diff --git a/src/loaders/calo_inn.py b/src/loaders/calo_inn.py
index 8fe8c5b..a25cd8e 100644
--- a/src/loaders/calo_inn.py
+++ b/src/loaders/calo_inn.py
@@ -60,8 +60,8 @@ def load(params: dict) -> list[DiscriminatorData]:
         )
         
         observables = []
-        if dataset['level']=='low' and dataset['normalize']==False:
-            observables = compute_energies(test_true, test_fake)
+        if dataset['level']=='low':
+            observables = compute_observables(test_true, test_fake)
         datasets.append(DiscriminatorData(
                 label = dataset['label'],
                 suffix = dataset['suffix'],
@@ -180,42 +180,72 @@ def create_data_high(data_path, dataset_list, **kwargs):
     ret = torch.cat((ret, incident_energy), 1)
     return ret.numpy()
 
-def compute_energies(true_data: np.ndarray, fake_data: np.ndarray) -> list[Observable]:
+def compute_observables(true_data: np.ndarray, fake_data: np.ndarray) -> list[Observable]:
     observables = []
-    en_0_true = np.sum(true_data[:, :288], axis=1)
-    en_0_fake = np.sum(fake_data[:, :288], axis=1)
-    en_1_true = np.sum(true_data[:,288:432], axis=1)
-    en_1_fake = np.sum(fake_data[:,288:432], axis=1)
-    en_2_true = np.sum(true_data[:,432:504], axis=1)
-    en_2_fake = np.sum(fake_data[:,432:504], axis=1)
+    phi0_true = center_of_energy(true_data[:,:288], 0, 'phi')
+    phi1_true = center_of_energy(true_data[:,288:432], 1, 'phi')
+    phi2_true = center_of_energy(true_data[:,432:504], 2, 'phi')
+
+    phi0_fake = center_of_energy(fake_data[:,:288], 0, 'phi')
+    phi1_fake = center_of_energy(fake_data[:,288:432], 1, 'phi')
+    phi2_fake = center_of_energy(fake_data[:,432:504], 2, 'phi')
+
+    sparsity0_true = layer_sparsity(true_data[:,:288], 0.0)
+    sparsity1_true = layer_sparsity(true_data[:,288:432], 0.0)
+    sparsity2_true = layer_sparsity(true_data[:,432:504], 0.0)
+    
+    sparsity0_fake = layer_sparsity(fake_data[:,:288], 0.0)
+    sparsity1_fake = layer_sparsity(fake_data[:,288:432], 0.0)
+    sparsity2_fake = layer_sparsity(fake_data[:,432:504], 0.0)
 
     observables.extend([
         Observable(
-            true_data = en_0_true,
-            fake_data = en_0_fake,
-            tex_label = f'E0',
-            bins = np.logspace(-5, -1, 100),
-            xscale = 'log',
+            true_data = phi0_true,
+            fake_data = phi0_fake,
+            tex_label = f'phi0',
+            bins = np.linspace(-125, 125, 50),
+            xscale = 'linear',
             yscale = 'log',
-            unit = 'GeV'
             ),
-        Observable(
-            true_data = en_1_true,
-            fake_data = en_1_fake,
-            tex_label = f'E1',
-            bins = np.logspace(-4, 0, 100),
-            xscale = 'log',
+         Observable(
+            true_data = phi1_true,
+            fake_data = phi1_fake,
+            tex_label = f'phi1',
+            bins = np.linspace(-125, 125, 50),
+            xscale = 'linear',
             yscale = 'log',
-            unit = 'GeV'
             ),
         Observable(
-            true_data = en_2_true,
-            fake_data = en_2_fake,
-            tex_label = f'E2',
-            bins = np.logspace(-5, -1, 100),
-            xscale = 'log',
+            true_data = phi2_true,
+            fake_data = phi2_fake,
+            tex_label = f'phi2',
+            bins = np.linspace(-125, 125, 50),
+            xscale = 'linear',
             yscale = 'log',
-            unit = 'GeV'
+            ),
+        Observable(
+            true_data = sparsity0_true,
+            fake_data = sparsity0_fake,
+            tex_label = f'sparsity0',
+            bins = np.linspace(0, 1, 20),
+            xscale = 'linear',
+            yscale = 'linear',
+            ),
+        Observable(
+            true_data = sparsity1_true,
+            fake_data = sparsity1_fake,
+            tex_label = f'sparsity1',
+            bins = np.linspace(0, 1, 20),
+            xscale = 'linear',
+            yscale = 'linear',
+            ),
+        Observable(
+            true_data = sparsity2_true,
+            fake_data = sparsity2_fake,
+            tex_label = f'sparsity2',
+            bins = np.linspace(0, 1, 20),
+            xscale = 'linear',
+            yscale = 'linear',
             ),
         ])