Hello community, here is the log from the commit of package python-networkx for openSUSE:Factory checked in at 2020-08-01 12:29:45 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Comparing /work/SRC/openSUSE:Factory/python-networkx (Old) and /work/SRC/openSUSE:Factory/.python-networkx.new.3592 (New) ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Package is "python-networkx" Sat Aug 1 12:29:45 2020 rev:23 rq:822138 version:2.4 Changes: -------- --- /work/SRC/openSUSE:Factory/python-networkx/python-networkx.changes 2020-07-17 20:45:39.240591270 +0200 +++ /work/SRC/openSUSE:Factory/.python-networkx.new.3592/python-networkx.changes 2020-08-01 12:30:03.966394350 +0200 @@ -1,0 +2,6 @@ +Tue Jul 21 16:10:13 UTC 2020 - Benjamin Greiner <code@bnavigator.de> + +- gh#networkx/networkx#4012 networkx-pr4012-use-mpl.patch + new matplotlib removed keyword argument 'warn' for mpl.use() + +------------------------------------------------------------------- New: ---- networkx-pr4012-use-mpl.patch ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Other differences: ------------------ ++++++ python-networkx.spec ++++++ --- /var/tmp/diff_new_pack.YJZr0C/_old 2020-08-01 12:30:05.774396043 +0200 +++ /var/tmp/diff_new_pack.YJZr0C/_new 2020-08-01 12:30:05.778396047 +0200 @@ -29,6 +29,8 @@ Patch0: numpy-38-test.patch # UPSTREAM PATCH: gh#networkx/networkx#3697 Patch1: matplotlib.patch +# UPSTREAM PATCH: gh#networkx/networkx#4012 +Patch2: networkx-pr4012-use-mpl.patch BuildRequires: %{python_module PyYAML} BuildRequires: %{python_module decorator >= 3.4.0} BuildRequires: %{python_module matplotlib >= 3.1} ++++++ networkx-pr4012-use-mpl.patch ++++++

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