rebdhuhn 0.4.1

Converts (already scraped) Entscheidungsbaumdiagramm tables to real graphs

rebdhuhn

🇩🇪 Dieses Repository enthält ein Python-Paket namens rebdhuhn (früher: ebdtable2graph), das genutzt werden kann, um aus .docx-Dateien extrahierte maschinenlesbare Tabellen, die einen Entscheidungsbaum (EBD) modellieren, in echte Graphen zu konvertieren. Diese Entscheidungsbäume sind Teil eines regulatorischen Regelwerks für die deutsche Energiewirtschaft und kommen in der Eingangsprüfung der Marktkommunikation zum Einsatz.

🇬🇧 This repository contains the source code of the Python package rebdhuhn (formerly known as ebdtable2graph).

Rationale

Assume, that you scraped the Entscheidungsbaumdiagramm tables by EDI@Energy from their somewhat "digitized" PDF/DOCX files. (To do so, you can use the package ebdamame (was: ebddocx2table).) Also assume, that the result of your scraping is a rebdhuhn.models.EbdTable.

The package rebdhuhn contains logic to convert your scraped data into a graph. This graph can then be exported e.g. as SVG and/or UML.

How to use rebdhuhn?

Install the package from pypi:

pip install rebdhuhn

Create an Instance of EbdTable

EbdTable contains the raw data by BDEW in a machine-readable format. Creating instances of EbdTable is out of scope for this package. Ask Hochfrequenz for support on this topic. In the following example we hard code the information.

from rebdhuhn.graph_conversion import convert_table_to_graph
from rebdhuhn.models import EbdCheckResult, EbdTable, EbdTableMetaData, EbdTableRow, EbdTableSubRow, EbdGraph

ebd_table: EbdTable  # this is the result of scraping the docx file
ebd_table = EbdTable(  # this data shouldn't be handwritten
    metadata=EbdTableMetaData(
        ebd_code="E_0003",
        chapter="7.39 AD: Bestellung der Aggregationsebene der Bilanzkreissummenzeitreihe auf Ebene der Regelzone",
        sub_chapter="7.39.1 E_0003_Bestellung der Aggregationsebene RZ prüfen",
        role="ÜNB",
    ),
    rows=[
        EbdTableRow(
            step_number="1",
            description="Erfolgt der Eingang der Bestellung fristgerecht?",
            sub_rows=[
                EbdTableSubRow(
                    check_result=EbdCheckResult(result=False, subsequent_step_number=None),
                    result_code="A01",
                    note="Fristüberschreitung",
                ),
                EbdTableSubRow(
                    check_result=EbdCheckResult(result=True, subsequent_step_number="2"),
                    result_code=None,
                    note=None,
                ),
            ],
        ),
        EbdTableRow(
            step_number="2",
            description="Erfolgt die Bestellung zum Monatsersten 00:00 Uhr?",
            sub_rows=[
                EbdTableSubRow(
                    check_result=EbdCheckResult(result=False, subsequent_step_number=None),
                    result_code="A02",
                    note="Gewählter Zeitpunkt nicht zulässig",
                ),
                EbdTableSubRow(
                    check_result=EbdCheckResult(result=True, subsequent_step_number="Ende"),
                    result_code=None,
                    note=None,
                ),
            ],
        ),
    ],
)
assert isinstance(ebd_table, EbdTable)

ebd_graph = convert_table_to_graph(ebd_table)
assert isinstance(ebd_graph, EbdGraph)

Export as PlantUML

from rebdhuhn import convert_graph_to_plantuml

plantuml_code = convert_graph_to_plantuml(ebd_graph)
with open("e_0003.puml", "w+", encoding="utf-8") as uml_file:
    uml_file.write(plantuml_code)

The file e_0003.puml now looks like this:

@startuml
...
if (<b>1: </b> Erfolgt der Eingang der Bestellung fristgerecht?) then (ja)
else (nein)
    :A01;
    note left
        Fristüberschreitung
    endnote
    kill;
endif
if (<b>2: </b> Erfolgt die Bestellung zum Monatsersten 00:00 Uhr?) then (ja)
    end
else (nein)
    :A02;
    note left
        Gewählter Zeitpunkt nicht zulässig
    endnote
    kill;
endif
@enduml

Export the graph as SVG

First, make sure to have a local instance of kroki up and running via docker (localhost:8125):

Add the required .env file to the repository root by opening a new terminal session, changing the directory to

cd path\to\rebdhuhn\repository\root

and executing the create_env_file.py script via

python create_env_file.py

Run the docker-desktop app on your local maschine and host the local kroki instance on PORT 8125 via

docker-compose up -d

To export the graph as SVG, use

from rebdhuhn import convert_plantuml_to_svg_kroki
from rebdhuhn.kroki import Kroki

kroki_client = Kroki()
svg_code = convert_plantuml_to_svg_kroki(plantuml_code, kroki_client)
with open("e_0003.svg", "w+", encoding="utf-8") as svg_file:
    svg_file.write(svg_code)

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.