Generate pdf images in the documentation by default

Images are now converted to png a posteriori for html output, but are
used as pdf for latex.

doc/.templates/layout.html

@@ -22,5 +22,8 @@
 {% block sidebarsearch %}
 {{ super() }}
 <a href="graph-tool.pdf">(Download PDF version)</a>
+
+<p/>
+Latest <a href="/graph-tool/doc/dev/">development version docs</a> are also available.
 {% endblock %}
 

doc/Makefile

-all:
+PDFIMAGES = $(shell ls *.pdf)
+PNGIMAGES = ${PDFIMAGES:.pdf=.png}
+HUGEIMAGES = $(shell find . -name "*.pdf"  -size +1000k)
+ORIGIMAGES = ${HUGEIMAGES:.pdf=.pdf-orig}
+
+PNGCONV = gm convert -density 600 -resample 50 -trim -antialias -quality 9 -filter Cubic
+
+all: $(ORIGIMAGES) $(PNGIMAGES)
 	sphinx-build -E -b html . build
 
+%.pdf-orig: %.pdf
+	cp $< $@ 
+	gm mogrify -resize 600 -trim -antialias -filter Cubic $<
+
+%.png: %.pdf
+	test -d $<-orig && $(PNGCONV) $<-orig $@  || true
+	test -d $<-orig || $(PNGCONV) $< $@ || true
+
 latex:
 	sphinx-build -E -b latex . build
 
@@ -9,3 +24,7 @@ test:
 
 push:
 	rsync -rEvpLz build/* root@skewed.de:/var/www/graph-tool-doc/
+
+push-dev:
+	rsync -rEvpLz build/* root@skewed.de:/var/www/graph-tool-doc/dev/
+

doc/conf.py

@@ -215,12 +215,19 @@ latex_documents = [
    ur'Tiago de Paula Peixoto', 'manual'),
 ]
 
-#latex_logo = "graph-draw.png"
+latex_preamble = """
+\setcounter{tocdepth}{2}
+"""
+
+latex_show_pagerefs = True
+latex_show_urls = False
+latex_paper_size = "a4"
+
+latex_logo = "blockmodel.pdf"
 
 latex_elements = {
     'papersize': "a4paper",
-    'fontpkg': r"\usepackage{palatino}\usepackage{eulervm}"}
-
+    'fontpkg': r"\usepackage{bookman}"}
 
 # Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {'python': ('http://docs.python.org', None),

doc/price.py

@@ -66,7 +66,7 @@ gca().set_xlim(0.8, 1e3)
 subplots_adjust(left=0.2, bottom=0.2)
 xlabel("$k_{in}$")
 ylabel("$NP(k_{in})$")
-savefig("deg-hist.png")
+savefig("deg-hist.pdf")
 
 # let's do a random walk on the graph and print the age of the vertices we find,
 # just for fun.

doc/quickstart.rst

@@ -83,10 +83,10 @@ visualize the graph we created so far with the
 
 .. doctest::
 
-   >>> graph_draw(g, vprops={"label": g.vertex_index}, output="two-nodes.png")
+   >>> graph_draw(g, vprops={"label": g.vertex_index}, output="two-nodes.pdf")
    <...>
 
-.. figure:: two-nodes.png
+.. figure:: two-nodes.*
    :align: center
 
    A simple directed graph with two vertices and one edge, created by
@@ -451,7 +451,7 @@ This is the degree distribution, with 100000 nodes. If you want to see a broader
 power law, try to increase the number of vertices to something like :math:`10^6`
 or :math:`10^7`.
 
-.. figure:: deg-hist.png
+.. figure:: deg-hist.*
    :align: center
 
    In-degree distribution of a price network with 100000 nodes.
@@ -463,9 +463,9 @@ use the :func:`~graph_tool.draw.graph_draw` function.
 
    g = load_graph("price.xml.gz")
    age = g.vertex_properties["age"]
-   graph_draw(g, size=(15,15), vcolor=age, output="price.png")
+   graph_draw(g, size=(15,15), vcolor=age, output="price.pdf")
 
-.. figure:: price.png
+.. figure:: price.*
    :align: center
 
    A Price network with :math:`10^5` nodes. The vertex colors represent
@@ -508,13 +508,13 @@ edge filtering.
 
    g, pos = triangulation(random((500, 2)) * 4, type="delaunay")
    tree = min_spanning_tree(g)
-   graph_draw(g, pos=pos, pin=True, size=(8, 8), ecolor=tree, output="min_tree.png")
+   graph_draw(g, pos=pos, pin=True, size=(8, 8), ecolor=tree, output="min_tree.pdf")
 
 The ``tree`` property map has a bool type, with value "1" if the edge belongs to
 the tree, and "0" otherwise. Below is an image of the original graph, with the
 marked edges.
 
