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LjaN5Qmܦ[M $o+S]:OAהtDtEEqZ)w7\ԘX3M^bمV|2,x-`PV]zg5`A @i!/YgF-'Lo:=\c9:r3t_#_[δۊOF ʲ2?l;|tzc:k^8^W\bv6w0EB$ V `eygFۣsrxG9tZ6ƎzoG;$T;̢?.AA(EFwU; Kx(^T(IT"lPR8#g7^эCs1r0 KcG;QiuyĒMdD(NF B h-rы`6~Do`Dz!O9Xvk_9>ǎwik3g>^j$|HNr %DE3 bU1-Zh`*hP*<.qoe,Ow#{a= >jn-hGigϜFю&^&%MI*O hqZE0Η-?Q/܉x,Ey;юv 43 aȈG(145fmzv"@B>rET$&Y>1Q>Хj^zqX!43hWWmeI*aX&2YQ+j ¶N}%O/ӁRoHZGp0B*s۩rd-x>79@@fiݝ1j=6N;GaussSum-3.0.1/GaussSum.py0000664000175000017500000000147712660404041014067 0ustar noelnoel#!/usr/bin/env python # -*- coding: cp1252 -*- # # Copyright (C) 2006-2009 Noel O'Boyle # # This program is free software; you can redistribute and/or modify it # under the terms of the GNU General Public License as published by the # Free Software Foundation; either version 2, or (at your option) any later # version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY, without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. from tkinter import * # GUI stuff import gausssum.gausssumgui if __name__=="__main__": root = Tk() root.title("GaussSum") app = gausssum.gausssumgui.App(root,sys.argv) # Set up the app... mainloop() # ...and sit back GaussSum-3.0.1/Docs/0000775000175000017500000000000012660404041012625 5ustar noelnoelGaussSum-3.0.1/Docs/ch01.html0000664000175000017500000000625112660404041014252 0ustar noelnoel Chapter 1. Installation

Chapter 1. Installation
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Chapter 1. Installation

Table of Contents

Installing an all-in-one bundle on Windows
Installing the GaussSum scripts on Windows
Installing on Linux

This chapter describes how to install and run GaussSum on computers running Windows or Linux. Note that there are two choices for installing in Windows. One is to install an all-in-one bundle of Python/Numpy/Matplotlib/GaussSum (the easier method), the other is to install the GaussSum sources and the required libraries separately.

Installing an all-in-one bundle on Windows

Download the quite large all-in-one bundle from here. Doubleclick on the .msi file to install it into something like C:\Program Files (x86)\GaussSum. A shortcut will be added to the Start Menu.

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Acknowledgments  Home Installing the GaussSum scripts on Windows
GaussSum-3.0.1/Docs/ch01s02.html0000664000175000017500000001262712660404041014603 0ustar noelnoel Installing the GaussSum scripts on Windows
Installing the GaussSum scripts on Windows
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Installing the GaussSum scripts on Windows

Install Python

GaussSum requires Python, which is a programming language commonly used for scripting.

Go to the Python install page. Download and run the Windows installer for a recent version of Python 3 (e.g. Python 3.3.2). Install it into something like C:\Python3.3. GaussSum also works well with Python 3.1 and 3.2.

Install Numpy

GaussSum requires Numpy, which is a Python extension that allows efficient matrix algebra.

Go to the Numpy download page. Download and run the Windows installer for your version of Python.

Install Matplotlib

In order to draw the graphs, GaussSum requires the Python package Matplotlib.

Go to the Matplotlib download page. Download and run the Windows installer for your version of Python.

Install the GaussSum Python scripts

The scripts can be found here. Extract the zip file into something like C:\Program Files (x86)\GaussSum.

Create a shortcut on your desktop to GaussSum.py (to do this, right-click on an empty part of the desktop, choose New -> Shortcut, browse to the folder where you installed GaussSum and choose GaussSum.py). To change the icon, right-click on the icon, choose Properties -> General -> Change Icon -> Browse. Find the folder where you installed GaussSum and choose GaussSum.ico. In addition, change the program used to open GaussSum by following these instructions: right-click on the icon, choose Properties -> General -> Opens with -> Change -> PythonW. If you want to set a startup folder, right-click on the icon, choose Properties -> Shortcut -> Start in:, and type the name of the startup folder.

If you have any questions or requests, or if you wish to be added to the mailing list for information on new versions and bug fixes, please send an email to gausssum-help@lists.sourceforge.net.

Start GaussSum

Just double-click the shortcut to GaussSum.py to start GaussSum.

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GaussSum-3.0.1/Docs/pr02.html0000664000175000017500000005357112660404041014311 0ustar noelnoel Citation
Citation
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Citation

If you use GaussSum to obtain results for publication, please cite it as follows:

N. M. O'Boyle, A. L. Tenderholt and K. M. Langner. J. Comp. Chem., 2008, 29, 839-845.

Here is a list of some papers that cite GaussSum from 2004 to 2009. There are many more papers since 2009 and I thank you for your continued support.

  1. Ligand-Selective Photodissociation from [Ru(bpy)(4AP)4]2+: a Spectroscopic and Computational Study Luca Salassa, Claudio Garino, Giovanni Salassa, Carlo Nervi, Roberto Gobetto, Carlo Lamberti, Diego Gianolio, Ranieri Bizzarri and Peter J. Sadler Inorg. Chem., 2009, 48 (4), pp 1469-1481

  2. Localised to intraligand charge-transfer states in cyclometalated platinum complexes: an experimental and theoretical study into the influence of electron-rich pendants and modulation of excited states by ion binding David L. Rochester, Stephanie Develay, Stanislav Zali, J. A. Gareth Williams, Dalton Trans., 2009, (10),1728-1741

  3. Synthetic, structural, photophysical and computational studies of -conjugated bis- and tris-1,3,2-benzodiazaboroles and related bis(boryl) dithiophenes Lothar Weber, Vanessa Werner, Mark A. Fox, Todd B. Marder, Stefanie Schwedler, Andreas Brockhinke, Hans-Georg Stammler, Beate Neumann, Dalton Trans., 2009, (8),1339-1351

  4. The Chromophore Structure of the Cyanobacterial Phytochrome Cph1 As Predicted by Time-Dependent Density Functional Theory Ricardo A. Matute and Renato Contreras, Guillermo Prez-Hernndez and Leticia Gonzlez J. Phys. Chem. B, 2008, 112 (51), pp 16253-16256

  5. Comparison of adsorption mechanism on colloidal silver surface of alafosfalin and its analogs Journal of Raman Spectroscopy Edyta Podstawka, Marcin Andrzejak, Pawelstrok Kafarski, Leonard M. Proniewicz Volume 39, Issue 9, Date: September 2008, Pages: 1238-1249

  6. Computational and Spectroscopic Studies of New Rhenium(I) Complexes Containing Pyridylimidazo[1,5-a]pyridine Ligands: Charge Transfer and Dual Emission by Fine-Tuning of Excited States Luca Salassa, Claudio Garino, Andrea Albertino, Giorgio Volpi, Carlo Nervi, Roberto Gobetto and Kenneth I. Hardcastle Organometallics, 2008, 27 (7), pp 1427-1435

  7. A Computational Study of the Ground and Excited State Structure and Absorption Spectra of Free-Base N-Confused Porphine and Free-Base N-Confused Tetraphenylporphyrin Shubham Vyas, Christopher M. Hadad and David A. Modarelli J. Phys. Chem. A, 2008, 112 (29), pp 6533-6549

  8. Determination of Absolute Configuration of Chiral Hemicage Metal Complexes Using Time-Dependent Density Functional Theory Frederick J. Coughlin, Karl D. Oyler, Robert A. Pascal, Jr., and Stefan Bernhard Inorg. Chem., 2008, 47 (3), pp 974-979

  9. Effect of an aliphatic spacer group on the adsorption mechanism of phosphonodipeptides containing N-terminal glycine on the colloidal silver surface Journal of Raman Spectroscopy Volume 39, Issue 10, Date: October 2008, Pages: 1396-1407 Edyta Podstawka, Pawelstrok Kafarski, Leonard M. Proniewicz

  10. Effect of an aliphatic spacer group on the adsorption mechanism on the colloidal silver surface of L-proline phosphonodipeptides Journal of Raman Spectroscopy Edyta Podstawka, Pawelstrok Kafarski, Leonard M. Proniewicz Volume 39, Issue 12, Date: December 2008, Pages: 1726-1739

  11. Electronic structure and reactivity analysis for a set of Zn-chelates with substituted 8-hydroxyquinoline ligands and their application in OLED Ricardo Vivas-Reyes, Francisco Nunez-Zarur, Emiliano Martinez Organic Electronics, Volume 9, Issue 5, October 2008, Pages 625-634

  12. A laser flash photolysis, matrix isolation, and DFT investigation of (?6-C6H5Y)Cr(CO)3 (Y = NH2, OCH3, H, CHO, or CO2CH3) Mohammed A.H. Alamiry, Peter Brennan, Conor Long, Mary T. Pryce, Journal of Organometallic Chemistry, Volume 693, Issue 17, 15 August 2008, Pages 2907-2914

  13. Mechanism of Forster-type hopping of charge transfer and excitation energy transfer along blocked oligothiophenes by Si-atoms Yong Ding, Xiangsi Wang, Fengcai Ma Chemical Physics, Volume 348, Issues 1-3, 2 June 2008, Pages 31-38

  14. Mechanism of Ligand Photodissociation in Photoactivable [Ru(bpy)2L2]2+ Complexes: A Density Functional Theory Study Luca Salassa, Claudio Garino, Giovanni Salassa, Roberto Gobetto and Carlo Nervi J. Am. Chem. Soc., 2008, 130 (29), pp 9590-9597

  15. Nature of Charge Carriers in Long Doped Oligothiophenes: The Effect of Counterions Natalia Zamoshchik, Ulrike Salzner and Michael Bendikov J. Phys. Chem. C, 2008, 112 (22), pp 8408-8418

  16. Photoinduced Se?C Insertion Following Photolysis of (?5-C4H4Se)Cr(CO)3. A Picosecond and Nanosecond Time-Resolved Infrared, Matrix Isolation, and DFT Investigation Peter Brennan, Michael W. George, Omar S. Jina, Conor Long, Jennifer McKenna, Mary T. Pryce, Xue-Zhong Sun and Khuong Q. Vuong Organometallics, 2008, 27 (15), pp 3671-3680

  17. Quantum chemical studies on the potentially important imidates Tarek M. El-Gogary Journal of Molecular Structure: THEOCHEM, Volume 861, Issues 1-3, 30 July 2008, Pages 62-67

  18. Reversible Intramolecular C?C Bond Formation/Breaking and Color Switching Mediated by a N,C-Chelate in (2-ph-py)BMes2 and (5-BMes2-2-ph-py)BMes2 Ying-Li Rao, Hazem Amarne, Shu-Bin Zhao, Theresa M. McCormick, Sanela Marti, Yi Sun, Rui-Yao Wang and Suning Wang J. Am. Chem. Soc., 2008, 130 (39), pp 12898-12900

  19. Ruthenium-carbonyl complexes of 1-alkyl-2-(arylazo)imidazoles: Synthesis, structure, spectra and redox properties T.K. Mondal, S.K. Sarker, P. Raghavaiah, C. Sinha Polyhedron, Volume 27, Issue 13, 10 September 2008, Pages 3020-3028

  20. Spectroscopic and theoretical studies on axial coordination of bis(pyrrol-2-ylmethyleneamine)phenyl complexes Jia-Mei Chen, Wen-Juan Ruan, Liang Meng, Feng Gao, Zhi-Ang Zhu Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Volume 71, Issue 1, 1 November 2008, Pages 191-198

  21. Structural Properties of l-X-l-Met-l-Ala Phosphonate Tripeptides: A Combined FT-IR, FT-RS, and SERS Spectroscopy Studies and DFT Calculations Edyta Podstawka, Pawe? Kafarski and Leonard M. Proniewicz J. Phys. Chem. A, 2008, 112 (46), pp 11744-11755

  22. Structures and Bonding on a Colloidal Silver Surface of the Various Length Carboxyl Terminal Fragments of Bombesin Edyta Podstawka, Yukihiro Ozaki and Leonard M. Proniewicz Langmuir, 2008, 24 (19), pp 10807-10816

  23. Structure?Property Relationships of Polyselenoethers [?(CH2)ySe?]x (y = 1, 2, and 3) and Related Polyethers and Polysulfides Yuji Sasanuma, Akinori Watanabe and Kenta Tamura J. Phys. Chem. B, 2008, 112 (32), pp 9613-9624

  24. Structure, spectra and electrochemistry of ruthenium-carbonyl complexes of naphthylazoimidazole Inorganica Chimica Acta, Volume 361, Issue 8, 2 June 2008, Pages 2431-2438 Tapan Kumar Mondal, Joydev Dinda, Jack Cheng, Tian-Huey Lu, Chittaranjan Sinha

  25. Structure, Stereodynamics and Absolute Configuration of the Atropisomers of Hindered Arylanthraquinones Lodovico Lunazzi, Michele Mancinelli and Andrea Mazzanti J. Org. Chem., 2009, 74 (3), pp 1345-1348

  26. Synthesis, Separation, and Circularly Polarized Luminescence Studies of Enantiomers of Iridium(III) Luminophores Frederick J. Coughlin, Michael S. Westrol, Karl D. Oyler, Neal Byrne, Christina Kraml, Eli Zysman-Colman, Michael S. Lowry and Stefan Bernhard Inorg. Chem., 2008, 47 (6), pp 2039-2048

  27. Theoretical analysis on the electronic structures and properties of PPV fused with electron-withdrawing unit: Monomer, oligomer and polymer Yangwu Fu, Wei Shen, Ming Li Polymer, Volume 49, Issue 10, 13 May 2008, Pages 2614-2620

  28. Computational Study of Iron(II) Systems Containing Ligands with Nitrogen Heterocyclic Groups R. A. Kirgan and D. P. Rillema J. Phys. Chem. A, 2007, 111 (50), pp 13157-13162

  29. Electronic Spectroscopy of Nonalternant Hydrocarbons Inside Helium Nanodroplets Ozgur Birer, Paolo Moreschini, Kevin K. Lehmann, and Giacinto Scoles J. Phys. Chem. A, 2007, 111 (49), pp 12200-12209

  30. Spectroscopic and Computational Studies of a Ru(II) Terpyridine Complex: The Importance of Weak Intermolecular Forces to Photophysical Properties Claudio Garino, Roberto Gobetto, Carlo Nervi, Luca Salassa, Edward Rosenberg, J. B. Alexander Ross, Xi Chu, Kenneth I. Hardcastle, and Cristiana Sabatini Inorg. Chem., 2007, 46 (21), pp 8752-8762

  31. Influence of the Substituted Side Group on the Molecular Structure and Electronic Properties of TPP and Related Implications on Organic Zeolites Use Godefroid Gahungu, Bin Zhang, and Jingping Zhang J. Phys. Chem. B, 2007, 111 (19), pp 5031-5033

  32. Syntheses and structures of mononuclear lutetium imido complexes with very short Lu-N bonds Tarun K. Panda, Soren Randoll, Cristian G. Hrib, Peter G. Jones, Thomas Bannenberg, Matthias Tamm, Chem. Commun., 2007, (47),5007-5009

  33. A DFT/TDDFT study of the structural and spectroscopic properties of Al(III) complexes with 4-nitrocatechol in acidic aqueous solution Jean-Paul Cornard, Christine Lapouge, Jean-Claude Merlin Chemical Physics, Volume 340, Issues 1-3, 9 November 2007, Pages 273-282

  34. Adsorption mechanism of physiologically active l-phenylalanine phosphonodipeptide analogues: Comparison of colloidal silver and macroscopic silver substrates E. Podstawka, A. Kudelski, L.M. Proniewicz Surface Science, Volume 601, Issue 21, 1 November 2007, Pages 4971-4983

  35. Theoretical studies on electrochemistry of p-aminophenol Yuanzhi Song Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Volume 67, Issues 3-4, July 2007, Pages 611-618

  36. Intramolecular hydrogen bonding and photoinduced intramolecular proton and electron transfer in 2-(2'-hydroxyphenyl)benzothiazole Dongjie Sun, Jinghai Fang, Guanghua Yu, Fengcai Ma Journal of Molecular Structure: THEOCHEM, Volume 806, Issues 1-3, 31 March 2007, Pages 105-112

  37. Photophysical properties and computational investigations of tricarbonylrhenium(I)[2-(4-methylpyridin-2-yl)benzo[d]-X-azole]L and tricarbonylrhenium(I)[2-(benzo[d]-X-azol-2-yl)-4-methylquinoline]L derivatives (X = N-CH3, O, or S; L = Cl-, pyridine) Andrea Albertino, Claudio Garino, Simona Ghiani, Roberto Gobetto, Carlo Nervi, Luca Salassa, Edward Rosenberg, Ayesha Sharmin, Guido Viscardi, Roberto Buscaino, Gianluca Croce, Marco Milanesio Journal of Organometallic Chemistry, Volume 692, Issue 6, 15 February 2007, Pages 1377-1391

  38. A Density Functional Theory Study of the Electronic Properties of Os(II) and Os(III) Complexes Immobilized on Au(111). O'Boyle, N. M.; Albrecht, T.; Murgida, D. H.; Cassidy, L.; Ulstrup, J.; Vos, J. G., Inorg. Chem., 2007, 46, 117.

  39. Photophysical and electrochemical properties of new ortho-metalated complexes of rhodium(III) containing 2,2-dipyridylketone and 2,2-dipyridylamine. An experimental and theoretical study Wei Lin Su, Yu Cheng Yu, Mei Ching Tseng, Shao Pin Wang and Wen Liang Huang Dalton Trans., 2007, 3440.

  40. Photochemical cis-trans Isomerization of cis-(eta6-1,2-Diphenylethene)Cr(CO)3 and the Molecular Structure of trans-(eta6-1,2-Diphenylethene)Cr(CO)3 A. Coleman, S.M. Draper, C. Long, and M.T. Pryce Organometallics, 2007, 26, 4128.

  41. Density Functional Studies on the Effects of Hydrogen Bonding on the Formation of a Charge-Transfer Complex between p-Benzoquinone and 2,6-Dimethoxyphenol Bangal, P. R. J. Phys. Chem. A.; (Article); 2007; 111(25); 5536-5543.

  42. Lone Pair-pi and pi-pi Interactions Play an Important Role in Proton-Coupled Electron Transfer Reactions DiLabio, G. A.; Johnson, E. R. J. Am. Chem. Soc.; (Article); 2007; 129(19); 6199-620

  43. Intramolecular hydrogen bonding and photoinduced intramolecular proton and electron transfer in 2-(2'-hydroxyphenyl)benzothiazole. D. Sun, J. Fang, G. Yu and F. Ma. J. Mol. Struct. THEOCHEM, 2007, 806, 105.

  44. Photophysical properties and computational investigations of tricarbonylrhenium(I)[2-(4-methylpyridin-2-yl)benzo[d]-X-azole]L and tricarbonylrhenium(I)[2-(benzo[d]-X-azol-2-yl)-4-methylquinoline]L derivatives (X = N--CH3, O, or S; L = Cl-, pyridine). A. Albertino, C. Garino, S. Ghiani, R. Gobetto, C. Nervi, L. Salassa, E. Rosenberg, A. Sharmin, G. Viscardi, R. Buscaino, G. Croce, and M. Milanesio, J. Organomet. Chem., 2007, 692, 1377.

  45. Electronic transitions and bonding properties in a series of five-coordinate "16-electron" complexes [Mn(CO)3(L2)]- (L2 = chelating redox-active p-donor ligand). F. Hartl, P. Rosa, L. Ricard, P. Le Floch and S. Zalis. Coord. Chem. Rev., 2007, 251, 557.

  46. Theoretical studies on electrochemistry of p-aminophenol Y. Song Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2007, 67, 611.

  47. The electronic and structural properties of nonclassical bicyclic thiophene: Monomer, oligomer and polymer W. Shen, M. Li, R. He, J. Zhang and W. Lei Polymer, 2007, 48, 3912-3918

  48. Spectroscopic and computational studies on self-assembly complexes of bis(pyrrol-2- ylmethyleneamine) ligands linked by alkyl spacers with Cu(II) W. Li, Y. Wang, L. Yang, A. Szeghalmi, Y. Ye, J. Ma, M. Luo, J.-m. Hu and W. Kiefer J. Raman. Spectros., 2007, 38, 483-495.

  49. CO2 Fixation and Transformation by a Dinuclear Copper Cryptate under Acidic Conditions J.-M. Chen, W. W., X.-L. Feng and T.-B. Lu Chemistry - An Asian Journal, 2007, 2, 710-719.

  50. A DFT study of the chemisorption of methoxy on clean and low oxygen precovered Ru(0 0 0 1) surfaces M.N.D.S. Cordeiro, A.S.S. Pinto and J.A.N.F. Gomes Surface Science, 2007, 601, 2473-2485

  51. Syntheses, crystallography and spectroelectrochemical studies of ruthenium azomethine complexes M.Z. Al-Noaimi, H. Saadeh, S.F. Haddad, M.I. El-Barghouthi, M. El-khateeb and R.J. Crutchley Polyhedron, 2007, 26, 3675.

  52. Field-induced conformational changes in bimetallic oligoaniline junctions J.C. Sotelo, L. Yan, M. Wang and J.M. Seminario Phys. Rev. A, 2007, 75, 022511

  53. Density functional theoretical study of Cun, Aln (n = 4-31) and copper doped aluminum clusters: Electronic properties and reactivity with atomic oxygen C. Lacaze-Dufaure, C. Blanc, G. Mankowski and C. Mijoule Surface Science, 2007, 601, 1544-1553

  54. Electronic Structure and Excited States of Rhenium(I) Amido and Phosphido Carbonyl-Bipyridine Complexes Studied by Picosecond Time-Resolved IR Spectroscopy and DFT Calculations. Gabrielsson, A.; Busby, M.; Matousek, P.; Towrie, M.; Hevia, E.; Cuesta, L.; Perez, J.; Zalis, S.; Vlcek, A., Jr., Inorg. Chem., 2006, 45, 9789.

  55. Spectroscopic and Computational Studies on the Coordination-Driven Self-Assembly Complexes (ZnL)2 and (NiL)2 [L= Bis(2,4-dimethyldipyrrin-3-yl)methane]. Li, W.; Wang, Y.-B.; Yang, L.-Y.; Shan, X.-F.; Cai, X.; Szeghalmi, A.; Ye, Y.; Ma, J.-S.; Luo, M.-D.; Hu, J.; Kiefer, W., J. Phys. Chem. B., 2006, 110, 21958.

  56. The hydrogen bond in the acetylene-2(HF) complex: A theoretical study about intramolecular and unusual PI...H interactions using DFT and AIM calculations. B.G. Oliveira, R.C.M.U. Araujo, A.B. Carvalho, E.F. Lima, W.L.V. Silva, M.N. Ramos and A.M. Tavares, J. Mol. Struct. THEOCHEM, 2006, 775, 39.

  57. Calculation of standard electrode potential of half reaction for benzoquinone and hydroquinone. Y. Song, J. Xie, Y. Song, H. Shu, G. Zhao, X. Lv and W. Xie, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2006, 65, 333.

  58. A theoretical quantum study on the distribution of electrophilic and nucleophilic active sites on Ag(100) surfaces modeled as Finite Clusters. C. H. Rios-Reyes, R. L. Camacho-Mendoza and L. H. Mendoza-Huizar, J. Mex. Chem. Soc., 2006, 50, 19.

  59. Computational studies of the interactions between emeraldine and palladium atom. B. Bialek, Surf. Sci., 2006, 600, 1679.

  60. Excited States of Nitro-Polypyridine Metal Complexes and Their Ultrafast Decay. Time-Resolved IR Absorption, Spectroelectrochemistry, and TD-DFT Calculations of fac-[Re(Cl)(CO)3(5-Nitro-1,10-phenanthroline)]. A. Gabrielsson, P. Matousek, M. Towrie, F. Hartl, S. Zalis, and A. Vlcek, Jr., J. Phys. Chem. A, 2005, 109, 6147.

  61. Molecular geometry, electronic structure and optical properties study of meridianal tris(8-hydroxyquinolinato)gallium(III) with ab initio and DFT methods. G. Gahungu, and J. Zhang, J. Mol. Struct. THEOCHEM, 2005, 755, 19.

  62. CH/N Substituted mer-Gaq3 and mer-Alq3 Derivatives: An Effective Approach for the Tuning of Emitting Color. G. Gahungu and J. Zhang. J. Phys. Chem. B, 2005, 109, 17762.

  63. Ground- and excited-state electronic structure of an emissive pyrazine-bridged ruthenium(II) dinuclear complex. W.R. Browne, N.M. O'Boyle, W. Henry, A.L. Guckian, S. Horn, T. Fett, C.M. O'Connor, M. Duati, L. De Cola, C.G. Coates, K.L. Ronayne, J.J. McGarvey, and J.G. Vos, J. Am. Chem. Soc., 2005, 127, 1229.

  64. Bimetallic Clusters Pt6Au: Geometric and Electronic Structures within Density Functional Theory W. Quan Tian, M. Ge, F. Gu, and Y. Aoki, J. Phys. Chem. A, 2005, 109, 9860.

  65. Ground vs. excited state interaction in ruthenium-thienyl dyads: implications for through bond interactions in multicomponent systems. W. Henry, W.R. Browne, K.L. Ronayne, N.M. O'Boyle, J.G. Vos, and J.J. McGarvey, J. Mol. Struct., 2005, 735-736, 123.

  66. (NH3CH2CH2NH3)Ag2SnS4: a quaternary sulfide-containing chiral layers. Y. An, B. Menghe, L. Ye, M. Ji, X. Liu, and G. Ning, Inorg. Chem. Commun., 2005, 8, 301.

  67. Ligand-to-Diimine/Metal-to-Diimine Charge-Transfer Excited States of [Re(NCS)(CO)3(alpha-diimine)] (alpha-diimine = 2,2'-bipyridine, di-iPr-N,N-1,4-diazabutadiene). A Spectroscopic and Computational Study. A.M. Blanco Rodriguez, A. Gabrielsson, M. Motevalli, P. Matousek, M. Towrie, J. Syebera, S. Zalis, and Antonin Vlcek, Jr., J. Phys. Chem. A, 2005, 109, 5016.

  68. DFT and HF Studies of the Geometry, Electronic Structure, and Vibrational Spectra of 2-Nitrotetraphenylporphyrin and Zinc 2-Nitrotetraphenylporphyrin. W. Li, Y.-B. Wang, I. Pavel, Y. Ye, Z.-P. Chen, M.-D. Luo, J.-M. Hu, and W. Kiefer, J. Phys. Chem. A, 2004, 108, 6052.

  69. Assessment of intercomponent interaction in phenylene bridged dinuclear ruthenium(II) and osmium(II) polypyridyl complexes. A.L. Guckian, M.Doering, M. Ciesielski, O. Walter, J. Hjelm, N.M. O'Boyle, W. Henry, W.R. Browne, J.J. McGarvey, and J.G. Vos, Dalton Trans., 2004, 3943.

  70. Ab initio study of the electronic and structural properties of the crystalline polyethyleneimine polymer. G. Herlem and B. Lakard, J. Chem. Phys., 2004, 120, 9376.

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Introduction  Home  Acknowledgments
GaussSum-3.0.1/Docs/ch03.html0000664000175000017500000000724312660404041014256 0ustar noelnoel Chapter 3. How do I follow the progress of an SCF convergence?
Chapter 3. How do I follow the progress of an SCF convergence?
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Chapter 3. How do I follow the progress of an SCF convergence?

For Gaussian to print SCF convergence information, it is necessary to specify additional print, by using #P in the route section.

Using GaussSum, open the file you want to examine. Usually this will be the output file of a job which is currently running.

Choose Monitor SCF and click on the GaussSum logo to run the script. A graph will be plotted of the progress of the SCF convergence versus step number. Details of the SCF cycles are written to the screen. Where there is information on several SCF convergence cycles in a log file (e.g. in the case of a geometry optimisation), GaussSum plots the one closest to the end of the file.

The progress of the SCF convergence is calculated by considering the convergence criteria. For example, for Gaussian, these criteria are the RMS density, MAX density and change in energy. Each of the current values is divided by the target values. The log is taken of any results greater than 1. The 'progress value' is then calculated by summing the logs. Note that convergence is achieved when the progress value equals 0.

You may wish to leave out the initial points, so as to focus on the last few points. You can do this by entering a non-zero value into the box labeled "Leave out the first n points", and re-running the script.

Note that, at the moment, this procedure does not work for SCF=QC (Gaussian).

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Chapter 2. How do I find all lines with a certain phrase? Home Chapter 4. How do I follow the progress of a geometry optimisation?
GaussSum-3.0.1/Docs/ch07s02.html0000664000175000017500000000620512660404041014604 0ustar noelnoel The circular dichroism spectrum
The circular dichroism spectrum
Prev Chapter 7. How do I get the UV-Vis or circular dichroism spectrum of a molecule? Next

The circular dichroism spectrum

Open a log file that is the result of a TD-DFT calculation. For best results, a Gaussian TD-DFT calculation should include the following keyword "IOP(9/40=2)".

From the list of operations on the left, choose Electronic transitions.

Choose the start and end (in nm) of the convoluted spectrum, as well as the number of points you wish to have in the calculated spectrum. Sigma refers to the width of the band at 1/e height for the gaussian curves used to convolute the spectrum. The units for sigma are eV. (The equation used is Equation 8 from Stephens and Harada, Chirality, 2010, 22, 229-233. Delta in that equation is Sigma/2.)

After you have set the various parameters, click on the GaussSum logo to convolute the spectrum. The details are written to gausssum3.0/CDSpectrum.txt.

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Chapter 7. How do I get the UV-Vis or circular dichroism spectrum of a molecule? Home The electron density difference map (EDDM) [Gaussian only]
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Chapter 5. How do I get the IR or Raman spectrum of a molecule?
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Chapter 5. How do I get the IR or Raman spectrum of a molecule?

(Gaussian,GAMESS)

Next, open a log file containing the results of a freq calculation. It isn't necessary to specify whether you wish to calculate the IR or the Raman spectrum - the IR spectrum will always be calculated, and if you ran a freq=raman job, then the Raman activity and Raman intensity spectra will be calculated.

Choose Frequencies from the list of operations on the left.

Parameters for Frequencies

Start, End

The spectra will be calculated for wavelengths between Start and End. The units of Start and End are cm-1.

Num pts

This parameter determines the number of points in the calculated spectra.

FWHM

The Full Width at Half Maximum of each peak.

Scaling factor

You can choose either a general or individual scaling factor (see below).The calculated frequencies are multiplied by the scaling factor. The scaled frequencies are then used to generate the spectra.

Exc. wavelength (nm)

(Only available for Raman) The value of the excitation wavelength is used to calculate the Raman intensities from the Raman activity (see below) using the equation described by Krishnakumar et al. (J. Mol. Struct., 2004, 702, 9) and Keresztury et al. (Spectrochimica Acta, 1993, 49A, 2007).

Temp. (K)

(Only available for Raman) This value determines the temperature used in the Raman intensity equation (see above).

Click on the GaussSum icon to run the script.

The spectra are convoluted with Lorentzian curves and then plotted with Gnuplot.

Information on the each spectrum and on the normal modes are written to gausssum3.0/IRSpectrum.txt and gausssum3.0/RamanSpectrum.txt.

The first few lines of an example IRSpectrum.txt are shown below. Tabs are used to separate each column of information. This allows easy import into spreadsheet software (e.g. Excel), just by right-clicking on the file and choosing "Open with". 

Spectrum			Normal Modes
Freq (cm-1)	IR act		Mode	Label	Freq (cm-1)	IR act	Scaling factors	Unscaled freq
8	0.000612391353264		1	A	466.3941	0.0	1.0	466.3941
16	0.000624493504806		2	A	466.3945	0.0	1.0	466.3945
24	0.000636968752613		3	A	698.2427	0.0	1.0	698.2427
32	0.000649832766662		4	A	698.2429	0.0	1.0	698.2429

If you want to use individual scaling factors, you should open a previously created IRSpectrum.txt or RamanSpectrum.txt and edit the column titled 'Scaling Factors'. You can do this in (for example) Excel, and then save as 'Tab Delimited'. Run the Frequencies option again but choose individual scaling factors this time. The new IRSpectrum.txt or RamanSpectrum.txt will contain the scaled frequencies.

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Chapter 4. How do I follow the progress of a geometry optimisation? Home Chapter 6. How do I extract molecular orbital information?
GaussSum-3.0.1/Docs/ch08.html0000664000175000017500000000712412660404041014261 0ustar noelnoel Chapter 8. How can I automate spectra generation for multiple files? [Advanced]
Chapter 8. How can I automate spectra generation for multiple files? [Advanced]
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Chapter 8. How can I automate spectra generation for multiple files? [Advanced]

Table of Contents

Generate UV-Vis spectra for multiple files

If you have a large number of log files, you may find it slow to use GaussSum to generate spectra for each file one-by-one. In order to speed things up, it is possible to write a Python script to automate this process. The following sections give example Python scripts that you could use. You should try to understand these before adapting them for your own purposes.

Generate UV-Vis spectra for multiple files

This example script generates a UV-Vis spectrum for each ".out" file in the current directory. Before running, you should set the PYTHONPATH to the directory containing GaussSum.py and make sure that there is no existing GaussSum output folder present. 

import os
import sys
import glob
import logging

from gausssum.electrontrans import ET
from gausssum.cclib.parser import ccopen

ver = "3.0"
def gaussdir(filename):
    return os.path.join(os.path.dirname(filename), "gausssum%s" % ver)

if __name__ == "__main__":
    start, end = 200, 500
    numpts, fwhm = 500, 3000

    filenames = glob.glob("*.out")
    for filename in filenames:
        log = ccopen(filename)
        log.logger.setLevel(logging.ERROR)
        data = log.parse(filename)

        ET(None, sys.stdout, data, filename, start, end, numpts, fwhm, True,
           False)
        os.rename(gaussdir(filename), filename.split(".")[0] +"_gs")
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The electron density difference map (EDDM) [Gaussian only] Home 
GaussSum-3.0.1/Docs/ch06s02.html0000664000175000017500000001503512660404041014604 0ustar noelnoel How to find the % contribution of a group to each molecular orbital (PDOS)
How to find the % contribution of a group to each molecular orbital (PDOS)
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How to find the % contribution of a group to each molecular orbital (PDOS)

In order to define the atoms which comprise a group, a file, gausssum3.0/Groups.txt, should be created with a format similar to the following: 

atoms
Ru
1
bpy1
2-11,14,17,22,27,34-35,42-43,50,57
bpy2
12,16,18-19,25-26,28-30,39-41,44-46,54-56,58,61
bpy3
13,15,20-21,23-24,31-33,36-38,47-49,51-53,59-60

The first line needs to be either "atoms", "orbitals", "allatoms" or "allorbitals". If it is "allatoms" or "allorbitals", then no further input is required and a separate group will be made for each atom or each orbital. Otherwise, as in the example above, you need to describe which atoms or which orbitals are in each group. The numbers correspond to the order of the atoms/orbitals in the output file. An easy way to obtain these for Gaussian calculations is to open the output file in GaussView and turn on the labels. Groups.txt needs to obey the following rules:

  • Every atom in the molecule must be listed

  • No atom may be listed more than once

A single point calculation should be done with the following keywords: (Gaussian) pop=full iop(3/33=1,3/36=-1), (GAMESS) NPRINT=3. This creates a large log file containing information on the overlap matrix among other things. (Note: the 3/36=-1 option for Gaussian prevents the calculation and printing of the multipole matrices; this is purely to keep the output file size as small as possible. In some cases, for example SCRF calculations, the multipole matrices must be calculated - if so, leave out the 3/36=-1. It will not affect the calculation of the PDOS.)

Using GaussSum open the log file and choose Orbitals. Pick the DOS option. See the previous section for information on the options.

Click on the GaussSum logo. GaussSum calculates the percent contributions of each of the groups to each of the molecular orbitals. This may take a few minutes.

Afterwards, the partial density of states spectra (PDOS) are plotted. Note that each one is stacked on top of the previous one, which means that the line at the greatest height is equal to the sum of all of the partial density of states, and hence equal to the total density of states spectrum. The stacking order is undefined. Information on the spectra is written to gausssum3.0/DOS_spectrum.txt which can be used to plot your own graphs.

Information on the molecular orbitals and the percent contributions of the groups is written to gausssum3.0/orbital_data.txt. The last few columns of orbital_data.txt contain more accurate values for the percent contributions and are used by the Electronic transitions operation. You should not edit this file if you wish to use the information in it to calculate the changes in charge density associated with electronic transitions, as described in Chapter 7, How do I get the UV-Vis or circular dichroism spectrum of a molecule?.

Note that the percent contributions are calculated based on Mulliken Population Analysis (MPA). MPA has some well-known deficiencies which can lead to unphysical values such as negative percentage contributions. If this happens for an orbital in which you are interested, remember that the exact figures are less important than the trend across a group of compounds.

Creation of PDOS spectra is also supported for unrestricted calculations with Gaussian. The spectrum plotted is of the total DOS broken down by the contribution of each of the groups. orbital_data.txt contains information on the breakdown of the alpha and beta electrons by group.

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Chapter 6. How do I extract molecular orbital information? Home How to find the nature of the overlap between different groups of atoms (COOP)
GaussSum-3.0.1/Docs/ch07.html0000664000175000017500000001103512660404041014254 0ustar noelnoel Chapter 7. How do I get the UV-Vis or circular dichroism spectrum of a molecule?
Chapter 7. How do I get the UV-Vis or circular dichroism spectrum of a molecule?
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Chapter 7. How do I get the UV-Vis or circular dichroism spectrum of a molecule?

Table of Contents

The UV-visible spectrum
The circular dichroism spectrum
The electron density difference map (EDDM) [Gaussian only]

The UV-visible spectrum

Open a log file that is the result of a TD-DFT calculation. For best results, a Gaussian TD-DFT calculation should include the following keyword "IOP(9/40=2)".

Choose the Electronic transitions from the list of operations on the left.

Choose the start and end (in nm) of the convoluted spectrum, as well as the number of points you wish to have in the calculated spectrum. FWHM refers to the full width at half-maximum of the gaussian curves used to convolute the spectrum. FWHM should be entered in cm-1.

After you have set the various parameters, click on the GaussSum logo to run convolute the spectrum.

The details are written to gausssum3.0/UVSpectrum.txt and gausssum3.0/UVData.txt. The file UVData.txt contains information on the contribution of singly-excited configurations to each electronic transition.

If there is a file in the gausssum3.0 directory called orbital_data.txt containing information on the percent contributions of various groups (e.g. ligands and metal centers) to the various molecular orbitals, GaussSum will use that data. It will calculate, for each transition, the change in charge density on each group. This information will be added to gausssum3.0/UVData.txt. (For more information, please see "How to find the % contribution of a group to each molecular orbital".)

For information on the formula used to convolute the UV-Vis spectrum, please see this pdf.

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How to find the nature of the overlap between different groups of atoms (COOP) Home The circular dichroism spectrum
GaussSum-3.0.1/Docs/index.html0000664000175000017500000001023212660404041014620 0ustar noelnoel GaussSum Version 3.0
GaussSum Version 3.0
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GaussSum Version 3.0

Table of Contents

Introduction
Citation
Acknowledgments
1. Installation
Installing an all-in-one bundle on Windows
Installing the GaussSum scripts on Windows
Installing on Linux
2. How do I find all lines with a certain phrase?
3. How do I follow the progress of an SCF convergence?
4. How do I follow the progress of a geometry optimisation?
5. How do I get the IR or Raman spectrum of a molecule?
6. How do I extract molecular orbital information?
How to extract basic information (DOS)
How to find the % contribution of a group to each molecular orbital (PDOS)
How to find the nature of the overlap between different groups of atoms (COOP)
7. How do I get the UV-Vis or circular dichroism spectrum of a molecule?
The UV-visible spectrum
The circular dichroism spectrum
The electron density difference map (EDDM) [Gaussian only]
8. How can I automate spectra generation for multiple files? [Advanced]
Generate UV-Vis spectra for multiple files
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    Introduction
GaussSum-3.0.1/Docs/pr01.html0000664000175000017500000002212412660404041014276 0ustar noelnoel Introduction
Introduction
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Introduction

GaussSum parses the output files of ADF, Dalton, Firefly, GAMESS, GAMESS-UK, Gaussian, Jaguar, Molpro, NWChem, ORCA, Psi and QChem calculations to extract useful information.

It is written by Noel O'Boyle and is available for free under the GNU Public License. For up-to-date information, please see http://gausssum.sf.net.

GaussSum version 3.0 can do the following:

  • display all lines containing a certain phrase

  • follow the progress of the SCF convergence

  • follow the progress of a geometry optimisation

  • extract molecular orbital information, including contributions of groups of atoms to the molecular orbitals

  • plot the density of states spectrum (and the partial density of states, in the case of groups of atoms)

  • plot the crystal orbital overlap population (COOP) spectrum, which gives information on the bonding/anti-bonding nature of an overlap between atoms/groups

  • extract information on the UV-Vis transitions, including the change in the charge density of groups of atoms

  • plot the UV-Vis spectrum and the circular dichroism spectrum

  • extract information on IR and Raman vibrations

  • plot the IR and Raman spectra, which may be scaled using general or individual scaling factors

  • Handle compressed log files (.zip, .gz, .bz2) as easily as regular log files.

Release Notes for Version 3.0.1:

  • Update in parsing library (to cclib 1.3.1+) adding support for several additional computational chemistry packages

  • Avoid use of deprecated "oldnumeric" numpy module (Clyde Fare, Erlendur Jónsson)

  • Correct name of output folder to gausssum3 (Angelo Rossi)

  • Correct the size of the About box (Daniel Liedert)

Release Notes for Version 3.0:

  • This is the first version of GaussSum that uses Python 3 instead of Python 2

  • Use Matplotlib instead of Gnuplot

  • Update in parsing library (to cclib r1064)

Release Notes for Version 2.2.6.1: Bugfix for 2.2.6. Gnuplot location undefined for new users of GaussSum (Guillaume Lamoureux).

Release Notes for Version 2.2.6: A patch from Thomas Pijper was integrated to enable calculation of Raman intensities (from Raman activity). Support has been added for calculating charge density changes for unrestricted calculations (requested by Phil Schauer). Blank lines in Groups.txt are now ignored.

Release Notes for Version 2.2.5: Parser updated to cclib r923 (Ben Stein, Marius Retegan). Removed minus signs in output for TD-DFT (previously these indicated the sign of the contribution). Corrected equation used for circular dichroism to match that from Stephens and Harada (see circular dichroism docs for ref). Thanks to Li-She Gan for identifying this problem and pointing me to this paper.

Release Notes for Version 2.2.4: Parser updated to cclib r912 (Tiago Silva).

Release Notes for Version 2.2.3: Fix serious bug with EDDM (Carlo Nervi, Carlos Silva Lopez). Parser updated to cclib r884 (Mahesh Kumar, Dan Matusek).

Release Notes for Version 2.2.2: Fix problems with Linux version (Daniel Liedert). Minor doc fixes (Irena Efremenko).

Release Notes for Version 2.2.1: Fix bug in importing cclib (Daniel Liedert). Parser updated to handle Gaussian09 CD output (Rino Pescitelli).

Release Notes for Version 2.2.0:

  • Update in parsing library (to cclib r877)

  • Added support for creating EDDM maps with Gaussian

Release Notes for Version 2.1.6: Can now handle unrestricted TD-DFT calculations correctly (previously there were errors in the contribution descriptions in UVData.txt).

Release Notes for Version 2.1.5: Updated cclib to cclib 0.9+ (r840). Fixed problem reparsing GeoOpts. Fixed problem with exe on Windows with default Gnuplot and Docs locations.

Release Notes for Version 2.1.4: Bugfixes for UVData.txt which had incorrect % contributions of singly-excited configurations and incorrect changes in electron density of groups.

Release Notes for Version 2.1.3: Bugfixes for incorrect data for energies in CDSpectrum.txt and incorrect units in heading in UVData.txt. Updated cclib to cclib 0.8 beta

Release Notes for Version 2.1.2: Bugfix for off-by-one error in orbital names in major and minor contributions in UVData.txt.

Release Notes for Version 2.1.1: Updated the code to save results to gausssum2.1 subfolder, along with some minor documentation fixes

Release Notes for Version 2.1.0:

  • Major update in parsing library (upgraded from cclib 0.6.1 to cclib 0.8dev)

  • Jaguar log files now supported

  • Compressed log files (.zip, .gz., .bz2) supported

  • Underlying code now uses Numpy for numerical calculation, rather than the deprecated Numeric

  • Fixed error in the output of DOS_spectrum.txt where the 'Total' column was equal to the values for the first group

  • Fixed problem plotting the COOP

Release Notes for Version 2.0.6: Plotting the PDOS was failing due to 'type' issues with Numeric arrays (due to changes in cclib 0.6.1).

Release Notes for Version 2.0.5: Parsing was failing for Gaussian files with "pop=regular".

Release Notes for Version 2.0.4: Plotting vibrational frequencies now works for GAMESS calculations that have imaginary frequencies (bug fix in cclib).

Release Notes for Version 2.0.3: Plotting vibrational frequencies now works for frequencies that don't have symmetry labels.

Release Notes for Version 2.0.2: COOP now works for unrestricted calculations.

Release Notes for Version 2.0: main differences compared to GaussSum 1.0

  • This is the first release of GaussSum that uses cclib to parse output files.

  • Calculation of the DOS and COOP uses matrix algebra now, and is almost instant.

  • Images can now be saved as .png files.

  • Groups can be described in terms of atomic orbitals now (and not just atoms).

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GaussSum Version 3.0 Home  Citation
GaussSum-3.0.1/Docs/ch06.html0000664000175000017500000001153712660404041014262 0ustar noelnoel Chapter 6. How do I extract molecular orbital information?
Chapter 6. How do I extract molecular orbital information?
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Chapter 6. How do I extract molecular orbital information?

Table of Contents

How to extract basic information (DOS)
How to find the % contribution of a group to each molecular orbital (PDOS)
How to find the nature of the overlap between different groups of atoms (COOP)

This chapter has been divided into three sections. The first section describes how to extract information on the eigenvalues and symmetries of the molecular orbitals. The second section describes how to extract information on the contributions of groups of atoms to each of the molecular orbitals. The third section describes how to extract information on the nature of the overlap between different groups of atoms.

How to extract basic information (DOS)

Open the log file containing the relevant information and choose Orbitals from the list of operations on the left. Pick the DOS option.

The boxes labeled "Start" and "End" are the start and end points (in eV) of the density of states spectrum. The box labeled "FWHM" is the full width at half-maximum (in eV) of the gaussian curves used to convolute the DOS and COOP spectra.

Click on the GaussSum logo to start the script. The molecular orbital information is written to gausssum3.0/orbital_data.txt.

The density of states (DOS) spectrum is convoluted using Gaussian curves of unit height and the specified full width at half-maximum. Gnuplot is then used to plot the spectrum. The details are written to gausssum3.0/DOS_spectrum.txt.

If you tick the box labelled "Create originorbs.txt?", a file gausssum3.0/orginorbs.txt will be created which can be used to plot the orbital energies as a series of bars, one above the other, using a program such as Origin (Windows) or Grace (Linux). See the worked example on the GaussSum web site for more information.

Unrestricted calculations are supported for Gaussian. The same files are created but with the data broken down into sections for alpha and beta electrons. The DOS spectrum plotted is also different, containing an alpha DOS, a beta DOS and a (scaled) total DOS. If you ticked the box to create originorbs.txt, the beta eigenvalues are listed after all of the alpha eigenvalues.

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Chapter 5. How do I get the IR or Raman spectrum of a molecule? Home How to find the % contribution of a group to each molecular orbital (PDOS)
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Installing on Linux
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Installing on Linux

Install Gnuplot

Check to see if you already have Gnuplot, by typing 'which gnuplot' in a shell window. If Gnuplot is installed, you need to make sure that it is version 4.0 or newer ('gnuplot --version').

If you need to install Gnuplot, rpms are available from rpmfind.net and gzipped tar files from SourceForge. If you are using Debian Linux, Gnuplot will be downloaded and installed if you issue the following command as root: 'apt-get install gnuplot'.

Install Python

Check to see if you already have Python, by typing 'which python3' in a shell window.

If you have Python, type 'python3'. The version number should be displayed. If this is less than 3.1, the scripts may not work with your currently installed Python version. (Press 'CTRL+D, ENTER' to exit.)

If you need to install Python, follow the appropriate instructions for your system at www.python.org. You should install the latest version of Python 3.3 (although Python 3.1 and 3.2 also work fine with GaussSum). If you are using Debian Linux, Python will be downloaded and installed if you issue the following command as root: 'apt-get install python3 python3-tk'.

Install Numpy

Numpy is an extension to Python that allows efficient matrix manipulation. You should try to find a binary package for your system rather than compiling it yourself (check your package management system). If you are using Debian Linux, Numpy will be downloaded and installed if you issue the following command as root: 'apt-get install python3-numpy'.

Install Matplotlib

Matplotlib is a Python package for drawing graphs. It should be available through your package manager. You should install the version for Python3. If it is not available, you should download and build it from source. Instructions are available from the Matplotlib website.

Download GaussSum

Download a gzipped tar of GaussSum here. This should be untarred into a folder in the usual way, using 'tar zxvf GaussSum-3.0.1.tar.gz'.

If you wish to be added to the mailing list for information on new versions and bug fixes, please send an email to gausssum-help@lists.sourceforge.net.

Start GaussSum

To run GaussSum, change directory to the GaussSum directory and type 'python GaussSum.py'. You can also pass a filename as an argument: for example, 'python GaussSum.py myoutputfile.out'.

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Installing the GaussSum scripts on Windows Home Chapter 2. How do I find all lines with a certain phrase?
GaussSum-3.0.1/Docs/ch06s03.html0000664000175000017500000000742712660404041014613 0ustar noelnoel How to find the nature of the overlap between different groups of atoms (COOP)
How to find the nature of the overlap between different groups of atoms (COOP)
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How to find the nature of the overlap between different groups of atoms (COOP)

Crystal Orbital Overlap Population (COOP) diagrams were first developed by Huckel. They are especially used in the area of extended solids. They are equivalent to a multiplication of the DOS spectrum and the overlap population (positive or negative) between two groups. In this way, the bond between two groups can be described as bonding or antibonding with respect to each of the molecular orbitals.

Exactly the same information as for creating a PDOS diagram is needed for creating a COOP diagram. That is, you need a log file which is the result of a single-point calculation containing the same keywords as described in "How to find the % contribution of a group to each molecular orbital". You also need a file Groups.txt describing the groups of interest in the molecule (see "How to find the % contribution of a group to each molecular orbital"). The rules for Groups.txt are not as strict in this case: no atom can be listed twice, but not every atom needs to be listed.

Choose COOP after picking Orbitals. The options for creating a COOP diagram are the same as for creating a DOS or PDOS diagram.

Click on the GaussSum logo to calculate the COOP. Information on the overlap is written to gausssum3.0/COOP_data.txt and gausssum3.0/COOP_spectrum.txt.

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How to find the % contribution of a group to each molecular orbital (PDOS) Home Chapter 7. How do I get the UV-Vis or circular dichroism spectrum of a molecule?
GaussSum-3.0.1/Docs/pr03.html0000664000175000017500000001430512660404041014302 0ustar noelnoel Acknowledgments
Acknowledgments
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Acknowledgments

A number of people provided helpful feedback during the development of GaussSum. The following names are listed in no particular order:

  • shaloncai reported an error in the data written in UVData.txt.

  • Rudy Coquet reported an error in the data written in CDSpectrum.txt.

  • Fabrizia Fabrizi de Biani reported an error in the labels used in UVData.txt.

  • Tong Glenna reported an error in the major and minor contributions in UVData.txt.

  • Christos Garoufalis reported a problem parsing PC-GAMESS files with large basis sets.

  • Emmanuel Koukaras reported a bug in the output of DOS_spectrum.txt, as well as a problem plotting the COOP.

  • Xinyu Huang reported a bug plotting the PDOS.

  • Yafei Dai reported problems parsing Gaussian files with "pop=regular".

  • Charles Bradshaw reported problems plotting the vibrational frequencies for GAMESS calculations.

  • Juan Sotelo-Campos pointed out that the COOP was only being calculated for alpha orbitals in unrestricted calculations.

  • Dr. Carlo Nervi, Torino, who found a bug in EDDM.py

  • James Hepburn, Aberdeen, who found that Hyperchem code had been neglected

  • Prof Ziyang Liu, Zhejiang University, P.R.China, who found a bug in the output of MO.py for unrestricted calculations

  • Fred Coughlin, Princeton, U.S., who found a couple of bugs in my code for CD spectra

  • Li Daobing and Jordan Mantha who have packaged GaussSum up for Debian and Ubuntu users

  • Neil Berry, Liverpool, who helped to increase GaussSum support for GAMESS files

  • Julien Chiron, Faculté des Sciences de Saint Jérôme, Marseille, France helped me add support for GAMESS and is perhaps the first Mac user of GaussSum (see screenshots)

  • Avril Coghlan, formerly of Trinity College Dublin, Ireland, thought of the name (it rhymes with awesome!) and suggested various web page improvements

  • Elmar Gerwalin, University of Kaiserslautern, Germany, who helped me enormously in getting GaussSum (a) to run in Linux and (b) to work for other people's calculations

  • Denis G. Golovanov, Russian Academy of Sciences, Moscow and his non-symmetry-containing logfile provided another challenge to GaussSum

  • Guillaume Herlam, Université de Franche-Comté, Besançon, France who requested and tested COOP diagrams

  • Roma Oakes, Queens University Belfast, made many useful comments regarding the initial development, especially regarding IR_Raman.py

  • Ullrich Siehl, University of Ulm, Germany, who made a number of suggestions that lead to the creation of version 0.9.

In order to create GaussSum, I had to learn a few things. I found the following resources very helpful:

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Citation  Home Chapter 1. Installation
GaussSum-3.0.1/Docs/ch04.html0000664000175000017500000000605412660404041014256 0ustar noelnoel Chapter 4. How do I follow the progress of a geometry optimisation?
Chapter 4. How do I follow the progress of a geometry optimisation?
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Chapter 4. How do I follow the progress of a geometry optimisation?

Using GaussSum open the file you want to examine. Usually this will be the output file of a job which is currently running.

Choose Monitor GeoOpt and click on the GaussSum logo to run the script.

Two graphs will be plotted. The first is of energy vs optimisation step. The second is the deviation from the targets vs optimisation step.

The deviation is found by considering the convergence criteria. Each of the current values is divided by the target values. The log is taken of any results greater than 1. The deviation is then calculated by summing the logs. Note that convergence is achieved when the progress value equals 0.

You may wish to leave out the initial points, so as to focus on the last few points. You can do this by entering a non-zero value into the box labeled "Leave out the first n points", and re-running the script.

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Chapter 3. How do I follow the progress of an SCF convergence? Home Chapter 5. How do I get the IR or Raman spectrum of a molecule?
GaussSum-3.0.1/Docs/ch02.html0000664000175000017500000000516312660404041014254 0ustar noelnoel Chapter 2. How do I find all lines with a certain phrase?
Chapter 2. How do I find all lines with a certain phrase?
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Chapter 2. How do I find all lines with a certain phrase?

Open the file you wish to search and choose Find from the list of operations on the left.

Either choose one of the default search terms or choose Custom. If you choose Custom, enter a search term into the box. Note: you can change the default search terms using the Settings dialog box.

Click on the GaussSum logo to start the script.

If you wish to find all lines containing either TERM_A or TERM_B, then use the following notation, TERM_A%TERM_B, for the search term. Any number of terms may be combined in this way.

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Installing on Linux Home Chapter 3. How do I follow the progress of an SCF convergence?
GaussSum-3.0.1/Docs/ch07s03.html0000664000175000017500000000665012660404041014611 0ustar noelnoel The electron density difference map (EDDM) [Gaussian only]
The electron density difference map (EDDM) [Gaussian only]
Prev Chapter 7. How do I get the UV-Vis or circular dichroism spectrum of a molecule? Next

The electron density difference map (EDDM) [Gaussian only]

An EDDM is a representation of the changes in electron density that occur for a given electronic transition. It is calculated using the information on the single-excited configurations that contribute to each transition. The relative contribution is based on the square of the configuration's coefficient. Note that for some programs (e.g. Gaussian) these squares are not guaranteed to sum to 1.0 and so should not be regarded as scientifically accurate. However, they should be sufficient for the purposes of generating a diagram.

After you have plotted the UV-Vis or circular dichroism spectrum, copy a checkpoint file (or formatted checkpoint file) into the gausssum3.0 directory. Then select "Electronic transitions" and choose the "Create EDDM script?" option. Click on the GaussSum logo to convolute the spectrum and generate gausssum3.0/eddm.bat (gausssum3.0/eddm.sh on Linux).

Next you need to set the environment variable G03DIR to the directory containing the Gaussian binaries. To generate the EDDM, at a command prompt run "eddm.bat N", where N is the transition number (starting from 1 for the lowest energy transition).

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The circular dichroism spectrum Home Chapter 8. How can I automate spectra generation for multiple files? [Advanced]
GaussSum-3.0.1/GaussSum.ico0000664000175000017500000002267612660404041014215 0ustar noelnoel00 %(0` %! !!! !!! !!! !!! !!! !!! !!! !!! !! !!!!!!!!!!!!!!!!!!!!!!!!!!!!! !!! !!! !!! !!! !!! !!! !!! !!! !!! !!! !!kikRUR 1RQZ!!km{{  1! !! 9Z]U)(9{189   )0RE{mmQ1uRQZ{{{sy{s}{s}{s}RYZ  49Mc]{U{UUaueJ}{yJIR!(1) ces{}{}sysususu{sy{s}{s}{sskqs  ,1iki]Y{]}}qaIs!(9)! 1km{sususususqsq{sy{s}{sy{ky{ky!UZ!i{]s]{emem]4Z9ARsqsususqsukq{kq{Zy{J1!!!!u{ek]ke{e{Y{U{]M$J JMR!R]kcm{RisBes1ek!akikqs!!!qsecYc]ke{e{Y{Q{Q{ BJMZJMR ! )()0189 # # This program is free software; you can redistribute and/or modify it # under the terms of the GNU General Public License as published by the # Free Software Foundation; either version 2, or (at your option) any later # version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY, without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. import os from tkinter import * # Matplotlib imports import matplotlib matplotlib.use('TkAgg') from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg from matplotlib.backend_bases import key_press_handler from matplotlib.figure import Figure class DisplayPlot(object): def __init__(self, root, g, title): self.popup = Toplevel(root) self.popup.focus_set() self.popup.title(title) self.title = title # do this better self.popup.resizable(False,False) self.frame1 = Frame(self.popup) self.frame1.pack() canvas = FigureCanvasTkAgg(g.figure, master=self.frame1) canvas.show() canvas.get_tk_widget().pack(side=TOP, fill=BOTH, expand=1) toolbar = NavigationToolbar2TkAgg( canvas, self.frame1 ) toolbar.update() canvas._tkcanvas.pack(side=TOP, fill=BOTH, expand=1) frame2 = Frame(self.frame1) frame2.pack(side=TOP) def on_key_event(event): key_press_handler(event, canvas, toolbar) def close(event=None): self.popup.destroy() Button(frame2, text="Close", underline=0, command=close).pack(side=LEFT) canvas.mpl_connect('key_press_event', on_key_event) self.popup.bind("",close) self.popup.bind("",close) self.popup.bind("",close) self.popup.protocol("WM_DELETE_WINDOW", close) GaussSum-3.0.1/gausssum/vibfreq.py0000664000175000017500000001514712660404041015624 0ustar noelnoel# # GaussSum (http://gausssum.sf.net) # Copyright (C) 2006-2013 Noel O'Boyle # # This program is free software; you can redistribute and/or modify it # under the terms of the GNU General Public License as published by the # Free Software Foundation; either version 2, or (at your option) any later # version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY, without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. import os import math import gausssum.utils from .plot import DisplayPlot from .mpl import MPLPlot from .folder import folder def activity_to_intensity(activity, frequency, excitation, temperature): """Convert Raman acitivity to Raman intensity according to Krishnakumar et al, J. Mol. Struct., 2004, 702, 9.""" excitecm = 1 / (1e-7 * excitation) f = 1e-13 above = f * (excitecm - frequency)**4 * activity exponential = -6.626068e-34 * 299792458 * frequency / (1.3806503e-23 * temperature) below = frequency * (1 - math.exp(exponential)) return above / below def get_scaling_factors(filename, scale): """Read in scaling factors from an existing output file. Note: Scale is prepopulated with the general scaling factor """ inputfile = open(filename, "r") line = inputfile.readline() line = inputfile.readline() i = 0 line = inputfile.readline().split('\t') while len(line) > 6: # Read in the individual scaling factors sf = line[-2] if sf != '': scale[i] = float(sf) i += 1 line = inputfile.readline().split('\t') inputfile.close() return scale def Vibfreq(root,screen,logfile,logfilename,start,end,numpts,FWHM,typeofscale,scalefactor,excitation,temperature): def dofreqs(name,act): screen.write("\n************* Doing the "+name+" *****************\n") filename=name+"Spectrum.txt" freq = logfile.vibfreqs.copy() # Copy so that it won't be changed by the routine if hasattr(logfile, "vibsyms"): vibsyms = logfile.vibsyms else: vibsyms = ['?'] * len(freq) # Handle the scaling of the frequencies scale = [scalefactor] * len(freq) if typeofscale == "Gen": screen.write("Going to scale with a scaling factor of "+str(scalefactor)+"\n") general = True else: screen.write("Going to use individual scaling factors\n") screen.write("Looking for scaling factors in "+filename+"...") inputfilename = os.path.join(gaussdir,filename) if not os.path.isfile(inputfilename): screen.write("not found\nGoing to use general scale factor of "+str(scalefactor)+" instead\n") general = True else: screen.write("found\n") general = False get_scaling_factors(inputfilename, scale) # Update scaling factors in 'scale' from file for i in range(len(freq)): # Scale the freqs freq[i] = freq[i]*scale[i] # Convolute the spectrum spectrum = gausssum.utils.Spectrum(start,end,numpts, [list(zip(freq,act))], FWHM,gausssum.utils.lorentzian) if name == "Raman": intensity = [activity_to_intensity(activity, frequency, excitation, temperature) for activity, frequency in zip(act, freq)] spectrum_intensity = gausssum.utils.Spectrum(start,end,numpts, [list(zip(freq, intensity))], FWHM,gausssum.utils.lorentzian) outputfile = open(os.path.join(gaussdir,filename),"w") screen.write("Writing scaled spectrum to "+filename+"\n") outputfile.write("Spectrum\t%s\t\tNormal Modes\n" % ["", "\t"][name=="Raman"]) outputfile.write("Freq (cm-1)\t%s act\t%s\tMode\tLabel\tFreq (cm-1)\t%s act\t" % (name, ["", "Intensity\t"][name=="Raman"],name)) outputfile.write("%sScaling factors\tUnscaled freq\n" % ["", "Intensity\t"][name=="Raman"]) width = end-start for x in range(0,numpts): if spectrum.spectrum[x,0]<1e-20: spectrum.spectrum[x,0] = 0. realx = width*(x+1)/numpts+start outputfile.write(str(realx)+"\t"+str(spectrum.spectrum[x,0])) if name == "Raman": outputfile.write("\t%f" % spectrum_intensity.spectrum[x,0]) if x # # This program is free software; you can redistribute and/or modify it # under the terms of the GNU General Public License as published by the # Free Software Foundation; either version 2, or (at your option) any later # version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY, without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. from tkinter import * # GUI stuff import tkinter.messagebox # For the About Dialog import tkinter.simpledialog import os class PreferencesPopupBox(simpledialog.Dialog): def __init__(self, parent, settings, title = None): # Override (just to set the geometry!) Toplevel.__init__(self, parent) self.transient(parent) self.settings=settings # Note that changes to self.settings will affect the global self.settings thru 'settings' self.oldsettings = settings.copy() # Remember the current settings if title: self.title(title) self.parent = parent self.result = None body = Frame(self) self.initial_focus = self.body(body) body.pack(padx=5, pady=5) self.buttonbox() if not self.initial_focus: self.initial_focus = self self.protocol("WM_DELETE_WINDOW", self.cancel) self.initial_focus.focus_set() self.wait_window(self) def body(self,master): # Override # The content of the settings dialog box # Creation of the main sections self.resizable(False,False) self.frame1=Frame(master,relief=SUNKEN,borderwidth=2) self.frame1.pack() Label(self.frame1,text="Search file").pack() self.frame3=Frame(self.frame1) self.frame3.pack() Label(self.frame1,text="").pack() Label(self.frame1,text="Frequencies").pack() self.frame4=Frame(self.frame1) self.frame4.pack() Label(self.frame1,text="").pack() Label(self.frame1,text="Orbitals").pack() self.frame5=Frame(self.frame1) self.frame5.pack() Label(self.frame1,text="").pack() Label(self.frame1,text="Electronic transitions").pack() self.frame6=Frame(self.frame1) self.frame6.pack() Label(self.frame1,text="").pack() # The Find.py section Label(self.frame3,text="Search for:").grid(row=0,column=0) self.find=[None]*4 self.find[0]=Entry(self.frame3,width=15) self.find[1]=Entry(self.frame3,width=15) self.find[2]=Entry(self.frame3,width=15) self.find[3]=Entry(self.frame3,width=15) self.find[0].grid(row=0,column=1) self.find[1].grid(row=1,column=1) self.find[2].grid(row=0,column=2) self.find[3].grid(row=1,column=2) for i in range(4): self.find[i].delete(0,END) self.find[i].insert(0,self.settings[ 'find.text%d'%(i+1) ]) # The IR_Raman.py section self.frame4a = Frame(self.frame4) self.frame4a.pack() self.irraman=[None]*6 Label(self.frame4a,text="Start:").grid(row=0,column=0) self.irraman[0]=Entry(self.frame4a,width=5) self.irraman[0].grid(row=0,column=1) Label(self.frame4a,text="End:").grid(row=0,column=2) self.irraman[1]=Entry(self.frame4a,width=5) self.irraman[1].grid(row=0,column=3) Label(self.frame4a,text="Num pts:").grid(row=0,column=4) self.irraman[2]=Entry(self.frame4a,width=5) self.irraman[2].grid(row=0,column=5) Label(self.frame4a,text="FWHM:").grid(row=0,column=6) self.irraman[3]=Entry(self.frame4a,width=5) self.irraman[3].grid(row=0,column=7) self.frame4b = Frame(self.frame4) self.frame4b.pack() Label(self.frame4b,text="Exc. wavelength:").grid(row=0,column=0) self.irraman[4]=Entry(self.frame4b,width=5) self.irraman[4].grid(row=0,column=1) Label(self.frame4b,text="Temp:").grid(row=0,column=2) self.irraman[5]=Entry(self.frame4b,width=6) self.irraman[5].grid(row=0,column=3) a=['start','end','numpoints','fwhm','excitation','temperature'] for i in range(6): self.irraman[i].delete(0,END) self.irraman[i].insert(0,self.settings[ 'ir_raman.%s'%a[i] ]) # The MO.py section self.mo=[None]*3 Label(self.frame5,text="Start:").grid(row=0,column=0) self.mo[0]=Entry(self.frame5,width=5) self.mo[0].grid(row=0,column=1) Label(self.frame5,text="End:").grid(row=0,column=2) self.mo[1]=Entry(self.frame5,width=5) self.mo[1].grid(row=0,column=3) Label(self.frame5,text="FWHM").grid(row=0,column=4) self.mo[2]=Entry(self.frame5,width=5) self.mo[2].grid(row=0,column=5) a=['start','end','fwhm'] for i in range(3): self.mo[i].delete(0,END) self.mo[i].insert(0,self.settings['mo.%s'%(a[i])]) # UVVis.py section self.uvvis=[None]*5 Label(self.frame6,text="Start:").grid(row=0,column=0) self.uvvis[0]=Entry(self.frame6,width=5) self.uvvis[0].grid(row=0,column=1) Label(self.frame6,text="End:").grid(row=0,column=2) self.uvvis[1]=Entry(self.frame6,width=5) self.uvvis[1].grid(row=0,column=3) Label(self.frame6,text="Num pts:").grid(row=0,column=4) self.uvvis[2]=Entry(self.frame6,width=5) self.uvvis[2].grid(row=0,column=5) Label(self.frame6,text="FWHM:").grid(row=0,column=6) self.uvvis[3]=Entry(self.frame6,width=5) self.uvvis[3].grid(row=0,column=7) Label(self.frame6,text="sigma:").grid(row=0,column=8) self.uvvis[4]=Entry(self.frame6,width=5) self.uvvis[4].grid(row=0,column=9) a=['start','end','numpoints','fwhm','sigma'] for i in range(5): self.uvvis[i].delete(0,END) self.uvvis[i].insert(0,self.settings['uvvis.%s'%(a[i])]) x = (652-450)//2 + self.parent.winfo_rootx() y = (480-410)//2 + self.parent.winfo_rooty() self.geometry("450x460+"+str(x)+"+"+str(y)) # Place it in the centre of the root window def buttonbox(self): # Override box = Frame(self) cancel=Button(box,text="Cancel",width=10,command=self.cancel,default=ACTIVE) cancel.pack(side=LEFT,padx=5,pady=5) self.save = Button(box, text="Save", width=10, command=self.ok, default=ACTIVE) self.save.pack(side=LEFT, padx=5, pady=5) self.bind("",self.ok) self.bind("",self.cancel) box.pack() def checkcubman(self, event=None): # Checks for existence of cubman at specificed location if not os.path.isfile(self.cubman.get()): messagebox.showerror(title="No such file", message=''' There isn't any file with this name. Make sure you include the full path, filename and extension (if any). For example (in Windows): C:\\Program Files\\Gaussian\\cubman.exe''' ) def checkformchk(self, event=None): # Checks for existence of formchk at specificed location if not os.path.isfile(self.formchk.get()): messagebox.showerror(title="No such file", message=''' There isn't any file with this name. Make sure you include the full path, filename and extension (if any). For example (in Windows): C:\\Program Files\\Gaussian\\formchk.exe''' ) def checkcubegen(self, event=None): # Checks for existence of formchk at specificed location if not os.path.isfile(self.cubegen.get()): messagebox.showerror(title="No such file", message=''' There isn't any file with this name. Make sure you include the full path, filename and extension (if any). For example (in Windows): C:\\Program Files\\Gaussian\\cubegen.exe''' ) def ok(self, event=None): # Override # Remembers the settings # If they are different from before, they will be saved by 'preferences()' a=['start','end','numpoints','fwhm','excitation','temperature'] for i in range(6): self.settings['ir_raman.%s'%(a[i])]=self.irraman[i].get() a=['start','end','numpoints','fwhm'] for i in range(4): self.settings['find.text%d'%(i+1)]=self.find[i].get() self.settings['uvvis.%s'%(a[i])]=self.uvvis[i].get() self.settings['uvvis.sigma']=self.uvvis[4].get() a=['start','end','fwhm'] for i in range(3): self.settings['mo.%s'%(a[i])]=self.mo[i].get() if not self.validate(): self.initial_focus.focus_set() # put focus back return self.withdraw() self.update_idletasks() self.apply() self.cancel() GaussSum-3.0.1/gausssum/electrontrans.py0000664000175000017500000004527612660404041017057 0ustar noelnoel# # GaussSum (http://gausssum.sf.net) # Copyright (C) 2006-2013 Noel O'Boyle # # This program is free software; you can redistribute and/or modify it # under the terms of the GNU General Public License as published by the # Free Software Foundation; either version 2, or (at your option) any later # version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY, without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details import os import sys import math import stat import numpy import glob from gausssum.utils import GaussianSpectrum, levelname, percent from gausssum.cclib.parser.utils import convertor # from Tkinter import * from .mpl import MPLPlot from .plot import DisplayPlot from .folder import folder def createEDDM(screen, logfile, contrib, gaussdir, unres): def cubman(cmds): if cmds[0] == "sc": output.write("echo Scaling %s by %s\n" % (cmds[1], cmds[-1])) elif cmds[0] == "a": output.write("echo Adding to %s\n" % cmds[3]) elif cmds[0] == "sq": output.write("echo Squaring %s\n" % cmds[1]) elif cmds[0] == "su": output.write("echo Subtracting %s from %s\n" % (cmds[1], cmds[3])) lines = [] lines.append("echo %s > tmp.txt" % cmds[0]) for cmd in cmds[1:]: lines.append("echo %s >> tmp.txt" % cmd) cmdname = syscmd[CAT] lines.append("%s tmp.txt | %s%scubman > tmp2.txt\n" % (cmdname, envvar('G03DIR'), os.sep)) output.write("\n".join(lines)) def envvar(x): if windows: return "%" + x + "%" else: return "${" + x + "}" def createcubes(mo): cube = "mo%s.cub" % getmo(mo) report = "echo Creating cubefile %s" % cube orb = mo[0] + 1 if unres: calctype = ['alpha', 'beta'][mo[1]] orb += len(logfile.moenergies[0]) * mo[1] cmd = "%s%scubegen 0 MO=%d %s %s -2 h\n" % ( envvar('G03DIR'), os.sep, orb, fchkpoint, cube) if windows: line = "if not exist %s %s & %s\n" % (cube, report, cmd) else: line = 'if [ ! -e "%s" ]\nthen\n %s\n %s\nfi\n' % (cube, report, cmd) output.write(line) def getmo(mo): suffix = "" if unres: suffix = ["A", "B"][mo[1]] return "%d%s" % (mo[0] + 1, suffix) def createsquares(mo): cube = "mo%s.cub" % getmo(mo) sq = "sq%s.cub" % getmo(mo) if windows: output.write("if not exist %s (\n" % sq) else: output.write("if [ ! -e %s ]\nthen\n" % sq) cubman(["sq", cube, "y", sq, "y"]) if windows: output.write(")\n") else: output.write("fi\n") def formchk(filename): filename = filename.split(fakeossep)[-1] fchkpoint = filename.replace('.chk', '.fck') report = "echo Formatting %s to %s" % (filename, fchkpoint) cmd = "%s%sformchk %s %s > tmp2.txt\n" % (envvar('G03DIR'), os.sep, filename, fchkpoint) if windows: line = "if not exist %s %s & %s\n" % (fchkpoint, report, cmd) else: line = 'if [ ! -e "%s" ]\nthen\n %s\n %s\nfi\n' % (fchkpoint, report, cmd) return fchkpoint, line windows = sys.platform == "win32" ## windows = False fakeossep = os.sep ## fakeossep = "\\" ## os.sep = "/" COPY, DELETE, CAT, MOVE = range(4) syscmd = ['cp', 'rm', 'cat', 'mv'] if windows: syscmd = ['copy', 'del', 'type', 'move'] fchkpoint = glob.glob(os.path.join(gaussdir, "*.fck")) + glob.glob(os.path.join(gaussdir, "*.fchk")) line = "" if len(fchkpoint) == 0: chkpoint = glob.glob(os.path.join(gaussdir, "*.chk")) fchkpoint, line = formchk(chkpoint[0]) else: fchkpoint = fchkpoint[0].split(fakeossep)[-1] # Error! screen.write("Creating the EDDM script file for %s\n" % fchkpoint) if windows: output = open(os.path.join(gaussdir, "eddm.bat"), "w") output.write("@echo off\n") else: finalsection = [] output = open(os.path.join(gaussdir, "eddm.sh"), "w") os.chmod(os.path.join(gaussdir, "eddm.sh"), stat.S_IRWXU) # Make executable if line: output.write(line) output.write("echo === Using formatted checkpoint %s ===\n" % fchkpoint) for i in range(len(logfile.etenergies)): # For each transition if windows: output.write("""if "%%1" == "%d" goto :tran%d\n""" % (i+1, i+1)) else: finalsection.append("""if [ "$1" == "%d" ]\nthen\n tran%d\nfi\n""" % (i+1, i+1)) line = "echo You need to specify a transition in the range 1 to %d\n" % len(logfile.etenergies) if windows: output.write("%s\ngoto :end\n" % line) else: finalsection.append(line) for i in range(len(logfile.etenergies)): # For each transition if windows: output.write(":tran%d\n" % (i+1)) else: output.write("function tran%d\n{\n" % (i+1)) totalcon = sum(contrib[i]) scales = [x / float(totalcon) for x in contrib[i]] minimum_scale = 0 # Don't include contributions that don't contribute! # Create any necessary cube and squared files alreadydone = set() for sec, scale in zip(logfile.etsecs[i], scales): if scale > minimum_scale: for M in range(2): if sec[M] not in alreadydone: createcubes(sec[M]) createsquares(sec[M]) alreadydone.add(sec[M]) N = 0 # Find the first transition with non-zero scale while N < len(scales) and scales[N] <= minimum_scale: N += 1 if N < len(scales): scale = scales[N] cubman(["sc","sq%s.cub" % getmo(logfile.etsecs[i][N][0]),"y","before.cub","y",str(scale)]) cubman(["sc","sq%s.cub" % getmo(logfile.etsecs[i][N][1]),"y","after.cub","y",str(scale)]) created_tmp = False for j in range(N + 1, len(logfile.etsecs[i])): scale = scales[j] if scale > minimum_scale: created_tmp = True cubman(["sc","sq%s.cub" % getmo(logfile.etsecs[i][j][0]),"y","tmp.cub","y",str(scale)]) cubman(["a","tmp.cub","y","before.cub","y","tmp2.cub","y"]) output.write("%s tmp2.cub before.cub\n" % syscmd[MOVE]) cubman(["sc","sq%s.cub" % getmo(logfile.etsecs[i][j][1]),"y","tmp.cub","y",str(scale)]) cubman(["a","tmp.cub","y","after.cub","y","tmp2.cub","y"]) output.write("%s tmp2.cub after.cub\n" % syscmd[MOVE]) if created_tmp: output.write("%s tmp.cub\n" % syscmd[DELETE]) cubman(["su","before.cub","y","after.cub","y", "trans%d.cub" % (i+1,),"y"]) else: output.write("echo No significant contributions found") if windows: output.write("goto :end\n") else: output.write("}\n") if windows: output.write(":end\n") else: output.write("\n".join(finalsection)) output.close() def readorbital_data(inputfile): # Reads in all data from orbital_data.txt line=inputfile.readline(); NBasis=int(line.split()[1]) line=inputfile.readline().split(); HOMO=[int(line[1])-1] unres=False if len(line)==3: unres=True # This is an unrestricted calculation HOMO.append(int(line[2])-1) line=inputfile.readline(); NGroups=int(line.split()[1]) groupname=[]; groupatoms=[] for i in range(NGroups): # Read in group info line=inputfile.readline() temp=line.split('\t') groupname.append(temp[0]) groupatoms.append(map(int,temp[1].split())) line=inputfile.readline() headers = inputfile.readline() # Contribs for orbital#1 will be in contrib[0][0-->NGroups] if not unres: contrib = numpy.zeros((1,NBasis,NGroups), "d") else: contrib = numpy.zeros((2,NBasis,NGroups), "d") evalues=[] for i in range(NBasis-1,-1,-1): line=inputfile.readline().split() evalue = [float(line[2])] more = line[4+NGroups:4+NGroups*2] # Strip off the crud at the start contrib[0,i,:] = [float(x) for x in more] if unres: evalue.append(float(line[6+NGroups*2])) more = line[8+NGroups*3:8+NGroups*4] contrib[1,i,:] = [float(x) for x in more] evalues.append(evalue) evalue.reverse() # Because you're reading them in backwards return HOMO,NBasis,NGroups,groupname,groupatoms,contrib,evalues def ET(root,screen,logfile,logfilename, start,end,numpts,FWHM,UVplot, EDDM): screen.write("Starting to analyse the electronic transitions\n") # Create output directory if necessary gaussdir=folder(screen,logfilename) unres = len(logfile.homos) > 1 CIS = logfile.etsecs if UVplot==True: ####################################################### # UV-Visible section # ####################################################### # Read in orbital_data.txt if it exists(which contains info on the contribs) NGroups=0 orbdata = False try: inputfile=open(os.path.join(gaussdir,"orbital_data.txt"),"r") except IOError: screen.write("orbital_data.txt not found\n") else: thisHOMO,NBasis,NGroups,groupname,groupatoms,contrib,evalue=readorbital_data(inputfile) inputfile.close() orbdata=True screen.write("Using orbital_data.txt\n") if thisHOMO != list(logfile.homos): screen.write("Disagreement on HOMO...orbital_data.txt says "+str(thisHOMO)+"\n"+logfile.filename+" says "+str(logfile.homos)+"\n") return False screenCD=[] for i in range(len(logfile.etenergies)): # For each transition screenCD.append("") # charge density [before, after] for each of the groups for each of the transitions majorCIS, minorCIS = [[] for x in logfile.etenergies], [[] for x in logfile.etenergies] allpercent = [[] for x in logfile.etenergies] for i in range(len(logfile.etenergies)): # For each transition if orbdata: CD=[ [0, 0] for x in range(NGroups)] totcontribs = 0 for j in range(len(CIS[i])): # For each contribution thisCIS = CIS[i][j] mycontrib = thisCIS[2] ** 2 totcontribs += mycontrib # tot up the contribs percontrib = int(mycontrib * 100 + .5) if orbdata: for k in range(NGroups): CD[k][0] += mycontrib * contrib[thisCIS[0][1], thisCIS[0][0], k] # Add to 'before' charge density CD[k][1] += mycontrib * contrib[thisCIS[1][1], thisCIS[1][0], k] # Add to 'after' charge density alphabeta = ["", ""] if unres: alphabeta = [["(A)", "(B)"][thisCIS[0][1]], ["(A)", "(B)"][thisCIS[1][1]]] CIStext = "%s%s->%s%s (%d%%)" % (levelname(thisCIS[0][0], logfile.homos[thisCIS[0][1]]), alphabeta[0], levelname(thisCIS[1][0], logfile.homos[thisCIS[1][1]]), alphabeta[1], percontrib) if percontrib >= 10: # Major contributions (>=10%) majorCIS[i].append(CIStext) elif percontrib >= 2: #Minor contributions (>=2%) minorCIS[i].append(CIStext) allpercent[i].append(percontrib) if orbdata: for j in range(len(groupname)): # The charge densities are scaled so that they add to one CD[j][0]=CD[j][0]/totcontribs CD[j][1]=CD[j][1]/totcontribs screenCD[i]=screenCD[i]+percent(CD[j][0])+"-->"+percent(CD[j][1])+" ("+percent(round(CD[j][1],2)-round(CD[j][0],2))+")\t" if EDDM: createEDDM(screen, logfile, allpercent, gaussdir, unres) # Write UVData.txt containing info on the transitions fileoutput = "" for i in range(len(logfile.etenergies)): # For each transition temp = [i+1,logfile.etenergies[i], convertor(logfile.etenergies[i],"cm-1","nm"), logfile.etoscs[i],logfile.etsyms[i], ", ".join(majorCIS[i]),", ".join(minorCIS[i])] if orbdata: temp.append(screenCD[i]) fileoutput += "\t".join(map(str,temp)) + "\n" screen.write("Writing the transition info to UVData.txt\n") outputfile=open(os.path.join(gaussdir,"UVData.txt"),"w") outputfile.write("HOMO is "+str(logfile.homos[0]+1)) if unres: outputfile.write("\t%d" % (logfile.homos[1] + 1,)) outputfile.write("\nNo.\tEnergy (cm-1)\tWavelength (nm)\tOsc. Strength\tSymmetry\tMajor contribs\tMinor contribs") for i in range(NGroups): outputfile.write("\t"+groupname[i]) outputfile.write("\n"+fileoutput) outputfile.close() endwaveno = convertor(start,"nm","cm-1") startwaveno = convertor(end,"nm","cm-1") t = GaussianSpectrum(startwaveno,endwaveno,numpts, ( logfile.etenergies,[[x*2.174e8/FWHM for x in logfile.etoscs]] ), FWHM) screen.write("Writing the spectrum to UVSpectrum.txt\n") outputfile=open(os.path.join(gaussdir,"UVSpectrum.txt"),"w") outputfile.write("Energy (cm-1)\tWavelength (nm)\tAbs\t<--UV Spectrum\tUV-Vis transitions-->\tEnergy (cm-1)\tWavelength (nm)\tOsc. strength\n") width=endwaveno-startwaveno for x in range(numpts): realx=width*x/numpts+startwaveno outputfile.write(str(realx)+"\t"+str(convertor(realx,"cm-1","nm"))+"\t"+str(t.spectrum[0,x])) if x\tWavelength (nm)\tEnergy (cm-1)\tR(length)\n") width = endwaveno - startwaveno for x in range(numpts): # Need to use float, otherwise it's an integer # realx = float(width)*x/numpts+start realx = width * x / numpts + startwaveno outputfile.write("%f\t%f\t%f" % (realx, 1.0e7/realx, t.spectrum[0,x])) if x < len(logfile.etenergies): # Write the R values out also outputfile.write( "\t\t\t%f\t%f\t%f" % (convertor(logfile.etenergies[x], "cm-1", "nm"), logfile.etenergies[x],logfile.etrotats[x]) ) outputfile.write("\n") outputfile.close() if root: # Plot the UV Spectrum using Gnuplot screen.write("Plotting using Gnuplot\n") if max(t.spectrum[0,:])<1E-8: # Gnuplot won't draw it if the spectrum is flat screen.write("There are no peaks in this wavelength range!\n") else: g = MPLPlot() g.setlabels("Wavelength (nm)", r"$\epsilon$") xvalues_nm = [convertor(x,"cm-1","nm") for x in t.xvalues] g.data(zip(xvalues_nm,t.spectrum[0,:])) energies_nm = [convertor(x,"cm-1","nm") for x in logfile.etenergies] oscdata = [(x, y) for x, y in zip(energies_nm, logfile.etrotats) if start < x < end] g.data(oscdata, vlines=True, y2axis="R (length) / $10^{-40}$") g.subplot.set_xlim(left=start, right=end) g.secondaxis.set_ylim(bottom=0) DisplayPlot(root, g, "Circular dichroism spectrum") screen.write("Finished") ####################################################### # End of main # ####################################################### GaussSum-3.0.1/gausssum/__init__.py0000664000175000017500000000000012660404041015703 0ustar noelnoelGaussSum-3.0.1/gausssum/geoopt.py0000664000175000017500000000372412660404041015461 0ustar noelnoel# # GaussSum (http://gausssum.sf.net) # Copyright (C) 2006-2013 Noel O'Boyle # # This program is free software; you can redistribute and/or modify it # under the terms of the GNU General Public License as published by the # Free Software Foundation; either version 2, or (at your option) any later # version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY, without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. import os import sys import math # from Tkinter import * from .plot import DisplayPlot from .mpl import MPLPlot def GeoOpt(root, screen, logfile, numpts): screen.write("Starting GeoOpt.py\n") deviation = [] for i in range(len(logfile.geovalues)): dev = 0 for j in range(len(logfile.geotargets)): if abs(logfile.geovalues[i][j]) > logfile.geotargets[j]: dev += math.log(abs(logfile.geovalues[i][j]) / logfile.geotargets[j]) deviation.append(dev) if len(logfile.scfenergies) >= numpts+2: # If there are two points to plot g = MPLPlot() g.setlabels("Optimisation Step", "Energy") data = list(zip(range(len(logfile.scfenergies)-numpts), logfile.scfenergies[numpts:])) g.data(data) g.data(data, lines=False) DisplayPlot(root, g, "Geometry optimisation") if len(deviation) >= numpts+2: h = MPLPlot() h.setlabels("Optimisation Step", "Deviation from targets") data = list(zip(range(len(deviation)-numpts), deviation[numpts:])) h.data(data) h.data(data, lines=False) h.subplot.set_ylim(bottom=0) DisplayPlot(root, h, "Deviation from targets") else: screen.write("I need at least two points to plot\n") screen.write("Finishing GeoOpt.py\n") GaussSum-3.0.1/gausssum/popanalysis.py0000664000175000017500000004776612660404041016544 0ustar noelnoel# # GaussSum (http://gausssum.sf.net) # Copyright (C) 2006-2013 Noel O'Boyle # # This program is free software; you can redistribute and/or modify it # under the terms of the GNU General Public License as published by the # Free Software Foundation; either version 2, or (at your option) any later # version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY, without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. import string import os import sys import numpy from math import exp, log from .plot import DisplayPlot from .mpl import MPLPlot from .folder import folder from gausssum.utils import levelname from gausssum.utils import GaussianSpectrum from gausssum.utils import Groups def Popanalysis(root,screen,logfile,logfilename,start,end,COOP,FWHM,makeorigin): def DOSconvolute(orb_MPA,evalue): """Convolute the DOS spectrum""" heights =[x for x in numpy.swapaxes(orb_MPA,0,1)] spectrum = GaussianSpectrum(start, end, 1000, (evalue, heights), FWHM) return spectrum def tidy(num): # Changes +-0.155648 into +-0.16 if num>=0: rounded=int(num*100+.5)/100. return str(rounded)[:4] else: rounded=int(num*100-.5)/100. return str(rounded)[:5] def originoutput(MPA): """Write a file suitable for reading with Origin.""" outputfile=open(os.path.join(gaussdir,"origin_orbs.txt"),"w") for i in range(len(evalue[0])): if numgroups>0 and pop: total=0 for j in range(numgroups): outputfile.write(str(total)+"\t"+str(evalue[0][i])+"\t") total += MPA[0][i,j] outputfile.write("\n") total=0 for j in range(numgroups): total += MPA[0][i,j] outputfile.write(str(total)+"\t"+str(evalue[0][i])+"\t") outputfile.write("\n") else: outputfile.write("0\t"+str(evalue[0][i])+"\n") outputfile.write("1\t"+str(evalue[0][i])+"\n") if unres: for i in range(len(evalue[1])): if numgroups>0 and pop: total=0 for j in range(numgroups): outputfile.write(str(total)+"\t"+str(evalue[1][i])+"\t") total += MPA[1][i,j] outputfile.write("\n") total=0 for j in range(numgroups): total += MPA[1][i,j] outputfile.write(str(total)+"\t"+str(evalue[1][i])+"\t") outputfile.write("\n") else: outputfile.write("0\t"+str(evalue[1][i])+"\n") outputfile.write("1\t"+str(evalue[1][i])+"\n") outputfile.close() def densityofstates(): """Do the density of status calculation.""" groupnames = list(groups.groups.keys()) if groups else [] if groups and pop: contrib = [x * numpy.dot(x,overlap) for x in MOCoeff] if not unres: MPA = [numpy.zeros( (logfile.nmo,numgroups), "d")] else: MPA = [numpy.zeros( (logfile.nmo,numgroups), "d") for x in range(2)] for i,groupname in enumerate(groupnames): for basisfn in groups.groups[groupname]: MPA[0][:,i] += contrib[0][:,basisfn] if unres: MPA[1][:,i] += contrib[1][:,basisfn] else: # Set MPA to be all ones MPA = [numpy.ones( (logfile.nmo,1), "d")] if unres: MPA = [numpy.ones( (logfile.nmo,1), "d") for x in range(2)] # Write DOS and PDOS data to orbital_data.txt screen.write("Writing orbital data to orbital_data.txt\n") outputfile=open(os.path.join(gaussdir,"orbital_data.txt"),"w") outputfile.write("NBasis:\t"+str(NBsUse)+"\n") outputfile.write("HOMO:\t%s" % "\t".join([str(x+1) for x in HOMO])) if not (groups and pop): # No point outputting group info since we don't have the %contribs outputfile.write("\nGroups:\t0\n") else: outputfile.write("\nGroups:\t"+str(len(groups.groups))+"\n") line = [] for groupname in groupnames: v = groups.groups[groupname] line.append("%s\t%s" % (groupname," ".join(map(str,v)))) outputfile.write("\n".join(line) + "\n") if unres: outputfile.write("\nAlpha MO\t\teV\tSymmetry") else: outputfile.write("\nMO\t\teV\tSymmetry") if groups and pop: t = "\t".join(groupnames) outputfile.write("\t" + t+"\tAccurate values (for the Electronic Transitions module)") if unres: if groups and pop: outputfile.write("\t"*(len(groups.groups)-1)) outputfile.write("\tBeta MO\t\teV\tSymmetry") if groups and pop: outputfile.write(t+"\tAccurate values (for UVVis.py)") outputfile.write("\n") for i in range(max([len(x) for x in evalue])-1,-1,-1): # Print them out backwards line=str(i+1)+"\t"+levelname(i, HOMO[0])+"\t"+str(round(evalue[0][i],2))+"\t"+symmetry[0][i] if groups and pop: for j in range(len(groups.groups)): line=line+"\t"+str(int(MPA[0][i,j]*100+.5)) for j in range(len(groups.groups)): line=line+"\t"+str(MPA[0][i,j]) if unres and i=len(evalue[1]): continue output.write(str(k+1)+"\t"+levelname(k, HOMO[j])+"\t"+str(evalue[j][k])) for fragx in range(len(groups.groups)-1): for fragy in range(fragx+1,len(groups.groups)): output.write("\t"+str(groupoverlap[j][k,fragx,fragy])) output.write("\n") output.close() # Convolute the COOP spectrum spectrum = [] for j in range(len(HOMO)): overlaps = [] for fragx in range(numgroups - 1): for fragy in range(fragx+1, numgroups): overlaps.append(groupoverlap[j][:,fragx,fragy]) spectrum.append(GaussianSpectrum(start, end, 1000, (evalue[j], overlaps), FWHM)) # Create COOP_spectrum.txt output = open(os.path.join(gaussdir,"COOP_spectrum.txt"),"w") output.write("\t") if unres: output.write("Alpha%sBeta%s" % ("\t"*numoverlaps,"\t"*numoverlaps)) output.write("Total\n") output.write("eV\t%s" % listofoverlaps) if unres: output.write("\t%s\t%s" % (listofoverlaps, listofoverlaps)) output.write("\n") for i in range(len(spectrum[0].xvalues)): output.write("%f" % spectrum[0].xvalues[i]) for k in range(len(spectrum)): for j in range(len(spectrum[k].spectrum[:,0])): output.write("\t%f" % spectrum[k].spectrum[j,i]) if unres: output.write("\t%f" % sum([spectrum[x].spectrum[j,i] for x in [0,1]])) output.write("\n") output.close() g = MPLPlot() i = 0 for fragx in range(numgroups-1): for fragy in range(fragx+1, numgroups): for j in range(len(spectrum)): if not unres: g.data( zip(spectrum[j].xvalues,spectrum[j].spectrum[i,:]), title='overlap of %s with %s' % (groupnames[fragx], groupnames[fragy])) else: g.data( zip(spectrum[j].xvalues,spectrum[j].spectrum[i,:]), title='%s overlap of %s with %s' % (['Alpha','Beta'][j], groupnames[fragx], groupnames[fragy])) ## if unres: # Plot the total or not? ## g.data( zip(spectrum[j].xvalues,spectrum[0].spectrum[i,:] + spectrum[1].spectrum[i,:]), ## title='Total overlap of %s with %s' % ( groupnames[fragx], groupnames[fragy])) i += 1 g.setlabels("Energy (eV)", "") g.subplot.set_xlim(left=start, right=end) g.subplot.legend(loc="upper right", prop={'size':8}) DisplayPlot(root,g,"COOP spectrum") return # End of crystalorbital() ############### START OF MAIN ################## screen.write("Starting to analyse the molecular orbitals\n") groupatoms=[]; groupname=[]; atomorb=[] # Create the output directory if necessary gaussdir=folder(screen,logfilename) # Read in the groups (if they exist!) filename = os.path.join(gaussdir,"Groups.txt") groups = False numgroups = 0 if not os.path.isfile(filename): screen.write("Groups.txt not found\n") elif not (hasattr(logfile, "atombasis") and hasattr(logfile, "atomnos")): screen.write("Groups.txt found but not used as logfile does not have" " atombasis or atomnos\n") # not necessary depending on the groups else: screen.write("Reading Groups.txt\n") if not hasattr(logfile, "aonames"): groups = Groups(filename, logfile.atomnos, None, logfile.atombasis) else: groups = Groups(filename, logfile.atomnos, logfile.aonames, logfile.atombasis) numgroups = len(groups.groups) screen.write("There are %d groups\n" % numgroups) NAtoms = logfile.natom NBasis = logfile.nbasis NBsUse = logfile.nmo # Verify that groups.txt is okay if groups: if COOP==False: status = groups.verify("DOS") else: status = groups.verify("COOP") if status: screen.write(status) return 1 screen.write("The number of atoms is "+str(NAtoms)+"\n") screen.write("NBasis is "+str(NBasis)+"\n") screen.write("NBsUse is "+str(NBsUse)+"\n") pop=False if hasattr(logfile, "aooverlaps") and hasattr(logfile, "mocoeffs"): screen.write("Found an overlap matrix and MO coefficients\n") pop=True # Assuming you never get an overlap matrix without the MOCoeffs MOCoeff = logfile.mocoeffs overlap = logfile.aooverlaps atomorb = logfile.aonames if len(MOCoeff)==2: screen.write("This is an unrestricted calculation - found the Beta MO coefficents\n") unres=False HOMO = logfile.homos if len(HOMO)==2: screen.write("This is an unrestricted calculation\n") unres=True evalue = logfile.moenergies if hasattr(logfile,"mosyms"): symmetry = logfile.mosyms else: symmetry = [["?" for x in evalue[0]]] if unres: symmetry.append(["?" for x in evalue[1]]) screen.write("Number of evalues found: %s\n" % " ".join([str(len(x)) for x in evalue])) screen.write("Number of orbital symmetries found: %s\n" % " ".join([str(len(x)) for x in symmetry])) if COOP==True: if not (groups and pop): screen.write("To calculate the COOP spectrum, you need Groups.txt and a log file containing a full population analysis\n") return 1 crystalorbital() else: # Density of States densityofstates() screen.write("Finished\n") GaussSum-3.0.1/gausssum/mesh.gif0000664000175000017500000010427412660404041015237 0ustar noelnoelGIF89a@      .' !%" */+#-&?",%%)*5( 6381176<9F42<9%?77a4S8 CACA G>>KEJH RINKYH QG7OFGKNMP jJ PRTQYPtI aO[QULMRT}H SUaNLXY ]XaW\[yR Z[iX sU mW eZQ_UTMOS_[__^^__ VLJl`Wdc}] icv` [pc V^j^^c``ig kijd;_ ]{gnlf qnj d {mwo g `so ps i/\uq5h?q uo[twxpL}tt o zwh {nmspqyzw yz tvsu q ~}y }vs xv ~ ||~v|r1tOM%m   LEYTΜR2˗0PMgncg0D%H;~@AMϞaժ pj]٪n CIG]Ygbߨ ޺Tq 8.,>[~IgllHz$Z\MW 3TY_GFB7B >h2*qJˆne3e0WvjW-[L^a{_}L~0eQ7 -Rp!R>5HE4n"G0j iMJu&!I\KAW+KzQ`abI]L%CFV`eO~`umBFh ٕ*ɄFHBiDbl 4uJ+DmWe*| u3`cON LyS]6PXvŧi$ꧥ^\dz rޢg2l6&k)Agi MAhnhpt[mz_/}pcUZQcl{mGVineںcbU_īW*`LAVnGM-M)WIk  c"ݺGضd#) iƨcUbg̒p3}nJK[XN^)e}AMbSeJȵIimL$i4,CRR}rv+w(2E-J5% 0xsϥl%^TG4cAS+i0<+T\^3@MVCВC0ɶZQH~xl=T˽ OOG+QX0}p:={slBƔ"_z|iKͺc0|VObcE"b0RT$oxظ~me0fK+'@/}1Fᬗ=_h g3x ISYQr 3l4)^a!LVct^ UͰ@"mBLH @(0*Y sKuY`r88Q⼄ 1$`L)@ 2HDq/DJCf`d9E-+,YYט5 @@/&OH _ @@$H}#է. 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LjaN5Qmܦ[M $o+S]:OAהtDtEEqZ)w7\ԘX3M^bمV|2,x-`PV]zg5`A @i!/YgF-'Lo:=\c9:r3t_#_[δۊOF ʲ2?l;|tzc:k^8^W\bv6w0EB$ V `eygFۣsrxG9tZ6ƎzoG;$T;̢?.AA(EFwU; Kx(^T(IT"lPR8#g7^эCs1r0 KcG;QiuyĒMdD(NF B h-rы`6~Do`Dz!O9Xvk_9>ǎwik3g>^j$|HNr %DE3 bU1-Zh`*hP*<.qoe,Ow#{a= >jn-hGigϜFю&^&%MI*O hqZE0Η-?Q/܉x,Ey;юv 43 aȈG(145fmzv"@B>rET$&Y>1Q>Хj^zqX!43hWWmeI*aX&2YQ+j ¶N}%O/ӁRoHZGp0B*s۩rd-x>79@@fiݝ1j=6N;GaussSum-3.0.1/gausssum/gausssumgui.py0000664000175000017500000006453212660404041016544 0ustar noelnoel# # GaussSum (http://gausssum.sf.net) # Copyright (C) 2006-2009 Noel O'Boyle # # This program is free software; you can redistribute and/or modify it # under the terms of the GNU General Public License as published by the # Free Software Foundation; either version 2, or (at your option) any later # version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY, without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. from tkinter import * # GUI stuff import tkinter.messagebox # For the About Dialog import tkinter.filedialog # For the Open File and Save File import webbrowser import tkinter.simpledialog import traceback import copy # For deepcopy...until I find a better way of doing this import configparser # For writing the settings to an .ini file import logging import glob from gausssum.cclib.parser import ADF, GAMESS, Gaussian, ccopen from gausssum.preferencesbox import PreferencesPopupBox from gausssum.aboutbox import AboutPopupBox from gausssum.popanalysis import Popanalysis from gausssum.electrontrans import ET from gausssum.geoopt import GeoOpt from gausssum.search import Search from gausssum.vibfreq import Vibfreq from gausssum.scf import SCF from gausssum.utils import * from gausssum.folder import folder import os, sys if hasattr(sys, "frozen"): # i.e. if using cx_Freeze installlocation = os.path.dirname(sys.executable) else: import gausssum installlocation = gausssum.__path__[0] class App: # This sets up the GUI def __init__(self,root,argv): self.root = root self.root.resizable(False,False) self.addmenubar() self.frame1=Frame(self.root) self.frame1.grid() frame2=Frame(self.root) frame2.grid(row=1) self.scrollbar=Scrollbar(frame2) self.scrollbar.pack(side=RIGHT,fill=Y) if sys.platform == "win32": self.txt=Text(frame2,font=("Courier New",8),yscrollcommand=self.scrollbar.set,width=90,height=21) else: # Avoid font problems (? I don't remember the reason for this) self.txt=Text(frame2,yscrollcommand=self.scrollbar.set,width=90,height=21) self.txt.pack(expand=1,fill=Y) self.scrollbar.config(command=self.txt.yview) self.screen=Redirect(self.txt.insert,self.root,[END,self.txt]) # Oh yeah! Could I have done it in a more roundabout way? self.screen.write("Support GaussSum by citing:\nN.M. O'Boyle, A.L. Tenderholt and K.M. Langner. J. Comp. Chem. 2008, 29, 839-845.\n") frame3=Frame(self.frame1,relief=GROOVE,borderwidth=3) frame3.pack(side=LEFT,anchor=W) self.middleframe=Frame(self.frame1,width=380,height=140) self.middleframe.pack(side=LEFT) # Thanks to the students from Ulm for the next line # which prevents self.middleframe resizing when you choose a different option self.middleframe.pack_propagate(0) self.frame4=Frame(self.middleframe) self.frame4.pack(side=LEFT) self.script = StringVar() self.b3=Radiobutton(frame3, text="Search file", variable=self.script, value="FIND",command=self.option,state=DISABLED) self.b3.pack(anchor=W) self.b0=Radiobutton(frame3, text="Monitor SCF", variable=self.script, value="SCF",command=self.option,state=DISABLED) self.b0.pack(anchor=W) self.b1=Radiobutton(frame3, text="Monitor GeoOpt", variable=self.script, value="GEOOPT",command=self.option,state=DISABLED) self.b1.pack(anchor=W) self.b2=Radiobutton(frame3, text="Frequencies", variable=self.script, value="IR_RAMAN",command=self.option,state=DISABLED) self.b2.pack(anchor=W) self.b5=Radiobutton(frame3, text="Orbitals", variable=self.script, value="MO",command=self.option,state=DISABLED) self.b5.pack(anchor=W) self.b4=Radiobutton(frame3, text="Electronic transitions", variable=self.script, value="UVVIS",command=self.option,state=DISABLED) self.b4.pack(anchor=W) ## self.b6=Radiobutton(frame3, text="EDDM.py", variable=self.script, value="EDDM", command=self.option,state=DISABLED) ## self.b6.pack(anchor=W) ## self.b7=Radiobutton(frame3, text="NMR.py (beta)", variable=self.script, value="NMR", command=self.option,state=DISABLED) ## self.b7.pack(anchor=W) self.b3.select() self.frame5=Frame(self.frame1) self.frame5.pack(side=LEFT) self.photo = PhotoImage(file=os.path.join(installlocation,"mesh2.gif")) # Doesn't work if don't use self. Button(self.frame5,image=self.photo,command=self.runscript).pack(side=LEFT) self.root.bind("", self.runscript) x = (self.root.winfo_screenwidth()-652)//2 # The window is 652x480 y = (self.root.winfo_screenheight()-580)//2 self.root.geometry("652x480+"+str(x)+"+"+str(y)) # Get the window dead-centre self.error=ErrorCatcher() # Read in the preferences from the preferences file. # If it doesn't exist, create one using the defaults. self.readprefs() if len(argv)>1: # What to do if a filename is passed as an argument if os.path.isfile(argv[1]): # Does it exist? self.inputfilename=os.path.basename(argv[1]) t=os.path.dirname(argv[1]) if t: os.chdir(t) # Create an instance of the parser self.logfile = ccopen(self.inputfilename) self.fileopenednow() else: self.screen.write(argv[1]+" does not exist or is not a valid filename\n") self.inputfilename=None else: self.inputfilename=None ### Read in the nmr standards file (if it exists) ## self.nmrstandards = NMRstandards(self.settings['nmr.data']) def option(self): # What to do when they choose a script s=self.script.get() self.frame4.destroy() self.frame4=Frame(self.middleframe,width=380) self.frame4.pack(side=LEFT) if s=="SCF" or s=="GEOOPT": Label(self.frame4,text="Leave out the first n points").pack(side=LEFT) self.numpts=Entry(self.frame4,width=3) self.numpts.pack(side=LEFT) self.numpts.insert(0,"0") self.reparse = IntVar() ckb = Checkbutton(self.frame4, text="Reparse first?", variable=self.reparse) ckb.pack(side=LEFT) elif s=="IR_RAMAN": frame5=Frame(self.frame4) frame5.pack(side=TOP) frame6=Frame(self.frame4) frame6.pack(side=TOP) frame7=Frame(self.frame4) frame7.pack(side=TOP) frame8=Frame(self.frame4) frame8.pack(side=TOP) frame9=Frame(self.frame4) frame9.pack(side=TOP) Label(frame5,text="Start:").pack(side=LEFT) self.start=Entry(frame5,width=5) self.start.pack(side=LEFT) self.start.insert(0,self.settings['ir_raman.start']) Label(frame5,text="End:").pack(side=LEFT) self.end=Entry(frame5,width=5) self.end.pack(side=LEFT) self.end.insert(0,self.settings['ir_raman.end']) Label(frame5,text="Num pts:").pack(side=LEFT) self.numpts=Entry(frame5,width=6) self.numpts.pack(side=LEFT) self.numpts.insert(0,self.settings['ir_raman.numpoints']) Label(frame5,text="FWHM").pack(side=LEFT) self.FWHM=Entry(frame5,width=3) self.FWHM.pack(side=LEFT) self.FWHM.insert(0,self.settings['ir_raman.fwhm']) Label(frame6,text="Scaling factors:").pack(side=LEFT) self.scale=StringVar() r=Radiobutton(frame6,text="General",variable=self.scale,value="Gen") r.pack(side=LEFT) self.scalefactor=Entry(frame6,width=5) self.scalefactor.insert(0,'1.00') self.scalefactor.pack(side=LEFT) r2=Radiobutton(frame6,text="Individual",variable=self.scale,value="Indiv") r2.pack(side=LEFT) r.select() Label(frame7, text="The following are used to calculate Raman intensities:").pack() Label(frame8, text="Exc. wavelength (nm)").pack(side=LEFT) self.excitation = Entry(frame8, width=6) self.excitation.pack(side=LEFT) self.excitation.insert(0, self.settings['ir_raman.excitation']) Label(frame8, text="Temp. (K)").pack(side=LEFT) self.temperature = Entry(frame8,width=6) self.temperature.pack(side=LEFT) self.temperature.insert(0, self.settings['ir_raman.temperature']) if not hasattr(self.data, "vibramans"): self.excitation.configure(state=DISABLED) self.temperature.configure(state=DISABLED) elif s=="FIND": frame6=Frame(self.frame4) frame6.pack(side=LEFT) frame7=Frame(self.frame4) frame7.pack(side=LEFT) self.searchrad=StringVar() for i in range(4): t="find.text%d" % (i+1) Radiobutton(frame6, text=self.settings[t], variable=self.searchrad, value=self.settings[t]).grid(sticky=W) r=Radiobutton(frame6, text="Custom", variable=self.searchrad, value="Custom") r.grid(sticky=W) r.select() self.customsearch=Entry(frame6,width=15) self.customsearch.grid(row=4,column=1) self.customsearch.insert(END,"Enter phrase here") self.casesensitive=IntVar() casetick=Checkbutton(frame6,text="Case sensitive",variable=self.casesensitive) casetick.grid(row=4,column=2) elif s=="MO": frame8=Frame(self.frame4) frame6=Frame(self.frame4) frame7=Frame(self.frame4) frame8.pack() frame6.pack(side=TOP) frame7.pack(side=TOP) self.MOplot=IntVar() self.MODOS=Radiobutton(frame8, text="DOS", variable=self.MOplot, value=False) self.MODOS.pack(anchor=W) self.MOCOOP=Radiobutton(frame8, text="COOP", variable=self.MOplot, value=True) self.MOCOOP.pack(anchor=W) self.MODOS.select() Label(frame6,text="Start:").pack(side=LEFT) self.start=Entry(frame6,width=5) self.start.pack(side=LEFT) self.start.insert(0,self.settings['mo.start']) Label(frame6,text="End:").pack(side=LEFT) self.end=Entry(frame6,width=5) self.end.pack(side=LEFT) self.end.insert(0,self.settings['mo.end']) Label(frame6,text="FWHM:").pack(side=LEFT) self.FWHM=Entry(frame6,width=5) self.FWHM.pack(side=LEFT) self.FWHM.insert(0,self.settings['mo.fwhm']) self.makeorigin=IntVar() self.makeoriginbtn=Checkbutton(frame7,text="Create originorbs.txt?",variable=self.makeorigin) self.makeoriginbtn.pack(anchor=W) elif s=="UVVIS": frame7=Frame(self.frame4) frame7.pack(side=TOP) frame6=Frame(self.frame4) frame6.pack() self.UVplot=IntVar() self.UVbox=Radiobutton(frame7, text="UV-Visible", variable=self.UVplot, command=self.UVupdate, value=True) self.UVbox.pack(anchor=W) self.CDbox=Radiobutton(frame7, text="Circular dichroism", variable=self.UVplot, command=self.UVupdate, value=False) self.CDbox.pack(anchor=W) self.UVbox.select() Label(frame6,text="Start:").grid(row=0,column=0) Label(frame6,text="nm").grid(row=1,column=1) self.start=Entry(frame6,width=5) self.start.grid(row=0,column=1) self.start.insert(0,self.settings['uvvis.start']) Label(frame6,text="End:").grid(row=0,column=2) Label(frame6,text="nm").grid(row=1,column=3) self.end=Entry(frame6,width=5) self.end.grid(row=0,column=3) self.end.insert(0,self.settings['uvvis.end']) Label(frame6,text="Num pts:").grid(row=0,column=4) self.numpts=Entry(frame6,width=5) self.numpts.grid(row=0,column=5) self.numpts.insert(0,self.settings['uvvis.numpoints']) self.fwhmlabel=Label(frame6,text="FWHM",width=6) self.fwhmlabel.grid(row=0,column=6) self.fwhmunits=Label(frame6,text="1/cm") self.fwhmunits.grid(row=1,column=7) self.FWHM=Entry(frame6,width=5) self.FWHM.grid(row=0,column=7) self.FWHM.insert(0,self.settings['uvvis.fwhm']) self.eddm = IntVar() self.eddmbtn = Checkbutton(frame7, text="Create EDDM script?", variable=self.eddm) self.eddmbtn.pack(anchor=W) gaussdir = folder(self.screen, self.logfile.filename, create=False) if (len(glob.glob(os.path.join(gaussdir, "*.fck"))) == 0 and len(glob.glob(os.path.join(gaussdir, "*.fchk"))) == 0 and len(glob.glob(os.path.join(gaussdir, "*.chk"))) == 0): self.eddmbtn.configure(state=DISABLED) def createnmrpanel(self,parent): # Sets up the Panel for the NMR options frame6=Frame(parent) frame6.grid(row=0,column=0) # Set up the Radiobuttons self.nmrrb = StringVar() # Extract, Comment, Listbox self.nmrrb1=Radiobutton(frame6, text="Just extract NMR data", variable=self.nmrrb, value="Extract",command=self.nmrrbcb) self.nmrrb1.pack(anchor=W) self.nmrrb2=Radiobutton(frame6, text="Take level of theory from comment", variable=self.nmrrb, value="Comment",command=self.nmrrbcb) self.nmrrb2.pack(anchor=W) self.nmrrb3=Radiobutton(frame6, text="Take level of theory from listbox", variable=self.nmrrb, value="Listbox",command=self.nmrrbcb) self.nmrrb3.pack(anchor=W) self.nmrrb1.select() # Set up the Listbox frame7=Frame(parent) frame7.grid(row=1,column=0) nmrscrlbar=Scrollbar(frame7,orient=VERTICAL) self.nmrlbx1=Listbox(frame7,yscrollcommand=nmrscrlbar.set,height=5) nmrscrlbar.config(command=self.nmrlbx1.yview) nmrscrlbar.pack(side=RIGHT,fill=Y) self.nmrlbx1.pack(side=LEFT,fill=BOTH,expand=1) for x in self.nmrstandards: self.nmrlbx1.insert(END,x['theory']) self.nmrlbx1.select_set(0) self.nmrlbx1.configure(state=DISABLED) # Set up more Radiobuttons frame8=Frame(parent) frame8.grid(row=0,column=1) self.nmrrb2 = StringVar() # Calculate, Standard self.nmrrb2a=Radiobutton(frame8, text="Calculate relative ppm", variable=self.nmrrb2, value="Calculate") self.nmrrb2a.pack(anchor=W) self.nmrrb2b=Radiobutton(frame8, text="Add new standard", variable=self.nmrrb2, value="Standard") self.nmrrb2b.pack(anchor=W) self.nmrrb2a.select() self.nmrrb2a.configure(state=DISABLED) self.nmrrb2b.configure(state=DISABLED) def nmrrbcb(self): # What to do when the user chooses one of the NMR radiobuttons # (NMR RadioButton CallBack) value=self.nmrrb.get() if value=="Listbox": # Configure the listbox self.nmrlbx1.configure(state=NORMAL) else: self.nmrlbx1.configure(state=DISABLED) if value=="Extract": # Configure everything self.nmrrb2a.configure(state=DISABLED) self.nmrrb2b.configure(state=DISABLED) elif value=="Listbox": self.nmrrb2a.configure(state=NORMAL) self.nmrrb2b.configure(state=DISABLED) else: self.nmrrb2a.configure(state=NORMAL) self.nmrrb2b.configure(state=NORMAL) def UVupdate(self): # What to do when the user chooses UVVis or CD for UVVis.py if self.UVplot.get()==False: # Choose CD self.fwhmlabel.configure(text="sigma:") self.fwhmunits.configure(text="eV") self.FWHM.delete(0,END) self.FWHM.insert(0,self.settings['uvvis.sigma']) else: # Choose UVVis self.fwhmlabel.configure(text="FWHM:") self.fwhmunits.configure(text="1/cm") self.FWHM.delete(0,END) self.FWHM.insert(0,self.settings['uvvis.fwhm']) def runscript(self,event=None): # What to do when the user clicks on the GaussSum logo # to run the script if not self.inputfilename: self.screen.write("You need to open a log file first\n") return # Do nothing if no file opened s = self.script.get() self.txt.delete(1.0, END) worked=False try: if s=="SCF": if self.reparse.get(): self.data = self.logfile.parse() worked = SCF(self.root,self.screen,self.data,int(self.numpts.get())) elif s=="GEOOPT": if self.reparse.get(): self.data = self.logfile.parse() worked = GeoOpt(self.root,self.screen,self.data,int(self.numpts.get())) elif s=="IR_RAMAN": worked = Vibfreq(self.root,self.screen,self.data,self.logfile.filename,int(self.start.get()),int(self.end.get()),int(self.numpts.get()),float(self.FWHM.get()),self.scale.get(),float(self.scalefactor.get()),float(self.excitation.get()),float(self.temperature.get())) elif s=="FIND": if self.searchrad.get()=="Custom": worked = Search(self.screen,self.logfile.filename,self.customsearch.get(),self.casesensitive.get()) else: worked = Search(self.screen,self.logfile.filename,self.searchrad.get(),1) elif s=="MO": worked = Popanalysis(self.root,self.screen,self.data,self.logfile.filename,float(self.start.get()),float(self.end.get()),self.MOplot.get(),float(self.FWHM.get()),self.makeorigin.get()) elif s=="UVVIS": worked = ET(self.root,self.screen,self.data,self.logfile.filename,int(self.start.get()),int(self.end.get()),int(self.numpts.get()),float(self.FWHM.get()),self.UVplot.get(),self.eddm.get()) ## elif s=="NMR": ## listboxvalue=self.nmrlbx1.curselection() ## try: # Works for both list of strings and list of ints (see Tkinter documentation for details) ## listboxvalue=map(int,listboxvalue) ## except ValueError: pass ## if len(listboxvalue)>0: ## selected = self.nmrlbx1.get(listboxvalue[0]) ## else: ## selected = [] ## worked=GaussSum.NMR.NMR(self.screen,self.logfile,self.nmrrb.get(), ## selected,self.nmrrb2.get(),self.nmrstandards) except: traceback.print_exc(file=self.error) # Catch errors from the python scripts (user GIGO errors, of course!) messagebox.showerror(title="The script is complaining...",message=self.error.log) self.error.clear() self.root.update() def addmenubar(self): """Set up the menubar.""" menu = Menu(self.root) self.root.config(menu=menu) filemenu = Menu(menu) menu.add_cascade(label="File", underline=0, menu=filemenu) filemenu.add_command(label="Open...", underline=0, command=self.fileopen) filemenu.add_command(label="Settings...",underline=0, command=self.preferences) filemenu.add_separator() filemenu.add_command(label="Exit", underline=1, command=self.fileexit) viewmenu=Menu(menu) menu.add_cascade(label="View", underline=0, menu=viewmenu) viewmenu.add_command(label="Error messages", underline=1, command=self.showerrors) helpmenu = Menu(menu) menu.add_cascade(label="Help", underline=0, menu=helpmenu) helpmenu.add_command(label="Documentation",underline=0, command=self.webdocs) helpmenu.add_separator() helpmenu.add_command(label="About...", underline=0, command=self.aboutdialog) def aboutdialog(self,event=None): # Soaks up event if provided d=AboutPopupBox(self.root,title="About GaussSum") def showerrors(self): self.screen.write("Log of error messages\n%s\n%s\n" % ('*'*20,self.error.longlog) ) def fileexit(self): self.root.destroy() def fileopen(self): if self.inputfilename!=None: mydir=os.path.dirname(self.inputfilename) else: mydir="." inputfilename = tkinter.filedialog.askopenfilename( filetypes=[ ("All files",".*"), ("Output Files",".out"), ("Log Files",".log"), ("ADF output",".adfout") ], initialdir=mydir ) if inputfilename!="": self.inputfilename=os.path.basename(inputfilename) os.chdir(os.path.dirname(inputfilename)) # Create an instance of the parser if hasattr(self, "logfile") and self.logfile!=None: self.logfile.logger.removeHandler(self.logfile.logger.handlers[0]) self.logfile = ccopen(self.inputfilename) self.fileopenednow() def fileopenednow(self): """What to do once a file is opened.""" if self.logfile==None: messagebox.showerror( title="Not a valid log file", message=("cclib does not recognise %s as a valid logfile.\n\n" "If the file *is* valid, please send an email to " "gausssum-help@lists.sourceforge.net\n describing the problem." % self.inputfilename) ) for button in [self.b0, self.b1, self.b2, self.b3, self.b4, self.b5]: button.configure(state=DISABLED) return # Prevent the logger writing to stdout; instead, write to screen self.logfile.logger.removeHandler(self.logfile.logger.handlers[0]) newhandler = logging.StreamHandler(self.screen) newhandler.setFormatter(logging.Formatter("[%(name)s %(levelname)s] %(message)s")) self.logfile.logger.addHandler(newhandler) try: self.data = self.logfile.parse() except: messagebox.showerror( title="Problems parsing the logfile", message=("cclib has problems parsing %s.\n\n" "If you think it shouldn't, please send an email to " "gausssum-help@lists.sourceforge.net\n describing the problem." % self.inputfilename) ) for button in [self.b0, self.b1, self.b2, self.b3, self.b4, self.b5]: button.configure(state=DISABLED) return self.screen.write("Opened and parsed %s.\n" % self.inputfilename) has = lambda x: hasattr(self.data, x) self.b3.configure(state=NORMAL) # Search if has("scftargets") and has("scfvalues"): self.b0.configure(state=NORMAL) else: self.b0.configure(state=DISABLED) if has("geotargets") and has("geovalues"): self.b1.configure(state=NORMAL) else: self.b1.configure(state=DISABLED) if has("vibfreqs") and (has("vibirs") or has("vibramans")): self.b2.configure(state=NORMAL) else: self.b2.configure(state=DISABLED) if has("etenergies") and has("etoscs"): self.b4.configure(state=NORMAL) else: self.b4.configure(state=DISABLED) self.b5.configure(state=NORMAL) # Orbitals ## self.b6.configure(state=NORMAL) # EDDM ## self.b7.configure(state=NORMAL) # NMR self.b3.invoke() # Click on Find.py self.root.title("GaussSum - "+self.inputfilename) def preferences(self): # The Preferences Dialog Box oldsettings=copy.deepcopy(self.settings) # Remember the old settings d=PreferencesPopupBox(self.root,self.settings,title="Settings") if self.settings!=oldsettings: # if there are any changes self.saveprefs() def getsettingsfile(self): # Check for GaussSum.ini in $APPDATA/GaussSum2.2 (XP) # or $HOME/.GaussSum2.2 (Linux) if sys.platform == "win32": settingsfile = os.path.join(os.getenv("APPDATA"),"GaussSum2.2","GaussSum.ini") else: settingsfile = os.path.join(os.getenv("HOME"), ".GaussSum2.2","GaussSum.ini") return settingsfile def saveprefs(self): # Save the settings settingsfile = self.getsettingsfile() writeoutconfigfile(self.settings,settingsfile) def readprefs(self): # The default settings are overwritten by any existing stored # settings. self.settings = { 'find.text1':'SCF Done', 'find.text2':'k 501%k502', 'find.text3':'imaginary', 'find.text4':'Framework', 'ir_raman.start':'0', 'ir_raman.end':'4000', 'ir_raman.numpoints':'500', 'ir_raman.fwhm':'10', 'ir_raman.excitation':'785', 'ir_raman.temperature':'293.15', 'mo.start':'-20', 'mo.end':'0', 'mo.fwhm':'0.3', 'uvvis.start':'300', 'uvvis.end':'800', 'uvvis.numpoints':'500', 'uvvis.fwhm':'3000', 'uvvis.sigma':'0.2', } settingsfile = self.getsettingsfile() if os.path.isfile(settingsfile): # Check for settings file # Found it! - so read it in. self.settings.update(readinconfigfile(settingsfile)) else: # Initialise the settings file (and directory) settingsdir = os.path.dirname(settingsfile) if not os.path.isdir(settingsdir): os.mkdir(settingsdir) self.saveprefs() # Save the inital settings file def webdocs(self): if hasattr(sys, "frozen"): # Using cx_Freeze webbrowser.open(os.path.join(installlocation, "Docs", "index.html")) else: webbrowser.open(os.path.join(installlocation, "..", "Docs", "index.html")) GaussSum-3.0.1/gausssum/aboutbox.py0000664000175000017500000000616712660404041016013 0ustar noelnoel# # GaussSum (http://gausssum.sf.net) # Copyright (C) 2006-2015 Noel O'Boyle # # This program is free software; you can redistribute and/or modify it # under the terms of the GNU General Public License as published by the # Free Software Foundation; either version 2, or (at your option) any later # version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY, without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. from tkinter import * # GUI stuff import webbrowser import tkinter.simpledialog import traceback import copy # For deepcopy...until I find a better way of doing this from gausssum.cclib.parser import ADF, GAMESS, Gaussian import os, sys if hasattr(sys, "frozen"): # i.e. if using py2exe installlocation = os.path.dirname(sys.executable) else: import gausssum installlocation = gausssum.__path__[0] class AboutPopupBox(tkinter.simpledialog.Dialog): def __init__(self, parent, title = None): # Override (just to set the geometry!) Toplevel.__init__(self, parent) self.transient(parent) if title: self.title(title) self.parent = parent self.result = None body = Frame(self) self.initial_focus = self.body(body) body.pack(padx=5, pady=5) self.buttonbox() self.grab_set() if not self.initial_focus: self.initial_focus = self self.protocol("WM_DELETE_WINDOW", self.cancel) self.initial_focus.focus_set() self.wait_window(self) def body(self,master): # Override self.resizable(False,False) self.canvas2 = Canvas(master,width=340,height=260) self.canvas2.pack(side=TOP) self.photo2 = PhotoImage(file=os.path.join(installlocation,"mesh.gif")) self.item2 = self.canvas2.create_image(11,11,anchor=NW,image=self.photo2) Label(master,text="(c) 2006-2016 Noel O'Boyle").pack(side=TOP) Label(master,text="http://gausssum.sf.net").pack(side=TOP) Label(master,text="").pack(side=TOP) # Creates a bit of spacing at the bottom Label(master,text="Support GaussSum by citing:").pack(side=TOP) Label(master,text="N.M. O'Boyle, A.L. Tenderholt and K.M. Langner.",font=("Times",10,"bold")).pack(side=TOP) Label(master,text="J. Comp. Chem. 2008, 29, 839-845.",font=("Times",10,"bold")).pack(side=TOP) # Label(master,text="").pack(side=TOP) # Creates a bit of spacing at the bottom width, height = 354, 455 x = (652-width)//2 + self.parent.winfo_rootx() y = (480-height)//2 + self.parent.winfo_rooty() self.geometry("%dx%d+%d+%d" % (width, height, x, y)) # Place it in the centre of the root window def openmybrowser(self): # New webbrowser.open("http://gausssum.sf.net") def buttonbox(self): # Override box = Frame(self) w = Button(box, text="OK", width=10, command=self.ok, default=ACTIVE) w.pack(side=LEFT, padx=5, pady=5) self.bind("", self.ok) box.pack() GaussSum-3.0.1/gausssum/scf.py0000664000175000017500000000332412660404041014733 0ustar noelnoel# # GaussSum (http://gausssum.sf.net) # Copyright (C) 2006-2013 Noel O'Boyle # # This program is free software; you can redistribute and/or modify it # under the terms of the GNU General Public License as published by the # Free Software Foundation; either version 2, or (at your option) any later # version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY, without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. import os import sys import math from .plot import DisplayPlot from .mpl import MPLPlot def SCF(root,screen,logfile,numpoints): screen.write("Starting to analyse the progress of the SCF\n") scfvalues = logfile.scfvalues[-1] # The most recent in the logfile scftargets = logfile.scftargets[-1] # Ditto deviation = [] for i in range(len(scfvalues)): # Which SCF cycle dev = 0 for j in range(len(scftargets)): # Which target if abs(scfvalues[i][j]) > scftargets[j]: dev += math.log(abs(scfvalues[i][j]) / scftargets[j]) deviation.append(dev) if len(deviation)>=numpoints+2: # If there are two points to plot h = MPLPlot() h.setlabels("SCF convergence step", "Deviation from targets") data = list(zip(range(len(deviation)-numpoints),deviation[numpoints:])) h.data(data) h.data(data, lines=False) h.subplot.set_ylim(bottom=0) DisplayPlot(root,h,"Plot of SCF deviation vs Iteration") else: screen.write("I need at least two points to plot\n") screen.write("Finished\n") GaussSum-3.0.1/gausssum/utils.py0000664000175000017500000003131612660404041015322 0ustar noelnoel# # GaussSum (http://gausssum.sf.net) # Copyright (C) 2006-2013 Noel O'Boyle # # This program is free software; you can redistribute and/or modify it # under the terms of the GNU General Public License as published by the # Free Software Foundation; either version 2, or (at your option) any later # version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY, without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. from tkinter import * import configparser # For writing the settings to an .ini file from gausssum.cclib.parser.utils import PeriodicTable import numpy import os import sys import math # This class allows Find.py, etc. to write directly to the 'console screen' # in GaussSum.py. # It also allows Find.py, etc. to write to stdout when testing. class Redirect: def __init__(self,write_function,root,other): self.outputfn=write_function self.args=other self.root=root def write(self,text): if self.args=="None": self.outputfn(text) else: self.outputfn(self.args[0],text) self.root.update() # so it shows it immediately self.args[1].see(END) # scroll if necessary to see last text inserted def flush(self): pass def readinconfigfile(inifile): # Taken from the Python Cookbook (O'Reilly): # reads in configuration information from a # Windows-style .ini file, and returns a # dictionary of settings. # # Uses the Python module configparser for the complicated stuff. cp = configparser.ConfigParser(interpolation=None) cp.read(inifile) config={} for sec in cp.sections(): name = sec.lower() for opt in cp.options(sec): config[name+"."