Metadata-Version: 1.0
Name: pygauss
Version: 0.4.3
Summary: Python API for Analysis of Gaussian Quantum Chemical Compuations
Home-page: https://github.com/chrisjsewell/PyGauss/wiki
Author: Chris Sewell
Author-email: chrisj_sewell@hotmail.com
License: GPL3
Description: Python Gaussian Analysis Tool (PyGauss)
        =======================================
        
        +------------+---------------------------------------------------------------------------+
        |Documents   | https://pygauss.readthedocs.org                                           |
        +            +---------------------------------------------------------------------------+
        |            |.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/https://readthedocs.org/projects/pygauss/badge/?version=stable  |
        +------------+---------------------------------------------------------------------------+
        |Conda       | https://pypi.python.org/pypi/pygauss                                      |
        +            +---------------------------------------------------------------------------+
        |            |.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/https://binstar.org/cjs14/pygauss/badges/version.svg            |
        +------------+---------------------------------------------------------------------------+
        |GitHub      | https://github.com/chrisjsewell/PyGauss                                   |
        +            +---------------------------------------------------------------------------+
        |            |.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/https://img.shields.io/github/release/chrisjsewell/PyGauss.svg  |
        +------------+---------------------------------------------------------------------------+
        
        
        PyGauss is designed to be an API for parsing one or more input/output
        files from a `Gaussian <http://www.gaussian.com/>`__ quantum chemical
        computation and provide functionality to assess **molecular geometry**
        and **electronic distribution** both visually and quantitatively.
        
        It is built on top of the
        `cclib <http://cclib.github.io/>`__/`chemview <http://chemview.readthedocs.org/en/latest/>`__/`chemlab <http://chemlab.readthedocs.org/en/latest/index.html>`__
        suite of packages and python scientific stack and is primarily designed
        to be used interactively in the `IPython
        Notebook <http://ipython.org/notebook.html>`__ (within which this readme
        was created). As shown below, a molecular optimisation can be assesed
        individually (much like in
        `gaussview <http://www.gaussian.com/g_prod/gv5b.htm>`__), but also as
        part of a group. The advantages of this package are then:
        
        -  Faster, more efficient analysis
        -  Reproducible analysis
        -  Trend analysis
        
        Instillation
        ------------
        
        OSX and Linux
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        The recommended was to use pygauss is to download the
        `Anaconda <http://continuum.io/downloads>`__ Scientific Python
        Distribution (64-bit). Once downloaded a new environment can be created
        in terminal and pygauss installed:
        
        ::
        
            conda create -n pg_env conda install -c https://conda.binstar.org/cjs14 pygauss
        
        
        Windows
        ~~~~~~~~~~~~~~~~~~~~~~
        
        There is currently no pygauss conda distributable for Windows or for
        chemlab, which has C-extensions that need to be built using a compiler. 
        Please see the documentation for guidance.
        
        
        Example Assessment
        ------------------
        
        You should then be able to open an assessment in IPython Notebook
        starting with the following:
        
        .. code:: python
        
            from IPython.display import display
            %matplotlib inline
            import pygauss as pg
            print 'pygauss version: {}'.format(pg.__version__)
        
        
        .. parsed-literal::
        
            pygauss version: 0.4.0
        
        
        and access the test folder with a number of example Gaussian outputs.
        
        .. code:: python
        
            folder = pg.get_test_folder()
            len(folder.list_files())
        
        
        
        
        .. parsed-literal::
        
            33
        
        
        
        **Note:** the *folder* object will act identical whether using a local
        path or one on a server over ssh (using
        `paramiko <http://www.paramiko.org/>`__):
        
        ::
        
            folder = pg.Folder('/path/to/folder', 
                            ssh_server='login.server.com',
                            ssh_username='username')
        
        Single Molecule Analysis
        ~~~~~~~~~~~~~~~~~~~~~~~~
        
        A *molecule* can be created containg data about the inital geometry,
        optimisation process and analysis of the final configuration. Molecules
        can be viewed statically or interactively (not currently supported by
        Firefox).
        
        .. code:: python
        
            mol = pg.molecule.Molecule(folder_obj=folder,
                            init_fname='CJS1_emim-cl_B_init.com', 
                            opt_fname=['CJS1_emim-cl_B_6-311+g-d-p-_gd3bj_opt-modredundant_difrz.log',
                                       'CJS1_emim-cl_B_6-311+g-d-p-_gd3bj_opt-modredundant_difrz_err.log',
                                       'CJS1_emim-cl_B_6-311+g-d-p-_gd3bj_opt-modredundant_unfrz.log'],
                            freq_fname='CJS1_emim-cl_B_6-311+g-d-p-_gd3bj_freq_unfrz.log',
                            nbo_fname='CJS1_emim-cl_B_6-311+g-d-p-_gd3bj_pop-nbo-full-_unfrz.log', 
                            atom_groups={'emim':range(20), 'cl':[20]},
                            alignto=[3,2,1])
            
            #mol.show_initial(active=True)
            display(mol.show_initial(represent='vdw', rotations=[[0,0,90], [-90, 90, 0]]))
            display(mol.show_optimisation(represent='ball_stick', rotations=[[0,0,90], [-90, 90, 0]]))
        
        
        
        .. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_11_0.png
        
        
        
        .. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_11_1.png
        
        
        Basic analysis of optimisation...
        
        .. code:: python
        
            print('Optimised? {0}, Conformer? {1}, Energy = {2} a.u.'.format(
                mol.is_optimised(), mol.is_conformer(), 
                round(mol.get_optimisation_E(units='hartree'),3)))
            ax = mol.plot_optimisation_E(units='hartree')
            ax.get_figure().set_size_inches(3, 2)
            ax = mol.plot_freq_analysis()
            ax.get_figure().set_size_inches(4, 2)
        
        
        .. parsed-literal::
        
            Optimised? True, Conformer? True, Energy = -805.105 a.u.
        
        
        
        .. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_13_1.png
        
        
        
        .. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_13_2.png
        
        
        Geometric analysis...
        
        .. code:: python
        
            print 'Cl optimised polar coords from aromatic ring : ({0}, {1},{2})'.format(
                *[round(i, 2) for i in mol.calc_polar_coords_from_plane(20,3,2,1)])
            ax = mol.plot_opt_trajectory(20, [3,2,1])
            ax.set_title('Cl optimisation path')
            ax.get_figure().set_size_inches(4, 3)
        
        
        .. parsed-literal::
        
            Cl optimised polar coords from aromatic ring : (0.11, -116.42,-170.06)
        
        
        
        .. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_15_1.png
        
        
        Potential Energy Scan analysis of geometric conformers...
        
        .. code:: python
        
            mol2 = pg.molecule.Molecule(folder_obj=folder, alignto=[3,2,1],
                        pes_fname=['CJS_emim_6311_plus_d3_scan.log', 
                                   'CJS_emim_6311_plus_d3_scan_bck.log'])   
            ax = mol2.plot_pes_scans([1,4,9,10], rotation=[0,0,90], img_pos='local_maxs', zoom=0.5)
            ax.set_title('Ethyl chain rotational conformer analysis')
            ax.get_figure().set_size_inches(7, 3)
        
        
        
        .. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_17_0.png
        
        
        Natural Bond Orbital and Second Order Perturbation Theory analysis...
        
        .. code:: python
        
            print '+ve charge centre polar coords from aromatic ring: ({0} {1},{2})'.format(
                *[round(i, 2) for i in mol.calc_nbo_charge_center(3, 2, 1)])
            display(mol.show_nbo_charges(represent='ball_stick', axis_length=0.4, 
                                          rotations=[[0,0,90], [-90, 90, 0]]))
        
        
        .. parsed-literal::
        
            +ve charge centre polar coords from aromatic ring: (0.02 -51.77,-33.15)
        
        
        
        .. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_19_1.png
        
        
        .. code:: python
        
            print 'H inter-bond energy = {} kJmol-1'.format(
                    mol.calc_hbond_energy(eunits='kJmol-1', atom_groups=['emim', 'cl']))
            print 'Other inter-bond energy = {} kJmol-1'.format(
                mol.calc_sopt_energy(eunits='kJmol-1', no_hbonds=True, atom_groups=['emim', 'cl']))
            display(mol.show_sopt_bonds(min_energy=1, eunits='kJmol-1',
                                        atom_groups=['emim', 'cl'],
                                        no_hbonds=True,
                                        rotations=[[0, 0, 90]]))
            display(mol.show_hbond_analysis(cutoff_energy=5.,alpha=0.6, 
                                            atom_groups=['emim', 'cl'],
                                            rotations=[[0, 0, 90], [90, 0, 0]]))
        
        
        .. parsed-literal::
        
            H inter-bond energy = 111.7128 kJmol-1
            Other inter-bond energy = 11.00392 kJmol-1
        
        
        
        .. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_20_1.png
        
        
        
        .. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_20_2.png
        
        
        Multiple Computations Analysis
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Multiple computations, for instance of different starting conformations,
        can be grouped into an *Analysis* class.
        
        .. code:: python
        
            analysis = pg.Analysis(folder_obj=folder)
            errors = analysis.add_runs(headers=['Cation', 'Anion', 'Initial'], 
                                           values=[['emim'], ['cl'],
                                                   ['B', 'BE', 'BM', 'F', 'FE']],
                        init_pattern='*{0}-{1}_{2}_init.com',
                        opt_pattern='*{0}-{1}_{2}_6-311+g-d-p-_gd3bj_opt*unfrz.log',
                        freq_pattern='*{0}-{1}_{2}_6-311+g-d-p-_gd3bj_freq*.log',
                        nbo_pattern='*{0}-{1}_{2}_6-311+g-d-p-_gd3bj_pop-nbo-full-*.log',
                        alignto=[3,2,1], atom_groups={'emim':range(20), 'cl':[20]})
            
            fig, caption = analysis.plot_mol_images(mtype='initial', max_cols=3,
                                    info_columns=['Cation', 'Anion', 'Initial'],
                                    rotations=[[0,0,90]])
            print caption
        
        
        .. parsed-literal::
        
            Figure: (A) emim, cl, B, (B) emim, cl, BE, (C) emim, cl, BM, (D) emim, cl, F, (E) emim, cl, FE
        
        
        
        .. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_23_1.png
        
        
        The methods mentioned for indivdiual molecules can then be applied to
        all or a subset of these computations.
        
        .. code:: python
        
            analysis.add_mol_property_subset('Opt', 'is_optimised', rows=[2,3])
            analysis.add_mol_property('Energy (au)', 'get_optimisation_E', units='hartree')
            analysis.add_mol_property('Cation chain, $\\psi$', 'calc_dihedral_angle', [1, 4, 9, 10])
            analysis.add_mol_property('Cation Charge', 'calc_nbo_charge', 'emim')
            analysis.add_mol_property('Anion Charge', 'calc_nbo_charge', 'cl')
            analysis.add_mol_property(['Anion-Cation, $r$', 'Anion-Cation, $\\theta$', 'Anion-Cation, $\\phi$'], 
                                           'calc_polar_coords_from_plane', 3, 2, 1, 20)
            analysis.add_mol_property('Anion-Cation h-bond', 'calc_hbond_energy', 
                                      eunits='kJmol-1', atom_groups=['emim', 'cl'])
            tbl = analysis.get_table(row_index=['Anion', 'Cation', 'Initial'], 
                               column_index=['Cation', 'Anion', 'Anion-Cation'])
        
        **NEW FEATURE:** there is now an option (requiring
        `pdflatex <http://www.tug.org/applications/pdftex/>`__ and
        `ghostscript <http://www.ghostscript.com/download/gsdnld.html>`__\ +\ `imagemagik <http://www.imagemagick.org/script/binary-releases.php>`__)
        to output the tables as a latex formatted image.
        
        .. code:: python
        
            analysis.get_table(row_index=['Anion', 'Cation', 'Initial'],
                               column_index=['Cation', 'Anion', 'Anion-Cation'],
                               as_image=True, font_size=12)
        
        
        
        
        .. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_27_0.png
        
        
        
        RadViz is a way of visualizing multi-variate data.
        
        .. code:: python
        
            ax = analysis.plot_radviz_comparison('Anion', columns=range(4, 10))
        
        
        
        .. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_29_0.png
        
        
        The KMeans algorithm clusters data by trying to separate samples into n
        groups of equal variance.
        
        .. code:: python
        
            pg.utils.imgplot_kmean_groups(
                analysis, 'Anion', 'cl', 4, range(4, 10), 
                output=['Initial'], mtype='optimised', 
                rotations=[[0, 0, 90], [-90, 90, 0]],
                axis_length=0.3)
        
        
        
        .. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_31_0.png
        
        
        .. parsed-literal::
        
            Figure: (A) B, (B) BE
        
        
        
        .. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_31_2.png
        
        
        .. parsed-literal::
        
            Figure: (A) BM
        
        
        
        .. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_31_4.png
        
        
        .. parsed-literal::
        
            Figure: (A) FE
        
        
        
        .. image:: https://github.com/chrisjsewell/PyGauss/raw/master/docs/source/images/output_31_6.png
        
        
        .. parsed-literal::
        
            Figure: (A) F
        
        
        MORE TO COME!!
        
Keywords: chemistry gaussian dft
Platform: Any.
