codecov[bot]: # [Codecov](https://codecov.io/gh/spacetelescope/jwst/pull/5112?src=pr&el=h1) Report
> Merging [#5112](https://codecov.io/gh/spacetelescope/jwst/pull/5112?src=pr&el=desc) into [master](https://codecov.io/gh/spacetelescope/jwst/commit/15bcd1cebe562a4f4d517f5f4951433432923e57&el=desc) will **decrease** coverage by `0.01%`.
> The diff coverage is `25.45%`.

[![Impacted file tree graph](https://codecov.io/gh/spacetelescope/jwst/pull/5112/graphs/tree.svg?width=650&height=150&src=pr&token=Utf5Zs9g7z)](https://codecov.io/gh/spacetelescope/jwst/pull/5112?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master    #5112      +/-   ##
==========================================
- Coverage   53.02%   53.00%   -0.02%     
==========================================
  Files         401      401              
  Lines       35609    35623      +14     
  Branches     5521     5521              
==========================================
+ Hits        18881    18883       +2     
- Misses      15588    15600      +12     
  Partials     1140     1140              
```

| Flag | Coverage Δ | |
|---|---|---|
| #unit | `53.00% <25.45%> (-0.02%)` | :arrow_down: |

| [Impacted Files](https://codecov.io/gh/spacetelescope/jwst/pull/5112?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [jwst/datamodels/ifuimage.py](https://codecov.io/gh/spacetelescope/jwst/pull/5112/diff?src=pr&el=tree#diff-andzdC9kYXRhbW9kZWxzL2lmdWltYWdlLnB5) | `72.72% <0.00%> (ø)` | |
| [jwst/datamodels/image.py](https://codecov.io/gh/spacetelescope/jwst/pull/5112/diff?src=pr&el=tree#diff-andzdC9kYXRhbW9kZWxzL2ltYWdlLnB5) | `100.00% <ø> (ø)` | |
| [jwst/datamodels/multiexposure.py](https://codecov.io/gh/spacetelescope/jwst/pull/5112/diff?src=pr&el=tree#diff-andzdC9kYXRhbW9kZWxzL211bHRpZXhwb3N1cmUucHk=) | `60.37% <0.00%> (-1.17%)` | :arrow_down: |
| [jwst/datamodels/multislit.py](https://codecov.io/gh/spacetelescope/jwst/pull/5112/diff?src=pr&el=tree#diff-andzdC9kYXRhbW9kZWxzL211bHRpc2xpdC5weQ==) | `73.52% <ø> (ø)` | |
| [jwst/datamodels/slit.py](https://codecov.io/gh/spacetelescope/jwst/pull/5112/diff?src=pr&el=tree#diff-andzdC9kYXRhbW9kZWxzL3NsaXQucHk=) | `65.38% <0.00%> (ø)` | |
| [jwst/pathloss/pathloss\_step.py](https://codecov.io/gh/spacetelescope/jwst/pull/5112/diff?src=pr&el=tree#diff-andzdC9wYXRobG9zcy9wYXRobG9zc19zdGVwLnB5) | `38.09% <0.00%> (ø)` | |
| [jwst/pathloss/pathloss.py](https://codecov.io/gh/spacetelescope/jwst/pull/5112/diff?src=pr&el=tree#diff-andzdC9wYXRobG9zcy9wYXRobG9zcy5weQ==) | `46.89% <34.14%> (-1.48%)` | :arrow_down: |
| [jwst/outlier\_detection/outlier\_detection.py](https://codecov.io/gh/spacetelescope/jwst/pull/5112/diff?src=pr&el=tree#diff-andzdC9vdXRsaWVyX2RldGVjdGlvbi9vdXRsaWVyX2RldGVjdGlvbi5weQ==) | `40.27% <0.00%> (-0.19%)` | :arrow_down: |

------

[Continue to review full report at Codecov](https://codecov.io/gh/spacetelescope/jwst/pull/5112?src=pr&el=continue).
> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)
> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`
> Powered by [Codecov](https://codecov.io/gh/spacetelescope/jwst/pull/5112?src=pr&el=footer). Last update [15bcd1c...d0b7bc0](https://codecov.io/gh/spacetelescope/jwst/pull/5112?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).

hbushouse: Also added some units tests in `datamodels`.
diff --git a/CHANGES.rst b/CHANGES.rst
index c7e7903a3..e7c4ac5d7 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -40,6 +40,8 @@ datamodels
 
 - Add TIMEUNIT keyword to schemas. [#5109]
 
+- Split ``pathloss`` object into ``pathloss_ps`` and ``pathloss_un``. [#5112]
+
 extract_1d
 ----------
 - rechecks the input model container in run_extract1d to select the correct processing [#5076]
@@ -65,11 +67,14 @@ outlier_detection
 
 - Fix outlier_detection bug when saving intermediate results. [#5108]
 
-  pathloss
+pathloss
 --------
-- fix bug in NIRSpec IFU data that causes valid pixel dq flags to set to
-  NON-SCIENCE in the region of an overlapping bounding box slice [#5047]
 
+- Fix bug in NIRSpec IFU data that causes valid pixel dq flags to set to 
+  NON-SCIENCE in the region of an overlapping bounding box slice [#5047]
+  
+- Update to save both point source and uniform source 2D pathloss correction
+  arrays to output. [#5112]
 
 pipeline
 --------
diff --git a/docs/jwst/data_products/science_products.rst b/docs/jwst/data_products/science_products.rst
index 9c27b4175..0d696d22a 100644
--- a/docs/jwst/data_products/science_products.rst
+++ b/docs/jwst/data_products/science_products.rst
@@ -259,9 +259,9 @@ The FITS file structure for a ``calints`` product is as follows:
 +-----+-------------+----------+-----------+-----------------------+
 |     | VAR_RNOISE  | IMAGE    | float32   | ncols x nrows x nints |
 +-----+-------------+----------+-----------+-----------------------+
-|     | AREA*       | IMAGE    |           | ncols x nrows         |
+|     | VAR_FLAT    | IMAGE    | float32   | ncols x nrows x nints |
 +-----+-------------+----------+-----------+-----------------------+
-|     | RELSENS*    | BINTABLE | N/A       | variable              |
+|     | AREA*       | IMAGE    |           | ncols x nrows         |
 +-----+-------------+----------+-----------+-----------------------+
 |     | WAVELENGTH* | IMAGE    | float32   | ncols x nrows         |
 +-----+-------------+----------+-----------+-----------------------+
@@ -280,10 +280,10 @@ The FITS file structure for a ``calints`` product is as follows:
    based on Poisson noise only, for each integration.
  - VAR_RNOISE: 3-D data array containing the variance estimate for each pixel,
    based on read noise only, for each integration.
+ - VAR_FLAT: 2-D data array containing the variance estimate for each pixel,
+   based on uncertainty in the flat-field.
  - AREA: 2-D data array containing pixel area values, added by the :ref:`photom <photom_step>`
    step, for imaging modes.
- - RELSENS: A table of sensitivity values as a function of wavelength, added by the
-   :ref:`photom <photom_step>` step, for some spectroscopic modes.
  - WAVELENGTH: 2-D data array of wavelength values for each pixel, for some spectroscopic modes.
  - ADSF: The data model meta data.
 
@@ -300,28 +300,22 @@ The FITS file structure for a ``cal`` product is as follows:
 +-----+---------------------------+----------+-----------+---------------+
 |  3  | DQ                        | IMAGE    | uint32    | ncols x nrows |
 +-----+---------------------------+----------+-----------+---------------+
-|     | VAR_POISSON               | IMAGE    | float32   | ncols x nrows |
-+-----+---------------------------+----------+-----------+---------------+
-|     | VAR_RNOISE                | IMAGE    | float32   | ncols x nrows |
+|  4  | VAR_POISSON               | IMAGE    | float32   | ncols x nrows |
 +-----+---------------------------+----------+-----------+---------------+
-|     | AREA*                     | IMAGE    | float32   | ncols x nrows |
-+-----+---------------------------+----------+-----------+---------------+
-|     | RELSENS*                  | BINTABLE | N/A       | variable      |
+|  5  | VAR_RNOISE                | IMAGE    | float32   | ncols x nrows |
 +-----+---------------------------+----------+-----------+---------------+
-|     | RELSENS2D*                | BINTABLE | N/A       | ncols x nrows |
+|  6  | VAR_FLAT                  | IMAGE    | float32   | ncols x nrows |
 +-----+---------------------------+----------+-----------+---------------+
-|     | PATHLOSS_POINTSOURCE*     | IMAGE    | float32   | ncols         |
+|     | AREA*                     | IMAGE    | float32   | ncols x nrows |
 +-----+---------------------------+----------+-----------+---------------+
-|     | WAVELENGTH_POINTSOURCE*   | IMAGE    | float32   | ncols         |
+|     | WAVELENGTH*               | IMAGE    | float32   | ncols x nrows |
 +-----+---------------------------+----------+-----------+---------------+
-|     | PATHLOSS_UNIFORMSOURCE*   | IMAGE    | float32   | ncols         |
+|     | PATHLOSS_PS*              | IMAGE    | float32   | ncols x nrows |
 +-----+---------------------------+----------+-----------+---------------+
-|     | WAVELENGTH_UNIFORMSOURCE* | IMAGE    | float32   | ncols         |
+|     | PATHLOSS_UN*              | IMAGE    | float32   | ncols x nrows |
 +-----+---------------------------+----------+-----------+---------------+
 |     | BARSHADOW*                | IMAGE    | float32   | ncols x nrows |
 +-----+---------------------------+----------+-----------+---------------+
-|     | WAVELENGTH*               | IMAGE    | float32   | ncols x nrows |
-+-----+---------------------------+----------+-----------+---------------+
 |     | ASDF                      | BINTABLE | N/A       | variable      |
 +-----+---------------------------+----------+-----------+---------------+
 
@@ -334,30 +328,22 @@ The FITS file structure for a ``cal`` product is as follows:
    based on Poisson noise only.
  - VAR_RNOISE: 2-D data array containing the variance estimate for each pixel,
    based on read noise only.
+ - VAR_FLAT: 2-D data array containing the variance estimate for each pixel,
+   based on uncertainty in the flat-field.
  - AREA: 2-D data array containing pixel area values, added by the :ref:`photom <photom_step>`
    step, for imaging modes.
- - RELSENS: A table of sensitivity values as a function of wavelength, added by the
-   :ref:`photom <photom_step>` step, for some spectroscopic modes.
- - RELSENS2D: 2-D data array of sensitivity values per pixel, added by the
-   :ref:`photom <photom_step>` step, for IFU spectroscopic modes.
- - PATHLOSS_POINTSOURCE: 1-D data array of point-source pathloss correction factors, added by
+ - WAVELENGTH: 2-D data array of wavelength values for each pixel, for some spectroscopic modes.
+ - PATHLOSS_PS: 2-D data array of point-source pathloss correction factors, added by
    the :ref:`pathloss <pathloss_step>` step, for some spectroscopic modes.
- - WAVELENGTH_POINTSOURCE: 1-D data array of wavelength values associated with the
-   PATHLOSS_POINTSOURCE correction factors, added by the :ref:`pathloss <pathloss_step>` step,
-   for some spectroscopic modes.
- - PATHLOSS_UNIFORMSOURCE: 1-D data array of uniform-source pathloss correction factors, added by
+ - PATHLOSS_UN: 1-D data array of uniform-source pathloss correction factors, added by
    the :ref:`pathloss <pathloss_step>` step, for some spectroscopic modes.
- - WAVELENGTH_UNIFORMSOURCE: 1-D data array of wavelength values associated with the
-   PATHLOSS_UNIFORMSOURCE correction factors, added by the :ref:`pathloss <pathloss_step>` step,
-   for some spectroscopic modes.
  - BARSHADOW: 2-D data array of NIRSpec MSA bar shadow correction factors, added by the
-   :ref:`barshadow <barshadow_step>` step, for NIRSpec MSA exposures only.
- - WAVELENGTH: 2-D data array of wavelength values for each pixel, for some spectroscopic modes.
+   :ref:`barshadow <barshadow_step>` step, for NIRSpec MOS exposures only.
  - ADSF: The data model meta data.
 
 For spectroscopic modes that contain data for multiple sources, such as NIRSpec MOS,
 NIRCam WFSS, and NIRISS WFSS, there will be multiple tuples of the SCI, ERR, DQ, VAR_POISSON,
-VAR_RNOISE, RELSENS, etc. extensions, where each tuple contains the data for a given source or
+VAR_RNOISE, etc. extensions, where each tuple contains the data for a given source or
 slit, as created by the :ref:`extract_2d <extract_2d_step>` step. FITS "EXTVER" keywords are
 used in each extension header to segregate the multiple instances of each extension type by
 source.
@@ -425,8 +411,6 @@ The FITS file structure for ``s2d`` products is as follows:
 +-----+-------------+----------+-----------+---------------+
 |  3  | WHT         | IMAGE    | float32   | ncols x nrows |
 +-----+-------------+----------+-----------+---------------+
-|  4  | RELSENS     | BINTABLE | N/A       | variable      |
-+-----+-------------+----------+-----------+---------------+
 |     | HDRTAB*     | BINTABLE | N/A       | variable      |
 +-----+-------------+----------+-----------+---------------+
 |     | ASDF        | BINTABLE | N/A       | variable      |
@@ -437,14 +421,12 @@ The FITS file structure for ``s2d`` products is as follows:
    to a specific output pixel.
  - WHT: 2-D weight image giving the relative weight of the output pixels (effectively a
    relative exposure time map).
- - RELSENS: A table of sensitivity values as a function of wavelength, carried along from the
-   input calibrated product.
  - HDRTAB: A table containing meta data (FITS keyword values) for all of the input images
    that were combined to produce the output image. Only appears when multiple inputs are used.
  - ADSF: The data model meta data.
 
 For exposure-based products that contain spectra for more than one source or slit
-(e.g. NIRSpec MOS) there will be multiple tuples of the SCI, CON, WHT, and RELSENS
+(e.g. NIRSpec MOS) there will be multiple tuples of the SCI, CON, and WHT
 extensions, one set for each source or slit. FITS "EXTVER" keywords are used in each
 extension header to segregate the multiple instances of each extension type by
 source.
diff --git a/docs/jwst/pathloss/description.rst b/docs/jwst/pathloss/description.rst
index ee146710e..e3334d83c 100644
--- a/docs/jwst/pathloss/description.rst
+++ b/docs/jwst/pathloss/description.rst
@@ -37,12 +37,13 @@ for both point source and uniform source data types.
 For the uniform source pathloss calculation, there is no dependence on position
 in the aperture/slit.
 
-Once the 1-D correction arrays have been computed, the appropriate form of the
-correction (point or uniform) is interpolated, as a function of wavelength, into
-the 2-D space of the slit or IFU data and divided into the SCI and ERR arrays of the
-science data.
-The 2-D correction array is also attached to the datamodel (extension "PATHLOSS")
-as a record of what was applied.
+Once the 1-D correction arrays have been computed, both forms of the correction
+(point and uniform) are interpolated, as a function of wavelength, into
+the 2-D space of the slit or IFU data and attached to the output data model
+(extensions "PATHLOSS_PS" and "PATHLOSS_UN") as a record of what was computed.
+The form of the 2-D correction (point or uniform) that's appropriate for the
+data is divided into the SCI and ERR arrays and propagated into the variance
+arrays of the science data.
 
 NIRISS
 ++++++
@@ -57,8 +58,8 @@ The algorithm calculates the correction for each column by simply interpolating
 along the Pupil Wheel position dimension of the reference file using linear
 interpolation.  The 1-D vector of correction vs. column number is interpolated,
 as a function of wavelength, into the 2-D space of the science image and divided
-into the SCI and ERR arrays.
-The 2-D correction array is also attached to the datamodel (extension "PATHLOSS")
+into the SCI and ERR arrays and propagated into the variance arrays.
+The 2-D correction array is also attached to the datamodel (extension "PATHLOSS_PS")
 as a record of what was applied.
 
 Error Propagation
diff --git a/docs/jwst/pipeline/calwebb_spec2.rst b/docs/jwst/pipeline/calwebb_spec2.rst
index 51fe75acc..c4f09c586 100644
--- a/docs/jwst/pipeline/calwebb_spec2.rst
+++ b/docs/jwst/pipeline/calwebb_spec2.rst
@@ -171,7 +171,7 @@ the output type will consist of either a single slit model or a mutli-slit model
 The multi-slit model is simply an array of multiple slit models, each one
 containing the data and relevant meta data for a particular extracted slit or
 source. A `~jwst.datamodels.MultiSlitModel` product will contain multiple
-tuples of SCI, ERR, DQ, WAVELENGTH, RELSENS, etc. arrays; one for each of the
+tuples of SCI, ERR, DQ, WAVELENGTH, etc. arrays; one for each of the
 extracted slits/sources.
 
 2D resampled data
diff --git a/jwst/datamodels/ifuimage.py b/jwst/datamodels/ifuimage.py
index f9f765a1f..55ae609ba 100644
--- a/jwst/datamodels/ifuimage.py
+++ b/jwst/datamodels/ifuimage.py
@@ -23,9 +23,6 @@ class IFUImageModel(DataModel):
     zeroframe : numpy float32 array
          Zeroframe array
 
-    area : numpy float32 array
-         Pixel area map array
-
     var_poisson : numpy float32 array
          variance due to poisson noise
 
@@ -35,8 +32,14 @@ class IFUImageModel(DataModel):
     wavelength : numpy float32 array
          wavelength
 
-    pathloss : numpy float32 array
-         pathloss correction
+    pathloss_point : numpy float32 array
+         pathloss correction for point source
+
+    pathloss_uniform : numpy float32 array
+         pathloss correction for uniform source
+
+    area : numpy float32 array
+         Pixel area map array
     """
     schema_url = "http://stsci.edu/schemas/jwst_datamodel/ifuimage.schema"
 
@@ -47,12 +50,12 @@ class IFUImageModel(DataModel):
             self.data = init.data
             self.dq = init.dq
             self.err = init.err
-            if init.hasattr('area'):
-                self.area = init.area
             if init.hasattr('var_poisson'):
                 self.var_poisson = init.var_poisson
             if init.hasattr('var_rnoise'):
                 self.var_rnoise = init.var_rnoise
+            if init.hasattr('area'):
+                self.area = init.area
             return
 
         super(IFUImageModel, self).__init__(init=init, **kwargs)
diff --git a/jwst/datamodels/image.py b/jwst/datamodels/image.py
index c723db55a..d6491efc7 100644
--- a/jwst/datamodels/image.py
+++ b/jwst/datamodels/image.py
@@ -22,17 +22,20 @@ class ImageModel(DataModel):
     zeroframe : numpy float32 array
          Zeroframe array
 
-    area : numpy float32 array
-         Pixel area map array
-
-    pathloss : numpy float32 array
-         Pathloss correction
-
     var_poisson : numpy float32 array
          variance due to poisson noise
 
     var_rnoise : numpy float32 array
          variance due to read noise
+
+    area : numpy float32 array
+         Pixel area map array
+
+    pathloss_point : numpy float32 array
+         Pathloss correction for point source
+
+    pathloss_uniform : numpy float32 array
+         Pathloss correction for uniform source
     """
     schema_url = "http://stsci.edu/schemas/jwst_datamodel/image.schema"
 
diff --git a/jwst/datamodels/multiexposure.py b/jwst/datamodels/multiexposure.py
index 6c9bdb80e..96ec90172 100644
--- a/jwst/datamodels/multiexposure.py
+++ b/jwst/datamodels/multiexposure.py
@@ -62,13 +62,14 @@ class MultiExposureModel(DataModel):
             self.exposures[0].data = init.data
             self.exposures[0].dq = init.dq
             self.exposures[0].err = init.err
-            self.exposures[0].wavelength = init.wavelength
-            self.exposures[0].barshadow = init.barshadow
-            self.exposures[0].area = init.area
             self.exposures[0].var_poisson = init.var_poisson
             self.exposures[0].var_rnoise = init.var_rnoise
             self.exposures[0].var_flat = init.var_flat
-            self.exposures[0].pathloss = init.pathloss
+            self.exposures[0].wavelength = init.wavelength
+            self.exposures[0].barshadow = init.barshadow
+            self.exposures[0].pathloss_point = init.pathloss_point
+            self.exposures[0].pathloss_uniform = init.pathloss_uniform
+            self.exposures[0].area = init.area
             return
 
         super(MultiExposureModel, self).__init__(
diff --git a/jwst/datamodels/multislit.py b/jwst/datamodels/multislit.py
index fd45f35d5..b412ecd6f 100644
--- a/jwst/datamodels/multislit.py
+++ b/jwst/datamodels/multislit.py
@@ -37,23 +37,26 @@ class MultiSlitModel(model_base.DataModel):
     slits.items.err : numpy float32 array
          Error array
 
+    slits.items.var_poisson : numpy float32 array
+         variance due to poisson noise
+
+    slits.items.var_rnoise : numpy float32 array
+         variance due to read noise
+
     slits.items.wavelength : numpy float32 array
          Wavelength array, corrected for zero-point
 
     slits.items.barshadow : numpy float32 array
          Bar shadow correction
 
-    slits.items.area : numpy float32 array
-         Pixel area map array
+    slits.items.pathloss_point : numpy float32 array
+         pathloss array for point source
 
-    slits.items.var_poisson : numpy float32 array
-         variance due to poisson noise
+    slits.items.pathloss_uniform : numpy float32 array
+         pathloss array for uniform source
 
-    slits.items.var_rnoise : numpy float32 array
-         variance due to read noise
-
-    slits.items.pathloss : numpy float32 array
-         pathloss array
+    slits.items.area : numpy float32 array
+         Pixel area map array
     """
     schema_url = "http://stsci.edu/schemas/jwst_datamodel/multislit.schema"
 
diff --git a/jwst/datamodels/schemas/pathlosscorr.schema.yaml b/jwst/datamodels/schemas/pathlosscorr.schema.yaml
index dd7f44497..ae2c11cba 100644
--- a/jwst/datamodels/schemas/pathlosscorr.schema.yaml
+++ b/jwst/datamodels/schemas/pathlosscorr.schema.yaml
@@ -4,8 +4,13 @@ $schema: "http://stsci.edu/schemas/fits-schema/fits-schema"
 id: "http://stsci.edu/schemas/jwst_datamodel/pathlosscorr.schema"
 type: object
 properties:
-  pathloss:
-    title: Path loss correction
-    fits_hdu: PATHLOSS
+  pathloss_point:
+    title: Pathloss point source correction
+    fits_hdu: PATHLOSS_PS
+    ndim: 2
+    datatype: float32
+  pathloss_uniform:
+    title: Pathloss uniformsource correction
+    fits_hdu: PATHLOSS_UN
     ndim: 2
     datatype: float32
diff --git a/jwst/datamodels/schemas/slitdata.schema.yaml b/jwst/datamodels/schemas/slitdata.schema.yaml
index f13354aed..60841afea 100644
--- a/jwst/datamodels/schemas/slitdata.schema.yaml
+++ b/jwst/datamodels/schemas/slitdata.schema.yaml
@@ -31,7 +31,6 @@ allOf:
       title: Bar shadow correction
       fits_hdu: BARSHADOW
       ndim: 2
-      default: 1.0
       datatype: float32
     area:
       title: Pixel area map array
@@ -48,9 +47,9 @@ allOf:
       fits_hdu: CON
       default: 0
       datatype: int32
-- $ref: slitmeta.schema
 - $ref: variance.schema
 - $ref: pathlosscorr.schema
+- $ref: slitmeta.schema
 - $ref: bunit.schema
 - $ref: photometry.schema
 - $ref: wcsinfo.schema
diff --git a/jwst/datamodels/slit.py b/jwst/datamodels/slit.py
index 982018e20..50350a842 100644
--- a/jwst/datamodels/slit.py
+++ b/jwst/datamodels/slit.py
@@ -20,24 +20,26 @@ class SlitDataModel(DataModel):
     err : numpy float32 array
          Error array
 
+    var_poisson : numpy float32 array
+         variance due to poisson noise
+
+    var_rnoise : numpy float32 array
+         variance due to read noise
+
     wavelength : numpy float32 array
          Wavelength array, corrected for zero-point
 
     barshadow : numpy float32 array
          Bar shadow correction
 
-    area : numpy float32 array
-         Pixel area map array
-
-    var_poisson : numpy float32 array
-         variance due to poisson noise
-
-    var_rnoise : numpy float32 array
-         variance due to read noise
+    pathloss_point : numpy float32 array
+         pathloss array for point source
 
-    pathloss : numpy float32 array
-         pathloss array
+    pathloss_uniform : numpy float32 array
+         pathloss array for uniform source
 
+    area : numpy float32 array
+         Pixel area map array
     """
 
     schema_url = "http://stsci.edu/schemas/jwst_datamodel/slitdata.schema"
@@ -49,12 +51,12 @@ class SlitDataModel(DataModel):
             self.dq = init.dq
             self.err = init.err
             self.area = init.area
-            if init.hasattr('wavelength'):
-                self.wavelength = init.wavelength
             if init.hasattr('var_poisson'):
                 self.var_poisson = init.var_poisson
             if init.hasattr('var_rnoise'):
                 self.var_rnoise = init.var_rnoise
+            if init.hasattr('wavelength'):
+                self.wavelength = init.wavelength
             for key in kwargs:
                 setattr(self, key, kwargs[key])
 
@@ -84,27 +86,29 @@ class SlitModel(DataModel):
     err : numpy float32 array
          Error array
 
+    var_poisson : numpy float32 array
+         variance due to poisson noise
+
+    var_rnoise : numpy float32 array
+         variance due to read noise
+
     wavelength : numpy float32 array
          Wavelength array, corrected for zero-point
 
     barshadow : numpy float32 array
          Bar shadow correction
 
-    area : numpy float32 array
-         Pixel area map array
-
-    var_poisson : numpy float32 array
-         variance due to poisson noise
+    pathloss_point : numpy float32 array
+         pathloss array for point source
 
-    var_rnoise : numpy float32 array
-         variance due to read noise
+    pathloss_uniform : numpy float32 array
+         pathloss array for uniform source
 
-    pathloss : numpy float32 array
-         pathloss array
+    area : numpy float32 array
+         Pixel area map array
 
     int_times : numpy table
          table of times for each integration
-
     """
     schema_url = "http://stsci.edu/schemas/jwst_datamodel/slit.schema"
 
@@ -116,12 +120,12 @@ class SlitModel(DataModel):
             self.dq = init.dq
             self.err = init.err
             self.area = init.area
-            if init.hasattr('wavelength'):
-                self.wavelength = init.wavelength
             if init.hasattr('var_poisson'):
                 self.var_poisson = init.var_poisson
             if init.hasattr('var_rnoise'):
                 self.var_rnoise = init.var_rnoise
+            if init.hasattr('wavelength'):
+                self.wavelength = init.wavelength
             if init.hasattr('int_times'):
                 self.int_times = init.int_times
             if init.meta.hasattr('wcs'):
diff --git a/jwst/pathloss/pathloss.py b/jwst/pathloss/pathloss.py
index a9e10ce00..7d6313c2d 100644
--- a/jwst/pathloss/pathloss.py
+++ b/jwst/pathloss/pathloss.py
@@ -10,9 +10,10 @@ log = logging.getLogger(__name__)
 log.setLevel(logging.DEBUG)
 
 
-# There are 30 slices in the NIRSPEC IFU, numbered from 0 to 29
+# There are 30 slices in the NIRSpec IFU, numbered from 0 to 29
 NIRSPEC_IFU_SLICES = np.arange(30)
 
+
 def get_center(exp_type, input):
     """Get the center of the target in the aperture.
     (0.0, 0.0) is the aperture center.  Coordinates go
@@ -22,9 +23,10 @@ def get_center(exp_type, input):
 
         # Currently assume IFU sources are centered
         return (0.0, 0.0)
+
     elif exp_type in ["NRS_MSASPEC", "NRS_FIXEDSLIT", "NRS_BRIGHTOBJ"]:
-        #
-        # MSA centering specified in the MiltiSlit model
+
+        # MSA centering is specified in the MiltiSlit model
         # "input" treated as a slit object
         try:
             xcenter = input.source_xpos
@@ -35,11 +37,13 @@ def get_center(exp_type, input):
             xcenter = 0.0
             ycenter = 0.0
         return (xcenter, ycenter)
+
     else:
-        log.warning("No method to get centering for exp_type {}".format(exp_type))
+        log.warning(f'No method to get centering for exp_type {exp_type}')
         log.warning("Using (0.0, 0.0)")
         return (0.0, 0.0)
 
+
 def get_aperture_from_model(input_model, match):
     """Figure out the correct aperture based on the value of the 'match'
     parameter.  For MSA, match is the number of shutters, for fixed slit,
@@ -56,12 +60,12 @@ def get_aperture_from_model(input_model, match):
             if aperture.name == match:
                 return aperture
     else:
-        log.warning("Unable to get aperture from "
-            "model type {0}".format(input_model.meta.exposure.type))
+        log.warning(f'Unable to get aperture from exp_type {input_model.meta.exposure.type}')
 
     # If nothing matches, return None
     return None
 
+
 def calculate_pathloss_vector(pathloss_refdata,
                               pathloss_wcs,
                               xcenter,
@@ -105,15 +109,15 @@ def calculate_pathloss_vector(pathloss_refdata,
     wavelength = np.zeros(wavesize, dtype=np.float32)
     pathloss_vector = np.zeros(wavesize, dtype=np.float32)
 
-    # uniformsource.data is 1-d, we just return it, along with
-    # a vector of wavelengths calculated using the WCS
+    # uniformsource.data is 1-d. We just return it, along with
+    # a vector of wavelengths calculated using the WCS.
