def reindex_all_results(conf):
db = DB(conf['db'])
es_exp = ESExporter(conf)
es_exp.delete_index(name='sm')
es_exp.create_index(name='sm')
ds_db_pairs = db.select("select name, config -> 'database'::text -> 'name'::text from dataset")
for ds_name, db_name in ds_db_pairs:
es_exp.index_ds(db, ds_name, db_name)
def reindex_all_results(conf):
db = DB(conf['db'])
es_exp = ESExporter(conf)
es_exp.delete_index(name='sm')
es_exp.create_index(name='sm')
ds_db_pairs = db.select(
"select name, config -> 'database'::text -> 'name'::text from dataset")
for ds_name, db_name in ds_db_pairs:
es_exp.index_ds(db, ds_name, db_name)
def test_foo(sm_config):
annotations = [('test_ds', 'test_db', 'H20', '+H', [], []), ('test_ds', 'test_db', 'Au', '+H', [], [])]
db_mock = MagicMock(DB)
db_mock.select.return_value = annotations
es_exp = ESExporter(sm_config)
es_exp.index_ds(db_mock, 'test_ds', 'test_db')
es = Elasticsearch()
d = es.get(index='sm', id='test_ds_test_db_H20_+H', doc_type='annotation', _source=True)
assert d['_source'] == {'ds_name': 'test_ds', 'db_name': 'test_db', 'sf': 'H20', 'adduct': '+H', 'comp_names': '', 'comp_ids': ''}
d = es.get(index='sm', id='test_ds_test_db_Au_+H', doc_type='annotation', _source=True)
assert d['_source'] == {'ds_name': 'test_ds', 'db_name': 'test_db', 'sf': 'Au', 'adduct': '+H', 'comp_names': '', 'comp_ids': ''}
def test_foo(sm_config):
annotations = [('test_ds', 'test_db', 'H20', '+H', [], [], 100), ('test_ds', 'test_db', 'Au', '+H', [], [], 200)]
db_mock = MagicMock(DB)
db_mock.select.return_value = annotations
es_exp = ESExporter(sm_config)
es_exp.index_ds(db_mock, 'test_ds', 'test_db')
es = Elasticsearch()
d = es.get(index='sm', id='test_ds_test_db_H20_+H', doc_type='annotation', _source=True)
assert d['_source'] == {'ds_name': 'test_ds', 'db_name': 'test_db', 'sf': 'H20', 'adduct': '+H',
'comp_names': '', 'comp_ids': '', 'mz': '00100.0000'}
d = es.get(index='sm', id='test_ds_test_db_Au_+H', doc_type='annotation', _source=True)
assert d['_source'] == {'ds_name': 'test_ds', 'db_name': 'test_db', 'sf': 'Au', 'adduct': '+H',
'comp_names': '', 'comp_ids': '', 'mz': '00200.0000'}
def test_index_ds_works(es_dsl_search, sm_index, sm_config):
ds_id = '2000-01-01_00h00m'
upload_dt = datetime.now().isoformat(' ')
mol_db_id = 0
last_finished = '2017-01-01T00:00:00'
def db_sel_side_effect(sql, params):
if sql == DATASET_SEL:
return [{
'ds_id': ds_id,
'ds_name': 'ds_name',
'ds_input_path': 'ds_input_path',
'ds_config': 'ds_config',
'ds_meta': {},
'ds_upload_dt': upload_dt,
'ds_status': 'ds_status',
'ds_last_finished': datetime.strptime(last_finished, '%Y-%m-%dT%H:%M:%S'),
'ds_is_public': True,
'ds_ion_img_storage': 'fs',
'ds_acq_geometry': {}
}]
elif sql == ANNOTATIONS_SEL:
return [{
'sf': 'H2O',
'sf_adduct': 'H2O+H',
'chaos': 1,
'image_corr': 1,
'pattern_match': 1,
'total_iso_ints': 100,
'min_iso_ints': 0,
'max_iso_ints': 100,
