parameters: (c, m=1, scl=1, axis=0)
return_statement: return c
docstring: Differentiate a Hermite series.
  
      Returns the Hermite series coefficients `c` differentiated `m` times
      along `axis`.  At each iteration the result is multiplied by `scl` (the
      scaling factor is for use in a linear change of variable). The argument
      `c` is an array of coefficients from low to high degree along each
      axis, e.g., [1,2,3] represents the series ``1*H_0 + 2*H_1 + 3*H_2``
      while [[1,2],[1,2]] represents ``1*H_0(x)*H_0(y) + 1*H_1(x)*H_0(y) +
      2*H_0(x)*H_1(y) + 2*H_1(x)*H_1(y)`` if axis=0 is ``x`` and axis=1 is
      ``y``.
  
      Parameters
      ----------
      c : array_like
          Array of Hermite series coefficients. If `c` is multidimensional the
          different axis correspond to different variables with the degree in
          each axis given by the corresponding index.
      m : int, optional
          Number of derivatives taken, must be non-negative. (Default: 1)
      scl : scalar, optional
          Each differentiation is multiplied by `scl`.  The end result is
          multiplication by ``scl**m``.  This is for use in a linear change of
          variable. (Default: 1)
      axis : int, optional
          Axis over which the derivative is taken. (Default: 0).
  
          .. versionadded:: 1.7.0
  
      Returns
      -------
      der : ndarray
          Hermite series of the derivative.
  
      See Also
      --------
      hermint
  
      Notes
      -----
      In general, the result of differentiating a Hermite series does not
      resemble the same operation on a power series. Thus the result of this
      function may be "unintuitive," albeit correct; see Examples section
      below.
  
      Examples
      --------
      >>> from numpy.polynomial.hermite import hermder
      >>> hermder([ 1. ,  0.5,  0.5,  0.5])
      array([1., 2., 3.])
      >>> hermder([-0.5,  1./2.,  1./8.,  1./12.,  1./16.], m=2)
      array([1., 2., 3.])
docstring_summary: Differentiate a Hermite series.
docstring_tokens: Differentiate a Hermite series .
function: def hermder(c, m=1, scl=1, axis=0):
      """
      Differentiate a Hermite series.
  
      Returns the Hermite series coefficients `c` differentiated `m` times
      along `axis`.  At each iteration the result is multiplied by `scl` (the
      scaling factor is for use in a linear change of variable). The argument
      `c` is an array of coefficients from low to high degree along each
      axis, e.g., [1,2,3] represents the series ``1*H_0 + 2*H_1 + 3*H_2``
      while [[1,2],[1,2]] represents ``1*H_0(x)*H_0(y) + 1*H_1(x)*H_0(y) +
      2*H_0(x)*H_1(y) + 2*H_1(x)*H_1(y)`` if axis=0 is ``x`` and axis=1 is
      ``y``.
  
      Parameters
      ----------
      c : array_like
          Array of Hermite series coefficients. If `c` is multidimensional the
          different axis correspond to different variables with the degree in
          each axis given by the corresponding index.
      m : int, optional
          Number of derivatives taken, must be non-negative. (Default: 1)
      scl : scalar, optional
          Each differentiation is multiplied by `scl`.  The end result is
          multiplication by ``scl**m``.  This is for use in a linear change of
          variable. (Default: 1)
      axis : int, optional
          Axis over which the derivative is taken. (Default: 0).
  
          .. versionadded:: 1.7.0
  
      Returns
      -------
      der : ndarray
          Hermite series of the derivative.
  
      See Also
      --------
      hermint
  
      Notes
      -----
      In general, the result of differentiating a Hermite series does not
      resemble the same operation on a power series. Thus the result of this
      function may be "unintuitive," albeit correct; see Examples section
      below.
  
      Examples
      --------
      >>> from numpy.polynomial.hermite import hermder
      >>> hermder([ 1. ,  0.5,  0.5,  0.5])
      array([1., 2., 3.])
      >>> hermder([-0.5,  1./2.,  1./8.,  1./12.,  1./16.], m=2)
      array([1., 2., 3.])
  
      """
      c = np.array(c, ndmin=1, copy=True)
      if c.dtype.char in '?bBhHiIlLqQpP':
          c = c.astype(np.double)
      cnt = pu._deprecate_as_int(m, "the order of derivation")
      iaxis = pu._deprecate_as_int(axis, "the axis")
      if cnt < 0:
          raise ValueError("The order of derivation must be non-negative")
      iaxis = normalize_axis_index(iaxis, c.ndim)
  
      if cnt == 0:
          return c
  
      c = np.moveaxis(c, iaxis, 0)
      n = len(c)
      if cnt >= n:
          c = c[:1]*0
      else:
          for i in range(cnt):
              n = n - 1
              c *= scl
              der = np.empty((n,) + c.shape[1:], dtype=c.dtype)
              for j in range(n, 0, -1):
                  der[j - 1] = (2*j)*c[j]
              c = der
      c = np.moveaxis(c, 0, iaxis)
      return c
function_tokens: def hermder ( c , m = 1 , scl = 1 , axis = 0 ) : c = np . array ( c , ndmin = 1 , copy = True ) if c . dtype . char in '?bBhHiIlLqQpP' : c = c . astype ( np . double ) cnt = pu . _deprecate_as_int ( m , "the order of derivation" ) iaxis = pu . _deprecate_as_int ( axis , "the axis" ) if cnt < 0 : raise ValueError ( "The order of derivation must be non-negative" ) iaxis = normalize_axis_index ( iaxis , c . ndim ) if cnt == 0 : return c c = np . moveaxis ( c , iaxis , 0 ) n = len ( c ) if cnt >= n : c = c [ : 1 ] * 0 else : for i in range ( cnt ) : n = n - 1 c *= scl der = np . empty ( ( n , ) + c . shape [ 1 : ] , dtype = c . dtype ) for j in range ( n , 0 , - 1 ) : der [ j - 1 ] = ( 2 * j ) * c [ j ] c = der c = np . moveaxis ( c , 0 , iaxis ) return c