Experiment IdsΒΆ

There are multiple ways to uniquely identify an experiment: - eID (str) : An experiment UUID as a string - np (int64) : An experiment UUID encoded as 2 int64s - path (Path) : A pathlib ALF path of the form /Subjects/// - ref (str) : An experiment reference string of the form yyyy-mm-dd_n_subject - url (str) : An remote http session path of the form /Subjects///

Internally Alyx and ONE uses eID strings to identify sessions. For example One.search returns a list of eID strings. In the ONE cache tables they are represented as a numpy array of 2 int64s because these are faster to search over. Session paths, URLs and ref strings are more readable.

from uuid import UUID

from one.api import ONE
from one.alf.spec import is_session_path, is_uuid_string, is_uuid

one = ONE(base_url='https://openalyx.internationalbrainlab.org')

# One.search returns experiment uuid strings
eids = one.search(data='channels.brainLocation')
assert is_uuid_string(eids[0])

# eID strings can be easily converted to other forms
session_path = one.eid2path(eids[0])  # returns a pathlib.Path object
assert is_session_path(session_path)
print(f'Session {"exists" if session_path.exists() else "does not exist"} on disk')

uuid = UUID(eids[0])
assert is_uuid(uuid)

# These conversion functions can except lists of experiment ids
ref_dict = one.eid2ref(eids)
assert len(ref_dict) == len(eids)

# ref strings can be sorted lexicographically (by date, number and subject in that order)
refs = sorted(one.dict2ref(ref_dict))

# Most ids can be interconverted also
eid = one.path2eid(
assert eid == eids[0]

# One load functions can accept most kinds of experiment identifiers
filepath = one.load_dataset(eid, 'channels.brainLocationIds_ccf_2017.npy',
dset = one.load_dataset(session_path, 'channels.brainLocationIds_ccf_2017.npy')
dset = one.load_dataset(filepath, 'channels.brainLocationIds_ccf_2017.npy')
short_path = '/'.join(session_path.parts[-3:])  # 'subject/date/number'
dset = one.load_dataset(short_path, 'channels.brainLocationIds_ccf_2017.npy')
url = one.path2url(filepath)
dset = one.load_dataset(url, 'channels.brainLocationIds_ccf_2017.npy')
dset = one.load_dataset(ref_dict[0], 'channels.brainLocationIds_ccf_2017.npy')
dset = one.load_dataset(refs[0], 'channels.brainLocationIds_ccf_2017.npy')

# Likewise with other load methods...
obj = one.load_object(short_path, 'channels', attribute='brainLocationIds')
Session exists on disk
{'subject': 'KS023', 'date': datetime.date(2019, 12, 10), 'sequence': 1}
['2019-12-10_1_KS023', '2020-01-08_1_CSHL049', '2020-09-19_1_CSH_ZAD_029']