Metadata-Version: 2.4
Name: correction_ea_vhr
Version: 0.2
Summary: Neural network correction tool for activation energy in VHR thermoluminescence
Home-page: https://github.com/Eduardo-TePe/correction_ea_vhr
Author: JF Benavente & JE Tenopala
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Requires-Dist: tensorflow
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: openpyxl
Dynamic: author
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# Correction of Activation Energy Affected by Variable Heating Rate (EaVHRCorrector)
===================================================================================

This project presents the development of a tool based on artificial intelligence to assist researchers in the correction of kinetic parameters obtained through the deconvolution of thermoluminescence TL glow curves. The correction is particularly useful when the results are affected by anomalous behavior associated with the heating rate, mainly due to Thermal Quenching.

Overview
--------
The model takes as input the area under each TL emission peak, which is a magnitude easily obtained from the analysis of experimental data. The input to the model is a vector of differences in area:

    X ≡ [∆A_medium, ∆A_median, ∆A_maximum, ∆A_minimum]

Features
--------
- AI-based correction of activation energy (Ea) affected by variable heating rate
- Designed for thermoluminescence glow curve analysis
- Accepts Excel files as input
- Provides corrected Ea, uncertainty, and model prediction


Usage
-----
Here is a minimal example of how to use the tool:

    from correction_ea_vhr.corrector import EaVHRCorrector
    import pandas as pd

    # Load your data
    df = pd.read_excel("TLGC.xlsx")
    X = df.iloc[:, 0:4].to_numpy()
    Y = df.iloc[:, 4:5].to_numpy()

    # Use the corrector
    modelo = EaVHRCorrector()
    media, incertidumbre, pred = modelo.corregir(X, Y)

Input Format
------------
The input Excel file should contain 5 columns:
1. ∆A_medium  
2. ∆A_median  
3. ∆A_maximum  
4. ∆A_minimum  
5. Experimental activation energy (Ea)

Output
------
- media: Corrected activation energy (mean)
- incertidumbre: Associated uncertainty
- pred: Model prediction array
