Python networkx.spring_layout() 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_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([]) 

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) 

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) 

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 

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) 

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, {}) 

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 build_word_ego_graph():
    import networkx as nx
    import matplotlib.pyplot as plt
    plt.rcParams['font.sans-serif'] = ['SimHei']  # 步骤一（替换sans-serif字体）
    plt.rcParams['axes.unicode_minus'] = False  # 步骤二（解决坐标轴负数的负号显示问题）
    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,"刘备",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, "刘备", min_freq=3, other_min_freq=2)
    pos = nx.spring_layout(G)
    nx.draw(G, pos)
    nx.draw_networkx_labels(G, pos)
    plt.show() 

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") 

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): 

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() 

def test_center_wrong_dimensions(self):
        G = nx.path_graph(1)
        assert_raises(ValueError, nx.random_layout, G, center=(1, 1, 1))
        assert_raises(ValueError, nx.circular_layout, G, center=(1, 1, 1))
        assert_raises(ValueError, nx.spring_layout, G, center=(1, 1, 1))
        assert_raises(ValueError, nx.fruchterman_reingold_layout, G, center=(1, 1, 1))
        assert_raises(ValueError, nx.fruchterman_reingold_layout, G, dim=3, center=(1, 1))
        assert_raises(ValueError, nx.spectral_layout, G, center=(1, 1, 1))
        assert_raises(ValueError, nx.spectral_layout, G, dim=3, center=(1, 1))
        assert_raises(ValueError, nx.shell_layout, G, center=(1, 1, 1)) 

def test_labels_and_colors(self):
        G = nx.cubical_graph()
        pos = nx.spring_layout(G)  # positions for all nodes
        # nodes
        nx.draw_networkx_nodes(G, pos,
                               nodelist=[0, 1, 2, 3],
                               node_color='r',
                               node_size=500,
                               alpha=0.8)
        nx.draw_networkx_nodes(G, pos,
                               nodelist=[4, 5, 6, 7],
                               node_color='b',
                               node_size=500,
                               alpha=0.8)
        # edges
        nx.draw_networkx_edges(G, pos, width=1.0, alpha=0.5)
        nx.draw_networkx_edges(G, pos,
                               edgelist=[(0, 1), (1, 2), (2, 3), (3, 0)],
                               width=8, alpha=0.5, edge_color='r')
        nx.draw_networkx_edges(G, pos,
                               edgelist=[(4, 5), (5, 6), (6, 7), (7, 4)],
                               width=8, alpha=0.5, edge_color='b')
        # some math labels
        labels = {}
        labels[0] = r'$a$'
        labels[1] = r'$b$'
        labels[2] = r'$c$'
        labels[3] = r'$d$'
        labels[4] = r'$\alpha$'
        labels[5] = r'$\beta$'
        labels[6] = r'$\gamma$'
        labels[7] = r'$\delta$'
        nx.draw_networkx_labels(G, pos, labels, font_size=16)
        plt.show() 

def test_smoke_string(self):
        G = self.Gs
        vpos = nx.random_layout(G)
        vpos = nx.circular_layout(G)
        vpos = nx.spring_layout(G)
        vpos = nx.fruchterman_reingold_layout(G)
        vpos = nx.spectral_layout(G)
        vpos = nx.shell_layout(G)
        if self.scipy is not None:
            vpos = nx.kamada_kawai_layout(G) 

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.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, {}) 

def plot_nhc_labels(self, ax, x, y, labels, k=0.01):

        G = nx.DiGraph()
        data_nodes = []
        init_pos = {}
        for xi, yi, label in zip(x, y, labels):
            data_str = 'data_{0}'.format(label)
            G.add_node(data_str)
            G.add_node(label)
            G.add_edge(label, data_str)
            data_nodes.append(data_str)
            init_pos[data_str] = (xi, yi)
            init_pos[label] = (xi, yi)

        pos = nx.spring_layout(G, pos=init_pos, fixed=data_nodes, k=k)

