def calibFlux(self, inIm, **kwargs):
print "\nCALIBRATING FLUX IN " + inIm
# determine which extinction file to use
hdu = fits.open(inIm)
if hdu[0].header["OBSERVAT"] == "Gemini-North":
self.extDat = "gmos$calib/mkoextinct.dat"
else:
self.extDat = "onedstds$ctioextinct.dat"
# determine which sensitivity function to use
centWave = str(int(hdu[0].header["CENTWAVE"]))
sFunc = self.sFunc.replace("X", centWave)
hdu.close()
# delete previous flux calibrated spectra and generate new one
iraf.imdelete("c" + inIm)
iraf.gscalibrate(inIm,
sfunction=sFunc,
extinction=self.extDat,
**kwargs)
# view the result
print "Displaying calibrated data cube"
self.viewCube("c" + inIm, version="1")
return
def calibrate(scifiles, extfile, observatory):
for f in scifiles:
redorblue = getredorblue(f)
iraf.unlearn(iraf.gscalibrate)
iraf.gscalibrate('et' + f[:-4] + '.fits',
sfunc='sens' + redorblue + '.fits', fl_ext=True,
extinction=extfile, observatory=observatory)
if os.path.exists('cet' + f[:-4] + '.fits'):
iraf.unlearn(iraf.splot)
iraf.splot('cet' + f.replace('.txt', '.fits') + '[sci]') # just to check
def calibrate(scifiles, extfile, observatory):
for f in scifiles:
redorblue = getredorblue(f)
iraf.unlearn(iraf.gscalibrate)
iraf.gscalibrate('et' + f[:-4] + '.fits',
sfunc='sens' + redorblue + '.fits', fl_ext=True, fl_vardq=dodq,
extinction=extfile, observatory=observatory)
if os.path.exists('cet' + f[:-4] + '.fits'):
iraf.unlearn(iraf.splot)
iraf.splot('cet' + f.replace('.txt', '.fits') + '[sci]') # just to check
def reduce_science(rawdir, rundir, flat, arc, twilight, twilight_flat, sciimg,
starimg, bias, overscan, vardq, observatory, lacos,
apply_lacos, lacos_xorder, lacos_yorder, lacos_sigclip,
lacos_objlim, bpm, instrument, slits, fl_gscrrej,
wltrim_frac, grow_gap, cube_bit_mask):
"""
Reduction pipeline for standard star.
Parameters
----------
rawdir: string
Directory containing raw images.
rundir: string
Directory where processed files are saved.
flat: string
Names of the files containing flat field images.
arc: string
Arc images.
twilight: string
Twilight flat images.
twilight_flat: string
Flat field for twilight image.
starimg: string
Name of the file containing the image to be reduced.
bias: string
Bias images.
grow_gap: number
Number of pixels by which to grow the bad pixel mask around
the chip gaps.
"""
iraf.set(stdimage='imtgmos')
iraf.gemini()
iraf.unlearn('gemini')
iraf.gmos()
iraf.unlearn('gmos')
iraf.gemtools()
iraf.unlearn('gemtools')
# os.path.isfile('arquivo')
iraf.unlearn('gemini')
iraf.unlearn('gmos')
iraf.task(lacos_spec=lacos)
# set directories
iraf.set(caldir=rawdir) #
iraf.set(rawdir=rawdir) # raw files
iraf.set(procdir=rundir) # processed files
iraf.gmos.logfile = 'logfile.log'
iraf.gfextract.verbose = 'no'
iraf.cd('procdir')
flat = flat.replace('.fits', '')
twilight = twilight.replace('.fits', '')
twilight_flat = twilight_flat.replace('.fits', '')
arc = arc.replace('.fits', '')
starimg = starimg.replace('.fits', '')
sciimg = sciimg.replace('.fits', '')
mdffile = 'mdf' + flat + '.fits'
iraf.gfreduce.bias = 'caldir$' + bias
iraf.gfreduce.fl_fulldq = 'yes'
iraf.gfreduce.fl_fixgaps = 'yes'
iraf.gfreduce.grow = grow_gap
iraf.gireduce.bpm = 'rawdir$' + bpm
iraf.gfextract.verbose = 'no'
cal_reduction(rawdir=rawdir,
rundir=rundir,
flat=flat,
arc=arc,
twilight=twilight,
bias=bias,
bpm=bpm,
overscan=overscan,
vardq=vardq,
instrument=instrument,
slits=slits,
twilight_flat=twilight_flat,
grow_gap=grow_gap)
#
# Actually reduce science
#
image_name = 'rg' + sciimg + '.fits'
if os.path.isfile(image_name):
pipe.skipwarn(image_name)
else:
iraf.gfreduce(sciimg,
slits='header',
rawpath='rawdir$',
fl_inter='no',
fl_addmdf='yes',
key_mdf='MDF',
mdffile=mdffile,
weights='no',
fl_over=overscan,
fl_trim='yes',
fl_bias='yes',
trace='no',
recenter='no',
fl_fulldq='yes',
fl_flux='no',
fl_gscrrej='no',
fl_extract='no',
fl_gsappwave='no',
fl_wavtran='no',
fl_novl='yes',
fl_skysub='no',
fl_vardq=vardq,
mdfdir='procdir$')
prefix = 'rg'
# Gemfix
image_name = 'p' + prefix + sciimg + '.fits'
if os.path.isfile(image_name):
pipe.skipwarn(image_name)
else:
iraf.gemfix(prefix + sciimg,
out='p' + prefix + sciimg,
method='fit1d',
bitmask=65535,
axis=1)
prefix = 'p' + prefix
# LA Cosmic - cosmic ray removal
if apply_lacos:
image_name = 'l' + prefix + sciimg + '.fits'
if os.path.isfile(image_name):
pipe.skipwarn(image_name)
else:
iraf.gemcrspec(prefix + sciimg,
out='l' + prefix + sciimg,
sigfrac=0.5,
niter=4,
fl_vardq=vardq,
xorder=lacos_xorder,
yorder=lacos_yorder,
sigclip=lacos_sigclip,
objlim=lacos_objlim)
prefix = 'l' + prefix
if fl_gscrrej:
image_name = 'ex' + prefix + sciimg + '.fits'
else:
image_name = 'e' + prefix + sciimg + '.fits'
if os.path.isfile(image_name):
pipe.skipwarn(image_name)
else:
iraf.gfreduce(prefix + sciimg,
slits='header',
rawpath='./',
fl_inter='no',
fl_addmdf='no',
key_mdf='MDF',
mdffile=mdffile,
fl_over='no',
fl_trim='no',
fl_bias='no',
trace='no',
recenter='no',
fl_flux='no',
fl_gscrrej=fl_gscrrej,
fl_extract='yes',
fl_gsappwave='yes',
fl_wavtran='no',
fl_novl='no',
fl_skysub='no',
grow=grow_gap,
reference='eprg' + flat,
weights='no',
wavtraname='eprg' + arc,
response='eprg' + flat + '_response.fits',
fl_vardq=vardq,
fl_fulldq='yes',
fl_fixgaps='yes')
if fl_gscrrej:
prefix = 'ex' + prefix
else:
prefix = 'e' + prefix
# if wl2 > 7550.0:
# wl2 = 7550.0
#
# Apply wavelength transformation
#
wl1, wl2 = wl_lims(prefix + sciimg + '.fits', wltrim_frac)
image_name = 't' + prefix + sciimg + '.fits'
if os.path.isfile(image_name):
pipe.skipwarn(image_name)
else:
iraf.gftransform(
prefix + sciimg,
wavtraname='eprg' + arc,
fl_vardq=vardq,
w1=wl1,
w2=wl2,
)
prefix = 't' + prefix
#
# Sky subtraction
#
image_name = 's' + prefix + sciimg + '.fits'
if os.path.isfile(image_name):
pipe.skipwarn(image_name)
else:
iraf.gfskysub(
prefix + sciimg,
expr='default',
combine='median',
reject='avsigclip',
scale='none',
zero='none',
weight='none',
sepslits='yes',
fl_inter='no',
lsigma=1,
hsigma=1,
)
prefix = 's' + prefix
#
# Apply flux calibration to galaxy
#
image_name = 'c' + prefix + sciimg + '.fits'
if os.path.isfile(image_name):
pipe.skipwarn(image_name)
else:
iraf.gscalibrate(prefix + sciimg,
sfuncti=starimg,
extinct='onedstds$ctioextinct.dat',
observatory=observatory,
fluxsca=1,
fl_vardq=vardq)
prefix = 'c' + prefix
#
# Remove spurious data with PCA
#
image_name = 'x' + prefix + sciimg + '.fits'
print(os.getcwd())
print(image_name)
if os.path.isfile(image_name):
pipe.skipwarn(image_name)
else:
t = pca.Tomography(prefix + sciimg + '.fits')
t.decompose()
t.remove_cosmic_rays(sigma_threshold=10.0)
t.write(image_name)
prefix = 'x' + prefix
#
# Create data cubes
#
image_name = 'd' + prefix + sciimg + '.fits'
if os.path.isfile(image_name):
pipe.skipwarn(image_name)
else:
data_cube = CubeBuilder(prefix + sciimg + '.fits')
data_cube.build_cube()
data_cube.fit_refraction_function()
data_cube.fix_atmospheric_refraction()
data_cube.write(image_name)
def reduce_stdstar(rawdir,
rundir,
caldir,
starobj,
stdstar,
flat,
arc,
twilight,
twilight_flat,
starimg,
bias,
overscan,
vardq,
lacos,
observatory,
apply_lacos,
lacos_xorder,
lacos_yorder,
lacos_objlim,
lacos_sigclip,
bpm,
instrument,
slits,
fl_gscrrej,
wltrim_frac=0.03,
sens_order=6,
sens_function='spline3',
apsum_radius=1):
"""
Reduction pipeline for standard star.
Parameters
----------
rawdir: string
Directory containing raw images.
rundi: string
Directory where processed files are saved.
caldir: string
Directory containing standard star calibration files.
starobj: string
Object keyword for the star image.
stdstar: string
Star name in calibration file.
flat: list
Names of the files containing flat field images.
arc: list
Arc images.
twilight: list
Twilight flat images.
starimg: string
Name of the file containing the image to be reduced.
bias: list
Bias images.
"""
iraf.set(stdimage='imtgmos')
iraf.task(lacos_spec=lacos)
iraf.gemini()
iraf.unlearn('gemini')
iraf.gmos()
iraf.unlearn('gmos')
iraf.gemtools()
iraf.unlearn('gemtools')
iraf.gmos.logfile = 'logfile.log'
iraf.gemtools.gloginit.logfile = 'logfile.log'
iraf.gfextract.verbose = 'no'
# set directories
iraf.set(caldir=rawdir) #
iraf.set(rawdir=rawdir) # raw files
iraf.set(procdir=rundir) # processed files
# os.path.isfile('arquivo')
iraf.cd('procdir')
flat = flat.strip('.fits')
twilight = twilight.strip('.fits')
twilight_flat = twilight_flat.strip('.fits')
arc = arc.strip('.fits')
starimg = starimg.strip('.fits')
mdffile = 'mdf' + flat + '.fits'
iraf.gfreduce.bias = 'rawdir$' + bias
iraf.gfreduce.fl_fulldq = 'yes'
iraf.gfreduce.fl_fixgaps = 'yes'
iraf.gireduce.bpm = 'rawdir$' + bpm
cal_reduction(rawdir=rawdir,
rundir=rundir,
flat=flat,
arc=arc,
twilight=twilight,
bias=bias,
bpm=bpm,
overscan=overscan,
vardq=vardq,
instrument=instrument,
slits=slits,
twilight_flat=twilight_flat)
#
# Actually reduce star
#
imageName = 'rg' + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
else:
imageName = 'g' + starimg + '.fits'
if os.path.isfile(imageName):
iraf.printlog(
'GIREDS: WARNING: Removing file {:s}'.format(imageName),
'logfile.log', 'yes')
iraf.delete(imageName)
iraf.gfreduce(starimg,
slits='header',
rawpath='rawdir$',
fl_inter='no',
fl_addmdf='yes',
key_mdf='MDF',
mdffile=mdffile,
weights='no',
fl_over=overscan,
fl_trim='yes',
fl_bias='yes',
trace='no',
