from __future__ import division, print_function
import sys
from scitbx.array_family import flex
from libtbx import group_args
from libtbx.utils import Sorry
from mmtbx.process_predicted_model import get_indices_as_ranges

##############################################################################
#################  domains_from_pae  by Tristan Croll#########################
##############################################################################
"""
This license applies to the routines: parse_pae_file,
domains_from_pae_matrix_networkx, domains_from_pae_matrix_igraph
in this file

These routines are from:
https://github.com/tristanic/pae_to_domains

MIT License

Copyright (c) 2021 Tristan Croll

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""


def parse_pae_file(pae_file):
    """Takes PAE file in PKL or JSON format and returns PAE matrix

    Arguments:
        *pae_file: file containing PAE matrix in PKL or JSON format

    Returns:
        pae_matrix as numpy array
    """
    import numpy
    import os
    _, ext = os.path.splitext(pae_file)
    if ext == '.json':
        import json

        try:
            with open(pae_file, 'rt') as f:
                data = json.load(f)
        except Exception:
            raise Sorry("Unable to read the json file: %s" %(pae_file))

        if isinstance(data, dict) and 'pae' in data:
            # ColabFold 1.3 produces a JSON file different from AlphaFold database.
            matrix = numpy.array(data['pae'])
        elif isinstance(data, list):
            data = data[0]
            if 'residue1' in data.keys() and 'distance' in data.keys():
                # Support original JSON file format.
                r1, d = data['residue1'], data['distance']
                size = max(r1)
                matrix = numpy.empty((size, size))
                matrix.ravel()[:] = d
            elif 'predicted_aligned_error' in data.keys():
                # Support new AlphaFold database JSON file format.
                matrix = numpy.asarray(
                  data['predicted_aligned_error']).astype(float)
                matrix[matrix == 0] = 0.2
            else:
                raise Sorry("PAE data not detected in json file: %s" %(
                    pae_file))
        else:
            raise Sorry("Data in %s is not in a recognised format" %(
              pae_file))
    elif ext == '.pkl':
        # Support PKL format when running AlphaFold locally in ptm or multimer modes.
        import pickle as pkl
        data = []
        try:
            with open(pae_file, 'rb') as f_in:
                data.append(pkl.load(f_in))
        except Exception:
            raise Sorry("Unable to read the pkl file: %s" %(pae_file))

        try:
            matrix = numpy.asarray(data[0]['predicted_aligned_error'])
        except KeyError:
            raise Sorry("PAE data not detected in pkl file: %s" %(pae_file))
    else:
        raise Sorry("PAE file extension: '%s' not recognised" %(ext))


    return matrix


def domains_from_pae_matrix_networkx(pae_matrix, pae_power=1,
                                     pae_cutoff=5, graph_resolution=1, weight_by_ca_ca_distance=False,
                                     distance_power=1, distance_model=None):
    """
    Takes a predicted aligned error (PAE) matrix representing the predicted error in distances between each
    pair of residues in a model, and uses a graph-based community clustering algorithm to partition the model
    into approximately rigid groups.

    Arguments:

        * pae_matrix: a (n_residues x n_residues) numpy array. Diagonal elements should be set to some non-zero
          value to avoid divide-by-zero warnings
        * pae_power (optional, default=1): each edge in the graph will be weighted proportional to (1/pae**pae_power)
        * pae_cutoff (optional, default=5): graph edges will only be created for residue pairs with pae<pae_cutoff
        * graph_resolution (optional, default=1): regulates how aggressively the clustering algorithm is. Smaller values
          lead to larger clusters. Value should be larger than zero, and values larger than 5 are unlikely to be useful.
        * weight_by_ca_ca_distance (optional, default=False): adjust the edge weighting for each residue pair according
          to the distance between CA residues. If this is True, then `distance_model` must be provided.
        * distance_power (optional, default=1): If `weight_by_ca_ca_distance` is True, then edge weights will be multiplied
          by 1/distance**distance_power.
        * distance_model (optional, default=None): Model corresponding to the PAE
          matrix. Only needed if `weight_by_ca_ca_distances is True.
    Returns: a series of lists, where each list contains the indices of residues belonging to one cluster.
    """
    try:
        import networkx as nx
    except ImportError:
        raise Sorry(
            'ERROR: This method requires NetworkX (>=2.6.2) to be installed. Please install it using "pip install networkx" '
            'in a Python >=3.7 environment and try again.')
    import numpy
    weights = 1 / pae_matrix ** pae_power

    if weight_by_ca_ca_distance:
        if distance_model is None:
            raise Sorry('If weight_by_ca_ca_distance is True, distance_model must be provided!')
        weights *= weights_from_distance_matrix(distance_model, distance_power)

    g = nx.Graph()
    size = weights.shape[0]
    g.add_nodes_from(range(size))
    edges = numpy.argwhere(pae_matrix < pae_cutoff)
    sel_weights = weights[edges.T[0], edges.T[1]]
    wedges = [(i, j, w) for (i, j), w in zip(edges, sel_weights)]
    g.add_weighted_edges_from(wedges)

    from networkx.algorithms import community

    try:
      clusters = community.greedy_modularity_communities(g, weight='weight',
                                           resolution=graph_resolution)
      return clusters
    except Exception as e:
      pass # run again below without resolution

    # failed... try again without resolution
    try:
      clusters = community.greedy_modularity_communities(g, weight='weight')
      return clusters
    except Exception as e: #  failed
      pass # run again
    return None

def domains_from_pae_matrix_igraph(pae_matrix, pae_power=1, pae_cutoff=5,
                                   graph_resolution=1, weight_by_ca_ca_distance=False,
                                   distance_power=1, distance_model=None):
    """
    Takes a predicted aligned error (PAE) matrix representing the predicted error in distances between each
    pair of residues in a model, and uses a graph-based community clustering algorithm to partition the model
    into approximately rigid groups.

    Arguments:

        * pae_matrix: a (n_residues x n_residues) numpy array. Diagonal elements should be set to some non-zero
          value to avoid divide-by-zero warnings
        * pae_power (optional, default=1): each edge in the graph will be weighted proportional to (1/pae**pae_power)
        * pae_cutoff (optional, default=5): graph edges will only be created for residue pairs with pae<pae_cutoff
        * graph_resolution (optional, default=1): regulates how aggressively the clustering algorithm is. Smaller values
          lead to larger clusters. Value should be larger than zero, and values larger than 5 are unlikely to be useful.
        * weight_by_ca_ca_distance (optional, default=False): adjust the edge weighting for each residue pair according
          to the distance between CA residues. If this is True, then `distance_model` must be provided.
        * distance_power (optional, default=1): If `weight_by_ca_ca_distance` is True, then edge weights will be multiplied
          by 1/distance**distance_power.
        * distance_model (optional, default=None):  Model corresponding to the PAE
          matrix. Only needed if `weight_by_ca_ca_distances is True.

    Returns: a series of lists, where each list contains the indices of residues belonging to one cluster.
    """
    try:
        import igraph
    except ImportError:
        raise Sorry(
            'ERROR: This method requires python-igraph to be installed. Please install it using "pip install python-igraph" '
            'in a Python >=3.6 environment and try again.')
    import numpy
    weights = 1 / pae_matrix ** pae_power

    if weight_by_ca_ca_distance:
        if distance_model is None:
            raise Sorry('If weight_by_ca_ca_distance is True, distance_model must be provided!')
        weights *= weights_from_distance_matrix(distance_model, distance_power)

    g = igraph.Graph()
    size = weights.shape[0]
    g.add_vertices(range(size))
    edges = numpy.argwhere(pae_matrix < pae_cutoff)
    sel_weights = weights[edges.T[0], edges.T[1]]
    g.add_edges(edges)
    g.es['weight'] = sel_weights

    vc = g.community_leiden(weights='weight',
                            resolution_parameter=graph_resolution / 100, n_iterations=-1)
    membership = numpy.array(vc.membership)
    from collections import defaultdict
    clusters = defaultdict(list)
    for i, c in enumerate(membership):
        clusters[c].append(i)
    clusters = list(sorted(clusters.values(), key=lambda l: (len(l)), reverse=True))
    return clusters


##############################################################################
#################  end of domains_from_pae  by Tristan Croll##################
##############################################################################


def distance_matrix(sites_cart):
    from scitbx.matrix import col
    distances = flex.double()
    n = sites_cart.size()
    for i in range(n):
        diffs = (sites_cart - col(sites_cart[i])).norms()
        # Set diagonal terms to 1 to avoid divide-by-zero issues
        diffs[i] = 1
        distances.extend(diffs)
    flex_grid = flex.grid((n, n))
    distances.reshape(flex_grid)
    return distances


def flex_matrix_as_numpy_matrix(distances):
    import numpy
    shape = distances.all()
    matrix = numpy.empty(shape)
    matrix.ravel()[:] = list(distances.as_1d())
    return matrix


def weights_from_distance_matrix(distance_model, distance_power):
    flex_matrix = distance_matrix(
        distance_model.apply_selection_string("name CA").get_sites_cart()
    )
    numpy_matrix = flex_matrix_as_numpy_matrix(flex_matrix)
    return 1 / numpy_matrix ** distance_power


