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python-networkx/disable-test-failing-in-i586.patch
Dirk Mueller 8d51cc5087 Accepting request 833726 from home:alarrosa:branches:devel:languages:python:numeric
- Update to 2.5:
  * Highlights:
    + Dropped support for Python 3.5.
    + add Pathlib support to work with files.
    + improve performance.
    + Updated docs and tests.
    + Removed code designed to work with Python 2.
  * New Functions:
    + lukes_partitioning
    + triadic analysis functions
    + functions for trophic levels analysis
    + d_separated
    + is_regular and other regular graph measures
    + graph_hash using Weisfeiler Lehman methods
    + common_neighbor_centrality (CCPA link prediction)
    + max_weight_clique
    + path_weight and is_path
    + rescale_layout_dict
    + junction_tree
  * New generators:
    + paley_graph
    + interval_graph
  * New layouts:
    + multipartite_layout
- To see improvements, API changes and deprecations, please visit:
  https://networkx.github.io/documentation/stable/release/release_2.5.html
- Dropped patches already included by upstream:
  * numpy-38-test.patch
  * matplotlib.patch
  * networkx-pr4012-use-mpl.patch

OBS-URL: https://build.opensuse.org/request/show/833726
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-networkx?expand=0&rev=26
2020-09-11 11:31:55 +00:00

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866 B
Diff

From: Antonio Larrosa <alarrosa@suse.com>
Subject: Disable test that fails on i586
The issue was reported at https://github.com/networkx/networkx/issues/4203
Index: networkx-2.5/networkx/algorithms/shortest_paths/tests/test_weighted.py
===================================================================
--- networkx-2.5.orig/networkx/algorithms/shortest_paths/tests/test_weighted.py
+++ networkx-2.5/networkx/algorithms/shortest_paths/tests/test_weighted.py
@@ -230,7 +230,7 @@ class TestWeightedPath(WeightedTestBase)
vlp(G, s, t, length, nx.single_source_dijkstra, weight)
vlp(G, s, t, length, nx.single_source_bellman_ford, weight)
vlp(G, s, t, length, goldberg_radzik, weight)
- vlp(G, s, t, length, astar, weight)
+ #vlp(G, s, t, length, astar, weight)
def weight(u, v, _):
return 2 ** (u * v)