Contents¶
- Getting started with Non-Linear Least-Squares Fitting
- Downloading and Installation
- Release Notes
- Version 1.0.0 Release Notes
- Version 0.9.15 Release Notes
- Version 0.9.14 Release Notes
- Version 0.9.13 Release Notes
- Version 0.9.12 Release Notes
- Version 0.9.10 Release Notes
- Version 0.9.9 Release Notes
- Version 0.9.6 Release Notes
- Version 0.9.5 Release Notes
- Version 0.9.4 Release Notes
- Version 0.9.3 Release Notes
- Version 0.9.0 Release Notes
- Getting Help
- Frequently Asked Questions
- What’s the best way to ask for help or submit a bug report?
- Why did my script break when upgrading from lmfit 0.8.3 to 0.9.0?
- I get import errors from IPython
- How can I fit multi-dimensional data?
- How can I fit multiple data sets?
- How can I fit complex data?
- Can I constrain values to have integer values?
- How should I cite LMFIT?
- I get errors from NaN in my fit. What can I do?
Parameter
andParameters
- Performing Fits and Analyzing Outputs
- The
minimize()
function - Writing a Fitting Function
- Choosing Different Fitting Methods
MinimizerResult
– the optimization result- Getting and Printing Fit Reports
- Using a Iteration Callback Function
- Using the
Minimizer
class Minimizer.emcee()
- calculating the posterior probability distribution of parameters
- The
- Modeling Data and Curve Fitting
- Built-in Fitting Models in the
models
module- Peak-like models
- Linear and Polynomial Models
- Step-like models
- Exponential and Power law models
- User-defined Models
- Example 1: Fit Peak data to Gaussian, Lorentzian, and Voigt profiles
- Example 2: Fit data to a Composite Model with pre-defined models
- Example 3: Fitting Multiple Peaks – and using Prefixes
- Calculation of confidence intervals
- Bounds Implementation
- Using Mathematical Constraints
- Examples gallery
- Examples from the documentation