Inclusion of ENCORE into MDAnalysis

.travis.yml

@@ -40,7 +40,7 @@ before_install:
   - conda config --add channels MDAnalysis
   - conda update --yes conda
 install:
-  - if [[ $SETUP == 'full' ]]; then conda create --yes -q -n pyenv python=$PYTHON_VERSION numpy scipy nose=1.3.7 sphinx=1.3 griddataformats six; fi
+  - if [[ $SETUP == 'full' ]]; then conda create --yes -q -n pyenv python=$PYTHON_VERSION numpy scipy nose=1.3.7 sphinx=1.3 griddataformats six scikit-learn; fi
   - if [[ $SETUP == 'minimal' ]]; then conda create --yes -q -n pyenv python=$PYTHON_VERSION numpy nose=1.3.7 sphinx=1.3 griddataformats six; fi
   - source activate pyenv
   - |

package/MDAnalysis/analysis/encore/__init__.py

@@ -0,0 +1,36 @@
+# __init__.py
+# Copyright (C) 2014 Wouter Boomsma, Matteo Tiberti
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+from __future__ import absolute_import
+
+__all__ = [
+    'covariance',
+    'similarity',
+    'confdistmatrix',
+    'clustering'
+]
+
+from .similarity import hes, ces, dres, \
+    ces_convergence, dres_convergence
+
+from .clustering.ClusterCollection import ClusterCollection, Cluster
+from .clustering.ClusteringMethod import *
+from .clustering.cluster import cluster
+from .dimensionality_reduction.DimensionalityReductionMethod import *
+from .dimensionality_reduction.reduce_dimensionality import (
+    reduce_dimensionality)
+from .confdistmatrix import get_distance_matrix
+from .utils import merge_universes

package/MDAnalysis/analysis/encore/bootstrap.py

@@ -0,0 +1,153 @@
+# bootstrap.py --- Bootstrap analysis for ensembles and distance matrices
+# Copyright (C) 2014 Wouter Boomsma, Matteo Tiberti
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+"""
+bootstrap procedures --- :mod:`MDAnalysis.analysis.ensemble.bootstrap`
+=====================================================================
+
+
+The module contains functions for bootstrapping either ensembles (Universe
+objects) or distance matrices, by resampling with replacement.
+
+:Author: Matteo Tiberti, Wouter Boomsma, Tone Bengtsen
+:Year: 2015--2016
+:Copyright: GNU Public License v3
+:Mantainer: Matteo Tiberti <matteo.tiberti@gmail.com>, mtiberti on github
+
+.. versionadded:: 0.16.0
+
+"""
+
+import numpy as np
+import logging
+import MDAnalysis as mda
+from .utils import TriangularMatrix, ParallelCalculation
+
+
+def bootstrapped_matrix(matrix, ensemble_assignment):
+    """
+    Bootstrap an input square matrix. The resulting matrix will have the same
+    shape as the original one, but the order of its elements will be drawn
+    (with repetition). Separately bootstraps each ensemble.
+
+    Parameters
+    ----------
+
+    matrix : encore.utils.TriangularMatrix
+        similarity/dissimilarity matrix
+
+    ensemble_assignment: numpy.array
+        array of ensemble assignments. This array must be matrix.size long.
+
+    Returns
+    -------
+
+    this_m : encore.utils.TriangularMatrix
+        bootstrapped similarity/dissimilarity matrix
+    """
+    ensemble_identifiers = np.unique(ensemble_assignment)
+    this_m = TriangularMatrix(size=matrix.size)
+    indexes = []
+    for ens in ensemble_identifiers:
+        old_indexes = np.where(ensemble_assignment == ens)[0]
+        indexes.append(np.random.randint(low=np.min(old_indexes),
+                                         high=np.max(old_indexes) + 1,
+                                         size=old_indexes.shape[0]))
+
+    indexes = np.hstack(indexes)
+    for j in range(this_m.size):
+        for k in range(j):
+            this_m[j, k] = matrix[indexes[j], indexes[k]]
+
+    logging.info("Matrix bootstrapped.")
+    return this_m
+
+
+def get_distance_matrix_bootstrap_samples(distance_matrix,
+                                          ensemble_assignment,
+                                          samples=100,
+                                          ncores=1):
+    """
+    Calculates distance matrices corresponding to bootstrapped ensembles, by
+    resampling with replacement.
+
+    Parameters
+    ----------
+
+    distance_matrix : encore.utils.TriangularMatrix
+        Conformational distance matrix
+
+    ensemble_assignment : str
+        Mapping from frames to which ensemble they are from (necessary because
+        ensembles are bootstrapped independently)
+
+    samples : int, optional
+        How many bootstrap samples to create.
+
+    ncores : int, optional
+        Maximum number of cores to be used (default is 1)
+
+    Returns
+    -------
+
+    confdistmatrix : list of encore.utils.TriangularMatrix
+    """
+
+    bs_args = \
+            [([distance_matrix, ensemble_assignment]) for i in range(samples)]
+
+    pc = ParallelCalculation(ncores, bootstrapped_matrix, bs_args)
+
+    pc_results = pc.run()
+
+    bootstrap_matrices = zip(*pc_results)[1]
+
+    return bootstrap_matrices
+
+
+def get_ensemble_bootstrap_samples(ensemble,
+                                   samples=100):
+    """
+    Generates a bootstrapped ensemble by resampling with replacement.
+
+    Parameters
+    ----------
+
+    ensemble : MDAnalysis.Universe
+        Conformational distance matrix
+
+    samples : int, optional
+        How many bootstrap samples to create.
+
+    Returns
+    -------
+
+    list of MDAnalysis.Universe objects
+    """
+
+    ensemble.transfer_to_memory()
+
+    ensembles = []
+    for i in range(samples):
+        indices = np.random.randint(
+            low=0,
+            high=ensemble.trajectory.timeseries().shape[1],
+            size=ensemble.trajectory.timeseries().shape[1])
+        ensembles.append(
+            mda.Universe(ensemble.filename,
+                        ensemble.trajectory.timeseries(format='afc')[:,indices,:],
+                         format=mda.coordinates.memory.MemoryReader))
+    return ensembles