Utilize Jdaviz.open for the CLI notebook

jdaviz/__init__.py

@@ -19,6 +19,7 @@
 from jdaviz.configs.cubeviz import Cubeviz  # noqa: F401
 from jdaviz.configs.imviz import Imviz  # noqa: F401
 from jdaviz.utils import enable_hot_reloading  # noqa: F401
+from jdaviz.core.data_formats import open  # noqa: F401
 
 # Clean up namespace.
 del os

jdaviz/cli.py

@@ -14,10 +14,12 @@
 __all__ = ['main']
 
 JDAVIZ_DIR = pathlib.Path(__file__).parent.resolve()
+DEFAULT_VERBOSITY = 'warning'
+DEFAULT_HISTORY_VERBOSITY = 'info'
 
 
 def main(filepaths=None, layout='default', instrument=None, browser='default',
-         theme='light', verbosity='warning', history_verbosity='info',
+         theme='light', verbosity=DEFAULT_VERBOSITY, history_verbosity=DEFAULT_HISTORY_VERBOSITY,
          hotreload=False):
     """
     Start a Jdaviz application instance with data loaded from FILENAME.

jdaviz/configs/__init__.py

@@ -1,5 +1,6 @@
 from .cubeviz import *  # noqa
 from .specviz import *  # noqa
+from .specviz2d import *  # noqa
 from .default import *  # noqa
 from .mosviz import *  # noqa
 from .imviz import *  # noqa

jdaviz/core/data_formats.py

@@ -11,6 +11,8 @@
 from stdatamodels import asdf_in_fits
 
 from jdaviz.core.config import list_configurations
+from jdaviz import configs as jdaviz_configs
+from jdaviz.cli import DEFAULT_VERBOSITY, DEFAULT_HISTORY_VERBOSITY
 
 __all__ = [
     'guess_dimensionality',
@@ -156,6 +158,9 @@ def identify_helper(filename, ext=1):
     -------
     helper_name : str
         Name of the best-guess helper for ``filename``.
+
+    Fits HDUList : astropy.io.fits.HDUList
+        The HDUList of the file opened to identify the helper
     """
     supported_dtypes = [
         Spectrum1D,
@@ -167,10 +172,11 @@ def identify_helper(filename, ext=1):
     if filename.lower().endswith('asdf'):
         # ASDF files are only supported in jdaviz for
         # Roman WFI 2D images, so suggest imviz:
-        return 'imviz'
+        return ('imviz', None)
 
-    header = fits.getheader(filename, ext=ext)
-    data = fits.getdata(filename, ext=ext)
+    hdul = fits.open(filename)
+    data = hdul[ext]
+    header = data.header
     wcs = _get_wcs(filename, header)
     has_spectral_axis = 'spectral' in wcs.world_axis_object_classes
 
@@ -201,10 +207,10 @@ def identify_helper(filename, ext=1):
             # could be 2D spectrum or 2D image. break tie with WCS:
             if has_spectral_axis:
                 if n_axes > 1:
-                    return 'specviz2d'
-                return 'specviz'
+                    return ('specviz2d', hdul)
+                return ('specviz', hdul)
             elif not isinstance(data, fits.BinTableHDU):
-                return 'imviz'
+                return ('imviz', hdul)
 
     # Ensure specviz is chosen when ``data`` is a table or recarray
     # and there's a "known" spectral column name:
@@ -230,7 +236,7 @@ def identify_helper(filename, ext=1):
 
         # if at least one spectral column is found:
         if sum(found_spectral_columns):
-            return 'specviz'
+            return ('specviz', hdul)
 
     # If the data could be spectral:
     for cls in [Spectrum1D, SpectrumList]:
@@ -240,10 +246,10 @@ def identify_helper(filename, ext=1):
             # first catch known JWST spectrum types:
             if (n_axes == 3 and
                     recognized_spectrum_format.find('s3d') > -1):
-                return 'cubeviz'
+                return ('cubeviz', hdul)
             elif (n_axes == 2 and
                   recognized_spectrum_format.find('x1d') > -1):
-                return 'specviz'
+                return ('specviz', hdul)
 
             # we intentionally don't choose specviz2d for
             # data recognized as 's2d' as we did with the cases above,
@@ -253,11 +259,11 @@ def identify_helper(filename, ext=1):
             # Use WCS to break the tie below:
             elif n_axes == 2:
                 if has_spectral_axis:
-                    return 'specviz2d'
-                return 'imviz'
+                    return ('specviz2d', hdul)
+                return ('imviz', hdul)
 
             elif n_axes == 1:
-                return 'specviz'
+                return ('specviz', hdul)
 
     try:
         # try using the specutils registry:
@@ -269,6 +275,46 @@ def identify_helper(filename, ext=1):
 
     if n_axes == 2 and not has_spectral_axis:
         # at this point, non-spectral 2D data are likely images:
-        return 'imviz'
+        return ('imviz', hdul)
 
