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@@ -278,6 +278,87 @@ if __name__ == "__main__":
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print("Converting to primap2 format.")
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data_trend_pm2 = pm2.pm2io.from_interchange_format(df_trend_IF)
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+ # ###
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+ # 3. Read in aggregated tables from 1990 - 2020
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+ # ###
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+
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+ # Work in progress
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+ inv_conf_per_sector = {
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+ "all": {
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+ "page_defs": {
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+ "32": {
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+ "area": ["64,649,547,106"],
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+ "cols": ["106,182,237,294,345,403,480"],
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+ },
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+ },
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+ "entity": "KYOTOGHG (SARGWP100)",
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+ "category_column": "Categories",
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+ "columns_to_drop": ["Categories"],
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+ "years": ["1990", "1995", "2000", "2005", "2010", "2015", "2020"],
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+ "unit": "Gg CO2e",
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+ "last_year": "2020",
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+ "rows_to_fix": {
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+ -3: [
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+ "Year",
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+ ],
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+ },
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+ "cat_codes_manual": {
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+ "Energy": "1",
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+ "IPPU": "2",
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+ "Agriculture": "3",
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+ "Waste": "4",
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+ "LULUCF": "M.LULUCF",
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+ "Total (excl. LULUCF)": "M.0.EL",
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+ "Total (incl. LULUCF)": "M.0",
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+ },
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+ },
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+ }
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+
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+ page = [*inv_conf_per_sector["all"]["page_defs"]][0] # noqa: RUF015
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+ sector = "all"
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+
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+ tables_inventory_original = camelot.read_pdf(
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+ str(input_folder / pdf_file),
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+ pages=page,
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+ table_areas=inv_conf_per_sector[sector]["page_defs"][page]["area"],
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+ columns=inv_conf_per_sector[sector]["page_defs"][page]["cols"],
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+ flavor="stream",
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+ split_text=True,
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+ )
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+
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+ df_sector = tables_inventory_original[0].df
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+
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+ last_row = df_sector.loc[df_sector[0] == "2020"].index[0]
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+
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+ df_sector = df_sector[0 : last_row + 1]
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+
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+ if "rows_to_fix" in inv_conf_per_sector[sector]:
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+ for n_rows in inv_conf_per_sector[sector]["rows_to_fix"].keys():
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+ print(f"Merge content for {n_rows=}")
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+ df_sector = fix_rows(
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+ df_sector,
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+ rows_to_fix=inv_conf_per_sector[sector]["rows_to_fix"][n_rows],
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+ col_to_use=0,
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+ n_rows=n_rows,
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+ )
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+
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+ df_sector = df_sector.set_index(0)
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+
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+ # transpose so categegories are in first columns
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+ df_sector = df_sector.T
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+
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+ df_sector = df_sector.rename(columns={" Year ": "category"})
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+
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+ # unit is always Gg
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+ df_sector.loc[:, "unit"] = inv_conf_per_sector[sector]["unit"]
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+
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+ # only one entity per table
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+ df_sector.loc[:, "entity"] = inv_conf_per_sector[sector]["entity"]
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+
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+ df_sector.loc[:, "category"] = df_sector.loc[:, "category"].replace(
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+ inv_conf_per_sector[sector]["cat_codes_manual"]
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+ )
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+
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# ###
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# Merge main and trend tables.
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# ###
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Daniel Busch