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from __future__ import division, absolute_import, print_function
import numpy as np
from numpy.testing import (
run_module_suite, assert_equal, assert_array_equal,
assert_raises
)
from numpy.lib.stride_tricks import as_strided, broadcast_arrays
def assert_shapes_correct(input_shapes, expected_shape):
# Broadcast a list of arrays with the given input shapes and check the
# common output shape.
inarrays = [np.zeros(s) for s in input_shapes]
outarrays = broadcast_arrays(*inarrays)
outshapes = [a.shape for a in outarrays]
expected = [expected_shape] * len(inarrays)
assert_equal(outshapes, expected)
def assert_incompatible_shapes_raise(input_shapes):
# Broadcast a list of arrays with the given (incompatible) input shapes
# and check that they raise a ValueError.
inarrays = [np.zeros(s) for s in input_shapes]
assert_raises(ValueError, broadcast_arrays, *inarrays)
def assert_same_as_ufunc(shape0, shape1, transposed=False, flipped=False):
# Broadcast two shapes against each other and check that the data layout
# is the same as if a ufunc did the broadcasting.
x0 = np.zeros(shape0, dtype=int)
# Note that multiply.reduce's identity element is 1.0, so when shape1==(),
# this gives the desired n==1.
n = int(np.multiply.reduce(shape1))
x1 = np.arange(n).reshape(shape1)
if transposed:
x0 = x0.T
x1 = x1.T
if flipped:
x0 = x0[::-1]
x1 = x1[::-1]
# Use the add ufunc to do the broadcasting. Since we're adding 0s to x1, the
# result should be exactly the same as the broadcasted view of x1.
y = x0 + x1
b0, b1 = broadcast_arrays(x0, x1)
assert_array_equal(y, b1)
def test_same():
x = np.arange(10)
y = np.arange(10)
bx, by = broadcast_arrays(x, y)
assert_array_equal(x, bx)
assert_array_equal(y, by)
def test_one_off():
x = np.array([[1, 2, 3]])
y = np.array([[1], [2], [3]])
bx, by = broadcast_arrays(x, y)
bx0 = np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3]])
by0 = bx0.T
assert_array_equal(bx0, bx)
assert_array_equal(by0, by)
def test_same_input_shapes():
# Check that the final shape is just the input shape.
data = [
(),
(1,),
(3,),
(0, 1),
(0, 3),
(1, 0),
(3, 0),
(1, 3),
(3, 1),
(3, 3),
]
for shape in data:
input_shapes = [shape]
# Single input.
assert_shapes_correct(input_shapes, shape)
# Double input.
input_shapes2 = [shape, shape]
assert_shapes_correct(input_shapes2, shape)
# Triple input.
input_shapes3 = [shape, shape, shape]
assert_shapes_correct(input_shapes3, shape)
def test_two_compatible_by_ones_input_shapes():
# Check that two different input shapes of the same length, but some have
# ones, broadcast to the correct shape.
data = [
[[(1,), (3,)], (3,)],
[[(1, 3), (3, 3)], (3, 3)],
[[(3, 1), (3, 3)], (3, 3)],
[[(1, 3), (3, 1)], (3, 3)],
[[(1, 1), (3, 3)], (3, 3)],
[[(1, 1), (1, 3)], (1, 3)],
[[(1, 1), (3, 1)], (3, 1)],
[[(1, 0), (0, 0)], (0, 0)],
[[(0, 1), (0, 0)], (0, 0)],
[[(1, 0), (0, 1)], (0, 0)],
[[(1, 1), (0, 0)], (0, 0)],
[[(1, 1), (1, 0)], (1, 0)],
[[(1, 1), (0, 1)], (0, 1)],
]
for input_shapes, expected_shape in data:
assert_shapes_correct(input_shapes, expected_shape)
# Reverse the input shapes since broadcasting should be symmetric.
assert_shapes_correct(input_shapes[::-1], expected_shape)
def test_two_compatible_by_prepending_ones_input_shapes():
