HyperCube is a python-based spectral fitting tool designed to make integral field spectroscopic (IFS), or hyperspectral data analysis more interactive and intuitive, while preserving automation and repeatability. The tool combines a user-friendly PyQT5 GUI with the robust and flexible fitting capabilities of lmfit, and is particularly well-suited for interactive and batch process spectral modeling of 3D spectral data.

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RedPack is a scalable Python3 pipeline that transforms sparse, noisy observational data into high-resolution spatial models using Gaussian Process Regression (GPR). Designed for datasets with nonuniform sampling and variable measurement errors, RedPack automates interpolation, uncertainty propagation, and validation—delivering actionable insights for industries where spatial modeling is critical (e.g., geospatial analytics, remote sensing, and industrial IoT).

Key Value Propositions

1. Data-Centric Preprocessing

   • Automatically filters noisy inputs using probabilistic membership scoring (e.g., akin to outlier detection in sensor networks).

   • Adapts to missing data and variable sampling density without manual tuning.

2. Machine Learning at Core

   • Leverages Gaussian Process Regression (scikit-learn) with a rational quadratic kernel to model multi-scale spatial trends.

   • Hyperparameters (length scale, mixture) are auto-optimized per dataset, ensuring robustness across domains.

3. Uncertainty-Aware Predictions

   • Propagates measurement errors via covariance ellipses, providing confidence intervals for every interpolated point.

   • Outputs continuous, high-resolution (5" equivalent) models from sparse inputs—ideal for gap-filling in irregularly sampled data.

4. Validation & Explainability

   • Benchmarked against ground-truth references (e.g., satellite thermal data in demos).

   • Quantifies performance via spatial correlation metrics (>90% in validation tests).

5. Modular & Scalable

   • Pipeline-ready: Integrates with existing data workflows (CSV, FITS, or APIs).

   • Use-case agnostic: Apply to logistics (demand mapping), environmental monitoring (pollution dispersion), or precision agriculture (soil variability).