def get_sync_data(sync_path):
sync_dataset = SyncDataset(sync_path)
meta_data = sync_dataset.meta_data
sample_freq = meta_data['ni_daq']['counter_output_freq']
# use rising edge for Scientifica, falling edge for Nikon http://confluence.corp.alleninstitute.org/display/IT/Ophys+Time+Sync
# 2P vsyncs
vs2p_r = sync_dataset.get_rising_edges('2p_vsync')
vs2p_f = sync_dataset.get_falling_edges(
'2p_vsync'
) # new sync may be able to do units = 'sec', so conversion can be skipped
frames_2p = vs2p_r / sample_freq
vs2p_fsec = vs2p_f / sample_freq
stimulus_times_no_monitor_delay = sync_dataset.get_falling_edges(
'stim_vsync') / sample_freq
if 'lick_times' in meta_data['line_labels']:
lick_times = sync_dataset.get_rising_edges('lick_1') / sample_freq
elif 'lick_sensor' in meta_data['line_labels']:
lick_times = sync_dataset.get_rising_edges('lick_sensor') / sample_freq
else:
lick_times = None
if '2p_trigger' in meta_data['line_labels']:
trigger = sync_dataset.get_rising_edges('2p_trigger') / sample_freq
elif 'acq_trigger' in meta_data['line_labels']:
trigger = sync_dataset.get_rising_edges('acq_trigger') / sample_freq
if 'stim_photodiode' in meta_data['line_labels']:
a = sync_dataset.get_rising_edges('stim_photodiode') / sample_freq
b = sync_dataset.get_falling_edges('stim_photodiode') / sample_freq
stim_photodiode = sorted(list(a) + list(b))
elif 'photodiode' in meta_data['line_labels']:
a = sync_dataset.get_rising_edges('photodiode') / sample_freq
b = sync_dataset.get_falling_edges('photodiode') / sample_freq
stim_photodiode = sorted(list(a) + list(b))
if 'cam1_exposure' in meta_data['line_labels']:
eye_tracking = sync_dataset.get_rising_edges(
'cam1_exposure') / sample_freq
elif 'eye_tracking' in meta_data['line_labels']:
eye_tracking = sync_dataset.get_rising_edges(
'eye_tracking') / sample_freq
if 'cam2_exposure' in meta_data['line_labels']:
behavior_monitoring = sync_dataset.get_rising_edges(
'cam2_exposure') / sample_freq
elif 'behavior_monitoring' in meta_data['line_labels']:
behavior_monitoring = sync_dataset.get_rising_edges(
'behavior_monitoring') / sample_freq
sync_data = {
'ophys_frames': frames_2p,
'lick_times': lick_times,
'ophys_trigger': trigger,
'eye_tracking': eye_tracking,
'behavior_monitoring': behavior_monitoring,
'stim_photodiode': stim_photodiode,
'stimulus_times_no_delay': stimulus_times_no_monitor_delay,
}
return sync_data
class OphysTimeAligner(object):
def __init__(self,
sync_file,
scanner=None,
dff_file=None,
stimulus_pkl=None,
eye_video=None,
behavior_video=None,
long_stim_threshold=LONG_STIM_THRESHOLD):
self.scanner = scanner if scanner is not None else "SCIVIVO"
self._dataset = Dataset(sync_file)
self._keys = get_keys(self._dataset)
self.long_stim_threshold = long_stim_threshold
self._monitor_delay = None
self._clipped_stim_ts_delta = None
self._clipped_stim_timestamp_values = None
if dff_file is not None:
self.ophys_data_length = get_ophys_data_length(dff_file)
else:
self.ophys_data_length = None
if stimulus_pkl is not None:
self.stim_data_length = get_stim_data_length(stimulus_pkl)
else:
self.stim_data_length = None
if eye_video is not None:
self.eye_data_length = get_video_length(eye_video)
else:
self.eye_data_length = None
if behavior_video is not None:
self.behavior_data_length = get_video_length(behavior_video)
else:
self.behavior_data_length = None
@property
def dataset(self):
return self._dataset
@property
def ophys_timestamps(self):
"""Get the timestamps for the ophys data."""
ophys_key = self._keys["2p"]
if self.scanner == "SCIVIVO":
# Scientifica data looks different than Nikon.
# http://confluence.corp.alleninstitute.org/display/IT/Ophys+Time+Sync
times = self.dataset.get_rising_edges(ophys_key, units="seconds")
elif self.scanner == "NIKONA1RMP":
# Nikon has a signal that indicates when it started writing to disk
acquiring_key = self._keys["acquiring"]
acquisition_start = self._dataset.get_rising_edges(
acquiring_key, units="seconds")[0]
ophys_times = self._dataset.get_falling_edges(ophys_key,
units="seconds")
times = ophys_times[ophys_times >= acquisition_start]
else:
raise ValueError("Invalid scanner: {}".format(self.scanner))
return times
@property
def corrected_ophys_timestamps(self):
times = self.ophys_timestamps
delta = 0
if self.ophys_data_length is not None:
if len(times) < self.ophys_data_length:
raise ValueError(
"Got too few timestamps ({}) for ophys data length "
"({})".format(len(times), self.ophys_data_length))
elif len(times) > self.ophys_data_length:
logging.info(
"Ophys data of length %s has timestamps of "
"length %s, truncating timestamps", self.ophys_data_length,
len(times))
delta = len(times) - self.ophys_data_length
times = times[:-delta]
else:
logging.info("No data length provided for ophys stream")
return times, delta
@property
def stim_timestamps(self):
stim_key = self._keys["stimulus"]
return self.dataset.get_falling_edges(stim_key, units="seconds")
def _get_clipped_stim_timestamps(self):
timestamps = self.stim_timestamps
delta = 0
if self.stim_data_length is not None and \
self.stim_data_length < len(timestamps):
stim_key = self._keys["stimulus"]
rising = self.dataset.get_rising_edges(stim_key, units="seconds")
# Some versions of camstim caused a spike when the DAQ is first
# initialized. Remove it.
if rising[1] - rising[0] > self.long_stim_threshold:
logging.info("Initial DAQ spike detected from stimulus, "
"removing it")
timestamps = timestamps[1:]
delta = len(timestamps) - self.stim_data_length
if delta != 0:
logging.info(
"Stim data of length %s has timestamps of "
"length %s", self.stim_data_length, len(timestamps))
elif self.stim_data_length is None:
logging.info("No data length provided for stim stream")
return timestamps, delta
@property
def clipped_stim_timestamps(self):
"""
Return the stimulus timestamps with the erroneous initial spike
removed (if relevant)
Returns
-------
timestamps: np.ndarray
An array of stimulus timestamps in seconds with th emonitor delay
added
delta: int
Difference between the length of timestamps
and the number of frames reported in the stimulus
pickle file, i.e.
len(timestamps) - len(pkl_file['items']['behavior']['intervalsms']
"""
if self._clipped_stim_ts_delta is None:
(self._clipped_stim_timestamp_values, self._clipped_stim_ts_delta
) = self._get_clipped_stim_timestamps()
return (self._clipped_stim_timestamp_values,
self._clipped_stim_ts_delta)
def _get_monitor_delay(self):
timestamps, delta = self.clipped_stim_timestamps
photodiode_key = self._keys["photodiode"]
delay = calculate_monitor_delay(self.dataset, timestamps,
photodiode_key)
return delay
@property
def monitor_delay(self):
"""
The monitor delay (in seconds) associated with the session
"""
if self._monitor_delay is None:
self._monitor_delay = self._get_monitor_delay()
return self._monitor_delay
@property
def corrected_stim_timestamps(self):
"""
The stimulus timestamps corrected for monitor delay
Returns
-------
timestamps: np.ndarray
An array of stimulus timestamps in seconds with th emonitor delay
added
delta: int
Difference between the length of timestamps and
the number of frames reported in the stimulus
pickle file, i.e.
len(timestamps) - len(pkl_file['items']['behavior']['intervalsms']
delay: float
The monitor delay in seconds
"""
timestamps, delta = self.clipped_stim_timestamps
delay = self.monitor_delay
return timestamps + delay, delta, delay
@property
def behavior_video_timestamps(self):
key = self._keys["behavior_camera"]
return self.dataset.get_falling_edges(key, units="seconds")
@property
def corrected_behavior_video_timestamps(self):
return corrected_video_timestamps("Behavior video",
self.behavior_video_timestamps,
self.behavior_data_length)
@property
def eye_video_timestamps(self):
key = self._keys["eye_camera"]
return self.dataset.get_falling_edges(key, units="seconds")
@property
def corrected_eye_video_timestamps(self):
return corrected_video_timestamps("Eye video",
self.eye_video_timestamps,
self.eye_data_length)
class OphysTimeAligner(object):
def __init__(self,
sync_file,
scanner=None,
dff_file=None,
stimulus_pkl=None,
eye_video=None,
behavior_video=None,
long_stim_threshold=LONG_STIM_THRESHOLD):
self.scanner = scanner if scanner is not None else "SCIVIVO"
self._dataset = Dataset(sync_file)
self._keys = get_keys(self._dataset)
self.long_stim_threshold = long_stim_threshold
if dff_file is not None:
self.ophys_data_length = get_ophys_data_length(dff_file)
else:
self.ophys_data_length = None
if stimulus_pkl is not None:
self.stim_data_length = get_stim_data_length(stimulus_pkl)
else:
self.stim_data_length = None
if eye_video is not None:
self.eye_data_length = get_video_length(eye_video)
else:
self.eye_data_length = None
if behavior_video is not None:
self.behavior_data_length = get_video_length(behavior_video)
else:
self.behavior_data_length = None
@property
def dataset(self):
return self._dataset
@property
def ophys_timestamps(self):
"""Get the timestamps for the ophys data."""
ophys_key = self._keys["2p"]
if self.scanner == "SCIVIVO":
# Scientifica data looks different than Nikon.
# http://confluence.corp.alleninstitute.org/display/IT/Ophys+Time+Sync
times = self.dataset.get_rising_edges(ophys_key, units="seconds")
elif self.scanner == "NIKONA1RMP":
# Nikon has a signal that indicates when it started writing to disk
acquiring_key = self._keys["acquiring"]
acquisition_start = self._dataset.get_rising_edges(
acquiring_key, units="seconds")[0]
ophys_times = self._dataset.get_falling_edges(ophys_key,
units="seconds")
times = ophys_times[ophys_times >= acquisition_start]
else:
raise ValueError("Invalid scanner: {}".format(self.scanner))
return times
@property
def corrected_ophys_timestamps(self):
times = self.ophys_timestamps
delta = 0
if self.ophys_data_length is not None:
if len(times) < self.ophys_data_length:
raise ValueError(
"Got too few timestamps ({}) for ophys data length "
"({})".format(len(times), self.ophys_data_length))
elif len(times) > self.ophys_data_length:
logging.info(
"Ophys data of length %s has timestamps of "
"length %s, truncating timestamps", self.ophys_data_length,
len(times))
delta = len(times) - self.ophys_data_length
times = times[:-delta]
else:
logging.info("No data length provided for ophys stream")
return times, delta
@property
def stim_timestamps(self):
stim_key = self._keys["stimulus"]
return self.dataset.get_falling_edges(stim_key, units="seconds")
@property
def corrected_stim_timestamps(self):
timestamps = self.stim_timestamps
delta = 0
if self.stim_data_length is not None and \
self.stim_data_length < len(timestamps):
stim_key = self._keys["stimulus"]
rising = self.dataset.get_rising_edges(stim_key, units="seconds")
# Some versions of camstim caused a spike when the DAQ is first
# initialized. Remove it.
if rising[1] - rising[0] > self.long_stim_threshold:
logging.info("Initial DAQ spike detected from stimulus, "
"removing it")
timestamps = timestamps[1:]
delta = len(timestamps) - self.stim_data_length
if delta != 0:
logging.info(
"Stim data of length %s has timestamps of "
"length %s", self.stim_data_length, len(timestamps))
elif self.stim_data_length is None:
logging.info("No data length provided for stim stream")
photodiode_key = self._keys["photodiode"]
delay = monitor_delay(self.dataset, timestamps, photodiode_key)
return timestamps + delay, delta, delay
@property
def behavior_video_timestamps(self):
key = self._keys["behavior_camera"]
return self.dataset.get_falling_edges(key, units="seconds")
@property
def corrected_behavior_video_timestamps(self):
return corrected_video_timestamps("Behavior video",
self.behavior_video_timestamps,
self.behavior_data_length)
@property
def eye_video_timestamps(self):
key = self._keys["eye_camera"]
return self.dataset.get_falling_edges(key, units="seconds")
@property
def corrected_eye_video_timestamps(self):
return corrected_video_timestamps("Eye video",
self.eye_video_timestamps,
self.eye_data_length)