In Summary, the Jupyter Notebook interface is an incredibly useful tool that allows for interactive changes to fitting parameters, immediate results, and a comprehensive analysis of all aspects of data analysis (code, analysis, and visualization) in one convenient location. Results can be tagged and compared for further analysis through the use of the versionization tool git. Figure Spectrafit's interactive Jupyter-Notebook interface is a powerful tool that allows users to explore the fit of complex 1 and 2. The top of the interface displays the fit for the pre-, rising-edge, and K-edge, and users can interactively explore the spectrum, fit, and contributions using the Plotly 108 feature. This feature includes built-in functions such as zooming, moving, and exporting, making it easy to manipulate and analyze the data. Additionally, the hover effects provide immediate energy and intensity feedback, enabling users to make quick and informed decisions. The bottom figures of the interface display important metrics, including BIC and AIC as bars, and the MSE is also displayed as an orange dotted line. As mentioned in the text, these three metric criteria should support the user in solving the fitting problem. Users can see a detailed comparison of three regions labeled I, II, and III. This comparison shows a strong energy similarity and a slight amplitude difference. This level of detail is important for users who need to understand the nuances of their data and make informed decisions based on that understanding. In this particular context, the utilization of box plots to represent the amplitude and center of complex 1 and 2 is a highly effective means of visualizing numerical data. The lower and higher extremes, as well as the interquartile range, are prominently displayed within these plots. Furthermore, the inclusion of a bar plot facilitates a direct comparison between the amplitude and center, with user-defined statistics employed for error estimation. Further details can be found in the SI -Jupyter. In essence, the user's chosen amplitude statistic is computed by taking the standard deviation of each complex and dividing it by the square root of three, which is the number of points. Meanwhile, the center statistic is determined by calculating the standard deviation of the difference between both complexes and dividing it by the square root of three as well. This process ensures accurate and precise measurements. The procedure to create these user-specific statistics is presented in Jupyter-SI -Advanced Section II. Adapted from Ref. ; Copyright 2023 America Chemical Society (ACS).