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| 3 | <title>Mopra 2005 Workshop - Data reduction tutorial worksheet</title>
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| 26 | </style>
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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 the 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.
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| 70 | <div class=code><pre>
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| 71 | q = s.auto_quotient()
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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 | </pre></div>
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| 118 | We start with the first spectrum (IF=0) bys setting the selection. We want to fit two gaussian components.
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| 119 | <div class=code><pre>
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| 120 | sel = selector()
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| 121 | sel.set_ifs(0)
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| 122 | q.set_selection(sel)
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| 123 | f.set_scan(q)
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| 124 | f.set_function(gauss=2) # fit two gaussians
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| 125 | f.fit()
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| 126 | f.plot(
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| 127 | </pre></div>
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| 128 | 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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| 129 | <div class=code><pre>
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| 130 | sel.set_ifs(1)
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| 131 | q.set_selection(sel)
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| 132 | f.set_function(gauss=7)
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| 133 | f.fit()
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| 134 | f.plot(residual=True)
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| 135 | f.plot(components=[0,1,2,3,4,5,6,-1])
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| 136 | f.plot(components=[0,1,2,3,4,5,6,-1],plotparms=True)
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| 137 | </pre></div>
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| 138 | </div>
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| 139 | <h1 class=custom>
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| 140 | Appendix
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| 141 | </h1>
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| 142 | <div class=main align=center>
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| 143 | More info can be found on the ASAP homepage
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| 144 | <a href="http://www.atnf.csiro.au/computing/software/asap">
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| 145 | http://www.atnf.csiro.au/computing/software/asap</a>
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| 146 | </div>
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| 147 | </body>
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| 148 | </html>
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