mini-project-EC463

Sprint information: https://github.com/ianballou/mini-project-EC463/projects/1
Demo website: http://128.31.24.130:5000

Pre-requirements

C compiler, general development tools installed, Python

Steps to run:

• cd into the root directory of this project
• Install python-virtualenv
• Run virtualenv .venv
• Run source .venv/bin/activate
• Run pip install flask
• Run export FLASK_APP=miniproject.py
• Run pip install flask-wtf
• Run pip install flask-sqlalchemy
• Run pip install flask-migrate
• Run pip install flask-login
• Run pip install flask-login
• Run pip install matplotlib
• Create ~/.config/matplotlib/matplotlibrc
• Run echo "backend: Agg" > ~/.config/matplotlib/matplotlibrc
• Install the python tkinter library with a package manager: apt-get, yum, dnf, etc
• Run flask db upgrade
• Start the site by running flask run --host=0.0.0.0

Design choices:

• Web framework: Python Flask
  
• Database management: SQLAlchemy
  
• Authentication: Database username/email/password
  
• Sensor simulation: Upon logging in and loading a user's homepage, sensor data is randomly (but realistically) generated and plotted.
  
• Plot display: Plots are generated as PNG files using the Python PyPlot library. These files are numbered and named based on their graph types and related users. They are then rendered in a user's homepage.

Workflow

1. Register with username, email, password, and # of sensors.
2. View graphs of sensor data on personal homepage.

Contributors

• Ian Ballou (https://github.com/ianballou)

  • VM Hosting
  • Front-end design & implementation
  • Web server API design & implementation
  • Database layout

• Lin Ma (https://github.com/lynnbgm)

  • Sensor simulation design & implementation
  • Sensor data display design & implementation
  • Application workflow architecture
  • Database research

  Acknowledgements

  The Flask Mega-Tutorial (https://blog.miguelgrinberg.com/post/the-flask-mega-tutorial-part-i-hello-world)