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File python-networkx.changes of Package python-networkx
------------------------------------------------------------------- Tue Oct 31 03:14:26 UTC 2017 - arun@gmx.de - specfile: * changes from tar.gz to zip * updated sed * INSTALL doesn't seem to be packaged anymore, deleted "rm" command - update to version 2.0: * Highlights + This release is the result of over two years of work with 1212 commits and 193 merges by 86 contributors. Highlights include: + We have made major changes to the methods in the Multi/Di/Graph classes. There is a migration guide for people moving from 1.X to 2.0. + We updated the documentation system. * full release notes at https://networkx.github.io/documentation/stable/release/release_2.0.html ------------------------------------------------------------------- Sun Aug 6 04:46:44 UTC 2017 - toddrme2178@gmail.com - Fix shebangs ------------------------------------------------------------------- Thu May 11 03:12:50 UTC 2017 - toddrme2178@gmail.com - Implement single-spec version. - Fix source URL. ------------------------------------------------------------------- Wed Aug 17 08:35:56 UTC 2016 - tbechtold@suse.com update to version networkx-1.11 * Update release and news info for v1.10.1 * Use utils.testing to handle testing edge and node equality * Update news to include 1.10 release highlights * Remove spurious line due to typo. * Fix algebraicconnectivity float conversion * Fix python3 numpy wont read in {}.values to array. * update requirements.txt on v1.11 branch * update doc/requirements.txt to point Sphinx-origin_stable * Update license, readme, and release.py for networkx-1.11 * adjust tutorial to mention import write_dot * Revert some API changes in layout.py due to bugs. * Update news and api for v1.11 * Update authors, copyrights and EOL space * Add release date in news * Add tests, convert center to np.array, fix domain_size * Put graphviz install outside check for python2.7 * Activate Appveyor-CI * Add layout tests and minor docs * networkx-1.11rc2 label * Remove all the symbolic links from the 'examples/' directory * v1.11 Add utils functions to flow variable __all__ * Fix Sphinx for v1.11 * Prepare release number and news.rst for v1.11 * simplify pydot imports, use testing.utils routines * Get the month right. * update release docs files for v1.11 * Use pydotplus for all supported python versions * Add note about pyggraphviz and pydotplus import changes * Modified release.py * change copyright year in doc build * For v1.11 drop support for python3.2 and add 3.5 * Update news.rst for v1.11 * Examples and doc changes * Re-add scaling inside fruchterman_reingold * Update conf.py to point to make_examples_rst.py * Reinstate v1.10 layout except center. Fix bugs * Adjust imports in drawing layouts with graphviz * Doc tweak on edges for v1.11 ------------------------------------------------------------------- Sun Mar 13 21:28:48 UTC 2016 - dmueller@suse.com - add license/readme ------------------------------------------------------------------- Wed Sep 9 12:32:21 UTC 2015 - tbechtold@suse.com - 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 dominance_frontiers function for computing dominance frontiers. * The GML reader/parser and writer/generator are rewritten to remove the dependence on pyparsing and enable handling of arbitrary graph data. * The network simplex method in the networkx.algorithms.flow package is rewritten to improve its performance and support multi- and disconnected networks. For some cases, the new implementation is two or three orders of magnitude faster than the old implementation. * Added the Margulis--Gabber--Galil graph to networkx.generators. * Added the chordal p-cycle graph, a mildly explicit algebraic construction of a family of 3-regular expander graphs. Also, moves both the existing expander graph generator function (for the Margulis-Gabber-Galil expander) and the new chordal cycle graph function to a new module, networkx.generators.expanders. * Allow overwriting of base class dict with dict-like: OrderedGraph, ThinGraph, LogGraph, etc. * Added to_pandas_dataframe and from_pandas_dataframe. * Added the Hopcroft--Karp algorithm for finding a maximum cardinality matching in bipartite graphs. * Expanded data keyword in G.edges and added default keyword. * Added support for finding optimum branchings and arborescences. * Added a from_pandas_dataframe function that accepts Pandas DataFrames and returns a new graph object. At a minimum, the DataFrame must have two columns, which define the nodes that make up an edge. However, the function can also process an arbitrary number of additional columns as edge attributes, such as 'weight'. * Expanded layout functions to add flexibility for drawing subsets of nodes with distinct layouts and for centering each layout around given coordinates. * Added ordered variants of default graph class. * Added harmonic centrality to network.algorithms.centrality. * The generators.bipartite have been moved to algorithms.bipartite.generators. The functions are not imported in the main namespace, so to use it, the bipartite package has to be imported. * Added Kanevsky's algorithm for finding all minimum-size separating node sets in an undirected graph. It is implemented as a generator of node cut sets. * Added power function for simple graphs * Added fast approximation for node connectivity based on White and Newman's approximation algorithm for finding node independent paths between two nodes. * Added transitive closure and antichains function for directed acyclic graphs in algorithms.dag. The antichains function was contributed by Peter Jipsen and Franco Saliola and originally developed for the SAGE project. * Added generator function for the complete multipartite graph. * Added nonisomorphic trees generator. * Added a generator function for circulant graphs to the networkx.generators.classic module. * Added function for computing quotient graphs; also created a new module, networkx.algorithms.minors. * Added longest_path and longest_path_length for DAG. * Added node and edge contraction functions to networkx.algorithms.minors. * Added a new modularity matrix module to networkx.linalg, and associated spectrum functions to the networkx.linalg.spectrum module. * Added function to generate all simple paths starting with the shortest ones based on Yen's algorithm for finding k shortest paths at algorithms.simple_paths. * Added the directed modularity matrix to the networkx.linalg.modularity_matrix module. * Adds triadic_census function; also creates a new module, networkx.algorithms.triads. * Adds functions for testing if a graph has weighted or negatively weighted edges. Also adds a function for testing if a graph is empty. These are is_weighted, is_negatively_weighted, and is_empty. * Added Johnson's algorithm; one more algorithm for shortest paths. It solves all pairs shortest path problem. This is johnson at algorithms.shortest_paths * Added Moody and White algorithm for identifying k_components in a graph, which is based on Kanevsky's algorithm for finding all minimum-size node cut-sets (implemented in all_node_cuts #1391). * Added fast approximation for k_components to the networkx.approximation package. This is based on White and Newman approximation algorithm for finding node independent paths between two nodes (see #1405). * The legacy ford_fulkerson maximum flow function is removed. Use edmonds_karp instead. * Support for Python 2.6 is dropped. ------------------------------------------------------------------- Sat Jul 25 12:36:58 UTC 2015 - seife+obs@b1-systems.com - fix rhel build by conditionalizing "Recommends:" tags - do not hardcode /usr/share/doc/packages but use %_docdir ------------------------------------------------------------------- Wed Apr 29 14:25:15 UTC 2015 - tbechtold@suse.com - Don't BuildRequires python-pygraphviz. It's not needed. ------------------------------------------------------------------- Thu Oct 30 10:46:52 UTC 2014 - tbechtold@suse.com - update to version 1.9.1: * Bugfix release for minor installation and documentation issues - Don't BuildRequire/Recommend matplotlib and scipy on SLE11 and SLE12. Both are not available there. ------------------------------------------------------------------- Fri Oct 24 09:35:49 UTC 2014 - toddrme2178@gmail.com - Add python-decorator in requires to buildrequires ------------------------------------------------------------------- Mon Sep 15 14:49:41 UTC 2014 - tbechtold@suse.com - update to version 1.9: * The flow package (networkx.algorithms.flow) is completely rewritten with backward incompatible changes. It introduces a new interface to flow algorithms. Existing code that uses the flow package will not work unmodified with NetworkX 1.9. * We added two new maximum flow algorithms (preflow_push and shortest_augmenting_path) and rewrote All maximum flow algorithm implementations (including the legacy ford_fulkerson) output now a residual network (i.e., a DiGraph) after computing the maximum flow. See maximum_flow documentation for the details on the conventions that NetworkX uses for defining a residual network. * We removed the old max_flow and min_cut functions. The main entry points to flow algorithms are now the functions maximum_flow, maximum_flow_value, minimum_cut and minimum_cut_value, which have new parameters that control maximum flow computation: flow_func for specifying the algorithm that will do the actual computation (it accepts a function as argument that implements a maximum flow algorithm), cutoff for suggesting a maximum flow value at which the algorithm stops, value_only for stopping the computation as soon as we have the value of the flow, and residual that accepts as argument a residual network to be reused in repeated maximum flow computation. * All flow algorithms are required to accept arguments for these parameters but may selectively ignored the inapplicable ones. For instance, preflow_push algorithm can stop after the preflow phase without computing a maximum flow if we only need the flow value, but both edmonds_karp and shortest_augmenting_path always compute a maximum flow to obtain the low value. * The new function minimum_cut returns the cut value and a node partition that defines the minimum cut. The function minimum_cut_value returns only the value of the cut, which is what the removed min_cut function used to return before 1.9. * The functions that implement flow algorithms (i.e., preflow_push, edmonds_karp, shortest_augmenting_path and ford_fulkerson) are not imported to the base NetworkX namespace. You have to explicitly import them from the flow package. * We also added a capacity-scaling minimum cost flow algorithm: capacity scaling. It supports MultiDiGraph and disconnected networks. - Add python-decorator as Requires ------------------------------------------------------------------- Mon Dec 9 13:26:37 UTC 2013 - toddrme2178@gmail.com - Add optional dependencies as Recommends ------------------------------------------------------------------- Sun Dec 8 13:49:40 UTC 2013 - p.drouand@gmail.com - Update to version 1.8.1 + No changelog available ------------------------------------------------------------------- Tue Jan 31 14:42:25 UTC 2012 - saschpe@suse.de - Don't package INSTALL.txt and other docs twice ------------------------------------------------------------------- Thu Jan 12 14:52:26 UTC 2012 - saschpe@suse.de - Spec file cosmetics ------------------------------------------------------------------- Wed Jan 11 14:56:08 UTC 2012 - toddrme2178@gmail.com - Cleaned up spec file - Renamed package from python-NetworkX to python-networkx to match the module name ------------------------------------------------------------------- Thu Sep 8 20:27:43 UTC 2011 - alinm.elena@gmail.com - initial commit ------------------------------------------------------------------- Fri Feb 6 00:00:00 UTC 2009 - urs.beyerle@env.ethz.ch - update to 0.99 ------------------------------------------------------------------- Thu Jun 26 00:00:00 UTC 2008 - ti.eugene@gmail.com - Initial build
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