mmodel: A workflow framework to accelerate the development of experimental simulations.

Area of Science:

• Computational science
• Scientific computing
• Software engineering for science

Background:

• Simulation is crucial for scientific experiment design and development.
• Traditional coding methods for complex simulations are often error-prone, inflexible, and difficult to interpret.
• Modifications like algorithm updates or parameter variations are challenging with conventional approaches.

Purpose of the Study:

• To present mmodel, a Python framework to streamline the development of experimental simulation packages.
• To address the limitations of conventional procedural coding in scientific simulations.
• To enhance the flexibility, readability, and testability of simulation code.

Main Methods:

• Utilizes a graph-theory approach to model experimental steps.
• Represents experimental procedures as a graph structure.
• Enables the framework to rewrite its own code for modifications.

Main Results:

• mmodel accelerates the writing of experimental simulation packages.
• The framework can automatically implement modifications, such as parameter looping.
• Reduces code duplication, improves readability and testability, and decreases development time.

Conclusions:

• mmodel offers a more efficient and robust method for developing scientific simulations.
• The graph-theory approach enhances the adaptability of simulation code.
• The framework is a valuable tool for researchers seeking to optimize simulation development.