def _merge_fastqc(samples):
"""
merge all fastqc samples into one by module
"""
fastqc_list = collections.defaultdict(list)
seen = set()
for data in samples:
name = dd.get_sample_name(data)
if name in seen:
continue
seen.add(name)
fns = glob.glob(os.path.join(dd.get_work_dir(data), "qc", dd.get_sample_name(data), "fastqc") + "/*")
for fn in fns:
if fn.endswith("tsv"):
metric = os.path.basename(fn)
fastqc_list[metric].append([name, fn])
for metric in fastqc_list:
dt_by_sample = []
for fn in fastqc_list[metric]:
dt = pd.read_csv(fn[1], sep="\t")
dt['sample'] = fn[0]
dt_by_sample.append(dt)
dt = utils.rbind(dt_by_sample)
dt.to_csv(metric, sep="\t", index=False, mode ='w')
return samples
def combine_sailfish(samples):
work_dir = dd.get_in_samples(samples, dd.get_work_dir)
dont_combine, to_combine = partition(dd.get_sailfish,
dd.sample_data_iterator(samples),
True)
if not to_combine:
return samples
out_file = os.path.join(work_dir, "sailfish", "combined.sf")
if not file_exists(out_file):
df = pd.DataFrame()
for data in to_combine:
sailfish_file = dd.get_sailfish(data)
samplename = dd.get_sample_name(data)
new_df = _sailfish_expression_parser(sailfish_file, samplename)
if df.empty:
df = new_df
else:
df = rbind([df, new_df])
with file_transaction(out_file) as tx_out_file:
df.to_csv(tx_out_file, sep="\t", index_label="name")
updated_samples = []
for data in dd.sample_data_iterator(samples):
data = dd.set_sailfish_combined(data, out_file)
updated_samples.append([data])
return updated_samples
def _get_module(fastq_list, module, wide=True):
dt_together = []
for sample in fastq_list:
dt = []
itern = fastq_list[sample].clean_data(module)
header = itern[0]
total = fastq_list[sample].clean_data("Basic Statistics")[4][1]
for data in itern[1:]:
if data[0].startswith("#"):
header = data
continue
if wide:
if data[0].find("-") > -1:
f, s = map(int, data[0].split("-"))
for pos in range(f, s):
dt.append([str(pos)] + data[1:])
else:
dt.append(data)
dt = pd.DataFrame(dt)
dt.columns = [h.replace(" ", "_") for h in header]
dt['sample'] = sample
dt['total'] = total
dt_together.append(dt)
dt_together = utils.rbind(dt_together)
return dt_together
def _merge_metrics(samples):
"""
parse project.yaml file to get metrics for each bam
"""
out_file = os.path.join("metrics", "metrics.tsv")
dt_together = []
cov = {}
with file_transaction(out_file) as out_tx:
for s in samples:
s = s[0]
m = tz.get_in(['summary', 'metrics'], s)
if m:
for me in m:
if isinstance(m[me], list):
m[me] = ":".join(m[me])
dt = pd.DataFrame(m, index=['1'])
dt['avg_coverage_per_region'] = _get_coverage_per_region(s['description'])
cov[s['description']] = dt['avg_coverage_per_region'][0]
# dt = pd.DataFrame.from_dict(m)
dt.columns = [k.replace(" ", "_").replace("(", "").replace(")", "") for k in dt.columns]
dt['sample'] = s['description']
dt_together.append(dt)
if len(dt_together) > 0:
dt_together = utils.rbind(dt_together)
dt_together.to_csv(out_tx, index=False, sep="\t")
for i, s in enumerate(samples):
if s[0]['description'] in cov:
samples[i][0]['summary']['metrics']['avg_coverage_per_region'] = cov[s[0]['description']]
return samples
def _get_module(fastq_list, module, wide=True):
dt_together = []
for sample in fastq_list:
dt = []
itern = fastq_list[sample].clean_data(module)
header = itern[0]
total = fastq_list[sample].clean_data("Basic Statistics")[4][1]
for data in itern[1:]:
if data[0].startswith("#"):
header = data
continue
if wide:
if data[0].find("-") > -1:
f, s = map(int, data[0].split("-"))
for pos in range(f, s):
dt.append([str(pos)] + data[1:])
