Working With SpectrumCollections¶

A spectrum collection is a way to keep a set of spectra data together and have the collection behave as if it were a single spectrum object. This means that it can be used in regular analysis functions to perform operations over entire sets of data.

Currently, all SpectrumCollection items must be the same shape. No assumptions are made about the dispersion solutions, and users are encouraged to ensure their spectrum collections make sense either by resampling them beforehand, or being aware that they do not share the same dispersion solution.

>>> import numpy as np
>>> import astropy.units as u
>>> from astropy.nddata import StdDevUncertainty
>>> from specutils import SpectrumCollection
>>> from specutils.utils.wcs_utils import gwcs_from_array

>>> flux = u.Quantity(np.random.sample((5, 10)), unit='Jy')
>>> spectral_axis = u.Quantity(np.arange(50).reshape((5, 10)), unit='AA')
>>> wcs = np.array([gwcs_from_array(x) for x in spectral_axis])
>>> uncertainty = StdDevUncertainty(np.random.sample((5, 10)), unit='Jy')
>>> mask = np.ones((5, 10)).astype(bool)
>>> meta = [{'test': 5, 'info': [1, 2, 3]} for i in range(5)]

>>> spec_coll = SpectrumCollection(
... flux=flux, spectral_axis=spectral_axis, wcs=wcs,
... uncertainty=uncertainty, mask=mask, meta=meta)

>>> spec_coll.shape
(5,)
>>> spec_coll.flux.unit
Unit("Jy")
>>> spec_coll.spectral_axis.shape
(5, 10)
>>> spec_coll.spectral_axis.unit
Unit("Angstrom")

Collections from 1D spectra¶

It is also possible to create a SpectrumCollection from a list of Spectrum1D:

>>> import warnings
>>> import numpy as np
>>> from astropy import units as u
>>> from specutils import Spectrum1D, SpectrumCollection
>>> spec = Spectrum1D(spectral_axis=np.linspace(0, 50, 50) * u.AA,
...                   flux=np.random.randn(50) * u.Jy,
...                   uncertainty=StdDevUncertainty(np.random.sample(50), unit='Jy'))
>>> with warnings.catch_warnings():  # Ignore warnings
...     warnings.simplefilter('ignore')
...     spec1 = Spectrum1D(spectral_axis=np.linspace(20, 60, 50) * u.AA,
...                        flux=np.random.randn(50) * u.Jy,
...                        uncertainty=StdDevUncertainty(np.random.sample(50), unit='Jy'))
...     spec_coll = SpectrumCollection.from_spectra([spec, spec1])

>>> spec_coll.shape
(2,)
>>> spec_coll.flux.unit
Unit("Jy")
>>> spec_coll.spectral_axis.shape
(2, 50)
>>> spec_coll.spectral_axis.unit
Unit("Angstrom")

SpectrumCollection objects can be treated just like Spectrum1D objects; calling a particular attribute on the object will return an array whose type depends on the type of the attribute in the Spectrum1D object.

>>> print(type(spec1.flux))
<class 'astropy.units.quantity.Quantity'>
>>> print(type(spec_coll.flux))
<class 'astropy.units.quantity.Quantity'>

The difference is their shape. The returned array from the SpectrumCollection object will have shape (N, M) where N is the number of input spectra and M is the length of the output dispersion grid.

>>> print(spec1.flux.shape)
(50,)
>>> print(spec_coll.flux.shape)
(2, 50)

Reference/API¶

Classes¶

`Conf`()	Configuration parameters for specutils.
`SpectrumCollection`(flux[, spectral_axis, ...])	A class to represent a heterogeneous set of spectra that are the same length but have different spectral axes.
`SpectrumList`([iterable])	A list that is used to hold a list of `Spectrum1D` objects