{
    "info": {
        "author": "Chris Sewell",
        "author_email": "chrisj_sewell@hotmail.com",
        "bugtrack_url": null,
        "classifiers": [],
        "description": "PyGauss Computational Chemistry Analysis\r\n=============================================\r\n\r\n**Documentation**: https://pygauss.readthedocs.org\r\n\r\n**Conda Distro**: https://binstar.org/cjs14/pygauss\r\n\r\n**Project**: https://github.com/chrisjsewell/PyGauss\r\n\r\n.. image:: https://readthedocs.org/projects/pygauss/badge/?version=stable\r\n   :target: https://pygauss.readthedocs.org\r\n.. image:: https://img.shields.io/github/release/chrisjsewell/PyGauss.svg\r\n   :target: https://github.com/chrisjsewell/PyGauss\r\n\r\nPyGauss is intended as an interactive tool for supporting the \r\nlifecycle of a computational molecular chemistry investigation.\r\nFrom visual and analytical exploration, \r\nthrough to documentation and publication.\r\n\r\nIntitally PyGauss has been designed for the purpose of examining one or more\r\n`Gaussian <http://www.gaussian.com/>`__ quantum chemical\r\ncomputations, both **geometrically** and **electronically**. It is built on top of the\r\n`cclib <http://cclib.github.io/>`__/`chemview <http://chemview.readthedocs.org/en/latest/>`__/`chemlab <http://chemlab.readthedocs.org/en/latest/index.html>`__\r\nsuite of packages and python scientific stack though, and so should be extensible \r\nto other types of computational chemical analysis. \r\nPyGauss is primarily designed to be used interactively in the \r\n`IPython Notebook <http://ipython.org/notebook.html>`__. \r\n\r\nAs shown in the examples, a molecular optimisation can be assesed\r\nindividually (much like in\r\n`gaussview <http://www.gaussian.com/g_prod/gv5b.htm>`__), but also as\r\npart of a group. The advantages of this package are then:\r\n\r\n-  Faster, more efficient analysis\r\n-  Extensible analysis\r\n-  Reproducible analysis\r\n\r\nQuick Start\r\n----------------\r\n\r\nOSX and Linux\r\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\n\r\nThe recommended was to use pygauss is to download the\r\n`Anaconda <http://continuum.io/downloads>`__ Scientific Python\r\nDistribution (64-bit). Once downloaded a new environment can be created\r\nin terminal and pygauss installed:\r\n\r\n::\r\n\r\n    conda create -n pg_env -c https://conda.binstar.org/cjs14 pygauss\r\n\r\n\r\nWindows\r\n~~~~~~~~~~~~~~~~~~~~~~\r\n\r\nThere is currently no pygauss conda distributable for Windows or for\r\nchemlab, which has C-extensions that need to be built using a compiler. \r\nPlease see the documentation for guidance.\r\n\r\n\r\n\r\nExample Assessment\r\n--------------------\r\n\r\nAfter installing PyGauss you should be able to open this IPython\r\nNotebook from;\r\nhttps://github.com/chrisjsewell/PyGauss/blob/master/Example\\_Assessment.ipynb,\r\nand run the following...\r\n\r\n.. code:: python\r\n\r\n    from IPython.display import display, Image\r\n    %matplotlib inline\r\n    import pygauss as pg\r\n    print 'pygauss version: {}'.format(pg.__version__)\r\n\r\n\r\n.. parsed-literal::\r\n\r\n    pygauss version: 0.6.0\r\n    \r\n\r\nThe test folder has a number of example Gaussian outputs to play around\r\nwith.\r\n\r\n.. code:: python\r\n\r\n    folder = pg.get_test_folder()\r\n    len(folder.list_files())\r\n\r\n\r\n\r\n\r\n.. parsed-literal::\r\n\r\n    33\r\n\r\n\r\n\r\n**Note:** the *folder* object will act identical whether using a local\r\npath or one on a server over ssh (using\r\n`paramiko <http://www.paramiko.org/>`__):\r\n\r\n::\r\n\r\n    folder = pg.Folder('/path/to/folder', \r\n                    ssh_server='login.server.com',\r\n                    ssh_username='username')\r\n\r\nSingle Molecule Analysis\r\n~~~~~~~~~~~~~~~~~~~~~~~~\r\n\r\nA *molecule* can be created containg data about the inital geometry,\r\noptimisation process and analysis of the final configuration.\r\n\r\n.. code:: python\r\n\r\n    mol = pg.molecule.Molecule(folder_obj=folder,\r\n                    init_fname='CJS1_emim-cl_B_init.com', \r\n                    opt_fname=['CJS1_emim-cl_B_6-311+g-d-p-_gd3bj_opt-modredundant_difrz.log',\r\n                               'CJS1_emim-cl_B_6-311+g-d-p-_gd3bj_opt-modredundant_difrz_err.log',\r\n                               'CJS1_emim-cl_B_6-311+g-d-p-_gd3bj_opt-modredundant_unfrz.log'],\r\n                    freq_fname='CJS1_emim-cl_B_6-311+g-d-p-_gd3bj_freq_unfrz.log',\r\n                    nbo_fname='CJS1_emim-cl_B_6-311+g-d-p-_gd3bj_pop-nbo-full-_unfrz.log', \r\n                    atom_groups={'emim':range(20), 'cl':[20]},\r\n                    alignto=[3,2,1])\r\n\r\nGeometric Analysis\r\n^^^^^^^^^^^^^^^^^^\r\n\r\nMolecules can be viewed statically or interactively.\r\n\r\n.. code:: python\r\n\r\n    #mol.show_initial(active=True)\r\n    vdw = mol.show_initial(represent='vdw', rotations=[[0,0,90], [-90, 90, 0]])\r\n    ball_stick = mol.show_optimisation(represent='ball_stick', rotations=[[0,0,90], [-90, 90, 0]])\r\n    display(vdw, ball_stick)\r\n\r\n\r\n\r\n.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_9_0.png\r\n\r\n\r\n\r\n.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_9_1.png\r\n\r\n\r\n.. code:: python\r\n\r\n    print 'Cl optimised polar coords from aromatic ring : ({0}, {1},{2})'.format(\r\n        *[round(i, 2) for i in mol.calc_polar_coords_from_plane(20,3,2,1)])\r\n    ax = mol.plot_opt_trajectory(20, [3,2,1])\r\n    ax.set_title('Cl optimisation path')\r\n    ax.get_figure().set_size_inches(4, 3)\r\n\r\n\r\n.. parsed-literal::\r\n\r\n    Cl optimised polar coords from aromatic ring : (0.11, -116.42,-170.06)\r\n    \r\n\r\n\r\n.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_10_1.png\r\n\r\n\r\nEnergetics and Frequency Analysis\r\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n\r\n.. code:: python\r\n\r\n    print('Optimised? {0}, Conformer? {1}, Energy = {2} a.u.'.format(\r\n        mol.is_optimised(), mol.is_conformer(), \r\n        round(mol.get_opt_energy(units='hartree'),3)))\r\n    ax = mol.plot_opt_energy(units='hartree')\r\n    ax.get_figure().set_size_inches(3, 2)\r\n    ax = mol.plot_freq_analysis()\r\n    ax.get_figure().set_size_inches(4, 2)\r\n\r\n\r\n.. parsed-literal::\r\n\r\n    Optimised? True, Conformer? True, Energy = -805.105 a.u.\r\n    \r\n\r\n\r\n.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_12_1.png\r\n\r\n\r\n\r\n.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_12_2.png\r\n\r\n\r\nPotential Energy Scan analysis of geometric conformers...\r\n\r\n.. code:: python\r\n\r\n    mol2 = pg.molecule.Molecule(folder_obj=folder, alignto=[3,2,1],\r\n                pes_fname=['CJS_emim_6311_plus_d3_scan.log', \r\n                           'CJS_emim_6311_plus_d3_scan_bck.log'])   \r\n    ax = mol2.plot_pes_scans([1,4,9,10], rotation=[0,0,90], img_pos='local_maxs', zoom=0.5)\r\n    ax.set_title('Ethyl chain rotational conformer analysis')\r\n    ax.get_figure().set_size_inches(7, 3)\r\n\r\n\r\n\r\n.