def eid2path(eid: Union[str, Iter], one=None, offline: bool = False) -> Union[Path, List]:
"""
Returns a local path from an eid, regardless of whether the path exists locally
:param eid: An experiment uuid
:param one: An instance of ONE
:param offline: If True, do not connect to database (not implemented)
:return: a Path instance
Examples:
>>> base = 'https://test.alyx.internationalbrainlab.org'
>>> one = ONE(username='******', password='******', base_url=base)
Connected to...
>>> eid = '4e0b3320-47b7-416e-b842-c34dc9004cf8'
>>> eid2path(eid, one=one)
WindowsPath('E:/FlatIron/zadorlab/Subjects/flowers/2018-07-13/001')
>>> eid2path([eid, '7dc3c44b-225f-4083-be3d-07b8562885f4'], one=one)
[WindowsPath('E:/FlatIron/zadorlab/Subjects/flowers/2018-07-13/001'),
WindowsPath('E:/FlatIron/cortexlab/Subjects/KS005/2019-04-11/001')]
"""
if not one:
one = ONE()
if offline:
raise NotImplementedError
# path = one.path_from_eid(eid, offline=True)
else:
d = one.get_details(eid)
path = d.get('local_path')
if not path:
root = Path(one._get_cache_dir(None)) / d['lab'] / 'Subjects'
path = root / d['subject'] / d['start_time'][:10] / ('%03d' % d['number'])
return path
def eid2ref(eid: Union[str, Iter], one=None, as_dict=True, parse=True) \
-> Union[str, Mapping, List]:
"""
Get human-readable session ref from path
:param eid: The experiment uuid to find reference for
:param one: An ONE instance
:param as_dict: If false a string is returned in the form 'subject_sequence_yyyy-mm-dd'
:param parse: If true, the reference date and sequence are parsed from strings to their
respective data types
:return: one or more objects with keys ('subject', 'date', 'sequence'), or strings with the
form yyyy-mm-dd_n_subject
Examples:
>>> base = 'https://test.alyx.internationalbrainlab.org'
>>> one = ONE(username='******', password='******', base_url=base)
Connected to...
>>> eid = '4e0b3320-47b7-416e-b842-c34dc9004cf8'
>>> eid2ref(eid, one=one)
{'subject': 'flowers', 'date': datetime.date(2018, 7, 13), 'sequence': 1}
>>> eid2ref(eid, parse=False, one=one)
{'subject': 'flowers', 'date': '2018-07-13', 'sequence': '001'}
>>> eid2ref(eid, as_dict=False, one=one)
'2018-07-13_1_flowers'
>>> eid2ref(eid, as_dict=False, parse=False, one=one)
'2018-07-13_001_flowers'
>>> eid2ref([eid, '7dc3c44b-225f-4083-be3d-07b8562885f4'], one=one)
[{'subject': 'flowers', 'date': datetime.date(2018, 7, 13), 'sequence': 1},
{'subject': 'KS005', 'date': datetime.date(2019, 4, 11), 'sequence': 1}]
"""
if not one:
one = ONE()
d = one.get_details(eid)
if parse:
date = datetime.fromisoformat(d['start_time']).date()
ref = {'subject': d['subject'], 'date': date, 'sequence': d['number']}
format_str = '{date:%Y-%m-%d}_{sequence:d}_{subject:s}'
else:
date = d['start_time'][:10]
ref = {'subject': d['subject'], 'date': date, 'sequence': '%03d' % d['number']}
format_str = '{date:s}_{sequence:s}_{subject:s}'
return Bunch(ref) if as_dict else format_str.format(**ref)
def criteria_opto_eids(eids,
max_lapse=0.2,
max_bias=0.3,
min_trials=200,
one=None):
if one is None:
one = ONE()
use_eids = []
for j, eid in enumerate(eids):
try:
trials = load_trials(eid, laser_stimulation=True)
lapse_l = 1 - (
np.sum(trials.loc[trials['signed_contrast'] == -1,
'choice'] == 1) /
trials.loc[trials['signed_contrast'] == -1, 'choice'].shape[0])
lapse_r = 1 - (
np.sum(trials.loc[trials['signed_contrast'] == 1,
'choice'] == -1) /
trials.loc[trials['signed_contrast'] == 1, 'choice'].shape[0])
bias = np.abs(0.5 -
(np.sum(trials.loc[trials['signed_contrast'] == 0,
'choice'] == 1) /
np.shape(trials.loc[trials['signed_contrast'] == 0,
'choice'] == 1)[0]))
details = one.get_details(eid)
if ((lapse_l < max_lapse) & (lapse_r < max_lapse) &
(trials.shape[0] > min_trials)
& (bias < max_bias) &
('laser_stimulation' in trials.columns)):
use_eids.append(eid)
elif 'laser_stimulation' not in trials.columns:
print('No laser_stimulation data for %s %s' %
(details['subject'], details['start_time'][:10]))
else:
print(
'%s %s excluded (n_trials: %d, lapse_l: %.2f, lapse_r: %.2f, bias: %.2f)'
% (details['subject'], details['start_time'][:10],
trials.shape[0], lapse_l, lapse_r, bias))
except Exception:
print('Could not load session %s' % eid)
return use_eids
eid = eids[i]
try:
spikes, clusters, channels = bbone.load_spike_sorting_with_channel(
eid, aligned=True, one=one)
ses_path = one.path_from_eid(eid)
trials = load_trials(eid)
except Exception as error_message:
print(error_message)
continue
# Check data integrity
if check_trials(trials) is False:
continue
# Extract session data
ses_info = one.get_details(eid)
subject = ses_info['subject']
date = ses_info['start_time'][:10]
probes_to_use = probes[i]
# Process per probe
for p, probe in enumerate(probes_to_use):
print('Processing %s (%d of %d)' % (probe, p + 1, len(probes_to_use)))
# Check if data is available for this probe
if probe not in clusters.keys():
continue
# Check if histology is available for this probe
if not hasattr(clusters[probe], 'acronym'):
continue
def plot_all(eid):
matplotlib.rcParams.update({'font.size': 10})
# report eid = '4a45c8ba-db6f-4f11-9403-56e06a33dfa4'
panels = {
'plot_paw_on_image': plot_paw_on_image,
'plot_wheel_position': plot_wheel_position,
'paw_speed_PSTH': paw_speed_PSTH,
'plot_licks': plot_licks,
'lick_raster': lick_raster,
'nose_speed_PSTH': nose_speed_PSTH,
'pupil_diameter_PSTH': pupil_diameter_PSTH,
'motion_energy_PSTH': motion_energy_PSTH
}
nrows = 2
ncols = 4
#plt.ioff()
plt.figure(figsize=(15, 10))
k = 1
for panel in panels:
plt.subplot(nrows, ncols, k)
add_panel_letter(k)
try:
panels[panel](eid)
#continue
except:
ax = plt.gca()
plt.text(.5,
.5,
f'error in \n {panel}',
color='r',
fontweight='bold',
bbox=dict(facecolor='white', alpha=0.5),
fontsize=10,
transform=ax.transAxes)
k += 1
plt.tight_layout()
# print QC outcome in title and DLC task version
one = ONE()
task = one.alyx.rest('tasks', 'list', session=eid, name='EphysDLC')[0]
det = one.get_details(eid, True)['extended_qc']
p = one.path_from_eid(eid)
s1 = ' '.join([str(p).split('/')[i] for i in [4, 6, 7, 8]])
dlc_qcs = [
'time_trace_length_match', 'trace_all_nan', 'mean_in_bbox',
'pupil_blocked', 'lick_detection'
]
qcs = [
'task', 'behavior', 'videoLeft', 'videoRight', 'videoBody', 'dlcLeft',
'dlcRight', 'dlcBody'
]
l = []
for q in qcs:
try:
if det[q] == 'FAIL':
if 'dlc' in q:
l.append('\n')
l.append(q + ':' + str(det[q]) + '-->')
video_type = q[3:]
for dlc_qc in dlc_qcs:
w = f'_dlc{video_type}_{dlc_qc}'
if not det[w]:
l.append(w + ':' + str(det[w]) + ',')
else:
l.append(q + ':' + str(det[q]) + ',')
except:
continue
s2 = ' '.join(l)
plt.suptitle(s1 + ', DLC version: ' + str(task['version']) + ' \n ' + s2,
backgroundcolor='white',
fontsize=6)
plt.tight_layout(rect=[0, 0.03, 1, 0.95])
#plt.tight_layout()
plt.savefig(f'/home/mic/reproducible_dlc/overviewJune/{eid}.png')
plt.close()
