mt_metadata.transfer_functions.tf.statistical_estimate
======================================================

.. py:module:: mt_metadata.transfer_functions.tf.statistical_estimate


Classes
-------

.. autoapisummary::

   mt_metadata.transfer_functions.tf.statistical_estimate.StatisticalEstimate


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

.. py:class:: StatisticalEstimate(**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:: name
      :type:  Annotated[str, Field(default='', description='Name of the statistical estimate', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['transfer function']})]


   .. py:attribute:: data_type
      :type:  Annotated[mt_metadata.common.enumerations.ArrayDTypeEnum, Field(default='complex', description='Type of number contained in the estimate', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['real']})]


   .. py:attribute:: description
      :type:  Annotated[str, Field(default='', description='Description of the statistical estimate', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['this is an estimate']})]


   .. py:attribute:: input_channels
      :type:  Annotated[list[str] | str, Field(default=[], description='List of input channels (sources)', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['hx, hy', ['hx', 'hy']]})]


   .. py:attribute:: output_channels
      :type:  Annotated[list[str] | str, Field(default=[], description='List of output channels (response).', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['hx, hy', ['hx', 'hy']]})]


   .. py:attribute:: units
      :type:  Annotated[str, Field(default='', description='Units of the estimate.', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['millivolts per kilometer per nanotesla']})]


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



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


      convert channels to a list of single channels