def sync_rotary_encoder(session_path, bpod_data=None, re_events=None):
if not bpod_data:
bpod_data = raw.load_data(session_path)
evt = re_events or raw.load_encoder_events(session_path)
# we work with stim_on (2) and closed_loop (3) states for the synchronization with bpod
tre = evt.re_ts.values / 1e6 # convert to seconds
# the first trial on the rotary encoder is a dud
rote = {'stim_on': tre[evt.sm_ev == 2][:-1],
'closed_loop': tre[evt.sm_ev == 3][:-1]}
bpod = {
'stim_on': np.array([tr['behavior_data']['States timestamps']
['stim_on'][0][0] for tr in bpod_data]),
'closed_loop': np.array([tr['behavior_data']['States timestamps']
['closed_loop'][0][0] for tr in bpod_data]),
}
if rote['closed_loop'].size <= 1:
raise err.SyncBpodWheelException("Not enough Rotary Encoder events to perform wheel"
" synchronization. Wheel data not extracted")
# bpod bug that spits out events in ms instead of us
if np.diff(bpod['closed_loop'][[-1, 0]])[0] / np.diff(rote['closed_loop'][[-1, 0]])[0] > 900:
_logger.error("Rotary encoder stores values in ms instead of us. Wheel timing inaccurate")
rote['stim_on'] *= 1e3
rote['closed_loop'] *= 1e3
# just use the closed loop for synchronization
# handle different sizes in synchronization:
sz = min(rote['closed_loop'].size, bpod['closed_loop'].size)
# if all the sample are contiguous and first samples match
diff_first_match = np.diff(rote['closed_loop'][:sz]) - np.diff(bpod['closed_loop'][:sz])
# if all the sample are contiguous and last samples match
diff_last_match = np.diff(rote['closed_loop'][-sz:]) - np.diff(bpod['closed_loop'][-sz:])
# 99% of the pulses match for a first sample lock
DIFF_THRESHOLD = 0.005
if np.mean(np.abs(diff_first_match) < DIFF_THRESHOLD) > 0.99:
re = rote['closed_loop'][:sz]
bp = bpod['closed_loop'][:sz]
indko = np.where(np.abs(diff_first_match) >= DIFF_THRESHOLD)[0]
# 99% of the pulses match for a last sample lock
elif np.mean(np.abs(diff_last_match) < DIFF_THRESHOLD) > 0.99:
re = rote['closed_loop'][-sz:]
bp = bpod['closed_loop'][-sz:]
indko = np.where(np.abs(diff_last_match) >= DIFF_THRESHOLD)[0]
# last resort is to use ad-hoc sync function
else:
bp, re = raw.sync_trials_robust(bpod['closed_loop'], rote['closed_loop'],
diff_threshold=DIFF_THRESHOLD, max_shift=5)
indko = np.array([])
# raise ValueError("Can't sync bpod and rotary encoder: non-contiguous sync pulses")
# remove faulty indices due to missing or bad syncs
indko = np.int32(np.unique(np.r_[indko + 1, indko]))
re = np.delete(re, indko)
bp = np.delete(bp, indko)
# check the linear drift
assert bp.size > 1
poly = np.polyfit(bp, re, 1)
assert np.all(np.abs(np.polyval(poly, bp) - re) < 0.002)
return interpolate.interp1d(re, bp, fill_value="extrapolate")
def align_with_bpod(session_path):
"""
Reads in trials.intervals ALF dataset from bpod and fpga.
Asserts consistency between datasets and compute the median time difference
:param session_path:
:return: dt: median time difference of trial start times (fpga - bpod)
"""
ITI_DURATION = 0.5
# check consistency
output_path = Path(session_path) / 'alf'
trials = alf.io.load_object(output_path, '_ibl_trials')
if alf.io.check_dimensions(trials) != 0:
# patching things up if the bpod and FPGA don't have the same recording span
_logger.warning(
"BPOD/FPGA synchronization: Bpod and FPGA don't have the same amount of"
" trial start events. Patching alf files.")
_, _, ibpod, ifpga = raw.sync_trials_robust(
trials['intervals_bpod'][:, 0],
trials['intervals'][:, 0],
return_index=True)
if ibpod.size == 0:
raise err.SyncBpodFpgaException(
'Can not sync BPOD and FPGA - no matching sync pulses '
'found.')
for k in trials:
if 'bpod' in k:
trials[k] = trials[k][ibpod]
else:
trials[k] = trials[k][ibpod]
alf.io.save_object_npy(output_path, trials, '_ibl_trials')
assert (alf.io.check_dimensions(trials) == 0)
tlen = (np.diff(trials['intervals_bpod']) -
np.diff(trials['intervals']))[:-1] - ITI_DURATION
assert (np.all(np.abs(tlen[np.invert(np.isnan(tlen))])[:-1] < 5 * 1e-3))
# dt is the delta to apply to bpod times in order to be on the ephys clock
dt = trials['intervals'][:, 0] - trials['intervals_bpod'][:, 0]
# compute the clock drift bpod versus dt
ppm = np.polyfit(trials['intervals'][:, 0], dt, 1)[0] * 1e6
if ppm > BPOD_FPGA_DRIFT_THRESHOLD_PPM:
_logger.warning(
'BPOD/FPGA synchronization shows values greater than 150 ppm')
# plt.plot(trials['intervals'][:, 0], dt, '*')
# so far 2 datasets concerned: goCueTrigger_times_bpod and response_times_bpod
for k in trials:
if not k.endswith('_times_bpod'):
continue
np.save(output_path.joinpath(f'_ibl_trials.{k[:-5]}.npy'),
trials[k] + dt)
return interpolate.interp1d(trials['intervals_bpod'][:, 0],
trials['intervals'][:, 0],
fill_value="extrapolate")
def bpod_fpga_sync(bpod_intervals=None,
ephys_intervals=None,
iti_duration=None):
"""
Computes synchronization function from bpod to fpga
:param bpod_intervals
:param ephys_intervals
:return: interpolation function
"""
if iti_duration is None:
iti_duration = 0.5
# check consistency
if bpod_intervals.size != ephys_intervals.size:
# patching things up if the bpod and FPGA don't have the same recording span
_logger.warning(
"BPOD/FPGA synchronization: Bpod and FPGA don't have the same amount of"
" trial start events. Patching alf files.")
_, _, ibpod, ifpga = raw_data_loaders.sync_trials_robust(
bpod_intervals[:, 0], ephys_intervals[:, 0], return_index=True)
if ibpod.size == 0:
raise err.SyncBpodFpgaException(
'Can not sync BPOD and FPGA - no matching sync pulses '
'found.')
bpod_intervals = bpod_intervals[ibpod, :]
ephys_intervals = ephys_intervals[ifpga, :]
else:
ibpod, ifpga = [
np.arange(bpod_intervals.shape[0]) for _ in np.arange(2)
]
tlen = (np.diff(bpod_intervals) -
np.diff(ephys_intervals))[:-1] - iti_duration
assert np.all(np.abs(tlen[np.invert(np.isnan(tlen))])[:-1] < 5 * 1e-3)
# dt is the delta to apply to bpod times in order to be on the ephys clock
dt = bpod_intervals[:, 0] - ephys_intervals[:, 0]
# compute the clock drift bpod versus dt
ppm = np.polyfit(bpod_intervals[:, 0], dt, 1)[0] * 1e6
if ppm > BPOD_FPGA_DRIFT_THRESHOLD_PPM:
_logger.warning(
'BPOD/FPGA synchronization shows values greater than %i ppm',
BPOD_FPGA_DRIFT_THRESHOLD_PPM)
# plt.plot(trials['intervals'][:, 0], dt, '*')
# so far 2 datasets concerned: goCueTrigger_times_bpod and response_times_bpod
fcn_bpod2fpga = interpolate.interp1d(bpod_intervals[:, 0],
ephys_intervals[:, 0],
fill_value="extrapolate")
return ibpod, ifpga, fcn_bpod2fpga