Python networkx.spring_layout方法代碼示例

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import spring_layout [as 別名]
def apply_network_layout(G, layout='kamada_kawai', verbose=True):

    if layout == 'kamada_kawai':

        if verbose:
            print('Applying the Kamada-Kawai network layout... (may take several minutes)')

        pos = nx.kamada_kawai_layout(G)

    elif layout == 'spring_embedded':

        if verbose:
            print('Applying the spring-embedded network layout... (may take several minutes)')

        pos = nx.spring_layout(G, k=0.2, iterations=100)

    for n in G:
        G.nodes[n]['x'] = pos[n][0]
        G.nodes[n]['y'] = pos[n][1]

    return G 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import spring_layout [as 別名]
def draw_wstate_tree(svm):
    import matplotlib.pyplot as plt
    import networkx as nx
    from networkx.drawing.nx_agraph import write_dot, graphviz_layout

    G = nx.DiGraph()
    pending_list = [svm.root_wstate]
    while len(pending_list):
        root = pending_list.pop()
        for trace, children in root.trace_to_children.items():
            for c in children:
                G.add_edge(repr(root), repr(c), label=trace)
                pending_list.append(c)
    # pos = nx.spring_layout(G)
    pos = graphviz_layout(G, prog='dot')
    edge_labels = nx.get_edge_attributes(G, 'label')
    nx.draw(G, pos)
    nx.draw_networkx_edge_labels(G, pos, edge_labels, font_size=8)
    nx.draw_networkx_labels(G, pos, font_size=10)
    plt.show() 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import spring_layout [as 別名]
def draw_spring(G, **kwargs):
    """Draw networkx graph with spring layout.

    Parameters
    ----------
    G : graph
       A networkx graph
    kwargs : optional keywords
       See hvplot.networkx.draw() for a description of optional
       keywords, with the exception of the pos parameter which is not
       used by this function.

    Returns
    -------
    graph : holoviews.Graph or holoviews.Overlay
       Graph element or Graph and Labels
    """
    return draw(G, nx.spring_layout, **kwargs) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import spring_layout [as 別名]
def plot_graph(plt, G):
    plt.title('num of nodes: '+str(G.number_of_nodes()), fontsize = 4)
    parts = community.best_partition(G)
    values = [parts.get(node) for node in G.nodes()]
    colors = []
    for i in range(len(values)):
        if values[i] == 0:
            colors.append('red')
        if values[i] == 1:
            colors.append('green')
        if values[i] == 2:
            colors.append('blue')
        if values[i] == 3:
            colors.append('yellow')
        if values[i] == 4:
            colors.append('orange')
        if values[i] == 5:
            colors.append('pink')
        if values[i] == 6:
            colors.append('black')
    plt.axis("off")
    pos = nx.spring_layout(G)
    # pos = nx.spectral_layout(G)
    nx.draw_networkx(G, with_labels=True, node_size=4, width=0.3, font_size = 3, node_color=colors,pos=pos) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import spring_layout [as 別名]
def plot_network(self, G, status, pos=None, nodelist=None, colordict={'S':'#009a80','I':'#ff2020', 'R':'gray'}, **nx_kwargs):
        r'''
        Plots the network in the axes self.networkx_ax.  Nodes are colored according to their status.
        if no ordered nodelist is provided, then the nodes are plotted so that 'I' nodes appear on top, while the order of the 'S' and 'R' nodes is random.  When the network is very dense this highlights 'I' nodes and allows the final state to more accurately represent the proportion of nodes having each status.
        '''
        colorlist = []
        if pos is None:
            pos = nx.spring_layout(G)
        if nodelist is None:
            nodelist = list(G.nodes())
            I_nodes = [node for node in nodelist if status[node] == 'I']
            other_nodes = [node for node in nodelist if status[node]!='I']
            random.shuffle(other_nodes)
            nodelist = other_nodes + I_nodes
            edgelist = list(G.edges())
        else:
            nodeset = set(nodelist)
            edgelist = [edge for edge in G.edges() if edge[0] in nodeset and edge[1] in nodeset]
        for node in nodelist:
            colorlist.append(colordict[status[node]])
        nx.draw_networkx_edges(G, pos, edgelist=edgelist, ax = self.network_axes, **nx_kwargs)
        nx.draw_networkx_nodes(G, pos, nodelist = nodelist, node_color=colorlist, ax=self.network_axes, **nx_kwargs)
        self.network_axes.set_xticks([])
        self.network_axes.set_yticks([]) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import spring_layout [as 別名]
def draw_graph(graph, output):
        """If matplotlib is available, draw the given graph to output file"""
        try:
            layout = nx.spring_layout(graph)
            metrics = {
                (src, dst): data['metric']
                for src, dst, data in graph.edges_iter(data=True)
            }
            nx.draw_networkx_edge_labels(graph, layout, edge_labels=metrics)
            nx.draw(graph, layout, node_size=20)
            nx.draw_networkx_labels(graph, layout,
                                    labels={n: n for n in graph})
            if os.path.exists(output):
                os.unlink(output)
            plt.savefig(output)
            plt.close()
            log.debug('Graph of %d nodes saved in %s', len(graph), output)
        except:
            pass 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import spring_layout [as 別名]
def build_word_ego_graph():
    import networkx as nx
    import matplotlib.pyplot as plt
    plt.rcParams['font.sans-serif'] = ['SimHei']  # 步驟一（替換sans-serif字體）
    plt.rcParams['axes.unicode_minus'] = False  # 步驟二（解決坐標軸負數的負號顯示問題）
    from harvesttext import get_sanguo, get_sanguo_entity_dict, get_baidu_stopwords

    ht0 = HarvestText()
    entity_mention_dict, entity_type_dict = get_sanguo_entity_dict()
    ht0.add_entities(entity_mention_dict, entity_type_dict)
    sanguo1 = get_sanguo()[0]
    stopwords = get_baidu_stopwords()
    docs = ht0.cut_sentences(sanguo1)
    G = ht0.build_word_ego_graph(docs,"劉備",min_freq=3,other_min_freq=2,stopwords=stopwords)
    pos = nx.kamada_kawai_layout(G)
    nx.draw(G,pos)
    nx.draw_networkx_labels(G,pos)
    plt.show()
    G = ht0.build_entity_ego_graph(docs, "劉備", min_freq=3, other_min_freq=2)
    pos = nx.spring_layout(G)
    nx.draw(G, pos)
    nx.draw_networkx_labels(G, pos)
    plt.show() 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import spring_layout [as 別名]
def plot_subgraph(self, region_start, region_stop, seq_name, neighbours=0):
		"""
		Return a plot of a sub-graph from a defined region with positions relative to some of the sequences.
		:param region_start: Start position (bp)
		:param region_stop: Stop position (bp)
		:param seq_name: Sequence ID that the bp positions are relative to.
		:param neighbours: Number of neighbours to be included. Currently testing, only 1 level available.
		:return: Plots a netowrkx + matplotlib figure of the region subgraph.
		"""

		sub_graph = extract_region_subgraph(self, region_start, region_stop, seq_name, neighbours=neighbours)
		pos = nx.spring_layout(sub_graph)
		nx.draw_networkx_nodes(sub_graph, pos, cmap=plt.get_cmap('jet'), node_size=300)
		nx.draw_networkx_labels(sub_graph, pos)
		nx.draw_networkx_edges(sub_graph, pos, arrows=True)
		plt.show() 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import spring_layout [as 別名]
def AddLayoutPreCC(g):
	pos = ABPUtils.BuildLayoutArray(g)
	if pos is None and len(g.nodes()) > 0:
		pos = nx.spring_layout(g, k=1/math.sqrt(len(g.nodes())), 
				weight=math.sqrt(len(g.nodes())), scale=100, iterations=1000)

	if pos is not None:
		nx.set_node_attributes(g, 'x', dict(zip(g.nodes(), [pos[n][0] for n in g.nodes()])))
		nx.set_node_attributes(g, 'y', dict(zip(g.nodes(), [pos[n][1] for n in g.nodes()])))
	


#starts = args.starts
#scores = {}
#scoreVals = []
#for idx in range(starts): 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import spring_layout [as 別名]
def test_nx_cluster(self):

        g = nx.karate_club_graph()
        coms = algorithms.louvain(g)
        pos = nx.spring_layout(g)
        viz.plot_network_clusters(g, coms, pos)

        plt.savefig("cluster.pdf")
        os.remove("cluster.pdf")

        coms = algorithms.demon(g, 0.25)
        pos = nx.spring_layout(g)
        viz.plot_network_clusters(g, coms, pos, plot_labels=True, plot_overlaps=True)

