saftig.filtering.wf

Classical static Wiener filter

Classes

WienerFilter

Satic Wiener filter implementation

Functions

mean_cross_correlation_offset(A, B, N, offset)

estimate the cross-correlation between A and B

wf_calculate(witness, target, n_filter[, idx_target])

caluclate the FIR coefficients for a wiener filter

wf_apply(WFC, witness)

apply the WF to witness data

Module Contents

saftig.filtering.wf.mean_cross_correlation_offset(A, B, N, offset)

estimate the cross-correlation between A and B :param A: First input array :param B: Second input array :param N: Number of steps to test. Defines length of output :param offset: Offset for the cross correlation

Parameters:

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

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

  • N (int) –

  • offset (int) –

Return type:

numpy.typing.NDArray

saftig.filtering.wf.wf_calculate(witness, target, n_filter, idx_target=0)

caluclate the FIR coefficients for a wiener filter

Parameters:

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

  • witness – Target sensor data

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

  • idx_target (int) – offset of the prediction relative to the end of the array

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

Returns:

filter coefficients, full_rank (bool)

Return type:

tuple[numpy.typing.NDArray, bool]

saftig.filtering.wf.wf_apply(WFC, witness)

apply the WF to witness data

Parameters:

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

  • target – Target sensor data

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

Returns:

prediction

Return type:

numpy.typing.NDArray

class saftig.filtering.wf.WienerFilter(n_filter, idx_target, n_channel=1)

Bases: saftig.filtering.common.FilterBase

Satic 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

>>> import saftig as sg
>>> n_filter = 128
>>> witness, target = sg.evaluation.TestDataGenerator(0.1).generate(int(1e5))
>>> filt = sg.filtering.WienerFilter(n_filter, 0, 1)
>>> _coefficients, full_rank = filt.condition(witness, target)
>>> full_rank
True
>>> 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
filter_state: numpy.typing.NDArray | None = None
filter_name: str = 'WF'
requires_apply_target = False
condition_multi_sequence(witness, target)

Use an input dataset to condition the filter

Parameters:

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

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

Return type:

tuple[numpy.typing.NDArray, bool]

apply_multi_sequence(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

Return type:

list[numpy.typing.NDArray]