Update docstrings

src/graph_tool/__init__.py

@@ -1485,8 +1485,8 @@ class Graph(object):
         >>> g.graph_properties["gnat"] = g.new_graph_property("string", "hi there!")
         >>> g.list_properties()
         gnat           (graph)   (type: string, val: hi there!)
-        foo            (vertex)  (type: double)
         bar            (vertex)  (type: python::object)
+        foo            (vertex)  (type: double)
         foo            (edge)    (type: vector<double>)
         """
 

src/graph_tool/centrality/__init__.py

@@ -695,7 +695,7 @@ def katz(g, alpha=0.01, beta=None, weight=None, vprop=None, epsilon=1e-6,
        >>> g = gt.collection.data["polblogs"]
        >>> g = gt.GraphView(g, vfilt=gt.label_largest_component(g))
        >>> w = g.new_edge_property("double")
-       >>> w.a = np.random.random(len(w.a)) * 42
+       >>> w.a = np.random.random(len(w.a))
        >>> x = gt.katz(g, weight=w)
        >>> gt.graph_draw(g, pos=g.vp["pos"], vertex_fill_color=x,
        ...               vertex_size=gt.prop_to_size(x, mi=5, ma=15),

src/graph_tool/clustering/__init__.py

@@ -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))
-    (0.0072, 0.00039746045691969764)
+    (0.007177777777777778, 0.0003966968553716155)
 
     References
     ----------
@@ -182,7 +182,7 @@ def global_clustering(g):
 
     >>> g = gt.random_graph(1000, lambda: (5,5))
     >>> print(gt.global_clustering(g))
-    (0.007182172103638073, 0.0003970213508956326)
+    (0.007177777777777778, 0.0003970939493209407)
 
     References
     ----------

src/graph_tool/community/blockmodel.py

@@ -1870,7 +1870,7 @@ def collect_edge_marginals(state, p=None):
        ...     ds, nmoves = gt.mcmc_sweep(state)
        ...     pe = gt.collect_edge_marginals(state, pe)
        >>> gt.bethe_entropy(state, pe)[0]
-       16.31947774487215
+       17.443089842818125
     """
 
     if p is None:
@@ -1922,7 +1922,7 @@ def collect_vertex_marginals(state, p=None):
        ...     ds, nmoves = gt.mcmc_sweep(state)
        ...     pv = gt.collect_vertex_marginals(state, pv)
        >>> gt.mf_entropy(state, pv)
-       18.348761352514927
+       19.94955528942717
        >>> gt.graph_draw(g, pos=g.vp["pos"], vertex_shape="pie", vertex_pie_fractions=pv, output="polbooks_blocks_soft_B4.pdf")
        <...>
 

src/graph_tool/community/nested_blockmodel.py

@@ -328,7 +328,7 @@ def nested_mcmc_sweep(state, beta=1., random_move=False, c=1., sequential=True,
        >>> state = gt.NestedBlockState(g, Bs=[10, 5, 3, 2, 1], deg_corr=True)
        >>> ret = gt.nested_mcmc_sweep(state)
        >>> print(ret)
-       [(-0.23943486013787463, 57), (-0.017558577944127587, 2), (0.0, 0), (-0.0012759602078996138, 1), (0.0, 0)]
+       [(-0.11881394061738723, 58), (-1.2855474804244876e-05, 1), (0.0, 0), (-0.003488958656098635, 1), (0.0, 0)]
 
     References
     ----------

src/graph_tool/correlations/__init__.py

@@ -115,7 +115,7 @@ def assortativity(g, deg):
     ...                     vertex_corr=lambda i,k: 1.0 / (1 + abs(i - k)), directed=False,
     ...                     n_iter=100)
     >>> gt.assortativity(g, "out")
-    (0.13903518011375607, 0.005051876804786422)
+    (0.1419534824769149, 0.005098622437085927)
 
     References
     ----------
@@ -191,13 +191,13 @@ def scalar_assortativity(g, deg):
     ...                     vertex_corr=lambda i,k: abs(i-k),
     ...                     directed=False, n_iter=100)
     >>> gt.scalar_assortativity(g, "out")
-    (-0.44070158356400696, 0.010592022444678632)
+    (-0.45182410730944034, 0.010288182870044639)
 
