Other GBTIDL Features and Examples

Other GBTIDL Features and Examples#

Customizing the output of the list procedure#

The list command can be used to view detailed information on records in the active input file. Because this file contains a great deal of information, it is possible to request that the list command show only that information of interest.

The following command shows how to create the sample data used in these examples:

sdfits -mode=raw -scans=177:180 -backends=acs /home/archive/test-data/tape-0002/TREG_040922

Then to access this data in GBTIDL use this command:

filein, ’TREG_040922.raw.acs.fits’

In the first example, we use list

list                    ; Show a brief description of everything

to show all the records, or spectra, currently found in the index file

#INDEX SOURCE SCAN PROCEDURE POL IFNUM FDNUM INT SIG CAL
   0    W3OH  177    OffOn   XX    0     0    0   T   T
   1    W3OH  177    OffOn   XX    0     0    0   T   F
   2    W3OH  177    OffOn   XX    0     0    1   T   T
   3    W3OH  177    OffOn   XX    0     0    1   T   F
   4    W3OH  177    OffOn   YY    0     0    0   T   T
   .
   .
   .
   30   W3OH  180    OffOn   YY    0     0    1   T   T
   31   W3OH  180    OffOn   YY    0     0    1   T   F

Next, we use a simple search parameter:

list, index=[0,1,2]     ; Show a description of the first three records

#INDEX SOURCE SCAN PROCEDURE POL IFNUM FDNUM INT SIG CAL
  W3OH  177    OffOn   XX    0     0    0   T   T
  W3OH  177    OffOn   XX    0     0    0   T   F
  W3OH  177    OffOn   XX    0     0    1   T   T

To see all of information associated with these records, use the verbose keyword.

list, index=[0,1,2], /verbose

This will return the parameters:

# INDEX, PROJECT, FILE, EXT, ROW, SOURCE, PROCEDURE, OBSID, E2ESC, PROCS, SCAN, POL,
PLNUM, IFNUM, FEED, FDNUM, INT, NUMCHN, SIG, CAL, SAMPLER, AZIMU, ELEV, LONGITUDE,
LATITUDE, TRGTLONG, TRGTLAT, LST, CENTFREQ, RESTFREQ, VELOCITY, FREQINT, FREQRES,
DATEOBS, TIMESTAMP, BANDWIDTH, EXPOSURE, TSYS, NSAVE

Obviously, the above example is not best if you are only interested in the values of a few specific columns. You can narrow the output like so:

list, index=[0,1,2], columns=["INDEX","INT","POLARIZATION"]

#INDEX INT POL
  0  XX
  0  XX
  1  XX

The list command prints records in the order of the index number by default. This can be changed using the sortcol keyword. Note that the full name of the column must be used.

list, scan=177, sortcol="INT" ; Sort by integration number

returns

#INDEX SOURCE SCAN PROCEDURE POL IFNUM FDNUM INT SIG CAL
  W3OH   177   OffOn   XX    0     0    0   T   T
  W3OH   177   OffOn   XX    0     0    0   T   F
  W3OH   177   OffOn   YY    0     0    0   T   T
  W3OH   177   OffOn   YY    0     0    0   T   F
  W3OH   177   OffOn   XX    0     0    1   T   T
  W3OH   177   OffOn   XX    0     0    1   T   F
  W3OH   177   OffOn   YY    0     0    1   T   T
  W3OH   177   OffOn   YY    0     0    1   T   F

The liststack command is identical to the list command except that it selects from records identified by the stack.

select, scan=177 ; Place scan 177’s records on the stack
liststack, col=["INDEX","INT","CAL"], sortcol="CAL"

returns

#INDEX INT CAL
  0   F
  1   F
  0   F
  1   F
  0   T
  1   T
  0   T
  1   T

Making postage stamp plots#

In displaying PointMap data, or just for displaying multiple spectra, it can be convenient to display spectra as postage stamp plots. GBTIDL currently does not have any inherent support for postage stamp plots, but it is easy to use the IDL plotter to duplicate plots as one might see from CLASS, for example.

For instance, a 3x3 PointMap might be stored as calibrated, reduced spectra in an SDFITS file, with the first 9 records representing the map. These can be displayed in a postage stamp plot as follows:

filein, ’postage.fits’
freeze
!p.multi = [0,3,3]
for i=0,8 do begin & $
    getrec, i & $
    x = getxarray() & $
    y = getdata() & $
    plot, x, y, xstyle=1 & $
endfor
unfreeze

For more flexibility in plot placement, the position parameter can be used, as in the following procedure:

pro plotpos, x, y, xpos, ypos, xsize, ysize
    if (n_elements(xsize)) eq 0 then xsize = 0.1
    if (n_elements(ysize)) eq 0 then ysize = 0.1
    freeze
    plot,x,y,position=[xpos-xsize/2,ypos-ysize/2,xpos+xsize/2,ypos+ysize/2], /noerase, xstyle=1
    unfreeze
end

The procedure might be used as follows:

erase
getrec,0
plotpos, getxarray(), getdata(), 0.5, 0.5, 0.25, 0.25
getrec,1
plotpos, getxarray(), getdata(), 0.2, 0.5, 0.25, 0.25
getrec,2
plotpos, getxarray(), getdata(), 0.8, 0.5, 0.25, 0.25
getrec,3
plotpos, getxarray(), getdata(), 0.5, 0.2, 0.25, 0.25
getrec,4
plotpos, getxarray(), getdata(), 0.5, 0.8, 0.25, 0.25

Example reduction sessions with sample data sets#

This section describes a few sample data sets for users who may wish to experiment with GBTIDL but who do not yet have any data to play with. You may wish to experiment with GBTIDL before you have an appropriate data set of your own. With each data set is an example of how the data might be reduced and analyzed in GBTIDL. The examples are simply guides, and there are many ways to reduce the data in each case.

HI Position Switched Data#

This is a strightforward observation of HI in a galaxy, observed using position switching. The data set is “clean”, so all the data can be included in the averaging. The example data reduction is terse in this case, and aims just to produce an HI spectrum calibrated as antenna temperature (K). The RMS noise and integrated flux density of the HI source are measured.

Retrieve the data: ngc5291.fits (http://safe.nrao.edu/wiki/pub/GB/Data/GBTIDLExampleAndSampleData/ngc5291.fits)
Example data reduction:
Get the data into GBTIDL and show a summary of the scans:
filein, ’ngc5291.fits’

summary

Calibrate and accumulate the data for each scan, and for each polarization:

for i=51,57,2 do begin getps, i, plnum=0 & accum & end
for i=51,57,2 do begin getps, i, plnum=1 & accum & end
ave

Set a baseline region and subtract the baseline:

chan
nregion,[3300,14800,17900,31000]
nfit,3
sety, 0.2, 0.5
bshape
baseline
unzoom

Apply some smoothing, then measure statistics:

hanning,/decimate
bdrop, 2500
edrop, 2500
velo
stats, 2000, 3000 ; this gives the RMS: 13.5 mJy
stats, 3900, 4800 ; this gives the integrated area: 60.439 K km/s
boxcar, 8 ; more smoothing

OH/HI Frequency Switched Data#

This is a slightly more involved data set than the previous one. In this case, there are 2 spectral windows, or “IFs”. One records the 1665/1667 MHz OH masers and the other records the HI emission toward W3(OH). The data are frequency switched. This data includes some integrations in which there are bad data, and so the observer must be careful to inspect and average the data selectively.

Retrieve the data: W3OH.fits (http://safe.nrao.edu/wiki/pub/GB/Data/GBTIDLExampleAndSampleData/W3OH.fits)

