def test_bw():
b = BigWig("libBigWig/test/test.bw")
assert repr(b) == "BigWig('libBigWig/test/test.bw')"
intervals = list(b("1", 0, 99))
assert intervals[0] == Interval(chrom='1', start=0, end=1, value=0.10000000149011612)
assert intervals[1] == Interval(chrom='1', start=1, end=2, value=0.20000000298023224)
assert intervals[2] == Interval(chrom='1', start=2, end=3, value=0.30000001192092896)
# default is to include all values
vals = b.values("1", 0, 9)
exp = array('f', [0.10000000149011612, 0.20000000298023224, 0.30000001192092896, nan, nan, nan, nan, nan, nan])
arr_equal(vals, exp)
vals = b.values("1", 0, 9, False)
exp = array('f', [0.10000000149011612, 0.20000000298023224, 0.30000001192092896])
arr_equal(vals, exp)
v = b.stats("1", 0, 9)
assert v == 0.2000000054637591
v = b.stats("1", 0, 9, stat="stdev")
assert v == 0.10000000521540645
v = b.stats("1", 0, 4, stat="coverage")
assert v == 0.75
v = b.stats("1", 0, 4, stat="coverage", nBins=2)
assert v == array('d', [1.0, 0.5])
b.close()
def test_bw():
b = BigWig("libBigWig/test/test.bw")
assert repr(b) == "BigWig('libBigWig/test/test.bw')"
intervals = list(b("1", 0, 99))
assert intervals[0] == Interval(chrom='1',
start=0,
end=1,
value=0.10000000149011612)
assert intervals[1] == Interval(chrom='1',
start=1,
end=2,
value=0.20000000298023224)
assert intervals[2] == Interval(chrom='1',
start=2,
end=3,
value=0.30000001192092896)
# default is to include all values
vals = b.values("1", 0, 9)
exp = array('f', [
0.10000000149011612, 0.20000000298023224, 0.30000001192092896, nan,
nan, nan, nan, nan, nan
])
arr_equal(vals, exp)
vals = b.values("1", 0, 9, False)
exp = array(
'f', [0.10000000149011612, 0.20000000298023224, 0.30000001192092896])
arr_equal(vals, exp)
v = b.stats("1", 0, 9)
assert v == 0.2000000054637591
v = b.stats("1", 0, 9, stat="stdev")
assert v == 0.10000000521540645
v = b.stats("1", 0, 4, stat="coverage")
assert v == 0.75
v = b.stats("1", 0, 4, stat="coverage", nBins=2)
assert v == array('d', [1.0, 0.5])
b.close()
def build_dnase_fc_scores(self):
path=DNASE_FOLD_COV_DIR
scores = np.zeros((len(self), len(self.samples)), dtype=float)
for sample_i, sample_name in enumerate(self.samples):
fname = "DNASE.{}.fc.signal.bigwig".format(sample_name)
b = BigWig(os.path.join(path, fname))
for region_i, region in enumerate(self.iter_regions()):
if region_i%1000000 == 0:
print "Sample %i/%i, row %i/%i" % (
sample_i+1, len(self.samples), region_i, len(self))
scores[region_i, sample_i] = b.stats(
region.contig, region.start, region.stop, 'mean')
b.close()
return pd.DataFrame(
np.nan_to_num(scores), columns=self.samples, index=self.data.index)
def build_dnase_fc_scores(self):
path = DNASE_FOLD_COV_DIR
scores = np.zeros((len(self), len(self.samples)), dtype=float)
for sample_i, sample_name in enumerate(self.samples):
fname = "DNASE.{}.fc.signal.bigwig".format(sample_name)
b = BigWig(os.path.join(path, fname))
for region_i, region in enumerate(self.iter_regions()):
if region_i % 1000000 == 0:
print "Sample %i/%i, row %i/%i" % (
sample_i + 1, len(self.samples), region_i, len(self))
scores[region_i,
sample_i] = b.stats(region.contig, region.start,
region.stop, 'mean')
b.close()
return pd.DataFrame(np.nan_to_num(scores),
columns=self.samples,
index=self.data.index)
def test_bad_chr():
b = BigWig("libBigWig/test/test.bw")
assert b.stats("chr1", 0, 10) is None
v = b.values("chr1", 0, 10)
assert len(v) == 0, v
def test_bad_chr():
b = BigWig("libBigWig/test/test.bw")
assert b.stats("chr1", 0, 10) is None
v = b.values("chr1", 0, 10)
assert len(v) == 0, v
