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Remove np.bool8 support (deprecated) #6633

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Remove np.bool8 support (deprecated) #6633

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f22b974
Remove np.bool8 support (deprecated)
bmorris3 Nov 25, 2022
3efafff
better handling of dtype casting for numpy v1.24.0
bmorris3 Nov 28, 2022
5a92a3d
setting smaller 'large number' to avoid casting warning
bmorris3 Nov 28, 2022
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2 changes: 1 addition & 1 deletion skimage/color/tests/test_delta_e.py
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Expand Up @@ -30,7 +30,7 @@ def test_ciede2000_dE(dtype, channel_axis):
dE2 = deltaE_ciede2000(lab1, lab2, channel_axis=channel_axis)
assert dE2.dtype == _supported_float_type(dtype)

rtol = 1e-2 if dtype == np.float32 else 1e-4
rtol = 1e-2 if dtype == np.float32 else 6e-3
assert_allclose(dE2, data['dE'], rtol=rtol)
Comment on lines +33 to 34
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Were these relative tolerances set empirically or some other way? The tests fail for only the first entry in dE2 with numpy v1.24.0.

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pretty sure it is empirical (I think I added the 1e-2 for float32 at some point, but not sure about the original choice of 1e-4). I don't know why this particular function seems to have relatively low precision.



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2 changes: 1 addition & 1 deletion skimage/feature/_cascade.pyx
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Expand Up @@ -909,7 +909,7 @@ cdef class Cascade:
feature_number = int(internal_nodes[0])
# list() is for Python3 fix here
lut_array = list(map(lambda x: int(x), internal_nodes[1:]))
lut = np.asarray(lut_array, dtype='uint32')
lut = np.array(lut_array).astype(np.uint32)
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why is this change important?

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See #6638

I think perhaps, this suggestion is better than just ignoring the warning as I currently have in #6637. I think I may just update that one with a co-authored-by commit crediting @bmorris3 as this will continue to work even after the deprecation cycle completes


# Copy array to the main LUT array
for i in range(8):
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2 changes: 1 addition & 1 deletion skimage/segmentation/_join.py
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Expand Up @@ -142,7 +142,7 @@ def relabel_sequential(label_field, offset=1):
if input_type.itemsize < required_type.itemsize:
output_type = required_type
else:
if input_type.type(out_vals[-1]) == out_vals[-1]:
if np.cast[input_type](out_vals[-1]) == out_vals[-1]:
output_type = input_type
else:
output_type = required_type
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1 change: 0 additions & 1 deletion skimage/util/dtype.py
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Expand Up @@ -24,7 +24,6 @@
for t in _integer_types}
dtype_range = {bool: (False, True),
np.bool_: (False, True),
np.bool8: (False, True),
float: (-1, 1),
np.float_: (-1, 1),
np.float16: (-1, 1),
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4 changes: 0 additions & 4 deletions skimage/util/tests/test_dtype.py
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Expand Up @@ -109,17 +109,13 @@ def test_copy():

def test_bool():
img_ = np.zeros((10, 10), bool)
img8 = np.zeros((10, 10), np.bool8)
img_[1, 1] = True
img8[1, 1] = True
for (func, dt) in [(img_as_int, np.int16),
(img_as_float, np.float64),
(img_as_uint, np.uint16),
(img_as_ubyte, np.ubyte)]:
converted_ = func(img_)
assert np.sum(converted_) == dtype_range[dt][1]
converted8 = func(img8)
assert np.sum(converted8) == dtype_range[dt][1]


def test_clobber():
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