Immersive Experience Platforms

# Immersive Experience Platform Implementation
import asyncio
import numpy as np
from typing import Dict, List, Tuple, Optional, Any
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from enum import Enum
import json
import threading
from concurrent.futures import ThreadPoolExecutor
import cv2

class DeviceType(Enum):
    VR_HEADSET = "vr_headset"
    AR_GLASSES = "ar_glasses"
    MOBILE_AR = "mobile_ar"
    DESKTOP = "desktop"
    HAPTIC_DEVICE = "haptic_device"

class ExperienceType(Enum):
    GAMING = "gaming"
    TRAINING = "training"
    SOCIAL = "social"
    COLLABORATION = "collaboration"
    ENTERTAINMENT = "entertainment"

@dataclass
class SpatialPose:
    position: Tuple[float, float, float]  # x, y, z
    rotation: Tuple[float, float, float, float]  # quaternion
    confidence: float
    timestamp: datetime

@dataclass
class UserSession:
    user_id: str
    session_id: str
    device_type: DeviceType
    current_pose: SpatialPose
    avatar_id: Optional[str] = None
    capabilities: Dict[str, bool] = field(default_factory=dict)
    network_quality: float = 1.0
    last_update: datetime = field(default_factory=datetime.now)

@dataclass
class VirtualObject:
    object_id: str
    position: Tuple[float, float, float]
    rotation: Tuple[float, float, float, float]
    scale: Tuple[float, float, float]
    mesh_id: str
    material_id: str
    physics_enabled: bool = False
    interaction_enabled: bool = True
    owner_user_id: Optional[str] = None

@dataclass
class RenderFrame:
    frame_id: int
    timestamp: datetime
    objects: List[VirtualObject]
    lighting_data: Dict
    camera_pose: SpatialPose
    user_id: str
    target_fps: int = 90

class ImmersiveExperienceEngine:
    """Core engine for immersive XR experiences"""
    
    def __init__(self, config: Dict):
        self.config = config
        self.active_sessions: Dict[str, UserSession] = {}
        self.virtual_objects: Dict[str, VirtualObject] = {}
        
        # Rendering and performance
        self.target_fps = config.get('target_fps', 90)
        self.frame_counter = 0
        self.last_frame_time = datetime.now()
        
        # Spatial computing
        self.spatial_tracker = SpatialTrackingSystem()
        self.environment_map = EnvironmentMap()
        
        # Rendering system
        self.render_engine = RenderingEngine(config.get('render_config', {}))
        self.interaction_system = InteractionSystem()
        
        # Social and networking
        self.session_manager = SessionManager()
        self.network_sync = NetworkSynchronizer()
        
        # Performance monitoring
        self.performance_metrics = PerformanceTracker()
        
        # Threading for concurrent processing
        self.executor = ThreadPoolExecutor(max_workers=config.get('workers', 8))
        self.running = False
        
    async def start_experience_session(
        self, 
        user_id: str, 
        device_type: DeviceType,
        experience_config: Dict
    ) -> UserSession:
        """Start a new immersive experience session"""
        
        session_id = self.generate_session_id()
        
        # Initialize user session
        session = UserSession(
            user_id=user_id,
            session_id=session_id,
            device_type=device_type,
            current_pose=SpatialPose(
                position=(0.0, 1.7, 0.0),  # Average head height
                rotation=(0.0, 0.0, 0.0, 1.0),  # Identity quaternion
                confidence=1.0,
                timestamp=datetime.now()
            ),
            capabilities=self.get_device_capabilities(device_type),
            network_quality=1.0
        )
        
        # Initialize spatial tracking for this session
        await self.spatial_tracker.initialize_tracking(session_id, device_type)
        
        # Set up rendering context
        render_context = await self.render_engine.create_context(
            session_id, 
            device_type,
            experience_config
        )
        
        # Configure interaction systems
        await self.interaction_system.setup_for_device(session_id, device_type)
        
        # Add to active sessions
        self.active_sessions[session_id] = session
        
        # Start session processing loop
        asyncio.create_task(self.session_processing_loop(session_id))
        
        return session
    
    async def session_processing_loop(self, session_id: str):
        """Main processing loop for an immersive session"""
        
        session = self.active_sessions.get(session_id)
        if not session:
            return
            
        frame_time = 1.0 / self.target_fps
        
        while session_id in self.active_sessions:
            loop_start = datetime.now()
            
            try:
                # Update spatial tracking
                pose_update = await self.spatial_tracker.get_latest_pose(session_id)
                if pose_update:
                    session.current_pose = pose_update
                
                # Process interactions
                interactions = await self.interaction_system.process_interactions(session_id)
                
                # Update virtual objects based on interactions
                await self.update_virtual_objects(session_id, interactions)
                
                # Prepare render frame
                render_frame = await self.prepare_render_frame(session)
                
                # Render frame
                await self.render_engine.render_frame(session_id, render_frame)
                
                # Network synchronization with other users
                await self.network_sync.sync_session_state(session)
                
                # Update performance metrics
                frame_time_ms = (datetime.now() - loop_start).total_seconds() * 1000
                self.performance_metrics.record_frame_time(session_id, frame_time_ms)
                
