python-networkx-2.4-bp152.1.1

- Drop incorrectly calculated dependency on python33
- Switch to using %pytest macro

- Update to version 2.4
  Highlights:
  * Remove deprecated code from 1.x
  * Support for Python 3.8
  * Switched to pytest for testing
  * Last release to support Python 3.5
  * Fifteen new fuctions, including onion decomposition and linear prufing
  * Three new generators, such as a directed joint degree generator
- Add numpy-38-test.patch, to correct test failure under Python 3.8
- Update URL, upstream changed to tarballs from zipfiles.

- Update to version 2.3
  Highlights:
  * Dropped support for Python 2. We are no longer supporting Python 2.7 and we will
    start changing code to take advantage of Python 3 features we couldn't before.
  * Added some Moral Graph analysis functions.
  * Enable matplotlib drawing using curved arrows via connectionstyle parameter.
  * Remove ticks and axes labels from matplotlib plots.
  * Two new generators of Harary Graphs.
  * Added Dual Barabasi-Albert model
  * Added VoteRank algorithm
  * Added Equitable coloring algorithms
  * Added planar layout algorithms
  * Les Miserables network example
  * Javascript example update

- Switch off test_subgraph_centrality_big_graph test, as it leads to
  crashes on i586, gh#networkx/networkx#3304.

- Fix incomplete summary. Trim filler wording from description.

- 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

- specfile:
  * update copyright year
  * removed devel from noarch package
- update to version 2.1:
  * Highlights
    + Arrows for drawing DiGraph edges are vastly improved! And an
    example to show them.
    + More than 12 new functions for graph generation, manipulation
    and/or new graph algorithms.
    o Add a large clique size heuristic function (#2830)
    o Add rooted product function (#2825)
    o Label Propagation Community Detection (#2821)
    o Minimum cycle basis (#2823)
    o Add Mycielski Operator (#2785)
    o Adds prefix_tree, dag_to_branching, and example. (#2784)
    o Add inverse_line_graph generator from #2241 (#2782)
    o Steiner tree and metric closure. (#2252)
    o Add flow based node and edge disjoint paths. (#2063)
    o Update geometric networks with new models (#2498)
    o Graph edit distance (#2729)
    o Added function for finding a k-edge-augmentation (#2572)
    + G.name is no longer processed by graph operators. It remains as
    a property mechanism to access G.graph['name'] but the user is
    in charge of updating or changing it for copies, subgraphs,
    unions and other graph operations.

- 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

- Fix shebangs

- Implement single-spec version.
- Fix source URL.

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

- add license/readme

- 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.

- fix rhel build by conditionalizing "Recommends:" tags
- do not hardcode /usr/share/doc/packages but use %_docdir

- Don't BuildRequires python-pygraphviz. It's not needed.

- 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.

- Add python-decorator in requires to buildrequires

- 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

- Add optional dependencies as Recommends

- Update to version 1.8.1
  + No changelog available

- Don't package INSTALL.txt and other docs twice

- Spec file cosmetics

- Cleaned up spec file
- Renamed package from python-NetworkX to python-networkx to match the module name

- initial commit

- update to 0.99

- Initial build