def resource_cutadapt_metrics(uri, **kwargs):
with open(uri) as fh:
data = "".join(fh)
sections = re.split("\n===.*===\n", data)
df = DataFrame.from_records([_split_x(x) for x in sections[1].split("\n") if x],
index=["statistic"], columns=["statistic", "value"])
df["value"] = pd.to_numeric(df["value"])
return df
def _reader(uri):
with open(uri) as fh:
data = [x.strip("\n").split("\t") for x in fh if not x.strip() == ""]
indices = list((i for i, val in enumerate(data)
if val[0].startswith("## METRICS CLASS")))
metrics = DataFrame.from_records(data[(indices[0] + 2):],
columns=data[(indices[0] + 1)],
index="CATEGORY")
return (metrics, None)
def _hist_reader(uri):
with open(uri) as fh:
data = [x.strip("\n").split("\t") for x in fh if not x.strip() == ""]
indices = list((i for i, val in enumerate(data)
if val[0].startswith("## METRICS CLASS")
or val[0].startswith("## HISTOGRAM")))
if len(indices) == 1:
indices.append(len(data))
metrics = DataFrame.from_records(data[(indices[0] + 2):(indices[1])],
columns=data[(indices[0] + 1)])
# We could be missing the histogram
try:
hist = DataFrame.from_records(data[(indices[1] + 2):],
columns=data[(indices[1] + 1)])
except:
logger.warn("No histogram data for {}".format(uri))
hist = None
return (metrics, hist)
def resource_genome_results(uri, key="Globals", **kwargs):
with open(uri) as fh:
data = "".join(fh)
sections = re.split(">+\s+[a-zA-Z ]+", data)
section_names = ["Header"] + [re.sub(" ", "_", x) for x in re.findall(">+\s+([a-zA-Z ]+)", data)]
d = dict()
for h, sec in zip(section_names, sections):
if h == "Coverage_per_contig":
d[h] = DataFrame.from_records([re.split("\s+", x.strip()) for x in sec.split("\n") if x],
columns=COVERAGE_PER_CONTIG_COLUMNS,
index="chr")
d[h] = d[h].apply(pd.to_numeric)
elif h in ["Coverage", "Header"]:
pass
else:
d[h] = DataFrame.from_records([_split_x(x) for x in sec.split("\n") if x],
columns=["statistic", "value"],
index="statistic")
if not h in ["Input"]:
d[h] = d[h].apply(pd.to_numeric)
return d[key]
