parameters: (data, axis=0, order=1, mode='clip')
return_statement: return argrelextrema(data, np.greater, axis, order, mode)
docstring: 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 one-dimensional.
  
      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 one-dimensional `data` `find_peaks` can be used to detect all
      local maxima, including flat ones.
  
      .. versionadded:: 0.11.0
  
      Examples
      --------
      >>> 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]))
docstring_summary: Calculate the relative maxima of `data`.
docstring_tokens: Calculate the relative maxima of data .
function: def 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 one-dimensional.
  
      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 one-dimensional `data` `find_peaks` can be used to detect all
      local maxima, including flat ones.
  
      .. versionadded:: 0.11.0
  
      Examples
      --------
      >>> 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]))
      """
      return argrelextrema(data, np.greater, axis, order, mode)
function_tokens: def argrelmax ( data , axis = 0 , order = 1 , mode = 'clip' ) : return argrelextrema ( data , np . greater , axis , order , mode )