91 lines
3.1 KiB
Python
91 lines
3.1 KiB
Python
# ucsxls2json.py
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# (c) 2024 Jamie Hardt
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#
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# This tool takes the "Full Translations" xlsx file and converts it into a
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# series of json files, one for each language in the table.
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#
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# This script is a part of the `ucs-community` project, a LICENSE file outling
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# your rights should be included in its distribution. For more information see
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# the project website on github: https://github.com/iluvcapra/ucs-community
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import pandas as pd
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import re
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import json
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import os
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from typing import Dict, List
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# this key is added to ever category entry created in the output files
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UCS_VERSION = "8.2.1"
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# This is the file to pull descriptions from
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EXCEL_FILE = 'UCS v8.2.1 Full Translations.xlsx'
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# Output directory, where all the output files will be written
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OUTPUT_DIR = 'json'
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data = pd.read_excel(EXCEL_FILE)
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# Create a table of all languages that maps language-column pairs to their
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# corresponding index in the xlsx
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# The English column headers aren't formatted like the other languages so we
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# just have to special-case them
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langs: Dict[str,Dict[int,str | List[str]]] = {'en': {0: 'Category', 1:
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'SubCategory', 2: 'CatID',
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3:'CatShort',
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4:'Explanations', 5:
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'Synonyms'}}
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# step through each column
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for i, (col_index, col_data) in enumerate(data.T.iterrows()):
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# the data is transposed, so indexing into col_data steps down in rows,
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# row[1] is where all the headers are.
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components = col_data[1].split("_")
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if len(components) == 2:
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# If this is true, we are in a translation column
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if components[0] == 'Category':
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langs[components[1]] = dict()
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langs[components[1]][i] = components[0]
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os.makedirs(OUTPUT_DIR, exist_ok=True)
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# Pull the rows into a list
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# skip the first three rows of the data, it's just header material
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rows = list(data.iterrows())[3:]
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# for each language
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for lang in langs:
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# construct a schedule for the language
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schedule = []
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# for each row
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for (_, row) in rows:
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# create a dict for the category on this row
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category: Dict[str, str | List[str]] = {'version':UCS_VERSION}
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for col_index in langs[lang]:
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key_name = str(langs[lang][col_index])
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category[key_name] = str(row.iloc[col_index])
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# Save the English CatID so this can be cross-referenced
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category['CatID'] = row.iloc[2]
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if key_name == 'Synonyms':
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# synonyms are stored in the spreadsheet as CSV, we should
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# normalize this.
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syns_raw = category['Synonyms']
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assert type(syns_raw) == str, \
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f"Synonym list (lang: {lang}, {category['CatID']}) was not readable"
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syn_list = re.split(r'\W+', syns_raw)
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category['Synonyms'] = [s.lower() for s in syn_list]
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schedule.append(category)
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# and dump it to json
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with open(f"{OUTPUT_DIR}/{lang}.json", "w") as fp:
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json.dump(schedule, indent=True, fp=fp)
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