Source code for MDAnalysis.analysis.polymer
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# MDAnalysis --- https://www.mdanalysis.org
# Copyright (c) 2006-2017 The MDAnalysis Development Team and contributors
# (see the file AUTHORS for the full list of names)
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# Released under the Lesser GNU Public Licence, v2.1 or any higher version
#
# Please cite your use of MDAnalysis in published work:
#
# 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
#
# 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
#
"""
Polymer analysis --- :mod:`MDAnalysis.analysis.polymer`
=======================================================
:Author: Richard J. Gowers
:Year: 2015, 2018
:Copyright: Lesser GNU Public License v2.1+
This module contains various commonly used tools in analysing polymers.
"""
import numpy as np
import scipy.optimize
import warnings
import logging
from .. import NoDataError
from ..core.groups import requires, AtomGroup
from ..lib.distances import calc_bonds
from .base import AnalysisBase, ResultsGroup
logger = logging.getLogger(__name__)
[docs]
@requires("bonds")
def sort_backbone(backbone):
"""Rearrange a linear AtomGroup into backbone order
Requires that the backbone has bond information,
and that only backbone atoms are provided (ie no side
chains or hydrogens).
Parameters
----------
backbone : AtomGroup
the backbone atoms, not necessarily in order
Returns
-------
sorted_backbone : AtomGroup
backbone in order, so `sorted_backbone[i]` is bonded to
`sorted_backbone[i - 1]` and `sorted_backbone[i + 1]`
.. versionadded:: 0.20.0
"""
degrees = [len(atom.bonded_atoms & backbone) for atom in backbone]
deg1_atoms = [atom for atom, d in zip(backbone, degrees) if d == 1]
wrong_atoms = [
atom for atom, d in zip(backbone, degrees) if d not in (1, 2)
]
if len(wrong_atoms) > 0:
raise ValueError(
"Backbone contains atoms with connectivity degree not equal to 1 or 2. "
"This suggests branches or isolated atoms. Problematic atoms: {}."
"".format(", ".join(str(a) for a in wrong_atoms))
)
if len(deg1_atoms) != 2:
raise ValueError(
"Backbone connectivity invalid: "
"expected exactly 2 atoms with connectivity degree 1 (caps). "
"Cyclical structures are not supported. "
"Atoms with connectivity degree 1 found: {}."
"".format(", ".join(str(a) for a in deg1_atoms))
)
# arbitrarily choose one of the capping atoms to be the startpoint
sorted_backbone = AtomGroup([deg1_atoms[0]])
for _ in range(len(backbone) - 1):
# current end of the chain
end_atom = sorted_backbone[-1]
# look at all bonded atoms which are also part of the backbone
# and subtract any that have already been added
next_atom = (end_atom.bonded_atoms & backbone) - sorted_backbone
# append this to the sorted backbone
sorted_backbone += next_atom
return sorted_backbone
[docs]
class PersistenceLength(AnalysisBase):
r"""Calculate the persistence length for polymer chains
The persistence length is the length at which two points on the polymer
chain become decorrelated. This is determined by first measuring the
autocorrelation (:math:`C(n)`) of two bond vectors
(:math:`\mathbf{a}_i, \mathbf{a}_{i + n}`) separated by :math:`n` bonds
.. math::
C(n) = \langle \cos\theta_{i, i+n} \rangle =
\langle \mathbf{a_i} \cdot \mathbf{a_{i+n}} \rangle
where :math:`a_i` and :math:`a_{i+n}` are unit vectors
along the bonds.
An exponential decay is then fitted to this, which yields the
persistence length
.. math::
C(n) \approx \exp\left( - \frac{n \bar{l_B}}{l_P} \right)
where :math:`\bar{l_B}` is the average bond length, and :math:`l_P` is
the persistence length which is fitted
Parameters
----------
atomgroups : iterable
List of AtomGroups. Each should represent a single
polymer chain, ordered in the correct order.
verbose : bool, optional
Show detailed progress of the calculation if set to ``True``.
Attributes
----------
results.bond_autocorrelation : numpy.ndarray
the measured bond autocorrelation
results.lb : float
the average bond length
.. versionadded:: 2.0.0
lb : float
Alias to the :attr:`results.lb`.
.. deprecated:: 2.0.0
Will be removed in MDAnalysis 3.0.0. Please use
:attr:`results.lb` instead.
results.x : numpy.ndarray
length of the decorrelation predicted by *lp*
.. versionadded:: 2.0.0
results.lp : float
calculated persistence length
.. versionadded:: 2.0.0
lp : float
Alias to the :attr:`results.lp`.
.. deprecated:: 2.0.0
Will be removed in MDAnalysis 3.0.0. Please use
:attr:`results.lp` instead.
results.fit : numpy.ndarray
the modelled backbone decorrelation predicted by *lp*
.. versionadded:: 2.0.0
fit : float
Alias to the :attr:`results.fit`.
.. deprecated:: 2.0.0
Will be removed in MDAnalysis 3.0.0. Please use
:attr:`results.fit` instead.
See Also
--------
:func:`sort_backbone`
for producing the sorted AtomGroup required for input.
Example
-------
.. code-block:: python
from MDAnalysis.tests.datafiles import TRZ_psf, TRZ
import MDAnalysis as mda
from MDAnalysis.analysis import polymer
u = mda.Universe(TRZ_psf, TRZ)
# this system is a pure polymer melt of polyamide,
# so we can select the chains by using the .fragments attribute
chains = u.atoms.fragments
# select only the backbone atoms for each chain
backbones = [chain.select_atoms('not name O* H*') for chain in chains]
# sort the chains, removing any non-backbone atoms
sorted_backbones = [polymer.sort_backbone(bb) for bb in backbones]
persistence_length = polymer.PersistenceLength(sorted_backbones)
# Run the analysis, this will average over all polymer chains
# and all timesteps in trajectory
persistence_length = persistence_length.run()
print(f'The persistence length is: {persistence_length.results.lp}')
# always check the visualisation of this:
persistence_length.plot()
.. versionadded:: 0.13.0
.. versionchanged:: 0.20.0
The run method now automatically performs the exponential fit
.. versionchanged:: 1.0.0
Deprecated :meth:`PersistenceLength.perform_fit` has now been removed.
.. versionchanged:: 2.0.0
Former ``results`` are now stored as ``results.bond_autocorrelation``.
:attr:`lb`, :attr:`lp`, :attr:`fit` are now stored in a
:class:`MDAnalysis.analysis.base.Results` instance.
.. versionchanged:: 2.10.0
introduced :meth:`get_supported_backends` allowing for parallel
execution on ``multiprocessing`` and ``dask`` backends.
"""
_analysis_algorithm_is_parallelizable = True
def __init__(self, atomgroups, **kwargs):
super(PersistenceLength, self).__init__(
atomgroups[0].universe.trajectory, **kwargs
)
self._atomgroups = atomgroups
# Check that all chains are the same length
lens = [len(ag) for ag in atomgroups]
chainlength = len(atomgroups[0])
if not all(l == chainlength for l in lens):
raise ValueError("Not all AtomGroups were the same size")
self.chainlength = chainlength
def _prepare(self):
self.results.raw_bond_autocorr = np.zeros(
self.chainlength - 1, dtype=np.float32
)
def _single_frame(self):
# could optimise this by writing a "self dot array"
# we're only using the upper triangle of np.inner
# function would accept a bunch of coordinates and spit out the
# decorrel for that
for chain in self._atomgroups:
# Vector from each atom to next
vecs = chain.positions[1:] - chain.positions[:-1]
# Normalized to unit vectors
vecs /= np.sqrt((vecs * vecs).sum(axis=1))[:, None]
inner_pr = np.inner(vecs, vecs)
for i in range(self.chainlength - 1):
self.results.raw_bond_autocorr[
: (self.chainlength - 1) - i
] += inner_pr[i, i:]
def _get_aggregator(self):
return ResultsGroup(
lookup={
"raw_bond_autocorr": ResultsGroup.ndarray_sum,
}
)
@property
def lb(self):
wmsg = (
"The `lb` attribute was deprecated in "
"MDAnalysis 2.0.0 and will be removed in MDAnalysis 3.0.0. "
"Please use `results.variance` instead."
)
warnings.warn(wmsg, DeprecationWarning)
return self.results.lb
@property
def lp(self):
wmsg = (
"The `lp` attribute was deprecated in "
"MDAnalysis 2.0.0 and will be removed in MDAnalysis 3.0.0. "
"Please use `results.variance` instead."
)
warnings.warn(wmsg, DeprecationWarning)
return self.results.lp
@property
def fit(self):
wmsg = (
"The `fit` attribute was deprecated in "
"MDAnalysis 2.0.0 and will be removed in MDAnalysis 3.0.0. "
"Please use `results.variance` instead."
)
warnings.warn(wmsg, DeprecationWarning)
return self.results.fit
def _conclude(self):
norm = np.linspace(self.chainlength - 1, 1, self.chainlength - 1)
norm *= len(self._atomgroups) * self._trajectory.n_frames
self.results.bond_autocorrelation = (
self.results.raw_bond_autocorr / norm
)
self._calc_bond_length()
self._perform_fit()
def _calc_bond_length(self):
"""calculate average bond length"""
bs = []
for ag in self._atomgroups:
pos = ag.positions
b = calc_bonds(pos[:-1], pos[1:]).mean()
bs.append(b)
self.results.lb = np.mean(bs)
def _perform_fit(self):
"""Fit the results to an exponential decay"""
try:
self.results.bond_autocorrelation
except AttributeError:
raise NoDataError("Use the run method first") from None
self.results.x = self.results.lb * np.arange(
len(self.results.bond_autocorrelation)
)
self.results.lp = fit_exponential_decay(
self.results.x, self.results.bond_autocorrelation
)
self.results.fit = np.exp(-self.results.x / self.results.lp)
[docs]
def plot(self, ax=None):
"""Visualize the results and fit
Parameters
----------
ax : matplotlib.Axes, optional
if provided, the graph is plotted on this axis
Returns
-------
ax : the axis that the graph was plotted on
"""
import matplotlib.pyplot as plt
if ax is None:
_, ax = plt.subplots()
ax.plot(
self.results.x,
self.results.bond_autocorrelation,
"ro",
label="Result",
)
ax.plot(self.results.x, self.results.fit, label="Fit")
ax.set_xlabel(r"x")
ax.set_ylabel(r"$C(x)$")
ax.set_xlim(0.0, 40 * self.results.lb)
ax.legend(loc="best")
return ax
[docs]
def fit_exponential_decay(x, y):
r"""Fit a function to an exponential decay
.. math:: y = \exp\left(- \frac{x}{a}\right)
Parameters
----------
x, y : array_like
The two arrays of data
Returns
-------
a : float
The coefficient *a* for this decay
Notes
-----
This function assumes that data starts at 1.0 and decays to 0.0
"""
def expfunc(x, a):
return np.exp(-x / a)
a = scipy.optimize.curve_fit(expfunc, x, y)[0][0]
return a