Update docstring tests

doc/demos/inference/_edge_weights.rst

@@ -245,9 +245,9 @@ Therefore, we can compute the posterior odds ratio between both models as:
 .. testoutput:: food-web
    :options: +NORMALIZE_WHITESPACE
 
-   ln Λ:  -70.145685...
+   ln Λ:  -24.246389...
 
-A value of :math:`\Lambda \approx \mathrm{e}^{-70} \approx 10^{-30}` in
+A value of :math:`\Lambda \approx \mathrm{e}^{-24} \approx 10^{-10}` in
 favor the exponential model indicates that the log-normal model does not
 provide a better fit for this particular data. Based on this, we
 conclude that the exponential model should be preferred in this case.

doc/demos/inference/_minimization.rst

@@ -134,7 +134,7 @@ illustrate its use with the neural network of the `C. elegans
 
 .. testsetup:: celegans
 
-   gt.seed_rng(47)
+   gt.seed_rng(51)
 
 .. testcode:: celegans
 
@@ -186,9 +186,9 @@ which shows the number of nodes and groups in all levels:
 
 .. testoutput:: celegans
 
-   l: 0, N: 297, B: 17
-   l: 1, N: 17, B: 9
-   l: 2, N: 9, B: 3
+   l: 0, N: 297, B: 16
+   l: 1, N: 16, B: 8
+   l: 2, N: 8, B: 3
    l: 3, N: 3, B: 1
 
 The hierarchical levels themselves are represented by individual
@@ -203,10 +203,10 @@ The hierarchical levels themselves are represented by individual
 
 .. testoutput:: celegans
 
-   <BlockState object with 17 blocks (17 nonempty), degree-corrected, for graph <Graph object, directed, with 297 vertices and 2359 edges at 0x...>, at 0x...>
-   <BlockState object with 9 blocks (9 nonempty), for graph <Graph object, directed, with 17 vertices and 156 edges at 0x...>, at 0x...>
-   <BlockState object with 3 blocks (3 nonempty), for graph <Graph object, directed, with 9 vertices and 57 edges at 0x...>, at 0x...>
-   <BlockState object with 1 blocks (1 nonempty), for graph <Graph object, directed, with 3 vertices and 9 edges at 0x...>, at 0x...>
+    <BlockState object with 16 blocks (16 nonempty), degree-corrected, for graph <Graph object, directed, with 297 vertices and 2359 edges at 0x...>, at 0x...>
+    <BlockState object with 8 blocks (8 nonempty), for graph <Graph object, directed, with 16 vertices and 134 edges at 0x...>, at 0x...>
+    <BlockState object with 3 blocks (3 nonempty), for graph <Graph object, directed, with 8 vertices and 50 edges at 0x...>, at 0x...>
+    <BlockState object with 1 blocks (1 nonempty), for graph <Graph object, directed, with 3 vertices and 8 edges at 0x...>, at 0x...>
 
 This means that we can inspect the hierarchical partition just as before:
 
@@ -221,6 +221,6 @@ This means that we can inspect the hierarchical partition just as before:
 
 .. testoutput:: celegans
 
-   7
-   0
+   2
+   1
    0

doc/demos/inference/_model_class_selection.rst

@@ -154,8 +154,8 @@ evidence efficiently, as we show below, using
 
 .. testoutput:: model-evidence
 
-   Model evidence for deg_corr = True: -569.590426... (mean field), -817.788531... (Bethe)
-   Model evidence for deg_corr = False: -587.028530... (mean field), -736.990655... (Bethe)
+   Model evidence for deg_corr = True: -579.300446... (mean field), -832.245049... (Bethe)
+   Model evidence for deg_corr = False: -586.652245... (mean field), -737.721423... (Bethe)
 
 If we consider the more accurate approximation, the outcome shows a
 preference for the non-degree-corrected model.
@@ -219,8 +219,8 @@ approach for the same network, using the nested model.
 
