.. _method-external_python-class_path_and_name:

"""""""""""""""""""
class_path_and_name
"""""""""""""""""""


Specify the module and class name of the external python method



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



**Specification**

- *Alias:* None

- *Arguments:* STRING


**Description**


Specify the module and class name of the external python method.
The python method will be treated by Dakota as a top-level Iterator
and must supply a ``core_run`` method.  Optionally, ``initialize_run``
``pre_run`` ``post_run`` and ``finalize_run`` methods may be supplied.




**Examples**



Use of external python methods in Dakota is enabled within the method
block via:

.. code-block::

  method,
      external_python
        class_path_and_name = "ext_py_methods.RandomSample"
        options_file = 'ext_py_options.json'


An example python class that implements simple random sampling in the
``core_run`` method could consist of the following:

.. code-block:: python

  import ext_method

  class RandomSample:
  
      def __init__(self, executor, params_file=None):
          self.executor = executor
          if params_file is not None:
              with open(params_file) as F:
                  all_methods_params = json.load(F)
              self.params = all_methods_params['RandomSample']
  
      def core_run(self):
          n_vars = self.executor.tv()
          init_pts = self.executor.initial_values()
          l_bounds = self.executor.continuous_lower_bounds()
          u_bounds = self.executor.continuous_upper_bounds()
  
          # Method specs
          max_evals = self.params['max_evals']
          rnd_seed  = self.params['rnd_seed']
          rnd.seed(rnd_seed)
  
          retval = {}
  
          i = 1
          x = init_pts
          xvals = []
          self.fns = []
          while  i<=max_evals:
              xvals.append(x)
              self.fns.append(self.executor.function_value(x))
              x = []
              for j in range(n_vars):
                  x.append(rnd.uniform(l_bounds[j], u_bounds[j]))
              i=i+1
  
          retval['x']   = x
          retval['fns'] = self.fns
  
          return retval
  
  
      def post_run(self):
          # Output using Dakota formatting
          self.executor.output_central_moments(self.fns)


A companion options file, ``ext_py_options.json`` which satisfies the
method specifications above might contain the following:

.. code-block::

    {
      "RandomSample" :
      {
        "max_evals" : 25,
        "rnd_seed"  : 231
      }
    }