Todo

Make the file available for download here.
Example data reduction:
Get the data into GBTIDL and show a summary of the scans:
filein, ’W3OH.fits’ summary
Begin by visually inspecting the data. Note not all the data is “good” so we will need to be selective in the averaging. The “wait” command simply pauses to give the observer a chance to look at the data.
for i=79, 83 do begin getfs, i, plnum=1, ifnum=0 & wait, 2 & end
Zoom in to the baseline and repeat:
sety, -2, 2 for i=79, 83 do begin getfs, i, plnum=1, ifnum=0 & wait, 2 & end
Inspect individual integrations within scan 83. Note that within a scan some integrations are good and some bad.
for i=0,5 do begin getfs, 83,intnum=i, plnum=1, ifnum=0 & wait, 2 & end
We must average only the good integrations. There are many ways to approach this problem. It would be natural to use the flagging commands, but here we use a different method which is terse but efficient. We loop through each integration of each scan, test the RMS in the data, and accumulate only the good integrations. The use of freeze before the loop and unfreeze after the loop speeds up the processing by turning off the automatic update of the plotter after each getfs call.
velo freeze for i=79,83 do begin & $ for j=0,5 do begin & $ for k=0,1 do begin & $ getfs, i, units=’Jy’, intnum=j, plnum=k, ifnum=0 & $ stats,-3000,-2000,ret=a,/quiet & $ if a.rms lt 0.5 then accum else print, ’Skipping’ ,i, j, k & $ end end end unfreeze ave
The next example illustrates the flagging approach. The bad integrations are first flagged and then the scans for ifnum=0 are averaged, using both polarizations. Note that the loop over integrations can now be eliminated. The getfs command averages all integrations and since the bad integrations are now flagged, they do not contribute to the average.
flag,[80,82], intnum=[1,3], plnum=1, ifnum=0, idstring=’corrupt’ flag, 83, intnum=[2,4], plnum=1, ifnum=0, idstring=’corrupt’ listflags,/summary freeze for i=79,83 do begin & $ for k=0,1 do begin & $ getfs,i,units=’Jy’, plnum=k, ifnum=0 & $ accum & $ end end unfreeze ave
Extract a region of interest:
chan my_spec = dcextract(!g.s[0],7500,9500) bdrop, 0 edrop, 0 show,my_spec !g.s[0] = my_spec show
Set the baseline regions using the mouse cursor and subtract a baseline.
sety, -0.2,0.4 ; Zoom in a bit setregion nfit, 7 bshape baseline
Fit Gaussians to one of the maser complexes. Use fitgauss to specify a 3-component fit.
velo setx, -60, -30 freey fitgauss
Follow the instructions for fitgauss.

H2O Total Power Nod Data#

This data set contains an observation of a maser line, observed in total power nod mode. In the first example below we show the simplest (but verbose) method to average and reduce the data. The second example is more involved. We use the stack to gather the scans for averaging. We store the individual scans in internal buffers, and display them all overlaid. Finally we average the data and write the final spectrum to disk.

Retrieve the data: IC1481.fits (http://safe.nrao.edu/wiki/pub/GB/Data/GBTIDLExampleAndSampleData/IC1481.fits)

Todo

Make the file available for download here.
Simple reduction of this data set:
Get the data into GBTIDL and accumulate some of the data:
filein, ’IC1481.fits’ getnod, 182, plnum=0 accum getnod, 182, plnum=1 accum getnod, 184, plnum=0 accum getnod, 184, plnum=1 accum
The other scans can be accumulated similarly. Now, average the accumulated data and fit a baseline.
ave
setregion nfit, 3 baseline

Alternative reduction:

Get the data into GBTIDL and show a summary of the scans:

filein, ’IC1481.fits’
summary

Clear the stack, then fill it with even scan numbers in the range 182-188.

emptystack
sclear
addstack, 182, 188, 2
tellstack

Now loop through each scan pair, retrieve the calibrated spectrum, accumulate it and also store it in a memory buffer. The use of freeze and unfreeze before and after the loop speeds up the processing by disabling the automatic update of the plotter after each getnod.

freeze
for i = 0, !g.acount-1 do begin & $
    getnod, astack(i), plnum=0, units=’Jy’, tsys=60 & accum & $
    copy, 0, i*2+2 & $
    getnod, astack(i), plnum=1, units=’Jy’, tsys=60 & accum & $
    copy, 0, i*2+3 & $
end
unfreeze
ave

Fit a baseline:

setregion
nfit, 3
bshape
baseline

Smooth the spectrum, then save it to disk.

hanning, /decimate
fileout, ’saved.fits’
keep

Create a plot showing each individual spectrum (2 polarizations per scan pair) on a single plot, with offsets to make it easier to see the spectra:

copy, 2, 0
baseline
show
copy, 0, 2
freeze
for i=3,9 do begin copy, i, 0 & baseline & bias, float(i-2)*0.2 & copy, 0, i & end
show, 2
unfreeze
for i=3,9 do oshow, i, color=!red

Other GBTIDL Features and Examples

Contents

Other GBTIDL Features and Examples#

Customizing the output of the list procedure#

Making postage stamp plots#

Example reduction sessions with sample data sets#

HI Position Switched Data#

OH/HI Frequency Switched Data#

H2O Total Power Nod Data#