                # Maintain target frame rate
                elapsed = (datetime.now() - loop_start).total_seconds()
                if elapsed < frame_time:
                    await asyncio.sleep(frame_time - elapsed)
                    
            except Exception as e:
                print(f"Error in session processing loop: {e}")
                await asyncio.sleep(0.01)  # Prevent tight error loops
    
    async def prepare_render_frame(self, session: UserSession) -> RenderFrame:
        """Prepare rendering frame with optimizations"""
        
        # Get visible objects based on frustum culling
        visible_objects = await self.get_visible_objects(
            session.current_pose,
            session.device_type
        )
        
        # Apply level-of-detail based on distance and device capabilities
        optimized_objects = await self.apply_lod_optimization(
            visible_objects,
            session.current_pose,
            session.device_type
        )
        
        # Prepare lighting data
        lighting_data = await self.calculate_lighting(
            session.current_pose.position,
            visible_objects
        )
        
        render_frame = RenderFrame(
            frame_id=self.frame_counter,
            timestamp=datetime.now(),
            objects=optimized_objects,
            lighting_data=lighting_data,
            camera_pose=session.current_pose,
            user_id=session.user_id,
            target_fps=self.target_fps
        )
        
        self.frame_counter += 1
        return render_frame
    
    async def get_visible_objects(
        self, 
        camera_pose: SpatialPose, 
        device_type: DeviceType
    ) -> List[VirtualObject]:
        """Get objects visible from camera pose using frustum culling"""
        
        # Get camera frustum parameters based on device
        frustum_params = self.get_device_frustum(device_type)
        
        visible_objects = []
        camera_pos = np.array(camera_pose.position)
        
        for obj_id, virtual_obj in self.virtual_objects.items():
            obj_pos = np.array(virtual_obj.position)
            
            # Simple distance check (in production, use proper frustum culling)
            distance = np.linalg.norm(obj_pos - camera_pos)
            
            if distance <= frustum_params['far_plane']:
                # Check if object is within field of view (simplified)
                if self.is_in_field_of_view(camera_pose, virtual_obj, frustum_params):
                    visible_objects.append(virtual_obj)
        
        return visible_objects
    
    def is_in_field_of_view(
        self, 
        camera_pose: SpatialPose, 
        obj: VirtualObject,
        frustum_params: Dict
    ) -> bool:
        """Check if object is within camera's field of view"""
        
        # Simplified FOV check - in production use proper frustum testing
        camera_pos = np.array(camera_pose.position)
        obj_pos = np.array(obj.position)
        
        # Vector from camera to object
        to_object = obj_pos - camera_pos
        to_object_normalized = to_object / np.linalg.norm(to_object)
        
        # Camera forward vector (simplified - assumes looking down -Z axis)
        camera_forward = np.array([0, 0, -1])
        
        # Check angle
        dot_product = np.dot(camera_forward, to_object_normalized)
        fov_radians = frustum_params['fov_degrees'] * np.pi / 180
        min_dot = np.cos(fov_radians / 2)
        
        return dot_product >= min_dot
    
    async def apply_lod_optimization(
        self,
        objects: List[VirtualObject],
        camera_pose: SpatialPose,
        device_type: DeviceType
    ) -> List[VirtualObject]:
        """Apply level-of-detail optimization based on distance and device"""
        
        optimized_objects = []
        camera_pos = np.array(camera_pose.position)
        device_performance = self.get_device_performance_level(device_type)
        
        for obj in objects:
            obj_pos = np.array(obj.position)
            distance = np.linalg.norm(obj_pos - camera_pos)
            
            # Determine LOD level
            lod_level = self.calculate_lod_level(distance, device_performance)
            
            # Create optimized version of object
            optimized_obj = VirtualObject(
                object_id=obj.object_id,
                position=obj.position,
                rotation=obj.rotation,
                scale=obj.scale,
                mesh_id=f"{obj.mesh_id}_lod{lod_level}",
                material_id=f"{obj.material_id}_lod{lod_level}",
                physics_enabled=obj.physics_enabled and lod_level <= 2,  # Disable physics for distant objects
                interaction_enabled=obj.interaction_enabled and distance <= 10.0,  # Interaction distance limit
                owner_user_id=obj.owner_user_id
            )
            
            optimized_objects.append(optimized_obj)
        
        return optimized_objects
    
    def calculate_lod_level(self, distance: float, device_performance: float) -> int:
        """Calculate appropriate level of detail"""
        
        # Base LOD on distance and device capability
        base_lod = 0
        
        if distance > 50:
            base_lod = 4  # Lowest detail
        elif distance > 25:
            base_lod = 3
        elif distance > 10:
            base_lod = 2
        elif distance > 5:
            base_lod = 1
        else:
            base_lod = 0  # Highest detail
        
        # Adjust based on device performance
        if device_performance < 0.5:  # Low-end device
            base_lod = min(4, base_lod + 2)
        elif device_performance < 0.7:  # Mid-range device
            base_lod = min(4, base_lod + 1)
        
        return base_lod
    
    async def calculate_lighting(
        self, 
        camera_position: Tuple[float, float, float],
        visible_objects: List[VirtualObject]
    ) -> Dict:
        """Calculate lighting data for the scene"""
        
