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Tiago Peixoto
graph-tool
Commits
4708d11d
Commit
4708d11d
authored
Nov 04, 2009
by
Tiago Peixoto
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Plain Diff
Docstring examples fixes
Small fixes of some examples in the docstrings.
parent
f8d0e225
Changes
6
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6 changed files
with
120 additions
and
129 deletions
+120
-129
doc/quickstart.rst
doc/quickstart.rst
+33
-32
src/graph_tool/centrality/__init__.py
src/graph_tool/centrality/__init__.py
+77
-86
src/graph_tool/clustering/__init__.py
src/graph_tool/clustering/__init__.py
+2
-2
src/graph_tool/correlations/__init__.py
src/graph_tool/correlations/__init__.py
+3
-3
src/graph_tool/generation/__init__.py
src/graph_tool/generation/__init__.py
+1
-1
src/graph_tool/stats/__init__.py
src/graph_tool/stats/__init__.py
+4
-5
No files found.
doc/quickstart.rst
View file @
4708d11d
...
...
@@ -425,21 +425,22 @@ Which outputs the following.
.. testoutput::
[ 1.21896533 0.2 0.28 0.36 0.28 0.2 0.2
0.2 0.2 0.44 0.2 0.53813333 0.344 0.2
0.52 0.4752 0.53066667 0.2 0.85066667 0.2
0.37813333 0.28 2.65013333 0.78133333 0.2 0.2 0.28
0.2 0.2 0.2 0.2 0.2 0.2 0.2
0.2 0.41333333 0.2 0.2 0.25333333 0.2
0.41333333 0.2 0.76 0.2 0.2 1.27370667
0.36 0.2 0.2 0.28 0.36 0.6 0.2
1.17517227 0.40266667 0.2 0.44533333 1.016 0.424 0.28
0.2 0.2 0.44 0.6 0.2 0.2 0.44
0.2 0.2 0.28 0.25333333 0.25333333 0.28 0.2
1.24213333 0.2 0.40266667 0.28 0.36 0.2 0.2
0.2 0.2 0.54133333 0.2 0.5552 0.2 0.44
0.44 0.2 0.2 0.28 0.2 0.6 0.2
0.44 0.2 0.28 0.344 0.472 ]
[ 0.25333333 0.2 0.2 0.2 0.2 0.79733333
0.49482667 0.52853333 0.44 0.36 0.2 0.42455467
0.2 0.552 0.28 0.36 0.36 0.2 0.2
0.25333333 1.89578667 1.06077099 0.2 0.2 1.26709333
0.36853333 0.2 0.2 0.2 0.488 0.49333333
0.28 0.2 0.2 0.2 0.648 0.2
0.29173333 0.28 0.56138667 0.42455467 0.2 0.36 0.504
0.2 1.17173333 0.2 0.28 0.36 0.488 0.52
0.2 0.2 0.44 0.648 0.2 0.6704 0.2
0.36 0.2 0.2 0.2 1.15701333 0.2 0.344
0.2 0.36 0.55733333 0.2 0.344 0.28 0.2
0.2 0.424 0.36 0.73333333 0.36853333 0.29173333
1.07596373 0.36 0.408 1.33386667 0.25333333 0.2 0.2
0.2 0.2 0.2 1.24533333 0.45173333 0.28 0.2
0.344 0.2 0.2 1.19626667 0.2 0.632 0.2
0.2 ]
The original graph can be recovered by setting the edge filter to ``None``.
...
...
@@ -453,23 +454,23 @@ Which outputs the following.
.. testoutput::
[ 0.
39689941 0.2 1.38764556 0.31699172 0.66893137 0.80523725
0.6
7802789 0.40784401 0.29361908 1.22559931 0.750107 1.02862225
0.
6472381
0.
78447305 0.54791497 0.73210888
1.00
329801 0.4320978
6
1.20758525 0.95797897 0.97106576 0.38080744 2.35690886 1.35609636
1.04694293 0.47676748 0.870367 0.9034519 0.60360189 0.2
4
0.35010316 0.81055356 0.59406634 0.68903488 0.3701726 0.50917786
0.
