Getting Started#

This page provides a quick introduction to get you started with Sigima through practical examples.

Interactive Notebook#

The best way to start learning Sigima is through an interactive Jupyter notebook. This example demonstrates image processing with Regions of Interest (ROI) and uses the matplotlib backend for web-friendly visualization.

Download simple_example.ipynb

What This Example Shows#

The notebook demonstrates:

  • Creating image objects from NumPy arrays

  • Defining Regions of Interest (ROI) for selective processing

  • Applying Gaussian filtering to images

  • Visualizing results with matplotlib backend (web-friendly, no Qt required)

  • Using the new visualization backend selection feature introduced in Sigima V1.1

Key Concepts#

Object-Oriented Approach

Sigima wraps NumPy arrays in rich objects (SignalObj, ImageObj) that carry metadata, units, and ROI information.

Region of Interest (ROI)

Process only specific areas of your data by defining ROIs. The example shows a circular ROI applied to an image.

Backend Flexibility

Choose between PlotPy (Qt-based, interactive) or Matplotlib (web-friendly) backends for visualization, making Sigima suitable for both desktop applications and web-based workflows.

Remote Control Example#

Sigima also enables remote control of DataLab sessions from external scripts or notebooks. This is useful for automation, batch processing, or integrating DataLab into larger workflows.

Download remote_example.ipynb

This notebook demonstrates:

  • Connecting to a running DataLab instance using SimpleRemoteProxy

  • Adding signals and images to DataLab from external Python code

  • Controlling DataLab programmatically without GUI interaction

Next Steps#