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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
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# Python in Science Conference, pages 102-109, Austin, TX, 2016. SciPy.
# doi: 10.25080/majora-629e541a-00e
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# MDAnalysis: A Toolkit for the Analysis of Molecular Dynamics Simulations.
# J. Comput. Chem. 32 (2011), 2319--2327, doi:10.1002/jcc.21787
#
r"""Principal Component Analysis (PCA) --- :mod:`MDAnalysis.analysis.pca`
=====================================================================
:Authors: John Detlefs
:Year: 2016
:Copyright: GNU Public License v3
.. versionadded:: 0.16.0
This module contains the linear dimensions reduction method Principal Component
Analysis (PCA). PCA sorts a simulation into 3N directions of descending
variance, with N being the number of atoms. These directions are called
the principal components. The dimensions to be analyzed are reduced by only
looking at a few projections of the first principal components. To learn how to
run a Principal Component Analysis, please refer to the :ref:`PCA-tutorial`.
The PCA problem is solved by solving the eigenvalue problem of the covariance
matrix, a :math:`3N \times 3N` matrix where the element :math:`(i, j)` is the
covariance between coordinates :math:`i` and :math:`j`. The principal
components are the eigenvectors of this matrix.
For each eigenvector, its eigenvalue is the variance that the eigenvector
explains. Stored in :attr:`PCA.results.cumulated_variance`, a ratio for each
number of eigenvectors up to index :math:`i` is provided to quickly find out
how many principal components are needed to explain the amount of variance
reflected by those :math:`i` eigenvectors. For most data,
:attr:`PCA.results.cumulated_variance`
will be approximately equal to one for some :math:`n` that is significantly
smaller than the total number of components. These are the components of
interest given by Principal Component Analysis.
From here, we can project a trajectory onto these principal components and
attempt to retrieve some structure from our high dimensional data.
For a basic introduction to the module, the :ref:`PCA-tutorial` shows how
to perform Principal Component Analysis.
.. _PCA-tutorial:
PCA Tutorial
------------
The example uses files provided as part of the MDAnalysis test suite
(in the variables :data:`~MDAnalysis.tests.datafiles.PSF` and
:data:`~MDAnalysis.tests.datafiles.DCD`). This tutorial shows how to use the
PCA class.
First load all modules and test data::
import MDAnalysis as mda
import MDAnalysis.analysis.pca as pca
from MDAnalysis.tests.datafiles import PSF, DCD
Given a universe containing trajectory data we can perform Principal Component
Analyis by using the class :class:`PCA` and retrieving the principal
components.::
u = mda.Universe(PSF, DCD)
PSF_pca = pca.PCA(u, select='backbone')
PSF_pca.run()
Inspect the components to determine the principal components you would like
to retain. The choice is arbitrary, but I will stop when 95 percent of the
variance is explained by the components. This cumulated variance by the
components is conveniently stored in the one-dimensional array attribute
:attr:`PCA.results.cumulated_variance`. The value at the ith index of
:attr:`PCA.results.cumulated_variance` is the sum of the variances from 0 to
i.::
n_pcs = np.where(PSF_pca.results.cumulated_variance > 0.95)[0][0]
atomgroup = u.select_atoms('backbone')
pca_space = PSF_pca.transform(atomgroup, n_components=n_pcs)
From here, inspection of the ``pca_space`` and conclusions to be drawn from the
data are left to the user.
Classes and Functions
---------------------
.. autoclass:: PCA
:members:
:inherited-members:
.. autofunction:: cosine_content
.. autofunction:: rmsip
.. autofunction:: cumulative_overlap
"""
import warnings
import numpy as np
import scipy.integrate
from MDAnalysis import Universe
from MDAnalysis.analysis.align import _fit_to
from MDAnalysis.lib.log import ProgressBar
from ..lib import util
from ..due import due, Doi
from .base import AnalysisBase
[docs]class PCA(AnalysisBase):
"""Principal component analysis on an MD trajectory.
After initializing and calling method with a universe or an atom group,
principal components ordering the atom coordinate data by decreasing
variance will be available for analysis. As an example:::
pca = PCA(universe, select='backbone').run()
pca_space = pca.transform(universe.select_atoms('backbone'), 3)
generates the principal components of the backbone of the atomgroup and
then transforms those atomgroup coordinates by the direction of those
variances. Please refer to the :ref:`PCA-tutorial` for more detailed
instructions.
