def test_get_padsize_too_large():
"""Pick one larger than the current maximum"""
with pytest.raises(TypeError) as te:
_psf.get_padsize(32401)
assert str(te.value) == 'Padding dimension length 32401 not supported'
def test_get_padsize(actual, expected):
"""These are taken from the hard-coded table, so will need updating
wenever we update it in the code.
"""
got = _psf.get_padsize(actual)
assert got == expected
def init_data(self, data):
if self.renorm_shape is None:
renorm_shape = []
for axis in self.dshape:
renorm_shape.append(get_padsize(2 * axis))
self.renorm_shape = tuple(renorm_shape)
# pad the data and convolve with unpadded kernel
datapad = pad_data(data, self.dshape, self.renorm_shape)
return (datapad, self.renorm_shape)
def init_data(self, data):
if self.renorm_shape is None:
renorm_shape = []
for axis in self.dshape:
renorm_shape.append(get_padsize(2 * axis))
self.renorm_shape = tuple(renorm_shape)
# pad the data and convolve with unpadded kernel
datapad = pad_data(data, self.dshape, self.renorm_shape)
return (datapad, self.renorm_shape)
def init_kernel(self, kernel):
if not self.frozen:
self._tcd.clear_kernel_fft()
renorm_shape = []
for axis in self.dshape:
renorm_shape.append(get_padsize(2 * axis))
self.renorm_shape = tuple(renorm_shape)
kernpad = pad_data(kernel, self.dshape, self.renorm_shape)
self.renorm = self._tcd.convolve(numpy.ones(len(kernel)), kernpad,
self.dshape, renorm_shape,
self.origin)
self.renorm = unpad_data(self.renorm, renorm_shape, self.dshape)
return (kernel, self.dshape)
def init_kernel(self, kernel):
if not self.frozen:
self._tcd.clear_kernel_fft()
renorm_shape = []
for axis in self.dshape:
renorm_shape.append(get_padsize(2 * axis))
self.renorm_shape = tuple(renorm_shape)
kernpad = pad_data(kernel, self.dshape, self.renorm_shape)
self.renorm = self._tcd.convolve(numpy.ones(len(kernel)), kernpad,
self.dshape, renorm_shape,
self.origin)
self.renorm = unpad_data(self.renorm, renorm_shape, self.dshape)
return (kernel, self.dshape)