code: def _calculate_comparison_stats(truth_vcf):
      
      # Avoid very small events for average calculations
      min_stat_size = 50
      min_median_size = 250
      sizes = []
      svtypes = set([])
      with utils.open_gzipsafe(truth_vcf) as in_handle:
          for call in (l.rstrip().split("\t") for l in in_handle if not l.startswith("#")):
              stats = _summarize_call(call)
              if stats["size"] > min_stat_size:
                  sizes.append(stats["size"])
              svtypes.add(stats["svtype"])
      pct10 = int(np.percentile(sizes, 10))
      pct25 = int(np.percentile(sizes, 25))
      pct50 = int(np.percentile(sizes, 50))
      pct75 = int(np.percentile(sizes, 75))
      ranges_detailed = [(int(min(sizes)), pct10), (pct10, pct25), (pct25, pct50),
                         (pct50, pct75), (pct75, max(sizes))]
      ranges_split = [(int(min(sizes)), pct50), (pct50, max(sizes))]
      return {"min_size": int(min(sizes) * 0.95), "max_size": int(max(sizes) + 1.05),
              "svtypes": svtypes, "merge_size": int(np.percentile([x for x in sizes if x > min_median_size], 50)),
              "ranges": []}
docs: Identify calls to validate from the input truth VCF.