This package contains Python bindings generating code for the MEmilio C++ library. It enables the automatic generation of a part of the Python Bindings that is common across multiple models. For a particular example, see the SEIR model with its files oseir.cpp and oseir.py.

This generating software was developed as a part of the Bachelor thesis Automatische Codegenerierung für nutzerfreundliche mathematisch-epidemiologische Modelle. The following figure from Chapter 5 outlines the workflow of the generator. Blue boxes represent parts of the code generator and orange ones the input and output. Rectangular boxes contain classes with logic, the rest represent data.

The package uses the Clang C++ library and the LibClang Python library to analyze the C++ code of a model. Both need to be installed and share the same version.

Use the provided pyproject.toml file to build and install the package. To install the package, use the command (from the directory containing pyproject.toml)

pip install -e .[dev]

During this step the package creates a compilation database (compile_commands.json) for the models of the C++ libraryC++ Library.

Afterwards you can use the following command to make a full installation of the package (not necessary for usage)

pip install .

The package provides an example script and README on how to use it in memilio/tools. The example uses the ode_seir model from the C++ Library.

You can print the AST of your model into a file (Usefull for development/debugging).

The package provides a test suite in the tests directory. To run the tests, make sure the package is installed, and you are in the source directory, then run:

cd tests
python -m unittest

This works with both normal (pip install .) and editable (pip install -e .) installations.

Alternatively, you can run the tests from outside the source directory:

cd /path/to/another/directory
python -m unittest discover -s /path/to/memilio/pycode/memilio-generation/tests

When implementing new model features you can follow these steps:

• For the features you want to implement, find the nodes in the abstract syntax tree (AST) (use method aviz.output_ast_formatted(); see the example in tools/).
• Add the extraction of those features. Therefore you need to change the "check_..."-methods corresponding to the CursorKind of your nodes in the Scanner class. If there is no corresponding "check_..."-method you need to write a new one and add it to the switch-method (scanner.switch_node_kind()).
• Extend the IntermediateRepresentation for the new model features.
• Adjust the cpp-template and the string-template-methods. If needed, use new identifiers and write new string-template-methods for them.
• Adjust the substitution dictionaries in the Generator class.
• Write new/Adjust script in the tool folder for the model and try to run.
• Add new strings in the Default dict
• Update tests.