Merge branch 'main' into improve_combine1d

CHANGES.rst

@@ -1,3 +1,12 @@
+1.16.1 (2024-10-30)
+===================
+
+resample_spec
+-------------
+
+- Update NIRSpec spectral resampling to add a missing correction factor in resampled
+  WCS tangent plane transformation. [#8908]
+
 1.16.0 (2024-09-20)
 ===================
 

README.md

@@ -216,12 +216,13 @@ the specified context and less than the context for the next release.
 
 | jwst tag            | DMS build | SDP_VER  | CRDS_CONTEXT | Released   | Ops Install | Notes                                         |
 |---------------------|-----------|----------|--------------|------------|-------------|-----------------------------------------------|
-| 1.16.0              | B11.1rc1  | TBD      | 1281         | 2024-09-20 | TBD         | First release candidate for B11.1             |
-| 1.15.1              | B11.0     | 2024.2.2 | 1242         | 2024-07-08 | 2024-09-12  | Final release candidate for B11.0             |
-| 1.15.0              | B11.0rc1  |          | 1241         | 2024-06-26 |             | First release candidate for B11.0             |
-| 1.14.1              |           |          | 1238         | 2024-06-27 |             | PyPI-only release for external users          |
-| 1.14.0              | B10.2.1   | 2024.1.1 | 1238         | 2024-03-29 | 2024-06-12  | Final release candidate for B10.2.1           |
-| 1.13.4              |           |          | 1185         | 2024-01-25 |             | PyPI-only release for external users          |
+| 1.16.1              | B11.1.1   | 2024.3.1 | 1298         | 2024-11-13 | TBD         | Final release candidate for B11.1             |
+| 1.16.0              | B11.1     | 2024.3.0 | 1298         | 2024-09-20 | TBD         | First release candidate for B11.1             |
+| 1.15.1              | B11.0     | 2024.2.2 | 1293         | 2024-07-08 | 2024-09-12  | Final release candidate for B11.0             |
+| 1.15.0              | B11.0rc1  |          | 1274         | 2024-06-26 |             | First release candidate for B11.0             |
+| 1.14.1              |           |          | 1240         | 2024-06-27 |             | PyPI-only release for external users          |
+| 1.14.0              | B10.2.1   | 2024.1.1 | 1240         | 2024-03-29 | 2024-06-12  | Final release candidate for B10.2.1           |
+| 1.13.4              |           |          | 1210         | 2024-01-25 |             | PyPI-only release for external users          |
 | 1.13.3              | B10.1     | 2023.4.0 | 1181         | 2024-01-05 |             | Final release candidate for B10.1             |
 | 1.13.2              | B10.1rc3  | 2023.4.0 | 1181         | 2023-12-21 |             | Third release candidate for B10.1             |
 | 1.13.1              | B10.1rc2  | 2023.4.0 | 1181         | 2023-12-19 |             | Second release candidate for B10.1            |

changes/8893.resample.rst

@@ -0,0 +1 @@
+Use s_region list to calculate output footprint instead of re-computing via WCS transforms

changes/8927.combine_1d.rst

@@ -0,0 +1 @@
+Fix wavelength sort order for single input spectrum.

changes/8927.master_background.rst

@@ -0,0 +1 @@
+Fix ModelContainer handling for user background input.

changes/8927.pipeline.rst

@@ -0,0 +1 @@
+Add 'mbsub' to the list of known suffixes, for ``master_background`` correction in ``calwebb_spec3``.

changes/8945.stpipe.rst

@@ -0,0 +1 @@
+Remove all uses of Step.__call__ to allow it's deprecation.

jwst/assign_mtwcs/moving_target_wcs.py

@@ -18,6 +18,8 @@
 
 from jwst.datamodels import ModelLibrary
 from jwst.stpipe.utilities import record_step_status
+from jwst.assign_wcs.util import update_s_region_imaging
+from jwst.lib.exposure_types import IMAGING_TYPES
 
 log = logging.getLogger(__name__)
 log.setLevel(logging.DEBUG)
@@ -69,6 +71,8 @@ def assign_moving_target_wcs(input_models):
                                     model.meta.wcsinfo.mt_ra, model.meta.wcsinfo.mt_dec)
                 del model.meta.wcs
                 model.meta.wcs = new_wcs
+            if model.meta.exposure.type.lower() in IMAGING_TYPES:
+                update_s_region_imaging(model)
             record_step_status(model, "assign_mtwcs", True)
             input_models.shelve(model, i, modify=True)
 

