def write_ip_NDF(data, bad_pixel_ref):
"""
This function writes out the array ip parameter data to an ndf_file.
Invocation:
result = write_ip_NDF(data,bad_pixel_ref)
Arguements:
data = The array ip parameter data
bad_ref = A NDF with bad pixel values to copy over.
Returned Value:
Writes NDF and returns handle.
"""
ndf_name_orig = NDG(1)
indf = ndf.open(ndf_name_orig[0], 'WRITE', 'NEW')
indf.new('_DOUBLE', 2, numpy.array([1, 1]), numpy.array([32, 40]))
ndfmap = indf.map('DATA', '_DOUBLE', 'WRITE')
ndfmap.numpytondf(data)
indf.annul()
# Copy bad pixels
ndf_name = NDG(1)
invoke("$KAPPA_DIR/copybad in={0} ref={1} out={2}".format(
ndf_name_orig, bad_pixel_ref, ndf_name))
return ndf_name
def normer( model, test, cmin, newmodel ):
"""
Normalise "model" to "test" returning result in "newmodel", so long as
the "correlation factor" (determined by function blanker) of test and
model is at least "cmin". Returns a boolean indicating the cmin value
was reached.
Invocation:
result = normer( model, test, cmin, newmodel )
Arguments:
model = string
The name of an existing NDF.
test = string
The name of an existing NDF.
cmin = float
The lowest acceptable absolute correlation factor.
newmodel = string
The name of an NDF to be created. The new NDF is only created if
the cmin value is reached.
Returned Value:
A boolean indicating if the cmin value was reached.
"""
btest = "{0}/btest".format(NDG.tempdir)
if abs( blanker( test, model, btest ) ) > cmin:
invoke( "$KAPPA_DIR/normalize in1={0} in2={2} out={1} device=!".format(model,newmodel,btest))
return True
else:
return False
def normer(model, test, cmin, newmodel):
"""
Normalise "model" to "test" returning result in "newmodel", so long as
the "correlation factor" (determined by function blanker) of test and
model is at least "cmin". Returns a boolean indicating the cmin value
was reached.
Invocation:
result = normer( model, test, cmin, newmodel )
Arguments:
model = string
The name of an existing NDF.
test = string
The name of an existing NDF.
cmin = float
The lowest acceptable absolute correlation factor.
newmodel = string
The name of an NDF to be created. The new NDF is only created if
the cmin value is reached.
Returned Value:
A boolean indicating if the cmin value was reached.
"""
btest = "{0}/btest".format(NDG.tempdir)
if abs(blanker(test, model, btest)) > cmin:
invoke("$KAPPA_DIR/normalize in1={0} in2={2} out={1} device=!".format(
model, newmodel, btest))
return True
else:
return False
def write_ip_NDF(data,bad_pixel_ref):
"""
This function writes out the array ip parameter data to an ndf_file.
Invocation:
result = write_ip_NDF(data,bad_pixel_ref)
Arguements:
data = The array ip parameter data
bad_ref = A NDF with bad pixel values to copy over.
Returned Value:
Writes NDF and returns handle.
"""
ndf_name_orig = NDG(1)
indf = ndf.open( ndf_name_orig[0], 'WRITE', 'NEW' )
indf.new('_DOUBLE', 2, numpy.array([1,1]),numpy.array([32,40]))
ndfmap = indf.map( 'DATA', '_DOUBLE', 'WRITE' )
ndfmap.numpytondf( data )
indf.annul()
# Copy bad pixels
ndf_name = NDG(1)
invoke( "$KAPPA_DIR/copybad in={0} ref={1} out={2}".format(ndf_name_orig,bad_pixel_ref,ndf_name) )
return ndf_name
def run_calcqu(input_data,config,harmonic):
# The following call to SMURF:CALCQU creates two HDS container files -
# one holding a set of Q NDFs and the other holding a set of U NDFs. Create
# these container files in the NDG temporary directory.
qcont = NDG(1)
qcont.comment = "qcont"
ucont = NDG(1)
ucont.comment = "ucont"
msg_out( "Calculating Q and U values for each bolometer...")
invoke("$SMURF_DIR/calcqu in={0} config=\"{1}\" lsqfit=no outq={2} outu={3} "
"harmonic={4} fix".format(input_data,starutil.shell_quote(config),
qcont,ucont,harmonic) )
return (qcont,ucont)
def run_calcqu(input_data, config, harmonic):
# The following call to SMURF:CALCQU creates two HDS container files -
# one holding a set of Q NDFs and the other holding a set of U NDFs. Create
# these container files in the NDG temporary directory.
qcont = NDG(1)
qcont.comment = "qcont"
ucont = NDG(1)
ucont.comment = "ucont"
msg_out("Calculating Q and U values for each bolometer...")
invoke(
"$SMURF_DIR/calcqu in={0} config=\"{1}\" lsqfit=no outq={2} outu={3} "
"harmonic={4} fix".format(input_data, starutil.shell_quote(config),
qcont, ucont, harmonic))
return (qcont, ucont)
def force_flat(ins, masks):
"""
Forces the background regions to be flat in a set of Q or U images.
Invocation:
result = force_flat( ins, masks )
Arguments:
in = NDG
An NDG object specifying a group of Q or U images from which
any low frequency background structure is to be removed.
masks = NDG
An NDG object specifying a corresponding group of Q or U images
in which source pixels are bad. These are only used to mask the
images specified by "in". It should have the same size as "in".
Returned Value:
A new NDG object containing the group of corrected Q or U images.
"""
# How many NDFs are we processing?
nndf = len(ins)
# Blank out sources by copy the bad pixels from "mask" into "in".
msg_out(" masking...")
qm = NDG(ins)
invoke("$KAPPA_DIR/copybad in={0} ref={1} out={2}".format(ins, masks, qm))
# Smooth the blanked NDFs using a 3 pixel Gaussian. Set wlim so that
# small holes are filled in by the smoothing process.
msg_out(" smoothing...")
qs = NDG(ins)
invoke("$KAPPA_DIR/gausmooth in={0} out={1} fwhm=3 wlim=0.5".format(
qm, qs))
# Fill remaining big holes using artifical data.
msg_out(" filling...")
qf = NDG(ins)
invoke("$KAPPA_DIR/fillbad in={0} out={1} niter=10 size=10 variance=no".
format(qs, qf))
# Subtract the filled low frequency data form the original to create the
# returned images.
msg_out(" removing low frequency background structure...")
result = NDG(ins)
invoke("$KAPPA_DIR/sub in1={0} in2={1} out={2}".format(ins, qf, result))
return result
def force_flat( ins, masks ):
"""
Forces the background regions to be flat in a set of Q or U images.
Invocation:
result = force_flat( ins, masks )
Arguments:
in = NDG
An NDG object specifying a group of Q or U images from which
any low frequency background structure is to be removed.
masks = NDG
An NDG object specifying a corresponding group of Q or U images
in which source pixels are bad. These are only used to mask the
images specified by "in". It should have the same size as "in".
Returned Value:
A new NDG object containing the group of corrected Q or U images.
"""
# How many NDFs are we processing?
nndf = len( ins )
# Blank out sources by copy the bad pixels from "mask" into "in".
msg_out( " masking...")
qm = NDG( ins )
invoke( "$KAPPA_DIR/copybad in={0} ref={1} out={2}".format(ins,masks,qm) )
# Smooth the blanked NDFs using a 3 pixel Gaussian. Set wlim so that
# small holes are filled in by the smoothing process.
msg_out( " smoothing...")
qs = NDG( ins )
invoke( "$KAPPA_DIR/gausmooth in={0} out={1} fwhm=3 wlim=0.5".format(qm,qs) )
# Fill remaining big holes using artifical data.
msg_out( " filling...")
qf = NDG( ins )
invoke( "$KAPPA_DIR/fillbad in={0} out={1} niter=10 size=10 variance=no".format(qs,qf) )
# Subtract the filled low frequency data form the original to create the
# returned images.
msg_out( " removing low frequency background structure...")
result = NDG( ins )
invoke( "$KAPPA_DIR/sub in1={0} in2={1} out={2}".format(ins,qf,result) )
return result
retain = parsys["RETAIN"].value
# The following call to SMURF:CALCQU creates two HDS container files -
# one holding a set of Q NDFs and the other holding a set of U NDFs. Create
# these container files in the NDG temporary directory.
qcont = NDG(1)
qcont.comment = "qcont"
ucont = NDG(1)
ucont.comment = "ucont"
# Create a set of Q images and a set of U images. These are put into the HDS
# container files "q_TMP.sdf" and "u_TMP.sdf". Each image contains Q or U
# values derived from a short section of raw data during which each bolometer
# moves less than half a pixel.
msg_out( "Calculating Q and U values for each bolometer...")
invoke("$SMURF_DIR/calcqu in={0} config={1} outq={2} outu={3} fix".
format(indata,config,qcont,ucont) )
# Remove spikes from the Q and U images. The cleaned NDFs are written to
# temporary NDFs specified by two new NDG objects "qff" and "uff", which
# inherit their size from the existing groups "qcont" and "ucont".
msg_out( "Removing spikes from bolometer Q and U values...")
qff = NDG(qcont)
qff.comment = "qff"
uff = NDG(ucont)
uff.comment = "uff"
invoke( "$KAPPA_DIR/ffclean in={0} out={1} box=3 clip=\[2,2,2\]"
.format(qcont,qff) )
invoke( "$KAPPA_DIR/ffclean in={0} out={1} box=3 clip=\[2,2,2\]"
.format(ucont,uff) )
# The next stuff we do independently for each subarray.
parsys["INSTRUMENT"].default = deflt
parsys["INSTRUMENT"].noprompt = True
# Get the JCMT instrument. Quote the string so that it can be used as
# a command line argument when running an atask from the shell.
instrument = starutil.shell_quote(parsys["INSTRUMENT"].value)
msg_out("Updating tiles for {0} data".format(instrument))
# See if temp files are to be retained.
retain = parsys["RETAIN"].value
# Set up the dynamic default for parameter "JSA". This is True if the
# dump of the WCS FrameSet in the first supplied NDF contains the string
# "HPX".
prj = invoke(
"$KAPPA_DIR/wcsattrib ndf={0} mode=get name=projection".format(
indata[0]))
parsys["JSA"].default = True if prj.strip() == "HEALPix" else False
# See if input NDFs are on the JSA all-sky pixel grid.
jsa = parsys["JSA"].value
if not jsa:
msg_out("The supplied NDFs will first be resampled onto the JSA "
"all-sky pixel grid")
# Report the tile directory.
tiledir = os.getenv('JSA_TILE_DIR')
if tiledir:
msg_out("Tiles will be written to {0}".format(tiledir))
else:
msg_out("Environment variable JSA_TILE_DIR is not set!")
fred = loadndg( "IN", True )
if indata != fred:
raise UsageError("\n\nThe directory specified by parameter RESTART ({0}) "
"refers to different time-series data".format(restart) )
msg_out( "Re-using data in {0}".format(restart) )
# Initialise the starlink random number seed to a known value so that
# results are repeatable.
os.environ["STAR_SEED"] = "65"
# Flat field the supplied template data
ff = loadndg( "FF" )
if not ff:
ff = NDG(indata)
msg_out( "Flatfielding template data...")
invoke("$SMURF_DIR/flatfield in={0} out={1}".format(indata,ff) )
ff = ff.filter()
savendg( "FF", ff )
else:
msg_out( "Re-using old flatfielded template data...")
# If required, create new artificial I, Q and U maps.
if newart:
msg_out( "Creating new artificial I, Q and U maps...")
# Get the parameters defining the artificial data
ipeak = parsys["IPEAK"].value
ifwhm = parsys["IFWHM"].value
pol = parsys["POL"].value
# Determine the spatial extent of the data on the sky.
if deflt != None:
parsys["INSTRUMENT"].default = deflt
parsys["INSTRUMENT"].noprompt = True
# Get the JCMT instrument. Quote the string so that it can be used as
# a command line argument when running an atask from the shell.
instrument = starutil.shell_quote( parsys["INSTRUMENT"].value )
msg_out( "Updating tiles for {0} data".format(instrument) )
# See if temp files are to be retained.
retain = parsys["RETAIN"].value
# Set up the dynamic default for parameter "JSA". This is True if the
# dump of the WCS FrameSet in the first supplied NDF contains the string
# "HPX".
prj = invoke("$KAPPA_DIR/wcsattrib ndf={0} mode=get name=projection".format(indata[0]) )
parsys["JSA"].default = True if prj.strip() == "HEALPix" else False
# See if input NDFs are on the JSA all-sky pixel grid.
jsa = parsys["JSA"].value
if not jsa:
msg_out( "The supplied NDFs will first be resampled onto the JSA "
"all-sky pixel grid" )
# Report the tile directory.
tiledir = os.getenv( 'JSA_TILE_DIR' )
if tiledir:
msg_out( "Tiles will be written to {0}".format(tiledir) )
else:
msg_out( "Environment variable JSA_TILE_DIR is not set!" )
msg_out( "Tiles will be written to the current directory ({0})".format(os.getcwd()) )
def remove_corr(ins, masks):
"""
Masks the supplied set of Q or U images and then looks for and removes
correlated components in the background regions.
Invocation:
result = remove_corr( ins, masks )
Arguments:
ins = NDG
An NDG object specifying a group of Q or U images from which
correlated background components are to be removed.
masks = NDG
An NDG object specifying a corresponding group of Q or U images
in which source pixels are bad. These are only used to mask the
images specified by "in". It should have the same size as "in".
Returned Value:
A new NDG object containing the group of corrected Q or U images.
"""
# How many NDFs are we processing?
nndf = len(ins)
# Blank out sources by copy the bad pixels from "mask" into "in". We refer
# to "q" below, but the same applies whether processing Q or U.
msg_out(" masking...")
qm = NDG(ins)
invoke("$KAPPA_DIR/copybad in={0} ref={1} out={2}".format(ins, masks, qm))
# Find the most correlated pair of imagtes. We use the basic correlation
# coefficient calculated by kappa:scatter for this.
msg_out(" Finding most correlated pair of images...")
cmax = 0
for i in range(0, nndf - 1):
for j in range(i + 1, nndf):
invoke("$KAPPA_DIR/scatter in1={0} in2={1} device=!".format(
qm[i], qm[j]))
c = starutil.get_task_par("corr", "scatter")
if abs(c) > abs(cmax):
cmax = c
cati = i
catj = j
if abs(cmax) < 0.3:
msg_out(" No correlated images found!")
return ins
msg_out(" Correlation for best pair of images = {0}".format(cmax))
# Find images that are reasonably correlated to the pair found above,
# and coadd them to form a model for the correlated background
# component. Note, the holes left by the masking are filled in by the
# coaddition using background data from other images.
msg_out(" Forming model...")
# Form the average of the two most correlated images, first normalising
# them to a common scale so that they both have equal weight.
norm = "{0}/norm".format(NDG.tempdir)
if not normer(qm[cati], qm[catj], 0.3, norm):
norm = qm[cati]
mslist = NDG([qm[catj], norm])
ave = "{0}/ave".format(NDG.tempdir)
invoke(
"$CCDPACK_DIR/makemos in={0} method=mean genvar=no usevar=no out={1}".
format(mslist, ave))
# Loop round each image finding the correlation factor of the image and
# the above average image.
temp = "{0}/temp".format(NDG.tempdir)
nlist = []
ii = 0
for i in range(0, nndf):
c = blanker(qm[i], ave, temp)
# If the correlation is high enough, normalize the image to the average
# image and then include the normalised image in the list of images to be
# coadded to form the final model.
if abs(c) > 0.3:
tndf = "{0}/t{1}".format(NDG.tempdir, ii)
ii += 1
invoke(
"$KAPPA_DIR/normalize in1={1} in2={2} out={0} device=!".format(
tndf, temp, ave))
nlist.append(tndf)
if ii == 0:
msg_out(" No secondary correlated images found!")
return ins
msg_out(
" Including {0} secondary correlated images in the model.".format(
ii))
# Coadded the images created above to form the model of the correlated
# background component. Fill any remaining bad pixels with artificial data.
model = "{0}/model".format(NDG.tempdir)
included = NDG(nlist)
invoke(
"$CCDPACK_DIR/makemos in={0} method=mean usevar=no genvar=no out={1}".
format(included, temp))
invoke("$KAPPA_DIR/fillbad in={1} variance=no out={0} size=10 niter=10".
format(model, temp))
# Now estimate how much of the model is present in each image and remove it.
msg_out(" Removing model...")
temp2 = "{0}/temp2".format(NDG.tempdir)
qnew = NDG(ins)
nbetter = 0
for i in range(0, nndf):
# Try to normalise the model to the current image. This fails if the
# correlation between them is too low.
if normer(model, qm[i], 0.3, temp):
# Remove the scaled model form the image.
invoke("$KAPPA_DIR/sub in1={0} in2={1} out={2}".format(
ins[i], temp, temp2))
# We now check that removing the correlated background component has in
# fact made the image flatter (poor fits etc can mean that images that
# are poorly correlated to the model have a large amount of model
# removed and so make the image less flat). FInd the standard deviation
# of the data in the original image and in the corrected image.
invoke("$KAPPA_DIR/stats {0} quiet".format(ins[i]))
oldsig = get_task_par("sigma", "stats")
invoke("$KAPPA_DIR/stats {0} quiet".format(temp2))
newsig = get_task_par("sigma", "stats")
# If the correction has made the image flatter, copy it to the returned NDG.
if newsig < oldsig:
nbetter += 1
invoke("$KAPPA_DIR/ndfcopy in={1} out={0}".format(
qnew[i], temp2))
else:
invoke("$KAPPA_DIR/ndfcopy in={0} out={1}".format(
ins[i], qnew[i]))
# If the input image is poorly correlated to the model, return the input
# image unchanged.
else:
invoke("$KAPPA_DIR/ndfcopy in={0} out={1}".format(ins[i], qnew[i]))
msg_out(" {0} out of {1} images have been improved.".format(
nbetter, nndf))
# Return the corrected images.
return qnew
# It's a good idea to get parameter values early if possible, in case
# the user goes off for a coffee whilst the script is running and does not
# see a later parameter prompt or error.
indata = parsys["IN"].value
retain = parsys["RETAIN"].value
# Erase any NDFs holding cleaned data or pointing data from previous runs.
for path in glob.glob("s*_con_res_cln.sdf"):
myremove(path)
base = path[:-16]
myremove("{0}_lat.sdf".format(base))
myremove("{0}_lon.sdf".format(base))
# Use sc2concat to concatenate and flatfield the data.
invoke("$SMURF_DIR/sc2concat in={0} out='./*_umap'".format(indata))
# Use makemap to generate quaity and pointing info.
concdata = NDG("*_umap")
confname = NDG.tempfile()
fd = open(confname,"w")
fd.write("^$STARLINK_DIR/share/smurf/dimmconfig.lis\n")
fd.write("numiter=1\n")
fd.write("exportclean=1\n")
fd.write("exportlonlat=1\n")
fd.write("dcfitbox=0\n")
fd.write("noisecliphigh=0\n")
fd.write("order=0\n")
fd.write("downsampscale=0\n")
fd.close()
indata = parsys["IN"].value
outdata = parsys["OUT"].value
niter = parsys["NITER"].value
pixsize = parsys["PIXSIZE"].value
config = parsys["CONFIG"].value
ref = parsys["REF"].value
mask2 = parsys["MASK2"].value
mask3 = parsys["MASK3"].value
extra = parsys["EXTRA"].value
retain = parsys["RETAIN"].value
itermap = parsys["ITERMAP"].value
# If requested, use numiter from the config file.
if niter == 0:
niter = int( invoke( "$KAPPA_DIR/configecho name=numiter config={0} "
"defaults=$SMURF_DIR/smurf_makemap.def "
"select=\"\'450=0,850=0\'\"".format(config)))
# If iterating to convergence, get the maximum allowed normalised map
# change between iterations, and set the number of iterations positive.
if niter < 0:
niter = -niter
maptol = float( invoke( "$KAPPA_DIR/configecho name=maptol config={0} "
"defaults=$SMURF_DIR/smurf_makemap.def "
"select=\"\'450=0,850=0\'\"".format(config)))
else:
maptol = 0
converged = False
# Determine the value of the (AST,COM,FLT).ZERO_NITER, ZERO_NOTLAST and
def remove_corr( ins, masks ):
"""
Masks the supplied set of Q or U images and then looks for and removes
correlated components in the background regions.
Invocation:
result = remove_corr( ins, masks )
Arguments:
ins = NDG
An NDG object specifying a group of Q or U images from which
correlated background components are to be removed.
masks = NDG
An NDG object specifying a corresponding group of Q or U images
in which source pixels are bad. These are only used to mask the
images specified by "in". It should have the same size as "in".
Returned Value:
A new NDG object containing the group of corrected Q or U images.
"""
# How many NDFs are we processing?
nndf = len( ins )
# Blank out sources by copy the bad pixels from "mask" into "in". We refer
# to "q" below, but the same applies whether processing Q or U.
msg_out( " masking...")
qm = NDG( ins )
invoke( "$KAPPA_DIR/copybad in={0} ref={1} out={2}".format(ins,masks,qm) )
# Find the most correlated pair of imagtes. We use the basic correlation
# coefficient calculated by kappa:scatter for this.
msg_out( " Finding most correlated pair of images...")
cmax = 0
for i in range(0,nndf-1):
for j in range(i + 1,nndf):
invoke( "$KAPPA_DIR/scatter in1={0} in2={1} device=!".format(qm[i],qm[j]) )
c = starutil.get_task_par( "corr", "scatter" )
if abs(c) > abs(cmax):
cmax = c
cati = i
catj = j
if abs(cmax) < 0.3:
msg_out(" No correlated images found!")
return ins
msg_out( " Correlation for best pair of images = {0}".format( cmax ) )
# Find images that are reasonably correlated to the pair found above,
# and coadd them to form a model for the correlated background
# component. Note, the holes left by the masking are filled in by the
# coaddition using background data from other images.
msg_out( " Forming model...")
