def advanceprocess(instrume, obsdate, propcode, median, function, order, rej_lo, rej_hi, niter, interp, sdbhost, sdbname, sdbuser, sdbpass, logfile, verbose):
"""Advance process the rss data"""
rawpath = instrume+'/raw'
prodpath = instrume+'/advance'
os.mkdir(prodpath)
instrume_name='RSS'
prefix = 'P'
img_list=[]
for img in glob.glob('%s/raw/%s*fits' % (instrume, prefix)):
struct=pyfits.open(img)
if struct[0].header['PROPID'].upper().strip() != 'JUNK':
img_list.append(img)
images=','.join(img_list)
obslog = '%s/%s%sOBSLOG.fits' % (prodpath, prefix, obsdate)
gaindb = iraf.osfn('pysalt$data/%s/%samps.dat' % (instrume, instrume_name))
xtalkfile = iraf.osfn('pysalt$data/%s/%sxtalk.dat' % (instrume, instrume_name))
geomfile = iraf.osfn('pysalt$data/%s/%sgeom.dat' % (instrume, instrume_name))
saltadvance(images, prodpath, obslogfile=obslog, gaindb=gaindb, xtalkfile=xtalkfile,
geomfile=geomfile, subover=True,trim=True,masbias=None,
subbias=False, median=False, function='polynomial', order=5,rej_lo=3,
rej_hi=3, niter=5,interp='linear', sdbhost=sdbhost, sdbname=sdbname,sdbuser=sdbuser, password=sdbpass,
clobber=True, logfile=logfile, verbose=verbose)
return
def processdata(instrume, obsdate, propcode, median, function, order, rej_lo, rej_hi, niter, interp, logfile, verbose):
"""Clean and process the data"""
#set up instrument specific naming
if instrume=='rss':
instrume_name='RSS'
prefix='P'
elif instrume=='scam':
instrume_name='SALTICAM'
prefix='S'
rawpath = instrume+'/raw'
prodpath = instrume+'/product'
img_list=[]
for img in glob.glob('%s/raw/%s*fits' % (instrume, prefix)):
struct=pyfits.open(img)
if struct[0].header['PROPID'].upper().strip() != 'JUNK':
img_list.append(img)
img_str=','.join(img_list)
obslog = '%s/%s%sOBSLOG.fits' % (prodpath, prefix, obsdate)
gaindb = iraf.osfn('pysalt$data/%s/%samps.dat' % (instrume, instrume_name))
#gaindb = ''
xtalkfile = iraf.osfn('pysalt$data/%s/%sxtalk.dat' % (instrume, instrume_name))
geomfile = iraf.osfn('pysalt$data/%s/%sgeom.dat' % (instrume, instrume_name))
if len(img_list)>0:
saltclean(images=img_str,outpath=prodpath,obslogfile=obslog,gaindb=gaindb,
xtalkfile=xtalkfile,geomfile=geomfile,subover=True,trim=True,
median=median,function=function,order=order,rej_lo=rej_lo,
rej_hi=rej_hi,niter=niter,masbias=True,subbias=False,interp=interp,
clobber=True,logfile=logfile,verbose=verbose)
rawsize = 0.
rawnum = 0
prodsize = 0.
prodnum = 0
if len(img_list)>0:
files = glob.glob('%s/raw/%s*.fits' % (instrume, prefix))
rawnum = len(files)
for file in files:
rawsize += os.stat(file).st_size
files = glob.glob('%s/product/*%s*.fits' % (instrume, prefix))
prodnum = len(files)
for file in files:
prodsize += os.stat(file).st_size
# collate RSS data for individual PIs
outpath = '.'
if len(img_list):
saltobsid(propcode=propcode,obslog=obslog,rawpath=rawpath,prodpath=prodpath, outpath=outpath,clobber=True,logfile=logfile,verbose=verbose)
return rawsize, rawnum, prodsize, prodnum
def specpolfilter(filterlist, infilelist):
obss = len(infilelist)
obsdict = obslog(infilelist)
for b in range(obss):
hdul = pyfits.open(infilelist[b])
dwav = float(hdul['SCI'].header['CDELT1'])
wav0 = float(hdul['SCI'].header['CRVAL1'])
wavs = int(hdul['SCI'].header['NAXIS1'])
ctypelist = (hdul['SCI'].header['CTYPE3']).split(',')
stokes_sw = hdul['SCI'].data[:, 0, :]
var_sw = hdul['VAR'].data[:, 0, :]
pstokess = len(ctypelist) - 1
ok_w = (hdul['BPM'].data[:, 0, :] == 0).all(axis=0)
wav_w = wav0 + dwav * np.arange(wavs)
print "\n" + infilelist[b]
print("Filter " + pstokess * "%5s Err ") % tuple(
ctypelist[1:])
for filter in filterlist:
if filter in ("U", "B", "V"):
filterfile = iraf.osfn("pysalt$data/scam/filters/Johnson_" +
filter + ".txt")
elif filter in ("R", "I"):
filterfile = iraf.osfn("pysalt$data/scam/filters/Cousins_" +
filter + ".txt")
# else:
# (filter file in cwd)
wav_l, feff_l = np.loadtxt(filterfile, dtype=float, unpack=True)
feff_l[feff_l < .0001] = 0.
feff_w = interp1d(wav_l, feff_l, kind='linear',
bounds_error=False)(wav_w)
okf_w = (ok_w & (feff_w > .0003))
feff_w[~okf_w] = 0.
if feff_w[okf_w].sum() == 0: continue
stokesfil_s = (feff_w[okf_w] * stokes_sw[:, okf_w]).sum(
axis=1) / feff_w[okf_w].sum()
varfil_s = (feff_w[okf_w]**2 * var_sw[:, okf_w]).sum(
axis=1) / (feff_w[okf_w].sum()**2)
nstokesfil_s = 100. * stokesfil_s / stokesfil_s[0]
nerrfil_s = 100. * np.sqrt(
varfil_s[:pstokess + 1]) / stokesfil_s[0]
print ("%4s "+pstokess*"%9.4f %7.4f ") % \
(tuple(filter)+tuple(np.vstack((nstokesfil_s[1:],nerrfil_s[1:])).T.ravel()))
return ()
def get_pedsub_pars( camera, pedparname, Trl, pedsub_file, saapername, debug=False ):
""" Get keyword parameter values for pedsub
@param camera: camera number
@type camera: int
@param pedparname: parameter file name
@type pedparname: string
@param Trl: trailer file name
@type Trl: string
@param pedsub_file: name of file with pedsub pars
@type pedsub_file: string
@param saapername: name of file for SAA persistence image
@type saapername: string
@return: kwpars
@rtype: dict
"""
# Get params from the pedsubtab
try:
kwpars = getkwpars(camera,iraf.osfn(pedparname))
except Exception as e:
set_keys_final(_error,_error, pedsub_file, donestring,saapername)
handle_exception(e, Trl, [], debug = debug)
return _error
return kwpars
def agncalibrate(img, outfile, calfile, specformat='lcogt'):
#set up some files that will be needed
logfile='specext.log'
hdu = fits.open(img)
w1 = hdu[0].header['CRVAL1']
p1 = hdu[0].header['CRPIX1']
dw = hdu[0].header['CD1_1']
f = hdu[0].data[0][0]
e = hdu[0].data[3][0]
xarr = np.arange(len(f))
w = (xarr)*dw+w1
cal_spectra=st.readspectrum(calfile, error=False, ftype='ascii')
airmass=hdu[0].header['AIRMASS']
exptime=hdu[0].header['EXPTIME']
extfile=iraf.osfn("pysalt$data/site/suth_extinct.dat")
ext_spectra=st.readspectrum(extfile, error=False, ftype='ascii')
flux_spec=Spectrum.Spectrum(w, f, e, stype='continuum')
flux_spec=calfunc(flux_spec, cal_spectra, ext_spectra, airmass, exptime, True)
hdu[0].data[0][0] = flux_spec.flux
hdu[0].data[3][0] = flux_spec.var
hdu.writeto(outfile, clobber=True)
def galextract(img, yc=None, dy=None, normalize=True, calfile=None, convert=True, specformat='ascii'):
#set up some files that will be needed
logfile='specext.log'
#create the spectra text files for all of our objects
spec_list=[]
#skynormalize the data
if normalize:
specslitnormalize(img, 'n'+img, '', response=None, response_output=None, order=3, conv=1e-2, niter=20,
startext=0, clobber=True,logfile='salt.log',verbose=True)
hdu=pyfits.open('n'+img)
target=hdu[0].header['OBJECT']
ofile='%s.%s_%i_%i.ltxt' % (target, extract_date(img), extract_number(img), yc)
#ofile = img.replace('fits', 'txt')
extract_spectra(hdu, yc, dy, ofile, smooth=False, grow=10, clobber=True, specformat=specformat, convert=convert)
if calfile is not None:
airmass=hdu[0].header['AIRMASS']
exptime=hdu[0].header['EXPTIME']
extfile=iraf.osfn("pysalt$data/site/suth_extinct.dat")
speccal(ofile, ofile.replace("txt", "spec"), calfile, extfile, airmass, exptime, clobber=True, logfile='salt.log', verbose=True)
def preprocessdata(instrume, prefix, obsdate, keyfile, log, logfile, verbose):
"""Run through all of the processing of the individual data files"""
log.message('Beginning pre-processing of %s data' % instrume.upper())
#set up the input path
inpath=instrume+'/raw/'
#creaate the product directory
prodpath=instrume+'/product/'
saltio.createdir(prodpath)
# convert any slot mode binary data to FITS
convertbin(inpath, iraf.osfn('pysalt$data/%s/%s_fits.config' % (instrume, instrume)), logfile, verbose)
# fix sec keywords for data of unequal binning obtained before 2006 Aug 12
if int(obsdate) < 20060812:
pinfiles = instrume+'/raw/*.fits'
log.message('Fixing SEC keywords in older data')
log.message('SALTFIXSEC -- infiles=' + pinfiles)
pipetools.saltfixsec(infiles=pinfiles)
#fix the key words for the data set
recfile = prodpath+prefix+ obsdate + 'KEYLOG.fits'
img=','.join(glob.glob(inpath+prefix+'*fits'))
if img:
salteditkey(images=img,outimages=img,outpref='',keyfile=keyfile,recfile=recfile,
clobber=True,logfile=logfile,verbose=verbose)
def galextract(img,
yc=None,
dy=None,
normalize=True,
calfile=None,
convert=True,
specformat='ascii'):
#set up some files that will be needed
logfile = 'specext.log'
#create the spectra text files for all of our objects
spec_list = []
#skynormalize the data
if normalize:
specslitnormalize(img,
'n' + img,
'',
response=None,
response_output=None,
order=3,
conv=1e-2,
niter=20,
startext=0,
clobber=True,
logfile='salt.log',
verbose=True)
hdu = pyfits.open('n' + img)
target = hdu[0].header['OBJECT']
ofile = '%s.%s_%i_%i.ltxt' % (target, extract_date(img),
extract_number(img), yc)
#ofile = img.replace('fits', 'txt')
extract_spectra(hdu,
yc,
dy,
ofile,
smooth=False,
grow=10,
clobber=True,
specformat=specformat,
convert=convert)
if calfile is not None:
airmass = hdu[0].header['AIRMASS']
exptime = hdu[0].header['EXPTIME']
extfile = iraf.osfn("pysalt$data/site/suth_extinct.dat")
speccal(ofile,
ofile.replace("txt", "spec"),
calfile,
extfile,
airmass,
exptime,
clobber=True,
logfile='salt.log',
verbose=True)
def specpolfilter(filterlist, infilelist):
obss = len(infilelist)
obsdict=obslog(infilelist)
for b in range(obss):
hdul = pyfits.open(infilelist[b])
dwav = float(hdul['SCI'].header['CDELT1'])
wav0 = float(hdul['SCI'].header['CRVAL1'])
wavs = int(hdul['SCI'].header['NAXIS1'])
ctypelist = (hdul['SCI'].header['CTYPE3']).split(',')
stokes_sw = hdul['SCI'].data[:,0,:]
var_sw = hdul['VAR'].data[:,0,:]
pstokess = len(ctypelist)-1
ok_w = (hdul['BPM'].data[:,0,:] == 0).all(axis=0)
wav_w = wav0 + dwav*np.arange(wavs)
print "\n"+infilelist[b]
print ("Filter "+pstokess*"%5s Err ") % tuple(ctypelist[1:])
for filter in filterlist:
if filter in ("U","B","V"):
filterfile = iraf.osfn("pysalt$data/scam/filters/Johnson_"+filter+".txt")
elif filter in ("R","I"):
filterfile = iraf.osfn("pysalt$data/scam/filters/Cousins_"+filter+".txt")
# else:
# (filter file in cwd)
wav_l,feff_l = np.loadtxt(filterfile,dtype=float,unpack=True)
feff_l[feff_l < .0001] = 0.
feff_w = interp1d(wav_l,feff_l,kind='linear',bounds_error=False)(wav_w)
okf_w = (ok_w & (feff_w > .0003))
feff_w[~okf_w] = 0.