def cluster_as_selection(c, first_resno=None):
    # first_resno is residue number for selections corresponding to index of zero
    c = sorted(c)
    ranges = get_indices_as_ranges(c)
    selection_string = ""
    if first_resno is not None:
        offset = first_resno
    else:
        offset = 0
    for r in ranges:
        r_start = r.start + offset
        r_end = r.end + offset
        if not selection_string:
            selection_string = "(resseq %s:%s)" % (r_start, r_end)
        else:
            selection_string = "%s or (resseq %s:%s)" % (
                selection_string, r_start, r_end)
    return selection_string


def read_model(filename):
    from iotbx.data_manager import DataManager
    dm = DataManager()
    dm.set_overwrite(True)
    dm.process_model_file(filename)
    model = dm.get_model(filename)
    model.add_crystal_symmetry_if_necessary()
    return model


def get_domain_selections_from_pae_matrix(pae_matrix=None,
                                          pae_file=None,
                                          library=None,
                                          pae_power=None,
                                          pae_cutoff=None,
                                          graph_resolution=None,
                                          weight_by_ca_ca_distance=None,
                                          distance_power=None,
                                          distance_model=None,
                                          first_resno=None):
    if library is None:
        library = 'networkx'
    if library == 'networkx':
        f = domains_from_pae_matrix_networkx
    elif library == 'igraph':
        f = domains_from_pae_matrix_igraph
    else:
        raise Sorry('Unrecognised library "%s"!', library)

    # first_resno is residue number of residue with index of zero

    if pae_matrix is None:
        pae_matrix = parse_pae_file(pae_file)
    kwargs = {
        "pae_power": pae_power,
        "pae_cutoff": pae_cutoff,
        "graph_resolution": graph_resolution,
        "weight_by_ca_ca_distance": weight_by_ca_ca_distance,
        "distance_power": distance_power,
        "distance_model": distance_model
    }
    # Use default values when argument is None
    kwargs = {key: value for key, value in kwargs.items() if value is not None}
    clusters = f(pae_matrix, **kwargs)  # NOTE graph_resolution not used in 2.7

    new_clusters = []
    for c in clusters:
        new_clusters.append(sorted(c))
    clusters = sorted(new_clusters,
                      key=lambda c: c[0])
    selections = []
    for c in clusters:
        selections.append(cluster_as_selection(c, first_resno=first_resno))
    return selections


def write_pae_file(pae_matrix, file_name, range_to_keep=None):
    pae_as_list_of_lists = pae_matrix.tolist()
    if range_to_keep:
        n = range_to_keep.end + 1 - range_to_keep.start
        shape = (n, n)
        pae_1d = []
        for l in pae_as_list_of_lists[range_to_keep.start:range_to_keep.end + 1]:
            new_l = l[range_to_keep.start:range_to_keep.end + 1]
            pae_1d += new_l
    else:
        assert type(pae_matrix.tolist()[0][0]) == type(float(1))

        shape = tuple(pae_matrix.shape)

        # Flatten it out
        pae_1d = pae_matrix.flatten().tolist()

    # Read in to flex array
    flex_array = flex.float(pae_1d)

    from scitbx.array_family.flex import grid
    flex_grid = grid(shape)

    n, n = shape

    # Reshape the flex array
    flex_array.reshape(flex_grid)

    # Write out array to text file as json
    residues_1 = []
    residues_2 = []
    distances = []
    for i in range(n):
        ii = i + 1
        for j in range(n):
            jj = j + 1
            residues_1.append(ii)
            residues_2.append(jj)
            distances.append(float("%.2f" % (flex_array[i, j])))

    residue_dict = {"residue1": residues_1,
                    "residue2": residues_2,
                    "distance": distances,
                    "max_predicted_aligned_error": 0}
    values = [residue_dict]

    text = str(values).replace(" ", "").replace("'", '"')

    f = open(file_name, 'w')
    print(text, file=f)
    f.close()
    print("Wrote json file to %s" % (file_name))


if __name__ == '__main__':
    input_file_name = sys.argv[1]
    args = group_args(
        group_args_type='parameters',
        pae_file=input_file_name,
        library='networkx',
        pae_power=1.0,
        pae_cutoff=5.0,
        resolution=1.0,
        select_range=False)

    if args.select_range:
        range_to_keep = group_args(
            group_args_type='range to keep',
            start=283,  # starts with 0
            end=364,
        )
        pae_matrix = parse_pae_file(args.pae_file)
        write_pae_file(pae_matrix, "pae.json", range_to_keep=range_to_keep)
    else:
        pae_matrix = parse_pae_file(args.pae_file)
        selections = get_domain_selections_from_pae_matrix(
            pae_matrix=pae_matrix,
            pae_power=args.pae_power, pae_cutoff=args.pae_cutoff,
            graph_resolution=args.pae_graph_resolution, )
        print("Selections:")
        for s in selections:
            print(s)
