Hey there! I just started using Fermentrack and I really like it! I could make use right away of my brand new Arduino Uno and the temperature control looks really good. I love that my Tilt was built in and easy to integrate.
Theree minor questions / observations:
- On the Arduino, the OneWire temperature devices need to go on pin A2. I had to dig to find that out, is there maybe a place to prompt people that A2 is the pin on the Arduino for OneWire?
That's awesome! I'm glad you like it!
Unfortunately, with the Arduino there are a number of different versions of the firmware floating around out there, many of which use different pins. I can add some notes to the Fermentrack documentation pointing people to sample pinouts, but there isn't a way to detect what pin configuration a specific build is using.
-The Tilt SG shows up on the dashboard trend with 2 decimal places (the axis just shows 2 decimals). What would be involved in getting it to show up with three decimals?
This will be fixed in an upcoming release of Fermentrack:
I'm guessing it's a few weeks out at this point, but I may release it sooner if I get all the features complete & tested.
-My beer log started with a couple of out-of-whack beer temperatures for unknown reasons a zero and a 138 or thereabouts). This zooms the temperature trend out quite a lot. I haven't managed to find where the brew data is being stored so I can edit those points and fix the zoom issue. Is it on a sqlite database somewhere!
Nope. Originally I
wanted it to be in a sqlite database, but found that after a few weeks of data had been stored, retrieving the data points would cause the graph to take several minutes to appear. Not good.
Instead, it's stored across three files - two CSV and an annotations file that is almost JSON. (It's JSON without the final, closing brackets)
In a default installation, the data files are stored here:
/home/fermentrack/fermentrack/data
As a side note, if anyone out there is an expert using pandas and would like to write some kind of "cleanup" script that could be used to delete data points that are outliers, I can certainly see where that would be helpful for situations like this. I've not yet managed to wrap my head around how to successfully use pandas, so it's not something I see myself creating any time soon, sadly.
If you have any other questions, don't hesitate to ask!