Add necessary checks for empty data frames.

config.yaml

@@ -240,6 +240,7 @@ PHONE_ESM:
     STRAW:
       COMPUTE: True
       SCALES: ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support"]
+      FEATURES: [mean]
       SRC_SCRIPT: src/features/phone_esm/straw/main.py
 
 # See https://www.rapids.science/latest/features/phone-keyboard/

rules/features.smk

@@ -343,7 +343,7 @@ rule esm_features:
         scales=lambda wildcards: config["PHONE_ESM"]["PROVIDERS"][wildcards.provider_key.upper()]["SCALES"]
     output: "data/interim/{pid}/phone_esm_features/phone_esm_clean_{provider_key}.csv"
     script:
-        "../src/features/entry.R"
+        "../src/features/entry.py"
 
 rule phone_keyboard_python_features:
     input:

src/features/phone_esm/straw/main.py

@@ -9,9 +9,14 @@ def straw_features(sensor_data_files, time_segment, provider, filter_data_by_seg
     # the subset of requested features this function can compute
     features_to_compute = list(set(requested_features) & set(base_features_names))
     esm_features = pd.DataFrame(columns=["local_segment"] + features_to_compute)
+    if not esm_data.empty:
+        esm_data = filter_data_by_segment(esm_data, time_segment)
 
-    esm_data = filter_data_by_segment(esm_data, time_segment)
-    esm_features["mean"] = esm_data.groupby(["local_segment"])["esm_user_score"].mean()
-    #TODO Create the column esm_user_score in esm_clean. Currently, this is only done when reversing.
-    esm_features = esm_features.reset_index()
+        if not esm_data.empty:
+            esm_features = pd.DataFrame()
+
+            esm_features["mean"] = esm_data.groupby(["local_segment"])["esm_user_score"].mean()
+            #TODO Create the column esm_user_score in esm_clean. Currently, this is only done when reversing.
+
+            esm_features = esm_features.reset_index()
     return esm_features