macros.py

"""@namespace IMP.pmi.macros
Protocols for sampling structures and analyzing them.
"""

from __future__ import print_function, division
import IMP
import IMP.pmi.tools
import IMP.pmi.samplers
import IMP.pmi.output
import IMP.pmi.analysis
import IMP.pmi.io
import IMP.pmi.alphabets
import IMP.rmf
import IMP.isd
import IMP.pmi.dof
import os
try:
    from pathlib import Path
except ImportError:  # Use bundled pathlib on Python 2 without pathlib
    from IMP._compat_pathlib import Path
import glob
from operator import itemgetter
from collections import defaultdict
import numpy as np
import itertools
import warnings
import math

import pickle


class _MockMPIValues(object):
    """Replace samplers.MPI_values when in test mode"""
    def get_percentile(self, name):
        return 0.


class _RMFRestraints(object):
    """All restraints that are written out to the RMF file"""
    def __init__(self, model, user_restraints):
        self._rmf_rs = IMP.pmi.tools.get_restraint_set(model, rmf=True)
        self._user_restraints = user_restraints if user_restraints else []

    def __len__(self):
        return (len(self._user_restraints)
                + self._rmf_rs.get_number_of_restraints())

    def __bool__(self):
        return len(self) > 0
    __nonzero__ = __bool__  # Python 2 compatibility

    def __getitem__(self, i):
        class FakePMIWrapper(object):
            def __init__(self, r):
                self.r = IMP.RestraintSet.get_from(r)

            def get_restraint(self):
                return self.r

        lenuser = len(self._user_restraints)
        if 0 <= i < lenuser:
            return self._user_restraints[i]
        elif 0 <= i - lenuser < self._rmf_rs.get_number_of_restraints():
            r = self._rmf_rs.get_restraint(i - lenuser)
            return FakePMIWrapper(r)
        else:
            raise IndexError("Out of range")


class ReplicaExchange(object):
    """A macro to help setup and run replica exchange.
    Supports Monte Carlo and molecular dynamics.
    Produces trajectory RMF files, best PDB structures,
    and output stat files.
    """
    def __init__(self, model, root_hier,
                 monte_carlo_sample_objects=None,
                 molecular_dynamics_sample_objects=None,
                 output_objects=[],
                 rmf_output_objects=None,
                 monte_carlo_temperature=1.0,
                 simulated_annealing=False,
                 simulated_annealing_minimum_temperature=1.0,
                 simulated_annealing_maximum_temperature=2.5,
                 simulated_annealing_minimum_temperature_nframes=100,
                 simulated_annealing_maximum_temperature_nframes=100,
                 replica_exchange_minimum_temperature=1.0,
                 replica_exchange_maximum_temperature=2.5,
                 replica_exchange_swap=True,
                 num_sample_rounds=1,
                 number_of_best_scoring_models=500,
                 monte_carlo_steps=10,
                 self_adaptive=False,
                 molecular_dynamics_steps=10,
                 molecular_dynamics_max_time_step=1.0,
                 number_of_frames=1000,
                 save_coordinates_mode="lowest_temperature",
                 nframes_write_coordinates=1,
                 write_initial_rmf=True,
                 initial_rmf_name_suffix="initial",
                 stat_file_name_suffix="stat",
                 best_pdb_name_suffix="model",
                 mmcif=False,
                 do_clean_first=True,
                 do_create_directories=True,
                 global_output_directory="./",
                 rmf_dir="rmfs/",
                 best_pdb_dir="pdbs/",
                 replica_stat_file_suffix="stat_replica",
                 em_object_for_rmf=None,
                 atomistic=False,
                 replica_exchange_object=None,
                 test_mode=False,
                 score_moved=False,
                 use_nestor=False,
                 nestor_restraints=None,
                 nestor_rmf_fname_prefix="nested",):
        """Constructor.
           @param model The IMP model
           @param root_hier Top-level (System)hierarchy
           @param monte_carlo_sample_objects Objects for MC sampling, which
                  should generally be a simple list of Mover objects, e.g.
                  from DegreesOfFreedom.get_movers().
           @param molecular_dynamics_sample_objects Objects for MD sampling,
                  which should generally be a simple list of particles.
           @param output_objects A list of structural objects and restraints
                  that will be included in output (ie, statistics "stat"
                  files). Any object that provides a get_output() method
                  can be used here. If None is passed
                  the macro will not write stat files.
           @param rmf_output_objects A list of structural objects and
                  restraints that will be included in rmf. Any object
                  that provides a get_output() method can be used here.
           @param monte_carlo_temperature  MC temp (may need to be optimized
                  based on post-sampling analysis)
           @param simulated_annealing If True, perform simulated annealing
           @param simulated_annealing_minimum_temperature Should generally be
                  the same as monte_carlo_temperature.
           @param simulated_annealing_minimum_temperature_nframes Number of
                  frames to compute at minimum temperature.
           @param simulated_annealing_maximum_temperature_nframes Number of
                  frames to compute at
                  temps > simulated_annealing_maximum_temperature.
           @param replica_exchange_minimum_temperature Low temp for REX; should
                  generally be the same as monte_carlo_temperature.
           @param replica_exchange_maximum_temperature High temp for REX
           @param replica_exchange_swap Boolean, enable disable temperature
                  swap (Default=True)
           @param num_sample_rounds        Number of rounds of MC/MD per cycle
           @param number_of_best_scoring_models Number of top-scoring PDB/mmCIF
                  models to keep around for analysis.
           @param mmcif If True, write best scoring models in mmCIF format;
                  if False (the default), write in legacy PDB format.
           @param best_pdb_dir The directory under `global_output_directory`
                  where best-scoring PDB/mmCIF files are written.
           @param best_pdb_name_suffix Part of the file name for best-scoring
                  PDB/mmCIF files.
           @param monte_carlo_steps        Number of MC steps per round
           @param self_adaptive     self adaptive scheme for Monte Carlo movers
           @param molecular_dynamics_steps  Number of MD steps per round
           @param molecular_dynamics_max_time_step Max time step for MD
           @param number_of_frames         Number of REX frames to run
           @param save_coordinates_mode  string: how to save coordinates.
                  "lowest_temperature" (default) only the lowest temperatures
                  is saved
                  "25th_score" all replicas whose score is below the 25th
                  percentile
                  "50th_score" all replicas whose score is below the 50th
                  percentile
                  "75th_score" all replicas whose score is below the 75th
                  percentile
           @param nframes_write_coordinates How often to write the coordinates
                  of a frame
           @param write_initial_rmf        Write the initial configuration
           @param global_output_directory Folder that will be created to house
                  output.
           @param test_mode Set to True to avoid writing any files, just test
                  one frame.
           @param score_moved   If True, attempt to speed up Monte Carlo
                  sampling by caching scoring function terms on particles
                  that didn't move.
           @param use_nestor   If True, follows the Nested Sampling workflow
                  of the NestOR module and skips writing stat files and
                  replica stat files.
           @param nestor_restraints   A list of restraints for which
                  likelihoods are to be computed for use by NestOR module.
           @param nestor_rmf_fname_prefix   Prefix to be used for storing .rmf3
                  files generated by NestOR .
        """
        self.model = model
        self.vars = {}

        # add check hierarchy is multistate
        if output_objects == []:
            # The "[]" in the default parameters is a global object, so make
            # our own copy here
            self.output_objects = []
        else:
            self.output_objects = output_objects
        self.rmf_output_objects = rmf_output_objects
        if (isinstance(root_hier, IMP.atom.Hierarchy)
                and not root_hier.get_parent()):
            if self.output_objects is not None:
                self.output_objects.append(
                    IMP.pmi.io.TotalScoreOutput(self.model))
            if self.rmf_output_objects is not None:
                self.rmf_output_objects.append(
                    IMP.pmi.io.TotalScoreOutput(self.model))
            self.root_hier = root_hier
            states = IMP.atom.get_by_type(root_hier, IMP.atom.STATE_TYPE)
            self.vars["number_of_states"] = len(states)
            if len(states) > 1:
                self.root_hiers = states
                self.is_multi_state = True
            else:
                self.root_hier = root_hier
                self.is_multi_state = False
        else:
            raise TypeError("Must provide System hierarchy (root_hier)")

        self._rmf_restraints = _RMFRestraints(model, None)
        self.em_object_for_rmf = em_object_for_rmf
        self.monte_carlo_sample_objects = monte_carlo_sample_objects
        self.vars["self_adaptive"] = self_adaptive
        self.molecular_dynamics_sample_objects = \
            molecular_dynamics_sample_objects
        self.replica_exchange_object = replica_exchange_object
        self.molecular_dynamics_max_time_step = \
            molecular_dynamics_max_time_step
        self.vars["monte_carlo_temperature"] = monte_carlo_temperature
        self.vars["replica_exchange_minimum_temperature"] = \
            replica_exchange_minimum_temperature
        self.vars["replica_exchange_maximum_temperature"] = \
            replica_exchange_maximum_temperature
        self.vars["replica_exchange_swap"] = replica_exchange_swap
        self.vars["simulated_annealing"] = simulated_annealing
        self.vars["simulated_annealing_minimum_temperature"] = \
            simulated_annealing_minimum_temperature
        self.vars["simulated_annealing_maximum_temperature"] = \
            simulated_annealing_maximum_temperature
        self.vars["simulated_annealing_minimum_temperature_nframes"] = \
            simulated_annealing_minimum_temperature_nframes
        self.vars["simulated_annealing_maximum_temperature_nframes"] = \
            simulated_annealing_maximum_temperature_nframes

        self.vars["num_sample_rounds"] = num_sample_rounds
        self.vars[
            "number_of_best_scoring_models"] = number_of_best_scoring_models
        self.vars["monte_carlo_steps"] = monte_carlo_steps
        self.vars["molecular_dynamics_steps"] = molecular_dynamics_steps
        self.vars["number_of_frames"] = number_of_frames
        if save_coordinates_mode not in ("lowest_temperature", "25th_score",
                                         "50th_score", "75th_score"):
            raise Exception("save_coordinates_mode has unrecognized value")
        else:
            self.vars["save_coordinates_mode"] = save_coordinates_mode
        self.vars["nframes_write_coordinates"] = nframes_write_coordinates
        self.vars["write_initial_rmf"] = write_initial_rmf
        self.vars["initial_rmf_name_suffix"] = initial_rmf_name_suffix
        self.vars["best_pdb_name_suffix"] = best_pdb_name_suffix
        self.vars["mmcif"] = mmcif
        self.vars["stat_file_name_suffix"] = stat_file_name_suffix
        self.vars["do_clean_first"] = do_clean_first
        self.vars["do_create_directories"] = do_create_directories
        self.vars["global_output_directory"] = global_output_directory
        self.vars["rmf_dir"] = rmf_dir
        self.vars["best_pdb_dir"] = best_pdb_dir
        self.vars["atomistic"] = atomistic
        self.vars["replica_stat_file_suffix"] = replica_stat_file_suffix
        self.vars["geometries"] = None
        self.test_mode = test_mode
        self.score_moved = score_moved
        self.nest = use_nestor
        self.nestor_restraints = nestor_restraints
        self.nestor_rmf_fname = nestor_rmf_fname_prefix

    def add_geometries(self, geometries):
        if self.vars["geometries"] is None:
            self.vars["geometries"] = list(geometries)
        else:
            self.vars["geometries"].extend(geometries)

    def show_info(self):
        print("ReplicaExchange: it generates initial.*.rmf3, stat.*.out, "
              "rmfs/*.rmf3 for each replica ")
        print("--- it stores the best scoring pdb models in pdbs/")
        print("--- the stat.*.out and rmfs/*.rmf3 are saved only at the "
              "lowest temperature")
        print("--- variables:")
        keys = list(self.vars.keys())
        keys.sort()
        for v in keys:
            print("------", v.ljust(30), self.vars[v])
        print("Use nestor: ", self.nest)

    def get_replica_exchange_object(self):
        return self.replica_exchange_object

