{"id":196291,"date":"2025-02-26T09:39:49","date_gmt":"2025-02-26T09:39:49","guid":{"rendered":"https:\/\/innovationspace.ansys.com\/knowledge\/?post_type=topic&p=196291"},"modified":"2025-05-22T13:17:51","modified_gmt":"2025-05-22T13:17:51","slug":"jupyter-notebook-programming-with-the-scade-python-wrapper","status":"publish","type":"topic","link":"https:\/\/innovationspace.ansys.com\/knowledge\/forums\/topic\/jupyter-notebook-programming-with-the-scade-python-wrapper\/","title":{"rendered":"Jupyter notebook programming with the SCADE Python Wrapper"},"content":{"rendered":"

Introduction<\/h3>\n

In the rapidly evolving landscape of safety-critical software development, integrating modern tools can significantly enhance productivity and flexibility. Ansys SCADE, renowned for its robust support in developing certified software, can be further empowered by leveraging Jupyter Notebooks<\/a>.<\/p>\n

This article explores how Jupyter Notebooks can extend SCADE’s capabilities, enabling developers to streamline workflows, automate tasks, and enhance data visualization. Discover how this synergy can revolutionize your development process.<\/p>\n

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\n Photo credit: Pixabay @ Pexels<\/a><\/em>\n<\/p>\n

A first-order low-pass filter example<\/h3>\n

In this article, we’ll demonstrate how to extend SCADE’s capabilities using a simple yet effective example: a first-order low-pass filter<\/a>.<\/p>\n

We will use the following recursive equation to represent our RC Filter:<\/p>\n

$$y[i]= y[i-1] + a(x[i] – y[i-1])$$<\/p>\n

Where:<\/p>\n

$$\\alpha = \\frac{\\Delta t}{\\frac{1}{2 \\pi \\omega_{c}}+ \\Delta t}$$<\/p>\n

$\\Delta t$ is the interval between each sampling <\/p>\n

$\\omega_{c}$ is the cutoff frequency<\/p>\n

Designing our filter in SCADE<\/h3>\n

Implementing a first order low pass filter in SCADE is quite simple and would give the following diagram:<\/p>\n

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\n Low Pass filter in SCADE<\/em>\n<\/p>\n

Note the use of the FBY<\/code> operator, which allows us to reference the value of y<\/code> from previous cycles ($y[i-1]$ in our equation above).<\/p>\n

Planning our tests<\/h3>\n

Testing a low-pass filter involves verifying its performance against expected behavior, such as attenuation of high-frequency components and preservation of low-frequency components. We will perform two kinds of tests to validate our filter.<\/p>\n

Time-domain testing<\/h4>\n

Test with step inputs to measure settling time, overshoot, and response characteristics.<\/p>\n

Frequency response testing<\/h4>\n

Verify the filter’s behavior across a range of frequencies:<\/p>\n