mt_metadata.features.weights.taper_weight_kernel
================================================

.. py:module:: mt_metadata.features.weights.taper_weight_kernel

.. autoapi-nested-parse::

   Module with a compound kernel, mixing multiple monotonic kernels.



Classes
-------

.. autoapisummary::

   mt_metadata.features.weights.taper_weight_kernel.TaperWeightKernel


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

.. py:class:: TaperWeightKernel(**data)

   Bases: :py:obj:`mt_metadata.features.weights.base.Base`


   A composite weight kernel that multiplies a low-cut and a high-cut monotonic taper kernel.

   :param low_cut: (lower_bound, upper_bound) for the low-cut transition region.
   :type low_cut: tuple[float, float]
   :param high_cut: (lower_bound, upper_bound) for the high-cut transition region.
   :type high_cut: tuple[float, float]
   :param style: The taper style to use (default is 'hann').
   :type style: str, optional
   :param \*\*kwargs: Additional keyword arguments passed to BaseWeightKernel.


   .. py:attribute:: low_cut
      :type:  Annotated[Tuple[float, float], Field(description='Low cut transition bounds', json_schema_extra={'units': None, 'required': True, 'examples': [[0.1, 0.5]]})]


   .. py:attribute:: high_cut
      :type:  Annotated[Tuple[float, float], Field(description='High cut transition bounds', json_schema_extra={'units': None, 'required': True, 'examples': [[0.5, 1.0]]})]


   .. py:attribute:: style
      :type:  Annotated[mt_metadata.processing.window.TypeEnum, Field(description='Taper style', json_schema_extra={'units': None, 'required': True, 'examples': ['hann', 'hamming', 'blackman']})]


   .. py:property:: low_kernel
      :type: mt_metadata.features.weights.taper_monotonic_weight_kernel.TaperMonotonicWeightKernel


      The low-cut taper kernel.


   .. py:property:: high_kernel
      :type: mt_metadata.features.weights.taper_monotonic_weight_kernel.TaperMonotonicWeightKernel


      The high-cut taper kernel.


   .. py:method:: evaluate(values)

      Evaluate the composite taper weight kernel on the input values.

      :param values: Input values to evaluate the kernel on.
      :type values: np.ndarray

      :returns: The product of the low-cut and high-cut kernel evaluations.
      :rtype: np.ndarray