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.. "examples/api/plot_doe.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

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

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

.. _sphx_glr_examples_api_plot_doe.py:


DOE algorithms
==============

In this example, we will discover the different functions of the API
related to design of experiments.

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.. code-block:: Python


    from __future__ import annotations

    from gemseo import configure_logger
    from gemseo import get_algorithm_options_schema
    from gemseo import get_available_doe_algorithms

    configure_logger()






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 .. code-block:: none


    <RootLogger root (INFO)>



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Get available DOE algorithms
----------------------------

The :func:`.get_available_doe_algorithms` function returns the list
of optimization algorithms available in |g| or in external modules

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.. code-block:: Python

    get_available_doe_algorithms()





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 .. code-block:: none


    ['CustomDOE', 'DiagonalDOE', 'OT_SOBOL', 'OT_RANDOM', 'OT_HASELGROVE', 'OT_REVERSE_HALTON', 'OT_HALTON', 'OT_FAURE', 'OT_MONTE_CARLO', 'OT_FACTORIAL', 'OT_COMPOSITE', 'OT_AXIAL', 'OT_OPT_LHS', 'OT_LHS', 'OT_LHSC', 'OT_FULLFACT', 'OT_SOBOL_INDICES', 'fullfact', 'ff2n', 'pbdesign', 'bbdesign', 'ccdesign', 'lhs', 'Halton', 'LHS', 'MC', 'PoissonDisk', 'Sobol']



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Get options schema
------------------
For a given optimization algorithm, e.g. ``"DiagonalDOE"``,
we can get the options; e.g.

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.. code-block:: Python

    get_algorithm_options_schema("DiagonalDOE")




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    {'$schema': 'http://json-schema.org/draft-04/schema', 'additionalProperties': False, 'description': 'Diagonal DOE algorithm', 'type': 'object', 'properties': {'normalize_design_space': {'type': 'boolean'}, 'round_ints': {'type': 'boolean'}, 'use_database': {'type': 'boolean'}, 'reset_iteration_counters': {'type': 'boolean'}, 'eval_jac': {'description': 'Whether to evaluate the Jacobian.', 'type': 'boolean'}, 'n_processes': {'minimum': 1, 'description': 'The maximum simultaneous number of processes\nused to parallelize the execution.', 'type': 'integer'}, 'wait_time_between_samples': {'minimum': 0, 'description': 'The waiting time between two samples.', 'type': 'number'}, 'n_samples': {'minimum': 2, 'description': 'The number of samples.\nThe number of samples must be greater than or equal to 2.', 'type': 'integer'}, 'max_time': {'minimum': 0.0, 'description': 'The maximum runtime in seconds.\nIf 0, no maximum runtime is set.', 'type': 'number'}, 'reverse': {'anyOf': [{'description': 'The dimensions or variables to sample from their upper bounds to\ntheir lower bounds.\nIf ``None``, every dimension will be sampled from its lower bound to its\nupper bound.', 'type': 'null'}, {'description': 'The dimensions or variables to sample from their upper bounds to\ntheir lower bounds.\nIf ``None``, every dimension will be sampled from its lower bound to its\nupper bound.', 'type': 'array', 'items': {'minItems': 1, 'type': 'string'}}]}, 'callbacks': {'description': 'The functions to be evaluated\nafter each call to :meth:`.OptimizationProblem.evaluate_functions`;\nto be called as ``callback(index, (output, jacobian))``.'}}, 'required': ['n_samples']}




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   **Total running time of the script:** (0 minutes 0.004 seconds)


.. _sphx_glr_download_examples_api_plot_doe.py:

.. only:: html

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

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

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

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

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


.. only:: html

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