Selficient Python backend and API

This backend has a RESTful API to interact with the Selficient home.

This project uses Flask for the development server, unit testing support and a RESTful API.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

You need:

• Python version 3.6.x to create a virtual environment and install the dependencies for this project
• 'pip' and 'py' as ENVIRONMENT VARIABLES (Windows)
• MariaDB running locally on your machine

Installing

A step by step series of examples that tell you how to get a development env running

Get the project from git

Clone the repo.

git clone https://github.com/WyTho/python_flask.git selficient-python-api

Change directory to the project folder

cd selficient-python-api

Setup a virtual environment (Windows)

Setting up a virtual environment on Linux or MacOS

Install virtual environments for Python

py -m pip install --user virtualenv

Setup a virtual environment in the project folder

py -m virtualenv env

Activate the virtual environment

.\env\Scripts\activate

Optional: More information about virtual environments

Install dependencies

Install the dependencies in the virtual environment

pip install -r requirements.txt

Setting up the database

You should have a MariaDB instance running on your local machine.

For debugging purposes it's handy to have a GUI for the database like HeidiSQL or Database Workbench. Some IDE's (like PyCharm) can connect to the database too.

Create a user for the DB with username: dev and password: secret

Create a database schema called WySmart

Import the database schema by running:

manage.py db upgrade

Optionally: Adding some fake data to the database

Run the following command to seed some dummy data into the database:

manage.py seed

This adds an example graph with its data called AVERAGE_TEMPERATURE The dummy data can be processed into averages using the Put call on /api/graph/AVERAGE_TEMPERATURE

Starting the API

Run

app.py

Running the tests

Run the test version of the application

test_app.py

Run in separate window

test_manage.py run_tests

Code structure

├── controllers (1)
├── migrations (2)
├── models (3)
├── processing (4)
└── tests (5)

1. controllers contains the resources that handle incoming requests
2. migrations contains the history of the database, these are used to initiate and update the database
3. models contains the businesslogic and database connection
4. processing contains scripts that are used to analyse event data in order to create graphs
5. tests contains the tests for this project.

Authors

• Rolf de Sterke - Initial work - Github page

See also the list of contributors who participated in this project.