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Commit 4708d11d authored by Tiago Peixoto's avatar Tiago Peixoto
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Docstring examples fixes 

Small fixes of some examples in the docstrings.
parent f8d0e225
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Showing with 120 additions and 129 deletions
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
0.67802789 0.40784401 0.29361908 1.22559931 0.750107 1.02862225
0.6472381 0.78447305 0.54791497 0.73210888 1.00329801 0.43209786
1.20758525 0.95797897 0.97106576 0.38080744 2.35690886 1.35609636
1.04694293 0.47676748 0.870367 0.9034519 0.60360189 0.24
0.35010316 0.81055356 0.59406634 0.68903488 0.3701726 0.50917786
0.819167 0.31490118 0.67404843 0.74766878 1.25501188 0.2
1.97251855 0.77583825 0.62509331 0.55088128 0.41242224 0.70903083
0.5918624 0.92565929 2.1083913 0.99864279 0.47676748 0.30583984
0.45919658 0.3551633 2.58583616 1.07020892 0.44358295 0.75860401
0.51277849 0.54073371 0.79816833 1.52121868 0.93996758 0.25077432
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 0.64233367 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 1.03787805
0.81171454 0.95811153 0.5728202 1.26472067]
[ 0.62729462 0.76981859 2.49409878 0.6482403 0.74615387 1.05405443
0.61325462 0.89427918 0.71954456 1.30133216 0.2826627 0.77604271
0.25073123 0.86915196 1.14884858 0.2826627 1.10094496 0.57026726
0.6198043 0.76768522 1.52240328 0.41022172 1.17159772 0.95765161
0.83490887 1.2136575 1.41449882 1.5489521 0.66412068 0.7352214
1.21037608 0.64396361 0.87802656 0.31938462 0.78743109 1.67050184
0.41200881 0.73928389 0.36523029 0.87377465 2.47043781 0.30561659
0.93662203 0.86383309 1.21911903 1.80271636 0.2 1.03872561
1.4359001 1.81688914 1.68310565 0.25073123 0.52549083 1.188486
1.31594365 0.2 1.52498274 0.66120137 0.66025516 0.63644263
0.26686166 0.88481433 1.34522024 0.31707021 1.06448852 0.51983431
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 1.11685236 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 1.52940485
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 0.39997616 0.80428057 0.43237369 0.2 0.75830329
0.41447482 1.56621542 0.30841665 0.86432715 0.79374139 0.54573086
0.89372179 0.93590145 0.25159724 1.12033843 0.2 0.98486039
0.28191404 0.88133806 0.31166878 1.73878838 0.6903469 0.94100349
0.25159724 0.32248278 1.03788472 0.58022932 0.35009064 0.94542317
0.85751934 0.69608227 1.11373543 1.13477707 0.2 0.71559888
0.30461189 0.2 1.02871995 1.14657561 0.2 0.25031945
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 1.71578696 0.85838961 0.41931136 0.96797602 0.61882367
1.07826603 0.2984934 1.1305187 0.75006564 0.48066231 1.61759314
0.73870051 1.08374044 0.38258693 0.98112013 0.2 0.25590818
1.17500568 1.2288973 0.29613246 1.45937444 0.39997616 1.18311783
0.67063807 0.39229458 0.72314004 0.88473325 0.32859279 0.40656244
0.51754349 0.5315028 0.55196274 0.2335463 1.56357203 0.91464458
0.46999727 1.06779933 0.4852867 0.48933035 0.58997931 0.52883683
0.79385874 0.59244805 0.99896399 1.0470592 ]
[ 0.89482844 1.37847566 0.24 1.30716676 0.2 0.70397009
0.40205781 0.74783725 1.37167015 0.66836587 0.5868133 0.47968714
1.52225854 1.07388611 0.76316432 0.39214247 0.9302883 0.86455762
0.77546264 1.87740317 0.25482139 0.29902553 0.2 0.24756383
0.97205301 0.29727392 1.34742309 0.30905457 0.55032542 0.56654712
0.40895463 0.77928729 0.73227413 0.59911926 1.39946277 0.72793699
2.27008393 0.88929335 0.48636962 0.73070609 0.2 0.232
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 0.72075953 0.66715456 0.58705749 0.74286661 0.37785867
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
0.31736266 0.42372513 0.79429328 1.44749664 1.20741669 0.65763236
0.40895463 0.62628812 0.32671006 0.85626447 0.59925496 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()
[ 0.06129648 0.02004734 0.04305659 0.01071136 0. 0.0252028
0.00679622 0.06981881 0.00541371 0.02462107 0.05328111 0.0107051
0.05981227 0. 0.01315561 0.00131498 0. 0.01883264
0.01663386 0.03195175 0.01942617 0.13693745 0.01378875 0.00962001
0.01325009 0.04685362 0.03839758 0.03395201 0.02160984 0.01727593
0.0478231 0. 0.03826993 0.05124999 0. 0.
