[Edit April 25 2019: Please note this is not an iPhone or Android app, and I have no plans to release it as such. You can use your phone or any other camera to take pictures of your ground coffee, but then you need to install the application on either OS X or through Python (on any operating system) to analyze the data. Download the application package here.]
At the present time I would tackle to contemporary an OS X software I if truth be told were developing for a couple of months. It turns out writing Python instrument for coffee is a spacious arrangement to relax after a day of writing Python instrument for astrophysics.
When I started being drawn to brewing distinctiveness coffee a couple of years within the past, one of many first issues that annoyed me changed into as soon as our inability to counsel grind sizes for a range of coffee brewing strategies, or to envision the good of varied grinders in an purpose arrangement. Sure, some laboratories have laser diffraction equipment that can measure the size of all particles popping out of a grinder, but rare are the coffee geeks which have get admission to to these multi-hundred thousands of bucks kinds of equipment.
Before every part, I made up my mind to use photography of my coffee grounds spread on a white sheet, and to use an worn fragment of instrument called ImageJ, developed by the Nationwide Institutes of Health mainly to analyze microscope photos, to create a distribution of the sizes of my coffee grounds. This labored decently properly, and allowed me to delivery up comparing varied grinders. Then Scott Rao made me realize that a stand-by myself software that doesn’t need an progressed set up and that is dedicated to coffee would be of interest to many contributors within the coffee industry. Potentially loyal the 10% geekiest of them, but that’s frosty.
I’m hoping that this software will again us perceive the results of particle dimension distributions on the style of coffee. I don’t mediate the industry if truth be told stored us within the loop with the total laser diffraction experiments, so optimistically we’re going to again ourselves as a group.
Must likelihood is you’ll per chance per chance even be drawn to measuring the particle dimension distribution of your grinder, then this app is for you ‒ and it’s free. I placed it as “delivery provide” on GitHub, so if likelihood is you’ll per chance per chance even be a developer, likelihood is you’ll per chance per chance even be welcome to send me solutions within the blueprint of push requests (the developers will know what that technique).
In expose so that you can delivery up, I counsel you learn this rapid set up recordsdata, that would point to tips on how to download the app and lumber it even supposing I am no longer a registered Apple Developer. Then, likelihood is you’ll per chance per chance also catch to both learn this rapid summary that will get you working with the fundamentals, or this very detailed and wordy individual handbook that will recordsdata you thru the total detailed alternatives the software gives you.
I would tackle to screen you an example of what is also done with the instrument. Beneath, I am comparing the particle dimension distribution of the Baratza Forté grinder, which makes use of 54 mm flat steel burrs, with that of the Lido 3 hand grinder, which makes use of 48 mm conical steel burrs. I dwelling each grinders in one arrangement that produces a same height of common-sized particles with diameters around 1 mm, but as likelihood is you’ll per chance per chance also search for, the particle dimension distributions are very varied ! The Forté generates arrangement much less fines (with diameters below 0.5 mm) and a cramped bit much less boulders (with diameters of roughly 2 mm), which is indicative of a bigger good grinder.
For now, the app is handiest supposed to be outdated on OS X computers. But while likelihood is you’ll per chance per chance even be working every other more or much less machine and know your arrangement around Python, likelihood is you’ll per chance per chance also at all times download it straight from GitHub and lumber it along with your private set up of Python 3.
I would tackle to thank Scott Rao for his pleasure when I shared this project belief with him, and for beta testing the instrument. I would furthermore tackle to thank Alex Levitt, Mitch Hale, Caleb Fischer, Francisco Quijano and Victor Malherbe for beta testing the instrument.