def fillgaps(profile): """Fill in the gaps""" hdu = pyfits.open(profile) #get the binning xbin, ybin = saltkey.ccdbin( hdu[0], '') #fill in the second gap x1 = 2044/xbin x2 = 2152/xbin d1=np.min(hdu[1].data[:,x1-10:x1], axis=1) d2=np.min(hdu[1].data[:,x2:x2+10], axis=1) d1 = 0.5 * (d1+d2) hdu[1].data[:,x1:x2] = d1.reshape(len(d1),1) #fill in the second gap x1 = 4096/xbin x2 = 4308/xbin d1=np.min(hdu[1].data[:,x1-10:x1], axis=1) d2=np.min(hdu[1].data[:,x2:x2+10], axis=1) d1 = 0.5 * (d1+d2) hdu[1].data[:,x1:x2] = d1.reshape(len(d1),1) if os.path.isfile(profile): os.remove(profile) hdu.writeto(profile)
def fillgaps(profile): """Fill in the gaps""" hdu = pyfits.open(profile) #get the binning xbin, ybin = saltkey.ccdbin( hdu[0], '') #fill in the second gap x1 = 2044/xbin x2 = 2152/xbin d1=np.min(hdu[1].data[:,x1-10:x1], axis=1) d2=np.min(hdu[1].data[:,x2:x2+10], axis=1) d1 = 0.5 * (d1+d2) hdu[1].data[:,x1:x2] = d1.reshape(len(d1),1) #fill in the second gap x1 = 4096/xbin x2 = 4308/xbin d1=np.min(hdu[1].data[:,x1-10:x1], axis=1) d2=np.min(hdu[1].data[:,x2:x2+10], axis=1) d1 = 0.5 * (d1+d2) hdu[1].data[:,x1:x2] = d1.reshape(len(d1),1) if os.path.isfile(profile): os.remove(profile) hdu.writeto(profile)
def calc_resolution(hdu):
"""Calculate the resolution for a setup"""
instrume=saltkey.get('INSTRUME', hdu[0]).strip()
grating=saltkey.get('GRATING', hdu[0]).strip()
grang=saltkey.get('GR-ANGLE', hdu[0])
grasteps=saltkey.get('GRTILT', hdu[0])
arang=saltkey.get('AR-ANGLE', hdu[0])
arsteps=saltkey.get('CAMANG', hdu[0])
rssfilter=saltkey.get('FILTER', hdu[0])
specmode=saltkey.get('OBSMODE', hdu[0])
masktype=saltkey.get('MASKTYP', hdu[0]).strip().upper()
slitname=saltkey.get('MASKID', hdu[0])
xbin, ybin = saltkey.ccdbin( hdu[0], '')
slit=st.getslitsize(slitname)
#create RSS Model
rss=RSSModel.RSSModel(grating_name=grating.strip(), gratang=grang, \
camang=arang,slit=slit, xbin=xbin, ybin=ybin, \
)
res=1e7*rss.calc_resolelement(rss.alpha(), -rss.beta())
return res
def calc_resolution(hdu):
"""Calculate the resolution for a setup"""
instrume=saltkey.get('INSTRUME', hdu[0]).strip()
grating=saltkey.get('GRATING', hdu[0]).strip()
grang=saltkey.get('GR-ANGLE', hdu[0])
grasteps=saltkey.get('GRTILT', hdu[0])
arang=saltkey.get('AR-ANGLE', hdu[0])
arsteps=saltkey.get('CAMANG', hdu[0])
rssfilter=saltkey.get('FILTER', hdu[0])
specmode=saltkey.get('OBSMODE', hdu[0])
masktype=saltkey.get('MASKTYP', hdu[0]).strip().upper()
slitname=saltkey.get('MASKID', hdu[0])
xbin, ybin = saltkey.ccdbin( hdu[0], '')
slit=st.getslitsize(slitname)
#create RSS Model
rss=RSSModel.RSSModel(grating_name=grating.strip(), gratang=grang, \
camang=arang,slit=slit, xbin=xbin, ybin=ybin, \
)
res=1e7*rss.calc_resolelement(rss.alpha(), -rss.beta())
return res
def specidentify(images, linelist, outfile, guesstype='rss', guessfile='',
automethod='Matchlines', function='poly', order=3, rstep=100,
rstart='middlerow', mdiff=5, thresh=3, niter=5, smooth=0,
subback=0, inter=True, startext=0, clobber=False,
textcolor='black', logfile='salt.log', verbose=True):
with logging(logfile, debug) as log:
# set up the variables
infiles = []
outfiles = []
# Check the input images
infiles = saltio.argunpack('Input', images)
# create list of output files
outfiles = saltio.argunpack('Output', outfile)
# open the line lists
slines, sfluxes = st.readlinelist(linelist)
# Identify the lines in each file
for img, ofile in zip(infiles, outfiles):
# open the image
hdu = saltio.openfits(img)
# get the basic information about the spectrograph
dateobs = saltkey.get('DATE-OBS', hdu[0])
try:
utctime = saltkey.get('UTC-OBS', hdu[0])
except SaltError:
utctime = saltkey.get('TIME-OBS', hdu[0])
instrume = saltkey.get('INSTRUME', hdu[0]).strip()
grating = saltkey.get('GRATING', hdu[0]).strip()
grang = saltkey.get('GR-ANGLE', hdu[0])
grasteps = saltkey.get('GRTILT', hdu[0])
arang = saltkey.get('AR-ANGLE', hdu[0])
arsteps = saltkey.get('CAMANG', hdu[0])
rssfilter = saltkey.get('FILTER', hdu[0])
specmode = saltkey.get('OBSMODE', hdu[0])
masktype = saltkey.get('MASKTYP', hdu[0]).strip().upper()
slitname = saltkey.get('MASKID', hdu[0])
xbin, ybin = saltkey.ccdbin(hdu[0], img)
for i in range(startext, len(hdu)):
if hdu[i].name == 'SCI':
log.message('Proccessing extension %i in %s' % (i, img))
# things that will change for each slit
if masktype == 'LONGSLIT':
slit = st.getslitsize(slitname)
xpos = -0.2666
ypos = 0.0117
objid = None
elif masktype == 'MOS':
slit = 1.5
#slit=saltkey.get('SLIT', hdu[i])
# set up the x and y positions
miny = hdu[i].header['MINY']
maxy = hdu[i].header['MAXY']
ras = hdu[i].header['SLIT_RA']
des = hdu[i].header['SLIT_DEC']
objid = hdu[i].header['SLITNAME']
# TODO: Check the perfomance of masks at different PA
rac = hdu[0].header['MASK_RA']
dec = hdu[0].header['MASK_DEC']
pac = hdu[0].header['PA']
# these are hard wired at the moment
xpixscale = 0.1267 * xbin
ypixscale = 0.1267 * ybin
cx = int(3162 / xbin)
cy = int(2050 / ybin)
x, y = mt.convert_fromsky(ras, des, rac, dec, xpixscale=xpixscale, ypixscale=ypixscale,
position_angle=-pac, ccd_cx=cx, ccd_cy=cy)
xpos = 0.015 * 2 * (cx - x[0])
ypos = 0.0117
else:
msg = '%s is not a currently supported masktype' % masktype
raise SALTSpecError(msg)
if instrume not in ['PFIS', 'RSS']:
msg = '%s is not a currently supported instrument' % instrume
raise SALTSpecError(msg)
# create RSS Model
rss = RSSModel.RSSModel(grating_name=grating.strip(), gratang=grang,
camang=arang, slit=slit, xbin=xbin, ybin=ybin,
xpos=xpos, ypos=ypos)
res = 1e7 * rss.calc_resolelement(rss.alpha(), -rss.beta())
dres = res / 10.0
wcen = 1e7 * rss.calc_centralwavelength()
R = rss.calc_resolution(
wcen / 1e7, rss.alpha(), -rss.beta())
logmsg = '\nGrating\tGR-ANGLE\tAR-ANGLE\tSlit\tWCEN\tR\n'
logmsg += '%s\t%8.3f\t%8.3f\t%4.2f\t%6.2f\t%4f\n' % (
grating, grang, arang, slit, wcen, R)
if log:
log.message(logmsg, with_header=False)
# set up the data for the source
try:
data = hdu[i].data
except Exception as e:
message = 'Unable to read in data array in %s because %s' % (
img, e)
raise SALTSpecError(message)
# set up the center row
if rstart == 'middlerow':
ystart = int(0.5 * len(data))
else:
ystart = int(rstart)
rss.gamma = 0.0
if masktype == 'MOS':
rss.gamma = 180.0 / math.pi * math.atan((y * rss.detector.pix_size * rss.detector.ybin
- 0.5 * rss.detector.find_height()) / rss.camera.focallength)
# set up the xarr array based on the image
xarr = np.arange(len(data[ystart]), dtype='int64')
# get the guess for the wavelength solution
if guesstype == 'rss':
# set up the rss model
ws = st.useRSSModel(
xarr, rss, function=function, order=order, gamma=rss.gamma)
elif guesstype == 'file':
soldict = {}
soldict = readsolascii(guessfile, soldict)
timeobs = enterdatetime('%s %s' % (dateobs, utctime))
exptime = saltkey.get('EXPTIME', hdu[0])
filtername = saltkey.get('FILTER', hdu[0]).strip()
try:
slitid = saltkey.get('SLITNAME', hdu[i])
except:
slitid = None
function, order, coef = findlinesol(
soldict, ystart, True, timeobs, exptime, instrume, grating, grang, arang, filtername, slitid, xarr=xarr)
ws = WavelengthSolution.WavelengthSolution(
xarr, xarr, function=function, order=order)
ws.set_coef(coef)
else:
raise SALTSpecError(
'This guesstype is not currently supported')
# identify the spectral lines
ImageSolution = identify(data, slines, sfluxes, xarr, ystart, ws=ws, function=function,
order=order, rstep=rstep, mdiff=mdiff, thresh=thresh, niter=niter,
method=automethod, res=res, dres=dres, smooth=smooth, inter=inter, filename=img,
subback=0, textcolor=textcolor, log=log, verbose=verbose)
if outfile and len(ImageSolution):
writeIS(ImageSolution, outfile, dateobs=dateobs, utctime=utctime, instrume=instrume,
grating=grating, grang=grang, grasteps=grasteps, arsteps=arsteps,
arang=arang, rfilter=rssfilter, slit=slit, xbin=xbin,
ybin=ybin, objid=objid,
filename=img, log=log, verbose=verbose)
def wavemap(
hdu,
soldict,
caltype="line",
function="poly",
order=3,
blank=0,
nearest=False,
array_only=False,
clobber=True,
log=None,
verbose=True,
):
"""Read in an image and a set of wavlength solutions. Calculate the best
wavelength solution for a given dataset and then apply that data set to the
image
return
"""
# set up the time of the observation
dateobs = saltkey.get("DATE-OBS", hdu[0])
utctime = saltkey.get("TIME-OBS", hdu[0])
exptime = saltkey.get("EXPTIME", hdu[0])
instrume = saltkey.get("INSTRUME", hdu[0]).strip()
grating = saltkey.get("GRATING", hdu[0]).strip()
if caltype == "line":
grang = saltkey.get("GRTILT", hdu[0])
arang = saltkey.get("CAMANG", hdu[0])
else:
grang = saltkey.get("GR-ANGLE", hdu[0])
arang = saltkey.get("AR-ANGLE", hdu[0])
filtername = saltkey.get("FILTER", hdu[0]).strip()
slitname = saltkey.get("MASKID", hdu[0])
slit = st.getslitsize(slitname)
xbin, ybin = saltkey.ccdbin(hdu[0])
timeobs = sr.enterdatetime("%s %s" % (dateobs, utctime))
# check to see if there is more than one solution
if caltype == "line":
if len(soldict) == 1:
sol = soldict.keys()[0]
slitid = None
if not sr.matchobservations(soldict[sol], instrume, grating, grang, arang, filtername, slitid):
msg = "Observations do not match setup for transformation but using the solution anyway"
if log:
log.warning(msg)
for i in range(1, len(hdu)):
if hdu[i].name == "SCI":
if log:
log.message("Correcting extension %i" % i)
istart = int(0.5 * len(hdu[i].data))
# open up the data
# set up the xarr and initial wavlength solution
xarr = np.arange(len(hdu[i].data[istart]), dtype="int64")
# get the slitid
try:
slitid = saltkey.get("SLITNAME", hdu[i])
except:
slitid = None
# check to see if wavext is already there and if so, then check update
# that for the transformation from xshift to wavelength
if saltkey.found("WAVEXT", hdu[i]):
w_ext = saltkey.get("WAVEXT", hdu[i]) - 1
wavemap = hdu[w_ext].data
function, order, coef = sr.findlinesol(
soldict,
istart,
nearest,
timeobs,
exptime,
instrume,
grating,
grang,
arang,
filtername,
slitid,
xarr,
)
ws = WavelengthSolution.WavelengthSolution(xarr, xarr, function=function, order=order)
ws.set_coef(coef)
for j in range(len(hdu[i].data)):
wavemap[j, :] = ws.value(wavemap[j, :])
if array_only:
return wavemap
hdu[w_ext].data = wavemap
continue
# set up a wavelength solution -- still in here for testing MOS data
try:
w_arr = sr.findsol(
xarr,
soldict,
istart,
caltype,
nearest,
timeobs,
exptime,
instrume,
grating,
grang,
arang,
filtername,
slit,
xbin,
ybin,
slitid,
function,
order,
)
except SALTSpecError as e:
if slitid:
msg = "SLITID %s: %s" % (slitid, e)
if log:
log.warning(msg)
continue
else:
raise SALTSpecError(e)
if w_arr is None:
w_arr = sr.findsol(
xarr,
soldict,
istart,
"rss",
nearest,
timeobs,
exptime,
instrume,
grating,
grang,
arang,
filtername,
slit,
xbin,
ybin,
slitid,
function,
order,
)
# for each line in the data, determine the wavelength solution
# for a given line in the image
wavemap = np.zeros_like(hdu[i].data)
for j in range(len(hdu[i].data)):
# find the wavelength solution for the data
w_arr = sr.findsol(
xarr,
soldict,
j,
caltype,
nearest,
timeobs,
exptime,
instrume,
grating,
grang,
arang,
filtername,
slit,
xbin,
ybin,
slitid,
function,
order,
)
if w_arr is not None:
wavemap[j, :] = w_arr
if array_only:
return wavemap
# write out the oimg
hduwav = fits.ImageHDU(data=wavemap, header=hdu[i].header, name="WAV")
hdu.append(hduwav)
saltkey.new("WAVEXT", len(hdu) - 1, "Extension for Wavelength Map", hdu[i])
return hdu
def specarcstraighten(images, outfile, function='poly', order=3, rstep=1,
rstart='middlerow', nrows=1,
y1=None, y2=None, sigma=5, sections=3, niter=5,
startext=0, clobber=False, logfile='salt.log', verbose=True):
with logging(logfile, debug) as log:
# Check the input images
infiles = saltio.argunpack('Input', images)
# create list of output files
outfiles = saltio.argunpack('Output', outfile)
# Identify the lines in each file
for img, ofile in zip(infiles, outfiles):
# open the image
hdu = saltio.openfits(img)
# get the basic information about the spectrograph
dateobs = saltkey.get('DATE-OBS', hdu[0])
try:
utctime = saltkey.get('UTC-OBS', hdu[0])
except SaltError:
utctime = saltkey.get('TIME-OBS', hdu[0])
instrume = saltkey.get('INSTRUME', hdu[0]).strip()
grating = saltkey.get('GRATING', hdu[0]).strip()
grang = saltkey.get('GR-ANGLE', hdu[0])
grasteps = saltkey.get('GRTILT', hdu[0])
arang = saltkey.get('AR-ANGLE', hdu[0])
arsteps = saltkey.get('CAMANG', hdu[0])
rssfilter = saltkey.get('FILTER', hdu[0])
specmode = saltkey.get('OBSMODE', hdu[0])
masktype = saltkey.get('MASKTYP', hdu[0]).strip().upper()
slitname = saltkey.get('MASKID', hdu[0])
xbin, ybin = saltkey.ccdbin(hdu[0], img)
for i in range(startext, len(hdu)):
if hdu[i].name == 'SCI':
log.message('Proccessing extension %i in %s' % (i, img))
# things that will change for each slit
if masktype == 'LONGSLIT':
slit = st.getslitsize(slitname)
objid = None
#elif masktype == 'MOS':
#slit = 1.5
# slit=saltkey.get('SLIT', hdu[i])
# set up the x and y positions
#miny = hdu[i].header['MINY']
#maxy = hdu[i].header['MAXY']
#ras = hdu[i].header['SLIT_RA']
#des = hdu[i].header['SLIT_DEC']
#objid = hdu[i].header['SLITNAME']
# Check the perfomance of masks at different PA
#rac = hdu[0].header['MASK_RA']
#dec = hdu[0].header['MASK_DEC']
#pac = hdu[0].header['PA']
else:
msg = '%s is not a currently supported masktype' % masktype
raise SALTSpecError(msg)
if instrume not in ['PFIS', 'RSS']:
msg = '%s is not a currently supported instrument' % instrume
raise SALTSpecError(msg)
# set up the data for the source
try:
data = hdu[i].data
except Exception as e:
message = 'Unable to read in data array in %s because %s' % (
img, e)
raise SALTSpecError(message)
# set up the center row
if rstart == 'middlerow':
ystart = int(0.5 * len(data))
else:
ystart = rstart
# set up the xarr array based on the image
xarr = np.arange(len(data[ystart]), dtype='int64')
# calculate the transformation
ImageSolution = arcstraight(data, xarr, ystart, function=function, order=order,
rstep=rstep, y1=y1, y2=y2, sigma=sigma, sections=sections,
niter=niter, log=log, verbose=verbose)
if outfile and len(ImageSolution):
writeIS(ImageSolution, outfile, dateobs=dateobs, utctime=utctime, instrume=instrume,
grating=grating, grang=grang, grasteps=grasteps, arsteps=arsteps,
arang=arang, rfilter=rssfilter, slit=slit, xbin=xbin,
ybin=ybin, objid=objid,
filename=img, log=log, verbose=verbose)
def make_mosaic(struct,
gap,
xshift,
yshift,
rotation,
interp_type='linear',
boundary='constant',
constant=0,
geotran=True,
fill=False,
cleanup=True,
log=None,
verbose=False):
"""Given a SALT image struct, combine each of the individual amplifiers and
apply the geometric CCD transformations to the image
"""
# get the name of the file
infile = saltkey.getimagename(struct[0], base=True)
outpath = './'
# identify instrument
instrume, keyprep, keygain, keybias, keyxtalk, keyslot = \
saltkey.instrumid(struct)
# how many amplifiers?
nsciext = saltkey.get('NSCIEXT', struct[0])
nextend = saltkey.get('NEXTEND', struct[0])
nccds = saltkey.get('NCCDS', struct[0])
amplifiers = nccds * 2
if nextend > nsciext:
varframe = True
else:
varframe = False
# CCD geometry coefficients
if (instrume == 'RSS' or instrume == 'PFIS'):
xsh = [0., xshift[0], 0., xshift[1]]
ysh = [0., yshift[0], 0., yshift[1]]
rot = [0., rotation[0], 0., rotation[1]]
elif instrume == 'SALTICAM':
xsh = [0., xshift[0], 0.]
ysh = [0., yshift[0], 0.]
rot = [0., rotation[0], 0]
# how many extensions?
nextend = saltkey.get('NEXTEND', struct[0])
# CCD on-chip binning
xbin, ybin = saltkey.ccdbin(struct[0])
# create temporary primary extension
outstruct = []
outstruct.append(struct[0])
# define temporary FITS file store tiled CCDs
tilefile = saltio.tmpfile(outpath)
tilefile += 'tile.fits'
if varframe:
tilehdu = [None] * (3 * int(nsciext / 2) + 1)
else:
tilehdu = [None] * int(nsciext / 2 + 1)
tilehdu[0] = fits.PrimaryHDU()
#tilehdu[0].header = struct[0].header
if log:
log.message('', with_stdout=verbose)
# iterate over amplifiers, stich them to produce file of CCD images
for i in range(int(nsciext / 2)):
hdu = i * 2 + 1
# amplifier = hdu%amplifiers
# if (amplifier == 0): amplifier = amplifiers
# read DATASEC keywords
datasec1 = saltkey.get('DATASEC', struct[hdu])
datasec2 = saltkey.get('DATASEC', struct[hdu + 1])
xdsec1, ydsec1 = saltstring.secsplit(datasec1)
xdsec2, ydsec2 = saltstring.secsplit(datasec2)
# read images
imdata1 = saltio.readimage(struct, hdu)
imdata2 = saltio.readimage(struct, hdu + 1)
# tile 2n amplifiers to yield n CCD images
outdata = numpy.zeros(
(int(ydsec1[1] + abs(ysh[i + 1] / ybin)),
int(xdsec1[1] + xdsec2[1] + abs(xsh[i + 1] / xbin))),
numpy.float32)
# set up the variance frame
if varframe:
vardata = outdata.copy()
vdata1 = saltio.readimage(struct, struct[hdu].header['VAREXT'])
vdata2 = saltio.readimage(struct, struct[hdu + 1].header['VAREXT'])
bpmdata = outdata.copy()
bdata1 = saltio.readimage(struct, struct[hdu].header['BPMEXT'])
bdata2 = saltio.readimage(struct, struct[hdu + 1].header['BPMEXT'])
x1 = xdsec1[0] - 1
if x1 != 0:
msg = 'The data in %s have not been trimmed prior to mosaicking.' \
% infile
log.error(msg)
if xsh[i + 1] < 0:
x1 += int(abs(xsh[i + 1] / xbin))
x2 = x1 + xdsec1[1]
y1 = ydsec1[0] - 1
if ysh[i + 1] < 0:
y1 += int(abs(ysh[i + 1] / ybin))
y2 = y1 + ydsec1[1]
outdata[y1:y2, x1:x2] =\
imdata1[ydsec1[0] - 1:ydsec1[1], xdsec1[0] - 1:xdsec1[1]]
if varframe:
vardata[y1:y2, x1:x2] =\
vdata1[ydsec1[0] - 1:ydsec1[1], xdsec1[0] - 1:xdsec1[1]]
bpmdata[y1:y2, x1:x2] =\
bdata1[ydsec1[0] - 1:ydsec1[1], xdsec1[0] - 1:xdsec1[1]]
x1 = x2
x2 = x1 + xdsec2[1]
y1 = ydsec2[0] - 1
if ysh[i + 1] < 0:
y1 += abs(ysh[i + 1] / ybin)
y2 = y1 + ydsec2[1]
outdata[y1:y2, x1:x2] =\
imdata2[ydsec1[0] - 1:ydsec1[1], xdsec1[0] - 1:xdsec1[1]]
if varframe:
vardata[y1:y2, x1:x2] =\
vdata2[ydsec1[0] - 1:ydsec1[1], xdsec1[0] - 1:xdsec1[1]]
bpmdata[y1:y2, x1:x2] =\
bdata2[ydsec1[0] - 1:ydsec1[1], xdsec1[0] - 1:xdsec1[1]]
# size of new image
naxis1 = str(xdsec1[1] + xdsec2[1])
naxis2 = str(ydsec1[1])
# add image and keywords to HDU list
tilehdu[i + 1] = fits.ImageHDU(outdata)
tilehdu[i + 1].header = struct[hdu].header
#tilehdu[
# i + 1].header['DATASEC'] = '[1:' + naxis1 + ',1:' + naxis2 + ']'
if varframe:
vext = i + 1 + int(nsciext / 2.)
tilehdu[vext] = fits.ImageHDU(vardata)
#tilehdu[vext].header = struct[struct[hdu].header['VAREXT']].header
#tilehdu[vext].header[
# 'DATASEC'] = '[1:' + naxis1 + ',1:' + naxis2 + ']'
bext = i + 1 + 2 * int(nsciext / 2.)
tilehdu[bext] = fits.ImageHDU(bpmdata)
#tilehdu[bext].header = struct[struct[hdu].header['BPMEXT']].header
#tilehdu[bext].header[
# 'DATASEC'] = '[1:' + naxis1 + ',1:' + naxis2 + ']'
# image tile log message #1
if log:
message = os.path.basename(infile) + '[' + str(hdu) + ']['
message += str(xdsec1[0]) + ':' + str(xdsec1[1]) + ','
message += str(ydsec1[0]) + ':' + str(ydsec1[1]) + '] --> '
message += os.path.basename(tilefile) + '[' + str(i + 1) + ']['
message += str(xdsec1[0]) + ':' + str(xdsec1[1]) + ','
message += str(ydsec1[0]) + ':' + str(ydsec1[1]) + ']'
log.message(message, with_stdout=verbose, with_header=False)
message = os.path.basename(infile) + '[' + str(hdu + 1) + ']['
message += str(xdsec1[0]) + ':' + str(xdsec1[1]) + ','
message += str(ydsec1[0]) + ':' + str(ydsec1[1]) + '] --> '
message += os.path.basename(tilefile) + '[' + str(i + 1) + ']['
message += str(xdsec1[1] + 1) + ':' + \
str(xdsec1[1] + xdsec2[1]) + ','
message += str(ydsec2[0]) + ':' + str(ydsec2[1]) + ']'
log.message(message, with_stdout=verbose, with_header=False)
# write temporary file of tiled CCDs
hdulist = fits.HDUList(tilehdu)
hdulist.writeto(tilefile)
# iterate over CCDs, transform and rotate images
yrot = [None] * 4
xrot = [None] * 4
tranfile = [' ']
tranhdu = [0]
if varframe:
tranfile = [''] * (3 * int(nsciext / 2) + 1)
tranhdu = [0] * (3 * int(nsciext / 2) + 1)
else:
tranfile = [''] * int(nsciext / 2 + 1)
tranhdu = [0] * int(nsciext / 2 + 1)
# this is hardwired for SALT where the second CCD is considered the
# fiducial
for hdu in range(1, int(nsciext / 2 + 1)):
tranfile[hdu] = saltio.tmpfile(outpath)
tranfile[hdu] += 'tran.fits'
if varframe:
tranfile[hdu + nccds] = saltio.tmpfile(outpath) + 'tran.fits'
tranfile[hdu + 2 * nccds] = saltio.tmpfile(outpath) + 'tran.fits'
ccd = hdu % nccds
if (ccd == 0):
ccd = nccds
# correct rotation for CCD binning
yrot[ccd] = rot[ccd] * ybin / xbin
xrot[ccd] = rot[ccd] * xbin / ybin
dxshift = xbin * int(float(int(gap) / xbin) + 0.5) - gap
# transformation using geotran IRAF task
# if (ccd == 1):
if (ccd != 2):
if geotran:
message = '\nSALTMOSAIC -- geotran ' + tilefile + \
'[' + str(ccd) + '] ' + tranfile[hdu]
message += ' \"\" \"\" xshift=' + \
str((xsh[ccd] + (2 - ccd) * dxshift) / xbin) + ' '
message += 'yshift=' + \
str(ysh[ccd] / ybin) + ' xrotation=' + str(xrot[ccd]) + ' '
message += 'yrotation=' + \
str(yrot[ccd]) + ' xmag=1 ymag=1 xmin=\'INDEF\''
message += 'xmax=\'INDEF\' ymin=\'INDEF\' ymax=\'INDEF\' '
message += 'ncols=\'INDEF\' '
message += 'nlines=\'INDEF\' verbose=\'no\' '
message += 'fluxconserve=\'yes\' nxblock=2048 '
message += 'nyblock=2048 interpolant=\'' + \
interp_type + '\' boundary=\'constant\' constant=0'
log.message(message, with_stdout=verbose)
yd, xd = tilehdu[ccd].data.shape
ncols = 'INDEF' # ncols=xd+abs(xsh[ccd]/xbin)
nlines = 'INDEF' # nlines=yd+abs(ysh[ccd]/ybin)
geo_xshift = xsh[ccd] + (2 - ccd) * dxshift / xbin
geo_yshift = ysh[ccd] / ybin
iraf.images.immatch.geotran(tilefile + "[" + str(ccd) + "]",
tranfile[hdu],
"",
"",
xshift=geo_xshift,
yshift=geo_yshift,
xrotation=xrot[ccd],
yrotation=yrot[ccd],
xmag=1,
ymag=1,
xmin='INDEF',
xmax='INDEF',
ymin='INDEF',
ymax='INDEF',
ncols=ncols,
nlines=nlines,
verbose='no',
fluxconserve='yes',
nxblock=2048,
nyblock=2048,
interpolant="linear",
boundary="constant",
constant=0)
if varframe:
var_infile = tilefile + "[" + str(ccd + nccds) + "]"
iraf.images.immatch.geotran(var_infile,
tranfile[hdu + nccds],
"",
"",
xshift=geo_xshift,
yshift=geo_yshift,
xrotation=xrot[ccd],
yrotation=yrot[ccd],
xmag=1,
ymag=1,
xmin='INDEF',
xmax='INDEF',
ymin='INDEF',
ymax='INDEF',
ncols=ncols,
nlines=nlines,
verbose='no',
fluxconserve='yes',
nxblock=2048,
nyblock=2048,
interpolant="linear",
boundary="constant",
constant=0)
var2_infile = tilefile + "[" + str(ccd + 2 * nccds) + "]"
iraf.images.immatch.geotran(var2_infile,
tranfile[hdu + 2 * nccds],
"",
"",
xshift=geo_xshift,
yshift=geo_yshift,
xrotation=xrot[ccd],
yrotation=yrot[ccd],
xmag=1,
ymag=1,
xmin='INDEF',
xmax='INDEF',
ymin='INDEF',
ymax='INDEF',
ncols=ncols,
nlines=nlines,
verbose='no',
fluxconserve='yes',
nxblock=2048,
nyblock=2048,
interpolant="linear",
boundary="constant",
constant=0)
# open the file and copy the data to tranhdu
tstruct = fits.open(tranfile[hdu])
tranhdu[hdu] = tstruct[0].data
tstruct.close()
if varframe:
tranhdu[hdu + nccds] = fits.open(tranfile[hdu +
nccds])[0].data
tranhdu[hdu + 2 * nccds] = fits.open(
tranfile[hdu + 2 * nccds])[0].data
else:
log.message("Transform CCD #%i using dx=%s, dy=%s, rot=%s" %
(ccd, xsh[ccd] / 2.0, ysh[ccd] / 2.0, xrot[ccd]),
with_stdout=verbose,
with_header=False)
tranhdu[hdu] = geometric_transform(
tilehdu[ccd].data,
tran_func,
prefilter=False,
order=1,
extra_arguments=(xsh[ccd] / 2, ysh[ccd] / 2, 1, 1,
xrot[ccd], yrot[ccd]))
tstruct = fits.PrimaryHDU(tranhdu[hdu])
tstruct.writeto(tranfile[hdu])
if varframe:
tranhdu[hdu + nccds] = geometric_transform(
tilehdu[hdu + 3].data,
tran_func,
prefilter=False,
order=1,
extra_arguments=(xsh[ccd] / 2, ysh[ccd] / 2, 1, 1,
xrot[ccd], yrot[ccd]))
tranhdu[hdu + 2 * nccds] = geometric_transform(
tilehdu[hdu + 6].data,
tran_func,
prefilter=False,
order=1,
extra_arguments=(xsh[ccd] / 2, ysh[ccd] / 2, 1, 1,
xrot[ccd], yrot[ccd]))
else:
log.message("Transform CCD #%i using dx=%s, dy=%s, rot=%s" %
(ccd, 0, 0, 0),
with_stdout=verbose,
with_header=False)
tranhdu[hdu] = tilehdu[ccd].data
if varframe:
tranhdu[hdu + nccds] = tilehdu[ccd + nccds].data
tranhdu[hdu + 2 * nccds] = tilehdu[ccd + 2 * nccds].data
# open outfile
if varframe:
outlist = 4 * [None]
else:
outlist = 2 * [None]
#outlist[0] = struct[0].copy()
outlist[0] = fits.PrimaryHDU()
outlist[0].header = struct[0].header
naxis1 = int(gap / xbin * (nccds - 1))
naxis2 = 0
for i in range(1, nccds + 1):
yw, xw = tranhdu[i].shape
naxis1 += xw + int(abs(xsh[ccd] / xbin)) + 1
naxis2 = max(naxis2, yw)
outdata = numpy.zeros((naxis2, naxis1), numpy.float32)
outdata.shape = naxis2, naxis1
if varframe:
vardata = outdata * 0
bpmdata = outdata * 0 + 1
# iterate over CCDs, stich them to produce a full image
hdu = 0
totxshift = 0
for hdu in range(1, nccds + 1):
# read DATASEC keywords
ydsec, xdsec = tranhdu[hdu].shape
# define size and shape of final image
# tile CCDs to yield mosaiced image
x1 = int((hdu - 1) * (xdsec + gap / xbin)) + int(totxshift)
x2 = xdsec + x1
y1 = int(0)
y2 = int(ydsec)
outdata[y1:y2, x1:x2] = tranhdu[hdu]
totxshift += int(abs(xsh[hdu] / xbin)) + 1
if varframe:
vardata[y1:y2, x1:x2] = tranhdu[hdu + nccds]
bpmdata[y1:y2, x1:x2] = tranhdu[hdu + 2 * nccds]
# make sure to cover up all the gaps include bad areas
if varframe:
baddata = (outdata == 0)
baddata = nd.maximum_filter(baddata, size=3)
bpmdata[baddata] = 1
# fill in the gaps if requested
if fill:
if varframe:
outdata = fill_gaps(outdata, 0)
else:
outdata = fill_gaps(outdata, 0)
# add to the file
outlist[1] = fits.ImageHDU(outdata)
if varframe:
outlist[2] = fits.ImageHDU(vardata, name='VAR')
outlist[3] = fits.ImageHDU(bpmdata, name='BPM')
# create the image structure
outstruct = fits.HDUList(outlist)
# update the head informaation
# housekeeping keywords
saltkey.put('NEXTEND', 2, outstruct[0])
saltkey.new('EXTNAME', 'SCI', 'Extension name', outstruct[1])
saltkey.new('EXTVER', 1, 'Extension number', outstruct[1])
if varframe:
saltkey.new('VAREXT', 2, 'Variance frame extension', outstruct[1])
saltkey.new('BPMEXT', 3, 'BPM Extension', outstruct[1])
try:
saltkey.copy(struct[1], outstruct[1], 'CCDSUM')
except:
pass
# Add keywords associated with geometry
saltkey.new('SGEOMGAP', gap, 'SALT Chip Gap', outstruct[0])
c1str = '{:3.2f} {:3.2f} {:3.4f}'.format(xshift[0], yshift[0], rotation[0])
saltkey.new('SGEOM1', c1str, 'SALT Chip 1 Transform', outstruct[0])
c2str = '{:3.2f} {:3.2f} {:3.4f}'.format(xshift[1], yshift[1], rotation[1])
saltkey.new('SGEOM2', c2str, 'SALT Chip 2 Transform', outstruct[0])
# WCS keywords
saltkey.new('CRPIX1', 0, 'WCS: X reference pixel', outstruct[1])
saltkey.new('CRPIX2', 0, 'WCS: Y reference pixel', outstruct[1])
saltkey.new('CRVAL1', float(xbin), 'WCS: X reference coordinate value',
outstruct[1])
saltkey.new('CRVAL2', float(ybin), 'WCS: Y reference coordinate value',
outstruct[1])
saltkey.new('CDELT1', float(xbin), 'WCS: X pixel size', outstruct[1])
saltkey.new('CDELT2', float(ybin), 'WCS: Y pixel size', outstruct[1])
saltkey.new('CTYPE1', 'pixel', 'X type', outstruct[1])
saltkey.new('CTYPE2', 'pixel', 'Y type', outstruct[1])
# cleanup temporary files
if cleanup:
for tfile in tranfile:
if os.path.isfile(tfile):
saltio.delete(tfile)
if os.path.isfile(tilefile):
status = saltio.delete(tilefile)
# return the file
return outstruct
def slotmerge(images, outimages, outpref, geomfile, clobber, logfile, verbose):
with logging(logfile, debug) as log:
# are the arguments defined
saltsafeio.argdefined('images', images)
saltsafeio.argdefined('geomfile', geomfile)
saltsafeio.argdefined('logfile', logfile)
# if the input file is a list, does it exist?
if images[0] == '@':
saltsafeio.listexists('Input', images)
# parse list of input files
infiles = saltsafeio.listparse('Raw image', images, '', '', '')
# check input files exist
saltsafeio.filesexist(infiles, '', 'r')
# load output name list: @list, * and comma separated
outimages = outimages.strip()
outpref = outpref.strip()
if len(outpref) == 0 and len(outimages) == 0:
raise SaltIOError('Output file(s) not specified')
# test output @filelist exists
if len(outimages) > 0 and outimages[0] == '@':
saltsafeio.listexists('Output', outimages)
# parse list of output files
outfiles = saltsafeio.listparse('Output image', outimages, outpref,
infiles, '')
# are input and output lists the same length?
saltsafeio.comparelists(infiles, outfiles, 'Input', 'output')
# do the output files already exist?
if not clobber:
saltsafeio.filesexist(outfiles, '', 'w')
# does CCD geometry definition file exist
geomfilefile = geomfile.strip()
saltsafeio.fileexists(geomfile)
# read geometry definition file
gap = 0
xshift = [0, 0]
yshift = [0, 0]
rotation = [0, 0]
gap, xshift, yshift, rotation = saltsafeio.readccdgeom(geomfile)
for ro in rotation:
if ro != 0:
log.warning('SLOTMERGE currently ignores CCD rotation')
# Begin processes each file
for infile, outfile in zip(infiles, outfiles):
# determine the name for the output file
outpath = outfile.rstrip(os.path.basename(outfile))
if (len(outpath) == 0):
outpath = '.'
# open each raw image
struct = saltsafeio.openfits(infile)
# identify instrument
instrume, keyprep, keygain, keybias, keyxtalk, keyslot = saltsafekey.instrumid(
struct, infile)
# how many amplifiers?
nccds = saltsafekey.get('NCCDS', struct[0], infile)
amplifiers = nccds * 2
#if (nccds != 2):
# raise SaltError('Can not currently handle more than two CCDs')
# CCD geometry coefficients
if instrume == 'RSS' or instrume == 'PFIS':
xsh = [xshift[0], 0., xshift[1]]
ysh = [yshift[0], 0., yshift[1]]
rot = [rotation[0], 0., rotation[1]]
refid = 1
if instrume == 'SALTICAM':
xsh = [xshift[0], 0.]
ysh = [yshift[0], 0.]
rot = [rotation[0], 0]
refid = 1
# how many extensions?
nextend = saltsafekey.get('NEXTEND', struct[0], infile)
# how many exposures
exposures = nextend / amplifiers
# CCD on-chip binning
xbin, ybin = saltsafekey.ccdbin(struct[0], infile)
gp = int(gap / xbin)
# create output hdu structure
outstruct = [None] * int(exposures + 1)
outstruct[0] = struct[0]
# iterate over exposures, stitch them to produce file of CCD images
for i in range(exposures):
# Determine the total size of the image
xsize = 0
ysize = 0
for j in range(amplifiers):
hdu = i * amplifiers + j + 1
try:
xsize += len(struct[hdu].data[0])
if ysize < len(struct[hdu].data):
ysize = len(struct[hdu].data)
except:
msg = 'Unable to access extension %i ' % hdu
raise SaltIOError(msg)
xsize += gp * (nccds - 1)
maxxsh, minxsh = determineshifts(xsh)
maxysh, minysh = determineshifts(ysh)
xsize += (maxxsh - minxsh)
ysize += (maxysh - minysh)
# Determine the x and y origins for each frame
xdist = 0
ydist = 0
shid = 0
x0 = np.zeros(amplifiers)
y0 = np.zeros(amplifiers)
for j in range(amplifiers):
x0[j] = xdist + xsh[shid] - minxsh
y0[j] = ysh[shid] - minysh
hdu = i * amplifiers + j + 1
darr = struct[hdu].data
xdist += len(darr[0])
if j % 2 == 1:
xdist += gp
shid += 1
# make the out image
outarr = np.zeros((ysize, xsize), np.float64)
# Embed each frame into the output array
for j in range(amplifiers):
hdu = i * amplifiers + j + 1
darr = struct[hdu].data
outarr = salttran.embed(darr, x0[j], y0[j], outarr)
# Add the outimage to the output structure
hdu = i * amplifiers + 1
outhdu = i + 1
outstruct[outhdu] = pyfits.ImageHDU(outarr)
outstruct[outhdu].header = struct[hdu].header
# Fix the headers in each extension
datasec = '[1:%4i,1:%4i]' % (xsize, ysize)
saltsafekey.put('DATASEC', datasec, outstruct[outhdu], outfile)
saltsafekey.rem('DETSIZE', outstruct[outhdu], outfile)
saltsafekey.rem('DETSEC', outstruct[outhdu], outfile)
saltsafekey.rem('CCDSEC', outstruct[outhdu], outfile)
saltsafekey.rem('AMPSEC', outstruct[outhdu], outfile)
# add housekeeping key words
outstruct[outhdu] = addhousekeeping(outstruct[outhdu], outhdu,
outfile)
# close input FITS file
saltsafeio.closefits(struct)
# housekeeping keywords
keymosaic = 'SLOTMERG'
fname, hist = history(level=1, wrap=False)
saltsafekey.housekeeping(struct[0], keymosaic,
'Amplifiers have been mosaiced', hist)
#saltsafekey.history(outstruct[0],hist)
# this is added for later use by
saltsafekey.put('NCCDS', 0.5, outstruct[0])
saltsafekey.put('NSCIEXT', exposures, outstruct[0])
saltsafekey.put('NEXTEND', exposures, outstruct[0])
# write FITS file of mosaiced image
outstruct = pyfits.HDUList(outstruct)
saltsafeio.writefits(outstruct, outfile, clobber=clobber)
def specslit(image,
outimage,
outpref,
exttype='auto',
slitfile='',
outputslitfile='',
regprefix='',
sections=3,
width=25,
sigma=2.2,
thres=6,
order=3,
padding=5,
yoffset=0,
inter=False,
clobber=True,
logfile='salt.log',
verbose=True):
with logging(logfile, debug) as log:
# check all the input and make sure that all the input needed is provided
# by the user
# read the image or image list and check if each in the list exist
infiles = saltio.argunpack('Input', image)
# unpack the outfiles
outfiles = saltio.listparse('Outimages', outimage, outpref, infiles,
'')
# from the extraction type, check whether the input file is specified.
# if the slitfile parameter is specified then use the slit files for
# the extraction. if the extraction type is auto then use image for the
# detection and the slit extraction
if exttype == 'rsmt' or exttype == 'fits' or exttype == 'ascii' or exttype == 'ds9':
slitfiles = saltio.argunpack('Slitfile', slitfile)
if len(slitfiles) == 1:
slitfiles = slitfiles * len(infiles)
saltio.comparelists(infiles, slitfiles, 'image', 'slitfile')
elif exttype == 'auto':
slitfiles = infiles
log.message(
'Extraction type is AUTO. Slit detection will be done from image'
)
# read in if an optional ascii file is requested
if len(outputslitfile) > 0:
outslitfiles = saltio.argunpack('Outslitfiles', outputslitfile)
saltio.comparelists(infiles, outslitfiles, 'image',
'outputslitfile')
else:
outslitfiles = [''] * len(infiles)
# check if the width and sigma parameters were specified.
# default is 25 and 2.2
if width < 10.:
msg = 'The width parameter needs be a value larger than 10'
raise SALTSpecError(msg)
if sigma < 0.0:
msg = 'Sigma must be greater than zero'
raise SaltSpecError(msg)
# check the treshold parameter. this needs to be specified by the user
if thres <= 0.0:
msg = 'Threshold must be greater than zero'
raise SaltSpecError(msg)
# check to make sure that the sections are greater than the order
if sections <= order:
msg = 'Number of sections must be greater than the order for the spline fit'
raise SaltSpecError(msg)
# run through each of the images and extract the slits
for img, oimg, sfile, oslit in zip(infiles, outfiles, slitfiles,
outslitfiles):
log.message('Proccessing image %s' % img)
# open the image
struct = saltio.openfits(img)
ylen, xlen = struct[1].data.shape
xbin, ybin = saltkey.ccdbin(struct[0], img)
# setup the VARIANCE and BPM frames
if saltkey.found('VAREXT', struct[1]):
varext = saltkey.get('VAREXT', struct[1])
varlist = []
else:
varext = None
# setup the BPM frames
if saltkey.found('BPMEXT', struct[1]):
bpmext = saltkey.get('BPMEXT', struct[1])
bpmlist = []
else:
bpmext = None
# open the slit definition file or identify the slits in the image
slitmask = None
ycheck = False
if exttype == 'rsmt':
log.message('Using slits from %s' % sfile)
if yoffset is None:
yoffset = 0
ycheck = True
slitmask = mt.read_slitmask_from_xml(sfile)
xpos = -0.3066
ypos = 0.0117
cx = int(xlen / 2.0)
cy = int(ylen / 2.0) + ypos / 0.015 / ybin + yoffset
order, slit_positions = mt.convert_slits_from_mask(
slitmask,
order=1,
xbin=xbin,
ybin=ybin,
pix_scale=0.1267,
cx=cx,
cy=cy)
sections = 1
elif exttype == 'fits':
log.message('Using slits from %s' % sfile)
order, slit_positions = read_slits_from_fits(sfile)
elif exttype == 'ascii':
log.message('Using slits from %s' % sfile)
order, slit_positions = mt.read_slits_from_ascii(sfile)
elif exttype == 'ds9':
log.message('Using slits from %s' % sfile)
order, slit_positions, slitmask = mt.read_slits_from_ds9(
sfile, order=order)
slitmask = None
sections = 1
elif exttype == 'auto':
log.message('Identifying slits in %s' % img)
# identify the slits in the image
order, slit_positions = identify_slits(struct[1].data, order,
sections, width, sigma,
thres)
# write out the slit identifications if ofile has been supplied
if oslit:
log.message('Writing slit positions to %s' % oslit)
mt.write_outputslitfile(slit_positions, oslit, order)
if ycheck:
slit_positions, dy = check_ypos(slit_positions, struct[1].data)
log.message('Using an offset of {}'.format(dy))
# extract the slits
spline_x = mt.divide_image(struct[1].data, sections)
spline_x = 0.5 * (np.array(spline_x[:-1]) + np.array(spline_x[1:]))
extracted_spectra, spline_positions = mt.extract_slits(
slit_positions,
spline_x,
struct[1].data,
order=order,
padding=padding)
if varext:
extracted_var, var_positions = mt.extract_slits(
slit_positions,
spline_x,
struct[varext].data,
order=order,
padding=padding)
if bpmext:
extracted_bpm, bpm_positions = mt.extract_slits(
slit_positions,
spline_x,
struct[bpmext].data,
order=order,
padding=padding)
# write out the data to the new array
# create the new file
hdulist = fits.HDUList([struct[0]])
# log the extracted spectra if needed
log.message('', with_stdout=verbose)
# setup output ds9 file
if regprefix:
regout = open(
regprefix + os.path.basename(img).strip('.fits') + '.reg',
'w')
regout.write('# Region file format: DS9 version 4.1\n')
regout.write('# Filename: %s\n' % img)
regout.write(
'global color=green dashlist=8 3 width=1 font="helvetica 10 normal roman" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 delete=1 include=1 source=1\nphysical\n'
)
# add each
imglist = []
nslits = len(spline_positions)
for i in range(nslits):
y1 = spline_positions[i][0].min()
y2 = spline_positions[i][1].max()
msg = 'Extracted Spectra %i between %i to %i' % (i + 1, y1, y2)
# log.message(msg, with_header=False, with_stdout=verbose)
sdu = fits.ImageHDU(extracted_spectra[i],
header=struct[1].header)
if varext:
vdu = fits.ImageHDU(extracted_var[i],
header=struct[varext].header)
sdu.header['VAREXT'] = i + nslits + 1
varlist.append(vdu)
if bpmext:
bdu = fits.ImageHDU(extracted_bpm[i],
header=struct[bpmext].header)
sdu.header['BPMEXT'] = i + 2 * nslits + 1
bpmlist.append(bdu)
imglist.append(sdu)
# add in some additional keywords
imglist[i].header['MINY'] = (y1,
'Lower Y value in original image')
imglist[i].header['MAXY'] = (y2,
'Upper Y value in original image')
if regprefix:
xsize = struct[1].data.shape[1]
xsize = int(0.5 * xsize)
rtext = ''
if slitmask:
# rtext='%s, %8.7f, %8.7f, %3.2f' % (slitmask.slitlets.data[i]['name'], slitmask.slitlets.data[i]['targ_ra'], slitmask.slitlets.data[i]['targ_dec'], slitmask.slitlets.data[i]['slit_width'])
pass
regout.write('box(%i,%i, %i, %i) #text={%s}\n' %
(xsize, 0.5 *
(y1 + y2), 2 * xsize, y2 - y1, rtext))
# add slit information
if slitmask:
imglist[i].header['SLITNAME'] = (
slitmask.slitlets.data[i]['name'], 'Slit Name')
imglist[i].header['SLIT_RA'] = (
slitmask.slitlets.data[i]['targ_ra'], 'Slit RA')
imglist[i].header['SLIT_DEC'] = (
slitmask.slitlets.data[i]['targ_dec'], 'Slit DEC')
imglist[i].header['SLIT'] = (
slitmask.slitlets.data[i]['slit_width'], 'Slit Width')
# add to the hdulist
hdulist += imglist
if varext:
hdulist += varlist
if bpmext:
hdulist += bpmlist
# write the slit positions to the header
# create the binary table HDU that contains the split positions
tbhdu = mt.slits_HDUtable(slit_positions, order)
bintable_hdr = tbhdu.header
# add the extname parameter to the extension
tbhdu.header['EXTNAME'] = 'BINTABLE'
# add the extname parameter to the extension
hdulist[0].header['SLITEXT'] = len(hdulist)
hdulist.append(tbhdu)
# add addition header information about the mask
if slitmask:
hdulist[0].header['MASKNAME'] = (slitmask.mask_name,
'SlitMask Name')
hdulist[0].header['MASK_RA'] = (slitmask.center_ra,
'SlitMask RA')
hdulist[0].header['MASK_DEC'] = (slitmask.center_dec,
'SlitMask DEC')
hdulist[0].header['MASK_PA'] = (slitmask.position_angle,
'SlitMask Position Angle')
# write out the image
saltio.writefits(hdulist, oimg, clobber)
def slotmerge(images,outimages,outpref,geomfile,clobber,logfile,verbose):
with logging(logfile,debug) as log:
# are the arguments defined
saltsafeio.argdefined('images',images)
saltsafeio.argdefined('geomfile',geomfile)
saltsafeio.argdefined('logfile',logfile)
# if the input file is a list, does it exist?
if images[0] == '@':
saltsafeio.listexists('Input',images)
# parse list of input files
infiles=saltsafeio.listparse('Raw image',images,'','','')
# check input files exist
saltsafeio.filesexist(infiles,'','r')
# load output name list: @list, * and comma separated
outimages = outimages.strip()
outpref = outpref.strip()
if len(outpref) == 0 and len(outimages) == 0:
raise SaltIOError('Output file(s) not specified')
# test output @filelist exists
if len(outimages) > 0 and outimages[0] == '@':
saltsafeio.listexists('Output',outimages)
# parse list of output files
outfiles=saltsafeio.listparse('Output image',outimages,outpref,infiles,'')
# are input and output lists the same length?
saltsafeio.comparelists(infiles,outfiles,'Input','output')
# do the output files already exist?
if not clobber:
saltsafeio.filesexist(outfiles,'','w')
# does CCD geometry definition file exist
geomfilefile = geomfile.strip()
saltsafeio.fileexists(geomfile)
# read geometry definition file
gap = 0
xshift = [0, 0]
yshift = [0, 0]
rotation = [0, 0]
gap, xshift, yshift, rotation=saltsafeio.readccdgeom(geomfile)
for ro in rotation:
if ro!=0:
log.warning('SLOTMERGE currently ignores CCD rotation')
# Begin processes each file
for infile, outfile in zip(infiles, outfiles):
# determine the name for the output file
outpath = outfile.rstrip(os.path.basename(outfile))
if (len(outpath) == 0):
outpath = '.'
# open each raw image
struct=saltsafeio.openfits(infile)
# identify instrument
instrume,keyprep,keygain,keybias,keyxtalk,keyslot=saltsafekey.instrumid(struct,infile)
# how many amplifiers?
nccds=saltsafekey.get('NCCDS',struct[0],infile)
amplifiers = nccds * 2
#if (nccds != 2):
# raise SaltError('Can not currently handle more than two CCDs')
# CCD geometry coefficients
if instrume == 'RSS' or instrume == 'PFIS':
xsh = [xshift[0], 0., xshift[1]]
ysh = [yshift[0], 0., yshift[1]]
rot = [rotation[0], 0., rotation[1]]
refid = 1
if instrume == 'SALTICAM':
xsh = [xshift[0], 0.]
ysh = [yshift[0], 0.]
rot = [rotation[0], 0]
refid = 1
# how many extensions?
nextend=saltsafekey.get('NEXTEND',struct[0],infile)
# how many exposures
exposures = nextend/amplifiers
# CCD on-chip binning
xbin, ybin=saltsafekey.ccdbin(struct[0],infile)
gp = int(gap / xbin)
# create output hdu structure
outstruct = [None] * int(exposures+1)
outstruct[0]=struct[0]
# iterate over exposures, stitch them to produce file of CCD images
for i in range(exposures):
# Determine the total size of the image
xsize=0
ysize=0
for j in range(amplifiers):
hdu=i*amplifiers+j+1
try:
xsize += len(struct[hdu].data[0])
if ysize < len(struct[hdu].data):
ysize=len(struct[hdu].data)
except:
msg='Unable to access extension %i ' % hdu
raise SaltIOError(msg)
xsize += gp* (nccds-1)
maxxsh, minxsh = determineshifts(xsh)
maxysh, minysh = determineshifts(ysh)
xsize += (maxxsh-minxsh)
ysize += (maxysh-minysh)
# Determine the x and y origins for each frame
xdist=0
ydist=0
shid=0
x0=np.zeros(amplifiers)
y0=np.zeros(amplifiers)
for j in range(amplifiers):
x0[j]=xdist+xsh[shid]-minxsh
y0[j]=ysh[shid]-minysh
hdu=i*amplifiers+j+1
darr=struct[hdu].data
xdist += len(darr[0])
if j%2==1:
xdist += gp
shid += 1
# make the out image
outarr=np.zeros((ysize, xsize), np.float64)
# Embed each frame into the output array
for j in range(amplifiers):
hdu=i*amplifiers+j+1
darr=struct[hdu].data
outarr=salttran.embed(darr, x0[j], y0[j], outarr)
# Add the outimage to the output structure
hdu=i*amplifiers+1
outhdu=i+1
outstruct[outhdu] = pyfits.ImageHDU(outarr)
outstruct[outhdu].header=struct[hdu].header
# Fix the headers in each extension
datasec='[1:%4i,1:%4i]' % (xsize, ysize)
saltsafekey.put('DATASEC',datasec, outstruct[outhdu], outfile)
saltsafekey.rem('DETSIZE',outstruct[outhdu],outfile)
saltsafekey.rem('DETSEC',outstruct[outhdu],outfile)
saltsafekey.rem('CCDSEC',outstruct[outhdu],outfile)
saltsafekey.rem('AMPSEC',outstruct[outhdu],outfile)
# add housekeeping key words
outstruct[outhdu]=addhousekeeping(outstruct[outhdu], outhdu, outfile)
# close input FITS file
saltsafeio.closefits(struct)
# housekeeping keywords
keymosaic='SLOTMERG'
fname, hist=history(level=1, wrap=False)
saltsafekey.housekeeping(struct[0],keymosaic,'Amplifiers have been mosaiced', hist)
#saltsafekey.history(outstruct[0],hist)
# this is added for later use by
saltsafekey.put('NCCDS', 0.5, outstruct[0])
saltsafekey.put('NSCIEXT', exposures, outstruct[0])
saltsafekey.put('NEXTEND', exposures, outstruct[0])
# write FITS file of mosaiced image
outstruct=pyfits.HDUList(outstruct)
saltsafeio.writefits(outstruct, outfile, clobber=clobber)
def specidentify(images,
linelist,
outfile,
guesstype='rss',
guessfile='',
automethod='Matchlines',
function='poly',
order=3,
rstep=100,
rstart='middlerow',
mdiff=5,
thresh=3,
niter=5,
smooth=0,
subback=0,
inter=True,
startext=0,
clobber=False,
textcolor='black',
preprocess=False,
logfile='salt.log',
verbose=True):
with logging(logfile, debug) as log:
# set up the variables
infiles = []
outfiles = []
# Check the input images
infiles = saltio.argunpack('Input', images)
# create list of output files
outfiles = saltio.argunpack('Output', outfile)
# open the line lists
slines, sfluxes = st.readlinelist(linelist)
# Identify the lines in each file
for img, ofile in zip(infiles, outfiles):
# open the image
hdu = saltio.openfits(img)
# get the basic information about the spectrograph
dateobs = saltkey.get('DATE-OBS', hdu[0])
try:
utctime = saltkey.get('UTC-OBS', hdu[0])
except SaltError:
utctime = saltkey.get('TIME-OBS', hdu[0])
instrume = saltkey.get('INSTRUME', hdu[0]).strip()
grating = saltkey.get('GRATING', hdu[0]).strip()
grang = saltkey.get('GR-ANGLE', hdu[0])
grasteps = saltkey.get('GRTILT', hdu[0])
arang = saltkey.get('AR-ANGLE', hdu[0])
arsteps = saltkey.get('CAMANG', hdu[0])
rssfilter = saltkey.get('FILTER', hdu[0])
specmode = saltkey.get('OBSMODE', hdu[0])
masktype = saltkey.get('MASKTYP', hdu[0]).strip().upper()
slitname = saltkey.get('MASKID', hdu[0])
xbin, ybin = saltkey.ccdbin(hdu[0], img)
for i in range(startext, len(hdu)):
if hdu[i].name == 'SCI':
log.message('Proccessing extension %i in %s' % (i, img))
# things that will change for each slit
if masktype == 'LONGSLIT':
slit = st.getslitsize(slitname)
xpos = -0.2666
ypos = 0.0117
objid = None
elif masktype == 'MOS':
slit = 1.
#slit=saltkey.get('SLIT', hdu[i])
# set up the x and y positions
miny = hdu[i].header['MINY']
maxy = hdu[i].header['MAXY']
ras = hdu[i].header['SLIT_RA']
des = hdu[i].header['SLIT_DEC']
objid = hdu[i].header['SLITNAME']
# TODO: Check the perfomance of masks at different PA
rac = hdu[0].header['MASK_RA']
dec = hdu[0].header['MASK_DEC']
pac = hdu[0].header['PA']
# these are hard wired at the moment
xpixscale = 0.1267 * xbin
ypixscale = 0.1267 * ybin
cx = int(3162 / xbin)
cy = int(2050 / ybin)
x, y = mt.convert_fromsky(ras,
des,
rac,
dec,
xpixscale=xpixscale,
ypixscale=ypixscale,
position_angle=-pac,
ccd_cx=cx,
ccd_cy=cy)
xpos = 0.015 * 2 * (cx - x[0])
ypos = 0.0117
else:
msg = '%s is not a currently supported masktype' % masktype
raise SALTSpecError(msg)
if instrume not in ['PFIS', 'RSS']:
msg = '%s is not a currently supported instrument' % instrume
raise SALTSpecError(msg)
# create RSS Model
rss = RSSModel.RSSModel(grating_name=grating.strip(),
gratang=grang,
camang=arang,
slit=slit,
xbin=xbin,
ybin=ybin,
xpos=xpos,
ypos=ypos)
res = 1e7 * rss.calc_resolelement(rss.alpha(), -rss.beta())
dres = res / 10.0
wcen = 1e7 * rss.calc_centralwavelength()
R = rss.calc_resolution(wcen / 1e7, rss.alpha(),
-rss.beta())
logmsg = '\nGrating\tGR-ANGLE\tAR-ANGLE\tSlit\tWCEN\tR\n'
logmsg += '%s\t%8.3f\t%8.3f\t%4.2f\t%6.2f\t%4f\n' % (
grating, grang, arang, slit, wcen, R)
if log:
log.message(logmsg, with_header=False)
# set up the data for the source
try:
data = hdu[i].data
except Exception, e:
message = 'Unable to read in data array in %s because %s' % (
img, e)
raise SALTSpecError(message)
# set up the center row
if rstart == 'middlerow':
ystart = int(0.5 * len(data))
else:
ystart = int(rstart)
rss.gamma = 0.0
if masktype == 'MOS':
rss.gamma = 180.0 / math.pi * math.atan(
(y * rss.detector.pix_size * rss.detector.ybin -
0.5 * rss.detector.find_height()) /
rss.camera.focallength)
# set up the xarr array based on the image
xarr = np.arange(len(data[ystart]), dtype='int64')
# get the guess for the wavelength solution
if guesstype == 'rss':
# set up the rss model
ws = st.useRSSModel(xarr,
rss,
function=function,
order=order,
gamma=rss.gamma)
if function in ['legendre', 'chebyshev']:
ws.func.func.domain = [xarr.min(), xarr.max()]
elif guesstype == 'file':
soldict = {}
soldict = readsolascii(guessfile, soldict)
timeobs = enterdatetime('%s %s' % (dateobs, utctime))
exptime = saltkey.get('EXPTIME', hdu[0])
filtername = saltkey.get('FILTER', hdu[0]).strip()
try:
slitid = saltkey.get('SLITNAME', hdu[i])
except:
slitid = None
function, order, coef, domain = findlinesol(soldict,
ystart,
True,
timeobs,
exptime,
instrume,
grating,
grang,
arang,
filtername,
slitid,
xarr=xarr)
ws = WavelengthSolution.WavelengthSolution(
xarr, xarr, function=function, order=order)
ws.func.func.domain = domain
ws.set_coef(coef)
else:
raise SALTSpecError(
'This guesstype is not currently supported')
# identify the spectral lines
ImageSolution = identify(data,
slines,
sfluxes,
xarr,
ystart,
ws=ws,
function=function,
order=order,
rstep=rstep,
mdiff=mdiff,
thresh=thresh,
niter=niter,
method=automethod,
res=res,
dres=dres,
smooth=smooth,
inter=inter,
filename=img,
subback=0,
textcolor=textcolor,
preprocess=preprocess,
log=log,
verbose=verbose)
if outfile and len(ImageSolution):
writeIS(ImageSolution,
outfile,
dateobs=dateobs,
utctime=utctime,
instrume=instrume,
grating=grating,
grang=grang,
grasteps=grasteps,
arsteps=arsteps,
arang=arang,
rfilter=rssfilter,
slit=slit,
xbin=xbin,
ybin=ybin,
objid=objid,
filename=img,
log=log,
verbose=verbose)
def make_mosaic(struct, gap, xshift, yshift, rotation, interp_type='linear',
boundary='constant', constant=0, geotran=True, fill=False,
cleanup=True, log=None, verbose=False):
"""Given a SALT image struct, combine each of the individual amplifiers and
apply the geometric CCD transformations to the image
"""
# get the name of the file
infile = saltkey.getimagename(struct[0], base=True)
outpath = './'
# identify instrument
instrume, keyprep, keygain, keybias, keyxtalk, keyslot = \
saltkey.instrumid(struct)
# how many amplifiers?
nsciext = saltkey.get('NSCIEXT', struct[0])
nextend = saltkey.get('NEXTEND', struct[0])
nccds = saltkey.get('NCCDS', struct[0])
amplifiers = nccds * 2
if nextend > nsciext:
varframe = True
else:
varframe = False
# CCD geometry coefficients
if (instrume == 'RSS' or instrume == 'PFIS'):
xsh = [0., xshift[0], 0., xshift[1]]
ysh = [0., yshift[0], 0., yshift[1]]
rot = [0., rotation[0], 0., rotation[1]]
elif instrume == 'SALTICAM':
xsh = [0., xshift[0], 0.]
ysh = [0., yshift[0], 0.]
rot = [0., rotation[0], 0]
# how many extensions?
nextend = saltkey.get('NEXTEND', struct[0])
# CCD on-chip binning
xbin, ybin = saltkey.ccdbin(struct[0])
# create temporary primary extension
outstruct = []
outstruct.append(struct[0])
# define temporary FITS file store tiled CCDs
tilefile = saltio.tmpfile(outpath)
tilefile += 'tile.fits'
if varframe:
tilehdu = [None] * (3 * int(nsciext / 2) + 1)
else:
tilehdu = [None] * int(nsciext / 2 + 1)
tilehdu[0] = fits.PrimaryHDU()
#tilehdu[0].header = struct[0].header
if log:
log.message('', with_stdout=verbose)
# iterate over amplifiers, stich them to produce file of CCD images
for i in range(int(nsciext / 2)):
hdu = i * 2 + 1
# amplifier = hdu%amplifiers
# if (amplifier == 0): amplifier = amplifiers
# read DATASEC keywords
datasec1 = saltkey.get('DATASEC', struct[hdu])
datasec2 = saltkey.get('DATASEC', struct[hdu + 1])
xdsec1, ydsec1 = saltstring.secsplit(datasec1)
xdsec2, ydsec2 = saltstring.secsplit(datasec2)
# read images
imdata1 = saltio.readimage(struct, hdu)
imdata2 = saltio.readimage(struct, hdu + 1)
# tile 2n amplifiers to yield n CCD images
outdata = numpy.zeros((ydsec1[1] +
abs(ysh[i +
1] /
ybin), xdsec1[1] +
xdsec2[1] +
abs(xsh[i +
1] /
xbin)), numpy.float32)
# set up the variance frame
if varframe:
vardata = outdata.copy()
vdata1 = saltio.readimage(struct, struct[hdu].header['VAREXT'])
vdata2 = saltio.readimage(struct, struct[hdu + 1].header['VAREXT'])
bpmdata = outdata.copy()
bdata1 = saltio.readimage(struct, struct[hdu].header['BPMEXT'])
bdata2 = saltio.readimage(struct, struct[hdu + 1].header['BPMEXT'])
x1 = xdsec1[0] - 1
if x1 != 0:
msg = 'The data in %s have not been trimmed prior to mosaicking.' \
% infile
log.error(msg)
if xsh[i + 1] < 0:
x1 += abs(xsh[i + 1] / xbin)
x2 = x1 + xdsec1[1]
y1 = ydsec1[0] - 1
if ysh[i + 1] < 0:
y1 += abs(ysh[i + 1] / ybin)
y2 = y1 + ydsec1[1]
outdata[y1:y2, x1:x2] =\
imdata1[ydsec1[0] - 1:ydsec1[1], xdsec1[0] - 1:xdsec1[1]]
if varframe:
vardata[y1:y2, x1:x2] =\
vdata1[ydsec1[0] - 1:ydsec1[1], xdsec1[0] - 1:xdsec1[1]]
bpmdata[y1:y2, x1:x2] =\
bdata1[ydsec1[0] - 1:ydsec1[1], xdsec1[0] - 1:xdsec1[1]]
x1 = x2
x2 = x1 + xdsec2[1]
y1 = ydsec2[0] - 1
if ysh[i + 1] < 0:
y1 += abs(ysh[i + 1] / ybin)
y2 = y1 + ydsec2[1]
outdata[y1:y2, x1:x2] =\
imdata2[ydsec1[0] - 1:ydsec1[1], xdsec1[0] - 1:xdsec1[1]]
if varframe:
vardata[y1:y2, x1:x2] =\
vdata2[ydsec1[0] - 1:ydsec1[1], xdsec1[0] - 1:xdsec1[1]]
bpmdata[y1:y2, x1:x2] =\
bdata2[ydsec1[0] - 1:ydsec1[1], xdsec1[0] - 1:xdsec1[1]]
# size of new image
naxis1 = str(xdsec1[1] + xdsec2[1])
naxis2 = str(ydsec1[1])
# add image and keywords to HDU list
tilehdu[i + 1] = fits.ImageHDU(outdata)
tilehdu[i + 1].header = struct[hdu].header
#tilehdu[
# i + 1].header['DATASEC'] = '[1:' + naxis1 + ',1:' + naxis2 + ']'
if varframe:
vext = i + 1 + int(nsciext / 2.)
