BrahMap quick start guide

Proceed with following steps:

1. Process the pointings, provided as pixel indices

   processed_pointings = ProcessTimeSamples(pixs=pointings, 
       npix=npix, 
       pol=3, 
       phi=pol_angles
   )
   npix_new, __ = processed_pointings.get_new_pixel
   

2. Create pointing matrix operator as sparse linear operator

   P = SparseLO(n=npix_new, 
       m=nsamp, 
       pix_samples=processed_pointings.pixs, 
       pol=3,
       angle_processed=processed_pointings
   )
   

3. Make a noise covariance matrix

   inv_N = BlockLO(blocksize=blocksize, t=inv_sigma2, offdiag=False)
   

4. Make a block-diagonal preconditioner operator

   Mbd = BlockDiagonalPreconditionerLO(CES=processed_pointings,
       n=npix_new, 
       pol=3
   )
   

5. Solve for sky maps

   A = P.T * inv_N * P
   b = P.T * inv_N * tod_array
   map_out = scipy.sparse.linalg.cg(A, b, M=Mbd)