Python networkx.draw_networkx_edge_labels() Examples

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() 

def _build_graph(show=False):
    """Load word dependencies into graph using networkx. Enables easy traversal of dependencies for parsing particular patterns.
    One graph is created for each sentence.

    Args:
        show (bool): If set to True, labeled visualization of network will be opened via matplotlib for each sentence

    Returns:
        None: Global variable G is set from within function

    """
    global G
    G = nx.Graph()
    node_labels, edge_labels = {}, {}
    for idx, dep in enumerate(A.deps):

        types = ["dependent", "governor"]

        # nodes, labels
        for x in types:
            G.add_node(str(dep[x]), word=dep[x + "Gloss"], pos=A.lookup[dep[x]]["pos"])
            node_labels[str(dep[x])] = dep[x + "Gloss"] + " : " + A.lookup[dep[x]]["pos"]

        # edges, labels
        G.add_edge(str(dep[types[0]]), str(dep[types[1]]), dep=dep["dep"])
        edge_labels[(str(dep[types[0]]), str(dep[types[1]]))] = dep["dep"]

    if show == True:
        pos = nx.spring_layout(G)
        nx.draw_networkx(G, pos=pos, labels=node_labels, node_color="white", alpha=.5)
        nx.draw_networkx_edge_labels(G, pos=pos, edge_labels=edge_labels)
        plt.show()


#########################################
# Dependency / POS parsing functions
######################################### 

def plot_alerts(_g, _bank_accts, _output_png):
    bank_ids = _bank_accts.keys()
    cmap = plt.get_cmap("tab10")
    pos = nx.nx_agraph.graphviz_layout(_g)

    plt.figure(figsize=(12.0, 8.0))
    plt.axis('off')

    for i, bank_id in enumerate(bank_ids):
        color = cmap(i)
        members = _bank_accts[bank_id]
        nx.draw_networkx_nodes(_g, pos, members, node_size=300, node_color=color, label=bank_id)
        nx.draw_networkx_labels(_g, pos, {n: n for n in members}, font_size=10)

    edge_labels = nx.get_edge_attributes(_g, "label")
    nx.draw_networkx_edges(_g, pos)
    nx.draw_networkx_edge_labels(_g, pos, edge_labels, font_size=6)

    plt.legend(numpoints=1)
    plt.subplots_adjust(left=0, right=1, bottom=0, top=1)
    plt.savefig(_output_png, dpi=120) 

def plot(self, file_path: str):
        try:
            import matplotlib.pyplot as plt
        except ImportError as err:
            raise('matplotlib not installed. Failed at: {}', err)

        try:
            pos = nx.nx_agraph.graphviz_layout(self.directed)
            nx.draw(
                self.directed,
                pos=pos,
                node_size=1200,
                node_color='lightblue',
                linewidths=0.25,
                font_size=8,
                font_weight='bold',
                with_labels=True,
                dpi=5000
            )
            # edge_labels = nx.get_edge_attributes(self.directed, name='attr_dict')
            # nx.draw_networkx_edge_labels(self.directed, pos=pos, edge_labels=edge_labels)
            plt.savefig(file_path)
        except IOError as err:
            raise('Could not create plot image: {}', err) 

def print_graph_nx(vertices, edges, print_dist=False):
  import networkx as nx
  G=nx.DiGraph()
  labels = []

  for v in vertices:
    G.add_node(v)

  for v in vertices:
    for e in edges:
      G.add_edge(e[0], e[1], weight=int(e[2]))
      #labels += [(e[0], e[1]), e[2]]

  print("Nodes of graph: ")
  print(G.nodes())
  print("Edges of graph: ")
  print(G.edges())

  nx.draw(G, with_labels=True, node_color='y')
  #nx.draw_networkx_edge_labels(G,labels)
  plt.savefig("simulation_graph.png") # save as png
  plt.show() 

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 

def draw_graph(self):
        nx.draw_shell(self.G, with_labels=True, font_weight='bold')