        # undo spring_layout's rescaling
        pos_after = np.vstack([pos[d] for d in data_nodes])
        pos_before = np.vstack([init_pos[d] for d in data_nodes])
        scale, shift_x = np.polyfit(pos_after[:,0], pos_before[:,0], 1)
        scale, shift_y = np.polyfit(pos_after[:,1], pos_before[:,1], 1)
        shift = np.array([shift_x, shift_y])
        for key, val in pos.items():
            pos[key] = (val*scale) + shift

        start = False
        for label, data_str in G.edges():
            if start == False:
                start = True
                continue
            self.ax.annotate(label,
                        xy=pos[data_str], xycoords='data',
                        xytext=pos[label], textcoords='data', fontweight='bold', ha='center', va='center',
                        arrowprops=dict(arrowstyle="-",#->
                                        shrinkA=0, shrinkB=0,
                                        connectionstyle="arc3", 
                                        color='k'),
                        transform=ccrs.PlateCarree()) 

def test_smoke_int(self):
        G = self.Gi
        vpos = nx.random_layout(G)
        vpos = nx.circular_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) 

def test_smoke_empty_graph(self):
        G = []
        vpos = nx.random_layout(G)
        vpos = nx.circular_layout(G)
        vpos = nx.spring_layout(G)
        vpos = nx.fruchterman_reingold_layout(G)
        vpos = nx.spectral_layout(G)
        vpos = nx.shell_layout(G)
        if self.scipy is not None:
            vpos = nx.kamada_kawai_layout(G) 

def draw_spring(G, **kwargs):
    """Draw the graph G with a spring layout.

    Parameters
    ----------
    G : graph
       A networkx graph

    kwargs : optional keywords
       See networkx.draw_networkx() for a description of optional keywords,
       with the exception of the pos parameter which is not used by this
       function.
    """
    draw(G, spring_layout(G), **kwargs) 

def test_scale_and_center_arg(self):
        sc = self.check_scale_and_center
        c = (4, 5)
        G = nx.complete_graph(9)
        G.add_node(9)
        sc(nx.random_layout(G, center=c), scale=0.5, center=(4.5, 5.5))
        # rest can have 2*scale length: [-scale, scale]
        sc(nx.spring_layout(G, scale=2, center=c), scale=2, center=c)
        sc(nx.spectral_layout(G, scale=2, center=c), scale=2, center=c)
        sc(nx.circular_layout(G, scale=2, center=c), scale=2, center=c)
        sc(nx.shell_layout(G, scale=2, center=c), scale=2, center=c)
        if self.scipy is not None:
            sc(nx.kamada_kawai_layout(G, scale=2, center=c), scale=2, center=c) 

def plot_graph(
    interactions, pos=None, node_color='orange',
    edge_color='black', title='',
    fixed_weight=None, weight_offset=1, weight_factor=4,
    layout='spring_layout', layout_parameters={},
    figsize=(12, 12), output_filepath=''
):
    """Plot graph from list of pandas df with interactions."""
    G = nx.Graph()
    G.add_weighted_edges_from(interactions.to_records(index=False))
    weights = [
        (
            fixed_weight if fixed_weight is not None else
            (weight_offset + G[u][v]['weight']) * weight_factor
        )
        for u, v in G.edges()
    ]
    if pos is None:
        pos = layout_factory[layout](G, **layout_parameters)
    plt.figure(figsize=figsize)
    plt.title(title)
    nx.draw(
        G, pos=pos, node_color=node_color, width=weights, edge_color=edge_color
    )
    nx.draw_networkx_labels(
        G, pos=pos,
        bbox=dict(
            boxstyle='round', ec=(0.0, 0.0, 0.0),
            alpha=0.9, fc=node_color, lw=1.5
        )
    )
    if output_filepath:
        plt.savefig(output_filepath, bbox_inches='tight')
    return pos 

def quickVisual(G, showLabel = False):
    """Just makes a simple _matplotlib_ figure and displays it, with each node coloured by its type. You can add labels with _showLabel_. This looks a bit nicer than the one provided my _networkx_'s defaults.

