mt_metadata.transfer_functions.tf.transfer_function
===================================================

.. py:module:: mt_metadata.transfer_functions.tf.transfer_function


Classes
-------

.. autoapisummary::

   mt_metadata.transfer_functions.tf.transfer_function.TransferFunction


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

.. py:class:: TransferFunction(**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='transfer function id', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['mt01_256']})]


   .. py:attribute:: sign_convention
      :type:  Annotated[mt_metadata.common.SignConventionEnum, Field(default='+', description='sign of the transfer function estimates', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['+']})]


   .. py:attribute:: units
      :type:  Annotated[str, Field(default='milliVolt per kilometer per nanoTesla', description='units of the impedance tensor estimates', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['milliVolt per kilometer per nanoTesla']})]


   .. py:attribute:: runs_processed
      :type:  Annotated[list[str], Field(default=[], description='list of runs used in the processing', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': [['MT001a', 'MT001c']]})]


   .. py:attribute:: remote_references
      :type:  Annotated[list[str], Field(default_factory=list, description='list of remote references', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': [['MT002b', 'MT002c']]})]


   .. py:attribute:: processed_date
      :type:  Annotated[mt_metadata.common.mttime.MTime | str | float | int | numpy.datetime64 | pandas.Timestamp, Field(default_factory=lambda: MTime(time_stamp=None), description='date the data were processed', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['2020-01-01T12:00:00']})]


   .. py:attribute:: processing_parameters
      :type:  Annotated[list[str], Field(default_factory=list, description='list of processing parameters with structure name = value', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': [['nfft=4096', 'n_windows=16']]})]


   .. py:attribute:: processed_by
      :type:  Annotated[mt_metadata.common.AuthorPerson, Field(default_factory=AuthorPerson, description='person who processed the data', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ["Person(name='John Doe', email='john.doe@example.com')"]})]


   .. py:attribute:: processing_type
      :type:  Annotated[str, Field(default='', description='Type of processing', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['robust remote reference']})]


   .. py:attribute:: software
      :type:  Annotated[mt_metadata.common.Software, Field(default_factory=Software, description='software used to process the data', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ["Software(name='Aurora', version='1.0.0')"]})]


   .. py:attribute:: data_quality
      :type:  Annotated[mt_metadata.common.DataQuality, Field(default_factory=DataQuality, description='data quality information', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['DataQuality()']})]


   .. py:attribute:: coordinate_system
      :type:  Annotated[mt_metadata.common.GeographicReferenceFrameEnum, Field(default=GeographicReferenceFrameEnum.geographic, description='coordinate system that the transfer function is in.  It is strongly recommended that the transfer functions be rotated to align with geographic coordinates with geographic north as 0 and east as 90.', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['geographic']})]


   .. py:attribute:: processing_config
      :type:  Annotated[str | None, Field(default=None, description='processing configuration', alias=None, json_schema_extra={'units': None, 'required': False, 'examples': ['aurora.processing']})]


   .. py:method:: validate_processed_date(field_value)
      :classmethod:



   .. py:method:: validate_units(value)
      :classmethod:



   .. py:method:: validate_coordinate_system(value)
      :classmethod: