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dashboard's Introduction

Aircraft Efficiency Dashboard

Setup Python Environment

Set up a Python virtual environment that includes all packages required to build the documentation. A Conda environment file is provided for convenient setup. The file is located at ./environment.yml. Install the environment dashboard by running from the repository root directory:

conda env create -f 'environment.yml'

and activate the environment:

conda activate dashboard

You are now ready to render the dashboard and thereafter convert it to a WASM Web Worker...

Build and Deploy

To convert the Panel application (.ipynb or py) to a standalone HTML file, run:

Note

Replace <PANEL_APP> and <REQUIREMENTS_FILE> with the file paths to the Panel application and the requirements file.

panel convert <PANEL_APP> --to pyodide-worker --index --requirements <REQUIREMENTS_FILE> --out pyodide

The GitHub Actions workflow deploy.yml will deploy the ./pyodide/index.html) to GitHub Pages on every push to the main branch.

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dashboard's Issues

Add new Figures (from "Aviation II" lecture)

Add these two figures:

to a GH pages site.

The relevant instructions for converting the .py files to WASM can be found at:

I already added three "test file" that show how to make any matplotlib figure interactive, using the bokeh package: sustainableaviation/dashboard/dev/tests. This includes the use of the servable() directive etc.

I would like the data to be "interactive" in the following sense: Mouse-hover to see the point metadata (eg. x, y, label, etc.). Use this application from the panel homepage for inspiration:

Screenshot 2023-12-20 at 10 19 10

For further information on how to get from matplotlib to WASM, compare this issue:

Determine best File-Loading Approach

In the current draft, .xlsx files are still read from GitHub like so:

ida = 'https://raw.githubusercontent.com/sustainableaviation/Aircraft-Performance/main/dashboard/data/Dashboard.xlsx'
ida = pd.read_excel(ida)

At the moment, 11 Excel sheets are read in this way. This is probably not the best way to feed data into the dashboard. A few ideas on how to improve the input performance:

  • Read Excel files during local build and package the resulting pd.dataframes into a pickle file

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