code: def PopulationSpread(tolerance=1e-6):
      """normalized absolute deviation from best candidate is < tolerance:
  
      ``abs(params - params[0]) <= tolerance``"""
      doc = "PopulationSpread with %s" % {"tolerance": tolerance}
  
      def _PopulationSpread(inst, info=False):
          if info:
              info = lambda x: x
          else:
              info = bool
          sim = numpy.array(inst.population)
          # if not len(sim[1:]):
          #    warn = "Warning: Invalid termination condition (nPop < 2)"
          #    print(warn)
          #    return warn
          if numpy.all(abs(sim - sim[0]) <= abs(tolerance * sim[0])):
              return info(doc)
          return info(null)
  
      _PopulationSpread.__doc__ = doc
      return _PopulationSpread
  
imports: import numpy
  import mystic.collapse
  from mystic.math.distance import Lnorm
  from mystic._scipy060optimize import approx_fprime, _epsilon
label_desc: Use image node_id 10 for calling a global function with example usage: PopulationSpread(tolerance) and returns: _PopulationSpread, info, info