mt_metadata.processing.time_series_decimation
=============================================

.. py:module:: mt_metadata.processing.time_series_decimation


Classes
-------

.. autoapisummary::

   mt_metadata.processing.time_series_decimation.MethodEnum
   mt_metadata.processing.time_series_decimation.TimeSeriesDecimation


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

.. py:class:: MethodEnum

   Bases: :py:obj:`mt_metadata.common.enumerations.StrEnumerationBase`


   str(object='') -> str
   str(bytes_or_buffer[, encoding[, errors]]) -> str

   Create a new string object from the given object. If encoding or
   errors is specified, then the object must expose a data buffer
   that will be decoded using the given encoding and error handler.
   Otherwise, returns the result of object.__str__() (if defined)
   or repr(object).
   encoding defaults to 'utf-8'.
   errors defaults to 'strict'.


   .. py:attribute:: default
      :value: 'default'



   .. py:attribute:: other
      :value: 'other'



.. py:class:: TimeSeriesDecimation(**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:: level
      :type:  Annotated[int | None, Field(default=None, description='Decimation level, must be a non-negative integer starting at 0', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['0']})]


   .. py:attribute:: factor
      :type:  Annotated[float, Field(default=1.0, description='Decimation factor between parent sample rate and decimated time series sample rate.', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['4.0']})]


   .. py:attribute:: method
      :type:  Annotated[MethodEnum, Field(default=MethodEnum.default, description='Type of decimation', alias=None, json_schema_extra={'units': '', 'required': True, 'examples': ['default']})]


   .. py:attribute:: sample_rate
      :type:  Annotated[float, Field(default=1.0, description='Sample rate of the decimation level data (after decimation).', alias=None, json_schema_extra={'units': 'samples per second', 'required': True, 'examples': ['256']})]


   .. py:attribute:: anti_alias_filter
      :type:  Annotated[str | None, Field(default='default', description='Type of anti alias filter for decimation.', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['default']})]


   .. py:method:: check_level(values)
      :classmethod:


      Check that level is a non-negative integer and set anti_alias_filter for level 0.