content: #!/usr/bin/env python
  
  import argparse
  import os
  import re
  import shutil
  
  import pandas
  import pysam
  from Bio import SeqIO
  
  
  def get_sample_name(file_path):
      base_file_name = os.path.basename(file_path)
      if base_file_name.find(".") > 0:
          # Eliminate the extension.
          return os.path.splitext(base_file_name)[0]
      return base_file_name
  
  
  def get_coverage_df(bam_file):
      # Create a coverage dictionary.
      coverage_dict = {}
      coverage_list = pysam.depth(bam_file, split_lines=True)
      for line in coverage_list:
          chrom, position, depth = line.split('\t')
          coverage_dict["%s-%s" % (chrom, position)] = depth
      # Convert it to a data frame.
      coverage_df = pandas.DataFrame.from_dict(coverage_dict, orient='index', columns=["depth"])
      return coverage_df
  
  
  def get_zero_df(reference):
      # Create a zero coverage dictionary.
      zero_dict = {}
      for record in SeqIO.parse(reference, "fasta"):
          chrom = record.id
          total_len = len(record.seq)
          for pos in list(range(1, total_len + 1)):
              zero_dict["%s-%s" % (str(chrom), str(pos))] = 0
      # Convert it to a data frame with depth_x
      # and depth_y columns - index is NaN.
      zero_df = pandas.DataFrame.from_dict(zero_dict, orient='index', columns=["depth"])
      return zero_df
  
  
  def output_zc_vcf_file(base_file_name, vcf_file, zero_df, total_zero_coverage, output_vcf):
      column_names = ["CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER", "INFO", "FORMAT", "Sample"]
      vcf_df = pandas.read_csv(vcf_file, sep='\t', header=None, names=column_names, comment='#')
      good_snp_count = len(vcf_df[(vcf_df['ALT'].str.len() == 1) & (vcf_df['REF'].str.len() == 1) & (vcf_df['QUAL'] > 150)])
      if total_zero_coverage > 0:
          header_file = "%s_header.csv" % base_file_name
          with open(header_file, 'w') as outfile:
              with open(vcf_file) as infile:
                  for line in infile:
                      if re.search('^#', line):
                          outfile.write("%s" % line)
          vcf_df_snp = vcf_df[vcf_df['REF'].str.len() == 1]
          vcf_df_snp = vcf_df_snp[vcf_df_snp['ALT'].str.len() == 1]
          vcf_df_snp['ABS_VALUE'] = vcf_df_snp['CHROM'].map(str) + "-" + vcf_df_snp['POS'].map(str)
          vcf_df_snp = vcf_df_snp.set_index('ABS_VALUE')
          cat_df = pandas.concat([vcf_df_snp, zero_df], axis=1, sort=False)
          cat_df = cat_df.drop(columns=['CHROM', 'POS', 'depth'])
          cat_df[['ID', 'ALT', 'QUAL', 'FILTER', 'INFO']] = cat_df[['ID', 'ALT', 'QUAL', 'FILTER', 'INFO']].fillna('.')
          cat_df['REF'] = cat_df['REF'].fillna('N')
          cat_df['FORMAT'] = cat_df['FORMAT'].fillna('GT')
          cat_df['Sample'] = cat_df['Sample'].fillna('./.')
          cat_df['temp'] = cat_df.index.str.rsplit('-', n=1)
          cat_df[['CHROM', 'POS']] = pandas.DataFrame(cat_df.temp.values.tolist(), index=cat_df.index)
          cat_df = cat_df[['CHROM', 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO', 'FORMAT', 'Sample']]
          cat_df['POS'] = cat_df['POS'].astype(int)
          cat_df = cat_df.sort_values(['CHROM', 'POS'])
          body_file = "%s_body.csv" % base_file_name
          cat_df.to_csv(body_file, sep='\t', header=False, index=False)
          with open(output_vcf, "w") as outfile:
              for cf in [header_file, body_file]:
                  with open(cf, "r") as infile:
                      for line in infile:
                          outfile.write("%s" % line)
      else:
          shutil.move(vcf_file, output_vcf)
      return good_snp_count
  
  
  def output_metrics_file(base_file_name, average_coverage, genome_coverage, good_snp_count, output_metrics):
      bam_metrics = [base_file_name, "", "%4f" % average_coverage, genome_coverage]
      vcf_metrics = [base_file_name, str(good_snp_count), "", ""]
      metrics_columns = ["File", "Number of Good SNPs", "Average Coverage", "Genome Coverage"]
      with open(output_metrics, "w") as fh:
          fh.write("# %s\n" % "\t".join(metrics_columns))
          fh.write("%s\n" % "\t".join(bam_metrics))
          fh.write("%s\n" % "\t".join(vcf_metrics))
  
  
  def output_files(vcf_file, total_zero_coverage, zero_df, output_vcf, average_coverage, genome_coverage, output_metrics):
      base_file_name = get_sample_name(vcf_file)
      good_snp_count = output_zc_vcf_file(base_file_name, vcf_file, zero_df, total_zero_coverage, output_vcf)
      output_metrics_file(base_file_name, average_coverage, genome_coverage, good_snp_count, output_metrics)
  
  
  def get_coverage_and_snp_count(bam_file, vcf_file, reference, output_metrics, output_vcf):
      coverage_df = get_coverage_df(bam_file)
      zero_df = get_zero_df(reference)
      coverage_df = zero_df.merge(coverage_df, left_index=True, right_index=True, how='outer')
      # depth_x "0" column no longer needed.
      coverage_df = coverage_df.drop(columns=['depth_x'])
      coverage_df = coverage_df.rename(columns={'depth_y': 'depth'})
      # Covert the NaN to 0 coverage and get some metrics.
      coverage_df = coverage_df.fillna(0)
      coverage_df['depth'] = coverage_df['depth'].apply(int)
      total_length = len(coverage_df)
      average_coverage = coverage_df['depth'].mean()
      zero_df = coverage_df[coverage_df['depth'] == 0]
      total_zero_coverage = len(zero_df)
      total_coverage = total_length - total_zero_coverage
      genome_coverage = "{:.2%}".format(total_coverage / total_length)
      # Output a zero-coverage vcf fil and the metrics file.
      output_files(vcf_file, total_zero_coverage, zero_df, output_vcf, average_coverage, genome_coverage, output_metrics)
  
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser()
  
      parser.add_argument('--bam_input', action='store', dest='bam_input', help='bam input file')
      parser.add_argument('--output_metrics', action='store', dest='output_metrics', required=False, default=None, help='Output metrics text file')
      parser.add_argument('--output_vcf', action='store', dest='output_vcf', required=False, default=None, help='Output VCF file')
      parser.add_argument('--reference', action='store', dest='reference', help='Reference dataset')
      parser.add_argument('--vcf_input', action='store', dest='vcf_input', help='vcf input file')
  
      args = parser.parse_args()
  
      get_coverage_and_snp_count(args.bam_input, args.vcf_input, args.reference, args.output_metrics, args.output_vcf)
  