JP-3325: bypass extract1d for SOSS observations in FULL subarray (#8225)

Co-authored-by: Ned Molter <[email protected]>
Co-authored-by: Howard Bushouse <[email protected]>

CHANGES.rst

@@ -39,6 +39,9 @@ extract_1d
 
 - Fixed a bug in the calling of optional MIRI MRS 1d residual fringe
   correction that could cause defringing to fail in some cases. [#8180]
+
+- Added a hook to bypass the ``extract_1d`` step for NIRISS SOSS data in 
+  the FULL subarray with warning. [#8225]
 
 outlier_detection
 -----------------
@@ -59,6 +62,10 @@ photom
   the pipeline put the (variable, calculated per pixel) dispersion back in.  Assumes that
   the dispersion needs to be in Angstroms/pixel to match the required factor of ~10. [#8207]
 
+- Added a hook to bypass the ``photom`` step when the ``extract_1d`` step 
+  was bypassed and came before the ``photom`` step, e.g. for NIRISS SOSS
+  data in the FULL subarray. [#8225]
+
 refpix
 ------
 

docs/jwst/references_general/drizpars_reffile.inc

@@ -46,6 +46,6 @@ filter       integer  The filter used to obtain the images
 pixfrac      float    The pixel "shrinkage" fraction
 kernel       string   The kernel function used to distribute flux
 fillval      float    Value assigned to pixels with no input flux
-wht_type     sting    The input image weighting type
+wht_type     string   The input image weighting type
 stepsize     integer  Output WCS grid interpolation step size
 ===========  =======  ===========================================

jwst/extract_1d/extract_1d_step.py

@@ -412,19 +412,16 @@ def process(self, input):
                     self.log.info('Exposure is in the SUBSTRIP256 subarray.')
                     self.log.info('Traces 1 and 2 will be modelled and decontaminated before extraction.')
                     subarray = 'SUBSTRIP256'
-                elif input_model.meta.subarray.name == 'FULL':
-                    self.log.info('Exposure is in the FULL subarray.')
-                    self.log.info('Traces 1 and 2 will be modelled and decontaminated before extraction.')
-                    subarray = 'FULL'
                 elif input_model.meta.subarray.name == 'SUBSTRIP96':
                     self.log.info('Exposure is in the SUBSTRIP96 subarray.')
                     self.log.info('Traces of orders 1 and 2 will be modelled but only order 1'
                                   ' will be decontaminated before extraction.')
                     subarray = 'SUBSTRIP96'
                 else:
-                    self.log.error('The SOSS extraction is implemented for the SUBSTRIP256,'
-                                   ' SUBSTRIP96 and FULL subarray only.')
-                    self.log.error('extract_1d will be skipped.')
+                    self.log.error('The SOSS extraction is implemented for the SUBSTRIP256'
+                                   'and SUBSTRIP96 subarrays only. Subarray is currently '
+                                   f'{input_model.meta.subarray.name}.')
+                    self.log.error('Extract1dStep will be skipped.')
                     input_model.meta.cal_step.extract_1d = 'SKIPPED'
                     return input_model
 

jwst/extract_1d/tests/test_expected_skips.py

@@ -0,0 +1,31 @@
+import stdatamodels.jwst.datamodels as dm
+from jwst.extract_1d import Extract1dStep
+from jwst.photom import PhotomStep
+import pytest
+import numpy as np
+
+
+@pytest.fixture(scope='module')
+def mock_niriss_full():
+    model = dm.CubeModel((3, 3, 3))
+    model.meta.instrument.name = 'NIRISS'
+    model.meta.instrument.detector = 'NIS'
+    model.meta.observation.date = '2023-07-22'
+    model.meta.observation.time = '06:24:45.569'
+    model.meta.instrument.name = 'NIRISS'
+    model.meta.instrument.detector = 'NIS'
+    model.meta.instrument.filter = 'CLEAR'
+    model.meta.exposure.type = 'NIS_SOSS'
+    model.meta.subarray.name = 'FULL'
+    model.data = np.arange(27).reshape((3, 3, 3))
+    return model
+
+
+def test_expected_skip_niriss_soss_full(mock_niriss_full):
+
+    with mock_niriss_full as model:
+        result = Extract1dStep().process(model)
+        result2 = PhotomStep().process(result)
+        assert result2.meta.cal_step.photom == 'SKIPPED'
+        assert result2.meta.cal_step.extract_1d == 'SKIPPED'
+        assert np.all(result2.data == model.data)

jwst/photom/photom.py

@@ -33,6 +33,14 @@ def __init__(self, message):
         super().__init__(message)
 
 
+class DataModelTypeError(Exception):
+
+    def __init__(self, message):
+        if message is None:
+            message = "Unexpected DataModel type."
+        super().__init__(message)
+
+
 def find_row(fits_table, match_fields):
     """
     Find a row in a FITS table matching fields.
@@ -413,6 +421,9 @@ def calc_niriss(self, ftab):
                     continue
                 self.photom_io(ftab.phot_table[row])
 
