scipy-curve-fit

What problem does it solve?

Fitting nonlinear models to experimental data often requires manual trial-and-error and can be error-prone. This Skill automates the process using scipy.optimize.curve_fit to estimate parameters from noisy measurements.

Core Features & Use Cases

• Nonlinear least-squares estimation: Fit arbitrary model functions to data with bounds and initial guesses.
• Quick model validation: Compute goodness-of-fit metrics such as R-squared and RMSE to assess model quality.
• Use Case: Analyze a temperature response by fitting a first-order or custom nonlinear model to recorded time-series data.

Quick Start

Fit your experimental time-series data to a nonlinear model using curve_fit and report the estimated parameters and fit quality.