This app visualizes ILI data of pipeline defects using the Python Bokeh library. It allows filtering and viewing the data in an interactive way.
- Interactive defect visualization technique that overlays transparent defect arcs
- Map showing location of defects
- Charts displaying analysis like max depth, average depth etc.
- Data table listing the individual defects
- Options to filter by:
- Distance along pipe
- Defect depth
- Pipe wall (internal or external)
- Defect type
- Mobile friendly responsive layout
The app is a single self-contained HTML file called bokeh_data_app.html. To run it, simply open this file in a modern web browser.
Filters can be adjusted using the sidebar widgets when on larger screens. On small screens like mobile, tap the menu button to access the filters.
View the app here
The main code for this project is main.py. Running it assembles the data, Bokeh models and HTML template into the final bokeh_data_app.html app.
To customize:
-
Update pipeline data by editing
df1.csv. -
Change Bokeh plots and widgets by editing
main.py. -
Revise the HTML layout and styles in
index.html.
- Bokeh - Interactive web plot generation (~40%)
- Pandas - Data processing (~5%)
- Jinja - Template rendering (~1%)
- NumPy - Numeric processing (~2%)
- HTML/Bootstrap - Markup and styling (~40%)
- JavaScript - Custom interactivity (~12%)
As a standalone HTML/JS application, this can be deployed to any basic web server or host. Some good simple options are:
- GitHub Pages
- Amazon S3
- Azure Blob Storage
The app can also be embedded into an existing web framework like Django.
MIT