Task qc fix

ibllib/io/extractors/ephys_fpga.py

@@ -658,15 +658,16 @@ def _update_var_names(self, bpod_fields=None, bpod_rsync_fields=None):
             self.settings = self.bpod_extractor.settings  # This is used by the TaskQC
             self.bpod_rsync_fields = bpod_rsync_fields
             if self.bpod_rsync_fields is None:
-                self.bpod_rsync_fields = tuple(self._time_fields(self.bpod_extractor.var_names))
+                self.bpod_rsync_fields = tuple([f for f in self._time_fields(self.bpod_extractor.var_names)
+                                                if 'wheel' not in f])
                 if 'table' in self.bpod_extractor.var_names:
                     if not self.bpod_trials:
                         self.bpod_trials = self.bpod_extractor.extract(save=False)
                     table_keys = alfio.AlfBunch.from_df(self.bpod_trials['table']).keys()
                     self.bpod_rsync_fields += tuple(self._time_fields(table_keys))
         elif bpod_rsync_fields:
             self.bpod_rsync_fields = bpod_rsync_fields
-        excluded = (*self.bpod_rsync_fields, 'table')
+        excluded = (*self.bpod_rsync_fields, 'table', *(f for f in self.bpod_extractor.var_names if 'wheel' in f))
         if bpod_fields:
             assert not set(self.bpod_fields).intersection(excluded), 'bpod_fields must not also be bpod_rsync_fields'
             self.bpod_fields = bpod_fields

ibllib/pipes/ephys_tasks.py

@@ -569,6 +569,8 @@ def _label_probe_qc(self, folder_probe, df_units, drift):
             qcdict['whitening_matrix_conditioning'] = np.linalg.cond(wm)
         # groom qc dict (this function will eventually go directly into the json field update)
         for k in qcdict:
+            if np.isnan(qcdict[k]):
+                qcdict[k] = None
             if isinstance(qcdict[k], np.int64):
                 qcdict[k] = int(qcdict[k])
             elif isinstance(qcdict[k], float):

ibllib/pipes/training_status.py

@@ -495,6 +495,8 @@ def get_data_collection(session_path):
         for i, (protocol, task_info) in enumerate(chain(*map(dict.items, task_protocols))):
             if 'passiveChoiceWorld' in protocol:
                 continue
+            elif 'ChoiceWorld' not in protocol:
+                continue
             collection = task_info.get('collection', f'raw_task_data_{i:02}')
             if collection == 'raw_passive_data':
                 continue

ibllib/qc/alignment_qc.py

@@ -383,7 +383,7 @@ def update_experimenter_evaluation(self, prev_alignments=None, override=False):
             self.alignments = prev_alignments
 
         outcomes = [align[2].split(':')[0] for key, align in self.alignments.items()
-                    if len(align) == 3]
+                    if len(align) >= 3]
         if len(outcomes) > 0:
             vals = list(map(QC.validate, outcomes))
             max_qc = np.argmax(vals)

ibllib/qc/task_qc_viewer/task_qc.py

@@ -78,16 +78,17 @@ def __init__(self, qc):
         map = {k: [] for k in set(outcomes.values())}
         for k, v in outcomes.items():
             map[v].append(k[6:])
+
         for k, v in map.items():
-            if k == 'PASS':
+            if k.name == 'PASS':
                 continue
-            print(f'The following checks were labelled {k}:')
+            print(f'The following checks were labelled {k.name}:')
             print('\n'.join(v), '\n')
 
         print('The following *critical* checks did not pass:')
         critical_checks = [f'_{x.replace("check", "task")}' for x in CRITICAL_CHECKS]
         for k, v in outcomes.items():
-            if v != 'PASS' and k in critical_checks:
+            if v.name != 'PASS' and k in critical_checks:
                 print(k[6:])
 
         # Make DataFrame from the trail level metrics
@@ -106,8 +107,8 @@ def n_trials(self):
         return self.qc.extractor.data['intervals'].shape[0]
 
     def get_wheel_data(self):
-        return {'re_pos': self.qc.extractor.data.get('wheel_position') or np.array([]),
-                're_ts': self.qc.extractor.data.get('wheel_timestamps') or np.array([])}
+        return {'re_pos': self.qc.extractor.data.get('wheel_position', np.array([])),
+                're_ts': self.qc.extractor.data.get('wheel_timestamps', np.array([]))}
 
     def create_plots(self, axes,
                      wheel_axes=None, trial_events=None, color_map=None, linestyle=None):
@@ -270,8 +271,10 @@ def show_session_task_qc(qc_or_session=None, bpod_only=False, local=False, one=N
         task.setUp()
         if local:  # currently setUp does not raise on missing data
             task.assert_expected_inputs(raise_error=True)
+        trials, _ = task.extract_behaviour(save=False)
+
         # Compute the QC and build the frame
-        task_qc = task.run_qc(update=False)
+        task_qc = task.run_qc(trials_data=trials, update=False)
         qc = QcFrame(task_qc)
 
     # Handle trial event names in habituationChoiceWorld