diff --git a/mlair/run_modules/pre_processing.py b/mlair/run_modules/pre_processing.py
index e265bd24b4408562653a80ab9f80745246bbfc9c..215c0bb80c05fd9ac267418961c2ca96e025b3e2 100644
--- a/mlair/run_modules/pre_processing.py
+++ b/mlair/run_modules/pre_processing.py
@@ -114,28 +114,29 @@ class PreProcessing(RunEnvironment):
                 Y = xr.concat([Y, station._Y], dim="Stations")
                 Y_extreme = xr.concat([Y_extreme, station._Y_extreme], dim="Stations")
 
-        fig, ax = plt.subplots(nrows=2, ncols=2)
-        fig.suptitle(f"Window Size=1, Bins={bins}, rates_cap={rates_cap}")
-        Y_hist = Y.plot.hist(bins=bin_edges, histtype="step", label="Before", ax=ax[0,0])[0]
-        Y_extreme_hist = Y_extreme.plot.hist(bins=bin_edges, histtype="step", label="After", ax=ax[0,0])[0]
-        ax[0,0].set_title(f"Histogram before-after oversampling")
-        ax[0,0].legend()
-        Y_hist_dens = Y.plot.hist(bins=bin_edges, density=True, histtype="step", label="Before", ax=ax[0,1])[0]
-        Y_extreme_hist_dens = Y_extreme.plot.hist(bins=bin_edges, density=True, histtype="step", label="After", ax=ax[0,1])[0]
-        ax[0,1].set_title(f"Density-Histogram before-after oversampling")
-        ax[0,1].legend()
-        real_oversampling = Y_extreme_hist/Y_hist
-        ax[1,0].plot(range(len(real_oversampling)), oversampling_rates_capped, label="Desired oversampling_rates")
-        ax[1,0].plot(range(len(real_oversampling)), real_oversampling, label="Actual Oversampling Rates")
-        ax[1,0].set_title(f"Oversampling rates")
-        ax[1,0].legend()
-        ax[1,1].plot(range(len(real_oversampling)), real_oversampling / oversampling_rates_capped,
-                 label="Actual/Desired Rate")
-        ax[1,1].set_title(f"Deviation from desired Oversampling rate")
-        ax[1,1].legend()
-        plt.show()
-        #data[1]._Y.where(data[1]._Y > bin_edges[9], drop=True)
-        #data[1]._Y_extreme.where(data[1]._Y_extreme > bin_edges[9], drop=True)
+        '''
+        if not on HPC:
+            fig, ax = plt.subplots(nrows=2, ncols=2)
+            fig.suptitle(f"Window Size=1, Bins={bins}, rates_cap={rates_cap}")
+            Y_hist = Y.plot.hist(bins=bin_edges, histtype="step", label="Before", ax=ax[0,0])[0]
+            Y_extreme_hist = Y_extreme.plot.hist(bins=bin_edges, histtype="step", label="After", ax=ax[0,0])[0]
+            ax[0,0].set_title(f"Histogram before-after oversampling")
+            ax[0,0].legend()
+            Y_hist_dens = Y.plot.hist(bins=bin_edges, density=True, histtype="step", label="Before", ax=ax[0,1])[0]
+            Y_extreme_hist_dens = Y_extreme.plot.hist(bins=bin_edges, density=True, histtype="step", label="After", ax=ax[0,1])[0]
+            ax[0,1].set_title(f"Density-Histogram before-after oversampling")
+            ax[0,1].legend()
+            real_oversampling = Y_extreme_hist/Y_hist
+            ax[1,0].plot(range(len(real_oversampling)), oversampling_rates_capped, label="Desired oversampling_rates")
+            ax[1,0].plot(range(len(real_oversampling)), real_oversampling, label="Actual Oversampling Rates")
+            ax[1,0].set_title(f"Oversampling rates")
+            ax[1,0].legend()
+            ax[1,1].plot(range(len(real_oversampling)), real_oversampling / oversampling_rates_capped,
+                     label="Actual/Desired Rate")
+            ax[1,1].set_title(f"Deviation from desired Oversampling rate")
+            ax[1,1].legend()
+            plt.show()
+            '''
 
     def report_pre_processing(self):
         """Log some metrics on data and create latex report."""
diff --git a/run_with_oversampling.py b/run_with_oversampling.py
index cbab9b4e579d41b975c3892f528a961341985366..b21e5e6f98df00a5f866c10db280f29e3366b014 100644
--- a/run_with_oversampling.py
+++ b/run_with_oversampling.py
@@ -9,8 +9,12 @@ from mlair.model_modules.model_class import IntelliO3_ts_architecture
 import os
 