     # Uniform source is always inside the slitlet
     if len(pathloss_refdata.shape) == 1:
         crpix1 = pathloss_wcs.crpix1
         crval1 = pathloss_wcs.crval1
         cdelt1 = pathloss_wcs.cdelt1
         for i in np.arange(wavesize):
-            wavelength[i] = crval1 +(float(i+1) - crpix1)*cdelt1
+            wavelength[i] = crval1 + (float(i+1) - crpix1)*cdelt1
         return wavelength, pathloss_refdata, True
 
     # pointsource.data is 3-d, so we have to extract a wavelength vector
@@ -123,7 +127,8 @@ def calculate_pathloss_vector(pathloss_refdata,
         crval3 = pathloss_wcs.crval3
         cdelt3 = pathloss_wcs.cdelt3
         for i in np.arange(wavesize):
-            wavelength[i] = crval3 +(float(i+1) - crpix3)*cdelt3
+            wavelength[i] = crval3 +(float(i+1) - crpix3) * cdelt3
+
         # Calculate python index of object center
         crpix1 = pathloss_wcs.crpix1
         crval1 = pathloss_wcs.crval1
@@ -153,8 +158,10 @@ def calculate_pathloss_vector(pathloss_refdata,
                                + a12*pathloss_refdata[:, i+1, j]
                                + a21*pathloss_refdata[:, i, j+1]
                                + a11*pathloss_refdata[:, i+1, j+1])
+
         return wavelength, pathloss_vector, is_inside_slitlet
 
+
 def do_correction(input_model, pathloss_model):
     """
     Short Summary
@@ -172,20 +179,26 @@ def do_correction(input_model, pathloss_model):
     Returns
     -------
     output_model : data model object
-        Science data with pathloss extensions added
+        Corrected science data with pathloss extensions added
 
     """
     exp_type = input_model.meta.exposure.type
-    log.info('Input exposure type is {}'.format(exp_type))
+    log.info(f'Input exposure type is {exp_type}')
     output_model = input_model.copy()
+
+    # NIRSpec MOS data
     if exp_type == 'NRS_MSASPEC':
         slit_number = 0
+
+        # Loop over all MOS slitlets
         for slit in output_model.slits:
             slit_number = slit_number + 1
-            log.info('Working on slit {}'.format(slit_number))
+            log.info(f'Working on slit {slit_number}')
             size = slit.data.size
-            # That has data.size > 0
+
+            # Only work on slits with data.size > 0
             if size > 0:
+
                 # Get centering
                 xcenter, ycenter = get_center(exp_type, slit)
                 # Calculate the 1-d wavelength and pathloss vectors
@@ -207,23 +220,30 @@ def do_correction(input_model, pathloss_model):
                     if is_inside_slitlet:
 
                         # Wavelengths in the reference file are in meters,
-                        #need them to be in microns
+                        # need them to be in microns
                         wavelength_pointsource *= 1.0e6
                         wavelength_uniformsource *= 1.0e6
 
                         wavelength_array = slit.wavelength
 
-                        # Compute the pathloss 2D correction
+                        # Compute the point source pathloss 2D correction
+                        pathloss_2d_ps = interpolate_onto_grid(
+                            wavelength_array,
+                            wavelength_pointsource,
+                            pathloss_pointsource_vector)
+
+                        # Compute the uniform source pathloss 2D correction
+                        pathloss_2d_un = interpolate_onto_grid(
+                            wavelength_array,
+                            wavelength_uniformsource,
+                            pathloss_uniform_vector)
+
+                        # Use the appropriate correction for this slit
                         if is_pointsource(slit.source_type):
-                            pathloss_2d = interpolate_onto_grid(
-                                wavelength_array,
-                                wavelength_pointsource,
-                                pathloss_pointsource_vector)
+                            pathloss_2d = pathloss_2d_ps
                         else:
-                            pathloss_2d = interpolate_onto_grid(
-                                wavelength_array,
-                                wavelength_uniformsource,
-                                pathloss_uniform_vector)
+                            pathloss_2d = pathloss_2d_un
+
                         # Apply the pathloss 2D correction and attach to datamodel
                         slit.data /= pathloss_2d
                         slit.err /= pathloss_2d
@@ -231,34 +251,41 @@ def do_correction(input_model, pathloss_model):
                         slit.var_rnoise /= pathloss_2d**2
                         if slit.var_flat is not None and np.size(slit.var_flat) > 0:
                             slit.var_flat /= pathloss_2d**2
-                        slit.pathloss = pathloss_2d
+                        slit.pathloss_point = pathloss_2d_ps
+                        slit.pathloss_uniform = pathloss_2d_un
                     else:
-                        log.warning("Source is outside slit.  Skipping "
-                        "pathloss correction for slit {}".format(slit_number))
+                        log.warning("Source is outside slit. Skipping "
+                                    f"pathloss correction for slit {slit_number}")
                 else:
-                    log.warning("Cannot find matching pathloss model for slit "
-                        "with {} shutters, skipping pathloss correction for this "
-                        "slit".format(nshutters))
+                    log.warning("Cannot find matching pathloss model for slit with"
+                                f"{nshutters} shutters")
+                    log.warning("Skipping pathloss correction for this slit")
                     continue
             else:
-                log.warning("Slit has data size = {}, skipping "
-                    "pathloss correction for this slitlet".format(size))
+                log.warning(f"Slit has data size = {size}")
+                log.warning("Skipping pathloss correction for this slitlet")
+
+        # Set step status to complete
         output_model.meta.cal_step.pathloss = 'COMPLETE'
+
+    # NIRSpec fixed-slit data
     elif exp_type in ['NRS_FIXEDSLIT', 'NRS_BRIGHTOBJ']:
         slit_number = 0
         is_inside_slit = True
-        # For each slit
+
+        # Loop over all slits contained in the input
         for slit in output_model.slits:
-            log.info('Working on slit {}'.format(slit.name))
+            log.info(f'Working on slit {slit.name}')
             slit_number = slit_number + 1
+
             # Get centering
             xcenter, ycenter = get_center(exp_type, slit)
-            # Calculate the 1-d wavelength and pathloss vectors
-            # for the source position
+            # Calculate the 1-d wavelength and pathloss vectors for the source position
             # Get the aperture from the reference file that matches the slit
             aperture = get_aperture_from_model(pathloss_model, slit.name)
+
             if aperture is not None:
-                log.info("Using aperture {}".format(aperture.name))
+                log.info(f'Using aperture {aperture.name}')
                 (wavelength_pointsource,
                  pathloss_pointsource_vector,
                  is_inside_slit) = calculate_pathloss_vector(aperture.pointsource_data,
@@ -271,24 +298,31 @@ def do_correction(input_model, pathloss_model):
                                                     xcenter, ycenter)
                 if is_inside_slit:
 
-                    # Wavelengths in the reference file are in meters, need them to be
-                    # in microns
+                    # Wavelengths in the reference file are in meters,
+                    # need them to be in microns
                     wavelength_pointsource *= 1.0e6
                     wavelength_uniformsource *= 1.0e6
 
                     wavelength_array = slit.wavelength
 
-                    # Compute the pathloss 2D correction
+                    # Compute the point source pathloss 2D correction
+                    pathloss_2d_ps = interpolate_onto_grid(
+                        wavelength_array,
+                        wavelength_pointsource,
+                        pathloss_pointsource_vector)
+
+                    # Compute the uniform source pathloss 2D correction
+                    pathloss_2d_un = interpolate_onto_grid(
+                        wavelength_array,
+                        wavelength_uniformsource,
+                        pathloss_uniform_vector)
+
+                    # Use the appropriate correction for this slit
                     if is_pointsource(slit.source_type):
-                        pathloss_2d = interpolate_onto_grid(
-                            wavelength_array,
-                            wavelength_pointsource,
-                            pathloss_pointsource_vector)
+                        pathloss_2d = pathloss_2d_ps
                     else:
-                        pathloss_2d = interpolate_onto_grid(
-                            wavelength_array,
-                            wavelength_uniformsource,
-                            pathloss_uniform_vector)
+                        pathloss_2d = pathloss_2d_un
+
                     # Apply the pathloss 2D correction and attach to datamodel
                     slit.data /= pathloss_2d
                     slit.err /= pathloss_2d
