Software & Models
Below is a curated list of software packages, machine-learning pipelines, and analysis tools that I have developed or supervised. These tools support research in helioseismology, solar imaging, space-weather forecasting, and automated feature detection.
1) FASTARR — FArSide Trained Active Region Recognition
Type: Python (Deep Learning)
Description:
A U-Net–based machine learning pipeline for detecting far-side solar active regions using helioseismic phase-shift maps. The model is trained on AR masks derived from far-side EUV observations and Earth-side magnetograms, providing robust scientific feature identification for heliophysics and space-weather applications.
Persistent Identifier / URL:
🔗 https://github.com/Amr1001Hamada/-FASTARR—FArSide-Trained-Active-Region-Recognition
2) Automated Coronal Hole Identification Algorithm
Type: MATLAB
Description:
Code for automated identification and mapping of coronal holes (CHs) from EUV synoptic maps. The workflow uses segmentation, thresholding, and statistical refinements to produce high-quality CH boundaries suitable for synoptic charts, space-weather modeling, and machine-learning pipelines.
Persistent Identifier / URL:
🔗 https://github.com/Amr1001Hamada?tab=repositories#:~:text=Automated%2DCoronal%2DHole%2DIdentification%2DCode
3) Far-Side Active Region Identification Algorithm
Type: MATLAB
Description:
Automated active region identification code for far-side helioseismic and EUV-based maps. Uses intensity, morphology, and statistical thresholds to produce AR masks for far-side magnetic reconstructions and validation of helioseismic signatures.
Persistent Identifier / URL:
🔗 https://github.com/Amr1001Hamada/FarSide-Automated-Active-Region-Identification-Code
4) Deep Learning Model for High-Speed Solar Wind Stream Prediction
Authors: Abraham-Alowonle, Joseph-Judah (Rights Holder), Hamada, Amr (Supervisor), et al.
Type: Python (Deep Learning)
Description:
A deep learning framework to predict high-speed solar wind streams based on spatio-temporal CH evolution. Includes preprocessing, model training scripts, and evaluation tools for forecasting studies.
Persistent Identifier / URL:
🔗 https://zenodo.org/records/14849922