Reference

Complete reference documentation for all NLSQ modules, functions, and classes.

Note

This section is for looking up specific functions and parameters. For learning how to use NLSQ, see Tutorials.

Reference Sections

• Core API Reference
  • Primary Functions
  • Classes
  • Return Values
  • Error Handling
  • See Also
• Model Functions Reference
  • Using Built-in Functions
  • Peak Functions
  • Exponential Functions
  • Sigmoid Functions
  • Power Functions
  • Creating Custom Functions
  • See Also
• Large Dataset API Reference
  • Overview
  • curve_fit_large
  • LargeDatasetFitter
  • AdaptiveHybridStreamingOptimizer
  • HybridStreamingConfig
  • Memory Estimation
  • Checkpointing
  • Parallel Processing
  • See Also
• Global Optimization
  • Overview
  • Installation
  • CMA-ES Global Optimization
  • Multi-Start Optimization
  • API Reference
• Orchestration Components
  • Overview
  • DataPreprocessor
  • OptimizationSelector
  • CovarianceComputer
  • StreamingCoordinator
  • Data Classes
  • Feature Flags
  • See Also
• Facades
  • Overview
  • OptimizationFacade
  • StabilityFacade
  • DiagnosticsFacade
  • Benefits
  • See Also
• Configuration Reference
  • YAML Configuration
  • Configuration Sections
  • Environment Variables
  • Programmatic Configuration
  • Presets
  • Default Values
  • See Also
• Visualization API
  • FitVisualizer
  • CurveFitResult Plotting
  • See Also
• CLI Reference
  • Entry Points
  • Global Options
  • Commands
  • Quick Reference
  • Workflow Configuration
  • Exit Codes
  • Scripting Examples
  • JSON Output Format
  • See Also

Core API

The main functions you’ll use for curve fitting:

Function	Description
nlsq.fit()	Unified fit function with preset-based configuration (recommended)
nlsq.curve_fit()	Drop-in replacement for scipy.optimize.curve_fit
nlsq.curve_fit_large()	Automatic handling for large datasets
nlsq.LeastSquares	Low-level least squares solver class

See Core API Reference for detailed API documentation.

Quick Links

Core Modules

• nlsq.minpack module - curve_fit implementation

• nlsq.least_squares module - Core optimization

• nlsq.functions module - Model functions library

Large Datasets

• nlsq.large_dataset module - Large dataset handling

• nlsq.adaptive_hybrid_streaming module - Streaming optimization

• nlsq.hybrid_streaming_config module - Hybrid streaming configuration

Utilities

• nlsq.validators module - Input validation

• nlsq.diagnostics package - Model Health Diagnostics (identifiability, gradient health, parameter sensitivity)

• nlsq.callbacks module - Progress callbacks

Full Module Documentation

For auto-generated documentation of all modules, see NLSQ API Reference.

See Also

• Tutorials - Step-by-step tutorials

• How-To Guides - Task-oriented guides

• Concepts & Explanations - Conceptual documentation