# Form the average of the two most correlated images, first normalising
# them to a common scale so that they both have equal weight.
norm = "{0}/norm".format(NDG.tempdir)
if not normer( qm[cati], qm[catj], 0.3, norm ):
norm = qm[cati]
mslist = NDG( [ qm[catj], norm ] )
ave = "{0}/ave".format(NDG.tempdir)
invoke( "$CCDPACK_DIR/makemos in={0} method=mean genvar=no usevar=no out={1}".format(mslist,ave) )
# Loop round each image finding the correlation factor of the image and
# the above average image.
temp = "{0}/temp".format(NDG.tempdir)
nlist = []
ii = 0
for i in range(0,nndf):
c = blanker( qm[i], ave, temp )
# If the correlation is high enough, normalize the image to the average
# image and then include the normalised image in the list of images to be
# coadded to form the final model.
if abs(c) > 0.3:
tndf = "{0}/t{1}".format(NDG.tempdir,ii)
ii += 1
invoke( "$KAPPA_DIR/normalize in1={1} in2={2} out={0} device=!".format(tndf,temp,ave))
nlist.append( tndf )
if ii == 0:
msg_out(" No secondary correlated images found!")
return ins
msg_out(" Including {0} secondary correlated images in the model.".format(ii) )
# Coadded the images created above to form the model of the correlated
# background component. Fill any remaining bad pixels with artificial data.
model = "{0}/model".format(NDG.tempdir)
included = NDG( nlist )
invoke( "$CCDPACK_DIR/makemos in={0} method=mean usevar=no genvar=no out={1}".format( included, temp ) )
invoke( "$KAPPA_DIR/fillbad in={1} variance=no out={0} size=10 niter=10".format(model,temp) )
# Now estimate how much of the model is present in each image and remove it.
msg_out(" Removing model...")
temp2 = "{0}/temp2".format(NDG.tempdir)
qnew = NDG(ins)
nbetter = 0
for i in range(0,nndf):
# Try to normalise the model to the current image. This fails if the
# correlation between them is too low.
if normer( model, qm[i], 0.3, temp ):
# Remove the scaled model form the image.
invoke( "$KAPPA_DIR/sub in1={0} in2={1} out={2}".format(ins[i],temp,temp2) )
# We now check that removing the correlated background component has in
# fact made the image flatter (poor fits etc can mean that images that
# are poorly correlated to the model have a large amount of model
# removed and so make the image less flat). FInd the standard deviation
# of the data in the original image and in the corrected image.
invoke( "$KAPPA_DIR/stats {0} quiet".format(ins[i]) )
oldsig = get_task_par( "sigma", "stats" )
invoke( "$KAPPA_DIR/stats {0} quiet".format(temp2) )
newsig = get_task_par( "sigma", "stats" )
# If the correction has made the image flatter, copy it to the returned NDG.
if newsig < oldsig:
nbetter += 1
invoke( "$KAPPA_DIR/ndfcopy in={1} out={0}".format(qnew[i],temp2) )
else:
invoke( "$KAPPA_DIR/ndfcopy in={0} out={1}".format(ins[i],qnew[i]) )
# If the input image is poorly correlated to the model, return the input
# image unchanged.
else:
invoke( "$KAPPA_DIR/ndfcopy in={0} out={1}".format(ins[i],qnew[i]) )
msg_out( " {0} out of {1} images have been improved.".format(nbetter,nndf) )
# Return the corrected images.
return qnew
def match( ref, imasked, fwhm1=4, fwhm2=100 ):
# To avoid creating hundreds of temp NDFs, re-use the same ones for each
# FWHM.
lof = NDG(1)
hif = NDG(1)
iscaled = NDG(1)
residuals = NDG(1)
# Create a logarithmically spaced list of 5 FWHM values, in pixels,
# between the supplied upper and lower FWHM limits. Try each smoothing FWHM
# in turn, finding the one that gives the best match (i.e. lowest RMS
# residuals) between high-pass filtered ref image and new I map. On each pass,
# low frequencies are removed from the ref image using the current FWHM,
# and the filtered ref image is compared to the new I map (allowing for
# a degradation in FCF).
minrms = 1.0E30
result = (0.0,0.0)
previous_fwhm = -1
fwhm1_next = -1
fwhm2_next = 0
for fwhm in np.logspace( math.log10(fwhm1), math.log10(fwhm2), 5 ):
# If required, record the current FWHM value as the upper limit for this
# function on the next level of recursion.
if fwhm2_next == -1:
fwhm2_next = fwhm
# If an error occurs estimating the RMS for a specific FWHM, ignore the
# FWHM and pass on to the next.
try:
# High-pass filter the ref image by smoothing it with a Gaussian of the
# current FWHM and then subtracting off the smoothed version.
invoke("$KAPPA_DIR/gausmooth in={0} out={1} fwhm={2}".
format( ref, lof, fwhm ))
invoke("$KAPPA_DIR/sub in1={0} in2={1} out={2}".
format( ref, lof, hif ))
# We will now use kappa:normalize to do a least squares fit between the
# pixel values in the filtered ref image and the corresponding pixel values
# in the new I map. This gives us the FCF degradation factor for the I
# map (the gradient of the fit), and scales the I map so that it has the same
# normalisation as the ref map. The scaling information is in the high
# data values (the source regions), and the fitting process will be
# confused if we include lots of background noise regions, so we use the
# masked I map instead of the full I map. We also tell kappa:normalise
# to use inly pixels that have a ref value above 2 times the noise value
# in ref map (to exclude any noise pixels that have been included in the
# masked I map). So first find the maximum value in the filtered ref map
# (the upper data limit for kappa:normalize).
invoke( "$KAPPA_DIR/stats ndf={0}".format(hif) )
highlimit = float( get_task_par( "MAXIMUM", "stats" ) )
# Get the noise level in the filtered ref map. This gives us the lower
# data limit for kappa:normalize. The filtered noise ref has no low
# frequencies ad so will be basically flat. So we can just the standard
# deviation of the pixel values as the noise. But we do 3 iterations of
# sigma clipping to exclude the bright source regions.
invoke( "$KAPPA_DIR/stats ndf={0} clip=\[3,3,3\]".format(hif) )
noise = float( get_task_par( "SIGMA", "stats" ) )
# Now use kappa:normalise to do the fit, using only ref values between
# lowlimit and highlimit. The slope gives the FCF degradation factor,
# and the offset indicates the difference in bowling between the filtered
# ref map and the I map (we do not use the offset).
invoke( "$KAPPA_DIR/normalize in1={0} in2={1} out={2} device=! "
"datarange=\[{3},{4}\]".format(imasked,hif,iscaled,2*noise,
highlimit))
degfac = float( get_task_par( "SLOPE", "normalize" ) )
# Now we have a version of the I map that is scaled so that it looks
# like the filtered ref map. Get the residuals between the filtered ref
# map and the scaled I map. Turn these residuals into SNR values by dividing
# them by the noise level in the filtered ref map, and then get the RMS
# of the residuals. We convert the residuals to SNR values because, if the
# ref map and I map were identical, heavier filtering would reduce the
# noise, and thus the RMS of the residuals. We want to minimise the RMS
# of the residuals, and so without conversion to SNR, the minimum would
# always be found at the heaviest possible filtering.
invoke( "$KAPPA_DIR/maths exp=\"'(ia-ib)/pa'\" ia={0} ib={1} pa={2} out={3}".
format(hif,iscaled,noise,residuals))
# Get the RMS of the residuals.
invoke( "$KAPPA_DIR/stats ndf={0}".format(residuals) )
mean = float( get_task_par( "MEAN", "stats" ) )
sigma = float( get_task_par( "SIGMA", "stats" ) )
rms = math.sqrt( mean*mean + sigma*sigma )
# If this is the lowest RMS found so far, remember it - together with
# the FWHM and degradation factor.
if rms < minrms:
minrms = rms
result = (degfac,fwhm)
fwhm1_next = previous_fwhm
fwhm2_next = -1
# If an error occurs estimating the RMS for a specific FWHM, ignore the
# FWHM and pass on to the next.
except starutil.AtaskError as err:
pass
# Record the current FWHM value for use on the next pass.
previous_fwhm = fwhm
# Progress report....
msg_out(" Smoothing with FWHM = {0} pixels gives RMS = {1}".format(fwhm,rms))
# If the range of FWHM values used by this invocation is greater than 1,
# invoke this function recursively to find the best FWHM within a smaller
# range centred on the best FWHM.
if minrms < 1.0E30 and (fwhm2 - fwhm1) > 1:
if fwhm1_next <= 0:
fwhm1_next = fwhm1
if fwhm2_next <= 0:
fwhm2_next = fwhm2
result = match( ref, imasked, fwhm1_next, fwhm2_next )
return result
if system == "ICRS":
parsys["CENTRE1"].prompt = "RA at centre of required circle"
parsys["CENTRE2"].prompt = "Dec at centre of required circle"
else:
parsys[
"CENTRE1"].prompt = "Galactic longitude at centre of required circle"
parsys[
"CENTRE2"].prompt = "Galactic latitude at centre of required circle"
centre1 = parsys["CENTRE1"].value
if centre1 is not None:
centre2 = parsys["CENTRE2"].value
radius = parsys["RADIUS"].value
frame = NDG.tempfile()
invoke("$ATOOLS_DIR/astskyframe \"'system={0}'\" {1}".format(
system, frame))
invoke("$ATOOLS_DIR/astunformat {0} 1 {1}".format(frame, centre1))
cen1 = starutil.get_task_par("DVAL", "astunformat")
invoke("$ATOOLS_DIR/astunformat {0} 2 {1}".format(frame, centre2))
cen2 = starutil.get_task_par("DVAL", "astunformat")
region = NDG.tempfile()
invoke(
"$ATOOLS_DIR/astcircle {0} 1 \[{1},{2}\] {3} ! ! {4}".format(
frame, cen1, cen2, math.radians(radius / 60.0), region))
# If a Region was supplied ,not we do not yet have the coordinates of
# the centre of the required region, and note if the Region is defined by
# an NDF.
else:
elif cval == "DAS":
instrument = "DAS"
# If so, set the default for the INSTRUMENT parameter and prevent the
# user being prompted for a value.
if instrument is not None:
parsys["INSTRUMENT"].default = instrument
parsys["INSTRUMENT"].noprompt = True
# Get the chosen instrument.
instrument = parsys["INSTRUMENT"].value
instrument = starutil.shell_quote(instrument)
# Get a list of the tiles that overlap the supplied NDF.
invoke("$SMURF_DIR/jsatilelist in={0} instrument={1} quiet".format(
inndf, instrument))
tiles = starutil.get_task_par("TILES", "jsatilelist")
# JSADICER requires the input array to be gridded on the JSA all-sky
# pixel grid. This is normally an HPX projection, but if the supplied
# NDF straddles a discontinuity in the HPX projection then we need to
# use a different flavour of HPX (either an HPX projection centred on
# RA=12h or am XPH (polar HEALPix) projection centred on the north or
# south pole). The above call to jsatileinfo will have determined the
# appropriate projection to use, so get it.
proj = starutil.get_task_par("PROJ", "jsatilelist")
# Create a file holding the FITS-WCS header for the first tile, using
# the type of projection determined above.
head = "{0}/header".format(NDG.tempdir)
invoke("$SMURF_DIR/jsatileinfo itile={0} instrument={1} header={2} "
# Get the radius of the map.
radius = 0.5 * math.sqrt(map_hght * map_hght + map_wdth * map_wdth)
# Create a Frame describing the coordinate system.
if tracksys == "GAL":
sys = "galactic"
elif tracksys == "J2000":
sys = "fk5"
else:
raise starutil.InvalidParameterError(
"The TRACKSYS header in {0} is {1} "
"- should be GAL or J2000".format(indata, tracksys))
frame = NDG.tempfile()
invoke("$ATOOLS_DIR/astskyframe \"'system={0}'\" {1}".format(sys, frame))
# Create a Circle describing the map.
if region is None:
region = NDG.tempfile()
display = True
else:
display = False
invoke(
"$ATOOLS_DIR/astcircle frame={0} form=1 centre=\[{1},{2}\] point={3} "
"unc=! options=! result={4}".format(frame, basec1, basec2, radius,
region))
if display:
f = open(region, "r")
ref = "!"