'msm': 1,
'adduct': '+H',
'job_id': 1,
'fdr': 0.1,
'iso_image_ids': ['iso_img_id_1', 'iso_img_id_2'],
'polarity': '+'
}, {
'sf': 'Au',
'sf_adduct': 'Au+H',
'chaos': 1,
'image_corr': 1,
'pattern_match': 1,
'total_iso_ints': 100,
'min_iso_ints': 0,
'max_iso_ints': 100,
'msm': 1,
'adduct': '+H',
'job_id': 1,
'fdr': 0.05,
'iso_image_ids': ['iso_img_id_1', 'iso_img_id_2'],
'polarity': '+'
}]
else:
logging.getLogger('engine').error('Wrong db_sel_side_effect arguments: ', args)
db_mock = MagicMock(spec=DB)
db_mock.select_with_fields.side_effect = db_sel_side_effect
mol_db_mock = MagicMock(MolecularDB)
mol_db_mock.id = mol_db_id
mol_db_mock.name = 'db_name'
mol_db_mock.version = '2017'
mol_db_mock.get_molecules.return_value = pd.DataFrame([('H2O', 'mol_id', 'mol_name'), ('Au', 'mol_id', 'mol_name')],
columns=['sf', 'mol_id', 'mol_name'])
isocalc_mock = MagicMock(IsocalcWrapper)
isocalc_mock.ion_centroids = lambda sf, adduct: {
('H2O', '+H'): ([100., 200.], None),
('Au', '+H'): ([10., 20.], None)
}[(sf, adduct)]
es_exp = ESExporter(db_mock)
es_exp.delete_ds(ds_id)
es_exp.index_ds(ds_id=ds_id, mol_db=mol_db_mock, isocalc=isocalc_mock)
wait_for_es(sec=1)
ds_d = es_dsl_search.filter('term', _type='dataset').execute().to_dict()['hits']['hits'][0]['_source']
assert ds_d == {
'ds_last_finished': last_finished, 'ds_config': 'ds_config', 'ds_meta': {},
'ds_status': 'ds_status', 'ds_name': 'ds_name', 'ds_input_path': 'ds_input_path', 'ds_id': ds_id,
'ds_upload_dt': upload_dt,
'annotation_counts': [{'db': {'name': 'db_name', 'version': '2017'},
'counts': [{'level': 5, 'n': 1}, {'level': 10, 'n': 2},
{'level': 20, 'n': 2}, {'level': 50, 'n': 2}]}],
'ds_is_public': True,
'ds_acq_geometry': {},
'ds_ion_img_storage': 'fs'
}
ann_1_d = es_dsl_search.filter('term', sf='H2O').execute().to_dict()['hits']['hits'][0]['_source']
assert ann_1_d == {
'pattern_match': 1, 'image_corr': 1, 'fdr': 0.1, 'chaos': 1, 'sf': 'H2O', 'min_iso_ints': 0,
'msm': 1, 'sf_adduct': 'H2O+H', 'total_iso_ints': 100, 'centroid_mzs': [100., 200.],
'iso_image_ids': ['iso_img_id_1', 'iso_img_id_2'], 'polarity': '+', 'job_id': 1, 'max_iso_ints': 100,
'adduct': '+H', 'ds_name': 'ds_name', 'annotation_counts': [], 'db_version': '2017', 'ds_status': 'ds_status',
'ion_add_pol': '[M+H]+', 'comp_names': ['mol_name'], 'db_name': 'db_name', 'mz': 100., 'ds_meta': {},
'comp_ids': ['mol_id'], 'ds_config': 'ds_config', 'ds_input_path': 'ds_input_path', 'ds_id': ds_id,
'ds_upload_dt': upload_dt, 'ds_last_finished': last_finished,
'ds_ion_img_storage': 'fs', 'ds_is_public': True
}
ann_2_d = es_dsl_search.filter('term', sf='Au').execute().to_dict()['hits']['hits'][0]['_source']
assert ann_2_d == {
'pattern_match': 1, 'image_corr': 1, 'fdr': 0.05, 'chaos': 1, 'sf': 'Au', 'min_iso_ints': 0,
'msm': 1, 'sf_adduct': 'Au+H', 'total_iso_ints': 100, 'centroid_mzs': [10., 20.],
'iso_image_ids': ['iso_img_id_1', 'iso_img_id_2'], 'polarity': '+', 'job_id': 1, 'max_iso_ints': 100,
'adduct': '+H', 'ds_name': 'ds_name', 'annotation_counts': [], 'db_version': '2017', 'ds_status': 'ds_status',
'ion_add_pol': '[M+H]+', 'comp_names': ['mol_name'], 'db_name': 'db_name', 'mz': 10., 'ds_meta': {},
'comp_ids': ['mol_id'], 'ds_config': 'ds_config', 'ds_input_path': 'ds_input_path', 'ds_id': ds_id,
'ds_upload_dt': upload_dt, 'ds_last_finished': last_finished,
'ds_ion_img_storage': 'fs', 'ds_is_public': True
}
def test_sm_daemon_es_export_fails(get_compute_img_metrics_mock, filter_sf_metrics_mock,
post_images_to_annot_service_mock,
MolDBServiceWrapperMock,
sm_config, test_db, es_dsl_search,
clean_isotope_storage):
init_mol_db_service_wrapper_mock(MolDBServiceWrapperMock)
get_compute_img_metrics_mock.return_value = lambda *args: (0.9, 0.9, 0.9, [100.], [0], [10.])
filter_sf_metrics_mock.side_effect = lambda x: x
url_dict = {
'iso_image_ids': ['iso_image_1', None, None, None]
}
post_images_to_annot_service_mock.return_value = {
35: url_dict,
44: url_dict
}
db = DB(sm_config['db'])
annotate_daemon = None
update_daemon = None
def throw_exception_function(*args, **kwargs):
raise Exception('Test')
es = ESExporter(db)
es.index_ds = throw_exception_function
try:
ds_id = '2000-01-01_00h00m'
upload_dt = datetime.now()
ds_config_str = open(ds_config_path).read()
db.insert(Dataset.DS_INSERT, [{
'id': ds_id,
'name': test_ds_name,
'input_path': input_dir_path,
'upload_dt': upload_dt,
'metadata': '{}',
'config': ds_config_str,
'status': DatasetStatus.QUEUED,
'is_public': True,
'mol_dbs': ['HMDB-v4'],
'adducts': ['+H', '+Na', '+K'],
'ion_img_storage': 'fs'
}])
ds = Dataset.load(db, ds_id)
queue_pub.publish({'ds_id': ds.id, 'ds_name': ds.name, 'action': 'annotate'})
run_daemons(db, es)
# dataset and job tables asserts
row = db.select_one('SELECT status from job')
assert row[0] == 'FINISHED'
row = db.select_one('SELECT status from dataset')
assert row[0] == 'FAILED'
finally:
db.close()
if annotate_daemon:
annotate_daemon.stop()
if update_daemon:
update_daemon.stop()
with warn_only():
local('rm -rf {}'.format(data_dir_path))
def test_index_ds_works(es_dsl_search, sm_index, sm_config):
ds_id = '2000-01-01_00h00m'
upload_dt = datetime.now().isoformat(' ')
mol_db_id = 0
last_finished = '2017-01-01T00:00:00'
def db_sel_side_effect(sql, params):
if sql == DATASET_SEL:
return [{
'ds_id': ds_id,
'ds_name': 'ds_name',
'ds_input_path': 'ds_input_path',
'ds_config': 'ds_config',
'ds_meta': {},
'ds_upload_dt': upload_dt,
'ds_status': 'ds_status',
'ds_last_finished': datetime.strptime(last_finished, '%Y-%m-%dT%H:%M:%S'),
'ds_is_public': True,
'ds_ion_img_storage': 'fs',
'ds_acq_geometry': {}
}]
elif sql == ANNOTATIONS_SEL:
return [{
'sf': 'H2O',
'sf_adduct': 'H2O+H',
'chaos': 1,
'image_corr': 1,
'pattern_match': 1,
'total_iso_ints': 100,
'min_iso_ints': 0,
'max_iso_ints': 100,
'msm': 1,
