Get the name of the questionnaire from Snakefile.

rules/features.smk

@@ -327,7 +327,7 @@ rule conversation_r_features:
 rule preprocess_esm:
     input: "data/raw/{pid}/phone_esm_with_datetime.csv"
     params:
-        questionnaire_names = lambda wildcards: config["PHONE_ESM"]["PROVIDERS"][wildcards.feature]["FEATURES"]
+        questionnaire_name = "{feature}"
     output: "data/interim/{pid}/phone_esm_{feature}_clean.csv"
     script:
         "../src/features/phone_esm/straw/preprocess.py"

src/features/phone_esm/straw/preprocess.py

@@ -1,17 +1,16 @@
 from esm_preprocess import *
 
-questionnaire_names = snakemake.params["questionnaire_names"]
+questionnaire_name = snakemake.params["questionnaire_name"]
 
 df_esm = pd.read_csv(snakemake.input[0])
 df_esm_preprocessed = preprocess_esm(df_esm)
 
-for questionnaire_name in questionnaire_names:
-    try:
-        questionnaire_id = QUESTIONNAIRE_IDS[questionnaire_name]
-    except ValueError:
-        print("The requested questionnaire name should be one of the following:")
-        print(QUESTIONNAIRE_IDS.keys())
-    else:
-        df_esm_selected = df_esm_preprocessed[df_esm_preprocessed["questionnaire_id"] == questionnaire_id]
-        df_esm_clean = clean_up_esm(df_esm_selected)
-        df_esm_clean.to_csv(snakemake.output[0])
+try:
+    questionnaire_id = QUESTIONNAIRE_IDS[questionnaire_name]
+except ValueError:
+    print("The requested questionnaire name should be one of the following:")
+    print(QUESTIONNAIRE_IDS.keys())
+else:
+    df_esm_selected = df_esm_preprocessed[df_esm_preprocessed["questionnaire_id"] == questionnaire_id]
+    df_esm_clean = clean_up_esm(df_esm_selected)
+    df_esm_clean.to_csv(snakemake.output[0])