oemAddSpeciesVMR

Workspace.oemAddSpeciesVMR(self, jac_targets: JacobianTargets = self.jac_targets, oem: OptimalEstimationData = self.oem, species: SpeciesEnum, d: Numeric = 0.1, matrix: BlockMatrix, inverse: BlockMatrix = []) → None

Set volume mixing ratio derivative.

See SpeciesEnum for valid species

This method wraps jac_targetsAddSpeciesVMR() together with adding the covariance matrices, to oem.covmat_diagonal_blocks for assembly by oemFinalizeDiagonal().

The input covariance matrices must fit the size of the later computed model state represented by jac_targets. The inverse block is optional.

Warning

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

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]

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

  • species (SpeciesEnum) – The species of interest. [IN]

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

  • matrix (BlockMatrix) – The covariance diagonal block matrix. [IN]

  • inverse (~pyarts3.arts.BlockMatrix, optional) – The inverse covariance diagonal block matrix. Defaults to pyarts3.arts.BlockMatrix() [IN]