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 speccal(specfile, outfile, calfile, extfile, airmass=None, exptime=None,
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)
cal_spectra = st.readspectrum(calfile, error=False, ftype='ascii')
# 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)
error = False
try:
if obs_spectra.var is not None:
error = True
except:
error = False
flux_spectra = calfunc(
obs_spectra,
cal_spectra,
ext_spectra,
airmass,
exptime,
error)
# write the spectra out
st.writespectrum(flux_spectra, outfile, ftype='ascii', error=error)
def speccal(
specfile, outfile, calfile, extfile, airmass=None, exptime=None, 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)
cal_spectra = st.readspectrum(calfile, error=False, ftype="ascii")
# 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)
error = False
try:
if obs_spectra.var is not None:
error = True
except:
error = False
flux_spectra = calfunc(obs_spectra, cal_spectra, ext_spectra, airmass, exptime, error)
# write the spectra out
st.writespectrum(flux_spectra, outfile, ftype="ascii", error=error)
def specsens(specfile, outfile, stdfile, extfile, airmass=None, exptime=None,
stdzp=3.68e-20, function='polynomial', order=3, thresh=3, niter=5,
fitter='gaussian', 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.strip(), error=True, ftype='ascii')
# smooth the observed spectrum
# 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.strip(), error=False, ftype='ascii')
std_spectra.flux = Spectrum.magtoflux(std_spectra.flux, stdzp)
std_spectra.flux = Spectrum.fnutofwave(
std_spectra.wavelength, std_spectra.flux)
# Get the typical bandpass of the standard star,
std_bandpass = np.diff(std_spectra.wavelength).mean()
# Smooth the observed spectrum to that bandpass
obs_spectra.flux = st.boxcar_smooth(obs_spectra, std_bandpass)
# read in the extinction file (leave in magnitudes)
ext_spectra = st.readspectrum(extfile.strip(), 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(exptime) 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)
# plot(cal_spectra.wavelength, cal_spectra.flux * std_spectra.flux)
# 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 = np.logical_and(mask, (cal_spectra.flux > 0))
# now fit the data
# Fit using a gaussian process.
if fitter=='gaussian':
from sklearn.gaussian_process import GaussianProcess
#Instanciate a Gaussian Process model
dy = obs_spectra.var[mask] ** 0.5
dy /= obs_spectra.flux[mask] / cal_spectra.flux[mask]
y = cal_spectra.flux[mask]
gp = GaussianProcess(corr='squared_exponential', theta0=1e-2,
thetaL=1e-4, thetaU=0.1, nugget=(dy / y) ** 2.0)
X = np.atleast_2d(cal_spectra.wavelength[mask]).T
# Fit to data using Maximum Likelihood Estimation of the parameters
gp.fit(X, y)
x = np.atleast_2d(cal_spectra.wavelength).T
# Make the prediction on the meshed x-axis (ask for MSE as well)
y_pred = gp.predict(x)
cal_spectra.flux = y_pred
else:
fit=interfit(cal_spectra.wavelength[mask], cal_spectra.flux[mask], function=function, order=order, thresh=thresh, niter=niter)
fit.interfit()
cal_spectra.flux=fit(cal_spectra.wavelength)
# write the spectra out
st.writespectrum(cal_spectra, outfile, ftype='ascii')
def specsens(specfile,
outfile,
stdfile,
extfile,
airmass=None,
exptime=None,
stdzp=3.68e-20,
function='polynomial',
order=3,
thresh=3,
niter=5,
fitter='gaussian',
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.strip(),
error=True,
ftype='ascii')
# smooth the observed spectrum
# 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.strip(),
error=False,
ftype='ascii')
std_spectra.flux = Spectrum.magtoflux(std_spectra.flux, stdzp)
std_spectra.flux = Spectrum.fnutofwave(std_spectra.wavelength,
std_spectra.flux)
# Get the typical bandpass of the standard star,
std_bandpass = np.diff(std_spectra.wavelength).mean()
# Smooth the observed spectrum to that bandpass
obs_spectra.flux = st.boxcar_smooth(obs_spectra, std_bandpass)
# read in the extinction file (leave in magnitudes)
ext_spectra = st.readspectrum(extfile.strip(),
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(exptime) 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)
# plot(cal_spectra.wavelength, cal_spectra.flux * std_spectra.flux)
# 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 = np.logical_and(mask, (cal_spectra.flux > 0))
# now fit the data
# Fit using a gaussian process.
if fitter == 'gaussian':
from sklearn.gaussian_process import GaussianProcess
#Instanciate a Gaussian Process model
dy = obs_spectra.var[mask]**0.5
dy /= obs_spectra.flux[mask] / cal_spectra.flux[mask]
y = cal_spectra.flux[mask]
gp = GaussianProcess(corr='squared_exponential',
theta0=1e-2,
thetaL=1e-4,
thetaU=0.1,
nugget=(dy / y)**2.0)
X = np.atleast_2d(cal_spectra.wavelength[mask]).T
# Fit to data using Maximum Likelihood Estimation of the parameters
gp.fit(X, y)
x = np.atleast_2d(cal_spectra.wavelength).T
# Make the prediction on the meshed x-axis (ask for MSE as well)
y_pred = gp.predict(x)
cal_spectra.flux = y_pred
else:
fit = interfit(cal_spectra.wavelength[mask],
cal_spectra.flux[mask],
function=function,
order=order,
thresh=thresh,
niter=niter)
fit.interfit()
cal_spectra.flux = fit(cal_spectra.wavelength)
# write the spectra out
st.writespectrum(cal_spectra, outfile, ftype='ascii')
