Update __str__ and __repr__ for Spectrum1D (#1123)

* Fixing numpy format error when printing Spectrum1D * Also shorten __repr__ to something more reasonable * Show actual spectral axis and flux if it's small * str test updated * Spectrum1D tests passing, more tweaks to format * Update docstrings * Fixing doc outputs * Fix typo * More docs fixes * Surprised this changed, testing... * Finish changing docs * Fix remote data doc tests * Went back to = over :, other improvements * Reconcile resample docstring updates * Add changelog
astropy · Feb 13, 2024 · c646007 · c646007
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diff --git a/CHANGES.rst b/CHANGES.rst
@@ -13,9 +13,13 @@ Bug Fixes
 - ``with_spectral_unit`` has been changed to ``with_spectral_axis_unit`` and actually works
   now. [#1119]
 
+- Fixed numpy error when printing a ``Spectrum1D`` object. [#1123]
+
 Other Changes and Additions
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
+- Updated the format of ``Spectrum1D.__str__`` and ``Spectrum1D.__repr__``. [#1123]
+
 1.12.0 (2023-10-17)
 -------------------
 

diff --git a/docs/arithmetic.rst b/docs/arithmetic.rst
@@ -29,23 +29,11 @@ Arithmetic support includes addition, subtract, multiplication, and division.
     >>> spec2 = Spectrum1D(spectral_axis=np.arange(1, 50) * u.nm, flux=rng.random(49)*u.Jy)
     >>> spec3 = spec1 + spec2
     >>> spec3  # doctest: +FLOAT_CMP
-    <Spectrum1D(flux=<Quantity [0.85594057, 0.59914105, 0.86545315, 1.29308365, 0.56743587,
-               0.63720232, 1.03919556, 0.33693653, 0.89068491, 1.41613598,
-               0.72461457, 1.20411351, 0.96480272, 0.37496506, 0.70241888,
-               1.36924151, 0.90943254, 0.82210346, 0.98018949, 1.05861761,
-               0.26172516, 1.02205189, 0.51839963, 1.21572449, 0.87754143,
-               1.02493144, 0.95316681, 0.37872965, 0.17723648, 1.21662474,
-               1.39171024, 1.23614795, 1.10636247, 0.97294585, 1.5453743 ,
-               1.01020945, 1.42125961, 1.36636734, 1.11338214, 0.58687869,
-               0.63074156, 0.67475105, 0.54093389, 1.77345469, 1.22990398,
-               0.78162068, 0.63760289, 0.63139356, 0.97112644] Jy>, spectral_axis=<SpectralAxis
+    <Spectrum1D(flux=[0.8559405665668484 ... 0.9711264429515736] Jy (shape=(49,), mean=0.91592 Jy); spectral_axis=<SpectralAxis
        (observer to target:
           radial_velocity=0.0 km / s
           redshift=0.0)
-      [ 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>)>
+      [ 1.  2.  3. ... 47. 48. 49.] nm> (length=49))>
 
 
 Propagation of Uncertainties

diff --git a/docs/manipulation.rst b/docs/manipulation.rst
@@ -59,19 +59,7 @@ along the spectral dimension.
     >>> spec1_gsmooth = gaussian_smooth(spec1, stddev=3)
     >>> spec1_tsmooth = trapezoid_smooth(spec1, width=3)
     >>> gaussian_smooth(spec1, stddev=3) # doctest: +FLOAT_CMP
-    <Spectrum1D(flux=<Quantity [0.25860917, 0.32640733, 0.38562205, 0.43159269, 0.46445898,
-               0.48828727, 0.50822537, 0.52741247, 0.54565787, 0.56035736,
-               0.56880835, 0.57015658, 0.56561802, 0.55700108, 0.54472603,
-               0.52720679, 0.50217622, 0.46914624, 0.43114785, 0.39432907,
-               0.36548049, 0.34885759, 0.34456742, 0.34936895, 0.35949514,
-               0.37376516, 0.39451092, 0.42591854, 0.47068509, 0.5272207 ,
-               0.58920337, 0.64763434, 0.69379067, 0.72157276, 0.7284874 ,
-               0.71560131, 0.68696137, 0.64870578, 0.60757162, 0.56898454,
-               0.53520565, 0.5047466 , 0.47338241, 0.43641424, 0.39090495,
-               0.3368543 , 0.27706956, 0.21605198, 0.15868783] Jy>, spectral_axis=<SpectralAxis [ 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>)>
+    <Spectrum1D(flux=[0.25860917267578276 ... 0.15868783272966752] Jy (shape=(49,), mean=0.48009 Jy); spectral_axis=<SpectralAxis [ 1.  2.  3. ... 47. 48. 49.] nm> (length=49))>
 
 Each of the specific smoothing methods create the appropriate `astropy.convolution.convolve`
 kernel and then call a helper function :func:`~specutils.manipulation.convolution_smooth`
@@ -87,19 +75,7 @@ that takes the spectrum and an astropy 1D kernel.  So, one could also do:
     >>> spec1 = Spectrum1D(spectral_axis=np.arange(1, 50) * u.nm,
     ...                    flux=np.random.default_rng(12345).random(49) * u.Jy)
     >>> convolution_smooth(spec1, box1d_kernel) # doctest: +FLOAT_CMP
-    <Spectrum1D(flux=<Quantity [0.18136479, 0.44715327, 0.59679282, 0.62157656, 0.46672605,
-               0.44074408, 0.37261896, 0.48593299, 0.60043103, 0.62093487,
-               0.71297658, 0.62145477, 0.57067218, 0.40165835, 0.47473917,
-               0.6752577 , 0.63680209, 0.58595151, 0.42684533, 0.34521923,
-               0.15387504, 0.19386345, 0.32172965, 0.35723812, 0.51579686,
-               0.42516394, 0.37951329, 0.13814274, 0.27323395, 0.51499156,
-               0.68497705, 0.79611717, 0.75279699, 0.83910701, 0.83822349,
-               0.77869171, 0.80439892, 0.65961564, 0.56881136, 0.40684661,
-               0.46353027, 0.48256952, 0.63312795, 0.4971397 , 0.50011884,
-               0.28525197, 0.31804274, 0.20644074, 0.12015627] Jy>, spectral_axis=<SpectralAxis [ 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>)>
+    <Spectrum1D(flux=[0.1813647873923075 ... 0.1201562712204726] Jy (shape=(49,), mean=0.49378 Jy); spectral_axis=<SpectralAxis [ 1.  2.  3. ... 47. 48. 49.] nm> (length=49))>
 
 In this case, the ``spec1_bsmooth2`` result should be equivalent to the ``spec1_bsmooth`` in
 the section above (assuming the flux data of the input ``spec`` is the same). Note that,
@@ -131,19 +107,7 @@ method applys the median filter across the flux.
     >>> spec1 = Spectrum1D(spectral_axis=np.arange(1, 50) * u.nm,
     ...                    flux=np.random.default_rng(12345).random(49) * u.Jy)
     >>> median_smooth(spec1, width=3) # doctest: +FLOAT_CMP
-    <Spectrum1D(flux=<Quantity [0.22733602, 0.31675834, 0.67625467, 0.67625467, 0.39110955,
-               0.39110955, 0.33281393, 0.59830875, 0.67275604, 0.67275604,
-               0.94180287, 0.66723745, 0.66723745, 0.44183967, 0.44183967,
-               0.6974535 , 0.6974535 , 0.6974535 , 0.32647286, 0.22013496,
-               0.1598956 , 0.1598956 , 0.34010018, 0.34010018, 0.46519315,
-               0.26642103, 0.19329439, 0.12946908, 0.12946908, 0.59856801,
-               0.60162124, 0.8547419 , 0.72478136, 0.86055132, 0.86055132,
-               0.86055132, 0.9293378 , 0.54618601, 0.49498794, 0.45177871,
-               0.45177871, 0.45177871, 0.66503892, 0.33089093, 0.33982834,
-               0.2588534 , 0.33982834, 0.2588534 , 0.00502233] Jy>, spectral_axis=<SpectralAxis [ 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>)>
+    <Spectrum1D(flux=[0.22733602246716966 ... 0.005022333717131788] Jy (shape=(49,), mean=0.48620 Jy); spectral_axis=<SpectralAxis [ 1.  2.  3. ... 47. 48. 49.] nm> (length=49))>
 
 Resampling
 ----------
@@ -374,14 +338,14 @@ the ``spectral_axis``. Therefore one can use a construct like this:
     >>> spectrum  # doctest: +FLOAT_CMP
     <Spectrum1D(flux=<Quantity [ 30.47170798, 103.99841062,  75.04511958,  94.05647164,
                195.10351887, 130.21795069,  12.78404032,  31.62425923,
-                 1.68011575,  85.30439276] Jy>, spectral_axis=<SpectralAxis [0., 1., 2., 3., 4., 5., 6., 7., 8., 9.] um>)>
+                 1.68011575,  85.30439276] Jy> (shape=(10,), mean=76.02860 Jy); spectral_axis=<SpectralAxis [0. 1. 2. ... 7. 8. 9.] um> (length=10))>
 
     >>> shift = 12300 * u.AA
     >>> new_spec = Spectrum1D(spectral_axis=spectrum.spectral_axis + shift, flux=spectrum.flux)
     >>> new_spec  # doctest: +FLOAT_CMP
     <Spectrum1D(flux=<Quantity [ 30.47170798, 103.99841062,  75.04511958,  94.05647164,
                195.10351887, 130.21795069,  12.78404032,  31.62425923,
-                 1.68011575,  85.30439276] Jy>, spectral_axis=<SpectralAxis [ 1.23,  2.23,  3.23,  4.23,  5.23,  6.23,  7.23,  8.23,  9.23, 10.23] um>)>
+                 1.68011575,  85.30439276] Jy> (shape=(10,), mean=76.02860 Jy); spectral_axis=<SpectralAxis [ 1.23  2.23  3.23 ...  8.23  9.23 10.23] um> (length=10))>
 
 Replacing a region
 ------------------
@@ -403,7 +367,7 @@ with the spline knots:
     >>> spline_knots = [3.5, 4.7, 6.8, 7.1] * u.AA
     >>> result = model_replace(input_spectrum, None, model=spline_knots)
     >>> result
-    <Spectrum1D(flux=<Quantity [ 2.,  4.,  6.,  8., 10., 12., 14., 16., 18., 20.] mJy>, spectral_axis=<SpectralAxis [ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.] Angstrom>)>
+    <Spectrum1D(flux=<Quantity [ 2.,  4.,  6.,  8., 10., 12., 14., 16., 18., 20.] mJy> (shape=(10,), mean=11.00000 mJy); spectral_axis=<SpectralAxis [ 1.  2.  3. ...  8.  9. 10.] Angstrom> (length=10))>
 
 The default behavior is to keep the data outside the replaced region unchanged.
 Alternatively, the spectrum outside the replaced region can be filled with zeros:
@@ -413,8 +377,7 @@ Alternatively, the spectrum outside the replaced region can be filled with zeros
     >>> spline_knots = [3.5, 4.7, 6.8, 7.1] * u.AA
     >>> result = model_replace(input_spectrum, None, model=spline_knots, extrapolation_treatment='zero_fill')
     >>> result
-    <Spectrum1D(flux=<Quantity [ 0.,  0.,  0.,  8., 10., 12., 14.,  0.,  0.,  0.] mJy>,
-        spectral_axis=<SpectralAxis [ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.] Angstrom>)>
+    <Spectrum1D(flux=<Quantity [ 0.,  0.,  0.,  8., 10., 12., 14.,  0.,  0.,  0.] mJy> (shape=(10,), mean=4.40000 mJy); spectral_axis=<SpectralAxis [ 1.  2.  3. ...  8.  9. 10.] Angstrom> (length=10))>
 
