def test_calc_empty_variants(fake_phenotype_data):
pheno_tool = PhenoTool(fake_phenotype_data, "i1.m1")
variants = Counter({})
res = pheno_tool.calc(variants)
assert isinstance(res, PhenoResult)
assert res.positive_count == 0
assert res.positive_mean == 0
assert res.positive_deviation == 0
assert res.negative_count == len(pheno_tool.pheno_df)
assert res.negative_mean
assert res.negative_deviation
assert res.pvalue == "NA"
resM, resF = pheno_tool.calc(variants, sex_split=True).values()
assert isinstance(resM, PhenoResult)
assert isinstance(resF, PhenoResult)
assert resM.positive_count == 0
assert resM.positive_mean == 0
assert resM.positive_deviation == 0
assert resM.negative_count == res.negative_count - resF.negative_count
assert resM.negative_mean
assert resM.negative_deviation
assert resM.pvalue == "NA"
assert resF.positive_count == 0
assert resF.positive_mean == 0
assert resF.positive_deviation == 0
assert resF.negative_count == res.negative_count - resM.negative_count
assert resF.negative_mean
assert resF.negative_deviation
assert resF.pvalue == "NA"
def test_calc_split_by_sex(mocker, fake_phenotype_data):
pheno_tool = PhenoTool(fake_phenotype_data, "i1.m1")
variants = Counter({
"f4.p1": 1,
"f5.p1": 1,
"f7.p1": 1,
"f10.p1": 1,
"f12.p1": 1,
"f16.p1": 1,
"f24.p1": 1,
"f25.p1": 1,
"f30.p1": 1,
"f32.p1": 1,
})
merged_df = PhenoTool.join_pheno_df_with_variants(pheno_tool.pheno_df,
variants)
mocker.spy(PhenoTool, "join_pheno_df_with_variants")
mocker.spy(pheno_tool, "_calc_stats")
pheno_tool.calc(variants, sex_split=True)
assert pheno_tool._calc_stats.call_count == 2
assert PhenoTool.join_pheno_df_with_variants.call_count == 1
call_arg_df, call_arg_sex_split = pheno_tool._calc_stats.call_args_list[0][
0]
assert merged_df.equals(call_arg_df)
assert call_arg_sex_split is Sex.M
call_arg_df, call_arg_sex_split = pheno_tool._calc_stats.call_args_list[1][
0]
assert merged_df.equals(call_arg_df)
assert call_arg_sex_split is Sex.F
def test_calc(mocker, fake_phenotype_data):
pheno_tool = PhenoTool(fake_phenotype_data, "i1.m1")
variants = Counter({
"f4.p1": 1,
"f5.p1": 1,
"f7.p1": 1,
"f10.p1": 1,
"f12.p1": 1,
"f16.p1": 1,
"f24.p1": 1,
"f25.p1": 1,
"f30.p1": 1,
"f32.p1": 1,
})
merged_df = PhenoTool.join_pheno_df_with_variants(pheno_tool.pheno_df,
variants)
mocker.spy(PhenoTool, "join_pheno_df_with_variants")
mocker.spy(pheno_tool, "_calc_stats")
pheno_tool.calc(variants, sex_split=False)
pheno_tool._calc_stats.assert_called_once()
PhenoTool.join_pheno_df_with_variants.assert_called_once()
call_arg_df, call_arg_sex_split = pheno_tool._calc_stats.call_args_list[0][
0]
assert merged_df.equals(call_arg_df)
assert call_arg_sex_split is None
def test_calc_empty_pheno_df(fake_phenotype_data):
pheno_tool = PhenoTool(fake_phenotype_data, "i1.m1", person_ids=[])
variants = Counter({
"f4.p1": 1,
"f5.p1": 1,
"f7.p1": 1,
"f10.p1": 1,
"f12.p1": 1,
"f16.p1": 1,
"f24.p1": 1,
"f25.p1": 1,
"f30.p1": 1,
"f32.p1": 1,
})
res = pheno_tool.calc(variants)
assert isinstance(res, PhenoResult)
assert res.positive_count == 0
assert res.positive_mean == 0
assert res.positive_deviation == 0
assert res.negative_count == 0
assert res.negative_mean == 0
assert res.negative_deviation == 0
assert res.pvalue == "NA"
resM, resF = pheno_tool.calc(variants, sex_split=True).values()
assert isinstance(resM, PhenoResult)
assert isinstance(resF, PhenoResult)
assert resM.positive_count == 0
assert resM.positive_mean == 0
assert resM.positive_deviation == 0
assert resM.negative_count == 0
assert resM.negative_mean == 0
assert resM.negative_deviation == 0
assert resM.pvalue == "NA"
assert resF.positive_count == 0
assert resF.positive_mean == 0
assert resF.positive_deviation == 0
assert resF.negative_count == 0
assert resF.negative_mean == 0
assert resF.negative_deviation == 0
assert resF.pvalue == "NA"