https://github.com/datalab-platform/sigima
Scientific computing engine for 1D signals and 2D images, part of the DataLab open-source platform
https://github.com/datalab-platform/sigima
Last synced: 2 months ago
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Scientific computing engine for 1D signals and 2D images, part of the DataLab open-source platform
- Host: GitHub
- URL: https://github.com/datalab-platform/sigima
- Owner: DataLab-Platform
- License: bsd-3-clause
- Created: 2025-05-29T13:17:15.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2026-01-30T12:03:31.000Z (2 months ago)
- Last Synced: 2026-01-31T01:11:37.629Z (2 months ago)
- Language: Python
- Size: 12.6 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Sigima - Scientific Image and Signal Processing Library
[](./LICENSE)
[](https://pypi.org/project/sigima/)
[](https://github.com/DataLab-Platform/Sigima)
[](https://pypi.org/project/sigima/)
**Sigima** is an **open-source Python library for scientific image and signal processing**,
designed as a modular and testable foundation for building advanced analysis pipelines.
🔬 Developed by the [DataLab Platform Developers](https://github.com/DataLab-Platform), Sigima powers the computation backend of [DataLab](https://datalab-platform.com/).
## 🌟 Project & Sponsors
| Project/Sponsor | Description |
|---------------------|-------------|
| 
| Open-source platform for scientific signal and image processing, powered by Sigima. |
| 
| European non-profit supporting open-source and internet projects. Sigima has received funding from NLnet for its development, through the DataLab project. |
---
## ✨ Highlights
- Unified processing model for **1D signals** and **2D images**
- Works with **object-oriented wrappers** (`SignalObj`, `ImageObj`) extending NumPy arrays
- Includes common processing tasks: filtering, smoothing, binning, thresholding, labeling, etc.
- Structured for **testability**, **modularity**, and **headless usage**
- 100% **independent of GUI frameworks** (no Qt/PlotPyStack dependencies)
---
## 💡 Use cases
Sigima is meant to be:
- A **processing backend** for scientific/industrial tools
- A library to **build reproducible analysis pipelines**
- A component for **headless automation or remote execution**
- A testbed for **developing and validating new signal/image operations**
---
## 📖 Design Philosophy
The main goal of **Sigima** is to provide a unified, high-level API for handling and processing **1D signals** and **2D images**, through dedicated Python objects: `SignalObj` and `ImageObj`.
The library is organized to separate concerns clearly:
- `sigima.objects`: defines the object model for signals and images.
- `sigima.params`: contains parameter classes for configuring processing functions.
- `sigima.proc`: provides high-level processing functions that operate directly on `SignalObj` and `ImageObj` instances.
- `sigima.io`: handles input/output operations (CSV files, image formats, etc.) for signals and images.
- `sigima.tools`: contains **low-level, NumPy-based functions** that implement the core logic behind many processing routines.
This structure supports a **layered programming model**:
- Developers can use `computation` to process full signal/image objects in an object-oriented manner.
- Or they can directly use `tools` to process raw NumPy arrays — for instance, in custom tools or when integrating Sigima into other projects.
> ⚠️ `sigima.tools` is not intended as a general-purpose NumPy extension. Its purpose is to **fill in the gaps** of common scientific libraries (NumPy, SciPy, scikit-image, etc.), offering consistent tools for signal/image processing in the context of Sigima and similar projects.
---
## Usage Outside Sigima
Although Sigima is designed primarily for object-based processing, some of its core functions are useful on their own.
For instance, the [DataLab](https://datalab-platform.com) project — an open-source platform for signal/image processing — uses many functions from `sigima.tools` independently of the object model. This demonstrates how `sigima.tools` can serve as a **lightweight utility layer** in scientific and industrial Python applications, even when the object model is not used directly.
To maintain this flexibility and avoid confusion, the distinction between `tools` (array-based) and `computation` (object-based) is intentional and explicit.
---
## 📦 Installation
```bash
pip install sigima
```
Or in a development environment:
```bash
git clone https://github.com/DataLab-Platform/Sigima.git
cd Sigima
pip install -e .
```
---
## 📚 Documentation
📖 Full documentation (in progress) is available at:
👉
> Want to use Sigima inside DataLab with GUI tools?
> Check out the full platform: [DataLab](https://datalab-platform.com/)
---
## ⚙️ Architecture
Sigima is organized by data type:
```text
sigima/
├── tools/ # Low-level NumPy-based algorithms supporting some computation functions
├── proc/ # High-level processing functions operating on SignalObj/ImageObj
│ ├── base/ # Common processing functions
│ ├── signal/ # 1D signal processing
│ └── image/ # 2D image processing
```
Each domain provides:
- Low-level functions operating on NumPy arrays
- High-level functions operating on `SignalObj` or `ImageObj`
---
## 🧪 Testing
Sigima comes with unit tests based on `pytest`.
To run all tests:
```bash
pytest
```
To run GUI-assisted validation tests (optional):
```bash
pytest --gui
```
---
## 🧠 License
Sigima is distributed under the terms of the BSD 3-Clause license.
See [LICENSE](./LICENSE) for details.
---
## 🤝 Contributing
Bug reports, feature requests and pull requests are welcome!
See the [CONTRIBUTING](https://datalab-platform.com/en/contributing) guide to get started.
---
---
© DataLab Platform Developers