diff --git a/src/ria_toolkit_oss/viz/onnx.py b/src/ria_toolkit_oss/viz/onnx.py
new file mode 100644
index 0000000..e260eeb
--- /dev/null
+++ b/src/ria_toolkit_oss/viz/onnx.py
@@ -0,0 +1,562 @@
+"""
+ONNX model visualization utilities.
+
+This module provides visualization functions for ONNX models following the same pattern
+as other ria-toolkit-oss visualization modules.
+"""
+
+from pathlib import Path
+
+import plotly.express as px
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+
+try:
+ import onnx
+ import onnx.helper
+ import onnx.numpy_helper
+
+ ONNX_AVAILABLE = True
+except ImportError:
+ ONNX_AVAILABLE = False
+
+
+def create_styled_error_figure(title: str, message: str, suggestion: str = None) -> go.Figure:
+ """Create a professional error figure with Qoherent dark theme styling."""
+ fig = go.Figure()
+
+ # Create a clean, centered text display using Plotly's text formatting
+ main_text = f"⚠️ {title}
"
+ main_text += f"{message}"
+
+ if suggestion:
+ main_text += "
💡 Suggestion:
"
+ main_text += f"{suggestion}"
+
+ # Add the main text annotation
+ fig.add_annotation(
+ text=main_text,
+ xref="paper",
+ yref="paper",
+ x=0.5,
+ y=0.5,
+ xanchor="center",
+ yanchor="middle",
+ showarrow=False,
+ align="center",
+ borderwidth=2,
+ bordercolor="#4a5568",
+ bgcolor="#2d3748",
+ font=dict(family="Arial, sans-serif", size=14, color="#e2e8f0"),
+ )
+
+ # Update layout with dark theme
+ fig.update_layout(
+ title="",
+ height=400,
+ template="plotly_dark",
+ margin=dict(l=40, r=40, t=40, b=40),
+ plot_bgcolor="#1a202c",
+ paper_bgcolor="#1a202c",
+ font=dict(color="#e2e8f0"),
+ )
+
+ # Remove axes and grid
+ fig.update_xaxes(visible=False)
+ fig.update_yaxes(visible=False)
+
+ return fig
+
+
+def graph_structure(file_path: Path) -> go.Figure:
+ """
+ Visualize the ONNX model graph structure showing nodes and connections.
+ Matches layout ID: graph_structure
+ """
+ if not ONNX_AVAILABLE:
+ return create_styled_error_figure(
+ "ONNX Not Available", "ONNX library is required for model analysis.", "Install with: pip install onnx"
+ )
+
+ try:
+ # Load ONNX model
+ model = onnx.load(str(file_path))
+ graph = model.graph
+ nodes = graph.node
+
+ 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."
+ )
+
+ # Create network diagram data
+ node_info = []
+ for i, node in enumerate(nodes):
+ node_info.append(
+ {
+ "id": i,
+ "name": node.name or f"{node.op_type}_{i}",
+ "op_type": node.op_type,
+ "inputs": len(node.input),
+ "outputs": len(node.output),
+ }
+ )
+
+ # Create visualization
+ fig = go.Figure()
+
+ # Simple linear layout for now
+ x_positions = list(range(len(node_info)))
+ y_positions = [0] * len(node_info)
+
+ # Add nodes as scatter points
+ fig.add_trace(
+ go.Scatter(
+ x=x_positions,
+ y=y_positions,
+ mode="markers+text",
+ marker=dict(
+ size=[min(max(info["inputs"] + info["outputs"] + 15, 20), 50) for info in node_info],
+ color=px.colors.qualitative.Set3[: len(node_info)],
+ opacity=0.8,
+ line=dict(width=2, color="white"),
+ ),
+ text=[f"{info['op_type']}" for info in node_info],
+ textposition="middle center",
+ textfont=dict(size=10, color="white"),
+ hovertemplate="%{text}
"
+ + "Name: %{customdata[0]}
"
+ + "Inputs: %{customdata[1]}
"
+ + "Outputs: %{customdata[2]}
"
+ + "",
+ customdata=[[info["name"], info["inputs"], info["outputs"]] for info in node_info],
+ name="Operators",
+ )
+ )
+
+ # Add connecting lines
+ for i in range(len(node_info) - 1):
+ fig.add_trace(
+ go.Scatter(
+ x=[x_positions[i], x_positions[i + 1]],
+ y=[y_positions[i], y_positions[i + 1]],
+ mode="lines",
+ line=dict(color="gray", width=1, dash="dot"),
+ showlegend=False,
+ hoverinfo="skip",
+ )
+ )
+
+ fig.update_layout(
+ title={
+ "text": (
+ "ONNX Graph Structure
"
+ f"{len(nodes)} Operators"
+ ),
+ "x": 0.5,
+ "xanchor": "center",
+ "font": {"size": 22},
+ },
+ xaxis_title="Execution Order",
+ yaxis_title="",
+ showlegend=False,
+ height=500,
+ template="plotly_dark",
+ yaxis=dict(showticklabels=False, showgrid=False),
+ xaxis=dict(showgrid=False),
+ margin=dict(l=50, r=50, t=80, b=50),
+ )
+
+ return fig
+
+ except Exception as e:
+ return create_styled_error_figure(
+ "Graph Analysis Error", "Could not analyze ONNX model structure.", f"Error: {str(e)}"
+ )
+
+
+def operator_analysis(file_path: Path) -> go.Figure:
+ """
+ Analyze the distribution and types of operators in the ONNX model.
