JP-3682: split outlier detection into multiple steps

jwst/outlier_detection/__init__.py

@@ -1,3 +1,11 @@
-from .outlier_detection_step import OutlierDetectionStep
+from .outlier_detection_coron_step import OutlierDetectionCoronStep
+from .outlier_detection_ifu_step import OutlierDetectionIFUStep
+from .outlier_detection_spec_step import OutlierDetectionSpecStep
+from .outlier_detection_imaging_step import OutlierDetectionImagingStep
+from .outlier_detection_tso_step import OutlierDetectionTSOStep
 
-__all__ = ['OutlierDetectionStep']
+__all__ = ['OutlierDetectionCoronStep',
+           'OutlierDetectionIFUStep',
+           'OutlierDetectionSpecStep',
+           'OutlierDetectionImagingStep',
+           'OutlierDetectionTSOStep']

jwst/outlier_detection/_fileio.py

@@ -100,7 +100,7 @@ def _make_blot_model(input_model, blot, blot_err):
 def _save_intermediate_output(model, suffix, make_output_path):
     """Save an intermediate output from outlier detection.
 
-    Ensure all intermediate outputs from OutlierDetectionStep have
+    Ensure all intermediate outputs from the steps have
     consistent file naming conventions.
 
     Parameters

jwst/outlier_detection/outlier_detection_coron_step.py

@@ -0,0 +1,81 @@
+"""
+Submodule for performing outlier detection on coronagraphy data.
+"""
+
+import logging
+
+import numpy as np
+
+from stdatamodels.jwst import datamodels
+from jwst.stpipe import Step
+
+from jwst.resample.resample_utils import build_mask
+
+from .utils import create_cube_median, flag_model_crs, OutlierDetectionStepBase
+from ._fileio import save_median
+
+log = logging.getLogger(__name__)
+log.setLevel(logging.DEBUG)
+
+
+__all__ = ["OutlierDetectionCoronStep"]
+
+
+class OutlierDetectionCoronStep(Step, OutlierDetectionStepBase):
+    """Flag outlier bad pixels and cosmic rays in DQ array of each input image.
+    Input images can be listed in an input association file or already opened
+    with a ModelContainer.  DQ arrays are modified in place.
+    Parameters
+    -----------
+    input_model : ~jwst.datamodels.CubeModel
+        CubeModel or filename pointing to a CubeModel
+    """
+
+    class_alias = "outlier_detection_coron"
+
+    spec = """
+        maskpt = float(default=0.7)
+        snr = float(default=5.0)
+        save_intermediate_results = boolean(default=False)
+        good_bits = string(default="~DO_NOT_USE")  # DQ flags to allow
+    """
+
+    def process(self, input_model):
+        """Perform outlier detection processing on input data."""
+
+        # determine the asn_id (if not set by the pipeline)
+        asn_id = self._get_asn_id(input_model)
+        self.log.info(f"Outlier Detection asn_id: {asn_id}")
+
+        if not isinstance(input_model, datamodels.JwstDataModel):
+            input_model = datamodels.open(input_model)
+
+        if not isinstance(input_model, datamodels.CubeModel):
+            raise TypeError(f"Input must be a CubeModel: {input_model}")
+
+        # FIXME weight_type could now be used here. Similar to tso data coron
+        # data was previously losing var_rnoise due to the conversion from a cube
+        # to a ModelContainer (which makes the default ivm weight ignore var_rnoise).
+        # Now that it's handled as a cube we could use the var_rnoise.
+        input_model.wht = build_mask(input_model.dq, self.good_bits).astype(np.float32)
+
+        # Perform median combination on set of drizzled mosaics
+        median_data = create_cube_median(input_model, self.maskpt)
+
+        if self.save_intermediate_results:
+            # make a median model
+            median_model = datamodels.ImageModel(median_data)
+            median_model.update(input_model)
+            median_model.meta.wcs = input_model.meta.wcs
+
+            save_median(median_model, self.make_output_path)
+            del median_model
+
+        # Perform outlier detection using statistical comparisons between
+        # each original input image and its blotted version of the median image
+        flag_model_crs(
+            input_model,
+            median_data,
+            self.snr,
+        )
+        return self._set_status(input_model, True)