diff --git a/figure_33_joint_plot.py b/figure_33_joint_plot.py
index 00ae6a80..ec3db4cd 100644
--- a/figure_33_joint_plot.py
+++ b/figure_33_joint_plot.py
@@ -131,6 +131,7 @@ def plot_boxplot(df, x_label, y_label="Balanced accuracy", order_ = None):
 
     plt.xlabel(x_label)
     plt.ylabel(y_label)
+    plt.xticks(rotation=90)
     plt.tight_layout()
     #plt.show(block=True)
 
@@ -200,6 +201,8 @@ def plot_per_train_time_relation(df, label):
         for norm_window in [0, 5, 10, 20, 30, 60, 120, 180, 300, 480, 720, 960, 1200, 1440]:
             OUT_FILE = f"d_out_patient_across_{label_name}_class_{class_}_{norm_window}.pkl"
             PATH_READ = os.path.join(PATH_PER, OUT_FILE)
+            if not os.path.exists(PATH_READ):
+                continue
 
             df = read_per_out(PATH_READ)
             #df = df.query("loc == 'ecog_stn'")
diff --git a/post_hoc_normalize.py b/post_hoc_normalize.py
index d3d4186d..7ff15828 100644
--- a/post_hoc_normalize.py
+++ b/post_hoc_normalize.py
@@ -12,8 +12,10 @@
 import time
 
 PATH_IN = "/Users/Timon/Documents/UCSF_Analysis/out/merged_std"
-PATH_OUT_BASE = "/Users/Timon/Documents/UCSF_Analysis/out/merged_normalized"
+PATH_IN = '/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/features/merged_std'
 
+PATH_OUT_BASE = "/Users/Timon/Documents/UCSF_Analysis/out/merged_normalized"
+PATH_OUT_BASE = '/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/features/merged_normalized'
 if __name__ == "__main__":
 
     df_all = pd.read_csv(os.path.join(PATH_IN, "all_merged_preprocessed.csv"), index_col=0)
@@ -36,15 +38,21 @@ def process_sub(sub):
             else:
                 time_before = df_sub.loc[idx, "pkg_dt"] - pd.Timedelta(minutes=normalization_window)
                 time_now = df_sub.loc[idx, "pkg_dt"]
-                df_range = df_sub.query("pkg_dt >= @time_before and pkg_dt <@time_now")
-                if df_range.shape[0] < 2:
-                    continue
+                
+                if normalization_window == 0:
+                    df_range = df_sub.query("pkg_dt == @time_now")
+                else:
+                    df_range = df_sub.query("pkg_dt >= @time_before and pkg_dt <@time_now")
+                    if df_range.shape[0] < 2:
+                        continue
                 
                 cols_use = [f for f in df_range.columns if "pkg_dt" not in f and f != "sub"]
                 mean_ = df_range[cols_use].mean()
-                std_ = df_range[cols_use].std()
-
-                row_add = (df_sub.loc[idx, cols_use] - mean_) / std_
+                if normalization_window != 0:
+                    std_ = df_range[cols_use].std()
+                    row_add = (df_sub.loc[idx, cols_use] - mean_) / std_
+                else:
+                    row_add = mean_
 
                 time_pkg_before = df_sub.loc[idx, "pkg_dt"] - pd.Timedelta(minutes=5)
                 time_pkg_after = df_sub.loc[idx, "pkg_dt"] + pd.Timedelta(minutes=5)
@@ -74,7 +82,7 @@ def process_sub(sub):
     
     #process_sub(subs[0])
     # parallelize
-    for normalization_window in [5, 10, 20, 30, 60, 120][::-1]:
+    for normalization_window in [0]:  # [5, 10, 20, 30, 60, 120][::-1]
         PATH_OUT = os.path.join(PATH_OUT_BASE, str(normalization_window))
         if not os.path.exists(PATH_OUT):
             os.makedirs(PATH_OUT)
diff --git a/run_decoding_ucsf_across_patients_diff_norm_windows.py b/run_decoding_ucsf_across_patients_diff_norm_windows.py
index f9634c47..b2a15614 100644
--- a/run_decoding_ucsf_across_patients_diff_norm_windows.py
+++ b/run_decoding_ucsf_across_patients_diff_norm_windows.py
@@ -29,12 +29,12 @@
             if  os.path.exists(os.path.join(PATH_PER, f"d_out_patient_across_{label_name}_class_{CLASSIFICATION}_{str(norm_window)}.pkl")):
                 continue
             
-            if norm_window == 0:
-                PATH_OUT = "/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/features/merged_std"
-                df_all = pd.read_csv(os.path.join(PATH_OUT, "all_merged_preprocessed.csv"), index_col=0)
-            else:
-                PATH_OUT = "/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/features/merged_normalized"
-                df_all = pd.read_csv(os.path.join(PATH_OUT, str(norm_window), "all_merged_normed.csv"), index_col=0)
+            # if norm_window == 0:
+            #     PATH_OUT = "/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/features/merged_std"
+            #     df_all = pd.read_csv(os.path.join(PATH_OUT, "all_merged_preprocessed.csv"), index_col=0)
+            # else:
+            PATH_OUT = "/Users/Timon/Library/CloudStorage/OneDrive-Charité-UniversitätsmedizinBerlin/Shared Documents - ICN Data World/General/Data/UCSF_OLARU/features/merged_normalized"
+            df_all = pd.read_csv(os.path.join(PATH_OUT, str(norm_window), "all_merged_normed.csv"), index_col=0)
             #df_all = df_all.drop(columns=["Unnamed: 0"])
             subs = df_all["sub"].unique()