Python reader for Bruker timsTOF (.d) data: DDA/DIA/PRM, fast centroiding and noise filtering
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Updated
Sep 25, 2026 - Python
Python reader for Bruker timsTOF (.d) data: DDA/DIA/PRM, fast centroiding and noise filtering
This repository hosts the benchmark suite for Kometra, featuring a synthetic dataset pipeline, testing for 16 centroiding algorithms, and metric reanalysis. It justifies selecting the Asymmetric Quadrant Profile (AQP), the only algorithm integrated into Kometra.
Fast Shack-Hartmann wavefront sensor pipeline: real-time wavefront reconstruction, atmospheric turbulence characterization (Fried parameter r0, coherence time τ0), and deformable-mirror actuator maps with inter-actuator coupling. Self-contained C real-time core (~0.3 ms/frame) + Python toolkit. ISRO BAH 2026 - Problem Statement 9.
Adaptive star-centroiding algorithm selection and stress-test benchmark under simulated spaceborne noise.
Synthetic star-image simulator and visualization layer for spacecraft attitude determination
To associate your repository with the centroiding topic, visit your repo's landing page and select "manage topics."