use outlier detection from stcal

romancal/outlier_detection/_fileio.py

@@ -0,0 +1,44 @@
+import logging
+
+from astropy.units import Quantity
+
+log = logging.getLogger(__name__)
+log.setLevel(logging.DEBUG)
+
+
+def save_median(example_model, median_data, median_wcs, make_output_path):
+    _save_intermediate_output(
+        _make_median_model(example_model, median_data, median_wcs),
+        "median",
+        make_output_path,
+    )
+
+
+def save_drizzled(drizzled_model, make_output_path):
+    _save_intermediate_output(drizzled_model, "outlier_i2d", make_output_path)
+
+
+def _make_median_model(example_model, data, wcs):
+    model = example_model.copy()
+    model.data = Quantity(data, unit=model.data.unit)
+    model.meta.wcs = wcs
+    return model
+
+
+def _save_intermediate_output(model, suffix, make_output_path):
+    """
+    Ensure all intermediate outputs from OutlierDetectionStep have consistent file naming conventions
+
+    Notes
+    -----
+    self.make_output_path() is updated globally for the step in the main pipeline
+    to include the asn_id in the output path, so no need to handle it here.
+    """
+
+    # outlier_?2d is not a known suffix, and make_output_path cannot handle an
+    # underscore in an unknown suffix, so do a manual string replacement
+    input_path = model.meta.filename.replace("_outlier_", "_")
+
+    output_path = make_output_path(input_path, suffix=suffix)
+    model.save(output_path)
+    log.info(f"Saved {suffix} model in {output_path}")