Project: s41467-025-62064-7 | Function: main (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Implements the complete viral consensus sequence generation algorithm by coordinating parallel processing of genomic segments, integrating variant calls across positions, and applying quality thresholds to generate the final consensus sequence. This orchestrates the entire computational workflow for viral genome reconstruction from sequencing data.

**KEY TERMS:** {
  "minAF": "minimum allele frequency threshold",
  "mindepth": "minimum read depth threshold",
  "variant_dict": "dictionary of genomic variants",
  "consensus": "final consensus sequence",
  "genome_length": "reference genome length",
  "batch": "parallel processing segments"
}

**CONTEXT CODE:**                     '--reference',
                    help='Reference genome fasta file',
                    type=str,
                   required = True)

parser.add_argument('-vf', 
                    '--variantfile',
                    help='Output path for variant tsv file',
                    type=str,
                   required = False)

parser.add_argument('-c', 
                    '--cores',
                    help='Number of cores to use for processing',
                    default=1,
                    type=int,
                   required = False)

parser.add_argument('-d', 
                    '--mindepth',
                    help='Minimal depth at which to not consider any alternative alleles',
                    default=30,
                    type=int,
                   required = False)

parser.add_argument('-af', 
                    '--minAF',
                    help='Minimal allele frequency to output',
                    default=0.1,
                    type=float,
                   required = False)

parser.add_argument('-k', 
                    '--keepindels',
                    help='Keep 1 and 2 nt indels',
                    action='store_true',
                   required = False)

def process_batch(start, stop, bamfile, reference):
    bamfile = pysam.AlignmentFile(bamfile, "rb")
    ref_name = bamfile.references[0]
    refseq = pysam.Fastafile(reference).fetch(ref_name)
    
    pileup = bamfile.pileup(contig=ref_name, start=start, stop=stop, ignore_orphans=False, min_mapping_quality=0, min_base_quality=0, truncate=True)
    
    return([parse_column(p.reference_pos, p.get_query_sequences(add_indels=True),p.get_num_aligned(), refseq) for p in pileup])

def parse_column(ref_pos, allele_list, num_aln, refseq):
    raise NotImplementedError("parse_column function has been masked for testing purposes")

def main():
    raise NotImplementedError("main function has been masked for testing purposes")

if __name__ == '__main__':
    main()

Complete the function implementation replacing "raise NotImplementedError".