    # Parameters

    _showLabel_ : `optional [bool]`

    > Default `False`, if `True` labels will be added to the nodes giving their IDs.
    """
    colours = "brcmykwg"
    f = plt.figure(1)
    ax = f.add_subplot(1,1,1)
    ndTypes = []
    ndColours = []
    layout = nx.spring_layout(G, k = 4 / math.sqrt(len(G.nodes())))
    for nd in G.nodes(data = True):
        if 'type' in nd[1]:
            if nd[1]['type'] not in ndTypes:
                ndTypes.append(nd[1]['type'])
            ndColours.append(colours[ndTypes.index(nd[1]['type']) % len(colours)])
        elif len(ndColours) > 1:
            raise RuntimeError("Some nodes do not have a type")
    if len(ndColours) < 1:
        nx.draw_networkx_nodes(G, pos = layout, node_color = colours[0], node_shape = '8', node_size = 100, ax = ax)
    else:
        nx.draw_networkx_nodes(G, pos = layout, node_color = ndColours, node_shape = '8', node_size = 100, ax = ax)
    nx.draw_networkx_edges(G, pos = layout, width = .7, ax = ax)
    if showLabel:
        nx.draw_networkx_labels(G, pos = layout, font_size = 8, ax = ax)
    plt.axis('off')
    f.set_facecolor('w') 

def test_spring_args(self):
        G = nx.complete_graph(9)
        vpos = nx.spring_layout(G, dim=3)
        assert_equal(vpos[0].shape, (3,))
        vpos = nx.spring_layout(G, fixed=[0,1], pos={1:(0,0), 2:(1,1)})
        vpos = nx.spring_layout(G, k=2, fixed=[0,1], pos={1:(0,0), 2:(1,1)})
        vpos = nx.spring_layout(G, scale=3, center=(2,5))
        vpos = nx.spring_layout(G, scale=3)
        vpos = nx.spring_layout(G, center=(2,5)) 

def test_single_node(self):
        G = nx.Graph()
        G.add_node(0)
        vpos = nx.random_layout(G)
        vpos = nx.circular_layout(G)
        vpos = nx.spring_layout(G)
        vpos = nx.fruchterman_reingold_layout(G)
        vpos = nx.shell_layout(G)
        vpos = nx.spectral_layout(G)
        # center arg
        vpos = nx.random_layout(G, scale=2, center=(4,5))
        vpos = nx.circular_layout(G, scale=2, center=(4,5))
        vpos = nx.spring_layout(G, scale=2, center=(4,5))
        vpos = nx.shell_layout(G, scale=2, center=(4,5))
        vpos = nx.spectral_layout(G, scale=2, center=(4,5)) 

def test_empty_graph(self):
        G=nx.Graph()
        vpos = nx.random_layout(G)
        vpos = nx.circular_layout(G)
        vpos = nx.spring_layout(G)
        vpos = nx.fruchterman_reingold_layout(G)
        vpos = nx.shell_layout(G)
        vpos = nx.spectral_layout(G)
        # center arg
        vpos = nx.random_layout(G, scale=2, center=(4,5))
        vpos = nx.circular_layout(G, scale=2, center=(4,5))
        vpos = nx.spring_layout(G, scale=2, center=(4,5))
        vpos = nx.shell_layout(G, scale=2, center=(4,5))
        vpos = nx.spectral_layout(G, scale=2, center=(4,5)) 

def test_smoke_string(self):
        G = self.Gs
        vpos = nx.random_layout(G)
        vpos = nx.circular_layout(G)
        vpos = nx.spring_layout(G)
        vpos = nx.fruchterman_reingold_layout(G)
        vpos = nx.spectral_layout(G)
        vpos = nx.shell_layout(G) 

def test_smoke_int(self):
        G=self.Gi
        vpos=nx.random_layout(G)
        vpos=nx.circular_layout(G)
        vpos=nx.spring_layout(G)
        vpos=nx.fruchterman_reingold_layout(G)
        vpos=nx.spectral_layout(G)
        vpos=nx.spectral_layout(self.bigG)
        vpos=nx.shell_layout(G) 

def draw_spring(G, **kwargs):
    """Draw the graph G with a spring layout.

    Parameters
    ----------
    G : graph
       A networkx graph

    kwargs : optional keywords
       See networkx.draw_networkx() for a description of optional keywords,
       with the exception of the pos parameter which is not used by this
       function.
    """
    draw(G, spring_layout(G), **kwargs)