---
title: Qimchi
---
# A Plotter for Experimental Data

**Qimchi** is a sleek, Plotly-based visualization tool built for scientists and engineers working with experimental (or really, *any*) data. It's tightly integrated with the [qcutils](https://gitlab.com/squad-lab/qcutils) ecosystem and is optimized to handle `zarr`-formatted `xarray` datasets out of the box.  
If you're new to Zarr and Xarray, [this guide](https://xarray.pydata.org/en/stable/io.html#zarr) is a great place to start.

# Why Qimchi?

Qimchi is designed for rapid, interactive feedback — perfect for high-velocity research and iterative experiments.  
Whether you're working at the bench or at your keyboard, Qimchi helps you visualize, explore, and understand your data in real time.

# Highlights

- **Modular by design**: Easily add custom filters, plot types, and new data formats.  
- **Built for scientists & engineers**: Especially tuned for dynamic measurement workflows.  
- **Data-agnostic**: If it fits into an `xarray.Dataset`, Qimchi can plot it.  
- **Lab-tested**: Optimized for datasets from [SQUAD Lab](https://squad-lab.org) at Forschungszentrum Jülich, Germany.

Looking for similar tools? Check out [plottr-inspectr](https://github.com/toolsforexperiments/plottr) or [quantify](https://docs.qblox.com/en/main/tutorials/quantify_tutorials.html).  
Qimchi aims to offer a smoother, faster experience for on-the-fly visualization and exploratory analysis.

# Get started
Qimchi is available as both a Python package **and** a ready-to-run Docker container (recommended for best compatibility).  
The Docker image is tested across Windows, macOS, and Linux.
- Full API reference available at .

```{toctree}
:caption: "Contents"
:maxdepth: 2

installation.md
datasets.md
api/index
design.md
changelog
```