mt_metadata.processing.aurora.station
=====================================

.. py:module:: mt_metadata.processing.aurora.station


Classes
-------

.. autoapisummary::

   mt_metadata.processing.aurora.station.Station


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

.. py:class:: Station(**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:: id
      :type:  Annotated[str, Field(default='', description='Station ID', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['mt001']})]


   .. py:attribute:: mth5_path
      :type:  Annotated[str | pathlib.Path, Field(default='', description='full path to MTH5 file where the station data is contained', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['/home/mt/experiment_01.h5']})]


   .. py:attribute:: remote
      :type:  Annotated[bool, Field(default=False, description='remote station (True) or local station (False)', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['False']})]


   .. py:attribute:: runs
      :type:  Annotated[list[mt_metadata.processing.aurora.run.Run], Field(default_factory=list, description='List of runs to process', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['001']})]


   .. py:method:: validate_mth5_path(value, info)
      :classmethod:



   .. py:method:: validate_runs(values, info)
      :classmethod:



   .. py:method:: get_run(run_id)

      Get a run by ID

      :param run_id: DESCRIPTION
      :type run_id: TYPE

      :returns: DESCRIPTION
      :rtype: Run | None



   .. py:property:: run_list
      :type: list[str]


      list of run names


   .. py:property:: run_dict
      :type: dict[str, mt_metadata.processing.aurora.run.Run]


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

      :returns: DESCRIPTION
      :rtype: dict[str, Run]


   .. py:method:: to_dataset_dataframe()

      Create a dataset definition dataframe that can be used in the
      processing

      [
          "station",
          "run",
          "start",
          "end",
          "mth5_path",
          "sample_rate",
          "input_channels",
          "output_channels",
          "remote",
      ]




   .. py:method:: from_dataset_dataframe(df)

      set a data frame

      [
          "station",
          "run",
          "start",
          "end",
          "mth5_path",
          "sample_rate",
          "input_channels",
          "output_channels",
          "remote",
      ]

      :param df: DESCRIPTION
      :type df: pd.DataFrame

      :returns: DESCRIPTION
      :rtype: TYPE