# 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)