pyacs.lib.glinalg.lscov_full

Solve least-squares with data covariance and return posterior covariance.

pyacs.lib.glinalg.lscov_full.lscov_full(G, d, cov, verbose=False)[source]

Solve least-squares with data covariance and return posterior covariance.

Parameters:

  • G (numpy.ndarray) – m x n design matrix.

  • d (numpy.ndarray) – m observation vector.

  • cov (numpy.ndarray) – m x m covariance matrix for d.

  • verbose (bool, optional) – If True, print lapack info. Default is False.

Returns:

  • X (numpy.ndarray) – Solution vector.

  • COV (numpy.ndarray) – Posterior covariance of the solution.

  • V (numpy.ndarray) – Residuals d - G*X.

  • chi2 (float) – Weighted residual sum of squares.