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selfcalv20.py
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selfcalv20.py
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import matplotlib
matplotlib.use('GTK')
import numpy
import os
import sys
from scipy import interpolate
import time
from subprocess import Popen, PIPE
import pyrap.tables as pt
import pyrap.images as pi
import uuid
pi = numpy.pi
import pwd
username = pwd.getpwuid(os.getuid())[0]
from facet_utilities import run, bg, angsep, getcpu, getmem
SCRIPTPATH = os.path.dirname(os.path.abspath(__file__))
# to run casa when not logged in
# Step 0. Run in screen
# Step 1. Xvfb :5 &
# Step 2. setenv DISPLAY :5
# run the program in screen and to exit screen: screen -d
def robust_sigma(in_y, zero=0):
"""
Calculate a resistant estimate of the dispersion of
a distribution. For an uncontaminated distribution,
this is identical to the standard deviation.
Use the median absolute deviation as the initial
estimate, then weight points using Tukey Biweight.
See, for example, Understanding Robust and
Exploratory Data Analysis, by Hoaglin, Mosteller
and Tukey, John Wiley and Sons, 1983.
.. note:: ROBUST_SIGMA routine from IDL ASTROLIB.
Examples
--------
>>> result = robust_sigma(in_y, zero=1)
Parameters
----------
in_y : array_like
Vector of quantity for which the dispersion is
to be calculated
zero : int
If set, the dispersion is calculated w.r.t. 0.0
rather than the central value of the vector. If
Y is a vector of residuals, this should be set.
Returns
-------
out_val : float
Dispersion value. If failed, returns -1.
"""
# Flatten array
y = in_y.ravel()
eps = 1.0E-20
c1 = 0.6745
c2 = 0.80
c3 = 6.0
c4 = 5.0
c_err = -1.0
min_points = 3
if zero:
y0 = 0.0
else:
y0 = numpy.median(y)
dy = y - y0
del_y = abs( dy )
# First, the median absolute deviation MAD about the median:
mad = numpy.median( del_y ) / c1
# If the MAD=0, try the MEAN absolute deviation:
if mad < eps:
mad = del_y.mean() / c2
if mad < eps:
return 0.0
# Now the biweighted value:
u = dy / (c3 * mad)
uu = u * u
q = numpy.where(uu <= 1.0)
count = len(q[0])
if count < min_points:
module_logger.warn('ROBUST_SIGMA: This distribution is TOO WEIRD! '
'Returning {}'.format(c_err))
return c_err
numerator = numpy.sum( (y[q] - y0)**2.0 * (1.0 - uu[q])**4.0 )
n = y.size
den1 = numpy.sum( (1.0 - uu[q]) * (1.0 - c4 * uu[q]) )
siggma = n * numerator / ( den1 * (den1 - 1.0) )
if siggma > 0:
out_val = numpy.sqrt( siggma )
else:
out_val = 0.0
return out_val
def meanclip(indata, clipsig=4.0, maxiter=10, converge_num=0.001, verbose=0):
"""
Computes an iteratively sigma-clipped mean on a
data set. Clipping is done about median, but mean
is returned.
.. note:: MYMEANCLIP routine from ACS library.
:History:
* 21/10/1998 Written by RSH, RITSS
* 20/01/1999 Added SUBS, fixed misplaced paren on float call, improved doc. RSH
* 24/11/2009 Converted to Python. PLL.
Examples
--------
>>> mean, sigma = meanclip(indata)
Parameters
----------
indata: array_like
Input data.
clipsig: float
Number of sigma at which to clip.
maxiter: int
Ceiling on number of clipping iterations.
converge_num: float
If the proportion of rejected pixels is less than
this fraction, the iterations stop.
verbose: {0, 1}
Print messages to screen?
Returns
-------
mean: float
N-sigma clipped mean.
sigma: float
Standard deviation of remaining pixels.
"""
# Flatten array
skpix = indata.reshape( indata.size, )
ct = indata.size
iter = 0; c1 = 1.0 ; c2 = 0.0
while (c1 >= c2) and (iter < maxiter):
lastct = ct
medval = numpy.median(skpix)
sig = numpy.std(skpix)
wsm = numpy.where( abs(skpix-medval) < clipsig*sig )
ct = len(wsm[0])
if ct > 0:
skpix = skpix[wsm]
c1 = abs(ct - lastct)
c2 = converge_num * lastct
iter += 1
# End of while loop
mean = numpy.mean( skpix )
sigma = robust_sigma( skpix )
if verbose:
prf = 'MEANCLIP:'
print '%s %.1f-sigma clipped mean' % (prf, clipsig)
print '%s Mean computed in %i iterations' % (prf, iter)
print '%s Mean = %.6f, sigma = %.6f' % (prf, mean, sigma)
return mean, sigma
def create_merged_parmdb_spline(parmdb_a,parmdb_p, parmdb_t, parmdb_out,cellsizetime_a, cellsizetime_b):
import lofar.parmdb
pdb_a = lofar.parmdb.parmdb(parmdb_a)
pdb_p = lofar.parmdb.parmdb(parmdb_p)
pdb_t = lofar.parmdb.parmdb(parmdb_t)
parms_a = pdb_a.getValuesGrid("*")
parms_p = pdb_p.getValuesGrid("*")
parms_t = pdb_t.getValuesGrid("*")
os.system('rm -rf ' + parmdb_out)
keynames_p = parms_p.keys()
keynames_a = parms_a.keys()
for key in keynames_p:
# copy over the scalar phases
scalarp = numpy.copy(parms_p[key]['values'][:,0])
parms_t[key]['values'][:,0] = scalarp
for key in keynames_a:
print key
tmp1 = numpy.copy(parms_t[key]['values'][:,0])
tmp2 = numpy.copy(parms_a[key]['values'][:,0])
time_t = 1.0*numpy.arange(len(tmp1))
time_a = (numpy.float(cellsizetime_a/cellsizetime_p)*numpy.arange(len(tmp2))) + (0.5*cellsizetime_a/cellsizetime_p)
el = ((numpy.max(time_t) - numpy.max(time_a[:-1]))/2 )+ numpy.max(time_a[:-1])
time_a[len(time_a)-1] = el
# to avoid edge effects in spline add values edges to be the last values from BBS
time_a = numpy.copy(numpy.append(0,time_a))
time_a = numpy.append(time_a, numpy.max(time_t))
val_start = tmp2[0]
val_end = tmp2[len(tmp2)-1]
tmp2 = numpy.copy(numpy.append(val_start,tmp2))
tmp2 = numpy.append(tmp2,val_end)
x = numpy.copy(time_a)
y = numpy.copy(tmp2)
# SPLINE INTERPOL of AMPS
tck = interpolate.splrep(x,y,s=0)
xnew = time_t
ynew = interpolate.splev(xnew,tck,der=0)
parms_t[key]['values'][:,0] = ynew
pdbnew = lofar.parmdb.parmdb(parmdb_out, create=True)
pdbnew.addValues(parms_t)
pdbnew.flush()
#lofar.expion.parmdbmain.store_parms(parmdb_out, parms_t, create_new = True)
return
def create_merged_parmdb(ms, parmdb_a,parmdb_p, parmdb_t, parmdb_out,cellsizetime_a, cellsizetime_b):
import lofar.parmdb
#pdb_pre = lofar.parmdb.parmdb(pre_apply_parmdb)
pdb_a = lofar.parmdb.parmdb(parmdb_a)
pdb_p = lofar.parmdb.parmdb(parmdb_p)
pdb_t = lofar.parmdb.parmdb(parmdb_t)
#parms_pre = pdb_pre.getValuesGrid("*")
parms_a = pdb_a.getValuesGrid("*")
parms_p = pdb_p.getValuesGrid("*")
parms_t = pdb_t.getValuesGrid("*")
os.system('rm -rf ' + parmdb_out)
keynames_p = parms_p.keys()
keynames_a = parms_a.keys()
length = numpy.int(cellsizetime_b)
for key in keynames_p:
# copy over the CommonScalar phases, TEC, clock
# just to get axis length
tmp1 = numpy.copy(parms_t[key]['values'][:, 0])
for idx in range(len(tmp1)):
el = numpy.float(idx)/numpy.float(length)
el = numpy.int(numpy.floor(el))
parms_t[key]['values'][idx,0] = numpy.copy(parms_p[key]['values'][el,0])
pol_list = ['0:0','1:1']
gain = 'Gain'
anttab = pt.table(ms + '/ANTENNA')
antenna_list = anttab.getcol('NAME')
anttab.close()
#length = numpy.int(cellsizetime_a/cellsizetime_b)
length = numpy.int(cellsizetime_a)
for pol in pol_list:
for antenna in antenna_list:
#real1 = numpy.copy(parms_pre[gain + ':' + pol + ':Real:'+ antenna]['values'][:, 0])
real2 = numpy.copy(parms_a[gain + ':' + pol + ':Real:'+ antenna]['values'][:, 0])
#imag1 = numpy.copy(parms_pre[gain + ':' + pol + ':Imag:'+ antenna]['values'][:, 0])
imag2 = numpy.copy(parms_a[gain + ':' + pol + ':Imag:'+ antenna]['values'][:, 0])
# just to get axis length
tmp1 = numpy.copy(parms_t[gain + ':' + pol + ':Imag:'+ antenna]['values'][:, 0])
#G1 = real1 + 1j*imag1
G2 = real2 + 1j*imag2
Gnew = G2
for idx in range(len(tmp1)):
el = numpy.float(idx)/numpy.float(length)
el = numpy.int(numpy.floor(el))
parms_t[gain + ':' + pol + ':Imag:'+ antenna]['values'][idx,0] = numpy.copy(numpy.imag(Gnew[el]))
parms_t[gain + ':' + pol + ':Real:'+ antenna]['values'][idx,0] = numpy.copy(numpy.real(Gnew[el]))
pdbnew = lofar.parmdb.parmdb(parmdb_out, create=True)
pdbnew.addValues(parms_t)
pdbnew.flush()
return
def create_merged_parmdb_scalarphase(ms, parmdb_p, parmdb_t, parmdb_out,cellsizetime_b):
import lofar.parmdb
pdb_p = lofar.parmdb.parmdb(parmdb_p)
pdb_t = lofar.parmdb.parmdb(parmdb_t)
parms_p = pdb_p.getValuesGrid("*")
parms_t = pdb_t.getValuesGrid("*")
os.system('rm -rf ' + parmdb_out)
keynames_p = parms_p.keys()
length = numpy.int(cellsizetime_b)
for key in keynames_p:
# copy over the CommonScalar phases
# just to get axis length
tmp1 = numpy.copy(parms_t[key]['values'][:, 0])
for idx in range(len(tmp1)):
el = numpy.float(idx)/numpy.float(length)
el = numpy.int(numpy.floor(el))
parms_t[key]['values'][idx,0] = numpy.copy(parms_p[key]['values'][el,0])
pdbnew = lofar.parmdb.parmdb(parmdb_out, create=True)
pdbnew.addValues(parms_t)
pdbnew.flush()
return
def make_image(mslist, cluster, callnumber, threshpix, threshisl, nterms, atrous_do, imsize, \
column, uvrange, increaseSNR_via_freqcoverage, numchanperms, nblockconcat):
do_mask = True
niter = 1000 # 7500 causes nasty clean artifacts
mscale = 'False'
if atrous_do:
mscale = 'True'
average = True # average the data a lot to speed up the imaging,
# ONLY average for small FOV, otherwise timesmearing is a problem
#average data
if average:
b=bg(maxp=10)
for ms in (mslist):
ndppp_parset = ms + '_NDPPP.parset'
ndppplog = ndppp_parset.replace('.parset','.log')
os.system('rm -f ' + ndppp_parset)
output = ms + '.tmpavg'
os.system('rm -rf ' + output)
f=open(ndppp_parset, 'w')
f.write('msin = %s\n' % ms)
f.write('msin.datacolumn = %s\n' % column)
if increaseSNR_via_freqcoverage == 'True':
f.write('msin.startchan = %s\n' % str(numchanperms*nblockconcat))
f.write('msin.nchan = %s\n' % str(numchanperms))
f.write('msout = %s\n' % output)
f.write('msout.writefullresflag=False\n')
f.write('steps=[avg]\n')
f.write('avg.type = squash\n')
f.write('avg.freqstep = 1\n')
if imsize <= 800:
f.write('avg.timestep = 12\n') # is the default
else:
if imsize <= 1200:
f.write('avg.timestep = 6\n')
else:
f.write('avg.timestep = 3\n')
f.close()
ndpppcmd = 'NDPPP ' + ndppp_parset+ ' >'+ndppplog+' 2>&1'
b.run(ndpppcmd)
#os.system (ndpppcmd)
b.wait()
ms = ''
for m in mslist:
if average:
ms = ms + ' ' + m + '.tmpavg'
else:
ms = ms + ' ' + m
imout = 'im'+ callnumber +'_cluster'+cluster+'nm'
print ms + ' ' + imout + ' ' + 'None' + ' ' + '1mJy' + ' ' + str(niter) + ' ' + str(nterms)
os.system('rm -rf ' + imout + '.*')
if do_mask:
if cluster == 'a2256': ## special case for a2256
niter = niter*15 # clean very deep here
os.system('casapy --nogui -c ' + SCRIPTPATH +'/casapy_cleanv4.py ' + ms + ' ' + imout + ' ' + 'None' +\
' ' + '1mJy' + ' ' + str(niter) + ' ' + str(nterms) + ' ' + str(imsize) + ' ' + mscale + ' ' + uvrange + ' ' +\
str(cellsize))
# make mask
if nterms > 1:
os.system('python ' + SCRIPTPATH + '/makecleanmask.py --threshpix '+str(threshpix)+\
' --threshisl '+str(threshisl) +' --atrous_do '+ str(atrous_do) +' ' +imout +'.image.tt0')
else:
os.system('python '+ SCRIPTPATH + '/makecleanmask.py --threshpix '+str(threshpix)+\
' --threshisl '+str(threshisl) +' --atrous_do '+ str(atrous_do) + ' ' + imout +'.image')
# clean image with manual mask
mask = imout+'.cleanmask'
niter = 1000
imout = 'im'+ callnumber +'_cluster'+cluster
os.system('rm -rf ' + imout + '.*')
if region != 'empty' : ## special cases
niter = niter*3 # deeper clean in region as they are usualy used for extended sources
os.system('casapy --nogui -c '+ SCRIPTPATH +'/casapy_cleanv4.py '+ ms + ' ' + imout + ' ' + mask+','+region + \
' ' + '1mJy' + ' ' + str(niter) + ' ' + str(nterms) + ' ' + str(imsize) + ' ' + mscale + ' ' + uvrange + ' ' +\
str(cellsize))
else:
os.system('casapy --nogui -c '+ SCRIPTPATH +'/casapy_cleanv4.py '+ ms + ' ' + imout + ' ' + mask + \
' ' + '1mJy' + ' ' + str(niter) + ' ' + str(nterms) + ' ' + str(imsize) + ' ' + mscale + ' ' + uvrange + ' ' +\
str(cellsize))
# convert to FITS
if nterms > 1:
os.system('image2fits in=' + imout +'.image.tt0' + ' ' + 'out='+ imout + '.fits')
else:
os.system('image2fits in=' + imout +'.image' + ' ' + 'out='+ imout + '.fits')
if average:
print 'rm -rf ' + ms
os.system('rm -rf ' + ms)
mask = imout+'.mask'
return imout,mask
def run_stefcal(mslist, skymodel, parmdb, solvetype, FFT, uvrange, timestep, inputcol, \
increaseSNR_via_freqcoverage, numchanperms, nblockconcat):
# solve type can be "diagonal", "scalarphase"
# if FFT == True then solve out of MODEL_DATA
import pyrap.tables as pt
freq_tab = pt.table(mslist[len(mslist)/2] + '/SPECTRAL_WINDOW')
freq = freq_tab.getcol('REF_FREQUENCY')[0]
freq_tab.close()
wav = 299792458./freq
timestep = numpy.int(timestep)
# make the parset
os.system('rm -f stefcal.parset')
f=open('stefcal.parset', 'w')
f.write('msin="tmp.ms"\n')
f.write('msin.datacolumn=%s\n' % inputcol)
if FFT:
f.write('msin.modelcolumn=MODEL_DATA\n')
f.write('msout=.\n')
f.write('steps=[f,c]\n')
f.write('f.type="filter"\n')
f.write('f.blrange=[%s,1e30]\n' % (numpy.float(uvrange)*wav)) # convert to meters
f.write('c.type="gaincal"\n')
if FFT:
f.write('c.usemodelcolumn=True\n')
else:
skydb = skymodel + '.skydb'
#print "makesourcedb in=" + skymodel + " out=" +skydb + " format='<'"
os.system('rm -rf ' + skydb)
os.system("makesourcedb in=" + skymodel + " out=" +skydb + " format='<'")
f.write('c.usemodelcolumn=False\n')
f.write('c.sourcedb=%s\n' % skydb)
f.write('c.parmdb="tmp_instrumentdb"\n')
f.write('c.maxiter=100\n')
f.write('c.tolerance=1e-6\n')
f.write('c.usebeammodel=False\n')
f.write('c.usechannelfreq=False\n')
f.write('c.caltype=%s\n' % solvetype) # can be diagonal, fulljones, phaseonly or scalarphase
f.write('c.solint=%s\n' % timestep)
f.write('c.debuglevel=1\n')
f.close()
if increaseSNR_via_freqcoverage == 'True':
print 'Updating weights for solvetype', solvetype
b=bg(maxp=numpy.int(128/(((2*nblockconcat) +1)*numchanperms))) # to limit number of taql processes
for ms in mslist:
if solvetype == 'phaseonly' or solvetype == 'scalarphase':
b.run("taql 'update " + ms + " set WEIGHT_SPECTRUM=WEIGHT_SPECTRUM_SC'")
if solvetype == 'diagonal' or solvetype == 'fulljones':
b.run("taql 'update " + ms + " set WEIGHT_SPECTRUM=WEIGHT_SPECTRUM_AP'")
# Check if all taql processes are finished
b.wait()
ndppp_stefcal=bg(maxp=32)
for ms in mslist:
log = ms + '.stefcallog'
instrument = ms + '/' + parmdb
os.system('rm -rf ' + instrument)
cmd = 'NDPPP stefcal.parset ' + 'msin=' + ms + ' c.parmdb=' + instrument #+ ' & ' #>' + log
print cmd
ndppp_stefcal.run(cmd)
#os.system(cmd)
#time.sleep(2)
#time.sleep(30)
ndppp_stefcal.wait()
# check if NDPPP has finished
#cmd = "ps -f -u "+ username+ " | grep NDPPP | grep stefcal |grep -v grep | wc -l"
#output=numpy.int(Popen(cmd, shell=True, stdout=PIPE).communicate()[0])
#while output > 0 :
# time.sleep(10)
# output=numpy.int(Popen(cmd, shell=True, stdout=PIPE).communicate()[0])
return
def runbbs(mslist, skymodel, parset, parmdb, applycal, TEC):
#NOTE WORK FROM MODEL_DATA (contains correct phase data from 10SB calibration)
#NOTE WORK FROM DATA if TEC
if TEC:
if len(mslist) == 10000: # 34 does not work now!!!!!!
