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transforms.loads

Convert IAM sectoral electricity demand to annual PyPSA loads.

Name-agnostic: operates on an already-loaded frame with [year, region, sector, value] (the Coupler handles the symbol choice + fallback via the loader).

convert_loads(raw, *, unit_factor=TWA_TO_MWH, regions=None, year_col='year', region_col='region', sector_col='sector', value_col='value', unit_label='MWh_el')

Convert IAM demand to annual MWh, one tidy row per (year, region, sector).

Source code in src/iampypsa/transforms/loads.py 
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def convert_loads(
    raw: pd.DataFrame,
    *,
    unit_factor: float = TWA_TO_MWH,
    regions: Sequence[str] | None = None,
    year_col: str = "year",
    region_col: str = "region",
    sector_col: str = "sector",
    value_col: str = "value",
    unit_label: str = "MWh_el",
) -> pd.DataFrame:
    """Convert IAM demand to annual MWh, one tidy row per (year, region, sector)."""

    df = raw[[year_col, region_col, sector_col, value_col]].copy()
    df[value_col] = df[value_col] * unit_factor
    df["unit"] = unit_label
    if regions is not None:
        df = df[df[region_col].isin(set(regions))]
    return (
        df.groupby([year_col, region_col, sector_col, "unit"], as_index=False, observed=True)[
            value_col
        ]
        .sum()
        .sort_values([year_col, region_col, sector_col])
        [[year_col, region_col, sector_col, value_col, "unit"]]
        .reset_index(drop=True)
    )