def gridtemplate(imagename10, image_length, direction_ra, direction_dec):
"""
myim02
"""
# get native grid information
num_x_pix = imhead(imagename10, mode="list")["shape"][0]
num_y_pix = imhead(imagename10, mode="list")["shape"][1]
pix_radian = imhead(imagename10, mode="list")["cdelt2"]
obsfreq = 115.27120 # imhead(imagename10,mode="list")["restfreq"][0]/1e9
pix_arcsec = round(pix_radian * 3600 * 180 / np.pi, 3)
# create tempalte image
blc_ra_tmp = imstat(imagename10)["blcf"].split(", ")[0]
blc_dec_tmp = imstat(imagename10)["blcf"].split(", ")[1]
blc_ra = blc_ra_tmp.replace(":", "h", 1).replace(":", "m", 1) + "s"
blc_dec = blc_dec_tmp.replace(".", "d", 1).replace(".", "m", 1) + "s"
beamsize = round(imhead(imagename10, "list")["beammajor"]["value"], 2)
pix_size = round(beamsize / 4.53, 2)
size_x = int(image_length / pix_size)
size_y = size_x
c = SkyCoord(blc_ra, blc_dec)
ra_dgr = str(c.ra.degree)
dec_dgr = str(c.dec.degree)
direction = "J2000 " + direction_ra + " " + direction_dec
mycl.done()
mycl.addcomponent(dir=direction,
flux=1.0,
fluxunit="Jy",
freq="230.0GHz",
shape="Gaussian",
majoraxis="0.1arcmin",
minoraxis="0.05arcmin",
positionangle="45.0deg")
myia.fromshape("template.im", [size_x, size_y, 1, 1], overwrite=True)
mycs = myia.coordsys()
mycs.setunits(["rad", "rad", "", "Hz"])
cell_rad = myqa.convert(myqa.quantity(str(pix_size) + "arcsec"),
"rad")["value"]
mycs.setincrement([-cell_rad, cell_rad], "direction")
mycs.setreferencevalue([
myqa.convert(direction_ra, "rad")["value"],
myqa.convert(direction_dec, "rad")["value"]
],
type="direction")
mycs.setreferencevalue(str(obsfreq) + "GHz", "spectral")
mycs.setincrement("1GHz", "spectral")
myia.setcoordsys(mycs.torecord())
myia.setbrightnessunit("Jy/pixel")
myia.modify(mycl.torecord(), subtract=False)
exportfits(imagename="template.im",
fitsimage="template.fits",
overwrite=True)
os.system("rm -rf template.image")
importfits(fitsimage="template.fits", imagename="template.image")
myia.close()
mycl.close()
def makeratio(im0, im1, name_im0, name_im1, cliplevel1, factor):
os.system("rm -rf " + dir_proj + "image_ratio/ratio_" + name_im0 + "_" +
name_im1 + ".image_tmp")
peak1 = imstat(im1)["max"][0]
os.system("rm -rf " + im1 + ".complete")
immath(imagename=im1,
expr="iif(IM0 >= " + str(peak1 * cliplevel1) + ", IM0, 0.0)",
outfile=im1 + ".complete")
ratio_image = dir_proj + "image_ratio/ratio_" + name_im0 + "_" + name_im1 + ".image"
os.system("rm -rf " + ratio_image)
immath(imagename=[im0, im1 + ".complete"],
expr="iif(IM0>=0,IM0/IM1/" + str(factor) + ", 0.0)",
outfile=ratio_image)
os.system("rm -rf " + im1 + ".complete")
"""
"""
import os
from os.path import join
from imstat import imstat
from makecube_defines import cube_dir, freq_min, freq_max
print """cube_dir = {0}""".format(cube_dir)
clean_number = int(os.getenv("CLEAN_NUMBER", -1))
cube_dir = join(cube_dir, "{0}".format(clean_number))
output_image = join(cube_dir, "cube_{0}~{1}".format(freq_min, freq_max))
print """output_image = {0}""".format(output_image)
imstat(imagename=output_image + ".image")
imstat(imagename=output_image + ".residual")
def selfcal(vis, spwn=6, doplots=True, INTERACTIVE=False, reclean=True, field='W51 Ku',
outdir_template="spw%i_selfcal_iter/", statsbox='170,50,229,97', ant1list=['ea14','ea05'],
ant2list=['ea16','ea07'], avgchannel_wide='128', avgchannel_narrow='8',
cleanboxes=cleanboxes, refant='ea27', solint='30s', niter=2,
multiscale=[0,5,10,15,25,50], imsize=512, robust=0.0 ):
"""
Docstring incomplete
"""
spw = int(spwn)
outdir = outdir_template % spwn
try:
os.mkdir(outdir)
except OSError:
pass
# you're supposed to pass in avg_data as input
avg_data = vis
mytb.open(vis+"/ANTENNA")
antnames = mytb.getcol("NAME")
# plot each antenna's ampl vs time for flagging purposes
for ant2 in ant2list:
for ant in ant1list:
plotms(vis=vis, spw=str(spwn), xaxis='time', yaxis='amp', avgchannel=avgchannel_wide,
avgscan=F, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
antenna=ant+"&"+ant2,
title='Amp vs Time before averaging for spw %i ant %s-%s' % (spwn,ant,ant2),
plotfile=outdir+'ampvstime_spw%i_ant%s-%s.png' % (spwn,ant,ant2),
field=field,
overwrite=True,
)
plotms(vis=vis, spw=str(spwn), xaxis='freq', yaxis='phase', avgtime='1e8',
avgscan=T, coloraxis='corr', iteraxis='baseline', xselfscale=T,
yselfscale=T,
antenna=ant+"&"+ant2,
title='Phase vs Freq with time averaging for spw %i ant %s-%s' % (spwn,ant,ant2),
plotfile=outdir+'phasevsfreq_spw%i_ant%s-%s.png' % (spwn,ant,ant2),
field=field,
overwrite=True,
)
plotms(vis=vis, spw=str(spwn), xaxis='amp', yaxis='phase', avgtime='1e8',
avgscan=T, coloraxis='corr', iteraxis='baseline', xselfscale=T,
yselfscale=T,
antenna=ant+"&"+ant2,
title='Phase vs Amp with time averaging for spw %i ant %s-%s' % (spwn,ant,ant2),
plotfile=outdir+'phasevsamp_spw%i_ant%s-%s.png' % (spwn,ant,ant2),
field=field,
overwrite=True,
)
# imagename = "noaverage_spw%i" % spwn
# os.system("rm -rf "+imagename+".image")
# os.system("rm -rf "+imagename+".model")
# os.system("rm -rf "+imagename+".flux")
# os.system("rm -rf "+imagename+".psf")
# os.system("rm -rf "+imagename+".residual")
# clean(vis=vis, field=field, imagename=imagename, mode='mfs',
# weighting='briggs', robust=robust, niter=500, imsize=512)
# viewer(imagename+".image",
# outfile=outdir+imagename+".image.png",
# outformat='png',
# gui=False)
# exportfits(imagename=imagename+".image", fitsimage=imagename+".fits", overwrite=True)
#width = 10 # for TW Hydra
# width = 4 # for NGC 3256
# (0) Using your split-off, calibrated data, plot the "model" in this MS using
# plotms. It should be unit-valued for all data. If not, run delmod to get
# rid of any model that might still be lurking in the header, and/or clearcal
# to set to 1 any MODEL data.
plotms(vis=avg_data, spw='0', xaxis='time', yaxis='amp',
avgchannel=avgchannel_wide, xdatacolumn='model', ydatacolumn='model', avgscan=F,
coloraxis='baseline', iteraxis='', xselfscale=T, yselfscale=T,
title='Model Amp vs Time after split for spw %i. Should be all 1s' % spwn,
plotfile=outdir+'ampvstime_model_shouldbe1.png', field=field,
overwrite=True,)
delmod(vis=avg_data)
plotms(vis=avg_data, spw='0', xaxis='phase', yaxis='amp',
avgchannel=avgchannel_wide, xdatacolumn='data', ydatacolumn='data', avgscan=F,
coloraxis='baseline', iteraxis='', xselfscale=T, yselfscale=T,
title='Corrected Phase vs Amp after split',
plotfile=outdir+'ampvsphase_corrected_avg_spw%i.png' % spwn, field=field,
overwrite=True,)
# (0.5) Run clean non-interactively with some set number of iterations, and be
# sure to keep the image around for comparison later. Run delmod to get rid of
# the model it saved to the MS header.
#if reclean:
# imagename="average_spw%i_shallowclean" % spwn
# for suffix in clean_output_suffixes:
# os.system("rm -rf "+imagename+suffix)
# clean(vis=avg_data, field=field, imagename=imagename, mode='mfs',
# weighting='briggs', robust=robust, niter=100, imsize=512)
# viewer(imagename+".image",
# outfile=outdir+imagename+".image.png",
# outformat='png',
# gui=False)
# exportfits(imagename=imagename+".image", fitsimage=imagename+".fits", overwrite=True)
# delmod(avg_data,scr=True)
# (1) Clean a single SPW *interactively*, boxing the brightest regions and not
# cleaning very deeply (maybe 100 iterations). Keep this model in the header
# -- it's what you'll use for the first round of self-calibration.
if reclean:
imagename="average_spw%i_shallowclean_masked" % spwn
for suffix in clean_output_suffixes:
os.system("rm -rf "+imagename+suffix)
clean(vis=avg_data, field=field, imagename=imagename, mode='mfs',
psfmode='hogbom',multiscale=multiscale,
weighting='briggs', robust=robust, niter=100, imsize=imsize,
mask=cleanboxes,
nterms=2,
usescratch=True)
viewer(imagename+".image.tt0",
outfile=outdir+imagename+".image.tt0.png",
outformat='png',
gui=False)
exportfits(imagename=imagename+".image.tt0", fitsimage=imagename+".fits", overwrite=True)
imrms = [imstat(imagename+".image.tt0",box=statsbox)['rms']]
# FAILS!!!!
#plotms(vis=avg_data, spw='0', xaxis='time', yaxis='amp',
# avgchannel='128', xdatacolumn='model', ydatacolumn='model', avgscan=F,
# coloraxis='baseline', iteraxis='', xselfscale=T, yselfscale=T,
# title='Model Amp vs Time after shallow clean for spw %i.' % spwn,
# plotfile=outdir+'ampvstime_model_shallowclean_spw%i.png' % spwn, field=field,
# overwrite=True,)
for calnum in xrange(niter):
# for Ku D W51 Ku spw 2
if reclean:
first_image = 'spw%i_C_C_firstim_selfcal%i' % (spwn,calnum)
for suffix in clean_output_suffixes:
os.system("rm -rf "+first_image+suffix)
clean(vis=avg_data,imagename=first_image,field=field, mode='mfs',
psfmode='hogbom',multiscale=multiscale,
weighting='briggs', robust=robust, niter=100, imsize=imsize,
mask=cleanboxes,
nterms=2,
usescratch=True)
exportfits(imagename=first_image+".image.tt0", fitsimage=first_image+".fits", overwrite=True)
viewer(first_image+".image.tt0",
outfile=outdir+first_image+".image.tt0.png",
outformat='png',
gui=False)
# this fails?
#plotms(vis=avg_data, spw='0', xaxis='time', yaxis='amp',
# avgchannel='128', xdatacolumn='model', ydatacolumn='model', avgscan=F,
# coloraxis='baseline', iteraxis='', xselfscale=T, yselfscale=T,
# title='Model Amp vs Time after shallow clean for spw %i iter %i.' % (spwn,calnum),
# plotfile=outdir+'ampvstime_model_shallowclean_spw%i_iter%i.png' % (spwn,calnum), field=field,
# overwrite=True,)
# DONE avg/split ing
caltable = 'selfcal%i_%s_spw%i.gcal' % (calnum,field.replace(" ",""),spwn)
if reclean:
os.system('rm -rf '+caltable)
gaincal(vis=avg_data,
field='',
caltable=caltable,
spw='',
# gaintype = 'T' could reduce failed fit errors by averaging pols...
gaintype='G', # 'G' from http://casaguides.nrao.edu/index.php?title=EVLA_Advanced_Topics_3C391
solint=solint,
refant=refant,
calmode='p',
combine='scan',
minblperant=4)
