Introducing SpectraFit: An Open-Source Tool for Interactive Spectral Analysis
Bibliographic record
Abstract
In chemistry, analyzing spectra through peak fitting is a crucial task that helps scientists extract useful quantitative information about a sample's chemical composition or electronic structure. To make this process more efficient, we have developed a new open-source software tool called SpectraFit. This tool allows users to perform quick data fitting using expressions of distribution and linear functions through the command line interface (CLI) or Jupyter Notebook, which can run on Linux, Windows, and MacOS, as well as in a Docker container. As part of our commitment to good scientific practice, we have introduced an output file-locking system to ensure the accuracy and consistency of information. This system collects input data, results data, and the initial fitting model in a single file, promoting transparency, reproducibility, collaboration, and innovation. To demonstrate SpectraFit's user-friendly interface and the advantages of its output file-locking system, we are focusing on a series of previously published iron-sulfur dimers and their XAS spectra. We will show how to analyze the XAS spectra via CLI and in a Jupyter Notebook by simultaneously fitting multiple data sets using SpectraFit. Additionally, we will demonstrate how SpectraFit can be used as a black box and white box solution, allowing users to apply their own algorithms to engineer the data further. This publication, along with its Supporting Information and the Jupyter Notebook, serves as a tutorial to guide users through each step of the process. SpectraFit will streamline the peak fitting process and provide a convenient, standardized platform for users to share fitting models, which we hope will improve transparency and reproducibility in the field of spectroscopy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".