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# Implement the section IV in Puzis 2012 paper
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# straight_lineent the section IV in Puzis 2012 paper
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# Heuristic Betweenness Centrality - Partitioning to Bi-connected Components
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import os
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import sys
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import pprint
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import networkx as nx
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import betweenness_centrality as centrality
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import utility
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from pdb import set_trace as debugger
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MAIN_CODE_DIR = os.environ.get('MAIN_CODE_DIR', '')
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class HeuristicBetweennessCentrality():
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    def __init__(self, subgraphs, bicomponents, cutpoints, num_vertices, link_weight, traffic_matrix):
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        self.subgraphs = subgraphs
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        self.bicomponents = bicomponents
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        self.cutpoints = cutpoints
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        self.num_vertices = num_vertices
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        self.link_weight = link_weight
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        self.traffic_matrix = traffic_matrix
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        self.bc_components = list()
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        self.calculate_bc_non_cutpoint()
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        self.calculate_bc_cutpoint()
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        self.bc = dict()
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        self.finalize()
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    def calculate_bc_non_cutpoint(self):
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        """BC for non cutpoint
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        """
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        for i, subgraphs in enumerate(self.subgraphs):
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            traffic_matrix = self.traffic_matrix[i]
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            results = centrality.weight_betweenness_centrality(subgraphs, traffic_matrix)
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            self.bc_components.append(results)
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    def calculate_bc_cutpoint(self):
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        self.bc_cutpoints = dict()
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        bc_inter = dict()
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        for v in self.cutpoints:
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            inter = 0
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            for i, comp in enumerate(self.bicomponents):
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                if v in comp:
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                    inter += self.link_weight.get_link_weight(i, v
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                        ) * self.link_weight.get_reverse_link_weight(i, v)
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            bc_inter[v] = inter
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        print 'XXXX bc_components = %s' % self.bc_components
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        print 'XXXX inter = %s' % bc_inter
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        for v in self.cutpoints:
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            bc_locally = 0
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            for i, comp in enumerate(self.bicomponents):
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                if v in comp:
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                    bc_locally += self.bc_components[i][v]
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            # self.bc_cutpoints[v] = bc_locally - bc_inter[v]
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            # TODO: do not minus the bc_inter
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            self.bc_cutpoints[v] = bc_locally
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    def finalize(self):
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        # Add the bc for non cutpoint vertices
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        for bc_component in self.bc_components:
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            for key, value in bc_component.iteritems():
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                if key not in self.bc:
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                    self.bc[key] = value
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        # Add the bc for cutpoint vertices
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        for key, value in self.bc_cutpoints.iteritems():
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            self.bc[key] = value
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        print '*** betweenness = %s' % self.bc
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        # Rescale the bc according to the original graph
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        factor = 1.0 / ((self.num_vertices - 1) * (self.num_vertices - 2))
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        # TODO: check the scaling factor, how much should it be?
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        # factor = 2.0 / (self.num_vertices*self.num_vertices - 3 * self.num_vertices + 2)
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        for key, value in self.bc.iteritems():
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            self.bc[key] = value * factor
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    def write(self, file_suffix=''):
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        filepath = '/output/heuristic_%s.csv'  % file_suffix
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        with open(MAIN_CODE_DIR + filepath, 'w') as output:
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            for node, centrality in self.bc.iteritems():
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                output.write('%s, %s\n' % (node, centrality))
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    def __str__(self):
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        return str(self.bc)
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class TrafficMatrix():
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    def __init__(self, bicomponents, cutpoints, link_weight):
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        self.bicomponents = bicomponents
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        self.cutpoints = cutpoints
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        self.num_components = len(bicomponents)
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        self.link_weight = link_weight
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        self.h = list()
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        self.generate_empty_traffic_matrix()
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        self.generate_traffic_matrix()
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    def generate_empty_traffic_matrix(self):
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        for i in range(self.num_components):
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            l = len(self.bicomponents[i])
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            matrix = [[1 for x in range(l)] for y in range(l)]
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            # update the main diagonal
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            for x in range(l):
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                matrix[x][x] = 0
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            self.h.append(matrix)
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    def generate_traffic_matrix(self):
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        # Update the value when one vertex is a cut-point, another vertex is not a cut-point
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        for i, components in enumerate(self.bicomponents):
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            normal_points = components.difference(self.cutpoints)
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            cutpoints = self.cutpoints.intersection(components)
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            for cutpoint in cutpoints:
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                for normal_point in normal_points:
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                    communication_intensity = self.link_weight.get_reverse_link_weight(i, cutpoint) + 1
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                    self.update(i, cutpoint, normal_point, communication_intensity)
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        # Update the value when both vertices are cut-points
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        for i, components in enumerate(self.bicomponents):
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            cutpoints = list(self.cutpoints.intersection(components))
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            len_cutpoints = len(cutpoints)
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            if len_cutpoints > 1:
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                for k in range(len_cutpoints - 1):
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                    for l in range(1, len_cutpoints):
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                        if k == l:
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                            continue
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                        communication_intensity = (
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                            self.link_weight.get_reverse_link_weight(i, cutpoints[k]) + 1) * (
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                            self.link_weight.get_reverse_link_weight(i, cutpoints[l]) + 1
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                        )
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                        self.update(i, cutpoints[k], cutpoints[l], communication_intensity)
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    def simple_update(self, comp_index, x_pos, y_pos, value):
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        self.h[comp_index][x_pos][y_pos] = value
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        # to keep the symmetric property of Traffic Matrix
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        self.h[comp_index][y_pos][x_pos] = value
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    def update(self, comp_index, x, y, value):
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        comp = sorted(self.bicomponents[comp_index])
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        try:
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            x_pos = list(comp).index(x)
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            y_pos = list(comp).index(y)
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        except:
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            debugger()
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            a = 2
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        self.simple_update(comp_index, x_pos, y_pos, value)
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    def __str__(self):
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        return str(self.h)
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    def __getitem__(self, key):
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        return self.h[key]
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class LinkWeight():
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    def __init__(self, graph, bicomponents, cutpoints):
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        self.num_vertices = nx.number_of_nodes(graph)
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        self.bicomponents = bicomponents
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        self.num_components = len(bicomponents)
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        self.cutpoints = cutpoints
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        self.Dv_B = [dict() for i in range(self.num_components)] # link weight
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        self.compute_component_tree_weight()
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        self.reverse_Dv_B = [dict() for i in range(self.num_components)] # reverse link weight
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        self.generate_reverse_link_weight()
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    def _components_sharing_cutpoint(self, B_cutpoints, point):
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        indices = list()
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        for i, cp in enumerate(B_cutpoints):
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            if point in cp:
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                indices.append(i)
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        return indices
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    def get_link_weight(self, comp_index, point):
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        Dv_B_comp = self.Dv_B[comp_index]
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        if point in Dv_B_comp:
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            return Dv_B_comp[point]
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        else:
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            return 0
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    def set_link_weight(self, comp_index, point, value):
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        self.Dv_B[comp_index][point] = value
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    def get_reverse_link_weight(self, comp_index, point):
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        reverse_Dv_B_comp = self.reverse_Dv_B[comp_index]
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        if point in reverse_Dv_B_comp:
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            return reverse_Dv_B_comp[point]
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        else:
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            return 0
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    def generate_link_weight(self):
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        # How many cutpoints does this component have
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        B_plus_v = list()
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        B_cutpoints = list() # number of cutpoints in component B
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        for comp in self.bicomponents:
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            points = comp.intersection(self.cutpoints)
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            B_plus_v.append(comp.difference(points))
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            B_cutpoints.append(points)
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        len_B_plus_v = [len(x) for x in B_plus_v]
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        len_B_cutpoints = [len(x) for x in B_cutpoints]
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        # Calculate the Dv_B
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        # For the leaf in the block-cut tree
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        level = 1
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        for (i, cp) in enumerate(B_cutpoints):
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            if len_B_cutpoints[i] == level:
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                point = list(cp)[0] # there is only 1 element
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                self.Dv_B[i][point] = len_B_plus_v[i]
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        # For other nodes in the block-cut tree
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        level += 1
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        while level <= max(len_B_cutpoints):
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            if level == 3:
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                debugger()
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            for (i, cp) in enumerate(B_cutpoints):
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                if len_B_cutpoints[i] == level:
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                    for point in cp:
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                        # 1st way
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                        shared_comp_indices = self._components_sharing_cutpoint(B_cutpoints, point)
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                        shared_comp_indices.remove(i)
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                        weight_shared_comp = list()
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                        for index in shared_comp_indices:
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                            weight_shared_comp.append(self.get_link_weight(index, point))
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                        weight = self.num_vertices - 1 - sum(weight_shared_comp)
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                        self.Dv_B[i][point] = weight
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            level += 1
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    def generate_reverse_link_weight(self):
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        for i, Dv_B_i in enumerate(self.Dv_B):
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            for key, value in Dv_B_i.iteritems():
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                self.reverse_Dv_B[i][key] = self.num_vertices - 1 - self.get_link_weight(i, key)
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    def compute_component_tree_weight(self):
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        """Follows exactly the Algorithm 1 [Puzis 2012]
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        """
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        B_cutpoints = list() # number of cutpoints in component B
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        for comp in self.bicomponents:
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            points = comp.intersection(self.cutpoints)
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            B_cutpoints.append(points)
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        Q = self._inititalize_component_tree_weight()
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        while Q:
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            pair = Q.pop(0)
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            if pair['type'] == 'component_vertex_pair':
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                B_index = pair['value'][0]
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                v = pair['value'][1]
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                size = len(self.bicomponents[B_index]) - 1;
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                all_cutpoints = self.bicomponents[B_index].intersection(self.cutpoints)
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                all_cutpoints.remove(v)
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                for cp in all_cutpoints:
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                    if self.get_link_weight(B_index, cp) != -1:
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                        size += self.num_vertices - self.get_link_weight(B_index, cp) - 1
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                link_weight = size
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                self._verify_link_weight(B_index, v, link_weight)
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                self.set_link_weight(B_index, v, link_weight)
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                # update Q
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                Q = self._find_unknown_weight_wrt_cutpoint(v, Q)
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            if pair['type'] == 'vertex_component_pair':
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                size = 0
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                B_index = pair['value'][0]
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                v = pair['value'][1]
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                shared_comp_indices = self._components_sharing_cutpoint(B_cutpoints, v)
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                shared_comp_indices.remove(B_index)
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                for i in shared_comp_indices:
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                    if self.get_link_weight(i, v) != - 1:
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                        size += self.get_link_weight(i, v)
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                link_weight = self.num_vertices - 1 - size
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                self._verify_link_weight(B_index, v, link_weight)
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                self.set_link_weight(B_index, v, link_weight)
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                # update Q
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                Q = self._find_unknown_weight_wrt_component(B_index, Q)
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    def _verify_link_weight(self, B_index, v, value):
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        """ If the old_value exist in self.Dv_B, then it must be equal to new value
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        Otherwise, do nothing
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        """
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        old_value = self.get_link_weight(B_index, v)
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        if old_value != -1: # -1 is unknown
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            if old_value != value:
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                print "BUGS FOUND in _verify_link_weight()"
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                sys.exit()
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    def _inititalize_component_tree_weight(self):
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        Q = []
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        for i, comp in enumerate(self.bicomponents):
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            current_cutpoints = self.cutpoints.intersection(comp)
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            if len(current_cutpoints) == 1:
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                Q.append({
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                    'type': 'component_vertex_pair',
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                    'value': (i, list(current_cutpoints)[0]) # (B_i, cutpoint) = (i-th component, the cutpoint name)
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                    })
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            for cp in current_cutpoints:
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                self.set_link_weight(i, cp, -1)
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        return Q
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    def _find_unknown_weight_wrt_cutpoint(self, cutpoint, Q):
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        # Cut-point v such that the weights of all but one of its links in T are already computed.
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        num_of_uncomputed_weight = 0
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        uncomputed_component_index = []
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        for i, Dv_B_comp in enumerate(self.Dv_B):
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            if cutpoint in Dv_B_comp:
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                if Dv_B_comp[cutpoint] == -1:
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                    num_of_uncomputed_weight += 1
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                    uncomputed_component_index.append(i)
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        if num_of_uncomputed_weight == 1:
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            pair = {
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                'type': 'vertex_component_pair',
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                'value': (uncomputed_component_index.pop(), cutpoint)
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            }
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            Q.append(pair)
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        return Q
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    def _find_unknown_weight_wrt_component(self, comp_index, Q):
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        # Component B such that weights of all but one of its links in T are already computed.
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        Dv_B_comp = self.Dv_B[comp_index]
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        values = Dv_B_comp.values()
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        # Check if -1 value appear only 1 time
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        flag = False
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        minus_one_value = [x for x in values if x == -1]
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        if len(minus_one_value) == 1:
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            for cp, value in Dv_B_comp.iteritems():
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                if value == -1:
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                    pair = {
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                        'type': 'component_vertex_pair',
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                        'value': (comp_index, cp)
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                    }
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                    Q.append(pair)
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        return Q
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    def __str__(self):
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        return str(self.Dv_B)
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def analyse_biconnected_component(graph):
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    bicomponents = list(nx.biconnected_components(graph))
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    print bicomponents
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    print [len(x) for x in bicomponents]
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def find_heuristic_bc(filepath):
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    filename = os.path.splitext(os.path.basename(filepath))[0]
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    pp = pprint.PrettyPrinter(indent=4)
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    graph = nx.read_weighted_edgelist(filepath)
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    if not nx.is_connected(graph):
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        print "Graph is not connected"
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        # sys.exit()
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    else:
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        print "Graph is connected"
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    # Find biconnected components
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    analyse_biconnected_component(graph)
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    subgraphs = list(nx.biconnected_component_subgraphs(graph))
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    bicomponents = list(nx.biconnected_components(graph))
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    component_edges = list(nx.biconnected_component_edges(graph))
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    cutpoints = set(nx.articulation_points(graph))
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    num_vertices = nx.number_of_nodes(graph)
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    lw = LinkWeight(graph, bicomponents, cutpoints)
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    tm = TrafficMatrix(bicomponents, cutpoints, lw)
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    bc = HeuristicBetweennessCentrality(subgraphs, bicomponents, cutpoints, num_vertices, lw, tm)
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    bc.write(file_suffix)
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    print bc
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def test_isomorphic_graphs(filepath1, filepath2):
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    graph1 = nx.read_weighted_edgelist(filepath1)
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    graph2 = nx.read_weighted_edgelist(filepath2)
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    print "Isomorphic = %s" % nx.is_isomorphic(graph1, graph2)
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if __name__ == '__main__':
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    utility.initialize()
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    f1 = MAIN_CODE_DIR + '/input/9_vertices_green.edges'
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    f2 = MAIN_CODE_DIR + '/input/9_vertices_blue.edges'
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    test_isomorphic_graphs(f1, f2)
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    # filepath = MAIN_CODE_DIR + '/input/simple_house.edges'
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    # filepath = MAIN_CODE_DIR + '/input/simple2.edges'
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    # filepath = MAIN_CODE_DIR + '/input/simple.edges'
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    # filepath = MAIN_CODE_DIR + '/input/ninux_simplified.edges'
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    # filepath = MAIN_CODE_DIR + '/input/ninux_simplified_connected.edges'
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    # filepath = MAIN_CODE_DIR + '/input/ninux_simplified_connected2.edges'
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    # filepath = MAIN_CODE_DIR + '/input/ninux_simplified_connected3.edges'
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    # filepath = MAIN_CODE_DIR + '/input/9_vertices_green.edges'
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    # filepath = MAIN_CODE_DIR + '/input/9_vertices_blue.edges'
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    # filepath = MAIN_CODE_DIR + '/input/simple_loop.edges'
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    # filepath = MAIN_CODE_DIR + '/input/straight_line.edges'
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    # filepath = MAIN_CODE_DIR + '/input/ninux_connected.edges'
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    # filepath = MAIN_CODE_DIR + '/input/ninux_unweighted.edges'
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    # filepath = MAIN_CODE_DIR + '/input/ninux_unweighted_simplified.edges'
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    # filepath = MAIN_CODE_DIR + '/input/ninux_unweighted_simplified_connected.edges'
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    # filepath = MAIN_CODE_DIR + '/input/simple3.edges'
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    # filepath = MAIN_CODE_DIR + '/input/simple4.edges'
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    # filepath = MAIN_CODE_DIR + '/input/simple5.edges'
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    filepath = MAIN_CODE_DIR + '/input/ninux.edges'
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    file_suffix = 'edge_list'
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    # Weight betweenness centrality
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    utility.weight_betweenness_centrality(filepath, file_suffix)
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    # Brandess betweenness centrality
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    utility.brandes_betweenness_centrality(filepath, file_suffix)
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    # Heuristic BC
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    find_heuristic_bc(filepath)
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