28 lines
1.0 KiB
Markdown
28 lines
1.0 KiB
Markdown
# MLXID: Eta Interpolation
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## Overview
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The project provides functions, scripts, and notebooks for:
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- Generating and visualizing eta interpolation lookup tables (LUTs)
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- Processing raw detector data (calibration and cluster finding)
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- Mapping non-uniform 2D distributions to uniform distributions using Rosenblatt or DoubleCDF methods
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## Example Workflows
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### Eta Interpolation for MC (see `Examples/etaInterpolation_MC.ipynb`)
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- Generate a non-uniform 2D distribution (e.g., spiral)
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- Build 2D histograms and LUTs using Rosenblatt or DoubleCDF methods
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- Map (x, y) to (u, v) for uniformity
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- Visualize results and evaluate residuals
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### Eta Interpolation for SiemenStar data (see `Examples/etaInterpolation_SiemenStar.ipynb`)
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- Configure measurement parameters (ROI, calibration files, LUTs)
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- Initialize and process raw detector frames
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- Generate and visualize interpolated 2D histograms
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## Requirements
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- Python 3.8+
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- numpy, matplotlib, glob
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- ROOT (for histogramming and advanced analysis)
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- Jupyter Notebook (for interactive analysis)
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