pygauss 0.4.0

Python Gaussian Analysis Tool (PyGauss)
=======================================

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
------------

- The source code is hosted on GitHub;
https://github.com/chrisjsewell/PyGauss
- A PyPi distribution is available at;
https://pypi-hypernode.com/pypi/pygauss
- A Conda distribution is available at; https://conda.binstar.org/cjs14

The Easy Way (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 -c https://conda.binstar.org/cjs14 pygauss

The Hard Way (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.
Therefore it will need to be cloned from GitHub. the extensions built,
dependancies installed and finally installed.

::

conda create -n pg_env python=2.7
conda install -n pg_env -c https://conda.binstar.org/cjs14 cclib
conda install -n pg_env -c https://conda.binstar.org/cjs14 chemview
conda install -n pg_env -c https://conda.binstar.org/cjs14 pyopengl
git clone --recursive https://github.com/chemlab/chemlab.git
cd chemlab
python setup.py build_ext --inplace
conda install -n pg_env <pil, pandas, matplotlib, scikit-learn, ...>
activate pg_env
pip install . # or add to PYTHONPATH
pip install pygauss

If you encounter difficulties it may be useful for you to look in
`working\_conda\_environments <https://github.com/chrisjsewell/PyGauss/tree/master/working_conda_environments>`__
at conda environments known to work.

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/readme_images/output_11_0.png



.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/readme_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/readme_images/output_13_1.png



.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/readme_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/readme_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/readme_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/readme_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/readme_images/output_20_1.png



.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/readme_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/readme_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/readme_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/readme_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/readme_images/output_31_0.png


.. parsed-literal::

Figure: (A) B, (B) BE



.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/readme_images/output_31_2.png


.. parsed-literal::

Figure: (A) BM



.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/readme_images/output_31_4.png


.. parsed-literal::

Figure: (A) FE



.. image:: https://github.com/chrisjsewell/PyGauss/raw/master/readme_images/output_31_6.png


.. parsed-literal::

Figure: (A) F


MORE TO COME!!