Convert
=======

>>> import os,tempfile
>>> from networkx import *
>>> from networkx.convert import *
>>> from networkx.operators import *
>>> from networkx.generators.classic import barbell_graph,cycle_graph
>>> import scipy
>>> import scipy.sparse

Bad shape
---------

>>> A=scipy.sparse.lil_matrix([[1,2,3],[4,5,6]])
>>> from_scipy_sparse_matrix(A)
Traceback (most recent call last):
...
NetworkXError: Adjacency matrix is not square. nx,ny=(2, 3)

Simple Graphs
--------------

>>> G=barbell_graph(10,3)

Scipy sparse matrix
~~~~~~~~~~~~~~~~~~~

>>> A=to_scipy_sparse_matrix(G)
>>> GG=from_scipy_sparse_matrix(A)
>>> sorted(G.nodes())==sorted(GG.nodes())
True
>>> sorted(G.edges())==sorted(GG.edges())
True
>>> GW=from_whatever(A)
>>> sorted(G.nodes())==sorted(GW.nodes())
True
>>> sorted(G.edges())==sorted(GW.edges())
True
>>> GI=Graph(A)
>>> sorted(G.nodes())==sorted(GI.nodes())
True
>>> sorted(G.edges())==sorted(GI.edges())
True
>>> ACSR=A.tocsr()
>>> GI=Graph(ACSR)
>>> sorted(G.nodes())==sorted(GI.nodes())
True
>>> sorted(G.edges())==sorted(GI.edges())
True
>>> ACOO=A.tocoo()
>>> GI=Graph(ACOO)
>>> sorted(G.nodes())==sorted(GI.nodes())
True
>>> sorted(G.edges())==sorted(GI.edges())
True
>>> ACSC=A.tocsc()
>>> GI=Graph(ACSC)
>>> sorted(G.nodes())==sorted(GI.nodes())
True
>>> sorted(G.edges())==sorted(GI.edges())
True
>>> AD=A.todense()
>>> GI=Graph(AD)
>>> sorted(G.nodes())==sorted(GI.nodes())
True
>>> sorted(G.edges())==sorted(GI.edges())
True
>>> AA=A.toarray()
>>> GI=Graph(AA)
>>> sorted(G.nodes())==sorted(GI.nodes())
True
>>> sorted(G.edges())==sorted(GI.edges())
True

DiGraphs
--------

>>> G=cycle_graph(10,create_using=DiGraph())

Scipy sparse matrix
~~~~~~~~~~~~~~~~~~~

>>> A=to_scipy_sparse_matrix(G)
>>> GG=from_scipy_sparse_matrix(A,create_using=DiGraph())
>>> sorted(G.nodes())==sorted(GG.nodes())
True
>>> sorted(G.edges())==sorted(GG.edges())
True
>>> GW=from_whatever(A,create_using=DiGraph())
>>> sorted(G.nodes())==sorted(GW.nodes())
True
>>> sorted(G.edges())==sorted(GW.edges())
True
>>> GI=DiGraph(A)
>>> sorted(G.nodes())==sorted(GI.nodes())
True
>>> sorted(G.edges())==sorted(GI.edges())
True


XGraph
------

>>> G=cycle_graph(4)
>>> e=G.edges()
>>> source=[u for u,v in e]
>>> dest=[v for u,v in e]
>>> weight=[s+10 for s in source]
>>> ex=zip(source,dest,weight)
>>> XG=XGraph()
>>> XG.add_edges_from(ex)


Numpy matrix
~~~~~~~~~~~~

>>> A=to_scipy_sparse_matrix(XG)
>>> GG=from_scipy_sparse_matrix(A,create_using=XGraph())
>>> sorted(XG.nodes())==sorted(GG.nodes())
True
>>> sorted(XG.edges())==sorted(GG.edges())
True
>>> GW=from_whatever(A,create_using=XGraph())
>>> sorted(XG.nodes())==sorted(GW.nodes())
True
>>> sorted(XG.edges())==sorted(GW.edges())
True
>>> GI=XGraph(A)
>>> sorted(XG.nodes())==sorted(GI.nodes())
True
>>> sorted(XG.edges())==sorted(GI.edges())
True


XDiGraph
------

>>> G=cycle_graph(4)
>>> e=G.edges()
>>> source=[u for u,v in e]
>>> dest=[v for u,v in e]
>>> weight=[s+10 for s in source]
>>> ex=zip(source,dest,weight)
>>> XG=XDiGraph()
>>> XG.add_edges_from(ex)


Scipy sparse matrix
~~~~~~~~~~~~~~~~~~~

>>> A=to_scipy_sparse_matrix(XG)
>>> GG=from_scipy_sparse_matrix(A,create_using=XDiGraph())
>>> sorted(XG.nodes())==sorted(GG.nodes())
True
>>> sorted(XG.edges())==sorted(GG.edges())
True
>>> GW=from_whatever(A,create_using=XDiGraph())
>>> sorted(XG.nodes())==sorted(GW.nodes())
True
>>> sorted(XG.edges())==sorted(GW.edges())
True
>>> GI=XDiGraph(A)
>>> sorted(XG.nodes())==sorted(GI.nodes())
True
>>> sorted(XG.edges())==sorted(GI.edges())
True

