argrelmax#
- scipy.signal.argrelmax(data, axis=0, order=1, mode='clip')[source]#
- Calculate the relative maxima of data. - Parameters:
- datandarray
- Array in which to find the relative maxima. 
- axisint, optional
- Axis over which to select from data. Default is 0. 
- orderint, optional
- How many points on each side to use for the comparison to consider - comparator(n, n+x)to be True.
- modestr, optional
- How the edges of the vector are treated. Available options are ‘wrap’ (wrap around) or ‘clip’ (treat overflow as the same as the last (or first) element). Default ‘clip’. See - numpy.take.
 
- Returns:
- extrematuple of ndarrays
- Indices of the maxima in arrays of integers. - extrema[k]is the array of indices of axis k of data. Note that the return value is a tuple even when data is 1-D.
 
 - See also - Notes - This function uses - argrelextremawith np.greater as comparator. Therefore, it requires a strict inequality on both sides of a value to consider it a maximum. This means flat maxima (more than one sample wide) are not detected. In case of 1-D data- find_peakscan be used to detect all local maxima, including flat ones.- Added in version 0.11.0. - Examples - >>> import numpy as np >>> from scipy.signal import argrelmax >>> x = np.array([2, 1, 2, 3, 2, 0, 1, 0]) >>> argrelmax(x) (array([3, 6]),) >>> y = np.array([[1, 2, 1, 2], ... [2, 2, 0, 0], ... [5, 3, 4, 4]]) ... >>> argrelmax(y, axis=1) (array([0]), array([1]))