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scipy.signal.

# argrelextrema

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

  Calculate the relative extrema of data.

  Parameters:
    data ndarray

      Array in which to find the relative extrema.

    comparator callable

      Function to use to compare two data points. Should take two arrays as arguments.

    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. ‘wrap’ (wrap around) or ‘clip’ (treat overflow as the same as the last (or first) element). Default is ‘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

  `argrelmin`, `argrelmax`

  Notes

  Added in version 0.11.0.

  Array API Standard Support

  `argrelextrema` has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable `SCIPY_ARRAY_API=1` and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

  Library | CPU | GPU
  --- | --- | ---
  NumPy | ✅ | n/a
  CuPy | n/a | ⛔
  PyTorch | ⛔ | ⛔
  JAX | ⛔ | ⛔
  Dask | ⛔ | n/a

  > See Support for the array API standard for more information.

  Examples

      >>> import numpy as np
      >>> from scipy.signal import argrelextrema
      >>> x = np.array([2, 1, 2, 3, 2, 0, 1, 0])
      >>> argrelextrema(x, np.greater)
      (array([3, 6]),)
      >>> y = np.array([[1, 2, 1, 2],
      ...               [2, 2, 0, 0],
      ...               [5, 3, 4, 4]])
      ...
      >>> argrelextrema(y, np.less, axis=1)
      (array([0, 2]), array([2, 1]))