JP-3768: Fix intermediate issues with non-resampled outlier methods

jwst/outlier_detection/tests/test_outlier_detection.py

@@ -203,6 +203,7 @@ def we_three_sci():
 def test_outlier_step_no_outliers(we_three_sci, do_resample, tmp_cwd):
  """Test whole step, no outliers"""
  container = ModelContainer(list(we_three_sci))
+ container[0].var_rnoise[10, 10] = 1E9
  pristine = ModelContainer([m.copy() for m in container])
  OutlierDetectionStep.call(container, in_memory=True, resample_data=do_resample)
 
@@ -261,7 +262,8 @@ def test_outlier_step_base(we_three_sci, tmp_cwd):
  assert len(median_files) != 0
 
 
-def test_outlier_step_spec(tmp_cwd, tmp_path):
+@pytest.mark.parametrize('resample', [True, False])
+def test_outlier_step_spec(tmp_cwd, tmp_path, resample):
  """Test outlier step for spec data including saving intermediate results."""
  output_dir = tmp_path / 'output'
  output_dir.mkdir(exist_ok=True)
@@ -274,18 +276,25 @@ def test_outlier_step_spec(tmp_cwd, tmp_path):
  # Make it an exposure type outlier detection expects
  miri_cal.meta.exposure.type = "MIR_LRS-FIXEDSLIT"
 
- # Make a couple copies, give them unique exposure numbers and filename
- container = ModelContainer([miri_cal, miri_cal.copy(), miri_cal.copy()])
- for i, model in enumerate(container):
- model.meta.filename = f'test_{i}_cal.fits'
+ def make_input():
+ # Make a couple copies, give them unique exposure numbers and filename
+ container = ModelContainer([miri_cal.copy(), miri_cal.copy(), miri_cal.copy()])
+ for i, model in enumerate(container):
+ model.meta.filename = f'test_{i}_cal.fits'
 
- # Drop a CR on the science array in the first image
- container[0].data[209, 37] += 1
+ # Drop a CR on the science array in the first image
+ container[0].data[209, 37] += 1
+
+ return container
 
  # Verify that intermediate files are removed when not saved
- # (s2d files are expected, i2d files are not, but we'll check
- # for them to make sure the imaging extension didn't creep back in)
- OutlierDetectionStep.call(container, output_dir=output_dir, save_results=True)
+ # If resampling, s2d files are expected, i2d files are not,
+ # but we'll check for them to make sure the imaging extension
+ # didn't creep back in.
+ container = make_input()
+ OutlierDetectionStep.call(container,
+ output_dir=output_dir, save_results=True,
+ resample_data=resample)
  for dirname in [output_dir, tmp_cwd]:
  result_files = glob(os.path.join(dirname, '*outlierdetectionstep.fits'))
  i2d_files = glob(os.path.join(dirname, '*i2d*.fits'))
@@ -306,9 +315,10 @@ def test_outlier_step_spec(tmp_cwd, tmp_path):
  assert len(result_files) == 0
 
  # Call again, but save intermediate to the output path
+ container = make_input()
  result = OutlierDetectionStep.call(
  container, save_results=True, save_intermediate_results=True,
- output_dir=output_dir
+ output_dir=output_dir, resample_data=resample
  )
 
  # Make sure nothing changed in SCI array
@@ -331,8 +341,13 @@ def test_outlier_step_spec(tmp_cwd, tmp_path):
  assert len(result_files) == len(container)
 
  # s2d, median, and blot files are written to the output directory
- assert len(s2d_files) == len(container)
- assert len(blot_files) == len(container)
+ if resample:
+ assert len(s2d_files) == len(container)
+ assert len(blot_files) == len(container)
+ else:
+ assert len(s2d_files) == 0
+ assert len(blot_files) == 0
+
  assert len(median_files) == 1
 
  # i2d files not written
@@ -674,4 +689,4 @@ def make_resamp(input_models):
  asn_id="test",
  allowed_memory=None,
  )
- return resamp
+ return resamp

jwst/outlier_detection/utils.py

@@ -78,16 +78,14 @@ def median_without_resampling(input_models,
  with input_models:
  for i in range(len(input_models)):
 
- drizzled_model = input_models.borrow(i)
+ input_model = input_models.borrow(i)
+ drizzled_model = copy.deepcopy(input_model)
+ input_models.shelve(input_model, i, modify=False)
+
  drizzled_model.wht = build_driz_weight(drizzled_model,
-  weight_type=weight_type,
-  good_bits=good_bits)
+ weight_type=weight_type,
+ good_bits=good_bits)
  median_wcs = copy.deepcopy(drizzled_model.meta.wcs)
- input_models.shelve(drizzled_model, i, modify=True)
-
- if save_intermediate_results:
- # write the drizzled model to file
- _fileio.save_drizzled(drizzled_model, make_output_path)
 
  if i == 0:
  input_shape = (ngroups,)+drizzled_model.data.shape