        # Simplified lighting calculation
        lighting_data = {
            'ambient_color': [0.2, 0.2, 0.3, 1.0],  # RGBA
            'directional_lights': [
                {
                    'direction': [-0.5, -1.0, -0.3],
                    'color': [1.0, 0.9, 0.8, 1.0],
                    'intensity': 1.0
                }
            ],
            'point_lights': [],
            'spot_lights': []
        }
        
        # Add dynamic point lights from objects that emit light
        for obj in visible_objects:
            if obj.material_id.endswith('_emissive'):
                lighting_data['point_lights'].append({
                    'position': obj.position,
                    'color': [1.0, 1.0, 1.0, 1.0],
                    'intensity': 2.0,
                    'range': 10.0
                })
        
        return lighting_data
    
    async def update_virtual_objects(
        self, 
        session_id: str, 
        interactions: List[Dict]
    ):
        """Update virtual objects based on user interactions"""
        
        for interaction in interactions:
            interaction_type = interaction.get('type')
            
            if interaction_type == 'grab':
                await self.handle_grab_interaction(session_id, interaction)
            elif interaction_type == 'release':
                await self.handle_release_interaction(session_id, interaction)
            elif interaction_type == 'gesture':
                await self.handle_gesture_interaction(session_id, interaction)
            elif interaction_type == 'voice_command':
                await self.handle_voice_command(session_id, interaction)
    
    async def handle_grab_interaction(self, session_id: str, interaction: Dict):
        """Handle object grabbing interaction"""
        
        object_id = interaction.get('target_object_id')
        if object_id and object_id in self.virtual_objects:
            virtual_obj = self.virtual_objects[object_id]
            
            # Check if object can be grabbed
            if virtual_obj.interaction_enabled:
                # Mark as grabbed by this user
                virtual_obj.owner_user_id = self.active_sessions[session_id].user_id
                
                # Update object position to hand position
                hand_pose = interaction.get('hand_pose')
                if hand_pose:
                    virtual_obj.position = hand_pose['position']
                    virtual_obj.rotation = hand_pose['rotation']
    
    def get_device_capabilities(self, device_type: DeviceType) -> Dict[str, bool]:
        """Get capabilities for different device types"""
        
        capabilities = {
            DeviceType.VR_HEADSET: {
                'hand_tracking': True,
                'eye_tracking': True,
                'spatial_audio': True,
                'haptic_feedback': True,
                'room_scale': True,
                'high_refresh_rate': True
            },
            DeviceType.AR_GLASSES: {
                'hand_tracking': True,
                'eye_tracking': True,
                'spatial_audio': True,
                'haptic_feedback': False,
                'room_scale': True,
                'high_refresh_rate': False
            },
            DeviceType.MOBILE_AR: {
                'hand_tracking': False,
                'eye_tracking': False,
                'spatial_audio': False,
                'haptic_feedback': True,
                'room_scale': False,
                'high_refresh_rate': False
            },
            DeviceType.DESKTOP: {
                'hand_tracking': False,
                'eye_tracking': False,
                'spatial_audio': True,
                'haptic_feedback': False,
                'room_scale': False,
                'high_refresh_rate': True
            }
        }
        
        return capabilities.get(device_type, {})
    
    def get_device_frustum(self, device_type: DeviceType) -> Dict:
        """Get rendering frustum parameters for device"""
        
        frustum_configs = {
            DeviceType.VR_HEADSET: {
                'fov_degrees': 110,
                'near_plane': 0.1,
                'far_plane': 1000.0,
                'aspect_ratio': 1.0
            },
            DeviceType.AR_GLASSES: {
                'fov_degrees': 45,
                'near_plane': 0.1,
                'far_plane': 100.0,
                'aspect_ratio': 16.0/9.0
            },
            DeviceType.MOBILE_AR: {
                'fov_degrees': 60,
                'near_plane': 0.1,
                'far_plane': 50.0,
                'aspect_ratio': 16.0/9.0
            },
            DeviceType.DESKTOP: {
                'fov_degrees': 75,
                'near_plane': 0.1,
                'far_plane': 1000.0,
                'aspect_ratio': 16.0/9.0
            }
        }
        
        return frustum_configs.get(device_type, frustum_configs[DeviceType.DESKTOP])
    
    def get_device_performance_level(self, device_type: DeviceType) -> float:
        """Get normalized performance level (0.0 to 1.0)"""
        
        performance_levels = {
            DeviceType.VR_HEADSET: 1.0,  # High-end
            DeviceType.AR_GLASSES: 0.7,  # Mid-high
            DeviceType.MOBILE_AR: 0.3,   # Low-mid
            DeviceType.DESKTOP: 0.9      # High
        }
        
        return performance_levels.get(device_type, 0.5)
    
    def generate_session_id(self) -> str:
        """Generate unique session ID"""
        import uuid
        return str(uuid.uuid4())
    
    async def end_session(self, session_id: str):
        """Clean up and end a session"""
        
        if session_id in self.active_sessions:
            session = self.active_sessions[session_id]
            
