FluxConservingResampler

class specutils.manipulation.FluxConservingResampler(extrapolation_treatment='nan_fill')

Bases: specutils.manipulation.ResamplerBase

This resampling algorithm conserves overall integrated flux (as opposed to flux density). Algorithm based on the equations documented in the following paper: https://ui.adsabs.harvard.edu/abs/2017arXiv170505165C/abstract

Parameters

extrapolation_treatmentstr

What to do when resampling off the edge of the spectrum. Can be 'nan_fill' to have points beyond the edges by set to NaN, or 'zero_fill' to be set to zero.

Examples

To resample an input spectrum to a user specified spectral grid using a flux conserving algorithm:

>>> import numpy as np
>>> import astropy.units as u
>>> from specutils import Spectrum1D
>>> from specutils.manipulation import FluxConservingResampler
>>> input_spectra = Spectrum1D(
...     flux=np.array([1, 3, 7, 6, 20]) * u.mJy,
...     spectral_axis=np.array([2, 4, 12, 16, 20]) * u.nm)
>>> resample_grid = [1, 5, 9, 13, 14, 17, 21, 22, 23]  *u.nm
>>> fluxc_resample = FluxConservingResampler()
>>> output_spectrum1D = fluxc_resample(input_spectra, resample_grid) 

Methods Summary

resample1d(self, orig_spectrum, fin_spec_axis)

Create a re-sampling matrix to be used in re-sampling spectra in a way that conserves flux.

Methods Documentation

resample1d(self, orig_spectrum, fin_spec_axis)

Create a re-sampling matrix to be used in re-sampling spectra in a way that conserves flux. If an uncertainty is present in the input spectra it will be propagated through to the final resampled output spectra as an InverseVariance uncertainty.

Parameters

orig_spectrumSpectrum1D

The original 1D spectrum.

fin_spec_axisQuantity

The desired spectral axis array.

Returns

resample_spectrumSpectrum1D

An output spectrum containing the resampled Spectrum1D