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 measuremment error covariance. Kelvin Man Yiu Leung and

Jayanth Jagalur Mohan (MIT) developed the noise clipping strategy.

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.