mt_metadata.features.feature_ts_run
===================================

.. py:module:: mt_metadata.features.feature_ts_run


Classes
-------

.. autoapisummary::

   mt_metadata.features.feature_ts_run.FeatureTSRun


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

.. py:class:: FeatureTSRun(**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='Suggested Run ID should be sample rate followed by a number or character.  Characters should only be used if the run number is small, if the run number is high consider using digits with zeros.  For example if you have 100 runs the run ID could be 001 or sr{sample_rate}_001. Should be the same as the time series run ID.', alias=None, pattern='^[a-zA-Z0-9_]*$', json_schema_extra={'units': None, 'required': True, 'examples': ['001']})]


   .. py:attribute:: sample_rate
      :type:  Annotated[float, Field(default=0.0, description='Digital sample rate for the run', alias=None, json_schema_extra={'units': 'samples per second', 'required': True, 'examples': ['100']})]


   .. py:attribute:: comments
      :type:  Annotated[mt_metadata.common.Comment, Field(default_factory=lambda: Comment(), description='Any comments about the feature', alias=None, json_schema_extra={'units': None, 'required': False, 'examples': ['estimated using hilburt transform.']})]


   .. py:attribute:: time_period
      :type:  Annotated[mt_metadata.common.TimePeriod, Field(default_factory=lambda: TimePeriod(), description='Time period for the feature', alias=None, json_schema_extra={'units': None, 'required': False, 'examples': ['2020-01-01/2020-01-31']})]


   .. py:method:: validate_comments(value, info)
      :classmethod: