Increment answers separately if needed.

exploration/expl_esm_labels.py

@@ -20,7 +20,13 @@ import datetime
 import seaborn as sns
 
 import participants.query_db
-from features.esm import QUESTIONNAIRE_IDS, clean_up_esm, get_esm_data, preprocess_esm
+from features.esm import (
+    QUESTIONNAIRE_IDS,
+    clean_up_esm,
+    get_esm_data,
+    increment_answers,
+    preprocess_esm,
+)
 from features.esm_COPE import reassign_question_ids
 from features.esm_JCQ import reverse_jcq_demand_control_scoring
 from features.esm_SAM import extract_stressful_events
@@ -308,6 +314,7 @@ if export_data:
             "esm_user_answer_numeric",
         ]
     ]
+    df_esm_SAM_for_export = increment_answers(df_esm_SAM_for_export)
     df_esm_SAM_for_export.sort_values(
         by=["participant_id", "device_id", "_id"], ignore_index=True, inplace=True
     )
@@ -413,6 +420,7 @@ df_esm_COPE = df_esm_preprocessed[
 
 # %%
 df_esm_COPE_clean = clean_up_esm(df_esm_COPE)
+df_esm_COPE_clean = increment_answers(df_esm_COPE_clean)
 df_esm_COPE_fixed = reassign_question_ids(df_esm_COPE_clean)
 
 # %%

features/esm_COPE.py

@@ -1,7 +1,5 @@
 import pandas as pd
 
-from features.esm import increment_answers
-
 COPE_ORIGINAL_MAX = 4
 COPE_ORIGINAL_MIN = 1
 
@@ -188,7 +186,4 @@ def reassign_question_ids(df_cope_cleaned: pd.DataFrame) -> pd.DataFrame:
         )
     )
 
-    # Finally, increment numeric answers.
-    df_cope_fixed = increment_answers(df_cope_fixed)
-
     return df_cope_fixed

features/esm_SAM.py

@@ -2,7 +2,6 @@ import numpy as np
 import pandas as pd
 
 import features.esm
-from features.esm import increment_answers
 
 SAM_ORIGINAL_MAX = 5
 SAM_ORIGINAL_MIN = 1
@@ -506,7 +505,4 @@ def reassign_question_ids(df_sam_cleaned: pd.DataFrame) -> pd.DataFrame:
         )
     )
 
-    # Finally, increment numeric answers.
-    df_sam_fixed = increment_answers(df_sam_fixed)
-
     return df_sam_fixed