Wires Vs Words — PCB Routing In Python

March 30, 2021 by Chris Lott 30 Comments

Preferring to spend hours typing code instead of graphically pushing traces around in a PCB layout tool, [James Bowman] over at ExCamera Labs has developed CuFlow, a method for routing PCBs in Python. Whether or not you’re on-board with the concept, you have to admit the results look pretty good.

Key to this project is a concept [James] calls rivers — the Dazzler board shown above contains only eight of them. Connections get to their destination by taking one or more of these rivers which can be split, joined, and merged along the way as needed in a very Pythonic manner. River navigation is performed using Turtle graphics-like commands such as left(90) and the appropriately named shimmy(d)that aligns two displaced rivers. He also makes extensive use of pin / gate swapping to make the routing smoother, and there’s a nifty shuffler feature which arbitrarily reorders signals in a crossbar manner. Routing to complex packages, like the BGA shown, is made easier by embedding signal escapes for each part’s library definition.

We completely agree with [James]’s frustration with so many schematics these days being nothing more than a visual net lists, not representing the logical flow and function of the design at all. However, CuFlow further obfuscates the interconnections by burying them deep inside the wire connection details. Perhaps, if CuFlow were melded with something like the SKiDL Python schematic description language, the concept would gain more traction?

That said, we like the concept and routing methodologies he has implemented in CuFlow. Check it out yourself by visiting the GitHub repository, where he writes in more detail about his motivation and various techniques. You may remember [James] two of his embedded systems development tools that we covered back in 2018 — the SPI Driver and the I2C driver.

An Algorithm For Art: Thread Portraits

March 18, 2021 by Kristina Panos 27 Comments

We’ve all been there — through the magic of the internet, you see someone else’s stunning project and you just have to replicate it. For [Jenny Ma], that project was computer-generated string art, as in the computer figures out the best nail order to replicate a given image, and you lay out the thread yourself.

So, how does it work? Although a few algorithms are out there already, [Jenny] wanted to make her own using Python. Essentially it crops the image into a circle and then lays out evenly-spaced software nails around the circumference. The algorithm starts from a random nail and then determines the best next nail to wrap around by drawing a line from that nail to every other nail and choosing the darkest one based on the darkness of the image underneath that little line. It repeats this one chord at a time, subtracting from the original image until every pixel has been replaced with a thread or lack thereof, and then it spits out an ordered list of nail numbers.

Once the software was ready, [Jenny] made a wood canvas that’s 80 cm (31.5″) in diameter and started laying out the nail hole locations. There wasn’t quite enough room for 300 nails, so instead of starting over, [Jenny] changed the algorithm to use 298 nails and re-ran it.

[Jenny] does a great job of discussing the many variables at play in this hardware representation of software-created art. The most obvious of course is that the more nails used, the higher the resolution would be, but she determined that 300 is the sweet spot — more than that, and the resolution doesn’t really improve. We have to wonder if 360 nails would make things any easier. Check out the build video after the break.

Want to cut out most of the manual labor altogether? Build yourself a string art machine.

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Custom Dummy Load With Data Logging

March 15, 2021 by Bryan Cockfield 10 Comments

While it might seem counterintuitive on the surface, there are a number of cases where dumping a large amount of energy into a resistor simply to turn it into heat is necessary to the operation of a circuit. Most of these cases involve testing electronic equipment such as power supplies or radio transmitters and while a simple resistor bank can be used in some situations, this active dummy load is comprised of different internals has some extra features to boot.

The load bank built by [Debraj] is actually an electronic load, which opens it up for a wider set of use cases than a simple passive dummy load like a resistor bank. It’s specifically designed for DC and also includes voltage measurement, current control, and temperature measurement and speed control of the fans on the heat sinks. It also includes a Bluetooth module that allows it to communicate to a computer using python via a custom protocol and GUI.

While this one does use a case and some other parts from another product and was specifically built to use them, the PCB schematics and code are all available to build your own or expand on this design. It’s intended for DC applications, but there are other dummy loads available for things such radio antenna design, and it turns out that you can learn a lot from them too.

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Tetris On Split-Flap Go Brrr

March 7, 2021 by Kristina Panos 9 Comments

It hardly seems possible, but engineer collective and split-flap display purveyors [Oat Foundry] were able to build a working implementation of Tetris on a 10 x 40 split-flap display in the span of a single day. Check it out in the video after the break.

This project is a bit understaffed in the details department, but we do know that [Oat Foundry] started with [Timur Bakibayev]’s open-source implementation of Tetris in Python and modified the draw function to work on a split-flap display. As you may have guessed, the biggest obstacle is the refresh rate and how it affects playability — particularly during those tense moments when a player rotates a piece before dropping it. Split-flaps flip quickly from on to off, but flipping back to on requires a full trip around through all the other characters.

We think this is nice work for a one-day build. Should they go further, we’d like to see the same things implemented as [Oat Foundry] does: a high score tracker and a preview of the next piece.

Don’t have a split-flap display? Yeah, us either, but we do have televisions. Turn on the tube and check out this Nano-scale Tetris.

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Circuit Impedance Calculations Without Cumbersome Simulations

March 2, 2021 by Bryan Cockfield 11 Comments

Using circuit simulating software like SPICE can be a powerful tool for modeling the behavior of a circuit in the real world. On the other hand, it’s not always necessary to have all of the features of SPICE available all the time, and these programs tend to be quite expensive as well. To that end, [Wes Hileman] noticed an opportunity for a specific, quick method for performing impedance calculations using python without bulky, expensive software and came up with a program which he calls fastZ.

The software works on any network of passive components (resistors, capacitors, and inductors) and the user can specify parallel and series connections using special operators. Not only can the program calculate the combined impedance but it can perform frequency analysis at a specified frequency or graph the frequency response over a wide range of frequencies. It’s also running in python which makes it as simple as importing any other python package, and is also easy to implement in any other python program compared to building a simulation and hoping for the best.

If you find yourself regularly drawing Bode plots or trying to cobble together a circuit simulation to work with your python code, this sort of solution is a great way to save a lot of headache. It is possible to get the a piece of software like SPICE to to work together with other python programs though, often with some pretty interesting results.

Python Settles Bet About Best Strategy In Children’s Board Game

February 21, 2021 by Donald Papp 17 Comments

Simulating a tabletop game can be done for several reasons: to play the game digitally, to create computer opponent(s), or to prove someone wrong. In [Everett]’s case, he used Python to prove which adult was right about basic strategy in a children’s game.

[Everett]’s 5-year-old loves a simple game called Hoot Owl Hoot! in which players cooperatively work to move owls along a track to the safety of a nest. Player pieces move on spaces according to the matching colors drawn from a deck of cards. If a space is already occupied, a piece may jump ahead to the next available spot. The game has a bit more to it than that, but those are the important parts. After a few games, the adults in the room found themselves disagreeing about which strategy was optimal in this simple game.

It seemed to [Everett] that it was best to move pieces in the rear, keeping player pieces grouped together and maximizing the chance of free moves gained by jumping over occupied spaces. [Everett]’s wife countered that a “longest move” strategy was best, and one should always select whichever piece would benefit the most (i.e. move the furthest distance) from any given move. Which approach wins games in the fewest moves? This small Python script simulates the game enough to iteratively determine that the two strategies are quite close in results, but the “longest move” strategy does ultimately come out on top.

As far as simulations go, it’s no Tamagotchi Singularity and [Everett] admits that the simulation isn’t a completely accurate one. But since its only purpose is to compare whether “no stragglers” or “longest move” wins in fewer moves, shortcuts like using random color generation in place of drawing the colors from a deck shouldn’t make a big difference. Or would it? Regardless, we can agree that board games can be fitting metaphors for the human condition.

Interfacing A Z80 CPU With The Raspberry Pi

February 9, 2021 by Lewin Day 25 Comments

The Z80 was a big deal in the 1970s and 1980s, and while its no longer a dominant architecture today, its legacy lives on. [James Andrew Fitzjohn] is a fan of the Z, and decided to interface the real silicon with the Raspberry Pi, by and large for the fun of it!

The Z80’s address and data lines, as well as the clock, are hooked up to the Raspberry Pi through several MCP23017 GPIO expanders. The Pi’s GPIO lines aren’t known for their speed, of course, and using expanders through I2C isn’t exactly quick either. However, speed isn’t necessary, as the clock only goes as fast as the Raspberry Pi desires, since it’s controlling the clock along with everything else. There’s also an LCD for viewing the Z80s status, along with some era-appropriate blinkenlights.

This setup allows the Pi to run code directly on the Z80 itself, while managing the CPU’s RAM in its own memory, all through a Python script. It’s a fun hack that lets you run retro code on retro silicon without using an emulator. Techniques like these are useful for finding undocumented or edge case performance of a processor. If this hack isn’t enough Zilog for your liking, consider throwing one in your pocket as well!