def mjd_fromheader(h):
"""
return modified Julian date from header
"""
datetu = h[0].header['DATE-OBS']
if len(datetu) < 23:
mjd_start = h[0].header['MJD-OBS']
mjd0 = 2400000.5
else:
#print datetu
mjd0, mjd, i = GLOBALutils.iau_cal2jd(int(datetu[:4]),
int(datetu[5:7]),
int(datetu[8:10]))
ho = int(datetu[11:13])
mi = int(datetu[14:16])
se = float(datetu[17:])
ut = float(ho) + float(mi) / 60.0 + float(se) / 3600.0
mjd_start = mjd + ut / 24.0
secinday = 24 * 3600.0
fraction = 0.5
texp = h[0].header['EXPTIME'] #sec
mjd = mjd_start + (fraction * texp) / secinday
return mjd, mjd0
def mjd_fromheader2(h):
"""
return modified Julian date from header
"""
rtime = 90.
secinday = 24 * 3600.0
date = h[0].header['DATE']
hour = date[11:]
date = date[:10]
datetu = date.replace('-', ':')
ut = hour.replace('-', ':')
datetu = date.replace(' ', '')
ut = hour.replace(' ', '')
#print datetu
#print ut
mjd0, mjd, i = GLOBALutils.iau_cal2jd(int(datetu[0:4]), int(datetu[5:7]),
int(datetu[8:10]))
ut = (float(ut[:2]) * 3600. + float(ut[3:5]) * 60. + float(ut[6:]))
mjd_start = mjd + ut / secinday
fraction = 0.5
texp = float(h[0].header['EXPTIME']) #sec
mjd = mjd_start - (fraction * texp + rtime) / secinday
return mjd, mjd0
def mjd_from_data(datetu, ut, texp, fraction):
secinday = 24 * 3600.0
mjd0, mjd, i = GLOBALutils.iau_cal2jd(int(datetu[0:4]), int(datetu[5:7]),
int(datetu[8:10]))
ut = (float(ut[:2]) * 3600. + float(ut[3:5]) * 60. + float(ut[6:]))
mjd_start = mjd + ut / secinday
mjd = mjd_start + (fraction * texp) / secinday
return mjd, mjd0
def get_thar_offsets(lines_thar, order_dir='wavcals/', pref='order_', suf='.iwdat', ior=20,fior=45, delt_or=3, del_width=200.,binning=1):
xcs = []
for ii in range(ior,fior):
thar_order = lines_thar[ii]
xct = []
for order in range(ii-delt_or,ii+delt_or):
order_s = str(order)
if (order < 10):
order_s = '0' + order_s
if os.access(order_dir+pref+order_s+suf,os.F_OK):
f = open(order_dir+pref+order_s+suf,'r')
llins = f.readlines()
if len(llins)>5:
pixel_centers_0 = []
for line in llins:
w = line.split()
nlines = int(w[0])
for j in range(nlines):
pixel_centers_0.append(float(w[2*j+1])*1./float(binning))
pixel_centers_0 = np.array(pixel_centers_0).astype('int')
#plot(thar_order)
#plot(pixel_centers_0,thar_order[pixel_centers_0],'ro')
#print order, order_s
#show()
ml = np.array(pixel_centers_0) - 2
mh = np.array(pixel_centers_0) + 2
if len(ml)>0:
xc,offs = GLOBALutils.XCorPix( thar_order, ml, mh, del_width=del_width)
else:
xc = np.zeros(len(offs))
else:
xc = np.array([])
if len(xct) == 0:
xct = xc.copy()
else:
xct = np.vstack((xct,xc))
if len(xcs) == 0:
xcs = xct.copy()
else:
xcs += xct
maxes, maxvels = [],[]
for i in range(xcs.shape[0]):
maxes.append(xcs[i].max())
maxvels.append(offs[np.argmax(xcs[i])])
#plot(offs,xcs[i])
#show()
maxes,maxvels = np.array(maxes),np.array(maxvels)
orders_offset = -delt_or + np.argmax(maxes)
rough_shift = maxvels[np.argmax(maxes)]
return orders_offset, rough_shift
def mjd_fromheader(h):
"""
return modified Julian date from header
"""
datetu = h[0].header['UT-DATE']
ut = h[0].header['UT-TIME']
mjd0,mjd,i = GLOBALutils.iau_cal2jd(int(datetu[0:4]),int(datetu[5:7]),int(datetu[8:]))
ut = float(ut[:2]) + float(ut[3:5])/60. + float(ut[6:])/3600.
mjd_start = mjd + ut/24.0
secinday = 24*3600.0
fraction = .5
texp = h[0].header['EXPTIME'] #sec
mjd = mjd_start + (fraction * texp) / secinday
return mjd, mjd0
def mjd_fromheader(h):
"""
return modified Julian date from header
"""
datetu = h[0].header['DATE-OBS']
mjd0,mjd,i = GLOBALutils.iau_cal2jd(int(datetu[:4]),int(datetu[5:7]),int(datetu[8:10]))
ut = float(datetu[11:13]) + float(datetu[14:16])/60. + float(datetu[17:])/3600.
mjd_start = mjd + ut/24.0
secinday = 24*3600.0
fraction = 0.5
texp = h[0].header['EXPTIME'] #sec
mjd = mjd_start + (fraction * texp) / secinday
return mjd, mjd0
def mjd_fromheader(h):
"""
return modified Julian date from header
"""
datetu = h[0].header['HIERARCH ESO CORA SHUTTER START DATE']
ut = h[0].header['HIERARCH ESO CORA SHUTTER START HOUR']
mjd0,mjd,i = GLOBALutils.iau_cal2jd(int(datetu[0:4]),int(datetu[4:6]),int(datetu[6:8]))
mjd_start = mjd + ut/24.0
secinday = 24*3600.0
fraction = h[0].header['HIERARCH ESO CORA PM FLUX TMMEAN']
texp = h[0].header['HIERARCH ESO OBS TEXP'] #sec
mjd = mjd_start + (fraction * texp) / secinday
return mjd, mjd0
def mjd_fromheader(h):
"""
return modified Julian date from header
"""
ih = 0
if len(h)>1:
ih = 1
datetu = h[ih].header['UT-DATE']
ut = h[ih].header['UT-TIME']
mjd0,mjd,i = GLOBALutils.iau_cal2jd(int(datetu[0:4]),int(datetu[5:7]),int(datetu[8:]))
ut = ut/3600.
mjd_start = mjd + ut/24.0
secinday = 24*3600.0
fraction = .5
texp = h[ih].header['EXPTIME'] #sec
mjd = mjd_start + (fraction * texp) / secinday
return mjd, mjd0
def mjd_fromheader(h):
"""
return modified Julian date from header
"""
secinday = 24*3600.0
datetu = h[0].header['DATE-OBS'][:10]
ut = h[0].header['DATE-OBS'][11:]
mjd0,mjd,i = GLOBALutils.iau_cal2jd(int(datetu[0:4]),int(datetu[5:7]),int(datetu[8:10]))
ut = (float(ut[:2])*3600. + float(ut[3:5])*60. + float(ut[6:]))
mjd_start = mjd + ut / secinday
fraction = h[0].header['HIERARCH ESO INS DET1 TMMEAN']
texp = h[0].header['EXPTIME'] #sec
mjd = mjd_start + (fraction * texp) / secinday
return mjd, mjd0
def mjd_fromheader(h):
"""
return modified Julian date from header
"""
datetu = h['DATE-OBS'].split('T')[0]
timetu = h['DATE-OBS'].split('T')[1]
mjd0,mjd,i = GLOBALutils.iau_cal2jd(int(datetu[:4]),int(datetu[5:7]),int(datetu[8:]))
ho = int(timetu[:2])
mi = int(timetu[3:5])
se = float(timetu[7:])
ut = float(ho) + float(mi)/60.0 + float(se)/3600.0
mjd_start = mjd + ut/24.0
secinday = 24*3600.0
fraction = 0.5
texp = h['EXPTIME'] #sec
mjd = mjd_start + (fraction * texp) / secinday
return mjd, mjd0
def mjd_fromheader2(hd):
"""
return modified Julian date from header
"""
secinday = 24*3600.0
date = hd['DATE-OBS']
ut = hd['UTC']
datetu = date.replace('-',':')
mjd0,mjd,i = GLOBALutils.iau_cal2jd(int(datetu[0:4]),int(datetu[5:7]),int(datetu[8:10]))
ut = (float(ut[:2])*3600. + float(ut[3:5])*60. + float(ut[6:]))
mjd_start = mjd + ut / secinday
fraction = 0.5
texp = float(hd['EXPTIME']) #sec
mjd = mjd_start + (fraction * texp) / secinday
return mjd, mjd0
def mjd_fromheader(h):
"""
return modified Julian date from header
"""
secinday = 24 * 3600.0
date = h[0].header['DATE']
stime = h[0].header['UT_START']
etime = h[0].header['UT_END']
date = date[:10]
if stime > etime:
date = yesterday(date)
hour = ra_from_sec(stime)
datetu = date.replace('-', ':')
ut = hour.replace('-', ':')
datetu = date.replace(' ', '')
ut = hour.replace(' ', '')
mjd0, mjd, i = GLOBALutils.iau_cal2jd(int(datetu[0:4]), int(datetu[5:7]),
int(datetu[8:10]))
ut = (float(ut[:2]) * 3600. + float(ut[3:5]) * 60. + float(ut[6:]))
mjd_start = mjd + ut / secinday
def mjd_fromheader(h):
"""
return modified Julian date from header
"""
datetu = h[0].header['DATE-OBS'][:10]
ut = h[0].header['DATE-OBS'][11:]
mjd0, mjd, i = GLOBALutils.iau_cal2jd(int(datetu[0:4]), int(datetu[5:7]),
int(datetu[8:10]))
ut = float(ut[:2]) + float(ut[3:5]) / 60. + float(ut[6:]) / 3600.
mjd_start = mjd + ut / 24.0
#print 'Warning!!! adding 5 hrs to comute MJD due to problem in header! CHECK in future!!'
#mjd_start += 5./24.
secinday = 24 * 3600.0
fraction = 0.5
texp = h[0].header['EXPTIME'] #sec
#print 'Warning!!! assuming that the provided date is at the end of the exposure!!! Check in the future!!'
#mjd = mjd_start - (fraction * texp) / secinday
mjd = mjd_start + (fraction * texp) / secinday
return mjd, mjd0
pre_process = 1
else:
print "\tPre-processing files found, going straight to extraction"
pre_process = 0
if pre_process == 1:
# Find orders & traces
print "\tTracing echelle orders..."
h = pyfits.open(dirin + stst)
if len(h) > 1:
h[0].data = h[1].data
d = h[0].data
d = mikeutils.OverscanTrim(d, bias_sec, ov_sec)
d = d.T
c_all, nord = GLOBALutils.get_them(d, ext_aperture, 4, mode=1)
c_all = c_all[1:-1]
nord = len(c_all)
print '\t\t', nord, 'orders found in red CCD'
hb = pyfits.open(dirin + 'b' + stst[1:])
if len(h) > 1:
hb[0].data = hb[1].data
db = hb[0].data
db = mikeutils.OverscanTrim(db, bias_sec, ov_sec)
db = db.T
c_all_b, nord_b = GLOBALutils.get_them(db, ext_aperture, 4, mode=1)
print '\t\t', nord_b, 'orders found in blue CCD'
# median combine list of ob flats
print "\tMedian combining flat frames"
hdu = pyfits.PrimaryHDU( MasterBias )
if (os.access(dirout+'MasterBias.fits',os.F_OK)):
os.remove(dirout+'MasterBias.fits')
hdu.writeto(dirout+'MasterBias.fits')
print("\t\t-> Masterbias: done!")
# median combine list of flats
Flat, RO_fl, GA_fl = cafeutils.MedianCombine(flats, zero=dirout+'MasterBias.fits')
hdu = pyfits.PrimaryHDU(Flat)
if (os.access(dirout+'Flat.fits',os.F_OK)):
os.remove(dirout+'Flat.fits')
hdu.writeto(dirout+'Flat.fits')
print("\t\t-> Masterflats: done!")
print("\tTracing echelle orders...")
c_all,nord = GLOBALutils.get_them(Flat, ext_aperture, trace_degree, maxords=-1, mode=1)
c_all, nord = GLOBALutils.good_orders(c_all,nord,Flat.shape[0],Flat.shape[1],ext_aperture)
if nord >= 90:
c_all = c_all[:90]
nord=90
print('\t\t'+ str(nord)+' orders found.')
