code: def logscale(x_min, x_max, n):
      
      if not (isinstance(n, int) and n > 0):
          raise ValueError('n must be a positive integer, got %s' % n)
      if x_min <= 0:
          raise ValueError('x_min must be positive, got %s' % x_min)
      if x_max <= x_min:
          raise ValueError('x_max (%s) must be bigger than x_min (%s)' %
                           (x_max, x_min))
      delta = numpy.log(x_max / x_min)
      return numpy.exp(delta * numpy.arange(n) / (n - 1)) * x_min
docs: :param x_min: minumum value
      :param x_max: maximum value
      :param n: number of steps
      :returns: an array of n values from x_min to x_max