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Autotune is a tool to help calculate potential adjustments to ISF, carb ratio, and basal rates.
The easiest way to run Autotune¶
The easiest way to run Autotune, if you don’t have an OpenAPS rig, is to use “AutotuneWeb”. It’s a website where you enter your Nightscout URL, confirm your profile, and get results emailed directly to you. Click here to go use AutotuneWeb.
What to expect when using AutotuneWeb¶After you check your Nightscout profile to make sure it’s up to date, and submit your URL, it will take you to the profile page. You should check again and make sure it’s pulling from a current profile. This is where you can tell it what type of insulin you’re using; how many days to run (up to 30, we recommend at least 7 to start); and provide your email address to get the results emailed to you.
When you get your email (note it may take 20 minutes), it will reference your NS URL at the top of the page and the date range you ran it on. The text will also tell you whether you ran with UAM on for basals.
On the left, you’ll see your starting values from your current NS profile; on the right is the tuned recommendation from Autotune.
Below the ISF and carb ratio, you’ll see the basal report.
If it’s your first time using AutotuneWeb:¶
Other sections on this page¶![]()
How Autotune works¶
There are two key pieces: oref0-autotune-prep and oref0-autotune-core. (For more autotune code, you can see oref0-autotune-(multiple files) listed in oref0/bin here - and there are also some autotune files in oref0/lib.
Note: Autotune does not read from the active profile (e.g. Pattern A or Pattern B if set). The Standard Basal Pattern is what will be pulled to be used and tuned by Autotune.
The difference between autotune and autosens:¶
Autosensitivity/resistance mode (aka “autosens”) is an advanced feature in OpenAPS that you can enable that looks at 24 hours of data and makes adjustments to ISF and targets based on the resulting sensitivity calculations. If you have a dying pump site, or have been sick and are resistant, your ISF is likely to be calculated down by autosens and then used in OpenAPS calculations accordingly. The opposite for being more sensitive is true as well. (Here’s a blog post describing autosensitivity during sick days.)
Autotune, by contrast, is designed to iteratively adjust basals, ISF, and carb ratio over the course of weeks. Because it makes changes more slowly than autosens, autotune ends up drawing on a larger pool of data, and is therefore able to differentiate whether and how basals and/or ISF need to be adjusted, and also whether carb ratio needs to be changed. Whereas we don’t recommend changing basals or ISF based on the output of autosens (because it’s only looking at 24h of data, and can’t tell apart the effects of basals vs. the effect of ISF), autotune is intended to be used to help guide basal, ISF, and carb ratio changes because it’s tracking trends over a large period of time. See below for how it can be used as a manual one-off calculation or in a closed loop setting, along with notes about the safety caps designed to go with it.
Different ways to utilize Autotune¶
Phase A: Running Autotune manually in OpenAPS¶
If you have an OpenAPS rig and want to run autotune manually, you can do so on the command line.
Running manually in your myopenaps directory¶
If you want to have OpenAPS use your autotune results (e.g. you changed pump settings and just want it to be tuned sooner than 4am), run the following:
Running manually in a different directory to not use the results automatically¶
You will want to run Autotune in a different directory on your rig if you do not want OpenAPS to use the autotune settings by default.
Note: If you did this correctly in your
newdirectory , settings will not be used by OpenAPS. You will need to cd~/newdirectory/autotune&&catautotune_recommendations.log to see your autotune recommendations, and autotune will only run when you manually run it. The recommended behavior is to run Autotune inside of your OpenAPS directory, per Phase B, which is the default and will automatically run every night and have OpenAPS use the settings from Autotune automatically.
Phase B: Running Autotune automatically in OpenAPS¶
In oref0 0.6.0 and beyond, autotune will run by default. This means that autotune would be iteratively running (as described in #261) and making changes to the underlying basals, ISF, and carb ratio being used by the loop, making small adjustments from the previously autotuned settings based on each day’s new data. However, there are safety caps (your autosens_max and autosens_min) in place to limit the amount of tuning that can be done at any time compared to the underlying pump profile. The autotune_recommendations will be tracked against the current pump profile, and if over time the tuning constantly recommends changes beyond the caps, you can use this to determine whether to tune the basals and ratios in those directions.