+        elif isinstance(self.input, datamodels.CubeModel):
+            raise DataModelTypeError(f"Unexpected input data model type for NIRISS: {self.input.__class__.__name__}")
+
         elif self.exptype in ['NIS_SOSS']:
             for spec in self.input.spec:
                 self.specnum += 1
@@ -481,13 +492,13 @@ def calc_miri(self, ftab):
                 photom_corr = miri_imager.time_corr_photom(ftab.timecoeff[row], mid_time)
 
                 data = np.array(
-                    [(self.filter, self.subarray, ftab.phot_table[row]['photmjsr']+photom_corr, ftab.phot_table[row]['uncertainty'])],
+                    [(self.filter, self.subarray, ftab.phot_table[row]['photmjsr'] + photom_corr, ftab.phot_table[row]['uncertainty'])],
                     dtype=[
                         ("filter", "O"),
                         ("subarray", "O"),
                         ("photmjsr", "<f4"),
                         ("uncertainty", "<f4")
-                        ],
+                    ],
                 )
                 fftab = datamodels.MirImgPhotomModel(phot_table=data)
                 self.photom_io(fftab.phot_table[0])
@@ -539,12 +550,12 @@ def calc_miri(self, ftab):
                 log.info("Skipping MRS MIRI time correction. Extensions not found in the reference file.")
                 self.mrs_time_correction = False
 
-            #if np.any(ftab.timecoeff_ch1['binwave']) and self.mrs_time_correction:
+            # if np.any(ftab.timecoeff_ch1['binwave']) and self.mrs_time_correction:
             if self.mrs_time_correction:
                 log.info("Applying MRS IFU time dependent correction.")
                 mid_time = self.input.meta.exposure.mid_time
                 correction = miri_mrs.time_correction(self.input, self.detector,
-                                                     ftab, mid_time)
+                                                      ftab, mid_time)
                 self.input.data /= correction
                 self.input.err /= correction
             else:

jwst/photom/photom_step.py

@@ -64,13 +64,21 @@ def process(self, input):
             result.meta.cal_step.photom = 'SKIPPED'
             return result
 
-        # Do the correction
-        phot = photom.DataSet(input_model, self.inverse, self.source_type,
-                              self.mrs_time_correction, correction_pars)
-        result = phot.apply_photom(phot_filename, area_filename)
-        result.meta.cal_step.photom = 'COMPLETE'
-
-        self.correction_pars = phot.correction_pars
-        self.correction_pars['refs'] = {'photom': phot_filename, 'area': area_filename}
-
+        try:
+            # Do the correction
+            phot = photom.DataSet(input_model, self.inverse, self.source_type,
+                                  self.mrs_time_correction, correction_pars)
+            result = phot.apply_photom(phot_filename, area_filename)
+            result.meta.cal_step.photom = 'COMPLETE'
+            self.correction_pars = phot.correction_pars
+            self.correction_pars['refs'] = {'photom': phot_filename, 'area': area_filename}
+
+        except photom.DataModelTypeError:
+            # should trip e.g. for NIRISS SOSS data in FULL subarray
+            self.log.error(f'Unexpected data model type {model_type} for '
+                           f'{input_model.meta.instrument.name.upper()}. '
+                           'Photom will be skipped.')
+            result = input_model.copy()
+            result.meta.cal_step.photom = 'SKIPPED'
+            return result
         return result

jwst/photom/tests/test_photom.py

@@ -341,7 +341,7 @@ def create_input(instrument, detector, exptype,
             input_model.var_flat = np.ones(shape, dtype=np.float32)
             input_model.meta.subarray.name = 'SUB256'       # matches 'GENERIC'
             input_model.meta.target.source_type = 'POINT'
-            input_model.meta.exposure.mid_time = 60000.0 # Added for new PHOTOM step
+            input_model.meta.exposure.mid_time = 60000.0  # Added for new PHOTOM step
             input_model.meta.photometry.pixelarea_arcsecsq = 0.0025
             input_model.meta.photometry.pixelarea_steradians = 0.0025 * A2_TO_SR
     elif instrument == 'FGS':
@@ -803,7 +803,7 @@ def create_photom_miri_image(min_wl=16.5, max_wl=19.5,
                       ('tau', '<f4'),
                       ('t0', '<f4')])
     reftabc = np.array(list(zip(timecoeff_amp, timecoeff_tau, timecoeff_t0)),
-                      dtype=dtypec)
+                       dtype=dtypec)
     ftab = datamodels.MirImgPhotomModel(phot_table=reftab, timecoeff=reftabc)
 
     return ftab
@@ -1342,6 +1342,21 @@ def test_niriss_image():
     assert np.allclose(ratio, compare, rtol=1.e-7)
 
 
+def test_expected_failure_niriss_cubemodel():
+    """
+    Test that passing a CubeModel to calc_niriss raises an exception
+    This occurs when extract_1d step is skipped, e.g. for NIRISS SOSS data
+    in FULL subarray.
+    """
+
+    input_model = create_input('NIRISS', 'NIS', 'NIS_SOSS',
+                               filter='CLEAR', pupil='GR700XD')
+    ds = photom.DataSet(input_model)
+    ds.input = datamodels.CubeModel()
+    with pytest.raises(photom.DataModelTypeError):
+        ds.calc_niriss(None)
+
+
 def test_miri_mrs():
     """Test calc_miri, MRS data"""
 
@@ -1431,7 +1446,7 @@ def test_miri_image():
     shape = input.shape
     ix = shape[1] // 2
     iy = shape[0] // 2
-    compare = photmjsr + amplitude * np.exp(-(60000 - t0)/tau) # Added for new PHOTOM step
+    compare = photmjsr + amplitude * np.exp(-(60000 - t0) / tau)  # Added for new PHOTOM step
     # Compare the values at the center pixel.
     ratio = output[iy, ix] / input[iy, ix]
     assert np.allclose(ratio, compare, rtol=1.e-7)