def dmeas_dinstrumentb(self, x_instrument, wl_hi, rdn_hi):
"""Jacobian of radiance with respect to the instrument parameters
that are unknown and not retrieved, i.e. the inevitable persisting
uncertainties in instrument spectral and radiometric calibration.
Input: meas, a vector of size n_chan
Returns: Kb_instrument, a matrix of size
[n_measurements x nb_instrument]"""
# Uncertainty due to radiometric calibration
meas = self.sample(x_instrument, wl_hi, rdn_hi)
dmeas_dinstrument = s.hstack(
(s.diagflat(meas), s.zeros((self.n_chan, 2))))
# Uncertainty due to spectral calibration
if self.bval[-2] > 1e-6:
dmeas_dinstrument[:, -2] = self.sample(
x_instrument, wl_hi, s.hstack((s.diff(rdn_hi), s.array([0]))))
# Uncertainty due to spectral stray light
if self.bval[-1] > 1e-6:
ssrf = srf(s.arange(-10, 11), 0, 4)
blur = convolve(meas, ssrf, mode='same')
dmeas_dinstrument[:, -1] = blur - meas
return dmeas_dinstrument
def dLs_dsurface(self, x_surface, geom):
'''Emission at surface includes fluorescence (here, a Gaussian)'''
dLs = s.zeros((len(self.wl), len(self.statevec)))
ngauss = srf(self.wl, self.fl_mu, self.fl_sigma)
ngauss = ngauss/max(ngauss)
dLs[:, self.flh_ind] = ngauss
return dLs
def sample(self, x_instrument, wl_hi, rdn_hi):
""" Apply instrument sampling to a radiance spectrum, returning the
predicted measurement"""
if self.calibration_fixed and all((self.wl_init - wl_hi) < wl_tol):
return rdn_hi
wl, fwhm = self.calibration(x_instrument)
if rdn_hi.ndim == 1:
return resample_spectrum(rdn_hi, wl_hi, wl, fwhm)
else:
resamp = []
# The "fast resample" option approximates a complete resampling
# by a convolution with a uniform FWHM.
if self.fast_resample:
for i, r in enumerate(rdn_hi):
ssrf = srf(s.arange(-10, 11), 0, fwhm[0])
blur = convolve(r, ssrf, mode='same')
resamp.append(interp1d(wl_hi, blur)(wl))
else:
for i, r in enumerate(rdn_hi):
r2 = resample_spectrum(r, wl_hi, wl, fwhm)
resamp.append(r2)
return s.array(resamp)
def calc_Ls(self, x_surface, geom):
'''Emission at surface includes fluorescence (here, a Gaussian)'''
ngauss = srf(self.wl, self.fl_mu, self.fl_sigma)
ngauss = ngauss/max(ngauss)
return ngauss * x_surface[self.flh_ind]