from typing import List, Dict, Tuple
from dataclasses import dataclass
from enum import Enum
from datetime import datetime

class DamageLevel(Enum):
    INTACT = 1
    MINOR = 2
    MODERATE = 3
    MAJOR = 4
    SEVERE = 5
    DESTROYED = 6

@dataclass
class BuildingDamage:
    building_id: str
    location: np.ndarray
    damage_level: DamageLevel
    confidence: float
    roof_damage: float
    wall_damage: float
    debris_present: bool
    accessible: bool

class RapidMapper:
    def __init__(self, grid_resolution: float = 10.0):
        self.grid_resolution = grid_resolution
        self.damage_grid = None
        self.buildings: List[BuildingDamage] = []

    def create_damage_map(self, rgb_frames: List[np.ndarray],
                         thermal_frames: List[np.ndarray],
                         positions: List[np.ndarray]) -> np.ndarray:
        """Create comprehensive damage assessment map"""
        h, w = rgb_frames[0].shape[:2]
        damage_map = np.zeros((h, w), dtype=int)

        for rgb, thermal, pos in zip(rgb_frames, thermal_frames, positions):
            # Analyze visual damage
            visual_damage = self._analyze_visual_damage(rgb)

            # Analyze thermal anomalies
            thermal_damage = self._analyze_thermal_damage(thermal)

            # Combine assessments
            combined = np.maximum(visual_damage, thermal_damage)

            # Update global map
            x, y = int(pos[0]), int(pos[1])
            if 0 <= x < h and 0 <= y < w:
                damage_map[max(0,x-5):min(h,x+5),
                          max(0,y-5):min(w,y+5)] = combined

        self.damage_grid = damage_map
        return damage_map

    def _analyze_visual_damage(self, rgb: np.ndarray) -> np.ndarray:
        """Analyze visual indicators of damage"""
        gray = np.mean(rgb, axis=2)

        # Edge detection for structural damage
        from scipy import ndimage
        edges = ndimage.sobel(gray)
        edge_density = ndimage.uniform_filter(edges, size=10)

        # Color analysis for fire/smoke damage
        hsv = self._rgb_to_hsv(rgb)
        fire_damage = (hsv[:,:,0] < 30) & (hsv[:,:,2] > 100)

        # Debris detection (irregular textures)
        texture = np.std(ndimage.uniform_filter(gray, size=5), axis=0)

        # Combine indicators
        damage = np.zeros_like(gray, dtype=int)
        damage[edge_density > 30] = DamageLevel.MODERATE.value
        damage[fire_damage] = DamageLevel.MAJOR.value
        damage[texture > 20] = DamageLevel.MINOR.value

        return damage

    def _analyze_thermal_damage(self, thermal: np.ndarray) -> np.ndarray:
        """Analyze thermal indicators"""
        damage = np.zeros_like(thermal, dtype=int)

        # Hot spots (fires, structural failures)
        hot_threshold = np.percentile(thermal, 95)
        damage[thermal > hot_threshold] = DamageLevel.MAJOR.value

        # Cold spots (exposed interiors, water damage)
        cold_threshold = np.percentile(thermal, 5)
        damage[thermal < cold_threshold] = DamageLevel.MODERATE.value

        return damage

    def _rgb_to_hsv(self, rgb: np.ndarray) -> np.ndarray:
        """Convert RGB to HSV"""
        r, g, b = rgb[:,:,0]/255.0, rgb[:,:,1]/255.0, rgb[:,:,2]/255.0
        max_c = np.maximum(np.maximum(r, g), b)
        min_c = np.minimum(np.minimum(r, g), b)
        v = max_c
        s = np.zeros_like(v)
        mask = max_c > 0
        s[mask] = (max_c[mask] - min_c[mask]) / max_c[mask]
        h = np.zeros_like(v)
        return np.stack([h * 180, s * 255, v * 255], axis=2)

    def identify_casualty_zones(self, damage_map: np.ndarray,
                               thermal_data: np.ndarray) -> List[np.ndarray]:
        """Identify areas likely to have casualties"""
        zones = []

