DOC: Fix ModelContainer ref

and clean up devel_io_design formatting

Author: pllim

docs/jwst/badpix_selfcal/description.rst

@@ -19,7 +19,7 @@ in the :ref:`calwebb_spec2 <calwebb_spec2>` pipeline.
 Input details
 -------------
 The input data must be in the form of a `~jwst.datamodels.IFUImageModel` or
-a `~jwst.datamodels.ModelContainer` containing exactly one
+a `~jwst.datamodels.container.ModelContainer` containing exactly one
 science exposure and any number of additional exposures.
 A fits or association file 
 that can be read into one of these data models is also acceptable.

docs/jwst/outlier_detection/outlier_detection_ifu.rst

@@ -5,7 +5,7 @@ Integral Field Unit (IFU) Data
 
 This module serves as the interface for applying ``outlier_detection`` to IFU
 observations, like those taken with NIRSpec and MIRI. A :ref:`Stage 3 association <asn-level3-techspecs>`,
-which is loaded into a :py:class:`~jwst.datamodels.ModelContainer` object,
+which is loaded into a :py:class:`~jwst.datamodels.container.ModelContainer` object,
 serves as the basic format for all processing performed by this step.
 
 After launch it was discovered that the bad pixels on the MIRI detectors vary with time.

docs/jwst/outlier_detection/outlier_detection_spec.rst

@@ -8,8 +8,8 @@ spectroscopic observations. The algorithm is very similar to the
 :ref:`imaging algorithm <outlier-detection-imaging>`, and much of the same code is used.
 Please refer to those docs for more information.
 A :ref:`Stage 3 association <asn-level3-techspecs>`,
-which is loaded into a :py:class:`~jwst.datamodels.ModelContainer` object,
-serves as the input and output to this step, and the :py:class:`~jwst.datamodels.ModelContainer`
+which is loaded into a :py:class:`~jwst.datamodels.container.ModelContainer` object,
+serves as the input and output to this step, and the :py:class:`~jwst.datamodels.container.ModelContainer`
 is converted into a :py:class:`~jwst.datamodels.library.ModelLibrary` object to allow sharing code
 with the imaging mode.
 

docs/jwst/skymatch/description.rst

@@ -95,9 +95,9 @@ step.
 
 Identification of images that belong to the same "exposure" and therefore
 can be grouped together is based on several attributes described in
-`jwst.datamodels.ModelContainer`. This grouping is performed automatically
+`jwst.datamodels.container.ModelContainer`. This grouping is performed automatically
 in the ``skymatch`` step using the
-`jwst.datamodels.ModelContainer.models_grouped` property or
+`jwst.datamodels.container.ModelContainer.models_grouped` property or
 :py:meth:`jwst.datamodels.library.ModelLibrary.group_indices`.
 
 However, when background across different detectors in a single "exposure"
@@ -107,7 +107,7 @@ it may be desirable to match image backgrounds independently. This can be
 achieved either by setting the ``image_model.meta.group_id`` attribute to a
 unique string or integer value for each image, or by adding the ``group_id``
 attribute to the ``members`` of the input ASN table - see
-`~jwst.datamodels.ModelContainer` for more details.
+`~jwst.datamodels.container.ModelContainer` for more details.
 
 .. note::
     Group ID (``group_id``) is used by both ``tweakreg`` and ``skymatch`` steps

docs/jwst/stack_refs/description.rst

@@ -32,7 +32,7 @@ multiple integrations within each exposure.
 It is assumed that the ``stack_refs`` step will be called from the
 :ref:`calwebb_coron3 <calwebb_coron3>` pipeline, which is given an ASN file as input,
 specifying one or more PSF target exposures.
-The actual input passed to the ``stack_refs`` step will be a `~jwst.datamodels.ModelContainer`
+The actual input passed to the ``stack_refs`` step will be a `~jwst.datamodels.container.ModelContainer`
 created by the :ref:`calwebb_coron3 <calwebb_coron3>` pipeline, containing a
 `~jwst.datamodels.CubeModel` data model for each PSF "_calints" exposure listed in the
 ASN file. See :ref:`calwebb_coron3 <calwebb_coron3>` for more details on the contents of

docs/jwst/stpipe/devel_io_design.rst

@@ -83,7 +83,7 @@ def bkg_for_container(input_data, tab_wavelength, tab_background, allow_mos=Fals
 
