with pytest.raises(numpy.AxisError):
xp.diff(a, axis=3)
with pytest.raises(numpy.AxisError):
xp.diff(a, axis=-4)
# This class compares CUB results against NumPy's
@testing.parameterize(*testing.product_dict(
testing.product({
'shape': [()],
'axis': [None, ()],
'spacing': [(), (1.2, )],
}) + testing.product({
'shape': [(33, )],
'axis': [None, 0, -1, (0, )],
'spacing': [(), (1.2, ), 'sequence of int', 'arrays'],
}) + testing.product({
'shape': [(10, 20), (10, 20, 30)],
'axis': [None, 0, -1, (0, -1), (1, 0)],
'spacing': [(), (1.2, ), 'sequence of int', 'arrays', 'mixed'],
}),
testing.product({
'edge_order': [1, 2],
}),
))
@testing.gpu
class TestGradient(unittest.TestCase):
def _gradient(self, xp, dtype, shape, spacing, axis, edge_order):
x = testing.shaped_random(shape, xp, dtype=dtype)
if axis is None:
normalized_axes = tuple(range(x.ndim))
else:
def test_product_dict(self):
self.assertListEqual(testing.product_dict(*self.actual), self.expect)
testing.product_dict(
# Filter-function specific params
testing.product({
'filter': ['convolve', 'correlate'],
}) + testing.product({
'filter': ['convolve1d', 'correlate1d', 'minimum_filter1d'],
'axis': [0, 1, -1],
}) + testing.product({
'filter': ['minimum_filter', 'median_filter'],
'footprint': [False, True],
}),
# Mode-specific params
testing.product({
**COMMON_PARAMS,
'mode': ['reflect'],
# With reflect test some of the other parameters as well
'origin': [0, 1, (-1, 1, -1, 1)],
'output': [None, numpy.uint8, numpy.float64],
}) + testing.product({
**COMMON_PARAMS,
'mode': ['constant'],
'cval': [-1.0, 0.0, 1.0],
}) + testing.product({
**COMMON_FLOAT_PARAMS,
'mode': ['constant'],
'cval': [numpy.nan, numpy.inf, -numpy.inf],
}) + testing.product({
**COMMON_PARAMS,
'mode': ['nearest', 'wrap'],
}) + testing.product({
**COMMON_PARAMS,
'shape': [(4, 5), (3, 4, 5)], # no (1,3,4,5) due to scipy bug
'mode': ['mirror'],
}))))
def test_product_dict(self):
assert testing.product_dict(*self.actual) == self.expect