 One can define the spline knots by providing an instance of `~specutils.SpectralRegion`,
 and the number of knots to be evenly spread along the region:
@@ -425,8 +388,7 @@ and the number of knots to be evenly spread along the region:
     >>> region = SpectralRegion(3.5*u.AA, 7.1*u.AA)
     >>> result = model_replace(input_spectrum, region, model=4)
     >>> result
-    <Spectrum1D(flux=<Quantity [ 2.,  4.,  6.,  8., 10., 12., 14., 16., 18., 20.] mJy>,
-        spectral_axis=<SpectralAxis [ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.] Angstrom>)>
+    <Spectrum1D(flux=<Quantity [ 2.,  4.,  6.,  8., 10., 12., 14., 16., 18., 20.] mJy> (shape=(10,), mean=11.00000 mJy); spectral_axis=<SpectralAxis [ 1.  2.  3. ...  8.  9. 10.] Angstrom> (length=10))>
 
 A model fitted over the region can also be used to replace the spectrum flux values:
 
@@ -442,7 +404,7 @@ A model fitted over the region can also be used to replace the spectrum flux val
     >>> result = model_replace(input_spectrum, region, model=fitted_model)
     >>> result  # doctest: +FLOAT_CMP
     <Spectrum1D(flux=<Quantity [1.        , 1.1       , 0.9       , 4.40801804, 9.58271877,
-               5.61238054, 0.88556096, 1.        , 1.2       , 1.1       ] mJy>, spectral_axis=<SpectralAxis [ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.] Angstrom>)>
+               5.61238054, 0.88556096, 1.        , 1.2       , 1.1       ] mJy> (shape=(10,), mean=2.67887 mJy); spectral_axis=<SpectralAxis [ 1.  2.  3. ...  8.  9. 10.] Angstrom> (length=10))>
 
 Reference/API
 -------------

diff --git a/docs/spectral_cube.rst b/docs/spectral_cube.rst
@@ -49,31 +49,11 @@ Print the contents of 3 spectral axis points in a 3x3 spaxel array:
 .. code-block:: python
 
     >>> sc[30:33,30:33,2000:2003] # doctest: +REMOTE_DATA
-    <Spectrum1D(flux=<Quantity [[[0.48920232, 0.4987253 , 0.5098349 ],
-                [0.493365  , 0.4964812 , 0.5223962 ],
-                [0.49446177, 0.4909543 , 0.5304416 ]],
-    <BLANKLINE>
-               [[0.53114057, 0.53538376, 0.5467784 ],
-                [0.53761804, 0.533159  , 0.554437  ],
-                [0.5470889 , 0.54905874, 0.57109433]],
-    <BLANKLINE>
-               [[0.5599331 , 0.554316  , 0.5618426 ],
-                [0.5763055 , 0.5668046 , 0.5774939 ],
-                [0.59571505, 0.60118765, 0.59942234]]] 1e-17 erg / (Angstrom s spaxel cm2)>, spectral_axis=<SpectralAxis
+    <Spectrum1D(flux=[[[0.4892023205757141 ... 0.5994223356246948]]] 1e-17 erg / (Angstrom s spaxel cm2) (shape=(3, 3, 3), mean=0.54165 1e-17 erg / (Angstrom s spaxel cm2)); spectral_axis=<SpectralAxis
        (observer to target:
           radial_velocity=0.0 km / s
           redshift=0.0)
-      [5.73984286e-07, 5.74116466e-07, 5.74248676e-07] m>, uncertainty=InverseVariance([[[4324.235 , 4326.87  , 4268.985 ],
-                      [5128.3867, 5142.5005, 4998.457 ],
-                      [4529.9097, 4545.8345, 4255.305 ]],
-    <BLANKLINE>
-                     [[4786.163 , 4811.216 , 4735.3135],
-                      [4992.71  , 5082.1294, 4927.881 ],
-                      [4992.9683, 5046.971 , 4798.005 ]],
-    <BLANKLINE>
-                     [[4831.2236, 4887.096 , 4806.84  ],
-                      [3895.8677, 4027.9104, 3896.0195],
-                      [4521.258 , 4630.997 , 4503.0396]]]))>
+      [5.73984286e-07 5.74116466e-07 5.74248676e-07] m> (length=3); uncertainty=InverseVariance)>
 
