def _calc(self, cars, nrb, ret_obj):
try:
# Assume that an nD nrb should be averaged to be 1D
# TODO: Change this in the future to accept matching NRB's
nrb = _mean_nd_to_1d(nrb)
if self.rng is None:
kkd = _kkrelation(bg=nrb + self.nrb_amp_offset,
cri=cars + self.cars_amp_offset,
hilb_kwargs=self.hilb_kwargs, **self.kk_kwargs)
ret_obj *= 0
ret_obj += kkd
else:
slice_cars_rng = cars.ndim*[slice(None)]
slice_cars_rng[self.kk_kwargs['axis']] = self.rng
slice_cars_rng = tuple(slice_cars_rng)
slice_ret_obj = ret_obj.ndim*[slice(None)]
slice_ret_obj[self.kk_kwargs['axis']] = self.rng
slice_ret_obj = tuple(slice_ret_obj)
kkd = _kkrelation(bg=nrb[self.rng] + self.nrb_amp_offset,
cri=cars[slice_cars_rng] + self.cars_amp_offset,
hilb_kwargs=self.hilb_kwargs, **self.kk_kwargs)
ret_obj *= 0
ret_obj[slice_ret_obj] += kkd
except Exception as e:
print('Error: {}'.format(e))
return False
else:
return True
def _calc(self, cars, nrb, ret_obj):
# Assume that an nD nrb should be averaged to be 1D
nrb = _mean_nd_to_1d(nrb)
shp = cars.shape[0:-2]
# Step row-by-row through image
for idx in _np.ndindex(shp):
if self.rng is None:
kkd = _kkrelation(bg=nrb + self.nrb_amp_offset,
cri=cars[idx] + self.cars_amp_offset,
phase_offset=self.phase_offset,
norm_by_bg=self.norm_to_nrb,
pad_factor=self.pad_factor)
else:
kkd = _kkrelation(bg=nrb[self.rng] + self.nrb_amp_offset,
cri=cars[idx][..., self.rng] + self.cars_amp_offset,
phase_offset=self.phase_offset,
norm_by_bg=self.norm_to_nrb,
pad_factor=self.pad_factor)
try:
ret_obj[idx] *= 0
if self.rng is None:
ret_obj[idx] += kkd
elif ret_obj[idx].size == kkd.size:
ret_obj[idx] += kkd
else:
ret_obj[idx][..., self.rng] += kkd
except:
return False
else:
pass
return True
def _calc(self, data, ret_obj):
# Assume that an nD dark should be averaged to be 1D
self.dark = _mean_nd_to_1d(self.dark, axis=-1)
# Expand dark dimensionality to match data.ndim
self.dark = _expand_1d_to_ndim(self.dark, data.ndim)
ret_obj -= self.dark
return True
def _calc(self, data, ret_obj):
# Assume that an nD dark should be averaged to be 1D
self.dark = _mean_nd_to_1d(self.dark, axis=-1)
# Expand dark dimensionality to match data.ndim
self.dark = _expand_1d_to_ndim(self.dark, data.ndim)
try:
ret_obj -= self.dark
except:
return False
else:
return True
def _calc(self, cars, nrb, ret_obj):
# Assume that an nD nrb should be averaged to be 1D
nrb = _mean_nd_to_1d(nrb)
shp = cars.shape[0:-2]
# Step row-by-row through image
for idx in _np.ndindex(shp):
if self.rng is None:
kkd = _kkrelation(bg=nrb + self.nrb_amp_offset,
cri=cars[idx] + self.cars_amp_offset,
phase_offset=self.phase_offset,
norm_by_bg=self.norm_to_nrb,
pad_factor=self.pad_factor)
else:
kkd = _kkrelation(bg=nrb[self.rng] + self.nrb_amp_offset,
cri=cars[idx][..., self.rng] +
self.cars_amp_offset,
phase_offset=self.phase_offset,
norm_by_bg=self.norm_to_nrb,
pad_factor=self.pad_factor)
try:
ret_obj[idx] *= 0
if self.rng is None:
ret_obj[idx] += kkd
elif ret_obj[idx].size == kkd.size:
ret_obj[idx] += kkd
else:
ret_obj[idx][..., self.rng] += kkd
except:
return False
else:
pass
return True