def parsebam(self):
        """
        Parse the dictionaries of the sorted bam files extracted using pysam
        """
        # Threading is actually the worst - need multiprocessing to make this work at all
        logging.info('Parsing BAM files')
        # The sample objects are too big to get pickled. To hack our way around this, try to dump the sample object to
        # json, and have the processing function turn the object into a dictionary.
        json_files = list()
        with tempfile.TemporaryDirectory() as tmpdir:
            best_assemblies = list()
            sample_names = list()
            for sample in self.runmetadata:
                json_name = os.path.join(tmpdir, '{sn}.json'.format(sn=sample.name))
                best_assemblies.append(sample.general.bestassemblyfile)
                sample_names.append(sample.name)
                with open(json_name, 'w') as f:
                    json.dump(sample[self.analysistype].dump(), f, sort_keys=True, indent=4)
                json_files.append(json_name)
            p = multiprocessing.Pool(processes=self.cpus)
            analysis_type_list = [self.analysistype] * len(self.runmetadata)
            iupac_list = [self.iupac] * len(self.runmetadata)
            cutoff_list = [self.cutoff] * len(self.runmetadata)
            depth_list = [self.averagedepth] * len(self.runmetadata)
            allow_soft_clip_list = [self.allow_soft_clips] * len(self.runmetadata)
            sample_results = p.starmap(Sippr.parse_one_sample,
                                       zip(json_files, sample_names, best_assemblies, analysis_type_list,
                                           iupac_list, cutoff_list, depth_list, allow_soft_clip_list))
            p.close()
            p.join()
        # Since we had to json-ize the sample objects, we now need to update the metadata for everything.
        for sample in self.runmetadata:
                sample[self.analysistype].faidict = dict()
                sample[self.analysistype].results = dict()
                sample[self.analysistype].avgdepth = dict()
                sample[self.analysistype].resultssnp = dict()
                sample[self.analysistype].snplocations = dict()
                sample[self.analysistype].resultsgap = dict()
                sample[self.analysistype].gaplocations = dict()
                sample[self.analysistype].sequences = dict()
                sample[self.analysistype].maxcoverage = dict()
                sample[self.analysistype].mincoverage = dict()
                sample[self.analysistype].standarddev = dict()
                # Figure out which of the sample results to use.
                for sample_result in sample_results:
                    if sample_result['name'] == sample.name:
                        sample[self.analysistype].faidict = sample_result['faidict']
                        sample[self.analysistype].results = sample_result['results']
                        sample[self.analysistype].avgdepth = sample_result['avgdepth']
                        sample[self.analysistype].resultssnp = sample_result['resultssnp']
                        sample[self.analysistype].snplocations = sample_result['snplocations']
                        sample[self.analysistype].resultsgap = sample_result['resultsgap']
                        sample[self.analysistype].gaplocations = sample_result['gaplocations']
                        sample[self.analysistype].sequences = sample_result['sequences']
                        sample[self.analysistype].maxcoverage = sample_result['maxcoverage']
                        sample[self.analysistype].mincoverage = sample_result['mincoverage']
                        sample[self.analysistype].standarddev = sample_result['standarddev']
        logging.info('Done parsing BAM files')
Parse the dictionaries of the sorted bam files extracted using pysam