adsg-core 1.4.0
Design Space Graph (ADSG Core)
License: MIT
Requires Python: >=3.9

# The Design Space Graph (ADSG Core)

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[GitHub Repository](https://github.com/jbussemaker/adsg-core) |
[Documentation](https://adsg-core.readthedocs.io/)

The Design Space Graph (DSG) allows you to model design spaces using a directed graph that contains three
types of architectural choices:

- Selection choices (see example below): selecting among mutually-exclusive options, used for *selecting* which nodes
  are part of an architecture instance
- Connection choices: connecting one or more source nodes to one or more target nodes, subject to connection constraints
  and optional node existence (due to selection choices)
- Additional design variables: continuous or discrete, subject to optional existence (due to selection choices)

![DSG with selection](https://raw.githubusercontent.com/jbussemaker/adsg-core/main/docs/figures/adsg_ex_sel.svg)

Modeling a design space like this allows you to:

- Model hierarchical relationships between choices, for example only activating a choice when another choice has some
  option selected, or restricting the available options for choices based on higher-up choices
- Formulate the design space as an optimization problem that can be solved using numerical optimization algorithms
- Generate architecture instances for a given design vector, automatically correct incorrect design variables, and get
  information about which design variables were active
- Implement an evaluation function (architecture instance --> metrics) and run the optimization problem

*Note: due to historical reasons the package and code refer to the ADSG (Architecture DSG), because originally it had
been developed to model system architecture design spaces. In the context of this library, the ADSG and DSG can be
considered to be equivalent.*

## Installation

First, create a conda environment (skip if you already have one):
```
conda create --name dsg python=3.12
conda activate dsg
```

Then install the package:
```
conda install numpy scipy~=1.9
pip install adsg-core
```

Optionally also install optimization algorithms ([SBArchOpt](sbarchopt.readthedocs.io/)):
```
pip install adsg-core[opt]
```

If you want to interact with the DSG from a [Jupyter notebook](https://jupyter.org/):
```
pip install adsg-core[nb]
jupyter notebook
```

## Documentation

Refer to the [documentation](https://adsg-core.readthedocs.io/) for more background on the DSG
and how to implement architecture optimization problems.

### Examples

An example DSG with two selection choices:

![DSG with selection](https://raw.githubusercontent.com/jbussemaker/adsg-core/main/docs/figures/adsg_ex_sel.svg)

An example DSG with a connection choice:

![DSG with connection](https://raw.githubusercontent.com/jbussemaker/adsg-core/main/docs/figures/adsg_ex_conn.svg)

The DSG of the [Apollo problem](https://adsg-core.readthedocs.io/en/latest/example_apollo/):

![GNC DSG](https://raw.githubusercontent.com/jbussemaker/adsg-core/main/docs/figures/adsg_ex_apollo.svg)

The DSG of the [GNC problem](https://adsg-core.readthedocs.io/en/latest/example_gnc/):

![GNC DSG](https://raw.githubusercontent.com/jbussemaker/adsg-core/main/docs/figures/adsg_ex_gnc.svg)

## Citing

If you use the DSG in your work, please cite it:

J.H. Bussemaker.
System Architecture Optimization: Function-Based Modeling, Optimization Algorithms, and Multidisciplinary Evaluation.
Dissertation, Delft University of Technology, July 2025.
DOI: [10.4233/uuid:246b18f9-1f8c-4ff7-b824-2b1786cf9d14](https://repository.tudelft.nl/record/uuid:246b18f9-1f8c-4ff7-b824-2b1786cf9d14)

## Contributing

The project is coordinated by: Jasper Bussemaker (*jasper.bussemaker at dlr.de*)

If you find a bug or have a feature request, please file an issue using the Github issue tracker.
If you require support for using the DSG or want to collaborate, feel free to contact me.

Contributions are appreciated too:
- Fork the repository
- Add your contributions to the fork
  - Update/add documentation
  - Add tests and make sure they pass (tests are run using `pytest`)
- Read and sign the [Contributor License Agreement (CLA)](https://github.com/jbussemaker/adsg-core/blob/main/ADSG%20Core%20DLR%20Individual%20Contributor%20License%20Agreement.docx)
  , and send it to the project coordinator
- Issue a pull request into the `dev` branch

**NOTE:** Do *NOT* directly contribute to the `adsg_core.optimization.assign_enc` and `.sel_choice_enc` modules!
Their development happens in separate repositories:
- [AssignmentEncoding](https://github.com/jbussemaker/AssignmentEncoding)
- [SelectionChoiceEncoding](https://github.com/jbussemaker/SelectionChoiceEncoding)

Use `update_enc_repos.py` to update the code in this repository.

### Adding Documentation

```
pip install -r requirements-docs.txt
mkdocs serve
```

Refer to [mkdocs](https://www.mkdocs.org/) and [mkdocstrings](https://mkdocstrings.github.io/) documentation
for more information.