Compress, Compute, and Conquer: Python-Blosc2 for Efficient Data Analysis (EuroSciPy 2025 tutorial)

Here you will find materials for the ironArray SLU tutorial on Python-Blosc2 for EuroSciPy 2025 in Krakow, Poland.

Preliminaries

Before the tutorial, it is advisable to have jupyter notebook installed (see here). This can be managed via Anaconda if you prefer (download here and follow the instructions), but in the command line (Windows/Linux/MacOS) it is very simple.

Before installing Jupyter Notebook, make sure you have Python (version 3.8 or later) installed and pip

python --version
pip --version

You may want to create an environment via conda create --name testenv python=3.12 if you have conda installed. You will still have to use pip to install the packages though.

Installing Jupyter Notebook

Install Jupyter using pip via pip install notebook

Opening Jupyter Notebook from the Command Line

Once installed, you can launch Jupyter Notebook by running jupyter notebook which will open a window in your browser, where you may manage notebooks.

Clone this git repo

Use either SSH, url or a zip file (click on the green 'Code' button in the top right), making sure to clone into the relevant directory. One may navigate to the repo via the command line, or from the jupyter notebook web browser interface.

Install dependencies

From the command line run

pip install caterva2[services]==2025.8.7 psutils blosc2==3.7.0 matplotlib ipympl

Alternatively, in a jupyter notebook cell, run (note the exclamation mark).

!pip install caterva2[services]==2025.8.7 psutils blosc2==3.7.0 matplotlib ipympl

That should be it! Open the first notebook and check that the first few cells all run to be sure!