def spectrum_1d_getitem(observed, part):
observed_part = Spectrum1D.from_array(observed.wavelength[part],
observed.flux[part])
if getattr(observed, 'uncertainty', None) is not None:
observed_part.uncertainty = getattr(observed.uncertainty, 'array',
observed.uncertainty)[part]
return observed_part
def spectrum_1d_getitem(observed, part):
observed_part = Spectrum1D.from_array(
observed.wavelength[part],
observed.flux[part])
if getattr(observed, 'uncertainty', None) is not None:
observed_part.uncertainty = getattr(observed.uncertainty, 'array',
observed.uncertainty)[part]
return observed_part
def __call__(self, model):
fit = np.zeros_like(model.wavelength.value)
for part, normalizer in zip(self.parts, self.normalizers):
fit[part] = normalizer(self.spectrum_1d_getitem(model, part)).flux
return Spectrum1D.from_array(model.wavelength.value,
fit,
unit=self.normalizers[0].flux_unit,
dispersion_unit=model.wavelength.unit)
def __call__(self, spectrum):
wavelength, flux = spectrum.wavelength.value, spectrum.flux
interpolated_flux = np.interp(self.observed.wavelength.value,
wavelength, flux)
return Spectrum1D.from_array(
self.observed.wavelength,
interpolated_flux,
dispersion_unit=self.observed.wavelength.unit,
unit=self.observed.unit)
def __call__(self, spectrum):
wavelength, flux = spectrum.wavelength.value, spectrum.flux
interpolated_flux = np.interp(self.observed.wavelength.value,
wavelength, flux)
return Spectrum1D.from_array(
self.observed.wavelength,
interpolated_flux,
dispersion_unit=self.observed.wavelength.unit,
unit=self.observed.unit)
def __call__(self, model):
fit = np.zeros_like(model.wavelength.value)
for part, normalizer in zip(self.parts, self.normalizers):
fit[part] = normalizer(self.spectrum_1d_getitem(model, part)).flux
return Spectrum1D.from_array(
model.wavelength.value,
fit, unit=self.normalizers[0].flux_unit,
dispersion_unit=model.wavelength.unit)
def __call__(self, spectrum):
from specutils import extinction
extinction_factor = 10**(-0.4*extinction.extinction_ccm89(
spectrum.wavelength, a_v=self.a_v, r_v=self.r_v))
return Spectrum1D.from_array(
spectrum.wavelength.value,
extinction_factor * spectrum.flux,
dispersion_unit=spectrum.wavelength.unit, unit=spectrum.unit)
def __call__(self, spectrum):
from specutils import extinction
extinction_factor = 10**(-0.4 * extinction.extinction_ccm89(
spectrum.wavelength, a_v=self.a_v, r_v=self.r_v))
return Spectrum1D.from_array(spectrum.wavelength.value,
extinction_factor * spectrum.flux,
dispersion_unit=spectrum.wavelength.unit,
unit=spectrum.unit)
def __call__(self, spectrum):
from specutils import extinction
extinction_factor = np.ones_like(spectrum.wavelength.value)
valid_wavelength = ((spectrum.wavelength > 910 * u.angstrom) &
(spectrum.wavelength < 33333 * u.angstrom))
extinction_factor[valid_wavelength] = 10 ** (-0.4 * extinction.extinction_ccm89(
spectrum.wavelength[valid_wavelength], a_v=self.a_v,
r_v=self.r_v).to(u.angstrom).value)
return Spectrum1D.from_array(spectrum.wavelength,
extinction_factor * spectrum.flux)
def __call__(self, spectrum):
from specutils import extinction
extinction_factor = np.ones_like(spectrum.wavelength.value)
valid_wavelength = ((spectrum.wavelength > 910 * u.angstrom) &
(spectrum.wavelength < 33333 * u.angstrom))
extinction_factor[valid_wavelength] = 10**(
-0.4 *
extinction.extinction_ccm89(spectrum.wavelength[valid_wavelength],
a_v=self.a_v,
r_v=self.r_v).to(u.angstrom).value)
return Spectrum1D.from_array(spectrum.wavelength,
extinction_factor * spectrum.flux)
def __call__(self, spectrum):
wavelength, flux = spectrum.wavelength.value, spectrum.flux
log_grid_log_wavelength = np.arange(np.log(wavelength.min()),
np.log(wavelength.max()),
self.resolution.to(1).value)
log_grid_wavelength = np.exp(log_grid_log_wavelength)
log_grid_flux = np.interp(log_grid_wavelength, wavelength, flux)
profile = self.rotational_profile()
log_grid_convolved = nd.convolve1d(log_grid_flux, profile)
convolved_flux = np.interp(wavelength, log_grid_wavelength,
log_grid_convolved)
return Spectrum1D.from_array(spectrum.wavelength,
convolved_flux,
dispersion_unit=spectrum.wavelength.unit,
unit=spectrum.unit)
def __call__(self, spectrum):
wavelength, flux = spectrum.wavelength.value, spectrum.flux
log_grid_log_wavelength = np.arange(np.log(wavelength.min()),
np.log(wavelength.max()),
self.resolution.to(1).value)
log_grid_wavelength = np.exp(log_grid_log_wavelength)
log_grid_flux = np.interp(log_grid_wavelength, wavelength, flux)
profile = self.rotational_profile()
log_grid_convolved = nd.convolve1d(log_grid_flux, profile)
convolved_flux = np.interp(wavelength, log_grid_wavelength,
log_grid_convolved)
return Spectrum1D.from_array(spectrum.wavelength,
convolved_flux,
dispersion_unit=spectrum.wavelength.unit,
unit=spectrum.unit)
def __call__(self, spectrum):
wavelength, flux = spectrum.wavelength.value, spectrum.flux
log_grid_log_wavelength = np.arange(
np.log(wavelength.min()), np.log(wavelength.max()),
1 / (self.sampling * self.R.to(1).value))
log_grid_wavelength = np.exp(log_grid_log_wavelength)
log_grid_flux = np.interp(log_grid_wavelength, wavelength, flux)
sigma = self.sampling / (2 * np.sqrt(2 * np.log(2)))
log_grid_convolved = nd.gaussian_filter1d(log_grid_flux, sigma)
convolved_flux = np.interp(wavelength, log_grid_wavelength,
log_grid_convolved)
return Spectrum1D.from_array(spectrum.wavelength,
convolved_flux,
dispersion_unit=spectrum.wavelength.unit,
unit=spectrum.unit)
def __call__(self, spectrum):
wavelength, flux = spectrum.wavelength.value, spectrum.flux
log_grid_log_wavelength = np.arange(np.log(wavelength.min()),
np.log(wavelength.max()),
1 / (self.sampling *
self.R.to(1).value))
log_grid_wavelength = np.exp(log_grid_log_wavelength)
log_grid_flux = np.interp(log_grid_wavelength, wavelength, flux)
sigma = self.sampling / (2 * np.sqrt(2 * np.log(2)))
log_grid_convolved = nd.gaussian_filter1d(log_grid_flux, sigma)
convolved_flux = np.interp(wavelength, log_grid_wavelength,
log_grid_convolved)
return Spectrum1D.from_array(spectrum.wavelength,
convolved_flux,
dispersion_unit=spectrum.wavelength.unit,
unit=spectrum.unit)
def __call__(self, spectrum):
R = self.resolution
Lambda = self.central_wavelength.value
wavelength = spectrum.dispersion.value
conversionfactor = 2 * np.sqrt(2 * np.log(2))
deltax = np.mean(wavelength[1:] - wavelength[0:-1])
FWHM = Lambda / R
sigma = (FWHM / deltax) / conversionfactor
flux = spectrum.flux
convolved_flux = gaussian_filter1d(flux, sigma, axis=0, order=0)
return Spectrum1D.from_array(spectrum.dispersion,
convolved_flux,
dispersion_unit=spectrum.dispersion.unit,
unit=spectrum.unit)
def __call__(self,spectrum):
R = self.resolution
Lambda = self.central_wavelength.value
wavelength = spectrum.dispersion.value
conversionfactor = 2 * np.sqrt(2 * np.log(2))
deltax = np.mean(wavelength[1:] - wavelength[0:-1])
FWHM = Lambda/R
sigma = (FWHM/deltax)/conversionfactor
flux = spectrum.flux
convolved_flux = gaussian_filter1d(flux, sigma, axis=0, order=0)
return Spectrum1D.from_array(
spectrum.dispersion,
convolved_flux,
dispersion_unit=spectrum.dispersion.unit, unit=spectrum.unit)
def eval(self, teff, logg, feh):
"""
Interpolating on the grid to the necessary parameters
Parameters
----------
teff: float
effective temperature
logg: float
base ten logarithm of surface gravity in cgs
feh: float
[Fe/H]
"""
flux = self.interpolate_grid(teff, logg, feh)
return Spectrum1D.from_array(self.wavelength,
flux,
dispersion_unit=u.angstrom,
unit=u.Unit('erg/ (cm2 s Angstrom)'))
def eval(self, *args):
"""
Interpolating on the grid to the necessary parameters
Parameters
----------
teff: float
effective temperature
logg: float
base ten logarithm of surface gravity in cgs
feh: float
[Fe/H]
"""
flux = self.interpolate_grid(*args)
return Spectrum1D.from_array(self.wavelength.value,
flux,
dispersion_unit=self.wavelength.unit,
unit=self.flux_unit)
def __call__(self, model):
# V[:,0]=mfi/e, Vp[:,1]=mfi/e*w, .., Vp[:,npol]=mfi/e*w**npol
V = self._Vp * (model.flux / self.uncertainty)[:, np.newaxis]
# normalizes different powers
scl = np.sqrt((V * V).sum(0))
if np.isfinite(scl[0].value): # check for validity before evaluating
sol, resids, rank, s = np.linalg.lstsq(V / scl,
self.signal_to_noise,
self._rcond)
sol = (sol.T / scl).T
if rank != self._Vp.shape[-1] - 1:
msg = "The fit may be poorly conditioned"
warnings.warn(msg)
fit = np.dot(V, sol) * self.uncertainty
# keep coefficients in case the outside wants to look at it
self.polynomial = Polynomial(sol,
domain=self.domain.value,
window=self.window.value)
return Spectrum1D.from_array(model.wavelength.value, fit)
else:
return model
def __call__(self, model):
# V[:,0]=mfi/e, Vp[:,1]=mfi/e*w, .., Vp[:,npol]=mfi/e*w**npol
V = self._Vp * (model.flux / self.uncertainty)[:, np.newaxis]
# normalizes different powers
scl = np.sqrt((V*V).sum(0))
if np.isfinite(scl[0].value): # check for validity before evaluating
sol, resids, rank, s = np.linalg.lstsq(V/scl, self.signal_to_noise,
self._rcond)
sol = (sol.T / scl).T
if rank != self._Vp.shape[-1] - 1:
msg = "The fit may be poorly conditioned"
warnings.warn(msg)
fit = np.dot(V, sol) * self.uncertainty
# keep coefficients in case the outside wants to look at it
self.polynomial = Polynomial(sol, domain=self.domain.value,
window=self.window.value)
return Spectrum1D.from_array(
model.wavelength.value,
fit)
else:
return model
def __call__(self, spectrum):
doppler_factor = 1. + (self.vrad / const.c)
return Spectrum1D.from_array(spectrum.wavelength * doppler_factor,
spectrum.flux)
def __call__(self, spectrum):
doppler_factor = 1. + self.vrad / const.c
return Spectrum1D.from_array(spectrum.wavelength * doppler_factor,
spectrum.flux,
dispersion_unit=spectrum.wavelength.unit)
def __call__(self, spectrum):
doppler_factor = 1. + self.vrad / const.c
return Spectrum1D.from_array(spectrum.wavelength * doppler_factor,
spectrum.flux,
dispersion_unit=spectrum.wavelength.unit)
def __call__(self, spectrum):
doppler_factor = 1. + (self.vrad / const.c)
return Spectrum1D.from_array(spectrum.wavelength * doppler_factor,
spectrum.flux)
def __call__(self):
return Spectrum1D.from_array(self.wavelength, self._interpolate_flux())
def __call__(self):
return Spectrum1D.from_array(self.wavelength, self._interpolate_flux())
