def testFrexpExecution(self):
data1 = np.random.random((5, 9, 4))
arr1 = tensor(data1.copy(), chunk_size=3)
o1, o2 = frexp(arr1)
o = o1 + o2
res = self.executor.execute_tensor(o, concat=True)[0]
expected = sum(np.frexp(data1))
self.assertTrue(np.allclose(res, expected))
arr1 = tensor(data1.copy(), chunk_size=3)
o1 = zeros(data1.shape, chunk_size=3)
o2 = zeros(data1.shape, dtype='i8', chunk_size=3)
frexp(arr1, o1, o2)
o = o1 + o2
res = self.executor.execute_tensor(o, concat=True)[0]
expected = sum(np.frexp(data1))
self.assertTrue(np.allclose(res, expected))
data1 = sps.random(5, 9, density=.1)
arr1 = tensor(data1.copy(), chunk_size=3)
o1, o2 = frexp(arr1)
o = o1 + o2
res = self.executor.execute_tensor(o, concat=True)[0]
expected = sum(np.frexp(data1.toarray()))
np.testing.assert_equal(res.toarray(), expected)
def test_frexp_execution(setup):
data1 = np.random.RandomState(0).random((5, 9, 4))
arr1 = tensor(data1.copy(), chunk_size=3)
o1, o2 = frexp(arr1)
o = o1 + o2
res = o.execute().fetch()
expected = sum(np.frexp(data1))
np.testing.assert_array_almost_equal(res, expected)
arr1 = tensor(data1.copy(), chunk_size=3)
o1 = zeros(data1.shape, chunk_size=3)
o2 = zeros(data1.shape, dtype='i8', chunk_size=3)
frexp(arr1, o1, o2)
o = o1 + o2
res = o.execute().fetch()
expected = sum(np.frexp(data1))
np.testing.assert_array_almost_equal(res, expected, decimal=3)
data1 = sps.random(5, 9, density=.1)
arr1 = tensor(data1.copy(), chunk_size=3)
o1, o2 = frexp(arr1)
o = o1 + o2
res = o.execute().fetch()
expected = sum(np.frexp(data1.toarray()))
np.testing.assert_equal(res.toarray(), expected)
def testZerosExecution(self):
t = zeros((20, 30), dtype='i8', chunk_size=5)
res = self.executor.execute_tensor(t, concat=True)
self.assertTrue(np.array_equal(res[0], np.zeros((20, 30), dtype='i8')))
self.assertEqual(res[0].dtype, np.int64)
t2 = zeros_like(t)
res = self.executor.execute_tensor(t2, concat=True)
self.assertTrue(np.array_equal(res[0], np.zeros((20, 30), dtype='i8')))
self.assertEqual(res[0].dtype, np.int64)
t = zeros((20, 30), dtype='i4', chunk_size=5, sparse=True)
res = self.executor.execute_tensor(t, concat=True)
self.assertEqual(res[0].nnz, 0)
def test_modf_execution(setup):
data1 = np.random.random((5, 9))
arr1 = tensor(data1.copy(), chunk_size=3)
o1, o2 = modf(arr1)
o = o1 + o2
res = o.execute().fetch()
expected = sum(np.modf(data1))
np.testing.assert_array_almost_equal(res, expected)
o1, o2 = modf([0, 3.5])
o = o1 + o2
res = o.execute().fetch()
expected = sum(np.modf([0, 3.5]))
np.testing.assert_array_almost_equal(res, expected)
arr1 = tensor(data1.copy(), chunk_size=3)
o1 = zeros(data1.shape, chunk_size=3)
o2 = zeros(data1.shape, chunk_size=3)
modf(arr1, o1, o2)
o = o1 + o2
res = o.execute().fetch()
expected = sum(np.modf(data1))
np.testing.assert_array_almost_equal(res, expected)
data1 = sps.random(5, 9, density=.1)
arr1 = tensor(data1.copy(), chunk_size=3)
o1, o2 = modf(arr1)
o = o1 + o2
res = o.execute().fetch()
expected = sum(np.modf(data1.toarray()))
np.testing.assert_equal(res.toarray(), expected)
def testZerosExecution(self):
t = zeros((20, 30), dtype='i8', chunk_size=5)
res = self.executor.execute_tensor(t, concat=True)
np.testing.assert_array_equal(res[0], np.zeros((20, 30), dtype='i8'))
