def test_frame_time_offset():
mock_data = {
"items": {
"behavior": {
"trial_log":
[
{
"events":
[
["trial_start", 2, 1],
["trial_end", 3, 2],
],
},
{
"events":
[
["trial_start", 4, 3],
["trial_start", 5, 4],
],
},
],
},
},
}
actual = frame_time_offset(mock_data)
assert 1. == actual
def get_rewards(self) -> pd.DataFrame:
"""Get reward data from pkl file, based on pkl file timestamps
(not sync file).
:returns: pd.DataFrame -- A dataframe containing timestamps of
delivered rewards.
"""
data = self._behavior_stimulus_file()
offset = frame_time_offset(data)
# No sync timestamps to rebase on, but do need to align to
# trial events, so add the offset as the "rebase" function
return get_rewards(data, lambda x: x + offset)
def get_licks(self):
behavior_stimulus_file = self.get_behavior_stimulus_file()
data = pd.read_pickle(behavior_stimulus_file)
rebase_function = self.get_stimulus_rebase_function()
# Get licks from pickle file (need to add an offset to align with
# the trial_log time stream)
lick_frames = (
data["items"]["behavior"]["lick_sensors"][0]["lick_events"])
vsyncs = data["items"]["behavior"]["intervalsms"]
vsync_times_raw = np.hstack(
(0, vsyncs)).cumsum() / 1000.0 # cumulative time
vsync_offset = frame_time_offset(data)
vsync_times = vsync_times_raw + vsync_offset
lick_times = [vsync_times[frame] for frame in lick_frames]
# Align pickle data with sync time stream
return pd.DataFrame({"time": list(map(rebase_function, lick_times))})
def get_stimulus_timestamps(self) -> np.ndarray:
"""Get stimulus timestamps (vsyncs) from pkl file. Align to the
(frame, time) points in the trial events.
NOTE: Located with behavior_session_id. Does not use the sync_file
which requires ophys_session_id.
Returns
-------
np.ndarray
Timestamps associated with stimulus presentations on the monitor
that do no account for monitor delay.
"""
data = self._behavior_stimulus_file()
vsyncs = data["items"]["behavior"]["intervalsms"]
cum_sum = np.hstack((0, vsyncs)).cumsum() / 1000.0 # cumulative time
offset = frame_time_offset(data)
return cum_sum + offset
def get_licks(self) -> pd.DataFrame:
"""Get lick data from pkl file.
This function assumes that the first sensor in the list of
lick_sensors is the desired lick sensor. If this changes we need
to update to get the proper line.
Since licks can occur outside of a trial context, the lick times
are extracted from the vsyncs and the frame number in `lick_events`.
Since we don't have a timestamp for when in "experiment time" the
vsync stream starts (from self.get_stimulus_timestamps), we compute
it by fitting a linear regression (frame number x time) for the
`start_trial` and `end_trial` events in the `trial_log`, to true
up these time streams.
:returns: pd.DataFrame -- A dataframe containing lick timestamps
"""
# Get licks from pickle file instead of sync
data = self._behavior_stimulus_file()
offset = frame_time_offset(data)
stimulus_timestamps = self.get_stimulus_timestamps() + offset
lick_frames = (data["items"]["behavior"]["lick_sensors"][0]
["lick_events"])
lick_times = [stimulus_timestamps[frame] for frame in lick_frames]
return pd.DataFrame({"time": lick_times})