else:
self.splines = [
InterpolatedUnivariateSpline(ti - t0, si, **kwargs)
for ti, si in zip(t, s)
]
def __len__(self):
return len(self.splines)
def __call__(self, t):
return np.vstack([s(t - self.t0) for s in self.splines])
m = 'animal_id in (20505, 20322, 20457, 20210, 20892)'
keys = (fuse.Activity() * stimulus.Sync * tune.StimulusType
& stimulus.Trial * stimulus.Condition & m
& 'stimulus_type = "stimulus.Trippy"').fetch('KEY')
key = keys[0]
pipe = (fuse.Activity() & key).module
num_frames = (pipe.ScanInfo() & key).fetch1('nframes')
num_depths = len(
dj.U('z') & (pipe.ScanInfo.Field().proj('z', nomatch='field') & key))
frame_times = (stimulus.Sync() & key).fetch1('frame_times',
squeeze=True) # one per depth
assert num_frames <= frame_times.size / num_depths <= num_frames + 1
frame_times = frame_times[:num_depths *
num_frames:num_depths] # one per volume
# reso/meso (up to SummaryImages)
for pipe in [reso, meso]:
pipe.ScanInfo().populate(next_scans, reserve_jobs=True, suppress_errors=True)
pipe.Quality().populate(next_scans, reserve_jobs=True, suppress_errors=True)
pipe.RasterCorrection().populate(next_scans, reserve_jobs=True, suppress_errors=True)
pipe.MotionCorrection().populate(next_scans, reserve_jobs=True, suppress_errors=True)
pipe.SummaryImages().populate(next_scans, reserve_jobs=True, suppress_errors=True)
# Field Registration
stack.InitialRegistration().populate(next_scans, reserve_jobs=True, suppress_errors=True)
stack.FieldRegistration().populate(next_scans, reserve_jobs=True, suppress_errors=True)
# fuse
fuse.MotionCorrection().populate(next_scans, reserve_jobs=True, suppress_errors=True)
fuse.ScanSet().populate(next_scans, reserve_jobs=True, suppress_errors=True)
fuse.Activity().populate(next_scans, reserve_jobs=True, suppress_errors=True)
fuse.ScanDone().populate(next_scans, reserve_jobs=True, suppress_errors=True)
# tune (these are memory intensive)
if POPULATE_TUNE:
tune_scans = next_scans & (experiment.Scan() & 'scan_ts > "2017-12-00 00:00:00"')
#stimulus.Sync needs to be run from Matlab
tune.STA().populate(tune_scans, reserve_jobs=True, suppress_errors=True)
tune.STAQual().populate(tune_scans, reserve_jobs=True, suppress_errors=True)
tune.STAExtent().populate(tune_scans, reserve_jobs=True)
tune.CaMovie().populate(tune_scans, reserve_jobs=True, suppress_errors=True)
tune.Drift().populate(tune_scans, reserve_jobs=True, suppress_errors=True)
tune.OriDesign().populate(tune_scans, reserve_jobs=True, suppress_errors=True)
tune.OriMap().populate(tune_scans, reserve_jobs=True, suppress_errors=True)
# sessions that have both Monet and Trippy from a few recent experiments
sessions = (fuse.Activity * stimulus.Sync
& 'animal_id in (20505, 20322, 20457, 20210, 20892)'
& (stimulus.Trial * stimulus.Monet2) &
(stimulus.Trial * stimulus.Trippy)).fetch('KEY')
key = sessions[2] # pick one
cache = {}
for key in sessions:
folder = os.path.join(os.path.abspath('..'), 'data', 'sessions',
dj.hash.key_hash(key)[:6])
if os.path.isdir(folder):
continue
print('load frame times.')
pipe = (fuse.Activity() & key).module
num_frames = (pipe.ScanInfo() & key).fetch1('nframes')
num_depths = len(
dj.U('z') & (pipe.ScanInfo.Field().proj('z', nomatch='field') & key))
frame_times = (stimulus.Sync() & key).fetch1('frame_times',
squeeze=True) # one per depth
assert num_frames <= frame_times.size / num_depths <= num_frames + 1
frame_times = frame_times[:num_depths *
num_frames:num_depths] # one per volume
print('load and cache soma traces')
trace_hash = dj.hash.key_hash(
{k: v
for k, v in key.items() if k not in {'stimulus_type'}})
archive = os.path.join('cache', trace_hash + '-traces.npz')
if os.path.isfile(archive):