Examples
========

This section provides practical examples demonstrating various features of egraph.

.. toctree::
   :maxdepth: 2

   sgd
   stress_majorization
   kamada_kawai
   sgd_3d
   sgd_hyperbolic_2d
   sgd_spherical_2d
   sgd_torus
   overwrap_removal

Overview
--------

These examples demonstrate real-world usage of egraph's features. Each example is self-contained and can be run independently.

Layout Algorithms
-----------------

* :doc:`sgd` - Stochastic Gradient Descent for fast, scalable layouts
* :doc:`stress_majorization` - High-quality layouts through stress minimization
* :doc:`kamada_kawai` - Spring-based layout algorithm

Advanced Drawing Spaces
------------------------

* :doc:`sgd_3d` - Three-dimensional graph layouts
* :doc:`sgd_hyperbolic_2d` - Hyperbolic space for hierarchical graphs
* :doc:`sgd_spherical_2d` - Spherical layouts for global networks
* :doc:`sgd_torus` - Torus layouts with periodic boundaries

Specialized Features
--------------------

* :doc:`overwrap_removal` - Eliminate node overlaps while preserving structure

Quick Example
-------------

Here's a simple example of creating a graph and applying a layout algorithm:

.. testcode:: python

    import networkx as nx
    import egraph as eg
    
    # Create a graph from NetworkX
    nx_graph = nx.les_miserables_graph()
    graph = eg.Graph()
    indices = {}
    for u in nx_graph.nodes:
        indices[u] = graph.add_node(u)
    for u, v in nx_graph.edges:
        graph.add_edge(indices[u], indices[v], (u, v))
    
    # Create an initial drawing
    drawing = eg.DrawingEuclidean2d.initial_placement(graph)
    
    # Apply a layout algorithm
    sm = eg.StressMajorization(graph, drawing, lambda _: 100)
    sm.run(drawing)
    
    # Extract node positions
    pos = {u: (drawing.x(i), drawing.y(i)) for u, i in indices.items()}
    
    # Visualize with NetworkX
    import matplotlib.pyplot as plt
    nx.draw(nx_graph, pos)