Contents

whereami 0.4.90

0

Uses WiFi to tell you where you are

whereami is a Python library that leverages the power of machine learning and WiFi signals to predict your location. It's a cross-platform solution that works on OSX, Windows, and Linux. The library uses sklearn's RandomForest for learning and prediction, making it capable of distinguishing between small distances, such as different rooms in a house or different seats in a room. The library provides a simple interface for training the model on different locations and predicting the current location.

Stars: 5088, Watchers: 5088, Forks: 250, Open Issues: 15

The kootenpv/whereami repo was created 7 years ago and the last code push was 5 months ago.
The project is extremely popular with a mindblowing 5088 github stars!

How to Install whereami

You can install whereami using pip

pip install whereami

or add it to a project with poetry

poetry add whereami

Package Details

Author
Pascal van Kooten
License
MIT
Homepage
https://github.com/kootenpv/whereami
PyPi:
https://pypi.org/project/whereami/
GitHub Repo:
https://github.com/kootenpv/whereami

Classifiers

  • Software Development
  • Software Development/Libraries
  • Software Development/Libraries/Python Modules
  • System/Software Distribution
  • System/Systems Administration
  • Utilities
No  whereami  pypi packages just yet.

Errors

A list of common whereami errors.

Code Examples

Here are some whereami code examples and snippets.

Related Packages & Articles

searoute 1.3.1

A python package for generating the shortest sea route between two points on Earth.

wpcsys 3.0.23

WPC Python driver APIs, the easiest way to Control & Data Acquisition (DAQ)

micrOSDevToolKit 2.0.6

Development and deployment environment for micrOS, the diy micropython automation OS (IoT)

mobly 1.12.3

Automation framework for special end-to-end test cases

dtw 1.4.0

The dtw Python package is a powerful tool for handling time-series data. It offers a complete implementation of Dynamic Time Warping (DTW) algorithms, which are used to optimally map one time-series (query) onto another (reference) by applying local stretch or compression to the time axes. This package is a Python equivalent of the R's DTW package and doesn't depend on any other Python packages. It's a great tool for any Python developer working with time-series data.

deepdiff 7.0.1

Deep Difference and Search of any Python object/data. Recreate objects by adding adding deltas to each other.