pyviewarr 0.1.1

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pyviewarr

A faster, more intuitive way to explore 2D data (i.e. monochromatic images) in Python notebooks.

• Server-side slicing — view 1 MB of a 100 GB data cube without downloading 100 GB of data
• Interactive re-stretching by right-clicking and dragging in X and Y (inspired by ds9)
• Stepper buttons for moving along the leading axes of N-D data where N > 2
• Linear, log, and symmetric linear (i.e. centered at zero with vmin == -vmax) scales
• Panning and zooming (0 key resets, - zooms out, = zooms in)
• A few good colormaps

Clanker code disclaimer: written in collaboration with Anthropic's Claude LLM. Use at your own risk.

Installation

pip install pyviewarr

or with uv:

uv add pyviewarr

Development

Be sure to clone with git clone --recurse-submodules (or, if you cloned first and then read this, git submodule update --init --recursive to initialize an existing clone).

The frontend part of the widget (i.e. viewarr itself) is written in Rust with egui, requiring a Rust compiler toolchain for wasm32-unknown-unknown and the wasm-pack tool installed.

Install rust with rustup: https://rustup.rs/

Now that you have cargo, install wasm-pack with cargo install wasm-pack.

Using uv for development will automatically manage virtual environments and dependencies for you.

uv run jupyter lab example.ipynb

Alternatively, create and manage your own virtual environment:

python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
jupyter lab example.ipynb

The widget front-end code bundles it's JavaScript dependencies. After setting up Python, make sure to install these dependencies locally:

npm install

While developing, you can run the following in a separate terminal to automatically rebuild JavaScript as you make changes:

npm run dev

Open example.ipynb in JupyterLab, VS Code, or your favorite editor to start developing. Changes made in js/ will be reflected in the notebook.