Notebooks

🙄 Daniels thoughts and reflections

This is placeholder for my own shareable scientific notebooks, great oportunity to excersice the muscle memory 💪🏻 using Python tools such as Polars - great for working with large datasets and Altair for making nice visual representation of the data. And big thanks to Marimo to glue everything together into a notebook including widgets allow the user to select and change the output. What also makes it awesome is that Marimo does allow one to use WebAssembly (WASM) so that allow user to run the notebook entirely within the browser with no other needs than a static web page. 😍

📕 Current Notebooks

Year 2025

• Health Data
  • This something I did develop before my annual doctors appointment allowing me to "share" my health data based on my custom view that I think I have best use of, and data collected across multiple sources such as Garmin Connect - where my smartwatch uploads its data. And Apple Health where my blood pressure meassures, weights are uploaded to. 🩺
• Energy consumption
  • As I found good reasons to do more in-depth analysis of our house energy consumption and costs involved so I made this notebook with charts to help more easily do this in the future. Click here to take part of this. ⚡️

..... original instructions from the Marimo project .....

🚀 Turn Marimo Notebooks into Github Pages

1. Fork this repository
2. Add your marimo files to the notebooks/ or apps/ directory
   1. notebooks/ notebooks are exported with --mode edit
   2. apps/ notebooks are exported with --mode run
3. Push to main branch
4. Go to repository Settings > Pages and change the "Source" dropdown to "GitHub Actions"
5. GitHub Actions will automatically build and deploy to Pages

Including data or assets

To include data or assets in your notebooks, add them to the public/ directory.

For example, the apps/charts.py notebook loads an image asset from the public/ directory.

<img src="public/logo.png" width="200" />

And the notebooks/penguins.py notebook loads a CSV dataset from the public/ directory.

import polars as pl
df = pl.read_csv(mo.notebook_location() / "public" / "penguins.csv")

🧪 Testing

To test the export process, run scripts/build.py from the root directory.

python scripts/build.py

This will export all notebooks in a folder called _site/ in the root directory. Then to serve the site, run:

python -m http.server -d _site

This will serve the site at http://localhost:8000.