Contents

• Getting started with Non-Linear Least-Squares Fitting
• Downloading and Installation
  • Prerequisites
  • Downloads
  • Installation
  • Development Version
  • Testing
  • Acknowledgements
  • Copyright, Licensing, and Re-distribution
• 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
    • Summary
    • Impact
    • Discussion
• 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?
    • nan_policy
    • Common sources of NaN
• Parameter and Parameters
  • The Parameter class
  • The Parameters class
  • Simple Example
• Performing Fits and Analyzing Outputs
  • The minimize() function
  • Writing a Fitting Function
  • Choosing Different Fitting Methods
  • MinimizerResult – the optimization result
    • Goodness-of-Fit Statistics
    • Akaike and Bayesian Information Criteria
    • Uncertainties in Variable Parameters, and their Correlations
  • Getting and Printing Fit Reports
  • Using a Iteration Callback Function
  • Using the Minimizer class
  • Minimizer.emcee() - calculating the posterior probability distribution of parameters
• Modeling Data and Curve Fitting
  • Motivation and simple example: Fit data to Gaussian profile
  • The Model class
    • Model class Methods
    • Model class Attributes
    • Determining parameter names and independent variables for a function
    • Explicitly specifying independent_vars
    • Functions with keyword arguments
    • Defining a prefix for the Parameters
    • Initializing model parameters
    • Using parameter hints
    • Saving and Loading Models
  • The ModelResult class
    • ModelResult methods
    • ModelResult attributes
    • Calculating uncertainties in the model function
    • Saving and Loading ModelResults
  • Composite Models : adding (or multiplying) Models
• Built-in Fitting Models in the models module
  • Peak-like models
    • GaussianModel
    • LorentzianModel
    • SplitLorentzianModel
    • VoigtModel
    • PseudoVoigtModel
    • MoffatModel
    • Pearson7Model
    • StudentsTModel
    • BreitWignerModel
    • LognormalModel
    • DampedOcsillatorModel
    • DampedHarmonicOcsillatorModel
    • ExponentialGaussianModel
    • SkewedGaussianModel
    • SkewedVoigtModel
    • DonaichModel
  • Linear and Polynomial Models
    • ConstantModel
    • LinearModel
    • QuadraticModel
    • PolynomialModel
  • Step-like models
    • StepModel
    • RectangleModel
  • Exponential and Power law models
    • ExponentialModel
    • PowerLawModel
  • User-defined Models
    • ExpressionModel
  • 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
  • Method used for calculating confidence intervals
  • A basic example
  • Working without standard error estimates
  • An advanced example for evaluating confidence intervals
  • Confidence Interval Functions
• Bounds Implementation
• Using Mathematical Constraints
  • Overview
  • Supported Operators, Functions, and Constants
  • Using Inequality Constraints
  • Advanced usage of Expressions in lmfit
• Examples gallery
• Examples from the documentation