def reporter(self):
        """
        Creates the metadata report by pulling specific attributes from the metadata objects
        """
        logging.info('Creating summary report')
        header = '{}\n'.format(','.join(self.headers))
        # Create a string to store all the results
        data = str()
        for sample in self.metadata:
            # Add the value of the appropriate attribute to the results string
            data += GenObject.returnattr(sample, 'name')
            # SampleName
            data += GenObject.returnattr(sample.run, 'SamplePlate')
            # Genus
            data += GenObject.returnattr(sample.general, 'closestrefseqgenus')
            # SequencingDate
            data += GenObject.returnattr(sample.run, 'Date')
            # Analyst
            data += GenObject.returnattr(sample.run, 'InvestigatorName')
            # SamplePurity
            data += GenObject.returnattr(sample.confindr, 'contam_status')
            # N50
            n50 = GenObject.returnattr(sample.quality_features_polished, 'n50',
                                       number=True)
            if n50 != '-,':
                data += n50
            else:
                data += '0,'
            # NumContigs
            data += GenObject.returnattr(sample.quality_features_polished, 'num_contigs',
                                         number=True)
            # TotalLength
            data += GenObject.returnattr(sample.quality_features_polished, 'genome_length',
                                         number=True)
            # MeanInsertSize
            data += GenObject.returnattr(sample.mapping, 'MeanInsertSize',
                                         number=True)
            # InsertSizeSTD
            data += GenObject.returnattr(sample.mapping, 'StdInsertSize',
                                         number=True)
            # AverageCoverageDepth
            data += GenObject.returnattr(sample.mapping, 'MeanCoveragedata',
                                         number=True)
            # CoverageDepthSTD
            data += GenObject.returnattr(sample.mapping, 'StdCoveragedata',
                                         number=True)
            # PercentGC
            data += GenObject.returnattr(sample.quality_features_polished, 'gc',
                                         number=True)
            # MASH_ReferenceGenome
            data += GenObject.returnattr(sample.mash, 'closestrefseq')
            # MASH_NumMatchingHashes
            data += GenObject.returnattr(sample.mash, 'nummatches')
            # 16S_result
            data += GenObject.returnattr(sample.sixteens_full, 'sixteens_match')
            # rMLST_Result
            try:
                # If the number of matches to the closest reference profile is 53, return the profile number
                if sample.rmlst.matches == 53:
                    rmlst_seq_type = GenObject.returnattr(sample.rmlst, 'sequencetype')
                    rmlst_seq_type = rmlst_seq_type if rmlst_seq_type != 'ND,' else 'new,'
                    data += rmlst_seq_type
                else:
                    # Otherwise the profile is set to new
                    data += 'new,'
            except AttributeError:
                data += 'new,'
            # MLST_Result
            try:
                if sample.mlst.matches == 7:
                    data += GenObject.returnattr(sample.mlst, 'sequencetype')
                else:
                    data += 'new,'
                    # # Create a set of all the genes present in the results (gene name split from allele)
                    # mlst_gene_set = {gene.split('_')[0] for gene in sample.mlst.results}
                    # # If there are all the genes present, but no perfect match to a reference profile, state that
                    # # the profile is new
                    # if len(mlst_gene_set) == 7:
                    #     data += 'new,'
                    # # Otherwise indicate that the profile is ND
                    # else:
                    #     data += 'ND,'
            except AttributeError:
                data += 'new,'
            # MLST_gene_X_alleles
            try:
                # Create a set of all the genes present in the results (gene name split from allele)
                gene_set = {gene.split('_')[0] for gene in sample.mlst.results}
                for gene in sorted(gene_set):
                    allele_list = list()
                    # Determine all the alleles that are present for each gene
                    for allele in sample.mlst.results:
                        if gene in allele:
                            allele_list.append(allele)
                    # If there is more than one allele in the sample, add both to the string separated by a ';'
                    if len(allele_list) > 1:
                        data += '{},'.format(';'.join(allele_list))
                    # Otherwise add the only allele
                    else:
                        data += allele_list[0] + ','
                # If there are fewer than seven matching alleles, add a ND for each missing result
                if len(gene_set) < 7:
                    data += (7 - len(gene_set)) * 'ND,'
            except AttributeError:
                # data += '-,-,-,-,-,-,-,'
                data += 'ND,ND,ND,ND,ND,ND,ND,'
            # CoreGenesPresent
            data += GenObject.returnattr(sample.coregenome, 'coreresults')
            # E_coli_Serotype
            try:
                # If no O-type was found, set the output to be O-untypeable
                if ';'.join(sample.serosippr.o_set) == '-':
                    otype = 'O-untypeable'
                else:
                    otype = '{oset} ({opid})'.format(oset=';'.join(sample.serosippr.o_set),
                                                     opid=sample.serosippr.best_o_pid)
                # Same as above for the H-type
                if ';'.join(sample.serosippr.h_set) == '-':
                    htype = 'H-untypeable'

                else:
                    htype = '{hset} ({hpid})'.format(hset=';'.join(sample.serosippr.h_set),
                                                     hpid=sample.serosippr.best_h_pid)
                serotype = '{otype}:{htype},'.format(otype=otype,
                                                     htype=htype)
                # Add the serotype to the data string unless neither O-type not H-type were found; add ND instead
                data += serotype if serotype != 'O-untypeable:H-untypeable,' else 'ND,'
            except AttributeError:
                data += 'ND,'
            # SISTR_serovar_antigen
            data += GenObject.returnattr(sample.sistr, 'serovar_antigen').rstrip(';')
            # SISTR_serovar_cgMLST
            data += GenObject.returnattr(sample.sistr, 'serovar_cgmlst')
            # SISTR_serogroup
            data += GenObject.returnattr(sample.sistr, 'serogroup')
            # SISTR_h1
            data += GenObject.returnattr(sample.sistr, 'h1').rstrip(';')
            # SISTR_h2
            data += GenObject.returnattr(sample.sistr, 'h2').rstrip(';')
            # SISTR_serovar
            data += GenObject.returnattr(sample.sistr, 'serovar')
            # GeneSeekr_Profile
            try:
                if sample.genesippr.report_output:
                    data += ';'.join(sample.genesippr.report_output) + ','
                else:
                    data += 'ND,'
            except AttributeError:
                data += 'ND,'
            # Vtyper_Profile
            data += GenObject.returnattr(sample.legacy_vtyper, 'toxinprofile')
            # AMR_Profile and resistant/sensitive status
            if sample.resfinder_assembled.pipelineresults:
                # Profile
                for resistance, resistance_set in sorted(sample.resfinder_assembled.pipelineresults.items()):
                    data += '{res}({r_set});'.format(res=resistance.replace(',', ';'),
                                                     r_set=';'.join(sorted(list(resistance_set))))
                data += ','
                # Resistant/Sensitive
                data += 'Resistant,'
            else:
                # Profile
                data += 'ND,'
                # Resistant/Sensitive
                data += 'Sensitive,'
            # Plasmid Result'
            if sample.mobrecon.pipelineresults:
                for plasmid, details in sorted(sample.mobrecon.pipelineresults.items()):
                    data += '{plasmid}({details});'.format(plasmid=plasmid,
                                                           details=details)
                data += ','
            else:
                data += 'ND,'
            # TotalPredictedGenes
            data += GenObject.returnattr(sample.prodigal, 'predictedgenestotal',
                                         number=True)
            # PredictedGenesOver3000bp
            data += GenObject.returnattr(sample.prodigal, 'predictedgenesover3000bp',
                                         number=True)
            # PredictedGenesOver1000bp
            data += GenObject.returnattr(sample.prodigal, 'predictedgenesover1000bp',
                                         number=True)
            # PredictedGenesOver500bp
            data += GenObject.returnattr(sample.prodigal, 'predictedgenesover500bp',
                                         number=True)
            # PredictedGenesUnder500bp
            data += GenObject.returnattr(sample.prodigal, 'predictedgenesunder500bp',
                                         number=True)
            # NumClustersPF
            data += GenObject.returnattr(sample.run, 'NumberofClustersPF')
            # Percent of reads mapping to PhiX control
            data += GenObject.returnattr(sample.run, 'phix_aligned')
            # Error rate calculated from PhiX control
            data += GenObject.returnattr(sample.run, 'error_rate')
            # LengthForwardRead
            data += GenObject.returnattr(sample.run, 'forwardlength',
                                         number=True)
            # LengthReverseRead
            data += GenObject.returnattr(sample.run, 'reverselength',
                                         number=True)
            # Real time strain
            data += GenObject.returnattr(sample.run, 'Description')
            # Flowcell
            data += GenObject.returnattr(sample.run, 'flowcell')
            # MachineName
            data += GenObject.returnattr(sample.run, 'instrument')
            # PipelineVersion
            data += self.commit + ','
            # AssemblyDate
            data += datetime.now().strftime('%Y-%m-%d')
            # Append a new line to the end of the results for this sample
            data += '\n'
        # Replace any NA values with -
        cleandata = data.replace('NA', 'ND')
        with open(os.path.join(self.reportpath, 'combinedMetadata.csv'), 'w') as metadatareport:
            metadatareport.write(header)
            metadatareport.write(cleandata)
Creates the metadata report by pulling specific attributes from the metadata objects