source: branches/newfiller/tutorials/tutorial_3.rst@ 2620

Last change on this file since 2620 was 1757, checked in by Kana Sugimoto, 14 years ago

New Development: Yes

JIRA Issue: Yes (CAS-2211)

Ready for Test: Yes

Interface Changes: Yes

What Interface Changed: ASAP 3.0.0 interface changes

Test Programs:

Put in Release Notes: Yes

Module(s): all the CASA sd tools and tasks are affected.

Description: Merged ATNF-ASAP 3.0.0 developments to CASA (alma) branch.

Note you also need to update casa/code/atnf.


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Tutorial 3 - Spectral-line gaussian component fitting and rms noise levels

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.. sectionauthor:: Shari Breen

Files

  • mon.asap: Data file

Data Log

  • ON-OFF Position switching mode with Hobart
  • 4 scans (2 OFF and 2 ON)
  • Dual pol, 1 IF

Instructions

  • Work through the list of commands given in the text file to calibrate the data and fit gaussian components to the emission. The commands should be typed into ASAP line-by-line until you get to the ADVANCED FITTING step which will require you to experiment with the parameters.
  • ADDITIONAL exercise: Determine the rms noise in the spectrum using the create mask function you learned in the first section and the stat command specified in the text file.

Examples - Fitting gaussians

If you only see a file called mon.asap.tar, untar it before getting started

# Load the scantable for the source into ASAP
mon = scantable("mon.asap")
mon.summary()
# Select the on-source scans
source=mon.get_scan([1,3])
# Select the reference scans
ref=mon.get_scan([0,2])
# Make the quotient spectra and plot it
quot=quotient(source,ref)
plotter.plot(quot)
# Remove the baseline and plot
quot.auto_poly_baseline()
plotter.plot(quot)
# Average the 2 scans together
av=quot.average_time(align=True)
plotter.plot(av)
# Check the the rest frequency is set correctly and define unit as km/s
print av.get_restfreqs()
av.set_unit("km/s")
plotter.plot(av)
# Scale the two polarizations separately
sel=selector()
sel.set_polarisations(0)
av.set_selection(sel)
av.scale(50.6)
sel.set_polarisations(1)
av.set_selection(sel)
av.scale(32.5)
# unset the selection
av.set_selection()
# Average the polarizations together
avpol=av.average_pol()
# Plot the data
plotter.plot(avpol)
# Create a mask that contains the emission
msk=avpol.create_mask([-10,30])
# Set up some fitting parameters
f=fitter()
f.set_scan(avpol,msk)
f.set_function(gauss=2)
# Fit a gaussian to the emission, plot the emission and fit
f.fit()
f.plot()
# Save the plot
plotter.save("monr2.eps")
# Get the fit parameters
f.get_parameters()
# Plot the fit residuals
f.plot(residual=True)
# Plot each of the fitted gaussians separately, with or without the fit parameters overlaid
f.plot(components=0,plotparms=True)

f.plot(components=1)

ADVANCED FITTING

Sometimes it is necessary to force some of the fitting pa- rameters such as peak or FWHM. For example, if you want to fix the peak of the first gaussian component to 39 Jy you can use the following command:

f.set_gauss_parameters(39, 10.2, 1, peakfixed=1, component=0)

You can now experiment with forcing the values of the different parameters. To look at the help file on this function type help fitter.set_gauss parameters Additional Exercise: Determine the rms noise in the spectrum.

  1. Create a mask that excludes the emission (if you leave it in the rms noise will be significantly inflated), using the same command as before except this time you will have to specify two ranges of values either side of the emission.
  2. Find the rms noise of the spectrum:
avpol.stats(stat="rms",mask=mymask)

The stats function can be used to extract many other statistics from the data such as the maximum and minimum values, the median value and many more. Use help to find out how to extract these statistics from the spectrum.

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