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JP-3744: set outlier step default to in-memory processing (#9214)
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Co-authored-by: Tyler Pauly <[email protected]>
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emolter and tapastro authored Feb 24, 2025
1 parent 298965a commit 382532d
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1 change: 1 addition & 0 deletions changes/9214.outlier_detection.rst
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Made in-memory processing the default for outlier detection imaging observations
4 changes: 2 additions & 2 deletions docs/jwst/outlier_detection/outlier_detection_imaging.rst
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Expand Up @@ -104,8 +104,8 @@ final output product. Specifically,
These concerns have been addressed by implementing an overall memory model for outlier detection that
includes options to minimize memory usage at the expense of temporary file I/O and runtime.
Control over this memory model happens
with the use of the ``in_memory`` parameter. The full impact of setting this parameter
to `False` includes:
with the use of the ``in_memory`` parameter, which defaults to True.
The full impact of setting this parameter to `False` includes:

#. The input :py:class:`~jwst.datamodels.ModelLibrary` object is loaded with `on_disk=True`.
This ensures that input models are loaded into memory one at at time,
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2 changes: 1 addition & 1 deletion jwst/outlier_detection/outlier_detection_step.py
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Expand Up @@ -52,7 +52,7 @@ class OutlierDetectionStep(Step):
resample_data = boolean(default=True)
good_bits = string(default="~DO_NOT_USE") # DQ flags to allow
search_output_file = boolean(default=False)
in_memory = boolean(default=False) # in_memory flag ignored if run within the pipeline; set at pipeline level instead
in_memory = boolean(default=True) # in_memory flag ignored if run within the pipeline; set at pipeline level instead
""" # noqa: E501

def process(self, input_data):
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