        # pos = graphviz_layout(self.G)
        # plt.axis('off')
        # nx.draw_networkx_nodes(self.G,pos,node_color='g',alpha = 0.8)
        # nx.draw_networkx_edges(self.G,pos,edge_color='b',alpha = 0.6)
        # nx.draw_networkx_edge_labels(self.G,pos,edge_labels = \
        # nx.get_edge_attributes(self.G,'weight'))
        # nx.draw_networkx_labels(self.G,pos) # node lables

        plt.savefig('graph.png') 

def _draw_edge_labels(self, edge_labels, **kwargs):
        pos = kwargs.pop('pos', self._pos)
        return nx.draw_networkx_edge_labels(self._G, pos, edge_labels=edge_labels, **kwargs) 

def draw_and_save_topology(graph: nx.Graph, edge_labels: List[Dict[Tuple[str, str], str]]) -> None:
    plt.figure(1, figsize=(12, 12))
    pos = nx.spring_layout(graph)
    nx.draw_networkx(graph, pos, node_size=1300, node_color='orange')
    nx.draw_networkx_edge_labels(graph, pos, edge_labels=edge_labels[0], label_pos=0.8)
    nx.draw_networkx_edge_labels(graph, pos, edge_labels=edge_labels[1], label_pos=0.2)
    filename = "topology.png"
    plt.savefig(filename)
    logger.info("The network topology diagram has been saved to %r", filename) 

def visualize(self):
        # import matplotlib.pyplot as plt
        pos = nx.spring_layout(self.graph_state)
        edge_labels = {e: self.graph_state.edges[e]['rel'] for e in self.graph_state.edges}
        print(edge_labels)
        nx.draw_networkx_edge_labels(self.graph_state, pos, edge_labels)
        nx.draw(self.graph_state, pos=pos, with_labels=True, node_size=200, font_size=10)
        #plt.show() 

def _plot_graph(graph, axis, weights=None, display_edge_labels=True):
    """Plot graph using networkx."""
    pos = nx.circular_layout(graph)
    nx.draw_circular(graph, with_labels=True, node_size=600, alpha=1.0,
                     ax=axis, node_color='Gainsboro', hold=True, font_size=14,
                     font_weight='bold')
    if display_edge_labels:
        edge_labels = nx.get_edge_attributes(graph, weights)
        nx.draw_networkx_edge_labels(graph, pos, edge_labels=edge_labels,
                                     font_size=13)  # font_weight='bold' 

def plot_mute_graph():
    """Plot MuTE example network.

    Network of 5 AR-processes, which is used as an example the paper
    on the MuTE toolbox (Montalto, PLOS ONE, 2014, eq. 14). The
    network consists of five autoregressive (AR) processes with model
    orders 2 and les and the following (non-linear) couplings:

        >>> 0 -> 1, u = 2
        >>> 0 -> 2, u = 3
        >>> 0 -> 3, u = 2 (non-linear)
        >>> 3 -> 4, u = 1
        >>> 4 -> 3, u = 1

    Returns:
        Figure handle
            Figure object from the matplotlib package
    """
    graph = nx.DiGraph()
    graph.add_nodes_from(np.arange(5))
    # graph.add_edges_from([(0, 1), (0, 2), (0, 3), (3, 4), (4, 3)])
    graph.add_weighted_edges_from([(0, 1, 2), (0, 2, 3), (0, 3, 2), (3, 4, 1),
                                   (4, 3, 1)], weight='delay')
    pos = {
        0: np.array([1, 1]),
        1: np.array([0, 2]),
        2: np.array([0, 0]),
        3: np.array([2, 1]),
        4: np.array([3, 1]),
    }
    fig = plt.figure()
    nx.draw(graph, pos=pos, with_labels=True, node_size=900, alpha=1.0,
            node_color='cadetblue', font_weight='bold',
            edge_color=['r', 'k', 'r', 'k', 'k'], hold=True)
    nx.draw_networkx_edge_labels(graph, pos=pos)
    plt.text(2, 0.1, 'non-linear interaction in red')
    # see here for an example on how to plot edge labels:
    # http://stackoverflow.com/questions/10104700/how-to-set-networkx-edge-labels-offset-to-avoid-label-overlap
    return fig 

def draw_stmt_graph(stmts):
    """Render the attributes of a list of Statements as directed graphs.

