Fix formatting.

config/models.py

@@ -232,17 +232,19 @@ class ESM(Base, AWAREsensor):
     esm_session = Column(Integer, nullable=False)
     esm_notification_id = Column(Integer, nullable=False)
     esm_expiration_threshold = Column(SmallInteger)
-    ESM_TYPE = {'text': 1,
-                'radio': 2,
-                'checkbox': 3,
-                'likert': 4,
-                'quick_answers': 5,
-                'scale': 6,
-                'datetime': 7,
-                'pam': 8,
-                'number': 9,
-                'web': 10,
-                'date': 11}
+    ESM_TYPE = {
+        "text": 1,
+        "radio": 2,
+        "checkbox": 3,
+        "likert": 4,
+        "quick_answers": 5,
+        "scale": 6,
+        "datetime": 7,
+        "pam": 8,
+        "number": 9,
+        "web": 10,
+        "date": 11,
+    }
 
 
 class Imperfection(Base):

exploration/expl_esm_labels.py

@@ -49,8 +49,7 @@ df_esm_PANAS_clean = clean_up_esm(df_esm_PANAS)
 
 # %%
 df_esm_PANAS_daily_means = (
-    df_esm_PANAS_clean.groupby(
-    ["participant_id", "date_lj", "questionnaire_id"])
+    df_esm_PANAS_clean.groupby(["participant_id", "date_lj", "questionnaire_id"])
     .esm_user_answer_numeric.agg("mean")
     .reset_index()
     .rename(columns={"esm_user_answer_numeric": "esm_numeric_mean"})
@@ -81,7 +80,10 @@ sns.displot(
 
 # %%
 sns.displot(
-    data=df_esm_PANAS_summary_participant, x="median", hue="PANAS_subscale", binwidth=0.2
+    data=df_esm_PANAS_summary_participant,
+    x="median",
+    hue="PANAS_subscale",
+    binwidth=0.2,
 )
 
 # %%
@@ -91,3 +93,31 @@ sns.displot(
 
 # %%
 df_esm_PANAS_summary_participant[df_esm_PANAS_summary_participant["std"] < 0.1]
+
+# %% [markdown]
+# # Stress appraisal measure
+
+# %%
+df_esm_SAM = df_esm_preprocessed[
+    (df_esm_preprocessed["questionnaire_id"] >= 87)
+    & (df_esm_preprocessed["questionnaire_id"] <= 93)
+]
+
+# %%
+clean_up_esm(df_esm_SAM)[["esm_user_answer", "esm_user_answer_numeric"]].head(9)
+
+# %%
+df_esm_PANAS_clean[["esm_user_answer", "esm_user_answer_numeric"]].head(n=10)
+
+# %%
+df_esm_SAM[
+    [
+        "esm_instructions",
+        "question_id",
+        "questionnaire_id",
+        "esm_user_answer",
+        "esm_type",
+    ]
+].head(n=10)
+
+# %%

features/esm.py

@@ -250,10 +250,14 @@ def clean_up_esm(df_esm_preprocessed: pd.DataFrame) -> pd.DataFrame:
         )
     ]
     df_esm_clean["esm_user_answer_numeric"] = np.nan
-    esm_type_numeric = [ESM.ESM_TYPE.get("radio"),
+    esm_type_numeric = [
+        ESM.ESM_TYPE.get("radio"),
         ESM.ESM_TYPE.get("scale"),
-                        ESM.ESM_TYPE.get("number")]
-    df_esm_clean[df_esm_clean["esm_type"].isin(esm_type_numeric)] = df_esm_clean[df_esm_clean["esm_type"].isin(esm_type_numeric)].assign(
+        ESM.ESM_TYPE.get("number"),
+    ]
+    df_esm_clean[df_esm_clean["esm_type"].isin(esm_type_numeric)] = df_esm_clean[
+        df_esm_clean["esm_type"].isin(esm_type_numeric)
+    ].assign(
         esm_user_answer_numeric=lambda x: x.esm_user_answer.str.slice(stop=1).astype(
             int
         )

statistical_analysis/adherence.py

@@ -242,8 +242,17 @@ df_session_workday[df_session_workday.time_diff_minutes < 30]
 
 # %%
 df_esm_preprocessed.loc[
-    (df_esm_preprocessed.participant_id == 35) & (df_esm_preprocessed.esm_session == 7) & (df_esm_preprocessed.device_id == "62a44038-3ccb-401e-a69c-6f22152c54a6"),
-    ["esm_trigger", "esm_session", "datetime_lj", "esm_instructions", "device_id", "_id"],
+    (df_esm_preprocessed.participant_id == 35)
+    & (df_esm_preprocessed.esm_session == 7)
+    & (df_esm_preprocessed.device_id == "62a44038-3ccb-401e-a69c-6f22152c54a6"),
+    [
+        "esm_trigger",
+        "esm_session",
+        "datetime_lj",
+        "esm_instructions",
+        "device_id",
+        "_id",
+    ],
 ]
 
 # %%