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makoralle 🪸

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makoralle provides pydantic models for the German MaKo (Marktkommunikation) processes together with serializers that turn those models into the output formats used downstream:

  • YAML – process and EBD (Entscheidungsbaum-Diagramm) representations
  • WSD – the websequencediagrams DSL for sequence diagrams
  • BPMN – business process model XML
  • Markdown – human-readable process documentation

It is the shared, dependency-light foundation (only pydantic and PyYAML) consumed by the private makorele parser and by the process-documentation webapp.

Separation of concerns

Part of the Hochfrequenz MaKo tooling — four repositories, one responsibility each:

Dependencies flow one way: makorele → makoralle; the dataset is produced by the tooling and consumed by the web app at a pinned tag. makoralle imports neither the parser nor the app.

Installation

pip install makoralle

Usage

The public serialization API is exposed at the package root and resolved lazily (importing makoralle, or a light submodule like makoralle.grouping, does not eagerly pull in the serialization stack):

from makoralle import emit_yaml, emit_wsd, emit_markdown, emit_bpmn
from makoralle.models.process import Process

# build or load a Process model, then serialize it
yaml_text = emit_yaml(process)
wsd_text = emit_wsd(process.sequence_diagrams[0])

The models live under makoralle.models (process, ebd, pid, activity, chunk) and the serializers under makoralle.serialization.

Development

This project uses the Hochfrequenz src-layout Python template with uv. To set up a development environment:

uv sync --group dev

Run the unit tests, linting, type checks and coverage (matching what CI runs):

uv run --group tests pytest
uv run --group linting ruff check src/makoralle unittests
uv run --group linting ruff format --check .
uv run --group type_check mypy --strict src/makoralle
uv run --group type_check mypy --strict unittests
cd unittests && uv run --group coverage coverage run -m pytest && cd ..

Metadata

Release files for makoralle 0.0.6

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0.0.24

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0.0.14

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