streambikes

Testing real time weather and bike APIs and online learning with Plotly Dash You need api keys from JCDecaux and OpenWeatherAPI to run the project. To run in docker:

• launch docker
• Add your API keys in src/settings/api_keys.yml
• run :

  docker secret create api_keys src/settings/api_keys.yml

  docker-compose build

  docker-compose up

Run for dev:

make create_environments

Activate the environment

Install packages

make requirements

If you are developing :

make requirements-dev

To run the dash app: export API keys as environment variables :

export DECAUX_API="yourbikeapikey"
export WEATHER_API="yourweatherapikey"

dash run src/application/dashapp.py

Project Organization

├── docker-compose.yml          <- Dash + Redis services docker compose 
├── Dockerfile                  <- Dash application docker file
├── Makefile                    <- Makefile with commands like `make data` or `make train`
├── LICENSE
├── README.md               
├── data                        <- data (essentially empty because we use online learning)
├── docs                        <- project documentation
├── requirements-dev.txt        <- python developpement modules requirements (tests, linting, etc)
├── requirements.txt            <- python packages requirements
├── mypy.ini                    <- mypy static type checking settings
├── setup.py                    <- makes project pip installable (pip install -e .) so src can be imported
├── src                         <- Source code for use in this project.
│   ├── __init__.py
│   ├── application             <- Scripts to create a Plotly Dash app to present results
│   │   ├── dashapp.py
│   │   └── data_for_dash.py
│   ├── data                    <- Scripts to download or generate data (calls APIs)
│   │   ├── __init__.py
│   │   └── collect_data.py
│   ├── features                <- Scripts to turn raw data into features for modeling
│   │   ├── __init__.py
│   │   └── build_features.py
│   ├── models                  <- Scripts for online learning (train and predict at the same time)
│   │   ├── __init__.py
│   │   └── online_model.py
│   └── settings                <- settings folder that manages changes between dev/staging(CI)/ and prod settings
│       ├── __init__.py
│       ├── api_keys.template
│       └── conf.py
├── tests
│   ├── __init__.py
│   ├── conftest.py
│   └── unit_tests
│       ├── application
│       ├── data
│       ├── features
│       ├── models
│       └── settings
└── tox.ini                     <- tox file with settings for running tox; see tox.readthedocs.io

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Project based on the cookiecutter data science project template. #cookiecutterdatascience