saftig.filtering.uwf

Updating Wiener Filter

Classes

UpdatingWienerFilter

Updating Wiener filter implementation

Module Contents

class saftig.filtering.uwf.UpdatingWienerFilter(n_filter, idx_target, n_channel=1, context_pre=0, context_post=0)

Bases: saftig.filtering.common.FilterBase

Updating Wiener filter 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

  • context_pre (int) – how many additional samples before the current block are used to update the filters

  • context_post (int) – how many additional samples after the current block are used to update the filters

>>> import saftig as sg
>>> n_filter = 128
>>> witness, target = sg.evaluation.TestDataGenerator(0.1).generate(int(1e5))
>>> filt = sg.filtering.UpdatingWienerFilter(n_filter, 0, 1, context_pre=20*n_filter, context_post=20*n_filter)
>>> prediction = filt.apply(witness, target) # check on the data used for conditioning
>>> residual_rms = sg.evaluation.rms(target-prediction)
>>> residual_rms > 0.05 and residual_rms < 0.15 # the expected RMS in this test scenario is 0.1
True
context_pre: int
context_post: int
filter_name: str = 'UWF'
filter_state: numpy.typing.NDArray | None = None
static supports_saving_loading()

Indicates whether saving and loading is supported.

Return type:

bool

condition_multi_sequence(witness, target, hide_warning=False)

Placeholder for compatibility to other filters; does nothing!

Parameters:

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

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

  • hide_warning (bool) –

Return type:

None

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

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

Returns:

prediction, bool indicating if all WF updates had full rank

Return type:

numpy.typing.NDArray

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

Apply the filter to input data

Similar to apply() but expects multiple sequences.

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]