saftig.evaluation.metrics

Methods of evaluation noise cancellation performance.

Attributes

Self

Classes

EvaluationMetric

Parent class for evaluation metrics

EvaluationMetricScalar

Parent class for evaluation metrics that yield a scalar value

EvaluationMetricPlottable

Parent class for evaluation metrics that provide a plotting feature

RMSMetric

The RMS of the residual signal

MSEMetric

The MSE of the residual signal

BandwidthPowerMetric

The signal power on a given frequency range

PSDMetric

Plots the PSD of the given signal

Functions

welch_multiple_sequences(arrays, nperseg, *args, **kwargs)

Apply scipy.signal.welch to a sequence of arrays

Module Contents

saftig.evaluation.metrics.Self
saftig.evaluation.metrics.welch_multiple_sequences(arrays, nperseg, *args, **kwargs)

Apply scipy.signal.welch to a sequence of arrays

Additional arguments are passed to the scipy Welch implementation. Spectra are combined with an average weighted by the array lengths. If sequences

Parameters:

  • arrays (collections.abc.Sequence[numpy.typing.NDArray]) – Sequence of arrays

  • nperseg – Length of FFT segments

Return type:

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

class saftig.evaluation.metrics.EvaluationMetric(**kwargs)

Bases: abc.ABC

Parent class for evaluation metrics

applied = False
prediction: collections.abc.Sequence[numpy.typing.NDArray]
dataset: saftig.evaluation.dataset.EvaluationDataset
residual: collections.abc.Sequence[numpy.typing.NDArray]
parameters: dict
name: str
method_hash_value: bytes
static init_wrapper(func)

A decorator for the __init__function

Saves a hash value for the configuration

apply(prediction, dataset)

Apply this filter

Parameters:
Return type:

Self

property result_full: tuple
Abstractmethod:

Return type:

tuple

The raw data of the result

property result: Any

The result of the metric evaluation

Return type:

Any

classmethod result_to_text(result_full)

String indicating the evaluation result

Parameters:

result_full (tuple[float | numpy.floating, Ellipsis]) –

Return type:

str

property text

The text representation of the evaluation result

classmethod _file_hash()

Calculates a hash value based on the file in which this method was defined.

Return type:

bytes

property method_hash: bytes

A hash representing the configured metric as a bytes object

Return type:

bytes

property method_hash_str: str

A hash representing the configured metric as a base64 like string

Return type:

str

static result_full_wrapper(func)

A decorator for the result_full member function.

Raises an exception if result is accessed on an object that was not applied to data. Caches the result to prevent double calculation.

class saftig.evaluation.metrics.EvaluationMetricScalar(**kwargs)

Bases: EvaluationMetric

Parent class for evaluation metrics that yield a scalar value

property result: float

The raw data of the result

Return type:

float

classmethod result_to_text(result_full)

String indicating the evaluation result

Parameters:

result_full (tuple[float | numpy.floating, Ellipsis]) –

Return type:

str

class saftig.evaluation.metrics.EvaluationMetricPlottable(**kwargs)

Bases: EvaluationMetric

Parent class for evaluation metrics that provide a plotting feature

abstract plot(ax)

Generate a result plot on the given axes object

Parameters:

ax (matplotlib.axes.Axes) –

save_plot(fname, figsize=(6, 3), tight_layout=True)

Save the plot to a file

Parameters:

  • fname (str | pathlib.Path) –

  • figsize (tuple[int, int]) –

  • tight_layout (bool) –

class saftig.evaluation.metrics.RMSMetric(**kwargs)

Bases: EvaluationMetricScalar

The RMS of the residual signal

name = 'Residual RMS'
property result_full: tuple[numpy.floating | float]

The raw data of the result

Return type:

tuple[numpy.floating | float]

static result_to_text(result_full)

String indicating the evaluation result

Parameters:

result_full (tuple[float | numpy.floating, Ellipsis]) –

Return type:

str

class saftig.evaluation.metrics.MSEMetric(**kwargs)

Bases: EvaluationMetricScalar

The MSE of the residual signal

name = 'Residual MSE'
property result_full: tuple[numpy.floating | float]

The raw data of the result

Return type:

tuple[numpy.floating | float]

class saftig.evaluation.metrics.BandwidthPowerMetric(f_start, f_stop, n_fft=1024, window='hann')

Bases: EvaluationMetricScalar

The signal power on a given frequency range

The spectrum is calculated with welch on each sequence. An average weighted by the sequence length is used to combine spectra from the sequences. The closes bins to f_start and f_stop is chosen as the integration borders.

Parameters:

  • f_start (float) – The frequency at which the power integration starts

  • f_stop (float) – The frequency at which the power integration stops

  • n_fft (int) – Sample count per FFT block used by welch

  • window – The FFT window type

name = 'Residual power on frequency range'
f_start
f_stop
n_fft = 1024
window = 'hann'
property result_full

The raw data of the result

class saftig.evaluation.metrics.PSDMetric(n_fft=1024, window='hann', logx=True, logy=True, show_target=True)

Bases: EvaluationMetricPlottable

Plots the PSD of the given signal

The spectrum is calculated with Welch on each sequence. An average weighted by the sequence length is used to combine spectra from the sequences. The closes bins to f_start and f_stop is chosen as the integration borders.

Parameters:

  • n_fft (int) – Sample count per FFT block used by Welch’s method

  • window (str) – fft window type

  • logx (bool) – Logarithmic x scale

  • logy (bool) – Logarithmic y scale

  • show_target (bool) –

name = 'Power spectral density'
n_fft = 1024
window = 'hann'
logx = True
logy = True
show_target = True
_welch_multiple_sequences(signal)

apply welch_multiple_sequences() with correct settings

Parameters:

signal (collections.abc.Sequence[numpy.typing.NDArray]) –

property result_full: tuple[numpy.typing.NDArray, numpy.typing.NDArray]

The raw data of the result

Return type:

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

plot(ax)

Plot to the given Axes object

Parameters:

ax (matplotlib.axes.Axes) –