This repository contains the example code talked about in this application note proposing a method of using the Disruptive Technologies (DT) Temperature Sensor for desk occupancy tracking. Written in Python 3, it implements the DT Developer API to communicate with a DT Studio project and its sensors. By calling sensor_stream.py, an estimate of desk occupancy will be calculated for previous history data and/or a live stream of datapoints from the moment of execution.
A DT Studio project containing desk- and reference temperature sensors should be made. All temperature sensors in the project will be assumed part of the desk occupancy estimation.
For best performance, the Wireless Temperature Sensor EN12830/330s, sampling at 5.5 minute intervals should be used. The Wireless Temperature Sensor will also work, but the lower sampling rate might reduce accuracy significantly.
Under each desk, a temperature sensor should be placed where it is most likely a person will sit. Closer to the edge is better.
(Recommended) For increased accuracy, one- or several temperature sensors can be used to track reference ambient temperature. These should be placed on a wall away from any window/sun, preferably at standing height. It should be away from any heat sources like air-condition vents or coffee machines etc.
Reference sensors should be given the label "reference" in the DT Studio project. If not it will be assumed to be a desk sensor.
Install dependencies.
pip install -r requirements.txt
Edit sensor_stream.py to provide the following authentication details of your project. Information about setting up your project for API authentication can be found in this streaming API guide.
USERNAME = "SERVICE_ACCOUNT_KEY" # this is the key
PASSWORD = "SERVICE_ACCOUT_SECRET" # this is the secret
PROJECT_ID = "PROJECT_ID" # this is the project idRunning python3 sensor_stream.py will start streaming data from the sensors in your project for which desk occupancy will be estimated for either historic data using --starttime flag, a stream, or both. Provide the --plot flag to visualise the results.
usage: sensor_stream.py [-h] [--starttime] [--endtime] [--plot] [--debug]
Desk Occupancy Estimation on Stream and Event History.
optional arguments:
-h, --help show this help message and exit
--starttime Event history UTC starttime [YYYY-MM-DDTHH:MM:SSZ].
--endtime Event history UTC endtime [YYYY-MM-DDTHH:MM:SSZ].
--plot Plot the estimated desk occupancy.
--debug Visualise algorithm operation.
Note: When using the --starttime argument for a date far back in time, if many sensors exist in the project, the paging process might take several minutes.