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_14_0.png\r\n\r\n\r\nPartial Charge Analysis\r\n^^^^^^^^^^^^^^^^^^^^^^^\r\n\r\nusing Natural Bond Orbital (NBO) analysis\r\n\r\n.. code:: python\r\n\r\n    print '+ve charge centre polar coords from aromatic ring: ({0} {1},{2})'.format(\r\n        *[round(i, 2) for i in mol.calc_nbo_charge_center(3, 2, 1)])\r\n    display(mol.show_nbo_charges(represent='ball_stick', axis_length=0.4, \r\n                                  rotations=[[0,0,90], [-90, 90, 0]]))\r\n\r\n\r\n.. parsed-literal::\r\n\r\n    +ve charge centre polar coords from aromatic ring: (0.02 -51.77,-33.15)\r\n    \r\n\r\n\r\n.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_16_1.png\r\n\r\n\r\nDensity of States Analysis\r\n^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n\r\n.. code:: python\r\n\r\n    print 'Number of Orbitals: {}'.format(mol.get_orbital_count())\r\n    homo, lumo = mol.get_orbital_homo_lumo()\r\n    homoe, lumoe = mol.get_orbital_energies([homo, lumo])\r\n    print 'HOMO at {} eV'.format(homoe)\r\n    print 'LUMO at {} eV'.format(lumoe)\r\n\r\n\r\n.. parsed-literal::\r\n\r\n    Number of Orbitals: 272\r\n    HOMO at -4.91492036773 eV\r\n    LUMO at -1.85989816817 eV\r\n    \r\n\r\n.. code:: python\r\n\r\n    ax = mol.plot_dos(per_energy=1, lbound=-20, ubound=10, legend_size=12)\r\n\r\n\r\n\r\n.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_19_0.png\r\n\r\n\r\nBonding Analysis\r\n^^^^^^^^^^^^^^^^\r\n\r\nUsing Second Order Perturbation Theory.\r\n\r\n.. code:: python\r\n\r\n    print 'H inter-bond energy = {} kJmol-1'.format(\r\n            mol.calc_hbond_energy(eunits='kJmol-1', atom_groups=['emim', 'cl']))\r\n    print 'Other inter-bond energy = {} kJmol-1'.format(\r\n        mol.calc_sopt_energy(eunits='kJmol-1', no_hbonds=True, atom_groups=['emim', 'cl']))\r\n    display(mol.show_sopt_bonds(min_energy=1, eunits='kJmol-1',\r\n                                atom_groups=['emim', 'cl'],\r\n                                no_hbonds=True,\r\n                                rotations=[[0, 0, 90]]))\r\n    display(mol.show_hbond_analysis(cutoff_energy=5.,alpha=0.6, \r\n                                    atom_groups=['emim', 'cl'],\r\n                                    rotations=[[0, 0, 90], [90, 0, 0]]))\r\n\r\n\r\n.. parsed-literal::\r\n\r\n    H inter-bond energy = 111.7128 kJmol-1\r\n    Other inter-bond energy = 11.00392 kJmol-1\r\n    \r\n\r\n\r\n.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_21_1.png\r\n\r\n\r\n\r\n.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_21_2.png\r\n\r\n\r\nMultiple Computations Analysis\r\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\n\r\nMultiple computations, for instance of different starting conformations,\r\ncan be grouped into an *Analysis* class and anlaysed collectively.\r\n\r\n.. code:: python\r\n\r\n    analysis = pg.Analysis(folder_obj=folder)\r\n    errors = analysis.add_runs(headers=['Cation', 'Anion', 'Initial'], \r\n                                   values=[['emim'], ['cl'],\r\n                                           ['B', 'BE', 'BM', 'F', 'FE']],\r\n                init_pattern='*{0}-{1}_{2}_init.com',\r\n                opt_pattern='*{0}-{1}_{2}_6-311+g-d-p-_gd3bj_opt*unfrz.log',\r\n                freq_pattern='*{0}-{1}_{2}_6-311+g-d-p-_gd3bj_freq*.log',\r\n                nbo_pattern='*{0}-{1}_{2}_6-311+g-d-p-_gd3bj_pop-nbo-full-*.log',\r\n                alignto=[3,2,1], atom_groups={'emim':range(1,20), 'cl':[20]}, \r\n                ipython_print=True)\r\n\r\n\r\n.. parsed-literal::\r\n\r\n    Reading data 5 of 5\r\n    \r\n\r\nMolecular Comparison\r\n^^^^^^^^^^^^^^^^^^^^\r\n\r\n.. code:: python\r\n\r\n    fig, caption = analysis.plot_mol_images(mtype='optimised', max_cols=3,\r\n                            info_columns=['Cation', 'Anion', 'Initial'],\r\n                            rotations=[[0,0,90]])\r\n    print caption\r\n\r\n\r\n.. parsed-literal::\r\n\r\n    (A) emim, cl, B, (B) emim, cl, BE, (C) emim, cl, BM, (D) emim, cl, F, (E) emim, cl, FE\r\n\r\n\r\n\r\n.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_26_1.png\r\n\r\n\r\nData Comparison\r\n^^^^^^^^^^^^^^^\r\n\r\n.. code:: python\r\n\r\n    fig, caption = analysis.plot_mol_graphs(gtype='dos', max_cols=3, \r\n                                            lbound=-20, ubound=10, legend_size=0,\r\n                                            band_gap_value=False,\r\n                                       info_columns=['Cation', 'Anion', 'Initial'])\r\n    print caption\r\n\r\n\r\n.. parsed-literal::\r\n\r\n    (A) emim, cl, B, (B) emim, cl, BE, (C) emim, cl, BM, (D) emim, cl, F, (E) emim, cl, FE\r\n\r\n\r\n\r\n.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_28_1.png\r\n\r\n\r\nThe methods mentioned for indivdiual molecules can be applied to all or\r\na subset of these computations.\r\n\r\n.. code:: python\r\n\r\n    analysis.add_mol_property_subset('Opt', 'is_optimised', rows=[2,3])\r\n    analysis.add_mol_property('Energy (au)', 'get_opt_energy', units='hartree')\r\n    analysis.add_mol_property('Cation chain, $\\\\psi$', 'calc_dihedral_angle', [1, 4, 9, 10])\r\n    analysis.add_mol_property('Cation Charge', 'calc_nbo_charge', 'emim')\r\n    analysis.add_mol_property('Anion Charge', 'calc_nbo_charge', 'cl')\r\n    analysis.add_mol_property(['Anion-Cation, $r$', 'Anion-Cation, $\\\\theta$', 'Anion-Cation, $\\\\phi$'], \r\n                                   'calc_polar_coords_from_plane', 3, 2, 1, 20)\r\n    analysis.add_mol_property('Anion-Cation h-bond', 'calc_hbond_energy', \r\n                              eunits='kJmol-1', atom_groups=['emim', 'cl'])\r\n    analysis.get_table(row_index=['Anion', 'Cation', 'Initial'], \r\n                       column_index=['Cation', 'Anion', 'Anion-Cation'])\r\n\r\n\r\n\r\nThere is also an option (requiring\r\n`pdflatex <http://www.tug.org/applications/pdftex/>`__ and\r\n`ghostscript <http://www.ghostscript.com/download/gsdnld.html>`__\\ +\\ `imagemagik <http://www.imagemagick.org/script/binary-releases.php>`__)\r\nto output the tables as a latex formatted image.\r\n\r\n.. code:: python\r\n\r\n    analysis.get_table(row_index=['Anion', 'Cation', 'Initial'],\r\n                       column_index=['Cation', 'Anion', 'Anion-Cation'],\r\n                       as_image=True, font_size=12)\r\n\r\n\r\n\r\n\r\n.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_32_0.png\r\n\r\n\r\n\r\nMulti-Variate Analysis\r\n^^^^^^^^^^^^^^^^^^^^^^\r\n\r\nRadViz is a way of visualizing multi-variate data.\r\n\r\n.. code:: python\r\n\r\n    ax = analysis.plot_radviz_comparison('Anion', columns=range(4, 10))\r\n\r\n\r\n\r\n.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_35_0.png\r\n\r\n\r\nThe KMeans algorithm clusters data by trying to separate samples into n\r\ngroups of equal variance.