def addLibMetrics(genomeinfodict, ionstats_alignment, ionstats_basecaller, keyPeak, BaseCallerMetrics, res, extra):
print 'addlibmetrics'
if keyPeak != None:
aveKeyCount = float(keyPeak.get('Library', 0.0))
else:
aveKeyCount = 0.0
align_sample = 0
if ionstats_alignment == None:
align_sample = -1
# check to see if this is a samled or full alignment
# if libMetrics.has_key('Total number of Sampled Reads'):
# align_sample = 1
if res.metaData.get('thumb', '0') == 1:
align_sample = 2
kwargs = {'report': res, 'aveKeyCounts': aveKeyCount, 'align_sample': align_sample}
kwargs['Genome_Version'] = genomeinfodict['genome_version']
kwargs['Index_Version'] = genomeinfodict['index_version']
kwargs['genome'] = genomeinfodict['genome_name']
kwargs['genomesize'] = genomeinfodict['genome_length']
kwargs['totalNumReads'] = ionstats_basecaller['full']['num_reads']
quallist = ['7', '10', '17', '20', '47'] # TODO Q30
bplist = [50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600]
if ionstats_alignment != None:
kwargs['total_mapped_reads'] = ionstats_alignment['aligned']['num_reads']
kwargs['total_mapped_target_bases'] = ionstats_alignment['aligned']['num_bases']
for q in quallist:
kwargs['q%s_longest_alignment' % q] = ionstats_alignment['AQ'+q]['max_read_length']
kwargs['q%s_mean_alignment_length' % q] = ionstats_alignment['AQ'+q]['mean_read_length']
kwargs['q%s_mapped_bases' % q] = ionstats_alignment['AQ'+q]['num_bases']
kwargs['q%s_alignments' % q] = ionstats_alignment['AQ'+q]['num_reads']
kwargs['q%s_coverage_percentage' % q] = 0.0 # 'N/A' # TODO
# 'Filtered %s Mean Coverage Depth' % q, '%.1f' % (float(ionstats_alignment['AQ'+q]['num_bases'])/float(3095693981)) ) TODO
for bp in bplist:
kwargs['i%sQ%s_reads' % (bp, q)] = sum(ionstats_alignment['AQ'+q]['read_length_histogram'][bp:])
try:
raw_accuracy = round((1 - float(sum(ionstats_alignment['error_by_position'])) / float(ionstats_alignment['aligned']['num_bases'])) * 100.0, 1)
kwargs['raw_accuracy'] = raw_accuracy
except:
kwargs['raw_accuracy'] = 0.0
else:
kwargs['total_mapped_reads'] = 0
kwargs['total_mapped_target_bases'] = 0
for q in quallist:
for bp in bplist:
kwargs['i%sQ%s_reads' % (bp, q)] = 0
kwargs['q%s_longest_alignment' % q] = 0
kwargs['q%s_mean_alignment_length' % q] = 0
kwargs['q%s_mapped_bases' % q] = 0
kwargs['q%s_alignments' % q] = 0
kwargs['q%s_coverage_percentage' % q] = 0.0
kwargs['raw_accuracy'] = 0.0
try:
kwargs['sysSNR'] = ionstats_basecaller['system_snr']
kwargs['cf'] = 100.0 * BaseCallerMetrics['Phasing']['CF']
kwargs['ie'] = 100.0 * BaseCallerMetrics['Phasing']['IE']
kwargs['dr'] = 100.0 * BaseCallerMetrics['Phasing']['DR']
except:
kwargs['sysSNR'] = 0.0
kwargs['cf'] = 0.0
kwargs['ie'] = 0.0
kwargs['dr'] = 0.0
if extra:
kwargs["duplicate_reads"] = extra.get("duplicate_reads", None)
libmetrics = res.libmetrics or models.LibMetrics()
for key, value in kwargs.items():
setattr(libmetrics, key, value)
libmetrics.save()
res.libmetrics = libmetrics
res.save()
def addLibMetrics(genomeinfodict, ionstats_alignment, ionstats_basecaller,
keyPeak, BaseCallerMetrics, res, extra):
print 'addlibmetrics'
metric_map = {
'genomelength': 'Genomelength',
