def read_hds2d(fitsecf,blazedf,wavlim=None):
import os
fitsecf_combined=fitsecf.replace(".fits","_c.fits")
blazedf_combined=blazedf.replace(".fits","_c.fits")
if not os.path.isfile(fitsecf_combined):
from pyraf import iraf
iraf.scombine(input=fitsecf,output=fitsecf_combined,combine="sum",group="images")
wav,data,header=read_hds_ecf(fitsecf_combined)#,wavlim=[5140,5200])
try:
if not os.path.isfile(blazedf_combined):
from pyraf import iraf
iraf.scombine(input=blazedf,output=blazedf_combined,combine="sum",group="images")
bwav,bdata,header_blaze=read_hds_ecf(blazedf_combined)#,wavlim=[5140,5200])
normspec=data/bdata
except:
normspec=data ###TEST####
if wavlim:
ind=np.searchsorted(wav,wavlim)
return wav[ind[0]:ind[1]], normspec[ind[0]:ind[1]], np.sqrt(data)[ind[0]:ind[1]], header
else:
return wav, normspec, np.sqrt(data), header
def run_scombine(listin, fn):
namefil = []
namelist = open(listin, "r")
for cols in (raw.strip().split() for raw in namelist):
namefil.append(cols[0])
namelist.close()
obs = []
count = -1
for i in range(len(namefil)):
if namefil[i - 1][0:2] != namefil[i][0:2]:
obs.append([])
count += 1
obs[count].append(namefil[i])
spec = []
for i in range(len(obs[0])):
spec.append([])
for j in range(len(obs)):
spec[i].append(obs[j][i])
iraf.noao()
iraf.onedspec()
for i in range(len(spec)):
temp = open('temp_list', 'w')
for j in range(len(spec[i])):
temp.write(spec[i][j] + '\n')
temp.close()
if (spec[i][0][-6] == 'L') or (spec[i][0][-6] == 'U'):
name_index = '0_' + spec[i][0][-6]
else:
name_index = spec[i][0][-6] + '_' + spec[i][0][-8]
if len(fibnum) != 0:
newname = ob_id[fibnum.index(name_index[0])] + '_' + name_index[-1]
name_index = newname
iraf.scombine('@temp_list', name_index, logfile='combine_log')
fn.append(name_index)
def spec_combine(red,
blue,
cname,
matchw1=5400,
matchw2=5500,
outw1=3500,
outw2=10000):
'''Combines blue and red spectra into single composite'''
iraf.scombine("%s,%s" % (red, blue),
cname,
combine="average",
reject="avsigclip",
w1=outw1,
w2=outw2,
dw=INDEF,
nw=INDEF,
scale="median",
zero="none",
weight="none",
sample="%i:%i" % (matchw1, matchw2))
#iraf.sarith(red, '/', blue, 'temp2', w1=matchw1, w2=matchw2)
#iraf.iterstat('temp2[*,1,1]', nsigrej=5, maxiter=3, prin=no, verbose=no)
#mean=float(iraf.iterstat.mean)
#iraf.sarith(red, '/', mean, 'temp3')
#iraf.scopy('temp3', 'red', w1=matchw1, w2=outw2)
#iraf.scopy(blue, 'blue', w1=outw1, w2=matchw2)
#iraf.scombine('red,blue', cname, w1=outw1, w2=outw2)
return
def scombine(inp, out, apt = '1'):
"""
Combine spectra.
"""
iraf.scombine.input = inp
iraf.scombine.output = out
iraf.scombine.apertures = apt
iraf.scombine(mode='h')
def stitch():
#Find the mean 3100 and 3125 of blue and uv
uvhdu = pyfits.open('13dh_uv.fits')
uvlam = fitshdr_to_wave(uvhdu[0].header)
w = np.logical_and(uvlam > 3050, uvlam < 3100)
uvmean = np.median(uvhdu[0].data[1,0,w])
uvhdu.close()
print("uv mean %e"%uvmean)
bluehdu = pyfits.open('13dh_blue.fits')
bluelam = fitshdr_to_wave(bluehdu[0].header)
w = np.logical_and(bluelam > 3050, bluelam < 3100)
blueuvmean = np.median(bluehdu[0].data[1,0,w])
print("blue mean uv %e"%blueuvmean)
# Find mean of red and blue between 5375 and 5600
w = np.logical_and(bluelam > 5375, bluelam < 5600)
blueredmean = bluehdu[0].data[1,0,w].mean()
bluehdu.close()
print("blue mean red %e"%blueredmean)
redhdu = pyfits.open('13dh_red.fits')
redlam = fitshdr_to_wave(redhdu[0].header)
w = np.logical_and(redlam > 5375, redlam < 5600)
redmean = redhdu[0].data[1,0,w].mean()
redhdu.close()
print("red mean %e"%redmean)
# trim uv at 3140
iraf.unlearn(iraf.scopy)
iraf.scopy('13dh_uv.fits', '13dh_uv_trim.fits', w1='INDEF', w2=3140, rebin='no')
# trim blue at 3130 and 5600
iraf.unlearn(iraf.scopy)
iraf.scopy('13dh_blue.fits', '13dh_blue_trim.fits', w1=3130, w2=5600, rebin='no')
# Trim red at 5375
iraf.unlearn(iraf.scopy)
iraf.scopy('13dh_red.fits', '13dh_red_trim.fits', w1=5375, w2='INDEF', rebin='no')
# Copy the spectra from the second extraction to the first
for im in ['13dh_uv_trim.fits', '13dh_blue_trim.fits', '13dh_red_trim.fits']:
hdu = pyfits.open(im, mode='update')
hdu[0].data[0, 0, :] = hdu[0].data[1, 0, :]
hdu.flush()
hdu.close()
# Write out the scale factors
lines = ['%f\n' % (blueuvmean / uvmean)**-1,'1.0\n','%f\n' % (blueredmean / redmean)**-1]
f = open('scales.dat','w')
f.writelines(lines)
f.close()
#Scombine with the correct scalings using average
iraf.unlearn(iraf.scombine)
iraf.scombine('13dh_uv_trim, 13dh_blue_trim, 13dh_red_trim', '13dh_hst.fits', scale='@scales.dat')
return
def combine_err(spectra_list, weight_file, outputname):
'''Combine two or more multispec error images using **scombine**.
The input weight file should be the weights used to combine the actual
data spectra. Error propagation is performed by
.. math::
\delta C = \sqrt{\sum_i(E_i/w_i)^2},
where :math:`E` is an error spectrum (.me_rf_lin.fits), :math:`w` is the
weight of the corresponding data spectrum, and dC is the error on the
combined data spectra.
Note that internally (in both this function and **scombine**) the weights
are normalized to a unity sum, which avoids the need to keep track of the
sum of the squares of the weights.
When combining images that are not on exactly the same wavelength grid
**scombine** will interpolate all spectra to have the same wavelengths as
the first image. A simplifying assumption made by this function is that
the interpolation is essentially linear. See the documentation for
:func:`dispcor_err` for more information.
'''
print 'Combining:'
for s in spectra_list:
print '\t{}'.format(s)
print 'Into {} using weights in {}'.format(outputname, weight_file)
#Read in weights
sq_weight_file = 'tmp_sq{}'.format(weight_file)
w = np.loadtxt(weight_file)
sqw = w**2
sqw /= np.sum(sqw)
np.savetxt(sq_weight_file, sqw, fmt='%5.4f')
#Construct list of squared outputs
sqlist = []
for spectrum in spectra_list:
tmpsq = 'tmp_sq{}'.format(spectrum)
pow_image(spectrum, tmpsq, 2.)
sqlist.append(tmpsq)
tmpoutput = 'tmp_sq{}'.format(outputname)
iraf.scombine(','.join(sqlist),
tmpoutput,
logfile='scombine_err.log',
weight='@{}'.format(sq_weight_file))
pow_image(tmpoutput, outputname, 0.5)
print 'Cleaning intermediates'
os.system('rm tmp_sq*')
return 0
def combine_err(spectra_list, weight_file, outputname):
'''Combine two or more multispec error images using **scombine**.
The input weight file should be the weights used to combine the actual
data spectra. Error propagation is performed by
.. math::
\delta C = \sqrt{\sum_i(E_i/w_i)^2},
where :math:`E` is an error spectrum (.me_rf_lin.fits), :math:`w` is the
weight of the corresponding data spectrum, and dC is the error on the
combined data spectra.
Note that internally (in both this function and **scombine**) the weights
are normalized to a unity sum, which avoids the need to keep track of the
sum of the squares of the weights.
When combining images that are not on exactly the same wavelength grid
**scombine** will interpolate all spectra to have the same wavelengths as
the first image. A simplifying assumption made by this function is that
the interpolation is essentially linear. See the documentation for
:func:`dispcor_err` for more information.
'''
print 'Combining:'
for s in spectra_list: print '\t{}'.format(s)
print 'Into {} using weights in {}'.format(outputname, weight_file)
#Read in weights
sq_weight_file = 'tmp_sq{}'.format(weight_file)
w = np.loadtxt(weight_file)
sqw = w**2
sqw /= np.sum(sqw)
np.savetxt(sq_weight_file,sqw,fmt='%5.4f')
#Construct list of squared outputs
sqlist = []
for spectrum in spectra_list:
tmpsq = 'tmp_sq{}'.format(spectrum)
pow_image(spectrum,tmpsq,2.)
sqlist.append(tmpsq)
tmpoutput = 'tmp_sq{}'.format(outputname)
iraf.scombine(','.join(sqlist),
tmpoutput,
logfile='scombine_err.log',
weight='@{}'.format(sq_weight_file))
pow_image(tmpoutput, outputname, 0.5)
print 'Cleaning intermediates'
os.system('rm tmp_sq*')
return 0
def speccombine(fs, outfile):
nsteps = 8001
lamgrid = np.linspace(3000.0, 11000.0, nsteps)
nfs = len(fs)
# for each aperture
# get all of the science images
specs = np.zeros((nfs, nsteps))
specerrs = np.zeros((nfs, nsteps))
for i, f in enumerate(fs):
hdu = pyfits.open(f)
lam = fitshdr_to_wave(hdu[0].header.copy())
# interpolate each spectrum onto a common wavelength scale
specs[i] = np.interp(lamgrid, lam, hdu[0].data,
left=0.0, right=0.0)
# Also calculate the errors. Right now we assume that the variances
# interpolate linearly. This is not strictly correct but it should be
# close. Also we don't include terms in the variance for the
# uncertainty in the wavelength solution.
specerrs[i] = 0.1 * specs[i]
# minimize the chi^2 given free parameters are multiplicative factors
# We could use linear or quadratic, but for now assume constant
# Assume 3 chips for now
p0 = np.ones(nfs / 3)
results = optimize.minimize(combine_spec_chi2, p0,
args=(lamgrid, specs, specerrs),
method='Nelder-Mead',
options={'maxfev': 1e5, 'maxiter': 1e5, 'ftol':1e-5})
# write the best fit parameters into the headers of the files
# Dump the list of spectra into a string that iraf can handle
iraf_filelist = str(fs).replace('[', '').replace(']', '').replace("'", '') #.replace(',', '[SCI],')
#iraf_filelist += '[SCI]'
# write the best fit results into a file
lines = []
for p in np.repeat(results['x'], 3):
lines.append('%f\n' % (1.0 / p))
f = open('scales.dat', 'w')
f.writelines(lines)
f.close()
# run scombine after multiplying the spectra by the best fit parameters
if os.path.exists(outfile):
os.remove(outfile)
iraf.unlearn(iraf.scombine)
iraf.scombine(iraf_filelist, outfile, scale='@scales.dat',
reject='avsigclip', lthreshold='INDEF', w1=bluecut)
def combine_normalize_images(WORK_DIR, combine=False):
if combine:
print '\n + Combine images\n'
try:
os.remove('combined_sum.fits')
except: pass
iraf.scombine(input=','.join([obj+'.ec.vh' for obj in observations[WORK_DIR]['objects']]), output='combined_sum', group='apertures', combine='sum', reject='none', Stdout="/dev/null")
# normalize spectra
# This step will require you to manually normalize all the spectra.
print '\n + Normalize spectra\n'
if combine:
try:
os.remove('combined_sum_cone_rv_echellet.fits')
except: pass
iraf.continuum(input='combined_sum', output='combined_sum_cont', type='fit', replace='no', listonly='no', functio='cheb', order=13, low_rej=2, high_rej=3, naverag=-3, niter=9, interac='no', markrej='no')
else:
for obj in observations[WORK_DIR]['objects']:
try:
os.remove(obj+'_cont.fits')
os.remove(obj+'_vh_norm.fits')
except: pass
iraf.continuum(input=obj+'.ec.vh', output=obj+'_cont', type='fit', replace='no', listonly='no', functio='cheb', order=13, low_rej=2, high_rej=3, naverag=-3, niter=9, interac='no', markrej='no', ask='yes')
iraf.sarith(input1=obj+'.ec.vh', op='/', input2=obj+'_cont', output=obj+'_vh_norm', format='multispec', Stdout="/dev/null")
#combine apertures
print '\n + Combine apertures\n'
if combine:
try:
os.remove('combined_sum_data.fits')
os.remove('combined_sum_norm.fits')
os.remove('combined_final.0001.fits')
except: pass
iraf.scombine(input='combined_sum', output='combined_sum_data', group='all', combine='sum', reject='none', Stdout="/dev/null")
iraf.scombine(input='combined_sum_cont', output='combined_sum_norm', group='all', combine='sum', reject='none', Stdout="/dev/null")
iraf.sarith(input1='combined_sum_data', op='/', input2='combined_sum_norm', output='combined_final', format='onedspec', Stdout="/dev/null")
else:
for obj in observations[WORK_DIR]['objects']:
try:
os.remove(obj+'_data1D.fits')
os.remove(obj+'_cont1D.fits')
os.remove(obj+'_1D_vh_norm.0001.fits')
os.remove(obj+'_1D_vh_norm.fits')
except: pass
iraf.scombine(input=obj+'.ec.vh', output=obj+'_data1D', group='all', combine='sum', reject='none', Stdout="/dev/null")
iraf.scombine(input=obj+'_cont', output=obj+'_cont1D', group='all', combine='sum', reject='none', Stdout="/dev/null")
iraf.sarith(input1=obj+'_data1D', op='/', input2=obj+'_cont1D', output=obj+'_1D_vh_norm', format='onedspec', Stdout="/dev/null")
def speccombine(fs, outfile):
nsteps = 8001
lamgrid = np.linspace(3000.0, 11000.0, nsteps)
nfs = len(fs)
# for each aperture
# get all of the science images
specs = np.zeros((nfs, nsteps))
specerrs = np.zeros((nfs, nsteps))
for i, f in enumerate(fs):
hdu = fits.open(f)
lam = fitshdr_to_wave(hdu[0].header.copy())
# interpolate each spectrum onto a common wavelength scale
specs[i] = np.interp(lamgrid, lam, hdu[0].data,
left=0.0, right=0.0)
# Also calculate the errors. Right now we assume that the variances
# interpolate linearly. This is not strictly correct but it should be
# close. Also we don't include terms in the variance for the
# uncertainty in the wavelength solution.
specerrs[i] = 0.1 * specs[i]
# minimize the chi^2 given free parameters are multiplicative factors
# We could use linear or quadratic, but for now assume constant
# Assume 3 chips for now
p0 = np.ones(nfs / 3)
results = optimize.minimize(combine_spec_chi2, p0,
args=(lamgrid, specs, specerrs),
method='Nelder-Mead',
options={'maxfev': 1e5, 'maxiter': 1e5, 'ftol':1e-5})
# write the best fit parameters into the headers of the files
# Dump the list of spectra into a string that iraf can handle
iraf_filelist = str(fs).replace('[', '').replace(']', '').replace("'", '') #.replace(',', '[SCI],')
#iraf_filelist += '[SCI]'
# write the best fit results into a file
lines = []
for p in np.repeat(results['x'], 3):
lines.append('%f\n' % (1.0 / p))
f = open('scales.dat', 'w')
f.writelines(lines)
f.close()
# run scombine after multiplying the spectra by the best fit parameters
if os.path.exists(outfile):
os.remove(outfile)
iraf.unlearn(iraf.scombine)
iraf.scombine(iraf_filelist, outfile, scale='@scales.dat',
reject='avsigclip', lthreshold='INDEF', w1=bluecut)
def scombine(fstr, oname, combine='sum'):
"""
Call iraf command scombine, generate a combined spectrum file.
If the file already exist, this function will delete the old one.
fstr : input string, like fstr=abc.fits,abd.fits,abe.fits
type : string
oname : output file name
type : string
combine : the spectrum combine method, the method include
'average', 'median', 'sum', default='sum'.
type : string
"""
if os.path.isfile(oname):
print('remove file ' + oname)
os.remove(oname)
iraf.scombine(input=fstr,
output=oname,
noutput='',
logfile='STDOUT',
apertures='',
group='apertures',
combine=combine,
reject='none',
first='No',
w1='INDEF',
w2='INDEF',
dw='INDEF',
nw='INDEF',
log='No',
scale='none',
zero='none',
weight='none',
sample='',
lthreshold='INDEF',
hthreshold='INDEF',
nlow=1,
nhigh=1,
nkeep=1,
mclip='Yes',
lsigma=3.0,
hsigma=3.0,
rdnoise='RDNOISE',
gain='GAIN',
snoise=0.0,
sigscale=0.1,
pclip=-0.5,
grow=0,
blank=1.0)
def recombine(imagelist, outputimage):
"""Recombine a group of multispec files by aperture
This uses the scombine task with group set to apertures. Because the
aperture information is preserved when doing sky subtraction all this does
is concatenate the individual .ms files into a single file.
Parameters
----------
imagelist : list of str
List of individual file names of the temporary, single-size .ms files created by skysub
outputimage : str
Name of the resulting image that has all apertures from the input list
Returns
-------
None
The result is a multispec file that is a concatination of all the files in the input list.
Notes
-----
To work as intended, each file in the input list should contain a unique set of aperture identifications. Any repeating apertures will be averaged together, which probably isn't what you want.
"""
print 'Combining appertures from:'
for image in imagelist:
print '\t' + image
iraf.scombine(
','.join(imagelist),
outputimage,
apertures='',
group='apertures',
combine='average', #These are irrelivant b/c
reject='avsigclip', # we aren't actually combining anything
first='yes', #Very important
w1='INDEF',
w2='INDEF',
dw='INDEF',
nw='INDEF',
weight='',
log='no',
gain=0.438,
rdnoise=3.9,
logfile='spool.txt')
return
def run_scombine(listin, fn, setup):
namefil = []
namelist = open(listin, "r")
for cols in (raw.strip().split() for raw in namelist):
namefil.append(cols[0])
namelist.close()
obs = []
count = -1
for i in range(len(namefil)):
if namefil[i - 1][namefil[i - 1].index('/') +
1:][0] != namefil[i][namefil[i].index('/') + 1:][0]:
obs.append([])
count += 1
obs[count].append(namefil[i])
spec = []
for i in range(len(obs[0])):
spec.append([])
for j in range(len(obs)):
Flag = True
try:
val = obs[j][i]
Flag = True
except IndexError:
Flag = False
if Flag:
spec[i].append(obs[j][i])
if len(spec[i]) != len(obs):
spec.pop(i)
iraf.noao()
iraf.onedspec()
for i in range(len(spec)):
temp = open(setup + '/temp_list', 'w')
for j in range(len(spec[i])):
temp.write(spec[i][j] + '\n')
temp.close()
longn = spec[i][0]
specname = longn[(longn.index('/') +
1):][(longn[(longn.index('/') + 1):]).index('/') +
1:]
iraf.scombine('@' + setup + '/temp_list',
setup + '/' + specname,
logfile=setup + '/combine_log')
fn.append(specname)
def spec_combine(red, blue, cname, matchw1=5400, matchw2=5500, outw1=3500,
outw2=10000):
'''Combines blue and red spectra into single composite'''
iraf.scombine("%s,%s" % (red, blue), cname, combine="average",
reject="avsigclip", w1=outw1, w2=outw2, dw=INDEF,
nw=INDEF, scale="median", zero="none", weight="none",
sample="%i:%i" % (matchw1, matchw2))
#iraf.sarith(red, '/', blue, 'temp2', w1=matchw1, w2=matchw2)
#iraf.iterstat('temp2[*,1,1]', nsigrej=5, maxiter=3, prin=no, verbose=no)
#mean=float(iraf.iterstat.mean)
#iraf.sarith(red, '/', mean, 'temp3')
#iraf.scopy('temp3', 'red', w1=matchw1, w2=outw2)
#iraf.scopy(blue, 'blue', w1=outw1, w2=matchw2)
#iraf.scombine('red,blue', cname, w1=outw1, w2=outw2)
return
def recombine(imagelist,outputimage):
"""Recombine a group of multispec files by aperture
This uses the scombine task with group set to apertures. Because the
aperture information is preserved when doing sky subtraction all this does
is concatenate the individual .ms files into a single file.
Parameters
----------
imagelist : list of str
List of individual file names of the temporary, single-size .ms files created by skysub
outputimage : str
Name of the resulting image that has all apertures from the input list
Returns
-------
None
The result is a multispec file that is a concatination of all the files in the input list.
Notes
-----
To work as intended, each file in the input list should contain a unique set of aperture identifications. Any repeating apertures will be averaged together, which probably isn't what you want.
"""
print 'Combining appertures from:'
for image in imagelist: print '\t'+image
iraf.scombine(','.join(imagelist),outputimage,
apertures='',
group='apertures',
combine='average', #These are irrelivant b/c
reject='avsigclip',# we aren't actually combining anything
first='yes', #Very important
w1='INDEF',w2='INDEF',dw='INDEF',nw='INDEF',
weight='',
log='no',
gain=0.438,
rdnoise=3.9,
logfile='spool.txt')
return
def combinespecs(inputre, scale='exposure', rdnoise='rdnoise', gain='gain'):
''' combine two or more spectra tha matches the input regular expression
'''
specfiles = glob.glob(specre)
specstring = ', '.join(specfiles)
print 'The following spectra will be combined: '
print specfiles
specout = str(raw_input('Enter output file name: '))
iraf.scombine.unlearn()
iraf.scombine.scale = scale
iraf.scombine.rdnoise = rdnoise
iraf.scombine.gain = gain
iraf.scombine(input=specstring, output=specout)
ask = str(raw_input('Plot output with splot? Y/N: '))
if (ask == 'y') or (ask == 'Y'):
iraf.splot.unlearn()
iraf.splot(specout)
def combinespecs(inputre, scale='exposure', rdnoise='rdnoise', gain='gain'):
''' combine two or more spectra tha matches the input regular expression
'''
specfiles = glob.glob(specre)
specstring = ', '.join(specfiles)
print 'The following spectra will be combined: '
print specfiles
specout = str(raw_input('Enter output file name: '))
iraf.scombine.unlearn()
iraf.scombine.scale = scale
iraf.scombine.rdnoise = rdnoise
iraf.scombine.gain = gain
iraf.scombine(input=specstring, output=specout)
ask = str(raw_input('Plot output with splot? Y/N: '))
if (ask == 'y') or (ask == 'Y'):
iraf.splot.unlearn()
iraf.splot(specout)
import glob
# Load third-party modules
from pyraf import iraf
# choose spectra to combine
specre = str(raw_input("Enter regular expression for spectra files: "))
specfiles = glob.glob(specre)
specstring = ', '.join(specfiles)
# choose spectra output name
specout = str(raw_input("Enter output combined spectra name: "))
# unlearn previous settings
iraf.scombine.unlearn()
# setup
iraf.scombine.combine = 'median'
iraf.scombine.reject = 'sigclip'
iraf.scombine.scale = 'exposure'
iraf.scombine.rdnoise = 'rdnoise'
iraf.scombine.gain = 'gain'
# call scombine
iraf.scombine(input=specstring, output=specout)
# visualize combined spectra with splot to check
iraf.splot.unlearn()
iraf.splot(specout)
print '--- DONE ---'
iraf.scombine(
input = combine_list_flux,\
output = "spec_" + file_name,\
noutput = "",\
logfile = "STDOUT",\
apertures = "*",\
group = "all",\
combine = "average",\
reject = "minmax",\
first = 1,\
w1 = spectrum_w1,\
w2 = spectrum_w2,\
dw = "INDEF",\
nw = "INDEF",\
log = 0,\
scale = "median",\
zero = "none",\
weight = "median",\
sample = sample_region,\
nlow = 1,\
nhigh = 1,\
nkeep = 1,\
mclip = 1,\
lsigma = 2.0,\
hsigma = 2.0,\
rdnoise = 0,\
gain = 1,\
snoise = 0,\
sigscale = 0.1,\
pclip = -0.5,\
grow = 0,\
blank = 0.0,\
mode = "al")
def deimos_extract(science, standard, dostandard=yes):
'''Extract and flux calibrate DEIMOS spectra (assuming they have been
processed into 2D images using deimos_pipe above).'''
if dostandard:
deimos_standard(standard)
for source in science:
images = iraffiles("%s_??_B.fits" % source)
joinstr = ""
for image in images:
bimage = image.split(".")[0]; rimage = bimage[:-1] + "R"
update_head(bimage, "FLUX_OBJ", standard)
update_head(rimage, "FLUX_OBJ", standard)
# Extract 1D spectra
if get_head("%s.fits" % bimage, "SKYSUB"):
iraf.apall(bimage, output="", inter=yes, find=yes, recenter=yes,
resize=yes, edit=yes, trace=yes, fittrace=yes, extract=yes,
extras=yes, review=no, background="none",
reference="%s_01_B" % standard)
iraf.apall(rimage, output="", inter=yes, find=yes, recenter=yes,
resize=yes, edit=yes, trace=yes, fittrace=yes, extract=yes,
extras=yes, review=no, background="none",
reference="%s_01_R" % standard)
else:
iraf.apall(bimage, output="", inter=yes, find=yes, recenter=yes,
resize=yes, edit=yes, trace=yes, fittrace=yes, extract=yes,
extras=yes, review=no, background="fit",
reference="%s_01_B" % standard)
iraf.apall(rimage, output="", inter=yes, find=yes, recenter=yes,
resize=yes, edit=yes, trace=yes, fittrace=yes, extract=yes,
extras=yes, review=no, background="fit",
reference="%s_01_R" % standard)
# Normalize by the standard continuua
iraf.sarith("%s.ms" % bimage, "/", "s%s_01_B.ms.fits" % standard,
"s%s.ms" % bimage)
iraf.sarith("%s.ms" % rimage, "/", "s%s_01_R.ms.fits" % standard,
"s%s.ms" % rimage)
# Telluric correct
bstd = "%s_01_B" % standard; rstd = "%s_01_R" % standard
iraf.telluric("s%s.ms" % bimage, "ts%s.ms" % bimage,
"telluric.B.%s.fits" % bstd, tweakrms=yes,
interactive=yes, sample='6850:6950,7575:7700')
iraf.telluric("s%s.ms" % rimage, "ts%s.ms" % rimage,
"telluric.R.%s.fits" % rstd, tweakrms=yes,
interactive=yes, sample='6850:6950,7575:7700')
# Flux calibration
iraf.calibrate("ts%s.ms" % bimage, "fts%s.ms" % bimage, extinct=yes,
flux=yes, extinction="home$extinct/maunakeaextinct.dat",
observatory="Keck", sensitivity="%s.B.sens" % standard,
airmass='', exptime='')
iraf.calibrate("ts%s.ms" % rimage, "fts%s.ms" % rimage, extinct=yes,
flux=yes, extinction="home$extinct/maunakeaextinct.dat",
observatory="Keck", sensitivity="%s.R.sens" % standard,
airmass='', exptime='')
joinstr += "fts%s.ms,fts%s.ms," % (bimage, rimage)
# Combine
iraf.scombine(joinstr[:-1], "%s.ms.fits" % source, combine="average",
reject="avsigclip", w1=INDEF, w2=INDEF, dw=INDEF, nw=INDEF,
scale="none", zero="none", weight="none", lsigma=3.0,
hsigma=3.0, gain=CCDGAIN, rdnoise=CCDRNOISE)
# Plot to enable final tweaks
iraf.splot("%s.ms.fits" % source)
return
## begin IRAFing
print('\n' + 'Setting airmass...')
iraf.setairmass('@speclist',
observ='lapalma',
ra='cat-ra',
dec='cat-dec',
equi='cat-equi',
st='lst',
ut='utstart')
print('\n' + 'Combining spectra...')
iraf.scombine(input='@speclist',
output='allspec',
group='all',
combine='median',
gain=2.45,
reject='crreject',
lthresh=1e-30,
hthresh=hthresh2)
print('\n' + 'Applying flux calibration to sensitivity function...')
iraf.calibrate(
'@speclist',
'allspeccal',
obs='lapalma',
sens=sensfile,
extinct='no',
ignoreaps='yes'
) ## If extinct='yes', try extinction ='onedstds$ctioextinct.dat'
print('\n' +
def stitch_flats(outputnames, pivots, outstring):
"""Take a list of multispec files and a list of pivots and stitch together a master flat.
The pivot values are inclusive, so if pivots = [72] then the master flat will contain fibers 1 - 72 from flat #1 and 73 - 109 from flat #2.
Parameters
----------
outputnames : list of str
Names of the multispec files to stitch together
pivots : list of int
The fibers that form the borders of the stitch. If outputnames is length N, pivots must be length N - 1
outstring : str
The special, IRAF scrunch string used to identify intermediate files associated with a **dohydra** run
Returns
-------
mastername : str
The name of the stitched master multispec flat
Notes
-----
The specifics of outstring depend on system and IRAF distribution. See :meth:`GradPak_flatfu.get_scrunch`
"""
pivots = [0] + pivots + [109]
tmpfiles = []
print 'Extracting flat apertures...'
for i, flat in enumerate(outputnames):
print '\ttaking {} from {} to {}'.format(flat, pivots[i] + 1,
pivots[i + 1])
name = 'tmp{}'.format(flat)
iraf.scopy(flat,
name,
apertur='{}-{}'.format(pivots[i] + 1, pivots[i + 1]),
w1='INDEF',
w2='INDEF',
format='multispec',
verbose=False)
tmpfiles.append(name)
mastername = 'dFlat_master{}.ms.fits'.format(outstring)
print 'Stitching together master flat {}'.format(mastername)
iraf.scombine(','.join(tmpfiles),
mastername,
apertur='',
group='apertures',
first=True,
w1='INDEF',
w2='INDEF',
dw='INDEF',
nw='INDEF',
log=False,
scale='none',
zero='none',
weight='none',
logfile='flatfu.log')
for tmp in tmpfiles:
os.remove(tmp)
return mastername
print smooth_files_list
iraf.scombine(
input = smooth_files_list,\
output = "master_smooth.fits",\
apertures = "",\
group = "all",\
combine = "average",\
reject = "none",\
first = 1,\
w1 = wave1,\
w2 = wave2,\
dw = "INDEF",\
nw = "INDEF",\
log = 0,\
scale = "median",\
zero = "none",\
weight = "median",\
sample = "",\
lthreshold="INDEF",\
hthreshold="INDEF",\
nlow = 1,\
nhigh = 1,\
nkeep = 1,\
mclip = 0,\
lsigma = 0,\
hsigma = 0,\
rdnoise = 0,\
gain = 1,\
snoise = 0,\
pclip = -0.5)
# Order 92 of the new grating was the only one that gave issues. CA H or K.
#if grating == 'new':
# iraf.continuum(input='@targets_extracted',
# output='fitcont//@targets_extracted',lines='92',type='fit',
# replace=no,wavescale=yes,logscale=no,listonly=no,
# interactive=no,sample='3860:3920,3945:3960',naverage=-3,
# function='spline3',order=5,niterate=10,low_reject=1.5,
# high_reject=5.0,markrej=no,grow=0,override=yes)
print 'Combining spectra...'
iraf.scombine('@targets_extracted',
'actual_flux//@targets_extracted',
group='images',
combine='average',
reject='none',
first=no,
w1=INDEF,
w2=INDEF,
dw=INDEF,
nw=INDEF,
log=no,
scale='none',
weight='none',
zero='none')
print 'Combining spectra...'
iraf.scombine('@targets_extracted',
'sum//@targets_extracted',
group='images',
combine='sum',
reject='none',
first=no,
w1=INDEF,
def salt_scombine(objlist):
obj = np.loadtxt(objlist, dtype="str", unpack=True, ndmin=1)
objlist1d = objlist + '1d'
objj = os.path.splitext(obj[0])[0]
if os.path.isfile(objj + 'wsf.fits'):
os.system('sed s/.fits/wsf1d.fits/ ' + objlist + ' > ' + objlist1d)
objnofits = string.split(obj[0], sep='.')[0] + 'wsf'
os.system('sed s/.fits/wsf1d_var.fits/ ' + objlist + ' > templist')
else:
os.system('sed s/.fits/ws1d.fits/ ' + objlist + ' > ' + objlist1d)
objnofits = string.split(obj[0], sep='.')[0] + 'ws'
os.system('sed s/.fits/ws1d_var.fits/ ' + objlist + ' > templist')
low_ap = float(
rt.getsh("grep -i 'low\t' database/ap" + objnofits +
" | awk '{print $3}'"))
high_ap = float(
rt.getsh("grep -i 'high\t' database/ap" + objnofits +
" | awk '{print $3}'"))
apradius = abs(low_ap) + abs(high_ap)
readnoise = 2.45 * np.sqrt(apradius)
print "ggg = %s" % readnoise
iraf.scombine.combine = 'average'
iraf.scombine.rdnoise = readnoise
iraf.scombine.reject = 'ccdclip'
iraf.scombine.scale = 'median'
outnam = ''.join(rt.header(obj[0], 'OBJECT').split()).lower()
outname = outnam + '_' + objlist
if os.path.isfile(outname + '.fits'):
os.system('rm ' + outname + '.fits')
inname = str('@' + objlist1d)
iraf.scombine(input=inname, output=outname, first='yes')
tempfile = 'temp.fits'
if os.path.isfile(tempfile):
os.system('rm ' + tempfile)
iraf.imsum('@templist', tempfile)
nspec = len(obj)
outname_var = outname + '_var'
if os.path.isfile(outname_var + '.fits'):
os.system('rm ' + outname_var + '.fits')
nspec = nspec * nspec
iraf.imarith(tempfile, '/', nspec, outname_var)
os.system('rm *temp.fits')
os.system('rm templist')
iraf.dispcor(outname_var, output='', table=outname)
outnametxt = outname + '.txt'
outname_vartxt = outname_var + '.txt'
iraf.wspectext(outname, outnametxt, header='no')
iraf.wspectext(outname_var, outname_vartxt, header='no')
rssdate = rt.header(obj[0], 'DATE-OBS')
rssdate = string.replace(rssdate, '-', '')
pastefinal = outname + '_final_' + rssdate + '.txt'
os.system('paste ' + outnametxt + ' ' + outname_vartxt + ' > ' +
pastefinal)
# Load third-party modules
from pyraf import iraf
# choose spectra to combine
specre = str(raw_input("Enter regular expression for spectra files: "))
specfiles = glob.glob(specre)
specstring = ", ".join(specfiles)
# choose spectra output name
specout = str(raw_input("Enter output combined spectra name: "))
# unlearn previous settings
iraf.scombine.unlearn()
# setup
iraf.scombine.combine = "median"
iraf.scombine.reject = "sigclip"
iraf.scombine.scale = "exposure"
iraf.scombine.rdnoise = "rdnoise"
iraf.scombine.gain = "gain"
# call scombine
iraf.scombine(input=specstring, output=specout)
# visualize combined spectra with splot to check
iraf.splot.unlearn()
iraf.splot(specout)
print "--- DONE ---"
BBparams, covar = curve_fit(bbody,wavev,fluxv,p0=(10000,1E-16)) #intial guess
T= BBparams[0]
Area = BBparams[1]
print '\nBlackbody temperature observed spectrum = %.0f +\- %.0f K\n' % (T,np.sqrt(covar[0,0]))
bbt = 'BBobs = %.0f +\- %.0f K' % (T,np.sqrt(covar[0,0]))
outputname = "bbody_sn_fit.dat" #% T
file = open(outputname,"w")
file.write("# Blackbody temperature = %.0f +\- %.0f K\n" % (T,np.sqrt(covar[0,0])))
w,f = [],[]
for wav in range(900,26000):
file.write("%g\t%g\n" % (wav,bbody(wav,T,Area)))
w.append(wav)
f.append(bbody(wav,T,Area))
iraf.rspec('bbody_sn_fit.dat','bbody_sn_fit.fits', title='bbodyfit',flux='no',dtype='interp',crval1=900,cdelt1=1)
iraf.scombine('bbody_sn_fit.fits,sn.fits,sn.fits,sn.fits', 'bsn_combo.fits',combine='median')
iraf.wspec('bsn_combo.fits','bsn_combo.txt',header='no')
lcf = open('bsn_combo.txt','r')
riga = lcf.readlines()
lcf.close()
wave,flux= [],[]
for line in riga:
p = line.split()
if float(line.split()[0]) >= fil_obs_min and float(line.split()[0]) <= fil_obs_max: #to match the spectrum wavelegnths to those of the filter
wave.append(float(p[0]))
flux.append(float(p[1]))
wavev = array(wave)
BBparams, covar = curve_fit(bbody,wavev,fluxv,p0=(10000,1E-16)) #intial guess
T= BBparams[0]
Area = BBparams[1]
print '\nBlackbody temperature observed spectrum = %.0f +\- %.0f K\n' % (T,np.sqrt(covar[0,0]))
bbt = 'BBobs = %.0f +\- %.0f K' % (T,np.sqrt(covar[0,0]))
outputname = "bbody_sn_fit.dat" #% T
file = open(outputname,"w")
file.write("# Blackbody temperature = %.0f +\- %.0f K\n" % (T,np.sqrt(covar[0,0])))
w,f = [],[]
for wav in range(900,26000):
file.write("%g\t%g\n" % (wav,bbody(wav,T,Area)))
w.append(wav)
f.append(bbody(wav,T,Area))
iraf.rspec('bbody_sn_fit.dat','bbody_sn_fit.fits', title='bbodyfit',flux='no',dtype='interp',crval1=900,cdelt1=1)
iraf.scombine('bbody_sn_fit.fits,sn.fits,sn.fits,sn.fits', 'bsn_combo.fits',combine='median')
iraf.wspec('bsn_combo.fits','bsn_combo.txt',header='no')
print '#######################################'
print '\033[4mSince now you are working with an hybrid spectrum+blackbody, if you want additional information, please use the normal version.\033[0m '
print '#######################################'
lcf = open('bsn_combo.txt','r')
riga = lcf.readlines()
lcf.close()
wave,flux= [],[]
for line in riga:
p = line.split()
if float(line.split()[0]) >= fil_obs_min and float(line.split()[0]) <= fil_obs_max: #to match the spectrum wavelegnths to those of the filter
wave.append(float(p[0]))
print '\n provide the following manually: \n'
print ptfsn2_name, ptfsn2_air, ptfsn2_exp
print '\n'
iraf.onedspec.calibrate(input=ptfsn2ms,
out=ptfsn2f,
extinction='mk_extinct.txt',
observatory='Keck',
ignoreaps=yes,
sensitivity='sensstar2.fits')
inputname = ptfsn1f + ',' + ptfsn2f
#Produce combined spectrum
iraf.scombine(input=inputname,
out='finalspectrum.ms',
reject='avsigclip',
scale='median',
sample='5500:6500')
l.write('calib=yes')
iraf.splot(images='finalspectrum.ms.fits')
iraf.wspectext(input='finalspectrum.ms.fits[*,1]',
output=ptfsn1_name + '.ascii',
header='NO')
iraf.wspectext(input='ptfsn1.f.fits[*,1]',
output=ptfsn1_name + '_r400.ascii',
header='NO')
iraf.wspectext(input='ptfsn2.f.fits[*,1]',
output=ptfsn1_name + '_b600.ascii',
def deimos_extract(science, standard, dostandard=yes):
'''Extract and flux calibrate DEIMOS spectra (assuming they have been
processed into 2D images using deimos_pipe above).'''
if dostandard:
deimos_standard(standard)
for source in science:
images = iraffiles("%s_??_B.fits" % source)
joinstr = ""
for image in images:
bimage = image.split(".")[0]
rimage = bimage[:-1] + "R"
update_head(bimage, "FLUX_OBJ", standard)
update_head(rimage, "FLUX_OBJ", standard)
# Extract 1D spectra
if get_head("%s.fits" % bimage, "SKYSUB"):
iraf.apall(bimage,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="none",
reference="%s_01_B" % standard)
iraf.apall(rimage,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="none",
reference="%s_01_R" % standard)
else:
iraf.apall(bimage,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="fit",
reference="%s_01_B" % standard)
iraf.apall(rimage,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="fit",
reference="%s_01_R" % standard)
# Normalize by the standard continuua
iraf.sarith("%s.ms" % bimage, "/", "s%s_01_B.ms.fits" % standard,
"s%s.ms" % bimage)
iraf.sarith("%s.ms" % rimage, "/", "s%s_01_R.ms.fits" % standard,
"s%s.ms" % rimage)
# Telluric correct
bstd = "%s_01_B" % standard
rstd = "%s_01_R" % standard
iraf.telluric("s%s.ms" % bimage,
"ts%s.ms" % bimage,
"telluric.B.%s.fits" % bstd,
tweakrms=yes,
interactive=yes,
sample='6850:6950,7575:7700')
iraf.telluric("s%s.ms" % rimage,
"ts%s.ms" % rimage,
"telluric.R.%s.fits" % rstd,
tweakrms=yes,
interactive=yes,
sample='6850:6950,7575:7700')
# Flux calibration
iraf.calibrate("ts%s.ms" % bimage,
"fts%s.ms" % bimage,
extinct=yes,
flux=yes,
extinction="home$extinct/maunakeaextinct.dat",
observatory="Keck",
sensitivity="%s.B.sens" % standard,
airmass='',
exptime='')
iraf.calibrate("ts%s.ms" % rimage,
"fts%s.ms" % rimage,
extinct=yes,
flux=yes,
extinction="home$extinct/maunakeaextinct.dat",
observatory="Keck",
sensitivity="%s.R.sens" % standard,
airmass='',
exptime='')
joinstr += "fts%s.ms,fts%s.ms," % (bimage, rimage)
# Combine
iraf.scombine(joinstr[:-1],
"%s.ms.fits" % source,
combine="average",
reject="avsigclip",
w1=INDEF,
w2=INDEF,
dw=INDEF,
nw=INDEF,
scale="none",
zero="none",
weight="none",
lsigma=3.0,
hsigma=3.0,
gain=CCDGAIN,
rdnoise=CCDRNOISE)
# Plot to enable final tweaks
iraf.splot("%s.ms.fits" % source)
return
def normalize_and_merge(reduced_science_files):
current_directory = os.getcwd()
for k in range(len(reduced_science_files)):
remove_file(current_directory, "norm.dummyI.fits")
remove_file(current_directory, "norm.dummyIa.fits")
remove_file(current_directory, "norm.dummyII.fits")
remove_file(current_directory, "norm.dummyIIa.fits")
remove_file(current_directory, "norm.dummyIII.fits")
remove_file(current_directory, "norm.dummyIV.fits")
remove_file(current_directory, "norm.dummyV.fits")
remove_file(current_directory, "norm.dummy1.fits")
remove_file(current_directory, "norm.dummy2.fits")
remove_file(current_directory, "norm.dummy3.fits")
remove_file(current_directory, "norm.dummy4.fits")
remove_file(current_directory, "norm.dummy5.fits")
remove_file(current_directory, "norm.dummy6.fits")
remove_file(current_directory, "norm.dummy7.fits")
remove_file(current_directory, "norm.dummy8.fits")
ranges = np.loadtxt("ranges.lis", delimiter=",")
aperturlist = open("test.norm.apertures.lis", "w")
merge = reduced_science_files[k].replace(".extracted.fits",
".norm.fits")
iraf.scopy(reduced_science_files[k],
place_here("norm.dummyI.fits"),
apertures="5:14",
format="multispec")
iraf.scopy(reduced_science_files[k],
place_here("norm.dummyII.fits"),
apertures="35:42",
format="multispec")
iraf.fit1d(place_here("norm.dummyI.fits"),
place_here("norm.dummyIa.fits"),
naverage=1,
axis=2,
type="fit",
low_rej=1.0,
high_rej=2.0,
order=2,
niterate=2,
func="spline3",
sample="*")
iraf.fit1d(place_here("norm.dummyII.fits"),
place_here("norm.dummyIIa.fits"),
naverage=1,
axis=2,
type="fit",
low_rej=1.0,
high_rej=2.0,
order=2,
niterate=2,
func="spline3",
sample="*")
iraf.scopy(reduced_science_files[k],
place_here("norm.dummy1.fits"),
apertures="1",
format="multispec",
w1="4544")
iraf.scopy(reduced_science_files[k],
place_here("norm.dummy2.fits"),
apertures="2",
format="multispec",
w1="4569")
iraf.scopy(reduced_science_files[k],
place_here("norm.dummy3.fits"),
apertures="3:7",
format="multispec")
iraf.scopy(place_here("norm.dummyIa.fits"),
place_here("norm.dummy4.fits"),
apertures="8:10",
format="multispec")
iraf.scopy(reduced_science_files[k],
place_here("norm.dummy5.fits"),
apertures="11:37",
format="multispec")
iraf.scopy(place_here("norm.dummyIIa.fits"),
place_here("norm.dummy6.fits"),
apertures="38:40",
format="multispec")
iraf.scopy(reduced_science_files[k],
place_here("norm.dummy7.fits"),
apertures="41:50",
format="multispec")
iraf.scopy(reduced_science_files[k],
place_here("norm.dummy8.fits"),
apertures="51",
format="multispec",
w2="7591")
iraf.scombine("@normalization.list.lis",
place_here("norm.dummyIII.fits"),
group='apertures')
iraf.fit1d(place_here("norm.dummyIII.fits"),
place_here("norm.dummyIV.fits"),
naverage=1,
axis=1,
type="fit",
low_rej=0.8,
high_rej=2.0,
order=7,
niterate=4,
func="spline3",
sample="*")
iraf.sarith(reduced_science_files[k], "/",
place_here("norm.dummyIV.fits"),
place_here("norm.dummyV.fits"))
iraf.fit1d(place_here("norm.dummyV.fits"),
merge,
naverage=1,
axis=1,
type="ratio",
low_rej=0.2,
high_rej=2.0,
order=1,
niterate=4,
func="chebyshev",
sample="*")
iraf.hedit(merge, "INSTRUME", 'TLS-echelle')
for i in range(len(ranges)):
apertures = int(ranges[i][0])
new_name = merge.replace(".fits", "." + str(apertures) + ".fits")
input1 = os.path.join(current_directory, merge)
output = os.path.join(current_directory, new_name)
iraf.scopy(input1,
output,
w1=ranges[i][1],
w2=ranges[i][2],
apertur=apertures,
format="multispec")
aperturlist.write(new_name + "\n")
aperturlist.close()
new_name_merged = reduced_science_files[k].replace(
".extracted.fits", ".merged.fits")
iraf.scombine("@test.norm.apertures.lis", new_name_merged, group='all')
cut_for_ston = new_name_merged.replace("merged", "cut")
iraf.scopy(new_name_merged,
cut_for_ston,
w1=5603,
w2=5612,
format="multispec",
apertures="")
stddev = iraf.imstat(cut_for_ston,
Stdout=1,
fields="stddev",
format="no")
ston = 1 / float(stddev[0])
iraf.hedit(new_name, "STON", ston)
for i in range(len(ranges)):
apertures = int(ranges[i][0])
os.remove(
os.path.join(
merge.replace(".fits", "." + str(apertures) + ".fits")))
os.remove(os.path.join(current_directory, "test.norm.apertures.lis"))
remove_file(current_directory, "norm.dummyI.fits")
remove_file(current_directory, "norm.dummyIa.fits")
remove_file(current_directory, "norm.dummyII.fits")
remove_file(current_directory, "norm.dummyIIa.fits")
remove_file(current_directory, "norm.dummyIII.fits")
remove_file(current_directory, "norm.dummyIV.fits")
remove_file(current_directory, "norm.dummyV.fits")
remove_file(current_directory, "norm.dummy1.fits")
remove_file(current_directory, "norm.dummy2.fits")
remove_file(current_directory, "norm.dummy3.fits")
remove_file(current_directory, "norm.dummy4.fits")
remove_file(current_directory, "norm.dummy5.fits")
remove_file(current_directory, "norm.dummy6.fits")
remove_file(current_directory, "norm.dummy7.fits")
remove_file(current_directory, "norm.dummy8.fits")
print("Success! Produced files *merged.fits, *norm.fits")
def lris_extract(science, standards, dostandard=yes):
'''Extract and flux calibrate LRIS spectra (assuming they have been
processed into 2D images using LRIS_pipe above).'''