jwst/combine_1d/combine1d.py

@@ -439,7 +439,7 @@ def count_input(input_spectra):
         # only include spectra that have more than 1 data point
         if len(input_wl) > 1:
             if wl is None:
-                wl = input_wl
+                wl = input_wl.copy()
             else:
                 wl = np.hstack((input_wl, wl))
     wl.sort()

jwst/lib/suffix.py

@@ -41,7 +41,7 @@
     'c1d', 'cal', 'calints', 'cat', 'crf', 'crfints',
     'dark',
     'i2d',
-    'median',
+    'mbsub', 'median',
     'phot', 'psf-amiavg', 'psfalign', 'psfstack', 'psfsub',
     'ramp', 'rate', 'rateints', 'residual_fringe',
     's2d', 's3d', 'snr',

jwst/master_background/master_background_mos_step.py

@@ -258,10 +258,10 @@ def _calc_master_background(
             self.barshadow.source_type = 'EXTENDED'
             self.photom.source_type = 'EXTENDED'
 
-            pre_calibrated = self.flat_field(data)
-            pre_calibrated = self.pathloss(pre_calibrated)
-            pre_calibrated = self.barshadow(pre_calibrated)
-            pre_calibrated = self.photom(pre_calibrated)
+            pre_calibrated = self.flat_field.run(data)
+            pre_calibrated = self.pathloss.run(pre_calibrated)
+            pre_calibrated = self.barshadow.run(pre_calibrated)
+            pre_calibrated = self.photom.run(pre_calibrated)
 
             # Create the 1D, fully calibrated master background.
             if user_background:
@@ -294,9 +294,9 @@ def _calc_master_background(
             self.flat_field.use_correction_pars = True
             self.flat_field.inverse = True
 
-            mb_multislit = self.photom(mb_multislit)
-            mb_multislit = self.barshadow(mb_multislit)
-            mb_multislit = self.pathloss(mb_multislit)
-            mb_multislit = self.flat_field(mb_multislit)
+            mb_multislit = self.photom.run(mb_multislit)
+            mb_multislit = self.barshadow.run(mb_multislit)
+            mb_multislit = self.pathloss.run(mb_multislit)
+            mb_multislit = self.flat_field.run(mb_multislit)
 
         return master_background, mb_multislit, bkg_x1d_spectra

jwst/master_background/master_background_step.py

@@ -97,7 +97,6 @@ def process(self, input):
                     del _
                     result = ModelContainer()
                     background_2d_collection = ModelContainer()
-                    background_2d_collection.update(input_data)
                     for model in input_data:
                         background_2d = expand_to_2d(model, self.user_background)
                         result.append(subtract_2d_background(model, background_2d))
@@ -109,7 +108,7 @@ def process(self, input):
                 else:
                     asn_id = None
                     background_2d = expand_to_2d(input_data, self.user_background)
-                    background_2d_collection = background_2d
+                    background_2d_collection = ModelContainer([background_2d])
                     result = subtract_2d_background(input_data, background_2d)
                     # Record name of user-supplied master background spectrum
                     result.meta.background.master_background_file = basename(self.user_background)

jwst/outlier_detection/tests/test_outlier_detection.py

@@ -6,6 +6,7 @@
 
 from gwcs.wcs import WCS
 from stdatamodels.jwst import datamodels
+from stcal.alignment.util import compute_s_region_imaging
 
 from jwst.datamodels import ModelContainer, ModelLibrary
 from jwst.assign_wcs import AssignWcsStep
@@ -151,6 +152,7 @@ def we_many_sci(
 
     # Replace the FITS-type WCS with an Identity WCS
     sci1.meta.wcs = create_fitswcs(sci1)
+    sci1.meta.wcsinfo.s_region = compute_s_region_imaging(sci1.meta.wcs, shape=shape, center=False)
     rng = np.random.default_rng(720)
     sci1.data = rng.normal(loc=background, size=shape, scale=sigma)
     sci1.err = np.zeros(shape) + sigma

jwst/pipeline/calwebb_ami3.py

@@ -74,7 +74,7 @@ def process(self, input):
 
             # Do the LG analysis for this image
             log.debug('Do LG processing for member %s', input_file)
-            result1, result2, result3 = self.ami_analyze(input_file)
+            result1, result2, result3 = self.ami_analyze.run(input_file)
 