if feff_w[okf_w].sum() == 0: continue
stokesfil_s = (feff_w[okf_w]*stokes_sw[:,okf_w]).sum(axis=1)/feff_w[okf_w].sum()
varfil_s = (feff_w[okf_w]**2*var_sw[:,okf_w]).sum(axis=1)/(feff_w[okf_w].sum()**2)
nstokesfil_s = 100.*stokesfil_s/stokesfil_s[0]
nerrfil_s = 100.*np.sqrt(varfil_s[:pstokess+1])/stokesfil_s[0]
print ("%4s "+pstokess*"%9.4f %7.4f ") % \
(tuple(filter)+tuple(np.vstack((nstokesfil_s[1:],nerrfil_s[1:])).T.ravel()))
return()
def init_northsouth(fs, topdir, rawpath):
# Get the correct directory for the standard star
base_stddir = 'spec50cal/'
extfile = iraf.osfn('gmisc$lib/onedstds/kpnoextinct.dat')
observatory = 'Gemini-North'
global is_GS
is_GS = pyfits.getval(fs[0], 'DETECTOR') == 'GMOS + Hamamatsu'
if is_GS:
global dooverscan
dooverscan = True
if not os.path.exists(topdir + '/raw_fixhdr'):
os.mkdir(topdir + '/raw_fixhdr')
rawpath = '%s/raw_fixhdr/' % topdir
os.system('gmoss_fix_headers.py --files="%s/raw/*.fits" --destination=%s' % (topdir, rawpath))
base_stddir = 'ctionewcal/'
observatory = 'Gemini-South'
extfile = iraf.osfn('gmisc$lib/onedstds/ctioextinct.dat')
return extfile, observatory, base_stddir, rawpath
def x1d_iraf(input,
outdir="",
update_input=iraf.no,
find_target=iraf.no,
cutoff=0.,
location="",
extrsize="",
verbosity=1,
version=""):
# Interpret input parameters
if outdir.strip() == "":
outdir = None
if outdir is not None:
outdir = iraf.osfn(outdir)
update_input = _toBoolean(update_input)
if cutoff <= 0.:
cutoff = None
find_targ = {"flag": _toBoolean(find_target), "cutoff": cutoff}
# convert from string to list
location = commaSep(location, "float")
extrsize = commaSep(extrsize, "int")
inlist = input.split(",")
more_input = []
for filename in inlist:
more_input.extend(filename.split())
all_input = []
for filename in more_input:
filename = iraf.osfn(filename)
all_input.extend(glob.glob(filename))
X1D.extractSpec(all_input,
outdir=outdir,
update_input=update_input,
location=location,
extrsize=extrsize,
find_target=find_targ,
verbosity=verbosity)
def mosred(infile_list, slitmask,propcode=None, dy=0, inter=True, guesstype='rss', guessfile='', rstep=100, automethod='Matchlines', preprocess=False):
#set up the files
infiles=','.join(['%s' % x for x in infile_list])
obsdate=os.path.basename(infile_list[0])[7:15]
#set up some files that will be needed
logfile='spec'+obsdate+'.log'
dbfile='spec%s.db' % obsdate
#create the observation log
obs_dict=obslog(infile_list)
#check the value of dy
#apply the mask to the data sets
for i in range(len(infile_list)):
specslit(image=infile_list[i], outimage='', outpref='s', exttype='rsmt', slitfile=slitmask,
outputslitfile='', regprefix='ds_', sections=3, width=25.0, sigma=2.2, thres=6.0, order=1, padding=5, yoffset=dy,
inter=False, clobber=True, logfile=logfile, verbose=True)
for i in range(len(infile_list)):
if obs_dict['OBJECT'][i].upper().strip()=='ARC' and (obs_dict['PROPID'][i].upper().strip()==propcode or propcode is None):
lamp=obs_dict['LAMPID'][i].strip().replace(' ', '')
arcimage='s'+os.path.basename(infile_list[i])
if lamp == 'NONE': lamp='CuAr'
lampfile=iraf.osfn("pysalt$data/linelists/%s.salt" % lamp)
specselfid(arcimage, '', 'a', arcimage, 'middlerow', 3, clobber=True, logfile=logfile, verbose=True)
specidentify('a'+arcimage, lampfile, dbfile, guesstype=guesstype,
guessfile=guessfile, automethod=automethod, function='legendre', order=3,
rstep=rstep, rstart='middlerow', mdiff=20, thresh=3, niter=5, smooth=3,
inter=True, clobber=True, preprocess=True, logfile=logfile, verbose=True)
#specrectify(arcimage, outimages='', outpref='x', solfile=dbfile, caltype='line',
# function='legendre', order=3, inttype='interp', w1=None, w2=None, dw=None, nw=None,
# blank=0.0, clobber=True, logfile=logfile, verbose=True)
objimages=''
spec_list=[]
for i in range(len(infile_list)):
if obs_dict['CCDTYPE'][i].count('OBJECT') and obs_dict['INSTRUME'][i].count('RSS') and \
(obs_dict['PROPID'][i].upper().strip()==propcode or propcode is None) and \
obs_dict['OBSMODE'][i].count('SPECTROSCOPY'):
img = infile_list[i]
##rectify it
specselfid('s'+img, '', 'a', arcimage, 'middlerow', 3, clobber=True, logfile=logfile, verbose=True)
specrectify('as'+img, outimages='', outpref='x', solfile=dbfile, caltype='line',
function='legendre', order=3, inttype='interp', w1=None, w2=None, dw=None, nw=None,
blank=0.0, clobber=True, logfile=logfile, verbose=True)
def init_northsouth(fs, topdir, rawpath):
# Get the correct directory for the standard star
base_stddir = 'spec50cal/'
extfile = iraf.osfn('gmisc$lib/onedstds/kpnoextinct.dat')
observatory = 'Gemini-North'
global is_GS
is_GS = fits.getval(fs[0], 'OBSERVAT') == 'Gemini-South'
if 'Hamamatsu' in fits.getval(fs[0], 'DETECTOR'):
global dooverscan
dooverscan = True
global do_qecorr
do_qecorr = True
if is_GS:
if not os.path.exists(topdir + '/raw_fixhdr'):
iraf.mkdir(topdir + '/raw_fixhdr')
#rawpath = '%s/raw_fixhdr/' % topdir
#os.system('gmoss_fix_headers.py --files="%s/raw/*.fits" --destination=%s' % (topdir, rawpath))
base_stddir = 'ctionewcal/'
observatory = 'Gemini-South'
extfile = iraf.osfn('gmisc$lib/onedstds/ctioextinct.dat')
return extfile, observatory, base_stddir, rawpath
def mosred(infile_list, slitmask,propcode=None, dy=0, inter=True, guesstype='rss', guessfile='', rstep=100, automethod='Matchlines'):
#set up the files
infiles=','.join(['%s' % x for x in infile_list])
obsdate=os.path.basename(infile_list[0])[7:15]
#set up some files that will be needed
logfile='spec'+obsdate+'.log'
dbfile='spec%s.db' % obsdate
#create the observation log
obs_dict=obslog(infile_list)
#apply the mask to the data sets
for i in range(len(infile_list)):
specslit(image=infile_list[i], outimage='', outpref='s', exttype='rsmt', slitfile='../../P001423N01.xml',
outputslitfile='', regprefix='ds_', sections=3, width=25.0, sigma=2.2, thres=6.0, order=1, padding=5, yoffset=dy,
inter=False, clobber=True, logfile=logfile, verbose=True)
for i in range(len(infile_list)):
if obs_dict['OBJECT'][i].upper().strip()=='ARC' and obs_dict['PROPID'][i].upper().strip()==propcode:
lamp=obs_dict['LAMPID'][i].strip().replace(' ', '')
arcimage='s'+os.path.basename(infile_list[i])
if lamp == 'NONE': lamp='CuAr'
lampfile=iraf.osfn("../../%s.salt" % lamp)
specselfid(arcimage, '', 'a', arcimage, 'middlerow', 3, clobber=True, logfile=logfile, verbose=True)
specidentify('a'+arcimage, lampfile, dbfile, guesstype=guesstype,
guessfile=guessfile, automethod=automethod, function='legendre', order=3,
rstep=rstep, rstart='middlerow', mdiff=20, thresh=3, niter=5, smooth=3,
inter=False, clobber=True, logfile=logfile, verbose=True)
#specrectify(arcimage, outimages='', outpref='x', solfile=dbfile, caltype='line',
# function='legendre', order=3, inttype='interp', w1=None, w2=None, dw=None, nw=None,
# blank=0.0, clobber=True, logfile=logfile, verbose=True)
objimages=''
spec_list=[]
for i in range(len(infile_list)):
if obs_dict['CCDTYPE'][i].count('OBJECT') and obs_dict['INSTRUME'][i].count('RSS') and \
obs_dict['PROPID'][i].upper().strip()==propcode and \
obs_dict['OBSMODE'][i].count('SPECTROSCOPY'):
img = infile_list[i]
##rectify it
specselfid('s'+img, '', 'a', arcimage, 'middlerow', 3, clobber=True, logfile=logfile, verbose=True)
specrectify('as'+img, outimages='', outpref='x', solfile=dbfile, caltype='line',
function='legendre', order=3, inttype='interp', w1=None, w2=None, dw=None, nw=None,
blank=0.0, clobber=True, logfile=logfile, verbose=True)
def buildtmc(tmcname):
from pyraf import iraf
from iraf import stsdas,hst_calib,synphot
out=open('buildtmc.log','w')
f=pyfits.open(tmcname)
flist=f[1].data.field('filename')
iraf.set(crrefer='./') #work locally
for k in range(len(flist)):
oldname=iraf.osfn(flist[k]).split('[')[0]
newname=fincre(oldname)
if os.path.exists(newname):
flist[k]=fincre(flist[k])
else:
out.write("%s: no change necessary\n"%oldname)
f.writeto(tmcname.replace(".fits","_new.fits"))
out.close()
def specred(infile_list, target, propcode, calfile=None, inter=True, automethod='Matchlines'):
#set up the files
infiles=','.join(['%s' % x for x in infile_list])
obsdate=os.path.basename(infile_list[0])[7:15]
#set up some files that will be needed
logfile='spec'+obsdate+'.log'
dbfile='spec%s.db' % obsdate
#create the observation log
obs_dict=obslog(infile_list)
for i in range(len(infile_list)):
if obs_dict['OBJECT'][i].upper().strip()=='ARC' and obs_dict['PROPID'][i].upper().strip()==propcode:
lamp=obs_dict['LAMPID'][i].strip().replace(' ', '')
arcimage=os.path.basename(infile_list[i])
if lamp == 'NONE': lamp='CuAr'
lampfile=iraf.osfn("pysalt$data/linelists/%s.salt" % lamp)
specidentify(arcimage, lampfile, dbfile, guesstype='rss',
guessfile='', automethod=automethod, function='legendre', order=3,
rstep=100, rstart='middlerow', mdiff=20, thresh=3, niter=5, smooth=3,
inter=False, clobber=True, logfile=logfile, verbose=True)
specrectify(arcimage, outimages='', outpref='x', solfile=dbfile, caltype='line',
function='legendre', order=3, inttype='interp', w1=None, w2=None, dw=None, nw=None,
blank=0.0, clobber=True, logfile=logfile, verbose=True)
objimages=''
spec_list=[]
for i in range(len(infile_list)):
if obs_dict['CCDTYPE'][i].count('OBJECT') and obs_dict['INSTRUME'][i].count('RSS') and obs_dict['PROPID'][i].upper().strip()==propcode:
img = infile_list[i]
##rectify it
specrectify(img, outimages='', outpref='x', solfile=dbfile, caltype='line',
function='legendre', order=3, inttype='interp', w1=None, w2=None, dw=None, nw=None,
blank=0.0, clobber=True, logfile=logfile, verbose=True)
#extract the spectra
spec_list.append(extract_spectra('x'+img, yc=1030, calfile=calfile, findobject=True, smooth=False, maskzeros=True, clobber=True))
#combine the results
w,f,e = speccombine(spec_list, obsdate)
outfile = "%s_%s.spec" % (target, obsdate)
write_spectra(outfile, w,f,e)
def buildtmc(tmcname):
from pyraf import iraf
from iraf import stsdas, hst_calib, synphot
out = open('buildtmc.log', 'w')
f = pyfits.open(tmcname)
flist = f[1].data.field('filename')
iraf.set(crrefer='./') #work locally
for k in range(len(flist)):
oldname = iraf.osfn(flist[k]).split('[')[0]
newname = fincre(oldname)
if os.path.exists(newname):
flist[k] = fincre(flist[k])
else:
out.write("%s: no change necessary\n" % oldname)
f.writeto(tmcname.replace(".fits", "_new.fits"))
out.close()
def setglobal(self, fname=None):
if fname is None:
fname = __file__
self.propername = self.id()
base, ext = os.path.splitext(os.path.basename(fname))
main, case, test = self.propername.split('.')
self.name = os.path.join(base, test, case)
self.wavename = self.name + '_wave.fits'
#Make sure the directories exist
dirname = os.path.dirname(self.name)
if not os.path.isdir(dirname):
os.makedirs(dirname)
self.file = self.name
self.thresh = 0.01
self.superthresh = 0.20
self.sigthresh = 0.01
self.discrep = -99
self.tda = {
'Obsmode': self.obsmode,
'Spectrum': self.spectrum,
'ETCid': None, #placeholder
'Thresh': self.thresh,
'Superthresh': self.superthresh,
'SigThresh': self.sigthresh,
'crrefer': iraf.osfn('crrefer$'),
'pysyn_cdbs': os.environ['PYSYN_CDBS'],
'SkyLines': self.hasSkyLines()
}
try:
self.tda['Subset'] = self.subset
except AttributeError:
pass
try:
self.tda['ETCid'] = self.etcid
except AttributeError:
pass
try:
self.tda['form'] = self.form
except AttributeError:
pass
self.tra = {}
def makesensfunc(scifiles, objname, base_stddir, extfile):
#TODO use individual standard star observations in each setting, not just red and blue
for f in scifiles:
redorblue = getredorblue(f)
# If this is a standard star, run standard
# Standards will have an observation class of either progCal or partnerCal
# Standards will have an observation class of either progCal or partnerCal
obsclass = fits.getval(f[:-4] + '.fits', 'OBSCLASS')
if obsclass == 'progCal' or obsclass == 'partnerCal':
# Figure out which directory the stardard star is in
stddir = iraf.osfn('gmisc$lib/onedstds/') + base_stddir
# iraf.gsstandard('est' + f[:-4], 'std' + redorblue,
# 'sens' + redorblue, starname=objname.lower(),
# caldir='gmisc$lib/onedstds/'+stddir, fl_inter=True)
specsens('et' + f[:-4] + '.fits', 'sens' + redorblue + '.fits',
stddir + objname + '.dat' , extfile,
float(fits.getval(f[:-4] + '.fits', 'AIRMASS')),
float(fits.getval(f[:-4] + '.fits', 'EXPTIME')))
def makesensfunc(scifiles, objname, base_stddir, extfile):
#TODO use individual standard star observations in each setting, not just red and blue
for f in scifiles:
redorblue = getredorblue(f)
# If this is a standard star, run standard
# Standards will have an observation class of either progCal or partnerCal
# Standards will have an observation class of either progCal or partnerCal
obsclass = pyfits.getval(f[:-4] + '.fits', 'OBSCLASS')
if obsclass == 'progCal' or obsclass == 'partnerCal':
# Figure out which directory the stardard star is in
stddir = iraf.osfn('gmisc$lib/onedstds/') + base_stddir
# iraf.gsstandard('est' + f[:-4], 'std' + redorblue,
# 'sens' + redorblue, starname=objname.lower(),
# caldir='gmisc$lib/onedstds/'+stddir, fl_inter=True)
specsens('et' + f[:-4] + '.fits', 'sens' + redorblue + '.fits',
stddir + objname + '.dat' , extfile,
float(pyfits.getval(f[:-4] + '.fits', 'AIRMASS')),
float(pyfits.getval(f[:-4] + '.fits', 'EXPTIME')))
def setglobal(self,fname=None):
if fname is None:
fname=__file__
self.propername=self.id()
base,ext=os.path.splitext(os.path.basename(fname))
main,case,test=self.propername.split('.')
