35 C36 112.6(3) loop_ _geom_bond_atom_site_label_1 _geom_bond_atom_site_label_2 _geom_bond_distance C1 O7 1.437(2) C1 C2 1.510(3) C1 C6 1.519(3) C2 C3 1.516(3) C3 C8 1.526(4) C3 C4 1.527(4) C4 C5 1.511(4) C5 C6 1.527(3) C6 C9 1.529(3) C9 C10 1.523(3) C9 C11 1.534(3) C20 C21 1.391(3) C20 C25 1.392(3) C20 C26 1.496(3) C21 C22 1.381(4) C22 C23 1.373(4) C23 C24 1.394(3) C24 C25 1.383(3) C24 C27 1.491(3) C25 N28 1.428(3) N28 C29 1.326(2) C29 O30 1.236(2) C29 C31 1.503(3) C31 N32 1.437(3) N32 C35 1.455(3) N32 C33 1.518(3) C33 C34 1.474(5) C35 C36 1.478(5) loop_ _geom_torsion_atom_site_label_1 _geom_torsion_atom_site_label_2 _geom_torsion_atom_site_label_3 _geom_torsion_atom_site_label_4 _geom_torsion O7 C1 C2 C3 -178.89(16) C6 C1 C2 C3 -56.8(2) C1 C2 C3 C8 178.91(19) C1 C2 C3 C4 54.2(3) C2 C3 C4 C5 -53.6(3) C8 C3 C4 C5 -177.8(2) C3 C4 C5 C6 55.5(3) O7 C1 C6 C5 177.93(17) C2 C1 C6 C5 54.7(2) O7 C1 C6 C9 -54.3(2) C2 C1 C6 C9 -177.50(18) C4 C5 C6 C1 -54.3(3) C4 C5 C6 C9 178.55(19) C1 C6 C9 C10 172.75(17) C5 C6 C9 C10 -62.3(3) C1 C6 C9 C11 -62.1(3) C5 C6 C9 C11 62.9(3) C25 C20 C21 C22 2.0(3) C26 C20 C21 C22 -178.4(2) C20 C21 C22 C23 -0.8(3) C21 C22 C23 C24 -0.5(4) C22 C23 C24 C25 0.6(3) C22 C23 C24 C27 179.8(2) C23 C24 C25 C20 0.7(3) C27 C24 C25 C20 -178.56(19) C23 C24 C25 N28 179.73(17) C27 C24 C25 N28 0.5(3) C21 C20 C25 C24 -2.0(3) C26 C20 C25 C24 178.5(2) C21 C20 C25 N28 179.00(17) C26 C20 C25 N28 -0.6(3) C24 C25 N28 C29 -76.1(2) C20 C25 N28 C29 103.0(2) C25 N28 C29 O30 2.1(3) C25 N28 C29 C31 -179.6(2) O30 C29 C31 N32 -176.6(2) N28 C29 C31 N32 5.0(3) C29 C31 N32 C35 119.6(3) C29 C31 N32 C33 -115.3(3) C31 N32 C33 C34 66.6(3) C35 N32 C33 C34 -164.8(3) C31 N32 C35 C36 -71.5(4) C33 N32 C35 C36 160.7(3) _journal_paper_doi 10.1021/jp101303j pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/ni.stru000066400000000000000000000034361470422267000234630ustar00rootroot00000000000000title structure Ni FCC format pdffit scale 1.000000 sharp 0.000000, 1.000000, 0.000000 spcgr Fm-3m cell 3.520000, 3.520000, 3.520000, 90.000000, 90.000000, 90.000000 dcell 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000 ncell 1, 1, 1, 4 atoms NI 0.00000000 0.00000000 0.00000000 1.0000 0.00000000 0.00000000 0.00000000 0.0000 0.00126651 0.00126651 0.00126651 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 NI 0.00000000 0.50000000 0.50000000 1.0000 0.00000000 0.00000000 0.00000000 0.0000 0.00126651 0.00126651 0.00126651 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 NI 0.50000000 0.00000000 0.50000000 1.0000 0.00000000 0.00000000 0.00000000 0.0000 0.00126651 0.00126651 0.00126651 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 NI 0.50000000 0.50000000 0.00000000 1.0000 0.00000000 0.00000000 0.00000000 0.0000 0.00126651 0.00126651 0.00126651 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/paracetamol.cif000066400000000000000000000242021470422267000251030ustar00rootroot00000000000000#------------------------------------------------------------------------------ #$Date: 2012-02-26 21:34:47 +0000 (Sun, 26 Feb 2012) $ #$Revision: 35909 $ #$URL: file:///home/coder/svn-repositories/cod/cif/7/10/39/7103910.cif $ #------------------------------------------------------------------------------ # # This file is available in the Crystallography Open Database (COD), # http://www.crystallography.net/ # # All data on this site have been placed in the public domain by the # contributors. # data_7103910 loop_ _publ_author_name 'Fabbiani, Francesca P A' 'Allan, David R' 'Dawson, Alice' 'David, William I F' 'McGregor, Pamela A' 'Oswald, Iain D H' 'Parsons, Simon' 'Pulham, Colin R' _publ_section_title ; Pressure-induced formation of a solvate of paracetamol. ; _journal_issue 24 _journal_name_full 'Chemical communications (Cambridge, England)' _journal_page_first 3004 _journal_page_last 3005 _journal_year 2003 _chemical_compound_source ; ? ; _chemical_formula_moiety ' C9 H13 N1 O3 ' _chemical_formula_sum 'C9 H13 N O3' _chemical_formula_weight 183.21 _chemical_name_common 'paracetamol monomethanolate' _chemical_name_systematic ; paracetamol monomethanolate ; _space_group_IT_number 14 _symmetry_cell_setting monoclinic _symmetry_space_group_name_Hall '-P 2ybc' _symmetry_space_group_name_H-M 'P 1 21/c 1' _atom_sites_solution_hydrogens geom _atom_sites_solution_primary direct _cell_angle_alpha 90 _cell_angle_beta 115.52(3) _cell_angle_gamma 90 _cell_formula_units_Z 4 _cell_length_a 7.630(2) _cell_length_b 17.209(3) _cell_length_c 7.3710(11) _cell_measurement_reflns_used 285 _cell_measurement_temperature 293 _cell_measurement_theta_max 18 _cell_measurement_theta_min 4 _cell_volume 873.4(4) _computing_cell_refinement ; SAINT (Siemens ,1995) ; _computing_data_collection ; SMART (Siemens, 1993) ; _computing_data_reduction ; SAINT (Siemens ,1995) ; _computing_molecular_graphics ; CAMERON (Watkin et al 1996) ; _computing_publication_material ; CRYSTALS (Watkin et al 2001) ; _computing_structure_refinement ; CRYSTALS (Watkin et al 2001) ; _computing_structure_solution DASH _diffrn_ambient_temperature 293 _diffrn_measured_fraction_theta_full 0.339 _diffrn_measured_fraction_theta_max 0.244 _diffrn_measurement_device_type 'Bruker SMART APEX CCD' _diffrn_measurement_method 'omega scans' _diffrn_radiation_monochromator graphite _diffrn_radiation_type 'Mo K\a' _diffrn_radiation_wavelength 0.71073 _diffrn_reflns_av_R_equivalents 0.05 _diffrn_reflns_limit_h_max 4 _diffrn_reflns_limit_h_min -4 _diffrn_reflns_limit_k_max 16 _diffrn_reflns_limit_k_min -17 _diffrn_reflns_limit_l_max 7 _diffrn_reflns_limit_l_min -7 _diffrn_reflns_number 801 _diffrn_reflns_theta_full 15.632 _diffrn_reflns_theta_max 20.843 _diffrn_reflns_theta_min 3.283 _diffrn_standards_decay_% 0.00 _diffrn_standards_interval_count 0 _diffrn_standards_interval_time 0 _diffrn_standards_number 0 _exptl_absorpt_coefficient_mu 0.105 _exptl_absorpt_correction_T_max 1.0000 _exptl_absorpt_correction_T_min 0.2895 _exptl_absorpt_correction_type multi-scan _exptl_absorpt_process_details 'SADABS (Siemens, 1996)' _exptl_crystal_colour ' block ' _exptl_crystal_density_diffrn 1.393 _exptl_crystal_description ' colourless ' _exptl_crystal_F_000 392.000 _exptl_crystal_size_max 0.20 _exptl_crystal_size_mid 0.20 _exptl_crystal_size_min 0.10 _refine_diff_density_max 0.33 _refine_diff_density_min -0.43 _refine_ls_extinction_method None _refine_ls_goodness_of_fit_ref 0.9312 _refine_ls_hydrogen_treatment noref _refine_ls_matrix_type full _refine_ls_number_parameters 14 _refine_ls_number_reflns 172 _refine_ls_number_restraints 2 _refine_ls_R_factor_all 0.1848 _refine_ls_R_factor_gt 0.1347 _refine_ls_shift/su_max 0.000032 _refine_ls_structure_factor_coef Fsqd _refine_ls_weighting_details ; Method, part 1, Chebychev polynomial, (Watkin, 1994, Prince, 1982) [weight] = 1.0/[A~0~*T~0~(x)+A~1~*T~1~(x) ... +A~n-1~]*T~n-1~(x)] where A~i~ are the Chebychev coefficients listed below and x= Fcalc/Fmax Method = Robust Weighting (Prince, 1982) W = [weight] * [1-(deltaF/6*sigmaF)^2]^2 A~i~ are: 9.45 10.7 2.51 ; _refine_ls_weighting_scheme calc _refine_ls_wR_factor_all 0.2161 _refine_ls_wR_factor_gt 0.1845 _refine_ls_wR_factor_ref 0.2161 _reflns_limit_h_max 1 _reflns_limit_h_min -4 _reflns_limit_k_max 17 _reflns_limit_k_min 0 _reflns_limit_l_max 7 _reflns_limit_l_min 0 _reflns_number_gt 118 _reflns_number_total 223 _reflns_threshold_expression I>2.00u(I) _[local]_cod_data_source_file b310394c.txt _[local]_cod_data_source_block compound_1 _[local]_cod_cif_authors_sg_H-M 'P 1 21/c 1 ' _[local]_cod_chemical_formula_sum_orig ' C9 H13 N1 O3 ' _cod_depositor_comments ; The following automatic conversions were performed: '_symmetry_cell_setting' value 'Monoclinic' changed to 'monoclinic' according to '/home/saulius/struct/CIF-dictionaries/cif_core.dic' dictionary named 'cif_core.dic' version 2.4.1 from 2010-06-29. Automatic conversion script Id: cif_fix_enum 1527 2010-12-29 10:47:43Z saulius ; _cod_database_code 7103910 loop_ _symmetry_equiv_pos_as_xyz x,y,z -x,-y,-z -x,y+1/2,-z+1/2 x,-y+1/2,z+1/2 loop_ _atom_site_label _atom_site_type_symbol _atom_site_fract_x _atom_site_fract_y _atom_site_fract_z _atom_site_U_iso_or_equiv _atom_site_occupancy _atom_site_adp_type _atom_site_refinement_flags _atom_site_attached_hydrogens O1 O 1.418(4) 0.2012(8) 0.453(2) 0.062(4) 1.0000 Uiso G . H11 H 1.482(4) 0.1617(9) 0.447(3) 0.049(4) 1.0000 Uiso G . O2 O 0.619(4) 0.0236(8) -0.131(2) 0.062(4) 1.0000 Uiso GD . N3 N 0.626(4) 0.1452(7) -0.007(2) 0.062(4) 1.0000 Uiso GD . C4 C 0.829(4) 0.1566(6) 0.106(2) 0.062(4) 1.0000 Uiso GD . C5 C 0.971(4) 0.1085(6) 0.094(2) 0.062(4) 1.0000 Uiso GD . C6 C 1.167(4) 0.1244(7) 0.212(2) 0.062(4) 1.0000 Uiso G . C7 C 1.223(4) 0.1874(6) 0.343(2) 0.062(4) 1.0000 Uiso G . C8 C 1.080(4) 0.2343(6) 0.358(3) 0.062(4) 1.0000 Uiso G . C9 C 0.886(4) 0.2188(7) 0.241(3) 0.062(4) 1.0000 Uiso G . C10 C 0.534(4) 0.0831(8) -0.118(2) 0.062(4) 1.0000 Uiso GD . C11 C 0.316(4) 0.091(1) -0.227(3) 0.062(4) 1.0000 Uiso G . O13 O 0.351(7) -0.0905(13) -0.448(4) 0.062(4) 1.0000 Uiso G . C12 C 0.157(7) -0.078(2) -0.566(6) 0.062(4) 1.0000 Uiso G . H51 H 0.9307 0.0630 -0.0005 0.073(4) 1.0000 Uiso . . H61 H 1.2667 0.0898 0.1991 0.073(4) 1.0000 Uiso . . H81 H 1.1207 0.2785 0.4548 0.073(4) 1.0000 Uiso . . H91 H 0.7856 0.2525 0.2543 0.073(4) 1.0000 Uiso . . H111 H 0.2747 0.1438 -0.1956 0.073(4) 1.0000 Uiso . . H112 H 0.2716 0.0866 -0.3739 0.073(4) 1.0000 Uiso . . H113 H 0.2518 0.0500 -0.1792 0.073(4) 1.0000 Uiso . . H31 H 0.5405 0.1895 -0.0019 0.074(4) 1.0000 Uiso . . H131 H 0.4337 -0.0522 -0.3521 0.039(4) 1.0000 Uiso . . H121 H 0.1026 -0.1282 -0.6557 0.075(4) 1.0000 Uiso . . H122 H 0.1286 -0.0339 -0.6528 0.075(4) 1.0000 Uiso . . H123 H 0.0850 -0.0765 -0.4785 0.075(4) 1.0000 Uiso . . loop_ _atom_type_symbol _atom_type_scat_dispersion_real _atom_type_scat_dispersion_imag _atom_type_scat_Cromer_Mann_a1 _atom_type_scat_Cromer_Mann_b1 _atom_type_scat_Cromer_Mann_a2 _atom_type_scat_Cromer_Mann_b2 _atom_type_scat_Cromer_Mann_a3 _atom_type_scat_Cromer_Mann_b3 _atom_type_scat_Cromer_Mann_a4 _atom_type_scat_Cromer_Mann_b4 _atom_type_scat_Cromer_Mann_c _atom_type_scat_source 'C ' 0.0033 0.0016 2.3100 20.8439 1.0200 10.2075 1.5886 0.5687 0.8650 51.6512 0.2156 International_Tables_Vol_IV_Table_2.2B 'H ' 0.0000 0.0000 0.4930 10.5109 0.3229 26.1257 0.1402 3.1424 0.0408 57.7998 0.0030 International_Tables_Vol_IV_Table_2.2B 'N ' 0.0061 0.0033 12.2126 0.0057 3.1322 9.8933 2.0125 28.9975 1.1663 0.5826 -11.5290 International_Tables_Vol_IV_Table_2.2B 'O ' 0.0106 0.0060 3.0485 13.2771 2.2868 5.7011 1.5463 0.3239 0.8670 32.9089 0.2508 International_Tables_Vol_IV_Table_2.2B loop_ _geom_angle_atom_site_label_1 _geom_angle_site_symmetry_1 _geom_angle_atom_site_label_2 _geom_angle_site_symmetry_2 _geom_angle_atom_site_label_3 _geom_angle_site_symmetry_3 _geom_angle _geom_angle_publ_flag C7 . O1 . H11 . 109.474 no H31 . N3 . C10 . 116.277 no H31 . N3 . C4 . 115.362 no C10 . N3 . C4 . 128.353 yes C9 . C4 . C5 . 119.15 yes C9 . C4 . N3 . 116.77 yes C5 . C4 . N3 . 124.05 yes H51 . C5 . C6 . 120.872 no H51 . C5 . C4 . 119.304 no C6 . C5 . C4 . 119.823 yes H61 . C6 . C7 . 120.681 no H61 . C6 . C5 . 118.513 no C7 . C6 . C5 . 120.80 yes C8 . C7 . O1 . 122.37 yes C8 . C7 . C6 . 119.35 yes O1 . C7 . C6 . 118.276 yes H81 . C8 . C9 . 120.811 no H81 . C8 . C7 . 119.196 no C9 . C8 . C7 . 119.991 yes H91 . C9 . C8 . 119.086 no H91 . C9 . C4 . 120.062 no C8 . C9 . C4 . 120.849 yes C11 . C10 . O2 . 121.759 yes C11 . C10 . N3 . 115.011 yes O2 . C10 . N3 . 123.229 yes H113 . C11 . H112 . 110.327 no H113 . C11 . H111 . 107.477 no H112 . C11 . H111 . 108.709 no H113 . C11 . C10 . 110.093 no H112 . C11 . C10 . 110.763 no H111 . C11 . C10 . 109.396 no H131 . O13 . C12 . 122.749 no H123 . C12 . H122 . 111.394 no H123 . C12 . H121 . 104.433 no H122 . C12 . H121 . 108.549 no H123 . C12 . O13 . 109.552 no H122 . C12 . O13 . 114.646 no H121 . C12 . O13 . 107.692 no loop_ _geom_bond_atom_site_label_1 _geom_bond_site_symmetry_1 _geom_bond_atom_site_label_2 _geom_bond_site_symmetry_2 _geom_bond_distance _geom_bond_publ_flag O1 . C7 . 1.3735 yes O1 . H11 . 0.85000 no O2 . C10 . 1.23881 yes N3 . H31 . 1.015 no N3 . C10 . 1.3458 yes N3 . C4 . 1.4208 yes C4 . C9 . 1.3952 yes C4 . C5 . 1.3995 yes C5 . H51 . 1.006 no C5 . C6 . 1.3918 yes C6 . H61 . 1.003 no C6 . C7 . 1.3913 yes C7 . C8 . 1.3963 yes C8 . H81 . 0.994 no C8 . C9 . 1.3869 yes C9 . H91 . 0.997 no C10 . C11 . 1.5077 yes C11 . H113 . 1.003 no C11 . H112 . 0.987 no C11 . H111 . 1.023 no O13 . H131 . 0.974 no O13 . C12 . 1.3717 yes C12 . H123 . 1.016 no C12 . H122 . 0.960 no C12 . H121 . 1.050 no pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata/paracetamol_monomethanolate.cif000066400000000000000000000242511470422267000303610ustar00rootroot00000000000000#------------------------------------------------------------------------------ #$Date: 2018-10-05 15:19:55 +0300 (Fri, 05 Oct 2018) $ #$Revision: 211332 $ #$URL: file:///home/coder/svn-repositories/cod/cif/2/20/15/2201530.cif $ #------------------------------------------------------------------------------ # # This file is available in the Crystallography Open Database (COD), # http://www.crystallography.net/. The original data for this entry # were provided by IUCr Journals, http://journals.iucr.org/. # # The file may be used within the scientific community so long as # proper attribution is given to the journal article from which the # data were obtained. # data_2201530 loop_ _publ_author_name 'Andrew Parkin' 'Simon Parsons' 'Colin R. Pulham' _publ_section_title ; Paracetamol monohydrate at 150K ; _journal_issue 12 _journal_name_full 'Acta Crystallographica Section E' _journal_page_first o1345 _journal_page_last o1347 _journal_paper_doi 10.1107/S1600536802019840 _journal_volume 58 _journal_year 2002 _chemical_formula_iupac 'C8 H9 N O2, H2 O' _chemical_formula_moiety 'C8 H9 N O2, H2 O' _chemical_formula_sum 'C8 H11 N O3' _chemical_formula_weight 169.18 _chemical_name_common 'Paracetamol monohydrate' _chemical_name_systematic ; 4-hydroxyacetanilide hydrate ; _space_group_IT_number 14 _symmetry_cell_setting monoclinic _symmetry_space_group_name_Hall '-P 2yn' _symmetry_space_group_name_H-M 'P 1 21/n 1' _atom_sites_solution_hydrogens mixed _atom_sites_solution_primary direct _atom_sites_solution_secondary difmap _audit_creation_method SHELXL-97 _cell_angle_alpha 90.00 _cell_angle_beta 96.399(3) _cell_angle_gamma 90.00 _cell_formula_units_Z 4 _cell_length_a 4.5039(6) _cell_length_b 10.5391(14) _cell_length_c 17.048(2) _cell_measurement_reflns_used 1700 _cell_measurement_temperature 150(2) _cell_measurement_theta_max 26.4 _cell_measurement_theta_min 2.2 _cell_volume 804.18(18) _computing_cell_refinement 'SAINT (Bruker, 2002)' _computing_data_collection 'SMART (Bruker, 2001) ' _computing_data_reduction SAINT _computing_molecular_graphics ; SHELXTL and CAMERON (Watkin et al., 1993) ; _computing_publication_material SHELXTL _computing_structure_refinement SHELXTL _computing_structure_solution 'SHELXTL (Sheldrick, 1997)' _diffrn_ambient_temperature 150(2) _diffrn_measured_fraction_theta_full 0.998 _diffrn_measured_fraction_theta_max 0.998 _diffrn_measurement_device_type 'Bruker SMART APEX CCD area-detector' _diffrn_measurement_method '\f and \w' _diffrn_radiation_monochromator graphite _diffrn_radiation_source 'fine-focus sealed tube' _diffrn_radiation_type MoK\a _diffrn_radiation_wavelength 0.71073 _diffrn_reflns_av_R_equivalents 0.0519 _diffrn_reflns_av_sigmaI/netI 0.0704 _diffrn_reflns_limit_h_max 5 _diffrn_reflns_limit_h_min -5 _diffrn_reflns_limit_k_max 13 _diffrn_reflns_limit_k_min -13 _diffrn_reflns_limit_l_max 21 _diffrn_reflns_limit_l_min -18 _diffrn_reflns_number 4516 _diffrn_reflns_theta_full 26.40 _diffrn_reflns_theta_max 26.40 _diffrn_reflns_theta_min 2.28 _exptl_absorpt_coefficient_mu 0.107 _exptl_absorpt_correction_T_max 0.962 _exptl_absorpt_correction_T_min 0.864 _exptl_absorpt_correction_type multi-scan _exptl_absorpt_process_details ; SADABS (Sheldrick, 1997), based on a procedure given by Blessing (1995) ; _exptl_crystal_colour Colourless _exptl_crystal_density_diffrn 1.397 _exptl_crystal_density_method 'not measured' _exptl_crystal_description needle _exptl_crystal_F_000 360 _exptl_crystal_size_max 0.45 _exptl_crystal_size_mid 0.07 _exptl_crystal_size_min 0.06 _refine_diff_density_max 0.351 _refine_diff_density_min -0.283 _refine_ls_extinction_coef 0 _refine_ls_extinction_method none _refine_ls_goodness_of_fit_ref 0.967 _refine_ls_hydrogen_treatment mixed _refine_ls_matrix_type full _refine_ls_number_parameters 126 _refine_ls_number_reflns 1650 _refine_ls_number_restraints 0 _refine_ls_restrained_S_all 0.967 _refine_ls_R_factor_all 0.0808 _refine_ls_R_factor_gt 0.0621 _refine_ls_shift/su_max <0.001 _refine_ls_shift/su_mean 0.000 _refine_ls_structure_factor_coef Fsqd _refine_ls_weighting_details 'calc w = 1/[\s^2^(Fo^2^)+(0.0834P)^2^] where P = (Fo^2^+2Fc^2^)/3' _refine_ls_weighting_scheme calc _refine_ls_wR_factor_gt 0.1433 _refine_ls_wR_factor_ref 0.1527 _reflns_number_gt 1232 _reflns_number_total 1650 _reflns_threshold_expression I>2\s(I) _cod_data_source_file na6176.cif _cod_data_source_block 1 _cod_depositor_comments ; The following automatic conversions were performed: '_atom_sites_solution_hydrogens' value 'geom + difmap' was changed to 'mixed'. Automatic conversion script Id: cif_fix_values 6452 2018-10-05 10:23:21Z andrius ; _cod_original_cell_volume 804.18(19) _cod_original_sg_symbol_Hall '-P 2yn ' _cod_original_sg_symbol_H-M 'P 21/n' _cod_database_code 2201530 _cod_database_fobs_code 2201530 loop_ _symmetry_equiv_pos_as_xyz 'x, y, z' '-x+1/2, y+1/2, -z+1/2' '-x, -y, -z' 'x-1/2, -y-1/2, z-1/2' loop_ _atom_site_label _atom_site_fract_x _atom_site_fract_y _atom_site_fract_z _atom_site_U_iso_or_equiv _atom_site_adp_type _atom_site_calc_flag _atom_site_refinement_flags _atom_site_occupancy _atom_site_disorder_assembly _atom_site_disorder_group _atom_site_type_symbol C1 0.5762(5) 0.6687(2) 0.15652(13) 0.0203(5) Uani d . 