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..edc5004
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+++ b/src/ria_toolkit_oss/viz/radio_dataset.py
@@ -0,0 +1,430 @@
+"""
+Simple, clean visualization utilities for RadioDataset analysis.
+"""
+
+import random
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+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 += f"
💡 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",
+ f"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 += f" (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 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
+ if class_key not in metadata.columns:
+ alternatives = ["class", "label", "modulation", "impairment", "use_case"]
+ for alt in alternatives:
+ if alt in metadata.columns:
+ class_key = alt
+ break
+
+ # Select sample
+ if sample_idx is None:
+ sample_idx = random.randint(0, len(metadata) - 1)
+
+ sample_metadata = metadata.iloc[sample_idx]
+
+ # Try to get actual sample data, fall back to synthetic
+ try:
+ sample_data = dataset[sample_idx]
+ except:
+ # 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))
+
+ # Ensure complex data
+ if not np.iscomplexobj(sample_data):
+ sample_data = sample_data.astype(complex)
+
+ # Simple FFT-based spectrogram
+ n_samples = len(sample_data)
+
+ # Ensure minimum viable data size
+ 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))
+
+ # Create spectrogram using numpy (no scipy dependency)
+ hop_length = max(1, nperseg // 2) # Prevent zero hop_length
+
+ # Ensure we can create at least one frame
+ 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) # Prevent zero frequency bins
+ 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) # Prevent index overflow
+
+ if end_idx > start_idx: # Ensure we have data to process
+ 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
+
+ # 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) # Prevent division by zero
+ 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
+
+ except Exception as e:
+ return create_styled_error_figure(
+ "Spectrogram Error",
+ "An error occurred while generating the spectrogram plot.",
+ f"Technical details: {str(e)}"
+ )
\ No newline at end of file