-.. figure:: min_tree.png
+.. figure:: min_tree.*
    :align: center
 
 We can now filter out the edges which don't belong to the minimum spanning tree.
@@ -522,11 +522,11 @@ We can now filter out the edges which don't belong to the minimum spanning tree.
 .. testcode::
 
     g.set_edge_filter(tree)
-    graph_draw(g, pos=pos, pin=True, size=(8, 8), output="min_tree_filtered.png")
+    graph_draw(g, pos=pos, pin=True, size=(8, 8), output="min_tree_filtered.pdf")
 
 This is how the graph looks when filtered:
 
-.. figure:: min_tree_filtered.png
+.. figure:: min_tree_filtered.*
    :align: center
 
 Everything should work transparently on the filtered graph, simply as if the
@@ -539,9 +539,9 @@ and draws them as colors and line thickness in the graph.
     bv, be = betweenness(g)
     be.a *= 10
     graph_draw(g, pos=pos, pin=True, size=(8,8), vsize=0.07, vcolor=bv,
-               eprops={"penwidth":be}, output="filtered-bt.png")
+               eprops={"penwidth":be}, output="filtered-bt.pdf")
 
-.. figure:: filtered-bt.png
+.. figure:: filtered-bt.*
    :align: center
 
 
@@ -553,9 +553,9 @@ The original graph can be recovered by setting the edge filter to ``None``.
     bv, be = betweenness(g)
     be.a *= 10
     graph_draw(g, pos=pos, pin=True, size=(8,8), vsize=0.07, vcolor=bv,
-               eprops={"penwidth":be}, output="nonfiltered-bt.png")
+               eprops={"penwidth":be}, output="nonfiltered-bt.pdf")
 
-.. figure:: nonfiltered-bt.png
+.. figure:: nonfiltered-bt.*
    :align: center
 
 Everything works in analogous fashion with vertex filtering.
@@ -609,11 +609,11 @@ Like above, the result should be the isolated minimum spanning tree:
     >>> bv, be = betweenness(tv)
     >>> be.a *= 10
     >>> graph_draw(tv, pos=pos, pin=True, size=(8,8), vsize=0.07, vcolor=bv,
-    ...            eprops={"penwidth":be}, output="mst-view.png")
+    ...            eprops={"penwidth":be}, output="mst-view.pdf")
     <...>
 
 
-.. figure:: mst-view.png
+.. figure:: mst-view.*
    :align: center
 
    A view of the Delaunay graph, isolating only the minimum spanning tree.
@@ -654,10 +654,10 @@ The graph view constructed above can be visualized as
 .. doctest::
 
     >>> graph_draw(u, pos=pos, pin=True, size=(8,8), vsize=0.07, vcolor=bv,
-    ...            output="central-edges-view.png")
+    ...            output="central-edges-view.pdf")
     <...>
 
-.. figure:: central-edges-view.png
+.. figure:: central-edges-view.*
    :align: center
 
    A view of the Delaunay graph, isolating only the edges with
@@ -682,10 +682,10 @@ The resulting graph view can be visualized as
 .. doctest::
 
     >>> graph_draw(u, pos=pos, pin=True, size=(8,8), vsize=0.07,
-    ...            output="composed-filter.png")
+    ...            output="composed-filter.pdf")
     <...>
 
-.. figure:: composed-filter.png
+.. figure:: composed-filter.*
    :align: center
 
    A composed view, obtained as the minimum spanning tree of all vertices

src/graph_tool/community/__init__.py

@@ -149,13 +149,13 @@ def community_structure(g, n_iter, n_spins, gamma=1.0, corr="erdos",
     >>> spins = gt.community_structure(g, 10000, 20, t_range=(5, 0.1),
     ...                                history_file="community-history1")
     >>> gt.graph_draw(g, pos=pos, pin=True, vsize=0.3, vcolor=spins,
-    ...               output="comm1.png", size=(10,10))
+    ...               output="comm1.pdf", size=(10,10))
     <...>
     >>> spins = gt.community_structure(g, 10000, 40, t_range=(5, 0.1),
     ...                                gamma=2.5,
     ...                                history_file="community-history2")
     >>> gt.graph_draw(g, pos=pos, pin=True, vsize=0.3, vcolor=spins,
-    ...               output="comm2.png", size=(10,10))
+    ...               output="comm2.pdf", size=(10,10))
     <...>
     >>> clf()
     >>> xlabel("iterations")
@@ -165,7 +165,7 @@ def community_structure(g, n_iter, n_spins, gamma=1.0, corr="erdos",
     >>> a = loadtxt("community-history1").transpose()
     >>> plot(a[0], a[2])
     [...]
-    >>> savefig("comm1-hist.png")
+    >>> savefig("comm1-hist.pdf")
     >>> clf()
     >>> xlabel("iterations")
     <...>
@@ -174,18 +174,18 @@ def community_structure(g, n_iter, n_spins, gamma=1.0, corr="erdos",
     >>> a = loadtxt("community-history2").transpose()
     >>> plot(a[0], a[2])
     [...]
-    >>> savefig("comm2-hist.png")
+    >>> savefig("comm2-hist.pdf")
 
 
     The community structure with :math:`\gamma=1`:
 
-    .. image:: comm1.png
-    .. image:: comm1-hist.png
+    .. image:: comm1.*
+    .. image:: comm1-hist.*
 
     The community structure with :math:`\gamma=2.5`:
 
-    .. image:: comm2.png
-    .. image:: comm2-hist.png
+    .. image:: comm2.*
+    .. image:: comm2-hist.*
 
 
     References
@@ -328,10 +328,10 @@ def condensation_graph(g, prop, weight=None):
     >>> size.a = log(ng[1].a+1)
     >>> gt.graph_draw(ng[0], vsize=size, vcolor=size, splines=True,
     ...               eprops={"len":20, "penwidth":10}, vprops={"penwidth":10},
-    ...               output="comm-network.png", size=(10,10))
+    ...               output="comm-network.pdf", size=(10,10))
     <...>
 
-    .. figure:: comm-network.png
+    .. figure:: comm-network.*
         :align: center
 
         Community network of a random graph. The color and sizes of the nodes

src/graph_tool/correlations/__init__.py

@@ -277,9 +277,9 @@ def corr_hist(g, deg_source, deg_target, bins=[[0, 1], [0, 1]], weight=None,
     <...>
     >>> colorbar()
     <...>
-    >>> savefig("corr.png")
+    >>> savefig("corr.pdf")
 
-    .. figure:: corr.png
+    .. figure:: corr.*
         :align: center
 
         Out/out-degree correlation histogram.
@@ -363,9 +363,9 @@ def combined_corr_hist(g, deg1, deg2, bins=[[0, 1], [0, 1]], float_count=True):
     <...>
     >>> colorbar()
     <...>
-    >>> savefig("combined_corr.png")
+    >>> savefig("combined_corr.pdf")
 
-    .. figure:: combined_corr.png
+    .. figure:: combined_corr.*
         :align: center
 
         Combined in/out-degree correlation histogram.
@@ -453,9 +453,9 @@ def avg_neighbour_corr(g, deg_source, deg_target, bins=[0, 1], weight=None):
     <...>
     >>> errorbar(h[2][:-1], h[0], yerr=h[1], fmt="o")
     (...)
-    >>> savefig("avg_corr.png")
+    >>> savefig("avg_corr.pdf")
 
-    .. figure:: avg_corr.png
+    .. figure:: avg_corr.*
         :align: center
 
         Average out/out degree correlation.
@@ -530,9 +530,9 @@ def avg_combined_corr(g, deg1, deg2, bins=[0, 1]):
     <...>
     >>> errorbar(h[2][:-1], h[0], yerr=h[1], fmt="o")
     (...)
-    >>> savefig("combined_avg_corr.png")
+    >>> savefig("combined_avg_corr.pdf")
 
-    .. figure:: combined_avg_corr.png
+    .. figure:: combined_avg_corr.*
         :align: center
 
