# Acquire information of the fit to the model of secondary
fit_pack = mu.model_secondary(if1='secondary_uvfmeas_7700_log.txt',
if2='secondary_uvfmeas_9500_log.txt')
# Now move things into a directory structure
mu.mv_uv('notsys')
# Reload the data with Tsys and use model as reference flux model
for index, freq in enumerate(FREQS):
ifsel = index + 1
primary = f"{mu.primary}.{freq}"
secondary = f"{mu.secondary}.{freq}"
mosaic = f"{mu.science}.{freq}"
# Obtain mflux string from the fitpack information
mfflux = mu.mfcal_flux(fit_pack, ifcal=ifsel)
# Load in the data
atlod = m(f"atlod in=raw/*C3171 options=rfiflag,birdie,xycorr,noauto "\
f"ifsel={ifsel} out=data{ifsel}.uv").run()
logger.log(logging.INFO, atlod)
# Flag the known bad channels out
if ifsel == 1:
mu.uvflag(atlod.out, mu.flags_7)
else:
mu.uvflag(atlod.out, mu.flags_9)
# Split the data up
uvsplit = m(f"uvsplit vis={atlod.out} options=mosaic ").run()
logger.log(logging.INFO, uvsplit)
print(uvsplit)
# Automated flagging
pgflag = mu.mirstr(f"pgflag vis={secondary} command='<b' stokes=i,q,u,v flagpar=8,5,5,3,6,3 options=nodisp").run()
print(pgflag)
pgflag = mu.mirstr(f"pgflag vis={secondary} command='<b' stokes=i,v,q,u flagpar=8,2,2,3,6,3 options=nodisp").run()
print(pgflag)
pgflag = mu.mirstr(f"pgflag vis={secondary} command='<b' stokes=i,v,u,q flagpar=8,2,2,3,6,3 options=nodisp").run()
print(pgflag)
sec_model = mu.model_secondary()
mfflux = mu.mfcal_flux(sec_model, ifcal=1)
print(mfflux)
# Primary calibration
mfcal = mu.mirstr(f'mfcal vis={secondary} flux={mfflux} interval=0.1').run()
gpcal = mu.mirstr(f"gpcal vis={secondary} interval=0.1 nfbin={NFBIN} flux={mfflux.split(',')[0]} spec={mfflux.split(',')[1]},{mfflux.split(',')[2]} options=xyvary,qusolve").run()
print(mfcal)
print(gpcal)
# Automated flagging
pgflag = mu.mirstr(f"pgflag vis={secondary} command='<b' stokes=i,q,u,v flagpar=8,5,5,3,6,3 options=nodisp").run()
print(pgflag)
pgflag = mu.mirstr(f"pgflag vis={secondary} command='<b' stokes=i,v,q,u flagpar=8,2,2,3,6,3 options=nodisp").run()