def convolveTelluric(lsf, telluric_data, alpha=1.0, airmass='1.5', pwv='1.5'): """ Return a convolved telluric transmission model given a telluric data and lsf. """ # get a telluric standard model wavelow = telluric_data.wave[0] - 50 wavehigh = telluric_data.wave[-1] + 50 telluric_model = nsp.getTelluric(wavelow=wavelow,wavehigh=wavehigh, airmass=airmass, pwv=pwv) telluric_model.flux **= alpha # lsf telluric_model.flux = nsp.broaden(wave=telluric_model.wave, flux=telluric_model.flux, vbroad=lsf, rotate=False, gaussian=True) # resample telluric_model.flux = np.array(nsp.integralResample(xh=telluric_model.wave, yh=telluric_model.flux, xl=telluric_data.wave)) telluric_model.wave = telluric_data.wave return telluric_model
def applyTelluric(model, alpha=1, airmass='1.5'):
"""
Apply the telluric model on the science model.
Parameters
----------
model : model object
BT Settl model
alpha : float
telluric scaling factor (the power on the flux)
Returns
-------
model : model object
BT Settl model times the corresponding model
"""
# read in a telluric model
wavelow = model.wave[0] - 10
wavehigh = model.wave[-1] + 10
telluric_model = nsp.getTelluric(wavelow=wavelow,
wavehigh=wavehigh, alpha=alpha, airmass=airmass)
# apply the telluric alpha parameter
#telluric_model.flux = telluric_model.flux**(alpha)
#if len(model.wave) > len(telluric_model.wave):
# print("The model has a higher resolution ({}) than the telluric model ({})."\
# .format(len(model.wave),len(telluric_model.wave)))
# model.flux = np.array(nsp.integralResample(xh=model.wave,
# yh=model.flux, xl=telluric_model.wave))
# model.wave = telluric_model.wave
# model.flux *= telluric_model.flux
#elif len(model.wave) < len(telluric_model.wave):
## This should be always true
telluric_model.flux = np.array(nsp.integralResample(xh=telluric_model.wave,
yh=telluric_model.flux, xl=model.wave))
telluric_model.wave = model.wave
model.flux *= telluric_model.flux
#elif len(model.wave) == len(telluric_model.wave):
# model.flux *= telluric_model.flux
return model
def convolveTelluric(lsf, airmass, pwv, telluric_data): """ Return a convolved telluric transmission model given a telluric data and lsf. """ # get a telluric standard model wavelow = telluric_data.wave[0] - 50 wavehigh = telluric_data.wave[-1] + 50 modelwave, modelflux = InterpTelluricModel(wavelow=wavelow, wavehigh=wavehigh, airmass=airmass, pwv=pwv) #modelflux **= alpha # lsf modelflux = nsp.broaden(wave=modelwave, flux=modelflux, vbroad=lsf, rotate=False, gaussian=True) # resample modelflux = np.array(nsp.integralResample(xh=modelwave, yh=modelflux, xl=telluric_data.wave)) modelwave = telluric_data.wave telluric_model = nsp.Model() telluric_model.flux = modelflux telluric_model.wave = modelwave return telluric_model
def makeModel(teff, logg, z, vsini, rv, alpha, wave_offset, flux_offset,
**kwargs):
"""
Return a forward model.
Parameters
----------
params : a dictionary that specifies the parameters such as teff, logg, z.