self.assertEqual(res[0].dtype, np.int64)
t2 = zeros_like(t)
res = self.executor.execute_tensor(t2, concat=True)
np.testing.assert_array_equal(res[0], np.zeros((20, 30), dtype='i8'))
self.assertEqual(res[0].dtype, np.int64)
t = zeros((20, 30), dtype='i4', chunk_size=5, sparse=True)
res = self.executor.execute_tensor(t, concat=True)
self.assertEqual(res[0].nnz, 0)
t = zeros((20, 30), dtype='i8', chunk_size=6, order='F')
res = self.executor.execute_tensor(t, concat=True)[0]
expected = np.zeros((20, 30), dtype='i8', order='F')
self.assertEqual(res.flags['C_CONTIGUOUS'], expected.flags['C_CONTIGUOUS'])
self.assertEqual(res.flags['F_CONTIGUOUS'], expected.flags['F_CONTIGUOUS'])
def test_zeros_execution(setup):
t = zeros((20, 30), dtype='i8', chunk_size=10)
res = t.execute().fetch()
np.testing.assert_array_equal(res, np.zeros((20, 30), dtype='i8'))
assert res[0].dtype == np.int64
t2 = zeros_like(t)
res = t2.execute().fetch()
np.testing.assert_array_equal(res, np.zeros((20, 30), dtype='i8'))
assert res[0].dtype == np.int64
t = zeros((20, 30), dtype='i4', chunk_size=5, sparse=True)
res = t.execute().fetch()
assert res[0].nnz == 0
t = zeros((20, 30), dtype='i8', chunk_size=6, order='F')
res = t.execute().fetch()
expected = np.zeros((20, 30), dtype='i8', order='F')
assert res.flags['C_CONTIGUOUS'] == expected.flags['C_CONTIGUOUS']
assert res.flags['F_CONTIGUOUS'] == expected.flags['F_CONTIGUOUS']
def testMoveaxisExecution(self):
x = zeros((3, 4, 5), chunk_size=2)
t = moveaxis(x, 0, -1)
res = self.executor.execute_tensor(t, concat=True)[0]
self.assertEqual(res.shape, (4, 5, 3))
t = moveaxis(x, -1, 0)
res = self.executor.execute_tensor(t, concat=True)[0]
self.assertEqual(res.shape, (5, 3, 4))
t = moveaxis(x, [0, 1], [-1, -2])
res = self.executor.execute_tensor(t, concat=True)[0]
self.assertEqual(res.shape, (5, 4, 3))
t = moveaxis(x, [0, 1, 2], [-1, -2, -3])
res = self.executor.execute_tensor(t, concat=True)[0]
self.assertEqual(res.shape, (5, 4, 3))
def testSetItemExecution(self):
rs = np.random.RandomState(0)
raw = data = rs.randint(0, 10, size=(11, 8, 12, 13))
arr = tensor(raw.copy(), chunk_size=3)
raw = raw.copy()
idx = slice(2, 9, 2), slice(3, 7), slice(-1, -9, -2), 2
arr[idx] = 20
res = self.executor.execute_tensor(arr, concat=True)[0]
raw[idx] = 20
np.testing.assert_array_equal(res, raw)
self.assertEqual(res.flags['C_CONTIGUOUS'], raw.flags['C_CONTIGUOUS'])
self.assertEqual(res.flags['F_CONTIGUOUS'], raw.flags['F_CONTIGUOUS'])
raw = data
shape = raw[idx].shape
arr2 = tensor(raw.copy(), chunk_size=3)
raw = raw.copy()
replace = rs.randint(10, 20, size=shape[:-1] + (1, )).astype('f4')
arr2[idx] = tensor(replace, chunk_size=4)
res = self.executor.execute_tensor(arr2, concat=True)[0]
raw[idx] = replace
np.testing.assert_array_equal(res, raw)
raw = np.asfortranarray(np.random.randint(0, 10, size=(11, 8, 12, 13)))
arr = tensor(raw.copy('A'), chunk_size=3)
raw = raw.copy('A')
idx = slice(2, 9, 2), slice(3, 7), slice(-1, -9, -2), 2
arr[idx] = 20
res = self.executor.execute_tensor(arr, concat=True)[0]
raw[idx] = 20
np.testing.assert_array_equal(res, raw)
self.assertEqual(res.flags['C_CONTIGUOUS'], raw.flags['C_CONTIGUOUS'])
self.assertEqual(res.flags['F_CONTIGUOUS'], raw.flags['F_CONTIGUOUS'])