# Check that two different input shapes (of different lengths) broadcast
# to the correct shape.
data = [
[[(), (3,)], (3,)],
[[(3,), (3, 3)], (3, 3)],
[[(3,), (3, 1)], (3, 3)],
[[(1,), (3, 3)], (3, 3)],
[[(), (3, 3)], (3, 3)],
[[(1, 1), (3,)], (1, 3)],
[[(1,), (3, 1)], (3, 1)],
[[(1,), (1, 3)], (1, 3)],
[[(), (1, 3)], (1, 3)],
[[(), (3, 1)], (3, 1)],
[[(), (0,)], (0,)],
[[(0,), (0, 0)], (0, 0)],
[[(0,), (0, 1)], (0, 0)],
[[(1,), (0, 0)], (0, 0)],
[[(), (0, 0)], (0, 0)],
[[(1, 1), (0,)], (1, 0)],
[[(1,), (0, 1)], (0, 1)],
[[(1,), (1, 0)], (1, 0)],
[[(), (1, 0)], (1, 0)],
[[(), (0, 1)], (0, 1)],
]
for input_shapes, expected_shape in data:
assert_shapes_correct(input_shapes, expected_shape)
# Reverse the input shapes since broadcasting should be symmetric.
assert_shapes_correct(input_shapes[::-1], expected_shape)
def test_incompatible_shapes_raise_valueerror():
# Check that a ValueError is raised for incompatible shapes.
data = [
[(3,), (4,)],
[(2, 3), (2,)],
[(3,), (3,), (4,)],
[(1, 3, 4), (2, 3, 3)],
]
for input_shapes in data:
assert_incompatible_shapes_raise(input_shapes)
# Reverse the input shapes since broadcasting should be symmetric.
assert_incompatible_shapes_raise(input_shapes[::-1])
def test_same_as_ufunc():
# Check that the data layout is the same as if a ufunc did the operation.
data = [
[[(1,), (3,)], (3,)],
[[(1, 3), (3, 3)], (3, 3)],
[[(3, 1), (3, 3)], (3, 3)],
[[(1, 3), (3, 1)], (3, 3)],
[[(1, 1), (3, 3)], (3, 3)],
[[(1, 1), (1, 3)], (1, 3)],
[[(1, 1), (3, 1)], (3, 1)],
[[(1, 0), (0, 0)], (0, 0)],
[[(0, 1), (0, 0)], (0, 0)],
[[(1, 0), (0, 1)], (0, 0)],
[[(1, 1), (0, 0)], (0, 0)],
[[(1, 1), (1, 0)], (1, 0)],
[[(1, 1), (0, 1)], (0, 1)],
[[(), (3,)], (3,)],
[[(3,), (3, 3)], (3, 3)],
[[(3,), (3, 1)], (3, 3)],
[[(1,), (3, 3)], (3, 3)],
[[(), (3, 3)], (3, 3)],
[[(1, 1), (3,)], (1, 3)],
[[(1,), (3, 1)], (3, 1)],
[[(1,), (1, 3)], (1, 3)],
[[(), (1, 3)], (1, 3)],
[[(), (3, 1)], (3, 1)],
[[(), (0,)], (0,)],
[[(0,), (0, 0)], (0, 0)],
[[(0,), (0, 1)], (0, 0)],
[[(1,), (0, 0)], (0, 0)],
[[(), (0, 0)], (0, 0)],
[[(1, 1), (0,)], (1, 0)],
[[(1,), (0, 1)], (0, 1)],
[[(1,), (1, 0)], (1, 0)],
[[(), (1, 0)], (1, 0)],
[[(), (0, 1)], (0, 1)],
]
for input_shapes, expected_shape in data:
assert_same_as_ufunc(input_shapes[0], input_shapes[1],
"Shapes: %s %s" % (input_shapes[0], input_shapes[1]))
# Reverse the input shapes since broadcasting should be symmetric.
assert_same_as_ufunc(input_shapes[1], input_shapes[0])
# Try them transposed, too.
assert_same_as_ufunc(input_shapes[0], input_shapes[1], True)
# ... and flipped for non-rank-0 inputs in order to test negative
# strides.
if () not in input_shapes:
assert_same_as_ufunc(input_shapes[0], input_shapes[1], False, True)
assert_same_as_ufunc(input_shapes[0], input_shapes[1], True, True)
def test_as_strided():
a = np.array([None])
a_view = as_strided(a)
expected = np.array([None])
assert_array_equal(a_view, np.array([None]))
a = np.array([1, 2, 3, 4])
a_view = as_strided(a, shape=(2,), strides=(2 * a.itemsize,))
expected = np.array([1, 3])
assert_array_equal(a_view, expected)
a = np.array([1, 2, 3, 4])
a_view = as_strided(a, shape=(3, 4), strides=(0, 1 * a.itemsize))
expected = np.array([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]])
assert_array_equal(a_view, expected)
if __name__ == "__main__":
run_module_suite()
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