+opt.lower()] = cp.get(sec,opt).strip() return config def writeoutconfigfile(config,inifile): # The companion to readinconfigile # Written by me! cp = configparser.ConfigParser(interpolation=None) for key,value in config.items(): sec=key.split('.')[0] opt=key.split('.')[1] if not cp.has_section(sec): cp.add_section(sec) cp.set(sec,opt,value) out=open(inifile,"w") cp.write(out) out.close() class NMRstandards(object): def __init__(self,location): """ Read in the nmr standards file (if it exists) Returns a list of dictionaries: nmrdata=[dict_1,dict_2,dict_3...] dict_n=['theory':'B3LYP/6-31G(d)','name':'TMS','C':12.23,'H':123] """ self.location = location self.nmrdata = [] # Check for nmr standards file in location described in settings try: readin = open(self.location,"r") except IOError: # File does not exist - i.e. no settings yet pass else: for line in readin: if not line.lstrip().startswith("#"): # Ignore comments temp=line.split("\t") adict={} adict['theory']=temp[0] adict['name']=temp[1] for i in range(2,len(temp),2): # Read in the calculated shifts adict[temp[i]]=float(temp[i+1]) self.nmrdata.append(adict) readin.close() def __getitem__(self,index): """Shortcut to access the data directly.""" return self.nmrdata[index] def save(self): """Write the NMR standards to disk.""" outputfile = open(self.location,"w") lines = [] for adict in self.nmrdata: output = [adict['theory'],adict['name']] for k,v in adict.items(): if k not in ["theory","name"]: output.append(k) output.append(str(v)) lines.append("\t".join(output)) outputfile.write("\n".join(lines)) outputfile.close() class ErrorCatcher: def __init__(self): self.log="" self.longlog="" def write(self,text): self.log=self.log+text def clear(self): self.longlog=self.longlog+self.log self.log="" def percent(number): return str(int(round(number*100))) # round leaves .0 at the end of a number def levelname(i,HOMO): if iHOMO+1: level='L+'+str(i-HOMO-1) elif i==HOMO+1: level="LUMO" else: level="HOMO" return level def lorentzian(x,peak,height,width): """The lorentzian curve. f(x) = a/(1+a) where a is FWHM**2/4 """ a = width**2./4. return float(height)*a/( (peak-x)**2 + a ) class Spectrum(object): """Convolutes and stores spectrum data. Usage: Spectrum(start,end,numpts,peaks,width,formula) where peaks is [(pos,height),...] formula is a function such as gaussianpeak or delta >>> t = Spectrum(0,50,11,[[(10,1),(30,0.9),(35,1)]],5,delta) >>> t.spectrum array([[ 0. ], [ 1. ], [ 1. ], [ 1. ], [ 0. ], [ 0.89999998], [ 1.89999998], [ 1.89999998], [ 1. ], [ 0. ], [ 0. ]],'d') """ def __init__(self,start,end,numpts,peaks,width,formula): self.start = start self.end = end self.numpts = numpts self.peaks = peaks self.width = width self.formula = formula # len(peaks) is the number of spectra in this object self.spectrum = numpy.zeros( (numpts,len(peaks)),"d") self.xvalues = numpy.arange(numpts)*float(end-start)/(numpts-1) + start for i in range(numpts): x = self.xvalues[i] for spectrumno in range(len(peaks)): for (pos,height) in peaks[spectrumno]: self.spectrum[i,spectrumno] = self.spectrum[i,spectrumno] + formula(x,pos,height,width) class GaussianSpectrum(object): """An optimised version of Spectrum for convoluting gaussian curves. Usage: GaussianSpectrum(start,end,numpts,peaks,width) where peaks -- ( [List of peaks],[ [list of heights],[list of heights],..] ) """ def __init__(self,start,end,numpts,peaks,width): self.start = start self.end = end self.numpts = numpts self.peaks = peaks[0] self.heights = peaks[1] self.width = width # make heights a local variable as it's accessed in the inner loop heights = self.heights # len(heights) is the number of spectra in this object data = [] self.xvalues = numpy.arange(self.numpts)*float(self.end-self.start)/(self.numpts-1) + self.start A = -2.7726/self.width**2 for x in self.xvalues: tot = [0]*len(self.heights) # The total for each spectrum for this x value for peakno in range(len(self.peaks)): # For each peak pos = self.peaks[peakno] exponent = math.exp(A*(pos-x)**2) for spectrumno in range(len(heights)): tot[spectrumno] += heights[spectrumno][peakno]*exponent data.append(tot) self.spectrum = numpy.swapaxes(numpy.array(data),0,1) class Groups(object): """Stores the groups to be used for a DOS or COOP calculation Essentially, the groups are a partitioning of the orbitals among atoms or a group of atoms. Usage: Groups(filename, atomnos, aonames, atombasis) where atomnos is a list of the atomic numbers of the atoms aonames is a list of the 'names' of each atomic orbital atombasis is a list for each atom of the indices of the basis fns on that atom Attributes: groups -- a dictionary of the partition of the orbitals among groups """ def __init__(self, filename, atomnos, aonames, atombasis): self.atomnos = atomnos self.aonames = aonames self.atombasis = atombasis self._makeatomnames() if not self.aonames: self._makeaonames() self._readfile(filename) self._setup() def _readfile(self,filename): """Read group info from a file. File format: First line must be one of "orbitals","atoms","allorbitals","allatoms". The remainder of the file is ignored in the case of allatoms/allorbitals. Otherwise a series of groups must be described with a name by itself on one line and a list of group members on the following line. As an example: atoms Phenyl ring 1,4-6,8 The rest 2,3,7 """ inputfile = open(filename,"r") grouptype = next(inputfile).strip() while not grouptype: # Ignore blank lines grouptype = next(inputfile).strip() groups = {} for line in inputfile: if not line.strip(): continue # Ignore blank lines groupname = line.strip() atoms = [] line = next(inputfile) parts = line.split(",") for x in parts: temp = x.split("-") if len(temp)==1: atoms.append(int(temp[0])) else: atoms.extend(range(int(temp[0]),int(temp[1])+1)) groups[groupname] = atoms inputfile.close() self.filegroups = groups self.grouptype = grouptype def _makeatomnames(self): """Create unique names for the atoms""" pt = PeriodicTable() d = {} names = [] for atomno in self.atomnos: if atomno in d: d[atomno] += 1 else: d[atomno] = 1 names.append(pt.element[atomno] + str(d[atomno])) self.atomnames = names def _makeaonames(self): """Create unique names for the atomic basis if needed""" d = {} for atomname, ab in zip(self.atomnames, self.atombasis): names.extend([atomname + "_" + x for x in range(len(ab))]) self.aonames = names def __str__(self): """Return a string representation of this object.""" ans = [] for k,v in self.groups.items(): ans.append("%s: %s" % (k,",".join(map(str,v)))) return "\n".join(ans) def _setup(self): """Create the groups attribute.""" type = self.grouptype.lower() if not self.filegroups: if type=="allorbitals": self.groups = {} for i in range(len(self.aonames)): orbital = self.aonames[i] self.groups[orbital] = [i] elif type=="allatoms": self.groups = dict(zip(self.atomnames, self.atombasis)) else: raise TypeError("You need to specify groups") else: if type=="orbitals": self.groups = {} for k,v in self.filegroups.items(): # Convert the indices to start from 0 self.groups[k] = [x-1 for x in v] elif type=="atoms": self.groups = {} for k,v in self.filegroups.items(): self.groups[k] = [] for x in v: # Convert the indices to start from 0 self.groups[k].extend(self.atombasis[x - 1]) else: raise TypeError("%s is not a valid type of group" % type) def verify(self,purpose): """Verify that the groups attribute is consistent. purpose is one of "DOS" or "COOP" """ status = "" # Create one big list of all of the atom orbitals in the groups all = [] for x in self.groups.values(): all += x all.sort() if purpose=="DOS": # Each atom orb must appear exactly once ok = (all==list(range(len(self.aonames)))) if not ok: status = "Problem with Groups.txt!\n(1)Every atom/orbital must " \ "be listed as a member of some group\n(2)No " \ "atom/orbital can be listed twice" elif purpose=="COOP": # No atom can appear twice (and cannot have any crazy atoms either) badatom = [x for x in all if x not in range(len(self.aonames))] if badatom: status = "Atomic orb number out of range in Groups.txt\n" else: for i in range(len(all)-1): if all[i]==all[i+1]: status += "Atomic orb %d has been included twice\n" % all[i] return status if __name__=="__main__": import doctest doctest.testmod(verbose=True) GaussSum-3.0.1/gausssum/mesh2.gif0000664000175000017500000001336312660404041015317 0ustar noelnoelGIF89aZC!!!!!!!!!!!!!)11))))!1!1)!!)!+#6#9- 91991!!!)!!1!!11!9)!)!))))1))9))91)))11)1111911919B!B)B1B1B9B9BBJ1J1J9JBJBR)W3ZBe<JJRJZJkJJJRRZRcRkRRRZZcZkZcckckkB1*A;5?1tbE&8L$홲cqrġ+Ƭ>1F|%玏5{FO`UMrV8XbbeL22|ⶍ;13fck?z2Lc `dc0c꤉fe1`r 3X $@` 5Cu cNt6@̐'&}Mט$Ly &>t|6qCP0`3!|LL2f 䐌7p7Uc(S'zq0HS7@L69w Ca̐]ǠMyL5c04cM'2$rt p )`a%Ѩ 9a@ :!Kz#,f1E}XB= qU;ġ!3 `SMV! -ҩQ $d TĢr uc<ڊ@ &y+DtCD`TCtv9&,TAUJO_iH!!@Hݰ^OWd`8C4c83 0͋ICwPjN9URAOρɴhJ Y  H縇=t[a+GXpy{& a pN #^ܪV%a4G*I-A fMk\MA^@$5BHny{bGrc+1hBSD$`8A `"%ɐxZd@! Eiњ/dxK0 4l! $,QM0(ʣ6,ya]]zHʣP<<`c' 6d SDoz4.ha C]H`?QCr yB6BL`Q= LcVB"bj!N'm ]0D$C kD$@سAd}$`m i$H.1'?Ϯ7KKȂh4׼NC$FA\E$ܬVC;X\zЇ> O4@L!phqz<Êx7U8- qVoG"_Jq 6ObD\נHg~{z'? tdJ b0#4W5% ЂIq U|3,ʍ tݜ4UT[ @4jS<"Qguۮ qud#LM#VIm4EMnO\*_(6)8H5z#3) p0q~#ߵx@" z' -:e/}Tw-n7@QPB xp@[ X$_:K+n߼% 4 (>CTQԣ dj0 wr_ji  GSP}cq g@ 6s7p fe*|"q'x*" fi(nbg p},0\wG%`~uf`k6@ElW`VTjT fncZ&r `j'vZ`^3(}FECwx6\ H7d  ii ^StZY0 gŇ|f憼 vTje^ifYPUs@g t 𐅍 heWI`fbpV!V)ƃ>xuxrbb׀'`U#ۀ 2~p] ` &n0Duf[XN`DuYVr]i`bf"`az` Y3 :` vq7Vjjfhb_`N(Rjب'mFvq  Ɛ ptPD4,:( T`Rfa&Nj jqE` N( mPj Jv tf {! yIS4fUf+RUu&pN^f*mƏe& O @0DyWD `"qaai[-fb5 7nZTn ]jT|jn:ɍ0& ϴYi7ℛeeV_^ɏqvrnEW%wtx^H`X_k ~)DQD"GIe yVY m`V pnj؈(ckj pԑ &ĊjovYVEg0 ʧ PFy&_ 0ٚY-zMZ""&u8iUy, `ujnjZ" pVwRJ ܆^Vv_:٣8yG &n6hpXvؠ ÂYqeq%N&wV=ʍf kj g '&n&nXP[ pO)p 0WmqgkH x5TE0]u&Vy_g[k XX L Ib]| $6Z!j|Pz prh4`kL2~yaP$N2ZEJ rjuuj [Gr8 u + "`,p#* Yvqgbu櫿j Cd\0g<Py@ h ^ɈVZ]kZǍ`Zuf ]F[yw8 2\3A#~X,Q{ewPR`1[miXT|*ٳ@l.fvr@[ H`,})j*-D`;fe^' rP0jfjj'gl u5tt;oX ЕTR&;Z;ˍ鳟Evq\ P뿬2 ְ/u\YL,f5 ` odžXI>+ g;;hYrGK 0Sу}~ U,i*J(BOe :0>k{HNY'xJ!;Ucsx`*Y`GA5`Pƌȟ ?˾Hܻ&`'TDL@ 0qǀK-" f\ ]ԝԁ O΢P EZ}J31$0}Jjm*T`P׸ PѬ׫ LPO0XϧdJА `2`X6a}P P  ؁mN0-S aM=& /Н p @ ؽ P = =MN" Ӥ`ߑQ:9fِ P~ Ͱ p `.} ,x=y2,P -ٰ 3tQ p 9@GGJ0 `0(.` p:];c>5ܲ- !֠ ` h0۵ mFK@ АRN m-cN:@&ݡ ` ` 0 wm }NP`8 QT96*a a ~7M%eLPPP`7 P˾}0zO;GaussSum-3.0.1/gausssum/folder.py0000664000175000017500000000225712660404041015437 0ustar noelnoel# # GaussSum (http://gausssum.sf.net) # Copyright (C) 2006-2009 Noel O'Boyle # # This program is free software; you can redistribute and/or modify it # under the terms of the GNU General Public License as published by the # Free Software Foundation; either version 2, or (at your option) any later # version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY, without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. # Creates a new output folder if it doesn't already exist # The folder is created in the same directory as the input file import os def folder(screen, logfilename, create=True): # Create the output directory if necessary logdir=os.path.dirname(logfilename) logname=os.path.basename(logfilename) gaussdir=os.path.join(logdir,"gausssum3") if create: if not os.path.isdir(gaussdir): screen.write("Creating new output folder\n") os.mkdir(gaussdir) else: screen.write("Using old output folder\n") return gaussdir GaussSum-3.0.1/gausssum/cclib/0000775000175000017500000000000012660404041014660 5ustar noelnoelGaussSum-3.0.1/gausssum/cclib/__init__.py0000664000175000017500000000000012660404041016757 0ustar noelnoelGaussSum-3.0.1/gausssum/cclib/parser/0000775000175000017500000000000012660404041016154 5ustar noelnoelGaussSum-3.0.1/gausssum/cclib/parser/gamessukparser.py0000664000175000017500000007172112660404041021572 0ustar noelnoel# -*- coding: utf-8 -*- # # This file is part of cclib (http://cclib.github.io), a library for parsing # and interpreting the results of computational chemistry packages. # # Copyright (C) 2006-2014, the cclib development team # # The library is free software, distributed under the terms of # the GNU Lesser General Public version 2.1 or later. You should have # received a copy of the license along with cclib. You can also access # the full license online at http://www.gnu.org/copyleft/lgpl.html. """Parser for GAMESS-UK output files""" import re import numpy from . import logfileparser from . import utils class GAMESSUK(logfileparser.Logfile): """A GAMESS UK log file""" SCFRMS, SCFMAX, SCFENERGY = list(range(3)) # Used to index self.scftargets[] def __init__(self, *args, **kwargs): # Call the __init__ method of the superclass super(GAMESSUK, self).__init__(logname="GAMESSUK", *args, **kwargs) def __str__(self): """Return a string representation of the object.""" return "GAMESS UK log file %s" % (self.filename) def __repr__(self): """Return a representation of the object.""" return 'GAMESSUK("%s")' % (self.filename) def normalisesym(self, label): """Use standard symmetry labels instead of GAMESS UK labels. >>> t = GAMESSUK("dummyfile.txt") >>> labels = ['a', 'a1', 'ag', "a'", 'a"', "a''", "a1''", 'a1"'] >>> labels.extend(["e1+", "e1-"]) >>> answer = [t.normalisesym(x) for x in labels] >>> answer ['A', 'A1', 'Ag', "A'", 'A"', 'A"', 'A1"', 'A1"', 'E1', 'E1'] """ label = label.replace("''", '"').replace("+", "").replace("-", "") ans = label[0].upper() + label[1:] return ans def before_parsing(self): # used for determining whether to add a second mosyms, etc. self.betamosyms = self.betamoenergies = self.betamocoeffs = False def extract(self, inputfile, line): """Extract information from the file object inputfile.""" if line[1:22] == "total number of atoms": natom = int(line.split()[-1]) self.set_attribute('natom', natom) if line[3:44] == "convergence threshold in optimization run": # Assuming that this is only found in the case of OPTXYZ # (i.e. an optimization in Cartesian coordinates) self.geotargets = [float(line.split()[-2])] if line[32:61] == "largest component of gradient": # This is the geotarget in the case of OPTXYZ if not hasattr(self, "geovalues"): self.geovalues = [] self.geovalues.append([float(line.split()[4])]) if line[37:49] == "convergence?": # Get the geovalues and geotargets for OPTIMIZE if not hasattr(self, "geovalues"): self.geovalues = [] self.geotargets = [] geotargets = [] geovalues = [] for i in range(4): temp = line.split() geovalues.append(float(temp[2])) if not self.geotargets: geotargets.append(float(temp[-2])) line = next(inputfile) self.geovalues.append(geovalues) if not self.geotargets: self.geotargets = geotargets # This is the only place coordinates are printed in single point calculations. Note that # in the following fragment, the basis set selection is not always printed: # # ****************** # molecular geometry # ****************** # # **************************************** # * basis selected is sto sto3g * # **************************************** # # ******************************************************************************* # * * # * atom atomic coordinates number of * # * charge x y z shells * # * * # ******************************************************************************* # * * # * * # * c 6.0 0.0000000 -2.6361501 0.0000000 2 * # * 1s 2sp * # * * # * * # * c 6.0 0.0000000 2.6361501 0.0000000 2 * # * 1s 2sp * # * * # ... # if line.strip() == "molecular geometry": self.updateprogress(inputfile, "Coordinates") self.skip_lines(inputfile, ['s', 'b', 's']) line = next(inputfile) if "basis selected is" in line: self.skip_lines(inputfile, ['s', 'b', 's', 's']) self.skip_lines(inputfile, ['header1', 'header2', 's', 's']) atomnos = [] atomcoords = [] line = next(inputfile) while line.strip(): line = next(inputfile) if line.strip()[1:10].strip() and list(set(line.strip())) != ['*']: atomcoords.append(list(map(float, line.split()[3:6]))) atomnos.append(int(round(float(line.split()[2])))) if not hasattr(self, "atomcoords"): self.atomcoords = [] self.atomcoords.append(atomcoords) self.set_attribute('atomnos', atomnos) # Each step of a geometry optimization will also print the coordinates: # # search 0 # ******************* # point 0 nuclear coordinates # ******************* # # x y z chg tag # ============================================================ # 0.0000000 -2.6361501 0.0000000 6.00 c # 0.0000000 2.6361501 0.0000000 6.00 c # .. # if line[40:59] == "nuclear coordinates": self.updateprogress(inputfile, "Coordinates") # We need not remember the first geometry in geometry optimizations, as this will # be already parsed from the "molecular geometry" section (see above). if not hasattr(self, 'firstnuccoords') or self.firstnuccoords: self.firstnuccoords = False return self.skip_lines(inputfile, ['s', 'b', 'colname', 'e']) atomcoords = [] atomnos = [] line = next(inputfile) while list(set(line.strip())) != ['=']: cols = line.split() atomcoords.append([utils.convertor(float(x), "bohr", "Angstrom") for x in cols[0:3]]) atomnos.append(int(float(cols[3]))) line = next(inputfile) if not hasattr(self, "atomcoords"): self.atomcoords = [] self.atomcoords.append(atomcoords) self.set_attribute('atomnos', atomnos) # This is printed when a geometry optimization succeeds, after the last gradient of the energy. if line[40:62] == "optimization converged": self.skip_line(inputfile, 's') if not hasattr(self, 'optdone'): self.optdone = [] self.optdone.append(len(self.geovalues)-1) # This is apparently printed when a geometry optimization is not converged but the job ends. if "minimisation not converging" in line: self.skip_line(inputfile, 's') self.optdone = [] if line[1:32] == "total number of basis functions": nbasis = int(line.split()[-1]) self.set_attribute('nbasis', nbasis) while line.find("charge of molecule")<0: line = next(inputfile) charge = int(line.split()[-1]) self.set_attribute('charge', charge) mult = int(next(inputfile).split()[-1]) self.set_attribute('mult', mult) alpha = int(next(inputfile).split()[-1])-1 beta = int(next(inputfile).split()[-1])-1 if self.mult == 1: self.homos = numpy.array([alpha], "i") else: self.homos = numpy.array([alpha, beta], "i") if line[37:69] == "s-matrix over gaussian basis set": self.aooverlaps = numpy.zeros((self.nbasis, self.nbasis), "d") self.skip_lines(inputfile, ['d', 'b']) i = 0 while i < self.nbasis: self.updateprogress(inputfile, "Overlap") self.skip_lines(inputfile, ['b', 'b', 'header', 'b', 'b']) for j in range(self.nbasis): temp = list(map(float, next(inputfile).split()[1:])) self.aooverlaps[j,(0+i):(len(temp)+i)] = temp i += len(temp) if line[18:43] == 'EFFECTIVE CORE POTENTIALS': self.skip_line(inputfile, 'stars') self.coreelectrons = numpy.zeros(self.natom, 'i') line = next(inputfile) while line[15:46] != "*"*31: if line.find("for atoms ...")>=0: atomindex = [] line = next(inputfile) while line.find("core charge")<0: broken = line.split() atomindex.extend([int(x.split("-")[0]) for x in broken]) line = next(inputfile) charge = float(line.split()[4]) for idx in atomindex: self.coreelectrons[idx-1] = self.atomnos[idx-1] - charge line = next(inputfile) if line[3:27] == "Wavefunction convergence": self.scftarget = float(line.split()[-2]) self.scftargets = [] if line[11:22] == "normal mode": if not hasattr(self, "vibfreqs"): self.vibfreqs = [] self.vibirs = [] units = next(inputfile) xyz = next(inputfile) equals = next(inputfile) line = next(inputfile) while line!=equals: temp = line.split() self.vibfreqs.append(float(temp[1])) self.vibirs.append(float(temp[-2])) line = next(inputfile) # Use the length of the vibdisps to figure out # how many rotations and translations to remove self.vibfreqs = self.vibfreqs[-len(self.vibdisps):] self.vibirs = self.vibirs[-len(self.vibdisps):] if line[44:73] == "normalised normal coordinates": self.skip_lines(inputfile, ['e', 'b', 'b']) self.vibdisps = [] freqnum = next(inputfile) while freqnum.find("=")<0: self.skip_lines(inputfile, ['b', 'e', 'freqs', 'e', 'b', 'header', 'e']) p = [ [] for x in range(9) ] for i in range(len(self.atomnos)): brokenx = list(map(float, next(inputfile)[25:].split())) brokeny = list(map(float, next(inputfile)[25:].split())) brokenz = list(map(float, next(inputfile)[25:].split())) for j,x in enumerate(list(zip(brokenx, brokeny, brokenz))): p[j].append(x) self.vibdisps.extend(p) self.skip_lines(inputfile, ['b', 'b']) freqnum = next(inputfile) if line[26:36] == "raman data": self.vibramans = [] self.skip_lines(inputfile, ['s', 'b', 'header', 'b']) line = next(inputfile) while line[1]!="*": self.vibramans.append(float(line.split()[3])) self.skip_line(inputfile, 'blank') line = next(inputfile) # Use the length of the vibdisps to figure out # how many rotations and translations to remove self.vibramans = self.vibramans[-len(self.vibdisps):] if line[3:11] == "SCF TYPE": self.scftype = line.split()[-2] assert self.scftype in ['rhf', 'uhf', 'gvb'], "%s not one of 'rhf', 'uhf' or 'gvb'" % self.scftype if line[15:31] == "convergence data": if not hasattr(self, "scfvalues"): self.scfvalues = [] self.scftargets.append([self.scftarget]) # Assuming it does not change over time while line[1:10] != "="*9: line = next(inputfile) line = next(inputfile) tester = line.find("tester") # Can be in a different place depending assert tester >= 0 while line[1:10] != "="*9: # May be two or three lines (unres) line = next(inputfile) scfvalues = [] line = next(inputfile) while line.strip(): if line[2:6] != "****": # e.g. **** recalulation of fock matrix on iteration 4 (examples/chap12/pyridine.out) scfvalues.append([float(line[tester-5:tester+6])]) line = next(inputfile) self.scfvalues.append(scfvalues) if line[10:22] == "total energy" and len(line.split()) == 3: if not hasattr(self, "scfenergies"): self.scfenergies = [] scfenergy = utils.convertor(float(line.split()[-1]), "hartree", "eV") self.scfenergies.append(scfenergy) # Total energies after Moller-Plesset corrections # Second order correction is always first, so its first occurance # triggers creation of mpenergies (list of lists of energies) # Further corrections are appended as found # Note: GAMESS-UK sometimes prints only the corrections, # so they must be added to the last value of scfenergies if line[10:32] == "mp2 correlation energy" or \ line[10:42] == "second order perturbation energy": if not hasattr(self, "mpenergies"): self.mpenergies = [] self.mpenergies.append([]) self.mp2correction = self.float(line.split()[-1]) self.mp2energy = self.scfenergies[-1] + self.mp2correction self.mpenergies[-1].append(utils.convertor(self.mp2energy, "hartree", "eV")) if line[10:41] == "third order perturbation energy": self.mp3correction = self.float(line.split()[-1]) self.mp3energy = self.mp2energy + self.mp3correction self.mpenergies[-1].append(utils.convertor(self.mp3energy, "hartree", "eV")) if line[40:59] == "molecular basis set": self.gbasis = [] line = next(inputfile) while line.find("contraction coefficients")<0: line = next(inputfile) equals = next(inputfile) blank = next(inputfile) atomname = next(inputfile) basisregexp = re.compile("\d*(\D+)") # Get everything after any digits shellcounter = 1 while line != equals: gbasis = [] # Stores basis sets on one atom blank = next(inputfile) blank = next(inputfile) line = next(inputfile) shellno = int(line.split()[0]) shellgap = shellno - shellcounter shellsize = 0 while len(line.split())!=1 and line!=equals: if line.split(): shellsize += 1 coeff = {} # coefficients and symmetries for a block of rows while line.strip() and line!=equals: temp = line.strip().split() # temp[1] may be either like (a) "1s" and "1sp", or (b) "s" and "sp" # See GAMESS-UK 7.0 distribution/examples/chap12/pyridine2_21m10r.out # for an example of the latter sym = basisregexp.match(temp[1]).groups()[0] assert sym in ['s', 'p', 'd', 'f', 'sp'], "'%s' not a recognized symmetry" % sym if sym == "sp": coeff.setdefault("S", []).append( (float(temp[3]), float(temp[6])) ) coeff.setdefault("P", []).append( (float(temp[3]), float(temp[10])) ) else: coeff.setdefault(sym.upper(), []).append( (float(temp[3]), float(temp[6])) ) line = next(inputfile) # either a blank or a continuation of the block if coeff: if sym == "sp": gbasis.append( ('S', coeff['S'])) gbasis.append( ('P', coeff['P'])) else: gbasis.append( (sym.upper(), coeff[sym.upper()])) if line == equals: continue line = next(inputfile) # either the start of the next block or the start of a new atom or # the end of the basis function section (signified by a line of equals) numtoadd = 1 + (shellgap // shellsize) shellcounter = shellno + shellsize for x in range(numtoadd): self.gbasis.append(gbasis) if line[50:70] == "----- beta set -----": self.betamosyms = True self.betamoenergies = True self.betamocoeffs = True # betamosyms will be turned off in the next # SYMMETRY ASSIGNMENT section if line[31:50] == "SYMMETRY ASSIGNMENT": if not hasattr(self, "mosyms"): self.mosyms = [] multiple = {'a':1, 'b':1, 'e':2, 't':3, 'g':4, 'h':5} equals = next(inputfile) line = next(inputfile) while line != equals: # There may be one or two lines of title (compare mg10.out and duhf_1.out) line = next(inputfile) mosyms = [] line = next(inputfile) while line != equals: temp = line[25:30].strip() if temp[-1] == '?': # e.g. e? or t? or g? (see example/chap12/na7mg_uhf.out) # for two As, an A and an E, and two Es of the same energy respectively. t = line[91:].strip().split() for i in range(1, len(t), 2): for j in range(multiple[t[i][0]]): # add twice for 'e', etc. mosyms.append(self.normalisesym(t[i])) else: for j in range(multiple[temp[0]]): mosyms.append(self.normalisesym(temp)) # add twice for 'e', etc. line = next(inputfile) assert len(mosyms) == self.nmo, "mosyms: %d but nmo: %d" % (len(mosyms), self.nmo) if self.betamosyms: # Only append if beta (otherwise with IPRINT SCF # it will add mosyms for every step of a geo opt) self.mosyms.append(mosyms) self.betamosyms = False elif self.scftype == 'gvb': # gvb has alpha and beta orbitals but they are identical self.mosysms = [mosyms, mosyms] else: self.mosyms = [mosyms] if line[50:62] == "eigenvectors": # Mocoeffs...can get evalues from here too # (only if using FORMAT HIGH though will they all be present) if not hasattr(self, "mocoeffs"): self.aonames = [] aonames = [] minus = next(inputfile) mocoeffs = numpy.zeros( (self.nmo, self.nbasis), "d") readatombasis = False if not hasattr(self, "atombasis"): self.atombasis = [] for i in range(self.natom): self.atombasis.append([]) readatombasis = True self.skip_lines(inputfile, ['b', 'b', 'evalues']) p = re.compile(r"\d+\s+(\d+)\s*(\w+) (\w+)") oldatomname = "DUMMY VALUE" mo = 0 while mo < self.nmo: self.updateprogress(inputfile, "Coefficients") self.skip_lines(inputfile, ['b', 'b', 'nums', 'b', 'b']) for basis in range(self.nbasis): line = next(inputfile) # Fill atombasis only first time around. if readatombasis: orbno = int(line[1:5])-1 atomno = int(line[6:9])-1 self.atombasis[atomno].append(orbno) if not self.aonames: pg = p.match(line[:18].strip()).groups() atomname = "%s%s%s" % (pg[1][0].upper(), pg[1][1:], pg[0]) if atomname != oldatomname: aonum = 1 oldatomname = atomname name = "%s_%d%s" % (atomname, aonum, pg[2].upper()) if name in aonames: aonum += 1 name = "%s_%d%s" % (atomname, aonum, pg[2].upper()) aonames.append(name) temp = list(map(float, line[19:].split())) mocoeffs[mo:(mo+len(temp)), basis] = temp # Fill atombasis only first time around. readatombasis = False if not self.aonames: self.aonames = aonames line = next(inputfile) # blank line while not line.strip(): line = next(inputfile) evalues = line if evalues[:17].strip(): # i.e. if these aren't evalues break # Not all the MOs are present mo += len(temp) mocoeffs = mocoeffs[0:(mo+len(temp)), :] # In case some aren't present if self.betamocoeffs: self.mocoeffs.append(mocoeffs) else: self.mocoeffs = [mocoeffs] if line[7:12] == "irrep": ########## eigenvalues ########### # This section appears once at the start of a geo-opt and once at the end # unless IPRINT SCF is used (when it appears at every step in addition) if not hasattr(self, "moenergies"): self.moenergies = [] equals = next(inputfile) while equals[1:5] != "====": # May be one or two lines of title (compare duhf_1.out and mg10.out) equals = next(inputfile) moenergies = [] line = next(inputfile) if not line.strip(): # May be a blank line here (compare duhf_1.out and mg10.out) line = next(inputfile) while line.strip() and line != equals: # May end with a blank or equals temp = line.strip().split() moenergies.append(utils.convertor(float(temp[2]), "hartree", "eV")) line = next(inputfile) self.nmo = len(moenergies) if self.betamoenergies: self.moenergies.append(moenergies) self.betamoenergies = False elif self.scftype == 'gvb': self.moenergies = [moenergies, moenergies] else: self.moenergies = [moenergies] # The dipole moment is printed by default at the beginning of the wavefunction analysis, # but the value is in atomic units, so we need to convert to Debye. It seems pretty # evident that the reference point is the origin (0,0,0) which is also the center # of mass after reorientation at the beginning of the job, although this is not # stated anywhere (would be good to check). # # ********************* # wavefunction analysis # ********************* # # commence analysis at 24.61 seconds # # dipole moments # # # nuclear electronic total # # x 0.0000000 0.0000000 0.0000000 # y 0.0000000 0.0000000 0.0000000 # z 0.0000000 0.0000000 0.0000000 # if line.strip() == "dipole moments": # In older version there is only one blank line before the header, # and newer version there are two. self.skip_line(inputfile, 'blank') line = next(inputfile) if not line.strip(): line = next(inputfile) self.skip_line(inputfile, 'blank') dipole = [] for i in range(3): line = next(inputfile) dipole.append(float(line.split()[-1])) reference = [0.0, 0.0, 0.0] dipole = utils.convertor(numpy.array(dipole), "ebohr", "Debye") if not hasattr(self, 'moments'): self.moments = [reference, dipole] else: assert self.moments[1] == dipole # Net atomic charges are not printed at all, it seems, # but you can get at them from nuclear charges and # electron populations, which are printed like so: # # --------------------------------------- # mulliken and lowdin population analyses # --------------------------------------- # # ----- total gross population in aos ------ # # 1 1 c s 1.99066 1.98479 # 2 1 c s 1.14685 1.04816 # ... # # ----- total gross population on atoms ---- # # 1 c 6.0 6.00446 5.99625 # 2 c 6.0 6.00446 5.99625 # 3 c 6.0 6.07671 6.04399 # ... if line[10:49] == "mulliken and lowdin population analyses": if not hasattr(self, "atomcharges"): self.atomcharges = {} while not "total gross population on atoms" in line: line = next(inputfile) self.skip_line(inputfile, 'blank') line = next(inputfile) mulliken, lowdin = [], [] while line.strip(): nuclear = float(line.split()[2]) mulliken.append(nuclear - float(line.split()[3])) lowdin.append(nuclear - float(line.split()[4])) line = next(inputfile) self.atomcharges["mulliken"] = mulliken self.atomcharges["lowdin"] = lowdin # ----- spinfree UHF natural orbital occupations ----- # # 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 # # 2.0000000 2.0000000 2.0000000 2.0000000 2.0000000 1.9999997 1.9999997 # ... if "natural orbital occupations" in line: occupations = [] self.skip_line(inputfile, "blank") line = inputfile.next() while line.strip(): occupations += map(float, line.split()) self.skip_line(inputfile, "blank") line = inputfile.next() self.set_attribute('nooccnos', occupations) if __name__ == "__main__": import doctest doctest.testmod() GaussSum-3.0.1/gausssum/cclib/parser/gaussianparser.py0000664000175000017500000017401712660404041021567 0ustar noelnoel# -*- coding: utf-8 -*- # # This file is part of cclib (http://cclib.github.io), a library for parsing # and interpreting the results of computational chemistry packages. # # Copyright (C) 2006-2014, the cclib development team # # The library is free software, distributed under the terms of # the GNU Lesser General Public version 2.1 or later. You should have # received a copy of the license along with cclib. You can also access # the full license online at http://www.gnu.org/copyleft/lgpl.html. """Parser for Gaussian output files""" from __future__ import print_function import re import numpy from . import logfileparser from . import utils class Gaussian(logfileparser.Logfile): """A Gaussian 98/03 log file.""" def __init__(self, *args, **kwargs): # Call the __init__ method of the superclass super(Gaussian, self).__init__(logname="Gaussian", *args, **kwargs) def __str__(self): """Return a string representation of the object.""" return "Gaussian log file %s" % (self.filename) def __repr__(self): """Return a representation of the object.""" return 'Gaussian("%s")' % (self.filename) def normalisesym(self, label): """Use standard symmetry labels instead of Gaussian labels. To normalise: (1) If label is one of [SG, PI, PHI, DLTA], replace by [sigma, pi, phi, delta] (2) replace any G or U by their lowercase equivalent >>> sym = Gaussian("dummyfile").normalisesym >>> labels = ['A1', 'AG', 'A1G', "SG", "PI", "PHI", "DLTA", 'DLTU', 'SGG'] >>> map(sym, labels) ['A1', 'Ag', 'A1g', 'sigma', 'pi', 'phi', 'delta', 'delta.u', 'sigma.g'] """ # note: DLT must come after DLTA greek = [('SG', 'sigma'), ('PI', 'pi'), ('PHI', 'phi'), ('DLTA', 'delta'), ('DLT', 'delta')] for k, v in greek: if label.startswith(k): tmp = label[len(k):] label = v if tmp: label = v + "." + tmp ans = label.replace("U", "u").replace("G", "g") return ans def before_parsing(self): # Used to index self.scftargets[]. SCFRMS, SCFMAX, SCFENERGY = list(range(3)) # Flag for identifying Coupled Cluster runs. self.coupledcluster = False # Fragment number for counterpoise or fragment guess calculations # (normally zero). self.counterpoise = 0 # Flag for identifying ONIOM calculations. self.oniom = False def after_parsing(self): # Correct the percent values in the etsecs in the case of # a restricted calculation. The following has the # effect of including each transition twice. if hasattr(self, "etsecs") and len(self.homos) == 1: new_etsecs = [[(x[0], x[1], x[2] * numpy.sqrt(2)) for x in etsec] for etsec in self.etsecs] self.etsecs = new_etsecs if hasattr(self, "scanenergies"): self.scancoords = [] self.scancoords = self.atomcoords if (hasattr(self, 'enthalpy') and hasattr(self, 'temperature') and hasattr(self, 'freeenergy')): self.set_attribute('entropy', (self.enthalpy - self.freeenergy) / self.temperature) # This bit is needed in order to trim coordinates that are printed a second time # at the end of geometry optimizations. Note that we need to do this for both atomcoords # and inputcoords. The reason is that normally a standard orientation is printed and that # is what we parse into atomcoords, but inputcoords stores the input (unmodified) coordinates # and that is copied over to atomcoords if no standard oritentation was printed, which happens # for example for jobs with no symmetry. This last step, however, is now generic for all parsers. # Perhaps then this part should also be generic code... # Regression that tests this: Gaussian03/cyclopropenyl.rhf.g03.cut.log if hasattr(self, 'optdone') and len(self.optdone) > 0: last_point = self.optdone[-1] if hasattr(self, 'atomcoords'): self.atomcoords = self.atomcoords[:last_point + 1] if hasattr(self, 'inputcoords'): self.inputcoords = self.inputcoords[:last_point + 1] def extract(self, inputfile, line): """Extract information from the file object inputfile.""" # This block contains some general information as well as coordinates, # which could be parsed in the future: # # Symbolic Z-matrix: # Charge = 0 Multiplicity = 1 # C 0.73465 0. 0. # C 1.93465 0. 0. # C # ... # # It also lists fragments, if there are any, which is potentially valuable: # # Symbolic Z-matrix: # Charge = 0 Multiplicity = 1 in supermolecule # Charge = 0 Multiplicity = 1 in fragment 1. # Charge = 0 Multiplicity = 1 in fragment 2. # B(Fragment=1) 0.06457 -0.0279 0.01364 # H(Fragment=1) 0.03117 -0.02317 1.21604 # ... # # Note, however, that currently we only parse information for the whole system # or supermolecule as Gaussian calls it. if line.strip() == "Symbolic Z-matrix:": self.updateprogress(inputfile, "Symbolic Z-matrix", self.fupdate) line = inputfile.next() while line.split()[0] == 'Charge': # For the supermolecule, we can parse the charge and multicplicity. regex = ".*=(.*)Mul.*=\s*-?(\d+).*" match = re.match(regex, line) assert match, "Something unusual about the line: '%s'" % line self.set_attribute('charge', int(match.groups()[0])) self.set_attribute('mult', int(match.groups()[1])) if line.split()[-2] == "fragment": self.nfragments = int(line.split()[-1].strip('.')) if line.strip()[-13:] == "model system.": self.nmodels = getattr(self, 'nmodels', 0) + 1 line = inputfile.next() # The remaining part will allow us to get the atom count. # When coordinates are given, there is a blank line at the end, but if # there is a Z-matrix here, there will also be variables and we need to # stop at those to get the right atom count. # Also, in older versions there is bo blank line (G98 regressions), # so we need to watch out for leaving the link. natom = 0 while line.split() and not "Variables" in line and not "Leave Link" in line: natom += 1 line = inputfile.next() self.set_attribute('natom', natom) # Continuing from above, there is not always a symbolic matrix, for example # if the Z-matrix was in the input file. In such cases, try to match the # line and get at the charge and multiplicity. # # Charge = 0 Multiplicity = 1 in supermolecule # Charge = 0 Multiplicity = 1 in fragment 1. # Charge = 0 Multiplicity = 1 in fragment 2. if line[1:7] == 'Charge' and line.find("Multiplicity") >= 0: self.updateprogress(inputfile, "Charge and Multiplicity", self.fupdate) if line.split()[-1] == "supermolecule" or not "fragment" in line: regex = ".*=(.*)Mul.*=\s*-?(\d+).*" match = re.match(regex, line) assert match, "Something unusual about the line: '%s'" % line self.set_attribute('charge', int(match.groups()[0])) self.set_attribute('mult', int(match.groups()[1])) if line.split()[-2] == "fragment": self.nfragments = int(line.split()[-1].strip('.')) # Number of atoms is also explicitely printed after the above. if line[1:8] == "NAtoms=": self.updateprogress(inputfile, "Attributes", self.fupdate) natom = int(line.split()[1]) self.set_attribute('natom', natom) # Catch message about completed optimization. if line[1:23] == "Optimization completed": if not hasattr(self, 'optdone'): self.optdone = [] self.optdone.append(len(self.geovalues) - 1) # Catch message about stopped optimization (not converged). if line[1:21] == "Optimization stopped": if not hasattr(self, "optdone"): self.optdone = [] # Extract the atomic numbers and coordinates from the input orientation, # in the event the standard orientation isn't available. if line.find("Input orientation") > -1 or line.find("Z-Matrix orientation") > -1: # If this is a counterpoise calculation, this output means that # the supermolecule is now being considered, so we can set: self.counterpoise = 0 self.updateprogress(inputfile, "Attributes", self.cupdate) if not hasattr(self, "inputcoords"): self.inputcoords = [] self.inputatoms = [] self.skip_lines(inputfile, ['d', 'cols', 'cols', 'd']) atomcoords = [] line = next(inputfile) while list(set(line.strip())) != ["-"]: broken = line.split() self.inputatoms.append(int(broken[1])) atomcoords.append(list(map(float, broken[3:6]))) line = next(inputfile) self.inputcoords.append(atomcoords) self.set_attribute('atomnos', self.inputatoms) self.set_attribute('natom', len(self.inputatoms)) # Extract the atomic masses. # Typical section: # Isotopes and Nuclear Properties: #(Nuclear quadrupole moments (NQMom) in fm**2, nuclear magnetic moments (NMagM) # in nuclear magnetons) # # Atom 1 2 3 4 5 6 7 8 9 10 # IAtWgt= 12 12 12 12 12 1 1 1 12 12 # AtmWgt= 12.0000000 12.0000000 12.0000000 12.0000000 12.0000000 1.0078250 1.0078250 1.0078250 12.0000000 12.0000000 # NucSpn= 0 0 0 0 0 1 1 1 0 0 # AtZEff= -3.6000000 -3.6000000 -3.6000000 -3.6000000 -3.6000000 -1.0000000 -1.0000000 -1.0000000 -3.6000000 -3.6000000 # NQMom= 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 # NMagM= 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 2.7928460 2.7928460 2.7928460 0.0000000 0.0000000 # ... with blank lines dividing blocks of ten, and Leave Link 101 at the end. # This is generally parsed before coordinates, so atomnos is not defined. # Note that in Gaussian03 the comments are not there yet and the labels are different. if line.strip() == "Isotopes and Nuclear Properties:": if not hasattr(self, "atommasses"): self.atommasses = [] line = next(inputfile) while line[1:16] != "Leave Link 101": if line[1:8] == "AtmWgt=": self.atommasses.extend(list(map(float,line.split()[1:]))) line = next(inputfile) # Extract the atomic numbers and coordinates of the atoms. if line.strip() == "Standard orientation:": self.updateprogress(inputfile, "Attributes", self.cupdate) # If this is a counterpoise calculation, this output means that # the supermolecule is now being considered, so we can set: self.counterpoise = 0 if not hasattr(self, "atomcoords"): self.atomcoords = [] self.skip_lines(inputfile, ['d', 'cols', 'cols', 'd']) atomnos = [] atomcoords = [] line = next(inputfile) while list(set(line.strip())) != ["-"]: broken = line.split() atomnos.append(int(broken[1])) atomcoords.append(list(map(float, broken[-3:]))) line = next(inputfile) self.atomcoords.append(atomcoords) self.set_attribute('natom', len(atomnos)) self.set_attribute('atomnos', atomnos) # This is a bit of a hack for regression Gaussian09/BH3_fragment_guess.pop_minimal.log # to skip output for all fragments, assuming the supermolecule is always printed first. # Eventually we want to make this more general, or even better parse the output for # all fragment, but that will happen in a newer version of cclib. if line[1:16] == "Fragment guess:" and getattr(self, 'nfragments', 0) > 1: if not "full" in line: inputfile.file.seek(0, 2) # Another hack for regression Gaussian03/ortho_prod_prod_freq.log, which is an ONIOM job. # Basically for now we stop parsing after the output for the real system, because # currently we don't support changes in system size or fragments in cclib. When we do, # we will want to parse the model systems, too, and that is what nmodels could track. if "ONIOM: generating point" in line and line.strip()[-13:] == 'model system.' and getattr(self, 'nmodels', 0) > 0: inputfile.file.seek(0,2) # With the gfinput keyword, the atomic basis set functios are: # # AO basis set in the form of general basis input (Overlap normalization): # 1 0 # S 3 1.00 0.000000000000 # 0.7161683735D+02 0.1543289673D+00 # 0.1304509632D+02 0.5353281423D+00 # 0.3530512160D+01 0.4446345422D+00 # SP 3 1.00 0.000000000000 # 0.2941249355D+01 -0.9996722919D-01 0.1559162750D+00 # 0.6834830964D+00 0.3995128261D+00 0.6076837186D+00 # 0.2222899159D+00 0.7001154689D+00 0.3919573931D+00 # **** # 2 0 # S 3 1.00 0.000000000000 # 0.7161683735D+02 0.1543289673D+00 # ... # # The same is also printed when the gfprint keyword is used, but the # interstitial lines differ and there are no stars between atoms: # # AO basis set (Overlap normalization): # Atom C1 Shell 1 S 3 bf 1 - 1 0.509245180608 -2.664678875191 0.000000000000 # 0.7161683735D+02 0.1543289673D+00 # 0.1304509632D+02 0.5353281423D+00 # 0.3530512160D+01 0.4446345422D+00 # Atom C1 Shell 2 SP 3 bf 2 - 5 0.509245180608 -2.664678875191 0.000000000000 # 0.2941249355D+01 -0.9996722919D-01 0.1559162750D+00 # ... #ONIOM calculations result basis sets reported for atoms that are not in order of atom number which breaks this code (line 390 relies on atoms coming in order) if line[1:13] == "AO basis set" and not self.oniom: self.gbasis = [] # For counterpoise fragment calcualtions, skip these lines. if self.counterpoise != 0: return atom_line = inputfile.next() self.gfprint = atom_line.split()[0] == "Atom" self.gfinput = not self.gfprint # Note how the shell information is on a separate line for gfinput, # whereas for gfprint it is on the same line as atom information. if self.gfinput: shell_line = inputfile.next() shell = [] while len(self.gbasis) < self.natom: if self.gfprint: cols = atom_line.split() subshells = cols[4] ngauss = int(cols[5]) else: cols = shell_line.split() subshells = cols[0] ngauss = int(cols[1]) parameters = [] for ig in range(ngauss): line = inputfile.next() parameters.append(list(map(self.float, line.split()))) for iss, ss in enumerate(subshells): contractions = [] for param in parameters: exponent = param[0] coefficient = param[iss+1] contractions.append((exponent, coefficient)) subshell = (ss, contractions) shell.append(subshell) if self.gfprint: line = inputfile.next() if line.split()[0] == "Atom": atomnum = int(re.sub(r"\D", "", line.split()[1])) if atomnum == len(self.gbasis) + 2: self.gbasis.append(shell) shell = [] atom_line = line else: self.gbasis.append(shell) else: line = inputfile.next() if line.strip() == "****": self.gbasis.append(shell) shell = [] atom_line = inputfile.next() shell_line = inputfile.next() else: shell_line = line # Find the targets for SCF convergence (QM calcs). if line[1:44] == 'Requested convergence on RMS density matrix': if not hasattr(self, "scftargets"): self.scftargets = [] # The following can happen with ONIOM which are mixed SCF # and semi-empirical if type(self.scftargets) == type(numpy.array([])): self.scftargets = [] scftargets = [] # The RMS density matrix. scftargets.append(self.float(line.split('=')[1].split()[0])) line = next(inputfile) # The MAX density matrix. scftargets.append(self.float(line.strip().split('=')[1][:-1])) line = next(inputfile) # For G03, there's also the energy (not for G98). if line[1:10] == "Requested": scftargets.append(self.float(line.strip().split('=')[1][:-1])) self.scftargets.append(scftargets) # Extract SCF convergence information (QM calcs). if line[1:10] == 'Cycle 1': if not hasattr(self, "scfvalues"): self.scfvalues = [] scfvalues = [] line = next(inputfile) while line.find("SCF Done") == -1: self.updateprogress(inputfile, "QM convergence", self.fupdate) if line.find(' E=') == 0: self.logger.debug(line) # RMSDP=3.74D-06 MaxDP=7.27D-05 DE=-1.73D-07 OVMax= 3.67D-05 # or # RMSDP=1.13D-05 MaxDP=1.08D-04 OVMax= 1.66D-04 if line.find(" RMSDP") == 0: parts = line.split() newlist = [self.float(x.split('=')[1]) for x in parts[0:2]] energy = 1.0 if len(parts) > 4: energy = parts[2].split('=')[1] if energy == "": energy = self.float(parts[3]) else: energy = self.float(energy) if len(self.scftargets[0]) == 3: # Only add the energy if it's a target criteria newlist.append(energy) scfvalues.append(newlist) try: line = next(inputfile) # May be interupted by EOF. except StopIteration: break self.scfvalues.append(scfvalues) # Extract SCF convergence information (AM1, INDO and other semi-empirical calcs). # The output (for AM1) looks like this: # Ext34=T Pulay=F Camp-King=F BShift= 0.00D+00 # It= 1 PL= 0.103D+01 DiagD=T ESCF= 31.564733 Diff= 0.272D+02 RMSDP= 0.152D+00. # It= 2 PL= 0.114D+00 DiagD=T ESCF= 7.265370 Diff=-0.243D+02 RMSDP= 0.589D-02. # ... # It= 11 PL= 0.184D-04 DiagD=F ESCF= 4.687669 Diff= 0.260D-05 RMSDP= 0.134D-05. # It= 12 PL= 0.105D-04 DiagD=F ESCF= 4.687669 Diff=-0.686D-07 RMSDP= 0.215D-05. # 4-point extrapolation. # It= 13 PL= 0.110D-05 DiagD=F ESCF= 4.687669 Diff=-0.111D-06 RMSDP= 0.653D-07. # Energy= 0.172272018655 NIter= 14. if line[1:4] == 'It=': scftargets = numpy.array([1E-7], "d") # This is the target value for the rms scfvalues = [[]] while line.find(" Energy") == -1: self.updateprogress(inputfile, "AM1 Convergence") if line[1:4] == "It=": parts = line.strip().split() scfvalues[0].append(self.float(parts[-1][:-1])) line = next(inputfile) # If an AM1 or INDO guess is used (Guess=INDO in the input, for example), # this will be printed after a single iteration, so that is the line # that should trigger a break from this loop. At least that's what we see # for regression Gaussian/Gaussian09/guessIndo_modified_ALT.out if line[:14] == " Initial guess": break # Attach the attributes to the object Only after the energy is found . if line.find(" Energy") == 0: self.scftargets = scftargets self.scfvalues = scfvalues # Note: this needs to follow the section where 'SCF Done' is used # to terminate a loop when extracting SCF convergence information. if line[1:9] == 'SCF Done': if not hasattr(self, "scfenergies"): self.scfenergies = [] self.scfenergies.append(utils.convertor(self.float(line.split()[4]), "hartree", "eV")) # gmagoon 5/27/09: added scfenergies reading for PM3 case # Example line: " Energy= -0.077520562724 NIter= 14." # See regression Gaussian03/QVGXLLKOCUKJST-UHFFFAOYAJmult3Fixed.out if line[1:8] == 'Energy=': if not hasattr(self, "scfenergies"): self.scfenergies = [] self.scfenergies.append(utils.convertor(self.float(line.split()[1]), "hartree", "eV")) # Total energies after Moller-Plesset corrections. # Second order correction is always first, so its first occurance # triggers creation of mpenergies (list of lists of energies). # Further MP2 corrections are appended as found. # # Example MP2 output line: # E2 = -0.9505918144D+00 EUMP2 = -0.28670924198852D+03 # Warning! this output line is subtly different for MP3/4/5 runs if "EUMP2" in line[27:34]: if not hasattr(self, "mpenergies"): self.mpenergies = [] self.mpenergies.append([]) mp2energy = self.float(line.split("=")[2]) self.mpenergies[-1].append(utils.convertor(mp2energy, "hartree", "eV")) # Example MP3 output line: # E3= -0.10518801D-01 EUMP3= -0.75012800924D+02 if line[34:39] == "EUMP3": mp3energy = self.float(line.split("=")[2]) self.mpenergies[-1].append(utils.convertor(mp3energy, "hartree", "eV")) # Example MP4 output lines: # E4(DQ)= -0.31002157D-02 UMP4(DQ)= -0.75015901139D+02 # E4(SDQ)= -0.32127241D-02 UMP4(SDQ)= -0.75016013648D+02 # E4(SDTQ)= -0.32671209D-02 UMP4(SDTQ)= -0.75016068045D+02 # Energy for most substitutions is used only (SDTQ by default) if line[34:42] == "UMP4(DQ)": mp4energy = self.float(line.split("=")[2]) line = next(inputfile) if line[34:43] == "UMP4(SDQ)": mp4energy = self.float(line.split("=")[2]) line = next(inputfile) if line[34:44] == "UMP4(SDTQ)": mp4energy = self.float(line.split("=")[2]) self.mpenergies[-1].append(utils.convertor(mp4energy, "hartree", "eV")) # Example MP5 output line: # DEMP5 = -0.11048812312D-02 MP5 = -0.75017172926D+02 if line[29:32] == "MP5": mp5energy = self.float(line.split("=")[2]) self.mpenergies[-1].append(utils.convertor(mp5energy, "hartree", "eV")) # Total energies after Coupled Cluster corrections. # Second order MBPT energies (MP2) are also calculated for these runs, # but the output is the same as when parsing for mpenergies. # Read the consecutive correlated energies # but append only the last one to ccenergies. # Only the highest level energy is appended - ex. CCSD(T), not CCSD. if line[1:10] == "DE(Corr)=" and line[27:35] == "E(CORR)=": self.ccenergy = self.float(line.split()[3]) if line[1:10] == "T5(CCSD)=": line = next(inputfile) if line[1:9] == "CCSD(T)=": self.ccenergy = self.float(line.split()[1]) if line[12:53] == "Population analysis using the SCF density": if hasattr(self, "ccenergy"): if not hasattr(self, "ccenergies"): self.ccenergies = [] self.ccenergies.append(utils.convertor(self.ccenergy, "hartree", "eV")) del self.ccenergy # Geometry convergence information. if line[49:59] == 'Converged?': if not hasattr(self, "geotargets"): self.geovalues = [] self.geotargets = numpy.array([0.0, 0.0, 0.0, 0.0], "d") newlist = [0]*4 for i in range(4): line = next(inputfile) self.logger.debug(line) parts = line.split() try: value = self.float(parts[2]) except ValueError: self.logger.error("Problem parsing the value for geometry optimisation: %s is not a number." % parts[2]) else: newlist[i] = value self.geotargets[i] = self.float(parts[3]) self.geovalues.append(newlist) # Gradients. # Read in the cartesian energy gradients (forces) from a block like this: # ------------------------------------------------------------------- # Center Atomic Forces (Hartrees/Bohr) # Number Number X Y Z # ------------------------------------------------------------------- # 1 1 -0.012534744 -0.021754635 -0.008346094 # 2 6 0.018984731 0.032948887 -0.038003451 # 3 1 -0.002133484 -0.006226040 0.023174772 # 4 1 -0.004316502 -0.004968213 0.023174772 # -2 -0.001830728 -0.000743108 -0.000196625 # ------------------------------------------------------------------ # # The "-2" line is for a dummy atom # # Then optimization is done in internal coordinates, Gaussian also # print the forces in internal coordinates, which can be produced from # the above. This block looks like this: # Variable Old X -DE/DX Delta X Delta X Delta X New X # (Linear) (Quad) (Total) # ch 2.05980 0.01260 0.00000 0.01134 0.01134 2.07114 # hch 1.75406 0.09547 0.00000 0.24861 0.24861 2.00267 # hchh 2.09614 0.01261 0.00000 0.16875 0.16875 2.26489 # Item Value Threshold Converged? if line[37:43] == "Forces": if not hasattr(self, "grads"): self.grads = [] self.skip_lines(inputfile, ['header', 'd']) forces = [] line = next(inputfile) while list(set(line.strip())) != ['-']: tmpforces = [] for N in range(3): # Fx, Fy, Fz force = line[23+N*15:38+N*15] if force.startswith("*"): force = "NaN" tmpforces.append(float(force)) forces.append(tmpforces) line = next(inputfile) self.grads.append(forces) #Extract PES scan data #Summary of the potential surface scan: # N A SCF #---- --------- ----------- # 1 109.0000 -76.43373 # 2 119.0000 -76.43011 # 3 129.0000 -76.42311 # 4 139.0000 -76.41398 # 5 149.0000 -76.40420 # 6 159.0000 -76.39541 # 7 169.0000 -76.38916 # 8 179.0000 -76.38664 # 9 189.0000 -76.38833 # 10 199.0000 -76.39391 # 11 209.0000 -76.40231 #---- --------- ----------- if "Summary of the potential surface scan:" in line: scanenergies = [] scanparm = [] colmnames = next(inputfile) hyphens = next(inputfile) line = next(inputfile) while line != hyphens: broken = line.split() scanenergies.append(float(broken[-1])) scanparm.append(map(float, broken[1:-1])) line = next(inputfile) if not hasattr(self, "scanenergies"): self.scanenergies = [] self.scanenergies = scanenergies if not hasattr(self, "scanparm"): self.scanparm = [] self.scanparm = scanparm if not hasattr(self, "scannames"): self.scannames = colmnames.split()[1:-1] # Orbital symmetries. if line[1:20] == 'Orbital symmetries:' and not hasattr(self, "mosyms"): # For counterpoise fragments, skip these lines. if self.counterpoise != 0: return self.updateprogress(inputfile, "MO Symmetries", self.fupdate) self.mosyms = [[]] line = next(inputfile) unres = False if line.find("Alpha Orbitals") == 1: unres = True line = next(inputfile) i = 0 while len(line) > 18 and line[17] == '(': if line.find('Virtual') >= 0: self.homos = numpy.array([i-1], "i") # 'HOMO' indexes the HOMO in the arrays parts = line[17:].split() for x in parts: self.mosyms[0].append(self.normalisesym(x.strip('()'))) i += 1 line = next(inputfile) if unres: line = next(inputfile) # Repeat with beta orbital information i = 0 self.mosyms.append([]) while len(line) > 18 and line[17] == '(': if line.find('Virtual')>=0: # Here we consider beta # If there was also an alpha virtual orbital, # we will store two indices in the array # Otherwise there is no alpha virtual orbital, # only beta virtual orbitals, and we initialize # the array with one element. See the regression # QVGXLLKOCUKJST-UHFFFAOYAJmult3Fixed.out # donated by Gregory Magoon (gmagoon). if (hasattr(self, "homos")): # Extend the array to two elements # 'HOMO' indexes the HOMO in the arrays self.homos.resize([2]) self.homos[1] = i-1 else: # 'HOMO' indexes the HOMO in the arrays self.homos = numpy.array([i-1], "i") parts = line[17:].split() for x in parts: self.mosyms[1].append(self.normalisesym(x.strip('()'))) i += 1 line = next(inputfile) # Some calculations won't explicitely print the number of basis sets used, # and will occasionally drop some without warning. We can infer the number, # however, from the MO symmetries printed here. Specifically, this fixes # regression Gaussian/Gaussian09/dvb_sp_terse.log (#23 on github). self.set_attribute('nmo', len(self.mosyms[-1])) # Alpha/Beta electron eigenvalues. if line[1:6] == "Alpha" and line.find("eigenvalues") >= 0: # For counterpoise fragments, skip these lines. if self.counterpoise != 0: return # For ONIOM calcs, ignore this section in order to bypass assertion failure. if self.oniom: return self.updateprogress(inputfile, "Eigenvalues", self.fupdate) self.moenergies = [[]] HOMO = -2 while line.find('Alpha') == 1: if line.split()[1] == "virt." and HOMO == -2: # If there aren't any symmetries, this is a good way to find the HOMO. # Also, check for consistency if homos was already parsed. HOMO = len(self.moenergies[0])-1 if hasattr(self, "homos"): assert HOMO == self.homos[0] else: self.homos = numpy.array([HOMO], "i") # Convert to floats and append to moenergies, but sometimes Gaussian # doesn't print correctly so test for ValueError (bug 1756789). part = line[28:] i = 0 while i*10+4 < len(part): s = part[i*10:(i+1)*10] try: x = self.float(s) except ValueError: x = numpy.nan self.moenergies[0].append(utils.convertor(x, "hartree", "eV")) i += 1 line = next(inputfile) # If, at this point, self.homos is unset, then there were not # any alpha virtual orbitals if not hasattr(self, "homos"): HOMO = len(self.moenergies[0])-1 self.homos = numpy.array([HOMO], "i") if line.find('Beta') == 2: self.moenergies.append([]) HOMO = -2 while line.find('Beta') == 2: if line.split()[1] == "virt." and HOMO == -2: # If there aren't any symmetries, this is a good way to find the HOMO. # Also, check for consistency if homos was already parsed. HOMO = len(self.moenergies[1])-1 if len(self.homos) == 2: assert HOMO == self.homos[1] else: self.homos.resize([2]) self.homos[1] = HOMO part = line[28:] i = 0 while i*10+4 < len(part): x = part[i*10:(i+1)*10] self.moenergies[1].append(utils.convertor(self.float(x), "hartree", "eV")) i += 1 line = next(inputfile) self.moenergies = [numpy.array(x, "d") for x in self.moenergies] # Start of the IR/Raman frequency section. # Caution is advised here, as additional frequency blocks # can be printed by Gaussian (with slightly different formats), # often doubling the information printed. # See, for a non-standard exmaple, regression Gaussian98/test_H2.log if line[1:14] == "Harmonic freq": self.updateprogress(inputfile, "Frequency Information", self.fupdate) removeold = False # The whole block should not have any blank lines. while line.strip() != "": # The line with indices if line[1:15].strip() == "" and line[15:23].strip().isdigit(): freqbase = int(line[15:23]) if freqbase == 1 and hasattr(self, 'vibfreqs'): # This is a reparse of this information removeold = True # Lines with symmetries and symm. indices begin with whitespace. if line[1:15].strip() == "" and not line[15:23].strip().isdigit(): if not hasattr(self, 'vibsyms'): self.vibsyms = [] syms = line.split() self.vibsyms.extend(syms) if line[1:15] == "Frequencies --": if not hasattr(self, 'vibfreqs'): self.vibfreqs = [] if removeold: # This is a reparse, so throw away the old info if hasattr(self, "vibsyms"): # We have already parsed the vibsyms so don't throw away! self.vibsyms = self.vibsyms[-len(line[15:].split()):] if hasattr(self, "vibirs"): self.vibirs = [] if hasattr(self, 'vibfreqs'): self.vibfreqs = [] if hasattr(self, 'vibramans'): self.vibramans = [] if hasattr(self, 'vibdisps'): self.vibdisps = [] removeold = False freqs = [self.float(f) for f in line[15:].split()] self.vibfreqs.extend(freqs) if line[1:15] == "IR Inten --": if not hasattr(self, 'vibirs'): self.vibirs = [] irs = [] for ir in line[15:].split(): try: irs.append(self.float(ir)) except ValueError: irs.append(self.float('nan')) self.vibirs.extend(irs) if line[1:15] == "Raman Activ --": if not hasattr(self, 'vibramans'): self.vibramans = [] ramans = [] for raman in line[15:].split(): try: ramans.append(self.float(raman)) except ValueError: ramans.append(self.float('nan')) self.vibramans.extend(ramans) # Block with displacement should start with this. if line.strip().split()[0:3] == ["Atom", "AN", "X"]: if not hasattr(self, 'vibdisps'): self.vibdisps = [] disps = [] for n in range(self.natom): line = next(inputfile) numbers = [float(s) for s in line[10:].split()] N = len(numbers) // 3 if not disps: for n in range(N): disps.append([]) for n in range(N): disps[n].append(numbers[3*n:3*n+3]) self.vibdisps.extend(disps) line = next(inputfile) # Electronic transitions. if line[1:14] == "Excited State": if not hasattr(self, "etenergies"): self.etenergies = [] self.etoscs = [] self.etsyms = [] self.etsecs = [] # Need to deal with lines like: # (restricted calc) # Excited State 1: Singlet-BU 5.3351 eV 232.39 nm f=0.1695 # (unrestricted calc) (first excited state is 2!) # Excited State 2: ?Spin -A 0.1222 eV 10148.75 nm f=0.0000 # (Gaussian 09 ZINDO) # Excited State 1: Singlet-?Sym 2.5938 eV 478.01 nm f=0.0000 =0.000 p = re.compile(":(?P.*?)(?P-?\d*\.\d*) eV") groups = p.search(line).groups() self.etenergies.append(utils.convertor(self.float(groups[1]), "eV", "cm-1")) self.etoscs.append(self.float(line.split("f=")[-1].split()[0])) self.etsyms.append(groups[0].strip()) line = next(inputfile) p = re.compile("(\d+)") CIScontrib = [] while line.find(" ->") >= 0: # This is a contribution to the transition parts = line.split("->") self.logger.debug(parts) # Has to deal with lines like: # 32 -> 38 0.04990 # 35A -> 45A 0.01921 frommoindex = 0 # For restricted or alpha unrestricted fromMO = parts[0].strip() if fromMO[-1] == "B": frommoindex = 1 # For beta unrestricted fromMO = int(p.match(fromMO).group())-1 # subtract 1 so that it is an index into moenergies t = parts[1].split() tomoindex = 0 toMO = t[0] if toMO[-1] == "B": tomoindex = 1 toMO = int(p.match(toMO).group())-1 # subtract 1 so that it is an index into moenergies percent = self.float(t[1]) # For restricted calculations, the percentage will be corrected # after parsing (see after_parsing() above). CIScontrib.append([(fromMO, frommoindex), (toMO, tomoindex), percent]) line = next(inputfile) self.etsecs.append(CIScontrib) # Circular dichroism data (different for G03 vs G09) # # G03 # # ## <0|r|b> * (Au), Rotatory Strengths (R) in # ## cgs (10**-40 erg-esu-cm/Gauss) # ## state X Y Z R(length) # ## 1 0.0006 0.0096 -0.0082 -0.4568 # ## 2 0.0251 -0.0025 0.0002 -5.3846 # ## 3 0.0168 0.4204 -0.3707 -15.6580 # ## 4 0.0721 0.9196 -0.9775 -3.3553 # # G09 # # ## 1/2[<0|r|b>* + (<0|rxdel|b>*)*] # ## Rotatory Strengths (R) in cgs (10**-40 erg-esu-cm/Gauss) # ## state XX YY ZZ R(length) R(au) # ## 1 -0.3893 -6.7546 5.7736 -0.4568 -0.0010 # ## 2 -17.7437 1.7335 -0.1435 -5.3845 -0.0114 # ## 3 -11.8655 -297.2604 262.1519 -15.6580 -0.0332 if (line[1:52] == "<0|r|b> * (Au), Rotatory Strengths (R)" or line[1:50] == "1/2[<0|r|b>* + (<0|rxdel|b>*)*]"): self.etrotats = [] self.skip_lines(inputfile, ['units']) headers = next(inputfile) Ncolms = len(headers.split()) line = next(inputfile) parts = line.strip().split() while len(parts) == Ncolms: try: R = self.float(parts[4]) except ValueError: # nan or -nan if there is no first excited state # (for unrestricted calculations) pass else: self.etrotats.append(R) line = next(inputfile) temp = line.strip().split() parts = line.strip().split() self.etrotats = numpy.array(self.etrotats, "d") # Number of basis sets functions. # Has to deal with lines like: # NBasis = 434 NAE= 97 NBE= 97 NFC= 34 NFV= 0 # and... # NBasis = 148 MinDer = 0 MaxDer = 0 # Although the former is in every file, it doesn't occur before # the overlap matrix is printed. if line[1:7] == "NBasis" or line[4:10] == "NBasis": # For counterpoise fragment, skip these lines. if self.counterpoise != 0: return # For ONIOM calcs, ignore this section in order to bypass assertion failure. if self.oniom: return # If nbasis was already parsed, check if it changed. If it did, issue a warning. # In the future, we will probably want to have nbasis, as well as nmo below, # as a list so that we don't need to pick one value when it changes. nbasis = int(line.split('=')[1].split()[0]) if hasattr(self, "nbasis"): try: assert nbasis == self.nbasis except AssertionError: self.logger.warning("Number of basis functions (nbasis) has changed from %i to %i" % (self.nbasis, nbasis)) self.nbasis = nbasis # Number of linearly-independent basis sets. if line[1:7] == "NBsUse": # For counterpoise fragment, skip these lines. if self.counterpoise != 0: return # For ONIOM calcs, ignore this section in order to bypass assertion failure. if self.oniom: return nmo = int(line.split('=')[1].split()[0]) self.set_attribute('nmo', nmo) # For AM1 calculations, set nbasis by a second method, # as nmo may not always be explicitly stated. if line[7:22] == "basis functions, ": nbasis = int(line.split()[0]) self.set_attribute('nbasis', nbasis) # Molecular orbital overlap matrix. # Has to deal with lines such as: # *** Overlap *** # ****** Overlap ****** # Note that Gaussian sometimes drops basis functions, # causing the overlap matrix as parsed below to not be # symmetric (which is a problem for population analyses, etc.) if line[1:4] == "***" and (line[5:12] == "Overlap" or line[8:15] == "Overlap"): # Ensure that this is the main calc and not a fragment if self.counterpoise != 0: return self.aooverlaps = numpy.zeros( (self.nbasis, self.nbasis), "d") # Overlap integrals for basis fn#1 are in aooverlaps[0] base = 0 colmNames = next(inputfile) while base < self.nbasis: self.updateprogress(inputfile, "Overlap", self.fupdate) for i in range(self.nbasis-base): # Fewer lines this time line = next(inputfile) parts = line.split() for j in range(len(parts)-1): # Some lines are longer than others k = float(parts[j+1].replace("D", "E")) self.aooverlaps[base+j, i+base] = k self.aooverlaps[i+base, base+j] = k base += 5 colmNames = next(inputfile) self.aooverlaps = numpy.array(self.aooverlaps, "d") # Molecular orbital coefficients (mocoeffs). # Essentially only produced for SCF calculations. # This is also the place where aonames and atombasis are parsed. if line[5:35] == "Molecular Orbital Coefficients" or line[5:41] == "Alpha Molecular Orbital Coefficients" or line[5:40] == "Beta Molecular Orbital Coefficients": # If counterpoise fragment, return without parsing orbital info if self.counterpoise != 0: return # Skip this for ONIOM calcs if self.oniom: return if line[5:40] == "Beta Molecular Orbital Coefficients": beta = True if self.popregular: return # This was continue before refactoring the parsers. #continue # Not going to extract mocoeffs # Need to add an extra array to self.mocoeffs self.mocoeffs.append(numpy.zeros((self.nmo, self.nbasis), "d")) else: beta = False self.aonames = [] self.atombasis = [] mocoeffs = [numpy.zeros((self.nmo, self.nbasis), "d")] base = 0 self.popregular = False for base in range(0, self.nmo, 5): self.updateprogress(inputfile, "Coefficients", self.fupdate) colmNames = next(inputfile) if not colmNames.split(): self.logger.warning("Molecular coefficients header found but no coefficients.") break; if base == 0 and int(colmNames.split()[0]) != 1: # Implies that this is a POP=REGULAR calculation # and so, only aonames (not mocoeffs) will be extracted self.popregular = True symmetries = next(inputfile) eigenvalues = next(inputfile) for i in range(self.nbasis): line = next(inputfile) if i == 0: # Find location of the start of the basis function name start_of_basis_fn_name = line.find(line.split()[3]) - 1 if base == 0 and not beta: # Just do this the first time 'round parts = line[:start_of_basis_fn_name].split() if len(parts) > 1: # New atom if i > 0: self.atombasis.append(atombasis) atombasis = [] atomname = "%s%s" % (parts[2], parts[1]) orbital = line[start_of_basis_fn_name:20].strip() self.aonames.append("%s_%s" % (atomname, orbital)) atombasis.append(i) part = line[21:].replace("D", "E").rstrip() temp = [] for j in range(0, len(part), 10): temp.append(float(part[j:j+10])) if beta: self.mocoeffs[1][base:base + len(part) / 10, i] = temp else: mocoeffs[0][base:base + len(part) / 10, i] = temp if base == 0 and not beta: # Do the last update of atombasis self.atombasis.append(atombasis) if self.popregular: # We now have aonames, so no need to continue break if not self.popregular and not beta: self.mocoeffs = mocoeffs # Natural orbital coefficients (nocoeffs) and occupation numbers (nooccnos), # which are respectively define the eigenvectors and eigenvalues of the # diagnolized one-electron density matrix. These orbitals are formed after # configuration interact (CI) calculations, but not only. Similarly to mocoeffs, # we can parse and check aonames and atombasis here. # # Natural Orbital Coefficients: # 1 2 3 4 5 # Eigenvalues -- 2.01580 2.00363 2.00000 2.00000 1.00000 # 1 1 O 1S 0.00000 -0.15731 -0.28062 0.97330 0.00000 # 2 2S 0.00000 0.75440 0.57746 0.07245 0.00000 # ... # if line[5:33] == "Natural Orbital Coefficients": self.aonames = [] self.atombasis = [] nocoeffs = numpy.zeros((self.nmo, self.nbasis), "d") nooccnos = [] base = 0 self.popregular = False for base in range(0, self.nmo, 5): self.updateprogress(inputfile, "Natural orbitals", self.fupdate) colmNames = next(inputfile) if base == 0 and int(colmNames.split()[0]) != 1: # Implies that this is a POP=REGULAR calculation # and so, only aonames (not mocoeffs) will be extracted self.popregular = True eigenvalues = next(inputfile) nooccnos.extend(map(float, eigenvalues.split()[2:])) for i in range(self.nbasis): line = next(inputfile) # Just do this the first time 'round. if base == 0: # Changed below from :12 to :11 to deal with Elmar Neumann's example. parts = line[:11].split() # New atom. if len(parts) > 1: if i > 0: self.atombasis.append(atombasis) atombasis = [] atomname = "%s%s" % (parts[2], parts[1]) orbital = line[11:20].strip() self.aonames.append("%s_%s" % (atomname, orbital)) atombasis.append(i) part = line[21:].replace("D", "E").rstrip() temp = [] for j in range(0, len(part), 10): temp.append(float(part[j:j+10])) nocoeffs[base:base + len(part) / 10, i] = temp # Do the last update of atombasis. if base == 0: self.atombasis.append(atombasis) # We now have aonames, so no need to continue. if self.popregular: break if not self.popregular: self.nocoeffs = nocoeffs self.nooccnos = nooccnos # For FREQ=Anharm, extract anharmonicity constants if line[1:40] == "X matrix of Anharmonic Constants (cm-1)": Nvibs = len(self.vibfreqs) self.vibanharms = numpy.zeros( (Nvibs, Nvibs), "d") base = 0 colmNames = next(inputfile) while base < Nvibs: for i in range(Nvibs-base): # Fewer lines this time line = next(inputfile) parts = line.split() for j in range(len(parts)-1): # Some lines are longer than others k = float(parts[j+1].replace("D", "E")) self.vibanharms[base+j, i+base] = k self.vibanharms[i+base, base+j] = k base += 5 colmNames = next(inputfile) # Pseudopotential charges. if line.find("Pseudopotential Parameters") > -1: self.skip_lines(inputfile, ['e', 'label1', 'label2', 'e']) line = next(inputfile) if line.find("Centers:") < 0: return # This was continue before parser refactoring. # continue # Needs to handle code like the following: # # Center Atomic Valence Angular Power Coordinates # Number Number Electrons Momentum of R Exponent Coefficient X Y Z # =================================================================================================================================== # Centers: 1 # Centers: 16 # Centers: 21 24 # Centers: 99100101102 # 1 44 16 -4.012684 -0.696698 0.006750 # F and up # 0 554.3796303 -0.05152700 centers = [] while line.find("Centers:") >= 0: temp = line[10:] for i in range(0, len(temp)-3, 3): centers.append(int(temp[i:i+3])) line = next(inputfile) centers.sort() # Not always in increasing order self.coreelectrons = numpy.zeros(self.natom, "i") for center in centers: front = line[:10].strip() while not (front and int(front) == center): line = next(inputfile) front = line[:10].strip() info = line.split() self.coreelectrons[center-1] = int(info[1]) - int(info[2]) line = next(inputfile) # This will be printed for counterpoise calcualtions only. # To prevent crashing, we need to know which fragment is being considered. # Other information is also printed in lines that start like this. if line[1:14] == 'Counterpoise:': if line[42:50] == "fragment": self.counterpoise = int(line[51:54]) # This will be printed only during ONIOM calcs; use it to set a flag # that will allow assertion failures to be bypassed in the code. if line[1:7] == "ONIOM:": self.oniom = True # Atomic charges are straightforward to parse, although the header # has changed over time somewhat. # # Mulliken charges: # 1 # 1 C -0.004513 # 2 C -0.077156 # ... # Sum of Mulliken charges = 0.00000 # Mulliken charges with hydrogens summed into heavy atoms: # 1 # 1 C -0.004513 # 2 C 0.002063 # ... # if line[1:25] == "Mulliken atomic charges:" or line[1:18] == "Mulliken charges:" or \ line[1:23] == "Lowdin Atomic Charges:" or line[1:16] == "Lowdin charges:": if not hasattr(self, "atomcharges"): self.atomcharges = {} ones = next(inputfile) charges = [] nline = next(inputfile) while not "Sum of" in nline: charges.append(float(nline.split()[2])) nline = next(inputfile) if "Mulliken" in line: self.atomcharges["mulliken"] = charges else: self.atomcharges["lowdin"] = charges if line.strip() == "Natural Population": if not hasattr(self, 'atomcharges'): self.atomcharges = {} line1 = next(inputfile) line2 = next(inputfile) if line1.split()[0] == 'Natural' and line2.split()[2] == 'Charge': dashes = next(inputfile) charges = [] for i in range(self.natom): nline = next(inputfile) charges.append(float(nline.split()[2])) self.atomcharges["natural"] = charges #Extract Thermochemistry #Temperature 298.150 Kelvin. Pressure 1.00000 Atm. #Zero-point correction= 0.342233 (Hartree/ #Thermal correction to Energy= 0. #Thermal correction to Enthalpy= 0. #Thermal correction to Gibbs Free Energy= 0.302940 #Sum of electronic and zero-point Energies= -563.649744 #Sum of electronic and thermal Energies= -563.636699 #Sum of electronic and thermal Enthalpies= -563.635755 #Sum of electronic and thermal Free Energies= -563.689037 if "Sum of electronic and thermal Enthalpies" in line: self.set_attribute('enthalpy', float(line.split()[6])) if "Sum of electronic and thermal Free Energies=" in line: self.set_attribute('freenergy', float(line.split()[7])) if line[1:12] == "Temperature": self.set_attribute('temperature', float(line.split()[1])) if __name__ == "__main__": import doctest, gaussianparser, sys if len(sys.argv) == 1: doctest.testmod(gaussianparser, verbose=False) if len(sys.argv) >= 2: parser = gaussianparser.Gaussian(sys.argv[1]) data = parser.parse() if len(sys.argv) > 2: for i in range(len(sys.argv[2:])): if hasattr(data, sys.argv[2 + i]): print(getattr(data, sys.argv[2 + i])) GaussSum-3.0.1/gausssum/cclib/parser/orcaparser.py0000664000175000017500000010742512660404041020700 0ustar noelnoel# -*- coding: utf-8 -*- # # This file is part of cclib (http://cclib.github.io), a library for parsing # and interpreting the results of computational chemistry packages. # # Copyright (C) 2007-2014, the cclib development team # # The library is free software, distributed under the terms of # the GNU Lesser General Public version 2.1 or later. You should have # received a copy of the license along with cclib. You can also access # the full license online at http://www.gnu.org/copyleft/lgpl.html. """Parser for ORCA output files""" from __future__ import print_function import numpy from . import logfileparser from . import utils class ORCA(logfileparser.Logfile): """An ORCA log file.""" def __init__(self, *args, **kwargs): # Call the __init__ method of the superclass super(ORCA, self).__init__(logname="ORCA", *args, **kwargs) def __str__(self): """Return a string representation of the object.""" return "ORCA log file %s" % (self.filename) def __repr__(self): """Return a representation of the object.""" return 'ORCA("%s")' % (self.filename) def normalisesym(self, label): """Use standard symmetry labels instead of Gaussian labels. To normalise: (1) If label is one of [SG, PI, PHI, DLTA], replace by [sigma, pi, phi, delta] (2) replace any G or U by their lowercase equivalent >>> sym = Gaussian("dummyfile").normalisesym >>> labels = ['A1', 'AG', 'A1G', "SG", "PI", "PHI", "DLTA", 'DLTU', 'SGG'] >>> map(sym, labels) ['A1', 'Ag', 'A1g', 'sigma', 'pi', 'phi', 'delta', 'delta.u', 'sigma.g'] """ def before_parsing(self): # A geometry optimization is started only when # we parse a cycle (so it will be larger than zero(). self.gopt_cycle = 0 # Keep track of whether this is a relaxed scan calculation self.is_relaxed_scan = False def extract(self, inputfile, line): """Extract information from the file object inputfile.""" if line[0:15] == "Number of atoms": natom = int(line.split()[-1]) self.set_attribute('natom', natom) if line[1:13] == "Total Charge": charge = int(line.split()[-1]) self.set_attribute('charge', charge) line = next(inputfile) mult = int(line.split()[-1]) self.set_attribute('mult', mult) # SCF convergence output begins with: # # -------------- # SCF ITERATIONS # -------------- # # However, there are two common formats which need to be handled, implemented as separate functions. if "SCF ITERATIONS" in line: self.skip_line(inputfile, 'dashes') line = next(inputfile) colums = line.split() if colums[1] == "Energy": self.parse_scf_condensed_format(inputfile, colums) elif colums[1] == "Starting": self.parse_scf_expanded_format(inputfile, colums) # Information about the final iteration, which also includes the convergence # targets and the convergence values, is printed separately, in a section like this: # # ***************************************************** # * SUCCESS * # * SCF CONVERGED AFTER 9 CYCLES * # ***************************************************** # # ... # # Total Energy : -382.04963064 Eh -10396.09898 eV # # ... # # ------------------------- ---------------- # FINAL SINGLE POINT ENERGY -382.049630637 # ------------------------- ---------------- # # We cannot use this last message as a stop condition in general, because # often there is vibrational output before it. So we use the 'Total Energy' # line. However, what comes after that is different for single point calculations # and in the inner steps of geometry optimizations. if "SCF CONVERGED AFTER" in line: if not hasattr(self, "scfenergies"): self.scfenergies = [] if not hasattr(self, "scfvalues"): self.scfvalues = [] if not hasattr(self, "scftargets"): self.scftargets = [] while not "Total Energy :" in line: line = next(inputfile) energy = float(line.split()[5]) self.scfenergies.append(energy) self._append_scfvalues_scftargets(inputfile, line) # Sometimes the SCF does not converge, but does not halt the # the run (like in bug 3184890). In this this case, we should # remain consistent and use the energy from the last reported # SCF cycle. In this case, ORCA print a banner like this: # # ***************************************************** # * ERROR * # * SCF NOT CONVERGED AFTER 8 CYCLES * # ***************************************************** if "SCF NOT CONVERGED AFTER" in line: if not hasattr(self, "scfenergies"): self.scfenergies = [] if not hasattr(self, "scfvalues"): self.scfvalues = [] if not hasattr(self, "scftargets"): self.scftargets = [] energy = self.scfvalues[-1][-1][0] self.scfenergies.append(energy) self._append_scfvalues_scftargets(inputfile, line) # The convergence targets for geometry optimizations are printed at the # beginning of the output, although the order and their description is # different than later on. So, try to standardize the names of the criteria # and save them for later so that we can get the order right. # # ***************************** # * Geometry Optimization Run * # ***************************** # # Geometry optimization settings: # Update method Update .... BFGS # Choice of coordinates CoordSys .... Redundant Internals # Initial Hessian InHess .... Almoef's Model # # Convergence Tolerances: # Energy Change TolE .... 5.0000e-06 Eh # Max. Gradient TolMAXG .... 3.0000e-04 Eh/bohr # RMS Gradient TolRMSG .... 1.0000e-04 Eh/bohr # Max. Displacement TolMAXD .... 4.0000e-03 bohr # RMS Displacement TolRMSD .... 2.0000e-03 bohr # if line[25:50] == "Geometry Optimization Run": stars = next(inputfile) blank = next(inputfile) line = next(inputfile) while line[0:23] != "Convergence Tolerances:": line = next(inputfile) if hasattr(self, 'geotargets'): self.logger.warning('The geotargets attribute should not exist yet. There is a problem in the parser.') self.geotargets = [] self.geotargets_names = [] # There should always be five tolerance values printed here. for i in range(5): line = next(inputfile) name = line[:25].strip().lower().replace('.','').replace('displacement', 'step') target = float(line.split()[-2]) self.geotargets_names.append(name) self.geotargets.append(target) # The convergence targets for relaxed surface scan steps are printed at the # beginning of the output, although the order and their description is # different than later on. So, try to standardize the names of the criteria # and save them for later so that we can get the order right. # # ************************************************************* # * RELAXED SURFACE SCAN STEP 12 * # * * # * Dihedral ( 11, 10, 3, 4) : 180.00000000 * # ************************************************************* # # Geometry optimization settings: # Update method Update .... BFGS # Choice of coordinates CoordSys .... Redundant Internals # Initial Hessian InHess .... Almoef's Model # # Convergence Tolerances: # Energy Change TolE .... 5.0000e-06 Eh # Max. Gradient TolMAXG .... 3.0000e-04 Eh/bohr # RMS Gradient TolRMSG .... 1.0000e-04 Eh/bohr # Max. Displacement TolMAXD .... 4.0000e-03 bohr # RMS Displacement TolRMSD .... 2.0000e-03 bohr if line[25:50] == "RELAXED SURFACE SCAN STEP": self.is_relaxed_scan = True blank = next(inputfile) info = next(inputfile) stars = next(inputfile) blank = next(inputfile) line = next(inputfile) while line[0:23] != "Convergence Tolerances:": line = next(inputfile) self.geotargets = [] self.geotargets_names = [] # There should always be five tolerance values printed here. for i in range(5): line = next(inputfile) name = line[:25].strip().lower().replace('.','').replace('displacement', 'step') target = float(line.split()[-2]) self.geotargets_names.append(name) self.geotargets.append(target) # After each geometry optimization step, ORCA prints the current convergence # parameters and the targets (again), so it is a good idea to check that they # have not changed. Note that the order of these criteria here are different # than at the beginning of the output, so make use of the geotargets_names created # before and save the new geovalues in correct order. # # ----------------------|Geometry convergence|--------------------- # Item value Tolerance Converged # ----------------------------------------------------------------- # Energy change 0.00006021 0.00000500 NO # RMS gradient 0.00031313 0.00010000 NO # RMS step 0.01596159 0.00200000 NO # MAX step 0.04324586 0.00400000 NO # .................................................... # Max(Bonds) 0.0218 Max(Angles) 2.48 # Max(Dihed) 0.00 Max(Improp) 0.00 # ----------------------------------------------------------------- # if line[33:53] == "Geometry convergence": if not hasattr(self, "geovalues"): self.geovalues = [] headers = next(inputfile) dashes = next(inputfile) names = [] values = [] targets = [] line = next(inputfile) while list(set(line.strip())) != ["."]: name = line[10:28].strip().lower() value = float(line.split()[2]) target = float(line.split()[3]) names.append(name) values.append(value) targets.append(target) line = next(inputfile) # The energy change is normally not printed in the first iteration, because # there was no previous energy -- in that case assume zero, but check that # no previous geovalues were parsed. newvalues = [] for i, n in enumerate(self.geotargets_names): if (n == "energy change") and (n not in names): if not self.is_relaxed_scan: assert len(self.geovalues) == 0 newvalues.append(0.0) else: newvalues.append(values[names.index(n)]) assert targets[names.index(n)] == self.geotargets[i] self.geovalues.append(newvalues) #if not an optimization, determine structure used if line[0:21] == "CARTESIAN COORDINATES" and not hasattr(self, "atomcoords"): self.skip_line(inputfile, 'dashes') atomnos = [] atomcoords = [] line = next(inputfile) while len(line) > 1: broken = line.split() atomnos.append(self.table.number[broken[0]]) atomcoords.append(list(map(float, broken[1:4]))) line = next(inputfile) self.set_attribute('natom', len(atomnos)) self.set_attribute('atomnos', atomnos) self.atomcoords = [atomcoords] # There's always a banner announcing the next geometry optimization cycle, # which looks something like this: # # ************************************************************* # * GEOMETRY OPTIMIZATION CYCLE 2 * # ************************************************************* if "GEOMETRY OPTIMIZATION CYCLE" in line: # Keep track of the current cycle jsut in case, because some things # are printed differently inside the first/last and other cycles. self.gopt_cycle = int(line.split()[4]) self.skip_lines(inputfile, ['s', 'd', 'text', 'd']) if not hasattr(self,"atomcoords"): self.atomcoords = [] atomnos = [] atomcoords = [] for i in range(self.natom): line = next(inputfile) broken = line.split() atomnos.append(self.table.number[broken[0]]) atomcoords.append(list(map(float, broken[1:4]))) self.atomcoords.append(atomcoords) self.set_attribute('atomnos', atomnos) if line[21:68] == "FINAL ENERGY EVALUATION AT THE STATIONARY POINT": if not hasattr(self, 'optdone'): self.optdone = [] self.optdone.append(len(self.atomcoords)) self.skip_lines(inputfile, ['text', 's', 'd', 'text', 'd']) atomcoords = [] for i in range(self.natom): line = next(inputfile) broken = line.split() atomcoords.append(list(map(float, broken[1:4]))) self.atomcoords.append(atomcoords) if "The optimization did not converge" in line: if not hasattr(self, 'optdone'): self.optdone = [] if line[0:16] == "ORBITAL ENERGIES": self.skip_lines(inputfile, ['d', 'text', 'text']) self.moenergies = [[]] self.homos = [[0]] line = next(inputfile) while len(line) > 20: #restricted calcs are terminated by ------ info = line.split() self.moenergies[0].append(float(info[3])) if float(info[1]) > 0.00: #might be 1 or 2, depending on restricted-ness self.homos[0] = int(info[0]) line = next(inputfile) line = next(inputfile) #handle beta orbitals if line[17:35] == "SPIN DOWN ORBITALS": text = next(inputfile) self.moenergies.append([]) self.homos.append(0) line = next(inputfile) while len(line) > 20: #actually terminated by ------ info = line.split() self.moenergies[1].append(float(info[3])) if float(info[1]) == 1.00: self.homos[1] = int(info[0]) line = next(inputfile) # So nbasis was parsed at first with the first pattern, but it turns out that # semiempirical methods (at least AM1 as reported by Julien Idé) do not use this. # For this reason, also check for the second patterns, and use it as an assert # if nbasis was already parsed. Regression PCB_1_122.out covers this test case. if line[1:32] == "# of contracted basis functions": self.set_attribute('nbasis', int(line.split()[-1])) if line[1:27] == "Basis Dimension Dim": self.set_attribute('nbasis', int(line.split()[-1])) if line[0:14] == "OVERLAP MATRIX": self.skip_line(inputfile, 'dashes') self.aooverlaps = numpy.zeros( (self.nbasis, self.nbasis), "d") for i in range(0, self.nbasis, 6): self.updateprogress(inputfile, "Overlap") header = next(inputfile) size = len(header.split()) for j in range(self.nbasis): line = next(inputfile) broken = line.split() self.aooverlaps[j, i:i+size] = list(map(float, broken[1:size+1])) # Molecular orbital coefficients. # This is also where atombasis is parsed. if line[0:18] == "MOLECULAR ORBITALS": self.skip_line(inputfile, 'dashes') mocoeffs = [ numpy.zeros((self.nbasis, self.nbasis), "d") ] self.aonames = [] self.atombasis = [] for n in range(self.natom): self.atombasis.append([]) for spin in range(len(self.moenergies)): if spin == 1: self.skip_line(inputfile, 'blank') mocoeffs.append(numpy.zeros((self.nbasis, self.nbasis), "d")) for i in range(0, self.nbasis, 6): self.updateprogress(inputfile, "Coefficients") self.skip_lines(inputfile, ['numbers', 'energies', 'occs']) dashes = next(inputfile) broken = dashes.split() size = len(broken) for j in range(self.nbasis): line = next(inputfile) broken = line.split() #only need this on the first time through if spin == 0 and i == 0: atomname = line[3:5].split()[0] num = int(line[0:3]) orbital = broken[1].upper() self.aonames.append("%s%i_%s"%(atomname, num+1, orbital)) self.atombasis[num].append(j) temp = [] vals = line[16:-1] #-1 to remove the last blank space for k in range(0, len(vals), 10): temp.append(float(vals[k:k+10])) mocoeffs[spin][i:i+size, j] = temp self.mocoeffs = mocoeffs if line[0:18] == "TD-DFT/TDA EXCITED": # Could be singlets or triplets if line.find("SINGLETS") >= 0: sym = "Singlet" elif line.find("TRIPLETS") >= 0: sym = "Triplet" else: sym = "Not specified" if not hasattr(self, "etenergies"): self.etsecs = [] self.etenergies = [] self.etsyms = [] lookup = {'a':0, 'b':1} line = next(inputfile) while line.find("STATE") < 0: line = next(inputfile) # Contains STATE or is blank while line.find("STATE") >= 0: broken = line.split() self.etenergies.append(float(broken[-2])) self.etsyms.append(sym) line = next(inputfile) sec = [] # Contains SEC or is blank while line.strip(): start = line[0:8].strip() start = (int(start[:-1]), lookup[start[-1]]) end = line[10:17].strip() end = (int(end[:-1]), lookup[end[-1]]) contrib = float(line[35:47].strip()) sec.append([start, end, contrib]) line = next(inputfile) self.etsecs.append(sec) line = next(inputfile) # This will parse etoscs for TD calculations, but note that ORCA generally # prints two sets, one based on the length form of transition dipole moments, # the other based on the velocity form. Although these should be identical # in the basis set limit, in practice they are rarely the same. Here we will # effectively parse just the spectrum based on the length-form. if (line[25:44] == "ABSORPTION SPECTRUM" or \ line[9:28] == "ABSORPTION SPECTRUM") and not hasattr(self, "etoscs"): self.skip_lines(inputfile, ['d', 'header', 'header', 'd']) self.etoscs = [] for x in self.etsyms: osc = next(inputfile).split()[3] if osc == "spin": # "spin forbidden" osc = 0 else: osc = float(osc) self.etoscs.append(osc) if line[0:23] == "VIBRATIONAL FREQUENCIES": self.skip_lines(inputfile, ['d', 'b']) self.vibfreqs = numpy.zeros((3 * self.natom,),"d") for i in range(3 * self.natom): line = next(inputfile) self.vibfreqs[i] = float(line.split()[1]) if numpy.any(self.vibfreqs[0:6] != 0): msg = "Modes corresponding to rotations/translations " msg += "may be non-zero." self.logger.warning(msg) self.vibfreqs = self.vibfreqs[6:] if line[0:12] == "NORMAL MODES": """ Format: NORMAL MODES ------------ These modes are the cartesian displacements weighted by the diagonal matrix M(i,i)=1/sqrt(m[i]) where m[i] is the mass of the displaced atom Thus, these vectors are normalized but *not* orthogonal 0 1 2 3 4 5 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... """ self.vibdisps = numpy.zeros(( 3 * self.natom, self.natom, 3), "d") self.skip_lines(inputfile, ['d', 'b', 'text', 'text', 'text', 'b']) for mode in range(0, 3 * self.natom, 6): header = next(inputfile) for atom in range(self.natom): x = next(inputfile).split()[1:] y = next(inputfile).split()[1:] z = next(inputfile).split()[1:] self.vibdisps[mode:mode + 6, atom, 0] = x self.vibdisps[mode:mode + 6, atom, 1] = y self.vibdisps[mode:mode + 6, atom, 2] = z self.vibdisps = self.vibdisps[6:] if line[0:11] == "IR SPECTRUM": self.skip_lines(inputfile, ['d', 'b', 'header', 'd']) self.vibirs = numpy.zeros((3 * self.natom,),"d") line = next(inputfile) while len(line) > 2: num = int(line[0:4]) self.vibirs[num] = float(line.split()[2]) line = next(inputfile) self.vibirs = self.vibirs[6:] if line[0:14] == "RAMAN SPECTRUM": self.skip_lines(inputfile, ['d', 'b', 'header', 'd']) self.vibramans = numpy.zeros((3 * self.natom,),"d") line = next(inputfile) while len(line) > 2: num = int(line[0:4]) self.vibramans[num] = float(line.split()[2]) line = next(inputfile) self.vibramans = self.vibramans[6:] # ORCA will print atomic charges along with the spin populations, # so care must be taken about choosing the proper column. # Population analyses are performed usually only at the end # of a geometry optimization or other run, so we want to # leave just the final atom charges. # Here is an example for Mulliken charges: # -------------------------------------------- # MULLIKEN ATOMIC CHARGES AND SPIN POPULATIONS # -------------------------------------------- # 0 H : 0.126447 0.002622 # 1 C : -0.613018 -0.029484 # 2 H : 0.189146 0.015452 # 3 H : 0.320041 0.037434 # ... # Sum of atomic charges : -0.0000000 # Sum of atomic spin populations: 1.0000000 if line[:23] == "MULLIKEN ATOMIC CHARGES": has_spins = "AND SPIN POPULATIONS" in line if not hasattr(self, "atomcharges"): self.atomcharges = { } if has_spins and not hasattr(self, "atomspins"): self.atomspins = {} self.skip_line(inputfile, 'dashes') charges = [] if has_spins: spins = [] line = next(inputfile) while line[:21] != "Sum of atomic charges": charges.append(float(line[8:20])) if has_spins: spins.append(float(line[20:])) line = next(inputfile) self.atomcharges["mulliken"] = charges if has_spins: self.atomspins["mulliken"] = spins # Things are the same for Lowdin populations, except that the sums # are not printed (there is a blank line at the end). if line[:22] == "LOEWDIN ATOMIC CHARGES": has_spins = "AND SPIN POPULATIONS" in line if not hasattr(self, "atomcharges"): self.atomcharges = { } if has_spins and not hasattr(self, "atomspins"): self.atomspins = {} self.skip_line(inputfile, 'dashes') charges = [] if has_spins: spins = [] line = next(inputfile) while line.strip(): charges.append(float(line[8:20])) if has_spins: spins.append(float(line[20:])) line = next(inputfile) self.atomcharges["lowdin"] = charges if has_spins: self.atomspins["lowdin"] = spins # It is not stated explicitely, but the dipole moment components printed by ORCA # seem to be in atomic units, so they will need to be converted. Also, they # are most probably calculated with respect to the origin . # # ------------- # DIPOLE MOMENT # ------------- # X Y Z # Electronic contribution: 0.00000 -0.00000 -0.00000 # Nuclear contribution : 0.00000 0.00000 0.00000 # ----------------------------------------- # Total Dipole Moment : 0.00000 -0.00000 -0.00000 # ----------------------------------------- # Magnitude (a.u.) : 0.00000 # Magnitude (Debye) : 0.00000 # if line.strip() == "DIPOLE MOMENT": self.skip_lines(inputfile, ['d', 'XYZ', 'electronic', 'nuclear', 'd']) total = next(inputfile) assert "Total Dipole Moment" in total reference = [0.0, 0.0, 0.0] dipole = numpy.array([float(d) for d in total.split()[-3:]]) dipole = utils.convertor(dipole, "ebohr", "Debye") if not hasattr(self, 'moments'): self.moments = [reference, dipole] else: try: assert numpy.all(self.moments[1] == dipole) except AssertionError: self.logger.warning('Overwriting previous multipole moments with new values') self.moments = [reference, dipole] def parse_scf_condensed_format(self, inputfile, line): """ Parse the SCF convergence information in condensed format """ # This is what it looks like # ITER Energy Delta-E Max-DP RMS-DP [F,P] Damp # *** Starting incremental Fock matrix formation *** # 0 -384.5203638934 0.000000000000 0.03375012 0.00223249 0.1351565 0.7000 # 1 -384.5792776162 -0.058913722842 0.02841696 0.00175952 0.0734529 0.7000 # ***Turning on DIIS*** # 2 -384.6074211837 -0.028143567475 0.04968025 0.00326114 0.0310435 0.0000 # 3 -384.6479682063 -0.040547022616 0.02097477 0.00121132 0.0361982 0.0000 # 4 -384.6571124353 -0.009144228947 0.00576471 0.00035160 0.0061205 0.0000 # 5 -384.6574659959 -0.000353560584 0.00191156 0.00010160 0.0025838 0.0000 # 6 -384.6574990782 -0.000033082375 0.00052492 0.00003800 0.0002061 0.0000 # 7 -384.6575005762 -0.000001497987 0.00020257 0.00001146 0.0001652 0.0000 # 8 -384.6575007321 -0.000000155848 0.00008572 0.00000435 0.0000745 0.0000 # **** Energy Check signals convergence **** assert line[2] == "Delta-E" assert line[3] == "Max-DP" if not hasattr(self, "scfvalues"): self.scfvalues = [] self.scfvalues.append([]) # Try to keep track of the converger (NR, DIIS, SOSCF, etc.). diis_active = True while not line == []: if 'Newton-Raphson' in line: diis_active = False elif 'SOSCF' in line: diis_active = False elif line[0].isdigit() and diis_active: energy = float(line[1]) deltaE = float(line[2]) maxDP = float(line[3]) rmsDP = float(line[4]) self.scfvalues[-1].append([deltaE, maxDP, rmsDP]) elif line[0].isdigit() and not diis_active: energy = float(line[1]) deltaE = float(line[2]) maxDP = float(line[5]) rmsDP = float(line[6]) self.scfvalues[-1].append([deltaE, maxDP, rmsDP]) line = next(inputfile).split() def parse_scf_expanded_format(self, inputfile, line): """ Parse SCF convergence when in expanded format. """ # The following is an example of the format # ----------------------------------------- # # *** Starting incremental Fock matrix formation *** # # ---------------------------- # ! ITERATION 0 ! # ---------------------------- # Total Energy : -377.960836651297 Eh # Energy Change : -377.960836651297 Eh # MAX-DP : 0.100175793695 # RMS-DP : 0.004437973661 # Actual Damping : 0.7000 # Actual Level Shift : 0.2500 Eh # Int. Num. El. : 43.99982197 (UP= 21.99991099 DN= 21.99991099) # Exchange : -34.27550826 # Correlation : -2.02540957 # # # ---------------------------- # ! ITERATION 1 ! # ---------------------------- # Total Energy : -378.118458080109 Eh # Energy Change : -0.157621428812 Eh # MAX-DP : 0.053240648588 # RMS-DP : 0.002375092508 # Actual Damping : 0.7000 # Actual Level Shift : 0.2500 Eh # Int. Num. El. : 43.99994143 (UP= 21.99997071 DN= 21.99997071) # Exchange : -34.00291075 # Correlation : -2.01607243 # # ***Turning on DIIS*** # # ---------------------------- # ! ITERATION 2 ! # ---------------------------- # .... # if not hasattr(self, "scfvalues"): self.scfvalues = [] self.scfvalues.append([]) line = "Foo" # dummy argument to enter loop while line.find("******") < 0: line = next(inputfile) info = line.split() if len(info) > 1 and info[1] == "ITERATION": dashes = next(inputfile) energy_line = next(inputfile).split() energy = float(energy_line[3]) deltaE_line = next(inputfile).split() deltaE = float(deltaE_line[3]) if energy == deltaE: deltaE = 0 maxDP_line = next(inputfile).split() maxDP = float(maxDP_line[2]) rmsDP_line = next(inputfile).split() rmsDP = float(rmsDP_line[2]) self.scfvalues[-1].append([deltaE, maxDP, rmsDP]) return # end of parse_scf_expanded_format def _append_scfvalues_scftargets(self, inputfile, line): # The SCF convergence targets are always printed after this, but apparently # not all of them always -- for example the RMS Density is missing for geometry # optimization steps. So, assume the previous value is still valid if it is # not found. For additional certainty, assert that the other targets are unchanged. while not "Last Energy change" in line: line = next(inputfile) deltaE_value = float(line.split()[4]) deltaE_target = float(line.split()[7]) line = next(inputfile) if "Last MAX-Density change" in line: maxDP_value = float(line.split()[4]) maxDP_target = float(line.split()[7]) line = next(inputfile) if "Last RMS-Density change" in line: rmsDP_value = float(line.split()[4]) rmsDP_target = float(line.split()[7]) else: rmsDP_value = self.scfvalues[-1][-1][2] rmsDP_target = self.scftargets[-1][2] assert deltaE_target == self.scftargets[-1][0] assert maxDP_target == self.scftargets[-1][1] self.scfvalues[-1].append([deltaE_value, maxDP_value, rmsDP_value]) self.scftargets.append([deltaE_target, maxDP_target, rmsDP_target]) if __name__ == "__main__": import sys import doctest, orcaparser if len(sys.argv) == 1: doctest.testmod(orcaparser, verbose=False) if len(sys.argv) == 2: parser = orcaparser.ORCA(sys.argv[1]) data = parser.parse() if len(sys.argv) > 2: for i in range(len(sys.argv[2:])): if hasattr(data, sys.argv[2 + i]): print(getattr(data, sys.argv[2 + i])) GaussSum-3.0.1/gausssum/cclib/parser/__init__.py0000664000175000017500000000250212660404041020264 0ustar noelnoel# -*- coding: utf-8 -*- # # This file is part of cclib (http://cclib.github.io), a library for parsing # and interpreting the results of computational chemistry packages. # # Copyright (C) 2006-2014, the cclib development team # # The library is free software, distributed under the terms of # the GNU Lesser General Public version 2.1 or later. You should have # received a copy of the license along with cclib. You can also access # the full license online at http://www.gnu.org/copyleft/lgpl.html. """Contains parsers for all supported programs""" # These import statements are added for the convenience of users... # Rather than having to type: # from cclib.parser.gaussianparser import Gaussian # they can use: # from cclib.parser import Gaussian from .adfparser import ADF from .daltonparser import DALTON from .gamessparser import GAMESS from .gamessukparser import GAMESSUK from .gaussianparser import Gaussian from .jaguarparser import Jaguar from .molproparser import Molpro from .nwchemparser import NWChem from .orcaparser import ORCA from .psiparser import Psi from .qchemparser import QChem # This allow users to type: # from cclib.parser import ccopen # from cclib.parser import ccread from .ccopen import ccopen from .ccopen import ccread from .data import ccData GaussSum-3.0.1/gausssum/cclib/parser/data.py0000664000175000017500000003050112660404041017436 0ustar noelnoel# -*- coding: utf-8 -*- # # This file is part of cclib (http://cclib.github.io), a library for parsing # and interpreting the results of computational chemistry packages. # # Copyright (C) 2007-2014, the cclib development team # # The library is free software, distributed under the terms of # the GNU Lesser General Public version 2.1 or later. You should have # received a copy of the license along with cclib. You can also access # the full license online at http://www.gnu.org/copyleft/lgpl.html. """Classes and tools for storing and handling parsed data""" import numpy class ccData(object): """Stores data extracted by cclib parsers Description of cclib attributes: aonames -- atomic orbital names (list of strings) aooverlaps -- atomic orbital overlap matrix (array[2]) atombasis -- indices of atomic orbitals on each atom (list of lists) atomcharges -- atomic partial charges (dict of arrays[1]) atomcoords -- atom coordinates (array[3], angstroms) atommasses -- atom masses (array[1], daltons) atomnos -- atomic numbers (array[1]) atomspins -- atomic spin densities (dict of arrays[1]) charge -- net charge of the system (integer) ccenergies -- molecular energies with Coupled-Cluster corrections (array[2], eV) coreelectrons -- number of core electrons in atom pseudopotentials (array[1]) enthalpy -- sum of electronic and thermal enthalpies (float, hartree/particle) entropy -- entropy (float, hartree/particle) etenergies -- energies of electronic transitions (array[1], 1/cm) etoscs -- oscillator strengths of electronic transitions (array[1]) etrotats -- rotatory strengths of electronic transitions (array[1], ??) etsecs -- singly-excited configurations for electronic transitions (list of lists) etsyms -- symmetries of electronic transitions (list of string) freeenergy -- sum of electronic and thermal free energies (float, hartree/particle) fonames -- fragment orbital names (list of strings) fooverlaps -- fragment orbital overlap matrix (array[2]) fragnames -- names of fragments (list of strings) frags -- indices of atoms in a fragment (list of lists) gbasis -- coefficients and exponents of Gaussian basis functions (PyQuante format) geotargets -- targets for convergence of geometry optimization (array[1]) geovalues -- current values for convergence of geometry optmization (array[1]) grads -- current values of forces (gradients) in geometry optimization (array[3]) hessian -- elements of the force constant matrix (array[1]) homos -- molecular orbital indices of HOMO(s) (array[1]) mocoeffs -- molecular orbital coefficients (list of arrays[2]) moenergies -- molecular orbital energies (list of arrays[1], eV) moments -- molecular multipole moments (list of arrays[], a.u.) mosyms -- orbital symmetries (list of lists) mpenergies -- molecular electronic energies with Møller-Plesset corrections (array[2], eV) mult -- multiplicity of the system (integer) natom -- number of atoms (integer) nbasis -- number of basis functions (integer) nmo -- number of molecular orbitals (integer) nocoeffs -- natural orbital coefficients (array[2]) nooccnos -- natural orbital occupation numbers (array[1]) optdone -- flags whether an optimization has converged (Boolean) scancoords -- geometries of each scan step (array[3], angstroms) scanenergies -- energies of potential energy surface (list) scannames -- names of varaibles scanned (list of strings) scanparm -- values of parameters in potential energy surface (list of tuples) scfenergies -- molecular electronic energies after SCF (Hartree-Fock, DFT) (array[1], eV) scftargets -- targets for convergence of the SCF (array[2]) scfvalues -- current values for convergence of the SCF (list of arrays[2]) temperature -- temperature used for Thermochemistry (float, kelvin) vibanharms -- vibrational anharmonicity constants (array[2], 1/cm) vibdisps -- cartesian displacement vectors (array[3], delta angstrom) vibfreqs -- vibrational frequencies (array[1], 1/cm) vibirs -- IR intensities (array[1], km/mol) vibramans -- Raman intensities (array[1], A^4/Da) vibsyms -- symmetries of vibrations (list of strings) (1) The term 'array' refers to a numpy array (2) The number of dimensions of an array is given in square brackets (3) Python indexes arrays/lists starting at zero, so if homos==[10], then the 11th molecular orbital is the HOMO """ # The expected types for all supported attributes. _attrtypes = { "aonames": list, "aooverlaps": numpy.ndarray, "atombasis": list, "atomcharges": dict, "atomcoords": numpy.ndarray, "atommasses": numpy.ndarray, "atomnos": numpy.ndarray, "atomspins": dict, "ccenergies": numpy.ndarray, "charge": int, "coreelectrons": numpy.ndarray, "enthalpy": float, "entropy": float, "etenergies": numpy.ndarray, "etoscs": numpy.ndarray, "etrotats": numpy.ndarray, "etsecs": list, "etsyms": list, "freeenergy": float, "fonames": list, "fooverlaps": numpy.ndarray, "fragnames": list, "frags": list, 'gbasis': list, "geotargets": numpy.ndarray, "geovalues": numpy.ndarray, "grads": numpy.ndarray, "hessian": numpy.ndarray, "homos": numpy.ndarray, "mocoeffs": list, "moenergies": list, "moments": list, "mosyms": list, "mpenergies": numpy.ndarray, "mult": int, "natom": int, "nbasis": int, "nmo": int, "nocoeffs": numpy.ndarray, "nooccnos": numpy.ndarray, "optdone": bool, "scancoords": numpy.ndarray, "scanenergies": list, "scannames": list, "scanparm": list, "scfenergies": numpy.ndarray, "scftargets": numpy.ndarray, "scfvalues": list, "temperature": float, "vibanharms": numpy.ndarray, "vibdisps": numpy.ndarray, "vibfreqs": numpy.ndarray, "vibirs": numpy.ndarray, "vibramans": numpy.ndarray, "vibsyms": list, } # The name of all attributes can be generated from the dictionary above. _attrlist = sorted(_attrtypes.keys()) # Arrays are double precision by default, but these will be integer arrays. _intarrays = ['atomnos', 'coreelectrons', 'homos'] # Attributes that should be lists of arrays (double precision). _listsofarrays = ['mocoeffs', 'moenergies', 'moments', 'scfvalues'] # Attributes that should be dictionaries of arrays (double precision). _dictsofarrays = ["atomcharges", "atomspins"] def __init__(self, attributes={}): """Initialize the cclibData object. Normally called in the parse() method of a Logfile subclass. Inputs: attributes - optional dictionary of attributes to load as data """ if attributes: self.setattributes(attributes) def listify(self): """Converts all attributes that are arrays or lists/dicts of arrays to lists.""" attrlist = [k for k in self._attrlist if hasattr(self, k)] for k in attrlist: v = self._attrtypes[k] if v == numpy.ndarray: setattr(self, k, getattr(self, k).tolist()) elif v == list and k in self._listsofarrays: setattr(self, k, [x.tolist() for x in getattr(self, k)]) elif v == dict and k in self._dictsofarrays: items = getattr(self, k).iteritems() pairs = [(key, val.tolist()) for key, val in items] setattr(self, k, dict(pairs)) def arrayify(self): """Converts appropriate attributes to arrays or lists/dicts of arrays.""" attrlist = [k for k in self._attrlist if hasattr(self, k)] for k in attrlist: v = self._attrtypes[k] precision = 'd' if k in self._intarrays: precision = 'i' if v == numpy.ndarray: a = getattr(self, k) setattr(self, k, numpy.array(getattr(self, k), precision)) elif v == list and k in self._listsofarrays: setattr(self, k, [numpy.array(x, precision) for x in getattr(self, k)]) elif v == dict and k in self._dictsofarrays: items = getattr(self, k).items() pairs = [(key, numpy.array(val, precision)) for key, val in items] setattr(self, k, dict(pairs)) def getattributes(self, tolists=False): """Returns a dictionary of existing data attributes. Inputs: tolists - flag to convert attributes to lists where applicable """ if tolists: self.listify() attributes = {} for attr in self._attrlist: if hasattr(self, attr): attributes[attr] = getattr(self, attr) if tolists: self.arrayify() return attributes def setattributes(self, attributes): """Sets data attributes given in a dictionary. Inputs: attributes - dictionary of attributes to set Outputs: invalid - list of attributes names that were not set, which means they are not specified in self._attrlist """ if type(attributes) is not dict: raise TypeError("attributes must be in a dictionary") valid = [a for a in attributes if a in self._attrlist] invalid = [a for a in attributes if a not in self._attrlist] for attr in valid: setattr(self, attr, attributes[attr]) self.arrayify() self.typecheck() return invalid def typecheck(self): """Check the types of all attributes. If an attribute does not match the expected type, then attempt to convert; if that fails, only then raise a TypeError. """ self.arrayify() for attr in [a for a in self._attrlist if hasattr(self, a)]: val = getattr(self, attr) if type(val) == self._attrtypes[attr]: continue try: val = self._attrtypes[attr](val) except ValueError: args = (attr, type(val), self._attrtypes[attr]) raise TypeError("attribute %s is %s instead of %s and could not be converted" % args) def write(self, filename=None, *args, **kwargs): """Write parsed attributes to a file. Possible extensions: .cjson or .json - output a chemical JSON file .cml - output a chemical markup language (CML) file .xyz - output a Cartesian XYZ file of the last coordinates available """ from ..writer import ccwrite outputstr = ccwrite(self, outputdest=filename, *args, **kwargs) return outputstr class ccData_optdone_bool(ccData): """This is the version of ccData where optdone is a Boolean.""" def __init__(self, *args, **kwargs): super(ccData_optdone_bool, self).__init__(*args, **kwargs) self._attrtypes['optdone'] = bool def setattributes(self, *args, **kwargs): invalid = super(ccData_optdone_bool, self).setattributes(*args, **kwargs) # Reduce optdone to a Boolean, because it will be parsed as a list. If this list has any element, # it means that there was an optimized structure and optdone should be True. if hasattr(self, 'optdone'): self.optdone = len(self.optdone) > 0 GaussSum-3.0.1/gausssum/cclib/parser/psiparser.py0000664000175000017500000011547012660404041020546 0ustar noelnoel# -*- coding: utf-8 -*- # # This file is part of cclib (http://cclib.github.io), a library for parsing # and interpreting the results of computational chemistry packages. # # Copyright (C) 2008-2014, the cclib development team # # The library is free software, distributed under the terms of # the GNU Lesser General Public version 2.1 or later. You should have # received a copy of the license along with cclib. You can also access # the full license online at http://www.gnu.org/copyleft/lgpl.html. """Parser for Psi3 and Psi4 output files""" import re import numpy from . import logfileparser from . import utils class Psi(logfileparser.Logfile): """A Psi log file.""" def __init__(self, *args, **kwargs): # Call the __init__ method of the superclass super(Psi, self).__init__(logname="Psi", *args, **kwargs) def __str__(self): """Return a string representation of the object.""" return "Psi log file %s" % (self.filename) def __repr__(self): """Return a representation of the object.""" return 'Psi("%s")' % (self.filename) def before_parsing(self): # There are some major differences between the output of Psi3 and Psi4, # so it will be useful to register which one we are dealing with. self.version = None # This is just used to track which part of the output we are in for Psi4, # with changes triggered by ==> things like this <== (Psi3 does not have this) self.section = None def normalisesym(self, label): """Use standard symmetry labels instead of NWChem labels. To normalise: (1) If label is one of [SG, PI, PHI, DLTA], replace by [sigma, pi, phi, delta] (2) replace any G or U by their lowercase equivalent >>> sym = NWChem("dummyfile").normalisesym >>> labels = ['A1', 'AG', 'A1G', "SG", "PI", "PHI", "DLTA", 'DLTU', 'SGG'] >>> map(sym, labels) ['A1', 'Ag', 'A1g', 'sigma', 'pi', 'phi', 'delta', 'delta.u', 'sigma.g'] """ # FIXME if necessary return label def extract(self, inputfile, line): """Extract information from the file object inputfile.""" # The version should always be detected. if "PSI3: An Open-Source Ab Initio" in line: self.version = 3 if "PSI4: An Open-Source Ab Initio" in line: self.version = 4 # This will automatically change the section attribute for Psi4, when encountering # a line that <== looks like this ==>, to whatever is in between. if (line.strip()[:3] == "==>") and (line.strip()[-3:] == "<=="): self.section = line.strip()[4:-4] # Psi3 print the coordinates in several configurations, and we will parse the # the canonical coordinates system in Angstroms as the first coordinate set, # although ir is actually somewhere later in the input, after basis set, etc. # We can also get or verify he number of atoms and atomic numbers from this block. if (self.version == 3) and (line.strip() == "-Geometry in the canonical coordinate system (Angstrom):"): self.skip_lines(inputfile, ['header', 'd']) coords = [] numbers = [] line = next(inputfile) while line.strip(): element = line.split()[0] numbers.append(self.table.number[element]) x = float(line.split()[1]) y = float(line.split()[2]) z = float(line.split()[3]) coords.append([x,y,z]) line = next(inputfile) self.set_attribute('natom', len(coords)) self.set_attribute('atomnos', numbers) if not hasattr(self, 'atomcoords'): self.atomcoords = [] self.atomcoords.append(coords) # ==> Geometry <== # # Molecular point group: c2h # Full point group: C2h # # Geometry (in Angstrom), charge = 0, multiplicity = 1: # # Center X Y Z # ------------ ----------------- ----------------- ----------------- # C -1.415253322400 0.230221785400 0.000000000000 # C 1.415253322400 -0.230221785400 0.000000000000 # ... # if (self.section == "Geometry") and ("Geometry (in Angstrom), charge" in line): assert line.split()[3] == "charge" charge = int(line.split()[5].strip(',')) self.set_attribute('charge', charge) assert line.split()[6] == "multiplicity" mult = int(line.split()[8].strip(':')) self.set_attribute('mult', mult) self.skip_line(inputfile, "blank") line = next(inputfile) # Usually there is the header and dashes, but, for example, the coordinates # printed when a geometry optimization finishes do not have it. if line.split()[0] == "Center": self.skip_line(inputfile, "dashes") line = next(inputfile) elements = [] coords = [] while line.strip(): el, x, y, z = line.split() elements.append(el) coords.append([float(x), float(y), float(z)]) line = next(inputfile) self.set_attribute('atomnos', [self.table.number[el] for el in elements]) if not hasattr(self, 'atomcoords'): self.atomcoords = [] # This condition discards any repeated coordinates that Psi print. For example, # geometry optimizations will print the coordinates at the beginning of and SCF # section and also at the start of the gradient calculation. if len(self.atomcoords) == 0 or self.atomcoords[-1] != coords: self.atomcoords.append(coords) # In Psi3 there are these two helpful sections. if (self.version == 3) and (line.strip() == '-SYMMETRY INFORMATION:'): line = next(inputfile) while line.strip(): if "Number of atoms" in line: self.set_attribute('natom', int(line.split()[-1])) line = next(inputfile) if (self.version == 3) and (line.strip() == "-BASIS SET INFORMATION:"): line = next(inputfile) while line.strip(): if "Number of AO" in line: self.set_attribute('nbasis', int(line.split()[-1])) line = next(inputfile) # Psi4 repeats the charge and multiplicity after the geometry. if (self.section == "Geometry") and (line[2:16].lower() == "charge ="): charge = int(line.split()[-1]) self.set_attribute('charge', charge) if (self.section == "Geometry") and (line[2:16].lower() == "multiplicity ="): mult = int(line.split()[-1]) self.set_attribute('mult', mult) # In Psi3, the section with the contraction scheme can be used to infer atombasis. if (self.version == 3) and line.strip() == "-Contraction Scheme:": self.skip_lines(inputfile, ['header', 'd']) indices = [] line = next(inputfile) while line.strip(): shells = line.split('//')[-1] expression = shells.strip().replace(' ', '+') expression = expression.replace('s', '*1') expression = expression.replace('p', '*3') expression = expression.replace('d', '*6') nfuncs = eval(expression) if len(indices) == 0: indices.append(range(nfuncs)) else: start = indices[-1][-1] + 1 indices.append(range(start, start+nfuncs)) line = next(inputfile) self.set_attribute('atombasis', indices) # In Psi3, the integrals program prints useful information when invoked. if (self.version == 3) and (line.strip() == "CINTS: An integrals program written in C"): self.skip_lines(inputfile, ['authors', 'd', 'b', 'b']) line = next(inputfile) assert line.strip() == "-OPTIONS:" while line.strip(): line = next(inputfile) line = next(inputfile) assert line.strip() == "-CALCULATION CONSTANTS:" while line.strip(): if "Number of atoms" in line: natom = int(line.split()[-1]) self.set_attribute('natom', natom) if "Number of atomic orbitals" in line: nbasis = int(line.split()[-1]) self.set_attribute('nbasis', nbasis) line = next(inputfile) # In Psi3, this part contains alot of important data pertaining to the SCF, but not only: if (self.version == 3) and (line.strip() == "CSCF3.0: An SCF program written in C"): self.skip_lines(inputfile, ['b', 'authors', 'b', 'd', 'b', 'mult', 'mult_comment', 'b']) line = next(inputfile) while line.strip(): if line.split()[0] == "multiplicity": mult = int(line.split()[-1]) self.set_attribute('mult', mult) if line.split()[0] == "charge": charge = int(line.split()[-1]) self.set_attribute('charge', charge) if line.split()[0] == "convergence": conv = float(line.split()[-1]) line = next(inputfile) if not hasattr(self, 'scftargets'): self.scftargets = [] self.scftargets.append([conv]) # The printout for Psi4 has a more obvious trigger for the SCF parameter printout. if (self.section == "Algorithm") and (line.strip() == "==> Algorithm <=="): self.skip_line(inputfile, 'blank') line = next(inputfile) while line.strip(): if "Energy threshold" in line: etarget = float(line.split()[-1]) if "Density threshold" in line: dtarget = float(line.split()[-1]) line = next(inputfile) if not hasattr(self, "scftargets"): self.scftargets = [] self.scftargets.append([etarget, dtarget]) # This section prints contraction information before the atomic basis set functions and # is a good place to parse atombasis indices as well as atomnos. However, the section this line # is in differs between HF and DFT outputs. # # -Contraction Scheme: # Atom Type All Primitives // Shells: # ------ ------ -------------------------- # 1 C 6s 3p // 2s 1p # 2 C 6s 3p // 2s 1p # 3 C 6s 3p // 2s 1p # ... if (self.section == "Primary Basis" or self.section == "DFT Potential") and line.strip() == "-Contraction Scheme:": self.skip_lines(inputfile, ['headers', 'd']) atomnos = [] atombasis = [] atombasis_pos = 0 line = next(inputfile) while line.strip(): element = line.split()[1] atomnos.append(self.table.number[element]) # To count the number of atomic orbitals for the atom, sum up the orbitals # in each type of shell, times the numbers of shells. Currently, we assume # the multiplier is a single digit and that there are only s and p shells, # which will need to be extended later when considering larger basis sets, # with corrections for the cartesian/spherical cases. ao_count = 0 shells = line.split('//')[1].split() for s in shells: count, type = s multiplier = 3*(type=='p') or 1 ao_count += multiplier*int(count) if len(atombasis) > 0: atombasis_pos = atombasis[-1][-1] + 1 atombasis.append(list(range(atombasis_pos, atombasis_pos+ao_count))) line = next(inputfile) self.set_attribute('natom', len(atomnos)) self.set_attribute('atomnos', atomnos) self.set_attribute('atombasis', atombasis) # The atomic basis set is straightforward to parse, but there are some complications # when symmetry is used, because in that case Psi4 only print the symmetry-unique atoms, # and the list of symmetry-equivalent ones is not printed. Therefore, for simplicity here # when an atomic is missing (atom indices are printed) assume the atomic orbitals of the # last atom of the same element before it. This might not work if a mixture of basis sets # is used somehow... but it should cover almost all cases for now. # # Note that Psi also print normalized coefficients (details below). # # ==> AO Basis Functions <== # # [ STO-3G ] # spherical # **** # C 1 # S 3 1.00 # 71.61683700 2.70781445 # 13.04509600 2.61888016 # ... if (self.section == "AO Basis Functions") and (line.strip() == "==> AO Basis Functions <=="): def get_symmetry_atom_basis(gbasis): """Get symmetry atom by replicating the last atom in gbasis of the same element.""" missing_index = len(gbasis) missing_atomno = self.atomnos[missing_index] ngbasis = len(gbasis) last_same = ngbasis - self.atomnos[:ngbasis][::-1].index(missing_atomno) - 1 return gbasis[last_same] dfact = lambda n: (n <= 0) or n * dfact(n-2) def get_normalization_factor(exp, lx, ly, lz): norm_s = (2*exp/numpy.pi)**0.75 if lx + ly + lz > 0: nom = (4*exp)**((lx+ly+lz)/2.0) den = numpy.sqrt(dfact(2*lx-1) * dfact(2*ly-1) * dfact(2*lz-1)) return norm_s * nom / den else: return norm_s self.skip_lines(inputfile, ['b', 'basisname']) line = next(inputfile) spherical = line.strip() == "spherical" if hasattr(self, 'spherical_basis'): assert self.spherical_basis == spherical else: self.spherical_basis = spherical gbasis = [] self.skip_line(inputfile, 'stars') line = next(inputfile) while line.strip(): element, index = line.split() atomno = self.table.number[element] index = int(index) # This is the code that adds missing atoms when symmetry atoms are excluded # from the basis set printout. Again, this will work only if all atoms of # the same element use the same basis set. while index > len(gbasis) + 1: gbasis.append(get_symmetry_atom_basis(gbasis)) gbasis.append([]) line = next(inputfile) while line.find("*") == -1: # The shell type and primitive count is in the first line. shell_type, nprimitives, smthg = line.split() nprimitives = int(nprimitives) # Get the angular momentum for this shell type. momentum = { 'S' : 0, 'P' : 1, 'D' : 2, 'F' : 3, 'G' : 4 }[shell_type.upper()] # Read in the primitives. primitives_lines = [next(inputfile) for i in range(nprimitives)] primitives = [list(map(float, pl.split())) for pl in primitives_lines] # Un-normalize the coefficients. Psi prints the normalized coefficient # of the highest polynomial, namely XX for D orbitals, XXX for F, and so on. for iprim, prim in enumerate(primitives): exp, coef = prim coef = coef / get_normalization_factor(exp, momentum, 0, 0) primitives[iprim] = [exp, coef] primitives = [tuple(p) for p in primitives] shell = [shell_type, primitives] gbasis[-1].append(shell) line = next(inputfile) line = next(inputfile) # We will also need to add symmetry atoms that are missing from the input # at the end of this block, if the symmetry atoms are last. while len(gbasis) < self.natom: gbasis.append(get_symmetry_atom_basis(gbasis)) self.gbasis = gbasis # A block called 'Calculation Information' prints these before starting the SCF. if (self.section == "Pre-Iterations") and ("Number of atoms" in line): natom = int(line.split()[-1]) self.set_attribute('natom', natom) if (self.section == "Pre-Iterations") and ("Number of atomic orbitals" in line): nbasis = int(line.split()[-1]) self.set_attribute('nbasis', nbasis) # ==> Iterations <== # Psi3 converges just the density elements, although it reports in the iterations # changes in the energy as well as the DIIS error. psi3_iterations_header = "iter total energy delta E delta P diiser" if (self.version == 3) and (line.strip() == psi3_iterations_header): if not hasattr(self, 'scfvalues'): self.scfvalues = [] self.scfvalues.append([]) line = next(inputfile) while line.strip(): ddensity = float(line.split()[-2]) self.scfvalues[-1].append([ddensity]) line = next(inputfile) # Psi4 converges both the SCF energy and density elements and reports both in the # iterations printout. However, the default convergence scheme involves a density-fitted # algorithm for efficiency, and this is often followed by a something with exact electron # repulsion integrals. In that case, there are actually two convergence cycles performed, # one for the density-fitted algorithm and one for the exact one, and the iterations are # printed in two blocks separated by some set-up information. if (self.section == "Iterations") and (line.strip() == "==> Iterations <=="): if not hasattr(self, 'scfvalues'): self.scfvalues = [] self.skip_line(inputfile, 'blank') header = next(inputfile) assert header.strip() == "Total Energy Delta E RMS |[F,P]|" scfvals = [] self.skip_line(inputfile, 'blank') line = next(inputfile) while line.strip() != "==> Post-Iterations <==": if line.strip() and line.split()[0] in ["@DF-RHF", "@RHF", "@DF-RKS", "@RKS"]: denergy = float(line.split()[4]) ddensity = float(line.split()[5]) scfvals.append([denergy, ddensity]) line = next(inputfile) self.section = "Post-Iterations" self.scfvalues.append(scfvals) # This section, from which we parse molecular orbital symmetries and # orbital energies, is quite similar for both Psi3 and Psi4, and in fact # the format for orbtials is the same, although the headers and spacers # are a bit different. Let's try to get both parsed with one code block. # # Here is how the block looks like for Psi4: # # Orbital Energies (a.u.) # ----------------------- # # Doubly Occupied: # # 1Bu -11.040586 1Ag -11.040524 2Bu -11.031589 # 2Ag -11.031589 3Bu -11.028950 3Ag -11.028820 # (...) # 15Ag -0.415620 1Bg -0.376962 2Au -0.315126 # 2Bg -0.278361 3Bg -0.222189 # # Virtual: # # 3Au 0.198995 4Au 0.268517 4Bg 0.308826 # 5Au 0.397078 5Bg 0.521759 16Ag 0.565017 # (...) # 24Ag 0.990287 24Bu 1.027266 25Ag 1.107702 # 25Bu 1.124938 # # The case is different in the trigger string. if "orbital energies (a.u.)" in line.lower(): # If this is Psi4, we will be in the appropriate section. assert (self.version == 3) or (self.section == "Post-Iterations") self.moenergies = [[]] self.mosyms = [[]] # Psi4 has dashes under the trigger line, but Psi3 did not. if self.version == 4: self.skip_line(inputfile, 'dashes') self.skip_line(inputfile, 'blank') # Both versions have this case insensisitive substring. doubly = next(inputfile) assert "doubly occupied" in doubly.lower() # Psi4 now has a blank line, Psi3 does not. if self.version == 4: self.skip_line(inputfile, 'blank') line = next(inputfile) while line.strip(): for i in range(len(line.split())//2): self.mosyms[0].append(line.split()[i*2][-2:]) self.moenergies[0].append(line.split()[i*2+1]) line = next(inputfile) # The last orbital energy here represented the HOMO. self.homos = [len(self.moenergies[0])-1] # Different numbers of blank lines in Psi3 and Psi4. if self.version == 3: self.skip_line(inputfile, 'blank') # The header for virtual orbitals is different for the two versions. unoccupied = next(inputfile) if self.version == 3: assert unoccupied.strip() == "Unoccupied orbitals" else: assert unoccupied.strip() == "Virtual:" # Psi4 now has a blank line, Psi3 does not. if self.version == 4: self.skip_line(inputfile, 'blank') line = next(inputfile) while line.strip(): for i in range(len(line.split())//2): self.mosyms[0].append(line.split()[i*2][-2:]) self.moenergies[0].append(line.split()[i*2+1]) line = next(inputfile) # Both Psi3 and Psi4 print the final SCF energy right after the orbital energies, # but the label is different. Psi4 also does DFT, and the label is also different in that case. if (self.version == 3 and "* SCF total energy" in line) or \ (self.section == "Post-Iterations" and ("@RHF Final Energy:" in line or "@RKS Final Energy" in line)): e = float(line.split()[-1]) if not hasattr(self, 'scfenergies'): self.scfenergies = [] self.scfenergies.append(utils.convertor(e, 'hartree', 'eV')) # ==> Molecular Orbitals <== # # 1 2 3 4 5 # # 1 0.7014827 0.7015412 0.0096801 0.0100168 0.0016438 # 2 0.0252630 0.0251793 -0.0037890 -0.0037346 0.0016447 # ... # 59 0.0000133 -0.0000067 0.0000005 -0.0047455 -0.0047455 # 60 0.0000133 0.0000067 0.0000005 0.0047455 -0.0047455 # # Ene -11.0288198 -11.0286067 -11.0285837 -11.0174766 -11.0174764 # Sym Ag Bu Ag Bu Ag # Occ 2 2 2 2 2 # # # 11 12 13 14 15 # # 1 0.1066946 0.1012709 0.0029709 0.0120562 0.1002765 # 2 -0.2753689 -0.2708037 -0.0102079 -0.0329973 -0.2790813 # ... # if (self.section == "Molecular Orbitals") and (line.strip() == "==> Molecular Orbitals <=="): self.skip_line(inputfile, 'blank') mocoeffs = [] indices = next(inputfile) while indices.strip(): indices = [int(i) for i in indices.split()] if len(mocoeffs) < indices[-1]: for i in range(len(indices)): mocoeffs.append([]) else: assert len(mocoeffs) == indices[-1] self.skip_line(inputfile, 'blank') line = next(inputfile) while line.strip(): iao = int(line.split()[0]) coeffs = [float(c) for c in line.split()[1:]] for i,c in enumerate(coeffs): mocoeffs[indices[i]-1].append(c) line = next(inputfile) energies = next(inputfile) symmetries = next(inputfile) occupancies = next(inputfile) self.skip_lines(inputfile, ['b', 'b']) indices = next(inputfile) if not hasattr(self, 'mocoeffs'): self.mocoeffs = [] self.mocoeffs.append(mocoeffs) # The formats for Mulliken and Lowdin atomic charges are the same, just with # the name changes, so use the same code for both. # # Properties computed using the SCF density density matrix # Mulliken Charges: (a.u.) # Center Symbol Alpha Beta Spin Total # 1 C 2.99909 2.99909 0.00000 0.00182 # 2 C 2.99909 2.99909 0.00000 0.00182 # ... for pop_type in ["Mulliken", "Lowdin"]: if line.strip() == "%s Charges: (a.u.)" % pop_type: if not hasattr(self, 'atomcharges'): self.atomcharges = {} header = next(inputfile) line = next(inputfile) while not line.strip(): line = next(inputfile) charges = [] while line.strip(): ch = float(line.split()[-1]) charges.append(ch) line = next(inputfile) self.atomcharges[pop_type.lower()] = charges mp_trigger = "MP2 Total Energy (a.u.)" if line.strip()[:len(mp_trigger)] == mp_trigger: mpenergy = utils.convertor(float(line.split()[-1]), 'hartree', 'eV') if not hasattr(self, 'mpenergies'): self.mpenergies = [] self.mpenergies.append([mpenergy]) # Note this is just a start and needs to be modified for CCSD(T), etc. ccsd_trigger = "* CCSD total energy" if line.strip()[:len(ccsd_trigger)] == ccsd_trigger: ccsd_energy = utils.convertor(float(line.split()[-1]), 'hartree', 'eV') if not hasattr(self, "ccenergis"): self.ccenergies = [] self.ccenergies.append(ccsd_energy) # The geometry convergence targets and values are printed in a table, with the legends # describing the convergence annotation. Probably exact slicing of the line needs # to be done in order to extract the numbers correctly. If there are no values for # a paritcular target it means they are not used (marked also with an 'o'), and in this case # we will set a value of numpy.inf so that any value will be smaller. # # ==> Convergence Check <== # # Measures of convergence in internal coordinates in au. # Criteria marked as inactive (o), active & met (*), and active & unmet ( ). # --------------------------------------------------------------------------------------------- # Step Total Energy Delta E MAX Force RMS Force MAX Disp RMS Disp # --------------------------------------------------------------------------------------------- # Convergence Criteria 1.00e-06 * 3.00e-04 * o 1.20e-03 * o # --------------------------------------------------------------------------------------------- # 2 -379.77675264 -7.79e-03 1.88e-02 4.37e-03 o 2.29e-02 6.76e-03 o ~ # --------------------------------------------------------------------------------------------- # if (self.section == "Convergence Check") and line.strip() == "==> Convergence Check <==": self.skip_lines(inputfile, ['b', 'units', 'comment', 'dash+tilde', 'header', 'dash+tilde']) # These are the position in the line at which numbers should start. starts = [27, 41, 55, 69, 83] criteria = next(inputfile) geotargets = [] for istart in starts: if criteria[istart:istart+9].strip(): geotargets.append(float(criteria[istart:istart+9])) else: geotargets.append(numpy.inf) self.skip_line(inputfile, 'dashes') values = next(inputfile) geovalues = [] for istart in starts: if values[istart:istart+9].strip(): geovalues.append(float(values[istart:istart+9])) # This assertion may be too restrictive, but we haven't seen the geotargets change. # If such an example comes up, update the value since we're interested in the last ones. if not hasattr(self, 'geotargets'): self.geotargets = geotargets else: assert self.geotargets == geotargets if not hasattr(self, 'geovalues'): self.geovalues = [] self.geovalues.append(geovalues) # This message signals a converged optimization, in which case we want # to append the index for this step to optdone, which should be equal # to the number of geovalues gathered so far. if line.strip() == "**** Optimization is complete! ****": if not hasattr(self, 'optdone'): self.optdone = [] self.optdone.append(len(self.geovalues)) # This message means that optimization has stopped for some reason, but we # still want optdone to exist in this case, although it will be an empty list. if line.strip() == "Optimizer: Did not converge!": if not hasattr(self, 'optdone'): self.optdone = [] # The reference point at which properties are evaluated in Psi4 is explicitely stated, # so we can save it for later. It is not, however, a part of the Properties section, # but it appears before it and also in other places where properies that might depend # on it are printed. # # Properties will be evaluated at 0.000000, 0.000000, 0.000000 Bohr # if (self.version == 4) and ("Properties will be evaluated at" in line.strip()): self.reference = numpy.array([float(x.strip(',')) for x in line.split()[-4:-1]]) assert line.split()[-1] == "Bohr" self.reference = utils.convertor(self.reference, 'bohr', 'Angstrom') # The properties section print the molecular dipole moment: # # ==> Properties <== # # #Properties computed using the SCF density density matrix # Nuclear Dipole Moment: (a.u.) # X: 0.0000 Y: 0.0000 Z: 0.0000 # # Electronic Dipole Moment: (a.u.) # X: 0.0000 Y: 0.0000 Z: 0.0000 # # Dipole Moment: (a.u.) # X: 0.0000 Y: 0.0000 Z: 0.0000 Total: 0.0000 # if (self.section == "Properties") and line.strip() == "Dipole Moment: (a.u.)": line = next(inputfile) dipole = numpy.array([float(line.split()[1]), float(line.split()[3]), float(line.split()[5])]) dipole = utils.convertor(dipole, "ebohr", "Debye") if not hasattr(self, 'moments'): self.moments = [self.reference, dipole] else: try: assert numpy.all(self.moments[1] == dipole) except AssertionError: self.logger.warning('Overwriting previous multipole moments with new values') self.logger.warning('This could be from post-HF properties or geometry optimization') self.moments = [self.reference, dipole] # Higher multipole moments are printed separately, on demand, in lexicographical order. # # Multipole Moments: # # ------------------------------------------------------------------------------------ # Multipole Electric (a.u.) Nuclear (a.u.) Total (a.u.) # ------------------------------------------------------------------------------------ # # L = 1. Multiply by 2.5417462300 to convert to Debye # Dipole X : 0.0000000 0.0000000 0.0000000 # Dipole Y : 0.0000000 0.0000000 0.0000000 # Dipole Z : 0.0000000 0.0000000 0.0000000 # # L = 2. Multiply by 1.3450341749 to convert to Debye.ang # Quadrupole XX : -1535.8888701 1496.8839996 -39.0048704 # Quadrupole XY : -11.5262958 11.4580038 -0.0682920 # ... # if line.strip() == "Multipole Moments:": self.skip_lines(inputfile, ['b', 'd', 'header', 'd', 'b']) # The reference used here should have been printed somewhere # before the properties and parsed above. moments = [self.reference] line = next(inputfile) while "----------" not in line.strip(): rank = int(line.split()[2].strip('.')) multipole = [] line = next(inputfile) while line.strip(): value = float(line.split()[-1]) fromunits = "ebohr" + (rank>1)*("%i" % rank) tounits = "Debye" + (rank>1)*".ang" + (rank>2)*("%i" % (rank-1)) value = utils.convertor(value, fromunits, tounits) multipole.append(value) line = next(inputfile) multipole = numpy.array(multipole) moments.append(multipole) line = next(inputfile) if not hasattr(self, 'moments'): self.moments = moments else: for im,m in enumerate(moments): if len(self.moments) <= im: self.moments.append(m) else: assert numpy.all(self.moments[im] == m) # We can also get some higher moments in Psi3, although here the dipole is not printed # separately and the order is not lexicographical. However, the numbers seem # kind of strange -- the quadrupole seems to be traceless, although I'm not sure # whether the standard transformation has been used. So, until we know what kind # of moment these are and how to make them raw again, we will only parse the dipole. # # -------------------------------------------------------------- # *** Electric multipole moments *** # -------------------------------------------------------------- # # CAUTION : The system has non-vanishing dipole moment, therefore # quadrupole and higher moments depend on the reference point. # # -Coordinates of the reference point (a.u.) : # x y z # -------------------- -------------------- -------------------- # 0.0000000000 0.0000000000 0.0000000000 # # -Electric dipole moment (expectation values) : # # mu(X) = -0.00000 D = -1.26132433e-43 C*m = -0.00000000 a.u. # mu(Y) = 0.00000 D = 3.97987832e-44 C*m = 0.00000000 a.u. # mu(Z) = 0.00000 D = 0.00000000e+00 C*m = 0.00000000 a.u. # |mu| = 0.00000 D = 1.32262368e-43 C*m = 0.00000000 a.u. # # -Components of electric quadrupole moment (expectation values) (a.u.) : # # Q(XX) = 10.62340220 Q(YY) = 1.11816843 Q(ZZ) = -11.74157063 # Q(XY) = 3.64633112 Q(XZ) = 0.00000000 Q(YZ) = 0.00000000 # if (self.version == 3) and line.strip() == "*** Electric multipole moments ***": self.skip_lines(inputfile, ['d', 'b', 'caution1', 'caution2', 'b']) coordinates = next(inputfile) assert coordinates.split()[-2] == "(a.u.)" self.skip_lines(inputfile, ['xyz', 'd']) line = next(inputfile) self.reference = numpy.array([float(x) for x in line.split()]) self.reference = utils.convertor(self.reference, 'bohr', 'Angstrom') self.skip_line(inputfile, "blank") line = next(inputfile) assert "Electric dipole moment" in line self.skip_line(inputfile, "blank") # Make sure to use the column that has the value in Debyes. dipole = [] for i in range(3): line = next(inputfile) dipole.append(float(line.split()[2])) if not hasattr(self, 'moments'): self.moments = [self.reference, dipole] else: assert self.moments[1] == dipole if __name__ == "__main__": import doctest, psiparser doctest.testmod(psiparser, verbose=False) GaussSum-3.0.1/gausssum/cclib/parser/molproparser.py0000664000175000017500000011365212660404041021263 0ustar noelnoel# -*- coding: utf-8 -*- # # This file is part of cclib (http://cclib.github.io), a library for parsing # and interpreting the results of computational chemistry packages. # # Copyright (C) 2007-2014, the cclib development team # # The library is free software, distributed under the terms of # the GNU Lesser General Public version 2.1 or later. You should have # received a copy of the license along with cclib. You can also access # the full license online at http://www.gnu.org/copyleft/lgpl.html. """Parser for Molpro output files""" import itertools import numpy from . import logfileparser from . import utils def create_atomic_orbital_names(orbitals): """Generate all atomic orbital names that could be used by Molpro. The names are returned in a dictionary, organized by subshell (S, P, D and so on). """ # We can write out the first two manually, since there are not that many. atomic_orbital_names = { 'S': ['s', '1s'], 'P': ['x', 'y', 'z', '2px', '2py', '2pz'], } # Although we could write out all names for the other subshells, it is better # to generate them if we need to expand further, since the number of functions quickly # grows and there are both Cartesian and spherical variants to consider. # For D orbitals, the Cartesian functions are xx, yy, zz, xy, xz and yz, and the # spherical ones are called 3d0, 3d1-, 3d1+, 3d2- and 3d2+. For F orbitals, the Cartesians # are xxx, xxy, xxz, xyy, ... and the sphericals are 4f0, 4f1-, 4f+ and so on. for i, orb in enumerate(orbitals): # Cartesian can be generated directly by combinations. cartesian = list(map(''.join, list(itertools.combinations_with_replacement(['x', 'y', 'z'], i+2)))) # For spherical functions, we need to construct the names. pre = str(i+3) + orb.lower() spherical = [pre + '0'] + [pre + str(j) + s for j in range(1, i+3) for s in ['-', '+']] atomic_orbital_names[orb] = cartesian + spherical return atomic_orbital_names class Molpro(logfileparser.Logfile): """Molpro file parser""" atomic_orbital_names = create_atomic_orbital_names(['D', 'F', 'G']) def __init__(self, *args, **kwargs): # Call the __init__ method of the superclass super(Molpro, self).__init__(logname="Molpro", *args, **kwargs) def __str__(self): """Return a string representation of the object.""" return "Molpro log file %s" % (self.filename) def __repr__(self): """Return a representation of the object.""" return 'Molpro("%s")' % (self.filename) def normalisesym(self, label): """Normalise the symmetries used by Molpro.""" ans = label.replace("`", "'").replace("``", "''") return ans def before_parsing(self): self.electronorbitals = "" self.insidescf = False def after_parsing(self): # If optimization thresholds are default, they are normally not printed and we need # to set them to the default after parsing. Make sure to set them in the same order that # they appear in the in the geometry optimization progress printed in the output, # namely: energy difference, maximum gradient, maximum step. if not hasattr(self, "geotargets"): self.geotargets = [] # Default THRENERG (required accuracy of the optimized energy). self.geotargets.append(1E-6) # Default THRGRAD (required accuracy of the optimized gradient). self.geotargets.append(3E-4) # Default THRSTEP (convergence threshold for the geometry optimization step). self.geotargets.append(3E-4) def extract(self, inputfile, line): """Extract information from the file object inputfile.""" if line[1:19] == "ATOMIC COORDINATES": if not hasattr(self,"atomcoords"): self.atomcoords = [] atomcoords = [] atomnos = [] self.skip_lines(inputfile, ['line', 'line', 'line']) line = next(inputfile) while line.strip(): temp = line.strip().split() atomcoords.append([utils.convertor(float(x), "bohr", "Angstrom") for x in temp[3:6]]) #bohrs to angs atomnos.append(int(round(float(temp[2])))) line = next(inputfile) self.atomcoords.append(atomcoords) self.set_attribute('atomnos', atomnos) self.set_attribute('natom', len(self.atomnos)) # Use BASIS DATA to parse input for gbasis, aonames and atombasis. If symmetry is used, # the function number starts from 1 for each irrep (the irrep index comes after the dot). # # BASIS DATA # # Nr Sym Nuc Type Exponents Contraction coefficients # # 1.1 A 1 1s 71.616837 0.154329 # 13.045096 0.535328 # 3.530512 0.444635 # 2.1 A 1 1s 2.941249 -0.099967 # 0.683483 0.399513 # ... # if line[1:11] == "BASIS DATA": # We can do a sanity check with the header. self.skip_line(inputfile, 'blank') header = next(inputfile) assert header.split() == ["Nr", "Sym", "Nuc", "Type", "Exponents", "Contraction", "coefficients"] self.skip_line(inputfile, 'blank') aonames = [] atombasis = [[] for i in range(self.natom)] gbasis = [[] for i in range(self.natom)] while line.strip(): # We need to read the line at the start of the loop here, because the last function # will be added when a blank line signalling the end of the block is encountered. line = next(inputfile) # The formatting here can exhibit subtle differences, including the number of spaces # or indentation size. However, we will rely on explicit slices since not all components # are always available. In fact, components not being there has some meaning (see below). line_nr = line[1:6].strip() line_sym = line[7:9].strip() line_nuc = line[11:14].strip() line_type = line[16:22].strip() line_exp = line[25:38].strip() line_coeffs = line[38:].strip() # If a new function type is printed or the BASIS DATA block ends with a blank line, # then add the previous function to gbasis, except for the first function since # there was no preceeding one. When translating the Molpro function name to gbasis, # note that Molpro prints all components, but we want it only once, with the proper # shell type (S,P,D,F,G). Molpro names also differ between Cartesian/spherical representations. if (line_type and aonames) or line.strip() == "": # All the possible AO names are created with the class. The function should always # find a match in that dictionary, so we can check for that here and will need to # update the dict if something unexpected comes up. funcbasis = None for fb, names in self.atomic_orbital_names.items(): if functype in names: funcbasis = fb assert funcbasis # There is a separate basis function for each column of contraction coefficients. Since all # atomic orbitals for a subshell will have the same parameters, we can simply check if # the function tuple is already in gbasis[i] before adding it. for i in range(len(coefficients[0])): func = (funcbasis, []) for j in range(len(exponents)): func[1].append((exponents[j], coefficients[j][i])) if func not in gbasis[funcatom-1]: gbasis[funcatom-1].append(func) # If it is a new type, set up the variables for the next shell(s). An exception is symmetry functions, # which we want to copy from the previous function and don't have a new number on the line. For them, # we just want to update the nuclear index. if line_type: if line_nr: exponents = [] coefficients = [] functype = line_type funcatom = int(line_nuc) # Add any exponents and coefficients to lists. if line_exp and line_coeffs: funcexp = float(line_exp) funccoeffs = [float(s) for s in line_coeffs.split()] exponents.append(funcexp) coefficients.append(funccoeffs) # If the function number is present then add to atombasis and aonames, which is different from # adding to gbasis since it enumerates AOs rather than basis functions. The number counts functions # in each irrep from 1 and we could add up the functions for each irrep to get the global count, # but it is simpler to just see how many aonames we have already parsed. Any symmetry functions # are also printed, but they don't get numbers so they are nor parsed. if line_nr: element = self.table.element[self.atomnos[funcatom-1]] aoname = "%s%i_%s" % (element, funcatom, functype) aonames.append(aoname) funcnr = len(aonames) atombasis[funcatom-1].append(funcnr-1) self.set_attribute('aonames', aonames) self.set_attribute('atombasis', atombasis) self.set_attribute('gbasis', gbasis) if line[1:23] == "NUMBER OF CONTRACTIONS": nbasis = int(line.split()[3]) self.set_attribute('nbasis', nbasis) # This is used to signalize whether we are inside an SCF calculation. if line[1:8] == "PROGRAM" and line[14:18] == "-SCF": self.insidescf = True # Use this information instead of 'SETTING ...', in case the defaults are standard. # Note that this is sometimes printed in each geometry optimization step. if line[1:20] == "NUMBER OF ELECTRONS": spinup = int(line.split()[3][:-1]) spindown = int(line.split()[4][:-1]) # Nuclear charges (atomnos) should be parsed by now. nuclear = numpy.sum(self.atomnos) charge = nuclear - spinup - spindown self.set_attribute('charge', charge) mult = spinup - spindown + 1 self.set_attribute('mult', mult) # Convergenve thresholds for SCF cycle, should be contained in a line such as: # CONVERGENCE THRESHOLDS: 1.00E-05 (Density) 1.40E-07 (Energy) if self.insidescf and line[1:24] == "CONVERGENCE THRESHOLDS:": if not hasattr(self, "scftargets"): self.scftargets = [] scftargets = list(map(float, line.split()[2::2])) self.scftargets.append(scftargets) # Usually two criteria, but save the names this just in case. self.scftargetnames = line.split()[3::2] # Read in the print out of the SCF cycle - for scfvalues. For RHF looks like: # ITERATION DDIFF GRAD ENERGY 2-EL.EN. DIPOLE MOMENTS DIIS # 1 0.000D+00 0.000D+00 -379.71523700 1159.621171 0.000000 0.000000 0.000000 0 # 2 0.000D+00 0.898D-02 -379.74469736 1162.389787 0.000000 0.000000 0.000000 1 # 3 0.817D-02 0.144D-02 -379.74635529 1162.041033 0.000000 0.000000 0.000000 2 # 4 0.213D-02 0.571D-03 -379.74658063 1162.159929 0.000000 0.000000 0.000000 3 # 5 0.799D-03 0.166D-03 -379.74660889 1162.144256 0.000000 0.000000 0.000000 4 if self.insidescf and line[1:10] == "ITERATION": if not hasattr(self, "scfvalues"): self.scfvalues = [] line = next(inputfile) energy = 0.0 scfvalues = [] while line.strip() != "": if line.split()[0].isdigit(): ddiff = float(line.split()[1].replace('D','E')) newenergy = float(line.split()[3]) ediff = newenergy - energy energy = newenergy # The convergence thresholds must have been read above. # Presently, we recognize MAX DENSITY and MAX ENERGY thresholds. numtargets = len(self.scftargetnames) values = [numpy.nan]*numtargets for n, name in zip(list(range(numtargets)),self.scftargetnames): if "ENERGY" in name.upper(): values[n] = ediff elif "DENSITY" in name.upper(): values[n] = ddiff scfvalues.append(values) line = next(inputfile) self.scfvalues.append(numpy.array(scfvalues)) # SCF result - RHF/UHF and DFT (RKS) energies. if (line[1:5] in ["!RHF", "!UHF", "!RKS"] and line[16:22].lower() == "energy"): if not hasattr(self, "scfenergies"): self.scfenergies = [] scfenergy = float(line.split()[4]) self.scfenergies.append(utils.convertor(scfenergy, "hartree", "eV")) # We are now done with SCF cycle (after a few lines). self.insidescf = False # MP2 energies. if line[1:5] == "!MP2": if not hasattr(self, 'mpenergies'): self.mpenergies = [] mp2energy = float(line.split()[-1]) mp2energy = utils.convertor(mp2energy, "hartree", "eV") self.mpenergies.append([mp2energy]) # MP2 energies if MP3 or MP4 is also calculated. if line[1:5] == "MP2:": if not hasattr(self, 'mpenergies'): self.mpenergies = [] mp2energy = float(line.split()[2]) mp2energy = utils.convertor(mp2energy, "hartree", "eV") self.mpenergies.append([mp2energy]) # MP3 (D) and MP4 (DQ or SDQ) energies. if line[1:8] == "MP3(D):": mp3energy = float(line.split()[2]) mp2energy = utils.convertor(mp3energy, "hartree", "eV") line = next(inputfile) self.mpenergies[-1].append(mp2energy) if line[1:9] == "MP4(DQ):": mp4energy = float(line.split()[2]) line = next(inputfile) if line[1:10] == "MP4(SDQ):": mp4energy = float(line.split()[2]) mp4energy = utils.convertor(mp4energy, "hartree", "eV") self.mpenergies[-1].append(mp4energy) # The CCSD program operates all closed-shel coupled cluster runs. if line[1:15] == "PROGRAM * CCSD": if not hasattr(self, "ccenergies"): self.ccenergies = [] while line[1:20] != "Program statistics:": # The last energy (most exact) will be read last and thus saved. if line[1:5] == "!CCD" or line[1:6] == "!CCSD" or line[1:9] == "!CCSD(T)": ccenergy = float(line.split()[-1]) ccenergy = utils.convertor(ccenergy, "hartree", "eV") line = next(inputfile) self.ccenergies.append(ccenergy) # Read the occupancy (index of HOMO s). # For restricted calculations, there is one line here. For unrestricted, two: # Final alpha occupancy: ... # Final beta occupancy: ... if line[1:17] == "Final occupancy:": self.homos = [int(line.split()[-1])-1] if line[1:23] == "Final alpha occupancy:": self.homos = [int(line.split()[-1])-1] line = next(inputfile) self.homos.append(int(line.split()[-1])-1) # Dipole is always printed on one line after the final RHF energy, and by default # it seems Molpro uses the origin as the reference point. if line.strip()[:13] == "Dipole moment": assert line.split()[2] == "/Debye" reference = [0.0, 0.0, 0.0] dipole = [float(d) for d in line.split()[-3:]] if not hasattr(self, 'moments'): self.moments = [reference, dipole] else: self.moments[1] == dipole # From this block aonames, atombasis, moenergies and mocoeffs can be parsed. The data is # flipped compared to most programs (GAMESS, Gaussian), since the MOs are in rows. Also, Molpro # does not cut the table into parts, rather each MO row has as many lines as it takes ro print # all of the MO coefficients. Each row normally has 10 coefficients, although this can be less # for the last row and when symmetry is used (each irrep has its own block). # # ELECTRON ORBITALS # ================= # # # Orb Occ Energy Couls-En Coefficients # # 1 1s 1 1s 1 2px 1 2py 1 2pz 2 1s (...) # 3 1s 3 1s 3 2px 3 2py 3 2pz 4 1s (...) # (...) # # 1.1 2 -11.0351 -43.4915 0.701460 0.025696 -0.000365 -0.000006 0.000000 0.006922 (...) # -0.006450 0.004742 -0.001028 -0.002955 0.000000 -0.701460 (...) # (...) # if line[1:18] == "ELECTRON ORBITALS" or self.electronorbitals: # For unrestricted calcualtions, ELECTRON ORBITALS is followed on the same line # by FOR POSITIVE SPIN or FOR NEGATIVE SPIN as appropriate. spin = (line[19:36] == "FOR NEGATIVE SPIN") or (self.electronorbitals[19:36] == "FOR NEGATIVE SPIN") if not self.electronorbitals: self.skip_line(inputfile, 'equals') self.skip_lines(inputfile, ['b', 'b', 'headers', 'b']) aonames = [] atombasis = [[] for i in range(self.natom)] moenergies = [] mocoeffs = [] line = next(inputfile) # Besides a double blank line, stop when the next orbitals are encountered for unrestricted jobs # or if there are stars on the line which always signifies the end of the block. while line.strip() and (not "ORBITALS" in line) and (not set(line.strip()) == {'*'}): # The function names are normally printed just once, but if symmetry is used then each irrep # has its own mocoeff block with a preceding list of names. is_aonames = line[:25].strip() == "" if is_aonames: # We need to save this offset for parsing the coefficients later. offset = len(aonames) aonum = len(aonames) while line.strip(): for s in line.split(): if s.isdigit(): atomno = int(s) atombasis[atomno-1].append(aonum) aonum += 1 else: functype = s element = self.table.element[self.atomnos[atomno-1]] aoname = "%s%i_%s" % (element, atomno, functype) aonames.append(aoname) line = next(inputfile) # Now there can be one or two blank lines. while not line.strip(): line = next(inputfile) # Newer versions of Molpro (for example, 2012 test files) will print some # more things here, such as HOMO and LUMO, but these have less than 10 columns. if "HOMO" in line or "LUMO" in line: break # Now parse the MO coefficients, padding the list with an appropriate amount of zeros. coeffs = [0.0 for i in range(offset)] while line.strip() != "": if line[:31].rstrip(): moenergy = float(line.split()[2]) moenergy = utils.convertor(moenergy, "hartree", "eV") moenergies.append(moenergy) # Coefficients are in 10.6f format and splitting does not work since there are not # always spaces between them. If the numbers are very large, there will be stars. str_coeffs = line[31:] ncoeffs = len(str_coeffs) // 10 coeff = [] for ic in range(ncoeffs): p = str_coeffs[ic*10:(ic+1)*10] try: c = float(p) except ValueError as detail: self.logger.warn("setting mocoeff element to zero: %s" % detail) c = 0.0 coeff.append(c) coeffs.extend(coeff) line = next(inputfile) mocoeffs.append(coeffs) # The loop should keep going until there is a double blank line, and there is # a single line between each coefficient block. line = next(inputfile) if not line.strip(): line = next(inputfile) # If symmetry was used (offset was needed) then we will need to pad all MO vectors # up to nbasis for all irreps before the last one. if offset > 0: for im,m in enumerate(mocoeffs): if len(m) < self.nbasis: mocoeffs[im] = m + [0.0 for i in range(self.nbasis - len(m))] self.set_attribute('atombasis', atombasis) self.set_attribute('aonames', aonames) # Consistent with current cclib conventions, reset moenergies/mocoeffs if they have been # previously parsed, since we want to produce only the final values. if not hasattr(self, "moenergies") or spin == 0: self.mocoeffs = [] self.moenergies = [] self.moenergies.append(moenergies) self.mocoeffs.append(mocoeffs) # Check if last line begins the next ELECTRON ORBITALS section, because we already used # this line and need to know when this method is called next time. if line[1:18] == "ELECTRON ORBITALS": self.electronorbitals = line else: self.electronorbitals = "" # If the MATROP program was called appropriately, # the atomic obital overlap matrix S is printed. # The matrix is printed straight-out, ten elements in each row, both halves. # Note that is the entire matrix is not printed, then aooverlaps # will not have dimensions nbasis x nbasis. if line[1:9] == "MATRIX S": if not hasattr(self, "aooverlaps"): self.aooverlaps = [[]] self.skip_lines(inputfile, ['b', 'symblocklabel']) line = next(inputfile) while line.strip() != "": elements = [float(s) for s in line.split()] if len(self.aooverlaps[-1]) + len(elements) <= self.nbasis: self.aooverlaps[-1] += elements else: n = len(self.aooverlaps[-1]) + len(elements) - self.nbasis self.aooverlaps[-1] += elements[:-n] self.aooverlaps.append([]) self.aooverlaps[-1] += elements[-n:] line = next(inputfile) # Thresholds are printed only if the defaults are changed with GTHRESH. # In that case, we can fill geotargets with non-default values. # The block should look like this as of Molpro 2006.1: # THRESHOLDS: # ZERO = 1.00D-12 ONEINT = 1.00D-12 TWOINT = 1.00D-11 PREFAC = 1.00D-14 LOCALI = 1.00D-09 EORDER = 1.00D-04 # ENERGY = 0.00D+00 ETEST = 0.00D+00 EDENS = 0.00D+00 THRDEDEF= 1.00D-06 GRADIENT= 1.00D-02 STEP = 1.00D-03 # ORBITAL = 1.00D-05 CIVEC = 1.00D-05 COEFF = 1.00D-04 PRINTCI = 5.00D-02 PUNCHCI = 9.90D+01 OPTGRAD = 3.00D-04 # OPTENERG= 1.00D-06 OPTSTEP = 3.00D-04 THRGRAD = 2.00D-04 COMPRESS= 1.00D-11 VARMIN = 1.00D-07 VARMAX = 1.00D-03 # THRDOUB = 0.00D+00 THRDIV = 1.00D-05 THRRED = 1.00D-07 THRPSP = 1.00D+00 THRDC = 1.00D-10 THRCS = 1.00D-10 # THRNRM = 1.00D-08 THREQ = 0.00D+00 THRDE = 1.00D+00 THRREF = 1.00D-05 SPARFAC = 1.00D+00 THRDLP = 1.00D-07 # THRDIA = 1.00D-10 THRDLS = 1.00D-07 THRGPS = 0.00D+00 THRKEX = 0.00D+00 THRDIS = 2.00D-01 THRVAR = 1.00D-10 # THRLOC = 1.00D-06 THRGAP = 1.00D-06 THRLOCT = -1.00D+00 THRGAPT = -1.00D+00 THRORB = 1.00D-06 THRMLTP = 0.00D+00 # THRCPQCI= 1.00D-10 KEXTA = 0.00D+00 THRCOARS= 0.00D+00 SYMTOL = 1.00D-06 GRADTOL = 1.00D-06 THROVL = 1.00D-08 # THRORTH = 1.00D-08 GRID = 1.00D-06 GRIDMAX = 1.00D-03 DTMAX = 0.00D+00 if line [1:12] == "THRESHOLDS": self.skip_line(input, 'blank') line = next(inputfile) while line.strip(): if "OPTENERG" in line: start = line.find("OPTENERG") optenerg = line[start+10:start+20] if "OPTGRAD" in line: start = line.find("OPTGRAD") optgrad = line[start+10:start+20] if "OPTSTEP" in line: start = line.find("OPTSTEP") optstep = line[start+10:start+20] line = next(inputfile) self.geotargets = [optenerg, optgrad, optstep] # The optimization history is the source for geovlues: # # END OF GEOMETRY OPTIMIZATION. TOTAL CPU: 246.9 SEC # # ITER. ENERGY(OLD) ENERGY(NEW) DE GRADMAX GRADNORM GRADRMS STEPMAX STEPLEN STEPRMS # 1 -382.02936898 -382.04914450 -0.01977552 0.11354875 0.20127947 0.01183997 0.12972761 0.20171740 0.01186573 # 2 -382.04914450 -382.05059234 -0.00144784 0.03299860 0.03963339 0.00233138 0.05577169 0.06687650 0.00393391 # 3 -382.05059234 -382.05069136 -0.00009902 0.00694359 0.01069889 0.00062935 0.01654549 0.02016307 0.00118606 # ... # # The above is an exerpt from Molpro 2006, but it is a little bit different # for Molpro 2012, namely the 'END OF GEOMETRY OPTIMIZATION occurs after the # actual history list. It seems there is a another consistent line before the # history, but this might not be always true -- so this is a potential weak link. if line[1:30] == "END OF GEOMETRY OPTIMIZATION." or line.strip() == "Quadratic Steepest Descent - Minimum Search": # I think this is the trigger for convergence, and it shows up at the top in Molpro 2006. geometry_converged = line[1:30] == "END OF GEOMETRY OPTIMIZATION." self.skip_line(inputfile, 'blank') # Newer version of Molpro (at least for 2012) print and additional column # with the timing information for each step. Otherwise, the history looks the same. headers = next(inputfile).split() if not len(headers) in (10,11): return # Although criteria can be changed, the printed format should not change. # In case it does, retrieve the columns for each parameter. index_ITER = headers.index('ITER.') index_THRENERG = headers.index('DE') index_THRGRAD = headers.index('GRADMAX') index_THRSTEP = headers.index('STEPMAX') line = next(inputfile) self.geovalues = [] while line.strip(): line = line.split() istep = int(line[index_ITER]) geovalues = [] geovalues.append(float(line[index_THRENERG])) geovalues.append(float(line[index_THRGRAD])) geovalues.append(float(line[index_THRSTEP])) self.geovalues.append(geovalues) line = next(inputfile) if line.strip() == "Freezing grid": line = next(inputfile) # The convergence trigger shows up somewhere at the bottom in Molpro 2012, # before the final stars. If convergence is not reached, there is an additional # line that can be checked for. This is a little tricky, though, since it is # not the last line... so bail out of the loop if convergence failure is detected. while "*****" not in line: line = next(inputfile) if line.strip() == "END OF GEOMETRY OPTIMIZATION.": geometry_converged = True if "No convergence" in line: geometry_converged = False break # Finally, deal with optdone, append the last step to it only if we had convergence. if not hasattr(self, 'optdone'): self.optdone = [] if geometry_converged: self.optdone.append(istep-1) # This block should look like this: # Normal Modes # # 1 Au 2 Bu 3 Ag 4 Bg 5 Ag # Wavenumbers [cm-1] 151.81 190.88 271.17 299.59 407.86 # Intensities [km/mol] 0.33 0.28 0.00 0.00 0.00 # Intensities [relative] 0.34 0.28 0.00 0.00 0.00 # CX1 0.00000 -0.01009 0.02577 0.00000 0.06008 # CY1 0.00000 -0.05723 -0.06696 0.00000 0.06349 # CZ1 -0.02021 0.00000 0.00000 0.11848 0.00000 # CX2 0.00000 -0.01344 0.05582 0.00000 -0.02513 # CY2 0.00000 -0.06288 -0.03618 0.00000 0.00349 # CZ2 -0.05565 0.00000 0.00000 0.07815 0.00000 # ... # Molpro prints low frequency modes in a subsequent section with the same format, # which also contains zero frequency modes, with the title: # Normal Modes of low/zero frequencies if line[1:13] == "Normal Modes": if line[1:37] == "Normal Modes of low/zero frequencies": islow = True else: islow = False self.skip_line(inputfile, 'blank') # Each portion of five modes is followed by a single blank line. # The whole block is followed by an additional blank line. line = next(inputfile) while line.strip(): if line[1:25].isspace(): numbers = list(map(int, line.split()[::2])) vibsyms = line.split()[1::2] if line[1:12] == "Wavenumbers": vibfreqs = list(map(float, line.strip().split()[2:])) if line[1:21] == "Intensities [km/mol]": vibirs = list(map(float, line.strip().split()[2:])) # There should always by 3xnatom displacement rows. if line[1:11].isspace() and line[13:25].strip().isdigit(): # There are a maximum of 5 modes per line. nmodes = len(line.split())-1 vibdisps = [] for i in range(nmodes): vibdisps.append([]) for n in range(self.natom): vibdisps[i].append([]) for i in range(nmodes): disp = float(line.split()[i+1]) vibdisps[i][0].append(disp) for i in range(self.natom*3 - 1): line = next(inputfile) iatom = (i+1)//3 for i in range(nmodes): disp = float(line.split()[i+1]) vibdisps[i][iatom].append(disp) line = next(inputfile) if not line.strip(): if not hasattr(self, "vibfreqs"): self.vibfreqs = [] if not hasattr(self, "vibsyms"): self.vibsyms = [] if not hasattr(self, "vibirs") and "vibirs" in dir(): self.vibirs = [] if not hasattr(self, "vibdisps") and "vibdisps" in dir(): self.vibdisps = [] if not islow: self.vibfreqs.extend(vibfreqs) self.vibsyms.extend(vibsyms) if "vibirs" in dir(): self.vibirs.extend(vibirs) if "vibdisps" in dir(): self.vibdisps.extend(vibdisps) else: nonzero = [f > 0 for f in vibfreqs] vibfreqs = [f for f in vibfreqs if f > 0] self.vibfreqs = vibfreqs + self.vibfreqs vibsyms = [vibsyms[i] for i in range(len(vibsyms)) if nonzero[i]] self.vibsyms = vibsyms + self.vibsyms if "vibirs" in dir(): vibirs = [vibirs[i] for i in range(len(vibirs)) if nonzero[i]] self.vibirs = vibirs + self.vibirs if "vibdisps" in dir(): vibdisps = [vibdisps[i] for i in range(len(vibdisps)) if nonzero[i]] self.vibdisps = vibdisps + self.vibdisps line = next(inputfile) if line[1:16] == "Force Constants": self.logger.info("Creating attribute hessian") self.hessian = [] line = next(inputfile) hess = [] tmp = [] while line.strip(): try: list(map(float, line.strip().split()[2:])) except: line = next(inputfile) line.strip().split()[1:] hess.extend([list(map(float, line.strip().split()[1:]))]) line = next(inputfile) lig = 0 while (lig==0) or (len(hess[0]) > 1): tmp.append(hess.pop(0)) lig += 1 k = 5 while len(hess) != 0: tmp[k] += hess.pop(0) k += 1 if (len(tmp[k-1]) == lig): break if k >= lig: k = len(tmp[-1]) for l in tmp: self.hessian += l if line[1:14] == "Atomic Masses" and hasattr(self,"hessian"): line = next(inputfile) self.amass = list(map(float, line.strip().split()[2:])) while line.strip(): line = next(inputfile) self.amass += list(map(float, line.strip().split()[2:])) #1PROGRAM * POP (Mulliken population analysis) # # # Density matrix read from record 2100.2 Type=RHF/CHARGE (state 1.1) # # Population analysis by basis function type # # Unique atom s p d f g Total Charge # 2 C 3.11797 2.88497 0.00000 0.00000 0.00000 6.00294 - 0.00294 # 3 C 3.14091 2.91892 0.00000 0.00000 0.00000 6.05984 - 0.05984 # ... if line.strip() == "1PROGRAM * POP (Mulliken population analysis)": self.skip_lines(inputfile, ['b', 'b', 'density_source', 'b', 'func_type', 'b']) header = next(inputfile) icharge = header.split().index('Charge') charges = [] line = next(inputfile) while line.strip(): cols = line.split() charges.append(float(cols[icharge]+cols[icharge+1])) line = next(inputfile) if not hasattr(self, "atomcharges"): self.atomcharges = {} self.atomcharges['mulliken'] = charges if __name__ == "__main__": import doctest, molproparser doctest.testmod(molproparser, verbose=False) GaussSum-3.0.1/gausssum/cclib/parser/logfileparser.py0000664000175000017500000003763112660404041021376 0ustar noelnoel# -*- coding: utf-8 -*- # # This file is part of cclib (http://cclib.github.io), a library for parsing # and interpreting the results of computational chemistry packages. # # Copyright (C) 2006-2014, the cclib development team # # The library is free software, distributed under the terms of # the GNU Lesser General Public version 2.1 or later. You should have # received a copy of the license along with cclib. You can also access # the full license online at http://www.gnu.org/copyleft/lgpl.html. """Generic output file parser and related tools""" import bz2 import fileinput import gzip import inspect import io import logging import os import random import sys import zipfile import numpy from . import utils from .data import ccData # This seems to avoid a problem with Avogadro. logging.logMultiprocessing = 0 class myBZ2File(bz2.BZ2File): """Return string instead of bytes""" def __next__(self): line = super().__next__() return line.decode("ascii", "replace") class myGzipFile(gzip.GzipFile): """Return string instead of bytes""" def __next__(self): line = super().__next__() return line.decode("ascii", "replace") class FileWrapper(object): """Wrap a file object so that we can maintain position""" def __init__(self, file): self.file = file self.pos = 0 self.file.seek(0, 2) self.size = self.file.tell() self.file.seek(0, 0) def next(self): line = next(self.file) self.pos += len(line) return line def __next__(self): return self.next() def __iter__(self): return self def close(self): self.file.close() def seek(self, pos, ref): self.file.seek(pos, ref) def openlogfile(filename): """Return a file object given a filename. Given the filename of a log file or a gzipped, zipped, or bzipped log file, this function returns a regular Python file object. Given an address starting with http://, this function retrieves the url and returns a file object using a temporary file. Given a list of filenames, this function returns a FileInput object, which can be used for seamless iteration without concatenation. """ # If there is a single string argument given. if type(filename) in [str, str]: extension = os.path.splitext(filename)[1] if extension == ".gz": fileobject = myGzipFile(filename, "r") elif extension == ".zip": zip = zipfile.ZipFile(filename, "r") assert len(zip.namelist()) == 1, "ERROR: Zip file contains more than 1 file" fileobject = io.StringIO(zip.read(zip.namelist()[0]).decode("ascii", "ignore")) elif extension in ['.bz', '.bz2']: # Module 'bz2' is not always importable. assert bz2 != None, "ERROR: module bz2 cannot be imported" fileobject = myBZ2File(filename, "r") else: fileobject = FileWrapper(io.open(filename, "r", errors='ignore')) return fileobject elif hasattr(filename, "__iter__"): # Compression (gzip and bzip) is supported as of Python 2.5. if sys.version_info[0] >= 2 and sys.version_info[1] >= 5: fileobject = fileinput.input(filename, openhook=fileinput.hook_compressed) else: fileobject = fileinput.input(filename) return fileobject class Logfile(object): """Abstract class for logfile objects. Subclasses defined by cclib: ADF, DALTON, GAMESS, GAMESSUK, Gaussian, Jaguar, Molpro, NWChem, ORCA, Psi, QChem """ def __init__(self, source, loglevel=logging.INFO, logname="Log", logstream=sys.stdout, datatype=ccData, **kwds): """Initialise the Logfile object. This should be called by a subclass in its own __init__ method. Inputs: source - a single logfile, a list of logfiles, or input stream """ # Set the filename to source if it is a string or a list of filenames. # In the case of an input stream, set some arbitrary name and the stream. # Elsewise, raise an Exception. if isinstance(source, str): self.filename = source self.isstream = False elif isinstance(source, list) and all([isinstance(s, str) for s in source]): self.filename = source self.isstream = False elif hasattr(source, "read"): self.filename = "stream %s" % str(type(source)) self.isstream = True self.stream = source else: raise ValueError # Set up the logger. # Note that calling logging.getLogger() with one name always returns the same instance. # Presently in cclib, all parser instances of the same class use the same logger, # which means that care needs to be taken not to duplicate handlers. self.loglevel = loglevel self.logname = logname self.logger = logging.getLogger('%s %s' % (self.logname, self.filename)) self.logger.setLevel(self.loglevel) if len(self.logger.handlers) == 0: handler = logging.StreamHandler(logstream) handler.setFormatter(logging.Formatter("[%(name)s %(levelname)s] %(message)s")) self.logger.addHandler(handler) # Periodic table of elements. self.table = utils.PeriodicTable() # This is the class that will be used in the data object returned by parse(), and should # normally be ccData or a subclass of it. self.datatype = datatype # Change the class used if we want optdone to be a list or if the 'future' option # is used, which might have more consequences in the future. optdone_as_list = kwds.get("optdone_as_list", False) or kwds.get("future", False) optdone_as_list = optdone_as_list if isinstance(optdone_as_list, bool) else False if not optdone_as_list: from .data import ccData_optdone_bool self.datatype = ccData_optdone_bool def __setattr__(self, name, value): # Send info to logger if the attribute is in the list self._attrlist. if name in getattr(self, "_attrlist", {}) and hasattr(self, "logger"): # Call logger.info() only if the attribute is new. if not hasattr(self, name): if type(value) in [numpy.ndarray, list]: self.logger.info("Creating attribute %s[]" %name) else: self.logger.info("Creating attribute %s: %s" %(name, str(value))) # Set the attribute. object.__setattr__(self, name, value) def parse(self, progress=None, fupdate=0.05, cupdate=0.002): """Parse the logfile, using the assumed extract method of the child.""" # Check that the sub-class has an extract attribute, # that is callable with the proper number of arguemnts. if not hasattr(self, "extract"): raise AttributeError("Class %s has no extract() method." %self.__class__.__name__) if not callable(self.extract): raise AttributeError("Method %s._extract not callable." %self.__class__.__name__) if len(inspect.getargspec(self.extract)[0]) != 3: raise AttributeError("Method %s._extract takes wrong number of arguments." %self.__class__.__name__) # Save the current list of attributes to keep after parsing. # The dict of self should be the same after parsing. _nodelete = list(set(self.__dict__.keys())) # Initiate the FileInput object for the input files. # Remember that self.filename can be a list of files. if not self.isstream: inputfile = openlogfile(self.filename) else: inputfile = self.stream # Intialize self.progress is_compressed = isinstance(inputfile, myGzipFile) or isinstance(inputfile, myBZ2File) if progress and not (is_compressed): self.progress = progress self.progress.initialize(inputfile.size) self.progress.step = 0 self.fupdate = fupdate self.cupdate = cupdate # Maybe the sub-class has something to do before parsing. self.before_parsing() # Loop over lines in the file object and call extract(). # This is where the actual parsing is done. for line in inputfile: self.updateprogress(inputfile, "Unsupported information", cupdate) # This call should check if the line begins a section of extracted data. # If it does, it parses some lines and sets the relevant attributes (to self). # Any attributes can be freely set and used across calls, however only those # in data._attrlist will be moved to final data object that is returned. self.extract(inputfile, line) # Close input file object. if not self.isstream: inputfile.close() # Maybe the sub-class has something to do after parsing. self.after_parsing() # If atomcoords were not parsed, but some input coordinates were ("inputcoords"). # This is originally from the Gaussian parser, a regression fix. if not hasattr(self, "atomcoords") and hasattr(self, "inputcoords"): self.atomcoords = numpy.array(self.inputcoords, 'd') # Set nmo if not set already - to nbasis. if not hasattr(self, "nmo") and hasattr(self, "nbasis"): self.nmo = self.nbasis # Creating deafult coreelectrons array. if not hasattr(self, "coreelectrons") and hasattr(self, "natom"): self.coreelectrons = numpy.zeros(self.natom, "i") # Create the data object we want to return. This is normally ccData, but can be changed # by passing the datatype argument to the constructor. All supported cclib attributes # are copied to this object, but beware that in order to be moved an attribute must be # included in the data._attrlist of ccData (or whatever else). # There is the possibility of passing assitional argument via self.data_args, but # we use this sparingly in cases where we want to limit the API with options, etc. data = self.datatype(attributes=self.__dict__) # Now make sure that the cclib attributes in the data object are all the correct type, # including arrays and lists of arrays. data.arrayify() # Delete all temporary attributes (including cclib attributes). # All attributes should have been moved to a data object, which will be returned. for attr in list(self.__dict__.keys()): if not attr in _nodelete: self.__delattr__(attr) # Update self.progress as done. if hasattr(self, "progress"): self.progress.update(inputfile.size, "Done") return data def before_parsing(self): """Set parser-specific variables and do other initial things here.""" pass def after_parsing(self): """Correct data or do parser-specific validation after parsing is finished.""" pass def updateprogress(self, inputfile, msg, xupdate=0.05): """Update progress.""" if hasattr(self, "progress") and random.random() < xupdate: newstep = inputfile.pos if newstep != self.progress.step: self.progress.update(newstep, msg) self.progress.step = newstep def normalisesym(self, symlabel): """Standardise the symmetry labels between parsers. This method should be overwritten by individual parsers, and should contain appropriate doctests. If is not overwritten, this is detected as an error by unit tests. """ return "ERROR: This should be overwritten by this subclass" def float(self, number): """Convert a string to a float. This method should perform certain checks that are specific to cclib, including avoiding the problem with Ds instead of Es in scientific notation. Another point is converting string signifying numerical problems (*****) to something we can manage (Numpy's NaN). >>> t = Logfile("dummyfile") >>> t.float("123.2323E+02") 12323.23 >>> t.float("123.2323D+02") 12323.23 >>> t.float("*****") nan """ if list(set(number)) == ['*']: return numpy.nan return float(number.replace("D","E")) def set_attribute(self, name, value, check=True): """Set an attribute and perform a check when it already exists. Note that this can be used for scalars and lists alike, whenever we want to set a value for an attribute. By default we want to check that the value does not change if the attribute already exists, and this function is a good place to add more tests in the future. """ if check and hasattr(self, name): try: assert getattr(self, name) == value except AssertionError: self.logger.warning("Attribute %s changed value (%s -> %s)" % (name, getattr(self, name), value)) setattr(self, name, value) def skip_lines(self, inputfile, sequence): """Read trivial line types and check they are what they are supposed to be. This function will read len(sequence) lines and do certain checks on them, when the elements of sequence have the appropriate values. Currently the following elements trigger checks: 'blank' or 'b' - the line should be blank 'dashes' or 'd' - the line should contain only dashes (or spaces) 'equals' or 'e' - the line should contain only equal signs (or spaces) 'stars' or 's' - the line should contain only stars (or spaces) """ expected_characters = { '-' : ['dashes', 'd'], '=' : ['equals', 'e'], '*' : ['stars', 's'], } lines = [] for expected in sequence: # Read the line we want to skip. line = next(inputfile) # Blank lines are perhaps the most common thing we want to check for. if expected in ["blank", "b"]: try: assert line.strip() == "" except AssertionError: frame, fname, lno, funcname, funcline, index = inspect.getouterframes(inspect.currentframe())[1] parser = fname.split('/')[-1] msg = "In %s, line %i, line not blank as expected: %s" % (parser, lno, line.strip()) self.logger.warning(msg) # All cases of heterogeneous lines can be dealt with by the same code. for character, keys in expected_characters.items(): if expected in keys: try: assert all([c == character for c in line.strip() if c != ' ']) except AssertionError: frame, fname, lno, funcname, funcline, index = inspect.getouterframes(inspect.currentframe())[1] parser = fname.split('/')[-1] msg = "In %s, line %i, line not all %s as expected: %s" % (parser, lno, keys[0], line.strip()) self.logger.warning(msg) continue # Save the skipped line, and we will return the whole list. lines.append(line) return lines skip_line = lambda self, inputfile, expected: self.skip_lines(inputfile, [expected]) if __name__ == "__main__": import doctest doctest.testmod() GaussSum-3.0.1/gausssum/cclib/parser/gamessparser.py0000664000175000017500000016112612660404041021231 0ustar noelnoel# -*- coding: utf-8 -*- # # This file is part of cclib (http://cclib.github.io), a library for parsing # and interpreting the results of computational chemistry packages. # # Copyright (C) 2006-2014, the cclib development team # # The library is free software, distributed under the terms of # the GNU Lesser General Public version 2.1 or later. You should have # received a copy of the license along with cclib. You can also access # the full license online at http://www.gnu.org/copyleft/lgpl.html. """Parser for GAMESS(US) output files""" from __future__ import print_function import re import numpy from . import logfileparser from . import utils class GAMESS(logfileparser.Logfile): """A GAMESS/Firefly log file.""" # Used to index self.scftargets[]. SCFRMS, SCFMAX, SCFENERGY = list(range(3)) def __init__(self, *args, **kwargs): # Call the __init__ method of the superclass super(GAMESS, self).__init__(logname="GAMESS", *args, **kwargs) def __str__(self): """Return a string representation of the object.""" return "GAMESS log file %s" % (self.filename) def __repr__(self): """Return a representation of the object.""" return 'GAMESS("%s")' % (self.filename) def normalisesym(self, label): """Normalise the symmetries used by GAMESS. To normalise, two rules need to be applied: (1) Occurences of U/G in the 2/3 position of the label must be lower-cased (2) Two single quotation marks must be replaced by a double >>> t = GAMESS("dummyfile").normalisesym >>> labels = ['A', 'A1', 'A1G', "A'", "A''", "AG"] >>> answers = map(t, labels) >>> print answers ['A', 'A1', 'A1g', "A'", 'A"', 'Ag'] """ if label[1:] == "''": end = '"' else: end = label[1:].replace("U", "u").replace("G", "g") return label[0] + end def before_parsing(self): self.firststdorient = True # Used to decide whether to wipe the atomcoords clean self.cihamtyp = "none" # Type of CI Hamiltonian: saps or dets. self.scftype = "none" # Type of SCF calculation: BLYP, RHF, ROHF, etc. def extract(self, inputfile, line): """Extract information from the file object inputfile.""" if line [1:12] == "INPUT CARD>": return # We are looking for this line: # PARAMETERS CONTROLLING GEOMETRY SEARCH ARE # ... # OPTTOL = 1.000E-04 RMIN = 1.500E-03 if line[10:18] == "OPTTOL =": if not hasattr(self, "geotargets"): opttol = float(line.split()[2]) self.geotargets = numpy.array([opttol, 3. / opttol], "d") # Has to deal with such lines as: # FINAL R-B3LYP ENERGY IS -382.0507446475 AFTER 10 ITERATIONS # FINAL ENERGY IS -379.7594673378 AFTER 9 ITERATIONS # ...so take the number after the "IS" if line.find("FINAL") == 1: if not hasattr(self, "scfenergies"): self.scfenergies = [] temp = line.split() self.scfenergies.append(utils.convertor(float(temp[temp.index("IS") + 1]), "hartree", "eV")) # For total energies after Moller-Plesset corrections, the output looks something like this: # # RESULTS OF MOLLER-PLESSET 2ND ORDER CORRECTION ARE # E(0)= -285.7568061536 # E(1)= 0.0 # E(2)= -0.9679419329 # E(MP2)= -286.7247480864 # where E(MP2) = E(0) + E(2) # # With GAMESS-US 12 Jan 2009 (R3), the preceding text is different: # DIRECT 4-INDEX TRANSFORMATION # SCHWARZ INEQUALITY TEST SKIPPED 0 INTEGRAL BLOCKS # E(SCF)= -76.0088477471 # E(2)= -0.1403745370 # E(MP2)= -76.1492222841 # if line.find("RESULTS OF MOLLER-PLESSET") >= 0 or line[6:37] == "SCHWARZ INEQUALITY TEST SKIPPED": if not hasattr(self, "mpenergies"): self.mpenergies = [] # Each iteration has a new print-out self.mpenergies.append([]) # GAMESS-US presently supports only second order corrections (MP2) # PC GAMESS also has higher levels (3rd and 4th), with different output # Only the highest level MP4 energy is gathered (SDQ or SDTQ) while re.search("DONE WITH MP(\d) ENERGY", line) is None: line = next(inputfile) if len(line.split()) > 0: # Only up to MP2 correction if line.split()[0] == "E(MP2)=": mp2energy = float(line.split()[1]) self.mpenergies[-1].append(utils.convertor(mp2energy, "hartree", "eV")) # MP2 before higher order calculations if line.split()[0] == "E(MP2)": mp2energy = float(line.split()[2]) self.mpenergies[-1].append(utils.convertor(mp2energy, "hartree", "eV")) if line.split()[0] == "E(MP3)": mp3energy = float(line.split()[2]) self.mpenergies[-1].append(utils.convertor(mp3energy, "hartree", "eV")) if line.split()[0] in ["E(MP4-SDQ)", "E(MP4-SDTQ)"]: mp4energy = float(line.split()[2]) self.mpenergies[-1].append(utils.convertor(mp4energy, "hartree", "eV")) # Total energies after Coupled Cluster calculations # Only the highest Coupled Cluster level result is gathered if line[12:23] == "CCD ENERGY:": if not hasattr(self, "ccenergies"): self.ccenergies = [] ccenergy = float(line.split()[2]) self.ccenergies.append(utils.convertor(ccenergy, "hartree", "eV")) if line.find("CCSD") >= 0 and line.split()[0:2] == ["CCSD", "ENERGY:"]: if not hasattr(self, "ccenergies"): self.ccenergies = [] ccenergy = float(line.split()[2]) line = next(inputfile) if line[8:23] == "CCSD[T] ENERGY:": ccenergy = float(line.split()[2]) line = next(inputfile) if line[8:23] == "CCSD(T) ENERGY:": ccenergy = float(line.split()[2]) self.ccenergies.append(utils.convertor(ccenergy, "hartree", "eV")) # Also collect MP2 energies, which are always calculated before CC if line [8:23] == "MBPT(2) ENERGY:": if not hasattr(self, "mpenergies"): self.mpenergies = [] self.mpenergies.append([]) mp2energy = float(line.split()[2]) self.mpenergies[-1].append(utils.convertor(mp2energy, "hartree", "eV")) # Extract charge and multiplicity if line[1:19] == "CHARGE OF MOLECULE": charge = int(line.split()[-1]) self.set_attribute('charge', charge) line = next(inputfile) mult = int(line.split()[-1]) self.set_attribute('mult', mult) # etenergies (originally used only for CIS runs, but now also TD-DFT) if "EXCITATION ENERGIES" in line and line.find("DONE WITH") < 0: if not hasattr(self, "etenergies"): self.etenergies = [] get_etosc = False header = next(inputfile).rstrip() if header.endswith("OSC. STR."): # water_cis_dets.out does not have the oscillator strength # in this table...it is extracted from a different section below get_etosc = True self.etoscs = [] self.skip_line(inputfile, 'dashes') line = next(inputfile) broken = line.split() while len(broken) > 0: # Take hartree value with more numbers, and convert. # Note that the values listed after this are also less exact! etenergy = float(broken[1]) self.etenergies.append(utils.convertor(etenergy, "hartree", "cm-1")) if get_etosc: etosc = float(broken[-1]) self.etoscs.append(etosc) broken = next(inputfile).split() # Detect the CI hamiltonian type, if applicable. # Should always be detected if CIS is done. if line[8:64] == "RESULTS FROM SPIN-ADAPTED ANTISYMMETRIZED PRODUCT (SAPS)": self.cihamtyp = "saps" if line[8:64] == "RESULTS FROM DETERMINANT BASED ATOMIC ORBITAL CI-SINGLES": self.cihamtyp = "dets" # etsecs (used only for CIS runs for now) if line[1:14] == "EXCITED STATE": if not hasattr(self, 'etsecs'): self.etsecs = [] if not hasattr(self, 'etsyms'): self.etsyms = [] statenumber = int(line.split()[2]) spin = int(float(line.split()[7])) if spin == 0: sym = "Singlet" if spin == 1: sym = "Triplet" sym += '-' + line.split()[-1] self.etsyms.append(sym) # skip 5 lines for i in range(5): line = next(inputfile) line = next(inputfile) CIScontribs = [] while line.strip()[0] != "-": MOtype = 0 # alpha/beta are specified for hamtyp=dets if self.cihamtyp == "dets": if line.split()[0] == "BETA": MOtype = 1 fromMO = int(line.split()[-3])-1 toMO = int(line.split()[-2])-1 coeff = float(line.split()[-1]) # With the SAPS hamiltonian, the coefficients are multiplied # by sqrt(2) so that they normalize to 1. # With DETS, both alpha and beta excitations are printed. # if self.cihamtyp == "saps": # coeff /= numpy.sqrt(2.0) CIScontribs.append([(fromMO,MOtype),(toMO,MOtype),coeff]) line = next(inputfile) self.etsecs.append(CIScontribs) # etoscs (used only for CIS runs now) if line[1:50] == "TRANSITION FROM THE GROUND STATE TO EXCITED STATE": if not hasattr(self, "etoscs"): self.etoscs = [] # This was the suggested as a fix in issue #61, and it does allow # the parser to finish without crashing. However, it seems that # etoscs is shorter in this case than the other transition attributes, # so that should be somehow corrected and tested for. if "OPTICALLY" in line: pass else: statenumber = int(line.split()[-1]) # skip 7 lines for i in range(8): line = next(inputfile) strength = float(line.split()[3]) self.etoscs.append(strength) # TD-DFT for GAMESS-US. # The format for excitations has changed a bit between 2007 and 2012. # Original format parser was written for: # # ------------------- # TRIPLET EXCITATIONS # ------------------- # # STATE # 1 ENERGY = 3.027228 EV # OSCILLATOR STRENGTH = 0.000000 # DRF COEF OCC VIR # --- ---- --- --- # 35 -1.105383 35 -> 36 # 69 -0.389181 34 -> 37 # 103 -0.405078 33 -> 38 # 137 0.252485 32 -> 39 # 168 -0.158406 28 -> 40 # # STATE # 2 ENERGY = 4.227763 EV # ... # # Here is the corresponding 2012 version: # # ------------------- # TRIPLET EXCITATIONS # ------------------- # # STATE # 1 ENERGY = 3.027297 EV # OSCILLATOR STRENGTH = 0.000000 # LAMBDA DIAGNOSTIC = 0.925 (RYDBERG/CHARGE TRANSFER CHARACTER) # SYMMETRY OF STATE = A # EXCITATION DE-EXCITATION # OCC VIR AMPLITUDE AMPLITUDE # I A X(I->A) Y(A->I) # --- --- -------- -------- # 35 36 -0.929190 -0.176167 # 34 37 -0.279823 -0.109414 # ... # # We discern these two by the presence of the arrow in the old version. # # The "LET EXCITATIONS" pattern used below catches both # singlet and triplet excitations output. if line[14:29] == "LET EXCITATIONS": self.etenergies = [] self.etoscs = [] self.etsecs = [] etsyms = [] self.skip_lines(inputfile, ['d', 'b']) # Loop while states are still being printed. line = next(inputfile) while line[1:6] == "STATE": self.updateprogress(inputfile, "Excited States") etenergy = utils.convertor(float(line.split()[-2]), "eV", "cm-1") etoscs = float(next(inputfile).split()[-1]) self.etenergies.append(etenergy) self.etoscs.append(etoscs) # Symmetry is not always present, especially in old versions. # Newer versions, on the other hand, can also provide a line # with lambda diagnostic and some extra headers. line = next(inputfile) if "LAMBDA DIAGNOSTIC" in line: line = next(inputfile) if "SYMMETRY" in line: etsyms.append(line.split()[-1]) line = next(inputfile) if "EXCITATION" in line and "DE-EXCITATION" in line: line = next(inputfile) if line.count("AMPLITUDE") == 2: line = next(inputfile) self.skip_line(inputfile, 'dashes') CIScontribs = [] line = next(inputfile) while line.strip(): cols = line.split() if "->" in line: i_occ_vir = [2, 4] i_coeff = 1 else: i_occ_vir = [0, 1] i_coeff = 2 fromMO, toMO = [int(cols[i]) - 1 for i in i_occ_vir] coeff = float(cols[i_coeff]) CIScontribs.append([(fromMO, 0), (toMO, 0), coeff]) line = next(inputfile) self.etsecs.append(CIScontribs) line = next(inputfile) # The symmetries are not always present. if etsyms: self.etsyms = etsyms # Maximum and RMS gradients. if "MAXIMUM GRADIENT" in line or "RMS GRADIENT" in line: parts = line.split() # Avoid parsing the following... ## YOU SHOULD RESTART "OPTIMIZE" RUNS WITH THE COORDINATES ## WHOSE ENERGY IS LOWEST. RESTART "SADPOINT" RUNS WITH THE ## COORDINATES WHOSE RMS GRADIENT IS SMALLEST. THESE ARE NOT ## ALWAYS THE LAST POINT COMPUTED! if parts[0] not in ["MAXIMUM", "RMS", "(1)"]: return if not hasattr(self, "geovalues"): self.geovalues = [] # Newer versions (around 2006) have both maximum and RMS on one line: # MAXIMUM GRADIENT = 0.0531540 RMS GRADIENT = 0.0189223 if len(parts) == 8: maximum = float(parts[3]) rms = float(parts[7]) # In older versions of GAMESS, this spanned two lines, like this: # MAXIMUM GRADIENT = 0.057578167 # RMS GRADIENT = 0.027589766 if len(parts) == 4: maximum = float(parts[3]) line = next(inputfile) parts = line.split() rms = float(parts[3]) # FMO also prints two final one- and two-body gradients (see exam37): # (1) MAXIMUM GRADIENT = 0.0531540 RMS GRADIENT = 0.0189223 if len(parts) == 9: maximum = float(parts[4]) rms = float(parts[8]) self.geovalues.append([maximum, rms]) # This is the input orientation, which is the only data available for # SP calcs, but which should be overwritten by the standard orientation # values, which is the only information available for all geoopt cycles. if line[11:50] == "ATOMIC COORDINATES": if not hasattr(self, "atomcoords"): self.atomcoords = [] line = next(inputfile) atomcoords = [] atomnos = [] line = next(inputfile) while line.strip(): temp = line.strip().split() atomcoords.append([utils.convertor(float(x), "bohr", "Angstrom") for x in temp[2:5]]) atomnos.append(int(round(float(temp[1])))) # Don't use the atom name as this is arbitary line = next(inputfile) self.set_attribute('atomnos', atomnos) self.atomcoords.append(atomcoords) if line[12:40] == "EQUILIBRIUM GEOMETRY LOCATED": # Prevent extraction of the final geometry twice if not hasattr(self, 'optdone'): self.optdone = [] self.optdone.append(len(self.geovalues) - 1) # Make sure we always have optdone for geomtry optimization, even if not converged. if "GEOMETRY SEARCH IS NOT CONVERGED" in line: if not hasattr(self, 'optdone'): self.optdone = [] # This is the standard orientation, which is the only coordinate # information available for all geometry optimisation cycles. # The input orientation will be overwritten if this is a geometry optimisation # We assume that a previous Input Orientation has been found and # used to extract the atomnos if line[1:29] == "COORDINATES OF ALL ATOMS ARE" and (not hasattr(self, "optdone") or self.optdone == []): self.updateprogress(inputfile, "Coordinates") if self.firststdorient: self.firststdorient = False # Wipes out the single input coordinate at the start of the file self.atomcoords = [] self.skip_lines(inputfile, ['line', '-']) atomcoords = [] line = next(inputfile) for i in range(self.natom): temp = line.strip().split() atomcoords.append(list(map(float, temp[2:5]))) line = next(inputfile) self.atomcoords.append(atomcoords) # Section with SCF information. # # The space at the start of the search string is to differentiate from MCSCF. # Everything before the search string is stored as the type of SCF. # SCF types may include: BLYP, RHF, ROHF, UHF, etc. # # For example, in exam17 the section looks like this (note that this is GVB): # ------------------------ # ROHF-GVB SCF CALCULATION # ------------------------ # GVB STEP WILL USE 119875 WORDS OF MEMORY. # # MAXIT= 30 NPUNCH= 2 SQCDF TOL=1.0000E-05 # NUCLEAR ENERGY= 6.1597411978 # EXTRAP=T DAMP=F SHIFT=F RSTRCT=F DIIS=F SOSCF=F # # ITER EX TOTAL ENERGY E CHANGE SQCDF DIIS ERROR # 0 0 -38.298939963 -38.298939963 0.131784454 0.000000000 # 1 1 -38.332044339 -0.033104376 0.026019716 0.000000000 # ... and will be terminated by a blank line. if line.rstrip()[-16:] == " SCF CALCULATION": # Remember the type of SCF. self.scftype = line.strip()[:-16] self.skip_line(inputfile, 'dashes') while line [:5] != " ITER": self.updateprogress(inputfile, "Attributes") # GVB uses SQCDF for checking convergence (for example in exam17). if "GVB" in self.scftype and "SQCDF TOL=" in line: scftarget = float(line.split("=")[-1]) # Normally, however, the density is used as the convergence criterium. # Deal with various versions: # (GAMESS VERSION = 12 DEC 2003) # DENSITY MATRIX CONV= 2.00E-05 DFT GRID SWITCH THRESHOLD= 3.00E-04 # (GAMESS VERSION = 22 FEB 2006) # DENSITY MATRIX CONV= 1.00E-05 # (PC GAMESS version 6.2, Not DFT?) # DENSITY CONV= 1.00E-05 elif "DENSITY CONV" in line or "DENSITY MATRIX CONV" in line: scftarget = float(line.split()[-1]) line = next(inputfile) if not hasattr(self, "scftargets"): self.scftargets = [] self.scftargets.append([scftarget]) if not hasattr(self,"scfvalues"): self.scfvalues = [] # Normally the iterations print in 6 columns. # For ROHF, however, it is 5 columns, thus this extra parameter. if "ROHF" in self.scftype: self.scf_valcol = 4 else: self.scf_valcol = 5 line = next(inputfile) # SCF iterations are terminated by a blank line. # The first four characters usually contains the step number. # However, lines can also contain messages, including: # * * * INITIATING DIIS PROCEDURE * * * # CONVERGED TO SWOFF, SO DFT CALCULATION IS NOW SWITCHED ON # DFT CODE IS SWITCHING BACK TO THE FINER GRID values = [] while line.strip(): try: temp = int(line[0:4]) except ValueError: pass else: values.append([float(line.split()[self.scf_valcol])]) line = next(inputfile) self.scfvalues.append(values) # Sometimes, only the first SCF cycle has the banner parsed for above, # so we must identify them from the header before the SCF iterations. # The example we have for this is the GeoOpt unittest for Firefly8. if line[1:8] == "ITER EX": # In this case, the convergence targets are not printed, so we assume # they do not change. self.scftargets.append(self.scftargets[-1]) values = [] line = next(inputfile) while line.strip(): try: temp = int(line[0:4]) except ValueError: pass else: values.append([float(line.split()[self.scf_valcol])]) line = next(inputfile) self.scfvalues.append(values) # Extract normal coordinate analysis, including vibrational frequencies (vibfreq), # IT intensities (vibirs) and displacements (vibdisps). # # This section typically looks like the following in GAMESS-US: # # MODES 1 TO 6 ARE TAKEN AS ROTATIONS AND TRANSLATIONS. # # FREQUENCIES IN CM**-1, IR INTENSITIES IN DEBYE**2/AMU-ANGSTROM**2, # REDUCED MASSES IN AMU. # # 1 2 3 4 5 # FREQUENCY: 52.49 41.45 17.61 9.23 10.61 # REDUCED MASS: 3.92418 3.77048 5.43419 6.44636 5.50693 # IR INTENSITY: 0.00013 0.00001 0.00004 0.00000 0.00003 # # ...or in the case of a numerical Hessian job... # # MODES 1 TO 5 ARE TAKEN AS ROTATIONS AND TRANSLATIONS. # # FREQUENCIES IN CM**-1, IR INTENSITIES IN DEBYE**2/AMU-ANGSTROM**2, # REDUCED MASSES IN AMU. # # 1 2 3 4 5 # FREQUENCY: 0.05 0.03 0.03 30.89 30.94 # REDUCED MASS: 8.50125 8.50137 8.50136 1.06709 1.06709 # # ...whereas PC-GAMESS has... # # MODES 1 TO 6 ARE TAKEN AS ROTATIONS AND TRANSLATIONS. # # FREQUENCIES IN CM**-1, IR INTENSITIES IN DEBYE**2/AMU-ANGSTROM**2 # # 1 2 3 4 5 # FREQUENCY: 5.89 1.46 0.01 0.01 0.01 # IR INTENSITY: 0.00000 0.00000 0.00000 0.00000 0.00000 # # If Raman is present we have (for PC-GAMESS)... # # MODES 1 TO 6 ARE TAKEN AS ROTATIONS AND TRANSLATIONS. # # FREQUENCIES IN CM**-1, IR INTENSITIES IN DEBYE**2/AMU-ANGSTROM**2 # RAMAN INTENSITIES IN ANGSTROM**4/AMU, DEPOLARIZATIONS ARE DIMENSIONLESS # # 1 2 3 4 5 # FREQUENCY: 5.89 1.46 0.04 0.03 0.01 # IR INTENSITY: 0.00000 0.00000 0.00000 0.00000 0.00000 # RAMAN INTENSITY: 12.675 1.828 0.000 0.000 0.000 # DEPOLARIZATION: 0.750 0.750 0.124 0.009 0.750 # # If GAMESS-US or PC-GAMESS has not reached the stationary point we have # and additional warning, repeated twice, like so (see n_water.log for an example): # # ******************************************************* # * THIS IS NOT A STATIONARY POINT ON THE MOLECULAR PES * # * THE VIBRATIONAL ANALYSIS IS NOT VALID !!! * # ******************************************************* # # There can also be additional warnings about the selection of modes, for example: # # * * * WARNING, MODE 6 HAS BEEN CHOSEN AS A VIBRATION # WHILE MODE12 IS ASSUMED TO BE A TRANSLATION/ROTATION. # PLEASE VERIFY THE PROGRAM'S DECISION MANUALLY! # if "NORMAL COORDINATE ANALYSIS IN THE HARMONIC APPROXIMATION" in line: self.vibfreqs = [] self.vibirs = [] self.vibdisps = [] # Need to get to the modes line, which is often preceeded by # a list of atomic weights and some possible warnings. # Pass the warnings to the logger if they are there. while not "MODES" in line: self.updateprogress(inputfile, "Frequency Information") line = next(inputfile) if "THIS IS NOT A STATIONARY POINT" in line: msg = "\n This is not a stationary point on the molecular PES" msg += "\n The vibrational analysis is not valid!!!" self.logger.warning(msg) if "* * * WARNING, MODE" in line: line1 = line.strip() line2 = next(inputfile).strip() line3 = next(inputfile).strip() self.logger.warning("\n " + "\n ".join((line1,line2,line3))) # In at least one case (regression zolm_dft3a.log) for older version of GAMESS-US, # the header concerning the range of nodes is formatted wrong and can look like so: # MODES 9 TO14 ARE TAKEN AS ROTATIONS AND TRANSLATIONS. # ... although it's unclear whether this happens for all two-digit values. startrot = int(line.split()[1]) if len(line.split()[2]) == 2: endrot = int(line.split()[3]) else: endrot = int(line.split()[2][2:]) self.skip_line(inputfile, 'blank') # Continue down to the first frequencies line = next(inputfile) while not line.strip() or not line.startswith(" 1"): line = next(inputfile) while not "SAYVETZ" in line: self.updateprogress(inputfile, "Frequency Information") # Note: there may be imaginary frequencies like this (which we make negative): # FREQUENCY: 825.18 I 111.53 12.62 10.70 0.89 # # A note for debuggers: some of these frequencies will be removed later, # assumed to be translations or rotations (see startrot/endrot above). for col in next(inputfile).split()[1:]: if col == "I": self.vibfreqs[-1] *= -1 else: self.vibfreqs.append(float(col)) line = next(inputfile) # Skip the symmetry (appears in newer versions), fixes bug #3476063. if line.find("SYMMETRY") >= 0: line = next(inputfile) # Skip the reduced mass (not always present). if line.find("REDUCED") >= 0: line = next(inputfile) # Not present in numerical Hessian calculations. if line.find("IR INTENSITY") >= 0: irIntensity = map(float, line.strip().split()[2:]) self.vibirs.extend([utils.convertor(x, "Debye^2/amu-Angstrom^2", "km/mol") for x in irIntensity]) line = next(inputfile) # Read in Raman vibrational intensities if present. if line.find("RAMAN") >= 0: if not hasattr(self,"vibramans"): self.vibramans = [] ramanIntensity = line.strip().split() self.vibramans.extend(list(map(float, ramanIntensity[2:]))) depolar = next(inputfile) line = next(inputfile) # This line seems always to be blank. assert line.strip() == '' # Extract the Cartesian displacement vectors. p = [ [], [], [], [], [] ] for j in range(self.natom): q = [ [], [], [], [], [] ] for coord in "xyz": line = next(inputfile)[21:] cols = list(map(float, line.split())) for i, val in enumerate(cols): q[i].append(val) for k in range(len(cols)): p[k].append(q[k]) self.vibdisps.extend(p[:len(cols)]) # Skip the Sayvetz stuff at the end. for j in range(10): line = next(inputfile) self.skip_line(inputfile, 'blank') line = next(inputfile) # Exclude rotations and translations. self.vibfreqs = numpy.array(self.vibfreqs[:startrot-1]+self.vibfreqs[endrot:], "d") self.vibirs = numpy.array(self.vibirs[:startrot-1]+self.vibirs[endrot:], "d") self.vibdisps = numpy.array(self.vibdisps[:startrot-1]+self.vibdisps[endrot:], "d") if hasattr(self, "vibramans"): self.vibramans = numpy.array(self.vibramans[:startrot-1]+self.vibramans[endrot:], "d") if line[5:21] == "ATOMIC BASIS SET": self.gbasis = [] line = next(inputfile) while line.find("SHELL")<0: line = next(inputfile) self.skip_lines(inputfile, ['blank', 'atomname']) # shellcounter stores the shell no of the last shell # in the previous set of primitives shellcounter = 1 while line.find("TOTAL NUMBER")<0: self.skip_line(inputfile, 'blank') line = next(inputfile) shellno = int(line.split()[0]) shellgap = shellno - shellcounter gbasis = [] # Stores basis sets on one atom shellsize = 0 while len(line.split())!=1 and line.find("TOTAL NUMBER")<0: shellsize += 1 coeff = {} # coefficients and symmetries for a block of rows while line.strip(): temp = line.strip().split() sym = temp[1] assert sym in ['S', 'P', 'D', 'F', 'G', 'L'] if sym == "L": # L refers to SP if len(temp)==6: # GAMESS US coeff.setdefault("S", []).append( (float(temp[3]), float(temp[4])) ) coeff.setdefault("P", []).append( (float(temp[3]), float(temp[5])) ) else: # PC GAMESS assert temp[6][-1] == temp[9][-1] == ')' coeff.setdefault("S", []).append( (float(temp[3]), float(temp[6][:-1])) ) coeff.setdefault("P", []).append( (float(temp[3]), float(temp[9][:-1])) ) else: if len(temp)==5: # GAMESS US coeff.setdefault(sym, []).append( (float(temp[3]), float(temp[4])) ) else: # PC GAMESS assert temp[6][-1] == ')' coeff.setdefault(sym, []).append( (float(temp[3]), float(temp[6][:-1])) ) line = next(inputfile) # either a blank or a continuation of the block if sym == "L": gbasis.append( ('S', coeff['S'])) gbasis.append( ('P', coeff['P'])) else: gbasis.append( (sym, coeff[sym])) line = next(inputfile) # either the start of the next block or the start of a new atom or # the end of the basis function section numtoadd = 1 + (shellgap // shellsize) shellcounter = shellno + shellsize for x in range(numtoadd): self.gbasis.append(gbasis) # The eigenvectors, which also include MO energies and symmetries, follow # the *final* report of evalues and the last list of symmetries in the log file: # # ------------ # EIGENVECTORS # ------------ # # 1 2 3 4 5 # -10.0162 -10.0161 -10.0039 -10.0039 -10.0029 # BU AG BU AG AG # 1 C 1 S 0.699293 0.699290 -0.027566 0.027799 0.002412 # 2 C 1 S 0.031569 0.031361 0.004097 -0.004054 -0.000605 # 3 C 1 X 0.000908 0.000632 -0.004163 0.004132 0.000619 # 4 C 1 Y -0.000019 0.000033 0.000668 -0.000651 0.005256 # 5 C 1 Z 0.000000 0.000000 0.000000 0.000000 0.000000 # 6 C 2 S -0.699293 0.699290 0.027566 0.027799 0.002412 # 7 C 2 S -0.031569 0.031361 -0.004097 -0.004054 -0.000605 # 8 C 2 X 0.000908 -0.000632 -0.004163 -0.004132 -0.000619 # 9 C 2 Y -0.000019 -0.000033 0.000668 0.000651 -0.005256 # 10 C 2 Z 0.000000 0.000000 0.000000 0.000000 0.000000 # 11 C 3 S -0.018967 -0.019439 0.011799 -0.014884 -0.452328 # 12 C 3 S -0.007748 -0.006932 0.000680 -0.000695 -0.024917 # 13 C 3 X 0.002628 0.002997 0.000018 0.000061 -0.003608 # ... # # There are blanks lines between each block. # # Warning! There are subtle differences between GAMESS-US and PC-GAMES # in the formatting of the first four columns. In particular, for F orbitals, # PC GAMESS: # 19 C 1 YZ 0.000000 0.000000 0.000000 0.000000 0.000000 # 20 C XXX 0.000000 0.000000 0.000000 0.000000 0.002249 # 21 C YYY 0.000000 0.000000 -0.025555 0.000000 0.000000 # 22 C ZZZ 0.000000 0.000000 0.000000 0.002249 0.000000 # 23 C XXY 0.000000 0.000000 0.001343 0.000000 0.000000 # GAMESS US # 55 C 1 XYZ 0.000000 0.000000 0.000000 0.000000 0.000000 # 56 C 1XXXX -0.000014 -0.000067 0.000000 0.000000 0.000000 # if line.find("EIGENVECTORS") == 10 or line.find("MOLECULAR OBRITALS") == 10: # This is the stuff that we can read from these blocks. self.moenergies = [[]] self.mosyms = [[]] if not hasattr(self, "nmo"): self.nmo = self.nbasis self.mocoeffs = [numpy.zeros((self.nmo, self.nbasis), "d")] readatombasis = False if not hasattr(self, "atombasis"): self.atombasis = [] self.aonames = [] for i in range(self.natom): self.atombasis.append([]) self.aonames = [] readatombasis = True self.skip_line(inputfile, 'dashes') for base in range(0, self.nmo, 5): self.updateprogress(inputfile, "Coefficients") line = next(inputfile) # This makes sure that this section does not end prematurely, # which happens in regression 2CO.ccsd.aug-cc-pVDZ.out. if line.strip() != "": break; numbers = next(inputfile) # Eigenvector numbers. # Sometimes there are some blank lines here. while not line.strip(): line = next(inputfile) # Eigenvalues for these orbitals (in hartrees). try: self.moenergies[0].extend([utils.convertor(float(x), "hartree", "eV") for x in line.split()]) except: self.logger.warning('MO section found but could not be parsed!') break; # Orbital symmetries. line = next(inputfile) if line.strip(): self.mosyms[0].extend(list(map(self.normalisesym, line.split()))) # Now we have nbasis lines. We will use the same method as in normalise_aonames() before. p = re.compile("(\d+)\s*([A-Z][A-Z]?)\s*(\d+)\s*([A-Z]+)") oldatom = '0' i_atom = 0 # counter to keep track of n_atoms > 99 flag_w = True # flag necessary to keep from adding 100's at wrong time for i in range(self.nbasis): line = next(inputfile) # If line is empty, break (ex. for FMO in exam37 which is a regression). if not line.strip(): break # Fill atombasis and aonames only first time around if readatombasis and base == 0: aonames = [] start = line[:17].strip() m = p.search(start) if m: g = m.groups() g2 = int(g[2]) # atom index in GAMESS file; changes to 0 after 99 # Check if we have moved to a hundred # if so, increment the counter and add it to the parsed value # There will be subsequent 0's as that atoms AO's are parsed # so wait until the next atom is parsed before resetting flag if g2 == 0 and flag_w: i_atom = i_atom + 100 flag_w = False # handle subsequent AO's if g2 != 0: flag_w = True # reset flag g2 = g2 + i_atom aoname = "%s%i_%s" % (g[1].capitalize(), g2, g[3]) oldatom = str(g2) atomno = g2-1 orbno = int(g[0])-1 else: # For F orbitals, as shown above g = [x.strip() for x in line.split()] aoname = "%s%s_%s" % (g[1].capitalize(), oldatom, g[2]) atomno = int(oldatom)-1 orbno = int(g[0])-1 self.atombasis[atomno].append(orbno) self.aonames.append(aoname) coeffs = line[15:] # Strip off the crud at the start. j = 0 while j*11+4 < len(coeffs): self.mocoeffs[0][base+j, i] = float(coeffs[j * 11:(j + 1) * 11]) j += 1 line = next(inputfile) # If it's a restricted calc and no more properties, we have: # # ...... END OF RHF/DFT CALCULATION ...... # # If there are more properties (such as the density matrix): # -------------- # # If it's an unrestricted calculation, however, we now get the beta orbitals: # # ----- BETA SET ----- # # ------------ # EIGENVECTORS # ------------ # # 1 2 3 4 5 # ... # line = next(inputfile) # This can come in between the alpha and beta orbitals (see #130). if line.strip() == "LZ VALUE ANALYSIS FOR THE MOS": while line.strip(): line = next(inputfile) line = next(inputfile) if line[2:22] == "----- BETA SET -----": self.mocoeffs.append(numpy.zeros((self.nmo, self.nbasis), "d")) self.moenergies.append([]) self.mosyms.append([]) for i in range(4): line = next(inputfile) for base in range(0, self.nmo, 5): self.updateprogress(inputfile, "Coefficients") blank = next(inputfile) line = next(inputfile) # Eigenvector no line = next(inputfile) self.moenergies[1].extend([utils.convertor(float(x), "hartree", "eV") for x in line.split()]) line = next(inputfile) self.mosyms[1].extend(list(map(self.normalisesym, line.split()))) for i in range(self.nbasis): line = next(inputfile) temp = line[15:] # Strip off the crud at the start j = 0 while j * 11 + 4 < len(temp): self.mocoeffs[1][base+j, i] = float(temp[j * 11:(j + 1) * 11]) j += 1 line = next(inputfile) self.moenergies = [numpy.array(x, "d") for x in self.moenergies] # Natural orbital coefficients and occupation numbers, presently supported only # for CIS calculations. Looks the same as eigenvectors, without symmetry labels. # # -------------------- # CIS NATURAL ORBITALS # -------------------- # # 1 2 3 4 5 # # 2.0158 2.0036 2.0000 2.0000 1.0000 # # 1 O 1 S 0.000000 -0.157316 0.999428 0.164938 0.000000 # 2 O 1 S 0.000000 0.754402 0.004472 -0.581970 0.000000 # ... # if line[10:30] == "CIS NATURAL ORBITALS": self.nocoeffs = numpy.zeros((self.nmo, self.nbasis), "d") self.nooccnos = [] self.skip_line(inputfile, 'dashes') for base in range(0, self.nmo, 5): self.skip_lines(inputfile, ['blank', 'numbers']) # The eigenvalues that go along with these natural orbitals are # their occupation numbers. Sometimes there are blank lines before them. line = next(inputfile) while not line.strip(): line = next(inputfile) eigenvalues = map(float, line.split()) self.nooccnos.extend(eigenvalues) # Orbital symemtry labels are normally here for MO coefficients. line = next(inputfile) # Now we have nbasis lines with the coefficients. for i in range(self.nbasis): line = next(inputfile) coeffs = line[15:] j = 0 while j*11+4 < len(coeffs): self.nocoeffs[base+j, i] = float(coeffs[j * 11:(j + 1) * 11]) j += 1 # We cannot trust this self.homos until we come to the phrase: # SYMMETRIES FOR INITAL GUESS ORBITALS FOLLOW # which either is followed by "ALPHA" or "BOTH" at which point we can say # for certain that it is an un/restricted calculations. # Note that MCSCF calcs also print this search string, so make sure # that self.homos does not exist yet. if line[1:28] == "NUMBER OF OCCUPIED ORBITALS" and not hasattr(self,'homos'): homos = [int(line.split()[-1])-1] line = next(inputfile) homos.append(int(line.split()[-1])-1) self.set_attribute('homos', homos) if line.find("SYMMETRIES FOR INITIAL GUESS ORBITALS FOLLOW") >= 0: # Not unrestricted, so lop off the second index. # In case the search string above was not used (ex. FMO in exam38), # we can try to use the next line which should also contain the # number of occupied orbitals. if line.find("BOTH SET(S)") >= 0: nextline = next(inputfile) if "ORBITALS ARE OCCUPIED" in nextline: homos = int(nextline.split()[0])-1 if hasattr(self,"homos"): try: assert self.homos[0] == homos except AssertionError: self.logger.warning("Number of occupied orbitals not consistent. This is normal for ECP and FMO jobs.") else: self.homos = [homos] self.homos = numpy.resize(self.homos, [1]) # Set the total number of atoms, only once. # Normally GAMESS print TOTAL NUMBER OF ATOMS, however in some cases # this is slightly different (ex. lower case for FMO in exam37). if not hasattr(self,"natom") and "NUMBER OF ATOMS" in line.upper(): natom = int(line.split()[-1]) self.set_attribute('natom', natom) # The first is from Julien's Example and the second is from Alexander's # I think it happens if you use a polar basis function instead of a cartesian one if line.find("NUMBER OF CARTESIAN GAUSSIAN BASIS") == 1 or line.find("TOTAL NUMBER OF BASIS FUNCTIONS") == 1: nbasis = int(line.strip().split()[-1]) self.set_attribute('nbasis', nbasis) elif line.find("TOTAL NUMBER OF CONTAMINANTS DROPPED") >= 0: nmos_dropped = int(line.split()[-1]) if hasattr(self, "nmo"): self.set_attribute('nmo', self.nmo - nmos_dropped) else: self.set_attribute('nmo', self.nbasis - nmos_dropped) # Note that this line is present if ISPHER=1, e.g. for C_bigbasis elif line.find("SPHERICAL HARMONICS KEPT IN THE VARIATION SPACE") >= 0: nmo = int(line.strip().split()[-1]) self.set_attribute('nmo', nmo) # Note that this line is not always present, so by default # NBsUse is set equal to NBasis (see below). elif line.find("TOTAL NUMBER OF MOS IN VARIATION SPACE") == 1: nmo = int(line.split()[-1]) self.set_attribute('nmo', nmo) elif line.find("OVERLAP MATRIX") == 0 or line.find("OVERLAP MATRIX") == 1: # The first is for PC-GAMESS, the second for GAMESS # Read 1-electron overlap matrix if not hasattr(self, "aooverlaps"): self.aooverlaps = numpy.zeros((self.nbasis, self.nbasis), "d") else: self.logger.info("Reading additional aooverlaps...") base = 0 while base < self.nbasis: self.updateprogress(inputfile, "Overlap") self.skip_lines(inputfile, ['b', 'basis_fn_number', 'b']) for i in range(self.nbasis - base): # Fewer lines each time line = next(inputfile) temp = line.split() for j in range(4, len(temp)): self.aooverlaps[base+j-4, i+base] = float(temp[j]) self.aooverlaps[i+base, base+j-4] = float(temp[j]) base += 5 # ECP Pseudopotential information if "ECP POTENTIALS" in line: if not hasattr(self, "coreelectrons"): self.coreelectrons = [0]*self.natom self.skip_lines(inputfile, ['d', 'b']) header = next(inputfile) while header.split()[0] == "PARAMETERS": name = header[17:25] atomnum = int(header[34:40]) # The pseudopotnetial is given explicitely if header[40:50] == "WITH ZCORE": zcore = int(header[50:55]) lmax = int(header[63:67]) self.coreelectrons[atomnum-1] = zcore # The pseudopotnetial is copied from another atom if header[40:55] == "ARE THE SAME AS": atomcopy = int(header[60:]) self.coreelectrons[atomnum-1] = self.coreelectrons[atomcopy-1] line = next(inputfile) while line.split() != []: line = next(inputfile) header = next(inputfile) # This was used before refactoring the parser, geotargets was set here after parsing. #if not hasattr(self, "geotargets"): # opttol = 1e-4 # self.geotargets = numpy.array([opttol, 3. / opttol], "d") #if hasattr(self,"geovalues"): self.geovalues = numpy.array(self.geovalues, "d") # This is quite simple to parse, but some files seem to print certain lines twice, # repeating the populations without charges, but not in proper order. # The unrestricted calculations are a bit tricky, since GAMESS-US prints populations # for both alpha and beta orbitals in the same format and with the same title, # but it still prints the charges only at the very end. if "TOTAL MULLIKEN AND LOWDIN ATOMIC POPULATIONS" in line: if not hasattr(self, "atomcharges"): self.atomcharges = {} header = next(inputfile) line = next(inputfile) # It seems that when population are printed twice (without charges), # there is a blank line along the way (after the first header), # so let's get a flag out of that circumstance. doubles_printed = line.strip() == "" if doubles_printed: title = next(inputfile) header = next(inputfile) line = next(inputfile) # Only go further if the header had five columns, which should # be the case when both populations and charges are printed. # This is pertinent for both double printing and unrestricted output. if not len(header.split()) == 5: return mulliken, lowdin = [], [] while line.strip(): if line.strip() and doubles_printed: line = next(inputfile) mulliken.append(float(line.split()[3])) lowdin.append(float(line.split()[5])) line = next(inputfile) self.atomcharges["mulliken"] = mulliken self.atomcharges["lowdin"] = lowdin # --------------------- # ELECTROSTATIC MOMENTS # --------------------- # # POINT 1 X Y Z (BOHR) CHARGE # -0.000000 0.000000 0.000000 -0.00 (A.U.) # DX DY DZ /D/ (DEBYE) # 0.000000 -0.000000 0.000000 0.000000 # if line.strip() == "ELECTROSTATIC MOMENTS": self.skip_lines(inputfile, ['d', 'b']) line = next(inputfile) # The old PC-GAMESS prints memory assignment information here. if "MEMORY ASSIGNMENT" in line: memory_assignment = next(inputfile) line = next(inputfile) # If something else ever comes up, we should get a signal from this assert. assert line.split()[0] == "POINT" # We can get the reference point from here, as well as # check here that the net charge of the molecule is correct. coords_and_charge = next(inputfile) assert coords_and_charge.split()[-1] == '(A.U.)' reference = numpy.array([float(x) for x in coords_and_charge.split()[:3]]) reference = utils.convertor(reference, 'bohr', 'Angstrom') charge = float(coords_and_charge.split()[-2]) self.set_attribute('charge', charge) dipoleheader = next(inputfile) assert dipoleheader.split()[:3] == ['DX', 'DY', 'DZ'] assert dipoleheader.split()[-1] == "(DEBYE)" dipoleline = next(inputfile) dipole = [float(d) for d in dipoleline.split()[:3]] # The dipole is always the first multipole moment to be printed, # so if it already exists, we will overwrite all moments since we want # to leave just the last printed value (could change in the future). if not hasattr(self, 'moments'): self.moments = [reference, dipole] else: try: assert self.moments[1] == dipole except AssertionError: self.logger.warning('Overwriting previous multipole moments with new values') self.logger.warning('This could be from post-HF properties or geometry optimization') self.moments = [reference, dipole] if __name__ == "__main__": import doctest, gamessparser, sys if len(sys.argv) == 1: doctest.testmod(gamessparser, verbose=False) if len(sys.argv) >= 2: parser = gamessparser.GAMESS(sys.argv[1]) data = parser.parse() if len(sys.argv) > 2: for i in range(len(sys.argv[2:])): if hasattr(data, sys.argv[2 + i]): print(getattr(data, sys.argv[2 + i])) GaussSum-3.0.1/gausssum/cclib/parser/ccopen.py0000664000175000017500000001355012660404041020001 0ustar noelnoel# -*- coding: utf-8 -*- # # This file is part of cclib (http://cclib.github.io), a library for parsing # and interpreting the results of computational chemistry packages. # # Copyright (C) 2009-2014, the cclib development team # # The library is free software, distributed under the terms of # the GNU Lesser General Public version 2.1 or later. You should have # received a copy of the license along with cclib. You can also access # the full license online at http://www.gnu.org/copyleft/lgpl.html. """Tools for identifying and working with files and streams for any supported program""" from __future__ import print_function import os import sys from . import data from . import logfileparser from .adfparser import ADF from .daltonparser import DALTON from .gamessparser import GAMESS from .gamessukparser import GAMESSUK from .gaussianparser import Gaussian from .jaguarparser import Jaguar from .molproparser import Molpro from .nwchemparser import NWChem from .orcaparser import ORCA from .psiparser import Psi from .qchemparser import QChem try: from ..bridge import cclib2openbabel _has_cclib2openbabel = True except ImportError: _has_cclib2openbabel = False # Parser choice is triggered by certain phrases occuring the logfile. Where these # strings are unique, we can set the parser and break. In other cases, the situation # is a little but more complicated. Here are the exceptions: # 1. The GAMESS trigger also works for GAMESS-UK files, so we can't break # after finding GAMESS in case the more specific phrase is found. # 2. Molro log files don't have the program header, but always contain # the generic string 1PROGRAM, so don't break here either to be cautious. # 3. The Psi header has two different strings with some variation # # The triggers are defined by the tuples in the list below like so: # (parser, phrases, flag whether we should break) triggers = [ (ADF, ["Amsterdam Density Functional"], True), (DALTON, ["Dalton - An Electronic Structure Program"], True), (GAMESS, ["GAMESS"], False), (GAMESS, ["GAMESS VERSION"], True), (GAMESSUK, ["G A M E S S - U K"], True), (Gaussian, ["Gaussian, Inc."], True), (Jaguar, ["Jaguar"], True), (Molpro, ["PROGRAM SYSTEM MOLPRO"], True), (Molpro, ["1PROGRAM"], False), (NWChem, ["Northwest Computational Chemistry Package"], True), (ORCA, ["O R C A"], True), (Psi, ["PSI", "Ab Initio Electronic Structure"], True), (QChem, ["A Quantum Leap Into The Future Of Chemistry"], True), ] def guess_filetype(inputfile): """Try to guess the filetype by searching for trigger strings.""" filetype = None for line in inputfile: for parser, phrases, do_break in triggers: if all([line.find(p) >= 0 for p in phrases]): filetype = parser if do_break: return filetype return filetype def ccread(source, *args, **kargs): """Attempt to open and read computational chemistry data from a file. If the file is not appropriate for cclib parsers, a fallback mechanism will try to recognize some common chemistry formats and read those using the appropriate bridge such as OpenBabel. Inputs: source - a single logfile, a list of logfiles, or an input stream Returns: a ccData object containing cclib data attributes """ log = ccopen(source, *args, **kargs) if log: if kargs['verbose']: print('Identified logfile to be in %s format' % log.logname) return log.parse() else: if kargs['verbose']: print('Attempting to use fallback mechanism to read file') return fallback(source) def ccopen(source, *args, **kargs): """Guess the identity of a particular log file and return an instance of it. Inputs: source - a single logfile, a list of logfiles, or an input stream Returns: one of ADF, DALTON, GAMESS, GAMESS UK, Gaussian, Jaguar, Molpro, NWChem, ORCA, Psi, QChem, or None (if it cannot figure it out or the file does not exist). """ # Try to open the logfile(s), using openlogfile. if isinstance(source, str) or \ isinstance(source, list) and all([isinstance(s, str) for s in source]): try: inputfile = logfileparser.openlogfile(source) except IOError as error: (errno, strerror) = error.args print("I/O error %s (%s): %s" % (errno, source, strerror)) return None isstream = False elif hasattr(source, "read"): inputfile = source isstream = True else: raise ValueError # Try to guess the filetype. filetype = guess_filetype(inputfile) # Need to close file before creating a instance. if not isstream: inputfile.close() # Return an instance of the logfile if one was chosen. if filetype: return filetype(source, *args, **kargs) def fallback(source): """Attempt to read standard molecular formats using other libraries. Currently this will read XYZ files with OpenBabel, but this can easily be extended to other formats and libraries, too. """ if isinstance(source, str): ext = os.path.splitext(source)[1][1:].lower() if _has_cclib2openbabel: if ext in ('xyz', ): return cclib2openbabel.readfile(source, ext) else: print("Could not import openbabel, fallback mechanism might not work.") GaussSum-3.0.1/gausssum/cclib/parser/jaguarparser.py0000664000175000017500000007450012660404041021222 0ustar noelnoel# -*- coding: utf-8 -*- # # This file is part of cclib (http://cclib.github.io), a library for parsing # and interpreting the results of computational chemistry packages. # # Copyright (C) 2006-2014, the cclib development team # # The library is free software, distributed under the terms of # the GNU Lesser General Public version 2.1 or later. You should have # received a copy of the license along with cclib. You can also access # the full license online at http://www.gnu.org/copyleft/lgpl.html. """Parser for Jaguar output files""" import re import numpy from . import logfileparser from . import utils class Jaguar(logfileparser.Logfile): """A Jaguar output file""" def __init__(self, *args, **kwargs): # Call the __init__ method of the superclass super(Jaguar, self).__init__(logname="Jaguar", *args, **kwargs) def __str__(self): """Return a string representation of the object.""" return "Jaguar output file %s" % (self.filename) def __repr__(self): """Return a representation of the object.""" return 'Jaguar("%s")' % (self.filename) def normalisesym(self, label): """Normalise the symmetries used by Jaguar. To normalise, three rules need to be applied: (1) To handle orbitals of E symmetry, retain everything before the / (2) Replace two p's by " (2) Replace any remaining single p's by ' >>> t = Jaguar("dummyfile").normalisesym >>> labels = ['A', 'A1', 'Ag', 'Ap', 'App', "A1p", "A1pp", "E1pp/Ap"] >>> answers = map(t, labels) >>> print answers ['A', 'A1', 'Ag', "A'", 'A"', "A1'", 'A1"', 'E1"'] """ ans = label.split("/")[0].replace("pp", '"').replace("p", "'") return ans def before_parsing(self): # We need to track whether we are inside geometry optimization in order # to parse SCF targets/values correctly. self.geoopt = False def after_parsing(self): # This is to make sure we always have optdone after geometry optimizations, # even if it is to be empty for unconverged runs. We have yet to test this # with a regression for Jaguar, though. if self.geoopt and not hasattr(self, 'optdone'): self.optdone = [] def extract(self, inputfile, line): """Extract information from the file object inputfile.""" # Extract charge and multiplicity if line[2:22] == "net molecular charge": self.set_attribute('charge', int(line.split()[-1])) self.set_attribute('mult', int(next(inputfile).split()[-1])) # The Gaussian basis set information is printed before the geometry, and we need # to do some indexing to get this into cclib format, because fn increments # for each engular momentum, but cclib does not (we have just P instead of # all three X/Y/Z with the same parameters. On the other hand, fn enumerates # the atomic orbitals correctly, so use it to build atombasis. # # Gaussian basis set information # # renorm mfac*renorm # atom fn prim L z coef coef coef # -------- ----- ---- --- ------------- ----------- ----------- ----------- # C1 1 1 S 7.161684E+01 1.5433E-01 2.7078E+00 2.7078E+00 # C1 1 2 S 1.304510E+01 5.3533E-01 2.6189E+00 2.6189E+00 # ... # C1 3 6 X 2.941249E+00 2.2135E-01 1.2153E+00 1.2153E+00 # 4 Y 1.2153E+00 # 5 Z 1.2153E+00 # C1 2 8 S 2.222899E-01 1.0000E+00 2.3073E-01 2.3073E-01 # C1 3 7 X 6.834831E-01 8.6271E-01 7.6421E-01 7.6421E-01 # ... # C2 6 1 S 7.161684E+01 1.5433E-01 2.7078E+00 2.7078E+00 # ... # if line.strip() == "Gaussian basis set information": self.skip_lines(inputfile, ['b', 'renorm', 'header', 'd']) # This is probably the only place we can get this information from Jaguar. self.gbasis = [] atombasis = [] line = next(inputfile) fn_per_atom = [] while line.strip(): if len(line.split()) > 3: aname = line.split()[0] fn = int(line.split()[1]) prim = int(line.split()[2]) L = line.split()[3] z = float(line.split()[4]) coef = float(line.split()[5]) # The primitive count is reset for each atom, so use that for adding # new elements to atombasis and gbasis. We could also probably do this # using the atom name, although that perhaps might not always be unique. if prim == 1: atombasis.append([]) fn_per_atom = [] self.gbasis.append([]) # Remember that fn is repeated when functions are contracted. if not fn-1 in atombasis[-1]: atombasis[-1].append(fn-1) # Here we use fn only to know when a new contraction is encountered, # so we don't need to decrement it, and we don't even use all values. # What's more, since we only wish to save the parameters for each subshell # once, we don't even need to consider lines for orbitals other than # those for X*, making things a bit easier. if not fn in fn_per_atom: fn_per_atom.append(fn) label = {'S': 'S', 'X': 'P', 'XX': 'D', 'XXX': 'F'}[L] self.gbasis[-1].append((label, [])) igbasis = fn_per_atom.index(fn) self.gbasis[-1][igbasis][1].append([z, coef]) else: fn = int(line.split()[0]) L = line.split()[1] # Some AO indices are only printed in these lines, for L > 0. if not fn-1 in atombasis[-1]: atombasis[-1].append(fn-1) line = next(inputfile) # The indices for atombasis can also be read later from the molecular orbital output. self.set_attribute('atombasis', atombasis) # This length of atombasis should always be the number of atoms. self.set_attribute('natom', len(self.atombasis)) # Effective Core Potential # # Atom Electrons represented by ECP # Mo 36 # Maximum angular term 3 # F Potential 1/r^n Exponent Coefficient # ----- -------- ----------- # 0 140.4577691 -0.0469492 # 1 89.4739342 -24.9754989 # ... # S-F Potential 1/r^n Exponent Coefficient # ----- -------- ----------- # 0 33.7771969 2.9278406 # 1 10.0120020 34.3483716 # ... # O 0 # Cl 10 # Maximum angular term 2 # D Potential 1/r^n Exponent Coefficient # ----- -------- ----------- # 1 94.8130000 -10.0000000 # ... if line.strip() == "Effective Core Potential": self.skip_line(inputfile, 'blank') line = next(inputfile) assert line.split()[0] == "Atom" assert " ".join(line.split()[1:]) == "Electrons represented by ECP" self.coreelectrons = [] line = next(inputfile) while line.strip(): if len(line.split()) == 2: self.coreelectrons.append(int(line.split()[1])) line = next(inputfile) if line[2:14] == "new geometry" or line[1:21] == "Symmetrized geometry" or line.find("Input geometry") > 0: # Get the atom coordinates if not hasattr(self, "atomcoords") or line[1:21] == "Symmetrized geometry": # Wipe the "Input geometry" if "Symmetrized geometry" present self.atomcoords = [] p = re.compile("(\D+)\d+") # One/more letters followed by a number atomcoords = [] atomnos = [] angstrom = next(inputfile) title = next(inputfile) line = next(inputfile) while line.strip(): temp = line.split() element = p.findall(temp[0])[0] atomnos.append(self.table.number[element]) atomcoords.append(list(map(float, temp[1:]))) line = next(inputfile) self.atomcoords.append(atomcoords) self.atomnos = numpy.array(atomnos, "i") self.set_attribute('natom', len(atomcoords)) # Hartree-Fock energy after SCF if line[1:18] == "SCFE: SCF energy:": if not hasattr(self, "scfenergies"): self.scfenergies = [] temp = line.strip().split() scfenergy = float(temp[temp.index("hartrees") - 1]) scfenergy = utils.convertor(scfenergy, "hartree", "eV") self.scfenergies.append(scfenergy) # Energy after LMP2 correction if line[1:18] == "Total LMP2 Energy": if not hasattr(self, "mpenergies"): self.mpenergies = [[]] lmp2energy = float(line.split()[-1]) lmp2energy = utils.convertor(lmp2energy, "hartree", "eV") self.mpenergies[-1].append(lmp2energy) if line[15:45] == "Geometry optimization complete": if not hasattr(self, 'optdone'): self.optdone = [] self.optdone.append(len(self.geovalues) - 1) if line.find("number of occupied orbitals") > 0: # Get number of MOs occs = int(line.split()[-1]) line = next(inputfile) virts = int(line.split()[-1]) self.nmo = occs + virts self.homos = numpy.array([occs-1], "i") self.unrestrictedflag = False if line[1:28] == "number of occupied orbitals": self.homos = numpy.array([float(line.strip().split()[-1])-1], "i") if line[2:27] == "number of basis functions": nbasis = int(line.strip().split()[-1]) self.set_attribute('nbasis', nbasis) if line.find("number of alpha occupied orb") > 0: # Get number of MOs for an unrestricted calc aoccs = int(line.split()[-1]) line = next(inputfile) avirts = int(line.split()[-1]) line = next(inputfile) boccs = int(line.split()[-1]) line = next(inputfile) bvirt = int(line.split()[-1]) self.nmo = aoccs + avirts self.homos = numpy.array([aoccs-1, boccs-1], "i") self.unrestrictedflag = True if line[0:4] == "etot": # Get SCF convergence information if not hasattr(self, "scfvalues"): self.scfvalues = [] self.scftargets = [[5E-5, 5E-6]] values = [] while line[0:4] == "etot": # Jaguar 4.2 # etot 1 N N 0 N -382.08751886450 2.3E-03 1.4E-01 # etot 2 Y Y 0 N -382.27486023153 1.9E-01 1.4E-03 5.7E-02 # Jaguar 6.5 # etot 1 N N 0 N -382.08751881733 2.3E-03 1.4E-01 # etot 2 Y Y 0 N -382.27486018708 1.9E-01 1.4E-03 5.7E-02 temp = line.split()[7:] if len(temp)==3: denergy = float(temp[0]) else: denergy = 0 # Should really be greater than target value # or should we just ignore the values in this line ddensity = float(temp[-2]) maxdiiserr = float(temp[-1]) if not self.geoopt: values.append([denergy, ddensity]) else: values.append([ddensity]) line = next(inputfile) self.scfvalues.append(values) # MO energies and symmetries. # Jaguar 7.0: provides energies and symmetries for both # restricted and unrestricted calculations, like this: # Alpha Orbital energies/symmetry label: # -10.25358 Bu -10.25353 Ag -10.21931 Bu -10.21927 Ag # -10.21792 Bu -10.21782 Ag -10.21773 Bu -10.21772 Ag # ... # Jaguar 6.5: prints both only for restricted calculations, # so for unrestricted calculations the output it looks like this: # Alpha Orbital energies: # -10.25358 -10.25353 -10.21931 -10.21927 -10.21792 -10.21782 # -10.21773 -10.21772 -10.21537 -10.21537 -1.02078 -0.96193 # ... # Presence of 'Orbital energies' is enough to catch all versions. if "Orbital energies" in line: # Parsing results is identical for restricted/unrestricted # calculations, just assert later that alpha/beta order is OK. spin = int(line[2:6] == "Beta") # Check if symmetries are printed also. issyms = "symmetry label" in line if not hasattr(self, "moenergies"): self.moenergies = [] if issyms and not hasattr(self, "mosyms"): self.mosyms = [] # Grow moeneriges/mosyms and make sure they are empty when # parsed multiple times - currently cclib returns only # the final output (ex. in a geomtry optimization). if len(self.moenergies) < spin+1: self.moenergies.append([]) self.moenergies[spin] = [] if issyms: if len(self.mosyms) < spin+1: self.mosyms.append([]) self.mosyms[spin] = [] line = next(inputfile).split() while len(line) > 0: if issyms: energies = [float(line[2*i]) for i in range(len(line)//2)] syms = [line[2*i+1] for i in range(len(line)//2)] else: energies = [float(e) for e in line] energies = [utils.convertor(e, "hartree", "eV") for e in energies] self.moenergies[spin].extend(energies) if issyms: syms = [self.normalisesym(s) for s in syms] self.mosyms[spin].extend(syms) line = next(inputfile).split() line = next(inputfile) # The second trigger string is in the version 8.3 unit test and the first one was # encountered in version 6.x and is followed by a bit different format. In particular, # the line with occupations is missing in each block. Here is a fragment of this block # from version 8.3: # # ***************************************** # # occupied + virtual orbitals: final wave function # # ***************************************** # # # 1 2 3 4 5 # eigenvalues- -11.04064 -11.04058 -11.03196 -11.03196 -11.02881 # occupations- 2.00000 2.00000 2.00000 2.00000 2.00000 # 1 C1 S 0.70148 0.70154 -0.00958 -0.00991 0.00401 # 2 C1 S 0.02527 0.02518 0.00380 0.00374 0.00371 # ... # if line.find("Occupied + virtual Orbitals- final wvfn") > 0 or \ line.find("occupied + virtual orbitals: final wave function") > 0: self.skip_lines(inputfile, ['b', 's', 'b', 'b']) if not hasattr(self,"mocoeffs"): self.mocoeffs = [] aonames = [] lastatom = "X" readatombasis = False if not hasattr(self, "atombasis"): self.atombasis = [] for i in range(self.natom): self.atombasis.append([]) readatombasis = True offset = 0 spin = 1 + int(self.unrestrictedflag) for s in range(spin): mocoeffs = numpy.zeros((len(self.moenergies[s]), self.nbasis), "d") if s == 1: #beta case self.skip_lines(inputfile, ['s', 'b', 'title', 'b', 's', 'b', 'b']) for k in range(0, len(self.moenergies[s]), 5): self.updateprogress(inputfile, "Coefficients") # All known version have a line with indices followed by the eigenvalues. self.skip_lines(inputfile, ['numbers', 'eigens']) # Newer version also have a line with occupation numbers here. line = next(inputfile) if "occupations-" in line: line = next(inputfile) for i in range(self.nbasis): info = line.split() # Fill atombasis only first time around. if readatombasis and k == 0: orbno = int(info[0]) atom = info[1] if atom[1].isalpha(): atomno = int(atom[2:]) else: atomno = int(atom[1:]) self.atombasis[atomno-1].append(orbno-1) if not hasattr(self,"aonames"): if lastatom != info[1]: scount = 1 pcount = 3 dcount = 6 #six d orbitals in Jaguar if info[2] == 'S': aonames.append("%s_%i%s"%(info[1], scount, info[2])) scount += 1 if info[2] == 'X' or info[2] == 'Y' or info[2] == 'Z': aonames.append("%s_%iP%s"%(info[1], pcount / 3, info[2])) pcount += 1 if info[2] == 'XX' or info[2] == 'YY' or info[2] == 'ZZ' or \ info[2] == 'XY' or info[2] == 'XZ' or info[2] == 'YZ': aonames.append("%s_%iD%s"%(info[1], dcount / 6, info[2])) dcount += 1 lastatom = info[1] for j in range(len(info[3:])): mocoeffs[j+k, i] = float(info[3+j]) line = next(inputfile) if not hasattr(self,"aonames"): self.aonames = aonames offset += 5 self.mocoeffs.append(mocoeffs) # Atomic charges from Mulliken population analysis: # # Atom C1 C2 C3 C4 C5 # Charge 0.00177 -0.06075 -0.05956 0.00177 -0.06075 # # Atom H6 H7 H8 C9 C10 # ... if line.strip() == "Atomic charges from Mulliken population analysis:": if not hasattr(self, 'atomcharges'): self.atomcharges = {} charges = [] self.skip_line(inputfile, "blank") line = next(inputfile) while "sum of atomic charges" not in line: assert line.split()[0] == "Atom" line = next(inputfile) assert line.split()[0] == "Charge" charges.extend([float(c) for c in line.split()[1:]]) self.skip_line(inputfile, "blank") line = next(inputfile) self.atomcharges['mulliken'] = charges if (line[2:6] == "olap") or (line.strip() == "overlap matrix:"): if line[6] == "-": return # This was continue (in loop) before parser refactoring. # continue # avoid "olap-dev" self.aooverlaps = numpy.zeros((self.nbasis, self.nbasis), "d") for i in range(0, self.nbasis, 5): self.updateprogress(inputfile, "Overlap") self.skip_lines(inputfile, ['b', 'header']) for j in range(i, self.nbasis): temp = list(map(float, next(inputfile).split()[1:])) self.aooverlaps[j, i:(i+len(temp))] = temp self.aooverlaps[i:(i+len(temp)), j] = temp if line[2:24] == "start of program geopt": if not self.geoopt: # Need to keep only the RMS density change info # if this is a geooptz self.scftargets = [[self.scftargets[0][0]]] if hasattr(self, "scfvalues"): self.scfvalues[0] = [[x[0]] for x in self.scfvalues[0]] self.geoopt = True else: self.scftargets.append([5E-5]) # Get Geometry Opt convergence information # # geometry optimization step 7 # energy: -382.30219111487 hartrees # [ turning on trust-radius adjustment ] # ** restarting optimization from step 6 ** # # # Level shifts adjusted to satisfy step-size constraints # Step size: 0.0360704 # Cos(theta): 0.8789215 # Final level shift: -8.6176299E-02 # # energy change: 2.5819E-04 . ( 5.0000E-05 ) # gradient maximum: 5.0947E-03 . ( 4.5000E-04 ) # gradient rms: 1.2996E-03 . ( 3.0000E-04 ) # displacement maximum: 1.3954E-02 . ( 1.8000E-03 ) # displacement rms: 4.6567E-03 . ( 1.2000E-03 ) # if line[2:28] == "geometry optimization step": if not hasattr(self, "geovalues"): self.geovalues = [] self.geotargets = numpy.zeros(5, "d") gopt_step = int(line.split()[-1]) energy = next(inputfile) blank = next(inputfile) # A quick hack for messages that show up right after the energy # at this point, which include: # ** restarting optimization from step 2 ** # [ turning on trust-radius adjustment ] # as found in regression file ptnh3_2_H2O_2_2plus.out and other logfiles. restarting_from_1 = False while blank.strip(): if blank.strip() == "** restarting optimization from step 1 **": restarting_from_1 = True blank = next(inputfile) # One or more blank lines, depending on content. line = next(inputfile) while not line.strip(): line = next(inputfile) # Note that the level shift message is followed by a blank, too. if "Level shifts adjusted" in line: while line.strip(): line = next(inputfile) line = next(inputfile) # The first optimization step does not produce an energy change, and # ther is also no energy change when the optimization is restarted # from step 1 (since step 1 had no change). values = [] target_index = 0 if (gopt_step == 1) or restarting_from_1: values.append(0.0) target_index = 1 while line.strip(): if len(line) > 40 and line[41] == "(": # A new geo convergence value values.append(float(line[26:37])) self.geotargets[target_index] = float(line[43:54]) target_index += 1 line = next(inputfile) self.geovalues.append(values) # IR output looks like this: # frequencies 72.45 113.25 176.88 183.76 267.60 312.06 # symmetries Au Bg Au Bu Ag Bg # intensities 0.07 0.00 0.28 0.52 0.00 0.00 # reduc. mass 1.90 0.74 1.06 1.42 1.19 0.85 # force const 0.01 0.01 0.02 0.03 0.05 0.05 # C1 X 0.00000 0.00000 0.00000 -0.05707 -0.06716 0.00000 # C1 Y 0.00000 0.00000 0.00000 0.00909 -0.02529 0.00000 # C1 Z 0.04792 -0.06032 -0.01192 0.00000 0.00000 0.11613 # C2 X 0.00000 0.00000 0.00000 -0.06094 -0.04635 0.00000 # ... etc. ... # This is a complete ouput, some files will not have intensities, # and older Jaguar versions sometimes skip the symmetries. if line[2:23] == "start of program freq": self.skip_line(inputfile, 'blank') # Version 8.3 has two blank lines here, earlier versions just one. line = next(inputfile) if not line.strip(): line = next(inputfile) self.vibfreqs = [] self.vibdisps = [] forceconstants = False intensities = False while line.strip(): if "force const" in line: forceconstants = True if "intensities" in line: intensities = True line = next(inputfile) # In older version, the last block had an extra blank line after it, # which could be caught. This is not true in newer version (including 8.3), # but in general it would be better to bound this loop more strictly. freqs = next(inputfile) while freqs.strip() and not "imaginary frequencies" in freqs: # Number of modes (columns printed in this block). nmodes = len(freqs.split())-1 # Append the frequencies. self.vibfreqs.extend(list(map(float, freqs.split()[1:]))) line = next(inputfile).split() # May skip symmetries (older Jaguar versions). if line[0] == "symmetries": if not hasattr(self, "vibsyms"): self.vibsyms = [] self.vibsyms.extend(list(map(self.normalisesym, line[1:]))) line = next(inputfile).split() if intensities: if not hasattr(self, "vibirs"): self.vibirs = [] self.vibirs.extend(list(map(float, line[1:]))) line = next(inputfile).split() if forceconstants: line = next(inputfile) # Start parsing the displacements. # Variable 'q' holds up to 7 lists of triplets. q = [ [] for i in range(7) ] for n in range(self.natom): # Variable 'p' holds up to 7 triplets. p = [ [] for i in range(7) ] for i in range(3): line = next(inputfile) disps = [float(disp) for disp in line.split()[2:]] for j in range(nmodes): p[j].append(disps[j]) for i in range(nmodes): q[i].append(p[i]) self.vibdisps.extend(q[:nmodes]) self.skip_line(inputfile, 'blank') freqs = next(inputfile) # Convert new data to arrays. self.vibfreqs = numpy.array(self.vibfreqs, "d") self.vibdisps = numpy.array(self.vibdisps, "d") if hasattr(self, "vibirs"): self.vibirs = numpy.array(self.vibirs, "d") # Parse excited state output (for CIS calculations). # Jaguar calculates only singlet states. if line[2:15] == "Excited State": if not hasattr(self, "etenergies"): self.etenergies = [] if not hasattr(self, "etoscs"): self.etoscs = [] if not hasattr(self, "etsecs"): self.etsecs = [] self.etsyms = [] etenergy = float(line.split()[3]) etenergy = utils.convertor(etenergy, "eV", "cm-1") self.etenergies.append(etenergy) self.skip_lines(inputfile, ['line', 'line', 'line', 'line']) line = next(inputfile) self.etsecs.append([]) # Jaguar calculates only singlet states. self.etsyms.append('Singlet-A') while line.strip() != "": fromMO = int(line.split()[0])-1 toMO = int(line.split()[2])-1 coeff = float(line.split()[-1]) self.etsecs[-1].append([(fromMO, 0), (toMO, 0), coeff]) line = next(inputfile) # Skip 3 lines for i in range(4): line = next(inputfile) strength = float(line.split()[-1]) self.etoscs.append(strength) if __name__ == "__main__": import doctest, jaguarparser doctest.testmod(jaguarparser, verbose=False) GaussSum-3.0.1/gausssum/cclib/parser/nwchemparser.py0000664000175000017500000014030112660404041021223 0ustar noelnoel# -*- coding: utf-8 -*- # # This file is part of cclib (http://cclib.github.io), a library for parsing # and interpreting the results of computational chemistry packages. # # Copyright (C) 2008-2014, the cclib development team # # The library is free software, distributed under the terms of # the GNU Lesser General Public version 2.1 or later. You should have # received a copy of the license along with cclib. You can also access # the full license online at http://www.gnu.org/copyleft/lgpl.html. """Parser for NWChem output files""" import itertools import re import numpy from . import logfileparser from . import utils class NWChem(logfileparser.Logfile): """An NWChem log file.""" def __init__(self, *args, **kwargs): # Call the __init__ method of the superclass super(NWChem, self).__init__(logname="NWChem", *args, **kwargs) def __str__(self): """Return a string representation of the object.""" return "NWChem log file %s" % (self.filename) def __repr__(self): """Return a representation of the object.""" return 'NWChem("%s")' % (self.filename) def normalisesym(self, label): """Use standard symmetry labels instead of NWChem labels. To normalise: (1) If label is one of [SG, PI, PHI, DLTA], replace by [sigma, pi, phi, delta] (2) replace any G or U by their lowercase equivalent >>> sym = NWChem("dummyfile").normalisesym >>> labels = ['A1', 'AG', 'A1G', "SG", "PI", "PHI", "DLTA", 'DLTU', 'SGG'] >>> map(sym, labels) ['A1', 'Ag', 'A1g', 'sigma', 'pi', 'phi', 'delta', 'delta.u', 'sigma.g'] """ # FIXME if necessary return label name2element = lambda self, lbl: "".join(itertools.takewhile(str.isalpha, str(lbl))) def extract(self, inputfile, line): """Extract information from the file object inputfile.""" # This is printed in the input module, so should always be the first coordinates, # and contains some basic information we want to parse as well. However, this is not # the only place where the coordinates are printed during geometry optimization, # since the gradients module has a separate coordinate printout, which happens # alongside the coordinate gradients. This geometry printout happens at the # beginning of each optimization step only. if line.strip() == 'Geometry "geometry" -> ""' or line.strip() == 'Geometry "geometry" -> "geometry"': self.skip_lines(inputfile, ['dashes', 'blank', 'units', 'blank', 'header', 'dashes']) if not hasattr(self, 'atomcoords'): self.atomcoords = [] line = next(inputfile) coords = [] atomnos = [] while line.strip(): # The column labeled 'tag' is usually empty, but I'm not sure whether it can have spaces, # so for now assume that it can and that there will be seven columns in that case. if len(line.split()) == 6: index, atomname, nuclear, x, y, z = line.split() else: index, atomname, tag, nuclear, x, y, z = line.split() coords.append(list(map(float, [x,y,z]))) atomnos.append(int(float(nuclear))) line = next(inputfile) self.atomcoords.append(coords) self.set_attribute('atomnos', atomnos) # If the geometry is printed in XYZ format, it will have the number of atoms. if line[12:31] == "XYZ format geometry": self.skip_line(inputfile, 'dashes') natom = int(next(inputfile).strip()) self.set_attribute('natom', natom) if line.strip() == "NWChem Geometry Optimization": self.skip_lines(inputfile, ['d', 'b', 'b', 'b', 'b', 'title', 'b', 'b']) line = next(inputfile) while line.strip(): if "maximum gradient threshold" in line: gmax = float(line.split()[-1]) if "rms gradient threshold" in line: grms = float(line.split()[-1]) if "maximum cartesian step threshold" in line: xmax = float(line.split()[-1]) if "rms cartesian step threshold" in line: xrms = float(line.split()[-1]) line = next(inputfile) self.set_attribute('geotargets', [gmax, grms, xmax, xrms]) # NWChem does not normally print the basis set for each atom, but rather # chooses the concise option of printing Gaussian coefficients for each # atom type/element only once. Therefore, we need to first parse those # coefficients and afterwards build the appropriate gbasis attribute based # on that and atom types/elements already parsed (atomnos). However, if atom # are given different names (number after element, like H1 and H2), then NWChem # generally prints the gaussian parameters for all unique names, like this: # # Basis "ao basis" -> "ao basis" (cartesian) # ----- # O (Oxygen) # ---------- # Exponent Coefficients # -------------- --------------------------------------------------------- # 1 S 1.30709320E+02 0.154329 # 1 S 2.38088610E+01 0.535328 # (...) # # H1 (Hydrogen) # ------------- # Exponent Coefficients # -------------- --------------------------------------------------------- # 1 S 3.42525091E+00 0.154329 # (...) # # H2 (Hydrogen) # ------------- # Exponent Coefficients # -------------- --------------------------------------------------------- # 1 S 3.42525091E+00 0.154329 # (...) # # This current parsing code below assumes all atoms of the same element # use the same basis set, but that might not be true, and this will probably # need to be considered in the future when such a logfile appears. if line.strip() == """Basis "ao basis" -> "ao basis" (cartesian)""": self.skip_line(inputfile, 'dashes') gbasis_dict = {} line = next(inputfile) while line.strip(): atomname = line.split()[0] atomelement = self.name2element(atomname) gbasis_dict[atomelement] = [] self.skip_lines(inputfile, ['d', 'labels', 'd']) shells = [] line = next(inputfile) while line.strip() and line.split()[0].isdigit(): shell = None while line.strip(): nshell, type, exp, coeff = line.split() nshell = int(nshell) assert len(shells) == nshell - 1 if not shell: shell = (type, []) else: assert shell[0] == type exp = float(exp) coeff = float(coeff) shell[1].append((exp,coeff)) line = next(inputfile) shells.append(shell) line = next(inputfile) gbasis_dict[atomelement].extend(shells) gbasis = [] for i in range(self.natom): atomtype = utils.PeriodicTable().element[self.atomnos[i]] gbasis.append(gbasis_dict[atomtype]) self.set_attribute('gbasis', gbasis) # Normally the indexes of AOs assigned to specific atoms are also not printed, # so we need to infer that. We could do that from the previous section, # it might be worthwhile to take numbers from two different places, hence # the code below, which builds atombasis based on the number of functions # listed in this summary of the AO basis. Similar to previous section, here # we assume all atoms of the same element have the same basis sets, but # this will probably need to be revised later. # The section we can glean info about aonmaes looks like: # # Summary of "ao basis" -> "ao basis" (cartesian) # ------------------------------------------------------------------------------ # Tag Description Shells Functions and Types # ---------------- ------------------------------ ------ --------------------- # C sto-3g 3 5 2s1p # H sto-3g 1 1 1s # # However, we need to make sure not to match the following entry lines: # # * Summary of "ao basis" -> "" (cartesian) # * Summary of allocated global arrays # # Unfortantely, "ao basis" isn't unique because it can be renamed to anything for # later reference: http://www.nwchem-sw.org/index.php/Basis # It also appears that we have to handle cartesian vs. spherical if line[1:11] == "Summary of": match = re.match(' Summary of "([^\"]*)" -> "([^\"]*)" \((.