isofit.core.instrument
======================

.. py:module:: isofit.core.instrument


Attributes
----------

.. autoapisummary::

   isofit.core.instrument.wl_tol


Classes
-------

.. autoapisummary::

   isofit.core.instrument.Instrument


Module Contents
---------------

.. py:data:: wl_tol
   :value: 0.01


.. py:class:: Instrument(full_config: Config)

   .. py:attribute:: n_chan


   .. py:attribute:: fast_resample


   .. py:attribute:: bounds


   .. py:attribute:: scale


   .. py:attribute:: init


   .. py:attribute:: prior_mean


   .. py:attribute:: prior_sigma


   .. py:attribute:: Sa_cached


   .. py:attribute:: Sa_normalized


   .. py:attribute:: statevec_names


   .. py:attribute:: n_state


   .. py:attribute:: integrations


   .. py:attribute:: dn_uncertainty_embedding
      :value: None



   .. py:attribute:: unknowns


   .. py:attribute:: bval


   .. py:attribute:: bvec


   .. py:attribute:: calibration_fixed
      :value: True



   .. py:method:: xa()

      Mean of prior distribution, calculated at state x.



   .. py:method:: Sa()

      Covariance of prior distribution (diagonal).



   .. py:method:: Sb(meas)

      Uncertainty due to unmodeled variables.



   .. py:method:: 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



   .. py:method:: 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.



   .. py:method:: 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]



   .. py:method:: sample(x_instrument, wl_hi, rdn_hi)

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



   .. py:method:: 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.



   .. py:method:: calibration(x_instrument)

      Calculate the measured wavelengths.



   .. py:method:: DN_additive_uncertainty(meas, rcc, interp, inflation)
      :staticmethod:



   .. py:method:: summarize(x_instrument, geom)

      Summary of state vector.