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 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( "$KAPPA_DIR/copybad in={0} ref={1} out={2}".format(qff,uff,bad_pixel_ref))
return( qdata_total,qvar_total,udata_total,uvar_total,elevation,opacity_term,bad_pixel_ref )
plot = parsys["PLOT"].value
# If any vectors are to be plotted, get the SNR limit for the plotted
# vectors.
if plot != None:
snr = parsys["SNR"].value
maxlen = parsys["MAXLEN"].value
# See if temp files are to be retained.
retain = parsys["RETAIN"].value
# The following call to SMURF:CALCQU creates two HDS container files -
# one holding a set of Q NDFs and the other holding a set of U NDFs. Create
# these container files in the NDG temporary directory.
qcont = NDG(1)
qcont.comment = "qcont"
ucont = NDG(1)
ucont.comment = "ucont"
# Create a set of Q images and a set of U images. These are put into the HDS
# container files "q_TMP.sdf" and "u_TMP.sdf". Each image contains Q or U
# values derived from a short section of raw data during which each bolometer
# moves less than half a pixel.
msg_out( "Calculating Q and U values for each bolometer...")
invoke("$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...")
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)
