def testClipOrderExecution(self):
a_data = np.asfortranarray(np.random.rand(4, 8))
a = tensor(a_data, chunk_size=3)
b = clip(a, 0.2, 0.8)
res = self.executor.execute_tensor(b, concat=True)[0]
expected = np.clip(a_data, 0.2, 0.8)
np.testing.assert_allclose(res, expected)
self.assertTrue(res.flags['F_CONTIGUOUS'])
self.assertFalse(res.flags['C_CONTIGUOUS'])
def testModfExecution(self):
data1 = np.random.random((5, 9))
arr1 = tensor(data1.copy(), chunk_size=3)
o1, o2 = modf(arr1)
o = o1 + o2
res = self.executor.execute_tensor(o, concat=True)[0]
expected = sum(np.modf(data1))
self.assertTrue(np.allclose(res, expected))
o1, o2 = modf([0, 3.5])
o = o1 + o2
res = self.executor.execute_tensor(o, concat=True)[0]
expected = sum(np.modf([0, 3.5]))
self.assertTrue(np.allclose(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 = self.executor.execute_tensor(o, concat=True)[0]
expected = sum(np.modf(data1))
self.assertTrue(np.allclose(res, expected))
data1 = sps.random(5, 9, density=.1)
arr1 = tensor(data1.copy(), chunk_size=3)
o1, o2 = modf(arr1)
o = o1 + o2
res = self.executor.execute_tensor(o, concat=True)[0]
expected = sum(np.modf(data1.toarray()))
np.testing.assert_equal(res.toarray(), expected)
def testModfOrderExecution(self):
data1 = np.random.random((5, 9))
t = tensor(data1, chunk_size=3)
o1, o2 = modf(t, order='F')
res1, res2 = self.executor.execute_tileables([o1, o2])
expected1, expected2 = np.modf(data1, order='F')
np.testing.assert_allclose(res1, expected1)
self.assertTrue(res1.flags['F_CONTIGUOUS'])
self.assertFalse(res1.flags['C_CONTIGUOUS'])
np.testing.assert_allclose(res2, expected2)
self.assertTrue(res2.flags['F_CONTIGUOUS'])
self.assertFalse(res2.flags['C_CONTIGUOUS'])
def test_arg_reduction(setup):
raw = np.random.random((20, 20, 20))
arr = tensor(raw, chunk_size=6)
assert raw.argmax() == arr.argmax().execute().fetch()
assert raw.argmin() == arr.argmin().execute().fetch()
np.testing.assert_array_equal(raw.argmax(axis=0),
arr.argmax(axis=0).execute().fetch())
np.testing.assert_array_equal(raw.argmin(axis=0),
arr.argmin(axis=0).execute().fetch())
raw_format = sps.random(20, 20, density=.1, format='lil')
random_min = np.random.randint(0, 200)
random_max = np.random.randint(200, 400)
raw_format[np.unravel_index(random_min, raw_format.shape)] = -1
raw_format[np.unravel_index(random_max, raw_format.shape)] = 2
raw = raw_format.tocoo()
arr = tensor(raw, chunk_size=6)
assert raw.argmax() == arr.argmax().execute().fetch()
assert raw.argmin() == arr.argmin().execute().fetch()
# test order
raw = np.asfortranarray(np.random.rand(10, 20, 30))
arr = tensor(raw, chunk_size=13)
arr2 = arr.argmax(axis=-1)
res = arr2.execute().fetch()
expected = raw.argmax(axis=-1)
np.testing.assert_allclose(res, expected)
assert res.flags['C_CONTIGUOUS'] == expected.flags['C_CONTIGUOUS']
assert res.flags['F_CONTIGUOUS'] == expected.flags['F_CONTIGUOUS']
with pytest.raises(TypeError):
tensor(list('abcdefghi'), dtype=object).argmax().execute()
def testMapChunk(self):
raw = np.random.rand(20)
a = tensor(raw, chunk_size=10)
mapped = a.map_chunk(lambda x: x * 0.5).tiles()
self.assertTrue(np.issubdtype(mapped.dtype, np.floating))
