479 lines
17 KiB
Python
479 lines
17 KiB
Python
import numpy as np
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from numpy.core._rational_tests import rational
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from numpy.testing import (
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assert_equal, assert_array_equal, assert_raises, assert_,
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assert_raises_regex, assert_warns,
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)
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from numpy.lib.stride_tricks import (
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as_strided, broadcast_arrays, _broadcast_shape, broadcast_to
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)
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def assert_shapes_correct(input_shapes, expected_shape):
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# Broadcast a list of arrays with the given input shapes and check the
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# common output shape.
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inarrays = [np.zeros(s) for s in input_shapes]
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outarrays = broadcast_arrays(*inarrays)
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outshapes = [a.shape for a in outarrays]
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expected = [expected_shape] * len(inarrays)
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assert_equal(outshapes, expected)
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def assert_incompatible_shapes_raise(input_shapes):
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# Broadcast a list of arrays with the given (incompatible) input shapes
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# and check that they raise a ValueError.
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inarrays = [np.zeros(s) for s in input_shapes]
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assert_raises(ValueError, broadcast_arrays, *inarrays)
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def assert_same_as_ufunc(shape0, shape1, transposed=False, flipped=False):
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# Broadcast two shapes against each other and check that the data layout
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# is the same as if a ufunc did the broadcasting.
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x0 = np.zeros(shape0, dtype=int)
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# Note that multiply.reduce's identity element is 1.0, so when shape1==(),
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# this gives the desired n==1.
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n = int(np.multiply.reduce(shape1))
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x1 = np.arange(n).reshape(shape1)
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if transposed:
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x0 = x0.T
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x1 = x1.T
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if flipped:
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x0 = x0[::-1]
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x1 = x1[::-1]
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# Use the add ufunc to do the broadcasting. Since we're adding 0s to x1, the
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# result should be exactly the same as the broadcasted view of x1.
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y = x0 + x1
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b0, b1 = broadcast_arrays(x0, x1)
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assert_array_equal(y, b1)
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def test_same():
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x = np.arange(10)
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y = np.arange(10)
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bx, by = broadcast_arrays(x, y)
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assert_array_equal(x, bx)
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assert_array_equal(y, by)
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def test_broadcast_kwargs():
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# ensure that a TypeError is appropriately raised when
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# np.broadcast_arrays() is called with any keyword
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# argument other than 'subok'
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x = np.arange(10)
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y = np.arange(10)
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with assert_raises_regex(TypeError, 'got an unexpected keyword'):
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broadcast_arrays(x, y, dtype='float64')
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def test_one_off():
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x = np.array([[1, 2, 3]])
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y = np.array([[1], [2], [3]])
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bx, by = broadcast_arrays(x, y)
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bx0 = np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3]])
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by0 = bx0.T
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assert_array_equal(bx0, bx)
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assert_array_equal(by0, by)
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def test_same_input_shapes():
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# Check that the final shape is just the input shape.
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data = [
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(),
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(1,),
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(3,),
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(0, 1),
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(0, 3),
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(1, 0),
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(3, 0),
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(1, 3),
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(3, 1),
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(3, 3),
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]
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for shape in data:
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input_shapes = [shape]
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# Single input.
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assert_shapes_correct(input_shapes, shape)
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# Double input.
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input_shapes2 = [shape, shape]
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assert_shapes_correct(input_shapes2, shape)
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# Triple input.
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input_shapes3 = [shape, shape, shape]
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assert_shapes_correct(input_shapes3, shape)
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def test_two_compatible_by_ones_input_shapes():
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# Check that two different input shapes of the same length, but some have
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# ones, broadcast to the correct shape.
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data = [
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[[(1,), (3,)], (3,)],
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[[(1, 3), (3, 3)], (3, 3)],
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[[(3, 1), (3, 3)], (3, 3)],
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[[(1, 3), (3, 1)], (3, 3)],
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[[(1, 1), (3, 3)], (3, 3)],
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[[(1, 1), (1, 3)], (1, 3)],
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[[(1, 1), (3, 1)], (3, 1)],
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[[(1, 0), (0, 0)], (0, 0)],
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[[(0, 1), (0, 0)], (0, 0)],
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[[(1, 0), (0, 1)], (0, 0)],
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[[(1, 1), (0, 0)], (0, 0)],
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[[(1, 1), (1, 0)], (1, 0)],
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[[(1, 1), (0, 1)], (0, 1)],
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]
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for input_shapes, expected_shape in data:
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assert_shapes_correct(input_shapes, expected_shape)
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# Reverse the input shapes since broadcasting should be symmetric.
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assert_shapes_correct(input_shapes[::-1], expected_shape)
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def test_two_compatible_by_prepending_ones_input_shapes():
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# Check that two different input shapes (of different lengths) broadcast
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# to the correct shape.
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data = [
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[[(), (3,)], (3,)],
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[[(3,), (3, 3)], (3, 3)],
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[[(3,), (3, 1)], (3, 3)],
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[[(1,), (3, 3)], (3, 3)],
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[[(), (3, 3)], (3, 3)],
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[[(1, 1), (3,)], (1, 3)],
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[[(1,), (3, 1)], (3, 1)],
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[[(1,), (1, 3)], (1, 3)],
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[[(), (1, 3)], (1, 3)],
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[[(), (3, 1)], (3, 1)],
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[[(), (0,)], (0,)],
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[[(0,), (0, 0)], (0, 0)],
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[[(0,), (0, 1)], (0, 0)],
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[[(1,), (0, 0)], (0, 0)],
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[[(), (0, 0)], (0, 0)],
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[[(1, 1), (0,)], (1, 0)],
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[[(1,), (0, 1)], (0, 1)],
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[[(1,), (1, 0)], (1, 0)],
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[[(), (1, 0)], (1, 0)],
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[[(), (0, 1)], (0, 1)],
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]
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for input_shapes, expected_shape in data:
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assert_shapes_correct(input_shapes, expected_shape)
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# Reverse the input shapes since broadcasting should be symmetric.
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assert_shapes_correct(input_shapes[::-1], expected_shape)
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def test_incompatible_shapes_raise_valueerror():
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# Check that a ValueError is raised for incompatible shapes.
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data = [
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[(3,), (4,)],
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[(2, 3), (2,)],
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[(3,), (3,), (4,)],
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[(1, 3, 4), (2, 3, 3)],
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]
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for input_shapes in data:
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assert_incompatible_shapes_raise(input_shapes)
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# Reverse the input shapes since broadcasting should be symmetric.
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assert_incompatible_shapes_raise(input_shapes[::-1])
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def test_same_as_ufunc():
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# Check that the data layout is the same as if a ufunc did the operation.
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data = [
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[[(1,), (3,)], (3,)],
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[[(1, 3), (3, 3)], (3, 3)],
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[[(3, 1), (3, 3)], (3, 3)],
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[[(1, 3), (3, 1)], (3, 3)],
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[[(1, 1), (3, 3)], (3, 3)],
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[[(1, 1), (1, 3)], (1, 3)],
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[[(1, 1), (3, 1)], (3, 1)],
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[[(1, 0), (0, 0)], (0, 0)],
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[[(0, 1), (0, 0)], (0, 0)],
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[[(1, 0), (0, 1)], (0, 0)],
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[[(1, 1), (0, 0)], (0, 0)],
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[[(1, 1), (1, 0)], (1, 0)],
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[[(1, 1), (0, 1)], (0, 1)],
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[[(), (3,)], (3,)],
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[[(3,), (3, 3)], (3, 3)],
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[[(3,), (3, 1)], (3, 3)],
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[[(1,), (3, 3)], (3, 3)],
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[[(), (3, 3)], (3, 3)],
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[[(1, 1), (3,)], (1, 3)],
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[[(1,), (3, 1)], (3, 1)],
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[[(1,), (1, 3)], (1, 3)],
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[[(), (1, 3)], (1, 3)],
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[[(), (3, 1)], (3, 1)],
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[[(), (0,)], (0,)],
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[[(0,), (0, 0)], (0, 0)],
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[[(0,), (0, 1)], (0, 0)],
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[[(1,), (0, 0)], (0, 0)],
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[[(), (0, 0)], (0, 0)],
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[[(1, 1), (0,)], (1, 0)],
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[[(1,), (0, 1)], (0, 1)],
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[[(1,), (1, 0)], (1, 0)],
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[[(), (1, 0)], (1, 0)],
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[[(), (0, 1)], (0, 1)],
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]
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for input_shapes, expected_shape in data:
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assert_same_as_ufunc(input_shapes[0], input_shapes[1],
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"Shapes: %s %s" % (input_shapes[0], input_shapes[1]))
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# Reverse the input shapes since broadcasting should be symmetric.
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assert_same_as_ufunc(input_shapes[1], input_shapes[0])
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# Try them transposed, too.
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assert_same_as_ufunc(input_shapes[0], input_shapes[1], True)
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# ... and flipped for non-rank-0 inputs in order to test negative
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# strides.
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if () not in input_shapes:
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assert_same_as_ufunc(input_shapes[0], input_shapes[1], False, True)
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assert_same_as_ufunc(input_shapes[0], input_shapes[1], True, True)
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def test_broadcast_to_succeeds():
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data = [
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[np.array(0), (0,), np.array(0)],
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[np.array(0), (1,), np.zeros(1)],
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[np.array(0), (3,), np.zeros(3)],
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[np.ones(1), (1,), np.ones(1)],
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[np.ones(1), (2,), np.ones(2)],
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[np.ones(1), (1, 2, 3), np.ones((1, 2, 3))],
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[np.arange(3), (3,), np.arange(3)],
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[np.arange(3), (1, 3), np.arange(3).reshape(1, -1)],
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[np.arange(3), (2, 3), np.array([[0, 1, 2], [0, 1, 2]])],
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# test if shape is not a tuple
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[np.ones(0), 0, np.ones(0)],
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[np.ones(1), 1, np.ones(1)],
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[np.ones(1), 2, np.ones(2)],
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# these cases with size 0 are strange, but they reproduce the behavior
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# of broadcasting with ufuncs (see test_same_as_ufunc above)
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[np.ones(1), (0,), np.ones(0)],
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[np.ones((1, 2)), (0, 2), np.ones((0, 2))],
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[np.ones((2, 1)), (2, 0), np.ones((2, 0))],
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]
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for input_array, shape, expected in data:
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actual = broadcast_to(input_array, shape)
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assert_array_equal(expected, actual)
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def test_broadcast_to_raises():
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data = [
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[(0,), ()],
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[(1,), ()],
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[(3,), ()],
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[(3,), (1,)],
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[(3,), (2,)],
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[(3,), (4,)],
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[(1, 2), (2, 1)],
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[(1, 1), (1,)],
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[(1,), -1],
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[(1,), (-1,)],
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[(1, 2), (-1, 2)],
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]
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for orig_shape, target_shape in data:
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arr = np.zeros(orig_shape)
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assert_raises(ValueError, lambda: broadcast_to(arr, target_shape))
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def test_broadcast_shape():
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# broadcast_shape is already exercized indirectly by broadcast_arrays
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assert_equal(_broadcast_shape(), ())
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assert_equal(_broadcast_shape([1, 2]), (2,))
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assert_equal(_broadcast_shape(np.ones((1, 1))), (1, 1))
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assert_equal(_broadcast_shape(np.ones((1, 1)), np.ones((3, 4))), (3, 4))
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assert_equal(_broadcast_shape(*([np.ones((1, 2))] * 32)), (1, 2))
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assert_equal(_broadcast_shape(*([np.ones((1, 2))] * 100)), (1, 2))
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# regression tests for gh-5862
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assert_equal(_broadcast_shape(*([np.ones(2)] * 32 + [1])), (2,))
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bad_args = [np.ones(2)] * 32 + [np.ones(3)] * 32
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assert_raises(ValueError, lambda: _broadcast_shape(*bad_args))
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def test_as_strided():
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a = np.array([None])
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a_view = as_strided(a)
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expected = np.array([None])
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assert_array_equal(a_view, np.array([None]))
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a = np.array([1, 2, 3, 4])
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a_view = as_strided(a, shape=(2,), strides=(2 * a.itemsize,))
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expected = np.array([1, 3])
