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.

See also

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.

Parallel implementation.

If enabled during compilation, this algorithm will run in parallel using OpenMP. See the parallel algorithms section for information about how to control several aspects of parallelization.

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. ])]

edge_hist

Contents

edge_hist#