recenter='no',
fl_flux='no',
fl_gscrrej='no',
fl_extract='no',
fl_gsappwave='no',
fl_wavtran='no',
fl_novl='yes',
fl_skysub='no',
fl_vardq=vardq,
mdfdir='procdir$')
prefix = 'rg'
#
# Gemfix
#
imageName = 'p' + prefix + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
else:
iraf.gemfix(prefix + starimg,
out='p' + prefix + starimg,
method='fit1d',
bitmask=1,
axis=1)
prefix = 'p' + prefix
#
# LA Cosmic
#
if apply_lacos:
imageName = 'l' + prefix + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
else:
if apply_lacos:
iraf.gemcrspec(prefix + starimg,
out='l' + prefix + starimg,
sigfrac=0.32,
niter=4,
fl_vardq=vardq,
xorder=lacos_xorder,
yorder=lacos_yorder,
objlim=lacos_objlim,
sigclip=lacos_sigclip)
prefix = 'l' + prefix
#
# Extraction and Gemini's comsmic ray rejection
#
if fl_gscrrej:
imageName = 'x' + prefix + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
fl_gscrrej = False
else:
imageName = 'ex' + prefix + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
else:
iraf.gfreduce(prefix + starimg,
slits='header',
rawpath='./',
fl_inter='no',
fl_addmdf='no',
key_mdf='MDF',
mdffile=mdffile,
fl_over='no',
fl_trim='no',
fl_bias='no',
trace='no',
recenter='no',
fl_flux='no',
fl_gscrrej=fl_gscrrej,
fl_extract='yes',
fl_gsappwave='yes',
fl_wavtran='no',
fl_novl='no',
fl_skysub='no',
reference='eprg' + flat,
weights='no',
wavtraname='eprg' + arc,
response='eprg' + flat + '_response.fits',
fl_vardq=vardq)
prefix = 'ex' + prefix
else:
imageName = 'e' + prefix + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
else:
iraf.gfreduce(prefix + starimg,
slits='header',
rawpath='./',
fl_inter='no',
fl_addmdf='no',
key_mdf='MDF',
mdffile=mdffile,
fl_over='no',
fl_trim='no',
fl_bias='no',
trace='no',
recenter='no',
fl_flux='no',
fl_gscrrej=fl_gscrrej,
fl_extract='yes',
fl_gsappwave='yes',
fl_wavtran='no',
fl_novl='no',
fl_skysub='no',
reference='eprg' + flat,
weights='no',
wavtraname='eprg' + arc,
response='eprg' + flat + '_response.fits',
fl_vardq=vardq)
prefix = 'e' + prefix
#
# Wavelength transform
#
wl1, wl2 = wl_lims(prefix + starimg + '.fits', wltrim_frac)
imageName = 't' + prefix + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
else:
iraf.gfreduce(prefix + starimg,
slits='header',
rawpath='./',
fl_inter='no',
fl_addmdf='no',
key_mdf='MDF',
mdffile=mdffile,
fl_over='no',
fl_trim='no',
fl_bias='no',
trace='no',
recenter='no',
fl_flux='no',
fl_gscrrej='no',
fl_extract='no',
fl_gsappwave='no',
fl_wavtran='yes',
fl_novl='no',
fl_skysub='no',
reference='eprg' + flat,
weights='no',
wavtraname='eprg' + arc,
response='eprg' + flat + '_response.fits',
fl_vardq=vardq,
w1=wl1,
w2=wl2)
prefix = 't' + prefix
#
# Sky subtraction
#
imageName = 's' + prefix + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
else:
iraf.gfreduce(prefix + starimg,
slits='header',
rawpath='./',
fl_inter='no',
fl_addmdf='no',
key_mdf='MDF',
mdffile=mdffile,
fl_over='no',
fl_trim='no',
fl_bias='no',
trace='no',
recenter='no',
fl_flux='no',
fl_gscrrej='no',
fl_extract='no',
fl_gsappwave='no',
fl_wavtran='no',
fl_novl='no',
fl_skysub='yes',
reference='eprg' + flat,
weights='no',
wavtraname='eprg' + arc,
response='eprg' + flat + '_response.fits',
fl_vardq=vardq,
w1=wl1,
w2=wl2)
prefix = 's' + prefix
#
# Apsumming the stellar spectra
#
xinst = pf.getdata(prefix + starimg + '.fits', ext=1)['XINST']
if instrument == 'GMOS-N':
x0 = np.average(xinst[xinst < 10])
elif instrument == 'GMOS-S':
x0 = np.average(xinst[xinst > 10])
ap_expression = '((XINST-{:.2f})**2 + '\
'(YINST-2.45)**2)**0.5 < {:.2f}'.format(x0, apsum_radius)
imageName = 'a' + prefix + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
else:
iraf.gfapsum(prefix + starimg,
fl_inter='no',
lthreshold='INDEF',
hthreshold='INDEF',
reject='avsigclip',
expr=ap_expression)
#
# Building sensibility function
#
if os.path.isfile('std' + starimg)\
and os.path.isfile('sens' + starimg + '.fits'):
skipwarn('std{0:s} and sens{0:s}.fits'.format(starimg))
else:
imageName = 'std' + starimg
if os.path.isfile(imageName):
iraf.printlog(
'GIREDS: WARNING: Removing file {:s}'.format(imageName),
'logfile.log', 'yes')
iraf.delete(imageName)
imageName = 'sens' + starimg + '.fits'
if os.path.isfile(imageName):
iraf.printlog(
'GIREDS: WARNING: Removing file {:s}'.format(imageName),
'logfile.log', 'yes')
iraf.delete(imageName)
iraf.gsstandard('a' + prefix + starimg,
starname=stdstar,
observatory=observatory,
sfile='std' + starimg,
sfunction='sens' + starimg,
caldir=caldir,
order=sens_order,
function=sens_function)
#
# Apply flux calibration to star
#
imageName = 'c' + prefix + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
else:
iraf.gscalibrate(prefix + starimg,
sfuncti='sens' + starimg,
extinct='onedstds$ctioextinct.dat',
observatory=observatory,
fluxsca=1,
fl_vardq=vardq)
#
# Create data cubes
#
imageName = 'dc' + prefix + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
else:
data_cube = CubeBuilder('c' + prefix + starimg + '.fits')
data_cube.build_cube()
data_cube.fit_refraction_function()
data_cube.fix_atmospheric_refraction()
data_cube.write(imageName)
#
# Test calibration
#
iraf.cd(caldir)
caldata = np.loadtxt(stdstar + '.dat', unpack=True)
iraf.cd('procdir')
calflux = mag2flux(caldata[0], caldata[1])
imageName = 'ca' + prefix + starimg + '.fits'
if os.path.isfile(imageName):
skipwarn(imageName)
else:
iraf.gscalibrate('a' + prefix + starimg,
sfuncti='sens' + starimg,
extinct='onedstds$ctioextinct.dat',
observatory=observatory,
fluxsca=1)
sumflux = pf.getdata('ca' + prefix + starimg + '.fits', ext=2)
sumhead = pf.getheader('ca' + prefix + starimg + '.fits', ext=2)
sumwl = sumhead['crval1'] + np.arange(
sumhead['naxis1']) * sumhead['cdelt1']
plt.close('all')
plt.plot(sumwl, sumflux, 'b', lw=.5)
plt.plot(caldata[0], calflux, 'r', lw=1.5)
plt.xlim(sumwl[0] * .99, sumwl[-1] * 1.01)
plt.ylim(min(calflux) * .8, max(calflux) * 1.2)
plt.savefig('calib' + starimg + '.eps')
import glob, os
import g0_init_cfg as ic
# ----- Importing IRAF from the root directory ----- #
current_dir = os.getcwd()
os.chdir(ic.dir_iraf)
from pyraf import iraf
from pyraf.iraf import gemini, gmos
os.chdir(current_dir)
iraf.chdir(current_dir)