'adduct': '+H',
'job_id': 1,
'fdr': 0.1,
'iso_image_ids': ['iso_img_id_1', 'iso_img_id_2'],
'polarity': '+'
}, {
'sf': 'Au',
'sf_adduct': 'Au+H',
'chaos': 1,
'image_corr': 1,
'pattern_match': 1,
'total_iso_ints': 100,
'min_iso_ints': 0,
'max_iso_ints': 100,
'msm': 1,
'adduct': '+H',
'job_id': 1,
'fdr': 0.05,
'iso_image_ids': ['iso_img_id_1', 'iso_img_id_2'],
'polarity': '+'
}]
else:
logging.getLogger('engine').error('Wrong db_sel_side_effect arguments: ', args)
db_mock = MagicMock(spec=DB)
db_mock.select_with_fields.side_effect = db_sel_side_effect
mol_db_mock = MagicMock(MolecularDB)
mol_db_mock.id = mol_db_id
mol_db_mock.name = 'db_name'
mol_db_mock.version = '2017'
mol_db_mock.get_molecules.return_value = pd.DataFrame([('H2O', 'mol_id', 'mol_name'), ('Au', 'mol_id', 'mol_name')],
columns=['sf', 'mol_id', 'mol_name'])
isocalc_mock = MagicMock(IsocalcWrapper)
isocalc_mock.ion_centroids = lambda sf, adduct: {
('H2O', '+H'): ([100., 200.], None),
('Au', '+H'): ([10., 20.], None)
}[(sf, adduct)]
es_exp = ESExporter(db_mock)
es_exp.delete_ds(ds_id)
es_exp.index_ds(ds_id=ds_id, mol_db=mol_db_mock, isocalc=isocalc_mock)
wait_for_es(sec=1)
ds_d = es_dsl_search.filter('term', _type='dataset').execute().to_dict()['hits']['hits'][0]['_source']
assert ds_d == {
'ds_last_finished': last_finished, 'ds_config': 'ds_config', 'ds_meta': {},
'ds_status': 'ds_status', 'ds_name': 'ds_name', 'ds_input_path': 'ds_input_path', 'ds_id': ds_id,
'ds_upload_dt': upload_dt,
'annotation_counts': [{'db': {'name': 'db_name', 'version': '2017'},
'counts': [{'level': 5, 'n': 1}, {'level': 10, 'n': 2},
{'level': 20, 'n': 2}, {'level': 50, 'n': 2}]}],
'ds_is_public': True,
'ds_acq_geometry': {},
'ds_ion_img_storage': 'fs'
}
ann_1_d = es_dsl_search.filter('term', sf='H2O').execute().to_dict()['hits']['hits'][0]['_source']
assert ann_1_d == {
'pattern_match': 1, 'image_corr': 1, 'fdr': 0.1, 'chaos': 1, 'sf': 'H2O', 'min_iso_ints': 0,
'msm': 1, 'sf_adduct': 'H2O+H', 'total_iso_ints': 100, 'centroid_mzs': [100., 200.],
'iso_image_ids': ['iso_img_id_1', 'iso_img_id_2'], 'polarity': '+', 'job_id': 1, 'max_iso_ints': 100,
'adduct': '+H', 'ds_name': 'ds_name', 'annotation_counts': [], 'db_version': '2017', 'ds_status': 'ds_status',
'ion_add_pol': '[M+H]+', 'comp_names': ['mol_name'], 'db_name': 'db_name', 'mz': 100., 'ds_meta': {},
'comp_ids': ['mol_id'], 'ds_config': 'ds_config', 'ds_input_path': 'ds_input_path', 'ds_id': ds_id,
'ds_upload_dt': upload_dt, 'ds_last_finished': last_finished,
'ds_ion_img_storage': 'fs', 'ds_is_public': True
}
ann_2_d = es_dsl_search.filter('term', sf='Au').execute().to_dict()['hits']['hits'][0]['_source']
assert ann_2_d == {
'pattern_match': 1, 'image_corr': 1, 'fdr': 0.05, 'chaos': 1, 'sf': 'Au', 'min_iso_ints': 0,
'msm': 1, 'sf_adduct': 'Au+H', 'total_iso_ints': 100, 'centroid_mzs': [10., 20.],
'iso_image_ids': ['iso_img_id_1', 'iso_img_id_2'], 'polarity': '+', 'job_id': 1, 'max_iso_ints': 100,