@@ -296,21 +330,26 @@ def do_correction(input_model, pathloss_model):
                     slit.var_rnoise /= pathloss_2d**2
                     if slit.var_flat is not None and np.size(slit.var_flat) > 0:
                         slit.var_flat /= pathloss_2d**2
-                    slit.pathloss = pathloss_2d
+                    slit.pathloss_point = pathloss_2d_ps
+                    slit.pathloss_uniform = pathloss_2d_un
+
                 else:
-                    log.warning("Source is outside slit.  Skipping "
-                        "pathloss correction for slit {}".format(slit.name))
+                    log.warning('Source is outside slit. Skipping '
+                                f'pathloss correction for slit {slit.name}')
             else:
-                log.warning("Cannot find matching pathloss model for aperture {} "
-                    "skipping pathloss correction for this slit".format(slit.name))
+                log.warning(f'Cannot find matching pathloss model for {slit.name}')
+                log.warning('Skipping pathloss correction for this slit')
                 continue
+
+        # Set step status to complete
         output_model.meta.cal_step.pathloss = 'COMPLETE'
+
+    # NIRSpec IFU
     elif exp_type == 'NRS_IFU':
         # IFU targets are always inside slit
         # Get centering
         xcenter, ycenter = get_center(exp_type, None)
-        # Calculate the 1-d wavelength and pathloss vectors
-        # for the source position
+        # Calculate the 1-d wavelength and pathloss vectors for the source position
         aperture = pathloss_model.apertures[0]
         (wavelength_pointsource,
          pathloss_pointsource_vector,
@@ -322,12 +361,12 @@ def do_correction(input_model, pathloss_model):
          dummy) = calculate_pathloss_vector(aperture.uniform_data,
                                             aperture.uniform_wcs,
                                             xcenter, ycenter)
-        # Wavelengths in the reference file are in meters, need them to be
-        # in microns
+        # Wavelengths in the reference file are in meters;
+        # need them to be in microns
         wavelength_pointsource *= 1.0e6
         wavelength_uniformsource *= 1.0e6
 
-        # Create the 2-d pathloss arrays, initialize with NaNs
+        # Create the 2-d wavelength arrays, initialize with NaNs
         wavelength_array = np.zeros(input_model.shape, dtype=np.float32)
         wavelength_array.fill(np.nan)
         for slice in NIRSPEC_IFU_SLICES:
@@ -339,17 +378,24 @@ def do_correction(input_model, pathloss_model):
             y = y[valid]
             wavelength_array[y.astype(int), x.astype(int)] = wavelength[valid]
 
-        # Compute the pathloss 2D correction
+        # Compute the point source pathloss 2D correction
+        pathloss_2d_ps = interpolate_onto_grid(
+            wavelength_array,
+            wavelength_pointsource,
+            pathloss_pointsource_vector)
+
+        # Compute the uniform source pathloss 2D correction
+        pathloss_2d_un = interpolate_onto_grid(
+            wavelength_array,
+            wavelength_uniformsource,
+            pathloss_uniform_vector)
+
+        # Use the appropriate correction for the source type
         if is_pointsource(input_model.meta.target.source_type):
-            pathloss_2d = interpolate_onto_grid(
-                wavelength_array,
-                wavelength_pointsource,
-                pathloss_pointsource_vector)
+            pathloss_2d = pathloss_2d_ps
         else:
-            pathloss_2d = interpolate_onto_grid(
-                wavelength_array,
-                wavelength_uniformsource,
-                pathloss_uniform_vector)
+            pathloss_2d = pathloss_2d_un
+
         # Apply the pathloss 2D correction and attach to datamodel
         output_model.data /= pathloss_2d
         output_model.err /= pathloss_2d
@@ -357,21 +403,24 @@ def do_correction(input_model, pathloss_model):
         output_model.var_rnoise /= pathloss_2d**2
         if output_model.var_flat is not None and np.size(output_model.var_flat) > 0:
             output_model.var_flat /= pathloss_2d**2
-        output_model.pathloss = pathloss_2d
+        output_model.pathloss_point = pathloss_2d_ps
+        output_model.pathloss_uniform = pathloss_2d_un
 
         # This might be useful to other steps
         output_model.wavelength = wavelength_array
 
+        # Set the step status to complete
         output_model.meta.cal_step.pathloss = 'COMPLETE'
 
+    # NIRISS SOSS
     elif exp_type == 'NIS_SOSS':
         """NIRISS SOSS pathloss correction is basically a correction for the
         flux from the 2nd and 3rd order dispersion that falls outside the
-        subarray aperture.  The correction depends
-        on the pupil wheel position and column number (or wavelength).  The
-        simple option is to do the correction by column number, then the only
-        interpolation needed is a 1-d interpolation into the pupil wheel position
-        dimension."""
+        subarray aperture.  The correction depends on the pupil wheel position
+        and column number (or wavelength).  The simple option is to do the
+        correction by column number, then the only interpolation needed is a
+        1-d interpolation into the pupil wheel position dimension.
+        """
 
         # Omit correction if this is a TSO observation
         if input_model.meta.visit.tsovisit:
@@ -379,26 +428,29 @@ def do_correction(input_model, pathloss_model):
             output_model.meta.cal_step.pathloss = 'SKIPPED'
             return output_model
 
+        # Get the pupil wheel position
         pupil_wheel_position = input_model.meta.instrument.pupil_position
         if pupil_wheel_position is None:
-            log.warning("Unable to get pupil wheel position from PWCPOS keyword "
-                "for {}".format(input_model.meta.filename))
+            log.warning('Unable to get pupil wheel position from PWCPOS keyword '
+                        f'for {input_model.meta.filename}')
             log.warning("Pathloss correction skipped")
             output_model.meta.cal_step.pathloss = 'SKIPPED'
             return output_model
 
-        subarray = input_model.meta.subarray.name
         # Get the aperture from the reference file that matches the subarray
+        subarray = input_model.meta.subarray.name
         aperture = get_aperture_from_model(pathloss_model, subarray)
         if aperture is None:
-            log.warning("Unable to get Aperture from reference file "
-                "for subarray {}".format(subarray))
+            log.warning('Unable to get Aperture from reference file '
+                        f'for subarray {subarray}')
             log.warning("Pathloss correction skipped")
             output_model.meta.cal_step.pathloss = 'SKIPPED'
             return output_model
 
         else:
-            log.info("Aperture {} selected from reference file".format(aperture.name))
+            log.info(f'Aperture {aperture.name} selected from reference file')
+
+        # Set up pathloss correction array
         pathloss_array = aperture.pointsource_data[0]
         nrows, ncols = pathloss_array.shape
         _, data_ncols = input_model.data.shape
@@ -409,8 +461,8 @@ def do_correction(input_model, pathloss_model):
         pupil_wheel_index = crpix1 + (pupil_wheel_position - crval1) / cdelt1 - 1
 
         if pupil_wheel_index < 0 or pupil_wheel_index > (ncols - 2):
-            log.info("Pupil Wheel position outside reference file coverage")
-            log.info("Setting pathloss correction to 1.0")
+            log.warning("Pupil Wheel position outside reference file coverage")
+            log.warning("Setting pathloss correction to 1.0")
         else:
             ix = int(pupil_wheel_index)
             dx = pupil_wheel_index - ix
@@ -426,6 +478,7 @@ def do_correction(input_model, pathloss_model):
                     correction[row] = (1.0 - dx) * pathloss_array[refrow_index, ix] + \
                                       dx * pathloss_array[refrow_index, ix + 1]
 
+        # Create and apply the 2D correction
         pathloss_2d = np.broadcast_to(correction, input_model.data.shape)
         output_model.data /= pathloss_2d
         output_model.err /= pathloss_2d
@@ -433,12 +486,14 @@ def do_correction(input_model, pathloss_model):
         output_model.var_rnoise /= pathloss_2d**2
         if output_model.var_flat is not None and np.size(output_model.var_flat) > 0:
             output_model.var_flat /= pathloss_2d**2
-        output_model.pathloss = pathloss_2d
+        output_model.pathloss_point = pathloss_2d
 
+        # Set step status to complete
         output_model.meta.cal_step.pathloss = 'COMPLETE'
 
     return output_model
 
+
 def interpolate_onto_grid(wavelength_grid, wavelength_vector, pathloss_vector):
     """
     Get the value of pathloss by interpolating each non-NaN element of
@@ -511,6 +566,7 @@ def interpolate_onto_grid(wavelength_grid, wavelength_vector, pathloss_vector):
 
     return pathloss_grid
 
+
 def is_pointsource(srctype):
     """Returns True if srctype is POINT"""
     if srctype is None:
diff --git a/jwst/pathloss/pathloss_step.py b/jwst/pathloss/pathloss_step.py
index 19a8f2f33..aa9d559e0 100755
--- a/jwst/pathloss/pathloss_step.py
+++ b/jwst/pathloss/pathloss_step.py
@@ -7,8 +7,7 @@ __all__ = ["PathLossStep"]
 
 class PathLossStep(Step):
     """
-    PathLossStep: Inserts the pathloss and wavelength arrays
-    into the data.
