tconnectsync 0.5

Syncs Tandem t:connect pump data to Nightscout for the t:slim X2

tconnectsync

Tconnectsync synchronizes data one-way from the Tandem Diabetes t:connect web/mobile application to Nightscout.

If you have a t:slim X2 pump with the companion t:connect mobile Android or iOS app, this will allow your pump bolus and basal data to be uploaded to Nightscout automatically. The t:connect Android app, by default, uploads pump data to Tandem's servers every hour, but using this tool you can update the frequency to as low as every five minutes! This allows for nearly real-time (but not instantaneous) pump data updates, almost like your pump uploads data directly to Nightscout!

At a high level, tconnectsync works by querying Tandem's undocumented APIs to receive basal and bolus data from t:connect, and then uploads that data as treatment objects to Nightscout. It contains features for checking for new Tandem pump data continuously, and updating that data along with the pump's reported IOB value to Nightscout whenever there is new data.

When you run the program with no arguments, it performs a single cycle of the following, and exits after completion:

• Queries for basal information via the t:connect ControlIQ API.
• Queries for bolus, basal, and IOB data via the t:connect non-ControlIQ API.
• Merges the basal information received from the two APIs. (If using ControlIQ, then basal information appears only on the ControlIQ API. If not using ControlIQ, it appears only on the legacy API.)
• Queries Nightscout for the most recently created Temp Basal object by tconnectsync, and uploads all data newer than that.
• Queries Nightscout for the most recently created Bolus object by tconnectsync, and uploads all data newer than that.
• Uploads a single Nightscout Activity object representing the current IOB as reported by the pump.

If run with the --auto-update flag, then the application performs the following steps:

• Queries an API endpoint used only by the t:connect mobile app which returns an internal event ID, corresponding to the most recent event published by the mobile app.
• Whenever the internal event ID changes (denoting that the mobile app uploaded new data to synchronize), perform all of the above mentioned steps to synchronize data.

Setup

To get started, you need to choose whether to install the application via Pip, Pipenv, or Docker.

After that, you can choose to run the program continuously via Supervisord or on a regular interval with Cron.

Installation

First, create a file named .env containing configuration values. You should specify the following parameters:

# Your credentials for t:connect
TCONNECT_EMAIL='[e-mail]'
TCONNECT_PASSWORD='password'

# Your pump's serial number (numeric)
PUMP_SERIAL_NUMBER=11111111

# URL and API secret for Nightscout
NS_URL='https://yournightscouturl/'
NS_SECRET='apisecret'

# Current timezone of the pump
TIMEZONE_NAME='America/New_York'

These values can alternatively be specified via environment variables.

The .env file contains your t:connect username and password, Tandem pump serial number (which is utilized in API calls to t:connect), your Nightscout URL and secret token (for uploading data to Nightscout), and local timezone (the timezone used in t:connect).

Installation via Pip

This is the easiest method to install.

First, ensure that you have Python 3 with Pip installed on your Linux machine. Then, install tconnectsync from pip:

$ pip3 install tconnectsync

After this, you should be able to view tconnectsync's help with:

$ tconnectsync --help
usage: tconnectsync [-h] [--version] [--pretend] [-v] [--start-date START_DATE] [--end-date END_DATE] [--days DAYS] [--auto-update] [--check-login]

Syncs bolus, basal, and IOB data from Tandem Diabetes t:connect to Nightscout.

optional arguments:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  --pretend             Pretend mode: do not upload any data to Nightscout.
  -v, --verbose         Verbose mode: show extra logging details
  --start-date START_DATE
                        The oldest date to process data from. Must be specified with --end-date.
  --end-date END_DATE   The newest date to process data until (inclusive). Must be specified with --start-date.
  --days DAYS           The number of days of t:connect data to read in. Cannot be used with --from-date and --until-date.
  --auto-update         If set, continuously checks for updates from t:connect and syncs with Nightscout.
  --check-login         If set, checks that the provided t:connect credentials can be used to log in.

Go to the folder where you created the .env file, and run:

$ tconnectsync --check-login

If you receive no errors, then you can move on to the Running Tconnectsync Continuously section.

Installing with Pipenv

You can run the application using Pipenv.

First, ensure you have Python 3 and pip installed, then install pipenv with pip3 install pipenv.

Clone the Git repository for tconnectsync and cd into it with:

$ git clone https://github.com/jwoglom/tconnectsync
$ cd tconnectsync

Then install tconnectsync's dependencies with pipenv install. Afterwards, you can launch the program with pipenv run tconnectsync so long as you are inside the checked-out tconnectsync folder.

$ git clone https://github.com/jwoglom/tconnectsync && cd tconnectsync
$ pip3 install pipenv
$ pipenv install
$ pipenv run tconnectsync --help
usage: main.py [-h] [--version] [--pretend] [-v] [--start-date START_DATE] [--end-date END_DATE] [--days DAYS] [--auto-update] [--check-login]

Syncs bolus, basal, and IOB data from Tandem Diabetes t:connect to Nightscout.

optional arguments:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  --pretend             Pretend mode: do not upload any data to Nightscout.
  -v, --verbose         Verbose mode: show extra logging details
  --start-date START_DATE
                        The oldest date to process data from. Must be specified with --end-date.
  --end-date END_DATE   The newest date to process data until (inclusive). Must be specified with --start-date.
  --days DAYS           The number of days of t:connect data to read in. Cannot be used with --from-date and --until-date.
  --auto-update         If set, continuously checks for updates from t:connect and syncs with Nightscout.
  --check-login         If set, checks that the provided t:connect credentials can be used to log in.

Move the .env file you created earlier into this folder, and run:

$ pipenv run tconnectsync --check-login

If you receive no errors, then you can move on to the Running Tconnectsync Continuously section.

Installing with Docker

First, ensure that you have Docker running and installed.

To download and run the prebuilt Docker image from GitHub Packages:

$ docker pull ghcr.io/jwoglom/tconnectsync/tconnectsync:latest
$ docker run ghcr.io/jwoglom/tconnectsync/tconnectsync --help

To instead build the image locally and launch the project:

$ git clone https://github.com/jwoglom/tconnectsync
$ cd tconnectsync
$ docker build -t tconnectsync .
$ docker run tconnectsync --help

Move the .env file you created earlier into this folder, and run:

$ docker run tconnectsync --check-login

If you receive no errors, then you can move on to the Running Tconnectsync Continuously section.

Running Tconnectsync Continuously

You most likely want tconnectsync to run either continuously (via the auto-update feature) or on a regular interval (via cron).

The supervisord approach is recommended for simplicity.

Running with Supervisord (recommended)

To configure tconnectsync to run continuously in the background using its --auto-update feature, you can use a tool such as Supervisord.

First, install supervisord via your Linux system's package manager. (For example, for Ubuntu/Debian-based systems, run sudo apt install supervisor)

Supervisord is configured by creating a configuration file in /etc/supervisor/conf.d.

Here is an example tconnectsync.conf which you can place in that folder:

[program:tconnectsync]
command=/path/to/tconnectsync/run.sh
directory=/path/to/tconnectsync/
stderr_logfile=/path/to/tconnectsync/stderr.log
stdout_logfile=/path/to/tconnectsync/stdout.log
user=<your username>
numprocs=1
autostart=true
autorestart=true

In order to create a run.sh file, see the section below which aligns with your choice of installation method.

After the configuration file has been created, ensure that Supervisor is running and configured to start on boot:

$ sudo systemctl daemon-reload
$ sudo systemctl start supervisord
$ sudo systemctl enable supervisord

Then use the supervisorctl command to manage the status of the tconnectsync program:

$ sudo supervisorctl status
tconnectsync                     STOPPED
$ sudo supervisorctl start tconnectsync
$ sudo supervisorctl status
tconnectsync                     RUNNING   pid 18810, uptime 00:00:05

You can look at the stderr.log and stdout.log files to check that tconnectsync is running and has started up properly:

$ tail -f /path/to/tconnectsync/stdout.log
Starting auto-update between 2021-09-30 00:06:39.942273 and 2021-10-01 00:06:39.942273
2021-10-01 00:06:39 DEBUG    Instantiating new AndroidApi
2021-10-01 00:06:39 DEBUG    Starting new HTTPS connection (1): tdcservices.tandemdiabetes.com:443
2021-10-01 00:06:40 DEBUG    https://tdcservices.tandemdiabetes.com:443 "POST /cloud/oauth2/token HTTP/1.1" 200 404
2021-10-01 00:06:40 INFO     Logged in to AndroidApi successfully (expiration: 2021-10-01T08:06:40.362Z, in 7 hours, 59 minutes)

With Pip Installation

In the tconnectsync.conf, you should set /path/to/tconnectsync to the folder containing your .env file.

