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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 confidence_interval(data, alpha):
 """ Computes the mean and alpha-confidence interval of the given sample set Parameters data : ndarray a 1D-array of samples alpha : float in [0,1] the confidence level, i.e. percentage of data included in the interval Returns ------- [m,l,r] where m is the mean of the data, and (l,r) are the m-alpha/2 and m+alpha/2 confidence interval boundaries. """

    if alpha < 0 or alpha > 1:
        raise ValueError('Not a meaningful confidence level: '+str(alpha))
    
    # compute mean
    m = np.mean(data)
    # sort data
    sdata = np.sort(data)
    # index of the mean
    im = np.searchsorted(sdata, m)
    if im == 0 or im == len(sdata):
        pm = im
    else:
        pm = (im-1) + (m-sdata[im-1]) / (sdata[im]-sdata[im-1])
    # left interval boundary
    pl = pm - alpha * pm
    il1 = max(0, int(math.floor(pl)))
    il2 = min(len(sdata)-1, int(math.ceil(pl)))
    l = sdata[il1] + (pl - il1)*(sdata[il2] - sdata[il1])
    # right interval boundary
    pr = pm + alpha * (len(data)-im)
    ir1 = max(0, int(math.floor(pr)))
    ir2 = min(len(sdata)-1, int(math.ceil(pr)))
    r = sdata[ir1] + (pr - ir1)*(sdata[ir2] - sdata[ir1])

    # return
    return m, l, r