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Tiago Peixoto
graph-tool
Commits
f74098a6
Commit
f74098a6
authored
Feb 11, 2013
by
Tiago Peixoto
Browse files
Fix output of docstring tests.
parent
ddd84961
Changes
7
Hide whitespace changes
Inline
Side-by-side
src/graph_tool/clustering/__init__.py
View file @
f74098a6
...
...
@@ -121,7 +121,7 @@ def local_clustering(g, prop=None, undirected=True):
>>> g = gt.random_graph(1000, lambda: (5,5))
>>> clust = gt.local_clustering(g)
>>> print(gt.vertex_average(g, clust))
(0.00
8622222222222222, 0.0004381250737482546
7)
(0.00
7988888888888889, 0.0004259396294091858
7)
References
----------
...
...
@@ -182,7 +182,7 @@ def global_clustering(g):
>>> g = gt.random_graph(1000, lambda: (5,5))
>>> print(gt.global_clustering(g))
(0.00
8641479099678457, 0.0004394563926611585
4)
(0.00
796839426811303, 0.0004262555515057513
4)
References
----------
...
...
@@ -261,11 +261,11 @@ def extended_clustering(g, props=None, max_depth=3, undirected=False):
>>> for i in range(0, 5):
... print(gt.vertex_average(g, clusts[i]))
...
(0.00
5646666666666667, 0.000485653786148116
)
(0.02
3786
66666666666
8
, 0.0009122
04314345811
)
(0.11
883, 0.0019560612322840113
)
(0.40
67333333333333, 0.0031162452478013408
)
(0.4
26033
3333333333, 0.003
097392092999815
)
(0.00
4208333333333333, 0.0004046856633377455
)
(0.02
0531
66666666666
7
, 0.0009122
518521037196
)
(0.11
366833333333333, 0.0019888303367389926
)
(0.40
287166666666663, 0.0032700366422513776
)
(0.4
39258
3333333333, 0.003
1463122041703218
)
References
----------
...
...
@@ -340,9 +340,9 @@ def motifs(g, k, p=1.0, motif_list=None):
>>> g = gt.random_graph(1000, lambda: (5,5))
>>> motifs, counts = gt.motifs(gt.GraphView(g, directed=False), 4)
>>> print(len(motifs))
10
55
>>> print(counts)
[
115392, 389974
, 66
8, 761, 3056, 1698, 770, 4, 10, 7
]
[
28946, 29051, 28786, 29009, 32700, 97746, 98018, 162496, 117, 86, 184, 96, 94, 17, 65, 267
,
2
66
, 85, 697, 665, 253, 261, 482, 435, 122, 228, 253, 124, 249, 346, 143, 44, 236, 268, 274, 1, 6, 1, 6, 8, 2, 2, 1, 2, 1, 2, 4, 2, 2, 1, 1, 3, 1, 2, 1
]
References
...
...
@@ -503,7 +503,7 @@ def motif_significance(g, k, n_shuffles=100, p=1.0, motif_list=None,
>>> print(len(motifs))
12
>>> print(zscores)
[
-0.26668839861225141, -0.37627454937420612, 0.63246648717833609, 1.9856284046854835
, -0.603
89512777130483, -0.35502673716019983
,
-
1.
223633276520342
8, 0.
87
, -0.
11
, -0.
47
, -0.
2
, -0.01]
[
1.1686248415493101, 1.1692144855989797, 0.28214101842848821
, -0.6
7
03
9795877350128, -0.8532661303912763, -0.85594303488376966
, 1.
980993037192153
8,
-
0.
09
, -0.
07
, -0.
2
, -0.
33
, -0.01]
"""
...
...
src/graph_tool/community/blockmodel.py
View file @
f74098a6
...
...
@@ -702,7 +702,7 @@ def min_dist(state, n=0):
>>> for i in range(1000):
... ds, nmoves = gt.mcmc_sweep(state)
>>> gt.min_dist(state)
(795.769450241863
5
, 2, 3)
(795.769450241863
3
, 2, 3)
"""
min_d
,
r
,
s
=
libcommunity
.
min_dist
(
state
.
bg
.
_Graph__graph
,
int
(
n
),
...
...
src/graph_tool/correlations/__init__.py
View file @
f74098a6
...
...
@@ -115,7 +115,7 @@ def assortativity(g, deg):
... lambda i,k: 1.0 / (1 + abs(i - k)), directed=False,
... mix_time=100)
>>> gt.assortativity(g, "out")
(0.1
4282704866231305, 0.005109451062660124
)
(0.1
3903518011375607, 0.005051876804786422
)
References
----------
...
...