self.assertEqual(mapped.shape, (np.nan, ))
self.assertEqual(len(mapped.chunks), 2)
mapped = a.map_chunk(lambda x: x * 0.5, elementwise=True).tiles()
self.assertTrue(np.issubdtype(mapped.dtype, np.floating))
self.assertEqual(mapped.shape, (20, ))
self.assertEqual(len(mapped.chunks), 2)
def testSliceExecution(self):
raw = np.random.random((11, 8, 12, 14))
arr = tensor(raw, chunk_size=3)
arr2 = arr[2:9:2, 3:7, -1:-9:-2, 12:-11:-4]
res = self.executor.execute_tensor(arr2, concat=True)
np.testing.assert_array_equal(res[0], raw[2:9:2, 3:7, -1:-9:-2,
12:-11:-4])
arr3 = arr[-4, 2:]
res = self.executor.execute_tensor(arr3, concat=True)
np.testing.assert_equal(res[0], raw[-4, 2:])
raw = sps.random(12, 14, density=.1)
arr = tensor(raw, chunk_size=3)
arr2 = arr[-1:-9:-2, 12:-11:-4]
res = self.executor.execute_tensor(arr2, concat=True)[0]
np.testing.assert_equal(res.toarray(),
raw.toarray()[-1:-9:-2, 12:-11:-4])
def testBaseOrderExecution(self):
raw = np.asfortranarray(np.random.rand(5, 6))
arr = tensor(raw, chunk_size=3)
res = self.executor.execute_tensor(arr + 1, concat=True)[0]
np.testing.assert_array_equal(res, raw + 1)
self.assertFalse(res.flags['C_CONTIGUOUS'])
self.assertTrue(res.flags['F_CONTIGUOUS'])
res2 = self.executor.execute_tensor(add(arr, 1, order='C'), concat=True)[0]
np.testing.assert_array_equal(res2, np.add(raw, 1, order='C'))
self.assertTrue(res2.flags['C_CONTIGUOUS'])
self.assertFalse(res2.flags['F_CONTIGUOUS'])
def testRechunkExecution(self):
raw = np.random.random((11, 8))
arr = tensor(raw, chunk_size=3)
arr2 = arr.rechunk(4)
res = self.executor.execute_tensor(arr2)
self.assertTrue(np.array_equal(res[0], raw[:4, :4]))
self.assertTrue(np.array_equal(res[1], raw[:4, 4:]))
self.assertTrue(np.array_equal(res[2], raw[4:8, :4]))
self.assertTrue(np.array_equal(res[3], raw[4:8, 4:]))
self.assertTrue(np.array_equal(res[4], raw[8:, :4]))
self.assertTrue(np.array_equal(res[5], raw[8:, 4:]))
def test_h_stack_execution(setup):
a_data = np.random.rand(10)
b_data = np.random.rand(20)
a = tensor(a_data, chunk_size=8)
b = tensor(b_data, chunk_size=8)
c = hstack([a, b])
res = c.execute().fetch()
expected = np.hstack([a_data, b_data])
assert np.array_equal(res, expected) is True
a_data = np.random.rand(10, 20)
b_data = np.random.rand(10, 5)
a = tensor(a_data, chunk_size=6)
b = tensor(b_data, chunk_size=8)
c = hstack([a, b])
res = c.execute().fetch()
expected = np.hstack([a_data, b_data])
assert np.array_equal(res, expected) is True
def test_map_chunk():
raw = np.random.rand(20)
a = tensor(raw, chunk_size=10)
mapped = tile(a.map_chunk(lambda x: x * 0.5))
assert np.issubdtype(mapped.dtype, np.floating) is True
assert mapped.shape == (np.nan, )
assert len(mapped.chunks) == 2
mapped = tile(a.map_chunk(lambda x: x * 0.5, elementwise=True))
assert np.issubdtype(mapped.dtype, np.floating) is True
assert mapped.shape == (20, )
assert len(mapped.chunks) == 2
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 test_base_order_execution(setup):
raw = np.asfortranarray(np.random.rand(5, 6))
arr = tensor(raw, chunk_size=3)
res = (arr + 1).execute().fetch()
np.testing.assert_array_equal(res, raw + 1)
