def extract_spectra(img,
minsize=5,
thresh=3,
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
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]
#use manual intervention to get section
os.system('ds9 %s &' % img)
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)
cleanspectra(ofile)
spec_list = [ofile, airmass, exptime, propcode]
return spec_list
def extract_spectra(hdu, yc, dy, outfile, minsize=5, thresh=3, grow=0, smooth=False, maskzeros=False, convert=True, cleanspectra=True, clobber=True, specformat='ascii'):
"""From an image, extract a spectra.
"""
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)
data[data<=0]=mean #rdata[data<=0]
y1=yc-dy
y2=yc+dy
#sy1=y2-2*dy
#sy2=y2+2*dy
#sdata = 1.0 * data
#y,x = np.indices(sdata.shape)
#for i in range(sdata.shape[1]):
# mask=(hdu[3].data[:,i]==0)
# mask[sy1:sy2] = 0
# if mask.sum()>0:
# sdata[y1:y2,i] = np.interp(y[y1:y2,i], y[:,i][mask], data[:,i][mask])
#hdu[1].data = sdata
#sk_list=extract(hdu, method='normal', section=[(y1,y2)], minsize=minsize, thresh=thresh, convert=True)
#ap_list[0].ldata=ap_list[0].ldata-sk_list[0].ldata
#ap_list[0].ldata=ap_list[0].ldata-float(y2-y1)/(sy2-sy1)*sk_list[0].ldata
convert=True
ap_list=extract(hdu, method='normal', section=[(y1,y2)], minsize=minsize, thresh=thresh, convert=convert)
sy1a=y2
sy2a=sy1a+2.0*dy
ska_list=extract(hdu, method='normal', section=[(sy1a,sy2a)], minsize=minsize, thresh=thresh, convert=convert)
sy2b=y1-dy
sy1b=sy2b-2.0*dy
skb_list=extract(hdu, method='normal', section=[(sy1b,sy2b)], minsize=minsize, thresh=thresh, convert=convert)
print sy1b, sy2b
sdata = 0.5*(ska_list[0].ldata/(sy2a-sy1a) + skb_list[0].ldata/(sy2b-sy1b))
#sdata = ska_list[0].ldata/(sy2a-sy1a)
#sdata = skb_list[0].ldata/(sy2b-sy1b)
ap_list[0].ldata=ap_list[0].ldata-float(y2-y1) * sdata
if cleanspectra:
clean_spectra(ap_list[0], grow=grow)
if specformat == 'ascii':
write_extract(outfile, [ap_list[0]], outformat='ascii', clobber=clobber)
elif specformat == 'lcogt':
write_lcogt(outfile, ap_list[0], hdu, sky=float(y2-y1) * sdata, clobber=clobber)
def extract_spectra(img, minsize=5, thresh=3, 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
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]
#use manual intervention to get section
os.system('ds9 %s &' % img)
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)
cleanspectra(ofile)
spec_list=[ofile, airmass, exptime, propcode]
return spec_list
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_spectra(hdu, yc, dy, outfile, minsize=5, thresh=3, grow=0, smooth=False, maskzeros=False,
convert=True, cleanspectra=True, calfile=None, clobber=True, specformat='ascii'):
"""From an image, extract a spectra.
"""
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)
data[data<=0]=mean #rdata[data<=0]
y1=yc-dy
y2=yc+dy
#sy1=y2-2*dy
#sy2=y2+2*dy
#sdata = 1.0 * data
#y,x = np.indices(sdata.shape)
#for i in range(sdata.shape[1]):
# mask=(hdu[3].data[:,i]==0)
# mask[sy1:sy2] = 0
# if mask.sum()>0:
# sdata[y1:y2,i] = np.interp(y[y1:y2,i], y[:,i][mask], data[:,i][mask])
#hdu[1].data = sdata
#sk_list=extract(hdu, method='normal', section=[(y1,y2)], minsize=minsize, thresh=thresh, convert=True)
#ap_list[0].ldata=ap_list[0].ldata-sk_list[0].ldata
#ap_list[0].ldata=ap_list[0].ldata-float(y2-y1)/(sy2-sy1)*sk_list[0].ldata
ap_list=extract(hdu, method='normal', section=[(y1,y2)], minsize=minsize, thresh=thresh, convert=convert)
sy1a=y2
sy2a=sy1a+2.0*dy
ska_list=extract(hdu, method='normal', section=[(sy1a,sy2a)], minsize=minsize, thresh=thresh, convert=convert)
sy2b=y1-dy
sy1b=sy2b-2.0*dy
skb_list=extract(hdu, method='normal', section=[(sy1b,sy2b)], minsize=minsize, thresh=thresh, convert=convert)
print sy1b, sy2b
sdata = 0.5*(ska_list[0].ldata/(sy2a-sy1a) + skb_list[0].ldata/(sy2b-sy1b))
