Source code for MDAnalysis.analysis.lineardensity
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# MDAnalysis --- https://www.mdanalysis.org
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# Please cite your use of MDAnalysis in published work:
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# R. J. Gowers, M. Linke, J. Barnoud, T. J. E. Reddy, M. N. Melo, S. L. Seyler,
# D. L. Dotson, J. Domanski, S. Buchoux, I. M. Kenney, and O. Beckstein.
# MDAnalysis: A Python package for the rapid analysis of molecular dynamics
# simulations. In S. Benthall and S. Rostrup editors, Proceedings of the 15th
# Python in Science Conference, pages 102-109, Austin, TX, 2016. SciPy.
# doi: 10.25080/majora-629e541a-00e
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# N. Michaud-Agrawal, E. J. Denning, T. B. Woolf, and O. Beckstein.
# MDAnalysis: A Toolkit for the Analysis of Molecular Dynamics Simulations.
# J. Comput. Chem. 32 (2011), 2319--2327, doi:10.1002/jcc.21787
#
"""
Linear Density --- :mod:`MDAnalysis.analysis.lineardensity`
===========================================================
A tool to compute mass and charge density profiles along the three
cartesian axes of the simulation cell. Works only for orthorombic,
fixed volume cells (thus for simulations in canonical NVT ensemble).
"""
from __future__ import division, absolute_import
import os.path as path
import warnings
import numpy as np
from MDAnalysis.analysis.base import AnalysisBase
[docs]class LinearDensity(AnalysisBase):
"""Linear density profile
Parameters
----------
select : AtomGroup
any atomgroup
grouping : str {'atoms', 'residues', 'segments', 'fragments'}
Density profiles will be computed on the center of geometry
of a selected group of atoms ['atoms']
binsize : float
Bin width in Angstrom used to build linear density
histograms. Defines the resolution of the resulting density
profile (smaller --> higher resolution) [0.25]
verbose : bool (optional)
Show detailed progress of the calculation if set to ``True``; the
default is ``False``.
Attributes
----------
results : dict
Keys 'x', 'y', and 'z' for the three directions. Under these
keys, find 'pos', 'pos_std' (mass-weighted density and
standard deviation), 'char', 'char_std' (charge density and
its standard deviation), 'slice_volume' (volume of bin).
.. deprecated:: 1.1.0
The structure of the ``results`` dictionary will change in
MDAnalysis 2.0
Example
-------
First create a LinearDensity object by supplying a selection,
then use the :meth:`run` method::
ldens = LinearDensity(selection)
ldens.run()
.. versionadded:: 0.14.0
.. versionchanged:: 1.0.0
Support for the ``start``, ``stop``, and ``step`` keywords has been
removed. These should instead be passed to :meth:`LinearDensity.run`.
The ``save()`` method was also removed, you can use ``np.savetxt()`` or
``np.save()`` on the :attr:`LinearDensity.results` dictionary contents
instead.
.. versionchanged:: 1.0.0
Changed `selection` keyword to `select`
"""
def __init__(self, select, grouping='atoms', binsize=0.25, **kwargs):
super(LinearDensity, self).__init__(select.universe.trajectory,
**kwargs)
# allows use of run(parallel=True)
self._ags = [select]
self._universe = select.universe
self.binsize = binsize
# group of atoms on which to compute the COM (same as used in
# AtomGroup.wrap())
self.grouping = grouping
# Dictionary containing results
self.results = {'x': {'dim': 0}, 'y': {'dim': 1}, 'z': {'dim': 2}}
warnings.warn(
"The structure of the `results` dictionary will change in "
"MDAnalysis version 2.0.",
category=DeprecationWarning
)
# Box sides
self.dimensions = self._universe.dimensions[:3]
self.volume = np.prod(self.dimensions)
# number of bins
bins = (self.dimensions // self.binsize).astype(int)
# Here we choose a number of bins of the largest cell side so that
# x, y and z values can use the same "coord" column in the output file
self.nbins = bins.max()
slices_vol = self.volume / bins
self.keys = ['pos', 'pos_std', 'char', 'char_std']
# Initialize results array with zeros
for dim in self.results:
idx = self.results[dim]['dim']
self.results[dim].update({'slice volume': slices_vol[idx]})
for key in self.keys:
self.results[dim].update({key: np.zeros(self.nbins)})
# Variables later defined in _prepare() method
self.masses = None
self.charges = None
self.totalmass = None
def _prepare(self):
# group must be a local variable, otherwise there will be
# issues with parallelization
group = getattr(self._ags[0], self.grouping)
# Get masses and charges for the selection
try: # in case it's not an atom
self.masses = np.array([elem.total_mass() for elem in group])
self.charges = np.array([elem.total_charge() for elem in group])
except AttributeError: # much much faster for atoms
self.masses = self._ags[0].masses
self.charges = self._ags[0].charges
self.totalmass = np.sum(self.masses)
def _single_frame(self):
self.group = getattr(self._ags[0], self.grouping)
self._ags[0].wrap(compound=self.grouping)
# Find position of atom/group of atoms
if self.grouping == 'atoms':
positions = self._ags[0].positions # faster for atoms
else:
# COM for res/frag/etc
positions = np.array([elem.centroid() for elem in self.group])
for dim in ['x', 'y', 'z']:
idx = self.results[dim]['dim']
key = 'pos'
key_std = 'pos_std'
# histogram for positions weighted on masses
hist, _ = np.histogram(positions[:, idx],
weights=self.masses,
bins=self.nbins,
range=(0.0, max(self.dimensions)))
self.results[dim][key] += hist
self.results[dim][key_std] += np.square(hist)
key = 'char'
key_std = 'char_std'
# histogram for positions weighted on charges
hist, _ = np.histogram(positions[:, idx],
weights=self.charges,
bins=self.nbins,
range=(0.0, max(self.dimensions)))
self.results[dim][key] += hist
self.results[dim][key_std] += np.square(hist)
def _conclude(self):
k = 6.022e-1 # divide by avodagro and convert from A3 to cm3
# Average results over the number of configurations
for dim in ['x', 'y', 'z']:
for key in ['pos', 'pos_std', 'char', 'char_std']:
self.results[dim][key] /= self.n_frames
# Compute standard deviation for the error
self.results[dim]['pos_std'] = np.sqrt(self.results[dim][
'pos_std'] - np.square(self.results[dim]['pos']))
self.results[dim]['char_std'] = np.sqrt(self.results[dim][
'char_std'] - np.square(self.results[dim]['char']))
for dim in ['x', 'y', 'z']:
self.results[dim]['pos'] /= self.results[dim]['slice volume'] * k
self.results[dim]['char'] /= self.results[dim]['slice volume'] * k
self.results[dim]['pos_std'] /= self.results[dim]['slice volume'] * k
self.results[dim]['char_std'] /= self.results[dim]['slice volume'] * k
def _add_other_results(self, other):
# For parallel analysis
results = self.results
for dim in ['x', 'y', 'z']:
key = 'pos'
key_std = 'pos_std'
results[dim][key] += other[dim][key]
results[dim][key_std] += other[dim][key_std]
key = 'char'
key_std = 'char_std'
results[dim][key] += other[dim][key]
results[dim][key_std] += other[dim][key_std]