GAMMA_FLOW: Guided Analysis of Multi-label spectra by MAtrix Factorization for Lightweight Operational Workflows
Viola Rädle, Tilman Hartwig, Benjamin Oesen, Emily Alice Kröger, Julius Vogt, Eike Gericke, Martin Baron
GAMMA_FLOW是一个开源的Python包,用于对光谱数据进行实时分析。 它支持单组分和多组分光谱的分类、去噪、分解和异常检测。 而不是依靠大型的计算密集型模型,它采用监督方法的非负矩阵因子化(NMF)来减小尺寸。 这确保了快速、高效和适应性强的分析,同时降低了计算成本。 gamma_flow 实现了 90% 以上的分类精度,并实现了可靠的自动化光谱解释。 最初为伽马射线光谱开发,适用于任何类型的一维光谱数据。 作为专有软件的开放和灵活的替代方案,它支持研究和行业的各种应用。
GAMMA_FLOW is an open-source Python package for real-time analysis of spectral data. It supports classification, denoising, decomposition, and outlier detection of both single- and multi-component spectra. Instead of relying on large, computationally intensive models, it employs a supervised approach to non-negative matrix factorization (NMF) for dimensionality reduction. This ensures a fast, efficient, and adaptable analysis while reducing computational costs. gamma_flow achieves classification...