            # Clean up spatial tracking
            await self.spatial_tracker.cleanup_tracking(session_id)
            
            # Clean up rendering context
            await self.render_engine.destroy_context(session_id)
            
            # Clean up interactions
            await self.interaction_system.cleanup_session(session_id)
            
            # Remove from active sessions
            del self.active_sessions[session_id]
            
            print(f"Session {session_id} ended for user {session.user_id}")

class SpatialTrackingSystem:
    """Handle spatial tracking and SLAM"""
    
    def __init__(self):
        self.tracking_sessions: Dict[str, Dict] = {}
        self.slam_engines: Dict[str, Any] = {}  # Placeholder for SLAM implementations
        
    async def initialize_tracking(self, session_id: str, device_type: DeviceType):
        """Initialize spatial tracking for a session"""
        
        tracking_config = {
            'tracking_frequency': 120 if device_type == DeviceType.VR_HEADSET else 60,
            'slam_enabled': device_type in [DeviceType.VR_HEADSET, DeviceType.AR_GLASSES],
            'imu_fusion': True,
            'visual_tracking': device_type != DeviceType.DESKTOP
        }
        
        self.tracking_sessions[session_id] = {
            'config': tracking_config,
            'last_pose': None,
            'tracking_quality': 1.0,
            'environment_map': {}
        }
        
        # Initialize SLAM if supported
        if tracking_config['slam_enabled']:
            await self.initialize_slam(session_id)
    
    async def initialize_slam(self, session_id: str):
        """Initialize SLAM system for spatial tracking"""
        
        # Placeholder for SLAM initialization
        # In production, integrate with ORB-SLAM, OpenVSLAM, or similar
        self.slam_engines[session_id] = {
            'keyframes': [],
            'map_points': [],
            'tracking_state': 'initialized'
        }
    
    async def get_latest_pose(self, session_id: str) -> Optional[SpatialPose]:
        """Get the latest tracked pose for a session"""
        
        if session_id not in self.tracking_sessions:
            return None
        
        # Simulate pose tracking - in production, get from actual tracking hardware
        current_time = datetime.now()
        
        # Create simulated pose with some movement
        import random
        pose = SpatialPose(
            position=(
                random.uniform(-1, 1) * 0.1,  # Small random movement
                1.7 + random.uniform(-1, 1) * 0.05,  # Head height with variation
                random.uniform(-1, 1) * 0.1
            ),
            rotation=(0.0, 0.0, 0.0, 1.0),  # Identity quaternion
            confidence=0.95,
            timestamp=current_time
        )
        
        self.tracking_sessions[session_id]['last_pose'] = pose
        return pose
    
    async def cleanup_tracking(self, session_id: str):
        """Clean up tracking resources"""
        
        if session_id in self.tracking_sessions:
            del self.tracking_sessions[session_id]
        
        if session_id in self.slam_engines:
            del self.slam_engines[session_id]

class RenderingEngine:
    """High-performance rendering system"""
    
    def __init__(self, config: Dict):
        self.config = config
        self.render_contexts: Dict[str, Dict] = {}
        self.shader_cache: Dict[str, Any] = {}
        self.texture_cache: Dict[str, Any] = {}
        self.mesh_cache: Dict[str, Any] = {}
        
    async def create_context(
        self, 
        session_id: str, 
        device_type: DeviceType,
        experience_config: Dict
    ) -> Dict:
        """Create rendering context for session"""
        
        context = {
            'device_type': device_type,
            'render_target_count': 2 if device_type == DeviceType.VR_HEADSET else 1,
            'resolution': self.get_target_resolution(device_type),
            'anti_aliasing': experience_config.get('anti_aliasing', True),
            'shadows_enabled': experience_config.get('shadows', True),
            'post_processing': experience_config.get('post_processing', True)
        }
        
        self.render_contexts[session_id] = context
        return context
    
    def get_target_resolution(self, device_type: DeviceType) -> Tuple[int, int]:
        """Get target rendering resolution for device"""
        
        resolutions = {
            DeviceType.VR_HEADSET: (2160, 1200),  # Per eye
            DeviceType.AR_GLASSES: (1920, 1080),
            DeviceType.MOBILE_AR: (1280, 720),
            DeviceType.DESKTOP: (1920, 1080)
        }
        
        return resolutions.get(device_type, (1920, 1080))
    
    async def render_frame(self, session_id: str, render_frame: RenderFrame):
        """Render a frame for the session"""
        
        context = self.render_contexts.get(session_id)
        if not context:
            return
        
        # Simulate rendering process
        render_start = datetime.now()
        
        # Render pipeline steps:
        # 1. Geometry pass
        await self.render_geometry_pass(render_frame, context)
        
        # 2. Lighting pass
        await self.render_lighting_pass(render_frame, context)
        
        # 3. Post-processing
        if context['post_processing']:
            await self.render_post_processing(render_frame, context)
        
        # 4. Present frame
        await self.present_frame(session_id, render_frame)
        
        render_time = (datetime.now() - render_start).total_seconds() * 1000
        
        # Track rendering performance
        if render_time > (1000.0 / render_frame.target_fps) * 0.8:  # 80% of frame budget
            print(f"Frame rendering took {render_time:.1f}ms (target: {1000.0/render_frame.target_fps:.1f}ms)")
    
    async def render_geometry_pass(self, render_frame: RenderFrame, context: Dict):
        """Render geometry pass"""
        # Simulate geometry rendering
        await asyncio.sleep(0.001)  # 1ms simulation
    
    async def render_lighting_pass(self, render_frame: RenderFrame, context: Dict):
        """Render lighting pass"""
        # Simulate lighting calculations
        await asyncio.sleep(0.002)  # 2ms simulation
    
    async def render_post_processing(self, render_frame: RenderFrame, context: Dict):
        """Apply post-processing effects"""
        # Simulate post-processing
        await asyncio.sleep(0.001)  # 1ms simulation
    
    async def present_frame(self, session_id: str, render_frame: RenderFrame):
        """Present rendered frame to display"""
        # Simulate frame presentation
        pass
    
    async def destroy_context(self, session_id: str):
        """Clean up rendering context"""
        if session_id in self.render_contexts:
            del self.render_contexts[session_id]

class InteractionSystem:
    """Handle user interactions and input"""
    
    def __init__(self):
        self.interaction_handlers: Dict[str, Dict] = {}
        self.gesture_recognizers: Dict[str, Any] = {}
        
    async def setup_for_device(self, session_id: str, device_type: DeviceType):
        """Set up interaction handling for device"""
        
        handlers = {
            'hand_tracking': device_type in [DeviceType.VR_HEADSET, DeviceType.AR_GLASSES],
            'eye_tracking': device_type == DeviceType.VR_HEADSET,
            'controller_input': device_type in [DeviceType.VR_HEADSET, DeviceType.DESKTOP],
            'touch_input': device_type == DeviceType.MOBILE_AR,
            'voice_commands': True
        }
        
        self.interaction_handlers[session_id] = handlers
    
    async def process_interactions(self, session_id: str) -> List[Dict]:
        """Process all interactions for a session"""
        
        interactions = []
        handlers = self.interaction_handlers.get(session_id, {})
        
        # Simulate different types of interactions
        if handlers.get('hand_tracking'):
            hand_interactions = await self.process_hand_tracking(session_id)
            interactions.extend(hand_interactions)
        
        if handlers.get('eye_tracking'):
            eye_interactions = await self.process_eye_tracking(session_id)
            interactions.extend(eye_interactions)
        
        if handlers.get('voice_commands'):
            voice_interactions = await self.process_voice_commands(session_id)
            interactions.extend(voice_interactions)
        
        return interactions
    
    async def process_hand_tracking(self, session_id: str) -> List[Dict]:
        """Process hand tracking interactions"""
        
        # Simulate hand tracking data
        interactions = []
        
        # Example: Detect grab gesture
        import random
        if random.random() < 0.01:  # 1% chance per frame
            interactions.append({
                'type': 'grab',
                'hand': 'right',
                'hand_pose': {
                    'position': (0.3, 1.2, -0.5),
                    'rotation': (0.0, 0.0, 0.0, 1.0)
                },
                'target_object_id': 'cube_001',  # Example object
                'confidence': 0.9
            })
        
        return interactions
    
    async def process_eye_tracking(self, session_id: str) -> List[Dict]:
        """Process eye tracking interactions"""
        
        interactions = []
        
        # Example: Eye gaze interaction
        import random
        if random.random() < 0.05:  # 5% chance per frame
            interactions.append({
                'type': 'eye_gaze',
                'gaze_direction': (0.0, 0.0, -1.0),
                'fixation_duration': 0.5,
                'target_object_id': 'button_001',
                'confidence': 0.8
            })
        
        return interactions
    
    async def process_voice_commands(self, session_id: str) -> List[Dict]:
        """Process voice command interactions"""
        
        interactions = []
        
        # Simulate voice command detection
        import random
        if random.random() < 0.001:  # 0.1% chance per frame
            interactions.append({
                'type': 'voice_command',
                'command': 'create cube',
                'confidence': 0.85,
                'language': 'en-US'
            })
        
        return interactions
    
    async def cleanup_session(self, session_id: str):
        """Clean up interaction handling"""
        if session_id in self.interaction_handlers:
            del self.interaction_handlers[session_id]

class PerformanceTracker:
    """Track and analyze performance metrics"""
    
    def __init__(self):
        self.frame_times: Dict[str, List[float]] = {}
        self.session_metrics: Dict[str, Dict] = {}
        
    def record_frame_time(self, session_id: str, frame_time_ms: float):
        """Record frame rendering time"""
        
        if session_id not in self.frame_times:
            self.frame_times[session_id] = []
        
        self.frame_times[session_id].append(frame_time_ms)
        
