Source code for MDAnalysis.analysis.encore.clustering.cluster

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clustering frontend --- :mod:`MDAnalysis.analysis.encore.clustering.cluster`

The module defines a function serving as front-end for various clustering
algorithms, wrapping them to allow them to be used interchangably.

:Author: Matteo Tiberti, Wouter Boomsma, Tone Bengtsen

.. versionadded:: 0.16.0

import numpy as np
from ..utils import ParallelCalculation, merge_universes
from .ClusterCollection import ClusterCollection
from ..confdistmatrix import get_distance_matrix
from . import ClusteringMethod

[docs]def cluster(ensembles, method = ClusteringMethod.AffinityPropagationNative(), select="name CA", distance_matrix=None, allow_collapsed_result=True, ncores=1, **kwargs): """Cluster frames from one or more ensembles, using one or more clustering methods. The function optionally takes pre-calculated distances matrices as an argument. Note that not all clustering procedure can work directly on distance matrices, so the distance matrices might be ignored for particular choices of method. Parameters ---------- ensembles : MDAnalysis.Universe, or list, or list of list thereof The function takes either a single Universe object, a list of Universe objects or a list of lists of Universe objects. If given a single universe, it simply clusters the conformations in the trajectory. If given a list of ensembles, it will merge them and cluster them together, keeping track of the ensemble to which each of the conformations belong. Finally, if passed a list of list of ensembles, the function will just repeat the functionality just described - merging ensembles for each ensemble in the outer loop. method: encore.ClusteringMethod or list thereof, optional A single or a list of instances of the Clustering classes from the clustering module. A separate analysis will be run for each method. Note that different parameters for the same clustering method can be explored by adding different instances of the same clustering class. select : str, optional Atom selection string in the MDAnalysis format. Default is "name CA" distance_matrix : encore.utils.TriangularMatrix or list thereof, optional Distance matrix used for clustering. If this parameter is not supplied the matrix will be calculated on the fly. If several distance matrices are supplied, an analysis will be done for each of them. The number of provided distance matrices should match the number of provided ensembles. allow_collapsed_result: bool, optional Whether a return value of a list of one value should be collapsed into just the value. ncores : int, optional Maximum number of cores to be used (default is 1). Returns ------- list of ClustersCollection objects (or potentially a single ClusteringCollection object if allow_collapsed_result is set to True) Example ------- Two ensembles are created as Universe object using a topology file and two trajectories. The topology- and trajectory files used are obtained from the MDAnalysis test suite for two different simulations of the protein AdK. Here, we cluster two ensembles :: >>> from MDAnalysis import Universe >>> import MDAnalysis.analysis.encore as encore >>> from MDAnalysis.tests.datafiles import PSF, DCD, DCD2 >>> ens1 = Universe(PSF, DCD) >>> ens2 = Universe(PSF, DCD2) >>> cluster_collection = encore.cluster([ens1,ens2]) >>> print cluster_collection You can change the parameters of the clustering method by explicitly specifying the method :: >>> cluster_collection = encore.cluster( [ens1,ens2], method=encore.AffinityPropagationNative(preference=-2.)) Here is an illustration using DBSCAN algorithm, instead of the default clustering method :: >>> cluster_collection = encore.cluster( [ens1,ens2], method=encore.DBSCAN()) You can also combine multiple methods in one call :: >>> cluster_collection = encore.cluster( [ens1,ens2], method=[encore.AffinityPropagationNative(preference=-1.), encore.AffinityPropagationNative(preference=-2.)]) In addition to standard cluster membership information, the `cluster_collection` output keep track of the origin of each conformation, so you check how the different trajectories are represented in each cluster. Here, for brevity, we print just the members of the two first clusters :: >>> print [cluster.metadata["ensemble_membership"] for cluster in cluster_collection][:2] [array([1, 1, 1, 1, 2]), array([1, 1, 1, 1, 1])] """ # Internally, ensembles are always transformed to a list of lists if ensembles is not None: if not hasattr(ensembles, '__iter__'): ensembles = [ensembles] ensembles_list = ensembles if not hasattr(ensembles[0], '__iter__'): ensembles_list = [ensembles] # Calculate merged ensembles and transfer to memory merged_ensembles = [] for ensembles in ensembles_list: # Transfer ensembles to memory for ensemble in ensembles: ensemble.transfer_to_memory() merged_ensembles.append(merge_universes(ensembles)) methods = method if not hasattr(method, '__iter__'): methods = [method] # Check whether any of the clustering methods can make use of a distance # matrix any_method_accept_distance_matrix = \ np.any([_method.accepts_distance_matrix for _method in methods]) # If distance matrices are provided, check that it matches the number # of ensembles if distance_matrix: if not hasattr(distance_matrix, '__iter__'): distance_matrix = [distance_matrix] if ensembles is not None and \ len(distance_matrix) != len(merged_ensembles): raise ValueError("Dimensions of provided list of distance matrices " "does not match that of provided list of " "ensembles: {0} vs {1}" .format(len(distance_matrix), len(merged_ensembles))) else: # Calculate distance matrices for all merged ensembles - if not provided if any_method_accept_distance_matrix: distance_matrix = [] for merged_ensemble in merged_ensembles: distance_matrix.append(get_distance_matrix(merged_ensemble, select=select, **kwargs)) args = [] for method in methods: if method.accepts_distance_matrix: args += [(d,) for d in distance_matrix] else: for merged_ensemble in merged_ensembles: coordinates = merged_ensemble.trajectory.timeseries(order="fac") # Flatten coordinate matrix into n_frame x n_coordinates coordinates = np.reshape(coordinates, (coordinates.shape[0], -1)) args.append((coordinates,)) # Execute clustering procedure pc = ParallelCalculation(ncores, methods, args) # Run parallel calculation results = # Keep track of which sample belongs to which ensembles metadata = None if ensembles is not None: ensemble_assignment = [] for i, ensemble in enumerate(ensembles): ensemble_assignment += [i+1]*len(ensemble.trajectory) ensemble_assignment = np.array(ensemble_assignment) metadata = {'ensemble_membership': ensemble_assignment} # Create clusters collections from clustering results, # one for each cluster. None if clustering didn't work. ccs = [ClusterCollection(clusters[1], metadata=metadata) for clusters in results] if allow_collapsed_result and len(ccs) == 1: ccs = ccs[0] return ccs