             # Save the averaged LG analysis results to a file
             result1.meta.asn.pool_name = asn['asn_pool']
@@ -90,7 +90,7 @@ def process(self, input):
 
             # Do the LG analysis for this image
             log.debug('Do LG processing for member %s', input_file)
-            result1, result2, result3 = self.ami_analyze(input_file)
+            result1, result2, result3 = self.ami_analyze.run(input_file)
 
             # Save the LG analysis results to a file
             result1.meta.asn.pool_name = asn['asn_pool']
@@ -104,7 +104,7 @@ def process(self, input):
         # assuming one ref star exposure per targ exposure
         if (len(psf_files) > 0) & (len(targ_files) > 0): 
             for (targ, psf) in zip(targ_lg,psf_lg):
-                result = self.ami_normalize(targ, psf)
+                result = self.ami_normalize.run(targ, psf)
                 # Save the result
                 result.meta.asn.pool_name = asn['asn_pool']
                 result.meta.asn.table_name = op.basename(asn.filename)

jwst/pipeline/calwebb_coron3.py

@@ -151,15 +151,15 @@ def process(self, user_input):
         # Perform outlier detection on the PSFs.
         if not skip_outlier_detection:
             for model in psf_models:
-                self.outlier_detection(model)
+                self.outlier_detection.run(model)
                 # step may have been skipped for this model;
                 # turn back on for next model
                 self.outlier_detection.skip = False
         else:
             self.log.info('Outlier detection skipped for PSF\'s')
 
         # Stack all the PSF images into a single CubeModel
-        psf_stack = self.stack_refs(psf_models)
+        psf_stack = self.stack_refs.run(psf_models)
         psf_models.close()
 
         # Save the resulting PSF stack
@@ -175,13 +175,13 @@ def process(self, user_input):
 
                 # Remove outliers from the target
                 if not skip_outlier_detection:
-                    target = self.outlier_detection(target)
+                    target = self.outlier_detection.run(target)
                     # step may have been skipped for this model;
                     # turn back on for next model
                     self.outlier_detection.skip = False
 
                 # Call align_refs
-                psf_aligned = self.align_refs(target, psf_stack)
+                psf_aligned = self.align_refs.run(target, psf_stack)
 
                 # Save the alignment results
                 self.save_model(
@@ -190,7 +190,7 @@ def process(self, user_input):
                 )
 
                 # Call KLIP
-                psf_sub = self.klip(target, psf_aligned)
+                psf_sub = self.klip.run(target, psf_aligned)
                 psf_aligned.close()
 
                 # Save the psf subtraction results
@@ -210,7 +210,7 @@ def process(self, user_input):
         resample_library = ModelLibrary(resample_input, on_disk=False)
 
         # Output is a single datamodel
-        result = self.resample(resample_library)
+        result = self.resample.run(resample_library)
 
         # Blend the science headers
         try:

jwst/pipeline/calwebb_dark.py

@@ -67,29 +67,29 @@ def process(self, input):
             # the steps are in a different order than NIR
             log.debug('Processing a MIRI exposure')
 
-            input = self.group_scale(input)
-            input = self.dq_init(input)
-            input = self.emicorr(input)
-            input = self.saturation(input)
-            input = self.ipc(input)
-            input = self.firstframe(input)
-            input = self.lastframe(input)
-            input = self.reset(input)
-            input = self.linearity(input)
-            input = self.rscd(input)
+            input = self.group_scale.run(input)
+            input = self.dq_init.run(input)
+            input = self.emicorr.run(input)
+            input = self.saturation.run(input)
+            input = self.ipc.run(input)
+            input = self.firstframe.run(input)
+            input = self.lastframe.run(input)
+            input = self.reset.run(input)
+            input = self.linearity.run(input)
+            input = self.rscd.run(input)
 
         else:
 
             # process Near-IR exposures
             log.debug('Processing a Near-IR exposure')
 
-            input = self.group_scale(input)
-            input = self.dq_init(input)
-            input = self.saturation(input)
-            input = self.ipc(input)
-            input = self.superbias(input)
-            input = self.refpix(input)
-            input = self.linearity(input)
+            input = self.group_scale.run(input)
+            input = self.dq_init.run(input)
+            input = self.saturation.run(input)
+            input = self.ipc.run(input)
+            input = self.superbias.run(input)
+            input = self.refpix.run(input)
+            input = self.linearity.run(input)
 
         log.info('... ending calwebb_dark')
 

jwst/pipeline/calwebb_detector1.py

@@ -89,18 +89,18 @@ def process(self, input):
             # the steps are in a different order than NIR
             log.debug('Processing a MIRI exposure')
 