819167 0.31490118 0.67404843 0.74766878 1.25501188 0.2
1
.9
7251855 0.77583825 0.62509331 0.55088128 0.41242224 0.70903083
0.5918624 0.92565929 2.1083913 0.99864279 0.47676748 0.30583984
0.45919
65
8
0.
3551633 2.58583616 1.07020892 0.44358295 0.75860401
0.
51277849 0.54073371 0.79816833 1.52121868 0.93996758 0.25077
43
2
0.
48392002 0.7181324 0.4789649 1.88431987 0.42763893 0.82713964
1.0133979
0.
8693855 1.94140631 0.39321364 1.27413606 3.16059924
1.32889816 0.89569002 0.27990067 0.64233
36
7
0.89888256 1.24365097
0.56000105 1.1805301 0.76991724 1.3304796 1.04003837 1.50556425
0.
39110528 0.2762828 0.2 1.57628517 1.38463963 1.0378780
5
0.81171454 0.95811153 0.5728202 1.26472067
]
[ 0.
62729462 0.76981859 2.49409878 0.6482403 0.74615387 1.05405443
0.6
1325462 0.89427918 0.71954456 1.30133216 0.2826627 0.77604271
0.
25073123
0.
86915196 1.14884858 0.2826627
1.
1
00
94496 0.5702672
6
0.6198043 0.76768522 1.52240328 0.41022172 1.17159772 0.95765161
0.83490887 1.2136575 1.41449882 1.5489521 0.66412068 0.735221
4
1.21037608 0.64396361 0.87802656 0.31938462 0.78743109 1.67050184
0.
41200881 0.73928389 0.36523029 0.87377465 2.47043781 0.30561659
0
.9
3662203 0.86383309 1.21911903 1.80271636 0.2 1.03872561
1.4359001 1.81688914 1.68310565 0.25073123 0.52549083 1.188486
1.315943
65 0.
2 1.52498274 0.66120137 0.66025516 0.63644263
0.
26686166 0.88481433 1.34522024 0.31707021 1.06448852 0.51983
43
1
0.
96831557 1.29751162 0.60525803 1.44864461 0.86032791 0.8863202
0.44530184
0.
97948075 1.5064464 1.34553188 1.23884369 0.91887273
0.89110859 1.08966816 1.116852
36
1.4889228 1.29937733 0.2
1.37848879 0.50230514 0.60896565 0.65921635 0.98165444 0.71947832
0.
56083022 0.604076 0.48384859 0.34872367 0.5166419 1.5294048
5
1.40411236 0.99922722 0.98348377 1.04335144
]
Everything works in analogous fashion with vertex filtering.
...
...
src/graph_tool/centrality/__init__.py
View file @
4708d11d
...
...
@@ -105,23 +105,23 @@ def pagerank(g, damping=0.8, prop=None, epslon=1e-6, max_iter=None,
>>> g = gt.random_graph(100, lambda: (poisson(3), poisson(3)))
>>> pr = gt.pagerank(g)
>>> print pr.get_array()
[ 0.
99988081
0
.3
9997616 0.80428057 0.43237369
0.2 0.7
583032
9
0.4
1447482 1.56621542 0.30841665
0.86
432715 0.79374139 0.54573086
0.89372179 0.93590145 0.25159724 1.12033843 0.2 0.98486039
0.
2819140
4
0
.8
8133806 0.31166878 1.73878838 0.6903469 0.94100349
0.
25159724 0.32248278 1.03788472 0.58022932
0.
3
50
09064 0.94542317
0.
85751934 0.69608227 1.11373543 1.13477707 0.2 0.71559888
0.30461189 0.2 1.02871995 1.14657561
0.2 0.2
5031945
0.
51841423 0.44709022 0.75239816 0.76551737 0.25638281 1.51657252
0.
30841665 0.59707408 0.34179258 1.0590272 2.16427996 0.51196274
1.
2264604
1
.7
1578696 0.85838961 0.41931136 0.96797602 0.618823
67
1.
07826603 0.2984934 1.1305187 0.75006564 0.48066231 1.61759314
0.
73870051 1.08374044 0.38258693 0.98112013 0.2
0.2
5590818
1.
17500568 1.2288973 0.29613246 1.45937444 0.39997616 1.18311783
0.
67063807 0.39229458 0.72314004 0.88473325 0.32859279 0.40656244
0.
5
17
54349 0.5315028 0.5519627
4
0
.2
335463 1.56357203 0.91464458
0.4
6999727 1.06779933 0.4852867 0.48933035
0.5
8
99
7931 0.52883683
0.
79385874 0.59244805 0.99896399 1.0470592
]
[ 0.
89482844
1
.3
7847566 0.24 1.30716676
0.2 0.7
039700
9
0.4
0205781 0.74783725 1.37167015 0.66836587
0.
5
86
8133 0.47968714
1.52225854 1.07388611 0.76316432 0.39214247 0.9302883 0.86455762
0.
7754626
4
1
.8
7740317 0.25482139 0.29902553 0.2 0.24756383
0.
97205301 0.29727392 1.34742309 0.30905457
0.
5
50
32542 0.56654712
0.
40895463 0.77928729 0.73227413 0.59911926 1.39946277 0.72793699
2.27008393 0.88929335 0.48636962 0.73070609
0.2 0.2
32
0.
96857512 2.97683022 0.58581032 0.80217847 0.37896569 0.93866821
0.
27337672 0.98201842 0.48551839 1.22651796 0.73263045 0.43013228
1.
00971133
0
.7
2075953 0.66715456 0.58705749 0.74286661 0.377858
67
1.
8475279 0.26432925 0.33994628 0.97319326 0.78104447 0.2
0.
33333761 0.51756267 0.47811583 0.85905246 1.46428623
0.2
1.
70687671 1.0107342 0.94504737 1.29858046 2.19707395 0.55931282
0.
85129509 1.09493368 1.22168331 0.64108136 0.70690188 0.2
0.
3
17
36266 0.42372513 0.79429328 1.4474966
4
1
.2
0741669 0.65763236
0.4
0895463 0.62628812 0.32671006 0.85626447
0.599
25496 0.3399879
0.
81215046 0.71506902 2.25678844 1.04882679
]
References
----------
...
...
@@ -202,23 +202,23 @@ def betweenness(g, vprop=None, eprop=None, weight=None, norm=True):
>>> g = gt.random_graph(100, lambda: (poisson(3), poisson(3)))
>>> vb, eb = gt.betweenness(g)
>>> print vb.get_array()
[ 0.0
6129648 0.02004734 0.04305659 0.01071136
0. 0.025
202
8
0.0
0679622 0.06981881 0.00541371 0.02462107 0.05328111 0.0107051
0.05
981227 0. 0.01315561 0.00131498
0. 0.0
1883264
0.0
1663386 0.03195175 0.01942617 0.13693745 0.01378875 0.00962001
0.0
1325009 0.04685362 0.03839758 0.03395201 0.02160984 0.0172759
3
0.0
478231
0. 0.0
3826993 0.05124999 0. 0.
0.0
0705917 0.
0.0
2190356 0.04505211
0. 0.00
676
41
9
0.0
0110802 0.00169839 0.08733666
0.
1
05
46473
0. 0.
12058932
0. 0.0
0907921 0.02182859 0.08865455 0. 0.041801
7
0.0
3500162 0.07492683 0.03856307
0.0
4
30
0598 0.02173347 0.00488363
0.0
3739852 0.01113193 0.04386369 0.02994719 0.03383728
0.
0.0
9230395 0.05449223 0.02507715 0.04944675 0. 0.00215935
0.0
4371057 0.01749238 0.00104315 0.04688928 0.00444627 0.0178016
0.0
1358585 0.02193068 0.03184527 0.05640358 0.00214389 0.03922583
0.0
2195544 0.02613584 0.02246488 0.00066481 0.0755375 0.03142692
0.0
4533332 0.03188087 0.04227853
0.0
39
263
2
8 0.00
810412 0.02888085
0.
0455241 0.01373183 0.07029039
0.04
38289
2]
[ 0.0
3047981 0.07396685 0.00270882 0.044637
0. 0.0
3
25
904
8
0.0
243547 0.04265909 0.06274696 0.01778475 0.03502657 0.02692273
0.05
170277 0.05522454 0.02303023 0.0038858
0. 0.0
4852871
0.0
2398655 0.00232365 0. 0.01064643 0. 0.01105872
0.0
3564021 0.0222059 0.05170383 0.00140447 0.03935299 0.0264481
3
0.0
1831885
0. 0.0
453981 0.04552396 0.1242787 0.04983878
0.0
7248363 0.04676976
0.0
3481327 0.04473583
0. 0.00
27
41
7
0.0
1061048 0.0470108 0.01059109
0.05
290495
0. 0.
02541583
0. 0.0
4012033 0.02616307 0.09056515 0.01640322 0.0159900
7
0.0
2784563 0.05008998 0.03788222
0.030
28745 0.01097982 0.00178571
0.0
5804645 0.01015181 0.0061582 0.0255485 0.05504439
0.
0.0
0179516 0.03367643 0.00304982 0.02333254 0.00843039 0.
0.0
5947385 0.01936996 0.0521946 0.04928937 0.03955121 0.01360865
0.0
2942447 0. 0.05149102 0.01054765 0. 0.
0.0
0537915 0.01251828 0.01097982 0.06667564 0.04090169 0.02161779
0.0
2941671 0.01793679 0.02360528
0.02638
257
0.00
62989 0.00946123
0.
0.02255701 0.05081734
0.04
84665
2]
References
----------
...
...
@@ -283,7 +283,7 @@ def central_point_dominance(g, betweenness):
>>> g = gt.random_graph(100, lambda: (poisson(3), poisson(3)))
>>> vb, eb = gt.betweenness(g)
>>> print gt.central_point_dominance(g, vb)
0.
108411171667
0.
0980212339559
References
----------
...
...
@@ -359,31 +359,31 @@ def eigentrust(g, trust_map, vprop=None, norm=False, epslon=1e-6, max_iter=0,
>>> trust.get_array()[:] = random(g.num_edges())*42
>>> t = gt.eigentrust(g, trust, norm=True)
>>> print t.get_array()
[ 1.
78295032e-02 1.10159977
e-0
3
8.27504534e-03 3.34579667
e-0
3
0.00000000e+00
9.28795883e-03 7.56225537e-03 2.0377228
8e-02
6.
87447
577
e-0
4
8.87085111e-03 2.84707349e-03 2.55095571
e-03
7.65302351
e-0
3
5.06044724
e-02
3
.9
8617107
e-0
4
1.
0289
782
2
e-0
2
0
.0
0000000e+00 6.76980749
e-0
3
6
.9
1342330
e-0
4
1.13998018
e-02
1.
91846222
e-0
3
3.74940757
e-0
2
8.65907932e-03 5.76596060
e-0
3
1
.1
1786939
e-0
5
8.20855949
e-0
4
9
.4
505608
5e-0
3
1.76099276
e-0
2
2.67746802e-03 1.0318216
4e-0
2
1.80748361
e-0
2
8.49781556
e-0
3
7.
89442825e-03 1.11838761
e-0
2
0
.0
0000000e+00 4.37095317
e-03
2.5
0451228
e-0
5
0.00000000e+00 6.04054677e-03 1.51361293
e-0
2
0.00000000e+00
1.62557422e-04 1.02859153
e-0
3
3.38079641
e-0
3
3.06115271
e-03 2.
96226918
e-0
3
7.40021010
e-0
5
1.6409693
2e-0
2
1.12026631e-03 3.33521569e-03 1.77214999e-03 6.62472745
e-0
3
3.17014482e-02 1.93793538e-03 5.24056364
e-0
2
4.04200200
e-02
2.96053927
e-0
2
2.06294202e-03 2.93045979e-02 1.87688605
e-03
1.13962350
e-02
6
.9
4033709
e-0
3
1.
57347756
e-0
2
3.97987237
e-03
1.
15994824
e-0
3
1.81252731e-02 2.06848985
e-0
2
3.73314296
e-0
3
1.27163202e-03 1.08081901
e-02 0.00000000e+00
2.25590063e-04
8.55970439
e-0
3
4.
15387826
e-0
2
8.6179207
6e-0
5
6
.4
8435253
e-02
5.61799591
e-0
3
4.