#sdata = ska_list[0].ldata/(sy2a-sy1a)
#sdata = skb_list[0].ldata/(sy2b-sy1b)
raw = 1.0 * ap_list[0].ldata
ap_list[0].ldata=ap_list[0].ldata-float(y2-y1) * sdata
print ap_list[0].wave[10], ap_list[0].ldata[10], ap_list[0].lvar[10]
flux_spec=Spectrum.Spectrum(ap_list[0].wave, ap_list[0].ldata, abs(ap_list[0].lvar)**0.5, stype='continuum')
print flux_spec.wavelength[10], flux_spec.flux[10], flux_spec.var[10]
if cleanspectra:
clean_spectra(ap_list[0], grow=grow)
if calfile:
cal_spectra=st.readspectrum(calfile, error=False, ftype='ascii')
airmass=hdu[0].header['AIRMASS']
exptime=hdu[0].header['EXPTIME']
extfile=iraf.osfn("pysalt$data/site/suth_extinct.dat")
ext_spectra=st.readspectrum(extfile, error=False, ftype='ascii')
flux_spec=Spectrum.Spectrum(ap_list[0].wave, ap_list[0].ldata, abs(ap_list[0].lvar)**0.5, stype='continuum')
print flux_spec.flux[10], flux_spec.var[10]
flux_spec=calfunc(flux_spec, cal_spectra, ext_spectra, airmass, exptime, True)
print flux_spec.flux[10], flux_spec.var[10]
else:
flux_spec = Spectrum.Spectrum(ap_list[0].wave, ap_list[0].ldata, abs(ap_list[0].lvar)**0.5, stype='continuum')
if specformat == 'ascii':
write_ascii(outfile, flux_spec, clobber=clobber)
elif specformat == 'lcogt':
write_lcogt(outfile, flux_spec, hdu, sky=float(y2-y1) * sdata, raw = raw, clobber=clobber)
def quickap(profile, lampid=None, findobj=False, objsection=None, skysection=None, clobber=False, logfile='saltclean.log', verbose=True):
profile = os.path.basename(profile)
outfile = profile.replace('fits', 'txt')
#open the file
struct=pyfits.open(profile)
data=struct[1].data
xlen=len(struct[1].data[0])
ylen=len(struct[1].data)
#determine the resolution element
dres = 1.0*calc_resolution(struct)
#correct for the response function
response = create_response(data, spatial_axis=1, order=3, conv=1e-2, niter=10)
data = data / response
if objsection is None:
y1=0.5*ylen-0.05*ylen
y2=0.5*ylen+0.05*ylen
objsection='[%i:%i]' % (y1,y2)
else:
y1=int(objsection[1:].split(':')[0])
y2=int(objsection[:-1].split(':')[1])
#extract object
minsize=5
thresh=5
ap_spec=extract(struct, method='normal', section=[(y1,y2)], minsize=minsize, thresh=thresh, convert=True)[0]
#if it is a lamp, do not sky subtract it
if saltsafeio.checkfornone(lampid):
write_extract(outfile, [ap_spec], outformat='ascii', clobber=clobber)
return y1,y2
if skysection is None:
skysection='%i:%i' % (y2+0.1*ylen,y2+0.2*ylen)
#sky spectrum
sy1, sy2 = skysection.split(':')
sy1=int(sy1)
sy2=int(sy2)
sk_spec=extract(struct, method='normal', section=[(sy1,sy2)], minsize=minsize, thresh=thresh, convert=True)[0]
sk_spec.ldata - sk_spec.ldata
w1=ap_spec.wave.min()
w2=ap_spec.wave.max()
nsect=10
dw=(w2-w1)/nsect
for w in np.arange(w1,w2,dw):
mask=(ap_spec.wave>w)*(ap_spec.wave<w+dw)
sk_spec=quickcross(ap_spec, sk_spec, mask=mask, dx=0.02, nstep=500)
ap_spec.ldata[mask]=ap_spec.ldata[mask]-float(y2-y1)/(sy2-sy1)*sk_spec.ldata[mask]
#set up masks for sky subtraction
amask = (ap_spec.ldata==0)
smask = (sk_spec.ldata==0)
#find offset between two spectra
d1 = ap_spec.ldata
d2 = sk_spec.ldata*float(y2-y1)/(sy2-sy1)
y=d1[d2>0]/d2[d2>0]
y=np.median(y)
#subtract the skyspectrum
clean_spectra(ap_spec, dres=2*dres, grow=dres)
#pl.plot(ap_spec.wave, ap_spec.ldata, ls='', marker='o', ms=2)
#pl.show()
#write it out and return
write_extract(outfile, [ap_spec], outformat='ascii', clobber=clobber)
#clean up the data
if clobber:
print 'Replacing pixels'
os.remove(profile)
struct[1].data[data==0]=np.median(data)
struct.writeto(profile)
return y1,y2
def extract_spectra(hdu,
yc,
dy,
outfile,
minsize=5,
thresh=3,
grow=0,
smooth=False,
maskzeros=False,
convert=True,
cleanspectra=True,
clobber=True,
specformat='ascii'):
"""From an image, extract a spectra.
"""
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)
data[data <= 0] = mean #rdata[data<=0]
y1 = yc - dy
y2 = yc + dy
#sy1=y2-2*dy
#sy2=y2+2*dy
#sdata = 1.0 * data
#y,x = np.indices(sdata.shape)
#for i in range(sdata.shape[1]):
# mask=(hdu[3].data[:,i]==0)
# mask[sy1:sy2] = 0
# if mask.sum()>0:
# sdata[y1:y2,i] = np.interp(y[y1:y2,i], y[:,i][mask], data[:,i][mask])
#hdu[1].data = sdata
#sk_list=extract(hdu, method='normal', section=[(y1,y2)], minsize=minsize, thresh=thresh, convert=True)
#ap_list[0].ldata=ap_list[0].ldata-sk_list[0].ldata
#ap_list[0].ldata=ap_list[0].ldata-float(y2-y1)/(sy2-sy1)*sk_list[0].ldata
convert = True
ap_list = extract(hdu,
method='normal',
section=[(y1, y2)],
minsize=minsize,
thresh=thresh,
convert=convert)
sy1a = y2
sy2a = sy1a + 2.0 * dy
ska_list = extract(hdu,
method='normal',
section=[(sy1a, sy2a)],
minsize=minsize,
thresh=thresh,
convert=convert)
sy2b = y1 - dy
sy1b = sy2b - 2.0 * dy
skb_list = extract(hdu,
method='normal',
section=[(sy1b, sy2b)],
minsize=minsize,
thresh=thresh,
convert=convert)
print sy1b, sy2b
sdata = 0.5 * (ska_list[0].ldata / (sy2a - sy1a) + skb_list[0].ldata /
(sy2b - sy1b))
#sdata = ska_list[0].ldata/(sy2a-sy1a)
#sdata = skb_list[0].ldata/(sy2b-sy1b)
ap_list[0].ldata = ap_list[0].ldata - float(y2 - y1) * sdata
if cleanspectra:
clean_spectra(ap_list[0], grow=grow)
if specformat == 'ascii':
write_extract(outfile, [ap_list[0]],
outformat='ascii',
clobber=clobber)
elif specformat == 'lcogt':
write_lcogt(outfile,
ap_list[0],
hdu,
sky=float(y2 - y1) * sdata,
clobber=clobber)
def quickap(profile, lampid=None, findobj=False, objsection=None, skysection=None, clobber=False, logfile='saltclean.log', verbose=True):
profile = os.path.basename(profile)
outfile = profile.replace('fits', 'txt')
#open the file
struct=pyfits.open(profile)
data=struct[1].data
xlen=len(struct[1].data[0])
ylen=len(struct[1].data)
#determine the resolution element
dres = 1.0*calc_resolution(struct)
#correct for the response function
response = create_response(data, spatial_axis=1, order=3, conv=1e-2, niter=10)
data = data / response
if objsection is None:
y1=0.5*ylen-0.05*ylen
y2=0.5*ylen+0.05*ylen
objsection='[%i:%i]' % (y1,y2)
else:
y1=int(objsection[1:].split(':')[0])
y2=int(objsection[:-1].split(':')[1])
#extract object
minsize=5
thresh=5
ap_spec=extract(struct, method='normal', section=[(y1,y2)], minsize=minsize, thresh=thresh, convert=True)[0]
#if it is a lamp, do not sky subtract it
if saltsafeio.checkfornone(lampid):
write_extract(outfile, [ap_spec], outformat='ascii', clobber=clobber)
return y1,y2
if skysection is None:
skysection='%i:%i' % (y2+0.1*ylen,y2+0.2*ylen)
#sky spectrum
sy1, sy2 = skysection.split(':')
sy1=int(sy1)
sy2=int(sy2)
sk_spec=extract(struct, method='normal', section=[(sy1,sy2)], minsize=minsize, thresh=thresh, convert=True)[0]
sk_spec.ldata - sk_spec.ldata
w1=ap_spec.wave.min()
w2=ap_spec.wave.max()
nsect=10
dw=(w2-w1)/nsect
for w in np.arange(w1,w2,dw):
mask=(ap_spec.wave>w)*(ap_spec.wave<w+dw)
sk_spec=quickcross(ap_spec, sk_spec, mask=mask, dx=0.02, nstep=500)
ap_spec.ldata[mask]=ap_spec.ldata[mask]-float(y2-y1)/(sy2-sy1)*sk_spec.ldata[mask]
#set up masks for sky subtraction
amask = (ap_spec.ldata==0)
smask = (sk_spec.ldata==0)
#find offset between two spectra
d1 = ap_spec.ldata
d2 = sk_spec.ldata*float(y2-y1)/(sy2-sy1)
y=d1[d2>0]/d2[d2>0]
y=np.median(y)
#subtract the skyspectrum
clean_spectra(ap_spec, dres=2*dres, grow=dres)
#pl.plot(ap_spec.wave, ap_spec.ldata, ls='', marker='o', ms=2)
#pl.show()
#write it out and return
write_extract(outfile, [ap_spec], outformat='ascii', clobber=clobber)
#clean up the data
if clobber:
print 'Replacing pixels'
os.remove(profile)
struct[1].data[data==0]=np.median(data)
struct.writeto(profile)
return y1,y2
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 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)
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