Images aren't just pixels—they are structured data.
BLITZ bridges the gap between lightweight desktop viewing and scientific array inspection.
(Click animation to open full size)
A high-performance, matrix-based image viewer designed for efficiently exploring both massive image datasets and single-image analysis workflows.
Download the latest release for Windows and Ubuntu
No installation required — simply download and run the executable.
BLITZ (Bulk Loading and Interactive Time series Zonal analysis) is a high-performance, matrix-based image exploration and analysis tool designed for efficiently managing both massive datasets and single-image analysis (e.g. high-speed camera footage, optical plasma diagnostics, and fast-framing scientific imaging).
It was originally developed and initially implemented by Philipp Mattern during his time at INP Greifswald (Leibniz Institute for Plasma Science and Technology) to analyze complex low-temperature plasma discharge dynamics.
It is actively maintained and further developed as part of his independent engineering work at M.E.S.S. – Mattern Engineering & Software Solutions.
Starting with Version 2.0 a fully refactored architecture with improved performance, stability, and maintainability was introduced.
BLITZ is the interactive viewer in the WETTER framework: Raw Data → DAMPF → KEIM → WOLKE → BLITZ. For the full pipeline, ecosystem overview, and links to all modules, see:
WETTER Framework — wetter.mess.engineering
DPG Symposium presentation (architecture and BLITZ–WOLKE integration):
📄 BLITZ_WOLKE_DPG25V2_Compact.pdf
- High-Performance Data Handling: Efficiently processes very large datasets (e.g. loading, scaling, and converting ~21,000 RGB images (~2.5 GB raw data) into ~6.2 GB of grayscale matrix data in ~30 s on a standard gaming laptop).
- Easy Data Handling: Drag-and-drop support for image, video, and NumPy matrix (*.npy) formats.
- Easy to Use: Automatic resource management for small and large datasets.
- User-Friendly Interface: Intuitive GUI with mouse-based navigation and shortcuts.
- Advanced Image Processing: Matrix-based processing with fast, Numba-accelerated statistics.
- Live View: Support for real USB cameras and simulated data streams.
- Built on Python: Using Qt and PyQtGraph for high performance and flexibility.
- Full Documentation Index
- Quick Start Guide
- Features & Tabs Explained
- Missing & Planned Features
- Optimization Report
- Data Sources & Build Variants
Run BLITZ in a browser via Docker. See: docker/README.md
Linux App Store packaging lives under flatpak/
(engineering.mess.BLITZ, M.E.S.S. branding). Build and Flathub notes are there.
To compile and develop locally:
-
Clone the repository:
$ git clone https://github.com/PiMaV/BLITZ.git $ cd BLITZ -
Set up a virtual environment and install dependencies with uv:
$ pip install uv $ uv sync $ uv run python -m blitz
Early development of BLITZ was supported by Richard Krieg (student assistant) until v1.3.0 / January 2025, including refactoring, bug fixing, and feature development during the INP-funded project phase.
- Example Dataset: KinPen Science Example Set
High-speed camera recordings of a cold atmospheric pressure plasma jet (kINPen), ideal for testing BLITZ's fast matrix loading and time-series analysis capabilities. - Plasma Data Repository: INPTDAT
Explore disciplinary datasets for low-temperature plasma physics or contribute your own.
BLITZ is licensed under the GNU General Public License v3.0.