jac_targetsAddSensorFrequencyPolyOffset

Workspace.jac_targetsAddSensorFrequencyPolyOffset(self, jac_targets: JacobianTargets = self.jac_targets, measurement_sensor: ArrayOfSensorObsel = self.measurement_sensor, d: Numeric = 0.1, sensor_elem: Index, polyorder: Index = 0) → None

Set sensor frequency derivative to use polynomial fitting offset

Order 0 means constant: \(f := f_0 + a\)

Order 1 means linear: \(f := f_0 + a + b f_0\)

and so on. The derivatives that are added to the model_state_vec are those with regards to a, b, etc..

Note

The rule for the sensor_elem GIN is a bit complex. Generally, methods such as measurement_sensorAddSimple() will simply add a single unique frequency grid to all the different SensorObsel that they add to the measurement_sensor. The GIN sensor_elem is 0 for the first unique frequency grid, 1 for the second, and so on. See ArrayOfSensorObsel member methods in python for help identifying and manipulating how many unique frequency grids are available in measurement_sensor.

Author: Richard Larsson

Parameters:

  • jac_targets (~pyarts3.arts.JacobianTargets, optional) – A list of targets for the Jacobian Matrix calculations. Defaults to self.jac_targets. [INOUT]

  • measurement_sensor (~pyarts3.arts.ArrayOfSensorObsel, optional) – A list of sensor elements that fully describe one or more observing sensor(s). Defaults to self.measurement_sensor. [IN]

  • d (~pyarts3.arts.Numeric, optional) – The perturbation used in methods that cannot compute derivatives analytically. Defaults to 0.1 [IN]

  • sensor_elem (Index) – The sensor element whose frequency grid to use. [IN]

  • polyorder (~pyarts3.arts.Index, optional) – The order of the polynomial fit. Defaults to 0 [IN]