6909668
6e-0
2
4.24627753
e-0
3
9.16721227
e-0
4
4.865223
62e-0
3
4.42735866e-03 5.50595265
e-0
4
3.12087221e-03
8.75442087
e-03
4
.25
588041
e-03
2.91851609
e-0
3
1.80331544
e-0
6
2.89281502
e-0
2
1.75099401
e-0
2
1.14704807
e-0
2
3.30940821
e-0
2
2.84005465
e-0
3
4.92435108e-03 4.34713976e-03 2.72336599
e-03
9.37679329
e-0
3
8.64912360
e-0
3
3.96113432
e-0
3
1.07637051
e-02]
[ 1.
04935746e-02 2.82745068
e-0
2
0.00000000e+00 1.81121002
e-0
2
0.00000000e+00
3.70898521e-03 1.00108703e-03 1.2962063
8e-02
1.71
874
0
47e-0
2
7.07523828e-03 8.29873222e-03 1.79259666
e-03
4.08925756
e-0
2
1.55855653
e-02
2
.9
2256968
e-0
3
1.
71520
782e-0
3
5
.0
4335865e-03 1.25678184
e-0
2
1
.9
2903241
e-0
2
2.46642649
e-02
1.
76431290
e-0
4
1.85066489
e-0
4
0.00000000e+00 4.52686439
e-0
4
7
.1
3943855
e-0
3
2.36002975
e-0
3
1
.4
436616
5e-0
2
4.39632543
e-0
4
7.50316671e-03 8.1352188
4e-0
3
3.98083843
e-0
3
1.04883920
e-0
2
7.
42099689e-03 2.46651355
e-0
3
2
.0
8148781e-02 8.02104873
e-03
2.5
9366573
e-0
2
2.11125347e-02 7.45781416e-03 6.62338254
e-0
3
0.00000000e+00
0.00000000e+00 1.72521147
e-0
2
4.74346499
e-0
2
8.10593668
e-03 2.
27229702
e-0
2
2.21525586
e-0
3
6.2422305
2e-0
3
2.59753300e-03 9.15181124e-03 3.67310718e-03 1.18998211
e-0
2
1.66177496e-02 6.44748287e-03 8.01978992
e-0
3
1.48621102
e-02
6.65606246
e-0
3
3.39887550e-03 1.20188240e-02 3.51012614
e-03
2.79661104
e-02
7
.9
0103914
e-0
5
1.
18015521
e-0
3
8.17179744
e-03
1.
05694658
e-0
2
0.00000000e+00 4.49123443
e-0
4
9.80728243
e-0
4
2.70933271e-03 1.61865322e-02 2.13504124
e-02 0.00000000e+00
1.17773123
e-0
2
4.
63490203
e-0
3
1.7933196
6e-0
2
1
.4
6366115
e-02
3.26856602
e-0
2
4.
3112600
6e-0
3
1.68787878
e-0
2
2.02752156
e-0
2
1.482030
62e-0
2
1.17346898e-03 7.87933309
e-0
3
0.00000000e+00
1.13274458
e-03
2
.25
418313
e-03
1.27966643
e-0
2
2.46154526
e-0
2
7.15248968
e-0
3
8.35660945
e-0
3
3.88259360
e-0
3
5.95428313
e-0
3
1.16751480
e-0
4
5.78637193e-03 6.50575506e-03 1.47111816
e-03
1.22855215
e-0
2
1.34294277
e-0
2
4.03141738
e-0
2
2.77313687
e-02]
References
----------
...
...
