xDrip VS orange

https://github.com/NightscoutFoundation/xDrip

xDrip+ is an unofficial and independent Android app which works as data hub and processor between many different devices.

It supports wireless connections to G4, G5, G6, G7, Medtrum A6, Libre via NFC and Bluetooth, 630G, 640G, 670G pumps, CareSens Air and Eversense CGM via companion apps. Bluetooth Glucose Meters such as the Contour Next One, AccuChek Guide, Verio Flex & Diamond Mini as well as devices like the Pendiq 2.0 Insulin Pen.

Heart-rate and step counter data is processed from Android Wear, Garmin, Fitbit and Pebble smart-watches and watch-faces for those that show glucose values and graphs.

On some Android Wear watches, it is possible for the G5 or G6 to talk directly to the watch so it can display values even when out of range of the phone.

The app contains sophisticated charting, customization and data entry features as well as a predictive simulation model.

xDrip, for sure, if you have an Android and a Dexcom. Diabetes-m is a pretty handy calculator, tracker and it has calorie/carb tracking. Tidepool is another that's a neat tool.

I'm using xdrip+ with cgm, which can help with it a lot, but theyre bolus recommendation isn't very user friendly. I think they are trying not to give recommendations because of the same fda issues. https://github.com/NightscoutFoundation/xDrip

linked from https://github.com/NightscoutFoundation/xDrip

https://apps.apple.com/gb/app/shuggah/id1586789452 for iOS or https://github.com/NightscoutFoundation/xDrip for android.

I know I've tooted its horn before, but Orange3 is a pretty neat Python-based GUI platform that makes this and a metric buttload of other statistical/ML techniques available to non-programmer types.

Just watch out for null character `x00` in the corpus. That always seems to kill it stone dead.

https://orangedatamining.com/

https://orange3.readthedocs.io/projects/orange-visual-progra...

For all you folks who aren't ace programmer types, the Orange3[1] platform gives you a very miniaturized[2] ability to turn out these sorts of visualizations very rapidly. It's not the most stable thing in the world, but the node-based ML workflow designer is worth the price of admission all by itself.

[1] https://orangedatamining.com/

[2] The Wikipedia extension in Text limits each search result to 25 articles, so sucking all of Wikipedia is . . well, Orange text analytics crashes when I look at it sideways with a null character, so let's not think about what would happen.

First is identifying the "similar" things in a corpus. Best way I know to do that, for non-programmer audiences, is the Orange Data Mining tool, which gives you a node-based text mining interface to perform statistical analysis on text. Hierarchical Clustering shows - very rapidly - how similar your "modules" are, which ones are most similar. There's many other techniques (semantic viewer, similarity hash, etc) as well - the right one will depend on how your content is laying about.

Both tools are ripoffs of a data mining framework named Orange 3

You should also check out Orange Data Mining, it allows to create a lot of charts, filter data from a chart to another, build ML models, predictions and a lot more. And you can do it with zero code.

You can start with a free GUI based tool Orange. It is a component based data science workflow tool, which you can use to handle 60-75% of the traditional data science tasks from classification, regression, to basic neural networks.