🇩🇪 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
).
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.
rebdhuhn
?Install the package from pypi:
pip install rebdhuhn
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="MaBiS",
section="7.39 AD: Bestellung der Aggregationsebene der Bilanzkreissummenzeitreihe auf Ebene der Regelzone",
ebd_name="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)
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
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)
Please follow the instructions in our Python Template Repository . And for further information, see the Tox Repository.
You are very welcome to contribute to this template repository by opening a pull request against the main branch.
This repository is part of the Hochfrequenz Libraries and Tools for a truly digitized market communication.