def test_get_qiime_minimal_mapping_single_reverse_primer(self):
conn_handler = SQLConnectionHandler()
conn_handler
sql = """INSERT INTO qiita.prep_columns
(prep_template_id, column_name, column_type)
VALUES (1, 'reverselinkerprimer', 'varchar');
ALTER TABLE qiita.prep_1
ADD COLUMN reverselinkerprimer varchar;
DELETE FROM qiita.prep_columns
WHERE prep_template_id = 1 AND column_name = 'run_prefix';
ALTER TABLE qiita.prep_1 DROP COLUMN run_prefix;
UPDATE qiita.prep_1 SET reverselinkerprimer = %s
"""
conn_handler.execute(sql, ('GTGCCAGCM',))
prep_template = PrepTemplate(1)
prep_template.generate_files()
out_dir = mkdtemp()
obs_fps = _get_qiime_minimal_mapping(prep_template, out_dir)
exp_fps = [join(out_dir, 'prep_1_MMF.txt')]
# Check that the returned list is as expected
self.assertEqual(obs_fps, exp_fps)
# Check that the file exists
self.assertTrue(exists(exp_fps[0]))
# Check the contents of the file
with open(exp_fps[0], "U") as f:
self.assertEqual(f.read(), EXP_PREP_RLP)
def test_get_qiime_minimal_mapping_multiple(self):
# We need to create a prep template in which we have different run
# prefix values, so we can test this case
metadata_dict = {
'SKB8.640193': {'center_name': 'ANL',
'center_project_name': 'Test Project',
'ebi_submission_accession': None,
'EMP_status': 'EMP',
'str_column': 'Value for sample 1',
'primer': 'GTGCCAGCMGCCGCGGTAA',
'barcode': 'GTCCGCAAGTTA',
'run_prefix': "s_G1_L001_sequences",
'platform': 'ILLUMINA',
'library_construction_protocol': 'AAA',
'experiment_design_description': 'BBB'},
'SKD8.640184': {'center_name': 'ANL',
'center_project_name': 'Test Project',
'ebi_submission_accession': None,
'EMP_status': 'EMP',
'str_column': 'Value for sample 2',
'primer': 'GTGCCAGCMGCCGCGGTAA',
'barcode': 'CGTAGAGCTCTC',
'run_prefix': "s_G1_L001_sequences",
'platform': 'ILLUMINA',
'library_construction_protocol': 'AAA',
'experiment_design_description': 'BBB'},
'SKB7.640196': {'center_name': 'ANL',
'center_project_name': 'Test Project',
'ebi_submission_accession': None,
'EMP_status': 'EMP',
'str_column': 'Value for sample 3',
'primer': 'GTGCCAGCMGCCGCGGTAA',
'barcode': 'CCTCTGAGAGCT',
'run_prefix': "s_G1_L002_sequences",
'platform': 'ILLUMINA',
'library_construction_protocol': 'AAA',
'experiment_design_description': 'BBB'}
}
md_template = pd.DataFrame.from_dict(metadata_dict, orient='index')
prep_template = PrepTemplate.create(md_template, Study(1), '16S')
for _, fp in prep_template.get_filepaths():
self.files_to_remove.append(fp)
out_dir = mkdtemp()
obs_fps = sorted(_get_qiime_minimal_mapping(prep_template, out_dir))
exp_fps = sorted([join(out_dir, 's_G1_L001_sequences_MMF.txt'),
join(out_dir, 's_G1_L002_sequences_MMF.txt')])
# Check that the returned list is as expected
self.assertEqual(obs_fps, exp_fps)
# Check that the file exists
for fp in exp_fps:
self.assertTrue(exists(fp))
# Check the contents of the file
for fp, contents in zip(exp_fps, [EXP_PREP_1, EXP_PREP_2]):
with open(fp, "U") as f:
self.assertEqual(f.read(), contents)
def test_get_qiime_minimal_mapping_single(self):
prep_template = PrepTemplate(1)
out_dir = mkdtemp()
obs_fps = _get_qiime_minimal_mapping(prep_template, out_dir)
exp_fps = [join(out_dir, 's_G1_L001_sequences_MMF.txt')]
# Check that the returned list is as expected
self.assertEqual(obs_fps, exp_fps)
# Check that the file exists
self.assertTrue(exists(exp_fps[0]))
# Check the contents of the file
with open(exp_fps[0], "U") as f:
self.assertEqual(f.read(), EXP_PREP)
def test_get_qiime_minimal_mapping_single(self):
prep_template = PrepTemplate(1)
out_dir = mkdtemp()
obs_fps = _get_qiime_minimal_mapping(prep_template, out_dir)
exp_fps = [join(out_dir, 's_G1_L001_sequences_MMF.txt')]
# Check that the returned list is as expected
self.assertEqual(obs_fps, exp_fps)
# Check that the file exists
self.assertTrue(exists(exp_fps[0]))
# Check the contents of the file
with open(exp_fps[0], "U") as f:
self.assertEqual(f.read(), EXP_PREP)
def test_get_qiime_minimal_mapping_single_no_run_prefix(self):
conn_handler = SQLConnectionHandler()
sql = """DELETE FROM qiita.prep_columns
WHERE prep_template_id = 1 AND column_name = 'run_prefix';
ALTER TABLE qiita.prep_1 DROP COLUMN run_prefix"""
conn_handler.execute(sql)
prep_template = PrepTemplate(1)
prep_template.generate_files()
out_dir = mkdtemp()
obs_fps = _get_qiime_minimal_mapping(prep_template, out_dir)
exp_fps = [join(out_dir, 'prep_1_MMF.txt')]
# Check that the returned list is as expected
self.assertEqual(obs_fps, exp_fps)
# Check that the file exists
self.assertTrue(exists(exp_fps[0]))
# Check the contents of the file
with open(exp_fps[0], "U") as f:
self.assertEqual(f.read(), EXP_PREP)
def test_get_qiime_minimal_mapping_numeric_sample_ids(self):
# Get minimal mapping file works correctly with numeric sample ids. A
# bug was found that samples of the type <study_id>.[0-9]*0 where
# truncated to <study_id>.[0-9]*
info = {"timeseries_type_id": 1,
"metadata_complete": True,
"mixs_compliant": True,
"number_samples_collected": 25,
"number_samples_promised": 28,
"study_alias": "testing",
"study_description": "Test description",
"study_abstract": "Test abstract",
"emp_person_id": StudyPerson(2),
"principal_investigator_id": StudyPerson(3),
"lab_person_id": StudyPerson(1)
}
new_study = Study.create(User('*****@*****.**'), "Test study", [1], info)
metadata_dict = {'1': {'host_subject_id': 'NotIdentified'},
'10': {'host_subject_id': 'NotIdentified'}}
metadata = pd.DataFrame.from_dict(metadata_dict, orient='index')
npt.assert_warns(QiitaDBWarning, SampleTemplate.create, metadata,
new_study)
metadata_dict = {'1': {'str_column': 'Value for sample 1',
'primer': 'GTGCCAGCMGCCGCGGTAA',
'barcode': 'GTCCGCAAGTTA'},
'10': {'str_column': 'Value for sample 1',
'primer': 'GTGCCAGCMGCCGCGGTAA',
'barcode': 'CGTAGAGCTCTC'}}
metadata = pd.DataFrame.from_dict(metadata_dict, orient='index')
pt = npt.assert_warns(QiitaDBWarning, PrepTemplate.create, metadata,
new_study, 2)
out_dir = mkdtemp()
obs_fps = _get_qiime_minimal_mapping(pt, out_dir)
exp_fps = [join(out_dir, 'prep_%s_MMF.txt' % pt.id)]
self.assertEqual(obs_fps, exp_fps)
self.assertTrue(exists(exp_fps[0]))
with open(exp_fps[0], 'U') as f:
self.assertEqual(f.read(), EXP_PREP_NUM.format(new_study.id))
def test_get_qiime_minimal_mapping_multiple(self):
# We need to create a prep template in which we have different run
# prefix values, so we can test this case
metadata_dict = {
'SKB8.640193': {
'center_name': 'ANL',
'center_project_name': 'Test Project',
'ebi_submission_accession': None,
'EMP_status': 'EMP',
'str_column': 'Value for sample 1',
'linkerprimersequence': 'GTGCCAGCMGCCGCGGTAA',
'barcodesequence': 'GTCCGCAAGTTA',
'run_prefix': "s_G1_L001_sequences",
'platform': 'ILLUMINA',
'library_construction_protocol': 'AAA',
'experiment_design_description': 'BBB'
},
'SKD8.640184': {
'center_name': 'ANL',
'center_project_name': 'Test Project',
'ebi_submission_accession': None,
'EMP_status': 'EMP',
'str_column': 'Value for sample 2',
'linkerprimersequence': 'GTGCCAGCMGCCGCGGTAA',
'barcodesequence': 'CGTAGAGCTCTC',
'run_prefix': "s_G1_L001_sequences",
'platform': 'ILLUMINA',
'library_construction_protocol': 'AAA',
'experiment_design_description': 'BBB'
},
'SKB7.640196': {
'center_name': 'ANL',
'center_project_name': 'Test Project',
'ebi_submission_accession': None,
'EMP_status': 'EMP',
'str_column': 'Value for sample 3',
'linkerprimersequence': 'GTGCCAGCMGCCGCGGTAA',
'barcodesequence': 'CCTCTGAGAGCT',
'run_prefix': "s_G1_L002_sequences",
'platform': 'ILLUMINA',
'library_construction_protocol': 'AAA',
'experiment_design_description': 'BBB'
}
}
md_template = pd.DataFrame.from_dict(metadata_dict, orient='index')
prep_template = PrepTemplate.create(md_template, RawData(2), Study(1),
'16S')
out_dir = mkdtemp()
obs_fps = sorted(_get_qiime_minimal_mapping(prep_template, out_dir))
exp_fps = sorted([
join(out_dir, 's_G1_L001_sequences_MMF.txt'),
join(out_dir, 's_G1_L002_sequences_MMF.txt')
])
# Check that the returned list is as expected
self.assertEqual(obs_fps, exp_fps)
# Check that the file exists
for fp in exp_fps:
self.assertTrue(exists(fp))
# Check the contents of the file
for fp, contents in zip(exp_fps, [EXP_PREP_1, EXP_PREP_2]):
with open(fp, "U") as f:
self.assertEqual(f.read(), contents)
