"
    Input:  {
              dict                  - existing flat dict 
              dict1                 - new flat dict to add
                                      if empty, no stat analysis

              (dict_compare)        - calculate improvements over this dict if present

              (skip_expected_value) - if 'yes', do not calculute expected value
              (skip_min_max)        - if 'yes', do not calculate min, max, mean, etc

                 For expected value (via gaussian_kde):

              (bins)                - number of bins (int, default = 100)
              (cov_factor)          - float covariance factor

              (skip_stat_analysis)  - if 'yes', just flatten array and add #min
            }

    Output: {
              return            - return code =  0, if successful
                                              >  0, if error
              (error)           - error text if return > 0

              dict              - updated dict
              max_range_percent - max % range in float/int data (useful to record points with unusual behavior)
              min               - 'yes', if one of monitored values reached min
              max               - 'yes', if one of monitored values reached max
            }

"