.. _method-external_python:

"""""""""""""""
external_python
"""""""""""""""


Use the external python methods interface



.. toctree::
   :hidden:
   :maxdepth: 1

   method-external_python-class_path_and_name
   method-external_python-options_file


**Specification**

- *Alias:* None

- *Arguments:* None


**Child Keywords:**

+-------------------------+--------------------+-------------------------+-----------------------------------------------+
| Required/Optional       | Description of     | Dakota Keyword          | Dakota Keyword Description                    |
|                         | Group              |                         |                                               |
+=========================+====================+=========================+===============================================+
| Required                                     | `class_path_and_name`__ | Specify the module and class name of the      |
|                                              |                         | external python method                        |
+----------------------------------------------+-------------------------+-----------------------------------------------+
| Optional                                     | `options_file`__        | Filename to pass to exteral python methods    |
+----------------------------------------------+-------------------------+-----------------------------------------------+

.. __: method-external_python-class_path_and_name.html
__ method-external_python-options_file.html



**Description**


This is an experimental capability that allows the user to expose
python algorithms to Dakota as top-level Iterators.  The Dakota model
is available to python and supplies function, gradient, and hessian
responses as appropriate in addition to helper functions for model
queries and output to the Dakota output stream.

The code example below shows class initialization which outputs the
wrapped model 'executor' docstring.




**Examples**



.. code-block::

  import ext_method

  class RandomSample:

      def __init__(self, executor, params_file=None):
          self.executor = executor
          help(self.executor)