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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def deriv1(x,y,i,n):
 """ alternative way to smooth the derivative of a noisy signal using least square fit. x=array of x axis y=array of y axis n=smoothing factor i= position in this method the slope in position i is calculated by least square fit of n points before and after position. """

    m_,x_,y_,xy_,x_2=0.,0.,0.,0.,0.
    for ix in range(i,i+n,1):
        x_=x_+x[ix]
        y_=y_+y[ix]
        xy_=xy_+x[ix]*y[ix]
        x_2=x_2+x[ix]**2
    m= old_div(( (n*xy_) - (x_*y_) ), ( n*x_2-(x_)**2))
    return(m)