Merge branch 'master' into header-cleanup

doc/demos/inference/_assortative.rst

+.. _planted_partition:
+
+Assortative community structure
+-------------------------------
+
+Traditionally, `"community structure"
+<https://en.wikipedia.org/wiki/Community_structure>`_ in the proper
+sense refers to groups of nodes that are more connected to each other
+than to nodes of other communities. The SBM is capable of representing
+this kind of structure without any problems, but in some circumstances
+it might make sense to search exclusively for assortative communities
+[lizhi-statistical-2020]_. A version of the SBM that is constrained in
+this way is called the "planted partition model", which can be inferred
+with graph-tool using
+:class:`~graph_tool.inference.planted_partition.PPBlockState`. This
+class behaves just like
+:class:`~graph_tool.inference.blockmodel.BlockState`, therefore all
+algorithms described in this documentation work in the same way. Below
+we show how this model can be inferred for the football network
+considered previously
+
+.. testsetup:: assortative
+
+   import os
+   try:
+       os.chdir("demos/inference")
+   except FileNotFoundError:
+       pass
+   gt.seed_rng(42)
+
+.. testcode:: assortative
+
+   g = gt.collection.data["football"]
+
+   state = gt.PPBlockState(g)
+
+   # Now we run 1,000 sweeps of the MCMC with zero temperature.
+   state.multiflip_mcmc_sweep(beta=np.inf, niter=1000)
+
+   state.draw(pos=g.vp.pos, output="football-pp.svg")
+
+.. figure:: football-pp.*
+   :align: center
+   :width: 350px
+
+   Best fit of the degree-corrected planted partition model to a
+   network of American college football teams.
+
+It is possible to perform model comparison with other model variations
+in the same manner as described in :ref:`sec_model_selection` below.

src/graph_tool/__init__.py

@@ -430,6 +430,7 @@ class PropertyMap(object):
                  "long double", "vector<bool>", "vector<int16_t>",
                  "vector<int32_t>", "vector<int64_t>", "vector<double>",
                  "vector<long double>", "string", "vector<string>"]
+        p = None
         for t in types:
             try:
                 p = self.copy(value_type=t, full=full)
@@ -439,8 +440,10 @@ class PropertyMap(object):
                         continue
                 if p.copy(value_type=self.value_type(), full=full) == self:
                     break
-            except (TypeError, ValueError, OverflowError):
+            except (TypeError, ValueError, OverflowError, AttributeError):
                 pass
+        if p is None:
+            p = self.copy()
         return p