Note: A filler routine is currently being developed to convert native MIRIAD format data into a CASA measurement set, but it has not yet been completed. The following steps describe how to use FITS format files to transfer CARMA data into CASA, and should still be useful even when the filler is operational. At that time, a description of how to use the CARMA filler routine will be added to this page.
Assumptions: 1. You have MIRIAD installed on your computer. 2. You are running the MIRIAD commands shown on this page in a shell script (csh), with a number of variables have been defined at the top of your script.
This example uses a single track from the CARMA STING observations of NGC4254 (M99); more details of the observations and analysis of this galaxy can be seen on the CARMA STING webpage () and in Rahman et al. (2010). The dataset can be downloaded here.
For this example, the following items would be defined at the beginning of the csh shell script:
# in csh script #!/bin/csh # FILE = name of original, raw MIRIAD-format file set FILE="../c0xxx.1E.miriad" # PROJECT = base file name for processed data files set PROJECT="c0xxx" # ANTPOS = the location of the file which contains updates to the CARMA antenna positions set ANTPOS="../antpos.YYMMDD"
Preparing MIRIAD data for export to FITS
To minimize the data you are transferring over, select only the astronomically useful data:
# in csh script uvcat vis=$FILE select='-source(NOISE),-auto' out=$PROJECT.vis
If you need to apply an update for the antenna positions, or linelength corrections, you should do these before exporting to FITS.
To update antenna positions:
# in csh script uvedit vis=$PROJECT.vis out=$PROJECT.ant apfile=$ANTPOS
To apply linelength corrections:
# in csh script linecal vis=$PROJECT.ant uvcat vis=$PROJECT.ant out=$PROJECT.lc
Currently, MIRIAD writes out the identity of the array as identified by Antenna 1, which is a formerly-OVRO antenna. Thus, without correction, the dataset will identify the array as OVRO rather than CARMA, which means that CASA will make the same mistake. To fix this problem, simply do a small header edit:
# in csh script puthd in=$PROJECT.lc/telescop value=CARMA
One last recommendation for preparing the data: it is still valuable to do some initial hanning smoothing to dampen ringing in the bandpass. (This was still true, at least, for data taken in summer 2009.)
To hanning smooth:
# in csh script uvcal vis=$PROJECT.lc options=hanning out=$PROJECT.hann
Writing out the FITS files
Now you should be ready to write out your CARMA visibility data. You will need to write out each spectral window as a separate FITS file in MIRIAD, and then concatenate them back together within CASA. In your shell script, just use a "foreach" loop.
In this example, I am writing out the upper sideband windows only (windows 4,5, and 6). This loop produces one file for each spectral window, including data from all sources observed with that window.
# in csh script foreach i (4 5 6) fits in=$PROJECT.hann op=uvout select="win($i)" out="./fits/$PROJECT.$i.fits" end
Constructing the CASA Measurement Set (MS)
You now have everything you need to tell CASA how to create an MS from these data; the remaining commands on this page are formatted as if they were part of an executable python script (e.g., "execfile 'script.py' in CASA).
If you have more than a couple spectral windows to import, I'd recommend using a little python to assemble the list of files for you, rather than having to tediously type out the whole list by hand. The following lines of code will produce an array of filenames that you can give CASA as an input for the "importuvfits" command:
# in CASA from glob import glob as filesearch import os fitsdir='fits/' msdir='./' project='c0xxx' catfile=msdir+project+'.ms' inlist = sorted(filesearch(fitsdir+'*.fits'))
In the same python script, you can now use the following "for" loop to read each of the individual FITS files into an MS of the same name (e.g., for a FITS file named "c0xxx.4.fits", produce an MS called "c0xxx.4.ms" .)
# in CASA for item in inlist: infile=item.split('/')[-1] outfile=infile.replace('.fits','.ms') importuvfits(fitsfile=item, vis=msdir+outfile)
Lastly, concatenate the individual spectral windows MS directories into one big MS containing all spectral windows, for all sources:
# in CASA files = sorted(filesearch('*.ms')) concat(vis=files,concatvis=catfile,freqtol="",dirtol="1arcsec",async=False)
A final check
Of course, if you'd like to see whether or not concat produced something sensible, just run a "listobs" to make sure things look all right:
# in CASA listobs(vis=catfile)
--Michele Thornley 28 December 2009