.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/gallery_hierarchical_viz.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_gallery_hierarchical_viz.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_gallery_hierarchical_viz.py:

Gallery Example: Hierarchical Parameter Visualization.

This example demonstrates the hierarchical visualization capabilities of Paroto,
showing how primary design parameters flow through models to constraints.

Features demonstrated:
- Automatic hierarchy extraction from OpenMDAO problems
- Multiple visualization formats (Mermaid, NetworkX, Cytoscape)
- Parameter level identification
- Graph analysis and statistics

.. GENERATED FROM PYTHON SOURCE LINES 12-169

.. code-block:: Python


    import openmdao.api as om

    from paroto.viz import (
        analyze_hierarchy,
        export_graphml,
        export_json,
        extract_hierarchy,
        generate_hierarchical_graph,
        generate_simple_graph,
        to_networkx,
    )


    def create_example_problem():
        """Create example optimization problem with parameter hierarchy.

        This creates a simple problem with:
        - 3 primary parameters (design variables)
        - 2 intermediate models
        - 2 constraints

        Returns
        -------
        om.Problem
            Configured OpenMDAO problem
        """
        prob = om.Problem()

        # Design variables (primary parameters)
        indeps = prob.model.add_subsystem("design", om.IndepVarComp(), promotes=["*"])
        indeps.add_output("length", val=10.0, units="m")
        indeps.add_output("width", val=5.0, units="m")
        indeps.add_output("height", val=3.0, units="m")

        # Intermediate model: compute volume
        prob.model.add_subsystem(
            "volume_calc",
            om.ExecComp(
                "volume = length * width * height",
                length={"units": "m"},
                width={"units": "m"},
                height={"units": "m"},
                volume={"units": "m**3"},
            ),
            promotes=["*"],
        )

        # Intermediate model: compute surface area
        prob.model.add_subsystem(
            "area_calc",
            om.ExecComp(
                "surface_area = 2 * (length*width + length*height + width*height)",
                length={"units": "m"},
                width={"units": "m"},
                height={"units": "m"},
                surface_area={"units": "m**2"},
            ),
            promotes=["*"],
        )

        # Constraint 1: Volume must be at least 100 m³
        prob.model.add_subsystem(
            "volume_constraint",
            om.ExecComp("volume_margin = volume - 100.0", units="m**3"),
            promotes=["*"],
        )

        # Constraint 2: Surface area must not exceed 200 m²
        prob.model.add_subsystem(
            "area_constraint",
            om.ExecComp("area_margin = 200.0 - surface_area", units="m**2"),
            promotes=["*"],
        )

        prob.setup()
        prob.run_model()

        return prob


    def main():
        """Run the hierarchical visualization example."""
        print("=" * 80)
        print("Hierarchical Parameter Visualization - Gallery Example")
        print("=" * 80)
        print()

        # Step 1: Create problem
        print("Step 1: Creating example optimization problem...")
        prob = create_example_problem()
        print("  [OK] Problem with 3 parameters, 2 models, 2 constraints")
        print()

        # Step 2: Extract hierarchy
        print("Step 2: Extracting parameter hierarchy...")
        primary_params = ["length", "width", "height"]
        hierarchy = extract_hierarchy(prob, primary_params)

        print(f"  [OK] Found {len(hierarchy['nodes'])} nodes")
        print(f"  [OK] Found {len(hierarchy['edges'])} edges")
        print(f"  [OK] Identified {len(hierarchy['primary_params'])} primary parameters")
        print()

        # Step 3: Analyze hierarchy
        print("Step 3: Analyzing hierarchy structure...")
        graph = to_networkx(hierarchy)
        stats = analyze_hierarchy(graph)

        print(f"  Levels in hierarchy: {stats['num_levels']}")
        print(f"  Nodes by level: {dict(sorted(stats['nodes_by_level'].items()))}")
        print(f"  Nodes by type: {stats['nodes_by_type']}")
        print()

        # Step 4: Generate visualizations
        print("Step 4: Generating visualizations...")

        # Simple all-to-all diagram
        simple_file = generate_simple_graph(
            parameters=["length", "width", "height"],
            models=["Volume Calculator", "Surface Area Calculator"],
            constraints=["Volume >= 100 m³", "Area <= 200 m²"],
            output_path="gallery_simple_diagram.mmd",
        )
        print(f"  [OK] Simple diagram: {simple_file}")

        # Hierarchical diagram showing actual levels
        hier_file = generate_hierarchical_graph(
            hierarchy, "gallery_hierarchical_diagram.mmd", orientation="TD"
        )
        print(f"  [OK] Hierarchical diagram: {hier_file}")

        # NetworkX exports for external tools
        graphml_file = export_graphml(graph, "gallery_graph.graphml")
        json_file = export_json(graph, "gallery_graph.json")
        print(f"  [OK] GraphML export: {graphml_file}")
        print(f"  [OK] JSON export: {json_file}")
        print()

        print("=" * 80)
        print("Gallery example complete!")
        print()
        print("Visualization files created:")
        print(f"  - {simple_file} (simple all-to-all)")
        print(f"  - {hier_file} (hierarchical levels)")
        print(f"  - {graphml_file} (for yEd, Gephi)")
        print(f"  - {json_file} (for D3.js, web apps)")
        print()
        print("Next steps:")
        print("  1. Open .mmd files at https://mermaid.live/")
        print("  2. Open .graphml in yEd for advanced layout editing")
        print("  3. Use .json for custom web visualizations")
        print("=" * 80)


    if __name__ == "__main__":
        main()


.. _sphx_glr_download_auto_examples_gallery_hierarchical_viz.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: gallery_hierarchical_viz.ipynb <gallery_hierarchical_viz.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: gallery_hierarchical_viz.py <gallery_hierarchical_viz.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: gallery_hierarchical_viz.zip <gallery_hierarchical_viz.zip>`