How to use the rdtools.soiling.NoValidIntervalError function in rdtools

results.loc[filt, 'run_slope_low'] = 0
        results.loc[filt, 'run_slope_high'] = 0
        results.loc[filt, 'valid'] = False

        # Calculate the next inferred start loss from next valid interval
        results['next_inferred_start_loss'] = np.clip(
                results[results.valid].inferred_start_loss.shift(-1),
                0, 1)
        # Calculate the inferred recovery at the end of each interval
        results['inferred_recovery'] = np.clip(
                results.next_inferred_start_loss - results.inferred_end_loss,
                0, 1)

        # Don't consider data outside of first and last valid interverals
        if len(results[results.valid]) == 0:
            raise NoValidIntervalError('No valid soiling intervals were found')
        new_start = results[results.valid].start.iloc[0]
        new_end = results[results.valid].end.iloc[-1]
        pm_frame_out = daily_df[new_start:new_end]
        pm_frame_out = pm_frame_out.reset_index() \
                                   .merge(results, how='left', on='run') \
                                   .set_index('date')

        pm_frame_out['loss_perfect_clean'] = np.nan
        pm_frame_out['loss_inferred_clean'] = np.nan
        pm_frame_out['days_since_clean'] = \
            (pm_frame_out.index - pm_frame_out.start).dt.days

        # Calculate the daily derate
        pm_frame_out['loss_perfect_clean'] = \
            pm_frame_out.start_loss + \
            pm_frame_out.days_since_clean * pm_frame_out.run_slope

}
            if len(run) > 2 and run.pi_norm.sum() > 0:
                fit = theilslopes(run.pi_norm, run.day)
                fit_poly = np.poly1d(fit[0:2])
                result_dict['run_slope'] = fit[0]
                result_dict['run_slope_low'] = fit[2]
                result_dict['run_slope_high'] = min([0.0, fit[3]])
                result_dict['inferred_start_loss'] = fit_poly(start_day)
                result_dict['inferred_end_loss'] = fit_poly(end_day)
                result_dict['valid'] = True
            result_list.append(result_dict)

        results = pd.DataFrame(result_list)

        if results.empty:
            raise NoValidIntervalError('No valid soiling intervals were found')

        # Filter results for each interval,
        # setting invalid interval to slope of 0
        results['slope_err'] = (results.run_slope_high-results.run_slope_low) \
                                    / abs(results.run_slope)
        # critera for exclusions
        filt = (
            (results.run_slope > 0) |
            (results.slope_err >= max_relative_slope_error / 100.0) |
            (results.max_neg_step <= -1.0 * max_negative_step)
        )

        results.loc[filt, 'run_slope'] = 0
        results.loc[filt, 'run_slope_low'] = 0
        results.loc[filt, 'run_slope_high'] = 0
        results.loc[filt, 'valid'] = False