MLXID: Eta Interpolation

Overview

The project provides functions, scripts, and notebooks for:

• Generating and visualizing eta interpolation lookup tables (LUTs)
• Processing raw detector data (calibration and cluster finding)
• Mapping non-uniform 2D distributions to uniform distributions using Rosenblatt or DoubleCDF methods

Example Workflows

Eta Interpolation for MC (see Examples/etaInterpolation_MC.ipynb)

• Generate a non-uniform 2D distribution (e.g., spiral)
• Build 2D histograms and LUTs using Rosenblatt or DoubleCDF methods
• Map (x, y) to (u, v) for uniformity
• Visualize results and evaluate residuals

Eta Interpolation for SiemenStar data (see Examples/etaInterpolation_SiemenStar.ipynb)

• Configure measurement parameters (ROI, calibration files, LUTs)
• Initialize and process raw detector frames
• Generate and visualize interpolated 2D histograms

Requirements

• Python 3.8+
• numpy, matplotlib, glob
• ROOT (for histogramming and advanced analysis)
• Jupyter Notebook (for interactive analysis)