        # Keep only recent frame times (last 1000 frames)
        if len(self.frame_times[session_id]) > 1000:
            self.frame_times[session_id] = self.frame_times[session_id][-1000:]
    
    def get_performance_stats(self, session_id: str) -> Dict:
        """Get performance statistics for session"""
        
        if session_id not in self.frame_times or not self.frame_times[session_id]:
            return {}
        
        frame_times = self.frame_times[session_id]
        
        return {
            'average_frame_time': sum(frame_times) / len(frame_times),
            'min_frame_time': min(frame_times),
            'max_frame_time': max(frame_times),
            'frame_count': len(frame_times),
            '95th_percentile': sorted(frame_times)[int(len(frame_times) * 0.95)],
            'dropped_frames': len([t for t in frame_times if t > 16.67])  # Assuming 60fps target
        }

# Supporting classes (simplified placeholders)
class EnvironmentMap:
    def __init__(self):
        self.spatial_anchors = {}
        self.occlusion_mesh = {}

class SessionManager:
    def __init__(self):
        self.active_sessions = {}

class NetworkSynchronizer:
    def __init__(self):
        pass
    
    async def sync_session_state(self, session: UserSession):
        # Placeholder for network synchronization
        pass

// Extended Reality Interaction Framework
interface XRDevice {
  type: 'vr' | 'ar' | 'mobile' | 'desktop';
  capabilities: DeviceCapabilities;
  displayInfo: DisplayConfiguration;
  trackingInfo: TrackingConfiguration;
}

interface DeviceCapabilities {
  handTracking: boolean;
  eyeTracking: boolean;
  spatialAudio: boolean;
  hapticFeedback: boolean;
  roomScale: boolean;
  passthrough: boolean;  // AR capability
}

interface SpatialInteraction {
  type: 'grab' | 'point' | 'gesture' | 'voice' | 'gaze';
  position: Vector3;
  direction: Vector3;
  confidence: number;
  timestamp: number;
  userId: string;
}

interface XRSession {
  sessionId: string;
  userId: string;
  device: XRDevice;
  spatialAnchors: Map<string, SpatialAnchor>;
  currentPose: Pose;
  interactionState: InteractionState;
}

class ImmersiveExperienceFramework {
  private sessions: Map<string, XRSession> = new Map();
  private spatialCompute: SpatialComputingEngine;
  private renderEngine: XRRenderingEngine;
  private interactionEngine: InteractionEngine;
  private socialSystem: SocialXRSystem;

  constructor(config: XRConfiguration) {
    this.spatialCompute = new SpatialComputingEngine(config.spatial);
    this.renderEngine = new XRRenderingEngine(config.rendering);
    this.interactionEngine = new InteractionEngine(config.interaction);
    this.socialSystem = new SocialXRSystem(config.social);
  }

  async createSession(
    userId: string, 
    deviceInfo: XRDevice
  ): Promise<XRSession> {
    
    const sessionId = this.generateSessionId();
    
    // Initialize spatial tracking
    await this.spatialCompute.initializeTracking(sessionId, deviceInfo);
    
    // Set up rendering context
    const renderContext = await this.renderEngine.createContext(
      sessionId, 
      deviceInfo
    );
    
    // Configure interactions
    await this.interactionEngine.setupDevice(sessionId, deviceInfo);
    
    // Create session
    const session: XRSession = {
      sessionId,
      userId,
      device: deviceInfo,
      spatialAnchors: new Map(),
      currentPose: this.getDefaultPose(),
      interactionState: this.createInteractionState()
    };
    
    this.sessions.set(sessionId, session);
    
    // Start session update loop
    this.startSessionLoop(sessionId);
    
    return session;
  }

  private async startSessionLoop(sessionId: string): Promise<void> {
    const session = this.sessions.get(sessionId);
    if (!session) return;

    const targetFrameTime = 1000 / this.getTargetFrameRate(session.device);
    
    while (this.sessions.has(sessionId)) {
      const frameStart = performance.now();
      
      try {
        // Update spatial tracking
        await this.updateSpatialTracking(session);
        
        // Process interactions
        const interactions = await this.processInteractions(session);
        
        // Update world state
        await this.updateWorldState(session, interactions);
        
        // Render frame
        await this.renderFrame(session);
        
        // Maintain frame rate
        const elapsed = performance.now() - frameStart;
        const remaining = targetFrameTime - elapsed;
        
        if (remaining > 0) {
          await this.sleep(remaining);
        } else {
          console.warn(`Frame overrun: ${elapsed.toFixed(2)}ms`);
        }
        
      } catch (error) {
        console.error('Session loop error:', error);
        await this.sleep(16); // Fallback frame time
      }
    }
  }

  private async updateSpatialTracking(session: XRSession): Promise<void> {
    // Get latest pose from tracking system
    const pose = await this.spatialCompute.getCurrentPose(session.sessionId);
    if (pose) {
      session.currentPose = pose;
    }
    
    // Update spatial anchors
    await this.updateSpatialAnchors(session);
    
    // Environmental understanding
    await this.updateEnvironmentMapping(session);
  }

  private async processInteractions(
    session: XRSession
  ): Promise<SpatialInteraction[]> {
    
    const interactions: SpatialInteraction[] = [];
    