# If no Q and U values were supplied, create a set of Q and U time
# streams from the supplied analysed intensity time streams. Put them in
# the QUDIR directory, or the temp directory if QUDIR is null.
if inqu == None:
north = parsys["NORTH"].value
qudir = parsys["QUDIR"].value
if not qudir:
qudir = NDG.tempdir
elif not os.path.exists(qudir):
os.makedirs(qudir)
msg_out( "Calculating Q, U and I time streams for each bolometer...")
invoke("$SMURF_DIR/calcqu in={0} lsqfit=yes config=def outq={1}/\*_QT "
"outu={1}/\*_UT outi={1}/\*_IT fix=yes north={2}".
format( indata, qudir, north ) )
# Get groups listing the time series files created by calcqu.
qts = NDG( "{0}/*_QT".format( qudir ) )
uts = NDG( "{0}/*_UT".format( qudir ) )
its = NDG( "{0}/*_IT".format( qudir ) )
# If pre-calculated Q and U values were supplied, identifiy the Q, U and I
# files.
else:
msg_out( "Using pre-calculating Q, U and I values...")
qndfs = []
undfs = []
indfs = []
iref = "!"
qref = parsys["QREF"].value
uref = parsys["UREF"].value
# If no Q and U values were supplied, create a set of Q and U time
# streams from the supplied analysed intensity time streams. Put them in
# the QUDIR directory, or the temp directory if QUDIR is null.
if inqu == None:
qudir = parsys["QUDIR"].value
if not qudir:
qudir = NDG.tempdir
elif not os.path.exists(qudir):
os.makedirs(qudir)
msg_out( "Calculating Q and U time streams for each bolometer...")
invoke("$SMURF_DIR/calcqu in={0} lsqfit=yes config=def outq={1}/\*_QT "
"outu={1}/\*_UT fix=yes".format( indata, qudir ) )
# Get groups listing the time series files created by calcqu.
qts = NDG( "{0}/*_QT".format( qudir ) )
uts = NDG( "{0}/*_UT".format( qudir ) )
# If pre-calculated Q and U values were supplied, identifiy the Q and U
# files.
else:
msg_out( "Using pre-calculating Q and U values...")
qndfs = []
undfs = []
for ndf in inqu:
invoke("$KAPPA_DIR/ndftrace ndf={0} quiet".format(ndf) )
label = starutil.get_task_par( "LABEL", "ndftrace" )
ipprms_dc_Q[row_val,col_val] = ipprms.x[2]
ipprms_dc_U[row_val,col_val] = ipprms.x[3]
chi2Vals[row_val,col_val] = ipprms.fun
else:
returnCode[row_val,col_val] = False
# Write NDFs.
out_p0 = write_ip_NDF(ip_prms['Pf_'+a[-1]],bad_pixel_ref)
out_p1 = write_ip_NDF(ipprms_pol_screen,bad_pixel_ref)
out_c0 = write_ip_NDF(ipprms_Co,bad_pixel_ref)
out_angc = write_ip_NDF(ip_prms['Theta_ip_'+a[-1]],bad_pixel_ref)
# Fill any bad pixels with smooth function to match surrounding pixels
msg_out( "Filling in bad pixel values for {0} bolometer IP parameters...".format(a))
out_p0_filled = NDG(1)
invoke( "$KAPPA_DIR/fillbad in={0} out={1} variance=true niter=10 size=15".format(out_p0,out_p0_filled) )
out_p1_filled = NDG(1)
invoke( "$KAPPA_DIR/fillbad in={0} out={1} variance=true niter=10 size=15".format(out_p1,out_p1_filled) )
out_c0_filled = NDG(1)
invoke( "$KAPPA_DIR/fillbad in={0} out={1} variance=true niter=10 size=15".format(out_c0,out_c0_filled) )
out_angc_filled = NDG(1)
invoke( "$KAPPA_DIR/fillbad in={0} out={1} variance=true niter=10 size=15".format(out_angc,out_angc_filled) )
# Copy individual NDFs to single output.
invoke( "$KAPPA_DIR/ndfcopy {0} {1}".format(out_p0,outdata+'_preclean.'+str.lower(a)+'p0'))
invoke( "$KAPPA_DIR/ndfcopy {0} {1}".format(out_p1,outdata+'_preclean.'+str.lower(a)+'p1'))
invoke( "$KAPPA_DIR/ndfcopy {0} {1}".format(out_c0,outdata+'_preclean.'+str.lower(a)+'c0'))
invoke( "$KAPPA_DIR/ndfcopy {0} {1}".format(out_angc,outdata+'_preclean.'+str.lower(a)+'angc'))
invoke( "$KAPPA_DIR/ndfcopy {0} {1}".format(out_p0_filled,outdata+'.'+str.lower(a)+'p0'))
invoke( "$KAPPA_DIR/ndfcopy {0} {1}".format(out_p1_filled,outdata+'.'+str.lower(a)+'p1'))
def blanker( test, model, newtest ):
"""
Blank out pixels in "test" that are not well correlated with "model",
returning result in newtest.
Invocation:
result = blanker( test, model, newtest )
Arguments:
test = string
The name of an existing NDF.
model = string
The name of an existing NDF.
newtest = string
The name of an NDF to be created.
Returned Value:
A value between +1 and -1 indicating the degree of correlation
between the model and test.
"""
# We want statistics of pixels that are present in both test and model,
# so first form a mask by adding them together, and then copy bad pixels
# form this mask into test and model
mask = "{0}/mask".format(NDG.tempdir)
tmask = "{0}/tmask".format(NDG.tempdir)
mmask = "{0}/mmask".format(NDG.tempdir)
invoke( "$KAPPA_DIR/add in1={0} in2={1} out={2}".format(test,model,mask) )
invoke( "$KAPPA_DIR/copybad in={0} ref={1} out={2}".format(test,mask,tmask) )
invoke( "$KAPPA_DIR/copybad in={0} ref={1} out={2}".format(model,mask,mmask) )
# Get the mean and standard deviation of the remaining pixels in the
# test NDF.
invoke( "$KAPPA_DIR/stats {0} clip=\[3,3,3\] quiet".format(tmask) )
tmean = get_task_par( "mean", "stats" )
tsigma = get_task_par( "sigma", "stats" )
# Also get the number of good pixels in the mask.
numgood1 = float( get_task_par( "numgood", "stats" ) )
# Get the mean and standard deviation of the remaining pixels in the
# model NDF.
invoke( "$KAPPA_DIR/stats {0} clip=\[3,3,3\] quiet".format(mmask) )
mmean = get_task_par( "mean", "stats" )
msigma = get_task_par( "sigma", "stats" )
# Normalize them both to have a mean of zero and a standard deviation of
# unity.
tnorm = "{0}/tnorm".format(NDG.tempdir)
invoke( "$KAPPA_DIR/maths exp='(ia-pa)/pb' ia={2} pa={0} pb={1} "
"out={3}".format(tmean,tsigma,tmask,tnorm))
mnorm = "{0}/mnorm".format(NDG.tempdir)
invoke( "$KAPPA_DIR/maths exp='(ia-pa)/pb' ia={2} pa={0} pb={1} "
"out={3}".format(mmean,msigma,mmask,mnorm))
# Find the difference between them.
diff = "{0}/diff".format(NDG.tempdir)
invoke( "$KAPPA_DIR/sub in1={0} in2={1} out={2}".format(mnorm,tnorm,diff) )
# Remove pixels that differ by more than 0.5 standard deviations.
mtmask = "{0}/mtmask".format(NDG.tempdir)
invoke( "$KAPPA_DIR/thresh in={0} thrlo=-0.5 newlo=bad thrhi=0.5 "
"newhi=bad out={1}".format(diff,mtmask) )
# See how many pixels remain (i.e. pixels that are very similar in the
# test and model NDFs).
invoke( "$KAPPA_DIR/stats {0} quiet".format(mtmask) )
numgood2 = float( get_task_par( "numgood", "stats" ) )
# It may be that the two NDFs are anti-correlated. To test for this we
# negate the model and do the above test again.
mnormn = "{0}/mnormn".format(NDG.tempdir)
invoke( "$KAPPA_DIR/cmult in={0} scalar=-1 out={1}".format(mnorm,mnormn) )
diffn = "{0}/diffn".format(NDG.tempdir)
invoke( "$KAPPA_DIR/sub in1={0} in2={1} out={2}".format(mnormn,tnorm,diffn ))
mtmaskn = "{0}/mtmaskn".format(NDG.tempdir)
invoke( "$KAPPA_DIR/thresh in={0} thrlo=-0.5 newlo=bad thrhi=0.5 "
"newhi=bad out={1}".format(diffn,mtmaskn) )
invoke( "$KAPPA_DIR/stats {0} quiet".format(mtmaskn) )
numgood2n = float( get_task_par( "numgood", "stats" ) )
# If we get more similar pixels by negating the model, the NDFs are
# anti-correlated.
if numgood2n > numgood2:
# Take a copy of the supplied test NDF, masking out pixels that are not
# anti-similar to the corresponding model pixels.
invoke( "$KAPPA_DIR/copybad in={0} ref={2} out={1}".format(test,newtest,mtmaskn) )
# The returned correlation factor is the ratio of the number of
# anti-similar pixels to the total number of pixels which the two NDFs
# have in common. But if there is not much difference between the number
# of similar and anti-similar pixels, we assume there is no correlation.
if numgood2n > 1.4*numgood2:
res = -(numgood2n/numgood1)
else:
res = 0.0
# If we get more similar pixels without negating the model, the NDFs are
# correlated. Do the equivalent to the above.
else:
invoke( "$KAPPA_DIR/copybad in={0} ref={2} out={1}".format(test,newtest,mtmask) )
if numgood2 > 1.4*numgood2n:
res = numgood2/numgood1
else:
res = 0.0
# If there are very few good pixels in common return zero correlation.
if numgood1 < 150:
res = 0.0
# Return the correlation factor.
return res
pixsize = parsys["PIXSIZE"].value
config = parsys["CONFIG"].value
ref = parsys["REF"].value
mask2 = parsys["MASK2"].value
mask3 = parsys["MASK3"].value
extra = parsys["EXTRA"].value
extra1 = parsys["EXTRA1"].value
itermap = parsys["ITERMAP"].value
# See if we are using pre-cleaned data, in which case there is no need
# to export the cleaned data on the first iteration. Note we need to
# convert the string returned by "invoke" to an int explicitly, otherwise
# the equality is never satisfied and we end up assuming that the raw
# data has been precleaned, even if it hasn't been precleaned.
if int( invoke( "$KAPPA_DIR/configecho name=doclean config={0} "
"defaults=$SMURF_DIR/smurf_makemap.def "
"select=\"\'450=0,850=1\'\" defval=1".format(config))) == 1:
precleaned = False
else:
precleaned = True
# If requested, use numiter from the config file. Arbitrarily choose 850 um
# values for the waveband-specific parameters, but these are not actually used.
if niter == 0:
niter = int( invoke( "$KAPPA_DIR/configecho name=numiter config={0} "
"defaults=$SMURF_DIR/smurf_makemap.def "
"select=\"\'450=0,850=1\'\" defval=5".format(config)))
# If iterating to convergence, get the maximum allowed normalised map
# change between iterations, and set the number of iterations positive.
if niter < 0:
def blanker(test, model, newtest):
"""
Blank out pixels in "test" that are not well correlated with "model",
returning result in newtest.