+ Matches layout ID: operator_analysis
+ """
+ if not ONNX_AVAILABLE:
+ return create_styled_error_figure(
+ "ONNX Not Available", "ONNX library is required for operator analysis.", "Install with: pip install onnx"
+ )
+
+ try:
+ model = onnx.load(str(file_path))
+ graph = model.graph
+
+ # Count operators
+ op_counts = {}
+ for node in graph.node:
+ op_type = node.op_type
+ op_counts[op_type] = op_counts.get(op_type, 0) + 1
+
+ if not op_counts:
+ return create_styled_error_figure(
+ "No Operators",
+ "This ONNX model contains no operators to analyze.",
+ "Please verify the model file is valid.",
+ )
+
+ # Sort by frequency
+ sorted_ops = sorted(op_counts.items(), key=lambda x: x[1], reverse=True)
+
+ # Create pie chart and bar chart
+ fig = make_subplots(
+ rows=2,
+ cols=1,
+ subplot_titles=("Operator Distribution", "Operator Frequency"),
+ specs=[[{"type": "pie"}], [{"type": "bar"}]],
+ )
+
+ # Pie chart for operator distribution
+ op_names, op_values = zip(*sorted_ops) if sorted_ops else ([], [])
+
+ fig.add_trace(
+ go.Pie(
+ labels=list(op_names),
+ values=list(op_values),
+ textinfo="label+percent",
+ textposition="auto",
+ showlegend=False,
+ ),
+ row=1,
+ col=1,
+ )
+
+ # Bar chart for frequency
+ fig.add_trace(
+ go.Bar(
+ x=list(op_names),
+ y=list(op_values),
+ marker_color=px.colors.qualitative.Set3[: len(op_names)],
+ showlegend=False,
+ ),
+ row=2,
+ col=1,
+ )
+
+ fig.update_layout(
+ title={
+ "text": (
+ "ONNX Operator Analysis
"
+ f"{len(op_counts)} Unique Types"
+ ),
+ "x": 0.5,
+ "xanchor": "center",
+ "font": {"size": 22},
+ },
+ height=700,
+ template="plotly_dark",
+ )
+
+ return fig
+
+ except Exception as e:
+ return create_styled_error_figure(
+ "Operator Analysis Error", "Could not analyze ONNX operators.", f"Error: {str(e)}"
+ )
+
+
+def model_metadata(file_path: Path) -> go.Figure:
+ """
+ Display comprehensive metadata about the ONNX model.
+ Matches layout ID: model_metadata
+ """
+ if not ONNX_AVAILABLE:
+ return create_styled_error_figure(
+ "ONNX Not Available", "ONNX library is required for metadata analysis.", "Install with: pip install onnx"
+ )
+
+ try:
+ model = onnx.load(str(file_path))
+ graph = model.graph
+
+ # Calculate basic statistics
+ total_nodes = len(graph.node)
+ total_inputs = len(graph.input)
+ total_outputs = len(graph.output)
+ total_initializers = len(graph.initializer)
+
+ # Calculate parameter count
+ total_params = 0
+ for initializer in graph.initializer:
+ try:
+ tensor = onnx.numpy_helper.to_array(initializer)
+ total_params += tensor.size
+ except Exception:
+ pass # Skip if tensor can't be loaded
+
+ # Get model file size
+ file_size_mb = file_path.stat().st_size / (1024 * 1024)
+
+ # Create metadata display
+ fig = make_subplots(
+ rows=2,
+ cols=2,
+ subplot_titles=("Model Size", "Architecture", "Inputs/Outputs", "Parameters"),
+ specs=[[{"type": "indicator"}, {"type": "bar"}], [{"type": "table"}, {"type": "indicator"}]],
+ )
+
+ # Model size indicator
+ fig.add_trace(
+ go.Indicator(
+ mode="number+gauge",
+ value=file_size_mb,
+ title={"text": "Model Size (MB)"},
+ number={"suffix": " MB", "valueformat": ".2f"},
+ gauge={
+ "axis": {"range": [0, max(100, file_size_mb * 1.5)]},
+ "bar": {"color": "darkblue"},
+ "steps": [
+ {"range": [0, 10], "color": "lightgreen"},
+ {"range": [10, 50], "color": "yellow"},
+ {"range": [50, 100], "color": "orange"},
+ ],
+ },
+ ),
+ row=1,
+ col=1,
+ )
+
+ # Architecture components
+ arch_data = ["Nodes", "Inputs", "Outputs", "Initializers"]
+ 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),
+ row=1,
+ col=2,
+ )
+
+ # I/O Table
+ io_data = []
+
+ # Add input info
+ for inp in graph.input[:5]: # Limit to first 5
+ shape = "Unknown"
+ dtype = "Unknown"
+ if inp.type and inp.type.tensor_type:
+ # Get shape
+ if inp.type.tensor_type.shape:
+ dims = [str(d.dim_value) if d.dim_value > 0 else "?" for d in inp.type.tensor_type.shape.dim]
+ shape = f"[{', '.join(dims)}]"
+
+ # Get data type
+ elem_type = inp.type.tensor_type.elem_type
+ type_map = {
+ 1: "float32",
+ 2: "uint8",
+ 3: "int8",
+ 6: "int32",
+ 7: "int64",
+ 9: "bool",
+ 10: "float16",
+ 11: "double",
+ }
+ dtype = type_map.get(elem_type, f"type_{elem_type}")
+
+ io_data.append(["Input", inp.name[:20], shape, dtype])
+
+ # Add output info
+ for out in graph.output[:5]: # Limit to first 5
+ shape = "Unknown"
+ dtype = "Unknown"
+ if out.type and out.type.tensor_type:
+ if out.type.tensor_type.shape:
+ dims = [str(d.dim_value) if d.dim_value > 0 else "?" for d in out.type.tensor_type.shape.dim]
+ shape = f"[{', '.join(dims)}]"
+
+ elem_type = out.type.tensor_type.elem_type
+ type_map = {
+ 1: "float32",
+ 2: "uint8",
+ 3: "int8",
+ 6: "int32",
+ 7: "int64",
+ 9: "bool",
+ 10: "float16",
+ 11: "double",
+ }
+ dtype = type_map.get(elem_type, f"type_{elem_type}")
+
+ io_data.append(["Output", out.name[:20], shape, dtype])
+
+ 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"),
+ ),
+ row=2,
+ col=1,
+ )
+
+ # Parameters indicator
+ fig.add_trace(
+ go.Indicator(
+ mode="number",
+ value=total_params,
+ title={"text": "Total Parameters"},
+ number={"suffix": "M", "valueformat": ".2f"},
+ number_font_size=30,
+ ),
+ row=2,
+ col=2,
+ )
+
+ fig.update_layout(
+ title={
+ "text": (
+ "ONNX Model Metadata
"
+ f"{total_params/1e6:.2f}M Parameters"
+ ),
+ "x": 0.5,
+ "xanchor": "center",
+ "font": {"size": 22},
+ },
+ height=600,
+ template="plotly_dark",
+ showlegend=False,
+ )
+
+ return fig
+
+ except Exception as e:
+ return create_styled_error_figure(
+ "Metadata Analysis Error", "Could not extract ONNX model metadata.", f"Error: {str(e)}"
+ )
+
+
+def performance_metrics(file_path: Path) -> go.Figure:
+ """
+ Display performance and computational metrics for the ONNX model.
+ Matches layout ID: performance_metrics
+ """
+ if not ONNX_AVAILABLE:
+ return create_styled_error_figure(
+ "ONNX Not Available",
+ "ONNX library is required for performance analysis.",
+ "Install with: pip install onnx",
+ )
+
+ try:
+ model = onnx.load(str(file_path))
+ graph = model.graph
+
+ # Calculate metrics
+ model_size_bytes = file_path.stat().st_size
+ model_size_mb = model_size_bytes / (1024 * 1024)
+
+ # Count parameters
+ total_params = 0
+ for initializer in graph.initializer:
+ try:
+ tensor = onnx.numpy_helper.to_array(initializer)
+ total_params += tensor.size
+ except Exception:
+ pass
+
+ # Estimate memory usage (rough approximation)
+ param_memory_mb = (total_params * 4) / (1024 * 1024) # Assume float32
+
+ # Count operations by complexity
+ compute_ops = ["Conv", "MatMul", "Gemm", "LSTM", "GRU"]
+ efficient_ops = ["Relu", "Add", "Mul", "BatchNormalization", "Dropout"]
+
+ compute_count = sum(1 for node in graph.node if any(op in node.op_type for op in compute_ops))
+ efficient_count = sum(1 for node in graph.node if any(op in node.op_type for op in efficient_ops))
+ total_ops = len(graph.node)
+ other_count = total_ops - compute_count - efficient_count
+
+ # Create performance dashboard
+ fig = make_subplots(
+ rows=2,
+ cols=2,
+ subplot_titles=("Model Efficiency", "Memory Usage", "Operation Types", "Complexity Score"),
+ specs=[[{"type": "bar"}, {"type": "bar"}], [{"type": "pie"}, {"type": "indicator"}]],
+ )
+
+ # Model efficiency metrics
+ efficiency_metrics = ["Model Size (MB)", "Parameters (M)", "Total Ops"]
+ efficiency_values = [model_size_mb, total_params / 1e6, total_ops]
+
+ fig.add_trace(
+ go.Bar(
+ x=efficiency_metrics, y=efficiency_values, marker_color=["blue", "green", "orange"], showlegend=False
+ ),
+ row=1,
+ col=1,
+ )
+
+ # Memory usage
+ memory_types = ["Parameters", "Est. Inference"]
+ 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),
+ row=1,
+ col=2,
+ )
+
+ # Operation types pie chart
+ fig.add_trace(
+ go.Pie(
+ labels=["Compute Ops", "Efficient Ops", "Other Ops"],
+ values=[compute_count, efficient_count, other_count],
+ marker_colors=["red", "green", "gray"],
+ ),
+ row=2,
+ col=1,
+ )
+
+ # Complexity score (simple heuristic)
+ complexity_score = min(100, (model_size_mb * 10 + total_params / 1e6 * 20 + compute_count))
+
+ fig.add_trace(
+ go.Indicator(
+ mode="gauge+number",
+ value=complexity_score,
+ title={"text": "Complexity Score"},
+ gauge={
+ "axis": {"range": [0, 100]},
+ "bar": {
+ "color": "darkred" if complexity_score > 70 else "orange" if complexity_score > 40 else "green"
+ },
+ "steps": [
+ {"range": [0, 40], "color": "lightgreen"},
+ {"range": [40, 70], "color": "yellow"},
+ {"range": [70, 100], "color": "lightcoral"},
+ ],
+ },
+ ),
+ row=2,
+ col=2,
+ )
+
+ fig.update_layout(
+ title={
+ "text": (
+ "ONNX Performance Metrics
"
+ f""
+ f"Complexity Score: {complexity_score:.0f}/100"
+ ),
+ "x": 0.5,
+ "xanchor": "center",
+ "font": {"size": 22},
+ },
+ height=600,
+ template="plotly_dark",
+ showlegend=False,
+ )
+
+ return fig
+
+ except Exception as e:
+ return create_styled_error_figure(
+ "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
new file mode 100644
index 0000000..6c625bc
--- /dev/null
+++ b/src/ria_toolkit_oss/viz/pytorch_state_dict.py
@@ -0,0 +1,194 @@
+import numpy as np
+import plotly.graph_objects as go
+from plotly.graph_objects import Figure
+
+
+def create_styled_error_figure(title: str, message: str, suggestion: str = None) -> go.Figure:
+ """Create a professional error figure with Qoherent dark theme styling."""
+ fig = go.Figure()
+
+ # Create a clean, centered text display using Plotly's text formatting
+ main_text = f"⚠️ {title}
"
+ main_text += f"{message}"
+
+ if suggestion:
+ main_text += "
💡 Suggestion:
"
+ main_text += f"{suggestion}"
+
+ # Add the main text annotation
+ fig.add_annotation(
+ text=main_text,
+ xref="paper",
+ yref="paper",
+ x=0.5,
+ y=0.5,
+ xanchor="center",
+ yanchor="middle",
+ showarrow=False,
+ align="center",
+ borderwidth=2,
+ bordercolor="#4a5568",
+ bgcolor="#2d3748",
+ font=dict(family="Arial, sans-serif", size=14, color="#e2e8f0"),
+ )
+
+ # Update layout with dark theme
+ fig.update_layout(
+ title="",
+ height=400,
+ template="plotly_dark",
+ margin=dict(l=40, r=40, t=40, b=40),
+ plot_bgcolor="#1a202c",
+ paper_bgcolor="#1a202c",
+ font=dict(color="#e2e8f0"),
+ )
+
+ # Remove axes and grid
+ fig.update_xaxes(visible=False)
+ fig.update_yaxes(visible=False)
+
+ return fig
+
+
+def model_summary_plot(state_dict: dict) -> Figure:
+ """Generate a summary plot of the PyTorch model state dict."""
+ if not state_dict:
+ return create_styled_error_figure(
+ "Empty State Dict",
+ "No parameters found in state dict",
+ "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:
+ try:
+ layer_name = key.replace(".weight", "")
+ param_count = (
+ 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 []
+ )
+ 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
+ if not layer_info:
+ 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",
+ )
+ # 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.update_layout(
+ title="Model Layer Parameter Counts",
+ xaxis_title="Layer",
+ yaxis_title="Number of Parameters",
+ template="plotly_dark",
+ )
+ return fig
+
+
+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"
+ )
+ if layer_name is None:
+ # Get first weight tensor
+ 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",
+ )
+ 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"):
+ 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")
+ 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")
+
+ return fig
+
+ except Exception as e:
+ 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",
+ )
+
+
+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"
+ )
+
+ all_weights = []
+ layer_names = []
+
+ for key, tensor in state_dict.items():
+ 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"):
+ weights_np = tensor.cpu().detach().numpy()
+ else:
+ weights_np = np.array(tensor)
+ flat_weights = weights_np.flatten()
+ all_weights.extend(flat_weights)
+ layer_names.extend([key] * len(flat_weights))
+ except Exception as e:
+ print(f"Warning: Could not process weights for layer {key}: {e}")
+ continue
+
+ if not all_weights:
+ 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",
+ )
+
+ 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",
+ )
+ return fig
diff --git a/src/ria_toolkit_oss/viz/radio_dataset.py b/src/ria_toolkit_oss/viz/radio_dataset.py
new file mode 100644
index 0000000..a96b4d2
--- /dev/null
+++ b/src/ria_toolkit_oss/viz/radio_dataset.py
@@ -0,0 +1,432 @@
+"""
+Simple, clean visualization utilities for RadioDataset analysis.
+"""
+
+import random
+from typing import Optional
+
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+from plotly.graph_objects import Figure
+from plotly.subplots import make_subplots
+
+
+def create_styled_error_figure(title: str, message: str, suggestion: str = None) -> Figure:
+ """Create a professional error figure with Qoherent dark theme styling."""
+ fig = go.Figure()
+
+ # Create a clean, centered text display using Plotly's text formatting
+ main_text = f"⚠️ {title}
"
+ main_text += f"{message}"
+
+ if suggestion:
+ main_text += "
💡 Suggestion:
"
+ main_text += f"{suggestion}"
+
+ # Add the main text annotation
+ fig.add_annotation(
+ text=main_text,
+ xref="paper",
+ yref="paper",
+ x=0.5,
+ y=0.5,
+ xanchor="center",
+ yanchor="middle",
+ showarrow=False,
+ align="center",
+ borderwidth=2,
+ bordercolor="#4a5568",
+ bgcolor="#2d3748",
+ font=dict(family="Arial, sans-serif", size=14, color="#e2e8f0"),
+ )
+
+ # Update layout with dark theme
+ fig.update_layout(
+ title="",
+ height=400,
+ template="plotly_dark",
+ margin=dict(l=40, r=40, t=40, b=40),
+ plot_bgcolor="#1a202c",
+ paper_bgcolor="#1a202c",
+ font=dict(color="#e2e8f0"),
+ )
+
+ # Remove axes and grid
+ fig.update_xaxes(visible=False)
+ fig.update_yaxes(visible=False)
+
+ return fig
+
+
+def _check_dataset_compatibility(dataset, plot_type: str) -> tuple[bool, str]:
+ """Check if dataset is compatible with a specific plot type.
+ Returns (is_compatible, error_message)
+ """
+ try:
+ metadata = dataset.metadata
+
+ if len(metadata) == 0:
+ return False, "Dataset is empty"
+
+ if plot_type == "class_distribution":
+ # Check if we have any categorical columns
+ categorical_cols = [col for col in metadata.columns if metadata[col].dtype == "object"]
+ alternatives = ["class", "label", "modulation", "impairment", "use_case", "category", "labels"]
+
+ has_class_col = any(alt in metadata.columns for alt in alternatives)
+ has_categorical = len(categorical_cols) > 0
+
+ if not has_class_col and not has_categorical:
+ return False, "No categorical columns found for class distribution"
+
+ elif plot_type == "sample_spectrogram":
+ # Check if we can generate a valid spectrogram
+ if len(metadata) < 1:
+ return False, "No samples available for spectrogram"
+
+ # Check if we can access sample data (basic test)
+ try:
+ sample_data = dataset[0] if hasattr(dataset, "__getitem__") else None
+ if sample_data is None or len(sample_data) < 32:
+ return False, "Insufficient sample data for spectrogram (need at least 32 points)"
+ except Exception:
+ # If we can't access data, we'll rely on synthetic data generation
+ pass
+
+ return True, ""
+
+ except Exception as e:
+ return False, f"Dataset compatibility check failed: {str(e)}"
+
+
+def class_distribution_plot(dataset, class_key: str = "modulation") -> Figure:
+ """Generate a bar plot showing the distribution of examples across classes."""
+ try:
+ # Check dataset compatibility first
+ is_compatible, error_msg = _check_dataset_compatibility(dataset, "class_distribution")
+ if not is_compatible:
+ return create_styled_error_figure(
+ "Dataset Not Compatible",
+ "This dataset doesn't have categorical labels needed for class distribution analysis.",
+ "Try using the Dataset Overview widget to explore the available data columns.",
+ )
+
+ metadata = dataset.metadata
+
+ # Find the class column
+ if class_key not in metadata.columns:
+ # Try common alternatives
+ alternatives = ["class", "label", "modulation", "impairment", "use_case", "category", "labels"]
+ for alt in alternatives:
+ if alt in metadata.columns:
+ class_key = alt
+ break
+ else:
+ # Use first categorical column
+ for col in metadata.columns:
+ if metadata[col].dtype == "object" or metadata[col].nunique() < 50:
+ class_key = col
+ break
+
+ if class_key not in metadata.columns:
+ 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."
+ ),
+ )
+
+ # Count examples per class (limit to top 20 for performance)
+ class_counts = metadata[class_key].value_counts()
+ if len(class_counts) > 20:
+ class_counts = class_counts.head(20)
+
+ class_counts = class_counts.sort_index()
+
+ # Create simple bar plot
+ fig = px.bar(x=class_counts.index, y=class_counts.values, title=f"Class Distribution: {class_key.title()}")
+
+ fig.update_traces(texttemplate="%{y}", textposition="outside")
+ fig.update_layout(
+ xaxis_title=class_key.title(),
+ yaxis_title="Number of Examples",
+ showlegend=False,
+ height=400,
+ template="plotly_dark",
+ )
+
+ return fig
+
+ except Exception as e:
+ return create_styled_error_figure(
+ "Class Distribution Error",
+ "An error occurred while generating the class distribution plot.",
+ f"Technical details: {str(e)}",
+ )
+
+
+def dataset_overview_plot(dataset) -> Figure:
+ """Generate an overview plot with key dataset statistics."""
+ try:
+ metadata = dataset.metadata
+ total_examples = len(metadata)
+
+ # Create subplot with multiple charts
+
+ # Determine subplot titles based on data type
+ categorical_cols = [col for col in metadata.columns if metadata[col].dtype == "object"]
+ numeric_cols = [col for col in metadata.columns if metadata[col].dtype in ["int64", "float64"]]
+
+ dist_title = "Value Distribution" if categorical_cols else "Data Distribution"
+
+ fig = make_subplots(
+ rows=2,
+ cols=2,
+ subplot_titles=("Dataset Size", "Data Types", dist_title, "Statistics Summary"),
+ specs=[
+ [{"type": "indicator"}, {"type": "bar"}],
+ [{"type": "histogram" if not categorical_cols else "bar"}, {"type": "table"}],
+ ],
+ )
+
+ # Top left: Dataset size indicator
+ fig.add_trace(
+ go.Indicator(
+ mode="number", value=total_examples, title={"text": "Total Examples"}, number={"font": {"size": 40}}
+ ),
+ row=1,
+ col=1,
+ )
+
+ # Top right: Data types distribution
+ dtype_counts = metadata.dtypes.value_counts()
+ fig.add_trace(
+ go.Bar(
+ x=[str(dt) for dt in dtype_counts.index], y=dtype_counts.values, name="Data Types", showlegend=False
+ ),
+ row=1,
+ col=2,
+ )
+
+ # Bottom left: Show distribution of numeric columns or categorical if available
+ categorical_cols = [col for col in metadata.columns if metadata[col].dtype == "object"]
+ numeric_cols = [col for col in metadata.columns if metadata[col].dtype in ["int64", "float64"]]
+
+ if categorical_cols:
+ col = categorical_cols[0] # Show first categorical column
+ value_counts = metadata[col].value_counts().head(10)
+ fig.add_trace(
+ go.Bar(x=value_counts.index, y=value_counts.values, name=f"{col} Distribution", showlegend=False),
+ row=2,
+ col=1,
+ )
+ elif numeric_cols:
+ # Show histogram of first numeric column
+ col = numeric_cols[0]
+ fig.add_trace(
+ go.Histogram(x=metadata[col], name=f"{col} Distribution", showlegend=False, nbinsx=20), row=2, col=1
+ )
+
+ # Bottom right: Basic statistics table
+ stats_data = []
+ display_cols = numeric_cols[:5] if len(numeric_cols) > 0 else metadata.columns[:5]
+
+ for col in display_cols:
+ if metadata[col].dtype in ["int64", "float64"]:
+ stats_data.append(
+ [
+ col[:15] + "..." if len(col) > 15 else col, # Truncate long column names
+ f"{metadata[col].mean():.3f}",
+ f"{metadata[col].std():.3f}",
+ f"{metadata[col].min():.3f}",
+ f"{metadata[col].max():.3f}",
+ ]
+ )
+ else:
+ unique_count = metadata[col].nunique()
+ stats_data.append(
+ [col[:15] + "..." if len(col) > 15 else col, "N/A", "N/A", f"{unique_count} unique", "N/A"]
+ )
+
+ if stats_data:
+ fig.add_trace(
+ go.Table(
+ header=dict(
+ values=["Column", "Mean", "Std", "Min/Unique", "Max"],
+ fill_color="rgba(30, 30, 30, 0.8)",
+ align="center",
+ font=dict(color="white", size=12),
+ ),
+ cells=dict(
+ values=list(zip(*stats_data)),
+ fill_color="rgba(50, 50, 50, 0.6)",
+ align="center",
+ font=dict(color="white", size=11),
+ ),
+ ),
+ row=2,
+ col=2,
+ )
+
+ # Create informative title
+ total_cols = len(metadata.columns)
+ title = f"Dataset Overview - {total_examples} samples, {total_cols} columns"
+ if total_cols > 5:
+ title += " (showing first 5)"
+
+ fig.update_layout(title=title, height=600, showlegend=False, template="plotly_dark")
+
+ return fig
+
+ except Exception as e:
+ return create_styled_error_figure(
+ "Dataset Overview Error",
+ "An error occurred while generating the dataset overview.",
+ f"Technical details: {str(e)}",
+ )
+
+
+def _find_class_column(metadata, class_key: str) -> str:
+ """Find the appropriate class column in metadata."""
+ if class_key in metadata.columns:
+ return class_key
+
+ alternatives = ["class", "label", "modulation", "impairment", "use_case"]
+ for alt in alternatives:
+ if alt in metadata.columns:
+ return alt
+ return class_key
+
+
+def _get_sample_data(dataset, sample_idx: int):
+ """Get sample data from dataset, with synthetic fallback."""
+ try:
+ return dataset[sample_idx]
+ except Exception:
+ # Generate synthetic signal based on class
+ n_samples = 1024
+ t = np.linspace(0, 1, n_samples)
+ freq = 0.1 + 0.05 * sample_idx % 5 # Vary frequency by sample
+ sample_data = np.exp(1j * 2 * np.pi * freq * t)
+ # Add some noise
+ sample_data += 0.1 * (np.random.randn(n_samples) + 1j * np.random.randn(n_samples))
+ return sample_data
+
+
+def _calculate_spectrogram_params(n_samples: int) -> tuple[int, int, int, int]:
+ """Calculate spectrogram parameters based on sample length."""
+ if n_samples < 32:
+ raise ValueError(f"Insufficient data: need at least 32 samples, got {n_samples}")
+
+ nperseg = min(256, max(32, n_samples // 4))
+ hop_length = max(1, nperseg // 2)
+
+ # Adjust for very short signals
+ if n_samples < nperseg:
+ nperseg = n_samples
+ hop_length = 1
+
+ n_frames = max(1, (n_samples - nperseg) // hop_length + 1)
+ freq_bins = max(1, nperseg // 2)
+
+ return nperseg, hop_length, n_frames, freq_bins
+
+
+def _compute_spectrogram(sample_data, nperseg: int, hop_length: int, n_frames: int, freq_bins: int):
+ """Compute spectrogram using FFT."""
+ n_samples = len(sample_data)
+ Sxx = np.zeros((freq_bins, n_frames))
+
+ for i in range(n_frames):
+ start_idx = i * hop_length
+ end_idx = min(start_idx + nperseg, n_samples)
+
+ if end_idx > start_idx:
+ windowed = sample_data[start_idx:end_idx]
+
+ # Pad if necessary to maintain nperseg size
+ if len(windowed) < nperseg:
+ windowed = np.pad(windowed, (0, nperseg - len(windowed)), mode="constant")
+
+ fft_result = np.fft.fft(windowed)
+ Sxx[:, i] = np.abs(fft_result[:freq_bins]) ** 2
+
+ 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:
+ """Create the plotly figure for the spectrogram."""
+ # Convert to dB
+ Sxx_db = 10 * np.log10(Sxx + 1e-10)
+
+ # Create time and frequency vectors
+ t = np.arange(n_frames) * hop_length / max(1, n_samples)
+ f = np.linspace(0, 0.5, freq_bins)
+
+ # Create plot
+ fig = go.Figure(data=go.Heatmap(z=Sxx_db, x=t, y=f, colorscale="viridis", colorbar=dict(title="Power (dB)")))
+
+ # Add title with metadata
+ title = f"Sample Spectrogram (Index: {sample_idx})"
+ if class_key in sample_metadata:
+ title += f" - {class_key}: {sample_metadata[class_key]}"
+
+ fig.update_layout(title=title, xaxis_title="Time", yaxis_title="Frequency", height=400, template="plotly_dark")
+ return fig
+
+
+def sample_spectrogram_plot(dataset, class_key: str = "modulation", sample_idx: Optional[int] = None) -> Figure:
+ """Generate a spectrogram plot from a sample in the dataset."""
+ try:
+ # Check dataset compatibility first
+ is_compatible, error_msg = _check_dataset_compatibility(dataset, "sample_spectrogram")
+ if not is_compatible:
+ return create_styled_error_figure(
+ "Spectrogram Not Available",
+ "This dataset doesn't have sufficient signal data for spectrogram visualization.",
+ "Ensure your dataset contains complex-valued signal samples with at least 32 data points per sample.",
+ )
+
+ metadata = dataset.metadata
+ if len(metadata) == 0:
+ raise ValueError("Dataset is empty")
+
+ # Find class column and select sample
+ class_key = _find_class_column(metadata, class_key)
+ if sample_idx is None:
+ sample_idx = random.randint(0, len(metadata) - 1)
+ sample_metadata = metadata.iloc[sample_idx]
+
+ # Get sample data and ensure it's complex
+ sample_data = _get_sample_data(dataset, sample_idx)
+ if not np.iscomplexobj(sample_data):
+ sample_data = sample_data.astype(complex)
+
+ # Calculate spectrogram parameters and compute spectrogram
+ n_samples = len(sample_data)
+ nperseg, hop_length, n_frames, freq_bins = _calculate_spectrogram_params(n_samples)
+ 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
+ )
+
+ except Exception as e:
+ return create_styled_error_figure(
+ "Spectrogram Error",
+ "An error occurred while generating the spectrogram plot.",
+ f"Technical details: {str(e)}",
+ )