wvrgcal.py

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#
# This file was generated using xslt from its XML file
#
# Copyright 2009, Associated Universities Inc., Washington DC
#
import sys
import os
from  casac import *
import string
from taskinit import casalog
#from taskmanager import tm
import task_wvrgcal
def wvrgcal(vis='', caltable='', toffset=-1, segsource=True, sourceflag=[''], tie=[''], nsol=1, disperse=False, wvrflag=[''], statfield='', statsource='', smooth=1, scale=1., reversespw='', cont=False):

        """Generate a gain table based on Water Vapour Radiometer data

Information about the observation and the performance of WVRGCAL is written to the CASA logger; 
see the CASA cookbook for a more detailed description of these parameters.

Of particular note is the discrepancy parameter (Disc): high values (> a few hundred microns) 
may indicate some levels of cloud contamination and the effect of applying the WVRGCAL correction 
should be checked; values > 1000 um in all antennas have currently been found to indicate that 
WVRGCAL correction should not be used.

      
  vis -- Name of input visibility file
              default: none; example: vis='ngc5921.ms'

  caltable -- Name of output gain calibration table
              default: none; example: caltable='ngc5921.wvr'

  toffset -- Time offset (sec) between interferometric and WVR data
             default: -1 (ALMA default)

  segsource -- Do a new coefficient calculation for each source
             default: True

  tie -- Prioritise tieing the phase of these sources as well as possible
         (requires segsource=True)
             default: [] example: ['3C273,NGC253', 'IC433,3C279']

  sourceflag -- Flag the WVR data for these source(s) as bad and do not produce corrections for it
               (requires segsource=True)
               default: [] (none) example: ['3C273']

  nsol -- Number of solutions for phase correction coefficients during this observation.
          By default only one set of coefficients is generated for the entire observation. 
          If more sets are requested, then they will be evenly distributed in time throughout 
          the observation. Values > 1 require segsource=False.
             default: 1

  disperse -- Apply correction for dispersion
             default: False

  wvrflag -- Flag the WVR data for these antenna(s) as bad and replace its data with interpolated values
               default: [] (none) example: ['DV03','DA05','PM02']           

  statfield -- Compute the statistics (Phase RMS, Disc) on this field only
               default: '' (all) 

  statsource -- Compute the statistics (Phase RMS, Disc) on this source only
               default: '' (all)             

  smooth -- Smooth WVR data by this many samples before applying the correction
             default: 1 (no smoothing) example: 3

  scale -- Scale the entire phase correction by this factor
             default: 1. (no scaling)

  reversespw -- Reverse the sign of the correction for the listed SPWs
                (only neede for early ALMA data before Cycle 0)
             default: '' (none), example: reversespw='0~2,4'; spectral windows 0,1,2,4

  cont -- Estimate the continuum (e.g., due to clouds)
             default: False

  
        """
        if type(sourceflag)==str: sourceflag=[sourceflag]
        if type(tie)==str: tie=[tie]
        if type(wvrflag)==str: wvrflag=[wvrflag]

#
#    The following is work around to avoid a bug with current python translation
#
        mytmp = {}

        mytmp['vis'] = vis
        mytmp['caltable'] = caltable
        mytmp['toffset'] = toffset
        mytmp['segsource'] = segsource
        mytmp['sourceflag'] = sourceflag
        mytmp['tie'] = tie
        mytmp['nsol'] = nsol
        mytmp['disperse'] = disperse
        mytmp['wvrflag'] = wvrflag
        mytmp['statfield'] = statfield
        mytmp['statsource'] = statsource
        mytmp['smooth'] = smooth
        mytmp['scale'] = scale
        mytmp['reversespw'] = reversespw
        mytmp['cont'] = cont
        pathname='file:///'+os.environ.get('CASAPATH').split()[0]+'/share/xml/'
        trec = casac.utils().torecord(pathname+'wvrgcal.xml')

        casalog.origin('wvrgcal')
        if trec.has_key('wvrgcal') and casac.utils().verify(mytmp, trec['wvrgcal']) :
            result = task_wvrgcal.wvrgcal(vis, caltable, toffset, segsource, sourceflag, tie, nsol, disperse, wvrflag, statfield, statsource, smooth, scale, reversespw, cont)

        else :
          result = False
        return result