Fit reflectivity data to models and generate parameter reports.
npx skills add https://github.com/mdoucet/analyzer --skill fitting
Please help me install this Agent Skill. Skill: fitting Source: https://github.com/mdoucet/analyzer/tree/main/skills/fitting Command: npx skills add https://github.com/mdoucet/analyzer --skill fitting
Automates the end-to-end fitting workflow for reflectivity data, enabling rapid fitting of combined data to a model, assessment of fit quality, and iterative model refinement.
Provide a data file and a model specification, then run the full fitting workflow to obtain results and a report.
High-intent search queries and answers about installing and using this skill.
Reflectivity data fitting is automated by providing combined data and model definitions to generate best-fit parameters, chi-squared assessments, and plots. The workflow evaluates fit quality across experiments and allows iterative model refinement by widening bounds.
Fitting combined reflectivity data from multiple experiments requires specifying a combined_data_dir and model definitions. The workflow processes these inputs to produce consolidated fitted parameters, reports, and plots for assessing overall model quality.
Iterating reflectivity models is supported by creating temporary models to re-fit and improve quality. You can refine parameters and widen bounds during model evaluation to achieve better fits and updated parameter estimates.
MCMC for reflectivity modeling helps assess parameter uncertainties by exploring the probability space to derive best-fit parameters. The workflow evaluates chi-squared and uncertainty metrics, generating reports to quantify fit confidence.
No specific dependencies or components are required to run reflectivity fitting workflows. You provide inputs from combined_data_dir and results_dir with model definitions and data files to output fitted parameters, reports, and plots.
Widening parameter bounds during reflectivity model evaluation is necessary when initial fitting yields poor chi-squared values or unrealistic parameter estimates. The workflow supports temporary model creation to re-fit and improve quality iteratively.
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