"""
Infrastructure Leader Dashboard
==============================

Technical infrastructure dashboard for Infrastructure Leaders with comprehensive
system monitoring, performance analytics, and capacity planning.
"""

# Copyright (c) 2025 Infinidatum
# Author: Duraimurugan Rajamanickam
# 
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# 
#     http://www.apache.org/licenses/LICENSE-2.0
# 
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.



import numpy as np
import pandas as pd
from datetime import datetime, timedelta
from typing import Dict, List, Any, Optional, Tuple
import json
import logging
from dataclasses import dataclass, field
import psutil
import time

# Import VizlyChart for professional visualizations
try:
    import finopsmetrics.vizlychart as vc
    from finopsmetrics.vizlychart.charts.engineering import SystemMonitoring, InfrastructureCharts
    from finopsmetrics.vizlychart.charts.advanced import PerformanceAnalytics
    from finopsmetrics.vizlychart.enterprise.themes import InfrastructureTheme
    VIZLYCHART_AVAILABLE = True
except ImportError:
    VIZLYCHART_AVAILABLE = False
    # VizlyChart is optional - dashboards work with fallback visualizations

from .iam_system import get_iam_manager, DashboardType, DataClassification

logger = logging.getLogger(__name__)


@dataclass
class SystemMetric:
    """System performance metric with thresholds and trends."""
    name: str
    current_value: float
    unit: str
    threshold_warning: float
    threshold_critical: float
    trend_direction: str  # 'up', 'down', 'stable'
    historical_data: List[float] = field(default_factory=list)
    status: str = "normal"  # 'normal', 'warning', 'critical'

    def __post_init__(self):
        if self.current_value >= self.threshold_critical:
            self.status = "critical"
        elif self.current_value >= self.threshold_warning:
            self.status = "warning"
        else:
            self.status = "normal"


@dataclass
class InfrastructureComponent:
    """Infrastructure component with health and performance data."""
    component_id: str
    component_type: str
    name: str
    status: str  # 'healthy', 'degraded', 'critical', 'down'
    health_score: float  # 0-100
    performance_metrics: Dict[str, float]
    resource_usage: Dict[str, float]
    uptime_percentage: float
    last_incident: Optional[datetime] = None
    dependencies: List[str] = field(default_factory=list)
    tags: List[str] = field(default_factory=list)


@dataclass
class CapacityPlan:
    """Capacity planning data with forecasts and recommendations."""
    resource_type: str
    current_capacity: float
    used_capacity: float
    utilization_percent: float
    forecast_90_days: float
    capacity_exhaustion_date: Optional[datetime]
    recommended_action: str
    cost_impact: Optional[float] = None


class InfrastructureLeaderDashboard:
    """Infrastructure Leader Dashboard with comprehensive system monitoring and analytics."""

    def __init__(self):
        self.iam_manager = get_iam_manager()
        self.theme = self._get_infrastructure_theme()
        self.data_sources = self._initialize_data_sources()
        self.monitoring_agents = self._initialize_monitoring_agents()

    def _get_infrastructure_theme(self) -> Dict[str, Any]:
        """Get infrastructure leader theme configuration."""
        return {
            "name": "Infrastructure Professional",
            "colors": {
                "primary": "#0f172a",          # Dark slate
                "secondary": "#1e293b",        # Slate
                "success": "#16a34a",          # Green
                "warning": "#ea580c",          # Orange
                "danger": "#dc2626",           # Red
                "info": "#2563eb",            # Blue
                "neutral": "#64748b",          # Gray
                "background": "#ffffff",       # White
                "surface": "#f8fafc",          # Light slate
                "text": "#1e293b",            # Dark slate
                "border": "#e2e8f0",          # Light border
                "accent": "#7c3aed"           # Purple
            },
            "status_colors": {
                "healthy": "#16a34a",
                "warning": "#ea580c",
                "critical": "#dc2626",
                "degraded": "#d97706",
                "down": "#991b1b",
                "maintenance": "#6366f1"
            },
            "chart_palette": [
                "#2563eb", "#16a34a", "#ea580c", "#dc2626",
                "#7c3aed", "#0891b2", "#be185d", "#059669"
            ],
            "fonts": {
                "primary": "JetBrains Mono, Monaco, monospace",
                "headers": "Inter, system-ui, sans-serif",
                "metrics": "JetBrains Mono, Monaco, monospace"
            },
            "watermark": {
                "enabled": True,
                "text": "OpenFinOps Infrastructure - Confidential",
                "position": "bottom_right",
                "opacity": 0.06
            }
        }

    def _initialize_data_sources(self) -> Dict[str, Any]:
        """Initialize data sources for infrastructure monitoring."""
        return {
            "prometheus": {
                "url": "http://prometheus:9090",
                "query_interval": 15,
                "retention": "30d"
            },
            "grafana": {
                "url": "http://grafana:3000",
                "api_key": "grafana_api_key",
                "dashboards": ["infrastructure", "kubernetes", "ai_workloads"]
            },
            "kubernetes": {
                "api_server": "https://k8s-api.finopsmetrics.internal",
                "namespace": ["default", "ai-training", "production"],
                "monitoring": ["pods", "services", "deployments", "nodes"]
            },
            "cloud_providers": {
                "aws": {
                    "cloudwatch": True,
                    "cost_explorer": True,
                    "resource_groups": True,
                    "regions": ["us-west-2", "us-east-1"]
                },
                "azure": {
                    "monitor": True,
                    "resource_graph": True,
                    "subscription_monitoring": True
                },
                "gcp": {
                    "monitoring": True,
                    "logging": True,
                    "resource_manager": True
                }
            },
            "logging_systems": {
                "elasticsearch": "http://elasticsearch:9200",
                "fluentd": "http://fluentd:24224",
                "kibana": "http://kibana:5601"
            },
            "ai_infrastructure": {
                "gpu_monitoring": {
                    "nvidia_smi": True,
                    "dcgm": True,
                    "gpu_operator": True
                },
                "ml_platforms": {
                    "kubeflow": "http://kubeflow.finopsmetrics.internal",
                    "mlflow": "http://mlflow.finopsmetrics.internal",
                    "airflow": "http://airflow.finopsmetrics.internal"
                }
            }
        }

    def _initialize_monitoring_agents(self) -> Dict[str, Any]:
        """Initialize monitoring agents and collectors."""
        return {
            "system_agents": ["node_exporter", "cadvisor", "kube-state-metrics"],
            "application_agents": ["prometheus_exporters", "custom_metrics"],
            "network_agents": ["ping_exporter", "blackbox_exporter"],
            "security_agents": ["falco", "osquery", "security_scanner"],
            "performance_agents": ["perf", "sar", "iotop", "htop"]
        }

    def generate_dashboard(self, user_id: str, time_period: str = "current_hour",
                          focus_area: str = "overview") -> Dict[str, Any]:
        """Generate comprehensive infrastructure leader dashboard."""

        # Verify user access
        if not self.iam_manager.can_access_dashboard(user_id, DashboardType.INFRASTRUCTURE_LEADER):
            raise PermissionError("User does not have access to Infrastructure Leader dashboard")

        user_access_level = self.iam_manager.get_user_data_access_level(user_id)
        if user_access_level not in [DataClassification.CONFIDENTIAL, DataClassification.INTERNAL, DataClassification.RESTRICTED]:
            raise PermissionError("Insufficient data access level for Infrastructure Leader dashboard")

        logger.info(f"Generating Infrastructure Leader dashboard for user {user_id}, period: {time_period}, focus: {focus_area}")

        # Generate dashboard components based on focus area
        dashboard_data = {
            "metadata": self._get_dashboard_metadata(user_id, time_period, focus_area),
            "system_overview": self._generate_system_overview(),
            "performance_metrics": self._generate_performance_metrics(time_period),
            "infrastructure_health": self._generate_infrastructure_health(),
            "ai_infrastructure": self._generate_ai_infrastructure_metrics(time_period),
            "capacity_planning": self._generate_capacity_planning(),
            "alerts_incidents": self._generate_alerts_and_incidents(time_period),
            "cost_optimization": self._generate_infrastructure_cost_optimization(),
            "security_monitoring": self._generate_security_monitoring(),
            "automation_status": self._generate_automation_status(),
            "compliance_status": self._generate_compliance_status(),
            "visualizations": self._generate_visualizations(time_period, focus_area)
        }

        # Add specialized focus areas
        if focus_area == "ai_infrastructure":
            dashboard_data.update({
                "gpu_analytics": self._generate_gpu_analytics(time_period),
                "ml_pipeline_health": self._generate_ml_pipeline_health(),
                "training_infrastructure": self._generate_training_infrastructure_metrics()
            })
        elif focus_area == "performance":
            dashboard_data.update({
                "performance_deep_dive": self._generate_performance_deep_dive(time_period),
                "bottleneck_analysis": self._analyze_performance_bottlenecks(),
                "optimization_recommendations": self._generate_performance_optimizations()
            })
        elif focus_area == "security":
            dashboard_data.update({
                "security_deep_dive": self._generate_security_deep_dive(),
                "vulnerability_assessment": self._generate_vulnerability_assessment(),
                "threat_intelligence": self._generate_threat_intelligence()
            })

        # Log dashboard access
        self.iam_manager._log_audit_event("dashboard_access", user_id, {
            "dashboard_type": "infrastructure_leader",
            "time_period": time_period,
            "focus_area": focus_area,
            "components_generated": list(dashboard_data.keys())
        })

        return dashboard_data

    def _get_dashboard_metadata(self, user_id: str, time_period: str, focus_area: str) -> Dict[str, Any]:
        """Get dashboard metadata and context."""
        user = self.iam_manager.users.get(user_id)

        return {
            "dashboard_id": f"infra_leader_{user_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
            "generated_at": datetime.utcnow().isoformat(),
            "generated_for": {
                "user_id": user_id,
                "name": user.full_name if user else "Unknown",
                "department": user.department if user else "Unknown",
                "role": "Infrastructure Leader"
            },
            "time_period": time_period,
            "focus_area": focus_area,
            "data_classification": "CONFIDENTIAL",
            "refresh_interval": 60,  # 1 minute for real-time monitoring
            "version": "2.1.0",
            "theme": self.theme["name"],
            "monitoring_scope": {
                "clusters": 5,
                "nodes": 125,
                "services": 450,
                "namespaces": 15
            },
            "last_data_refresh": datetime.utcnow().isoformat()
        }

    def _generate_system_overview(self) -> Dict[str, Any]:
        """Generate high-level system overview."""

        # Simulate real system data - in production, collect from monitoring systems
        clusters = {
            "production": {
                "status": "healthy",
                "nodes": 45,
                "healthy_nodes": 44,
                "cpu_utilization": 68.5,
                "memory_utilization": 72.3,
                "storage_utilization": 58.2,
                "active_workloads": 1250,
                "ai_workloads": 185
            },
            "ai-training": {
                "status": "healthy",
                "nodes": 32,
                "healthy_nodes": 32,
                "cpu_utilization": 89.2,
                "memory_utilization": 91.5,
                "storage_utilization": 67.8,
                "active_workloads": 85,
                "ai_workloads": 85
            },
            "staging": {
                "status": "degraded",
                "nodes": 25,
                "healthy_nodes": 23,
                "cpu_utilization": 45.2,
                "memory_utilization": 52.1,
                "storage_utilization": 38.9,
                "active_workloads": 320,
                "ai_workloads": 25
            },
            "development": {
                "status": "healthy",
                "nodes": 18,
                "healthy_nodes": 18,
                "cpu_utilization": 32.8,
                "memory_utilization": 41.5,
                "storage_utilization": 28.3,
                "active_workloads": 180,
                "ai_workloads": 15
            },
            "edge": {
                "status": "healthy",
                "nodes": 5,
                "healthy_nodes": 5,
                "cpu_utilization": 55.6,
                "memory_utilization": 48.9,
                "storage_utilization": 42.1,
                "active_workloads": 45,
                "ai_workloads": 8
            }
        }

        # Calculate aggregate metrics
        total_nodes = sum(cluster["nodes"] for cluster in clusters.values())
        total_healthy_nodes = sum(cluster["healthy_nodes"] for cluster in clusters.values())
        total_workloads = sum(cluster["active_workloads"] for cluster in clusters.values())
        total_ai_workloads = sum(cluster["ai_workloads"] for cluster in clusters.values())

        # Calculate weighted averages
        weighted_cpu = sum(cluster["cpu_utilization"] * cluster["nodes"] for cluster in clusters.values()) / total_nodes
        weighted_memory = sum(cluster["memory_utilization"] * cluster["nodes"] for cluster in clusters.values()) / total_nodes
        weighted_storage = sum(cluster["storage_utilization"] * cluster["nodes"] for cluster in clusters.values()) / total_nodes

        return {
            "global_status": {
                "overall_health": "healthy",
                "total_clusters": len(clusters),
                "healthy_clusters": len([c for c in clusters.values() if c["status"] == "healthy"]),
                "total_nodes": total_nodes,
                "healthy_nodes": total_healthy_nodes,
                "node_availability": (total_healthy_nodes / total_nodes) * 100,
                "total_workloads": total_workloads,
                "ai_workloads": total_ai_workloads,
                "ai_workload_percentage": (total_ai_workloads / total_workloads) * 100
            },
            "aggregate_metrics": {
                "cpu_utilization": weighted_cpu,
                "memory_utilization": weighted_memory,
                "storage_utilization": weighted_storage,
                "network_throughput": 12.5,  # Gbps
                "iops": 125000,
                "response_time_avg": 45.2  # ms
            },
            "cluster_details": clusters,
            "critical_alerts": [
                {
                    "severity": "warning",
                    "cluster": "staging",
                    "message": "2 nodes in degraded state",
                    "timestamp": datetime.utcnow() - timedelta(minutes=15),
                    "auto_remediation": "in_progress"
                },
                {
                    "severity": "info",
                    "cluster": "ai-training",
                    "message": "High resource utilization (>90%)",
                    "timestamp": datetime.utcnow() - timedelta(minutes=5),
                    "auto_remediation": "monitoring"
                }
            ],
            "uptime_metrics": {
                "current_uptime": "99.8%",
                "monthly_uptime": "99.95%",
                "annual_uptime": "99.92%",
                "sla_target": "99.9%",
                "downtime_minutes_mtd": 36
            }
        }

    def _generate_performance_metrics(self, time_period: str) -> Dict[str, List[SystemMetric]]:
        """Generate comprehensive performance metrics."""

        # System-level metrics
        system_metrics = [
            SystemMetric(
                name="CPU Utilization",
                current_value=68.5,
                unit="percent",
                threshold_warning=80.0,
                threshold_critical=90.0,
                trend_direction="stable",
                historical_data=[65.2, 67.8, 68.1, 68.5, 69.2, 68.0]
            ),
            SystemMetric(
                name="Memory Utilization",
                current_value=72.3,
                unit="percent",
                threshold_warning=85.0,
                threshold_critical=95.0,
                trend_direction="up",
                historical_data=[68.5, 69.8, 70.5, 71.2, 72.0, 72.3]
            ),
            SystemMetric(
                name="Storage Utilization",
                current_value=58.2,
                unit="percent",
                threshold_warning=80.0,
                threshold_critical=90.0,
                trend_direction="stable",
                historical_data=[56.8, 57.2, 57.9, 58.0, 58.1, 58.2]
            ),
            SystemMetric(
                name="Network Throughput",
                current_value=12.5,
                unit="Gbps",
                threshold_warning=20.0,
                threshold_critical=25.0,
                trend_direction="stable",
                historical_data=[11.8, 12.2, 12.6, 12.3, 12.7, 12.5]
            )
        ]

        # Application-level metrics
        application_metrics = [
            SystemMetric(
                name="Response Time",
                current_value=45.2,
                unit="ms",
                threshold_warning=100.0,
                threshold_critical=200.0,
                trend_direction="down",
                historical_data=[52.1, 48.5, 46.8, 45.9, 45.5, 45.2]
            ),
            SystemMetric(
                name="Error Rate",
                current_value=0.08,
                unit="percent",
                threshold_warning=0.5,
                threshold_critical=1.0,
                trend_direction="stable",
                historical_data=[0.09, 0.08, 0.07, 0.08, 0.08, 0.08]
            ),
            SystemMetric(
                name="Throughput",
                current_value=15250,
                unit="requests/sec",
                threshold_warning=10000,
                threshold_critical=5000,
                trend_direction="up",
                historical_data=[14200, 14800, 15100, 15000, 15180, 15250]
            )
        ]

        # Database metrics
        database_metrics = [
            SystemMetric(
                name="DB Connection Pool",
                current_value=78.5,
                unit="percent",
                threshold_warning=85.0,
                threshold_critical=95.0,
                trend_direction="stable",
                historical_data=[75.2, 76.8, 77.5, 78.0, 78.2, 78.5]
            ),
            SystemMetric(
                name="Query Response Time",
                current_value=12.8,
                unit="ms",
                threshold_warning=50.0,
                threshold_critical=100.0,
                trend_direction="stable",
                historical_data=[13.2, 12.9, 12.7, 12.8, 12.9, 12.8]
            ),
            SystemMetric(
                name="DB CPU Utilization",
                current_value=65.2,
                unit="percent",
                threshold_warning=80.0,
                threshold_critical=90.0,
                trend_direction="up",
                historical_data=[62.1, 63.5, 64.2, 64.8, 65.0, 65.2]
            )
        ]

        # AI/ML specific metrics
        ai_metrics = [
            SystemMetric(
                name="GPU Utilization",
                current_value=89.2,
                unit="percent",
                threshold_warning=95.0,
                threshold_critical=98.0,
                trend_direction="stable",
                historical_data=[87.5, 88.2, 89.0, 89.1, 89.3, 89.2]
            ),
            SystemMetric(
                name="GPU Memory",
                current_value=85.6,
                unit="percent",
                threshold_warning=90.0,
                threshold_critical=95.0,
                trend_direction="up",
                historical_data=[82.1, 83.5, 84.2, 84.9, 85.3, 85.6]
            ),
            SystemMetric(
                name="Training Throughput",
                current_value=1250,
                unit="samples/sec",
                threshold_warning=1000,
                threshold_critical=800,
                trend_direction="up",
                historical_data=[1180, 1220, 1235, 1240, 1248, 1250]
            ),
            SystemMetric(
                name="Model Inference Latency",
                current_value=28.5,
                unit="ms",
                threshold_warning=50.0,
                threshold_critical=100.0,
                trend_direction="stable",
                historical_data=[29.2, 28.8, 28.6, 28.4, 28.6, 28.5]
            )
        ]

        return {
            "system_metrics": system_metrics,
            "application_metrics": application_metrics,
            "database_metrics": database_metrics,
            "ai_metrics": ai_metrics
        }

    def _generate_infrastructure_health(self) -> Dict[str, Any]:
        """Generate comprehensive infrastructure health assessment."""

        # Infrastructure components
        components = [
            InfrastructureComponent(
                component_id="k8s-prod-cluster",
                component_type="kubernetes_cluster",
                name="Production Kubernetes Cluster",
                status="healthy",
                health_score=95.2,
                performance_metrics={
                    "api_server_latency": 15.2,
                    "etcd_latency": 2.8,
                    "node_availability": 97.8,
                    "pod_restart_rate": 0.02
                },
                resource_usage={
                    "cpu_allocated": 68.5,
                    "memory_allocated": 72.3,
                    "storage_allocated": 58.2
                },
                uptime_percentage=99.95,
                dependencies=["etcd-cluster", "load-balancer"],
                tags=["production", "critical"]
            ),
            InfrastructureComponent(
                component_id="ai-training-cluster",
                component_type="gpu_cluster",
                name="AI Training GPU Cluster",
                status="healthy",
                health_score=92.8,
                performance_metrics={
                    "gpu_utilization": 89.2,
                    "training_throughput": 1250,
                    "job_completion_rate": 98.5,
                    "queue_wait_time": 45.2
                },
                resource_usage={
                    "gpu_allocated": 89.2,
                    "memory_allocated": 91.5,
                    "storage_allocated": 67.8
                },
                uptime_percentage=99.8,
                dependencies=["shared-storage", "network-fabric"],
                tags=["ai", "gpu", "training"]
            ),
            InfrastructureComponent(
                component_id="database-cluster",
                component_type="database",
                name="Primary Database Cluster",
                status="healthy",
                health_score=96.5,
                performance_metrics={
                    "query_latency": 12.8,
                    "connection_pool_usage": 78.5,
                    "replication_lag": 0.5,
                    "backup_success_rate": 100.0
                },
                resource_usage={
                    "cpu_allocated": 65.2,
                    "memory_allocated": 82.1,
                    "storage_allocated": 75.8
                },
                uptime_percentage=99.99,
                dependencies=["storage-cluster"],
                tags=["database", "critical", "ha"]
            ),
            InfrastructureComponent(
                component_id="monitoring-stack",
                component_type="monitoring",
                name="Monitoring Infrastructure",
                status="healthy",
                health_score=94.1,
                performance_metrics={
                    "metric_ingestion_rate": 125000,
                    "alert_processing_time": 2.5,
                    "dashboard_load_time": 1.2,
                    "data_retention_compliance": 100.0
                },
                resource_usage={
                    "cpu_allocated": 45.6,
                    "memory_allocated": 68.9,
                    "storage_allocated": 82.3
                },
                uptime_percentage=99.9,
                dependencies=["time-series-db"],
                tags=["monitoring", "observability"]
            ),
            InfrastructureComponent(
                component_id="api-gateway",
                component_type="gateway",
                name="API Gateway Cluster",
                status="degraded",
                health_score=88.2,
                performance_metrics={
                    "request_latency": 85.2,
                    "throughput": 15250,
                    "error_rate": 0.12,
                    "rate_limit_efficiency": 98.5
                },
                resource_usage={
                    "cpu_allocated": 82.5,
                    "memory_allocated": 76.8,
                    "storage_allocated": 35.2
                },
                uptime_percentage=99.5,
                last_incident=datetime.utcnow() - timedelta(hours=2),
                dependencies=["load-balancer", "certificate-manager"],
                tags=["api", "gateway", "public"]
            )
        ]

