Source code for MDAnalysis.analysis.encore.dimensionality_reduction.reduce_dimensionality

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dimensionality reduction frontend --- :mod:`MDAnalysis.analysis.encore.dimensionality_reduction.reduce_dimensionality`

The module defines a function serving as front-end for various dimensionality
reduction 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 ..confdistmatrix import get_distance_matrix
from ..utils import ParallelCalculation, merge_universes
from ..dimensionality_reduction.DimensionalityReductionMethod import (

[docs]def reduce_dimensionality(ensembles, method=StochasticProximityEmbeddingNative(), select="name CA", distance_matrix=None, allow_collapsed_result=True, ncores=1, **kwargs): """ Reduce dimensions in frames from one or more ensembles, using one or more dimensionality reduction methods. The function optionally takes pre-calculated distances matrices as an argument. Note that not all dimensionality reduction 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 works on the conformations in the trajectory. If given a list of ensembles, it will merge them and analyse 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 : MDAnalysis.analysis.encore.dimensionality_reduction.DimensionalityReductionMethod or list A single or a list of instances of the DimensionalityReductionMethod classes from the dimensionality_reduction module. A separate analysis will be run for each method. Note that different parameters for the same method can be explored by adding different instances of the same dimensionality reduction class. Options are Stochastic Proximity Embedding or Principal Component Analysis. select : str, optional Atom selection string in the MDAnalysis format (default is "name CA") distance_matrix : encore.utils.TriangularMatrix, optional Distance matrix for stochastic proximity embedding. If this parameter is not supplied an RMSD distance matrix will be calculated on the fly (default). 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 (default = True). ncores : int, optional Maximum number of cores to be used (default is 1). Returns ------- list of coordinate arrays in the reduced dimensions (or potentially a single coordinate array 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 reduce two ensembles to two dimensions, and plot the result using matplotlib: :: >>> 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) >>> coordinates, details = encore.reduce_dimensionality([ens1,ens2]) >>> plt.scatter(coordinates[0], coordinates[1], color=[["red", "blue"][m-1] for m in details["ensemble_membership"]]) Note how we extracted information about which conformation belonged to which ensemble from the details variable. You can change the parameters of the dimensionality reduction method by explicitly specifying the method :: >>> coordinates, details = encore.reduce_dimensionality([ens1,ens2], method=encore.StochasticProximityEmbeddingNative(dimension=3)) Here is an illustration using Principal Component Analysis, instead of the default dimensionality reduction method :: >>> coordinates, details = encore.reduce_dimensionality( [ens1,ens2], method=encore.PrincipalComponentAnalysis(dimension=2)) You can also combine multiple methods in one call :: >>> coordinates, details = encore.reduce_dimensionality( [ens1,ens2], method=[encore.PrincipalComponentAnalysis(dimension=2), encore.StochasticProximityEmbeddingNative(dimension=2)]) """ 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 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 dimensionality reduction procedure pc = ParallelCalculation(ncores, methods, args) # Run parallel calculation results = # Keep track of which sample belongs to which ensembles details = {} 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) details['ensemble_membership'] = ensemble_assignment coordinates = [] for result in results: coordinates.append(result[1][0]) # details.append(result[1][1]) if allow_collapsed_result and len(coordinates)==1: coordinates = coordinates[0] # details = details[0] return coordinates, details