Sigima#
Sigima is an advanced Python library for scientific image and signal processing. It provides a wide range of functionalities for analyzing and processing data, including signal filtering, image enhancement, and feature extraction. Sigima is based on a simple but effective object-oriented design, making it easy to use and extend.
With Sigima, do in 3 lines of code what would normally take dozens of lines:
import numpy as np
import sigima.objects
import sigima.proc.image
data = np.random.normal(100, 30, (100, 100)) # Prepare test image data
img = sigima.objects.create_image("Noisy", data) # Create the image object
img.roi = sigima.objects.create_image_roi("circle", [30, 30, 20]) # Define ROI
result = sigima.proc.image.gaussian_filter(img, sigma=5.0) # Apply Gaussian filter
Developed and maintained by the DataLab Platform Developers, Sigima powers the computation backend of DataLab.#
Installation, overview, and features
Gallery of examples
Reference documentation
Getting involved in the project
Try it Online#
Experience Sigima instantly in your browser — no installation required!
Click the badge above to open a basic example 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.
Simply run the cells to explore:
Creating signal and image objects
Applying processing functions
Visualizing results inline
Sigima has been funded by the following stakeholders:
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NLnet Foundation, as part of the NGI0 Commons Fund, backed by the European Commission, has funded the redesign of DataLab’s core architecture. |
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CEA, the French Alternative Energies and Atomic Energy Commission, is the major investor in DataLab, and is the main contributor to the project. |
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CODRA, a software engineering and editor firm, has supported DataLab open-source journey since its inception (see here). |