#
# Watch out for failed solutions noted in the terminal during this
# solution. If you see a large fraction (really more than 1 or 2) of
# your antennas failing to converge in many time intervals then you
# may need to lengthen the solution interval.
#
# =%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%
# INSPECT THE CALIBRATION
# =%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%
#
# After you have run the gaincal, you want to inspect the
# solution. Use PLOTCAL to look at the solution (here broken into
# panels by SPW with individual antennas mapped to colors). Look at
# the overall magnitude of the correction to get an idea of how
# important the selfcal is and at how quickly it changes with time to
# get an idea of how stable the instrument and atmosphere were.
#
if doplots:
for ant2 in ant2list:
for ant in ant1list:
# (4) Have a look at the gain solutions by antenna. Which antennas
# have the largest phase corrections? Before applying the
# calibration, use plotms to display the corrected phase vs. amp
# for these antennas, to compare with *after* the correction is
# applied.
plotcal(caltable=caltable,
xaxis='time', yaxis='phase',
showgui=False,
antenna=ant+'&'+ant2,
figfile=outdir+'selfcal%i_spw%i_phasevstime_ant%s-%s.png' % (calnum,spwn,ant,ant2),
iteration='')#, subplot = 221)
#plotcal(caltable=caltable, xaxis='amp', yaxis='phase',
# showgui=False,
# antenna=ant,
# figfile=outdir+'selfcal%i_spw%i_phasevsamp_ant%s.png' % (calnum,spwn,ant),
# iteration='')#, subplot = 221)
if calnum == 0:
datacol='data'
else:
datacol='corrected'
plotms(vis=avg_data, xaxis='time', yaxis='phase',
xdatacolumn=datacol, ydatacolumn=datacol,
avgtime='15s', avgchannel=avgchannel_narrow, coloraxis='corr',
antenna=ant+'&'+ant2,
overwrite=True, title='Iteration %i for spw %i and ant %s-%s. datacol=%s' % (calnum,spwn,ant,ant2,datacol),
plotfile=outdir+'selfcal%i_spw%i_ant%s-%s_phasetime.png' % (calnum,spwn,ant,ant2),)
plotms(vis=avg_data, xaxis='time', yaxis='amp',
xdatacolumn=datacol, ydatacolumn=datacol,
avgtime='15s', avgchannel=avgchannel_narrow, coloraxis='corr',
antenna=ant+'&'+ant2,
overwrite=True, title='Iteration %i for spw %i and ant %s-%s. datacol=%s' % (calnum,spwn,ant,ant2,datacol),
plotfile=outdir+'selfcal%i_spw%i_ant%s-%s_amptime.png' % (calnum,spwn,ant,ant2),)
plotms(vis=avg_data, xaxis='phase', yaxis='amp',
xdatacolumn=datacol, ydatacolumn=datacol,
avgtime='60s', avgchannel=avgchannel_narrow, coloraxis='corr',
antenna=ant+'&'+ant2,
overwrite=True, title='Iteration %i for spw %i and ant %s-%s. datacol=%s' % (calnum,spwn,ant,ant2,datacol),
plotfile=outdir+'selfcal%i_spw%i_ant%s-%s_phaseamp.png' % (calnum,spwn,ant,ant2),)
plotcal(caltable=caltable,
xaxis='time', yaxis='phase',
plotrange=[0,0,-180,180],
showgui=INTERACTIVE,
figfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_phasevstime.png' % (calnum,spwn),
iteration='spw' if INTERACTIVE else '')#, subplot = 221)
plotcal(caltable=caltable,
xaxis='antenna', yaxis='phase',
showgui=INTERACTIVE,
figfile=outdir+'selfcal%i_spw%i_phasevsantenna.png' % (calnum,spwn),
iteration='')
plotcal(caltable=caltable,
xaxis='time', yaxis='amp',
plotrange=[0,0,0.5,1.5],
showgui=INTERACTIVE,
figfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_ampvstime.png' % (calnum,spwn),
iteration='spw' if INTERACTIVE else '')#, subplot = 221)
#plotcal(caltable=caltable,
# xaxis='phase', yaxis='amp',
# plotrange=[-50,50,0.5,1.5],
# showgui=INTERACTIVE,
# figfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_ampvsphase.png' % (calnum,spwn),
# iteration='spw' if INTERACTIVE else '')#, subplot = 221)
# THERE WILL BE WEIRD "LUSTRE" ERRORS GENERATED BY THE FILE SYSTEM. DO
# NOT FREAK OUT. These are just a feature of our fast file
# system. Plotcal will still work.
# It can be useful useful to plot the X-Y solutions (i.e., differences
# between polarizations) as an indicator of the noise in the
# solutions.
plotcal(caltable=caltable,
xaxis='time',
yaxis='phase',
plotrange=[0,0,-25, 25],
poln = '/',
showgui=INTERACTIVE,
iteration='spw,antenna' if INTERACTIVE else '',
figfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_poldiff.png' % (calnum,spwn),
subplot = 221 if INTERACTIVE else 111)
plotms(vis=avg_data,
xaxis='uvdist',
yaxis='amp',
xdatacolumn='corrected',
ydatacolumn='corrected',
avgtime='1e8s',
avgchannel=avgchannel_narrow,
coloraxis='baseline',
overwrite=True,
title='Iteration %i for spw %i' % (calnum,spw),
plotfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_uvdistamp.png' % (calnum,spwn),
)
#plotms(vis=avg_data,
# xaxis='phase',
# yaxis='amp',
# xdatacolumn='corrected',
# ydatacolumn='corrected',
# avgtime='60s',
# avgchannel=avgchannel_narrow,
# coloraxis='corr',
# overwrite=True,
# title='Iteration %i for spw %i' % (calnum,spw),
# plotfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_phaseamp.png' % (calnum,spwn),
# )
plotms(vis=avg_data,
xaxis='time',
yaxis='amp',
xdatacolumn='corrected',
ydatacolumn='corrected',
avgtime='10s',
avgchannel=avgchannel_narrow,
coloraxis='baseline',
overwrite=True,
title='Iteration %i for spw %i' % (calnum,spw),
plotfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_amptime.png' % (calnum,spwn),
)
# The rms noise is about 4 to 8 deg, depending on antenna, but the
# phase changes are considerably larger. This indicates that the
# application of this solution will improve the image.
# =%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%
# APPLY THE CALIBRATION
# =%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%
#
# If you are satisfied with your solution, you can now apply it to the
# data to generate a new corrected data column, which you can then
# image. Be sure to save the previous flags before you do so because
# applycal will flag data without good solutions. The commented
# command after the applycal will roll back to the saved solution in
# case you get in trouble.
#
# flagmanager(vis=avg_data,
# mode='save',
# versionname='before_selfcal_apply')
# 2013-03-04 19:53:37 SEVERE agentflagger:: (file /opt/casa/stable-2013-02/gcwrap/tools/flagging/agentflagger_cmpt.cc, line 37) Exception Reported: Invalid Table operation: ArrayColumn::setShape; shape cannot be changed for row 0 column FLAG
# *** Error *** Invalid Table operation: ArrayColumn::setShape; shape cannot be changed for row 0 column FLAG
if reclean:
applycal(vis=avg_data,
gaintable=caltable,
interp='linear',
flagbackup=True) # was False when flagmanager was used
# (6) Plot corrected phase vs. amp for the antennas you picked out in (4),
# to check that in fact the corrections have been applied as expected.
for ant2 in ant2list:
for ant in ant1list:
plotms(vis=avg_data, xaxis='time', yaxis='phase',
xdatacolumn='corrected', ydatacolumn='corrected',
avgtime='15s', avgchannel=avgchannel_narrow, coloraxis='corr',
antenna=ant+'&'+ant2,
overwrite=True, title='Iteration %i for spw %i and ant %s-%s' % (calnum,spwn,ant,ant2),
plotfile=outdir+'selfcal%i_spw%i_ant%s-%s_phasetime_applied.png' % (calnum,spwn,ant,ant2),)
plotms(vis=avg_data, xaxis='time', yaxis='amp',
xdatacolumn='corrected', ydatacolumn='corrected',
avgtime='60s', avgchannel=avgchannel_narrow, coloraxis='corr',
antenna=ant+'&'+ant2,
overwrite=True, title='Iteration %i for spw %i and ant %s-%s' % (calnum,spwn,ant,ant2),
plotfile=outdir+'selfcal%i_spw%i_ant%s-%s_amptime_applied.png' % (calnum,spwn,ant,ant2),)
plotms(vis=avg_data, xaxis='phase', yaxis='amp',
xdatacolumn='corrected', ydatacolumn='corrected',
avgtime='60s', avgchannel=avgchannel_narrow, coloraxis='corr',
antenna=ant+'&'+ant2,
overwrite=True, title='Iteration %i for spw %i and ant %s-%s' % (calnum,spwn,ant,ant2),
plotfile=outdir+'selfcal%i_spw%i_ant%s-%s_phaseamp_applied.png' % (calnum,spwn,ant,ant2),)
plotms(vis=vis, spw='0', xaxis='freq', yaxis='phase', avgtime='1e8',
avgscan=T, coloraxis='corr', iteraxis='baseline', xselfscale=T,
yselfscale=T,
antenna=ant+'&'+ant2,
title='Phase vs Freq with time averaging for spw %i ant %s-%s iter %i' % (spwn,ant,ant2,calnum),
plotfile=outdir+'phasevsfreq_spw%i_ant%s-%s_selfcal%i.png' % (spwn,ant,ant2,calnum),
field=field,
overwrite=True,
)
# Use this command to roll back to the previous flags in the event of
# an unfortunate applycal.
#flagmanager(vis=avg_data,
# mode='restore',
# versionname='before_selfcal_apply')
if reclean:
selfcal_image = 'spw%i_C_C_selfcal%i' % (spwn,calnum)
for suffix in clean_output_suffixes:
os.system("rm -rf "+selfcal_image+suffix)
clean(vis=avg_data,imagename=selfcal_image,field=field, mode='mfs',
psfmode='hogbom',multiscale=multiscale,
weighting='briggs', robust=robust, niter=1000, imsize=imsize,
nterms=2,
mask=cleanboxes,
usescratch=True)
exportfits(imagename=selfcal_image+".image.tt0", fitsimage=selfcal_image+".fits", overwrite=True)
plotms(vis=avg_data, spw='0', xaxis='baseline', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Baseline after CSCLEAN iter %i' % calnum,
plotfile=outdir+'post_selfcal%i_spw%i_residVSbaseline.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=avg_data, spw='0', xaxis='time', yaxis='amp', avgtime='5s',
ydatacolumn='corrected-model',
coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Time after CSCLEAN iter %i' % (calnum),
plotfile=outdir+'post_selfcal%i_spw%i_residVStime.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=avg_data, spw='0', xaxis='uvdist', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. UVDIST after CSCLEAN iter %i' % (calnum) ,
plotfile=outdir+'post_selfcal%i_spw%i_residVSuvdist.png' % (calnum,spwn),
field=field,
overwrite=True,
)
imrms.append(imstat(selfcal_image+".image.tt0",box=statsbox)['rms'])
viewer(selfcal_image+".image.tt0",
outfile=outdir+selfcal_image+".image.tt0.png",
outformat='png',
gui=False)
print "FINISHED ITERATION %i" % calnum
print "FINISHED ITERATING!!! YAY!"
# final phase + gain cal:
# http://casaguides.nrao.edu/index.php?title=Calibrating_a_VLA_5_GHz_continuum_survey#One_Last_Iteration:_Amplitude_.26_Phase_Self_Calibration
aptable = 'selfcal_ap_%s_spw%i.gcal' % (field.replace(" ",""),spwn)
gaincal(vis=avg_data, field='', caltable=aptable, gaintable=caltable, spw='',
solint='inf', refant=refant, calmode='ap', combine='', minblperant=4)
plotcal(caltable=aptable,
xaxis='phase', yaxis='amp',
plotrange=[-50,50,0.5,1.5],
showgui=INTERACTIVE,
figfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_ampvsphase_final.png' % (calnum,spwn),
iteration='spw' if INTERACTIVE else '')#, subplot = 221)
applycal(vis=avg_data,
gaintable=[aptable,caltable],
interp='linear',
flagbackup=True) # was False when flagmanager was used
selfcal_image = 'spw%i_C_C_selfcal%i_final' % (spwn,calnum)
for suffix in clean_output_suffixes:
os.system("rm -rf "+selfcal_image+suffix)
clean(vis=avg_data,imagename=selfcal_image,field=field, mode='mfs', mask=cleanboxes,
weighting='briggs', robust=robust, niter=10000, imsize=imsize,
nterms=2,
usescratch=True)
exportfits(imagename=selfcal_image+".image.tt0", fitsimage=selfcal_image+".fits", overwrite=True)
plotms(vis=avg_data, spw='0', xaxis='baseline', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Baseline after CSCLEAN iter %i' % calnum,
plotfile=outdir+'post_selfcal%i_spw%i_residVSbaseline.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=avg_data, spw='0', xaxis='time', yaxis='amp', avgtime='5s',
ydatacolumn='corrected-model',
coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Time after CSCLEAN iter %i' % (calnum),
plotfile=outdir+'post_selfcal%i_spw%i_residVStime.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=avg_data, spw='0', xaxis='uvdist', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. UVDIST after CSCLEAN iter %i' % (calnum) ,
plotfile=outdir+'post_selfcal%i_spw%i_residVSuvdist.png' % (calnum,spwn),
field=field,
overwrite=True,
)
selfcal_image = 'spw%i_C_C_selfcal%i_final_multiscale' % (spwn,calnum)
for suffix in clean_output_suffixes:
os.system("rm -rf "+selfcal_image+suffix)
clean(vis=avg_data,imagename=selfcal_image,field=field, mode='mfs', imagermode='csclean',# mask=cleanboxes,
multiscale=multiscale, psfmode='hogbom',
nterms=2,
weighting='briggs', robust=robust, niter=10000, imsize=imsize,
usescratch=True)
exportfits(imagename=selfcal_image+".image.tt0", fitsimage=selfcal_image+".fits", overwrite=True)
plotms(vis=avg_data, spw='0', xaxis='baseline', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Baseline after multiscale CLEAN iter %i' % (calnum),
plotfile=outdir+'post_selfcal%i_spw%i_residVSbaseline_multiscale.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=avg_data, spw='0', xaxis='time', yaxis='amp', avgtime='5s',
ydatacolumn='corrected-model',
coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Time after multiscale CLEAN iter %i' % (calnum),
plotfile=outdir+'post_selfcal%i_spw%i_residVStime_multiscale.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=avg_data, spw='0', xaxis='uvdist', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. UVDIST after multiscale CLEAN iter %i' % (calnum),
plotfile=outdir+'post_selfcal%i_spw%i_residVSuvdist_multiscale.png' % (calnum,spwn),
field=field,
overwrite=True,
)
return imrms
def CMEuvmodelfit(MS, args, truth=True, qs=None, dqs=None, largestBL=1.5e4):
'''
This function fits a uvmodel fit to a MS and compares the fit with actual
values from the truth image.
MS: the name of teh MS file
sourceInitial: a text file which contains initial estimates of the component to be fitted
truthPos: actual position of the component in the truth image
'''
print 'start of uvmodelfit'
res = 1024
#No breaky if not truth
raimout = -1.
decimout = -1.
majimout = -1.
minimout = -1.
paimout = -1.
diff = defaultdict(list)
##get the info for truth image
if truth:
###
xmax, ymax = imstat('CME-' + MS[9:18] +
'.truth')['maxposf'].split(',')[:2]
raTruth = qa.toangle(xmax)['value'] * np.pi / 180.
decTruth = qa.toangle(ymax)['value'] * np.pi / 180.
print imstat('CME-' + MS[9:18] + '.truth')['maxposf']
##Imstat truth stuff into log??
CMEimstatst = imstat('CME-' + MS[9:18] + '.truth')
xmaxp, ymaxp = CMEimstatst['maxpos'][:2]
boxhalfwidth = 100
bx = str(max(xmaxp - boxhalfwidth, 0)) + ', ' + str(
max(ymaxp - boxhalfwidth, 0)) + ', ' + str(
min(xmaxp + boxhalfwidth, res - 1)) + ', ' + str(
min(ymaxp + boxhalfwidth, res - 1))
##addcomponent
try:
imfitout = imfit(imagename='CME-' + MS[9:18] + '.truth', box=bx)
except:
print 'failed imfit on truth image ' + MS
return
raimout = imfitout['results']['component0']['shape']['direction'][
'm0']['value']
decimout = imfitout['results']['component0']['shape']['direction'][
'm1']['value']
majimout = imfitout['results']['component0']['shape']['majoraxis'][
'value']
minimout = imfitout['results']['component0']['shape']['minoraxis'][
'value']
paimout = imfitout['results']['component0']['shape']['positionangle'][
'value']
##ADDing imfitout
diff[MS].append([raimout, decimout])
diff[MS].append([majimout, minimout])
diff[MS].append([paimout, 0.0])
###
if not truth:
#Hegedus add in initial pos prediction from brightest point in dirty image
if args.correlate:
#create baseline plots
#plots
print 'blah1'
polarfig = figure()
print 'figure created'
absq = abs(qs.flatten())
angq = arcsin(qs.flatten().imag)
todel = []
for i in range(len(angq)):
if math.isnan(abs(angq[i])):
todel.append(i)
print i, ' wah bad angle'
continue
polar([0, angq[i]], [0, absq[i]], marker='o')
for i in range(len(todel) - 1, -1, -1):
angq = np.delete(angq, todel[i])
absq = np.delete(absq, todel[i])
title(
"Ratios of CASA Computed and Correlated Visibilities\n 1.0 at 0 deg is perfect"
)
# xlabel('Relative Error')
# ylabel('Bin Count')
savefig('VisRelDiff%s.png' % MS, bbox_inches='tight', pad_inches=1)
hist(angq, bins=4)
title("Histogram of Visibility Ratios, 0 deg is perfect")
#
savefig('VisRelDiffHist%s.png' % MS)
plt.close(polarfig)
#plot hist of absq
histplot = figure()
hist(absq, bins=20)
title("Histogram of abs(Visibility) Ratios, 1.0 is perfect")
xlabel("abs(casa vis/correlated vis)")
savefig('VisAbsRatioHist%s.png' % MS)
plt.close(histplot)
#
#create dirty baseline plots
#plots
polarfig = figure()
absq = abs(dqs.flatten())
angq = arcsin(dqs.flatten().imag)
todel = []
for i in range(len(angq)):
if math.isnan(abs(angq[i])):
todel.append(i)
print i, ' wah bad angle'
continue
polar([0, angq[i]], [0, absq[i]], marker='o')
for i in range(len(todel) - 1, -1, -1):
angq = np.delete(angq, todel[i])
absq = np.delete(absq, todel[i])
title(
"Ratios of CASA Computed and Noisy Correlated Visibilities \n 1.0 at 0 deg is perfect"
)
# xlabel('Relative Error')
# ylabel('Bin Count')
savefig('NoisyVisRelDiff%s.png' % MS,
bbox_inches='tight',
pad_inches=1)
hist(angq)
title("Histogram of Noisy Visibility Ratios, 0 deg is perfect")
#
savefig('NoisyVisRelDiffHist%s.png' % MS)
plt.close(polarfig)
#plot hist of absq
histplot = figure()
hist(absq, bins=20)
title("Histogram of Noisy abs(Visibility) Ratios, 1.0 is perfect")
savefig('NoisyVisAbsRatioHist%s.png' % MS)
plt.close(histplot)
print 'cleaning dirty image'
hz = float(MS[11:16]) * 10**6
wavelen = 3e8 / hz
cellsizerad = wavelen / largestBL / 16.
csas = cellsizerad * 180. / np.pi * 3600
imWidth = 1024. * 60 #arcsec, 1024 res built in
#csas = cellsizerad
imsize = int(imWidth / csas) #int(1024/60.*3600./csas)
print 'pre imsize is ' + str(imsize)
if imsize < 1:
imsize = 1024
#use next power of 2
imsize = int(pow(2, math.ceil(math.log(imsize, 2)))) / 2
print 'imsize used is ' + str(imsize)
print 'cell size for ' + MS + ' is ' + str(csas) + ' arcsec'
try:
clean(
vis=MS,
imagename=MS + '.dirty',
outlierfile='',
field='',
spw='',
selectdata=False,
mode='mfs',
nterms=1,
gridmode='', #widefield', wprojplanes = -1, facets = 1,
niter=0,
gain=0.1,
threshold='0.0mJy',
interactive=False,
mask=[],
imsize=[imsize, imsize], #[1024, 1024],
cell=[str(csas) + 'arcsec',
str(csas) + 'arcsec'], #cell=['1arcsec', '1arcsec'],
phasecenter='',
stokes='I',
weighting='briggs',
robust=-0.5,
uvtaper=False,
usescratch=False,
allowchunk=False)
except:
print 'Clean wanted to crash on ' + MS
pass
print 'done cleaning, now getting brightest dirty point'
try:
CMEimstats = imstat(MS + '.dirty.image')
except:
print "Error, Cannot read image, exiting on " + str(MS)
return
if type(CMEimstats) == type(None) or type(CMEimstats) == type(True):
print "Error Couldn't clean to image, exiting on " + MS
return
print CMEimstats['maxposf']
#Get importuvfits
xmaxp, ymaxp = CMEimstats['maxpos'][:2]
print 'max x y of dirty is ' + str(xmaxp) + ', ' + str(ymaxp)
boxhalfwidth = int(imsize / 40) #[25, 25, 10]
# boxhalfwidth = boxhalfwidths[ind]
print 'boxhalfwidth ', boxhalfwidth
bx = str(max(xmaxp - boxhalfwidth, 0)) + ', ' + str(
max(ymaxp - boxhalfwidth, 0)) + ', ' + str(
min(xmaxp + boxhalfwidth, imsize - 1)) + ', ' + str(
min(ymaxp + boxhalfwidth, imsize - 1))
print 'box is ' + bx
try:
imfitout = imfit(imagename=MS + '.dirty.image', box=bx)
except:
print "Error, Cannot fit dirty image, exiting on " + str(MS)
xmax, ymax = CMEimstats['maxposf'].split(',')[:2]
raTruth = qa.toangle(xmax)['value'] * np.pi / 180.
decTruth = qa.toangle(ymax)['value'] * np.pi / 180.
diff[MS].append([raTruth, decTruth])
diff[MS].append([-1., -1.])
diff[MS].append([0.0, 0.0])
np.savetxt('uvfits_' + MS + '.txt', diff[MS])
return
try:
raimout = imfitout['results']['component0']['shape']['direction'][
'm0']['value']
except:
print "Error, Cannot fit dirty image get component 0 or output, exiting on " + str(
MS)
xmax, ymax = CMEimstats['maxposf'].split(',')[:2]
raTruth = qa.toangle(xmax)['value'] * np.pi / 180.
decTruth = qa.toangle(ymax)['value'] * np.pi / 180.
diff[MS].append([raTruth, decTruth])
diff[MS].append([-1., -1.])
diff[MS].append([0.0, 0.0])
np.savetxt('uvfits_' + MS + '.txt', diff[MS])
return
raimout = imfitout['results']['component0']['shape']['direction'][
'm0']['value']
decimout = imfitout['results']['component0']['shape']['direction'][
'm1']['value']
majimout = imfitout['results']['component0']['shape']['majoraxis'][
'value']
minimout = imfitout['results']['component0']['shape']['minoraxis'][
'value']
paimout = imfitout['results']['component0']['shape']['positionangle'][
'value']
cl.close()
diff[MS].append([raimout, decimout])
diff[MS].append([majimout, minimout])
diff[MS].append([paimout, 0.0])
np.savetxt('uvfits_' + MS + '.txt', diff[MS])
return
def runFiltering(self, antennaCfg, trackDuration, frequency, dec,
imageTotalSize, resolutionStart, resolutionEnd,
resolutionStep):
"""
use the disk100.fits image of a uniform disk of 100 pixel size (diameter)
Run a set of simulations to account for the filtering at different scales.
antennaCfg: antenna configuration file
trackDuration : trackDuration
resolutionStart,resolutionEnd,resolutionStep: range for the resolution to simulate
dec: declination
OUTPUT:
resolution, flux: resolution and flux output (1 Jy in entry)
"""
maskClean = [70, 70, 180, 180]
projectName = "tempCurveFiltering"
nStep = math.floor((resolutionEnd - resolutionStart) / resolutionStep)
resolutionArr = np.arange(nStep) * resolutionStep + resolutionStart
flux = np.zeros(nStep)
boxStat = '%d,%d,%d,%d' % (maskClean[0], maskClean[1], maskClean[2],
maskClean[3])
os.system("rm -Rf *%s*" % (projectName))
if dec <= -10:
decString = "J2000 10h00m00s %3dd00m00s" % (dec)
elif dec < 0 and dec > -10:
decString = "J2000 10h00m00s -0%1dd00m00s" % (-dec)
elif dec >= 0 and dec < 10:
decString = "J2000 10h00m00s +0%1dd00m00s" % (dec)
elif dec >= 10:
decString = "J2000 10h00m00s +%2dd00m00s" % (dec)
# simulayion with one arcsec component
##os.system("rm -Rf *%s*"%(projectName))
print decString
index = 0
print resolutionArr
for res in resolutionArr:
resolutionDisk = "%4.2farcsec" % (res / 100.)
print resolutionDisk
print antennaCfg
so.sim_observe(
project=projectName + "%d" % (index),
skymodel='disk100.fits',
inbright='1Jy/pixel',
indirection=decString,
incell=resolutionDisk,
incenter=frequency,
inwidth='8GHz',
antennalist=antennaCfg,
totaltime=trackDuration,
integration='10s',
direction=decString,
maptype='ALMA',
# mapsize = ['2arcmin','2arcmin'],
# pointingspacing = "2arcmin"
)
sa.sim_analyze(
project=projectName + "%d" % (index),
image=True,
imsize=imageTotalSize,
# cell = cellsize,
mask=maskClean,
niter=2000,
threshold='0.1mJy',
graphics='file')
imageName = projectName + "%d/" % (
index) + projectName + "%d.%s.image" % (
index, antennaCfg.split('.')[0])
# imageName=projectName+"%d/"%(index)+projectName+"%d.image"%(index)
print "Read %s" % (imageName)
ia.open(imageName)
# flux0=ia.statistics()['flux'][0]
stat = ims.imstat(
imagename=imageName,
box=boxStat,
)
flux0 = stat['flux'][0]
print flux0
if res == resolutionArr[0]:
fluxNormalization = flux0
fluxRes = flux0 / fluxNormalization
print "Flux: %4.3f" % (fluxRes)
flux[index] = fluxRes
ia.close()
index += 1
return (resolutionArr, flux)
imWidthDeg=10.,
nIter=0,
Threshold='3000000.0Jy')
#add noise
addThermalNoise(lunarMS, SEFD=-1., dv=10000., dt=60.)
#image
imageData(lunarMS,
truthImage,
imWidthDeg=10.,
nIter=0,
Threshold='3000000.0Jy')
#fit data
truthData = imstat(truthImage)
brightPoint = truthData['maxposf'].split(',')
bFlux = truthData['max'][0]
rastring = brightPoint[0]
decstring = brightPoint[1]
brightDirTruth = me.direction('J2000', rastring, decstring)
ravaltruth = brightDirTruth['m0']['value'] #returns radians
decvaltruth = brightDirTruth['m1']['value'] #returns radians
print(
'True Input Params (flux (Jy), ra (rad), dec (rad), major (arcsec), minor (arcsec), PA (degrees))'
)
print(
'%2.4f, %2.4f, %2.4f, %2.4f, %2.4f, %2.4f' %
(bFlux, ravaltruth, decvaltruth, 0., 0., 0.)
def selfcal(vis, spwn=6, doplots=True, INTERACTIVE=False, reclean=True, field='W51 Ku',
outdir_template="spw%i_selfcal_iter/", statsbox='170,50,229,97', ant1list=['ea14','ea05'],
ant2list=['ea16','ea07'], avgchannel_wide='128', avgchannel_narrow='8',
cleanboxes=cleanboxes, refant='ea27', solint='30s', niter=2,
multiscale=[0,5,10,15,25,50], imsize=512, ):
"""
Docstring incomplete
"""
spw = int(spwn)
outdir = outdir_template % spwn
try:
os.mkdir(outdir)
except OSError:
pass
# you're supposed to pass in avg_data as input
avg_data = vis
mytb.open(vis+"/ANTENNA")
antnames = mytb.getcol("NAME")
# (1) Clean a single SPW *interactively*, boxing the brightest regions and not
# cleaning very deeply (maybe 100 iterations). Keep this model in the header
# -- it's what you'll use for the first round of self-calibration.
if reclean:
imagename="average_spw%i_shallowclean_masked" % spwn
for suffix in clean_output_suffixes:
os.system("rm -rf "+imagename+suffix)
clean(vis=avg_data, field=field, imagename=imagename, mode='mfs',
psfmode='hogbom',multiscale=multiscale,
weighting='briggs', robust=0.0, niter=100, imsize=imsize,
mask=cleanboxes,
nterms=2,
usescratch=True)
viewer(imagename+".image.tt0",
outfile=outdir+imagename+".image.tt0.png",
outformat='png',
gui=False)
exportfits(imagename=imagename+".image.tt0", fitsimage=imagename+".fits", overwrite=True)
imrms = [imstat(imagename+".image.tt0",box=statsbox)['rms']]
for calnum in xrange(niter):
# for Ku D W51 Ku spw 2
if reclean:
first_image = 'spw%i_ku_d_firstim_selfcal%i' % (spwn,calnum)
for suffix in clean_output_suffixes:
os.system("rm -rf "+first_image+suffix)
clean(vis=avg_data,imagename=first_image,field=field, mode='mfs',
psfmode='hogbom',multiscale=multiscale,
weighting='briggs', robust=0.0, niter=100, imsize=imsize,
mask=cleanboxes,
nterms=2,
usescratch=True)
exportfits(imagename=first_image+".image.tt0", fitsimage=first_image+".fits", overwrite=True)
viewer(first_image+".image.tt0",
outfile=outdir+first_image+".image.tt0.png",
outformat='png',
gui=False)
caltable = 'selfcal%i_%s_spw%i.pcal' % (calnum,field.replace(" ",""),spwn)
if reclean:
os.system('rm -rf '+caltable)
gaincal(vis=avg_data,
field='',
caltable=caltable,
spw='',
# gaintype = 'T' could reduce failed fit errors by averaging pols...
gaintype='G', # 'G' from http://casaguides.nrao.edu/index.php?title=EVLA_Advanced_Topics_3C391
solint=solint,
refant=refant,
calmode='p',
combine='scan',
minblperant=4)
if reclean:
applycal(vis=avg_data,
gaintable=caltable,
interp='linear',
flagbackup=True) # was False when flagmanager was used
if reclean:
selfcal_image = 'spw%i_ku_d_selfcal%i' % (spwn,calnum)
for suffix in clean_output_suffixes:
os.system("rm -rf "+selfcal_image+suffix)
clean(vis=avg_data,imagename=selfcal_image,field=field, mode='mfs',
psfmode='hogbom',multiscale=multiscale,
weighting='briggs', robust=0.5, niter=1000, imsize=imsize,
nterms=2,
mask=cleanboxes,
usescratch=True)
exportfits(imagename=selfcal_image+".image.tt0", fitsimage=selfcal_image+".fits", overwrite=True)
imrms.append(imstat(selfcal_image+".image.tt0",box=statsbox)['rms'])
viewer(selfcal_image+".image.tt0",
outfile=outdir+selfcal_image+".image.tt0.png",
outformat='png',
gui=False)
print "FINISHED ITERATION %i" % calnum
print "FINISHED ITERATING!!! YAY!"
# final phase + gain cal:
# http://casaguides.nrao.edu/index.php?title=Calibrating_a_VLA_5_GHz_continuum_survey#One_Last_Iteration:_Amplitude_.26_Phase_Self_Calibration
aptable = 'selfcal_ap_%s_spw%i.gcal' % (field.replace(" ",""),spwn)
gaincal(vis=avg_data, field='', caltable=aptable, gaintable=caltable, spw='',
solint='inf', refant=refant, calmode='ap', combine='', minblperant=4)
plotcal(caltable=aptable,
xaxis='phase', yaxis='amp',
plotrange=[-50,50,0.5,1.5],
showgui=INTERACTIVE,
figfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_ampvsphase_final.png' % (calnum,spwn),
iteration='spw' if INTERACTIVE else '')#, subplot = 221)
applycal(vis=avg_data,
gaintable=[aptable,caltable],
interp='linear',
flagbackup=True) # was False when flagmanager was used
selfcal_image = 'spw%i_ku_d_selfcal%i_final' % (spwn,calnum)
for suffix in clean_output_suffixes:
os.system("rm -rf "+selfcal_image+suffix)
clean(vis=avg_data,imagename=selfcal_image,field=field, mode='mfs', mask=cleanboxes,
weighting='briggs', robust=0.5, niter=10000, imsize=imsize,
nterms=2,
usescratch=True)
exportfits(imagename=selfcal_image+".image.tt0", fitsimage=selfcal_image+".fits", overwrite=True)
plotms(vis=avg_data, spw='0', xaxis='baseline', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Baseline after CSCLEAN iter %i' % calnum,
plotfile=outdir+'post_selfcal%i_spw%i_residVSbaseline.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=avg_data, spw='0', xaxis='time', yaxis='amp', avgtime='5s',
ydatacolumn='corrected-model',
coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Time after CSCLEAN iter %i' % (calnum),
plotfile=outdir+'post_selfcal%i_spw%i_residVStime.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=avg_data, spw='0', xaxis='uvdist', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. UVDIST after CSCLEAN iter %i' % (calnum) ,
plotfile=outdir+'post_selfcal%i_spw%i_residVSuvdist.png' % (calnum,spwn),
field=field,
overwrite=True,
)
selfcal_image = 'spw%i_ku_d_selfcal%i_final_multiscale' % (spwn,calnum)
for suffix in clean_output_suffixes:
os.system("rm -rf "+selfcal_image+suffix)
clean(vis=avg_data,imagename=selfcal_image,field=field, mode='mfs', imagermode='csclean',# mask=cleanboxes,
multiscale=multiscale, psfmode='hogbom',
nterms=2,
weighting='briggs', robust=0.5, niter=10000, imsize=imsize,
usescratch=True)
exportfits(imagename=selfcal_image+".image.tt0", fitsimage=selfcal_image+".fits", overwrite=True)
plotms(vis=avg_data, spw='0', xaxis='baseline', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Baseline after multiscale CLEAN iter %i' % (calnum),
plotfile=outdir+'post_selfcal%i_spw%i_residVSbaseline_multiscale.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=avg_data, spw='0', xaxis='time', yaxis='amp', avgtime='5s',
ydatacolumn='corrected-model',
coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Time after multiscale CLEAN iter %i' % (calnum),
plotfile=outdir+'post_selfcal%i_spw%i_residVStime_multiscale.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=avg_data, spw='0', xaxis='uvdist', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. UVDIST after multiscale CLEAN iter %i' % (calnum),
plotfile=outdir+'post_selfcal%i_spw%i_residVSuvdist_multiscale.png' % (calnum,spwn),
field=field,
overwrite=True,
)
return imrms
def eazy_immoments(dir_proj,
imagename,
galname,
noise,
beamp,
snr_mom,
percent,
nchan,
maskname=None,
myim="03"):
"""
myim03, myim05
use co10 mask for co10, co21 mask for co21
This is be replace by eazy_immoments_r21.
"""
if myim == "03":
# prepare workinf directory e.g., ngc0628_co10
name_line = imagename.split(galname + "_")[1].split("_")[0]
dir_image = dir_proj + "../" + galname + "_" + name_line + "/"
cubeimage = dir_image + name_line + "_cube.image"
os.system("mkdir " + dir_image)
os.system("cp -r " + imagename + " " + cubeimage)
elif myim == "05":
name_line = imagename.split(galname + "_")[1].split("/")[0]
dir_image = dir_proj
cubeimage = imagename
print("### woking on " + galname + " " + name_line + " " + beamp)
if maskname == None:
os.system("rm -rf " + cubeimage + ".masked")
#os.system("rm -rf " + dir_image+"*.noise")
os.system("rm -rf " + dir_image + "*.mask*")
# imsmooth
cubesmooth1 = cubeimage.replace(".image", ".smooth1") # 4.0 mJy
bmaj = imhead(cubeimage, "list")["beammajor"]["value"]
imsmooth(
imagename=cubeimage,
targetres=True,
major=str(bmaj * 3.0) + "arcsec", #1.2) + "arcsec",
minor=str(bmaj * 3.0) + "arcsec", #1.2) + "arcsec",
pa="0deg",
outfile=cubesmooth1)
cubesmooth2 = cubeimage.replace(".image", ".smooth2") # 10 mJy
imsmooth(imagename=cubeimage,
targetres=True,
major=str(bmaj * 5.0) + "arcsec",
minor=str(bmaj * 5.0) + "arcsec",
pa="0deg",
outfile=cubesmooth2)
cubesmooth3 = cubeimage.replace(".image", ".smooth3") # 10 mJy
imsmooth(imagename=cubeimage,
targetres=True,
major=str(bmaj * 7.0) + "arcsec",
minor=str(bmaj * 7.0) + "arcsec",
pa="0deg",
outfile=cubesmooth3)
# noise
noisesmooth1 = noisehist(cubesmooth1,
0.02,
"test",
3.0,
bins=200,
thres=0.0001,
plotter=False)
noisesmooth2 = noisehist(cubesmooth2,
0.02,
"test",
3.0,
bins=200,
thres=0.0001,
plotter=False)
noisesmooth3 = noisehist(cubesmooth3,
0.02,
"test",
3.0,
bins=200,
thres=0.0001,
plotter=False)
# create mask
#tscreatemask(cubeimage,noise*1.*2.,dir_image+name_line+"_mask0.image")
tscreatemask(cubesmooth1, noisesmooth1 * 0.0,
dir_image + name_line + "_mask1.image")
tscreatemask(cubesmooth2, noisesmooth2 * 0.0,
dir_image + name_line + "_mask2.image")
tscreatemask(cubesmooth3, noisesmooth3 * 0.0,
dir_image + name_line + "_mask3.image")
immath(imagename=[
dir_image + name_line + "_mask1.image",
dir_image + name_line + "_mask2.image",
dir_image + name_line + "_mask3.image"
],
expr="iif(IM0+IM1 >= 2.0, 1.0, 0.0)",
outfile=dir_image + name_line + "_" + beamp + "_mask.image")
os.system("rm -rf " + cubesmooth1)
os.system("rm -rf " + cubesmooth2)
os.system("rm -rf " + cubesmooth3)
os.system("rm -rf " + dir_image + name_line + "_mask0.image")
os.system("rm -rf " + dir_image + name_line + "_mask1.image")
os.system("rm -rf " + dir_image + name_line + "_mask2.image")
mask_use_here = dir_image + name_line + "_" + beamp + "_mask.image"
else:
mask_use_here = dir_image + name_line + "_" + beamp + "_mask.image"
os.system("cp -r " + maskname + " " + mask_use_here)
immath(imagename=[cubeimage, mask_use_here],
expr="iif( IM0>=" + str(noise * snr_mom) + ", IM0*IM1, 0.0)",
outfile=cubeimage + ".masked")
#os.system("rm -rf " + mask_use_here)
vch = abs(imhead(cubeimage,
mode="list")["cdelt4"]) / 115.27120e9 * 299792.458
immath(imagename=cubeimage + ".masked",
expr="iif( IM0>0, 1.0/" + str(vch) + ", 0.0)",
outfile=cubeimage + ".maskedTF")
immoments(imagename=cubeimage + ".maskedTF",
moments=[0],
outfile=dir_image + name_line + ".moment0.noise_tmp") # Nch
beamarea_pix = tsbeam_area(dir_image + name_line + ".moment0.noise_tmp")
immath(
dir_image + name_line + ".moment0.noise_tmp",
expr=str(vch) + "*sqrt(IM0)*" +
str(noise), #+"/"+str(np.sqrt(beamarea_pix)),
outfile=dir_image + name_line + "_" + beamp + ".moment0.noise")
immoments(
imagename=cubeimage + ".masked",
moments=[0],
#includepix = [noises[i]*3.0,10000.],
outfile=dir_image + name_line + ".moment0_tmp")
immoments(
imagename=cubeimage + ".masked",
moments=[1],
#includepix = [noises[i]*3.0,10000.],
outfile=dir_image + name_line + ".moment1_tmp")
immoments(
imagename=cubeimage + ".masked",
moments=[8],
#includepix = [noises[i]*3.0,10000.],
outfile=dir_image + name_line + ".moment8_tmp")
# masking
peak = imstat(dir_image + name_line + ".moment0_tmp")["max"][0]
#
immath(imagename=[
dir_image + name_line + ".moment0_tmp",
dir_image + name_line + ".moment0_tmp"
],
expr="iif( IM0>=" + str(peak * percent) + ", IM1, 0.0)",
outfile=dir_image + name_line + "_" + beamp + ".moment0_tmp2")
immath(imagename=[
dir_image + name_line + "_" + beamp + ".moment0_tmp2",
dir_image + name_line + ".moment0.noise_tmp"
],
expr="iif( IM1>=" + str(nchan) + ", IM0, 0.0)",
outfile=dir_image + name_line + "_" + beamp + ".moment0")
#
immath(imagename=[
dir_image + name_line + ".moment0_tmp",
dir_image + name_line + ".moment1_tmp"
],
expr="iif( IM0>=" + str(peak * percent) + ", IM1, 0.0)",
outfile=dir_image + name_line + "_" + beamp + ".moment1_tmp2")
immath(imagename=[
dir_image + name_line + "_" + beamp + ".moment1_tmp2",
dir_image + name_line + ".moment0.noise_tmp"
],
expr="iif( IM1>=" + str(nchan) + ", IM0, 0.0)",
outfile=dir_image + name_line + "_" + beamp + ".moment1")
#
immath(imagename=[
dir_image + name_line + ".moment0_tmp",
dir_image + name_line + ".moment8_tmp"
],
expr="iif( IM0>=" + str(peak * percent) + ", IM1, 0.0)",
outfile=dir_image + name_line + "_" + beamp + ".moment8_tmp2")
immath(imagename=[
dir_image + name_line + "_" + beamp + ".moment8_tmp2",
dir_image + name_line + ".moment0.noise_tmp"
],
expr="iif( IM1>=" + str(nchan) + ", IM0, 0.0)",
outfile=dir_image + name_line + "_" + beamp + ".moment8")
#
immath(imagename=[
dir_image + name_line + "_" + beamp + ".moment0",
dir_image + name_line + "_" + beamp + ".moment0.noise"
],
expr="IM0/IM1",
outfile=dir_image + name_line + "_" + beamp +
".moment0.snratio_tmp")
immath(imagename=[
dir_image + name_line + "_" + beamp + ".moment0.snratio_tmp",
dir_image + name_line + ".moment0.noise_tmp"
],
expr="iif( IM1>=" + str(nchan) + ", IM0, 0.0)",
outfile=dir_image + name_line + "_" + beamp + ".moment0.snratio")
os.system("rm -rf " + cubeimage + ".maskedTF")
os.system("rm -rf " + dir_image + name_line + ".moment0.noise_tmp")
os.system("rm -rf " + dir_image + name_line + "_" + beamp +
".moment0.snratio_tmp")
os.system("rm -rf " + dir_image + name_line + ".moment0_tmp")
os.system("rm -rf " + dir_image + name_line + ".moment1_tmp")
os.system("rm -rf " + dir_image + name_line + ".moment8_tmp")
os.system("rm -rf " + dir_image + name_line + "_" + beamp +
".moment0_tmp2")
os.system("rm -rf " + dir_image + name_line + "_" + beamp +
".moment1_tmp2")
os.system("rm -rf " + dir_image + name_line + "_" + beamp +
".moment8_tmp2")
return mask_use_here
outfile=dir_image + name_line + ".moment0_tmp")
immoments(
imagename=cubeimage + ".pbcor.masked",
moments=[1],
#includepix = [noises[i]*3.0,10000.],
outfile=dir_image + name_line + ".moment1_tmp")
immoments(
imagename=cubeimage + ".pbcor.masked",
moments=[8],
#includepix = [noises[i]*3.0,10000.],
outfile=dir_image + name_line + ".moment8_tmp")
# masking
peak = imstat(dir_image + name_line + ".moment0_tmp")["max"][0]
immath(imagename=[
dir_image + name_line + ".moment0_tmp",
dir_image + name_line + ".moment0_tmp"
],
expr="iif( IM0>=" + str(peak * percents[i]) + ", IM1, 0.0)",
outfile=dir_image + name_line + ".moment0")
immath(imagename=[
dir_image + name_line + ".moment0_tmp",
dir_image + name_line + ".moment1_tmp"
],
expr="iif( IM0>=" + str(peak * percents[i]) + ", IM1, 0.0)",
outfile=dir_image + name_line + ".moment1")
title(
'RMSE as a function of Time with %i Spacecraft and cleaned for %i iterations'
% (numsc, iters))
ylabel("RMSE")
xlabel("Minutes used")
savefig('rmses%iiters%i.png' % (numsc, iters))
plt.close(fig1)
np.savetxt('rmses' + str(numsc) + 'iters%d.txt' % iters,
np.array(rmses).flatten(),
fmt='%.7f')
#now analyze pointing errors
truthMaxPos = imstat(imageName)['maxposf'].split()
maxRA = truthMaxPos[0][:-1]
maxDec = truthMaxPos[1][:-1]
tFeatDir = me.direction('J2000', maxRA, maxDec)
maxRArad = tFeatDir['m0']['value']
maxDecrad = tFeatDir['m1']['value']
dirtyMaxPos = imstat(RelicMergeMS + '.' + str(iters) + 'dirty' +
str(length - 1) + '.image')['maxposf'].split()
dmaxRA = dirtyMaxPos[0][:-1]
dmaxDec = dirtyMaxPos[1][:-1]
dFeatDir = me.direction('J2000', dmaxRA, dmaxDec)
def selfcal(vis, spw='6', doplots=True, INTERACTIVE=False, reclean=True,
field='W51 Ku', outdir_template="spw%i_selfcal_iter/",
statsbox='170,50,229,97', ant1list=['ea14','ea05'],
ant2list=['ea16','ea07'], avgchannel_wide='128',
avgchannel_narrow='8', cleanboxes="", refant='ea27', solint='30s',
niter=2, multiscale=[0,3,6,12,24,48,96], imsize=512,
cell='0.1arcsec', weighting='uniform', robust=0.0,
minsnr=3, psfmode='clark', openviewer=False,
shallowniter=100,
midniter=1000,
deepniter=1e4,
minblperant=4,
gaintype='G'):
"""
Docstring incomplete
"""
# Jan 2016: the fact that I have to make these declarations indicates that
# this code never actually worked.
spwn = int(spw)
split(vis=vis, outputvis="selfcal_copy_{0}".format(vis))
vis_for_selfcal = "selfcal_copy_{0}".format(vis)
outdir = outdir_template % spwn
if not os.path.exists(outdir):
os.mkdir(outdir)
shallowniter = int(shallowniter)
midniter = int(midniter)
deepniter = int(deepniter)
fieldstr = field.replace(" ","")
# (1) Clean a single SPW *interactively*, boxing the brightest regions and not
# cleaning very deeply (maybe 100 iterations). Keep this model in the header
# -- it's what you'll use for the first round of self-calibration.
#
# Those are the official directions. They are nonsense when dealing with
# the extended emission of W51.
if reclean:
imagename="selfcal_spw%i_shallowclean_iter0" % int(spw)
for suffix in clean_output_suffixes:
os.system("rm -rf "+imagename+suffix)
clean(vis=vis_for_selfcal, field=field, imagename=imagename, mode='mfs',
psfmode=psfmode, multiscale=multiscale,
weighting=weighting, robust=robust, niter=shallowniter,
imsize=imsize, cell=cell,
mask=cleanboxes,
nterms=1,
interactive=INTERACTIVE,
usescratch=True)
exportfits(imagename=imagename+".image", fitsimage=imagename+".fits",
overwrite=True, dropdeg=True)
imrms = [imstat(imagename+".image",box=statsbox)['rms']]
for calnum in xrange(niter):
if reclean:
first_image = 'selfcal_{0}_{1}_firstim_selfcal{2}'.format(fieldstr,
spw,
calnum)
for suffix in clean_output_suffixes:
os.system("rm -rf "+first_image+suffix)
clean(vis=vis_for_selfcal,imagename=first_image,field=field,
mode='mfs', psfmode=psfmode, multiscale=multiscale,
weighting=weighting, robust=robust, niter=shallowniter,
imsize=imsize, mask=cleanboxes, cell=cell, nterms=1,
usescratch=True)
exportfits(imagename=first_image+".image",
fitsimage=first_image+".fits", overwrite=True,
dropdeg=True)
caltable = 'selfcal%i_%s_spw%i.pcal' % (calnum,fieldstr,spwn)
if reclean:
os.system('rm -rf '+caltable)
gaincal(vis=vis_for_selfcal,
field='',
caltable=caltable,
spw='',
# gaintype = 'T' could reduce failed fit errors by averaging pols...
gaintype=gaintype, # 'G' from http://casaguides.nrao.edu/index.php?title=EVLA_Advanced_Topics_3C391
solint=solint,
refant=refant,
calmode='p',
combine='scan',
minsnr=minsnr,
minblperant=minblperant)
# Watch out for failed solutions noted in the terminal during this
# solution. If you see a large fraction (really more than 1 or 2) of
# your antennas failing to converge in many time intervals then you
# may need to lengthen the solution interval.
# =%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%
# INSPECT THE CALIBRATION
# =%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%
# After you have run the gaincal, you want to inspect the
# solution. Use PLOTCAL to look at the solution (here broken into
# panels by SPW with individual antennas mapped to colors). Look at
# the overall magnitude of the correction to get an idea of how
# important the selfcal is and at how quickly it changes with time to
# get an idea of how stable the instrument and atmosphere were.
#
if doplots:
for ant2 in ant2list:
for ant in ant1list:
# (4) Have a look at the gain solutions by antenna. Which antennas
# have the largest phase corrections? Before applying the
# calibration, use plotms to display the corrected phase vs. amp
# for these antennas, to compare with *after* the correction is
# applied.
plotcal(caltable=caltable,
xaxis='time', yaxis='phase',
showgui=INTERACTIVE,
antenna=ant+'&'+ant2,
figfile=outdir+'selfcal%i_spw%i_phasevstime_ant%s-%s.png' % (calnum,spwn,ant,ant2),
iteration='')#, subplot = 221)
#plotcal(caltable=caltable, xaxis='amp', yaxis='phase',
# showgui=INTERACTIVE,
# antenna=ant,
# figfile=outdir+'selfcal%i_spw%i_phasevsamp_ant%s.png' % (calnum,spwn,ant),
# iteration='')#, subplot = 221)
if calnum == 0:
datacol='data'
else:
datacol='corrected'
plotms(vis=vis_for_selfcal, xaxis='time', yaxis='phase',
xdatacolumn=datacol, ydatacolumn=datacol,
avgtime='15s', avgchannel=avgchannel_narrow, coloraxis='corr',
antenna=ant+'&'+ant2,
overwrite=True, title='Iteration %i for spw %i and ant %s-%s. datacol=%s' % (calnum,spwn,ant,ant2,datacol),
showgui=INTERACTIVE,
plotfile=outdir+'selfcal%i_spw%i_ant%s-%s_phasetime.png' % (calnum,spwn,ant,ant2),)
plotms(vis=vis_for_selfcal, xaxis='time', yaxis='amp',
xdatacolumn=datacol, ydatacolumn=datacol,
avgtime='15s', avgchannel=avgchannel_narrow, coloraxis='corr',
antenna=ant+'&'+ant2,
overwrite=True, title='Iteration %i for spw %i and ant %s-%s. datacol=%s' % (calnum,spwn,ant,ant2,datacol),
showgui=INTERACTIVE,
plotfile=outdir+'selfcal%i_spw%i_ant%s-%s_amptime.png' % (calnum,spwn,ant,ant2),)
plotms(vis=vis_for_selfcal, xaxis='phase', yaxis='amp',
xdatacolumn=datacol, ydatacolumn=datacol,
avgtime='60s', avgchannel=avgchannel_narrow, coloraxis='corr',
antenna=ant+'&'+ant2,
overwrite=True, title='Iteration %i for spw %i and ant %s-%s. datacol=%s' % (calnum,spwn,ant,ant2,datacol),
showgui=INTERACTIVE,
plotfile=outdir+'selfcal%i_spw%i_ant%s-%s_phaseamp.png' % (calnum,spwn,ant,ant2),)
plotcal(caltable=caltable,
xaxis='time', yaxis='phase',
plotrange=[0,0,-180,180],
showgui=INTERACTIVE,
figfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_phasevstime.png' % (calnum,spwn),
iteration='spw' if INTERACTIVE else '')#, subplot = 221)
plotcal(caltable=caltable,
xaxis='antenna', yaxis='phase',
showgui=INTERACTIVE,
figfile=outdir+'selfcal%i_spw%i_phasevsantenna.png' % (calnum,spwn),
iteration='')
plotcal(caltable=caltable,
xaxis='time', yaxis='amp',
plotrange=[0,0,0.5,1.5],
showgui=INTERACTIVE,
figfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_ampvstime.png' % (calnum,spwn),
iteration='spw' if INTERACTIVE else '')#, subplot = 221)
#plotcal(caltable=caltable,
# xaxis='phase', yaxis='amp',
# plotrange=[-50,50,0.5,1.5],
# showgui=INTERACTIVE,
# figfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_ampvsphase.png' % (calnum,spwn),
# iteration='spw' if INTERACTIVE else '')#, subplot = 221)
# THERE WILL BE WEIRD "LUSTRE" ERRORS GENERATED BY THE FILE SYSTEM. DO
# NOT FREAK OUT. These are just a feature of our fast file
# system. Plotcal will still work.
# It can be useful useful to plot the X-Y solutions (i.e., differences
# between polarizations) as an indicator of the noise in the
# solutions.
plotcal(caltable=caltable,
xaxis='time',
yaxis='phase',
plotrange=[0,0,-25, 25],
poln = '/',
showgui=INTERACTIVE,
iteration='spw,antenna' if INTERACTIVE else '',
figfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_poldiff.png' % (calnum,spwn),
subplot = 221 if INTERACTIVE else 111)
plotms(vis=vis_for_selfcal,
xaxis='uvdist',
yaxis='amp',
xdatacolumn='corrected',
ydatacolumn='corrected',
avgtime='1e8s',
avgchannel=avgchannel_narrow,
coloraxis='baseline',
overwrite=True,
showgui=INTERACTIVE,
title='Iteration %i for spw %i' % (calnum,spwn),
plotfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_uvdistamp.png' % (calnum,spwn),
)
#plotms(vis=vis_for_selfcal,
# xaxis='phase',
# yaxis='amp',
# xdatacolumn='corrected',
# ydatacolumn='corrected',
# avgtime='60s',
# avgchannel=avgchannel_narrow,
# coloraxis='corr',
# overwrite=True,
# title='Iteration %i for spw %i' % (calnum,spw),
# plotfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_phaseamp.png' % (calnum,spwn),
# )
plotms(vis=vis_for_selfcal,
xaxis='time',
yaxis='amp',
xdatacolumn='corrected',
ydatacolumn='corrected',
avgtime='10s',
avgchannel=avgchannel_narrow,
coloraxis='baseline',
overwrite=True,
showgui=INTERACTIVE,
title='Iteration %i for spw %i' % (calnum,spwn),
plotfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_amptime.png' % (calnum,spwn),
)
# The rms noise is about 4 to 8 deg, depending on antenna, but the
# phase changes are considerably larger. This indicates that the
# application of this solution will improve the image.
# =%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%
# APPLY THE CALIBRATION
# =%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%
#
# If you are satisfied with your solution, you can now apply it to the
# data to generate a new corrected data column, which you can then
# image. Be sure to save the previous flags before you do so because
# applycal will flag data without good solutions. The commented
# command after the applycal will roll back to the saved solution in
# case you get in trouble.
#
# flagmanager(vis=vis_for_selfcal,
# mode='save',
# versionname='before_selfcal_apply')
# 2013-03-04 19:53:37 SEVERE agentflagger:: (file /opt/casa/stable-2013-02/gcwrap/tools/flagging/agentflagger_cmpt.cc, line 37) Exception Reported: Invalid Table operation: ArrayColumn::setShape; shape cannot be changed for row 0 column FLAG
# *** Error *** Invalid Table operation: ArrayColumn::setShape; shape cannot be changed for row 0 column FLAG
if reclean:
flagmanager(vis=vis_for_selfcal, mode='backup', versionname='pre_applycal1')
applycal(vis=vis_for_selfcal,
gaintable=caltable,
interp='linear',
flagbackup=False) # no need when using flagmanager
flagmanager(vis=vis_for_selfcal, mode='restore', versionname='pre_applycal_1')
# (6) Plot corrected phase vs. amp for the antennas you picked out in (4),
# to check that in fact the corrections have been applied as expected.
if doplots:
for ant2 in ant2list:
for ant in ant1list:
plotms(vis=vis_for_selfcal, xaxis='time', yaxis='phase',
xdatacolumn='corrected', ydatacolumn='corrected',
avgtime='15s', avgchannel=avgchannel_narrow, coloraxis='corr',
antenna=ant+'&'+ant2,
overwrite=True, title='Iteration %i for spw %i and ant %s-%s' % (calnum,spwn,ant,ant2),
showgui=INTERACTIVE,
plotfile=outdir+'selfcal%i_spw%i_ant%s-%s_phasetime_applied.png' % (calnum,spwn,ant,ant2),)
plotms(vis=vis_for_selfcal, xaxis='time', yaxis='amp',
xdatacolumn='corrected', ydatacolumn='corrected',
avgtime='60s', avgchannel=avgchannel_narrow, coloraxis='corr',
antenna=ant+'&'+ant2,
overwrite=True, title='Iteration %i for spw %i and ant %s-%s' % (calnum,spwn,ant,ant2),
showgui=INTERACTIVE,
plotfile=outdir+'selfcal%i_spw%i_ant%s-%s_amptime_applied.png' % (calnum,spwn,ant,ant2),)
plotms(vis=vis_for_selfcal, xaxis='phase', yaxis='amp',
xdatacolumn='corrected', ydatacolumn='corrected',
avgtime='60s', avgchannel=avgchannel_narrow, coloraxis='corr',
antenna=ant+'&'+ant2,
overwrite=True, title='Iteration %i for spw %i and ant %s-%s' % (calnum,spwn,ant,ant2),
showgui=INTERACTIVE,
plotfile=outdir+'selfcal%i_spw%i_ant%s-%s_phaseamp_applied.png' % (calnum,spwn,ant,ant2),)
plotms(vis=vis, spw='0', xaxis='freq', yaxis='phase', avgtime='1e8',
avgscan=T, coloraxis='corr', iteraxis='baseline', xselfscale=T,
yselfscale=T,
antenna=ant+'&'+ant2,
title='Phase vs Freq with time averaging for spw %i ant %s-%s iter %i' % (spwn,ant,ant2,calnum),
plotfile=outdir+'phasevsfreq_spw%i_ant%s-%s_selfcal%i.png' % (spwn,ant,ant2,calnum),
field=field,
overwrite=True,
showgui=INTERACTIVE,
)
# Use this command to roll back to the previous flags in the event of
# an unfortunate applycal.
#flagmanager(vis=vis_for_selfcal,
# mode='restore',
# versionname='before_selfcal_apply')
if reclean:
selfcal_image = 'selfcal_{0} {1}_selfcal{2}'.format(fieldstr,spwn,calnum)
for suffix in clean_output_suffixes:
os.system("rm -rf "+selfcal_image+suffix)
clean(vis=vis_for_selfcal, imagename=selfcal_image, field=field,
mode='mfs', psfmode=psfmode, multiscale=multiscale,
weighting=weighting, robust=robust, niter=midniter,
imsize=imsize, cell=cell, nterms=1, mask=cleanboxes,
psfmode=psfmode,
usescratch=True)
exportfits(imagename=selfcal_image+".image", fitsimage=selfcal_image+".fits", overwrite=True)
if doplots:
plotms(vis=vis_for_selfcal, spw='0', xaxis='baseline', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Baseline after CSCLEAN iter %i' % calnum,
plotfile=outdir+'post_selfcal%i_spw%i_residVSbaseline.png' % (calnum,spwn),
field=field,
overwrite=True,
showgui=INTERACTIVE,
)
plotms(vis=vis_for_selfcal, spw='0', xaxis='time', yaxis='amp', avgtime='5s',
ydatacolumn='corrected-model',
coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Time after CSCLEAN iter %i' % (calnum),
plotfile=outdir+'post_selfcal%i_spw%i_residVStime.png' % (calnum,spwn),
field=field,
overwrite=True,
showgui=INTERACTIVE,
)
plotms(vis=vis_for_selfcal, spw='0', xaxis='uvdist', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. UVDIST after CSCLEAN iter %i' % (calnum) ,
plotfile=outdir+'post_selfcal%i_spw%i_residVSuvdist.png' % (calnum,spwn),
field=field,
overwrite=True,
showgui=INTERACTIVE,
)
imrms.append(imstat(selfcal_image+".image",box=statsbox)['rms'])
print "FINISHED ITERATION %i" % calnum
print "FINISHED ITERATING!!! YAY!"
# final phase + gain cal:
# http://casaguides.nrao.edu/index.php?title=Calibrating_a_VLA_5_GHz_continuum_survey#One_Last_Iteration:_Amplitude_.26_Phase_Self_Calibration
aptable = 'selfcal_ap_%s_spw%i.gcal' % (field.replace(" ",""),spwn)
gaincal(vis=vis_for_selfcal, field='', caltable=aptable, gaintable=caltable, spw='',
solint='inf', refant=refant, calmode='ap', combine='', minblperant=4,
gaintype=gaintype, minsnr=minsnr)
if doplots:
plotcal(caltable=aptable,
xaxis='phase', yaxis='amp',
plotrange=[-50,50,0.5,1.5],
showgui=INTERACTIVE,
figfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_ampvsphase_final.png' % (calnum,spwn),
iteration='spw' if INTERACTIVE else '')#, subplot = 221)
applycal(vis=vis_for_selfcal,
gaintable=[aptable,caltable],
interp='linear',
flagbackup=True) # was False when flagmanager was used
flagmanager(vis=vis_for_selfcal, mode='restore', versionname='applycal_1')
selfcal_image = 'spw%i_ku_d_selfcal%i_final' % (spwn,calnum)
for suffix in clean_output_suffixes:
os.system("rm -rf "+selfcal_image+suffix)
clean(vis=vis_for_selfcal,imagename=selfcal_image,field=field, mode='mfs',
mask=cleanboxes, weighting=weighting, robust=robust, niter=deepniter,
psfmode=psfmode, imsize=imsize, cell=cell, nterms=1, usescratch=True)
exportfits(imagename=selfcal_image+".image", fitsimage=selfcal_image+".fits", overwrite=True)
if doplots:
plotms(vis=vis_for_selfcal, spw='0', xaxis='baseline', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Baseline after CSCLEAN iter %i' % calnum,
plotfile=outdir+'post_selfcal%i_spw%i_residVSbaseline.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=vis_for_selfcal, spw='0', xaxis='time', yaxis='amp', avgtime='5s',
ydatacolumn='corrected-model',
coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Time after CSCLEAN iter %i' % (calnum),
plotfile=outdir+'post_selfcal%i_spw%i_residVStime.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=vis_for_selfcal, spw='0', xaxis='uvdist', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. UVDIST after CSCLEAN iter %i' % (calnum) ,
plotfile=outdir+'post_selfcal%i_spw%i_residVSuvdist.png' % (calnum,spwn),
field=field,
overwrite=True,
)
selfcal_image = 'spw%i_ku_d_selfcal%i_final_multiscale' % (spwn,calnum)
for suffix in clean_output_suffixes:
os.system("rm -rf "+selfcal_image+suffix)
clean(vis=vis_for_selfcal,imagename=selfcal_image,field=field, mode='mfs',
psfmode=psfmode, nterms=1, weighting=weighting, robust=robust,
niter=deepniter, imsize=imsize, cell=cell, usescratch=True)
exportfits(imagename=selfcal_image+".image", fitsimage=selfcal_image+".fits", overwrite=True)
if doplots:
plotms(vis=vis_for_selfcal, spw='0', xaxis='baseline', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Baseline after multiscale CLEAN iter %i' % (calnum),
plotfile=outdir+'post_selfcal%i_spw%i_residVSbaseline_multiscale.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=vis_for_selfcal, spw='0', xaxis='time', yaxis='amp', avgtime='5s',
ydatacolumn='corrected-model',
coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Time after multiscale CLEAN iter %i' % (calnum),
plotfile=outdir+'post_selfcal%i_spw%i_residVStime_multiscale.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=vis_for_selfcal, spw='0', xaxis='uvdist', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. UVDIST after multiscale CLEAN iter %i' % (calnum),
plotfile=outdir+'post_selfcal%i_spw%i_residVSuvdist_multiscale.png' % (calnum,spwn),
field=field,
overwrite=True,
)
return imrms
def moments_to_ratio(dir_data, galname, suffix, threesigma_co10,
threesigma_co21, threesigma8_co10, threesigma8_co21):
"""
"""
print("###########################")
print("### running moments_to_ratio")
print("###########################")
### setup
dir_data1 = dir_data + galname + "/"
im_co10 = glob.glob(dir_data1 + galname + "*co10*" + suffix +
"*moment0")[0]
im_co21 = glob.glob(dir_data1 + galname + "*co21*" + suffix +
"*moment0")[0]
outmask_co10 = im_co10.replace(".moment0", "_mom.mask")
outmask_co21 = im_co21.replace(".moment0", "_mom.mask")
m8_co10 = glob.glob(dir_data1 + galname + "*co10*" + suffix +
"*moment8")[0]
m8_co21 = glob.glob(dir_data1 + galname + "*co21*" + suffix +
"*moment8")[0]
outmask8_co10 = m8_co10.replace(".moment8", "_mom8.mask")
outmask8_co21 = m8_co21.replace(".moment8", "_mom8.mask")
### create a combined mask
# mom-0
peak = imstat(im_co10)["max"][0]
createmask(im_co10, threesigma_co10, outmask_co10)
peak = imstat(im_co21)["max"][0]
createmask(im_co21, threesigma_co21, outmask_co21)
outfile = dir_data1 + galname + "_r21_" + suffix + ".mask"
os.system("rm -rf " + outfile)
immath(imagename=[outmask_co10, outmask_co21],
mode="evalexpr",
expr="IM0*IM1",
outfile=outfile)
makemask(mode="copy",
inpimage=outfile,
inpmask=outfile,
output=outfile + ":mask0",
overwrite=True)
#mom-8
peak = imstat(m8_co10)["max"][0]
createmask(m8_co10, threesigma8_co10, outmask8_co10)
peak = imstat(im_co21)["max"][0]
createmask(m8_co21, threesigma8_co21, outmask8_co21)
outfile = dir_data1 + galname + "_r21_" + suffix + "_m8.mask"
os.system("rm -rf " + outfile)
immath(imagename=[outmask8_co10, outmask8_co21],
mode="evalexpr",
expr="IM0*IM1",
outfile=outfile)
makemask(mode="copy",
inpimage=outfile,
inpmask=outfile,
output=outfile + ":mask0",
overwrite=True)
### create line ratio map
#mom-0
outfile = dir_data1 + galname + "_r21_" + suffix + ".image"
mask = dir_data1 + galname + "_r21_" + suffix + ".mask"
line_ratio(dir_data="",
im1=im_co21,
im2=im_co10,
outfile=outfile,
diff="4.",
mask=mask)
#mom-8
outfile = dir_data1 + galname + "_r21_" + suffix + "_m8.image"
mask = dir_data1 + galname + "_r21_" + suffix + "_m8.mask"
line_ratio(dir_data="",
im1=m8_co21,
im2=m8_co10,
outfile=outfile,
diff="4.",
mask=mask)
def selfcal(vis, spw='6', spwname=None, INTERACTIVE=False, field='W51 Ku',
statsbox='170,50,229,97', cleanboxes="", refant='ea27',
solint='30s', niter=2, multiscale=[0,3,6,12,24,48,96], imsize=512,
cell='0.1arcsec', weighting='uniform', robust=0.0, minsnr=3,
psfmode='clark', shallowniter=100, midniter=1000, deepniter=1e4,
threshold='0.0mJy', pointclean=False,
minblperant=4, gaintype='G', **kwargs):
"""
Docstring incomplete
"""
# Jan 2016: the fact that I have to make these declarations indicates that
# this code never actually worked.
spwn = int(spw)
if spwname is None:
spwname = str(spw)
vis_for_selfcal = "selfcal_copy_{0}".format(vis)
os.system('rm -rf {0}'.format(vis_for_selfcal))
os.system('rm -rf {0}.flagversions'.format(vis_for_selfcal))
assert split(vis=vis, outputvis=vis_for_selfcal)
flagmanager(vis=vis_for_selfcal, mode='save', versionname='original')
shallowniter = int(shallowniter)
midniter = int(midniter)
deepniter = int(deepniter)
fieldstr = field.replace(" ","")
# (1) Clean a single SPW *interactively*, boxing the brightest regions and not
# cleaning very deeply (maybe 100 iterations). Keep this model in the header
# -- it's what you'll use for the first round of self-calibration.
#
# Those are the official directions. They are nonsense when dealing with
# the extended emission of W51.
# This first image is effectively discarded
imagename="selfcal_{1}_spw{0}_shallowclean_iter0".format(spwname, fieldstr)
for suffix in clean_output_suffixes:
os.system("rm -rf "+imagename+suffix)
clean(vis=vis_for_selfcal, field=field, imagename=imagename,
threshold=threshold,
mode='mfs', psfmode=psfmode, multiscale=multiscale,
weighting=weighting, robust=robust, niter=shallowniter,
imsize=imsize, cell=cell, mask=cleanboxes, nterms=1,
interactive=INTERACTIVE, usescratch=True, **kwargs)
if pointclean:
clean(vis=vis_for_selfcal, field=field, imagename=imagename,
threshold=threshold,
mode='mfs', psfmode=psfmode,
weighting=weighting, robust=robust, niter=shallowniter,
imsize=imsize, cell=cell, mask=cleanboxes, nterms=1,
interactive=INTERACTIVE, usescratch=True, **kwargs)
exportfits(imagename=imagename+".image", fitsimage=imagename+".fits",
overwrite=True, dropdeg=True)
exportfits(imagename=imagename+".model", fitsimage=imagename+".model.fits",
overwrite=True, dropdeg=True)
exportfits(imagename=imagename+".residual", fitsimage=imagename+".residual.fits",
overwrite=True, dropdeg=True)
imrms = [imstat(imagename+".image",box=statsbox)['rms']]
for calnum in xrange(niter):
first_image = 'selfcal_{0}_{1}_firstim_selfcal{2}'.format(fieldstr,
spwname,
calnum)
clearcal(vis=vis_for_selfcal)
for suffix in clean_output_suffixes:
os.system("rm -rf "+first_image+suffix)
clean(vis=vis_for_selfcal, imagename=first_image, field=field,
threshold=threshold,
mode='mfs', psfmode=psfmode, multiscale=multiscale,
weighting=weighting, robust=robust, niter=midniter,
imsize=imsize, mask=cleanboxes, cell=cell, nterms=1,
usescratch=True, interactive=INTERACTIVE, **kwargs)
if pointclean:
clean(vis=vis_for_selfcal, imagename=first_image, field=field,
threshold=threshold,
mode='mfs', psfmode=psfmode,
weighting=weighting, robust=robust, niter=midniter,
imsize=imsize, mask=cleanboxes, cell=cell, nterms=1,
usescratch=True, interactive=INTERACTIVE, **kwargs)
exportfits(imagename=first_image+".image",
fitsimage=first_image+".fits", overwrite=True,
dropdeg=True)
exportfits(imagename=first_image+".model",
fitsimage=first_image+".model.fits", overwrite=True,
dropdeg=True)
exportfits(imagename=first_image+".residual",
fitsimage=first_image+".residual.fits", overwrite=True,
dropdeg=True)
caltable = 'selfcal{0}_{1}_spw{2}.pcal'.format(calnum,fieldstr,spwname)
os.system('rm -rf '+caltable)