1 . . C C2 0.6405(5) 0.5890(2) 0.09617(14) 0.0228(6) Uani d . 1 . . C H2 0.5360 0.5979 0.0449 0.027 Uiso calc R 1 . . H C3 0.8575(5) 0.4962(2) 0.11063(14) 0.0227(6) Uani d . 1 . . C H3 0.9020 0.4418 0.0691 0.027 Uiso calc R 1 . . H C4 1.0105(5) 0.4823(2) 0.18545(14) 0.0201(5) Uani d . 1 . . C O4 1.2257(4) 0.39115(15) 0.20327(10) 0.0237(4) Uani d . 1 . . O H4 1.256(6) 0.344(3) 0.1589(17) 0.032(7) Uiso d . 1 . . H C5 0.9479(5) 0.5626(2) 0.24524(14) 0.0231(6) Uani d . 1 . . C H5 1.0530 0.5541 0.2965 0.028 Uiso calc R 1 . . H C6 0.7335(5) 0.6550(2) 0.23077(14) 0.0228(6) Uani d . 1 . . C H6 0.6924 0.7104 0.2722 0.027 Uiso calc R 1 . . H N7 0.3541(5) 0.76490(18) 0.14821(12) 0.0210(5) Uani d . 1 . . N H7 0.317(5) 0.802(2) 0.1921(15) 0.017(6) Uiso d . 1 . . H C8 0.1973(5) 0.8114(2) 0.08327(13) 0.0197(5) Uani d . 1 . . C O8 0.2264(4) 0.77490(15) 0.01541(9) 0.0256(4) Uani d . 1 . . O C9 -0.0181(6) 0.9152(2) 0.09736(15) 0.0251(6) Uani d . 1 . . C H9A 0.0620 0.9968 0.0819 0.038 Uiso calc R 1 . . H H9B -0.0470 0.9175 0.1535 0.038 Uiso calc R 1 . . H H9C -0.2101 0.8991 0.0659 0.038 Uiso calc R 1 . . H O1W 0.1960(5) 0.73246(17) 0.41504(11) 0.0283(5) Uani d . 1 . . O H1W 0.363(9) 0.725(3) 0.452(2) 0.062(11) Uiso d . 1 . . H H2W 0.045(9) 0.739(3) 0.446(2) 0.060(10) Uiso d . 1 . . H loop_ _atom_site_aniso_label _atom_site_aniso_U_11 _atom_site_aniso_U_22 _atom_site_aniso_U_33 _atom_site_aniso_U_12 _atom_site_aniso_U_13 _atom_site_aniso_U_23 C1 0.0202(13) 0.0170(11) 0.0232(13) -0.0018(9) -0.0003(9) 0.0017(9) C2 0.0266(14) 0.0190(11) 0.0210(13) -0.0012(10) -0.0051(9) 0.0017(9) C3 0.0269(14) 0.0182(11) 0.0226(13) -0.0013(10) 0.0005(10) -0.0032(10) C4 0.0168(13) 0.0183(11) 0.0244(13) -0.0013(9) -0.0014(9) 0.0044(10) O4 0.0286(10) 0.0211(9) 0.0204(10) 0.0044(7) -0.0020(7) 0.0001(7) C5 0.0244(14) 0.0219(12) 0.0214(13) -0.0027(10) -0.0046(9) 0.0027(10) C6 0.0257(14) 0.0213(11) 0.0204(13) -0.0012(10) -0.0012(9) -0.0043(10) N7 0.0251(12) 0.0192(10) 0.0181(11) 0.0001(8) -0.0006(8) -0.0029(8) C8 0.0212(13) 0.0172(11) 0.0198(12) -0.0040(9) -0.0015(9) -0.0012(9) O8 0.0265(10) 0.0274(9) 0.0214(10) 0.0038(7) -0.0032(7) -0.0023(7) C9 0.0268(14) 0.0219(12) 0.0250(13) -0.0001(10) -0.0034(10) 0.0005(10) O1W 0.0301(11) 0.0299(10) 0.0237(10) -0.0025(8) -0.0019(8) 0.0016(8) loop_ _atom_type_symbol _atom_type_description _atom_type_scat_dispersion_real _atom_type_scat_dispersion_imag _atom_type_scat_source C C 0.0033 0.0016 'International Tables Vol C Tables 4.2.6.8 and 6.1.1.4' H H 0.0000 0.0000 'International Tables Vol C Tables 4.2.6.8 and 6.1.1.4' N N 0.0061 0.0033 'International Tables Vol C Tables 4.2.6.8 and 6.1.1.4' O O 0.0106 0.0060 'International Tables Vol C Tables 4.2.6.8 and 6.1.1.4' loop_ _geom_angle_atom_site_label_1 _geom_angle_atom_site_label_2 _geom_angle_atom_site_label_3 _geom_angle C2 C1 C6 119.2(2) C2 C1 N7 124.2(2) C6 C1 N7 116.6(2) C1 C2 C3 119.9(2) C1 C2 H2 120.1 C3 C2 H2 120.1 C2 C3 C4 120.4(2) C2 C3 H3 119.8 C4 C3 H3 119.8 O4 C4 C5 117.6(2) O4 C4 C3 122.9(2) C5 C4 C3 119.6(2) C4 O4 H4 110.9(18) C6 C5 C4 120.0(2) C6 C5 H5 120.0 C4 C5 H5 120.0 C5 C6 C1 121.0(2) C5 C6 H6 119.5 C1 C6 H6 119.5 C8 N7 C1 130.2(2) C8 N7 H7 114.1(16) C1 N7 H7 115.6(16) O8 C8 N7 123.9(2) O8 C8 C9 120.9(2) N7 C8 C9 115.23(19) C8 C9 H9A 109.5 C8 C9 H9B 109.5 H9A C9 H9B 109.5 C8 C9 H9C 109.5 H9A C9 H9C 109.5 H9B C9 H9C 109.5 H1W O1W H2W 102(3) loop_ _geom_bond_atom_site_label_1 _geom_bond_atom_site_label_2 _geom_bond_site_symmetry_2 _geom_bond_distance _geom_bond_publ_flag C1 C2 . 1.384(3) ? C1 C6 . 1.387(3) ? C1 N7 . 1.420(3) ? C2 C3 . 1.386(3) ? C2 H2 . 0.9500 ? C3 C4 . 1.388(3) ? C3 H3 . 0.9500 ? C4 O4 . 1.374(3) ? C4 C5 . 1.378(3) ? O4 H4 . 0.93(3) ? C5 C6 . 1.375(3) ? C5 H5 . 0.9500 ? C6 H6 . 0.9500 ? N7 C8 . 1.338(3) ? N7 H7 . 0.88(3) ? C8 O8 . 1.240(3) ? C8 C9 . 1.499(3) ? C9 H9A . 0.9800 ? C9 H9B . 0.9800 ? C9 H9C . 0.9800 ? O1W H1W . 0.93(4) ? O1W H2W . 0.90(4) ? loop_ _geom_hbond_atom_site_label_D _geom_hbond_atom_site_label_H _geom_hbond_atom_site_label_A _geom_hbond_site_symmetry_A _geom_hbond_distance_DH _geom_hbond_distance_HA _geom_hbond_distance_DA _geom_hbond_angle_DHA N7 H7 O4 2_655 0.88(3) 2.04(3) 2.918(3) 174(2) O4 H4 O1W 2_645 0.93(3) 1.76(3) 2.673(2) 170(2) O1W H1W O8 4_676 0.93(4) 1.85(4) 2.780(3) 171(3) O1W H2W O8 4_576 0.90(4) 1.97(4) 2.866(3) 171(3) loop_ _cod_related_entry_id _cod_related_entry_database _cod_related_entry_code 1 ChemSpider 21132048 paracetamol_monomethanolate_data_single_crystal.cif000066400000000000000000001312431470422267000343750ustar00rootroot00000000000000pyobjcryst-2024.2.1/src/pyobjcryst/tests/testdata#------------------------------------------------------------------------------ #$Date: 2016-04-02 23:58:55 +0300 (Sat, 02 Apr 2016) $ #$Revision: 180935 $ #$URL: file:///home/coder/svn-repositories/cod/hkl/2/20/15/2201530.hkl $ #------------------------------------------------------------------------------ # # This file is available in the Crystallography Open Database (COD), # http://www.crystallography.net/. The original data for this entry # were provided by IUCr Journals, http://journals.iucr.org/. # # The file may be used within the scientific community so long as # proper attribution is given to the journal article from which the # data were obtained. # data_2201530_Fobs loop_ _publ_author_name 'Parkin, Andrew' 'Parsons, Simon' 'Pulham, Colin R.' _publ_section_title ; Paracetamol monohydrate at 150K ; _journal_issue 12 _journal_name_full 'Acta Crystallographica Section E' _journal_page_first o1345 _journal_page_last o1347 _journal_volume 58 _journal_year 2002 _space_group_IT_number 14 _symmetry_space_group_name_Hall '-P 2yn' _symmetry_space_group_name_H-M 'P 1 21/n 1' _cell_angle_alpha 90.000 _cell_angle_beta 96.399 _cell_angle_gamma 90.000 _cell_length_a 4.5039 _cell_length_b 10.5391 _cell_length_c 17.0480 _exptl_crystal_F_000 360.00 _reflns_d_resolution_high 0.7993 _cod_data_source_file na61761sup2.hkl _cod_data_source_block 1 #BEGIN Tags that were not found in dictionaries: _shelx_title 'paracetamol monohydrate in P2(1)/n' _shelx_refln_list_code 4 _shelx_f_calc_maximum 111.90 _shelx_f_squared_multiplier 1.000 #END Tags that were not found in dictionaries loop_ _symmetry_equiv_pos_as_xyz 'x, y, z' '-x+1/2, y+1/2, -z+1/2' '-x, -y, -z' 'x-1/2, -y-1/2, z-1/2' loop_ _refln_index_h _refln_index_k _refln_index_l _refln_F_squared_calc _refln_F_squared_meas _refln_F_squared_sigma _refln_observed_status 2 0 0 1505.76 1436.12 41.04 o 4 0 0 206.70 156.32 19.60 o 1 1 0 110.92 94.97 6.84 o 2 1 0 310.08 282.99 13.93 o 3 1 0 4.50 6.35 4.38 o 4 1 0 102.54 108.50 12.63 o 5 1 0 5.68 15.03 8.76 o 0 2 0 1159.71 1244.79 25.29 o 1 2 0 5942.08 5886.67 105.23 o 2 2 0 3517.90 3663.85 73.06 o 3 2 0 90.83 83.66 10.58 o 4 2 0 0.07 1.18 4.41 o 5 2 0 39.79 66.37 14.05 o 1 3 0 1870.35 1836.42 48.83 o 2 3 0 19.91 20.87 4.55 o 3 3 0 0.34 0.63 3.48 o 4 3 0 7.00 8.91 5.42 o 5 3 0 13.16 21.22 9.11 o 0 4 0 887.14 890.46 82.43 o 1 4 0 482.30 481.36 21.90 o 2 4 0 9.52 9.43 3.20 o 3 4 0 36.70 39.93 6.66 o 4 4 0 22.21 36.82 9.70 o 5 4 0 7.88 14.25 10.85 o 1 5 0 456.57 455.76 10.71 o 2 5 0 59.90 58.88 5.43 o 3 5 0 9.44 11.57 3.42 o 4 5 0 132.77 140.97 21.83 o 5 5 0 0.11 4.15 5.37 o 0 6 0 664.85 592.17 15.54 o 1 6 0 0.01 2.12 2.78 o 2 6 0 55.59 62.96 7.50 o 3 6 0 28.07 23.74 7.72 o 4 6 0 0.22 2.61 4.90 o 1 7 0 28.94 28.76 4.68 o 2 7 0 63.40 70.48 8.23 o 3 7 0 0.43 3.54 4.99 o 4 7 0 1.59 5.76 6.14 o 0 8 0 110.12 88.94 10.84 o 1 8 0 83.37 70.74 7.10 o 2 8 0 164.93 148.59 11.60 o 3 8 0 19.85 27.39 7.94 o 4 8 0 1.24 4.18 6.01 o 1 9 0 98.69 86.78 8.04 o 2 9 0 26.56 20.72 6.56 o 3 9 0 111.30 104.14 14.63 o 4 9 0 11.92 12.54 6.70 o 0 10 0 63.26 59.93 10.05 o 1 10 0 4.65 5.30 2.99 o 2 10 0 3.92 7.32 5.21 o 3 10 0 31.63 32.60 8.84 o 1 11 0 122.42 97.72 10.80 o 2 11 0 0.46 4.69 5.16 o 3 11 0 10.29 11.61 6.44 o 0 12 0 319.10 240.98 28.62 o 1 12 0 3.10 2.73 3.31 o 2 12 0 19.72 16.51 6.17 o -5 0 1 19.07 24.96 10.46 o -3 0 1 194.70 185.98 19.87 o -1 0 1 736.78 536.78 21.83 o 1 0 1 62.19 50.71 6.53 o 3 0 1 938.58 844.97 36.86 o -5 1 1 20.38 24.02 7.06 o -4 1 1 107.26 120.20 13.49 o -3 1 1 386.97 408.36 20.92 o -2 1 1 14.24 12.65 4.76 o -1 1 1 409.94 366.59 14.12 o 0 1 1 471.90 586.25 26.03 o 1 1 1 38.53 31.62 3.66 o 2 1 1 48.08 44.95 6.28 o 3 1 1 1.50 2.17 3.74 o 4 1 1 19.60 20.82 6.00 o 5 1 1 8.49 18.72 8.05 o -5 2 1 43.21 58.42 16.47 o -4 2 1 205.16 228.19 19.52 o -3 2 1 29.42 25.51 6.49 o -2 2 1 6.08 1.23 3.89 o -1 2 1 0.48 2.98 2.33 o 0 2 1 11.18 16.48 2.49 o 1 2 1 4562.27 4949.41 36.98 o 2 2 1 463.49 479.93 36.69 o 3 2 1 14.67 12.39 5.31 o 4 2 1 2.19 3.78 3.91 o 5 2 1 53.79 97.31 23.40 o -5 3 1 11.89 25.67 9.22 o -4 3 1 3.51 5.36 8.50 o -3 3 1 74.50 78.06 10.40 o -2 3 1 1417.55 1427.59 37.12 o -1 3 1 2302.03 2394.71 33.44 o 0 3 1 1497.31 1560.99 16.73 o 1 3 1 290.35 288.47 8.75 o 2 3 1 37.08 49.23 5.45 o 3 3 1 190.82 197.06 29.75 o 4 3 1 3.11 2.97 5.89 o 5 3 1 34.52 56.67 15.42 o -5 4 1 3.38 9.67 11.24 o -4 4 1 0.31 4.41 3.92 o -3 4 1 57.81 78.64 9.01 o -2 4 1 76.73 73.26 5.98 o -1 4 1 263.82 264.67 6.83 o 0 4 1 143.49 167.15 6.59 o 1 4 1 51.28 43.80 4.18 o 2 4 1 193.15 211.88 10.82 o 3 4 1 35.91 41.98 6.52 o 4 4 1 284.02 383.32 28.37 o 5 4 1 7.87 16.60 9.93 o -5 5 1 0.79 3.16 5.34 o -4 5 1 39.67 43.65 9.60 o -3 5 1 12.32 18.67 4.48 o -2 5 1 629.13 586.29 24.05 o -1 5 1 22.06 23.79 2.66 o 0 5 1 686.63 701.67 13.95 o 1 5 1 256.89 273.45 9.58 o 2 5 1 1056.00 950.36 31.70 o 3 5 1 8.40 10.23 4.00 o 4 5 1 68.10 93.32 21.43 o 5 5 1 6.75 12.94 10.06 o -5 6 1 2.42 2.34 5.38 o -4 6 1 8.45 7.38 5.84 o -3 6 1 72.00 67.30 9.26 o -2 6 1 2.12 3.54 3.54 o -1 6 1 246.82 256.10 9.73 o 0 6 1 17.60 15.58 2.94 o 1 6 1 883.18 901.18 16.88 o 2 6 1 64.06 58.10 5.44 o 3 6 1 542.68 512.47 28.83 o 4 6 1 56.08 83.88 14.31 o -4 7 1 136.79 141.80 17.14 o -3 7 1 34.83 35.54 9.21 o -2 7 1 200.63 189.80 12.94 o -1 7 1 175.86 159.48 9.44 o 0 7 1 1256.13 1302.58 36.81 o 1 7 1 35.38 31.59 4.90 o 2 7 1 20.47 20.10 4.79 o 3 7 1 189.85 147.60 16.96 o 4 7 1 0.11 2.81 5.45 o -4 8 1 49.30 65.97 12.54 o -3 8 1 40.99 31.63 8.50 o -2 8 1 54.70 50.29 7.06 o -1 8 1 105.08 99.98 8.21 o 0 8 1 179.37 154.80 9.81 o 1 8 1 102.97 93.99 8.05 o 2 8 1 66.01 67.99 10.93 o 3 8 1 3.98 3.52 4.80 o 4 8 1 0.79 1.27 5.49 o -4 9 1 36.03 33.38 9.30 o -3 9 1 95.00 91.78 13.56 o -2 9 1 83.97 78.71 9.38 o -1 9 1 92.95 87.84 8.27 o 0 9 1 103.84 94.07 8.20 o 1 9 1 91.76 67.67 6.93 o 2 9 1 3.40 6.74 4.59 o 3 9 1 4.00 7.66 5.12 o 4 9 1 0.13 2.80 5.41 o -3 10 1 88.03 81.46 13.09 o -2 10 1 0.59 6.02 4.32 o -1 10 1 156.58 131.18 10.04 o 0 10 1 261.56 238.80 13.45 o 1 10 1 2.54 5.46 2.68 o 2 10 1 58.53 49.90 10.16 o 3 10 1 10.38 12.85 6.04 o -3 11 1 30.41 37.09 10.09 o -2 11 1 91.51 69.11 11.41 o -1 11 1 80.20 69.17 7.58 o 0 11 1 192.06 159.41 11.63 o 1 11 1 3.52 4.90 4.02 o 2 11 1 24.56 26.11 7.35 o 3 11 1 25.04 25.43 8.56 o -2 12 1 2.51 2.95 4.35 o -1 12 1 55.33 56.53 8.89 o 0 12 1 204.53 167.16 12.26 o 1 12 1 57.73 51.08 10.72 o 2 12 1 5.91 7.09 5.07 o 0 13 1 6.09 9.23 4.34 o -4 0 2 1.19 5.23 7.06 o -2 0 2 651.08 560.96 29.80 o 0 0 2 494.07 378.60 26.34 o 2 0 2 50.42 44.96 8.63 o 4 0 2 103.69 106.39 17.25 o -5 1 2 4.98 9.29 5.43 o -4 1 2 13.18 16.19 6.47 o -3 1 2 280.87 283.85 19.00 o -2 1 2 80.97 64.03 7.90 o -1 1 2 4347.16 3847.83 170.44 o 0 1 2 2427.58 2581.80 26.00 o 1 1 2 116.09 114.93 7.88 o 2 1 2 890.96 918.18 22.08 o 3 1 2 8.07 9.92 4.19 o 4 1 2 280.48 302.96 17.65 o 5 1 2 182.78 349.36 53.06 o -5 2 2 174.79 221.03 20.71 o -4 2 2 333.15 379.44 24.68 o -3 2 2 85.51 74.88 9.46 o -2 2 2 224.29 235.85 15.02 o -1 2 2 6234.72 5526.72 135.69 o 0 2 2 2249.82 2727.46 55.53 o 1 2 2 165.30 178.51 11.08 o 2 2 2 6.90 8.24 3.38 o 3 2 2 0.01 0.43 3.24 o 4 2 2 43.95 58.71 11.24 o 5 2 2 15.05 23.14 9.11 o -5 3 2 21.51 27.19 13.33 o -4 3 2 59.06 61.56 10.53 o -3 3 2 116.57 117.54 13.31 o -2 3 2 139.24 140.99 9.50 o -1 3 2 0.15 0.99 2.10 o 0 3 2 104.25 116.36 5.32 o 1 3 2 778.30 929.40 18.21 o 2 3 2 112.04 110.54 7.97 o 3 3 2 150.46 166.17 11.74 o 4 3 2 89.17 106.94 14.60 o 5 3 2 18.41 24.29 8.97 o -5 4 2 25.69 34.90 16.99 o -4 4 2 332.11 410.63 24.16 o -3 4 2 12.88 11.28 4.56 o -2 4 2 4591.60 4734.31 86.07 o -1 4 2 371.49 393.65 11.96 o 0 4 2 1458.92 1555.56 24.26 o 1 4 2 32.18 33.52 3.80 o 2 4 2 13.17 11.52 3.47 o 3 4 2 54.84 68.90 10.51 o 4 4 2 7.21 10.48 6.56 o 5 4 2 26.26 50.03 11.96 o -5 5 2 16.53 21.57 14.25 o -4 5 2 13.07 13.87 4.46 o -3 5 2 5.05 6.17 4.18 o -2 5 2 216.03 225.57 10.24 o -1 5 2 917.75 987.45 21.52 o 0 5 2 63.75 63.60 4.84 o 1 5 2 1618.77 1602.51 77.47 o 2 5 2 219.60 195.52 9.29 o 3 5 2 51.77 56.45 9.18 o 4 5 2 8.15 7.35 5.13 o 5 5 2 0.46 3.66 9.02 o -5 6 2 1.81 3.01 8.36 o -4 6 2 10.89 13.93 6.65 o -3 6 2 297.78 293.69 17.51 o -2 6 2 149.52 142.07 10.80 o -1 6 2 171.81 169.07 8.29 o 0 6 2 298.72 302.02 10.12 o 1 6 2 380.03 376.60 12.11 o 2 6 2 21.62 27.71 3.82 o 3 6 2 204.12 222.16 20.65 o 4 6 2 73.97 72.07 12.76 o -4 7 2 1.74 7.08 5.67 o -3 7 2 80.27 85.76 10.10 o -2 7 2 34.74 42.47 6.96 o -1 7 2 398.00 350.43 11.87 o 0 7 2 12.65 13.27 3.07 o 1 7 2 4.06 5.89 3.23 o 2 7 2 396.22 431.07 27.22 o 3 7 2 3.40 3.65 5.49 o 4 7 2 77.89 97.33 14.47 o -4 8 2 14.27 13.83 6.60 o -3 8 2 0.19 5.34 4.63 o -2 8 2 102.25 80.72 9.06 o -1 8 2 39.08 43.89 4.94 o 0 8 2 0.54 1.82 2.77 o 1 8 2 32.80 30.81 5.54 o 2 8 2 402.90 371.62 25.22 o 3 8 2 28.73 24.79 7.92 o 4 8 2 73.40 68.95 13.10 o -4 9 2 18.20 24.91 8.18 o -3 9 2 9.88 15.60 5.93 o -2 9 2 6.79 11.34 4.34 o -1 9 2 54.81 47.60 6.13 o 0 9 2 350.42 334.94 21.32 o 1 9 2 17.07 18.16 4.11 o 2 9 2 20.70 18.58 6.78 o 3 9 2 10.91 17.38 7.19 o 4 9 2 8.33 10.06 9.67 o -3 10 2 1.36 4.99 4.75 o -2 10 2 20.20 16.62 4.61 o -1 10 2 48.07 38.48 5.57 o 0 10 2 46.85 42.73 5.96 o 1 10 2 44.39 38.18 8.46 o 2 10 2 5.63 4.34 4.68 o 3 10 2 10.94 4.10 5.12 o -3 11 2 13.10 15.85 6.59 o -2 11 2 21.59 26.77 9.41 o -1 11 2 3.76 4.06 2.74 o 0 11 2 151.37 129.69 10.28 o 1 11 2 0.62 2.94 3.85 o 2 11 2 14.43 17.43 8.30 o -2 12 2 0.75 4.72 4.79 o -1 12 2 47.00 38.01 5.64 o 0 12 2 1.24 1.51 3.42 o 1 12 2 75.67 