BUG: wrong columns for reponse angles

spatialtopology · Aug 25, 2024 · 3d7fafb · 3d7fafb
1 parent 19037cb
commit 3d7fafb
Showing 1 changed file with 9 additions and 41 deletions.
diff --git a/spacetop_prep/events/bidsify_social_ENH.py b/spacetop_prep/events/bidsify_social_ENH.py
@@ -490,12 +490,8 @@ def parse_args():
     expect['trial_type'] = 'expectrating'
     expect['trial_index'] =  beh_df.index +1
     expect['rating_value'] =  beh_df['event02_expect_angle'].round(2)
-    expect['rating_glmslabel'] = expect['rating_value'].apply(categorize_rating)
-    # expect['rating_glmslabel'] = pd.cut(expect['rating_value'], 
-    #                                     bins=bins, labels=labels, right=True)
     expect['rating_value_fillna'] = (beh_df['event02_expect_fillna']).round(2)
-    # expect['rating_glmslabel_fillna'] = pd.cut(expect['rating_value_fillna'], 
-                                            #    bins=bins, labels=labels, right=True)
+    expect['rating_glmslabel'] = expect['rating_value'].apply(categorize_rating)
     expect['rating_glmslabel_fillna'] = expect['rating_value_fillna'].apply(categorize_rating)
     expect['rating_mouseonset'] = (traj_df['expect_motiononset']).round(2)
     expect['rating_mousedur'] = (traj_df['expect_motiondur']).round(2)
@@ -547,13 +543,9 @@ def parse_args():
     outcome['trial_type'] = 'outcomerating'
     outcome['trial_index'] =  beh_df.index +1
     outcome['rating_value'] =  (beh_df['event04_actual_angle']).round(2)
-    # outcome['rating_glmslabel'] = pd.cut(outcome['rating_value'], 
-    #                                      bins=bins, labels=labels, right=True)
-    outcome['rating_glmslabel'] = outcome['rating_value'].apply(categorize_rating)
     outcome['rating_value_fillna'] = beh_df['event04_outcome_fillna']
     outcome['rating_glmslabel'] = outcome['rating_value'].apply(categorize_rating)
-    # outcome['rating_glmslabel_fillna'] = pd.cut(outcome['rating_value_fillna'], 
-    #                                             bins=bins, labels=labels, right=True)
+    outcome['rating_glmslabel_fillna'] = outcome['rating_value_fillna'].apply(categorize_rating)
     outcome['rating_mouseonset'] = (traj_df['outcome_motiononset']).round(2)
     outcome['rating_mousedur'] = (traj_df['outcome_motiondur']).round(2)
     outcome['cue'] = beh_df['event01_cue_type'] 
@@ -785,17 +777,10 @@ def parse_args():
     expect['run_type'] = task_name
     expect['trial_type'] = 'expectrating'
     expect['trial_index'] =  beh_df.index +1
-
     expect['rating_value'] =  beh_df['event02_expect_angle'].round(2)
-    # expect['rating_glmslabel'] = pd.cut(expect['rating_value'], 
-    #                                     bins=bins, labels=labels, right=True)
-
     expect['rating_value_fillna'] = (beh_df['event02_expect_fillna']).round(2)
-    # expect['rating_glmslabel_fillna'] = pd.cut(expect['rating_value_fillna'], 
-    #                                            bins=bins, labels=labels, right=True)
-    expect['rating_glmslabel'] = expect['rating_value'].apply(categorize_rating)
-    # outcome['rating_value_fillna'] = beh_df['event04_outcome_fillna']
     expect['rating_glmslabel'] = expect['rating_value'].apply(categorize_rating)
+    expect['rating_glmslabel_fillna'] = expect['rating_value_fillna'].apply(categorize_rating)
     expect['rating_mouseonset'] = (traj_df['expect_motiononset']).round(2)
     expect['rating_mousedur'] = (traj_df['expect_motiondur']).round(2)
     expect['cue'] = beh_df['event01_cue_type'] # if same as param_cue_type
@@ -879,16 +864,10 @@ def parse_args():
     outcome['run_type'] = task_name
     outcome['trial_type'] = 'outcomerating'
     outcome['trial_index'] =  beh_df.index +1
-    outcome['rating_value'] =  (beh_df['event04_actual_angle']).round(2)
-    # outcome['rating_glmslabel'] = pd.cut(outcome['rating_value'], 
-    #                                      bins=bins, labels=labels, right=True)
-    outcome['rating_value_fillna'] = beh_df['event04_outcome_fillna']
-    # outcome['rating_glmslabel_fillna'] = pd.cut(outcome['rating_value_fillna'], 
-    #                                             bins=bins, labels=labels, right=True)
-
-    outcome['rating_glmslabel'] = outcome['rating_value'].apply(categorize_rating)
-    # outcome['rating_value_fillna'] = beh_df['event04_outcome_fillna']
+    outcome['rating_value'] =  beh_df['event04_actual_angle'].round(2)
+    outcome['rating_value_fillna'] = (beh_df['event04_outcome_fillna']).round(2)
     outcome['rating_glmslabel'] = outcome['rating_value'].apply(categorize_rating)
+    outcome['rating_glmslabel_fillna'] = outcome['rating_value_fillna'].apply(categorize_rating)
     outcome['rating_mouseonset'] = (traj_df['outcome_motiononset']).round(2)
     outcome['rating_mousedur'] = (traj_df['outcome_motiondur']).round(2)
 
@@ -1107,15 +1086,10 @@ def parse_args():
     expect['trial_index'] =  beh_df.index +1
 
     expect['rating_value'] =  beh_df['event02_expect_angle'].round(2)
-    # expect['rating_glmslabel'] = pd.cut(expect['rating_value'], 
-    #                                     bins=bins, labels=labels, right=True)
     expect['rating_value_fillna'] = (beh_df['event02_expect_fillna']).round(2)
-    # expect['rating_glmslabel_fillna'] = pd.cut(expect['rating_value_fillna'], 
-    #                                            bins=bins, labels=labels, right=True)
-
-    expect['rating_glmslabel'] = expect['rating_value'].apply(categorize_rating)
-    # outcome['rating_value_fillna'] = beh_df['event04_outcome_fillna']
     expect['rating_glmslabel'] = expect['rating_value'].apply(categorize_rating)
+    expect['rating_glmslabel_fillna'] = expect['rating_value_fillna'].apply(categorize_rating)
+
     expect['rating_mouseonset'] = (traj_df['expect_motiononset']).round(2)
     expect['rating_mousedur'] = (traj_df['expect_motiondur']).round(2)
     expect['cue'] = beh_df['event01_cue_type'] # if same as param_cue_type
@@ -1163,15 +1137,9 @@ def parse_args():
     outcome['trial_type'] = 'outcomerating'
     outcome['trial_index'] =  beh_df.index +1
     outcome['rating_value'] =  (beh_df['event04_actual_angle']).round(2)
-    # outcome['rating_glmslabel'] = pd.cut(outcome['rating_value'], 
-    #                                      bins=bins, labels=labels, right=True)
     outcome['rating_value_fillna'] = beh_df['event04_outcome_fillna']
-    # outcome['rating_glmslabel_fillna'] = pd.cut(outcome['rating_value_fillna'], 
-    #                                             bins=bins, labels=labels, right=True)
-
-    outcome['rating_glmslabel'] = outcome['rating_value'].apply(categorize_rating)
-    # outcome['rating_value_fillna'] = beh_df['event04_outcome_fillna']
     outcome['rating_glmslabel'] = outcome['rating_value'].apply(categorize_rating)
+    outcome['rating_glmslabel_fillna'] = outcome['rating_value_fillna'].apply(categorize_rating)
     outcome['rating_mouseonset'] = (traj_df['outcome_motiononset']).round(2)
     outcome['rating_mousedur'] = (traj_df['outcome_motiondur']).round(2)
     outcome['cue'] = beh_df['event01_cue_type']