def test_multi_open2(self):
from idiva.io.vcf import ReadVCF
with ReadVCF.open(vcf_file) as vcf:
assert isinstance(vcf, ReadVCF)
a = list(map(str, vcf))
with ReadVCF.open(vcf) as vcf:
b = list(map(str, vcf))
self.assertListEqual(a, b)
def test_dataline_types(self):
from idiva.io.vcf import ReadVCF, RawDataline
with ReadVCF.open(vcf_file) as vcf:
candidate = first(vcf)
self.assertIsInstance(candidate, RawDataline)
self.assertIsInstance(candidate.pos, int)
self.assertIsInstance(candidate.qual, float)
self.assertIsInstance(candidate.info, str)
def test_rewinds(self):
from idiva.io.vcf import ReadVCF
with ReadVCF.open(vcf_file) as vcf:
assert isinstance(vcf, ReadVCF)
with vcf.rewind_when_done:
a = list(map(str, vcf))
with vcf.rewind_when_done:
b = list(map(str, vcf))
self.assertListEqual(a, b)
def test_datalines_accuracy(self):
from idiva.io.vcf import ReadVCF
with ReadVCF.open(vcf_file) as vcf:
reference = [
"20 14370 rs6054257 G A 29 PASS NS=3;DP=14;AF=0.5;DB;H2 GT:GQ:DP:HQ 0|0:48:1:51,51 1|0:48:8:51,51 1/1:43:5:.,.",
"20 17330 None T A 3 q10 NS=3;DP=11;AF=0.017 GT:GQ:DP:HQ 0|0:49:3:58,50 0|1:3:5:65,3 0/0:41:3",
"20 1110696 rs6040355 A G,T 67 PASS NS=2;DP=10;AF=0.333,0.667;AA=T;DB GT:GQ:DP:HQ 1|2:21:6:23,27 2|1:2:0:18,2 2/2:35:4",
"20 1230237 None T None 47 PASS NS=3;DP=13;AA=T GT:GQ:DP:HQ 0|0:54:7:56,60 0|0:48:4:51,51 0/0:61:2",
"20 1234567 microsat1 GTCT G,GTACT 50 PASS NS=3;DP=9;AA=G GT:GQ:DP 0/1:35:4 0/2:17:2 1/1:40:3",
]
candidate = list(map(str, vcf))
self.assertListEqual(reference, candidate)
def test_db_clf(self):
with ReadVCF.open(URLS['case']) as case:
result = db_classifier(case=case, ctrl=None)
self.assertTrue(len(result.df))
def test_sanity2(self):
from idiva.io.vcf import ReadVCF
with ReadVCF.open(vcf_file) as vcf:
with ReadVCF.open(vcf) as vcf:
list(vcf)
def test_db_clf(self):
with ReadVCF.open(URLS['case']) as case:
result = phenomenet_classifier(case=case)
self.assertTrue(len(result.df))
def test_phenom_basic(self):
with ReadVCF.open(URLS['case']) as case:
result = phenomenet_classifier_basic(case=case, ctrl=None)
self.assertTrue(len(result.df))
log.info('passed!')
def test_phenom_clf(self):
with ReadVCF.open(URLS['case']) as case, ReadVCF.open(
URLS['ctrl']) as ctrl:
result = phenomenet_classifier(case=case, ctrl=ctrl)
self.assertTrue(len(result.df))
log.info('passed!')