# test bool indexing set
raw = data
arr = tensor(raw.copy(), chunk_size=3)
raw1 = rs.rand(11)
arr[tensor(raw1, chunk_size=4) < 0.6, 2:7] = 3
res = self.executor.execute_tileable(arr, concat=True)[0]
raw[raw1 < 0.6, 2:7] = 3
np.testing.assert_array_equal(res, raw)
raw = np.random.randint(3, size=10).astype(np.int64)
raw2 = np.arange(3)
arr = zeros((10, 3))
arr[tensor(raw) == 1, tensor(raw2) == 1] = 1
res = self.executor.execute_tileable(arr, concat=True)[0]
expected = np.zeros((10, 3))
expected[raw == 1, raw2 == 1] = 1
np.testing.assert_array_equal(res, expected)
ctx, executor = self._create_test_context(self.executor)
with ctx:
raw = data
arr = tensor(raw.copy(), chunk_size=3)
raw1 = rs.rand(11)
set_data = rs.rand((raw1 < 0.8).sum(), 8, 12, 13)
arr[tensor(raw1, chunk_size=4) < 0.8] = tensor(set_data)
res = self.executor.execute_tileables([arr])[0]
raw[raw1 < 0.8] = set_data
np.testing.assert_array_equal(res, raw)
# test error
with self.assertRaises(ValueError):
t = tensor(raw, chunk_size=3)
t[0, 0, 0, 0] = zeros(2, chunk_size=10)
_ = self.executor.execute_tensor(t)
def test_setitem_execution(setup):
rs = np.random.RandomState(0)
raw = data = rs.randint(0, 10, size=(11, 8, 12, 13))
arr = tensor(raw.copy(), chunk_size=6)
raw = raw.copy()
idx = slice(2, 9, 2), slice(3, 7), slice(-1, -9, -2), 2
arr[idx] = 20
res = arr.execute().fetch()
raw[idx] = 20
np.testing.assert_array_equal(res, raw)
assert res.flags['C_CONTIGUOUS'] == raw.flags['C_CONTIGUOUS']
assert res.flags['F_CONTIGUOUS'] == raw.flags['F_CONTIGUOUS']
raw = data
shape = raw[idx].shape
arr2 = tensor(raw.copy(), chunk_size=6)
raw = raw.copy()
replace = rs.randint(10, 20, size=shape[:-1] + (1,)).astype('f4')
arr2[idx] = tensor(replace, chunk_size=7)
res = arr2.execute().fetch()
raw[idx] = replace
np.testing.assert_array_equal(res, raw)
raw = np.asfortranarray(np.random.randint(0, 10, size=(11, 8, 12, 13)))
arr = tensor(raw.copy('A'), chunk_size=6)
raw = raw.copy('A')
idx = slice(2, 9, 2), slice(3, 7), slice(-1, -9, -2), 2
arr[idx] = 20
res = arr.execute().fetch()
raw[idx] = 20
np.testing.assert_array_equal(res, raw)
assert res.flags['C_CONTIGUOUS'] == raw.flags['C_CONTIGUOUS']
assert res.flags['F_CONTIGUOUS'] == raw.flags['F_CONTIGUOUS']
# test bool indexing set
raw = data
arr = tensor(raw.copy(), chunk_size=6)
raw1 = rs.rand(11)
arr[tensor(raw1, chunk_size=4) < 0.6, 2: 7] = 3
res = arr.execute().fetch()
raw[raw1 < 0.6, 2: 7] = 3
np.testing.assert_array_equal(res, raw)
raw = np.random.randint(3, size=10).astype(np.int64)
raw2 = np.arange(3)
arr = zeros((10, 3))
arr[tensor(raw) == 1, tensor(raw2) == 1] = 1
res = arr.execute().fetch()
expected = np.zeros((10, 3))
expected[raw == 1, raw2 == 1] = 1
np.testing.assert_array_equal(res, expected)
raw = data
arr = tensor(raw.copy(), chunk_size=6)
raw1 = rs.rand(11)
set_data = rs.rand((raw1 < 0.8).sum(), 8, 12, 13)
arr[tensor(raw1, chunk_size=4) < 0.8] = tensor(set_data)
res = arr.execute().fetch()
raw[raw1 < 0.8] = set_data
np.testing.assert_array_equal(res, raw)
# test error
with pytest.raises(ValueError):
t = tensor(raw, chunk_size=3)
t[0, 0, 0, 0] = zeros(2, chunk_size=10)
t.execute()
def test_block_execution(setup):
# arrays is a tuple.
with pytest.raises(TypeError):
block((1, 2, 3))
