Source code for MDAnalysis.analysis.polymer

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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
# 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: GNU Public License v3

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

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 """ if not backbone.n_fragments == 1: raise ValueError("{} fragments found in backbone. " "backbone must be a single contiguous AtomGroup" "".format(backbone.n_fragments)) branches = [at for at in backbone if len(at.bonded_atoms & backbone) > 2] if branches: # find which atom has too many bonds for easier debug raise ValueError( "Backbone is not linear. " "The following atoms have more than two bonds in backbone: {}." "".format(','.join(str(a) for a in branches))) caps = [atom for atom in backbone if len(atom.bonded_atoms & backbone) == 1] if not caps: # cyclical structure raise ValueError("Could not find starting point of backbone, " "is the backbone cyclical?") # arbitrarily choose one of the capping atoms to be the startpoint sorted_backbone = AtomGroup([caps[0]]) # iterate until the sorted chain length matches the backbone size while len(sorted_backbone) < len(backbone): # 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 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 : float the average bond length .. versionadded:: 2.0.0 lb : float Alias to the :attr:``. .. deprecated:: 2.0.0 Will be removed in MDAnalysis 3.0.0. Please use :attr:`` 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. : numpy.ndarray the modelled backbone decorrelation predicted by *lp* .. versionadded:: 2.0.0 fit : float Alias to the :attr:``. .. deprecated:: 2.0.0 Will be removed in MDAnalysis 3.0.0. Please use :attr:`` 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 = 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. """ 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._results = np.zeros(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 n = len(self._atomgroups[0]) 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(n-1): self._results[:(n-1)-i] += inner_pr[i, i:] @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 @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 def _conclude(self): n = len(self._atomgroups[0]) norm = np.linspace(n - 1, 1, n - 1) norm *= len(self._atomgroups) * self.n_frames self.results.bond_autocorrelation = self._results / 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) = 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 = *\ np.arange(len(self.results.bond_autocorrelation)) self.results.lp = fit_exponential_decay(self.results.x, self.results.bond_autocorrelation) = 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: fig, ax = plt.subplots() ax.plot(self.results.x, self.results.bond_autocorrelation, 'ro', label='Result') ax.plot(self.results.x,, label='Fit') ax.set_xlabel(r'x') ax.set_ylabel(r'$C(x)$') ax.set_xlim(0.0, 40 * 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