Creates an output that is compatible with the legacy metadata reports. This method will be removed once
        a new database scheme is implemented

def legacy_reporter(self):
        """
        Creates an output that is compatible with the legacy metadata reports. This method will be removed once
        a new database scheme is implemented
        """
        from collections import OrderedDict
        logging.info('Creating legacy summary report')
        row = ''
        # Create a dictionary of tuples to be printed in the final report
        for sample in self.metadata:
            data = OrderedDict([
                ('SampleName', sample.name),
                ('N50', str(sample.quality_features_polished.n50)),
                ('NumContigs', str(sample.quality_features_polished.num_contigs)),
                ('TotalLength', str(sample.quality_features_polished.genome_length)),
                ('MeanInsertSize', sample.mapping.MeanInsertSize),
                ('AverageCoverageDepth', sample.mapping.MeanCoveragedata.split("X")[0]),
                ('ReferenceGenome', sample.mash.closestrefseq),
                ('RefGenomeAlleleMatches', '-'),
                ('16sPhylogeny', sample.sixteens_full.genus),
                ('rMLSTsequenceType', sample.rmlst.sequencetype),
                ('MLSTsequencetype', sample.mlst.sequencetype),
                ('MLSTmatches', str(sample.mlst.matchestosequencetype)),
                ('coreGenome', GenObject.returnattr(sample.coregenome, 'coreresults').rstrip(',')),
                ('SeroType', '{oset}:{hset}'
                    .format(oset=';'.join(sample.serosippr.o_set),
                            hset=';'.join(sample.serosippr.h_set))),
                ('geneSeekrProfile', ';'.join(result for result, pid in sorted(sample.genesippr.results.items()))),
                ('vtyperProfile', ';'.join(sorted(sample.legacy_vtyper.toxinprofile))),
                ('percentGC', str(sample.quality_features_polished.gc)),
                ('TotalPredictedGenes', str(sample.prodigal.predictedgenestotal)),
                ('predictedgenesover3000bp', str(sample.prodigal.predictedgenesover3000bp)),
                ('predictedgenesover1000bp', str(sample.prodigal.predictedgenesover1000bp)),
                ('predictedgenesover500bp', str(sample.prodigal.predictedgenesover500bp)),
                ('predictedgenesunder500bp', str(sample.prodigal.predictedgenesunder500bp)),
                ('SequencingDate', sample.run.Date),
                ('Investigator', sample.run.InvestigatorName),
                ('TotalClustersinRun', str(sample.run.TotalClustersinRun)),
                ('NumberofClustersPF', str(sample.run.NumberofClustersPF)),
                ('PercentOfClusters', str(sample.run.PercentOfClusters)),
                ('LengthofForwardRead', str(sample.run.forwardlength)),
                ('LengthofReverseRead', str(sample.run.reverselength)),
                ('Project', str(sample.run.SampleProject)),
                ('PipelineVersion', self.commit)
            ])

            if not row:
                row += ','.join([key for key, value in data.items()])
            row += '\n'
            row += ','.join([value for key, value in data.items()])
        cleanrow = row.replace('NA', '').replace(',-,', ',,')
        with open(os.path.join(self.reportpath, 'legacy_combinedMetadata.csv'), 'w') as metadatareport:
            metadatareport.write(cleanrow)