68.55 11.71 o 2 12 2 2.88 8.20 5.29 o 0 13 2 20.90 19.63 5.75 o -5 0 3 0.03 -0.52 6.27 o -3 0 3 1141.01 1211.19 51.50 o -1 0 3 1124.71 1052.77 87.83 o 1 0 3 682.91 749.68 28.75 o 3 0 3 4.53 4.97 4.84 o 5 0 3 150.70 210.43 37.90 o -5 1 3 4.83 5.06 5.32 o -4 1 3 15.64 19.14 7.19 o -3 1 3 0.05 1.53 3.60 o -2 1 3 67.68 59.37 7.90 o -1 1 3 570.26 535.19 11.27 o 0 1 3 3198.11 3361.49 52.76 o 1 1 3 1495.18 1668.75 40.20 o 2 1 3 41.12 36.34 4.84 o 3 1 3 582.27 588.98 20.16 o 4 1 3 232.22 269.56 21.90 o 5 1 3 609.00 906.86 53.21 o -5 2 3 0.02 0.71 4.71 o -4 2 3 19.61 21.85 6.93 o -3 2 3 366.40 313.35 18.99 o -2 2 3 0.19 0.22 3.11 o -1 2 3 12521.73 11707.18 114.68 o 0 2 3 513.26 592.12 9.29 o 1 2 3 623.31 621.53 57.32 o 2 2 3 330.32 345.74 15.15 o 3 2 3 18.46 20.28 4.50 o 4 2 3 157.29 202.91 22.09 o 5 2 3 102.38 127.19 20.57 o -5 3 3 9.66 10.78 7.56 o -4 3 3 2.49 3.47 4.67 o -3 3 3 54.38 52.96 9.09 o -2 3 3 430.30 395.70 16.46 o -1 3 3 905.86 892.41 40.33 o 0 3 3 199.35 224.29 6.10 o 1 3 3 2.06 2.92 2.52 o 2 3 3 136.58 134.37 8.69 o 3 3 3 406.14 426.40 61.62 o 4 3 3 42.27 76.97 17.06 o 5 3 3 7.90 24.47 9.03 o -5 4 3 7.91 23.66 14.25 o -4 4 3 4.53 7.73 4.10 o -3 4 3 326.80 326.84 16.92 o -2 4 3 1644.92 1670.22 45.54 o -1 4 3 584.39 670.37 32.18 o 0 4 3 18.53 18.47 2.61 o 1 4 3 965.86 1071.94 38.07 o 2 4 3 233.11 224.70 10.56 o 3 4 3 63.06 71.81 10.61 o 4 4 3 0.05 4.54 5.17 o 5 4 3 2.82 8.55 6.73 o -5 5 3 0.06 4.84 10.33 o -4 5 3 18.78 21.84 5.83 o -3 5 3 79.51 77.85 8.92 o -2 5 3 857.77 883.36 23.39 o -1 5 3 13.07 15.30 3.24 o 0 5 3 178.71 204.13 6.56 o 1 5 3 166.78 194.16 8.37 o 2 5 3 8.01 9.61 2.82 o 3 5 3 15.49 16.60 5.68 o 4 5 3 23.74 22.96 7.25 o 5 5 3 20.45 24.57 12.81 o -5 6 3 1.55 4.97 10.46 o -4 6 3 25.26 30.87 8.48 o -3 6 3 0.35 4.05 3.79 o -2 6 3 86.57 97.59 9.67 o -1 6 3 2.26 4.37 2.44 o 0 6 3 228.37 284.25 12.82 o 1 6 3 46.64 53.16 4.93 o 2 6 3 26.95 23.29 4.77 o 3 6 3 181.52 156.60 11.97 o 4 6 3 34.36 33.91 8.82 o -4 7 3 6.90 9.39 5.68 o -3 7 3 268.55 256.11 17.30 o -2 7 3 653.41 632.33 24.46 o -1 7 3 0.42 1.65 2.23 o 0 7 3 166.21 193.35 9.49 o 1 7 3 53.81 52.09 5.13 o 2 7 3 52.94 51.86 10.11 o 3 7 3 50.54 53.44 10.69 o 4 7 3 4.98 11.47 6.48 o -4 8 3 21.80 31.58 9.04 o -3 8 3 11.39 11.94 4.61 o -2 8 3 187.67 154.86 12.33 o -1 8 3 127.70 125.01 7.78 o 0 8 3 135.87 136.30 9.61 o 1 8 3 0.61 3.44 3.87 o 2 8 3 310.54 261.22 21.70 o 3 8 3 6.10 9.20 5.95 o 4 8 3 1.29 2.96 5.87 o -4 9 3 155.37 179.24 20.69 o -3 9 3 0.02 1.99 3.94 o -2 9 3 50.56 42.76 7.22 o -1 9 3 10.29 11.15 3.70 o 0 9 3 40.59 35.08 5.62 o 1 9 3 5.34 8.56 5.11 o 2 9 3 89.81 70.59 11.86 o 3 9 3 28.67 26.38 7.87 o -3 10 3 2.80 5.10 4.87 o -2 10 3 0.01 2.00 2.93 o -1 10 3 464.52 394.70 17.31 o 0 10 3 151.14 138.47 11.53 o 1 10 3 37.83 36.01 8.18 o 2 10 3 0.65 3.19 4.41 o 3 10 3 12.89 22.60 8.54 o -3 11 3 20.61 25.07 8.23 o -2 11 3 52.71 41.36 6.90 o -1 11 3 2.97 3.96 2.72 o 0 11 3 40.35 40.42 7.62 o 1 11 3 43.82 37.02 8.73 o 2 11 3 2.14 2.97 4.61 o -2 12 3 0.77 2.88 4.09 o -1 12 3 0.11 2.16 2.62 o 0 12 3 30.77 33.76 7.65 o 1 12 3 19.51 13.06 5.99 o 2 12 3 42.57 33.78 8.62 o 0 13 3 1.14 3.32 3.43 o -4 0 4 258.75 265.71 28.23 o -2 0 4 375.01 361.25 25.88 o 0 0 4 1009.28 712.88 51.76 o 2 0 4 217.37 196.88 13.14 o 4 0 4 518.05 666.43 55.94 o -5 1 4 6.29 8.79 5.82 o -4 1 4 140.41 146.39 15.80 o -3 1 4 267.24 267.43 18.91 o -2 1 4 19.69 10.19 4.92 o -1 1 4 9.83 8.26 2.50 o 0 1 4 4.41 3.66 2.32 o 1 1 4 2875.20 3222.35 85.11 o 2 1 4 98.57 90.05 5.93 o 3 1 4 13.51 15.16 3.53 o 4 1 4 15.39 25.49 6.59 o 5 1 4 2.02 4.04 7.96 o -5 2 4 45.10 61.37 12.09 o -4 2 4 2.96 5.15 4.94 o -3 2 4 53.20 47.41 8.21 o -2 2 4 25.97 25.94 3.91 o -1 2 4 63.26 80.45 5.87 o 0 2 4 15.60 18.55 3.86 o 1 2 4 487.52 541.27 33.51 o 2 2 4 175.88 163.20 8.36 o 3 2 4 215.82 237.64 17.84 o 4 2 4 45.14 50.48 8.46 o 5 2 4 117.87 185.53 25.55 o -5 3 4 3.81 8.11 7.02 o -4 3 4 0.11 4.42 5.26 o -3 3 4 114.71 111.26 9.82 o -2 3 4 129.46 106.42 7.88 o -1 3 4 11.86 10.73 2.35 o 0 3 4 3657.13 4864.06 68.60 o 1 3 4 318.50 328.15 25.32 o 2 3 4 571.63 646.01 16.74 o 3 3 4 258.97 253.00 18.99 o 4 3 4 8.62 10.31 5.51 o 5 3 4 20.19 30.97 10.84 o -5 4 4 106.10 145.73 28.23 o -4 4 4 21.68 23.23 5.61 o -3 4 4 56.17 59.56 7.70 o -2 4 4 168.88 166.89 9.41 o -1 4 4 81.57 90.33 5.56 o 0 4 4 296.33 382.80 8.60 o 1 4 4 0.59 1.81 2.15 o 2 4 4 102.87 91.33 6.64 o 3 4 4 516.26 525.44 26.41 o 4 4 4 19.43 26.39 7.81 o 5 4 4 14.84 21.30 6.86 o -5 5 4 10.65 25.09 15.29 o -4 5 4 35.43 39.63 7.89 o -3 5 4 114.92 112.87 10.78 o -2 5 4 82.97 89.46 7.51 o -1 5 4 416.89 422.64 19.99 o 0 5 4 15.56 12.42 2.41 o 1 5 4 451.96 507.43 12.92 o 2 5 4 267.15 243.00 17.38 o 3 5 4 58.83 62.31 9.44 o 4 5 4 11.06 19.72 6.99 o -4 6 4 24.72 26.98 6.45 o -3 6 4 217.97 228.06 15.67 o -2 6 4 74.15 73.24 7.24 o -1 6 4 2.36 1.80 2.26 o 0 6 4 3.17 6.15 1.72 o 1 6 4 27.24 24.61 3.62 o 2 6 4 25.50 22.03 4.71 o 3 6 4 163.87 144.16 11.76 o 4 6 4 36.20 40.76 10.57 o -4 7 4 5.06 10.10 5.39 o -3 7 4 111.52 118.73 11.74 o -2 7 4 93.56 91.19 8.28 o -1 7 4 1.43 2.14 2.44 o 0 7 4 21.25 21.10 3.52 o 1 7 4 443.83 446.75 14.17 o 2 7 4 14.22 20.10 7.39 o 3 7 4 7.15 5.12 5.86 o 4 7 4 21.44 29.79 9.26 o -4 8 4 167.40 160.50 18.26 o -3 8 4 3.06 2.54 3.41 o -2 8 4 74.90 62.92 8.30 o -1 8 4 185.09 186.74 11.50 o 0 8 4 27.58 32.00 4.27 o 1 8 4 375.90 363.34 33.07 o 2 8 4 2.65 2.65 4.17 o 3 8 4 8.83 20.37 7.98 o 4 8 4 2.41 8.10 7.25 o -4 9 4 23.14 30.53 8.62 o -3 9 4 77.26 77.63 9.97 o -2 9 4 6.06 12.48 4.65 o -1 9 4 859.67 807.82 30.68 o 0 9 4 181.85 170.24 10.96 o 1 9 4 7.07 9.63 5.44 o 2 9 4 252.10 192.00 18.73 o 3 9 4 1.26 6.21 5.31 o -3 10 4 0.00 1.51 5.16 o -2 10 4 0.99 4.37 3.25 o -1 10 4 143.98 126.52 9.94 o 0 10 4 106.99 105.83 11.66 o 1 10 4 26.76 25.10 7.76 o 2 10 4 5.85 9.52 5.53 o 3 10 4 93.60 94.64 15.29 o -3 11 4 19.07 22.83 7.23 o -2 11 4 11.84 14.26 4.57 o -1 11 4 8.58 10.41 3.42 o 0 11 4 273.15 267.69 19.44 o 1 11 4 113.27 98.82 14.14 o 2 11 4 10.31 12.85 6.18 o -2 12 4 10.57 17.94 5.93 o -1 12 4 2.10 2.87 2.41 o 0 12 4 167.27 142.48 14.32 o 1 12 4 127.18 109.70 19.34 o -5 0 5 36.92 46.53 14.64 o -3 0 5 201.52 149.13 20.00 o -1 0 5 10.94 12.91 4.65 o 1 0 5 0.10 1.54 3.38 o 3 0 5 23.63 26.99 5.92 o 5 0 5 25.22 35.94 17.25 o -5 1 5 7.65 9.24 6.19 o -4 1 5 147.85 158.54 22.87 o -3 1 5 98.40 78.20 10.05 o -2 1 5 6.21 4.66 2.29 o -1 1 5 348.22 296.26 15.96 o 0 1 5 917.38 1025.01 58.22 o 1 1 5 1777.90 2146.96 91.78 o 2 1 5 149.29 160.62 7.87 o 3 1 5 126.71 112.86 9.92 o 4 1 5 778.16 815.23 41.44 o 5 1 5 1.32 6.27 8.27 o -5 2 5 73.85 100.21 15.15 o -4 2 5 16.42 11.56 5.80 o -3 2 5 175.91 171.59 13.27 o -2 2 5 79.36 71.45 5.78 o -1 2 5 198.03 215.75 11.24 o 0 2 5 148.93 144.38 5.83 o 1 2 5 7.20 6.81 3.63 o 2 2 5 102.37 105.24 6.17 o 3 2 5 122.30 108.51 13.12 o 4 2 5 4.73 8.78 4.53 o 5 2 5 68.01 79.95 15.95 o -5 3 5 14.95 26.61 10.39 o -4 3 5 21.85 17.14 6.29 o -3 3 5 11.31 12.80 3.87 o -2 3 5 1001.67 994.23 29.81 o -1 3 5 856.08 998.79 37.60 o 0 3 5 654.34 811.56 15.51 o 1 3 5 1575.73 1970.09 33.17 o 2 3 5 129.07 122.79 14.68 o 3 3 5 1248.67 1024.53 69.01 o 4 3 5 12.79 17.44 5.42 o 5 3 5 86.48 112.85 18.74 o -5 4 5 114.77 121.85 16.25 o -4 4 5 7.72 12.43 5.66 o -3 4 5 4.56 6.36 3.26 o -2 4 5 1111.65 1065.33 27.51 o -1 4 5 353.66 392.59 13.35 o 0 4 5 234.11 320.01 8.93 o 1 4 5 5.61 4.24 3.33 o 2 4 5 16.68 16.91 4.29 o 3 4 5 416.18 391.83 22.36 o 4 4 5 19.92 23.60 6.02 o 5 4 5 23.54 34.90 13.00 o -5 5 5 9.49 14.25 11.63 o -4 5 5 13.45 17.64 6.81 o -3 5 5 42.66 40.57 6.30 o -2 5 5 7.39 6.10 3.02 o -1 5 5 352.55 399.10 17.92 o 0 5 5 91.39 100.91 5.10 o 1 5 5 1696.03 1888.66 33.56 o 2 5 5 204.69 190.62 15.03 o 3 5 5 274.90 270.45 19.17 o 4 5 5 40.19 50.89 8.14 o -4 6 5 12.65 15.72 6.53 o -3 6 5 1802.71 1711.47 100.21 o -2 6 5 6.94 9.93 3.64 o -1 6 5 5.30 3.19 2.45 o 0 6 5 65.38 67.93 5.37 o 1 6 5 65.84 68.37 6.01 o 2 6 5 161.72 141.38 10.78 o 3 6 5 100.72 96.10 10.51 o 4 6 5 77.49 73.36 12.54 o -4 7 5 4.28 4.83 4.38 o -3 7 5 110.09 106.84 10.99 o -2 7 5 11.47 14.72 4.09 o -1 7 5 306.64 329.94 12.03 o 0 7 5 4.98 9.71 2.79 o 1 7 5 35.26 30.95 5.37 o 2 7 5 119.62 107.31 9.50 o 3 7 5 16.74 14.40 6.72 o 4 7 5 25.08 28.27 6.94 o -4 8 5 106.30 90.18 21.57 o -3 8 5 0.18 4.50 3.78 o -2 8 5 125.41 113.44 9.47 o -1 8 5 31.71 31.43 4.33 o 0 8 5 3.99 7.04 2.59 o 1 8 5 57.91 53.53 9.56 o 2 8 5 0.48 4.90 4.89 o 3 8 5 0.23 3.23 5.08 o 4 8 5 51.09 57.31 9.42 o -4 9 5 115.99 159.58 30.19 o -3 9 5 18.57 26.94 6.08 o -2 9 5 42.33 41.35 6.19 o -1 9 5 2.24 4.03 2.65 o 0 9 5 3.64 1.83 4.07 o 1 9 5 16.29 15.70 6.51 o 2 9 5 7.50 11.51 5.73 o 3 9 5 65.21 79.09 13.55 o -3 10 5 0.03 3.42 3.66 o -2 10 5 7.98 5.19 3.63 o -1 10 5 29.47 32.62 5.32 o 0 10 5 60.11 70.73 10.03 o 1 10 5 92.22 92.15 14.00 o 2 10 5 0.95 4.55 4.99 o 3 10 5 12.31 10.86 6.33 o -2 11 5 14.84 18.42 4.90 o -1 11 5 0.01 2.50 2.54 o 0 11 5 4.73 5.11 4.23 o 1 11 5 115.34 95.37 14.03 o 2 11 5 0.11 1.77 5.11 o -2 12 5 23.18 27.06 5.79 o -1 12 5 21.45 28.40 6.73 o 0 12 5 99.07 104.74 12.34 o 1 12 5 5.32 8.76 5.69 o -4 0 6 94.32 93.58 17.78 o -2 0 6 582.25 567.11 47.35 o 0 0 6 1380.06 1276.62 87.18 o 2 0 6 64.53 46.90 8.82 o 4 0 6 87.42 96.06 21.83 o -5 1 6 17.41 19.63 7.58 o -4 1 6 141.11 142.97 15.80 o -3 1 6 0.12 -1.74 3.92 o -2 1 6 109.61 98.28 5.51 o -1 1 6 801.35 718.01 15.10 o 0 1 6 343.53 377.35 15.34 o 1 1 6 173.37 190.75 7.38 o 2 1 6 561.91 591.66 18.13 o 3 1 6 4.91 4.51 3.18 o 4 1 6 492.53 456.38 29.71 o 5 1 6 117.17 141.67 21.76 o -5 2 6 3.20 7.74 5.85 o -4 2 6 140.19 141.44 17.64 o -3 2 6 56.14 60.32 6.60 o -2 2 6 93.48 87.36 5.63 o -1 2 6 1.77 1.50 1.75 o 0 2 6 429.63 430.26 13.53 o 1 2 6 62.01 80.37 7.59 o 2 2 6 29.03 30.29 5.24 o 3 2 6 11.69 9.85 5.11 o 4 2 6 17.53 20.57 7.70 o 5 2 6 47.94 55.72 13.18 o -5 3 6 7.52 11.41 5.80 o -4 3 6 86.10 80.50 11.76 o -3 3 6 88.34 86.35 8.01 o -2 3 6 0.98 1.97 2.21 o -1 3 6 137.44 144.57 5.76 o 0 3 6 590.53 706.72 15.54 o 1 3 6 1.12 0.86 3.07 o 2 3 6 423.57 404.03 24.16 o 3 3 6 189.60 175.08 13.04 o 4 3 6 69.76 82.18 11.19 o 5 3 6 1.54 4.92 6.56 o -5 4 6 0.02 -0.41 4.39 o -4 4 6 19.09 17.57 6.84 o -3 4 6 164.48 164.67 11.77 o -2 4 6 231.54 234.75 11.31 o -1 4 6 95.77 105.93 5.68 o 0 4 6 172.98 233.47 7.58 o 1 4 6 187.67 209.05 11.97 o 2 4 6 79.64 73.51 8.34 o 3 4 6 58.86 56.18 7.72 o 4 4 6 32.09 27.22 6.02 o -5 5 6 2.04 7.23 4.74 o -4 5 6 2.02 2.65 4.73 o -3 5 6 354.62 336.28 17.18 o -2 5 6 43.95 38.47 5.13 o -1 5 6 299.69 321.24 11.25 o 0 5 6 47.63 56.49 4.25 o 1 5 6 34.02 33.99 4.98 o 2 5 6 153.42 147.05 10.83 o 3 5 6 66.19 62.78 8.86 o 4 5 6 3.09 6.86 3.83 o -4 6 6 0.69 0.99 4.00 o -3 6 6 47.10 48.66 7.54 o -2 6 6 465.25 454.30 17.06 o -1 6 6 88.21 88.92 6.40 o 0 6 6 141.68 176.13 8.96 o 1 6 6 189.96 199.18 10.72 o 2 6 6 431.43 369.18 14.70 o 3 6 6 4.41 7.03 3.30 o 4 6 6 3.57 7.19 5.46 o -4 7 6 8.67 10.02 6.01 o -3 7 6 54.20 58.04 8.52 o -2 7 6 398.67 385.82 25.11 o -1 7 6 2.79 4.74 2.67 o 0 7 6 186.98 196.73 9.36 o 1 7 6 15.55 17.00 4.50 o 2 7 6 0.91 2.92 2.49 o 3 7 6 2.91 3.96 3.60 o 4 7 6 8.84 12.15 5.28 o -4 8 6 1.06 5.24 4.02 o -3 8 6 46.68 40.36 7.42 o -2 8 6 172.06 162.48 11.39 o -1 8 6 207.62 205.01 11.33 o 0 8 6 152.69 166.36 11.31 o 1 8 6 110.56 98.18 9.53 o 2 8 6 71.45 71.22 11.76 o 3 8 6 305.67 300.90 26.25 o -3 9 6 35.35 28.72 6.60 o -2 9 6 4.98 6.47 3.06 o -1 9 6 448.45 453.34 20.55 o 0 9 6 13.27 14.88 5.51 o 1 9 6 8.67 6.01 5.27 o 2 9 6 17.66 18.69 7.12 o 3 9 6 110.22 91.76 13.83 o -3 10 6 33.18 42.17 7.63 o -2 10 6 4.08 7.50 3.81 o -1 10 6 59.59 56.99 6.86 o 0 10 6 21.36 25.59 6.52 o 1 10 6 603.83 547.66 32.35 o 2 10 6 3.03 5.88 4.71 o 3 10 6 31.40 31.53 9.19 o -2 11 6 48.54 43.11 7.47 o -1 11 6 132.90 137.93 14.13 o 0 11 6 26.18 23.55 7.33 o 1 11 6 1.53 4.16 4.33 o 2 11 6 1.31 2.77 4.24 o -1 12 6 62.58 68.30 10.07 o 0 12 6 31.45 34.12 8.84 o 1 12 6 60.51 64.17 12.05 o -5 0 7 14.70 18.95 10.06 o -3 0 7 4.83 5.88 6.27 o -1 0 7 330.62 216.23 12.13 o 1 0 7 3071.05 3663.70 96.78 o 3 0 7 22.18 12.68 9.02 o 5 0 7 7.36 16.86 12.68 o -5 1 7 4.67 6.92 6.38 o -4 1 7 0.67 3.21 4.45 o -3 1 7 204.89 135.80 18.30 o -2 1 7 484.74 499.76 24.09 o -1 1 7 4033.13 3403.51 53.10 o 0 1 7 201.03 222.12 17.81 o 1 1 7 692.96 789.09 14.08 o 2 1 7 1.47 1.95 2.69 o 3 1 7 242.23 197.31 12.93 o 4 1 7 64.26 69.00 13.17 o 5 1 7 5.28 11.98 7.68 o -5 2 7 51.11 72.52 13.77 o -4 2 7 1.00 1.12 4.24 o -3 2 7 43.52 46.96 7.31 o -2 2 7 73.76 60.09 4.91 o -1 2 7 186.67 191.26 6.27 o 0 2 7 577.60 599.17 12.68 o 1 2 7 86.75 91.57 8.36 o 2 2 7 612.23 658.56 40.07 o 3 2 7 240.20 203.31 13.41 o 4 2 7 6.66 10.64 6.33 o 5 2 7 34.55 48.68 12.75 o -5 3 7 60.73 77.59 13.68 o -4 3 7 1.82 4.96 4.66 o -3 3 7 280.11 269.30 16.13 o -2 3 7 28.05 30.82 3.83 o -1 3 7 5.22 6.88 1.98 o 0 3 7 1.42 -1.18 2.34 o 1 3 7 371.47 457.21 17.80 o 2 3 7 327.63 320.30 16.18 o 3 3 7 257.81 240.11 17.89 o 4 3 7 15.86 15.04 6.84 o -5 4 7 3.28 9.66 5.29 o -4 4 7 10.88 15.78 6.49 o -3 4 7 14.37 18.08 5.02 o -2 4 7 9.81 12.75 3.29 o -1 4 7 5.84 4.60 1.88 o 0 4 7 60.59 88.52 5.11 o 1 4 7 12.38 22.74 5.01 o 2 4 7 421.28 402.07 18.16 o 3 4 7 96.59 85.62 9.04 o 4 4 7 36.37 39.72 7.19 o -5 5 7 3.03 5.43 5.48 o -4 5 7 0.03 0.41 4.05 o -3 5 7 128.78 120.41 10.62 o -2 5 7 0.69 2.52 2.74 o -1 5 7 861.05 950.50 18.55 o 0 5 7 300.27 333.28 9.28 o 1 5 7 88.91 89.45 7.33 o 2 5 7 438.24 378.47 17.26 o 3 5 7 1.11 4.40 3.60 o 4 5 7 3.89 7.47 4.25 o -4 6 7 3.64 7.36 5.34 o -3 6 7 18.98 23.49 5.00 o -2 6 7 182.87 184.35 11.22 o -1 6 7 90.65 98.79 6.84 o 0 6 7 61.12 73.13 5.75 o 1 6 7 0.05 1.61 2.57 o 2 6 7 0.05 2.07 2.94 o 3 6 7 65.96 51.67 7.35 o 4 6 7 23.29 22.86 5.51 o -4 7 7 96.50 95.06 14.81 o -3 7 7 25.35 31.93 6.55 o -2 7 7 96.94 102.85 9.04 o -1 7 7 406.46 457.50 19.26 o 0 7 7 4.92 5.84 3.54 o 1 7 7 1014.28 988.86 28.89 o 2 7 7 50.16 41.91 5.47 o 3 7 7 0.62 2.04 3.22 o 4 7 7 5.83 12.41 5.09 o -4 8 7 180.50 206.19 26.01 o -3 8 7 13.42 15.60 4.94 o -2 8 7 119.37 122.31 10.12 o -1 8 7 100.58 109.92 7.80 o 0 8 7 51.80 59.65 8.05 o 1 8 7 27.54 31.82 6.02 o 2 8 7 139.93 124.72 14.87 o 3 8 7 5.19 6.59 5.08 o -3 9 7 23.89 31.10 6.53 o -2 9 7 0.53 4.48 3.20 o -1 9 7 85.81 82.49 6.74 o 0 9 7 166.01 156.05 16.43 o 1 9 7 98.22 91.42 13.02 o 2 9 7 25.23 28.23 8.06 o 3 9 7 1.79 5.75 5.31 o -3 10 7 11.87 20.56 5.74 o -2 10 7 8.28 13.12 3.94 o -1 10 7 16.94 18.26 5.42 o 0 10 7 132.45 152.01 17.47 o 1 10 7 8.49 13.22 5.67 o 2 10 7 8.61 10.41 5.68 o -2 11 7 3.96 5.47 3.64 o -1 11 7 13.95 17.63 5.51 o 0 11 7 66.68 66.63 11.52 o 1 11 7 1.31 7.95 5.37 o 2 11 7 11.39 10.72 6.51 o -1 12 7 1.40 3.74 3.77 o 0 12 7 45.43 47.00 10.05 o 1 12 7 58.15 46.86 10.43 o -2 0 8 0.76 5.49 4.34 o 0 0 8 0.82 5.96 3.21 o 2 0 8 0.60 2.48 2.85 o 4 0 8 252.55 206.77 29.28 o -5 1 8 132.05 147.76 16.96 o -4 1 8 0.68 1.97 4.48 o -3 1 8 8.64 6.27 3.78 o -2 1 8 65.31 51.63 5.71 o -1 1 8 62.24 55.61 5.22 o 0 1 8 944.10 1014.91 22.81 o 1 1 8 69.32 85.34 5.07 o 2 1 8 284.36 242.35 12.73 o 3 1 8 42.81 38.77 8.50 o 4 1 8 15.77 21.43 7.53 o -5 2 8 86.23 109.13 21.43 o -4 2 8 55.76 44.49 9.11 o -3 2 8 35.37 37.36 6.55 o -2 2 8 9.26 8.77 2.58 o -1 2 8 93.92 76.51 4.09 o 0 2 8 21.02 27.20 3.65 o 1 2 8 84.48 82.36 8.14 o 2 2 8 0.00 3.14 2.90 o 3 2 8 193.55 180.44 14.40 o 4 2 8 77.50 70.91 13.59 o -5 3 8 3.88 5.92 5.11 o -4 3 8 88.19 85.15 11.78 o -3 3 8 9.50 8.86 3.95 o -2 3 8 0.33 1.86 1.88 o -1 3 8 2.01 3.51 1.90 o 0 3 8 1611.76 1850.94 35.54 o 1 3 8 7.52 12.57 4.52 o 2 3 8 31.95 29.82 5.55 o 3 3 8 393.93 353.66 18.62 o 4 3 8 27.68 27.02 8.04 o -5 4 8 21.97 29.19 8.45 o -4 4 8 225.95 218.30 19.64 o -3 4 8 224.77 182.76 13.21 o -2 4 8 54.73 53.34 4.87 o -1 4 8 2.06 3.81 1.76 o 0 4 8 69.13 72.52 6.01 o 1 4 8 276.73 305.38 15.80 o 2 4 8 477.24 413.24 19.42 o 3 4 8 6.61 7.57 3.83 o 4 4 8 37.11 39.98 9.21 o -4 5 8 9.40 12.68 5.82 o -3 5 8 351.38 336.75 19.03 o -2 5 8 246.87 234.26 12.03 o -1 5 8 36.52 32.17 3.96 o 0 5 8 277.02 330.87 9.80 o 1 5 8 95.79 88.13 7.65 o 2 5 8 758.89 684.06 24.39 o 3 5 8 0.98 5.99 3.73 o 4 5 8 0.08 3.35 3.72 o -4 6 8 1.39 2.68 4.33 o -3 6 8 46.67 47.68 8.06 o -2 6 8 3.14 5.63 3.04 o -1 6 8 142.09 160.69 8.59 o 0 6 8 15.36 17.43 4.85 o 1 6 8 99.43 