tilehdu[vext] = fits.ImageHDU(vardata)
#tilehdu[vext].header = struct[struct[hdu].header['VAREXT']].header
#tilehdu[vext].header[
# 'DATASEC'] = '[1:' + naxis1 + ',1:' + naxis2 + ']'
bext = i + 1 + 2 * int(nsciext / 2.)
tilehdu[bext] = fits.ImageHDU(bpmdata)
#tilehdu[bext].header = struct[struct[hdu].header['BPMEXT']].header
#tilehdu[bext].header[
# 'DATASEC'] = '[1:' + naxis1 + ',1:' + naxis2 + ']'
# image tile log message #1
if log:
message = os.path.basename(infile) + '[' + str(hdu) + ']['
message += str(xdsec1[0]) + ':' + str(xdsec1[1]) + ','
message += str(ydsec1[0]) + ':' + str(ydsec1[1]) + '] --> '
message += os.path.basename(tilefile) + '[' + str(i + 1) + ']['
message += str(xdsec1[0]) + ':' + str(xdsec1[1]) + ','
message += str(ydsec1[0]) + ':' + str(ydsec1[1]) + ']'
log.message(message, with_stdout=verbose, with_header=False)
message = os.path.basename(infile) + '[' + str(hdu + 1) + ']['
message += str(xdsec1[0]) + ':' + str(xdsec1[1]) + ','
message += str(ydsec1[0]) + ':' + str(ydsec1[1]) + '] --> '
message += os.path.basename(tilefile) + '[' + str(i + 1) + ']['
message += str(xdsec1[1] + 1) + ':' + \
str(xdsec1[1] + xdsec2[1]) + ','
message += str(ydsec2[0]) + ':' + str(ydsec2[1]) + ']'
log.message(message, with_stdout=verbose, with_header=False)
# write temporary file of tiled CCDs
hdulist = fits.HDUList(tilehdu)
hdulist.writeto(tilefile)
# iterate over CCDs, transform and rotate images
yrot = [None] * 4
xrot = [None] * 4
tranfile = [' ']
tranhdu = [0]
if varframe:
tranfile = [''] * (3 * int(nsciext / 2) + 1)
tranhdu = [0] * (3 * int(nsciext / 2) + 1)
else:
tranfile = [''] * int(nsciext / 2 + 1)
tranhdu = [0] * int(nsciext / 2 + 1)
# this is hardwired for SALT where the second CCD is considered the
# fiducial
for hdu in range(1, int(nsciext / 2 + 1)):
tranfile[hdu] = saltio.tmpfile(outpath)
tranfile[hdu] += 'tran.fits'
if varframe:
tranfile[hdu + nccds] = saltio.tmpfile(outpath) + 'tran.fits'
tranfile[hdu + 2 * nccds] = saltio.tmpfile(outpath) + 'tran.fits'
ccd = hdu % nccds
if (ccd == 0):
ccd = nccds
# correct rotation for CCD binning
yrot[ccd] = rot[ccd] * ybin / xbin
xrot[ccd] = rot[ccd] * xbin / ybin
dxshift = xbin * int(float(int(gap) / xbin) + 0.5) - gap
# transformation using geotran IRAF task
# if (ccd == 1):
if (ccd != 2):
if geotran:
message = '\nSALTMOSAIC -- geotran ' + tilefile + \
'[' + str(ccd) + '] ' + tranfile[hdu]
message += ' \"\" \"\" xshift=' + \
str((xsh[ccd] + (2 - ccd) * dxshift) / xbin) + ' '
message += 'yshift=' + \
str(ysh[ccd] / ybin) + ' xrotation=' + str(xrot[ccd]) + ' '
message += 'yrotation=' + \
str(yrot[ccd]) + ' xmag=1 ymag=1 xmin=\'INDEF\''
message += 'xmax=\'INDEF\' ymin=\'INDEF\' ymax=\'INDEF\' '
message += 'ncols=\'INDEF\' '
message += 'nlines=\'INDEF\' verbose=\'no\' '
message += 'fluxconserve=\'yes\' nxblock=2048 '
message += 'nyblock=2048 interpolant=\'' + \
interp_type + '\' boundary=\'constant\' constant=0'
log.message(message, with_stdout=verbose)
yd, xd = tilehdu[ccd].data.shape
ncols = 'INDEF' # ncols=xd+abs(xsh[ccd]/xbin)
nlines = 'INDEF' # nlines=yd+abs(ysh[ccd]/ybin)
geo_xshift = xsh[ccd] + (2 - ccd) * dxshift / xbin
geo_yshift = ysh[ccd] / ybin
iraf.images.immatch.geotran(tilefile + "[" + str(ccd) + "]",
tranfile[hdu],
"",
"",
xshift=geo_xshift,
yshift=geo_yshift,
xrotation=xrot[ccd],
yrotation=yrot[ccd],
xmag=1, ymag=1, xmin='INDEF',
xmax='INDEF', ymin='INDEF',
ymax='INDEF', ncols=ncols,
nlines=nlines, verbose='no',
fluxconserve='yes', nxblock=2048,
nyblock=2048, interpolant="linear",
boundary="constant", constant=0)
if varframe:
var_infile = tilefile + "[" + str(ccd + nccds) + "]"
iraf.images.immatch.geotran(var_infile,
tranfile[hdu + nccds],
"",
"",
xshift=geo_xshift,
yshift=geo_yshift,
xrotation=xrot[ccd],
yrotation=yrot[ccd],
xmag=1, ymag=1, xmin='INDEF',
xmax='INDEF', ymin='INDEF',
ymax='INDEF', ncols=ncols,
nlines=nlines, verbose='no',
fluxconserve='yes',
nxblock=2048, nyblock=2048,
interpolant="linear",
boundary="constant",
constant=0)
var2_infile = tilefile + "[" + str(ccd + 2 * nccds) + "]"
iraf.images.immatch.geotran(var2_infile,
tranfile[hdu + 2 * nccds],
"",
"",
xshift=geo_xshift,
yshift=geo_yshift,
xrotation=xrot[ccd],
yrotation=yrot[ccd],
xmag=1, ymag=1, xmin='INDEF',
xmax='INDEF', ymin='INDEF',
ymax='INDEF', ncols=ncols,
nlines=nlines, verbose='no',
fluxconserve='yes',
nxblock=2048, nyblock=2048,
interpolant="linear",
boundary="constant",
constant=0)
# open the file and copy the data to tranhdu
tstruct = fits.open(tranfile[hdu])
tranhdu[hdu] = tstruct[0].data
tstruct.close()
if varframe:
tranhdu[
hdu +
nccds] = fits.open(
tranfile[
hdu +
nccds])[0].data
tranhdu[
hdu +
2 *
nccds] = fits.open(
tranfile[
hdu +
2 *
nccds])[0].data
else:
log.message(
"Transform CCD #%i using dx=%s, dy=%s, rot=%s" %
(ccd,
xsh[ccd] /
2.0,
ysh[ccd] /
2.0,
xrot[ccd]),
with_stdout=verbose,
with_header=False)
tranhdu[hdu] = geometric_transform(
tilehdu[ccd].data,
tran_func,
prefilter=False,
order=1,
extra_arguments=(
xsh[ccd] / 2,
ysh[ccd] / 2,
1,
1,
xrot[ccd],
yrot[ccd]))
tstruct = fits.PrimaryHDU(tranhdu[hdu])
tstruct.writeto(tranfile[hdu])
if varframe:
tranhdu[hdu + nccds] = geometric_transform(
tilehdu[hdu + 3].data,
tran_func,
prefilter=False,
order=1,
extra_arguments=(
xsh[ccd] / 2, ysh[ccd] / 2,
1, 1,
xrot[ccd], yrot[ccd]))
tranhdu[hdu + 2 * nccds] = geometric_transform(
tilehdu[hdu + 6].data,
tran_func,
prefilter=False,
order=1,
extra_arguments=(
xsh[ccd] / 2, ysh[ccd] / 2,
1, 1,
xrot[ccd], yrot[ccd]))
else:
log.message(
"Transform CCD #%i using dx=%s, dy=%s, rot=%s" %
(ccd, 0, 0, 0), with_stdout=verbose, with_header=False)
tranhdu[hdu] = tilehdu[ccd].data
if varframe:
tranhdu[hdu + nccds] = tilehdu[ccd + nccds].data
tranhdu[hdu + 2 * nccds] = tilehdu[ccd + 2 * nccds].data
# open outfile
if varframe:
outlist = 4 * [None]
else:
outlist = 2 * [None]
#outlist[0] = struct[0].copy()
outlist[0] = fits.PrimaryHDU()
outlist[0].header = struct[0].header
naxis1 = int(gap / xbin * (nccds - 1))
naxis2 = 0
for i in range(1, nccds + 1):
yw, xw = tranhdu[i].shape
naxis1 += xw + int(abs(xsh[ccd] / xbin)) + 1
naxis2 = max(naxis2, yw)
outdata = numpy.zeros((naxis2, naxis1), numpy.float32)
outdata.shape = naxis2, naxis1
if varframe:
vardata = outdata * 0
bpmdata = outdata * 0 + 1
# iterate over CCDs, stich them to produce a full image
hdu = 0
totxshift = 0
for hdu in range(1, nccds + 1):
# read DATASEC keywords
ydsec, xdsec = tranhdu[hdu].shape
# define size and shape of final image
# tile CCDs to yield mosaiced image
x1 = int((hdu - 1) * (xdsec + gap / xbin)) + int(totxshift)
x2 = xdsec + x1
y1 = int(0)
y2 = int(ydsec)
outdata[y1:y2, x1:x2] = tranhdu[hdu]
totxshift += int(abs(xsh[hdu] / xbin)) + 1
if varframe:
vardata[y1:y2, x1:x2] = tranhdu[hdu + nccds]
bpmdata[y1:y2, x1:x2] = tranhdu[hdu + 2 * nccds]
# make sure to cover up all the gaps include bad areas
if varframe:
baddata = (outdata == 0)
baddata = nd.maximum_filter(baddata, size=3)
bpmdata[baddata] = 1
# fill in the gaps if requested
if fill:
if varframe:
outdata = fill_gaps(outdata, 0)
else:
outdata = fill_gaps(outdata, 0)
# add to the file
outlist[1] = fits.ImageHDU(outdata)
if varframe:
outlist[2] = fits.ImageHDU(vardata,name='VAR')
outlist[3] = fits.ImageHDU(bpmdata,name='BPM')
# create the image structure
outstruct = fits.HDUList(outlist)
# update the head informaation
# housekeeping keywords
saltkey.put('NEXTEND', 2, outstruct[0])
saltkey.new('EXTNAME', 'SCI', 'Extension name', outstruct[1])
saltkey.new('EXTVER', 1, 'Extension number', outstruct[1])
if varframe:
saltkey.new('VAREXT', 2, 'Variance frame extension', outstruct[1])
saltkey.new('BPMEXT', 3, 'BPM Extension', outstruct[1])
try:
saltkey.copy(struct[1], outstruct[1], 'CCDSUM')
except:
pass
# Add keywords associated with geometry
saltkey.new('SGEOMGAP', gap, 'SALT Chip Gap', outstruct[0])
c1str = '{:3.2f} {:3.2f} {:3.4f}'.format(xshift[0],
yshift[0],
rotation[0])
saltkey.new('SGEOM1', c1str, 'SALT Chip 1 Transform', outstruct[0])
c2str = '{:3.2f} {:3.2f} {:3.4f}'.format(xshift[1],
yshift[1],
rotation[1])
saltkey.new('SGEOM2', c2str, 'SALT Chip 2 Transform', outstruct[0])
# WCS keywords
saltkey.new('CRPIX1', 0, 'WCS: X reference pixel', outstruct[1])
saltkey.new('CRPIX2', 0, 'WCS: Y reference pixel', outstruct[1])
saltkey.new(
'CRVAL1',
float(xbin),
'WCS: X reference coordinate value',
outstruct[1])
saltkey.new(
'CRVAL2',
float(ybin),
'WCS: Y reference coordinate value',
outstruct[1])
saltkey.new('CDELT1', float(xbin), 'WCS: X pixel size', outstruct[1])
saltkey.new('CDELT2', float(ybin), 'WCS: Y pixel size', outstruct[1])
saltkey.new('CTYPE1', 'pixel', 'X type', outstruct[1])
saltkey.new('CTYPE2', 'pixel', 'Y type', outstruct[1])
# cleanup temporary files
if cleanup:
for tfile in tranfile:
if os.path.isfile(tfile):
saltio.delete(tfile)
if os.path.isfile(tilefile):
status = saltio.delete(tilefile)
# return the file
return outstruct
def rectify(hdu, soldict, caltype='line', function='poly', order=3, inttype='interp',
w1=None, w2=None, dw=None, nw=None,
blank=0, pixscale=0.0, time_interp=False, clobber=True, log=None, verbose=True):
"""Read in an image and a set of wavlength solutions. Calculate the best
wavelength solution for a given dataset and then apply that data set to the
image
return
"""
#set the
set_w1=(w1 is None)
set_w2=(w2 is None)
set_dw=(dw is None)
set_nw=(nw is None)
#set up the time of the observation
dateobs=saltkey.get('DATE-OBS', hdu[0])
utctime=saltkey.get('TIME-OBS', hdu[0])
exptime=saltkey.get('EXPTIME', hdu[0])
instrume=saltkey.get('INSTRUME', hdu[0]).strip()
grating=saltkey.get('GRATING', hdu[0]).strip()
if caltype=='line':
grang=saltkey.get('GRTILT', hdu[0])
arang=saltkey.get('CAMANG', hdu[0])
else:
grang=saltkey.get('GR-ANGLE', hdu[0])
arang=saltkey.get('AR-ANGLE', hdu[0])
filtername=saltkey.get('FILTER', hdu[0]).strip()
slitname=saltkey.get('MASKID', hdu[0])
slit=st.getslitsize(slitname)
xbin, ybin = saltkey.ccdbin( hdu[0])
timeobs=enterdatetime('%s %s' % (dateobs, utctime))
#check to see if there is more than one solution
if caltype=='line':
if len(soldict)==1:
sol=soldict.keys()[0]
slitid=None
if not matchobservations(soldict[sol], instrume, grating, grang, arang, filtername, slitid):
msg='Observations do not match setup for transformation but using the solution anyway'
if log: log.warning(msg)
for i in range(1,len(hdu)):
if hdu[i].name=='SCI':
if log: log.message('Correcting extension %i' % i)
istart=int(0.5*len(hdu[i].data))
#open up the data
#set up the xarr and initial wavlength solution
xarr=np.arange(len(hdu[i].data[istart]), dtype='int64')
#get the slitid
try:
slitid=saltkey.get('SLITNAME', hdu[i])
except:
slitid=None
#set up a wavelength solution
try:
w_arr=findsol(xarr, soldict, istart, caltype, timeobs, exptime, instrume, grating, grang, arang, filtername,
slit, xbin, ybin, slitid, function, order )
except SALTSpecError, e:
if slitid:
msg='SLITID %s: %s' % (slitid, e)
if log: log.warning(msg)
continue
else:
raise SALTSpecError(e)
if w_arr is None:
w_arr=findsol(xarr, soldict, istart, 'rss', timeobs, exptime, instrume, grating, grang, arang, filtername,
slit, xbin, ybin, slitid, function, order )
#set up the output x-axis
if set_w1: w1=w_arr.min()
if set_w2: w2=w_arr.max()
if set_nw: nw=len(xarr)
if set_dw: dw=float(w2-w1)/nw
nw_arr=createoutputxaxis(w1, w2, nw)
#setup the VARIANCE and BPM frames
if saltkey.found('VAREXT', hdu[i]):
varext=saltkey.get('VAREXT', hdu[i])
else:
varext=None
#setup the BPM frames
if saltkey.found('BPMEXT', hdu[i]):
bpmext=saltkey.get('BPMEXT', hdu[i])
else:
bpmext=None
#for each line in the data, determine the wavelength solution
#for a given line in the image
for j in range(len(hdu[i].data)):
#find the wavelength solution for the data
w_arr=findsol(xarr, soldict, j, caltype, timeobs, exptime, instrume, grating, grang, arang, filtername,
slit, xbin, ybin, slitid, function, order )
#apply that wavelength solution to the data
if w_arr is not None:
try:
hdu[i].data[j,:]=st.interpolate(nw_arr, w_arr, hdu[i].data[j,:], inttype, left=blank, right=blank)
except Exception, e:
hdu[i].data[j,:]=hdu[i].data[j,:]*0.0+blank
msg='In row %i, solution cannot be found due to %s' % (i, e)
#correct the variance frame
if varext:
try:
hdu[varext].data[j,:]=st.interpolate(nw_arr, w_arr, hdu[varext].data[j,:], inttype, left=blank, right=blank)
except Exception, e:
msg='In row %i, solution cannot be found due to %s' % (i, e)
#correct the BPM frame
if bpmext:
try:
hdu[bpmext].data[j,:]=st.interpolate(nw_arr, w_arr, hdu[bpmext].data[j,:], inttype, left=blank, right=blank)
except Exception, e:
msg='In row %i, solution cannot be found due to %s' % (i, e)
def auto_arc_lens(arc_image, dbfile='wav.db', ndstep=20, logfile='salt.log'):
"""Automatically process an arc image for the SALT Lens project
"""
hdu = fits.open(arc_image)
hdr = hdu[0].header
if hdr['LAMPID'] == 'Xe' and hdr['GRATING'] == 'PG0900' and hdr['GRTILT'] == 15.875:
print('Automatically processing arc image')
data = hdu[1].data
ystart = int(0.5 * len(data))
xarr = np.arange(len(data[ystart]), dtype='int64')
farr = data[ystart]
lampfile = os.path.dirname(__file__)+'/data/Xe.lens'
guessfile = os.path.dirname(__file__)+'/data/lens.db'
slines, sfluxes = st.readlinelist(lampfile)
spectrum = Spectrum.Spectrum(
slines, sfluxes, dw=0.1, stype='line', sigma=6)
swarr = spectrum.wavelength
sfarr = spectrum.flux * farr.max() / spectrum.flux.max()
soldict = readsolascii(guessfile, {})
soldict = (soldict[soldict.keys()[0]])
ws = WavelengthSolution.WavelengthSolution(
xarr, xarr, function=soldict[7], order=soldict[8])
ws.func.func.domain = soldict[11]
ws.set_coef(soldict[10][2])
# start the pre processing
dcoef = ws.coef * 0.0
dcoef[0] = 0.05 * ndstep
dcoef[1] = 0.01 * ws.coef[1]
ws = st.findxcor(xarr, farr, swarr, sfarr, ws,
dcoef=dcoef, ndstep=ndstep, best=False, inttype='interp')
xp, wp = st.crosslinematch(xarr, farr, slines, sfluxes, ws,
res=6, mdiff=20, wdiff=10,
sections=3, sigma=5, niter=5)
ws = st.findfit(np.array(xp), np.array(wp), ws=ws, thresh=ws.thresh)
print(ws)
with logging(logfile, True) as log:
iws = ai.AutoIdentify(xarr, data, slines, sfluxes, ws, farr=farr,
method='Matchlines', rstep=100, istart=ystart, nrows=1,
res=6, dres=0.25, mdiff=20, sigma=5,
smooth=3, niter=5, dc=5, ndstep=ndstep,
oneline=False, log=log, verbose=True)
# get the basic information about the spectrograph
dateobs = saltkey.get('DATE-OBS', hdu[0])
try:
utctime = saltkey.get('UTC-OBS', hdu[0])
except SaltError:
utctime = saltkey.get('TIME-OBS', hdu[0])
instrume = saltkey.get('INSTRUME', hdu[0]).strip()
grating = saltkey.get('GRATING', hdu[0]).strip()
grang = saltkey.get('GR-ANGLE', hdu[0])
grasteps = saltkey.get('GRTILT', hdu[0])
arang = saltkey.get('AR-ANGLE', hdu[0])
arsteps = saltkey.get('CAMANG', hdu[0])
rssfilter = saltkey.get('FILTER', hdu[0])
specmode = saltkey.get('OBSMODE', hdu[0])
masktype = saltkey.get('MASKTYP', hdu[0]).strip().upper()
slitname = saltkey.get('MASKID', hdu[0])
slit = st.getslitsize(slitname)
xbin, ybin = saltkey.ccdbin(hdu[0], arc_image)
writeIS(iws, dbfile, dateobs=dateobs, utctime=utctime, instrume=instrume,
grating=grating, grang=grang, grasteps=grasteps, arsteps=arsteps,
arang=arang, rfilter=rssfilter, slit=slit, xbin=xbin,
ybin=ybin, objid=None, filename=arc_image, log=log, verbose=True)
print(iws)
#self.findfit()
else:
lamp = hdr['LAMPID']
lampfile=iraf.osfn("pysalt$data/linelists/%s.salt" % lamp)
lampfile = 'Xe.lens'
specidentify(arc_image, lampfile, dbfile, guesstype='rss',
guessfile=None, automethod='Matchlines', function='legendre', order=3,
rstep=100, rstart='middlerow', mdiff=20, thresh=5, niter=5, smooth=3,
inter=True, clobber=True, preprocess=True, logfile=logfile, verbose=True)
print("Running specidenity in interactive mode")
def wavemap(hdu,
soldict,
caltype='line',
function='poly',
order=3,
blank=0,
nearest=False,
array_only=False,
clobber=True,
log=None,
verbose=True):
"""Read in an image and a set of wavlength solutions. Calculate the best
wavelength solution for a given dataset and then apply that data set to the
image
return
"""
# set up the time of the observation
dateobs = saltkey.get('DATE-OBS', hdu[0])
utctime = saltkey.get('TIME-OBS', hdu[0])
exptime = saltkey.get('EXPTIME', hdu[0])
instrume = saltkey.get('INSTRUME', hdu[0]).strip()
grating = saltkey.get('GRATING', hdu[0]).strip()
if caltype == 'line':
grang = saltkey.get('GRTILT', hdu[0])
arang = saltkey.get('CAMANG', hdu[0])
else:
grang = saltkey.get('GR-ANGLE', hdu[0])
arang = saltkey.get('AR-ANGLE', hdu[0])
filtername = saltkey.get('FILTER', hdu[0]).strip()
slitname = saltkey.get('MASKID', hdu[0])
slit = st.getslitsize(slitname)
xbin, ybin = saltkey.ccdbin(hdu[0])
timeobs = sr.enterdatetime('%s %s' % (dateobs, utctime))
# check to see if there is more than one solution
if caltype == 'line':
if len(soldict) == 1:
sol = soldict.keys()[0]
slitid = None
if not sr.matchobservations(soldict[sol], instrume, grating, grang,
arang, filtername, slitid):
msg = 'Observations do not match setup for transformation but using the solution anyway'
if log:
log.warning(msg)
for i in range(1, len(hdu)):
if hdu[i].name == 'SCI':
if log:
log.message('Correcting extension %i' % i)
istart = int(0.5 * len(hdu[i].data))
# open up the data
# set up the xarr and initial wavlength solution
xarr = np.arange(len(hdu[i].data[istart]), dtype='int64')
# get the slitid
try:
slitid = saltkey.get('SLITNAME', hdu[i])
except:
slitid = None
#check to see if wavext is already there and if so, then check update
#that for the transformation from xshift to wavelength
if saltkey.found('WAVEXT', hdu[i]):
w_ext = saltkey.get('WAVEXT', hdu[i]) - 1
wavemap = hdu[w_ext].data
function, order, coef = sr.findlinesol(
soldict, istart, nearest, timeobs, exptime, instrume,
grating, grang, arang, filtername, slitid, xarr)
ws = WavelengthSolution.WavelengthSolution(xarr,
xarr,
function=function,
order=order)
ws.set_coef(coef)
for j in range(len(hdu[i].data)):
wavemap[j, :] = ws.value(wavemap[j, :])
if array_only: return wavemap
hdu[w_ext].data = wavemap
continue
# set up a wavelength solution -- still in here for testing MOS data
try:
w_arr = sr.findsol(xarr, soldict, istart, caltype, nearest,
timeobs, exptime, instrume, grating, grang,
arang, filtername, slit, xbin, ybin, slitid,
function, order)
except SALTSpecError as e:
if slitid:
msg = 'SLITID %s: %s' % (slitid, e)
if log:
log.warning(msg)
continue
else:
raise SALTSpecError(e)
if w_arr is None:
w_arr = sr.findsol(xarr, soldict, istart, 'rss', nearest,
timeobs, exptime, instrume, grating, grang,
arang, filtername, slit, xbin, ybin, slitid,
function, order)
# for each line in the data, determine the wavelength solution
# for a given line in the image
wavemap = np.zeros_like(hdu[i].data)
for j in range(len(hdu[i].data)):
# find the wavelength solution for the data
w_arr = sr.findsol(xarr, soldict, j, caltype, nearest, timeobs,
exptime, instrume, grating, grang, arang,
filtername, slit, xbin, ybin, slitid,
function, order)
if w_arr is not None: wavemap[j, :] = w_arr
if array_only: return wavemap
# write out the oimg
hduwav = fits.ImageHDU(data=wavemap,
header=hdu[i].header,
name='WAV')
hdu.append(hduwav)
saltkey.new('WAVEXT',
len(hdu) - 1, 'Extension for Wavelength Map', hdu[i])
return hdu
def identify(img, oimg, slines, sfluxes, guesstype, guessfile, function, order,
rstep, interact, clobber, log, verbose):
"""For a given image, find the solution for each row in the file. Use the appropriate first guess and
guess type along with the appropriate function and order for the fit.
Write out the new image with the solution in the headers and/or as a table in the multi-extension
fits file
returns the status
"""
status = 0
ImageSolution = {}
dcstep = 3
nstep = 50
res = 2.0
dres = 0.1
centerrow = None
nrows = 1
sigma = 3
niter = 5
xdiff = 2 * res
method = 'MatchZero'
# Open up the image
hdu = saltsafeio.openfits(img)
# Read in important keywords
# determine the central row and read it in
try:
data = hdu[1].data
midline = int(0.5 * len(data))
xarr = np.arange(len(data[midline]))
specarr = data
except Exception as e:
message = 'Unable to read in data array in %s because %s' % (img, e)
raise SALTSpecError(message)
# determine the type of first guess. Assumes none
if guesstype == 'user':
pass
elif guesstype == 'rss':
dateobs = saltsafekey.get('DATE-OBS', hdu[0], img)
utctime = saltsafekey.get('UTC-OBS', hdu[0], img)
instrume = saltsafekey.get('INSTRUME', hdu[0], img)
grating = saltsafekey.get('GRATING', hdu[0], img)
grang = saltsafekey.get('GR-ANGLE', hdu[0], img)
arang = saltsafekey.get('AR-ANGLE', hdu[0], img)
filter = saltsafekey.get('FILTER', hdu[0], img)
slit = float(saltsafekey.get('MASKID', hdu[0], img))
xbin, ybin = saltsafekey.ccdbin(hdu[0], img)
# set up the rss model
rssmodel = RSSModel.RSSModel(grating_name=grating.strip(),
gratang=grang,
camang=arang,
slit=slit,
xbin=xbin,
ybin=ybin)
rss = rssmodel.rss
res = 1e7 * rss.calc_resolelement(rss.gratang,
rss.gratang - rss.camang)
if not instrume in ['PFIS', 'RSS']:
msg = '%s is not a currently supported instrument' % instrume
raise SALTSpecError(msg)
ws = useRSSModel(xarr, rss, function=function, order=order)
elif guesstype == 'image':
pass
else:
ws = None
# run in either interactive or non-interactive mode
if interact:
ImageSolution = InterIdentify(xarr,
specarr,
slines,
sfluxes,
ws,
xdiff=xdiff,
function=function,
order=order,
verbose=True)
else:
ImageSolution = AutoIdentify(xarr,
specarr,
slines,
sfluxes,
ws,
rstep=rstep,
method=method,
icenter=centerrow,
nrows=nrows,
res=res,
dres=dres,
dc=dcstep,
nstep=nstep,
sigma=sigma,
niter=niter,
verbose=verbose)
# set up the list of solutions to into an array
key_arr = np.array(ImageSolution.keys())
arg_arr = key_arr.argsort()
ws_arr = np.zeros((len(arg_arr), len(ws.coef) + 1), dtype=float)
# write the solution to an array
for j, i in enumerate(arg_arr):
if isinstance(ImageSolution[key_arr[i]],
WavelengthSolution.WavelengthSolution):
ws_arr[j, 0] = key_arr[i]
ws_arr[j, 1:] = ImageSolution[key_arr[i]].coef
# write the solution as an file
if outfile:
# write header to the file that should include the order and function
if os.path.isfile(outfile) and not clobber:
dout = open(outfile, 'a')
else:
dout = open(outfile, 'w')
msg = '#WS: Wavelength solution for image %s\n' % img
msg += '#The following parameters were used in determining the solution:\n'
msg += '#name=%s\n' % img
msg += '#time-obs=%s %s\n' % (dateobs, utctime)
msg += '#instrument=%s\n' % instrume
msg += '#grating=%s\n' % grating.strip()
msg += '#graang=%s\n' % grang
msg += '#arang=%s\n' % arang
msg += '#filter=%s\n' % filter.strip()
msg += '#Function=%s\n' % function
msg += '#Order=%s\n' % order
msg += '#Starting Data\n'
dout.write(msg)
for i in range(len(ws_arr)):
if ws_arr[i, 0]:
msg = '%5.2f ' % ws_arr[i, 0]
msg += ' '.join(['%e' % k for k in ws_arr[i, 1:]])
dout.write(msg + '\n')
dout.write('\n')
dout.close()
# write the solution as an extension
hdu.close()
return
def saltadvance(images, outpath, obslogfile=None, gaindb=None,xtalkfile=None,
geomfile=None,subover=True,trim=True,masbias=None,
subbias=False, median=False, function='polynomial', order=5,rej_lo=3,
rej_hi=3,niter=5,interp='linear', sdbhost='',sdbname='',sdbuser='', password='',
clobber=False, cleanup=True, logfile='salt.log', verbose=True):
"""SALTADVANCE provides advanced data reductions for a set of data. It will
sort the data, and first process the biases, flats, and then the science
frames. It will record basic quality control information about each of
the steps.