assert res.flags['C_CONTIGUOUS'] is False
assert res.flags['F_CONTIGUOUS'] is True
res2 = add(arr, 1, order='C').execute().fetch()
np.testing.assert_array_equal(res2, np.add(raw, 1, order='C'))
assert res2.flags['C_CONTIGUOUS'] is True
assert res2.flags['F_CONTIGUOUS'] is False
def testDStackExecution(self):
a_data = np.random.rand(10)
b_data = np.random.rand(10)
a = tensor(a_data, chunk_size=4)
b = tensor(b_data, chunk_size=4)
c = dstack([a, b])
res = self.executor.execute_tensor(c, concat=True)[0]
expected = np.dstack([a_data, b_data])
self.assertTrue(np.array_equal(res, expected))
a_data = np.random.rand(10, 20)
b_data = np.random.rand(10, 20)
a = tensor(a_data, chunk_size=3)
b = tensor(b_data, chunk_size=4)
c = dstack([a, b])
res = self.executor.execute_tensor(c, concat=True)[0]
expected = np.dstack([a_data, b_data])
self.assertTrue(np.array_equal(res, expected))
def testSetItemExecution(self):
raw = data = np.random.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 = np.random.randint(10, 20, size=shape[:-1] + (1,)).astype('f4')
arr2[idx] = tensor(replace, chunk_size=4)
res = self.executor.execute_tensor(arr2, concat=True)
raw[idx] = replace
np.testing.assert_array_equal(res[0], 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'])
def test_modf_order_execution(setup):
data1 = np.random.random((5, 9))
t = tensor(data1, chunk_size=3)
o1, o2 = modf(t, order='F')
res1, res2 = execute(o1, o2)
expected1, expected2 = np.modf(data1, order='F')
np.testing.assert_allclose(res1, expected1)
assert res1.flags['F_CONTIGUOUS'] is True
assert res1.flags['C_CONTIGUOUS'] is False
np.testing.assert_allclose(res2, expected2)
assert res2.flags['F_CONTIGUOUS'] is True
assert res2.flags['C_CONTIGUOUS'] is False
def test_clip_order_execution(setup):
a_data = np.asfortranarray(np.random.rand(4, 8))
a = tensor(a_data, chunk_size=3)
b = clip(a, 0.2, 0.8)
res = b.execute().fetch()
expected = np.clip(a_data, 0.2, 0.8)
np.testing.assert_allclose(res, expected)
assert res.flags['F_CONTIGUOUS'] is True
assert res.flags['C_CONTIGUOUS'] is False
def test_unravel_index():
indices = tensor([22, 41, 37], chunk_size=1)
t = unravel_index(indices, (7, 6))
assert len(t) == 2
t = [tile(r) for r in t]
assert len(t[0].chunks) == 3
assert len(t[1].chunks) == 3
with pytest.raises(TypeError):
unravel_index([22, 41, 37], (7, 6), order='B')
def testCumReduction(self):
raw = np.random.randint(5, size=(8, 8, 8))
arr = tensor(raw, chunk_size=3)
res1 = self.executor.execute_tensor(arr.cumsum(axis=1), concat=True)
res2 = self.executor.execute_tensor(arr.cumprod(axis=1), concat=True)
expected1 = raw.cumsum(axis=1)
expected2 = raw.cumprod(axis=1)
np.testing.assert_array_equal(res1[0], expected1)
np.testing.assert_array_equal(res2[0], expected2)
raw = sps.random(8, 8, density=.1)
arr = tensor(raw, chunk_size=3)
res1 = self.executor.execute_tensor(arr.cumsum(axis=1), concat=True)
res2 = self.executor.execute_tensor(arr.cumprod(axis=1), concat=True)
expected1 = raw.A.cumsum(axis=1)
expected2 = raw.A.cumprod(axis=1)
self.assertTrue(np.allclose(res1[0], expected1))
self.assertTrue(np.allclose(res2[0], expected2))
def testCopytoExecution(self):