    The layout works well for a single Statement or a few Statements at a time.
    This function displays the plot of the graph using plt.show().

    Parameters
    ----------
    stmts : list[indra.statements.Statement]
        A list of one or more INDRA Statements whose attribute graph should
        be drawn.
    """
    import networkx
    try:
        import matplotlib.pyplot as plt
    except Exception:
        logger.error('Could not import matplotlib, not drawing graph.')
        return
    try:  # This checks whether networkx has this package to work with.
        import pygraphviz
    except Exception:
        logger.error('Could not import pygraphviz, not drawing graph.')
        return
    import numpy
    g = networkx.compose_all([stmt.to_graph() for stmt in stmts])
    plt.figure()
    plt.ion()
    g.graph['graph'] = {'rankdir': 'LR'}
    pos = networkx.drawing.nx_agraph.graphviz_layout(g, prog='dot')
    g = g.to_undirected()

    # Draw nodes
    options = {
        'marker': 'o',
        's': 200,
        'c': [0.85, 0.85, 1],
        'facecolor': '0.5',
        'lw': 0,
    }
    ax = plt.gca()
    nodelist = list(g)
    xy = numpy.asarray([pos[v] for v in nodelist])
    node_collection = ax.scatter(xy[:, 0], xy[:, 1], **options)
    node_collection.set_zorder(2)
    # Draw edges
    networkx.draw_networkx_edges(g, pos, arrows=False, edge_color='0.5')
    # Draw labels
    edge_labels = {(e[0], e[1]): e[2].get('label') for e in g.edges(data=True)}
    networkx.draw_networkx_edge_labels(g, pos, edge_labels=edge_labels)
    node_labels = {n[0]: n[1].get('label') for n in g.nodes(data=True)}
    for key, label in node_labels.items():
        if len(label) > 25:
            parts = label.split(' ')
            parts.insert(int(len(parts)/2), '\n')
            label = ' '.join(parts)
            node_labels[key] = label
    networkx.draw_networkx_labels(g, pos, labels=node_labels)
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
    plt.show() 

def showTask(task, root="ROOT()", filename="task.dot"):
    import matplotlib.pyplot as plt

    g = nx.DiGraph()

    nodes = [root]
    visited = set()
    nodelist = []

    print(root)
    print(task.nodeSummary())

    while len(nodes) > 0:
        node = nodes.pop()
        visited.add(node)
        children = task.children[node]
        print("NODE =", node, "CHILDREN =")
        weights = task.weights[node]
        if len(weights) > 0:
            for child, wt in zip(children, weights):
                print("\t",child,"weight =", wt)
                g.add_edge(node, child, weight=int(wt))
                nodelist.append(child)
                if child not in visited:
                    print("\t\tadding", child)
                    nodes.append(child)
        elif len(children) > 0:
            raise RuntimeError('weights not initialized')

    pos = nx.nx_agraph.graphviz_layout(g, prog="dot")
    nx.draw_networkx_edges(g, pos, width=1.0, alpha=1., arrows=False)
    nx.draw(g, pos, prog='dot', node_size=1000, nodelist=nodelist,
            width=1.0, alpha=1., arrows=True, with_labels=True,)
    labels = nx.get_edge_attributes(g,'weight')
    nx.draw_networkx_edge_labels(g,pos,edge_labels=labels)
    #a = nx.nx_agraph.to_agraph(g)
    #a.draw('ex.png', prog='dot')
    plt.axis('off')
    plt.show() 

def plot_graph(G, out_file=None, node_size=1500, font_size=10,
				node_and_labels=None, edge_and_labels=None):
	"""
	plot a graph
	"""

	pos = nx.get_node_attributes(G, 'pos')
	if not pos:
		pos = nx.spring_layout(G)

	nx.draw_networkx(G, pos, node_size=node_size, 
						node_color='w', 
						edge_color='k', 
						labels = node_and_labels,
						font_size=font_size,
						font_color='r',
						arrows=True,
						with_labels=True)

	plt.axis('off')	

	if edge_and_labels:
		nx.draw_networkx_edge_labels(G, pos, 
										edge_labels=edge_and_labels, 
										font_color='r',
										label_pos=0.4, 
										alpha=0.0)

	if out_file:
		plt.savefig(out_file, bbox_inches='tight')

	plt.show() 

def plot(self, density=1.0):
        """Plots a network diagram from (triple) nodes and edges in the KB.

