def ronchi(ronchilist, ronchiflat, calflat, over, flatdark, log):
"""Establish Spatial-distortion calibration with nfsdist.
NFSDIST uses the information in the "Ronchi" Calibration images
to calibrate the spatial dimension of the NIFS IFU field. The
Ronchi frame is a dispersed flat field image with a slit-mask
in the field so that the illumination on the IFU is in a
pattern of ~10 different slitlets that are stacked in the
y-dimension on the field. Proper alignment of the slits across
the image slicer pattern can be used for spatial rectification
of the on-sky science data. The spatial solution determined by
NFSDIST is linked to the science data in NFFITCOORDS.
"""
# Update ronchi flat frames with offset value and generate variance and data quality extensions.
# Output: "n"+image+".fits".
for image in ronchilist:
image = str(image).strip()
if over:
iraf.delete("n"+image+'.fits')
iraf.nfprepare(image,rawpath=".", shiftimage="s"+calflat, \
bpm="rgn"+calflat+"_sflat_bpm.pl", fl_vardq="yes",fl_corr="no",fl_nonl="no", \
logfile=log)
ronchilist = checkLists(ronchilist, '.', 'n', '.fits')
# Combine nfprepared ronchi flat images "n"+image+".fits". Output: combined file will
# have the name of the first ronchi flat file, "gn"+ronchiflat+".fits".
if over:
iraf.delete("gn"+ronchiflat+".fits")
if len(ronchilist) > 1:
iraf.gemcombine(listit(ronchilist,"n"),output="gn"+ronchiflat,fl_dqpr="yes", \
masktype="none",fl_vardq="yes",logfile=log)
else:
iraf.copy("n"+ronchiflat+".fits","gn"+ronchiflat+".fits")
# NSREDUCE combined ronchi frame, "gn"+ronchiflat+".fits", to extract the slices into unique MEF extensions and
# apply an approximate wavelength calibration to each slice. Output: "rgn"+ronchiflat+".fits".
if over:
iraf.delete("rgn"+ronchiflat+".fits")
iraf.nsreduce("gn"+ronchiflat, outpref="r", dark="rgn"+flatdark+"_dark", flatimage="rgn"+calflat+"_flat", fl_cut="yes", fl_nsappw="yes", fl_flat="yes", fl_sky="no", fl_dark="yes", fl_vardq="no", logfile=log)
if os.path.exists("ronchifile"):
if over:
iraf.delete("ronchifile")
iraf.delete("rgn"+ronchiflat)
else:
print "\nOutput file exists and -over not set - ",\
"not performing ronchi calibration with iraf.nfsdist.\n"
return
else:
# Determine the spatial distortion correction. Output: overwrites "rgn"+ronchiflat+".fits" and makes
# changes to files in /database directory.
iraf.nfsdist("rgn"+ronchiflat,fwidth=6.0, cradius=8.0, glshift=2.8, minsep=6.5, thresh=2000.0, nlost=3, fl_inter='no',logfile=log)
# Put the name of the spatially referenced ronchi flat "rgn"+ronchiflat into a
# text file called ronchifile to be used by the pipeline later. Also associated files
# are in the "database/" directory.
open("ronchifile", "w").write("rgn"+ronchiflat)
def makeArcDark(arcdarklist, arcdark, calflat, over, log):
""""Prepare with iraf.nfprepare and combine the daytime arc darks.
Processing with NFPREPARE will rename the data extension and add
variance and data quality extensions. By default (see NSHEADERS)
the extension names are SCI for science data, VAR for variance, and
DQ for data quality (0 = good). Generation of the data quality
plane (DQ) is important in order to fix hot and dark pixels on the
NIFS detector in subsequent steps in the data reduction process.
Various header keywords (used later) are also added in NFPREPARE.
NFPREPARE will also add an MDF file (extension MDF) describing the
NIFS image slicer pattern and how the IFU maps to the sky field.
