pipeline.src.flows.vessels
==========================

.. py:module:: pipeline.src.flows.vessels


Functions
---------

.. autoapisummary::

   pipeline.src.flows.vessels.extract_french_vessels
   pipeline.src.flows.vessels.extract_eu_vessels
   pipeline.src.flows.vessels.extract_non_eu_vessels
   pipeline.src.flows.vessels.extract_vessels_operators
   pipeline.src.flows.vessels.extract_vessels_logbook_equipement
   pipeline.src.flows.vessels.extract_french_vessels_navigation_licences
   pipeline.src.flows.vessels.extract_control_charters
   pipeline.src.flows.vessels.concat_merge_vessels
   pipeline.src.flows.vessels.clean_vessels
   pipeline.src.flows.vessels.add_unknown_vessel
   pipeline.src.flows.vessels.load_vessels
   pipeline.src.flows.vessels.vessels_flow


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

.. py:function:: extract_french_vessels() -> pandas.DataFrame

   Extracts french vessels from Navpro.

   :returns: french vessels
   :rtype: pd.DataFrame


.. py:function:: extract_eu_vessels() -> pandas.DataFrame

   Extracts EU vessels from Navpro.

   :returns: EU vessels
   :rtype: pd.DataFrame


.. py:function:: extract_non_eu_vessels() -> pandas.DataFrame

   Extracts non-EU vessels from Navpro.

   :returns: non-EU vessels
   :rtype: pd.DataFrame


.. py:function:: extract_vessels_operators() -> pandas.DataFrame

   Extracts vessel operators (in the sense of "the people or organisation that operate
   or manage the operations of the vessel") data from Poséidon (name, contact info).

   :returns: Vessels operators
   :rtype: pd.DataFrame


.. py:function:: extract_vessels_logbook_equipement() -> pandas.DataFrame

   Extracts vessels logbook equipement data

   :returns: Vessels logbook equipment data
   :rtype: pd.DataFrame


.. py:function:: extract_french_vessels_navigation_licences() -> pandas.DataFrame

   Extracts the navigation licence sailing category and expiration date of french
   vessels from Gina.

   :returns: French vessels navigation licence information
   :rtype: pd.DataFrame


.. py:function:: extract_control_charters() -> pandas.DataFrame

   Extracts vessels with the information of whether they are under control charter or
   not from Monitorfish, based on historical control data.

   :returns: Vessels with a boolean column `under_charter`
   :rtype: pd.DataFrame


.. py:function:: concat_merge_vessels(french_vessels: pandas.DataFrame, eu_vessels: pandas.DataFrame, non_eu_vessels: pandas.DataFrame, vessels_logbook_equipement: pandas.DataFrame, vessels_operators: pandas.DataFrame, licences: pandas.DataFrame, control_charters: pandas.DataFrame) -> pandas.DataFrame

   Concatenates `french_vessels`, `eu_vessels` and `non_eu_vessels`, then performs a
   left join of the resulting DataFrame with `vessels_operators`, `licences` and
   `control_charters` successively.

   Vessels, identified by their `id` should be unique :

     - accross `french_vessels`, `eu_vessels` and `non_eu_vessels` : a given `id`
       cannot be in more than one of the three DataFrames, and it must be present just
       once (a single row)
     - in  `vessels_operators`, `licences` and `control_charters`: a given `id` can be
       in 1, 2 or all 3 DataFrames, but it cannot have more than one row in each
       DataFrame.

   :param french_vessels: French vessels
   :type french_vessels: pd.DataFrame
   :param eu_vessels: EU vessels
   :type eu_vessels: pd.DataFrame
   :param non_eu_vessels: non-EU vessels
   :type non_eu_vessels: pd.DataFrame
   :param vessels_logbook_equipement: vessels logbook equipment data
   :type vessels_logbook_equipement: pd.DataFrame
   :param vessels_operators: vessels' operators data
   :type vessels_operators: pd.DataFrame
   :param licences: french vessels navigation licences data
   :type licences: pd.DataFrame
   :param control_charters: vessels under_charter status
   :type control_charters: pd.DataFrame

   :raises ValueError: if a vessel `id` is duplicated

   :returns: merged vessels data
   :rtype: pd.DataFrame


.. py:function:: clean_vessels(all_vessels: pandas.DataFrame) -> pandas.DataFrame

   Combines and concatenates data of some columns as coalesced values or lists (phone
   numbers, emails...)

   :param all_vessels: Output of concat_merge_vessels
   :type all_vessels: pd.DataFrame

   :returns: vessels data ready to be loaded.
   :rtype: pd.DataFrame


.. py:function:: add_unknown_vessel(all_vessels: pandas.DataFrame) -> pandas.DataFrame

   Adds an "UNKNOWN" vessel to the list, to be used when reporting an action on a
   vessel that is not part of the officiel vessels list.

   :param all_vessels: List of vessels
   :type all_vessels: pd.DataFrame

   :returns: Same as input with one added "Unknown vessel"
   :rtype: pd.DataFrame

   :raises AssertionError: if one of the input vessels has the id reserved for the UNKNOWN
   :raises vessel:


.. py:function:: load_vessels(all_vessels: pandas.DataFrame)

   Replaces the content of the `vessels` table with the content of the `all_vessels`
   DataFrame.

   :param all_vessels: vessels data to load
   :type all_vessels: pd.DataFrame


.. py:function:: vessels_flow()