library and scripts to optimize cuts and build IRFs from event-lists
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Updated
Mar 23, 2018 - Python
library and scripts to optimize cuts and build IRFs from event-lists
Minimal code and trained models to recreate the results in the paper 'Deep learning with photosensor timing information as a background rejection method for the Cherenkov Telescope Array' by S. Spencer et al. (2021).
Gamma-Ray Event Reconstruction with Uncertainty Models
Display CTA train and bus arrival times on an LCD using an Arduino Yun
Python Client for Chicago Transit Authority
TableAnnotation is a semantic annotation tool for tables leveraging three steps: table preprocessing, entity lookup and annotation (Cell-Entity Annotation, Column-Type Annotation, Column-Pair Annotation)
Software plugins for EGOPY platform
Chicago Transit Autority's station and Lines data
CTA simulator based on ctools with additional native functionalities
Unified trend-following systems implemented in pure NumPy with comprehensive validation and testing. This implementation follows the theoretical framework presented in Sepp & Lucic (2025) "The Science and Practice of Trend-following Systems".
64x64 LED matrix display that shows transit and commute ETAs using CTA, Divvy, and Transit App APIs
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