'adduct': '+H', 'ds_name': 'ds_name', 'annotation_counts': [], 'db_version': '2017', 'ds_status': 'ds_status',
'ion_add_pol': '[M+H]+', 'comp_names': ['mol_name'], 'db_name': 'db_name', 'mz': 10., 'ds_meta': {},
'comp_ids': ['mol_id'], 'ds_config': 'ds_config', 'ds_input_path': 'ds_input_path', 'ds_id': ds_id,
'ds_upload_dt': upload_dt, 'ds_last_finished': last_finished,
'ds_ion_img_storage': 'fs', 'ds_is_public': True
}
def test_index_ds_works(sm_config, test_db, es, es_dsl_search, sm_index,
ds_config, metadata, annotation_stats):
ds_id = '2000-01-01_00h00m'
upload_dt = datetime.now().isoformat()
last_finished = '2017-01-01 00:00:00'
iso_image_ids = ['iso_img_id_1', 'iso_img_id_2']
stats = json.dumps(annotation_stats)
db = DB()
db.insert(
"INSERT INTO dataset(id, name, input_path, config, metadata, upload_dt, status, "
"status_update_dt, is_public, acq_geometry, ion_thumbnail) "
"VALUES (%s, 'ds_name', 'ds_input_path', %s, %s, %s, 'ds_status', %s, true, '{}', %s)",
[[
ds_id,
json.dumps(ds_config),
json.dumps(metadata), upload_dt, upload_dt, 'thumb-id'
]],
)
moldb = create_test_molecular_db()
(job_id, ) = db.insert_return(
"INSERT INTO job(ds_id, moldb_id, status, start, finish) "
"VALUES (%s, %s, 'job_status', %s, %s) RETURNING id",
rows=[(ds_id, moldb.id, last_finished, last_finished)],
)
(user_id, ) = db.insert_return(
"INSERT INTO graphql.user (email, name, role) "
"VALUES ('email', 'user_name', 'user') RETURNING id",
[[]],
)
(group_id, ) = db.insert_return(
"INSERT INTO graphql.group (name, short_name) VALUES ('group name', 'grp') RETURNING id",
[[]],
)
db.insert(
"INSERT INTO graphql.dataset(id, user_id, group_id) VALUES (%s, %s, %s)",
[[ds_id, user_id, group_id]],
)
ion_id1, ion_id2 = db.insert_return(
"INSERT INTO graphql.ion(ion, formula, chem_mod, neutral_loss, adduct, charge, ion_formula) "
"VALUES (%s, %s, %s, %s, %s, %s, %s) RETURNING id",
[
['H2O-H+O-H+H', 'H2O', '-H+O', '-H', '+H', 1, 'HO2'],
['Au+H', 'Au', '', '', '+H', 1, 'HAu'],
],
)
db.insert(
"INSERT INTO annotation(job_id, formula, chem_mod, neutral_loss, adduct, "
"msm, fdr, stats, iso_image_ids, ion_id) "
"VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)",
[
[
job_id, 'H2O', '-H+O', '-H', '+H', 1, 0.1, stats,
iso_image_ids, ion_id1
],
[
job_id, 'Au', '', '', '+H', 1, 0.05, stats, iso_image_ids,
ion_id2
],
],
)
isocalc_mock = MagicMock(IsocalcWrapper)
isocalc_mock.centroids = lambda formula: {
'H2O+H': ([100.0, 200.0], None),
'H2O-H+O-H+H': ([100.0, 200.0, 300.0], None),
'Au+H': ([10.0, 20.0], None),
}[formula]
isocalc_mock.mass_accuracy_bounds = lambda mzs: (mzs, mzs)
with patch(
'sm.engine.es_export.molecular_db.fetch_molecules',
return_value=pd.DataFrame(
[('H2O', 'mol_id', 'mol_name'), ('Au', 'mol_id', 'mol_name')],
columns=['formula', 'mol_id', 'mol_name'],
),
):
es_exp = ESExporter(db, sm_config)
es_exp.delete_ds(ds_id)
es_exp.index_ds(
ds_id=ds_id,
moldb=moldb,
isocalc=isocalc_mock,
)
wait_for_es(es, sm_config['elasticsearch']['index'])
ds_d = (es_dsl_search.filter(
'term',
_type='dataset').execute().to_dict()['hits']['hits'][0]['_source'])
expected_ds_fields = {
'ds_last_finished': last_finished,
'ds_config': ds_config,
'ds_adducts': ds_config['isotope_generation']['adducts'],
'ds_moldb_ids': ds_config['database_ids'],
'ds_chem_mods': [],
'ds_neutral_losses': [],
'ds_project_ids': [],
'ds_project_names': [],
'ds_meta': metadata,
'ds_status': 'ds_status',
'ds_status_update_dt': upload_dt,
'ds_name': 'ds_name',
'ds_input_path': 'ds_input_path',
'ds_id': ds_id,
'ds_upload_dt': upload_dt,
'ds_is_public': True,
'ds_submitter_email': 'email',
'ds_submitter_id': user_id,
'ds_submitter_name': 'user_name',
'ds_group_approved': False,
'ds_group_id': group_id,
'ds_group_name': 'group name',
'ds_group_short_name': 'grp',
}
assert ds_d == {
**expected_ds_fields,
'ds_acq_geometry': {},
'annotation_counts': [{
'db': {
'id': moldb.id,
'name': moldb.name
},
'counts': [
{
'level': 5,
'n': 1
},
{
'level': 10,
'n': 2
},
{
'level': 20,
'n': 2
},
{
'level': 50,
'n': 2
},
],
}],
}
ann_1_d = (es_dsl_search.filter(
'term',
formula='H2O').execute().to_dict()['hits']['hits'][0]['_source'])
top_level_stats = {
'pattern_match': annotation_stats['spectral'],
'image_corr': annotation_stats['spatial'],
'chaos': annotation_stats['chaos'],
**{
key: value
for key, value in annotation_stats.items() if key in NON_METRIC_STATS
},
}
metrics = {
key: value
for key, value in annotation_stats.items()
if key not in NON_METRIC_STATS
}
assert ann_1_d == {
**expected_ds_fields,
**top_level_stats,
'metrics':
metrics,
'fdr':
0.1,
'formula':
'H2O',
'msm':
1.0,
'ion':
'H2O-H+O-H+H+',
'ion_formula':
'HO2',
'centroid_mzs': [100.0, 200.0, 300.0],
'iso_image_ids': ['iso_img_id_1', 'iso_img_id_2'],
'iso_image_urls': [
f'http://localhost:9000/{sm_config["image_storage"]["bucket"]}/iso/{ds_id}/iso_img_id_1',
f'http://localhost:9000/{sm_config["image_storage"]["bucket"]}/iso/{ds_id}/iso_img_id_2',
],
'isobars': [],
'isomer_ions': [],
'polarity':
'+',
'job_id':
1,
'adduct':
'+H',
'neutral_loss':
'-H',
'chem_mod':
'-H+O',
'annotation_counts': [],
'comp_names': ['mol_name'],
'comps_count_with_isomers':
1,
'db_id':
moldb.id,
'db_name':
moldb.name,
'db_version':
moldb.version,
'mz':
100.0,
'comp_ids': ['mol_id'],
'annotation_id':
1,
'off_sample_label':
None,
'off_sample_prob':
None,
}
ann_2_d = (es_dsl_search.filter(
'term',
formula='Au').execute().to_dict()['hits']['hits'][0]['_source'])
assert ann_2_d == {
**expected_ds_fields,
**top_level_stats,
'metrics':
metrics,
'fdr':
0.05,
'formula':
'Au',
'msm':
1.0,
'ion':
'Au+H+',
'ion_formula':
'HAu',
'centroid_mzs': [10.0, 20.0],
'iso_image_ids': ['iso_img_id_1', 'iso_img_id_2'],
'iso_image_urls': [
f'http://localhost:9000/{sm_config["image_storage"]["bucket"]}/iso/{ds_id}/iso_img_id_1',
f'http://localhost:9000/{sm_config["image_storage"]["bucket"]}/iso/{ds_id}/iso_img_id_2',
],
'isobars': [],
'isomer_ions': [],
'polarity':
'+',
'job_id':
1,
'adduct':
'+H',
'neutral_loss':
'',
'chem_mod':
'',
'annotation_counts': [],
'comp_names': ['mol_name'],
'comps_count_with_isomers':
1,
'db_id':
moldb.id,
'db_name':
moldb.name,
'db_version':
moldb.version,
'mz':
10.0,
'comp_ids': ['mol_id'],
'annotation_id':
2,
'off_sample_label':
None,
'off_sample_prob':
None,
}
def test_sm_daemon_es_export_fails(
MSMSearchMock,
post_images_to_image_store_mock,
test_db,
es_dsl_search,
clean_isotope_storage,
reset_queues,
metadata,
ds_config,
queue_pub,
local_sm_config,
):
moldb = init_moldb()
formula_metrics_df = pd.DataFrame({
'formula_i': [0, 1, 2],
'ion_formula': ['C12H24O-H2O+H', 'C12H24O-H2+O2-CO+Na', 'C12H24O+K'],
'formula': ['C12H24O', 'C12H24O', 'C12H24O'],
'modifier': ['-H2O+H', '-H2+O2-CO+Na', '+K'],
'chem_mod': ['', '-H2+O2', ''],
'neutral_loss': ['-H2O', '-CO', ''],
'adduct': ['+H', '+Na', '+K'],
'chaos': [0.9, 0.9, 0.9],
'spatial': [0.9, 0.9, 0.9],
'spectral': [0.9, 0.9, 0.9],
'msm': [0.9**3, 0.9**3, 0.9**3],
'total_iso_ints': [[100.0], [100.0], [100.0]],
'min_iso_ints': [[0], [0], [0]],
'max_iso_ints': [[10.0], [10.0], [10.0]],
'fdr': [0.1, 0.1, 0.1],
}).set_index('formula_i')
search_algo_mock = MSMSearchMock()
search_algo_mock.search.return_value = [
(formula_metrics_df, [], create_test_fdr_diagnostics_bundle())
]
search_algo_mock.metrics = OrderedDict([
('chaos', 0),
('spatial', 0),
('spectral', 0),
('msm', 0),
('total_iso_ints', []),
('min_iso_ints', []),
('max_iso_ints', []),
])
image_ids = ['iso_image_1', None, None, None]
post_images_to_image_store_mock.return_value = {
0: image_ids,
1: image_ids,
2: image_ids
}
db = DB()
def throw_exception_function(*args, **kwargs):
raise Exception('Test')
es = ESExporter(db, local_sm_config)
es.index_ds = throw_exception_function
ds = create_test_ds(
name=test_ds_name,
input_path=input_dir_path,
config={
**ds_config, 'database_ids': [moldb.id]
},
status=DatasetStatus.QUEUED,
es=es,
)
queue_pub.publish({
'ds_id': ds.id,
'ds_name': test_ds_name,
'action': DaemonAction.ANNOTATE
})
run_daemons(db, es, local_sm_config)
# dataset and job tables asserts
row = db.select_one('SELECT status from job')
assert row[0] == 'FINISHED'
row = db.select_one('SELECT status from dataset')
assert row[0] == 'FAILED'
def run(self, input_path, ds_config_path, clean=False):
""" Entry point of the engine. Molecule search is completed in several steps:
* Copying input data to the engine work dir
* Conversion input data (imzML+ibd) to plain text format. One line - one spectrum data
* Generation and saving to the database theoretical peaks for all formulas from the molecule database
* Molecules search. The most compute intensive part. Spark is used to run it in distributed manner.
