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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 entropy(data, bins=256):
 """Compute a threshold using Ray's entropy measurement data - an array of intensity values between zero and one bins - we bin the data into this many equally-spaced bins, then pick the bin index that optimizes the metric """

    
    data = np.atleast_1d(data)
    data = data[~ np.isnan(data)]
    if len(data) == 0:
        return 0
    elif len(data) == 1:
        return data[0]

    if bins > len(data):
        bins = len(data)
    data.sort()
    var = running_variance(data)+1.0/512.0
    rvar = np.flipud(running_variance(np.flipud(data)))+1.0/512.0 
    thresholds = data[1:len(data):len(data)//bins]
    w = np.arange(0,len(data)-1,len(data)//bins)
    score_low = w * np.log(var[0:len(data)-1:len(data)//bins] *
                           w * np.sqrt(2*np.pi*np.exp(1)))
    score_low[np.isnan(score_low)]=0
    
    w = len(data) - np.arange(1,len(data),len(data)//bins)
    score_high = w * np.log(rvar[1:len(data):len(data)//bins] * w *
                            np.sqrt(2*np.pi*np.exp(1)))
    score_high[np.isnan(score_high)]=0
    scores = score_low + score_high
    index = np.argwhere(scores == scores.min()).flatten()
    if len(index)==0:
        return thresholds[0]
    #
    # Take the average of the thresholds to either side of
    # the chosen value to get an intermediate in cases where there is
    # a steep step between the background and foreground
    index = index[0]
    if index == 0:
        index_low = 0
    else:
        index_low = index-1
    if index == len(thresholds)-1:
        index_high = len(thresholds)-1
    else:
        index_high = index+1 
    return (thresholds[index_low]+thresholds[index_high]) / 2