@@ -488,32 +488,23 @@ def absolute_trust(g, trust_map, source = None, vprop=None, n_paths=10000,
>>> trust.get_array()[:] = random(g.num_edges())
>>> t = gt.absolute_trust(g, trust, source=g.vertex(0))
>>> print t.a
[ 0.00000000e+00 5.14258135e-02 2.42874582e-04 1.05347472e-06
0.00000000e+00 3.13429149e-04 1.53697222e-04 3.83063399e-05
2.65668937e-06 2.04029901e-05 1.19582153e-05 2.67743821e-06
1.50606560e-04 1.51595650e-05 5.72684475e-05 2.16466381e-06
0.00000000e+00 4.08340061e-05 3.26896572e-06 7.80860267e-05
7.31033290e-05 7.81690832e-05 2.93440658e-04 1.19013202e-05
1.60601849e-06 6.79167712e-05 9.35414301e-05 1.98991248e-05
2.08142130e-05 1.28565785e-04 2.83893891e-03 8.45362053e-05
1.15751883e-05 1.97248846e-05 0.00000000e+00 7.51004486e-06
5.49704676e-07 0.00000000e+00 1.06219388e-04 9.64852468e-04
0.00000000e+00 4.70496027e-05 5.49108602e-05 6.23617670e-06
1.32625806e-06 7.35202433e-05 2.09546902e-06 1.99138155e-03
4.32934771e-06 2.61887887e-05 2.55099939e-05 3.90874553e-06
9.07765143e-05 2.59243068e-06 7.50032403e-06 8.36211398e-05
7.80814352e-04 8.12133072e-06 6.24066931e-04 2.19465770e-06
4.15039190e-05 5.41464668e-05 1.84421073e-03 8.02449156e-06
4.01472852e-06 3.76746767e-01 7.02886863e-05 1.52365123e-04
4.58687938e-06 3.70470973e-02 0.00000000e+00 1.85922960e-06
2.05481272e-05 1.41021895e-04 1.45217040e-06 3.18562543e-06
2.62264044e-01 7.41140347e-06 1.39150089e-05 3.86583428e-06
2.85681164e-06 4.12923146e-06 7.05705402e-07 2.12584322e-05
1.65948868e-04 3.10144404e-05 5.08749580e-06 0.00000000e+00
1.45435603e-03 4.19224443e-03 4.88198531e-05 3.00152848e-04
5.61591759e-05 2.31951396e-04 1.19051653e-05 2.34710286e-05
6.27636571e-04 1.65759606e-02 1.30944429e-05 1.26282526e-05]
[ 0. 0.02313155 0. 0.0137347 0. 0.01102793
0.01128784 0.04625048 0.03853915 0.01845147 0.04615215 0.0029449
0.00694276 0.18236335 0.00217966 0.00339272 0.00850374 0.01893049
0.03348913 0.01321992 0.00080411 0.04414003 0. 0.13552437
0.00941916 0.19501805 0.02914234 0.01086888 0.03168659 0.00628033
0.05111872 0.06860108 0.01768409 0.01173521 0.0298894 0.02298583
0.03934682 0.06823432 0.19336846 0.02223112 0. 0. 0.0286787
0.01942249 0.03179068 0.00325739 0.34593499 0.00958355 0.02272858
0.01339147 0.02373504 0.0395046 0.02559305 0.06796198 0.0190701
0.59591656 0.01690353 0.03425196 0.04172327 0.04237095 0.03286538
0.00342536 0.01641873 0.02524374 0.02455111 0. 0.01524952
0.00146199 0.11204837 0.03967154 0.01779094 0. 0.0111206
0.00494046 0.03037591 0.0248954 0.02045298 0.02783211 0.04825638
0.01445001 0.01134507 0.00710798 0.02194733 0. 0.02747093
0.02335361 0.00816605 0.01416937 0.01496241 0.00783877 0.05209053
0.02135097 0.00102158 0.01213873 0.11390882 0.03516289 0.01956201
0.02973489 0.01396339 0.02814348]
"""
if
vprop
==
None
:
...
...
src/graph_tool/clustering/__init__.py
View file @
4708d11d
...
...
@@ -499,9 +499,9 @@ def motif_significance(g, k, n_shuffles=100, p=1.0, motif_list=None,
>>> g = gt.random_graph(100, lambda: (3,3))
>>> motifs, zscores = gt.motif_significance(g, 3)
>>> print len(motifs)
1
1
1
2
>>> print zscores
[-0.
44792287521793828, -0.44849469953471233
,
-
0.
12780221806490794, 0.11231267421795425, 0.55559062898180811, 0.55359702531968369, -0.56824732989514348
, -0.
07
000000000000000
7
, -0.
070000000000000007, -0.44
, -0.1
2
]
[-0.
16076033462706543, -0.16176522339544466
, 0.
010436730454036206, 0.0944284162896325, 0.20360864249886917, 0.20675511240829708, -0.42478047102781324
, -0.
1
000000000000000
1
, -0.
10000000000000001, -0.28000000000000003, -0.14000000000000001
, -0.
0
1]
"""
s_ms
,
counts
=
motifs
(
g
,
k
,
p
,
motif_list
,
undirected
)
...
...
src/graph_tool/correlations/__init__.py
View file @
4708d11d
...
...
@@ -105,7 +105,7 @@ def assortativity(g, deg):
>>> g = gt.random_graph(1000, lambda: sample_k(40),
... lambda i,k: 1.0/(1+abs(i-k)), directed=False)
>>> gt.assortativity(g, "out")
(0.14
754098360655737, 0.005171061784557
046
3
)
(0.14
264767490573943, 0.00509391318271
046
51
)
References
----------
...
...
@@ -174,12 +174,12 @@ def scalar_assortativity(g, deg):
>>> g = gt.random_graph(1000, lambda: sample_k(40), lambda i,k: abs(i-k),
... directed=False)
>>> gt.scalar_assortativity(g, "out")
(-0.4
5583464361842779, 0.010751070629208364
)
(-0.4
7490876200787641, 0.010404791498106225
)
>>> g = gt.random_graph(1000, lambda: sample_k(40),
... lambda i,k: 1.0/(1+abs(i-k)),
... directed=False)
>>> gt.scalar_assortativity(g, "out")
(0.
59350810559389722, 0.011785797817251774
)
(0.
62401111084836425, 0.011049051007488318
)
References
----------
...
...
src/graph_tool/generation/__init__.py
View file @
4708d11d
...
...
@@ -126,7 +126,7 @@ def random_graph(N, deg_sampler, deg_corr=None, directed=True,
>>> g = gt.random_graph(1000, lambda: sample_k(40),
... lambda i,k: 1.0/(1+abs(i-k)), directed=False)
>>> gt.scalar_assortativity(g, "out")
(0.
59472179721535989, 0.011919463022240388
)
(0.
60296352140954257, 0.011780362691333932
)
The following samples an in,out-degree pair from the joint distribution:
...
...
src/graph_tool/stats/__init__.py
View file @
4708d11d
...
...
@@ -102,9 +102,8 @@ def vertex_hist(g, deg, bins=[1], float_count=True):
>>> seed(42)
>>> g = gt.random_graph(1000, lambda: (poisson(5), poisson(5)))
>>> print gt.vertex_hist(g, "out")
[array([ 8., 33., 100., 141., 167., 165., 142., 114., 76.,
25., 21., 7., 0., 1.]), array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], dtype=uint64)]
[array([ 10., 30., 86., 138., 166., 154., 146., 129., 68.,
36., 23., 8., 3., 2., 0., 1.]), array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], dtype=uint64)]
"""
ret
=
libgraph_tool_stats
.
\
...
...
@@ -206,7 +205,7 @@ def vertex_average(g, deg):
>>> seed(42)
>>> g = gt.random_graph(1000, lambda: (poisson(5), poisson(5)))
>>> print gt.vertex_average(g, "in")
(
4.9320000000000004, 0.0678334430793
543
08
)
(
5.0919999999999996, 0.071885575743677
543)
"""
ret
=
libgraph_tool_stats
.
\
...
...
@@ -358,7 +357,7 @@ def distance_histogram(g, weight=None, bins=[1], samples=None,
[array([ 0., 300., 862., 2147., 3766., 2588., 237.]), array([0, 1, 2, 3, 4, 5, 6], dtype=uint64)]
>>> hist = gt.distance_histogram(g, samples=10)
>>> print hist
[array([ 0., 30., 8
7
., 21
9
., 375., 2
55
., 2
4
.]), array([0, 1, 2, 3, 4, 5, 6], dtype=uint64)]
[array([ 0., 30., 8
4
., 21
0
., 375., 2
64
., 2
7
.]), array([0, 1, 2, 3, 4, 5, 6], dtype=uint64)]
"""
if
samples
!=
None
:
seed
=
numpy
.
random
.
randint
(
0
,
sys
.
maxint
)
...
...
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