Python is used to handle all the data management and calculations at the backend.

SQL database hosts registered user information, blogs, well data and pre-trained machine learning models. SQL database satisfies both data querying convenience and data security requirement.

Map related shapes (e.g. polygons, lines and part of point coordinates) and their attributes are stored in the Json files (including GeoJson) for better scalability and tansformability.

The Django framework are deployed for Python framing. Django ORM is used for structured database mapping. Python and JavaScript are used for Json and other NoSQL file mapping.

HTML and CSS coupling with Bootstrap and JQuery libraries are used to handle frontend page layout. JavaScript and Jinja are used to process data transitions and some calculations embedded in HTML pages or quoted in web pages.

Maps are coded with Leaflet and Folium libraries.

A weather API is used to fetch the Calgary local weather information that is displayed at the bottom of the sidebar.

This web is being hosted on an Apache web server with Ubuntu OS. Mod_WSGI serves as the gateway interface.


Fun Apps:

Three apps so far. Two GIS informative maps and one machine learning app. Since the Machine Learning Well Optimization app is built on a database of wells, user is required to register for access.

Knowledge Share:

Four articles/ presentation with contents in machine learning data modeling and python web development.

Blog, Login and Register:

User management functions. User need setup an account to access to the machine learning app. With the convenience of user management database, a blog app is an easy bonus for us to share ideas.


It is an area to release or share information.

For more information about me, you may click on the icons at the top to access my LinkedIn, or leave me a message in Blog.