Detect Peaks

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

Citation: Duarte, Marcos. (2015) Notes on Scientific Computing for Biomechanics and Motor Control. GitHub repository, https://github.com/demotu/BMC.

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pyteck.detect_peaks.detect_peaks(x, mph=None, mpd=1, threshold=0, edge='rising', kpsh=False, valley=False, show=False, ax=None)[source]

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

Parameters

  • x (1D array_like) – data.

  • mph ({None, number}, optional (default = None)) – detect peaks that are greater than minimum peak height (if parameter valley is False) or peaks that are smaller than maximum peak height (if parameter valley is True).

  • mpd (positive integer, optional (default = 1)) – detect peaks that are at least separated by minimum peak distance (in number of data).

  • threshold (positive number, optional (default = 0)) – detect peaks (valleys) that are greater (smaller) than threshold in relation to their immediate neighbors.

  • edge ({None, 'rising', 'falling', 'both'}, optional (default = 'rising')) – for a flat peak, keep only the rising edge (‘rising’), only the falling edge (‘falling’), both edges (‘both’), or don’t detect a flat peak (None).

  • kpsh (bool, optional (default = False)) – keep peaks with same height even if they are closer than mpd.

  • valley (bool, optional (default = False)) – if True (1), detect valleys (local minima) instead of peaks.

  • show (bool, optional (default = False)) – if True (1), plot data in matplotlib figure.

  • ax (a matplotlib.axes.Axes instance, optional (default = None)) –

Returns

ind – indeces of the peaks in x.

Return type

1D array_like

Notes

The detection of valleys instead of peaks is performed internally by simply negating the data: ind_valleys = detect_peaks(-x)

The function can handle NaN’s

See this IPython Notebook 1.

References

1

http://nbviewer.ipython.org/github/demotu/BMC/blob/master/notebooks/DetectPeaks.ipynb

Examples

>>> from detect_peaks import detect_peaks
>>> x = np.random.randn(100)
>>> x[60:81] = np.nan
>>> # detect all peaks and plot data
>>> ind = detect_peaks(x, show=True)
>>> print(ind)

>>> x = np.sin(2*np.pi*5*np.linspace(0, 1, 200)) + np.random.randn(200)/5
>>> # set minimum peak height = 0 and minimum peak distance = 20
>>> detect_peaks(x, mph=0, mpd=20, show=True)

>>> x = [0, 1, 0, 2, 0, 3, 0, 2, 0, 1, 0]
>>> # set minimum peak distance = 2
>>> detect_peaks(x, mpd=2, show=True)

>>> x = np.sin(2*np.pi*5*np.linspace(0, 1, 200)) + np.random.randn(200)/5
>>> # detection of valleys instead of peaks
>>> detect_peaks(x, mph=-1.2, mpd=20, valley=True, show=True)

>>> x = [0, 1, 1, 0, 1, 1, 0]
>>> # detect both edges
>>> detect_peaks(x, edge='both', show=True)

>>> x = [-2, 1, -2, 2, 1, 1, 3, 0]
>>> # set threshold = 2
>>> detect_peaks(x, threshold = 2, show=True)