Parameters
----------
universe : Universe
Universe
select : string, optional
A valid selection statement for choosing a subset of atoms from
the atomgroup.
align : boolean, optional
If True, the trajectory will be aligned to a reference
structure.
mean : array_like, optional
Optional reference positions to be be used as the mean of the
covariance matrix.
n_components : int, optional
The number of principal components to be saved, default saves
all principal components
verbose : bool (optional)
Show detailed progress of the calculation if set to ``True``.
Attributes
----------
results.p_components: array, (n_atoms * 3, n_components)
Principal components of the feature space,
representing the directions of maximum variance in the data.
The column vector p_components[:, i] is the eigenvector
corresponding to the variance[i].
.. versionadded:: 2.0.0
p_components: array, (n_atoms * 3, n_components)
Alias to the :attr:`results.p_components`.
.. deprecated:: 2.0.0
Will be removed in MDAnalysis 3.0.0. Please use
:attr:`results.p_components` instead.
results.variance : array (n_components, )
Raw variance explained by each eigenvector of the covariance
matrix.
.. versionadded:: 2.0.0
variance : array (n_components, )
Alias to the :attr:`results.variance`.
.. deprecated:: 2.0.0
Will be removed in MDAnalysis 3.0.0. Please use
:attr:`results.variance` instead.
results.cumulated_variance : array, (n_components, )
Percentage of variance explained by the selected components and the sum
of the components preceding it. If a subset of components is not chosen
then all components are stored and the cumulated variance will converge
to 1.
.. versionadded:: 2.0.0
cumulated_variance : array, (n_components, )
Alias to the :attr:`results.cumulated_variance`.
.. deprecated:: 2.0.0
Will be removed in MDAnalysis 3.0.0. Please use
:attr:`results.cumulated_variance` instead.
Notes
-----
Computation can be sped up by supplying precalculated mean positions.
.. versionchanged:: 0.19.0
The start frame is used when performing selections and calculating
mean positions. Previously the 0th frame was always used.
.. versionchanged:: 1.0.0
``n_components`` now limits the correct axis of ``p_components``.
``cumulated_variance`` now accurately represents the contribution of
each principal component and does not change when ``n_components`` is
given. If ``n_components`` is not None or is less than the number of
``p_components``, ``cumulated_variance`` will not sum to 1.
``align=True`` now correctly aligns the trajectory and computes the
correct means and covariance matrix.
.. versionchanged:: 2.0.0
``mean_atoms`` removed, as this did not reliably contain the mean
positions.
``mean`` input now accepts coordinate arrays instead of atomgroup.
:attr:`p_components`, :attr:`variance` and :attr:`cumulated_variance`
are now stored in a :class:`MDAnalysis.analysis.base.Results` instance.
"""
def __init__(self, universe, select='all', align=False, mean=None,
n_components=None, **kwargs):
super(PCA, self).__init__(universe.trajectory, **kwargs)
self._u = universe
# for transform function
self.align = align
self._calculated = False
self._n_components = n_components
self._select = select
self._mean = mean
def _prepare(self):
# access start index
self._u.trajectory[self.start]
# reference will be start index
self._reference = self._u.select_atoms(self._select)
self._atoms = self._u.select_atoms(self._select)
self._n_atoms = self._atoms.n_atoms
if self._mean is None:
self.mean = np.zeros((self._n_atoms, 3))
self._calc_mean = True
else:
self.mean = np.asarray(self._mean)
if self.mean.shape[0] != self._n_atoms:
raise ValueError('Number of atoms in reference ({}) does '
'not match number of atoms in the '
'selection ({})'.format(self._n_atoms,
self.mean.shape[0]))
self._calc_mean = False
if self.n_frames == 1:
raise ValueError('No covariance information can be gathered from a'
'single trajectory frame.\n')
n_dim = self._n_atoms * 3
self.cov = np.zeros((n_dim, n_dim))
self._ref_atom_positions = self._reference.positions
self._ref_cog = self._reference.center_of_geometry()
self._ref_atom_positions -= self._ref_cog
if self._calc_mean:
for ts in ProgressBar(self._u.trajectory[self.start:self.stop:self.step],
verbose=self._verbose, desc="Mean Calculation"):
if self.align:
mobile_cog = self._atoms.center_of_geometry()
mobile_atoms, old_rmsd = _fit_to(self._atoms.positions - mobile_cog,
self._ref_atom_positions,
self._atoms,
mobile_com=mobile_cog,
ref_com=self._ref_cog)
self.mean += self._atoms.positions
self.mean /= self.n_frames
self._xmean = np.ravel(self.mean)
def _single_frame(self):
if self.align:
mobile_cog = self._atoms.center_of_geometry()
mobile_atoms, old_rmsd = _fit_to(self._atoms.positions - mobile_cog,
self._ref_atom_positions,
self._atoms,
mobile_com=mobile_cog,
ref_com=self._ref_cog)
# now all structures are aligned to reference
x = mobile_atoms.positions.ravel()
else:
x = self._atoms.positions.ravel()
x -= self._xmean
self.cov += np.dot(x[:, np.newaxis], x[:, np.newaxis].T)
def _conclude(self):
self.cov /= self.n_frames - 1
e_vals, e_vects = np.linalg.eig(self.cov)
sort_idx = np.argsort(e_vals)[::-1]
self._variance = e_vals[sort_idx]
self._p_components = e_vects[:, sort_idx]
self._calculated = True
self.n_components = self._n_components
@property
def p_components(self):
wmsg = ("The `p_components` attribute was deprecated in "
"MDAnalysis 2.0.0 and will be removed in MDAnalysis 3.0.0. "
"Please use `results.p_components` instead.")