data : an input science data used for continuum correction
Returns
-------
model: a synthesized model
"""
# read in the parameters
order = kwargs.get('order', 33)
modelset = kwargs.get('modelset', 'btsettl08')
lsf = kwargs.get('lsf', 6.0) # instrumental LSF
tell = kwargs.get('tell', True) # apply telluric
data = kwargs.get('data', None) # for continuum correction and resampling
if data is not None:
order = data.order
# read in a model
#model = nsp.Model(teff=teff, logg=logg, feh=z, order=order, modelset=modelset)
model = nsp.Model()
model.wave, model.flux = InterpModel(teff,
logg,
modelset=modelset,
order=order)
model.wave *= 10000
# wavelength offset
#model.wave += wave_offset
# apply vsini
model.flux = nsp.broaden(wave=model.wave,
flux=model.flux,
vbroad=vsini,
rotate=True,
gaussian=False)
# apply rv (including the barycentric correction)
model.wave = nsp.rvShift(model.wave, rv=rv)
# apply telluric
if tell is True:
model = nsp.applyTelluric(model=model, alpha=alpha, airmass='1.5')
# NIRSPEC LSF
model.flux = nsp.broaden(wave=model.wave,
flux=model.flux,
vbroad=lsf,
rotate=False,
gaussian=True)
# add a fringe pattern to the model
#model.flux *= (1+amp*np.sin(freq*(model.wave-phase)))
# wavelength offset
model.wave += wave_offset
# integral resampling
if data is not None:
model.flux = np.array(
nsp.integralResample(xh=model.wave, yh=model.flux, xl=data.wave))
model.wave = data.wave
# contunuum correction
model = nsp.continuum(data=data, mdl=model)
# flux offset
model.flux += flux_offset
#model.flux **= (1 + flux_exponent_offset)
return model
# apply rv (including the barycentric correction)
model.wave = nsp.rvShift(model.wave, rv=rv)
model_notell = copy.deepcopy(model)
# apply telluric
model = nsp.applyTelluric(model=model, alpha=alpha)
# NIRSPEC LSF
model.flux = nsp.broaden(wave=model.wave,
flux=model.flux,
vbroad=lsf,
rotate=False,
gaussian=True)
# integral resampling
model.flux = np.array(
nsp.integralResample(xh=model.wave, yh=model.flux, xl=data.wave))
model.wave = data.wave
# contunuum correction
model, cont_factor = nsp.continuum(data=data, mdl=model, prop=True)
# NIRSPEC LSF
model_notell.flux = nsp.broaden(wave=model_notell.wave,
flux=model_notell.flux,
vbroad=lsf,
rotate=False,
gaussian=True)
# integral resampling
model_notell.flux = np.array(
nsp.integralResample(xh=model_notell.wave,
def makeModel(teff, logg, z, vsini, rv, wave_offset, flux_offset, **kwargs):
"""
Return a forward model.
Parameters
----------
teff : effective temperature
data : an input science data used for continuum correction
Optional Parameters
-------------------
Returns
-------
model: a synthesized model
"""
# read in the parameters
order = kwargs.get('order', 33)
modelset = kwargs.get('modelset', 'btsettl08')
instrument = kwargs.get('instrument', 'nirspec')
lsf = kwargs.get('lsf', 6.0) # instrumental LSF
tell = kwargs.get('tell', True) # apply telluric
data = kwargs.get('data', None) # for continuum correction and resampling
if data is not None and instrument == 'nirspec':
order = data.order
# read in a model
#print('teff ',teff,'logg ',logg, 'z', z, 'order', order, 'modelset', modelset)
#print('teff ',type(teff),'logg ',type(logg), 'z', type(z), 'order', type(order), 'modelset', type(modelset))
model = nsp.Model(teff=teff,
logg=logg,
feh=z,
order=order,
modelset=modelset,
instrument=instrument)
elif data is not None and instrument == 'apogee':
model = nsp.Model(teff=teff,
logg=logg,
feh=z,
modelset=modelset,
instrument=instrument)
# wavelength offset
#model.wave += wave_offset
# apply vsini
model.flux = nsp.broaden(wave=model.wave,
flux=model.flux,
vbroad=vsini,
rotate=True,
gaussian=False)
# apply rv (including the barycentric correction)
model.wave = nsp.rvShift(model.wave, rv=rv)
# instrumental LSF
model.flux = nsp.broaden(wave=model.wave,
flux=model.flux,
vbroad=lsf,
rotate=False,
gaussian=True)
# add a fringe pattern to the model
#model.flux *= (1+amp*np.sin(freq*(model.wave-phase)))
# wavelength offset
model.wave += wave_offset
# integral resampling
if data is not None:
model.flux = np.array(
nsp.integralResample(xh=model.wave, yh=model.flux, xl=data.wave))
model.wave = data.wave
# contunuum correction
model = nsp.continuum(data=data, mdl=model)
# flux offset
model.flux += flux_offset
#model.flux **= (1 + flux_exponent_offset)
return model
def makeModel(teff,logg,z,vsini,rv,alpha,wave_offset,flux_offset,**kwargs):
"""
Return a forward model.
Parameters
----------
teff : effective temperature
data : an input science data used for continuum correction
Optional Parameters
-------------------
Returns
-------
model: a synthesized model
"""
# read in the parameters
order = kwargs.get('order', 33)
modelset = kwargs.get('modelset', 'btsettl08')
instrument = kwargs.get('instrument', 'nirspec')
lsf = kwargs.get('lsf', 6.0) # instrumental LSF
tell = kwargs.get('tell', True) # apply telluric
data = kwargs.get('data', None) # for continuum correction and resampling
output_stellar_model = kwargs.get('output_stellar_model', False)
if data is not None and instrument == 'nirspec':
order = data.order
# read in a model
#print('teff ',teff,'logg ',logg, 'z', z, 'order', order, 'modelset', modelset)
#print('teff ',type(teff),'logg ',type(logg), 'z', type(z), 'order', type(order), 'modelset', type(modelset))
model = nsp.Model(teff=teff, logg=logg, feh=z, order=order, modelset=modelset, instrument=instrument)
#elif data is not None and instrument == 'apogee':
elif instrument == 'apogee':
model = nsp.Model(teff=teff, logg=logg, feh=z, modelset=modelset, instrument=instrument)
elif data is None and instrument == 'nirspec':
model = nsp.Model(teff=teff, logg=logg, feh=z, order=order, modelset=modelset, instrument=instrument)
# wavelength offset
#model.wave += wave_offset
# apply vsini
model.flux = nsp.broaden(wave=model.wave,
flux=model.flux, vbroad=vsini, rotate=True, gaussian=False)
# apply rv (including the barycentric correction)
model.wave = rvShift(model.wave, rv=rv)
if output_stellar_model:
stellar_model = copy.deepcopy(model)
# apply telluric
if tell is True:
model = nsp.applyTelluric(model=model, alpha=alpha, airmass='1.5')
# instrumental LSF
model.flux = nsp.broaden(wave=model.wave,
flux=model.flux, vbroad=lsf, rotate=False, gaussian=True)
if output_stellar_model:
stellar_model.flux = nsp.broaden(wave=stellar_model.wave,
flux=stellar_model.flux, vbroad=lsf, rotate=False, gaussian=True)
# add a fringe pattern to the model
#model.flux *= (1+amp*np.sin(freq*(model.wave-phase)))
# wavelength offset
model.wave += wave_offset
if output_stellar_model: stellar_model.wave += wave_offset
# integral resampling
if data is not None:
model.flux = np.array(nsp.integralResample(xh=model.wave,
yh=model.flux, xl=data.wave))
model.wave = data.wave
if output_stellar_model:
stellar_model.flux = np.array(nsp.integralResample(xh=stellar_model.wave,
yh=stellar_model.flux, xl=data.wave))
stellar_model.wave = data.wave
# contunuum correction
if data.instrument == 'nirspec':
if output_stellar_model:
model, cont_factor = nsp.continuum(data=data, mdl=model, prop=True)
stellar_model.flux *= cont_factor
else:
model = nsp.continuum(data=data, mdl=model)
elif data.instrument == 'apogee' and data.datatype =='apvisit':
## set the order in the continuum fit
deg = 5
## because of the APOGEE bands, continuum is corrected from three pieces of the spectra
data0 = copy.deepcopy(data)
model0 = copy.deepcopy(model)
range0 = np.where((data0.wave >= data.oriWave0[0][-1]) & (data0.wave <= data.oriWave0[0][0]))
data0.wave = data0.wave[range0]
data0.flux = data0.flux[range0]
data0.noise = data0.noise[range0]
model0.wave = model0.wave[range0]
model0.flux = model0.flux[range0]
model0 = nsp.continuum(data=data0, mdl=model0, deg=deg)
data1 = copy.deepcopy(data)
model1 = copy.deepcopy(model)
range1 = np.where((data1.wave >= data.oriWave0[1][-1]) & (data1.wave <= data.oriWave0[1][0]))
data1.wave = data1.wave[range1]
data1.flux = data1.flux[range1]
data1.noise = data1.noise[range1]
model1.wave = model1.wave[range1]
model1.flux = model1.flux[range1]
model1 = nsp.continuum(data=data1, mdl=model1, deg=deg)
data2 = copy.deepcopy(data)
model2 = copy.deepcopy(model)
range2 = np.where((data2.wave >= data.oriWave0[2][-1]) & (data2.wave <= data.oriWave0[2][0]))
data2.wave = data2.wave[range2]
data2.flux = data2.flux[range2]
data2.noise = data2.noise[range2]
model2.wave = model2.wave[range2]
model2.flux = model2.flux[range2]
model2 = nsp.continuum(data=data2, mdl=model2, deg=deg)
model.flux = np.array( list(model0.flux) + list(model1.flux) + list(model2.flux) )
model.wave = np.array( list(model0.wave) + list(model1.wave) + list(model2.wave) )
elif data.instrument == 'apogee' and data.datatype =='apstar':
model = nsp.continuum(data=data, mdl=model)
# flux offset
model.flux += flux_offset
if output_stellar_model: stellar_model.flux += flux_offset
#model.flux **= (1 + flux_exponent_offset)
if output_stellar_model:
return model, stellar_model
else:
return model
def continuumTelluric(data, model=None, order=None):
"""
Return a continnum telluric standard data.
Default: return a telluric flux of mean 1.
Parameters
----------
data: spectrum object
The input telluric data to be continuum
corrected
model: (optional) model object
The telluric model to obtain the mean flux
Instead of 1 as in default, it returns a constant
shift by the difference between the mean flux of
the telluric data and that of the telluric model
Returns
-------
data: spectrum object
continuum corrected telluric data
Examples
--------
>>> import nirspec_pip as nsp
>>> nsp.continuumTelluric(data)
>>> nsp.continuumTelluric(data,model)
"""
if model is None:
wavelow = data.wave[0] - 20
wavehigh = data.wave[-1] + 20
model = nsp.getTelluric(wavelow, wavehigh)
if not data.applymask:
data2 = copy.deepcopy(data)
data.maskBySigmas(sigma=1.5)
else:
data2 = copy.deepcopy(data)
data.wave = data.wave[10:-30]
data.flux = data.flux[10:-30]
data.noise = data.noise[10:-30]
# plt.plot(data.wave,data.flux, alpha=0.5)
# plt.plot(data2.wave,data2.flux, alpha=0.5)
# plt.show()
# plt.close()
if data.order == 35:
# O35 has a voigt absorption profile
popt, pcov = curve_fit(
voigt_profile,
data.wave[20:-20],
data.flux[20:-20],
p0=[21660, 2000, 0.1, 0.1, 0.01, 0.1, 10000, 1000],
maxfev=10000)
#plt.plot(data.wave,data.flux,'k-',alpha=0.5)
#plt.plot(data.wave,voigt_profile(data.wave,*popt),'r-',alpha=0.5)
#plt.show()
#plt.close()
const = np.mean(data.flux/voigt_profile(data.wave, *popt))\
-np.mean(model.flux)
data.flux = data.flux / voigt_profile(data.wave, *popt) - const
data.noise = data.noise / voigt_profile(data.wave, *popt)
#if not data.applymask:
data2.flux = data2.flux / voigt_profile(data2.wave, *popt) - const
data2.noise = data2.noise / voigt_profile(data2.wave, *popt)
data = data2
elif data.order == 38 or data.order == 30:
# O38 has rich absorption features
def fit_continuum_O38(x, a, b, **kwargs):
flux = kwargs.get('flux', data.flux)
linear = a * x + b
return flux / linear
model2 = copy.deepcopy(model)
model2.flux = nsp.broaden(wave=model2.wave,
flux=model2.flux,
vbroad=4.8,
rotate=False,
gaussian=True)
model2.flux = np.array(
nsp.integralResample(xh=model2.wave, yh=model2.flux, xl=data.wave))
model2.wave = data.wave
popt, pcov = curve_fit(fit_continuum_O38,
data.wave,
model2.flux,
p0=[8.54253062e+00, -166000])
#const = np.mean(data.flux/linear_fit(data.wave, *popt))-np.mean(model.flux)
#data.flux = data.flux/linear_fit(data.wave, *popt) - const
data.flux = data.flux / linear_fit(data.wave, *popt)
data.noise = data.noise / linear_fit(data.wave, *popt)
#if not data.applymask:
data2.flux /= linear_fit(data2.wave, *popt)
data2.noise /= linear_fit(data2.wave, *popt)
data = data2
elif data.order == 55:
popt, pcov = curve_fit(_continuumFit, data.wave, data.flux)
#if data.applymask:
# data.flux = data.flux/_continuumFit(data.wave, *popt)
# data.noise = data.noise/_continuumFit(data.wave, *popt)
#
# factor = np.max(data.flux)
# data.flux /= factor
# data.noise /= factor
# data.flux /= 0.93
# data.noise /= 0.93
#elif not data.applymask:
data2.flux = data2.flux / _continuumFit(data2.wave, *popt)
data2.noise = data2.noise / _continuumFit(data2.wave, *popt)
factor = np.max(data2.flux)
data2.flux = data2.flux / factor
data2.noise = data2.noise / factor
data2.flux /= 0.93
data2.noise /= 0.93
data = data2
elif data.order == 56:
# select the highest points to fit a polynomial
x1 = np.max(data.flux[0:100])
x2 = np.max(data.flux[100:150])
x3 = np.max(data.flux[200:300])
x4 = np.max(data.flux[300:400])
x5 = np.max(data.flux[600:700])
x6 = np.max(data.flux[700:800])
a = [
float(data.wave[np.where(data.flux == x1)]),
float(data.wave[np.where(data.flux == x2)]),
float(data.wave[np.where(data.flux == x3)]),
float(data.wave[np.where(data.flux == x4)]),
float(data.wave[np.where(data.flux == x5)]),
float(data.wave[np.where(data.flux == x6)])
]
b = [x1, x2, x3, x4, x5, x6]
popt, pcov = curve_fit(_continuumFit, a, b)
data.flux = data.flux / _continuumFit(data.wave, *popt) * 0.85
data.noise = data.noise / _continuumFit(data.wave, *popt) * 0.85
#if not data.applymask:
data2.flux = data2.flux / _continuumFit(data2.wave, *popt) * 0.85
data2.noise = data2.noise / _continuumFit(data2.wave, *popt) * 0.85
data = data2
elif data.order == 59:
#wave0 = int(data.wave[np.where(data.flux==np.min(data.flux))])
popt, pcov = curve_fit(
voigt_profile,
data.wave,
data.flux,
p0=[12820, 2000, 0.1, 0.1, 0.01, 0.1, 10000, 1000],
maxfev=100000)
#p0=[wave0,2000,0.1,0.1,0.01,0.1,10000,1000],
#maxfev=10000)
data.flux /= voigt_profile(data.wave, *popt)
data.noise /= voigt_profile(data.wave, *popt)
#if not data.applymask:
data2.flux /= voigt_profile(data2.wave, *popt)
data2.noise /= voigt_profile(data2.wave, *popt)
data = data2
#plt.plot(data.wave,data.flux)
#plt.show()
#plt.close()
## this is not true in general!!
elif data.order == 65:
# O65 is best mateched by a gaussian absorption feature
popt, pcov = curve_fit(gaus_absorption_only,
data.wave,
data.flux,
p0=[11660, 50, 2000, 2000],
maxfev=100000)
const = np.mean(
data.flux / gaus_absorption_only(data.wave, *popt)) - np.mean(
model.flux)
data.flux = data.flux / gaus_absorption_only(data.wave, *popt) - const
data.noise /= gaus_absorption_only(data.wave, *popt)
#if not data.applymask:
data2.flux = data2.flux / gaus_absorption_only(data2.wave, *
popt) - const
data2.noise /= gaus_absorption_only(data2.wave, *popt)
data = data2
else:
# this second order polynomial continnum correction
# works for the O33, O34, O36, and O37
popt, pcov = curve_fit(_continuumFit, data.wave, data.flux)
const = np.mean(data.flux / _continuumFit(data.wave, *popt)) - np.mean(
model.flux)
if data.order == 57: const = 0
data.flux = data.flux / _continuumFit(data.wave, *popt) - const
data.noise = data.noise / _continuumFit(data.wave, *popt)
#if not data.applymask:
data2.flux = data2.flux / _continuumFit(data2.wave, *popt) - const
data2.noise = data2.noise / _continuumFit(data2.wave, *popt)
data = data2
return data