@@ -190,13 +190,13 @@ def scalar_assortativity(g, deg):
>>> g = gt.random_graph(1000, lambda: sample_k(40), lambda i,k: abs(i-k),
... directed=False, mix_time=100)
>>> gt.scalar_assortativity(g, "out")
(-0.43
719843848745943, 0.010593923895499584
)
(-0.43
34085753985522, 0.010571027872280038
)
>>> g = gt.random_graph(1000, lambda: sample_k(40),
... lambda i, k: 1.0 / (1 + abs(i - k)),
... directed=False, mix_time=100)
>>> gt.scalar_assortativity(g, "out")
(0.
6018887530895891, 0.011474042583027698
)
(0.
5994169713159511, 0.011668093738666827
)
References
----------
...
...
src/graph_tool/generation/__init__.py
View file @
f74098a6
...
...
@@ -204,7 +204,7 @@ def random_graph(N, deg_sampler, deg_corr=None, cache_probs=True, directed=True,
... lambda i, k: 1.0 / (1 + abs(i - k)), directed=False,
... mix_time=100)
>>> gt.scalar_assortativity(g, "out")
(0.6
06974415793802, 0.010937932958096811
)
(0.6
285094791115295, 0.010745128857935755
)
The following samples an in,out-degree pair from the joint distribution:
...
...
src/graph_tool/spectral/__init__.py
View file @
f74098a6
...
...
@@ -90,13 +90,13 @@ def adjacency(g, sparse=True, weight=None):
>>> g = gt.random_graph(100, lambda: (10, 10))
>>> m = gt.adjacency(g)
>>> print(m.todense())
[[ 0. 0. 0. ..., 0.
0
. 0.]
[[ 0. 0. 0. ..., 0.
1
. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ...,
1
. 0. 1.]
[ 0. 0. 0. ...,
0
. 0. 1.]
...,
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0.
1
. ..., 0. 0. 0.]
[ 0. 0.
0
. ...,
1
. 0. 0.]]
[ 0. 0.
0
. ..., 0. 0. 0.]
[ 0. 0.
1
. ...,
0
. 0. 0.]]
References
----------
...
...
@@ -165,31 +165,34 @@ def laplacian(g, deg="total", normalized=True, sparse=True, weight=None):
Notes
-----
The Laplacian matrix is defined as
The
weighted
Laplacian matrix is defined as
.. math::
\ell_{i
,
j} =
\ell_{ij} =
\begin{cases}
\Gamma(v_i) & \text{if } i = j \\
-
1
& \text{if } i \neq j \text{ and } v_i \text{ is adjacent to } v_j \\
-
w_{ij}
& \text{if } i \neq j \text{ and } v_i \text{ is adjacent to } v_j \\
0 & \text{otherwise}.
\end{cases}
Where :math:`\Gamma(v_i)
` is the degree of vertex :math:`v_i`. The
normalized version is
Where :math:`\Gamma(v_i)
=\sum_j A_{ij}w_{ij}` is sum of the weights of
vertex :math:`v_i`. The
normalized version is
.. math::
\ell_{i
,
j} =
\ell_{ij} =
\begin{cases}
1 & \text{ if } i = j \text{ and } \Gamma(v_i) \neq 0 \\
-\frac{
1
}{\sqrt{\Gamma(v_i)\Gamma(v_j)}} & \text{ if } i \neq j \text{ and } v_i \text{ is adjacent to } v_j \\
-\frac{
w_{ij}
}{\sqrt{\Gamma(v_i)\Gamma(v_j)}} & \text{ if } i \neq j \text{ and } v_i \text{ is adjacent to } v_j \\
0 & \text{otherwise}.
\end{cases}
In the case of weighted edges, the value 1 is replaced the weight of the
respective edge.
In the case of unweighted edges, it is assumed :math:`w_{ij} = 1`.
For directed graphs, it is assumed :math:`\Gamma(v_i)=\sum_j A_{ij}w_{ij} +
\sum_j A_{ji}w_{ji}` if ``deg=="total"``, :math:`\Gamma(v_i)=\sum_j A_{ij}w_{ij}`
if ``deg=="out"`` or :math:`\Gamma(v_i)=\sum_j A_{ji}w_{ji}` ``deg=="in"``.
Examples
--------
...
...
@@ -200,13 +203,13 @@ def laplacian(g, deg="total", normalized=True, sparse=True, weight=None):
>>> g = gt.random_graph(100, lambda: (10,10))
>>> m = gt.laplacian(g)
>>> print(m.todense())
[[ 1.
0.
0. ..., 0. 0. 0. ]
[ 0. 1. 0. ..., 0. 0.
