Update spectrum class name from Spectrum1D to Spectrum

CHANGES.rst

@@ -7,14 +7,16 @@ New Features
 - Spectral axis can now be any axis, rather than being forced to be last. See docs
   for more details. [#1033]
 
-- Spectrum1D now properly handles GWCS input for wcs attribute. [#1074]
+- Spectrum now properly handles GWCS input for wcs attribute. [#1074]
 
 - JWST reader no longer transposes the input data cube for 3D data and retains
   full GWCS information (including spatial). [#1074]
 
 Other Changes and Additions
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
+- Spectrum1D renamed to Spectrum. [#1126]
+
 1.13.0 (2024-02-19)
 -------------------
 

docs/analysis.rst

@@ -8,7 +8,7 @@ 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
 sub-sections below - a gaussian-profile line with flux of 5 GHz Jy.  See
-:doc:`spectrum1d` for more on creating spectra:
+:doc:`spectrum` for more on creating spectra:
 
 .. plot::
     :include-source: true
@@ -18,14 +18,14 @@ sub-sections below - a gaussian-profile line with flux of 5 GHz Jy.  See
     >>> from astropy import units as u
     >>> from astropy.nddata import StdDevUncertainty
     >>> from astropy.modeling import models
-    >>> from specutils import Spectrum1D, SpectralRegion
+    >>> from specutils import Spectrum, 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)
+    >>> noisy_gaussian = Spectrum(spectral_axis=spectral_axis, flux=flux, uncertainty=uncertainty)
     >>> import matplotlib.pyplot as plt #doctest:+SKIP
     >>> plt.step(noisy_gaussian.spectral_axis, noisy_gaussian.flux) #doctest:+SKIP
 
@@ -48,7 +48,7 @@ spectrum:
 
 
 A second method to calculate SNR does not require the uncertainty defined
-on the `~specutils.Spectrum1D` object. This computes the signal to noise
+on the `~specutils.Spectrum` object. This computes the signal to noise
 ratio DER_SNR following the definition set forth by the Spectral
 Container Working Group of ST-ECF, MAST and CADC. This algorithm is described at
 https://esahubble.org/static/archives/stecfnewsletters/pdf/hst_stecf_0042.pdf
@@ -257,15 +257,15 @@ An example of how to do template matching with an unknown redshift is:
    >>> 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'))
+   >>> observed_spectrum = Spectrum(spectral_axis=spec_axis*(1+observed_redshift), flux=np.random.randn(50) * u.Jy, uncertainty=StdDevUncertainty(np.random.sample(50), unit='Jy'))
+   >>> spectral_template = Spectrum(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) # doctest:+FLOAT_CMP
 
 
 Dust extinction
 ---------------
 
-Dust extinction can be applied to Spectrum1D instances via their internal arrays, using
+Dust extinction can be applied to Spectrum instances via their internal arrays, using
 the ``dust_extinction`` package (http://dust-extinction.readthedocs.io/en/latest)
 
 Below is an example of how to apply extinction.
@@ -277,14 +277,14 @@ Below is an example of how to apply extinction.
 
     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)
+    spec = Spectrum(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)
+    spec_ext = Spectrum(spectral_axis=wave, flux=flux_ext)
 
 
 Template Cross-correlation
@@ -311,8 +311,8 @@ value set.
     >>> 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)
+    >>> ospec = Spectrum(spectral_axis=spec_axis, flux=flux1, uncertainty=uncertainty, velocity_convention='optical', rest_value=rest_value)
+    >>> tspec = Spectrum(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

docs/arithmetic.rst

@@ -20,16 +20,16 @@ Arithmetic support includes addition, subtract, multiplication, and division.
 
 .. code-block:: python
 
-    >>> from specutils import Spectrum1D
+    >>> from specutils import Spectrum
     >>> import astropy.units as u
     >>> import numpy as np
 
     >>> rng = np.random.default_rng(12345)
-    >>> spec1 = Spectrum1D(spectral_axis=np.arange(1, 50) * u.nm, flux=rng.random(49)*u.Jy)
-    >>> spec2 = Spectrum1D(spectral_axis=np.arange(1, 50) * u.nm, flux=rng.random(49)*u.Jy)
+    >>> spec1 = Spectrum(spectral_axis=np.arange(1, 50) * u.nm, flux=rng.random(49)*u.Jy)
+    >>> spec2 = Spectrum(spectral_axis=np.arange(1, 50) * u.nm, flux=rng.random(49)*u.Jy)
     >>> spec3 = spec1 + spec2
     >>> spec3  # doctest: +FLOAT_CMP
-    <Spectrum1D(flux=[0.8559405665668484 ... 0.9711264429515736] Jy (shape=(49,), mean=0.91592 Jy); spectral_axis=<SpectralAxis
+    <Spectrum(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)
@@ -47,10 +47,10 @@ Arithmetic operations also support the propagation of unceratinty information.
 
