text: import os, sys
  import numpy as np
  execfile('/home/sandrews/mypy/keplerian_mask/keplerian_mask.py')
  
  # simulation
  fname = 'simp3_std_medr_medv_shortint_noiseless.tavg'
  
  # frequencies
  chanstart, chanwidth, nchan = '-5.6km/s', '0.16km/s', 121
  
  # imaging
  robust = 2.0
  thresh = '7.4mJy'
  restfreq = 230.538e9
  imsize = 512
  cell = '0.02arcsec'
  
  # mask parameters
  inc, PA, mstar, dist, zr, Vsys = 40., 310., 0.7, 150., 0.23, 4.
  rmax, nbeams = 1.35, 1.3
  
  #########
  
  
  # - fixed imaging parameters
  cleanscales = [0, 5, 10, 15]
  extens = ['.image', '.mask', '.model', '.pb', '.psf', '.residual', '.sumwt']
  
  # import the FITS into MS format
  os.system('rm -rf sim_MS/'+fname+'.ms')
  importuvfits(fitsfile='sim_uvfits/'+fname+'.uvfits', vis='sim_MS/'+fname+'.ms')
  
  # make a dirty image to guide a Keplerian mask
  for ext in extens:
      os.system('rm -rf sim_images/im_'+fname+'_dirty'+ext)
  tclean(vis='sim_MS/'+fname+'.ms', 
         imagename='sim_images/im_'+fname+'_dirty', specmode='cube', 
         start=chanstart, width=chanwidth, nchan=nchan, outframe='LSRK', 
         restfreq=str(restfreq / 1e9)+'GHz', imsize=imsize, cell=cell, 
         deconvolver='multiscale', scales=cleanscales, niter=0, 
         weighting='briggs', robust=robust, interactive=False, nterms=1, 
         restoringbeam='common')
  
  # - Make a keplerian mask from the (noise-free) dirty image
  os.system('rm -rf sim_images/im_'+fname+'_dirty.mask.image')
  make_mask('sim_images/im_'+fname+'_dirty.image', inc=inc, PA=PA, zr=zr,
            mstar=mstar, dist=dist, vlsr=Vsys, r_max=rmax, nbeams=nbeams)
  
  # - Make a CLEAN image 
  for ext in extens:
      os.system('rm -rf sim_images/im_'+fname+ext)
  tclean(vis='sim_MS/'+fname+'.ms', 
         imagename='sim_images/im_'+fname, specmode='cube', 
         start=chanstart, width=chanwidth, nchan=nchan, outframe='LSRK', 
         restfreq=str(restfreq / 1e9)+'GHz', imsize=imsize, cell=cell, 
         deconvolver='multiscale', scales=cleanscales, niter=10000000, 
         threshold=thresh, weighting='briggs', robust=robust, 
         mask='sim_images/im_'+fname+'_dirty.mask.image', interactive=False, 
         nterms=1, restoringbeam='common')
  
  # export to a FITS cube
  exportfits('sim_images/im_'+fname+'.image', 
             'sim_images/im_'+fname+'.image.fits', overwrite=True)
  
  # cleanup
  os.system('rm -rf *.last')
  os.system('rm -rf *.log')
  