warnings.warn(wmsg, DeprecationWarning)
return self.results.p_components
@property
def variance(self):
wmsg = ("The `variance` 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.variance
@property
def cumulated_variance(self):
wmsg = ("The `cumulated_variance` attribute was deprecated in "
"MDAnalysis 2.0.0 and will be removed in MDAnalysis 3.0.0. "
"Please use `results.cumulated_variance` instead.")
warnings.warn(wmsg, DeprecationWarning)
return self.results.cumulated_variance
@property
def n_components(self):
return self._n_components
@n_components.setter
def n_components(self, n):
if self._calculated:
if n is None:
n = len(self._variance)
self.results.variance = self._variance[:n]
self.results.cumulated_variance = (np.cumsum(self._variance) /
np.sum(self._variance))[:n]
self.results.p_components = self._p_components[:, :n]
self._n_components = n
[docs] @due.dcite(
Doi('10.1002/(SICI)1097-0134(19990901)36:4<419::AID-PROT5>3.0.CO;2-U'),
Doi('10.1529/biophysj.104.052449'),
description="RMSIP",
path='MDAnalysis.analysis.pca',
)
def rmsip(self, other, n_components=None):
"""Compute the root mean square inner product between subspaces.
This is only symmetric if the number of components is the same for
both instances. The RMSIP effectively measures how
correlated the vectors of this instance are to those of ``other``.
Please cite [Amadei1999]_ and [Leo-Macias2004]_ if you use this function.
Parameters
----------
other : :class:`~MDAnalysis.analysis.pca.PCA`
Another PCA class. This must have already been run.
n_components : int or tuple of ints, optional
number of components to compute for the inner products.
``None`` computes all of them.
Returns
-------
float:
Root mean square inner product of the selected subspaces.
0 indicates that they are mutually orthogonal, whereas 1 indicates
that they are identical.
See also
--------
:func:`~MDAnalysis.analysis.pca.rmsip`
.. versionadded:: 1.0.0
"""
try:
a = self.results.p_components
except AttributeError:
raise ValueError('Call run() on the PCA before using rmsip')
try:
b = other.p_components
except AttributeError:
if isinstance(other, type(self)):
raise ValueError(
'Call run() on the other PCA before using rmsip')
else:
raise ValueError('other must be another PCA class')
return rmsip(a.T, b.T, n_components=n_components)
[docs] @due.dcite(
Doi('10.1016/j.str.2007.12.011'),
description="Cumulative overlap",
path='MDAnalysis.analysis.pca',
)
def cumulative_overlap(self, other, i=0, n_components=None):
"""Compute the cumulative overlap of a vector in a subspace.
This is not symmetric. The cumulative overlap measures the overlap of
the chosen vector in this instance, in the ``other`` subspace.
Please cite [Yang2008]_ if you use this function.
Parameters
----------
other : :class:`~MDAnalysis.analysis.pca.PCA`
Another PCA class. This must have already been run.
i : int, optional
The index of eigenvector to be analysed.
n_components : int, optional
number of components in ``other`` to compute for the cumulative overlap.
``None`` computes all of them.
Returns
-------
float:
Cumulative overlap of the chosen vector in this instance to
the ``other`` subspace. 0 indicates that they are mutually
orthogonal, whereas 1 indicates that they are identical.
See also
--------
:func:`~MDAnalysis.analysis.pca.cumulative_overlap`
.. versionadded:: 1.0.0
"""
try:
a = self.results.p_components
except AttributeError:
raise ValueError(
'Call run() on the PCA before using cumulative_overlap')
try:
b = other.p_components
except AttributeError:
if isinstance(other, type(self)):
raise ValueError(
'Call run() on the other PCA before using cumulative_overlap')
else:
raise ValueError('other must be another PCA class')
return cumulative_overlap(a.T, b.T, i=i, n_components=n_components)
[docs]def cosine_content(pca_space, i):
"""Measure the cosine content of the PCA projection.
The cosine content of pca projections can be used as an indicator if a
simulation is converged. Values close to 1 are an indicator that the
simulation isn't converged. For values below 0.7 no statement can be made.
If you use this function please cite [BerkHess1]_.
Parameters
----------
pca_space : array, shape (number of frames, number of components)
The PCA space to be analyzed.
i : int
The index of the pca_component projection to be analyzed.
Returns
-------
A float reflecting the cosine content of the ith projection in the PCA
space. The output is bounded by 0 and 1, with 1 reflecting an agreement
with cosine while 0 reflects complete disagreement.
References
----------
.. [BerkHess1] Berk Hess. Convergence of sampling in protein simulations.
Phys. Rev. E 65, 031910 (2002).
"""
t = np.arange(len(pca_space))
T = len(pca_space)
cos = np.cos(np.pi * t * (i + 1) / T)
return ((2.0 / T) * (scipy.integrate.simps(cos*pca_space[:, i])) ** 2 /
scipy.integrate.simps(pca_space[:, i] ** 2))
[docs]@due.dcite(
Doi('10.1002/(SICI)1097-0134(19990901)36:4<419::AID-PROT5>3.0.CO;2-U'),
Doi('10.1529/biophysj.104.052449'),
description="RMSIP",
path='MDAnalysis.analysis.pca',
)
def rmsip(a, b, n_components=None):
"""Compute the root mean square inner product between subspaces.
This is only symmetric if the number of components is the same for
``a`` and ``b``. The RMSIP effectively measures how
correlated the vectors of ``a`` are to those of ``b``.
Please cite [Amadei1999]_ and [Leo-Macias2004]_ if you use this function.
Parameters
----------
a : array, shape (n_components, n_features)
The first subspace. Must have the same number of features as ``b``.
b : array, shape (n_components, n_features)
The second subspace. Must have the same number of features as ``a``.
n_components : int or tuple of ints, optional
number of components to compute for the inner products.
``None`` computes all of them.
Returns
-------
float:
Root mean square inner product of the selected subspaces.
0 indicates that they are mutually orthogonal, whereas 1 indicates
that they are identical.
.. versionadded:: 1.0.0
"""
n_components = util.asiterable(n_components)
if len(n_components) == 1:
n_a = n_b = n_components[0]
elif len(n_components) == 2:
n_a, n_b = n_components
else:
raise ValueError('Too many values provided for n_components')
if n_a is None:
n_a = len(a)
if n_b is None:
n_b = len(b)
sip = np.matmul(a[:n_a], b[:n_b].T) ** 2
msip = sip.sum()/n_a
return msip**0.5
[docs]@due.dcite(
Doi('10.1016/j.str.2007.12.011'),
description="Cumulative overlap",
path='MDAnalysis.analysis.pca',
)
def cumulative_overlap(a, b, i=0, n_components=None):
"""Compute the cumulative overlap of a vector in a subspace.
This is not symmetric. The cumulative overlap measures the overlap of
the chosen vector in ``a``, in the ``b`` subspace.
Please cite [Yang2008]_ if you use this function.
Parameters
----------
a : array, shape (n_components, n_features) or vector, length n_features
The first subspace containing the vector of interest. Alternatively,
the actual vector. Must have the same number of features as ``b``.
b : array, shape (n_components, n_features)
The second subspace. Must have the same number of features as ``a``.
i : int, optional
The index of eigenvector to be analysed.
n_components : int, optional
number of components in ``b`` to compute for the cumulative overlap.
``None`` computes all of them.
Returns
-------
float:
Cumulative overlap of the chosen vector in ``a`` to the ``b`` subspace.
0 indicates that they are mutually orthogonal, whereas 1 indicates
that they are identical.
.. versionadded:: 1.0.0
"""
if len(a.shape) < len(b.shape):
a = a[np.newaxis, :]
vec = a[i][np.newaxis, :]
vec_norm = (vec**2).sum() ** 0.5
if n_components is None:
n_components = len(b)
b = b[:n_components]
b_norms = (b**2).sum(axis=1) ** 0.5
o = np.abs(np.matmul(vec, b.T)) / (b_norms*vec_norm)
return (o**2).sum() ** 0.5