Python networkx.Graph方法代碼示例

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import Graph [as 別名]
def __init__(self, model_nm, logfile=None, props=None,
                 loglevel=logging.INFO):
        self.model_nm = model_nm
        self.graph = nx.Graph()
        if props is None:
            self.props = {}
        else:
            self.props = props
            logfile = self.get("log_fname")
        self.logger = Logger(self, model_name=model_nm,logfile=logfile)
        self.graph.add_edge(self, self.logger)
        self["OS"] = platform.system()
        self["model"] = model_nm
        # process command line args and set them as properties:
        prop_nm = None
        for arg in sys.argv:
            # the first arg (-prop) names the property
            if arg.startswith(SWITCH):
                prop_nm = arg.lstrip(SWITCH)
            # the second arg is the property value
            elif prop_nm is not None:
                self[prop_nm] = arg
                prop_nm = None 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import Graph [as 別名]
def form_cliques(p_values, nnames):
    """
    This method forms the cliques
    """
    # first form the numpy matrix data
    m = len(nnames)
    g_data = np.zeros((m, m), dtype=np.int64)
    for p in p_values:
        if p[3] == False:
            i = np.where(nnames == p[0])[0][0]
            j = np.where(nnames == p[1])[0][0]
            min_i = min(i, j)
            max_j = max(i, j)
            g_data[min_i, max_j] = 1

    g = networkx.Graph(g_data)
    return networkx.find_cliques(g) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import Graph [as 別名]
def __init_unrestorables(self):
        # anything that can't be restored from JSON file init here
        pop_name = ""
        if self.model_nm:
            pop_name += self.model_nm + " "
        pop_name += "Agents"
        self.agents = ap.AgentPop(pop_name)

        self.womb = []
        self.graph = nx.Graph()
        self.graph.add_edge(self, self.agents)
        if self.props is not None:
            self.graph.add_edge(self, self.props)
        self.graph.add_edge(self, self.user)
        self.graph.add_edge(self, self.menu)
        self.graph.add_edge(self, node.universals) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import Graph [as 別名]
def convert_to_graph_tool(G):
  timer = utils.Timer()
  timer.tic()
  gtG = gt.Graph(directed=G.is_directed())
  gtG.ep['action'] = gtG.new_edge_property('int')

  nodes_list = G.nodes()
  nodes_array = np.array(nodes_list)

  nodes_id = np.zeros((nodes_array.shape[0],), dtype=np.int64)

  for i in range(nodes_array.shape[0]):
    v = gtG.add_vertex()
    nodes_id[i] = int(v)

  # d = {key: value for (key, value) in zip(nodes_list, nodes_id)}
  d = dict(itertools.izip(nodes_list, nodes_id))

  for src, dst, data in G.edges_iter(data=True):
    e = gtG.add_edge(d[src], d[dst])
    gtG.ep['action'][e] = data['action']
  nodes_to_id = d
  timer.toc(average=True, log_at=1, log_str='src.graph_utils.convert_to_graph_tool')
  return gtG, nodes_array, nodes_to_id 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import Graph [as 別名]
def setUp(self):
        # Create the graph for testing
        G = nx.Graph()
        G.add_edge('a',2)
        G.add_edge(2,'c')
        G.add_edge('a','c')
        G.add_edge('a',4)
        G.add_edge(4,'c')
        G.add_edge('out','c')
        G.add_edge('c','d')
        G.add_edge('d',2)
        # Growth edges
        G.add_edge('out',11)
        G.add_edge('out',12)
        G.add_edge(12, 'd')
        G.add_edge('TTTTT',11)
        G.add_edge('qqqq', 'TTTTT')
        G.add_node('alone')
        self.input_G = G.copy()

        # Viewer under test
        self.a = nxv.GraphCanvas(G) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import Graph [as 別名]
def _radial_behind(self, home_node, behind_node):
        """Detect what nodes create a radial string behind the edge from
        home_node to behind_node"""

        base_islands = nx.number_connected_components(self.dispG)

        # If we remove the edge in question, it should radialize the system
        #  and we can then detect the side to remove
        G = nx.Graph()
        G.add_nodes_from(self.dispG.nodes())
        G.add_edges_from(self.dispG.edges())
        G.remove_edge(home_node, behind_node)

        node_sets = list(nx.connected_components(G))

        if len(node_sets) == base_islands:
            # There is no radial path behind this node
            return None
        else:
            for ns in node_sets:
                if behind_node in ns:
                    # We know know what nodes to remove from the display graph
                    #  to remove the radial string
                    return ns 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import Graph [as 別名]
def text_to_graph(text):
    import networkx as nx
    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.neighbors import kneighbors_graph

    # use tfidf to transform texts into feature vectors
    vectorizer = TfidfVectorizer()
    vectors = vectorizer.fit_transform(text)

    # build the graph which is full-connected
    N = vectors.shape[0]
    mat = kneighbors_graph(vectors, N, metric='cosine', mode='distance', include_self=True)
    mat.data = 1 - mat.data  # to similarity

    g = nx.from_scipy_sparse_matrix(mat, create_using=nx.Graph())

    return g 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import Graph [as 別名]
def mol_to_graph(mol):
  """Convert RDKit Mol to NetworkX graph

  Convert mol into a graph representation atoms are nodes, and bonds
  are vertices stored as graph

  Parameters
  ----------
  mol: rdkit Mol
    The molecule to convert into a graph. 