\r\n\r\n.. code:: python\r\n\r\n    pg.utils.imgplot_kmean_groups(\r\n        analysis, 'Anion', 'cl', 4, range(4, 10), \r\n        output=['Initial'], mtype='optimised', \r\n        rotations=[[0, 0, 90], [-90, 90, 0]],\r\n        axis_length=0.3)\r\n\r\n\r\n\r\n.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_37_0.png\r\n\r\n\r\n.. parsed-literal::\r\n\r\n    (A) BM\r\n    \r\n\r\n\r\n.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_37_2.png\r\n\r\n\r\n.. parsed-literal::\r\n\r\n    (A) FE\r\n    \r\n\r\n\r\n.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_37_4.png\r\n\r\n\r\n.. parsed-literal::\r\n\r\n    (A) B, (B) BE\r\n    \r\n\r\n\r\n.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_37_6.png\r\n\r\n\r\n.. parsed-literal::\r\n\r\n    (A) F\r\n    \r\n\r\nDocumentation (MS Word)\r\n~~~~~~~~~~~~~~~~~~~~~~~\r\n\r\nAfter analysing the computations, it would be reasonable to want to\r\ndocument some of our findings. This can be achieved by outputting\r\nindividual figure or table images via the folder object.\r\n\r\n.. code:: python\r\n\r\n    file_path = folder.save_ipyimg(vdw, 'image_of_molecule')\r\n    Image(file_path)\r\n\r\n\r\n\r\n\r\n.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_40_0.png\r\n\r\n\r\n\r\nBut you may also want to produce a more full record of your analysis,\r\nand this is where `python-docx <https://python-docx.readthedocs.org>`__\r\nsteps in. Building on this package the pygauss MSDocument class can\r\nproduce a full document of your analysis.\r\n\r\n.. code:: python\r\n\r\n    import matplotlib.pyplot as plt\r\n    d = pg.MSDocument()\r\n    d.add_heading('A Pygauss Example Assessment', level=0)\r\n\r\n    d.add_docstring(\"\"\"\r\n    # Introduction\r\n\r\n    We have looked at the following aspects\r\n    of [EMIM]^{+}[Cl]^{-} (C_{6}H_{11}ClN_{2});\r\n\r\n    - Geometric conformers\r\n    - Electronic structure\r\n\r\n    # Geometric Conformers\r\n    \"\"\")\r\n\r\n    fig, caption = analysis.plot_mol_images(max_cols=2, \r\n                    rotations=[[90,0,0], [0,0,90]], \r\n                    info_columns=['Anion', 'Cation', 'Initial'])\r\n    d.add_mpl(fig, dpi=96, height=9, caption=caption)\r\n    plt.close()\r\n    d.add_paragraph()\r\n    df = analysis.get_table(\r\n            columns=['Anion Charge', 'Cation Charge'],\r\n            row_index=['Anion', 'Cation', 'Initial'])\r\n    d.add_dataframe(df, incl_indx=True, style='Medium Shading 1 Accent 1',\r\n                    caption='Analysis of Conformer Charge')\r\n\r\n    d.add_docstring(\"\"\"\r\n    # Molecular Orbital Analysis\r\n    ## Density of States\r\n\r\n    It is **important** to *emphasise* that the\r\n    computations have only been run in the gas phase.\r\n    \"\"\")\r\n    fig, caption = analysis.plot_mol_graphs(gtype='dos', max_cols=3, \r\n                                        lbound=-20, ubound=10, legend_size=0,\r\n                                        band_gap_value=False,\r\n                                   info_columns=['Cation', 'Anion', 'Initial'])\r\n    d.add_mpl(fig, dpi=96, height=9, caption=caption)\r\n    plt.close()\r\n\r\n    d.save('exmpl_assess.docx')\r\n\r\nWhich gives us the following:\r\n\r\n.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/example_docx1.png\r\n\r\n.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/example_docx2.png\r\n\r\nMORE TO COME!!",
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