diff --git a/SP2XR_toolkit.py b/SP2XR_toolkit.py
index c4bb5a6..9682b15 100644
--- a/SP2XR_toolkit.py
+++ b/SP2XR_toolkit.py
@@ -1680,7 +1680,7 @@ def process_hist_and_dist(df, col, flag_col, flag_value, bin_lims, bin_ctrs, dt_
 
     # Resample and calculate histogram
     ddf_hist_compact = df_filtered[col].resample(dt_str).agg(
-        {'result': lambda x: calculate_histogram(x, bin_lims=bin_lims)})
+        {'result': lambda x: calculate_histogram(x, bin_lims=bin_lims)})  # I might want to add a .fillna(0) here, this would solve the problem of fillna in the resampling function
     
     # Add and filter based on 'original_idx'
     ddf_hist_compact[['original_idx']] = df_filtered[['temporary_col']].resample(dt_str).count()
@@ -1859,7 +1859,7 @@ def process_pbp_parquet(dir_path_pbp, dir_path_hk,
     ddf_pbp['cnts_thin_low_inc_scatt_ratio'] = 0
     ddf_pbp['cnts_particles_for_tl_dist'] = 0 # this flag is used for the calculation of time lag distributions below (it includes the particles classfified as "thin" or "thick")
     
-    ddf_pbp.loc[~flag_scatt & flag_inc_in_range_tl_analysis, 'thin_noScatt'] = 1
+    ddf_pbp.loc[~flag_scatt & flag_inc_in_range_tl_analysis, 'cnts_thin_noScatt'] = 1
     ddf_pbp.loc[flag_scatt & flag_scatt_not_sat & flag_inc_in_range_tl_analysis & flag_timelag_0_50 & ~flag_low_ratio_inc_scatt, 'cnts_thin'] = 1
     ddf_pbp.loc[flag_scatt & flag_scatt_not_sat & flag_inc_in_range_tl_analysis & flag_timelag_0_50 & flag_low_ratio_inc_scatt, 'cnts_thin_low_inc_scatt_ratio'] = 1
     ddf_pbp.loc[flag_scatt & flag_scatt_not_sat & flag_inc_in_range_tl_analysis & flag_timelag_greater_50 & ~flag_extreme_positive_timelag, 'cnts_thick'] = 1