This module contains several files that are all in one way or another related to correcting the effects of the primary beam on MeerKAT radio images.
Calculate and apply primary beam correction for wideband data using primary beams and multiple frequencies. These can be generated using katbeam, or a series of primary beam images can be given at different frequencies. Several modes are supported, chosen through the parameter mfs_mode, related to how the image was generated and the file structure. Several usage examples are given in the examples directory.
The mfs_mode options are:
none: A simple primary beam correction at a single (central) frequency.casa: Primary beam models are fit with the a Taylor polynomial and applied to the corresponding Taylor term images. If the number of Taylor terms is two or more, a spectral index image is automatically generated. The procedure to do the primary beam correction is meant to emulate the procedure as it is done in the CASAwidebandpbcortask.wsclean: Assumes the images were created using the WSClean MFS imaging mode, and a weighted primary beam is created using the weights from the individual channel images.wsclean-poly: Just aswsclean, assumes the images were created using WSClean MFS imaging mode, but with-fit-spectral-poloption engaged, fitting a polynomial to the data. The primary beam is generated is thus generated in a similar manner to thecasaoption.weighted: Create a weighted primary beam model (likewscleanmode), manually specifying the frequencies and corresponding weights.
Two different types of primary beam models are supported in the model keyword, either katbeam (default) which uses katbeam models, or images where the primary beam model images on file can be specified. This latter option is useful, for instance, when you have generated holographic primary beam models with the make_holo_beams.py script (see below). Beware that when you supply lists of model images, weights, and frequencies, the order must be correct! The script can not and will not check beyond whether the lists are the same length.
usage: meerkat_widebandpbcor.py [-h] [-m MODEL] [-b BAND] [-t THRESH] [-T]
[--model_images MODEL_IMAGES [MODEL_IMAGES ...]]
[--freqs FREQS [FREQS ...]]
[--weights WEIGHTS [WEIGHTS ...]]
[--invert_weights] [--nterms NTERMS]
[--alpha_thresh ALPHA_THRESH] [--write_beams]
[--outdir OUTDIR]
image_name mfs_mode
positional arguments:
image_name An image to correct. In 'casa' mode CASA file
structure is assumed, i.e. image_name.image.tt0,
image_name.image.tt1. In 'wsclean' mode, wsclean
output will be assumed, i.e.
imagename-0000-image.fits, imagename-0001-image.fits
for channel images and imagename-MFS-image.fits for
the mfs image.
mfs_mode Which multi-frequency synthesis mode has been used for
the image to determine the primary beam model. 'casa'
assumes Taylor term imaging with casa file structure,
while 'wsclean' assumes weighted imaging with a
wsclean structure. 'weighted' will assume weighted
imaging but without file structure, so frequencies and
weights are input manually. To do primary beam
correction at a single frequency without wideband mfs,
choose 'none'
optional arguments:
-h, --help show this help message and exit
-m MODEL, --model MODEL
Which primary beam model to use, options are 'katbeam'
or 'images', in the latter case input images must be
specified by model_images parameter (default=katbeam).
-b BAND, --band BAND If using katbeam, specify band for which primary beam
model will be used, can be L, UHF, or S (default = L).
-t THRESH, --threshold THRESH
Threshold (at central frequency) below which image
pixels will be set to blank values (nan). Use to
remove areas where the primary beam correction is
large (default=0.3).
-T, --trim Trim image outside valid region (set by --threshold)
to reduce size.
--model_images MODEL_IMAGES [MODEL_IMAGES ...]
Specify files with PB images to to fit wideband
primary beam. Corresponding frequencies must be given
with the freqs parameter.
--freqs FREQS [FREQS ...]
Frequencies (in MHz) to use to generate the primary
beam (katbeam) or frequencies corresponding to input
model images (images). If using Taylor terms, the
number of frequencies should be equal or greater than
the number of Taylor terms. For the most accurate
results should resemble the frequency structure of the
data used for imaging
--weights WEIGHTS [WEIGHTS ...]
Weights associated with the input frequencies for a
weighted primary beam
--invert_weights Invert the weights
--nterms NTERMS Number of Taylor coefficients
--alpha_thresh ALPHA_THRESH
Mask all pixels below this flux level in the spectral
index image (default=0).
--write_beams Write derived beams to fits files (default=do not
write files).
--outdir OUTDIR Output directory of images.
Script to create MeerKAT holographic primary beam models for different frequencies and polarisations based on data from de Villiers (2023). These can be used as input model images in meerkat_widebandpbcor.py.
usage: make_holo_beams.py [-h] [--freqs FREQS [FREQS ...]] [--outdir OUTDIR]
[--stokes STOKES [STOKES ...]] [--cell CELL]
[--imsize IMSIZE]
holodir
positional arguments:
holodir Directory containing holographic images, assuming npz
format from data by de Villiers (2023).
optional arguments:
-h, --help show this help message and exit
--freqs FREQS [FREQS ...]
Frequencies of output images, in MHz.
--outdir OUTDIR Output directory of images.
--stokes STOKES [STOKES ...]
Beam polarization(s).
--cell CELL Cell size, in arcsec.
--imsize IMSIZE Image size, in pixels
This script assumes a catalog created from an image that was corrected with a primary beam which did not take into account wideband effects. It takes a catalog of sources and calculates and applies residual corrections to the fluxes and spectral indices of sources. If imaging products are not available or wideband primary beam correction in the image plane is too much of a hassle, these residual corrections can do a decent job of correcting for the effects of the wideband primary beam. The derivation and motivation behind these corrections is given in Wagenveld et al. (2023).
usage: alpha_flux_corrections.py [-h] [-d DIST_COL]
[-f [FLUX_COLS [FLUX_COLS ...]]]
[-a [ALPHA_COLS [ALPHA_COLS ...]]]
[--alpha ALPHA]
catalog
positional arguments:
catalog Input catalog to correct spectral index and flux
optional arguments:
-h, --help show this help message and exit
-d DIST_COL, --dist_col DIST_COL
Catalog column containing the distance of the source
to the pointing center (default = 'Sep_PC')
-f [FLUX_COLS [FLUX_COLS ...]], --flux_cols [FLUX_COLS [FLUX_COLS ...]]
Flux column for to apply flux correction on (default =
none, don't apply flux corrections)
-a [ALPHA_COLS [ALPHA_COLS ...]], --alpha_cols [ALPHA_COLS [ALPHA_COLS ...]]
Spectral index column(s) to apply corrections on.
(default = none, don't apply spectral index
corrections)
--alpha ALPHA Spectral index to calculate flux corrections for. Can
be either float, or column in the catalog (default =
-0.8)
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