normalise_sumwt

ska_sdp_func_python.imaging.base.normalise_sumwt(im: Image, sumwt, min_weight=0.1, flat_sky=False) → Image

Normalise out the sum of weights.

The gridding weights are accumulated as a function of channel and polarisation. This function corrects for this sum of weights. The sum of weights can be a 2D array or an image the same shape as the image (as for primary beam correction).

The parameter flat_sky controls whether the sensitivity (sumwt) is divided out pixel by pixel or instead the maximum value is divided out.

Parameters:

• im – Image, im[“pixels”].data has shape [nchan, npol, ny, nx]

• sumwt – Sum of weights [nchan, npol] or [nchan, npol, ny, nx]

• min_weight – Minimum (fractional) weight to be used in dividing by the sumwt images

• flat_sky – Make the flux values correct instead of noise (default is False)

Returns:

Image with sum of weights normalised out