A Python 3 headless implementation of the Capella modeling tool
capellambse allows you reading and writing Capella models from Python without
Java or the Capella tool on any
(reasonable) platform. We wanted to "talk" to Capella models from Python, but
without any Java on the way. We thought this project will help individuals and
organisations getting through the MBSE adoption journey with Capella faster and
so we made it public and open-source.
With capellambse you can access all (or almost all) Capella model elements,
and render diagrams as SVG and PNG. We made it for automation of Systems
Engineering work, so it integrates nicely into most CI/CD toolchains. We also
found it at the core of our artifact generation pipelines (model to documents,
model to SW interfaces).
The library works with PVMT and Requirements extensions without any additional efforts.
It started as a basic library somewhere mid 2019. Since then it was re-architected a few times and now has a full read/write capability for most of the present Capella ontology. We are continuously improving the API (introducing shortcuts), increasing the meta-model coverage and have more engineering automations and improvements in the pipeline to share.
capellambse-context-diagrams
— A capellambse extension that visualizes the context of Capella objects, and
exposes it on element attributes like .context_diagram, .tree_view, etc.
capella-diff-tools — A set
of tools to compare Capella models.
capella-polarion —
Synchronize information from the Capella model into a
Polarion project
capella-ros-tools — Import
and export ROS *.msg files to/from Capella models, or transform *.msg
files into a declarative YAML file.
Did you make something cool that is using or extending capellambse? Tell us about it, so we can add it to this list!
The library is designed to be easy to use and discover, especially in an interactive environment such as JupyterLab. Additionally, API documentation is automatically generated and published whenever new features and bug fixes are added.
You are encouraged to explore our test models and demo notebooks. Click on the button below to launch a Jupyter notebook server on the public myBinder service, and get started in seconds:
Warning: Do not enter confidential
information,
such as passwords for non-public models, into a notebook hosted on myBinder. If
you want to try out capellambse with those models, please install and
run it in a local, trusted environment!
The docs/source/examples directory contains several hands-on example
notebooks that you can immediately run and start experimenting with. Below is a
short summary of each notebook's goal. If you are in the JupyterLab
environment, you can click the notebook names to directly open them in a new
lab tab. On Github, you will be shown a statically rendered preview of the
notebook.
01 Introduction.ipynb provides a high-level overview of the library features. It visualizes examples like a Component - Function allocation table by leveraging Jupyter's and IPython's rich display functionality.
02 Intro to Physical
Architecture.ipynb
explores some more advanced concepts on the example of the Physical
Architecture Layer. It shows how to derive tabular data, like a Bill of
Materials or a Software to Hardware allocation table, by using pandas
dataframes.
03 Data Values.ipynb shows how the API can be used to explore classes, class instances and other objects related to data modeling.
04 Intro to Jinja
templating.ipynb
demonstrates how to effectively combine capellambse with the powerful
Jinja templating engine. This enables
the creation of all sorts of model-derived documents and artifacts, including
interactive web pages, PDF documents and any other textual representations of
your models.
05 Introduction to Libraries.ipynb shows how to use Capella Library Projects within capellambse. In this example you'll learn how the API can be used to open a project that is based on a library and find objects in both models.
06 Introduction to Requirement access and management.ipynb shows how the API can be used to work with requirements objects, introduced by the Capella Requirements Viewpoint. In this example you'll see how to find requirements in the model, see which objects requirements are linked / traced to and even export requirements to Excel or ReqIF formats.
07 Code
Generation.ipynb
shows how to generate code from class diagrams. In particular, we focus on
Interface Descriptive Languages with concrete examples for Class to ROS2
IDL and
Google Protocol Buffers. We
also show how simple Python stubs could be generated given a Class
object.
08 Property Values.ipynb shows how to access property values and property value groups, as well as the Property Value Management (PVMT) extension.
09 Context Diagrams.ipynb shows the capellambse-context-diagrams extension that visualizes contexts of Capella objects. The extension is external to the capellambse library and needs to be installed separately.
10 Declarative Modeling.ipynb demonstrates a basic application of the declarative approach to modeling on a coffee machine example.
11 Complexity Assessment.ipynb quickly demonstrates how to use and view the model complexity badge for a Capella model.
We are constantly working on improving everything shown here, as well as adding even more useful functionality and helpful demos. If you have any new ideas that were not mentioned yet, don't hesitate to contribute!
In order to use private models that are not publicly available, please install
and use capellambse in a local, trusted environment.
You can install the latest released version directly from PyPI.
pip install capellambse
On supported platforms, the downloaded wheel includes an optional native module, which provides faster implementations of some functions. Other platforms will fall back to a pure Python wheel without this speedup. To build the native module, a Rust compiler is required.
For details on how to set up a local development environment, please refer to the CONTRIBUTING guide.
As we started in mid 2019 and there was no such thing as Python4Capella yet, we are not API compatible with that project.
The generated diagrams are currently not persisted in .aird files, and
currently there is no plan to implement this. If there is a genuine usecase for
that we may re-consider it - feel free to create an issue or add comments to an
existing one.
We'd love to see your bug reports and improvement suggestions! Please take a look at our guidelines for contributors for details.
This project is compliant with the REUSE Specification Version 3.0.
Copyright DB InfraGO AG, licensed under Apache 2.0 (see full text in LICENSES/Apache-2.0.txt)
Dot-files are licensed under CC0-1.0 (see full text in LICENSES/CC0-1.0.txt)
To provide the same look and feel across platforms, we distribute our library
bundled with the OpenSans font (capellambse/OpenSans-Regular.ttf). The
OpenSans font is Copyright 2020 The Open Sans Project
Authors, licensed under OFL-1.1 (see
full text in LICENSES/OFL-1.1.txt).
FAQs
Provides access to Capella MBSE projects in Python
We found that capellambse demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 4 open source maintainers collaborating on the project.
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