orca.tasks.imaging_peel_pipeline
================================

.. py:module:: orca.tasks.imaging_peel_pipeline

.. autoapi-nested-parse::

   Peeled imaging pipeline Celery task.

   Implements a complete imaging pipeline with peeling:
       copy → applycal → flag_ants → AOFlagger → TTCal peel → WSClean

   Results are generated on NVMe scratch and moved to Lustre on completion.

   This module extends the base imaging_tasks without modifying them.



Attributes
----------

.. autoapisummary::

   orca.tasks.imaging_peel_pipeline.LOG


Functions
---------

.. autoapisummary::

   orca.tasks.imaging_peel_pipeline.peel_with_ttcal_maxiter5
   orca.tasks.imaging_peel_pipeline.peel_imaging_pipeline_task


Module Contents
---------------

.. py:function:: peel_with_ttcal_maxiter5(ms: str, sources: str)

   Run TTCal peeling with reduced iteration count.

   Local wrapper forcing --maxiter=5 for faster peeling.

   :param ms: Path to measurement set.
   :param sources: Path to sources.json file.

   :raises RuntimeError: If TTCal returns non-zero exit code.


.. py:data:: LOG

.. py:function:: peel_imaging_pipeline_task(self, ms_path: str, delay_table: str, bandpass_table: str, final_dir: str, bad_corrs: Optional[List[int]] = None, aoflag_strategy: str | None = 'LWA_opt_GH1.lua', peel_sources_json: str | None = '/home/pipeline/sources.json', extra_wsclean: Optional[List[str]] = None) -> Dict[str, str]

   Full-featured imaging pipeline **added** on top of the old tasks.

   Returns  {'dirty_png': <file>, 'workspace': <directory>}