DataLab-Kernel#

DataLab-Kernel is a standalone Xeus-Python-based Jupyter kernel for scientific data processing with optional live synchronization to DataLab.

It runs seamlessly in both native Jupyter (JupyterLab, Jupyter Notebook, VS Code) and JupyterLite (browser-based, no server required), enabling flexible notebook-based workflows for signal and image analysis with Sigima.

from datalab_kernel import Workspace, Plotter, create_signal
import numpy as np

# Create workspace and plotter
workspace = Workspace()
plotter = Plotter(workspace)

# Create and add signal
x = np.linspace(0, 10, 1000)
y = np.sin(2 * np.pi * x) * np.exp(-x / 5)
signal = create_signal("Damped Sine", x, y)
workspace.add("my_signal", signal)

# Visualize
plotter.plot("my_signal")
_images/DataLab-Banner.svg

Part of the DataLab Platform.#

User Guide

Installation, overview, and features

User Guide
API Reference

Reference documentation

API Reference
Examples

Notebooks and tutorials

Examples
Contributing

Getting involved in the project

Contributing

Key Features#

Xeus-Python Foundation

Built on Xeus-Python for improved performance, native debugger support, and excellent Qt event loop integration.

Dual Operating Modes

  • Standalone mode: Work independently with HDF5 persistence

  • Live mode: Synchronize with running DataLab via Web API

Cross-Platform Jupyter Support

Works in native Jupyter (JupyterLab, Notebook, VS Code) and JupyterLite (browser-based, no server required).

Try it Online#

Experience DataLab-Kernel instantly in your browser — no installation required!

Quick Start notebook

Introduction to Workspace, Plotter, and basic Sigima processing

Advanced Showcase notebook

Advanced plotting, signal/image processing, ROI, and analysis

Click a badge to open the notebook in a live JupyterLite environment powered by notebook.link. This service, developed by QuantStack, enables sharing and running Jupyter notebooks directly in the browser with zero setup.

Unified API

Same code works in both modes - switch seamlessly between local notebooks and DataLab-connected workflows.

Scientific Data Objects

Full access to Sigima’s SignalObj and ImageObj with metadata, units, labels, and ROI support.

Interactive Visualization

Built-in plotting with matplotlib integration.

Reproducibility

HDF5-based workspace persistence ensures complete reproducibility.