self.name=os.path.join(base,test,case)
self.wavename=self.name+'_wave.fits'
#Make sure the directories exist
dirname=os.path.dirname(self.name)
if not os.path.isdir(dirname):
os.makedirs(dirname)
self.file=self.name
self.thresh=0.01
self.superthresh=0.20
self.sigthresh=0.01
self.discrep=-99
self.tda={'Obsmode':self.obsmode,
'Spectrum':self.spectrum,
'ETCid':None, #placeholder
'Thresh':self.thresh,
'Superthresh':self.superthresh,
'SigThresh':self.sigthresh,
'crrefer':iraf.osfn('crrefer$'),
'pysyn_cdbs':os.environ['PYSYN_CDBS'],
'SkyLines':self.hasSkyLines()}
try:
self.tda['Subset']=self.subset
except AttributeError:
pass
try:
self.tda['ETCid']=self.etcid
except AttributeError:
pass
try:
self.tda['form']=self.form
except AttributeError:
pass
self.tra={}
def specred(infile_list, propcode=None, inter=True, automethod='Matchlines'):
#set up the files
infiles=','.join(['%s' % x for x in infile_list])
obsdate=os.path.basename(infile_list[0])[7:15]
#set up some files that will be needed
logfile='spec'+obsdate+'.log'
dbfile='spec%s.db' % obsdate
#create the observation log
obs_dict=obslog(infile_list)
for i in range(len(infile_list)):
if obs_dict['OBJECT'][i].upper().strip()=='ARC' and obs_dict['PROPID'][i].upper().strip()==propcode:
lamp=obs_dict['LAMPID'][i].strip().replace(' ', '')
arcimage=os.path.basename(infile_list[i])
if lamp == 'NONE': lamp='CuAr'
lampfile=iraf.osfn("pysalt$data/linelists/%s.salt" % lamp)
#lampfile='/Users/crawford/research/kepler/Xe.salt'
specidentify(arcimage, lampfile, dbfile, guesstype='rss',
guessfile='', automethod=automethod, function='legendre', order=3,
rstep=100, rstart='middlerow', mdiff=20, thresh=3, niter=5, smooth=3,
inter=inter, clobber=True, logfile=logfile, verbose=True)
#specrectify(arcimage, outimages='', outpref='x', solfile=dbfile, caltype='line',
# function='legendre', order=3, inttype='interp', w1=None, w2=None, dw=None, nw=None,
# blank=0.0, clobber=True, logfile=logfile, verbose=True)
objimages=''
spec_list=[]
for i in range(len(infile_list)):
if obs_dict['CCDTYPE'][i].count('OBJECT') and obs_dict['INSTRUME'][i].count('RSS') and obs_dict['PROPID'][i].upper().strip()==propcode:
img = infile_list[i]
##rectify it
specrectify(img, outimages='', outpref='x', solfile=dbfile, caltype='line',
function='legendre', order=3, inttype='interp', w1=None, w2=None, dw=None, nw=None,
blank=0.0, nearest=True, clobber=True, logfile=logfile, verbose=True)
# extract the order of the fit and the positions of each of the slits
order, slit_positions = mt.read_slits_HDUtable(struct[slitext])
return order, slit_positions
def check_ypos(slit_positions, data):
"""Check the y-position"""
from scipy.signal import find_peaks_cwt
# determine the points that
y = data.sum(axis=1)
yp = find_peaks_cwt(np.gradient(y), np.array([5]))
x = np.arange(len(y))
s_min= x.max()
s_max= x.min()
for n, ym, yx in slit_positions:
if ym < s_min: s_min = ym
if yx > s_max: s_max = yx
dy = yp[yp>10].min()-s_min
for i in range(len(slit_positions)):
n, ym, yx = slit_positions[i]
slit_positions[i] = [n, ym+dy, yx+dy]
return slit_positions, dy
# main code
parfile = iraf.osfn("saltspec$specslit.par")
t = iraf.IrafTaskFactory(taskname="specslit", value=parfile, function=specslit,
pkgname='saltspec')
])
parser.add_argument('--verbose',
action='store_true',
help='Write to a log file?')
parser.add_argument('--logfile',
'-l',
default='kepdeltapix.log',
help='Name of ascii log file',
dest='logfile',
type=str)
parser.add_argument('--status',
'-e',
help='Exit status (0=good)',
default=0,
dest='status',
type=int)
args = parser.parse_args()
cmdLine = True
kepdeltapix(args.infile, args.rownum, args.columns, args.rows, args.fluxes,
args.prfdir, args.interpolation, args.tolerance, args.fittype,
args.imscale, args.cmap, args.verbose, args.logfile,
args.status, cmdLine)
else:
from pyraf import iraf
parfile = iraf.osfn("kepler$kepdeltapix.par")
t = iraf.IrafTaskFactory(taskname="kepdeltapix",
value=parfile,
function=kepdeltapix)
#set binning
binstr = saltstring.makebinstr(flatkeys[2])
#set gain
gnstr = saltstring.makegainstr(flatkeys[3])
#set readout
rostr = saltstring.makereadoutstr(flatkeys[4])
fltstr = flatkeys[5].strip()
if flatkeys[6].count('SKY'):
skystr = 'Sky'
else:
skystr = ''
flatname = '%s%s%sFlat%s%s%s%s%s.fits' % (instr, obsdate, skystr, mdstr,
binstr, gnstr, rostr, fltstr)
return flatname
# -----------------------------------------------------------
# main code
if not iraf.deftask('saltclean'):
parfile = iraf.osfn("saltred$saltclean.par")
t = iraf.IrafTaskFactory(taskname="saltclean",
value=parfile,
function=saltclean,
pkgname='saltred')
# update the header values with the image name and extension number
try:
hdue = pyfits.ImageHDU(oarray)
hdue.header=header
hdue.header.update('ONAME',infile,'Original image name')
hdue.header.update('OEXT',hdu,'Original extension number')
except Exception as e:
msg='SALTPHOT--WARNING: Could not update image in newfits file for %s ext %i because %s' \
% (infile, hdu, e)
raise SaltIOError(msg)
hduList.append(hdue)
j +=1
# close FITS file
saltio.closefits(struct)
# close the output fits file:
try:
# write out the file
hduList.flush()
hduList.close()
except Exception as e:
raise SaltIOError('Failed to write %s because %s' % (outfits, e))
# -----------------------------------------------------------
# main code
parfile = iraf.osfn("slottools$slotback.par")
t = iraf.IrafTaskFactory(taskname="slotback",value=parfile,function=slotback,pkgname='slottools')
return t_real, t_wrong, t_start, t_arr, ysum_arr
def find_real_time(dt, t_min, t_max):
"""Find the real exposure+dead time
returns float
"""
t_e=np.arange(t_min,t_max,0.0001)
ysum=t_e*0.0
for i in range(len(t_e)):
ysum[i]=ntime_func(dt, t_e[i])
return t_e, ysum
def ntime_func(dt, t_e):
"""Merit function to determine best time
Weighted for the number of objects in each step
return float
"""
y=0
for j in range(len(dt)):
i=dt[j]/t_e
y += abs(i-round(i))
return y
# -----------------------------------------------------------
# main code
parfile = iraf.osfn("slottools$slotutcfix.par")
t = iraf.IrafTaskFactory(taskname="slotutcfix",value=parfile,function=slotutcfix,pkgname='slottools')
# -----------------------------------------------------------
# main
if '--shell' in sys.argv:
import argparse
parser = argparse.ArgumentParser(description='Plot, create or edit custom light curve extraction masks for target pixel files')
parser.add_argument('--shell', action='store_true', help='Are we running from the shell?')
parser.add_argument('infile', help='name of input target pixel FITS file', type=str)
parser.add_argument('maskfile', help='name of ASCII custom aperture definition file', type=str)
parser.add_argument('--plotfile', default='', help='name of output PNG plot file', type=str)
parser.add_argument('--tabrow', default=2177, help='The table row containing the image to plot', type=int)
parser.add_argument('--imin', default=1.5e5, help='minimum of image intensity scale [e-]', type=float)
parser.add_argument('--imax', default=5.0e5, help='maximum of image intensity scale [e-]', type=float)
parser.add_argument('--iscale', default='logarithmic', help='type of image intensity scale',
type=str, choices=['linear','logarithmic','squareroot'])
parser.add_argument('--cmap', default='PuBu', help='image colormap', type=str)
parser.add_argument('--verbose', action='store_true', help='Write to a log file?')
parser.add_argument('--logfile', '-l', help='Name of ascii log file', default='kepcotrend.log', dest='logfile', type=str)
parser.add_argument('--status', '-e', help='Exit status (0=good)', default=0, dest='status', type=int)
args = parser.parse_args()
cmdLine=True
kepmask(args.infile, args.maskfile, args.plotfile, args.tabrow, args.imin, args.imax, args.iscale,
args.cmap, args.verbose, args.logfile, args.status, cmdLine)
else:
from pyraf import iraf
parfile = iraf.osfn("kepler$kepmask.par")
t = iraf.IrafTaskFactory(taskname="kepmask", value=parfile, function=kepmask)
def findwskeyword(keyword, sol):
"""Find and return a value for a keyword in the list of the wavelength solution"""
i = sol.index(keyword)
j = sol[i:].index('\n')
return sol[i:i + j].split('=')[1].strip()
def enterdatetime(dstr):
"""Break up the datetime string to create a datetime object
return datetime
"""
dlist = dstr.split()
year, month, day = dlist[0].split('-')
hour, minute, second = dlist[1].split(':')
return datetime.datetime(
int(year), int(month), int(day), int(hour), int(minute), int(float(second)))
def subtracttime(d1, d2):
"""Return the difference in two dates in seconds"""
dt = max(d1, d2) - min(d1, d2)
return 86400 * dt.days + dt.seconds
# main code
if not iraf.deftask('specrectify'):
parfile = iraf.osfn("saltspec$specrectify.par")
t = iraf.IrafTaskFactory(taskname="specrectify", value=parfile,
function=specrectify, pkgname='saltspec')
bpm: bad pixel mask array
"""
if bpm is None:
bpm=arr*0.0+1.0
wei=None
else:
# correct the weights for the bad pixel mask
if ivar is None: ivar=arr*0.0+1.0
wei=ivar*(1-bpm)
#TODO: need to add a check to make sure that there are is a place
#to make sure that one of the weights is at least zero
check_wei=wei.sum(axis=0)
wei[0][check_wei==0]=wei.min()
if method=='average':
c_arr, s_arr=np.average(arr, axis=0, weights=wei, returned=True)
return c_arr, s_arr
elif method=='median':
return np.median(arr, axis=0), bpm.sum(axis=0)
else:
msg='%s is not a method for combining arrays' % method
raise SaltError(msg)
# -----------------------------------------------------------
# main code
if not iraf.deftask('saltcombine'):
parfile = iraf.osfn("saltred$saltcombine.par")
t = iraf.IrafTaskFactory(taskname="saltcombine",value=parfile,function=saltcombine, pkgname='saltred')
parser.add_argument('--verbose', action='store_true', help='Write to a log file?')
parser.add_argument('--logfile', '-l', help='Name of ascii log file', default='keptransit.log', dest='logfile', type=str)
parser.add_argument('--status', '-e', help='Exit status (0=good)', default=0, dest='status', type=int)
cmdLine=True
args = parser.parse_args()
keptransit(args.inputfile, args.outputfile, args.datacol, args.errorcol,
args.periodini_d, args.rprsini, args.T0ini,
args.Eccini, args.arsini, args.incini, args.omegaini, args.LDparams, args.secini,
args.fixperiod, args.fixrprs, args.fixT0,
args.fixEcc, args.fixars, args.fixinc, args.fixomega, args.fixsec, args.fixfluxoffset,
args.removeflaggeddata, args.ftol,
args.fitter, args.norm,
args.clobber, args.plot, args.verbose, args.logfile, args.status,
cmdLine)
else:
from pyraf import iraf
parfile = iraf.osfn("kepler$keptransit.par")
t = iraf.IrafTaskFactory(taskname="keptransit", value=parfile, function=keptransit)
parser.add_argument('--verbose',
action='store_true',
help='Write to a log file?')
parser.add_argument('--logfile',
'-l',
help='Name of ascii log file',
default='kepcotrend.log',
dest='logfile',
type=str)
parser.add_argument('--status',
'-e',
help='Exit status (0=good)',
default=0,
dest='status',
type=int)
args = parser.parse_args()
cmdLine = True
kepdiffim(args.infile, args.outfile, args.plotfile, args.imscale,
args.cmap, args.filter, args.function, args.cutoff, args.clobber,
args.verbose, args.logfile, args.status, cmdLine)
else:
from pyraf import iraf
parfile = iraf.osfn("kepler$kepdiffim.par")
t = iraf.IrafTaskFactory(taskname="kepdiffim",
value=parfile,
function=kepdiffim)
# create the columns and the
for ap in ap_list:
fvar = abs(ap.lvar)**0.5
# create the columns
col1 = pyfits.Column(name='wavelength',
format='D',
unit='Angstroms',
array=ap.wave)
col2 = pyfits.Column(name='counts',
format='D',
unit='Counts',
array=ap.ldata)
col3 = pyfits.Column(name='counts_err', format='D', array=fvar)
# add to the table
tbhdu = pyfits.new_table([col1, col2, col3])
hdulist.append(tbhdu)
# write it out
hdulist.writeto(ofile)
return
# main code
parfile = iraf.osfn("saltspec$specextract.par")
t = iraf.IrafTaskFactory(taskname="specextract",
value=parfile,
function=specextract,
pkgname='saltspec')
# convert from string to list
location = commaSep(location, "float")
extrsize = commaSep(extrsize, "int")
inlist = input.split(",")
more_input = []
for filename in inlist:
more_input.extend(filename.split())
all_input = []
for filename in more_input:
filename = iraf.osfn(filename)
all_input.extend(glob.glob(filename))
X1D.extractSpec(all_input,
outdir=outdir,
update_input=update_input,
location=location,
extrsize=extrsize,
find_target=find_targ,
verbosity=verbosity)
# Initialize IRAF Task definition now...
parfile = iraf.osfn(_parfile)
junk = iraf.IrafTaskFactory(taskname=_taskname,
value=parfile,
pkgname=PkgName,
pkgbinary=PkgBinary,
function=x1d_iraf)
iraf.x1dcorr.version = _version
parser.add_argument('--verbose',
action='store_true',
help='Write to a log file?')
parser.add_argument('--logfile',
'-l',
default='kepprfphot.log',
help='Name of ascii log file',
dest='logfile',
type=str)
parser.add_argument('--status',
'-e',
help='Exit status (0=good)',
default=0,
dest='status',
type=int)
args = parser.parse_args()
cmdLine = True
kepprf(args.infile, args.plotfile, args.rownum, args.columns,
args.rows, args.fluxes, args.border, args.background,
args.focus, args.prfdir, args.xtol, args.ftol, args.imscale,
args.cmap, args.labcol, args.apercol, args.plot, args.verbose,
args.logfile, args.status, cmdLine)
else:
from pyraf import iraf
parfile = iraf.osfn("kepler$kepprf.par")
t = iraf.IrafTaskFactory(taskname="kepprf",
value=parfile,
function=kepprf)
parser.add_argument('--verbose',
action='store_true',
help='Write to a log file?')
parser.add_argument('--logfile',
'-l',
help='Name of ascii log file',
default='keptimefix.log',
dest='logfile',
type=str)
parser.add_argument('--status',
'-e',
help='Exit status (0=good)',
default=0,
dest='status',
type=int)
cmdLine = True
args = parser.parse_args()
cmdLine = True
keptimefix(args.infile, args.outfile, args.clobber, args.verbose,
args.logfile, args.status, cmdLine)
else:
from pyraf import iraf
parfile = iraf.osfn("kepler$keptimefix.par")
t = iraf.IrafTaskFactory(taskname="keptimefix",
value=parfile,
function=keptimefix)
parser.add_argument('plotfile', help='Name of output PNG plot file', type=str)
parser.add_argument('--rownum', '-r', default=2200, help='Row number of image stored in infile', dest='rownum', type=int)
parser.add_argument('--columns', help='Column number of each source to be fit', type=str)
parser.add_argument('--rows', help='Row number of each source to be fit', type=str)
parser.add_argument('--fluxes', help='Relative flux of each source to be fit', type=str)
parser.add_argument('--border', '-b', help='Order of background polynmial fit', default=1, dest='border', type=int)
parser.add_argument('--background', action='store_true', help='Fit background?', default=False)
parser.add_argument('--focus', action='store_true', help='Fit focus changes?', default=False)
parser.add_argument('--prfdir', help='Folder containing Point Response Function FITS files', type=str)
parser.add_argument('--xtol', '-x', default=1.0e-4, help='Fit parameter tolerance', dest='xtol', type=float)
parser.add_argument('--ftol', '-f', default=1.0, help='Fit minimization tolerance', dest='ftol', type=float)
parser.add_argument('--imscale', '-i', help='Type of image intensity scale', default='linear', dest='imscale', type=str,choices=['linear','logarithmic','squareroot'])
parser.add_argument('--colmap', '-c', help='Image colormap', default='YlOrBr', dest='cmap', type=str,choices=['Accent','Blues','BrBG','BuGn','BuPu','Dark2','GnBu','Greens','Greys','OrRd','Oranges','PRGn','Paired','Pastel1','Pastel2','PiYG','PuBu','PuBuGn','PuOr','PuRd','Purples','RdBu','RdGy','RdPu','RdYlBu','RdYlGn','Reds','Set1','Set2','Set3','Spectral','YlGn','YlGnBu','YlOrBr','YlOrRd','afmhot','autumn','binary','bone','brg','bwr','cool','copper','flag','gist_earth','gist_gray','gist_heat','gist_ncar','gist_rainbow','gist_yarg','gnuplot','gnuplot2','gray','hot','hsv','jet','ocean','pink','prism','rainbow','seismic','spectral','spring','summer','terrain','winter','browse'])
parser.add_argument('--labcol', help='Label color', default='#ffffff', type=str)
parser.add_argument('--apercol', help='Aperture color', default='#ffffff', type=str)
parser.add_argument('--plot', action='store_true', help='Plot fit results?', default=False)
parser.add_argument('--verbose', action='store_true', help='Write to a log file?')
parser.add_argument('--logfile', '-l', default='kepprfphot.log', help='Name of ascii log file', dest='logfile', type=str)
parser.add_argument('--status', '-e', help='Exit status (0=good)', default=0, dest='status', type=int)
args = parser.parse_args()
cmdLine=True
kepprf(args.infile,args.plotfile,args.rownum,args.columns,args.rows,args.fluxes,args.border,
args.background,args.focus,args.prfdir,args.xtol,args.ftol,args.imscale,args.cmap,
args.labcol,args.apercol,args.plot,args.verbose,args.logfile,args.status,cmdLine)
else:
from pyraf import iraf
parfile = iraf.osfn("kepler$kepprf.par")
t = iraf.IrafTaskFactory(taskname="kepprf", value=parfile, function=kepprf)
for i in range(ys):
x = numpy.arange(xs)
rdata = data[i, :]
rmask = mask[i, :]
rmask = nd.minimum_filter(rmask, size=3)
if rmask.any() == True:
rdata = numpy.interp(x, x[rmask], rdata[rmask])
data[i, rmask == 0] = rdata[rmask == 0]
return data
def tran_func(a, xshift, yshift, xmag, ymag, xrot, yrot):
xtran = ymag * a[0] * cos(yrot * pi / 180.0) \
- xmag * a[1] * sin(xrot * pi / 180) \
- yshift
ytran = ymag * a[0] * sin(yrot * pi / 180.0) \
+ xmag * a[1] * cos(xrot * pi / 180) \
- xshift
return xtran, ytran
# -----------------------------------------------------------
# main code
if not iraf.deftask('saltmosaic'):
parfile = iraf.osfn("saltred$saltmosaic.par")
t = iraf.IrafTaskFactory(taskname="saltmosaic",
value=parfile,
function=saltmosaic,
pkgname='saltred')
parser.add_argument('--verbose',
action='store_true',
help='Write to a log file?')
parser.add_argument('--logfile',
'-l',
help='Name of ascii log file',
default='kepcotrend.log',
dest='logfile',
type=str)
parser.add_argument('--status',
'-e',
help='Exit status (0=good)',
default=0,
dest='status',
type=int)
args = parser.parse_args()
cmdLine = True
kepwindow(args.infile, args.outfile, args.fcol, args.fmax, args.nfreq,
args.plot, args.clobber, args.verbose, args.logfile, args.status,
cmdLine)
else:
from pyraf import iraf
parfile = iraf.osfn("kepler$kepwindow.par")
t = iraf.IrafTaskFactory(taskname="kepwindow",
value=parfile,
function=kepwindow)
header.update('SCIEXT', sci_ext, comment='Extension of science frame')
return pyfits.ImageHDU(data=data, header=header, name='BPM')
def createvariance(inhdu, sci_ext, var_ext):
"""Create a variance hdu from an input hdu"""
# create the variance array
data = inhdu.data.copy()
if (data <= 0).any():
j = numpy.where(data > 0)
min_pos = data[j].min()
j = numpy.where(data <= 0)
data[j] = min_pos
data = data ** 0.5
header = inhdu.header.copy()
header['EXTVER'] = var_ext
header.update('SCIEXT', sci_ext, comment='Extension of science frame')
return pyfits.ImageHDU(data=data, header=header, name='VAR')
# -----------------------------------------------------------
# main code
parfile = iraf.osfn("saltspec$specprepare.par")
t = iraf.IrafTaskFactory(
taskname="specprepare",
value=parfile,
function=specprepare,
pkgname='saltspec')
"of the number of frames per combined exposure (", frames_per_combined,
") so", total_frames % frames_per_combined, "frames from the end of "
"the range will be left off.")
toidx = toidx - (total_frames % frames_per_combined)
total_frames = toidx - fromidx
total_combined_frames = int(total_frames / frames_per_combined)
info("Output data cube will have", total_combined_frames, "total frames of", newexposure, "sec exposure")
info("Processing input data cube frames", fromidx, "to", toidx)
target_dir = tempfile.mkdtemp()
info("Created working directory {0} for intermediate data".format(target_dir))
try:
range_pairs = list((
fromidx + n * frames_per_combined,
fromidx + (n+1) * frames_per_combined - 1
) for n in range(0, total_combined_frames))
frame_paths = combine_cube_frames(cubefile, range_pairs, target_dir)
# frames_to_cube(frame_paths, outfile)
finally:
# Don't leave clutter if the task fails
# shutil.rmtree(target_dir)
info("Removed working directory {0}".format(target_dir))
# def frames_to_cube(frames, outfile):
parfile = iraf.osfn("aotools$aoavgcube.par")
t = iraf.IrafTaskFactory(taskname="aoavgcube", value=parfile, function=aoavgcube)
parser.add_argument('--verbose',
action='store_true',
help='Write to a log file?')