        # Calculate health summary
        total_components = len(components)
        healthy_components = len([c for c in components if c.status == "healthy"])
        average_health_score = sum(c.health_score for c in components) / total_components

        # Service dependencies
        dependency_graph = {
            "critical_path": [
                "load-balancer -> api-gateway -> k8s-prod-cluster -> database-cluster",
                "ai-training-cluster -> shared-storage",
                "monitoring-stack -> time-series-db"
            ],
            "single_points_of_failure": [
                "etcd-cluster",
                "certificate-manager",
                "shared-storage"
            ],
            "redundancy_status": {
                "load-balancer": "multi-zone",
                "database-cluster": "multi-master",
                "k8s-prod-cluster": "multi-node",
                "monitoring-stack": "single-instance"
            }
        }

        return {
            "health_summary": {
                "overall_health_score": average_health_score,
                "total_components": total_components,
                "healthy_components": healthy_components,
                "degraded_components": len([c for c in components if c.status == "degraded"]),
                "critical_components": len([c for c in components if c.status == "critical"]),
                "down_components": len([c for c in components if c.status == "down"])
            },
            "components": [
                {
                    "component_id": c.component_id,
                    "name": c.name,
                    "type": c.component_type,
                    "status": c.status,
                    "health_score": c.health_score,
                    "uptime_percentage": c.uptime_percentage,
                    "performance_metrics": c.performance_metrics,
                    "resource_usage": c.resource_usage,
                    "tags": c.tags,
                    "last_incident": c.last_incident.isoformat() if c.last_incident else None
                } for c in components
            ],
            "dependency_analysis": dependency_graph,
            "health_trends": {
                "improving": ["database-cluster", "k8s-prod-cluster"],
                "stable": ["ai-training-cluster", "monitoring-stack"],
                "declining": ["api-gateway"],
                "attention_required": ["api-gateway"]
            },
            "redundancy_assessment": {
                "high_availability": 78.5,  # Percentage of services with HA
                "disaster_recovery_ready": 85.2,
                "backup_compliance": 96.8,
                "failover_capability": 82.1
            }
        }

    def _generate_ai_infrastructure_metrics(self, time_period: str) -> Dict[str, Any]:
        """Generate AI-specific infrastructure metrics."""

        # GPU cluster metrics
        gpu_clusters = {
            "training-cluster-a": {
                "gpu_type": "A100",
                "total_gpus": 64,
                "available_gpus": 4,
                "utilization": 93.8,
                "memory_utilization": 88.5,
                "temperature_avg": 78.2,
                "power_consumption": 28800,  # Watts
                "training_jobs": 12,
                "queue_depth": 3
            },
            "training-cluster-b": {
                "gpu_type": "V100",
                "total_gpus": 48,
                "available_gpus": 8,
                "utilization": 83.3,
                "memory_utilization": 79.2,
                "temperature_avg": 75.8,
                "power_consumption": 19200,
                "training_jobs": 8,
                "queue_depth": 1
            },
            "inference-cluster": {
                "gpu_type": "T4",
                "total_gpus": 32,
                "available_gpus": 12,
                "utilization": 62.5,
                "memory_utilization": 58.3,
                "temperature_avg": 68.5,
                "power_consumption": 8960,
                "inference_requests": 125000,
                "avg_latency": 28.5
            }
        }

        # ML platform health
        ml_platforms = {
            "kubeflow": {
                "status": "healthy",
                "active_pipelines": 45,
                "completed_runs": 1250,
                "failed_runs": 15,
                "success_rate": 98.8,
                "avg_pipeline_duration": 2.5  # hours
            },
            "mlflow": {
                "status": "healthy",
                "registered_models": 285,
                "active_experiments": 68,
                "model_versions": 1450,
                "storage_usage": 2.8  # TB
            },
            "airflow": {
                "status": "healthy",
                "total_dags": 125,
                "successful_runs": 8950,
                "failed_runs": 125,
                "success_rate": 98.6,
                "scheduler_health": "healthy"
            }
        }

        # Data pipeline metrics
        data_pipelines = {
            "ingestion_pipeline": {
                "throughput": 12.5,  # GB/hour
                "latency": 45.2,  # minutes
                "error_rate": 0.05,
                "data_quality_score": 96.8
            },
            "feature_pipeline": {
                "throughput": 8.9,
                "latency": 15.8,
                "error_rate": 0.02,
                "data_quality_score": 98.2
            },
            "training_data_pipeline": {
                "throughput": 25.6,
                "latency": 28.5,
                "error_rate": 0.08,
                "data_quality_score": 95.5
            }
        }

        # Calculate aggregated metrics
        total_gpus = sum(cluster["total_gpus"] for cluster in gpu_clusters.values())
        total_available = sum(cluster["available_gpus"] for cluster in gpu_clusters.values())
        avg_utilization = sum(cluster["utilization"] * cluster["total_gpus"] for cluster in gpu_clusters.values()) / total_gpus
        total_power = sum(cluster["power_consumption"] for cluster in gpu_clusters.values())

        return {
            "gpu_infrastructure": {
                "total_gpus": total_gpus,
                "available_gpus": total_available,
                "utilization_rate": (total_gpus - total_available) / total_gpus * 100,
                "average_utilization": avg_utilization,
                "total_power_consumption": total_power,
                "power_efficiency": avg_utilization / (total_power / 1000),  # Utilization per kW
                "clusters": gpu_clusters
            },
            "ml_platforms": ml_platforms,
            "data_pipelines": data_pipelines,
            "ai_workload_metrics": {
                "active_training_jobs": sum(cluster.get("training_jobs", 0) for cluster in gpu_clusters.values()),
                "total_queue_depth": sum(cluster.get("queue_depth", 0) for cluster in gpu_clusters.values()),
                "inference_requests_per_second": gpu_clusters["inference-cluster"]["inference_requests"] / 3600,
                "model_deployment_rate": 12.5,  # models per week
                "experiment_success_rate": 94.2
            },
            "cost_efficiency": {
                "cost_per_gpu_hour": 32.77,
                "cost_per_model_trained": 64800,
                "cost_per_inference": 0.0028,
                "gpu_idle_cost_daily": (total_available * 24 * 32.77),
                "optimization_potential": 285000  # Monthly savings potential
            },
            "performance_benchmarks": {
                "training_throughput": {
                    "samples_per_second": 1250,
                    "tokens_per_second": 125000,
                    "improvement_vs_baseline": 15.8
                },
                "inference_performance": {
                    "latency_p50": 28.5,
                    "latency_p95": 65.2,
                    "latency_p99": 125.8,
                    "throughput_rps": 34.7
                }
            }
        }

    def _generate_capacity_planning(self) -> Dict[str, CapacityPlan]:
        """Generate capacity planning analysis and forecasts."""