+    PathLossStep: Apply the path loss correction to a science exposure.
 
     Pathloss depends on the centering of the source in the aperture if the
     source is a point source.
@@ -24,16 +23,14 @@ class PathLossStep(Step):
         # Open the input data model
         with datamodels.open(input) as input_model:
 
-            # Get the name of the path loss reference file to use
-            self.pathloss_name = self.get_reference_file(input_model,
-                                                         'pathloss')
-            self.log.info('Using PATHLOSS reference file %s',
-                          self.pathloss_name)
+            # Get the name of the pathloss reference file to use
+            self.pathloss_name = self.get_reference_file(input_model, 'pathloss')
+            self.log.info(f'Using PATHLOSS reference file {self.pathloss_name}')
 
             # Check for a valid reference file
             if self.pathloss_name == 'N/A':
                 self.log.warning('No PATHLOSS reference file found')
-                self.log.warning('Path loss step will be skipped')
+                self.log.warning('Pathloss step will be skipped')
                 result = input_model.copy()
                 result.meta.cal_step.pathloss = 'SKIPPED'
                 return result

Save both pathloss point source and uniform source correction arrays
_Issue [JP-1556](https://jira.stsci.edu/browse/JP-1556) was created on JIRA by [Howard Bushouse](https://jira.stsci.edu/secure/ViewProfile.jspa?name=bushouse):_

As part of the implementation of NIRSpec master background updates outlined in JP-1213, it would facilitate corrections needed to support IFU mode if the calwebb_spec2 pathloss step computed and saved both point source and uniform source versions of the correction, at least for the NIRSpec IFU mode (and possibly FS and MOS too). Modify the pathloss step to compute and save both versions to the output data model. The data model schema itself may need to be updated to define both versions of the extension.
diff --git a/jwst/datamodels/tests/test_models.py b/jwst/datamodels/tests/test_models.py
index 1ed44b0db..662f43231 100644
--- a/jwst/datamodels/tests/test_models.py
+++ b/jwst/datamodels/tests/test_models.py
@@ -141,7 +141,6 @@ def test_open():
             assert isinstance(dm, QuadModel)
 
 
-
 def test_open_warning():
     with warnings.catch_warnings(record=True) as warners:
         # Cause all warnings to always be triggered.
@@ -258,6 +257,21 @@ def test_default_value_anyof_schema():
         assert val.instance == {}
 
 
+def test_imagemodel():
+    dims = (10,10)
+    with ImageModel(dims) as dm:
+        assert dm.data.shape == dims
+        assert dm.err.shape == dims
+        assert dm.dq.shape == dims
+        assert dm.data.mean() == 0.0
+        assert dm.err.mean() == 0.0
+        assert dm.dq.mean() == 0.0
+        assert dm.zeroframe.shape == dims
+        assert dm.area.shape == dims
+        assert dm.pathloss_point.shape == dims
+        assert dm.pathloss_uniform.shape == dims
+
+
 def test_multislit():
     with MultiSlitModel() as dm:
         dm.slits.append(dm.slits.item())
@@ -265,6 +279,10 @@ def test_multislit():
         slit.data = np.random.rand(5, 5)
         slit.dm = np.random.rand(5, 5)
         slit.err = np.random.rand(5, 5)
+        assert slit.wavelength.shape == (0,0)
+        assert slit.pathloss_point.shape == (0,0)
+        assert slit.pathloss_uniform.shape == (0,0)
+        assert slit.barshadow.shape == (0,0)
 
 
 def test_secondary_shapes():

pip install -e .[test]
parse_log_pytest
requirements.txt
pytest pytest-cov pytest-xdist pytest-mock pytest-asyncio
apt-get update apt-get install -y gcc
3.8
pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning
asdf==2.15.0
asdf-astropy==0.4.0
asdf-coordinates-schemas==0.2.0
asdf-standard==1.0.3
asdf-transform-schemas==0.4.0
asdf-wcs-schemas==0.3.0
asdf_unit_schemas==0.2.0
astropy==5.2.2
attrs==25.3.0
certifi==2025.1.31
charset-normalizer==3.4.1
ci-watson==0.6.1
codecov==2.1.13
coverage==7.6.1
crds==12.1.4
drizzle==1.14.4
exceptiongroup==1.2.2
execnet==2.1.1
filelock==3.16.1
gwcs==0.18.3
idna==3.10
importlib_metadata==8.5.0
importlib_resources==6.4.5
iniconfig==2.1.0
jmespath==1.0.1
jsonschema==4.17.3
-e git+https://github.com/spacetelescope/jwst.git@8e5b573c66cb124e6f32e957c17226ac0c524ec4#egg=jwst
numpy==1.24.4
packaging==24.2
Parsley==1.3
photutils==1.8.0
pkgutil_resolve_name==1.3.10
pluggy==1.5.0
psutil==7.0.0
pyerfa==2.0.0.3
pyparsing==3.1.4
pyrsistent==0.20.0
pytest==8.3.5
pytest-asyncio==0.24.0
pytest-cov==5.0.0
pytest-doctestplus==1.3.0
pytest-mock==3.14.0
pytest-openfiles==0.6.0
pytest-xdist==3.6.1
PyYAML==6.0.2
requests==2.32.3
requests-mock==1.12.1
scipy==1.10.1
semantic-version==2.10.0
spherical-geometry==1.2.23
stsci.image==2.3.5
stsci.imagestats==1.6.3
stsci.stimage==0.2.6
tomli==2.2.1
tweakwcs==0.8.7
urllib3==2.2.3
zipp==3.20.2

name: jwst
channels:
  - defaults
  - https://repo.anaconda.com/pkgs/main
  - https://repo.anaconda.com/pkgs/r
  - conda-forge
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=5.1=1_gnu
  - ca-certificates=2025.2.25=h06a4308_0
  - ld_impl_linux-64=2.40=h12ee557_0
  - libffi=3.4.4=h6a678d5_1
  - libgcc-ng=11.2.0=h1234567_1
  - libgomp=11.2.0=h1234567_1
  - libstdcxx-ng=11.2.0=h1234567_1
  - ncurses=6.4=h6a678d5_0
  - openssl=3.0.16=h5eee18b_0
  - pip=24.2=py38h06a4308_0
  - python=3.8.20=he870216_0
  - readline=8.2=h5eee18b_0
  - setuptools=75.1.0=py38h06a4308_0
  - sqlite=3.45.3=h5eee18b_0
  - tk=8.6.14=h39e8969_0
  - wheel=0.44.0=py38h06a4308_0
  - xz=5.6.4=h5eee18b_1
  - zlib=1.2.13=h5eee18b_1
  - pip:
      - asdf==2.15.0
      - asdf-astropy==0.4.0
      - asdf-coordinates-schemas==0.2.0
      - asdf-standard==1.0.3
      - asdf-transform-schemas==0.4.0
      - asdf-unit-schemas==0.2.0
      - asdf-wcs-schemas==0.3.0
      - astropy==5.2.2
      - attrs==25.3.0
      - certifi==2025.1.31
      - charset-normalizer==3.4.1
      - ci-watson==0.6.1
      - codecov==2.1.13
      - coverage==7.6.1
      - crds==12.1.4
      - drizzle==1.14.4
      - exceptiongroup==1.2.2
      - execnet==2.1.1
      - filelock==3.16.1
      - gwcs==0.18.3
      - idna==3.10
      - importlib-metadata==8.5.0
      - importlib-resources==6.4.5
      - iniconfig==2.1.0
      - jmespath==1.0.1
      - jsonschema==4.17.3
      - jwst==0.16.3.dev50+g8e5b573c6
      - numpy==1.24.4
      - packaging==24.2
      - parsley==1.3
      - photutils==1.8.0
      - pkgutil-resolve-name==1.3.10
      - pluggy==1.5.0
      - psutil==7.0.0
      - pyerfa==2.0.0.3
      - pyparsing==3.1.4
      - pyrsistent==0.20.0
      - pytest==8.3.5
      - pytest-asyncio==0.24.0
      - pytest-cov==5.0.0
      - pytest-doctestplus==1.3.0
      - pytest-mock==3.14.0
      - pytest-openfiles==0.6.0
      - pytest-xdist==3.6.1
      - pyyaml==6.0.2
      - requests==2.32.3
      - requests-mock==1.12.1
      - scipy==1.10.1
      - semantic-version==2.10.0
      - spherical-geometry==1.2.23