if dostandard:
lris_standard(standards)
for src in science:
bimages = iraffiles("%s_??_B.fits" % src)
rimages = iraffiles("%s_??_R.fits" % src)
joinstr = ""
for bimage in bimages:
bimage = bimage.split(".")[0]
# Find appropriate standard for this image
bstd = get_head("%s.fits" % bimage, "STDNAME")
# Extract 1D spectra
if get_head("%s.fits" % bimage, "SKYSUB"):
iraf.apall(bimage, output="", inter=yes, find=yes, recenter=yes,
resize=yes, edit=yes, trace=yes, fittrace=yes, extract=yes,
extras=yes, review=no, background="none",
reference=bstd)
else:
iraf.apall(bimage, output="", inter=yes, find=yes, recenter=yes,
resize=yes, edit=yes, trace=yes, fittrace=yes, extract=yes,
extras=yes, review=no, background="fit",
reference=bstd)
# Normalize by the standard continuua
iraf.sarith("%s.ms" % bimage, "/", "../standards/s%s.ms.fits" % bstd,
"s%s.ms" % bimage, w1=INDEF, w2=INDEF)
# Telluric correct
iraf.telluric("s%s.ms" % bimage, "ts%s.ms" % bimage,
"../standards/telluric.%s.fits" % bstd, tweakrms=yes,
interactive=yes, sample='6850:6950,7575:7700,8750:9925')
# Flux calibration
iraf.calibrate("ts%s.ms" % bimage, "fts%s.ms" % bimage, extinct=yes,
flux=yes, extinction="home$extinct/maunakeaextinct.dat",
observatory="Keck", sens="../standards/%s.sens" % bstd,
airmass='', exptime='')
joinstr += "fts%s.ms," % bimage
for rimage in rimages:
rimage = rimage.split(".")[0]
# Find appropriate standard for this image
rstd = get_head("%s.fits" % rimage, "STDNAME")
# Extract 1D spectra
if get_head("%s.fits" % rimage, "SKYSUB"):
iraf.apall(rimage, output="", inter=yes, find=yes, recenter=yes,
resize=yes, edit=yes, trace=yes, fittrace=yes, extract=yes,
extras=yes, review=no, background="none",
reference=rstd)
else:
iraf.apall(rimage, output="", inter=yes, find=yes, recenter=yes,
resize=yes, edit=yes, trace=yes, fittrace=yes, extract=yes,
extras=yes, review=no, background="fit",
reference=rstd)
# Normalize by the standard continuua
iraf.sarith("%s.ms" % rimage, "/", "../standards/s%s.ms.fits" % rstd,
"s%s.ms" % rimage, w1=5500, w2=INDEF)
# Telluric correct
iraf.telluric("s%s.ms" % rimage, "ts%s.ms" % rimage,
"../standards/telluric.%s.fits" % rstd, tweakrms=yes,
interactive=yes, sample='6850:6950,7575:7700')
# Flux calibration
iraf.calibrate("ts%s.ms" % rimage, "fts%s.ms" % rimage, extinct=yes,
flux=yes, extinction="home$extinct/maunakeaextinct.dat",
observatory="Keck", sens="../standards/%s.sens" % rstd,
airmass='', exptime='')
joinstr += "fts%s.ms," % rimage
# Combine
iraf.scombine(joinstr[:-1], "%s.ms.fits" % src, combine="average",
reject="avsigclip", w1=INDEF, w2=INDEF, dw=INDEF, nw=INDEF,
scale="median", zero="none", weight="none", sample="5450:5600",
lsigma=3.0, hsigma=3.0, gain=RCCDGAIN, rdnoise=RCCDRNOISE)
# Plot to enable final tweaks
iraf.splot("%s.ms.fits" % src)
return
def lris_extract(science, standards, dostandard=yes):
'''Extract and flux calibrate LRIS spectra (assuming they have been
processed into 2D images using LRIS_pipe above).'''
if dostandard:
lris_standard(standards)
for src in science:
bimages = iraffiles("%s_??_B.fits" % src)
rimages = iraffiles("%s_??_R.fits" % src)
joinstr = ""
for bimage in bimages:
bimage = bimage.split(".")[0]
# Find appropriate standard for this image
bstd = get_head("%s.fits" % bimage, "STDNAME")
# Extract 1D spectra
if get_head("%s.fits" % bimage, "SKYSUB"):
iraf.apall(bimage,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="none",
reference=bstd)
else:
iraf.apall(bimage,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="fit",
reference=bstd)
# Normalize by the standard continuua
iraf.sarith("%s.ms" % bimage,
"/",
"../standards/s%s.ms.fits" % bstd,
"s%s.ms" % bimage,
w1=INDEF,
w2=INDEF)
# Telluric correct
iraf.telluric("s%s.ms" % bimage,
"ts%s.ms" % bimage,
"../standards/telluric.%s.fits" % bstd,
tweakrms=yes,
interactive=yes,
sample='6850:6950,7575:7700,8750:9925')
# Flux calibration
iraf.calibrate("ts%s.ms" % bimage,
"fts%s.ms" % bimage,
extinct=yes,
flux=yes,
extinction="home$extinct/maunakeaextinct.dat",
observatory="Keck",
sens="../standards/%s.sens" % bstd,
airmass='',
exptime='')
joinstr += "fts%s.ms," % bimage
for rimage in rimages:
rimage = rimage.split(".")[0]
# Find appropriate standard for this image
rstd = get_head("%s.fits" % rimage, "STDNAME")
# Extract 1D spectra
if get_head("%s.fits" % rimage, "SKYSUB"):
iraf.apall(rimage,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="none",
reference=rstd)
else:
iraf.apall(rimage,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="fit",
reference=rstd)
# Normalize by the standard continuua
iraf.sarith("%s.ms" % rimage,
"/",
"../standards/s%s.ms.fits" % rstd,
"s%s.ms" % rimage,
w1=5500,
w2=INDEF)
# Telluric correct
iraf.telluric("s%s.ms" % rimage,
"ts%s.ms" % rimage,
"../standards/telluric.%s.fits" % rstd,
tweakrms=yes,
interactive=yes,
sample='6850:6950,7575:7700')
# Flux calibration
iraf.calibrate("ts%s.ms" % rimage,
"fts%s.ms" % rimage,
extinct=yes,
flux=yes,
extinction="home$extinct/maunakeaextinct.dat",
observatory="Keck",
sens="../standards/%s.sens" % rstd,
airmass='',
exptime='')
joinstr += "fts%s.ms," % rimage
# Combine
iraf.scombine(joinstr[:-1],
"%s.ms.fits" % src,
combine="average",
reject="avsigclip",
w1=INDEF,
w2=INDEF,
dw=INDEF,
nw=INDEF,
scale="median",
zero="none",
weight="none",
sample="5450:5600",
lsigma=3.0,
hsigma=3.0,
gain=RCCDGAIN,
rdnoise=RCCDRNOISE)
# Plot to enable final tweaks
iraf.splot("%s.ms.fits" % src)
return
def speccombine(fs=None):
iraf.cd('work')
if fs is None:
fs = glob('trm/sci*c?.fits')
if len(fs)==0:
print("No flux calibrated images to combine.")
iraf.cd('..')