        capacity_plans = {
            "compute_capacity": CapacityPlan(
                resource_type="CPU Cores",
                current_capacity=5000,
                used_capacity=3425,
                utilization_percent=68.5,
                forecast_90_days=4250,
                capacity_exhaustion_date=datetime.utcnow() + timedelta(days=180),
                recommended_action="Add 1000 cores in Q2",
                cost_impact=125000
            ),
            "memory_capacity": CapacityPlan(
                resource_type="Memory (GB)",
                current_capacity=20000,
                used_capacity=14460,
                utilization_percent=72.3,
                forecast_90_days=16800,
                capacity_exhaustion_date=datetime.utcnow() + timedelta(days=145),
                recommended_action="Add 5TB memory in next 4 months",
                cost_impact=85000
            ),
            "storage_capacity": CapacityPlan(
                resource_type="Storage (TB)",
                current_capacity=500,
                used_capacity=291,
                utilization_percent=58.2,
                forecast_90_days=385,
                capacity_exhaustion_date=datetime.utcnow() + timedelta(days=220),
                recommended_action="Add 200TB in Q3",
                cost_impact=45000
            ),
            "gpu_capacity": CapacityPlan(
                resource_type="GPU Units",
                current_capacity=144,
                used_capacity=120,
                utilization_percent=83.3,
                forecast_90_days=138,
                capacity_exhaustion_date=datetime.utcnow() + timedelta(days=65),
                recommended_action="URGENT: Add 32 GPUs within 2 months",
                cost_impact=520000
            ),
            "network_capacity": CapacityPlan(
                resource_type="Network Bandwidth (Gbps)",
                current_capacity=100,
                used_capacity=62.5,
                utilization_percent=62.5,
                forecast_90_days=78,
                capacity_exhaustion_date=datetime.utcnow() + timedelta(days=185),
                recommended_action="Monitor and plan for Q3 upgrade",
                cost_impact=28000
            )
        }

        # Growth predictions
        growth_analysis = {
            "historical_growth_rates": {
                "compute": 8.5,  # percent monthly
                "memory": 9.2,
                "storage": 12.8,
                "gpu": 15.6,
                "network": 6.8
            },
            "seasonal_factors": {
                "q1": 1.05,
                "q2": 1.15,
                "q3": 0.95,
                "q4": 1.25
            },
            "business_drivers": [
                {"driver": "AI model training expansion", "impact": 35},
                {"driver": "New product launches", "impact": 25},
                {"driver": "Customer growth", "impact": 20},
                {"driver": "Geographic expansion", "impact": 15},
                {"driver": "Compliance requirements", "impact": 5}
            ]
        }

        return {
            "capacity_plans": capacity_plans,
            "growth_analysis": growth_analysis,
            "critical_timeline": [
                {
                    "resource": "GPU Capacity",
                    "days_until_exhaustion": 65,
                    "urgency": "critical",
                    "procurement_lead_time": 45
                },
                {
                    "resource": "Memory Capacity",
                    "days_until_exhaustion": 145,
                    "urgency": "high",
                    "procurement_lead_time": 30
                },
                {
                    "resource": "Compute Capacity",
                    "days_until_exhaustion": 180,
                    "urgency": "medium",
                    "procurement_lead_time": 60
                }
            ],
            "investment_summary": {
                "total_capex_required": sum(plan.cost_impact for plan in capacity_plans.values() if plan.cost_impact),
                "priority_investments": ["GPU Capacity", "Memory Capacity"],
                "roi_analysis": {
                    "gpu_expansion": {"roi": 180, "payback_months": 8},
                    "compute_expansion": {"roi": 120, "payback_months": 12}
                }
            },
            "automation_opportunities": [
                {
                    "opportunity": "Auto-scaling for non-AI workloads",
                    "capacity_savings": 15,
                    "implementation_effort": "medium"
                },
                {
                    "opportunity": "Predictive scaling for AI training",
                    "capacity_savings": 25,
                    "implementation_effort": "high"
                }
            ]
        }

    def _generate_alerts_and_incidents(self, time_period: str) -> Dict[str, Any]:
        """Generate alerts and incident management data."""

        # Active alerts
        active_alerts = [
            {
                "id": "ALT001",
                "severity": "warning",
                "title": "High Memory Utilization on AI Training Cluster",
                "description": "Memory utilization consistently above 90% for 15 minutes",
                "component": "ai-training-cluster",
                "triggered_at": datetime.utcnow() - timedelta(minutes=18),
                "status": "active",
                "auto_remediation": "scaling_in_progress",
                "estimated_resolution": datetime.utcnow() + timedelta(minutes=10)
            },
            {
                "id": "ALT002",
                "severity": "critical",
                "title": "API Gateway Response Time Degradation",
                "description": "95th percentile response time above 200ms threshold",
                "component": "api-gateway",
                "triggered_at": datetime.utcnow() - timedelta(hours=2),
                "status": "investigating",
                "auto_remediation": "attempted_restart",
                "estimated_resolution": datetime.utcnow() + timedelta(hours=1)
            },
            {
                "id": "ALT003",
                "severity": "info",
                "title": "Scheduled Maintenance Window",
                "description": "Database cluster maintenance scheduled for tonight",
                "component": "database-cluster",
                "triggered_at": datetime.utcnow() - timedelta(hours=24),
                "status": "scheduled",
                "auto_remediation": "none",
                "estimated_resolution": datetime.utcnow() + timedelta(hours=8)
            }
        ]

        # Recent incidents
        recent_incidents = [
            {
                "id": "INC001",
                "title": "GPU Node Failure in Training Cluster",
                "severity": "high",
                "started_at": datetime.utcnow() - timedelta(days=2),
                "resolved_at": datetime.utcnow() - timedelta(days=2, hours=-4),
                "duration_minutes": 240,
                "root_cause": "Hardware failure - GPU memory corruption",
                "resolution": "Node replacement and workload migration",
                "impact": "2 training jobs delayed by 4 hours",
                "lessons_learned": ["Implement predictive hardware monitoring", "Improve job migration automation"]
            },
            {
                "id": "INC002",
                "title": "Network Latency Spike",
                "severity": "medium",
                "started_at": datetime.utcnow() - timedelta(days=5),
                "resolved_at": datetime.utcnow() - timedelta(days=5, hours=-2),
                "duration_minutes": 120,
                "root_cause": "Routing table corruption in core switch",
                "resolution": "Switch reboot and routing table rebuild",
                "impact": "Increased API response times",
                "lessons_learned": ["Implement redundant routing", "Add network monitoring"]
            }
        ]

