Python networkx.draw_graphviz函数代码示例

    def render_community_graph(self, show_single_nodes=True):
        added_nodes = set()
        graph = nx.Graph()
        for edge, _ in self.adjacency_matrix.iteritems():
            player_one = self.__players[edge.player_one]
            player_two = self.__players[edge.player_two]
            added = False
            if show_single_nodes:
                graph.add_node(edge.player_one)
                graph.add_node(edge.player_two)
                added = True
            if player_one.community == player_two.community:
                graph.add_edge(edge.player_one, edge.player_two)
                added = True
            if added:
                added_nodes.add(edge.player_one)
                added_nodes.add(edge.player_two)

        for node in self.nodes:
            if node.fide_id in added_nodes:
                graph.node[node.fide_id]['elo_rank'] = math.floor(node.elo/100) * 100

        min_val = self.__min_elo
        max_val = 2900
        elo_levels = range(min_val, max_val, 100)
        color_levels = np.linspace(1, 0, num=len(elo_levels), endpoint=True)
        color_value_map = {elo: color for (elo, color) in zip(elo_levels, color_levels)}
        color_values = [color_value_map.get(graph.node[n]['elo_rank'], 0.0) for n in graph.nodes()]

        nx.draw_graphviz(graph, cmap=pylab.get_cmap('jet'), node_color=color_values,
                         node_size=100)

def minimalColoring (probMatrix, contigs, cutoff = 0.01):
	# create a graph based on the probMatrix
	probG = nx.Graph()
	for i in range (len(contigs)):
		contigi = contigs[i]
		if contigi in excluded_utgs:
			print "yo"
			continue
		probG.add_node(contigi)
		for j in range (i+1, len(contigs)):
			contigj = contigs[j]
			if contigi in excluded_utgs:
				print "yo"
				continue
			if np.isnan(probMatrix[i][j]):
				continue
			if probMatrix[i][j] > cutoff:
				probG.add_edge(contigi, contigj)
	nx.draw_graphviz(probG)
	plt.savefig(out_tag + "_color_groups.png")
	plt.clf()	
	#print probG.nodes()
	#print probG.edges()
	#print nx.find_cliques(probG)
	components = list(nx.connected_components(probG))
	#components = list(nx.find_cliques(probG))
	return components

def salva_grafoNX_imagem(G):
    """
    Salva grafos em formato png e dot
    """
    nx.draw_graphviz(G)
    nx.write_dot(G, 'relatorios/grafo_lei_vs_lei.dot')
    P.savefig('relatorios/grafo_lei_vs_lei.png')

def main():

    if len(sys.argv) < 2:
        sys.exit('Usage: %s $json_file' % sys.argv[0])

    if not os.path.exists(sys.argv[1]):
        sys.exit('ERROR: %s was not found!' % sys.argv[1])

    if len(sys.argv) == 2:
        G = merge_same_node(parse_saaf_json(sys.argv[1]))
        nx.draw_graphviz(G, prog = 'dot')
        plt.axis('off')
        plt.savefig("merged_by_networkx.png")
        json.dump(
            json_graph.node_link_data(G),
            open('ford3js.json', 'w'),
            sort_keys = True,
            indent = 4
        )
    if len(sys.argv) == 3:
        G1 = merge_same_node(parse_saaf_json(sys.argv[1]))
        G2 = merge_same_node(parse_saaf_json(sys.argv[2]))
        GM = isomorphism.DiGraphMatcher(G2, G1, node_match = op_match)
        #GM = isomorphism.DiGraphMatcher(G2, G1)
        print GM.is_isomorphic()
        print GM.subgraph_is_isomorphic()
        print GM.mapping

    def render_graph(self, max_edges=None, min_games=1):
        added_nodes = set()
        graph = nx.Graph()
        for i, (edge, num) in enumerate(self.adjacency_matrix.iteritems()):
            if max_edges and i > max_edges:
                break
            if num < min_games:
                continue
            graph.add_edge(edge.player_one, edge.player_two)
            added_nodes.add(edge.player_one)
            added_nodes.add(edge.player_two)

        for node in self.nodes:
            if node.fide_id in added_nodes:
                graph.node[node.fide_id]['elo_rank'] = int(math.floor(node.elo/100) * 100)

        min_val = self.__min_elo
        max_val = 2900
        elo_levels = range(min_val, max_val, 100)
        color_levels = np.linspace(1, 0, num=len(elo_levels), endpoint=True)
        color_value_map = {elo: color for (elo, color) in zip(elo_levels, color_levels)}
        color_values = [color_value_map.get(graph.node[n]['elo_rank'], 0.0) for n in graph.nodes()]