else:
dt.append(data)
dt = pd.DataFrame(dt)
dt.columns = [h.replace(" ", "_") for h in header]
dt['sample'] = sample
dt['total'] = total
dt_together.append(dt)
dt_together = utils.rbind(dt_together)
return dt_together
def _merge_fastqc(samples):
"""
merge all fastqc samples into one by module
"""
fastqc_list = collections.defaultdict(list)
seen = set()
for data in samples:
name = dd.get_sample_name(data)
if name in seen:
continue
seen.add(name)
fns = glob.glob(
os.path.join(dd.get_work_dir(data), "qc", dd.get_sample_name(data),
"fastqc") + "/*")
for fn in fns:
if fn.endswith("tsv"):
metric = os.path.basename(fn)
fastqc_list[metric].append([name, fn])
for metric in fastqc_list:
dt_by_sample = []
for fn in fastqc_list[metric]:
dt = pd.read_csv(fn[1], sep="\t")
dt['sample'] = fn[0]
dt_by_sample.append(dt)
dt = utils.rbind(dt_by_sample)
dt.to_csv(metric, sep="\t", index=False, mode='w')
return samples
def combine_spikein(samples):
work_dir = dd.get_in_samples(samples, dd.get_work_dir)
sailfish_dir = os.path.join(work_dir, "spikein")
dont_combine, to_combine = partition(dd.get_spikein_counts,
dd.sample_data_iterator(samples),
True)
if not to_combine:
return samples
tidy_file = os.path.join(sailfish_dir, "spikein.sf")
if not file_exists(tidy_file):
logger.info("Combining count files into %s." % tidy_file)
df = pd.DataFrame()
for data in to_combine:
sailfish_file = dd.get_spikein_counts(data)
samplename = dd.get_sample_name(data)
new_df = sailfish._sailfish_expression_parser(
sailfish_file, samplename)
if df.empty:
df = new_df
else:
df = rbind([df, new_df])
df["id"] = df.index
# some versions of the transcript annotations can have duplicated entries
df = df.drop_duplicates(["id", "sample"])
with file_transaction(tidy_file) as tx_out_file:
df.to_csv(tx_out_file, sep="\t", index_label="name")
logger.info("Finished combining count files into %s." % tidy_file)
updated_samples = []
for data in dd.sample_data_iterator(samples):
data = dd.set_spikein_counts(data, tidy_file)
updated_samples.append([data])
return updated_samples
def combine_spikein(samples):
work_dir = dd.get_in_samples(samples, dd.get_work_dir)
sailfish_dir = os.path.join(work_dir, "spikein")
dont_combine, to_combine = partition(dd.get_spikein_counts,
dd.sample_data_iterator(samples), True)
if not to_combine:
return samples
tidy_file = os.path.join(sailfish_dir, "spikein.sf")
if not file_exists(tidy_file):
logger.info("Combining count files into %s." % tidy_file)
df = pd.DataFrame()
for data in to_combine:
sailfish_file = dd.get_spikein_counts(data)
samplename = dd.get_sample_name(data)
new_df = sailfish._sailfish_expression_parser(sailfish_file, samplename)
if df.empty:
df = new_df
else:
df = rbind([df, new_df])
df["id"] = df.index
# some versions of the transcript annotations can have duplicated entries
df = df.drop_duplicates(["id", "sample"])
with file_transaction(tidy_file) as tx_out_file:
df.to_csv(tx_out_file, sep="\t", index_label="name")
logger.info("Finished combining count files into %s." % tidy_file)
updated_samples = []
for data in dd.sample_data_iterator(samples):
data = dd.set_spikein_counts(data, tidy_file)
updated_samples.append([data])
return updated_samples
def combine_sailfish(samples):
work_dir = dd.get_in_samples(samples, dd.get_work_dir)
sailfish_dir = os.path.join(work_dir, "sailfish")
gtf_file = dd.get_in_samples(samples, dd.get_gtf_file)
dont_combine, to_combine = partition(dd.get_sailfish,
dd.sample_data_iterator(samples),
True)
if not to_combine:
return samples
