circuitpython VS NumPy

The keypad itself can mimic any keypresses on a regular keyboard if the Pico is installed with CircuitPython, so the Pico-to-Mac communication is already set up. For the Mac to communicate with the Pico, I managed to piece the instructions together from various sources. That’s the bit I’ll be bringing together into this article.

As another point for those interested, it (to my knowledge) is currently the ONLY implementation of a scoring machine that the firmware for the device is written in Circuit Python. which gives this unit an advantage when it comes to any and all multi tasking that the unit may need to do.

The barrier to entry has never been lower. Last night I prototyped some code in Python on my Mac to talk to a Bluetooth peripheral, and then had ChatGPT translate it to Arduino C++ code for a $5 ESP32 which mostly worked on the first go.

You can even run Python on microcontrollers these days. See Adafruit's https://circuitpython.org for which they publish modules for many (almost all?) of the sensors they sell. The modern microcontroller frameworks hide much of the complexity of Wi-Fi, Bluetooth, filesystems, etc. so you can do complicated things with minimal effort. You can really cobble something together in an afternoon.

The Pico runs CircuitPython and uses Adafruit's HID library to emulate a keyboard. It's also possible to emulate a mouse and a gamepad (there are some experiments for gamepad emulation in experiments).

Asked your question in the adafruit discord and they said there's no HT1632c library for circuit python yet. There is a library for micropython. You can request to have it ported from the Micropython library I linked to by opening a feature request in the Circuit Python Github. That would be your best option for now or attempt to port the library yourself.

Microcontrollers can run Python now https://circuitpython.org/

I'm aware of two versions of Python for microcontrollers. Micro Python and Circuit Python

I tried to look in https://github.com/adafruit/circuitpython/tree/main/shared-bindings at audiocore, audioio, an audiomixer looking for a way to access the sample counter manually but I have no clue what I'm looking at :(

AI starts with math and coding. You don’t need a PhD—just high school math like algebra and some geometry. Linear algebra (think matrices) and calculus (like slopes) help understand how AI models work. Python is the main language for AI, thanks to tools like TensorFlow and NumPy. If you know JavaScript from Vue.js, Python’s syntax is straightforward.

As is the case with most Python libraries, it is open-source and free-to-use, making it easily accessible by anyone willing to learn machine learning, and it is built upon other open-source libraries within Python, like SciPy for advanced scientific operations, NumPy for efficient numerical computations, Matplotlib for data visualization, and Cython for increased efficiency and speed, similar to that of C/C++.

I hit a similar issue in 2017 which is still the case today: Python's builtin `random.shuffle` destroys numpy arrays passed into it [0]. This is apparently a design limitation within numpy and cannot be detected or fixed, so it still stands today.

[0] https://github.com/numpy/numpy/issues/10215

The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis.

In my testing, I found that when checking out 500 commits sequentially from the numpy repository, disabling this feature required 13.8 seconds to complete on average across 10 runs. Enabling this feature took on average 11.2 seconds across 10 runs. Not an astounding difference in testing, but if core.fsmonitor can save me 2.6 seconds per 500 commits, on a project with 37,775 commits that could add up to a time savings of 211.54 seconds, or 3 minutes and 32 seconds! More testing on my end needs to be done if this feature scales linearly, but for now I will keep it on and use version 1 of the tool.

Readers of this blog who are familiar with LAPACK are likely to not be intimately familiar with the wild world of web technologies. For those coming from the world of numerical and scientific computation and have familiarity with the scientific Python ecosystem, the easiest way to think of stdlib is as an open source scientific computing library in the mold of NumPy and SciPy. It provides multi-dimensional array data structures and associated routines for mathematics, statistics, and linear algebra, but uses JavaScript, rather than Python, as its primary scripting language. As such, stdlib is laser-focused on the web ecosystem and its application development paradigms. This focus necessitates some interesting design and project architecture decisions, which make stdlib rather unique when compared to more traditional libraries designed for numerical computation.

numpy

This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays.