Plot module¶

Content¶

Details¶

This modules provides the following features:

• plotting the distribution of some attribute over the graph
• basic graph plotting
• animation of some recorded activity

nngt.plot.Animation2d¶

nngt.plot.AnimationNetwork¶

nngt.plot.draw_network(network, nsize='total-degree', ncolor='group', nshape='o', nborder_color='k', nborder_width=0.5, esize=1.0, ecolor='k', max_nsize=5.0, max_esize=2.0, threshold=0.5, decimate=None, spatial=True, size=(600, 600), xlims=None, ylims=None, dpi=75, axis=None, show=False, **kwargs)[source]¶

Draw a given graph/network.

Parameters:

network (Graph or subclass) – The graph/network to plot. nsize (float, array of float or string, optional (default: “total-degree”)) – Size of the nodes as a percentage of the canvas length. Otherwise, it can be a string that correlates the size to a node attribute among “in/out/total-degree”, or “betweenness”. ncolor (float, array of floats or string, optional (default: 0.5)) – Color of the nodes; if a float in [0, 1], position of the color in the current palette, otherwise a string that correlates the color to a node attribute among “in/out/total-degree”, “betweenness” or “group”. nshape (char or array of chars, optional (default: “o”)) – Shape of the nodes (see Matplotlib markers). nborder_color (char, float or array, optional (default: “k”)) – Color of the node’s border using predefined Matplotlib colors). or floats in [0, 1] defining the position in the palette. nborder_width (float or array of floats, optional (default: 0.5)) – Width of the border in percent of canvas size. esize (float, str, or array of floats, optional (default: 0.5)) – Width of the edges in percent of canvas length. Available string values are “betweenness” and “weight”. ecolor (char, float or array, optional (default: “k”)) – Edge color. max_esize (float, optional (default: 5.)) – If a custom property is entered as esize, this normalizes the edge width between 0. and max_esize. decimate (int, optional (default: keep all connections)) – Plot only one connection every decimate. spatial (bool, optional (default: True)) – If True, use the neurons’ positions to draw them. size (tuple of ints, optional (default: (600,600))) – (width, height) tuple for the canvas size (in px). dpi (int, optional (default: 75)) – Resolution (dot per inch).

nngt.plot.palette(numbers)[source]¶

nngt.plot.degree_distribution(network, deg_type='total', nodes=None, num_bins='doane', use_weights=False, logx=False, logy=False, axis=None, axis_num=None, colors=None, norm=False, show=False, **kwargs)[source]¶

Plotting the degree distribution of a graph.

Parameters:

graph (Graph or subclass) – The graph to analyze. deg_type (string or N-tuple, optional (default: “total”)) – Type of degree to consider (“in”, “out”, or “total”) nodes (list or numpy.array of ints, optional (default: all nodes)) – Restrict the distribution to a set of nodes. num_bins (int or N-tuple, optional (default: ‘auto’):) – Number of bins used to sample the distribution. Defaults to unsupervised Bayesian blocks method. use_weights (bool, optional (default: False)) – Use weighted degrees (do not take the sign into account : only the magnitude of the weights is considered). logx (bool, optional (default: False)) – Use log-spaced bins. logy (bool, optional (default: False)) – Use logscale for the degree count. axis (matplotlib.axes.Axes instance, optional (default: new one)) – Axis which should be used to plot the histogram, if None, a new one is created. show (bool, optional (default: True)) – Show the Figure right away if True, else keep it warm for later use. **kwargs (keyword arguments for matplotlib.axes.Axes.bar().)

nngt.plot.betweenness_distribution(network, btype='both', use_weights=True, nodes=None, logx=False, logy=False, num_nbins='auto', num_ebins=None, axes=None, colors=None, norm=False, show=True, **kwargs)[source]¶

Plotting the betweenness distribution of a graph.

Parameters:

graph (Graph or subclass) – the graph to analyze. btype (string, optional (default: “both”)) – type of betweenness to display (“node”, “edge” or “both”) use_weights (bool, optional (default: True)) – use weighted degrees (do not take the sign into account : all weights are positive). nodes (list or numpy.array of ints, optional (default: all nodes)) – Restrict the distribution to a set of nodes (taken into account only for the node attribute). logx (bool, optional (default: False)) – use log-spaced bins. logy (bool, optional (default: False)) – use logscale for the degree count. num_nbins (int or ‘auto’, optional (default: ‘auto’):) – Number of bins used to sample the node distribution. Defaults to unsupervised Bayesian blocks method. num_ebins (int or ‘auto’, optional (default: None):) – Number of bins used to sample the edge distribution. Defaults to max(num_edges / 500., 10) (‘auto’ method will be slow). axes (list of matplotlib.axis.Axis, optional (default: new ones)) – Axes which should be used to plot the histogram, if None, new ones are created. show (bool, optional (default: True)) – Show the Figure right away if True, else keep it warm for later use.

nngt.plot.node_attributes_distribution(network, attributes, nodes=None, num_bins='auto', logx=False, logy=False, norm=False, title=None, colors=None, show=True, **kwargs)[source]¶

Return node attributes for a set of nodes.

Parameters:

network (Graph) – The graph where the nodes belong. attributes (str or list) – Attributes which should be returned, among: * “betweenness” * “clustering” * “in-degree”, “out-degree”, “total-degree” * “subgraph_centrality” * “b2” (requires NEST) * “firing_rate” (requires NEST) nodes (list, optional (default: all nodes)) – Nodes for which the attributes should be returned. num_bins (int or list, optional (default: ‘auto’)) – Number of bins to plot the distributions. If only one int is provided, it is used for all attributes, otherwize a list containing one int per attribute in attributes is required. Defaults to unsupervised Bayesian blocks method. logx (bool or list, optional (default: False)) – Use log-spaced bins. logy (bool or list, optional (default: False)) – use logscale for the node count.

nngt.plot.compare_population_attributes(network, attributes, nodes=None, reference_nodes=None, num_bins='auto', reference_color='gray', title=None, logx=False, logy=False, show=True, **kwargs)[source]¶

Compare node attributes between two sets of nodes. Since number of nodes can vary, normalized distributions are used.

Parameters:

network (Graph) – The graph where the nodes belong. attributes (str or list) – Attributes which should be returned, among: * “betweenness” * “clustering” * “in-degree”, “out-degree”, “total-degree” * “subgraph_centrality” * “b2” (requires NEST) * “firing_rate” (requires NEST) nodes (list, optional (default: all nodes)) – Nodes for which the attributes should be returned. reference_nodes (list, optional (default: all nodes)) – Reference nodes for which the attributes should be returned in order to compare with nodes. num_bins (int or list, optional (default: ‘auto’)) – Number of bins to plot the distributions. If only one int is provided, it is used for all attributes, otherwize a list containing one int per attribute in attributes is required. Defaults to unsupervised Bayesian blocks method. logx (bool or list, optional (default: False)) – Use log-spaced bins. logy (bool or list, optional (default: False)) – use logscale for the node count.

nngt.plot.correlation_to_attribute(network, reference_attribute, other_attributes, nodes=None, title=None, show=True)[source]¶

For each node plot the value of reference_attributes against each of the other_attributes to check for correlations.

Parameters:

network (Graph) – The graph where the nodes belong. reference_attribute (str or array-like) – Attribute which should serve as reference, among: “betweenness” “clustering” “in-degree”, “out-degree”, “total-degree” “subgraph_centrality” “b2” (requires NEST) “firing_rate” (requires NEST) a custom array of values, in which case one entry per node in nodes is required. other_attributes (str or list) – Attributes that will be compared to the reference. nodes (list, optional (default: all nodes)) – Nodes for which the attributes should be returned.