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✈️📉 Interactive Dashboard showing Historical Efficiency Improvements of Commercial Aircraft

Home Page: https://sustainableaviation.github.io/dashboard

License: BSD 3-Clause "New" or "Revised" License

Jupyter Notebook 78.46% HTML 16.08% JavaScript 5.46%

aviation efficiency sustainable

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.

dashboard's People

Contributors

Watchers

dashboard's Issues

Evaluate Dashboard Tools

Embed Panel Application in Sphinx/Markdown Page

Once we have converted a matplotlib figure into a panel application, how can we include it in a markdown page, like so:

Compare also:

Discussion on the Panel Discourse Forum

Host Panel Application on Webserver

Compare the Panel documentation:

Pin Pyodide in `panel convert`

...to ensure consistency across releases.

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~~

`panel` Issues (Pyodide)

The recommended way to install panel into a Pyodide environment is presently not working (try in this JupyterLite environment):

Current versions: Bokeh, Panel

bokeh=3.2.0
panel=1.1.1

await micropip.install("https://cdn.holoviz.org/panel/1.1.1/dist/wheels/bokeh-3.2.0-py3-none-any.whl")
await micropip.install("https://cdn.holoviz.org/panel/1.1.1/wheels/panel-1.1.1-py3-none-any.whl")

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:

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

Panel WASM Setup

Compare:

Workflow:

Convert Jupyter Notebook to Pyodide web worker (html+js): dev/tests/test_bokeh_static.ipynb
Render WASM page locally: open http://localhost:8000/pyodide/test_bokeh_static.html
Create GH Actions custom workflow to ~~build~~ + deploy

Edit: best to build locally to ensure the files really work

Test WASM Page Load Speed

...for instance, dependent on the requirements.txt:

pandas
numpy
scipy
bokeh
...

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