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, 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
# Simulated spectrum
CARS = _np.abs(E + X)**2
NRB = _np.abs(E + XNR)**2
nrb.data = NRB
# Copies of spectrum
temp = _np.dot(_np.ones((30, 30, 1)), CARS[None, :])
# Create an HSData class instance
hsi.data = temp
num_spectra = int(hsi.size / WN.size)
hsi.freq.data = WN
start = _timeit.default_timer()
kkd = _kkrelation(NRB, CARS)
stop = _timeit.default_timer()
print('Single spectrum -- Total time: {:.6f} sec'.format(stop - start))
_timeit.time.sleep(2)
start = _timeit.default_timer()
kkd = _kkrelation(NRB, temp)
stop = _timeit.default_timer()
print('Data-based -- Total time: {:.6f} sec ({:.6f} sec/spectrum)'.format(
stop - start, (stop - start) / num_spectra))
_timeit.time.sleep(2)
kk = KramersKronig(cars_amp_offset=0,
nrb_amp_offset=0,
norm_to_nrb=False,
pad_factor=1)
# Simulated spectrum
CARS = _np.abs(E+X)**2
NRB = _np.abs(E+XNR)**2
nrb.data = NRB
# Copies of spectrum
temp = _np.dot(_np.ones((30,30,1)),CARS[None,:])
# Create an HSData class instance
hsi.data = temp
num_spectra = int(hsi.size/WN.size)
hsi.freq.data = WN
start = _timeit.default_timer()
kkd = _kkrelation(NRB,CARS)
stop = _timeit.default_timer()
print('Single spectrum -- Total time: {:.6f} sec'.format(stop-start))
_timeit.time.sleep(2)
start = _timeit.default_timer()
kkd = _kkrelation(NRB,temp)
stop = _timeit.default_timer()
print('Data-based -- Total time: {:.6f} sec ({:.6f} sec/spectrum)'.format(stop-start,
(stop-start)/num_spectra))
_timeit.time.sleep(2)
kk = KramersKronig(cars_amp_offset=0, nrb_amp_offset=0, norm_to_nrb=False, pad_factor=1)
start = _timeit.default_timer()
kk.calculate(hsi.data, nrb.data)