def main(args):
ncontrib, nloci, ploci = mhapi.contrib(Profile(fromfile=args.result))
data = {
"min_num_contrib": ncontrib,
"num_loci_max_alleles": nloci,
"perc_loci_max_alleles": ploci,
}
with mhopen(args.out, "w") as fh:
json.dump(data, fh, indent=4)
def main(args):
contained, total = mhapi.contain(Profile(fromfile=args.profile1),
Profile(fromfile=args.profile2))
data = {
"containment": round(contained / total, 4),
"contained_alleles": contained,
"total_alleles": total,
}
with mhopen(args.out, "w") as fh:
json.dump(data, fh, indent=4)
def main(args):
prof1 = Profile(fromfile=args.profile1)
prof2 = Profile(fromfile=args.profile2) if args.profile2 else None
frequencies = load_marker_frequencies(args.freq)
result = mhapi.prob(frequencies, prof1, prof2=prof2, erate=args.erate)
key = "random_match_probability" if prof2 is None else "likelihood_ratio"
data = {
key: "{:.3E}".format(result),
}
with mhopen(args.out, "w") as fh:
json.dump(data, fh, indent=4)
def __init__(self, fromfile=None):
global SCHEMA
if fromfile:
if isinstance(fromfile, str) or isinstance(fromfile, Path):
with mhopen(str(fromfile), "r") as fh:
self.data = json.load(fh)
else:
self.data = json.load(fromfile)
if SCHEMA is None:
SCHEMA = load_schema()
jsonschema.validate(instance=self.data, schema=SCHEMA)
else:
self.data = self.initialize()
def read_length_dist(fastq,
outfile,
xlabel="Read Length (bp)",
xlim=None,
scale=1000,
title=None):
"""Plot distribution of read lengths
:param str fastq: path of a FASTQ file containing NGS reads
:param str outfile: path of a graphic file to create
:param str xlabel: label for the X axis
:param tuple xlim: a 2-tuple of numbers (x1, x2) representing the start and end points of the portion of the X axis to be displayed; by default this is determined automatically
:param float scale: scaling factor for the Y axis
:param str title: title for the plot
"""
backend = matplotlib.get_backend()
plt.switch_backend("Agg")
lengths = list()
with mhopen(fastq, "r") as fh:
for record in SeqIO.parse(fh, "fastq"):
lengths.append(len(record))
fig = plt.figure(figsize=(6, 4), dpi=200)
plt.hist(lengths,
bins=25,
weights=[1 / scale] * len(lengths),
edgecolor="#000099")
if xlim is None:
xlim = (min(lengths) * 0.9, max(lengths) * 1.1)
plt.xlim(*xlim)
ax = plt.gca()
ax.yaxis.grid(True, color="#DDDDDD")
ax.set_axisbelow(True)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
ax.spines["bottom"].set_color("#CCCCCC")
ax.tick_params(left=False)
ax.set_xlabel(xlabel, labelpad=15, fontsize=16)
ax.set_ylabel(f"Frequency (× {scale})", labelpad=15, fontsize=16)
if title:
ax.set_title(title, pad=25, fontsize=18)
plt.savefig(outfile, bbox_inches="tight")
plt.switch_backend(backend)
def populate_from_bed(bedfile):
with mhopen(bedfile, "r") as fh:
line = next(fh)
ploidy = line.count("|") + 1
fh.seek(0)
marker_alleles = defaultdict(
lambda: [list() for _ in range(ploidy)])
for line in fh:
line = line.strip()
if line == "":
continue
marker, start, end, allelestr = line.split("\t")
alleles = allelestr.split("|")
for i, a in enumerate(alleles):
marker_alleles[marker][i].append(a)
profile = SimulatedProfile(ploidy=ploidy)
for marker, allele_list in marker_alleles.items():
for i, haplotype in enumerate(allele_list):
profile.add(i, marker, ",".join(haplotype))
return profile
def main(args):
profiles = [SimulatedProfile(pfile) for pfile in args.profiles]
combined = SimulatedProfile.merge(profiles)
with mhopen(args.out, "w") as fh:
combined.dump(fh)
def load_schema():
with mhopen(package_file("data/profile-schema.json"), "r") as fh:
return json.load(fh)
def dump(self, outfile):
if isinstance(outfile, str) or isinstance(outfile, Path):
with mhopen(str(outfile), "w") as fh:
json.dump(self.data, fh, indent=4, sort_keys=True)
else:
json.dump(self.data, outfile, indent=4, sort_keys=True)