        nx.draw_graphviz(graph, cmap=pylab.get_cmap('jet'), node_color=color_values,
                         node_size=100)

 def displayGraph(self, g, label=False):
     axon, sd = axon_dendrites(g)
     sizes = node_sizes(g) * 50
     if len(sizes) == 0:
         print('Empty graph for cell. Make sure proto file has `*asymmetric` on top. I cannot handle symmetric compartmental connections')
         return
     weights = np.array([g.edge[e[0]][e[1]]['weight'] for e in g.edges()])
     pos = nx.graphviz_layout(g, prog='twopi')
     xmin, ymin, xmax, ymax = 1e9, 1e9, -1e9, -1e9
     for p in list(pos.values()):
         if xmin > p[0]:
             xmin = p[0]
         if xmax < p[0]:
             xmax = p[0]
         if ymin > p[1]:
             ymin = p[1]
         if ymax < p[1]:
             ymax = p[1]        
     edge_widths = 10.0 * weights / max(weights)
     node_colors = ['k' if x in axon else 'gray' for x in g.nodes()]
     lw = [1 if n.endswith('comp_1') else 0 for n in g.nodes()]
     self.axes.clear()
     try:
         nx.draw_graphviz(g, ax=self.axes, prog='twopi', node_color=node_colors, lw=lw)
     except (NameError, AttributeError) as e:
         nx.draw_spectral(g, ax=self.axes, node_color=node_colors, lw=lw, with_labels=False, )

def draw_all(filename='out.png'):
  """
  draw every graph connected and everything yeah
  """
  def _make_abbreviation(string):
    s = string.split(" ")
    return ''.join([word[0] for word in s])
  import matplotlib.pyplot as plt
  plt.clf()
  this = sys.modules[__name__]
  relationals = [getattr(this, i) for i in this.__dict__ if isinstance(getattr(this,i), Relational)]
  biggraph = nx.DiGraph()
  for r in relationals:
    for n in r._nk.nodes():
      biggraph.add_edges_from(n._nk.edges())
  for n in biggraph.nodes():
    if n.external_parents:
      for p in n.external_parents:
        biggraph.add_edges_from(p._nk.edges())
    if n.external_childs:
      for c in n.external_childs:
        biggraph.add_edges_from(c._nk.edges())
  for n in biggraph.nodes():
    if "." not in n.name:
      n.name = n.name+"."+_make_abbreviation(n.container.name)
  nx.draw_graphviz(biggraph,prog='neato',width=1,node_size=300,font_size=4,overlap='scalexy')
  plt.savefig(filename)

def map_flows(catalog):
    import analysis as trans
    fm = trans.FlowMapper()

    read_exceptions = {}
    for i,fn in enumerate(os.listdir('.\\repository_data\\')):
        print i, fn
        try:
            sys = catalog.read(''.join(['.\\repository_data\\',fn]))
        except Exception as e:
            read_exceptions[fn] = e
            print '\t',e.message
        fm.add_system(sys)
        if i > 5:
            break

    graph = fm.transformation_graph()
    fm.stats()
    nx.draw_graphviz(graph,prog='dot',root='energy')
    print nx.to_numpy_matrix(graph) > 0
#    pdg = nx.to_pydot(graph)
#    pdg.write_png('transform.png')
#    nx.graphviz_layout(graph,prog='neato')
#    nx.draw_graphviz(graph)
    plt.show()

def plot_co_x(cox, start, end, size = (20,20), title = '', weighted=False, weight_threshold=10):

        """ Plotting function for keyword graphs

        Parameters
        --------------------
        cox: the coword networkx graph; assumes that nodes have attribute 'topic'
        start: start year
        end: end year
        """

        plt.figure(figsize=size)
        plt.title(title +' %s - %s'%(start,end), fontsize=18)
        if weighted:
            elarge=[(u,v) for (u,v,d) in cox.edges(data=True) if d['weight'] >weight_threshold]
            esmall=[(u,v) for (u,v,d) in cox.edges(data=True) if d['weight'] <=weight_threshold]
            pos=nx.graphviz_layout(cox) # positions for all nodes
            nx.draw_networkx_nodes(cox,pos,
                node_color= [s*4500 for s in nx.eigenvector_centrality(cox).values()],
                node_size = [s*6+20  for s in nx.degree(cox).values()],
                alpha=0.7)
            # edges
            nx.draw_networkx_edges(cox,pos,edgelist=elarge,
                                width=1, alpha=0.5, edge_color='black') #, edge_cmap=plt.cm.Blues
            nx.draw_networkx_edges(cox,pos,edgelist=esmall,
                                width=0.3,alpha=0.5,edge_color='yellow',style='dotted')
            # labels
            nx.draw_networkx_labels(cox,pos,font_size=10,font_family='sans-serif')
            plt.axis('off')
        else:
            nx.draw_graphviz(cox, with_labels=True,
                         alpha = 0.8, width=0.1,
                         fontsize=9,
                         node_color = [s*4 for s in nx.eigenvector_centrality(cox).values()],
                         node_size = [s*6+20 for s in nx.degree(cox).values()])

