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 extract(hdu, ext=1, method='normal', section=[],
minsize=3.0, thresh=3.0, convert=True):
"""For a given image, extract a 1D spectra from the image
and write the spectra to the output file
"""
ap_list = []
i = ext
if hdu[i].name == 'SCI':
# set up the data, variance, and bad pixel frames
# first step is to find the region to extract
data_arr = hdu[i].data
try:
var_arr = hdu[hdu[i].header['VAREXT']].data
except:
var_arr = None
try:
bpm_arr = hdu[hdu[i].header['BPMEXT']].data
except:
bpm_arr = None
var_arr = None
bpm_arr = None
xarr = np.arange(len(data_arr[0]))
# convert using the WCS information
try:
w0 = hdu[i].header['CRVAL1']
dw = hdu[i].header['CD1_1']
except Exception as e:
msg = 'Error on Ext %i: %s' % (i, e)
raise Exception(msg)
warr = w0 + dw * xarr
# convert from air to vacuum
if convert:
warr = Spectrum.air2vac(warr)
# set up the sections in case of findobj
if section is None:
section = findobj.findObjects(
data_arr,
method='median',
specaxis=1,
minsize=minsize,
thresh=thresh,
niter=5)
# extract all of the regions
for sec in section:
ap = apext.apext(warr, data_arr, ivar=var_arr)
y1, y2 = sec
ap.flatten(y1, y2)
ap_list.append(ap)
return ap_list
def extract(hdu,
ext=1,
method='normal',
section=[],
minsize=3.0,
thresh=3.0,
convert=True):
"""For a given image, extract a 1D spectra from the image
and write the spectra to the output file
"""
ap_list = []
i = ext
if hdu[i].name == 'SCI':
# set up the data, variance, and bad pixel frames
# first step is to find the region to extract
data_arr = hdu[i].data
try:
var_arr = hdu[hdu[i].header['VAREXT']].data
except:
var_arr = None
try:
bpm_arr = hdu[hdu[i].header['BPMEXT']].data
except:
bpm_arr = None
var_arr = None
bpm_arr = None
xarr = np.arange(len(data_arr[0]))
# convert using the WCS information
try:
w0 = saltkey.get('CRVAL1', hdu[i])
dw = saltkey.get('CD1_1', hdu[i])
except Exception as e:
msg = 'Error on Ext %i: %s' % (i, e)
raise SALTSpecError(msg)
warr = w0 + dw * xarr
# convert from air to vacuum
if convert:
warr = Spectrum.air2vac(warr)
# set up the sections in case of findobj
if section is None:
section = findobj.findObjects(data_arr,
method='median',
specaxis=1,
minsize=minsize,
thresh=thresh,
niter=5)
# extract all of the regions
for sec in section:
ap = apext.apext(warr, data_arr, ivar=var_arr)
y1, y2 = sec
ap.flatten(y1, y2)
ap_list.append(ap)
return ap_list
def extract_spectra(img, yc=None, oy=10, dy=50, minsize=5, thresh=3, findobject=False,
niter=5, calfile=None, smooth=False, maskzeros=False, 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
#skynormalize the data
#specslitnormalize(img, 'n'+img, '', response=None, response_output=None, order=3, conv=1e-2, niter=20,
# startext=0, clobber=False,logfile='salt.log',verbose=True)
print 'Extract Spectra from ', img
hdu=pyfits.open(img)
target=hdu[0].header['OBJECT'].replace(' ', '')
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=mean np.random.normal(mean, std, size=data.shape)
print mean, std
data[data<=0]=mean #rdata[data<=0]
#use manual intervention to get section
if findobject:
section=findobj.findObjects(data, method='median', specaxis=1, minsize=minsize, thresh=thresh, niter=niter)
if yc is None and len(section)>0:
yc = np.mean(section[0])
elif len(section)>0:
diff = 1e6
for i in range(len(section)):
y = np.mean(section[i])
if abs(y-yc) < diff:
bestyc = y
diff = abs(y-yc)
yc = bestyc
if yc is None:
os.system('ds9 %s &' % img)
print len(hdu)
if len(hdu)==2:
print 'Using basic extraction'
if yc is None:
y1=int(raw_input('y1:'))
y2=int(raw_input('y2:'))
sy1=int(raw_input('sky y1:'))
sy2=int(raw_input('sky y2:'))
ap_list=extract(hdu, method='normal', section=[(y1,y2)], minsize=minsize, thresh=thresh, convert=True)
sk_list=extract(hdu, method='normal', section=[(sy1,sy2)], minsize=minsize, thresh=thresh, convert=True)
ap_list[0].ldata=ap_list[0].ldata-float(y2-y1)/(sy2-sy1)*sk_list[0].ldata
