@numba.jit('<STR_LIT>', nopython=True)<EOL>def subsample_snps_map(seqchunk, nmask, maparr):
<EOL>rmask = np.zeros(seqchunk.shape[<NUM_LIT:1>], dtype=np.bool_)<EOL>last_loc = -<NUM_LIT:1><EOL>for idx in xrange(maparr.shape[<NUM_LIT:0>]):<EOL><INDENT>if maparr[idx] != last_loc:<EOL><INDENT>if not nmask[idx]:<EOL><INDENT>rmask[idx] = True<EOL><DEDENT>last_loc = maparr[idx]<EOL><DEDENT><DEDENT>return rmask<EOL>
removes ncolumns from snparray prior to matrix calculation, and 
subsamples 'linked' snps (those from the same RAD locus) such that
for these four samples only 1 SNP per locus is kept. This information
comes from the 'map' array (map file).