'rNumAlignments': 'Filtered relaxed BLAST Alignments',
'rMeanAlignLen': 'Filtered relaxed BLAST Mean Alignment Length',
'rLongestAlign': 'Filtered relaxed BLAST Longest Alignment',
'rCoverage': 'Filtered relaxed BLAST coverage percentage',
'r50Q10': 'Filtered relaxed BLAST 50Q10 Reads',
'r100Q10': 'Filtered relaxed BLAST 100Q10 Reads',
'r200Q10': 'Filtered relaxed BLAST 200Q10 Reads',
'r50Q17': 'Filtered relaxed BLAST 50Q17 Reads',
'r100Q17': 'Filtered relaxed BLAST 100Q17 Reads',
'r200Q17': 'Filtered relaxed BLAST 200Q17 Reads',
'r50Q20': 'Filtered relaxed BLAST 50Q20 Reads',
'r100Q20': 'Filtered relaxed BLAST 100Q20 Reads',
'r200Q20': 'Filtered relaxed BLAST 200Q20 Reads',
'sNumAlignments': 'Filtered strict BLAST Alignments',
'sMeanAlignLen': 'Filtered strict BLAST Mean Alignment Length',
'sCoverage': 'Filtered strict BLAST coverage percentage',
'sLongestAlign': 'Filtered strict BLAST Longest Alignment',
's50Q10': 'Filtered strict BLAST 50Q10 Reads',
's100Q10': 'Filtered strict BLAST 100Q10 Reads',
's200Q10': 'Filtered strict BLAST 200Q10 Reads',
's50Q17': 'Filtered strict BLAST 50Q17 Reads',
's100Q17': 'Filtered strict BLAST 100Q17 Reads',
's200Q17': 'Filtered strict BLAST 200Q17 Reads',
's50Q20': 'Filtered strict BLAST 50Q20 Reads',
's100Q20': 'Filtered strict BLAST 100Q20 Reads',
's200Q20': 'Filtered strict BLAST 200Q20 Reads',
"q7_qscore_bases": "Filtered Mapped Q7 Bases",
"q10_qscore_bases": "Filtered Mapped Q10 Bases",
"q17_qscore_bases": "Filtered Mapped Q17 Bases",
"q20_qscore_bases": "Filtered Mapped Q20 Bases",
"q47_qscore_bases": "Filtered Mapped Q47 Bases",
'total_number_of_sampled_reads': 'Total number of Sampled Reads',
'sampled_q7_coverage_percentage':
'Sampled Filtered Q7 Coverage Percentage',
'sampled_q7_mean_coverage_depth':
'Sampled Filtered Q7 Mean Coverage Depth',
'sampled_q7_alignments': 'Sampled Filtered Q7 Alignments',
'sampled_q7_mean_alignment_length':
'Sampled Filtered Q7 Mean Alignment Length',
'sampled_mapped_bases_in_q7_alignments':
'Sampled Filtered Mapped Bases in Q7 Alignments',
'sampled_q7_longest_alignment':
'Sampled Filtered Q7 Longest Alignment',
'sampled_50q7_reads': 'Sampled Filtered 50Q7 Reads',
'sampled_100q7_reads': 'Sampled Filtered 100Q7 Reads',
'sampled_200q7_reads': 'Sampled Filtered 200Q7 Reads',
'sampled_300q7_reads': 'Sampled Filtered 300Q7 Reads',
'sampled_400q7_reads': 'Sampled Filtered 400Q7 Reads',
'sampled_q10_coverage_percentage':
'Sampled Filtered Q10 Coverage Percentage',
'sampled_q10_mean_coverage_depth':
'Sampled Filtered Q10 Mean Coverage Depth',
'sampled_q10_alignments': 'Sampled Filtered Q10 Alignments',
'sampled_q10_mean_alignment_length':
'Sampled Filtered Q10 Mean Alignment Length',
'sampled_mapped_bases_in_q10_alignments':
'Sampled Filtered Mapped Bases in Q10 Alignments',
'sampled_q10_longest_alignment':
'Sampled Filtered Q10 Longest Alignment',
'sampled_50q10_reads': 'Sampled Filtered 50Q10 Reads',
'sampled_100q10_reads': 'Sampled Filtered 100Q10 Reads',
'sampled_200q10_reads': 'Sampled Filtered 200Q10 Reads',
'sampled_300q10_reads': 'Sampled Filtered 300Q10 Reads',
'sampled_400q10_reads': 'Sampled Filtered 400Q10 Reads',
