- Update to version 2.2:
* Add support for Python 3.7. This is the last release to support
Python 2.
* Uniform random number generator (RNG) handling which defaults
to global RNGs but allows specification of a single RNG for all
random numbers in NX.
* Improved GraphViews to ease subclassing and remove cyclic
references which caused trouble with deepcopy and pickle.
* New Graph method G.update(H)
- Run tests
- Update project url
OBS-URL: https://build.opensuse.org/request/show/662473
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-networkx?expand=0&rev=5
- update to 1.10:
* connected_components, weakly_connected_components, and
strongly_connected_components return now a generator of
sets of nodes. Previously the generator was of lists of
nodes. This PR also refactored the connected_components
and weakly_connected_components implementations making them
faster, especially for large graphs.
* The func_iter functions in Di/Multi/Graphs classes are slated
for removal in NetworkX 2.0 release. func will behave like func_iter
and return an iterator instead of list. These functions are deprecated
in NetworkX 1.10 release.
* A enumerate_all_cliques function is added in the clique package
(networkx.algorithms.clique) for enumerating all cliques
(including nonmaximal ones) of undirected graphs.
* A coloring package (networkx.algorithms.coloring) is created for graph
coloring algorithms. Initially, a greedy_color function is provided
for coloring graphs using various greedy heuristics.
* A new generator edge_dfs, added to networkx.algorithms.traversal, implements
a depth-first traversal of the edges in a graph. This complements
functionality provided by a depth-first traversal of the nodes in
a graph. For multigraphs, it allows the user to know precisely which
edges were followed in a traversal. All NetworkX graph types are
supported. A traversal can also reverse edge orientations or ignore them.
* A find_cycle function is added to the networkx.algorithms.cycles package
to find a cycle in a graph. Edge orientations can be optionally
reversed or ignored.
* Add a random generator for the duplication-divergence model.
* A new networkx.algorithms.dominance package is added for dominance/dominator
algorithms on directed graphs. It contains a immediate_dominators
function for computing immediate dominators/dominator trees and a (forwarded request 330044 from tbechtold)
OBS-URL: https://build.opensuse.org/request/show/330047
OBS-URL: https://build.opensuse.org/package/show/openSUSE:Factory/python-networkx?expand=0&rev=8