def plot_graph(graph=None, path=None, dist=None, best=None, best_dist=None,
			   save=False, name=None, title=None):
	"""
	Plot a TSP graph.

	Parameters
	----------
	graph: An XML TSP graph. If None, use the default graph from parse_xml_graph.
	path: An ordered list of node indices. If given, plot the path. Otherwise, plot
		the underlying graph.
	dist: Distances of the paths in path.
	best: Empirical best path.
	best_dist: Empirical best path distance.
	save: If True, saves the graph as name.png. Otherwise, draws the graph.
	name: Graph is saved as name.png
	title: Caption of the graph.
	"""
	# Check input parameters 
	if save:
		assert (name is not None), 'If saving graph, must provide name'

	# Initialize graph
	if graph is not None:
		g = parse_xml_graph(graph)
	else:
		g = parse_xml_graph()
	G = nx.from_numpy_matrix(g)

	# Plot path, if applicable
	edges = list()
	edge_colors = list()
	if path is not None:
		edges.extend([(path[i], path[i+1]) for i in range(len(path)-1)])
		edges.append((path[-1], path[0]))
		edge_colors.extend(['r' for i in range(len(path))])
	if best is not None:
		edges.extend([(best[i], best[i+1]) for i in range(len(best)-1)])
		edges.append((best[-1], best[0]))
		edge_colors.extend(['b' for i in range(len(best))])
	if path is None and best is None:
		edges = G.edges()

	plt.clf()
	fig = plt.figure(figsize=(14, 5.5))
	ax1 = fig.add_subplot(121)
	ax2 = fig.add_subplot(122)
	nx.draw_graphviz(G, edgelist=edges, edge_color=edge_colors, with_labels=None,
		node_color='k', node_size=100, ax=ax1)
	ax1.set_title(title)
	ax2.plot(np.arange(1, len(dist)+1), dist, color='r', alpha=0.9, label='Best found path')
	ax2.hlines(best_dist, 0, len(dist)+1, color='b', label='Best path')
	ax2.set_xlim(1, max(len(dist), 2));
	ax2.legend()

	if not save:
		plt.show()
	else:
		plt.savefig('temp/{}.png'.format(name))
	fig.clf()

def s_draw(listdata):
    '''Draw the weighted graph associated with the Seifert data 'listdata'.'''
    startree = make_graph(listdata)
    labels = dict((n, '%s,%s' %(n,a['weight'])) for n,a in 
                  startree.nodes(data=True))
    nx.draw_graphviz(startree, labels=labels, node_size=700, width=3, 
                     alpha=0.7)    
    plt.show()    

def graph_draw(graph):
    nx.draw_graphviz(
        graph,
        node_size=[16 * graph.degree(n) for n in graph],
        node_color=[graph.depth[n] for n in graph],
        with_labels=False,
    )
    matplotlib.pyplot.show()

 def update_graph(self):
     '''Redraw graph in matplotlib.pyplot.'''
     plt.clf() # erase figure
     labels = dict((n, '%s,%s' %(n,a['weight'])) \
                   for n,a in self.graph.nodes(data=True))
     nx.draw_graphviz(self.graph, labels=labels, node_size=700, width=3, 
                      alpha=0.7)
     plt.show()

def debug_graph( graph, message="" ):
    import matplotlib.pyplot as plt
    print message

    pos = nx.layout.fruchterman_reingold_layout( graph )
    nx.draw( graph, pos )
    nx.draw_graphviz( graph )
    plt.show()

 def test_lobster(self):
     import networkx as nx
     import matplotlib.pyplot as plt
     #g = nx.random_lobster(15, 0.8, 0.1)
     g = nx.barbell_graph(7, 5)
     #g = nx.erdos_renyi_graph(15, 0.2)
     nx.draw_graphviz(g)
     plt.savefig("/tmp/lobster.png")
     print distancematrix.matrix_calls(g.edges(), 7)