ganX -- generate artificially new XRF a python library to generate MA-XRF raw data out of RGB images

TL;DR

ganX is a Python library that synthesizes XRF macro maps from RGB images using a Monte Carlo approach based on pigment signal databases, enabling realistic XRF data generation for research and analysis.

Contribution

The paper introduces ganX, the first tool to generate synthetic XRF data from RGB images using a novel Monte Carlo method based on pigment signal databases.

Findings

Library is available on PyPi and GitHub.

Generates realistic XRF maps from RGB images.

Uses a Monte Carlo sampling approach.

Abstract

In this paper we present the first version of ganX -- generate artificially new XRF, a Python library to generate X-ray fluorescence Macro maps (MA-XRF) from a coloured RGB image. To do that, a Monte Carlo method is used, where each MA-XRF pixel signal is sampled out of an XRF signal probability function. Such probability function is computed using a database of couples (pigment characteristic XRF signal, RGB), by a weighted sum of such pigment XRF signal by proximity of the image RGB to the pigment characteristic RGB. The library is released to PyPi and the code is available open source on GitHub.