def test_block(self):
A = self.data
B = numpy.array([((1, 0), 10)], dtype=sparse.dtype([4, 2]))
C = E = numpy.array([((0, 2), 20)], dtype=sparse.dtype([1, 5]))
D = F = numpy.array([((0, 1), 30)], dtype=sparse.dtype([1, 2]))
for a, b, c, d, e, f in numpy.ndindex(2, 2, 2, 2, 2, 2):
with self.subTest(A=a, B=b, C=c, D=d, E=e, F=f):
datas = [[a and A, b and B], [c and C, d and D],
[e and E, f and F]]
if (a or c or e) and (b or d or f) and (a or b) and (
c or d) and (e or f):
retval = sparse.block(datas)
self.assertEqual(sparse.shape(retval), (6, 7))
self.assertEqual(retval.tolist(),
([((2, 4), 10), ((3, 4), 20), ((2, 3), 1),
((1, 2), 30), ((0, 1), 40),
((1, 2), 50), ((2, 3), -1), ((3, 0), 0),
((2, 0), 60)] if a else []) +
([((1, 5), 10)] if b else []) +
([((4, 2), 20)] if c else []) +
([((4, 6), 30)] if d else []) +
([((5, 2), 20)] if e else []) +
([((5, 6), 30)] if f else []))
else:
with self.assertRaises(Exception):
sparse.blocks(datas)
def test_extract(self):
indices, values, shape = sparse.extract(self.data)
self.assertEqual(indices[0].tolist(),
sparse.indices(self.data)[0].tolist())
self.assertEqual(indices[1].tolist(),
sparse.indices(self.data)[1].tolist())
self.assertEqual(values.tolist(), sparse.values(self.data).tolist())
self.assertEqual(shape, sparse.shape(self.data))
def test_block(self):
A = self.data
B = C = numpy.array([
((1,), 10)], dtype=sparse.dtype([3]))
for a, b, c in numpy.ndindex(2,2,2):
with self.subTest(A=a, B=b, C=c):
datas = [a and A, b and B, c and C]
if a and b and c:
retval = sparse.block(datas)
self.assertEqual(sparse.shape(retval), (12,))
self.assertEqual(retval.tolist(),
[((4,),10), ((4,),20), ((3,),1), ((2,),30), ((1,),40), ((2,),50), ((3,),-1), ((0,),0), ((0,),60), ((7,),10), ((10,),10)])
else:
with self.assertRaises(Exception):
sparse.blocks(datas)
def test_shape(self):
self.assertEqual(sparse.shape(self.data), (5, ))
