'**SUMMARY**
A function to generate a spline curve fitting over the points in LineScan with
order of precision given by the parameter degree
**PARAMETERS**
* *degree* - the precision of the generated spline
**RETURNS**
The spline as a LineScan fitting over the initial values of LineScan
**EXAMPLE**
>>> import matplotlib.pyplot as plt
>>> img = Image("lenna")
>>> ls = img.getLineScan(pt1=(10,10)),pt2=(20,20)).normalize()
>>> spline = ls.fitSpline()
>>> plt.plot(ls)
>>> plt.show()
>>> plt.plot(spline)
>>> plt.show()
**NOTES**
Implementation taken from http://www.scipy.org/Cookbook/Interpolation'

def fitSpline(self, degree=2):

    if (degree > 4):
        degree = 4
    if (degree < 1):
        warnings.warn('LineScan.fitSpline    -    degree    needs    to    be    >=    1')
        return None
    retVal = None
    y = np.array(self)
    x = np.arange(0, len(y), 1)
    dx = 1
    newx = np.arange(0, (len(y) - 1), pow(0.1, degree))
    cj = sps.cspline1d(y)
    retVal = sps.cspline1d_eval(cj, newx, dx=dx, x0=x[0])
    return retVal