Create a run.sh file containing:

#!/bin/bash

tconnectsync --auto-update

With Pipenv Installation

In the tconnectsync.conf, you should set /path/to/tconnectsync to the folder where you checked-out the GitHub repository.

An example run.sh which launches tconnectsync within its pipenv-configured virtual environment:

#!/bin/bash

PIPENV=/home/$(whoami)/.local/bin/pipenv
VENV=$($PIPENV --venv)

source $VENV/bin/activate

cd /path/to/tconnectsync
exec python3 -u main.py --auto-update

With Docker Installation

In the tconnectsync.conf, you should set /path/to/tconnectsync to the folder where you checked-out the GitHub repository.

An example run.sh if you installed tconnectsync via the GitHub Docker Registry:

#!/bin/bash

docker run ghcr.io/jwoglom/tconnectsync/tconnectsync --auto-update

An example run.sh if you built tconnectsync locally:

#!/bin/bash

docker run tconnectsync --auto-update

Running with Cron

If you choose not to run tconnectsync with --auto-update continuously, you can instead run it at a periodic interval (i.e. every 15 minutes) by just invoking tconnectsync with no arguments via cron.

If using Pipenv or a virtualenv, make sure that you either prefix the call to main.py with pipenv run or source the bin/activate file within the virtualenv, so that the proper dependencies are loaded. If not using any kind of virtualenv, you can instead just install the necessary dependencies as specified inside Pipfile globally.

An example configuration in /etc/crontab which runs every 15 minutes:

# m         h  dom mon dow user   command
0,15,30,45  *  *   *   *   root   /path/to/tconnectsync/run.sh

You can use one of the same run.sh files referenced above, but remove the --auto-update flag since you are handling the functionality for running the script periodically yourself.

Tandem APIs

This application utilizes three separate Tandem APIs for obtaining t:connect data, referenced here by the identifying part of their URLs:

• controliq - Contains Control:IQ related data, namely a timeline of all Basal events uploaded by the pump, separated by type (temp basals, algorithmically-updated basals, or profile-updated basals).
• android - Used internally by the t:connect Android app, these API endpoints were discovered by reverse-engineering the Android app. Most of the API endpoints are used for uploading pump data, and tconnectsync uses one endpoint which returns the most recent event ID uploaded by the pump, so we know when more data has been uploaded.
• tconnectws2 - More legacy than the others, this seems to power the bulk of the main t:connect website. It is used to retrieve a CSV export of non-ControlIQ basal data, as well as bolus and IOB data. (I haven't found any mentions of bolus or IOB data in the Control:IQ-specific API.)

I have only tested tconnectsync with a Tandem pump set in the US Eastern timezone. Tandem's (to us, undocumented) APIs are a bit loose with timezones, so please let me know if you notice any timezone-related bugs.

Backfilling t:connect Data

To backfill existing t:connect data in to Nightscout, you can use the --start-date and --end-date options. For example, the following will upload all t:connect data between January 1st and March 1st, 2020 to Nightscout:

python3 main.py --start-date 2020-01-01 --end-date 2020-03-01

In order to bulk-import a lot of data, you may need to use shorter intervals, and invoke tconnectsync multiple times. Tandem's API endpoints occasionally return invalid data if you request too large of a data window which causes tconnectsync to error out mid-way through.

One oddity when backfilling data is that the Control:IQ specific API endpoints return errors if they are queried before you updated your pump to utilize Control:IQ. This is partially worked around in tconnectsync's code, but you might need to update the logic if you did not switch to a Control:IQ enabled pump immediately after launch.