def deinit(self, vals):
if self.renorm is not None:
vals = unpad_data(vals, self.renorm_shape, self.dshape)
vals = vals / self.renorm
if self.do_pad:
vals = vals[self.pad_mask]
return vals
def deinit(self, vals):
if self.renorm is not None:
vals = unpad_data(vals, self.renorm_shape, self.dshape)
vals = vals / self.renorm
if self.do_pad:
vals = vals[self.pad_mask]
return vals
def test_unpad_data_basic():
"""Not 100% sure what it's meant to do but check something.
Check we can "invert" pad_data
"""
out = _psf.unpad_data([1, 2, 3, 4, 5, 6, 7, 0, 0, 0], [10], [7])
assert len(out) == 7
assert isinstance(out, np.ndarray)
assert out == pytest.approx([1, 2, 3, 4, 5, 6, 7])
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)
def test_unpad_data_error3():
with pytest.raises(TypeError) as te:
_psf.unpad_data([1, 2, 3], [4], [3])
assert str(te.value) == 'input array sizes do not match dimensions, kernel size: 3 vs kernel dim: 4'
