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

    # -*- Mode: python; tab-width: 4; indent-tabs-mode:nil; coding:utf-8 -*-
    # vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4
    #
    # MDAnalysis --- https://www.mdanalysis.org
    # Copyright (c) 2006-2017 The MDAnalysis Development Team and contributors
    # (see the file AUTHORS for the full list of names)
    #
    # Released under the Lesser GNU Public Licence, v2.1 or any higher version
    #
    # Please cite your use of MDAnalysis in published work:
    #
    # 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
    #
    """
    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

    .. deprecated:: 2.8.0
       This module is deprecated in favour of the
       MDAKit `mdaencore <https://mdanalysis.org/mdaencore/>`_ and will be removed
       in MDAnalysis 3.0.0.

    """
    import numpy as np
    from ..confdistmatrix import get_distance_matrix
    from ..utils import ParallelCalculation, merge_universes
    from ..dimensionality_reduction.DimensionalityReductionMethod import (
        StochasticProximityEmbeddingNative,
    )

    def reduce_dimensionality(
        ensembles,
        method=None,
        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 method is None:
            method = StochasticProximityEmbeddingNative()
        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 = pc.run()

        # 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