def test_combine_sample_snps_index3():
# check index_type
df = combine_sample_snps(
SNP_DATA,
1,
[lambda x: x],
index_type='taxon'
)
assert df.index.tolist() == [838, 839]
def test_combine_sample_snps_index2():
# check index_type
df = combine_sample_snps(
SNP_DATA,
1,
[lambda x: x],
index_type='gene'
)
# Can't expect the list to be sorted
assert sorted(df.index.tolist()) == ['gene1', 'gene2', 'gene3']
def test_combine_sample_snps_index1():
# check index_type
df = combine_sample_snps(SNP_DATA, 1, [lambda x: x])
assert df.index.tolist() == [('gene1', 839), ('gene2', 838), ('gene3', 838)]
def test_combine_sample_snps_values():
# check if values are correct is working
df = combine_sample_snps(SNP_DATA, 1, [lambda x: x])
assert (df.min().min(), df.max().max()) == (0.5, 1.)
def test_combine_sample_snps_min_num2():
# check if min_num is working
df = combine_sample_snps(SNP_DATA, 2, [lambda x: x])
assert df.shape == (2, 2)
def test_combine_sample_min_num1():
# check if min_num is working
df = combine_sample_snps(SNP_DATA, 1, [lambda x: x])
df.shape == (3, 2)