+)\)', line) if match and match.group(1) == match.group(2): self.skip_lines(inputfile, ['d', 'title', 'd']) self.shells = {} self.shells["type"] = match.group(3) atombasis_dict = {} line = next(inputfile) while line.strip(): atomname, desc, shells, funcs, types = line.split() atomelement = self.name2element(atomname) self.shells[atomname] = types atombasis_dict[atomelement] = int(funcs) line = next(inputfile) last = 0 atombasis = [] for atom in self.atomnos: atomelement = utils.PeriodicTable().element[atom] nfuncs = atombasis_dict[atomelement] atombasis.append(list(range(last, last+nfuncs))) last = atombasis[-1][-1] + 1 self.set_attribute('atombasis', atombasis) # This section contains general parameters for Hartree-Fock calculations, # which do not contain the 'General Information' section like most jobs. if line.strip() == "NWChem SCF Module": self.skip_lines(inputfile, ['d', 'b', 'b', 'title', 'b', 'b', 'b']) line = next(inputfile) while line.strip(): if line[2:8] == "charge": charge = int(float(line.split()[-1])) self.set_attribute('charge', charge) if line[2:13] == "open shells": unpaired = int(line.split()[-1]) self.set_attribute('mult', 2*unpaired + 1) if line[2:7] == "atoms": natom = int(line.split()[-1]) self.set_attribute('natom', natom) if line[2:11] == "functions": nfuncs = int(line.split()[-1]) self.set_attribute("nbasis", nfuncs) line = next(inputfile) # This section contains general parameters for DFT calculations, as well as # for the many-electron theory module. if line.strip() == "General Information": if hasattr(self, 'linesearch') and self.linesearch: return while line.strip(): if "No. of atoms" in line: self.set_attribute('natom', int(line.split()[-1])) if "Charge" in line: self.set_attribute('charge', int(line.split()[-1])) if "Spin multiplicity" in line: mult = line.split()[-1] if mult == "singlet": mult = 1 self.set_attribute('mult', int(mult)) if "AO basis - number of function" in line: nfuncs = int(line.split()[-1]) self.set_attribute('nbasis', nfuncs) # These will be present only in the DFT module. if "Convergence on energy requested" in line: target_energy = float(line.split()[-1].replace('D', 'E')) if "Convergence on density requested" in line: target_density = float(line.split()[-1].replace('D', 'E')) if "Convergence on gradient requested" in line: target_gradient = float(line.split()[-1].replace('D', 'E')) line = next(inputfile) # Pretty nasty temporary hack to set scftargets only in the SCF module. if "target_energy" in dir() and "target_density" in dir() and "target_gradient" in dir(): if not hasattr(self, 'scftargets'): self.scftargets = [] self.scftargets.append([target_energy, target_density, target_gradient]) # If the full overlap matrix is printed, it looks like this: # # global array: Temp Over[1:60,1:60], handle: -996 # # 1 2 3 4 5 6 # ----------- ----------- ----------- ----------- ----------- ----------- # 1 1.00000 0.24836 -0.00000 -0.00000 0.00000 0.00000 # 2 0.24836 1.00000 0.00000 -0.00000 0.00000 0.00030 # 3 -0.00000 0.00000 1.00000 0.00000 0.00000 -0.00014 # ... if "global array: Temp Over[" in line: self.set_attribute('nbasis', int(line.split('[')[1].split(',')[0].split(':')[1])) self.set_attribute('nmo', int(line.split(']')[0].split(',')[1].split(':')[1])) aooverlaps = [] while len(aooverlaps) < self.nbasis: self.skip_line(inputfile, 'blank') indices = [int(i) for i in inputfile.next().split()] assert indices[0] == len(aooverlaps) + 1 self.skip_line(inputfile, "dashes") data = [inputfile.next().split() for i in range(self.nbasis)] indices = [int(d[0]) for d in data] assert indices == list(range(1, self.nbasis+1)) for i in range(1, len(data[0])): vector = [float(d[i]) for d in data] aooverlaps.append(vector) self.set_attribute('aooverlaps', aooverlaps) if line.strip() in ("The SCF is already converged", "The DFT is already converged"): if self.linesearch: return self.scftargets.append(self.scftargets[-1]) self.scfvalues.append(self.scfvalues[-1]) # The default (only?) SCF algorithm for Hartree-Fock is a preconditioned conjugate # gradient method that apparently "always" converges, so this header should reliably # signal a start of the SCF cycle. The convergence targets are also printed here. if line.strip() == "Quadratically convergent ROHF": if hasattr(self, 'linesearch') and self.linesearch: return while not "Final" in line: # Only the norm of the orbital gradient is used to test convergence. if line[:22] == " Convergence threshold": target = float(line.split()[-1]) if not hasattr(self, "scftargets"): self.scftargets = [] self.scftargets.append([target]) # This is critical for the stop condition of the section, # because the 'Final Fock-matrix accuracy' is along the way. # It would be prudent to find a more robust stop condition. while list(set(line.strip())) != ["-"]: line = next(inputfile) if line.split() == ['iter', 'energy', 'gnorm', 'gmax', 'time']: values = [] self.skip_line(inputfile, 'dashes') line = next(inputfile) while line.strip(): iter,energy,gnorm,gmax,time = line.split() gnorm = float(gnorm.replace('D','E')) values.append([gnorm]) line = next(inputfile) if not hasattr(self, 'scfvalues'): self.scfvalues = [] self.scfvalues.append(values) line = next(inputfile) # The SCF for DFT does not use the same algorithm as Hartree-Fock, but always # seems to use the following format to report SCF convergence: # convergence iter energy DeltaE RMS-Dens Diis-err time # ---------------- ----- ----------------- --------- --------- --------- ------ # d= 0,ls=0.0,diis 1 -382.2544324446 -8.28D+02 1.42D-02 3.78D-01 23.2 # d= 0,ls=0.0,diis 2 -382.3017298534 -4.73D-02 6.99D-03 3.82D-02 39.3 # d= 0,ls=0.0,diis 3 -382.2954343173 6.30D-03 4.21D-03 7.95D-02 55.3 # ... if line.split() == ['convergence', 'iter', 'energy', 'DeltaE', 'RMS-Dens', 'Diis-err', 'time']: if hasattr(self, 'linesearch') and self.linesearch: return self.skip_line(inputfile, 'dashes') line = next(inputfile) values = [] while line.strip(): # Sometimes there are things in between iterations with fewer columns, # and we want to skip those lines, most probably. An exception might # unrestricted calcualtions, which show extra RMS density and DIIS # errors, although it is not clear yet whether these are for the # beta orbitals or somethine else. The iterations look like this in that case: # convergence iter energy DeltaE RMS-Dens Diis-err time # ---------------- ----- ----------------- --------- --------- --------- ------ # d= 0,ls=0.0,diis 1 -382.0243202601 -8.28D+02 7.77D-03 1.04D-01 30.0 # 7.68D-03 1.02D-01 # d= 0,ls=0.0,diis 2 -382.0647539758 -4.04D-02 4.64D-03 1.95D-02 59.2 # 5.39D-03 2.36D-02 # ... if len(line[17:].split()) == 6: iter, energy, deltaE, dens, diis, time = line[17:].split() val_energy = float(deltaE.replace('D', 'E')) val_density = float(dens.replace('D', 'E')) val_gradient = float(diis.replace('D', 'E')) values.append([val_energy, val_density, val_gradient]) line = next(inputfile) if not hasattr(self, 'scfvalues'): self.scfvalues = [] self.scfvalues.append(values) # These triggers are supposed to catch the current step in a geometry optimization search # and determine whether we are currently in the main (initial) SCF cycle of that step # or in the subsequent line search. The step is printed between dashes like this: # # -------- # Step 0 # -------- # # and the summary lines that describe the main SCF cycle for the frsit step look like this: # #@ Step Energy Delta E Gmax Grms Xrms Xmax Walltime #@ ---- ---------------- -------- -------- -------- -------- -------- -------- #@ 0 -379.76896249 0.0D+00 0.04567 0.01110 0.00000 0.00000 4.2 # ok ok # # However, for subsequent step the format is a bit different: # # Step Energy Delta E Gmax Grms Xrms Xmax Walltime # ---- ---------------- -------- -------- -------- -------- -------- -------- #@ 2 -379.77794602 -7.4D-05 0.00118 0.00023 0.00440 0.01818 14.8 # ok # # There is also a summary of the line search (which we don't use now), like this: # # Line search: # step= 1.00 grad=-1.8D-05 hess= 8.9D-06 energy= -379.777955 mode=accept # new step= 1.00 predicted energy= -379.777955 # if line[10:14] == "Step": self.geostep = int(line.split()[-1]) self.skip_line(inputfile, 'dashes') self.linesearch = False if line[0] == "@" and line.split()[1] == "Step": at_and_dashes = next(inputfile) line = next(inputfile) assert int(line.split()[1]) == self.geostep == 0 gmax = float(line.split()[4]) grms = float(line.split()[5]) xrms = float(line.split()[6]) xmax = float(line.split()[7]) if not hasattr(self, 'geovalues'): self.geovalues = [] self.geovalues.append([gmax, grms, xmax, xrms]) self.linesearch = True if line[2:6] == "Step": self.skip_line(inputfile, 'dashes') line = next(inputfile) assert int(line.split()[1]) == self.geostep if self.linesearch: #print(line) return gmax = float(line.split()[4]) grms = float(line.split()[5]) xrms = float(line.split()[6]) xmax = float(line.split()[7]) if not hasattr(self, 'geovalues'): self.geovalues = [] self.geovalues.append([gmax, grms, xmax, xrms]) self.linesearch = True # There is a clear message when the geometry optimization has converged: # # ---------------------- # Optimization converged # ---------------------- # if line.strip() == "Optimization converged": self.skip_line(inputfile, 'dashes') if not hasattr(self, 'optdone'): self.optdone = [] self.optdone.append(len(self.geovalues) - 1) if "Failed to converge" in line and hasattr(self, 'geovalues'): if not hasattr(self, 'optdone'): self.optdone = [] # The line containing the final SCF energy seems to be always identifiable like this. if "Total SCF energy" in line or "Total DFT energy" in line: # NWChem often does a line search during geometry optimization steps, reporting # the SCF information but not the coordinates (which are not necessarily 'intermediate' # since the step size can become smaller). We want to skip these SCF cycles, # unless the coordinates can also be extracted (possibly from the gradients?). if hasattr(self, 'linesearch') and self.linesearch: return if not hasattr(self, "scfenergies"): self.scfenergies = [] energy = float(line.split()[-1]) energy = utils.convertor(energy, "hartree", "eV") self.scfenergies.append(energy) # The final MO orbitals are printed in a simple list, but apparently not for # DFT calcs, and often this list does not contain all MOs, so make sure to # parse them from the MO analysis below if possible. This section will be like this: # # Symmetry analysis of molecular orbitals - final # ----------------------------------------------- # # Numbering of irreducible representations: # # 1 ag 2 au 3 bg 4 bu # # Orbital symmetries: # # 1 bu 2 ag 3 bu 4 ag 5 bu # 6 ag 7 bu 8 ag 9 bu 10 ag # ... if line.strip() == "Symmetry analysis of molecular orbitals - final": self.skip_lines(inputfile, ['d', 'b', 'numbering', 'b', 'reps', 'b', 'syms', 'b']) if not hasattr(self, 'mosyms'): self.mosyms = [[None]*self.nbasis] line = next(inputfile) while line.strip(): ncols = len(line.split()) assert ncols % 2 == 0 for i in range(ncols//2): index = int(line.split()[i*2]) - 1 sym = line.split()[i*2+1] sym = sym[0].upper() + sym[1:] if self.mosyms[0][index]: if self.mosyms[0][index] != sym: self.logger.warning("Symmetry of MO %i has changed" % (index+1)) self.mosyms[0][index] = sym line = next(inputfile) # The same format is used for HF and DFT molecular orbital analysis. We want to parse # the MO energies from this section, although it is printed already before this with # less precision (might be useful to parse that if this is not available). Also, this # section contains coefficients for the leading AO contributions, so it might also # be useful to parse and use those values if the full vectors are not printed. # # The block looks something like this (two separate alpha/beta blocks in the unrestricted case): # # ROHF Final Molecular Orbital Analysis # ------------------------------------- # # Vector 1 Occ=2.000000D+00 E=-1.104059D+01 Symmetry=bu # MO Center= 1.4D-17, 0.0D+00, -6.5D-37, r^2= 2.1D+00 # Bfn. Coefficient Atom+Function Bfn. Coefficient Atom+Function # ----- ------------ --------------- ----- ------------ --------------- # 1 0.701483 1 C s 6 -0.701483 2 C s # # Vector 2 Occ=2.000000D+00 E=-1.104052D+01 Symmetry=ag # ... # Vector 12 Occ=2.000000D+00 E=-1.020253D+00 Symmetry=bu # MO Center= -1.4D-17, -5.6D-17, 2.9D-34, r^2= 7.9D+00 # Bfn. Coefficient Atom+Function Bfn. Coefficient Atom+Function # ----- ------------ --------------- ----- ------------ --------------- # 36 -0.298699 11 C s 41 0.298699 12 C s # 2 0.270804 1 C s 7 -0.270804 2 C s # 48 -0.213655 15 C s 53 0.213655 16 C s # ... # if "Final" in line and "Molecular Orbital Analysis" in line: # Unrestricted jobs have two such blocks, for alpha and beta orbitals, and # we need to keep track of which one we're parsing (always alpha in restricted case). unrestricted = ("Alpha" in line) or ("Beta" in line) alphabeta = int("Beta" in line) self.skip_lines(inputfile, ['dashes', 'blank']) energies = [] symmetries = [None]*self.nbasis line = next(inputfile) homo = 0 while line[:7] == " Vector": # Note: the vector count starts from 1 in NWChem. nvector = int(line[7:12]) # A nonzero occupancy for SCF jobs means the orbital is occupied. if ("Occ=2.0" in line) or ("Occ=1.0" in line): homo = nvector-1 # If the printout does not start from the first MO, assume None for all previous orbitals. if len(energies) == 0 and nvector > 1: for i in range(1,nvector): energies.append(None) energy = float(line[34:47].replace('D','E')) energy = utils.convertor(energy, "hartree", "eV") energies.append(energy) # When symmetry is not used, this part of the line is missing. if line[47:58].strip() == "Symmetry=": sym = line[58:].strip() sym = sym[0].upper() + sym[1:] symmetries[nvector-1] = sym line = next(inputfile) if "MO Center" in line: line = next(inputfile) if "Bfn." in line: line = next(inputfile) if "-----" in line: line = next(inputfile) while line.strip(): line = next(inputfile) line = next(inputfile) self.set_attribute('nmo', nvector) if not hasattr(self, 'moenergies') or (len(self.moenergies) > alphabeta): self.moenergies = [] self.moenergies.append(energies) if not hasattr(self, 'mosyms') or (len(self.mosyms) > alphabeta): self.mosyms = [] self.mosyms.append(symmetries) if not hasattr(self, 'homos') or (len(self.homos) > alphabeta): self.homos = [] self.homos.append(homo) # This is where the full MO vectors are printed, but a special directive is needed for it: # # Final MO vectors # ---------------- # # # global array: alpha evecs[1:60,1:60], handle: -995 # # 1 2 3 4 5 6 # ----------- ----------- ----------- ----------- ----------- ----------- # 1 -0.69930 -0.69930 -0.02746 -0.02769 -0.00313 -0.02871 # 2 -0.03156 -0.03135 0.00410 0.00406 0.00078 0.00816 # 3 0.00002 -0.00003 0.00067 0.00065 -0.00526 -0.00120 # ... # if line.strip() == "Final MO vectors": if not hasattr(self, 'mocoeffs'): self.mocoeffs = [] self.skip_lines(inputfile, ['d', 'b', 'b']) # The columns are MOs, rows AOs, but that's and educated guess since no # atom information is printed alongside the indices. This next line gives # the dimensions, which we can check. if set before this. Also, this line # specifies whether we are dealing with alpha or beta vectors. array_info = next(inputfile) while ("global array" in array_info): alphabeta = int(line.split()[2] == "beta") size = array_info.split('[')[1].split(']')[0] nbasis = int(size.split(',')[0].split(':')[1]) nmo = int(size.split(',')[1].split(':')[1]) self.set_attribute('nbasis', nbasis) self.set_attribute('nmo', nmo) self.skip_line(inputfile, 'blank') mocoeffs = [] while len(mocoeffs) < self.nmo: nmos = list(map(int,next(inputfile).split())) assert len(mocoeffs) == nmos[0]-1 for n in nmos: mocoeffs.append([]) self.skip_line(inputfile, 'dashes') for nb in range(nbasis): line = next(inputfile) index = int(line.split()[0]) assert index == nb+1 coefficients = list(map(float,line.split()[1:])) assert len(coefficients) == len(nmos) for i,c in enumerate(coefficients): mocoeffs[nmos[i]-1].append(c) self.skip_line(inputfile, 'blank') self.mocoeffs.append(mocoeffs) array_info = next(inputfile) # For Hartree-Fock, the atomic Mulliken charges are typically printed like this: # # Mulliken analysis of the total density # -------------------------------------- # # Atom Charge Shell Charges # ----------- ------ ------------------------------------------------------- # 1 C 6 6.00 1.99 1.14 2.87 # 2 C 6 6.00 1.99 1.14 2.87 # ... if line.strip() == "Mulliken analysis of the total density": if not hasattr(self, "atomcharges"): self.atomcharges = {} self.skip_lines(inputfile, ['d', 'b', 'header', 'd']) charges = [] line = next(inputfile) while line.strip(): index, atomname, nuclear, atom = line.split()[:4] shells = line.split()[4:] charges.append(float(atom)-float(nuclear)) line = next(inputfile) self.atomcharges['mulliken'] = charges # Not the the 'overlap population' as printed in the Mulliken population analysis, # is not the same thing as the 'overlap matrix'. In fact, it is the overlap matrix # multiplied elementwise times the density matrix. # # ---------------------------- # Mulliken population analysis # ---------------------------- # # ----- Total overlap population ----- # # 1 2 3 4 5 6 7 # # 1 1 C s 2.0694818227 -0.0535883400 -0.0000000000 -0.0000000000 -0.0000000000 -0.0000000000 0.0000039991 # 2 1 C s -0.0535883400 0.8281341291 0.0000000000 -0.0000000000 0.0000000000 0.0000039991 -0.0009906747 # ... # # DFT does not seem to print the separate listing of Mulliken charges # by default, but they are printed by this modules later on. They are also print # for Hartree-Fock runs, though, so in that case make sure they are consistent. if line.strip() == "Mulliken population analysis": self.skip_lines(inputfile, ['d', 'b', 'total_overlap_population', 'b']) overlaps = [] line= next(inputfile) while all([c.isdigit() for c in line.split()]): # There is always a line with the MO indices printed in thie block. indices = [int(i)-1 for i in line.split()] for i in indices: overlaps.append([]) # There is usually a blank line after the MO indices, but # there are exceptions, so check if line is blank first. line = next(inputfile) if not line.strip(): line = next(inputfile) # Now we can iterate or atomic orbitals. for nao in range(self.nbasis): data = list(map(float, line.split()[4:])) for i,d in enumerate(data): overlaps[indices[i]].append(d) line = next(inputfile) line = next(inputfile) # This header should be printed later, before the charges are print, which of course # are just sums of the overlaps and could be calculated. But we just go ahead and # parse them, make sure they're consistent with previously parsed values and # use these since they are more precise (previous precision could have been just 0.01). while "Total gross population on atoms" not in line: line = next(inputfile) self.skip_line(inputfile, 'blank') charges = [] for i in range(self.natom): line = next(inputfile) iatom, element, ncharge, epop = line.split() iatom = int(iatom) ncharge = float(ncharge) epop = float(epop) assert iatom == (i+1) charges.append(epop-ncharge) if not hasattr(self, 'atomcharges'): self.atomcharges = {} if not "mulliken" in self.atomcharges: self.atomcharges['mulliken'] = charges else: assert max(self.atomcharges['mulliken'] - numpy.array(charges)) < 0.01 self.atomcharges['mulliken'] = charges # NWChem prints the dipole moment in atomic units first, and we could just fast forward # to the values in Debye, which are also printed. But we can also just convert them # right away and so parse a little bit less. Note how the reference point is print # here within the block nicely, as it is for all moment later. # # ------------- # Dipole Moment # ------------- # # Center of charge (in au) is the expansion point # X = 0.0000000 Y = 0.0000000 Z = 0.0000000 # # Dipole moment 0.0000000000 Debye(s) # DMX 0.0000000000 DMXEFC 0.0000000000 # DMY 0.0000000000 DMYEFC 0.0000000000 # DMZ -0.0000000000 DMZEFC 0.0000000000 # # ... # if line.strip() == "Dipole Moment": self.skip_lines(inputfile, ['d', 'b']) reference_comment = next(inputfile) assert "(in au)" in reference_comment reference = next(inputfile).split() self.reference = [reference[-7], reference[-4], reference[-1]] self.reference = numpy.array([float(x) for x in self.reference]) self.reference = utils.convertor(self.reference, 'bohr', 'Angstrom') self.skip_line(inputfile, 'blank') magnitude = next(inputfile) assert magnitude.split()[-1] == "A.U." dipole = [] for i in range(3): line = next(inputfile) dipole.append(float(line.split()[1])) dipole = utils.convertor(numpy.array(dipole), "ebohr", "Debye") if not hasattr(self, 'moments'): self.moments = [self.reference, dipole] else: self.moments[1] == dipole # The quadrupole moment is pretty straightforward to parse. There are several # blocks printed, and the first one called 'second moments' contains the raw # moments, and later traceless values are printed. The moments, however, are # not in lexicographical order, so we need to sort them. Also, the first block # is in atomic units, so remember to convert to Buckinghams along the way. # # ----------------- # Quadrupole Moment # ----------------- # # Center of charge (in au) is the expansion point # X = 0.0000000 Y = 0.0000000 Z = 0.0000000 # # < R**2 > = ********** a.u. ( 1 a.u. = 0.280023 10**(-16) cm**2 ) # ( also called diamagnetic susceptibility ) # # Second moments in atomic units # # Component Electronic+nuclear Point charges Total # -------------------------------------------------------------------------- # XX -38.3608511210 0.0000000000 -38.3608511210 # YY -39.0055467347 0.0000000000 -39.0055467347 # ... # if line.strip() == "Quadrupole Moment": self.skip_lines(inputfile, ['d', 'b']) reference_comment = next(inputfile) assert "(in au)" in reference_comment reference = next(inputfile).split() self.reference = [reference[-7], reference[-4], reference[-1]] self.reference = numpy.array([float(x) for x in self.reference]) self.reference = utils.convertor(self.reference, 'bohr', 'Angstrom') self.skip_lines(inputfile, ['b', 'units', 'susc', 'b']) line = next(inputfile) assert line.strip() == "Second moments in atomic units" self.skip_lines(inputfile, ['b', 'header', 'd']) # Parse into a dictionary and then sort by the component key. quadrupole = {} for i in range(6): line = next(inputfile) quadrupole[line.split()[0]] = float(line.split()[-1]) lex = sorted(quadrupole.keys()) quadrupole = [quadrupole[key] for key in lex] quadrupole = utils.convertor(numpy.array(quadrupole), "ebohr2", "Buckingham") # The checking of potential previous values if a bit more involved here, # because it turns out NWChem has separate keywords for dipole, quadrupole # and octupole output. So, it is perfectly possible to print the quadrupole # and not the dipole... if that is the case set the former to None and # issue a warning. Also, a regression has been added to cover this case. if not hasattr(self, 'moments') or len(self.moments) <2: self.logger.warning("Found quadrupole moments but no previous dipole") self.moments = [self.reference, None, quadrupole] else: if len(self.moments) == 2: self.moments.append(quadrupole) else: assert self.moments[2] == quadrupole # The octupole moment is analogous to the quadrupole, but there are more components # and the checking of previously parsed dipole and quadrupole moments is more involved, # with a corresponding test also added to regressions. # # --------------- # Octupole Moment # --------------- # # Center of charge (in au) is the expansion point # X = 0.0000000 Y = 0.0000000 Z = 0.0000000 # # Third moments in atomic units # # Component Electronic+nuclear Point charges Total # -------------------------------------------------------------------------- # XXX -0.0000000000 0.0000000000 -0.0000000000 # YYY -0.0000000000 0.0000000000 -0.0000000000 # ... # if line.strip() == "Octupole Moment": self.skip_lines(inputfile, ['d', 'b']) reference_comment = next(inputfile) assert "(in au)" in reference_comment reference = next(inputfile).split() self.reference = [reference[-7], reference[-4], reference[-1]] self.reference = numpy.array([float(x) for x in self.reference]) self.reference = utils.convertor(self.reference, 'bohr', 'Angstrom') self.skip_line(inputfile, 'blank') line = next(inputfile) assert line.strip() == "Third moments in atomic units" self.skip_lines(inputfile, ['b', 'header', 'd']) octupole = {} for i in range(10): line = next(inputfile) octupole[line.split()[0]] = float(line.split()[-1]) lex = sorted(octupole.keys()) octupole = [octupole[key] for key in lex] octupole = utils.convertor(numpy.array(octupole), "ebohr3", "Debye.ang2") if not hasattr(self, 'moments') or len(self.moments) < 2: self.logger.warning("Found octupole moments but no previous dipole or quadrupole moments") self.moments = [self.reference, None, None, octupole] elif len(self.moments) == 2: self.logger.warning("Found octupole moments but no previous quadrupole moments") self.moments.append(None) self.moments.append(octupole) else: if len(self.moments) == 3: self.moments.append(octupole) else: assert self.moments[3] == octupole if "Total MP2 energy" in line: mpenerg = float(line.split()[-1]) if not hasattr(self, "mpenergies"): self.mpenergies = [] self.mpenergies.append([]) self.mpenergies[-1].append(utils.convertor(mpenerg, "hartree", "eV")) if "CCSD(T) total energy / hartree" in line: ccenerg = float(line.split()[-1]) if not hasattr(self, "ccenergies"): self.ccenergies = [] self.ccenergies.append([]) self.ccenergies[-1].append(utils.convertor(ccenerg, "hartree", "eV")) def after_parsing(self): """NWChem-specific routines for after parsing file. Currently, expands self.shells() into self.aonames. """ # setup a few necessary things, including a regular expression # for matching the shells table = utils.PeriodicTable() elements = [ table.element[x] for x in self.atomnos ] pattern = re.compile("(\ds)+(\dp)*(\dd)*(\df)*(\dg)*") labels = {} labels['s'] = ["%iS"] labels['p'] = ["%iPX", "%iPY", "%iPZ"] if self.shells['type'] == 'spherical': labels['d'] = ['%iD-2', '%iD-1', '%iD0', '%iD1', '%iD2'] labels['f'] = ['%iF-3', '%iF-2', '%iF-1', '%iF0', '%iF1', '%iF2', '%iF3'] labels['g'] = ['%iG-4', '%iG-3', '%iG-2', '%iG-1', '%iG0', '%iG1', '%iG2', '%iG3', '%iG4'] elif self.shells['type'] == 'cartesian': labels['d'] = ['%iDXX', '%iDXY', '%iDXZ', '%iDYY', '%iDYZ', '%iDZZ'] labels['f'] = ['%iFXXX', '%iFXXY', '%iFXXZ', '%iFXYY', '%iFXYZ', '%iFXZZ', '%iFYYY', '%iFYYZ', '%iFYZZ', '%iFZZZ'] labels['g'] = ['%iGXXXX', '%iGXXXY', '%iGXXXZ', '%iGXXYY', '%iGXXYZ', '%iGXXZZ', '%iGXYYY', '%iGXYYZ', '%iGXYZZ', '%iGXZZZ', '%iGYYYY', '%iGYYYZ', '%iGYYZZ', '%iGYZZZ', '%iGZZZZ'] else: self.logger.warning("Found a non-standard aoname representation type.") return # now actually build aonames # involves expanding 2s1p into appropriate types self.aonames = [] for i, element in enumerate(elements): try: shell_text = self.shells[element] except KeyError: del self.aonames msg = "Cannot determine aonames for at least one atom." self.logger.warning(msg) break prefix = "%s%i_" % (element, i + 1) # (e.g. C1_) matches = pattern.match(shell_text) for j, group in enumerate(matches.groups()): if group is None: continue count = int(group[:-1]) label = group[-1] for k in range(count): temp = [ x % (j + k + 1) for x in labels[label] ] self.aonames.extend( [ prefix + x for x in temp ] ) if __name__ == "__main__": import doctest, nwchemparser doctest.testmod(nwchemparser, verbose=False) GaussSum-3.0.1/gausssum/cclib/parser/utils.py0000664000175000017500000001110512660404041017664 0ustar noelnoel# -*- coding: utf-8 -*- # # This file is part of cclib (http://cclib.github.io), a library for parsing # and interpreting the results of computational chemistry packages. # # Copyright (C) 2006-2014, the cclib development team # # The library is free software, distributed under the terms of # the GNU Lesser General Public version 2.1 or later. You should have # received a copy of the license along with cclib. You can also access # the full license online at http://www.gnu.org/copyleft/lgpl.html. """Utilities often used by cclib parsers""" def convertor(value, fromunits, tounits): """Convert from one set of units to another. Sources: NIST 2010 CODATA (http://physics.nist.gov/cuu/Constants/index.html) Documentation of GAMESS-US or other programs as noted >>> print "%.1f" % convertor(8, "eV", "cm-1") 64524.8 """ _convertor = { "Angstrom_to_bohr": lambda x: x * 1.8897261245, "bohr_to_Angstrom": lambda x: x * 0.5291772109, "cm-1_to_eV": lambda x: x / 8065.54429, "cm-1_to_hartree": lambda x: x / 219474.6313708, "cm-1_to_kcal": lambda x: x / 349.7550112, "cm-1_to_kJmol-1": lambda x: x / 83.5934722814, "cm-1_to_nm": lambda x: 1e7 / x, "eV_to_cm-1": lambda x: x * 8065.54429, "eV_to_hartree": lambda x: x / 27.21138505, "eV_to_kcal": lambda x: x * 23.060548867, "eV_to_kJmol-1": lambda x: x * 96.4853364596, "hartree_to_cm-1": lambda x: x * 219474.6313708, "hartree_to_eV": lambda x: x * 27.21138505, "hartree_to_kcal": lambda x: x * 627.50947414, "hartree_to_kJmol-1":lambda x: x * 2625.4996398, "kcal_to_cm-1": lambda x: x * 349.7550112, "kcal_to_eV": lambda x: x / 23.060548867, "kcal_to_hartree": lambda x: x / 627.50947414, "kcal_to_kJmol-1": lambda x: x * 4.184, "kJmol-1_to_cm-1": lambda x: x * 83.5934722814, "kJmol-1_to_eV": lambda x: x / 96.4853364596, "kJmol-1_to_hartree": lambda x: x / 2625.49963978, "kJmol-1_to_kcal": lambda x: x / 4.184, "nm_to_cm-1": lambda x: 1e7 / x, # Taken from GAMESS docs, "Further information", # "Molecular Properties and Conversion Factors" "Debye^2/amu-Angstrom^2_to_km/mol": lambda x: x * 42.255, # Conversion for charges and multipole moments. "e_to_coulomb": lambda x: x * 1.602176565 * 1e-19, "e_to_statcoulomb": lambda x: x * 4.80320425 * 1e-10, "coulomb_to_e": lambda x: x * 0.6241509343 * 1e19, "statcoulomb_to_e": lambda x: x * 0.2081943527 * 1e10, "ebohr_to_Debye": lambda x: x * 2.5417462300, "ebohr2_to_Buckingham": lambda x: x * 1.3450341749, "ebohr2_to_Debye.ang": lambda x: x * 1.3450341749, "ebohr3_to_Debye.ang2": lambda x: x * 0.7117614302, "ebohr4_to_Debye.ang3": lambda x: x * 0.3766479268, "ebohr5_to_Debye.ang4": lambda x: x * 0.1993134985, } return _convertor["%s_to_%s" % (fromunits, tounits)] (value) class PeriodicTable(object): """Allows conversion between element name and atomic no. >>> t = PeriodicTable() >>> t.element[6] 'C' >>> t.number['C'] 6 >>> t.element[44] 'Ru' >>> t.number['Au'] 79 """ def __init__(self): self.element = [None, 'H', 'He', 'Li', 'Be', 'B', 'C', 'N', 'O', 'F', 'Ne', 'Na', 'Mg', 'Al', 'Si', 'P', 'S', 'Cl', 'Ar', 'K', 'Ca', 'Sc', 'Ti', 'V', 'Cr', 'Mn', 'Fe', 'Co', 'Ni', 'Cu', 'Zn', 'Ga', 'Ge', 'As', 'Se', 'Br', 'Kr', 'Rb', 'Sr', 'Y', 'Zr', 'Nb', 'Mo', 'Tc', 'Ru', 'Rh', 'Pd', 'Ag', 'Cd', 'In', 'Sn', 'Sb', 'Te', 'I', 'Xe', 'Cs', 'Ba', 'La', 'Ce', 'Pr', 'Nd', 'Pm', 'Sm', 'Eu', 'Gd', 'Tb', 'Dy', 'Ho', 'Er', 'Tm', 'Yb', 'Lu', 'Hf', 'Ta', 'W', 'Re', 'Os', 'Ir', 'Pt', 'Au', 'Hg', 'Tl', 'Pb', 'Bi', 'Po', 'At', 'Rn', 'Fr', 'Ra', 'Ac', 'Th', 'Pa', 'U', 'Np', 'Pu', 'Am', 'Cm', 'Bk', 'Cf', 'Es', 'Fm', 'Md', 'No', 'Lr', 'Rf', 'Db', 'Sg', 'Bh', 'Hs', 'Mt', 'Ds', 'Rg', 'Uub'] self.number = {} for i in range(1, len(self.element)): self.number[self.element[i]] = i if __name__ == "__main__": import doctest, utils doctest.testmod(utils, verbose=False) GaussSum-3.0.1/gausssum/cclib/parser/qchemparser.py0000664000175000017500000017165512660404041021057 0ustar noelnoel# -*- coding: utf-8 -*- # # This file is part of cclib (http://cclib.github.io), a library for parsing # and interpreting the results of computational chemistry packages. # # Copyright (C) 2014-2015, the cclib development team # # The library is free software, distributed under the terms of # the GNU Lesser General Public version 2.1 or later. You should have # received a copy of the license along with cclib. You can also access # the full license online at http://www.gnu.org/copyleft/lgpl.html. """Parser for Q-Chem output files""" from __future__ import print_function import re import numpy import itertools from . import logfileparser from . import utils class QChem(logfileparser.Logfile): """A Q-Chem 4 log file.""" def __init__(self, *args, **kwargs): # Call the __init__ method of the superclass super(QChem, self).__init__(logname="QChem", *args, **kwargs) def __str__(self): """Return a string representation of the object.""" return "QChem log file %s" % (self.filename) def __repr__(self): """Return a representation of the object.""" return 'QChem("%s")' % (self.filename) def normalisesym(self, label): """Q-Chem does not require normalizing symmetry labels.""" def before_parsing(self): # Keep track of whether or not we're performing an # (un)restricted calculation. self.unrestricted = False self.is_rohf = False # Compile the dashes-and-or-spaces-only regex. self.re_dashes_and_spaces = re.compile('^[\s-]+$') # Compile the regex for extracting the atomic index from an # aoname. self.re_atomindex = re.compile('(\d+)_') # A maximum of 6 columns per block when printing matrices. self.ncolsblock = 6 # By default, when asked to print orbitals via # `scf_print`/`scf_final_print` and/or `print_orbitals`, # Q-Chem will print all occupieds and the first 5 virtuals. # # When the number is set for `print_orbitals`, that section of # the output will display (NOcc + that many virtual) MOs, but # any other sections present due to # `scf_print`/`scf_final_print` will still only display (NOcc # + 5) MOs. # # Note that the density matrix is always (NBasis * NBasis)! self.norbdisp_alpha = self.norbdisp_beta = 5 self.norbdisp_alpha_aonames = self.norbdisp_beta_aonames = 5 self.norbdisp_set = False self.alpha_mo_coefficient_headers = ( 'RESTRICTED (RHF) MOLECULAR ORBITAL COEFFICIENTS', 'ALPHA MOLECULAR ORBITAL COEFFICIENTS' ) def after_parsing(self): # If parsing a fragment job, each of the geometries appended to # `atomcoords` may be of different lengths, which will prevent # conversion from a list to NumPy array. # Take the length of the first geometry as correct, and remove # all others with different lengths. if len(self.atomcoords) > 1: correctlen = len(self.atomcoords[0]) self.atomcoords[:] = [coords for coords in self.atomcoords if len(coords) == correctlen] # At the moment, there is no similar correction for other properties! # QChem does not print all MO coefficients by default, but rather # up to HOMO+5. So, fill up the missing values with NaNs. If there are # other cases where coefficient are missing, but different ones, this # general afterthought might not be appropriate and the fix will # need to be done while parsing. if hasattr(self, 'mocoeffs'): for im in range(len(self.mocoeffs)): _nmo, _nbasis = self.mocoeffs[im].shape if (_nmo, _nbasis) != (self.nmo, self.nbasis): coeffs = numpy.empty((self.nmo, self.nbasis)) coeffs[:] = numpy.nan coeffs[0:_nmo, 0:_nbasis] = self.mocoeffs[im] self.mocoeffs[im] = coeffs # When parsing the 'MOLECULAR ORBITAL COEFFICIENTS' block for # `aonames`, Q-Chem doesn't print the principal quantum number # for each shell; this needs to be added. if hasattr(self, 'aonames') and hasattr(self, 'atombasis'): angmom = ('', 'S', 'P', 'D', 'F', 'G', 'H', 'I') for atom in self.atombasis: bfcounts = dict() for bfindex in atom: atomname, bfname = self.aonames[bfindex].split('_') # Keep track of how many times each shell type has # appeared. if bfname in bfcounts: bfcounts[bfname] += 1 else: # Make sure the starting number for type of # angular momentum begins at the appropriate # principal quantum number (1S, 2P, 3D, 4F, # ...). bfcounts[bfname] = angmom.index(bfname[0]) newbfname = '{}{}'.format(bfcounts[bfname], bfname) self.aonames[bfindex] = '_'.join([atomname, newbfname]) def extract(self, inputfile, line): """Extract information from the file object inputfile.""" # If the input section is repeated back, parse the $rem and # $molecule sections. if line[0:11] == 'User input:': self.skip_line(inputfile, 'd') while list(set(line.strip())) != ['-']: if '$rem' in line: while '$end' not in line: line = next(inputfile) if 'print_orbitals' in line.lower(): # Stay with the default value if a number isn't # specified. if line.split()[-1].lower() in ('true', 'false'): continue else: norbdisp_aonames = int(line.split()[-1]) self.norbdisp_alpha_aonames = norbdisp_aonames self.norbdisp_beta_aonames = norbdisp_aonames self.norbdisp_set = True # Charge and multiplicity are present in the input file, which is generally # printed once at the beginning. However, it is also prined for fragment # calculations, so make sure we parse only the first occurance. if '$molecule' in line: line = next(inputfile) charge, mult = map(int, line.split()) if not hasattr(self, 'charge'): self.set_attribute('charge', charge) if not hasattr(self, 'mult'): self.set_attribute('mult', mult) line = next(inputfile) # Parse the general basis for `gbasis`, in the style used by # Gaussian. if 'Basis set in general basis input format:' in line: self.skip_lines(inputfile, ['d', '$basis']) line = next(inputfile) if not hasattr(self, 'gbasis'): self.gbasis = [] # The end of the general basis block. while '$end' not in line: atom = [] # 1. Contains element symbol and atomic index of # basis functions; if 0, applies to all atoms of # same element. assert len(line.split()) == 2 line = next(inputfile) # The end of each atomic block. while '****' not in line: # 2. Contains the type of basis function {S, SP, # P, D, F, G, H, ...}, the number of primitives, # and the weight of the final contracted function. bfsplitline = line.split() assert len(bfsplitline) == 3 bftype = bfsplitline[0] nprim = int(bfsplitline[1]) line = next(inputfile) # 3. The primitive basis functions that compose # the contracted basis function; there are `nprim` # of them. The first value is the exponent, and # the second value is the contraction # coefficient. If `bftype == 'SP'`, the primitives # are for both S- and P-type basis functions but # with separate contraction coefficients, # resulting in three columns. if bftype == 'SP': primitives_S = [] primitives_P = [] else: primitives = [] # For each primitive in the contracted basis # function... for iprim in range(nprim): primsplitline = line.split() exponent = float(primsplitline[0]) if bftype == 'SP': assert len(primsplitline) == 3 coefficient_S = float(primsplitline[1]) coefficient_P = float(primsplitline[2]) primitives_S.append((exponent, coefficient_S)) primitives_P.append((exponent, coefficient_P)) else: assert len(primsplitline) == 2 coefficient = float(primsplitline[1]) primitives.append((exponent, coefficient)) line = next(inputfile) if bftype == 'SP': bf_S = ('S', primitives_S) bf_P = ('P', primitives_P) atom.append(bf_S) atom.append(bf_P) else: bf = (bftype, primitives) atom.append(bf) # Move to the next contracted basis function # as long as we don't hit the '****' atom # delimiter. self.gbasis.append(atom) line = next(inputfile) # Extract the atomic numbers and coordinates of the atoms. if 'Standard Nuclear Orientation (Angstroms)' in line: if not hasattr(self, 'atomcoords'): self.atomcoords = [] self.skip_lines(inputfile, ['cols', 'dashes']) atomelements = [] atomcoords = [] line = next(inputfile) while list(set(line.strip())) != ['-']: entry = line.split() atomelements.append(entry[1]) atomcoords.append(list(map(float, entry[2:]))) line = next(inputfile) self.atomcoords.append(atomcoords) if not hasattr(self, 'atomnos'): self.atomnos = [] self.atomelements = [] for atomelement in atomelements: self.atomelements.append(atomelement) if atomelement == 'GH': self.atomnos.append(0) else: self.atomnos.append(utils.PeriodicTable().number[atomelement]) self.natom = len(self.atomnos) self.atommap = self.generate_atom_map() self.formula_histogram = self.generate_formula_histogram() # Number of electrons. # Useful for determining the number of occupied/virtual orbitals. if 'Nuclear Repulsion Energy' in line: if not hasattr(self, 'nalpha'): line = next(inputfile) nelec_re_string = 'There are(\s+[0-9]+) alpha and(\s+[0-9]+) beta electrons' match = re.findall(nelec_re_string, line.strip()) self.nalpha = int(match[0][0].strip()) self.nbeta = int(match[0][1].strip()) self.norbdisp_alpha += self.nalpha self.norbdisp_alpha_aonames += self.nalpha self.norbdisp_beta += self.nbeta self.norbdisp_beta_aonames += self.nbeta # Number of basis functions. # Because Q-Chem's integral recursion scheme is defined using # Cartesian basis functions, there is often a distinction between the # two in the output. We only parse for *pure* functions. # Examples: # Only one type: # There are 30 shells and 60 basis functions # Both Cartesian and pure: # ... if 'basis functions' in line: if not hasattr(self, 'nbasis'): self.set_attribute('nbasis', int(line.split()[-3])) # Check for whether or not we're peforming an # (un)restricted calculation. if 'calculation will be' in line: if ' restricted' in line: self.unrestricted = False if 'unrestricted' in line: self.unrestricted = True if hasattr(self, 'nalpha') and hasattr(self, 'nbeta'): if self.nalpha != self.nbeta: self.unrestricted = True self.is_rohf = True # Section with SCF iterations goes like this: # # SCF converges when DIIS error is below 1.0E-05 # --------------------------------------- # Cycle Energy DIIS Error # --------------------------------------- # 1 -381.9238072190 1.39E-01 # 2 -382.2937212775 3.10E-03 # 3 -382.2939780242 3.37E-03 # ... # scf_success_messages = ( 'Convergence criterion met', 'corrected energy' ) scf_failure_messages = ( 'SCF failed to converge', 'Convergence failure' ) if 'SCF converges when ' in line: if not hasattr(self, 'scftargets'): self.scftargets = [] target = float(line.split()[-1]) self.scftargets.append([target]) # We should have the header between dashes now, # but sometimes there are lines before the first dashes. while not 'Cycle Energy' in line: line = next(inputfile) self.skip_line(inputfile, 'd') values = [] iter_counter = 1 line = next(inputfile) while not any(message in line for message in scf_success_messages): # Some trickery to avoid a lot of printing that can occur # between each SCF iteration. entry = line.split() if len(entry) > 0: if entry[0] == str(iter_counter): # Q-Chem only outputs one error metric. error = float(entry[2]) values.append([error]) iter_counter += 1 line = next(inputfile) # We've converged, but still need the last iteration. if any(message in line for message in scf_success_messages): entry = line.split() error = float(entry[2]) values.append([error]) iter_counter += 1 # This is printed in regression QChem4.2/dvb_sp_unconverged.out # so use it to bail out when convergence fails. if any(message in line for message in scf_failure_messages): break if not hasattr(self, 'scfvalues'): self.scfvalues = [] self.scfvalues.append(numpy.array(values)) # Molecular orbital coefficients. # Try parsing them from this block (which comes from # `scf_final_print = 2``) rather than the combined # aonames/mocoeffs/moenergies block (which comes from # `print_orbitals = true`). if 'Final Alpha MO Coefficients' in line: if not hasattr(self, 'mocoeffs'): self.mocoeffs = [] mocoeffs = numpy.empty(shape=(self.nbasis, self.norbdisp_alpha)) self.parse_matrix(inputfile, mocoeffs) self.mocoeffs.append(mocoeffs.transpose()) if 'Final Beta MO Coefficients' in line: mocoeffs = numpy.empty(shape=(self.nbasis, self.norbdisp_beta)) self.parse_matrix(inputfile, mocoeffs) self.mocoeffs.append(mocoeffs.transpose()) if 'Total energy in the final basis set' in line: if not hasattr(self, 'scfenergies'): self.scfenergies = [] scfenergy = float(line.split()[-1]) self.scfenergies.append(utils.convertor(scfenergy, 'hartree', 'eV')) # Geometry optimization. if 'Maximum Tolerance Cnvgd?' in line: line_g = list(map(float, next(inputfile).split()[1:3])) line_d = list(map(float, next(inputfile).split()[1:3])) line_e = next(inputfile).split()[2:4] if not hasattr(self, 'geotargets'): self.geotargets = [line_g[1], line_d[1], self.float(line_e[1])] if not hasattr(self, 'geovalues'): self.geovalues = [] try: ediff = abs(self.float(line_e[0])) except ValueError: ediff = numpy.nan geovalues = [line_g[0], line_d[0], ediff] self.geovalues.append(geovalues) if '** OPTIMIZATION CONVERGED **' in line: if not hasattr(self, 'optdone'): self.optdone = [] self.optdone.append(len(self.atomcoords)) if '** MAXIMUM OPTIMIZATION CYCLES REACHED **' in line: if not hasattr(self, 'optdone'): self.optdone = [] # Moller-Plesset corrections. # There are multiple modules in Q-Chem for calculating MPn energies: # cdman, ccman, and ccman2, all with different output. # # MP2, RI-MP2, and local MP2 all default to cdman, which has a simple # block of output after the regular SCF iterations. # # MP3 is handled by ccman2. # # MP4 and variants are handled by ccman. # This is the MP2/cdman case. if 'MP2 total energy' in line: if not hasattr(self, 'mpenergies'): self.mpenergies = [] mp2energy = float(line.split()[4]) mp2energy = utils.convertor(mp2energy, 'hartree', 'eV') self.mpenergies.append([mp2energy]) # This is the MP3/ccman2 case. if line[1:11] == 'MP2 energy' and line[12:19] != 'read as': if not hasattr(self, 'mpenergies'): self.mpenergies = [] mpenergies = [] mp2energy = float(line.split()[3]) mpenergies.append(mp2energy) line = next(inputfile) line = next(inputfile) # Just a safe check. if 'MP3 energy' in line: mp3energy = float(line.split()[3]) mpenergies.append(mp3energy) mpenergies = [utils.convertor(mpe, 'hartree', 'eV') for mpe in mpenergies] self.mpenergies.append(mpenergies) # This is the MP4/ccman case. if 'EHF' in line: if not hasattr(self, 'mpenergies'): self.mpenergies = [] mpenergies = [] while list(set(line.strip())) != ['-']: if 'EMP2' in line: mp2energy = float(line.split()[2]) mpenergies.append(mp2energy) if 'EMP3' in line: mp3energy = float(line.split()[2]) mpenergies.append(mp3energy) if 'EMP4SDQ' in line: mp4sdqenergy = float(line.split()[2]) mpenergies.append(mp4sdqenergy) # This is really MP4SD(T)Q. if 'EMP4 ' in line: mp4sdtqenergy = float(line.split()[2]) mpenergies.append(mp4sdtqenergy) line = next(inputfile) mpenergies = [utils.convertor(mpe, 'hartree', 'eV') for mpe in mpenergies] self.mpenergies.append(mpenergies) # Coupled cluster corrections. # Hopefully we only have to deal with ccman2 here. if 'CCD total energy' in line: if not hasattr(self, 'ccenergies'): self.ccenergies = [] ccdenergy = float(line.split()[-1]) ccdenergy = utils.convertor(ccdenergy, 'hartree', 'eV') self.ccenergies.append(ccdenergy) if 'CCSD total energy' in line: has_triples = False if not hasattr(self, 'ccenergies'): self.ccenergies = [] ccsdenergy = float(line.split()[-1]) # Make sure we aren't actually doing CCSD(T). line = next(inputfile) line = next(inputfile) if 'CCSD(T) total energy' in line: has_triples = True ccsdtenergy = float(line.split()[-1]) ccsdtenergy = utils.convertor(ccsdtenergy, 'hartree', 'eV') self.ccenergies.append(ccsdtenergy) if not has_triples: ccsdenergy = utils.convertor(ccsdenergy, 'hartree', 'eV') self.ccenergies.append(ccsdenergy) # Electronic transitions. Works for both CIS and TDDFT. if 'Excitation Energies' in line: # Restricted: # --------------------------------------------------- # TDDFT/TDA Excitation Energies # --------------------------------------------------- # # Excited state 1: excitation energy (eV) = 3.6052 # Total energy for state 1: -382.167872200685 # Multiplicity: Triplet # Trans. Mom.: 0.0000 X 0.0000 Y 0.0000 Z # Strength : 0.0000 # D( 33) --> V( 3) amplitude = 0.2618 # D( 34) --> V( 2) amplitude = 0.2125 # D( 35) --> V( 1) amplitude = 0.9266 # # Unrestricted: # Excited state 2: excitation energy (eV) = 2.3156 # Total energy for state 2: -381.980177630969 # : 0.7674 # Trans. Mom.: -2.7680 X -0.1089 Y 0.0000 Z # Strength : 0.4353 # S( 1) --> V( 1) amplitude = -0.3105 alpha # D( 34) --> S( 1) amplitude = 0.9322 beta self.skip_lines(inputfile, ['dashes', 'blank']) line = next(inputfile) etenergies = [] etsyms = [] etoscs = [] etsecs = [] spinmap = {'alpha': 0, 'beta': 1} while list(set(line.strip())) != ['-']: # Take the total energy for the state and subtract from the # ground state energy, rather than just the EE; # this will be more accurate. if 'Total energy for state' in line: energy = utils.convertor(float(line.split()[-1]), 'hartree', 'cm-1') etenergy = energy - utils.convertor(self.scfenergies[-1], 'eV', 'cm-1') etenergies.append(etenergy) # if 'excitation energy' in line: # etenergy = utils.convertor(float(line.split()[-1]), 'eV', 'cm-1') # etenergies.append(etenergy) if 'Multiplicity' in line: etsym = line.split()[1] etsyms.append(etsym) if 'Strength' in line: strength = float(line.split()[-1]) etoscs.append(strength) # This is the list of transitions. if 'amplitude' in line: sec = [] while line.strip() != '': if self.unrestricted: spin = spinmap[line[42:47].strip()] else: spin = 0 # There is a subtle difference between TDA and RPA calcs, # because in the latter case each transition line is # preceeded by the type of vector: X or Y, name excitation # or deexcitation (see #154 for details). For deexcitations, # we will need to reverse the MO indices. Note also that Q-Chem # starts reindexing virtual orbitals at 1. if line[5] == '(': ttype = 'X' startidx = int(line[6:9]) - 1 endidx = int(line[17:20]) - 1 + self.nalpha contrib = float(line[34:41].strip()) else: assert line[5] == ":" ttype = line[4] startidx = int(line[9:12]) - 1 endidx = int(line[20:23]) - 1 + self.nalpha contrib = float(line[37:44].strip()) start = (startidx, spin) end = (endidx, spin) if ttype == 'X': sec.append([start, end, contrib]) elif ttype == 'Y': sec.append([end, start, contrib]) else: raise ValueError('Unknown transition type: %s' % ttype) line = next(inputfile) etsecs.append(sec) line = next(inputfile) self.set_attribute('etenergies', etenergies) self.set_attribute('etsyms', etsyms) self.set_attribute('etoscs', etoscs) self.set_attribute('etsecs', etsecs) # Molecular orbital energies and symmetries. if 'Orbital Energies (a.u.) and Symmetries' in line: # -------------------------------------------------------------- # Orbital Energies (a.u.) and Symmetries # -------------------------------------------------------------- # # Alpha MOs, Restricted # -- Occupied -- # -10.018 -10.018 -10.008 -10.008 -10.007 -10.007 -10.006 -10.005 # 1 Bu 1 Ag 2 Bu 2 Ag 3 Bu 3 Ag 4 Bu 4 Ag # -9.992 -9.992 -0.818 -0.755 -0.721 -0.704 -0.670 -0.585 # 5 Ag 5 Bu 6 Ag 6 Bu 7 Ag 7 Bu 8 Bu 8 Ag # -0.561 -0.532 -0.512 -0.462 -0.439 -0.410 -0.400 -0.397 # 9 Ag 9 Bu 10 Ag 11 Ag 10 Bu 11 Bu 12 Bu 12 Ag # -0.376 -0.358 -0.349 -0.330 -0.305 -0.295 -0.281 -0.263 # 13 Bu 14 Bu 13 Ag 1 Au 15 Bu 14 Ag 15 Ag 1 Bg # -0.216 -0.198 -0.160 # 2 Au 2 Bg 3 Bg # -- Virtual -- # 0.050 0.091 0.116 0.181 0.280 0.319 0.330 0.365 # 3 Au 4 Au 4 Bg 5 Au 5 Bg 16 Ag 16 Bu 17 Bu # 0.370 0.413 0.416 0.422 0.446 0.469 0.496 0.539 # 17 Ag 18 Bu 18 Ag 19 Bu 19 Ag 20 Bu 20 Ag 21 Ag # 0.571 0.587 0.610 0.627 0.646 0.693 0.743 0.806 # 21 Bu 22 Ag 22 Bu 23 Bu 23 Ag 24 Ag 24 Bu 25 Ag # 0.816 # 25 Bu # # Beta MOs, Restricted # -- Occupied -- # -10.018 -10.018 -10.008 -10.008 -10.007 -10.007 -10.006 -10.005 # 1 Bu 1 Ag 2 Bu 2 Ag 3 Bu 3 Ag 4 Bu 4 Ag # -9.992 -9.992 -0.818 -0.755 -0.721 -0.704 -0.670 -0.585 # 5 Ag 5 Bu 6 Ag 6 Bu 7 Ag 7 Bu 8 Bu 8 Ag # -0.561 -0.532 -0.512 -0.462 -0.439 -0.410 -0.400 -0.397 # 9 Ag 9 Bu 10 Ag 11 Ag 10 Bu 11 Bu 12 Bu 12 Ag # -0.376 -0.358 -0.349 -0.330 -0.305 -0.295 -0.281 -0.263 # 13 Bu 14 Bu 13 Ag 1 Au 15 Bu 14 Ag 15 Ag 1 Bg # -0.216 -0.198 -0.160 # 2 Au 2 Bg 3 Bg # -- Virtual -- # 0.050 0.091 0.116 0.181 0.280 0.319 0.330 0.365 # 3 Au 4 Au 4 Bg 5 Au 5 Bg 16 Ag 16 Bu 17 Bu # 0.370 0.413 0.416 0.422 0.446 0.469 0.496 0.539 # 17 Ag 18 Bu 18 Ag 19 Bu 19 Ag 20 Bu 20 Ag 21 Ag # 0.571 0.587 0.610 0.627 0.646 0.693 0.743 0.806 # 21 Bu 22 Ag 22 Bu 23 Bu 23 Ag 24 Ag 24 Bu 25 Ag # 0.816 # 25 Bu # -------------------------------------------------------------- self.skip_line(inputfile, 'dashes') line = next(inputfile) # Sometimes Q-Chem gets a little confused... while 'Warning : Irrep of orbital' in line: line = next(inputfile) line = next(inputfile) energies_alpha = [] symbols_alpha = [] if self.unrestricted: energies_beta = [] symbols_beta = [] line = next(inputfile) # The end of the block is either a blank line or only dashes. while not self.re_dashes_and_spaces.search(line): if 'Occupied' in line or 'Virtual' in line: # A nice trick to find where the HOMO is. if 'Virtual' in line: self.homos = [len(energies_alpha)-1] line = next(inputfile) # Parse the energies and symmetries in pairs of lines. # energies = [utils.convertor(energy, 'hartree', 'eV') # for energy in map(float, line.split())] # This convoluted bit handles '*******' when present. energies = [] energy_line = line.split() for e in energy_line: try: energy = utils.convertor(self.float(e), 'hartree', 'eV') except ValueError: energy = numpy.nan energies.append(energy) energies_alpha.extend(energies) line = next(inputfile) symbols = line.split()[1::2] symbols_alpha.extend(symbols) line = next(inputfile) line = next(inputfile) # Only look at the second block if doing an unrestricted calculation. # This might be a problem for ROHF/ROKS. if self.unrestricted: assert 'Beta MOs' in line self.skip_line(inputfile, '-- Occupied --') line = next(inputfile) while not self.re_dashes_and_spaces.search(line): if 'Occupied' in line or 'Virtual' in line: # This will definitely exist, thanks to the above block. if 'Virtual' in line: if len(self.homos) == 1: self.homos.append(len(energies_beta)-1) line = next(inputfile) energies = [] energy_line = line.split() for e in energy_line: try: energy = utils.convertor(self.float(e), 'hartree', 'eV') except ValueError: energy = numpy.nan energies.append(energy) energies_beta.extend(energies) line = next(inputfile) symbols = line.split()[1::2] symbols_beta.extend(symbols) line = next(inputfile) # For now, only keep the last set of MO energies, even though it is # printed at every step of geometry optimizations and fragment jobs. self.moenergies = [[]] self.mosyms = [[]] self.moenergies[0] = numpy.array(energies_alpha) self.mosyms[0] = symbols_alpha if self.unrestricted: self.moenergies.append([]) self.mosyms.append([]) self.moenergies[1] = numpy.array(energies_beta) self.mosyms[1] = symbols_beta self.set_attribute('nmo', len(self.moenergies[0])) # Molecular orbital energies, no symmetries. if line.strip() == 'Orbital Energies (a.u.)': # In the case of no orbital symmetries, the beta spin block is not # present for restricted calculations. # -------------------------------------------------------------- # Orbital Energies (a.u.) # -------------------------------------------------------------- # # Alpha MOs # -- Occupied -- # ******* -38.595 -34.580 -34.579 -34.578 -19.372 -19.372 -19.364 # -19.363 -19.362 -19.362 -4.738 -3.252 -3.250 -3.250 -1.379 # -1.371 -1.369 -1.365 -1.364 -1.362 -0.859 -0.855 -0.849 # -0.846 -0.840 -0.836 -0.810 -0.759 -0.732 -0.729 -0.704 # -0.701 -0.621 -0.610 -0.595 -0.587 -0.584 -0.578 -0.411 # -0.403 -0.355 -0.354 -0.352 # -- Virtual -- # -0.201 -0.117 -0.099 -0.086 0.020 0.031 0.055 0.067 # 0.075 0.082 0.086 0.092 0.096 0.105 0.114 0.148 # # Beta MOs # -- Occupied -- # ******* -38.561 -34.550 -34.549 -34.549 -19.375 -19.375 -19.367 # -19.367 -19.365 -19.365 -4.605 -3.105 -3.103 -3.102 -1.385 # -1.376 -1.376 -1.371 -1.370 -1.368 -0.863 -0.858 -0.853 # -0.849 -0.843 -0.839 -0.818 -0.765 -0.738 -0.737 -0.706 # -0.702 -0.624 -0.613 -0.600 -0.591 -0.588 -0.585 -0.291 # -0.291 -0.288 -0.275 # -- Virtual -- # -0.139 -0.122 -0.103 0.003 0.014 0.049 0.049 0.059 # 0.061 0.070 0.076 0.081 0.086 0.090 0.098 0.106 # 0.138 # -------------------------------------------------------------- self.skip_lines(inputfile, ['dashes', 'blank']) line = next(inputfile) energies_alpha = [] if self.unrestricted: energies_beta = [] line = next(inputfile) # The end of the block is either a blank line or only dashes. while not self.re_dashes_and_spaces.search(line): if 'Occupied' in line or 'Virtual' in line: # A nice trick to find where the HOMO is. if 'Virtual' in line: self.homos = [len(energies_alpha)-1] line = next(inputfile) energies = [] energy_line = line.split() for e in energy_line: try: energy = utils.convertor(self.float(e), 'hartree', 'eV') except ValueError: energy = numpy.nan energies.append(energy) energies_alpha.extend(energies) line = next(inputfile) line = next(inputfile) # Only look at the second block if doing an unrestricted calculation. # This might be a problem for ROHF/ROKS. if self.unrestricted: assert 'Beta MOs' in line self.skip_line(inputfile, '-- Occupied --') line = next(inputfile) while not self.re_dashes_and_spaces.search(line): if 'Occupied' in line or 'Virtual' in line: # This will definitely exist, thanks to the above block. if 'Virtual' in line: if len(self.homos) == 1: self.homos.append(len(energies_beta)-1) line = next(inputfile) energies = [] energy_line = line.split() for e in energy_line: try: energy = utils.convertor(self.float(e), 'hartree', 'eV') except ValueError: energy = numpy.nan energies.append(energy) energies_beta.extend(energies) line = next(inputfile) # For now, only keep the last set of MO energies, even though it is # printed at every step of geometry optimizations and fragment jobs. self.moenergies = [[]] self.moenergies[0] = numpy.array(energies_alpha) if self.unrestricted: self.moenergies.append([]) self.moenergies[1] = numpy.array(energies_beta) self.set_attribute('nmo', len(self.moenergies[0])) # If we've asked to display more virtual orbitals than there # are MOs present in the molecule, fix that now. if hasattr(self, 'nmo') and hasattr(self, 'nalpha') and hasattr(self, 'nbeta'): if self.norbdisp_alpha_aonames > self.nmo: self.norbdisp_alpha_aonames = self.nmo if self.norbdisp_beta_aonames > self.nmo: self.norbdisp_beta_aonames = self.nmo # Molecular orbital coefficients. # This block comes from `print_orbitals = true/{int}`. Less # precision than `scf_final_print >= 2` for `mocoeffs`, but # important for `aonames` and `atombasis`. if any(header in line for header in self.alpha_mo_coefficient_headers): if not hasattr(self, 'mocoeffs'): self.mocoeffs = [] if not hasattr(self, 'atombasis'): self.atombasis = [] for n in range(self.natom): self.atombasis.append([]) if not hasattr(self, 'aonames'): self.aonames = [] # We could also attempt to parse `moenergies` here, but # nothing is gained by it. mocoeffs = numpy.empty(shape=(self.nbasis, self.norbdisp_alpha_aonames)) self.parse_matrix_aonames(inputfile, mocoeffs) # Only use these MO coefficients if we don't have them # from `scf_final_print`. if len(self.mocoeffs) == 0: self.mocoeffs.append(mocoeffs.transpose()) # Go back through `aonames` to create `atombasis`. assert len(self.aonames) == self.nbasis for aoindex, aoname in enumerate(self.aonames): atomindex = int(self.re_atomindex.search(aoname).groups()[0]) - 1 self.atombasis[atomindex].append(aoindex) assert len(self.atombasis) == len(self.atomnos) if 'BETA MOLECULAR ORBITAL COEFFICIENTS' in line: mocoeffs = numpy.empty(shape=(self.nbasis, self.norbdisp_beta_aonames)) self.parse_matrix_aonames(inputfile, mocoeffs) if len(self.mocoeffs) == 1: self.mocoeffs.append(mocoeffs.transpose()) # Population analysis. if 'Ground-State Mulliken Net Atomic Charges' in line: self.parse_charge_section(inputfile, 'mulliken') if 'Hirshfeld Atomic Charges' in line: self.parse_charge_section(inputfile, 'hirshfeld') if 'Ground-State ChElPG Net Atomic Charges' in line: self.parse_charge_section(inputfile, 'chelpg') # Multipole moments are not printed in lexicographical order, # so we need to parse and sort them. The units seem OK, but there # is some uncertainty about the reference point and whether it # can be changed. # # Notice how the letter/coordinate labels change to coordinate ranks # after hexadecapole moments, and need to be translated. Additionally, # after 9-th order moments the ranks are not necessarily single digits # and so there are spaces between them. # # ----------------------------------------------------------------- # Cartesian Multipole Moments # LMN = < X^L Y^M Z^N > # ----------------------------------------------------------------- # Charge (ESU x 10^10) # 0.0000 # Dipole Moment (Debye) # X 0.0000 Y 0.0000 Z 0.0000 # Tot 0.0000 # Quadrupole Moments (Debye-Ang) # XX -50.9647 XY -0.1100 YY -50.1441 # XZ 0.0000 YZ 0.0000 ZZ -58.5742 # ... # 5th-Order Moments (Debye-Ang^4) # 500 0.0159 410 -0.0010 320 0.0005 # 230 0.0000 140 0.0005 050 0.0012 # ... # ----------------------------------------------------------------- # if "Cartesian Multipole Moments" in line: # This line appears not by default, but only when # `multipole_order` > 4: line = inputfile.next() if 'LMN = < X^L Y^M Z^N >' in line: line = inputfile.next() # The reference point is always the origin, although normally the molecule # is moved so that the center of charge is at the origin. self.reference = [0.0, 0.0, 0.0] self.moments = [self.reference] # Watch out! This charge is in statcoulombs without the exponent! # We should expect very good agreement, however Q-Chem prints # the charge only with 5 digits, so expect 1e-4 accuracy. charge_header = inputfile.next() assert charge_header.split()[0] == "Charge" charge = float(inputfile.next().strip()) charge = utils.convertor(charge, 'statcoulomb', 'e') * 1e-10 # Allow this to change until fragment jobs are properly implemented. # assert abs(charge - self.charge) < 1e-4 # This will make sure Debyes are used (not sure if it can be changed). line = inputfile.next() assert line.strip() == "Dipole Moment (Debye)" while "-----" not in line: # The current multipole element will be gathered here. multipole = [] line = inputfile.next() while ("-----" not in line) and ("Moment" not in line): cols = line.split() # The total (norm) is printed for dipole but not other multipoles. if cols[0] == 'Tot': line = inputfile.next() continue # Find and replace any 'stars' with NaN before moving on. for i in range(len(cols)): if '***' in cols[i]: cols[i] = numpy.nan # The moments come in pairs (label followed by value) up to the 9-th order, # although above hexadecapoles the labels are digits representing the rank # in each coordinate. Above the 9-th order, ranks are not always single digits, # so there are spaces between them, which means moments come in quartets. if len(self.moments) < 5: for i in range(len(cols)//2): lbl = cols[2*i] m = cols[2*i + 1] multipole.append([lbl, m]) elif len(self.moments) < 10: for i in range(len(cols)//2): lbl = cols[2*i] lbl = 'X'*int(lbl[0]) + 'Y'*int(lbl[1]) + 'Z'*int(lbl[2]) m = cols[2*i + 1] multipole.append([lbl, m]) else: for i in range(len(cols)//4): lbl = 'X'*int(cols[4*i]) + 'Y'*int(cols[4*i + 1]) + 'Z'*int(cols[4*i + 2]) m = cols[4*i + 3] multipole.append([lbl, m]) line = inputfile.next() # Sort should use the first element when sorting lists, # so this should simply work, and afterwards we just need # to extract the second element in each list (the actual moment). multipole.sort() multipole = [m[1] for m in multipole] self.moments.append(multipole) # For `method = force` or geometry optimizations, # the gradient is printed. if 'Gradient of SCF Energy' in line: if not hasattr(self, 'grads'): self.grads = [] grad = numpy.empty(shape=(3, self.natom)) self.parse_matrix(inputfile, grad) self.grads.append(grad.T) # For IR-related jobs, the Hessian is printed (dim: 3*natom, 3*natom). # Note that this is *not* the mass-weighted Hessian. if 'Hessian of the SCF Energy' in line: if not hasattr(self, 'hessian'): dim = 3*self.natom self.hessian = numpy.empty(shape=(dim, dim)) self.parse_matrix(inputfile, self.hessian) # Start of the IR/Raman frequency section. if 'VIBRATIONAL ANALYSIS' in line: while 'STANDARD THERMODYNAMIC QUANTITIES' not in line: ## IR, optional Raman: # ********************************************************************** # ** ** # ** VIBRATIONAL ANALYSIS ** # ** -------------------- ** # ** ** # ** VIBRATIONAL FREQUENCIES (CM**-1) AND NORMAL MODES ** # ** FORCE CONSTANTS (mDYN/ANGSTROM) AND REDUCED MASSES (AMU) ** # ** INFRARED INTENSITIES (KM/MOL) ** ##** RAMAN SCATTERING ACTIVITIES (A**4/AMU) AND DEPOLARIZATION RATIOS ** # ** ** # ********************************************************************** # Mode: 1 2 3 # Frequency: -106.88 -102.91 161.77 # Force Cnst: 0.0185 0.0178 0.0380 # Red. Mass: 2.7502 2.8542 2.4660 # IR Active: NO YES YES # IR Intens: 0.000 0.000 0.419 # Raman Active: YES NO NO ##Raman Intens: 2.048 0.000 0.000 ##Depolar: 0.750 0.000 0.000 # X Y Z X Y Z X Y Z # C 0.000 0.000 -0.100 -0.000 0.000 -0.070 -0.000 -0.000 -0.027 # C 0.000 0.000 0.045 -0.000 0.000 -0.074 0.000 -0.000 -0.109 # C 0.000 0.000 0.148 -0.000 -0.000 -0.074 0.000 0.000 -0.121 # C 0.000 0.000 0.100 -0.000 -0.000 -0.070 0.000 0.000 -0.027 # C 0.000 0.000 -0.045 0.000 -0.000 -0.074 -0.000 -0.000 -0.109 # C 0.000 0.000 -0.148 0.000 0.000 -0.074 -0.000 -0.000 -0.121 # H -0.000 0.000 0.086 -0.000 0.000 -0.082 0.000 -0.000 -0.102 # H 0.000 0.000 0.269 -0.000 -0.000 -0.091 0.000 0.000 -0.118 # H 0.000 0.000 -0.086 0.000 -0.000 -0.082 -0.000 0.000 -0.102 # H -0.000 0.000 -0.269 0.000 0.000 -0.091 -0.000 -0.000 -0.118 # C 0.000 -0.000 0.141 -0.000 -0.000 -0.062 -0.000 0.000 0.193 # C -0.000 -0.000 -0.160 0.000 0.000 0.254 -0.000 0.000 0.043 # H 0.000 -0.000 0.378 -0.000 0.000 -0.289 0.000 0.000 0.519 # H -0.000 -0.000 -0.140 0.000 0.000 0.261 -0.000 -0.000 0.241 # H -0.000 -0.000 -0.422 0.000 0.000 0.499 -0.000 0.000 -0.285 # C 0.000 -0.000 -0.141 0.000 0.000 -0.062 -0.000 -0.000 0.193 # C -0.000 -0.000 0.160 -0.000 -0.000 0.254 0.000 0.000 0.043 # H 0.000 -0.000 -0.378 0.000 -0.000 -0.289 -0.000 0.000 0.519 # H -0.000 -0.000 0.140 -0.000 -0.000 0.261 0.000 0.000 0.241 # H -0.000 -0.000 0.422 -0.000 -0.000 0.499 0.000 0.000 -0.285 # TransDip 0.000 -0.000 -0.000 0.000 -0.000 -0.000 -0.000 0.000 0.021 # Mode: 4 5 6 # ... # There isn't any symmetry information for normal modes present # in Q-Chem. # if not hasattr(self, 'vibsyms'): # self.vibsyms = [] if 'Frequency:' in line: if not hasattr(self, 'vibfreqs'): self.vibfreqs = [] vibfreqs = map(float, line.split()[1:]) self.vibfreqs.extend(vibfreqs) if 'IR Intens:' in line: if not hasattr(self, 'vibirs'): self.vibirs = [] vibirs = map(float, line.split()[2:]) self.vibirs.extend(vibirs) if 'Raman Intens:' in line: if not hasattr(self, 'vibramans'): self.vibramans = [] vibramans = map(float, line.split()[2:]) self.vibramans.extend(vibramans) # This is the start of the displacement block. if line.split()[0:3] == ['X', 'Y', 'Z']: if not hasattr(self, 'vibdisps'): self.vibdisps = [] disps = [] for k in range(self.natom): line = next(inputfile) numbers = list(map(float, line.split()[1:])) N = len(numbers) // 3 if not disps: for n in range(N): disps.append([]) for n in range(N): disps[n].append(numbers[3*n:(3*n)+3]) self.vibdisps.extend(disps) line = next(inputfile) # Anharmonic vibrational analysis. # Q-Chem includes 3 theories: VPT2, TOSH, and VCI. # For now, just take the VPT2 results. # if 'VIBRATIONAL ANHARMONIC ANALYSIS' in line: # while list(set(line.strip())) != ['=']: # if 'VPT2' in line: # if not hasattr(self, 'vibanharms'): # self.vibanharms = [] # self.vibanharms.append(float(line.split()[-1])) # line = next(inputfile) if 'STANDARD THERMODYNAMIC QUANTITIES AT' in line: if not hasattr(self, 'temperature'): self.temperature = float(line.split()[4]) # Not supported yet. if not hasattr(self, 'pressure'): self.pressure = float(line.split()[7]) self.skip_lines(inputfile, ['blank', 'Imaginary']) line = next(inputfile) # Not supported yet. if 'Zero point vibrational energy' in line: if not hasattr(self, 'zpe'): # Convert from kcal/mol to Hartree/particle. self.zpe = utils.convertor(float(line.split()[4]), 'kcal', 'hartree') atommasses = [] while 'Archival summary' not in line: if 'Has Mass' in line: atommass = float(line.split()[6]) atommasses.append(atommass) if 'Total Enthalpy' in line: if not hasattr(self, 'enthalpy'): enthalpy = float(line.split()[2]) self.enthalpy = utils.convertor(enthalpy, 'kcal', 'hartree') if 'Total Entropy' in line: if not hasattr(self, 'entropy'): entropy = float(line.split()[2]) * self.temperature / 1000 # This is the *temperature dependent* entropy. self.entropy = utils.convertor(entropy, 'kcal', 'hartree') if not hasattr(self, 'freeenergy'): self.freeenergy = self.enthalpy - self.entropy line = next(inputfile) if not hasattr(self, 'atommasses'): self.atommasses = numpy.array(atommasses) # TODO: # 'enthalpy' (incorrect) # 'entropy' (incorrect) # 'freeenergy' (incorrect) # 'nocoeffs' # 'nooccnos' # 'vibanharms' def parse_charge_section(self, inputfile, chargetype): """Parse the population analysis charge block.""" self.skip_line(inputfile, 'blank') line = next(inputfile) has_spins = False if 'Spin' in line: if not hasattr(self, 'atomspins'): self.atomspins = dict() has_spins = True spins = [] self.skip_line(inputfile, 'dashes') if not hasattr(self, 'atomcharges'): self.atomcharges = dict() charges = [] line = next(inputfile) while list(set(line.strip())) != ['-']: elements = line.split() charge = self.float(elements[2]) charges.append(charge) if has_spins: spin = self.float(elements[3]) spins.append(spin) line = next(inputfile) self.atomcharges[chargetype] = numpy.array(charges) if has_spins: self.atomspins[chargetype] = numpy.array(spins) def parse_matrix(self, inputfile, nparray): """Q-Chem prints most matrices in a standard format; parse the matrix into a preallocated NumPy array of the appropriate shape. """ nrows, ncols = nparray.shape line = next(inputfile) assert len(line.split()) == min(self.ncolsblock, ncols) colcounter = 0 while colcounter < ncols: # If the line is just the column header (indices)... if line[:5].strip() == '': line = next(inputfile) rowcounter = 0 while rowcounter < nrows: row = list(map(float, line.split()[1:])) assert len(row) == min(self.ncolsblock, (ncols - colcounter)) nparray[rowcounter][colcounter:colcounter + self.ncolsblock] = row line = next(inputfile) rowcounter += 1 colcounter += self.ncolsblock def parse_matrix_aonames(self, inputfile, nparray): """Q-Chem prints most matrices in a standard format; parse the matrix into a preallocated NumPy array of the appropriate shape. Rather than have one routine for parsing all general matrices and the 'MOLECULAR ORBITAL COEFFICIENTS' block, use a second which handles `aonames`. """ bigmom = ('d', 'f', 'g', 'h') nrows, ncols = nparray.shape line = next(inputfile) assert len(line.split()) == min(self.ncolsblock, ncols) colcounter = 0 while colcounter < ncols: # If the line is just the column header (indices)... if line[:5].strip() == '': line = next(inputfile) # Do nothing for now. if 'eigenvalues' in line: line = next(inputfile) rowcounter = 0 while rowcounter < nrows: row = line.split() # Only take the AO names on the first time through. if colcounter == 0: if len(self.aonames) != self.nbasis: # Apply the offset for rows where there is # more than one atom of any element in the # molecule. offset = int(self.formula_histogram[row[1]] != 1) if offset: name = self.atommap.get(row[1] + str(row[2])) else: name = self.atommap.get(row[1] + '1') # For l > 1, there is a space between l and # m_l when using spherical functions. shell = row[2 + offset] if shell in bigmom: shell = ''.join([shell, row[3 + offset]]) aoname = ''.join([name, '_', shell.upper()]) self.aonames.append(aoname) row = list(map(float, row[-min(self.ncolsblock, (ncols - colcounter)):])) nparray[rowcounter][colcounter:colcounter + self.ncolsblock] = row line = next(inputfile) rowcounter += 1 colcounter += self.ncolsblock def generate_atom_map(self): """Generate the map to go from Q-Chem atom numbering: 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'H1', 'H2', 'H3', 'H4', 'C7', ... to cclib atom numbering: 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'H7', 'H8', 'H9', 'H10', 'C11', ... for later use. """ # Generate the desired order. order_proper = [element + str(num) for element, num in zip(self.atomelements, itertools.count(start=1))] # We need separate counters for each element. element_counters = {element: itertools.count(start=1) for element in set(self.atomelements)} # Generate the Q-Chem printed order. order_qchem = [element + str(next(element_counters[element])) for element in self.atomelements] # Combine the orders into a mapping. atommap = {k:v for k, v, in zip(order_qchem, order_proper)} return atommap def generate_formula_histogram(self): """From the atomnos, generate a histogram that represents the molecular formula. """ histogram = dict() for element in self.atomelements: if element in histogram.keys(): histogram[element] += 1 else: histogram[element] = 1 return histogram if __name__ == '__main__': import sys import doctest, qchemparser if len(sys.argv) == 1: doctest.testmod(qchemparser, verbose=False) if len(sys.argv) == 2: parser = qchemparser.QChem(sys.argv[1]) data = parser.parse() if len(sys.argv) > 2: for i in range(len(sys.argv[2:])): if hasattr(data, sys.argv[2 + i]): print(getattr(data, sys.argv[2 + i])) GaussSum-3.0.1/gausssum/cclib/parser/daltonparser.py0000664000175000017500000005054712660404041021237 0ustar noelnoel# This file is part of cclib (http://cclib.github.io), a library for parsing # and interpreting the results of computational chemistry packages. # # Copyright (C) 2006-2015, the cclib development team # # The library is free software, distributed under the terms of # the GNU Lesser General Public version 2.1 or later. You should have # received a copy of the license along with cclib. You can also access # the full license online at http://www.gnu.org/copyleft/lgpl.html. """Parser for DALTON output files""" from __future__ import print_function import numpy from . import logfileparser from . import utils class DALTON(logfileparser.Logfile): """A DALTON log file.""" def __init__(self, *args, **kwargs): # Call the __init__ method of the superclass super(DALTON, self).__init__(logname="DALTON", *args, **kwargs) def __str__(self): """Return a string representation of the object.""" return "DALTON log file %s" % (self.filename) def __repr__(self): """Return a representation of the object.""" return 'DALTON("%s")' % (self.filename) def normalisesym(self, label): """Normalise the symmetries used by DALTON.""" # It appears that DALTON is using the correct labels. return label def before_parsing(self): # Used to decide whether to wipe the atomcoords clean. self.firststdorient = True def extract(self, inputfile, line): """Extract information from the file object inputfile.""" # This section is close to the beginning of the file, and can be used # to parse natom, nbasis and atomnos. Note that DALTON operates on the # idea of atom type, which are not necessarily unique. # # Atoms and basis sets # -------------------- # # Number of atom types : 6 # Total number of atoms: 20 # # Basis set used is "STO-3G" from the basis set library. # # label atoms charge prim cont basis # ---------------------------------------------------------------------- # C 6 6.0000 15 5 [6s3p|2s1p] # H 4 1.0000 3 1 [3s|1s] # C 2 6.0000 15 5 [6s3p|2s1p] # H 2 1.0000 3 1 [3s|1s] # C 2 6.0000 15 5 [6s3p|2s1p] # H 4 1.0000 3 1 [3s|1s] # ---------------------------------------------------------------------- # total: 20 70.0000 180 60 # ---------------------------------------------------------------------- # # Threshold for neglecting AO integrals: 1.00D-12 # if line.strip() == "Atoms and basis sets": self.skip_lines(inputfile, ['d', 'b']) line = next(inputfile) assert "Number of atom types" in line self.ntypes = int(line.split()[-1]) line = next(inputfile) assert "Total number of atoms:" in line self.set_attribute("natom", int(line.split()[-1])) self.skip_lines(inputfile, ['b', 'basisname', 'b']) line = next(inputfile) cols = line.split() iatoms = cols.index('atoms') icharge = cols.index('charge') icont = cols.index('cont') self.skip_line(inputfile, 'dashes') atomnos = [] atombasis = [] nbasis = 0 for itype in range(self.ntypes): line = next(inputfile) cols = line.split() atoms = int(cols[iatoms]) charge = float(cols[icharge]) assert int(charge) == charge charge = int(charge) cont = int(cols[icont]) for at in range(atoms): atomnos.append(charge) atombasis.append(list(range(nbasis, nbasis + cont))) nbasis += cont self.set_attribute('atomnos', atomnos) self.set_attribute('atombasis', atombasis) # Since DALTON sometimes uses symmetry labels (Ag, Au, etc.) and sometimes # just the symmetry group index, we need to parse and keep a mapping between # these two for later. # # Symmetry Orbitals # ----------------- # # Number of orbitals in each symmetry: 25 5 25 5 # # # Symmetry Ag ( 1) # # 1 C 1s 1 + 2 # 2 C 1s 3 + 4 # ... # if line.strip() == "Symmetry Orbitals": self.skip_lines(inputfile, ['d', 'b']) line = inputfile.next() self.symcounts = [int(c) for c in line.split()[-4:]] self.symlabels = [] for sc in self.symcounts: self.skip_lines(inputfile, ['b', 'b']) # If the number of orbitals for a symmetry is zero, the printout # is different (see MP2 unittest logfile for an example). line = inputfile.next() if sc == 0: assert "No orbitals in symmetry" in line else: assert line.split()[0] == "Symmetry" self.symlabels.append(line.split()[1]) self.skip_line(inputfile, 'blank') for i in range(sc): orbital = inputfile.next() # Wave function specification # ============================ # @ Wave function type >>> KS-DFT <<< # @ Number of closed shell electrons 70 # @ Number of electrons in active shells 0 # @ Total charge of the molecule 0 # # @ Spin multiplicity and 2 M_S 1 0 # @ Total number of symmetries 4 (point group: C2h) # @ Reference state symmetry 1 (irrep name : Ag ) # # This is a DFT calculation of type: B3LYP # ... # if "@ Total charge of the molecule" in line: self.set_attribute("charge", int(line.split()[-1])) if "@ Spin multiplicity and 2 M_S 1 0" in line: self.set_attribute("mult", int(line.split()[-2])) # Orbital specifications # ====================== # Abelian symmetry species All | 1 2 3 4 # | Ag Au Bu Bg # --- | --- --- --- --- # Total number of orbitals 60 | 25 5 25 5 # Number of basis functions 60 | 25 5 25 5 # # ** Automatic occupation of RKS orbitals ** # # -- Initial occupation of symmetries is determined from extended Huckel guess. # -- Initial occupation of symmetries is : # @ Occupied SCF orbitals 35 | 15 2 15 3 # # Maximum number of Fock iterations 0 # Maximum number of DIIS iterations 60 # Maximum number of QC-SCF iterations 60 # Threshold for SCF convergence 1.00D-05 # This is a DFT calculation of type: B3LYP # ... # if "Total number of orbitals" in line: # DALTON 2015 adds a @ in front of number of orbitals index = 4 if "@" in line: index = 5 self.set_attribute("nbasis", int(line.split()[index])) if "@ Occupied SCF orbitals" in line and not hasattr(self, 'homos'): temp = line.split() homos = int(temp[4]) self.set_attribute('homos', [homos-1]) # it is the index (python counting, so -1) if "Threshold for SCF convergence" in line: if not hasattr(self, "scftargets"): self.scftargets = [] scftarget = self.float(line.split()[-1]) self.scftargets.append([scftarget]) # ********************************************* # ***** DIIS optimization of Hartree-Fock ***** # ********************************************* # # C1-DIIS algorithm; max error vectors = 8 # # Automatic occupation of symmetries with 70 electrons. # # Iter Total energy Error norm Delta(E) SCF occupation # ----------------------------------------------------------------------------- # K-S energy, electrons, error : -46.547567739269 69.9999799123 -2.01D-05 # @ 1 -381.645762476 4.00D+00 -3.82D+02 15 2 15 3 # Virial theorem: -V/T = 2.008993 # @ MULPOP C _1 0.15; C _2 0.15; C _1 0.12; C _2 0.12; C _1 0.11; C _2 0.11; H _1 -0.15; H _2 -0.15; H _1 -0.14; H _2 -0.14; # @ C _1 0.23; C _2 0.23; H _1 -0.15; H _2 -0.15; C _1 0.08; C _2 0.08; H _1 -0.12; H _2 -0.12; H _1 -0.13; H _2 -0.13; # ----------------------------------------------------------------------------- # K-S energy, electrons, error : -46.647668038900 69.9999810430 -1.90D-05 # @ 2 -381.949410128 1.05D+00 -3.04D-01 15 2 15 3 # Virial theorem: -V/T = 2.013393 # ... # # Wwith and without symmetry, the "Total energy" line is shifted a little. if "Iter" in line and "Total energy" in line: iteration = 0 converged = False values = [] if not hasattr(self, "scfvalues"): self.scfvalues = [] while not converged: line = next(inputfile) # each iteration is bracketed by "-------------" if "-------------------" in line: iteration += 1 continue # the first hit of @ n where n is the current iteration strcompare = "@{0:>3d}".format(iteration) if strcompare in line: temp = line.split() values.append([float(temp[2])]) #print(line.split()) if line[0] == "@" and "converged in" in line: converged = True self.scfvalues.append(values) # DALTON organizes the energies by symmetry, so we need to parser first, # and then sort the energies (and labels) before we store them. # # The formatting varies depending on RHF/DFT and/or version. Here is # an example from a DFT job: # # *** SCF orbital energy analysis *** # # Only the five lowest virtual orbital energies printed in each symmetry. # # Number of electrons : 70 # Orbital occupations : 15 2 15 3 # # Sym Kohn-Sham orbital energies # # 1 Ag -10.01616533 -10.00394288 -10.00288640 -10.00209612 -9.98818062 # -0.80583154 -0.71422407 -0.58487249 -0.55551093 -0.50630125 # ... # # Here is an example from an RHF job that only has symmetry group indices: # # *** SCF orbital energy analysis *** # # Only the five lowest virtual orbital energies printed in each symmetry. # # Number of electrons : 70 # Orbital occupations : 15 2 15 3 # # Sym Hartree-Fock orbital energies # # 1 -11.04052518 -11.03158921 -11.02882211 -11.02858563 -11.01747921 # -1.09029777 -0.97492511 -0.79988247 -0.76282547 -0.69677619 # ... # if "*** SCF orbital energy analysis ***" in line: # to get ALL orbital energies, the .PRINTLEVELS keyword needs # to be at least 0,10 (up from 0,5). I know, obvious, right? # this, however, will conflict with the scfvalues output that # changes into some weird form of DIIS debug output. mosyms = [] moenergies = [] self.skip_line(inputfile, 'blank') # if the second line has "Only the five" present in it, then we have a reduced # number of virtual orbital energies printed. If it does not, then it contains # the number of electrons. # DALTON 2015 increases the output to 20 virtuals, so only check # for a slightly smaller part of the string line = next(inputfile) if "Only the" in line: self.skip_line(inputfile, 'blank') line = next(inputfile) nelectrons = int(line.split()[-1]) line = next(inputfile) occupations = [int(o) for o in line.split()[3:]] nsym = len(occupations) self.skip_lines(inputfile, ['b', 'header', 'b']) # now parse nsym symmetries for isym in range(nsym): # For unoccupied symmetries, nothing is printed here. if occupations[isym] == 0: continue # When there are exactly five energies printed (on just one line), it seems # an extra blank line is printed after a block. line = next(inputfile) if not line.strip(): line = next(inputfile) cols = line.split() # The first line has the orbital symmetry information, but sometimes # it's the label and sometimes it's the index. There are always five # energies per line, though, so we can deduce if we have the labels or # not just the index. In the latter case, we depend on the labels # being read earlier into the list `symlabels`. if 'A' in cols[1] or 'B' in cols[1]: sym = self.normalisesym(cols[1]) energies = [float(t) for t in cols[2:]] else: sym = self.normalisesym(self.symlabels[int(cols[0]) - 1]) energies = [float(t) for t in cols[1:]] while len(energies) > 0: moenergies.extend(energies) mosyms.extend(len(energies)*[sym]) line = next(inputfile) energies = [float(col) for col in line.split()] # now sort the data about energies and symmetries. see the following post for the magic # http://stackoverflow.com/questions/19339/a-transpose-unzip-function-in-python-inverse-of-zip sdata = sorted(zip(moenergies, mosyms), key=lambda x: x[0]) moenergies, mosyms = zip(*sdata) self.moenergies = [[]] self.moenergies[0] = moenergies self.mosyms = [[]] self.mosyms[0] = mosyms if not hasattr(self, "nmo"): self.nmo = self.nbasis if len(self.moenergies[0]) != self.nmo: self.set_attribute('nmo', len(self.moenergies[0])) #self.mocoeffs = [numpy.zeros((self.nmo, self.nbasis), "d")] # .-----------------------------------. # | >>> Final results from SIRIUS <<< | # `-----------------------------------' # # # @ Spin multiplicity: 1 # @ Spatial symmetry: 1 ( irrep Ag in C2h ) # @ Total charge of molecule: 0 # # @ Final DFT energy: -382.050716652387 # @ Nuclear repulsion: 445.936979976608 # @ Electronic energy: -827.987696628995 # # @ Final gradient norm: 0.000003746706 # ... # if "Final DFT energy" in line or "Final HF energy" in line: if not hasattr(self, "scfenergies"): self.scfenergies = [] temp = line.split() self.scfenergies.append(utils.convertor(float(temp[-1]), "hartree", "eV")) if "@ = MP2 second order energy" in line: energ = utils.convertor(float(line.split()[-1]), 'hartree', 'eV') if not hasattr(self, "mpenergies"): self.mpenergies = [] self.mpenergies.append([]) self.mpenergies[-1].append(energ) if "Total energy CCSD(T)" in line: energ = utils.convertor(float(line.split()[-1]), 'hartree', 'eV') if not hasattr(self, "ccenergies"): self.ccenergies = [] self.ccenergies.append(energ) # The molecular geometry requires the use of .RUN PROPERTIES in the input. # Note that the second column is not the nuclear charge, but the atom type # index used internally by DALTON. # # Molecular geometry (au) # ----------------------- # # C _1 1.3498778652 2.3494125195 0.0000000000 # C _2 -1.3498778652 -2.3494125195 0.0000000000 # C _1 2.6543517307 0.0000000000 0.0000000000 # ... # if "Molecular geometry (au)" in line: if not hasattr(self, "atomcoords"): self.atomcoords = [] if self.firststdorient: self.firststdorient = False line = next(inputfile) line = next(inputfile) #line = next(inputfile) atomcoords = [] for i in range(self.natom): line = next(inputfile) temp = line.split() # if symmetry has been enabled, extra labels are printed. if not, the list is one shorter coords = [1, 2, 3] try: float(temp[1]) except ValueError: coords = [2, 3, 4] atomcoords.append([utils.convertor(float(temp[i]), "bohr", "Angstrom") for i in coords]) self.atomcoords.append(atomcoords) # ------------------------------------------------- # extract the center of mass line if "Center-of-mass coordinates (a.u.):" in line: temp = line.split() reference = [utils.convertor(float(temp[i]), "bohr", "Angstrom") for i in [3, 4, 5]] if not hasattr(self, 'moments'): self.moments = [reference] # ------------------------------------------------- # Extract the dipole moment if "Dipole moment components" in line: dipole = numpy.zeros(3) line = next(inputfile) line = next(inputfile) line = next(inputfile) if not "zero by symmetry" in line: line = next(inputfile) line = next(inputfile) temp = line.split() for i in range(3): dipole[i] = float(temp[2]) # store the Debye value if hasattr(self, 'moments'): self.moments.append(dipole) if __name__ == "__main__": import doctest, daltonparser, sys if len(sys.argv) == 1: doctest.testmod(daltonparser, verbose=False) if len(sys.argv) >= 2: parser = daltonparser.DALTON(sys.argv[1]) data = parser.parse() if len(sys.argv) > 2: for i in range(len(sys.argv[2:])): if hasattr(data, sys.argv[2 + i]): print(getattr(data, sys.argv[2 + i])) GaussSum-3.0.1/gausssum/cclib/parser/adfparser.py0000664000175000017500000013764012660404041020510 0ustar noelnoel# -*- coding: utf-8 -*- # # This file is part of cclib (http://cclib.github.io), a library for parsing # and interpreting the results of computational chemistry packages. # # Copyright (C) 2006-2014, the cclib development team # # The library is free software, distributed under the terms of # the GNU Lesser General Public version 2.1 or later. You should have # received a copy of the license along with cclib. You can also access # the full license online at http://www.gnu.org/copyleft/lgpl.html. """Parser for ADF output files""" from __future__ import print_function import re import numpy from . import logfileparser from . import utils class ADF(logfileparser.Logfile): """An ADF log file""" def __init__(self, *args, **kwargs): # Call the __init__ method of the superclass super(ADF, self).__init__(logname="ADF", *args, **kwargs) def __str__(self): """Return a string representation of the object.""" return "ADF log file %s" % (self.filename) def __repr__(self): """Return a representation of the object.""" return 'ADF("%s")' % (self.filename) def normalisesym(self, label): """Use standard symmetry labels instead of ADF labels. To normalise: (1) any periods are removed (except in the case of greek letters) (2) XXX is replaced by X, and a " added. (3) XX is replaced by X, and a ' added. (4) The greek letters Sigma, Pi, Delta and Phi are replaced by their lowercase equivalent. >>> sym = ADF("dummyfile").normalisesym >>> labels = ['A','s','A1','A1.g','Sigma','Pi','Delta','Phi','Sigma.g','A.g','AA','AAA','EE1','EEE1'] >>> map(sym,labels) ['A', 's', 'A1', 'A1g', 'sigma', 'pi', 'delta', 'phi', 'sigma.g', 'Ag', "A'", 'A"', "E1'", 'E1"'] """ greeks = ['Sigma', 'Pi', 'Delta', 'Phi'] for greek in greeks: if label.startswith(greek): return label.lower() ans = label.replace(".", "") if ans[1:3] == "''": temp = ans[0] + '"' ans = temp l = len(ans) if l > 1 and ans[0] == ans[1]: # Python only tests the second condition if the first is true if l > 2 and ans[1] == ans[2]: ans = ans.replace(ans[0]*3, ans[0]) + '"' else: ans = ans.replace(ans[0]*2, ans[0]) + "'" return ans def normalisedegenerates(self, label, num, ndict=None): """Generate a string used for matching degenerate orbital labels To normalise: (1) if label is E or T, return label:num (2) if label is P or D, look up in dict, and return answer """ if not ndict: ndict = { 'P': {0:"P:x", 1:"P:y", 2:"P:z"}, \ 'D': {0:"D:z2", 1:"D:x2-y2", 2:"D:xy", 3:"D:xz", 4:"D:yz"}} if label in ndict: if num in ndict[label]: return ndict[label][num] else: return "%s:%i" % (label, num+1) else: return "%s:%i" % (label, num+1) def before_parsing(self): # Used to avoid extracting the final geometry twice in a GeoOpt self.NOTFOUND, self.GETLAST, self.NOMORE = list(range(3)) self.finalgeometry = self.NOTFOUND # Used for calculating the scftarget (variables names taken from the ADF manual) self.accint = self.SCFconv = self.sconv2 = None # keep track of nosym and unrestricted case to parse Energies since it doens't have an all Irreps section self.nosymflag = False self.unrestrictedflag = False SCFCNV, SCFCNV2 = list(range(2)) #used to index self.scftargets[] maxelem, norm = list(range(2)) # used to index scf.values def extract(self, inputfile, line): """Extract information from the file object inputfile.""" # If a file contains multiple calculations, currently we want to print a warning # and skip to the end of the file, since cclib parses only the main system, which # is usually the largest. Here we test this by checking if scftargets has already # been parsed when another INPUT FILE segment is found, although this might # not always be the best indicator. if line.strip() == "(INPUT FILE)" and hasattr(self, "scftargets"): self.logger.warning("Skipping remaining calculations") inputfile.seek(0, 2) return # We also want to check to make sure we aren't parsing "Create" jobs, # which normally come before the calculation we actually want to parse. if line.strip() == "(INPUT FILE)": while True: self.updateprogress(inputfile, "Unsupported Information", self.fupdate) line = next(inputfile) if line.strip() == "(INPUT FILE)" else None if line and not line[:6] in ("Create", "create"): break line = next(inputfile) # In ADF 2014.01, there are (INPUT FILE) messages, so we need to use just # the lines that start with 'Create' and run until the title or something # else we are sure is is the calculation proper. It would be good to combine # this with the previous block, if possible. if line[:6] == "Create": while line[:5] != "title": line = inputfile.next() if line[1:10] == "Symmetry:": info = line.split() if info[1] == "NOSYM": self.nosymflag = True # Use this to read the subspecies of irreducible representations. # It will be a list, with each element representing one irrep. if line.strip() == "Irreducible Representations, including subspecies": self.skip_line(inputfile, 'dashes') self.irreps = [] line = next(inputfile) while line.strip() != "": self.irreps.append(line.split()) line = next(inputfile) if line[4:13] == 'Molecule:': info = line.split() if info[1] == 'UNrestricted': self.unrestrictedflag = True if line[1:6] == "ATOMS": # Find the number of atoms and their atomic numbers # Also extract the starting coordinates (for a GeoOpt anyway) # and the atommasses (previously called vibmasses) self.updateprogress(inputfile, "Attributes", self.cupdate) self.atomcoords = [] self.skip_lines(inputfile, ['header1', 'header2', 'header3']) atomnos = [] atommasses = [] atomcoords = [] coreelectrons = [] line = next(inputfile) while len(line)>2: #ensure that we are reading no blank lines info = line.split() element = info[1].split('.')[0] atomnos.append(self.table.number[element]) atomcoords.append(list(map(float, info[2:5]))) coreelectrons.append(int(float(info[5]) - float(info[6]))) atommasses.append(float(info[7])) line = next(inputfile) self.atomcoords.append(atomcoords) self.set_attribute('natom', len(atomnos)) self.set_attribute('atomnos', atomnos) self.set_attribute('atommasses', atommasses) self.set_attribute('coreelectrons', coreelectrons) if line[1:10] == "FRAGMENTS": header = next(inputfile) self.frags = [] self.fragnames = [] line = next(inputfile) while len(line) > 2: #ensure that we are reading no blank lines info = line.split() if len(info) == 7: #fragment name is listed here self.fragnames.append("%s_%s" % (info[1], info[0])) self.frags.append([]) self.frags[-1].append(int(info[2]) - 1) elif len(info) == 5: #add atoms into last fragment self.frags[-1].append(int(info[0]) - 1) line = next(inputfile) # Extract charge if line[1:11] == "Net Charge": charge = int(line.split()[2]) self.set_attribute('charge', charge) line = next(inputfile) if len(line.strip()): # Spin polar: 1 (Spin_A minus Spin_B electrons) # (Not sure about this for higher multiplicities) mult = int(line.split()[2]) + 1 else: mult = 1 self.set_attribute('mult', mult) if line[1:22] == "S C F U P D A T E S": # find targets for SCF convergence if not hasattr(self,"scftargets"): self.scftargets = [] self.skip_lines(inputfile, ['e', 'b', 'numbers']) line = next(inputfile) self.SCFconv = float(line.split()[-1]) line = next(inputfile) self.sconv2 = float(line.split()[-1]) # In ADF 2013, the default numerical integration method is fuzzy cells, # although it used to be Voronoi polyhedra. Both methods apparently set # the accint parameter, although the latter does so indirectly, based on # a 'grid quality' setting. This is translated into accint using a # dictionary with values taken from the documentation. if "Numerical Integration : Voronoi Polyhedra (Te Velde)" in line: self.integration_method = "voronoi_polyhedra" if line[1:27] == 'General Accuracy Parameter': # Need to know the accuracy of the integration grid to # calculate the scftarget...note that it changes with time self.accint = float(line.split()[-1]) if "Numerical Integration : Fuzzy Cells (Becke)" in line: self.integration_method = 'fuzzy_cells' if line[1:19] == "Becke grid quality": self.grid_quality = line.split()[-1] quality2accint = { 'BASIC' : 2.0, 'NORMAL' : 4.0, 'GOOD' : 6.0, 'VERYGOOD' : 8.0, 'EXCELLENT' : 10.0, } self.accint = quality2accint[self.grid_quality] # Half of the atomic orbital overlap matrix is printed since it is symmetric, # but this requires "PRINT Smat" to be in the input. There are extra blank lines # at the end of the block, which are used to terminate the parsing. # # ====== smat # # column 1 2 3 4 # row # 1 1.00000000000000E+00 # 2 2.43370854175315E-01 1.00000000000000E+00 # 3 0.00000000000000E+00 0.00000000000000E+00 1.00000000000000E+00 # ... # if "====== smat" in line: # Initialize the matrix with Nones so we can easily check all has been parsed. overlaps = [[None] * self.nbasis for i in range(self.nbasis)] self.skip_line(inputfile, 'blank') line = inputfile.next() while line.strip(): colline = line assert colline.split()[0] == "column" columns = [int(i) for i in colline.split()[1:]] rowline = inputfile.next() assert rowline.strip() == "row" line = inputfile.next() while line.strip(): i = int(line.split()[0]) vals = [float(col) for col in line.split()[1:]] for j, o in enumerate(vals): k = columns[j] overlaps[k-1][i-1] = o overlaps[i-1][k-1] = o line = inputfile.next() line = inputfile.next() # Now all values should be parsed, and so no Nones remaining. assert all([all([x != None for x in ao]) for ao in overlaps]) self.set_attribute('aooverlaps', overlaps) if line[1:11] == "CYCLE 1": self.updateprogress(inputfile, "QM convergence", self.fupdate) newlist = [] line = next(inputfile) if not hasattr(self,"geovalues"): # This is the first SCF cycle self.scftargets.append([self.sconv2*10, self.sconv2]) elif self.finalgeometry in [self.GETLAST, self.NOMORE]: # This is the final SCF cycle self.scftargets.append([self.SCFconv*10, self.SCFconv]) else: # This is an intermediate SCF cycle in a geometry optimization, # in which case the SCF convergence target needs to be derived # from the accint parameter. For Voronoi polyhedra integration, # accint is printed and parsed. For fuzzy cells, it can be inferred # from the grid quality setting, as is done somewhere above. if self.accint: oldscftst = self.scftargets[-1][1] grdmax = self.geovalues[-1][1] scftst = max(self.SCFconv, min(oldscftst, grdmax/30, 10**(-self.accint))) self.scftargets.append([scftst*10, scftst]) while line.find("SCF CONVERGED") == -1 and line.find("SCF not fully converged, result acceptable") == -1 and line.find("SCF NOT CONVERGED") == -1: if line[4:12] == "SCF test": if not hasattr(self, "scfvalues"): self.scfvalues = [] info = line.split() newlist.append([float(info[4]), abs(float(info[6]))]) try: line = next(inputfile) except StopIteration: #EOF reached? self.logger.warning("SCF did not converge, so attributes may be missing") break if line.find("SCF not fully converged, result acceptable") > 0: self.logger.warning("SCF not fully converged, results acceptable") if line.find("SCF NOT CONVERGED") > 0: self.logger.warning("SCF did not converge! moenergies and mocoeffs are unreliable") if hasattr(self, "scfvalues"): self.scfvalues.append(newlist) # Parse SCF energy for SP calcs from bonding energy decomposition section. # It seems ADF does not print it earlier for SP calculations. # Geometry optimization runs also print this, and we want to parse it # for them, too, even if it repeats the last "Geometry Convergence Tests" # section (but it's usually a bit different). if line[:21] == "Total Bonding Energy:": if not hasattr(self, "scfenergies"): self.scfenergies = [] energy = utils.convertor(float(line.split()[3]), "hartree", "eV") self.scfenergies.append(energy) if line[51:65] == "Final Geometry": self.finalgeometry = self.GETLAST # Get the coordinates from each step of the GeoOpt. if line[1:24] == "Coordinates (Cartesian)" and self.finalgeometry in [self.NOTFOUND, self.GETLAST]: self.skip_lines(inputfile, ['e', 'b', 'title', 'title', 'd']) atomcoords = [] line = next(inputfile) while list(set(line.strip())) != ['-']: atomcoords.append(list(map(float, line.split()[5:8]))) line = next(inputfile) if not hasattr(self, "atomcoords"): self.atomcoords = [] self.atomcoords.append(atomcoords) # Don't get any more coordinates in this case. # KML: I think we could combine this with optdone (see below). if self.finalgeometry == self.GETLAST: self.finalgeometry = self.NOMORE # There have been some changes in the format of the geometry convergence information, # and this is how it is printed in older versions (2007.01 unit tests). # # ========================== # Geometry Convergence Tests # ========================== # # Energy old : -5.14170647 # new : -5.15951374 # # Convergence tests: # (Energies in hartree, Gradients in hartree/angstr or radian, Lengths in angstrom, Angles in degrees) # # Item Value Criterion Conv. Ratio # ------------------------------------------------------------------------- # change in energy -0.01780727 0.00100000 NO 0.00346330 # gradient max 0.03219530 0.01000000 NO 0.30402650 # gradient rms 0.00858685 0.00666667 NO 0.27221261 # cart. step max 0.07674971 0.01000000 NO 0.75559435 # cart. step rms 0.02132310 0.00666667 NO 0.55335378 # if line[1:27] == 'Geometry Convergence Tests': if not hasattr(self, "geotargets"): self.geovalues = [] self.geotargets = numpy.array([0.0, 0.0, 0.0, 0.0, 0.0], "d") if not hasattr(self, "scfenergies"): self.scfenergies = [] self.skip_lines(inputfile, ['e', 'b']) energies_old = next(inputfile) energies_new = next(inputfile) self.scfenergies.append(utils.convertor(float(energies_new.split()[-1]), "hartree", "eV")) self.skip_lines(inputfile, ['b', 'convergence', 'units', 'b', 'header', 'd']) values = [] for i in range(5): temp = next(inputfile).split() self.geotargets[i] = float(temp[-3]) values.append(float(temp[-4])) self.geovalues.append(values) # This is to make geometry optimization always have the optdone attribute, # even if it is to be empty for unconverged runs. if not hasattr(self, 'optdone'): self.optdone = [] # After the test, there is a message if the search is converged: # # *************************************************************************************************** # Geometry CONVERGED # *************************************************************************************************** # if line.strip() == "Geometry CONVERGED": self.skip_line(inputfile, 'stars') self.optdone.append(len(self.geovalues) - 1) # Here is the corresponding geometry convergence info from the 2013.01 unit test. # Note that the step number is given, which it will be prudent to use in an assertion. # #---------------------------------------------------------------------- #Geometry Convergence after Step 3 (Hartree/Angstrom,Angstrom) #---------------------------------------------------------------------- #current energy -5.16274478 Hartree #energy change -0.00237544 0.00100000 F #constrained gradient max 0.00884999 0.00100000 F #constrained gradient rms 0.00249569 0.00066667 F #gradient max 0.00884999 #gradient rms 0.00249569 #cart. step max 0.03331296 0.01000000 F #cart. step rms 0.00844037 0.00666667 F if line[:31] == "Geometry Convergence after Step": stepno = int(line.split()[4]) # This is to make geometry optimization always have the optdone attribute, # even if it is to be empty for unconverged runs. if not hasattr(self, 'optdone'): self.optdone = [] # The convergence message is inline in this block, not later as it was before. if "** CONVERGED **" in line: if not hasattr(self, 'optdone'): self.optdone = [] self.optdone.append(len(self.geovalues) - 1) self.skip_line(inputfile, 'dashes') current_energy = next(inputfile) energy_change = next(inputfile) constrained_gradient_max = next(inputfile) constrained_gradient_rms = next(inputfile) gradient_max = next(inputfile) gradient_rms = next(inputfile) cart_step_max = next(inputfile) cart_step_rms = next(inputfile) if not hasattr(self, "scfenergies"): self.scfenergies = [] energy = utils.convertor(float(current_energy.split()[-2]), "hartree", "eV") self.scfenergies.append(energy) if not hasattr(self, "geotargets"): self.geotargets = numpy.array([0.0, 0.0, 0.0, 0.0, 0.0], "d") self.geotargets[0] = float(energy_change.split()[-2]) self.geotargets[1] = float(constrained_gradient_max.split()[-2]) self.geotargets[2] = float(constrained_gradient_rms.split()[-2]) self.geotargets[3] = float(cart_step_max.split()[-2]) self.geotargets[4] = float(cart_step_rms.split()[-2]) if not hasattr(self, "geovalues"): self.geovalues = [] self.geovalues.append([]) self.geovalues[-1].append(float(energy_change.split()[-3])) self.geovalues[-1].append(float(constrained_gradient_max.split()[-3])) self.geovalues[-1].append(float(constrained_gradient_rms.split()[-3])) self.geovalues[-1].append(float(cart_step_max.split()[-3])) self.geovalues[-1].append(float(cart_step_rms.split()[-3])) if line.find('Orbital Energies, per Irrep and Spin') > 0 and not hasattr(self, "mosyms") and self.nosymflag and not self.unrestrictedflag: #Extracting orbital symmetries and energies, homos for nosym case #Should only be for restricted case because there is a better text block for unrestricted and nosym self.mosyms = [[]] self.moenergies = [[]] self.skip_lines(inputfile, ['e', 'header', 'd', 'label']) line = next(inputfile) info = line.split() if not info[0] == '1': self.logger.warning("MO info up to #%s is missing" % info[0]) #handle case where MO information up to a certain orbital are missing while int(info[0]) - 1 != len(self.moenergies[0]): self.moenergies[0].append(99999) self.mosyms[0].append('A') homoA = None while len(line) > 10: info = line.split() self.mosyms[0].append('A') self.moenergies[0].append(utils.convertor(float(info[2]), 'hartree', 'eV')) if info[1] == '0.000' and not hasattr(self, 'homos'): self.set_attribute('homos', [len(self.moenergies[0]) - 2]) line = next(inputfile) self.moenergies = [numpy.array(self.moenergies[0], "d")] if line[1:29] == 'Orbital Energies, both Spins' and not hasattr(self, "mosyms") and self.nosymflag and self.unrestrictedflag: #Extracting orbital symmetries and energies, homos for nosym case #should only be here if unrestricted and nosym self.mosyms = [[], []] moenergies = [[], []] self.skip_lines(inputfile, ['d', 'b', 'header', 'd']) homoa = 0 homob = None line = next(inputfile) while len(line) > 5: info = line.split() if info[2] == 'A': self.mosyms[0].append('A') moenergies[0].append(utils.convertor(float(info[4]), 'hartree', 'eV')) if info[3] != '0.00': homoa = len(moenergies[0]) - 1 elif info[2] == 'B': self.mosyms[1].append('A') moenergies[1].append(utils.convertor(float(info[4]), 'hartree', 'eV')) if info[3] != '0.00': homob = len(moenergies[1]) - 1 else: print(("Error reading line: %s" % line)) line = next(inputfile) self.moenergies = [numpy.array(x, "d") for x in moenergies] self.set_attribute('homos', [homoa, homob]) # Extracting orbital symmetries and energies, homos. if line[1:29] == 'Orbital Energies, all Irreps' and not hasattr(self, "mosyms"): self.symlist = {} self.mosyms = [[]] self.moenergies = [[]] self.skip_lines(inputfile, ['e', 'b', 'header', 'd']) homoa = None homob = None #multiple = {'E':2, 'T':3, 'P':3, 'D':5} # The above is set if there are no special irreps names = [irrep[0].split(':')[0] for irrep in self.irreps] counts = [len(irrep) for irrep in self.irreps] multiple = dict(list(zip(names, counts))) irrepspecies = {} for n in range(len(names)): indices = list(range(counts[n])) subspecies = self.irreps[n] irrepspecies[names[n]] = dict(list(zip(indices, subspecies))) line = next(inputfile) while line.strip(): info = line.split() if len(info) == 5: #this is restricted #count = multiple.get(info[0][0],1) count = multiple.get(info[0], 1) for repeat in range(count): # i.e. add E's twice, T's thrice self.mosyms[0].append(self.normalisesym(info[0])) self.moenergies[0].append(utils.convertor(float(info[3]), 'hartree', 'eV')) sym = info[0] if count > 1: # add additional sym label sym = self.normalisedegenerates(info[0], repeat, ndict=irrepspecies) try: self.symlist[sym][0].append(len(self.moenergies[0])-1) except KeyError: self.symlist[sym] = [[]] self.symlist[sym][0].append(len(self.moenergies[0])-1) if info[2] == '0.00' and not hasattr(self, 'homos'): self.homos = [len(self.moenergies[0]) - (count + 1)] #count, because need to handle degenerate cases line = next(inputfile) elif len(info) == 6: #this is unrestricted if len(self.moenergies) < 2: #if we don't have space, create it self.moenergies.append([]) self.mosyms.append([]) # count = multiple.get(info[0][0], 1) count = multiple.get(info[0], 1) if info[2] == 'A': for repeat in range(count): # i.e. add E's twice, T's thrice self.mosyms[0].append(self.normalisesym(info[0])) self.moenergies[0].append(utils.convertor(float(info[4]), 'hartree', 'eV')) sym = info[0] if count > 1: #add additional sym label sym = self.normalisedegenerates(info[0], repeat) try: self.symlist[sym][0].append(len(self.moenergies[0])-1) except KeyError: self.symlist[sym] = [[], []] self.symlist[sym][0].append(len(self.moenergies[0])-1) if info[3] == '0.00' and homoa == None: homoa = len(self.moenergies[0]) - (count + 1) #count because degenerate cases need to be handled if info[2] == 'B': for repeat in range(count): # i.e. add E's twice, T's thrice self.mosyms[1].append(self.normalisesym(info[0])) self.moenergies[1].append(utils.convertor(float(info[4]), 'hartree', 'eV')) sym = info[0] if count > 1: #add additional sym label sym = self.normalisedegenerates(info[0], repeat) try: self.symlist[sym][1].append(len(self.moenergies[1])-1) except KeyError: self.symlist[sym] = [[], []] self.symlist[sym][1].append(len(self.moenergies[1])-1) if info[3] == '0.00' and homob == None: homob = len(self.moenergies[1]) - (count + 1) line = next(inputfile) else: #different number of lines print(("Error", info)) if len(info) == 6: #still unrestricted, despite being out of loop self.set_attribute('homos', [homoa, homob]) self.moenergies = [numpy.array(x, "d") for x in self.moenergies] # Section on extracting vibdisps # Also contains vibfreqs, but these are extracted in the # following section (see below) if line[1:28] == "Vibrations and Normal Modes": self.vibdisps = [] self.skip_lines(inputfile, ['e', 'b', 'header', 'header', 'b', 'b']) freqs = next(inputfile) while freqs.strip()!="": minus = next(inputfile) p = [ [], [], [] ] for i in range(len(self.atomnos)): broken = list(map(float, next(inputfile).split()[1:])) for j in range(0, len(broken), 3): p[j//3].append(broken[j:j+3]) self.vibdisps.extend(p[:(len(broken)//3)]) self.skip_lines(inputfile, ['b', 'b']) freqs = next(inputfile) self.vibdisps = numpy.array(self.vibdisps, "d") if line[1:24] == "List of All Frequencies": # Start of the IR/Raman frequency section self.updateprogress(inputfile, "Frequency information", self.fupdate) # self.vibsyms = [] # Need to look into this a bit more self.vibirs = [] self.vibfreqs = [] for i in range(8): line = next(inputfile) line = next(inputfile).strip() while line: temp = line.split() self.vibfreqs.append(float(temp[0])) self.vibirs.append(float(temp[2])) # or is it temp[1]? line = next(inputfile).strip() self.vibfreqs = numpy.array(self.vibfreqs, "d") self.vibirs = numpy.array(self.vibirs, "d") if hasattr(self, "vibramans"): self.vibramans = numpy.array(self.vibramans, "d") #******************************************************************************************************************8 #delete this after new implementation using smat, eigvec print,eprint? # Extract the number of basis sets if line[1:49] == "Total nr. of (C)SFOs (summation over all irreps)": nbasis = int(line.split(":")[1].split()[0]) self.set_attribute('nbasis', nbasis) # now that we're here, let's extract aonames self.fonames = [] self.start_indeces = {} self.skip_line(inputfile, 'blank') note = next(inputfile) symoffset = 0 self.skip_line(inputfile, 'blank') line = next(inputfile) if len(line) > 2: #fix for ADF2006.01 as it has another note self.skip_line(inputfile, 'blank') line = next(inputfile) self.skip_line(inputfile, 'blank') self.nosymreps = [] while len(self.fonames) < self.nbasis: symline = next(inputfile) sym = symline.split()[1] line = next(inputfile) num = int(line.split(':')[1].split()[0]) self.nosymreps.append(num) #read until line "--------..." is found while line.find('-----') < 0: line = next(inputfile) line = next(inputfile) # the start of the first SFO while len(self.fonames) < symoffset + num: info = line.split() #index0 index1 occ2 energy3/4 fragname5 coeff6 orbnum7 orbname8 fragname9 if not sym in list(self.start_indeces.keys()): #have we already set the start index for this symmetry? self.start_indeces[sym] = int(info[1]) orbname = info[8] orbital = info[7] + orbname.replace(":", "") fragname = info[5] frag = fragname + info[9] coeff = float(info[6]) line = next(inputfile) while line.strip() and not line[:7].strip(): # while it's the same SFO # i.e. while not completely blank, but blank at the start info = line[43:].split() if len(info)>0: # len(info)==0 for the second line of dvb_ir.adfout frag += "+" + fragname + info[-1] coeff = float(info[-4]) if coeff < 0: orbital += '-' + info[-3] + info[-2].replace(":", "") else: orbital += '+' + info[-3] + info[-2].replace(":", "") line = next(inputfile) # At this point, we are either at the start of the next SFO or at # a blank line...the end self.fonames.append("%s_%s" % (frag, orbital)) symoffset += num # blankline blankline next(inputfile); next(inputfile) if line[1:32] == "S F O P O P U L A T I O N S ,": #Extract overlap matrix # self.fooverlaps = numpy.zeros((self.nbasis, self.nbasis), "d") symoffset = 0 for nosymrep in self.nosymreps: line = next(inputfile) while line.find('===') < 10: #look for the symmetry labels line = next(inputfile) self.skip_lines(inputfile, ['b', 'b']) text = next(inputfile) if text[13:20] != "Overlap": # verify this has overlap info break self.skip_lines(inputfile, ['b', 'col', 'row']) if not hasattr(self,"fooverlaps"): # make sure there is a matrix to store this self.fooverlaps = numpy.zeros((self.nbasis, self.nbasis), "d") base = 0 while base < nosymrep: #have we read all the columns? for i in range(nosymrep - base): self.updateprogress(inputfile, "Overlap", self.fupdate) line = next(inputfile) parts = line.split()[1:] for j in range(len(parts)): k = float(parts[j]) self.fooverlaps[base + symoffset + j, base + symoffset +i] = k self.fooverlaps[base + symoffset + i, base + symoffset + j] = k #blank, blank, column for i in range(3): next(inputfile) base += 4 symoffset += nosymrep base = 0 # The commented code below makes the atombasis attribute based on the BAS function in ADF, # but this is probably not so useful, since SFOs are used to build MOs in ADF. # if line[1:54] == "BAS: List of all Elementary Cartesian Basis Functions": # # self.atombasis = [] # # # There will be some text, followed by a line: # # (power of) X Y Z R Alpha on Atom # while not line[1:11] == "(power of)": # line = inputfile.next() # dashes = inputfile.next() # blank = inputfile.next() # line = inputfile.next() # # There will be two blank lines when there are no more atom types. # while line.strip() != "": # atoms = [int(i)-1 for i in line.split()[1:]] # for n in range(len(atoms)): # self.atombasis.append([]) # dashes = inputfile.next() # line = inputfile.next() # while line.strip() != "": # indices = [int(i)-1 for i in line.split()[5:]] # for i in range(len(indices)): # self.atombasis[atoms[i]].append(indices[i]) # line = inputfile.next() # line = inputfile.next() if line[48:67] == "SFO MO coefficients": self.mocoeffs = [numpy.zeros((self.nbasis, self.nbasis), "d")] spin = 0 symoffset = 0 lastrow = 0 # Section ends with "1" at beggining of a line. while line[0] != "1": line = next(inputfile) # If spin is specified, then there will be two coefficient matrices. if line.strip() == "***** SPIN 1 *****": self.mocoeffs = [numpy.zeros((self.nbasis, self.nbasis), "d"), numpy.zeros((self.nbasis, self.nbasis), "d")] # Bump up the spin. if line.strip() == "***** SPIN 2 *****": spin = 1 symoffset = 0 lastrow = 0 # Next symmetry. if line.strip()[:4] == "=== ": sym = line.split()[1] if self.nosymflag: aolist = list(range(self.nbasis)) else: aolist = self.symlist[sym][spin] # Add to the symmetry offset of AO ordering. symoffset += lastrow # Blocks with coefficient always start with "MOs :". if line[1:6] == "MOs :": # Next line has the MO index contributed to. monumbers = [int(n) for n in line[6:].split()] self.skip_lines(inputfile, ['occup', 'label']) # The table can end with a blank line or "1". row = 0 line = next(inputfile) while not line.strip() in ["", "1"]: info = line.split() if int(info[0]) < self.start_indeces[sym]: #check to make sure we aren't parsing CFs line = next(inputfile) continue self.updateprogress(inputfile, "Coefficients", self.fupdate) row += 1 coeffs = [float(x) for x in info[1:]] moindices = [aolist[n-1] for n in monumbers] # The AO index is 1 less than the row. aoindex = symoffset + row - 1 for i in range(len(monumbers)): self.mocoeffs[spin][moindices[i], aoindex] = coeffs[i] line = next(inputfile) lastrow = row # ************************************************************************** # * * # * Final excitation energies from Davidson algorithm * # * * # ************************************************************************** # # Number of loops in Davidson routine = 20 # Number of matrix-vector multiplications = 24 # Type of excitations = SINGLET-SINGLET # # Symmetry B.u # # ... several blocks ... # # Normal termination of EXCITATION program part if line[4:53] == "Final excitation energies from Davidson algorithm": while line[1:9] != "Symmetry" and "Normal termination" not in line: line = next(inputfile) symm = self.normalisesym(line.split()[1]) # Excitation energies E in a.u. and eV, dE wrt prev. cycle, # oscillator strengths f in a.u. # # no. E/a.u. E/eV f dE/a.u. # ----------------------------------------------------- # 1 0.17084 4.6488 0.16526E-01 0.28E-08 # ... while line.split() != ['no.', 'E/a.u.', 'E/eV', 'f', 'dE/a.u.'] and "Normal termination" not in line: line = next(inputfile) self.skip_line(inputfile, 'dashes') etenergies = [] etoscs = [] etsyms = [] line = next(inputfile) while len(line) > 2: info = line.split() etenergies.append(utils.convertor(float(info[2]), "eV", "cm-1")) etoscs.append(float(info[3])) etsyms.append(symm) line = next(inputfile) # There is another section before this, with transition dipole moments, # but this should just skip past it. while line[1:53] != "Major MO -> MO transitions for the above excitations": line = next(inputfile) # Note that here, and later, the number of blank lines can vary between # version of ADF (extra lines are seen in 2013.01 unit tests, for example). self.skip_line(inputfile, 'blank') excitation_occupied = next(inputfile) header = next(inputfile) while not header.strip(): header = next(inputfile) header2 = next(inputfile) x_y_z = next(inputfile) line = next(inputfile) while not line.strip(): line = next(inputfile) # Before we start handeling transitions, we need to create mosyms # with indices; only restricted calcs are possible in ADF. counts = {} syms = [] for mosym in self.mosyms[0]: if list(counts.keys()).count(mosym) == 0: counts[mosym] = 1 else: counts[mosym] += 1 syms.append(str(counts[mosym]) + mosym) etsecs = [] printed_warning = False for i in range(len(etenergies)): etsec = [] info = line.split() while len(info) > 0: match = re.search('[^0-9]', info[1]) index1 = int(info[1][:match.start(0)]) text = info[1][match.start(0):] symtext = text[0].upper() + text[1:] sym1 = str(index1) + self.normalisesym(symtext) match = re.search('[^0-9]', info[3]) index2 = int(info[3][:match.start(0)]) text = info[3][match.start(0):] symtext = text[0].upper() + text[1:] sym2 = str(index2) + self.normalisesym(symtext) try: index1 = syms.index(sym1) except ValueError: if not printed_warning: self.logger.warning("Etsecs are not accurate!") printed_warning = True try: index2 = syms.index(sym2) except ValueError: if not printed_warning: self.logger.warning("Etsecs are not accurate!") printed_warning = True etsec.append([(index1, 0), (index2, 0), float(info[4])]) line = next(inputfile) info = line.split() etsecs.append(etsec) # Again, the number of blank lines between transition can vary. line = next(inputfile) while not line.strip(): line = next(inputfile) if not hasattr(self, "etenergies"): self.etenergies = etenergies else: self.etenergies += etenergies if not hasattr(self, "etoscs"): self.etoscs = etoscs else: self.etoscs += etoscs if not hasattr(self, "etsyms"): self.etsyms = etsyms else: self.etsyms += etsyms if not hasattr(self, "etsecs"): self.etsecs = etsecs else: self.etsecs += etsecs if "M U L L I K E N P O P U L A T I O N S" in line: if not hasattr(self, "atomcharges"): self.atomcharges = {} while line[1:5] != "Atom": line = next(inputfile) self.skip_line(inputfile, 'dashes') mulliken = [] line = next(inputfile) while line.strip(): mulliken.append(float(line.split()[2])) line = next(inputfile) self.atomcharges["mulliken"] = mulliken # Dipole moment is always printed after a point calculation, # and the reference point for this is always the origin (0,0,0) # and not necessarily the center of mass, as explained on the # ADF user mailing list (see cclib/cclib#113 for details). # # ============= # Dipole Moment *** (Debye) *** # ============= # # Vector : 0.00000000 0.00000000 0.00000000 # Magnitude: 0.00000000 # if line.strip()[:13] == "Dipole Moment": self.skip_line(inputfile, 'equals') # There is not always a blank line here, for example when the dipole and quadrupole # moments are printed after the multipole derived atomic charges. Still, to the best # of my knowledge (KML) the values are still in Debye. line = next(inputfile) if not line.strip(): line = next(inputfile) assert line.split()[0] == "Vector" dipole = [float(d) for d in line.split()[-3:]] reference = [0.0, 0.0, 0.0] if not hasattr(self, 'moments'): self.moments = [reference, dipole] else: try: assert self.moments[1] == dipole except AssertionError: self.logger.warning('Overwriting previous multipole moments with new values') self.moments = [reference, dipole] if __name__ == "__main__": import doctest, adfparser doctest.testmod(adfparser, verbose=False) GaussSum-3.0.1/gausssum/mpl.py0000664000175000017500000000374412660404041014756 0ustar noelnoel# # GaussSum (http://gausssum.sf.net) # Copyright (C) 2006-2013 Noel O'Boyle # # This program is free software; you can redistribute and/or modify it # under the terms of the GNU General Public License as published by the # Free Software Foundation; either version 2, or (at your option) any later # version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY, without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. from matplotlib.figure import Figure from matplotlib import rcParams from matplotlib import pyplot rcParams.update({'figure.autolayout': True}) # Uncomment the following for XKCD-style plots # pyplot.xkcd() colors = "bgrcmyk" class MPLPlot(object): def __init__(self): self.figure = Figure(figsize=(6,5), dpi=100) self.subplot = self.figure.add_subplot(111) self.cindex =0 def data(self, mtuples, lines=True, title=None, vlines=False, y2axis=""): color = colors[self.cindex] if y2axis: new_axis = self.subplot.twinx() new_axis.set_ylabel(y2axis) self.secondaxis = new_axis axis = new_axis else: axis = self.subplot if not mtuples: return xvals, yvals = zip(*mtuples) if not lines: axis.plot(xvals, yvals, "x", color=color, label=title) else: if not vlines: axis.plot(xvals, yvals, color=color, label=title) else: for i, (xval, yval) in enumerate(zip(xvals, yvals)): axis.vlines(xval, yval, 0, color=color, label=title if i==0 else "") self.cindex += 1 if self.cindex == len(colors): self.cindex = 0 def setlabels(self, xaxis, yaxis): self.subplot.set_xlabel(xaxis) self.subplot.set_ylabel(yaxis) GaussSum-3.0.1/gausssum/search.py0000664000175000017500000000254212660404041015426 0ustar noelnoel# # GaussSum (http://gausssum.sf.net) # Copyright (C) 2006-2009 Noel O'Boyle # # This program is free software; you can redistribute and/or modify it # under the terms of the GNU General Public License as published by the # Free Software Foundation; either version 2, or (at your option) any later # version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY, without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. def Search(screen, logfilename, searchstring, case_sensitive): screen.write("Starting to search\n") searchterm = searchstring.split("%") inputfile = open(logfilename, "r") if not case_sensitive: # change all the searchterms to lowercase temp=[] for x in searchterm: temp.append(x.lower()) searchterm=temp screenoutput = [] for line in inputfile: searchline = line if not case_sensitive: searchline = line.lower() for x in searchterm: if searchline.find(x) >= 0: screenoutput.append(line) break inputfile.close() screen.write("".join(screenoutput)) screen.write("Finished searching\n") GaussSum-3.0.1/mesh2.gif0000664000175000017500000001336312660404041013450 0ustar noelnoelGIF89aZC!!!!!!!!!!!!!)11))))!1!1)!!)!+#6#9- 91991!!!)!!1!!11!9)!)!))))1))9))91)))11)1111911919B!B)B1B1B9B9BBJ1J1J9JBJBR)W3ZBe<JJRJZJkJJJRRZRcRkRRRZZcZkZcckckkB1*A;5?1tbE&8L$홲cqrġ+Ƭ>1F|%玏5{FO`UMrV8XbbeL22|ⶍ;13fck?z2Lc `dc0c꤉fe1`r 3X $@` 5Cu cNt6@̐'&}Mט$Ly &>t|6qCP0`3!|LL2f 䐌7p7Uc(S'zq0HS7@L69w Ca̐]ǠMyL5c04cM'2$rt p )`a%Ѩ 9a@ :!Kz#,f1E}XB= qU;ġ!3 `SMV! -ҩQ $d TĢr uc<ڊ@ &y+DtCD`TCtv9&,TAUJO_iH!!@Hݰ^OWd`8C4c83 0͋ICwPjN9URAOρɴhJ Y  H縇=t[a+GXpy{& a pN #^ܪV%a4G*I-A fMk\MA^@$5BHny{bGrc+1hBSD$`8A `"%ɐxZd@! 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