    // Hand tracking interactions
    if (session.device.capabilities.handTracking) {
      const handInteractions = await this.processHandTracking(session);
      interactions.push(...handInteractions);
    }
    
    // Eye tracking interactions
    if (session.device.capabilities.eyeTracking) {
      const eyeInteractions = await this.processEyeTracking(session);
      interactions.push(...eyeInteractions);
    }
    
    // Voice interactions
    const voiceInteractions = await this.processVoiceCommands(session);
    interactions.push(...voiceInteractions);
    
    // Controller interactions
    const controllerInteractions = await this.processControllerInput(session);
    interactions.push(...controllerInteractions);
    
    return interactions;
  }

  private async processHandTracking(
    session: XRSession
  ): Promise<SpatialInteraction[]> {
    
    // Get hand tracking data
    const handData = await this.getHandTrackingData(session.sessionId);
    if (!handData) return [];
    
    const interactions: SpatialInteraction[] = [];
    
    // Detect grab gestures
    for (const hand of ['left', 'right']) {
      const handInfo = handData[hand];
      if (!handInfo) continue;
      
      // Check for grab gesture
      if (this.isGrabGesture(handInfo)) {
        interactions.push({
          type: 'grab',
          position: handInfo.palmPosition,
          direction: handInfo.palmNormal,
          confidence: handInfo.confidence,
          timestamp: performance.now(),
          userId: session.userId
        });
      }
      
      // Check for pointing gesture
      if (this.isPointingGesture(handInfo)) {
        interactions.push({
          type: 'point',
          position: handInfo.indexTip,
          direction: handInfo.pointingDirection,
          confidence: handInfo.confidence,
          timestamp: performance.now(),
          userId: session.userId
        });
      }
    }
    
    return interactions;
  }

  private async processEyeTracking(
    session: XRSession
  ): Promise<SpatialInteraction[]> {
    
    const eyeData = await this.getEyeTrackingData(session.sessionId);
    if (!eyeData) return [];
    
    const interactions: SpatialInteraction[] = [];
    
    // Gaze interaction
    if (eyeData.gazeConfidence > 0.8) {
      interactions.push({
        type: 'gaze',
        position: eyeData.gazeOrigin,
        direction: eyeData.gazeDirection,
        confidence: eyeData.gazeConfidence,
        timestamp: performance.now(),
        userId: session.userId
      });
    }
    
    return interactions;
  }

  private async updateWorldState(
    session: XRSession,
    interactions: SpatialInteraction[]
  ): Promise<void> {
    
    // Process each interaction
    for (const interaction of interactions) {
      await this.handleInteraction(session, interaction);
    }
    
    // Update physics simulation
    await this.updatePhysics(session);
    
    // Update social presence
    await this.socialSystem.updateUserPresence(session);
  }

  private async handleInteraction(
    session: XRSession,
    interaction: SpatialInteraction
  ): Promise<void> {
    
    switch (interaction.type) {
      case 'grab':
        await this.handleGrabInteraction(session, interaction);
        break;
        
      case 'point':
        await this.handlePointInteraction(session, interaction);
        break;
        
      case 'gesture':
        await this.handleGestureInteraction(session, interaction);
        break;
        
      case 'voice':
        await this.handleVoiceInteraction(session, interaction);
        break;
        
      case 'gaze':
        await this.handleGazeInteraction(session, interaction);
        break;
    }
  }

  private async renderFrame(session: XRSession): Promise<void> {
    // Get visible objects with frustum culling
    const visibleObjects = await this.getVisibleObjects(session);
    
    // Apply level-of-detail optimization
    const optimizedObjects = this.applyLODOptimization(
      visibleObjects, 
      session.currentPose.position,
      session.device
    );
    
    // Render frame
    await this.renderEngine.renderFrame({
      sessionId: session.sessionId,
      pose: session.currentPose,
      objects: optimizedObjects,
      lighting: await this.calculateLighting(session),
      effects: await this.getActiveEffects(session)
    });
    
    // Apply post-processing
    if (session.device.capabilities.passthrough) {
      await this.renderEngine.compositeWithPassthrough(session.sessionId);
    }
  }

  // Spatial Computing Engine
  private async updateSpatialAnchors(session: XRSession): Promise<void> {
    // Update existing anchors
    for (const [anchorId, anchor] of session.spatialAnchors) {
      const updated = await this.spatialCompute.trackAnchor(anchorId);
      if (updated) {
        session.spatialAnchors.set(anchorId, updated);
      }
    }
    
    // Detect new anchors
    const newAnchors = await this.spatialCompute.detectNewAnchors(
      session.sessionId
    );
    
    for (const anchor of newAnchors) {
      session.spatialAnchors.set(anchor.id, anchor);
    }
  }

  private applyLODOptimization(
    objects: VirtualObject[],
    viewerPosition: Vector3,
    device: XRDevice
  ): VirtualObject[] {
    
    const devicePerformance = this.getDevicePerformance(device);
    
    return objects.map(obj => {
      const distance = this.calculateDistance(obj.position, viewerPosition);
      const lodLevel = this.calculateLODLevel(distance, devicePerformance);
      
      return {
        ...obj,
        meshLOD: lodLevel,
        textureLOD: lodLevel,
        shadowCasting: lodLevel <= 2,
        physicsSim: lodLevel === 0 && distance < 5
      };
    });
  }

  private async calculateLighting(session: XRSession): Promise<LightingData> {
    const lightingData: LightingData = {
      ambientColor: [0.2, 0.2, 0.3, 1.0],
      directionalLights: [],
      pointLights: [],
      environmentProbe: null
    };
    
