code: def inverseHistogram(hist, bin_range):
      
      data = hist.astype(float) / np.min(hist[np.nonzero(hist)])
      new_data = np.empty(shape=np.sum(data, dtype=int))
      i = 0
      xvals = np.linspace(bin_range[0], bin_range[1], len(data))
      for d, x in zip(data, xvals):
          new_data[i:i + d] = x
          i += int(d)
      return new_data
docs: sample data from given histogram and min, max values within range
  
      Returns:
          np.array: data that would create the same histogram as given