More documentation fixes.

doc/draw.rst

 .. automodule:: graph_tool.draw
+   :no-members:
 
     Layout algorithms
     =================

doc/search_example.xml

@@ -5,7 +5,8 @@
 
   <!-- property keys -->
   <key id="key0" for="node" attr.name="name" attr.type="string" />
-  <key id="key1" for="edge" attr.name="weight" attr.type="float" />
+  <key id="key1" for="node" attr.name="pos" attr.type="vector_float" />
+  <key id="key2" for="edge" attr.name="weight" attr.type="float" />
 
   <graph id="G" edgedefault="undirected" parse.nodeids="canonical" parse.edgeids="canonical" parse.order="nodesfirst">
 
@@ -14,86 +15,96 @@
    <!-- vertices -->
     <node id="n0">
       <data key="key0">Bob</data>
+      <data key="key1">-0x1.ef066a9bc7566p+8, -0x1.8cba14a4daf9ep+8</data>
     </node>
     <node id="n1">
       <data key="key0">Alice</data>
+      <data key="key1">-0x1.eff87ddcb5dcdp+8, -0x1.8be648e13bf73p+8</data>
     </node>
     <node id="n2">
       <data key="key0">Carol</data>
+      <data key="key1">-0x1.eec36defefc65p+8, -0x1.8bfb011626325p+8</data>
     </node>
     <node id="n3">
       <data key="key0">Carlos</data>
+      <data key="key1">-0x1.ef434147d196bp+8, -0x1.8bf303e0a8156p+8</data>
     </node>
     <node id="n4">
       <data key="key0">Chuck</data>
+      <data key="key1">-0x1.ee23a5c21581dp+8, -0x1.8c946519352c7p+8</data>
     </node>
     <node id="n5">
       <data key="key0">Dave</data>
+      <data key="key1">-0x1.f07f37873d595p+8, -0x1.8b75d14dbc907p+8</data>
     </node>
     <node id="n6">
       <data key="key0">Eve</data>
+      <data key="key1">-0x1.ee9849cf138ffp+8, -0x1.8c6143c2dad9p+8</data>
     </node>
     <node id="n7">
       <data key="key0">Isaac</data>
+      <data key="key1">-0x1.ee7057c39cfedp+8, -0x1.8cf90ebb37008p+8</data>
     </node>
     <node id="n8">
       <data key="key0">Oscar</data>
+      <data key="key1">-0x1.f0a22f1b03f88p+8, -0x1.8c165cbc20962p+8</data>
     </node>
     <node id="n9">
       <data key="key0">Imothep</data>
+      <data key="key1">-0x1.ee3d366d42ab8p+8, -0x1.8b9e54a9a27b3p+8</data>
     </node>
 
    <!-- edges -->
     <edge id="e0" source="n0" target="n6">
-      <data key="key1">0x1.9e7393907213cp+1</data>
+      <data key="key2">0x1.9e7393907213cp+1</data>
     </edge>
     <edge id="e1" source="n0" target="n4">
-      <data key="key1">0x1.015b0db294a3dp+2</data>
+      <data key="key2">0x1.015b0db294a3dp+2</data>
     </edge>
     <edge id="e2" source="n0" target="n3">
-      <data key="key1">0x1.12bcc343cdc2ap+2</data>
+      <data key="key2">0x1.12bcc343cdc2ap+2</data>
     </edge>
     <edge id="e3" source="n0" target="n7">
-      <data key="key1">0x1.ba3952afb7e9ep+1</data>
+      <data key="key2">0x1.ba3952afb7e9ep+1</data>
     </edge>
     <edge id="e4" source="n1" target="n8">
-      <data key="key1">0x1.0ff8065948f03p+1</data>
+      <data key="key2">0x1.0ff8065948f03p+1</data>
     </edge>
     <edge id="e5" source="n1" target="n5">
-      <data key="key1">0x1.a872068a0a4e5p+1</data>
+      <data key="key2">0x1.a872068a0a4e5p+1</data>
     </edge>
     <edge id="e6" source="n1" target="n3">
-      <data key="key1">0x1.2816bc78b034fp+2</data>
+      <data key="key2">0x1.2816bc78b034fp+2</data>
     </edge>
     <edge id="e7" source="n2" target="n6">
-      <data key="key1">0x1.82250c1278b54p+2</data>
+      <data key="key2">0x1.82250c1278b54p+2</data>
     </edge>
     <edge id="e8" source="n2" target="n9">
-      <data key="key1">0x1.8bb78dae3ee5ap+0</data>
+      <data key="key2">0x1.8bb78dae3ee5ap+0</data>
     </edge>
     <edge id="e9" source="n3" target="n6">
-      <data key="key1">0x1.587ad2707187fp+2</data>
+      <data key="key2">0x1.587ad2707187fp+2</data>
     </edge>
     <edge id="e10" source="n3" target="n9">
-      <data key="key1">0x1.7df6a3095905ep+2</data>
+      <data key="key2">0x1.7df6a3095905ep+2</data>
     </edge>
     <edge id="e11" source="n4" target="n6">
-      <data key="key1">0x1.62ac71123a64ap+0</data>
+      <data key="key2">0x1.62ac71123a64ap+0</data>
     </edge>
     <edge id="e12" source="n4" target="n7">
-      <data key="key1">0x1.2c156b7e6a49p+1</data>
+      <data key="key2">0x1.2c156b7e6a49p+1</data>
     </edge>
     <edge id="e13" source="n4" target="n9">
-      <data key="key1">0x1.543c16e43a9bfp+2</data>
+      <data key="key2">0x1.543c16e43a9bfp+2</data>
     </edge>
     <edge id="e14" source="n5" target="n8">
-      <data key="key1">0x1.5a4ab7454f0e2p+1</data>
+      <data key="key2">0x1.5a4ab7454f0e2p+1</data>
     </edge>
     <edge id="e15" source="n6" target="n7">
-      <data key="key1">0x1.841d9db6417f3p+1</data>
+      <data key="key2">0x1.841d9db6417f3p+1</data>
     </edge>
     <edge id="e16" source="n6" target="n9">
-      <data key="key1">0x1.20af02e97ed9ep+2</data>
+      <data key="key2">0x1.20af02e97ed9ep+2</data>
     </edge>
 
   </graph>

src/graph_tool/community/blockmodel.py

@@ -601,9 +601,9 @@ def model_entropy(B, N, E, directed=False, nr=None):
     References
     ----------
 
-    .. [peixoto-parsimonious-2014] Tiago P. Peixoto, "Parsimonious module inference in large networks",
-       Phys. Rev. Lett. 110, 148701 (2014), :doi:`10.1103/PhysRevLett.110.148701`, :arxiv:`1212.4794`.
-    .. [peixoto-hierarchical-2014] Tiago P. Peixoto, "Hierarchical block structures and high-resolution
+    .. [peixoto-parsimonious-2013] Tiago P. Peixoto, "Parsimonious module inference in large networks",
+       Phys. Rev. Lett. 110, 148701 (2013), :doi:`10.1103/PhysRevLett.110.148701`, :arxiv:`1212.4794`.
+    .. [peixoto-hierarchical-2013] Tiago P. Peixoto, "Hierarchical block structures and high-resolution
        model selection in large networks ", :arxiv:`1310.4377`.
 
     """
@@ -647,8 +647,8 @@ def get_max_B(N, E, directed=False):
 
     References
     ----------
-    .. [peixoto-parsimonious-2014] Tiago P. Peixoto, "Parsimonious module inference in large networks",
-       Phys. Rev. Lett. 110, 148701 (2014), :doi:`10.1103/PhysRevLett.110.148701`, :arxiv:`1212.4794`.
+    .. [peixoto-parsimonious-2013] Tiago P. Peixoto, "Parsimonious module inference in large networks",
+       Phys. Rev. Lett. 110, 148701 (2013), :doi:`10.1103/PhysRevLett.110.148701`, :arxiv:`1212.4794`.
 
 
     """
@@ -698,8 +698,8 @@ def get_akc(B, I, N=float("inf"), directed=False):
 
     References
     ----------
-    .. [peixoto-parsimonious-2014] Tiago P. Peixoto, "Parsimonious module inference in large networks",
-       Phys. Rev. Lett. 110, 148701 (2014), :doi:`10.1103/PhysRevLett.110.148701`, :arxiv:`1212.4794`.
+    .. [peixoto-parsimonious-2013] Tiago P. Peixoto, "Parsimonious module inference in large networks",
+       Phys. Rev. Lett. 110, 148701 (2013), :doi:`10.1103/PhysRevLett.110.148701`, :arxiv:`1212.4794`.
 
