Reliable parameter extraction from experimental data is essential for quantitative analysis across spectroscopy, diffraction, photoluminescence, chromatography, microscopy, and time-resolved measurements. However, nonlinear fitting often remains difficult to reproduce, especially when complex models, correlated parameters, uncertain derived quantities, and user-dependent fitting choices are involved. We present FitED, a Python-based desktop application for nonlinear fitting of one-dimensional scientific data that combines an accessible graphical interface with a transparent and flexible numerical backend. FitED supports conventional peak profiles, including Gaussian, Lorentzian, Pseudo-Voigt, and exact area-normalized Voigt functions, as well as arbitrary user-defined analytical models for broader experimental applications. The software integrates local and global-search-assisted optimization strategies, automated model initialization, repeated stability testing, parameter-correlation analysis, and covariance-based propagation of uncertainty for derived quantities. By combining interactive usability with uncertainty-aware analysis and structured export of fitting results, FitED provides a practical platform for reproducible and interpretable fitting of experimental data. The software is intended to support both routine analysis and advanced model evaluation while preserving the parameter-level control required by experimental researchers.

翻译：从实验数据中可靠提取参数是光谱学、衍射、光致发光、色谱、显微成像及时间分辨测量等领域定量分析的关键。然而，非线性拟合往往难以复现，尤其是在涉及复杂模型、参数相关性、不确定衍生量及用户依赖的拟合选择时。我们提出FitED——一款基于Python的一维科学数据非线性拟合桌面应用程序，它兼具易用的图形界面与透明灵活的计算后端。FitED支持常规峰形函数，包括高斯、洛伦兹、伪Voigt及精确面积归一化Voigt函数，并可加载任意用户自定义的解析模型以拓展实验应用。该软件整合了局部搜索与全局搜索辅助优化策略、自动模型初始化、重复稳定性测试、参数相关性分析以及基于协方差的衍生量不确定度传播方法。通过将交互式易用性、不确定度感知分析与结构化的拟合结果导出相结合，FitED为可复现且可解释的实验数据拟合提供了实用平台。该软件旨在支持常规分析及高级模型评估，同时保留实验研究人员所需的参数级控制能力。