mt_metadata.features.feature_decimation_channel
===============================================

.. py:module:: mt_metadata.features.feature_decimation_channel


Classes
-------

.. autoapisummary::

   mt_metadata.features.feature_decimation_channel.FeatureDecimationChannel


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

.. py:class:: FeatureDecimationChannel(**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 channel', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['ex']})]


   .. py:attribute:: frequency_max
      :type:  Annotated[float, Field(default=0.0, description='Highest frequency present in the sprectrogam data.', alias=None, json_schema_extra={'units': 'samples per second', 'required': True, 'examples': [77.0]})]


   .. py:attribute:: frequency_min
      :type:  Annotated[float, Field(default=0.0, description='Lowest frequency present in the sprectrogam data.', alias=None, json_schema_extra={'units': 'samples per second', 'required': True, 'examples': [99.0]})]


   .. py:attribute:: sample_rate_decimation_level
      :type:  Annotated[float, Field(default=1.0, description='Sample rate of the time series that was Fourier transformed to generate the FC decimation level.', alias=None, json_schema_extra={'units': 'samples per second', 'required': True, 'examples': [60]})]


   .. py:attribute:: sample_rate_window_step
      :type:  Annotated[float, Field(default=1.0, description='Sample rate of the windows.', alias=None, json_schema_extra={'units': 'samples per second', 'required': True, 'examples': [4]})]


   .. py:attribute:: units
      :type:  Annotated[str, Field(default='count', description='Units of the channel', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['milliVolts']})]


   .. py:attribute:: time_period
      :type:  Annotated[mt_metadata.common.TimePeriod, Field(default_factory=TimePeriod, description='Time period of the channel', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': [{'start': '2020-01-01', 'end': '2020-01-02'}]})]


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