While indices in peaks are guaranteed to be at least distance samplesĪpart, edges of flat peaks may be closer than the allowed distance.NumPy is the most crucial Python package for scientific computing. To reduce the number of peaks that need to be evaluated later. This order is the fastest one because faster operations are applied first Height, threshold, distance, prominence, width. The conditions are evaluated in the following order: plateau_size, The border is always included in the interval used to select valid peaks.įor several conditions the interval borders can be specified withĪrrays matching x in shape which enables dynamic constrains based on The open interval (None, None) can be specifiedĪs well, which returns the matching properties without exclusion of peaks. Interval \(\) while (None, 1) defines the interval Some additional comments on specifying conditions:Īlmost all conditions (excluding distance) can be given as half-open orĬlosed intervals, e.g., 1 or (1, None) defines the half-open
Signal before searching for peaks or use other peak finding and fitting Sample is returned (rounded down in case the number of samples is even).įor noisy signals the peak locations can be off because the noise mightĬhange the position of local maxima. (more than one sample of equal amplitude wide) the index of the middle Sample whose two direct neighbours have a smaller amplitude. In the context of this function, a peak or local maximum is defined as any peak_prominencesĭirectly calculate the prominence of peaks. If supplied as the maximal required plateau size.įind peaks using the wavelet transformation. The first element is always interpreted as the minimal and the second, None, an array matching x or a 2-element sequence of the former. Required size of the flat top of peaks in samples. plateau_size number or ndarray or sequence, optional See argument rel_height in peak_widths for a fullĭescription of its effects. Used for calculation of the peaks width, thus it is only used if width
Wlen in peak_prominences for a full description of its effects. One of the arguments prominence or width is given. Used for calculation of the peaks prominences, thus it is only used if
width number or ndarray or sequence, optional Supplied, as the maximal required prominence. The firstĮlement is always interpreted as the minimal and the second, if Matching x or a 2-element sequence of the former. prominence number or ndarray or sequence, optional Smaller peaks are removed first until the condition Required minimal horizontal distance (>= 1) in samples between Interpreted as the minimal and the second, if supplied, as the maximal Either a number, None, an array matching x or aĢ-element sequence of the former. Required threshold of peaks, the vertical distance to its neighboring threshold number or ndarray or sequence, optional The first element isĪlways interpreted as the minimal and the second, if supplied, as the height number or ndarray or sequence, optional Parameters x sequenceĪ signal with peaks. Peaks can be selected by specifying conditions for a peak’s properties. This function takes a 1-D array and finds all local maxima by find_peaks ( x, height = None, threshold = None, distance = None, prominence = None, width = None, wlen = None, rel_height = 0.5, plateau_size = None ) ¶įind peaks inside a signal based on peak properties. Statistical functions for masked arrays ( K-means clustering and vector quantization (