docs: def get_baseparser_extended_df(sample, bp_lines, ref, alt):
      """Turn baseParser results into a dataframe"""
      columns = "chrom\tpos\tref\tcov\tA\tC\tG\tT\t*\t-\t+".split()
      if bp_lines is None:
          return None
  
      # change baseparser output to get most common maf per indel
      bpdf = pd.DataFrame([[sample] + l.rstrip('\n').split("\t") for l in bp_lines if len(l) > 0],
                          columns=["sample"] + columns, dtype=np.object)
      bpdf[bpdf == ""] = None
  
      # remove zero coverage rows
      bpdf = bpdf[bpdf["cov"].astype(int) > 0]
  
      if len(bpdf) == 0:
          return None
  
      if ref and alt:
          # add columns for validation allele
          bpdf = pd.concat([bpdf, pd.DataFrame({"val_ref": pd.Series(ref), "val_alt": pd.Series(alt)})], axis=1)
          bpdf = pd.concat([bpdf, bpdf.apply(_val_al, axis=1)], axis=1)
  
      bpdf = pd.concat([bpdf, bpdf.apply(_most_common_indel, axis=1)], axis=1)
      bpdf = pd.concat([bpdf, bpdf.apply(_most_common_al, axis=1)], axis=1)
      bpdf["most_common_count"] = bpdf.apply(lambda x: max([x.most_common_al_count, x.most_common_indel_count]), axis=1)
      bpdf["most_common_maf"] = bpdf.apply(lambda x: max([x.most_common_al_maf, x.most_common_indel_maf]), axis=1)
  
      return bpdf