vertex_hist | Return the vertex histogram of the given degree type or property. |
edge_hist | Return the edge histogram of the given property. |
vertex_average | Return the average of the given degree or vertex property. |
edge_average | Return the average of the given degree or vertex property. |
label_parallel_edges | Label edges which are parallel, i.e, have the same source and target vertices. |
remove_parallel_edges | Remove all parallel edges from the graph. |
label_self_loops | Label edges which are self-loops, i.e, the source and target vertices are the same. |
remove_self_loops | Remove all self-loops edges from the graph. |
remove_labeled_edges | Remove every edge e such that label[e] != 0. |
distance_histogram | Return the shortest-distance histogram for each vertex pair in the graph. |
Return the vertex histogram of the given degree type or property.
Parameters : | g : Graph
deg : string or PropertyMap
bins : list of bins (optional, default: [0, 1])
float_count : bool (optional, default: True)
|
---|---|
Returns : | counts : ndarray
bins : ndarray
|
See also
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([ 10., 36., 90., 147., 164., 165., 142., 109., 70.,
31., 28., 7., 1.]), array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], dtype=uint64)]
Return the edge histogram of the given property.
Parameters : | g : Graph
eprop : PropertyMap
bins : list of bins (optional, default: [0, 1])
float_count : bool (optional, default: True)
|
---|---|
Returns : | counts : ndarray
bins : ndarray
|
See also
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([ 483., 462., 467., 493., 498., 486., 515., 552., 496., 548.]), array([ 0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])]
Return the average of the given degree or vertex property.
Parameters : | g : Graph
deg : string or PropertyMap
|
---|---|
Returns : | average : float
std : float
|
See also
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"))
(4.982, 0.06855418295042251)
Return the average of the given degree or vertex property.
Parameters : | g : Graph
eprop : PropertyMap
|
---|---|
Returns : | average : float
std : float
|
See also
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))
(0.49849732125677476, 0.004086182531863621)
Remove every edge e such that label[e] != 0.
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 \(|PE|-1\). (If count_all==True, the range is 0 to \(|PE|\) instead). If mark_only==True, all parallel edges are simply marked with the value 1. If the eprop parameter is given (a PropertyMap), the labelling is stored there.
Remove all parallel edges from the graph. Only one edge from each parallel edge set is left.
Label edges which are self-loops, i.e, the source and target vertices are the same. For each self-loop edge set \(SL\), the labelling starts from 0 to \(|SL|-1\). If mark_only == True, self-loops are labeled with 1 and others with 0. If the eprop parameter is given (a PropertyMap), the labelling is stored there.
Remove all self-loops edges from the graph.
Return the shortest-distance histogram for each vertex pair in the graph.
Parameters : | g : Graph
weight : PropertyMap (optional, default: None)
bins : list of bins (optional, default: [0, 1])
samples : int (optional, default: None)
float_count : bool (optional, default: True)
|
---|---|
Returns : | counts : ndarray
bins : ndarray
|
See also
Notes
The algorithm runs in \(O(V^2)\) time, or \(O(V^2\log V)\) if weight != 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., 866., 2206., 3893., 2476., 159.]), array([0, 1, 2, 3, 4, 5, 6, 7], dtype=uint64)]
>>> hist = gt.distance_histogram(g, samples=10)
>>> print(hist)
[array([ 0., 30., 84., 217., 385., 249., 25.]), array([0, 1, 2, 3, 4, 5, 6, 7], dtype=uint64)]