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assert_array_equal(a_view, expected)
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a = np.array([1, 2, 3, 4])
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a_view = as_strided(a, shape=(3, 4), strides=(0, 1 * a.itemsize))
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expected = np.array([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]])
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assert_array_equal(a_view, expected)
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# Regression test for gh-5081
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dt = np.dtype([('num', 'i4'), ('obj', 'O')])
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a = np.empty((4,), dtype=dt)
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a['num'] = np.arange(1, 5)
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a_view = as_strided(a, shape=(3, 4), strides=(0, a.itemsize))
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expected_num = [[1, 2, 3, 4]] * 3
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expected_obj = [[None]*4]*3
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assert_equal(a_view.dtype, dt)
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assert_array_equal(expected_num, a_view['num'])
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assert_array_equal(expected_obj, a_view['obj'])
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# Make sure that void types without fields are kept unchanged
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a = np.empty((4,), dtype='V4')
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a_view = as_strided(a, shape=(3, 4), strides=(0, a.itemsize))
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assert_equal(a.dtype, a_view.dtype)
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# Make sure that the only type that could fail is properly handled
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dt = np.dtype({'names': [''], 'formats': ['V4']})
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a = np.empty((4,), dtype=dt)
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a_view = as_strided(a, shape=(3, 4), strides=(0, a.itemsize))
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assert_equal(a.dtype, a_view.dtype)
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# Custom dtypes should not be lost (gh-9161)
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r = [rational(i) for i in range(4)]
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a = np.array(r, dtype=rational)
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a_view = as_strided(a, shape=(3, 4), strides=(0, a.itemsize))
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assert_equal(a.dtype, a_view.dtype)
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assert_array_equal([r] * 3, a_view)
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def as_strided_writeable():
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arr = np.ones(10)
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view = as_strided(arr, writeable=False)
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assert_(not view.flags.writeable)
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# Check that writeable also is fine:
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view = as_strided(arr, writeable=True)
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assert_(view.flags.writeable)
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view[...] = 3
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assert_array_equal(arr, np.full_like(arr, 3))
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# Test that things do not break down for readonly:
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arr.flags.writeable = False
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view = as_strided(arr, writeable=False)
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view = as_strided(arr, writeable=True)
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assert_(not view.flags.writeable)
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class VerySimpleSubClass(np.ndarray):
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def __new__(cls, *args, **kwargs):
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return np.array(*args, subok=True, **kwargs).view(cls)
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class SimpleSubClass(VerySimpleSubClass):
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def __new__(cls, *args, **kwargs):
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self = np.array(*args, subok=True, **kwargs).view(cls)
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self.info = 'simple'
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return self
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def __array_finalize__(self, obj):
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self.info = getattr(obj, 'info', '') + ' finalized'
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def test_subclasses():
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# test that subclass is preserved only if subok=True
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a = VerySimpleSubClass([1, 2, 3, 4])
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assert_(type(a) is VerySimpleSubClass)
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a_view = as_strided(a, shape=(2,), strides=(2 * a.itemsize,))
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assert_(type(a_view) is np.ndarray)
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a_view = as_strided(a, shape=(2,), strides=(2 * a.itemsize,), subok=True)
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assert_(type(a_view) is VerySimpleSubClass)
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# test that if a subclass has __array_finalize__, it is used
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a = SimpleSubClass([1, 2, 3, 4])
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a_view = as_strided(a, shape=(2,), strides=(2 * a.itemsize,), subok=True)
