2013 overview table

src/unfccc_ghg_data/unfccc_reader/Bangladesh/config_bgd_bur1.py

@@ -0,0 +1,25 @@
+"""
+Configuration file to read Bangladesh's BUR 1.
+"""
+coords_terminologies = {
+    "area": "ISO3",
+    "category": "IPCC2006_PRIMAP",
+    "scenario": "PRIMAP",
+}
+
+inv_conf_per_year = {
+    "2013": {
+        "page_defs": {
+            "207": {
+                "area": ["60,630,534,79"],
+                "cols": ["387,444,495"],
+                "skip_rows": 0,
+            },
+            "208": {
+                "area": ["65,687,527,120"],
+                "cols": ["380,437,491"],
+                "skip_rows": 5,
+            },
+        },
+    },
+}

src/unfccc_ghg_data/unfccc_reader/Bangladesh/read_BGD_BUR1_from_pdf.py

@@ -1,3 +1,145 @@
 """
-Read Burundi's BUR1 from pdf
+Read Bangladesh's BUR1 from pdf
 """
+
+import camelot
+import pandas as pd
+from config_bgd_bur1 import coords_terminologies, inv_conf_per_year
+
+from unfccc_ghg_data.helper import (
+    downloaded_data_path,
+    extracted_data_path,
+)
+
+if __name__ == "__main__":
+    # ###
+    # configuration
+    # ###
+
+    input_folder = downloaded_data_path / "UNFCCC" / "Bangladesh" / "BUR1"
+    output_folder = extracted_data_path / "UNFCCC" / "Bangladesh"
+
+    if not output_folder.exists():
+        output_folder.mkdir()
+
+    pdf_file = "Updated_BUR1_Report_15_11_2023.pdf"
+    output_filename = "BGD_BUR1_2023_"
+    category_column = f"category ({coords_terminologies['category']})"
+    compression = dict(zlib=True, complevel=9)
+
+    # ###
+    # 1. Read in main tables from the Annex
+    # ###
+
+    df_year = None
+    for year in inv_conf_per_year.keys():
+        print("-" * 60)
+        print(f"Reading year {year}.")
+        print("-" * 60)
+        df_year = None
+        for page in inv_conf_per_year[year]["page_defs"].keys():
+            print(f"Reading table from page {page}.")
+            tables_inventory_original = camelot.read_pdf(
+                str(input_folder / pdf_file),
+                pages=page,
+                table_areas=inv_conf_per_year[year]["page_defs"][page]["area"],
+                columns=inv_conf_per_year[year]["page_defs"][page]["cols"],
+                flavor="stream",
+                split_text=False,
+            )
+            print("Reading complete.")
+
+            df_page = tables_inventory_original[0].df
+
+            if df_year is None:
+                df_year = df_page[
+                    inv_conf_per_year[year]["page_defs"][page]["skip_rows"] :
+                ]
+            else:
+                df_year = pd.concat(
+                    [
+                        df_year,
+                        df_page[
+                            inv_conf_per_year[year]["page_defs"][page]["skip_rows"] :
+                        ],
+                    ],
+                    axis=0,
+                    join="outer",
+                ).reset_index(drop=True)
+
+        pass
+        # # fix content that spreads across multiple rows
+        # if "rows_to_fix" in inv_conf_per_year[year]:
+        #     for n_rows in inv_conf_per_year[year]["rows_to_fix"].keys():
+        #         print(f"Merge content for {n_rows=}")
+        #         df_year = fix_rows(
+        #             df_year,
+        #             rows_to_fix=inv_conf_per_year[year]["rows_to_fix"][n_rows],
+        #             col_to_use=0,
+        #             n_rows=n_rows,
+        #         )
+        #
+        # df_header = pd.DataFrame([inv_conf["header"], inv_conf["unit"]])
+        #
+        # skip_rows = 11
+        # df_year = pd.concat(
+        #     [df_header, df_year[skip_rows:]], axis=0, join="outer"
+        # ).reset_index(drop=True)
+        #
+        # df_year = pm2.pm2io.nir_add_unit_information(
+        #     df_year,
+        #     unit_row=inv_conf["unit_row"],
+        #     entity_row=inv_conf["entity_row"],
+        #     regexp_entity=".*",
+        #     regexp_unit=".*",
+        #     default_unit="Gg",
+        # )
+        #
+        # print("Added unit information.")
+        #
+        # # set index
+        # df_year = df_year.set_index(inv_conf["index_cols"])
+        #
+        # # convert to long format
+        # df_year_long = pm2.pm2io.nir_convert_df_to_long(
+        #     df_year, year, inv_conf["header_long"]
+        # )
+        #
+        # # extract from tuple
+        # df_year_long["orig_cat_name"] = df_year_long["orig_cat_name"].str[0]
+        #
+        # # prep for conversion to PM2 IF and native format
+        # # make a copy of the categories row
+        # df_year_long["category"] = df_year_long["orig_cat_name"]
+        #
+        # # replace cat names by codes in col "category"
+        # # first the manual replacements
+        #
+        # df_year_long["category"] = df_year_long["category"].replace(
+        #     inv_conf["cat_codes_manual"]
+        # )
+        #
+        # df_year_long["category"] = df_year_long["category"].str.replace(".", "")
+        #
+        # # then the regex replacements
+        # df_year_long["category"] = df_year_long["category"].str.replace(
+        #     inv_conf["cat_code_regexp"], repl, regex=True
+        # )
+        #
+        # df_year_long = df_year_long.reset_index(drop=True)
+        #
+        # df_year_long["data"] = df_year_long["data"].str.replace(",", "")
+        #
+        # # make sure all col headers are str
+        # df_year_long.columns = df_year_long.columns.map(str)
+        #
+        # df_year_long = df_year_long.drop(columns=["orig_cat_name"])
+        #
+        # if df_main is None:
+        #     df_main = df_year_long
+        # else:
+        #     df_main = pd.concat(
+        #         [df_main, df_year_long],
+        #         axis=0,
+        #         join="outer",
+        #     ).reset_index(drop=True)