Remove all uses of Step.__call__

changes/1499.stpipe.rst

@@ -0,0 +1 @@
+Remove Step.__call__ usage (which will be deprecated in stpipe).

docs/roman/outlier_detection/outlier_examples.rst

@@ -14,7 +14,7 @@ the outlier detection step.
                 # read the file list into a ModelLibrary object
                 mc = ModelLibrary(["img_1.asdf", "img_2.asdf"])
                 step = OutlierDetectionStep()
-                step.process(mc)
+                step.run(mc)
 
 2. To run the outlier detection step (with the default parameters) on an ASN file
    called "asn_sample.json" with the following content:
@@ -56,7 +56,7 @@ the outlier detection step.
                 # read the file list into a ModelLibrary object
                 mc = ModelLibrary("asn_sample.json")
                 step = OutlierDetectionStep()
-                step.process(mc)
+                step.run(mc)
 
 #. To run the outlier detection step (with the default parameters) on an ASN file
    called "asn_sample.json" (the files listed in the association file must have been

docs/roman/stpipe/call_via_run.rst

@@ -22,9 +22,6 @@ parameter file will be ignored.
  pipe.output_dir = '/my/data/pipeline_outputs'
 
  # Run the pipeline
- result = pipe('r0000101001001001001_01101_0001_WFI01_uncal.asdf')
-
- # Or, execute the pipeline using the run method
  result = pipe.run('r0000101001001001001_01101_0001_WFI01_uncal.asdf')
 
 To run a single step:
@@ -41,8 +38,5 @@ To run a single step:
  step.save_results = True
  step.output_dir = '/my/data/jump_data'
 
- # Execute by calling the instance directly
- result = step('r0000101001001001001_01101_0001_WFI01_linearity.asdf')
-
- # Or, execute using the run method
+ # Execute using the run method
  result = step.run('r0000101001001001001_01101_0001_WFI01_linearity.asdf')

docs/roman/stpipe/user_pipeline.rst

@@ -183,10 +183,10 @@ step in the example above, one can do::
 From Python
 -----------
 
-Once the pipeline has been configured (as above), just call the
-instance to run it.
+Once the pipeline has been configured (as above) it can be executed
+using run.
 
-    pipe(input_data)
+    pipe.run(input_data)
 
 Caching details
 ---------------

docs/roman/stpipe/user_step.rst

@@ -243,8 +243,3 @@ the step. The previous example could be re-written as::
 
     mystep = FlatFieldStep(override_sflat='sflat.asdf')
     output = mystep.run(input)
-
-Using the ``.run()`` method is the same as calling the instance directly.
-They are equivalent::
-
-    output = mystep(input)

docs/roman/tweakreg/tweakreg_examples.rst

@@ -9,11 +9,11 @@ or a Roman datamodel `ImageModel`.
 
                 from romancal.tweakreg.tweakreg_step import TweakRegStep
                 step = TweakRegStep()
-                step.call([img])
+                step.run([img])
 
         .. note::
             If the input is a single Roman ``DataModel``,
-            either ``step.call([img])`` or ``step.call(img)`` will work. For multiple elements as input,
+            either ``step.run([img])`` or ``step.run(img)`` will work. For multiple elements as input,
             they must be passed in as either a list or a ModelLibrary.
 
 #. To run TweakReg in a Python session on an association file with the default parameters:
@@ -22,7 +22,7 @@ or a Roman datamodel `ImageModel`.
 
                 from romancal.tweakreg.tweakreg_step import TweakRegStep
                 step = TweakRegStep()
-                step.call("asn_file.json")
+                step.run("asn_file.json")
 
 #. To run TweakReg on a Roman's exposure with default astrometric parameters and save
    the absolute catalog data:
@@ -34,7 +34,7 @@ or a Roman datamodel `ImageModel`.
                 step.save_abs_catalog = True # save the catalog data used for absolute astrometry
                 step.abs_refcat = 'GAIADR3' # use Gaia DR3 for absolute astrometry
                 step.catalog_path = '/path/for/the/abs/catalog' # save the Gaia catalog to this path
-                step.call([img])
+                step.run([img])
 
 #. To run TweakReg using a custom source catalog with the default parameters:
 
@@ -93,4 +93,4 @@ or a Roman datamodel `ImageModel`.
                 step.use_custom_catalogs = True # use custom catalogs
                 step.catalog_format = "ascii.ecsv" # custom catalogs format
                 step.catfile = '/path/to/catfile/catfilename' # path to datamodel:catalog mapping
-                step.call([img])
+                step.run([img])

romancal/outlier_detection/tests/test_outlier_detection.py

@@ -115,7 +115,7 @@ def test_outlier_do_detection_write_files_to_custom_location(tmp_path, base_imag
     # set output dir for all files created by the step
     outlier_step.output_dir = tmp_path.as_posix()
 