     Parameters
     ----------
-    input_data : JWST association or `~jwst.datamodels.ModelContainer`
+    input_data : JWST association or `~jwst.datamodels.container.ModelContainer`
         The input science data.
     tab_wavelength : 1-D ndarray
         The wavelength column read from the 1-D background table.
@@ -99,7 +99,7 @@ def bkg_for_container(input_data, tab_wavelength, tab_background, allow_mos=Fals
 
     Returns
     -------
-    background : `~jwst.datamodels.ModelContainer`
+    background : `~jwst.datamodels.container.ModelContainer`
         A copy of `input_data` but with the data replaced by the background,
         "expanded" from 1-D to 2-D.
     """

jwst/master_background/expand_to_2d.py

@@ -33,14 +33,14 @@ def process(self, input_data):
         Parameters
         ----------
         input_data : `~jwst.datamodels.ImageModel`, `~jwst.datamodels.IFUImageModel`, \
-                     `~jwst.datamodels.ModelContainer`, str
+                     `~jwst.datamodels.container.ModelContainer`, str
             Input target datamodel(s) or association file to which master background
             subtraction is to be applied.
 
         Returns
         -------
         result : `~jwst.datamodels.ImageModel`,  `~jwst.datamodels.IFUImageModel`, \
-                 `~jwst.datamodels.ModelContainer`
+                 `~jwst.datamodels.container.ModelContainer`
             The background-subtracted science datamodel(s)
         """
         with datamodels.open(input_data) as input_model:
@@ -326,11 +326,11 @@ def subtract_2d_background(source, background):
 
     Parameters
     ----------
-    source : `~jwst.datamodels.JwstDataModel` or `~jwst.datamodels.ModelContainer`
+    source : `~jwst.datamodels.JwstDataModel` or `~jwst.datamodels.container.ModelContainer`
         The input science data.
     background : `~jwst.datamodels.JwstDataModel`
         The input background data.  Must be the same datamodel type as `source`.
-        For a `~jwst.datamodels.ModelContainer`, the source and background
+        For a `~jwst.datamodels.container.ModelContainer`, the source and background
         models in the input containers must match one-to-one.
 
     Returns

jwst/master_background/master_background_step.py

@@ -61,15 +61,16 @@ def process(self, input_data):
 
         Parameters
         ----------
-        input_data : asn file, `~jwst.datamodels.ModelContainer`, or \
+        input_data : asn file, `~jwst.datamodels.container.ModelContainer`, or \
                      `~jwst.datamodels.library.ModelLibrary`
             The input association.
             For imaging modes a ModelLibrary is expected, whereas for spectroscopic modes a
             ModelContainer is expected.
 
         Returns
         -------
-        result_models : `~jwst.datamodels.ModelContainer` or `~jwst.datamodels.library.ModelLibrary`
+        result_models : `~jwst.datamodels.container.ModelContainer` or \
+                        `~jwst.datamodels.library.ModelLibrary`
             The modified input data with DQ flags set for detected outliers.
         """
         # Open the input data, making a copy as needed.

jwst/outlier_detection/outlier_detection_step.py

@@ -470,7 +470,7 @@ def flag_crs_in_models_with_resampling(
 
     Parameters
     ----------
-    input_models : `~jwst.datamodels.ModelContainer`
+    input_models : `~jwst.datamodels.container.ModelContainer`
         The input datamodels.
     median_data : ndarray
         The median data array.