a = ones((2, 3), chunk_size=1)
b = tensor([3, -1, 3], chunk_size=2)
copyto(a, b, where=b > 1)
res = self.executor.execute_tensor(a, concat=True)[0]
expected = np.array([[3, 1, 3], [3, 1, 3]])
np.testing.assert_equal(res, expected)
a = ones((2, 3), chunk_size=1)
b = tensor(np.asfortranarray(np.random.rand(2, 3)), chunk_size=2)
copyto(b, a)
res = self.executor.execute_tensor(b, concat=True)[0]
expected = np.asfortranarray(np.ones((2, 3)))
np.testing.assert_array_equal(res, expected)
self.assertTrue(res.flags['F_CONTIGUOUS'])
self.assertFalse(res.flags['C_CONTIGUOUS'])
def test_vdot_execution(setup):
a_data = np.array([1 + 2j, 3 + 4j])
b_data = np.array([5 + 6j, 7 + 8j])
a = tensor(a_data, chunk_size=1)
b = tensor(b_data, chunk_size=1)
t = vdot(a, b)
res = t.execute().fetch()
expected = np.vdot(a_data, b_data)
np.testing.assert_equal(res, expected)
a_data = np.array([[1, 4], [5, 6]])
b_data = np.array([[4, 1], [2, 2]])
a = tensor(a_data, chunk_size=1)
b = tensor(b_data, chunk_size=1)
t = vdot(a, b)
res = t.execute().fetch()
expected = np.vdot(a_data, b_data)
np.testing.assert_equal(res, expected)
def testUnravelIndex(self):
indices = tensor([22, 41, 37], chunk_size=1)
t = unravel_index(indices, (7, 6))
self.assertEqual(len(t), 2)
[r.tiles() for r in t]
self.assertEqual(len(t[0].chunks), 3)
self.assertEqual(len(t[1].chunks), 3)
with self.assertRaises(TypeError):
unravel_index([22, 41, 37], (7, 6), order='B')
def testVdotExecution(self):
a_data = np.array([1 + 2j, 3 + 4j])
b_data = np.array([5 + 6j, 7 + 8j])
a = tensor(a_data, chunk_size=1)
b = tensor(b_data, chunk_size=1)
t = vdot(a, b)
res = self.executor.execute_tensor(t)[0]
expected = np.vdot(a_data, b_data)
np.testing.assert_equal(res, expected)
a_data = np.array([[1, 4], [5, 6]])
b_data = np.array([[4, 1], [2, 2]])
a = tensor(a_data, chunk_size=1)
b = tensor(b_data, chunk_size=1)
t = vdot(a, b)
res = self.executor.execute_tensor(t)[0]
expected = np.vdot(a_data, b_data)
np.testing.assert_equal(res, expected)
def testSplitExecution(self):
x = arange(48, chunk_size=3).reshape(2, 3, 8)
ss = split(x, 4, axis=2)
res = [self.executor.execute_tensor(i, concat=True)[0] for i in ss]
expected = np.split(np.arange(48).reshape(2, 3, 8), 4, axis=2)
self.assertEqual(len(res), len(expected))
[np.testing.assert_equal(r, e) for r, e in zip(res, expected)]
ss = split(x, [3, 5, 6, 10], axis=2)
res = [self.executor.execute_tensor(i, concat=True)[0] for i in ss]
expected = np.split(np.arange(48).reshape(2, 3, 8), [3, 5, 6, 10], axis=2)
self.assertEqual(len(res), len(expected))
[np.testing.assert_equal(r, e) for r, e in zip(res, expected)]
# hsplit
x = arange(120, chunk_size=3).reshape(2, 12, 5)
ss = hsplit(x, 4)
res = [self.executor.execute_tensor(i, concat=True)[0] for i in ss]
expected = np.hsplit(np.arange(120).reshape(2, 12, 5), 4)
self.assertEqual(len(res), len(expected))
[np.testing.assert_equal(r, e) for r, e in zip(res, expected)]
# vsplit
x = arange(48, chunk_size=3).reshape(8, 3, 2)
ss = vsplit(x, 4)
res = [self.executor.execute_tensor(i, concat=True)[0] for i in ss]
expected = np.vsplit(np.arange(48).reshape(8, 3, 2), 4)
self.assertEqual(len(res), len(expected))