"""
# Update arc dark frames with mdf offset value and generate variance and data quality extensions.
for image in arcdarklist:
image = str(image).strip()
if over:
iraf.delete("n"+image+".fits")
iraf.nfprepare(image, rawpath='.', shiftimage="s"+calflat, \
bpm=sflat_bpm,fl_vardq='yes',fl_corr='no',fl_nonl='no', logfile=log)
arcdarklist = checkLists(arcdarklist, '.', 'n', '.fits')
# Combine arc dark frames, "n"+image+".fits". Output combined file will have the name of the first arc dark file.
if over:
iraf.delete("gn"+arcdark+".fits")
if len(arcdarklist) > 1:
iraf.gemcombine(listit(arcdarklist,"n"),output="gn"+arcdark, fl_dqpr='yes',fl_vardq='yes',masktype="none",logfile=log)
else:
iraf.copy('n'+arcdark+'.fits', 'gn'+arcdark+'.fits')
# Put the name of the combined and prepared arc dark frame "gn"+arcdark into a text
# file called arcdarkfile to be used by the pipeline later.
open("arcdarkfile", "w").write("gn"+arcdark)
def reduceArc(arclist, arc, arcdarklist, arcdark, log, over):
"""Flat field and cut the arc data with iraf.nfprepare and
iraf.nsreduce.
Processing with NFPREPARE will rename the data extension and add
variance and data quality extensions. By default (see NSHEADERS)
the extension names are SCI for science data, VAR for variance, and
DQ for data quality (0 = good). Generation of the data quality
plane (DQ) is important in order to fix hot and dark pixels on the
NIFS detector in subsequent steps in the data reduction process.
Various header keywords (used later) are also added in NFPREPARE.
NFPREPARE will also add an MDF file (extension MDF) describing the
NIFS image slicer pattern and how the IFU maps to the sky field.
NSREDUCE is used for basic reduction of raw data - it provides a
single, unified interface to several tasks and also allows for
the subtraction of dark frames and dividing by the flat. For
NIFS reduction, NSREDUCE is used to call the NSCUT and NSAPPWAVE
routines. NSREDUCE resides in the GNIRS package.
"""
# Store the name of the shift image in "shiftima".
shiftima = open("shiftfile", "r").readlines()[0].strip()
# Store the name of the bad pixel mask in "sflat_bpm".
sflat_bpm = open("sflat_bpmfile", "r").readlines()[0].strip()
# Store the name of the final flat field frame in "flat".
flat = open("flatfile", "r").readlines()[0].strip()
# Update arc images with offset value and generate variance and data
# quality extensions. Results in "n"+image+".fits"
for image in arclist:
image = str(image).strip()
if os.path.exists("n"+image+".fits"):
if over:
iraf.delete("n"+image+".fits")
else:
print "\nOutput file exists and -over not set - skipping nfprepare of arcs."
continue
iraf.nfprepare(image, rawpath=".", shiftimage=shiftima,bpm=sflat_bpm,\
fl_vardq="yes",fl_corr='no',fl_nonl='no',logfile=log)
# Check that output files for all arc images exists from nfprepare; if output does not
# exist remove corresponding arc images from arclist.
arclist = checkLists(arclist, '.', 'n', '.fits')
# Update arc dark frames with mdf offset value and generate variance and data
# quality extensions. Results in "n"+image+".fits"
for image in arcdarklist:
image = str(image).strip()
if os.path.exists("n"+image+".fits"):
if over:
iraf.delete("n"+image+".fits")
else:
print "\nOutput file exists and -over not set - skipping nfprepare of arcdarks."