ofile='%s.%s_%i.txt' % (target, extract_date(img), extract_number(img))
write_extract(ofile, [ap_list[0]], outformat='ascii', clobber=clobber)
w, f, e = np.loadtxt(ofile, usecols=(0,1,2), unpack=True)
w, f, e=cleanspectra(w, f, e, neg=True)
m = (w>3900)*(w<8100)
write_spectra(ofile, w[m], f[m], e[m])
else:
print 'Using advanced extraction'
if yc is None: yc=int(raw_input('yc:'))
w0=hdu[1].header['CRVAL1']
dw=hdu[1].header['CD1_1']
xarr = np.arange(len(hdu[1].data[0]))
warr=w0+dw*xarr
print warr.min(), warr.max()
print hdu[1].data[yc, 1462], hdu[2].data[yc,1462]
warr, madata, var = skysub_region(warr, hdu[1].data, hdu[2].data, hdu[3].data, yc, oy, dy)
print warr.min(), warr.max()
w, f, e = masked_extract(warr, madata[yc-oy:yc+oy, :], var[yc-oy:yc+oy, :])
print yc
ofile='%s.%s_%i_%i.txt' % (target, extract_date(img), extract_number(img), yc)
write_spectra(ofile, w, f, e)
if calfile is not None:
extfile=iraf.osfn("pysalt$data/site/suth_extinct.dat")
speccal(ofile, ofile.replace("txt", "spec"), calfile, extfile, airmass, exptime, clobber=True, logfile='salt.log', verbose=True)
spec_list=[ofile, airmass, exptime, propcode]
return spec_list
def extract_spectra(img,
yc=None,
oy=10,
dy=50,
minsize=5,
thresh=3,
findobject=False,
niter=5,
calfile=None,
smooth=False,
maskzeros=False,
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
#skynormalize the data
#specslitnormalize(img, 'n'+img, '', response=None, response_output=None, order=3, conv=1e-2, niter=20,
# startext=0, clobber=False,logfile='salt.log',verbose=True)
print 'Extract Spectra from ', img
hdu = pyfits.open(img)
target = hdu[0].header['OBJECT'].replace(' ', '')
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=mean np.random.normal(mean, std, size=data.shape)
print mean, std
data[data <= 0] = mean #rdata[data<=0]
#use manual intervention to get section
if findobject:
section = findobj.findObjects(data,
method='median',
specaxis=1,
minsize=minsize,
thresh=thresh,
niter=niter)
if yc is None and len(section) > 0:
yc = np.mean(section[0])
elif len(section) > 0:
diff = 1e6
for i in range(len(section)):
y = np.mean(section[i])
if abs(y - yc) < diff:
bestyc = y
diff = abs(y - yc)
yc = bestyc
if yc is None:
os.system('ds9 %s &' % img)
print len(hdu)
if len(hdu) == 2:
print 'Using basic extraction'
if yc is None:
y1 = int(raw_input('y1:'))
y2 = int(raw_input('y2:'))
sy1 = int(raw_input('sky y1:'))
sy2 = int(raw_input('sky y2:'))
ap_list = extract(hdu,
method='normal',
section=[(y1, y2)],
minsize=minsize,
thresh=thresh,
convert=True)
sk_list = extract(hdu,
method='normal',
section=[(sy1, sy2)],
minsize=minsize,
thresh=thresh,
convert=True)
ap_list[0].ldata = ap_list[0].ldata - float(y2 - y1) / (
sy2 - sy1) * sk_list[0].ldata
ofile = '%s.%s_%i.txt' % (target, extract_date(img),
extract_number(img))
write_extract(ofile, [ap_list[0]], outformat='ascii', clobber=clobber)
w, f, e = np.loadtxt(ofile, usecols=(0, 1, 2), unpack=True)
w, f, e = cleanspectra(w, f, e, neg=True)
m = (w > 3900) * (w < 8100)
write_spectra(ofile, w[m], f[m], e[m])
else:
print 'Using advanced extraction'
if yc is None: yc = int(raw_input('yc:'))
w0 = hdu[1].header['CRVAL1']
dw = hdu[1].header['CD1_1']
xarr = np.arange(len(hdu[1].data[0]))
warr = w0 + dw * xarr
print warr.min(), warr.max()
print hdu[1].data[yc, 1462], hdu[2].data[yc, 1462]
warr, madata, var = skysub_region(warr, hdu[1].data, hdu[2].data,
hdu[3].data, yc, oy, dy)
print warr.min(), warr.max()
w, f, e = masked_extract(warr, madata[yc - oy:yc + oy, :],
var[yc - oy:yc + oy, :])
print yc
ofile = '%s.%s_%i_%i.txt' % (target, extract_date(img),
extract_number(img), yc)
write_spectra(ofile, w, f, e)
if calfile is not None:
extfile = iraf.osfn("pysalt$data/site/suth_extinct.dat")
speccal(ofile,
ofile.replace("txt", "spec"),
calfile,
extfile,
airmass,
exptime,
clobber=True,
logfile='salt.log',
verbose=True)
spec_list = [ofile, airmass, exptime, propcode]
return spec_list