# pickle traces
trace_dict = {'c_all':c_all,
'nord':nord,
'GA_bias': GA_bias, 'RO_bias' : RO_bias,
'GA_fl': GA_fl, 'RO_fl': RO_fl}
pickle.dump( trace_dict, open( dirout+"trace.pkl", 'w' ) )
else:
trace_dict = pickle.load( open( dirout+"trace.pkl", 'r' ) )
os.remove(dirout + 'Flat.fits')
hdu.writeto(dirout + 'Flat.fits')
print "\t\t-> Masterflat: done!"
#Flat = pyfits.getdata(dirout+'Flat.fits')
#RO_flat, GA_flat = 1.,1.
# Find orders & traces
print "\tTracing echelle orders..."
h = pyfits.open(dirin + stst)[0]
hth = pyfits.getheader(dirin + stst)
d = h.data
d = pfsutils.OverscanTrim(d, bias_section, over_section)
d -= MasterBias
c_all, nord = GLOBALutils.get_them(d,
ext_aperture - 1,
trace_degree,
mode=1,
nsigmas=5)
trace_dict = {
'c_all': c_all,
'nord': nord,
'GA_bias': GA_bias,
'RO_bias': RO_bias,
'GA_flat': GA_flat,
'RO_flat': RO_flat
}
pickle.dump(trace_dict, open(dirout + 'trace.pkl', 'w'))
else:
print '\t\tLoading Masterbias, Masterflat and traces'
if (pre_process == 1):
# median combine list of flats
print "\t\tGenerating Master calibration frames..."
Flat, RO_fl, GA_fl = hiresutils.MedianCombine(flats,chip=chip)
hdu = pyfits.PrimaryHDU(Flat)
if (os.access(dirout+'Flat_' + str(int(chip)) + '.fits',os.F_OK)):
os.remove(dirout+'Flat_' + str(int(chip)) + '.fits')
hdu.writeto(dirout+'Flat_' + str(int(chip)) + '.fits')
print "\t\t-> Masterflat: done!"
hdu = pyfits.open(dirin+stst)
d = hdu[chip].data
d = hiresutils.OverscanTrim(d,hdu[chip].header['DATASEC'])
d = d.T
print "\tTracing echelle orders..."
c_all, nord = GLOBALutils.get_them(d,ext_aperture+5,trace_degree,mode=1,nsigmas=10,nc2=1)
print '\t\t'+str(nord)+' orders found...'
# pickle traces
trace_dict = {'c_all':c_all,
'nord':nord,
'GA_fl': GA_fl, 'RO_fl': RO_fl}
pickle.dump( trace_dict, open( dirout+'trace_' + str(int(chip)) + '.pkl', 'w' ) )
else:
trace_dict = pickle.load( open( dirout+'trace_' + str(int(chip)) + '.pkl' ) )
c_all = trace_dict['c_all']
nord = trace_dict['nord']
# recover GA*, RO*
GA_fl = trace_dict['GA_fl']
RO_fl = trace_dict['RO_fl']
for time in dark_times:
group = []
for dark in darks:
hd = pyfits.getheader(dark)
if hd['EXPTIME'] == time:
group.append(dark)
if len(group)>2:
dark_groups.append(group)
ndark_times.append(hd['EXPTIME'])
dark_times = ndark_times
i = 0
while i < len(dark_times):
DARK, RON, GAIN = vbtutils.MedianCombine(dark_groups[i], zero_bo=True, zero=dirout+'MasterBias.fits',dark_bo=False)
hdu = pyfits.PrimaryHDU( DARK )
hdu = GLOBALutils.update_header(hdu,'EXPTIME',dark_times[i])
if os.access(dirout+'DARK_'+str(int(dark_times[i]))+'s.fits',os.F_OK):
os.remove(dirout+'DARK_'+str(int(dark_times[i]))+'s.fits')
hdu.writeto( dirout+'DARK_'+str(int(dark_times[i]))+'s.fits' )
MDARKS.append(dirout+'DARK_'+str(int(dark_times[i]))+'s.fits')
i+=1
print "\t\t-> Masterdarks: done!"
Flat, RO_flat, GA_flat = vbtutils.MedianCombine(flats, zero_bo=True, dark_bo=False, flat_bo=False,zero=dirout+'MasterBias.fits')
hdu = pyfits.PrimaryHDU( Flat )
if (os.access(dirout+'MasterFlat.fits',os.F_OK)):
os.remove(dirout+'MasterFlat.fits')
hdu.writeto(dirout+'MasterFlat.fits')
print "\t\t-> Masterflat: done!"
MasDarl = []
for t in dark_times:
sirven = []
i = 0
while i < len(darks):
if each_time[i] == t:
sirven.append(darks[i])
i += 1
Mdark, roD, gaF = pucherosutils.MedianCombine(
sirven, True, dirout + 'MasterBias.fits')
nMdark = 'MasterDark_' + str(t) + 's.fits'
if (os.access(dirout + nMdark, os.F_OK)):
os.remove(dirout + nMdark)
hdu = pyfits.PrimaryHDU(Mdark)
hdu = GLOBALutils.update_header(hdu, 'EXPTIME', t)
hdu.writeto(dirout + nMdark)
MasDarl.append(dirout + nMdark)
darks_dict = {'d_names': MasDarl, 'd_times': dark_times}
pickle.dump(darks_dict, open(dirout + "darks.pkl", 'w'))
print "\t\t-> Masterdarks: done!"
else:
dark_times = []
MasDarl = []
darks_dict = {'d_names': MasDarl, 'd_times': dark_times}
pickle.dump(darks_dict, open(dirout + "darks.pkl", 'w'))
print "\t\t-> 0 Masterdarks found!"
# median combine list of flats
(force_pre_process) ):
print "\tNo previous pre-processing files or found"
pre_process = 1
else:
print "\tPre-processing files found, going straight to extraction"
pre_process = 0
trace_file = dirout + 'trace.pkl'
if (pre_process == 1):
#c_ob, nord_ob = GLOBALutils.get_them(Flat_ob, 10.,trace_degree,maxords=-1)
#c_ob, nord_ob = FIDEOSutils.good_orders(c_ob,nord_ob,Flat_ob.shape[0],Flat_ob.shape[1],ext_aperture)
#print nord_ob, 'object orders found'
#c_co, nord_co = GLOBALutils.get_them(Flat_co, 10.,trace_degree,maxords=-1)
#c_co, nord_co = FIDEOSutils.good_orders(c_co,nord_co,Flat_co.shape[0],Flat_co.shape[1],ext_aperture)
#print nord_co, 'comparison orders found'
MasterBias = GLOBALutils.MedianCombine_simple(biases, ZF=0.)
hdu = pyfits.PrimaryHDU(MasterBias)
if (os.access(dirout + 'MasterBias.fits', os.F_OK)):
os.remove(dirout + 'MasterBias.fits')
hdu.writeto(dirout + 'MasterBias.fits')
MasterFlat = GLOBALutils.MedianCombine_simple(flats, ZF=MasterBias)
MasterFlat = np.fliplr(MasterFlat.T)
hdu = pyfits.PrimaryHDU(MasterFlat)
if (os.access(dirout + 'MasterFlat.fits', os.F_OK)):
os.remove(dirout + 'MasterFlat.fits')
hdu.writeto(dirout + 'MasterFlat.fits')
MasterFlat_co = GLOBALutils.MedianCombine_simple(flats_co, ZF=MasterBias)
MasterFlat_co = np.fliplr(MasterFlat_co.T)
hdu = pyfits.PrimaryHDU(MasterFlat_co)
def fill_list_header(hdu, ofiles):
for i in range(len(ofiles)):
hdu = GLOBALutils.update_header(hdu, 'RAW' + str(i),
ofiles[i].split('/')[-1])
return hdu
for i in range(nspec.shape[1]):
npix = np.arange(nspec.shape[2])
wav = np.polyval(coefs[i][1:][::-1], npix) * 10
nspec[0, i, :] = wav
spl = interpolate.splrep(np.arange(wav.shape[0]), wav, k=3)
dlambda_dx = interpolate.splev(np.arange(wav.shape[0]), spl, der=1)
NN = np.average(dlambda_dx)
dlambda_dx /= NN
nspec[9, i, :] = nspec[5, i, :] * (dlambda_dx**1)
nspec[8, i, :] = 100.
ml_v = tlines - 0.01
mh_v = tlines + 0.01
weight = np.ones(len(tlines))
vels, xc_full, sn, nlines_ccf, W_ccf = \
GLOBALutils.XCor(nspec, ml_v, mh_v, weight, 0, 1., vel_width=300,vel_step=3.,\
spec_order=9,iv_order=10,sn_order=8,max_vel_rough=300)
xc_av = GLOBALutils.Average_CCF(xc_full, sn, sn_min=3., Simple=True, W=W_ccf)
#plot(vels,xc_av)
#show()
j = np.median(xc_av)
im = np.argmax(xc_av)
rv = vels[im]
vels, xc_full, sn, nlines_ccf, W_ccf = \
GLOBALutils.XCor(nspec, ml_v, mh_v, weight, rv, 1., vel_width=20,vel_step=0.3,\
spec_order=9,iv_order=10,sn_order=8,max_vel_rough=300)
xc_av = GLOBALutils.Average_CCF(xc_full, sn, sn_min=3., Simple=True, W=W_ccf)
cent = np.sum(xc_av * vels) / np.sum(xc_av)
print cent
#axvline(cent)
#plot(vels,xc_av-j)
Flat, RO_fl, GA_fl = uvesutils.MedianCombine(flats, bias=MasterBias)
hdu = pyfits.PrimaryHDU(Flat)
if (os.access(dirout + 'Flat.fits', os.F_OK)):
os.remove(dirout + 'Flat.fits')
hdu.writeto(dirout + 'Flat.fits')
print "\t\t-> MasterFlat: done!"
hdu = pyfits.open(orderref[0])
d1 = hdu[1].data - MasterBias[:, :, 0]
d2 = hdu[2].data - MasterBias[:, :, 1]
d1 = d1.T
d2 = d2.T
print "\tTracing echelle orders..."
c_all1, nord1 = GLOBALutils.get_them(d1,
int(0.2 * ext_aperture),
trace_degree,
mode=1,
nsigmas=10)
print '\t\t' + str(nord1) + ' orders for chip1 found...'
c_all2, nord2 = GLOBALutils.get_them(d2,
int(0.2 * ext_aperture),
trace_degree,
mode=1,
nsigmas=10,
endat=2030)
print '\t\t' + str(nord2) + ' orders for chip2 found...'
trace_dict = {'c_all1':c_all1,'nord1':nord1,\
'c_all2':c_all2,'nord2':nord2,\
'GA_bias': GA_bias, 'RO_bias': RO_bias,\
'GA_fl': GA_fl, 'RO_fl': RO_fl}
# save this file for later reference
hdu = pyfits.PrimaryHDU(FlatR)
if (os.access(dirout + 'FlatR.fits', os.F_OK)):
os.remove(dirout + 'FlatR.fits')
hdu.writeto(dirout + 'FlatR.fits')
hdu = pyfits.PrimaryHDU(FlatB)
if (os.access(dirout + 'FlatB.fits', os.F_OK)):
os.remove(dirout + 'FlatB.fits')
hdu.writeto(dirout + 'FlatB.fits')
# Find orders & traces
print "\tTracing echelle orders..."
c_all, nord = GLOBALutils.get_them(FlatB + FlatR,
ext_aperture,
trace_degree,
maxords=-1,
mode=1)
print "\t\t\t", nord, 'orders found ...'
trace_dict = {'c_all':c_all, 'nord':nord, 'GA_bias': GA_bias, 'RO_bias': RO_bias, \
'GA_flatB': GA_flatB, 'RO_flatB': RO_flatB, 'GA_flatR': GA_flatR, 'RO_flatR': RO_flatR}
pickle.dump(trace_dict, open(dirout + "trace.pkl", 'w'))
else:
print '\tLoading Masterbias, Masterflat and traces'
trace_dict = pickle.load(open(dirout + "trace.pkl", 'r'))
c_all = trace_dict['c_all']
nord = trace_dict['nord']
GA_bias = trace_dict['GA_bias']
RO_bias = trace_dict['RO_bias']