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assert_(type(a_view) is SimpleSubClass)
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assert_(a_view.info == 'simple finalized')
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# similar tests for broadcast_arrays
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b = np.arange(len(a)).reshape(-1, 1)
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a_view, b_view = broadcast_arrays(a, b)
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assert_(type(a_view) is np.ndarray)
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assert_(type(b_view) is np.ndarray)
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assert_(a_view.shape == b_view.shape)
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a_view, b_view = broadcast_arrays(a, b, subok=True)
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assert_(type(a_view) is SimpleSubClass)
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assert_(a_view.info == 'simple finalized')
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assert_(type(b_view) is np.ndarray)
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assert_(a_view.shape == b_view.shape)
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# and for broadcast_to
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shape = (2, 4)
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a_view = broadcast_to(a, shape)
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assert_(type(a_view) is np.ndarray)
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assert_(a_view.shape == shape)
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a_view = broadcast_to(a, shape, subok=True)
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assert_(type(a_view) is SimpleSubClass)
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assert_(a_view.info == 'simple finalized')
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assert_(a_view.shape == shape)
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def test_writeable():
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# broadcast_to should return a readonly array
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original = np.array([1, 2, 3])
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result = broadcast_to(original, (2, 3))
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assert_equal(result.flags.writeable, False)
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assert_raises(ValueError, result.__setitem__, slice(None), 0)
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# but the result of broadcast_arrays needs to be writeable, to
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# preserve backwards compatibility
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for is_broadcast, results in [(False, broadcast_arrays(original,)),
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(True, broadcast_arrays(0, original))]:
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for result in results:
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# This will change to False in a future version
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if is_broadcast:
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with assert_warns(FutureWarning):
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assert_equal(result.flags.writeable, True)
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with assert_warns(DeprecationWarning):
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result[:] = 0
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# Warning not emitted, writing to the array resets it
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assert_equal(result.flags.writeable, True)
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else:
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# No warning:
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assert_equal(result.flags.writeable, True)
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for results in [broadcast_arrays(original),
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broadcast_arrays(0, original)]:
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for result in results:
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# resets the warn_on_write DeprecationWarning
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|
result.flags.writeable = True
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|
# check: no warning emitted
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|
assert_equal(result.flags.writeable, True)
|
|
result[:] = 0
|
|
|
|
# keep readonly input readonly
|
|
original.flags.writeable = False
|
|
_, result = broadcast_arrays(0, original)
|
|
assert_equal(result.flags.writeable, False)
|
|
|
|
# regression test for GH6491
|
|
shape = (2,)
|
|
strides = [0]
|
|
tricky_array = as_strided(np.array(0), shape, strides)
|
|
other = np.zeros((1,))
|
|
first, second = broadcast_arrays(tricky_array, other)
|
|
assert_(first.shape == second.shape)
|
|
|
|
|
|
def test_writeable_memoryview():
|
|
# The result of broadcast_arrays exports as a non-writeable memoryview
|
|
# because otherwise there is no good way to opt in to the new behaviour
|
|
# (i.e. you would need to set writeable to False explicitly).
|
|
# See gh-13929.
|
|
original = np.array([1, 2, 3])
|
|
|
|
for is_broadcast, results in [(False, broadcast_arrays(original,)),
|
|
(True, broadcast_arrays(0, original))]:
|
|
for result in results:
|
|
# This will change to False in a future version
|
|
if is_broadcast:
|
|
# memoryview(result, writable=True) will give warning but cannot
|
|
# be tested using the python API.
|
|
assert memoryview(result).readonly
|
|
else:
|
|
assert not memoryview(result).readonly
|
|
|
|
|
|
def test_reference_types():
|
|
input_array = np.array('a', dtype=object)
|
|
expected = np.array(['a'] * 3, dtype=object)
|
|
actual = broadcast_to(input_array, (3,))
|
|
assert_array_equal(expected, actual)
|
|
|
|
actual, _ = broadcast_arrays(input_array, np.ones(3))
|
|
assert_array_equal(expected, actual)
|