RetrievalAddSpeciesVMR

Workspace.RetrievalAddSpeciesVMR(self, jacobian_targets: pyarts.arts.JacobianTargets | None = None, covariance_matrix_diagonal_blocks: pyarts.arts.JacobianTargetsDiagonalCovarianceMatrixMap | None = None, species: pyarts.arts.SpeciesEnum | None = None, d: pyarts.arts.Numeric | None = None, matrix: pyarts.arts.BlockMatrix | None = None, inverse: pyarts.arts.BlockMatrix | None = None) → None

Set volume mixing ratio derivative

See SpeciesEnum for valid species

This method wraps jacobian_targetsAddSpeciesVMR() together with adding the covariance matrices, to the covariance_matrix_diagonal_blocks, which are required to perform OEM().

The input covariance matrices must fit the size of the later computed model state represented by the jacobian_targets. The covariance matrix inverse

Author(s): 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]

  • covariance_matrix_diagonal_blocks (JacobianTargetsDiagonalCovarianceMatrixMap, optional) – A helper map for setting the covariance matrix. See covariance_matrix_diagonal_blocks, defaults to self.covariance_matrix_diagonal_blocks [INOUT]

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

  • d (Numeric, optional) – , optionalThe perturbation used in methods that cannot compute derivatives analytically. [IN]

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

  • inverse (BlockMatrix, optional) – , optionalThe inverse covariance diagonal block matrix. [IN]