https://github.com/hochfrequenz/ebd_toolchain
🐓🫛rebdhuhn meets ebdamame
https://github.com/hochfrequenz/ebd_toolchain
bdew ebd ebdamame entscheidungsbaumdiagramm kroki networkx rebdhuhn
Last synced: 3 months ago
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🐓🫛rebdhuhn meets ebdamame
- Host: GitHub
- URL: https://github.com/hochfrequenz/ebd_toolchain
- Owner: Hochfrequenz
- License: gpl-3.0
- Created: 2024-05-02T07:05:08.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-24T21:39:32.000Z (3 months ago)
- Last Synced: 2025-03-24T22:28:56.582Z (3 months ago)
- Topics: bdew, ebd, ebdamame, entscheidungsbaumdiagramm, kroki, networkx, rebdhuhn
- Language: Python
- Homepage:
- Size: 223 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# EBD Toolchain
[](LICENSE)
🇩🇪 Dieses Repository enthält ein Python-Script, welches die beiden Python-Pakete [ebdamame](https://github.com/Hochfrequenz/ebdamame) 🫛 und [rebdhuhn](https://github.com/Hochfrequenz/rebdhuhn) 🐓 kombiniert, um aus .docx-Dateien maschinenlesbare Tabellen zur Modellierung von [edi@energy](https://www.edi-energy.de) Entscheidungsbäumen (EBD) zu extrahieren und anschließend in Form [echter Graphen](https://github.com/Hochfrequenz/machine-readable_entscheidungsbaumdiagramme/) zu visualisieren.
Diese Entscheidungsbäume sind Teil eines regulatorischen Regelwerks für die deutsche Energiewirtschaft und kommen in der Eingangsprüfung der Marktkommunikation zum Einsatz.
🇺🇸 This repository provides a Python script combining the libraries [ebdamame](https://github.com/Hochfrequenz/ebdamame) and [rebdhuhn](https://github.com/Hochfrequenz/rebdhuhn) in order to render [edi@energy](https://www.edi-energy.de) _Entscheidungsbaumdiagramme_ (EBD) as both machine-readable tables as well as corresponding graphs in `.svg` and `.uml` format.
## How to use EBD Toolchain
You can either run the toolchain via Python (requires Docker AND Python to be installed on your machine).
Or you can run the entire toolchain in a docker container (requires only Docker).
### Option A: Via Python + Kroki in a Docker Container
#### Install both libraries from PiPy:
```bash
pip install -r requirements.txt
```
Further, make sure to have a local instance of [kroki](https://kroki.io) up and running via docker (localhost:8125) as described in the [rebdhuhn](https://github.com/Hochfrequenz/rebdhuhn) readme.
Run the `docker-desktop` app on your local maschine and start the local kroki container via
```bash
docker-compose up -d
```
#### Execute the EBD toolchain script:
Run `main.py` using your IDE or inside a terminal session via
```bash
python main.py
```
Keep following the on-screen prompts given by the terminal and provide
- the path of the directory containing the `.docx` edi@energy EBD file
- the path to the output directory
- the desired data formats `.json`, `.dot`, `.svg` or `.puml`.
Alternatively, the script can simply be executed using the single command
```bash
main.py -i -o -t json -t dot -t svg -t puml
```
where `-i`, `-o` and `-t` denote the input directory path, the output directory path and the supported data format, respectively.
### Option B: Docker only (no Python required)
In this repository:
1. create an `.env` file with a structure similar to [`env.example`](env.example).
2. set the environment variables to meaningful values.
3. Login to GitHub Container Registry (GHCR); Use a [Personal Access Token](https://github.com/settings/tokens/new) (PAT) to login that has access to this repository and at least `read:packages` scope
```bash
docker login ghcr.io -u YOUR_GITHUB_USERNAME
```
4. create a docker-compose.yml that looks like this:
https://github.com/Hochfrequenz/ebd_toolchain/blob/f71c4285f1d1515b36b604487da298708b8858fa/docker-compose.yaml#L1-L22
5. then run:
```bash
docker compose up --abort-on-container-exit
```
You can find a [Github Action with exactly this setup](https://github.com/Hochfrequenz/edi_energy_mirror/blob/master/.github/workflows/ebdamame_rebdhuhn.yml) our edi_energy_mirror where it's used to automatically scrape the latest `.docx` files and pushes the results to [machine-readable_entscheidungsbaumdiagramme](https://github.com/Hochfrequenz/machine-readable_entscheidungsbaumdiagramme/) which is then used by our frontend [`entscheidungbaumdiagramm`](https://github.com/Hochfrequenz/entscheidungsbaumdiagramm/).
## How to use this Repository on Your Machine (for development)
Please follow the instructions in our
[Python Template Repository](https://github.com/Hochfrequenz/python_template_repository#how-to-use-this-repository-on-your-machine).
And for further information, see the [Tox Repository](https://github.com/tox-dev/tox).
## 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](https://github.com/Hochfrequenz/digital_market_communication/).