# this is a very special case for a full run, manually here to run with 3 solver controls
vdslist = ''
os.system('makevds /home/'+username+'/cep2.clusterdesc ' + ms)
vdslist = vdslist + ms + '.vds '
else:
vdslist = ''
for ms in mslist:
os.system('makevds /home/'+username+'/cep2.clusterdesc ' + ms)
vdslist = vdslist + ms + '.vds '
gds = 'tec.gds'
print vdslist
os.system('combinevds ' + gds + ' '+ vdslist)
key = str(uuid.uuid4())
key = key[0:12]
cmd1= 'calibrate -f --key ' + key + ' --cluster-desc /home/'+username+'/cep2.clusterdesc --instrument-name ' + parmdb + ' '
cmd2= '--db ldb001 --db-user postgres ' + gds + ' ' + parset + ' ' + skymodel + ' ' + '. > ' + gds + '.bbslog'
cmd = cmd1 + cmd2
print cmd
os.system(cmd)
else:
for ms in mslist:
log = ms + '.bbslog'
if applycal:
cmd = 'calibrate-stand-alone --parmdb-name ' + parmdb + ' ' + ms + ' ' + parset + ' ' + skymodel + '>' + log + ' 2>&1 &'
#cmd = 'calibrate-stand-alone -t 4 --parmdb-name ' + parmdb + ' ' + ms + ' ' + parset + ' ' + skymodel + '>' + log + '&'
else:
cmd = 'calibrate-stand-alone -f --parmdb-name ' + parmdb + ' ' + ms + ' ' + parset + ' ' + skymodel + '>' + log + ' 2>&1 &'
#cmd = 'calibrate-stand-alone -t 4 -f --parmdb-name ' + parmdb + ' ' + ms + ' ' + parset + ' ' + skymodel + '>' + log + '&'
print cmd
os.system(cmd)
time.sleep(10)
done = 0
while(done < len(mslist)):
done = 0
for ms in mslist:
cmd = "grep 'bbs-reducer terminated successfully.' " + ms + ".bbslog"
output=Popen(cmd, shell=True, stdout=PIPE).communicate()[0]
if 'INFO' in output:
done = done + 1
print ms, 'is done'
time.sleep(5)
return
def create_scalarphase_parset(timestep, TEC, clock, groups, FFT, uvrange):
bbs_parset = 'scalarphase.parset'
os.system('rm -f ' + bbs_parset)
f=open(bbs_parset, 'w')
if TEC:
f.write('Strategy.InputColumn = DATA\n')
f.write('Strategy.UseSolver = T\n')
else:
f.write('Strategy.InputColumn = MODEL_DATA\n')
f.write('Strategy.UseSolver = F\n')
f.write('Strategy.ChunkSize = 960\n')
f.write('Strategy.Steps = [solve,correct]\n')
if FFT:
f.write('Step.solve.Model.Sources = [@MODEL_DATA]\n')
else:
f.write('Step.solve.Model.Sources = []\n')
f.write('Step.solve.Model.Cache.Enable = T\n')
f.write('Step.solve.Model.Phasors.Enable = F\n')
f.write('Step.solve.Model.DirectionalGain.Enable = F\n')
f.write('Step.solve.Model.Gain.Enable = F\n')
if TEC:
f.write('Step.solve.Model.TEC.Enable = T\n')
f.write('Step.solve.Solve.CalibrationGroups = [%s]\n' % groups)
if clock:
f.write('Step.solve.Model.Clock.Enable = T\n')
f.write('Step.solve.Model.Rotation.Enable = F\n')
f.write('Step.solve.Model.CommonScalarPhase.Enable = T \n')
f.write('Step.solve.Operation = SOLVE\n')
if TEC:
if clock:
f.write('Step.solve.Solve.Parms = ["CommonScalarPhase:*","TEC:*","Clock:*"]\n')
else:
f.write('Step.solve.Solve.Parms = ["CommonScalarPhase:*","TEC:*"]\n')
else:
f.write('Step.solve.Solve.Parms = ["CommonScalarPhase:*"]\n')
f.write('Step.solve.Solve.CellSize.Freq = 0\n')
f.write('Step.solve.Solve.CellSize.Time = %s\n' % str(timestep))
f.write('Step.solve.Solve.CellChunkSize = 120\n')
f.write('Step.solve.Solve.PropagateSolutions = T\n')
f.write('Step.solve.Solve.Options.MaxIter = 100\n')
f.write('Step.solve.Solve.Options.LMFactor = 1.0\n')
f.write('Step.solve.Solve.Options.BalancedEqs = F\n')
f.write('Step.solve.Solve.Options.UseSVD = T\n')
f.write('Step.solve.Model.Beam.Enable = F\n')
f.write('Step.solve.Solve.UVRange = [%s]\n' % uvrange)
f.write('Step.solve.Solve.Mode = COMPLEX \n')
f.write('Step.correct.Model.Sources = []\n')
f.write('Step.correct.Model.CommonScalarPhase.Enable= T\n')
f.write('Step.correct.Model.Cache.Enable = T\n')
if TEC:
f.write('Step.correct.Model.TEC.Enable = T\n')
if clock:
f.write('Step.correct.Model.Clock.Enable = T\n')
f.write('Step.correct.Model.DirectionalGain.Enable = F\n')
f.write('Step.correct.Model.Gain.Enable = F\n')
f.write('Step.correct.Model.Phasors.Enable = F\n')
f.write('Step.correct.Operation = CORRECT\n')
f.write('Step.correct.Output.Column = CORRECTED_DATA\n')
f.write('Step.correct.Model.Beam.Enable = F\n')
f.write('Step.correct.Output.WriteCovariance = F\n')
f.close()
return bbs_parset
def create_scalarphase_parset_p(timestep, TEC, clock, groups, FFT, uvrange):
bbs_parset = 'scalarphase_p.parset'
os.system('rm -f ' + bbs_parset)
f=open(bbs_parset, 'w')
if TEC:
f.write('Strategy.InputColumn = DATA\n')
f.write('Strategy.UseSolver = T\n')
else:
f.write('Strategy.InputColumn = MODEL_DATA\n')
f.write('Strategy.UseSolver = F\n')
f.write('Strategy.ChunkSize = 960\n')
f.write('Strategy.Steps = [solve,correct]\n')
if FFT:
f.write('Step.solve.Model.Sources = [@MODEL_DATA]\n')
else:
f.write('Step.solve.Model.Sources = []\n')
f.write('Step.solve.Model.Cache.Enable = T\n')
f.write('Step.solve.Model.Phasors.Enable = F\n')
f.write('Step.solve.Model.DirectionalGain.Enable = F\n')
f.write('Step.solve.Model.Gain.Enable = F\n')
if TEC:
f.write('Step.solve.Model.TEC.Enable = T\n')
f.write('Step.solve.Solve.CalibrationGroups = [%s]\n' % groups)
if clock:
f.write('Step.solve.Model.Clock.Enable = T\n')
f.write('Step.solve.Model.Rotation.Enable = F\n')
f.write('Step.solve.Model.CommonScalarPhase.Enable = T \n')
f.write('Step.solve.Operation = SOLVE\n')
if TEC:
if clock:
f.write('Step.solve.Solve.Parms = ["CommonScalarPhase:*","TEC:*","Clock:*"]\n')
else:
f.write('Step.solve.Solve.Parms = ["CommonScalarPhase:*","TEC:*"]\n')
else:
f.write('Step.solve.Solve.Parms = ["CommonScalarPhase:*"]\n')
f.write('Step.solve.Solve.CellSize.Freq = 0\n')
f.write('Step.solve.Solve.CellSize.Time = %s\n' % str(timestep))
f.write('Step.solve.Solve.CellChunkSize = 120\n')
f.write('Step.solve.Solve.PropagateSolutions = T\n')
f.write('Step.solve.Solve.Options.MaxIter = 100\n')
f.write('Step.solve.Solve.Options.LMFactor = 1.0\n')
f.write('Step.solve.Solve.Options.BalancedEqs = F\n')
f.write('Step.solve.Solve.Options.UseSVD = T\n')
f.write('Step.solve.Model.Beam.Enable = F\n')
f.write('Step.solve.Solve.UVRange = [%s]\n' % uvrange)
f.write('Step.solve.Solve.Mode = COMPLEX \n')
f.write('Step.correct.Model.Sources = []\n')
f.write('Step.correct.Model.CommonScalarPhase.Enable= T\n')
f.write('Step.correct.Model.Cache.Enable = T\n')
f.write('Step.correct.Model.DirectionalGain.Enable = F\n')
if TEC:
f.write('Step.correct.Model.TEC.Enable = T\n')
if clock:
f.write('Step.correct.Model.Clock.Enable = T\n')
f.write('Step.correct.Model.Gain.Enable = F\n')
f.write('Step.correct.Model.Phasors.Enable = F\n')
f.write('Step.correct.Operation = CORRECT\n')
f.write('Step.correct.Output.Column = CORRECTED_DATA_PHASE\n')
f.write('Step.correct.Model.Beam.Enable = F\n')
f.write('Step.correct.Output.WriteCovariance = F\n')
f.close()
return bbs_parset
def create_amponly_parset(timestep, FFT, uvrange):
bbs_parset = 'amplitudeonly.parset'
os.system('rm -f ' + bbs_parset)
f=open(bbs_parset, 'w')
f.write('Strategy.InputColumn = CORRECTED_DATA_PHASE\n')
f.write('Strategy.ChunkSize = 1440\n')
f.write('Strategy.UseSolver = F\n')
f.write('Strategy.Steps = [solve]\n')
if FFT:
f.write('Step.solve.Model.Sources = [@MODEL_DATA]\n')
else:
f.write('Step.solve.Model.Sources = []\n')
f.write('Step.solve.Model.Cache.Enable = T\n')
f.write('Step.solve.Model.Phasors.Enable = F\n') ## T
f.write('Step.solve.Model.DirectionalGain.Enable = F\n')
f.write('Step.solve.Model.Gain.Enable = T\n')
f.write('Step.solve.Model.Rotation.Enable = F\n')
f.write('Step.solve.Model.CommonScalarPhase.Enable = F\n')
f.write('Step.solve.Operation = SOLVE\n')
f.write('Step.solve.Solve.Parms = ["Gain:0:0:*","Gain:1:1:*"]\n') ## ["Gain:0:0:Ampl:*","Gain:1:1:Ampl:*"]
# to test
f.write('Step.solve.Solve.CellSize.Freq = 0\n')
f.write('Step.solve.Solve.CellSize.Time = %s\n' %str(timestep))
f.write('Step.solve.Solve.CellChunkSize = 12\n')
f.write('Step.solve.Solve.PropagateSolutions = T\n')
f.write('Step.solve.Solve.Options.MaxIter = 100\n')
f.write('Step.solve.Solve.Options.LMFactor = 1.0\n')
f.write('Step.solve.Solve.Options.BalancedEqs = F\n')
f.write('Step.solve.Solve.Options.UseSVD = T\n')
f.write('Step.solve.Model.Beam.Enable = F\n')
f.write('Step.solve.Solve.UVRange = [%s]\n' % uvrange) ## [600]
f.write('Step.solve.Solve.Mode = COMPLEX\n')
f.close()
return bbs_parset
def create_scalarphase_parset_p2(timestep, TEC, clock, groups, FFT, uvrange):
bbs_parset = 'scalarphase_p2.parset'
os.system('rm -f ' + bbs_parset)
f=open(bbs_parset, 'w')
f.write('Strategy.InputColumn = CORRECTED_DATA_AMP\n')
f.write('Strategy.ChunkSize = 960\n')
if TEC:
f.write('Strategy.UseSolver = T\n')
else:
f.write('Strategy.UseSolver = F\n')
f.write('Strategy.Steps = [solve,correct]\n')
if FFT:
f.write('Step.solve.Model.Sources = [@MODEL_DATA]\n')
else:
f.write('Step.solve.Model.Sources = []\n')
f.write('Step.solve.Model.Cache.Enable = T\n')
f.write('Step.solve.Model.Phasors.Enable = F\n')
f.write('Step.solve.Model.DirectionalGain.Enable = F\n')
f.write('Step.solve.Model.Gain.Enable = F\n')
if TEC:
f.write('Step.solve.Model.TEC.Enable = T\n')
f.write('Step.solve.Solve.CalibrationGroups = [%s]\n' % groups)
if clock:
f.write('Step.solve.Model.Clock.Enable = T\n')
f.write('Step.solve.Model.Rotation.Enable = F\n')
f.write('Step.solve.Model.CommonScalarPhase.Enable = T \n')
f.write('Step.solve.Operation = SOLVE\n')
if TEC:
if clock:
f.write('Step.solve.Solve.Parms = ["CommonScalarPhase:*","TEC:*","Clock:*"]\n')
else:
f.write('Step.solve.Solve.Parms = ["CommonScalarPhase:*","TEC:*"]\n')
else:
f.write('Step.solve.Solve.Parms = ["CommonScalarPhase:*"]\n')
f.write('Step.solve.Solve.CellSize.Freq = 0\n')
f.write('Step.solve.Solve.CellSize.Time = %s\n' % str(timestep))
f.write('Step.solve.Solve.CellChunkSize = 120\n')
f.write('Step.solve.Solve.PropagateSolutions = T\n')
f.write('Step.solve.Solve.Options.MaxIter = 100\n')
f.write('Step.solve.Solve.Options.LMFactor = 1.0\n')
f.write('Step.solve.Solve.Options.BalancedEqs = F\n')
f.write('Step.solve.Solve.Options.UseSVD = T\n')
f.write('Step.solve.Model.Beam.Enable = F\n')
f.write('Step.solve.Solve.UVRange = [%s]\n' % uvrange)
f.write('Step.solve.Solve.Mode = COMPLEX \n')
f.write('Step.correct.Model.Sources = []\n')
f.write('Step.correct.Model.CommonScalarPhase.Enable= T\n')
f.write('Step.correct.Model.Cache.Enable = T\n')
f.write('Step.correct.Model.DirectionalGain.Enable = F\n')
f.write('Step.correct.Model.Gain.Enable = F\n')
if TEC:
f.write('Step.correct.Model.TEC.Enable = T\n')
if clock:
f.write('Step.correct.Model.Clock.Enable = T\n')
f.write('Step.correct.Model.Phasors.Enable = F\n')
f.write('Step.correct.Operation = CORRECT\n')
f.write('Step.correct.Output.Column = CORRECTED_DATA_PHASE\n')
f.write('Step.correct.Model.Beam.Enable = F\n')
f.write('Step.correct.Output.WriteCovariance = F\n')
f.close()
return bbs_parset
def find_imagenoise(imagename):
import pyrap.images as pi
im = pi.image(imagename)
image = numpy.copy(im.getdata())
mean, rms = meanclip(image)
#im.close()
return rms, numpy.abs(numpy.max(image)/numpy.min(image))
def plotds9image(mslist, cluster):
import glob
sclmax = 0.04*numpy.sqrt(8.)/numpy.sqrt(numpy.float(len(mslist)))
sclmin = -0.5*sclmax
listofimages = glob.glob('im*_cluster' + cluster + '.fits')
if len(listofimages) > 9: # to get the image order right in ds9
cmd = 'ds9 im?_cluster' + cluster + '.fits ' +'im??_cluster' + cluster + '.fits'+ ' -view buttons no -view info no ' + \
'-view panner no -view magnifier no -geometry 800x800 ' + \
'-scale limits ' + str(sclmin) + ' ' + str(sclmax) + ' ' + \
'-scale match -cmap bb -cmap match -zoom to fit -match frame wcs -colorbar no ' + \
'-saveimage jpg 90 im_cluster' + cluster +'.jpg' +' -quit&'
else:
cmd = 'ds9 im?_cluster' + cluster + '.fits' + ' -view buttons no -view info no ' + \
'-view panner no -view magnifier no -geometry 800x800 ' + \
'-scale limits ' + str(sclmin) + ' ' + str(sclmax) + ' ' + \
'-scale match -cmap bb -cmap match -zoom to fit -match frame wcs -colorbar no ' + \
'-saveimage jpg 90 im_cluster' + cluster +'.jpg' +' -quit&'
print cmd
os.system(cmd)
return
def concat_neighbor_block(basename, resolution, source, mslist, nblockconcat):
ndp=bg(maxp=32)
for ms in mslist:
number = numpy.int(ms.split(basename)[1].split('.')[0].split('SB')[1].split('-')[0])
BANDS = range(number-(10*nblockconcat),number+(10*nblockconcat)+9,10)
ndppplist = ["{name:s}_SB{b1:03d}-{b2:03d}.{sr:s}.ms".format(name=basename,sr=source,b1=b,b2=b+9) for b in BANDS]
f=open(ms+'.concatparset','w')
f.write('msin='+str(ndppplist)+'\nmsin.datacolumn = DATA\nmsin.missingdata=True\nmsin.orderms=False\nmsout='+ ms+'.concat'+'\nsteps=[]\n')
f.close()
os.system('rm -rf ' + ms + '.concat')
cmd = 'NDPPP ' + ms + '.concatparset'
print cmd
ndp.run(cmd)
ndp.wait()
return
def create_correct_dde_scalarphase_ap_parset(parset, numchanperms):
f=open(parset, 'w')
f.write('Strategy.InputColumn = DATA\n')
f.write('Strategy.ChunkSize = %s\n' % numpy.int(7200/numchanperms))
f.write('Strategy.Steps = [correct]\n')
f.write('Step.correct.Model.Sources = []\n')
f.write('Step.correct.Model.CommonScalarPhase.Enable= T\n')
f.write('Step.correct.Model.Cache.Enable = T\n')
f.write('Step.correct.Model.DirectionalGain.Enable = F\n')
f.write('Step.correct.Model.Gain.Enable = T\n')
f.write('Step.correct.Model.Phasors.Enable = F\n')
f.write('Step.correct.Operation = CORRECT\n')
f.write('Step.correct.Output.Column = CORRECTED_DATA\n')
f.write('Step.correct.Model.Beam.Enable = F\n')
f.write('Step.correct.Output.WriteCovariance = F\n')
f.close()
return
def create_correct_scalarphasep2_parset(parset, numchanperms):
f=open(parset, 'w')
f.write('Strategy.InputColumn = DATA\n')
f.write('Strategy.ChunkSize = %s\n' % numpy.int(7200/numchanperms))
f.write('Strategy.Steps = [correct]\n')
f.write('Step.correct.Model.Sources = []\n')
f.write('Step.correct.Model.CommonScalarPhase.Enable= T\n')
f.write('Step.correct.Model.Cache.Enable = T\n')
f.write('Step.correct.Model.DirectionalGain.Enable = F\n')
f.write('Step.correct.Model.Gain.Enable = F\n')
f.write('Step.correct.Model.Phasors.Enable = F\n')
f.write('Step.correct.Operation = CORRECT\n')
f.write('Step.correct.Output.Column = CORRECTED_DATA_PHASE\n')
f.write('Step.correct.Model.Beam.Enable = F\n')
f.write('Step.correct.Output.WriteCovariance = F\n')
f.close()
return
def create_correct_scalarphase_parset(parset, numchanperms):
f=open(parset, 'w')
f.write('Strategy.InputColumn = DATA\n')
f.write('Strategy.ChunkSize = %s\n' % numpy.int(7200/numchanperms))
f.write('Strategy.Steps = [correct]\n')
f.write('Step.correct.Model.Sources = []\n')
f.write('Step.correct.Model.CommonScalarPhase.Enable= T\n')
f.write('Step.correct.Model.Cache.Enable = T\n')
f.write('Step.correct.Model.DirectionalGain.Enable = F\n')
f.write('Step.correct.Model.Gain.Enable = F\n')
f.write('Step.correct.Model.Phasors.Enable = F\n')
f.write('Step.correct.Operation = CORRECT\n')
f.write('Step.correct.Output.Column = CORRECTED_DATA\n')
f.write('Step.correct.Model.Beam.Enable = F\n')
f.write('Step.correct.Output.WriteCovariance = F\n')
f.close()
return
#print sys.argv
el=len(sys.argv)
mslist = sys.argv[1:el-17]
cluster = str(sys.argv[el-17])
atrous_do = str(sys.argv[el-16])
imsize = numpy.int(sys.argv[el-15])
nterms = numpy.int(sys.argv[el-14]) # only 1 to 3 is supported !!
cellsizetime_a = numpy.int(sys.argv[el-13])
cellsizetime_p = numpy.int(sys.argv[el-12])
TECi = str(sys.argv[el-11])
clocki = str(sys.argv[el-10])
HRi = str(sys.argv[el-9])
region = str(sys.argv[el-8])
uvrange = str(sys.argv[el-7]) # in wavelengths
peelskymodel = str(sys.argv[el-6])
cellsize = str(sys.argv[el-5])
basename = str(sys.argv[el-4])
resolutionstr = str(sys.argv[el-3])
increaseSNR_via_freqcoverage = str(sys.argv[el-2])
nblockconcat = numpy.int(sys.argv[el-1])
ScaleMaskThreshold = (numpy.float(len(mslist))/5.)**(0.15)
#uvrange = str(120.)
TEC = False
FFT = False
clock= False
HR = False # high dynamic range
if TECi == "True":
TEC = True