Factory Functions

Convenience functions for creating SPA instances from different data sources.

from_leontief()

Create SPA from a pre-computed Leontief inverse.

from_leontief(
    L: ArrayLike,
    direct_intensities: Union[ArrayLike, Dict[str, ArrayLike]],
    sectors: Optional[Sequence[str]] = None,
    mode: Literal["sector_specific", "system_wide"] = "system_wide",
) -> SPA

Parameters

Parameter	Type	Default	Description
L	ArrayLike	-	Leontief inverse matrix (I - A)^(-1)
direct_intensities	ArrayLike or Dict	-	Direct intensity vector(s)
sectors	Sequence[str]	None	Optional sector names
mode	str	"system_wide"	Analysis mode

Example

from fastspa import from_leontief

# If you already have L computed
spa = from_leontief(L_matrix, intensities, sectors)

from_dataframe()

Create SPA from pandas DataFrames.

from_dataframe(
    A_df: pd.DataFrame,
    intensities_df: pd.DataFrame,
    intensity_columns: Optional[List[str]] = None,
) -> SPA

Parameters

Parameter	Type	Description
A_df	DataFrame	A-matrix with sector names as index and columns
intensities_df	DataFrame	Intensities with sector index
intensity_columns	List[str]	Which columns to use (None = all numeric)

Example

from fastspa import from_dataframe
import pandas as pd

A_df = pd.read_csv("A.csv", index_col=0)
intensities_df = pd.read_csv("intensities.csv", index_col=0)

spa = from_dataframe(A_df, intensities_df, intensity_columns=["CO2", "Water"])

from_csv()

Create SPA from CSV files.

from_csv(
    A_path: str,
    intensities_path: str,
    intensity_columns: Optional[List[str]] = None,
    sectors_path: Optional[str] = None,
    mode: Literal["sector_specific", "system_wide"] = "sector_specific",
) -> SPA

Parameters

Parameter	Type	Default	Description
A_path	str	-	Path to A-matrix CSV
intensities_path	str	-	Path to intensities CSV
intensity_columns	List[str]	None	Columns to use (None = all numeric)
sectors_path	str	None	Path to sector names file
mode	str	"sector_specific"	Analysis mode

Example

from fastspa import from_csv

spa = from_csv(
    "A_matrix.csv",
    "intensities.csv",
    intensity_columns=["ghg", "water"],
)

from_io_table()

Create SPA from an IO table in XLSX format (experimental).

from_io_table(
    xlsx_path: str,
    sheet_name: str = "Table 5",
    intensities: Optional[ArrayLike] = None,
) -> SPA

Parameters

Parameter	Type	Default	Description
xlsx_path	str	-	Path to XLSX file
sheet_name	str	"Table 5"	Sheet containing IO table
intensities	ArrayLike	None	Direct intensities (random if not provided)

Notes

This function automatically:

• Extracts the largest continuous numeric matrix as transactions
• Identifies sector names from adjacent rows/columns
• Computes technical coefficients

Example

from fastspa import from_io_table

spa = from_io_table(
    "national_io_table.xlsx",
    sheet_name="Transactions",
    intensities=my_emissions_vector
)