continue
iraf.nfprepare(image, rawpath=".", shiftimage=shiftima, bpm=sflat_bpm, \
fl_vardq='yes',fl_corr='no',fl_nonl='no',logfile=log)
# Check that output files for all arc images exists from nfprepare; if output does not
# exist remove corresponding arc images from arclist.
arcdarklist = checkLists(arcdarklist, '.', 'n', '.fits')
# Combine arc frames, "n"+image+".fits". Output combined file will have the name of the first arc file.
if os.path.exists("gn"+arc+".fits"):
if over:
iraf.delete("gn"+arc+".fits")
if len(arclist) > 1:
iraf.gemcombine(listit(arclist,"n"),output="gn"+arc, fl_dqpr='yes',fl_vardq='yes',masktype="none",logfile=log)
else:
iraf.copy('n'+arc+'.fits', 'gn'+arc+'.fits')
else:
print "\nOutput file exists and -over not set - skipping gemcombine of arcs."
else:
if len(arclist) > 1:
iraf.gemcombine(listit(arclist,"n"),output="gn"+arc, fl_dqpr='yes',fl_vardq='yes',masktype="none",logfile=log)
else:
iraf.copy('n'+arc+'.fits', 'gn'+arc+'.fits')
# Combine arc dark frames, "n"+image+".fits". Output combined file will have the name of the first arc dark file.
if os.path.exists("gn"+arcdark+".fits"):
if over:
iraf.delete("gn"+arcdark+".fits")
if len(arcdarklist) > 1:
iraf.gemcombine(listit(arcdarklist,"n"),output="gn"+arcdark, fl_dqpr='yes',fl_vardq='yes',masktype="none",logfile=log)
else:
iraf.copy('n'+arcdark+'.fits', 'gn'+arcdark+'.fits')
else:
print "\nOutput file exists and -over not set - skipping gemcombine of arcdarks."
else:
if len(arcdarklist) > 1:
iraf.gemcombine(listit(arcdarklist,"n"),output="gn"+arcdark, fl_dqpr='yes',fl_vardq='yes',masktype="none",logfile=log)
else:
iraf.copy('n'+arcdark+'.fits', 'gn'+arcdark+'.fits')
# Put the name of the combined and prepared arc dark frame "gn"+arcdark into a text
# file called arcdarkfile to be used by the pipeline later.
open("arcdarkfile", "w").write("gn"+arcdark)
# NSREDUCE on arc images "gn"+arc+".fits" to extract the slices and apply an approximate
# wavelength calibration. Results in "rgn"+image+".fits"
if os.path.exists("rgn"+arc+".fits"):
if over:
iraf.delete("rgn"+arc+".fits")
else:
print "Output file exists and -over not set - skipping apply_flat_arc."
return
fl_dark = "no"
if arcdark != "":
fl_dark = "yes"
hdulist = astropy.io.fits.open(arc+'.fits')
if 'K_Long' in hdulist[0].header['GRATING']:
iraf.nsreduce("gn"+arc, darki=arcdark, fl_cut="yes", fl_nsappw="yes", crval = 23000., fl_dark="yes", fl_sky="no", fl_flat="yes", flatimage=flat, fl_vardq="no",logfile=log)
else:
iraf.nsreduce("gn"+arc, darki="gn"+arcdark, flatimage=flat, \
fl_vardq="no", fl_cut="yes", fl_nsappw="yes", fl_sky="no", fl_dark="yes", fl_flat="yes", \
logfile=log)
# Put the name of the combined and prepared arc dark frame "gn"+arcdark into a text
# file called arcdarkfile to be used by the pipeline later.
open("arcdarkfile", "w").write("gn"+arcdark)
def makeFlat(flatlist, flatdarklist, calflat, flatdark, over, log):
"""Make flat and bad pixel mask.
Use NFPREPARE on the lamps on/lamps off flats to update the
raw data headers and attach the mask definition file (MDF) as a
binary table on all files. Note that dark frames will not have an
MDF attached by default. Instead, the appropriate MDF is added in
NSREDUCE or NSFLAT to match the data being reduced.
Use NSREDUCE to cut the calibration (flat/arc) spectra to
the size specified by the MDF, placing different IFU slices in
separate image extensions.
Use NSFLAT to generate a normalized flat field (for each
IFU slice or cross-dispersed order) from lamp flats. A mask (BPM)
will also be generated by thresholding - this can be used to flag
bad pixels in other data.
