gedih3.doctor.diagnoses.backfill
================================

.. py:module:: gedih3.doctor.diagnoses.backfill

.. autoapi-nested-parse::

   backfill diagnosis — fill NaN gaps in product columns from source HDF5s.

   The detection runs row-level: for each partition × product, we count nulls per
   granule by grouping on ``root_file_l2a``. Findings include the partition file,
   the product, and the per-granule null counts.

   The fix:
     1. Group findings by source granule × product to compute the minimal set of
        HDF5 files to read.
     2. (S3 mode) run :func:`s3_etl_subset` to populate a temp dir with just those
        granules' source files for the requested products. Cleanup in ``finally``.
     3. For each affected partition file, read the relevant ``[shot_number, vars]``
        from the source HDF5(s), suffix columns with ``_<prod>``, write to a
        temporary patch parquet, and call :func:`parquet_fill_columns` to merge.
     4. Mark each healed granule × product as ``INDEXED`` in the build log.

   ..
       !! processed by numpydoc !!


Attributes
----------

.. autoapisummary::

   gedih3.doctor.diagnoses.backfill.GranuleKey


Functions
---------

.. autoapisummary::

   gedih3.doctor.diagnoses.backfill.backfill_check
   gedih3.doctor.diagnoses.backfill.backfill_fix


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

.. py:data:: GranuleKey

.. py:function:: backfill_check(ctx: gedih3.doctor.report.DoctorContext) -> gedih3.doctor.report.Report

.. py:function:: backfill_fix(ctx: gedih3.doctor.report.DoctorContext, report: gedih3.doctor.report.Report) -> gedih3.doctor.report.Report