How to use the eemeter.modeling.exceptions.DataSufficiencyException function in eemeter

def test_min_contiguous_months(input_df):
    min_contiguous_months = 9
    model = HourlyDayOfWeekModel(min_contiguous_months=min_contiguous_months)
    with pytest.raises(model_exceptions.DataSufficiencyException) as sufficiency_exception:
        model.fit(input_df)

def meets_sufficiency_or_error(self, df):
        if len(df) < self.min_contiguous_months * 30 * 24:
            raise model_exceptions.\
                DataSufficiencyException("Min Contigous Month criteria not satisifed: Min Months Reqd:  " +
                                         str(self.min_contiguous_months))

direction=direction,
                        req=_n,
                        mp=mp_type,
                        upd_n=upd_n_non_nan,
                        temp_n=temp_n_non_nan
                    )
                )

        if reason is not None:
            raise model_exceptions.DataSufficiencyException(
                'Data does not meet minimum contiguous months requirement. {}'
                .format(reason)
            )

        if not np.nansum(upd) > 0.01:
            raise model_exceptions.DataSufficiencyException(
                "Energy trace data is all or nearly all zero")

        return

def meets_sufficiency_or_error(self, df):
        if np.sum(np.isfinite(df['usage'])) < self.min_fraction_coverage * len(df):
            raise model_exceptions.DataSufficiencyException("Insufficient coverage")
        if len(df) < self.min_contiguous_months * 30:
            raise model_exceptions.DataSufficiencyException("Insufficient data")
        return

def meets_sufficiency_or_error(self, df):
        if np.sum(np.isfinite(df['usage'])) < self.min_fraction_coverage * len(df):
            raise model_exceptions.DataSufficiencyException("Insufficient coverage")
        if len(df) < self.min_contiguous_months * 30:
            raise model_exceptions.DataSufficiencyException("Insufficient data")
        return

def billing_to_monthly_avg(self, trace_and_temp):
        ''' Helper function to handle monthly billing or other irregular data.
        '''
        (energy_data, temp_data) = trace_and_temp

        # Handle empty series
        if energy_data.empty:
            raise model_exceptions.DataSufficiencyException("No energy trace data")
        if temp_data.empty:
            raise model_exceptions.DataSufficiencyException("No temperature data")

        # Convert billing multiindex to straight index
        temp_data.index = temp_data.index.droplevel()

        # Resample temperature data to daily
        temp_data_daily = temp_data.resample('D').apply(np.mean)[0]

        # Drop any duplicate indices
        energy_data = energy_data[
            ~energy_data.index.duplicated(keep='last')].sort_index()

        # Check for empty series post-resampling and deduplication
        if energy_data.empty:
            raise model_exceptions.DataSufficiencyException(

def billing_to_monthly_avg(self, trace_and_temp):
        ''' Helper function to handle monthly billing or other irregular data.
        '''
        (energy_data, temp_data) = trace_and_temp

        # Handle empty series
        if energy_data.empty:
            raise model_exceptions.DataSufficiencyException("No energy trace data")
        if temp_data.empty:
            raise model_exceptions.DataSufficiencyException("No temperature data")

        # Convert billing multiindex to straight index
        temp_data.index = temp_data.index.droplevel()

        # Resample temperature data to daily
        temp_data_daily = temp_data.resample('D').apply(np.mean)[0]

        # Drop any duplicate indices
        energy_data = energy_data[
            ~energy_data.index.duplicated(keep='last')].sort_index()

        # Check for empty series post-resampling and deduplication
        if energy_data.empty:
            raise model_exceptions.DataSufficiencyException(
                "No energy trace data after deduplication")
        if temp_data_daily.empty:

# Convert billing multiindex to straight index
        temp_data.index = temp_data.index.droplevel()

        # Resample temperature data to daily
        temp_data_daily = temp_data.resample('D').apply(np.mean)[0]

        # Drop any duplicate indices
        energy_data = energy_data[
            ~energy_data.index.duplicated(keep='last')].sort_index()

        # Check for empty series post-resampling and deduplication
        if energy_data.empty:
            raise model_exceptions.DataSufficiencyException(
                "No energy trace data after deduplication")
        if temp_data_daily.empty:
            raise model_exceptions.DataSufficiencyException(
                "No temperature data after resampling")

        # get daily mean values
        upd_data_daily_mean_values = [
            value / (e - s).days for value, s, e in
            zip(energy_data, energy_data.index, energy_data.index[1:])
        ] + [np.nan]  # add missing last data point, which is null by convention anyhow
        usage_data_daily_mean_values = [
            value for value, s, e in
            zip(energy_data, energy_data.index, energy_data.index[1:])
        ] + [np.nan]  # add missing last data point, which is null by convention anyhow

        # Create arrays to hold computed CDD and HDD for each
        # balance point temperature.
        cdd = {i: [0] for i in self.bp_cdd}
        hdd = {i: [0] for i in self.bp_hdd}