docs: def filter_out_mutations_in_normal(tumordf, normaldf, most_common_maf_min=0.2,
                                     most_common_count_maf_threshold=20,
                                     most_common_count_min=1):
      """Remove mutations that are in normal"""
      df = tumordf.merge(normaldf, on=["chrom", "pos"], suffixes=("_T", "_N"))
  
      # filters
      common_al = (df.most_common_al_count_T == df.most_common_count_T) & (df.most_common_al_T == df.most_common_al_N)
      common_indel = (df.most_common_indel_count_T == df.most_common_count_T) & \
          (df.most_common_indel_T == df.imost_common_indel_N)
      normal_criteria = ((df.most_common_count_N >= most_common_count_maf_threshold) &
                         (df.most_common_maf_N > most_common_maf_min)) | \
          ((df.most_common_count_N < most_common_count_maf_threshold) &
           (df.most_common_count_N > most_common_count_min))
      df = df[~(common_al | common_indel) & normal_criteria]
  
      # restore column names of tumor
      for c in df.columns:
          if c.endswith("_N"):
              del df[c]
              df.columns = [c[:-2] if c.endswith("_T") else c for c in df.columns]
  
      return df