Only extract periods where laser is on

iblrig_custom_tasks/nate_optoBiasedChoiceWorld/task.py

@@ -110,7 +110,7 @@ def extra_parser():
             default=DEFAULTS['OPTO_STIM_STATES'],
             nargs='+',
             type=str,
-            help=f'list of the state machine states where opto stim should be delivered',
+            help='list of the state machine states where opto stim should be delivered',
         )
         return parser
 

projects/nate_optoBiasedChoiceWorld.py

@@ -52,12 +52,13 @@ def _extract(self, extractor_classes=None, **kwargs) -> dict:
             settings=settings, save=False, task_collection=self.task_collection)
 
         # Extract laser dataset
-        out['laser_intervals'] = np.full((len(self.bpod_trials), 2), np.nan)
-        for i, trial in enumerate(self.bpod_trials):
+        laser_intervals = []
+        for trial in filter(lambda t: t['opto_stimulation'], self.bpod_trials):
             states = trial['behavior_data']['States timestamps']
             # Assumes one of these states per trial: takes the timestamp of the first matching state
             start = next((v[0][0] for k, v in states.items() if k in settings['OPTO_TTL_STATES']), np.nan)
             stop = next((v[0][0] for k, v in states.items() if k in settings['OPTO_STOP_STATES']), np.nan)
-            out['laser_intervals'][i, :] = (start, stop)
+            laser_intervals.append((start, stop))
+        out['laser_intervals'] = np.array(laser_intervals, dtype=np.float64)
 
         return {k: out[k] for k in self.var_names}  # Ensures all datasets present and ordered

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name            = "project_extraction"
-version         = "0.2.0"
+version         = "0.2.1"
 description     = "Custom extractors for satellite tasks"
 dynamic         = [ "readme" ]
 keywords        = [ "IBL", "neuro-science" ]