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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 _linear_normalization_from_0to1( data, mask, theoretical_max, theoretical_min=0, message="normalizing equalized data to fit in 0 to 1 range"):
 """Do a linear normalization so all data is in the 0 to 1 range. This is a sloppy but fast calculation that relies on parameters giving it the correct theoretical current max and min so it can scale the data accordingly. """


    LOG.debug(message)
    if theoretical_min != 0:
        data[mask] = data[mask] - theoretical_min
        theoretical_max = theoretical_max - theoretical_min
    data[mask] = data[mask] / theoretical_max