0.00705917 0. 0.02190356 0.04505211 0. 0.00676419
0.00110802 0.00169839 0.08733666 0.10546473 0. 0.12058932
0. 0.00907921 0.02182859 0.08865455 0. 0.0418017
0.03500162 0.07492683 0.03856307 0.04300598 0.02173347 0.00488363
0.03739852 0.01113193 0.04386369 0.02994719 0.03383728 0.
0.09230395 0.05449223 0.02507715 0.04944675 0. 0.00215935
0.04371057 0.01749238 0.00104315 0.04688928 0.00444627 0.0178016
0.01358585 0.02193068 0.03184527 0.05640358 0.00214389 0.03922583
0.02195544 0.02613584 0.02246488 0.00066481 0.0755375 0.03142692
0.04533332 0.03188087 0.04227853 0.03926328 0.00810412 0.02888085
0.0455241 0.01373183 0.07029039 0.04382892]
[ 0.03047981 0.07396685 0.00270882 0.044637 0. 0.03259048
0.0243547 0.04265909 0.06274696 0.01778475 0.03502657 0.02692273
0.05170277 0.05522454 0.02303023 0.0038858 0. 0.04852871
0.02398655 0.00232365 0. 0.01064643 0. 0.01105872
0.03564021 0.0222059 0.05170383 0.00140447 0.03935299 0.02644813
0.01831885 0. 0.0453981 0.04552396 0.1242787 0.04983878
0.07248363 0.04676976 0.03481327 0.04473583 0. 0.0027417
0.01061048 0.0470108 0.01059109 0.05290495 0. 0.02541583
0. 0.04012033 0.02616307 0.09056515 0.01640322 0.01599007
0.02784563 0.05008998 0.03788222 0.03028745 0.01097982 0.00178571
0.05804645 0.01015181 0.0061582 0.0255485 0.05504439 0.
0.00179516 0.03367643 0.00304982 0.02333254 0.00843039 0.
0.05947385 0.01936996 0.0521946 0.04928937 0.03955121 0.01360865
0.02942447 0. 0.05149102 0.01054765 0. 0.
0.00537915 0.01251828 0.01097982 0.06667564 0.04090169 0.02161779
0.02941671 0.01793679 0.02360528 0.02638257 0.0062989 0.00946123
0. 0.02255701 0.05081734 0.04846652]
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 1.10159977e-03 8.27504534e-03 3.34579667e-03
0.00000000e+00 9.28795883e-03 7.56225537e-03 2.03772288e-02
6.87447577e-04 8.87085111e-03 2.84707349e-03 2.55095571e-03
7.65302351e-03 5.06044724e-02 3.98617107e-04 1.02897822e-02
0.00000000e+00 6.76980749e-03 6.91342330e-04 1.13998018e-02
1.91846222e-03 3.74940757e-02 8.65907932e-03 5.76596060e-03
1.11786939e-05 8.20855949e-04 9.45056085e-03 1.76099276e-02
2.67746802e-03 1.03182164e-02 1.80748361e-02 8.49781556e-03
7.89442825e-03 1.11838761e-02 0.00000000e+00 4.37095317e-03
2.50451228e-05 0.00000000e+00 6.04054677e-03 1.51361293e-02
0.00000000e+00 1.62557422e-04 1.02859153e-03 3.38079641e-03
3.06115271e-03 2.96226918e-03 7.40021010e-05 1.64096932e-02
1.12026631e-03 3.33521569e-03 1.77214999e-03 6.62472745e-03
3.17014482e-02 1.93793538e-03 5.24056364e-02 4.04200200e-02
2.96053927e-02 2.06294202e-03 2.93045979e-02 1.87688605e-03
1.13962350e-02 6.94033709e-03 1.57347756e-02 3.97987237e-03
1.15994824e-03 1.81252731e-02 2.06848985e-02 3.73314296e-03
1.27163202e-03 1.08081901e-02 0.00000000e+00 2.25590063e-04
8.55970439e-03 4.15387826e-02 8.61792076e-05 6.48435253e-02
5.61799591e-03 4.69096686e-02 4.24627753e-03 9.16721227e-04
4.86522362e-03 4.42735866e-03 5.50595265e-04 3.12087221e-03
8.75442087e-03 4.25588041e-03 2.91851609e-03 1.80331544e-06
