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transforms.co2_prices

Extract and convert a CO2 price pathway.

The transform is name-agnostic: it takes an already-loaded frame with canonical columns [region, year, value] (the loader/Coupler handles the GDX symbol + renames).

convert_co2_prices(prices, currency_factor=1.0, *, carbon_to_co2=True, value_col='value')

Convert CO2 prices to PyPSA units: tC→tCO2 (optional) and a currency factor.

Source code in src/iampypsa/transforms/co2_prices.py 
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def convert_co2_prices(
    prices: pd.DataFrame,
    currency_factor: float = 1.0,
    *,
    carbon_to_co2: bool = True,
    value_col: str = "value",
) -> pd.DataFrame:
    """Convert CO2 prices to PyPSA units: tC→tCO2 (optional) and a currency factor."""
    out = prices.copy()
    factor = currency_factor * (TONNE_C_TO_TONNE_CO2 if carbon_to_co2 else 1.0)
    out[value_col] = out[value_col] * factor
    return out

extract_co2_prices(raw, regions=None, years=None, *, region_col='region', year_col='year', value_col='value')

Extract the per-(region, year) CO2 price pathway, filtered and reindexed.

Filters to regions if given and, if years is given, reindexes to the full regions × years grid (missing entries filled with 0, matching the Eur rule).

Source code in src/iampypsa/transforms/co2_prices.py 
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def extract_co2_prices(
    raw: pd.DataFrame,
    regions: Sequence[str] | None = None,
    years: Sequence[int] | None = None,
    *,
    region_col: str = "region",
    year_col: str = "year",
    value_col: str = "value",
) -> pd.DataFrame:
    """Extract the per-(region, year) CO2 price pathway, filtered and reindexed.

    Filters to ``regions`` if given and, if ``years`` is given, reindexes to the full
    ``regions × years`` grid (missing entries filled with 0, matching the Eur rule).
    """
    df = raw[[region_col, year_col, value_col]].copy()
    df[year_col] = df[year_col].astype(int)
    if regions is not None:
        df = df[df[region_col].isin(set(regions))]
    if years is not None:
        grid = pd.MultiIndex.from_product(
            [sorted(set(df[region_col]) if regions is None else set(regions)),
             [int(y) for y in years]],
            names=[region_col, year_col],
        )
        df = (
            df.set_index([region_col, year_col])[value_col]
            .reindex(grid, fill_value=0.0)
            .reset_index()
        )
    return df.sort_values([region_col, year_col]).reset_index(drop=True)