        # High damage areas
        severe_mask = damage_map >= DamageLevel.MAJOR.value

        # With thermal signatures (survivors)
        thermal_mask = thermal_data > 30  # Body temperature

        # Combine
        casualty_risk = severe_mask & thermal_mask

        from scipy import ndimage
        labeled, num_features = ndimage.label(casualty_risk)

        for i in range(1, num_features + 1):
            centroid = ndimage.center_of_mass(labeled == i)
            zones.append(np.array(centroid))

        return zones

    def prioritize_search_areas(self, damage_map: np.ndarray,
                               thermal_data: np.ndarray) -> List[Dict]:
        """Prioritize areas for search and rescue"""
        priorities = []

        # Calculate priority score for each grid cell
        h, w = damage_map.shape
        cell_size = 50

        for y in range(0, h, cell_size):
            for x in range(0, w, cell_size):
                cell_damage = damage_map[y:y+cell_size, x:x+cell_size]
                cell_thermal = thermal_data[y:y+cell_size, x:x+cell_size]

                # Priority factors
                damage_score = np.mean(cell_damage) / 6
                heat_score = np.mean(cell_thermal > 30) if cell_thermal.size > 0 else 0
                accessibility = 1 - damage_score * 0.5

                priority_score = (damage_score * 0.4 +
                                heat_score * 0.4 +
                                accessibility * 0.2)

                if priority_score > 0.3:
                    priorities.append({
                        'location': np.array([x + cell_size//2, y + cell_size//2]),
                        'priority_score': priority_score,
                        'damage_level': int(np.max(cell_damage)),
                        'heat_signatures': heat_score > 0.1
                    })

        # Sort by priority
        priorities.sort(key=lambda p: p['priority_score'], reverse=True)
        return priorities

    def generate_situation_report(self) -> Dict:
        """Generate comprehensive situation report"""
        if self.damage_grid is None:
            return {'error': 'No damage map available'}

        total_cells = self.damage_grid.size
        damage_counts = {}

        for level in DamageLevel:
            count = np.sum(self.damage_grid == level.value)
            damage_counts[level.name] = {
                'count': int(count),
                'percentage': count / total_cells * 100
            }

        return {
            'total_area_assessed': total_cells * (self.grid_resolution ** 2) / 10000,
            'damage_summary': damage_counts,
            'critical_areas': int(np.sum(self.damage_grid >= DamageLevel.MAJOR.value)),
            'estimated_displaced': self._estimate_displaced(),
            'priority_response_needed': self._estimate_response_needs()
        }

    def _estimate_displaced(self) -> int:
        """Estimate number of displaced people"""
        major_damage = np.sum(self.damage_grid >= DamageLevel.MAJOR.value)
        return int(major_damage * 10)  # ~10 people per major damage unit

    def _estimate_response_needs(self) -> Dict:
        """Estimate response resource needs"""
        severe = np.sum(self.damage_grid >= DamageLevel.SEVERE.value)
        major = np.sum(self.damage_grid == DamageLevel.MAJOR.value)

        return {
            'rescue_teams': int(severe / 10) + 1,
            'medical_units': int(major / 20) + 1,
            'shelter_capacity': int(major * 50),
            'supply_trucks': int(major / 15) + 1
        }

from typing import List, Dict, Tuple
from dataclasses import dataclass
from enum import Enum

class StructureType(Enum):
    RESIDENTIAL = "residential"
    COMMERCIAL = "commercial"
    INDUSTRIAL = "industrial"
    INFRASTRUCTURE = "infrastructure"

@dataclass
class StructureAssessment:
    structure_id: str
    structure_type: StructureType
    location: np.ndarray
    damage_percentage: float
    structural_integrity: float
    fire_damage: float
    water_damage: float
    roof_condition: float
    wall_condition: float
    accessible: bool
    search_priority: int

class DamageAssessor:
    def __init__(self):
        self.integrity_thresholds = {
            'safe': 0.8,
            'caution': 0.5,
            'dangerous': 0.2
        }

    def assess_structure(self, rgb: np.ndarray,
                        thermal: np.ndarray,
                        lidar: np.ndarray = None) -> StructureAssessment:
        """Comprehensive structure assessment"""
        # Visual damage analysis
        visual_damage = self._analyze_visual_damage(rgb)

        # Thermal damage analysis
        thermal_damage = self._analyze_thermal_damage(thermal)

        # Structural integrity (if LiDAR available)
        integrity = self._assess_integrity(lidar) if lidar is not None else 0.5

        # Calculate overall damage
        overall_damage = (visual_damage * 0.5 +
                         thermal_damage * 0.3 +
                         (1 - integrity) * 0.2)

        return StructureAssessment(
            structure_id=f"STRUCT-{np.random.randint(1000, 9999)}",
            structure_type=self._classify_structure(rgb),
            location=np.array([rgb.shape[0]//2, rgb.shape[1]//2]),
            damage_percentage=overall_damage * 100,
            structural_integrity=integrity,
            fire_damage=thermal_damage,
            water_damage=self._detect_water_damage(rgb),
            roof_condition=self._assess_roof(rgb),
            wall_condition=self._assess_walls(rgb),
            accessible=overall_damage < 0.7,
            search_priority=self._calculate_priority(overall_damage, thermal_damage)
        )

    def _analyze_visual_damage(self, rgb: np.ndarray) -> float:
        """Analyze visual damage indicators"""
        gray = np.mean(rgb, axis=2)

        # Edge density indicates structural damage
        from scipy import ndimage
        edges = ndimage.sobel(gray)
        edge_score = np.mean(edges > 50)

        # Color irregularities
        color_std = np.std(rgb, axis=(0,1))
        color_anomaly = np.mean(color_std) / 100

        return min(1.0, edge_score * 0.6 + color_anomaly * 0.4)

    def _analyze_thermal_damage(self, thermal: np.ndarray) -> float:
        """Analyze thermal damage indicators"""
        # Extreme temperatures indicate fire damage
        hot_ratio = np.mean(thermal > 60)
        cold_ratio = np.mean(thermal < 5)

        return min(1.0, hot_ratio * 2 + cold_ratio)

    def _assess_integrity(self, lidar: np.ndarray) -> float:
        """Assess structural integrity from LiDAR data"""
        # Simple planarity check
        h, w = lidar.shape
        x, y = np.meshgrid(np.arange(w), np.arange(h))

        A = np.column_stack([x.ravel(), y.ravel(), np.ones_like(x.ravel())])
        result = np.linalg.lstsq(A, lidar.ravel(), rcond=None)
        plane = (result[0][0] * x + result[0][1] * y + result[0][2])

        deviation = np.std(lidar - plane)
        integrity = max(0, 1 - deviation / 0.5)

        return integrity

    def _detect_water_damage(self, rgb: np.ndarray) -> float:
        """Detect water damage indicators"""
        # Blue tint and stains
        b_channel = rgb[:,:,2]
        water_indicator = np.mean(b_channel > 150)
        return min(1.0, water_indicator * 2)

    def _assess_roof(self, rgb: np.ndarray) -> float:
        """Assess roof condition"""
        # Top portion of image
        top_third = rgb[:rgb.shape[0]//3, :]
        damage = self._analyze_visual_damage(top_third)
        return max(0, 1 - damage)

    def _assess_walls(self, rgb: np.ndarray) -> float:
        """Assess wall condition"""
        middle = rgb[rgb.shape[0]//3:2*rgb.shape[0]//3, :]
        damage = self._analyze_visual_damage(middle)
        return max(0, 1 - damage)

    def _classify_structure(self, rgb: np.ndarray) -> StructureType:
        """Classify structure type"""
        # Simplified - would use trained model
        return StructureType.RESIDENTIAL

    def _calculate_priority(self, damage: float,
                          thermal_damage: float) -> int:
        """Calculate search priority (1=highest)"""
        if damage > 0.8 and thermal_damage > 0.3:
            return 1
        elif damage > 0.6:
            return 2
        elif damage > 0.4:
            return 3
        return 4

from typing import List, Dict, Tuple
from dataclasses import dataclass
from enum import Enum

class RelayStatus(Enum):
    ACTIVE = "active"
    LOW_BATTERY = "low_battery"
    OFFLINE = "offline"

@dataclass
class CommunicationRelay:
    relay_id: str
    position: np.ndarray
    status: RelayStatus
    battery_level: float
    coverage_radius: float
    connected_users: int

class CommRestorationSystem:
    def __init__(self, disaster_area: Tuple[float, float]):
        self.area = disaster_area
        self.relays: List[CommunicationRelay] = []
        self.base_station = np.array([0, 0])

    def deploy_relays(self, positions: List[np.ndarray],
                     battery_levels: List[float] = None):
        """Deploy communication relays"""
        if battery_levels is None:
            battery_levels = [100.0] * len(positions)

        for i, (pos, batt) in enumerate(zip(positions, battery_levels)):
            self.relays.append(CommunicationRelay(
                relay_id=f"RELAY-{i+1:03d}",
                position=pos,
                status=RelayStatus.ACTIVE,
                battery_level=batt,
                coverage_radius=200,
                connected_users=0
            ))

    def optimize_relay_positions(self, priority_zones: List[np.ndarray],
                                num_relays: int = 8) -> List[np.ndarray]:
        """Optimize relay positions for maximum coverage"""
        # K-means like placement
        if len(priority_zones) <= num_relays:
            return priority_zones[:num_relays]

        # Initialize with priority zones
        positions = priority_zones[:num_relays].copy()

        # Iterative optimization
        for _ in range(10):
            # Assign zones to nearest relay
            assignments = [[] for _ in range(num_relays)]
            for zone in priority_zones:
                distances = [np.linalg.norm(zone - pos) for pos in positions]
                nearest = np.argmin(distances)
                assignments[nearest].append(zone)

            # Update relay positions to center of assigned zones
            for i in range(num_relays):
                if assignments[i]:
                    positions[i] = np.mean(assignments[i], axis=0)

        return positions

    def calculate_coverage(self) -> float:
        """Calculate percentage of area covered"""
        total_area = self.area[0] * self.area[1]
        covered_area = 0

        for relay in self.relays:
            if relay.status != RelayStatus.OFFLINE:
                # Approximate coverage as circle
                coverage = np.pi * relay.coverage_radius ** 2
                covered_area += coverage

        # Remove overlaps (simplified)
        coverage_ratio = min(1.0, covered_area / total_area * 0.7)
        return coverage_ratio * 100

    def monitor_network_health(self) -> Dict:
        """Monitor communication network health"""
        active = sum(1 for r in self.relays if r.status == RelayStatus.ACTIVE)
        low_battery = sum(1 for r in self.relays if r.status == RelayStatus.LOW_BATTERY)
        offline = sum(1 for r in self.relays if r.status == RelayStatus.OFFLINE)

        total_users = sum(r.connected_users for r in self.relays)

        return {
            'active_relays': active,
            'low_battery_relays': low_battery,
            'offline_relays': offline,
            'total_relays': len(self.relays),
            'coverage_percentage': self.calculate_coverage(),
            'connected_users': total_users,
            'network_resilience': active / max(1, len(self.relays))
        }

    def simulate_battery_drain(self, hours: float = 24):
        """Simulate battery drain over time"""
        drain_rate = 2.0  # % per hour

        for relay in self.relays:
            relay.battery_level -= drain_rate * hours
            relay.battery_level = max(0, relay.battery_level)

            if relay.battery_level < 20:
                relay.status = RelayStatus.LOW_BATTERY
            if relay.battery_level < 5:
                relay.status = RelayStatus.OFFLINE

from typing import List, Dict, Tuple
from dataclasses import dataclass
from enum import Enum

class SupplyType(Enum):
    MEDICAL = "medical"
    WATER = "water"
    FOOD = "food"
    SHELTER = "shelter"
    COMMUNICATION = "communication"

class Priority(Enum):
    CRITICAL = 1
    HIGH = 2
    MEDIUM = 3
    LOW = 4

@dataclass
class SupplyRequest:
    request_id: str
    supply_type: SupplyType
    location: np.ndarray
    quantity: int
    priority: Priority
    timestamp: float

class SupplyDeliverySystem:
    def __init__(self, depot_location: np.ndarray):
        self.depot = depot_location
        self.pending_requests: List[SupplyRequest] = []
        self.active_deliveries: Dict[str, SupplyRequest] = {}
        self.completed_deliveries: List[str] = []

    def add_request(self, request: SupplyRequest):
        """Add supply request"""
        self.pending_requests.append(request)
        self._prioritize_requests()

    def _prioritize_requests(self):
        """Sort requests by priority and urgency"""
        self.pending_requests.sort(
            key=lambda r: (r.priority.value, -r.timestamp))

    def plan_delivery_batch(self, max_deliveries: int = 5) -> List[Dict]:
        """Plan batch of deliveries"""
        batch = []

        for request in self.pending_requests[:max_deliveries]:
            route = self._plan_route(request)
            batch.append({
                'request': request,
                'route': route,
                'estimated_time': self._estimate_time(route),
                'drone_requirements': self._get_drone_requirements(request)
            })

        return batch

    def _plan_route(self, request: SupplyRequest) -> List[np.ndarray]:
        """Plan delivery route"""
        waypoints = [
            self.depot.copy(),
            request.location.copy()
        ]

        # Add intermediate waypoints for obstacle avoidance
        mid_point = (self.depot + request.location) / 2
        mid_point[2] = 50  # Altitude
        waypoints.insert(1, mid_point)

        return waypoints

    def _estimate_time(self, route: List[np.ndarray]) -> float:
        """Estimate delivery time in minutes"""
        total_distance = sum(
            np.linalg.norm(route[i+1] - route[i])
            for i in range(len(route)-1)
        )
        speed = 10  # m/s
        return total_distance / speed / 60

    def _get_drone_requirements(self, request: SupplyRequest) -> Dict:
        """Get drone requirements for delivery"""
        weight_map = {
            SupplyType.MEDICAL: 2.0,
            SupplyType.WATER: 5.0,
            SupplyType.FOOD: 3.0,
            SupplyType.SHELTER: 8.0,
            SupplyType.COMMUNICATION: 1.5
        }

        weight = weight_map.get(request.supply_type, 3.0)
        volume = weight * 0.001  # Approximate

        return {
            'min_payload_kg': weight,
            'min_volume_m3': volume,
            'special_equipment': request.supply_type == SupplyType.MEDICAL,
            'priority': request.priority.name
        }

    def track_delivery(self, request_id: str) -> Dict:
        """Track delivery status"""
        if request_id in self.active_deliveries:
            request = self.active_deliveries[request_id]
            return {
                'status': 'in_transit',
                'location': request.location.tolist(),
                'supply_type': request.supply_type.value
            }
        elif request_id in self.completed_deliveries:
            return {'status': 'delivered'}
        return {'status': 'pending'}

    def generate_delivery_report(self) -> str:
        """Generate delivery status report"""
        report = "=" * 50 + "\n"
        report += "SUPPLY DELIVERY REPORT\n"
        report += "=" * 50 + "\n\n"

        report += f"Pending Requests: {len(self.pending_requests)}\n"
        report += f"Active Deliveries: {len(self.active_deliveries)}\n"
        report += f"Completed: {len(self.completed_deliveries)}\n\n"

        # Breakdown by type
        type_counts = {}
        for req in self.pending_requests:
            stype = req.supply_type.value
            type_counts[stype] = type_counts.get(stype, 0) + 1

        report += "Pending by Type:\n"
        for stype, count in type_counts.items():
            report += f"  {stype}: {count}\n"

        return report

# disaster_response_system.py
from typing import List, Dict
from dataclasses import dataclass
from enum import Enum

class ResponsePhase(Enum):
    INITIAL_ASSESSMENT = "initial_assessment"
    SEARCH_RESCUE = "search_rescue"
    RECOVERY = "recovery"
    REHABILITATION = "rehabilitation"

class DisasterResponseSystem:
    def __init__(self, disaster_type: str, affected_area: tuple):
        self.disaster_type = disaster_type
        self.affected_area = affected_area
        self.mapper = RapidMapper()
        self.assessor = DamageAssessor()
        self.comm_system = CommRestorationSystem(affected_area)
        self.supply_system = SupplyDeliverySystem(np.array([0, 0]))

        self.phase = ResponsePhase.INITIAL_ASSESSMENT
        self.situation_report = {}

    def initial_assessment(self, sensor_data: Dict) -> Dict:
        """Perform initial disaster assessment"""
        print("PHASE 1: Initial Assessment")

        # Create damage map
        damage_map = self.mapper.create_damage_map(
            sensor_data['rgb_frames'],
            sensor_data['thermal_frames'],
            sensor_data['positions']
        )

        # Identify priority areas
        priorities = self.mapper.prioritize_search_areas(
            damage_map, sensor_data['thermal_frames'][0])

        # Generate report
        self.situation_report = self.mapper.generate_situation_report()

        # Deploy communication relays
        relay_positions = self.comm_system.optimize_relay_positions(
            [p['location'] for p in priorities[:8]])
        self.comm_system.deploy_relays(relay_positions)

        self.phase = ResponsePhase.SEARCH_RESCUE

        return {
            'damage_map': damage_map,
            'priority_areas': priorities[:10],
            'situation_report': self.situation_report,
            'communication_status': self.comm_system.monitor_network_health()
        }

    def search_rescue_operations(self, survivor_detections: List[Dict]) -> Dict:
        """Coordinate search and rescue"""
        print("PHASE 2: Search & Rescue")

        # Prioritize survivors
        survivor_priorities = self._prioritize_survivors(survivor_detections)

        # Deploy supply deliveries for critical survivors
        critical_survivors = [s for s in survivor_priorities if s['priority'] == 1]
        for survivor in critical_survivors:
            request = SupplyRequest(
                request_id=f"SUPPLY-{np.random.randint(1000, 9999)}",
                supply_type=SupplyType.MEDICAL,
                location=survivor['location'],
                quantity=1,
                priority=Priority.CRITICAL,
                timestamp=0
            )
            self.supply_system.add_request(request)

        # Plan delivery batch
        delivery_batch = self.supply_system.plan_delivery_batch()

        return {
            'survivor_count': len(survivor_priorities),
            'critical_survivors': len(critical_survivors),
            'deliveries_planned': len(delivery_batch),
            'communication_status': self.comm_system.monitor_network_health()
        }

    def _prioritize_survivors(self, detections: List[Dict]) -> List[Dict]:
        """Prioritize survivors for rescue"""
        prioritized = []

        for det in detections:
            # Priority factors
            heat_score = det.get('heat_signature', 0)
            movement_score = det.get('movement', 0)
            accessibility = det.get('accessibility', 0.5)

            priority_score = (heat_score * 0.4 +
                            movement_score * 0.3 +
                            accessibility * 0.3)

            priority = 1 if priority_score > 0.7 else \
                      2 if priority_score > 0.4 else 3

            prioritized.append({
                'location': det['location'],
                'priority': priority,
                'priority_score': priority_score
            })

        return sorted(prioritized, key=lambda x: x['priority_score'], reverse=True)

    def recovery_operations(self) -> Dict:
        """Coordinate recovery operations"""
        print("PHASE 3: Recovery")

        # Assess infrastructure
        infrastructure_status = {
            'roads': self._assess_roads(),
            'utilities': self._assess_utilities(),
            'buildings': self._assess_buildings()
        }

        # Resource needs
        resource_needs = self.situation_report.get('priority_response_needed', {})

        return {
            'infrastructure_status': infrastructure_status,
            'resource_needs': resource_needs,
            'timeline_estimate': self._estimate_recovery_timeline()
        }

    def _assess_roads(self) -> Dict:
        return {'functional': 45, 'blocked': 35, 'damaged': 20}

    def _assess_utilities(self) -> Dict:
        return {'power': 30, 'water': 50, 'comms': 70}

    def _assess_buildings(self) -> Dict:
        return self.situation_report.get('damage_summary', {})

    def _estimate_recovery_timeline(self) -> Dict:
        return {
            'critical_repairs': '1-2 weeks',
            'infrastructure': '1-3 months',
            'full_recovery': '6-12 months'
        }

    def generate_mission_report(self) -> str:
        """Generate comprehensive mission report"""
        report = "=" * 60 + "\n"
        report += "DISASTER RESPONSE MISSION REPORT\n"
        report += "=" * 60 + "\n\n"

        report += f"Disaster Type: {self.disaster_type}\n"
        report += f"Affected Area: {self.affected_area[0]} km²\n"
        report += f"Current Phase: {self.phase.value}\n\n"

        report += "SITUATION SUMMARY:\n"
        for key, value in self.situation_report.items():
            report += f"  {key}: {value}\n"

        report += f"\nCOMMUNICATION STATUS:\n"
        comm_status = self.comm_system.monitor_network_health()
        for key, value in comm_status.items():
            report += f"  {key}: {value}\n"

        return report


# Usage
system = DisasterResponseSystem(
    disaster_type="Earthquake",
    affected_area=(5.0, 5.0)  # 5km x 5km
)

# Simulate sensor data
sensor_data = {
    'rgb_frames': [np.random.randint(50, 200, (100, 100, 3), dtype=np.uint8)],
    'thermal_frames': [np.random.uniform(15, 45, (100, 100))],
    'positions': [np.array([50, 50, 0])]
}

# Run initial assessment
results = system.initial_assessment(sensor_data)

# Run search and rescue
survivor_detections = [
    {'location': np.array([30, 40, 0]), 'heat_signature': 0.9, 'movement': 0.7},
    {'location': np.array([70, 60, 0]), 'heat_signature': 0.8, 'movement': 0.3}
]
sar_results = system.search_rescue_operations(survivor_detections)

print(system.generate_mission_report())