Use NSSLITFUNCTION to produce the final flat.
NSSLITFUNCTION extends the flatfield produced by correcting the
NSFLAT normalization for inter-slice variations. The output
from this task is used as the flatfield image for further
reduction.
"""
# Update lamps on flat frames with mdf offset value and generate variance and data quality extensions.
for image in flatlist:
image = str(image).strip()
if os.path.exists('n'+image+'.fits'):
if over:
os.remove('n'+image+'.fits')
iraf.nfprepare(image+'.fits',rawpath='.',shiftim="s"+calflat, fl_vardq='yes',fl_corr='no',fl_nonl='no', logfile=log)
else:
print "Output exists and -over- not set - skipping nfprepare of lamps on flats"
else:
iraf.nfprepare(image+'.fits',rawpath='.',shiftim="s"+calflat, fl_vardq='yes',fl_corr='no',fl_nonl='no', logfile=log)
flatlist = checkLists(flatlist, '.', 'n', '.fits')
# Update lamps off flat images with offset value and generate variance and data quality extensions.
for image in flatdarklist:
image = str(image).strip()
if os.path.exists('n'+image+'.fits'):
if over:
iraf.delete('n'+image+'.fits')
iraf.nfprepare(image+'.fits',rawpath='.',shiftim="s"+calflat, fl_vardq='yes',fl_corr='no',fl_nonl='no', logfile=log)
else:
print "\nOutput exists and -over- not set - skipping nfprepare of lamps off flats."
else:
iraf.nfprepare(image+'.fits',rawpath='.',shiftim="s"+calflat, fl_vardq='yes',fl_corr='no',fl_nonl='no', logfile=log)
flatdarklist = checkLists(flatdarklist, '.', 'n', '.fits')
# Combine lamps on flat images, "n"+image+".fits". Output combined file will have name of the first flat file with "gn" prefix.
if os.path.exists('gn'+calflat+'.fits'):
if over:
iraf.delete("gn"+calflat+".fits")
iraf.gemcombine(listit(flatlist,"n"),output="gn"+calflat,fl_dqpr='yes', fl_vardq='yes',masktype="none",logfile=log)
else:
print "\nOutput exists and -over- not set - skipping gemcombine of lamps on flats."
else:
iraf.gemcombine(listit(flatlist,"n"),output="gn"+calflat,fl_dqpr='yes', fl_vardq='yes',masktype="none",logfile=log)
# Combine lamps off flat images, "n"+image+".fits". Output combined file will have name of the first darkflat file with "gn" prefix.
if os.path.exists('gn'+flatdark+'.fits'):
if over:
iraf.delete("gn"+flatdark+".fits")
iraf.gemcombine(listit(flatdarklist,"n"),output="gn"+flatdark,fl_dqpr='yes', fl_vardq='yes',masktype="none",logfile=log)
else:
print "\nOutput exists and -over- not set - skipping gemcombine of lamps off flats."
else:
iraf.gemcombine(listit(flatdarklist,"n"),output="gn"+flatdark,fl_dqpr='yes', fl_vardq='yes',masktype="none",logfile=log)
# NSREDUCE on lamps on flat images, "gn"+calflat+".fits", to extract the slices and apply an approximate wavelength calibration.
if os.path.exists('rgn'+calflat+'.fits'):
if over:
iraf.delete("rgn"+calflat+".fits")
iraf.nsreduce ("gn"+calflat,fl_cut='yes',fl_nsappw='yes',fl_vardq='yes', fl_sky='no',fl_dark='no',fl_flat='no',logfile=log)
else:
print "\nOutput exists and -over- not set - skipping nsreduce of lamps on flats."
else:
iraf.nsreduce ("gn"+calflat,fl_cut='yes',fl_nsappw='yes',fl_vardq='yes', fl_sky='no',fl_dark='no',fl_flat='no',logfile=log)
# NSREDUCE on lamps off flat frames, "gn"+flatdark+".fits", to extract the slices and apply an approximate wavelength calibration.
if os.path.exists('rgn'+flatdark+'.fits'):
if over:
iraf.delete("rgn"+flatdark+".fits")
iraf.nsreduce ("gn"+flatdark,fl_cut='yes',fl_nsappw='yes',fl_vardq='yes', fl_sky='no',fl_dark='no',fl_flat='no',logfile=log)
else:
print "\nOutput exists and -over- not set - skipping nsreduce of lamps off flats."
else:
iraf.nsreduce ("gn"+flatdark,fl_cut='yes',fl_nsappw='yes',fl_vardq='yes', fl_sky='no',fl_dark='no',fl_flat='no',logfile=log)
# Create slice-by-slice flat field image and BPM image from the darkflats, using NSFLAT.
# Lower and upper limit of bad pixels are 0.15 and 1.55.
if over:
iraf.delete("rgn"+flatdark+"_dark.fits")
iraf.delete("rgn"+calflat+"_sflat.fits")
iraf.delete("rgn"+calflat+"_sflat_bpm.pl")
# Get the spectral band so we can fine tune thr_flo and thr_fup. This fine tunes the number of
# bad pixels caught to an approximately constant level for each band. A few bad pixels will
# not be caught, but it was found that catching them marked large portions of the top and bottom
# rows of pixels of each slice as bad pixels.
header = astropy.io.fits.open(calflat+'.fits')
grat = header[0].header['GRATING'][0:1]
if grat == 'Z' or grat == 'J':
flo = 0.07
fup = 1.55
inter = 'no'
elif grat == 'H' or grat == 'K':
flo = 0.05
fup = 1.55
inter = 'no'
else:
print "\n#####################################################################"
print "#####################################################################"
print ""
print " WARNING in baselineCalibration: nsflat detected a non-standard wavelength "
print " configuration. Running interactively. "
print ""
print "#####################################################################"
print "#####################################################################\n"
# Arbitrary; not tested with non-standard configurations.
flo = 0.15
fup = 1.55
inter = 'yes'
iraf.nsflat("rgn"+calflat,darks="rgn"+flatdark,flatfile="rgn"+calflat+"_sflat", \
darkfile="rgn"+flatdark+"_dark", fl_save_dark='yes',process="fit", \
thr_flo=flo,thr_fup=fup, fl_vardq='yes', fl_int=inter, logfile=log )
# Renormalize the slices to account for slice-to-slice variations using NSSLITFUNCTION - make the final flat field image.
if over:
iraf.delete("rgn"+calflat+"_flat.fits")
iraf.nsslitfunction("rgn"+calflat,"rgn"+calflat+"_flat", \
flat="rgn"+calflat+"_sflat",dark="rgn"+flatdark+"_dark",combine="median", \
order=3,fl_vary='no',logfile=log)
# Put the name of the final flat field and bad pixel mask (BPM) into text files of fixed name to be used by the pipeline later.
open("flatfile", "w").write("rgn"+calflat+"_flat") # Final flat field
open("sflatfile", "w").write("rgn"+calflat+"_sflat") # Flat field before renormalization (before nsslitfunction)
open("sflat_bpmfile", "w").write("rgn"+calflat+"_sflat_bpm.pl") # Bad Pixel Mask
def makeWaveCal(arclist, arc, arcdarklist, arcdark, log, over, path):
"""Determine the wavelength solution of each slice of the observation and
set the arc coordinate file.
If the user wishes to change the coordinate file to a different
one, they need only to change the "clist" variable to their line list
in the coordli= parameter in the nswavelength call.
Uses NSWAVELENGTH to calibrate arc data (after cutting and
optionally applying a flatfield with NSREDUCE in a previous step).
DATA REDUCTION HINT -
For the nswavelength call, the different wavelength settings
use different vaues for some of the parameters. For optimal auto
results, use:
K-band: thresho=50.0, cradius=8.0 --> (gives rms of 0.1 to 0.3)
H-band: thresho=100.0, cradius=8.0 --> (gives rms of 0.05 to 0.15)
J-band: thresho=100.0 --> (gives rms of 0.03 to 0.09)
Z-band: Currently not working very well for non-interactive mode
Note that better RMS fits can be obtained by running the wavelength
calibration interactively and identifying all of the lines
manually. Tedious, but will give more accurate results than the
automatic mode (i.e., fl_inter-). Use fl_inter+ for manual mode.
"""
# Store the name of the shift image in "shiftima".
shiftima = open("shiftfile", "r").readlines()[0].strip()
# Store the name of the bad pixel mask in "sflat_bpm".
sflat_bpm = open("sflat_bpmfile", "r").readlines()[0].strip()
# Store the name of the final flat field frame in "flat".
flat = open("flatfile", "r").readlines()[0].strip()
# Update arc images with offset value and generate variance and data
# quality extensions. Results in "n"+image+".fits"
for image in arclist:
image = str(image).strip()
if os.path.exists("n" + image + ".fits"):
if over:
iraf.delete("n" + image + ".fits")
else:
print "\nOutput file exists and -over not set - skipping nfprepare of arcs."
continue
iraf.nfprepare(image, rawpath=".", shiftimage=shiftima,bpm=sflat_bpm,\
fl_vardq="yes",fl_corr='no',fl_nonl='no',logfile=log)
# Check that output files for all arc images exists from nfprepare; if output does not
# exist remove corresponding arc images from arclist.
arclist = checkLists(arclist, '.', 'n', '.fits')
# Update arc dark frames with mdf offset value and generate variance and data
# quality extensions. Results in "n"+image+".fits"
for image in arcdarklist:
image = str(image).strip()
if os.path.exists("n" + image + ".fits"):
if over:
iraf.delete("n" + image + ".fits")
else:
print "\nOutput file exists and -over not set - skipping nfprepare of arcdarks."
continue
iraf.nfprepare(image, rawpath=".", shiftimage=shiftima, bpm=sflat_bpm, \
fl_vardq='yes',fl_corr='no',fl_nonl='no',logfile=log)
# Check that output files for all arc images exists from nfprepare; if output does not
# exist remove corresponding arc images from arclist.
arcdarklist = checkLists(arcdarklist, '.', 'n', '.fits')
# Combine arc frames, "n"+image+".fits". Output combined file will have the name of the first arc file.
if os.path.exists("gn" + arc + ".fits"):
if over:
iraf.delete("gn" + arc + ".fits")
if len(arclist) > 1:
iraf.gemcombine(listit(arclist, "n"),
output="gn" + arc,
fl_dqpr='yes',
fl_vardq='yes',
masktype="none",
logfile=log)
else:
iraf.copy('n' + arc + '.fits', 'gn' + arc + '.fits')
else:
print "\nOutput file exists and -over not set - skipping gemcombine of arcs."
else:
if len(arclist) > 1:
iraf.gemcombine(listit(arclist, "n"),
output="gn" + arc,
fl_dqpr='yes',
fl_vardq='yes',
masktype="none",
logfile=log)
else:
iraf.copy('n' + arc + '.fits', 'gn' + arc + '.fits')
# Combine arc dark frames, "n"+image+".fits". Output combined file will have the name of the first arc dark file.
if os.path.exists("gn" + arcdark + ".fits"):
if over:
iraf.delete("gn" + arcdark + ".fits")
if len(arcdarklist) > 1:
iraf.gemcombine(listit(arcdarklist, "n"),
output="gn" + arcdark,
fl_dqpr='yes',
fl_vardq='yes',
masktype="none",
logfile=log)
else:
iraf.copy('n' + arcdark + '.fits', 'gn' + arcdark + '.fits')
else:
print "\nOutput file exists and -over not set - skipping gemcombine of arcdarks."
