If you’ve ever been curious if there’s a way to program microcontrollers without actually writing software, you might be interested in FlowCode. It isn’t a free product, but there is a free demo available. [Web learning] did a demo of programming a Nucleo board using the system. You can check it out below.
The product looks slick and it supports a dizzying number of processors ranging from AVR (yes, it will do Arduino), PIC, and ARM targets. However, the pricing can add up if you actually want to target all of those processors as you wind up paying for the CPU as well as components. For example, the non-commercial starter pack costs about $75 and supports a few popular processors and components like LEDs, PWM, rotary encoders, and so on.
Python is the Arduino of software projects. It has a critical mass of libraries for anything from facial recognition and neural networks to robotics and remote sensing. And just like Arduino, I have yet to find the killer IDE for Python. Perhaps I just haven’t tried the right one yet, but it could be that I’m just doing Python wrong.
For Years I’ve Been IDLE
I’m a Linux-only type of a guy so using IDLE for Python is a natural fit. It’s in the repositories for super quick and easy install and there’s basically zero configuration to be done. Generally speaking my preferred development environment is text editor and command line compiler. IDLE is just one step above that. You get a separate window for the shell and each Python file you’re working on. Have IDLE run your code and it saves the file, then launches it in the shell window.
For me, there are two important features of IDLE’s shell. The first is that it keeps an interactive session open after you run your Python code. This means that any globals that your script uses are still available, and that you can experiment with your code by calling functions (and classes, etc) in real time. The second desirable feature is that while using this interactive shell, IDLE supports code completion and docstring support (it gives you hints for what parameters a function accepts/requires).
But simplicity has a tough time scaling. I’m working on larger and larger projects spread over many files and the individual nature of IDLE editor windows and lack of robust navigation has me looking to move forward.
I’ve tried perhaps a half-dozen different Python IDEs now, spending the most time on two of them: Geany and Atom. Both are easy to install on Linux and provide the more advanced features I want for larger projects: better navigation, cross-file code completion (and warnings), variable type and scope indication.
The look of Geany brings to mind an “IDE 1.0” layout style and theme. It’s the familiar three-pane layout that places symbols to the left, code to the right, and status along the bottom. When you run your program it launches in an interactive terminal, which I like, but you lose all IDE features at this point, which I despise. There is no code completion, and no syntax highlighting.
I have been using Atom much more than Geany and have grown to like it enough to stick with it for now. I’d call Atom the “IDE 2.0” layout. It launches with a dark theme and everything is a tab.
Atom depends heavily on packages (plugins that anyone may write). The package management is good, and the packages I’ve tried have been superb. I’m using autocomplete-python and tabs-to-spaces, but again I come up short when it comes to running Python files. I’ve tried platformio-ide-terminal, script, and runner plugins. The first brings up a terminal as a bottom pane but doesn’t automatically run the file in that terminal. Script also uses a bottom pane but I can’t get it to run interactively. I’m currently using runner which has an okay display but is not interactive. I’ve resorted to using a “fake” python file in my projects as a workaround for commands and tests I would normally run in the interactive shell.
Tell Us How You Python
It’s entirely possible I’ve just been using Python wrong all these years and that tinkering with your code in an interactive shell is a poor choose of development processes.
What do you prefer for your Python development? Does an interactive shell matter to you? Did you start with IDLE and move to a more mature IDE. Which IDE did you end up with and what kind of compromises did you make during that change. Let us know in the comments below.
[Michael Becker] has been using FreeRTOS for about seven years. He decided to start adding some features and has a very interesting C++ class wrapper for the OS available.
Real Time Operating Systems (RTOS) add functionality for single-thread microcontrollers to run multiple programs at the same time without threatening the firmware developer’s sanity. This project adds C++ to the rest of the FreeRTOS benefits. We know that people have strong feelings one way or the other about using C++ in embedded systems. However, as the 24 demo projects illustrate, it is possible.
One nice thing about the library is that it is carefully documented. A large number of examples don’t hurt either. The library is clean with just under 30 classes. It seems to have resisted the trend of having classes for everything. You know the kind of library we mean. To create an Integer object, just build a configuration object to pass to the class factory generator which…. This library doesn’t entertain any of that. It has simple abstractions around threads and timers, queues, and mutexes.
One of the issues with getting started with any Arm-based project is picking a toolset. Some of us here just use the command line with our favorite editor, but we know that doesn’t suit many people–they want a modern IDE. But which one to choose? User [Wassim] faced this problem, evaluated six different options for STM32 and was kind enough to document his findings over on Hackaday.io.
Many of the tools are Windows-only and at least two of them are not totally free, but it is still a good list with some great observations. Of course, the choice of an IDE is a highly personal thing, but just having a good list is a great start.
Lisp is one of those interesting computer languages that you either love or hate. But it has certainly stood the test of time. Of all the ancient languages that are still in practical use, only FORTRAN is older, and only by one year. If you ever wanted to learn Lisp, [Kanaka] has an interesting approach: Study how to build your own Lisp in your favorite language.
What if your favorite language is something obscure? [Kanaka’s] GitHub page has no fewer than 64 different implementations of Mal (Make a Lisp), each in a different language. Unsurprisingly, C and Python are on the list. However, so is Forth and Go and Awk. Not strange enough for you? How about Make? Yes, Make, like you use to build programs. Bash, Postscript, and even VHDL have entries, although–surprisingly–no Verilog; we don’t know why.
Each implementation of Mal is separated into eleven incremental, self-contained, and testable steps that demonstrate core concepts of Lisp. The last step can actually run a copy of itself–typical for a mind-bending language like Lisp. There is a guide to help you navigate through the process in the language of your choice. The suggestion is to not look at the code in the repository until after you’ve written it yourself. You can see [Kanaka] (also known as [Joel Martin]) giving a recent talk about the Mal process in the videos below.
If you’ve looked at machine learning, you may have noticed that a lot of the examples are interesting but hard to follow. That’s why [Jostmey] created Naked Tensor, a bare-minimum example of using TensorFlow. The example is simple, just doing some straight line fits on some data points. One example shows how it is done in series, one in parallel, and another for an 8-million point dataset. All the code is in Python.
If you haven’t run into it yet, TensorFlow is an open source library from Google. To quote from its website:
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
We all know that hacker that won’t use a regular compiler. If he’s not using assembly language, he uses a compiler he wrote. If you don’t know him, maybe it is you! If you really don’t know one, then meet these two. [Nathan Fuller] and [Andy Baldwin] want to encourage you to write your own 3D slicer.
Their post is very detailed and uses Autodesk Dynamo as a graphical programming language. However, the details aren’t really specific to Dynamo. It is like a compiler. You sort of know what it must be doing, but until you’ve seen one taken apart, there are a lot of subtleties you probably wouldn’t think of right away if you were building one from scratch.