  Returns
  -------
  graph: networkx.Graph
    Contains atoms indices as nodes, edges as bonds.
  """
  import networkx as nx
  G = nx.Graph()
  num_atoms = mol.GetNumAtoms()
  G.add_nodes_from(range(num_atoms))
  for i in range(mol.GetNumBonds()):
    from_idx = mol.GetBonds()[i].GetBeginAtomIdx()
    to_idx = mol.GetBonds()[i].GetEndAtomIdx()
    G.add_edge(from_idx, to_idx)
  return G 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import Graph [as 別名]
def calculate_max_depth_over_max_width(comment_tree):
    comment_tree_nx = nx.from_scipy_sparse_matrix(comment_tree, create_using=nx.Graph())

    if len(comment_tree_nx) == 0:
        max_depth_over_max_width = 0.0
    else:
        node_to_depth = nx.shortest_path_length(comment_tree_nx, 0)
        depth_to_nodecount = collections.defaultdict(int)

        for k, v in node_to_depth.items():
            depth_to_nodecount[v] += 1

        max_depth = max(node_to_depth.values())
        max_width = max(depth_to_nodecount.values())

        max_depth_over_max_width = max_depth/max_width

    return max_depth_over_max_width 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import Graph [as 別名]
def calculate_comment_tree_hirsch(comment_tree):
    comment_tree_nx = nx.from_scipy_sparse_matrix(comment_tree, create_using=nx.Graph())

    if len(comment_tree_nx) == 0:
        comment_tree_hirsch = 0.0
    else:
        node_to_depth = nx.shortest_path_length(comment_tree_nx, 0)

        depth_to_nodecount = collections.defaultdict(int)

        for k, v in node_to_depth.items():
            depth_to_nodecount[v] += 1

        comment_tree_hirsch = max(node_to_depth.values())
        while True:
            if depth_to_nodecount[comment_tree_hirsch] >= comment_tree_hirsch:
                break
            else:
                comment_tree_hirsch -= 1

    return comment_tree_hirsch 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import Graph [as 別名]
def load_equivalences(paths):
  """Loads equivalences from a text file.

  Args:
    paths: sequence of paths to the text files of equivalences; id0,id1 per
      line, or id0,id1,x,y,z.

  Returns:
    NX graph object representing the equivalences
  """
  equiv_graph = nx.Graph()

  for path in paths:
    with open(path, "r") as f:
      reader = pd.read_csv(
          f, sep=",", engine="c", comment="#", chunksize=4096, header=None)
      for chunk in reader:
        if len(chunk.columns) not in (2, 5):
          logging.critical("Unexpected # of columns (%d), want 2 or 5",
                           len(chunk.columns))

        edges = chunk.values[:, :2]
        equiv_graph.add_edges_from(edges)

  return equiv_graph 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import Graph [as 別名]
def test_no_cheapest_neighbor_first():
    # Example demonstrating that we should not always visit the cheapest
    # neighbor first
    # Visit order should be
    # C B F D E
    global G
    G = nx.Graph()
    G.add_edge('A', 'B', cost=3)
    G.add_edge('A', 'C', cost=1)
    G.add_edge('B', 'F', cost=1)
    G.add_edge('C', 'E', cost=5)
    G.add_edge('C', 'D', cost=4)
    visited = init('A')

    print('Visited : ',  visited)

    assert visited == ['A', 'C', 'B', 'F', 'D', 'E']
    print('\n\n') 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import Graph [as 別名]
def test_one_loop_2():
    global G
    G = nx.Graph()
    G.add_edge('A', 'B', cost=2)
    G.add_edge('B', 'C', cost=1)
    G.add_edge('B', 'D', cost=2)
    G.add_edge('C', 'E', cost=2)
    G.add_edge('D', 'E', cost=2)
    G.add_edge('E', 'F', cost=5)
    G.add_edge('D', 'G', cost=1)
    visited = init('A')
    print('Visited : ',  visited)

    # order between E and G does not matter, the cost of the path from A to
    # them is the same (5)
    assert visited == ['A', 'B', 'C', 'D', 'E', 'G', 'F'] or \
        visited == ['A', 'B', 'C', 'D', 'G', 'E', 'F']
    print('\n\n') 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import Graph [as 別名]
def generate_scalefree_graph(variables_count, m_edge, allow_subgraph):
    if not allow_subgraph:
        graph = nx.barabasi_albert_graph(variables_count, m_edge)
        is_connected = nx.is_connected(graph)
        while not is_connected:
            graph = nx.barabasi_albert_graph(variables_count, m_edge)
            is_connected = nx.is_connected(graph)
    else:
        graph = nx.barabasi_albert_graph(variables_count, m_edge)

    # In the obtained graph, low rank nodes will have a much higher edge count
    # than high rank nodes. We shuffle the nodes names to avoid this effect:
    new_nodes = list(range(variables_count))
    random.shuffle(new_nodes)
    node_mapping = {n: nn for n, nn in zip(graph.nodes, new_nodes)}

    new_graph = nx.Graph((node_mapping[e1], node_mapping[e2]) for e1, e2 in graph.edges)
    return new_graph 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import Graph [as 別名]
def generate_binary_constraints(
    grid_graph: nx.Graph, variables, bin_range: float, extensive: bool
) -> Dict[str, Constraint]:
    constraints: Dict[str, Constraint] = {}
    for nodes in grid_graph.edges:
        (r1, c1), (r2, c2) = sorted(nodes)
        name1 = f"v_{r1}_{c1}"
        name2 = f"v_{r2}_{c2}"
        v1 = variables[name1]
        v2 = variables[name2]

        if extensive:
            constraint = generate_binary_extensive_constraint(v1, v2, bin_range)
        else:
            constraint = generate_binary_intentional_constraint(v1, v2, bin_range)
        constraints[constraint.name] = constraint
    return constraints