saftig.evaluation

Tooling to automate evaluation of filtering techniques on datasets.

Submodules

Classes

TestDataGenerator

Generate simple test data for correlated noise mitigation techniques

Functions

rms(a)

Calculate the root mean square value of an array

total_power(a)

calculate the total power of a signal (square or RMS)

residual_power_ratio(target, prediction[, start, ...])

Calculate the ratio between residual power of the residual and the target signal

residual_amplitude_ratio(*args, **kwargs)

Calculate the ratio between residual amplitude of the residual and the target signal

measure_runtime(filter_classes[, n_samples, n_filter, ...])

Measure the runtime of filers for a specific scenario

Package Contents

saftig.evaluation.rms(a)

Calculate the root mean square value of an array

Parameters:

a (collections.abc.Sequence | numpy.typing.NDArray) –

Return type:

float

saftig.evaluation.total_power(a)

calculate the total power of a signal (square or RMS)

>>> import saftig, numpy
>>> signal = numpy.ones(10) * 2
>>> saftig.evaluation.total_power(signal)
4.0
Parameters:

a (collections.abc.Sequence | numpy.typing.NDArray) –

Return type:

float

class saftig.evaluation.TestDataGenerator(witness_noise_level=0.1, target_noise_level=0, transfer_function=1, sample_rate=1.0, rng_seed=None)

Generate simple test data for correlated noise mitigation techniques The channel count is implicitly defined by the shape of witness_noise_level

Parameters:

  • witness_noise_level (float | collections.abc.Sequence) – amplitude ratio of the sensor noise to the correlated noise in the witness sensor Scalar or 1D-vector for multiple sensors

  • target_noise_level (float) – amplitude ratio of the sensor noise to the correlated noise in the target sensor

  • transfer_functon – ratio between the amplitude in the target and witness signals

  • sample_rate (float) – The outputs are referenced to an ASD of 1/sqrt(Hz) if a sample rate is provided

  • transfer_function (float) –

  • rng_seed (Optional[int]) – 

>>> import saftig as sg
>>> # create data with two witness sensors with relative noise amplitudes of 0.1
>>> tdg = sg.evaluation.TestDataGenerator(witness_noise_level=[0.1, 0.1])
>>> # generate a dataset with 1000 samples
>>> witness, target = tdg.generate(1000)
>>> witness.shape, target.shape
((2, 1000), (1000,))
rng: Any
witness_noise_level
target_noise_level
transfer_function
sample_rate = 1.0
scaled_whitenoise(shape)

Generate whitenoise with an ASD of one

Parameters:

shape – shape of the new array

Returns:

Array of white noise

Return type:

numpy.typing.NDArray

generate(n)

Generate sequences of samples

Parameters:

  • N – number of samples

  • n (int) –

Returns:

witness signal, target signal

Return type:

tuple[numpy.typing.NDArray, numpy.typing.NDArray]

saftig.evaluation.residual_power_ratio(target, prediction, start=None, stop=None, remove_dc=True)

Calculate the ratio between residual power of the residual and the target signal

Parameters:

  • target (collections.abc.Sequence) – target signal array

  • prediction (collections.abc.Sequence) – prediction array (same length as target

  • start (int | None) – use only a section of the arrays, start at this index

  • stop (int | None) – use only a section of the arrays, stop at this index

  • component (remove DC) – remove DC component before calculation

  • remove_dc (bool) –

Return type:

float

saftig.evaluation.residual_amplitude_ratio(*args, **kwargs)

Calculate the ratio between residual amplitude of the residual and the target signal

Parameters:

  • target – target signal array

  • prediction – prediction array (same length as target

  • start – use only a section of the arrays, start at this index

  • stop – use only a section of the arrays, stop at this index

  • component (remove DC) – remove DC component before calculation

Return type:

float

saftig.evaluation.measure_runtime(filter_classes, n_samples=int(10000.0), n_filter=128, idx_target=0, n_channel=1, additional_filter_settings=None, repititions=1)

Measure the runtime of filers for a specific scenario Be aware that this gives no feedback upon how much multithreading is used!

Parameters:

  • n_samples (int) – Length of the test data

  • n_filter (int) – Length of the FIR filters / input block size

  • idx_target (int) – Position of the prediction

  • n_channel (int) – Number of witness sensor channels

  • additional_filter_settings (collections.abc.Sequence[dict] | None) – optional settings passed to the filters

  • repititions (int) – how manu repititions to perform during the timing measurement

  • filter_classes (collections.abc.Sequence[saftig.filtering.FilterBase]) –

Returns:

(time_conditioning, time_apply) each in seconds

Return type:

tuple[collections.abc.Sequence, collections.abc.Sequence]