    def _add_provenance(self, sampler_md, sampler_mc):
        """Record details about the sampling in the IMP Hierarchies"""
        iterations = 0
        if sampler_md:
            method = "Molecular Dynamics"
            iterations += self.vars["molecular_dynamics_steps"]
        if sampler_mc:
            method = "Hybrid MD/MC" if sampler_md else "Monte Carlo"
            iterations += self.vars["monte_carlo_steps"]
        # If no sampling is actually done, no provenance to write
        if iterations == 0 or self.vars["number_of_frames"] == 0:
            return
        iterations *= self.vars["num_sample_rounds"]

        pi = self.model.add_particle("sampling")
        p = IMP.core.SampleProvenance.setup_particle(
                self.model, pi, method, self.vars["number_of_frames"],
                iterations)
        p.set_number_of_replicas(
                self.replica_exchange_object.get_number_of_replicas())
        IMP.pmi.tools._add_pmi_provenance(self.root_hier)
        IMP.core.add_provenance(self.model, self.root_hier, p)

    def execute_macro(self):
        temp_index_factor = 100000.0
        samplers = []
        sampler_mc = None
        sampler_md = None
        if self.monte_carlo_sample_objects is not None:
            print("Setting up MonteCarlo")
            sampler_mc = IMP.pmi.samplers.MonteCarlo(
                self.model, self.monte_carlo_sample_objects,
                self.vars["monte_carlo_temperature"],
                score_moved=self.score_moved)
            if self.vars["simulated_annealing"]:
                tmin = self.vars["simulated_annealing_minimum_temperature"]
                tmax = self.vars["simulated_annealing_maximum_temperature"]
                nfmin = self.vars[
                    "simulated_annealing_minimum_temperature_nframes"]
                nfmax = self.vars[
                    "simulated_annealing_maximum_temperature_nframes"]
                sampler_mc.set_simulated_annealing(tmin, tmax, nfmin, nfmax)
            if self.vars["self_adaptive"]:
                sampler_mc.set_self_adaptive(
                    isselfadaptive=self.vars["self_adaptive"])
            if self.output_objects is not None:
                self.output_objects.append(sampler_mc)
            if self.rmf_output_objects is not None:
                self.rmf_output_objects.append(sampler_mc)
            samplers.append(sampler_mc)

        if self.molecular_dynamics_sample_objects is not None:
            print("Setting up MolecularDynamics")
            sampler_md = IMP.pmi.samplers.MolecularDynamics(
                self.model, self.molecular_dynamics_sample_objects,
                self.vars["monte_carlo_temperature"],
                maximum_time_step=self.molecular_dynamics_max_time_step)
            if self.vars["simulated_annealing"]:
                tmin = self.vars["simulated_annealing_minimum_temperature"]
                tmax = self.vars["simulated_annealing_maximum_temperature"]
                nfmin = self.vars[
                    "simulated_annealing_minimum_temperature_nframes"]
                nfmax = self.vars[
                    "simulated_annealing_maximum_temperature_nframes"]
                sampler_md.set_simulated_annealing(tmin, tmax, nfmin, nfmax)
            if self.output_objects is not None:
                self.output_objects.append(sampler_md)
            if self.rmf_output_objects is not None:
                self.rmf_output_objects.append(sampler_md)
            samplers.append(sampler_md)
# -------------------------------------------------------------------------

        print("Setting up ReplicaExchange")
        rex = IMP.pmi.samplers.ReplicaExchange(
            self.model, self.vars["replica_exchange_minimum_temperature"],
            self.vars["replica_exchange_maximum_temperature"], samplers,
            replica_exchange_object=self.replica_exchange_object)
        self.replica_exchange_object = rex.rem

        myindex = rex.get_my_index()
        if self.output_objects is not None:
            self.output_objects.append(rex)
        if self.rmf_output_objects is not None:
            self.rmf_output_objects.append(rex)
        # must reset the minimum temperature due to the
        # different binary length of rem.get_my_parameter double and python
        # float
        min_temp_index = int(min(rex.get_temperatures()) * temp_index_factor)

# -------------------------------------------------------------------------

        globaldir = self.vars["global_output_directory"] + "/"
        rmf_dir = globaldir + self.vars["rmf_dir"]
        pdb_dir = globaldir + self.vars["best_pdb_dir"]

        if not self.test_mode and not self.nest:
            if self.vars["do_clean_first"]:
                pass

            if self.vars["do_create_directories"]:

                try:
                    os.makedirs(globaldir)
                except:  # noqa: E722
                    pass
                try:
                    os.makedirs(rmf_dir)
                except:  # noqa: E722
                    pass

                if not self.is_multi_state:
                    try:
                        os.makedirs(pdb_dir)
                    except:  # noqa: E722
                        pass
                else:
                    for n in range(self.vars["number_of_states"]):
                        try:
                            os.makedirs(pdb_dir + "/" + str(n))
                        except:  # noqa: E722
                            pass

# -------------------------------------------------------------------------

        sw = IMP.pmi.tools.Stopwatch()
        if self.output_objects is not None:
            self.output_objects.append(sw)
        if self.rmf_output_objects is not None:
            self.rmf_output_objects.append(sw)

        output = IMP.pmi.output.Output(atomistic=self.vars["atomistic"])

        if not self.nest:
            print("Setting up stat file")
            low_temp_stat_file = globaldir + \
                self.vars["stat_file_name_suffix"] + "." + \
                str(myindex) + ".out"

        # Ensure model is updated before saving init files
        if not self.test_mode:
            self.model.update()

        if not self.test_mode and not self.nest:
            if self.output_objects is not None:
                output.init_stat2(low_temp_stat_file,
                                  self.output_objects,
                                  extralabels=["rmf_file", "rmf_frame_index"])
        else:
            print("Stat file writing is disabled")

        if self.rmf_output_objects is not None and not self.nest:
            print("Stat info being written in the rmf file")

        if not self.test_mode and not self.nest:
            print("Setting up replica stat file")
            replica_stat_file = globaldir + \
                self.vars["replica_stat_file_suffix"] + "." + \
                str(myindex) + ".out"
            if not self.test_mode:
                output.init_stat2(replica_stat_file, [rex],
                                  extralabels=["score"])

            print("Setting up best pdb files")
            if not self.is_multi_state:
                if self.vars["number_of_best_scoring_models"] > 0:
                    output.init_pdb_best_scoring(
                        pdb_dir + "/" + self.vars["best_pdb_name_suffix"],
                        self.root_hier,
                        self.vars["number_of_best_scoring_models"],
                        replica_exchange=True,
                        mmcif=self.vars['mmcif'],
                        best_score_file=globaldir + "best.scores.rex.py")
                    pdbext = ".0.cif" if self.vars['mmcif'] else ".0.pdb"
                    output.write_psf(
                        pdb_dir + "/" + "model.psf",
                        pdb_dir + "/" +
                        self.vars["best_pdb_name_suffix"] + pdbext)
            else:
                if self.vars["number_of_best_scoring_models"] > 0:
                    for n in range(self.vars["number_of_states"]):
                        output.init_pdb_best_scoring(
                            pdb_dir + "/" + str(n) + "/" +
                            self.vars["best_pdb_name_suffix"],
                            self.root_hiers[n],
                            self.vars["number_of_best_scoring_models"],
                            replica_exchange=True,
                            mmcif=self.vars['mmcif'],
                            best_score_file=globaldir + "best.scores.rex.py")
                        pdbext = ".0.cif" if self.vars['mmcif'] else ".0.pdb"
                        output.write_psf(
                            pdb_dir + "/" + str(n) + "/" + "model.psf",
                            pdb_dir + "/" + str(n) + "/" +
                            self.vars["best_pdb_name_suffix"] + pdbext)
# ---------------------------------------------

        if self.em_object_for_rmf is not None:
            output_hierarchies = [
                self.root_hier,
                self.em_object_for_rmf.get_density_as_hierarchy(
                )]
        else:
            output_hierarchies = [self.root_hier]

        if not self.test_mode and not self.nest:
            print("Setting up and writing initial rmf coordinate file")
            init_suffix = globaldir + self.vars["initial_rmf_name_suffix"]
            output.init_rmf(init_suffix + "." + str(myindex) + ".rmf3",
                            output_hierarchies,
                            listofobjects=self.rmf_output_objects)
            if self._rmf_restraints:
                output.add_restraints_to_rmf(
                    init_suffix + "." + str(myindex) + ".rmf3",
                    self._rmf_restraints)
            output.write_rmf(init_suffix + "." + str(myindex) + ".rmf3")
            output.close_rmf(init_suffix + "." + str(myindex) + ".rmf3")

        if not self.test_mode:
            mpivs = IMP.pmi.samplers.MPI_values(self.replica_exchange_object)
        else:
            mpivs = _MockMPIValues()

        self._add_provenance(sampler_md, sampler_mc)

        if not self.test_mode and not self.nest:
            print("Setting up production rmf files")
            rmfname = rmf_dir + "/" + str(myindex) + ".rmf3"
            output.init_rmf(rmfname, output_hierarchies,
                            geometries=self.vars["geometries"],
                            listofobjects=self.rmf_output_objects)

            if self._rmf_restraints:
                output.add_restraints_to_rmf(rmfname, self._rmf_restraints)

        if not self.test_mode and self.nest:
            print("Setting up NestOR rmf files")
            nestor_rmf_fname = str(self.nestor_rmf_fname) + '_' + \
                str(self.replica_exchange_object.get_my_index()) + '.rmf3'

            output.init_rmf(nestor_rmf_fname, output_hierarchies,
                            geometries=self.vars["geometries"],
                            listofobjects=self.rmf_output_objects)

        ntimes_at_low_temp = 0

        if myindex == 0 and not self.nest:
            self.show_info()
        self.replica_exchange_object.set_was_used(True)
        nframes = self.vars["number_of_frames"]
        if self.test_mode:
            nframes = 1

        sampled_likelihoods = []
        for i in range(nframes):
            if self.test_mode:
                score = 0.
            else:
                for nr in range(self.vars["num_sample_rounds"]):
                    if sampler_md is not None:
                        sampler_md.optimize(
                                  self.vars["molecular_dynamics_steps"])
                    if sampler_mc is not None:
                        sampler_mc.optimize(self.vars["monte_carlo_steps"])
                score = IMP.pmi.tools.get_restraint_set(
                    self.model).evaluate(False)
                mpivs.set_value("score", score)
            if not self.nest:
                output.set_output_entry("score", score)

            my_temp_index = int(rex.get_my_temp() * temp_index_factor)

            if self.vars["save_coordinates_mode"] == "lowest_temperature":
                save_frame = (min_temp_index == my_temp_index)
            elif self.vars["save_coordinates_mode"] == "25th_score":
                score_perc = mpivs.get_percentile("score")
                save_frame = (score_perc*100.0 <= 25.0)
            elif self.vars["save_coordinates_mode"] == "50th_score":
                score_perc = mpivs.get_percentile("score")
                save_frame = (score_perc*100.0 <= 50.0)
            elif self.vars["save_coordinates_mode"] == "75th_score":
                score_perc = mpivs.get_percentile("score")
                save_frame = (score_perc*100.0 <= 75.0)

            # Ensure model is updated before saving output files
            if save_frame and not self.test_mode:
                self.model.update()

            if save_frame:
                print("--- frame %s score %s " % (str(i), str(score)))

                if self.nest:
                    if math.isnan(score):
                        sampled_likelihoods.append(math.nan)
                    else:
                        likelihood_for_sample = 1
                        for rstrnt in self.nestor_restraints:
                            likelihood_for_sample *= rstrnt.get_likelihood()
                        sampled_likelihoods.append(likelihood_for_sample)
                        output.write_rmf(nestor_rmf_fname)

                if not self.test_mode and not self.nest:
                    if i % self.vars["nframes_write_coordinates"] == 0:
                        print('--- writing coordinates')
                        if self.vars["number_of_best_scoring_models"] > 0:
                            output.write_pdb_best_scoring(score)
                        output.write_rmf(rmfname)
                        output.set_output_entry("rmf_file", rmfname)
                        output.set_output_entry("rmf_frame_index",
                                                ntimes_at_low_temp)
                    else:
                        output.set_output_entry("rmf_file", rmfname)
                        output.set_output_entry("rmf_frame_index", '-1')
                    if self.output_objects is not None:
                        output.write_stat2(low_temp_stat_file)
                ntimes_at_low_temp += 1

            if not self.test_mode and not self.nest:
                output.write_stat2(replica_stat_file)
            if self.vars["replica_exchange_swap"]:
                rex.swap_temp(i, score)

        if self.nest and len(sampled_likelihoods) > 0:
            with open("likelihoods_"
                      + str(self.replica_exchange_object.get_my_index()),
                      "wb") as lif:
                pickle.dump(sampled_likelihoods, lif)

            output.close_rmf(nestor_rmf_fname)

        for p, state in IMP.pmi.tools._all_protocol_outputs(self.root_hier):
            p.add_replica_exchange(state, self)

        if not self.test_mode and not self.nest:
            print("closing production rmf files")
            output.close_rmf(rmfname)


class BuildSystem(object):
    """A macro to build a IMP::pmi::topology::System based on a
       TopologyReader object.

    Easily create multi-state systems by calling this macro
    repeatedly with different TopologyReader objects!
    A useful function is get_molecules() which returns the PMI Molecules
    grouped by state as a dictionary with key = (molecule name),
    value = IMP.pmi.topology.Molecule
    Quick multi-state system:
    @code{.python}
    model = IMP.Model()
    reader1 = IMP.pmi.topology.TopologyReader(tfile1)
    reader2 = IMP.pmi.topology.TopologyReader(tfile2)
    bs = IMP.pmi.macros.BuildSystem(model)
    bs.add_state(reader1)
    bs.add_state(reader2)
    bs.execute_macro() # build everything including degrees of freedom
    IMP.atom.show_molecular_hierarchy(bs.get_hierarchy())
    ### now you have a two state system, you add restraints etc
    @endcode
    @note The "domain name" entry of the topology reader is not used.
    All molecules are set up by the component name, but split into rigid bodies
    as requested.
    """

    _alphabets = {'DNA': IMP.pmi.alphabets.dna,
                  'RNA': IMP.pmi.alphabets.rna}

    def __init__(self, model, sequence_connectivity_scale=4.0,
                 force_create_gmm_files=False, resolutions=[1, 10],
                 name='System'):
        """Constructor
        @param model An IMP Model
        @param sequence_connectivity_scale For scaling the connectivity
               restraint
        @param force_create_gmm_files If True, will sample and create GMMs
                  no matter what. If False, will only sample if the
                  files don't exist. If number of Gaussians is zero, won't
                  do anything.
        @param resolutions The resolutions to build for structured regions
        @param name The name of the top-level hierarchy node.
        """
        self.model = model
        self.system = IMP.pmi.topology.System(self.model, name=name)
        self._readers = []    # the TopologyReaders (one per state)
        # TempResidues for each domain key=unique name,
        # value=(atomic_res,non_atomic_res).
        self._domain_res = []
        self._domains = []    # key = domain unique name, value = Component
        self.force_create_gmm_files = force_create_gmm_files
        self.resolutions = resolutions

    def add_state(self, reader, keep_chain_id=False, fasta_name_map=None,
                  chain_ids=None):
        """Add a state using the topology info in a
           IMP::pmi::topology::TopologyReader object.
        When you are done adding states, call execute_macro()
        @param reader The TopologyReader object
        @param fasta_name_map dictionary for converting protein names
               found in the fasta file
        @param chain_ids A list or string of chain IDs for assigning to
               newly-created molecules, e.g.
               `string.ascii_uppercase+string.ascii_lowercase+string.digits`.
               If not specified, chain IDs A through Z are assigned, then
               AA through AZ, then BA through BZ, and so on, in the same
               fashion as PDB.
        """
        state = self.system.create_state()
        self._readers.append(reader)
        # key is unique name, value is (atomic res, nonatomicres)
        these_domain_res = {}
        these_domains = {}       # key is unique name, value is _Component
        if chain_ids is None:
            chain_ids = IMP.pmi.output._ChainIDs()
        numchain = 0

        # setup representation
        # loop over molecules, copies, then domains
        for molname in reader.get_molecules():
            copies = reader.get_molecules()[molname].domains
            for nc, copyname in enumerate(copies):
                print("BuildSystem.add_state: setting up molecule %s copy "
                      "number %s" % (molname, str(nc)))
                copy = copies[copyname]
                # option to not rename chains
                if keep_chain_id:
                    all_chains = [c for c in copy if c.chain is not None]
                    if all_chains:
                        chain_id = all_chains[0].chain
                    else:
                        chain_id = chain_ids[numchain]
                        warnings.warn(
                            "No PDBs specified for %s, so keep_chain_id has "
                            "no effect; using default chain ID '%s'"
                            % (molname, chain_id), IMP.pmi.ParameterWarning)
                else:
                    chain_id = chain_ids[numchain]
                if nc == 0:
                    alphabet = IMP.pmi.alphabets.amino_acid
                    fasta_flag = copy[0].fasta_flag
                    if fasta_flag in self._alphabets:
                        alphabet = self._alphabets[fasta_flag]
                    seqs = IMP.pmi.topology.Sequences(
                        copy[0].fasta_file, fasta_name_map)
                    seq = seqs[copy[0].fasta_id]
                    print("BuildSystem.add_state: molecule %s sequence has "
                          "%s residues" % (molname, len(seq)))
                    orig_mol = state.create_molecule(
                        molname, seq, chain_id, alphabet=alphabet,
                        uniprot=seqs.uniprot.get(copy[0].fasta_id))
                    mol = orig_mol
                    numchain += 1
                else:
                    print("BuildSystem.add_state: creating a copy for "
                          "molecule %s" % molname)
                    mol = orig_mol.create_copy(chain_id)
                    numchain += 1

                for domainnumber, domain in enumerate(copy):
                    print("BuildSystem.add_state: ---- setting up domain %s "
                          "of molecule %s" % (domainnumber, molname))
                    # we build everything in the residue range, even if it
                    #  extends beyond what's in the actual PDB file
                    these_domains[domain.get_unique_name()] = domain
                    if domain.residue_range == [] or \
                            domain.residue_range is None:
                        domain_res = mol.get_residues()
                    else:
                        start = domain.residue_range[0]+domain.pdb_offset
                        if domain.residue_range[1] == 'END':
                            end = len(mol.sequence)
                        else:
                            end = domain.residue_range[1]+domain.pdb_offset
                        domain_res = mol.residue_range(start-1, end-1)
                        print("BuildSystem.add_state: -------- domain %s of "
                              "molecule %s extends from residue %s to "
                              "residue %s "
                              % (domainnumber, molname, start, end))
                    if domain.pdb_file == "BEADS":
                        print("BuildSystem.add_state: -------- domain %s of "
                              "molecule %s represented by BEADS "
                              % (domainnumber, molname))
                        mol.add_representation(
                            domain_res,
                            resolutions=[domain.bead_size],
                            setup_particles_as_densities=(
                                domain.em_residues_per_gaussian != 0),
                            color=domain.color)
                        these_domain_res[domain.get_unique_name()] = \
                            (set(), domain_res)
                    elif domain.pdb_file == "IDEAL_HELIX":
                        print("BuildSystem.add_state: -------- domain %s of "
                              "molecule %s represented by IDEAL_HELIX "
                              % (domainnumber, molname))
                        emper = domain.em_residues_per_gaussian
                        mol.add_representation(
                            domain_res,
                            resolutions=self.resolutions,
                            ideal_helix=True,
                            density_residues_per_component=emper,
                            density_prefix=domain.density_prefix,
                            density_force_compute=self.force_create_gmm_files,
                            color=domain.color)
                        these_domain_res[domain.get_unique_name()] = \
                            (domain_res, set())
                    else:
                        print("BuildSystem.add_state: -------- domain %s of "
                              "molecule %s represented by pdb file %s "
                              % (domainnumber, molname, domain.pdb_file))
                        domain_atomic = mol.add_structure(domain.pdb_file,
                                                          domain.chain,
                                                          domain.residue_range,
                                                          domain.pdb_offset,
                                                          soft_check=True)
                        domain_non_atomic = domain_res - domain_atomic
                        if not domain.em_residues_per_gaussian:
                            mol.add_representation(
                                domain_atomic, resolutions=self.resolutions,
                                color=domain.color)
                            if len(domain_non_atomic) > 0:
                                mol.add_representation(
                                    domain_non_atomic,
                                    resolutions=[domain.bead_size],
                                    color=domain.color)
                        else:
                            print("BuildSystem.add_state: -------- domain %s "
                                  "of molecule %s represented by gaussians "
                                  % (domainnumber, molname))
                            emper = domain.em_residues_per_gaussian
                            creategmm = self.force_create_gmm_files
                            mol.add_representation(
                                domain_atomic,
                                resolutions=self.resolutions,
                                density_residues_per_component=emper,
                                density_prefix=domain.density_prefix,
                                density_force_compute=creategmm,
                                color=domain.color)
                            if len(domain_non_atomic) > 0:
                                mol.add_representation(
                                    domain_non_atomic,
                                    resolutions=[domain.bead_size],
                                    setup_particles_as_densities=True,
                                    color=domain.color)
                        these_domain_res[domain.get_unique_name()] = (
                            domain_atomic, domain_non_atomic)
        self._domain_res.append(these_domain_res)
        self._domains.append(these_domains)
        print('BuildSystem.add_state: State', len(self.system.states), 'added')
        return state

    def get_molecules(self):
        """Return list of all molecules grouped by state.
        For each state, it's a dictionary of Molecules where key is the
        molecule name
        """
        return [s.get_molecules() for s in self.system.get_states()]

    def get_molecule(self, molname, copy_index=0, state_index=0):
        return self.system.get_states()[state_index].get_molecules()[
                molname][copy_index]

    def execute_macro(self, max_rb_trans=4.0, max_rb_rot=0.04,
                      max_bead_trans=4.0, max_srb_trans=4.0, max_srb_rot=0.04):
        """Builds representations and sets up degrees of freedom"""
        print("BuildSystem.execute_macro: building representations")
        self.root_hier = self.system.build()

        print("BuildSystem.execute_macro: setting up degrees of freedom")
        self.dof = IMP.pmi.dof.DegreesOfFreedom(self.model)
        for nstate, reader in enumerate(self._readers):
            rbs = reader.get_rigid_bodies()
            srbs = reader.get_super_rigid_bodies()
            csrbs = reader.get_chains_of_super_rigid_bodies()

            # add rigid bodies
            domains_in_rbs = set()
            for rblist in rbs:
                print("BuildSystem.execute_macro: -------- building rigid "
                      "body %s" % (str(rblist)))
                all_res = IMP.pmi.tools.OrderedSet()
                bead_res = IMP.pmi.tools.OrderedSet()
                for dname in rblist:
                    domain = self._domains[nstate][dname]
                    print("BuildSystem.execute_macro: -------- adding %s"
                          % (str(dname)))
                    all_res |= self._domain_res[nstate][dname][0]
                    bead_res |= self._domain_res[nstate][dname][1]
                    domains_in_rbs.add(dname)
                all_res |= bead_res
                print("BuildSystem.execute_macro: -------- creating rigid "
                      "body with max_trans %s max_rot %s "
                      "non_rigid_max_trans %s"
                      % (str(max_rb_trans), str(max_rb_rot),
                         str(max_bead_trans)))
                self.dof.create_rigid_body(all_res,
                                           nonrigid_parts=bead_res,
                                           max_trans=max_rb_trans,
                                           max_rot=max_rb_rot,
                                           nonrigid_max_trans=max_bead_trans,
                                           name="RigidBody %s" % dname)

            # if you have any domains not in an RB, set them as flexible beads
            for dname, domain in self._domains[nstate].items():
                if dname not in domains_in_rbs:
                    if domain.pdb_file != "BEADS":
                        warnings.warn(
                            "No rigid bodies set for %s. Residues read from "
                            "the PDB file will not be sampled - only regions "
                            "missing from the PDB will be treated flexibly. "
                            "To sample the entire sequence, use BEADS instead "
                            "of a PDB file name" % dname,
                            IMP.pmi.StructureWarning)
                    self.dof.create_flexible_beads(
                            self._domain_res[nstate][dname][1],
                            max_trans=max_bead_trans)

            # add super rigid bodies
            for srblist in srbs:
                print("BuildSystem.execute_macro: -------- building "
                      "super rigid body %s" % (str(srblist)))
                all_res = IMP.pmi.tools.OrderedSet()
                for dname in srblist:
                    print("BuildSystem.execute_macro: -------- adding %s"
                          % (str(dname)))
                    all_res |= self._domain_res[nstate][dname][0]
                    all_res |= self._domain_res[nstate][dname][1]

                print("BuildSystem.execute_macro: -------- creating super "
                      "rigid body with max_trans %s max_rot %s "
                      % (str(max_srb_trans), str(max_srb_rot)))
                self.dof.create_super_rigid_body(
                    all_res, max_trans=max_srb_trans, max_rot=max_srb_rot)

            # add chains of super rigid bodies
            for csrblist in csrbs:
                all_res = IMP.pmi.tools.OrderedSet()
                for dname in csrblist:
                    all_res |= self._domain_res[nstate][dname][0]
                    all_res |= self._domain_res[nstate][dname][1]
                all_res = list(all_res)
                all_res.sort(key=lambda r: r.get_index())
                self.dof.create_main_chain_mover(all_res)
        return self.root_hier, self.dof


@IMP.deprecated_object("2.8", "Use AnalysisReplicaExchange instead")
class AnalysisReplicaExchange0(object):
    """A macro for running all the basic operations of analysis.
    Includes clustering, precision analysis, and making ensemble density maps.
    A number of plots are also supported.
    """
    def __init__(self, model,
                 merge_directories=["./"],
                 stat_file_name_suffix="stat",
                 best_pdb_name_suffix="model",
                 do_clean_first=True,
                 do_create_directories=True,
                 global_output_directory="output/",
                 replica_stat_file_suffix="stat_replica",
                 global_analysis_result_directory="./analysis/",
                 test_mode=False):
        """Constructor.
           @param model                           The IMP model
           @param stat_file_name_suffix
           @param merge_directories The directories containing output files
           @param best_pdb_name_suffix
           @param do_clean_first
           @param do_create_directories
           @param global_output_directory          Where everything is
           @param replica_stat_file_suffix
           @param global_analysis_result_directory
           @param test_mode If True, nothing is changed on disk
        """

        try:
            from mpi4py import MPI
            self.comm = MPI.COMM_WORLD
            self.rank = self.comm.Get_rank()
            self.number_of_processes = self.comm.size
        except ImportError:
            self.rank = 0
            self.number_of_processes = 1

        self.test_mode = test_mode
        self._protocol_output = []
        self.cluster_obj = None
        self.model = model
        stat_dir = global_output_directory
        self.stat_files = []
        # it contains the position of the root directories
        for rd in merge_directories:
            stat_files = glob.glob(os.path.join(rd, stat_dir, "stat.*.out"))
            if len(stat_files) == 0:
                warnings.warn("no stat files found in %s"
                              % os.path.join(rd, stat_dir),
                              IMP.pmi.MissingFileWarning)
            self.stat_files += stat_files

    def add_protocol_output(self, p):
        """Capture details of the modeling protocol.
           @param p an instance of IMP.pmi.output.ProtocolOutput or a subclass.
        """
        # Assume last state is the one we're interested in
        self._protocol_output.append((p, p._last_state))

    def get_modeling_trajectory(self,
                                score_key="Total_Score",
                                rmf_file_key="rmf_file",
                                rmf_file_frame_key="rmf_frame_index",
                                outputdir="./",
                                get_every=1,
                                nframes_trajectory=10000):
        """ Get a trajectory of the modeling run, for generating
        demonstrative movies

        @param score_key           The score for ranking models
        @param rmf_file_key        Key pointing to RMF filename
        @param rmf_file_frame_key  Key pointing to RMF frame number
        @param outputdir           The local output directory used in the run
        @param get_every           Extract every nth frame
        @param nframes_trajectory  Total number of frames of the trajectory
        """
        import math

        trajectory_models = IMP.pmi.io.get_trajectory_models(
            self.stat_files, score_key, rmf_file_key, rmf_file_frame_key,
            get_every)
        score_list = list(map(float, trajectory_models[2]))

        max_score = max(score_list)
        min_score = min(score_list)

        bins = [(max_score-min_score)*math.exp(-float(i))+min_score
                for i in range(nframes_trajectory)]
        binned_scores = [None]*nframes_trajectory
        binned_model_indexes = [-1]*nframes_trajectory

        for model_index, s in enumerate(score_list):
            bins_score_diffs = [abs(s-b) for b in bins]
            bin_index = min(enumerate(bins_score_diffs), key=itemgetter(1))[0]
            if binned_scores[bin_index] is None:
                binned_scores[bin_index] = s
                binned_model_indexes[bin_index] = model_index
            else:
                old_diff = abs(binned_scores[bin_index]-bins[bin_index])
                new_diff = abs(s-bins[bin_index])
                if new_diff < old_diff:
                    binned_scores[bin_index] = s
                    binned_model_indexes[bin_index] = model_index

        print(binned_scores)
        print(binned_model_indexes)

    def _expand_ambiguity(self, prot, d):
        """If using PMI2, expand the dictionary to include copies as
           ambiguous options

           This also keeps the states separate.
        """
        newdict = {}
        for key in d:
            val = d[key]
            if '..' in key or (isinstance(val, tuple) and len(val) >= 3):
                newdict[key] = val
                continue
            states = IMP.atom.get_by_type(prot, IMP.atom.STATE_TYPE)
            if isinstance(val, tuple):
                start = val[0]
                stop = val[1]
                name = val[2]
            else:
                start = 1
                stop = -1
                name = val
            for nst in range(len(states)):
                sel = IMP.atom.Selection(prot, molecule=name, state_index=nst)
                copies = sel.get_selected_particles(with_representation=False)
                if len(copies) > 1:
                    for nc in range(len(copies)):
                        if len(states) > 1:
                            newdict['%s.%i..%i' % (name, nst, nc)] = \
                                (start, stop, name, nc, nst)
                        else:
                            newdict['%s..%i' % (name, nc)] = \
                                (start, stop, name, nc, nst)
                else:
                    newdict[key] = val
        return newdict

    def clustering(self,
                   score_key="Total_Score",
                   rmf_file_key="rmf_file",
                   rmf_file_frame_key="rmf_frame_index",
                   state_number=0,
                   prefiltervalue=None,
                   feature_keys=[],
                   outputdir="./",
                   alignment_components=None,
                   number_of_best_scoring_models=10,
                   rmsd_calculation_components=None,
                   distance_matrix_file='distances.mat',
                   load_distance_matrix_file=False,
                   skip_clustering=False,
                   number_of_clusters=1,
                   display_plot=False,
                   exit_after_display=True,
                   get_every=1,
                   first_and_last_frames=None,
                   density_custom_ranges=None,
                   write_pdb_with_centered_coordinates=False,
                   voxel_size=5.0):
        """Get the best scoring models, compute a distance matrix,
           cluster them, and create density maps.

        Tuple format: "molname" just the molecule,
        or (start,stop,molname,copy_num(optional),state_num(optional)
        Can pass None for copy or state to ignore that field.
        If you don't pass a specific copy number

        @param score_key              The score for ranking models.
        @param rmf_file_key           Key pointing to RMF filename
        @param rmf_file_frame_key     Key pointing to RMF frame number
        @param state_number           State number to analyze
        @param prefiltervalue         Only include frames where the
               score key is below this value
        @param feature_keys           Keywords for which you want to
               calculate average, medians, etc.
               If you pass "Keyname" it'll include everything that matches
               "*Keyname*"
        @param outputdir               The local output directory used in
               the run
        @param alignment_components    Dictionary with keys=groupname,
               values are tuples for aligning the structures
               e.g. {"Rpb1": (20,100,"Rpb1"),"Rpb2":"Rpb2"}
        @param number_of_best_scoring_models  Num models to keep per run
        @param rmsd_calculation_components    For calculating RMSD
               (same format as alignment_components)
        @param distance_matrix_file           Where to store/read the
               distance matrix
        @param load_distance_matrix_file      Try to load the distance
               matrix file
        @param skip_clustering                Just extract the best scoring
               models and save the pdbs
        @param number_of_clusters             Number of k-means clusters
        @param display_plot                   Display the distance matrix
        @param exit_after_display             Exit after displaying distance
               matrix
        @param get_every                      Extract every nth frame
        @param first_and_last_frames          A tuple with the first and last
               frames to be analyzed. Values are percentages!
               Default: get all frames
        @param density_custom_ranges          For density calculation
               (same format as alignment_components)
        @param write_pdb_with_centered_coordinates
        @param voxel_size                     Used for the density output
        """
        # Track provenance information to be added to each output model
        prov = []
        self._outputdir = Path(outputdir).absolute()
        self._number_of_clusters = number_of_clusters
        for p, state in self._protocol_output:
            p.add_replica_exchange_analysis(state, self, density_custom_ranges)

        if self.test_mode:
            return

        if self.rank == 0:
            try:
                os.mkdir(outputdir)
            except:  # noqa: E722
                pass

        if not load_distance_matrix_file:
            if len(self.stat_files) == 0:
                print("ERROR: no stat file found in the given path")
                return
            my_stat_files = IMP.pmi.tools.chunk_list_into_segments(
                self.stat_files, self.number_of_processes)[self.rank]

            # read ahead to check if you need the PMI2 score key instead
            for k in (score_key, rmf_file_key, rmf_file_frame_key):
                if k in feature_keys:
                    warnings.warn(
                            "no need to pass " + k + " to feature_keys.",
                            IMP.pmi.ParameterWarning)
                    feature_keys.remove(k)

            best_models = IMP.pmi.io.get_best_models(
                my_stat_files, score_key, feature_keys, rmf_file_key,
                rmf_file_frame_key, prefiltervalue, get_every, provenance=prov)
            rmf_file_list = best_models[0]
            rmf_file_frame_list = best_models[1]
            score_list = best_models[2]
            feature_keyword_list_dict = best_models[3]

# ------------------------------------------------------------------------
# collect all the files and scores
# ------------------------------------------------------------------------

            if self.number_of_processes > 1:
                score_list = IMP.pmi.tools.scatter_and_gather(score_list)
                rmf_file_list = IMP.pmi.tools.scatter_and_gather(rmf_file_list)
                rmf_file_frame_list = IMP.pmi.tools.scatter_and_gather(
                    rmf_file_frame_list)
                for k in feature_keyword_list_dict:
                    feature_keyword_list_dict[k] = \
                        IMP.pmi.tools.scatter_and_gather(
                            feature_keyword_list_dict[k])

            # sort by score and get the best scoring ones
            score_rmf_tuples = list(zip(score_list,
                                        rmf_file_list,
                                        rmf_file_frame_list,
                                        list(range(len(score_list)))))

            if density_custom_ranges:
                for k in density_custom_ranges:
                    if not isinstance(density_custom_ranges[k], list):
                        raise Exception("Density custom ranges: values must "
                                        "be lists of tuples")

            # keep subset of frames if requested
            if first_and_last_frames is not None:
                nframes = len(score_rmf_tuples)
                first_frame = int(first_and_last_frames[0] * nframes)
                last_frame = int(first_and_last_frames[1] * nframes)
                if last_frame > len(score_rmf_tuples):
                    last_frame = -1
                score_rmf_tuples = score_rmf_tuples[first_frame:last_frame]

            # sort RMFs by the score_key in ascending order, and store the rank
            best_score_rmf_tuples = sorted(
                score_rmf_tuples,
                key=lambda x: float(x[0]))[:number_of_best_scoring_models]
            best_score_rmf_tuples = [t+(n,) for n, t in
                                     enumerate(best_score_rmf_tuples)]
            # Note in the provenance info that we only kept best-scoring models
            prov.append(IMP.pmi.io.FilterProvenance(
                "Best scoring", 0, number_of_best_scoring_models))
            # sort the feature scores in the same way
            best_score_feature_keyword_list_dict = defaultdict(list)
            for tpl in best_score_rmf_tuples:
                index = tpl[3]
                for f in feature_keyword_list_dict:
                    best_score_feature_keyword_list_dict[f].append(
                        feature_keyword_list_dict[f][index])
            my_best_score_rmf_tuples = IMP.pmi.tools.chunk_list_into_segments(
                best_score_rmf_tuples,
                self.number_of_processes)[self.rank]

            # expand the dictionaries to include ambiguous copies
            prot_ahead = IMP.pmi.analysis.get_hiers_from_rmf(
                self.model, 0, my_best_score_rmf_tuples[0][1])[0]
            if rmsd_calculation_components is not None:
                tmp = self._expand_ambiguity(
                    prot_ahead, rmsd_calculation_components)
                if tmp != rmsd_calculation_components:
                    print('Detected ambiguity, expand rmsd components to',
                          tmp)
                    rmsd_calculation_components = tmp
            if alignment_components is not None:
                tmp = self._expand_ambiguity(prot_ahead,
                                             alignment_components)
                if tmp != alignment_components:
                    print('Detected ambiguity, expand alignment '
                          'components to', tmp)
                    alignment_components = tmp

# -------------------------------------------------------------
# read the coordinates
# ------------------------------------------------------------
            rmsd_weights = IMP.pmi.io.get_bead_sizes(
                self.model, my_best_score_rmf_tuples[0],
                rmsd_calculation_components, state_number=state_number)
            got_coords = IMP.pmi.io.read_coordinates_of_rmfs(
                self.model, my_best_score_rmf_tuples, alignment_components,
                rmsd_calculation_components, state_number=state_number)

            # note! the coordinates are simply float tuples, NOT decorators,
            # NOT Vector3D, NOR particles, because these object cannot be
            # serialized. We need serialization
            # for the parallel computation based on mpi.

            # dict:key=component name,val=coords per hit
            all_coordinates = got_coords[0]

            # same as above, limited to alignment bits
            alignment_coordinates = got_coords[1]

            # same as above, limited to RMSD bits
            rmsd_coordinates = got_coords[2]

            # dictionary with key=RMF, value=score rank
            rmf_file_name_index_dict = got_coords[3]

            # RMF file per hit
            all_rmf_file_names = got_coords[4]

# ------------------------------------------------------------------------
# optionally don't compute distance matrix or cluster, just write top files
# ------------------------------------------------------------------------
            if skip_clustering:
                if density_custom_ranges:
                    DensModule = IMP.pmi.analysis.GetModelDensity(
                        density_custom_ranges, voxel=voxel_size)

                dircluster = os.path.join(outputdir,
                                          "all_models."+str(self.rank))
                try:
                    os.mkdir(outputdir)
                except:  # noqa: E722
                    pass
                try:
                    os.mkdir(dircluster)
                except:  # noqa: E722
                    pass
                clusstat = open(os.path.join(
                    dircluster, "stat."+str(self.rank)+".out"), "w")
                for cnt, tpl in enumerate(my_best_score_rmf_tuples):
                    rmf_name = tpl[1]
                    rmf_frame_number = tpl[2]
                    tmp_dict = {}
                    index = tpl[4]
                    for key in best_score_feature_keyword_list_dict:
                        tmp_dict[key] = \
                            best_score_feature_keyword_list_dict[key][index]

                    if cnt == 0:
                        prots, rs = \
                            IMP.pmi.analysis.get_hiers_and_restraints_from_rmf(
                                self.model, rmf_frame_number, rmf_name)
                    else:
                        linking_successful = \
                            IMP.pmi.analysis.link_hiers_and_restraints_to_rmf(
                                self.model, prots, rs, rmf_frame_number,
                                rmf_name)
                        if not linking_successful:
                            continue

                    if not prots:
                        continue

                    states = IMP.atom.get_by_type(
                        prots[0], IMP.atom.STATE_TYPE)
                    prot = states[state_number]

                    # get transformation aligning coordinates of
                    # requested tuples to the first RMF file
                    if cnt == 0:
                        coords_f1 = alignment_coordinates[cnt]
                    if cnt > 0:
                        coords_f2 = alignment_coordinates[cnt]
                        if coords_f2:
                            Ali = IMP.pmi.analysis.Alignment(
                                coords_f1, coords_f2)
                            transformation = Ali.align()[1]
                        else:
                            transformation = \
                                IMP.algebra.get_identity_transformation_3d()

                        rbs = set()
                        for p in IMP.atom.get_leaves(prot):
                            if not IMP.core.XYZR.get_is_setup(p):
                                IMP.core.XYZR.setup_particle(p)
                                IMP.core.XYZR(p).set_radius(0.0001)
                                IMP.core.XYZR(p).set_coordinates((0, 0, 0))

                            if IMP.core.RigidBodyMember.get_is_setup(p):
                                rbm = IMP.core.RigidBodyMember(p)
                                rb = rbm.get_rigid_body()
                                rbs.add(rb)
                            else:
                                IMP.core.transform(IMP.core.XYZ(p),
                                                   transformation)
                        for rb in rbs:
                            IMP.core.transform(rb, transformation)

                    o = IMP.pmi.output.Output()
                    self.model.update()
                    out_pdb_fn = os.path.join(
                        dircluster, str(cnt)+"."+str(self.rank)+".pdb")
                    out_rmf_fn = os.path.join(
                        dircluster, str(cnt)+"."+str(self.rank)+".rmf3")
                    o.init_pdb(out_pdb_fn, prot)
                    tc = write_pdb_with_centered_coordinates
                    o.write_pdb(out_pdb_fn,
                                translate_to_geometric_center=tc)

                    tmp_dict["local_pdb_file_name"] = \
                        os.path.basename(out_pdb_fn)
                    tmp_dict["rmf_file_full_path"] = rmf_name
                    tmp_dict["local_rmf_file_name"] = \
                        os.path.basename(out_rmf_fn)
                    tmp_dict["local_rmf_frame_number"] = 0

                    clusstat.write(str(tmp_dict)+"\n")

                    # create a single-state System and write that
                    h = IMP.atom.Hierarchy.setup_particle(
                        IMP.Particle(self.model))
                    h.set_name("System")
                    h.add_child(prot)
                    o.init_rmf(out_rmf_fn, [h], rs)

                    o.write_rmf(out_rmf_fn)
                    o.close_rmf(out_rmf_fn)
                    # add the density
                    if density_custom_ranges:
                        DensModule.add_subunits_density(prot)

                if density_custom_ranges:
                    DensModule.write_mrc(path=dircluster)
                    del DensModule
                return

            # broadcast the coordinates
            if self.number_of_processes > 1:
                all_coordinates = IMP.pmi.tools.scatter_and_gather(
                    all_coordinates)
                all_rmf_file_names = IMP.pmi.tools.scatter_and_gather(
                    all_rmf_file_names)
                rmf_file_name_index_dict = IMP.pmi.tools.scatter_and_gather(
                    rmf_file_name_index_dict)
                alignment_coordinates = IMP.pmi.tools.scatter_and_gather(
                    alignment_coordinates)
                rmsd_coordinates = IMP.pmi.tools.scatter_and_gather(
                    rmsd_coordinates)

            if self.rank == 0:
                # save needed information in external files
                self.save_objects(
                    [best_score_feature_keyword_list_dict,
                     rmf_file_name_index_dict],
                    ".macro.pkl")

# ------------------------------------------------------------------------
# Calculate distance matrix and cluster
# ------------------------------------------------------------------------
            print("setup clustering class")
            self.cluster_obj = IMP.pmi.analysis.Clustering(rmsd_weights)

            for n, model_coordinate_dict in enumerate(all_coordinates):
                # let's try to align
                if (alignment_components is not None
                        and len(self.cluster_obj.all_coords) == 0):
                    # set the first model as template coordinates
                    self.cluster_obj.set_template(alignment_coordinates[n])
                self.cluster_obj.fill(all_rmf_file_names[n],
                                      rmsd_coordinates[n])
            print("Global calculating the distance matrix")

            # calculate distance matrix, all against all
            self.cluster_obj.dist_matrix()

            # perform clustering and optionally display
            if self.rank == 0:
                self.cluster_obj.do_cluster(number_of_clusters)
                if display_plot:
                    if self.rank == 0:
                        self.cluster_obj.plot_matrix(
                            figurename=os.path.join(outputdir,
                                                    'dist_matrix.pdf'))
                    if exit_after_display:
                        exit()
                self.cluster_obj.save_distance_matrix_file(
                    file_name=distance_matrix_file)

# ------------------------------------------------------------------------
# Alternatively, load the distance matrix from file and cluster that
# ------------------------------------------------------------------------
        else:
            if self.rank == 0:
                print("setup clustering class")
                self.cluster_obj = IMP.pmi.analysis.Clustering()
                self.cluster_obj.load_distance_matrix_file(
                    file_name=distance_matrix_file)
                print("clustering with %s clusters" % str(number_of_clusters))
                self.cluster_obj.do_cluster(number_of_clusters)
                [best_score_feature_keyword_list_dict,
                 rmf_file_name_index_dict] = self.load_objects(".macro.pkl")
                if display_plot:
                    if self.rank == 0:
                        self.cluster_obj.plot_matrix(figurename=os.path.join(
                            outputdir, 'dist_matrix.pdf'))
                    if exit_after_display:
                        exit()
        if self.number_of_processes > 1:
            self.comm.Barrier()

# ------------------------------------------------------------------------
# now save all information about the clusters
# ------------------------------------------------------------------------

        if self.rank == 0:
            print(self.cluster_obj.get_cluster_labels())
            for n, cl in enumerate(self.cluster_obj.get_cluster_labels()):
                print("rank %s " % str(self.rank))
                print("cluster %s " % str(n))
                print("cluster label %s " % str(cl))
                print(self.cluster_obj.get_cluster_label_names(cl))
                cluster_size = \
                    len(self.cluster_obj.get_cluster_label_names(cl))
                cluster_prov = \
                    prov + [IMP.pmi.io.ClusterProvenance(cluster_size)]

                # first initialize the Density class if requested
                if density_custom_ranges:
                    DensModule = IMP.pmi.analysis.GetModelDensity(
                        density_custom_ranges,
                        voxel=voxel_size)

                dircluster = outputdir + "/cluster." + str(n) + "/"
                try:
                    os.mkdir(dircluster)
                except:   # noqa: E722
                    pass

                rmsd_dict = {
                    "AVERAGE_RMSD":
                    str(self.cluster_obj.get_cluster_label_average_rmsd(cl))}
                clusstat = open(dircluster + "stat.out", "w")
                for k, structure_name in enumerate(
                        self.cluster_obj.get_cluster_label_names(cl)):
                    # extract the features
                    tmp_dict = {}
                    tmp_dict.update(rmsd_dict)
                    index = rmf_file_name_index_dict[structure_name]
                    for key in best_score_feature_keyword_list_dict:
                        tmp_dict[
                            key] = best_score_feature_keyword_list_dict[
                            key][
                            index]

                    # get the rmf name and the frame number from the list of
                    # frame names
                    rmf_name = structure_name.split("|")[0]
                    rmf_frame_number = int(structure_name.split("|")[1])
                    clusstat.write(str(tmp_dict) + "\n")

                    # extract frame (open or link to existing)
                    if k == 0:
                        prots, rs = \
                            IMP.pmi.analysis.get_hiers_and_restraints_from_rmf(
                                self.model, rmf_frame_number, rmf_name)
                    else:
                        linking_successful = \
                            IMP.pmi.analysis.link_hiers_and_restraints_to_rmf(
                                self.model, prots, rs, rmf_frame_number,
                                rmf_name)
                        if not linking_successful:
                            continue
                    if not prots:
                        continue

                    states = IMP.atom.get_by_type(
                        prots[0], IMP.atom.STATE_TYPE)
                    prot = states[state_number]
                    if k == 0:
                        IMP.pmi.io.add_provenance(cluster_prov, (prot,))

                    # transform clusters onto first
                    if k > 0:
                        co = self.cluster_obj
                        model_index = co.get_model_index_from_name(
                            structure_name)
                        transformation = co.get_transformation_to_first_member(
                            cl, model_index)
                        rbs = set()
                        for p in IMP.atom.get_leaves(prot):
                            if not IMP.core.XYZR.get_is_setup(p):
                                IMP.core.XYZR.setup_particle(p)
                                IMP.core.XYZR(p).set_radius(0.0001)
                                IMP.core.XYZR(p).set_coordinates((0, 0, 0))

