paroto.io.stone_loader
======================

.. py:module:: paroto.io.stone_loader

.. autoapi-nested-parse::

   Load and instantiate architectures from STONE format YAML files.



Functions
---------

.. autoapisummary::

   paroto.io.stone_loader.load_stone_config
   paroto.io.stone_loader.create_problem_from_stone
   paroto.io.stone_loader.get_default_values_from_stone


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

.. py:function:: load_stone_config(yaml_path)

   Load STONE format configuration from YAML file.

   :param yaml_path: Path to STONE format YAML file
   :type yaml_path: :py:class:`str`

   :returns: **config** -- Configuration dictionary
   :rtype: :py:class:`dict`

   :raises ValueError: If file is not in STONE format


.. py:function:: create_problem_from_stone(yaml_path)

   Create OpenMDAO problem from STONE format configuration.

   Dynamically instantiates systems based on types specified in the YAML config.

   :param yaml_path: Path to STONE format YAML file
   :type yaml_path: :py:class:`str`

   :returns: **prob** -- Configured OpenMDAO problem
   :rtype: :py:class:`om.Problem`

   .. rubric:: Examples

   >>> prob = create_problem_from_stone("config_example.yml")
   >>> prob.setup()
   >>> prob.run_model()

   .. rubric:: Notes

   The config can specify either:
   - A single top-level system (e.g., TorchDesignGroup)
   - Multiple subsystems to be added to the problem


.. py:function:: get_default_values_from_stone(yaml_path)

   Extract default values for all parameters from STONE config.

   :param yaml_path: Path to STONE format YAML file
   :type yaml_path: :py:class:`str`

   :returns: **defaults** -- Dictionary mapping variable names to default values
   :rtype: :py:class:`dict`