tidy_file = os.path.join(sailfish_dir, "combined.sf")
transcript_tpm_file = os.path.join(sailfish_dir, "combined.isoform.sf.tpm")
gene_tpm_file = os.path.join(sailfish_dir, "combined.gene.sf.tpm")
tx2gene = os.path.join(sailfish_dir, "tx2gene.csv")
if not all([
file_exists(x)
for x in [gene_tpm_file, tidy_file, transcript_tpm_file, tx2gene]
]):
logger.info("Combining count files into %s." % tidy_file)
df = pd.DataFrame()
for data in to_combine:
sailfish_file = dd.get_sailfish(data)
samplename = dd.get_sample_name(data)
new_df = _sailfish_expression_parser(sailfish_file, samplename)
if df.empty:
df = new_df
else:
df = rbind([df, new_df])
df["id"] = df.index
# some versions of the transcript annotations can have duplicated entries
df = df.drop_duplicates(["id", "sample"])
with file_transaction(tidy_file) as tx_out_file:
df.to_csv(tx_out_file, sep="\t", index_label="name")
with file_transaction(transcript_tpm_file) as tx_out_file:
df.pivot("id", "sample", "tpm").to_csv(tx_out_file, sep="\t")
with file_transaction(gene_tpm_file) as tx_out_file:
pivot = df.pivot("id", "sample", "tpm")
tdf = pd.DataFrame.from_dict(gtf.transcript_to_gene(gtf_file),
orient="index")
tdf.columns = ["gene_id"]
pivot = pivot.join(tdf)
pivot = pivot.groupby("gene_id").agg(np.sum)
pivot.to_csv(tx_out_file, sep="\t")
tx2gene = gtf.tx2genefile(gtf_file, tx2gene)
logger.info("Finished combining count files into %s." % tidy_file)
updated_samples = []
for data in dd.sample_data_iterator(samples):
data = dd.set_sailfish_tidy(data, tidy_file)
data = dd.set_sailfish_transcript_tpm(data, transcript_tpm_file)
data = dd.set_sailfish_gene_tpm(data, gene_tpm_file)
data = dd.set_tx2gene(data, tx2gene)
updated_samples.append([data])
return updated_samples
def _combine_regional_coverage(in_bams, samplenames, chrom, start, end, work_dir):
"""
given a list of bam files, sample names and a region, calculate the
coverage in the region for each of the samples and return a tidy pandas
dataframe of the format:
chrom position coverage name
"""
dfs = [_calc_regional_coverage(bam, chrom, start, end, sample, work_dir) for bam, sample
in zip(in_bams, samplenames)]
return rbind(dfs)
def _combine_regional_coverage(in_bams, samplenames, chrom, start, end, work_dir):
"""
given a list of bam files, sample names and a region, calculate the
coverage in the region for each of the samples and return a tidy pandas
dataframe of the format:
chrom position coverage name
"""
dfs = [_calc_regional_coverage(bam, chrom, start, end, sample, work_dir) for bam, sample
in zip(in_bams, samplenames)]
return rbind(dfs)
def combine_sailfish(samples):
work_dir = dd.get_in_samples(samples, dd.get_work_dir)
sailfish_dir = os.path.join(work_dir, "sailfish")
gtf_file = dd.get_in_samples(samples, dd.get_gtf_file)
dont_combine, to_combine = partition(dd.get_sailfish,
dd.sample_data_iterator(samples), True)
if not to_combine:
return samples
tidy_file = os.path.join(sailfish_dir, "combined.sf")
transcript_tpm_file = os.path.join(sailfish_dir, "combined.isoform.sf.tpm")
gene_tpm_file = os.path.join(sailfish_dir, "combined.gene.sf.tpm")
tx2gene = os.path.join(sailfish_dir, "tx2gene.csv")
if not all([file_exists(x) for x in [gene_tpm_file, tidy_file,
transcript_tpm_file, tx2gene]]):
logger.info("Combining count files into %s." % tidy_file)
df = pd.DataFrame()
for data in to_combine:
sailfish_file = dd.get_sailfish(data)
samplename = dd.get_sample_name(data)
new_df = _sailfish_expression_parser(sailfish_file, samplename)
if df.empty:
df = new_df
else:
df = rbind([df, new_df])
df["id"] = df.index
# some versions of the transcript annotations can have duplicated entries
df = df.drop_duplicates(["id", "sample"])
with file_transaction(tidy_file) as tx_out_file:
df.to_csv(tx_out_file, sep="\t", index_label="name")
with file_transaction(transcript_tpm_file) as tx_out_file:
df.pivot("id", "sample", "tpm").to_csv(tx_out_file, sep="\t")
with file_transaction(gene_tpm_file) as tx_out_file:
pivot = df.pivot("id", "sample", "tpm")
tdf = pd.DataFrame.from_dict(gtf.transcript_to_gene(gtf_file),
orient="index")
tdf.columns = ["gene_id"]
pivot = pivot.join(tdf)
pivot = pivot.groupby("gene_id").agg(np.sum)
pivot.to_csv(tx_out_file, sep="\t")
tx2gene = gtf.tx2genefile(gtf_file, tx2gene)
logger.info("Finished combining count files into %s." % tidy_file)
updated_samples = []
for data in dd.sample_data_iterator(samples):
data = dd.set_sailfish_tidy(data, tidy_file)
data = dd.set_sailfish_transcript_tpm(data, transcript_tpm_file)
data = dd.set_sailfish_gene_tpm(data, gene_tpm_file)
data = dd.set_tx2gene(data, tx2gene)
updated_samples.append([data])
return updated_samples
def _merge_metrics(samples):
"""
parse project.yaml file to get metrics for each bam
"""
out_file = os.path.join("metrics", "metrics.tsv")
dt_together = []
cov = {}
with file_transaction(out_file) as out_tx:
for s in samples:
sample_name = dd.get_sample_name(s)
s = _add_disambiguate(s)
if sample_name in cov:
continue
m = tz.get_in(['summary', 'metrics'], s)
sample_file = os.path.abspath(
os.path.join("metrics", "%s_bcbio.txt" % sample_name))
if not tz.get_in(['summary', 'qc'], s):
s['summary'] = {"qc": {}}
if m:
for me in m.keys():
if isinstance(m[me], list) or isinstance(
m[me], dict) or isinstance(m[me], tuple):
m.pop(me, None)
dt = pd.DataFrame(m, index=['1'])
dt['avg_coverage_per_region'] = _get_coverage_per_region(s)
cov[sample_name] = dt['avg_coverage_per_region'][0]
dt.columns = [
k.replace(" ", "_").replace("(", "").replace(")", "")
for k in dt.columns
]
dt['sample'] = sample_name
dt['rRNA_rate'] = m.get('rRNA_rate', "NA")
df = _fix_duplicated_rate(dt)
dt.transpose().to_csv(sample_file, sep="\t", header=False)
dt_together.append(dt)
s['summary']['qc'].update(
{'bcbio': {
'base': sample_file,
'secondary': []
}})
if len(dt_together) > 0:
dt_together = utils.rbind(dt_together)
dt_together.to_csv(out_tx, index=False, sep="\t")
out = []
for s in samples:
if sample_name in cov:
s['summary']['metrics']['avg_coverage_per_region'] = cov[
sample_name]
out.append(s)
return out
def bcbio_metrics(args):
"""
parse project.yaml file to get metrics for each bam
"""
project = yaml.load(open(args.bams[0]))
out_dir = safe_makedir(args.out)
out_file = op.join(out_dir, "metrics.tsv")
dt_together = []
with file_transaction(out_file) as out_tx:
for s in project['samples']:
m = s['summary']['metrics']
dt = pd.DataFrame.from_dict(m)
dt.columns = [k.replace(" ", "_").replace("(", "").replace(")", "") for k in dt.columns]
dt['sample'] = s['description']
dt_together.append(dt)
dt_together = rbind(dt_together)
dt_together.to_csv(out_tx, index=False, sep="\t")
def combine_sailfish(samples):
work_dir = dd.get_in_samples(samples, dd.get_work_dir)
gtf_file = dd.get_in_samples(samples, dd.get_gtf_file)
dont_combine, to_combine = partition(dd.get_sailfish,
dd.sample_data_iterator(samples), True)
if not to_combine:
return samples
tidy_file = os.path.join(work_dir, "sailfish", "combined.sf")
transcript_tpm_file = os.path.join(work_dir, "sailfish",
"combined.isoform.sf.tpm")
gene_tpm_file = os.path.join(work_dir, "sailfish",
"combined.gene.sf.tpm")
if not all([file_exists(x) for x in [gene_tpm_file, tidy_file,
transcript_tpm_file]]):
df = pd.DataFrame()
for data in to_combine:
sailfish_file = dd.get_sailfish(data)
samplename = dd.get_sample_name(data)
new_df = _sailfish_expression_parser(sailfish_file, samplename)
if df.empty:
df = new_df
else:
df = rbind([df, new_df])
with file_transaction(tidy_file) as tx_out_file:
df.to_csv(tx_out_file, sep="\t", index_label="name")
with file_transaction(transcript_tpm_file) as tx_out_file:
df.pivot(None, "sample", "tpm").to_csv(tx_out_file, sep="\t")
with file_transaction(gene_tpm_file) as tx_out_file:
pivot = df.pivot(None, "sample", "tpm")
tdf = pd.DataFrame.from_dict(gtf.transcript_to_gene(gtf_file),
orient="index")
tdf.columns = ["gene_id"]
pivot = pivot.join(tdf)
pivot = pivot.groupby("gene_id").agg(np.sum)
pivot.to_csv(tx_out_file, sep="\t")
updated_samples = []
for data in dd.sample_data_iterator(samples):
data = dd.set_sailfish_tidy(data, tidy_file)
data = dd.set_sailfish_transcript_tpm(data, transcript_tpm_file)
data = dd.set_sailfish_gene_tpm(data, gene_tpm_file)
updated_samples.append([data])
return updated_samples
def combine_sailfish(samples):
work_dir = dd.get_in_samples(samples, dd.get_work_dir)
gtf_file = dd.get_in_samples(samples, dd.get_gtf_file)
dont_combine, to_combine = partition(dd.get_sailfish,
dd.sample_data_iterator(samples), True)
if not to_combine:
return samples
tidy_file = os.path.join(work_dir, "sailfish", "combined.sf")
transcript_tpm_file = os.path.join(work_dir, "sailfish",
"combined.isoform.sf.tpm")
gene_tpm_file = os.path.join(work_dir, "sailfish",
"combined.gene.sf.tpm")
if not all([file_exists(x) for x in [gene_tpm_file, tidy_file,
transcript_tpm_file]]):
df = pd.DataFrame()
for data in to_combine:
sailfish_file = dd.get_sailfish(data)
samplename = dd.get_sample_name(data)
new_df = _sailfish_expression_parser(sailfish_file, samplename)
if df.empty:
df = new_df
else:
df = rbind([df, new_df])
with file_transaction(tidy_file) as tx_out_file:
df.to_csv(tx_out_file, sep="\t", index_label="name")
with file_transaction(transcript_tpm_file) as tx_out_file:
df.pivot(None, "sample", "tpm").to_csv(tx_out_file, sep="\t")
with file_transaction(gene_tpm_file) as tx_out_file:
pivot = df.pivot(None, "sample", "tpm")
tdf = pd.DataFrame.from_dict(gtf.transcript_to_gene(gtf_file),
orient="index")
tdf.columns = ["gene_id"]
pivot = pivot.join(tdf)
pivot = pivot.groupby("gene_id").agg(np.sum)
pivot.to_csv(tx_out_file, sep="\t")
updated_samples = []
for data in dd.sample_data_iterator(samples):
data = dd.set_sailfish_tidy(data, tidy_file)
data = dd.set_sailfish_transcript_tpm(data, transcript_tpm_file)
data = dd.set_sailfish_gene_tpm(data, gene_tpm_file)
updated_samples.append([data])
return updated_samples
def _merge_metrics(yaml_data):
"""
parse project.yaml file to get metrics for each bam
"""
project = yaml_data
out_file = os.path.join("metrics", "metrics.tsv")
dt_together = []
with file_transaction(out_file) as out_tx:
for s in project['samples']:
m = s['summary']['metrics']
for me in m:
if isinstance(m[me], list):
m[me] = ":".join(m[me])
dt = pd.DataFrame(m, index=['1'])
# dt = pd.DataFrame.from_dict(m)
dt.columns = [k.replace(" ", "_").replace("(", "").replace(")", "") for k in dt.columns]
dt['sample'] = s['description']
dt_together.append(dt)
dt_together = utils.rbind(dt_together)
dt_together.to_csv(out_tx, index=False, sep="\t")
def _merge_metrics(yaml_data):
"""
parse project.yaml file to get metrics for each bam
"""
project = yaml_data
out_file = os.path.join("metrics", "metrics.tsv")
dt_together = []
with file_transaction(out_file) as out_tx:
for s in project['samples']:
m = s['summary']['metrics']
for me in m:
if isinstance(m[me], list):
m[me] = ":".join(m[me])
dt = pd.DataFrame(m, index=['1'])
# dt = pd.DataFrame.from_dict(m)
dt.columns = [k.replace(" ", "_").replace("(", "").replace(")", "") for k in dt.columns]
dt['sample'] = s['description']
dt_together.append(dt)
dt_together = utils.rbind(dt_together)
dt_together.to_csv(out_tx, index=False, sep="\t")
def _merge_metrics(samples):
"""
parse project.yaml file to get metrics for each bam
"""
out_file = os.path.join("metrics", "metrics.tsv")
dt_together = []
cov = {}
with file_transaction(out_file) as out_tx:
for s in samples:
sample_name = dd.get_sample_name(s)
s = _add_disambiguate(s)
if sample_name in cov:
continue
m = tz.get_in(['summary', 'metrics'], s)
sample_file = os.path.abspath(os.path.join("metrics", "%s_bcbio.txt" % sample_name))
if not tz.get_in(['summary', 'qc'], s):
s['summary'] = {"qc": {}}
if m:
for me in m.keys():
if isinstance(m[me], list) or isinstance(m[me], dict) or isinstance(m[me], tuple):
m.pop(me, None)
dt = pd.DataFrame(m, index=['1'])
dt['avg_coverage_per_region'] = _get_coverage_per_region(s)
cov[sample_name] = dt['avg_coverage_per_region'][0]
dt.columns = [k.replace(" ", "_").replace("(", "").replace(")", "") for k in dt.columns]
dt['sample'] = sample_name
dt['rRNA_rate'] = m.get('rRNA_rate', "NA")
df = _fix_duplicated_rate(dt)
dt.transpose().to_csv(sample_file, sep="\t", header=False)
dt_together.append(dt)
s['summary']['qc'].update({'bcbio':{'base': sample_file, 'secondary': []}})
if len(dt_together) > 0:
dt_together = utils.rbind(dt_together)
dt_together.to_csv(out_tx, index=False, sep="\t")
out = []
for s in samples:
if sample_name in cov:
s['summary']['metrics']['avg_coverage_per_region'] = cov[sample_name]
out.append(s)
return out
def _merge_fastqc(data):
"""
merge all fastqc samples into one by module
"""
fastqc_list = defaultdict(list)
for sample in data:
name = dd.get_sample_name(sample[0])
fns = glob.glob(os.path.join(dd.get_work_dir(sample[0]), "qc", dd.get_sample_name(sample[0]), "fastqc") + "/*")
for fn in fns:
if fn.endswith("tsv"):
metric = os.path.basename(fn)
fastqc_list[metric].append([name, fn])
for metric in fastqc_list:
dt_by_sample = []
for fn in fastqc_list[metric]:
dt = pd.read_csv(fn[1], sep="\t")
dt['sample'] = fn[0]
dt_by_sample.append(dt)
dt = utils.rbind(dt_by_sample)
dt.to_csv(metric, sep="\t", index=False, mode = 'w')
return [data]
def _merge_metrics(samples):
"""
parse project.yaml file to get metrics for each bam
"""
out_file = os.path.join("metrics", "metrics.tsv")
dt_together = []
cov = {}
with file_transaction(out_file) as out_tx:
for s in samples:
s = s[0]
if s['description'] in cov:
continue
m = tz.get_in(['summary', 'metrics'], s)
if m:
for me in m:
if isinstance(m[me], list):
m[me] = ":".join(m[me])
dt = pd.DataFrame(m, index=['1'])
dt['avg_coverage_per_region'] = _get_coverage_per_region(
s['description'])
cov[s['description']] = dt['avg_coverage_per_region'][0]
# dt = pd.DataFrame.from_dict(m)
dt.columns = [
k.replace(" ", "_").replace("(", "").replace(")", "")
for k in dt.columns
]
dt['sample'] = s['description']
dt_together.append(dt)
if len(dt_together) > 0:
dt_together = utils.rbind(dt_together)
dt_together.to_csv(out_tx, index=False, sep="\t")
for i, s in enumerate(samples):
if s[0]['description'] in cov:
samples[i][0]['summary']['metrics'][
'avg_coverage_per_region'] = cov[s[0]['description']]
return samples
