def test_run(self):
'''test run'''
# use the test ini file. But we don't want the db line,
# because we're making a new db
ini_file = os.path.join(modules_dir, 'tests', 'data', 'db.ini')
# in case database already exists
try:
db_connection.DbConnection(ini_file, destroy=True)
except:
pass
# thorws error because database doesn't exist
with self.assertRaises(db_connection.Error):
db_connection.DbConnection(ini_file)
dbm = db_maker.DbMaker(ini_file)
dbm.run()
# We'll just check that the database got created, and the
# expected tables are in there. OTT to check complete
# schema, methinks.
dbc = db_connection.DbConnection(ini_file)
cursor = dbc.connection.cursor()
cursor.execute('USE ' + dbc.db)
cursor.execute('show tables')
got_tables = list(cursor.fetchall())
got_tables.sort()
expected_tables = [(x, ) for x in sorted(db_schema.tables)]
self.assertEqual(expected_tables, got_tables)
# check version got added
got_rows = cursor.execute('SELECT * FROM Version;')
expected_rows = [(db_schema.version)]
dbc.close()
db_connection.DbConnection(ini_file, destroy=True)
def setUp(self):
try:
db_connection.DbConnection(db_ini_file, destroy=True)
except:
pass
dbm = db_maker.DbMaker(db_ini_file)
dbm.run()
self.db = db.Db(db_ini_file)
def setUp(self):
self.pipeline_root = os.path.abspath('piperoot')
os.mkdir(self.pipeline_root)
try:
db_connection.DbConnection(ini_file, destroy=True)
except:
pass
dbm = db_maker.DbMaker(ini_file)
dbm.run()
self.db = db.Db(ini_file)
sample_dicts = [
{
'subject_id': 'subject_1',
'site_id': '01',
'lab_id': 'lab_id_1',
'isolate_number': '1',
'sequence_replicate_number': 1,
'submission_date': datetime.date(2018, 4, 4),
'reads_file_1': 'reads_1_1.fq',
'reads_file_1_md5': 'md5_1_1',
'reads_file_2_md5': 'md5_1_2',
'reads_file_2': 'reads_1_2.fq',
'dataset_name': 'set1',
'submit_to_ena': '0',
'instrument_model': 'Illumina HiSeq 2500',
'ena_center_name': 'Centre 1',
'ena_on_hold': '0',
'ena_run_accession': 'ERR123456',
'ena_sample_accession': 'ERS123456',
},
{
'subject_id': 'subject_2',
'site_id': '01',
'lab_id': 'lab_id_2',
'isolate_number': '1',
'sequence_replicate_number': 1,
'submission_date': datetime.date(2018, 4, 4),
'reads_file_1': 'reads_2_1.fq',
'reads_file_1_md5': 'md5_2_1',
'reads_file_2_md5': 'md5_2_2',
'reads_file_2': 'reads_2_2.fq',
'dataset_name': 'set1',
'submit_to_ena': '0',
'instrument_model': 'Illumina HiSeq 2500',
'ena_center_name': 'Centre 1',
'ena_on_hold': '0',
'ena_run_accession': 'ERR123457',
'ena_sample_accession': 'ERS123457',
},
{
'subject_id': 'subject_3',
'site_id': '02',
'lab_id': 'lab_id_3',
'isolate_number': '1',
'sequence_replicate_number': 1,
'submission_date': datetime.date(2018, 4, 4),
'reads_file_1': 'reads_3_1.fq',
'reads_file_1_md5': 'md5_3_1',
'reads_file_2_md5': 'md5_3_2',
'reads_file_2': 'reads_3_2.fq',
'dataset_name': 'set2',
'submit_to_ena': '0',
'instrument_model': 'Illumina HiSeq 2500',
'ena_center_name': 'Centre 2',
'ena_on_hold': '0',
'ena_run_accession': None,
'ena_sample_accession': None,
},
{
'subject_id': 'subject_3',
'site_id': '02',
'lab_id': 'lab_id_3',
'isolate_number': '1',
'sequence_replicate_number': 2,
'submission_date': datetime.date(2018, 4, 4),
'reads_file_1': 'reads_4_1.fq',
'reads_file_1_md5': 'md5_4_1',
'reads_file_2_md5': 'md5_4_2',
'reads_file_2': 'reads_4_2.fq',
'dataset_name': 'set2',
'submit_to_ena': '0',
'instrument_model': 'Illumina HiSeq 2500',
'ena_center_name': 'Centre 2',
'ena_on_hold': '0',
'ena_run_accession': None,
'ena_sample_accession': None,
},
]
for d in sample_dicts:
self.db.add_one_seqrep(d)
where_dict = {'original_reads_file_1_md5': d['reads_file_1_md5']}
update_dict = {
'remove_contam_reads_file_1_md5':
d['reads_file_1_md5'] + '.remove_contam',
'remove_contam_reads_file_2_md5':
d['reads_file_2_md5'] + '.remove_contam',
}
self.db.update_row('Seqrep', where_dict, update_dict)
seqrep_to_isolate = {1: 1, 2: 2, 3: 3, 4: 3}
for seqrep, isolate in seqrep_to_isolate.items():
ref_id = 1 if seqrep in {1, 2} else 2
version = '0.1.1' if seqrep in {1, 2} else '0.1.3'
d = {
'isolate_id': isolate,
'seqrep_id': seqrep,
'seqrep_pool': None,
'version': version,
'pipeline_name': 'remove_contam',
'status': 1,
'reference_id': ref_id
}
self.db.add_row_to_table('Pipeline', d)
d = {
'isolate_id': isolate,
'seqrep_id': seqrep,
'seqrep_pool': None,
'version': version,
'pipeline_name': 'qc',
'status': 1,
'reference_id': ref_id + 2
}
self.db.add_row_to_table('Pipeline', d)
var_call_rows = [
{
'isolate_id': 1,
'seqrep_id': None,
'seqrep_pool': '1',
'version': '1.2.3',
'pipeline_name': 'variant_call',
'status': 1,
'reference_id': 10
},
{
'isolate_id': 2,
'seqrep_id': None,
'seqrep_pool': '2',
'version': '1.2.3',
'pipeline_name': 'variant_call',
'status': 1,
'reference_id': 10
},
{
'isolate_id': 3,
'seqrep_id': None,
'seqrep_pool': '1_2',
'version': '1.2.3',
'pipeline_name': 'variant_call',
'status': 1,
'reference_id': 10
},
]
for d in var_call_rows:
self.db.add_row_to_table('Pipeline', d)
d['pipeline_name'] = 'mykrobe_predict'
self.db.add_row_to_table('Pipeline', d)
self.db.commit()
def tearDown(self):
self.db.commit_and_close()
db_connection.DbConnection(db_ini_file, destroy=True, must_exist=True)
def setUp(self):
self.pipeline_root = os.path.abspath("piperoot")
os.mkdir(self.pipeline_root)
try:
db_connection.DbConnection(ini_file, destroy=True)
except:
pass
dbm = db_maker.DbMaker(ini_file)
dbm.run()
self.db = db.Db(ini_file)
sample_dicts = [
{
"subject_id": "subject_1",
"site_id": "01",
"lab_id": "lab_id_1",
"isolate_number": "1",
"sequence_replicate_number": 1,
"submission_date": datetime.date(2018, 4, 4),
"reads_file_1": "reads_1_1.fq",
"reads_file_1_md5": "md5_1_1",
"reads_file_2_md5": "md5_1_2",
"reads_file_2": "reads_1_2.fq",
"dataset_name": "set1",
"submit_to_ena": "0",
"instrument_model": "Illumina HiSeq 2500",
"ena_center_name": "Centre 1",
"ena_on_hold": "0",
"ena_run_accession": "ERR123456",
"ena_sample_accession": "ERS123456",
},
{
"subject_id": "subject_2",
"site_id": "01",
"lab_id": "lab_id_2",
"isolate_number": "1",
"sequence_replicate_number": 1,
"submission_date": datetime.date(2018, 4, 4),
"reads_file_1": "reads_2_1.fq",
"reads_file_1_md5": "md5_2_1",
"reads_file_2_md5": "md5_2_2",
"reads_file_2": "reads_2_2.fq",
"dataset_name": "set1",
"submit_to_ena": "0",
"instrument_model": "Illumina HiSeq 2500",
"ena_center_name": "Centre 1",
"ena_on_hold": "0",
"ena_run_accession": "ERR123457",
"ena_sample_accession": "ERS123457",
},
{
"subject_id": "subject_3",
"site_id": "02",
"lab_id": "lab_id_3",
"isolate_number": "1",
"sequence_replicate_number": 1,
"submission_date": datetime.date(2018, 4, 4),
"reads_file_1": "reads_3_1.fq",
"reads_file_1_md5": "md5_3_1",
"reads_file_2_md5": "md5_3_2",
"reads_file_2": "reads_3_2.fq",
"dataset_name": "set2",
"submit_to_ena": "0",
"instrument_model": "Illumina HiSeq 2500",
"ena_center_name": "Centre 2",
"ena_on_hold": "0",
"ena_run_accession": None,
"ena_sample_accession": None,
},
{
"subject_id": "subject_3",
"site_id": "02",
"lab_id": "lab_id_3",
"isolate_number": "1",
"sequence_replicate_number": 2,
"submission_date": datetime.date(2018, 4, 4),
"reads_file_1": "reads_4_1.fq",
"reads_file_1_md5": "md5_4_1",
"reads_file_2_md5": "md5_4_2",
"reads_file_2": "reads_4_2.fq",
"dataset_name": "set2",
"submit_to_ena": "0",
"instrument_model": "Illumina HiSeq 2500",
"ena_center_name": "Centre 2",
"ena_on_hold": "0",
"ena_run_accession": None,
"ena_sample_accession": None,
},
]
for d in sample_dicts:
self.db.add_one_seqrep(d)
where_dict = {"original_reads_file_1_md5": d["reads_file_1_md5"]}
update_dict = {
"remove_contam_reads_file_1_md5":
d["reads_file_1_md5"] + ".remove_contam",
"remove_contam_reads_file_2_md5":
d["reads_file_2_md5"] + ".remove_contam",
}
self.db.update_row("Seqrep", where_dict, update_dict)
seqrep_to_isolate = {1: 1, 2: 2, 3: 3, 4: 3}
for seqrep, isolate in seqrep_to_isolate.items():
ref_id = 1 if seqrep in {1, 2} else 2
version = "0.1.1" if seqrep in {1, 2} else "0.1.3"
d = {
"isolate_id": isolate,
"seqrep_id": seqrep,
"seqrep_pool": None,
"version": version,
"pipeline_name": "remove_contam",
"status": 1,
"reference_id": ref_id,
}
self.db.add_row_to_table("Pipeline", d)
d = {
"isolate_id": isolate,
"seqrep_id": seqrep,
"seqrep_pool": None,
"version": version,
"pipeline_name": "qc",
"status": 1,
"reference_id": ref_id + 2,
}
self.db.add_row_to_table("Pipeline", d)
var_call_rows = [
{
"isolate_id": 1,
"seqrep_id": None,
"seqrep_pool": "1",
"version": "1.2.3",
"pipeline_name": "variant_call",
"status": 1,
"reference_id": 10,
},
{
"isolate_id": 2,
"seqrep_id": None,
"seqrep_pool": "2",
"version": "1.2.3",
"pipeline_name": "variant_call",
"status": 1,
"reference_id": 10,
},
{
"isolate_id": 3,
"seqrep_id": None,
"seqrep_pool": "1_2",
"version": "1.2.3",
"pipeline_name": "variant_call",
"status": 1,
"reference_id": 10,
},
]
for d in var_call_rows:
self.db.add_row_to_table("Pipeline", d)
d["pipeline_name"] = "mykrobe_predict"
self.db.add_row_to_table("Pipeline", d)
self.db.commit()
def __init__(self, ini_file):
try:
self.dbc = db_connection.DbConnection(ini_file, create=True)
except:
raise Error("Error connecting to database")
def test_nextflow_import(self):
'''test nextflow_import'''
nextflow_helper.write_config_file()
pipeline_root = 'tmp.nextflow_import.pipeline_root'
os.mkdir(pipeline_root)
try:
db_connection.DbConnection(db_ini_file, destroy=True)
except:
pass
dbm = db_maker.DbMaker(db_ini_file)
dbm.run()
dropbox_dir = 'tmp.nextflow_import.dropbox'
shutil.copytree(os.path.join(data_dir, 'dropbox'), dropbox_dir)
xlsx_archive_dir = 'tmp.nextflow_import.xlsx_archive'
os.mkdir(xlsx_archive_dir)
expected_xlsx_files = [
os.path.basename(x)
for x in glob.glob(os.path.join(dropbox_dir, '*.xlsx'))
]
nextflow_file = os.path.join(nextflow_helper.nextflow_dir, 'import.nf')
work_dir = 'tmp.nextflow_import.work'
dag_file = 'nextflow.import.dag.pdf'
try:
os.unlink(dag_file)
except:
pass
command = ' '.join([
'nextflow run', '--dropbox_dir', dropbox_dir, '--pipeline_root',
pipeline_root, '--db_config_file', db_ini_file,
'--xlsx_archive_dir', xlsx_archive_dir, '-with-dag', dag_file,
'-c', nextflow_helper.config_file, '-w', work_dir, nextflow_file
])
utils.syscall(command)
os.unlink(nextflow_helper.config_file)
shutil.rmtree(work_dir)
# All files should be gone from the dropbox
self.assertEqual([], os.listdir(dropbox_dir))
shutil.rmtree(dropbox_dir)
# The two spreadsheets should have been archived
got_xlsx_files = [
os.path.basename(x)
for x in glob.glob(os.path.join(xlsx_archive_dir, '**', '*.xlsx'))
]
self.assertEqual(expected_xlsx_files, got_xlsx_files)
shutil.rmtree(xlsx_archive_dir)
# Check database updated correctly
database = db.Db(db_ini_file)
expected_sample_rows = [
{
'subject_id': 'p1',
'site_id': 's1',
'sample_id_from_lab': 'l1',
'dataset_name': 'g1',
'ena_center_name': 'Center A',
'ena_sample_accession': 'ERS123456',
'ena_study_accession': None
},
{
'subject_id': 'p2',
'site_id': 's2',
'sample_id_from_lab': 'l2',
'dataset_name': 'g2',
'ena_center_name': 'Center A',
'ena_sample_accession': None,
'ena_study_accession': None
},
{
'subject_id': 'p1',
'site_id': 's3',
'sample_id_from_lab': 'l1',
'dataset_name': 'g1',
'ena_center_name': 'Center B',
'ena_sample_accession': None,
'ena_study_accession': None
},
]
got_sample_rows = sorted(database.get_rows_from_table('Sample'),
key=itemgetter('site_id'))
