parameters: (a, axis=None, out=None, keepdims=False)
docstring: Return the maximum of an array or maximum along an axis.
  
      Parameters
      ----------
      a : array_like
          Input data.
      axis : int, optional
          Axis along which to operate.  By default, flattened input is used.
      out : ndarray, optional
          Alternative output array in which to place the result.  Must
          be of the same shape and buffer length as the expected output.
          See `doc.ufuncs` (Section "Output arguments") for more details.
      keepdims : bool, optional
          If this is set to True, the axes which are reduced are left
          in the result as dimensions with size one. With this option,
          the result will broadcast correctly against the original `arr`.
  
      Returns
      -------
      amax : ndarray or scalar
          Maximum of `a`. If `axis` is None, the result is a scalar value.
          If `axis` is given, the result is an array of dimension
          ``a.ndim - 1``.
  
      See Also
      --------
      amin :
          The minimum value of an array along a given axis, propagating any NaNs.
      nanmax :
          The maximum value of an array along a given axis, ignoring any NaNs.
      maximum :
          Element-wise maximum of two arrays, propagating any NaNs.
      fmax :
          Element-wise maximum of two arrays, ignoring any NaNs.
      argmax :
          Return the indices of the maximum values.
  
      nanmin, minimum, fmin
  
      Notes
      -----
      NaN values are propagated, that is if at least one item is NaN, the
      corresponding max value will be NaN as well. To ignore NaN values
      (MATLAB behavior), please use nanmax.
  
      Don't use `amax` for element-wise comparison of 2 arrays; when
      ``a.shape[0]`` is 2, ``maximum(a[0], a[1])`` is faster than
      ``amax(a, axis=0)``.
  
      Examples
      --------
      >>> a = np.arange(4).reshape((2,2))
      >>> a
      array([[0, 1],
             [2, 3]])
      >>> np.amax(a)           # Maximum of the flattened array
      3
      >>> np.amax(a, axis=0)   # Maxima along the first axis
      array([2, 3])
      >>> np.amax(a, axis=1)   # Maxima along the second axis
      array([1, 3])
  
      >>> b = np.arange(5, dtype=np.float)
      >>> b[2] = np.NaN
      >>> np.amax(b)
      nan
      >>> np.nanmax(b)
      4.0
docstring_summary: Return the maximum of an array or maximum along an axis.
docstring_tokens: Return the maximum of an array or maximum along an axis .
function: def amax(a, axis=None, out=None, keepdims=False):
      """
      Return the maximum of an array or maximum along an axis.
  
      Parameters
      ----------
      a : array_like
          Input data.
      axis : int, optional
          Axis along which to operate.  By default, flattened input is used.
      out : ndarray, optional
          Alternative output array in which to place the result.  Must
          be of the same shape and buffer length as the expected output.
          See `doc.ufuncs` (Section "Output arguments") for more details.
      keepdims : bool, optional
          If this is set to True, the axes which are reduced are left
          in the result as dimensions with size one. With this option,
          the result will broadcast correctly against the original `arr`.
  
      Returns
      -------
      amax : ndarray or scalar
          Maximum of `a`. If `axis` is None, the result is a scalar value.
          If `axis` is given, the result is an array of dimension
          ``a.ndim - 1``.
  
      See Also
      --------
      amin :
          The minimum value of an array along a given axis, propagating any NaNs.
      nanmax :
          The maximum value of an array along a given axis, ignoring any NaNs.
      maximum :
          Element-wise maximum of two arrays, propagating any NaNs.
      fmax :
          Element-wise maximum of two arrays, ignoring any NaNs.
      argmax :
          Return the indices of the maximum values.
  
      nanmin, minimum, fmin
  
      Notes
      -----
      NaN values are propagated, that is if at least one item is NaN, the
      corresponding max value will be NaN as well. To ignore NaN values
      (MATLAB behavior), please use nanmax.
  
      Don't use `amax` for element-wise comparison of 2 arrays; when
      ``a.shape[0]`` is 2, ``maximum(a[0], a[1])`` is faster than
      ``amax(a, axis=0)``.
  
      Examples
      --------
      >>> a = np.arange(4).reshape((2,2))
      >>> a
      array([[0, 1],
             [2, 3]])
      >>> np.amax(a)           # Maximum of the flattened array
      3
      >>> np.amax(a, axis=0)   # Maxima along the first axis
      array([2, 3])
      >>> np.amax(a, axis=1)   # Maxima along the second axis
      array([1, 3])
  
      >>> b = np.arange(5, dtype=np.float)
      >>> b[2] = np.NaN
      >>> np.amax(b)
      nan
      >>> np.nanmax(b)
      4.0
  
      """
      if type(a) is not mu.ndarray:
          try:
              amax = a.max
          except AttributeError:
              return _methods._amax(a, axis=axis,
                                  out=out, keepdims=keepdims)
          # NOTE: Dropping the keepdims parameter
          return amax(axis=axis, out=out)
      else:
          return _methods._amax(a, axis=axis,
                              out=out, keepdims=keepdims)
function_tokens: def amax ( a , axis = None , out = None , keepdims = False ) : if type ( a ) is not mu . ndarray : try : amax = a . max except AttributeError : return _methods . _amax ( a , axis = axis , out = out , keepdims = keepdims ) # NOTE: Dropping the keepdims parameter return amax ( axis = axis , out = out ) else : return _methods . _amax ( a , axis = axis , out = out , keepdims = keepdims )