# ----- Spectrophotometric calibration ----- #
iraf.imdelete('cstxeqxbrg@'+ic.lst_sci, verify='no')
for sci in iraf.type(ic.lst_sci, Stdout=1):
sci = sci.strip()
iraf.gscalibrate('stxeqxbrg'+sci, sfunction=ic.caldir+ic.sensfunc,
obs=ic.obs_site, extinction=ic.extinction,
fl_ext='yes', fl_vardq='yes')
os.system('ds9 &')
iraf.sleep(5.0)
for sci in iraf.type(ic.lst_sci, Stdout=1):
iraf.gfdisplay('cstxeqxbrg'+sci, 1, version=1)
# Printing the running time
print('--- %s seconds ---' %(time.time()-start_time))
vardq = 'yes'
for i in gal:
iraf.gsreduce(rawdir + i,
rawpath=rawdir,
bias=rawdir + bias,
fl_fixpix='yes',
flat='flat.fits',
fl_over='no',
fl_gscrrej='yes',
fl_vardq=vardq,
fl_fulldq=vardq)
iraf.gstransform('gs' + i,
wavtraname='gs' + arc.replace('.fits', ''),
fl_vardq=vardq)
iraf.gsskysub('tgs' + i, long_sample='800:900', fl_vardq=vardq)
iraf.gscalibrate('stgs' + i, fl_vardq=vardq)
a = iraf.hselect((3 * 'cstgs{:s}[0],').format(*gal),
'xoffset,yoffset',
'yes',
Stdout=1)
pixscale = 0.292 # arcsec/pix along the direction of the slit
b = np.array([[float(j) for j in i.split('\t')] for i in a]) / pixscale
np.savetxt('offsets.dat', b)
iraf.imcombine((3 * 'cstgs{:s}[2],').format(*gal),
'ngc3081_545.fits',
offsets='offsets.dat',
combine='average',
reject='sigclip')
iraf.gsskysub(
'tgs' + star, long_sample='150:200')
iraf.gsextract(
'stgs' + star)
iraf.gsstandard(
'estgs' + star, caldir='onedstds$ctionewcal/', starname='l3218')
for i in gal:
iraf.gsreduce(
rawdir + i, rawpath=rawdir, bias=rawdir + bias,
fl_fixpix='yes', flat='flat.fits', fl_over='no',
fl_gscrrej='yes')
iraf.gstransform(
'gs' + i, wavtraname='gs' + arc.replace('.fits', ''))
iraf.gsskysub(
'tgs' + i, long_sample='800:900')
iraf.gscalibrate(
'stgs' + i)
a = iraf.hselect(
(3*'cstgs{:s}[0],').format(*gal), 'xoffset,yoffset', 'yes', Stdout=1)
pixscale = 0.292 # arcsec/pix along the direction of the slit
b = np.array([[float(j) for j in i.split('\t')] for i in a]) / pixscale
np.savetxt('offsets.dat', b)
iraf.imcombine(
(3*'cstgs{:s}[2],').format(*gal), 'ngc3081_545.fits',
offsets='offsets.dat', combine='average', reject='sigclip')
iraf.gsstandard(
'astexlrg'+n, starname=stdstar[m],
observatory=observatory[m], sfile='std'+str(m)+'_'+str(i),
sfunction='sens'+str(m), caldir=caldir[m])
'''
# (B)Test calibration
import matplotlib.pyplot as plt
starflux = [locrep+starinfo[i]['flux'] for i in starinfo_idx]
for m, i in enumerate(star):
iraf.delete('castexlrg'+i+'.fits')
iraf.gscalibrate(
'astexlrg'+i, sfuncti='sens'+str(m),
observa=observatory[m], fluxsca=1)
starcal_fl = pf.getdata('castexlrg'+i+'.fits', ext=2)
starcal_he = pf.getheader('castexlrg'+i+'.fits', ext=2)
starcal_wl = starcal_he['crval1'] + np.arange(starcal_he['naxis1'])*starcal_he['cdelt1']
starrep = np.loadtxt(starflux[m], unpack=True)
# Need: baixar arquivos de todas estrelas com contagem em fluxo
# ou converter o arquivo do gmos de mag>fluxo
# Need: limitar range(x) do arquivo do repositorio
# Need: limitar range(y) do plot.
plt.close('all')
plt.plot(starcal_wl, starcal_fl, 'b')
plt.plot(starrep[0], starrep[1], 'r', lw=1.56)
plt.xlim(starcal_wl[0], starcal_wl[-1])