0.
]
[ 0. 0. 1. ..., 0.
0.
0.
05
]
[[ 1.
-
0.
05
0. ..., 0. 0. 0. ]
[ 0. 1. 0. ..., 0. 0.
-
0.
05
]
[ 0. 0. 1. ..., 0.
-
0.
05
0.
]
...,
[ 0. 0. 0.
05
..., 1. 0. 0.
05
]
[
0.05 0. 0. ..., 0. 1. 0. ]
[ 0. 0.
05
0. ...,
0.
0. 1. ]]
[ 0. 0. 0.
..., 1. 0. 0.
]
[
-
0.05 0. 0. ..., 0. 1. 0. ]
[ 0. 0.
0. ...,
-
0.
05
0. 1. ]]
References
----------
...
...
@@ -298,13 +301,13 @@ def incidence(g, sparse=True):
>>> g = gt.random_graph(100, lambda: (2,2))
>>> m = gt.incidence(g)
>>> print(m.todense())
[[
0. 0
. 0. ..., 0. 0. 0.]
[ 0. 0.
0
. ..., 0. 0. 0.]
[[
-1. -1
. 0. ..., 0. 0. 0.]
[ 0. 0.
-1
. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ...,
0
. 0. 0.]
[ 0. 0. 0. ..., 0.
0. 0
.]]
[ 0. 0. 0. ...,
-1
. 0. 0.]
[ 0. 0. 0. ..., 0.
-1. -1
.]]
References
----------
...
...
src/graph_tool/stats/__init__.py
View file @
f74098a6
...
...
@@ -408,10 +408,10 @@ def distance_histogram(g, weight=None, bins=[0, 1], samples=None,
>>> g = gt.random_graph(100, lambda: (3, 3))
>>> hist = gt.distance_histogram(g)
>>> print(hist)
[array([ 0., 300., 86
2
., 215
3
., 38
13
., 25
47
., 2
25
.]), array([0, 1, 2, 3, 4, 5, 6, 7], dtype=uint64)]
[array([ 0., 300., 8
5
6., 21
8
5., 38
40
., 25
16
., 2
03
.]), array([0, 1, 2, 3, 4, 5, 6, 7], dtype=uint64)]
>>> hist = gt.distance_histogram(g, samples=10)
>>> print(hist)
[array([ 0., 30., 8
8
., 2
16
., 3
84
., 2
5
2.,
20
.]), array([0, 1, 2, 3, 4, 5, 6, 7], dtype=uint64)]
[array([ 0., 30., 8
7
., 2
27
., 3
90
., 2
4
2.,
14
.]), array([0, 1, 2, 3, 4, 5, 6, 7], dtype=uint64)]
"""
if
samples
!=
None
:
...
...
src/graph_tool/topology/__init__.py
View file @
f74098a6
...
...
@@ -127,7 +127,7 @@ def similarity(g1, g2, label1=None, label2=None, norm=True):
1.0
>>> gt.random_rewire(u);
>>> gt.similarity(u, g)
0.0
4333333333333333
5
0.05
"""
if
label1
is
None
:
...
...
@@ -232,7 +232,7 @@ def subgraph_isomorphism(sub, g, max_n=0, random=False):
>>> sub = gt.random_graph(10, lambda: (poisson(1.9), poisson(1.9)))
>>> vm, em = gt.subgraph_isomorphism(sub, g)
>>> print(len(vm))
21
56
56
32
>>> for i in range(len(vm)):
... g.set_vertex_filter(None)
... g.set_edge_filter(None)
...
...
@@ -537,10 +537,10 @@ def dominator_tree(g, root, dom_map=None):
>>> root = [v for v in g.vertices() if v.in_degree() == 0]
>>> dom = gt.dominator_tree(g, root[0])
>>> print(dom.a)
[ 0 0 72 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 78 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0]
0 0 0 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
References
----------
...
...
@@ -587,8 +587,8 @@ def topological_sort(g):
>>> g.set_edge_filter(tree)
>>> sort = gt.topological_sort(g)
>>> print(sort)
[
0 10 1 18 2 3 16 27 24 20 14 4 15 23 9 21 13 22 12 5 6 7 29 28 8
1
9 25 26 11 1
7]
[
17 1 20 5 6 8 28 0 3 9 11 24 29 2 22 4 7 14 19 26 23 10 12 13 15
1
6 18 21 25 2
7]
References
----------
...
...
