diff --git a/src/policies/policy_executer.py b/src/policies/policy_executer.py
index 95bde10..e343231 100644
--- a/src/policies/policy_executer.py
+++ b/src/policies/policy_executer.py
@@ -124,6 +124,9 @@ def get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc,
     predicted_nrv_profits_cycles = {i: [0, 0] for i in penalties}
     baseline_profits_cycles = {i: [0, 0] for i in penalties}
 
+    _charge_thresholds = {}
+    _discharge_thresholds = {}
+
     initial, nrvs, target = predict_NRV(model, date, data_processor, test_loader)
 
     initial = np.repeat(initial, nrvs.shape[0])
@@ -139,6 +142,10 @@ def get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc,
 
     for penalty in penalties:
         found_charge_thresholds, found_discharge_thresholds = baseline_policy.get_optimal_thresholds(reconstructed_imbalance_prices, charge_thresholds, discharge_thresholds, penalty)
+        
+        _charge_thresholds[penalty] = found_charge_thresholds
+        _discharge_thresholds[penalty] = found_discharge_thresholds
+
         next_day_charge_threshold = found_charge_thresholds.mean(axis=0)
         next_day_discharge_threshold = found_discharge_thresholds.mean(axis=0)
         yesterday_charge_thresholds, yesterday_discharge_thresholds = baseline_policy.get_optimal_thresholds(yesterday_imbalance_prices, charge_thresholds, discharge_thresholds, penalty)
@@ -153,23 +160,29 @@ def get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc,
         baseline_profits_cycles[penalty][0] += yesterday_profit.item()
         baseline_profits_cycles[penalty][1] += yesterday_charge_cycles.item()
 
-    return predicted_nrv_profits_cycles, baseline_profits_cycles
+    return predicted_nrv_profits_cycles, baseline_profits_cycles, _charge_thresholds, _discharge_thresholds
 
 def next_day_test_set(model, data_processor, test_loader, ipc, predict_NRV: callable):
     penalties = [0, 10, 50, 150, 300, 500, 600, 800, 1000, 1500, 2000, 2500]
     predicted_nrv_profits_cycles = {i: [0, 0] for i in penalties}
     baseline_profits_cycles = {i: [0, 0] for i in penalties}
 
+    charge_thresholds = {}
+    discharge_thresholds = {}
+
     # get all dates in test set
     dates = baseline_policy.test_data["DateTime"].dt.date.unique()
 
     # dates back to datetime
     dates = pd.to_datetime(dates)
 
-    for date in tqdm(dates):
+    for date in tqdm(dates[:10]):
         try:
-            new_predicted_nrv_profits_cycles, new_baseline_profits_cycles = get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc, predict_NRV, penalties)
+            new_predicted_nrv_profits_cycles, new_baseline_profits_cycles, new_charge_thresholds, new_discharge_thresholds = get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc, predict_NRV, penalties)
             
+            charge_thresholds[date] = new_charge_thresholds
+            discharge_thresholds[date] = new_discharge_thresholds
+
             for penalty in penalties:
                 predicted_nrv_profits_cycles[penalty][0] += new_predicted_nrv_profits_cycles[penalty][0]
                 predicted_nrv_profits_cycles[penalty][1] += new_predicted_nrv_profits_cycles[penalty][1]
@@ -179,15 +192,15 @@ def next_day_test_set(model, data_processor, test_loader, ipc, predict_NRV: call
 
         except Exception as e:
             # print(f"Error for date {date}")
-            continue
+            raise e
 
-    return predicted_nrv_profits_cycles, baseline_profits_cycles
+    return predicted_nrv_profits_cycles, baseline_profits_cycles, charge_thresholds, discharge_thresholds
 
 def main():
     clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
     task = clearml_helper.get_task(task_name="Policy Test")
 
-    task.execute_remotely(queue_name="default", exit_process=True)
+    # task.execute_remotely(queue_name="default", exit_process=True)
 
     configuration, model, data_processor, test_loader = load_model(args.task_id)
 
@@ -205,7 +218,69 @@ def main():
 
     ipc = ImbalancePriceCalculator(data_path="")
 
