divand
divand performs an n-dimensional variational analysis (interpolation) of arbitrarily located observations.
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Compute a variational analysis of arbitrarily located observations.
Compute metric scale factors.
Return the analytical kernel and normalization factor.
Add a constraint to the cost function.
Form the different components of the background error covariance matrix.
Form the inverse of the background error covariance matrix.
Create the advection constrain.
Computes diagnostics for the analysis.
Include constraint from EOFs.
Compute the expected error variance of the analysis.
Factorize some matrices to increase performance.
Create the laplacian operator.
Include the constrain from the observations.
Generate the gradient and Laplacian operators.
Orthogonalize EOF modes.
No preconditioner is used.
Compute a preconditioner using a modified incomplete Cholesky decomposition.
Compute a preconditioner using the Cholesky decomposition.
The RMS error between two variables.
Solve the variational problem with the contraints from EOFs.
Solve the variational problem.
Concatenate a cell array.
Display a message in a color (followed by a newline).
Display a message in a color (without a newline).
Solve a linear system with the preconditioned conjugated-gradient method.
Interpolation matrix for a n-dimensional interpolation.
Derive fractional indices on a regular grid.
Derive fractional indices on a separable grid.
Product between a Gaussian covariance matrix and a vector.
Initialize structure for packing and unpacking given their mask.
Pack a series of variables into a vector under the control of a mask.
Unpack a vector into different variables under the control of a mask.
Create a matrix which represents the concatenation of a series of matrices.
Append a matrix to a CatBlockMat matrix.
Conjugate transpose of a CatBlockMat matrix.
Convert a CatBlockMat matrix to a regular matrix.
Last index of a CatBlockMat matrix.
Convert a CatBlockMat matrix to a regular matrix.
Matrix left division A \ B.
Matrix product A*B.
Size of a CatBlockMat matrix.
Subreference of a CatBlockMat matrix.
Create a covariance matrix based on a function handler.
Matrix left division A \ B.
Covariance matrix with a sparse inverse matrix.
Diagonal elements.
Factorize matrix.
Convert a CovarIS matrix to a regular matrix.
Inverse of a CovarIS matrix.
Matrix left division A \ B.
Matrix product A*B.
Subreference of a CovarIS matrix.
Covariance matrix reconstructed from Lanczos vectors.
Diagonal elements.
Matrix product A*B.
Parametric covariance matrix with a guassian kernel.
Diagonal elements.
Convert a CovarParam matrix to a regular matrix.
Return true if argument is a scalar.
Return true if argument is a vector.
Matrix left division A \ B.
Matrix product with correlation matrix.
Matrix product A*B.
Size of a CovarParam matrix.
Subreference of a CovarParam matrix.
Covariance matrix than can be inverted using the Sherman-Morrison-Woodbury forumla.
Diagonal elements.
Convert a CovarSWM matrix to a regular matrix.
Matrix left division A \ B.
Matrix product A*B.
Size of a CovarSWM matrix.
Append a matrix to a DiagBlockMat matrix.
Block diagonal matrix.
Diagonal elements.
Convert a DiagBlockMat matrix to a regular matrix.
Inverse of a DiagBlockMat matrix.
Matrix left division A \ B.
Matrix product A*B.
Size of a DiagBlockMat matrix.
Compute the sum.
Conjugate transpose of a MatFun matrix.
Convert a MatFun matrix to a regular matrix.
Last index of a MatFun matrix.
Convert a MatFun matrix to a regular matrix.
Matrix operator object based on a function handel.
Matrix product A*B.
Size of a MatFun matrix.
Create diagonal sparse matrix.
Sparse operator for differentiation.
Sparse operator for a gradient.
Create a sparse interpolation matrix.
Create a sparse matrix which packs an array under the control of a mask.
Sparse operator shifting a field in a given dimension.
Sparse operator for staggering.
Sparse operator for trimming.
Testing divand in 1 dimension.
Testing divand in 2 dimensions with advection.
Testing divand in 2 dimensions with correlated errors.
Testing divand in 2 dimensions with independent verification.
Testing divand in 2 dimensions with a custom constrain.
Testing divand in 2 dimensions in a cyclic domain.
Testing divand in 2 dimensions with EOF constraints.
Testing divand in 2 dimensions with lenx /= leny.
Testing divand in 2 dimensions.
Testing divand in 2 dimensions with pcg solver.
Testing divand in 2 dimensions with relative correlation length.
Testing divand in 3 dimensions without correlation in the 3rd dimension (vertically stacked).
Testing divand in 3 dimensions.
Testing divand in 4 dimensions.
Test if divand is working correctly.
Testing 1D linear interpolation.
Testing 2D linear interpolation.
Testing linear interpolation on regular grid.
Testing sparse operators.
A simple example of divand in 2 dimensions with observations from an analytical function.
A realistic example of divand in 2 dimensions with salinity observations in the Mediterranean Sea at 30m.
Package: divand
