63ef8bfa3b63090437bd27bf1e04db7ae1eae3cf,nilmtk/stats/totalenergy.py,,get_total_energy,#Any#Any#,39

Before Change

    energy = {}
    data_source_rank = {} // overwrite Power with Energy with Energy(cumulative)
    for (physical_quantity, ac_type), series in df.iteritems():
        if physical_quantity == "power":
            // Preference is to calculate energy from 
            // native Energy data rather than Power data
            // so don"t overwrite with Power data.
            if not energy.has_key(ac_type):
                energy[ac_type] = _energy_for_power_series(
                    series, max_sample_period)
                data_source_rank[ac_type] = 3 // least favourite
        elif physical_quantity == "cumulative energy":
            energy[ac_type] = series.iloc[-1] - series.iloc[0]
            data_source_rank[ac_type] = 1 // favourite
        elif (physical_quantity == "energy" and 
              data_source_rank.get(ac_type, 3) > 2):
            energy[ac_type] = series.sum()
            data_source_rank[ac_type] = 2
    return energy


def _energy_for_power_series(series, max_sample_period):

After Change

    // Select a column based on ordered preferences
    PHYSICAL_QUANTITY_PREFS = ["cumulative energy", "energy", "power"]
    selected_columns = []
    for ac_type in AC_TYPES:
        physical_quantities = [physical_quantity 
                               for (physical_quantity, col_ac_type) in df.keys()
                               if col_ac_type == ac_type]
        for pq in PHYSICAL_QUANTITY_PREFS:
            if pq in physical_quantities:
                selected_columns.append((pq, ac_type))
                break

    energy = {}
    for col in selected_columns:
        print("Calculating energy for column", col)
        (physical_quantity, ac_type) = col
        series = df[col]
        if physical_quantity == "power":
            energy[ac_type] = _energy_for_power_series(series, max_sample_period)
        elif physical_quantity == "cumulative energy":
            energy[ac_type] = series.iloc[-1] - series.iloc[0]
        elif physical_quantity == "energy":
            energy[ac_type] = series.sum()

    return energy


def _energy_for_power_series(series, max_sample_period):

Instances