mt_metadata.processing.aurora.regression
========================================

.. py:module:: mt_metadata.processing.aurora.regression


Classes
-------

.. autoapisummary::

   mt_metadata.processing.aurora.regression.Regression


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

.. py:class:: Regression(**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:: minimum_cycles
      :type:  Annotated[int, Field(default=1, description='Minimum number of cycles in the regression', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['10']})]


   .. py:attribute:: max_iterations
      :type:  Annotated[int, Field(default=10, description='Max iterations of the regression', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['10']})]


   .. py:attribute:: max_redescending_iterations
      :type:  Annotated[int, Field(default=2, description='Max redescending iterations of the regression', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['2']})]


   .. py:attribute:: r0
      :type:  Annotated[float, Field(default=1.5, description='The number of standard deviations where the influence function changes from linear to quadratic', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['1.4']})]


   .. py:attribute:: u0
      :type:  Annotated[float, Field(default=2.8, description='Control for redescending Huber regression weights.', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['2.8']})]


   .. py:attribute:: tolerance
      :type:  Annotated[float, Field(default=0.005, description='Control for convergence of RME algorithm.  Lower means more iterations', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['0.005']})]


   .. py:attribute:: verbosity
      :type:  Annotated[int, Field(default=1, description='Control for logging messages during regression -- Higher means more messages', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['1']})]