match.lib.scoringfileframe
==========================

.. py:module:: match.lib.scoringfileframe


Attributes
----------

.. autoapisummary::

   match.lib.scoringfileframe.logger


Classes
-------

.. autoapisummary::

   match.lib.scoringfileframe.ScoringFileFrame


Functions
---------

.. autoapisummary::

   match.lib.scoringfileframe.match_variants


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

.. py:class:: ScoringFileFrame(paths, chrom=None, cleanup=True, tmpdir=None)

   Like :class:`pgscatalog.core.NormalisedScoringFile`, but backed by the polars dataframe library

   Instantiated with a :class:`pgscatalog.core.NormalisedScoringFile` written to
   a file. This is a long format/melted CSV file containing normalised variant data
   (i.e. the output of combine scorefiles application):

   >>> from ._config import Config
   >>> path = Config.ROOT_DIR.parent / "pgscatalog.core" / "tests" / "data" / "combined.txt.gz"
   >>> x = ScoringFileFrame(path)
   >>> x  # doctest: +ELLIPSIS
   ScoringFileFrame([NormalisedScoringFile('.../combined.txt.gz')])

   Using a context manager is important to prepare a polars dataframe:

   >>> with x as arrow:
   ...     assert all(os.path.exists(x) for x in x.arrowpaths)
   ...     arrow.collect().shape
   (154, 11)
   >>> assert not any(os.path.exists(x) for x in x.arrowpaths)  # all cleaned up

   >>> from .variantframe import VariantFrame
   >>> path = Config.ROOT_DIR.parent / "pgscatalog.core" / "tests" / "data" / "hapnest.bim"
   >>> target = VariantFrame(path, dataset="hapnest")
   >>> with target as target_df, x as score_df:
   ...     match_variants(score_df=score_df, target_df=target_df, target=target)  # doctest: +ELLIPSIS
   MatchResult(dataset=hapnest, matchresult=[<LazyFrame ...


   .. py:method:: save_ipc(destination)

      Save the dataframe prepared by the context manager to an Arrow IPC file

      Useful because the context manager will clean up the IPC files while exiting.

      This method allows data to be persisted.



   .. py:attribute:: arrowpaths
      :value: None



   .. py:attribute:: chrom
      :value: None



.. py:function:: match_variants(score_df, target_df, target)

   Get all match candidates for a VariantFrame dataframe and ScoringFileFrame dataframe

   Returns a :class:`MatchResult`


.. py:data:: logger