Invocation:
result = blanker( test, model, newtest )
Arguments:
test = string
The name of an existing NDF.
model = string
The name of an existing NDF.
newtest = string
The name of an NDF to be created.
Returned Value:
A value between +1 and -1 indicating the degree of correlation
between the model and test.
"""
# We want statistics of pixels that are present in both test and model,
# so first form a mask by adding them together, and then copy bad pixels
# form this mask into test and model
mask = "{0}/mask".format(NDG.tempdir)
tmask = "{0}/tmask".format(NDG.tempdir)
mmask = "{0}/mmask".format(NDG.tempdir)
invoke("$KAPPA_DIR/add in1={0} in2={1} out={2}".format(test, model, mask))
invoke("$KAPPA_DIR/copybad in={0} ref={1} out={2}".format(
test, mask, tmask))
invoke("$KAPPA_DIR/copybad in={0} ref={1} out={2}".format(
model, mask, mmask))
# Get the mean and standard deviation of the remaining pixels in the
# test NDF.
invoke("$KAPPA_DIR/stats {0} clip=\[3,3,3\] quiet".format(tmask))
tmean = get_task_par("mean", "stats")
tsigma = get_task_par("sigma", "stats")
# Also get the number of good pixels in the mask.
numgood1 = float(get_task_par("numgood", "stats"))
# Get the mean and standard deviation of the remaining pixels in the
# model NDF.
invoke("$KAPPA_DIR/stats {0} clip=\[3,3,3\] quiet".format(mmask))
mmean = get_task_par("mean", "stats")
msigma = get_task_par("sigma", "stats")
# Normalize them both to have a mean of zero and a standard deviation of
# unity.
tnorm = "{0}/tnorm".format(NDG.tempdir)
invoke("$KAPPA_DIR/maths exp=\"'(ia-pa)/pb'\" ia={2} pa={0} pb={1} "
"out={3}".format(tmean, tsigma, tmask, tnorm))
mnorm = "{0}/mnorm".format(NDG.tempdir)
invoke("$KAPPA_DIR/maths exp=\"'(ia-pa)/pb'\" ia={2} pa={0} pb={1} "
"out={3}".format(mmean, msigma, mmask, mnorm))
# Find the difference between them.
diff = "{0}/diff".format(NDG.tempdir)
invoke("$KAPPA_DIR/sub in1={0} in2={1} out={2}".format(mnorm, tnorm, diff))
# Remove pixels that differ by more than 0.5 standard deviations.
mtmask = "{0}/mtmask".format(NDG.tempdir)
invoke("$KAPPA_DIR/thresh in={0} thrlo=-0.5 newlo=bad thrhi=0.5 "
"newhi=bad out={1}".format(diff, mtmask))
# See how many pixels remain (i.e. pixels that are very similar in the
# test and model NDFs).
invoke("$KAPPA_DIR/stats {0} quiet".format(mtmask))
numgood2 = float(get_task_par("numgood", "stats"))
# It may be that the two NDFs are anti-correlated. To test for this we
# negate the model and do the above test again.
mnormn = "{0}/mnormn".format(NDG.tempdir)
invoke("$KAPPA_DIR/cmult in={0} scalar=-1 out={1}".format(mnorm, mnormn))
diffn = "{0}/diffn".format(NDG.tempdir)
invoke("$KAPPA_DIR/sub in1={0} in2={1} out={2}".format(
mnormn, tnorm, diffn))
mtmaskn = "{0}/mtmaskn".format(NDG.tempdir)
invoke("$KAPPA_DIR/thresh in={0} thrlo=-0.5 newlo=bad thrhi=0.5 "
"newhi=bad out={1}".format(diffn, mtmaskn))
invoke("$KAPPA_DIR/stats {0} quiet".format(mtmaskn))
numgood2n = float(get_task_par("numgood", "stats"))
# If we get more similar pixels by negating the model, the NDFs are
# anti-correlated.
if numgood2n > numgood2:
# Take a copy of the supplied test NDF, masking out pixels that are not
# anti-similar to the corresponding model pixels.
invoke("$KAPPA_DIR/copybad in={0} ref={2} out={1}".format(
test, newtest, mtmaskn))
# The returned correlation factor is the ratio of the number of
# anti-similar pixels to the total number of pixels which the two NDFs
# have in common. But if there is not much difference between the number
# of similar and anti-similar pixels, we assume there is no correlation.
if numgood2n > 1.4 * numgood2:
res = -(numgood2n / numgood1)
else:
res = 0.0
# If we get more similar pixels without negating the model, the NDFs are
# correlated. Do the equivalent to the above.
else:
invoke("$KAPPA_DIR/copybad in={0} ref={2} out={1}".format(
test, newtest, mtmask))
if numgood2 > 1.4 * numgood2n:
res = numgood2 / numgood1
else:
res = 0.0
# If there are very few good pixels in common return zero correlation.
if numgood1 < 150:
res = 0.0
# Return the correlation factor.
return res
in2 = parsys["IN2"].value
# See if temp files are to be retained.
retain = parsys["RETAIN"].value
# Get the name of any report file to create.
report = parsys["REPORT"].value
# Create an empty list to hold the lines of the report.
report_lines = []
# Use kappa:ndfcompare to compare the main NDFs holding the map data
# array. Include a check that the root ancestors of the two maps are the
# same. Always create a report file so we can echo it to the screen.
report0 = os.path.join(NDG.tempdir, "report0")
invoke("$KAPPA_DIR/ndfcompare in1={0} in2={1} report={2} skiptests=! "
"accdat=0.3v accvar=1E-3 quiet".format(in1, in2, report0))
# See if any differences were found. If so, append the lines of the
# report to the report_lines list.
similar = starutil.get_task_par("similar", "ndfcompare")
if not similar:
with open(report0) as f:
report_lines.extend(f.readlines())
# Now compare the WEIGHTS extension NDF (no need for the roots ancestor
# check since its already been done).
report1 = os.path.join(NDG.tempdir, "report1")
invoke("$KAPPA_DIR/ndfcompare in1={0}.more.smurf.weights accdat=1E-3 "
"in2={1}.more.smurf.weights report={2} quiet".format(
in1, in2, report1))
basec2 = math.radians( basec2 )
# Get the radius of the map.
radius = 0.5*math.sqrt( map_hght*map_hght + map_wdth*map_wdth )
# Create a Frame describing the coordinate system.
if tracksys == "GAL":
sys = "galactic";
elif tracksys == "J2000":
sys = "fk5"
else:
raise starutil.InvalidParameterError("The TRACKSYS header in {0} is {1} "
"- should be GAL or J2000".format(indata,tracksys) )
frame = NDG.tempfile()
invoke( "$ATOOLS_DIR/astskyframe \"'system={0}'\" {1}".format(sys,frame) )
# Create a Circle describing the map.
if region == None:
region = NDG.tempfile()
display = True
else:
display = False
invoke( "$ATOOLS_DIR/astcircle frame={0} form=1 centre=\[{1},{2}\] point={3} "
"unc=! options=! result={4}".format(frame,basec1,basec2,radius,region) )
if display:
f = open( region, "r" )
print( f.read() )
f.close()
# Do tests for 5 different peak values
for ipeak in range(0, 1):
starutil.msg_out( ">>> Doing sep={0} and peak={1}....".format(clump_separation,peak_value))
# Get the dimensions of a square image that would be expected to
# contain the target number of clumps at the current separation.
npix = int( clump_separation*math.sqrt( nclump_target ) )
# Create a temporary file containing circular clumps of constant size
# and shape (except for the effects of noise).
model = NDG(1)
out = NDG(1)
outcat = NDG.tempfile(".fit")
invoke( "$CUPID_DIR/makeclumps angle=\[0,0\] beamfwhm=0 deconv=no "
"fwhm1=\[{0},0\] fwhm2=\[{0},0\] lbnd=\[1,1\] ubnd=\[{1},{1}\] "
"model={2} nclump={3} out={4} outcat={5} pardist=normal "
"peak = \[{6},0\] rms={7} trunc=0.1".
format(clump_fwhm,npix,model,nclump_target,out,outcat,
peak_value,noise) )
# Run fellwalker on the data.
mask = NDG(1)
outcat_fw = NDG.tempfile(".fit")
invoke( "$CUPID_DIR/findclumps config=def deconv=no in={0} "
"method=fellwalker out={1} outcat={2} rms={3}".
format(out,mask,outcat_fw,noise) )
# Get the number of clumps found by FellWalker.
nfw = starutil.get_task_par( "nclumps", "findclumps" )
if nfw > 0:
# See how many of the clump peaks found by FellWalker match real clumps to
ref = "!"
# If no Q and U values were supplied, create a set of Q and U time
# streams from the supplied analysed intensity time streams. Put them in
# the QUDIR directory, or the temp directory if QUDIR is null.
if inqu == None:
north = parsys["NORTH"].value
qudir = parsys["QUDIR"].value
if not qudir:
qudir = NDG.tempdir
elif not os.path.exists(qudir):
os.makedirs(qudir)
msg_out( "Calculating Q, U and I time streams for each bolometer...")
invoke("$SMURF_DIR/calcqu in={0} lsqfit=yes config=def outq={1}/\*_QT "
"outu={1}/\*_UT outi={1}/\*_IT fix=yes north={2}".
format( indata, qudir, north ) )
# Get groups listing the time series files created by calcqu.
qts = NDG( "{0}/*_QT".format( qudir ) )
uts = NDG( "{0}/*_UT".format( qudir ) )
its = NDG( "{0}/*_IT".format( qudir ) )
# If pre-calculated Q and U values were supplied, identifiy the Q, U and I
# files.
else:
msg_out( "Using pre-calculating Q, U and I values...")
qndfs = []
undfs = []
indfs = []
niter = parsys["NITER"].value
pixsize = parsys["PIXSIZE"].value
config = parsys["CONFIG"].value
ref = parsys["REF"].value
mask2 = parsys["MASK2"].value
mask3 = parsys["MASK3"].value
extra = parsys["EXTRA"].value
itermap = parsys["ITERMAP"].value
# See if we are using pre-cleaned data, in which case there is no need
# to export the cleaned data on the first iteration. Note we need to
# convert the string returned by "invoke" to an int explicitly, otherwise
# the equality is never satisfied and we end up assuming that the raw
# data has been precleaned, even if it hasn't been precleaned.
if int( invoke( "$KAPPA_DIR/configecho name=doclean config={0} "
"defaults=$SMURF_DIR/smurf_makemap.def "
"select=\"\'450=0,850=1\'\" defval=1".format(config))) == 1:
precleaned = False
else:
precleaned = True
# If requested, use numiter from the config file. Arbitrarily choose 850 um
# values for the waveband-specific parameters, but these are not actually used.
if niter == 0:
niter = int( invoke( "$KAPPA_DIR/configecho name=numiter config={0} "
"defaults=$SMURF_DIR/smurf_makemap.def "
"select=\"\'450=0,850=1\'\" defval=5".format(config)))
# If iterating to convergence, get the maximum allowed normalised map
# change between iterations, and set the number of iterations positive.
if niter < 0:
returnCode[row_val, col_val] = False
# Write NDFs.
out_p0 = write_ip_NDF(ip_prms['Pf_' + a[-1]], bad_pixel_ref)
out_p1 = write_ip_NDF(ipprms_pol_screen, bad_pixel_ref)
out_c0 = write_ip_NDF(ipprms_Co, bad_pixel_ref)
out_angc = write_ip_NDF(ip_prms['Theta_ip_' + a[-1]],
bad_pixel_ref)
# Fill any bad pixels with smooth function to match surrounding pixels
msg_out(
"Filling in bad pixel values for {0} bolometer IP parameters..."