return
#diagnostic()
nsteps = 8001
lamgrid = np.linspace(2000.0, 10000.0, nsteps)
nfs = len(fs)
# for each aperture
# get all of the science images
specs = np.zeros((nfs, nsteps))
specerrs = np.zeros((nfs, nsteps))
ap = 0
for i, f in enumerate(fs):
hdu = pyfits.open(f)
# print ('---hdu.data---')
# print (hdu[0].data)
w=WCS(f)
# print ('-----w-----')
# print(w)
# get the wavelengths of the pixels
npix = hdu[0].data.shape[2]
# print('-----npix-----')
# print(npix)
lam = w.all_pix2world(np.linspace(0, npix - 1, npix), 0, 0, 0)[0]
# print('-----lam-----')
# print(lam)
# interpolate each spectrum onto a comman wavelength scale
specs[i] = interp(lamgrid, lam, hdu[0].data[0][ap],
left=0.0, right=0.0)
# Also calculate the errors. Right now we assume that the variances
# interpolate linearly. This is not stricly correct but it should be
# close. Also we don't include terms in the variance for the
# uncertainty in the wavelength solution.
specerrs[i] = interp(lamgrid, lam, hdu[0].data[3][ap] ** 2.0) ** 0.5
#print ('-----specs-----')
#print (specs)
# minimize the chi^2 given free parameters are multiplicative factors
# We could use linear or quadratic, but for now assume constant
p0 = np.ones(nfs)
results = optimize.minimize(combine_spec_chi2, p0,
args=(lamgrid, specs, specerrs),
method='Nelder-Mead',
options={'maxfev': 1e5, 'maxiter': 1e5})
# write the best fit parameters into the headers of the files
# Dump the list of spectra into a string that iraf can handle
iraf_filelist = str(fs).replace('[', '').replace(']', '').replace("'", '')
# write the best fit results into a file
lines = []
for p in results['x']:
lines.append('%f\n' % (1.0 / p))
f = open('flx/scales.dat', 'w')
f.writelines(lines)
f.close()
# run scombine after multiplying the spectra by the best fit parameters
combfile = 'sci_com.fits'
if os.path.exists(combfile):
os.remove(combfile)
iraf.scombine(iraf_filelist, combfile, scale='@flx/scales.dat',
reject='avsigclip', lthreshold=-2e-16)
# Remove the other apertures [TBD]
# remove the sky and arc bands from the combined spectra. (or add back?? TBD)
# remove some header keywords that don't make sense in the combined file
delkws = ['GR-ANGLE','FILTER','BANDID2','BANDID3','BANDID4']
for kw in delkws:
pyfits.delval(combfile,kw)
# combine JD (average), AIRMASS (average), EXPTIME (sum)
# we assume there is a c1.fits file for each image
c1fs = [f for f in fs if 'c1.fits' in f]
avgjd = np.mean([pyfits.getval(f,'JD') for f in c1fs])
pyfits.setval(combfile,'JD',value=avgjd, comment='average of multiple exposures')
print "average JD = " + str(avgjd)
sumet = np.sum([pyfits.getval(f,'EXPTIME') for f in c1fs])
pyfits.setval(combfile,'EXPTIME',value=sumet,comment='sum of multiple exposures')
print "total EXPTIME = " + str(sumet)
avgam = np.mean([pyfits.getval(f,'AIRMASS') for f in c1fs])
pyfits.setval(combfile,'AIRMASS',value=avgam,comment='average of multiple exposures')
print "avg AIRMASS = " + str(avgam)
# update this to used avg jd midpoint of all exposures?
print "barycentric velocity correction (km/s) = ",
iraf.bcvcorr(spectra=combfile,keytime='UTC-OBS',keywhen='mid',
obslong="339:11:16.8",obslat="-32:22:46.2",obsalt='1798',obsname='saao',
savebcv='yes',savejd='yes',printmode=2)
pyfits.setval(combfile,'UTMID',comment='added by RVSAO task BCVCORR')
pyfits.setval(combfile,'GJDN',comment='added by RVSAO task BCVCORR')
pyfits.setval(combfile,'HJDN',comment='added by RVSAO task BCVCORR')
pyfits.setval(combfile,'BCV',comment='added by RVSAO task BCVCORR (km/s)')
pyfits.setval(combfile,'HCV',comment='added by RVSAO task BCVCORR (km/s)')
iraf.dopcor(input=combfile,output='',redshift=-iraf.bcvcorr.bcv,isvelocity='yes',
add='no',dispersion='yes',flux='no',verbose='yes')
pyfits.setval(combfile,'DOPCOR01',comment='barycentric velocity correction applied')
iraf.cd('..')
for i, obj in enumerate(objs):
print "File: {0} ({1}/{2})".format(obj, i+1, len(objs))
lis = [x for x in fits if x.endswith(obj)]
goodlis = select_specs(lis)
filenames = ", ".join(goodlis)
output = os.path.join(outdir, obj)
if len(goodlis) == 1:
shutil.copy(goodlis[0], os.path.join(outdir2,
goodlis[0].replace("_hcg_", "_").replace("_h62_", "_")))
continue
if os.path.exists(output):
continue
iraf.scombine(input = filenames, output = output, group = 'all',
combine = 'sum', reject="none",
weight="none", w1 = 4000., w2=6500.,dw=1.25)
ax = plt.subplot(111)
for l in lis:
w = wavelength_array(l)
intens = pf.getdata(l)
c = "k" if l in goodlis else "0.5"
ax.plot(w, intens, "-", color=c, lw=0.4)
w = wavelength_array(output)
intens = pf.getdata(output)
ax.plot(w, intens, "-r", lw=2)
ax.set_xlabel("Wavelength (Angstrom)")
ax.set_ylabel("Counts")
plt.title(obj.replace("_", "-"))
plt.pause(0.1)
plt.show(block=0)
order = order + 1
print "No order exist"
print "Combining spectra"
os.chdir(outdir+"temp")
os.system("ls *.fits > combine_list")
iraf.scombine(
input = "@combine_list",\
output = "combine.fits",\
group = "all",\
combine = "average",\
reject = "sigclip",\
first = 1,\
w1 = "INDEF",\
w2 = "INDEF",\
dw = "INDEF",\
nw = "INDEF",\
log = 0,\
scale = "median",\
zero = "none",\
weight = "median",\
nlow = 5,\
nhigh = 5,\
mclip = 1,\
lsigma = 2.0,\
hsigma = 2.0)
os.system("mv combine.fits ../"+object_name+".fits")
def speccombine(fs=None):
iraf.cd('work')
if fs is None:
fs = glob('flx/sci*c?.fits')
if len(fs)==0:
print("No flux calibrated images to combine.")
iraf.cd('..')
return
nsteps = 8001
lamgrid = np.linspace(2000.0, 10000.0, nsteps)
nfs = len(fs)
# for each aperture
# get all of the science images
specs = np.zeros((nfs, nsteps))
specerrs = np.zeros((nfs, nsteps))
ap = 0
for i, f in enumerate(fs):
hdu = pyfits.open(f)
w = WCS(f)
# get the wavelengths of the pixels
npix = hdu[0].data.shape[2]
lam = w.all_pix2world(np.linspace(0, npix - 1, npix), 0, 0, 0)[0]
# interpolate each spectrum onto a comman wavelength scale
specs[i] = interp(lamgrid, lam, hdu[0].data[0][ap],
left=0.0, right=0.0)
# Also calculate the errors. Right now we assume that the variances
# interpolate linearly. This is not stricly correct but it should be
# close. Also we don't include terms in the variance for the
# uncertainty in the wavelength solution.
specerrs[i] = interp(lamgrid, lam, hdu[0].data[3][ap] ** 2.0) ** 0.5
# minimize the chi^2 given free parameters are multiplicative factors
# We could use linear or quadratic, but for now assume constant
p0 = np.ones(nfs)
results = optimize.minimize(combine_spec_chi2, p0,
args=(lamgrid, specs, specerrs),
method='Nelder-Mead',
options={'maxfev': 1e5, 'maxiter': 1e5})
# write the best fit parameters into the headers of the files
# Dump the list of spectra into a string that iraf can handle
iraf_filelist = str(fs).replace('[', '').replace(']', '').replace("'", '')
# write the best fit results into a file
lines = []
for p in results['x']:
lines.append('%f\n' % (1.0 / p))
f = open('flx/scales.dat', 'w')
f.writelines(lines)
f.close()
# run scombine after multiplying the spectra by the best fit parameters
combfile = 'sci_com.fits'
if os.path.exists(combfile):
os.remove(combfile)
iraf.scombine(iraf_filelist, combfile, scale='@flx/scales.dat',
reject='avsigclip', lthreshold=1e-19)
# Remove the other apertures [TBD]
# remove the sky and arc bands from the combined spectra. (or add back?? TBD)
# remove some header keywords that don't make sense in the combined file
delkws = ['GRATING','GR-ANGLE','FILTER','BANDID2','BANDID3','BANDID4']
for kw in delkws:
pyfits.delval(combfile,kw)
# combine JD (average), AIRMASS (average), EXPTIME (sum)
# we assume there is a c1.fits file for each image
c1fs = [f for f in fs if 'c1.fits' in f]
avgjd = np.mean([pyfits.getval(f,'JD') for f in c1fs])
pyfits.setval(combfile,'JD',value=avgjd)
print "average JD = " + str(avgjd)
sumet = np.sum([pyfits.getval(f,'EXPTIME') for f in c1fs])
pyfits.setval(combfile,'EXPTIME',value=sumet)
print "total EXPTIME = " + str(sumet)
avgam = np.mean([pyfits.getval(f,'AIRMASS') for f in c1fs])
pyfits.setval(combfile,'AIRMASS',value=avgam)
print "avg AIRMASS = " + str(avgam)
iraf.cd('..')
return specs
def stitch_flats(outputnames,pivots,outstring):
"""Take a list of multispec files and a list of pivots and stitch together a master flat.
The pivot values are inclusive, so if pivots = [72] then the master flat will contain fibers 1 - 72 from flat #1 and 73 - 109 from flat #2.
Parameters
----------
outputnames : list of str
Names of the multispec files to stitch together
pivots : list of int
The fibers that form the borders of the stitch. If outputnames is length N, pivots must be length N - 1
outstring : str
The special, IRAF scrunch string used to identify intermediate files associated with a **dohydra** run
Returns
-------
mastername : str
The name of the stitched master multispec flat
Notes
-----
The specifics of outstring depend on system and IRAF distribution. See :meth:`GradPak_flatfu.get_scrunch`
"""
pivots = [0] + pivots + [109]
tmpfiles = []
print 'Extracting flat apertures...'
for i, flat in enumerate(outputnames):
print '\ttaking {} from {} to {}'.format(flat,pivots[i]+1,pivots[i+1])
name = 'tmp{}'.format(flat)
iraf.scopy(flat,name,
apertur='{}-{}'.format(pivots[i]+1,pivots[i+1]),
w1='INDEF',
w2='INDEF',
format='multispec',
verbose=False)
tmpfiles.append(name)
mastername = 'dFlat_master{}.ms.fits'.format(outstring)
print 'Stitching together master flat {}'.format(mastername)
iraf.scombine(','.join(tmpfiles),mastername,
apertur='',
group='apertures',
first=True,
w1='INDEF',
w2='INDEF',
dw='INDEF',
nw='INDEF',
log=False,
scale='none',
zero='none',
weight='none',
logfile='flatfu.log')
for tmp in tmpfiles:
os.remove(tmp)
return mastername
def stitch():
#Find the mean 3100 and 3125 of blue and uv
uvhdu = pyfits.open('13dh_uv.fits')
uvlam = fitshdr_to_wave(uvhdu[0].header)
w = np.logical_and(uvlam > 3050, uvlam < 3100)
uvmean = np.median(uvhdu[0].data[1, 0, w])
uvhdu.close()
print("uv mean %e" % uvmean)
bluehdu = pyfits.open('13dh_blue.fits')
bluelam = fitshdr_to_wave(bluehdu[0].header)
w = np.logical_and(bluelam > 3050, bluelam < 3100)
blueuvmean = np.median(bluehdu[0].data[1, 0, w])
print("blue mean uv %e" % blueuvmean)
# Find mean of red and blue between 5375 and 5600
w = np.logical_and(bluelam > 5375, bluelam < 5600)
blueredmean = bluehdu[0].data[1, 0, w].mean()
bluehdu.close()
print("blue mean red %e" % blueredmean)
redhdu = pyfits.open('13dh_red.fits')
redlam = fitshdr_to_wave(redhdu[0].header)
w = np.logical_and(redlam > 5375, redlam < 5600)
redmean = redhdu[0].data[1, 0, w].mean()
redhdu.close()
print("red mean %e" % redmean)
# trim uv at 3140
iraf.unlearn(iraf.scopy)
iraf.scopy('13dh_uv.fits',
'13dh_uv_trim.fits',
w1='INDEF',
w2=3140,
rebin='no')
# trim blue at 3130 and 5600
iraf.unlearn(iraf.scopy)
iraf.scopy('13dh_blue.fits',
'13dh_blue_trim.fits',
w1=3130,
w2=5600,
rebin='no')
# Trim red at 5375
iraf.unlearn(iraf.scopy)
iraf.scopy('13dh_red.fits',
'13dh_red_trim.fits',
w1=5375,
w2='INDEF',
rebin='no')
# Copy the spectra from the second extraction to the first
for im in [
'13dh_uv_trim.fits', '13dh_blue_trim.fits', '13dh_red_trim.fits'
]:
hdu = pyfits.open(im, mode='update')
hdu[0].data[0, 0, :] = hdu[0].data[1, 0, :]
hdu.flush()
hdu.close()
# Write out the scale factors
lines = [
'%f\n' % (blueuvmean / uvmean)**-1, '1.0\n',
'%f\n' % (blueredmean / redmean)**-1
]
f = open('scales.dat', 'w')
f.writelines(lines)
f.close()
#Scombine with the correct scalings using average
iraf.unlearn(iraf.scombine)
iraf.scombine('13dh_uv_trim, 13dh_blue_trim, 13dh_red_trim',
'13dh_hst.fits',
scale='@scales.dat')
return
(T, np.sqrt(covar[0, 0])))
w, f = [], []
for wav in range(900, 26000):
file.write("%g\t%g\n" % (wav, bbody(wav, T, Area)))
w.append(wav)
f.append(bbody(wav, T, Area))
iraf.rspec('bbody_sn_fit.dat',
'bbody_sn_fit.fits',
title='bbodyfit',
flux='no',
dtype='interp',
crval1=900,
cdelt1=1)
iraf.scombine('bbody_sn_fit.fits,sn.fits,sn.fits,sn.fits',
'bsn_combo.fits',
combine='median')
iraf.wspec('bsn_combo.fits', 'bsn_combo.txt', header='no')
lcf = open('bsn_combo.txt', 'r')
riga = lcf.readlines()
lcf.close()
wave, flux = [], []
for line in riga:
p = line.split()
if float(line.split()[0]) >= fil_obs_min and float(
line.split()[0]
) <= fil_obs_max: #to match the spectrum wavelegnths to those of the filter
wave.append(float(p[0]))
flux.append(float(p[1]))
def speccombine(fs=None):
iraf.cd('work')
if fs is None:
fs = glob('flx/sci*c?.fits')
if len(fs)==0:
print("No flux calibrated images to combine.")
iraf.cd('..')
return
nsteps = 8001
lamgrid = np.linspace(2000.0, 10000.0, nsteps)
nfs = len(fs)
# for each aperture
# get all of the science images
specs = np.zeros((nfs, nsteps))
specerrs = np.zeros((nfs, nsteps))
ap = 0
for i, f in enumerate(fs):
hdu = pyfits.open(f)
w = WCS(f)
# get the wavelengths of the pixels
npix = hdu[0].data.shape[2]
lam = w.all_pix2world(np.linspace(0, npix - 1, npix), 0, 0, 0)[0]
# interpolate each spectrum onto a comman wavelength scale
specs[i] = interp(lamgrid, lam, hdu[0].data[0][ap],
left=0.0, right=0.0)
# Also calculate the errors. Right now we assume that the variances
# interpolate linearly. This is not stricly correct but it should be
# close. Also we don't include terms in the variance for the
# uncertainty in the wavelength solution.
specerrs[i] = interp(lamgrid, lam, hdu[0].data[3][ap] ** 2.0) ** 0.5
# minimize the chi^2 given free parameters are multiplicative factors
# We could use linear or quadratic, but for now assume constant
p0 = np.ones(nfs)
results = optimize.minimize(combine_spec_chi2, p0,
args=(lamgrid, specs, specerrs),
method='Nelder-Mead',
options={'maxfev': 1e5, 'maxiter': 1e5})
# write the best fit parameters into the headers of the files
# Dump the list of spectra into a string that iraf can handle
iraf_filelist = str(fs).replace('[', '').replace(']', '').replace("'", '')
# write the best fit results into a file
lines = []
for p in results['x']:
lines.append('%f\n' % (1.0 / p))
f = open('flx/scales.dat', 'w')
f.writelines(lines)
f.close()
# run scombine after multiplying the spectra by the best fit parameters
combfile = 'sci_com.fits'
if os.path.exists(combfile):
os.remove(combfile)
iraf.scombine(iraf_filelist, combfile, scale='@flx/scales.dat',
reject='avsigclip', lthreshold=-1e-17)
# Remove the other apertures [TBD]
# remove the sky and arc bands from the combined spectra. (or add back?? TBD)
# remove some header keywords that don't make sense in the combined file
delkws = ['GR-ANGLE','FILTER','BANDID2','BANDID3','BANDID4']
for kw in delkws:
pyfits.delval(combfile,kw)
# combine JD (average), AIRMASS (average), EXPTIME (sum)
# we assume there is a c1.fits file for each image
c1fs = [f for f in fs if 'c1.fits' in f]
avgjd = np.mean([pyfits.getval(f,'JD') for f in c1fs])
pyfits.setval(combfile,'JD',value=avgjd)
print "average JD = " + str(avgjd)
sumet = np.sum([pyfits.getval(f,'EXPTIME') for f in c1fs])
pyfits.setval(combfile,'EXPTIME',value=sumet)
print "total EXPTIME = " + str(sumet)
avgam = np.mean([pyfits.getval(f,'AIRMASS') for f in c1fs])
pyfits.setval(combfile,'AIRMASS',value=avgam)
print "avg AIRMASS = " + str(avgam)
iraf.cd('..')
return specs
for file in os.listdir(os.getcwd()):
if file.startswith('dspec'):
iraf.calibrate(file, 'f'+file)
#combine spectra
finalcomb = []
for file in os.listdir(os.getcwd()):
if file.startswith('fds'):
finalcomb.append(file)
file1 = open('listascombine', 'w')
file1.writelines(["%s\n" % item for item in finalcomb])
file1.close()
iraf.scombine('@listascombine', 'temp_quick.fits')
iraf.scopy('temp_quick.fits',target+'_quick.fits',w1='4000',w2='9000')
#mv final file in a dedicated folder
shutil.copy(target+'_quick.fits','./output/.')
#remove temp FILES
os.remove('lapalmaextinct.dat')
for file in os.listdir(os.getcwd()):
if file.endswith('fits'):
os.remove(file)
elif file.startswith('lista'):
os.remove(file)
elif file.startswith('log'):
os.remove(file)
airmass=objects_pars[imname][1],
exptime=objects_pars[imname][2])
combname.append(imname + '_calib')
combstr = ','.join(combname)
combname = []
for imname in objects_pars:
if 'sn' in objects_pars[imname][3]:
combname.append(imname + '_calib')
combstr = ','.join(combname)
iraf.scombine(input=combstr,
out='combinedspec_rmlow.ms',
combine='average',
reject='minmax',
scale='none',
sample='5000:7500',
nlow=1,
nhigh=1,
nkeep=2)
iraf.scombine(input=combstr,
out='combinedspec.ms',
combine='average',
reject='minmax',
scale='none',
sample='5000:7500',
nlow=0,
nhigh=1,
nkeep=1)
l.write('calib=yes')
