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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 remove_noise(time, magnitude, error, error_limit=3, std_limit=5):
 """Points within 'std_limit' standard deviations from the mean and with errors greater than 'error_limit' times the error mean are considered as noise and thus are eliminated. """

    data, mjd = magnitude, time

    data_len = len(mjd)
    error_mean = np.mean(error)
    error_tolerance = error_limit * (error_mean or 1)
    data_mean = np.mean(data)
    data_std = np.std(data)

    mjd_out, data_out, error_out = [], [], []
    for i in range(data_len):
        is_not_noise = (
            error[i] < error_tolerance and
            (np.absolute(data[i] - data_mean) / data_std) < std_limit)

        if is_not_noise:
            mjd_out.append(mjd[i])
            data_out.append(data[i])
            error_out.append(error[i])

    data_out = np.asarray(data_out)
    mjd_out = np.asarray(mjd_out)
    error_out = np.asarray(error_out)

    return mjd_out, data_out, error_out