-            input = self.group_scale(input)
-            input = self.dq_init(input)
-            input = self.emicorr(input)
-            input = self.saturation(input)
-            input = self.ipc(input)
-            input = self.firstframe(input)
-            input = self.lastframe(input)
-            input = self.reset(input)
-            input = self.linearity(input)
-            input = self.rscd(input)
-            input = self.dark_current(input)
-            input = self.refpix(input)
+            input = self.group_scale.run(input)
+            input = self.dq_init.run(input)
+            input = self.emicorr.run(input)
+            input = self.saturation.run(input)
+            input = self.ipc.run(input)
+            input = self.firstframe.run(input)
+            input = self.lastframe.run(input)
+            input = self.reset.run(input)
+            input = self.linearity.run(input)
+            input = self.rscd.run(input)
+            input = self.dark_current.run(input)
+            input = self.refpix.run(input)
 
             # skip until MIRI team has figured out an algorithm
             # input = self.persistence(input)
@@ -110,28 +110,28 @@ def process(self, input):
             # process Near-IR exposures
             log.debug('Processing a Near-IR exposure')
 
-            input = self.group_scale(input)
-            input = self.dq_init(input)
-            input = self.saturation(input)
-            input = self.ipc(input)
-            input = self.superbias(input)
-            input = self.refpix(input)
-            input = self.linearity(input)
+            input = self.group_scale.run(input)
+            input = self.dq_init.run(input)
+            input = self.saturation.run(input)
+            input = self.ipc.run(input)
+            input = self.superbias.run(input)
+            input = self.refpix.run(input)
+            input = self.linearity.run(input)
 
             # skip persistence for NIRSpec
             if instrument != 'NIRSPEC':
-                input = self.persistence(input)
+                input = self.persistence.run(input)
 
-            input = self.dark_current(input)
+            input = self.dark_current.run(input)
 
         # apply the charge_migration step
-        input = self.charge_migration(input)
+        input = self.charge_migration.run(input)
 
         # apply the jump step
-        input = self.jump(input)
+        input = self.jump.run(input)
 
         # apply the clean_flicker_noise step
-        input = self.clean_flicker_noise(input)
+        input = self.clean_flicker_noise.run(input)
 
         # save the corrected ramp data, if requested
         if self.save_calibrated_ramp:
@@ -143,23 +143,23 @@ def process(self, input):
         # objects, but when the step is skipped due to `skip = True`,
         # only the input is returned when the step is invoked.
         if self.ramp_fit.skip:
-            input = self.ramp_fit(input)
+            input = self.ramp_fit.run(input)
             ints_model = None
         else:
-            input, ints_model = self.ramp_fit(input)
+            input, ints_model = self.ramp_fit.run(input)
 
         # apply the gain_scale step to the exposure-level product
         if input is not None:
             self.gain_scale.suffix = 'gain_scale'
-            input = self.gain_scale(input)
+            input = self.gain_scale.run(input)
         else:
             log.info("NoneType returned from ramp_fit.  Gain Scale step skipped.")
 
         # apply the gain scale step to the multi-integration product,
         # if it exists, and then save it
         if ints_model is not None:
             self.gain_scale.suffix = 'gain_scaleints'
-            ints_model = self.gain_scale(ints_model)
+            ints_model = self.gain_scale.run(ints_model)
             self.save_model(ints_model, 'rateints')
 
         # setup output_file for saving
@@ -176,4 +176,4 @@ def setup_output(self, input):
         if input.meta.cal_step.ramp_fit == 'COMPLETE':
             self.suffix = 'rate'
         else:
-            self.suffix = 'ramp'
+            self.suffix = 'ramp'

jwst/pipeline/calwebb_guider.py

@@ -52,9 +52,9 @@ def process(self, input):
             input = datamodels.GuiderRawModel(input)
 
         # Apply the steps
-        input = self.dq_init(input)
-        input = self.guider_cds(input)
-        input = self.flat_field(input)
+        input = self.dq_init.run(input)
+        input = self.guider_cds.run(input)
+        input = self.flat_field.run(input)
 
         log.info('... ending calwebb_guider')