stack_cm¶
- sofia_redux.instruments.forcast.stack.stack_cm(data, header, variance=None, extra=None)[source]¶
Run the stacking algorithm on CM data (multi-position chop)
result = frame1 - frame2 - frame3 - … - frameN
- Parameters:
- datanumpy.ndarray
(nframe, nrow, ncol)
- headerastropy.io.fits.header.Header
FITS header to update with HISTORY messages
- variancenumpy.ndarray, optional
variance array to propagate (nframe, nrow, ncol)
- extradict, optional
- If set will be updated with:
- chopsub -> numpy.ndarray (nrow, ncol)
chop-subtracted data (same as output data in this case)
- Returns:
- 2-tuple
numpy.ndarray : The stacked data array (nrow, ncol) numpy.ndarray : The propagated variance array (nrow, ncol)