helium-positioning-api 0.0.2.dev1683125793

Helium Positioning API

Helium Positioning API

Features

Prediction of the location of devices belonging to an organization in the Helium Console. Several different methods and models are available.

Docker

The easiest way to run the positioning-API is to prepare an .env file (see below for further instructions what should be in it) and run it as docker-container:

docker run -p 8000:8000 --env-file=.env ghcr.io/emergotechnologies/helium-positioning-api:main

Installation

Developer install

The following allows a user to create a developer install of the positioning api.

pip install -r requirements.txt
poetry install
poetry shell
pip install git+https://github.com/emergotechnologies/helium-api-wrapper

Prerequisites

Before use, ensure that there is an .env file in the root directory of the repository where the API_KEY variable is entered (see .env.sample). You can generate and copy the API_KEY at https://console.helium.com/profile.

Usage

The service allows usage via command line interface or locally hosted REST interface.

CLI

Get Device Position

python -m helium_positioning_api predict --uuid 92f23793-6647-40aa-b255-fa1d4baec75d

Currently defaults to the "nearest_neighbor" model.

Advanced Requests

The location prediction command is

python -m helium_positioning_api predict --uuid 'your uuid' --model 'your model selection'

See the table below for a list of currently available models.

Model	Position estimation explanation	Suggested use
nearest_neighbor	Location of hotspot with the best signal	Purchase of at most one packet from a device (see Packet Configurations for more details)
midpoint	Point of equal distance from the two hotspots with the best signals	Purchase of at least two packets from a device (see Packet Configurations for more details)
linear_regression (experimental)	Trilateration with an linear regression distance estimator	Experimental. Purchase of at least three packets from a device (see Packet Configurations for more details)
gradient_boosting (experimental)	Trilateration with a gradient boosted regression distance estimator	Experimental. Purchase of at least three packets from a device (see Packet Configurations for more details)

REST-API

1. Start local REST-API (default)

   python -m helium_positioning_api serve
   

2. Open Browser and navigate to 127.0.0.1:8000/docs
3. Click on predict_tf path to drop down the endpoint details
4. Click on the Try it out button.
5. Fill in the uuid of your device and click on the button Execute to get an estimation using the nearest_neighbor model
6. You can see the location prediction response in the Responses section below.

The mapping of available models to paths can be seen in the table below.

model	path
nearest_neighbor	predict_tf
midpoint	predict_mp
linear_regression	predict_tl_lin
gradient_boosting	predict_tl_grad

Contributing

Contributions are very welcome. To learn more, see the Contributor Guide.

License

Distributed under the terms of the MIT license, Helium Positioning API is free and open source software.

Issues

If you encounter any problems, please file an issue along with a detailed description.

Credits

This project was generated from @cjolowicz's Hypermodern Python Cookiecutter template.