Generate and modify Refl1d Python model definitions for neutron reflectometry fitting.
npx skills add https://github.com/mdoucet/analyzer --skill models-mdoucet
Please help me install this Agent Skill. Skill: models Source: https://github.com/mdoucet/analyzer/tree/main/skills/models Command: npx skills add https://github.com/mdoucet/analyzer --skill models-mdoucet
This Skill documents and explains Refl1d Python model files used to describe layered samples for neutron reflectometry fitting, helping users choose, create, and modify models so they can run reliable fits and sensitivity studies.
Use the models skill to inspect the cu_thf model, summarize its layer stack and parameter ranges, and generate a standalone fit script for the data file REFL_218281_combined_data_auto.txt.
High-intent search queries and answers about installing and using this skill.
Refl1d layer models require a create_fit_experiment(q, dq, data, errors) function that constructs a refl1d.experiment.Experiment from SLD and Slab definitions. You must set explicit parameter.range values for thickness, material rho, and interface widths to run reliable fits.
The required signature is create_fit_experiment(q, dq, data, errors), accepting q, dq, data, and errors arrays. It must return a refl1d.experiment.Experiment with explicit parameter.range settings for slab thickness, material rho, and interface widths.
You set parameter ranges by applying explicit parameter.range settings within your Refl1d model definition. This directly constrains the fitting bounds for material rho, layer thickness, and interface widths during the neutron reflectometry analysis.
Yes, you can generate a standalone fit script from an existing Refl1d model and an experimental reflectivity data file. The skill produces these scripts to run sensitivity studies and focused fitting campaigns without modifying the original Python model definition.
You adjust parameter ranges by creating a temporary Refl1d model with modified thickness and SLD parameter.range settings. This allows you to target specific variables for a focused fitting campaign without altering the original Python model file in the models directory.
Yes, the skill supports selecting between available reflectometry layer model variants, such as cu_thf and its minimal or oxide versions. It documents how layers are ordered and explains how to choose the appropriate model for your experimental data.
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