-    outlier_step(input_models)
+    outlier_step.run(input_models)
 
     # meta.filename for the median image created by OutlierDetection.do_detection()
     median_path = tmp_path / "drizzled_median.asdf"
@@ -183,7 +183,7 @@ def test_find_outliers(tmp_path, base_image, on_disk):
     # make sure files are written out to disk
     outlier_step.in_memory = not on_disk
 
-    result = outlier_step(input_models)
+    result = outlier_step.run(input_models)
 
     expected_crs = [img_0_input_coords, img_1_input_coords, None]
     with result:
@@ -222,7 +222,7 @@ def test_identical_images(tmp_path, base_image, caplog):
     # make sure files are written out to disk
     outlier_step.in_memory = False
 
-    result = outlier_step(input_models)
+    result = outlier_step.run(input_models)
 
     # assert that log shows no new outliers detected
     assert "0 pixels marked as outliers" in {x.message for x in caplog.records}

romancal/pipeline/exposure_pipeline.py

@@ -124,12 +124,12 @@
             log.info(f"Processing a WFI exposure {input_filename}")
 
             self.dq_init.suffix = "dq_init"
-            result = self.dq_init(input)
+            result = self.dq_init.run(input)
 
             if input_filename:
                 result.meta.filename = input_filename
 
-            result = self.saturation(result)
+            result = self.saturation.run(result)
 
             # Test for fully saturated data
             if is_fully_saturated(result):
@@ -155,16 +155,16 @@
                 results.append(result)
                 return results
 
-            result = self.refpix(result)
-            result = self.linearity(result)
-            result = self.dark_current(result)
-            result = self.rampfit(result)
-            result = self.assign_wcs(result)
+            result = self.refpix.run(result)
+            result = self.linearity.run(result)
+            result = self.dark_current.run(result)
+            result = self.rampfit.run(result)
+            result = self.assign_wcs.run(result)
 
             if result.meta.exposure.type == "WFI_IMAGE":
-                result = self.flatfield(result)
-                result = self.photom(result)
-                result = self.source_catalog(result)
+                result = self.flatfield.run(result)
+                result = self.photom.run(result)
+                result = self.source_catalog.run(result)
                 tweakreg_input.append(result)
                 log.info(
                     f"Number of models to tweakreg:   {len(tweakreg_input), n_members}"
@@ -185,7 +185,7 @@
         # Now that all the exposures are collated, run tweakreg
         # Note: this does not cover the case where the asn mixes imaging and spectral
         #          observations. This should not occur on-prem
-        result = self.tweakreg(results)
+        result = self.tweakreg.run(results)
 
         log.info("Roman exposure calibration pipeline ending...")
 

romancal/pipeline/mosaic_pipeline.py

@@ -72,11 +72,11 @@
         if file_type == "asn":
             input = ModelLibrary(input, on_disk=self.on_disk)
             self.flux.suffix = "flux"
-            result = self.flux(input)
+            result = self.flux.run(input)
             self.skymatch.suffix = "skymatch"
-            result = self.skymatch(result)
+            result = self.skymatch.run(result)
             self.outlier_detection.suffix = "outlier_detection"
-            result = self.outlier_detection(result)
+            result = self.outlier_detection.run(result)
             #
             # check to see if the product name contains a skycell name & if true get the skycell record
             product_name = input.asn["products"][0]["name"]
@@ -131,19 +131,19 @@
                         wcs_file = asdf.open(self.resample.output_wcs)
                         self.suffix = "i2d"
                         self.output_file = input.asn["products"][0]["name"]
-                        result = self.resample(result)
+                        result = self.resample.run(result)
                         self.sourcecatalog.output_file = self.output_file
-                        result_catalog = self.sourcecatalog(result)
+                        result_catalog = self.sourcecatalog.run(result)
                     else:
                         log.info("resampling a mosaic file is not yet supported")
                         exit(0)
 
             else:
                 self.resample.suffix = "i2d"
                 self.output_file = input.asn["products"][0]["name"]
-                result = self.resample(result)
+                result = self.resample.run(result)
                 self.sourcecatalog.output_file = self.output_file
-                result_catalog = self.sourcecatalog(result)  # noqa: F841
+                result_catalog = self.sourcecatalog.run(result)  # noqa: F841
                 self.suffix = "i2d"
                 if input_filename:
                     result.meta.filename = self.output_file