[np.testing.assert_equal(r, e) for r, e in zip(res, expected)]
# dsplit
x = arange(48, chunk_size=3).reshape(2, 3, 8)
ss = dsplit(x, 4)
res = [self.executor.execute_tensor(i, concat=True)[0] for i in ss]
expected = np.dsplit(np.arange(48).reshape(2, 3, 8), 4)
self.assertEqual(len(res), len(expected))
[np.testing.assert_equal(r, e) for r, e in zip(res, expected)]
x_data = sps.random(12, 8, density=.1)
x = tensor(x_data, chunk_size=3)
ss = split(x, 4, axis=0)
res = [self.executor.execute_tensor(i, concat=True)[0] for i in ss]
expected = np.split(x_data.toarray(), 4, axis=0)
self.assertEqual(len(res), len(expected))
[np.testing.assert_equal(r.toarray(), e) for r, e in zip(res, expected)]
def testSqureFormExecution(self):
from scipy.spatial.distance import pdist as sp_pdist, \
squareform as sp_squareform
raw_a = np.random.rand(80, 10)
raw_pdsit = sp_pdist(raw_a)
raw_square = sp_squareform(raw_pdsit)
# tomatrix, test 1 chunk
vec = tensor(raw_pdsit, chunk_size=raw_pdsit.shape[0])
mat = distance.squareform(vec, chunk_size=100)
result = self._executor.execute_tensor(mat, concat=True)[0]
np.testing.assert_array_equal(result, raw_square)
# tomatrix, test more than 1 chunk
vec = tensor(raw_pdsit, chunk_size=33)
self.assertGreater(len(vec.tiles().chunks), 1)
mat = distance.squareform(vec, chunk_size=34)
result = self._executor.execute_tensor(mat, concat=True)[0]
np.testing.assert_array_equal(result, raw_square)
# tovec, test 1 chunk
mat = tensor(raw_square)
vec = distance.squareform(mat, chunk_size=raw_pdsit.shape[0])
self.assertEqual(len(mat.tiles().chunks), 1)
self.assertEqual(len(vec.tiles().chunks), 1)
result = self._executor.execute_tensor(vec, concat=True)[0]
np.testing.assert_array_equal(result, raw_pdsit)
# tovec, test more than 1 chunk
mat = tensor(raw_square, chunk_size=31)
vec = distance.squareform(mat, chunk_size=40)
self.assertGreater(len(vec.tiles().chunks), 1)
result = self._executor.execute_tensor(vec, concat=True)[0]
np.testing.assert_array_equal(result, raw_pdsit)
# test checks
# generate non-symmetric matrix
non_sym_arr = np.random.RandomState(0).rand(10, 10)
# 1 chunk
mat = tensor(non_sym_arr)
vec = distance.squareform(mat, checks=True, chunk_size=100)
with self.assertRaises(ValueError):
_ = self._executor.execute_tensor(vec, concat=True)[0]
# force checks=False
vec = distance.squareform(mat, checks=False, chunk_size=100)
_ = self._executor.execute_tensor(vec, concat=True)[0]
# more than 1 chunk
mat = tensor(non_sym_arr, chunk_size=6)
vec = distance.squareform(mat, checks=True, chunk_size=8)
self.assertGreater(len(vec.tiles().chunks), 1)
with self.assertRaises(ValueError):
_ = self._executor.execute_tensor(vec, concat=True)[0]
# force checks=False
vec = distance.squareform(mat, checks=False, chunk_size=100)
_ = self._executor.execute_tensor(vec, concat=True)[0]
def test_squareform_execution(setup):
from scipy.spatial.distance import pdist as sp_pdist, \
squareform as sp_squareform
raw_a = np.random.rand(80, 10)
raw_pdsit = sp_pdist(raw_a)
raw_square = sp_squareform(raw_pdsit)
# tomatrix, test 1 chunk
vec = tensor(raw_pdsit, chunk_size=raw_pdsit.shape[0])
