Analysis¶
The specutils package comes with a set of tools for doing common analysis tasks on astronomical spectra. Some examples of applying these tools are described below. The basic spectrum shown here is used in the examples in the subsections below  a gaussianprofile line with flux of 5 GHz Jy. See Working with Spectrum1Ds for more on creating spectra:
>>> import numpy as np
>>> from astropy import units as u
>>> from astropy.nddata import StdDevUncertainty
>>> from astropy.modeling import models
>>> from specutils import Spectrum1D, SpectralRegion
>>> np.random.seed(42)
>>> spectral_axis = np.linspace(11., 1., 200) * u.GHz
>>> spectral_model = models.Gaussian1D(amplitude=5*(2*np.pi*0.8**2)**0.5*u.Jy, mean=5*u.GHz, stddev=0.8*u.GHz)
>>> flux = spectral_model(spectral_axis)
>>> flux += np.random.normal(0., 0.05, spectral_axis.shape) * u.Jy
>>> uncertainty = StdDevUncertainty(0.2*np.ones(flux.shape)*u.Jy)
>>> noisy_gaussian = Spectrum1D(spectral_axis=spectral_axis, flux=flux, uncertainty=uncertainty)
>>> import matplotlib.pyplot as plt
>>> plt.step(noisy_gaussian.spectral_axis, noisy_gaussian.flux)
(Source code, png, hires.png, pdf)
SNR¶
The signaltonoise ratio of a spectrum is often a valuable quantity for
evaluating the quality of a spectrum. The snr
function
performs this task, either on the spectrum as a whole, or subregions of a
spectrum:
>>> from specutils.analysis import snr
>>> snr(noisy_gaussian)
<Quantity 2.47730726>
>>> snr(noisy_gaussian, SpectralRegion(6*u.GHz, 4*u.GHz))
<Quantity 9.8300873>
A second method to calculate SNR does not require the uncertainty defined
on the Spectrum1D
object. This computes the signal to noise
ratio DER_SNR following the definition set forth by the Spectral
Container Working Group of STECF, MAST and CADC. This algorithm is described at
https://esahubble.org/static/archives/stecfnewsletters/pdf/hst_stecf_0042.pdf
>>> from specutils.analysis import snr_derived
>>> snr_derived(noisy_gaussian)
<Quantity 1.13359867>
>>> snr_derived(noisy_gaussian, SpectralRegion(6*u.GHz, 4*u.GHz))
<Quantity 42.10020601>
The conditions on the data for this implementation for it to be an unbiased estimator of the SNR are strict. In particular:
the noise is uncorrelated in wavelength bins spaced two pixels apart
for large wavelength regions, the signal over the scale of 5 or more pixels can be approximated by a straight line
Line Flux Estimates¶
While linefitting (see Line/Spectrum Fitting) is a more thorough way to measure
spectral line fluxes, direct measures of line flux are very useful for either
quicklook settings or for spectra not amedable to fitting. The
line_flux
function addresses that use case. The closely
related specutils.analysis.equivalent_width
computes the equivalent width
of a spectral feature, a flux measure that is normalized against the continuum
of a spectrum. Both are demonstrated below:
Note
The line_flux
function assumes the spectrum has
already been continuumsubtracted, while
equivalent_width
assumes the continuum is at a fixed,
known level (defaulting to 1, meaning continuumnormalized).
Continuum Fitting describes how continuua can be generated
to prepare a spectrum for use with these functions.
>>> from specutils.analysis import line_flux
>>> line_flux(noisy_gaussian, SpectralRegion(7*u.GHz, 3*u.GHz))
<Quantity 4.93784874 GHz Jy>
>>> line_flux(noisy_gaussian).to(u.erg * u.cm**2 * u.s**1)
<Quantity 4.97826284e14 erg / (cm2 s)>
These line_flux measurements also include uncertainties if the spectrum itself has uncertainties:
>>> flux = line_flux(noisy_gaussian)
>>> flux.uncertainty.to(u.erg * u.cm**2 * u.s**1)
<Quantity 1.42132016e15 erg / (cm2 s)>
>>> line_flux(noisy_gaussian, SpectralRegion(7*u.GHz, 3*u.GHz))
<Quantity 4.93784874 GHz Jy>
For the equivalent width, note the need to add a continuum level:
>>> from specutils.analysis import equivalent_width
>>> noisy_gaussian_with_continuum = noisy_gaussian + 1*u.Jy
>>> equivalent_width(noisy_gaussian_with_continuum)
<Quantity 4.97951 GHz>
>>> equivalent_width(noisy_gaussian_with_continuum, regions=SpectralRegion(7*u.GHz, 3*u.GHz))
<Quantity 4.93785 GHz>
Centroid¶
The centroid
function provides a firstmoment analysis to
estimate the center of a spectral feature:
>>> from specutils.analysis import centroid
>>> centroid(noisy_gaussian, SpectralRegion(7*u.GHz, 3*u.GHz))
<Quantity 4.99909151 GHz>
While this example is “presubtracted”, this function only performs well if the contiuum has already been subtracted, as for the other functions above and below.
Moment¶
The moment
function computes moments of any order:
>>> from specutils.analysis import moment
>>> moment(noisy_gaussian, SpectralRegion(7*u.GHz, 3*u.GHz))
<Quantity 98.26318995 Jy>
>>> moment(noisy_gaussian, SpectralRegion(7*u.GHz, 3*u.GHz), order=1)
<Quantity 4.99909151 GHz>
>>> moment(noisy_gaussian, SpectralRegion(7*u.GHz, 3*u.GHz), order=2)
<Quantity 0.58586695 GHz2>
Line Widths¶
There are several width statistics that are provided by the
specutils.analysis
submodule.
The gaussian_sigma_width
function estimates the width of the spectrum by
computing a secondmomentbased approximation of the standard deviation.
The gaussian_fwhm
function estimates the width of the spectrum at half max,
again by computing an approximation of the standard deviation.
Both of these functions assume that the spectrum is approximately gaussian.
The function fwhm
provides an estimate of the full width of the spectrum at
half max that does not assume the spectrum is gaussian. It locates the maximum,
and then locates the value closest to half of the maximum on either side, and
measures the distance between them.
A function to calculate the full width at zero intensity (i.e. the width of a
spectral feature at the continuum) is provided as fwzi
. Like the fwhm
calculation, it does not make assumptions about the shape of the feature
and calculates the width by finding the points at either side of maximum
that reach the continuum value. In this case, it assumes the provided
spectrum has been continuum subtracted.
Each of the width analysis functions are applied to this spectrum below:
>>> from specutils.analysis import gaussian_sigma_width, gaussian_fwhm, fwhm, fwzi
>>> gaussian_sigma_width(noisy_gaussian)
<Quantity 0.74075431 GHz>
>>> gaussian_fwhm(noisy_gaussian)
<Quantity 1.74434311 GHz>
>>> fwhm(noisy_gaussian)
<Quantity 1.86047666 GHz>
>>> fwzi(noisy_gaussian)
<Quantity 94.99997484 GHz>
Template comparison¶
The template_match
function takes an
observed spectrum and n
template spectra and returns the best template that
matches the observed spectrum via chisquare minimization.
If the redshift is known, the user can set that for the redshift
parameter
and then run the
template_match
function.
This function will:
Match the resolution and wavelength spacing of the observed spectrum.
Compute the chisquare between the observed spectrum and each template.
Return the lowest chisquare and its corresponding template spectrum, normalized to the observed spectrum (and the index of the template spectrum if the list of templates is iterable). It also
If the redshift is unknown, the user specifies a grid of redshift values in the form of an iterable object such as a list, tuple, or numpy array with the redshift values to use. As an example, a simple linear grid can be built with:
>>> rs_values = np.arange(1., 3.25, 0.25)
The template_match
function will then:
Move each template to the first term in the redshift grid.
Run steps 1 and 2 of the case with known redshift.
Move to the next term in the redshift grid.
Run steps 1 and 2 of the case with known redshift.
Repeat the steps until the end of the grid is reached.
Return the best redshift, the lowest chisquare and its corresponding template spectrum, and a list with all chi2 values, one per template. The returned template spectrum corresponding to the lowest chi2 is redshifted and normalized to the observed spectrum (and the index of the template spectrum if the list of templates is iterable). When multiple templates are matched with a redshift grid, a listoflists is returned with the trial chisquare values computed for every combination redshifttemplate. The external list spans the range of templates in the collection/list, while each internal list contains all chi2 values for a given template.
An example of how to do template matching with an unknown redshift is:
>>> from specutils.analysis import template_comparison
>>> spec_axis = np.linspace(0, 50, 50) * u.AA
>>> observed_redshift = 2.0
>>> min_redshift = 1.0
>>> max_redshift = 3.0
>>> delta_redshift = .25
>>> resample_method = "flux_conserving"
>>> rs_values = np.arange(min_redshift, max_redshift+delta_redshift, delta_redshift)
>>> observed_spectrum = Spectrum1D(spectral_axis=spec_axis*(1+observed_redshift), flux=np.random.randn(50) * u.Jy, uncertainty=StdDevUncertainty(np.random.sample(50), unit='Jy'))
>>> spectral_template = Spectrum1D(spectral_axis=spec_axis, flux=np.random.randn(50) * u.Jy, uncertainty=StdDevUncertainty(np.random.sample(50), unit='Jy'))
>>> tm_result = template_comparison.template_match(observed_spectrum=observed_spectrum, spectral_templates=spectral_template, resample_method=resample_method, redshift=rs_values)
Dust extinction¶
Dust extinction can be applied to Spectrum1D instances via their internal arrays, using
the dust_extinction
package (http://dustextinction.readthedocs.io/en/latest)
Below is an example of how to apply extinction.
from astropy.modeling.blackbody import blackbody_lambda
from dust_extinction.parameter_averages import F99
wave = np.logspace(np.log10(1000), np.log10(3e4), num=10) * u.AA
flux = blackbody_lambda(wave, 10000 * u.K)
spec = Spectrum1D(spectral_axis=wave, flux=flux)
# define the model
ext = F99(Rv=3.1)
# extinguish (redden) the spectrum
flux_ext = spec.flux * ext.extinguish(spec.spectral_axis, Ebv=0.5)
spec_ext = Spectrum1D(spectral_axis=wave, flux=flux_ext)
Template Crosscorrelation¶
The crosscorrelation function between an observed spectrum and a template spectrum that both share a common spectral
axis can be calculated with the function template_correlate
in the analysis
module.
An example of how to get the cross correlation follows. Note that the observed spectrum must have a rest wavelength value set.
>>> from specutils.analysis import correlation
>>> size = 200
>>> spec_axis = np.linspace(4500., 6500., num=size) * u.AA
>>> f1 = np.random.randn(size)*0.5 * u.Jy
>>> f2 = np.random.randn(size)*0.5 * u.Jy
>>> rest_value = 6000. * u.AA
>>> mean1 = 5035. * u.AA
>>> mean2 = 5015. * u.AA
>>> g1 = models.Gaussian1D(amplitude=30 * u.Jy, mean=mean1, stddev=10. * u.AA)
>>> g2 = models.Gaussian1D(amplitude=30 * u.Jy, mean=mean2, stddev=10. * u.AA)
>>> flux1 = f1 + g1(spec_axis)
>>> flux2 = f2 + g2(spec_axis)
>>> uncertainty = StdDevUncertainty(0.2*np.ones(size)*u.Jy)
>>> ospec = Spectrum1D(spectral_axis=spec_axis, flux=flux1, uncertainty=uncertainty, velocity_convention='optical', rest_value=rest_value)
>>> tspec = Spectrum1D(spectral_axis=spec_axis, flux=flux2, uncertainty=uncertainty)
>>> corr, lag = correlation.template_correlate(ospec, tspec)
The lag values are reported in km/s units. The correlation values are computed after the template spectrum is normalized in order to have the same total flux as the observed spectrum.
Reference/API¶
Functions¶

Calculate the centroid of a region, or regions, of the spectrum. 

Computes the equivalent width of a region of the spectrum. 

Compute the true full width half max of the spectrum. 

Compute the true full width at zero intensity (i.e. 

Estimate the width of the spectrum using a secondmoment analysis. 

Estimate the width of the spectrum using a secondmoment analysis. 

Determine if the baseline of this spectrum is less than a threshold. 

Computes the integrated flux in a spectrum or region of a spectrum. 

Estimate the moment of the spectrum. 

Calculate the mean S/N of the spectrum based on the flux and uncertainty in the spectrum. 

This function computes the signal to noise ratio DER_SNR following the definition set forth by the Spectral Container Working Group of STECF, MAST and CADC. 

Compute crosscorrelation of the observed and template spectra. 

Resample a spectrum and template onto a common logspaced spectral grid. 

Find which spectral templates is the best fit to an observed spectrum by computing the chisquared. 

Find the bestfit redshift for template_spectrum to match observed_spectrum using chi2. 

Decorator for methods that should warn if the baseline of the spectrum does not appear to be below a threshold. 