Metadata-Version: 2.1
Name: workoutizer
Version: 0.12.0
Summary: 🏋️ Browser based Sport and Workout Organizer 🏃‍♀️
Home-page: https://github.com/fgebhart/workoutizer
Author: Fabian Gebhart
License: UNKNOWN
Description: # Workoutizer
        [![PyPI](https://badge.fury.io/py/workoutizer.svg)](https://badge.fury.io/py/workoutizer) [![Python](https://img.shields.io/pypi/pyversions/workoutizer.svg?style=plastic)](https://badge.fury.io/py/workoutizer) [![Build Status](https://github.com/fgebhart/workoutizer/workflows/Test/badge.svg)](https://github.com/fgebhart/workoutizer/actions?query=workflow%3ATest) [![Coverage Badge](https://raw.githubusercontent.com/fgebhart/workoutizer/master/.github/badges/coverage.svg)](https://raw.githubusercontent.com/fgebhart/workoutizer/master/.github/badges/coverage.svg)
        
        The Workoutizer is a simple web application for organizing your workouts and sports activities. It is designed to work
        locally on any UNIX-like system running Python.
        
        Track your activities to get an overview of your overall training, similar to platforms like
        [strava](https://www.strava.com/) or [garmin connect](https://connect.garmin.com/) - but without
        uploading all your sensitive health data to some 3rd party cloud.
        
        ## Features
        * Automatic import of Garmin `.fit` files and `.gpx` files
        * Automatic naming of activities based on daytime, sport and geo location
        * Render your activity gps data on different OSM maps
        * Plot your activity specific data e.g. heart rate, pace, temperature, cadence and altitude
        * Integrate laps into both plots and maps
        * Connected plots and map via mouse hovering
        * Find the fastest sections in your activities using [sportgems](https://github.com/fgebhart/sportgems) and highlight on map
        * Keyboard navigation in browser
        * Add untracked activities manually via the GUI
        * Export activities as `.gpx` files
        * Add as many different sports as you want
        * Convenience CLI for installing, running, stopping, updating, ...
        
        
        ## Getting Started
        
        Install workoutizer
        ```shell script
        pip install workoutizer
        ```
        
        Initialize workoutizer to provide some demo data and run it:
        ```shell script
        wkz init --demo
        wkz run
        ```
        
        See the help description of the CLI with `wkz --help` and subcommands, e.g.: `wkz manage --help`. 
        
        In case you want to run workoutizer on a Raspberry Pi in your local network, follow the 
        [Raspberry Pi setup instructions](https://github.com/fgebhart/workoutizer/tree/master/setup).
        
        ## Gallery 
        
         Dashboard             |  Sport Page
        :-------------------------:|:-------------------------:
        ![](https://i.imgur.com/FcB5JDl.png)  |  ![](https://i.imgur.com/6fwcEZX.png)
        
         Activity Page 1/2             |  Activity Page 2/2
        :-------------------------:|:-------------------------:
        ![](https://i.imgur.com/tcS6L4Y.png)  |  ![](https://i.imgur.com/QSf3Dpo.png)
        
        ## Thanks
        
        Thanks to the authors of projects I integrated into workoutizer:
        * [leaflet-ui](https://github.com/Raruto/leaflet-ui) by [Raruto](https://github.com/Raruto)
        * [django-colorfield](https://github.com/fabiocaccamo/django-colorfield) by [Fabio Caccamo](https://github.com/fabiocaccamo)
        * [python-fitparse](https://github.com/dtcooper/python-fitparse) by [dtcooper](https://github.com/dtcooper)
        * [leaflet-color-markers](https://github.com/pointhi/leaflet-color-markers) by [pointhi](https://github.com/pointhi)
        * [Font Awesome Icons](https://fontawesome.com/)
        
        ## Changelog
        
        See the [releases section](https://github.com/fgebhart/workoutizer/releases).
        
        ## Contributing
        
        Contributions are welcome! Feel free to pick an [open issue](https://github.com/fgebhart/workoutizer/issues), open up 
        a pull request or file a new issue.
        
        For local development I recommend to run the development docker container. First clone the repo:
        ```
        git clone git@github.com:fgebhart/workoutizer.git
        cd workoutizer
        ```
        and then start workoutizer in docker using the convenience script:
        ```
        ./run_docker.sh
        ```
        This might take a while to build the image, run the container and initialize workoutizer. Once up and running, run the
        tests with
        ```
        pytest wizer/tests/ -n4
        ```
        Once this was successful you are good to go.
        
        In order to run workoutizer you could either run it using django's `manage.py` script
        ```
        python manage.py runserver
        ```
        or using the `wkz` cli
        ```
        wkz run
        ```
        In case you encounter any issues in the setup process, feel free to file an issue.
        
        Note: If you are using VS-Code you might want to open the folder of this repo in a docker container directly using the
        Remote - Containers extension.
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Framework :: Django
Classifier: Framework :: Django :: 2.2
Classifier: Framework :: Django :: 3.0
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Provides-Extra: testing
