ebdamame 0.3.0

A scraper to library to scrape .docx files with 'Entscheidungsbaumdiagramm' tables into a truely machine readable structure

ebdamame

🇩🇪 Dieses Repository enthält ein Python-Paket namens ebdamame (früher: ebddocx2table), das genutzt werden kann, um aus .docx-Dateien maschinenlesbare Tabellen, die einen Entscheidungsbaum (EBD) modellieren, zu extrahieren (scrapen). Diese Entscheidungsbäume sind Teil eines regulatorischen Regelwerks für die deutsche Energiewirtschaft und kommen in der Eingangsprüfung der Marktkommunikation zum Einsatz. Die mit diesem Paket erstellten maschinenlesbaren Tabellen können mit rebdhuhn (früher: ebdtable2graph) in echte Graphen und Diagramme umgewandelt werden. Exemplarische Ergebnisse des Scrapings finden sich als .json-Dateien im Repository machine-readable_entscheidungsbaumdiagramme.

🇬🇧 This repository contains the source code of the Python package ebdamame (formerly published as ebddocx2table).

Rationale

Assume that you want to analyse or visualize the Entscheidungsbaumdiagramme (EBD) by EDI@Energy. The website edi-energy.de, as always, only provides you with PDF or Word files instead of really digitized data.

The package ebdamame scrapes the .docx files and returns data in a model defined in the "sister" package rebdhuhn (formerly known as ebdtable2graph).

Once you scraped the data (using this package) you can plot it with rebdhuhn.

How to use the package

In any case, install the repo from PyPI:

pip install ebdamame

Use as a library

import json
from pathlib import Path

import cattrs

from ebdamame import TableNotFoundError, get_all_ebd_keys, get_ebd_docx_tables  # type:ignore[import]
from ebdamame.docxtableconverter import DocxTableConverter  # type:ignore[import]

docx_file_path = Path("unittests/test_data/ebd20230629_v34.docx")
# download this .docx File from edi-energy.de or find it in the unittests of this repository.
# https://github.com/Hochfrequenz/ebddocx2table/blob/main/unittests/test_data/ebd20230629_v34.docx
docx_tables = get_ebd_docx_tables(docx_file_path, ebd_key="E_0003")
converter = DocxTableConverter(
    docx_tables,
    ebd_key="E_0003",
    chapter="MaBiS",
    sub_chapter="7.42.1: AD: Bestellung der Aggregationsebene der Bilanzkreissummenzeitreihe auf Ebene der Regelzone",
)
result = converter.convert_docx_tables_to_ebd_table()
with open(Path("E_0003.json"), "w+", encoding="utf-8") as result_file:
    # the result file can be found here:
    # https://github.com/Hochfrequenz/machine-readable_entscheidungsbaumdiagramme/tree/main/FV2310
    json.dump(cattrs.unstructure(result), result_file, ensure_ascii=False, indent=2, sort_keys=True)

Use as a CLI tool

to be written

How to use this Repository on Your Machine (for development)

Please follow the instructions in our Python Template Repository. And for further information, see the Tox Repository.

Contribute

You are very welcome to contribute to this template repository by opening a pull request against the main branch.

Related Tools and Context

This repository is part of the Hochfrequenz Libraries and Tools for a truly digitized market communication.