def test_hartigan_diptest_for_modality(self):
"""
Ensure that the package runs Fisher exact if cell counts are <=5 and it's a 2x2
"""
dist_1_peak = modality.generate_data(peaks=1, n=[10000])
t1 = modality.hartigan_diptest(dist_1_peak)
assert t1 > 0.95
dist_2_peak = modality.generate_data(peaks=2, n=[10000, 10000])
t2 = modality.hartigan_diptest(dist_2_peak)
assert t2 < 0.05
dist_3_peak = modality.generate_data(peaks=3, n=[10000, 10000, 10000])
t3 = modality.hartigan_diptest(dist_3_peak)
assert t3 < 0.05
def _diptest(self, x):
"""
Compute Hartigan Dip Test for modality.
p < 0.05 suggests possible multimodality.
"""
return modality.hartigan_diptest(x.values)
def _diptest(self,x):
"""
Compute Hartigan Dip Test for modality.
p < 0.05 suggests possible multimodality.
"""
p = modality.hartigan_diptest(x.values)
# dropna=False argument in pivot_table does not function as expected
# return -1 instead of None
if pd.isnull(p):
return -1
return p
def test_hartigan_diptest(self):
t0 = time.time()
hartigan_diptest(self.data)
t1 = time.time()
print("Hartigan diptest: {}".format(t1-t0))