The wood-burning heater [g3gg0] has at home works perfectly, except for one flaw: the pellet reservoir needs to be manually refilled every few days. Humans being notoriously unreliable creatures, this critical task is sometimes overlooked, which naturally leads to literally chilling results.
With automatic fill systems expensive and difficult to install, [g3gg0] wanted to find some kind of way for the heater to notify its caretakers about any potential fault conditions. Not just the fact that it was out of fuel (though that would naturally be the most common alert), but any other issue which would potentially keep the heater from doing it’s job. In short, the heater was going to get a one-way ticket to the Internet of Things.
As it turns out, this task was not quite as difficult as you might expect. The Windhager heater already had upgrade bays where the user could insert additional modules and sensors, as well as a rudimentary data bus over RS-485. All [g3gg0] had to do was tap into this bus, decode what the packets contained, and use the information to generate alerts over the network. The ESP32 was more than up to the task, it just needed a custom PCB and 3D printed enclosure that would allow it to slot into the heater like an official expansion module.
When an interesting message flashes across the bus, the ESP32 captures it and relays the appropriate message to an MQTT broker. From there, the automation possibilities are nearly endless. In this case, the heater’s status information is being visualized with tools like Grafana, and important alerts are sent out to mobile devices with PushingBox. With a setup like this, the Windhager will never go hungry again.
The web-based tool works on the idea of agents. Each agent has one or more behaviors that run each time step. In the example simulation, which models wildfires in forests, the agent is named forest, although it really models one virtual tree. There’s also a behavior called forest which controls the tree’s rate of growth and chance of burning based on nearby trees and lightning. Other behaviors simulate a burning tree and what happens to a tree after burning — an ember — which may or may not grow back.
Have you ever taken a look at all the information that Google has collected about you over all these years? That is, of course, assuming you have a Google account, but that’s quite a given if you own an Android device and have privacy concerns overruled by convenience. And considering that GPS is a pretty standard smartphone feature nowadays, you shouldn’t be surprised that your entire location history is very likely part of the collected data as well. So unless you opted out from an everchanging settings labyrinth in the past, it’s too late now, that data exists — period. Well, we might as well use it for our own benefit then and visualize what we’ve got there.
Location data naturally screams for maps as visualization method, and [luka1199] thought what would be better than an interactive Geo Heatmap written in Python, showing all the hotspots of your life. Built around the Folium library, the script reads the JSON dump of your location history that you can request from Google’s Takeout service, and overlays the resulting heatmap on the OpenStreetMap world map, ready for you to explore in your browser. Being Python, that’s pretty much all there is, which makes [Luka]’s script also a good starting point to play around with Folium and map visualization yourself.
While simply just looking at the map and remembering the places your life has taken you to can be fun on its own, you might also realize some time optimization potential in alternative route plannings, or use it to turn your last road trip route into an art piece. Just, whatever you do, be careful that you don’t accidentally leak the location of some secret military facilities.
If your only exposure to seismologists at work is through film and television, you can be forgiven for thinking they still lay out rolls of paper to examine lines of ink under a magnifying glass. The reality is far more interesting in a field that has eagerly adopted all available technology. A dramatic demonstration of modern earthquake data gathering, processing, and visualization was Tweeted by @IRIS_EPO following a central California quake on July 4th, 2019. In this video can see the quake’s energy propagate across the continental United States in multiple waves of varying speed and intensity. The video is embedded below, but click through to the Twitter thread too as it has a lot more explanation.
The acronym IRIS EPO expands out to Incorporated Research Institutions for Seismology, Education and Public Outreach. We agree with their publicity mission; more people need to know how cool modern seismology is. By combining information from thousands of seismometers, we could see forces that we could not see from any individual location. IRIS makes seismic data available to researchers (or curious data science hackers) in a vast historical database or a real time data stream. Data compilations are presented in several different forms, this particular video is a GMV or Ground Motion Visualization. Significant events like the 4th of July earthquake get their own GMV page where we can see additional details, like the fact this visualization compiled data from 2,132 stations.
The finished product shows very well the progression as the youngster adapts to a regular sleep pattern, and even shows a shift to the right at the very bottom as a result of a trip across time zones to see relatives. It’s both a good visualisation and a unique keepsake that the baby will treasure one day as an adult. (Snarky Ed Note: Or bring along to the therapist as evidence.)
March 14th is “Pi Day”, for reasons which should be obvious to our more mathematically inclined readers. As you are not reading this post on March 14th, that must mean we’re either fashionably late to Pi Day 2019, or exceptionally early for Pi Day 2020. But in either event, we’ve got a hack for you that celebrates the day using two things we have it on good authority most hackers overindulge in: food and needless complexity.
One might say that this project takes a three dimensional pie and converts it to a two dimensional facsimile, but perhaps that’s over-analyzing it. In reality, it was a fun little hack [Mike] put together just because he thought it would be fun. Which is certainly enough of a motive for us. More practically though, if you’re looking for a good example for how to get a load cell talking to your non-edible Raspberry Pi, you could do worse than checking this out.
Like many people who have a solar power setup at home, [Jeroen Boeye] was curious to see just how much energy his panels were putting out. But unlike most people, it just so happens that he’s a data scientist with a deep passion for programming and a flair for visualizations. In his latest blog post, [Jeroen] details how his efforts to explain some anomalous data ended with the discovery that his solar array was effectively acting as an extremely low-resolution camera.
It all started when he noticed that in some months, the energy produced by his panels was not following the expected curve. Generally speaking, the energy output of stationary solar panels should follow a clear bell curve: increasing output until the sun is in the ideal position, and then decreasing output as the sun moves away. Naturally cloud cover can impact this, but cloud cover should come and go, not show up repeatedly in the data.
[Jeroen] eventually came to realize that the dips in power generation were due to two large trees in his yard. This gave him the idea of seeing if he could turn his solar panels into a rudimentary camera. In theory, if he compared the actual versus expected output of his panels at any given time, the results could be used as “pixels” in an image.
He started by creating a model of the ideal energy output of his panels throughout the year, taking into account not only obvious variables such as the changing elevation of the sun, but also energy losses through atmospheric dispersion. This model was then compared with the actual power output of his solar panels, and periods of low efficiency were plotted as darker dots to represent an obstruction. Finally, the plotted data was placed over a panoramic image taken from the perspective of the solar panels. Sure enough, the periods of low panel efficiency lined up with the trees and buildings that are in view of the panels.