diff --git a/src/features/all_cleaning_overall/straw/main.py b/src/features/all_cleaning_overall/straw/main.py
index b0fc74a2..5fba061e 100644
--- a/src/features/all_cleaning_overall/straw/main.py
+++ b/src/features/all_cleaning_overall/straw/main.py
@@ -96,18 +96,29 @@ def straw_cleaning(sensor_data_files, provider, target):
     impute_w_sn3 = [col for col in features.columns if "loglocationvariance" in col]
     features[impute_w_sn2] = features[impute_w_sn2].fillna(-1000000) # Special case of imputation - loglocation
 
-    # Impute selected phone features with 0 + impute ESM features with 0
+    # Impute location features
+    impute_locations = [col for col in features \
+        if col.startswith('phone_locations_doryab_') and
+        'radiusgyration' not in col    
+    ]
+
+    # Impute selected phone, location, and esm features with 0
     impute_zero = [col for col in features if \
         col.startswith('phone_applications_foreground_rapids_') or
+        col.startswith('phone_activity_recognition_') or
         col.startswith('phone_battery_rapids_') or
         col.startswith('phone_bluetooth_rapids_') or
         col.startswith('phone_light_rapids_') or
         col.startswith('phone_calls_rapids_') or
         col.startswith('phone_messages_rapids_') or
         col.startswith('phone_screen_rapids_') or
-        col.startswith('phone_wifi_visible')]
-        
-    features[impute_zero+list(esm_cols.columns)] = features[impute_zero+list(esm_cols.columns)].fillna(0)
+        col.startswith('phone_bluetooth_doryab_') or
+        col.startswith('phone_wifi_visible')
+        ]
+
+    features[impute_zero+impute_locations+list(esm_cols.columns)] = features[impute_zero+impute_locations+list(esm_cols.columns)].fillna(0)
+
+    pd.set_option('display.max_rows', None)
 
     graph_bf_af(features, "4context_imp")
  
@@ -138,15 +149,14 @@ def straw_cleaning(sensor_data_files, provider, target):
     if features.empty:
         return pd.DataFrame(columns=excluded_columns)
 
-
-    # (7) STANDARDIZATION TODO: exclude nominal features from standardization 
-
+    # (7) STANDARDIZATION
     if provider["STANDARDIZATION"]:
+        nominal_cols = [col for col in features.columns if "mostcommonactivity" in col or "homelabel" in col] # Excluded nominal features
         # Expected warning within this code block
         with warnings.catch_warnings():
             warnings.simplefilter("ignore", category=RuntimeWarning)
-            features.loc[:, ~features.columns.isin(excluded_columns + ["pid"])] = \
-                features.loc[:, ~features.columns.isin(excluded_columns)].groupby('pid').transform(lambda x: StandardScaler().fit_transform(x.values[:,np.newaxis]).ravel())
+            features.loc[:, ~features.columns.isin(excluded_columns + ["pid"] + nominal_cols)] = \
+                features.loc[:, ~features.columns.isin(excluded_columns + nominal_cols)].groupby('pid').transform(lambda x: StandardScaler().fit_transform(x.values[:,np.newaxis]).ravel())
 
     graph_bf_af(features, "8standardization")