 .. testoutput:: model-evidence
 
-   Model evidence for deg_corr = True: -551.228195... (mean field), -740.460493... (Bethe)
-   Model evidence for deg_corr = False: -544.660366... (mean field), -649.135026... (Bethe)
+   Model evidence for deg_corr = True: -555.768070... (mean field), -731.501041... (Bethe)
+   Model evidence for deg_corr = False: -544.346500... (mean field), -630.951518... (Bethe)
 
 The results are similar: If we consider the most accurate approximation,
 the non-degree-corrected model possesses the largest evidence. Note also

doc/demos/inference/_model_selection.rst

@@ -25,8 +25,8 @@ we have
 .. testoutput:: model-selection
    :options: +NORMALIZE_WHITESPACE
 
-   Non-degree-corrected DL:     8456.994339...
-   Degree-corrected DL:         8233.850036...
+   Non-degree-corrected DL:     8524.911216...
+   Degree-corrected DL:         8274.075603...
    
 Since it yields the smallest description length, the degree-corrected
 fit should be preferred. The statistical significance of the choice can
@@ -52,12 +52,12 @@ fits. In our particular case, we have
 .. testoutput:: model-selection
    :options: +NORMALIZE_WHITESPACE
 
-   ln Λ:  -223.144303...
+   ln Λ:  -250.835612...
 
 The precise threshold that should be used to decide when to `reject a
 hypothesis <https://en.wikipedia.org/wiki/Hypothesis_testing>`_ is
 subjective and context-dependent, but the value above implies that the
-particular degree-corrected fit is around :math:`\mathrm{e}^{233} \approx 10^{96}`
+particular degree-corrected fit is around :math:`\mathrm{e}^{251} \approx 10^{109}`
 times more likely than the non-degree corrected one, and hence it can be
 safely concluded that it provides a substantially better fit.
 
@@ -79,11 +79,11 @@ example, for the American football network above, we have:
 .. testoutput:: model-selection
    :options: +NORMALIZE_WHITESPACE
 
-   Non-degree-corrected DL:     1734.814739...
+   Non-degree-corrected DL:     1733.525685...
    Degree-corrected DL:         1780.576716...
-   ln Λ:                         -45.761977...
+   ln Λ:                         -47.051031...
 
-Hence, with a posterior odds ratio of :math:`\Lambda \approx \mathrm{e}^{-45} \approx
-10^{-19}` in favor of the non-degree-corrected model, it seems like the
+Hence, with a posterior odds ratio of :math:`\Lambda \approx \mathrm{e}^{-47} \approx
+10^{-20}` in favor of the non-degree-corrected model, we conclude that the
 degree-corrected variant is an unnecessarily complex description for
 this network.

doc/demos/inference/_reconstruction.rst

@@ -185,9 +185,9 @@ Which yields the following output:
    
 .. testoutput:: measured
 
-    Posterior probability of edge (11, 36): 0.801980...
-    Posterior probability of non-edge (15, 73): 0.097309...
-    Estimated average local clustering: 0.572154 ± 0.004853...
+   Posterior probability of edge (11, 36): 0.812881...
+   Posterior probability of non-edge (15, 73): 0.160516...
+   Estimated average local clustering: 0.57309 ± 0.005985...
 
 We have a successful reconstruction, where both ambiguous adjacency
 matrix entries are correctly recovered. The value for the average
@@ -306,9 +306,9 @@ Which yields:
    
 .. testoutput:: measured
 
-    Posterior probability of edge (11, 36): 0.790179...
-    Posterior probability of non-edge (15, 73): 0.109010...
-    Estimated average local clustering: 0.572504 ± 0.005453...
+   Posterior probability of edge (11, 36): 0.693369...
+   Posterior probability of non-edge (15, 73): 0.170517...
+   Estimated average local clustering: 0.570545 ± 0.006892...
 
 The results are very similar to the ones obtained with the uniform model
 in this case, but can be quite different in situations where a large
@@ -434,9 +434,9 @@ The above yields the output:
    
 .. testoutput:: uncertain
 
-    Posterior probability of edge (11, 36): 0.950495...
-    Posterior probability of non-edge (15, 73): 0.067406...
-    Estimated average local clustering: 0.552333 ± 0.019183...
+   Posterior probability of edge (11, 36): 0.881188...
+   Posterior probability of non-edge (15, 73): 0.043004...
+   Estimated average local clustering: 0.557825 ± 0.014038...
 