      - stsci-image==2.3.5
      - stsci-imagestats==1.6.3
      - stsci-stimage==0.2.6
      - tomli==2.2.1
      - tweakwcs==0.8.7
      - urllib3==2.2.3
      - zipp==3.20.2
prefix: /opt/conda/envs/jwst

jwst/datamodels/tests/test_models.py::test_imagemodel jwst/datamodels/tests/test_models.py::test_multislit
jwst/datamodels/tests/test_models.py::test_from_fits_write jwst/datamodels/tests/test_models.py::test_multislit_from_fits_image jwst/datamodels/tests/test_models.py::test_multislit_copy jwst/datamodels/tests/test_models.py::test_open_fits_model_s3 jwst/datamodels/tests/test_models.py::test_image_with_extra_keyword_to_multislit jwst/datamodels/tests/test_models.py::test_update_from_dict
jwst/datamodels/tests/test_models.py::test_set_shape jwst/datamodels/tests/test_models.py::test_broadcast jwst/datamodels/tests/test_models.py::test_broadcast2 jwst/datamodels/tests/test_models.py::test_from_hdulist jwst/datamodels/tests/test_models.py::test_subarray jwst/datamodels/tests/test_models.py::test_delete jwst/datamodels/tests/test_models.py::test_open jwst/datamodels/tests/test_models.py::test_open_warning jwst/datamodels/tests/test_models.py::test_copy jwst/datamodels/tests/test_models.py::test_stringify jwst/datamodels/tests/test_models.py::test_section jwst/datamodels/tests/test_models.py::test_init_with_array jwst/datamodels/tests/test_models.py::test_init_with_array2 jwst/datamodels/tests/test_models.py::test_init_with_array3 jwst/datamodels/tests/test_models.py::test_set_array jwst/datamodels/tests/test_models.py::test_set_array2 jwst/datamodels/tests/test_models.py::test_base_model_has_no_arrays jwst/datamodels/tests/test_models.py::test_array_type jwst/datamodels/tests/test_models.py::test_copy_model jwst/datamodels/tests/test_models.py::test_dtype_match jwst/datamodels/tests/test_models.py::test_default_value_anyof_schema jwst/datamodels/tests/test_models.py::test_secondary_shapes jwst/datamodels/tests/test_models.py::test_multislit_from_image jwst/datamodels/tests/test_models.py::test_multislit_metadata jwst/datamodels/tests/test_models.py::test_multislit_metadata2 jwst/datamodels/tests/test_models.py::test_model_with_nonstandard_primary_array jwst/datamodels/tests/test_models.py::test_initialize_arrays_with_arglist jwst/datamodels/tests/test_models.py::test_open_asdf_model[None] jwst/datamodels/tests/test_models.py::test_open_asdf_model[/jwst/jwst/datamodels/tests/data/collimator_fake.asdf] jwst/datamodels/tests/test_models.py::test_open_asdf_model_s3 jwst/datamodels/tests/test_models.py::test_modelcontainer_iteration jwst/datamodels/tests/test_models.py::test_modelcontainer_indexing jwst/datamodels/tests/test_models.py::test_modelcontainer_group1 jwst/datamodels/tests/test_models.py::test_modelcontainer_group2 jwst/datamodels/tests/test_models.py::test_modelcontainer_group_names jwst/datamodels/tests/test_models.py::test_modelcontainer_error_from_asn jwst/datamodels/tests/test_models.py::test_object_node_iterator jwst/datamodels/tests/test_models.py::test_hasattr jwst/datamodels/tests/test_models.py::test_validate_on_read jwst/datamodels/tests/test_models.py::test_validate_required_field jwst/datamodels/tests/test_models.py::test_multislit_model jwst/datamodels/tests/test_models.py::test_slit_from_image jwst/datamodels/tests/test_models.py::test_ifuimage jwst/datamodels/tests/test_models.py::test_datamodel_raises_filenotfound jwst/datamodels/tests/test_models.py::test_abvega_offset_model jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[None-False-False] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[None-True-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[0-False-False] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[0-True-False] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[1-False-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[1-True-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[2-False-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[2-True-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[f-False-False] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[f-True-False] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[F-False-False] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[F-True-False] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[false-False-False] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[false-True-False] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[False-False-False] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[False-True-False] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[FALSE-False-False] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[FALSE-True-False] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[t-False-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[t-True-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[T-False-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[T-True-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[true-False-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[true-True-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[True-False-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[True-True-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[TRUE-False-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[TRUE-True-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[y-False-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[y-True-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[Y-False-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[Y-True-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[yes-False-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[yes-True-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[Yes-False-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[Yes-True-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[YES-False-True] jwst/datamodels/tests/test_models.py::test_get_envar_as_boolean[YES-True-True] jwst/datamodels/tests/test_models.py::test_getarray_noinit_valid jwst/datamodels/tests/test_models.py::test_getarray_noinit_raises jwst/datamodels/tests/test_models.py::test_getarray_noinit_noinit
jwst/datamodels/multislit.py jwst/datamodels/slit.py docs/jwst/pathloss/description.rst jwst/datamodels/image.py jwst/pathloss/pathloss.py jwst/datamodels/schemas/pathlosscorr.schema.yaml jwst/datamodels/schemas/slitdata.schema.yaml jwst/datamodels/ifuimage.py docs/jwst/data_products/science_products.rst jwst/datamodels/multiexposure.py CHANGES.rst jwst/pathloss/pathloss_step.py docs/jwst/pipeline/calwebb_spec2.rst
jwst/datamodels/multislit.py jwst/datamodels/slit.py docs/jwst/pathloss/description.rst jwst/datamodels/image.py jwst/pathloss/pathloss.py jwst/datamodels/schemas/pathlosscorr.schema.yaml jwst/datamodels/schemas/slitdata.schema.yaml jwst/datamodels/ifuimage.py docs/jwst/data_products/science_products.rst jwst/datamodels/multiexposure.py CHANGES.rst jwst/pathloss/pathloss_step.py docs/jwst/pipeline/calwebb_spec2.rst