parser.add_argument('--logfile',
'-l',
help='Name of ascii log file',
default='kepcotrend.log',
dest='logfile',
type=str)
parser.add_argument('--status',
'-e',
help='Exit status (0=good)',
default=0,
dest='status',
type=int)
args = parser.parse_args()
cmdLine = True
keptrial(args.infile, args.outfile, args.datacol, args.errcol, args.fmin,
args.fmax, args.nfreq, args.method, args.ntrials, args.plot,
args.clobber, args.verbose, args.logfile, args.status, cmdLine)
else:
from pyraf import iraf
parfile = iraf.osfn("kepler$keptrial.par")
t = iraf.IrafTaskFactory(taskname="keptrial",
value=parfile,
function=keptrial)
x = numpy.arange(xs)
rdata = data[i, :]
rmask = mask[i, :]
rmask = nd.minimum_filter(rmask, size=3)
if rmask.any() == True:
rdata = numpy.interp(x, x[rmask], rdata[rmask])
data[i, rmask == 0] = rdata[rmask == 0]
return data
def tran_func(a, xshift, yshift, xmag, ymag, xrot, yrot):
xtran = ymag * a[0] * cos(yrot * pi / 180.0) \
- xmag * a[1] * sin(xrot * pi / 180) \
- yshift
ytran = ymag * a[0] * sin(yrot * pi / 180.0) \
+ xmag * a[1] * cos(xrot * pi / 180) \
- xshift
return xtran, ytran
# -----------------------------------------------------------
# main code
if not iraf.deftask('saltmosaic'):
parfile = iraf.osfn("saltred$saltmosaic.par")
t = iraf.IrafTaskFactory(
taskname="saltmosaic",
value=parfile,
function=saltmosaic,
pkgname='saltred')
def flexure_rssspec(imagefits, fitslist, option=""):
print str(datetime.now())
if option == "filesave": prefix = raw_input("\nFile prefix: ")
pixel = 15. # pixel size in microns
pix_scale = 0.125
sexparams = ["X_IMAGE","Y_IMAGE","FLUX_ISO","FLUX_MAX","FLAGS","CLASS_STAR", \
"X2WIN_IMAGE","Y2WIN_IMAGE","XYWIN_IMAGE","ERRX2WIN_IMAGE"]
np.savetxt("qred_thrufoc.param", sexparams, fmt="%s")
fmaxcol, flagcol, xvarcol, yvarcol, xerrcol = (
3, 4, 6, 7, 9) # column nos (from 0) of data in sextractor
imagestooclosefactor = 3.0 # too close if factor*sep < sqrt(var)
gaptooclose = 1.25 # arcsec
edgetooclose = 1.25 # arcsec
rattolerance = 0.25
toofaint = 250. # FMAX counts
galaxydelta = 0.4 # arcsec
MOSimagelimit = 1. # arcsec
deblend = .005 # default
imagehdr = pyfits.getheader(imagefits)
if imagehdr["GR-STATE"][1] == "4":
print "First fits file " + imagefits + " is not image of mask"
exit()
flexposns = len(fitslist)
obsdict = obslog(fitslist)
image_f = [fitslist[fpos].split(".")[0][-12:] for fpos in range(flexposns)]
dateobs = obsdict["DATE-OBS"][0].replace("-", "")
if int(dateobs) > 20110928:
rho_f = np.array(obsdict["TRKRHO"]).astype(float)
else:
rho_f = np.array(obsdict["TELRHO"]).astype(float)
catpos = np.argmin(np.abs(rho_f))
cbin, rbin = np.array(obsdict["CCDSUM"][catpos].split(" ")).astype(int)
maskid = obsdict["MASKID"][catpos].strip()
filter = obsdict["FILTER"][catpos].strip()
grating = obsdict["GRATING"][catpos].strip()
rows, cols = pyfits.getdata(fitslist[catpos]).shape
isspec = (obsdict["GR-STATE"][catpos][1] == "4")
if not isspec:
print "Use flexure_rssimage for image flexure analysis"
exit()
grang = float(obsdict["GRTILT"][catpos])
artic = float(obsdict["CAMANG"][catpos])
lamp = obsdict["LAMPID"][catpos].strip()
print "\nMask: ", maskid
print "Filter: ", filter
print "Grating: ", grating
print "Artic (deg): ", artic
print "Gr Angle (deg): ", grang
print "Lamp: ", lamp
# map the mask spots _m using the imaging fits file
sex_js = sextract(imagefits, deblend=deblend)
flux_s = sex_js[2]
fluxmedian = np.median(np.sort(flux_s)[-10:])
okm_s = (flux_s > fluxmedian / 10) # cull bogus spots
maskholes = okm_s.sum()
r_m = sex_js[1, okm_s]
c_m = sex_js[0, okm_s]
# find mask rows _R, tabulate
histr_b, binr_b = np.histogram(r_m, bins=rows / 10, range=(0, rows))
bin0_R = np.where((histr_b[1:] > 0) & (histr_b[:-1] == 0))[0]
bin1_R = np.where((histr_b[1:] == 0) & (histr_b[:-1] > 0))[0]
maskRows = bin0_R.shape[0]
bin_m = np.digitize(r_m, binr_b) - 1
R_m = np.array([np.where((bin_m[m] >= bin0_R) & (bin_m[m] <= bin1_R))[0][0] \
for m in range(maskholes)])
# find mask cols _C, tabulate
histc_b, binc_b = np.histogram(c_m, bins=cols / 10, range=(0, cols))
bin0_C = np.where((histc_b[1:] > 0) & (histc_b[:-1] == 0))[0]
bin1_C = np.where((histc_b[1:] == 0) & (histc_b[:-1] > 0))[0]
maskCols = bin0_C.shape[0]
bin_m = np.digitize(c_m, binc_b) - 1
C_m = np.array([np.where((bin_m[m] >= bin0_C) & (bin_m[m] <= bin1_C))[0][0] \
for m in range(maskholes)])
# identify mask center = optical axis
if maskid == 'P000000N99': # symmetric mask
Raxis = maskRows / 2
Caxis = maskCols / 2
elif maskid == 'P000000N03': # mask with centered cross
Raxis = np.where((np.argmax(histr_b) >= bin0_R)
& (np.argmax(histr_b) <= bin1_R))[0][0]
Caxis = np.where((np.argmax(histc_b) >= bin0_C)
& (np.argmax(histc_b) <= bin1_C))[0][0]
else:
print "Not a valid flexure mask"
exit()
maxis = np.where((R_m == Raxis) & (C_m == Caxis))[0][0]
raxis = r_m[maxis]
caxis = c_m[maxis]
print "\nMask_Holes Rows Cols r axis c axis \n pixels pixels"
print " %5i %5i %5i %8.1f %8.1f" % (maskholes, maskRows, maskCols,
raxis * rbin, caxis * cbin)
# np.savetxt(dateobs+'_'+"mask.txt",np.vstack((r_m,c_m,sex_js[2,okm_s],R_m)).T,fmt="%10.2f")
# get linelist, predict spots in spectral image
wavcent = rsslam(grating, grang, artic, 0., dateobs)
specfile = datedfile(datadir + "spectrograph/spec_yyyymmdd.txt", dateobs)
FCampoly = np.loadtxt(specfile, usecols=(1, ))[5:11]
fcam = np.polyval(FCampoly, (wavcent / 1000. - 4.))
lampfile = iraf.osfn("pysalt$data/linelists/" + lamp + ".salt")
wav_l, int_l = np.loadtxt(lampfile, unpack=True)
maxdalpha = -np.degrees((cols / 2) * cbin * pixel / (1000. * fcam))
maxgamma = np.degrees((rows / 2) * rbin * pixel / (1000. * fcam))
maxwav = rsslam(grating, grang, artic, cols * cbin / 2, dateobs,
-maxdalpha, 0)
minwav = rsslam(grating, grang, artic, -cols * cbin / 2, dateobs,
maxdalpha, maxgamma)
ok_l = (wav_l >= minwav) & (wav_l <= maxwav)
wav_l = wav_l[ok_l]
int_l = int_l[ok_l]
lines = wav_l.shape[0]
col_ml = np.zeros((maskholes, lines))
dcol_c = np.arange(-(cols * cbin / 2), (cols * cbin / 2))
for m in range(maskholes):
dalpha = -np.degrees((c_m[m] - caxis) * cbin * pixel / (1000. * fcam))
gamma = np.degrees((r_m[m] - raxis) * rbin * pixel / (1000. * fcam))
wav0, wav1 = rsslam(grating,
grang,
artic,
dcol_c[[0, -1]],
dateobs,
dalpha,
gamma=gamma)
ok_l = ((wav_l > wav0) & (wav_l < wav1))
colwav = interp1d(rsslam(grating,grang,artic,dcol_c, \
dateobs,dalpha=dalpha,gamma=gamma), dcol_c)
col_ml[m, ok_l] = colwav(wav_l[ok_l]) + caxis * cbin
# np.savetxt(dateobs+"_col_ml.txt",np.vstack((R_m,C_m,col_ml.T)),fmt="%8.1f")
# identify mask hole and wavelength for spots in spec image closest to rho=0
os.remove("sexwt.fits")
sex_js = sextract(fitslist[catpos], "", deblend=deblend)
r_s = sex_js[1]
c_s = sex_js[0]
flux_s = sex_js[2]
spots = r_s.shape[0]
fluxmedian = np.median(np.sort(sex_js[2])[-10:])
ok_s = (flux_s > fluxmedian / 30) # cull bogus spots
# find spectral bin rows RR in candidates R0, cull non-spectra
histr_b, binr_b = np.histogram(r_s[ok_s], bins=rows / 10, range=(0, rows))
histr_b[[0, -1]] = 0
bin0_R0 = np.where((histr_b[1:] > 0) & (histr_b[:-1] == 0))[0] + 1
bin1_R0 = np.where((histr_b[1:] == 0) & (histr_b[:-1] > 0))[0]
bin_s = np.digitize(r_s, binr_b) - 1
maxcount_R0 = np.array([(histr_b[bin0_R0[R0]:bin1_R0[R0]+1]).max() \
for R0 in range(bin0_R0.shape[0])])
ok_R0 = (maxcount_R0 > 3)
specrows = ok_R0.sum() # cull down to spectra RR
bin0_RR = bin0_R0[ok_R0]
bin1_RR = bin1_R0[ok_R0]
ok_s &= ((bin_s >= bin0_RR[:, None]) &
(bin_s <= bin1_RR[:, None])).any(axis=0)
RR_s = -np.ones(spots)
r_RR = np.zeros(specrows)
for RR in range(specrows):
isRR_s = ok_s & np.in1d(bin_s, np.arange(bin0_RR[RR], bin1_RR[RR] + 1))
RR_s[isRR_s] = RR
r_RR[RR] = r_s[isRR_s].mean()
count_RR = (RR_s[:, None] == range(specrows)).sum(axis=0)
if maskid == 'P000000N99':
RRaxis = np.argmin((raxis - r_RR)**2)
elif maskid == 'P000000N03':
RRaxis = np.argmax(count_RR)
# cull weak lines
ptile = 100. * min(
1., 5. * maskCols / count_RR.max()) # want like 5 brightest lines
for RR in range(specrows):
isRR_s = ok_s & np.in1d(bin_s, np.arange(bin0_RR[RR], bin1_RR[RR] + 1))
fluxmin = np.percentile(sex_js[2, isRR_s], 100. - ptile)
ok_s[isRR_s] &= (sex_js[2, isRR_s] > fluxmin)
# identify with mask rows R (assuming no gaps)
RR_m = R_m + RRaxis - Raxis
# find approximate grating shift in dispersion direction by looking for most common id error
histc_b = np.zeros(60)
for RR in range(specrows):
isRR_s = ((RR_s == RR) & ok_s)
cerr_MS = (c_s[None, isRR_s] - col_ml[RR_m == RR].ravel()[:, None])
histc_b += np.histogram(cerr_MS.ravel(), bins=60, range=(-150, 150))[0]
cshift = 5 * np.argmax(histc_b) - 150
col_ml += cshift
# identify wavelength and mask column with spots in each spectrum
isfound_s = np.zeros((spots), dtype=bool)
bintol = 16 / cbin # 2 arcsec tolerance for line ID
R_s = -np.ones(spots, dtype=int)
C_s = -np.ones(spots, dtype=int)
l_s = -np.ones(spots, dtype=int)
m_s = -np.ones(spots, dtype=int)
cerr_s = np.zeros(spots)
rmscol = 0.
for RR in range(specrows): # _S spot in spectrum, _P (mask column, line)
isRR_m = (RR_m == RR)
isRR_s = ((RR_s == RR) & ok_s)
cerr_PS = (c_s[None, isRR_s] - col_ml[isRR_m].ravel()[:, None])
Spots = isRR_s.sum()
Possibles = col_ml[isRR_m].size
Cols = Possibles / lines
P_S = np.argmin(np.abs(cerr_PS), axis=0)
cerr_S = cerr_PS[P_S, range(isRR_s.sum())]
isfound_S = (np.abs(cerr_S) < bintol)
M_P, l_P = np.unravel_index(np.arange(Possibles), (Cols, lines))
m_P = np.where(isRR_m)[0][M_P]
m_S = m_P[P_S]
C_P = C_m[m_P]
C_S = C_P[P_S]
l_S = l_P[P_S]
s_S = np.where(isRR_s)[0]
R_s[isRR_s] = RR + Raxis - RRaxis
cerr_s[s_S] = cerr_S
C_s[s_S[isfound_S]] = C_S[isfound_S]
l_s[s_S[isfound_S]] = l_S[isfound_S]
m_s[s_S[isfound_S]] = m_S[isfound_S]
isfound_s[s_S] |= isfound_S
rmscol += (cerr_S[isfound_S]**2).sum()
# cull wavelengths to _L with < 1/2 Mask Rows or Cols
ok_s &= isfound_s
ok_l = np.zeros((lines), dtype=bool)
for line in range(lines):
lRows = np.unique(R_s[l_s == line]).shape[0]
lCols = np.unique(C_s[l_s == line]).shape[0]
ok_l[line] = ((lRows >= maskRows / 2) & (lCols >= maskCols / 2))
l_L = np.where(ok_l)[0]
wav_L = wav_l[l_L]
Lines = l_L.shape[0]
ok_s &= np.in1d(l_s, l_L)
# tabulate good catalog spots (final _S)
s_S = np.where(ok_s)[0]
r_S = r_s[s_S]
c_S = c_s[s_S]
cerr_S = cerr_s[s_S]
R_S = R_s[s_S]
C_S = C_s[s_S]
l_S = l_s[s_S]
Spots = ok_s.sum()
rshift = r_S[R_S == Raxis].mean() - raxis
cshift += (c_S - col_ml[m_s[s_S], l_S]).mean()
rmscol = np.sqrt(rmscol / Spots)
np.savetxt("cat_S.txt",np.vstack((s_S,r_S,c_S,R_S,C_S,l_S,cerr_S)).T, \
fmt="%5i %8.2f %8.2f %5i %5i %5i %8.2f")
print "\nSpec_Spots Lines rshift cshift rms\n pixels pixels pixels"
print " %5i %5i %8.1f %8.1f %8.1f" % (Spots, np.unique(l_S).shape[0],
rshift, cshift, rmscol)
print "\nLineno Wavel spots Rows Cols"
for L in range(Lines):
line = l_L[L]
lRows = np.unique(R_S[l_S == line]).shape[0]
lCols = np.unique(C_S[l_S == line]).shape[0]
lspots = (l_S == line).sum()
print " %5i %8.2f %5i %5i %5i" % (line, wav_l[line], lspots, lRows,
lCols)
sexcols = sex_js.shape[0]
sexdata_jfS = np.zeros((sexcols, flexposns, Spots))
sexdata_jfS[:, catpos] = sex_js[:, ok_s]
xcenter_L = col_ml[maxis, l_L]
ycenter = raxis + rshift
if option == "filesave":
np.savetxt(prefix+"Spots.txt",sexdata_jfS[:,catpos].T, \
fmt=2*"%9.2f "+"%9.0f "+"%9.1f "+"%4i "+"%6.2f "+3*"%7.2f "+"%11.3e")
# find spots in flexure series, in order of increasing abs(rho), and store sextractor output
row_fLd = np.zeros((flexposns, Lines, 2))
col_fLd = np.zeros((flexposns, Lines, 2))
print "\n fits rho line spots rshift cshift rslope cslope rmserr "
print " deg Ang arcsec arcsec arcmin arcmin bins"
for dirn in (1, -1):
refpos = catpos
posdirlist = np.argsort(dirn * rho_f)
poslist = posdirlist[dirn * rho_f[posdirlist] > rho_f[refpos]]
for fpos in poslist:
col_S, row_S = sexdata_jfS[0:2, refpos, :]
sex_js = sextract(fitslist[fpos], "sexwt.fits", deblend=deblend)
binsqerr_sS = (sex_js[1, :, None] - row_S[None, :])**2 + (
sex_js[0, :, None] - col_S[None, :])**2
S_s = np.argmin(binsqerr_sS, axis=1)
# First compute image shift by averaging small errors
rowerr_s = sex_js[1] - row_S[S_s]
colerr_s = sex_js[0] - col_S[S_s]
hist_r, bin_r = np.histogram(rowerr_s,
bins=32,
range=(-2 * bintol, 2 * bintol))
drow = rowerr_s[(rowerr_s > bin_r[np.argmax(hist_r)]-bintol) & \
(rowerr_s < bin_r[np.argmax(hist_r)]+bintol)].mean()
hist_c, bin_c = np.histogram(colerr_s,
bins=32,
range=(-2 * bintol, 2 * bintol))
dcol = colerr_s[(colerr_s > bin_c[np.argmax(hist_c)]-bintol) & \
(colerr_s < bin_c[np.argmax(hist_c)]+bintol)].mean()
# Now refind the closest ID
binsqerr_sS = (sex_js[1,:,None] - row_S[None,:] -drow)**2 + \
(sex_js[0,:,None] - col_S[None,:] -dcol)**2
binsqerr_s = binsqerr_sS.min(axis=1)
isfound_s = binsqerr_s < bintol**2
S_s = np.argmin(binsqerr_sS, axis=1)
isfound_s &= (binsqerr_s == binsqerr_sS[:, S_s].min(axis=0))
isfound_S = np.array([S in S_s[isfound_s] for S in range(Spots)])
sexdata_jfS[:, fpos, S_s[isfound_s]] = sex_js[:, isfound_s]
drow_S = sexdata_jfS[1, fpos] - sexdata_jfS[1, catpos]
dcol_S = sexdata_jfS[0, fpos] - sexdata_jfS[0, catpos]
# np.savetxt("motion_"+str(fpos)+".txt",np.vstack((isfound_S,l_S,drow_S,dcol_S)).T,fmt="%3i %3i %8.2f %8.2f")
# Compute flexure image motion parameters for each line
for L in range(Lines):
ok_S = ((l_S == l_L[L]) & isfound_S)
row_fLd[fpos,L],rowchi,d,d,d = \
np.polyfit(sexdata_jfS[0,catpos,ok_S]-xcenter_L[L],drow_S[ok_S],deg=1,full=True)
col_fLd[fpos,L],colchi,d,d,d = \
np.polyfit(sexdata_jfS[1,catpos,ok_S]-ycenter,dcol_S[ok_S],deg=1,full=True)
rms = np.sqrt((rowchi + colchi) / (2 * ok_S.sum()))
print ("%12s %5.0f %5i %5i "+5*"%7.2f ") % (image_f[fpos], rho_f[fpos], wav_L[L], \
ok_S.sum(),row_fLd[fpos,L,1]*rbin*pix_scale, col_fLd[fpos,L,1]*cbin*pix_scale, \
60.*np.degrees(row_fLd[fpos,L,0]),-60.*np.degrees(col_fLd[fpos,L,0]), rms)
if option == "filesave":
np.savetxt(prefix+"flex_"+str(fpos)+".txt",np.vstack((isfound_S,drow_S,dcol_S)).T, \
fmt = "%2i %8.3f %8.3f")
np.savetxt(prefix + "sextr_" + str(fpos) + ".txt",
sexdata_jfS[:, fpos].T)
print
# make plots
fig, plot_s = plt.subplots(2, 1, sharex=True)
plt.xlabel('Rho (deg)')
plt.xlim(-120, 120)
plt.xticks(range(-120, 120, 30))
fig.set_size_inches((8.5, 11))
fig.subplots_adjust(left=0.175)
plot_s[0].set_title(
str(dateobs) + [" Imaging", " Spectral"][isspec] + " Flexure")
plot_s[0].set_ylabel('Mean Position (arcsec)')
plot_s[0].set_ylim(-0.5, 4.)
plot_s[1].set_ylabel('Rotation (arcmin ccw)')
plot_s[1].set_ylim(-10., 6.)
lbl_L = [("%5.0f") % (wav_L[L]) for L in range(Lines)]
color_L = 'bgrcmykw'
for L in range(Lines):
plot_s[0].plot(rho_f,row_fLd[:,L,1]*rbin*pix_scale, \
color=color_L[L],marker='D',markersize=8,label='row '+lbl_L[L])
plot_s[1].plot(rho_f,
60. * np.degrees(row_fLd[:, L, 0]),
color=color_L[L],
marker='D',
markersize=8,
label='row ' + lbl_L[L])
collbl = 'col' + lbl_L[0]
for L in range(Lines):
plot_s[0].plot(rho_f,col_fLd[:,L,1]*cbin*pix_scale, \
color=color_L[L],marker='s',markersize=8,label=collbl)
plot_s[1].plot(rho_f,-60.*np.degrees(col_fLd[:,L,0]), \
color=color_L[L],marker='s',markersize=8,label=collbl)
collbl = ''
plot_s[0].legend(fontsize='medium', loc='upper center')
plotfile = str(dateobs) + ['_imflex.pdf', '_grflex.pdf'][isspec]
plt.savefig(plotfile, orientation='portrait')
if os.name == 'posix':
if os.popen('ps -C evince -f').read().count(plotfile) == 0:
os.system('evince ' + plotfile + ' &')
os.remove("out.txt")
os.remove("qred_thrufoc.param")
os.remove("sexwt.fits")
return
type=str)
parser.add_argument('--verbose',
action='store_true',
help='Write to a log file?')
parser.add_argument('--logfile',
'-l',
help='Name of ascii log file',
default='kepcotrend.log',
dest='logfile',
type=str)
parser.add_argument('--status',
'-e',
help='Exit status (0=good)',
default=0,
dest='status',
type=int)
args = parser.parse_args()
cmdLine = True
kepmask(args.infile, args.maskfile, args.plotfile, args.tabrow, args.imin,
args.imax, args.iscale, args.cmap, args.verbose, args.logfile,
args.status, cmdLine)
else:
from pyraf import iraf
parfile = iraf.osfn("kepler$kepmask.par")
t = iraf.IrafTaskFactory(taskname="kepmask",
value=parfile,
function=kepmask)
kepmsg.clock(message,logfile,verbose)
# main
if '--shell' in sys.argv:
import argparse
parser = argparse.ArgumentParser(description='Append multiple month short cadence and/or multiple quarter long cadence data')
parser.add_argument('--shell', action='store_true', help='Are we running from the shell?')
parser.add_argument('infiles', help='List of input files', type=str)
parser.add_argument('outfile', help='Name of FITS file to output', type=str)
parser.add_argument('--clobber', action='store_true', help='Overwrite output file?')
parser.add_argument('--verbose', action='store_true', help='Write to a log file?')
parser.add_argument('--logfile', '-l', help='Name of ascii log file', default='kepcotrend.log', dest='logfile', type=str)
parser.add_argument('--status', '-e', help='Exit status (0=good)', default=0, dest='status', type=int)
args = parser.parse_args()
kepstitch(args.infiles,args.outfile,args.clobber,args.verbose,args.logfile,args.status)
else:
from pyraf import iraf
parfile = iraf.osfn("kepler$kepstitch.par")
t = iraf.IrafTaskFactory(taskname="kepstitch", value=parfile, function=kepstitch)
x1=0
y2=y2
x2=x2
else:
y1=0
x1=x2
x2=x1+x2
elif detector=='hrdet':
y1=0
if i%2==1: x1=0
if i%2==0:
x1=x2
x2=x1+x2
if i>2:
y1=y2
y2=y1+y2
data[y1:y2,x1:x2]=hdu[i].data
ihdu = pyfits.ImageHDU(data)
nhdu = pyfits.HDUList([hdu[0], ihdu])
return nhdu
if not iraf.deftask('hrsstack'):
parfile = iraf.osfn("salthrs$hrsstack.par")
t = iraf.IrafTaskFactory(taskname="hrsstack",value=parfile,function=hrsstack, pkgname='salthrs')
msg += '#gratilt=%s\n' % grasteps
msg += '#arang=%s\n' % arang
msg += '#artilt=%s\n' % arsteps
msg += '#filter=%s\n' % rfilter.strip()
if objid:
msg += '#slitid=%s\n' % objid
msg += '#Function=%s\n' % function
msg += '#Order=%s\n' % order
msg += '#Starting Data\n'
dout.write(msg)
for i in range(len(ws_arr)):
if ws_arr[i, 0]:
msg = '%5.2f ' % ws_arr[i, 0]
msg += ' '.join(['%e' % k for k in ws_arr[i, 1:]])
dout.write(msg + '\n')
dout.write('\n')
dout.close()
return
# main code
parfile = iraf.osfn("saltspec$specarcstraighten.par")
t = iraf.IrafTaskFactory(
taskname="specarcstraighten",
value=parfile,
function=specarcstraighten,
pkgname='saltspec')
from pyraf.iraf import pysalt
import os, string, sys, glob, pyfits, time
from PyQt4 import QtCore, QtGui
from pySlitMask import SlitMaskGui
from saltsafelog import logging, history
from salterror import SaltError
debug=True
# -----------------------------------------------------------
# core routine
def masktool(catalog='', image='', logfile='salt.log', verbose=True):
with logging(logfile,debug) as log:
app = QtGui.QApplication([])
myapp = SlitMaskGui(infile=catalog, inimage=image)
myapp.show()
app.exec_()
# -----------------------------------------------------------
# main code
parfile = iraf.osfn("proptools$masktool.par")
t = iraf.IrafTaskFactory(taskname="masktool",value=parfile,function=masktool, pkgname='proptools')
msg += '#grating=%s\n' % grating.strip()
msg += '#graang=%s\n' % grang
msg += '#gratilt=%s\n' % grasteps
msg += '#arang=%s\n' % arang
msg += '#artilt=%s\n' % arsteps
msg += '#filter=%s\n' % rfilter.strip()
if objid:
msg += '#slitid=%s\n' % objid
msg += '#Function=%s\n' % function
msg += '#Order=%s\n' % order
msg += '#Starting Data\n'
dout.write(msg)
for i in range(len(ws_arr)):
if ws_arr[i, 0]:
msg = '%5.2f ' % ws_arr[i, 0]
msg += ' '.join(['%e' % k for k in ws_arr[i, 1:]])
dout.write(msg + '\n')
dout.write('\n')
dout.close()
return
# main code
if not iraf.deftask('specidentify'):
parfile = iraf.osfn("saltspec$specidentify.par")
t = iraf.IrafTaskFactory(
taskname="specidentify", value=parfile, function=specidentify, pkgname='saltspec')
def createbadpixel(inhdu, bphdu, sci_ext, bp_ext):
"""Create the bad pixel hdu from a bad pixel hdu"""
if bphdu is None:
data=inhdu[sci_ext].data*0.0
else:
infile=inhdu._HDUList__file.name
bpfile=bphdu._HDUList__file.name
if not saltkey.compare(inhdu[0], bphdu[0], 'INSTRUME', infile, bpfile):
message = '%s and %s are not the same %s' % (infile,bpfile, 'INSTRUME')
raise SaltError(message)
for k in ['CCDSUM', 'NAXIS1', 'NAXIS2']:
if not saltkey.compare(inhdu[sci_ext], bphdu[sci_ext], k, infile, bpfile):
message = '%s and %s are not the same %s' % (infile,bpfile, k)
raise SaltError(message)
data=bphdu[sci_ext].data.copy()
header=inhdu[sci_ext].header.copy()
header['EXTVER']=bp_ext
header.update('SCIEXT',sci_ext,comment='Extension of science frame')
return pyfits.ImageHDU(data=data, header=header, name='BPM')
# -----------------------------------------------------------
# main code
if not iraf.deftask('saltprepare'):
parfile = iraf.osfn("saltred$saltprepare.par")
t = iraf.IrafTaskFactory(taskname="saltprepare",value=parfile,function=saltprepare, pkgname='saltred')
parser.add_argument('outfile', help='Name of FITS file to output', type=str)
parser.add_argument('--period', help='Period to fold data upon [days]', type=float)
parser.add_argument('--bjd0', help='time of zero phase for the folded period [BJD]', type=float)
parser.add_argument('--bindata', action='store_true', help='Bin output data?')
parser.add_argument('--binmethod', default='mean', help='Binning method', type=str, choices=['mean','median','sigclip'])
parser.add_argument('--threshold', default=1.0, help='Sigma clipping threshold [sigma]', type=float)
parser.add_argument('--niter', default=5, help='Number of sigma clipping iterations before giving up', type=int)
parser.add_argument('--nbins', default=1000, help='Number of period bins', type=int)
parser.add_argument('--quality', action='store_true', help='Reject bad quality timestamps?')
parser.add_argument('--plottype', default='sap', help='plot type', type=str, choices=['sap','pdc','cbv','det','none'])
parser.add_argument('--plotlab', default='e$^-$ s$^{-1}$', help='Plot axis label', type=str)
parser.add_argument('--clobber', action='store_true', help='Overwrite output file?')
parser.add_argument('--verbose', action='store_true', help='Write to a log file?')