115.64 8.66 o 2 6 8 423.42 373.04 16.05 o 3 6 8 3.65 7.06 3.90 o 4 6 8 47.31 55.25 8.75 o -4 7 8 48.10 62.24 12.12 o -3 7 8 108.83 97.78 11.42 o -2 7 8 421.11 436.07 18.55 o -1 7 8 0.43 3.57 2.39 o 0 7 8 248.63 252.24 15.24 o 1 7 8 77.14 83.43 9.20 o 2 7 8 350.26 330.78 18.06 o 3 7 8 13.20 14.83 3.92 o 4 7 8 15.64 24.00 6.33 o -4 8 8 170.61 172.15 20.64 o -3 8 8 0.15 2.37 3.56 o -2 8 8 36.45 33.78 5.81 o -1 8 8 289.34 295.81 14.24 o 0 8 8 14.60 18.16 4.98 o 1 8 8 120.19 138.68 12.08 o 2 8 8 66.60 67.55 12.74 o 3 8 8 25.55 24.18 7.68 o -3 9 8 38.84 38.63 9.07 o -2 9 8 12.33 15.24 4.03 o -1 9 8 127.07 144.60 11.20 o 0 9 8 260.17 262.49 16.46 o 1 9 8 2.74 4.11 5.08 o 2 9 8 0.50 2.27 4.42 o 3 9 8 159.84 147.49 17.64 o -3 10 8 44.11 55.46 11.19 o -2 10 8 0.65 0.59 2.92 o -1 10 8 22.48 25.75 5.95 o 0 10 8 63.21 61.99 11.48 o 1 10 8 4.19 6.42 5.43 o 2 10 8 14.43 16.16 6.81 o -2 11 8 63.97 61.41 11.93 o -1 11 8 34.64 37.28 7.11 o 0 11 8 1.35 5.26 4.52 o 1 11 8 54.09 53.23 11.37 o 2 11 8 0.84 4.74 5.37 o -1 12 8 143.93 171.39 21.53 o 0 12 8 14.33 20.58 7.07 o -5 0 9 113.70 119.20 22.09 o -3 0 9 3.99 5.85 4.80 o -1 0 9 11.33 11.00 4.42 o 1 0 9 1286.03 1393.17 79.40 o 3 0 9 93.02 65.87 15.42 o -5 1 9 29.95 34.14 9.33 o -4 1 9 65.50 58.50 10.03 o -3 1 9 50.66 45.35 6.49 o -2 1 9 632.89 548.18 17.13 o -1 1 9 59.55 55.05 3.54 o 0 1 9 127.58 132.29 8.15 o 1 1 9 243.95 239.35 8.52 o 2 1 9 62.99 62.89 7.10 o 3 1 9 121.52 97.76 13.25 o 4 1 9 11.40 17.32 6.97 o -5 2 9 8.91 12.43 5.86 o -4 2 9 149.05 131.78 14.71 o -3 2 9 22.57 24.37 5.76 o -2 2 9 9.08 10.14 3.41 o -1 2 9 11.02 16.56 2.52 o 0 2 9 767.13 814.13 28.13 o 1 2 9 34.92 37.37 6.14 o 2 2 9 3.95 7.31 3.58 o 3 2 9 14.25 13.51 5.96 o 4 2 9 62.92 63.21 11.89 o -5 3 9 41.60 47.46 9.75 o -4 3 9 116.27 107.17 13.63 o -3 3 9 357.18 314.65 17.63 o -2 3 9 4.54 7.35 3.06 o -1 3 9 1014.55 909.97 25.64 o 0 3 9 28.75 40.27 6.30 o 1 3 9 6.61 4.45 3.42 o 2 3 9 81.42 77.11 8.75 o 3 3 9 97.96 76.04 8.79 o 4 3 9 18.97 21.11 7.26 o -5 4 9 0.15 4.87 4.80 o -4 4 9 28.12 30.66 7.72 o -3 4 9 362.57 321.13 18.51 o -2 4 9 10.71 11.27 3.11 o -1 4 9 200.04 184.91 10.20 o 0 4 9 173.43 191.37 9.63 o 1 4 9 110.33 120.16 10.54 o 2 4 9 179.50 170.69 12.59 o 3 4 9 46.97 47.51 7.47 o 4 4 9 86.48 75.78 12.36 o -4 5 9 32.65 33.12 8.40 o -3 5 9 373.81 358.19 20.54 o -2 5 9 293.61 297.66 13.80 o -1 5 9 0.17 1.99 2.35 o 0 5 9 63.15 66.49 6.03 o 1 5 9 78.35 89.76 7.95 o 2 5 9 27.45 31.27 6.19 o 3 5 9 20.88 20.77 5.26 o 4 5 9 4.56 6.88 5.55 o -4 6 9 3.77 10.42 5.59 o -3 6 9 91.64 95.43 11.37 o -2 6 9 386.33 418.44 16.97 o -1 6 9 59.75 72.45 6.08 o 0 6 9 101.88 110.55 9.93 o 1 6 9 71.80 81.36 7.42 o 2 6 9 158.72 155.64 10.87 o 3 6 9 7.60 9.73 3.92 o 4 6 9 0.43 2.77 4.20 o -4 7 9 28.58 41.21 9.86 o -3 7 9 0.48 3.00 3.45 o -2 7 9 4.71 5.80 3.36 o -1 7 9 4.12 7.19 2.47 o 0 7 9 80.13 86.30 9.03 o 1 7 9 97.13 97.14 9.76 o 2 7 9 206.17 169.58 13.03 o 3 7 9 166.26 142.29 11.27 o -4 8 9 12.98 17.05 6.75 o -3 8 9 18.87 23.82 6.41 o -2 8 9 15.69 31.46 5.60 o -1 8 9 5.71 9.87 3.44 o 0 8 9 256.74 300.05 17.18 o 1 8 9 8.83 13.76 4.30 o 2 8 9 25.43 24.38 7.83 o 3 8 9 189.45 165.43 18.38 o -3 9 9 16.70 24.70 6.62 o -2 9 9 1.80 4.36 3.03 o -1 9 9 1.32 5.15 3.12 o 0 9 9 24.09 36.71 6.52 o 1 9 9 1.63 5.98 5.19 o 2 9 9 35.89 35.93 9.18 o 3 9 9 99.46 87.02 14.44 o -3 10 9 11.06 16.51 6.65 o -2 10 9 28.79 34.36 7.26 o -1 10 9 5.31 3.19 4.97 o 0 10 9 179.27 186.84 19.18 o 1 10 9 3.96 9.93 5.00 o 2 10 9 7.84 9.56 5.80 o -2 11 9 38.34 41.27 9.67 o -1 11 9 0.87 7.57 6.97 o 0 11 9 5.49 7.81 5.17 o 1 11 9 43.23 50.60 10.64 o -4 0 10 13.08 14.64 8.76 o -2 0 10 537.99 506.91 26.07 o 0 0 10 885.14 719.51 106.58 o 2 0 10 211.74 197.75 25.75 o 4 0 10 247.12 212.52 31.76 o -5 1 10 0.36 1.53 5.61 o -4 1 10 234.91 219.22 18.73 o -3 1 10 18.73 20.03 4.73 o -2 1 10 693.15 550.30 21.41 o -1 1 10 35.96 33.20 4.29 o 0 1 10 306.56 335.95 16.17 o 1 1 10 0.02 0.08 2.52 o 2 1 10 1.11 0.67 3.91 o 3 1 10 10.72 14.13 6.18 o 4 1 10 7.29 11.52 5.65 o -5 2 10 15.77 24.25 7.58 o -4 2 10 9.46 11.59 5.24 o -3 2 10 156.96 122.60 9.65 o -2 2 10 3.24 2.49 2.65 o -1 2 10 399.89 379.96 12.42 o 0 2 10 213.00 221.34 9.47 o 1 2 10 7.45 11.31 4.22 o 2 2 10 332.85 314.98 16.69 o 3 2 10 150.18 111.80 14.31 o 4 2 10 106.76 96.03 14.37 o -5 3 10 31.29 27.36 7.44 o -4 3 10 100.37 102.96 13.62 o -3 3 10 93.30 84.48 9.58 o -2 3 10 403.40 356.43 14.96 o -1 3 10 8.43 8.19 2.20 o 0 3 10 255.11 262.03 14.48 o 1 3 10 15.87 22.10 5.43 o 2 3 10 46.86 43.76 6.95 o 3 3 10 14.94 14.11 5.90 o 4 3 10 62.99 59.63 11.59 o -4 4 10 0.00 1.39 3.26 o -3 4 10 37.64 30.44 6.05 o -2 4 10 6.48 4.29 3.48 o -1 4 10 75.71 74.55 4.92 o 0 4 10 0.01 1.53 2.64 o 1 4 10 29.95 41.29 7.12 o 2 4 10 0.81 1.92 3.37 o 3 4 10 29.00 28.08 5.72 o 4 4 10 30.41 22.40 7.30 o -4 5 10 5.81 11.29 5.43 o -3 5 10 35.59 37.71 7.24 o -2 5 10 316.46 315.95 17.68 o -1 5 10 164.12 195.74 12.48 o 0 5 10 217.81 243.00 11.64 o 1 5 10 298.09 357.67 19.25 o 2 5 10 266.43 272.77 16.94 o 3 5 10 25.00 27.91 6.00 o 4 5 10 0.09 2.03 4.40 o -4 6 10 4.25 11.54 5.96 o -3 6 10 12.62 13.25 4.80 o -2 6 10 9.59 11.25 4.34 o -1 6 10 0.45 1.75 3.20 o 0 6 10 74.94 81.35 8.73 o 1 6 10 181.86 196.99 14.35 o 2 6 10 180.58 177.71 13.91 o 3 6 10 4.93 4.21 3.20 o -4 7 10 12.71 11.05 5.60 o -3 7 10 0.14 2.72 3.46 o -2 7 10 206.05 201.97 12.81 o -1 7 10 143.20 148.61 17.30 o 0 7 10 25.17 34.06 6.43 o 1 7 10 647.27 733.42 27.23 o 2 7 10 1.02 2.33 3.59 o 3 7 10 15.02 11.05 4.21 o -3 8 10 70.43 91.91 11.66 o -2 8 10 27.55 25.50 5.00 o -1 8 10 60.98 85.71 13.44 o 0 8 10 96.00 117.13 11.21 o 1 8 10 116.45 115.65 10.72 o 2 8 10 3.36 5.91 3.44 o 3 8 10 3.55 6.93 5.10 o -3 9 10 6.24 7.07 4.28 o -2 9 10 11.15 20.31 4.48 o -1 9 10 0.59 6.08 4.94 o 0 9 10 189.30 243.12 17.03 o 1 9 10 5.17 6.05 5.15 o 2 9 10 21.84 21.26 7.61 o -2 10 10 42.12 59.38 9.38 o -1 10 10 0.73 6.76 4.89 o 0 10 10 499.64 510.39 31.04 o 1 10 10 0.03 2.74 5.11 o 2 10 10 8.19 13.38 5.88 o -1 11 10 35.29 43.13 15.42 o 0 11 10 14.31 17.42 6.12 o 1 11 10 6.02 9.51 5.22 o -5 0 11 453.67 521.10 44.44 o -3 0 11 0.90 1.84 4.05 o -1 0 11 530.14 383.12 28.23 o 1 0 11 18.05 16.92 4.28 o 3 0 11 142.21 118.28 21.57 o -5 1 11 89.96 98.95 14.73 o -4 1 11 8.53 9.96 5.84 o -3 1 11 61.23 51.67 6.82 o -2 1 11 10.47 8.96 3.31 o -1 1 11 29.86 25.64 4.01 o 0 1 11 4.95 5.82 2.62 o 1 1 11 18.80 20.43 4.34 o 2 1 11 8.21 6.02 4.42 o 3 1 11 39.34 36.60 8.93 o 4 1 11 44.78 47.57 10.36 o -5 2 11 10.52 12.31 5.73 o -4 2 11 134.57 136.82 15.28 o -3 2 11 99.11 93.64 8.75 o -2 2 11 9.02 7.07 3.18 o -1 2 11 9.90 6.68 2.79 o 0 2 11 528.01 545.96 15.30 o 1 2 11 32.54 27.50 5.46 o 2 2 11 2.84 2.59 4.52 o 3 2 11 57.80 41.65 9.49 o 4 2 11 5.04 13.06 6.21 o -4 3 11 246.46 205.92 19.17 o -3 3 11 49.22 41.48 6.81 o -2 3 11 42.20 33.87 5.00 o -1 3 11 118.10 105.21 9.07 o 0 3 11 506.57 548.23 21.65 o 1 3 11 55.97 60.36 8.29 o 2 3 11 55.56 51.73 7.61 o 3 3 11 25.65 28.19 8.06 o 4 3 11 0.81 1.84 4.88 o -4 4 11 8.14 12.24 5.56 o -3 4 11 1.32 2.51 3.18 o -2 4 11 238.45 229.75 14.59 o -1 4 11 109.86 103.90 7.23 o 0 4 11 3.53 3.01 2.64 o 1 4 11 408.10 446.52 21.35 o 2 4 11 100.13 95.52 10.51 o 3 4 11 1.58 7.72 4.71 o 4 4 11 59.65 56.66 11.34 o -4 5 11 14.39 15.08 5.93 o -3 5 11 65.99 58.89 8.66 o -2 5 11 15.11 14.08 5.04 o -1 5 11 108.40 111.77 9.87 o 0 5 11 34.58 40.20 6.69 o 1 5 11 397.39 458.13 21.89 o 2 5 11 970.93 982.99 32.85 o 3 5 11 10.69 14.46 4.57 o -4 6 11 26.57 39.64 9.90 o -3 6 11 0.30 -0.87 3.03 o -2 6 11 3.34 5.23 3.63 o -1 6 11 0.06 0.22 4.95 o 0 6 11 0.89 4.13 3.47 o 1 6 11 117.23 128.33 11.85 o 2 6 11 125.86 133.95 12.50 o 3 6 11 168.76 140.20 12.68 o -4 7 11 4.00 4.61 4.65 o -3 7 11 2.78 6.80 4.05 o -2 7 11 1.14 3.39 3.87 o -1 7 11 212.30 251.49 21.92 o 0 7 11 53.18 60.80 7.87 o 1 7 11 195.09 199.94 14.65 o 2 7 11 102.59 107.89 11.65 o 3 7 11 2.11 4.54 3.97 o -3 8 11 67.97 102.02 13.16 o -2 8 11 44.13 47.64 7.75 o -1 8 11 2.68 8.33 5.81 o 0 8 11 5.70 7.97 3.63 o 1 8 11 16.58 21.96 5.37 o 2 8 11 78.02 68.44 9.09 o 3 8 11 173.98 129.45 15.95 o -3 9 11 30.51 32.37 7.14 o -2 9 11 5.35 10.50 6.37 o -1 9 11 7.61 6.79 5.74 o 0 9 11 52.91 57.17 8.21 o 1 9 11 0.35 2.34 4.38 o 2 9 11 47.37 42.17 9.64 o -2 10 11 247.21 320.98 28.51 o -1 10 11 3.03 6.93 5.41 o 0 10 11 3.67 3.07 5.03 o 1 10 11 14.39 19.46 7.01 o -1 11 11 10.66 15.55 11.89 o 0 11 11 7.91 9.67 10.59 o -4 0 12 9.46 14.25 8.10 o -2 0 12 56.85 56.33 9.10 o 0 0 12 929.95 846.08 71.43 o 2 0 12 17.53 29.02 11.89 o 4 0 12 34.36 22.09 11.50 o -4 1 12 157.03 162.46 17.34 o -3 1 12 42.67 33.29 5.56 o -2 1 12 167.56 138.31 9.66 o -1 1 12 26.44 20.46 3.86 o 0 1 12 42.92 39.11 4.77 o 1 1 12 15.42 13.18 4.02 o 2 1 12 141.65 125.86 16.06 o 3 1 12 76.01 61.02 11.08 o 4 1 12 41.26 45.12 10.43 o -4 2 12 33.67 31.48 6.48 o -3 2 12 66.74 62.51 7.43 o -2 2 12 110.78 93.88 7.92 o -1 2 12 42.77 38.70 4.94 o 0 2 12 341.58 351.49 17.21 o 1 2 12 79.97 83.09 9.01 o 2 2 12 575.86 495.88 30.36 o 3 2 12 65.13 48.17 10.15 o 4 2 12 39.54 35.59 9.23 o -4 3 12 63.88 60.22 9.12 o -3 3 12 4.15 3.89 3.80 o -2 3 12 165.37 147.25 10.04 o -1 3 12 6.67 7.46 3.18 o 0 3 12 0.10 3.71 3.87 o 1 3 12 220.90 237.84 24.65 o 2 3 12 10.70 13.33 6.08 o 3 3 12 0.57 2.68 4.24 o 4 3 12 108.38 86.78 14.83 o -4 4 12 49.49 49.11 8.05 o -3 4 12 0.02 1.13 3.01 o -2 4 12 107.61 103.51 10.36 o -1 4 12 315.79 303.70 13.01 o 0 4 12 132.58 137.62 12.01 o 1 4 12 31.06 38.56 7.42 o 2 4 12 206.95 201.73 15.70 o 3 4 12 110.00 94.43 13.80 o -4 5 12 118.94 134.90 13.87 o -3 5 12 36.42 31.51 6.50 o -2 5 12 17.44 21.63 5.19 o -1 5 12 38.44 44.57 9.78 o 0 5 12 47.16 51.45 7.04 o 1 5 12 18.80 24.77 6.17 o 2 5 12 133.93 134.96 12.62 o 3 5 12 32.64 33.63 8.30 o -4 6 12 47.12 63.49 10.02 o -3 6 12 1.26 5.08 3.61 o -2 6 12 304.54 346.38 21.97 o -1 6 12 4.85 8.09 6.49 o 0 6 12 37.34 47.44 7.16 o 1 6 12 205.04 214.90 15.25 o 2 6 12 170.11 178.61 18.55 o 3 6 12 4.57 4.57 3.91 o -3 7 12 39.00 49.35 8.50 o -2 7 12 20.82 23.79 5.55 o -1 7 12 4.10 8.53 6.60 o 0 7 12 93.95 112.32 10.55 o 1 7 12 98.55 113.74 11.24 o 2 7 12 41.52 49.79 8.13 o 3 7 12 69.52 73.31 9.82 o -3 8 12 67.05 86.96 11.76 o -2 8 12 0.24 5.66 5.02 o -1 8 12 150.01 190.17 20.24 o 0 8 12 149.83 185.06 19.40 o 1 8 12 2.83 4.75 3.72 o 2 8 12 3.23 8.09 4.05 o -2 9 12 0.04 5.83 6.72 o -1 9 12 84.88 84.87 14.53 o 0 9 12 194.54 244.70 22.36 o 1 9 12 49.22 52.60 7.97 o 2 9 12 27.54 25.05 7.28 o -2 10 12 0.55 0.13 4.92 o -1 10 12 142.33 194.24 22.01 o 0 10 12 32.92 33.20 15.29 o 1 10 12 9.55 14.27 6.63 o -3 0 13 31.76 30.68 7.39 o -1 0 13 295.21 208.33 18.49 o 1 0 13 175.15 194.09 28.10 o 3 0 13 356.96 268.98 33.59 o -4 1 13 0.20 4.69 4.31 o -3 1 13 21.12 19.03 4.46 o -2 1 13 1.63 3.56 2.51 o -1 1 13 37.88 31.89 4.63 o 0 1 13 117.00 127.83 9.11 o 1 1 13 97.54 99.72 14.19 o 2 1 13 10.76 14.84 6.60 o 3 1 13 56.47 51.40 10.72 o 4 1 13 1.38 5.64 5.11 o -4 2 13 36.56 25.97 6.08 o -3 2 13 4.62 6.05 3.77 o -2 2 13 77.04 62.85 6.74 o -1 2 13 271.77 219.02 11.06 o 0 2 13 78.93 71.88 8.54 o 1 2 13 365.79 383.24 19.30 o 2 2 13 7.98 10.93 6.18 o 3 2 13 77.35 66.74 12.15 o -4 3 13 13.16 15.07 4.96 o -3 3 13 285.58 278.41 17.82 o -2 3 13 75.69 59.87 6.31 o -1 3 13 4.28 3.03 3.14 o 0 3 13 64.32 67.63 8.73 o 1 3 13 0.67 3.92 3.88 o 2 3 13 47.07 49.38 10.66 o 3 3 13 108.40 86.90 13.61 o -4 4 13 38.46 35.04 7.13 o -3 4 13 7.45 10.61 4.40 o -2 4 13 54.12 61.45 8.46 o -1 4 13 37.74 31.31 8.41 o 0 4 13 86.95 102.87 10.67 o 1 4 13 517.43 565.73 25.38 o 2 4 13 110.41 102.54 14.64 o 3 4 13 112.65 101.23 14.65 o -4 5 13 13.21 13.29 4.75 o -3 5 13 9.22 9.75 4.04 o -2 5 13 22.49 18.73 4.85 o -1 5 13 3.22 2.49 5.21 o 0 5 13 140.53 162.24 17.04 o 1 5 13 127.23 135.77 12.92 o 2 5 13 44.39 47.17 7.89 o 3 5 13 1.95 6.03 4.85 o -4 6 13 82.84 110.86 13.20 o -3 6 13 5.29 4.80 3.62 o -2 6 13 122.48 129.54 12.82 o -1 6 13 8.96 9.42 6.12 o 0 6 13 49.74 64.08 11.93 o 1 6 13 88.84 95.57 10.47 o 2 6 13 21.06 27.76 7.74 o 3 6 13 133.52 126.77 16.07 o -3 7 13 2.92 2.33 3.86 o -2 7 13 124.32 141.85 17.70 o -1 7 13 3.27 8.62 5.85 o 0 7 13 31.50 45.69 9.83 o 1 7 13 84.82 91.02 10.00 o 2 7 13 88.47 79.25 9.95 o -3 8 13 123.55 161.08 15.87 o -2 8 13 0.97 4.75 5.30 o -1 8 13 97.04 109.41 16.60 o 0 8 13 18.60 29.12 8.51 o 1 8 13 37.46 44.54 7.53 o 2 8 13 21.76 25.30 5.86 o -2 9 13 6.86 16.90 7.87 o -1 9 13 3.67 4.71 5.62 o 0 9 13 499.28 590.66 56.66 o 1 9 13 73.08 76.15 9.48 o -1 10 13 18.71 28.11 9.12 o 0 10 13 1.64 7.84 9.80 o -4 0 14 2.61 4.96 4.57 o -2 0 14 206.89 158.47 14.70 o 0 0 14 2.07 3.85 3.72 o 2 0 14 523.10 410.00 42.61 o -4 1 14 6.73 6.43 4.95 o -3 1 14 2.85 4.34 2.99 o -2 1 14 39.94 34.95 5.31 o -1 1 14 489.99 420.93 15.66 o 0 1 14 749.43 721.15 44.89 o 1 1 14 58.14 62.52 11.95 o 2 1 14 70.53 63.03 12.28 o 3 1 14 32.47 23.23 7.74 o -4 2 14 3.62 3.88 3.61 o -3 2 14 3.10 3.44 3.02 o -2 2 14 0.56 1.55 2.42 o -1 2 14 9.13 15.61 3.76 o 0 2 14 792.41 779.74 51.69 o 1 2 14 176.77 183.98 20.91 o 2 2 14 8.70 13.70 6.68 o 3 2 14 73.38 54.87 11.36 o -4 3 14 11.41 16.48 5.09 o -3 3 14 33.82 34.17 6.80 o -2 3 14 0.02 1.68 3.44 o -1 3 14 148.36 132.78 11.13 o 0 3 14 173.65 155.99 12.88 o 1 3 14 741.05 779.18 30.01 o 2 3 14 14.15 14.75 6.87 o 3 3 14 33.04 36.13 9.38 o -4 4 14 8.15 8.66 4.10 o -3 4 14 69.01 75.93 9.60 o -2 4 14 43.58 47.87 7.36 o -1 4 14 3.89 3.99 5.41 o 0 4 14 0.98 3.11 3.45 o 1 4 14 135.41 138.53 13.06 o 2 4 14 12.30 22.29 7.44 o 3 4 14 0.06 4.31 4.10 o -4 5 14 13.43 12.28 4.84 o -3 5 14 6.93 11.94 4.36 o -2 5 14 75.37 81.22 10.04 o -1 5 14 167.73 195.90 19.67 o 0 5 14 4.23 9.94 6.40 o 1 5 14 23.25 31.44 8.70 o 2 5 14 22.44 40.64 9.75 o 3 5 14 0.00 2.99 4.59 o -3 6 14 30.46 31.64 6.77 o -2 6 14 184.98 204.64 34.90 o -1 6 14 2.65 4.08 5.15 o 0 6 14 11.10 26.31 8.43 o 1 6 14 0.00 4.32 3.70 o 2 6 14 4.42 4.21 4.73 o -3 7 14 1.22 1.51 3.56 o -2 7 14 17.03 23.63 8.57 o -1 7 14 0.62 7.92 5.59 o 0 7 14 3.51 6.52 5.40 o 1 7 14 205.59 191.03 14.43 o 2 7 14 88.30 76.59 9.82 o -2 8 14 25.62 36.56 9.95 o -1 8 14 5.64 11.05 6.91 o 0 8 14 4.92 18.60 7.81 o 1 8 14 25.77 33.69 6.25 o 2 8 14 38.87 39.07 6.97 o -2 9 14 6.04 6.89 5.43 o -1 9 14 3.29 12.10 6.42 o 0 9 14 23.52 37.19 9.72 o 1 9 14 77.92 84.41 13.43 o -3 0 15 227.71 205.86 18.04 o -1 0 15 618.62 483.68 24.18 o 1 0 15 646.10 632.06 52.80 o 3 0 15 0.00 0.65 6.53 o -4 1 15 61.27 58.33 8.57 o -3 1 15 45.51 44.33 6.25 o -2 1 15 88.95 73.49 7.43 o -1 1 15 78.74 65.75 6.76 o 0 1 15 10.04 14.95 4.38 o 1 1 15 9.18 12.97 7.27 o 2 1 15 41.51 37.02 9.88 o 3 1 15 1.09 5.04 4.97 o -4 2 15 20.25 21.35 5.61 o -3 2 15 64.57 67.55 11.59 o -2 2 15 111.54 85.90 7.66 o -1 2 15 364.23 295.44 16.36 o 0 2 15 25.79 28.71 6.13 o 1 2 15 122.95 142.99 18.69 o 2 2 15 18.94 17.04 7.20 o 3 2 15 117.29 81.25 14.16 o -4 3 15 6.73 5.33 3.52 o -3 3 15 3.78 3.58 3.43 o -2 3 15 0.84 1.10 3.46 o -1 3 15 0.99 4.41 3.26 o 0 3 15 9.20 10.36 4.64 o 1 3 15 16.92 17.34 7.71 o 2 3 15 1.71 3.28 4.94 o 3 3 15 140.63 103.99 15.65 o -4 4 15 5.08 1.75 2.96 o -3 4 15 53.56 55.58 8.42 o -2 4 15 36.21 30.55 6.03 o -1 4 15 195.18 194.28 19.59 o 0 4 15 1.46 4.46 3.69 o 1 4 15 379.60 402.55 22.64 o 2 4 15 88.85 96.94 14.64 o 3 4 15 77.78 65.52 13.11 o -3 5 15 1.88 6.27 3.79 o -2 5 15 17.59 18.93 8.17 o -1 5 15 2.99 8.90 6.80 o 0 5 15 78.57 90.95 13.81 o 1 5 15 0.10 0.26 4.80 o 2 5 15 1.11 0.98 4.11 o -3 6 15 46.66 69.08 10.23 o -2 6 15 0.03 7.23 5.62 o -1 6 15 13.92 22.55 7.97 o 0 6 15 22.51 31.59 8.87 o 1 6 15 133.09 153.20 18.02 o 2 6 15 55.18 51.63 15.68 o -3 7 15 1.08 6.89 6.82 o -2 7 15 0.06 6.70 6.76 o -1 7 15 12.54 16.23 7.19 o 0 7 15 11.60 24.50 8.10 o 1 7 15 38.65 49.10 10.58 o 2 7 15 6.84 10.53 4.57 o -2 8 15 0.00 2.06 6.01 o -1 8 15 52.12 61.35 12.92 o 0 8 15 131.82 150.65 19.06 o 1 8 15 21.35 26.80 8.00 o -1 9 15 7.78 20.16 8.41 o 0 9 15 51.59 63.10 12.69 o -4 0 16 8.07 9.03 4.83 o -2 0 16 111.60 87.06 11.25 o 0 0 16 275.05 283.22 35.81 o 2 0 16 30.02 21.30 10.72 o -4 1 16 10.14 11.99 5.28 o -3 1 16 10.43 12.37 3.59 o -2 1 16 26.28 28.10 4.80 o -1 1 16 619.31 587.35 20.18 o 0 1 16 0.02 6.42 5.92 o 1 1 16 63.81 66.54 13.54 o 2 1 16 44.41 33.93 9.43 o 3 1 16 29.69 26.11 8.16 o -4 2 16 0.33 0.49 3.24 o -3 2 