isofit.core.instrument

class Instrument(full_config)

Bases: object

xa()

Mean of prior distribution, calculated at state x.

Sa()

Covariance of prior distribution (diagonal).

Sy(meas, geom)

Calculate measurement error covariance.

Input: meas, the instrument measurement Returns: Sy, the measurement error covariance due to instrument noise

dmeas_dinstrument(x_instrument, wl_hi, rdn_hi)

Jacobian of measurement with respect to the instrument free parameter state vector. We use finite differences for now.

dmeas_dinstrumentb(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]

sample(x_instrument, wl_hi, rdn_hi)

Apply instrument sampling to a radiance spectrum, returning predicted measurement.

simulate_measurement(meas, geom)

Simulate a measurement by the given sensor, for a true radiance sampled to instrument wavelengths. This basically just means drawing a sample from the noise distribution.

calibration(x_instrument)

Calculate the measured wavelengths.

summarize(x_instrument, geom)

Summary of state vector.