def testEmptyInit(self):
"""Key functionality for things that don't have any data that is loaded with the dataset"""
pc = PhenoCovar()
for i in range(10):
pc.add_subject(i + 1, None, None)
self.assertEqual(0, len(pc.phenotype_names))
self.assertEqual(10, len(pc.pedigree_data))
def test_basic_population(self):
# Indicate that we want to use sex as a covariate
PhenoCovar.sex_as_covariate = True
pc = PhenoCovar()
#sex = [1, 1, 2, 2, 1, 1]
for line in self.ped:
fam, ind, sex, ph = line.split()
pc.add_subject("%s:%s" % (fam, ind),
sex=int(sex),
phenotype=float(ph))
self.assertEqual(1, len(pc.covariate_data))
self.assertEqual(1, len(pc.phenotype_data))
self.assertEqual(6, len(pc.covariate_data[0]))
self.assertEqual(6, len(pc.phenotype_data[0]))
i = 0
for line in self.ped:
fam, ind, sex, ph = line.split()
iid = "%s:%s" % (fam, ind)
pdata = pc.pedigree_data[iid]
self.assertEqual(pdata, i)
self.assertAlmostEqual(float(ph), pc.phenotype_data[0][i])
self.assertEqual(int(sex), pc.covariate_data[0][i])
i += 1
# Indicate that we do not want to use sex as a covariate
PhenoCovar.sex_as_covariate = False
newpc = PhenoCovar()
for line in self.ped:
fam, ind, sex, ph = line.split()
newpc.add_subject("%s:%s" % (fam, ind),
sex=int(sex),
phenotype=float(ph))
# Test that sex wasn't loaded as a covariate due to the setting of PhenoCovar.sex_as_covariate
self.assertEqual(0, len(newpc.covariate_data))
self.assertEqual(1, len(newpc.phenotype_data))
self.assertEqual(6, len(newpc.pedigree_data))
self.assertEqual(6, len(newpc.phenotype_data[0]))
i = 0
for line in self.ped:
fam, ind, sex, ph = line.split()
iid = "%s:%s" % (fam, ind)
pdata = pc.pedigree_data[iid]
self.assertEqual(pdata, i)
self.assertAlmostEqual(float(ph), pc.phenotype_data[0][i])
i += 1
def testPhenoWithAllMissing(self):
pc = PhenoCovar()
count = 0
invalid = 0
missingness = 0
pc.add_subject("1:1", phenotype=1)
pc.add_subject("2:2", phenotype=1)
pc.add_subject("3:3", phenotype=1)
pc.add_subject("4:4", phenotype=1)
pc.freeze_subjects()
try:
for test in pc:
(pheno, covars, nonmissing) = test.get_variables(
numpy.array([True, True, True, True]))
count += 1
except InvariantVar as e:
invalid += 1
except TooMuchMissingpPhenoCovar as e:
missingness += 1
self.assertEqual(0, invalid)
self.assertEqual(1, missingness)
self.assertEqual(0, count)
