- summary_results = gerald.summary.lane_results
- for end in range(len(summary_results)):
- eland_result = gerald.eland_results.results[end][lane_id]
- report.append("Sample name %s" % (eland_result.sample_name))
- report.append("Lane id %s end %s" % (eland_result.lane_id, end))
- if end < len(summary_results) and summary_results[end].has_key(eland_result.lane_id):
- cluster = summary_results[end][eland_result.lane_id].cluster
- report.append("Clusters %d +/- %d" % (cluster[0], cluster[1]))
- report.append("Total Reads: %d" % (eland_result.reads))
-
- if hasattr(eland_result, 'match_codes'):
- mc = eland_result.match_codes
- nm = mc['NM']
- nm_percent = float(nm) / eland_result.reads * 100
- qc = mc['QC']
- qc_percent = float(qc) / eland_result.reads * 100
-
- report.append("No Match: %d (%2.2g %%)" % (nm, nm_percent))
- report.append("QC Failed: %d (%2.2g %%)" % (qc, qc_percent))
- report.append('Unique (0,1,2 mismatches) %d %d %d' % \
- (mc['U0'], mc['U1'], mc['U2']))
- report.append('Repeat (0,1,2 mismatches) %d %d %d' % \
- (mc['R0'], mc['R1'], mc['R2']))
-
- if hasattr(eland_result, 'genome_map'):
- report.append("Mapped Reads")
- mapped_reads = summarize_mapped_reads(eland_result.genome_map, eland_result.mapped_reads)
- for name, counts in mapped_reads.items():
+ lane_results = gerald.summary.lane_results
+ eland_result = gerald.eland_results[lane_id]
+ report.append("Sample name %s" % (eland_result.sample_name))
+ report.append("Lane id %s end %s" % (lane_id.lane, lane_id.read))
+
+ if lane_id.read < len(lane_results) and \
+ lane_id.lane in lane_results[lane_id.read]:
+ summary_results = lane_results[lane_id.read][lane_id.lane]
+ cluster = summary_results.cluster
+ report.append("Clusters %d +/- %d" % (cluster[0], cluster[1]))
+ report.append("Total Reads: %d" % (eland_result.reads))
+
+ if hasattr(eland_result, 'match_codes'):
+ mc = eland_result.match_codes
+ nm = mc['NM']
+ nm_percent = float(nm) / eland_result.reads * 100
+ qc = mc['QC']
+ qc_percent = float(qc) / eland_result.reads * 100
+
+ report.append("No Match: %d (%2.2g %%)" % (nm, nm_percent))
+ report.append("QC Failed: %d (%2.2g %%)" % (qc, qc_percent))
+ report.append('Unique (0,1,2 mismatches) %d %d %d' % \
+ (mc['U0'], mc['U1'], mc['U2']))
+ report.append('Repeat (0,1,2 mismatches) %d %d %d' % \
+ (mc['R0'], mc['R1'], mc['R2']))
+
+ if hasattr(eland_result, 'genome_map'):
+ report.append("Mapped Reads")
+ mapped_reads = summarize_mapped_reads(eland_result.genome_map,
+ eland_result.mapped_reads)
+ for name, counts in mapped_reads.items():