2.89281502e-02 1.75099401e-02 1.14704807e-02 3.30940821e-02
2.84005465e-03 4.92435108e-03 4.34713976e-03 2.72336599e-03
9.37679329e-03 8.64912360e-03 3.96113432e-03 1.07637051e-02]
[ 1.04935746e-02 2.82745068e-02 0.00000000e+00 1.81121002e-02
0.00000000e+00 3.70898521e-03 1.00108703e-03 1.29620638e-02
1.71874047e-02 7.07523828e-03 8.29873222e-03 1.79259666e-03
4.08925756e-02 1.55855653e-02 2.92256968e-03 1.71520782e-03
5.04335865e-03 1.25678184e-02 1.92903241e-02 2.46642649e-02
1.76431290e-04 1.85066489e-04 0.00000000e+00 4.52686439e-04
7.13943855e-03 2.36002975e-03 1.44366165e-02 4.39632543e-04
7.50316671e-03 8.13521884e-03 3.98083843e-03 1.04883920e-02
7.42099689e-03 2.46651355e-03 2.08148781e-02 8.02104873e-03
2.59366573e-02 2.11125347e-02 7.45781416e-03 6.62338254e-03
0.00000000e+00 0.00000000e+00 1.72521147e-02 4.74346499e-02
8.10593668e-03 2.27229702e-02 2.21525586e-03 6.24223052e-03
2.59753300e-03 9.15181124e-03 3.67310718e-03 1.18998211e-02
1.66177496e-02 6.44748287e-03 8.01978992e-03 1.48621102e-02
6.65606246e-03 3.39887550e-03 1.20188240e-02 3.51012614e-03
2.79661104e-02 7.90103914e-05 1.18015521e-03 8.17179744e-03
1.05694658e-02 0.00000000e+00 4.49123443e-04 9.80728243e-04
2.70933271e-03 1.61865322e-02 2.13504124e-02 0.00000000e+00
1.17773123e-02 4.63490203e-03 1.79331966e-02 1.46366115e-02
3.26856602e-02 4.31126006e-03 1.68787878e-02 2.02752156e-02
1.48203062e-02 1.17346898e-03 7.87933309e-03 0.00000000e+00
1.13274458e-03 2.25418313e-03 1.27966643e-02 2.46154526e-02
7.15248968e-03 8.35660945e-03 3.88259360e-03 5.95428313e-03
1.16751480e-04 5.78637193e-03 6.50575506e-03 1.47111816e-03
1.22855215e-02 1.34294277e-02 4.03141738e-02 2.77313687e-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)
11
12
>>> print zscores
[-0.44792287521793828, -0.44849469953471233, -0.12780221806490794, 0.11231267421795425, 0.55559062898180811, 0.55359702531968369, -0.56824732989514348, -0.070000000000000007, -0.070000000000000007, -0.44, -0.12]
[-0.16076033462706543, -0.16176522339544466, 0.010436730454036206, 0.0944284162896325, 0.20360864249886917, 0.20675511240829708, -0.42478047102781324, -0.10000000000000001, -0.10000000000000001, -0.28000000000000003, -0.14000000000000001, -0.01]
"""
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")
(0.14754098360655737, 0.0051710617845570463)
(0.14264767490573943, 0.0050939131827104651)
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")
(-0.45583464361842779, 0.010751070629208364)
(-0.47490876200787641, 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, 0.067833443079354308)
(5.0919999999999996, 0.071885575743677543)
"""
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., 87., 219., 375., 255., 24.]), array([0, 1, 2, 3, 4, 5, 6], dtype=uint64)]
[array([ 0., 30., 84., 210., 375., 264., 27.]), array([0, 1, 2, 3, 4, 5, 6], dtype=uint64)]
"""
if samples != None:
seed = numpy.random.randint(0, sys.maxint)
......
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