File networkx-pr4012-use-mpl.patch of Package python-networkx

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File networkx-pr4012-use-mpl.patch of Package python-networkx (Revision d47ed62cf22005009bcec900aa418bbe)

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From 83bf28a8f46a311f2bc277eab66226f6b9117c1d Mon Sep 17 00:00:00 2001
From: Ram Rachum <ram@rachum.com>
Date: Sun, 21 Jun 2020 22:24:11 +0300
Subject: [PATCH 1/2] Fix exception causes and messages in 12 modules

---
 examples/subclass/plot_antigraph.py           |  4 +--
 .../algorithms/approximation/kcomponents.py   |  4 +--
 .../algorithms/assortativity/correlation.py   | 18 +++++-----
 networkx/algorithms/bipartite/cluster.py      |  4 +--
 networkx/algorithms/bipartite/edgelist.py     | 19 +++++-----
 networkx/algorithms/bipartite/matching.py     |  4 +--
 networkx/algorithms/bipartite/matrix.py       |  4 +--
 networkx/algorithms/bipartite/spectral.py     |  4 +--
 .../centrality/current_flow_betweenness.py    | 36 +++++++++----------
 .../current_flow_betweenness_subset.py        | 20 +++++------
 networkx/algorithms/centrality/katz.py        | 12 +++----
 .../algorithms/centrality/second_order.py     |  4 +--
 12 files changed, 68 insertions(+), 65 deletions(-)

Index: networkx-2.4/examples/subclass/plot_antigraph.py
===================================================================
--- networkx-2.4.orig/examples/subclass/plot_antigraph.py
+++ networkx-2.4/examples/subclass/plot_antigraph.py
@@ -71,8 +71,8 @@ class AntiGraph(nx.Graph):
         """
         try:
             return iter(set(self.adj) - set(self.adj[n]) - set([n]))
-        except KeyError:
-            raise NetworkXError("The node %s is not in the graph." % (n,))
+        except KeyError as e:
+            raise NetworkXError("The node %s is not in the graph." % (n,)) from e
 
     def degree(self, nbunch=None, weight=None):
         """Return an iterator for (node, degree) in the dense graph.
Index: networkx-2.4/networkx/algorithms/approximation/kcomponents.py
===================================================================
--- networkx-2.4.orig/networkx/algorithms/approximation/kcomponents.py
+++ networkx-2.4/networkx/algorithms/approximation/kcomponents.py
@@ -245,8 +245,8 @@ class _AntiGraph(nx.Graph):
         """
         try:
             return iter(set(self._adj) - set(self._adj[n]) - set([n]))
-        except KeyError:
-            raise NetworkXError("The node %s is not in the graph." % (n,))
+        except KeyError as e:
+            raise NetworkXError("The node %s is not in the graph." % (n,)) from e
 
     class AntiAtlasView(Mapping):
         """An adjacency inner dict for AntiGraph"""
Index: networkx-2.4/networkx/algorithms/assortativity/correlation.py
===================================================================
--- networkx-2.4.orig/networkx/algorithms/assortativity/correlation.py
+++ networkx-2.4/networkx/algorithms/assortativity/correlation.py
@@ -132,9 +132,9 @@ def degree_pearson_correlation_coefficie
     """
     try:
         import scipy.stats as stats
-    except ImportError:
-        raise ImportError(
-            "Assortativity requires SciPy: http://scipy.org/ ")
+    except ImportError as e:
+        raise ImportError("Assortativity requires SciPy:"
+                          "http://scipy.org/ ") from e
     xy = node_degree_xy(G, x=x, y=y, nodes=nodes, weight=weight)
     x, y = zip(*xy)
     return stats.pearsonr(x, y)[0]
@@ -254,9 +254,9 @@ def attribute_ac(M):
     """
     try:
         import numpy
-    except ImportError:
-        raise ImportError(
-            "attribute_assortativity requires NumPy: http://scipy.org/ ")
+    except ImportError as e:
+        raise ImportError('attribute_assortativity requires '
+                          'NumPy: http://scipy.org/') from e
     if M.sum() != 1.0:
         M = M / float(M.sum())
     M = numpy.asmatrix(M)
@@ -271,9 +271,9 @@ def numeric_ac(M):
     # numeric assortativity coefficient, pearsonr
     try:
         import numpy
-    except ImportError:
-        raise ImportError('numeric_assortativity requires ',
-                          'NumPy: http://scipy.org/')
+    except ImportError as e:
+        raise ImportError('numeric_assortativity requires '
+                          'NumPy: http://scipy.org/') from e
     if M.sum() != 1.0:
         M = M / float(M.sum())
     nx, ny = M.shape  # nx=ny
Index: networkx-2.4/networkx/algorithms/bipartite/cluster.py
===================================================================
--- networkx-2.4.orig/networkx/algorithms/bipartite/cluster.py
+++ networkx-2.4/networkx/algorithms/bipartite/cluster.py
@@ -115,9 +115,9 @@ def latapy_clustering(G, nodes=None, mod
 