 
 Spectral slab extraction
@@ -90,31 +70,11 @@ spectral regions from the cube.
     >>> ss.shape  # doctest: +REMOTE_DATA
     (74, 74, 3)
     >>> ss[30:33,30:33,::] # doctest: +REMOTE_DATA
-    <Spectrum1D(flux=<Quantity [[[0.6103081 , 0.95697385, 1.0791174 ],
-                [0.5663384 , 0.8872061 , 1.0814004 ],
-                [0.520966  , 0.7819859 , 1.024845  ]],
-    <BLANKLINE>
-               [[0.64514536, 0.96376216, 1.083235  ],
-                [0.6112465 , 0.89025146, 1.058679  ],
-                [0.56316894, 0.77895504, 0.99165994]],
-    <BLANKLINE>
-               [[0.65954393, 0.9084677 , 0.9965009 ],
-                [0.6255246 , 0.84401435, 0.9930112 ],
-                [0.59066033, 0.762025  , 0.9361185 ]]] 1e-17 erg / (Angstrom s spaxel cm2)>, spectral_axis=<SpectralAxis
+    <Spectrum1D(flux=[[[0.6103081107139587 ... 0.936118483543396]]] 1e-17 erg / (Angstrom s spaxel cm2) (shape=(3, 3, 3), mean=0.83004 1e-17 erg / (Angstrom s spaxel cm2)); spectral_axis=<SpectralAxis
        (observer to target:
           radial_velocity=0.0 km / s
           redshift=0.0)
-      [5.00034537e-07, 5.00149688e-07, 5.00264865e-07] m>, uncertainty=InverseVariance([[[3449.242 , 2389.292 , 2225.105 ],
-                      [4098.7485, 2965.88  , 2632.497 ],
-                      [3589.92  , 2902.7622, 2292.3823]],
-    <BLANKLINE>
-                     [[3563.3342, 2586.58  , 2416.039 ],
-                      [4090.8855, 3179.1702, 2851.823 ],
-                      [4158.919 , 3457.0115, 2841.1965]],
-    <BLANKLINE>
-                     [[3684.6013, 3056.2   , 2880.6592],
-                      [3221.7888, 2801.3518, 2525.541 ],
-                      [3936.68  , 3461.534 , 3047.6135]]]))>
+      [5.00034537e-07 5.00149688e-07 5.00264865e-07] m> (length=3); uncertainty=InverseVariance)>
 
 
 Spectral Bounding Region

diff --git a/docs/spectrum1d.rst b/docs/spectrum1d.rst
@@ -64,7 +64,7 @@ a loader.
     >>> import urllib
     >>> specs = urllib.request.urlopen('https://data.sdss.org/sas/dr14/sdss/spectro/redux/26/spectra/0751/spec-0751-52251-0160.fits') # doctest: +REMOTE_DATA
     >>> Spectrum1D.read(specs, format="SDSS-III/IV spec") # doctest: +REMOTE_DATA
-    <Spectrum1D(flux=<Quantity [30.596626,...]...>
+    <Spectrum1D(flux=[30.59662628173828 ... 51.70271682739258] 1e-17 erg / (Angstrom s cm2) (shape=(3841,), mean=51.88042 1e-17 erg / (Angstrom s cm2)); spectral_axis=<SpectralAxis [3799.2686 3800.1426 3801.0188 ... 9193.905  9196.0205 9198.141 ] Angstrom> (length=3841); uncertainty=InverseVariance)>
 
 Note that the same spectrum could be more conveniently downloaded via
 astroquery, if the user has that package installed:
@@ -74,7 +74,7 @@ astroquery, if the user has that package installed:
      >>> from astroquery.sdss import SDSS  # doctest: +REMOTE_DATA
      >>> specs = SDSS.get_spectra(plate=751, mjd=52251, fiberID=160, data_release=14)  # doctest: +REMOTE_DATA
      >>> Spectrum1D.read(specs[0], format="SDSS-III/IV spec")  # doctest: +REMOTE_DATA
-     <Spectrum1D(flux=<Quantity [30.596626,...]...>
+     <Spectrum1D(flux=[30.59662628173828 ... 51.70271682739258] 1e-17 erg / (Angstrom s cm2) (shape=(3841,), mean=51.88042 1e-17 erg / (Angstrom s cm2)); spectral_axis=<SpectralAxis [3799.2686 3800.1426 3801.0188 ... 9193.905  9196.0205 9198.141 ] Angstrom> (length=3841); uncertainty=InverseVariance)>
 