16 8.32 9.25 5.08 o -2 2 16 60.64 57.40 6.52 o -1 2 16 47.79 44.86 7.42 o 0 2 16 260.95 272.49 17.98 o 1 2 16 34.44 30.94 9.46 o 2 2 16 45.51 39.82 10.41 o 3 2 16 10.85 8.02 5.56 o -3 3 16 35.64 31.87 6.43 o -2 3 16 138.58 140.06 12.64 o -1 3 16 40.66 41.97 6.88 o 0 3 16 317.65 336.72 20.82 o 1 3 16 10.83 17.59 7.69 o 2 3 16 1.03 3.50 5.06 o 3 3 16 1.48 0.39 7.32 o -3 4 16 3.82 7.81 3.84 o -2 4 16 48.05 42.76 9.59 o -1 4 16 16.46 18.33 7.16 o 0 4 16 26.00 31.21 8.82 o 1 4 16 3.53 7.63 5.59 o 2 4 16 63.09 72.02 13.58 o -3 5 16 105.19 124.53 13.32 o -2 5 16 69.50 65.78 12.89 o -1 5 16 204.03 219.84 21.58 o 0 5 16 35.07 40.59 9.61 o 1 5 16 110.31 112.25 15.52 o 2 5 16 24.99 36.73 15.55 o -3 6 16 3.40 7.22 6.32 o -2 6 16 56.69 60.20 13.03 o -1 6 16 0.00 1.59 5.20 o 0 6 16 42.48 65.63 13.03 o 1 6 16 3.18 10.70 6.12 o 2 6 16 26.19 28.88 12.68 o -2 7 16 0.00 4.73 6.21 o -1 7 16 150.54 185.32 22.15 o 0 7 16 33.60 43.53 10.80 o 1 7 16 237.48 306.75 26.96 o -1 8 16 1.00 7.35 7.00 o 0 8 16 2.93 6.47 7.11 o -3 0 17 58.93 50.86 9.28 o -1 0 17 811.32 813.60 53.59 o 1 0 17 71.03 76.72 21.17 o -3 1 17 40.02 35.25 5.46 o -2 1 17 1.63 4.34 2.62 o -1 1 17 39.07 48.57 7.47 o 0 1 17 41.82 53.09 11.37 o 1 1 17 27.39 40.14 10.94 o 2 1 17 85.59 75.17 14.83 o -3 2 17 58.50 47.97 9.69 o -2 2 17 72.95 59.18 6.93 o -1 2 17 37.60 41.57 7.47 o 0 2 17 62.52 59.46 12.23 o 1 2 17 53.80 50.28 12.24 o 2 2 17 44.12 38.34 10.36 o -3 3 17 0.00 -0.25 3.16 o -2 3 17 199.79 189.99 19.59 o -1 3 17 66.33 73.58 12.72 o 0 3 17 1.20 4.63 3.95 o 1 3 17 35.40 37.30 10.51 o 2 3 17 3.10 7.23 6.21 o -3 4 17 74.35 83.84 11.04 o -2 4 17 13.40 24.42 7.79 o -1 4 17 111.18 116.30 15.47 o 0 4 17 124.22 133.27 16.59 o 1 4 17 26.08 34.25 9.51 o 2 4 17 4.57 3.86 5.14 o -3 5 17 7.21 3.78 5.58 o -2 5 17 54.11 57.41 11.85 o -1 5 17 11.31 21.54 8.16 o 0 5 17 262.82 290.96 25.46 o 1 5 17 5.01 16.60 10.06 o 2 5 17 188.19 191.47 31.89 o -2 6 17 6.65 8.65 7.59 o -1 6 17 1.07 5.99 6.47 o 0 6 17 4.01 9.15 6.90 o 1 6 17 0.64 2.29 5.50 o -2 7 17 17.00 33.30 10.03 o -1 7 17 81.49 126.95 19.34 o 0 7 17 6.88 11.41 7.40 o 1 7 17 38.82 53.59 11.41 o -2 0 18 8.39 7.47 4.65 o 0 0 18 186.53 203.76 31.50 o 2 0 18 26.71 30.06 13.46 o -3 1 18 0.01 1.39 2.66 o -2 1 18 123.00 107.54 9.25 o -1 1 18 37.68 39.82 9.80 o 0 1 18 0.86 5.98 5.25 o 1 1 18 0.22 10.51 6.81 o 2 1 18 138.88 168.24 22.22 o -3 2 18 0.92 0.00 3.36 o -2 2 18 223.77 215.25 20.41 o -1 2 18 21.24 26.10 5.64 o 0 2 18 58.18 63.14 12.88 o 1 2 18 146.57 173.98 21.97 o 2 2 18 17.18 26.28 9.47 o -3 3 18 145.89 119.52 11.76 o -2 3 18 41.01 50.82 10.57 o -1 3 18 28.51 32.75 8.44 o 0 3 18 12.16 17.59 6.94 o 1 3 18 11.53 22.38 8.94 o 2 3 18 0.81 3.60 5.55 o -2 4 18 72.08 69.30 12.50 o -1 4 18 3.53 3.62 4.86 o 0 4 18 19.58 19.37 7.43 o 1 4 18 34.48 43.78 16.08 o -2 5 18 1.33 2.67 5.50 o -1 5 18 3.81 12.78 7.26 o 0 5 18 17.13 25.55 8.52 o 1 5 18 0.00 5.36 9.02 o -2 6 18 31.44 34.61 10.80 o -1 6 18 17.63 23.23 9.04 o 0 6 18 128.67 187.11 21.75 o 1 6 18 1.66 10.46 9.28 o -3 0 19 2.67 2.17 3.29 o -1 0 19 192.37 214.61 33.20 o 1 0 19 0.47 6.53 9.41 o -3 1 19 5.52 3.58 2.96 o -2 1 19 138.54 127.90 12.42 o -1 1 19 5.11 11.10 6.17 o 0 1 19 2.90 12.80 6.74 o 1 1 19 0.78 4.34 5.72 o -2 2 19 80.33 78.84 12.97 o -1 2 19 11.12 16.20 6.28 o 0 2 19 4.98 10.78 6.28 o 1 2 19 11.50 18.63 8.25 o -2 3 19 10.29 16.68 6.55 o -1 3 19 0.08 2.43 4.76 o 0 3 19 60.41 64.83 19.08 o 1 3 19 82.73 99.60 16.62 o -2 4 19 4.53 7.27 6.15 o -1 4 19 3.12 3.79 4.83 o 0 4 19 12.31 8.63 9.41 o 1 4 19 0.36 10.59 10.06 o -1 5 19 24.61 35.91 10.06 o 0 5 19 16.14 24.18 12.81 o -2 0 20 37.14 36.20 12.42 o 0 0 20 148.25 228.59 36.86 o -2 1 20 19.36 17.89 4.71 o -1 1 20 0.01 5.63 5.30 o 0 1 20 1.33 8.07 5.65 o 1 1 20 16.46 17.46 7.62 o -2 2 20 80.33 88.53 13.33 o -1 2 20 13.60 31.26 7.64 o 0 2 20 29.28 42.42 11.02 o 1 2 20 18.03 25.90 8.93 o -2 3 20 0.25 3.96 5.09 o -1 3 20 28.55 31.63 13.20 o 0 3 20 113.39 144.42 28.36 o -1 4 20 46.64 55.37 11.59 o 0 4 20 23.49 29.93 14.77 o -1 0 21 5.91 8.63 8.10 o -1 1 21 2.28 7.85 5.25 o 0 1 21 10.19 22.06 8.39 o -1 2 21 6.04 11.24 8.50 o pyobjcryst-2024.2.1/src/pyobjcryst/tests/testglobaloptim.py000066400000000000000000000075401470422267000241100ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # File coded by: Vincent Favre-Nicolin # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Tests for MonteCarlo module.""" import unittest from pyobjcryst.tests.pyobjcrysttestutils import loadcifdata from pyobjcryst.diffractiondatasinglecrystal import DiffractionDataSingleCrystal from pyobjcryst.globaloptim import MonteCarlo, AnnealingSchedule, GlobalOptimType from pyobjcryst import refinableobj class TestGlobalOptim(unittest.TestCase): def setUp(self): self.c = loadcifdata('caffeine.cif') self.d = DiffractionDataSingleCrystal(self.c) self.d.GenHKLFullSpace2(0.4, True) self.d.SetIobsToIcalc() def tearDown(self): del self.c del self.d def test_mc_create(self): """Check Creating a basic Monte-Carlo object """ mc = MonteCarlo() mc.AddRefinableObj(self.c) mc.AddRefinableObj(self.d) def test_mc_name(self): """Check Creating a basic Monte-Carlo object """ mc = MonteCarlo() mc.AddRefinableObj(self.c) mc.AddRefinableObj(self.d) mc.SetName('caffeine') self.assertEqual(mc.GetName(), 'caffeine') def test_mc_llk(self): """Check Creating a basic Monte-Carlo object """ mc = MonteCarlo() mc.AddRefinableObj(self.c) mc.AddRefinableObj(self.d) junk = mc.GetLogLikelihood() def test_mc_fix_use_pars(self): mc = MonteCarlo() mc.AddRefinableObj(self.c) mc.AddRefinableObj(self.d) junk = mc.GetLogLikelihood() mc.FixAllPar() mc.SetParIsUsed("Scale factor", False) mc.SetParIsUsed("Scale factor", True) mc.SetParIsFixed("Scale factor", True) mc.SetParIsFixed("Scale factor", False) mc.SetParIsUsed(refinableobj.refpartype_scatt, False) mc.SetParIsUsed(refinableobj.refpartype_scatt, True) mc.SetParIsFixed(refinableobj.refpartype_scatt, True) mc.SetParIsFixed(refinableobj.refpartype_scatt, False) def test_mc_optim(self): mc = MonteCarlo() mc.AddRefinableObj(self.c) mc.AddRefinableObj(self.d) mc.RandomizeStartingConfig() mc.Optimize(nb_step=1000) def test_mc_optim_multi(self): mc = MonteCarlo() mc.AddRefinableObj(self.c) mc.AddRefinableObj(self.d) mc.RandomizeStartingConfig() mc.MultiRunOptimize(nb_run=2, nb_step=1000) def test_mc_sa(self): mc = MonteCarlo() mc.AddRefinableObj(self.c) mc.AddRefinableObj(self.d) mc.RandomizeStartingConfig() mc.RunSimulatedAnnealing(nb_step=1000) def test_mc_pt(self): mc = MonteCarlo() mc.AddRefinableObj(self.c) mc.AddRefinableObj(self.d) mc.RandomizeStartingConfig() mc.RunParallelTempering(nb_step=1000) # TODO: this is experimental and leads to segfault if testcrystal:testDummyAtom() has been run before (?!) # def test_mc_lsq(self): # mc = MonteCarlo() # mc.AddRefinableObj(self.c) # mc.AddRefinableObj(self.d) # mc.RandomizeStartingConfig() # mc.InitLSQ() # # print(mc.GetLSQObj().GetCompiledRefinedObj()) # mc.RunRandomLSQ(nbCycle=2) def test_mc_set_algo(self): mc = MonteCarlo() mc.AddRefinableObj(self.c) mc.AddRefinableObj(self.d) mc.RandomizeStartingConfig() mc.SetAlgorithmSimulAnnealing(AnnealingSchedule.SMART, 1000.0, 1.0) mc.Optimize(nb_step=1000) mc.SetAlgorithmParallTempering(AnnealingSchedule.SMART, 1000.0, 1.0) mc.Optimize(nb_step=1000) if __name__ == "__main__": unittest.main() pyobjcryst-2024.2.1/src/pyobjcryst/tests/testindexing.py000066400000000000000000000057061470422267000234060ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # File coded by: Vincent Favre-Nicolin # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Tests for indexing module.""" import unittest from numpy import pi from pyobjcryst.indexing import CrystalSystem, CrystalCentering, \ EstimateCellVolume, RecUnitCell, PeakList, CellExplorer, quick_index class TestIndexing(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def test_estimate_cell_volume(self): """Check EstimateCellVolume """ # 20 reflections observed from d=47.326A to 1.537A v = EstimateCellVolume(1 / 1.537, 1 / 47.326, 20, CrystalSystem.CUBIC, CrystalCentering.LATTICE_P, 1.2) self.assertAlmostEqual(v, 309, delta=2) v = EstimateCellVolume(1 / 1.537, 1 / 47.326, 20, CrystalSystem.CUBIC, CrystalCentering.LATTICE_P, 0.3) self.assertAlmostEqual(v, 2475, delta=2) v = EstimateCellVolume(1 / 1.537, 1 / 47.326, 20, CrystalSystem.ORTHOROMBIC, CrystalCentering.LATTICE_F, 1.2) self.assertAlmostEqual(v, 308, delta=2) v = EstimateCellVolume(1 / 1.537, 1 / 47.326, 20, CrystalSystem.ORTHOROMBIC, CrystalCentering.LATTICE_I, 0.3) self.assertAlmostEqual(v, 666, delta=2) def test_recunitcell(self): r = RecUnitCell(0, 0.1, 0, 0, 0, 0, 0, CrystalSystem.CUBIC, CrystalCentering.LATTICE_P, 0) d = r.hkl2d(1, 1, 1, None, 0) self.assertAlmostEqual(d, 0.03, 5) u = r.DirectUnitCell() self.assertAlmostEqual(u[0], 10, 5) self.assertAlmostEqual(u[3], pi / 2) def test_quick_index(self): # Try to index cimetidine powder pattern from experimental list of points v = [0.106317, 0.113542, 0.146200, 0.152765, 0.161769, 0.166021, 0.186157, 0.188394, 0.189835, 0.200636, 0.207603, 0.211856, 0.212616, 0.215067, 0.220722, 0.221532, 0.223939, 0.227054, 0.231044, 0.235053] pl = PeakList() pl.set_dobs_list(v) ex = quick_index(pl, verbose=False, continue_on_sol=False) self.assertGreater(ex.GetBestScore(), 120) sols = ex.GetSolutions() # Without continue_on_sol=True, this should yield only one solution self.assertEqual(len(sols), 1) self.assertGreater(sols[0][1], 120) ruc = sols[0][0] # Check lattice type self.assertEqual(ruc.centering, CrystalCentering.LATTICE_P) self.assertEqual(ruc.lattice, CrystalSystem.MONOCLINIC) # Cell volume self.assertAlmostEqual(ruc.DirectUnitCell()[-1], 1280, delta=2) if __name__ == "__main__": unittest.main() pyobjcryst-2024.2.1/src/pyobjcryst/tests/testlsq.py000066400000000000000000000055121470422267000223730ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # File coded by: Vincent Favre-Nicolin # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Tests for LSQ module.""" import unittest from pyobjcryst.tests.pyobjcrysttestutils import loadcifdata from pyobjcryst.diffractiondatasinglecrystal import DiffractionDataSingleCrystal from pyobjcryst.lsq import LSQ from pyobjcryst import refinableobj class TestGlobalOptim(unittest.TestCase): def setUp(self): self.c = loadcifdata('caffeine.cif') self.d = DiffractionDataSingleCrystal(self.c) self.d.GenHKLFullSpace2(0.4, True) self.d.SetIobsToIcalc() def tearDown(self): del self.c del self.d def test_lsq_create(self): """Check Creating a basic LSQ object """ lsq = LSQ() lsq.SetRefinedObj(self.d) def test_lsq_get_obs_calc(self): """Check Creating a basic LSQ object & get obs&calc arrays """ lsq = LSQ() lsq.SetRefinedObj(self.d, 0, True, True) junk = lsq.GetLSQObs(), lsq.GetLSQCalc(), lsq.ChiSquare() def test_lsq_get_refined_obj(self): """Check Creating a basic LSQ object & get obs&calc arrays """ lsq = LSQ() lsq.SetRefinedObj(self.d, 0, True, True) lsq.PrepareRefParList() # print(lsq.GetCompiledRefinedObj()) def test_lsq_set_pr_fixed(self): """Check Creating a basic LSQ object & get obs&calc arrays """ lsq = LSQ() lsq.SetRefinedObj(self.d, 0, True, True) lsq.PrepareRefParList() lsq.SetParIsFixed(refinableobj.refpartype_objcryst, False) lsq.SetParIsFixed(refinableobj.refpartype_scattdata, True) lsq.SetParIsFixed(refinableobj.refpartype_scattdata_scale, False) lsq.SetParIsFixed(refinableobj.refpartype_unitcell, True) lsq.SetParIsFixed(refinableobj.refpartype_scattpow, True) lsq.SetParIsFixed(refinableobj.refpartype_scattdata_radiation, True) def test_lsq_refine(self): lsq = LSQ() lsq.SetRefinedObj(self.d) # Refine structural parameters lsq.SetParIsFixed(refinableobj.refpartype_objcryst, False) lsq.SetParIsFixed(refinableobj.refpartype_scattdata, True) lsq.SetParIsFixed(refinableobj.refpartype_scattdata_scale, False) lsq.SetParIsFixed(refinableobj.refpartype_unitcell, True) lsq.SetParIsFixed(refinableobj.refpartype_scattpow, True) lsq.SetParIsFixed(refinableobj.refpartype_scattdata_radiation, True) for i in range(5): self.c.RandomizeConfiguration() lsq.Refine(10, False, True) if __name__ == "__main__": unittest.main() pyobjcryst-2024.2.1/src/pyobjcryst/tests/testmolecule.py000066400000000000000000000635061470422267000234100ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Tests for molecule module.""" import io import unittest from pkg_resources import resource_filename from pyobjcryst import ObjCrystException from pyobjcryst.crystal import Crystal from pyobjcryst.molecule import ( GetBondLength, StretchModeBondLength, GetBondAngle, StretchModeBondAngle, GetDihedralAngle, StretchModeTorsion, ImportFenskeHallZMatrix) from pyobjcryst.refinableobj import RefParType, RefinablePar from pyobjcryst.tests.pyobjcrysttestutils import makeC60, makeMnO6 from numpy import pi numplaces = 6 class TestMolecule(unittest.TestCase): def setUp(self): self.c = makeC60() self.m = self.c.GetScatterer("c60") return def tearDown(self): del self.c del self.m return def testProperties(self): """Make sure we can access the python-only properties.""" self.m.Q0 *= 1.001 self.m.Q1 *= 1.001 self.m.Q2 *= 1.001 self.m.Q3 *= 1.001 self.m.X *= 1.001 self.m.Y *= 1.001 self.m.Z *= 1.001 self.m.Occupancy *= 1.001 return def testContainment(self): """Make sure we can still use the molecule if the crystal is out of scope.""" c = makeC60() m = self.c.GetScatterer("c60") self.assertEqual("c60", m.GetName()) del c self.assertEqual("c60", m.GetName()) return def testAddPar(self): """See if we crash if we add a parameter and delete the molecule.""" c = makeC60() m = self.c.GetScatterer("c60") rpt = RefParType("test") par = RefinablePar("testpar", 3, 0, 10, rpt) m.AddPar(par) self.assertAlmostEqual(3, par.GetValue()) del m self.assertAlmostEqual(3, par.GetValue()) del c self.assertAlmostEqual(3, par.GetValue()) return def testAtoms(self): """Make sure the atoms are there. This tests AddAtom by association. This tests GetAtom. """ self.assertEqual(60, self.m.GetNbAtoms()) for i in range(60): a1 = self.m.GetAtom(i) self.assertEqual(a1.GetName(), "C%i"%i) a = self.m.GetAtom(0) x = a.X self.assertEqual(60, self.m.GetNbAtoms()) self.m.RemoveAtom(a) self.assertEqual(59, self.m.GetNbAtoms()) # Make sure the atom is still valid. We don't want RemoveAtom deleting # the memory for an object we still have access to. self.assertEqual(a.X, x) # Check to see if a is in our list for i in range(59): self.assertNotEqual(a.GetName(), self.m.GetAtom(i)) # What happens if we try to remove an atom that is not in the molecule? # First, try the same atom again. This will throw an objcryst error. self.assertRaises(ObjCrystException, self.m.RemoveAtom, a) ## Try to remove an atom from another molecule c = makeC60() m = c.GetScatterer("c60") self.assertRaises(ObjCrystException, self.m.RemoveAtom, m.GetAtom(1)) # Remove all the atoms. for a in self.m[:]: self.m.RemoveAtom(a) atoms = self.m.GetAtomList() self.assertEqual(0, len(atoms)) self.assertEqual(0, self.m.GetNbAtoms()) return def testGetAtom(self): "check Molecule.GetAtom." m = self.m a0 = m.GetAtom(0) b0 = m.GetAtom("C0") c0 = m.GetAtom(-60) self.assertEqual([a0.X, a0.Y, a0.Z], [b0.X, b0.Y, b0.Z]) self.assertEqual([a0.X, a0.Y, a0.Z], [c0.X, c0.Y, c0.Z]) a0.X = 0.123 self.assertEqual(a0.X, b0.X) self.assertEqual(a0.X, c0.X) self.assertRaises(IndexError, m.GetAtom, 60) self.assertRaises(IndexError, m.GetAtom, -61) self.assertRaises(ValueError, m.GetAtom, "invalid") return def testFindAtom(self): "check Molecule.FindAtom." m = self.m a0 = m.GetAtom(0) b0 = m.FindAtom("C0") self.assertEqual([a0.X, a0.Y, a0.Z], [b0.X, b0.Y, b0.Z]) a0.X = 0.123 self.assertEqual(a0.X, b0.X) self.assertIs(None, m.FindAtom("invalid")) return def testBonds(self): """Test the Bond methods.""" a1 = self.m.GetAtom(0) a2 = self.m.GetAtom(1) a3 = self.m.GetAtom(2) a4 = self.m.GetAtom(3) # Check for a bond that doesn't exist bond = self.m.FindBond(a1, a2) self.assertTrue(bond is None) # Make a bond and try to find it self.m.AddBond(a1, a2, 5, 0, 0) bond1 = self.m.FindBond(a1, a2) bond2 = self.m.FindBond(a2, a1) bond3 = self.m.FindBond(a1, a3) self.assertTrue(bond1 is not None) # Cannot expect the python objects to be the same, but they should point # to the same internal object self.assertEqual(bond1.GetName(), bond2.GetName()) # Try some bad bonds self.assertTrue(bond3 is None) # Remove an atom, the bond should disappear as well. self.m.RemoveAtom(a1) bond4 = self.m.FindBond(a1, a2) self.assertTrue(bond4 is None) # Try to find a bond from an atom outside of the molecule. m = makeC60().GetScatterer("c60") b1 = m.GetAtom(0) b2 = m.GetAtom(1) bond5 = self.m.FindBond(b1, b2) self.assertTrue(bond5 is None) # Try to make a bond using an atom that is not in the structure... # This seems to be allowed by ObjCryst++, and causes no errors. This # might be necessary to constrain a distance between two molecules, # thus it is allowed. # make a good bond. bond6 = self.m.AddBond(a3, a4, 5, 0, 0) bond7 = self.m.GetBond(0) self.assertEqual(bond6.GetName(), bond7.GetName()) # Delete some bonds and see what happens name = bond6.GetName() del bond6 del bond7 bond8 = self.m.GetBond(0) self.assertEqual(name, bond8.GetName()) # Try to get a bond that doesn't exist by index self.assertRaises(IndexError, self.m.GetBond, 1) # Remove the bond bonds = self.m.GetBondList() self.assertEqual(1, len(bonds)) self.m.RemoveBond(bonds[0]) # is the bond still in existance? self.assertEqual(name, bond8.GetName()) # Can we get it from the engine? self.assertRaises(IndexError, self.m.GetBond, 0) bond9 = self.m.FindBond(a3, a4) self.assertTrue(bond9 is None) # make a good bond again bond10 = self.m.AddBond(a3, a4, 5, 0, 0) # Get an atom from that a = bond10.GetAtom1() # Try to remove that atom self.m.RemoveAtom(a) self.assertEqual(0, self.m.GetNbBonds()) return def testBondAngles(self): """Test the BondAngle accessors.""" a1 = self.m.GetAtom(0) a2 = self.m.GetAtom(1) a3 = self.m.GetAtom(2) a4 = self.m.GetAtom(3) # Check for a bondangle angle that doesn't exist ba = self.m.FindBondAngle(a1, a2, a3) self.assertTrue(ba is None) # Make a ba and try to find it self.m.AddBondAngle(a2, a1, a3, 90, 0, 0) ba1 = self.m.FindBondAngle(a2, a1, a3) ba2 = self.m.FindBondAngle(a3, a1, a2) ba3 = self.m.FindBondAngle(a1, a2, a4) self.assertTrue(ba1 is not None) self.assertEqual(ba1.GetName(), ba2.GetName()) # Try some bad bond angles self.assertTrue(ba3 is None) # Remove an atom, the bondangle should disappear as well. self.m.RemoveAtom(a1) ba4 = self.m.FindBondAngle(a2, a1, a3) self.assertTrue(ba4 is None) # Try to find a bondangle from an atom outside of the molecule. m = makeC60().GetScatterer("c60") b1 = m.GetAtom(0) b2 = m.GetAtom(1) b3 = m.GetAtom(1) ba5 = self.m.FindBondAngle(b1, b2, b3) self.assertTrue(ba5 is None) # make a good bond angle ba6 = self.m.AddBondAngle(a2, a3, a4, 5, 0, 0) ba7 = self.m.GetBondAngle(0) self.assertEqual(ba6.GetName(), ba7.GetName()) # Delete some bond angles and see what happens name = ba6.GetName() del ba6 del ba7 ba8 = self.m.GetBondAngle(0) self.assertEqual(name, ba8.GetName()) # Try to get a bond angle that doesn't exist by index self.assertRaises(IndexError, self.m.GetBondAngle, 1) # Remove the bond angle angles = self.m.GetBondAngleList() self.assertEqual(1, len(angles)) self.m.RemoveBondAngle(angles[0]) # is the object still in existance? self.assertEqual(name, ba8.GetName()) # Can we get it from the engine? self.assertRaises(IndexError, self.m.GetBondAngle, 0) ba9 = self.m.FindBondAngle(a2, a3, a4) self.assertTrue(ba9 is None) # make a good bond angle again ba10 = self.m.AddBondAngle(a2, a3, a4, 5, 0, 0) # Get an atom from that a = ba10.GetAtom1() # Try to remove that atom self.m.RemoveAtom(a) self.assertEqual(0, self.m.GetNbBondAngles()) return def testDihedralAngles(self): """Test the FindDihedralAngle method.""" a1 = self.m.GetAtom(0) a2 = self.m.GetAtom(1) a3 = self.m.GetAtom(2) a4 = self.m.GetAtom(3) a5 = self.m.GetAtom(5) # Check for a dihedral angle that doesn't exist da = self.m.FindDihedralAngle(a1, a2, a3, a4) self.assertTrue(da is None) # Make a da and try to find it self.m.AddDihedralAngle(a1, a2, a3, a4, 90, 0, 0) da1 = self.m.FindDihedralAngle(a1, a2, a3, a4) da2 = self.m.FindDihedralAngle(a1, a2, a3, a4) self.assertTrue(da1 is not None) self.assertEqual(da1.GetName(), da2.GetName()) # Remove an atom, the dihedral angle should disappear as well. self.m.RemoveAtom(a1) da4 = self.m.FindDihedralAngle(a2, a1, a3, a4) self.assertTrue(da4 is None) # Try to find a dihedral angle from an atom outside of the molecule. m = makeC60().GetScatterer("c60") b1 = m.GetAtom(0) b2 = m.GetAtom(1) b3 = m.GetAtom(1) b4 = m.GetAtom(1) da5 = self.m.FindDihedralAngle(b1, b2, b3, b4) self.assertTrue(da5 is None) # make a good dihedral angle da6 = self.m.AddDihedralAngle(a2, a3, a4, a5, 5, 0, 0) da7 = self.m.GetDihedralAngle(0) self.assertEqual(da6.GetName(), da7.GetName()) # Delete some dihedral angles and see what happens name = da6.GetName() del da6 del da7 da8 = self.m.GetDihedralAngle(0) self.assertEqual(name, da8.GetName()) # Try to get a dihedral angle that doesn't exist by index self.assertRaises(IndexError, self.m.GetDihedralAngle, 1) # Remove the dihedral angle angles = self.m.GetDihedralAngleList() self.assertEqual(1, len(angles)) self.m.RemoveDihedralAngle(angles[0]) # is the object still in existance? self.assertEqual(name, da8.GetName()) # Can we get it from the engine? self.assertRaises(IndexError, self.m.GetDihedralAngle, 0) da9 = self.m.FindDihedralAngle(a2, a3, a4, a5) self.assertTrue(da9 is None) # make a good dihedral angle again da10 = self.m.AddDihedralAngle(a2, a3, a4, a5, 5, 0, 0) # Get an atom from that a = da10.GetAtom1() # Try to remove that atom self.m.RemoveAtom(a) self.assertEqual(0, self.m.GetNbDihedralAngles()) return def testRigidGroup(self): """Test adding and manipulating a rigid group.""" # A rigid group has the interface of a set self.assertEqual(0, len(self.m.GetRigidGroupList())) self.assertEqual(0, self.m.GetNbRigidGroups()) rg = self.m.AddRigidGroup(self.m.GetAtomList()) self.assertEqual(1, self.m.GetNbRigidGroups()) rgl = self.m.GetRigidGroupList() self.assertEqual(1, len(rgl)) self.assertEqual(60, len(rgl[0])) # We would like to check to see if the atoms are the same, but the # rigid group is a set, not a list. # Test to see if we can remove the list. self.m.RemoveRigidGroup(rg) self.assertEqual(0, self.m.GetNbRigidGroups()) rgl = self.m.GetRigidGroupList() self.assertEqual(0, len(rgl)) return def testManipulation(self): """Test moving the atoms.""" ac = self.m.AddAtom(0, 0, 0, None, "center") self.assertTrue(ac.IsDummy()) self.m.SetCenterAtom(ac) a0 = self.m.GetAtom(0) x = a0.X y = a0.Y z = a0.Z # Translate the atoms self.m.TranslateAtomGroup(self.m.GetAtomList(), 0, 0, 0.5) self.assertAlmostEqual(x, a0.X) self.assertAlmostEqual(y, a0.Y) self.assertAlmostEqual(z+0.5, a0.Z) # Move them back self.m.TranslateAtomGroup(self.m.GetAtomList(), 0, 0, -0.5) self.assertAlmostEqual(x, a0.X) self.assertAlmostEqual(y, a0.Y) self.assertAlmostEqual(z, a0.Z) # Rotate the atoms import numpy xyz = [numpy.array([a.X, a.Y, a.Z]) for a in self.m] self.m.RotateAtomGroup((0,0,0), (0,0,1), self.m.GetAtomList(), pi/2) rm = numpy.array([[0, -1, 0], [1, 0, 0], [0, 0, 1]]) for i in range(len(self.m)): xyzi = xyz[i] newxyz = numpy.dot(rm, xyzi) self.assertAlmostEqual(newxyz[0], self.m[i].X, 6) self.assertAlmostEqual(newxyz[1], self.m[i].Y, 6) self.assertAlmostEqual(newxyz[2], self.m[i].Z, 6) return def testZMatrix(self): """Test creating a Molecule from a z-matrix""" fname = resource_filename(__name__, "testdata/cime.fhz") c= Crystal() m = ImportFenskeHallZMatrix(c, fname) assert m.GetNbAtoms() == 17 # Test how changing a name to one that is already taken messes things up. class TestMolAtom(unittest.TestCase): def setUp(self): c = makeC60() self.m = c.GetScatterer("c60") self.a = self.m.GetAtom("C0") return def tearDown(self): del self.m del self.a return def testAccessors(self): a = self.a # Test name Get/Set self.assertEqual("C0", a.GetName()) a.SetName("test") self.assertEqual("test", a.GetName()) # Test xyz & occ Get/Set self.assertAlmostEqual(3.451266498, a.X, numplaces) self.assertAlmostEqual(0.685, a.Y, numplaces) self.assertAlmostEqual(0, a.Z, numplaces) self.assertAlmostEqual(1.0, a.Occupancy, numplaces) a.x = 3.40 a.y = 0.68 a.z = 0.1 a.occ = 1.02 self.assertAlmostEqual(3.40, a.x, numplaces) self.assertAlmostEqual(0.68, a.y, numplaces) self.assertAlmostEqual(0.1, a.z, numplaces) self.assertAlmostEqual(1.02, a.occ, numplaces) # Test GetMolecule. We can't expect the python object to be the same as # our molecule above. However, we can verify that it points to the same # object. m = a.GetMolecule() self.assertEqual(m.GetName(), self.m.GetName()) # Change something with the molecule, and check to see if it appears in # self.m m.GetAtom("C1").Occupancy = 0.1 self.assertAlmostEqual(0.1, self.m.GetAtom("C1").Occupancy, numplaces) # Test IsDummy self.assertFalse(a.IsDummy()) # Test GetScatteringPower sp = a.GetScatteringPower() self.assertEqual("ScatteringPowerAtom", sp.GetClassName()) self.assertEqual("C", sp.GetName()) # Test Ring Get/Set self.assertFalse(a.IsInRing()) a.SetIsInRing(True) self.assertTrue(a.IsInRing()) a.SetIsInRing(False) self.assertFalse(a.IsInRing()) return # End class TestMolAtom class TestMolBond(unittest.TestCase): def setUp(self): c = makeC60() self.m = c.GetScatterer("c60") # Add a bond self.a1 = self.m.GetAtom(0) self.a2 = self.m.GetAtom(1) self.b = self.m.AddBond(self.a1, self.a2, 5, 1, 2) return def tearDown(self): del self.m del self.a1 del self.a2 del self.b return def testAccessors(self): m = self.m b = self.b a1 = self.a1 a2 = self.a2 # Check the name self.assertEqual("C0-C1", b.GetName()) # Get the atoms at1 = b.GetAtom1() at2 = b.GetAtom2() self.assertEqual(at1.GetName(), a1.GetName()) self.assertEqual(at2.GetName(), a2.GetName()) # Data self.assertAlmostEqual(5, b.Length0, numplaces) self.assertAlmostEqual(1, b.LengthSigma, numplaces) self.assertAlmostEqual(2, b.LengthDelta, numplaces) self.assertAlmostEqual(1, b.BondOrder, numplaces) b.Length0 = 1.2 b.LengthSigma = 2 b.LengthDelta = 1 b.BondOrder = 2 self.assertAlmostEqual(1.2, b.Length0, numplaces) self.assertAlmostEqual(2, b.LengthSigma, numplaces) self.assertAlmostEqual(1, b.LengthDelta, numplaces) self.assertAlmostEqual(2, b.BondOrder, numplaces) # Check the log likelihood of the bond and the containing molecule b.Length0 = 4 ll = ((b.Length - (b.Length0-b.LengthDelta))/b.LengthSigma)**2 self.assertAlmostEqual(ll, b.GetLogLikelihood(), numplaces) self.assertAlmostEqual(ll, m.GetLogLikelihood(), numplaces) return # End class TestMolBond class TestMolBondAngle(unittest.TestCase): def setUp(self): c = makeC60() self.m = c.GetScatterer("c60") # Add a bond self.a1 = self.m.GetAtom(0) self.a2 = self.m.GetAtom(1) self.a3 = self.m.GetAtom(2) self.ba = self.m.AddBondAngle(self.a1, self.a2, self.a3, 5, 1, 2) return def tearDown(self): del self.m del self.a1 del self.a2 del self.a3 del self.ba return def testAccessors(self): m = self.m ba = self.ba a1 = self.a1 a2 = self.a2 a3 = self.a3 # Check the name self.assertEqual("C0-C1-C2", ba.GetName()) # Get the atoms at1 = ba.GetAtom1() at2 = ba.GetAtom2() at3 = ba.GetAtom3() self.assertEqual(at1.GetName(), a1.GetName()) self.assertEqual(at2.GetName(), a2.GetName()) self.assertEqual(at3.GetName(), a3.GetName()) # Data self.assertAlmostEqual(5, ba.Angle0, numplaces) self.assertAlmostEqual(1, ba.AngleSigma, numplaces) self.assertAlmostEqual(2, ba.AngleDelta, numplaces) ba.Angle0 = 1.2 ba.AngleSigma = 2 ba.AngleDelta = 1 self.assertAlmostEqual(1.2, ba.Angle0, numplaces) self.assertAlmostEqual(2, ba.AngleSigma, numplaces) self.assertAlmostEqual(1, ba.AngleDelta, numplaces) # Check the log likelihood of the bond and the containing molecule ba.Angle0 = 4 ll = ((ba.Angle - (ba.Angle0-ba.AngleDelta))/ba.AngleSigma)**2 self.assertAlmostEqual(ll, ba.GetLogLikelihood(), numplaces) self.assertAlmostEqual(ll, m.GetLogLikelihood(), numplaces) return # End class TestMolBondAngle class TestMolDihedralAngle(unittest.TestCase): def setUp(self): c = makeC60() self.m = c.GetScatterer("c60") # Add a bond self.a1 = self.m.GetAtom(0) self.a2 = self.m.GetAtom(1) self.a3 = self.m.GetAtom(2) self.a4 = self.m.GetAtom(3) self.da = self.m.AddDihedralAngle(self.a1, self.a2, self.a3, self.a4, 5, 1, 2) return def tearDown(self): del self.m del self.a1 del self.a2 del self.a3 del self.a4 del self.da return def testAccessors(self): m = self.m da = self.da a1 = self.a1 a2 = self.a2 a3 = self.a3 a4 = self.a4 # Check the name self.assertEqual("C0-C1-C2-C3", da.GetName()) # Get the atoms at1 = da.GetAtom1() at2 = da.GetAtom2() at3 = da.GetAtom3() at4 = da.GetAtom4() self.assertEqual(at1.GetName(), a1.GetName()) self.assertEqual(at2.GetName(), a2.GetName()) self.assertEqual(at3.GetName(), a3.GetName()) self.assertEqual(at4.GetName(), a4.GetName()) # Data # Note that the angle is in [-pi, pi] from math import pi self.assertAlmostEqual(5-2*pi, da.Angle0, numplaces) self.assertAlmostEqual(1, da.AngleSigma, numplaces) self.assertAlmostEqual(2, da.AngleDelta, numplaces) da.Angle0 = 1.2 da.AngleSigma = 2 da.AngleDelta = 1 self.assertAlmostEqual(1.2, da.Angle0, numplaces) self.assertAlmostEqual(2, da.AngleSigma, numplaces) self.assertAlmostEqual(1, da.AngleDelta, numplaces) # Check the log likelihood of the bond and the containing molecule da.Angle0 = pi-0.2 da.AngleDelta = 0 da.AngleSigma = 0.1 angle = da.Angle + (da.Angle0-da.AngleDelta) - 2*pi ll = (angle/da.AngleSigma)**2 # For some reason these are not very close in value. self.assertAlmostEqual(ll, da.GetLogLikelihood(), 2) self.assertAlmostEqual(ll, m.GetLogLikelihood(), 2) return # End class TestMolDihedralAngle class TestStretchModeBondLength(unittest.TestCase): def setUp(self): self.c = makeMnO6() self.m = self.c.GetScatterer("MnO6") return def tearDown(self): del self.m del self.c return def testStretchModeBondLength(self): """Test the StretchModeBondLength class.""" # Measure the distance ac = self.m[0] # The 0, 0, z atom atop = self.m[1] # The 0, 0, -z atom abot = self.m[6] d0 = GetBondLength(atop, abot) dc0 = GetBondLength(ac, atop) # Now create a stretch mode with just these two sm = StretchModeBondLength(atop, abot, None) sm.AddAtom(abot) self.assertEqual(sm.mpAtom0.GetName(), atop.GetName()) self.assertEqual(sm.mpAtom1.GetName(), abot.GetName()) # Stretch the bond by 5% delta = 0.05 * d0 sm.Stretch(delta) # Make sure this does what we expected d1 = GetBondLength(atop, abot) self.assertAlmostEqual(d0+delta, d1, 6) # Note that only the second atom has moved dc1 = GetBondLength(ac, atop) self.assertAlmostEqual(dc0, dc1) return # End class TestStretchModeBondLength class TestStretchModeBondAngle(unittest.TestCase): def setUp(self): self.c = makeMnO6() self.m = self.c.GetScatterer("MnO6") return def tearDown(self): del self.m del self.c return def testStretchModeBondAngle(self): """Test the StretchModeBondLength class.""" a1 = self.m[1] ac = self.m[0] a2 = self.m[2] # Measure the angle angle0 = GetBondAngle(a1, ac, a2) # Now create a stretch mode with these sm = StretchModeBondAngle(a1, ac, a2, None) sm.AddAtom(a2) self.assertEqual(sm.mpAtom0.GetName(), a1.GetName()) self.assertEqual(sm.mpAtom1.GetName(), ac.GetName()) self.assertEqual(sm.mpAtom2.GetName(), a2.GetName()) # Stretch the angle by 5% delta = 0.05 * angle0 sm.Stretch(delta) # Make sure this does what we expected angle1 = GetBondAngle(a1, ac, a2) self.assertAlmostEqual(angle0+delta, angle1, 6) return # End class TestStretchModeBondAngle class TestStretchModeTorsion(unittest.TestCase): def setUp(self): self.c = makeMnO6() self.m = self.c.GetScatterer("MnO6") return def tearDown(self): del self.m del self.c return def testStretchModeTorsion(self): """Test the StretchModeBondLength class.""" a1 = self.m[1] ac0 = self.m[3] ac1 = self.m[0] a2 = self.m[2] # Measure the angle angle0 = GetDihedralAngle(a1, ac0, ac1, a2) # Now create a stretch mode with the central bond sm = StretchModeTorsion(ac0, ac1, None) # Add the last atom so it can rotate sm.AddAtom(a2) self.assertEqual(sm.mpAtom1.GetName(), ac0.GetName()) self.assertEqual(sm.mpAtom2.GetName(), ac1.GetName()) # Stretch the angle by 5% delta = 0.25 * angle0 sm.Stretch(delta) # Make sure this does what we expected angle1 = GetDihedralAngle(a1, ac0, ac1, a2) self.assertAlmostEqual(angle0+delta, angle1, 6) return def testDummy(self): """Test adding a dummy atom.""" # In this past, dummy atoms would cause seg-faults in crystal::Print. # We test that here. self.m.AddAtom(0, 0, 0, None, "center") sp = self.m[-1].GetScatteringPower() self.assertTrue(sp is None) sm = self.m.xml() self.assertEqual(8, sm.count('Atom Name')) sc = str(self.c) sclines = sc.splitlines() self.assertTrue(sclines[2].endswith(' 8')) self.assertTrue('ScattPow: dummy' in sc) return # End class TestStretchTorsion if __name__ == "__main__": unittest.main() pyobjcryst-2024.2.1/src/pyobjcryst/tests/testpowderpattern.py000066400000000000000000000265051470422267000244770ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst Complex Modeling Initiative # (c) 2018 Brookhaven Science Associates, # Brookhaven National Laboratory. # All rights reserved. # # File coded by: Pavol Juhas # # See AUTHORS.txt for a list of people who contributed. # See LICENSE.txt for license information. # ############################################################################## """Unit tests for pyobjcryst.powderpattern (with indexing & """ import unittest import numpy as np from pyobjcryst import ObjCrystException from pyobjcryst.powderpattern import PowderPattern, SpaceGroupExplorer from pyobjcryst.radiation import RadiationType, WavelengthType from pyobjcryst.crystal import * from pyobjcryst.reflectionprofile import ReflectionProfileType from pyobjcryst.indexing import * from pyobjcryst.tests.pyobjcrysttestutils import loadcifdata, datafile # ---------------------------------------------------------------------------- class TestRoutines(unittest.TestCase): pass # def test_CreatePowderPatternFromCIF(self): assert False # End of class TestRoutines # ---------------------------------------------------------------------------- class TestPowderPattern(unittest.TestCase): def setUp(self): self.pp = PowderPattern() return def test___init__(self): self.assertEqual(0, self.pp.GetNbPowderPatternComponent()) self.assertEqual(0, len(self.pp.GetPowderPatternX())) self.assertEqual(0, len(self.pp.GetPowderPatternObs())) self.assertEqual(0, len(self.pp.GetPowderPatternCalc())) return # def test_AddPowderPatternBackground(self): assert False # def test_AddPowderPatternDiffraction(self): assert False # def test_FitScaleFactorForIntegratedR(self): assert False # def test_FitScaleFactorForIntegratedRw(self): assert False # def test_FitScaleFactorForR(self): assert False # def test_FitScaleFactorForRw(self): assert False # def test_GetMaxSinThetaOvLambda(self): assert False # def test_GetNbPowderPatternComponent(self): assert False # def test_GetPowderPatternCalc(self): assert False # def test_GetPowderPatternComponent(self): assert False def test_GetPowderPatternObs(self): self.assertTrue(np.array_equal([], self.pp.GetPowderPatternObs())) return def test_GetPowderPatternX(self): self.assertTrue(np.array_equal([], self.pp.GetPowderPatternX())) return # def test_GetScaleFactor(self): assert False # def test_ImportPowderPattern2ThetaObs(self): assert False # def test_ImportPowderPattern2ThetaObsSigma(self): assert False # def test_ImportPowderPatternFullprof(self): assert False # def test_ImportPowderPatternFullprof4(self): assert False # def test_ImportPowderPatternGSAS(self): assert False # def test_ImportPowderPatternILL_D1A5(self): assert False # def test_ImportPowderPatternMultiDetectorLLBG42(self): assert False # def test_ImportPowderPatternPSI_DMC(self): assert False # def test_ImportPowderPatternSietronicsCPI(self): assert False # def test_ImportPowderPatternTOF_ISIS_XYSigma(self): assert False # def test_ImportPowderPatternXdd(self): assert False # def test_Prepare(self): assert False # def test_SetEnergy(self): assert False # def test_SetMaxSinThetaOvLambda(self): assert False def test_SetPowderPatternObs(self): pp = self.pp obs = np.array([1.0, 3.0, 7.0]) self.assertRaises(ObjCrystException, pp.SetPowderPatternObs, obs) pp.SetPowderPatternPar(0, 0.5, 3) pp.SetPowderPatternObs(obs) self.assertTrue(np.array_equal(obs, pp.GetPowderPatternObs())) pp.SetPowderPatternObs(list(obs)[::-1]) self.assertTrue(np.array_equal(obs[::-1], pp.GetPowderPatternObs())) return def test_SetPowderPatternPar(self): pp = self.pp pp.SetPowderPatternPar(0, 0.25, 5) tth = np.linspace(0, 1, 5) self.assertTrue(np.array_equal(tth, pp.GetPowderPatternX())) pp.SetPowderPatternPar(0, 0.25, 0) self.assertEqual(0, len(pp.GetPowderPatternX())) return def test_SetPowderPatternX(self): pp = self.pp tth0 = np.array([0, 0.1, 0.3, 0.7]) tth1 = np.array([0, 0.1, 0.3, 0.7, 0.75, 0.77, 0.80]) pp.SetPowderPatternX(tth0) self.assertTrue(np.array_equal(tth0, pp.GetPowderPatternX())) pp.SetPowderPatternX(list(tth1)) self.assertTrue(np.array_equal(tth1, pp.GetPowderPatternX())) pp.SetPowderPatternX(tuple(2 * tth0)) self.assertTrue(np.array_equal(2 * tth0, pp.GetPowderPatternX())) return def test_SetPowderPatternXempty(self): pp = self.pp pp.SetPowderPatternX([0, 0.1, 0.2, 0.3]) pp.SetPowderPatternX([]) self.assertEqual(0, len(pp.GetPowderPatternX())) return def test_SetWavelength(self): pp = self.pp pp.SetWavelength(1.2345) self.assertAlmostEqual(pp.GetWavelength(), 1.2345, places=4) def test_SetWavelengthXrayTube(self): pp = self.pp t = pp.GetRadiation().GetWavelengthType() w = pp.GetWavelength() pp.SetWavelength("Cu") self.assertAlmostEqual(pp.GetWavelength(), 1.5418, places=4) self.assertEqual(pp.GetRadiation().GetWavelengthType(), WavelengthType.WAVELENGTH_ALPHA12) pp.GetRadiation().SetWavelengthType(t) pp.SetWavelength(w) def test_SetRadiationType(self): pp = self.pp t = pp.GetRadiationType() pp.SetRadiationType(RadiationType.RAD_NEUTRON) self.assertEqual(pp.GetRadiationType(), RadiationType.RAD_NEUTRON) pp.SetRadiationType(t) def test_quick_fit(self): c = loadcifdata("paracetamol.cif") p = PowderPattern() p.SetWavelength(0.7) x = np.linspace(0, 40, 8001) p.SetPowderPatternX(np.deg2rad(x)) p.SetPowderPatternObs(np.ones_like(x)) pd = p.AddPowderPatternDiffraction(c) pd.SetReflectionProfilePar(ReflectionProfileType.PROFILE_PSEUDO_VOIGT, 1e-6) # p.plot(hkl=True) calc = p.GetPowderPatternCalc() obs = np.random.poisson(calc * 1e5 / calc.max() + 50).astype(np.float64) p.SetPowderPatternObs(obs) p.SetMaxSinThetaOvLambda(0.3) p.quick_fit_profile(auto_background=True, verbose=False, plot=False) def test_peaklist_index(self): c = loadcifdata("paracetamol.cif") p = PowderPattern() p.SetWavelength(0.7) x = np.linspace(0, 40, 16001) p.SetPowderPatternX(np.deg2rad(x)) p.SetPowderPatternObs(np.ones_like(x)) pd = p.AddPowderPatternDiffraction(c) pd.SetReflectionProfilePar(ReflectionProfileType.PROFILE_PSEUDO_VOIGT, 1e-7) # p.plot(hkl=True) calc = p.GetPowderPatternCalc() obs = np.random.poisson(calc * 1e6 / calc.max() + 50).astype(np.float64) p.SetPowderPatternObs(obs) p.SetMaxSinThetaOvLambda(0.2) p.FitScaleFactorForIntegratedRw() pl = p.FindPeaks() ex = quick_index(pl, verbose=False) sols = ex.GetSolutions() self.assertGreater(len(sols), 0) ruc = sols[0][0] # Check lattice type self.assertEqual(ruc.centering, CrystalCentering.LATTICE_P) self.assertEqual(ruc.lattice, CrystalSystem.MONOCLINIC) # Cell volume self.assertAlmostEqual(ruc.DirectUnitCell()[-1], c.GetVolume(), delta=5) def test_spacegroup_explorer(self): c = loadcifdata("paracetamol.cif") p = PowderPattern() p.SetWavelength(0.7) x = np.linspace(0, 40, 8001) p.SetPowderPatternX(np.deg2rad(x)) p.SetPowderPatternObs(np.ones_like(x)) pd = p.AddPowderPatternDiffraction(c) pd.SetReflectionProfilePar(ReflectionProfileType.PROFILE_PSEUDO_VOIGT, 1e-6, 0, 0, 0, 0) # p.plot(hkl=True) calc = p.GetPowderPatternCalc() obs = np.random.poisson(calc * 1e6 / calc.max() + 50).astype(np.float64) p.SetPowderPatternObs(obs) # NB: with max(stol)=0.2 this fails and best result is P1 p.SetMaxSinThetaOvLambda(0.3) # Do the profile optimisation in P1 pd.GetCrystal().GetSpaceGroup().ChangeSpaceGroup("P1") p.FitScaleFactorForIntegratedRw() p.quick_fit_profile(auto_background=True, init_profile=False, verbose=False, plot=False) spgex = SpaceGroupExplorer(pd) spgex.Run("P 1 21/c 1") spgex.RunAll(verbose=False) spg = spgex.GetScores()[0] # This fails about XX% of the time (fit not converging well enough ?) # self.assertEqual(spg.hermann_mauguin, 'P 1 21/c 1') # if True: #spg.hermann_mauguin != 'P 1 21/c 1': # print() # for s in spgex.GetScores(): # print(s) def test_update_nbrefl(self): c = loadcifdata("paracetamol.cif") p = PowderPattern() p.SetWavelength(1.5) x = np.linspace(0, 40, 4000) p.SetPowderPatternX(np.deg2rad(x)) p.SetPowderPatternObs(np.ones_like(x)) pd = p.AddPowderPatternDiffraction(c) p.GetPowderPatternCalc() self.assertEqual(pd.GetNbRefl(), 89) # Change lattice parameter, the reflection list is updated # during the next powder pattern calculation c.a *= 1.1 p.GetPowderPatternCalc() self.assertEqual(pd.GetNbRefl(), 92) # Change the spacegroup, the reflection list is updated # during the next powder pattern calculation c.GetSpaceGroup().ChangeSpaceGroup("P1") p.GetPowderPatternCalc() self.assertEqual(pd.GetNbRefl(), 187) # def test_SetScaleFactor(self): assert False # End of class TestPowderPattern # ---------------------------------------------------------------------------- class TestPowderPatternComponent(unittest.TestCase): pass # def test___init__(self): assert False # def test_GetParentPowderPattern(self): assert False # End of class TestPowderPatternComponent # ---------------------------------------------------------------------------- class TestPowderPatternBackground(unittest.TestCase): pass # def test___init__(self): assert False # def test_FixParametersBeyondMaxresolution(self): assert False # def test_GetPowderPatternCalc(self): assert False # def test_ImportUserBackground(self): assert False # def test_OptimizeBayesianBackground(self): assert False # def test_SetInterpPoints(self): assert False # End of class TestPowderPatternBackground # ---------------------------------------------------------------------------- class TestPowderPatternDiffraction(unittest.TestCase): pass # def test___init__(self): assert False # def test_ExtractLeBail(self): assert False # def test_GetExtractionMode(self): assert False # def test_GetNbReflBelowMaxSinThetaOvLambda(self): assert False # def test_GetPowderPatternCalc(self): assert False # def test_GetProfile(self): assert False # def test_SetCrystal(self): assert False # def test_SetExtractionMode(self): assert False # def test_SetReflectionProfilePar(self): assert False # End of class TestPowderPatternDiffraction # ---------------------------------------------------------------------------- if __name__ == '__main__': unittest.main() pyobjcryst-2024.2.1/src/pyobjcryst/tests/testradiation.py000066400000000000000000000030561470422267000235470ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # File coded by: Vincent Favre-Nicolin # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Tests for Radiation module.""" import unittest from pyobjcryst.radiation import Radiation, RadiationType, WavelengthType from pyobjcryst.diffractiondatasinglecrystal import DiffractionDataSingleCrystal from pyobjcryst.powderpattern import PowderPattern class TestRadiation(unittest.TestCase): def testRadiation(self): """Test Radiation creation""" r = Radiation() return def testWavelength(self): """Test setting & reading wavelength""" r = Radiation() r.SetWavelength(1.24) self.assertAlmostEqual(r.GetWavelength(), 1.24, places=3) return def testType(self): """Test setting & reading X-ray Tube wavelength""" r = Radiation() r.SetWavelengthType(WavelengthType.WAVELENGTH_ALPHA12) self.assertEqual(r.GetWavelengthType(), WavelengthType.WAVELENGTH_ALPHA12) r.SetRadiationType(RadiationType.RAD_NEUTRON) self.assertEqual(r.GetRadiationType(), RadiationType.RAD_NEUTRON) r.SetWavelength("Cu") self.assertAlmostEqual(r.GetWavelength(), 1.5418, places=4) self.assertEqual(r.GetWavelengthType(), WavelengthType.WAVELENGTH_ALPHA12) return if __name__ == "__main__": unittest.main() pyobjcryst-2024.2.1/src/pyobjcryst/tests/testrefinableobj.py000066400000000000000000000315141470422267000242170ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Tests for refinableobj module.""" from pyobjcryst.refinableobj import RefinableObjClock, RefParType, Restraint from pyobjcryst.refinableobj import RefinablePar, RefinableObj from pyobjcryst import ObjCrystException import unittest import numpy from pyobjcryst.tests.pyobjcrysttestutils import makeScatterer, makeCrystal class TestRefinableObjClock(unittest.TestCase): def testRelations(self): """Test clicking! Chances are that someone will someday read this code for an example on how to use clocks. If not, then I've wasted my time writing this. Anyway, clocks are more complex then they appear. This is because ObjCryst++ has an internal clock that gets incremented whenever any clock is Clicked. So, one cannot trust that a clock will increment by only one value when it is clicked. Furthermore, clocks only alert their parents to a change. So, it is possible to decrease the value of a parent clock with SetEqual below the values of its children clocks. Callling Click on the parent or child will restore the proper parent > child relationship. """ c1 = RefinableObjClock() c2 = RefinableObjClock() parent = RefinableObjClock() ref = RefinableObjClock() ref2 = RefinableObjClock() c1.Reset() c2.Reset() parent.Reset() ref.Reset() ref2.Reset() # See if these things are at the same spot self.assertTrue(not (c1 < c2 or c2 < c1)) # Click one to make greater than other c1.Click() self.assertTrue(c1 > c2) self.assertFalse(c2 > c1) # Adding children advances the parent beyond all children parent.AddChild(c1) parent.AddChild(c2) self.assertTrue(parent > c1) self.assertTrue(parent > c2) self.assertTrue(c1 > c2) # Clicking parent leaves the children ref.SetEqual(c1) parent.Click() self.assertTrue(parent > ref) self.assertTrue(parent > c1) self.assertTrue(parent > c2) self.assertTrue(c1 > c2) self.assertTrue(not (c1 < ref or ref < c1)) # Resetting parent does not reset children parent.Reset() self.assertTrue(parent < c1) # Resetting child does not affect parent ref.SetEqual(parent) c1.Reset() self.assertTrue(not (parent < ref or ref < parent)) # Clicking children advances parent ref.SetEqual(parent) c1.Click() self.assertTrue(parent > c1) self.assertTrue(parent > c2) # Reset child does not affect parent or other children ref.SetEqual(parent) ref2.SetEqual(c1) c2.Reset() self.assertTrue(not (parent < ref or ref < parent)) self.assertTrue(not (c1 < ref2 or ref2 < c1)) # Increasing child above parent with SetEqual will increase parent to # child's value ref.SetEqual(parent) ref2.SetEqual(c1) ref.Click() ref.Click() self.assertTrue(ref > parent) c1.SetEqual(ref) self.assertTrue(c1 > ref2) self.assertTrue(not (parent < c1 or c1 < parent)) ref.Reset() self.assertTrue(parent > ref) # Decreasing child with SetEqual will not affect parent. c1.Click() ref2.SetEqual(c1) c1.Click() self.assertTrue(ref2 < c1) self.assertTrue(ref2 < parent) ref.SetEqual(parent) c1.SetEqual(ref2) self.assertTrue(not (ref < parent or parent < ref)) # Increasing child with SetEqual, so that it is still smaller than # parent, will increment parent parent.SetEqual(ref2) parent.Click() self.assertTrue(c1 < parent) self.assertTrue(ref2 < parent) ref.SetEqual(parent) c1.SetEqual(ref2) self.assertTrue(c1 < parent) self.assertTrue(not (ref < parent or parent < ref)) # Reducing parent with SetEqual will not affect children. ref.Reset() c1.Click() c2.Click() parent.SetEqual(ref) self.assertTrue(parent < c1) self.assertTrue(parent < c2) return def testRemoveChild(self): """Test the RemoveChild method.""" c1 = RefinableObjClock() c2 = RefinableObjClock() parent = RefinableObjClock() # Test equality values after removing child parent.AddChild(c1) c1.Click() self.assertTrue(parent > c1) parent.RemoveChild(c1) c1.Click() self.assertTrue(c1 > parent) # Try to remove a clock that is not part of another. This should do # nothing, just as in ObjCryst++. parent.RemoveChild(c2) return class TestRestraint(unittest.TestCase): def testEquality(self): """See if we get back what we put in.""" rpt = RefParType("test") res1 = Restraint(rpt) rpt2 = res1.GetType() self.assertEqual(rpt2, rpt) return class TestRefinablePar(unittest.TestCase): def setUp(self): self.rpt = RefParType("test") self.testpar = RefinablePar("test", 3.0, 0, 10, self.rpt) return def testToFromPython(self): """See if refinable parameters can be created from within python and within c++.""" c = makeCrystal(*makeScatterer()) # Get a parameter created from c++ par = c.GetPar("a") self.assertAlmostEqual(3.52, par.GetValue()) # pass a parameter and pass it into c++ c.AddPar(self.testpar); # get it back testpar2 = c.GetPar("test") self.assertAlmostEqual(self.testpar.GetValue(), testpar2.GetValue()) testpar2.SetValue(2.17) self.assertAlmostEqual(2.17, testpar2.GetValue(), places=6) self.assertAlmostEqual(self.testpar.GetValue(), testpar2.GetValue()) return def testGetType(self): """See if we can get the proper RefParType from a RefinablePar.""" rpt2 = self.testpar.GetType() self.assertEqual(rpt2, self.rpt) return class TestRefinableObj(unittest.TestCase): def setUp(self): """Make a RefinableObj and add some RefinablePars.""" self.r = RefinableObj() self.r.SetName("test1") # Add some parameters self.rpt = RefParType("test") p1 = RefinablePar("p1", 3, 0, 10, self.rpt) p2 = RefinablePar("p2", -3, -10, 0, self.rpt) self.r.AddPar(p1) self.r.AddPar(p2) return def _getPars(self): """Convenience function.""" p1 = self.r.GetPar(0) p2 = self.r.GetPar(1) return p1, p2 def testNames(self): """Test the naming methods.""" self.assertEqual("RefinableObj", self.r.GetClassName()) self.assertEqual("test1", self.r.GetName()) return def testGetPar(self): """Test GetPar.""" p1 = self.r.GetPar(0) p2 = self.r.GetPar(1) self.assertEqual(2, self.r.GetNbPar()) self.assertEqual("p1", p1.GetName()) self.assertEqual("p2", p2.GetName()) return def testFixUnFix(self): """Test FixAllPar.""" p1, p2 = self._getPars() r = self.r r.FixAllPar() self.assertTrue(p1.IsFixed()) self.assertTrue(p2.IsFixed()) r.PrepareForRefinement() self.assertEqual(0, r.GetNbParNotFixed()) r.UnFixAllPar() self.assertFalse(p1.IsFixed()) self.assertFalse(p2.IsFixed()) r.PrepareForRefinement() self.assertEqual(2, r.GetNbParNotFixed()) r.FixAllPar() self.assertTrue(p1.IsFixed()) self.assertTrue(p2.IsFixed()) r.SetParIsFixed(0, True) r.SetParIsFixed(1, False) self.assertTrue(p1.IsFixed()) self.assertFalse(p2.IsFixed()) r.PrepareForRefinement() self.assertEqual(1, r.GetNbParNotFixed()) r.SetParIsFixed("p1", False) r.SetParIsFixed("p2", True) self.assertFalse(p1.IsFixed()) self.assertTrue(p2.IsFixed()) r.PrepareForRefinement() self.assertEqual(1, r.GetNbParNotFixed()) return def testUsedUnUsed(self): """Test FixAllPar.""" p1, p2 = self._getPars() r = self.r r.SetParIsUsed("p1", False) r.SetParIsUsed("p2", True) self.assertFalse(p1.IsUsed()) self.assertTrue(p2.IsUsed()) r.SetParIsUsed(self.rpt, True) self.assertTrue(p1.IsUsed()) self.assertTrue(p2.IsUsed()) return def testAddParRefinableObj(self): """Test adding another object.""" r2 = RefinableObj() r2.SetName("test2") # Add some parameters p3 = RefinablePar("p3", 3, 0, 10, self.rpt) p4 = RefinablePar("p4", -3, -10, 0, self.rpt) r2.AddPar(p3) r2.AddPar(p4) self.r.AddPar(r2) self.assertEqual(4, self.r.GetNbPar()) return def testAddParTwice(self): """Try to add the same parameter twice. We could stop this in the bindings, but since RefinableObj doesn't delete its parameters in the destructor, it shouldn't lead to trouble. """ p3 = RefinablePar("p3", 3, 0, 10, self.rpt) self.r.AddPar(p3) self.r.AddPar(p3) return def testParmSets(self): """Test creation of parameter sets.""" self.assertRaises(ObjCrystException, self.r.SaveParamSet, 3) p1, p2 = self._getPars() r = self.r # Test saving and retrieval of parameters save1 = r.CreateParamSet("save1") savevals1 = r.GetParamSet(save1) self.assertTrue(numpy.array_equal([3, -3], savevals1)) # Change a parameter test new value p1.SetValue(8.0) save2 = r.CreateParamSet("save2") savevals2 = r.GetParamSet(save2) self.assertTrue(numpy.array_equal([8, -3], savevals2)) # Restore the old set r.RestoreParamSet(save1) self.assertEqual(3, p1.GetValue()) # Get the names self.assertEqual(r.GetParamSetName(save1), "save1") self.assertEqual(r.GetParamSetName(save2), "save2") # Delete parameter sets r.ClearParamSet(save2) self.assertRaises(ObjCrystException, r.SaveParamSet, save2) r.EraseAllParamSet() self.assertRaises(ObjCrystException, r.SaveParamSet, save1) return def testLimits(self): """Test the limit-setting functions.""" p1, p2 = self._getPars() r = self.r # Check setting absolute limits by name r.SetLimitsAbsolute("p1", 0, 1) p1.SetValue(8) self.assertEqual(1, p1.GetValue()) p1.SetValue(-1) self.assertEqual(0, p1.GetValue()) # Check setting absolute limits by type r.SetLimitsAbsolute(self.rpt, 0, 1) p1.SetValue(10) p2.SetValue(10) self.assertEqual(1, p1.GetValue()) self.assertEqual(1, p2.GetValue()) # Check setting relative limits by name r.SetLimitsRelative("p1", 0, 1) p1.SetValue(8) self.assertEqual(2, p1.GetValue()) p1.SetValue(-1) self.assertEqual(1, p1.GetValue()) # Check setting relative limits by type r.SetLimitsRelative(self.rpt, 0, 1) p1.SetValue(10) p2.SetValue(10) self.assertEqual(2, p1.GetValue()) self.assertEqual(2, p2.GetValue()) # Check setting proportional limits by name p1.SetValue(1) r.SetLimitsProportional("p1", 0, 3) p1.SetValue(8) self.assertEqual(3, p1.GetValue()) p1.SetValue(-1) self.assertEqual(0, p1.GetValue()) # Check setting proportional limits by type p1.SetValue(1) p2.SetValue(2) r.SetLimitsProportional(self.rpt, 1, 2) p1.SetValue(10) p2.SetValue(10) self.assertEqual(2, p1.GetValue()) self.assertEqual(4, p2.GetValue()) return def testOptimStep(self): """Test SetGlobalOptimStep.""" p1, p2 = self._getPars() self.r.SetGlobalOptimStep(self.rpt, 1) self.r.SetGlobalOptimStep(self.rpt, 0.1) self.assertAlmostEqual(0.1, p1.GetGlobalOptimStep()) self.assertAlmostEqual(0.1, p2.GetGlobalOptimStep()) return def test_xml(self): """Test xml() function""" x = self.r.xml() if __name__ == "__main__": unittest.main() pyobjcryst-2024.2.1/src/pyobjcryst/tests/testspacegroup.py000066400000000000000000000033221470422267000237410ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst Complex Modeling Initiative # (c) 2019 Brookhaven Science Associates, # Brookhaven National Laboratory. # All rights reserved. # # File coded by: Pavol Juhas # # See AUTHORS.txt for a list of people who contributed. # See LICENSE.txt for license information. # ############################################################################## """Unit tests for pyobjcryst.spacegroup """ import unittest from pyobjcryst.spacegroup import SpaceGroup # ---------------------------------------------------------------------------- class TestSpaceGroup(unittest.TestCase): def setUp(self): return def test___init__(self): "check SpaceGroup.__init__()" sg = SpaceGroup() self.assertEqual(1, sg.GetSpaceGroupNumber()) self.assertRaises(ValueError, SpaceGroup, "invalid") sgfm3m = SpaceGroup("F m -3 m") self.assertEqual(225, sgfm3m.GetSpaceGroupNumber()) sg3 = SpaceGroup("3") self.assertEqual(3, sg3.GetSpaceGroupNumber()) return def test_ChangeSpaceGroup(self): "check SpaceGroup.ChangeSpaceGroup()" sg = SpaceGroup("F m -3 m") self.assertEqual("F m -3 m", sg.GetName()) self.assertRaises(ValueError, sg.ChangeSpaceGroup, "invalid") self.assertEqual("F m -3 m", sg.GetName()) sg.ChangeSpaceGroup("P1") self.assertEqual("P 1", sg.GetName()) return # End of class TestSpaceGroup # ---------------------------------------------------------------------------- if __name__ == '__main__': unittest.main() pyobjcryst-2024.2.1/src/pyobjcryst/tests/testutils.py000066400000000000000000000030641470422267000227340ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2010 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Tests for crystal module.""" import unittest import numpy from pyobjcryst.tests.pyobjcrysttestutils import loadcifdata from pyobjcryst.utils import putAtomsInMolecule class TestPutAtomsInMolecule(unittest.TestCase): def test_caffeine(self): """Check molecule conversion for caffeine. """ c = loadcifdata('caffeine.cif') xyz0 = [(sc.X, sc.Y, sc.Z) for sc in c.GetScatteringComponentList()] self.assertEqual(24, c.GetNbScatterer()) putAtomsInMolecule(c, name='espresso') self.assertEqual(1, c.GetNbScatterer()) mol = c.GetScatterer(0) self.assertEqual('espresso', mol.GetName()) self.assertEqual(24, mol.GetNbAtoms()) xyz1 = [(sc.X, sc.Y, sc.Z) for sc in c.GetScatteringComponentList()] uc0 = numpy.array(xyz0) - numpy.floor(xyz0) uc1 = numpy.array(xyz1) - numpy.floor(xyz1) self.assertTrue(numpy.allclose(uc0, uc1)) return # End of class TestPutAtomsInMolecule if __name__ == "__main__": unittest.main() pyobjcryst-2024.2.1/src/pyobjcryst/unitcell.py000066400000000000000000000013311470422267000213440ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Python wrapping of UnitCell.h See the online ObjCryst++ documentation (https://objcryst.readthedocs.io). """ __all__ = ["UnitCell"] from pyobjcryst._pyobjcryst import UnitCell pyobjcryst-2024.2.1/src/pyobjcryst/utils.py000066400000000000000000000066471470422267000207040ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Utilities for crystals.""" # FIXME: check if this function does any meaningful job. def putAtomsInMolecule(crystal, alist = None, name = None): """Place atoms from a crystal into a molecule inside the crystal. Selected atoms are put into a new Molecule object, which is then placed inside of the Crystal. The atoms are then removed from the crystal. The molecule is placed at the center of mass of the moved atoms. crystal -- The crystal containing the atoms. alist -- A list of indices or names identifying the atoms. If alist is None (default), all atoms from the crystal are placed into a molecule. name -- A name for the molecule. If name is None (default), the name m_cname will be given, where cname is substituted for the crystal's name. Raises TypeError if idxlist identifies a non-atom. """ c = crystal if name is None: name = "m_%s" % c.GetName() if alist is None: alist = range(c.GetNbScatterer()) from pyobjcryst.molecule import Molecule from pyobjcryst.atom import Atom m = Molecule(c, name) # center of mass cx = cy = cz = 0.0 # mapping fractional coords back into [0, 1) from math import floor f = lambda v: v - floor(v) scat = [] for idx in alist: s = c.GetScatt(idx) if not isinstance(s, Atom): raise TypeError("identifier '%s' does not specify an Atom") sp = s.GetScatteringPower() scat.append(s) x, y, z = map(f, [s.X, s.Y, s.Z]) x, y, z = c.FractionalToOrthonormalCoords(x, y, z) m.AddAtom(x, y, z, sp, s.GetName()) cx += x cy += y cz += z # Adjust center of mass cx /= len(alist) cy /= len(alist) cz /= len(alist) # Remove scatterers from the crystal for s in scat: c.RemoveScatterer(s) # Put the molecule at the CoM m.X, m.Y, m.Z = c.OrthonormalToFractionalCoords(cx, cy, cz) # Add the molecule to the crystal c.AddScatterer(m) m.UpdateScattCompList() return def _xyztostring(crystal): """Helper function to write xyz coordinates of a crystal to a string.""" nsc = 0 out = "" scl = crystal.GetScatteringComponentList() out += "...\n" for s in scl: sp = s.mpScattPow if sp is None: continue nsc += 1 el = sp.GetSymbol() xyz = [s.X, s.Y, s.Z] xyz = crystal.FractionalToOrthonormalCoords(*xyz) x, y, z = xyz out += "%s %f %f %f\n"%(el, x, y, z) out = "%i\n"%nsc + out return out def printxyz(crystal): """Print a crystal in xyz format.""" print(_xyztostring(crystal)) return def writexyz(crystal, filename): """Write a crystal to an xyz file.""" f = open(filename, 'w') out = _xyztostring(crystal) f.write(out) f.close() return pyobjcryst-2024.2.1/src/pyobjcryst/version.py000066400000000000000000000045531470422267000212230ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """ Definition of __version__, __date__, __timestamp__, __git_commit__, libobjcryst_version_info. Notes ----- Variable `__gitsha__` is deprecated as of version 2.1. Use `__git_commit__` instead. """ __all__ = ['__date__', '__git_commit__', '__timestamp__', '__version__', 'libobjcryst_version_info'] import os.path from pkg_resources import resource_filename # obtain version information from the version.cfg file cp = dict(version='', date='', commit='', timestamp='0') fcfg = resource_filename(__name__, 'version.cfg') if not os.path.isfile(fcfg): # pragma: no cover from warnings import warn warn('Package metadata not found, execute "./setup.py egg_info".') fcfg = os.devnull with open(fcfg) as fp: kwords = [[w.strip() for w in line.split(' = ', 1)] for line in fp if line[:1].isalpha() and ' = ' in line] assert all(w[0] in cp for w in kwords), "received unrecognized keyword" cp.update(kwords) __version__ = cp['version'] __date__ = cp['date'] __git_commit__ = cp['commit'] __timestamp__ = int(cp['timestamp']) # TODO remove deprecated __gitsha__ in version 2.2. __gitsha__ = __git_commit__ del cp, fcfg, fp, kwords # version information on the active libObjCryst library ---------------------- from collections import namedtuple from pyobjcryst._pyobjcryst import _get_libobjcryst_version_info_dict libobjcryst_version_info = namedtuple('libobjcryst_version_info', "major minor micro patch version_number version date git_commit") vd = _get_libobjcryst_version_info_dict() libobjcryst_version_info = libobjcryst_version_info( version = vd['version_str'], version_number = vd['version'], major = vd['major'], minor = vd['minor'], micro = vd['micro'], patch = vd['patch'], date = vd['date'], git_commit = vd['git_commit']) del vd pyobjcryst-2024.2.1/src/pyobjcryst/zscatterer.py000066400000000000000000000041551470422267000217220ustar00rootroot00000000000000#!/usr/bin/env python ############################################################################## # # pyobjcryst by DANSE Diffraction group # Simon J. L. Billinge # (c) 2009 The Trustees of Columbia University # in the City of New York. All rights reserved. # # File coded by: Chris Farrow # # See AUTHORS.txt for a list of people who contributed. # See LICENSE_DANSE.txt for license information. # ############################################################################## """Python wrapping of Zscatterer. See the online ObjCryst++ documentation (https://objcryst.readthedocs.io). Changes from ObjCryst::ZAtom - XMLOutput and Input are not wrapped. Changes from ObjCryst++ - XMLOutput and Input are not wrapped. """ __all__ = ["ZScatterer", "ZAtom", "ZPolyhedron", "RegularPolyhedraType", "GlobalScatteringPower"] from urllib.request import urlopen from pyobjcryst._pyobjcryst import ZScatterer as ZScatterer_orig from pyobjcryst._pyobjcryst import ZAtom from pyobjcryst._pyobjcryst import ZPolyhedron from pyobjcryst._pyobjcryst import RegularPolyhedraType from pyobjcryst._pyobjcryst import GlobalScatteringPower class ZScatterer(ZScatterer_orig): def ImportFenskeHallZMatrix(self, src, named=False): """ Import atoms from a Fenske-Hall z-matrix :param src: either a python filed (opened in 'rb' mode), or a filename, or an url ("http://...") to a text file with the z-matrix :param named: if True, allows to read a named Z-matrix - the formatting is similar to a Fenske-Hall z-matrix but only relies on spaces between the different fields instead of a strict number of characters. """ if isinstance(src, str): if len(src) > 4: if src[:4].lower() == 'http': return super().ImportFenskeHallZMatrix(urlopen(src), named) with open(src, 'rb') as fhz: # Make sure file object is closed super().ImportFenskeHallZMatrix(fhz, named) else: super().ImportFenskeHallZMatrix(src, named)