# List depths are mismatched.
with pytest.raises(ValueError):
block([[1, 2], [[3, 4]]])
# List at arrays cannot be empty.
with pytest.raises(ValueError):
block([])
# List at arrays[1] cannot be empty.
with pytest.raises(ValueError):
block([[1, 2], []])
# Mismatched array shapes.
with pytest.raises(ValueError):
block([eye(512), eye(512), ones((511, 1))])
# Test large block.
block([eye(512), eye(512), ones((512, 1))])
# Test block inputs a single array.
c = block(array([1, 2, 3]))
r = c.execute().fetch()
np.testing.assert_array_equal(r, array([1, 2, 3]))
a = eye(2) * 2
b = eye(3) * 3
c = block([[a, zeros((2, 3))], [ones((3, 2)), b]])
r = c.execute().fetch()
expected = array([[2., 0., 0., 0., 0.], [0., 2., 0., 0., 0.],
[1., 1., 3., 0., 0.], [1., 1., 0., 3., 0.],
[1., 1., 0., 0., 3.]])
np.testing.assert_array_equal(r, expected)
# eye with different chunk sizes
a = eye(5, chunk_size=2) * 2
b = eye(4, chunk_size=3) * 3
c = block([[a, zeros((5, 4), chunk_size=4)],
[ones((4, 5), chunk_size=5), b]])
r = c.execute().fetch()
expected = array([[2., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 2., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 2., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 2., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 2., 0., 0., 0., 0.],
[1., 1., 1., 1., 1., 3., 0., 0., 0.],
[1., 1., 1., 1., 1., 0., 3., 0., 0.],
[1., 1., 1., 1., 1., 0., 0., 3., 0.],
[1., 1., 1., 1., 1., 0., 0., 0., 3.]])
np.testing.assert_array_equal(r, expected)
# hstack([1, 2, 3])
c = block([1, 2, 3])
r = c.execute().fetch()
expected = array([1, 2, 3])
np.testing.assert_array_equal(r, expected)
# hstack([a, b, 10])
a = array([1, 2, 3])
b = array([2, 3, 4])
c = block([a, b, 10])
r = c.execute().fetch()
expected = array([1, 2, 3, 2, 3, 4, 10])
np.testing.assert_array_equal(r, expected)
# hstack([a, b, 10]) with different chunk sizes
a = array([1, 2, 3, 4, 5, 6, 7], chunk_size=3)
b = array([2, 3, 4, 5], chunk_size=4)
c = block([a, b, 10])
r = c.execute().fetch()
expected = array([1, 2, 3, 4, 5, 6, 7, 2, 3, 4, 5, 10])
np.testing.assert_array_equal(r, expected)
# hstack([A, B])
A = ones((2, 2), int)
B = 2 * A
c = block([A, B])
r = c.execute().fetch()
expected = array([[1, 1, 2, 2], [1, 1, 2, 2]])
np.testing.assert_array_equal(r, expected)
# vstack([a, b])
a = array([1, 2, 3])
b = array([2, 3, 4])
c = block([[a], [b]])
r = c.execute().fetch()
expected = array([[1, 2, 3], [2, 3, 4]])
np.testing.assert_array_equal(r, expected)
# vstack([a, b]) with different chunk sizes
a = array([1, 2, 3, 4, 5, 6, 7], chunk_size=5)
b = array([2, 3, 4, 5, 6, 7, 8], chunk_size=6)
c = block([[a], [b]])
r = c.execute().fetch()
expected = array([[1, 2, 3, 4, 5, 6, 7], [2, 3, 4, 5, 6, 7, 8]])
np.testing.assert_array_equal(r, expected)
# vstack([A, B])
A = ones((2, 2), int)
B = 2 * A
c = block([[A], [B]])
r = c.execute().fetch()
expected = array([[1, 1], [1, 1], [2, 2], [2, 2]])
np.testing.assert_array_equal(r, expected)
a = array(0)
b = array([1])
# atleast_1d(a)
c = block([a])
r = c.execute().fetch()
expected = array([0])
np.testing.assert_array_equal(r, expected)
# atleast_1d(b)
c = block([b])
r = c.execute().fetch()
expected = array([1])
np.testing.assert_array_equal(r, expected)
# atleast_2d(a)
c = block([[a]])
r = c.execute().fetch()
expected = array([[0]])
np.testing.assert_array_equal(r, expected)
# atleast_2d(b)
c = block([[b]])
r = c.execute().fetch()
expected = array([[1]])
np.testing.assert_array_equal(r, expected)
