parameters: (c)
return_statement: return prd
docstring: Multiply a Laguerre series by x.
  
      Multiply the Laguerre series `c` by x, where x is the independent
      variable.
  
  
      Parameters
      ----------
      c : array_like
          1-D array of Laguerre series coefficients ordered from low to
          high.
  
      Returns
      -------
      out : ndarray
          Array representing the result of the multiplication.
  
      See Also
      --------
      lagadd, lagsub, lagmul, lagdiv, lagpow
  
      Notes
      -----
      The multiplication uses the recursion relationship for Laguerre
      polynomials in the form
  
      .. math::
  
      xP_i(x) = (-(i + 1)*P_{i + 1}(x) + (2i + 1)P_{i}(x) - iP_{i - 1}(x))
  
      Examples
      --------
      >>> from numpy.polynomial.laguerre import lagmulx
      >>> lagmulx([1, 2, 3])
      array([-1.,  -1.,  11.,  -9.])
docstring_summary: Multiply a Laguerre series by x.
docstring_tokens: Multiply a Laguerre series by x .
function: def lagmulx(c):
      """Multiply a Laguerre series by x.
  
      Multiply the Laguerre series `c` by x, where x is the independent
      variable.
  
  
      Parameters
      ----------
      c : array_like
          1-D array of Laguerre series coefficients ordered from low to
          high.
  
      Returns
      -------
      out : ndarray
          Array representing the result of the multiplication.
  
      See Also
      --------
      lagadd, lagsub, lagmul, lagdiv, lagpow
  
      Notes
      -----
      The multiplication uses the recursion relationship for Laguerre
      polynomials in the form
  
      .. math::
  
      xP_i(x) = (-(i + 1)*P_{i + 1}(x) + (2i + 1)P_{i}(x) - iP_{i - 1}(x))
  
      Examples
      --------
      >>> from numpy.polynomial.laguerre import lagmulx
      >>> lagmulx([1, 2, 3])
      array([-1.,  -1.,  11.,  -9.])
  
      """
      # c is a trimmed copy
      [c] = pu.as_series([c])
      # The zero series needs special treatment
      if len(c) == 1 and c[0] == 0:
          return c
  
      prd = np.empty(len(c) + 1, dtype=c.dtype)
      prd[0] = c[0]
      prd[1] = -c[0]
      for i in range(1, len(c)):
          prd[i + 1] = -c[i]*(i + 1)
          prd[i] += c[i]*(2*i + 1)
          prd[i - 1] -= c[i]*i
      return prd
function_tokens: def lagmulx ( c ) : # c is a trimmed copy [ c ] = pu . as_series ( [ c ] ) # The zero series needs special treatment if len ( c ) == 1 and c [ 0 ] == 0 : return c prd = np . empty ( len ( c ) + 1 , dtype = c . dtype ) prd [ 0 ] = c [ 0 ] prd [ 1 ] = - c [ 0 ] for i in range ( 1 , len ( c ) ) : prd [ i + 1 ] = - c [ i ] * ( i + 1 ) prd [ i ] += c [ i ] * ( 2 * i + 1 ) prd [ i - 1 ] -= c [ i ] * i return prd