diff --git a/src/ria_toolkit_oss/viz/onnx.py b/src/ria_toolkit_oss/viz/onnx.py
index b92c3e4..e260eeb 100644
--- a/src/ria_toolkit_oss/viz/onnx.py
+++ b/src/ria_toolkit_oss/viz/onnx.py
@@ -75,9 +75,7 @@ def graph_structure(file_path: Path) -> go.Figure:
     """
     if not ONNX_AVAILABLE:
         return create_styled_error_figure(
-            "ONNX Not Available",
-            "ONNX library is required for model analysis.",
-            "Install with: pip install onnx"
+            "ONNX Not Available", "ONNX library is required for model analysis.", "Install with: pip install onnx"
         )
 
     try:
@@ -88,9 +86,7 @@ def graph_structure(file_path: Path) -> go.Figure:
 
         if len(nodes) == 0:
             return create_styled_error_figure(
-                "Empty Model",
-                "This ONNX model contains no operators.",
-                "Please check if the model file is valid."
+                "Empty Model", "This ONNX model contains no operators.", "Please check if the model file is valid."
             )
 
         # Create network diagram data
@@ -153,8 +149,10 @@ def graph_structure(file_path: Path) -> go.Figure:
 
         fig.update_layout(
             title={
-                "text": ("ONNX Graph Structure<br>"
-                         f"<span style='font-size:14px; color:#a0a0a0;'>{len(nodes)} Operators</span>"),
+                "text": (
+                    "ONNX Graph Structure<br>"
+                    f"<span style='font-size:14px; color:#a0a0a0;'>{len(nodes)} Operators</span>"
+                ),
                 "x": 0.5,
                 "xanchor": "center",
                 "font": {"size": 22},
@@ -173,9 +171,7 @@ def graph_structure(file_path: Path) -> go.Figure:
 
     except Exception as e:
         return create_styled_error_figure(
-            "Graph Analysis Error",
-            "Could not analyze ONNX model structure.",
-            f"Error: {str(e)}"
+            "Graph Analysis Error", "Could not analyze ONNX model structure.", f"Error: {str(e)}"
         )
 
 
@@ -186,9 +182,7 @@ def operator_analysis(file_path: Path) -> go.Figure:
     """
     if not ONNX_AVAILABLE:
         return create_styled_error_figure(
-            "ONNX Not Available",
-            "ONNX library is required for operator analysis.",
-            "Install with: pip install onnx"
+            "ONNX Not Available", "ONNX library is required for operator analysis.", "Install with: pip install onnx"
         )
 
     try:
@@ -248,8 +242,10 @@ def operator_analysis(file_path: Path) -> go.Figure:
 
         fig.update_layout(
             title={
-                "text": ("ONNX Operator Analysis<br>"
-                         f"<span style='font-size:14px; color:#a0a0a0;'>{len(op_counts)} Unique Types</span>"),
+                "text": (
+                    "ONNX Operator Analysis<br>"
+                    f"<span style='font-size:14px; color:#a0a0a0;'>{len(op_counts)} Unique Types</span>"
+                ),
                 "x": 0.5,
                 "xanchor": "center",
                 "font": {"size": 22},
@@ -262,9 +258,7 @@ def operator_analysis(file_path: Path) -> go.Figure:
 
     except Exception as e:
         return create_styled_error_figure(
-            "Operator Analysis Error",
-            "Could not analyze ONNX operators.",
-            f"Error: {str(e)}"
+            "Operator Analysis Error", "Could not analyze ONNX operators.", f"Error: {str(e)}"
         )
 
 
@@ -275,9 +269,7 @@ def model_metadata(file_path: Path) -> go.Figure:
     """
     if not ONNX_AVAILABLE:
         return create_styled_error_figure(
-            "ONNX Not Available",
-            "ONNX library is required for metadata analysis.",
-            "Install with: pip install onnx"
+            "ONNX Not Available", "ONNX library is required for metadata analysis.", "Install with: pip install onnx"
         )
 
     try:
@@ -336,12 +328,7 @@ def model_metadata(file_path: Path) -> go.Figure:
         arch_values = [total_nodes, total_inputs, total_outputs, total_initializers]
 
         fig.add_trace(
-            go.Bar(
-                x=arch_data,
-                y=arch_values,
-                marker_color=["blue", "green", "orange", "red"],
-                showlegend=False
-            ),
+            go.Bar(x=arch_data, y=arch_values, marker_color=["blue", "green", "orange", "red"], showlegend=False),
             row=1,
             col=2,
         )
@@ -402,16 +389,8 @@ def model_metadata(file_path: Path) -> go.Figure:
         if io_data:
             fig.add_trace(
                 go.Table(
-                    header=dict(
-                        values=["Type", "Name", "Shape", "Data Type"],
-                        fill_color="lightblue",
-                        align="left"
-                    ),
-                    cells=dict(
-                        values=list(zip(*io_data)),
-                        fill_color="white",
-                        align="left"
-                    ),
+                    header=dict(values=["Type", "Name", "Shape", "Data Type"], fill_color="lightblue", align="left"),
+                    cells=dict(values=list(zip(*io_data)), fill_color="white", align="left"),
                 ),
                 row=2,
                 col=1,
@@ -432,8 +411,10 @@ def model_metadata(file_path: Path) -> go.Figure:
 
         fig.update_layout(
             title={
-                "text": ("ONNX Model Metadata<br>"
-                         f"<span style='font-size:14px; color:#a0a0a0;'>{total_params/1e6:.2f}M Parameters</span>"),
+                "text": (
+                    "ONNX Model Metadata<br>"
+                    f"<span style='font-size:14px; color:#a0a0a0;'>{total_params/1e6:.2f}M Parameters</span>"
+                ),
                 "x": 0.5,
                 "xanchor": "center",
                 "font": {"size": 22},
@@ -447,9 +428,7 @@ def model_metadata(file_path: Path) -> go.Figure:
 
     except Exception as e:
         return create_styled_error_figure(
-            "Metadata Analysis Error",
-            "Could not extract ONNX model metadata.",
-            f"Error: {str(e)}"
+            "Metadata Analysis Error", "Could not extract ONNX model metadata.", f"Error: {str(e)}"
         )
 
 
@@ -508,10 +487,7 @@ def performance_metrics(file_path: Path) -> go.Figure:
 
         fig.add_trace(
             go.Bar(
-                x=efficiency_metrics,
-                y=efficiency_values,
-                marker_color=["blue", "green", "orange"],
-                showlegend=False
+                x=efficiency_metrics, y=efficiency_values, marker_color=["blue", "green", "orange"], showlegend=False
             ),
             row=1,
             col=1,
@@ -522,12 +498,7 @@ def performance_metrics(file_path: Path) -> go.Figure:
         memory_values = [param_memory_mb, param_memory_mb * 2]  # Rough estimate
 
         fig.add_trace(
-            go.Bar(
-                x=memory_types,
-                y=memory_values,
-                marker_color=["purple", "red"],
-                showlegend=False
-            ),
+            go.Bar(x=memory_types, y=memory_values, marker_color=["purple", "red"], showlegend=False),
             row=1,
             col=2,
         )
@@ -569,9 +540,11 @@ def performance_metrics(file_path: Path) -> go.Figure:
 
         fig.update_layout(
             title={
-                "text": ("ONNX Performance Metrics<br>"
-                         f"<span style='font-size:14px; color:#a0a0a0;'>"
-                         f"Complexity Score: {complexity_score:.0f}/100</span>"),
+                "text": (
+                    "ONNX Performance Metrics<br>"
+                    f"<span style='font-size:14px; color:#a0a0a0;'>"
+                    f"Complexity Score: {complexity_score:.0f}/100</span>"
+                ),
                 "x": 0.5,
                 "xanchor": "center",
                 "font": {"size": 22},
@@ -585,7 +558,5 @@ def performance_metrics(file_path: Path) -> go.Figure:
 
     except Exception as e:
         return create_styled_error_figure(
-            "Performance Analysis Error",
-            "Could not analyze ONNX model performance.",
-            f"Error: {str(e)}"
+            "Performance Analysis Error", "Could not analyze ONNX model performance.", f"Error: {str(e)}"
         )
diff --git a/src/ria_toolkit_oss/viz/pytorch_state_dict.py b/src/ria_toolkit_oss/viz/pytorch_state_dict.py
index 578ebd0..6c625bc 100644
--- a/src/ria_toolkit_oss/viz/pytorch_state_dict.py
+++ b/src/ria_toolkit_oss/viz/pytorch_state_dict.py
@@ -56,29 +56,25 @@ def model_summary_plot(state_dict: dict) -> Figure:
         return create_styled_error_figure(
             "Empty State Dict",
             "No parameters found in state dict",
-            "Ensure the model state dictionary contains weight parameters"
+            "Ensure the model state dictionary contains weight parameters",
         )
     # Count parameters by layer type
     layer_info = []
     for key, tensor in state_dict.items():
-        if 'weight' in key:
+        if "weight" in key:
             try:
-                layer_name = key.replace('.weight', '')
+                layer_name = key.replace(".weight", "")
                 param_count = (
-                    tensor.numel() if hasattr(tensor, 'numel')
-                    else len(tensor.flatten()) if hasattr(tensor, 'flatten')
-                    else 0
+                    tensor.numel()
+                    if hasattr(tensor, "numel")
+                    else len(tensor.flatten()) if hasattr(tensor, "flatten") else 0
                 )
                 shape = (
-                    list(tensor.shape) if hasattr(tensor, 'shape')
-                    else [len(tensor)] if hasattr(tensor, '__len__')
-                    else []
+                    list(tensor.shape)
+                    if hasattr(tensor, "shape")
+                    else [len(tensor)] if hasattr(tensor, "__len__") else []
                 )
-                layer_info.append({
-                    'layer': layer_name,
-                    'parameters': param_count,
-                    'shape': shape
-                })
+                layer_info.append({"layer": layer_name, "parameters": param_count, "shape": shape})
             except Exception as e:
                 print(f"Warning: Could not process layer {key}: {e}")
                 continue
@@ -86,22 +82,24 @@ def model_summary_plot(state_dict: dict) -> Figure:
         return create_styled_error_figure(
             "No Weight Layers Found",
             "No weight layers found in state dict",
-            "Ensure the state dictionary contains layers with '.weight' parameters"
+            "Ensure the state dictionary contains layers with '.weight' parameters",
         )
     # Create bar chart of parameter counts
-    fig = go.Figure(data=[
-        go.Bar(
-            x=[info['layer'] for info in layer_info],
-            y=[info['parameters'] for info in layer_info],
-            text=[f"Shape: {info['shape']}" for info in layer_info],
-            textposition='auto',
-        )
-    ])
+    fig = go.Figure(
+        data=[
+            go.Bar(
+                x=[info["layer"] for info in layer_info],
+                y=[info["parameters"] for info in layer_info],
+                text=[f"Shape: {info['shape']}" for info in layer_info],
+                textposition="auto",
+            )
+        ]
+    )
     fig.update_layout(
         title="Model Layer Parameter Counts",
         xaxis_title="Layer",
         yaxis_title="Number of Parameters",
-        template="plotly_dark"
+        template="plotly_dark",
     )
     return fig
 
@@ -110,48 +108,36 @@ def layer_weights_plot(state_dict: dict, layer_name: str = None) -> Figure:
     """Visualize weights for a specific layer."""
     if not state_dict:
         return create_styled_error_figure(
-            "Empty State Dict",
-            "No data in state dict",
-            "Ensure the model state dictionary contains data"
+            "Empty State Dict", "No data in state dict", "Ensure the model state dictionary contains data"
         )
     if layer_name is None:
         # Get first weight tensor
-        weight_keys = [k for k in state_dict.keys() if 'weight' in k]
+        weight_keys = [k for k in state_dict.keys() if "weight" in k]
         if not weight_keys:
             return create_styled_error_figure(
                 "No Weight Tensors Found",
                 "No weight tensors found in state dict",
-                "Ensure the state dictionary contains layers with '.weight' parameters"
+                "Ensure the state dictionary contains layers with '.weight' parameters",
             )
         layer_name = weight_keys[0]
     try:
         weights = state_dict[layer_name]
         # Convert to numpy if it's a torch tensor
-        if hasattr(weights, 'numpy'):
-            weights_np = weights.detach().numpy() if hasattr(weights, 'detach') else weights.numpy()
-        elif hasattr(weights, 'cpu'):
+        if hasattr(weights, "numpy"):
+            weights_np = weights.detach().numpy() if hasattr(weights, "detach") else weights.numpy()
+        elif hasattr(weights, "cpu"):
             weights_np = weights.cpu().detach().numpy()
         else:
             weights_np = np.array(weights)
         # For 2D weights, create heatmap
         if len(weights_np.shape) == 2:
-            fig = go.Figure(data=go.Heatmap(
-                z=weights_np,
-                colorscale='RdBu',
-                zmid=0
-            ))
-            fig.update_layout(
-                title=f"Weights Heatmap: {layer_name}",
-                template="plotly_dark"
-            )
+            fig = go.Figure(data=go.Heatmap(z=weights_np, colorscale="RdBu", zmid=0))
+            fig.update_layout(title=f"Weights Heatmap: {layer_name}", template="plotly_dark")
         else:
             # For other shapes, flatten and show histogram
             flat_weights = weights_np.flatten()
             fig = go.Figure(data=[go.Histogram(x=flat_weights, nbinsx=50)])
-            fig.update_layout(
-                title=f"Weight Distribution: {layer_name}",
-                template="plotly_dark"
-            )
+            fig.update_layout(title=f"Weight Distribution: {layer_name}", template="plotly_dark")
 
         return fig
 
@@ -159,7 +145,7 @@ def layer_weights_plot(state_dict: dict, layer_name: str = None) -> Figure:
         return create_styled_error_figure(
             "Layer Processing Error",
             f"Error processing layer {layer_name}: {str(e)}",
-            "Check that the layer name exists and contains valid tensor data"
+            "Check that the layer name exists and contains valid tensor data",
         )
 
 
@@ -167,21 +153,19 @@ def weight_distribution_plot(state_dict: dict) -> Figure:
     """Show distribution of weights across all layers."""
     if not state_dict:
         return create_styled_error_figure(
-            "Empty State Dict",
-            "No data in state dict",
-            "Ensure the model state dictionary contains data"
+            "Empty State Dict", "No data in state dict", "Ensure the model state dictionary contains data"
         )
 
     all_weights = []
     layer_names = []
 
     for key, tensor in state_dict.items():
-        if 'weight' in key:
+        if "weight" in key:
             try:
                 # Convert to numpy if it's a torch tensor
-                if hasattr(tensor, 'numpy'):
-                    weights_np = tensor.detach().numpy() if hasattr(tensor, 'detach') else tensor.numpy()
-                elif hasattr(tensor, 'cpu'):
+                if hasattr(tensor, "numpy"):
+                    weights_np = tensor.detach().numpy() if hasattr(tensor, "detach") else tensor.numpy()
+                elif hasattr(tensor, "cpu"):
                     weights_np = tensor.cpu().detach().numpy()
                 else:
                     weights_np = np.array(tensor)
@@ -196,21 +180,15 @@ def weight_distribution_plot(state_dict: dict) -> Figure:
         return create_styled_error_figure(
             "No Weight Data Found",
             "No weight data found in state dict",
-            "Ensure the state dictionary contains layers with '.weight' parameters"
+            "Ensure the state dictionary contains layers with '.weight' parameters",
         )
 
-    fig = go.Figure(data=[
-        go.Histogram(
-            x=all_weights,
-            nbinsx=100,
-            name="All Weights"
-        )
-    ])
+    fig = go.Figure(data=[go.Histogram(x=all_weights, nbinsx=100, name="All Weights")])
 
     fig.update_layout(
         title="Overall Weight Distribution",
         xaxis_title="Weight Value",
         yaxis_title="Frequency",
-        template="plotly_dark"
+        template="plotly_dark",
     )
     return fig
diff --git a/src/ria_toolkit_oss/viz/radio_dataset.py b/src/ria_toolkit_oss/viz/radio_dataset.py
index cae4084..a96b4d2 100644
--- a/src/ria_toolkit_oss/viz/radio_dataset.py
+++ b/src/ria_toolkit_oss/viz/radio_dataset.py
@@ -133,8 +133,10 @@ def class_distribution_plot(dataset, class_key: str = "modulation") -> Figure:
             return create_styled_error_figure(
                 "No Class Labels Found",
                 "This dataset contains numerical data without categorical labels.",
-                ("Try using the Dataset Overview widget for data analysis, "
-                 "or check if your dataset has hidden categorical columns."),
+                (
+                    "Try using the Dataset Overview widget for data analysis, "
+                    "or check if your dataset has hidden categorical columns."
+                ),
             )
 
         # Count examples per class (limit to top 20 for performance)
@@ -355,8 +357,16 @@ def _compute_spectrogram(sample_data, nperseg: int, hop_length: int, n_frames: i
     return Sxx
 
 
-def _create_spectrogram_figure(Sxx, n_frames: int, hop_length: int, n_samples: int, freq_bins: int,
-                               sample_idx: int, class_key: str, sample_metadata) -> Figure:
+def _create_spectrogram_figure(
+    Sxx,
+    n_frames: int,
+    hop_length: int,
+    n_samples: int,
+    freq_bins: int,
+    sample_idx: int,
+    class_key: str,
+    sample_metadata,
+) -> Figure:
     """Create the plotly figure for the spectrogram."""
     # Convert to dB
     Sxx_db = 10 * np.log10(Sxx + 1e-10)
@@ -410,8 +420,9 @@ def sample_spectrogram_plot(dataset, class_key: str = "modulation", sample_idx:
         Sxx = _compute_spectrogram(sample_data, nperseg, hop_length, n_frames, freq_bins)
 
         # Create and return the figure
-        return _create_spectrogram_figure(Sxx, n_frames, hop_length, n_samples, freq_bins,
-                                          sample_idx, class_key, sample_metadata)
+        return _create_spectrogram_figure(
+            Sxx, n_frames, hop_length, n_samples, freq_bins, sample_idx, class_key, sample_metadata
+        )
 
     except Exception as e:
         return create_styled_error_figure(