mat = distance.squareform(vec, chunk_size=100)
result = mat.execute().fetch()
np.testing.assert_array_equal(result, raw_square)
# tomatrix, test more than 1 chunk
vec = tensor(raw_pdsit, chunk_size=33)
assert len(tile(vec).chunks) > 1
mat = distance.squareform(vec, chunk_size=34)
result = mat.execute().fetch()
np.testing.assert_array_equal(result, raw_square)
# tovec, test 1 chunk
mat = tensor(raw_square)
vec = distance.squareform(mat, chunk_size=raw_pdsit.shape[0])
assert len(tile(mat).chunks) == 1
assert len(tile(vec).chunks) == 1
result = vec.execute().fetch()
np.testing.assert_array_equal(result, raw_pdsit)
# tovec, test more than 1 chunk
mat = tensor(raw_square, chunk_size=31)
vec = distance.squareform(mat, chunk_size=40)
assert len(tile(vec).chunks) > 1
result = vec.execute().fetch()
np.testing.assert_array_equal(result, raw_pdsit)
# test checks
# generate non-symmetric matrix
non_sym_arr = np.random.RandomState(0).rand(10, 10)
# 1 chunk
mat = tensor(non_sym_arr)
vec = distance.squareform(mat, checks=True, chunk_size=100)
with pytest.raises(ValueError):
_ = vec.execute().fetch()
# force checks=False
vec = distance.squareform(mat, checks=False, chunk_size=100)
_ = vec.execute().fetch()
# more than 1 chunk
mat = tensor(non_sym_arr, chunk_size=6)
vec = distance.squareform(mat, checks=True, chunk_size=8)
assert len(tile(vec).chunks) > 1
with pytest.raises(ValueError):
_ = vec.execute().fetch()
# force checks=False
vec = distance.squareform(mat, checks=False, chunk_size=100)
_ = vec.execute().fetch()
def testDiffExecution(self):
data = np.array([1, 2, 4, 7, 0])
x = tensor(data, chunk_size=2)
t = diff(x)
res = self.executor.execute_tensor(t, concat=True)[0]
expected = np.diff(data)
np.testing.assert_equal(res, expected)
t = diff(x, n=2)
res = self.executor.execute_tensor(t, concat=True)[0]
expected = np.diff(data, n=2)
np.testing.assert_equal(res, expected)
data = np.array([[1, 3, 6, 10], [0, 5, 6, 8]])
x = tensor(data, chunk_size=2)
t = diff(x)
res = self.executor.execute_tensor(t, concat=True)[0]
expected = np.diff(data)
np.testing.assert_equal(res, expected)
t = diff(x, axis=0)
res = self.executor.execute_tensor(t, concat=True)[0]
expected = np.diff(data, axis=0)
np.testing.assert_equal(res, expected)
x = mt.arange('1066-10-13', '1066-10-16', dtype=mt.datetime64)
t = diff(x)
res = self.executor.execute_tensor(t, concat=True)[0]
expected = np.diff(
np.arange('1066-10-13', '1066-10-16', dtype=np.datetime64))
np.testing.assert_equal(res, expected)
def testMixedIndexingExecution(self):
raw = np.random.random((11, 8, 12, 13))
arr = tensor(raw, chunk_size=3)
raw_cond = raw[0, :, 0, 0] < .5
cond = tensor(raw[0, :, 0, 0], chunk_size=3) < .5
arr2 = arr[10::-2, cond, None, ..., :5]
size_res = self.executor.execute_tensor(arr2, mock=True)
res = self.executor.execute_tensor(arr2, concat=True)
new_shape = list(arr2.shape)
new_shape[1] = cond.shape[0]
self.assertEqual(sum(s[0] for s in size_res),
int(np.prod(new_shape) * arr2.dtype.itemsize))
np.testing.assert_array_equal(res[0], raw[10::-2, raw_cond, None,
..., :5])
b_raw = np.random.random(8)
cond = tensor(b_raw, chunk_size=2) < .5
arr3 = arr[-2::-3, cond, ...]