     raise ValueError(f"No helper could be auto-identified for {filename}.")
+
+
+def open(filename, show=True, **kwargs):
+    '''
+    Automatically detect the correct configuration based on a given file,
+    load the data, and display the configuration
+
+    Parameters
+    ----------
+    filename : str (path-like)
+        Name for a local data file.
+    show : bool
+        Determines whether to immediately show the application
+
+    Returns
+    -------
+    Jdaviz ConfigHelper : jdaviz.core.helpers.ConfigHelper
+        The autoidentified ConfigHelper for the given data
+    '''
+    # Identify the correct config
+    helper_str, hdul = identify_helper(filename)
+    viz_class = getattr(jdaviz_configs, helper_str.capitalize())
+
+    # Create config instance
+    verbosity = kwargs.pop('verbosity', DEFAULT_VERBOSITY)
+    history_verbosity = kwargs.pop('history_verbosity', DEFAULT_HISTORY_VERBOSITY)
+    viz_helper = viz_class(verbosity=verbosity, history_verbosity=history_verbosity)
+
+    # Load data
+    data = hdul if (hdul is not None) else filename
+    if helper_str == "specviz":
+        viz_helper.load_spectrum(data, **kwargs)
+    else:
+        viz_helper.load_data(data, **kwargs)
+
+    # Display app
+    if show:
+        viz_helper.show()
+
+    return viz_helper

jdaviz/core/tests/test_autoconfig.py

@@ -0,0 +1,33 @@
+# Tests automatic config detection against our example notebook data
+
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+import pytest
+from astroquery.mast import Observations
+
+from jdaviz import open as jdaviz_open
+from jdaviz.configs import Specviz2d, Cubeviz, Imviz  # , Specviz
+
+
+@pytest.mark.remote_data
+@pytest.mark.filterwarnings('ignore')
+@pytest.mark.parametrize('uris', (
+    # ("mast:JWST/product/jw02732-o004_t004_miri_ch1-shortmediumlong_x1d.fits", Specviz),
+    # Specviz check disabled due to https://github.com/spacetelescope/jdaviz/issues/2229
+    ("mast:JWST/product/jw01538-o160_s00004_nirspec_f170lp-g235h-s1600a1-sub2048_s2d.fits", Specviz2d),  # noqa
+    ("mast:JWST/product/jw02727-o002_t062_nircam_clear-f090w_i2d.fits", Imviz),
+    ("mast:JWST/product/jw02732-o004_t004_miri_ch1-shortmediumlong_s3d.fits", Cubeviz))
+)
+def test_autoconfig(uris):
+    # Setup temporary directory
+    with TemporaryDirectory(ignore_cleanup_errors=True) as tempdir:
+        uri = uris[0]
+        helper_class = uris[1]
+        download_path = str(Path(tempdir) / Path(uri).name)
+        Observations.download_file(uri, local_path=download_path)
+
+        viz_helper = jdaviz_open(download_path, show=False)
+
+        assert type(viz_helper) == helper_class
+        assert len(viz_helper.app.data_collection) > 0

jdaviz/jdaviz_cli.ipynb

@@ -7,12 +7,9 @@
    "outputs": [],
    "source": [
     "# PREFIX\n",
-    "from jdaviz import CONFIG\n",
+    "from jdaviz import open as jdaviz_open\n",
     "\n",
-    "viz = CONFIG(verbosity='JDAVIZ_VERBOSITY', history_verbosity='JDAVIZ_HISTORY_VERBOSITY')\n",
-    "for data in DATA_LIST:\n",
-    "    viz.load_data(data) #ADDITIONAL_LOAD_DATA_ARGS\n",
-    "viz.show()"
+    "viz = jdaviz_open(verbosity='JDAVIZ_VERBOSITY', history_verbosity='JDAVIZ_HISTORY_VERBOSITY') #ADDITIONAL_LOAD_DATA_ARGS"
    ]
   }
  ],

notebooks/CubevizExample.ipynb

@@ -85,6 +85,13 @@
     "    cubeviz.load_data(f'{data_dir}/{fn}')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, the data and the configuration can be autodetected and loaded simultaneously by calling `jdaviz.open(f'{data_dir}/{fn}')`"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -225,7 +232,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,

notebooks/ImvizExample.ipynb

@@ -152,6 +152,14 @@
     "imviz.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "99d4bdef",
+   "metadata": {},
+   "source": [
+    "Alternatively, the data and the configuration can be autodetected and loaded simultaneously by calling `jdaviz.open(f'{data_dir}/{fn}')`"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "3e78efeb",
@@ -720,7 +728,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,

notebooks/Specviz2dExample.ipynb

@@ -96,6 +96,14 @@
    "source": [
     "specviz2d.load_data(f'{data_dir}/{fn}')"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "795bc520",
+   "metadata": {},
+   "source": [
+    "Alternatively, the data and the configuration can be autodetected and loaded simultaneously by calling `jdaviz.open(f'{data_dir}/{fn}')`"
+   ]
   }
  ],
  "metadata": {
@@ -114,7 +122,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,

notebooks/SpecvizExample.ipynb

@@ -111,6 +111,13 @@
     "specviz.load_spectrum(f'{data_dir}/{fn}', \"myfile\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, the data and the configuration can be autodetected and loaded simultaneously by calling `jdaviz.open(f'{data_dir}/{fn}')`"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -293,7 +300,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,