graph_tool.stats
 Miscellaneous statistics¶
Summary¶
Return the vertex histogram of the given degree type or property. 

Return the edge histogram of the given property. 

Return the average of the given degree or vertex property. 

Return the average of the given degree or vertex property. 

Label edges which are parallel, i.e, have the same source and target vertices. 

Remove all parallel edges from the graph. 

Label edges which are selfloops, i.e, the source and target vertices are the same. 

Remove all selfloops edges from the graph. 

Remove every edge e such that label[e] != 0. 

Return the shortestdistance histogram for each vertex pair in the graph. 
Contents¶

graph_tool.stats.
vertex_hist
(g, deg, bins=[0, 1], float_count=True)[source]¶ Return the vertex histogram of the given degree type or property.
 Parameters
 g
Graph
Graph to be used.
 degstring or
VertexPropertyMap
Degree or property to be used for the histogram. It can be either “in”, “out” or “total”, for in, out, or total degree of the vertices. It can also be a vertex property map.
 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.
 g
 Returns
 counts
numpy.ndarray
The bin counts.
 bins
numpy.ndarray
The bin edges.
 counts
See also
edge_hist
Edge histograms.
vertex_average
Average of vertex properties, degrees.
edge_average
Average of edge properties.
distance_histogram
Shortestdistance histogram.
Notes
The algorithm runs in \(O(V)\) time.
If enabled during compilation, this algorithm runs in parallel.
Examples
>>> from numpy.random import poisson >>> g = gt.random_graph(1000, lambda: (poisson(5), poisson(5))) >>> print(gt.vertex_hist(g, "out")) [array([ 5., 32., 85., 148., 152., 182., 160., 116., 53., 25., 23., 13., 3., 2., 1.]), array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], dtype=uint64)]

graph_tool.stats.
edge_hist
(g, eprop, bins=[0, 1], float_count=True)[source]¶ Return the edge histogram of the given property.
 Parameters
 g
Graph
Graph to be used.
 eprop
EdgePropertyMap
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.
 g
 Returns
 counts
numpy.ndarray
The bin counts.
 bins
numpy.ndarray
The bin edges.
 counts
See also
vertex_hist
Vertex histograms.
vertex_average
Average of vertex properties, degrees.
edge_average
Average of edge properties.
distance_histogram
Shortestdistance 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([485., 538., 502., 505., 474., 497., 544., 465., 492., 498.]), array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])]

graph_tool.stats.
vertex_average
(g, deg)[source]¶ Return the average of the given degree or vertex property.
 Parameters
 g
Graph
Graph to be used.
 degstring or
VertexPropertyMap
Degree or property to be used for the histogram. It can be either “in”, “out” or “total”, for in, out, or total degree of the vertices. It can also be a vertex property map.
 g
 Returns
 averagefloat
The average of the given degree or property.
 stdfloat
The standard deviation of the average.
See also
vertex_hist
Vertex histograms.
edge_hist
Edge histograms.
edge_average
Average of edge properties.
distance_histogram
Shortestdistance histogram.
Notes
The algorithm runs in \(O(V)\) time.
If enabled during compilation, this algorithm runs in parallel.
Examples
>>> from numpy.random import poisson >>> g = gt.random_graph(1000, lambda: (poisson(5), poisson(5))) >>> print(gt.vertex_average(g, "in")) (4.986, 0.07323799560337517)

graph_tool.stats.
edge_average
(g, eprop)[source]¶ Return the average of the given degree or vertex property.
 Parameters
 g
Graph
Graph to be used.
 eprop
EdgePropertyMap
Edge property to be used for the histogram.
 g
 Returns
 averagefloat
The average of the given property.
 stdfloat
The standard deviation of the average.
See also
vertex_hist
Vertex histograms.
edge_hist
Edge histograms.
vertex_average
Average of vertex degree, properties.
distance_histogram
Shortestdistance 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_average(g, eprop)) (0.5027850372071281, 0.004073940886690715)

graph_tool.stats.
label_parallel_edges
(g, mark_only=False, eprop=None)[source]¶ Label edges which are parallel, i.e, have the same source and target vertices. For each parallel edge set \(PE\), the labelling starts from 0 to \(PE1\). If mark_only==True, all parallel edges are simply marked with the value 1. If the eprop parameter is given (a
EdgePropertyMap
), the labelling is stored there.

graph_tool.stats.
remove_parallel_edges
(g)[source]¶ Remove all parallel edges from the graph. Only one edge from each parallel edge set is left.

graph_tool.stats.
label_self_loops
(g, mark_only=False, eprop=None)[source]¶ Label edges which are selfloops, i.e, the source and target vertices are the same. For each selfloop edge set \(SL\), the labelling starts from 0 to \(SL1\). If mark_only == True, selfloops are labeled with 1 and others with 0. If the eprop parameter is given (a
EdgePropertyMap
), the labelling is stored there.

graph_tool.stats.
remove_labeled_edges
(g, label)[source]¶ Remove every edge e such that label[e] != 0.

graph_tool.stats.
distance_histogram
(g, weight=None, bins=[0, 1], samples=None, float_count=True)[source]¶ Return the shortestdistance histogram for each vertex pair in the graph.
 Parameters
 g
Graph
Graph to be used.
 weight
EdgePropertyMap
(optional, default: None) Edge weights.
 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.
 samplesint (optional, default: None)
If supplied, the distances will be randomly sampled from a number of source vertices given by this parameter. It samples is None (default), all pairs are used.
 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.
 g
 Returns
 counts
numpy.ndarray
The bin counts.
 bins
numpy.ndarray
The bin edges.
 counts
See also
vertex_hist
Vertex histograms.
edge_hist
Edge histograms.
vertex_average
Average of vertex degree, properties.
distance_histogram
Shortestdistance histogram.
Notes
The algorithm runs in \(O(V^2)\) time, or \(O(V^2\log V)\) if weight is not None. If samples is supplied, the complexities are \(O(\text{samples}\times V)\) and \(O(\text{samples}\times V\log V)\), respectively.
If enabled during compilation, this algorithm runs in parallel.
Examples
>>> g = gt.random_graph(100, lambda: (3, 3)) >>> hist = gt.distance_histogram(g) >>> print(hist) [array([ 0., 300., 862., 2195., 3850., 2518., 175.]), array([0, 1, 2, 3, 4, 5, 6, 7], dtype=uint64)] >>> hist = gt.distance_histogram(g, samples=10) >>> print(hist) [array([ 0., 30., 86., 213., 378., 262., 21.]), array([0, 1, 2, 3, 4, 5, 6, 7], dtype=uint64)]