The (Data) Plot Thickens

You’ve generated a ton of data. How do you analyze it and present it? Sure, you can use a spreadsheet. Or break out some programming tools. Or try LabPlot. Sure, it is sort of like a spreadsheet. But it does more. It has object management features, worksheets like a Juypter notebook, and a software development kit, in case it doesn’t do what you want out of the box.

The program is made to deal with very large data sets. There are tons of output options, including the usual line plots, histograms, and more exotic things like Q-Q plots. You can have hierarchies of spreadsheets (for example, a child spreadsheet can compute statistics about a parent spreadsheet). There are tons of regression analysis tools, likelihood estimation, and numerical integration and differentiation built in.

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Winners Of Hackaday’s Data Loggin’ Contest: Bluetooth Gardening, Counting Cups, And Predicting Rainfall

The votes for Hackaday’s Data Loggin’ Contest have been received, saved to SD, pushed out to MQTT, and graphed. Now it’s time to announce the three projects that made the most sense out of life’s random data and earned themselves a $100 gift certificate for Tindie, the Internet’s foremost purveyor of fine hand-crafted artisanal electronics.

First up, and winner of the Data Wizard category, is this whole-garden soil moisture monitor by [Joseph Eoff]. You might not realize it from the picture at the top of the page, but lurking underneath the mulch of that lovely garden is more than 20 Bluetooth soil sensors arranged in a grid pattern. All of the data is sucked up by a series of solar powered ESP32 access points, and ultimately ends up on a Raspberry Pi by way of MQTT. Here, custom Python software generates a heatmap that indicates possible trouble spots in the garden. With its easy to understand visualization of what’s happening under the surface, this project perfectly captured the spirit of the category.