def test_create_kegg_df():
"""Unit tests for create_kegg_df()
"""
df1 = metamoles.create_kegg_df(
data_path + "/test_kegg_enzyme_records.txt.gz", "enzyme")
df2 = metamoles.create_kegg_df(
data_path + "/test_kegg_compound_records.txt.gz", "compound")
# for enzyme database
assert df1.shape == (3, 16)
assert df1.columns.tolist() == [
'classname', 'cofactor', 'comment', 'dblinks', 'disease', 'effector',
'entry', 'genes', 'inhibitor', 'name', 'pathway', 'product',
'reaction', 'structures', 'substrate', 'sysname'
]
assert df1['entry'].tolist() == ['1.1.1.1', '1.1.1.2', '1.1.1.3']
# for compound database
assert df2.shape == (55, 8)
assert df2.columns.tolist() == [
'dblinks', 'entry', 'enzyme', 'formula', 'mass', 'name', 'pathway',
'structures'
]
assert df2['entry'].tolist() == [
'C00001', 'C00002', 'C00003', 'C00004', 'C00005', 'C00006', 'C00007',
'C00008', 'C00009', 'C00010', 'C00011', 'C00012', 'C00013', 'C00014',
'C00015', 'C00016', 'C00017', 'C00018', 'C00019', 'C00020', 'C00021',
'C00022', 'C00023', 'C00024', 'C00025', 'C00026', 'C00027', 'C00028',
'C00029', 'C00030', 'C00031', 'C00032', 'C00033', 'C00034', 'C00035',
'C00036', 'C00037', 'C00038', 'C00039', 'C00040', 'C00041', 'C00042',
'C00043', 'C00044', 'C00045', 'C00046', 'C00047', 'C00048', 'C00049',
'C00050', 'C00051', 'C00052', 'C00053', 'C00054', 'C00055'
]
return
def test_join_pubchem_ids():
"""Unit tests for join_pubchem_ids()
"""
pubchemdf = metamoles.create_kegg_df(
data_path + "/test_kegg_compound_records.txt.gz", "compound")
masterdf = metamoles.create_kegg_df(
data_path + "/test_kegg_enzyme_records.txt.gz", "enzyme")
return
def test_parse_reversible_reactions():
"""Unit tests for parse_reversible_reactions()
"""
df = metamoles.create_kegg_df("test_kegg_enzyme_records.txt.gz", "enzyme")
reaction_list = metamoles.parse_reaction_ids(df)
assert metamoles.parse_reversible_reactions(reaction_list) == reaction_list
return
def test_binarize_enzyme_class():
"""Unit tests for binarize_enzyme_class()
"""
df = metamoles.create_kegg_df("test_kegg_enzyme_records.txt.gz","enzyme")
actual = metamoles.binarize_enzyme_class(df,column="entry").enzyme_class_1.tolist()
assert actual == [1, 1, 1]
return
def test_parse_reaction_ids():
"""Unit tests for parse_reaction_ids()
"""
df = metamoles.create_kegg_df("test_kegg_enzyme_records.txt.gz", "enzyme")
assert metamoles.parse_reaction_ids(df) == [
'R00623', 'R00624', 'R07328', 'R01773', 'R01775'
]
return
def test_explode_dataframe():
"""Unit tests for explode_dataframe()
"""
df = metamoles.create_kegg_df("test_kegg_enzyme_records.txt.gz","enzyme")
exploded_df = metamoles.explode_dataframe(df, metamoles.parse_compound_ids,
'product', ['entry'])
assert exploded_df.shape == (11, 2)
assert exploded_df['product'].tolist() == ['C00071','C00004','C00080','C01450',
'C00071','C00005','C00080','C00441','C00004','C00005', 'C00080']
return
def test_parse_compound_ids():
"""Unit tests for parse_compound_ids()
"""
df = metamoles.create_kegg_df("test_kegg_enzyme_records.txt.gz","enzyme")
expected = ['C00071','C00004','C00080','C01450','C00071','C00005',
'C00080','C00441','C00004','C00005','C00080']
actual = metamoles.parse_compound_ids(df["product"])
assert expected == actual, "expected result of parse_compound_ids does not match the actual result"
return
def test_select_promiscuous_enzymes():
"""Unit tests for select_promiscuous_enzymes()
"""
df = metamoles.create_kegg_df("test_kegg_enzyme_records.txt.gz", "enzyme")
test_prom_df = metamoles.select_promiscuous_enzymes(df)
expected_column = ['entry', 'reaction', 'product', 'substrate']
actual_column = test_prom_df.columns.tolist()
assert test_prom_df.shape == (1, 4)
assert expected_column == actual_column, "expected column names and actual column names do not match"
return
def test_create_negative_matches():
"""Unit tests for create_negative_matches()
"""
df = metamoles.create_kegg_df("test_kegg_enzyme_records.txt.gz", "enzyme")
exploded_df = metamoles.explode_dataframe(df, metamoles.parse_compound_ids,
'product', ['entry'])
pos_df, neg_df = metamoles.create_negative_matches(exploded_df, 'entry',
'product')
assert pos_df.shape == (11, 3)
assert neg_df.shape == (7, 3)
# assert pos_df['enzyme'].tolist() == ['1.1.1.2', '1.1.1.2', '1.1.1.2', '1.1.1.1', '1.1.1.1', '1.1.1.1', '1.1.1.1', '1.1.1.3', '1.1.1.3', '1.1.1.3', '1.1.1.3'], "ValueError enzyme listing is wrong"
# assert neg_df['product'].tolist() == ['C00004', 'C01450', 'C00441', 'C00441', 'C00005', 'C01450', 'C00071'], "ValueError compound listing is wrong"
return
def test_parse_pubchem_ids():
"""Unit tests for parse_pubchem_ids()
"""
compound_df = metamoles.create_kegg_df("test_kegg_compound_records.txt.gz","compound")
PubChemID_list = []
for _, row in compound_df.iterrows():
pubchem_id = metamoles.parse_pubchem_ids(row['dblinks'])
PubChemID_list.append(pubchem_id)
assert PubChemID_list == ['3303', '3304', '3305', '3306', '3307', '3308',
'3309', '3310', '3311', '3312', '3313', '3314', '3315', '3316', '3317',
'3318', '3319', '3320', '3321', '3322', '3323', '3324', '3325', '3326',
'3327', '3328', '3329', '3330', '3331', '3332', '3333', '3334', '3335',
'3336', '3337', '3338', '3339', '3340', '3341', '3342', '3343', '3344',
'3345', '3346', '3347', '3348', '3349', '3350', '3351', '3352', '3353',
'3354', '3355', '3356', '3357']
return