     >>> g = gt.random_graph(1000, lambda: sample_k(40), model="probabilistic",
     ...                     vertex_corr=lambda i, k: 1.0 / (1 + abs(i - k)),
     ...                     directed=False, n_iter=100)
     >>> gt.scalar_assortativity(g, "out")
-    (0.6007430887839058, 0.011569809783643956)
+    (0.6421210898147616, 0.010632143985746339)
 
     References
     ----------

src/graph_tool/generation/__init__.py

@@ -189,7 +189,7 @@ def random_graph(N, deg_sampler, directed=True,
     ...                     vertex_corr=lambda i, k: 1.0 / (1 + abs(i - k)), directed=False,
     ...                     n_iter=100)
     >>> gt.scalar_assortativity(g, "out")
-    (0.6285094791115295, 0.010745128857935755)
+    (0.6197157767573332, 0.010781011616659146)
 
     The following samples an in,out-degree pair from the joint distribution:
 
@@ -584,7 +584,7 @@ def random_rewire(g, model="uncorrelated", n_iter=1, edge_sweep=True,
        gt.graph_draw(g, pos=pos, output="rewire_orig.png", output_size=(300, 300))
 
     >>> gt.random_rewire(g, "correlated")
-    189
+    212
     >>> pos = gt.arf_layout(g)
     >>> gt.graph_draw(g, pos=pos, output="rewire_corr.pdf", output_size=(300, 300))
     <...>
@@ -595,7 +595,7 @@ def random_rewire(g, model="uncorrelated", n_iter=1, edge_sweep=True,
        gt.graph_draw(g, pos=pos, output="rewire_corr.png", output_size=(300, 300))
 
     >>> gt.random_rewire(g)
-    197
+    207
     >>> pos = gt.arf_layout(g)
     >>> gt.graph_draw(g, pos=pos, output="rewire_uncorr.pdf", output_size=(300, 300))
     <...>
@@ -606,7 +606,7 @@ def random_rewire(g, model="uncorrelated", n_iter=1, edge_sweep=True,
        gt.graph_draw(g, pos=pos, output="rewire_uncorr.png", output_size=(300, 300))
 
     >>> gt.random_rewire(g, "erdos")
-    26
+    21
     >>> pos = gt.arf_layout(g)
     >>> gt.graph_draw(g, pos=pos, output="rewire_erdos.pdf", output_size=(300, 300))
     <...>
@@ -637,17 +637,17 @@ def random_rewire(g, model="uncorrelated", n_iter=1, edge_sweep=True,
     >>> errorbar(corr[2][:-1], corr[0], yerr=corr[1], fmt="o-", label="Original")
     <...>
     >>> gt.random_rewire(g, "correlated")
-    206
+    212
     >>> corr = gt.avg_neighbour_corr(g, "out", "out")
     >>> errorbar(corr[2][:-1], corr[0], yerr=corr[1], fmt="*", label="Correlated")
     <...>
     >>> gt.random_rewire(g)
-    109
+    120
     >>> corr = gt.avg_neighbour_corr(g, "out", "out")
     >>> errorbar(corr[2][:-1], corr[0], yerr=corr[1], fmt="o-", label="Uncorrelated")
     <...>
     >>> gt.random_rewire(g, "erdos")
-    13
+    20
     >>> corr = gt.avg_neighbour_corr(g, "out", "out")
     >>> errorbar(corr[2][:-1], corr[0], yerr=corr[1], fmt="o-", label=r"Erd\H{o}s")
     <...>
@@ -693,7 +693,7 @@ def random_rewire(g, model="uncorrelated", n_iter=1, edge_sweep=True,
     ...          label=r"$\left<\text{i}\right>$ vs o")
     <...>
     >>> gt.random_rewire(g, "correlated")
-    4323
+    4130
     >>> corr = gt.avg_neighbour_corr(g, "in", "out")
     >>> errorbar(corr[2][:-1], corr[0], yerr=corr[1], fmt="o-",
     ...          label=r"$\left<\text{o}\right>$ vs i, corr.")
@@ -703,7 +703,7 @@ def random_rewire(g, model="uncorrelated", n_iter=1, edge_sweep=True,
     ...          label=r"$\left<\text{i}\right>$ vs o, corr.")
     <...>
     >>> gt.random_rewire(g, "uncorrelated")
-    153
+    194
     >>> corr = gt.avg_neighbour_corr(g, "in", "out")
     >>> errorbar(corr[2][:-1], corr[0], yerr=corr[1], fmt="o-",
     ...          label=r"$\left<\text{o}\right>$ vs i, uncorr.")

