JP-3630: Add slit-specific processing metadata

changes/9254.barshadow.rst

@@ -0,0 +1 @@
+For NIRSpec multislit data, add a metadata keyword to each slit to record whether it has been barshadow corrected.

changes/9254.pathloss.rst

@@ -0,0 +1 @@
+For NIRSpec multislit data, add a metadata keyword to each slit to record the pathloss correction type applied (POINT or UNIFORM).

changes/9254.wavecorr.rst

@@ -0,0 +1 @@
+For NIRSpec multislit data, add a metadata keyword to each slit to record whether it has been wavelength corrected.

docs/jwst/barshadow/description.rst

@@ -54,3 +54,8 @@ Output product
 The output is a new copy of the input `~jwst.datamodels.MultiSlitModel`, with the
 corrections applied to the slit data arrays. The 2-D correction array for each slit
 is also added to the datamodel in the "BARSHADOW" extension.
+
+Upon successful completion of the step, the status keyword "S_BARSHA"
+in the primary header is set to "COMPLETE".  For each SCI extension, the "BARSHDW"
+keyword is set to True if the slit was barshadow corrected (it is an extended
+source) or False if it was not corrected (it is a point source).

docs/jwst/pathloss/description.rst

@@ -109,6 +109,12 @@ Like for the point source case, the 1-d arrays of pathloss correction and wavele
 are used to interpolate the correction for each pixel in the science data, using the
 wavelength of each pixel to interpolate into the pathloss correction array.
 
+Upon successful completion of the step, the status keyword "S_PTHLOS"
+in the primary header is set to "COMPLETE".  For each SCI extension, the "PTHLOSS"
+keyword is set to "POINT" if the point source correction type was applied to the
+slit data. It is set to "UNIFORM" if the uniform correction type was applied.
+The keyword is not set if no correction was applied.
+
 MIRI LRS
 ++++++++
 The algorithm for MIRI LRS mode is largely the same as that for NIRSpec described

docs/jwst/wavecorr/description.rst

@@ -9,6 +9,11 @@ The wavelength correction (``wavecorr``) step in the
 assignments for NIRSpec fixed-slit (FS) and MOS point sources that are
 known to be off center (in the dispersion direction) in their slit.
 
+Upon successful completion of the step, the status keyword "S_WAVCOR"
+in the primary header is set to "COMPLETE".  For each SCI extension, the "WAVECOR"
+keyword is set to True if the slit was wavelength corrected (it is a point
+source) or False if it was not corrected (it is a point source).
+
 NIRSpec MOS
 -----------
 For NIRSpec MOS exposures (EXP_TYPE="NRS_MSASPEC"), wavelength
@@ -47,7 +52,4 @@ target coordinates and aperture reference point during the
 :ref:`extract_2d <extract_2d_step>` step. This estimated position (in the 
 dispersion direction) is used in the same manner as described above
 for MOS slitlets to update the slit WCS and compute corrected wavelengths
-for the primary slit only. 
-
-Upon successful completion of the step, the status keyword "S_WAVCOR"
-is set to "COMPLETE".
+for the primary slit only.

jwst/barshadow/bar_shadow.py

@@ -73,6 +73,20 @@ def do_correction(
             correction = _calc_correction(slitlet, barshadow_model, source_type)
         corrections.slits.append(correction)
 
+        if correction is None:
+            # For point sources, there is no real correction applied.
+            # Record the correction status as False in this case.
+            slitlet.barshadow_corrected = False
+
+            # Store a blank barshadow image
+            slitlet.barshadow = np.ones_like(slitlet.data)
+
+            # No further processing needed
+            continue
+
+        # Otherwise, record the correction status as True.
+        slitlet.barshadow_corrected = True
+
         # Apply the correction by dividing into the science and uncertainty arrays:
         #     var_poisson and var_rnoise are divided by correction**2,
         #     because they're variance, while err is standard deviation
@@ -118,7 +132,7 @@ def _calc_correction(slitlet, barshadow_model, source_type):
     slitlet_number = slitlet.slitlet_id
 
     # Correction only applies to extended/uniform sources
-    correction = datamodels.SlitModel(data=np.ones(slitlet.data.shape))
+    correction = None
     if not has_uniform_source(slitlet, source_type):
         log.info(f"Bar shadow correction skipped for slitlet {slitlet_number} (source not uniform)")
         return correction
@@ -171,6 +185,7 @@ def _calc_correction(slitlet, barshadow_model, source_type):
     wcol = (wavelength - w0) / wave_increment
 
