Python nx_agraph.graphviz_layout方法代碼示例

# 需要導入模塊: from networkx.drawing import nx_agraph [as 別名]
# 或者: from networkx.drawing.nx_agraph import graphviz_layout [as 別名]
def _layoutBlockGraph(self):
        # note that graphviz expects sizes in inches, so we scale them back
        self._setGraphNodeSizes(1. / self.GRAPHVIZ_SCALE_FACTOR)

        # dot is for directed graphs
        layout = graphviz_layout(self.blockGraph, prog='dot')

        layout = self._fixGraphvizLayout(layout)

        for blockAddr in self.blockAddrs:
            # this both updates the HTML element, and the graph node's
            # x and y attributes
            self._setBlockPos(blockAddr, layout[blockAddr])

        # now set node sizes for edge layout algo
        self._setGraphNodeSizes(scalingFactor=1.0)

        layoutAlgo = _EdgeLayoutAlgo(self.blockGraph)
        edgePaths = layoutAlgo.doLayout()

        for b1Addr, b2Addr in self.blockGraph.edges_iter():
            self._setEdgePath(b1Addr, b2Addr, edgePaths[b1Addr, b2Addr])

        self._updateSceneRect(edgePaths) 

# 需要導入模塊: from networkx.drawing import nx_agraph [as 別名]
# 或者: from networkx.drawing.nx_agraph import graphviz_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: from networkx.drawing import nx_agraph [as 別名]
# 或者: from networkx.drawing.nx_agraph import graphviz_layout [as 別名]
def recalculate_positions(self, prog='sfdp', *args, **kwargs):
        try:
            self.positions = graphviz_layout(self._G, prog=prog, *args, **kwargs)
        except ImportError:
            import warnings
            warnings.warn("Unable to use graphviz, please install pygraphviz. Using networkx spring layout by default")
            self.positions = nx.spring_layout(self._G, *args, **kwargs)
        return self.positions 

# 需要導入模塊: from networkx.drawing import nx_agraph [as 別名]
# 或者: from networkx.drawing.nx_agraph import graphviz_layout [as 別名]
def main():
    # Create a directed graph
    G = nx.DiGraph()

    # An example
    l=[ ('a','b'),
        ('b','c'),
        ('c','d'),
        ('d','e'),
        ('e','f'),
        ('w','x'),
        ('w','t'),
        ('t','q'),
        ('q','r'),
        ('q','u')]

    # Build up a graph
    for t in l:
        G.add_edge(t[0], t[1])

    # Plot trees
    pos = graphviz_layout(G, prog='dot')
    nx.draw(G, pos, with_labels=True, arrows=False)

    plt.savefig('draw_trees_with_pygraphviz.png', bbox_inches='tight')   
    plt.show() 

# 需要導入模塊: from networkx.drawing import nx_agraph [as 別名]
# 或者: from networkx.drawing.nx_agraph import graphviz_layout [as 別名]
def plot_nx(bn,**kwargs):
	"""
	Draw BayesNet object from networkx engine
	"""
	g = nx.DiGraph(bn.E)
	pos = graphviz_layout(g,'dot')
	#node_size=600,node_color='w',with_labels=False
	nx.draw_networkx(g,pos=pos, **kwargs)
	plt.axis('off')
	plt.show() 

# 需要導入模塊: from networkx.drawing import nx_agraph [as 別名]
# 或者: from networkx.drawing.nx_agraph import graphviz_layout [as 別名]
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)') 

