mt_metadata.processing.aurora.stations
======================================

.. py:module:: mt_metadata.processing.aurora.stations


Classes
-------

.. autoapisummary::

   mt_metadata.processing.aurora.stations.Stations


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

.. py:class:: Stations(**data)

   Bases: :py:obj:`mt_metadata.base.MetadataBase`


   Base class for all metadata objects with Pydantic validation.

   MetadataBase extends DotNotationBaseModel (which inherits from Pydantic's
   BaseModel) to provide automatic validation according to metadata standards.
   It adds functionality beyond dictionaries, supporting JSON, XML, pandas
   Series, and other formats for metadata interchange.

   .. attribute:: _skip_equals

      Private attribute listing fields to skip in equality comparisons

      :type: list[str]

   .. attribute:: _fields

      Private attribute caching field information

      :type: dict[str, Any]

   .. rubric:: Notes

   - All field assignments are validated automatically via Pydantic
   - None values are converted to appropriate defaults (empty string or 0.0)
   - Supports nested attribute access via dot notation
   - Thread-safe for read operations after initialization


   .. py:attribute:: remote
      :type:  Annotated[list[mt_metadata.processing.aurora.station.Station], Field(default_factory=list, description='list of remote sites', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['10']})]


   .. py:attribute:: local
      :type:  Annotated[mt_metadata.processing.aurora.station.Station, Field(default_factory=Station, description='local site', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['10']})]


   .. py:method:: validate_remote(value, info)

          Method for unpacking rr_station info into mt_metadata object.

          Developmnent Notes:
          This function was raising an exception when trying to populate an aurora.Processing object
          from a json.loads() dict.
          TODO: add a description of input variable and use cases, ... it seems that we may not want
          to support multiple rr stations yet.

      :param rr_station:

      :rtype: list of Station objects



   .. py:method:: add_remote(rr)

      add a remote station

      :param rr: remote station to add
      :type rr: Station | dict



   .. py:property:: remote_dict
      :type: dict[str, mt_metadata.processing.aurora.station.Station]


      need to have a dictionary, but it can't be an attribute cause that
      gets confusing when reading in a json file

      :returns: dictionary of remote stations
      :rtype: dict[str, Station]


   .. py:method:: from_dataset_dataframe(df)

      from a dataset dataframe

      :param df: dataset dataframe to read from
      :type df: pd.DataFrame

      :rtype: None



   .. py:method:: to_dataset_dataframe()

      output a dataframe

      :returns: dataframe representation of the station
      :rtype: pd.DataFrame



   .. py:method:: get_station(station_id)

      get a station object from the id

      :param station_id: ID of the station to retrieve
      :type station_id: str

      :returns: Station object corresponding to the given ID
      :rtype: Station