Signal

anomaly

pyCGM2.Signal.anomaly.anomaly_rolling(values, aprioriError=0, window=10, threshold=3, method='median', plot=False, label='Unknow', referenceValues=None)

anomaly detection from rolling windows

Parameters

  • values (np.array) – values

  • aprioriError (int,Optional[0]) – a priori error.

  • window (int,Optional[10]) – size of the window.

  • threshold (int,Optional[3]) – standard deviation factor

  • method (str,Optional[median]) – descriptive statistic method

  • plot (bool,Optional[False]) – enable plot

  • label (str,Optional[Unknown]) – Description of parameter label

  • referenceValues (np.array,Optional[None]) – values used as reference instead of the values computing from the rolling windows

detect_changes

The module only contains the function “detect_changes” implemenented by Marcos Duarte, available in [BMC](https://github.com/demotu/BMC)

see BMC documentation for details

pyCGM2.Signal.detect_changes.detect_cusum(x, threshold=1, drift=0, ending=False, show=True, ax=None)

Cumulative sum algorithm (CUSUM) to detect abrupt changes in data

detect_onset

The module only contains the function “detect_onset” implemenented by Marcos Duarte, available in [BMC](https://github.com/demotu/BMC)

see BMC documentation for details

pyCGM2.Signal.detect_onset.detect_onset(x, threshold=0, n_above=1, n_below=0, threshold2=None, n_above2=1, show=False, ax=None)

Detects onset in data based on amplitude threshold.

detect_peaks

The module only contains the function “detect_peaks” implemenented by Marcos Duarte, available in [BMC](https://github.com/demotu/BMC)

see BMC documentation for details

pyCGM2.Signal.detect_peaks.detect_peaks(x, mph=None, mpd=1, threshold=0, edge='rising', kpsh=False, valley=False, show=False, ax=None)

Detect peaks in data based on their amplitude and other features.

signal_processing

The module only contains functions for filtering data

pyCGM2.Signal.signal_processing.arrayLowPassFiltering(valuesArray, freq, order=2, fc=6)

Low-pass filtering of a numpy.array

Parameters

  • valuesArray (numpy.array(n,n)) –

  • fc (float,Optional) – cut-off frequency. Default set to 6 Hz

  • order (int,optional) – order of the low-pass filter, Default set to 2

pyCGM2.Signal.signal_processing.enveloppe(array, fc, fa)

Get signal enveloppe from a low pass filter

Parameters

  • array (numpy.array(n,n)) – array

  • fc (float) – cut-off frequency

  • fa (float) – sample frequency

pyCGM2.Signal.signal_processing.forcePlateFiltering(btkAcq, order=4, fc=5)

Low-pass filtering of Force plate outputs

Parameters

  • btkAcq (btk.Acquisition) – btk acquisition instance

  • fc (float,Optional) – cut-off frequency. Default set to 5 Hz

  • order (int,optional) – order of the low-pass filter, Default set to 4

pyCGM2.Signal.signal_processing.highPass(array, lowerFreq, upperFreq, fa)

butterworth bandpass filter.

Parameters

  • array (numpy.array(n,n)) – array

  • lowerFreq (float) – lower frequency

  • upperFreq (float) – upper frequency

  • fa (float) – sample frequency

pyCGM2.Signal.signal_processing.markerFiltering(btkAcq, markers, order=2, fc=6, zerosFiltering=True)

Low-pass filtering of all points in an acquisition

Parameters

  • btkAcq (btk.Acquisition) – btk acquisition instance

  • fc (float,Optional) – cut-off frequency. Default set to 6 Hz

  • order (int,optional) – order of the low-pass filter, Default set to 2

  • zerosFiltering (bool,optional) – enable zero filtering, Default set to True

pyCGM2.Signal.signal_processing.rectify(array)

rectify a signal ( i.e get absolute values)

Parameters

array (numpy.array(n,n)) – array

pyCGM2.Signal.signal_processing.remove50hz(array, fa)

Remove the 50Hz signal

Args

array (array(n,n): array fa (float): sample frequency