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', ealpha=0.5, max_nsize=5.0, max_esize=2.0, curved_edges=False, threshold=0.5, decimate=None, spatial=True, restrict_sources=None, restrict_targets=None, show_environment=True, fast=False, 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 (str, char, float or array, optional (default: “k”)) – Edge color. If ecolor=”groups”, edges color will depend on the source and target groups, i.e. only edges from and toward same groups will have the same 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. Use -1 to hide all edges. spatial (bool, optional (default: True)) – If True, use the neurons’ positions to draw them. restrict_sources (str or list, optional (default: all)) – Only draw edges starting from a restricted set of source nodes. restrict_targets (str or list, optional (default: all)) – Only draw edges ending on a restricted set of target nodes. show_environment (bool, optional (default: True)) – Plot the environment if the graph is spatial. fast (bool, optional (default: False)) – Use a faster algorithm to plot the edges. This method leads to less pretty plots and zooming on the graph will make the edges start or ending in places that will differ more or less strongly from the actual node positions. 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). show (bool, optional (default: True)) – Display the plot immediately.

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.