paroto.viz.problem_graph
========================

.. py:module:: paroto.viz.problem_graph

.. autoapi-nested-parse::

   Problem graph abstraction for OpenMDAO visualization.

   This module provides a unified graph representation that can be consumed by
   different visualization backends (Mermaid, Cytoscape, NetworkX, etc.).
   Uses OpenMDAO's connection model as the foundation.



Attributes
----------

.. autoapisummary::

   paroto.viz.problem_graph.DEFAULT_CONSTRAINT_MARKERS
   paroto.viz.problem_graph.UNCONNECTED_LEVEL
   paroto.viz.problem_graph.PRIMARY_LEVEL


Classes
-------

.. autoapisummary::

   paroto.viz.problem_graph.ProblemGraph


Module Contents
---------------

.. py:data:: DEFAULT_CONSTRAINT_MARKERS
   :value: ['margin', 'constraint', 'satisfied', 'error']


.. py:data:: UNCONNECTED_LEVEL
   :value: -1


.. py:data:: PRIMARY_LEVEL
   :value: 0


.. py:class:: ProblemGraph(prob, primary_params = None, constraint_markers = None)

   Unified graph abstraction for OpenMDAO Problem visualization.

   This class wraps an OpenMDAO Problem and provides a clean, unified interface
   for extracting graph structure that can be consumed by any visualization backend.

   :param prob: OpenMDAO problem (must have setup() called)
   :type prob: :py:class:`openmdao.api.Problem`
   :param primary_params: List of primary parameter names (design variables).
                          Can be partial names that will be matched
   :type primary_params: :py:class:`list` of :py:class:`str`, *optional*
   :param constraint_markers: Keywords to identify constraint variables (e.g., ['margin', 'constraint'])
   :type constraint_markers: :py:class:`list` of :py:class:`str`, *optional*

   .. attribute:: nodes

      Mapping of node_id -> node attributes (type, level, units, value, label, subsystem)

      :type: :py:class:`dict`

   .. attribute:: edges

      List of (source, target) connections

      :type: :py:class:`list` of :py:class:`tuple`

   .. attribute:: primary_params

      Full names of primary parameters

      :type: :py:class:`list`


   .. py:attribute:: prob


   .. py:attribute:: model


   .. py:attribute:: nodes
      :type:  Dict[str, Dict[str, Any]]


   .. py:attribute:: edges
      :type:  List[Tuple[str, str]]
      :value: []



   .. py:attribute:: primary_params
      :type:  List[str]
      :value: []



   .. py:method:: get_nodes_by_level(max_level = None)

      Get nodes organized by hierarchy level.

      :param max_level: Maximum level to include. If None, includes all levels
      :type max_level: :py:class:`int`, *optional*

      :returns: Mapping of level -> list of node_ids
      :rtype: :py:class:`dict`



   .. py:method:: get_nodes_by_subsystem()

      Get nodes organized by subsystem.

      :returns: Mapping of subsystem_name -> list of node_ids
      :rtype: :py:class:`dict`



   .. py:method:: get_nodes_by_type()

      Get nodes organized by type.

      :returns: Mapping of node_type -> list of node_ids
      :rtype: :py:class:`dict`



   .. py:method:: filter_by_level(max_level)

      Get filtered view with nodes up to max_level.

      :param max_level: Maximum hierarchy level to include
      :type max_level: :py:class:`int`

      :returns: (filtered_nodes, filtered_edges) dictionaries
      :rtype: :py:class:`tuple`



   .. py:method:: get_statistics()

      Get graph statistics.

      :returns: Statistics including node counts, edge counts, level distribution
      :rtype: :py:class:`dict`