 The reconstruction is accurate, despite the two ambiguous entries having
 the same measurement probability. The reconstructed network is visualized below.

src/graph_tool/__init__.py

@@ -1837,9 +1837,9 @@ class Graph(object):
         --------
         >>> g = gt.random_graph(6, lambda: 1, directed=False)
         >>> g.get_edges()
-        array([[2, 1, 2],
-               [3, 4, 0],
-               [5, 0, 1]], dtype=uint64)
+        array([[0, 3, 2],
+               [1, 4, 1],
+               [2, 5, 0]], dtype=uint64)
         """
         edges = libcore.get_edge_list(self.__graph)
         E = edges.shape[0] // 3

src/graph_tool/clustering/__init__.py

@@ -112,16 +112,10 @@ def local_clustering(g, prop=None, undirected=True):
 
     Examples
     --------
-    .. testcode::
-       :hide:
-
-       np.random.seed(42)
-       gt.seed_rng(42)
-
-    >>> g = gt.random_graph(1000, lambda: (5,5))
+    >>> g = gt.collection.data["karate"]
     >>> clust = gt.local_clustering(g)
     >>> print(gt.vertex_average(g, clust))
-    (0.008177777777777779, 0.00042080229075093...)
+    (0.5706384782..., 0.05869813676...)
 
     References
     ----------
@@ -174,15 +168,9 @@ def global_clustering(g):
 
     Examples
     --------
-    .. testcode::
-       :hide:
-
-       np.random.seed(42)
-       gt.seed_rng(42)
-
-    >>> g = gt.random_graph(1000, lambda: (5,5))
+    >>> g = gt.collection.data["karate"]
     >>> print(gt.global_clustering(g))
-    (0.008177777777777779, 0.0004212235142651...)
+    (0.2556818181..., 0.06314746595...)
 
     References
     ----------
@@ -252,22 +240,16 @@ def extended_clustering(g, props=None, max_depth=3, undirected=False):
 
     Examples
     --------
-    .. testcode::
-       :hide:
-
-       np.random.seed(42)
-       gt.seed_rng(42)
-
-    >>> g = gt.random_graph(1000, lambda: (5,5))
+    >>> g = gt.collection.data["karate"]
     >>> clusts = gt.extended_clustering(g, max_depth=5)
     >>> for i in range(0, 5):
     ...    print(gt.vertex_average(g, clusts[i]))
     ...
-    (0.0050483333333333335, 0.0004393940240073...)
-    (0.024593787878787878, 0.0009963004021144...)
-    (0.11238924242424242, 0.001909615401971...)
-    (0.40252272727272725, 0.003113987400030...)
-    (0.43629378787878786, 0.003144159256565...)
+    (0.5706384782076..., 0.05869813676256...)
+    (0.3260389360735..., 0.04810773205917...)
+    (0.0530678759917..., 0.01513061504691...)
+    (0.0061658977316..., 0.00310690511463...)
+    (0.0002162629757..., 0.00021305890271...)
 
     References
     ----------
@@ -349,9 +331,9 @@ def motifs(g, k, p=1.0, motif_list=None, return_maps=False):
     >>> g = gt.random_graph(1000, lambda: (5,5))
     >>> motifs, counts = gt.motifs(gt.GraphView(g, directed=False), 4)
     >>> print(len(motifs))
-    18
+    11
     >>> print(counts)
-    [115557, 390005, 627, 700, 1681, 2815, 820, 12, 27, 44, 15, 7, 12, 4, 6, 1, 2, 1]
+    [116386, 392916, 443, 507, 2574, 1124, 741, 5, 5, 8, 2]
 
     References
     ----------
@@ -520,9 +502,9 @@ 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)
-    [0.22728646681107012, 0.21409572051644973, 0.007022040788902111, 0.5872141967123348, -0.37770179603294357, -0.3484733504783734, 0.8861811801325502, -0.08, -0.2, -0.38, -0.2]
+    [2.59252643351441, 2.5966529814390387, 2.3459237708258587, -1.0829180621127024, -1.3368754665984663, -2.33027728409781, -3.055817397993647, -0.1, -0.15, -0.19, -0.4, -0.01]
 
     References
     ----------

src/graph_tool/inference/blockmodel.py

@@ -2067,7 +2067,7 @@ class BlockState(object):
            ...     ret = state.mcmc_sweep(niter=10)
            ...     pe = state.collect_edge_marginals(pe)
            >>> gt.bethe_entropy(g, pe)[0]
-           -0.901611...
+           12.204791...
         """
 
         if p is None:
@@ -2124,7 +2124,7 @@ class BlockState(object):
            ...     ret = state.mcmc_sweep(niter=10)
            ...     pv = state.collect_vertex_marginals(pv)
            >>> gt.mf_entropy(g, pv)
-           26.887021...
+           16.904653...
            >>> gt.graph_draw(g, pos=g.vp["pos"], vertex_shape="pie",
            ...               vertex_pie_fractions=pv, output="polbooks_blocks_soft_B4.pdf")
            <...>
@@ -2193,8 +2193,7 @@ class BlockState(object):
            ...     ret = state.mcmc_sweep(niter=10)
            ...     ph = state.collect_partition_histogram(ph)
            >>> gt.microstate_entropy(ph)
-           129.330077...
-
+           137.024741...
         """
 
         if h is None:

src/graph_tool/spectral/__init__.py

@@ -374,7 +374,7 @@ def incidence(g, vindex=None, eindex=None):
     >>> print(m.todense())
     [[-1. -1.  0. ...  0.  0.  0.]
      [ 0.  0.  0. ...  0.  0.  0.]
-     [ 0.  0.  0. ...  0.  0.  0.]
+     [ 1.  0.  0. ...  0.  0.  0.]
      ...
      [ 0.  0. -1. ...  0.  0.  0.]
      [ 0.  0.  0. ...  0.  0.  0.]

src/graph_tool/stats/__init__.py

@@ -113,8 +113,8 @@ def vertex_hist(g, deg, bins=[0, 1], float_count=True):
     >>> from numpy.random import poisson
     >>> g = gt.random_graph(1000, lambda: (poisson(5), poisson(5)))
     >>> print(gt.vertex_hist(g, "out"))
-    [array([  7.,  33.,  91., 145., 165., 164., 152., 115.,  62.,  29.,  28.,
-             6.,   1.,   1.,   0.,   1.]), array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16],
+    [array([  5.,  32.,  85., 148., 152., 182., 160., 116.,  53.,  25.,  23.,
+            13.,   3.,   2.,   1.]), array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15],
           dtype=uint64)]
     """
 
@@ -178,7 +178,7 @@ def edge_hist(g, eprop, bins=[0, 1], float_count=True):
     >>> eprop = g.new_edge_property("double")
     >>> eprop.get_array()[:] = random(g.num_edges())
     >>> print(gt.edge_hist(g, eprop, linspace(0, 1, 11)))
-    [array([501., 441., 478., 480., 506., 494., 507., 535., 499., 559.]), array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])]
+    [array([485., 538., 502., 505., 474., 497., 544., 465., 492., 498.]), array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])]
 
     """
 
@@ -232,7 +232,7 @@ def vertex_average(g, deg):
     >>> from numpy.random import poisson
     >>> g = gt.random_graph(1000, lambda: (poisson(5), poisson(5)))
     >>> print(gt.vertex_average(g, "in"))
-    (4.96, 0.0679882342762334)
+    (4.986, 0.07323799560337517)
     """
 
     if isinstance(deg, PropertyMap) and "string" in deg.value_type():
@@ -294,7 +294,7 @@ def edge_average(g, eprop):
     >>> eprop = g.new_edge_property("double")
     >>> eprop.get_array()[:] = random(g.num_edges())
     >>> print(gt.edge_average(g, eprop))
-    (0.49888156584192045, 0.004096739923418754)
+    (0.5027850372071281, 0.004073940886690715)
     """
 
     if "string" in eprop.value_type():
@@ -427,10 +427,10 @@ 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.,  880., 2269., 3974., 2358.,  119.]), array([0, 1, 2, 3, 4, 5, 6, 7], dtype=uint64)]
+    [array([   0.,  300.,  862., 2195., 3850., 2518.,  175.]), array([0, 1, 2, 3, 4, 5, 6, 7], dtype=uint64)]
     >>> hist = gt.distance_histogram(g, samples=10)
     >>> print(hist)
-    [array([  0.,  30.,  87., 223., 394., 239.,  17.]), array([0, 1, 2, 3, 4, 5, 6, 7], dtype=uint64)]
+    [array([  0.,  30.,  86., 213., 378., 262.,  21.]), array([0, 1, 2, 3, 4, 5, 6, 7], dtype=uint64)]
     """
 
     if samples is not None: