def apply_flat(scifilename, maskname, band):
''' Divides the contents of scifilename by the flat field and
overwrites scifilename with the same file divided by the flat
Args:
scifilename: Path to science file name.
maskname: The mask name
band: The filter bands
Results:
Overwrites scifilename where the data contents of the file
are divided by the pixel flat
'''
flat = IO.load_flat(maskname, band, {})
flat_data = flat[1].filled(1.0)
header, data = IO.readfits(scifilename)
print("Applying flat to file {0}".format(scifilename))
IO.writefits(data / flat_data,
maskname,
scifilename, {},
header=header,
overwrite=True)
def imcombine(filelist, maskname, fname, options, sum_type):
""" combine the images in file list into fname.
Sum type:
rate -- filelist is in cnt/s
ivar-rate -- filelist is in s/cnt
snr-rate -- filelist is in SNR
"""
ARR = None
hdr = None
i = 1
itime = 0
for file in filelist:
this_hdr, img = IO.readfits(file)
cards = this_hdr.ascardlist()
thisitime = this_hdr["truitime"]
itime += thisitime
if ARR is None:
ARR = np.zeros(img.shape)
if sum_type == "rate":
ARR += img * thisitime
if sum_type == "ivar-rate":
ARR += thisitime / img
if sum_type == "snr-rate":
ARR += img * thisitime
if hdr is None:
hdr = this_hdr
hdr.update("fno%2.2i" % i, file, "--")
for card in cards:
key, value, comment = (card.key, card.value, card.comment)
if hdr.has_key(key) and hdr[key] != value:
key = key + ("_img%2.2i" % i)
if len(key) > 8:
key = "HIERARCH " + key
try:
hdr.update(key, value, comment)
except ValueError:
pass
hdr.update("itime", itime, "Itime for %i rectified images" % len(filelist))
if sum_type == "rate":
ARR /= itime
if sum_type == "ivar-rate":
ARR = itime / ARR
if sum_type == "snr-rate":
ARR /= itime
IO.writefits(ARR, maskname, fname, options, header=hdr, overwrite=True, lossy_compress=True)
def imcombine(filelist, maskname, fname, options, sum_type):
''' combine the images in file list into fname.
Sum type:
rate -- filelist is in cnt/s
ivar-rate -- filelist is in s/cnt
snr-rate -- filelist is in SNR
'''
ARR = None
hdr = None
i = 1
itime = 0
for file in filelist:
this_hdr, img = IO.readfits(file)
cards = this_hdr.ascardlist()
thisitime = this_hdr['truitime']
itime += thisitime
if ARR is None: ARR = np.zeros(img.shape)
if sum_type == 'rate': ARR += img * thisitime
if sum_type == 'ivar-rate': ARR += thisitime / img
if sum_type == 'snr-rate': ARR += img * thisitime
if hdr is None:
hdr = this_hdr
hdr["fno%2.2i" % i] = (file, "--")
for card in cards:
key, value, comment = (card.key, card.value, card.comment)
if hdr.has_key(key) and hdr[key] != value:
key = key + ("_img%2.2i" % i)
if len(key) > 8: key = 'HIERARCH ' + key
try:
hdr[key] = (value, comment)
except ValueError:
pass
hdr['itime'] = (itime, 'Itime for %i rectified images' % len(filelist))
if sum_type == 'rate': ARR /= itime
if sum_type == 'ivar-rate': ARR = itime / ARR
if sum_type == 'snr-rate': ARR /= itime
IO.writefits(ARR,
maskname,
fname,
options,
header=hdr,
overwrite=True,
lossy_compress=True)
def rectify(dname, lamdat, A, B, maskname, band, wavoptions, longoptions):
header, data = IO.readfits(dname)
raw_img = data * Detector.gain / header['TRUITIME']
dlam = Wavelength.grating_results(band)
hpp = np.array(Filters.hpp[band])
ll_fid = np.arange(hpp[0], hpp[1], dlam)
rectified = np.zeros((2048, len(ll_fid)))
from scipy.interpolate import interp1d
for i in xrange(2048):
ll = lamdat[i, :]
ss = raw_img[i, :]
ok = np.isfinite(ll) & np.isfinite(ss) & (ll < hpp[1]) & (ll > hpp[0])
if len(np.where(ok)[0]) < 30:
continue
f = interp1d(ll[ok], ss[ok], bounds_error=False)
rectified[i, :] = f(ll_fid)
header.update("wat0_001", "system=world")
header.update("wat1_001", "wtype=linear")
header.update("wat2_001", "wtype=linear")
header.update("dispaxis", 1)
header.update("dclog1", "Transform")
header.update("dc-flag", 0)
header.update("ctype1", "AWAV")
header.update("cunit1", "Angstrom")
header.update("crval1", ll_fid[0])
header.update("crval2", 0)
header.update("crpix1", 1)
header.update("crpix2", 1)
header.update("cdelt1", 1)
header.update("cdelt2", 1)
header.update("cname1", "angstrom")
header.update("cname2", "pixel")
header.update("cd1_1", dlam)
header.update("cd1_2", 0)
header.update("cd2_1", 0)
header.update("cd2_2", 1)
header.update("object", "rectified [eps]")
IO.writefits(rectified,
maskname,
"rectified_%s" % (dname),
wavoptions,
header=header,
overwrite=True,
lossy_compress=True)
def rectify(dname, lamdat, A, B, maskname, band, wavoptions,
longoptions):
header, data = IO.readfits(dname)
raw_img = data * Detector.gain / header['TRUITIME']
dlam = Wavelength.grating_results(band)
hpp = np.array(Filters.hpp[band])
ll_fid = np.arange(hpp[0], hpp[1], dlam)
rectified = np.zeros((2048, len(ll_fid)))
from scipy.interpolate import interp1d
for i in xrange(2048):
ll = lamdat[i,:]
ss = raw_img[i,:]
ok = np.isfinite(ll) & np.isfinite(ss) & (ll < hpp[1]) & (ll >
hpp[0])
if len(np.where(ok)[0]) < 30:
continue
f = interp1d(ll[ok], ss[ok], bounds_error=False)
rectified[i,:] = f(ll_fid)
header.update("wat0_001", "system=world")
header.update("wat1_001", "wtype=linear")
header.update("wat2_001", "wtype=linear")
header.update("dispaxis", 1)
header.update("dclog1", "Transform")
header.update("dc-flag", 0)
header.update("ctype1", "AWAV")
header.update("cunit1", "Angstrom")
header.update("crval1", ll_fid[0])
header.update("crval2", 0)
header.update("crpix1", 1)
header.update("crpix2", 1)
header.update("cdelt1", 1)
header.update("cdelt2", 1)
header.update("cname1", "angstrom")
header.update("cname2", "pixel")
header.update("cd1_1", dlam)
header.update("cd1_2", 0)
header.update("cd2_1", 0)
header.update("cd2_2", 1)
header.update("object", "rectified [eps]")
IO.writefits(rectified, maskname, "rectified_%s" % (dname),
wavoptions, header=header, overwrite=True, lossy_compress=True)
def apply_flat(scifilename, maskname, band):
''' Divides the contents of scifilename by the flat field and
overwrites scifilename with the same file divided by the flat
Args:
scifilename: Path to science file name.
maskname: The mask name
band: The filter bands
Results:
Overwrites scifilename where the data contents of the file
are divided by the pixel flat
'''
flat = IO.load_flat(maskname, band, {})
flat_data = flat[1].filled(1.0)
header, data = IO.readfits(scifilename)
print("Applying flat to file {0}".format(scifilename))
IO.writefits(data/flat_data, maskname, scifilename, {}, header=header,
overwrite=True)
def imcombine(files, maskname, options, flat, outname=None, shifts=None,
extension=None):
'''
From a list of files it imcombine returns the imcombine of several values.
The imcombine code also estimates the readnoise ad RN/sqrt(numreads) so
that the variance per frame is equal to (ADU + RN^2) where RN is computed
in ADUs.
Arguments:
files[]: list of full path to files to combine
maskname: Name of mask
options: Options dictionary
flat[2048x2048]: Flat field (values should all be ~ 1.0)
outname: If set, will write (see notes below for details)
eps_[outname].fits: electron/sec file
itimes_[outname].fits: integration time
var_[outname].fits: Variance files
shifts[len(files)]: If set, will "roll" each file by the
amount in the shifts vector in pixels. This argument
is used when telescope tracking is poor. If you need
to use this, please notify Keck staff about poor
telescope tracking.
Returns 6-element tuple:
header: The combined header
electrons [2048x2048]: e- (in e- units)
var [2048x2048]: electrons + RN**2 (in e-^2 units)
bs: The MOSFIRE.Barset instance
itimes [2048x2048]: itimes (in s units)
Nframe: The number of frames that contribute to the summed
arrays above. If Nframe > 5 I use the sigma-clipping
Cosmic Ray Rejection tool. If Nframe < 5 then I drop
the max/min elements.
Notes:
header -- fits header
ADUs -- The mean # of ADUs per frame
var -- the Variance [in adu] per frame.
bs -- Barset
itimes -- The _total_ integration time in second
Nframe -- The number of frames in a stack.
Thus the number of electron per second is derived as:
e-/sec = (ADUs * Gain / Flat) * (Nframe/itimes)
The total number of electrons is:
el = ADUs * Gain * Nframe
'''
ADUs = np.zeros((len(files), 2048, 2048))
itimes = np.zeros((len(files), 2048, 2048))
prevssl = None
prevmn = None
patternid = None
maskname = None
header = None
if shifts is None:
shifts = np.zeros(len(files))
warnings.filterwarnings('ignore')
for i in xrange(len(files)):
fname = files[i]
thishdr, data, bs = IO.readmosfits(fname, options, extension=extension)
itimes[i,:,:] = thishdr["truitime"]
base = os.path.basename(fname).rstrip(".fits")
fnum = int(base.split("_")[1])
if shifts[i] == 0:
ADUs[i,:,:] = data.filled(0.0) / flat
else:
ADUs[i,:,:] = np.roll(data.filled(0.0) / flat, np.int(shifts[i]), axis=0)
''' Construct Header'''
if header is None:
header = thishdr
header["imfno%3.3i" % (fnum)] = (fname, "img%3.3i file name" % fnum)
map(lambda x: rem_header_key(header, x), ["CTYPE1", "CTYPE2", "WCSDIM",
"CD1_1", "CD1_2", "CD2_1", "CD2_2", "LTM1_1", "LTM2_2", "WAT0_001",
"WAT1_001", "WAT2_001", "CRVAL1", "CRVAL2", "CRPIX1", "CRPIX2",
"RADECSYS"])
for card in header.cards:
if card == '': continue
key,val,comment = card
if key in thishdr:
if val != thishdr[key]:
newkey = key + ("_img%2.2i" % fnum)
try: header[newkey.rstrip()] = (thishdr[key], comment)
except: pass
''' Now handle error checking'''
if maskname is not None:
if thishdr["maskname"] != maskname:
raise Exception("File %s uses mask '%s' but the stack is of '%s'" %
(fname, thishdr["maskname"], maskname))
maskname = thishdr["maskname"]
if thishdr["aborted"]:
raise Exception("Img '%s' was aborted and should not be used" %
fname)
if prevssl is not None:
if len(prevssl) != len(bs.ssl):
# todo Improve these checks
raise Exception("The stack of input files seems to be of "
"different masks")
prevssl = bs.ssl
if patternid is not None:
if patternid != thishdr["frameid"]:
raise Exception("The stack should be of '%s' frames only, but "
"the current image is a '%s' frame." % (patternid,
thishdr["frameid"]))
patternid = thishdr["frameid"]
if maskname is not None:
if maskname != thishdr["maskname"]:
raise Exception("The stack should be of CSU mask '%s' frames "
"only but contains a frame of '%s'." % (maskname,
thishdr["maskname"]))
maskname = thishdr["maskname"]
if thishdr["BUNIT"] != "ADU per coadd":
raise Exception("The units of '%s' are not in ADU per coadd and "
"this violates an assumption of the DRP. Some new code "
"is needed in the DRP to handle the new units of "
"'%s'." % (fname, thishdr["BUNIT"]))
''' Error checking is complete'''
print "%s %s[%s]/%s: %5.1f s, Shift: %i px" % (fname, maskname, patternid,
header['filter'], np.mean(itimes[i]), shifts[i])
warnings.filterwarnings('always')
# the electrons and el_per_sec arrays are:
# [2048, 2048, len(files)] and contain values for
# each individual frame that is being combined.
# These need to be kept here for CRR reasons.
electrons = np.array(ADUs) * Detector.gain
el_per_sec = electrons / itimes
output = np.zeros((2048, 2048))
exptime = np.zeros((2048, 2048))
numreads = header["READS0"]
RN_adu = Detector.RN / np.sqrt(numreads) / Detector.gain
RN = Detector.RN / np.sqrt(numreads)
# Cosmic ray rejection code begins here. This code construction the
# electrons and itimes arrays.
if len(files) >= 9:
print "Sigclip CRR"
srt = np.argsort(electrons, axis=0, kind='quicksort')
shp = el_per_sec.shape
sti = np.ogrid[0:shp[0], 0:shp[1], 0:shp[2]]
electrons = electrons[srt, sti[1], sti[2]]
el_per_sec = el_per_sec[srt, sti[1], sti[2]]
itimes = itimes[srt, sti[1], sti[2]]
# Construct the mean and standard deviation by dropping the top and bottom two
# electron fluxes. This is temporary.
mean = np.mean(el_per_sec[2:-2,:,:], axis = 0)
std = np.std(el_per_sec[2:-2,:,:], axis = 0)
drop = np.where( (el_per_sec > (mean+std*4)) | (el_per_sec < (mean-std*4)) )
print "dropping: ", len(drop[0])
electrons[drop] = 0.0
itimes[drop] = 0.0
electrons = np.sum(electrons, axis=0)
itimes = np.sum(itimes, axis=0)
Nframe = len(files)
elif len(files) > 5:
print "WARNING: Drop min/max CRR"
srt = np.argsort(el_per_sec,axis=0)
shp = el_per_sec.shape
sti = np.ogrid[0:shp[0], 0:shp[1], 0:shp[2]]
electrons = electrons[srt, sti[1], sti[2]]
itimes = itimes[srt, sti[1], sti[2]]
electrons = np.sum(electrons[1:-1,:,:], axis=0)
itimes = np.sum(itimes[1:-1,:,:], axis=0)
Nframe = len(files) - 2
else:
print "WARNING: CRR through median filtering"
for i in xrange(len(files)):
el = electrons[i,:,:]
it = itimes[i,:,:]
el_mf = scipy.signal.medfilt(el, 5)
bad = np.abs(el - el_mf) / np.abs(el) > 10.0
el[bad] = 0.0
it[bad] = 0.0
electrons[i,:,:] = el
itimes[i,:,:] = it
electrons = np.sum(electrons, axis=0)
itimes = np.sum(itimes, axis=0)
Nframe = len(files)
''' Now handle variance '''
numreads = header["READS0"]
RN_adu = Detector.RN / np.sqrt(numreads) / Detector.gain
RN = Detector.RN / np.sqrt(numreads)
var = (electrons + RN**2)
''' Now mask out bad pixels '''
electrons[data.mask] = np.nan
var[data.mask] = np.inf
if "RN" in header: raise Exception("RN Already populated in header")
header['RN'] = ("%1.3f" , "Read noise in e-")
header['NUMFRM'] = (Nframe, 'Typical number of frames in stack')
if outname is not None:
header['BUNIT'] = 'ELECTRONS/SECOND'
IO.writefits(np.float32(electrons/itimes), maskname, "eps_%s" % (outname),
options, header=header, overwrite=True)
# Update itimes after division in order to not introduce nans
itimes[data.mask] = 0.0
header['BUNIT'] = 'ELECTRONS^2'
IO.writefits(var, maskname, "var_%s" % (outname),
options, header=header, overwrite=True, lossy_compress=True)
header['BUNIT'] = 'SECOND'
IO.writefits(np.float32(itimes), maskname, "itimes_%s" % (outname),
options, header=header, overwrite=True, lossy_compress=True)
return header, electrons, var, bs, itimes, Nframe
def write_outputs(solutions, itime, header, maskname, band_name, plan, options):
sky_sub_out = np.zeros((2048, 2048), dtype=np.float)
sky_model_out = np.zeros((2048, 2048), dtype=np.float)
p0 = plan[0].replace("'", "p")
p1 = plan[1].replace("'", "p")
suffix = "%s-%s" % (p0,p1)
xroi = slice(0,2048)
for sol in solutions:
if not sol["ok"]:
continue
yroi = slice(sol["bottom"], sol["top"])
sky_sub_out[yroi, xroi] = sol["output"]
sky_model_out[yroi, xroi] = sol["model"]
header['BUNIT'] = 'SECOND'
IO.writefits(itime, maskname, "itime_%s_%s_%s.fits" % (maskname, band,
suffix), options, header=header, overwrite=True, lossy_compress=True)
header['BUNIT'] = 'ELECTRONS/SECOND'
IO.writefits(data, maskname, "sub_%s_%s_%s.fits" % (maskname, band,
suffix), options, header=header, overwrite=True, lossy_compress=True)
header['BUNIT'] = 'ELECTRONS/SECOND'
IO.writefits(sky_sub_out, maskname, "bsub_%s_%s_%s.fits" % (maskname, band,
suffix), options, header=header, overwrite=True)
header['BUNIT'] = 'ELECTRONS'
IO.writefits(Var, maskname, "var_%s_%s_%s.fits" % (maskname, band,
suffix), options, header=header, overwrite=True, lossy_compress=True)
header['BUNIT'] = 'ELECTRONS/SECOND'
IO.writefits(sky_model_out, maskname, "bmod_%s_%s_%s.fits" % (maskname,
band, suffix), options, header=header, overwrite=True,
lossy_compress=True)
'''Now create rectified solutions'''
dlam = Wavelength.grating_results(band)
hpp = np.array(Filters.hpp[band])
ll_fid = np.arange(hpp[0], hpp[1], dlam)
nspec = len(ll_fid)
rectified = np.zeros((2048, nspec), dtype=np.float32)
rectified_var = np.zeros((2048, nspec), dtype=np.float32)
rectified_itime = np.zeros((2048, nspec), dtype=np.float32)
from scipy.interpolate import interp1d
for i in xrange(2048):
ll = lam[1][i,:]
ss = sky_sub_out[i,:]
ok = np.isfinite(ll) & np.isfinite(ss) & (ll < hpp[1]) & (ll >
hpp[0])
if len(np.where(ok)[0]) < 100:
continue
f = interp1d(ll[ok], ss[ok], bounds_error=False)
rectified[i,:] = f(ll_fid)
f = interp1d(ll, Var[i,:], bounds_error=False)
rectified_var[i,:] = f(ll_fid)
f = interp1d(ll, itime[i,:], bounds_error=False)
rectified_itime[i,:] = f(ll_fid)
header["wat0_001"] = "system=world"
header["wat1_001"] = "type=linear"
header["wat2_001"] = "type=linear"
header["dispaxis"] = 1
header["dclog1"] = "Transform"
header["dc-flag"] = 0
header["type1"] = "AWAV"
header["cunit1"] = "Angstrom"
header["crval1"] = (ll_fid[0], "Starting wavelength Angstrom")
header["crval2"] = 0
header["crpix1"] = 1
header["crpix2"] = 1
header["cdelt1"] = 1
header["cdelt2"] = 1
header["cname1"] = "angstrom"
header["cname2"] = "pixel"
header["cd1_1"] = (dlam, "Angstrom/pixel")
header["cd1_2"] = 0
header["cd2_1"] = 0
header["cd2_2"] = (1, "pixel/pixel")
IO.writefits(rectified_itime, maskname,
"%s_rectified_itime_%s_%s.fits" % (maskname, band_name,
suffix), options, header=header, overwrite=True, lossy_compress=True)
IO.writefits(rectified, maskname, "%s_rectified_%s_%s.fits" % (maskname,
band_name, suffix), options, header=header, overwrite=True,
lossy_compress=True)
IO.writefits(rectified_var, maskname, "%s_rectified_var_%s_%s.fits" %
(maskname, band_name, suffix), options, header=header, overwrite=True,
lossy_compress=True)
IO.writefits(rectified*rectified_itime/np.sqrt(rectified_var), maskname,
"%s_rectified_sn_%s_%s.fits" % (maskname, band_name,
suffix), options, header=header, overwrite=True, lossy_compress=True)
def handle_rectification(maskname, in_files, wavename, band_pass, barset_file, options,
commissioning_shift=3.0):
'''Handle slit rectification and coaddition.
Args:
maskname: The mask name string
in_files: List of stacked spectra in electron per second. Will look
like ['electrons_Offset_1.5.txt.fits', 'electrons_Offset_-1.5.txt.fits']
wavename: path (relative or full) to the wavelength stack file, string
band_pass: Band pass name, string
barset_file: Path to a mosfire fits file containing the full set of
FITS extensions for the barset. It can be any file in the list
of science files.
Returns:
None
Writes files:
[maskname]_[band]_[object name]_eps.fits --
The rectified, background subtracted, stacked eps spectrum
[maskname]_[band]_[object name]_sig.fits --
Rectified, background subtracted, stacked weight spectrum (STD/itime)
[maskname]_[band]_[object_name]_itime.fits
Rectified, CRR stacked integration time spectrum
[maskname]_[band]_[object_name]_snrs.fits
Rectified signal to noise spectrum
'''
global edges, dats, vars, itimes, shifts, lambdas, band, fidl, all_shifts
band = band_pass
dlambda = Wavelength.grating_results(band)
hpp = Filters.hpp[band]
fidl = np.arange(hpp[0], hpp[1], dlambda)
lambdas = IO.readfits(wavename, options)
if np.any(lambdas[1].data < 0) or np.any(lambdas[1].data > 29000):
print "***********WARNING ***********"
print "The file {0} may not be a wavelength file.".format(wavename)
print "Check before proceeding."
print "***********WARNING ***********"
edges, meta = IO.load_edges(maskname, band, options)
shifts = []
posnames = []
postoshift = {}
for file in in_files:
print ":: ", file
II = IO.read_drpfits(maskname, file, options)
off = np.array((II[0]["decoff"], II[0]["raoff"]),dtype=np.float64)
if "yoffset" in II[0]:
off = -II[0]["yoffset"]
else:
# Deal with data taken during commissioning
if II[0]["frameid"] == 'A': off = 0.0
else: off = commissioning_shift
try: off0
except: off0 = off
shift = off - off0
shifts.append(shift)
posnames.append(II[0]["frameid"])
postoshift[II[0]['frameid']] = shift
print "Position {0} shift: {1:2.2f} as".format(off, shift)
plans = Background.guess_plan_from_positions(set(posnames))
all_shifts = []
for plan in plans:
to_append = []
for pos in plan:
to_append.append(postoshift[pos])
all_shifts.append(to_append)
# Reverse the elements in all_shifts to deal with an inversion
all_shifts.reverse()
theBPM = IO.badpixelmask()
all_solutions = []
cntr = 0
for plan in plans:
p0 = plan[0].replace("'", "p")
p1 = plan[1].replace("'", "p")
suffix = "%s-%s" % (p0,p1)
print "Handling plan %s" % suffix
fname = "bsub_{0}_{1}_{2}.fits".format(maskname,band,suffix)
EPS = IO.read_drpfits(maskname, fname, options)
EPS[1] = np.ma.masked_array(EPS[1], theBPM, fill_value=0)
fname = "var_{0}_{1}_{2}.fits".format(maskname, band, suffix)
VAR = IO.read_drpfits(maskname, fname, options)
VAR[1] = np.ma.masked_array(VAR[1], theBPM, fill_value=np.inf)
fname = "itime_{0}_{1}_{2}.fits".format(maskname, band, suffix)
ITIME = IO.read_drpfits(maskname, fname, options)
ITIME[1] = np.ma.masked_array(ITIME[1], theBPM, fill_value=0)
dats = EPS
vars = VAR
itimes = ITIME
EPS[0]["ORIGFILE"] = fname
tock = time.time()
sols = range(len(edges)-1,-1,-1)
shifts = all_shifts[cntr]
cntr += 1
p = Pool()
solutions = p.map(handle_rectification_helper, sols)
#solutions = map(handle_rectification_helper, [15])
p.close()
all_solutions.append(solutions)
tick = time.time()
print "-----> Mask took %i. Writing to disk." % (tick-tock)
output = np.zeros((1, len(fidl)))
snrs = np.zeros((1, len(fidl)))
sdout= np.zeros((1, len(fidl)))
itout= np.zeros((1, len(fidl)))
# the barset [bs] is used for determining object position
x, x, bs = IO.readmosfits(barset_file, options)
for i_slit in xrange(len(solutions)):
solution = all_solutions[0][i_slit]
header = EPS[0].copy()
obj = header['OBJECT']
target_name = bs.ssl[-(i_slit+1)]['Target_Name']
header['OBJECT'] = target_name
pixel_dist = np.float(bs.ssl[-(i_slit+1)]['Target_to_center_of_slit_distance'])/0.18
pixel_dist -= solution['offset']
ll = solution["lambda"]
header["wat0_001"] = "system=world"
header["wat1_001"] = "wtype=linear"
header["wat2_001"] = "wtype=linear"
header["dispaxis"] = 1
header["dclog1"] = "Transform"
header["dc-flag"] = 0
header["ctype1"] = "AWAV"
header["cunit1"] = "Angstrom"
header["crval1"] = ll[0]
header["crval2"] = -solution["eps_img"].shape[0]/2 - pixel_dist
header["crpix1"] = 1
header["crpix2"] = 1
header["cdelt1"] = 1
header["cdelt2"] = 1
header["cname1"] = "angstrom"
header["cname2"] = "pixel"
header["cd1_1"] = ll[1]-ll[0]
header["cd1_2"] = 0
header["cd2_1"] = 0
header["cd2_2"] = 1
S = output.shape
img = solution["eps_img"]
std = solution["sd_img"]
tms = solution["itime_img"]
for i_solution in xrange(1,len(all_solutions)):
print "Combining solution %i" %i_solution
solution = all_solutions[i_solution][i_slit]
img += solution["eps_img"]
std += solution["sd_img"]
tms += solution["itime_img"]
output = np.append(output, img, 0)
output = np.append(output, np.nan*np.zeros((3,S[1])), 0)
snrs = np.append(snrs, img*tms/std, 0)
snrs = np.append(snrs, np.nan*np.zeros((3,S[1])), 0)
sdout = np.append(sdout, std, 0)
sdout = np.append(sdout, np.nan*np.zeros((3,S[1])), 0)
itout = np.append(itout, tms, 0)
itout = np.append(itout, np.nan*np.zeros((3,S[1])), 0)
header['bunit'] = ('electron/second', 'electron power')
IO.writefits(img, maskname,
"{0}_{1}_{2}_eps.fits".format(maskname, band, target_name), options,
overwrite=True, header=header, lossy_compress=False)
header['bunit'] = ('electron/second', 'sigma/itime')
IO.writefits(std/tms, maskname,
"{0}_{1}_{2}_sig.fits".format(maskname, band, target_name), options,
overwrite=True, header=header, lossy_compress=False)
header['bunit'] = ('second', 'exposure time')
IO.writefits(tms, maskname,
"{0}_{1}_{2}_itime.fits".format(maskname, band, target_name), options,
overwrite=True, header=header, lossy_compress=False)
header['bunit'] = ('', 'SNR')
IO.writefits(img*tms/std, maskname,
"{0}_{1}_{2}_snrs.fits".format(maskname, band, target_name), options,
overwrite=True, header=header, lossy_compress=False)
header = EPS[0].copy()
header["wat0_001"] = "system=world"
header["wat1_001"] = "wtype=linear"
header["wat2_001"] = "wtype=linear"
header["dispaxis"] = 1
header["dclog1"] = "Transform"
header["dc-flag"] = 0
header["ctype1"] = "AWAV"
header["cunit1"] = ("Angstrom", 'Start wavelength')
header["crval1"] = ll[0]
header["crval2"] = 1
header["crpix1"] = 1
header["crpix2"] = 1
header["cdelt1"] = 1
header["cdelt2"] = 1
header["cname1"] = "angstrom"
header["cname2"] = "pixel"
header["cd1_1"] = (ll[1]-ll[0], 'Angstrom/pixel')
header["cd1_2"] = 0
header["cd2_1"] = 0
header["cd2_2"] = 1
header["bunit"] = "ELECTRONS/SECOND"
IO.writefits(output, maskname, "{0}_{1}_eps.fits".format(maskname,
band), options, overwrite=True, header=header,
lossy_compress=False)
header["bunit"] = ""
IO.writefits(snrs, maskname, "{0}_{1}_snrs.fits".format(maskname,
band), options, overwrite=True, header=header,
lossy_compress=False)
header["bunit"] = "ELECTRONS/SECOND"
IO.writefits(sdout/itout, maskname, "{0}_{1}_sig.fits".format(maskname,
band), options, overwrite=True, header=header,
lossy_compress=False)
header["bunit"] = "SECOND"
IO.writefits(itout, maskname, "{0}_{1}_itime.fits".format(maskname,
band), options, overwrite=True, header=header,
lossy_compress=False)
def write_outputs(solutions, itime, header, maskname, band_name, plan,
options):
sky_sub_out = np.zeros((2048, 2048), dtype=np.float)
sky_model_out = np.zeros((2048, 2048), dtype=np.float)
p0 = plan[0].replace("'", "p")
p1 = plan[1].replace("'", "p")
suffix = "%s-%s" % (p0, p1)
xroi = slice(0, 2048)
for sol in solutions:
if not sol["ok"]:
continue
yroi = slice(sol["bottom"], sol["top"])
sky_sub_out[yroi, xroi] = sol["output"]
sky_model_out[yroi, xroi] = sol["model"]
header['BUNIT'] = 'SECOND'
IO.writefits(itime,
maskname,
"itime_%s_%s_%s.fits" % (maskname, band, suffix),
options,
header=header,
overwrite=True,
lossy_compress=True)
header['BUNIT'] = 'ELECTRONS/SECOND'
IO.writefits(data,
maskname,
"sub_%s_%s_%s.fits" % (maskname, band, suffix),
options,
header=header,
overwrite=True,
lossy_compress=True)
header['BUNIT'] = 'ELECTRONS/SECOND'
IO.writefits(sky_sub_out,
maskname,
"bsub_%s_%s_%s.fits" % (maskname, band, suffix),
options,
header=header,
overwrite=True)
header['BUNIT'] = 'ELECTRONS'
IO.writefits(Var,
maskname,
"var_%s_%s_%s.fits" % (maskname, band, suffix),
options,
header=header,
overwrite=True,
lossy_compress=True)
header['BUNIT'] = 'ELECTRONS/SECOND'
IO.writefits(sky_model_out,
maskname,
"bmod_%s_%s_%s.fits" % (maskname, band, suffix),
options,
header=header,
overwrite=True,
lossy_compress=True)
'''Now create rectified solutions'''
dlam = Wavelength.grating_results(band)
hpp = np.array(Filters.hpp[band])
ll_fid = np.arange(hpp[0], hpp[1], dlam)
nspec = len(ll_fid)
rectified = np.zeros((2048, nspec), dtype=np.float32)
rectified_var = np.zeros((2048, nspec), dtype=np.float32)
rectified_itime = np.zeros((2048, nspec), dtype=np.float32)
from scipy.interpolate import interp1d
for i in xrange(2048):
ll = lam[1][i, :]
ss = sky_sub_out[i, :]
ok = np.isfinite(ll) & np.isfinite(ss) & (ll < hpp[1]) & (ll > hpp[0])
if len(np.where(ok)[0]) < 100:
continue
f = interp1d(ll[ok], ss[ok], bounds_error=False)
rectified[i, :] = f(ll_fid)
f = interp1d(ll, Var[i, :], bounds_error=False)
rectified_var[i, :] = f(ll_fid)
f = interp1d(ll, itime[i, :], bounds_error=False)
rectified_itime[i, :] = f(ll_fid)
header["wat0_001"] = "system=world"
header["wat1_001"] = "type=linear"
header["wat2_001"] = "type=linear"
header["dispaxis"] = 1
header["dclog1"] = "Transform"
header["dc-flag"] = 0
header["type1"] = "AWAV"
header["cunit1"] = "Angstrom"
header["crval1"] = (ll_fid[0], "Starting wavelength Angstrom")
header["crval2"] = 0
header["crpix1"] = 1
header["crpix2"] = 1
header["cdelt1"] = 1
header["cdelt2"] = 1
header["cname1"] = "angstrom"
header["cname2"] = "pixel"
header["cd1_1"] = (dlam, "Angstrom/pixel")
header["cd1_2"] = 0
header["cd2_1"] = 0
header["cd2_2"] = (1, "pixel/pixel")
IO.writefits(rectified_itime,
maskname,
"%s_rectified_itime_%s_%s.fits" %
(maskname, band_name, suffix),
options,
header=header,
overwrite=True,
lossy_compress=True)
IO.writefits(rectified,
maskname,
"%s_rectified_%s_%s.fits" % (maskname, band_name, suffix),
options,
header=header,
overwrite=True,
lossy_compress=True)
IO.writefits(rectified_var,
maskname,
"%s_rectified_var_%s_%s.fits" % (maskname, band_name, suffix),
options,
header=header,
overwrite=True,
lossy_compress=True)
IO.writefits(rectified * rectified_itime / np.sqrt(rectified_var),
maskname,
"%s_rectified_sn_%s_%s.fits" % (maskname, band_name, suffix),
options,
header=header,
overwrite=True,
lossy_compress=True)
def imcombine(files,
maskname,
options,
flat,
outname=None,
shifts=None,
extension=None):
'''
From a list of files it imcombine returns the imcombine of several values.
The imcombine code also estimates the readnoise ad RN/sqrt(numreads) so
that the variance per frame is equal to (ADU + RN^2) where RN is computed
in ADUs.
Arguments:
files[]: list of full path to files to combine
maskname: Name of mask
options: Options dictionary
flat[2048x2048]: Flat field (values should all be ~ 1.0)
outname: If set, will write (see notes below for details)
eps_[outname].fits: electron/sec file
itimes_[outname].fits: integration time
var_[outname].fits: Variance files
shifts[len(files)]: If set, will "roll" each file by the
amount in the shifts vector in pixels. This argument
is used when telescope tracking is poor. If you need
to use this, please notify Keck staff about poor
telescope tracking.
Returns 6-element tuple:
header: The combined header
electrons [2048x2048]: e- (in e- units)
var [2048x2048]: electrons + RN**2 (in e-^2 units)
bs: The MOSFIRE.Barset instance
itimes [2048x2048]: itimes (in s units)
Nframe: The number of frames that contribute to the summed
arrays above. If Nframe > 5 I use the sigma-clipping
Cosmic Ray Rejection tool. If Nframe < 5 then I drop
the max/min elements.
Notes:
header -- fits header
ADUs -- The mean # of ADUs per frame
var -- the Variance [in adu] per frame.
bs -- Barset
itimes -- The _total_ integration time in second
Nframe -- The number of frames in a stack.
Thus the number of electron per second is derived as:
e-/sec = (ADUs * Gain / Flat) * (Nframe/itimes)
The total number of electrons is:
el = ADUs * Gain * Nframe
'''
ADUs = np.zeros((len(files), 2048, 2048))
itimes = np.zeros((len(files), 2048, 2048))
prevssl = None
prevmn = None
patternid = None
maskname = None
header = None
if shifts is None:
shifts = np.zeros(len(files))
warnings.filterwarnings('ignore')
for i in xrange(len(files)):
fname = files[i]
thishdr, data, bs = IO.readmosfits(fname, options, extension=extension)
itimes[i, :, :] = thishdr["truitime"]
base = os.path.basename(fname).rstrip(".fits")
fnum = int(base.split("_")[1])
if shifts[i] == 0:
ADUs[i, :, :] = data.filled(0.0) / flat
else:
ADUs[i, :, :] = np.roll(data.filled(0.0) / flat,
np.int(shifts[i]),
axis=0)
''' Construct Header'''
if header is None:
header = thishdr
header["imfno%3.3i" % (fnum)] = (fname, "img%3.3i file name" % fnum)
map(lambda x: rem_header_key(header, x), [
"CTYPE1", "CTYPE2", "WCSDIM", "CD1_1", "CD1_2", "CD2_1", "CD2_2",
"LTM1_1", "LTM2_2", "WAT0_001", "WAT1_001", "WAT2_001", "CRVAL1",
"CRVAL2", "CRPIX1", "CRPIX2", "RADECSYS"
])
for card in header.cards:
if card == '': continue
key, val, comment = card
if key in thishdr:
if val != thishdr[key]:
newkey = key + ("_img%2.2i" % fnum)
try:
header[newkey.rstrip()] = (thishdr[key], comment)
except:
pass
''' Now handle error checking'''
if maskname is not None:
if thishdr["maskname"] != maskname:
raise Exception(
"File %s uses mask '%s' but the stack is of '%s'" %
(fname, thishdr["maskname"], maskname))
maskname = thishdr["maskname"]
if thishdr["aborted"]:
raise Exception("Img '%s' was aborted and should not be used" %
fname)
if prevssl is not None:
if len(prevssl) != len(bs.ssl):
# todo Improve these checks
raise Exception("The stack of input files seems to be of "
"different masks")
prevssl = bs.ssl
if patternid is not None:
if patternid != thishdr["frameid"]:
raise Exception("The stack should be of '%s' frames only, but "
"the current image is a '%s' frame." %
(patternid, thishdr["frameid"]))
patternid = thishdr["frameid"]
if maskname is not None:
if maskname != thishdr["maskname"]:
raise Exception("The stack should be of CSU mask '%s' frames "
"only but contains a frame of '%s'." %
(maskname, thishdr["maskname"]))
maskname = thishdr["maskname"]
if thishdr["BUNIT"] != "ADU per coadd":
raise Exception(
"The units of '%s' are not in ADU per coadd and "
"this violates an assumption of the DRP. Some new code "
"is needed in the DRP to handle the new units of "
"'%s'." % (fname, thishdr["BUNIT"]))
''' Error checking is complete'''
print "%s %s[%s]/%s: %5.1f s, Shift: %i px" % (
fname, maskname, patternid, header['filter'], np.mean(
itimes[i]), shifts[i])
warnings.filterwarnings('always')
# the electrons and el_per_sec arrays are:
# [2048, 2048, len(files)] and contain values for
# each individual frame that is being combined.
# These need to be kept here for CRR reasons.
electrons = np.array(ADUs) * Detector.gain
el_per_sec = electrons / itimes
output = np.zeros((2048, 2048))
exptime = np.zeros((2048, 2048))
numreads = header["READS0"]
RN_adu = Detector.RN / np.sqrt(numreads) / Detector.gain
RN = Detector.RN / np.sqrt(numreads)
# Cosmic ray rejection code begins here. This code construction the
# electrons and itimes arrays.
if len(files) >= 9:
print "Sigclip CRR"
srt = np.argsort(electrons, axis=0, kind='quicksort')
shp = el_per_sec.shape
sti = np.ogrid[0:shp[0], 0:shp[1], 0:shp[2]]
electrons = electrons[srt, sti[1], sti[2]]
el_per_sec = el_per_sec[srt, sti[1], sti[2]]
itimes = itimes[srt, sti[1], sti[2]]
# Construct the mean and standard deviation by dropping the top and bottom two
# electron fluxes. This is temporary.
mean = np.mean(el_per_sec[2:-2, :, :], axis=0)
std = np.std(el_per_sec[2:-2, :, :], axis=0)
drop = np.where((el_per_sec > (mean + std * 4))
| (el_per_sec < (mean - std * 4)))
print "dropping: ", len(drop[0])
electrons[drop] = 0.0
itimes[drop] = 0.0
electrons = np.sum(electrons, axis=0)
itimes = np.sum(itimes, axis=0)
Nframe = len(files)
elif len(files) > 5:
print "WARNING: Drop min/max CRR"
srt = np.argsort(el_per_sec, axis=0)
shp = el_per_sec.shape
sti = np.ogrid[0:shp[0], 0:shp[1], 0:shp[2]]
electrons = electrons[srt, sti[1], sti[2]]
itimes = itimes[srt, sti[1], sti[2]]
electrons = np.sum(electrons[1:-1, :, :], axis=0)
itimes = np.sum(itimes[1:-1, :, :], axis=0)
Nframe = len(files) - 2
else:
print "WARNING: CRR through median filtering"
for i in xrange(len(files)):
el = electrons[i, :, :]
it = itimes[i, :, :]
el_mf = scipy.signal.medfilt(el, 5)
bad = np.abs(el - el_mf) / np.abs(el) > 10.0
el[bad] = 0.0
it[bad] = 0.0
electrons[i, :, :] = el
itimes[i, :, :] = it
electrons = np.sum(electrons, axis=0)
itimes = np.sum(itimes, axis=0)
Nframe = len(files)
''' Now handle variance '''
numreads = header["READS0"]
RN_adu = Detector.RN / np.sqrt(numreads) / Detector.gain
RN = Detector.RN / np.sqrt(numreads)
var = (electrons + RN**2)
''' Now mask out bad pixels '''
electrons[data.mask] = np.nan
var[data.mask] = np.inf
if "RN" in header: raise Exception("RN Already populated in header")
header['RN'] = ("%1.3f", "Read noise in e-")
header['NUMFRM'] = (Nframe, 'Typical number of frames in stack')
if outname is not None:
header['BUNIT'] = 'ELECTRONS/SECOND'
IO.writefits(np.float32(electrons / itimes),
maskname,
"eps_%s" % (outname),
options,
header=header,
overwrite=True)
# Update itimes after division in order to not introduce nans
itimes[data.mask] = 0.0
header['BUNIT'] = 'ELECTRONS^2'
IO.writefits(var,
maskname,
"var_%s" % (outname),
options,
header=header,
overwrite=True,
lossy_compress=True)
header['BUNIT'] = 'SECOND'
IO.writefits(np.float32(itimes),
maskname,
"itimes_%s" % (outname),
options,
header=header,
overwrite=True,
lossy_compress=True)
return header, electrons, var, bs, itimes, Nframe
def handle_rectification(maskname, in_files, wavename, band_pass, files, options,
commissioning_shift=3.0, target='default'):
'''Handle slit rectification and coaddition.
Args:
maskname: The mask name string
in_files: List of stacked spectra in electron per second. Will look
like ['electrons_Offset_1.5.txt.fits', 'electrons_Offset_-1.5.txt.fits']
wavename: path (relative or full) to the wavelength stack file, string
band_pass: Band pass name, string
barset_file: Path to a mosfire fits file containing the full set of
FITS extensions for the barset. It can be any file in the list
of science files.
Returns:
None
Writes files:
[maskname]_[band]_[object name]_eps.fits --
The rectified, background subtracted, stacked eps spectrum
[maskname]_[band]_[object name]_sig.fits --
Rectified, background subtracted, stacked weight spectrum (STD/itime)
[maskname]_[band]_[object_name]_itime.fits
Rectified, CRR stacked integration time spectrum
[maskname]_[band]_[object_name]_snrs.fits
Rectified signal to noise spectrum
'''
global edges, dats, vars, itimes, shifts, lambdas, band, fidl, all_shifts
band = band_pass
dlambda = Wavelength.grating_results(band)
hpp = Filters.hpp[band]
fidl = np.arange(hpp[0], hpp[1], dlambda)
lambdas = IO.readfits(wavename, options)
if np.any(lambdas[1].data < 0) or np.any(lambdas[1].data > 29000):
info("***********WARNING ***********")
info("The file {0} may not be a wavelength file.".format(wavename))
info("Check before proceeding.")
info("***********WARNING ***********")
edges, meta = IO.load_edges(maskname, band, options)
shifts = []
posnames = []
postoshift = {}
for file in in_files:
info(":: "+str(file))
II = IO.read_drpfits(maskname, file, options)
off = np.array((II[0]["decoff"], II[0]["raoff"]),dtype=np.float64)
if "yoffset" in II[0]:
off = -II[0]["yoffset"]
else:
# Deal with data taken during commissioning
if II[0]["frameid"] == 'A': off = 0.0
else: off = commissioning_shift
try: off0
except: off0 = off
shift = off - off0
shifts.append(shift)
posnames.append(II[0]["frameid"])
postoshift[II[0]['frameid']] = shift
info("Position {0} shift: {1:2.2f} as".format(off, shift))
# this is to deal with cases in which we want to rectify one single file
if len(set(posnames)) is 1:
plans = [['A']]
else:
plans = Background.guess_plan_from_positions(set(posnames))
all_shifts = []
for plan in plans:
to_append = []
for pos in plan:
to_append.append(postoshift[pos])
all_shifts.append(to_append)
# Reverse the elements in all_shifts to deal with an inversion
all_shifts.reverse()
theBPM = IO.badpixelmask()
all_solutions = []
cntr = 0
if target is 'default':
outname = maskname
else:
outname = target
for plan in plans:
if len(plan) is 1:
p0 = 'A'
p1 = 'B'
else:
p0 = plan[0].replace("'", "p")
p1 = plan[1].replace("'", "p")
suffix = "%s-%s" % (p0,p1)
info("Handling plan %s" % suffix)
fname = "bsub_{0}_{1}_{2}.fits".format(outname,band,suffix)
EPS = IO.read_drpfits(maskname, fname, options)
EPS[1] = np.ma.masked_array(EPS[1], theBPM, fill_value=0)
fname = "var_{0}_{1}_{2}.fits".format(outname, band, suffix)
VAR = IO.read_drpfits(maskname, fname, options)
VAR[1] = np.ma.masked_array(VAR[1], theBPM, fill_value=np.inf)
fname = "itime_{0}_{1}_{2}.fits".format(outname, band, suffix)
ITIME = IO.read_drpfits(maskname, fname, options)
ITIME[1] = np.ma.masked_array(ITIME[1], theBPM, fill_value=0)
dats = EPS
vars = VAR
itimes = ITIME
EPS[0]["ORIGFILE"] = fname
tock = time.time()
sols = range(len(edges)-1,-1,-1)
shifts = all_shifts[cntr]
cntr += 1
p = Pool()
solutions = p.map(handle_rectification_helper, sols)
p.close()
all_solutions.append(solutions)
tick = time.time()
info("-----> Mask took %i. Writing to disk." % (tick-tock))
output = np.zeros((1, len(fidl)))
snrs = np.zeros((1, len(fidl)))
sdout= np.zeros((1, len(fidl)))
itout= np.zeros((1, len(fidl)))
# the barset [bs] is used for determining object position
files = IO.list_file_to_strings(files)
info("Using "+str(files[0])+" for slit configuration.")
x, x, bs = IO.readmosfits(files[0], options)
for i_slit in xrange(len(solutions)):
solution = all_solutions[0][i_slit]
header = EPS[0].copy()
obj = header['OBJECT']
target_name = bs.ssl[-(i_slit+1)]['Target_Name']
header['OBJECT'] = target_name
pixel_dist = np.float(bs.ssl[-(i_slit+1)]['Target_to_center_of_slit_distance'])/0.18
pixel_dist -= solution['offset']
ll = solution["lambda"]
header["wat0_001"] = "system=world"
header["wat1_001"] = "wtype=linear"
header["wat2_001"] = "wtype=linear"
header["dispaxis"] = 1
header["dclog1"] = "Transform"
header["dc-flag"] = 0
header["ctype1"] = "AWAV"
header["cunit1"] = "Angstrom"
header["crval1"] = ll[0]
header["crval2"] = -solution["eps_img"].shape[0]/2 - pixel_dist
header["crpix1"] = 1
header["crpix2"] = 1
#remove redundant CDELTi due to wavelength issues with ds9
#see: https://github.com/Keck-DataReductionPipelines/MosfireDRP/issues/44
#header["cdelt1"] = 1
#header["cdelt2"] = 1
header["cname1"] = "angstrom"
header["cname2"] = "pixel"
header["cd1_1"] = ll[1]-ll[0]
header["cd1_2"] = 0
header["cd2_1"] = 0
header["cd2_2"] = 1
try:
header["BARYCORR"]= (lambdas[0]['BARYCORR'],lambdas[0].comments['BARYCORR'])
except KeyError:
warning( "Barycentric corrections not applied to the wavelength solution")
pass
S = output.shape
img = solution["eps_img"]
std = solution["sd_img"]
tms = solution["itime_img"]
for i_solution in xrange(1,len(all_solutions)):
info("Combining solution %i" %i_solution)
solution = all_solutions[i_solution][i_slit]
img += solution["eps_img"]
std += solution["sd_img"]
tms += solution["itime_img"]
#print "adding in quadrature"
output = np.append(output, img, 0)
output = np.append(output, np.nan*np.zeros((3,S[1])), 0)
snrs = np.append(snrs, img*tms/std, 0)
snrs = np.append(snrs, np.nan*np.zeros((3,S[1])), 0)
sdout = np.append(sdout, std, 0)
sdout = np.append(sdout, np.nan*np.zeros((3,S[1])), 0)
itout = np.append(itout, tms, 0)
itout = np.append(itout, np.nan*np.zeros((3,S[1])), 0)
header['bunit'] = ('electron/second', 'electron power')
IO.writefits(img, maskname,
"{0}_{1}_{2}_eps.fits".format(outname, band, target_name), options,
overwrite=True, header=header, lossy_compress=False)
header['bunit'] = ('electron/second', 'sigma/itime')
IO.writefits(std/tms, maskname,
"{0}_{1}_{2}_sig.fits".format(outname, band, target_name), options,
overwrite=True, header=header, lossy_compress=False)
header['bunit'] = ('second', 'exposure time')
IO.writefits(tms, maskname,
"{0}_{1}_{2}_itime.fits".format(outname, band, target_name), options,
overwrite=True, header=header, lossy_compress=False)
header['bunit'] = ('', 'SNR')
IO.writefits(img*tms/std, maskname,
"{0}_{1}_{2}_snrs.fits".format(outname, band, target_name), options,
overwrite=True, header=header, lossy_compress=False)
header = EPS[0].copy()
header["wat0_001"] = "system=world"
header["wat1_001"] = "wtype=linear"
header["wat2_001"] = "wtype=linear"
header["dispaxis"] = 1
header["dclog1"] = "Transform"
header["dc-flag"] = 0
header["ctype1"] = "AWAV"
header["cunit1"] = ("Angstrom", 'Start wavelength')
header["crval1"] = ll[0]
header["crval2"] = 1
header["crpix1"] = 1
header["crpix2"] = 1
#remove redundant CDELTi due to wavelength issues with ds9
#see: https://github.com/Keck-DataReductionPipelines/MosfireDRP/issues/44
#header["cdelt1"] = 1
#header["cdelt2"] = 1
header["cname1"] = "angstrom"
header["cname2"] = "pixel"
header["cd1_1"] = (ll[1]-ll[0], 'Angstrom/pixel')
header["cd1_2"] = 0
header["cd2_1"] = 0
header["cd2_2"] = 1
try:
header["BARYCORR"]= (lambdas[0]['BARYCORR'],lambdas[0].comments['BARYCORR'])
except KeyError:
warning( "Barycentric corrections not applied to the wavelength solution")
pass
header["bunit"] = "ELECTRONS/SECOND"
info("############ Final reduced file: {0}_{1}_eps.fits".format(outname,band))
IO.writefits(output, maskname, "{0}_{1}_eps.fits".format(outname,
band), options, overwrite=True, header=header,
lossy_compress=False)
header["bunit"] = ""
IO.writefits(snrs, maskname, "{0}_{1}_snrs.fits".format(outname,
band), options, overwrite=True, header=header,
lossy_compress=False)
header["bunit"] = "ELECTRONS/SECOND"
IO.writefits(sdout/itout, maskname, "{0}_{1}_sig.fits".format(outname,
band), options, overwrite=True, header=header,
lossy_compress=False)
header["bunit"] = "SECOND"
IO.writefits(itout, maskname, "{0}_{1}_itime.fits".format(outname,
band), options, overwrite=True, header=header,
lossy_compress=False)