gaincal(vis=vis_for_selfcal,
field=field,
caltable=caltable,
spw='',
# gaintype = 'T' could reduce failed fit errors by averaging pols...
gaintype=gaintype, # 'G' from http://casaguides.nrao.edu/index.php?title=EVLA_Advanced_Topics_3C391
solint=solint,
refant=refant,
calmode='p',
combine='scan',
minsnr=minsnr,
minblperant=minblperant)
# Watch out for failed solutions noted in the terminal during this
# solution. If you see a large fraction (really more than 1 or 2) of
# your antennas failing to converge in many time intervals then you
# may need to lengthen the solution interval.
applycal(vis=vis_for_selfcal,
gaintable=caltable,
interp='linear',
flagbackup=False) # no need when using flagmanager
flagmanager(vis=vis_for_selfcal, mode='restore', versionname='original')
new_vis_for_selfcal = "selfcal{1}_copy_{0}".format(vis, calnum)
os.system('rm -rf {0}'.format(new_vis_for_selfcal))
os.system('rm -rf {0}.flagversions'.format(new_vis_for_selfcal))
split(vis=vis_for_selfcal, outputvis=new_vis_for_selfcal,
datacolumn='corrected')
flagmanager(vis=new_vis_for_selfcal, mode='save',
versionname='original')
vis_for_selfcal = new_vis_for_selfcal
# (6) Plot corrected phase vs. amp for the antennas you picked out in (4),
# to check that in fact the corrections have been applied as expected.
selfcal_image = 'selfcal_{0}_{1}_selfcal{2}'.format(fieldstr,spwname,calnum)
for suffix in clean_output_suffixes:
os.system("rm -rf "+selfcal_image+suffix)
clean(vis=vis_for_selfcal, imagename=selfcal_image, field=field,
threshold=threshold,
mode='mfs', psfmode=psfmode, multiscale=multiscale,
weighting=weighting, robust=robust, niter=midniter,
imsize=imsize, cell=cell, nterms=1, mask=cleanboxes,
usescratch=False, interactive=INTERACTIVE, **kwargs)
if pointclean:
clean(vis=vis_for_selfcal, imagename=selfcal_image, field=field,
threshold=threshold,
mode='mfs', psfmode=psfmode,
weighting=weighting, robust=robust, niter=midniter,
imsize=imsize, cell=cell, nterms=1, mask=cleanboxes,
usescratch=False, interactive=INTERACTIVE, **kwargs)
exportfits(imagename=selfcal_image+".image",
fitsimage=selfcal_image+".fits", overwrite=True,
dropdeg=True)
exportfits(imagename=selfcal_image+".model",
fitsimage=selfcal_image+".model.fits", overwrite=True,
dropdeg=True)
exportfits(imagename=selfcal_image+".residual",
fitsimage=selfcal_image+".residual.fits", overwrite=True,
dropdeg=True)
imrms.append(imstat(selfcal_image+".image",box=statsbox)['rms'])
print "FINISHED ITERATION %i" % calnum
print "FINISHED ITERATING!!! YAY!"
# final phase + gain cal:
# http://casaguides.nrao.edu/index.php?title=Calibrating_a_VLA_5_GHz_continuum_survey#One_Last_Iteration:_Amplitude_.26_Phase_Self_Calibration
aptable = 'selfcal_ap_{0}_spw{1}.gcal'.format(field.replace(" ",""),spwname)
os.system('rm -rf '+aptable)
gaincal(vis=vis_for_selfcal, field=field, caltable=aptable,
gaintable=caltable, spw='', solint='inf', refant=refant,
calmode='ap', combine='', minblperant=minblperant,
gaintype=gaintype, minsnr=minsnr)
applycal(vis=vis_for_selfcal,
gaintable=[aptable],
interp='linear',
flagbackup=False)
flagmanager(vis=vis_for_selfcal, mode='restore', versionname='original')
new_vis_for_selfcal = "selfcal{1}{2}_copy_{0}".format(vis, "final", calnum)
os.system('rm -rf {0}'.format(new_vis_for_selfcal))
os.system('rm -rf {0}.flagversions'.format(new_vis_for_selfcal))
split(vis=vis_for_selfcal, outputvis=new_vis_for_selfcal,
datacolumn='corrected')
flagmanager(vis=new_vis_for_selfcal, mode='save', versionname='original')
vis_for_selfcal = new_vis_for_selfcal
# pointsource cleaning only (this doesn't work very well for W51)
# don't bother doing it if multiscale is empty
if multiscale:
selfcal_image = 'selfcal_{0}_{1}_final'.format(fieldstr,spwname,calnum)
for suffix in clean_output_suffixes:
os.system("rm -rf "+selfcal_image+suffix)
clean(vis=vis_for_selfcal,imagename=selfcal_image,field=field, mode='mfs',
threshold=threshold,
mask=cleanboxes, weighting=weighting, robust=robust, niter=deepniter,
psfmode=psfmode, imsize=imsize, cell=cell, nterms=1,
usescratch=False, **kwargs)
exportfits(imagename=selfcal_image+".image",
fitsimage=selfcal_image+".fits", overwrite=True,
dropdeg=True)
exportfits(imagename=selfcal_image+".model",
fitsimage=selfcal_image+".model.fits", overwrite=True,
dropdeg=True)
exportfits(imagename=selfcal_image+".residual",
fitsimage=selfcal_image+".residual.fits", overwrite=True,
dropdeg=True)
selfcal_image = 'selfcal_{0}_{1}_final_multiscale'.format(fieldstr,spwname,calnum)
for suffix in clean_output_suffixes:
os.system("rm -rf "+selfcal_image+suffix)
clean(vis=vis_for_selfcal,imagename=selfcal_image,field=field, mode='mfs',
threshold=threshold,
psfmode=psfmode, nterms=1, weighting=weighting, robust=robust,
multiscale=multiscale, mask=cleanboxes, niter=deepniter,
imsize=imsize, cell=cell, usescratch=False, **kwargs)
if pointclean:
clean(vis=vis_for_selfcal,imagename=selfcal_image,field=field, mode='mfs',
threshold=threshold,
psfmode=psfmode, nterms=1, weighting=weighting, robust=robust,
mask=cleanboxes, niter=deepniter,
imsize=imsize, cell=cell, usescratch=False, **kwargs)
exportfits(imagename=selfcal_image+".image",
fitsimage=selfcal_image+".fits", overwrite=True,
dropdeg=True)
exportfits(imagename=selfcal_image+".model",
fitsimage=selfcal_image+".model.fits", overwrite=True,
dropdeg=True)
exportfits(imagename=selfcal_image+".residual",
fitsimage=selfcal_image+".residual.fits", overwrite=True,
dropdeg=True)
return imrms
def imsmooth3(imagename,outmask,chans,beam_size,template,pixelmin=10):
chnoise = ch_noise(imagename,chans)
factor1, factor2, factor3 = 3, 9, 15
# imsmooth
os.system("rm -rf "+imagename+".smooth1")
imsmooth(imagename=imagename,
targetres=True,
major=str(beam_size*factor1)+"arcsec",
minor=str(beam_size*factor1)+"arcsec",
pa="0deg",
outfile=imagename+".smooth1")
rms1 = imstat(imagename+".smooth1",chans=chnoise)["rms"][0]
os.system("rm -rf "+imagename+".smooth2")
imsmooth(imagename=imagename,
targetres=True,
major=str(beam_size*factor2)+"arcsec",
minor=str(beam_size*factor2)+"arcsec",
pa="0deg",
outfile=imagename+".smooth2")
rms2 = imstat(imagename+".smooth2",chans=chnoise)["rms"][0]
os.system("rm -rf "+imagename+".smooth3")
imsmooth(imagename=imagename,
targetres=True,
major=str(beam_size*factor3)+"arcsec",
minor=str(beam_size*factor3)+"arcsec",
pa="0deg",
outfile=imagename+".smooth3")
rms3 = imstat(imagename+".smooth3",chans=chnoise)["rms"][0]
# imregrid
easy_imregrid(imagename+".smooth1",template,axes=[0,1])
easy_imregrid(imagename+".smooth2",template,axes=[0,1])
easy_imregrid(imagename+".smooth3",template,axes=[0,1])
# immath: snr should be 4, but 8 for iras13120
os.system("rm -rf "+imagename+".smooth1.mask")
immath(imagename=imagename+".smooth1.regrid",
mode="evalexpr",
expr="iif(IM0>="+str(rms1*4)+",1.0,0.0)",
outfile=imagename+".smooth1.mask")
os.system("rm -rf "+imagename+".smooth2.mask")
immath(imagename=imagename+".smooth2.regrid",
mode="evalexpr",
expr="iif(IM0>="+str(rms2*4)+",1.0,0.0)",
outfile=imagename+".smooth2.mask")
os.system("rm -rf "+imagename+".smooth3.mask")
immath(imagename=imagename+".smooth3.regrid",
mode="evalexpr",
expr="iif(IM0>="+str(rms3*4)+",1.0,0.0)",
outfile=imagename+".smooth3.mask")
os.system("rm -rf "+outmask)
immath(imagename=[imagename+".smooth1.mask",
imagename+".smooth2.mask",
imagename+".smooth3.mask"],
mode="evalexpr",
expr="iif(IM0+IM1+IM2>=3.0,1.0,0.0)",
outfile=outmask)
os.system("rm -rf "+imagename+".smooth1.regrid")
os.system("rm -rf "+imagename+".smooth2.regrid")
os.system("rm -rf "+imagename+".smooth3.regrid")
beamarea = beam_area(imagename)
remove_smallmask(outmask,beamarea,pixelmin)
easy_imsmooth(image_cube_depb,beams[i],delete_original=True)
image_cube_depb_sm = image_cube_depb + ".smooth"
# mv to galname/ directory
os.system("mkdir "+dir_gal)
easy_imregrid(image_cube_sm,temp_rebinpix,axes=[0,1],delete_original=True)
easy_imregrid(image_cube_depb_sm,temp_rebinpix,axes=[0,1],delete_original=True)
image_cube2 = dir_gal+galnames[i]+"_co21_"+suffix+".cube"
os.system("rm -rf "+image_cube2)
os.system("cp -r "+image_cube_sm+".regrid "+image_cube2)
os.system("cp -r "+image_cube_depb_sm+".regrid "+image_cube2+".depb")
# immoments
chnoise = ch_noise(image_cube2,chanss[i])
rms = imstat(image_cube2,chans=chnoise)["rms"][0]
moment_maps(image_cube2,chanss[i],outmask,rms*snr_moms[i],beams[i])
# pb masking
mask_pb = dir_gal+galnames[i]+"_pb_"+suffix+".mask"
peak = imhead(pb_map,mode="list")["datamax"]
createmask(pb_map,peak*pbcuts[i],mask_pb)
images_moment = glob.glob(image_cube2 + ".moment*")
for k in range(len(images_moment)):
outfile = images_moment[k].replace(".cube","")
if galnames[i] == "ngc3110":
os.system("mv " + images_moment[k] + " " \
+ images_moment[k].replace(".cube",""))
else:
os.system("rm -rf " + outfile)
