jacobian_targetsAddSensorFrequencyPolyFit
- Workspace.jacobian_targetsAddSensorFrequencyPolyFit(self, jacobian_targets: pyarts.arts.JacobianTargets | None = None, measurement_sensor: pyarts.arts.ArrayOfSensorObsel | None = None, d: pyarts.arts.Numeric | None = None, sensor_elem: pyarts.arts.Index | None = None, polyorder: pyarts.arts.Index | None = None) None
Set sensor frequency derivative to use polynomial fitting offset
Order 0 means constant: f := f0 + a Order 1 means linear: f := f0 + a + b * f0 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 asmeasurement_sensorAddSimple()
will simply add a single unique frequency grid to all the differentSensorObsel
that they add to themeasurement_sensor
. The GINsensor_elem
is 0 for the first unique frequency grid, 1 for the second, and so on. SeeArrayOfSensorObsel
member methods in python for help identifying and manipulating how many unique frequency grids are available inmeasurement_sensor
.Author(s): Richard Larsson
- Parameters:
jacobian_targets (JacobianTargets, optional) – A list of targets for the Jacobian Matrix calculations. See
jacobian_targets
, defaults toself.jacobian_targets
[INOUT]measurement_sensor (ArrayOfSensorObsel, optional) – A list of sensor elements. See
measurement_sensor
, defaults toself.measurement_sensor
[IN]d (Numeric, optional) – , optionalThe perturbation used in methods that cannot compute derivatives analytically. [IN]
sensor_elem (Index) – The sensor element whose frequency grid to use. [IN]
polyorder (Index, optional) – , optionalThe order of the polynomial fit. [IN]