         Average combined in/out-degree correlation.

src/graph_tool/draw/__init__.py

@@ -283,10 +283,10 @@ def graph_draw(g, pos=None, size=(15, 15), pin=False, layout=None, maxiter=None,
     >>> ebet.a += 10
     >>> gt.graph_draw(g, vsize=deg, vcolor=deg, vorder=deg, elen=10,
     ...               ecolor=ebet, eorder=ebet, penwidth=ebet,
-    ...               overlap="prism", output="graph-draw.png")
+    ...               overlap="prism", output="graph-draw.pdf")
     <...>
 
-    .. figure:: graph-draw.png
+    .. figure:: graph-draw.*
         :align: center
 
         Kamada-Kawai force-directed layout of a graph with a power-law degree
@@ -686,10 +686,10 @@ def fruchterman_reingold_layout(g, weight=None, a=None, r=1., scale=None,
     >>> seed(42)
     >>> g = gt.price_network(300)
     >>> pos = gt.fruchterman_reingold_layout(g, n_iter=1000)
-    >>> gt.graph_draw(g, pos=pos, pin=True, output="graph-draw-fr.png")
+    >>> gt.graph_draw(g, pos=pos, pin=True, output="graph-draw-fr.pdf")
     <...>
 
-    .. figure:: graph-draw-fr.png
+    .. figure:: graph-draw-fr.*
         :align: center
 
         Fruchterman-Reingold layout of a Price network.
@@ -767,10 +767,10 @@ def arf_layout(g, weight=None, d=0.5, a=10, dt=0.001, epsilon=1e-6,
     >>> seed(42)
     >>> g = gt.price_network(300)
     >>> pos = gt.arf_layout(g, max_iter=0)
-    >>> gt.graph_draw(g, pos=pos, pin=True, output="graph-draw-arf.png")
+    >>> gt.graph_draw(g, pos=pos, pin=True, output="graph-draw-arf.pdf")
     <...>
 
-    .. figure:: graph-draw-arf.png
+    .. figure:: graph-draw-arf.*
         :align: center
 
         ARF layout of a Price network.

src/graph_tool/flow/__init__.py

@@ -59,10 +59,10 @@ The following network will be used as an example throughout the documentation.
     g.edge_properties["cap"] = cap
     g.vertex_properties["pos"] = pos
     g.save("flow-example.xml.gz")
-    gt.graph_draw(g, pos=pos, pin=True, penwidth=cap, output="flow-example.png")
+    gt.graph_draw(g, pos=pos, pin=True, penwidth=cap, output="flow-example.pdf")
 
 
-.. figure:: flow-example.png
+.. figure:: flow-example.*
     :align: center
 
     Example network used in the documentation below. The edge capacities are
@@ -129,10 +129,10 @@ def edmonds_karp_max_flow(g, source, target, capacity, residual=None):
     >>> print max_flow
     6.92759897841
     >>> pos = g.vertex_properties["pos"]
-    >>> gt.graph_draw(g, pos=pos, pin=True, penwidth=res, output="example-edmonds-karp.png")
+    >>> gt.graph_draw(g, pos=pos, pin=True, penwidth=res, output="example-edmonds-karp.pdf")
     <...>
 
-    .. figure:: example-edmonds-karp.png
+    .. figure:: example-edmonds-karp.*
         :align: center
 
         Edge flows obtained with the Edmonds-Karp algorithm. The source and
@@ -205,10 +205,10 @@ def push_relabel_max_flow(g, source, target, capacity, residual=None):
     >>> print max_flow
     6.92759897841
     >>> pos = g.vertex_properties["pos"]
-    >>> gt.graph_draw(g, pos=pos, pin=True, penwidth=res, output="example-push-relabel.png")
+    >>> gt.graph_draw(g, pos=pos, pin=True, penwidth=res, output="example-push-relabel.pdf")
     <...>
 
-    .. figure:: example-push-relabel.png
+    .. figure:: example-push-relabel.*
         :align: center
 
 
@@ -282,10 +282,10 @@ def boykov_kolmogorov_max_flow(g, source, target, capacity, residual=None):
     >>> print max_flow
     6.92759897841
     >>> pos = g.vertex_properties["pos"]
-    >>> gt.graph_draw(g, pos=pos, pin=True, penwidth=res, output="example-kolmogorov.png")
+    >>> gt.graph_draw(g, pos=pos, pin=True, penwidth=res, output="example-kolmogorov.pdf")
     <...>
 
-    .. figure:: example-kolmogorov.png
+    .. figure:: example-kolmogorov.*
         :align: center
 
         Edge flows obtained with the Boykov-Kolmogorov algorithm. The source and
@@ -353,10 +353,10 @@ def max_cardinality_matching(g, match=None):
     >>> res = gt.max_cardinality_matching(g)
     >>> print res[1]
     True
-    >>> gt.graph_draw(g, ecolor=res[0], output="max_card_match.png")
+    >>> gt.graph_draw(g, ecolor=res[0], output="max_card_match.pdf")
     <...>
 
-    .. figure:: max_card_match.png
+    .. figure:: max_card_match.*
         :align: center
 
         Edges belonging to the matching are in red.

src/graph_tool/generation/__init__.py

@@ -134,7 +134,7 @@ def random_graph(N, deg_sampler, deg_corr=None, directed=True,
     ``mix_time`` parameter passed as ``n_iter``.
 
     The complexity is :math:`O(V + E)` if parallel edges are allowed, and
-    :math:`O(V + E \times\text{mix_time})` if parallel edges are not allowed.
+    :math:`O(V + E \times\text{mix-time})` if parallel edges are not allowed.
 
 
     .. note ::
@@ -213,9 +213,9 @@ def random_graph(N, deg_sampler, deg_corr=None, directed=True,
     <...>
     >>> ylabel("out-degree")
     <...>
-    >>> savefig("combined-deg-hist.png")
+    >>> savefig("combined-deg-hist.pdf")
 
-    .. figure:: combined-deg-hist.png
+    .. figure:: combined-deg-hist.*
         :align: center
 
         Combined degree histogram.
@@ -266,9 +266,9 @@ def random_graph(N, deg_sampler, deg_corr=None, directed=True,
     <...>
     >>> ylabel("average target degree")
     <...>
-    >>> savefig("deg-corr-dir.png")
+    >>> savefig("deg-corr-dir.pdf")
 
-    .. figure:: deg-corr-dir.png
+    .. figure:: deg-corr-dir.*
         :align: center
 
         Average nearest neighbour correlations.
@@ -292,10 +292,10 @@ def random_graph(N, deg_sampler, deg_corr=None, directed=True,
     >>> g, bm = gt.random_graph(1000, lambda: poisson(10), directed=False,
     ...                         blockmodel=lambda: randint(10), deg_corr=corr,
     ...                         mix_time=500)
-    >>> gt.graph_draw(g, vcolor=bm, layout="sfdp", output="blockmodel.png")
+    >>> gt.graph_draw(g, vcolor=bm, layout="sfdp", output="blockmodel.pdf")
     <...>
 
-    .. figure:: blockmodel.png
+    .. figure:: blockmodel.*
         :align: center
 
         Simple blockmodel with 10 blocks.
@@ -477,8 +477,8 @@ def random_rewire(g, strat="uncorrelated", n_iter=1, edge_sweep=True,
 
 
     Each edge is tentatively swapped once per iteration, so the overall
-    complexity is :math:`O(V + E \times \text{n_iter})`. If ``edge_sweep ==
-    False``, the complexity becomes :math:`O(V + E + \text{n_iter})`.
+    complexity is :math:`O(V + E \times \text{n-iter})`. If ``edge_sweep ==
+    False``, the complexity becomes :math:`O(V + E + \text{n-iter})`.
 
     Examples
     --------
@@ -489,24 +489,24 @@ def random_rewire(g, strat="uncorrelated", n_iter=1, edge_sweep=True,
     >>> from pylab import *
     >>> seed(43)
     >>> g, pos = gt.triangulation(random((1000,2)))
-    >>> gt.graph_draw(g, layout="arf", output="rewire_orig.png", size=(6,6))
+    >>> gt.graph_draw(g, layout="arf", output="rewire_orig.pdf", size=(6,6))
     <...>
     >>> gt.random_rewire(g, "correlated")
-    >>> gt.graph_draw(g, layout="arf", output="rewire_corr.png", size=(6,6))
+    >>> gt.graph_draw(g, layout="arf", output="rewire_corr.pdf", size=(6,6))
     <...>
     >>> gt.random_rewire(g)
-    >>> gt.graph_draw(g, layout="arf", output="rewire_uncorr.png", size=(6,6))
+    >>> gt.graph_draw(g, layout="arf", output="rewire_uncorr.pdf", size=(6,6))
     <...>
     >>> gt.random_rewire(g, "erdos")
-    >>> gt.graph_draw(g, layout="arf", output="rewire_erdos.png", size=(6,6))
+    >>> gt.graph_draw(g, layout="arf", output="rewire_erdos.pdf", size=(6,6))
     <...>
 
     Some `ridiculograms <http://www.youtube.com/watch?v=YS-asmU3p_4>`_ :
 
-    .. image:: rewire_orig.png
-    .. image:: rewire_corr.png
-    .. image:: rewire_uncorr.png
-    .. image:: rewire_erdos.png
+    .. image:: rewire_orig.*
+    .. image:: rewire_corr.*
+    .. image:: rewire_uncorr.*
+    .. image:: rewire_erdos.*
 
     *From left to right:* Original graph --- Shuffled graph, with degree
     correlations --- Shuffled graph, without degree correlations --- Shuffled graph,
@@ -540,9 +540,9 @@ def random_rewire(g, strat="uncorrelated", n_iter=1, edge_sweep=True,
     <...>
     >>> legend(loc="best")
     <...>
-    >>> savefig("shuffled-stats.png")
+    >>> savefig("shuffled-stats.pdf")
 
-    .. figure:: shuffled-stats.png
+    .. figure:: shuffled-stats.*
         :align: center
 
         Average degree correlations for the different shuffled and non-shuffled
@@ -592,9 +592,9 @@ def random_rewire(g, strat="uncorrelated", n_iter=1, edge_sweep=True,
     <...>
     >>> ylabel("average target degree")
     <...>
-    >>> savefig("shuffled-deg-corr-dir.png")
+    >>> savefig("shuffled-deg-corr-dir.pdf")
 
-    .. figure:: shuffled-deg-corr-dir.png
+    .. figure:: shuffled-deg-corr-dir.*
         :align: center
 
         Average degree correlations for the different shuffled and non-shuffled
@@ -722,16 +722,16 @@ def graph_union(g1, g2, props=None, include=False):
     >>> 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(g, layout="arf", size=(8,8), output="graph_original.pdf")
     <...>
-    >>> gt.graph_draw(ug, layout="arf", size=(8,8), output="graph_union.png")
+    >>> gt.graph_draw(ug, layout="arf", size=(8,8), output="graph_union.pdf")
     <...>
-    >>> gt.graph_draw(uug, layout="arf", size=(8,8), output="graph_union2.png")
+    >>> gt.graph_draw(uug, layout="arf", size=(8,8), output="graph_union2.pdf")
     <...>
 
-    .. image:: graph_original.png
-    .. image:: graph_union.png
-    .. image:: graph_union2.png
+    .. image:: graph_original.*
+    .. image:: graph_union.*
+    .. image:: graph_union2.*
 
     """
     if props == None:
@@ -837,7 +837,7 @@ def triangulation(points, type="simple", periodic=False):
     >>> b = gt.betweenness(g, weight=weight)
     >>> b[1].a *= 100
     >>> gt.graph_draw(g, pos=pos, pin=True, size=(8,8), vsize=0.07, vcolor=b[0],
-    ...               eprops={"penwidth":b[1]}, output="triang.png")
+    ...               eprops={"penwidth":b[1]}, output="triang.pdf")
     <...>
     >>> g, pos = gt.triangulation(points, type="delaunay")
     >>> weight = g.new_edge_property("double")
@@ -847,13 +847,13 @@ def triangulation(points, type="simple", periodic=False):
     >>> b = gt.betweenness(g, weight=weight)
     >>> b[1].a *= 120
     >>> gt.graph_draw(g, pos=pos, pin=True, size=(8,8), vsize=0.07, vcolor=b[0],
-    ...               eprops={"penwidth":b[1]}, output="triang-delaunay.png")
+    ...               eprops={"penwidth":b[1]}, output="triang-delaunay.pdf")
     <...>
 
     2D triangulation of random points:
 
-    .. image:: triang.png
-    .. image:: triang-delaunay.png
+    .. image:: triang.*
+    .. image:: triang-delaunay.*
 
     *Left:* Simple triangulation. *Right:* Delaunay triangulation. The vertex
     colors and the edge thickness correspond to the weighted betweenness
@@ -904,18 +904,18 @@ def lattice(shape, periodic=False):
     Examples
     --------
     >>> g = gt.lattice([10,10])
-    >>> gt.graph_draw(g, size=(8,8), output="lattice.png")
+    >>> gt.graph_draw(g, size=(8,8), output="lattice.pdf")
     <...>
     >>> g = gt.lattice([10,20], periodic=True)
-    >>> gt.graph_draw(g, size=(8,8), output="lattice_periodic.png")
+    >>> gt.graph_draw(g, size=(8,8), output="lattice_periodic.pdf")
     <...>
     >>> g = gt.lattice([10,10,10])
-    >>> gt.graph_draw(g, size=(8,8), output="lattice_3d.png")
+    >>> gt.graph_draw(g, size=(8,8), output="lattice_3d.pdf")
     <...>
 
-    .. image:: lattice.png
-    .. image:: lattice_periodic.png
-    .. image:: lattice_3d.png
+    .. image:: lattice.*
+    .. image:: lattice_periodic.*
+    .. image:: lattice_3d.*
 
     *Left:* 10x10 2D lattice. *Middle:* 10x20 2D periodic lattice (torus).
     *Right:* 10x10x10 3D lattice.
@@ -974,14 +974,14 @@ def geometric_graph(points, radius, ranges=None):
     >>> seed(42)
     >>> points = random((500, 2)) * 4
     >>> g, pos = gt.geometric_graph(points, 0.3)
-    >>> gt.graph_draw(g, pos=pos, pin=True, size=(8,8), output="geometric.png")
+    >>> gt.graph_draw(g, pos=pos, pin=True, size=(8,8), output="geometric.pdf")
     <...>
     >>> g, pos = gt.geometric_graph(points, 0.3, [(0,4), (0,4)])
-    >>> gt.graph_draw(g, size=(8,8), output="geometric_periodic.png")
+    >>> gt.graph_draw(g, size=(8,8), output="geometric_periodic.pdf")
     <...>
 
-    .. image:: geometric.png
-    .. image:: geometric_periodic.png
+    .. image:: geometric.*
+    .. image:: geometric_periodic.*
 
     *Left:* Geometric network with random points. *Right:* Same network, but
      with periodic boundary conditions.
@@ -1092,15 +1092,15 @@ def price_network(N, m=1, c=None, gamma=1, directed=True, seed_graph=None):
     >>> seed(42)
     >>> g = gt.price_network(100000)
     >>> gt.graph_draw(g, layout="sfdp", size=(12,12), vcolor=g.vertex_index,
-    ...               output="price-network.png")
+    ...               output="price-network.pdf")
     <...>
     >>> g = gt.price_network(100000, c=0.1)
     >>> gt.graph_draw(g, layout="sfdp", size=(12,12), vcolor=g.vertex_index,
-    ...               output="price-network-broader.png")
+    ...               output="price-network-broader.pdf")
     <...>
 
-    .. image:: price-network.png
-    .. image:: price-network-broader.png
+    .. image:: price-network.*
+    .. image:: price-network-broader.*
 
     Price networks with :math:`N=10^5` nodes. **Left:** :math:`c=1`, **Right:**
     :math:`c=0.1`. The colors represent the order in which vertices were

src/graph_tool/search/__init__.py

@@ -60,10 +60,10 @@ In this module, most documentation examples will make use of the network
 >>> gt.graph_draw(g, vprops={"label": name, "shape": "doublecircle",
 ...                          "fillcolor": "#729fcf"},
 ...               penwidth=weight, overlap=False, splines=True,
-...               output="search_example.png")
+...               output="search_example.pdf")
 <...>
 
-.. figure:: search_example.png
+.. figure:: search_example.*
    :alt: search example
    :align: center
 
@@ -1358,10 +1358,10 @@ def astar_search(g, source, weight, visitor=AStarVisitor(),
     >>> gt.graph_draw(g, pos=pos, pin=True, size=(8,8), vsize=0.07,
     ...               vcolor=touch_v, vcmap=matplotlib.cm.binary,