Python json_graph.node_link_graph函数代码示例

 def iso_json(string1,string2):
     dataG1 = json.loads(string1)
     graph1 = json_graph.node_link_graph(dataG1)
     dataG2 = json.loads(string2)
     graph2 = json_graph.node_link_graph(dataG2)
    # return nx.is_isomorphic(graph1, graph2)
     return nx.faster_could_be_isomorphic(graph1, graph2)

def restore_anm_nidb_from_json(data):

    d = ank_json_custom_loads(data)
    anm = autonetkit.anm.AbstractNetworkModel()
    nidb = autonetkit.nidb.NIDB()

    for overlay_id, overlay_data in d.items():
        if overlay_id == "nidb":
            continue # don't restore nidb graph to anm
        anm._overlays[overlay_id] = json_graph.node_link_graph(overlay_data)

    nidb._graph = json_graph.node_link_graph(d['nidb'])
    rebind_interfaces(anm)

    return anm, nidb

def graphs_json2networkx(input_dict):
    from json import loads
    from networkx.readwrite import json_graph

    gtext = loads(input_dict['graph'])
    g =  json_graph.node_link_graph(gtext)
    return {'nxgraph': g}

def main(json_file, output_prefix, source, target):
    
    with open(json_file) as data_file:    
        data = json.load(data_file)

    G = json_graph.node_link_graph(data, directed=False)

    print "Finished Reading in Graph: {0}".format(datetime.datetime.now())

    id_seq = networkx.get_node_attributes(G, "sequence")

    seq_id = { seq : node_id for node_id, seq in id_seq.items()}

    print "Created inverse lookup table: {0}".format(datetime.datetime.now())

    if ',' in target:
        targets = target.split(',')

    for target in targets:
        paths = networkx.all_shortest_paths(G, seq_id[source], seq_id[target])

        with open("{0}_paths_{1}_{2}.txt".format(output_prefix, source, target), 'w') as o:
            for path in paths:
                o.write(",".join( [id_seq[node_id] for node_id in path ] ))
	        o.write("\n")

    print "Output paths: {0}".format(datetime.datetime.now())

def restore_anm_nidb_from_json(data):
    # This can be used to extract from the json used to send to webserver

    d = ank_json_custom_loads(data)
    anm = autonetkit.anm.AbstractNetworkModel()
    nidb = autonetkit.nidb.DeviceModel()

    for overlay_id, overlay_data in d.items():
        if overlay_id == "nidb":
            continue # don't restore nidb graph to anm
        anm._overlays[overlay_id] = json_graph.node_link_graph(overlay_data)

    nidb._graph = json_graph.node_link_graph(d['nidb'])
    rebind_interfaces(anm)

    return anm, nidb

 def graph(self, node_links_data):
     self.g = json_graph.node_link_graph(node_links_data)
     remove = list()
     for _id in self.g.node:
         if self.g.node[_id]["node_type"] not in self.valid_type:
             remove.append(_id)
     self.g.remove_nodes_from(remove)

 def as_tree(graph, root=OPENSTACK_CLUSTER, reverse=False):
     linked_graph = json_graph.node_link_graph(graph)
     if 0 == nx.number_of_nodes(linked_graph):
         return {}
     if reverse:
         linked_graph = linked_graph.reverse()
     return json_graph.tree_data(linked_graph, root=root)

def load_json(stream):
    """
    Args:
        stream: Open stream containing js

    Assumes the js is in networkx link-node format
    """
    
    js = json.load(stream)
    g = json_graph.node_link_graph(js)

    assert all([nd.has_key('coords') for nd in g.node.values()]),\
           "json node-link graph must have nodes with coords for GeoGraph"

    # get coords
    coords = [v['coords'] for v in g.node.values()]

    # set default projection
    input_proj = ""
    if gm.is_in_lon_lat(coords):
        input_proj = gm.PROJ4_LATLONG
    else:
        input_proj = gm.PROJ4_FLAT_EARTH

    coords_dict = {k: v['coords'] for k, v in g.node.items()}
    # now get rid of 'coords' key,val for each node
    for node in g.node.values():
        node.pop('coords', None)

    geo_nodes = GeoGraph(srs=input_proj, coords=coords_dict, data=g)
    return geo_nodes

def get_selected_reaction(jsonGraph, nodeDic, reacIDs, org):
    """
    Filtering selected Reactions and show Results from PyNetMet calculation.
    It returns a subgraph of the Graph from the jsonGraph. The output is a DOT-Language String.
    @param jsonGraph: Graph in JSON-Format
    @param nodeDic: dict mapping names to ids
    @param reacIDs: Name of reactions that contained in the nodeDic
    @param org: organism
    @return Subgraph
    """
    # Translate reac names to IDs
    # Get substrates and products of all reacs
    metabolites = []
    for reac in reacIDs:
        metabolites += org.get_reaction(reac).metabolites

    met_ids = list(map(lambda x: nodeDic[x], metabolites))
    g = json_graph.node_link_graph(jsonGraph)

    g.remove_edges_from(list(filter(lambda x: g.get_edge_data(*x)["object"].name not in reacIDs, g.edges(met_ids))))

    # Get products/substrates directly connected to filter
    #reacIDs += flatten(g.in_edges(reacIDs)) + flatten(g.out_edges(reacIDs))

    h = g.subgraph(met_ids)
    return h

def read_from_json_gexf(fname=None,label_field_name='APIs',conv_undir = False):
    '''
    Load the graph files (.gexf or .json only supported)
    :param fname: graph file name
    :param label_field_name: filed denoting the node label
    :param conv_undir: convert to undirected graph or not
    :return: graph in networkx format
    '''
    if not fname:
        logging.error('no valid path or file name')
        return None
    else:
        try:
            try:
                with open(fname, 'rb') as File:
                    org_dep_g = json_graph.node_link_graph(json.load(File))
            except:
                org_dep_g = nx.read_gexf (path=fname)

            g = nx.DiGraph()
            for n, d in org_dep_g.nodes_iter(data=True):
                g.add_node(n, attr_dict={'label': '-'.join(d[label_field_name].split('\n'))})
            g.add_edges_from(org_dep_g.edges_iter())
        except:
            logging.error("unable to load graph from file: {}".format(fname))
            # return 0
    logging.debug('loaded {} a graph with {} nodes and {} egdes'.format(fname, g.number_of_nodes(),g.number_of_edges()))
    if conv_undir:
        g = nx.Graph (g)
        logging.debug('converted {} as undirected graph'.format (g))
    return g

def find_min_spanning_tree(A):
	"""
		Input:
			A : Adjecency matrix in scipy.sparse format.
		Output:
			T : Minimum spanning tree.
			run_time : Total runtime to find minimum spanning tree 

	"""
	# Record start time.
	start = time.time()

	# Check if graph is pre-processed, if yes then don't process it again.
	if os.path.exists('../Data/dcg_graph.json'):
		with open('../Data/dcg_graph.json') as data:
			d = json.load(data)
		G = json_graph.node_link_graph(d)

	# If graph is not preprocessed then convert it to a Graph and save it to a JSON file.
	else:
		G = from_scipy_sparse_matrix(A)
		data = json_graph.node_link_data(G)
		with open('../Data/dcg_graph.json', 'w') as outfile:
			json.dump(data, outfile)

	# Find MST.
	T = minimum_spanning_tree(G)

	#Record total Runtime
	run_time = time.time()-start
	return T, run_time

def main(json_file, output_prefix, metric):
    
    with open(json_file) as data_file:    
        data = json.load(data_file)

    G = json_graph.node_link_graph(data)

    metrics = {}

    #metrics["degree"] = degree(G)
    metrics["closeness"] = closeness_centrality(G).values()
    #TODO: add any other metrics here using a similar format to above line.
    sequences = {}    	

    cleaved_seq = { key : val for key, val in sequences.items() if val["type"] == "CLEAVED" }

    if metric != "metrics":
	labels_to_plot = [metric]
    else:
	labels_to_plot = metrics.keys()
    n_to_plot = len(labels_to_plot)
    fig, axarr = pconv.create_ax(n_to_plot, 1, shx=False, shy=False)

    nbins = 20    

    for ind, key in enumerate(labels_to_plot):
	normed = True
        hist.draw_actual_plot(axarr[0,ind], metrics["key"], "", key.capitalize(), normed=normed, nbins=nbins)    
        axarr[0,ind].ticklabel_format(axis='x', style='sci', scilimits=(-2,2))

        #pconv.add_legend(axarr[0,ind], location="middle right")
    pconv.save_fig(fig, output_prefix, "metrics", n_to_plot*5, 5, tight=True, size=12) 

def transferRedditDataFormat(dataset_dir, output_file):
    G = json_graph.node_link_graph(json.load(open(dataset_dir + "/reddit-G.json")))
    labels = json.load(open(dataset_dir + "/reddit-class_map.json"))

    train_ids = [n for n in G.nodes() if not G.node[n]['val'] and not G.node[n]['test']]
    test_ids = [n for n in G.nodes() if G.node[n]['test']]
    val_ids = [n for n in G.nodes() if G.node[n]['val']]
    train_labels = [labels[i] for i in train_ids]
    test_labels = [labels[i] for i in test_ids]
    val_labels = [labels[i] for i in val_ids]
    feats = np.load(dataset_dir + "/reddit-feats.npy")
    ## Logistic gets thrown off by big counts, so log transform num comments and score
    feats[:, 0] = np.log(feats[:, 0] + 1.0)
    feats[:, 1] = np.log(feats[:, 1] - min(np.min(feats[:, 1]), -1))
    feat_id_map = json.load(open(dataset_dir + "reddit-id_map.json"))
    feat_id_map = {id: val for id, val in feat_id_map.iteritems()}

    # train_feats = feats[[feat_id_map[id] for id in train_ids]]
    # test_feats = feats[[feat_id_map[id] for id in test_ids]]

    # numNode = len(feat_id_map)
    # adj = sp.lil_matrix(np.zeros((numNode,numNode)))
    # for edge in G.edges():
    #     adj[feat_id_map[edge[0]], feat_id_map[edge[1]]] = 1

    train_index = [feat_id_map[id] for id in train_ids]
    val_index = [feat_id_map[id] for id in val_ids]
    test_index = [feat_id_map[id] for id in test_ids]
    np.savez(output_file, feats = feats, y_train=train_labels, y_val=val_labels, y_test = test_labels, train_index = train_index,
             val_index=val_index, test_index = test_index)

def read_json_graph(istream):
    """
    Reads a json graph output by the algorithm and returns it
    """
    data = json.loads(istream.read())
    G = json_graph.node_link_graph(data)
    return G

def simple_to_nx(j_data):
    port_to_index_mapping = defaultdict(dict)
    for node in j_data['nodes']:
        if not "ports" in node:
            continue
        node_id = node['id']
        # first check for loopback zero
        ports = node['ports']
        _ports = {}  # output format
        try:
            lo_zero = [p for p in ports if p['id'] == "Loopback0"].pop()
        except IndexError:
            # can't pop -> no loopback zero, append
            lo_zero = {'category': 'loopback',
                       'description': "Loopback Zero"}
        else:
            ports.remove(lo_zero)
        finally:
            _ports[0] = lo_zero
        '''Sharad: below change is for 2nd loopback. currently commenting it out.
           change start in below loop to 2 while adding another loopback
        lo_one = {'category': 'loopback',
                      'description': "Loopback One",
                   'id':'loopback1'}
        _ports[1] = lo_one
        '''
        for index, port in enumerate(ports, start=1):
            _ports[index] = port
            port_to_index_mapping[node_id][port['id']] = index

        del node['ports']
        node['_ports'] = _ports

    nodes_by_id = {n['id']: i for i, n
                   in enumerate(j_data['nodes'])}

    unmapped_links = []

    if "links" in j_data:
        mapped_links = j_data['links']
        for link in mapped_links:
            src = link['src']
            dst = link['dst']
            src_pos = nodes_by_id[src]
            dst_pos = nodes_by_id[dst]
            src_port_id = port_to_index_mapping[src][link['src_port']]
            dst_port_id = port_to_index_mapping[dst][link['dst_port']]

            interfaces = {src: src_port_id,
                          dst: dst_port_id}

            unmapped_links.append({'source': src_pos,
                                   'target': dst_pos,
                                   '_ports': interfaces,
                                   'link_type': link['link_type']
                                   })

    j_data['links'] = unmapped_links
    return json_graph.node_link_graph(j_data)