else:
if len(arcdarklist) > 1:
iraf.gemcombine(listit(arcdarklist, "n"),
output="gn" + arcdark,
fl_dqpr='yes',
fl_vardq='yes',
masktype="none",
logfile=log)
else:
iraf.copy('n' + arcdark + '.fits', 'gn' + arcdark + '.fits')
# Put the name of the combined and prepared arc dark frame "gn"+arcdark into a text
# file called arcdarkfile to be used by the pipeline later.
open("arcdarkfile", "w").write("gn" + arcdark)
# NSREDUCE on arc images "gn"+arc+".fits" to extract the slices and apply an approximate
# wavelength calibration. Results in "rgn"+image+".fits"
if os.path.exists("rgn" + arc + ".fits"):
if over:
iraf.delete("rgn" + arc + ".fits")
else:
print "Output file exists and -over not set - skipping apply_flat_arc."
return
fl_dark = "no"
if arcdark != "":
fl_dark = "yes"
hdulist = astropy.io.fits.open(arc + '.fits')
if 'K_Long' in hdulist[0].header['GRATING']:
iraf.nsreduce("gn" + arc,
darki=arcdark,
fl_cut="yes",
fl_nsappw="yes",
crval=23000.,
fl_dark="yes",
fl_sky="no",
fl_flat="yes",
flatimage=flat,
fl_vardq="no",
logfile=log)
else:
iraf.nsreduce("gn"+arc, darki="gn"+arcdark, flatimage=flat, \
fl_vardq="no", fl_cut="yes", fl_nsappw="yes", fl_sky="no", fl_dark="yes", fl_flat="yes", \
logfile=log)
# Put the name of the combined and prepared arc dark frame "gn"+arcdark into a text
# file called arcdarkfile to be used by the pipeline later.
open("arcdarkfile", "w").write("gn" + arcdark)
if os.path.exists("wrgn" + arc + ".fits"):
if over:
iraf.delete("wrgn" + arc + ".fits")
else:
print "\nOutput file exists and -over not set - ",\
"not determining wavelength solution and recreating the wavelength reference arc.\n"
return
# Determine the wavelength setting.
hdulist = astropy.io.fits.open("rgn" + arc + ".fits")
band = hdulist[0].header['GRATING'][0:1]
central_wavelength = float(hdulist[0].header['GRATWAVE'])
# Set interactive mode. Default False for standard configurations (and True for non-standard wavelength configurations ).
pauseFlag = False
if band == "K" and central_wavelength == 2.20:
clist = RUNTIME_DATA_PATH + "k_ar.dat"
my_thresh = 50.0
elif band == "J":
clist = RUNTIME_DATA_PATH + "j_ar.dat"
my_thresh = 100.0
elif band == "H":
clist = RUNTIME_DATA_PATH + "h_ar.dat"
my_thresh = 100.0
elif band == "Z":
clist = "nifs$data/ArXe_Z.dat"
my_thresh = 100.0
else:
# Print a warning that the pipeline is being run with non-standard grating.
print "\n#####################################################################"
print "#####################################################################"
print ""
print " WARNING in calibrate: found a non-standard (non Z, J, H or K) "
print " wavelength configuration."
print " NSWAVELENGTH will be run interactively."
print ""
print "#####################################################################"
print "#####################################################################\n"
clist = "gnirs$data/argon.dat"
my_thresh = 100.0
interactive = 'yes'
if not pauseFlag:
# Establish wavelength calibration for arclamp spectra. Output: A series of
# files in a "database/" directory containing the wavelength solutions of
# each slice and a reduced arc frame "wrgn"+ARC+".fits".
iraf.nswavelength("rgn"+arc, coordli=clist, nsum=10, thresho=my_thresh, \
trace='yes', fwidth=2.0, match=-6,cradius=8.0,fl_inter='no',nfound=10,nlost=10, \
logfile=log)
else:
a = raw_input("For now, interactive Z or non-standard wavelength calibrations are unsupported. " + \
"Bugs running IRAF tasks interactively from python mean iraf.nswavelength cannot be activated automatically. " + \
"Therefore please run iraf.nswavelength() interactively from Pyraf to do a wavelength calibration by hand.")