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Name: Python product dev tools for electronics
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Author(s):	Barrie Duncan
Location	Auditorium 2
Date	aug Sat 24	Days Raw Files
Start	11:00	First Raw Start	10:34
Duration	00:50:00	Offset	0:25:15
End	11:50	Last Raw End	12:04
Chapters	00:00 0:01:22 0:01:30 0:03:22 0:30:24 0:31:56 0:33:22
Total cuts_time	41 min.

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Description:	"Automation dev tools allow us to work smarter not harder. We have integrated Python into a large range of our electronic product development lifecycle. This includes: 1. Simulators for new technology. 2. Code generators for embedded C firmware. 3. Automation testing and data analysis." "High volume electronics product development can be very complex. Microcontrollers used in these products can run multiple control systems all operating in parallel. Automation with Python in each step of the product development life-cycle allows us to be very efficient and thorough in our development and validation processes. The Python numba package has been key to developing a simulator of new motor technology recently invented. This allows prototyping control systems without any hardware, and also helps understand how the new technology will work. Python code generators have been a major component to our latest embedded products developed. These write large sections of our embedded code based on high level specifications. This includes features such as: creating fully redundant database configuration storage on only a few kB of flash; allows for blackbox storage features; configurable user interface, etc. Over the last few years we have created an automation test system for helping to validate our product development. This test system was written in Python to ensure our developed products are of the highest quality capturing millions of test combinations when validating final products in their real environment without simulators. It is used for testing various motors and controllers and is designed to capture their data. With the comprehensive information captured from 24/7 testing sequences, we are able to provide both on-device analysis and post-processing analysis. The system allows building new firmware using Python code generators, loading firmware onto real devices under test, exercising the system and analysing all the results. Abnormalities are detected as they happen, and more complex faults can be found by post-processing the large amount of data collected from the automation regression test system. This system even allows checking for data trends and relationships that were never thought of when the tests were originally ran. Currently clients both locally in NZ and also in the USA use this system." Barrie Duncan has been involved in the electronics and embedded software industry for over 20 years. He worked at Schneider Electric in Australia, then in the USA developing high quality power industry products that must keep running 24/7. Currently he runs a consulting company specialising in hard real-time systems which control high quality reliable products. This includes using a range of software languages (C/C++/assembly), and electronics design to ensure a quality solution. Barrie has recently switched to using Python to help in his development processes including simulations, code generators, automation testing and data analysis. markdown
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