def createspectra(img, obsdate, minsize=5, thresh=3, skysection=[800,1000], smooth=False, maskzeros=True, clobber=True):
"""Create a list of spectra for each of the objects in the images"""
#okay we need to identify the objects for extraction and identify the regions for sky extraction
#first find the objects in the image
hdu=pyfits.open(img)
target=hdu[0].header['OBJECT']
propcode=hdu[0].header['PROPID']
airmass=hdu[0].header['AIRMASS']
exptime=hdu[0].header['EXPTIME']
if smooth:
data=smooth_data(hdu[1].data)
else:
data=hdu[1].data
#replace the zeros with the average from the frame
if maskzeros:
mean,std=iterstat(data[data>0])
rdata=np.random.normal(mean, std, size=data.shape)
print mean, std
data[data<=0]=rdata[data<=0]
#find the sections in the images
section=findobj.findObjects(data, method='median', specaxis=1, minsize=minsize, thresh=thresh, niter=5)
print section
#use a region near the center to create they sky
skysection=findskysection(section, skysection)
print skysection
#sky subtract the frames
shdu=skysubtract(hdu, method='normal', section=skysection)
if os.path.isfile('s'+img): os.remove('s'+img)
shdu.writeto('s'+img)
spec_list=[]
#extract the spectra
#extract the comparison spectrum
section=findobj.findObjects(shdu[1].data, method='median', specaxis=1, minsize=minsize, thresh=thresh, niter=5)
print section
for j in range(len(section)):
ap_list=extract(shdu, method='normal', section=[section[j]], minsize=minsize, thresh=thresh, convert=True)
ofile='%s.%s_%i_%i.txt' % (target, obsdate, extract_number(img), j)
write_extract(ofile, [ap_list[0]], outformat='ascii', clobber=clobber)
spec_list.append([ofile, airmass, exptime, propcode])
return spec_list
def specreduce(images, badpixelimage=None, caltype='rss',
function='polynomial', order=3,
skysub=True, skysection=None, findobj=False,
objsection=None, thresh=3.0,
clobber=True, logfile='salt.log', verbose=True):
with logging(logfile, debug) as log:
# Check the input images
infiles = saltio.argunpack('Input', images)
# open the badpixelstruct
if saltio.checkfornone(badpixelimage):
badpixelstruct = saltio.openfits(badpixelimage)
else:
badpixelstruct = None
# set up the section for sky estimate
if skysection is not None:
skysection = makesection(skysection)
else:
skysub = False
# set up the section for sky estimate
section = saltio.checkfornone(objsection)
if section is not None:
sections = saltio.getSection(section, iraf_format=False)
objsection = []
for i in range(0, len(sections), 2):
objsection.append((sections[i], sections[i + 1]))
# determine the wavelength solutions
if caltype == 'line':
calc_wavesol(infiles)
# correct the images
for img in infiles:
# open the fits file
struct = saltio.openfits(img)
# prepare filep
log.message('Preparing %s' % img)
struct = prepare(struct, badpixelstruct)
# rectify the spectrum
log.message('Rectifying %s using %s' % (img, caltype))
struct = rectify(
struct,
None,
caltype=caltype,
function=function,
order=order)
# sky subtract the spectrum
# assumes the data is long slit and in the middle of the field
if skysub:
log.message('Subtracting the sky from %s' % img)
struct = skysubtract(
struct,
method='normal',
section=skysection)
# extract the spectrum
log.message('Extracting the spectrum from %s' % img)
print objsection
aplist = extract(
struct,
method='normal',
section=objsection,
thresh=thresh)
oimg = os.path.dirname(
os.path.abspath(img)) + '/s' + os.path.basename(img.strip())
ofile = oimg[:-5] + '.txt'
write_extract(ofile, aplist, clobber=clobber)
# write FITS file
log.message('Writing 2-D corrected image as %s' % oimg)
saltio.writefits(struct, oimg, clobber=clobber)
saltio.closefits(struct)
def specreduce(
images,
badpixelimage=None,
caltype="rss",
function="polynomial",
order=3,
skysub=True,
skysection=None,
findobj=False,
objsection=None,
thresh=3.0,
clobber=True,
logfile="salt.log",
verbose=True,
):
with logging(logfile, debug) as log:
# Check the input images
infiles = saltio.argunpack("Input", images)
# open the badpixelstruct
if saltio.checkfornone(badpixelimage):
badpixelstruct = saltio.openfits(badpixelimage)
else:
badpixelstruct = None
# set up the section for sky estimate
if skysection is not None:
skysection = makesection(skysection)
else:
skysub = False
# set up the section for sky estimate
section = saltio.checkfornone(objsection)
if section is not None:
sections = saltio.getSection(section, iraf_format=False)
objsection = []
for i in range(0, len(sections), 2):
objsection.append((sections[i], sections[i + 1]))
# determine the wavelength solutions
if caltype == "line":
calc_wavesol(infiles)
# correct the images
for img in infiles:
# open the fits file
struct = saltio.openfits(img)
# prepare filep
log.message("Preparing %s" % img)
struct = prepare(struct, badpixelstruct)
# rectify the spectrum
log.message("Rectifying %s using %s" % (img, caltype))
struct = rectify(struct, None, caltype=caltype, function=function, order=order)
# sky subtract the spectrum
# assumes the data is long slit and in the middle of the field
if skysub:
log.message("Subtracting the sky from %s" % img)
struct = skysubtract(struct, method="normal", section=skysection)
# extract the spectrum
log.message("Extracting the spectrum from %s" % img)
print objsection
aplist = extract(struct, method="normal", section=objsection, thresh=thresh)
oimg = os.path.dirname(os.path.abspath(img)) + "/s" + os.path.basename(img.strip())
ofile = oimg[:-5] + ".txt"
write_extract(ofile, aplist, clobber=clobber)
# write FITS file
log.message("Writing 2-D corrected image as %s" % oimg)
saltio.writefits(struct, oimg, clobber=clobber)
saltio.closefits(struct)