Signal Attention Visualizer

PODCAST: explore the RF_QUANTUM_SCYTHE Signal Attention Visualizer Server, a WebSocket server designed to display real-time signal attention maps. This Python-based server utilizes asynchronous operations to manage client connections, register and unregister users, and broadcast attention data to connected clients. It can operate in a demo mode that generates synthetic attention data or in a standard mode where external components provide the data for visualization, which is then processed by a SignalAttentionVisualizer to create radar sweep data for display. The server is configured to run in a background thread, ensuring continuous operation for visualizing complex signal patterns.

# RF_QUANTUM_SCYTHE Signal Attention Visualizer
# WebSocket server that displays signal attention maps in real-time

The RF_QUANTUM_SCYTHE Signal Attention Visualizer Server handles and broadcasts real-time signal attention data to clients through a series of coordinated components, primarily utilizing a queue and WebSocket communication.

Here’s a breakdown of the process:

• Data Ingestion: Signal attention data is added to a centralized attention_queue. This is done via the add_attention_data function, which simply puts attention_features into the queue. In a normal operational mode, external components would call this function to provide visualization data. In demo mode, synthetic data generated by generate_demo_data is periodically added to this queue.
• Queuing Mechanism: The attention_queue acts as a buffer, storing incoming attention data until it can be processed and sent to clients.
• Producer Handler: For each connected client, a producer_task runs a producer_handler asynchronously. This handler is responsible for sending attention data to the specific client’s WebSocket.
  • The producer_handler continuously checks the attention_queue for new data, specifically every 100 milliseconds using asyncio.sleep(0.1).
  • When new attention_data is available in the queue (i.e., while not attention_queue.empty():), it retrieves the data using attention_queue.get().
  • The retrieved attention_data is then processed by an instance of SignalAttentionVisualizer to generate_radar_sweep_data. This suggests the raw attention data is transformed into a format suitable for visualization, perhaps for a radar-like display.
  • The processed radar_data is then converted into a JSON string (json.dumps(radar_data)).
  • Finally, this JSON message is sent directly to the connected client via await websocket.send(message).
• Client Connection Management:
  • When a new client connects, the server’s handler function calls register to add the client’s WebSocket to a set of connected clients, and logs the new connection.
  • If a client disconnects or an error occurs (like websockets.exceptions.ConnectionClosed), the unregister method is called to remove the client from the set.
  • While the producer_handler sends data to individual clients, the server also has a broadcast method that can send a message to all connected clients simultaneously, although the attention data flow specifically uses the producer_handler for each client.
• Server Operation: The AttentionVisualizerServer initializes with a SignalAttentionVisualizer instance. The server can be started in a background thread using start_background_server, which runs the start_server method using asyncio.run. This setup ensures the server continuously listens for new connections and manages data flow.

In essence, incoming attention data is queued, and multiple “producer” tasks, one for each connected client, pull from this shared queue, transform the data, and send it in real-time over individual WebSocket connections.