Python networkx.random_layout() Examples

def draw_random(G, **kwargs):
    """Draw networkx graph with random 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
    """
    return draw(G, nx.random_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 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 test_default_scale_and_center(self):
        sc = self.check_scale_and_center
        c = (0, 0)
        G = nx.complete_graph(9)
        G.add_node(9)
        sc(nx.random_layout(G), scale=0.5, center=(0.5, 0.5))
        sc(nx.spring_layout(G), scale=1, center=c)
        sc(nx.spectral_layout(G), scale=1, center=c)
        sc(nx.circular_layout(G), scale=1, center=c)
        sc(nx.shell_layout(G), scale=1, center=c)
        if self.scipy is not None:
            sc(nx.kamada_kawai_layout(G), scale=1, center=c) 

def random_layout(G, center=None, dim=2):
    """Position nodes uniformly at random in the unit square.

    For every node, a position is generated by choosing each of dim
    coordinates uniformly at random on the interval [0.0, 1.0).

    NumPy (http://scipy.org) is required for this function.

    Parameters
    ----------
    G : NetworkX graph or list of nodes
        A position will be assigned to every node in G.

    center : array-like or None
        Coordinate pair around which to center the layout.

    dim : int
        Dimension of layout.

    Returns
    -------
    pos : dict
        A dictionary of positions keyed by node

    Examples
    --------
    >>> G = nx.lollipop_graph(4, 3)
    >>> pos = nx.random_layout(G)

    """
    import numpy as np

    G, center = _process_params(G, center, dim)
    shape = (len(G), dim)
    pos = np.random.random(shape) + center
    pos = pos.astype(np.float32)
    pos = dict(zip(G, pos))

    return pos 

def test_alpha_iter(self):
        pos = nx.random_layout(self.G)
        # with fewer alpha elements than nodes
        plt.subplot(131)
        nx.draw_networkx_nodes(self.G, pos, alpha=[0.1, 0.2])
        # with equal alpha elements and nodes
        num_nodes = len(self.G.nodes)
        alpha = [x / num_nodes for x in range(num_nodes)]
        colors = range(num_nodes)
        plt.subplot(132)
        nx.draw_networkx_nodes(self.G, pos, node_color=colors, alpha=alpha)
        # with more alpha elements than nodes
        alpha.append(1)
        plt.subplot(133)
        nx.draw_networkx_nodes(self.G, pos, alpha=alpha) 

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 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_default_scale_and_center(self):
        sc = self.check_scale_and_center
        c = (0, 0)
        G = nx.complete_graph(9)
        G.add_node(9)
        sc(nx.random_layout(G), scale=0.5, center=(0.5, 0.5))
        sc(nx.spring_layout(G), scale=1, center=c)
        sc(nx.spectral_layout(G), scale=1, center=c)
        sc(nx.circular_layout(G), scale=1, center=c)
        sc(nx.shell_layout(G), scale=1, center=c)
        if self.scipy is not None:
            sc(nx.kamada_kawai_layout(G), scale=1, center=c) 

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 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_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 test_alpha_iter(self):
        pos = nx.random_layout(self.G)
        # with fewer alpha elements than nodes
        plt.subplot(131)
        nx.draw_networkx_nodes(self.G, pos, alpha=[0.1, 0.2])
        # with equal alpha elements and nodes
        num_nodes = len(self.G.nodes)
        alpha = [x / num_nodes for x in range(num_nodes)]
        colors = range(num_nodes)
        plt.subplot(132)
        nx.draw_networkx_nodes(self.G, pos, node_color=colors, alpha=alpha)
        # with more alpha elements than nodes
        alpha.append(1)
        plt.subplot(133)
        nx.draw_networkx_nodes(self.G, pos, alpha=alpha) 

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.planar_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 plot_graph(self):
    import matplotlib.pyplot as plt

    pos=networkx.spring_layout(self.G,iterations=2000)
    #pos=networkx.spectral_layout(G)
    #pos = networkx.random_layout(G)
    networkx.draw_networkx_nodes(self.G, pos)
    networkx.draw_networkx_edges(self.G, pos, arrows=True)
    networkx.draw_networkx_labels(self.G, pos)
    plt.show() 

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 test_smoke_string(self):
        G = self.Gs
        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.spectral_layout(G)
        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 test_smoke_empty_graph(self):
        G = []
        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.spectral_layout(G)
        vpos = nx.shell_layout(G)
        vpos = nx.bipartite_layout(G, G)
        if self.scipy is not None:
            vpos = nx.kamada_kawai_layout(G) 

def test_scale_and_center_arg(self):
        G = nx.complete_graph(9)
        G.add_node(9)
        vpos = nx.random_layout(G, scale=2, center=(4,5))
        self.check_scale_and_center(vpos, scale=2, center=(4,5))
        vpos = nx.spring_layout(G, scale=2, center=(4,5))
        self.check_scale_and_center(vpos, scale=2, center=(4,5))
        vpos = nx.spectral_layout(G, scale=2, center=(4,5))
        self.check_scale_and_center(vpos, scale=2, center=(4,5))
        # circular can have twice as big length
        vpos = nx.circular_layout(G, scale=2, center=(4,5))
        self.check_scale_and_center(vpos, scale=2*2, center=(4,5))
        vpos = nx.shell_layout(G, scale=2, center=(4,5))
        self.check_scale_and_center(vpos, scale=2*2, center=(4,5))

        # check default center and scale
        vpos = nx.random_layout(G)
        self.check_scale_and_center(vpos, scale=1, center=(0.5,0.5))
        vpos = nx.spring_layout(G)
        self.check_scale_and_center(vpos, scale=1, center=(0.5,0.5))
        vpos = nx.spectral_layout(G)
        self.check_scale_and_center(vpos, scale=1, center=(0.5,0.5))
        vpos = nx.circular_layout(G)
        self.check_scale_and_center(vpos, scale=2, center=(0,0))
        vpos = nx.shell_layout(G)
        self.check_scale_and_center(vpos, scale=2, center=(0,0)) 

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 display_retweet_network(network, outfile=None, show=False):
    """
    Take a DiGraph (retweet network?) and display+/save it to file.
    Nodes must have a 'color' property, represented literally and indicating their type
    Edges must have a 'weight' property, represented as edge width
    """

    # Create a color list corresponding to nodes.
    node_colors = [ n[1]["color"] for n in network.nodes(data=True) ]

    # Get edge weights from graph
    edge_weights = [ e[2]["weight"] for e in network.edges(data=True) ]

    # Build up graph figure
    #pos = nx.random_layout(network)
    pos = nx.spring_layout(network)
    nx.draw_networkx_edges(network, pos, alpha=0.3 , width=edge_weights, edge_color='m')
    nx.draw_networkx_nodes(network, pos, node_size=400, node_color=node_colors, alpha=0.4)
    #nx.draw_networkx_labels(network, pos, fontsize=6)

    plt.title("Retweet Network", { 'fontsize': 12 })
    plt.axis('off')

    if outfile:
        print "Saving network to file: {0}".format(outfile)
        plt.savefig(outfile)

    if show:
        print "Displaying graph. Close graph window to resume python execution"
        plt.show() 

def plot_graph(G, filename='prova.png', values=None, colorbar_obj=None):
    
    func_types_dic = {
                'spring' : nx.spring_layout,
                'random' : nx.random_layout,
                'shell' : nx.shell_layout,
                'spectral' : nx.spectral_layout,
                'viz' : graphviz_layout
                }

    print(G.edges())
    print(G.nodes())

    fig = plt.figure(figsize=(10, 10))
    ax = fig.add_subplot(111)

    color_nodes = [values.get(node, 0.2) for node in G.nodes()] 

    pos = func_types_dic[params.plot_type_str](G)

    nodes = nx.draw_networkx_nodes(G, pos, node_color=color_nodes, \
            alpha=.6)#, cmap=plt.get_cmap('brg'))

    nx.draw_networkx_edges(G, pos, width=2.)
    nx.draw_networkx_labels(G, pos, font_color='k', font_weight='10')
    plt.title("|V| = %d, |E| = %d"%(len(G.nodes()), len(G.edges())))
    colorbar_obj.set_array(color_nodes)
    plt.colorbar(colorbar_obj)
    plt.axis('off')
    fig.savefig(filename, format='png')
    plt.close() 

def random_layout(G, center=None, dim=2, seed=None):
    """Position nodes uniformly at random in the unit square.

    For every node, a position is generated by choosing each of dim
    coordinates uniformly at random on the interval [0.0, 1.0).

    NumPy (http://scipy.org) is required for this function.

    Parameters
    ----------
    G : NetworkX graph or list of nodes
        A position will be assigned to every node in G.

    center : array-like or None
        Coordinate pair around which to center the layout.

    dim : int
        Dimension of layout.

    seed : int, RandomState instance or None  optional (default=None)
        Set the random state for deterministic node layouts.
        If int, `seed` is the seed used by the random number generator,
        if numpy.random.RandomState instance, `seed` is the random
        number generator,
        if None, the random number generator is the RandomState instance used
        by numpy.random.

    Returns
    -------
    pos : dict
        A dictionary of positions keyed by node

    Examples
    --------
    >>> G = nx.lollipop_graph(4, 3)
    >>> pos = nx.random_layout(G)

    """
    import numpy as np

    G, center = _process_params(G, center, dim)
    pos = seed.rand(len(G), dim) + center
    pos = pos.astype(np.float32)
    pos = dict(zip(G, pos))

    return pos