code: def qspline1d(signal, lamb=0.0):
      """Compute quadratic spline coefficients for rank-1 array.
  
      Find the quadratic spline coefficients for a 1-D signal assuming
      mirror-symmetric boundary conditions.   To obtain the signal back from the
      spline representation mirror-symmetric-convolve these coefficients with a
      length 3 FIR window [1.0, 6.0, 1.0]/ 8.0 .
  
      Parameters
      ----------
      signal : ndarray
          A rank-1 array representing samples of a signal.
      lamb : float, optional
          Smoothing coefficient (must be zero for now).
  
      Returns
      -------
      c : ndarray
          Cubic spline coefficients.
  
      """
      if lamb != 0.0:
          raise ValueError(
              "Smoothing quadratic splines not supported yet."
          )
      else:
          return _quadratic_coeff(signal)
  
imports: from __future__ import division, print_function, absolute_import
  from scipy._lib.six import xrange
  from numpy import logical_and, asarray, pi, zeros_like, piecewise, array, arctan2, tan, zeros, arange, floor
  from numpy.core.umath import sqrt, exp, greater, less, cos, add, sin, less_equal, greater_equal
  from .spline import cspline2d, sepfir2d
  from scipy.special import comb, gamma
label_desc: Use image node_id 15 for calling a global function with example usage: qspline1d(signal, lamb) and returns: _quadratic_coeff