# graph_tool.stats.edge_hist#

graph_tool.stats.edge_hist(g, eprop, bins=[0, 1], float_count=True)[source]#

Return the edge histogram of the given property.

Parameters:
gGraph

Graph to be used.

epropEdgePropertyMap

Edge property to be used for the histogram.

binslist of bins (optional, default: [0, 1])

List of bins to be used for the histogram. The values given represent the edges of the bins (i.e. lower and upper bounds). If the list contains two values, this will be used to automatically create an appropriate bin range, with a constant width given by the second value, and starting from the first value.

float_countbool (optional, default: True)

If True, the counts in each histogram bin will be returned as floats. If False, they will be returned as integers.

Returns:
countsnumpy.ndarray

The bin counts.

binsnumpy.ndarray

The bin edges.

vertex_hist

Vertex histograms.

vertex_average

Average of vertex properties, degrees.

edge_average

Average of edge properties.

distance_histogram

Shortest-distance histogram.

Notes

The algorithm runs in $$O(|E|)$$ time.

If enabled during compilation, this algorithm runs in parallel.

Examples

>>> from numpy import arange
>>> from numpy.random import random
>>> g = gt.random_graph(1000, lambda: (5, 5))
>>> eprop = g.new_edge_property("double")
>>> eprop.get_array()[:] = random(g.num_edges())
>>> print(gt.edge_hist(g, eprop, linspace(0, 1, 11)))
[array([525., 504., 502., 502., 467., 499., 531., 471., 520., 479.]), array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])]