Fixed database typo and removed unnecessary class identifier.
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venv/Lib/site-packages/skimage/restoration/tests/test_unwrap.py
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venv/Lib/site-packages/skimage/restoration/tests/test_unwrap.py
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import numpy as np
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from skimage.restoration import unwrap_phase
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import sys
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import warnings
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from skimage._shared import testing
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from skimage._shared.testing import (assert_array_almost_equal_nulp,
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assert_almost_equal, assert_array_equal,
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assert_, skipif)
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from skimage._shared._warnings import expected_warnings
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def assert_phase_almost_equal(a, b, *args, **kwargs):
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"""An assert_almost_equal insensitive to phase shifts of n*2*pi."""
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shift = 2 * np.pi * np.round((b.mean() - a.mean()) / (2 * np.pi))
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with expected_warnings([r'invalid value encountered|\A\Z',
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r'divide by zero encountered|\A\Z']):
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print('assert_phase_allclose, abs', np.max(np.abs(a - (b - shift))))
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print('assert_phase_allclose, rel',
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np.max(np.abs((a - (b - shift)) / a)))
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if np.ma.isMaskedArray(a):
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assert_(np.ma.isMaskedArray(b))
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assert_array_equal(a.mask, b.mask)
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assert_(a.fill_value == b.fill_value)
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au = np.asarray(a)
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bu = np.asarray(b)
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with expected_warnings([r'invalid value encountered|\A\Z',
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r'divide by zero encountered|\A\Z']):
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print('assert_phase_allclose, no mask, abs',
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np.max(np.abs(au - (bu - shift))))
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print('assert_phase_allclose, no mask, rel',
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np.max(np.abs((au - (bu - shift)) / au)))
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assert_array_almost_equal_nulp(a + shift, b, *args, **kwargs)
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def check_unwrap(image, mask=None):
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image_wrapped = np.angle(np.exp(1j * image))
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if mask is not None:
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print('Testing a masked image')
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image = np.ma.array(image, mask=mask, fill_value=0.5)
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image_wrapped = np.ma.array(image_wrapped, mask=mask, fill_value=0.5)
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image_unwrapped = unwrap_phase(image_wrapped, seed=0)
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assert_phase_almost_equal(image_unwrapped, image)
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def test_unwrap_1d():
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image = np.linspace(0, 10 * np.pi, 100)
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check_unwrap(image)
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# Masked arrays are not allowed in 1D
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with testing.raises(ValueError):
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check_unwrap(image, True)
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# wrap_around is not allowed in 1D
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with testing.raises(ValueError):
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unwrap_phase(image, True, seed=0)
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@testing.parametrize("check_with_mask", (False, True))
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def test_unwrap_2d(check_with_mask):
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mask = None
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x, y = np.ogrid[:8, :16]
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image = 2 * np.pi * (x * 0.2 + y * 0.1)
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if check_with_mask:
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mask = np.zeros(image.shape, dtype=np.bool)
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mask[4:6, 4:8] = True
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check_unwrap(image, mask)
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@testing.parametrize("check_with_mask", (False, True))
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def test_unwrap_3d(check_with_mask):
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mask = None
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x, y, z = np.ogrid[:8, :12, :16]
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image = 2 * np.pi * (x * 0.2 + y * 0.1 + z * 0.05)
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if check_with_mask:
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mask = np.zeros(image.shape, dtype=np.bool)
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mask[4:6, 4:6, 1:3] = True
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check_unwrap(image, mask)
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def check_wrap_around(ndim, axis):
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# create a ramp, but with the last pixel along axis equalling the first
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elements = 100
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ramp = np.linspace(0, 12 * np.pi, elements)
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ramp[-1] = ramp[0]
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image = ramp.reshape(tuple([elements if n == axis else 1
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for n in range(ndim)]))
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image_wrapped = np.angle(np.exp(1j * image))
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index_first = tuple([0] * ndim)
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index_last = tuple([-1 if n == axis else 0 for n in range(ndim)])
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# unwrap the image without wrap around
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# We do not want warnings about length 1 dimensions
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with expected_warnings([r'Image has a length 1 dimension|\A\Z']):
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image_unwrap_no_wrap_around = unwrap_phase(image_wrapped, seed=0)
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print('endpoints without wrap_around:',
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image_unwrap_no_wrap_around[index_first],
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image_unwrap_no_wrap_around[index_last])
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# without wrap around, the endpoints of the image should differ
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assert_(abs(image_unwrap_no_wrap_around[index_first] -
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image_unwrap_no_wrap_around[index_last]) > np.pi)
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# unwrap the image with wrap around
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wrap_around = [n == axis for n in range(ndim)]
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# We do not want warnings about length 1 dimensions
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with expected_warnings([r'Image has a length 1 dimension.|\A\Z']):
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image_unwrap_wrap_around = unwrap_phase(image_wrapped, wrap_around,
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seed=0)
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print('endpoints with wrap_around:',
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image_unwrap_wrap_around[index_first],
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image_unwrap_wrap_around[index_last])
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# with wrap around, the endpoints of the image should be equal
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assert_almost_equal(image_unwrap_wrap_around[index_first],
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image_unwrap_wrap_around[index_last])
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dim_axis = [(ndim, axis) for ndim in (2, 3) for axis in range(ndim)]
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@skipif(sys.version_info[:2] == (3, 4),
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reason="Doesn't work with python 3.4. See issue #3079")
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@testing.parametrize("ndim, axis", dim_axis)
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def test_wrap_around(ndim, axis):
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check_wrap_around(ndim, axis)
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def test_mask():
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length = 100
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ramps = [np.linspace(0, 4 * np.pi, length),
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np.linspace(0, 8 * np.pi, length),
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np.linspace(0, 6 * np.pi, length)]
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image = np.vstack(ramps)
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mask_1d = np.ones((length,), dtype=np.bool)
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mask_1d[0] = mask_1d[-1] = False
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for i in range(len(ramps)):
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# mask all ramps but the i'th one
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mask = np.zeros(image.shape, dtype=np.bool)
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mask |= mask_1d.reshape(1, -1)
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mask[i, :] = False # unmask i'th ramp
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image_wrapped = np.ma.array(np.angle(np.exp(1j * image)), mask=mask)
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image_unwrapped = unwrap_phase(image_wrapped)
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image_unwrapped -= image_unwrapped[0, 0] # remove phase shift
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# The end of the unwrapped array should have value equal to the
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# endpoint of the unmasked ramp
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assert_array_almost_equal_nulp(image_unwrapped[:, -1], image[i, -1])
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assert_(np.ma.isMaskedArray(image_unwrapped))
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# Same tests, but forcing use of the 3D unwrapper by reshaping
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with expected_warnings(['length 1 dimension']):
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shape = (1,) + image_wrapped.shape
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image_wrapped_3d = image_wrapped.reshape(shape)
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image_unwrapped_3d = unwrap_phase(image_wrapped_3d)
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# remove phase shift
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image_unwrapped_3d -= image_unwrapped_3d[0, 0, 0]
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assert_array_almost_equal_nulp(image_unwrapped_3d[:, :, -1],
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image[i, -1])
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def test_invalid_input():
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with testing.raises(ValueError):
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unwrap_phase(np.zeros([]))
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with testing.raises(ValueError):
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unwrap_phase(np.zeros((1, 1, 1, 1)))
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with testing.raises(ValueError):
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unwrap_phase(np.zeros((1, 1)), 3 * [False])
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with testing.raises(ValueError):
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unwrap_phase(np.zeros((1, 1)), 'False')
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def test_unwrap_3d_middle_wrap_around():
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# Segmentation fault in 3D unwrap phase with middle dimension connected
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# GitHub issue #1171
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image = np.zeros((20, 30, 40), dtype=np.float32)
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unwrap = unwrap_phase(image, wrap_around=[False, True, False])
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assert_(np.all(unwrap == 0))
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def test_unwrap_2d_compressed_mask():
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# ValueError when image is masked array with a compressed mask (no masked
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# elements). GitHub issue #1346
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image = np.ma.zeros((10, 10))
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unwrap = unwrap_phase(image)
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assert_(np.all(unwrap == 0))
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def test_unwrap_2d_all_masked():
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# Segmentation fault when image is masked array with a all elements masked
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# GitHub issue #1347
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# all elements masked
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image = np.ma.zeros((10, 10))
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image[:] = np.ma.masked
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unwrap = unwrap_phase(image)
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assert_(np.ma.isMaskedArray(unwrap))
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assert_(np.all(unwrap.mask))
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# 1 unmasked element, still zero edges
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image = np.ma.zeros((10, 10))
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image[:] = np.ma.masked
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image[0, 0] = 0
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unwrap = unwrap_phase(image)
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assert_(np.ma.isMaskedArray(unwrap))
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assert_(np.sum(unwrap.mask) == 99) # all but one masked
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assert_(unwrap[0, 0] == 0)
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def test_unwrap_3d_all_masked():
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# all elements masked
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image = np.ma.zeros((10, 10, 10))
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image[:] = np.ma.masked
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unwrap = unwrap_phase(image)
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assert_(np.ma.isMaskedArray(unwrap))
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assert_(np.all(unwrap.mask))
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# 1 unmasked element, still zero edges
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image = np.ma.zeros((10, 10, 10))
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image[:] = np.ma.masked
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image[0, 0, 0] = 0
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unwrap = unwrap_phase(image)
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assert_(np.ma.isMaskedArray(unwrap))
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assert_(np.sum(unwrap.mask) == 999) # all but one masked
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assert_(unwrap[0, 0, 0] == 0)
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