diff --git a/doc/draw.rst b/doc/draw.rst
index cc302f5e1973f1f5fd0f9ff280c65b4d353a0a68..ae025c5da7dd42fd75ead4601f5d74ebf48ba1f7 100644
--- a/doc/draw.rst
+++ b/doc/draw.rst
@@ -1,4 +1,5 @@
.. automodule:: graph_tool.draw
+ :no-members:
Layout algorithms
=================
diff --git a/doc/search_example.xml b/doc/search_example.xml
index 7e67162c4a378bbf346cdc025cce8f0027666a33..57b97b75051b5f7fb3d440f97e05588d9046d6a6 100644
--- a/doc/search_example.xml
+++ b/doc/search_example.xml
@@ -5,7 +5,8 @@
-
+
+
@@ -14,86 +15,96 @@
Bob
+ -0x1.ef066a9bc7566p+8, -0x1.8cba14a4daf9ep+8
Alice
+ -0x1.eff87ddcb5dcdp+8, -0x1.8be648e13bf73p+8
Carol
+ -0x1.eec36defefc65p+8, -0x1.8bfb011626325p+8
Carlos
+ -0x1.ef434147d196bp+8, -0x1.8bf303e0a8156p+8
Chuck
+ -0x1.ee23a5c21581dp+8, -0x1.8c946519352c7p+8
Dave
+ -0x1.f07f37873d595p+8, -0x1.8b75d14dbc907p+8
Eve
+ -0x1.ee9849cf138ffp+8, -0x1.8c6143c2dad9p+8
Isaac
+ -0x1.ee7057c39cfedp+8, -0x1.8cf90ebb37008p+8
Oscar
+ -0x1.f0a22f1b03f88p+8, -0x1.8c165cbc20962p+8
Imothep
+ -0x1.ee3d366d42ab8p+8, -0x1.8b9e54a9a27b3p+8
- 0x1.9e7393907213cp+1
+ 0x1.9e7393907213cp+1
- 0x1.015b0db294a3dp+2
+ 0x1.015b0db294a3dp+2
- 0x1.12bcc343cdc2ap+2
+ 0x1.12bcc343cdc2ap+2
- 0x1.ba3952afb7e9ep+1
+ 0x1.ba3952afb7e9ep+1
- 0x1.0ff8065948f03p+1
+ 0x1.0ff8065948f03p+1
- 0x1.a872068a0a4e5p+1
+ 0x1.a872068a0a4e5p+1
- 0x1.2816bc78b034fp+2
+ 0x1.2816bc78b034fp+2
- 0x1.82250c1278b54p+2
+ 0x1.82250c1278b54p+2
- 0x1.8bb78dae3ee5ap+0
+ 0x1.8bb78dae3ee5ap+0
- 0x1.587ad2707187fp+2
+ 0x1.587ad2707187fp+2
- 0x1.7df6a3095905ep+2
+ 0x1.7df6a3095905ep+2
- 0x1.62ac71123a64ap+0
+ 0x1.62ac71123a64ap+0
- 0x1.2c156b7e6a49p+1
+ 0x1.2c156b7e6a49p+1
- 0x1.543c16e43a9bfp+2
+ 0x1.543c16e43a9bfp+2
- 0x1.5a4ab7454f0e2p+1
+ 0x1.5a4ab7454f0e2p+1
- 0x1.841d9db6417f3p+1
+ 0x1.841d9db6417f3p+1
- 0x1.20af02e97ed9ep+2
+ 0x1.20af02e97ed9ep+2
diff --git a/src/graph_tool/community/blockmodel.py b/src/graph_tool/community/blockmodel.py
index 0a23e647ac029dca3720ef6d5690f37c6759490f..40d55241ff2bf8a20885d2aaa4402302ba862f17 100644
--- a/src/graph_tool/community/blockmodel.py
+++ b/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`.
"""
diff --git a/src/graph_tool/community/nested_blockmodel.py b/src/graph_tool/community/nested_blockmodel.py
index 72b5007870079360f104b2ebf5afa059e4382eae..6054362923b62dcaab457d300ca805b115afd619 100644
--- a/src/graph_tool/community/nested_blockmodel.py
+++ b/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`.
"""
diff --git a/src/graph_tool/draw/cairo_draw.py b/src/graph_tool/draw/cairo_draw.py
index e2a677b7cc323e702367cf19b8466c0ea045d2b3..bbf05b5acb55dcf03cc02417ad6ae566870555b8 100644
--- a/src/graph_tool/draw/cairo_draw.py
+++ b/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
diff --git a/src/graph_tool/search/__init__.py b/src/graph_tool/search/__init__.py
index a98937526c9e341d0283093445ed0089ce41453b..26d4491e520e3dbb826a3d3f51d0f5ad0af67a2d 100644
--- a/src/graph_tool/search/__init__.py
+++ b/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 `, 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: