diff --git a/scripts/model_builder/plot_data.py b/scripts/model_builder/plot_data.py
index 8acc1ff..d5bb542 100644
--- a/scripts/model_builder/plot_data.py
+++ b/scripts/model_builder/plot_data.py
@@ -13,132 +13,162 @@ from helpers.app_settings import get_app_settings
 
 
 def load_validation_data():
-    val_dataset = ModulationH5Dataset(
-        "data/dataset/val.h5", label_name="modulation", data_key="validation_data"
-    )
+   val_dataset = ModulationH5Dataset(
+       "data/dataset/val.h5", label_name="modulation", data_key="validation_data"
+   )
 
-    X = np.stack([x.numpy() for x, _ in val_dataset])  # shape: (N, C, L)
-    y = np.array([y.item() for _, y in val_dataset])  # shape: (N,)
-    class_names = list(val_dataset.label_encoder.classes_)
 
-    return X, y, class_names
+   x = np.stack([x.numpy() for x, _ in val_dataset])  # shape: (N, C, L)
+   y = np.array([y.item() for _, y in val_dataset])  # shape: (N,)
+   class_names = list(val_dataset.label_encoder.classes_)
+
+
+   return x, y, class_names
 
 
 def build_model_from_ckpt(
-    ckpt_path: str, in_channels: int, num_classes: int
+   ckpt_path: str, in_channels: int, num_classes: int
 ) -> torch.nn.Module:
-    """
-    Build and return a PyTorch model loaded from a checkpoint.
-    """
-    model = RFClassifier(
-        model=mobilenetv3(
-            model_size="mobilenetv3_small_050",
-            num_classes=num_classes,
-            in_chans=in_channels,
-        )
-    )
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    checkpoint = torch.load(ckpt_path, weights_only=True, map_location=device)
-    model.load_state_dict(checkpoint["state_dict"])
-    model.eval()
-    return model
+   """
+   Build and return a PyTorch model loaded from a checkpoint.
+   """
+   model = RFClassifier(
+       model=mobilenetv3(
+           model_size="mobilenetv3_small_050",
+           num_classes=num_classes,
+           in_chans=in_channels,
+       )
+   )
+   device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+   checkpoint = torch.load(ckpt_path, weights_only=True, map_location=device)
+   model.load_state_dict(checkpoint["state_dict"])
+   model.eval()
+   return model
+
+
 
 
 def evaluate_checkpoint(ckpt_path: str):
-    """
-    Loads the model from checkpoint and evaluates it on a validation set.
-    Prints classification metrics and plots a confusion matrix.
-    """
-
-    # Load validation data
-    X_val, y_true, class_names = load_validation_data()
-    num_classes = len(class_names)
-    in_channels = X_val.shape[1]
-
-    # Load model
-    model = build_model_from_ckpt(
-        ckpt_path, in_channels=in_channels, num_classes=num_classes
-    )
-
-    # Inference
-    y_pred = []
-    with torch.no_grad():
-        for x in X_val:
-            x_tensor = torch.tensor(x[np.newaxis, ...], dtype=torch.float32)
-            logits = model(x_tensor)
-            pred = torch.argmax(logits, dim=1).item()
-            y_pred.append(pred)
-
-    # Print classification report
-    print("\nClassification Report:")
-    print(
-        classification_report(y_true, y_pred, target_names=class_names, zero_division=0)
-    )
-
-    plot_confusion_matrix_with_counts(
-        y_true=np.array(y_true),
-        y_pred=np.array(y_pred),
-        classes=class_names,
-        normalize=True,
-        title="Normalized Confusion Matrix",
-    )
+   """
+   Loads the model from checkpoint and evaluates it on a validation set.
+   Prints classification metrics and plots a confusion matrix.
+   """
 
 
-def plot_confusion_matrix_with_counts(
-    y_true: np.ndarray,
-    y_pred: np.ndarray,
-    classes: list[str],
-    normalize: bool = True,
-    title: str = "Confusion Matrix (counts and normalized)",
+   # Load validation data
+   X_val, y_true, class_names = load_validation_data()
+   num_classes = len(class_names)
+   in_channels = X_val.shape[1]
+
+
+   # Load model
+   model = build_model_from_ckpt(
+       ckpt_path, in_channels=in_channels, num_classes=num_classes
+   )
+
+
+   # Inference
+   y_pred = []
+   with torch.no_grad():
+       for x in X_val:
+           x_tensor = torch.tensor(x[np.newaxis, ...], dtype=torch.float32)
+           logits = model(x_tensor)
+           pred = torch.argmax(logits, dim=1).item()
+           y_pred.append(pred)
+
+
+   # Print classification report
+   print("\nClassification Report:")
+   print(
+       classification_report(y_true, y_pred, target_names=class_names, zero_division=0)
+   )
+  
+  
+  
+   print_confusion_matrix(
+       y_true=np.array(y_true),
+       y_pred=np.array(y_pred),
+       classes=class_names,
+       normalize=True,
+       title="Normalized Confusion Matrix",
+   )
+
+
+
+
+def print_confusion_matrix(
+   y_true: np.ndarray,
+   y_pred: np.ndarray,
+   classes: list[str],
+   normalize: bool = True,
+   title: str = "Confusion Matrix (counts and normalized)",
 ) -> None:
+   """
+   Plot a confusion matrix showing both raw counts and (optionally) normalized values.
+
+
+   Args:
+       y_true: true labels (integers 0..C-1)
+       y_pred: predicted labels (same shape as y_true)
+       classes: list of class‐name strings in index order
+       normalize: if True, each row is normalized to sum=1
+       title: title for the plot
+   """
+   # 1) build raw CM
+   c = len(classes)
+   cm = np.zeros((c, c), dtype=int)
+   for t, p in zip(y_true, y_pred):
+       cm[t, p] += 1
+      
+  
+   # 2) normalize if requested
+   if normalize:
+       with np.errstate(divide="ignore", invalid="ignore"):
+           cm_norm = cm.astype(float) / cm.sum(axis=1)[:, None]
+           cm_norm = np.nan_to_num(cm_norm)
+       print_confusion_matrix_helper(cm_norm, classes)
+   else:
+       print_confusion_matrix_helper(cm, classes)
+
+
+
+
+import numpy as np
+
+def print_confusion_matrix_helper(matrix, classes=None, normalize=False, digits=2):
     """
-    Plot a confusion matrix showing both raw counts and (optionally) normalized values.
+    Pretty prints a confusion matrix with x/y labels.
 
-    Args:
-        y_true: true labels (integers 0..C-1)
-        y_pred: predicted labels (same shape as y_true)
-        classes: list of class‐name strings in index order
-        normalize: if True, each row is normalized to sum=1
-        title: title for the plot
+    Parameters:
+    - matrix: square 2D numpy array
+    - labels: list of class labels (default: range(num_classes))
+    - normalize: whether to normalize rows to sum to 1
+    - digits: number of decimal places to show for normalized values
     """
-    # 1) build raw CM
-    C = len(classes)
-    cm = np.zeros((C, C), dtype=int)
-    for t, p in zip(y_true, y_pred):
-        cm[t, p] += 1
-
-    # 2) normalize if requested
-    if normalize:
-        with np.errstate(divide="ignore", invalid="ignore"):
-            cm_norm = cm.astype(float) / cm.sum(axis=1)[:, None]
-            cm_norm = np.nan_to_num(cm_norm)
-    else:
-        cm_norm = cm
-
-    # 3) plot
-    fig, ax = plt.subplots(figsize=(8, 8))
-    im = ax.imshow(cm_norm, interpolation="nearest")
-    ax.set_title(title)
-    ax.set_xlabel("Predicted label")
-    ax.set_ylabel("True label")
-    ax.set_xticks(np.arange(C))
-    ax.set_yticks(np.arange(C))
-    ax.set_xticklabels(classes, rotation=45, ha="right")
-    ax.set_yticklabels(classes)
-
-    # 4) annotate
-    for i in range(C):
-        for j in range(C):
-            count = cm[i, j]
-            val = cm_norm[i, j]
-            txt = f"{count}\n{val:.2f}"
-            ax.text(j, i, txt, ha="center", va="center")
-
-    fig.colorbar(im, ax=ax, label="Normalized value" if normalize else "Count")
-    plt.tight_layout()
-    plt.show()
-
+    matrix = np.array(matrix)
+    num_classes = matrix.shape[0]
+    labels = classes or list(range(num_classes))
+    
+    # Header
+    print(" " * 9 + "Ground Truth →")
+    header = "Pred ↓ | " + "  ".join([f"{str(label):>6}" for label in labels])
+    print(header)
+    print("-" * len(header))
+    
+    # Rows
+    for i in range(num_classes):
+        row_vals = matrix[i]
+        if normalize:
+            row_sum = row_vals.sum()
+            row_vals = row_vals / row_sum if row_sum != 0 else row_vals
+            row_str = "  ".join([f"{val:>6.{digits}f}" for val in row_vals])
+        else:
+            row_str = "  ".join([f"{int(val):>6}" for val in row_vals])
+        print(f"{str(labels[i]):>7} | {row_str}")
 
+      
 if __name__ == "__main__":
-    settings = get_app_settings()
-    evaluate_checkpoint(os.path.join("checkpoint_files", "inference_recognition_model.ckpt"))
+   settings = get_app_settings()
+   evaluate_checkpoint(os.path.join("checkpoint_files", "inference_recognition_model.ckpt"))
+
+