src/graph_tool/topology/__init__.py

@@ -797,12 +797,12 @@ 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 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0
-     0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0
-     0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1]
+    [0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0
+     0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0
+     0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 1 1 1 0]
     >>> u = gt.GraphView(g, vfilt=l)   # extract the largest component as a graph
     >>> print(u.num_vertices())
-    16
+    18
     """
 
     label = g.new_vertex_property("bool")
@@ -848,18 +848,18 @@ 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 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 0
-     1 1 0 0 1 1 0 1 1 0 0 1 1 1 1 0 1 0 0 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 0 0]
+    [1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 0
+     1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 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 0 1 1 1 0 1 1 1 0 1 1 1 0]
 
     The in-component can be obtained by reversing the graph.
 
     >>> l = gt.label_out_component(gt.GraphView(g, reversed=True, directed=True),
     ...                            g.vertex(1))
     >>> print(l.a)
-    [1 1 1 0 0 1 1 0 1 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 0 0
-     0 1 0 0 0 1 0 1 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 1 0 0 1 1 0 1
-     1 0 0 0 0 1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 0 0 1 0 0 0]
+    [0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0
+     0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0]
     """
 
     label = g.new_vertex_property("bool")
@@ -931,18 +931,17 @@ def label_biconnected_components(g, eprop=None, vprop=None):
     >>> g = gt.random_graph(100, lambda: poisson(2), directed=False)
     >>> comp, art, hist = gt.label_biconnected_components(g)
     >>> print(comp.a)
-    [51 51 51 51 51 51 11 52 51 51 44 42 41 45 49 23 19 51 51 32 38 51 24 37 51
-     51 51 10  8 51 20 43 51 51 51 51 51 47 46 51 51 13 14 51 51 51 51 33 30 51
-      1 21 51 51 51 35 36  6 51 26 27  7 12  4  3 29 28 51 51 51 31 51 51  0 39
-     51 51 51 34 40 51 51  9 17 51 51 18 15 22  2 16 50  5 48 51 51 53 51 51 25]
+    [35 23 35 35 32 32 35 35  1 37 32 28 32 35 32  2 32 29 35 35 13 14 34 12 17
+     35 35 35 35 35  3 35 35 35 35 35 35 28 28 35 33 35 19 35 35 35 35  6 35 35
+     24 39 35 31 35 10  9 22 32 35  4 25 26 35 35  7 35 35 35 35 35 35 36 35 35
+     35 32 35  0 35 35 35 32 35 28 32 35 20 30 27 18 38 16  5 15 11 28 35  8 21]
     >>> print(art.a)
-    [1 0 1 0 0 1 0 0 1 1 0 0 1 1 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 1 1 0
-     0 1 0 0 1 1 0 1 1 0 0 0 1 1 0 1 1 0 0 0 1 0 0 1 0 1 0 0 1 1 0 0 0 0 0 1 1
-     1 0 1 1 0 1 0 1 1 0 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0]
+    [1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 1 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0
+     0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 1 1 0 0 0 0 0 1 0
+     1 0 1 1 0 0 0 0 1 0 0 1 0 1 1 1 0 0 0 0 0 0 0 1 0 0]
     >>> print(hist)
     [ 1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
-      1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
-      1 47  1  1]
+      1  1  1  5  1  1  1 10  1  1 48  1  1  1  1]
     """
 
     if vprop is None: