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===================
Spectrum Arithmetic
===================
Specutils sports the ability to perform arithmetic operations over spectrum
data objects. There is full support for propagating unit information.
.. note:: Spectrum arithmetic requires that the two spectrum objects have
compatible WCS information.
.. warning:: Specutils does not currently implement interpolation techniques
for converting spectral axes information from one WCS source to
another.
Basic Arithmetic
----------------
Arithmetic support includes addition, subtract, multiplication, and division.
.. code-block:: python
>>> from specutils import Spectrum1D
>>> import astropy.units as u
>>> import numpy as np
>>> spec1 = Spectrum1D(spectral_axis=np.arange(1, 50) * u.nm, flux=np.random.sample(49)*u.Jy)
>>> spec2 = Spectrum1D(spectral_axis=np.arange(1, 50) * u.nm, flux=np.random.sample(49)*u.Jy)
>>> spec3 = spec1 + spec2
>>> spec3 #doctest:+SKIP
<Spectrum1D(flux=<Quantity [1.85874504, 0.61621214, 0.83535041, 0.79151086, 0.45719958,
1.31989271, 0.27674835, 0.70582565, 1.32792166, 1.2417933 ,
0.80759589, 0.30630131, 0.67328771, 1.00480227, 0.34217072,
0.35003 , 1.2829507 , 1.47479322, 0.69394109, 1.85822987,
0.98505397, 1.77968666, 0.86440386, 0.8547567 , 1.49315833,
0.66677101, 1.26206613, 1.27884653, 1.42401291, 1.256702 ,
0.97971943, 0.82923743, 1.34281845, 0.70388224, 1.19961318,
0.98068789, 1.02384543, 0.31453759, 1.8461661 , 0.79451612,
1.24044751, 0.48436633, 0.31850599, 0.82791702, 1.23084396,
0.40616336, 0.60637889, 1.88098282, 0.76899619] Jy>, spectral_axis=<Quantity [ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13.,
14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26.,
27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39.,
40., 41., 42., 43., 44., 45., 46., 47., 48., 49.] nm>)>
Propagation of Uncertainties
----------------------------
Arithmetic operations also support the propagation of unceratinty information.
.. code-block:: python
>>> from astropy.nddata import StdDevUncertainty
>>> spec1 = Spectrum1D(spectral_axis=np.arange(10) * u.nm, flux=np.random.sample(10)*u.Jy, uncertainty=StdDevUncertainty(np.random.sample(10) * 0.1))
>>> spec2 = Spectrum1D(spectral_axis=np.arange(10) * u.nm, flux=np.random.sample(10)*u.Jy, uncertainty=StdDevUncertainty(np.random.sample(10) * 0.1))
>>> spec1.uncertainty #doctest:+SKIP
StdDevUncertainty([0.04386832, 0.09909487, 0.07589192, 0.0311604 ,
0.07973579, 0.04687858, 0.01161918, 0.06013496,
0.00476118, 0.06720447])
>>> spec2.uncertainty #doctest:+SKIP
StdDevUncertainty([0.00889175, 0.00890437, 0.05194229, 0.08794455,
0.09918037, 0.04815417, 0.06464564, 0.0164324 ,
0.04358771, 0.08260218])
>>> spec3 = spec1 + spec2
>>> spec3.uncertainty #doctest:+SKIP
StdDevUncertainty([0.04476039, 0.09949412, 0.09196513, 0.09330174,
0.12725778, 0.06720435, 0.06568154, 0.06233969,
0.04384698, 0.10648737])
Reference/API
-------------
.. automodapi:: specutils.spectra.spectrum_mixin
:no-heading:
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