"""
plotover=False
#start logging
with logging(logfile,debug) as log:
# Check the input images
infiles = saltio.argunpack ('Input',images)
infiles.sort()
# create list of output files
outpath=saltio.abspath(outpath)
#log into the database
sdb=saltmysql.connectdb(sdbhost, sdbname, sdbuser, password)
#does the gain database file exist
if gaindb:
dblist= saltio.readgaindb(gaindb)
else:
dblist=[]
# does crosstalk coefficient data exist
if xtalkfile:
xtalkfile = xtalkfile.strip()
xdict = saltio.readxtalkcoeff(xtalkfile)
else:
xdict=None
#does the mosaic file exist--raise error if no
saltio.fileexists(geomfile)
# Delete the obslog file if it already exists
if os.path.isfile(obslogfile) and clobber: saltio.delete(obslogfile)
#read in the obsveration log or create it
if os.path.isfile(obslogfile):
msg='The observing log already exists. Please either delete it or run saltclean with clobber=yes'
raise SaltError(msg)
else:
headerDict=obslog(infiles, log)
obsstruct=createobslogfits(headerDict)
saltio.writefits(obsstruct, obslogfile)
#create the list of bias frames and process them
filename=obsstruct.data.field('FILENAME')
detmode=obsstruct.data.field('DETMODE')
obsmode=obsstruct.data.field('OBSMODE')
ccdtype=obsstruct.data.field('CCDTYPE')
propcode=obsstruct.data.field('PROPID')
masktype=obsstruct.data.field('MASKTYP')
#set the bias list of objects
biaslist=filename[(ccdtype=='ZERO')*(propcode=='CAL_BIAS')]
masterbias_dict={}
for img in infiles:
if os.path.basename(img) in biaslist:
#open the image
struct=fits.open(img)
bimg=outpath+'bxgp'+os.path.basename(img)
#print the message
if log:
message='Processing Zero frame %s' % img
log.message(message, with_stdout=verbose)
#process the image
struct=clean(struct, createvar=True, badpixelstruct=None, mult=True,
dblist=dblist, xdict=xdict, subover=subover, trim=trim, subbias=False,
bstruct=None, median=median, function=function, order=order,
rej_lo=rej_lo, rej_hi=rej_hi, niter=niter, plotover=plotover, log=log,
verbose=verbose)
#update the database
updatedq(os.path.basename(img), struct, sdb)
#write the file out
# housekeeping keywords
fname, hist=history(level=1, wrap=False, exclude=['images', 'outimages', 'outpref'])
saltkey.housekeeping(struct[0],'SPREPARE', 'Images have been prepared', hist)
saltkey.new('SGAIN',time.asctime(time.localtime()),'Images have been gain corrected',struct[0])
saltkey.new('SXTALK',time.asctime(time.localtime()),'Images have been xtalk corrected',struct[0])
saltkey.new('SBIAS',time.asctime(time.localtime()),'Images have been de-biased',struct[0])
# write FITS file
saltio.writefits(struct,bimg, clobber=clobber)
saltio.closefits(struct)
#add files to the master bias list
masterbias_dict=compareimages(struct, bimg, masterbias_dict, keylist=biasheader_list)
#create the master bias frame
for i in masterbias_dict.keys():
bkeys=masterbias_dict[i][0]
blist=masterbias_dict[i][1:]
mbiasname=outpath+createmasterbiasname(blist, bkeys)
bfiles=','.join(blist)
saltcombine(bfiles, mbiasname, method='median', reject='sigclip', mask=False,
weight=False, blank=0, scale=None, statsec=None, lthresh=3, \
hthresh=3, clobber=False, logfile=logfile,verbose=verbose)
#create the list of flatfields and process them
flatlist=filename[ccdtype=='FLAT']
masterflat_dict={}
for img in infiles:
if os.path.basename(img) in flatlist:
#open the image
struct=fits.open(img)
fimg=outpath+'bxgp'+os.path.basename(img)
#print the message
if log:
message='Processing Flat frame %s' % img
log.message(message, with_stdout=verbose)
#process the image
struct=clean(struct, createvar=True, badpixelstruct=None, mult=True,
dblist=dblist, xdict=xdict, subover=subover, trim=trim, subbias=False,
bstruct=None, median=median, function=function, order=order,
rej_lo=rej_lo, rej_hi=rej_hi, niter=niter, plotover=plotover, log=log,
verbose=verbose)
#update the database
updatedq(os.path.basename(img), struct, sdb)
#write the file out
# housekeeping keywords
fname, hist=history(level=1, wrap=False, exclude=['images', 'outimages', 'outpref'])
saltkey.housekeeping(struct[0],'SPREPARE', 'Images have been prepared', hist)
saltkey.new('SGAIN',time.asctime(time.localtime()),'Images have been gain corrected',struct[0])
saltkey.new('SXTALK',time.asctime(time.localtime()),'Images have been xtalk corrected',struct[0])
saltkey.new('SBIAS',time.asctime(time.localtime()),'Images have been de-biased',struct[0])
# write FITS file
saltio.writefits(struct,fimg, clobber=clobber)
saltio.closefits(struct)
#add files to the master bias list
masterflat_dict=compareimages(struct, fimg, masterflat_dict, keylist=flatheader_list)
#create the master flat frame
for i in masterflat_dict.keys():
fkeys=masterflat_dict[i][0]
flist=masterflat_dict[i][1:]
mflatname=outpath+createmasterflatname(flist, fkeys)
ffiles=','.join(flist)
saltcombine(ffiles, mflatname, method='median', reject='sigclip', mask=False,
weight=False, blank=0, scale=None, statsec=None, lthresh=3, \
hthresh=3, clobber=False, logfile=logfile,verbose=verbose)
#process the arc data
arclist=filename[(ccdtype=='ARC') * (obsmode=='SPECTROSCOPY') * (masktype=='LONGSLIT')]
for i, img in enumerate(infiles):
nimg=os.path.basename(img)
if nimg in arclist:
#open the image
struct=fits.open(img)
simg=outpath+'bxgp'+os.path.basename(img)
obsdate=os.path.basename(img)[1:9]
#print the message
if log:
message='Processing ARC frame %s' % img
log.message(message, with_stdout=verbose)
struct=clean(struct, createvar=False, badpixelstruct=None, mult=True,
dblist=dblist, xdict=xdict, subover=subover, trim=trim, subbias=False,
bstruct=None, median=median, function=function, order=order,
rej_lo=rej_lo, rej_hi=rej_hi, niter=niter, plotover=plotover,
log=log, verbose=verbose)
# write FITS file
saltio.writefits(struct,simg, clobber=clobber)
saltio.closefits(struct)
#mosaic the images
mimg=outpath+'mbxgp'+os.path.basename(img)
saltmosaic(images=simg, outimages=mimg,outpref='',geomfile=geomfile,
interp=interp,cleanup=True,clobber=clobber,logfile=logfile,
verbose=verbose)
#remove the intermediate steps
saltio.delete(simg)
#measure the arcdata
arcimage=outpath+'mbxgp'+nimg
dbfile=outpath+obsdate+'_specid.db'
lamp = obsstruct.data.field('LAMPID')[i]
lamp = lamp.replace(' ', '')
lampfile = iraf.osfn("pysalt$data/linelists/%s.salt" % lamp)
print arcimage, lampfile, os.getcwd()
specidentify(arcimage, lampfile, dbfile, guesstype='rss',
guessfile='', automethod='Matchlines', function='legendre',
order=3, rstep=100, rstart='middlerow', mdiff=20, thresh=3,
startext=0, niter=5, smooth=3, inter=False, clobber=True, logfile=logfile,
verbose=verbose)
try:
ximg = outpath+'xmbxgp'+os.path.basename(arcimage)
specrectify(images=arcimage, outimages=ximg, outpref='', solfile=dbfile, caltype='line',
function='legendre', order=3, inttype='interp', w1=None, w2=None, dw=None,
nw=None, blank=0.0, conserve=True, nearest=True, clobber=True,
logfile=logfile, verbose=verbose)
except:
pass
#process the science data
for i, img in enumerate(infiles):
nimg=os.path.basename(img)
if not (nimg in flatlist or nimg in biaslist or nimg in arclist):
#open the image
struct=fits.open(img)
if struct[0].header['PROPID'].count('CAL_GAIN'): continue
simg=outpath+'bxgp'+os.path.basename(img)
#print the message
if log:
message='Processing science frame %s' % img
log.message(message, with_stdout=verbose)
#Check to see if it is RSS 2x2 and add bias subtraction
instrume=saltkey.get('INSTRUME', struct[0]).strip()
gainset = saltkey.get('GAINSET', struct[0])
rospeed = saltkey.get('ROSPEED', struct[0])
target = saltkey.get('OBJECT', struct[0]).strip()
exptime = saltkey.get('EXPTIME', struct[0])
obsmode = saltkey.get('OBSMODE', struct[0]).strip()
detmode = saltkey.get('DETMODE', struct[0]).strip()
masktype = saltkey.get('MASKTYP', struct[0]).strip()
xbin, ybin = saltkey.ccdbin( struct[0], img)
obsdate=os.path.basename(img)[1:9]
bstruct=None
crtype=None
thresh=5
mbox=11
bthresh=5.0,
flux_ratio=0.2
bbox=25
gain=1.0
rdnoise=5.0
fthresh=5.0
bfactor=2
gbox=3
maxiter=5
subbias=False
if instrume=='RSS' and gainset=='FAINT' and rospeed=='SLOW':
bfile='P%sBiasNM%ix%iFASL.fits' % (obsdate, xbin, ybin)
if os.path.exists(bfile):
bstruct=fits.open(bfile)
subbias=True
if detmode=='Normal' and target!='ARC' and xbin < 5 and ybin < 5:
crtype='edge'
thresh=5
mbox=11
bthresh=5.0,
flux_ratio=0.2
bbox=25
gain=1.0
rdnoise=5.0
fthresh=5.0
bfactor=2
gbox=3
maxiter=3
#process the image
struct=clean(struct, createvar=True, badpixelstruct=None, mult=True,
dblist=dblist, xdict=xdict, subover=subover, trim=trim, subbias=subbias,
bstruct=bstruct, median=median, function=function, order=order,
rej_lo=rej_lo, rej_hi=rej_hi, niter=niter, plotover=plotover,
crtype=crtype,thresh=thresh,mbox=mbox, bbox=bbox, \
bthresh=bthresh, flux_ratio=flux_ratio, gain=gain, rdnoise=rdnoise,
bfactor=bfactor, fthresh=fthresh, gbox=gbox, maxiter=maxiter,
log=log, verbose=verbose)
#update the database
updatedq(os.path.basename(img), struct, sdb)
#write the file out
# housekeeping keywords
fname, hist=history(level=1, wrap=False, exclude=['images', 'outimages', 'outpref'])
saltkey.housekeeping(struct[0],'SPREPARE', 'Images have been prepared', hist)
saltkey.new('SGAIN',time.asctime(time.localtime()),'Images have been gain corrected',struct[0])
saltkey.new('SXTALK',time.asctime(time.localtime()),'Images have been xtalk corrected',struct[0])
saltkey.new('SBIAS',time.asctime(time.localtime()),'Images have been de-biased',struct[0])
# write FITS file
saltio.writefits(struct,simg, clobber=clobber)
saltio.closefits(struct)
#mosaic the files--currently not in the proper format--will update when it is
if not saltkey.fastmode(saltkey.get('DETMODE', struct[0])):
mimg=outpath+'mbxgp'+os.path.basename(img)
saltmosaic(images=simg, outimages=mimg,outpref='',geomfile=geomfile,
interp=interp,fill=True, cleanup=True,clobber=clobber,logfile=logfile,
verbose=verbose)
#remove the intermediate steps
saltio.delete(simg)
#if the file is spectroscopic mode, apply the wavelength correction
if obsmode == 'SPECTROSCOPY' and masktype.strip()=='LONGSLIT':
dbfile=outpath+obsdate+'_specid.db'
try:
ximg = outpath+'xmbxgp'+os.path.basename(img)
specrectify(images=mimg, outimages=ximg, outpref='', solfile=dbfile, caltype='line',
function='legendre', order=3, inttype='interp', w1=None, w2=None, dw=None,
nw=None, blank=0.0, conserve=True, nearest=True, clobber=True,
logfile=logfile, verbose=verbose)
except Exception, e:
log.message('%s' % e)
#clean up the results
if cleanup:
#clean up the bias frames
for i in masterbias_dict.keys():
blist=masterbias_dict[i][1:]
for b in blist: saltio.delete(b)
#clean up the flat frames
for i in masterflat_dict.keys():
flist=masterflat_dict[i][1:]
for f in flist: saltio.delete(f)
def identify(img, oimg, slines, sfluxes, guesstype, guessfile, function, order, rstep, interact, clobber, log, verbose):
"""For a given image, find the solution for each row in the file. Use the appropriate first guess and
guess type along with the appropriate function and order for the fit.
Write out the new image with the solution in the headers and/or as a table in the multi-extension
fits file
returns the status
"""
status = 0
ImageSolution = {}
dcstep = 3
nstep = 50
res = 2.0
dres = 0.1
centerrow = None
nrows = 1
sigma = 3
niter = 5
xdiff = 2 * res
method = "MatchZero"
# Open up the image
hdu = saltsafeio.openfits(img)
# Read in important keywords
# determine the central row and read it in
try:
data = hdu[1].data
midline = int(0.5 * len(data))
xarr = np.arange(len(data[midline]))
specarr = data
except Exception as e:
message = "Unable to read in data array in %s because %s" % (img, e)
raise SALTSpecError(message)
# determine the type of first guess. Assumes none
if guesstype == "user":
pass
elif guesstype == "rss":
dateobs = saltsafekey.get("DATE-OBS", hdu[0], img)
utctime = saltsafekey.get("UTC-OBS", hdu[0], img)
instrume = saltsafekey.get("INSTRUME", hdu[0], img)
grating = saltsafekey.get("GRATING", hdu[0], img)
grang = saltsafekey.get("GR-ANGLE", hdu[0], img)
arang = saltsafekey.get("AR-ANGLE", hdu[0], img)
filter = saltsafekey.get("FILTER", hdu[0], img)
slit = float(saltsafekey.get("MASKID", hdu[0], img))
xbin, ybin = saltsafekey.ccdbin(hdu[0], img)
# set up the rss model
rssmodel = RSSModel.RSSModel(
grating_name=grating.strip(), gratang=grang, camang=arang, slit=slit, xbin=xbin, ybin=ybin
)
rss = rssmodel.rss
res = 1e7 * rss.calc_resolelement(rss.gratang, rss.gratang - rss.camang)
if not instrume in ["PFIS", "RSS"]:
msg = "%s is not a currently supported instrument" % instrume
raise SALTSpecError(msg)
ws = useRSSModel(xarr, rss, function=function, order=order)
elif guesstype == "image":
pass
else:
ws = None
# run in either interactive or non-interactive mode
if interact:
ImageSolution = InterIdentify(
xarr, specarr, slines, sfluxes, ws, xdiff=xdiff, function=function, order=order, verbose=True
)
else:
ImageSolution = AutoIdentify(
xarr,
specarr,
slines,
sfluxes,
ws,
rstep=rstep,
method=method,
icenter=centerrow,
nrows=nrows,
res=res,
dres=dres,
dc=dcstep,
nstep=nstep,
sigma=sigma,
niter=niter,
verbose=verbose,
)
# set up the list of solutions to into an array
key_arr = np.array(ImageSolution.keys())
arg_arr = key_arr.argsort()
ws_arr = np.zeros((len(arg_arr), len(ws.coef) + 1), dtype=float)
# write the solution to an array
for j, i in enumerate(arg_arr):
if isinstance(ImageSolution[key_arr[i]], WavelengthSolution.WavelengthSolution):
ws_arr[j, 0] = key_arr[i]
ws_arr[j, 1:] = ImageSolution[key_arr[i]].coef
# write the solution as an file
if outfile:
# write header to the file that should include the order and function
if os.path.isfile(outfile) and not clobber:
dout = open(outfile, "a")
else:
dout = open(outfile, "w")
msg = "#WS: Wavelength solution for image %s\n" % img
msg += "#The following parameters were used in determining the solution:\n"
msg += "#name=%s\n" % img
msg += "#time-obs=%s %s\n" % (dateobs, utctime)
msg += "#instrument=%s\n" % instrume
msg += "#grating=%s\n" % grating.strip()
msg += "#graang=%s\n" % grang
msg += "#arang=%s\n" % arang
msg += "#filter=%s\n" % filter.strip()
msg += "#Function=%s\n" % function
msg += "#Order=%s\n" % order
msg += "#Starting Data\n"
dout.write(msg)
for i in range(len(ws_arr)):
if ws_arr[i, 0]:
msg = "%5.2f " % ws_arr[i, 0]
msg += " ".join(["%e" % k for k in ws_arr[i, 1:]])
dout.write(msg + "\n")
dout.write("\n")
dout.close()
# write the solution as an extension
hdu.close()
return
def rectify(hdu, soldict, caltype='line', function='poly', order=3, inttype='interp',
w1=None, w2=None, dw=None, nw=None, blank=0, pixscale=0.0, time_interp=False,
conserve=False, nearest=False, clobber=True, log=None, verbose=True):
"""Read in an image and a set of wavlength solutions. Calculate the best
wavelength solution for a given dataset and then apply that data set to the
image
return
"""
# set the basic values
set_w1 = (w1 is None)
set_w2 = (w2 is None)
set_dw = (dw is None)
set_nw = (nw is None)
# set up the time of the observation
dateobs = saltkey.get('DATE-OBS', hdu[0])
utctime = saltkey.get('TIME-OBS', hdu[0])
exptime = saltkey.get('EXPTIME', hdu[0])
instrume = saltkey.get('INSTRUME', hdu[0]).strip()
grating = saltkey.get('GRATING', hdu[0]).strip()
if caltype == 'line':
grang = saltkey.get('GRTILT', hdu[0])
arang = saltkey.get('CAMANG', hdu[0])
else:
grang = saltkey.get('GR-ANGLE', hdu[0])
arang = saltkey.get('AR-ANGLE', hdu[0])
filtername = saltkey.get('FILTER', hdu[0]).strip()
slitname = saltkey.get('MASKID', hdu[0])
slit = st.getslitsize(slitname)
xbin, ybin = saltkey.ccdbin(hdu[0])
timeobs = enterdatetime('%s %s' % (dateobs, utctime))
# check to see if there is more than one solution
if caltype == 'line':
if len(soldict) == 1:
sol = soldict.keys()[0]
slitid = None
if not matchobservations(
soldict[sol], instrume, grating, grang, arang, filtername, slitid):
msg = 'Observations do not match setup for transformation but using the solution anyway'
if log:
log.warning(msg)
for i in range(1, len(hdu)):
if hdu[i].name == 'SCI':
if log:
log.message('Correcting extension %i' % i)
istart = int(0.5 * len(hdu[i].data))
# open up the data
# set up the xarr and initial wavlength solution
xarr = np.arange(len(hdu[i].data[istart]), dtype='int64')
# get the slitid
try:
slitid = saltkey.get('SLITNAME', hdu[i])
except:
slitid = None
# set up a wavelength solution
try:
w_arr = findsol(xarr, soldict, istart, caltype, nearest, timeobs, exptime, instrume, grating, grang, arang, filtername,
slit, xbin, ybin, slitid, function, order)
except SALTSpecError as e:
if slitid:
msg = 'SLITID %s: %s' % (slitid, e)
if log:
log.warning(msg)
continue
else:
raise SALTSpecError(e)
if w_arr is None:
w_arr = findsol(xarr, soldict, istart, 'rss', nearest, timeobs, exptime, instrume, grating, grang, arang, filtername,
slit, xbin, ybin, slitid, function, order)
# set up the output x-axis
if set_w1:
w1 = w_arr.min()
if set_w2:
w2 = w_arr.max()
if set_nw:
nw = len(xarr)
if set_dw:
dw = float(w2 - w1) / nw
nw_arr = createoutputxaxis(w1, w2, nw)
# setup the VARIANCE and BPM frames
if saltkey.found('VAREXT', hdu[i]):
varext = saltkey.get('VAREXT', hdu[i])
else:
varext = None
# setup the BPM frames
if saltkey.found('BPMEXT', hdu[i]):
bpmext = saltkey.get('BPMEXT', hdu[i])
else:
bpmext = None
# for each line in the data, determine the wavelength solution
# for a given line in the image
for j in range(len(hdu[i].data)):
# find the wavelength solution for the data
w_arr = findsol(xarr, soldict, j, caltype, nearest, timeobs, exptime, instrume, grating, grang, arang, filtername,
slit, xbin, ybin, slitid, function, order)
# apply that wavelength solution to the data
if w_arr is not None:
try:
hdu[i].data[
j,
:] = st.interpolate(
nw_arr,
w_arr,
hdu[i].data[
j,
:],
inttype,
left=blank,
right=blank)
except Exception as e:
hdu[i].data[j, :] = hdu[i].data[j, :] * 0.0 + blank
msg = 'In row %i, solution cannot be found due to %s' % (
i, e)
# correct the variance frame
if varext:
try:
hdu[varext].data[
j,
:] = st.interpolate(
nw_arr,
w_arr,
hdu[varext].data[
j,
:],
inttype,
left=blank,
right=blank)
except Exception as e:
msg = 'In row %i, solution cannot be found due to %s' % (
i, e)
# correct the BPM frame
if bpmext:
try:
hdu[bpmext].data[
j,
:] = st.interpolate(
nw_arr,
w_arr,
hdu[bpmext].data[
j,
:],
inttype,
left=blank,
right=blank)
except Exception as e:
msg = 'In row %i, solution cannot be found due to %s' % (
i, e)
else:
hdu[i].data[j, :] = hdu[i].data[j, :] * 0.0 + blank
if conserve:
hdu[i].data = hdu[i].data / dw
if varext:
hdu[varext].data = hdu[varext].data / dw
# Add WCS information
saltkey.new('CTYPE1', 'LAMBDA', 'Coordinate Type', hdu[i])
saltkey.new('CTYPE2', 'PIXEL', 'Coordinate Type', hdu[i])
saltkey.new(
'CD1_1',
dw,
'WCS: Wavelength Dispersion in angstrom/pixel',
hdu[i])
saltkey.new('CD2_1', 0.0, 'WCS: ', hdu[i])
saltkey.new('CD1_2', 0.0, 'WCS: ', hdu[i])
saltkey.new('CD2_2', ybin * pixscale, 'WCS: ', hdu[i])
saltkey.new('CRPIX1', 0.0, 'WCS: X Reference pixel', hdu[i])
saltkey.new('CRPIX2', 0.0, 'WCS: Y Reference pixel', hdu[i])
saltkey.new('CRVAL1', w1, 'WCS: X Reference pixel', hdu[i])
saltkey.new('CRVAL2', 0.0, 'WCS: Y Reference pixel', hdu[i])
saltkey.new('CDELT1', 1.0, 'WCS: X pixel size', hdu[i])
saltkey.new('CDELT2', 1.0, 'WCS: Y pixel size', hdu[i])
saltkey.new('DC-FLAG', 0, 'Dispesion Corrected image', hdu[i])
return hdu
message = 'Unable to read in data array in %s because %s' % (img, e)
raise SALTSpecError(message)
#determine the type of first guess. Assumes none
if guesstype=='user':
pass
elif guesstype=='rss':
dateobs=saltsafekey.get('DATE-OBS', hdu[0], img)
utctime=saltsafekey.get('UTC-OBS', hdu[0], img)
instrume=saltsafekey.get('INSTRUME', hdu[0], img)
grating=saltsafekey.get('GRATING', hdu[0], img)
grang=saltsafekey.get('GR-ANGLE', hdu[0], img)
arang=saltsafekey.get('AR-ANGLE', hdu[0], img)
filter=saltsafekey.get('FILTER', hdu[0], img)
xbin, ybin = saltsafekey.ccdbin( hdu[0], img)
if not instrume in ['PFIS', 'RSS']:
msg='%s is not a currently supported instrument' % instrume
raise SALTSpecError(msg)
ws=useRSSModel(xarr, grating, grang, arang, xbin, function=function, order=order)
elif guesstype=='image':
pass
else:
ws=None
#if interactive is selected launch the tool in interactive mode
#if not, produce a wavelength solution from the information already
#provided.