     try:
         cc_func = modes[mode]
-    except KeyError:
+    except KeyError as e:
         raise nx.NetworkXError(
-            "Mode for bipartite clustering must be: dot, min or max")
+            "Mode for bipartite clustering must be: dot, min or max") from e
 
     if nodes is None:
         nodes = G
Index: networkx-2.4/networkx/algorithms/bipartite/edgelist.py
===================================================================
--- networkx-2.4.orig/networkx/algorithms/bipartite/edgelist.py
+++ networkx-2.4/networkx/algorithms/bipartite/edgelist.py
@@ -139,8 +139,8 @@ def generate_edgelist(G, delimiter=' ',
     """
     try:
         part0 = [n for n, d in G.nodes.items() if d['bipartite'] == 0]
-    except:
-        raise AttributeError("Missing node attribute `bipartite`")
+    except BaseException as e:
+        raise AttributeError("Missing node attribute `bipartite`") from e
     if data is True or data is False:
         for n in part0:
             for e in G.edges(n, data=data):
@@ -242,9 +242,9 @@ def parse_edgelist(lines, comments='#',
             try:
                 u = nodetype(u)
                 v = nodetype(v)
-            except:
+            except BaseException as e:
                 raise TypeError("Failed to convert nodes %s,%s to type %s."
-                                % (u, v, nodetype))
+                                % (u, v, nodetype)) from e
 
         if len(d) == 0 or data is False:
             # no data or data type specified
@@ -253,9 +253,9 @@ def parse_edgelist(lines, comments='#',
             # no edge types specified
             try:  # try to evaluate as dictionary
                 edgedata = dict(literal_eval(' '.join(d)))
-            except:
-                raise TypeError(
-                    "Failed to convert edge data (%s) to dictionary." % (d))
+            except BaseException as e:
+                raise TypeError("Failed to convert edge data"
+                                " (%s) to dictionary." % (d)) from e
         else:
             # convert edge data to dictionary with specified keys and type
             if len(d) != len(data):
@@ -266,10 +266,10 @@ def parse_edgelist(lines, comments='#',
             for (edge_key, edge_type), edge_value in zip(data, d):
                 try:
                     edge_value = edge_type(edge_value)
-                except:
+                except BaseException as e:
                     raise TypeError(
                         "Failed to convert %s data %s to type %s."
-                        % (edge_key, edge_value, edge_type))
+                        % (edge_key, edge_value, edge_type)) from e
                 edgedata.update({edge_key: edge_value})
         G.add_node(u, bipartite=0)
         G.add_node(v, bipartite=1)
Index: networkx-2.4/networkx/algorithms/bipartite/matching.py
===================================================================
--- networkx-2.4.orig/networkx/algorithms/bipartite/matching.py
+++ networkx-2.4/networkx/algorithms/bipartite/matching.py
@@ -555,9 +555,9 @@ def minimum_weight_full_matching(G, top_
     """
     try:
         import scipy.optimize
-    except ImportError:
+    except ImportError as e:
         raise ImportError('minimum_weight_full_matching requires SciPy: ' +
-                          'https://scipy.org/')
+                          'https://scipy.org/') from e
     left, right = nx.bipartite.sets(G, top_nodes)
     # Ensure that the graph is complete. This is currently a requirement in
     # the underlying  optimization algorithm from SciPy, but the constraint
Index: networkx-2.4/networkx/algorithms/bipartite/matrix.py
===================================================================
--- networkx-2.4.orig/networkx/algorithms/bipartite/matrix.py
+++ networkx-2.4/networkx/algorithms/bipartite/matrix.py
@@ -109,8 +109,9 @@ def biadjacency_matrix(G, row_order, col
         return M.asformat(format)
     # From Scipy 1.1.0, asformat will throw a ValueError instead of an
     # AttributeError if the format if not recognized.
-    except (AttributeError, ValueError):
-        raise nx.NetworkXError("Unknown sparse matrix format: %s" % format)
+    except (AttributeError, ValueError) as e:
+        raise nx.NetworkXError(
+		"Unknown sparse matrix format: %s" % format) from e
 
 
 def from_biadjacency_matrix(A, create_using=None, edge_attribute='weight'):
Index: networkx-2.4/networkx/algorithms/bipartite/spectral.py
===================================================================
--- networkx-2.4.orig/networkx/algorithms/bipartite/spectral.py
+++ networkx-2.4/networkx/algorithms/bipartite/spectral.py
@@ -56,9 +56,9 @@ def spectral_bipartivity(G, nodes=None,
     """
     try:
         import scipy.linalg
-    except ImportError:
+    except ImportError as e:
         raise ImportError('spectral_bipartivity() requires SciPy: ',
-                          'http://scipy.org/')
+                          'http://scipy.org/') from e
     nodelist = list(G)  # ordering of nodes in matrix
     A = nx.to_numpy_matrix(G, nodelist, weight=weight)
     expA = scipy.linalg.expm(A)
Index: networkx-2.4/networkx/algorithms/centrality/current_flow_betweenness.py
===================================================================
--- networkx-2.4.orig/networkx/algorithms/centrality/current_flow_betweenness.py
+++ networkx-2.4/networkx/algorithms/centrality/current_flow_betweenness.py
@@ -90,15 +90,15 @@ def approximate_current_flow_betweenness
     """
     try:
         import numpy as np
-    except ImportError:
-        raise ImportError('current_flow_betweenness_centrality requires NumPy ',
-                          'http://scipy.org/')
+    except ImportError as e:
+        raise ImportError('current_flow_betweenness_centrality requires NumPy '
+                          'http://scipy.org/') from e
     try:
         from scipy import sparse
         from scipy.sparse import linalg
-    except ImportError:
-        raise ImportError('current_flow_betweenness_centrality requires SciPy ',
-                          'http://scipy.org/')
+    except ImportError as e:
+        raise ImportError('current_flow_betweenness_centrality requires SciPy '
+                          'http://scipy.org/') from e
     if not nx.is_connected(G):
         raise nx.NetworkXError("Graph not connected.")
     solvername = {"full": FullInverseLaplacian,
@@ -214,14 +214,14 @@ def current_flow_betweenness_centrality(
     """
     try:
         import numpy as np
-    except ImportError:
-        raise ImportError('current_flow_betweenness_centrality requires NumPy ',
-                          'http://scipy.org/')
+    except ImportError as e:
+        raise ImportError('current_flow_betweenness_centrality requires NumPy '
+                          'http://scipy.org/') from e
     try:
         import scipy
-    except ImportError:
-        raise ImportError('current_flow_betweenness_centrality requires SciPy ',
-                          'http://scipy.org/')
+    except ImportError as e:
+        raise ImportError('current_flow_betweenness_centrality requires SciPy '
+                          'http://scipy.org/') from e
     if not nx.is_connected(G):
         raise nx.NetworkXError("Graph not connected.")
     n = G.number_of_nodes()
@@ -326,14 +326,14 @@ def edge_current_flow_betweenness_centra
     from networkx.utils import reverse_cuthill_mckee_ordering
     try:
         import numpy as np
-    except ImportError:
-        raise ImportError('current_flow_betweenness_centrality requires NumPy ',
-                          'http://scipy.org/')
+    except ImportError as e:
+        raise ImportError('current_flow_betweenness_centrality requires NumPy '
+                          'http://scipy.org/') from e
     try:
         import scipy
-    except ImportError:
-        raise ImportError('current_flow_betweenness_centrality requires SciPy ',
-                          'http://scipy.org/')
+    except ImportError as e:
+        raise ImportError('current_flow_betweenness_centrality requires SciPy '
+                          'http://scipy.org/') from e
     if not nx.is_connected(G):
         raise nx.NetworkXError("Graph not connected.")
     n = G.number_of_nodes()
Index: networkx-2.4/networkx/algorithms/centrality/current_flow_betweenness_subset.py
===================================================================
--- networkx-2.4.orig/networkx/algorithms/centrality/current_flow_betweenness_subset.py
+++ networkx-2.4/networkx/algorithms/centrality/current_flow_betweenness_subset.py
@@ -99,14 +99,14 @@ def current_flow_betweenness_centrality_
     from networkx.utils import reverse_cuthill_mckee_ordering
     try:
         import numpy as np
-    except ImportError:
+    except ImportError as e:
         raise ImportError('current_flow_betweenness_centrality requires NumPy ',
-                          'http://scipy.org/')
+                          'http://scipy.org/') from e
     try:
         import scipy
-    except ImportError:
+    except ImportError as e:
         raise ImportError('current_flow_betweenness_centrality requires SciPy ',
-                          'http://scipy.org/')
+                          'http://scipy.org/') from e
     if not nx.is_connected(G):
         raise nx.NetworkXError("Graph not connected.")
     n = G.number_of_nodes()
@@ -214,14 +214,14 @@ def edge_current_flow_betweenness_centra
     """
     try:
         import numpy as np
-    except ImportError:
-        raise ImportError('current_flow_betweenness_centrality requires NumPy ',
-                          'http://scipy.org/')
+    except ImportError as e:
+        raise ImportError('current_flow_betweenness_centrality requires NumPy '
+                          'http://scipy.org/') from e
     try:
         import scipy
-    except ImportError:
-        raise ImportError('current_flow_betweenness_centrality requires SciPy ',
-                          'http://scipy.org/')
+    except ImportError as e:
+        raise ImportError('current_flow_betweenness_centrality requires SciPy '
+                          'http://scipy.org/') from e
     if not nx.is_connected(G):
         raise nx.NetworkXError("Graph not connected.")
     n = G.number_of_nodes()
Index: networkx-2.4/networkx/algorithms/centrality/katz.py
===================================================================
--- networkx-2.4.orig/networkx/algorithms/centrality/katz.py
+++ networkx-2.4/networkx/algorithms/centrality/katz.py
@@ -160,11 +160,11 @@ def katz_centrality(G, alpha=0.1, beta=1
 
     try:
         b = dict.fromkeys(G, float(beta))
-    except (TypeError, ValueError, AttributeError):
+    except (TypeError, ValueError, AttributeError) as e:
         b = beta
         if set(beta) != set(G):
             raise nx.NetworkXError('beta dictionary '
-                                   'must have a value for every node')
+                                   'must have a value for every node') from e
 
     # make up to max_iter iterations
     for i in range(max_iter):
@@ -308,8 +308,8 @@ def katz_centrality_numpy(G, alpha=0.1,
     """
     try:
         import numpy as np
-    except ImportError:
-        raise ImportError('Requires NumPy: http://scipy.org/')
+    except ImportError as e:
+        raise ImportError('Requires NumPy: http://numpy.org/') from e
     if len(G) == 0:
         return {}
     try:
@@ -322,8 +322,8 @@ def katz_centrality_numpy(G, alpha=0.1,
         nodelist = list(G)
         try:
             b = np.ones((len(nodelist), 1)) * float(beta)
-        except (TypeError, ValueError, AttributeError):
-            raise nx.NetworkXError('beta must be a number')
+        except (TypeError, ValueError, AttributeError) as e:
+            raise nx.NetworkXError('beta must be a number') from e
 
     A = nx.adj_matrix(G, nodelist=nodelist, weight=weight).todense().T
     n = A.shape[0]
Index: networkx-2.4/networkx/algorithms/centrality/second_order.py
===================================================================
--- networkx-2.4.orig/networkx/algorithms/centrality/second_order.py
+++ networkx-2.4/networkx/algorithms/centrality/second_order.py
@@ -99,8 +99,8 @@ def second_order_centrality(G):
 
     try:
         import numpy as np
-    except ImportError:
-        raise ImportError('Requires NumPy: http://scipy.org/')
+    except ImportError as e:
+        raise ImportError('Requires NumPy: http://numpy.org/') from e
 
     n = len(G)
 
Index: networkx-2.4/networkx/drawing/tests/test_pylab.py
===================================================================
--- networkx-2.4.orig/networkx/drawing/tests/test_pylab.py
+++ networkx-2.4/networkx/drawing/tests/test_pylab.py
@@ -4,7 +4,7 @@ import itertools
 import pytest
 
 mpl = pytest.importorskip('matplotlib')
-mpl.use('PS', warn=False)
+mpl.use('PS')
 plt = pytest.importorskip('matplotlib.pyplot')
 plt.rcParams['text.usetex'] = False

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