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from __future__ import division, print_function, absolute_import
import numpy as np
from numpy.testing import assert_array_almost_equal
from scipy.sparse import csr_matrix
from scipy.sparse.csgraph import csgraph_from_dense, csgraph_to_dense
def test_csgraph_from_dense():
np.random.seed(1234)
G = np.random.random((10, 10))
some_nulls = (G < 0.4)
all_nulls = (G < 0.8)
for null_value in [0, np.nan, np.inf]:
G[all_nulls] = null_value
olderr = np.seterr(invalid="ignore")
try:
G_csr = csgraph_from_dense(G, null_value=0)
finally:
np.seterr(**olderr)
G[all_nulls] = 0
assert_array_almost_equal(G, G_csr.toarray())
for null_value in [np.nan, np.inf]:
G[all_nulls] = 0
G[some_nulls] = null_value
olderr = np.seterr(invalid="ignore")
try:
G_csr = csgraph_from_dense(G, null_value=0)
finally:
np.seterr(**olderr)
G[all_nulls] = 0
assert_array_almost_equal(G, G_csr.toarray())
def test_csgraph_to_dense():
np.random.seed(1234)
G = np.random.random((10, 10))
nulls = (G < 0.8)
G[nulls] = np.inf
G_csr = csgraph_from_dense(G)
for null_value in [0, 10, -np.inf, np.inf]:
G[nulls] = null_value
assert_array_almost_equal(G, csgraph_to_dense(G_csr, null_value))
def test_multiple_edges():
# create a random sqare matrix with an even number of elements
np.random.seed(1234)
X = np.random.random((10, 10))
Xcsr = csr_matrix(X)
# now double-up every other column
Xcsr.indices[::2] = Xcsr.indices[1::2]
# normal sparse toarray() will sum the duplicated edges
Xdense = Xcsr.toarray()
assert_array_almost_equal(Xdense[:, 1::2],
X[:, ::2] + X[:, 1::2])
# csgraph_to_dense chooses the minimum of each duplicated edge
Xdense = csgraph_to_dense(Xcsr)
assert_array_almost_equal(Xdense[:, 1::2],
np.minimum(X[:, ::2], X[:, 1::2]))
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