FitED: A User-Centric, Extensible Software Environment for Robust Peak-Profile and General Functional Data Fitting
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866917548019154944 |
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| author | Aboulsaad, Mustafa Mahmoud |
| author_facet | Aboulsaad, Mustafa Mahmoud |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_00172 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FitED: A User-Centric, Extensible Software Environment for Robust Peak-Profile and General Functional Data Fitting Aboulsaad, Mustafa Mahmoud Data Analysis, Statistics and Probability Materials Science Mathematical Software 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. |
| title | FitED: A User-Centric, Extensible Software Environment for Robust Peak-Profile and General Functional Data Fitting |
| topic | Data Analysis, Statistics and Probability Materials Science Mathematical Software |
| url | https://arxiv.org/abs/2605.00172 |