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|  | 3 | <title>Mopra 2005 Workshop - Data reduction tutorial worksheet</title> | 
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|  | 27 | </head> | 
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|  | 28 | <body> | 
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|  | 29 | <div> | 
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|  | 30 | <h1 class=custom> | 
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|  | 31 | Introduction | 
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|  | 32 | </h1> | 
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|  | 33 | <div class=main align=center> | 
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|  | 34 | This is a short tutorial on how to use ASAP for simple on of dual IF | 
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|  | 35 | observations. It is highly customisable via ".aipsrc" parameters. It | 
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|  | 36 | can read rpfits,sdfits and measurement sets, and export to sdfits, ms, | 
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|  | 37 | ASCII and image fits.  ASAP provides a lot more functionality, such as | 
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|  | 38 | frequency alignment, averaging which can't be presented here. | 
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|  | 39 | </div> | 
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|  | 40 | <h1 class=custom> | 
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|  | 41 | Part I - Reduction | 
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|  | 42 | </h1> | 
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|  | 43 | <div class=main align=center> | 
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|  | 44 | To start ASAP type the follwing at  the *nix command line prompt | 
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|  | 45 | <div class=shell> | 
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|  | 46 | <pre> | 
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|  | 47 | localhost> asap | 
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|  | 48 | </pre> | 
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|  | 49 | </div> | 
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|  | 50 | To get the list of available commands in asap type: | 
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|  | 51 | <div class=code><pre> | 
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|  | 52 | commands() | 
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|  | 53 | </pre></div> | 
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|  | 54 | Help can be accessed by using <em>help</em>: | 
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|  | 55 | <div class=code><pre> | 
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|  | 56 | help(scantable) | 
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|  | 57 | help(scantable.summary) | 
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|  | 58 | </pre></div> | 
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|  | 59 | To start we are reading in the data into a scantable, which can be accessed via th variable <em>s</em>. After reading in we have a look at the data. | 
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|  | 60 | <div class=code><pre> | 
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|  | 61 | s = scantable("2005-05-08_0350.rpf") | 
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|  | 62 | s.summary() | 
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|  | 63 | </pre></div> | 
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|  | 64 | We can plot the scan now. First we set up the plotter to plot "IF" as stcked colours and "time" across panels. Then we issue th plot command. | 
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|  | 65 | <div class=code><pre> | 
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|  | 66 | plotter.set_mode("IF","time") | 
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|  | 67 | plotter.plot(s) # plot s | 
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|  | 68 | </pre></div> | 
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|  | 69 | Now we can build the quotient. This applies the quotient to both IFs. We are taking all scans ending in "_S" to be "on" source and all "_R" to be "off". | 
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|  | 70 | <div class=code><pre> | 
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|  | 71 | q = quotient(s.get_scan("*_S"),s.get_scan("*_R")) | 
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|  | 72 | plotter.plot(q) # plot q | 
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|  | 73 | </pre></div> | 
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|  | 74 | Now we can set some information anbout the velocity setup. We set the rest frequencies for 13Co and SiO and want to operate in "LSRK". | 
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|  | 75 | <div class=code><pre> | 
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|  | 76 | restfreqs = [110.201,86.243]     # 13CO-1/0, SiO the two IF | 
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|  | 77 | q.set_restfreqs(restfreqs,"GHz") # set the restfrequencies, as not in data | 
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|  | 78 | q.set_unit("km/s")               # set the unit to be used from now on | 
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|  | 79 | q.set_freqframe("LSRK")          # set frequency frame | 
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|  | 80 | plotter.plot()                   # replot, should show velocity now | 
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|  | 81 | </pre></div> | 
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|  | 82 | Now we can subtract a baseline. ASAP can do this automatically if ther is good S/N and the lines aren't too broad. | 
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|  | 83 | <div class=code><pre> | 
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|  | 84 | q.auto_poly_baseline() # determine and subtract a poly baseline automatically | 
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|  | 85 | plotter.plot() # replot | 
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|  | 86 | </pre></div> | 
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|  | 87 | We can zoom in on the spectrum and format the title and legend. ASAP accepts LATEX math expressions. | 
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|  | 88 | <div class=code><pre> | 
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|  | 89 | plotter.set_range(-35,35)                                  # zoom in  bit | 
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|  | 90 | plotter.set_legend([r"$^{13}CO(1\leftarrow 0)$",r"$SiO$"]) # make nice latex IF labels | 
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|  | 91 | plotter.set_title(['Mopra Tutorial 2005'])                 # set the title | 
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|  | 92 | </pre></div> | 
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|  | 93 | Now convert Brightness temperature to Flux density. | 
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|  | 94 | <div class=code><pre> | 
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|  | 95 | q.convert_flux() # K -> Jy | 
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|  | 96 | plotter.plot() | 
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|  | 97 | </pre></div> | 
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|  | 98 | This "final" plot can be saved now as "png", "ps" or "eps" | 
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|  | 99 | <div class=code><pre> | 
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|  | 100 | plotter.save("tutorial.png") | 
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|  | 101 | plotter.save("tutorial.eps") | 
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|  | 102 | </pre></div> | 
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|  | 103 | We can also do some stats on the spectra. We select a line free region first, which get applied to the statistics. | 
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|  | 104 | <div class=code><pre> | 
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|  | 105 | msk = q.create_mask([-70,20], [20,70]) # line free region - two windows | 
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|  | 106 | rms = q.stats("rms",msk) | 
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|  | 107 | med = q.stats("median",msk) | 
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|  | 108 | </pre></div> | 
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|  | 109 | </div> | 
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|  | 110 | <h1 class=custom> | 
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|  | 111 | Part II - Fitting | 
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|  | 112 | </h1> | 
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|  | 113 | <div class=main align=center> | 
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|  | 114 | This part shows how easy it is to fit gaussian line profiles. | 
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|  | 115 | <div class=code><pre> | 
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|  | 116 | f = fitter() | 
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|  | 117 | f.set_scan(q) | 
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|  | 118 | </pre></div> | 
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|  | 119 | We start with the first spectrum (IF=0) bys setting the cursor. We want to fits to gaussian components. | 
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|  | 120 | <div class=code><pre> | 
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|  | 121 | q.set_cursor(IF=0) | 
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|  | 122 | f.set_function(gauss=2) # fit two gaussians | 
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|  | 123 | f.fit() | 
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|  | 124 | f.plot( | 
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|  | 125 | </pre></div> | 
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|  | 126 | The second IFs spectrum is more complex. We select it and fit seven gaussians to it. Here we also plot residuals, the individual components and the fit parameters. | 
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|  | 127 | <div class=code><pre> | 
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|  | 128 | q.set_cursor(IF=1) | 
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|  | 129 | f.set_function(gauss=7) | 
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|  | 130 | f.fit() | 
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|  | 131 | f.plot(residual=True) | 
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|  | 132 | f.plot(components=[0,1,2,3,4,5,6,-1]) | 
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|  | 133 | f.plot(components=[0,1,2,3,4,5,6,-1],plotparms=True) | 
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|  | 134 | </pre></div> | 
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|  | 135 | </div> | 
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|  | 136 | <h1 class=custom> | 
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|  | 137 | Appendix | 
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|  | 138 | </h1> | 
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|  | 139 | <div class=main align=center> | 
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|  | 140 | More info can be found on the ASAP homepage | 
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|  | 141 | <a href="http://www.atnf.csiro.au/computing/software/asap"> | 
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|  | 142 | http://www.atnf.csiro.au/computing/software/asap</a> | 
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|  | 143 | </div> | 
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|  | 144 | </body> | 
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|  | 145 | </html> | 
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