jacobian_targetsAddErrorPolyFit

Workspace.jacobian_targetsAddErrorPolyFit(self, jacobian_targets: pyarts3.arts.JacobianTargets | None = None, measurement_sensor: pyarts3.arts.ArrayOfSensorObsel | None = None, t: pyarts3.arts.Vector | None = None, sensor_elem: pyarts3.arts.Index | None = None, polyorder: pyarts3.arts.Index | None = None) → None

Set a measurement error to polynomial fit.

This is a generic error that is simply added to measurement_vector as if

\[y = y_0 + \epsilon(p_0,\; p_1,\; \cdots,\; p_n),\]

where \(y\) represents measurement_vector and \(y_0\) is the measurement vector without any errors)

Order 0 means constant: \(y = y_0 + a\)

Order 1 means linear: \(y = y_0 + a + b t\)

and so on. The derivatives that are added to the model_state_vector 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:

  • jacobian_targets (JacobianTargets, optional) – A list of targets for the Jacobian Matrix calculations. See jacobian_targets, defaults to self.jacobian_targets [INOUT]

  • measurement_sensor (ArrayOfSensorObsel, optional) – A list of sensor elements. See measurement_sensor, defaults to self.measurement_sensor [IN]

  • t (Vector) – The grid of \(y\). As \(t\) above. [IN]

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

  • polyorder (Index, optional) – The order of the polynomial fit. Maximum \(n\) above. Defaults to 0 [IN]