def prepare_observed(observed):
"""
Preparing an observed spectrum
Parameters
----------
observed: Spectrum1D object
"""
wavelength, flux = observed.wavelength, observed.flux
if getattr(observed, 'uncertainty', None) is None:
uncertainty = np.ones_like(flux)
else:
uncertainty = getattr(observed.uncertainty, 'array',
observed.uncertainty).value
wavelength_order = np.argsort(wavelength)
spec = Spectrum1D.from_array(wavelength[wavelength_order],
flux[wavelength_order])
spec.uncertainty = uncertainty[wavelength_order]
return spec
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 evaluate_slow(self, wavelength, flux):
spec = Spectrum1D.from_array(wavelength * u.angstrom,
flux * u.erg/u.s/u.cm**2/u.angstrom)
return np.array(u.Quantity(self.calculate_magnitudes(spec)).value)