.format(a))
out_p0_filled = NDG(1)
invoke(
"$KAPPA_DIR/fillbad in={0} out={1} variance=true niter=10 size=15"
.format(out_p0, out_p0_filled))
out_p1_filled = NDG(1)
invoke(
"$KAPPA_DIR/fillbad in={0} out={1} variance=true niter=10 size=15"
.format(out_p1, out_p1_filled))
out_c0_filled = NDG(1)
invoke(
"$KAPPA_DIR/fillbad in={0} out={1} variance=true niter=10 size=15"
.format(out_c0, out_c0_filled))
out_angc_filled = NDG(1)
invoke(
"$KAPPA_DIR/fillbad in={0} out={1} variance=true niter=10 size=15"
.format(out_angc, out_angc_filled))
# Copy individual NDFs to single output.
tiledir = os.getenv( 'JSA_TILE_DIR' )
if tiledir:
msg_out( "Tiles will be read from {0}".format(tiledir) )
else:
msg_out( "Environment variable JSA_TILE_DIR is not set!" )
msg_out( "Tiles will be read from the current directory ({0})".format(os.getcwd()) )
# Create an empty list to hold the NDFs for the tiles holding the
# required data.
tilendf = []
itilelist = []
# Identify the tiles that overlap the specified region, and loop round
# them.
invoke("$SMURF_DIR/tilelist region={0} instrument={1}".format(region,instrument) )
for itile in starutil.get_task_par( "tiles", "tilelist" ):
# Get information about the tile, including the 2D spatial pixel index
# bounds of its overlap with the required Region.
invoke("$SMURF_DIR/tileinfo itile={0} instrument={1} "
"target={2}".format(itile,instrument,region) )
# Skip this tile if it does not exist (i.e. is empty).
if starutil.get_task_par( "exists", "tileinfo" ):
# Get the 2D spatial pixel index bounds of the part of the master tile that
# overlaps the required region.
tlbnd = starutil.get_task_par( "tlbnd", "tileinfo" )
tubnd = starutil.get_task_par( "tubnd", "tileinfo" )
def get_filtered_skydip_data(qarray, uarray, clip, a):
"""
This function takes q and u array data (output from calcqu), applies ffclean to remove spikes
and puts in numpy array variable
It borrows (copies) heavily from pol2cat.py (2015A)
Invocation:
( qdata_total,qvar_total,udata_total,uvar_total,elevation,opacity_term,bad_pixel_ref ) = ...
get_filtered_skydip_data(qarray,uarray,clip,a)
Arguments:
qarray = An NDF of Q array data (output from calcqu).
uarray = An NDF of U array data (output form calcqu).
clip = The sigma cut for ffclean.
a = A string indicating the array (eg. 'S8A').
Returned Value:
qdata_total = A numpy array with the cleaned qarray data.
qvar_total = A numpy array with the qarray variance data.
udata_total = A numpy array with the cleaned uarray data.
uvar_total = A numpy array with the uarray variance data.
elevation = A numpy array with the elevation data
opacity_term = A numpy array with the opacity brightness term (1-exp(-tau*air_mass))
Here tau is calculated using the WVM data as input.
"""
# Remove spikes from the Q images for the current subarray. The cleaned NDFs
# are written to temporary NDFs specified by the new NDG object "qff", which
# inherit its size from the existing group "qarray"".
msg_out("Removing spikes from {0} bolometer Q values...".format(a))
qff = NDG(qarray)
qff.comment = "qff"
invoke("$KAPPA_DIR/ffclean in={0} out={1} genvar=yes box=3 clip=\[{2}\]".
format(qarray, qff, clip))
# Remove spikes from the U images for the current subarray. The cleaned NDFs
# are written to temporary NDFs specified by the new NDG object "uff", which
# inherit its size from the existing group "uarray"".
msg_out("Removing spikes from {0} bolometer U values...".format(a))
uff = NDG(uarray)
uff.comment = "uff"
invoke("$KAPPA_DIR/ffclean in={0} out={1} genvar=yes box=3 clip=\[{2}\]".
format(uarray, uff, clip))
elevation = []
opacity_term = []
for stare in range(len(qff[:])):
# Stack Q data in numpy array
# Get elevation information
elevation.append(
numpy.array(
float(
invoke(
"$KAPPA_DIR/fitsmod ndf={0} edit=print keyword=ELSTART"
.format(qff[stare])))))
# Get Tau (Opacity) information
tau_temp = numpy.array(
float(
invoke(
"$KAPPA_DIR/fitsmod ndf={0} edit=print keyword=WVMTAUST".
format(qff[stare]))))
# Convert to obs band.
if '4' in a:
tau_temp = 19.04 * (tau_temp - 0.018) # Eq from Dempsey et al
elif '8' in a:
tau_temp = 5.36 * (tau_temp - 0.006) # Eq from Dempsey et al.
opacity_term.append(1 -
numpy.exp(-1 * tau_temp /
numpy.sin(numpy.radians(elevation[-1]))))
invoke("$KAPPA_DIR/ndftrace {0} quiet".format(qff[stare]))
nx = get_task_par("dims(1)", "ndftrace")
ny = get_task_par("dims(2)", "ndftrace")
qdata_temp = numpy.reshape(Ndf(qff[stare]).data, (ny, nx))
qdata_temp[numpy.abs(qdata_temp) > 1e300] = numpy.nan
if stare == 0:
qdata_total = qdata_temp
else:
qdata_total = numpy.dstack((qdata_total, qdata_temp))
qvar_temp = numpy.reshape(Ndf(qff[stare]).var, (ny, nx))
qdata_temp[numpy.abs(qvar_temp) > 1e300] = numpy.nan
if stare == 0:
qvar_total = qvar_temp
else:
qvar_total = numpy.dstack((qvar_total, qvar_temp))
# Stack U data in numpy array
invoke("$KAPPA_DIR/ndftrace {0} quiet".format(uff[stare]))
nx = get_task_par("dims(1)", "ndftrace")
ny = get_task_par("dims(2)", "ndftrace")
udata_temp = numpy.reshape(Ndf(uff[stare]).data, (ny, nx))
udata_temp[numpy.abs(udata_temp) > 1e300] = numpy.nan
if stare == 0:
udata_total = udata_temp
else:
udata_total = numpy.dstack((udata_total, udata_temp))
uvar_temp = numpy.reshape(Ndf(uff[stare]).var, (ny, nx))
udata_temp[numpy.abs(uvar_temp) > 1e300] = numpy.nan
if stare == 0:
uvar_total = uvar_temp
else:
uvar_total = numpy.dstack((uvar_total, uvar_temp))
# Create bad pixel reference.
bad_pixel_ref = NDG(1)
invoke("$KAPPA_DIR/copybad in={0} ref={1} out={2}".format(
qff, uff, bad_pixel_ref))
return (qdata_total, qvar_total, udata_total, uvar_total, elevation,
opacity_term, bad_pixel_ref)
filter = 850
msg_out( "No value found for FITS header 'FILTER' in {0} - assuming 850".format(qin[0]))
if filter == 450:
fcf1 = 962.0
fcf2 = 491.0
elif filter == 850:
fcf1 = 725.0
fcf2 = 537.0
else:
raise starutil.InvalidParameterError("Invalid FILTER header value "
"'{0} found in {1}.".format( filter, qin[0] ) )
# Remove any spectral axes
qtrim = NDG(qin)
invoke( "$KAPPA_DIR/ndfcopy in={0} out={1} trim=yes".format(qin,qtrim) )
utrim = NDG(uin)
invoke( "$KAPPA_DIR/ndfcopy in={0} out={1} trim=yes".format(uin,utrim) )
itrim = NDG(iin)
invoke( "$KAPPA_DIR/ndfcopy in={0} out={1} trim=yes".format(iin,itrim) )
# Rotate them to use the same polarimetric reference direction.
qrot = NDG(qtrim)
urot = NDG(utrim)
invoke( "$POLPACK_DIR/polrotref qin={0} uin={1} like={2} qout={3} uout={4} ".
format(qtrim,utrim,qtrim[0],qrot,urot) )
# Mosaic them into a single set of Q, U and I images, aligning them
# with the first I image.
qmos = NDG( 1 )
invoke( "$KAPPA_DIR/wcsmosaic in={0} out={1} ref={2} method=bilin accept".format(qrot,qmos,itrim[0]) )
print "prepared with the command: oracdr_scuba2_<band> <observation-date>"
print "where band is: 450 or 850"
print "and observation-date has the form: YYYYMMDD"
sys.exit(0)
# print "band={0}".format(band)
# Get WNFACT value and nFrames from data file
wnfact = float(starutil.get_fits_header(indata, "WNFACT"))
# print "wnfact={0}".format(wnfact)
nFrames = int(starutil.get_fits_header(indata, "MIRSTOP")) + 1
# print "nFrames={0}".format(nFrames)
# Gather statistics on the central region of the input spectrum
# We are interested in the z position of the maximum pixel value (peak)
instats = invoke("$KAPPA_DIR/stats ndf={0} quiet".format(indata))
maxpos = starutil.get_task_par("MAXPOS", "stats")
maxposz = maxpos[2]
# print "maxposz={0}".format(maxposz)
# Calculate the band pass frames centered on the peak
if band == "SCUBA2_850":
wnlbound = 11.2
wnubound = 12.2
else:
if band == "SCUBA2_450":
wnlbound = 22.1
wnubound = 23.3
# print "wnlbound={0}".format(wnlbound)
# print "wnubound={0}".format(wnubound)
bandwidth = wnubound - wnlbound
if inqui:
qin = inqui.filter("'\.Q$'" )
uin = inqui.filter("'\.U$'" )
iin = inqui.filter("'\.I$'" )
# If not supplied, try again using INQ, INU and INI (i.e. scan & spin
# data).
else:
qin = parsys["INQ"].value
uin = parsys["INU"].value
iin = parsys["INI"].value
# Check they are all in units of pW.
for quilist in (qin,uin,iin):
for sdf in quilist:
invoke("$KAPPA_DIR/ndftrace ndf={0} quiet".format(sdf) )
units = starutil.get_task_par( "UNITS", "ndftrace" ).replace(" ", "")
if units != "pW":
raise starutil.InvalidParameterError("All supplied I, Q and U "
"maps must be in units of 'pW', but '{0}' has units '{1}'.".
format(sdf,units))
# Now get the PI value to use.
pimap = parsys["PI"].value
# Now get the QUI value to use.
qui = parsys["QUI"].value
# Get the output catalogue now to avoid a long wait before the user gets
# prompted for it.
outcat = parsys["CAT"].value
def get_filtered_skydip_data(qarray,uarray,clip,a):
"""
This function takes q and u array data (output from calcqu), applies ffclean to remove spikes
and puts in numpy array variable
It borrows (copies) heavily from pol2cat.py (2015A)
Invocation:
( qdata_total,qvar_total,udata_total,uvar_total,elevation,opacity_term,bad_pixel_ref ) = ...
get_filtered_skydip_data(qarray,uarray,clip,a)
Arguments:
qarray = An NDF of Q array data (output from calcqu).
uarray = An NDF of U array data (output form calcqu).
clip = The sigma cut for ffclean.
a = A string indicating the array (eg. 'S8A').
Returned Value:
qdata_total = A numpy array with the cleaned qarray data.
qvar_total = A numpy array with the qarray variance data.
udata_total = A numpy array with the cleaned uarray data.
uvar_total = A numpy array with the uarray variance data.
elevation = A numpy array with the elevation data
opacity_term = A numpy array with the opacity brightness term (1-exp(-tau*air_mass))
Here tau is calculated using the WVM data as input.
"""
# Remove spikes from the Q images for the current subarray. The cleaned NDFs
# are written to temporary NDFs specified by the new NDG object "qff", which
# inherit its size from the existing group "qarray"".
msg_out( "Removing spikes from {0} bolometer Q values...".format(a))
qff = NDG(qarray)
qff.comment = "qff"
invoke( "$KAPPA_DIR/ffclean in={0} out={1} genvar=yes box=3 clip=\[{2}\]".format(qarray,qff,clip) )
# Remove spikes from the U images for the current subarray. The cleaned NDFs
# are written to temporary NDFs specified by the new NDG object "uff", which
# inherit its size from the existing group "uarray"".
msg_out( "Removing spikes from {0} bolometer U values...".format(a))
uff = NDG(uarray)
uff.comment = "uff"
invoke( "$KAPPA_DIR/ffclean in={0} out={1} genvar=yes box=3 clip=\[{2}\]"
.format(uarray,uff,clip) )
elevation = []
opacity_term = []
for stare in range(len(qff[:])):
# Stack Q data in numpy array
# Get elevation information
elevation.append(numpy.array( float( invoke( "$KAPPA_DIR/fitsmod ndf={0} edit=print keyword=ELSTART".format( qff[ stare ] ) ) ) ) )
# Get Tau (Opacity) information
tau_temp = numpy.array( float( invoke( "$KAPPA_DIR/fitsmod ndf={0} edit=print keyword=WVMTAUST".format( qff[ stare ] ) ) ) )
# Convert to obs band.
if '4' in a:
tau_temp = 19.04*(tau_temp-0.018) # Eq from Dempsey et al
elif '8' in a:
tau_temp = 5.36*(tau_temp-0.006) # Eq from Dempsey et al.
opacity_term.append(1-numpy.exp(-1*tau_temp/numpy.sin(numpy.radians(elevation[-1]))))
invoke( "$KAPPA_DIR/ndftrace {0} quiet".format(qff[ stare ]))
nx = get_task_par( "dims(1)", "ndftrace" )
ny = get_task_par( "dims(2)", "ndftrace" )
qdata_temp = numpy.reshape( Ndf( qff[ stare ] ).data, (ny,nx))
qdata_temp[numpy.abs(qdata_temp)>1e300] = numpy.nan;
if stare == 0:
qdata_total = qdata_temp
else:
qdata_total = numpy.dstack((qdata_total,qdata_temp))
qvar_temp = numpy.reshape( Ndf( qff[ stare ] ).var, (ny,nx))
qdata_temp[numpy.abs(qvar_temp)>1e300] = numpy.nan;
if stare == 0:
qvar_total = qvar_temp
else:
qvar_total = numpy.dstack((qvar_total,qvar_temp))
# Stack U data in numpy array
invoke( "$KAPPA_DIR/ndftrace {0} quiet".format(uff[ stare ]))
nx = get_task_par( "dims(1)", "ndftrace" )
ny = get_task_par( "dims(2)", "ndftrace" )
udata_temp = numpy.reshape( Ndf( uff[ stare ] ).data, (ny,nx))
udata_temp[numpy.abs(udata_temp)>1e300] = numpy.nan;
if stare == 0:
udata_total = udata_temp
else:
udata_total = numpy.dstack((udata_total,udata_temp))
uvar_temp = numpy.reshape( Ndf( uff[ stare ] ).var, (ny,nx))
udata_temp[numpy.abs(uvar_temp)>1e300] = numpy.nan;
if stare == 0:
uvar_total = uvar_temp
else:
uvar_total = numpy.dstack((uvar_total,uvar_temp))
# Create bad pixel reference.
bad_pixel_ref = NDG(1)
invoke( "$KAPPA_DIR/copybad in={0} ref={1} out={2}".format(qff,uff,bad_pixel_ref))
return( qdata_total,qvar_total,udata_total,uvar_total,elevation,opacity_term,bad_pixel_ref )
in2 = parsys["IN2"].value
# See if temp files are to be retained.
retain = parsys["RETAIN"].value
# Get the name of any report file to create.
report = parsys["REPORT"].value
# Create an empty list to hold the lines of the report.
report_lines = []
# Use kappa:ndfcompare to compare the main NDFs holding the map data
# array. Include a check that the root ancestors of the two maps are the
# same. Always create a report file so we can echo it to the screen.
report0 = os.path.join(NDG.tempdir,"report0")
invoke( "$KAPPA_DIR/ndfcompare in1={0} in2={1} report={2} skiptests=! "
"accdat=0.1v accvar=1E-4 quiet".format(in1,in2,report0) )
# See if any differences were found. If so, append the lines of the
# report to the report_lines list.
similar = starutil.get_task_par( "similar", "ndfcompare" )
if not similar:
with open(report0) as f:
report_lines.extend( f.readlines() )
# Now compare the WEIGHTS extension NDF (no need for the roots ancestor
# check since its already been done).
report1 = os.path.join(NDG.tempdir,"report1")
invoke( "$KAPPA_DIR/ndfcompare in1={0}.more.smurf.weights accdat=1E-4 "
"in2={1}.more.smurf.weights report={2} quiet".format(in1,in2,report1) )
# See if any differences were found. If so, append the report to any
# and determine the waveband. If neither header is found, look for
# "s4a", etc, in the NDF's provenance info.
if isndf1:
try:
subarray = starutil.get_fits_header( config1, "SUBARRAY" )
if subarray is None:
filter = starutil.get_fits_header( config1, "FILTER" )
if filter is not None:
filter = filter.strip()
if filter == "450":
subarray = "s4"
elif filter == "850":
subarray = "s8"
if subarray is None:
text = starutil.invoke( "$KAPPA_DIR/provshow {0}".format(config1) )
if "s4a" in text or "s4b" in text or "s4c" in text or "s4d" in text:
subarray = "s4"
elif "s8a" in text or "s8b" in text or "s8c" in text or "s8d" in text:
subarray = "s8"
else:
subarray = None
except:
print( "\n!! It looks like NDF '{0}' either does not exist or is "
"corrupt.".format(config1) )
os._exit(1)
if isndf1:
if subarray is None:
msg_out("Cannot determine the SCUBA-2 waveband for NDF '{0}' "
"- was it really created by MAKEMAP?".format(config1), starutil.CRITICAL )
fred = NDG.load("IN", True)
if indata != fred:
raise UsageError(
"\n\nThe directory specified by parameter RESTART ({0}) "
"refers to different time-series data".format(restart))
msg_out("Re-using data in {0}".format(restart))
# Initialise the starlink random number seed to a known value so that
# results are repeatable.
os.environ["STAR_SEED"] = "65"
# Has the indput data been flatfielded? Test the first supplied input NDF.
ff = None
try:
if "smf_flatfield" in invoke(
"$HDSTRACE_DIR/hdstrace {0}.more.smurf.smurfhist".format(
indata[0])):
msg_out("Input data has already been flatfielded.")
ff = indata
except starutil.StarUtilError:
pass
# Flat field the supplied template data
if not ff:
ff = NDG.load("FF")
if ff:
msg_out("Re-using old flatfielded template data...")
if not ff:
ffdir = NDG.subdir()
msg_out("Flatfielding template data...")
elif cval == "DAS":
instrument = "DAS"
# If so, set the default for the INSTRUMENT parameter and prevent the
# user being prompted for a value.
if instrument != None:
parsys["INSTRUMENT"].default = instrument
parsys["INSTRUMENT"].noprompt = True
# Get the chosen instrument.
instrument = parsys["INSTRUMENT"].value
instrument = starutil.shell_quote( instrument )
# Get a list of the tiles that overlap the supplied NDF.
invoke( "$SMURF_DIR/jsatilelist in={0} instrument={1} quiet".format(inndf,instrument) )
tiles = starutil.get_task_par( "TILES", "jsatilelist" )
# JSADICER requires the input array to be gridded on the JSA all-sky
# pixel grid. This is normally an HPX projection, but if the supplied
# NDF straddles a discontinuity in the HPX projection then we need to
# use a different flavour of HPX (either an HPX projection centred on
# RA=12h or am XPH (polar HEALPix) projection centred on the north or
# south pole). The above call to jsatileinfo will have determined the
# appropriate projection to use, so get it.
proj = starutil.get_task_par( "PROJ", "jsatilelist" )
# Create a file holding the FITS-WCS header for the first tile, using
# the type of projection determined above.
head = "{0}/header".format(NDG.tempdir)
invoke( "$SMURF_DIR/jsatileinfo itile={0} instrument={1} header={2} "
# See if temp files are to be retained.
retain = parsys["RETAIN"].value
# See statistical debiasing is to be performed.
debias = parsys["DEBIAS"].value
# Get groups containing all the Q, U and I images.
qin = inqui.filter("'\.Q$'" )
uin = inqui.filter("'\.U$'" )
iin = inqui.filter("'\.I$'" )
# Rotate them to use the same polarimetric reference direction.
qrot = NDG(qin)
urot = NDG(uin)
invoke( "$POLPACK_DIR/polrotref qin={0} uin={1} like={2} qout={3} uout={4} ".
format(qin,uin,qin[0],qrot,urot) )
# Mosaic them into a single set of Q, U and I images.
qmos = NDG( 1 )
invoke( "$KAPPA_DIR/wcsmosaic in={0} out={1} method=bilin accept".format(qrot,qmos) )
umos = NDG( 1 )
invoke( "$KAPPA_DIR/wcsmosaic in={0} out={1} method=bilin accept".format(urot,umos) )
imos = NDG( 1 )
invoke( "$KAPPA_DIR/wcsmosaic in={0} out={1} method=bilin accept".format(iin,imos) )
# If required, save the Q, U and I images.
if qui != None:
invoke( "$KAPPA_DIR/ndfcopy {0} out={1}.Q".format(qmos,qui) )
invoke( "$KAPPA_DIR/ndfcopy {0} out={1}.U".format(umos,qui) )
invoke( "$KAPPA_DIR/ndfcopy {0} out={1}.I".format(imos,qui) )
def pca( indata, ncomp ):
"""
Identifies and returns the strongest PCA components in a 3D NDF.
Invocation:
result = pca( indata, ncomp )
Arguments:
indata = NDG
An NDG object specifying a single 3D NDF. Each plane in the cube
is a separate image, and the images are compared using PCA.
ncomp = int
The number of PCA components to include in the returned NDF.
Returned Value:
A new NDG object containing a single 3D NDF containing just the
strongest "ncomp" PCA components found in the input NDF.
"""
msg_out( " finding strongest {0} components using Principal Component Analysis...".format(ncomp) )
# Get the shape of the input NDF.
invoke( "$KAPPA_DIR/ndftrace {0} quiet".format(indata) )
nx = get_task_par( "dims(1)", "ndftrace" )
ny = get_task_par( "dims(2)", "ndftrace" )
nz = get_task_par( "dims(3)", "ndftrace" )
# Fill any bad pixels.
tmp = NDG(1)
invoke( "$KAPPA_DIR/fillbad in={0} out={1} variance=no niter=10 size=10".format(indata,tmp) )
# Read the planes from the supplied NDF. Note, numpy axis ordering is the
# reverse of starlink axis ordering. We want a numpy array consisting of
# "nz" elements, each being a vectorised form of a plane from the 3D NDF.
ndfdata = numpy.reshape( Ndf( tmp[0] ).data, (nz,nx*ny) )
# Normalize each plane to a mean of zero and standard deviation of 1.0
means = []
sigmas = []
newdata = []
for iplane in range(0,nz):
plane = ndfdata[ iplane ]
mn = plane.mean()
sg = math.sqrt( plane.var() )
means.append( mn )
sigmas.append( sg )
if sg > 0.0:
newdata.append( (plane-mn)/sg )
newdata= numpy.array( newdata )
# Transpose as required by MDP.
pcadata = numpy.transpose( newdata )
# Find the required number of PCA components (these are the strongest
# components).
pca = mdp.nodes.PCANode( output_dim=ncomp )
comp = pca.execute( pcadata )
# Re-project the components back into the space of the input 3D NDF.
ip = numpy.dot( comp, pca.get_recmatrix() )
# Transpose the array so that each row is an image.
ipt = numpy.transpose(ip)
# Normalise them back to the original scales.
jplane = 0
newdata = []
for iplane in range(0,nz):
if sigmas[ iplane ] > 0.0:
newplane = sigmas[ iplane ] * ipt[ jplane ] + means[ iplane ]
jplane += 1
else:
newplane = ndfdata[ iplane ]
newdata.append( newplane )
newdata= numpy.array( newdata )
# Dump the re-projected images out to a 3D NDF.
result = NDG(1)
indf = ndf.open( result[0], 'WRITE', 'NEW' )
indf.new('_DOUBLE', 3, numpy.array([1,1,1]),numpy.array([nx,ny,nz]))
ndfmap = indf.map( 'DATA', '_DOUBLE', 'WRITE' )
ndfmap.numpytondf( newdata )
indf.annul()
# Uncomment to dump the components.
# msg_out( "Dumping PCA comps to {0}-comps".format(result[0]) )
# compt = numpy.transpose(comp)
# indf = ndf.open( "{0}-comps".format(result[0]), 'WRITE', 'NEW' )
# indf.new('_DOUBLE', 3, numpy.array([1,1,1]),numpy.array([nx,ny,ncomp]))
# ndfmap = indf.map( 'DATA', '_DOUBLE', 'WRITE' )
# ndfmap.numpytondf( compt )
# indf.annul()
return result
print "where band is: 450 or 850"
print "and observation-date has the form: YYYYMMDD"
sys.exit(0)
# print "band={0}".format(band)
# Get WNFACT value and nFrames from data file
wnfact = float(starutil.get_fits_header(indata, "WNFACT"))
# print "wnfact={0}".format(wnfact)
nFrames = int(starutil.get_fits_header(indata, "MIRSTOP")) + 1
# print "nFrames={0}".format(nFrames)
# Gather statistics on the central region of the input spectrum
# We are interested in the z position of the maximum pixel value (peak)
instats = invoke("$KAPPA_DIR/stats ndf={0} quiet".format(indata))
maxpos = starutil.get_task_par("MAXPOS", "stats")
maxposz = maxpos[2]
# print "maxposz={0}".format(maxposz)
# Calculate the band pass frames centered on the peak
if band == "SCUBA2_850":
wnlbound = 11.2
wnubound = 12.2
else:
if band == "SCUBA2_450":
wnlbound = 22.1
wnubound = 23.3
# print "wnlbound={0}".format(wnlbound)
# print "wnubound={0}".format(wnubound)
bandwidth = wnubound - wnlbound
fred = NDG.load( "IN", True )
if indata != fred:
raise UsageError("\n\nThe directory specified by parameter RESTART ({0}) "
"refers to different time-series data".format(restart) )
msg_out( "Re-using data in {0}".format(restart) )
# Initialise the starlink random number seed to a known value so that
# results are repeatable.
os.environ["STAR_SEED"] = "65"
# Flat field the supplied template data
ff = NDG.load( "FF" )
if not ff:
ffdir = NDG.subdir()
msg_out( "Flatfielding template data...")
invoke("$SMURF_DIR/flatfield in={0} out=\"{1}/*\"".format(indata,ffdir) )
ff = NDG("{0}/\*".format(ffdir))
ff.save( "FF" )
else:
msg_out( "Re-using old flatfielded template data...")
# Output files. Base the modification on "ff" rather than "indata",
# since "indata" may include non-science files (flatfields, darks etc)
# for which no corresponding output file should be created.
gexp = parsys["OUT"].value
outdata = NDG( ff, gexp )
# If required, create new artificial I, Q and U maps.
if newart:
msg_out( "Creating new artificial I, Q and U maps...")
# To be a group expression, it must contain at least one of the
# following characters: ^,= (NDFs are not allowed any of these).
gexp_chars = set( '^=,' )
if any( (c in gexp_chars) for c in config1 ):
isndf1 = False
else:
isndf1 = True
# If it is an NDF, attempt to get the SUBARRAY fits header, and
# determine the waveband. If no SUBARRAY header is found, look for
# "s4a", etc, in the NDF's provenance info.
if isndf1:
try:
subarray = starutil.get_fits_header( config1, "SUBARRAY" )
if subarray == None:
text = starutil.invoke( "$KAPPA_DIR/provshow {0}".format(config1) )
if "s4a" in text or "s4b" in text or "s4c" in text or "s4d" in text:
subarray = "s4"
elif "s8a" in text or "s8b" in text or "s8c" in text or "s8d" in text:
subarray = "s8"
else:
subarray = None
except:
print( "\n!! It looks like NDF '{0}' either does not exist or is "
"corrupt.".format(config1) )
os._exit(1)
if isndf1:
if subarray == None:
msg_out("Cannot determine the SCUBA-2 waveband for NDF '{0}' "
"- was it really created by MAKEMAP?".format(config1), starutil.CRITICAL )
tiledir = os.getenv('JSA_TILE_DIR')
if tiledir:
msg_out("Tiles will be read from {0}".format(tiledir))
else:
msg_out("Environment variable JSA_TILE_DIR is not set!")
msg_out("Tiles will be read from the current directory ({0})".format(
os.getcwd()))
# Create an empty list to hold the NDFs for the tiles holding the
# required data.
tilendf = []
itilelist = []
# Identify the tiles that overlap the specified region, and loop round
# them.
invoke("$SMURF_DIR/tilelist region={0} instrument={1}".format(
region, instrument))
for itile in starutil.get_task_par("tiles", "tilelist"):
# Get information about the tile, including the 2D spatial pixel index
# bounds of its overlap with the required Region.
invoke("$SMURF_DIR/tileinfo itile={0} instrument={1} "
"target={2}".format(itile, instrument, region))
# Skip this tile if it does not exist (i.e. is empty).
if starutil.get_task_par("exists", "tileinfo"):
# Get the 2D spatial pixel index bounds of the part of the master tile that
# overlaps the required region.
tlbnd = starutil.get_task_par("tlbnd", "tileinfo")
tubnd = starutil.get_task_par("tubnd", "tileinfo")
if region == None :
system = parsys["SYSTEM"].value
if system == "ICRS" :
parsys["CENTRE1"].prompt = "RA at centre of required circle"
parsys["CENTRE2"].prompt = "Dec at centre of required circle"
else:
parsys["CENTRE1"].prompt = "Galactic longitude at centre of required circle"
parsys["CENTRE2"].prompt = "Galactic latitude at centre of required circle"
centre1 = parsys["CENTRE1"].value
if centre1 != None:
centre2 = parsys["CENTRE2"].value
radius = parsys["RADIUS"].value
frame = NDG.tempfile()
invoke( "$ATOOLS_DIR/astskyframe \"'system={0}'\" {1}".format(system,frame) )
invoke( "$ATOOLS_DIR/astunformat {0} 1 {1}".format(frame,centre1) )
cen1 = starutil.get_task_par( "DVAL", "astunformat" )
invoke( "$ATOOLS_DIR/astunformat {0} 2 {1}".format(frame,centre2) )
cen2 = starutil.get_task_par( "DVAL", "astunformat" )
region = NDG.tempfile()
invoke( "$ATOOLS_DIR/astcircle {0} 1 \[{1},{2}\] {3} ! ! {4}".
format(frame,cen1,cen2,math.radians(radius/60.0),region) )
# If a Region was supplied ,not we do not yet have the coordinates of
# the centre of the required region, and note if the Region is defined by
# an NDF.
else:
try:
outbase = parsys["OUT"].value
fakemap = parsys["FAKEMAP"].value
# Erase any NDFs holding cleaned data, exteinction or pointing data from
# previous runs.
for path in glob.glob("*_con_res_cln.sdf"):
myremove(path)
base = path[:-16]
myremove("{0}_lat.sdf".format(base))
myremove("{0}_lon.sdf".format(base))
myremove("{0}_con_ext.sdf".format(base))
# Use sc2concat to concatenate and flatfield the data.
msg_out("Concatenating and flatfielding...")
concbase = NDG.tempfile("")
invoke("$SMURF_DIR/sc2concat in={0} outbase={1} maxlen=360".format(
indata, concbase))
concdata = NDG("{0}_*".format(concbase))
# Use makemap to generate quality, extinction and pointing info.
confname = NDG.tempfile()
fd = open(confname, "w")
fd.write("^$STARLINK_DIR/share/smurf/dimmconfig.lis\n")
fd.write("numiter=1\n")
fd.write("exportclean=1\n")
fd.write("exportndf=ext\n")
fd.write("exportlonlat=1\n")
fd.write("dcfitbox=0\n")
fd.write("noisecliphigh=0\n")
fd.write("order=0\n")
fd.write("downsampscale=0\n")
if fakemap != None:
