Remove all (including pipeline) uses of Step.__call__

changes/8945.stpipe.rst

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

jwst/master_background/master_background_mos_step.py

@@ -235,10 +235,10 @@
             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:
@@ -269,9 +269,9 @@
             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

jwst/pipeline/calwebb_ami3.py

@@ -74,7 +74,7 @@
 
             # 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 @@
 
             # 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 @@
         # 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 @@
         # 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 @@
 
                 # 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 @@
                 )
 
                 # 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 @@
         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 @@
             # 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 @@
             # 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 @@
             # 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 @@
         # 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 @@
         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 @@
             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')
 

jwst/pipeline/calwebb_image2.py

@@ -150,20 +150,20 @@
                 self.bkg_subtract.save_results = True
 
             # Call the background subtraction step
-            input = self.bkg_subtract(input, members_by_type['background'])
+            input = self.bkg_subtract.run(input, members_by_type['background'])
 
         # work on slope images
-        input = self.assign_wcs(input)
-        input = self.flat_field(input)
-        input = self.photom(input)
+        input = self.assign_wcs.run(input)
+        input = self.flat_field.run(input)
+        input = self.photom.run(input)
 
         # Resample individual exposures, but only if it's one of the
         # regular 2D science image types
         if input.meta.exposure.type.upper() in self.image_exptypes and \
                 len(input.data.shape) == 2:
             self.resample.save_results = self.save_results
             self.resample.suffix = 'i2d'
-            self.resample(input)
+            self.resample.run(input)
 
         # That's all folks
         self.log.info(

jwst/pipeline/calwebb_image3.py

@@ -84,24 +84,24 @@
                 is_moving = is_moving_target(model)
                 input_models.shelve(model, 0, modify=False)
             if is_moving:
-                input_models = self.assign_mtwcs(input_models)
+                input_models = self.assign_mtwcs.run(input_models)
             else:
-                input_models = self.tweakreg(input_models)
+                input_models = self.tweakreg.run(input_models)
 
-            input_models = self.skymatch(input_models)
-            input_models = self.outlier_detection(input_models)
+            input_models = self.skymatch.run(input_models)
+            input_models = self.outlier_detection.run(input_models)
 
         elif self.skymatch.skymethod == 'match':
             self.log.warning("Turning 'skymatch' step off for a single "
                                 "input image when 'skymethod' is 'match'")
 
         else:
-            input_models = self.skymatch(input_models)
+            input_models = self.skymatch.run(input_models)
 
-        result = self.resample(input_models)
+        result = self.resample.run(input_models)
         del input_models
         if isinstance(result, datamodels.ImageModel) and result.meta.cal_step.resample == 'COMPLETE':
-            self.source_catalog(result)
+            self.source_catalog.run(result)
 
 
     def _load_input_as_library(self, input):
@@ -128,4 +128,4 @@
         elif isinstance(input, datamodels.JwstDataModel):
             return ModelLibrary([input], asn_exptypes=['science'], on_disk=not self.in_memory)
         else:
-            raise TypeError(f"Input type {type(input)} not supported.")
+            raise TypeError(f"Input type {type(input)} not supported.")