    // Add main directional light
    lightingData.directionalLights.push({
      direction: [-0.3, -0.8, -0.5],
      color: [1.0, 0.9, 0.8, 1.0],
      intensity: 1.0,
      castShadows: true
    });
    
    // For AR, blend with real-world lighting
    if (session.device.type === 'ar') {
      const realWorldLighting = await this.estimateRealWorldLighting(session);
      lightingData.ambientColor = this.blendColors(
        lightingData.ambientColor,
        realWorldLighting.ambient,
        0.7 // 70% real world influence
      );
    }
    
    return lightingData;
  }

  // Social XR System
  private async initializeSocialFeatures(session: XRSession): Promise<void> {
    // Set up avatar system
    await this.socialSystem.createAvatar(session.userId, {
      appearancePrefs: await this.getUserAvatarPrefs(session.userId),
      deviceCapabilities: session.device.capabilities
    });
    
    // Join social spaces if applicable
    const socialSpaces = await this.getSocialSpacesForUser(session.userId);
    for (const space of socialSpaces) {
      await this.socialSystem.joinSpace(session.sessionId, space.id);
    }
  }

  // Performance optimization
  private getTargetFrameRate(device: XRDevice): number {
    switch (device.type) {
      case 'vr': return 90; // VR needs high frame rate
      case 'ar': return 60; // AR can be slightly lower
      case 'mobile': return 60;
      case 'desktop': return 60;
      default: return 60;
    }
  }

  private getDevicePerformance(device: XRDevice): number {
    // Return normalized performance level (0.0 to 1.0)
    const performanceLevels = {
      'vr': 1.0,      // High-end VR headsets
      'ar': 0.8,      // AR glasses
      'mobile': 0.4,  // Mobile AR
      'desktop': 0.9  // Desktop systems
    };
    
    return performanceLevels[device.type] || 0.5;
  }

  // Utility methods
  private generateSessionId(): string {
    return `xr_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
  }

  private sleep(ms: number): Promise<void> {
    return new Promise(resolve => setTimeout(resolve, ms));
  }

  private calculateDistance(pos1: Vector3, pos2: Vector3): number {
    const dx = pos1.x - pos2.x;
    const dy = pos1.y - pos2.y;
    const dz = pos1.z - pos2.z;
    return Math.sqrt(dx * dx + dy * dy + dz * dz);
  }

  private calculateLODLevel(distance: number, performance: number): number {
    let lodLevel = 0;
    
    if (distance > 50) lodLevel = 4;
    else if (distance > 25) lodLevel = 3;
    else if (distance > 10) lodLevel = 2;
    else if (distance > 5) lodLevel = 1;
    
    // Adjust for device performance
    if (performance < 0.5) lodLevel = Math.min(4, lodLevel + 2);
    else if (performance < 0.7) lodLevel = Math.min(4, lodLevel + 1);
    
    return lodLevel;
  }

  async destroy(): Promise<void> {
    // Clean up all sessions
    for (const [sessionId] of this.sessions) {
      await this.destroySession(sessionId);
    }
    
    // Clean up engines
    await this.spatialCompute.destroy();
    await this.renderEngine.destroy();
    await this.interactionEngine.destroy();
    await this.socialSystem.destroy();
  }

  private async destroySession(sessionId: string): Promise<void> {
    const session = this.sessions.get(sessionId);
    if (!session) return;
    
    // Clean up spatial tracking
    await this.spatialCompute.cleanupSession(sessionId);
    
    // Clean up rendering
    await this.renderEngine.destroyContext(sessionId);
    
    // Clean up interactions
    await this.interactionEngine.cleanupSession(sessionId);
    
    // Remove from social systems
    await this.socialSystem.removeUser(session.userId);
    
    this.sessions.delete(sessionId);
  }
}

// Supporting interfaces and types
interface Vector3 {
  x: number;
  y: number;
  z: number;
}

interface Quaternion {
  x: number;
  y: number;
  z: number;
  w: number;
}

interface Pose {
  position: Vector3;
  rotation: Quaternion;
}

interface VirtualObject {
  id: string;
  position: Vector3;
  rotation: Quaternion;
  scale: Vector3;
  meshLOD: number;
  textureLOD: number;
  shadowCasting: boolean;
  physicsSim: boolean;
}

interface LightingData {
  ambientColor: [number, number, number, number];
  directionalLights: DirectionalLight[];
  pointLights: PointLight[];
  environmentProbe: any;
}

interface DirectionalLight {
  direction: [number, number, number];
  color: [number, number, number, number];
  intensity: number;
  castShadows: boolean;
}