        :param float density: Probability (0-1) that a given edge will be plotted, \
        useful to thin out dense graphs for visualization."""
        edgelist = [e for e in self.G.edges(data=True) if random.random() < density]
        newg = nx.DiGraph(edgelist)
        pos = nx.spring_layout(newg)
        plt.figure(1,figsize=(12,12))
        nx.draw_networkx_nodes(newg, pos, node_size=200)
        nx.draw_networkx_edges(newg, pos, edgelist=edgelist, width=1, font_size=8)
        nx.draw_networkx_labels(newg, pos, font_size=10, font_family='sans-serif')
        nx.draw_networkx_edge_labels(newg, pos)
        plt.axis('off')
        plt.show() 

def plot_network_with_results(psstc, model, time=0):
    G = create_network(psstc)

    fig, axs = plt.subplots(1, 1, figsize=(12, 9))
    ax = axs

    line_color_dict = dict()
    hour = 0
    for i, b in branch_df.iterrows():
        if model.ThermalLimit[i] != 0:
            line_color_dict[(b['F_BUS'], b['T_BUS'])] = round(abs(model.LinePower[i, hour].value / model.ThermalLimit[i]), 2)
        else:
            line_color_dict[(b['F_BUS'], b['T_BUS'])] = 0

    gen_color_dict = dict()
    hour = 0
    for i, g in generator_df.iterrows():
        gen_color_dict[(i, g['GEN_BUS'])] = round(abs(model.PowerGenerated[i, hour].value / model.MaximumPowerOutput[i]), 2)

    color_dict = line_color_dict.copy()
    color_dict.update(gen_color_dict)

    edge_color = list()

    for e in G.edges():
        try:
            edge_color.append( color_dict[(e[0], e[1])] )
        except KeyError:
            edge_color.append( color_dict[(e[1], e[0])] )

    ax.axis('off')
    pos = graphviz_layout(G, prog='sfdp')
    nx.draw_networkx_nodes(G, pos, list(generator_df.index),)
    nx.draw_networkx_nodes(G, pos, list(bus_df.index), node_color='black',)
    edges = nx.draw_networkx_edges(G, pos, edge_color=edge_color, edge_cmap=cmap, width=3)
    nx.draw_networkx_edge_labels(G, pos, edge_labels=color_dict)

    divider = make_axes_locatable(ax)
    cax = divider.append_axes("left", size="5%", pad=0.05)
    cb = plt.colorbar(edges, cax=cax)
    cax.yaxis.set_label_position('left')
    cax.yaxis.set_ticks_position('left')
    # cb.set_label('Voltage (V)') 

def plot_graph(cls, G, filename=None, node_attribute_name='id', edge_attribute_name=None, 
                    colored_nodes=None, colored_edges=None, colored_path=None, **kwargs):
    #def plot_graph(self, G, out_file, **kwd):
        """plot graph"""
        plt.clf()

        # get the layout of G
        pos = nx.get_node_attributes(G, 'pos')
        if not pos:
            pos = nx.spring_layout(G)

        # get node attributes
        with_labels = False
        node_labels = None
        if node_attribute_name == 'id':
            with_labels = True
        elif node_attribute_name:
            node_labels = nx.get_node_attributes(G, node_attribute_name)

        # get edge attributes
        if not edge_attribute_name:
            edge_labels = nx.get_edge_attributes(G, edge_attribute_name)