 
 List of Loaders
@@ -326,69 +326,11 @@ value will apply to the lower bound input.
     >>> lower = [SpectralCoord(4.9, unit=u.um), SkyCoord(ra=205, dec=26, unit=u.deg)]
     >>> upper = [SpectralCoord(4.9, unit=u.um), SkyCoord(ra=205.5, dec=27.5, unit=u.deg)]
     >>> spec.crop(lower, upper)  # doctest: +IGNORE_WARNINGS +FLOAT_CMP
-    <Spectrum1D(flux=<Quantity [[[0.70861236],
-                [0.5663815 ],
-                [0.0606386 ],
-                [0.13811995],
-                [0.8974065 ]],
-    <BLANKLINE>
-               [[0.97618597],
-                [0.870499  ],
-                 [0.01522275],
-                 [0.59180312],
-                 [0.29160346]],
-    <BLANKLINE>
-               [[0.13274479],
-                [0.99381789],
-                [0.767089  ],
-                [0.73765335],
-                [0.36185756]],
-    <BLANKLINE>
-               [[0.09635759],
-                [0.38060021],
-                [0.63263223],
-                [0.60785285],
-                [0.4914904 ]],
-    <BLANKLINE>
-               [[0.83173506],
-                [0.1679683 ],
-                [0.39415721],
-                [0.25540459],
-                [0.39779061]],
-    <BLANKLINE>
-               [[0.27625437],
-                [0.90381118],
-                [0.65453332],
-                [0.64141404],
-                [0.36941242]],
-    <BLANKLINE>
-               [[0.90620901],
-                [0.41437874],
-                [0.71279457],
-                [0.41105785],
-                [0.7459662 ]],
-    <BLANKLINE>
-               [[0.57160214],
-                [0.45227951],
-                [0.29112881],
-                [0.44654224],
-                [0.65356417]],
-    <BLANKLINE>
-               [[0.60171961],
-                [0.46916617],
-                [0.3919754 ],
-                [0.01304226],
-                [0.32085301]],
-    <BLANKLINE>
-               [[0.00470581],
-                [0.54743546],
-                [0.24740782],
-                [0.77903598],
-                [0.63457146]]] Jy>, spectral_axis=<SpectralAxis
-       (observer to target:
-          radial_velocity=0.0 km / s
-          redshift=0.0)
-      [4.90049987e-06] m>)>
+    <Spectrum1D(flux=[[[0.708612359963129 ... 0.6345714580773677]]] Jy (shape=(10, 5, 1), mean=0.49653 Jy); spectral_axis=<SpectralAxis
+        (observer to target:
+           radial_velocity=0.0 km / s
+           redshift=0.0)
+      [4.90049987e-06] m> (length=1))>
 
 Collapsing
 ----------
@@ -415,11 +357,11 @@ spectral axis, or 'spatial', which will collapse along all non-spectral axes.
 
     >>> spec.mean(axis='spatial')  # doctest: +FLOAT_CMP
     <Spectrum1D(flux=<Quantity [0.37273938, 0.53843905, 0.61351648, 0.57311623, 0.44339915,
-               0.66084728, 0.45881921, 0.38715911, 0.39967185, 0.53257671] Jy>, spectral_axis=<SpectralAxis
+               0.66084728, 0.45881921, 0.38715911, 0.39967185, 0.53257671] Jy> (shape=(10,), mean=0.49803 Jy); spectral_axis=<SpectralAxis
        (observer to target:
           radial_velocity=0.0 km / s
           redshift=0.0)
-      [5000., 5001., 5002., 5003., 5004., 5005., 5006., 5007., 5008., 5009.] Angstrom>)>
+      [5000. 5001. 5002. ... 5007. 5008. 5009.] Angstrom> (length=10))>
 
 Note that in this case, the result of the collapse operation is a
 `~specutils.Spectrum1D` rather than an `astropy.units.Quantity`, because the