     # Interpolate the bar shadow correction for non-Nan pixels
+    correction = datamodels.SlitModel()
     correction.data = ndimage.map_coordinates(shadow, [yrow, wcol], cval=np.nan, order=1)
 
     return correction

jwst/barshadow/tests/test_barshadow_step.py

@@ -24,15 +24,16 @@ def log_watcher(monkeypatch):
     return watcher
 
 
-@pytest.fixture(scope="module")
-def nirspec_mos_model():
+def create_nirspec_mos_model():
     hdul = create_nirspec_mos_file()
     msa_meta = os.path.join(
         jwst.__path__[0], *["assign_wcs", "tests", "data", "msa_configuration.fits"]
     )
     hdul[0].header["MSAMETFL"] = msa_meta
     hdul[0].header["MSAMETID"] = 12
     im = datamodels.ImageModel(hdul)
+    hdul.close()
+
     im.data = np.full((2048, 2048), 1.0)
     im_wcs = AssignWcsStep.call(im)
     im_ex2d = Extract2dStep.call(im_wcs)
@@ -43,17 +44,19 @@ def nirspec_mos_model():
     im_ex2d.slits[0].var_poisson = im_ex2d.slits[0].data * 0.01
     im_ex2d.slits[0].var_flat = im_ex2d.slits[0].data * 0.01
 
-    yield im_ex2d
-    im_ex2d.close()
-    im_wcs.close()
-    im.close()
-    hdul.close()
+    return im_ex2d
+
+
+@pytest.fixture(scope="module")
+def nirspec_mos_model():
+    return create_nirspec_mos_model()
 
 
 def test_barshadow_step(nirspec_mos_model):
     model = nirspec_mos_model.copy()
     result = BarShadowStep.call(model)
     assert result.meta.cal_step.barshadow == "COMPLETE"
+    assert result.slits[0].barshadow_corrected is True
 
     # 5 shutter slitlet, correction should not be uniform
     shadow = result.slits[0].barshadow
@@ -95,9 +98,13 @@ def test_barshadow_step_zero_length(nirspec_mos_model, log_watcher):
     result = BarShadowStep.call(model)
     log_watcher.assert_seen()
 
-    # correction ran, but is all 1s
+    # correction step ran, but is all 1s
     assert result.meta.cal_step.barshadow == "COMPLETE"
     assert np.all(result.slits[0].barshadow == 1)
+
+    # correction status is False
+    assert result.slits[0].barshadow_corrected is False
+
     result.close()
 
 
@@ -112,6 +119,7 @@ def test_barshadow_step_not_uniform(nirspec_mos_model, log_watcher):
     # correction ran, but is all 1s
     assert result.meta.cal_step.barshadow == "COMPLETE"
     assert np.all(result.slits[0].barshadow == 1)
+    assert result.slits[0].barshadow_corrected is False
     result.close()
 
 
@@ -126,6 +134,7 @@ def test_barshadow_no_reffile(monkeypatch, nirspec_mos_model):
     # correction did not run
     assert result.meta.cal_step.barshadow == "SKIPPED"
     assert result.slits[0].barshadow.size == 0
+    assert result.slits[0].barshadow_corrected is None
     result.close()
 
 
@@ -138,6 +147,7 @@ def test_barshadow_wrong_exptype():
     # correction did not run
     assert result.meta.cal_step.barshadow == "SKIPPED"
     assert result.slits[0].barshadow.size == 0
+    assert result.slits[0].barshadow_corrected is None
 
     result.close()
 

jwst/extract_1d/extract.py

@@ -742,6 +742,9 @@ def copy_keyword_info(slit, slitname, spec):
         "quadrant",
         "slit_xscale",
         "slit_yscale",
+        "wavelength_corrected",
+        "pathloss_correction_type",
+        "barshadow_corrected",
     ]
     for key in copy_populated_attributes:
         if getattr(slit, key, None) is not None:

jwst/extract_1d/tests/test_extract.py

@@ -473,6 +473,9 @@ def test_copy_keyword_info(mock_nirspec_fs_one_slit, mock_one_spec):
         "source_ra": 10.0,
         "source_dec": 10.0,
         "shutter_state": "x",
+        "wavelength_corrected": True,
+        "pathloss_correction_type": "POINT",
+        "barshadow_corrected": False
     }
     for key, value in expected.items():
         setattr(mock_nirspec_fs_one_slit, key, value)

jwst/pathloss/pathloss.py

@@ -430,14 +430,6 @@
             log.warning("Forcing of source type with NIS_SOSS is not implemented. Skipping")
             output_model.meta.cal_step.pathloss = "SKIPPED"
             corrections = None
-        elif inverse:
-            log.warning("Use of inversion with NIS_SOSS is not implemented. Skipping")
-            output_model.meta.cal_step.pathloss = "SKIPPED"
-            corrections = None
-        elif source_type is not None:
-            log.warning("Forcing of source type with NIS_SOSS is not implemented. Skipping")
-            output_model.meta.cal_step.pathloss = "SKIPPED"
-            corrections = None
         else:
             corrections = do_correction_soss(output_model, pathloss_model)
 
@@ -584,15 +576,21 @@
 
         if not inverse:
             slit.data /= correction.data
+            slit.err /= correction.data
+            slit.var_poisson /= correction.data**2
+            slit.var_rnoise /= correction.data**2
+            if slit.var_flat is not None and np.size(slit.var_flat) > 0:
+                slit.var_flat /= correction.data**2
         else:
             slit.data *= correction.data
-        slit.err /= correction.data
-        slit.var_poisson /= correction.data**2
-        slit.var_rnoise /= correction.data**2
-        if slit.var_flat is not None and np.size(slit.var_flat) > 0:
-            slit.var_flat /= correction.data**2
+            slit.err *= correction.data
+            slit.var_poisson *= correction.data**2
+            slit.var_rnoise *= correction.data**2
+            if slit.var_flat is not None and np.size(slit.var_flat) > 0:
+                slit.var_flat *= correction.data**2
         slit.pathloss_point = correction.pathloss_point
         slit.pathloss_uniform = correction.pathloss_uniform
+        slit.pathloss_correction_type = correction.pathloss_correction_type
 
