Change Spectrum1D to Spectrum

docs/create_products.rst

@@ -5,14 +5,14 @@ Creating Jdaviz-readable Products
 
 Spectroscopic data products (1D, 2D, and 3D) can be loaded
 in the different ``jdaviz`` configurations using
-essentially two methods, i.e., loading :class:`~specutils.Spectrum1D` objects or
+essentially two methods, i.e., loading :class:`~specutils.Spectrum` objects or
 from FITS files. Here, we list a few ways in which data can be packaged to be easily loaded
 into a ``jdaviz`` configuration.
 
 Data in a database
 ------------------
 
-If the data are stored in a database, we recommend storing a :class:`~specutils.Spectrum1D` object
+If the data are stored in a database, we recommend storing a :class:`~specutils.Spectrum` object
 per entry. This would allow the user to query the data and visualize it in
 ``jdaviz`` with few lines of code; also see :ref:`create_product_spectrum1d_obj`.
 
@@ -34,8 +34,8 @@ Available loaders can be listed with the following commands:
 
 .. code-block:: python
 
-    from specutils import Spectrum1D
-    Spectrum1D.read.list_formats()
+    from specutils import Spectrum
+    Spectrum.read.list_formats()
 
 The majority are fairly specific to missions and instruments. Four formats
 are more generic and adaptable: ``ASCII``, ``ECSV``, ``tabular-fits``, and
@@ -55,14 +55,14 @@ is available. We are working on the necessary documentation to prompt
 
 .. _create_product_spectrum1d_obj:
 
-Providing scripts to load the data as Spectrum1D objects
+Providing scripts to load the data as Spectrum objects
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 If none of the above is an acceptable option, the user can create the data
 products with their custom format and provide scripts or Jupyter Notebooks
-that show how to read the products and create :class:`~specutils.Spectrum1D` objects
+that show how to read the products and create :class:`~specutils.Spectrum` objects
 that can be read into ``jdaviz``. More about
-how to create :class:`~specutils.Spectrum1D` objects for the 1D, 2D, and 3D cases can be
+how to create :class:`~specutils.Spectrum` objects for the 1D, 2D, and 3D cases can be
 found in the corresponding "Importing data" sections of the various configurations:
 
 * :ref:`specviz-import-data`

docs/cubeviz/export_data.rst

@@ -74,12 +74,12 @@ with the name of the data you want to extract):
 
     mydata = cubeviz.get_data(data_label="data_name")
 
-The data is returned as a 3D `specutils.Spectrum1D` object.
+The data is returned as a 3D `specutils.Spectrum` object.
 
-To write out a `specutils.Spectrum1D` cube from Cubeviz
+To write out a `specutils.Spectrum` cube from Cubeviz
 (e.g., a fitted cube from :ref:`model-fitting`),
 where the mask (if available) is as defined in
-`Spectrum1D masks <https://specutils.readthedocs.io/en/latest/spectrum1d.html#including-masks>`_:
+`Spectrum masks <https://specutils.readthedocs.io/en/latest/spectrum1d.html#including-masks>`_:
 
 .. code-block:: python
 

docs/cubeviz/import_data.rst

@@ -5,24 +5,24 @@ Importing Data into Cubeviz
 ***************************
 
 By design, Cubeviz only supports data that can be parsed as
-:class:`~specutils.Spectrum1D` objects. Despite the name, :class:`~specutils.Spectrum1D`
+:class:`~specutils.Spectrum` objects. Despite the name, :class:`~specutils.Spectrum`
 now supports 3D cubes and allows the Python-level interface and parsing tools to
 be defined in ``specutils`` instead of being duplicated in Jdaviz.
-:class:`~specutils.Spectrum1D` objects are very flexible in their capabilities, however,
+:class:`~specutils.Spectrum` objects are very flexible in their capabilities, however,
 and hence should address most astronomical spectrum use cases.
 If you are creating your own data products, please read the page :ref:`create_products`.
 
 Cubeviz will automatically parse the data into the multiple viewers as described in
 :ref:`cubeviz-display-cubes`. For the best experience, data loaded into Cubeviz should contain valid WCS
-keywords. For more information on how :class:`~specutils.Spectrum1D`
-uses WCS, please go to the `Spectrum1D defining WCS section <https://specutils.readthedocs.io/en/stable/spectrum1d.html#defining-wcs>`_.
+keywords. For more information on how :class:`~specutils.Spectrum`
+uses WCS, please go to the `Spectrum defining WCS section <https://specutils.readthedocs.io/en/stable/spectrum1d.html#defining-wcs>`_.
 To check if your FITS file contains valid WCS keywords, please use
 `Astropy WCS validate <astropy.wcs.validate>`.
 For an example on loading a cube with valid WCS keywords, please see the :ref:`cubeviz-import-api`
 section below.
 
 Loading data without WCS is also possible as long as they are compatible
-with :class:`~specutils.Spectrum1D`. However, not all plugins will work with this data.
+with :class:`~specutils.Spectrum`. However, not all plugins will work with this data.
 
 .. _cubeviz-viewers:
 
@@ -60,7 +60,7 @@ Importing data through the GUI
 Users may load data into the Cubeviz application
 by clicking the :guilabel:`Import Data` button at the top left of the application's
 user interface. This opens a dialogue with a prompt to select a file
-that can be parsed as a :class:`~specutils.Spectrum1D` object.
+that can be parsed as a :class:`~specutils.Spectrum` object.
 
 After clicking :guilabel:`Import`, the data file will be parsed and loaded into the
 application. A notification will appear to confirm whether the data import
@@ -75,7 +75,7 @@ Importing data via the API
 Alternatively, users who work in a coding environment like a Jupyter
 notebook can access the Cubeviz helper class API. Using this API, users can
 load data into the application through code with the :py:meth:`~jdaviz.configs.specviz.helper.Specviz.load_data`
-method, which takes as input a :class:`~specutils.Spectrum1D` object.
+method, which takes as input a :class:`~specutils.Spectrum` object.
 
 FITS Files
 ----------
@@ -89,33 +89,33 @@ The example below loads a FITS file into Cubeviz:
     cubeviz.load_data("/path/to/data/file.fits")
     cubeviz.show()
 
-Spectrum1D (from file)
+Spectrum (from file)
 ----------------------
 
 For cases where the built-in parser is unable to understand your file format,
-you can try the `~specutils.Spectrum1D` parser directly and then pass the object to the
+you can try the `~specutils.Spectrum` parser directly and then pass the object to the
 :py:meth:`~jdaviz.core.helpers.ConfigHelper.load_data` method:
 
 .. code-block:: python
 
-    from specutils import Spectrum1D
+    from specutils import Spectrum
     from jdaviz import Cubeviz
-    spec3d = Spectrum1D.read("/path/to/data/file.fits")
+    spec3d = Spectrum.read("/path/to/data/file.fits")
     cubeviz = Cubeviz()
     cubeviz.load_data(spec3d, data_label='My Cube')
     cubeviz.show()
 
-Spectrum1D (from array)
+Spectrum (from array)
 -----------------------
 
-You can create your own :class:`~specutils.Spectrum1D` object by hand to load into Cubeviz:
+You can create your own :class:`~specutils.Spectrum` object by hand to load into Cubeviz:
 
 .. code-block:: python
 
     import numpy as np
     from astropy import units as u
     from astropy.wcs import WCS
-    from specutils import Spectrum1D
+    from specutils import Spectrum
     from jdaviz import Cubeviz
 
     flux = np.arange(16).reshape((2, 2, 4)) * u.Jy
@@ -125,7 +125,7 @@ You can create your own :class:`~specutils.Spectrum1D` object by hand to load in
                 "CRPIX1": 0, "CRPIX2": 0, "CRPIX3": 0}
     w = WCS(wcs_dict)
 
-    cube = Spectrum1D(flux=flux, wcs=w)
+    cube = Spectrum(flux=flux, wcs=w)
     cubeviz = Cubeviz()
     cubeviz.load_data(cube, data_label='My Cube')
     cubeviz.show()
@@ -143,7 +143,7 @@ object, you can load it into Cubeviz as follows:
     from jdaviz import Cubeviz
 
     # mydatamodel is a jwst.datamodels object
-    # Due to current schema in jwst.datamodels, you'll need to create your own WCS object before you create your Spectrum1D object
+    # Due to current schema in jwst.datamodels, you'll need to create your own WCS object before you create your Spectrum object
     wcs_dict = {"CTYPE1": mydatamodel.meta.wcsinfo.ctype3, "CTYPE2": mydatamodel.meta.wcsinfo.ctype2,
             "CTYPE3": mydatamodel.meta.wcsinfo.ctype1,
             "CRVAL1": mydatamodel.meta.wcsinfo.crval3, "CRVAL2": mydatamodel.meta.wcsinfo.crval2,
@@ -160,7 +160,7 @@ object, you can load it into Cubeviz as follows:
     data = np.swapaxes(data, 1, 2)
 
     # Create your spectrum1
-    spec3d = Spectrum1D(data, wcs=my_wcs)
+    spec3d = Spectrum(data, wcs=my_wcs)
     cubeviz = Cubeviz()
     cubeviz.load_data(spec3d, data_label='My Cube')
     cubeviz.show()

docs/cubeviz/plugins.rst

@@ -12,7 +12,7 @@ icon in the upper right corner of the Cubeviz application.
 
 The data analysis plugins are meant to aid quick-look analysis
 of both 3D and 1D spectroscopic data. In many cases, functions can be applied to
-`~specutils.Spectrum1D` objects, which include both 3D and 1D datasets.
+`~specutils.Spectrum` objects, which include both 3D and 1D datasets.
 Plugins that are specific to 1D spectra are described in
 more detail under :ref:`Specviz: Data Analysis Plugins <specviz-plugins>`.
 In many cases, these capabilities can be further applied on a per spaxel basis

docs/mosviz/plugins.rst

@@ -3,7 +3,7 @@ Data Analysis Plugins
 *********************
 
 The Mosviz data analysis plugins include operations on both
-2D images and Spectrum1D one dimensional datasets.
+2D images and Spectrum one dimensional datasets.
 Plugins that are specific to 1D spectra are described in
 more detail under Specviz:Data Analysis Plugins.  All plugins
 are accessed via the plugin icon in the upper right corner
@@ -120,5 +120,5 @@ The :guilabel:`Remove` button can be used to remove a slit once it has been appl
 
 In order to plot a slit onto the image viewer, we need WCS information from an image and slit position from a 2D spectrum.
 The slit position is calculated using the ``S_REGION`` header extension value, located in the
-`~specutils.Spectrum1D.meta` attribute of the :class:`~specutils.Spectrum1D` object
+`~specutils.Spectrum.meta` attribute of the :class:`~specutils.Spectrum` object
 that is active in the 2D spectrum viewer.

docs/specviz/export_data.rst

@@ -14,8 +14,8 @@ those data currently back into your Jupyter notebook:
 
     specviz.get_spectra()
 
-which yields a either a single `specutils.Spectrum1D` object or a dictionary of 
-`specutils.Spectrum1D` (if there are multiple displayed spectra) that you can
+which yields a either a single `specutils.Spectrum` object or a dictionary of 
+`specutils.Spectrum` (if there are multiple displayed spectra) that you can
 manipulate however you wish.  You can then load the modified spectrum back into
 the notebook via the API described in :ref:`specviz-import-api`.
 
@@ -35,15 +35,15 @@ To extract a spectrum with a spectral subset applied:
 
     specviz.get_data(spectral_subset='Subset 1')
 
-In this case, the returned `specutils.Spectrum1D` object will have a ``mask``
+In this case, the returned `specutils.Spectrum` object will have a ``mask``
 attribute, where ``True`` corresponds to the region outside the selected subset
 (i.e., the region that has been masked out). You could load back in a copy of the
 spectrum containing only your subset by running:
 