-    predicted_nrv_profits_cycles, baseline_profits_cycles = next_day_test_set(model, data_processor, test_loader, ipc, predict_NRV)
+    predicted_nrv_profits_cycles, baseline_profits_cycles, charge_thresholds, discharge_thresholds = next_day_test_set(model, data_processor, test_loader, ipc, predict_NRV)
+    # the charge_thresholds is a dictionary with date as key. The values of the dictionary is another dictionary with keys as penalties and values as the charge thresholds
+    # create density plot that shows a density plot of the charge thresholds for each penalty (use seaborn displot) (One plot with a different color for each penalty)
+
+    import seaborn as sns
+    import matplotlib.pyplot as plt
+
+    charge_thresholds_for_penalty = {}
+    for d in charge_thresholds.values():
+        for penalty, thresholds in d.items():
+            if penalty not in charge_thresholds_for_penalty:
+                charge_thresholds_for_penalty[penalty] = []
+            charge_thresholds_for_penalty[penalty].extend(thresholds)
+
+    discharge_thresholds_for_penalty = {}
+    for d in discharge_thresholds.values():
+        for penalty, thresholds in d.items():
+            if penalty not in discharge_thresholds_for_penalty:
+                discharge_thresholds_for_penalty[penalty] = []
+            discharge_thresholds_for_penalty[penalty].extend(thresholds)
+
+    ### Plot charge thresholds distribution ###
+    data_to_plot = []
+    for penalty, values in charge_thresholds_for_penalty.items():
+        for value in values:
+            data_to_plot.append({'Penalty': penalty, 'Value': value.item()})
+    df = pd.DataFrame(data_to_plot)
+    print(df.head())
+    palette = sns.color_palette("bright", len(charge_thresholds.keys()))
+    fig = sns.displot(data=df, x="Value", hue="Penalty", kind="kde", palette=palette)
+    plt.title('Density of Charge Thresholds by Penalty')
+    plt.xlabel('Charge Threshold')
+    plt.ylabel('Density')
+    plt.legend(title='Penalty')
+    task.get_logger().report_matplotlib_figure(
+        "Policy Results", 
+        "Charge Thresholds", 
+        iteration=0, 
+        figure=fig
+    )
+    plt.close()
+
+    ### Plot discharge thresholds distribution ###
+    data_to_plot = []
+    for penalty, values in discharge_thresholds_for_penalty.items():
+        for value in values:
+            data_to_plot.append({'Penalty': penalty, 'Value': value.item()})
+    df = pd.DataFrame(data_to_plot)
+    palette = sns.color_palette("bright", len(discharge_thresholds.keys()))
+    fig = sns.displot(data=df, x="Value", hue="Penalty", kind="kde", palette=palette)
+    plt.title('Density of Charge Thresholds by Penalty')
+    plt.xlabel('Charge Threshold')
+    plt.ylabel('Density')
+    plt.legend(title='Penalty')
+    task.get_logger().report_matplotlib_figure(
+        "Policy Results", 
+        "Discharge Thresholds", 
+        iteration=0, 
+        figure=fig
+    )
+    plt.close()
+
+
 
     # create dataframe with columns "name", "penalty", "profit", "cycles"
     df = pd.DataFrame(columns=["name", "penalty", "profit", "cycles"])
diff --git a/src/utils/clearml.py b/src/utils/clearml.py
index 0fbf3f8..516b0b4 100644
--- a/src/utils/clearml.py
+++ b/src/utils/clearml.py
@@ -10,6 +10,6 @@ class ClearMLHelper:
         Task.ignore_requirements("torchvision")
         Task.ignore_requirements("tensorboard")
         task = Task.init(project_name=self.project_name, task_name=task_name, continue_last_task=False)
-        task.set_base_docker(f"docker.io/clearml/pytorch-cuda-gcc:2.0.0-cuda11.7-cudnn8-runtime --env GIT_SSL_NO_VERIFY=true --env CLEARML_AGENT_GIT_USER=VictorMylle --env CLEARML_AGENT_GIT_PASS=Voetballer1" )
+        task.set_base_docker(f"docker.io/clearml/pytorch-cuda-gcc:2.0.0-cuda11.7-cudnn8-runtime")
         task.set_packages("requirements.txt")
         return task
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