Spectrum1D¶

class
specutils.
Spectrum1D
(flux=None, spectral_axis=None, wcs=None, velocity_convention=None, rest_value=None, *args, **kwargs)[source]¶ Bases:
specutils.spectra.spectrum_mixin.OneDSpectrumMixin
,astropy.nddata.NDDataRef
Spectrum container for 1D spectral data.
 Parameters
 flux
astropy.units.Quantity
or astropy.nddata.NDData`like The flux data for this spectrum.
 spectral_axis
astropy.units.Quantity
Dispersion information with the same shape as the last (or only) dimension of flux.
 wcs
astropy.wcs.WCS
orgwcs.wcs.WCS
WCS information object.
 velocity_convention{“doppler_relativistic”, “doppler_optical”, “doppler_radio”}
Convention used for velocity conversions.
 rest_value
Quantity
Any quantity supported by the standard spectral equivalencies (wavelength, energy, frequency, wave number). Describes the rest value of the spectral axis for use with velocity conversions.
 uncertainty
NDUncertainty
Contains uncertainty information along with propagation rules for spectrum arithmetic. Can take a unit, but if none is given, will use the unit defined in the flux.
 metadict
Arbitrary container for any userspecific information to be carried around with the spectrum container object.
 flux
Attributes Summary
The energy of the spectral axis as a
Quantity
in units of eV.The frequency as a
Quantity
in units of GHzThe flux density of photons as a
Quantity
, in units of photons per cm^2 per second per spectral_axis unitThe wavelength as a
Quantity
in units of AngstromsMethods Summary
spectral_resolution
(self, true_dispersion, …)Evaluate the probability distribution of the spectral resolution.
Attributes Documentation

bin_edges
¶

photon_flux
¶ The flux density of photons as a
Quantity
, in units of photons per cm^2 per second per spectral_axis unit

shape
¶
Methods Documentation

spectral_resolution
(self, true_dispersion, delta_dispersion, axis=1)[source]¶ Evaluate the probability distribution of the spectral resolution.
 Parameters
 true_dispersion
Quantity
True value(s) of dispersion for which the resolution should be evaluated.
 delta_dispersion
Quantity
Array of (observed  true) dispersion bin edges to integrate the resolution probability density over.
 axisint
Which axis of
delta_dispersion
contains the strictly increasing dispersion values to interpret as bin edges. The dimension ofdelta_dispersion
along this axis must be at least two.
 true_dispersion
 Returns
 numpy array
Array of dimensionless probabilities calculated as the integral of P(observed  true) over each bin in (observed  true). The output shape is the result of broadcasting the input shapes.
Examples
To tabulate a binned resolution function at 6000A covering +/10A in 0.2A steps:
>>> R = spectrum1d.spectral_resolution( ... 6000 * u.Angstrom, np.linspace(10, 10, 51) * u.Angstrom) >>> assert R.shape == (50,) >>> assert np.allclose(R.sum(), 1.)
To build a sparse resolution matrix for true wavelengths 40008000A in 0.1A steps:
>>> R = spectrum1d.spectral_resolution( ... np.linspace(4000, 8000, 40001)[:, np.newaxis] * u.Angstrom, ... np.linspace(10, +10, 201) * u.Angstrom) >>> assert R.shape == (40000, 200) >>> assert np.allclose(R.sum(axis=1), 1.)