parser.add_argument('--logfile', '-l', help='Name of ascii log file', default='kepcotrend.log', dest='logfile', type=str)
parser.add_argument('--status', '-e', help='Exit status (0=good)', default=0, dest='status', type=int)
args = parser.parse_args()
cmdLine=True
kepfold(args.infile,args.outfile,args.period,args.bjd0,args.bindata,args.binmethod,args.threshold,
args.niter,args.nbins,args.quality,args.plottype,args.plotlab,args.clobber,args.verbose,
args.logfile,args.status,cmdLine)
else:
from pyraf import iraf
parfile = iraf.osfn("kepler$kepfold.par")
t = iraf.IrafTaskFactory(taskname="kepfold", value=parfile, function=kepfold)
def imred(rawdir, prodir, cleanup=True):
print rawdir
print prodir
#get the name of the files
infile_list=glob.glob(rawdir+'*.fits')
infiles=','.join(['%s' % x for x in infile_list])
#get the current date for the files
obsdate=os.path.basename(infile_list[0])[1:9]
print obsdate
#set up some files that will be needed
logfile='imred'+obsdate+'.log'
flatimage='FLAT%s.fits' % (obsdate)
dbfile='spec%s.db' % obsdate
#create the observation log
obs_dict=obslog(infile_list)
#prepare the data
saltprepare(infiles, '', 'p', createvar=False, badpixelimage='', clobber=True, logfile=logfile, verbose=True)
#bias subtract the data
saltbias('pP*fits', '', 'b', subover=True, trim=True, subbias=False, masterbias='',
median=False, function='polynomial', order=5, rej_lo=3.0, rej_hi=5.0,
niter=10, plotover=False, turbo=False,
clobber=True, logfile=logfile, verbose=True)
#gain correct the data
saltgain('bpP*fits', '', 'g', usedb=False, mult=True, clobber=True, logfile=logfile, verbose=True)
#cross talk correct the data
saltxtalk('gbpP*fits', '', 'x', xtalkfile = "", usedb=False, clobber=True, logfile=logfile, verbose=True)
#cosmic ray clean the data
#only clean the object data
for i in range(len(infile_list)):
if obs_dict['CCDTYPE'][i].count('OBJECT') and obs_dict['INSTRUME'][i].count('RSS'):
img='xgbp'+os.path.basename(infile_list[i])
saltcrclean(img, img, '', 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=5, multithread=True, clobber=True, logfile=logfile, verbose=True)
#flat field correct the data
flat_imgs=''
for i in range(len(infile_list)):
if obs_dict['CCDTYPE'][i].count('FLAT'):
if flat_imgs: flat_imgs += ','
flat_imgs += 'xgbp'+os.path.basename(infile_list[i])
if len(flat_imgs)!=0:
saltcombine(flat_imgs,flatimage, method='median', reject=None, mask=False, \
weight=True, blank=0, scale='average', statsec='[200:300, 600:800]', lthresh=3, \
hthresh=3, clobber=True, logfile=logfile, verbose=True)
#saltillum(flatimage, flatimage, '', mbox=11, clobber=True, logfile=logfile, verbose=True)
saltflat('xgbpP*fits', '', 'f', flatimage, minflat=500, clobber=True, logfile=logfile, verbose=True)
else:
flats=None
imfiles=glob.glob('xgbpP*fits')
for f in imfiles:
shutil.copy(f, 'f'+f)
#mosaic the data
geomfile=iraf.osfn("pysalt$data/rss/RSSgeom.dat")
saltmosaic('fxgbpP*fits', '', 'm', geomfile, interp='linear', cleanup=True, geotran=True, clobber=True, logfile=logfile, verbose=True)
#clean up the images
if cleanup:
for f in glob.glob('p*fits'): os.remove(f)
for f in glob.glob('bp*fits'): os.remove(f)
for f in glob.glob('gbp*fits'): os.remove(f)
for f in glob.glob('xgbp*fits'): os.remove(f)
for f in glob.glob('fxgbp*fits'): os.remove(f)
j=sol[i:].index('\n')
return i+j+1
def findwskeyword(keyword, sol):
"""Find and return a value for a keyword in the list of the wavelength solution"""
i=sol.index(keyword)
j=sol[i:].index('\n')
return sol[i:i+j].split('=')[1].strip()
def enterdatetime(dstr):
"""Break up the datetime string to create a datetime object
return datetime
"""
dlist=dstr.split()
year, month, day=dlist[0].split('-')
hour, minute, second = dlist[1].split(':')
return datetime.datetime(int(year), int(month), int(day), int(hour), int(minute),int(float(second)))
def subtracttime(d1, d2):
"""Return the difference in two dates in seconds"""
dt=max(d1,d2)-min(d1,d2)
return 86400*dt.days+dt.seconds
# main code
parfile = iraf.osfn("saltspec$specrectify.par")
t = iraf.IrafTaskFactory(taskname="specrectify",value=parfile,function=specrectify, pkgname='saltspec')
help='Overwrite output file?')
parser.add_argument('--verbose',
action='store_true',
help='Write to a log file?')
parser.add_argument('--logfile',
'-l',
help='Name of ascii log file',
default='kepcotrend.log',
dest='logfile',
type=str)
parser.add_argument('--status',
'-e',
help='Exit status (0=good)',
default=0,
dest='status',
type=int)
args = parser.parse_args()
cmdLine = True
kepclip(args.infile, args.outfile, args.ranges, args.plot, args.plotcol,
args.clobber, args.verbose, args.logfile, args.status, cmdLine)
else:
from pyraf import iraf
parfile = iraf.osfn("kepler$kepclip.par")
t = iraf.IrafTaskFactory(taskname="kepclip",
value=parfile,
function=kepclip)
"""Return the keyword value. Throw a warning if it doesn't work """
try:
value = saltkey.get(keyword, struct)
if isinstance(default, str): value = value.strip()
except SaltIOError:
value = default
infile = struct._file.name
message = 'WARNING: cannot find keyword %s in %s' % (keyword, infile)
if warn and log: log.message(message, with_header=False)
if (str(value).strip() == ''): value = default
if (type(value) != type(default)):
infile = struct._file.name
message = 'WARNING: Type mismatch for %s for %s in %s[0]' % (
str(value), keyword, infile)
message += '/n ' + str(type(value)) + ' ' + str(type(default))
if warn and log: log.message(message, with_header=False)
value = default
return value
# -----------------------------------------------------------
# main code
if not iraf.deftask('saltobslog'):
parfile = iraf.osfn("saltred$saltobslog.par")
t = iraf.IrafTaskFactory(taskname="saltobslog",
value=parfile,
function=saltobslog,
pkgname='saltred')
def flexure_rssspec(imagefits,fitslist,option=""):
print str(datetime.now())
if option == "filesave": prefix = raw_input("\nFile prefix: ")
pixel = 15. # pixel size in microns
pix_scale=0.125
sexparams = ["X_IMAGE","Y_IMAGE","FLUX_ISO","FLUX_MAX","FLAGS","CLASS_STAR", \
"X2WIN_IMAGE","Y2WIN_IMAGE","XYWIN_IMAGE","ERRX2WIN_IMAGE"]
np.savetxt("qred_thrufoc.param",sexparams,fmt="%s")
fmaxcol,flagcol,xvarcol,yvarcol,xerrcol = (3,4,6,7,9) # column nos (from 0) of data in sextractor
imagestooclosefactor = 3.0 # too close if factor*sep < sqrt(var)
gaptooclose = 1.25 # arcsec
edgetooclose = 1.25 # arcsec
rattolerance = 0.25
toofaint = 250. # FMAX counts
galaxydelta = 0.4 # arcsec
MOSimagelimit = 1. # arcsec
deblend = .005 # default
imagehdr = pyfits.getheader(imagefits)
if imagehdr["GR-STATE"][1] == "4":
print "First fits file "+imagefits+" is not image of mask"
exit()
flexposns = len(fitslist)
obsdict=obslog(fitslist)
image_f = [fitslist[fpos].split(".")[0][-12:] for fpos in range(flexposns)]
dateobs = obsdict["DATE-OBS"][0].replace("-","")
if int(dateobs) > 20110928: rho_f = np.array(obsdict["TRKRHO"]).astype(float)
else: rho_f = np.array(obsdict["TELRHO"]).astype(float)
catpos = np.argmin(np.abs(rho_f))
cbin,rbin = np.array(obsdict["CCDSUM"][catpos].split(" ")).astype(int)
maskid = obsdict["MASKID"][catpos].strip()
filter = obsdict["FILTER"][catpos].strip()
grating = obsdict["GRATING"][catpos].strip()
rows,cols = pyfits.getdata(fitslist[catpos]).shape
isspec = (obsdict["GR-STATE"][catpos][1] =="4")
if not isspec:
print "Use flexure_rssimage for image flexure analysis"
exit()
grang = float(obsdict["GRTILT"][catpos])
artic = float(obsdict["CAMANG"][catpos])
lamp = obsdict["LAMPID"][catpos].strip()
print "\nMask: ", maskid
print "Filter: ", filter
print "Grating: ", grating
print "Artic (deg): ", artic
print "Gr Angle (deg): ", grang
print "Lamp: ", lamp
# map the mask spots _m using the imaging fits file
sex_js = sextract(imagefits,deblend=deblend)
flux_s = sex_js[2]
fluxmedian = np.median(np.sort(flux_s)[-10:])
okm_s = (flux_s > fluxmedian/10) # cull bogus spots
maskholes = okm_s.sum()
r_m = sex_js[1,okm_s]
c_m = sex_js[0,okm_s]
# find mask rows _R, tabulate
histr_b, binr_b = np.histogram(r_m,bins=rows/10,range=(0,rows))
bin0_R = np.where((histr_b[1:]>0) & (histr_b[:-1]==0))[0]
bin1_R = np.where((histr_b[1:]==0) & (histr_b[:-1]>0))[0]
maskRows = bin0_R.shape[0]
bin_m = np.digitize(r_m,binr_b) - 1
R_m = np.array([np.where((bin_m[m] >= bin0_R) & (bin_m[m] <= bin1_R))[0][0] \
for m in range(maskholes)])
# find mask cols _C, tabulate
histc_b, binc_b = np.histogram(c_m,bins=cols/10,range=(0,cols))
bin0_C = np.where((histc_b[1:]>0) & (histc_b[:-1]==0))[0]
bin1_C = np.where((histc_b[1:]==0) & (histc_b[:-1]>0))[0]
maskCols = bin0_C.shape[0]
bin_m = np.digitize(c_m,binc_b) - 1
C_m = np.array([np.where((bin_m[m] >= bin0_C) & (bin_m[m] <= bin1_C))[0][0] \
for m in range(maskholes)])
# identify mask center = optical axis
if maskid == 'P000000N99': # symmetric mask
Raxis = maskRows/2
Caxis = maskCols/2
elif maskid == 'P000000N03': # mask with centered cross
Raxis = np.where((np.argmax(histr_b) >= bin0_R) & (np.argmax(histr_b) <= bin1_R))[0][0]
Caxis = np.where((np.argmax(histc_b) >= bin0_C) & (np.argmax(histc_b) <= bin1_C))[0][0]
else:
print "Not a valid flexure mask"
exit()
maxis = np.where((R_m==Raxis)&(C_m==Caxis))[0][0]
raxis = r_m[maxis]
caxis = c_m[maxis]
print "\nMask_Holes Rows Cols r axis c axis \n pixels pixels"
print " %5i %5i %5i %8.1f %8.1f" % (maskholes,maskRows,maskCols,raxis*rbin,caxis*cbin)
# np.savetxt(dateobs+'_'+"mask.txt",np.vstack((r_m,c_m,sex_js[2,okm_s],R_m)).T,fmt="%10.2f")
# get linelist, predict spots in spectral image
wavcent = rsslam(grating, grang, artic, 0.,dateobs)
specfile = datedfile(datadir+"spectrograph/spec_yyyymmdd.txt",dateobs)
FCampoly=np.loadtxt(specfile,usecols=(1,))[5:11]
fcam = np.polyval(FCampoly,(wavcent/1000. - 4.))
lampfile=iraf.osfn("pysalt$data/linelists/"+lamp+".salt")
wav_l,int_l = np.loadtxt(lampfile,unpack=True)
maxdalpha = -np.degrees((cols/2)*cbin*pixel/(1000.*fcam))
maxgamma = np.degrees((rows/2)*rbin*pixel/(1000.*fcam))
maxwav = rsslam(grating,grang,artic, cols*cbin/2,dateobs,-maxdalpha,0)
minwav = rsslam(grating,grang,artic,-cols*cbin/2,dateobs, maxdalpha,maxgamma)
ok_l = (wav_l >= minwav) & (wav_l <= maxwav)
wav_l = wav_l[ok_l]
int_l = int_l[ok_l]
lines = wav_l.shape[0]
col_ml = np.zeros((maskholes,lines))
dcol_c = np.arange(-(cols*cbin/2),(cols*cbin/2))
for m in range(maskholes):
dalpha = -np.degrees((c_m[m]-caxis)*cbin*pixel/(1000.*fcam))
gamma = np.degrees((r_m[m]-raxis)*rbin*pixel/(1000.*fcam))
wav0,wav1 = rsslam(grating,grang,artic,dcol_c[[0,-1]],dateobs,dalpha,gamma=gamma)
ok_l = ((wav_l > wav0) & (wav_l < wav1))
colwav = interp1d(rsslam(grating,grang,artic,dcol_c, \
dateobs,dalpha=dalpha,gamma=gamma), dcol_c)
col_ml[m,ok_l] = colwav(wav_l[ok_l]) + caxis*cbin
# np.savetxt(dateobs+"_col_ml.txt",np.vstack((R_m,C_m,col_ml.T)),fmt="%8.1f")
# identify mask hole and wavelength for spots in spec image closest to rho=0
os.remove("sexwt.fits")
sex_js = sextract(fitslist[catpos],"",deblend=deblend)
r_s = sex_js[1]
c_s = sex_js[0]
flux_s = sex_js[2]
spots = r_s.shape[0]
fluxmedian = np.median(np.sort(sex_js[2])[-10:])
ok_s = (flux_s > fluxmedian/30) # cull bogus spots
# find spectral bin rows RR in candidates R0, cull non-spectra
histr_b, binr_b = np.histogram(r_s[ok_s],bins=rows/10,range=(0,rows))
histr_b[[0,-1]] = 0
bin0_R0 = np.where((histr_b[1:]>0) & (histr_b[:-1]==0))[0] + 1
bin1_R0 = np.where((histr_b[1:]==0) & (histr_b[:-1]>0))[0]
bin_s = np.digitize(r_s,binr_b) - 1
maxcount_R0 = np.array([(histr_b[bin0_R0[R0]:bin1_R0[R0]+1]).max() \
for R0 in range(bin0_R0.shape[0])])
ok_R0 = (maxcount_R0 > 3)
specrows = ok_R0.sum() # cull down to spectra RR
bin0_RR = bin0_R0[ok_R0]
bin1_RR = bin1_R0[ok_R0]
ok_s &= ((bin_s >= bin0_RR[:,None]) & (bin_s <= bin1_RR[:,None])).any(axis=0)
RR_s = -np.ones(spots)
r_RR = np.zeros(specrows)
for RR in range(specrows):
isRR_s = ok_s & np.in1d(bin_s,np.arange(bin0_RR[RR],bin1_RR[RR]+1))
RR_s[isRR_s] = RR
r_RR[RR] = r_s[isRR_s].mean()
count_RR = (RR_s[:,None]==range(specrows)).sum(axis=0)
if maskid == 'P000000N99':
RRaxis = np.argmin((raxis-r_RR)**2)
elif maskid == 'P000000N03':
RRaxis = np.argmax(count_RR)
# cull weak lines
ptile = 100.*min(1.,5.*maskCols/count_RR.max()) # want like 5 brightest lines
for RR in range(specrows):
isRR_s = ok_s & np.in1d(bin_s,np.arange(bin0_RR[RR],bin1_RR[RR]+1))
fluxmin = np.percentile(sex_js[2,isRR_s],100.-ptile)
ok_s[isRR_s] &= (sex_js[2,isRR_s] > fluxmin)
# identify with mask rows R (assuming no gaps)
RR_m = R_m + RRaxis - Raxis
# find approximate grating shift in dispersion direction by looking for most common id error
histc_b = np.zeros(60)
for RR in range(specrows):
isRR_s = ((RR_s==RR) & ok_s)
cerr_MS = (c_s[None,isRR_s] - col_ml[RR_m==RR].ravel()[:,None])
histc_b += np.histogram(cerr_MS.ravel(),bins=60,range=(-150,150))[0]
cshift = 5*np.argmax(histc_b) - 150
col_ml += cshift
# identify wavelength and mask column with spots in each spectrum
isfound_s = np.zeros((spots),dtype=bool)
bintol = 16/cbin # 2 arcsec tolerance for line ID
R_s = -np.ones(spots,dtype=int)
C_s = -np.ones(spots,dtype=int)
l_s = -np.ones(spots,dtype=int)
m_s = -np.ones(spots,dtype=int)
cerr_s = np.zeros(spots)
rmscol = 0.