                            if IMP.core.RigidBodyMember.get_is_setup(p):
                                rbm = IMP.core.RigidBodyMember(p)
                                rb = rbm.get_rigid_body()
                                rbs.add(rb)
                            else:
                                IMP.core.transform(IMP.core.XYZ(p),
                                                   transformation)
                        for rb in rbs:
                            IMP.core.transform(rb, transformation)

                    # add the density
                    if density_custom_ranges:
                        DensModule.add_subunits_density(prot)

                    # pdb writing should be optimized!
                    o = IMP.pmi.output.Output()
                    self.model.update()
                    o.init_pdb(dircluster + str(k) + ".pdb", prot)
                    o.write_pdb(dircluster + str(k) + ".pdb")

                    # create a single-state System and write that
                    h = IMP.atom.Hierarchy.setup_particle(
                        IMP.Particle(self.model))
                    h.set_name("System")
                    h.add_child(prot)
                    o.init_rmf(dircluster + str(k) + ".rmf3", [h], rs)
                    o.write_rmf(dircluster + str(k) + ".rmf3")
                    o.close_rmf(dircluster + str(k) + ".rmf3")

                    del o
                    # IMP.atom.destroy(prot)

                if density_custom_ranges:
                    DensModule.write_mrc(path=dircluster)
                    del DensModule

        if self.number_of_processes > 1:
            self.comm.Barrier()

    def get_cluster_rmsd(self, cluster_num):
        if self.cluster_obj is None:
            raise Exception("Run clustering first")
        return self.cluster_obj.get_cluster_label_average_rmsd(cluster_num)

    def save_objects(self, objects, file_name):
        import pickle
        outf = open(file_name, 'wb')
        pickle.dump(objects, outf)
        outf.close()

    def load_objects(self, file_name):
        import pickle
        inputf = open(file_name, 'rb')
        objects = pickle.load(inputf)
        inputf.close()
        return objects


class AnalysisReplicaExchange(object):

    """
    This class contains analysis utilities to investigate ReplicaExchange
    results.
    """

    ########################
    # Construction and Setup
    ########################

    def __init__(self, model, stat_files, best_models=None, score_key=None,
                 alignment=True):
        """
        Construction of the Class.
        @param model IMP.Model()
        @param stat_files list of string. Can be ascii stat files,
               rmf files names
        @param best_models Integer. Number of best scoring models,
               if None: all models will be read
        @param alignment boolean (Default=True). Align before computing
               the rmsd.
        """

        self.model = model
        self.best_models = best_models
        self.stath0 = IMP.pmi.output.StatHierarchyHandler(
            model, stat_files, self.best_models, score_key, cache=True)
        self.stath1 = IMP.pmi.output.StatHierarchyHandler(
            StatHierarchyHandler=self.stath0)

        self.rbs1, self.beads1 = IMP.pmi.tools.get_rbs_and_beads(
            IMP.pmi.tools.select_at_all_resolutions(self.stath1))
        self.rbs0, self.beads0 = IMP.pmi.tools.get_rbs_and_beads(
            IMP.pmi.tools.select_at_all_resolutions(self.stath0))
        self.sel0_rmsd = IMP.atom.Selection(self.stath0)
        self.sel1_rmsd = IMP.atom.Selection(self.stath1)
        self.sel0_alignment = IMP.atom.Selection(self.stath0)
        self.sel1_alignment = IMP.atom.Selection(self.stath1)
        self.clusters = []
        # fill the cluster list with a single cluster containing all models
        c = IMP.pmi.output.Cluster(0)
        self.clusters.append(c)
        for n0 in range(len(self.stath0)):
            c.add_member(n0)
        self.pairwise_rmsd = {}
        self.pairwise_molecular_assignment = {}
        self.alignment = alignment
        self.symmetric_molecules = {}
        self.issymmetricsel = {}
        self.update_seldicts()
        self.molcopydict0 = IMP.pmi.tools.get_molecules_dictionary_by_copy(
            IMP.atom.get_leaves(self.stath0))
        self.molcopydict1 = IMP.pmi.tools.get_molecules_dictionary_by_copy(
            IMP.atom.get_leaves(self.stath1))

    def set_rmsd_selection(self, **kwargs):
        """
        Setup the selection onto which the rmsd is computed
        @param kwargs use IMP.atom.Selection keywords
        """
        self.sel0_rmsd = IMP.atom.Selection(self.stath0, **kwargs)
        self.sel1_rmsd = IMP.atom.Selection(self.stath1, **kwargs)
        self.update_seldicts()

    def set_symmetric(self, molecule_name):
        """
        Store names of symmetric molecules
        """
        self.symmetric_molecules[molecule_name] = 0
        self.update_seldicts()

    def set_alignment_selection(self, **kwargs):
        """
        Setup the selection onto which the alignment is computed
        @param kwargs use IMP.atom.Selection keywords
        """
        self.sel0_alignment = IMP.atom.Selection(self.stath0, **kwargs)
        self.sel1_alignment = IMP.atom.Selection(self.stath1, **kwargs)

    ######################
    # Clustering functions
    ######################
    def clean_clusters(self):
        for c in self.clusters:
            del c
        self.clusters = []

    def cluster(self, rmsd_cutoff=10, metric=IMP.atom.get_rmsd):
        """
        Cluster the models based on RMSD.
        @param rmsd_cutoff Float the distance cutoff in Angstrom
        @param metric (Default=IMP.atom.get_rmsd) the metric that will
               be used to compute rmsds
        """
        self.clean_clusters()
        not_clustered = set(range(len(self.stath1)))
        while len(not_clustered) > 0:
            self.aggregate(not_clustered, rmsd_cutoff, metric)
        self.update_clusters()

    def refine(self, rmsd_cutoff=10):
        """
        Refine the clusters by merging the ones whose centers are close
        @param rmsd_cutoff cutoff distance in Angstorms
        """
        clusters_copy = self.clusters
        for c0, c1 in itertools.combinations(self.clusters, 2):
            if c0.center_index is None:
                self.compute_cluster_center(c0)
            if c1.center_index is None:
                self.compute_cluster_center(c1)
            _ = self.stath0[c0.center_index]
            _ = self.stath1[c1.center_index]
            rmsd, molecular_assignment = self.rmsd()
            if rmsd <= rmsd_cutoff:
                if c1 in self.clusters:
                    clusters_copy.remove(c1)
                c0 += c1
        self.clusters = clusters_copy
        self.update_clusters()

    ####################
    # Input Output
    ####################

    def set_cluster_assignments(self, cluster_ids):
        if len(cluster_ids) != len(self.stath0):
            raise ValueError('cluster ids has to be same length as '
                             'number of frames')

        self.clusters = []
        for i in sorted(list(set(cluster_ids))):
            self.clusters.append(IMP.pmi.output.Cluster(i))
        for i, (idx, d) in enumerate(zip(cluster_ids, self.stath0)):
            self.clusters[idx].add_member(i, d)

    def get_cluster_data(self, cluster):
        """
        Return the model data from a cluster
        @param cluster IMP.pmi.output.Cluster object
        """
        data = []
        for m in cluster:
            data.append(m)
        return data

    def save_data(self, filename='data.pkl'):
        """
        Save the data for the whole models into a pickle file
        @param filename string
        """
        self.stath0.save_data(filename)

    def set_data(self, data):
        """
        Set the data from an external IMP.pmi.output.Data
        @param data IMP.pmi.output.Data
        """
        self.stath0.data = data
        self.stath1.data = data

    def load_data(self, filename='data.pkl'):
        """
        Load the data from an external pickled file
        @param filename string
        """
        self.stath0.load_data(filename)
        self.stath1.load_data(filename)
        self.best_models = len(self.stath0)

    def add_cluster(self, rmf_name_list):
        c = IMP.pmi.output.Cluster(len(self.clusters))
        print("creating cluster index "+str(len(self.clusters)))
        self.clusters.append(c)
        current_len = len(self.stath0)

        for rmf in rmf_name_list:
            print("adding rmf "+rmf)
            self.stath0.add_stat_file(rmf)
            self.stath1.add_stat_file(rmf)

        for n0 in range(current_len, len(self.stath0)):
            d0 = self.stath0[n0]
            c.add_member(n0, d0)
        self.update_clusters()

    def save_clusters(self, filename='clusters.pkl'):
        """
        Save the clusters into a pickle file
        @param filename string
        """
        try:
            import cPickle as pickle
        except ImportError:
            import pickle
        fl = open(filename, 'wb')
        pickle.dump(self.clusters, fl)

    def load_clusters(self, filename='clusters.pkl', append=False):
        """
        Load the clusters from a pickle file
        @param filename string
        @param append bool (Default=False), if True. append the clusters
               to the ones currently present
        """
        try:
            import cPickle as pickle
        except ImportError:
            import pickle
        fl = open(filename, 'rb')
        self.clean_clusters()
        if append:
            self.clusters += pickle.load(fl)
        else:
            self.clusters = pickle.load(fl)
        self.update_clusters()

    ####################
    # Analysis Functions
    ####################

    def compute_cluster_center(self, cluster):
        """
        Compute the cluster center for a given cluster
        """
        member_distance = defaultdict(float)

        for n0, n1 in itertools.combinations(cluster.members, 2):
            _ = self.stath0[n0]
            _ = self.stath1[n1]
            rmsd, _ = self.rmsd()
            member_distance[n0] += rmsd

        if len(member_distance) > 0:
            cluster.center_index = min(member_distance,
                                       key=member_distance.get)
        else:
            cluster.center_index = cluster.members[0]

    def save_coordinates(self, cluster, rmf_name=None, reference="Absolute",
                         prefix="./"):
        """
        Save the coordinates of the current cluster a single rmf file
        """
        print("saving coordinates", cluster)
        if self.alignment:
            self.set_reference(reference, cluster)
        o = IMP.pmi.output.Output()
        if rmf_name is None:
            rmf_name = prefix+'/'+str(cluster.cluster_id)+".rmf3"

        _ = self.stath1[cluster.members[0]]
        self.model.update()
        o.init_rmf(rmf_name, [self.stath1])
        for n1 in cluster.members:
            _ = self.stath1[n1]
            self.model.update()
            self.apply_molecular_assignments(n1)
            if self.alignment:
                self.align()
            o.write_rmf(rmf_name)
            self.undo_apply_molecular_assignments(n1)
        o.close_rmf(rmf_name)

    def prune_redundant_structures(self, rmsd_cutoff=10):
        """
        remove structures that are similar
        append it to a new cluster
        """
        print("pruning models")
        selected = 0
        filtered = [selected]
        remaining = range(1, len(self.stath1), 10)

        while len(remaining) > 0:
            d0 = self.stath0[selected]
            rm = []
            for n1 in remaining:
                _ = self.stath1[n1]
                if self.alignment:
                    self.align()
                d, _ = self.rmsd()
                if d <= rmsd_cutoff:
                    rm.append(n1)
                    print("pruning model %s, similar to model %s, rmsd %s"
                          % (str(n1), str(selected), str(d)))
            remaining = [x for x in remaining if x not in rm]
            if len(remaining) == 0:
                break
            selected = remaining[0]
            filtered.append(selected)
            remaining.pop(0)
        c = IMP.pmi.output.Cluster(len(self.clusters))
        self.clusters.append(c)
        for n0 in filtered:
            d0 = self.stath0[n0]
            c.add_member(n0, d0)
        self.update_clusters()

    def precision(self, cluster):
        """
        Compute the precision of a cluster
        """
        npairs = 0
        rmsd = 0.0
        precision = None

        if cluster.center_index is not None:
            members1 = [cluster.center_index]
        else:
            members1 = cluster.members

        for n0 in members1:
            _ = self.stath0[n0]
            for n1 in cluster.members:
                if n0 != n1:
                    npairs += 1
                    _ = self.stath1[n1]
                    self.apply_molecular_assignments(n1)
                    tmp_rmsd, _ = self.rmsd()
                    rmsd += tmp_rmsd
                    self.undo_apply_molecular_assignments(n1)

        if npairs > 0:
            precision = rmsd/npairs
        cluster.precision = precision
        return precision

    def bipartite_precision(self, cluster1, cluster2, verbose=False):
        """
        Compute the bipartite precision (ie the cross-precision)
        between two clusters
        """
        npairs = 0
        rmsd = 0.0
        for cn0, n0 in enumerate(cluster1.members):
            _ = self.stath0[n0]
            for cn1, n1 in enumerate(cluster2.members):
                _ = self.stath1[n1]
                tmp_rmsd, _ = self.rmsd()
                if verbose:
                    print("--- rmsd between structure %s and structure "
                          "%s is %s" % (str(cn0), str(cn1), str(tmp_rmsd)))
                rmsd += tmp_rmsd
                npairs += 1
        precision = rmsd/npairs
        return precision

    def rmsf(self, cluster, molecule, copy_index=0, state_index=0,
             cluster_ref=None, step=1):
        """
        Compute the Root mean square fluctuations
        of a molecule in a cluster
        Returns an IMP.pmi.tools.OrderedDict() where the keys are the
        residue indexes and the value is the rmsf
        """
        rmsf = IMP.pmi.tools.OrderedDict()

