saftig.external.spicypy_wf
==========================

.. py:module:: saftig.external.spicypy_wf

.. autoapi-nested-parse::

   A wrapper for spicypy.signal.WienerFilter with the saftig.common.FilterBase interface.
   This is intended to allow comparisons between the implementations.



Classes
-------

.. autoapisummary::

   saftig.external.spicypy_wf.SpicypyWienerFilter


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

.. py:class:: SpicypyWienerFilter(n_filter, idx_target, n_channel = 1)

   Bases: :py:obj:`saftig.filtering.common.FilterBase`


   A wrapper for the spicypy WF implementation

   :param n_filter: Length of the FIR filter
       (how many samples are in the input window per output sample)
   :param idx_target: Position of the prediction
   :param n_channel: Number of witness sensor channels

   >>> import saftig as sg
   >>> n_filter = 10
   >>> witness, target = sg.evaluation.TestDataGenerator(0.1).generate(int(1e3))
   >>> filt = sg.external.SpicypyWienerFilter(n_filter, 0, 1)
   >>> sp_filt = filt.condition(witness, target)
   >>> prediction = filt.apply(witness, target) # apply to training data



   .. py:attribute:: filter_name
      :value: 'SpicypyWF'



   .. py:attribute:: conditioned
      :value: False



   .. py:attribute:: filter_state
      :type:  Optional[spicypy.signal.WienerFilter]


   .. py:method:: supports_saving_loading()
      :staticmethod:


      Indicates whether saving and loading is supported.



   .. py:method:: make_spicypy_time_series(witness, target, sample_rate = 1.0)
      :staticmethod:


      Convert the given witness and target signals to the format requried by spicypy.

      :param witness: Witness sensor data
      :param target: Target sensor data
      :param sample_rate: The sample rate of the time series



   .. py:method:: condition(witness, target, sample_rate = 1.0, use_multiprocessing = False)

      Use an input dataset to condition the filter

      :param witness: Witness sensor data
      :param target: Target sensor data
      :param sample_rate: The sample rate of the time series



   .. py:method:: apply(witness, target, pad = True, update_state = False, sample_rate = 1.0)

      Apply the filter to input data

      :param witness: Witness sensor data
      :param target: Target sensor data (is ignored)
      :param pad: if True, apply padding zeros so that the length matches the target signal
      :param update_state: ignored

      :return: prediction