Update Jdaviz for specutils 2 compatibility

Fix coords mouseover when SkyCoord is before SpectralCoord

More debugging

Fix slice with different spectral_axis_index, add specutils_format kwarg to cubeviz parser

Remove unused import

Remove ESA parser from commissioning [skip ci]

Generalizing coords_info to handle spectral axis first or last [ci skip]

Working on debugging aperture photometry

Need to pull meta from data not comp

Fix aperture photometry and wrong coord order in coords_info

Fix codestyle, delete defunct case in parser

Change Spectrum1D to Spectrum

Change class name in recent code additions

Get spectral extraction working again for cylinder case

Debugging wavelength dependent case

Make sure spectral extraction result is linked when added to data collection

Moved fix to correct place

Fix indents

Remove stray code from rebase

Update missed Spectrum1D

Debugging cubeviz parser

First start on fixing testt

Fix bad rebase in spectral extraction

Starting to work through test failures

Working through test failures

Debugging spectral extraction

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

@@ -65,12 +65,12 @@ 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`
-supports 3D cubes and allows the Python-level interface and parsing tools to
+:class:`~specutils.Spectrum` objects. :class:`~specutils.Spectrum` 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()
@@ -149,7 +149,7 @@ object, you can load it into Cubeviz as follows:
     mydatamodel = datamodels.open(file)
 
     # 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,
@@ -166,7 +166,7 @@ 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.
 
 .. code-block:: python