        # assumes that residue indexes are identical for stath0 and stath1
        if cluster_ref is not None:
            if cluster_ref.center_index is not None:
                members0 = [cluster_ref.center_index]
            else:
                members0 = cluster_ref.members
        else:
            if cluster.center_index is not None:
                members0 = [cluster.center_index]
            else:
                members0 = cluster.members

        s0 = IMP.atom.Selection(self.stath0, molecule=molecule, resolution=1,
                                copy_index=copy_index, state_index=state_index)
        ps0 = s0.get_selected_particles()
        # get the residue indexes
        residue_indexes = list(IMP.pmi.tools.OrderedSet(
            [IMP.pmi.tools.get_residue_indexes(p)[0] for p in ps0]))

        # get the corresponding particles
        npairs = 0
        for n0 in members0:
            d0 = self.stath0[n0]
            for n1 in cluster.members[::step]:
                if n0 != n1:
                    print("--- rmsf %s %s" % (str(n0), str(n1)))
                    self.apply_molecular_assignments(n1)

                    s1 = IMP.atom.Selection(
                        self.stath1, molecule=molecule,
                        residue_indexes=residue_indexes, resolution=1,
                        copy_index=copy_index, state_index=state_index)
                    ps1 = s1.get_selected_particles()

                    d1 = self.stath1[n1]
                    if self.alignment:
                        self.align()
                    for n, (p0, p1) in enumerate(zip(ps0, ps1)):
                        r = residue_indexes[n]
                        d0 = IMP.core.XYZ(p0)
                        d1 = IMP.core.XYZ(p1)
                        if r in rmsf:
                            rmsf[r] += IMP.core.get_distance(d0, d1)
                        else:
                            rmsf[r] = IMP.core.get_distance(d0, d1)
                    npairs += 1
                    self.undo_apply_molecular_assignments(n1)
        for r in rmsf:
            rmsf[r] /= npairs

            for stath in [self.stath0, self.stath1]:
                if molecule not in self.symmetric_molecules:
                    s = IMP.atom.Selection(
                        stath, molecule=molecule, residue_index=r,
                        resolution=1, copy_index=copy_index,
                        state_index=state_index)
                else:
                    s = IMP.atom.Selection(
                        stath, molecule=molecule, residue_index=r,
                        resolution=1, state_index=state_index)

                ps = s.get_selected_particles()
                for p in ps:
                    if IMP.pmi.Uncertainty.get_is_setup(p):
                        IMP.pmi.Uncertainty(p).set_uncertainty(rmsf[r])
                    else:
                        IMP.pmi.Uncertainty.setup_particle(p, rmsf[r])

        return rmsf

    def save_densities(self, cluster, density_custom_ranges, voxel_size=5,
                       reference="Absolute",  prefix="./", step=1):
        if self.alignment:
            self.set_reference(reference, cluster)
        dens = IMP.pmi.analysis.GetModelDensity(density_custom_ranges,
                                                voxel=voxel_size)

        for n1 in cluster.members[::step]:
            print("density "+str(n1))
            _ = self.stath1[n1]
            self.apply_molecular_assignments(n1)
            if self.alignment:
                self.align()
            dens.add_subunits_density(self.stath1)
            self.undo_apply_molecular_assignments(n1)
        dens.write_mrc(path=prefix+'/', suffix=str(cluster.cluster_id))
        del dens

    def contact_map(self, cluster, contact_threshold=15, log_scale=False,
                    consolidate=False, molecules=None, prefix='./',
                    reference="Absolute"):
        if self.alignment:
            self.set_reference(reference, cluster)
        import numpy as np
        import matplotlib.pyplot as plt
        import matplotlib.cm as cm
        from scipy.spatial.distance import cdist
        import IMP.pmi.topology
        if molecules is None:
            mols = [IMP.pmi.topology.PMIMoleculeHierarchy(mol)
                    for mol in IMP.pmi.tools.get_molecules(
                        IMP.atom.get_leaves(self.stath1))]
        else:
            mols = [IMP.pmi.topology.PMIMoleculeHierarchy(mol)
                    for mol in IMP.pmi.tools.get_molecules(
                        IMP.atom.Selection(
                            self.stath1,
                            molecules=molecules).get_selected_particles())]
        unique_copies = [mol for mol in mols if mol.get_copy_index() == 0]
        mol_names_unique = dict((mol.get_name(), mol) for mol in unique_copies)
        total_len_unique = sum(max(mol.get_residue_indexes())
                               for mol in unique_copies)

        index_dict = {}
        prev_stop = 0

        if not consolidate:
            for mol in mols:
                seqlen = max(mol.get_residue_indexes())
                index_dict[mol] = range(prev_stop, prev_stop + seqlen)
                prev_stop += seqlen

        else:
            for mol in unique_copies:
                seqlen = max(mol.get_residue_indexes())
                index_dict[mol] = range(prev_stop, prev_stop + seqlen)
                prev_stop += seqlen

        for ncl, n1 in enumerate(cluster.members):
            print(ncl)
            _ = self.stath1[n1]
            coord_dict = IMP.pmi.tools.OrderedDict()
            for mol in mols:
                rindexes = mol.get_residue_indexes()
                coords = np.ones((max(rindexes), 3))
                for rnum in rindexes:
                    sel = IMP.atom.Selection(mol, residue_index=rnum,
                                             resolution=1)
                    selpart = sel.get_selected_particles()
                    if len(selpart) == 0:
                        continue
                    selpart = selpart[0]
                    coords[rnum - 1, :] = \
                        IMP.core.XYZ(selpart).get_coordinates()
                coord_dict[mol] = coords

            if not consolidate:
                coords = np.concatenate(list(coord_dict.values()))
                dists = cdist(coords, coords)
                binary_dists = np.where((dists <= contact_threshold)
                                        & (dists >= 1.0), 1.0, 0.0)
            else:
                binary_dists_dict = {}
                for mol1 in mols:
                    len1 = max(mol1.get_residue_indexes())
                    for mol2 in mols:
                        name1 = mol1.get_name()
                        name2 = mol2.get_name()
                        dists = cdist(coord_dict[mol1], coord_dict[mol2])
                        if (name1, name2) not in binary_dists_dict:
                            binary_dists_dict[(name1, name2)] = \
                                np.zeros((len1, len1))
                        binary_dists_dict[(name1, name2)] += \
                            np.where((dists <= contact_threshold)
                                     & (dists >= 1.0), 1.0, 0.0)
                binary_dists = np.zeros((total_len_unique, total_len_unique))

                for name1, name2 in binary_dists_dict:
                    r1 = index_dict[mol_names_unique[name1]]
                    r2 = index_dict[mol_names_unique[name2]]
                    binary_dists[min(r1):max(r1)+1, min(r2):max(r2)+1] = \
                        np.where((binary_dists_dict[(name1, name2)] >= 1.0),
                                 1.0, 0.0)

            if ncl == 0:
                dist_maps = [dists]
                av_dist_map = dists
                contact_freqs = binary_dists
            else:
                dist_maps.append(dists)
                av_dist_map += dists
                contact_freqs += binary_dists

        if log_scale:
            contact_freqs = -np.log(1.0-1.0/(len(cluster)+1)*contact_freqs)
        else:
            contact_freqs = 1.0/len(cluster)*contact_freqs
        av_dist_map = 1.0/len(cluster)*contact_freqs

        fig = plt.figure(figsize=(100, 100))
        ax = fig.add_subplot(111)
        ax.set_xticks([])
        ax.set_yticks([])
        gap_between_components = 50
        colormap = cm.Blues
        colornorm = None

        if not consolidate:
            sorted_tuple = sorted(
                (IMP.pmi.topology.PMIMoleculeHierarchy(
                    mol).get_extended_name(), mol) for mol in mols)
            prot_list = list(zip(*sorted_tuple))[1]
        else:
            sorted_tuple = sorted(
                (IMP.pmi.topology.PMIMoleculeHierarchy(mol).get_name(), mol)
                for mol in unique_copies)
            prot_list = list(zip(*sorted_tuple))[1]

        prot_listx = prot_list
        nresx = gap_between_components + \
            sum([max(mol.get_residue_indexes())
                + gap_between_components for mol in prot_listx])

        # set the list of proteins on the y axis
        prot_listy = prot_list
        nresy = gap_between_components + \
            sum([max(mol.get_residue_indexes())
                + gap_between_components for mol in prot_listy])

        # this is the residue offset for each protein
        resoffsetx = {}
        resendx = {}
        res = gap_between_components
        for mol in prot_listx:
            resoffsetx[mol] = res
            res += max(mol.get_residue_indexes())
            resendx[mol] = res
            res += gap_between_components

        resoffsety = {}
        resendy = {}
        res = gap_between_components
        for mol in prot_listy:
            resoffsety[mol] = res
            res += max(mol.get_residue_indexes())
            resendy[mol] = res
            res += gap_between_components

        resoffsetdiagonal = {}
        res = gap_between_components
        for mol in IMP.pmi.tools.OrderedSet(prot_listx + prot_listy):
            resoffsetdiagonal[mol] = res
            res += max(mol.get_residue_indexes())
            res += gap_between_components

        # plot protein boundaries
        xticks = []
        xlabels = []
        for n, prot in enumerate(prot_listx):
            res = resoffsetx[prot]
            end = resendx[prot]
            for proty in prot_listy:
                resy = resoffsety[proty]
                endy = resendy[proty]
                ax.plot([res, res], [resy, endy], linestyle='-',
                        color='gray', lw=0.4)
                ax.plot([end, end], [resy, endy], linestyle='-',
                        color='gray', lw=0.4)
            xticks.append((float(res) + float(end)) / 2)
            xlabels.append(IMP.pmi.topology.PMIMoleculeHierarchy(
                prot).get_extended_name())

        yticks = []
        ylabels = []
        for n, prot in enumerate(prot_listy):
            res = resoffsety[prot]
            end = resendy[prot]
            for protx in prot_listx:
                resx = resoffsetx[protx]
                endx = resendx[protx]
                ax.plot([resx, endx], [res, res], linestyle='-',
                        color='gray', lw=0.4)
                ax.plot([resx, endx], [end, end], linestyle='-',
                        color='gray', lw=0.4)
            yticks.append((float(res) + float(end)) / 2)
            ylabels.append(IMP.pmi.topology.PMIMoleculeHierarchy(
                prot).get_extended_name())

        # plot the contact map

        tmp_array = np.zeros((nresx, nresy))
        ret = {}
        for px in prot_listx:
            for py in prot_listy:
                resx = resoffsetx[px]
                lengx = resendx[px] - 1
                resy = resoffsety[py]
                lengy = resendy[py] - 1
                indexes_x = index_dict[px]
                minx = min(indexes_x)
                maxx = max(indexes_x)
                indexes_y = index_dict[py]
                miny = min(indexes_y)
                maxy = max(indexes_y)
                tmp_array[resx:lengx, resy:lengy] = \
                    contact_freqs[minx:maxx, miny:maxy]
                ret[(px, py)] = np.argwhere(
                    contact_freqs[minx:maxx, miny:maxy] == 1.0) + 1

        ax.imshow(tmp_array, cmap=colormap, norm=colornorm,
                  origin='lower', alpha=0.6, interpolation='nearest')

        ax.set_xticks(xticks)
        ax.set_xticklabels(xlabels, rotation=90)
        ax.set_yticks(yticks)
        ax.set_yticklabels(ylabels)
        plt.setp(ax.get_xticklabels(), fontsize=6)
        plt.setp(ax.get_yticklabels(), fontsize=6)

