仿真-使用共享库使用Python运行Simulink仿真模型-附项目源码+例程-优质项目实战.zip

# Python-Simulink Run your Simulink models & libraries in Python. ## Motivation 1. Running software-in-the-loop tests with Simulink becomes time consuming with Matlab & Simulink overhead. There are ways to reduce it (Model reference, etc) however nothing has shown to be as fast as a precompiled shared library. 2. Python has a very mature set of tools and packages to automate testing. 3. Testing can be distributed to machines without Matlab/Simulink licenses. 4. There are more and more Python developers. Being able to hand off a Simulink Library for further use there is a good feature. ## Use cases 1. Use Python and it's ecosystem to run complex Simulink models. 2. Use Python & `pytest` to run Software-in-the-Loop (SIL) tests on Simulink subsystems. 3. Give Simulink algorithms to developers without Matlab/Simulink licenses to use. 4. Use GitHub/GitLab actions and Python to automate testing in the cloud. 5. Start a programming language war at your company. ## Disclaimer This repository is a set of instructions, with examples, on how to create a Pythonic wrapper for Simulink models. It **is not** a turnkey Python module to do this: ```python import simulinkdll simulinkdll.run("my_model.slx") do_stuff() ``` For a given library or model configuring the Python should only need done when the Simulink Parameters/Signals change. The end developer will then be able do a `import simulink_model`, but it takes development time. ### High level instructions. 1. [Create a shared library in Simulink.](https://www.mathworks.com/help/ecoder/ug/creating-and-using-host-based-shared-libraries.html) 2. Create Python representations of all items in the header file. 3. Open the shared library (`.dll`, `.so`) in Python and run the model. # Examples ### [Simple DLL Export](https://nbviewer.jupyter.org/github/dapperfu/python_SimulinkDLL/blob/master/Example1/dllModel.ipynb) - For demonstrating minimal dll functionality and the steps required to run a model in Python. - Demonstrate implementions of `SimulinkGlobal` vs `ExportedGlobal` in `Simulink.Parameter` and `Simulink.Signal` variables.  ### [Discrete Transfer Function](https://nbviewer.jupyter.org/github/dapperfu/python_SimulinkDLL/blob/master/Example2/discrete_tf-python_class.ipynb)  A simple discrete transfer function. Compiled with a 1st order low pass filter. There are two example notebooks for Example 2. 1. [Simple Example](https://nbviewer.jupyter.org/github/dapperfu/python_SimulinkDLL/blob/master/Example2/discrete_tf.ipynb) - A simple low-level ctypes wrapper. 2. [Pythonic Example](https://nbviewer.jupyter.org/github/dapperfu/python_SimulinkDLL/blob/master/Example2/discrete_tf-python_class.ipynb) - Use Python syntactic sugar to create a high level [TransferTF python](https://github.com/dapperfu/python_SimulinkDLL/blob/master/Example2/discretetf.py) class to interact with the model. Adds datalogging and pandas integration. - Example 2 also contains sample `pytest` tests in the [`tests`](https://github.com/dapperfu/python_SimulinkDLL/tree/master/Example2/tests) directory. This demonstrates how you can use `pytest` to test Simulink models. Sample test results are shown shown in [Example2/test_results.md.](https://github.com/dapperfu/python_SimulinkDLL/blob/master/Example2/test_results.md) - Tests can be run on [GitHub actions](https://github.com/features/actions) as well. This is an example badge: [](https://github.com/dapperfu/python_SimulinkDLL/actions/workflows/blank.yml)  ### [Bouncing Ball](https://nbviewer.jupyter.org/github/dapperfu/python_SimulinkDLL/blob/master/Example3/bouncing_ball.ipynb) Adapted from [Mathworks's Simulation of a Bouncing Ball](https://www.mathworks.com/help/simulink/slref/simulation-of-a-bouncing-ball.html)  Running a Simulation in Simulink also has some overhead. By compiling the model to a shared library and executing it, this overhead is eliminated. `bouncing_ball_benchmark.m` benchmarks the model by testing increasingly smaller time steps. The model was then compiled and tested in Python and the corresponding times are recorded below. | Time Step | Simulink Duration (s) | Python Duration (s) | | --------- | --------------------- | ------------------- | | 1e-4 | 0.5905 | 0.06 | | 1e-5 | 1.0461 | 0.61 | | 1e-6 | 8.1991 | 6.08 | | 1e-7 | 78.9901 | 60.18 | # Jenkins Build Automation This project also serves as a proof of concept for using [CI/CD devops techniques](https://www.atlassian.com/continuous-delivery/principles/continuous-integration-vs-delivery-vs-deployment) with Simulink Models. There is a [`Jenkinsfile` ](Jenkinsfile) that will build each of the examples and archive the artifacts: - shared library (`.dll`) - header files (`.h`) Jenkins Pipeline screenshot:  Jenkins Artifacts screenshot: