diff --git a/features/esm_SAM.py b/features/esm_SAM.py
index dd81404..3c52501 100644
--- a/features/esm_SAM.py
+++ b/features/esm_SAM.py
@@ -20,7 +20,8 @@ GROUP_QUESTIONNAIRES_BY = [
     "device_id",
     "esm_session",
 ]
-# Each questionnaire occurs only once within each esm_session on the same device within the same participant.
+# Each questionnaire occurs only once within each esm_session on the same device
+# within the same participant.
 
 
 def extract_stressful_events(df_esm: pd.DataFrame) -> pd.DataFrame:
@@ -78,7 +79,8 @@ def extract_stressful_events(df_esm: pd.DataFrame) -> pd.DataFrame:
 
 def calculate_threat_challenge_means(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
     """
-    This function calculates challenge and threat (two Stress Appraisal Measure subscales) means,
+    This function calculates challenge and threat
+        (two Stress Appraisal Measure subscales) means,
         for each ESM session (within participants and devices).
     It creates a grouped dataframe with means in two columns.
 
@@ -90,7 +92,8 @@ def calculate_threat_challenge_means(df_esm_sam_clean: pd.DataFrame) -> pd.DataF
     Returns
     -------
     df_esm_event_threat_challenge_mean_wide: pd.DataFrame
-        A dataframe of unique ESM sessions (by participants and devices) with threat and challenge means.
+        A dataframe of unique ESM sessions (by participants and devices)
+        with threat and challenge means.
     """
     # Select only threat and challenge assessments for events
     df_esm_event_threat_challenge = df_esm_sam_clean[
@@ -112,8 +115,8 @@ def calculate_threat_challenge_means(df_esm_sam_clean: pd.DataFrame) -> pd.DataF
         aggfunc="mean",
     )
     # Drop unnecessary column values.
-    df_esm_event_threat_challenge_mean_wide.columns = df_esm_event_threat_challenge_mean_wide.columns.get_level_values(
-        1
+    df_esm_event_threat_challenge_mean_wide.columns = (
+        df_esm_event_threat_challenge_mean_wide.columns.get_level_values(1)
     )
     df_esm_event_threat_challenge_mean_wide.columns.name = None
     df_esm_event_threat_challenge_mean_wide.rename(
@@ -189,10 +192,12 @@ def detect_event_work_related(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
 
 def convert_event_time(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
     """
-    This function only serves to convert the string datetime answer into a real datetime type.
-    Errors during this conversion are coerced, meaning that non-datetime answers are assigned Not a Time (NaT).
-    NOTE: Since the only available non-datetime answer to this question was "0 - I do not remember",
-        the NaTs can be interpreted to mean this.
+    This function only serves to convert the string datetime answer
+        into a real datetime type.
+    Errors during this conversion are coerced, meaning that non-datetime answers
+        are assigned Not a Time (NaT).
+    NOTE: Since the only available non-datetime answer to this question was
+        "0 - I do not remember", the NaTs can be interpreted to mean this.
 
     Parameters
     ----------
@@ -208,9 +213,10 @@ def convert_event_time(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
         df_esm_sam_clean["questionnaire_id"] == QUESTIONNAIRE_ID_SAM.get("event_time")
     ].assign(
         event_time=lambda x: pd.to_datetime(
-            x.esm_user_answer, errors="coerce", infer_datetime_format=True, exact=True
+            x.esm_user_answer, errors="coerce", format="Y-m-d H:M:S %z", exact=True
         )
     )
+    # Example answer: 2020-09-29 00:05:00 +0200
     return df_esm_event_time
 
 
@@ -241,9 +247,12 @@ def extract_event_duration(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
         == QUESTIONNAIRE_ID_SAM.get("event_duration")
     ].assign(
         event_duration=lambda x: pd.to_datetime(
-            x.esm_user_answer.str.slice(start=0, stop=-6), errors="coerce"
+            x.esm_user_answer.str.slice(start=0, stop=-6),
+            errors="coerce",
+            format="Y-m-d H:M:S",
         ).dt.time
     )
+    # Example answer: 2020-09-29 00:05:00 +0200
     # TODO Explore the values recorded in event_duration and possibly fix mistakes.
     # For example, participants reported setting 23:50:00 instead of 00:50:00.
 
@@ -251,7 +260,7 @@ def extract_event_duration(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
     # we can determine whether:
     #   - this event is still going on ("1 - It is still going on")
     #   - the participant couldn't remember it's duration ("0 - I do not remember")
-    # Generally, these answers were converted to esm_user_answer_numeric in clean_up_esm,
+    # Generally, these answers were converted to esm_user_answer_numeric in clean_up_esm
     # but only the numeric types of questions and answers.
     # Since this was of "datetime" type, convert these specific answers here again.
     df_esm_event_duration["event_duration_info"] = np.nan
@@ -264,4 +273,5 @@ def extract_event_duration(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
     return df_esm_event_duration
 
 
-# TODO: How many questions about the stressfulness of the period were asked and how does this relate to events?
+# TODO: How many questions about the stressfulness of the period were asked
+#  and how does this relate to events?