def _call(self, x, out):
"""Implement ``self(x, out)``."""
with writable_array(out) as out_arr:
resize_array(x.asarray(), op.domain.shape,
offset=op.offset, pad_mode=op.pad_mode,
pad_const=0, direction='adjoint',
out=out_arr)
def test_resize_array_adj(resize_setup, odl_floating_dtype):
dtype = odl_floating_dtype
pad_mode, pad_const, newshp, offset, array, _ = resize_setup
if pad_const != 0:
# Not well-defined
return
array = np.array(array, dtype=dtype)
if is_real_dtype(dtype):
other_arr = np.random.uniform(-10, 10, size=newshp)
else:
other_arr = (np.random.uniform(-10, 10, size=newshp) +
1j * np.random.uniform(-10, 10, size=newshp))
resized = resize_array(array,
newshp,
offset,
pad_mode,
pad_const,
direction='forward')
resized_adj = resize_array(other_arr,
array.shape,
offset,
pad_mode,
pad_const,
direction='adjoint')
assert (np.vdot(resized.ravel(),
other_arr.ravel()) == pytest.approx(np.vdot(
array.ravel(), resized_adj.ravel()),
rel=dtype_tol(dtype)))
def _call(self, x, out):
"""Implement ``self(x, out)``."""
with writable_array(out) as out_arr:
resize_array(x.asarray(),
self.range.shape,
offset=self.offset,
pad_mode=self.pad_mode,
pad_const=self.pad_const,
direction='forward',
out=out_arr)
def test_resize_array_fwd(resize_setup, scalar_dtype):
pad_mode, pad_const, newshp, offset, array_in, true_out = resize_setup
array_in = np.array(array_in, dtype=scalar_dtype)
true_out = np.array(true_out, dtype=scalar_dtype)
resized = resize_array(array_in, newshp, offset, pad_mode, pad_const,
direction='forward')
out = np.empty(newshp, dtype=scalar_dtype)
resize_array(array_in, newshp, offset, pad_mode, pad_const,
direction='forward', out=out)
assert np.array_equal(resized, true_out)
assert np.array_equal(out, true_out)
def test_resize_array_fwd(resize_setup, scalar_dtype):
pad_mode, pad_const, newshp, offset, array_in, true_out = resize_setup
array_in = np.array(array_in, dtype=scalar_dtype)
true_out = np.array(true_out, dtype=scalar_dtype)
resized = resize_array(array_in, newshp, offset, pad_mode, pad_const,
direction='forward')
out = np.empty(newshp, dtype=scalar_dtype)
resize_array(array_in, newshp, offset, pad_mode, pad_const,
direction='forward', out=out)
assert np.array_equal(resized, true_out)
assert np.array_equal(out, true_out)
def _call(self, x, out):
"""Implement ``self(x, out)``."""
# TODO: simplify once context manager is available
out[:] = resize_array(
x.asarray(), self.range.shape, offset=self.offset,
pad_mode=self.pad_mode, pad_const=0, direction='adjoint',
out=out.asarray())
def _call(self, x, out):
"""Implement ``self(x, out)``."""
# TODO: simplify once context manager is available
out[:] = resize_array(x.asarray(),
self.range.shape,
offset=self.offset,
pad_mode=self.pad_mode,
pad_const=0,
direction='adjoint',
out=out.asarray())
def test_resize_array_corner_cases(odl_scalar_dtype, padding):
# Test extreme cases of resizing that are still valid for several
# `pad_mode`s
dtype = odl_scalar_dtype
pad_mode, pad_const = padding
# Test array
arr = np.arange(12, dtype=dtype).reshape((3, 4))
# Resize to and from 0 total size
squashed_arr = resize_array(arr, (3, 0), pad_mode=pad_mode)
assert squashed_arr.size == 0
squashed_arr = resize_array(arr, (0, 0), pad_mode=pad_mode)
assert squashed_arr.size == 0
if pad_mode == 'constant':
# Blowing up from size 0 only works with constant padding
true_blownup_arr = np.empty_like(arr)
true_blownup_arr.fill(pad_const)
blownup_arr = resize_array(np.ones((3, 0), dtype=dtype), (3, 4),
pad_mode=pad_mode,
pad_const=pad_const)
assert np.array_equal(blownup_arr, true_blownup_arr)
blownup_arr = resize_array(np.ones((0, 0), dtype=dtype), (3, 4),
pad_mode=pad_mode,
pad_const=pad_const)
assert np.array_equal(blownup_arr, true_blownup_arr)
# Resize from 0 axes to 0 axes
zero_axes_arr = resize_array(np.array(0, dtype=dtype), (),
pad_mode=pad_mode)
assert zero_axes_arr == np.array(0, dtype=dtype)
if pad_mode == 'periodic':
# Resize with periodic padding, using all values from the original
# array on both sides
max_per_shape = (9, 12)
res_arr = resize_array(arr,
max_per_shape,
pad_mode='periodic',
offset=arr.shape)
assert np.array_equal(res_arr, np.tile(arr, (3, 3)))
elif pad_mode == 'symmetric':
# Symmetric padding, maximum number is one less compared to periodic
# padding since the boundary value is not repeated
arr = np.arange(6).reshape((2, 3))
max_sym_shape = (4, 7)
res_arr = resize_array(arr,
max_sym_shape,
pad_mode='symmetric',
offset=[1, 2])
true_arr = np.array([[5, 4, 3, 4, 5, 4, 3], [2, 1, 0, 1, 2, 1, 0],
[5, 4, 3, 4, 5, 4, 3], [2, 1, 0, 1, 2, 1, 0]])
assert np.array_equal(res_arr, true_arr)
def test_resize_array_adj(resize_setup, floating_dtype):
pad_mode, pad_const, newshp, offset, array, _ = resize_setup
if pad_const != 0:
# Not well-defined
return
array = np.array(array, dtype=floating_dtype)
if is_real_dtype(floating_dtype):
other_arr = np.random.uniform(-10, 10, size=newshp)
else:
other_arr = (np.random.uniform(-10, 10, size=newshp) +
1j * np.random.uniform(-10, 10, size=newshp))
resized = resize_array(array, newshp, offset, pad_mode, pad_const,
direction='forward')
resized_adj = resize_array(other_arr, array.shape, offset, pad_mode,
pad_const, direction='adjoint')
assert almost_equal(np.vdot(resized.ravel(), other_arr.ravel()),
np.vdot(array.ravel(), resized_adj.ravel()))
def test_resize_array_corner_cases(scalar_dtype, padding):
# Test extreme cases of resizing that are still valid for several
# `pad_mode`s
pad_mode, pad_const = padding
# Test array
arr = np.arange(12, dtype=scalar_dtype).reshape((3, 4))
# Resize to and from 0 total size
squashed_arr = resize_array(arr, (3, 0), pad_mode=pad_mode)
assert squashed_arr.size == 0
squashed_arr = resize_array(arr, (0, 0), pad_mode=pad_mode)
assert squashed_arr.size == 0
if pad_mode == 'constant':
# Blowing up from size 0 only works with constant padding
true_blownup_arr = np.empty_like(arr)
true_blownup_arr.fill(pad_const)
blownup_arr = resize_array(np.ones((3, 0), dtype=scalar_dtype), (3, 4),
pad_mode=pad_mode, pad_const=pad_const)
assert np.array_equal(blownup_arr, true_blownup_arr)
blownup_arr = resize_array(np.ones((0, 0), dtype=scalar_dtype), (3, 4),
pad_mode=pad_mode, pad_const=pad_const)
assert np.array_equal(blownup_arr, true_blownup_arr)
# Resize from 0 axes to 0 axes
zero_axes_arr = resize_array(np.array(0, dtype=scalar_dtype), (),
pad_mode=pad_mode)
assert zero_axes_arr == np.array(0, dtype=scalar_dtype)
if pad_mode == 'periodic':
# Resize with periodic padding, using all values from the original
# array on both sides
max_per_shape = (9, 12)
res_arr = resize_array(arr, max_per_shape, pad_mode='periodic',
offset=arr.shape)
assert np.array_equal(res_arr, np.tile(arr, (3, 3)))
elif pad_mode == 'symmetric':
# Symmetric padding, maximum number is one less compared to periodic
# padding since the boundary value is not repeated
arr = np.arange(6).reshape((2, 3))
max_sym_shape = (4, 7)
res_arr = resize_array(arr, max_sym_shape, pad_mode='symmetric',
offset=[1, 2])
true_arr = np.array(
[[5, 4, 3, 4, 5, 4, 3],
[2, 1, 0, 1, 2, 1, 0],
[5, 4, 3, 4, 5, 4, 3],
[2, 1, 0, 1, 2, 1, 0]])
assert np.array_equal(res_arr, true_arr)
def test_resize_array_raise():
arr_1d = np.arange(6)
arr_2d = np.arange(6).reshape((2, 3))
# Shape not a sequence
with pytest.raises(TypeError):
resize_array(arr_1d, 19)