# the rows also have the sample_id, which is made by mysql auto increment,
# We don't know the order in which things are added, so can't check the sample_id.
for row in got_sample_rows:
del row['sample_id']
self.assertEqual(expected_sample_rows, got_sample_rows)
expected_rows = [
{
'sequence_replicate_number': 1,
'original_reads_file_1_md5':
'edc176f367fe8e5a014c819b9ec9b05c',
'original_reads_file_2_md5':
'0dd551a0d76d90059808f6f7ddbb0e02',
'remove_contam_reads_file_1_md5': None,
'remove_contam_reads_file_2_md5': None,
'pool_sequence_replicates': 1,
'withdrawn': 0,
'import_status': 1,
'submission_date': datetime.date(2017, 12, 25),
'submit_to_ena': 0,
'ena_run_accession': 'ERR123456',
'ena_on_hold': 0,
'isolate_number_from_lab': '1',
'pool_sequence_replicates': 1,
'ena_experiment_accession': None,
'instrument_model': 'Illumina HiSeq 2000'
},
{
'sequence_replicate_number': 1,
'original_reads_file_1_md5':
'fe5cd28cf9394be14794f0a56a2fe845',
'original_reads_file_2_md5':
'd026fd9a439294ed42795bd7f1e7df10',
'remove_contam_reads_file_1_md5': None,
'remove_contam_reads_file_2_md5': None,
'pool_sequence_replicates': 1,
'withdrawn': 0,
'import_status': 1,
'submission_date': datetime.date(2017, 12, 26),
'submit_to_ena': 1,
'ena_run_accession': None,
'ena_on_hold': 1,
'isolate_number_from_lab': '1',
'pool_sequence_replicates': 1,
'ena_experiment_accession': None,
'instrument_model': 'Illumina HiSeq 2000'
},
{
'sequence_replicate_number': 1,
'original_reads_file_1_md5':
'aa8f077673c158c4f2a19fc3c50e3fa7',
'original_reads_file_2_md5':
'ae6bafef67da3c26576e799c32985ac9',
'remove_contam_reads_file_1_md5': None,
'remove_contam_reads_file_2_md5': None,
'pool_sequence_replicates': 1,
'withdrawn': 0,
'import_status': 1,
'submission_date': datetime.date(2017, 12, 26),
'submit_to_ena': 1,
'ena_run_accession': None,
'ena_on_hold': 1,
'isolate_number_from_lab': '2',
'pool_sequence_replicates': 1,
'ena_experiment_accession': None,
'instrument_model': 'Illumina HiSeq 2000'
},
{
'sequence_replicate_number': 1,
'original_reads_file_1_md5':
'6b9a34ed492dad739ac03e084f3b2ab9',
'original_reads_file_2_md5':
'7ceffc5314ff7e305b4ab5bd859850c9',
'remove_contam_reads_file_1_md5': None,
'remove_contam_reads_file_2_md5': None,
'pool_sequence_replicates': 1,
'withdrawn': 0,
'import_status': 1,
'submission_date': datetime.date(2017, 12, 25),
'submit_to_ena': 1,
'ena_run_accession': None,
'ena_on_hold': 0,
'isolate_number_from_lab': '1',
'pool_sequence_replicates': 1,
'ena_experiment_accession': None,
'instrument_model': 'Illumina HiSeq 2500'
},
{
'sequence_replicate_number': 2,
'original_reads_file_1_md5':
'ec0377e321c59c0b1b6392a3c6dfc2dc',
'original_reads_file_2_md5':
'd541ffdb43a0648233ec7408c3626bfd',
'remove_contam_reads_file_1_md5': None,
'remove_contam_reads_file_2_md5': None,
'pool_sequence_replicates': 1,
'withdrawn': 0,
'import_status': 1,
'submission_date': datetime.date(2017, 12, 25),
'submit_to_ena': 1,
'ena_run_accession': None,
'ena_on_hold': 0,
'isolate_number_from_lab': '1',
'pool_sequence_replicates': 1,
'ena_experiment_accession': None,
'instrument_model': 'Illumina HiSeq 2500'
},
]
expected_rows.sort(key=itemgetter('original_reads_file_1_md5'))
query = 'SELECT * FROM (Seqrep JOIN Isolate ON Seqrep.isolate_id = Isolate.isolate_id)'
got_rows = database.query_to_dict(query)
got_rows.sort(key=itemgetter('original_reads_file_1_md5'))
# Check reads files etc written correctly
for isolate_data in got_rows:
iso_dir = isolate_dir.IsolateDir(pipeline_root,
isolate_data['sample_id'],
isolate_data['isolate_id'])
self.assertTrue(os.path.exists(iso_dir.reads_dir))
for i in [1, 2]:
self.assertTrue(
os.path.exists(
iso_dir.reads_filename(
'original',
isolate_data['sequence_replicate_number'], i)))
# similar to above, we don't know the sample_id, seqrep_id or isolate_id, which are auto generated.
for row in got_rows:
del row['sample_id']
del row['seqrep_id']
del row['isolate_id']
self.assertEqual(expected_rows, got_rows)
shutil.rmtree(pipeline_root)
nextflow_helper.clean_files()
database.commit_and_close()
db_connection.DbConnection(db_ini_file, destroy=True, must_exist=True)