        # display and write to file
        fig.set_size_inches(0.005 * nresx, 0.005 * nresy)
        [i.set_linewidth(2.0) for i in ax.spines.values()]

        plt.savefig(prefix+"/contact_map."+str(cluster.cluster_id)+".pdf",
                    dpi=300, transparent="False")
        return ret

    def plot_rmsd_matrix(self, filename):
        self.compute_all_pairwise_rmsd()
        distance_matrix = np.zeros(
            (len(self.stath0), len(self.stath1)))
        for (n0, n1) in self.pairwise_rmsd:
            distance_matrix[n0, n1] = self.pairwise_rmsd[(n0, n1)]

        import matplotlib as mpl
        mpl.use('Agg')
        import matplotlib.pylab as pl
        from scipy.cluster import hierarchy as hrc

        fig = pl.figure(figsize=(10, 8))
        ax = fig.add_subplot(212)
        dendrogram = hrc.dendrogram(
            hrc.linkage(distance_matrix),
            color_threshold=7,
            no_labels=True)
        leaves_order = dendrogram['leaves']
        ax.set_xlabel('Model')
        ax.set_ylabel('RMSD [Angstroms]')

        ax2 = fig.add_subplot(221)
        cax = ax2.imshow(
            distance_matrix[leaves_order, :][:, leaves_order],
            interpolation='nearest')
        cb = fig.colorbar(cax)
        cb.set_label('RMSD [Angstroms]')
        ax2.set_xlabel('Model')
        ax2.set_ylabel('Model')

        pl.savefig(filename, dpi=300)
        pl.close(fig)

    ####################
    # Internal Functions
    ####################

    def update_clusters(self):
        """
        Update the cluster id numbers
        """
        for n, c in enumerate(self.clusters):
            c.cluster_id = n

    def get_molecule(self, hier, name, copy):
        s = IMP.atom.Selection(hier, molecule=name, copy_index=copy)
        return IMP.pmi.tools.get_molecules(s.get_selected_particles()[0])[0]

    def update_seldicts(self):
        """
        Update the seldicts
        """
        self.seldict0 = IMP.pmi.tools.get_selections_dictionary(
            self.sel0_rmsd.get_selected_particles())
        self.seldict1 = IMP.pmi.tools.get_selections_dictionary(
            self.sel1_rmsd.get_selected_particles())
        for mol in self.seldict0:
            for sel in self.seldict0[mol]:
                self.issymmetricsel[sel] = False
        for mol in self.symmetric_molecules:
            self.symmetric_molecules[mol] = len(self.seldict0[mol])
            for sel in self.seldict0[mol]:
                self.issymmetricsel[sel] = True

    def align(self):
        tr = IMP.atom.get_transformation_aligning_first_to_second(
            self.sel1_alignment, self.sel0_alignment)

        for rb in self.rbs1:
            IMP.core.transform(rb, tr)

        for bead in self.beads1:
            try:
                IMP.core.transform(IMP.core.XYZ(bead), tr)
            except:  # noqa: E722
                continue

        self.model.update()

    def aggregate(self, idxs, rmsd_cutoff=10, metric=IMP.atom.get_rmsd):
        '''
        initial filling of the clusters.
        '''
        n0 = idxs.pop()
        print("clustering model "+str(n0))
        d0 = self.stath0[n0]
        c = IMP.pmi.output.Cluster(len(self.clusters))
        print("creating cluster index "+str(len(self.clusters)))
        self.clusters.append(c)
        c.add_member(n0, d0)
        clustered = set([n0])
        for n1 in idxs:
            print("--- trying to add model " + str(n1) + " to cluster "
                  + str(len(self.clusters)))
            d1 = self.stath1[n1]
            if self.alignment:
                self.align()
            rmsd, _ = self.rmsd(metric=metric)
            if rmsd < rmsd_cutoff:
                print("--- model "+str(n1)+" added, rmsd="+str(rmsd))
                c.add_member(n1, d1)
                clustered.add(n1)
            else:
                print("--- model "+str(n1)+" NOT added, rmsd="+str(rmsd))
        idxs -= clustered

    def merge_aggregates(self, rmsd_cutoff, metric=IMP.atom.get_rmsd):
        """
        merge the clusters that have close members
        @param rmsd_cutoff cutoff distance in Angstorms
        """
        # before merging, clusters are spheres of radius rmsd_cutoff
        # centered on the 1st element
        # here we only try to merge clusters whose centers are closer
        # than 2*rmsd_cutoff
        to_merge = []
        print("merging...")
        for c0, c1 in filter(lambda x: len(x[0].members) > 1,
                             itertools.combinations(self.clusters, 2)):
            n0, n1 = [c.members[0] for c in (c0, c1)]
            _ = self.stath0[n0]
            _ = self.stath1[n1]
            rmsd, _ = self.rmsd()
            if (rmsd < 2*rmsd_cutoff and
                    self.have_close_members(c0, c1, rmsd_cutoff, metric)):
                to_merge.append((c0, c1))

        for c0, c in reversed(to_merge):
            self.merge(c0, c)

        # keep only full clusters
        self.clusters = [c for c in
                         filter(lambda x: len(x.members) > 0, self.clusters)]

    def have_close_members(self, c0, c1, rmsd_cutoff, metric):
        '''
        returns true if c0 and c1 have members that are closer than rmsd_cutoff
        '''
        print("check close members for clusters " + str(c0.cluster_id) +
              " and " + str(c1.cluster_id))
        for n0, n1 in itertools.product(c0.members[1:], c1.members):
            _ = self.stath0[n0]
            _ = self.stath1[n1]
            rmsd, _ = self.rmsd(metric=metric)
            if rmsd < rmsd_cutoff:
                return True

        return False

    def merge(self, c0, c1):
        '''
        merge two clusters
        '''
        c0 += c1
        c1.members = []
        c1.data = {}

    def rmsd_helper(self, sels0, sels1, metric):
        '''
        a function that returns the permutation best_sel of sels0 that
        minimizes metric
        '''
        best_rmsd2 = float('inf')
        best_sel = None
        if self.issymmetricsel[sels0[0]]:
            # this cases happens when symmetries were defined
            N = len(sels0)
            for offset in range(N):
                sels = [sels0[(offset+i) % N] for i in range(N)]
                sel0 = sels[0]
                sel1 = sels1[0]
                r = metric(sel0, sel1)
                rmsd2 = r*r*N
                if rmsd2 < best_rmsd2:
                    best_rmsd2 = rmsd2
                    best_sel = sels
        else:
            for sels in itertools.permutations(sels0):
                rmsd2 = 0.0
                for sel0, sel1 in itertools.takewhile(
                        lambda x: rmsd2 < best_rmsd2, zip(sels, sels1)):
                    r = metric(sel0, sel1)
                    rmsd2 += r*r
                if rmsd2 < best_rmsd2:
                    best_rmsd2 = rmsd2
                    best_sel = sels
        return best_sel, best_rmsd2

    def compute_all_pairwise_rmsd(self):
        for d0 in self.stath0:
            for d1 in self.stath1:
                rmsd, _ = self.rmsd()

    def rmsd(self, metric=IMP.atom.get_rmsd):
        '''
        Computes the RMSD. Resolves ambiguous pairs assignments
        '''
        # here we memoize the rmsd and molecular assignment so that it's
        # not done multiple times
        n0 = self.stath0.current_index
        n1 = self.stath1.current_index
        if ((n0, n1) in self.pairwise_rmsd) \
                and ((n0, n1) in self.pairwise_molecular_assignment):
            return (self.pairwise_rmsd[(n0, n1)],
                    self.pairwise_molecular_assignment[(n0, n1)])

        if self.alignment:
            self.align()
        # if it's not yet memoized
        total_rmsd = 0.0
        total_N = 0
        # this is a dictionary which keys are the molecule names, and values
        # are the list of IMP.atom.Selection for all molecules that share
        # the molecule name
        molecular_assignment = {}
        for molname, sels0 in self.seldict0.items():
            sels_best_order, best_rmsd2 = \
                self.rmsd_helper(sels0, self.seldict1[molname], metric)

            Ncoords = len(sels_best_order[0].get_selected_particles())
            Ncopies = len(self.seldict1[molname])
            total_rmsd += Ncoords*best_rmsd2
            total_N += Ncoords*Ncopies

            for sel0, sel1 in zip(sels_best_order, self.seldict1[molname]):
                p0 = sel0.get_selected_particles()[0]
                p1 = sel1.get_selected_particles()[0]
                m0 = IMP.pmi.tools.get_molecules([p0])[0]
                m1 = IMP.pmi.tools.get_molecules([p1])[0]
                c0 = IMP.atom.Copy(m0).get_copy_index()
                c1 = IMP.atom.Copy(m1).get_copy_index()
                molecular_assignment[(molname, c0)] = (molname, c1)

        total_rmsd = math.sqrt(total_rmsd/total_N)

        self.pairwise_rmsd[(n0, n1)] = total_rmsd
        self.pairwise_molecular_assignment[(n0, n1)] = molecular_assignment
        self.pairwise_rmsd[(n1, n0)] = total_rmsd
        self.pairwise_molecular_assignment[(n1, n0)] = molecular_assignment
        return total_rmsd, molecular_assignment

    def set_reference(self, reference, cluster):
        """
        Fix the reference structure for structural alignment, rmsd and
        chain assignment

        @param reference can be either "Absolute" (cluster center of the
               first cluster) or Relative (cluster center of the current
               cluster)
        """
        if reference == "Absolute":
            _ = self.stath0[0]
        elif reference == "Relative":
            if cluster.center_index:
                n0 = cluster.center_index
            else:
                n0 = cluster.members[0]
            _ = self.stath0[n0]

    def apply_molecular_assignments(self, n1):
        """
        compute the molecular assignments between multiple copies
        of the same sequence. It changes the Copy index of Molecules
        """
        _ = self.stath1[n1]
        _, molecular_assignment = self.rmsd()
        for (m0, c0), (m1, c1) in molecular_assignment.items():
            mol0 = self.molcopydict0[m0][c0]
            mol1 = self.molcopydict1[m1][c1]
            cik0 = IMP.atom.Copy(mol0).get_copy_index_key()
            p1 = IMP.atom.Copy(mol1).get_particle()
            p1.set_value(cik0, c0)

    def undo_apply_molecular_assignments(self, n1):
        """
        Undo the Copy index assignment
        """
        _ = self.stath1[n1]
        _, molecular_assignment = self.rmsd()
        for (m0, c0), (m1, c1) in molecular_assignment.items():
            mol0 = self.molcopydict0[m0][c0]
            mol1 = self.molcopydict1[m1][c1]
            cik0 = IMP.atom.Copy(mol0).get_copy_index_key()
            p1 = IMP.atom.Copy(mol1).get_particle()
            p1.set_value(cik0, c1)

    ####################
    # Container Functions
    ####################

    def __repr__(self):
        s = "AnalysisReplicaExchange\n"
        s += "---- number of clusters %s \n" % str(len(self.clusters))
        s += "---- number of models %s \n" % str(len(self.stath0))
        return s

    def __getitem__(self, int_slice_adaptor):
        if isinstance(int_slice_adaptor, int):
            return self.clusters[int_slice_adaptor]
        elif isinstance(int_slice_adaptor, slice):
            return self.__iter__(int_slice_adaptor)
        else:
            raise TypeError("Unknown Type")

    def __len__(self):
        return len(self.clusters)

    def __iter__(self, slice_key=None):
        if slice_key is None:
            for i in range(len(self)):
                yield self[i]
        else:
            for i in range(len(self))[slice_key]:
                yield self[i]