        # colored nodes
        node_default_color = '0.75' # Gray shades 

        node_color = node_default_color
        if colored_nodes:
            node_color = ['r' if node in colored_nodes else node_default_color
                            for node in G.nodes()]

        # colored path
        if colored_path:
            nrof_nodes = len(colored_path)
            idx = 0
            colored_edges = list()
            while idx < nrof_nodes-1:
                colored_edges.append((colored_path[idx], colored_path[idx+1]))
                idx += 1

        # colored edges
        edge_default_color = 'k' # black
        edge_color = edge_default_color
        if colored_edges:
            set_colored_edges = {frozenset(t) for t in colored_edges}  # G.edges returns a list of 2-tuples

            edge_color = ['r' if frozenset([u, v]) in set_colored_edges else edge_default_color
                            for u, v in G.edges()]


        # draw 
        nx.draw(G, pos, with_labels=with_labels, node_color=node_color, edge_color=edge_color, **kwargs)
        if node_labels:
            nx.draw_networkx_labels(G, pos, labels=node_labels)
        nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)

        if filename:
            plt.savefig(filename, bbox_inches='tight', pad_inches=0)
        else:
            plt.show() 

def visualize_diagram(bpmn_diagram):
    """
    Shows a simple visualization of diagram

    :param bpmn_diagram: an instance of BPMNDiagramGraph class.
    """
    g = bpmn_diagram.diagram_graph
    pos = bpmn_diagram.get_nodes_positions()
    nx.draw_networkx_nodes(g, pos, node_shape='s', node_color='white',
                           nodelist=bpmn_diagram.get_nodes_id_list_by_type(consts.Consts.task))
    nx.draw_networkx_nodes(g, pos, node_shape='s', node_color='white',
                           nodelist=bpmn_diagram.get_nodes_id_list_by_type(consts.Consts.subprocess))
    nx.draw_networkx_nodes(g, pos, node_shape='d', node_color='white',
                           nodelist=bpmn_diagram.get_nodes_id_list_by_type(consts.Consts.complex_gateway))
    nx.draw_networkx_nodes(g, pos, node_shape='o', node_color='white',
                           nodelist=bpmn_diagram.get_nodes_id_list_by_type(consts.Consts.event_based_gateway))
    nx.draw_networkx_nodes(g, pos, node_shape='d', node_color='white',
                           nodelist=bpmn_diagram.get_nodes_id_list_by_type(consts.Consts.inclusive_gateway))
    nx.draw_networkx_nodes(g, pos, node_shape='d', node_color='white',
                           nodelist=bpmn_diagram.get_nodes_id_list_by_type(consts.Consts.exclusive_gateway))
    nx.draw_networkx_nodes(g, pos, node_shape='d', node_color='white',
                           nodelist=bpmn_diagram.get_nodes_id_list_by_type(consts.Consts.parallel_gateway))
    nx.draw_networkx_nodes(g, pos, node_shape='o', node_color='white',
                           nodelist=bpmn_diagram.get_nodes_id_list_by_type(consts.Consts.start_event))
    nx.draw_networkx_nodes(g, pos, node_shape='o', node_color='white',
                           nodelist=bpmn_diagram.get_nodes_id_list_by_type(consts.Consts.intermediate_catch_event))
    nx.draw_networkx_nodes(g, pos, node_shape='o', node_color='white',
                           nodelist=bpmn_diagram.get_nodes_id_list_by_type(consts.Consts.end_event))
    nx.draw_networkx_nodes(g, pos, node_shape='o', node_color='white',
                           nodelist=bpmn_diagram.get_nodes_id_list_by_type(consts.Consts.intermediate_throw_event))

    node_labels = {}
    for node in g.nodes(data=True):
        node_labels[node[0]] = node[1].get(consts.Consts.node_name)
    nx.draw_networkx_labels(g, pos, node_labels)

    nx.draw_networkx_edges(g, pos)

    edge_labels = {}
    for edge in g.edges(data=True):
        edge_labels[(edge[0], edge[1])] = edge[2].get(consts.Consts.name)
    nx.draw_networkx_edge_labels(g, pos, edge_labels)

    plt.show()