JP-3452: Update logging in imprint step (#8041)

CHANGES.rst

@@ -8,6 +8,12 @@ general
 
 - Increase asdf maximum version to 4 [#8018]
 
+imprint
+-------
+
+- Updated the logging to report which imprint image is being subtracted from the
+  science image. [#8041]
+
 resample
 --------
 

docs/jwst/imprint/description.rst

@@ -9,12 +9,21 @@ MOS and IFU exposures by the MSA structure. This is accomplished by
 subtracting a dedicated exposure taken with all MSA shutters closed and the
 IFU entrance aperture blocked.
 
-The step has two input parameters: the target exposure and a list of imprint
-exposures.  These arguments can be provided as either a file name
-or a JWST data model.
+The step has two input parameters: the target exposure and a list of one
+or more imprint exposures. These arguments can be provided as either file names
+or JWST data models.
 
-The SCI data array of the imprint exposure is subtracted from the SCI array
-of the target exposure. The DQ arrays of the two exposures are combined using
+In the event that multiple imprint images are provided, the step uses the
+meta data of the target and imprint exposures to find the imprint exposure
+that matches the observation number (keyword "OBSERVTN") and dither pattern
+position number (keyword "PATT_NUM") of the target exposure. The matching
+imprint image is then subtracted from the target image. If no matching imprint
+image is found, the step will be skipped, returning the input target image
+unaltered.
+
+When subtracting the imprint data model from the target data model,
+the SCI data array of the imprint exposure is subtracted from the SCI array
+of the target exposure, and the DQ arrays of the two exposures are combined using
 a bitwise logical OR operation. The ERR and variance arrays are not
 currently used or modified.
 

jwst/imprint/imprint_step.py

@@ -9,7 +9,7 @@
 class ImprintStep(Step):
     """
     ImprintStep: Removes NIRSpec MSA imprint structure from an exposure
-    by subtracting an imprint exposure.
+    by subtracting an imprint (a.k.a. leakcal) exposure.
     """
 
     class_alias = "imprint"
@@ -19,46 +19,46 @@ class ImprintStep(Step):
 
     def process(self, input, imprint):
 
-        # subtract leakcal (imprint) image
-        # If there is only one imprint image is in the association use it  for all the data.
-        # If there is more than one imprint image in the association then select
-        # the imprint  image to subtract based on position number (DataModel.meta.dither.position_number) &
-        # observation number (Datamodel.meta.observation.observation_number)
-
-        # Open the input data model and get position number of image
+        # Open the input science image and get its dither pattern position number
         input_model = datamodels.open(input)
         pos_no = input_model.meta.dither.position_number
         obs_no = input_model.meta.observation.observation_number
 
-        # find imprint that goes with input image - if there is only 1 imprint image - use it.
+        # If there is only one imprint image listed in the association,
+        # use it for all science images.
+        # If there is more than one imprint image in the association,
+        # then select the imprint image to be used by matching the dither pattern position
+        # number and observation number to the science image.
         num_imprint = len(imprint)
-        match = None
         if num_imprint == 1:
-            match = 0
+            match = 0  # use the first, and only, imprint image
         else:
-            i = 0
-            while match is None and i < num_imprint:
-                # open imprint images and read in pos_no to find match
+            match = None
+            for i in range(num_imprint):
+                # open imprint image and read in pos_no to find match
                 imprint_model = datamodels.open(imprint[i])
                 imprint_pos_no = imprint_model.meta.dither.position_number
                 imprint_obs_no = imprint_model.meta.observation.observation_number
                 imprint_model.close()
                 if pos_no == imprint_pos_no and obs_no == imprint_obs_no:
                     match = i
-                i = i + 1
+                    break
 
-        # Initialize result to be input (just in case no matching imprint image was found)
+        # Copy the input image to the output (just in case no matching imprint image was found)
         result = input_model.copy()
 
         if match is not None:
+            # Subtract the matching imprint image
             imprint_model = datamodels.open(imprint[match])
+            self.log.info(f"Subtracting imprint image {imprint_model.meta.filename}")
             result = subtract_images.subtract(input_model, imprint_model)
 
             # Update the step status and close the imprint model
             result.meta.cal_step.imprint = 'COMPLETE'
             imprint_model.close()
         else:
-            self.log.info(f'No imprint image was found for {input}')
+            self.log.warning(f'No matching imprint image was found for {input}')
+            self.log.warning('Step will be skipped')
             result.meta.cal_step.imprint = 'SKIPPED'
 
         input_model.close()

jwst/regtest/test_nirspec_imprint.py

@@ -0,0 +1,51 @@
+"""Regression test for NIRSpec imprint subtraction"""
+import pytest
+
+from jwst.regtest import regtestdata as rt
+
+# Define artifactory source and truth
+INPUT_PATH = 'nirspec/imprint'
+TRUTH_PATH = 'truth/test_nirspec_imprint'
+
+
+@pytest.fixture(scope='module')
+def run_spec2(jail, rtdata_module):
+    """Run the Spec2Pipeline on a spec2 ASN containing a single exposure
+    with multiple imprint exposures"""
+    rtdata = rtdata_module
+
+    # Setup the inputs
+    asn_name = 'jw01802-o011_spec2_00046_asn.json'
+    asn_path = INPUT_PATH + '/' + asn_name
+
+    # Run the pipeline
+    step_params = {
+        'input_path': asn_path,
+        'step': 'calwebb_spec2',
+        'args': [
+            '--steps.assign_wcs.save_results=true',
+            '--steps.imprint_subtract.save_results=true',
+            '--steps.msa_flagging.skip=true',
+            '--steps.srctype.skip=true',
+            '--steps.flat_field.skip=true',
+            '--steps.pathloss.skip=true',
+            '--steps.photom.skip=true',
+            '--steps.cube_build.skip=true',
+            '--steps.extract_1d.skip=true',
+        ]
+    }
+
+    rtdata = rt.run_step_from_dict(rtdata, **step_params)
+    return rtdata
+
+
+@pytest.mark.bigdata
+@pytest.mark.parametrize(
+    'suffix',
+    ['assign_wcs', 'imprint_subtract', 'cal']
+)
+def test_nirspec_imprint(run_spec2, fitsdiff_default_kwargs, suffix):
+    """Regression test matching output files"""
+    rt.is_like_truth(run_spec2, fitsdiff_default_kwargs, suffix,
+                     truth_path=TRUTH_PATH)
+