parameters: (data, axis=0, order=1, mode='clip')
return_statement: return argrelextrema(data, np.less, axis, order, mode)
docstring: Calculate the relative minima of `data`.
  
      Parameters
      ----------
      data : ndarray
          Array in which to find the relative minima.
      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 minima 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, argrelmax, find_peaks
  
      Notes
      -----
      This function uses `argrelextrema` with np.less as comparator. Therefore it
      requires a strict inequality on both sides of a value to consider it a
      minimum. This means flat minima (more than one sample wide) are not detected.
      In case of one-dimensional `data` `find_peaks` can be used to detect all
      local minima, including flat ones, by calling it with negated `data`.
  
      .. versionadded:: 0.11.0
  
      Examples
      --------
      >>> from scipy.signal import argrelmin
      >>> x = np.array([2, 1, 2, 3, 2, 0, 1, 0])
      >>> argrelmin(x)
      (array([1, 5]),)
      >>> y = np.array([[1, 2, 1, 2],
      ...               [2, 2, 0, 0],
      ...               [5, 3, 4, 4]])
      ...
      >>> argrelmin(y, axis=1)
      (array([0, 2]), array([2, 1]))
docstring_summary: Calculate the relative minima of `data`.
docstring_tokens: Calculate the relative minima of data .
function: def argrelmin(data, axis=0, order=1, mode='clip'):
      """
      Calculate the relative minima of `data`.
  
      Parameters
      ----------
      data : ndarray
          Array in which to find the relative minima.
      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 minima 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, argrelmax, find_peaks
  
      Notes
      -----
      This function uses `argrelextrema` with np.less as comparator. Therefore it
      requires a strict inequality on both sides of a value to consider it a
      minimum. This means flat minima (more than one sample wide) are not detected.
      In case of one-dimensional `data` `find_peaks` can be used to detect all
      local minima, including flat ones, by calling it with negated `data`.
  
      .. versionadded:: 0.11.0
  
      Examples
      --------
      >>> from scipy.signal import argrelmin
      >>> x = np.array([2, 1, 2, 3, 2, 0, 1, 0])
      >>> argrelmin(x)
      (array([1, 5]),)
      >>> y = np.array([[1, 2, 1, 2],
      ...               [2, 2, 0, 0],
      ...               [5, 3, 4, 4]])
      ...
      >>> argrelmin(y, axis=1)
      (array([0, 2]), array([2, 1]))
  
      """
      return argrelextrema(data, np.less, axis, order, mode)
function_tokens: def argrelmin ( data , axis = 0 , order = 1 , mode = 'clip' ) : return argrelextrema ( data , np . less , axis , order , mode )