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math/pynetworkx,
Python package for creating and manipulating graphs and networks
Branch: CURRENT,
Version: 1.11,
Package name: py27networkx1.11,
Maintainer: pkgsrcusersNetworkX (NX) is a Python package for the creation, manipulation, and
study of the structure, dynamics, and functions of complex networks.
Features:
 Includes standard graphtheoretic 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. timeseries, text, images, XML records)
 Exploits existing code from highquality legacy software in C,
C++, Fortran, etc.
 Open source (encourages community input)
 Unittested
Required to run:[
devel/pysetuptools] [
lang/python27] [
devel/pydecorator]
Required to build:[
devel/pynose]
Master sites:
SHA1: ac24380b13dfe92633370ad2091c0c04b6d098a2
RMD160: f8338d0bf5327dc29e25a7a94bfe363ca25d228b
Filesize: 1284.899 KB
Version history: (Expand)
 (20160812) Updated to version: py27networkx1.11
 (20151101) Updated to version: py27networkx1.10
 (20140731) Updated to version: py27networkx1.9
 (20140610) Updated to version: py27networkx1.8.1
 (20121004) Updated to version: py27networkx1.7nb1
 (20120713) Updated to version: py27networkx1.7
CVS history: (Expand)
20160911 18:55:17 by Thomas Klausner  Files touched by this commit (1) 
Log message:
This package does in fact support python3.x.

20160812 15:38:22 by Wen Heping  Files touched by this commit (3)  
Log message:
Update to 1.11
Update DEPENDS
Based on PR/51271 from kamelderouiche@yahoo.com
Upstream changes:
1.11
API changes
[#1930] No longer import nx_agraph and nx_pydot into the toplevel \
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 \
warnings.
Enable the docs to be automatically built on readthedocs.org by changing \
requirements.txt

20160709 15:04:18 by Thomas Klausner  Files touched by this commit (599) 
Log message:
Remove python33: adapt all packages that refer to it.

20160610 11:06:54 by Thomas Klausner  Files touched by this commit (1) 
Log message:
Fix MASTER_SITES.

20160608 19:43:49 by Thomas Klausner  Files touched by this commit (356) 
Log message:
Switch to MASTER_SITES_PYPI.

20151205 22:26:09 by Adam Ciarcinski  Files touched by this commit (578) 
Log message:
Extend PYTHON_VERSIONS_INCOMPATIBLE to 35

20151101 10:58:28 by Thomas Klausner  Files touched by this commit (3)  
Log message:
Update pynetworkx 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 \
release.
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 depthfirst traversal of the edges in a graph. This complements \
functionality provided by a depthfirst 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 duplicationdivergence 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 pcycle graph, a mildly explicit algebraic \
construction of a family of 3regular expander graphs. Also, moves both the \
existing expander graph generator function (for the MargulisGabberGalil \
expander) and the new chordal cycle graph function to a new module, \
networkx.generators.expanders.
[#1314] Allow overwriting of base class dict with dictlike: 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 \
coordinates.
[#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 minimumsize separating \
node sets in an undirected graph. It is implemented as a generator of node cut \
sets.
[#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 \
algorithms.simple_paths.
[#1455] Added the directed modularity matrix to the \
networkx.linalg.modularity_matrix module.
[#1474] Adds triadic_census function; also creates a new module, \
networkx.algorithms.triads.
[#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 \
algorithms.shortest_paths
[#1414] Added Moody and White algorithm for identifying k_components in a \
graph, which is based on Kanevsky’s algorithm for finding all minimumsize \
node cutsets (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.

20140728 14:16:23 by Wen Heping  Files touched by this commit (3)  
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.
Highlights
Completely rewritten maximum flow and flowbased connectivity algorithms with \
backwards incompatible interfaces
Community graph generators
StoerWagner minimum cut algorithm
Lineartime Eulerian circuit algorithm
Linear algebra package changed to use SciPy sparse matrices
Algebraic connectivity, Fiedler vector, spectral ordering algorithms
Link prediction algorithms
GoldbergRadzik shortest path algorithm
Semiconnected graph and tree recognition algorithms
