./math/py-networkx, Python package for creating and manipulating graphs and networks

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Branch: CURRENT, Version: 1.11, Package name: py27-networkx-1.11, Maintainer: pkgsrc-users

NetworkX (NX) is a Python package for the creation, manipulation, and
study of the structure, dynamics, and functions of complex networks.


- Includes standard graph-theoretic and statistical physics functions
- Easy exchange of network algorithms between applications,
disciplines, and platforms
- Includes many classic graphs and synthetic networks
- Nodes and edges can be "anything"
(e.g. time-series, text, images, XML records)
- Exploits existing code from high-quality legacy software in C,
C++, Fortran, etc.
- Open source (encourages community input)
- Unit-tested

Required to run:
[devel/py-setuptools] [lang/python27] [devel/py-decorator]

Required to build:

Master sites:

SHA1: ac24380b13dfe92633370ad2091c0c04b6d098a2
RMD160: f8338d0bf5327dc29e25a7a94bfe363ca25d228b
Filesize: 1284.899 KB

Version history: (Expand)

CVS history: (Expand)

   2016-09-11 18:55:17 by Thomas Klausner | Files touched by this commit (1)
Log message:
This package does in fact support python-3.x.
   2016-08-12 15:38:22 by Wen Heping | Files touched by this commit (3) | Package updated
Log message:
Update to 1.11
Based on PR/51271 from kamelderouiche@yahoo.com

Upstream changes:

API changes

    [#1930] No longer import nx_agraph and nx_pydot into the top-level \ 
namespace. They can be accessed within networkx as e.g. nx.nx_agraph.write_dot \ 
or imported as from networkx.drawing.nx_agraph import write_dot.
    [#1750] Arguments center and scale are now available for all layout \ 
functions. The defaul values revert to the v1.9 values (center is the origin for \ 
circular layouts and domain is [0, scale) for others.
    [#1924] Replace pydot with pydotplus for drawing with the pydot interface.
    [#1888] Replace support for Python3.2 with support for Python 3.5.

Miscellaneous changes

    [#1763] Set up appveyor to automatically test installation on Windows \ 
machines. Remove symbolic links in examples to help such istallation.

Change many doc_string typos to allow sphinx to build the docs without errors or \ 

Enable the docs to be automatically built on readthedocs.org by changing \ 
   2016-07-09 15:04:18 by Thomas Klausner | Files touched by this commit (599)
Log message:
Remove python33: adapt all packages that refer to it.
   2016-06-10 11:06:54 by Thomas Klausner | Files touched by this commit (1)
Log message:
   2016-06-08 19:43:49 by Thomas Klausner | Files touched by this commit (356)
Log message:
   2015-12-05 22:26:09 by Adam Ciarcinski | Files touched by this commit (578)
Log message:
   2015-11-01 10:58:28 by Thomas Klausner | Files touched by this commit (3) | Package updated
Log message:
Update py-networkx to 1.10, based on PR 50383 by Derouiche.

API changes

    [#1501] 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.
    [#1547] 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 \ 

New functionalities

    [#823] A enumerate_all_cliques function is added in the clique package \ 
(networkx.algorithms.clique) for enumerating all cliques (including nonmaximal \ 
ones) of undirected graphs.
    [#1105] 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.
    [#1193] 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.
    [#1194] 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.
    [#1210] Add a random generator for the duplication-divergence model.
    [#1241] 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.
    [#1269] The GML reader/parser and writer/generator are rewritten to remove \ 
the dependence on pyparsing and enable handling of arbitrary graph data.
    [#1280] 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.
    [#1286] Added the Margulis–Gabber–Galil graph to networkx.generators.
    [#1306] 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, \ 
    [#1314] Allow overwriting of base class dict with dict-like: OrderedGraph, \ 
ThinGraph, LogGraph, etc.
    [#1321] Added to_pandas_dataframe and from_pandas_dataframe.
    [#1322] Added the Hopcroft–Karp algorithm for finding a maximum \ 
cardinality matching in bipartite graphs.
    [#1336] Expanded data keyword in G.edges and added default keyword.
    [#1338] Added support for finding optimum branchings and arborescences.
    [#1340] 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’.
    [#1354] Expanded layout functions to add flexibility for drawing subsets of \ 
nodes with distinct layouts and for centering each layout around given \ 
    [#1356] Added ordered variants of default graph class.
    [#1360] Added harmonic centrality to network.algorithms.centrality.
    [#1390] 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.
    [#1391] 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 \ 
    [#1399] Added power function for simple graphs
    [#1405] Added fast approximation for node connectivity based on White and \ 
Newman’s approximation algorithm for finding node independent paths between \ 
two nodes.
    [#1413] 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.
    [#1425] Added generator function for the complete multipartite graph.
    [#1427] Added nonisomorphic trees generator.
    [#1436] Added a generator function for circulant graphs to the \ 
networkx.generators.classic module.
    [#1437] Added function for computing quotient graphs; also created a new \ 
module, networkx.algorithms.minors.
    [#1438] Added longest_path and longest_path_length for DAG.
    [#1439] Added node and edge contraction functions to networkx.algorithms.minors.
    [#1445] Added a new modularity matrix module to networkx.linalg, and \ 
associated spectrum functions to the networkx.linalg.spectrum module.
    [#1447] Added function to generate all simple paths starting with the \ 
shortest ones based on Yen’s algorithm for finding k shortest paths at \ 
    [#1455] Added the directed modularity matrix to the \ 
networkx.linalg.modularity_matrix module.
    [#1474] Adds triadic_census function; also creates a new module, \ 
    [#1476] 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.
    [#1481] Added Johnson’s algorithm; one more algorithm for shortest paths. \ 
It solves all pairs shortest path problem. This is johnson at \ 
    [#1414] 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).
    [#1415] 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).

Removed functionalities

    [#1236] The legacy ford_fulkerson maximum flow function is removed. Use \ 
edmonds_karp instead.

Miscellaneous changes

    [#1192] Support for Python 2.6 is dropped.
   2014-07-28 14:16:23 by Wen Heping | Files touched by this commit (3) | Package updated
Log message:
Update to 1.9
Add missing DEPENDS

Upstream changes:
NetworkX 1.9
Release date: 21 June 2014

Support for Python 3.1 is dropped in this release.

Completely rewritten maximum flow and flow-based connectivity algorithms with \ 
backwards incompatible interfaces
Community graph generators
Stoer-Wagner minimum cut algorithm
Linear-time Eulerian circuit algorithm
Linear algebra package changed to use SciPy sparse matrices
Algebraic connectivity, Fiedler vector, spectral ordering algorithms
Link prediction algorithms
Goldberg-Radzik shortest path algorithm
Semiconnected graph and tree recognition algorithms