# coding KLIP

Here I rewrite the recipe of the KLIP PSF-subtraction algorithm by Soummer & Pueyo & Larkin (2012) in vector form — less of the confusing indices and easier to code:

• Assemble the reference images into matrix $\mathbf{R}$, containing K rows, each one a reference image vector of length N pixels. Subtract the mean of each image vector (row) from itself.
• Get the eigendecomposition of $\mathbf{R} \mathbf{R}^T$. This yields the ranked eigenvalue vector $\mathbf{\Lambda}$, and corresponding eigenvectors as rows of the KxK matrix $\mathbf{V}$.
• Divide each (kth) row of $\mathbf{V}$ by its singular value, $\sqrt{\lambda_k}$, call this new matrix $\mathbf{V'}$.
• The principle component basis, $\mathbf{Z}$ (dim. KxN), is formed by a transformation of $\mathbf{R}$ (S&P&L eq 5): $\mathbf{Z} = \mathbf{V'R}$. The kth row of $\mathbf{Z}$ is the kth principal component of the reference data matrix $\mathbf{R}$.
• Choose $K_{\rm KLIP}$ principal components to retain. The truncated basis, $\mathbf{Z}_{\rm KL}$ (with $K_{\rm KLIP} \le K$ rows) is the new K-L basis for describing the PSF.
• To form the PSF estimate, $\mathbf{\hat{I}}$, project the target row vector $\mathbf{T}$ (dim. 1xN) onto the K-L basis (S&P&L eq 8): $\mathbf{\hat{I}} = \mathbf{T} \mathbf{Z}_{\rm KL}^T \mathbf{Z}_{\rm KL}$. This last step is by far the most time- and memory-consuming part, because of the large NxN operand.

and here’s a python implementation:

def get_klip_basis(R, cutoff): w, V = np.linalg.eig(np.dot(R, np.transpose(R))) sort_ind = np.argsort(w)[::-1] #indices of eigenvals sorted in descending order sv = np.sqrt(w[sort_ind]).reshape(-1,1) #column of ranked singular values Z = np.dot(1./sv*np.transpose(V[:, sort_ind]), R) return Z[0:cutoff, :], sv