Source code for dakotathon.method.multidim_parameter_study

#! /usr/bin/env python
"""Implementation of a Dakota multidim parameter study."""

from .base import MethodBase
from ..utils import to_iterable


classname = "MultidimParameterStudy"


class MultidimParameterStudy(MethodBase):

    """Define parameters for a Dakota multidim parameter study."""

    def __init__(self, partitions=(10, 8), **kwargs):
        """Create a new Dakota multidim parameter study.

        Parameters
        ----------
        partitions : array_like of int
          Number of intervals between lower and upper bounds for each study
          parameter.

        Examples
        --------
        Create a default multidim parameter study experiment:

        >>> m = MultidimParameterStudy()

        """
        MethodBase.__init__(self, **kwargs)
        self.method = self.__module__.rsplit(".")[-1]
        self._partitions = partitions

    @property
    def partitions(self):
        """The number of evaluation intervals for each parameter."""
        return self._partitions

    @partitions.setter
    def partitions(self, value):
        """Set the number of evaluation intervals for each parameter.

        Parameters
        ----------
        value : list or tuple of int
          The new number of partitions.

        """
        if not isinstance(value, (tuple, list)):
            raise TypeError("Partitions must be a tuple or a list")
        self._partitions = value

    def __str__(self):
        """Define a multidim parameter study method block.

        See Also
        --------
        dakotathon.method.base.MethodBase.__str__

        """
        s = MethodBase.__str__(self)
        partitions = to_iterable(self.partitions)
        s += "    partitions ="
        for p in partitions:
            s += " {}".format(p)
        s += "\n\n"
        return s