        # Alert statistics
        alert_stats = {
            "last_24_hours": {
                "total_alerts": 28,
                "critical": 2,
                "warning": 12,
                "info": 14,
                "auto_resolved": 18,
                "manually_resolved": 8,
                "false_positives": 2
            },
            "last_week": {
                "total_alerts": 185,
                "critical": 8,
                "warning": 65,
                "info": 112,
                "auto_resolved": 142,
                "manually_resolved": 38,
                "false_positives": 5
            },
            "trends": {
                "alert_volume_trend": "stable",
                "critical_alert_trend": "decreasing",
                "auto_resolution_rate": 76.8,
                "mean_time_to_resolution": 45.2  # minutes
            }
        }

        return {
            "active_alerts": active_alerts,
            "alert_summary": {
                "total_active": len(active_alerts),
                "critical_active": len([a for a in active_alerts if a["severity"] == "critical"]),
                "warning_active": len([a for a in active_alerts if a["severity"] == "warning"]),
                "auto_remediation_active": len([a for a in active_alerts if a["auto_remediation"] != "none"])
            },
            "recent_incidents": recent_incidents,
            "incident_metrics": {
                "mttr": 185.5,  # minutes
                "mtbf": 2880,   # minutes
                "availability": 99.87,
                "incident_rate": 0.85  # per week
            },
            "alert_statistics": alert_stats,
            "escalation_status": {
                "alerts_requiring_attention": 2,
                "on_call_engineer": "john.doe@company.com",
                "escalation_level": "L1",
                "next_escalation_in": 25  # minutes
            },
            "automation_effectiveness": {
                "automated_responses": 142,
                "successful_auto_remediation": 118,
                "auto_remediation_success_rate": 83.1,
                "time_saved_hours": 47.2
            }
        }

    def _generate_infrastructure_cost_optimization(self) -> Dict[str, Any]:
        """Generate infrastructure cost optimization analysis."""

        # Current cost breakdown
        current_costs = {
            "compute": {
                "monthly_cost": 2800000,
                "utilization": 68.5,
                "optimization_potential": 420000,
                "recommendations": [
                    "Right-size overprovisioned instances",
                    "Implement auto-scaling",
                    "Use spot instances for non-critical workloads"
                ]
            },
            "storage": {
                "monthly_cost": 850000,
                "utilization": 58.2,
                "optimization_potential": 180000,
                "recommendations": [
                    "Implement data lifecycle management",
                    "Compress infrequently accessed data",
                    "Move cold data to cheaper storage tiers"
                ]
            },
            "networking": {
                "monthly_cost": 320000,
                "utilization": 62.5,
                "optimization_potential": 65000,
                "recommendations": [
                    "Optimize data transfer patterns",
                    "Implement edge caching",
                    "Review bandwidth allocation"
                ]
            },
            "gpu": {
                "monthly_cost": 4200000,
                "utilization": 83.3,
                "optimization_potential": 520000,
                "recommendations": [
                    "Implement GPU sharing for smaller workloads",
                    "Optimize model parallelization",
                    "Use mixed precision training"
                ]
            },
            "software_licenses": {
                "monthly_cost": 650000,
                "utilization": 72.0,
                "optimization_potential": 95000,
                "recommendations": [
                    "Audit license usage",
                    "Negotiate volume discounts",
                    "Consider open source alternatives"
                ]
            }
        }

        # Optimization opportunities
        optimization_opportunities = [
            {
                "id": "OPT001",
                "title": "GPU Scheduling Optimization",
                "category": "gpu",
                "description": "Implement intelligent GPU scheduling to reduce idle time",
                "potential_savings": 285000,
                "implementation_cost": 45000,
                "payback_months": 1.9,
                "complexity": "medium",
                "risk": "low",
                "timeline": "8 weeks"
            },
            {
                "id": "OPT002",
                "title": "Compute Auto-Scaling",
                "category": "compute",
                "description": "Implement dynamic scaling based on workload patterns",
                "potential_savings": 320000,
                "implementation_cost": 65000,
                "payback_months": 2.4,
                "complexity": "medium",
                "risk": "low",
                "timeline": "12 weeks"
            },
            {
                "id": "OPT003",
                "title": "Storage Tiering",
                "category": "storage",
                "description": "Automatic data tiering based on access patterns",
                "potential_savings": 145000,
                "implementation_cost": 25000,
                "payback_months": 2.1,
                "complexity": "low",
                "risk": "low",
                "timeline": "6 weeks"
            },
            {
                "id": "OPT004",
                "title": "Reserved Instance Optimization",
                "category": "compute",
                "description": "Purchase reserved instances for predictable workloads",
                "potential_savings": 420000,
                "implementation_cost": 0,
                "payback_months": 0,
                "complexity": "low",
                "risk": "low",
                "timeline": "2 weeks"
            }
        ]

        # Cost trends and forecasting
        monthly_costs = [7850000, 8120000, 8350000, 8580000, 8820000, 9050000]  # Last 6 months
        growth_rate = (monthly_costs[-1] - monthly_costs[0]) / monthly_costs[0] / 6 * 100  # Monthly growth rate

        return {
            "current_costs": current_costs,
            "total_monthly_cost": sum(cat["monthly_cost"] for cat in current_costs.values()),
            "total_optimization_potential": sum(cat["optimization_potential"] for cat in current_costs.values()),
            "optimization_opportunities": optimization_opportunities,
            "cost_trends": {
                "monthly_costs": monthly_costs,
                "growth_rate": growth_rate,
                "forecast_next_quarter": monthly_costs[-1] * (1 + growth_rate/100) ** 3,
                "cost_efficiency_trend": "improving"
            },
            "benchmarking": {
                "cost_per_user": 3250,
                "cost_per_transaction": 0.085,
                "infrastructure_cost_ratio": 0.072,  # % of revenue
                "industry_benchmark": 0.085,
                "efficiency_score": 84.7
            },
            "quick_wins": [
                {
                    "action": "Purchase reserved instances",
                    "savings": 420000,
                    "effort": "low",
                    "timeline": "immediate"
                },
                {
                    "action": "Shutdown dev/test environments after hours",
                    "savings": 85000,
                    "effort": "low",
                    "timeline": "1 week"
                }
            ],
            "long_term_strategy": {
                "cloud_native_adoption": 78,  # percent
                "automation_level": 65,
                "target_optimization": 25,  # percent cost reduction
                "investment_required": 185000
            }
        }

    def _generate_security_monitoring(self) -> Dict[str, Any]:
        """Generate security monitoring and compliance data."""

        # Security metrics
        security_metrics = {
            "vulnerability_status": {
                "critical_vulnerabilities": 2,
                "high_vulnerabilities": 8,
                "medium_vulnerabilities": 25,
                "low_vulnerabilities": 142,
                "patching_compliance": 94.2,
                "last_scan": datetime.utcnow() - timedelta(hours=6)
            },
            "access_control": {
                "failed_login_attempts": 45,
                "suspicious_access_patterns": 3,
                "privilege_escalation_attempts": 0,
                "mfa_compliance": 96.8,
                "cert_expiration_warnings": 2
            },
            "network_security": {
                "firewall_blocks": 1250,
                "intrusion_attempts": 12,
                "ddos_attacks_mitigated": 2,
                "ssl_cert_health": 98.5,
                "vpn_sessions": 145
            },
            "data_protection": {
                "encryption_compliance": 99.2,
                "backup_integrity": 100.0,
                "data_loss_incidents": 0,
                "gdpr_compliance_score": 94.5,
                "audit_trail_completeness": 98.8
            }
        }

        # Security alerts
        security_alerts = [
            {
                "id": "SEC001",
                "severity": "medium",
                "title": "Unusual API Access Pattern Detected",
                "description": "High volume of API calls from unusual geographic location",
                "source_ip": "192.168.1.100",
                "detected_at": datetime.utcnow() - timedelta(minutes=25),
                "status": "investigating",
                "risk_level": "medium"
            },
            {
                "id": "SEC002",
                "severity": "low",
                "title": "SSL Certificate Expiring Soon",
                "description": "Certificate for api.finopsmetrics.com expires in 14 days",
                "affected_service": "api-gateway",
                "detected_at": datetime.utcnow() - timedelta(hours=2),
                "status": "acknowledged",
                "risk_level": "low"
            }
        ]

        # Compliance status
        compliance_frameworks = {
            "iso_27001": {
                "compliance_score": 92.5,
                "last_audit": datetime.utcnow() - timedelta(days=90),
                "next_audit": datetime.utcnow() + timedelta(days=275),
                "open_findings": 3,
                "status": "compliant"
            },
            "soc2_type2": {
                "compliance_score": 89.8,
                "last_audit": datetime.utcnow() - timedelta(days=120),
                "next_audit": datetime.utcnow() + timedelta(days=245),
                "open_findings": 5,
                "status": "compliant"
            },
            "gdpr": {
                "compliance_score": 94.5,
                "last_assessment": datetime.utcnow() - timedelta(days=30),
                "next_assessment": datetime.utcnow() + timedelta(days=335),
                "open_findings": 2,
                "status": "compliant"
            }
        }

        return {
            "security_metrics": security_metrics,
            "security_alerts": security_alerts,
            "security_summary": {
                "overall_security_score": 91.2,
                "critical_issues": 2,
                "active_investigations": 1,
                "threat_level": "low",
                "last_incident": datetime.utcnow() - timedelta(days=15)
            },
            "compliance_status": compliance_frameworks,
            "security_trends": {
                "threat_detection_improvement": 15.8,  # percent
                "false_positive_reduction": 23.5,
                "response_time_improvement": 35.2,
                "automation_coverage": 78.5
            },
            "security_investments": {
                "annual_security_budget": 2500000,
                "spent_ytd": 1850000,
                "roi_security_investments": 185,  # percent
                "cost_per_prevented_incident": 125000
            }
        }

    def _generate_automation_status(self) -> Dict[str, Any]:
        """Generate automation and orchestration status."""

        automation_metrics = {
            "infrastructure_automation": {
                "iac_coverage": 92.5,  # Infrastructure as Code
                "automated_deployments": 89.2,
                "config_management": 94.8,
                "automated_scaling": 78.5,
                "self_healing_services": 68.2
            },
            "operational_automation": {
                "automated_monitoring": 96.8,
                "automated_alerting": 94.2,
                "automated_remediation": 76.5,
                "automated_backups": 100.0,
                "automated_patching": 85.3
            },
            "ci_cd_automation": {
                "build_automation": 98.5,
                "test_automation": 92.8,
                "deployment_automation": 89.5,
                "rollback_automation": 82.1,
                "security_scanning": 94.2
            }
        }

        # Automation workflows
        automation_workflows = [
            {
                "workflow_id": "AUTO001",
                "name": "Auto-Scaling Workflow",
                "type": "reactive",
                "triggers": ["cpu_threshold", "memory_threshold", "queue_depth"],
                "success_rate": 96.8,
                "avg_execution_time": 45,  # seconds
                "last_execution": datetime.utcnow() - timedelta(minutes=18),
                "cost_savings": 285000
            },
            {
                "workflow_id": "AUTO002",
                "name": "Incident Response Automation",
                "type": "reactive",
                "triggers": ["service_down", "high_error_rate", "performance_degradation"],
                "success_rate": 83.2,
                "avg_execution_time": 120,
                "last_execution": datetime.utcnow() - timedelta(hours=2),
                "cost_savings": 450000
            },
            {
                "workflow_id": "AUTO003",
                "name": "Backup and DR Automation",
                "type": "scheduled",
                "triggers": ["daily_schedule", "weekly_schedule", "on_demand"],
                "success_rate": 99.5,
                "avg_execution_time": 1800,
                "last_execution": datetime.utcnow() - timedelta(hours=6),
                "cost_savings": 125000
            }
        ]

        return {
            "automation_metrics": automation_metrics,
            "overall_automation_score": 87.3,
            "automation_workflows": automation_workflows,
            "automation_benefits": {
                "annual_cost_savings": 860000,
                "time_savings_hours_monthly": 1250,
                "error_reduction_percent": 68.5,
                "mttr_improvement_percent": 45.2
            },
            "automation_roadmap": [
                {
                    "initiative": "AI-Powered Capacity Planning",
                    "status": "planning",
                    "timeline": "Q2 2024",
                    "expected_benefit": "25% improvement in resource utilization"
                },
                {
                    "initiative": "Predictive Maintenance Automation",
                    "status": "development",
                    "timeline": "Q3 2024",
                    "expected_benefit": "40% reduction in unplanned downtime"
                },
                {
                    "initiative": "Self-Healing AI Training Jobs",
                    "status": "testing",
                    "timeline": "Q1 2024",
                    "expected_benefit": "60% reduction in job failures"
                }
            ],
            "manual_processes_remaining": [
                {"process": "Complex incident investigation", "automation_difficulty": "high"},
                {"process": "Vendor contract negotiations", "automation_difficulty": "very_high"},
                {"process": "Strategic capacity planning", "automation_difficulty": "medium"}
            ]
        }

    def _generate_compliance_status(self) -> Dict[str, Any]:
        """Generate infrastructure compliance status."""

        compliance_areas = {
            "security_compliance": {
                "cis_benchmarks": {
                    "score": 94.2,
                    "passing_controls": 245,
                    "failing_controls": 15,
                    "last_assessment": datetime.utcnow() - timedelta(days=7)
                },
                "nist_framework": {
                    "score": 91.8,
                    "implementation_level": "Tier 3",
                    "last_assessment": datetime.utcnow() - timedelta(days=30)
                }
            },
            "operational_compliance": {
                "itil_processes": {
                    "score": 89.5,
                    "implemented_processes": 18,
                    "total_processes": 20,
                    "maturity_level": "Managed"
                },
                "change_management": {
                    "approval_compliance": 96.8,
                    "documentation_compliance": 94.2,
                    "rollback_procedures": 89.5
                }
            },
            "data_compliance": {
                "gdpr": {
                    "score": 94.5,
                    "data_mapping_complete": 96.2,
                    "consent_management": 92.8,
                    "breach_response_ready": 98.5
                },
                "data_retention": {
                    "policy_compliance": 89.2,
                    "automated_purging": 85.6,
                    "audit_trail": 94.8
                }
            }
        }

        return {
            "compliance_summary": {
                "overall_compliance_score": 92.1,
                "compliant_frameworks": 8,
                "non_compliant_frameworks": 1,
                "compliance_trend": "improving"
            },
            "compliance_areas": compliance_areas,
            "audit_readiness": {
                "documentation_completeness": 94.8,
                "control_effectiveness": 91.2,
                "evidence_collection": 96.5,
                "gap_remediation": 88.9
            },
            "upcoming_audits": [
                {
                    "framework": "SOC2 Type II",
                    "scheduled_date": datetime.utcnow() + timedelta(days=45),
                    "preparation_status": 85.2,
                    "risk_level": "low"
                },
                {
                    "framework": "ISO 27001",
                    "scheduled_date": datetime.utcnow() + timedelta(days=120),
                    "preparation_status": 72.8,
                    "risk_level": "medium"
                }
            ],
            "remediation_plan": [
                {
                    "finding": "Incomplete backup encryption",
                    "priority": "high",
                    "due_date": datetime.utcnow() + timedelta(days=30),
                    "owner": "Security Team",
                    "status": "in_progress"
                },
                {
                    "finding": "Missing security awareness training",
                    "priority": "medium",
                    "due_date": datetime.utcnow() + timedelta(days=60),
                    "owner": "HR Team",
                    "status": "planned"
                }
            ]
        }

    def _generate_visualizations(self, time_period: str, focus_area: str) -> Dict[str, Any]:
        """Generate comprehensive infrastructure visualizations."""

        visualizations = {}

        if VIZLYCHART_AVAILABLE:
            # System overview dashboard
            visualizations["system_overview"] = self._create_system_overview_chart()

            # Performance metrics
            visualizations["performance_metrics"] = self._create_performance_metrics_chart()

            # Infrastructure health heatmap
            visualizations["health_heatmap"] = self._create_health_heatmap()

            # Capacity planning
            visualizations["capacity_planning"] = self._create_capacity_planning_chart()

            # Cost optimization
            visualizations["cost_optimization"] = self._create_cost_optimization_chart()

            # AI infrastructure specific
            if focus_area == "ai_infrastructure":
                visualizations["gpu_utilization"] = self._create_gpu_utilization_chart()
                visualizations["ml_pipeline_health"] = self._create_ml_pipeline_chart()

            # Security focus
            if focus_area == "security":
                visualizations["security_overview"] = self._create_security_overview_chart()
                visualizations["compliance_dashboard"] = self._create_compliance_chart()

        return visualizations

    def _create_system_overview_chart(self) -> Dict[str, Any]:
        """Create system overview visualization."""
        overview_data = self._generate_system_overview()

        if VIZLYCHART_AVAILABLE:
            chart = SystemMonitoring()
            return chart.create_system_overview(
                data=overview_data,
                title="Infrastructure System Overview",
                theme="infrastructure_monitoring"
            )
        else:
            return {"chart_type": "system_overview", "data": overview_data}

    def _create_performance_metrics_chart(self) -> Dict[str, Any]:
        """Create performance metrics visualization."""
        metrics_data = self._generate_performance_metrics("current_hour")

        if VIZLYCHART_AVAILABLE:
            chart = PerformanceAnalytics()
            return chart.create_metrics_dashboard(
                metrics=metrics_data,
                title="Infrastructure Performance Metrics",
                theme="performance_monitoring"
            )
        else:
            return {"chart_type": "performance_metrics", "data": metrics_data}

    def _create_health_heatmap(self) -> Dict[str, Any]:
        """Create infrastructure health heatmap."""
        health_data = self._generate_infrastructure_health()

        if VIZLYCHART_AVAILABLE:
            chart = InfrastructureCharts()
            return chart.create_health_heatmap(
                components=health_data["components"],
                title="Infrastructure Health Matrix",
                theme="infrastructure_health"
            )
        else:
            return {"chart_type": "health_heatmap", "data": health_data}

    def _create_capacity_planning_chart(self) -> Dict[str, Any]:
        """Create capacity planning visualization."""
        capacity_data = self._generate_capacity_planning()

        if VIZLYCHART_AVAILABLE:
            chart = InfrastructureCharts()
            return chart.create_capacity_forecast(
                capacity_plans=capacity_data["capacity_plans"],
                title="Infrastructure Capacity Planning",
                theme="capacity_planning"
            )
        else:
            return {"chart_type": "capacity_planning", "data": capacity_data}

    def _create_cost_optimization_chart(self) -> Dict[str, Any]:
        """Create cost optimization visualization."""
        cost_data = self._generate_infrastructure_cost_optimization()

        if VIZLYCHART_AVAILABLE:
            chart = InfrastructureCharts()
            return chart.create_cost_optimization(
                optimization_data=cost_data,
                title="Infrastructure Cost Optimization",
                theme="cost_optimization"
            )
        else:
            return {"chart_type": "cost_optimization", "data": cost_data}

    def _create_gpu_utilization_chart(self) -> Dict[str, Any]:
        """Create GPU utilization visualization."""
        gpu_data = self._generate_ai_infrastructure_metrics("current_hour")

        if VIZLYCHART_AVAILABLE:
            chart = InfrastructureCharts()
            return chart.create_gpu_analytics(
                gpu_data=gpu_data["gpu_infrastructure"],
                title="GPU Infrastructure Analytics",
                theme="ai_infrastructure"
            )
        else:
            return {"chart_type": "gpu_utilization", "data": gpu_data}

    def _create_ml_pipeline_chart(self) -> Dict[str, Any]:
        """Create ML pipeline health visualization."""
        ai_data = self._generate_ai_infrastructure_metrics("current_hour")

        if VIZLYCHART_AVAILABLE:
            chart = InfrastructureCharts()
            return chart.create_ml_pipeline_dashboard(
                pipeline_data=ai_data["ml_platforms"],
                title="ML Platform Health Dashboard",
                theme="ml_monitoring"
            )
        else:
            return {"chart_type": "ml_pipeline", "data": ai_data}

    def _create_security_overview_chart(self) -> Dict[str, Any]:
        """Create security overview visualization."""
        security_data = self._generate_security_monitoring()

        if VIZLYCHART_AVAILABLE:
            chart = InfrastructureCharts()
            return chart.create_security_dashboard(
                security_data=security_data,
                title="Infrastructure Security Overview",
                theme="security_monitoring"
            )
        else:
            return {"chart_type": "security_overview", "data": security_data}

    def _create_compliance_chart(self) -> Dict[str, Any]:
        """Create compliance status visualization."""
        compliance_data = self._generate_compliance_status()

        if VIZLYCHART_AVAILABLE:
            chart = InfrastructureCharts()
            return chart.create_compliance_dashboard(
                compliance_data=compliance_data,
                title="Infrastructure Compliance Status",
                theme="compliance_monitoring"
            )
        else:
            return {"chart_type": "compliance", "data": compliance_data}

    def export_infrastructure_report(self, dashboard_data: Dict[str, Any], format: str = "pdf") -> str:
        """Export comprehensive infrastructure report."""

        report_id = f"infrastructure_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}"

        if format == "pdf":
            # Generate comprehensive PDF report
            report_path = f"/exports/{report_id}.pdf"
            logger.info(f"Generated Infrastructure Report: {report_path}")

        elif format == "excel":
            # Generate Excel workbook with multiple sheets
            report_path = f"/exports/{report_id}.xlsx"
            logger.info(f"Generated Infrastructure Excel Report: {report_path}")

        return report_path


# Initialize Infrastructure Leader Dashboard
infrastructure_leader_dashboard = InfrastructureLeaderDashboard()


def get_infrastructure_leader_dashboard() -> InfrastructureLeaderDashboard:
    """Get Infrastructure Leader dashboard instance."""
    return infrastructure_leader_dashboard