.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/example_problem_graph.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_example_problem_graph.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_example_problem_graph.py:

Example: Using ProblemGraph abstraction for unified visualization.

This example demonstrates the new ProblemGraph abstraction layer that provides
a unified interface for all visualization backends (Mermaid, Cytoscape, NetworkX).

.. GENERATED FROM PYTHON SOURCE LINES 6-118

.. code-block:: Python


    import openmdao.api as om

    from paroto.viz import (
        ProblemGraph,
        export_graphml,
        generate_hierarchical_graph,
        to_networkx,
    )
    from paroto.viz.cytoscape import hierarchy_to_cytoscape


    def create_example_problem():
        """Create a simple example problem."""
        prob = om.Problem()

        # Design variables
        indeps = prob.model.add_subsystem("design", om.IndepVarComp(), promotes=["*"])
        indeps.add_output("x", val=2.0)
        indeps.add_output("y", val=3.0)

        # Intermediate computation
        prob.model.add_subsystem(
            "compute",
            om.ExecComp("z = x**2 + y**2"),
            promotes=["*"],
        )

        # Constraint
        prob.model.add_subsystem(
            "constraint",
            om.ExecComp("z_margin = z - 10.0"),
            promotes=["*"],
        )

        prob.setup()
        prob.run_model()

        return prob


    def main():
        """Demonstrate ProblemGraph abstraction."""
        print("=" * 80)
        print("ProblemGraph Abstraction Example")
        print("=" * 80)
        print()

        # Create problem
        print("Step 1: Creating OpenMDAO problem...")
        prob = create_example_problem()
        print("  [OK] Problem created")
        print()

        # Create ProblemGraph abstraction
        print("Step 2: Creating ProblemGraph abstraction...")
        graph = ProblemGraph(prob, primary_params=["x", "y"])
        print(f"  [OK] Graph with {len(graph.nodes)} nodes, {len(graph.edges)} edges")
        print()

        # Inspect graph structure
        print("Step 3: Inspecting graph structure...")
        stats = graph.get_statistics()
        print(f"  Primary parameters: {stats['num_primary_params']}")
        print(f"  Hierarchy levels: {stats['num_levels']}")
        print(f"  Nodes by level: {stats['nodes_by_level']}")
        print(f"  Nodes by type: {stats['nodes_by_type']}")
        print()

        # Get organized views
        print("Step 4: Getting organized views...")
        nodes_by_level = graph.get_nodes_by_level()
        nodes_by_subsystem = graph.get_nodes_by_subsystem()
        print(f"  Nodes by level: {len(nodes_by_level)} levels")
        print(f"  Nodes by subsystem: {len(nodes_by_subsystem)} subsystems")
        print()

        # Generate visualizations using the same graph
        print("Step 5: Generating visualizations from single graph...")

        # Mermaid
        generate_hierarchical_graph(graph, "example_graph_mermaid.mmd")
        print("  [OK] Mermaid diagram generated")

        # Cytoscape
        cyto_elements = hierarchy_to_cytoscape(graph)
        print(f"  [OK] Cytoscape elements generated ({len(cyto_elements)} elements)")

        # NetworkX
        nx_graph = to_networkx(graph)
        export_graphml(nx_graph, "example_graph.graphml")
        print("  [OK] NetworkX/GraphML exported")

        print()

        # Show filtering capability
        print("Step 6: Filtering by level...")
        filtered_nodes, filtered_edges = graph.filter_by_level(max_level=1)
        print(f"  Filtered to level 0-1: {len(filtered_nodes)} nodes, {len(filtered_edges)} edges")
        print()

        print("=" * 80)
        print("ProblemGraph provides a unified abstraction that:")
        print("  - Wraps OpenMDAO Problem connections")
        print("  - Provides consistent interface for all viz backends")
        print("  - Supports filtering, organization, and statistics")
        print("  - Eliminates code duplication between generators")
        print("=" * 80)


    if __name__ == "__main__":
        main()


.. _sphx_glr_download_auto_examples_example_problem_graph.py:

.. only:: html

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

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

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

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

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

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

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