def test_columnwise_iteration_with_function():
input = lambda i, j: 2 * i + j
m = LazyArray(input, shape=(4, 3))
cols = [col for col in m.by_column()]
assert_array_equal(cols[0], np.array([0, 2, 4, 6]))
assert_array_equal(cols[1], np.array([1, 3, 5, 7]))
assert_array_equal(cols[2], np.array([2, 4, 6, 8]))
def test_columnwise_iteration_with_structured_array_and_mask():
input = np.arange(12).reshape((4, 3))
m = LazyArray(input, shape=(4, 3)) # 4 rows, 3 columns
mask = np.array([False, True, True])
cols = [col for col in m.by_column(mask=mask)]
assert_array_equal(cols[0], input[:, 1])
assert_array_equal(cols[1], input[:, 2])
def test_evaluate_with_functional_array():
input = lambda i,j: 2*i + j
m = LazyArray(input, shape=(4,3))
assert_array_equal(m.evaluate(),
np.array([[0, 1, 2],
[2, 3, 4],
[4, 5, 6],
[6, 7, 8]]))
def test_multiple_operations_with_structured_array():
input = np.arange(12).reshape((4, 3))
m0 = LazyArray(input, shape=(4, 3))
m1 = (m0 + 2) < 5
m2 = (m0 < 5) + 2
assert_array_equal(m1.evaluate(simplify=True), (input + 2) < 5)
assert_array_equal(m2.evaluate(simplify=True), (input < 5) + 2)
assert_array_equal(m0.evaluate(simplify=True), input)
def test_columnwise_iteration_with_random_array_parallel_safe_no_mask():
random.mpi_rank=0; random.num_processes=2
input = random.RandomDistribution(rng=MockRNG(parallel_safe=True))
copy_input = random.RandomDistribution(rng=MockRNG(parallel_safe=True))
m = LazyArray(input, shape=(4,3))
cols = [col for col in m.by_column()]
assert_array_equal(cols[0], copy_input.next(4, mask_local=False))
assert_array_equal(cols[1], copy_input.next(4, mask_local=False))
assert_array_equal(cols[2], copy_input.next(4, mask_local=False))
def test_columnwise_iteration_with_random_array_parallel_safe_with_mask():
random.mpi_rank=0; random.num_processes=2
input = random.RandomDistribution(rng=MockRNG(parallel_safe=True))
copy_input = random.RandomDistribution(rng=MockRNG(parallel_safe=True))
m = LazyArray(input, shape=(4,3))
mask = np.array([False, False, True])
cols = [col for col in m.by_column(mask=mask)]
assert_equal(len(cols), 1)
assert_array_almost_equal(cols[0], copy_input.next(12, mask_local=False)[8:], 15)
def test_columnwise_iteration_with_random_array_parallel_safe_with_mask():
orig_get_mpi_config = random.get_mpi_config
random.get_mpi_config = lambda: (0, 2)
input = random.RandomDistribution("uniform", (0, 1), rng=MockRNG(parallel_safe=True))
copy_input = random.RandomDistribution("gamma", (2, 3), rng=MockRNG(parallel_safe=True))
m = LazyArray(input, shape=(4, 3))
mask = np.array([False, False, True])
cols = [col for col in m.by_column(mask=mask)]
assert_equal(len(cols), 1)
assert_array_almost_equal(cols[0], copy_input.next(12, mask_local=False)[8:], 15)
random.get_mpi_config = orig_get_mpi_config
def test_columnwise_iteration_with_random_array_parallel_safe_no_mask():
orig_get_mpi_config = random.get_mpi_config
random.get_mpi_config = lambda: (0, 2)
input = random.RandomDistribution("uniform", (0, 1), rng=MockRNG(parallel_safe=True))
copy_input = random.RandomDistribution("normal", (0, 1), rng=MockRNG(parallel_safe=True))
m = LazyArray(input, shape=(4, 3))
cols = [col for col in m.by_column()]
assert_array_equal(cols[0], copy_input.next(4, mask_local=False))
assert_array_equal(cols[1], copy_input.next(4, mask_local=False))
assert_array_equal(cols[2], copy_input.next(4, mask_local=False))
random.get_mpi_config = orig_get_mpi_config
def test_columnwise_iteration_with_random_array_parallel_safe_no_mask():
orig_get_mpi_config = random.get_mpi_config
# first, with a single MPI node
random.get_mpi_config = lambda: (0, 2)
input = random.RandomDistribution('uniform', (0, 1), rng=MockRNG(parallel_safe=True))
m = LazyArray(input, shape=(4, 3))
cols_np1 = [col for col in m.by_column()]
# now, on one node of two
random.get_mpi_config = lambda: (1, 2)
input = random.RandomDistribution('uniform', (0, 1), rng=MockRNG(parallel_safe=True))
m = LazyArray(input, shape=(4, 3))
cols_np2_1 = [col for col in m.by_column()]
for i in range(3):
assert_array_equal(cols_np1[i], cols_np2_1[i])
random.get_mpi_config = orig_get_mpi_config
def test_columnwise_iteration_with_random_array_parallel_safe_with_mask():
orig_get_mpi_config = random.get_mpi_config
mask = np.array([False, False, True])
# first, with a single MPI node
random.get_mpi_config = lambda: (0, 2)
input = random.RandomDistribution('uniform', (0, 1), rng=MockRNG(parallel_safe=True))
m = LazyArray(input, shape=(4, 3))
cols_np1 = [col for col in m.by_column(mask=mask)]
# now, on one node of two
random.get_mpi_config = lambda: (0, 2)
input = random.RandomDistribution('uniform', (0, 1), rng=MockRNG(parallel_safe=True))
m = LazyArray(input, shape=(4, 3))
cols_np2_0 = [col for col in m.by_column(mask=mask)]
# now, on the other node of two
random.get_mpi_config = lambda: (1, 2)
input = random.RandomDistribution('uniform', (0, 1), rng=MockRNG(parallel_safe=True))
m = LazyArray(input, shape=(4, 3))
cols_np2_1 = [col for col in m.by_column(mask=mask)]
assert_equal(len(cols_np1), 1)
assert_equal(len(cols_np2_0), 1)
assert_equal(len(cols_np2_1), 1)
assert_array_equal(cols_np1[0], cols_np2_0[0])
random.get_mpi_config = orig_get_mpi_config
def connect(self, projection):
"""Connect-up a Projection."""
connection_map = LazyArray(lambda i, j: i == j, shape=projection.shape)
self._connect_with_map(projection, connection_map)
def test_iadd_with_flat_array():
m = LazyArray(5, shape=(4, 3))
m += 2
assert_array_equal(m.evaluate(), 7 * np.ones((4, 3)))
assert_equal(m.base_value, 5)
assert_equal(m.evaluate(simplify=True), 7)
def test_add_incommensurate_arrays():
m0 = LazyArray(5, shape=(4, 3))
m1 = LazyArray(7, shape=(5, 3))
assert_raises(ValueError, m0.__add__, m1)
def _generate_distance_map(self, projection):
position_generators = (projection.pre.position_generator,
projection.post.position_generator)
return LazyArray(
projection.space.distance_generator(*position_generators),
shape=projection.shape)
def test_evaluate_with_structured_array():
input = np.arange(12).reshape((4, 3))
m = LazyArray(input, shape=(4, 3))
assert_array_equal(m.evaluate(), input)
def test_lt_with_flat_array():
m0 = LazyArray(5, shape=(4, 3))
m1 = m0 < 10
assert_equal(m1.evaluate(simplify=True), True)
assert_equal(m0.evaluate(simplify=True), 5)
def test_setitem_nonexpanded_same_value():
A = LazyArray(3, shape=(5,))
assert A.evaluate(simplify=True) == 3
A[0] = 3
assert A.evaluate(simplify=True) == 3
def _connection_map(self):
return LazyArray(~numpy.isnan(
self._prev_connected.get(['weight'], 'array', gather='all')[0]))
def test_create_with_invalid_string():
A = LazyArray("d+2", shape=3)
def test_create_with_float():
A = LazyArray(3.0, shape=(5,))
assert A.shape == (5,)
assert A.evaluate(simplify=True) == 3.0
def test_create_with_array():
A = LazyArray(np.array([1, 2, 3]), shape=(3, ))
assert A.shape == (3, )
assert_array_equal(A.evaluate(simplify=True), np.array([1, 2, 3]))
def test_create_with_float():
A = LazyArray(3.0, shape=(5, ))
assert A.shape == (5, )
assert A.evaluate(simplify=True) == 3.0
def test_getitem_from_constant_array():
m = LazyArray(3 * np.ones((4, 3)), shape=(4, 3))
assert m[0, 0] == m[3, 2] == m[-1, 2] == m[-4, 2] == m[2, -3] == 3
assert_raises(IndexError, m.__getitem__, (4, 0))
assert_raises(IndexError, m.__getitem__, (2, -4))
def test_columnwise_iteration_with_flat_array_and_mask():
m = LazyArray(5, shape=(4, 3)) # 4 rows, 3 columns
mask = np.array([True, False, True])
cols = [col for col in m.by_column(mask=mask)]
assert_equal(cols, [5, 5])
def test_columnwise_iteration_with_flat_array():
m = LazyArray(5, shape=(4, 3)) # 4 rows, 3 columns
cols = [col for col in m.by_column()]
assert_equal(cols, [5, 5, 5])
def test_evaluate_with_functional_array():
input = lambda i, j: 2 * i + j
m = LazyArray(input, shape=(4, 3))
assert_array_equal(m.evaluate(),
np.array([[0, 1, 2], [2, 3, 4], [4, 5, 6], [6, 7, 8]]))
def test_lt_with_structured_array():
input = np.arange(12).reshape((4, 3))
m0 = LazyArray(input, shape=(4, 3))
m1 = m0 < 5
assert_array_equal(m1.evaluate(simplify=True), input < 5)
def test_evaluate_with_flat_array():
m = LazyArray(5, shape=(4, 3))
assert_array_equal(m.evaluate(), 5 * np.ones((4, 3)))
def test_evaluate_with_flat_array():
m = LazyArray(5, shape=(4, 3))
assert_array_equal(m.evaluate(), 5 * np.ones((4, 3)))
def test_iadd_with_flat_array():
m = LazyArray(5, shape=(4, 3))
m += 2
assert_array_equal(m.evaluate(), 7 * np.ones((4, 3)))
assert_equal(m.base_value, 5)
assert_equal(m.evaluate(simplify=True), 7)
def test_lt_with_flat_array():
m0 = LazyArray(5, shape=(4, 3))
m1 = m0 < 10
assert_equal(m1.evaluate(simplify=True), True)
assert_equal(m0.evaluate(simplify=True), 5)
def test_setitem_nonexpanded_same_value():
A = LazyArray(3, shape=(5, ))
assert A.evaluate(simplify=True) == 3
A[0] = 3
assert A.evaluate(simplify=True) == 3
def test_structured_array_lt_array():
input = np.arange(12).reshape((4, 3))
m0 = LazyArray(input, shape=(4, 3))
comparison = 5 * np.ones((4, 3))
m1 = m0 < comparison
assert_array_equal(m1.evaluate(simplify=True), input < comparison)
def test_create_with_array():
A = LazyArray(np.array([1, 2, 3]), shape=(3,))
assert A.shape == (3,)
assert_array_equal(A.evaluate(simplify=True), np.array([1, 2, 3]))
def test_setitem_invalid_value():
A = LazyArray(3, shape=(5, ))
assert_raises(TypeError, A.__setitem__, "abc")
def test_setitem_nonexpanded_different_value():
A = LazyArray(3, shape=(5,))
assert A.evaluate(simplify=True) == 3
A[0] = 4
A[4] = 5
assert_array_equal(A.evaluate(simplify=True), np.array([4, 3, 3, 3, 5]))
def test_setitem_nonexpanded_different_value():
A = LazyArray(3, shape=(5, ))
assert A.evaluate(simplify=True) == 3
A[0] = 4
A[4] = 5
assert_array_equal(A.evaluate(simplify=True), np.array([4, 3, 3, 3, 5]))
def test_columnwise_iteration_with_structured_array():
input = np.arange(12).reshape((4, 3))
m = LazyArray(input, shape=(4, 3)) # 4 rows, 3 columns
cols = [col for col in m.by_column()]
assert_array_equal(cols[0], input[:, 0])
assert_array_equal(cols[2], input[:, 2])
def test_columnwise_iteration_with_flat_array():
m = LazyArray(5, shape=(4, 3)) # 4 rows, 3 columns
cols = [col for col in m.by_column()]
assert_equal(cols, [5, 5, 5])
def test_columnwise_iteration_with_flat_array_and_mask():
m = LazyArray(5, shape=(4, 3)) # 4 rows, 3 columns
mask = np.array([True, False, True])
cols = [col for col in m.by_column(mask=mask)]
assert_equal(cols, [5, 5])
def test_columnwise_iteration_with_structured_array():
input = np.arange(12).reshape((4, 3))
m = LazyArray(input, shape=(4, 3)) # 4 rows, 3 columns
cols = [col for col in m.by_column()]
assert_array_equal(cols[0], input[:, 0])
assert_array_equal(cols[2], input[:, 2])
def test_evaluate_with_structured_array():
input = np.arange(12).reshape((4, 3))
m = LazyArray(input, shape=(4, 3))
assert_array_equal(m.evaluate(), input)
def connect(self, projection):
connection_map = LazyArray(self.array, projection.shape)
self._connect_with_map(projection, connection_map)