 
-def load_stations():
+def load_stations(external_station_list):
     import json
+    if external_station_list is None:
+        filename = 'supplement/station_list_north_german_plain_rural.json'
+    else:
+        filename = external_station_list
     try:
         filename = 'supplement/station_list_north_german_plain_rural.json'
         with open(filename, 'r') as jfile:
@@ -22,15 +26,14 @@ def load_stations():
 
 def main(parser_args):
     plots = remove_items(DEFAULT_PLOT_LIST, "PlotConditionalQuantiles")
-    workflow = DefaultWorkflow(  # stations=load_stations(),
-        # stations=["DEBW087","DEBW013", "DEBW107",  "DEBW076"],
-        stations=["DEBW013", "DEBW087", "DEBW107", "DEBW076"],
-        train_model=False, create_new_model=True, network="UBA",
+    workflow = DefaultWorkflow(stations=load_stations('supplement/German_background_station.json')[:75],
+        #stations=["DEBW013", "DEBW087", "DEBW107", "DEBW076"],
+        train_model=True, create_new_model=True, network="UBA",
         model=IntelliO3_ts_architecture, oversampling_method="bin_oversampling",
         evaluate_bootstraps=False,  # plot_list=["PlotCompetitiveSkillScore"],
         competitors=["test_model", "test_model2"],
         competitor_path=os.path.join(os.getcwd(), "data", "comp_test"),
-        window_lead_time=1, oversampling_bins=10, oversampling_rates_cap=100,
+        window_lead_time=2, oversampling_bins=10, oversampling_rates_cap=100,
         **parser_args.__dict__)
     workflow.run()
 
diff --git a/run_without_oversampling.py b/run_without_oversampling.py
index 3c69b4508b644a1b2ef3ffc1b18a9ee6796eac02..5b51ffa0d73c0a92491f0cf52285dd53dcec5cd3 100644
--- a/run_without_oversampling.py
+++ b/run_without_oversampling.py
@@ -9,8 +9,12 @@ from mlair.model_modules.model_class import IntelliO3_ts_architecture
 import os
 
 
-def load_stations():
+def load_stations(external_station_list = None):
     import json
+    if external_station_list is None:
+        filename = 'supplement/station_list_north_german_plain_rural.json'
+    else:
+        filename = external_station_list
     try:
         filename = 'supplement/station_list_north_german_plain_rural.json'
         with open(filename, 'r') as jfile:
@@ -22,15 +26,14 @@ def load_stations():
 
 def main(parser_args):
     plots = remove_items(DEFAULT_PLOT_LIST, "PlotConditionalQuantiles")
-    workflow = DefaultWorkflow(  # stations=load_stations(),
-        # stations=["DEBW087","DEBW013", "DEBW107",  "DEBW076"],
-        stations=["DEBW013", "DEBW087", "DEBW107", "DEBW076"],
-        train_model=False, create_new_model=True, network="UBA",
+    workflow = DefaultWorkflow(stations=load_stations('supplement/German_background_station.json')[:75],
+        #stations=["DEBW013", "DEBW087", "DEBW107", "DEBW076"],
+        train_model=True, create_new_model=True, network="UBA",
         model=IntelliO3_ts_architecture,
         evaluate_bootstraps=False,  # plot_list=["PlotCompetitiveSkillScore"],
         competitors=["test_model", "test_model2"],
         competitor_path=os.path.join(os.getcwd(), "data", "comp_test"),
-        window_lead_time=1, oversampling_bins=10, oversampling_rates_cap=100,
+        window_lead_time=2, oversampling_bins=10, oversampling_rates_cap=100,
         **parser_args.__dict__)
     workflow.run()