Python networkx.read_gpickle函数代码示例

def load_train_test_graphs(dataset, recache_input):
    raw_mat_path = 'data/{}.npz'.format(dataset)
    train_graph_path = 'data/{}/train_graph.pkl'.format(dataset)
    test_graph_path = 'data/{}/test_graph.pkl'.format(dataset)

    if recache_input:
        print('loading sparse matrix from {}'.format(raw_mat_path))
        m = load_sparse_csr(raw_mat_path)

        print('splitting train and test...')
        train_m, test_m = split_train_test(
            m,
            weights=[0.9, 0.1])

        print('converting to nx.DiGraph')
        train_g = nx.from_scipy_sparse_matrix(train_m, create_using=nx.DiGraph(), edge_attribute='sign')
        test_g = nx.from_scipy_sparse_matrix(test_m, create_using=nx.DiGraph(), edge_attribute='sign')
                
        print('saving train and test graphs...')
        nx.write_gpickle(train_g, train_graph_path)
        nx.write_gpickle(test_g, test_graph_path)
    else:
        print('loading train and test graphs...')
        train_g = nx.read_gpickle(train_graph_path)
        test_g = nx.read_gpickle(test_graph_path)
    return train_g, test_g

def main():
    rev_forward_g = nx.read_gpickle("rev_forward.gpickle")
    rev_backward_g = nx.read_gpickle("rev_backward.gpickle")
    with open("ids.pickle", "rb") as f:
        ids = pickle.load(f)

    cutoff = 12
    dists = []
    for start, forward_rounds, backward_rounds, end in ids:
        s = count(start)
        if not 4 <= s <= 6:
            continue

        # backward extension
        backward_extension_rounds = 3
        rounds = forward_rounds + backward_rounds + backward_extension_rounds

        for p, w in propagate(
            rev_backward_g,
            end,
            backward_extension_rounds - 1,
            cutoff
        ):
            p = add_last_round(p)
            dists.append((start, p, w, rounds))

            print("{} ... X ... {} with probability {}, {} rounds".format(
                start,
                " <- ".join(str(v) for v in p),
                math.exp(-w),
                rounds
            ))

    with open("dists.pickle", "wb") as f:
        pickle.dump(dists, f)

def ntwks_to_matrices(in_files, edge_key):
    first = nx.read_gpickle(in_files[0])
    files = len(in_files)
    nodes = len(first.nodes())
    matrix = np.zeros((nodes, nodes, files))
    for idx, name in enumerate(in_files):
        graph = nx.read_gpickle(name)
        for u, v, d in graph.edges(data=True):
            graph[u][v]['weight'] = d[edge_key]  # Setting the edge requested edge value as weight value
        matrix[:, :, idx] = nx.to_numpy_matrix(graph)  # Retrieve the matrix
    return matrix

def _build_and_store_new_graph(data_file, name=""):
    """
    Reads the nodes and edges files stored in the 1.1 version and build a new Graph compatible with 2.0
    :param data_file: path to temporary directory
    :param name: name of the network
    :return: new Graph compatible with version 2.0
    """
    data_file += name
    edges = networkx.read_gpickle(data_file + "_edges" + ".gpickle")
    nodes = networkx.read_gpickle(data_file + "_nodes" + ".gpickle")
    net = networkx.Graph()
    net.add_nodes_from(nodes)
    net.add_edges_from(edges)
    return net

def diff_history(directory, length = 1):
    glob_dir = os.path.join(directory, "*.pickle.tar.gz")
    pickle_files = glob.glob(glob_dir)
    pickle_files = sorted(pickle_files)
    pairs = [(a, b) for (a, b) in zip(pickle_files, pickle_files[1:])]
    pairs = pairs[-1*length:]
    diffs = []
    for fileA, fileB in pairs:
        graphA = nx.read_gpickle(fileA)
        graphB = nx.read_gpickle(fileB)
        diff = compare(graphA, graphB)
        # remove render folder which is timestamps
        diffs.append(diff)

    return diffs

	def __init__(self, handle):
		super(SourcePairCombiner, self).__init__()
		self.handle = handle
		self.dirhandle = 'reassortant_edges'
		self.G = nx.read_gpickle('{0} Full Complement Graph.pkl'.format(self.handle))
		self.current_sourcepair = None # current sourcepair graph
		self.current_noi = None # current node of interest

 def __init__(self,filename) :
     #self.graph = nx.barabasi_albert_graph(100,5)
     self.graph = nx.Graph()
     self.graph = nx.read_gpickle(filename)
     self.nodes = self.graph.nodes()
     self.graph = self.mapper()
     self.edges = self.graph.edges()

def main():
  seed(0) #set seed
  #get graph info
  G = nx.read_gpickle("input/graphMTC_CentroidsLength6.gpickle") #noCentroidsLength15.gpickle") #does not have centroidal links. There is also the choice of a proper multidigraph: nx.read_gpickle("input/graphMTC_CentroidsLength5.gpickle")
  G = nx.freeze(G) #prevents edges or nodes to be added or deleted
  #get od info. This is in format of a dict keyed by od, like demand[sd1][sd2] = 200000.
  demand = bd.build_demand('input/BATS2000_34SuperD_TripTableData.csv', 'input/superdistricts_centroids.csv')
  #get earthquake info
  q = QuakeMaps('input/20130210_mtc_total_lnsas3.pkl', 'input/20130210_mtc_magnitudes3.pkl', 'input/20130210_mtc_faults3.pkl', 'input/20130210_mtc_weights3.pkl', 'input/20130210_mtc_scenarios3.pkl') #input/20130107_mtc_total_lnsas1.pkl', 'input/20130107_mtc_magnitudes1.pkl','input/20130107_mtc_faults1.pkl', 'input/20130107_mtc_weights1.pkl', 'input/20130107_mtc_scenarios1.pkl') #'input/20130210_mtc_total_lnsas3.pkl', 'input/20130210_mtc_magnitudes3.pkl', 'input/20130210_mtc_faults3.pkl', 'input/20130210_mtc_weights3.pkl', 'input/20130210_mtc_scenarios3.pkl') #('input/20130107_mtc_total_lnsas1.pkl', 'input/20130107_mtc_magnitudes1.pkl',  #totalfilename=None, magfilename=None, faultfilename=None, weightsfilename=None, scenariofilename=None):
  print 'weights: ', q.weights
  q.num_sites = len(q.lnsas[0])
  #determine which scenarios you want to run
  good_indices = pick_scenarios(q.lnsas, q.weights)
  
  travel_index_times = []
  index = 0
  #loop over scenarios
  print 'size of lnsas: ', len(q.lnsas)
  for scenario in q.lnsas: #each 'scenario' has 1557 values of lnsa, i.e. one per site
    if index in good_indices:
      print 'index: ', index
      (bridges, flow, path, path2) = run_simple_iteration(G, scenario, demand, False)
      travel_index_times.append((index, bridges, flow, path, path2))
#      print 'new travel times: ', travel_index_times
      if index%1000 ==0:
        util.write_2dlist(time.strftime("%Y%m%d")+'_bridges_flow_paths4.txt',travel_index_times)
    index += 1 #IMPORTANT
  util.write_2dlist(time.strftime("%Y%m%d")+'_bridges_flow_paths4.txt',travel_index_times)
  print 'the number of scenarios I considered doing: ', index
  print 'the number of scenarios I actually did: ', len(travel_index_times)

    def _read_celltype_graph(self, celltypes_file, format="gml"):
        """
        Read celltype-celltype connectivity graph from file.

        celltypes_file -- the path of the file containing
        the graph.
        
        format -- format of the file. allowed values: gml, graphml, edgelist, pickle, yaml.

        """
        start = datetime.now()
        celltype_graph = None
        try:
            if format == "gml":
                celltype_graph = nx.read_gml(celltypes_file)
            elif format == "edgelist":
                celltype_graph = nx.read_edgelist(celltypes_file)
            elif format == "graphml":
                celltype_graph = nx.read_graphml(celltypes_file)
            elif format == "pickle":
                celltype_graph = nx.read_gpickle(celltypes_file)
            elif format == "yaml":
                celltype_graph = nx.read_yaml(celltypes_file)
            else:
                print "Unrecognized format %s" % (format)
        except Exception, e:
            print e

def load_data():
    start = time.time()
    try:
        print("Loading data from /data pickles and hfd5 adj matrices")
        f = h5py.File('data/cosponsorship_data.hdf5', 'r')
        for chamber in ['house', 'senate']:
            for congress in SUPPORTED_CONGRESSES:
                adj_matrix_lookup[(chamber, congress)] = np.asarray(f[chamber + str(congress)])

                igraph_graph = igraph.load("data/" + chamber + str(congress) + "_igraph.pickle", format="pickle")
                igraph_graph_lookup[(chamber, congress, False)] = igraph_graph

                nx_graph = nx.read_gpickle("data/" + chamber + str(congress) + "_nx.pickle")
                nx_graph_lookup[(chamber, congress, False)] = nx_graph
    except IOError as e:
        print("Loading data from cosponsorship files")
        f = h5py.File("data/cosponsorship_data.hdf5", "w")
        for chamber in ['house', 'senate']:
            for congress in SUPPORTED_CONGRESSES:
                print("Starting %s %s" % (str(congress), chamber))
                adj_matrix = load_adjacency_matrices(congress, chamber)
                data = f.create_dataset(chamber + str(congress), adj_matrix.shape, dtype='f')
                data[0: len(data)] = adj_matrix

                # igraph
                get_cosponsorship_graph(congress, chamber, False).save("data/" + chamber + str(congress) + "_igraph.pickle", "pickle")
                # networkx
                nx.write_gpickle(get_cosponsorship_graph_nx(congress, chamber, False), "data/" + chamber + str(congress) + "_nx.pickle")

                print("Done with %s %s" % (str(congress), chamber))
    print("Data loaded in %d seconds" % (time.time() - start))

def main():
  seed(0) #set seed
  #get graph info
  G = nx.read_gpickle("input/graphMTC_CentroidsLength5.gpickle") #noCentroidsLength15.gpickle") #does not have centroidal links
  print '|V| = ', len(G.nodes())
  print '|E| = ', len(G.edges())
  G = nx.freeze(G) #prevents edges or nodes to be added or deleted
  #get od info. This is in format of a dict keyed by od, like demand[sd1][sd2] = 200000.
  demand = bd.build_demand('input/BATS2000_34SuperD_TripTableData.csv', 'input/superdistricts_centroids.csv') #bd.build_demand('input/BATS2000_34SuperD_TripTableData.csv', 'input/superdistricts_centroids.csv')
  #get earthquake info
  q = QuakeMaps('input/20130210_mtc_total_lnsas3.pkl', 'input/20130210_mtc_magnitudes3.pkl', 'input/20130210_mtc_faults3.pkl', 'input/20130210_mtc_weights3.pkl', 'input/20130210_mtc_scenarios3.pkl') #(input/20130107_mtc_total_lnsas1.pkl', 'input/20130107_mtc_magnitudes1.pkl', 'input/20130107_mtc_faults1.pkl', 'input/20130107_mtc_weights1.pkl', 'input/20130107_mtc_scenarios1.pkl') #totalfilename=None, magfilename=None, faultfilename=None, weightsfilename=None, scenariofilename=None): 'input/20130210_mtc_total_lnsas3.pkl', 'input/20130210_mtc_magnitudes3.pkl', 'input/20130210_mtc_faults3.pkl', 'input/20130210_mtc_weights3.pkl', 'input/20130210_mtc_scenarios3.pkl') #(


  q.num_sites = len(q.lnsas[0])
  #determine which scenarios you want to run
  good_indices = pick_scenarios(q.lnsas, q.weights)
  
  travel_index_times = []
  index = 0
  #loop over scenarios
  for scenario in q.lnsas: #each 'scenario' has 1557 values of lnsa, i.e. one per site
    if index in good_indices:
      print 'index: ', index
      (travel_time, vmt) = run_iteration(G, scenario, demand)
      travel_index_times.append((index, travel_time, vmt))
#      print 'new travel times: ', travel_index_times
      if index%100 ==0:
        util.write_2dlist(time.strftime("%Y%m%d")+'_travel_time.txt',travel_index_times)
    index += 1 #IMPORTANT
  util.write_2dlist(time.strftime("%Y%m%d")+'_travel_time.txt',travel_index_times)

def experiment_4():
    G = nx.Graph()
    G.add_edge(0, 11, weight=91)
    G.add_edge(1, 11, weight=72)
    G.add_edge(1, 13, weight=96)
    G.add_edge(2, 13, weight=49)
    G.add_edge(2, 6, weight=63)
    G.add_edge(2, 3, weight=31)
    G.add_edge(3, 9, weight=98)
    G.add_edge(3, 7, weight=1)
    G.add_edge(3, 12, weight=59)
    G.add_edge(4, 7, weight=6)
    G.add_edge(4, 9, weight=6)
    G.add_edge(4, 8, weight=95)
    G.add_edge(5, 11, weight=44)
    G.add_edge(6, 11, weight=53)
    G.add_edge(8, 10, weight=2)
    G.add_edge(8, 12, weight=48)
    G.add_edge(9, 12, weight=32)
    G.add_edge(10, 14, weight=16)
    G.add_edge(11, 13, weight=86)

    G = nx.read_gpickle('G.gpickle')
    
    path_nx = nx.dijkstra_path(G, 0, 14)
    path = dijkstra(G, 0, 14, True)
    if path_cost(G, path) > path_cost(G, path_nx):
        print 'Error'
    else:
        print 'Correct'
        
    return locals()

def remove_unconnected_graphs_and_threshold(in_file):
    import nipype.interfaces.cmtk as cmtk
    import nipype.pipeline.engine as pe
    import os
    import os.path as op
    import networkx as nx
    from nipype.utils.filemanip import split_filename
    connected = []
    if in_file == None or in_file == [None]:
        return None
    elif len(in_file) == 0:
        return None
    graph = nx.read_gpickle(in_file)
    if not graph.number_of_edges() == 0:
        connected.append(in_file)
        _, name, ext = split_filename(in_file)
        filtered_network_file = op.abspath(name + '_filt' + ext)
    if connected == []:
        return None

    #threshold_graphs = pe.Node(interface=cmtk.ThresholdGraph(), name="threshold_graphs")
    threshold_graphs = cmtk.ThresholdGraph()
    from nipype.interfaces.cmtk.functional import tinv
    weight_threshold = 1  # tinv(0.95, 198-30-1)
    threshold_graphs.inputs.network_file = in_file
    threshold_graphs.inputs.weight_threshold = weight_threshold
    threshold_graphs.inputs.above_threshold = True
    threshold_graphs.inputs.edge_key = "weight"
    threshold_graphs.inputs.out_filtered_network_file = op.abspath(
        filtered_network_file)
    threshold_graphs.run()
    return op.abspath(filtered_network_file)

def topology(data, ell):
    """
    Computation of topological characteristics.
    
    Parameters
    ------------
    data : array of pathes to the graphs
    ell : list of length scales
    """    

    for i in data:
        G = nx.read_gpickle(i)
        B = nx.number_of_edges(G)
        V = nx.number_of_nodes(G)
        Euler = V - B
        C = (B-V)/float(V)
        eu.append(Euler)
        c_t.append(C)
        vert.append(V)
        bran.append(B)

    plt.plot(ell, c_t, '.', label='v23')
    #
    #np.save('/backup/yuliya/v23/graphs_largedom/Euler.npy', eu)
    #np.save('/backup/yuliya/v23/graphs_largedom/C_t.npy', c_t)
    #np.save('/backup/yuliya/v23/graphs_largedom/V.npy', vert)
    #np.save('/backup/yuliya/v23/graphs_largedom/B.npy', bran)
    #np.save('/backup/yuliya/vsi01/graphs_largdom/time.npv23/graphs_largedom/y', t)
    plt.yscale('log')

def short_branches():
    """
    Visualization of short branches of the skeleton.
    
    """
    data1_sk = glob.glob('/backup/yuliya/vsi05/skeletons_largdom/*.h5')
    data1_sk.sort()

    for i,j, k in zip(d[1][37:47], data1_sk[46:56], ell[1][37:47]):
        g = nx.read_gpickle(i)
        dat = tb.openFile(j)
        skel = np.copy(dat.root.skel)
        bra = np.copy(dat.root.branches)
        mask = np.zeros_like(skel)    
        dat.close()
    
        length = nx.get_edge_attributes(g, 'length')
        number = nx.get_edge_attributes(g, 'number')
        num_dict = {}
        for m in number:
            for v in number[m]:
                num_dict.setdefault(v, []).append(m)
        find_br = ndimage.find_objects(bra)
        for l in list(length.keys()):
            if length[l]<0.5*k: #Criteria
                for b in number[l]:
                    mask[find_br[b-1]] = bra[find_br[b-1]]==b
        mlab.figure(bgcolor=(1,1,1), size=(1200,1200))
        mlab.contour3d(skel, colormap='hot')
        mlab.contour3d(mask)
        mlab.savefig('/backup/yuliya/vsi05/skeletons/short_bran/'+ i[42:-10] + '.png')
        mlab.close()