Important When autotune is enabled in your loop to run automatically, changes to your basal profile within the pump during the middle of the day will NOT cause an immediate change to the basal profile the loop is using. The loop will continue to use your autotune-generated profile until a new one is updated just after midnight each night. Each autotune nightly run will pull the current pump profile as its baseline for being able to make adjustments. If you have reason to want a want a mid-day change to your basal program immediately, you should run autotune manually (see A for directions) to have it re-pull the settings from the pump and tune from the new settings.
How to copy over autotune files from another rig:¶
Log into the NEW rig and run the following command:
scp-r[email protected]:~/myopenaps/autotune/~/myopenaps/autotune (where “my-edison-original” is substituted for your rig name that you want to copy files from)
Phase C: Running Autotune for suggested adjustments without an OpenAPS rig¶
Note:* the easiest way of running Autotune is now “AutotuneWeb”. See the top of this page for instructions on running it via the web service, without having to set it up on your own computer. If you do want to manually set up your own computer to be able to run it over a time period >30 days or other reasons, see below.
Caution for AndroidAPS users: Currently, the master oref0 version with Autotune does not parse AndroidAPS entries correctly. You must set AndroidAPS to upload all temp basals as “absolute” rates, instead of %, and use the dev branch of oref0. If you do not do both of these things, your results will be wrong! Future versions of Autotune will allow using AndroidAPS data as long as the option to upload temp basals as absolute values instead of / in addition to percent is enabled in AndroidAPS.
If you are not running autotune as part of a closed loop, you can still run it as a “one-off”.(OpenAPS/existing oref0 users may want to use the above instructions instead, however, from phase A or phase B on this page.) For more about autotune, you can read Dana’s autotune blog post for some background/additional detail and scroll up in the page to see more details about how autotune works.
Requirements: You should have Nightscout BG and treatment data. If you do not regularly enter carbs (meals) into Nightscout (this happens automatically when you use the “Bolus Wizard” on the Medtronic pump and should not be manually added to Nightscout if you use the Bolus Wizard), autotune will try to raise basals at those times of days to compensate. However, you could still look at overnight basal recommendations and probably even ISF recommendations overall. Read this page for more details on what you should/not pay attention to with missing data.
Note: this is currently based on one ISF and carb ratio throughout the day. Here is the issue if you want to keep track of the work to make autotune work with multiple ISF or carb ratios.
Feedback: Please note autotune is brand new, and still a work in progress (WIP). Please provide feedback along the way, or after you run it. You can share your thoughts in Gitter, or via this short Google form.
Step 0: Decide where to run Autotune
Step 1a: Run via a cloud-based virtual machine
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Step 1b: Run via a Windows-based virtual machine
Step 1c: Prep your Mac
Mac install commands:
Step 1d: Run on a Windows 10 computer using the Windows Subsystem for Linux (WSL)
Step 2: Install oref0
Step 3: Create a profile.json with your settings
Every comma, quote mark, and bracket matter on this file, so please double-check carefully.
Step 4: Run autotune on retrospective data from Nightscout
Step 5: Upload resulting profile to Nightscout
Step 5a: Upload resulting profile to Nightscout and switch to it
Optional configurations¶
Re-Running Autotune¶
Remember, to initially set-up Autotune follow the instructions above
To subsequently re-run Autotune at a later time:
Why Isn’t It Working At All?¶
(First - breathe, and have patience!) Here are some things to check:
If you get the error
ERROR:API_SECRETisnotsetwhencallingoref0-autotune.sh and autotune won’t run, try this (note: as of oref 0.5.5, this error has been downgraded to a warning as this will only prevent autotune from running if you have “locked down” your NS to prevent anonymous read access):
To test this fix, type
echo$API_SECRET and hit enter. If this returns the API Secret that you set in the environment file, then it should work for you to run autotune.
Other things to check:
What does this output from autotune mean?¶
Go here to read more about understanding the output, to see an example visual of what the output might look like, and scenarios when you may want to disregard portions of the output based on the data you provide it.
Remember, autotune is still a work in progress (WIP). Please provide feedback along the way, or after you run it. You can share your thoughts in Gitter, or via this short Google form.
(If you have issues running it, questions about reviewing the data, or want to provide input for direction of the feature, please comment on this issue in Github.)
Yay, It Worked! This is Cool!¶
Great! We’d love to hear if it worked well, plus any additional feedback - please also provide input via this short Google form and/or comment on this issue in Github for more detailed feedback about the tool. You can also help us tackle some of the known issues and feature requests listed here.
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Next: Understanding autotune
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