Useful scripts for RACS
The recommended way to install. First obtain conda from Anaconda or Miniconda. Clone this repo, build the environment, and activate:
git clone https://github.com/AlecThomson/RACS-tools
cd RACS-tools
conda env create
conda activate racs-toolsA Dockerfile is provided if you wish to build your own container. Otherwise, images are provided on DockerHub. You can pull these by running e.g.
docker pull alecthomson/racstoolsor
singularity pull docker://alecthomson/racstoolsNOTE: These builds are still experimental, and have not been widely tested. In particular, parallelisation may not work as expected.
You can also use the package manager pip to install RACS-tools.
# Stable
pip install RACS-tools
# Latest
pip install git+https://github.com/AlecThomson/RACS-tools$ beamcon_2D -h
usage: beamcon_2D [-h] [-p PREFIX] [-s SUFFIX] [-o OUTDIR] [--conv_mode {robust,scipy,astropy,astropy_fft}] [-v] [-d] [--bmaj BMAJ] [--bmin BMIN]
[--bpa BPA] [--log LOG] [--logfile LOGFILE] [-c CUTOFF] [--circularise] [-t TOLERANCE] [-e EPSILON] [-n NSAMPS] [--ncores NCORES]
[--executor {thread,process,mpi}]
infile [infile ...]
Smooth a field of 2D images to a common resolution. - Parallelisation can run using multiprocessing or MPI. - Default names of output files are
/path/to/beamlog{infile//.fits/.{SUFFIX}.fits} - By default, the smallest common beam will be automatically computed. - Optionally, you can specify a
target beam to use.
positional arguments:
infile Input FITS image(s) to smooth (can be a wildcard) - beam info must be in header.
options:
-h, --help show this help message and exit
-p PREFIX, --prefix PREFIX
Add prefix to output filenames. (default: None)
-s SUFFIX, --suffix SUFFIX
Add suffix to output filenames [sm]. (default: sm)
-o OUTDIR, --outdir OUTDIR
Output directory of smoothed FITS image(s) [same as input file]. (default: None)
--conv_mode {robust,scipy,astropy,astropy_fft}
Which method to use for convolution [robust]. 'robust' computes the analytic FT of the convolving Gaussian. Note this mode can
now handle NaNs in the data. Can also be 'scipy', 'astropy', or 'astropy_fft'. Note these other methods cannot cope well with
small convolving beams. (default: robust)
-v, --verbosity Increase output verbosity (default: 0)
-d, --dryrun Compute common beam and stop [False]. (default: False)
--bmaj BMAJ Target BMAJ (arcsec) to convolve to [None]. (default: None)
--bmin BMIN Target BMIN (arcsec) to convolve to [None]. (default: None)
--bpa BPA Target BPA (deg) to convolve to [None]. (default: None)
--log LOG Name of beamlog file. If provided, save beamlog data to a file [None - not saved]. (default: None)
--logfile LOGFILE Save logging output to file (default: None)
-c CUTOFF, --cutoff CUTOFF
Cutoff BMAJ value (arcsec) -- Blank channels with BMAJ larger than this [None -- no limit] (default: None)
--circularise Circularise the final PSF -- Sets the BMIN = BMAJ, and BPA=0. (default: False)
-t TOLERANCE, --tolerance TOLERANCE
tolerance for radio_beam.commonbeam. (default: 0.0001)
-e EPSILON, --epsilon EPSILON
epsilon for radio_beam.commonbeam. (default: 0.0005)
-n NSAMPS, --nsamps NSAMPS
nsamps for radio_beam.commonbeam. (default: 200)
--ncores NCORES Number of cores to use for parallelisation. If None, use all available cores. (default: None)
--executor {thread,process,mpi}
Executor to use for parallelisation (default: thread)
$ beamcon_3D -h
usage: beamcon_3D [-h] [--uselogs] [--mode MODE] [--conv_mode {robust,scipy,astropy,astropy_fft}] [-v] [--logfile LOGFILE] [-d] [-p PREFIX] [-s SUFFIX]
[-o OUTDIR] [--bmaj BMAJ] [--bmin BMIN] [--bpa BPA] [-c CUTOFF] [--circularise] [--ref_chan {first,last,mid}] [-t TOLERANCE]
[-e EPSILON] [-n NSAMPS] [--ncores NCORES] [--executor_type {thread,process,mpi}]
infile [infile ...]
Smooth a field of 3D cubes to a common resolution. - Default names of output files are /path/to/beamlog{infile//.fits/.{SUFFIX}.fits} - By default, the
smallest common beam will be automatically computed. - Optionally, you can specify a target beam to use. - It is currently assumed that cubes will be
4D with a dummy Stokes axis. - Iterating over Stokes axis is not yet supported.
positional arguments:
infile Input FITS image(s) to smooth (can be a wildcard) - CASA beamtable will be used if present i.e. if CASAMBM = T - Otherwise beam
info must be in co-located beamlog files. - beamlog must have the name /path/to/beamlog{infile//.fits/.txt}
options:
-h, --help show this help message and exit
--uselogs Get convolving information from previous run [False]. (default: False)
--mode MODE Common resolution mode [natural]. natural -- allow frequency variation. total -- smooth all plans to a common resolution.
(default: natural)
--conv_mode {robust,scipy,astropy,astropy_fft}
Which method to use for convolution [robust]. 'robust' computes the analytic FT of the convolving Gaussian. Note this mode can
now handle NaNs in the data. Can also be 'scipy', 'astropy', or 'astropy_fft'. Note these other methods cannot cope well with
small convolving beams. (default: robust)
-v, --verbosity Increase output verbosity (default: 0)
--logfile LOGFILE Save logging output to file (default: None)
-d, --dryrun Compute common beam and stop. (default: False)
-p PREFIX, --prefix PREFIX
Add prefix to output filenames. (default: None)
-s SUFFIX, --suffix SUFFIX
Add suffix to output filenames [{MODE}]. (default: None)
-o OUTDIR, --outdir OUTDIR
Output directory of smoothed FITS image(s) [None - same as input]. (default: None)
--bmaj BMAJ BMAJ to convolve to [max BMAJ from given image(s)]. (default: None)
--bmin BMIN BMIN to convolve to [max BMAJ from given image(s)]. (default: None)
--bpa BPA BPA to convolve to [0]. (default: None)
-c CUTOFF, --cutoff CUTOFF
Cutoff BMAJ value (arcsec) -- Blank channels with BMAJ larger than this [None -- no limit] (default: None)
--circularise Circularise the final PSF -- Sets the BMIN = BMAJ, and BPA=0. (default: False)
--ref_chan {first,last,mid}
Reference psf for header [None]. first -- use psf for first frequency channel. last -- use psf for the last frequency channel.
mid -- use psf for the centre frequency channel. Will use the CRPIX channel if not set. (default: None)
-t TOLERANCE, --tolerance TOLERANCE
tolerance for radio_beam.commonbeam. (default: 0.0001)
-e EPSILON, --epsilon EPSILON
epsilon for radio_beam.commonbeam. (default: 0.0005)
-n NSAMPS, --nsamps NSAMPS
nsamps for radio_beam.commonbeam. (default: 200)
--ncores NCORES Number of cores to use for parallelisation. If None, use all available cores. (default: None)
--executor_type {thread,process,mpi}
Executor type for parallelisation. (default: thread)
$ getnoise_list -h
usage: getnoise_list [-h] [-s] [-b] [-c CLIPLEV] [-i ITERATE] [-f FILE] qfile ufile
Find bad channels by checking statistics of each channel image.
positional arguments:
qfile Stokes Q fits file
ufile Stokes U fits file
options:
-h, --help show this help message and exit
-s, --save_noise Save noise values to disk [default False]
-b, --blank Blank bad channels? [default False - just print out bad channels]
-c CLIPLEV, --cliplev CLIPLEV
Clip level in sigma, make this number lower to be more aggressive [default 5]
-i ITERATE, --iterate ITERATE
Iterate flagging check N times [dafult 1 -- one pass only]
-f FILE, --file FILE Filename to write bad channel indices to file [None -- do not write]
If finding a common beam fails, try tweaking the tolerance, epsilon, and nsamps parameters. See radio-beam for more details.
Profiling for beamcon_3D suggests this program requires a minimum of ~15X the memory of a data cube slice per process to perform convolution to a common beam. So for a 800 MB slice (e.g. typical POSSUM cube) you would want to allow 15 GB memory per worker (I use 20 GB). Choose ncores appropriately given your machine memory availability and this limit to ensure optimal performance with multiprocessing.
An example slurm header for beamcon_3D:
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=<ncores>
#SBATCH --mem-per-cpu=20G
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.