# 需要導入模塊: from networkx.drawing import nx_agraph [as 別名]
# 或者: from networkx.drawing.nx_agraph import graphviz_layout [as 別名]
def __init__(self, name, table, rootlane='timepoint', shift=None, log2=True, posgl=False, csv=False, groups=True):
        """
        Initializes the class by providing the the common name ('name') of .gexf and .json files produced by
        e.g. ParseAyasdiGraph(), the name of the file containing the filtered raw data ('table'), as produced by
        Preprocess.save(), and the name of the column that contains sampling time points. Optional argument
        'shift' can be an integer n specifying that the first n columns of the table should be ignored, or a
        list of columns that should only be considered. If optional argument 'log2' is False, it is assumed that
        the filtered raw data is in units of TPM instead of log_2(1+TPM). When optional argument 'posgl' is False,
        a files name.posg and name.posgl are generated with the positions of the graph nodes for visualization.
        When 'posgl' is True, instead of generating new positions, the positions stored in files name.posg and
        name.posgl are used for visualization of the topological graph.
        """
        UnrootedGraph.__init__(self, name, table, shift, log2, posgl, csv, groups)
        self.rootlane = rootlane
        self.root, self.leaf = self.find_root(self.get_dendrite())
        self.g3, self.dicdend = self.dendritic_graph()
        self.edgesize = []
        self.dicedgesize = {}
        self.edgesizeprun = []
        self.nodesize = []
        self.dicmelisa = {}
        self.nodesizeprun = []
        self.dicmelisaprun = {}
        for ee in self.g3.edges():
            yu = self.dicdend[int(ee[0].split('_')[0])][int(ee[0].split('_')[1])]
            yu2 = self.dicdend[int(ee[1].split('_')[0])][int(ee[1].split('_')[1])]
            self.edgesize.append(self.gl.subgraph(list(yu)+list(yu2)).number_of_edges()-self.gl.subgraph(yu).number_of_edges()
                                 - self.gl.subgraph(yu2).number_of_edges())
            self.dicedgesize[ee] = self.edgesize[-1]
        for ee in self.g3.nodes():
            lisa = []
            for uu in self.dicdend[int(ee.split('_')[0])][int(ee.split('_')[1])]:
                lisa += self.dic[uu]
            self.nodesize.append(len(set(lisa)))
            self.dicmelisa[ee] = set(lisa)
        try:
            from networkx.drawing.nx_agraph import graphviz_layout
            self.posg3 = graphviz_layout(self.g3, 'sfdp')
        except:
            self.posg3 = networkx.spring_layout(self.g3)
        self.dicdis = self.get_distroot(self.root)
        pel2, tol = self.get_gene(self.rootlane, ignore_log=True)
        self.pel = numpy.array([pel2[m] for m in self.pl])*tol
        dr2 = self.get_distroot(self.root)
        self.dr = numpy.array([dr2[m] for m in self.pl])
        self.po = scipy.stats.linregress(self.pel, self.dr) 

# 需要導入模塊: from networkx.drawing import nx_agraph [as 別名]
# 或者: from networkx.drawing.nx_agraph import graphviz_layout [as 別名]
def render(self, mode='human', close=False):
        if self.simple_render:
            observation = np.zeros((20, 20*self.chain_length))
            observation[:, self.state*20:(self.state+1)*20] = 255
            return observation
        else:
            # lazy loading of networkx and matplotlib to allow using the environment without installing them if
            # necessary
            import networkx as nx
            from networkx.drawing.nx_agraph import graphviz_layout
            import matplotlib.pyplot as plt

            if not hasattr(self, 'G'):
                self.states = list(range(self.chain_length))
                self.G = nx.DiGraph(directed=True)
                for i, origin_state in enumerate(self.states):
                    if i < self.chain_length - 1:
                        self.G.add_edge(origin_state,
                                        origin_state + 1,
                                        weight=0.5)
                    if i > 0:
                        self.G.add_edge(origin_state,
                                        origin_state - 1,
                                        weight=0.5, )
                    if i == 0 or i < self.chain_length - 1:
                        self.G.add_edge(origin_state,
                                        origin_state,
                                        weight=0.5, )

            fig = plt.gcf()
            if np.all(fig.get_size_inches() != [10, 2]):
                fig.set_size_inches(5, 1)
            color = ['y']*(len(self.G))
            color[self.state] = 'r'
            options = {
                'node_color': color,
                'node_size': 50,
                'width': 1,
                'arrowstyle': '-|>',
                'arrowsize': 5,
                'font_size': 6
            }
            pos = graphviz_layout(self.G, prog='dot', args='-Grankdir=LR')
            nx.draw_networkx(self.G, pos, arrows=True, **options)
            fig.canvas.draw()
            data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
            data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
            return data