 .. code-block:: python
 
     spec = specviz.get_data(spectral_subset='Subset 1')
-    subset_spec = Spectrum1D(flux=spec.flux[~spec.mask],
+    subset_spec = Spectrum(flux=spec.flux[~spec.mask],
                              spectral_axis=spec.spectral_axis[~spec.mask])
     specviz.load_data(subset_spec)
 

docs/specviz/import_data.rst

@@ -4,17 +4,17 @@
 Importing Data Into Specviz
 ***************************
 
-By design, Specviz only supports data that can be parsed as :class:`~specutils.Spectrum1D` objects,
+By design, Specviz only supports data that can be parsed as :class:`~specutils.Spectrum` objects,
 as that allows the Python-level interface and parsing tools to be defined in ``specutils``
 instead of being duplicated in Jdaviz.
-:class:`~specutils.Spectrum1D` objects are very flexible in their capabilities, however,
+:class:`~specutils.Spectrum` objects are very flexible in their capabilities, however,
 and hence should address most astronomical spectrum use cases.
 If you are creating your own data products, please read the page :ref:`create_products`.
 
 .. seealso::
 
     `Reading from a File <https://specutils.readthedocs.io/en/stable/spectrum1d.html#reading-from-a-file>`_
-        Specutils documentation on loading data as :class:`~specutils.Spectrum1D` objects.
+        Specutils documentation on loading data as :class:`~specutils.Spectrum` objects.
 
 .. _specviz-import-commandline:
 
@@ -36,7 +36,7 @@ Importing data through the GUI
 You can load your data into the Specviz application
 by clicking the :guilabel:`Import Data` button at the top left of the application's
 user interface. This opens a dialogue where the user can select a file
-that can be parsed as a :class:`~specutils.Spectrum1D`.
+that can be parsed as a :class:`~specutils.Spectrum`.
 
 After clicking :guilabel:`Import`, the data file will be parsed and loaded into the
 application. A notification will appear to let users know if the data import
@@ -52,7 +52,7 @@ Alternatively, users who work in a coding environment like a Jupyter
 notebook can access the Specviz helper class API. Using this API, users can
 load data into the application through code with the
 :py:meth:`~jdaviz.configs.specviz.helper.Specviz.load_data`
-method, which takes as input a :class:`~specutils.Spectrum1D` object.
+method, which takes as input a :class:`~specutils.Spectrum` object.
 
 FITS Files
 ----------
@@ -61,13 +61,13 @@ The example below loads a FITS file into Specviz:
 
 .. code-block:: python
 
-    from specutils import Spectrum1D
-    spec1d = Spectrum1D.read("/path/to/data/file")
+    from specutils import Spectrum
+    spec1d = Spectrum.read("/path/to/data/file")
     specviz = Specviz()
     specviz.load_data(spec1d, data_label="my_spec")
     specviz.show()
 
-You can also pass the path to a file that `~specutils.Spectrum1D` understands directly to the
+You can also pass the path to a file that `~specutils.Spectrum` understands directly to the
 :py:meth:`~jdaviz.configs.specviz.helper.Specviz.load_data` method:
 
 .. code-block:: python
@@ -83,12 +83,12 @@ You can create your own array to load into Specviz:
 
     import numpy as np
     import astropy.units as u
-    from specutils import Spectrum1D
+    from specutils import Spectrum
     from jdaviz import Specviz
 
     flux = np.random.randn(200) * u.Jy
     wavelength = np.arange(5100, 5300) * u.AA
-    spec1d = Spectrum1D(spectral_axis=wavelength, flux=flux)
+    spec1d = Spectrum(spectral_axis=wavelength, flux=flux)
     specviz = Specviz()
     specviz.load_data(spec1d, data_label="my_spec")
     specviz.show()
@@ -101,15 +101,15 @@ object, you can load it into Specviz as follows:
 
 .. code-block:: python
 
-    from specutils import Spectrum1D
+    from specutils import Spectrum
     from jdaviz import Specviz
 
     # mydatamodel is a jwst.datamodels.MultiSpecModel object
     a = mydatamodel.spec[0]
     flux = a.spec_table['FLUX']
     wave = a.spec_table['WAVELENGTH']
 
-    spec1d = Spectrum1D(flux=flux, spectral_axis=wave)
+    spec1d = Spectrum(flux=flux, spectral_axis=wave)
     specviz = Specviz()
     specviz.load_data(spec1d, data_label="MultiSpecModel")
     specviz.show()
@@ -124,7 +124,7 @@ Importing a SpectrumList
 
 The :py:meth:`~jdaviz.configs.specviz.helper.Specviz.load_data` also accepts
 a `~specutils.SpectrumList` object, in which case it will both load the
-individual `~specutils.Spectrum1D` objects in the list and additionally attempt
+individual `~specutils.Spectrum` objects in the list and additionally attempt
 to stitch together the spectra into a single data object so that
 they can be manipulated and analyzed in the application as a single entity:
 

docs/specviz/plugins.rst

@@ -65,12 +65,12 @@ Markers
 Gaussian Smooth
 ===============
 
-Gaussian Smooth is performed on a Spectrum1D data object.
+Gaussian Smooth is performed on a Spectrum data object.
 The spectrum is convolved with a Gaussian function.
 The Gaussian standard deviation in pixels must be entered into the
 :guilabel:`Standard deviation` field in the plugin.
 
-A new Spectrum1D object is generated and is added to the spectrum
+A new Spectrum object is generated and is added to the spectrum
 viewer.
 It can be selected and shown in the viewer via the
 :guilabel:`Data` icon in the viewer toolbar.

docs/specviz2d/export_data.rst

@@ -9,7 +9,7 @@ Exporting Data From Specviz2D
 2D Spectra
 ==========
 
-Images in the 2D spectrum viewer can be exported as `specutils.Spectrum1D` objects into
+Images in the 2D spectrum viewer can be exported as `specutils.Spectrum` objects into
 the notebook (replace "2D data" with the label of the desired data):
 
 .. code-block:: python

docs/specviz2d/import_data.rst

@@ -4,17 +4,17 @@
 Importing Data Into Specviz2D
 *****************************
 
-By design, Specviz2D only supports data that can be parsed as :class:`~specutils.Spectrum1D` objects,
+By design, Specviz2D only supports data that can be parsed as :class:`~specutils.Spectrum` objects,
 as that allows the Python-level interface and parsing tools to be defined in ``specutils``
 instead of being duplicated in Jdaviz.
-:class:`~specutils.Spectrum1D` objects are very flexible in their capabilities, however,
+:class:`~specutils.Spectrum` objects are very flexible in their capabilities, however,
 and hence should address most astronomical spectrum use cases.
 If you are creating your own data products, please read the page :ref:`create_products`.
 
 .. seealso::
 
     `Reading from a File <https://specutils.readthedocs.io/en/stable/spectrum1d.html#reading-from-a-file>`_
-        Specutils documentation on loading data as :class:`~specutils.Spectrum1D` objects.
+        Specutils documentation on loading data as :class:`~specutils.Spectrum` objects.
 
 Specviz2D can either take both a 2D and 1D spectrum as input, or can automatically extract a 1D
 spectrum if only a 2D spectrum is provided.  To view the extraction parameters and override the
@@ -42,7 +42,7 @@ Importing data through the GUI
 You can load your data into the Specviz2D application
 by clicking the :guilabel:`Import Data` button at the top left of the application's
 user interface. This opens a dialogue where the user can select a file
-that can be parsed as a :class:`~specutils.Spectrum1D`.
+that can be parsed as a :class:`~specutils.Spectrum`.
 
 After clicking :guilabel:`Import`, the data file will be parsed and loaded into the
 application.
@@ -56,7 +56,7 @@ Alternatively, users who work in a coding environment like a Jupyter
 notebook can access the Specviz2D helper class API. Using this API, users can
 load data into the application through code with the
 :meth:`~jdaviz.configs.specviz2d.helper.Specviz2d.load_data`
-method, which takes as input a :class:`~specutils.Spectrum1D` object or filename for the
+method, which takes as input a :class:`~specutils.Spectrum` object or filename for the
 2D spectrum and (optionally) the 1D spectrum.
 
 By default, extension 1 of the 2D