class_docstr: Powell Direction Search optimization,
  adapted from scipy.optimize.fmin_powell.
      
class_docstr_tok: ["Powell","Direction","Search","optimization",",","adapted","from","scipy.optimize.fmin_powell","."]
docstr: set the evaluation limits
  
  Args:
      iterscale (int, default=1000): scale factor for iteration upper limit
      evalscale (int, default=1000): scale factor for evaluation upper limit
  
  Notes:
      - ``iterscale`` and ``evalscale`` are used to set the maximum iteration
        and evaluation limits, respectively. The new limit is defined as
        ``limit = (nDim * nPop * scale) + count``, where ``count`` is the number
        of existing iterations or evaluations, respectively.
          
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code: def _SetEvaluationLimits(self, iterscale=1000, evalscale=1000):
      """set the evaluation limits
  
      Args:
          iterscale (int, default=1000): scale factor for iteration upper limit
          evalscale (int, default=1000): scale factor for evaluation upper limit
  
      Notes:
          - ``iterscale`` and ``evalscale`` are used to set the maximum iteration
            and evaluation limits, respectively. The new limit is defined as
            ``limit = (nDim * nPop * scale) + count``, where ``count`` is the number
            of existing iterations or evaluations, respectively.
      """
      super(PowellDirectionalSolver, self)._SetEvaluationLimits(
          iterscale, evalscale
      )
      return
  
imports: from mystic.tools import wrap_function, unpair, wrap_nested
  from mystic.tools import wrap_bounds, wrap_penalty, reduced
  import numpy
  from numpy import eye, zeros, shape, asarray, absolute, asfarray
  from numpy import clip, squeeze
  from mystic._scipy060optimize import brent
  from mystic.abstract_solver import AbstractSolver
label_desc: Use image node_id 3 for calling the PowellDirectionalSolver obj's underlying member method code with example usage: obj._SetEvaluationLimits(iterscale, evalscale) without return types