@@ -688,11 +688,11 @@ def label_components(g, vprop=None, directed=None):
>>> g = gt.random_graph(100, lambda: (1, 1))
>>> comp, hist = gt.label_components(g)
>>> print(comp.a)
[0 1 1 1
1
1
1
1
1 1 1 1 1 1 1 1
1 1
1
1
1 2 1 1 1 1 3
1 1 1 1
1 1 1 2
1
0
0
2
1
1 1 1 1
1
1
3
1 1 1 1 1 1 1 1 1 1
1 2
1
1 1
1 1 1 1 1 1 1 1 1 3 2 1
1 1
1
1 1 2 1
1 1 1 0 1 1 1 1 1 1 1
1 1 1
1 1 1
1 1]
[0 1 1 1
2
1
3
1
4 0 0 4 2 0 0 2
1 1
2
1
0 4 1 5 2 4 0
1 1 1 1
0 4 5 1
1
4
0
4
1
4 4 2 1 4 4
1
2
3
0 0 4 2 4 2 4 4 4 4
1
4
2
0
1 1
2 4 2 2 4 5 4 0 2
1 1
4
1
0
1 2 1
0 0 4 0 1 4 2 4 0 4 4
1 1 1
0 2
1 1]
>>> print(hist)
[
4 87 6
3]
[
19 32 17 2 27
3]
"""
if
vprop
is
None
:
...
...
@@ -745,12 +745,12 @@ def label_largest_component(g, directed=None):
>>> g = gt.random_graph(100, lambda: poisson(1), directed=False)
>>> l = gt.label_largest_component(g)
>>> print(l.a)
[
1 1
0 0 1
1
0 0 0
1
0 1 0 0 0 0
0
0 0 0 1 0 0 0
1 1 0
0 0 1 0 0 0 0 0 1 0
0 0
1
0
1
0 0 0
1
0 0 0
1
0 0 0 0 0 0 0
1
0 0 0 0 0
1
0
1
0 0 0 0
1
0 0 0
0 0 0 0
1
0 0 0
1
0 0 0 0 0
1 1 1 0 1
0 0 1 0 0 0 0]
[
0 0
0 0 1
0
0 0 0
0
0 1 0 0 0 0
1
0 0 0 1 0 0 0
0 0 1
0 0 1 0 0 0 0 0 1 0
0 0
0
0
0
0 0 0
0
0 0 0
0
0 0 0 0 0 0 0
0
0 0 0 0 0
0
0
0
0 0 0 0
0
0 0 0
0
1
0 0 0 0 0 0
0
0 0 0 0 0
0 0
0 0 1 0 0 0
0 0 1
0]
>>> u = gt.GraphView(g, vfilt=l) # extract the largest component as a graph
>>> print(u.num_vertices())
26
10
"""
label
=
g
.
new_vertex_property
(
"bool"
)
...
...
@@ -796,9 +796,9 @@ def label_out_component(g, root):
>>> g = gt.random_graph(100, lambda: poisson(2.2), directed=False)
>>> l = gt.label_out_component(g, g.vertex(2))
>>> print(l.a)
[1
0
1 1 0 1 1
0
1 1 1 0 1 1
0
1 0 1 1 1 1 1 0 1 1
0
1 0 1 1 1 0 0 1 1
1
0
1 1 0 1 1 1 1 1 1 1 1
0
1 1 1
1
1 0 1 1 1 1
0
1 1 1 1 1 0 0
0 1
1 1 1 1 1
1 1 0 1 1 1 1
0
1 1 1 1 1 1
0
1 1 0 1 1 1 0 1 1 1 0]
[1
1
1 1 0 1
0
1 1 1 1 0 1 1 1 0 1 1 1 1 1 0 1 1
1 1
1 0 1 1 1 0 0 1 1
0
0
1 1 0 1 1 1 1 1 1 1 1
1
1 1 1
0
1 0 1 1 1 1
1
1 1 1 1 1 0 0
1 0
1 1 1 1 1
1 1 0 1 1 1 1
1
1 1 1 1 1 1
1
1 1 0 1 1 1 0 1 1 1 0]
The in-component can be obtained by reversing the graph.
...
...
@@ -806,7 +806,7 @@ def label_out_component(g, root):
... g.vertex(1))
>>> print(l.a)
[0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0
1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0
0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
"""
...
...
