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# scipy.signal.argrelmax

scipy.signal.argrelmax(data, axis=0, order=1, mode='clip')

  Calculate the relative maxima of data.

  Parameters:
    data ndarray

      Array in which to find the relative maxima.

    axis int, optional

      Axis over which to select from data. Default is 0.

    order int, optional

      How many points on each side to use for the comparison to consider `comparator(n, n+x)` to be True.

    mode str, 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:
    extrema tuple 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

  `argrelextrema`, `argrelmin`, `find_peaks`

  Notes

  This function uses `argrelextrema` with 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_peaks` can be used to detect all local maxima, including flat ones.

  New 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]))