begin coding for Korea inventory 2021

code/UNFCCC_reader/Republic_of_Korea/read_KOR_Inventory2021_from_xlsx.py

@@ -0,0 +1,174 @@
+# this script reads data from Chile's 2020 national inventory which is underlying BUR4
+# Data is read from the xlsx file
+
+import os
+import sys
+import pandas as pd
+import primap2 as pm2
+from pathlib import Path
+
+from config_KOR_BUR4 import cat_name_translations, cat_codes
+from primap2.pm2io._data_reading import filter_data
+
+# ###
+# configuration
+# ###
+
+input_folder = Path('..') / '..' / '..' / 'downloaded_data' / 'non-UNFCCC' / 'Republic_of_Korea' / '2021_Inventory'
+output_folder = Path('..') / '..' / '..' / 'extracted_data' / 'Republic_of_Korea'
+
+output_filename = 'KOR_INV2021_2021_'
+
+inventory_file = 'Republic_of_Korea_National_GHG_Inventory_(1990_2019).xlsx'
+years_to_read = range(1990, 2019 + 1)
+
+sheets_to_read = ['온실가스', 'CO2', 'CH4', 'N2O', 'HFCs', 'PFCs', 'SF6']
+cols_to_read = range(1, 2019 - 1990 + 3)
+
+# columns for category code and original category name
+index_cols = ['분야·부문/연도']
+
+sheet_metadata = {
+    'entity': {
+        '온실가스': 'KYOTOGHG (SARGWP100)',
+        'CO2': 'CO2',
+        'CH4': 'CH4 (SARGWP100)',
+        'N2O': 'N2O (SARGWP100)',
+        'HFCs': 'HFCS (SARGWP100)',
+        'PFCs': 'PFCS (SARGWP100)',
+        'SF6': 'SF6 (SARGWP100)',
+    },
+    'unit': {
+        '온실가스': 'Gg CO2 / yr',
+        'CO2': 'Gg CO2 / yr',
+        'CH4': 'Gg CO2 / yr',
+        'N2O': 'Gg CO2 / yr',
+        'HFCs': 'Gg CO2 / yr',
+        'PFCs': 'Gg CO2 / yr',
+        'SF6': 'Gg CO2 / yr',
+    }
+}
+
+# definitions for conversion to interchange format
+time_format = "%Y"
+
+coords_cols = {
+    "category": "category",
+    "entity": "entity",
+    "unit": "unit",
+}
+
+add_coords_cols = {
+    "orig_cat_name": ["orig_cat_name", "category"],
+    "cat_name_translation": ["cat_name_translation", "category"]
+}
+
+coords_terminologies = {
+    "area": "ISO3",
+    "category": "IPCC1996_KOR_INV",
+    "scenario": "PRIMAP",
+}
+
+coords_defaults = {
+    "source": "KOR-GHG-Inventory",
+    "provenance": "measured",
+    "area": "KOR",
+    "scenario": "INV2021",
+}
+
+coords_value_mapping = {
+    "cat_name_translation": cat_name_translations,
+    "category": cat_codes,
+}
+
+# filtering after IF creation to be able to use the IPCC codes
+filter_remove = {
+    "f1": {
+        "category (IPCC1996_KOR_INV)": "\IGNORE",
+    },
+}
+
+filter_keep = {}
+
+meta_data = {
+    "references": "http://www.gir.go.kr/home/file/readDownloadFile.do?fileId=5240&fileSeq=1",
+    "rights": "",
+    "contact": "mail@johannes-guetschow.de",
+    "title": "Republic of Korea: National Greenhouse Gas Inventory Report 2021",
+    "comment": "Read fom xlsx file by Johannes Gütschow",
+    "institution": "Republic of Korea, Ministry of Environment, Greenhouse Gas Inventory and Research Center",
+}
+
+cols_for_space_stripping = []
+
+compression = dict(zlib=True, complevel=9)
+
+# ###
+# start data reading
+# ###
+
+# change working directory to script directory for proper folder names
+script_path = os.path.abspath(sys.argv[0])
+script_dir_name = os.path.dirname(script_path)
+os.chdir(script_dir_name)
+
+df_all = None
+
+for sheet in sheets_to_read:
+    # read current sheet (one sheet per gas)
+    df_current = pd.read_excel(input_folder / inventory_file, sheet_name=sheet, skiprows=3, nrows=146, usecols=cols_to_read,
+                               engine="openpyxl")
+    # drop all rows where the index cols (category code and name) are both NaN
+    # as without one of them there is no category information
+    df_current.dropna(axis=0, how='all', subset=index_cols, inplace=True)
+    # set index. necessary for the stack operation in the conversion to long format
+    # df_current = df_current.set_index(index_cols)
+    # add columns
+    for col in sheet_metadata.keys():
+        df_current.insert(1, col, sheet_metadata[col][sheet])
+    # aggregate to one df
+    if df_all is None:
+        df_all = df_current
+    else:
+        df_all = pd.concat([df_all, df_current])
+
+df_all = df_all.reset_index(drop=True)
+# rename category col because filtering produces problems with korean col names
+df_all.rename(columns={"분야·부문/연도": "category"}, inplace=True)
+
+# create copies of category col for further processing
+df_all["orig_cat_name"] = df_all["category"]
+df_all["cat_name_translation"] = df_all["category"]
+
+# make sure all col headers are str
+df_all.columns = df_all.columns.map(str)
+
+# ###
+# convert to PRIMAP2 interchange format
+# ###
+data_if = pm2.pm2io.convert_wide_dataframe_if(
+    df_all,
+    coords_cols=coords_cols,
+    add_coords_cols=add_coords_cols,
+    coords_defaults=coords_defaults,
+    coords_terminologies=coords_terminologies,
+    coords_value_mapping=coords_value_mapping,
+    #coords_value_filling=coords_value_filling,
+    #filter_remove=filter_remove,
+    #filter_keep=filter_keep,
+    meta_data=meta_data,
+    convert_str=True
+    )
+
+filter_data(data_if, filter_remove=filter_remove)
+
+# ###
+# save data to IF and native format
+# ###
+if not output_folder.exists():
+    output_folder.mkdir()
+pm2.pm2io.write_interchange_format(output_folder / (output_filename + coords_terminologies["category"]), data_if)
+
+data_pm2 = pm2.pm2io.from_interchange_format(data_if)
+encoding = {var: compression for var in data_pm2.data_vars}
+data_pm2.pr.to_netcdf(output_folder / (output_filename + coords_terminologies["category"] + ".nc"), encoding=encoding)