Merge pull request #33 from int-brain-lab/princeton

Princeton

projects/alejandro_FPLROptoChoiceWorld.py

@@ -0,0 +1,40 @@
+"""Bpod extractor for alejandro's FPLOptoChoiceWorld and FPROptoChoiceWorld task.
+
+This is the same as biasedChoiceWorld with the addition of one dataset, `trials.laserStimulation`; The trials which the
+laser was on. For th FPLOptoChoiceWorld protocol the laser was on when the stimulus was on the left hand side and for
+the FPROptoChoiceWorld protocol the laser was on when the stimulus was on the right hand side of the screen
+
+"""
+import numpy as np
+from ibllib.io.extractors.base import BaseBpodTrialsExtractor, run_extractor_classes
+from ibllib.io.extractors.bpod_trials import BiasedTrials
+
+
+class TrialsFPLROpto(BaseBpodTrialsExtractor):
+    var_names = BiasedTrials.var_names + ('laser_stimulation', 'laser_intervals',)
+    save_names = BiasedTrials.save_names + ('_ibl_trials.laserStimulation.npy', '_ibl_trials.laserIntervals.npy',)
+
+    def _extract(self, extractor_classes=None, **kwargs) -> dict:
+
+        # Extract common biased choice world datasets
+        out, _ = run_extractor_classes(
+            [BiasedTrials], session_path=self.session_path, bpod_trials=self.bpod_trials,
+            settings=self.settings, save=False, task_collection=self.task_collection)
+
+        # Extract laser stimulation dataset
+        laser_stimulation = np.zeros_like(out['table']['contrastLeft'])
+        laser_intervals = np.full((out['table']['contrastLeft'].size, 2), np.nan)
+        if 'FPR' in self.settings['PYBPOD_PROTOCOL']:
+            idx = ~np.isnan(out['table']['contrastRight'])
+        elif 'FPL' in self.settings['PYBPOD_PROTOCOL']:
+            idx = ~np.isnan(out['table']['contrastLeft'])
+
+        laser_stimulation[idx] = 1
+        # Laser stimulated at goCue for 200 ms
+        laser_intervals[idx, 0] = out['table']['goCue_times'][idx]
+        laser_intervals[idx, 1] = out['table']['goCue_times'][idx] + 200 / 1e3
+
+        out['laser_stimulation'] = laser_stimulation
+        out['laser_intervals'] = laser_intervals
+
+        return {k: out[k] for k in self.var_names}  # Ensures all datasets present and ordered

projects/biased_fibrephotometry.py

@@ -511,8 +511,14 @@ def sync_timestamps(daq_data, fp_data, trials):
         mask = state.isin(CHANNELS['L470'])
         if ttl_interval == 40:
             frame_number = fp_data['FrameCounter'][mask]
+        elif ttl_interval == 20:
+            frame_number = fp_data['FrameCounter'][mask]
         elif ttl_interval == 10:
             frame_number = fp_data['FrameCounter']
+        elif ttl_interval == 25:
+            frame_number = fp_data['FrameCounter']
+        elif ttl_interval == 50:
+            frame_number = fp_data['FrameCounter'][mask]
 
         ttl_diff = np.diff(daq_data.loc[daq_data['photometry_ttl'] == 1].index)
         while ttl_diff.max() > ttl_interval * 3:
@@ -531,16 +537,23 @@ def sync_timestamps(daq_data, fp_data, trials):
         daq_data.loc[np.where(daq_data['bpod'].diff() == -1)[0], 'ttl_off'] = 1
         daq_data.loc[np.where(daq_data['bpod'].diff() == 1)[0], 'ttl_duration'] = \
             daq_data.loc[daq_data['ttl_off'] == 1].index - daq_data.loc[daq_data['ttl_on'] == 1].index
-        # Valve and error tones have pulses > 100
-        daq_data.loc[daq_data['ttl_duration'] > 100, 'feedback_times'] = 1
+        # Valve and error tones have pulses > 105
+        daq_data.loc[daq_data['ttl_duration'] > 105, 'feedback_times'] = 1
 
         n_daq_trials = (daq_data['feedback_times'] == 1).sum()
         if n_daq_trials == trials['feedback_times'].size:
             daq_data.loc[daq_data['feedback_times'] == 1, 'bpod_times'] = trials['feedback_times']
         elif n_daq_trials - trials['feedback_times'].size == 1:
             daq_data.loc[daq_data['feedback_times'] == 1, 'bpod_times'] = np.r_[trials['feedback_times'], np.nan]
         else:
-            assert n_daq_trials == trials['feedback_times'].size, "Trials don't match up"
+            trial_diff = n_daq_trials - trials['feedback_times'].size
+            nan_trials = np.where(trials.choice == 0)[0]
+            if len(nan_trials) == -1 * trial_diff:
+                feedback_times = np.delete(trials['feedback_times'], nan_trials)
+                assert len(feedback_times) == n_daq_trials, "Trials don't match up"
+                daq_data.loc[daq_data['feedback_times'] == 1, 'bpod_times'] = feedback_times
+            else:
+                assert n_daq_trials == trials['feedback_times'].size, "Trials don't match up"
 
         daq_data['bpod_times'].interpolate(inplace=True)
         # Set values after last pulse to nan

projects/task_extractor_map.json

@@ -1,5 +1,11 @@
 {
   "samuel_cuedBiasedChoiceWorld": "BiasedTrials",
   "_iblrig_tasks_neuromodulatorChoiceWorld": "projects.neuromodulators.TrialsTableNeuromodulator",
-  "nate_optoBiasedChoiceWorld": "projects.nate_optoBiasedChoiceWorld.TrialsOpto"
+  "nate_optoBiasedChoiceWorld": "projects.nate_optoBiasedChoiceWorld.TrialsOpto",
+  "FPChoiceWorld": "BiasedTrials",
+  "FPROptoChoiceWorld": "projects.alejandro_FPLROptoChoiceWorld.TrialsFPLROpto",
+  "FPLOptoChoiceWorld": "projects.alejandro_FPLROptoChoiceWorld.TrialsFPLROpto",
+  "_bandit_biasedChoiceWorld": "projects.training_bandit.TrialsBandit",
+  "_bandit_100_0_biasedChoiceWorld": "projects.training_bandit.TrialsBandit",
+  "_bandit_alllaser_cued_ephysChoiceWorld": "projects.training_bandit.TrialsLaserBandit"
 }