* Saving results (isotope images and their metrics of quality for each putative molecule) to the database
Args
-------
input_path : string
Path to the dataset folder with .imzML and .ibd files
ds_config_path: string
Path to the dataset config file
clean : bool
Clean all interim data files before starting molecule search
"""
try:
self.wd_manager = WorkDirManager(self.ds_name)
if clean:
self.wd_manager.clean()
self.wd_manager.copy_input_data(input_path, ds_config_path)
self._read_ds_config()
logger.info('Dataset config:\n%s', pformat(self.ds_config))
self._configure_spark()
self._init_db()
if not self.wd_manager.exists(self.wd_manager.txt_path):
imzml_converter = ImzmlTxtConverter(
self.ds_name, self.wd_manager.local_dir.imzml_path,
self.wd_manager.local_dir.txt_path,
self.wd_manager.local_dir.coord_path)
imzml_converter.convert()
if not self.wd_manager.local_fs_only:
self.wd_manager.upload_to_remote()
self.ds = Dataset(self.sc, self.ds_name, self.client_email,
input_path, self.ds_config, self.wd_manager,
self.db)
self.ds.save_ds_meta()
self.store_job_meta()
theor_peaks_gen = TheorPeaksGenerator(self.sc, self.sm_config,
self.ds_config)
theor_peaks_gen.run()
target_adducts = self.ds_config['isotope_generation']['adducts']
self.fdr = FDR(self.job_id,
self.sf_db_id,
decoy_sample_size=20,
target_adducts=target_adducts,
db=self.db)
self.fdr.decoy_adduct_selection()
self.formulas = FormulasSegm(self.job_id, self.sf_db_id,
self.ds_config, self.db)
# search_alg = MSMBasicSearch(self.sc, self.ds, self.formulas, self.fdr, self.ds_config)
search_alg = MSMExtraFeats(self.sc, self.ds, self.formulas,
self.fdr, self.ds_config)
sf_metrics_df, sf_iso_images = search_alg.search()
search_results = SearchResults(
self.sf_db_id, self.ds_id, self.job_id, self.ds_name,
self.formulas.get_sf_adduct_peaksn(), self.db, self.sm_config,
self.ds_config)
search_results.sf_metrics_df = sf_metrics_df
search_results.sf_iso_images = sf_iso_images
search_results.metrics = search_alg.metrics
search_results.nrows, search_results.ncols = self.ds.get_dims()
search_results.store()
es = ESExporter(self.sm_config)
es.index_ds(self.db, self.ds_name,
self.ds_config['database']['name'])
except Exception:
exc_type, exc_value, exc_traceback = sys.exc_info()
logger.error('\n'.join(
traceback.format_exception(exc_type, exc_value,
exc_traceback)))
finally:
if self.sc:
# self.sc.show_profiles()
self.sc.stop()
if self.db:
self.db.close()