diff --git a/src/data/datetime/assign_to_time_segment.R b/src/data/datetime/assign_to_time_segment.R
index c18f042a..b7b9d8da 100644
--- a/src/data/datetime/assign_to_time_segment.R
+++ b/src/data/datetime/assign_to_time_segment.R
@@ -5,15 +5,11 @@ options(scipen=999)
 
 assign_rows_to_segments <- function(data, segments){
   # This function is used by all segment types, we use data.tables because they are fast
-  print(nrow(data))
-  print(ncol(data))
 
   data <- data.table::as.data.table(data)
   data[, assigned_segments := ""]
   for(i in seq_len(nrow(segments))) {
     segment <- segments[i,]
-    print(segment)
-    print(data[segment$segment_start_ts<= timestamp & segment$segment_end_ts >= timestamp])
 
     data[segment$segment_start_ts<= timestamp & segment$segment_end_ts >= timestamp,
          assigned_segments := stringi::stri_c(assigned_segments, segment$segment_id, sep = "|")]
@@ -21,12 +17,6 @@ assign_rows_to_segments <- function(data, segments){
   
   data[,assigned_segments:=substring(assigned_segments, 2)]
   data
-
-  test <- # print multiple columns
-    data %>%
-    dplyr::filter(is.na(assigned_segments))
-
-  test %>% as_tibble() %>% print(n=50)
 }
 
 assign_to_time_segment <- function(sensor_data, time_segments, time_segments_type, include_past_periodic_segments, most_common_tz){
diff --git a/src/features/all_cleaning_overall/straw/main.py b/src/features/all_cleaning_overall/straw/main.py
index 1963808a..b0fc74a2 100644
--- a/src/features/all_cleaning_overall/straw/main.py
+++ b/src/features/all_cleaning_overall/straw/main.py
@@ -192,7 +192,7 @@ def straw_cleaning(sensor_data_files, provider, target):
         if esm not in features:
             features[esm] = esm_cols[esm]
 
-    graph_bf_af(features, "11correlation_drop")
+    graph_bf_af(features, "10correlation_drop")
 
     # (10) VERIFY IF THERE ARE ANY NANS LEFT IN THE DATAFRAME
     if features.isna().any().any():