pygauss 0.1.6

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/>`__/`chemlab <http://chemlab.readthedocs.org/en/latest/index.html>`__/`chemview <http://chemview.readthedocs.org/en/latest/>`__
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
has been written). 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

Detail instillation...

::

pip install pygauss

conda install -c http://conda.binstar.org/gabrielelanaro chemlab

You should then be able to start an ipython notebook...

.. code:: python

from IPython.display import display
%matplotlib inline
import pygauss as pg
folder = pg.get_test_folder()

Single Molecule Analysis
------------------------

A *molecule* can be created containg data about the inital geometry,
optimisation process and analysis of the final configuration.

.. code:: python

mol = pg.molecule.Molecule(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',
alignto=[3,2,1])

#mol.show_initial(active=True)
display(mol.show_initial(zoom=0.5, rotations=[[0,0,90], [-90, 90, 0]]))
display(mol.show_optimisation(ball_stick=True, rotations=[[0,0,90], [-90, 90, 0]]))



.. image:: https://github.com/chrisjsewell/PyGauss/readmeraw/master/output_6_0.png



.. image:: https://github.com/chrisjsewell/PyGauss/readmeraw/master/output_6_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)


.. parsed-literal::

Optimised? True, Conformer? True, Energy = -805.105 a.u.



.. image:: https://github.com/chrisjsewell/PyGauss/readmeraw/master/output_8_1.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/readmeraw/master/output_10_1.png


Potential Energy Scan analysis of geometric conformers...

.. code:: python

mol2 = pg.molecule.Molecule(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/readmeraw/master/output_12_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(ball_stick=True, axis_length=0.4,
rotations=[[0,0,90], [-90, 90, 0]]))
display(mol.show_SOPT_bonds(min_energy=15., rotations=[[0, 0, 90]]))


.. parsed-literal::

+ve charge centre polar coords from aromatic ring: (0.02 -51.77,-33.15)



.. image:: https://github.com/chrisjsewell/PyGauss/readmeraw/master/output_14_1.png



.. image:: https://github.com/chrisjsewell/PyGauss/readmeraw/master/output_14_2.png


Multiple Computations Analysis
------------------------------

a

.. code:: python

analysis = pg.analysis.Analysis(folder)
df, errors = analysis.add_runs(headers=['Cation', 'Anion', 'Initial'],
values=[['emim'], ['cl'],
['B', 'BE', 'BM', 'F', 'FE', 'FM']],
init_pattern='CJS1_{0}-{1}_{2}_init.com',
opt_pattern='CJS1_{0}-{1}_{2}_6-311+g-d-p-_gd3bj_opt-modredundant_unfrz.log',
freq_pattern='CJS1_{0}-{1}_{2}_6-311+g-d-p-_gd3bj_freq_unfrz.log',
nbo_pattern='CJS1_{0}-{1}_{2}_6-311+g-d-p-_gd3bj_pop-nbo-full-_unfrz.log')
print 'Read Errors:', errors


.. parsed-literal::

Read Errors: [{'Cation': 'emim', 'Initial': 'FM', 'Anion': 'cl'}]


.. code:: python

analysis.add_mol_property('Opt', 'is_optimised')
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', range(1, 20))
analysis.add_mol_property('Anion Charge', 'calc_nbo_charge', [20])
analysis.add_mol_property(['Anion-Cation, $r$', 'Anion-Cation, $\\theta$', 'Anion-Cation, $\\phi$'],
'calc_polar_coords_from_plane', 3, 2, 1, 20)
df = analysis.get_table(row_index=['Anion', 'Cation', 'Initial'],
column_index=['Cation', 'Anion', 'Anion-Cation'])
df




.. raw:: html

<div style="max-height:1000px;max-width:1500px;overflow:auto;">
<table border="1" class="dataframe">
<thead>
<tr>
<th></th>
<th></th>
<th></th>
<th colspan="2" halign="left"></th>
<th colspan="2" halign="left">Cation</th>
<th>Anion</th>
<th colspan="3" halign="left">Anion-Cation</th>
</tr>
<tr>
<th></th>
<th></th>
<th></th>
<th>Opt</th>
<th>Energy (au)</th>
<th>chain, $\psi$</th>
<th>Charge</th>
<th>Charge</th>
<th>$r$</th>
<th>$\theta$</th>
<th>$\phi$</th>
</tr>
<tr>
<th>Anion</th>
<th>Cation</th>
<th>Initial</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th rowspan="5" valign="top">cl</th>
<th rowspan="5" valign="top">emim</th>
<th>B</th>
<td>True</td>
<td>-805.105</td>
<td>80.794</td>
<td>0.888</td>
<td>-0.888</td>
<td>0.420</td>
<td>-123.392</td>
<td>172.515</td>
</tr>
<tr>
<th>BE</th>
<td>True</td>
<td>-805.105</td>
<td>80.622</td>
<td>0.887</td>
<td>-0.887</td>
<td>0.420</td>
<td>-123.449</td>
<td>172.806</td>
</tr>
<tr>
<th>BM</th>
<td>True</td>
<td>-805.104</td>
<td>73.103</td>
<td>0.874</td>
<td>-0.874</td>
<td>0.420</td>
<td>124.121</td>
<td>-166.774</td>
</tr>
<tr>
<th>F</th>
<td>True</td>
<td>-805.118</td>
<td>147.026</td>
<td>0.840</td>
<td>-0.840</td>
<td>0.420</td>
<td>10.393</td>
<td>0.728</td>
</tr>
<tr>
<th>FE</th>
<td>True</td>
<td>-805.117</td>
<td>85.310</td>
<td>0.851</td>
<td>-0.851</td>
<td>0.417</td>
<td>-13.254</td>
<td>-4.873</td>
</tr>
</tbody>
</table>
</div>



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/readmeraw/master/output_20_0.png


The KMeans algorithm clusters data by trying to separate samples in n
groups of equal variance

.. code:: python

kwargs = {'mtype':'optimised', 'align_to':[3,2,1],
'rotations':[[0, 0, 90], [-90, 90, 0]],
'axis_length':0.3}
def show_groups(df):
for cat, gf in df.groupby('Category'):
print 'Category {0}:'.format(cat)
mols = analysis.yield_mol_images(rows=gf.index.tolist(), **kwargs)
for mol, row in zip(mols, gf.index.tolist()):
print '(row {0})'.format(row)
display(mol)
show_groups(analysis.calc_kmean_groups('Anion', 'cl', 4, columns=range(4, 10)))


.. parsed-literal::

Category 0:
(row 2)



.. image:: https://github.com/chrisjsewell/PyGauss/readmeraw/master/output_22_1.png


.. parsed-literal::

Category 1:
(row 0)



.. image:: https://github.com/chrisjsewell/PyGauss/readmeraw/master/output_22_3.png


.. parsed-literal::

(row 1)



.. image:: https://github.com/chrisjsewell/PyGauss/readmeraw/master/output_22_5.png


.. parsed-literal::

Category 2:
(row 4)



.. image:: https://github.com/chrisjsewell/PyGauss/readmeraw/master/output_22_7.png


.. parsed-literal::

Category 3:
(row 3)



.. image:: https://github.com/chrisjsewell/PyGauss/readmeraw/master/output_22_9.png