def sensfunc(obs_spectra, std_spectra, ext_spectra, airmass, exptime):
#re-interpt the std_spectra over the same wavelength
std_spectra.interp(obs_spectra.warr)
#re-interp the ext_spetra over the sam ewavelength
ext_spectra.interp(obs_spectra.warr)
#create the calibration spectra
cal_spectra=Spectra(obs_spectra.warr, obs_spectra.farr.copy())
#set up the bandpass
bandpass=np.diff(obs_spectra.warr).mean()
print bandpass
#correct for extinction
cal_spectra.farr=cal_spectra.farr/10**(-0.4*airmass*ext_spectra.farr)
#correct for the exposure time and calculation the sensitivity curve
cal_spectra.farr=cal_spectra.farr/exptime/bandpass/std_spectra.farr
#now fit cal_spectra with a low order fit
mask=(cal_spectra.farr>0)
fit=interfit(cal_spectra.warr[mask], cal_spectra.farr[mask], function='polynomial', order=5, niter=5)
fit.fit()
figure()
warr=cal_spectra.warr
plot(warr, fit(warr))
plot(warr, cal_spectra.farr)
show()
cal_spectra.farr=fit(warr)
return cal_spectra
def specsens(specfile, outfile, stdfile, extfile, airmass=None, exptime=None,
stdzp=3.68e-20, function='polynomial', order=3, thresh=3, niter=5,
clobber=True, logfile='salt.log',verbose=True):
with logging(logfile,debug) as log:
#read in the specfile and create a spectrum object
obs_spectra=st.readspectrum(specfile, error=True, ftype='ascii')
#read in the std file and convert from magnitudes to fnu
#then convert it to fwave (ergs/s/cm2/A)
std_spectra=st.readspectrum(stdfile, error=False, ftype='ascii')
std_spectra.flux=Spectrum.magtoflux(std_spectra.flux, stdzp)
std_spectra.flux=Spectrum.fnutofwave(std_spectra.wavelength, std_spectra.flux)
#read in the extinction file (leave in magnitudes)
ext_spectra=st.readspectrum(extfile, error=False, ftype='ascii')
#determine the airmass if not specified
if saltio.checkfornone(airmass) is None:
message='Airmass was not supplied'
raise SALTSpecError(message)
#determine the exptime if not specified
if saltio.checkfornone(airmass) is None:
message='Exposure Time was not supplied'
raise SALTSpecError(message)
#calculate the calibrated spectra
log.message('Calculating the calibration curve for %s' % specfile)
cal_spectra=sensfunc(obs_spectra, std_spectra, ext_spectra, airmass, exptime)
#fit the spectra--first take a first cut of the spectra
#using the median absolute deviation to throw away bad points
cmed=np.median(cal_spectra.flux)
cmad=saltstat.mad(cal_spectra.flux)
mask=(abs(cal_spectra.flux-cmed)<thresh*cmad)
mask=(cal_spectra.flux>0)
#now fit the data
fit=interfit(cal_spectra.wavelength[mask], cal_spectra.flux[mask], function=function, order=order, thresh=thresh, niter=niter)
fit.interfit()
#print 'plotting...'
#figure()
#plot(cal_spectra.wavelength, cal_spectra.flux)
#plot(obs_spectra.wavelength, obs_spectra.flux*cal_spectra.flux.mean()/obs_spectra.flux.mean())
#plot(std_spectra.wavelength, std_spectra.flux*cal_spectra.flux.mean()/std_spectra.flux.mean())
#plot(cal_spectra.wavelength, fit(cal_spectra.wavelength))
#show()
#write the spectra out
cal_spectra.flux=fit(cal_spectra.wavelength)
st.writespectrum(cal_spectra, outfile, ftype='ascii')
def fitsky(xarr, data, var_arr, function='polynomial', order=2, thresh=3):
"""For each column, fit a function to the column after rejecting
sources and then create a sky image from that
"""
sdata = 0.0 * data
for i in xarr:
yarr = data[:, i]
yind = np.arange(len(yarr))
m = np.median(yarr)
s = stats.median_absolute_deviation(yarr)
mask = (abs(yarr - m) < thresh * s)
try:
it = interfit(yind[mask], yarr[mask], function='poly', order=2)
it.fit()
sdata[:, i] = it(yind)
except:
sdata[:, i] = 0.0 * sdata[:, i] + m
return sdata
def fitsky(xarr, data, var_arr, function='polynomial', order=2, thresh=3):
"""For each column, fit a function to the column after rejecting
sources and then create a sky image from that
"""
sdata = 0.0 * data
for i in xarr:
yarr = data[:, i]
yind = np.arange(len(yarr))
m = np.median(yarr)
s = stats.median_absolute_deviation(yarr)
mask = (abs(yarr - m) < thresh * s)
try:
it = interfit(yind[mask], yarr[mask], function='poly', order=2)
it.fit()
sdata[:, i] = it(yind)
except:
sdata[:, i] = 0.0 * sdata[:, i] + m
return sdata
def sensfunc(obs_spectra, std_spectra, ext_spectra, airmass, exptime):
# re-interpt the std_spectra over the same wavelength
std_spectra.interp(obs_spectra.warr)
# re-interp the ext_spetra over the sam ewavelength
ext_spectra.interp(obs_spectra.warr)
# create the calibration spectra
cal_spectra = Spectra(obs_spectra.warr, obs_spectra.farr.copy())
# set up the bandpass
bandpass = np.diff(obs_spectra.warr).mean()
print bandpass
# correct for extinction
cal_spectra.farr = cal_spectra.farr / \
10 ** (-0.4 * airmass * ext_spectra.farr)
# correct for the exposure time and calculation the sensitivity curve
cal_spectra.farr = cal_spectra.farr / exptime / bandpass / std_spectra.farr
# now fit cal_spectra with a low order fit
mask = (cal_spectra.farr > 0)
fit = interfit(cal_spectra.warr[mask],
cal_spectra.farr[mask],
function='polynomial',
order=5,
niter=5)
fit.fit()
figure()
warr = cal_spectra.warr
plot(warr, fit(warr))
plot(warr, cal_spectra.farr)
show()
cal_spectra.farr = fit(warr)
return cal_spectra
def continuum_subtract(spec, function='polynomial', order=7):
"""Fit a function to a spectra and subtract the continuum"""
wc=interfit(spec.wavelength, spec.flux, function=function, order=order)
wc.interfit()
return spec.flux-wc(spec.wavelength)
def continuum_subtract(spec, function='polynomial', order=7):
"""Fit a function to a spectra and subtract the continuum"""
wc=interfit(spec.wavelength, spec.flux, function=function, order=order)
wc.interfit()
return spec.flux-wc(spec.wavelength)
