mt_metadata.processing.fourier_coefficients.fc
==============================================

.. py:module:: mt_metadata.processing.fourier_coefficients.fc


Classes
-------

.. autoapisummary::

   mt_metadata.processing.fourier_coefficients.fc.MethodEnum
   mt_metadata.processing.fourier_coefficients.fc.FC


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:: fft
      :value: 'fft'



   .. py:attribute:: wavelet
      :value: 'wavelet'



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



.. py:class:: FC(**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:: decimation_levels
      :type:  Annotated[list[str], Field(default_factory=list, description='List of decimation levels', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['[1, 2, 3]']})]


   .. py:attribute:: id
      :type:  Annotated[str, Field(default='', description='ID given to the FC group', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['aurora_01']})]


   .. py:attribute:: channels_estimated
      :type:  Annotated[list[str], Field(default_factory=list, description='list of channels estimated', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': [['ex', 'hy']]})]


   .. py:attribute:: starting_sample_rate
      :type:  Annotated[float, Field(default=1.0, description='Starting sample rate of the time series used to estimate FCs.', alias=None, json_schema_extra={'units': 'samples per second', 'required': True, 'examples': [60]})]


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


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


   .. py:attribute:: levels
      :type:  Annotated[mt_metadata.common.ListDict, Field(default_factory=ListDict, description='ListDict of decimation levels and their parameters', alias=None, json_schema_extra={'units': None, 'required': True, 'examples': ['ListDict containing Decimation objects']})]


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



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



   .. py:method:: synchronize_levels()

      Ensure that decimation_levels and levels are synchronized.
      - Creates Decimation objects for any levels in decimation_levels that don't exist in levels
      - Adds level names to decimation_levels for any existing levels not in the list



   .. py:method:: has_decimation_level(level)

      Check to see if the decimation_level already exists

      :param level: decimation_level level to look for
      :type level: string
      :return: True if found, False if not
      :rtype: boolean




   .. py:method:: decimation_level_index(level)

      get index of the decimation_level in the decimation_level list



   .. py:method:: get_decimation_level(level)

      Get a decimation_level

      :param level: decimation_level level to look for
      :type level: string
      :return: decimation_level object based on decimation_level type
      :rtype: :class:`mt_metadata.timeseries.decimation_level`




   .. py:method:: add_decimation_level(fc_decimation)

      Add a decimation_level to the list, check if one exists if it does overwrite it

      :param fc_decimation: decimation level object to add
      :type fc_decimation: :class:`mt_metadata.processing.fourier_coefficients.decimation_basemodel.Decimation`




   .. py:method:: remove_decimation_level(decimation_level_id)

      remove a ch from the survey

      :param level: decimation_level level to look for
      :type level: string




   .. py:property:: n_decimation_levels


   .. py:method:: update_time_period()

      update time period from ch information