animal_response no longer replaced by choice

Author: rachelstephlee

src/rachel_analysis_utils/analysis_utils.py

@@ -140,9 +140,10 @@ def enrich_df_trials(df_trials):
 
 
     ##### PART IV: GETTING STAY/LEAVE #######
-    _choice_shifted = df_trials.groupby('ses_idx')['choice'].shift(1)
-    df_trials['stay'] = df_trials['choice'] == _choice_shifted
-    df_trials['switch'] = df_trials['choice'] != _choice_shifted
+    
+    _choice_shifted = df_trials.groupby('ses_idx')['animal_response'].shift(1)
+    df_trials['stay'] = df_trials['animal_response'] == _choice_shifted
+    df_trials['switch'] = df_trials['animal_response'] != _choice_shifted
     df_trials['response_time'] = df_trials['choice_time_in_trial'] -  df_trials['goCue_start_time_in_trial']
 
 

src/rachel_analysis_utils/enrich_df_sess.py

@@ -105,25 +105,25 @@ def get_mean_side_bias(nwb):
 
 def get_baited_rate(nwb):
   df_trials = nwb.df_trials
-  mask = ((df_trials['bait_left'] == True) & (df_trials['choice'] == 0.0)) | ((df_trials['bait_right'] == True) & (df_trials['choice'] == 1.0))
+  mask = ((df_trials['bait_left'] == True) & (df_trials['animal_response'] == 0.0)) | ((df_trials['bait_right'] == True) & (df_trials['animal_response'] == 1.0))
   return float(mask.sum()) / float(len(df_trials))
 
 def get_left_choice_rate(nwb):
   df_trials = nwb.df_trials
-  left_count = (df_trials['choice'] == 0.0).sum()
+  left_count = (df_trials['animal_response'] == 0.0).sum()
   return float(left_count) / float(len(df_trials))
 
 def get_ignore_choice_rate(nwb):
   df_trials = nwb.df_trials
-  ignore_count = (df_trials['choice'] == 2.0).sum()
+  ignore_count = (df_trials['animal_response'] == 2.0).sum()
   return float(ignore_count) / float(len(df_trials))
 
 def get_left_right_diff(nwb):
     df = getattr(nwb, 'df_trials', None)
     if df is None or len(df) == 0:
         return float('nan')
-    left = (df['choice'] == 0.0).sum()
-    right = (df['choice'] == 1.0).sum()
+    left = (df['animal_response'] == 0.0).sum()
+    right = (df['animal_response'] == 1.0).sum()
     return float(left - right) / float(len(df))   # signed difference normalized by total trials
 
 def get_left_right_abs_diff(nwb):

src/rachel_analysis_utils/nwb_utils.py

@@ -33,8 +33,8 @@ def subsample_lr_thirds(nwb, per_seg_min = 50):
 
     positions = df_trials['trial'].values
 
-    left_mask = df_trials['choice'].to_numpy() == 0
-    right_mask = df_trials['choice'].to_numpy() == 1
+    left_mask = df_trials['animal_response'].to_numpy() == 0
+    right_mask = df_trials['animal_response'].to_numpy() == 1
 
     left_pos = positions[left_mask]
     right_pos = positions[right_mask]
@@ -62,9 +62,9 @@ def subsample_lr_thirds(nwb, per_seg_min = 50):
 
 def split_nwb_by_choice(nwb):
     nwb_split = copy.deepcopy(nwb)
-    nwb_split.df_trials_left = nwb.df_trials.query('choice == 0.0')
-    nwb_split.df_trials_right = nwb.df_trials.query('choice == 1.0')
-    nwb_split.df_trials_ignore = nwb.df_trials.query('choice == 2.0')
+    nwb_split.df_trials_left = nwb.df_trials.query('animal_response == 0.0')
+    nwb_split.df_trials_right = nwb.df_trials.query('animal_response == 1.0')
+    nwb_split.df_trials_ignore = nwb.df_trials.query('animal_response == 2.0')
     return nwb_split
 
 def split_nwb_by_time(nwb):
@@ -255,7 +255,7 @@ def load_nwb_list(plot_loc, add_fip = False):
 
             print(f"loading {session_folder.name}")
 
-            nwb = dummy_nwb.load(session_folder)
+            nwb = dummy_nwb.load(session_folder, load_fip = add_fip)
             nwbs.append(nwb)
 
     return nwbs, df_sess, df_slope