# out given, but not an ndarray
with pytest.raises(TypeError):
resize_array(arr_1d, (10,), out=[])
# out has wrong shape
with pytest.raises(ValueError):
out = np.empty((4, 5))
resize_array(arr_2d, (5, 5), out=out)
# Input and output arrays differ in dimensionality
with pytest.raises(ValueError):
out = np.empty((4, 5))
resize_array(arr_1d, (4, 5))
with pytest.raises(ValueError):
out = np.empty((4, 5))
resize_array(arr_1d, (4, 5), out=out)
# invalid pad mode
with pytest.raises(ValueError):
resize_array(arr_1d, (10,), pad_mode='madeup_mode')
# padding constant cannot be cast to output data type
with pytest.raises(ValueError):
resize_array(arr_1d, (10,), pad_const=1.0) # arr_1d has dtype int
with pytest.raises(ValueError):
arr_1d_float = arr_1d.astype(float)
resize_array(arr_1d_float, (10,), pad_const=1.0j)
# Too few entries for order 0 or 1 padding modes
empty_arr = np.ones((3, 0))
with pytest.raises(ValueError):
resize_array(empty_arr, (3, 1), pad_mode='order0')
small_arr = np.ones((3, 1))
with pytest.raises(ValueError):
resize_array(small_arr, (3, 3), pad_mode='order1')
# Too large padding sizes for symmetric
small_arr = np.ones((3, 1))
with pytest.raises(ValueError):
resize_array(small_arr, (3, 2), pad_mode='symmetric')
with pytest.raises(ValueError):
resize_array(small_arr, (6, 1), pad_mode='symmetric')
with pytest.raises(ValueError):
resize_array(small_arr, (4, 3), offset=(0, 1), pad_mode='symmetric')
# Too large padding sizes for periodic
small_arr = np.ones((3, 1))
with pytest.raises(ValueError):
resize_array(small_arr, (3, 3), pad_mode='periodic')
with pytest.raises(ValueError):
resize_array(small_arr, (7, 1), pad_mode='periodic')
with pytest.raises(ValueError):
resize_array(small_arr, (3, 4), offset=(0, 1), pad_mode='periodic')
def test_resize_array_raise():
arr_1d = np.arange(6)
arr_2d = np.arange(6).reshape((2, 3))
# Shape not a sequence
with pytest.raises(TypeError):
resize_array(arr_1d, 19)
# out given, but not an ndarray
with pytest.raises(TypeError):
resize_array(arr_1d, (10,), out=[])
# out has wrong shape
with pytest.raises(ValueError):
out = np.empty((4, 5))
resize_array(arr_2d, (5, 5), out=out)
# Input and output arrays differ in dimensionality
with pytest.raises(ValueError):
out = np.empty((4, 5))
resize_array(arr_1d, (4, 5))
with pytest.raises(ValueError):
out = np.empty((4, 5))
resize_array(arr_1d, (4, 5), out=out)
# invalid pad mode
with pytest.raises(ValueError):
resize_array(arr_1d, (10,), pad_mode='madeup_mode')
# padding constant cannot be cast to output data type
with pytest.raises(ValueError):
resize_array(arr_1d, (10,), pad_const=1.0) # arr_1d has dtype int
with pytest.raises(ValueError):
arr_1d_float = arr_1d.astype(float)
resize_array(arr_1d_float, (10,), pad_const=1.0j)
# Too few entries for order 0 or 1 padding modes
empty_arr = np.ones((3, 0))
with pytest.raises(ValueError):
resize_array(empty_arr, (3, 1), pad_mode='order0')
small_arr = np.ones((3, 1))
with pytest.raises(ValueError):
resize_array(small_arr, (3, 3), pad_mode='order1')
# Too large padding sizes for symmetric
small_arr = np.ones((3, 1))
with pytest.raises(ValueError):
resize_array(small_arr, (3, 2), pad_mode='symmetric')
with pytest.raises(ValueError):
resize_array(small_arr, (6, 1), pad_mode='symmetric')
with pytest.raises(ValueError):
resize_array(small_arr, (4, 3), offset=(0, 1), pad_mode='symmetric')
# Too large padding sizes for periodic
small_arr = np.ones((3, 1))
with pytest.raises(ValueError):
resize_array(small_arr, (3, 3), pad_mode='periodic')
with pytest.raises(ValueError):
resize_array(small_arr, (7, 1), pad_mode='periodic')
with pytest.raises(ValueError):
resize_array(small_arr, (3, 4), offset=(0, 1), pad_mode='periodic')