        plt.savefig("cluster.pdf")
        os.remove("cluster.pdf") 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import spring_layout [as 別名]
def test_smoke_int(self):
        G = self.Gi
        vpos = nx.random_layout(G)
        vpos = nx.circular_layout(G)
        vpos = nx.planar_layout(G)
        vpos = nx.spring_layout(G)
        vpos = nx.fruchterman_reingold_layout(G)
        vpos = nx.fruchterman_reingold_layout(self.bigG)
        vpos = nx.spectral_layout(G)
        vpos = nx.spectral_layout(G.to_directed())
        vpos = nx.spectral_layout(self.bigG)
        vpos = nx.spectral_layout(self.bigG.to_directed())
        vpos = nx.shell_layout(G)
        if self.scipy is not None:
            vpos = nx.kamada_kawai_layout(G)
            vpos = nx.kamada_kawai_layout(G, dim=1) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import spring_layout [as 別名]
def test_empty_graph(self):
        G = nx.empty_graph()
        vpos = nx.random_layout(G, center=(1, 1))
        assert_equal(vpos, {})
        vpos = nx.circular_layout(G, center=(1, 1))
        assert_equal(vpos, {})
        vpos = nx.planar_layout(G, center=(1, 1))
        assert_equal(vpos, {})
        vpos = nx.bipartite_layout(G, G)
        assert_equal(vpos, {})
        vpos = nx.spring_layout(G, center=(1, 1))
        assert_equal(vpos, {})
        vpos = nx.fruchterman_reingold_layout(G, center=(1, 1))
        assert_equal(vpos, {})
        vpos = nx.spectral_layout(G, center=(1, 1))
        assert_equal(vpos, {})
        vpos = nx.shell_layout(G, center=(1, 1))
        assert_equal(vpos, {}) 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import spring_layout [as 別名]
def draw_adjacency_graph(adjacency_matrix,
                         node_color=None,
                         size=10,
                         layout='graphviz',
                         prog='neato',
                         node_size=80,
                         colormap='autumn'):
    """draw_adjacency_graph."""
    graph = nx.from_scipy_sparse_matrix(adjacency_matrix)

    plt.figure(figsize=(size, size))
    plt.grid(False)
    plt.axis('off')

    if layout == 'graphviz':
        pos = nx.graphviz_layout(graph, prog=prog)
    else:
        pos = nx.spring_layout(graph)

    if len(node_color) == 0:
        node_color = 'gray'
    nx.draw_networkx_nodes(graph, pos,
                           node_color=node_color,
                           alpha=0.6,
                           node_size=node_size,
                           cmap=plt.get_cmap(colormap))
    nx.draw_networkx_edges(graph, pos, alpha=0.5)
    plt.show()


# draw a whole set of graphs:: 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import spring_layout [as 別名]
def malt_demo(nx=False):
    """
    A demonstration of the result of reading a dependency
    version of the first sentence of the Penn Treebank.
    """
    dg = DependencyGraph("""Pierre  NNP     2       NMOD
Vinken  NNP     8       SUB
,       ,       2       P
61      CD      5       NMOD
years   NNS     6       AMOD
old     JJ      2       NMOD
,       ,       2       P
will    MD      0       ROOT
join    VB      8       VC
the     DT      11      NMOD
board   NN      9       OBJ
as      IN      9       VMOD
a       DT      15      NMOD
nonexecutive    JJ      15      NMOD
director        NN      12      PMOD
Nov.    NNP     9       VMOD
29      CD      16      NMOD
.       .       9       VMOD
""")
    tree = dg.tree()
    tree.pprint()
    if nx:
        # currently doesn't work
        import networkx
        from matplotlib import pylab

        g = dg.nx_graph()
        g.info()
        pos = networkx.spring_layout(g, dim=1)
        networkx.draw_networkx_nodes(g, pos, node_size=50)
        # networkx.draw_networkx_edges(g, pos, edge_color='k', width=8)
        networkx.draw_networkx_labels(g, pos, dg.nx_labels)
        pylab.xticks([])
        pylab.yticks([])
        pylab.savefig('tree.png')
        pylab.show() 

# 需要導入模塊: import networkx [as 別名]
# 或者: from networkx import spring_layout [as 別名]
def plot(cg):
    """
    Plot the call graph using matplotlib
    For larger graphs, this should not be used, as it is very slow
    and probably you can not see anything on it.

    :param cg: A networkx call graph to plot
    """
    from androguard.core.analysis.analysis import ExternalMethod
    import matplotlib.pyplot as plt
    import networkx as nx
    pos = nx.spring_layout(cg)

    internal = []
    external = []

    for n in cg.node:
        if isinstance(n, ExternalMethod):
            external.append(n)
        else:
            internal.append(n)

    nx.draw_networkx_nodes(cg, pos=pos, node_color='r', nodelist=internal)
    nx.draw_networkx_nodes(cg, pos=pos, node_color='b', nodelist=external)
    nx.draw_networkx_edges(cg, pos, arrow=True)
    nx.draw_networkx_labels(cg, pos=pos,
                            labels={x: "{} {}".format(x.get_class_name(),
                                                      x.get_name())
                                    for x in cg.edge})
    plt.draw()
    plt.show()