for RR in range(specrows): # _S spot in spectrum, _P (mask column, line)
isRR_m = (RR_m==RR)
isRR_s = ((RR_s==RR) & ok_s)
cerr_PS = (c_s[None,isRR_s] - col_ml[isRR_m].ravel()[:,None])
Spots = isRR_s.sum()
Possibles = col_ml[isRR_m].size
Cols = Possibles/lines
P_S = np.argmin(np.abs(cerr_PS),axis=0)
cerr_S = cerr_PS[P_S,range(isRR_s.sum())]
isfound_S = (np.abs(cerr_S) < bintol)
M_P,l_P = np.unravel_index(np.arange(Possibles),(Cols,lines))
m_P = np.where(isRR_m)[0][M_P]
m_S = m_P[P_S]
C_P = C_m[m_P]
C_S = C_P[P_S]
l_S = l_P[P_S]
s_S = np.where(isRR_s)[0]
R_s[isRR_s] = RR + Raxis-RRaxis
cerr_s[s_S] = cerr_S
C_s[s_S[isfound_S]] = C_S[isfound_S]
l_s[s_S[isfound_S]] = l_S[isfound_S]
m_s[s_S[isfound_S]] = m_S[isfound_S]
isfound_s[s_S] |= isfound_S
rmscol += (cerr_S[isfound_S]**2).sum()
# cull wavelengths to _L with < 1/2 Mask Rows or Cols
ok_s &= isfound_s
ok_l = np.zeros((lines),dtype=bool)
for line in range(lines):
lRows = np.unique(R_s[l_s==line]).shape[0]
lCols = np.unique(C_s[l_s==line]).shape[0]
ok_l[line] = ((lRows>=maskRows/2) & (lCols>=maskCols/2))
l_L = np.where(ok_l)[0]
wav_L = wav_l[l_L]
Lines = l_L.shape[0]
ok_s &= np.in1d(l_s,l_L)
# tabulate good catalog spots (final _S)
s_S = np.where(ok_s)[0]
r_S = r_s[s_S]
c_S = c_s[s_S]
cerr_S = cerr_s[s_S]
R_S = R_s[s_S]
C_S = C_s[s_S]
l_S = l_s[s_S]
Spots = ok_s.sum()
rshift = r_S[R_S==Raxis].mean() - raxis
cshift += (c_S - col_ml[m_s[s_S],l_S]).mean()
rmscol = np.sqrt(rmscol/Spots)
np.savetxt("cat_S.txt",np.vstack((s_S,r_S,c_S,R_S,C_S,l_S,cerr_S)).T, \
fmt="%5i %8.2f %8.2f %5i %5i %5i %8.2f")
print "\nSpec_Spots Lines rshift cshift rms\n pixels pixels pixels"
print " %5i %5i %8.1f %8.1f %8.1f" % (Spots,np.unique(l_S).shape[0],rshift,cshift,rmscol)
print "\nLineno Wavel spots Rows Cols"
for L in range(Lines):
line = l_L[L]
lRows = np.unique(R_S[l_S==line]).shape[0]
lCols = np.unique(C_S[l_S==line]).shape[0]
lspots = (l_S==line).sum()
print " %5i %8.2f %5i %5i %5i" % (line,wav_l[line],lspots,lRows,lCols)
sexcols = sex_js.shape[0]
sexdata_jfS = np.zeros((sexcols,flexposns,Spots))
sexdata_jfS[:,catpos] = sex_js[:,ok_s]
xcenter_L = col_ml[maxis,l_L]
ycenter = raxis + rshift
if option == "filesave":
np.savetxt(prefix+"Spots.txt",sexdata_jfS[:,catpos].T, \
fmt=2*"%9.2f "+"%9.0f "+"%9.1f "+"%4i "+"%6.2f "+3*"%7.2f "+"%11.3e")
# find spots in flexure series, in order of increasing abs(rho), and store sextractor output
row_fLd = np.zeros((flexposns,Lines,2))
col_fLd = np.zeros((flexposns,Lines,2))
print "\n fits rho line spots rshift cshift rslope cslope rmserr "
print " deg Ang arcsec arcsec arcmin arcmin bins"
for dirn in (1,-1):
refpos = catpos
posdirlist = np.argsort(dirn*rho_f)
poslist = posdirlist[dirn*rho_f[posdirlist] > rho_f[refpos]]
for fpos in poslist:
col_S,row_S = sexdata_jfS[0:2,refpos,:]
sex_js = sextract(fitslist[fpos],"sexwt.fits",deblend=deblend)
binsqerr_sS = (sex_js[1,:,None] - row_S[None,:])**2 + (sex_js[0,:,None] - col_S[None,:])**2
S_s = np.argmin(binsqerr_sS,axis=1)
# First compute image shift by averaging small errors
rowerr_s = sex_js[1] - row_S[S_s]
colerr_s = sex_js[0] - col_S[S_s]
hist_r,bin_r = np.histogram(rowerr_s,bins=32,range=(-2*bintol,2*bintol))
drow = rowerr_s[(rowerr_s > bin_r[np.argmax(hist_r)]-bintol) & \
(rowerr_s < bin_r[np.argmax(hist_r)]+bintol)].mean()
hist_c,bin_c = np.histogram(colerr_s,bins=32,range=(-2*bintol,2*bintol))
dcol = colerr_s[(colerr_s > bin_c[np.argmax(hist_c)]-bintol) & \
(colerr_s < bin_c[np.argmax(hist_c)]+bintol)].mean()
# Now refind the closest ID
binsqerr_sS = (sex_js[1,:,None] - row_S[None,:] -drow)**2 + \
(sex_js[0,:,None] - col_S[None,:] -dcol)**2
binsqerr_s = binsqerr_sS.min(axis=1)
isfound_s = binsqerr_s < bintol**2
S_s = np.argmin(binsqerr_sS,axis=1)
isfound_s &= (binsqerr_s == binsqerr_sS[:,S_s].min(axis=0))
isfound_S = np.array([S in S_s[isfound_s] for S in range(Spots)])
sexdata_jfS[:,fpos,S_s[isfound_s]] = sex_js[:,isfound_s]
drow_S = sexdata_jfS[1,fpos]-sexdata_jfS[1,catpos]
dcol_S = sexdata_jfS[0,fpos]-sexdata_jfS[0,catpos]
# np.savetxt("motion_"+str(fpos)+".txt",np.vstack((isfound_S,l_S,drow_S,dcol_S)).T,fmt="%3i %3i %8.2f %8.2f")
# Compute flexure image motion parameters for each line
for L in range(Lines):
ok_S = ((l_S == l_L[L]) & isfound_S)
row_fLd[fpos,L],rowchi,d,d,d = \
np.polyfit(sexdata_jfS[0,catpos,ok_S]-xcenter_L[L],drow_S[ok_S],deg=1,full=True)
col_fLd[fpos,L],colchi,d,d,d = \
np.polyfit(sexdata_jfS[1,catpos,ok_S]-ycenter,dcol_S[ok_S],deg=1,full=True)
rms = np.sqrt((rowchi+colchi)/(2*ok_S.sum()))
print ("%12s %5.0f %5i %5i "+5*"%7.2f ") % (image_f[fpos], rho_f[fpos], wav_L[L], \
ok_S.sum(),row_fLd[fpos,L,1]*rbin*pix_scale, col_fLd[fpos,L,1]*cbin*pix_scale, \
60.*np.degrees(row_fLd[fpos,L,0]),-60.*np.degrees(col_fLd[fpos,L,0]), rms)
if option == "filesave":
np.savetxt(prefix+"flex_"+str(fpos)+".txt",np.vstack((isfound_S,drow_S,dcol_S)).T, \
fmt = "%2i %8.3f %8.3f")
np.savetxt(prefix+"sextr_"+str(fpos)+".txt",sexdata_jfS[:,fpos].T)
print
# make plots
fig,plot_s = plt.subplots(2,1,sharex=True)
plt.xlabel('Rho (deg)')
plt.xlim(-120,120)
plt.xticks(range(-120,120,30))
fig.set_size_inches((8.5,11))
fig.subplots_adjust(left=0.175)
plot_s[0].set_title(str(dateobs)+[" Imaging"," Spectral"][isspec]+" Flexure")
plot_s[0].set_ylabel('Mean Position (arcsec)')
plot_s[0].set_ylim(-0.5,4.)
plot_s[1].set_ylabel('Rotation (arcmin ccw)')
plot_s[1].set_ylim(-10.,6.)
lbl_L = [("%5.0f") % (wav_L[L]) for L in range(Lines)]
color_L = 'bgrcmykw'
for L in range(Lines):
plot_s[0].plot(rho_f,row_fLd[:,L,1]*rbin*pix_scale, \
color=color_L[L],marker='D',markersize=8,label='row '+lbl_L[L])
plot_s[1].plot(rho_f,60.*np.degrees(row_fLd[:,L,0]),
color=color_L[L],marker='D',markersize=8,label='row '+lbl_L[L])
collbl = 'col'+lbl_L[0]
for L in range(Lines):
plot_s[0].plot(rho_f,col_fLd[:,L,1]*cbin*pix_scale, \
color=color_L[L],marker='s',markersize=8,label=collbl)
plot_s[1].plot(rho_f,-60.*np.degrees(col_fLd[:,L,0]), \
color=color_L[L],marker='s',markersize=8,label=collbl)
collbl = ''
plot_s[0].legend(fontsize='medium',loc='upper center')
plotfile = str(dateobs)+['_imflex.pdf','_grflex.pdf'][isspec]
plt.savefig(plotfile,orientation='portrait')
if os.name=='posix':
if os.popen('ps -C evince -f').read().count(plotfile)==0: os.system('evince '+plotfile+' &')
os.remove("out.txt")
os.remove("qred_thrufoc.param")
os.remove("sexwt.fits")
return
parser.add_argument('--verbose',
action='store_true',
help='Write to a log file?')
parser.add_argument('--logfile',
'-l',
help='Name of ascii log file',
default='kepcotrend.log',
dest='logfile',
type=str)
parser.add_argument('--status',
'-e',
help='Exit status (0=good)',
default=0,
dest='status',
type=int)
args = parser.parse_args()
kepcotrendsc(args.infile, args.outfile, args.bvfile, args.listbv,
args.fitmethod, args.fitpower, args.iterate, args.sigma,
args.maskfile, args.scinterp, args.plot, args.clobber,
args.verbose, args.logfile, args.status)
else:
from pyraf import iraf
parfile = iraf.osfn("kepler$kepcotrend.par")
t = iraf.IrafTaskFactory(taskname="kepcotrend",
value=parfile,
function=kepcotrendsc)
saltkey.put('DATASEC',datasec,struct[bhdu])
#subtract the master bias if necessary
if subbias and bstruct:
struct[i].data -= bstruct[i].data
#update the variance frame
if saltkey.found('VAREXT', struct[i]):
vhdu=saltkey.get('VAREXT', struct[i])
try:
vdata=struct[vhdu].data
struct[vhdu].data=vdata+bstruct[vhdu].data
except Exception, e:
msg='Cannot update the variance frame in %s[%i] because %s' % (infile, vhdu, e)
raise SaltError(msg)
if plotover:
plt.ioff()
plt.show()
return struct
# -----------------------------------------------------------
# main code
if not iraf.deftask('saltbias'):
parfile = iraf.osfn("saltred$saltbias.par")
t = iraf.IrafTaskFactory(taskname="saltbias",value=parfile,function=saltbias, pkgname='saltred')
print dir, ' directory with data'
outfile = outfile.strip()
if os.path.isfile(outfile):
print 'output file exists, appending'
# saltsafeio.delete(outfile)
# check whether the calibrate logfile is defined
saltsafeio.filedefined('Log', calibratelogfile)
# Get current working directory as the Fortran code changes dir
startdir = os.getcwd()
# If all looks OK, run the FORTRAN code
calibrate_wrapper.calibrate(plottype, infile, outfile,
calibratelogfile)
# go back to starting directory
os.chdir(startdir)
# -----------------------------------------------------------
# main code
parfile = iraf.osfn("saltfp$saltfpcalibrate.par")
t = iraf.IrafTaskFactory(taskname="saltfpcalibrate",
value=parfile,
function=saltfpcalibrate,
pkgname='saltfp')