oemInitFromData

Workspace.oemInitFromData(self, oem: OptimalEstimationData = self.oem, model_state_vec: Vector = self.model_state_vec, measurement_vec: Vector = self.measurement_vec, model_state_covmat: CovarianceMatrix = self.model_state_covmat, measurement_vec_error_covmat: CovarianceMatrix = self.measurement_vec_error_covmat) → None

Initialize oem from a complete numerical input problem.

Consumes model_state_vec as the prior, measurement_vec as the observations, model_state_covmat and measurement_vec_error_covmat, leaving them empty. Both covariances must cover their respective vectors; dimensions are checked before any input is consumed. Replaces the entire OEM object and leaves it unchecked. Use oem.check() for numerical validation.

No physical fields or target mappings are changed. Fitted measurements and Jacobians are modeling results and are not imported. The initial current state is empty, so the calculation starts from the prior. Use oemInit() and the oemAdd methods instead to construct a problem through target-based builders.

Warning

Automatic size constraints are not checked for this method. Group-invariant checks on read-only inputs still apply.

Author: Richard Larsson

Parameters:

  • oem (~pyarts3.arts.OptimalEstimationData, optional) – Numerical problem and results for oemCalc() and oemCalcReduced(). Defaults to self.oem. [OUT]

  • model_state_vec (~pyarts3.arts.Vector, optional) – A state vector of the model. Defaults to self.model_state_vec. [INOUT]

  • measurement_vec (~pyarts3.arts.Vector, optional) – The measurement vector for, e.g., a sensor. Defaults to self.measurement_vec. [INOUT]

  • model_state_covmat (~pyarts3.arts.CovarianceMatrix, optional) – Covariance matrix of a priori distribution. Defaults to self.model_state_covmat. [INOUT]

  • measurement_vec_error_covmat (~pyarts3.arts.CovarianceMatrix, optional) – Covariance matrix for observation uncertainties. Defaults to self.measurement_vec_error_covmat. [INOUT]