Quick, reliable data logging is a key requirement for tests from component characterization to prolonged endurance testing to the evaluation of large dataset. This project sets out a way to simulate real-world signals collection, filter and store them in a simple database. This project also provides a platform to interact with stored signal data through Web front-end page. The system invloves the following steps:
- Signal collection
- Filtering
- Storing in local DB
- Visualization through a local Web-server
- Change home directory to \web-server\black-dashboard-flask
- Install dependencies: pip3 install -r requirements.txt
Set the FLASK_APP environment variable
# (Unix/Mac) export FLASK_APP=run.py
# (Windows) set FLASK_APP=run.py
# (Powershell) $env:FLASK_APP = ".\run.py"
Set up the DEBUG environment
# (Unix/Mac) export FLASK_ENV=development
# (Windows) set FLASK_ENV=development
# (Powershell) $env:FLASK_ENV = "development"
- run run.py
- Create username and password or use 'admin' as a username and 'pass' as a password
- change home directory to \sensor
- Run main.py
Backend
[Create and simulate Signals - Done!]
[Create single database class for different DBMS - Done!]
[Create MariaDB database class - Done!]
[Create SQLite database class - Done!]
Frontend
[Setup FLASK - Done!]
[Create a live stream of sensor data recored - Done!]
GNU General Public License V3.0
Solomon Gugsa
Free Design Template used from Black Dashboard Flask - Provided by Creative Tim and AppSeed
Initial