Commit 360c2570 by Tiago Peixoto

### Assorted documentation improvements in generation module.

parent d1aa20b7
 ... ... @@ -185,7 +185,10 @@ def random_graph(N, deg_sampler, deg_corr=None, directed=True, Lets plot the average degree correlations to check. >>> clf() >>> figure(figsize=(6,3)) <...> >>> axes([0.1,0.15,0.63,0.8]) <...> >>> corr = gt.avg_neighbour_corr(g, "in", "in") >>> errorbar(corr[2], corr[0], yerr=corr[1], fmt="o-", ... label=r"$\left<\text{in}\right>$ vs in") ... ... @@ -202,7 +205,7 @@ def random_graph(N, deg_sampler, deg_corr=None, directed=True, >>> errorbar(corr[2], corr[0], yerr=corr[1], fmt="o-", ... label=r"$\left<\text{out}\right>$ vs out") (...) >>> legend(loc="best") >>> legend(loc=(1.05,0.5)) <...> >>> xlabel("source degree") <...> ... ... @@ -269,11 +272,10 @@ def random_rewire(g, strat= "uncorrelated", parallel_edges = False, Some small graphs for visualization. >>> from numpy.random import zipf, seed >>> from numpy.random import random, seed >>> from pylab import * >>> seed(42) >>> g = gt.random_graph(1000, lambda: sample_k(10), ... lambda i,j: exp(abs(i-j)), directed=False) >>> g, pos = gt.triangulation(random((1000,2))) >>> gt.graph_draw(g, layout="arf", output="rewire_orig.png", size=(6,6)) <...> >>> gt.random_rewire(g, "correlated") ... ... @@ -294,11 +296,12 @@ def random_rewire(g, strat= "uncorrelated", parallel_edges = False, We can try some larger graphs to get better statistics. >>> clf() >>> figure() <...> >>> g = gt.random_graph(20000, lambda: sample_k(20), ... lambda i,j: exp(abs(i-j)), directed=False) >>> corr = gt.avg_neighbour_corr(g, "out", "out") >>> errorbar(corr[2], corr[0], yerr=corr[1], fmt="o-", label="original") >>> errorbar(corr[2], corr[0], yerr=corr[1], fmt="*-", label="original") (...) >>> gt.random_rewire(g, "correlated") >>> corr = gt.avg_neighbour_corr(g, "out", "out") ... ... @@ -330,7 +333,10 @@ def random_rewire(g, strat= "uncorrelated", parallel_edges = False, >>> g = gt.random_graph(20000, lambda: (sample_k(19), sample_k(19)), ... lambda a,b: (p.pmf(a[0],b[1])* ... p.pmf(a[1],20-b[0]))) >>> clf() >>> figure(figsize=(6,3)) <...> >>> axes([0.1,0.15,0.6,0.8]) <...> >>> corr = gt.avg_neighbour_corr(g, "in", "out") >>> errorbar(corr[2], corr[0], yerr=corr[1], fmt="o-", ... label=r"$\left<\text{o}\right>$ vs i") ... ... @@ -357,7 +363,7 @@ def random_rewire(g, strat= "uncorrelated", parallel_edges = False, >>> errorbar(corr[2], corr[0], yerr=corr[1], fmt="o-", ... label=r"$\left<\text{i}\right>$ vs o, uncorr.") (...) >>> legend(loc="best") >>> legend(loc=(1.05,0.45)) <...> >>> xlabel("source degree") <...> ... ... @@ -447,6 +453,26 @@ def graph_union(g1, g2, props=[], include=False): props : list of :class:~graph_tool.PropertyMap objects List of propagated properties. This is only returned if props is not empty. Examples -------- >>> from numpy.random import random, seed >>> seed(42) >>> g = gt.triangulation(random((300,2)))[0] >>> ug = gt.graph_union(g, g) >>> uug = gt.graph_union(g, ug) >>> gt.graph_draw(g, layout="arf", size=(8,8), output="graph_original.png") <...> >>> gt.graph_draw(ug, layout="arf", size=(8,8), output="graph_union.png") <...> >>> gt.graph_draw(uug, layout="arf", size=(8,8), output="graph_union2.png") <...> .. image:: graph_original.png .. image:: graph_union.png .. image:: graph_union2.png """ if not include: g1 = Graph(g1) ... ...
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