# this block is to transform the data and normalize it into ranges
def adapt_bin(data, bins = 20, filt = 1000):
    data = data[np.nonzero(data)]
    data = data[np.where(abs(data) < filt)]
    bins = np.interp(np.linspace(0, len(data), bins), np.arange(len(data)), np.sort(data))
    return bins

bins = adapt_bin(data.signal.values, bins = 1000)
data['sig_dig'] = np.digitize(data.signal, bins) - round(np.average(np.digitize(data.signal, bins)))