Basic usageΒΆ
All datasets in alchemtest
are accessible via load_*
functions, organized in submodules by the software package that
generated them. The current set of submodules are:
Gromacs molecular dynamics simulation datasets. |
|
Amber molecular dynamics simulation datasets. |
|
NAMD molecular dynamics simulation datasets. |
|
GOMC Monte Carlo simulation datasets. |
As an example, we can access the Gromacs: Benzene in water dataset with:
>>> from alchemtest.gmx import load_benzene
>>> bz = load_benzene()
and use the resulting Bunch
object to introspect what this
dataset includes. In particular, it features a DESCR
attribute
with a human-readable description of the dataset:
>>> print(bz.DESCR)
Gromacs: Benzene in water
=========================
Benzene in water, alchemically turned into benzene in vacuum separated from water
Notes
-----
Data Set Characteristics:
:Number of Legs: 2 (Coulomb, VDW)
:Number of Windows: 5 for Coulomb, 16 for VDW
:Length of Windows: 40ns
:Missing Values: None
:Creator: \I. Kenney
:Donor: Ian Kenney (ian.kenney@asu.edu)
:Date: March 2017
:License: `CC0
<https://creativecommons.org/publicdomain/zero/1.0/>`_
Public Domain Dedication
This dataset was generated using `MDPOW <https://github.com/Becksteinlab/MDPOW>`_, with
the `Gromacs <http://www.gromacs.org/>`_ molecular dynamics engine.
as well as the dataset itself:
>>> bz.data.keys()
['VDW', 'Coulomb']
which consists in this case of two alchemical legs, each having several files. For this dataset each file happens to correspond to a simulation sampling a particular \(\lambda\):
>>> bz.data['Coulomb']
['/usr/local/python3.6/site-packages/alchemtest/gmx/benzene/Coulomb/0000/dhdl.xvg.bz2',
'/usr/local/python3.6/site-packages/alchemtest/gmx/benzene/Coulomb/0250/dhdl.xvg.bz2',
'/usr/local/python3.6/site-packages/alchemtest/gmx/benzene/Coulomb/0500/dhdl.xvg.bz2',
'/usr/local/python3.6/site-packages/alchemtest/gmx/benzene/Coulomb/0750/dhdl.xvg.bz2',
'/usr/local/python3.6/site-packages/alchemtest/gmx/benzene/Coulomb/1000/dhdl.xvg.bz2']
These paths can be read by any appropriate parser for further
analysis. For this particular dataset, see
alchemlyb.parsing.gmx
for a good set of parsers.