@@ -879,15 +879,15 @@ def label_biconnected_components(g, eprop=None, vprop=None):
>>> g = gt.random_graph(100, lambda: 2, directed=False)
>>> comp, art, hist = gt.label_biconnected_components(g)
>>> print(comp.a)
[
2 3 0 0 2 0 0 0 1 0 0 1 0 0
1 3
0
2
0
0
0 0 0 1 0 0 2 0 0 0 2 3 0 0 2
0
3
0
0 3 0 0 0 0 0 0 2 1 2 0 0 0
0
0
0
0 0 1 3 0 2 1 1 0 0 0 0
0 0 1 0
0
1 2
0 0 2 0 2 0 0 0 2 0 0 0 0 3 1 0 0 1 0 0 0 0 2 0 0
3]
[
1 1 0 1 0 1 1 2 1 1 3 3 0 2 2 1
1 3 2
1
0
1 1 1 1 3 1 2 1 3 4 3 1 1 4
0
0
0
1 1 1 1 2 1 1 2 2 2 2
0
1
0
1 1 1 1 2 2 1 1 1 1 1 0 1 1
0 0 1 0
1 4
1 2
1 1 1 1 0 1 2 1 1 1 1 1 1 1 1 1 4 1 1 1 1 3 1 3 1
3]
>>> print(art.a)
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
>>> print(hist)
[
66 12
14
8
]
[
14 59
14
9 4
]
"""
if
vprop
is
None
:
...
...
@@ -973,43 +973,43 @@ def shortest_distance(g, source=None, weights=None, max_dist=None,
>>> g = gt.random_graph(100, lambda: (poisson(3), poisson(3)))
>>> dist = gt.shortest_distance(g, source=g.vertex(0))
>>> print(dist.a)
[ 0
5
9
6
2147483647 2147483647
3
5
7
4 5
7
5
3
5
6
4 6
5
3
7
4
5
1
8
7
6
2
8
6
7
6
3
6
3
3
4
6
4
3
2147483647
4
5
4
6
5
9
6
8
7
6
4
4
7
2
7
7
6
4
4
6
9
4
4
2
3
7
5
6
5
2147483647 2147483647
6
5
4
5
6
6
6 5 7 5 5
7
5
6
4
3
6
4
6
5 5
5 4
7
5
5
4
3
]
[ 0
4
4
4
2147483647 2147483647
6
7
6
4 5
5
6
4
6
7
4 6
6
1
7
5
6
3
5
7
5
6
7
7
8
6
5
4
5
7
6
7
6
6
2147483647
6
3
7
5
6
7
5
8
5
6
5
4
6
6
4
7
9
6
3
7
6
3
5
7
4
6
8
7
6
2147483647 2147483647
2
5
6
5
7
6
6 5 7 5 5
4
7
6
6
5
3
6
6
8
5 4
5 6
5
6
7
4
]
>>> dist = gt.shortest_distance(g)
>>> print(dist[g.vertex(0)].a)
[ 0
5
9
6
2147483647 2147483647
3
5
7
4 5
7
5
3
5
6
4 6
5
3
7
4
5
1
8
7
6
2
8
6
7
6
3
6
3
3
4
6
4
3
2147483647
4
5
4
6
5
9
6
8
7
6
4
4
7
2
7
7
6
4
4
6
9
4
4
2
3
7
5
6
5
2147483647 2147483647
6
5
4
5
6
6
6 5 7 5 5
7
5
6
4
3
6
4
6
5 5
5 4
7
5
5
4
3
]
[ 0
4
4
4
2147483647 2147483647
6
7
6
4 5
5
6
4
6
7
4 6
6
1
7
5
6
3
5
7
5
6
7
7
8
6
5
4
5
7
6
7
6
6
2147483647
6
3
7
5
6
7
5
8
5
6
5
4
6
6
4
7
9
6
3
7
6
3
5
7
4
6
8
7
6
2147483647 2147483647
2
5
6
5
7
6
6 5 7 5 5
4
7
6
6
5
3
6
6
8
5 4
5 6
5
6
7
4
]
References
----------
...
...
@@ -1118,9 +1118,9 @@ def shortest_path(g, source, target, weights=None, pred_map=None):
>>> g = gt.random_graph(300, lambda: (poisson(4), poisson(4)))
>>> vlist, elist = gt.shortest_path(g, g.vertex(10), g.vertex(11))
>>> print([str(v) for v in vlist])
['10', '
149
', '58', '
227
', '
267
', '11']
['10', '
267', '212
', '
1
58', '
112
', '
160
', '11']
>>> print([str(e) for e in elist])
['(10,
149
)', '(
149
, 58)', '(58,
227
)', '(
227, 267
)', '(
267
, 11)']
['(10,
267)', '(267, 212
)', '(
212
,
1
58)', '(
1
58,
112
)', '(
112, 160
)', '(
160
, 11)']
References
----------
...
...
@@ -1224,7 +1224,7 @@ def pseudo_diameter(g, source=None, weights=None):
>>> print(dist)
10.0
>>> print(int(ends[0]), int(ends[1]))
116 196
0 295
References
----------
...
...
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