pipeline.src.helpers.segments
=============================

.. py:module:: pipeline.src.helpers.segments


Functions
---------

.. autoapisummary::

   pipeline.src.helpers.segments.allocate_segments_to_catches


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

.. py:function:: allocate_segments_to_catches(catches: pandas.DataFrame, segments: pandas.DataFrame, catch_id_column: str, batch_id_column: str) -> pandas.DataFrame

   Takes a pandas DataFrame of catches, a pandas DataFrame defining fleet segments and
   a pandas DataFrame defining control priorities, and returns a pandas DataFrame
   of catches with (at most) one allocated segment per catch and the corresponding
   impact (from `segments`).

   The catches DataFrame must have columns:

     - one id column identifying each catch (the `catch_id_column`)
     - one id column identifying bathes of catches, which can be a trip id, a vessel
       id, a PNO report id... which identifies catches that somehow belong to the same
       "batch" of catches. This is used to compute the share of target species and the
       main SCIP species type of each group (the `batch_id_column`), which is a
       criterion for certain segments.
     - year `int`
     - fao_area `str`
     - gear `str`
     - mesh `float` (can be null if gear has no mesh)
     - species `str`
     - scip_species_type `str`
     - weight `float`
     - vessel_type `str`


   The segments DataFrame must have columns:

     - segment `str`
     - segment_name `str`
     - year `int`
     - gears List[`str`]
     - min_mesh `float`
     - max_mesh `float`
     - fao_areas `List[str]`
     - target_species `List[str]`
     - min_share_of_target_species `float`
     - main_scip_species_type `str`
     - vessel_types `List[str]`
     - impact_risk_factor `float`
     - priority `float`