fix docs

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])