'sampled_q17_coverage_percentage':
'Sampled Filtered Q17 Coverage Percentage',
'sampled_q17_mean_coverage_depth':
'Sampled Filtered Q17 Mean Coverage Depth',
'sampled_q17_alignments': 'Sampled Filtered Q17 Alignments',
'sampled_q17_mean_alignment_length':
'Sampled Filtered Q17 Mean Alignment Length',
'sampled_mapped_bases_in_q17_alignments':
'Sampled Filtered Mapped Bases in Q17 Alignments',
'sampled_q17_longest_alignment':
'Sampled Filtered Q17 Longest Alignment',
'sampled_50q17_reads': 'Sampled Filtered 50Q17 Reads',
'sampled_100q17_reads': 'Sampled Filtered 100Q17 Reads',
'sampled_200q17_reads': 'Sampled Filtered 200Q17 Reads',
'sampled_300q17_reads': 'Sampled Filtered 300Q17 Reads',
'sampled_400q17_reads': 'Sampled Filtered 400Q17 Reads',
'sampled_q20_coverage_percentage':
'Sampled Filtered Q20 Coverage Percentage',
'sampled_q20_mean_coverage_depth':
'Sampled Filtered Q20 Mean Coverage Depth',
'sampled_q20_alignments': 'Sampled Filtered Q20 Alignments',
'sampled_q20_mean_alignment_length':
'Sampled Filtered Q20 Mean Alignment Length',
'sampled_mapped_bases_in_q20_alignments':
'Sampled Filtered Mapped Bases in Q20 Alignments',
'sampled_q20_longest_alignment':
'Sampled Filtered Q20 Longest Alignment',
'sampled_50q20_reads': 'Sampled Filtered 50Q20 Reads',
'sampled_100q20_reads': 'Sampled Filtered 100Q20 Reads',
'sampled_200q20_reads': 'Sampled Filtered 200Q20 Reads',
'sampled_300q20_reads': 'Sampled Filtered 300Q20 Reads',
'sampled_400q20_reads': 'Sampled Filtered 400Q20 Reads',
'sampled_q47_coverage_percentage':
'Sampled Filtered Q47 Coverage Percentage',
'sampled_q47_mean_coverage_depth':
'Sampled Filtered Q47 Mean Coverage Depth',
'sampled_q47_alignments': 'Sampled Filtered Q47 Alignments',
'sampled_q47_mean_alignment_length':
'Sampled Filtered Q47 Mean Alignment Length',
'sampled_mapped_bases_in_q47_alignments':
'Sampled Filtered Mapped Bases in Q47 Alignments',
'sampled_q47_longest_alignment':
'Sampled Filtered Q47 Longest Alignment',
'sampled_50q47_reads': 'Sampled Filtered 50Q47 Reads',
'sampled_100q47_reads': 'Sampled Filtered 100Q47 Reads',
'sampled_200q47_reads': 'Sampled Filtered 200Q47 Reads',
'sampled_300q47_reads': 'Sampled Filtered 300Q47 Reads',
'sampled_400q47_reads': 'Sampled Filtered 400Q47 Reads',
'extrapolated_from_number_of_sampled_reads':
'Extrapolated from number of Sampled Reads',
'extrapolated_q7_coverage_percentage':
'Extrapolated Filtered Q7 Coverage Percentage',
'extrapolated_q7_mean_coverage_depth':
'Extrapolated Filtered Q7 Mean Coverage Depth',
'extrapolated_q7_alignments': 'Extrapolated Filtered Q7 Alignments',
'extrapolated_q7_mean_alignment_length':
'Extrapolated Filtered Q7 Mean Alignment Length',
'extrapolated_mapped_bases_in_q7_alignments':
'Extrapolated Filtered Mapped Bases in Q7 Alignments',
'extrapolated_q7_longest_alignment':
'Extrapolated Filtered Q7 Longest Alignment',
'extrapolated_50q7_reads': 'Extrapolated Filtered 50Q7 Reads',
'extrapolated_100q7_reads': 'Extrapolated Filtered 100Q7 Reads',
'extrapolated_200q7_reads': 'Extrapolated Filtered 200Q7 Reads',
'extrapolated_300q7_reads': 'Extrapolated Filtered 300Q7 Reads',
'extrapolated_400q7_reads': 'Extrapolated Filtered 400Q7 Reads',
'extrapolated_q10_coverage_percentage':
'Extrapolated Filtered Q10 Coverage Percentage',
'extrapolated_q10_mean_coverage_depth':
'Extrapolated Filtered Q10 Mean Coverage Depth',
'extrapolated_q10_alignments': 'Extrapolated Filtered Q10 Alignments',
'extrapolated_q10_mean_alignment_length':
'Extrapolated Filtered Q10 Mean Alignment Length',
'extrapolated_mapped_bases_in_q10_alignments':
'Extrapolated Filtered Mapped Bases in Q10 Alignments',
'extrapolated_q10_longest_alignment':
'Extrapolated Filtered Q10 Longest Alignment',
'extrapolated_50q10_reads': 'Extrapolated Filtered 50Q10 Reads',
'extrapolated_100q10_reads': 'Extrapolated Filtered 100Q10 Reads',
'extrapolated_200q10_reads': 'Extrapolated Filtered 200Q10 Reads',
'extrapolated_300q10_reads': 'Extrapolated Filtered 300Q10 Reads',
'extrapolated_400q10_reads': 'Extrapolated Filtered 400Q10 Reads',
'extrapolated_q17_coverage_percentage':
'Extrapolated Filtered Q17 Coverage Percentage',
'extrapolated_q17_mean_coverage_depth':
'Extrapolated Filtered Q17 Mean Coverage Depth',
'extrapolated_q17_alignments': 'Extrapolated Filtered Q17 Alignments',
'extrapolated_q17_mean_alignment_length':
'Extrapolated Filtered Q17 Mean Alignment Length',
'extrapolated_mapped_bases_in_q17_alignments':
'Extrapolated Filtered Mapped Bases in Q17 Alignments',
'extrapolated_q17_longest_alignment':
'Extrapolated Filtered Q17 Longest Alignment',
'extrapolated_50q17_reads': 'Extrapolated Filtered 50Q17 Reads',
'extrapolated_100q17_reads': 'Extrapolated Filtered 100Q17 Reads',
'extrapolated_200q17_reads': 'Extrapolated Filtered 200Q17 Reads',
'extrapolated_300q17_reads': 'Extrapolated Filtered 300Q17 Reads',
'extrapolated_400q17_reads': 'Extrapolated Filtered 400Q17 Reads',
'extrapolated_q20_coverage_percentage':
'Extrapolated Filtered Q20 Coverage Percentage',
'extrapolated_q20_mean_coverage_depth':
'Extrapolated Filtered Q20 Mean Coverage Depth',
'extrapolated_q20_alignments': 'Extrapolated Filtered Q20 Alignments',
'extrapolated_q20_mean_alignment_length':
'Extrapolated Filtered Q20 Mean Alignment Length',
'extrapolated_mapped_bases_in_q20_alignments':
'Extrapolated Filtered Mapped Bases in Q20 Alignments',
'extrapolated_q20_longest_alignment':
'Extrapolated Filtered Q20 Longest Alignment',
'extrapolated_50q20_reads': 'Extrapolated Filtered 50Q20 Reads',
'extrapolated_100q20_reads': 'Extrapolated Filtered 100Q20 Reads',
'extrapolated_200q20_reads': 'Extrapolated Filtered 200Q20 Reads',
'extrapolated_300q20_reads': 'Extrapolated Filtered 300Q20 Reads',
'extrapolated_400q20_reads': 'Extrapolated Filtered 400Q20 Reads',
'extrapolated_q47_coverage_percentage':
'Extrapolated Filtered Q47 Coverage Percentage',
'extrapolated_q47_mean_coverage_depth':
'Extrapolated Filtered Q47 Mean Coverage Depth',
'extrapolated_q47_alignments': 'Extrapolated Filtered Q47 Alignments',
'extrapolated_q47_mean_alignment_length':
'Extrapolated Filtered Q47 Mean Alignment Length',
'extrapolated_mapped_bases_in_q47_alignments':
'Extrapolated Filtered Mapped Bases in Q47 Alignments',
'extrapolated_q47_longest_alignment':
'Extrapolated Filtered Q47 Longest Alignment',
'extrapolated_50q47_reads': 'Extrapolated Filtered 50Q47 Reads',
'extrapolated_100q47_reads': 'Extrapolated Filtered 100Q47 Reads',
'extrapolated_200q47_reads': 'Extrapolated Filtered 200Q47 Reads',
'extrapolated_300q47_reads': 'Extrapolated Filtered 300Q47 Reads',
'extrapolated_400q47_reads': 'Extrapolated Filtered 400Q47 Reads',
'duplicate_reads': 'Count of Duplicate Reads',
}
if keyPeak != None:
aveKeyCount = float(keyPeak.get('Library', 0.0))
else:
aveKeyCount = 0.0
align_sample = 0
if ionstats_alignment == None:
align_sample = -1
#check to see if this is a samled or full alignment
#if libMetrics.has_key('Total number of Sampled Reads'):
# align_sample = 1
if res.metaData.get('thumb', '0') == 1:
align_sample = 2
kwargs = {
'report': res,
'aveKeyCounts': aveKeyCount,
'align_sample': align_sample
}
for dbname, key in metric_map.iteritems():
kwargs[dbname] = set_type('0')
kwargs['Genome_Version'] = genomeinfodict['genome_version']
kwargs['Index_Version'] = genomeinfodict['index_version']
kwargs['genome'] = genomeinfodict['genome_name']
kwargs['genomesize'] = genomeinfodict['genome_length']
quallist = ['7', '10', '17', '20', '47'] #TODO Q30
bplist = [50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600]
if ionstats_alignment != None:
kwargs['totalNumReads'] = ionstats_alignment['full']['num_reads']
kwargs['total_mapped_reads'] = ionstats_alignment['aligned'][
'num_reads']
kwargs['total_mapped_target_bases'] = ionstats_alignment['aligned'][
'num_bases']
for q in quallist:
kwargs['q%s_longest_alignment' %
q] = ionstats_alignment['AQ' + q]['max_read_length']
kwargs['q%s_mean_alignment_length' %
q] = ionstats_alignment['AQ' + q]['mean_read_length']
kwargs['q%s_mapped_bases' % q] = ionstats_alignment['AQ' +
q]['num_bases']
kwargs['q%s_alignments' % q] = ionstats_alignment['AQ' +
q]['num_reads']
kwargs['q%s_coverage_percentage' % q] = 0.0 #'N/A' # TODO
# 'Filtered %s Mean Coverage Depth' % q, '%.1f' % (float(ionstats_alignment['AQ'+q]['num_bases'])/float(3095693981)) ) TODO
for bp in bplist:
kwargs['i%sQ%s_reads' % (bp, q)] = sum(
ionstats_alignment['AQ' + q]['read_length_histogram'][bp:])
try:
raw_accuracy = round(
(1 - float(sum(ionstats_alignment['error_by_position'])) /
float(ionstats_alignment['aligned']['num_bases'])) * 100.0, 1)
kwargs['raw_accuracy'] = raw_accuracy
except:
kwargs['raw_accuracy'] = 0.0
else:
kwargs['totalNumReads'] = 0
kwargs['total_mapped_reads'] = 0
kwargs['total_mapped_target_bases'] = 0
for q in quallist:
for bp in bplist:
kwargs['i%sQ%s_reads' % (bp, q)] = 0
kwargs['q%s_longest_alignment' % q] = 0
kwargs['q%s_mean_alignment_length' % q] = 0
kwargs['q%s_mapped_bases' % q] = 0
kwargs['q%s_alignments' % q] = 0
kwargs['q%s_coverage_percentage' % q] = 0.0
kwargs['raw_accuracy'] = 0.0
try:
kwargs['sysSNR'] = ionstats_basecaller['system_snr']
kwargs['cf'] = 100.0 * BaseCallerMetrics['Phasing']['CF']
kwargs['ie'] = 100.0 * BaseCallerMetrics['Phasing']['IE']
kwargs['dr'] = 100.0 * BaseCallerMetrics['Phasing']['DR']
except:
kwargs['sysSNR'] = 0.0
kwargs['cf'] = 0.0
kwargs['ie'] = 0.0
kwargs['dr'] = 0.0
if extra:
kwargs["duplicate_reads"] = extra.get("duplicate_reads", None)
libmetrics = res.libmetrics or models.LibMetrics()
for key, value in kwargs.items():
setattr(libmetrics, key, value)
libmetrics.save()
res.libmetrics = libmetrics
res.save()
def addLibMetrics(
genomeinfodict,
ionstats_alignment,
ionstats_basecaller,
keyPeak,
BaseCallerMetrics,
res,
extra,
):
print("addlibmetrics")
if keyPeak != None:
aveKeyCount = float(keyPeak.get("Library", 0.0))
else:
aveKeyCount = 0.0
align_sample = 0
if ionstats_alignment == None:
align_sample = -1
# check to see if this is a samled or full alignment
# if libMetrics.has_key('Total number of Sampled Reads'):
# align_sample = 1
if res.metaData.get("thumb", "0") == 1:
align_sample = 2
kwargs = {
"report": res,
"aveKeyCounts": aveKeyCount,
"align_sample": align_sample
}
kwargs["Genome_Version"] = genomeinfodict["genome_version"]
kwargs["Index_Version"] = genomeinfodict["index_version"]
kwargs["genome"] = genomeinfodict["genome_name"]
kwargs["genomesize"] = genomeinfodict["genome_length"]
kwargs["totalNumReads"] = ionstats_basecaller["full"]["num_reads"]
quallist = ["7", "10", "17", "20", "47"] # TODO Q30
bplist = [50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600]
if ionstats_alignment != None:
kwargs["total_mapped_reads"] = ionstats_alignment["aligned"][
"num_reads"]
kwargs["total_mapped_target_bases"] = ionstats_alignment["aligned"][
"num_bases"]
for q in quallist:
kwargs["q%s_longest_alignment" %
q] = ionstats_alignment["AQ" + q]["max_read_length"]
kwargs["q%s_mean_alignment_length" %
q] = ionstats_alignment["AQ" + q]["mean_read_length"]
kwargs["q%s_mapped_bases" % q] = ionstats_alignment["AQ" +
q]["num_bases"]
kwargs["q%s_alignments" % q] = ionstats_alignment["AQ" +
q]["num_reads"]
kwargs["q%s_coverage_percentage" % q] = 0.0 # 'N/A' # TODO
# 'Filtered %s Mean Coverage Depth' % q, '%.1f' % (float(ionstats_alignment['AQ'+q]['num_bases'])/float(3095693981)) ) TODO
for bp in bplist:
kwargs["i%sQ%s_reads" % (bp, q)] = sum(
ionstats_alignment["AQ" + q]["read_length_histogram"][bp:])
try:
raw_accuracy = round(
(1 - float(sum(ionstats_alignment["error_by_position"])) /
float(ionstats_alignment["aligned"]["num_bases"])) * 100.0,
2,
)
kwargs["raw_accuracy"] = raw_accuracy
except Exception:
kwargs["raw_accuracy"] = 0.0
else:
kwargs["total_mapped_reads"] = 0
kwargs["total_mapped_target_bases"] = 0
for q in quallist:
for bp in bplist:
kwargs["i%sQ%s_reads" % (bp, q)] = 0
kwargs["q%s_longest_alignment" % q] = 0
kwargs["q%s_mean_alignment_length" % q] = 0
kwargs["q%s_mapped_bases" % q] = 0
kwargs["q%s_alignments" % q] = 0
kwargs["q%s_coverage_percentage" % q] = 0.0
kwargs["raw_accuracy"] = 0.0
try:
kwargs["sysSNR"] = ionstats_basecaller["system_snr"]
kwargs["cf"] = 100.0 * BaseCallerMetrics["Phasing"]["CF"]
kwargs["ie"] = 100.0 * BaseCallerMetrics["Phasing"]["IE"]
kwargs["dr"] = 100.0 * BaseCallerMetrics["Phasing"]["DR"]
except Exception:
kwargs["sysSNR"] = 0.0
kwargs["cf"] = 0.0
kwargs["ie"] = 0.0
kwargs["dr"] = 0.0
if extra:
kwargs["duplicate_reads"] = extra.get("duplicate_reads", None)
libmetrics = res.libmetrics or models.LibMetrics()
for key, value in list(kwargs.items()):
setattr(libmetrics, key, value)
libmetrics.save()
res.libmetrics = libmetrics
res.save()