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4 changes: 4 additions & 0 deletions docs/api/index.md
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---
description: Complete API reference for lonkit. Documentation for LON, CMLON, BasinHoppingSampler, LONVisualizer, and configuration classes.
---

# API Reference

Complete API documentation for lonkit.
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4 changes: 4 additions & 0 deletions docs/getting-started/concepts.md
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description: Description of Local Optima Networks, fitness landscapes, basin hopping, and CMLONs. Core concepts for optimization analysis.
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# Core Concepts

This page explains the key concepts behind Local Optima Networks and how lonkit implements them.
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4 changes: 4 additions & 0 deletions docs/getting-started/installation.md
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description: Installation of lonkit Python library via pip or from source.
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# Installation

## Requirements
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4 changes: 4 additions & 0 deletions docs/getting-started/quickstart.md
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description: Getting started with lonkit. Basic API reference and guides on constructing, analyzing, and visualizing Local Optima Networks.
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# Quick Start

This guide will get you up and running with lonkit in just a few minutes.
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4 changes: 4 additions & 0 deletions docs/index.md
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description: lonkit - Python library for constructing, analyzing, and visualizing Local Optima Networks (LONs) for continuous and discrete optimization.
---

# lonkit

**Local Optima Networks for Continuous Optimization**
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23 changes: 23 additions & 0 deletions docs/overrides/main.html
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{% extends "base.html" %} {% block extrahead %}
{{ super() }}
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "SoftwareApplication",
"name": "lonkit",
"description": "Local Optima Networks (LONs) for continuous optimization - Python library for constructing, analyzing, and visualizing fitness landscapes.",
"url": "https://helix-agh.github.io/lonkit",
"applicationCategory": "DeveloperApplication",
"operatingSystem": "Python 3.10+",
"programmingLanguage": "Python",
"license": "https://opensource.org/licenses/MIT",
"author": {
"@type": "Organization",
"name": "HELIX AGH",
"url": "https://github.com/helix-agh"
},
"downloadUrl": "https://pypi.org/project/lonkit/",
"keywords": "optimization, fitness landscape, local optima networks, basin hopping, evolutionary algorithms, visualization, multi-dimensional"
}
</script>
{% endblock %}
4 changes: 4 additions & 0 deletions docs/user-guide/analysis.md
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description: Analyzing Local Optima Networks using computed metrics. Funnel analysis, neutrality, strength metrics, and sink identification.
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# Analysis Guide

This guide explains how to analyze LONs and interpret the computed metrics.
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4 changes: 4 additions & 0 deletions docs/user-guide/examples.md
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description: Complete examples demonstrating lonkit capabilities on benchmark optimization problems like Rastrigin, Ackley, and Sphere functions.
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# Examples

Complete examples demonstrating lonkit's capabilities.
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4 changes: 4 additions & 0 deletions docs/user-guide/sampling.md
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description: Configuration and usage of Basin-Hopping sampling for constructing Local Optima Networks. Perturbation, step size, and stopping condition controls.
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# Sampling Guide

This guide covers how to configure Basin-Hopping sampling for LON construction.
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4 changes: 4 additions & 0 deletions docs/user-guide/visualization.md
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description: 2D and 3D visualizations of Local Optima Networks. Export plots as PNG, generate animated GIFs, and customize visual styling.
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# Visualization Guide

This guide covers how to create visualizations of Local Optima Networks.
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1 change: 1 addition & 0 deletions mkdocs.yml
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theme:
name: material
custom_dir: docs/overrides
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palette:
- scheme: default
primary: indigo
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30 changes: 30 additions & 0 deletions pyproject.toml
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description = "Local Optima Networks (LONs) for continuous optimization - Python implementation"
readme = "README.md"
requires-python = ">=3.10"
authors = [{name = "HELIX AGH"}]
license = {text = "MIT"}
urls = {Homepage = "https://helix-agh.github.io/lonkit", Documentation = "https://helix-agh.github.io/lonkit", Repository = "https://github.com/helix-agh/lonkit", Issues = "https://github.com/helix-agh/lonkit/issues"}
keywords = [
"optimization",
"fitness-landscape",
"local-optima-networks",
"basin-hopping",
"evolutionary-algorithms",
"continuous-optimization",
"discrete-optimization",
"metaheuristics",
"fitness-landscape-analysis",
"visualization",
"multi-dimensional"
]
classifiers = [
"Development Status :: 3 - Alpha",
"Intended Audience :: Science/Research",
"Intended Audience :: Developers",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Topic :: Scientific/Engineering :: Mathematics",
"Topic :: Scientific/Engineering :: Visualization",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
]
dependencies = [
"numpy>=1.24.0",
"scipy>=1.10.0",
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