         # Make sure all NaNs and flags match up in the output slit model
         match_nans_and_flags(slit)
@@ -651,15 +649,22 @@
 
         if not inverse:
             slit.data /= correction.data
+            slit.err /= correction.data
+            slit.var_poisson /= correction.data**2
+            slit.var_rnoise /= correction.data**2
+            if slit.var_flat is not None and np.size(slit.var_flat) > 0:
+                slit.var_flat /= correction.data**2
         else:
             slit.data *= correction.data
-        slit.err /= correction.data
-        slit.var_poisson /= correction.data**2
-        slit.var_rnoise /= correction.data**2
-        if slit.var_flat is not None and np.size(slit.var_flat) > 0:
-            slit.var_flat /= correction.data**2
+            slit.err *= correction.data
+            slit.var_poisson *= correction.data**2
+            slit.var_rnoise *= correction.data**2
+            if slit.var_flat is not None and np.size(slit.var_flat) > 0:
+                slit.var_flat *= correction.data**2
+
         slit.pathloss_point = correction.pathloss_point
         slit.pathloss_uniform = correction.pathloss_uniform
+        slit.pathloss_correction_type = correction.pathloss_correction_type
 
         # Make sure all NaNs and flags match up in the output slit model
         match_nans_and_flags(slit)
@@ -705,15 +710,21 @@
 
     if not inverse:
         data.data /= correction.data
+        data.err /= correction.data
+        data.var_poisson /= correction.data**2
+        data.var_rnoise /= correction.data**2
+        if data.var_flat is not None and np.size(data.var_flat) > 0:
+            data.var_flat /= correction.data**2
     else:
         data.data *= correction.data
-    data.err /= correction.data
-    data.var_poisson /= correction.data**2
-    data.var_rnoise /= correction.data**2
-    if data.var_flat is not None and np.size(data.var_flat) > 0:
-        data.var_flat /= correction.data**2
+        data.err *= correction.data
+        data.var_poisson *= correction.data**2
+        data.var_rnoise *= correction.data**2
+        if data.var_flat is not None and np.size(data.var_flat) > 0:
+            data.var_flat *= correction.data**2
     data.pathloss_point = correction.pathloss_point
     data.pathloss_uniform = correction.pathloss_uniform
+    data.pathloss_correction_type = correction.pathloss_correction_type
 
     # This might be useful to other steps
     data.wavelength = correction.wavelength
@@ -962,14 +973,18 @@
                 # Use the appropriate correction for this slit
                 if is_pointsource(source_type or slit.source_type):
                     pathloss_2d = pathloss_2d_ps
+                    correction_type = "POINT"
                 else:
                     pathloss_2d = pathloss_2d_un
+                    correction_type = "UNIFORM"
 
                 # Save the corrections. The `data` portion is the correction used.
                 # The individual ones will be saved in the respective attributes.
                 correction = datamodels.SlitModel(data=pathloss_2d)
                 correction.pathloss_point = pathloss_2d_ps
                 correction.pathloss_uniform = pathloss_2d_un
+                correction.pathloss_correction_type = correction_type
+
             else:
                 log.warning("Source is outside slit.")
         else:
@@ -1046,7 +1061,7 @@
                 )
 
                 pathloss_2d = pathloss_2d_ps
-
+                correction_type = "POINT"
             else:
                 wavelength_array = slit.wavelength
 
@@ -1061,12 +1076,14 @@
                 )
 
                 pathloss_2d = pathloss_2d_un
+                correction_type = "UNIFORM"
 
             # Save the corrections. The `data` portion is the correction used.
             # The individual ones will be saved in the respective attributes.
             correction = datamodels.SlitModel(data=pathloss_2d)
             correction.pathloss_point = pathloss_2d_ps
             correction.pathloss_uniform = pathloss_2d_un
+            correction.pathloss_correction_type = correction_type
 
         else:
             log.warning(
@@ -1150,15 +1167,18 @@
     # Use the appropriate correction for the source type
     if is_pointsource(source_type or data.meta.target.source_type):
         pathloss_2d = pathloss_2d_ps
+        correction_type = "POINT"
     else:
         pathloss_2d = pathloss_2d_un
+        correction_type = "UNIFORM"
 
     # Save the corrections. The `data` portion is the correction used.
     # The individual ones will be saved in the respective attributes.
     correction = type(data)(data=pathloss_2d)
     correction.pathloss_point = pathloss_2d_ps
     correction.pathloss_uniform = pathloss_2d_un
     correction.wavelength = wavelength_array
+    correction.pathloss_correction_type = correction_type
 
     return correction