     """
     if N != float("inf"):
@@ -746,7 +746,7 @@ def mcmc_sweep(state, beta=1., random_move=False, c=1., dense=False,
         random order. Otherwise a total of `N` moves attempts are made, where
         `N` is the number of vertices, where each vertex can be selected with
         equal probability.
-    vertices: ``list of ints`` (optional, default: ``None``)
+    vertices : ``list of ints`` (optional, default: ``None``)
         A list of vertices which will be attempted to be moved. If ``None``, all
         vertices will be attempted.
     verbose : ``bool`` (optional, default: ``False``)
@@ -798,7 +798,7 @@ def mcmc_sweep(state, beta=1., random_move=False, c=1., dense=False,
     the Markov chain by proposing membership moves :math:`r\to s` with
     probability :math:`p(r\to s|t) \propto e_{ts} + c`, where :math:`t` is the
     block label of a random neighbour of the vertex being moved. See
-    [peixoto-efficient-2014]_ for more details.
+    [peixoto-efficient-2013]_ for more details.
 
     This algorithm has a complexity of :math:`O(E)`, where :math:`E` is the
     number of edges in the network.
@@ -847,9 +847,9 @@ def mcmc_sweep(state, beta=1., random_move=False, c=1., dense=False,
     .. [peixoto-entropy-2012] Tiago P. Peixoto "Entropy of Stochastic Blockmodel
        Ensembles." Physical Review E 85, no. 5 (2012): 056122. :doi:`10.1103/PhysRevE.85.056122`,
        :arxiv:`1112.6028`.
-    .. [peixoto-parsimonious-2014] Tiago P. Peixoto, "Parsimonious module inference in large networks",
-       Phys. Rev. Lett. 110, 148701 (2014), :doi:`10.1103/PhysRevLett.110.148701`, :arxiv:`1212.4794`.
-    .. [peixoto-efficient-2014] Tiago P. Peixoto, "Efficient Monte Carlo and greedy
+    .. [peixoto-parsimonious-2013] Tiago P. Peixoto, "Parsimonious module inference in large networks",
+       Phys. Rev. Lett. 110, 148701 (2013), :doi:`10.1103/PhysRevLett.110.148701`, :arxiv:`1212.4794`.
+    .. [peixoto-efficient-2013] Tiago P. Peixoto, "Efficient Monte Carlo and greedy
        heuristic for the inference of stochastic block models", :arxiv:`1310.4378`.
     """
 
@@ -1016,9 +1016,7 @@ def multilevel_minimize(state, B, nsweeps=10, adaptive_sweeps=True, epsilon=0,
                         random_move=False, c=1., dense=False, multigraph=False,
                         sequential=True, checkpoint=None,
                         minimize_state=None, verbose=False):
-    r"""Performs an agglomerative heuristic, which progressively merges blocks
-    together (while allowing individual node moves) to achieve a good partition
-    in ``B`` blocks.
+    r"""Performs an agglomerative heuristic, which progressively merges blocks together (while allowing individual node moves) to achieve a good partition in ``B`` blocks.
 
     Parameters
     ----------
@@ -1115,7 +1113,7 @@ def multilevel_minimize(state, B, nsweeps=10, adaptive_sweeps=True, epsilon=0,
 
     This algorithm performs an agglomerative heuristic on the current block state,
     where blocks are progressively merged together, using repeated applications of
-    the :func:`mcmc_sweep` moves, at different scales. See [peixoto-efficient-2014]_
+    the :func:`mcmc_sweep` moves, at different scales. See [peixoto-efficient-2013]_
     for more details.
 
     This algorithm has a complexity of :math:`O(N\ln^2 N)`, where :math:`N` is the
@@ -1149,7 +1147,7 @@ def multilevel_minimize(state, B, nsweeps=10, adaptive_sweeps=True, epsilon=0,
      References
     ----------
 
-    .. [peixoto-efficient-2014] Tiago P. Peixoto, "Efficient Monte Carlo and greedy
+    .. [peixoto-efficient-2013] Tiago P. Peixoto, "Efficient Monte Carlo and greedy
        heuristic for the inference of stochastic block models", :arxiv:`1310.4378`.
     """
 
@@ -1614,7 +1612,7 @@ def minimize_blockmodel_dl(g, eweight=None, vweight=None, deg_corr=True, dense=F
     specific values of :math:`B` via :func:`mcmc_sweep` (with :math:`\beta = 1`
     and :math:`\beta\to\infty`), and minimizing :math:`\Sigma_{t/c}` via an
     one-dimensional Fibonacci search on :math:`B`. See
-    [peixoto-parsimonious-2014]_ for more details.
+    [peixoto-parsimonious-2013]_ for more details.
 
     This algorithm has a complexity of :math:`O(\tau N\ln^2 B_{\text{max}})`,
     where :math:`N` is the number of nodes in the network, :math:`\tau` is the
@@ -1663,9 +1661,9 @@ def minimize_blockmodel_dl(g, eweight=None, vweight=None, deg_corr=True, dense=F
     .. [peixoto-entropy-2012] Tiago P. Peixoto "Entropy of Stochastic Blockmodel
        Ensembles." Physical Review E 85, no. 5 (2012): 056122. :doi:`10.1103/PhysRevE.85.056122`,
        :arxiv:`1112.6028`.
-    .. [peixoto-parsimonious-2014] Tiago P. Peixoto, "Parsimonious module inference in large networks",
-       Phys. Rev. Lett. 110, 148701 (2014), :doi:`10.1103/PhysRevLett.110.148701`, :arxiv:`1212.4794`.
-    .. [peixoto-efficient-2014] Tiago P. Peixoto, "Efficient Monte Carlo and greedy
+    .. [peixoto-parsimonious-2013] Tiago P. Peixoto, "Parsimonious module inference in large networks",
+       Phys. Rev. Lett. 110, 148701 (2013), :doi:`10.1103/PhysRevLett.110.148701`, :arxiv:`1212.4794`.
+    .. [peixoto-efficient-2013] Tiago P. Peixoto, "Efficient Monte Carlo and greedy
        heuristic for the inference of stochastic block models", :arxiv:`1310.4378`.
 
     """

src/graph_tool/community/nested_blockmodel.py

@@ -333,9 +333,9 @@ def nested_mcmc_sweep(state, beta=1., random_move=False, c=1., sequential=True,
     References
     ----------
 
-    .. [peixoto-efficient-2014] Tiago P. Peixoto, "Efficient Monte Carlo and greedy
+    .. [peixoto-efficient-2013] Tiago P. Peixoto, "Efficient Monte Carlo and greedy
        heuristic for the inference of stochastic block models", :arxiv:`1310.4378`.
-    .. [peixoto-hierarchical-2014] Tiago P. Peixoto, "Hierarchical block structures and high-resolution
+    .. [peixoto-hierarchical-2013] Tiago P. Peixoto, "Hierarchical block structures and high-resolution
        model selection in large networks ", :arxiv:`1310.4377`.
     """
 
@@ -576,9 +576,9 @@ def nested_tree_sweep(state, nsweeps=10, epsilon=0., r=2.,
     References
     ----------
 
-    .. [peixoto-efficient-2014] Tiago P. Peixoto, "Efficient Monte Carlo and greedy
+    .. [peixoto-efficient-2013] Tiago P. Peixoto, "Efficient Monte Carlo and greedy
        heuristic for the inference of stochastic block models", :arxiv:`1310.4378`.
-    .. [peixoto-hierarchical-2014] Tiago P. Peixoto, "Hierarchical block structures
+    .. [peixoto-hierarchical-2013] Tiago P. Peixoto, "Hierarchical block structures
        and high-resolution model selection in large networks ", :arxiv:`1310.4377`.
     """
 
@@ -811,9 +811,9 @@ def init_nested_state(g, Bs, deg_corr=True, dense=False,
     References
     ----------
 
-    .. [peixoto-efficient-2014] Tiago P. Peixoto, "Efficient Monte Carlo and greedy
+    .. [peixoto-efficient-2013] Tiago P. Peixoto, "Efficient Monte Carlo and greedy
        heuristic for the inference of stochastic block models", :arxiv:`1310.4378`.
-    .. [peixoto-hierarchical-2014] Tiago P. Peixoto, "Hierarchical block structures
+    .. [peixoto-hierarchical-2013] Tiago P. Peixoto, "Hierarchical block structures
        and high-resolution model selection in large networks ", :arxiv:`1310.4377`.
     """
 
@@ -999,7 +999,7 @@ def minimize_nested_blockmodel_dl(g, Bs=None, bs=None, deg_corr=True,
         \mathcal{L}^l_t = \ln\left(\!\!{B_l\choose B_{l-1}}\!\!\right) + \ln B_{l-1}! - \sum_r \ln n_r^l!.
 
 
-    See [peixoto-hierarchical-2014]_ for details on the algorithm.
+    See [peixoto-hierarchical-2013]_ for details on the algorithm.
 
     This algorithm has a complexity of :math:`O(N \ln^2 N)`, where :math:`N`
     is the number of nodes in the network.
@@ -1037,9 +1037,9 @@ def minimize_nested_blockmodel_dl(g, Bs=None, bs=None, deg_corr=True,
     References
     ----------
 
-    .. [peixoto-hierarchical-2014] Tiago P. Peixoto, "Hierarchical block structures and high-resolution
+    .. [peixoto-hierarchical-2013] Tiago P. Peixoto, "Hierarchical block structures and high-resolution
        model selection in large networks ", :arxiv:`1310.4377`.
-    .. [peixoto-efficient-2014] Tiago P. Peixoto, "Efficient Monte Carlo and greedy
+    .. [peixoto-efficient-2013] Tiago P. Peixoto, "Efficient Monte Carlo and greedy
        heuristic for the inference of stochastic block models", :arxiv:`1310.4378`.
     """
 

src/graph_tool/draw/cairo_draw.py

@@ -1019,8 +1019,7 @@ def transform_scale(M, scale):
     return np.sqrt(p[0] ** 2 + p[1] ** 2)
 
 def get_hierarchy_control_points(g, t, tpos, beta=0.8):
-    r"""Return the Bézier spline control points for the edges in ``g``, given
-    the hierarchical structure encoded in graph `t`.
+    r"""Return the Bézier spline control points for the edges in ``g``, given the hierarchical structure encoded in graph `t`.
 
     Parameters
     ----------
@@ -1030,7 +1029,7 @@ def get_hierarchy_control_points(g, t, tpos, beta=0.8):
         Directed graph containing the hierarchy of ``g``. It must be a directed
         tree with a single root. The direction of the edges point from the root
         to the leaves, and the vertices in ``t`` with index in the range
-        :math:`[0, N-1]`, with `:math:`N` being the number of vertices in ``g``,
+        :math:`[0, N-1]`, with :math:`N` being the number of vertices in ``g``,
         must correspond to the respective vertex in ``g``.
     tpos : :class:`~graph_tool.PropertyMap`
         Vector-valued vertex property map containing the x and y coordinates of

src/graph_tool/search/__init__.py

@@ -52,13 +52,14 @@ Examples
 In this module, most documentation examples will make use of the network
 :download:`search_example.xml <search_example.xml>`, shown below.
 
->>> gt.seed_rng(42)
 >>> g = gt.load_graph("search_example.xml")
->>> name = g.vertex_properties["name"]
->>> weight = g.edge_properties["weight"]
->>> pos = gt.graph_draw(g, vertex_text=name, vertex_font_size=12, vertex_shape="double_circle",
-...                     vertex_fill_color="#729fcf", vertex_pen_width=3,
-...                     edge_pen_width=weight, output="search_example.pdf")
+>>> name = g.vp["name"]
+>>> weight = g.ep["weight"]
+>>> pos = g.vp["pos"]
+>>> gt.graph_draw(g, pos, vertex_text=name, vertex_font_size=12, vertex_shape="double_circle",
+...               vertex_fill_color="#729fcf", vertex_pen_width=3,
+...               edge_pen_width=weight, output="search_example.pdf")
+<...>
 
 .. testcode::
    :hide: