NGC 5921: red-shifted HI emission 5.7.2: Difference between revisions

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The technique used to calibrate and image continuum datasets generally applies to spectral line observations, except that an additional calibration step is required. '''Bandpass calibration''' flattens the spectral response of the observations, ensuring that spectral channel images are properly calibrated in amplitude and phase.
The technique used to calibrate and image continuum datasets generally applies to spectral line observations, except that an additional calibration step is required. '''Bandpass calibration''' flattens the spectral response of the observations, ensuring that spectral channel images are properly calibrated in amplitude and phase.
The following tutorial derives from an annotated script provided in the [http://casa.nrao.edu/Doc/Cookbook/casa_cookbook.pdf CASA Cookbook]. The script is largely reproduced in sections and additionally annotated with figures and illustrations.
The data are included with the CASA installation.
== Setting up the CASA Environment ==
Start up CASA in the directory you want to use.
<source lang="bash">
# in bash
mkdir NGC5921
cd NGC5921
casapy
</source>
Now, in CASA, set up paths and global variables to facilitate the data reduction. These operations can be performed on-the-fly if you are reducing data interactively, but it's better to get them out of the way in the scripting environment.
<source lang="python">
import os
scriptmode = True
prefix = 'ngc5921.demo' # The prefix to use for all output files
# Set up some useful variables (these will be altered later on)
msfile = prefix + '.ms'
btable = prefix + '.bcal'
gtable = prefix + '.gcal'
ftable = prefix + '.fluxscale'
splitms = prefix + '.src.split.ms'
imname = prefix + '.cleanimg'
</source>
We'll use a python ''os'' command to get the appropriate CASA path for your installation. The use of '''os.environ.get''' is explained in [[#os.environ.get | the Appendix]].
<source lang="python">
pathname=os.environ.get('CASAPATH').split()[0]
fitsdata=pathname+'/data/demo/NGC5921.fits'
</source>
== Appendix: Some Python Notes ==
=== os.environ.get ===
It's worth having a look at the output of the '''os.environ.get''' command to understand the python syntax. You can think of '''os.environ''' as a list of operating system environment variables, and '''get''' is a method that extracts information about the requested environment variable, here, CASAPATH. '''Get''' returns a string of whitespace separated information, and '''.split()''' turns that string into a list. The array index '''[0]''' extracts the first element of that list, which contains the path.
To illustrate, here is some example python I/O in CASA.
<pre>
CASA <12>: print os.environ.get('CASAPATH')
/usr/lib64/casapy/30.0.9709test-001 linux local el5bld64b
CASA <13>: print os.environ.get('CASAPATH').split()
['/usr/lib64/casapy/30.0.9709test-001', 'linux', 'local', 'el5bld64b']
CASA <14>: print os.environ.get('CASAPATH').split()[0]
/usr/lib64/casapy/30.0.9709test-001
</pre>

Revision as of 11:33, 3 December 2009

↵ VLA Tutorials

Overview

The technique used to calibrate and image continuum datasets generally applies to spectral line observations, except that an additional calibration step is required. Bandpass calibration flattens the spectral response of the observations, ensuring that spectral channel images are properly calibrated in amplitude and phase.

The following tutorial derives from an annotated script provided in the CASA Cookbook. The script is largely reproduced in sections and additionally annotated with figures and illustrations.

The data are included with the CASA installation.

Setting up the CASA Environment

Start up CASA in the directory you want to use. 

# in bash
mkdir NGC5921
cd NGC5921
casapy

Now, in CASA, set up paths and global variables to facilitate the data reduction. These operations can be performed on-the-fly if you are reducing data interactively, but it's better to get them out of the way in the scripting environment. 

import os
scriptmode = True

prefix = 'ngc5921.demo' # The prefix to use for all output files
# Set up some useful variables (these will be altered later on)
msfile = prefix + '.ms'
btable = prefix + '.bcal'
gtable = prefix + '.gcal'
ftable = prefix + '.fluxscale'
splitms = prefix + '.src.split.ms'
imname = prefix + '.cleanimg'

We'll use a python os command to get the appropriate CASA path for your installation. The use of os.environ.get is explained in the Appendix. 

pathname=os.environ.get('CASAPATH').split()[0]
fitsdata=pathname+'/data/demo/NGC5921.fits'

Appendix: Some Python Notes

os.environ.get

It's worth having a look at the output of the os.environ.get command to understand the python syntax. You can think of os.environ as a list of operating system environment variables, and get is a method that extracts information about the requested environment variable, here, CASAPATH. Get returns a string of whitespace separated information, and .split() turns that string into a list. The array index [0] extracts the first element of that list, which contains the path.

To illustrate, here is some example python I/O in CASA. 

CASA <12>: print os.environ.get('CASAPATH')
/usr/lib64/casapy/30.0.9709test-001 linux local el5bld64b

CASA <13>: print os.environ.get('CASAPATH').split()
['/usr/lib64/casapy/30.0.9709test-001', 'linux', 'local', 'el5bld64b']

CASA <14>: print os.environ.get('CASAPATH').split()[0]
/usr/lib64/casapy/30.0.9709test-001
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