     >>> rng = np.random.default_rng(12345)
     >>> wave = np.arange(10) * u.nm
-    >>> spec1 = Spectrum1D(spectral_axis=wave,
+    >>> spec1 = Spectrum(spectral_axis=wave,
     ...                    flux=rng.random(10)*u.Jy,
     ...                    uncertainty=StdDevUncertainty(rng.random(10) * 0.1))
-    >>> spec2 = Spectrum1D(spectral_axis=wave,
+    >>> spec2 = Spectrum(spectral_axis=wave,
     ...                    flux=rng.random(10)*u.Jy,
     ...                    uncertainty=StdDevUncertainty(rng.random(10) * 0.1))
     >>> spec1.uncertainty

docs/custom_loading.rst

@@ -12,21 +12,21 @@ a separate python file and place the file in their ``~/.specutils`` directory.
 Loading from a FITS File
 ------------------------
 A spectra with a *Linear Wavelength Solution* can be read using the ``read``
-method of the :class:`~specutils.Spectrum1D` class to parse the file name and
+method of the :class:`~specutils.Spectrum` class to parse the file name and
 format
 
 
 .. code-block:: python
 
   import os
-  from specutils import Spectrum1D
+  from specutils import Spectrum
 
   file_path = os.path.join('path/to/folder', 'file_with_1d_wcs.fits')
 
-  spec = Spectrum1D.read(file_path, format='wcs1d-fits')
+  spec = Spectrum.read(file_path, format='wcs1d-fits')
 
 
-This will create a :class:`~specutils.Spectrum1D` object that you can manipulate later.
+This will create a :class:`~specutils.Spectrum` object that you can manipulate later.
 
 For instance, you could plot the spectrum.
 
@@ -51,7 +51,7 @@ Defining a custom loader consists of importing the
 `~specutils.io.registers.data_loader` decorator from specutils and attaching
 it to a function that knows how to parse the user's data.  The return object
 of this function must be an instance of one of the spectral classes
-(:class:`~specutils.Spectrum1D`, :class:`~specutils.SpectrumCollection`,
+(:class:`~specutils.Spectrum`, :class:`~specutils.SpectrumCollection`,
 :class:`~specutils.SpectrumList`).
 
 Optionally, the user may define an identifier function. This function acts to
@@ -69,11 +69,11 @@ ensure that the data file being loaded is compatible with the loader function.
     from astropy.wcs import WCS
 
     from specutils.io.registers import data_loader
-    from specutils import Spectrum1D
+    from specutils import Spectrum
 
 
     # Define an optional identifier. If made specific enough, this circumvents the
-    # need to add ``format="my-format"`` in the ``Spectrum1D.read`` call.
+    # need to add ``format="my-format"`` in the ``Spectrum.read`` call.
     def identify_generic_fits(origin, *args, **kwargs):
         return (isinstance(args[0], str) and
                 os.path.splitext(args[0].lower())[1] == '.fits')
@@ -92,7 +92,7 @@ ensure that the data file being loaded is compatible with the loader function.
             uncertainty = StdDevUncertainty(tab["err"])
             data = tab["flux"] * Unit("Jy")
 
-        return Spectrum1D(flux=data, wcs=wcs, uncertainty=uncertainty, meta=meta)
+        return Spectrum(flux=data, wcs=wcs, uncertainty=uncertainty, meta=meta)
 
 
 An ``extensions`` keyword can be provided. This allows for basic filename
@@ -109,17 +109,17 @@ with a particular extension.
     loaders = get_loaders_by_extension('fits')
 
 The returned list contains the format labels that can be fed into the ``format``
-keyword argument of the ``Spectrum1D.read`` method.
+keyword argument of the ``Spectrum.read`` method.
 
 After placing this python file in the user's ``~/.specutils`` directory, it
 can be utilized by referencing its name in the ``read`` method of the
-:class:`~specutils.Spectrum1D` class
+:class:`~specutils.Spectrum` class
 
 .. code-block:: python
 
-    from specutils import Spectrum1D
+    from specutils import Spectrum
 
-    spec = Spectrum1D.read("path/to/data", format="my-format")
+    spec = Spectrum.read("path/to/data", format="my-format")
 
 .. _multiple_spectra:
 
@@ -134,11 +134,11 @@ custom JWST data loader as an example:
 .. literalinclude:: ../specutils/io/default_loaders/jwst_reader.py
     :language: python
 
-Note that by default, any loader that uses ``dtype=Spectrum1D`` will also
+Note that by default, any loader that uses ``dtype=Spectrum`` will also
 automatically add a reader for `~specutils.SpectrumList`. This enables user
 code to call `specutils.SpectrumList.read <astropy.nddata.NDIOMixin.read>` in
 all cases if it can't make assumptions about whether a loader returns one or
-many `~specutils.Spectrum1D` objects. This method is available since
+many `~specutils.Spectrum` objects. This method is available since
 `~specutils.SpectrumList` makes use of the Astropy IO registry (see
 `astropy.io.registry.read`).
 
@@ -171,11 +171,11 @@ This again will be done in a separate python file and placed in the user's
 
 The custom writer can be used by passing the name of the custom writer to the
 ``format`` argument of the ``write`` method on the
-:class:`~specutils.Spectrum1D`.
+:class:`~specutils.Spectrum`.
 
 .. code-block:: python
 
-    spec = Spectrum1D(flux=np.random.sample(100) * u.Jy,
+    spec = Spectrum(flux=np.random.sample(100) * u.Jy,
                       spectral_axis=np.arange(100) * u.AA)
 
     spec.write("my_output.fits", format="fits-writer")