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from nose import SkipTest
from nose.tools import assert_raises, assert_true, assert_equal
import networkx as nx
from networkx.generators.classic import barbell_graph,cycle_graph,path_graph
class TestConvertNumpy(object):
numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
@classmethod
def setupClass(cls):
global np
global np_assert_equal
try:
import numpy as np
np_assert_equal=np.testing.assert_equal
except ImportError:
raise SkipTest('NumPy not available.')
def __init__(self):
self.G1 = barbell_graph(10, 3)
self.G2 = cycle_graph(10, create_using=nx.DiGraph())
self.G3 = self.create_weighted(nx.Graph())
self.G4 = self.create_weighted(nx.DiGraph())
def create_weighted(self, G):
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)
G.add_weighted_edges_from(ex)
return G
def assert_equal(self, G1, G2):
assert_true( sorted(G1.nodes())==sorted(G2.nodes()) )
assert_true( sorted(G1.edges())==sorted(G2.edges()) )
def identity_conversion(self, G, A, create_using):
GG = nx.from_numpy_matrix(A, create_using=create_using)
self.assert_equal(G, GG)
GW = nx.to_networkx_graph(A, create_using=create_using)
self.assert_equal(G, GW)
GI = create_using.__class__(A)
self.assert_equal(G, GI)
def test_shape(self):
"Conversion from non-square array."
A=np.array([[1,2,3],[4,5,6]])
assert_raises(nx.NetworkXError, nx.from_numpy_matrix, A)
def test_identity_graph_matrix(self):
"Conversion from graph to matrix to graph."
A = nx.to_numpy_matrix(self.G1)
self.identity_conversion(self.G1, A, nx.Graph())
def test_identity_graph_array(self):
"Conversion from graph to array to graph."
A = nx.to_numpy_matrix(self.G1)
A = np.asarray(A)
self.identity_conversion(self.G1, A, nx.Graph())
def test_identity_digraph_matrix(self):
"""Conversion from digraph to matrix to digraph."""
A = nx.to_numpy_matrix(self.G2)
self.identity_conversion(self.G2, A, nx.DiGraph())
def test_identity_digraph_array(self):
"""Conversion from digraph to array to digraph."""
A = nx.to_numpy_matrix(self.G2)
A = np.asarray(A)
self.identity_conversion(self.G2, A, nx.DiGraph())
def test_identity_weighted_graph_matrix(self):
"""Conversion from weighted graph to matrix to weighted graph."""
A = nx.to_numpy_matrix(self.G3)
self.identity_conversion(self.G3, A, nx.Graph())
def test_identity_weighted_graph_array(self):
"""Conversion from weighted graph to array to weighted graph."""
A = nx.to_numpy_matrix(self.G3)
A = np.asarray(A)
self.identity_conversion(self.G3, A, nx.Graph())
def test_identity_weighted_digraph_matrix(self):
"""Conversion from weighted digraph to matrix to weighted digraph."""
A = nx.to_numpy_matrix(self.G4)
self.identity_conversion(self.G4, A, nx.DiGraph())
def test_identity_weighted_digraph_array(self):
"""Conversion from weighted digraph to array to weighted digraph."""
A = nx.to_numpy_matrix(self.G4)
A = np.asarray(A)
self.identity_conversion(self.G4, A, nx.DiGraph())
def test_nodelist(self):
"""Conversion from graph to matrix to graph with nodelist."""
P4 = path_graph(4)
P3 = path_graph(3)
nodelist = P3.nodes()
A = nx.to_numpy_matrix(P4, nodelist=nodelist)
GA = nx.Graph(A)
self.assert_equal(GA, P3)
# Make nodelist ambiguous by containing duplicates.
nodelist += [nodelist[0]]
assert_raises(nx.NetworkXError, nx.to_numpy_matrix, P3, nodelist=nodelist)
def test_weight_keyword(self):
WP4 = nx.Graph()
WP4.add_edges_from( (n,n+1,dict(weight=0.5,other=0.3)) for n in range(3) )
P4 = path_graph(4)
A = nx.to_numpy_matrix(P4)
np_assert_equal(A, nx.to_numpy_matrix(WP4,weight=None))
np_assert_equal(0.5*A, nx.to_numpy_matrix(WP4))
np_assert_equal(0.3*A, nx.to_numpy_matrix(WP4,weight='other'))
def test_from_numpy_matrix_type(self):
A=np.matrix([[1]])
G=nx.from_numpy_matrix(A)
assert_equal(type(G[0][0]['weight']),int)
A=np.matrix([[1]]).astype(np.float)
G=nx.from_numpy_matrix(A)
assert_equal(type(G[0][0]['weight']),float)
A=np.matrix([[1]]).astype(np.str)
G=nx.from_numpy_matrix(A)
assert_equal(type(G[0][0]['weight']),str)
A=np.matrix([[1]]).astype(np.bool)
G=nx.from_numpy_matrix(A)
assert_equal(type(G[0][0]['weight']),bool)
A=np.matrix([[1]]).astype(np.complex)
G=nx.from_numpy_matrix(A)
assert_equal(type(G[0][0]['weight']),complex)
A=np.matrix([[1]]).astype(np.object)
assert_raises(TypeError,nx.from_numpy_matrix,A)
def test_from_numpy_matrix_dtype(self):
dt=[('weight',float),('cost',int)]
A=np.matrix([[(1.0,2)]],dtype=dt)
G=nx.from_numpy_matrix(A)
assert_equal(type(G[0][0]['weight']),float)
assert_equal(type(G[0][0]['cost']),int)
assert_equal(G[0][0]['cost'],2)
assert_equal(G[0][0]['weight'],1.0)
def test_to_numpy_recarray(self):
G=nx.Graph()
G.add_edge(1,2,weight=7.0,cost=5)
A=nx.to_numpy_recarray(G,dtype=[('weight',float),('cost',int)])
assert_equal(sorted(A.dtype.names),['cost','weight'])
assert_equal(A.weight[0,1],7.0)
assert_equal(A.weight[0,0],0.0)
assert_equal(A.cost[0,1],5)
assert_equal(A.cost[0,0],0)
def test_numpy_multigraph(self):
G=nx.MultiGraph()
G.add_edge(1,2,weight=7)
G.add_edge(1,2,weight=70)
A=nx.to_numpy_matrix(G)
assert_equal(A[1,0],77)
A=nx.to_numpy_matrix(G,multigraph_weight=min)
assert_equal(A[1,0],7)
A=nx.to_numpy_matrix(G,multigraph_weight=max)
assert_equal(A[1,0],70)
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