xdiff=20
ws= findwavelengthsolution(xarr, specarr, slines, sfluxes, ws, xdiff, function,
order, zeropoint=False, interact=interact, verbose=verbose)
def specslit(image, outimage, outpref, exttype='auto', slitfile='', outputslitfile='',
regprefix='', sections=3, width=25, sigma=2.2, thres=6, order=3, padding=5, yoffset=0,
inter=False, clobber=True, logfile='salt.log', verbose=True):
with logging(logfile, debug) as log:
# check all the input and make sure that all the input needed is provided
# by the user
# read the image or image list and check if each in the list exist
infiles = saltio.argunpack('Input', image)
# unpack the outfiles
outfiles = saltio.listparse(
'Outimages',
outimage,
outpref,
infiles,
'')
# from the extraction type, check whether the input file is specified.
# if the slitfile parameter is specified then use the slit files for
# the extraction. if the extraction type is auto then use image for the
# detection and the slit extraction
if exttype == 'rsmt' or exttype == 'fits' or exttype == 'ascii' or exttype == 'ds9':
slitfiles = saltio.argunpack('Slitfile', slitfile)
if len(slitfiles) == 1:
slitfiles = slitfiles * len(infiles)
saltio.comparelists(infiles, slitfiles, 'image', 'slitfile')
elif exttype == 'auto':
slitfiles = infiles
log.message(
'Extraction type is AUTO. Slit detection will be done from image')
# read in if an optional ascii file is requested
if len(outputslitfile) > 0:
outslitfiles = saltio.argunpack('Outslitfiles', outputslitfile)
saltio.comparelists(
infiles,
outslitfiles,
'image',
'outputslitfile')
else:
outslitfiles = [''] * len(infiles)
# check if the width and sigma parameters were specified.
# default is 25 and 2.2
if width < 10.:
msg = 'The width parameter needs be a value larger than 10'
raise SALTSpecError(msg)
if sigma < 0.0:
msg = 'Sigma must be greater than zero'
raise SaltSpecError(msg)
# check the treshold parameter. this needs to be specified by the user
if thres <= 0.0:
msg = 'Threshold must be greater than zero'
raise SaltSpecError(msg)
# check to make sure that the sections are greater than the order
if sections <= order:
msg = 'Number of sections must be greater than the order for the spline fit'
raise SaltSpecError(msg)
# run through each of the images and extract the slits
for img, oimg, sfile, oslit in zip(
infiles, outfiles, slitfiles, outslitfiles):
log.message('Proccessing image %s' % img)
# open the image
struct = saltio.openfits(img)
ylen, xlen = struct[1].data.shape
xbin, ybin = saltkey.ccdbin(struct[0], img)
# setup the VARIANCE and BPM frames
if saltkey.found('VAREXT', struct[1]):
varext = saltkey.get('VAREXT', struct[1])
varlist = []
else:
varext = None
# setup the BPM frames
if saltkey.found('BPMEXT', struct[1]):
bpmext = saltkey.get('BPMEXT', struct[1])
bpmlist = []
else:
bpmext = None
# open the slit definition file or identify the slits in the image
slitmask = None
ycheck = False
if exttype == 'rsmt':
log.message('Using slits from %s' % sfile)
if yoffset is None:
yoffset = 0
ycheck = True
slitmask = mt.read_slitmask_from_xml(sfile)
xpos = -0.3066
ypos = 0.0117
cx = int(xlen / 2.0)
cy = int(ylen / 2.0) + ypos / 0.015 / ybin + yoffset
order, slit_positions = mt.convert_slits_from_mask(
slitmask, order=1, xbin=xbin, ybin=ybin, pix_scale=0.1267, cx=cx, cy=cy)
sections = 1
elif exttype == 'fits':
log.message('Using slits from %s' % sfile)
order, slit_positions = read_slits_from_fits(sfile)
elif exttype == 'ascii':
log.message('Using slits from %s' % sfile)
order, slit_positions = mt.read_slits_from_ascii(sfile)
elif exttype == 'ds9':
log.message('Using slits from %s' % sfile)
order, slit_positions, slitmask = mt.read_slits_from_ds9(
sfile, order=order)
slitmask = None
sections = 1
elif exttype == 'auto':
log.message('Identifying slits in %s' % img)
# identify the slits in the image
order, slit_positions = identify_slits(
struct[1].data, order, sections, width, sigma, thres)
# write out the slit identifications if ofile has been supplied
if oslit:
log.message('Writing slit positions to %s' % oslit)
mt.write_outputslitfile(slit_positions, oslit, order)
if ycheck:
slit_positions, dy = check_ypos(slit_positions, struct[1].data)
log.message('Using an offset of {}'.format(dy))
# extract the slits
spline_x = mt.divide_image(struct[1].data, sections)
spline_x = 0.5 * (np.array(spline_x[:-1]) + np.array(spline_x[1:]))
extracted_spectra, spline_positions = mt.extract_slits(slit_positions,
spline_x, struct[1].data, order=order, padding=padding)
if varext:
extracted_var, var_positions = mt.extract_slits(slit_positions,
spline_x, struct[varext].data, order=order, padding=padding)
if bpmext:
extracted_bpm, bpm_positions = mt.extract_slits(slit_positions,
spline_x, struct[bpmext].data, order=order, padding=padding)
# write out the data to the new array
# create the new file
hdulist = fits.HDUList([struct[0]])
# log the extracted spectra if needed
log.message('', with_stdout=verbose)
# setup output ds9 file
if regprefix:
regout = open(
regprefix +
os.path.basename(img).strip('.fits') +
'.reg',
'w')
regout.write('# Region file format: DS9 version 4.1\n')
regout.write('# Filename: %s\n' % img)
regout.write(
'global color=green dashlist=8 3 width=1 font="helvetica 10 normal roman" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 delete=1 include=1 source=1\nphysical\n')
# add each
imglist = []
nslits = len(spline_positions)
for i in range(nslits):
y1 = spline_positions[i][0].min()
y2 = spline_positions[i][1].max()
msg = 'Extracted Spectra %i between %i to %i' % (i + 1, y1, y2)
# log.message(msg, with_header=False, with_stdout=verbose)
sdu = fits.ImageHDU(
extracted_spectra[i],
header=struct[1].header)
if varext:
vdu = fits.ImageHDU(
extracted_var[i],
header=struct[varext].header)
sdu.header['VAREXT'] = i + nslits + 1
varlist.append(vdu)
if bpmext:
bdu = fits.ImageHDU(
extracted_bpm[i],
header=struct[bpmext].header)
sdu.header['BPMEXT']= i + 2 * nslits + 1
bpmlist.append(bdu)
imglist.append(sdu)
# add in some additional keywords
imglist[i].header['MINY'] = (y1,
'Lower Y value in original image')
imglist[i].header['MAXY'] = (y2,
'Upper Y value in original image')
if regprefix:
xsize = struct[1].data.shape[1]
xsize = int(0.5 * xsize)
rtext = ''
if slitmask:
# rtext='%s, %8.7f, %8.7f, %3.2f' % (slitmask.slitlets.data[i]['name'], slitmask.slitlets.data[i]['targ_ra'], slitmask.slitlets.data[i]['targ_dec'], slitmask.slitlets.data[i]['slit_width'])
pass
regout.write('box(%i,%i, %i, %i) #text={%s}\n' % (
xsize, 0.5 * (y1 + y2), 2 * xsize, y2 - y1, rtext))
# add slit information
if slitmask:
imglist[i].header['SLITNAME'] = (slitmask.slitlets.data[i]['name'],
'Slit Name')
imglist[i].header['SLIT_RA'] = (slitmask.slitlets.data[i]['targ_ra'],
'Slit RA')
imglist[i].header['SLIT_DEC'] = (slitmask.slitlets.data[i]['targ_dec'],
'Slit DEC')
imglist[i].header['SLIT'] = (slitmask.slitlets.data[i]['slit_width'],
'Slit Width')
# add to the hdulist
hdulist += imglist
if varext:
hdulist += varlist
if bpmext:
hdulist += bpmlist
# write the slit positions to the header
# create the binary table HDU that contains the split positions
tbhdu = mt.slits_HDUtable(slit_positions, order)
bintable_hdr = tbhdu.header
# add the extname parameter to the extension
tbhdu.header['EXTNAME'] = 'BINTABLE'
# add the extname parameter to the extension
hdulist[0].header['SLITEXT'] = len(hdulist)
hdulist.append(tbhdu)
# add addition header information about the mask
if slitmask:
hdulist[0].header['MASKNAME'] = (slitmask.mask_name, 'SlitMask Name')
hdulist[0].header['MASK_RA'] = (slitmask.center_ra,
'SlitMask RA')
hdulist[0].header['MASK_DEC'] = ( slitmask.center_dec,
'SlitMask DEC')
hdulist[0].header['MASK_PA'] = ( slitmask.position_angle,
'SlitMask Position Angle')
# write out the image
saltio.writefits(hdulist, oimg, clobber)
def prepare(hdu):
"""Prepare HRS data to be similar to other SALT file formats
This includes splitting each amplifier into multi-extension
formats
"""
#set the detector
detname=saltkey.get('DETNAM', hdu[0])
if detname=='08443-03-01' or detname=='HRDET':
detector='hrdet'
elif detname=='04434-23-02' or detname=='HBDET':
detector='hbdet'
else:
raise SaltError('%s is not an HRS detector' % detnam)
#get key parameters
try:
nccd=saltkey.get('CCDNAMPS', hdu[0])
except:
nccd=saltkey.get('CCDAMPS', hdu[0])
xbin, ybin=saltkey.ccdbin(hdu[0])
gain=saltkey.get('GAIN', hdu[0])
gain=gain.split()
rospeed=saltkey.get('ROSPEED', hdu[0])
ccdshape=hdu[0].data.shape
#create a multiexention fits file
phdu=pyfits.PrimaryHDU()
phdu.header=hdu[0].header
nhdu = pyfits.HDUList(phdu)
#these keywords need to be added
saltkey.new('DETMODE', value='Normal', comment='Detector mode', hdu=nhdu[0])
saltkey.new('NCCDS', value=1, comment='Detector mode', hdu=nhdu[0])
saltkey.new('GAINSET', value='SLOW', comment='Detector mode', hdu=nhdu[0])
value='OBJECT'
if hdu[0].header['OBJECT']=='Bias': value='ZERO'
if hdu[0].header['OBJECT']=='Arc': value='ARC'
if hdu[0].header['OBJECT']=='Flat': value='FLAT'
saltkey.new('CCDTYPE', value=value, comment='CCD Type', hdu=nhdu[0])
if nccd==1:
nhdu.append(pyfits.ImageHDU(hdu[0].data))
j=1
saltkey.new('GAIN', value=float(gain[0]), comment='Nominal CCD gain (e/ADU)', hdu=nhdu[j])
saltkey.new('RDNOISE', value=0, comment='Nominal readout noise in e', hdu=nhdu[j])
saltkey.new('SATURATE', value=1, comment='Pixel saturation level in ADU', hdu=nhdu[j])
saltkey.new('XTALK', value=0.0, comment='Cross talk coefficient', hdu=nhdu[j])
detsize=getdetsize(ccdshape, xbin, ybin)
ampshape=nhdu[j].data.shape
ampsec=getdetsize(ccdshape, 1, 1)
saltkey.new('DETSIZE', value=detsize, comment='Detector size', hdu=nhdu[j])
saltkey.new('BIASSEC', value=getbiassec(j, ampshape, xbin, ybin), comment='Bias section', hdu=nhdu[j])
saltkey.new('DATASEC', value=getdatasec(j, ampshape, xbin, ybin), comment='Data section', hdu=nhdu[j])
saltkey.new('AMPSEC', value=ampsec, comment='Amplifier section', hdu=nhdu[j])
saltkey.new('CCDSEC', value=detsize, comment='CCD section', hdu=nhdu[j])
saltkey.new('DETSEC', value=detsize, comment='Detector section', hdu=nhdu[j])
return nhdu
for i in range(nccd):
j=i+1
x1,x2,y1,y2=definesection(j, detector, ccdshape)
nhdu.append(pyfits.ImageHDU(hdu[0].data[y1:y2,x1:x2]))
#keywords that need to be added include
#gain, rdnoise, xtalk, saturate,
saltkey.new('GAIN', value=float(gain[i]), comment='Nominal CCD gain (e/ADU)', hdu=nhdu[j])
saltkey.new('RDNOISE', value=0, comment='Nominal readout noise in e', hdu=nhdu[j])
saltkey.new('SATURATE', value=1, comment='Pixel saturation level in ADU', hdu=nhdu[j])
saltkey.new('XTALK', value=0.0, comment='Cross talk coefficient', hdu=nhdu[j])
#DETSIZE, BIASSEC, DATASEC,
#AMPSEC, CCDSEC, DETSEC
ampsec='[%i:%i,%i:%i]' % (x1+1,x2,y1+1,y2)
ampshape=nhdu[j].data.shape
saltkey.new('DETSIZE', value=getdetsize(ccdshape, xbin, ybin), comment='Detector size', hdu=nhdu[j])
saltkey.new('BIASSEC', value=getbiassec(j, ampshape, xbin, ybin), comment='Bias section', hdu=nhdu[j])
saltkey.new('DATASEC', value=getdatasec(j, ampshape, xbin, ybin), comment='Data section', hdu=nhdu[j])
saltkey.new('AMPSEC', value=ampsec, comment='Amplifier section', hdu=nhdu[j])
saltkey.new('CCDSEC', value=getdetsize(ccdshape, xbin, ybin), comment='CCD section', hdu=nhdu[j])
saltkey.new('DETSEC', value=getdetsize(ccdshape, xbin, ybin), comment='Detector section', hdu=nhdu[j])
#BSCALE, BZERO
#nhdu[j].header.set('BSCALE', value=1.0, comment='Val=BSCALE*pix+BZERO')
#nhdu[j].header.set('BZERO', value=32768., comment='Val=BSCALE*pix+BZERO')
#ATM1_1, ATM2_2,
#not implimented
return nhdu