res = self.executor.execute_tensor(arr3, concat=True)
np.testing.assert_array_equal(res[0], raw[-2::-3, b_raw < .5, ...])
def test_fancy_indexing_numpy_execution(setup):
# test fancy index of type numpy ndarray
raw = np.random.random((11, 8, 12, 14))
arr = tensor(raw, chunk_size=(6, 5, 7, 8))
index = [9, 10, 3, 1, 8, 10]
arr2 = arr[index]
res = arr2.execute().fetch()
np.testing.assert_array_equal(res, raw[index])
index = np.random.permutation(8)
arr3 = arr[:2, ..., index]
res = arr3.execute().fetch()
np.testing.assert_array_equal(res, raw[:2, ..., index])
index = [1, 3, 9, 10]
arr4 = arr[..., index, :5]
res = arr4.execute().fetch()
np.testing.assert_array_equal(res, raw[..., index, :5])
index1 = [8, 10, 3, 1, 9, 10]
index2 = [1, 3, 9, 10, 2, 7]
arr5 = arr[index1, :, index2]
res = arr5.execute().fetch()
np.testing.assert_array_equal(res, raw[index1, :, index2])
index1 = [1, 3, 5, 7, 9, 10]
index2 = [1, 9, 9, 10, 2, 7]
arr6 = arr[index1, :, index2]
res = arr6.execute().fetch()
np.testing.assert_array_equal(res, raw[index1, :, index2])
index1 = [[8, 10, 3], [1, 9, 10]]
index2 = [[1, 3, 9], [10, 2, 7]]
arr7 = arr[index1, :, index2]
res = arr7.execute().fetch()
np.testing.assert_array_equal(res, raw[index1, :, index2])
index1 = [[1, 3], [3, 7], [7, 7]]
index2 = [1, 9]
arr8 = arr[0, index1, :, index2]
res = arr8.execute().fetch()
np.testing.assert_array_equal(res, raw[0, index1, :, index2])
def test_base_execution(setup):
arr = ones((10, 8), chunk_size=2)
arr2 = arr + 1
res = arr2.execute().fetch()
np.testing.assert_array_equal(res, np.ones((10, 8)) + 1)
data = np.random.random((10, 8, 3))
arr = tensor(data, chunk_size=2)
arr2 = arr + 1
res = arr2.execute().fetch()
np.testing.assert_array_equal(res, data + 1)
def test_to_cpu():
x = tensor(np.random.rand(10, 10), chunk_size=3, gpu=True)
cx = to_cpu(x)
assert cx.dtype == x.dtype
assert cx.order == x.order
assert cx.op.gpu is False
cx, x = tile(cx, x)
assert cx.chunks[0].dtype == x.chunks[0].dtype
assert cx.chunks[0].order == x.chunks[0].order
assert cx.chunks[0].op.gpu is False
