/MALATANG

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MALATANG

Part-1 convert JCMT file format to CLASS format

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UPDATE 03 Oct. 2017:

revised the bug in velocity definition:

add velo or freq separation

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test data: in 71.202

/home/malatang/2015raw/20151202/a20151202_00030_01_0001.sdf

In linux/mac, please copy the script files to the data folder and run:

usage:

 ./all_convert.sh coordinate.dat 
 coordinate.dat contains RA Dec in one line 
 Here RA and Dec are in  Radian (Equatorial J2000 ), found in NED or in reference CO files.

Example (for NGC 253):

 ./all_convert.sh coordinate.dat 
 coordinate file: 
 11.888002  -25.288220

The rest should be automatic and you will get all .jcmt files in the CLASS format.

or, if you like to run each script individually:

1). ./initialisation                            a20151202_00030_01_0001.sdf 
2). ipython convert_JCMT_to_sdfits.py           a20151202_00030_01_0001.fits  
1). class @ convert_fits_to_class.class         a20151202_00030_01_0001   RA Dec

initialisation.sh : use Starlink to convert sdf to fits, get coordinates and system temperature for each subscan and each receptor

convert_JCMT_to_sdfits.py : use python to convert fits file (generated by starlink) to sdfits format (readable for class) for each subscan and each receptor

convert_fits_to_class.class : use CLASS to read all fits files (each subscan and each receptor) into a CLASS file (.jcmt)

Note: 1). If you use Mac, please install rename by :

    brew install rename

2). convert_JCMT_to_sdfits.py uses python 3.5, please install it using anaconda: https://www.continuum.io/downloads

Please install the plugins (most of them are installed by anaconda by default):

matplotlib
scipy 
numpy
astropy

3). please specify the coordinate of the reference position in convert_fits_to_class.class

Please see the note header in convert_fits_to_class.class

TODO:

      1). remove temperary files -- Done 
      2). input output variables -- Done  
      3). more careful check     -- half done

Part-2 test automated pipeline for quality control

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UPDATE 04 Jan. 2017:

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Method:

1) use CO mapping data (e.g. Nobeyama 45m CO survey ) as reference, for setting windows (line free channels) in the baseline subtraction.  
2). Use the ratios of theoritical rms and measured rms and the allan variation to qualify each spectrum.

CO data: Nobeyama CO atlas survey

http://www.nro.nao.ac.jp/~nro45mrt/download/COatlas/data/n253/N253RD_TMB.FITS.gz

Useage:

class @ qualify.class 

qualify.class calls run.class which calls the following: 

@ run0.class  ! switch the HCN/HCOP data to relative velocity frame
@ run1.class  ! qualify all data and output qualify.dat
@ run2.class  ! analysis spectral qualities and output plots
@ run3.class  ! final baseline subtraction with CO reference data

Details :

  @ run molecule redshift jcmt_file reference_file velocity_resolution_for_qualify weak_or_not

  molecule:        
                  Molecule to select HCN or HCOP for the rest frequencies
  reference_file:  
                  The name of the .fits file, but you need to lmv an output class format first if there is no reference .fits file, then please set it to none, then the code automatically recognises it and set window -100 100 accordingly.

  velocity_resolution_for_qualify:  
                  The velocity resolution for the baseline qualification
  weak_or_not: 
                  if .TRUE. then weak line, then do not set window using the CO data during the qualification but still set CO window in the last baseline subtraction step
                  if .False. then strong line, then set window using the CO data both in qualification and in the last baseline subtraction step.
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  Note: two folders are needed for the qualification : tagged, quality
  tagged will contain all the tagged (qualified) data.
  quality will contain all the plots of the qualification and the quality data of each spectrum







TODO: 1). automatically cut edge channels  -- done  
      2). output qualified spectra         -- done. 
      3). more statistic analysis (Histogram fitting, find outliers, and more)  for the output array. -- done. 
      4). check the discripance between resolution and channel width. -- done

Part-3 A semi-manual interactive pipeline for data quality inspection

Useage:

@flag_idx.class  input.jcmt  N   ! N means how many polygons you want to use for setting windows

firstly set a polygon to set window for all spectra: click four corners of setting window regions. 

Then start data quality inspection and flagging (selection). -- when you
find some regions with bad data, please click the upper side then the lower
side of this region. Then the code will automatically neglect this region
in the output.

Part-4: A generic automatic Qualification of long time integration spectral data (Gildas format) observed with single dish telescopes.

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UPDATE 06 Mar. 2017:

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Aims:

  • to automatically do the qualification using RMS ratio (measured/theoretical) and the Allan deviation (measured/theoretical)
  • to output a qualify.dat file for future use
  • to output quality plots in quality folder
  • to output qualified spectral files in tagged folder

Usage:

 Example: @ qualify.class E-097.A-0973A-2016-2016-04-06.apex 20 SPT0125-50 AP-G101-XF02
          @ qualify.class filename  velo_resolution  source_name   spectrometer