saftig.external.spicypy_wf

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

Classes

SpicypyWienerFilter

A wrapper for the spicypy WF implementation

Module Contents

class saftig.external.spicypy_wf.SpicypyWienerFilter(n_filter, idx_target, n_channel=1)

Bases: saftig.filtering.common.FilterBase

A wrapper for the spicypy WF implementation

Parameters:

  • n_filter (int) – Length of the FIR filter (how many samples are in the input window per output sample)

  • idx_target (int) – Position of the prediction

  • n_channel (int) – 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
supports_multi_sequence = False
filter_name = 'SpicypyWF'
conditioned = False
filter_state: spicypy.signal.WienerFilter | None
static supports_saving_loading()

Indicates whether saving and loading is supported.

static make_spicypy_time_series(witness: collections.abc.Sequence | numpy.typing.NDArray, target: None, sample_rate: float = 1.0) → tuple[collections.abc.Sequence[spicypy.signal.TimeSeries], None]
static make_spicypy_time_series(witness: collections.abc.Sequence | numpy.typing.NDArray, target: collections.abc.Sequence | numpy.typing.NDArray, sample_rate: float = 1.0) → tuple[collections.abc.Sequence[spicypy.signal.TimeSeries], spicypy.signal.TimeSeries]

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

Parameters:

  • witness – Witness sensor data

  • target – Target sensor data

  • sample_rate – The sample rate of the time series

condition(witness, target, sample_rate=1.0, use_multiprocessing=False)

Use an input dataset to condition the filter

Parameters:

  • witness (collections.abc.Sequence | numpy.typing.NDArray) – Witness sensor data

  • target (collections.abc.Sequence | numpy.typing.NDArray) – Target sensor data

  • sample_rate (float) – The sample rate of the time series

  • use_multiprocessing (bool) –

Return type:

spicypy.signal.WienerFilter

condition_multi_sequence(witness, target)

Not supported

Parameters:

  • witness (collections.abc.Sequence | collections.abc.Sequence[collections.abc.Sequence] | numpy.typing.NDArray) –

  • target (collections.abc.Sequence | numpy.typing.NDArray) –

apply(witness, target=None, pad=True, update_state=False, sample_rate=1.0)

Apply the filter to input data

Parameters:

  • witness (collections.abc.Sequence | numpy.typing.NDArray) – Witness sensor data

  • target (collections.abc.Sequence | numpy.typing.NDArray | None) – Target sensor data (is ignored)

  • pad (bool) – if True, apply padding zeros so that the length matches the target signal

  • update_state (bool) – ignored

  • sample_rate (float) –

Returns:

prediction

Return type:

numpy.typing.NDArray

apply_multi_sequence(witness, target, pad=True, update_state=False)

Not supported

Parameters:

  • witness (collections.abc.Sequence | numpy.typing.NDArray) –

  • target (collections.abc.Sequence | numpy.typing.NDArray | None) –

  • pad (bool) –

  • update_state (bool) –

Return type:

collections.abc.Sequence[numpy.typing.NDArray]