Heater Joins The Internet Of Things With ESP32 Board

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.

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Simulate Your World With Hash.ai

We will admit that we often throw together software simulations of real-world things, but we’ll also admit they are usually quick and dirty and just dump out text that we might graph in a spreadsheet or using GNUPlot. But with Hash.ai, you can quickly generate simulations of just about anything quickly and easily. The simulations will have beautiful visualizations and graphs, too. The tool works with JavaScript or Python and you don’t have to waste your time writing the parts that don’t change.

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.

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The Heat Of The Moments – Location Visualization In Python

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.

[via r/dataisbeautiful]

Watch Earthquake Roll Across A Continent In Seismograph Visualization Video

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.

If this stirred up interest in seismology, you can join in the fun of networked seismic data. A simple seismograph can be built from quite humble components, but of course there are specially designed chips for the task as well.

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A Baby’s First Year In Data, As A Blanket

New parents will tell you that a baby takes a few months to acquire something close to a day/night sleep pattern, and during that time Mom and Dad also find their sleep becomes a a rarely-snatched luxury. [Seung Lee] has turned this experience into a unique data visualisation, by taking the sleep pattern data of his son’s first year of life and knitting it into a blanket.

The data was recorded using the Baby Connect app, from which it was exported and converted to JSON. This was in turn fed to some HTML/Javascript which generated a knitting pattern in a handy format that could be displayed on any mobile or portable device for knitting on the go. The blanket was then knitted by hand as a series of panels that were later joined into one, providing relief as the rows lined up.

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.)

This blanket was hand-knitted, but it’s not the first knitted project we’ve seen. How about a map of the Universe created on a hacked knitting machine?

Let Your Pi Make A Pie Chart For Your Pie

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.

This project comes from [Mike MacHenry], and it’s just as straightforward as it looks. Put simply, he’s using a load cell connected to the Raspberry Pi to weigh an actual pie and monitor its change over time. As the pie is consumed by hungry hackers, a pie graph (what else?) is rendered on the attached screen to show you how much of the dessert is left.

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.

We’ve also got to give [Mike] extra credit for including the recipe and procedure for actually baking the apple pie used in the project. While we’re not 100% sure the MIT license [Mike] used is actually valid for foodstuffs, but believe it or not this isn’t the first time we’ve seen Git used in the production of baked goods.

Imaging The Neighborhood With Solar Panels

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.

Expected versus actual power output.

[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.

We’ve seen plenty of solar hacks, but this one has to be something of a first. Usually people are more worried about maximizing efficiency or tracking the sun with them.