franc.filtering.bypass

A filtering method that does conditioning

Classes

BypassFilter

Implementation of a filtering method that just predicts zeros.

Module Contents

class franc.filtering.bypass.BypassFilter(n_channel, n_filter=1, idx_target=0)

Bases: franc.filtering.common.FilterBase

Implementation of a filtering method that just predicts zeros.

Intended for testing and to get apply metrics to input data data.

Parameters:

  • n_channel (int) – Number of witness sensor channels

  • n_filter (int) –

  • idx_target (int) –

all other parameters are ignored

>>> import franc as fnc
>>> n_filter = 128
>>> witness, target = fnc.evaluation.TestDataGenerator(0.1).generate(int(1e5))
>>> filt = fnc.filtering.BypassFilter(1, n_filter, 0)
>>> filt.condition(witness, target)
>>> prediction = filt.apply(witness, target) # check on the data used for conditioning
>>> float(sum(prediction))
0.0
filter_name: str = 'Bypass'
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:

None

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]