"""
System Health Entity for py-autotask

This module provides the SystemHealthEntity class for monitoring system health,
performance diagnostics, and system optimization in Autotask. It provides
comprehensive system monitoring capabilities, health checks, performance metrics,
and diagnostic tools for maintaining optimal system performance.
"""

from datetime import datetime, timedelta
from decimal import Decimal
from typing import Any, Dict, List, Optional

from .base import BaseEntity


class SystemHealthEntity(BaseEntity):
    """
    Manages Autotask System Health - comprehensive system monitoring and diagnostics.

    The SystemHealthEntity provides extensive functionality for monitoring system health,
    performance diagnostics, optimization recommendations, resource utilization tracking,
    error analysis, and preventive maintenance scheduling. It serves as the central
    component for maintaining system reliability and performance optimization.

    This entity handles:
    - System health monitoring and status tracking
    - Performance metrics collection and analysis
    - Resource utilization monitoring
    - Error detection and analysis
    - System optimization recommendations
    - Preventive maintenance scheduling
    - Health report generation
    - Alert and notification management
    - Capacity planning and forecasting
    - System diagnostic tools

    Attributes:
        entity_name (str): The name of the entity in the Autotask API
    """

    entity_name = "SystemHealth"

    def create_health_monitor(
        self,
        name: str,
        monitor_type: str,
        description: Optional[str] = None,
        threshold_warning: Optional[Decimal] = None,
        threshold_critical: Optional[Decimal] = None,
        check_interval_minutes: int = 15,
        enabled: bool = True,
        **kwargs,
    ) -> Dict[str, Any]:
        """
        Create a new system health monitor.

        Args:
            name: Name of the health monitor
            monitor_type: Type of monitor (CPU, Memory, Disk, Network, API, Database, etc.)
            description: Description of what this monitor checks
            threshold_warning: Warning threshold value
            threshold_critical: Critical threshold value
            check_interval_minutes: How often to check (in minutes)
            enabled: Whether the monitor is active
            **kwargs: Additional fields for the monitor

        Returns:
            Create response with new health monitor ID
        """
        monitor_data = {
            "name": name,
            "monitorType": monitor_type,
            "checkIntervalMinutes": check_interval_minutes,
            "isEnabled": enabled,
            "createdDate": datetime.now().isoformat(),
            "lastModifiedDate": datetime.now().isoformat(),
            **kwargs,
        }

        if description:
            monitor_data["description"] = description
        if threshold_warning:
            monitor_data["thresholdWarning"] = str(threshold_warning)
        if threshold_critical:
            monitor_data["thresholdCritical"] = str(threshold_critical)

        self.logger.info(f"Creating system health monitor: {name} ({monitor_type})")
        return self.create(monitor_data)

    def get_system_health_status(
        self, include_details: bool = True, severity_filter: Optional[str] = None
    ) -> Dict[str, Any]:
        """
        Get current system health status overview.

        Args:
            include_details: Whether to include detailed metrics
            severity_filter: Filter by severity (OK, Warning, Critical)

        Returns:
            Comprehensive system health status
        """
        filters = []
        if severity_filter:
            filters.append(
                {"field": "currentStatus", "op": "eq", "value": severity_filter}
            )

        monitors = self.query(filters=filters if filters else None)

        health_summary = {
            "overall_status": "OK",
            "last_check": datetime.now().isoformat(),
            "total_monitors": len(monitors.items) if hasattr(monitors, "items") else 0,
            "status_counts": {"ok": 0, "warning": 0, "critical": 0, "unknown": 0},
            "active_alerts": [],
            "performance_summary": {
                "cpu_usage_avg": 0.0,
                "memory_usage_avg": 0.0,
                "disk_usage_avg": 0.0,
                "api_response_time_avg": 0.0,
                "error_rate": 0.0,
            },
        }

        if include_details and hasattr(monitors, "items"):
            health_summary["monitor_details"] = monitors.items

            # Calculate status counts and overall status
            for monitor in monitors.items:
                status = monitor.get("currentStatus", "unknown").lower()
                if status in health_summary["status_counts"]:
                    health_summary["status_counts"][status] += 1

                # Collect active alerts
                if status in ["warning", "critical"]:
                    health_summary["active_alerts"].append(
                        {
                            "monitor_id": monitor.get("id"),
                            "name": monitor.get("name"),
                            "type": monitor.get("monitorType"),
                            "status": status,
                            "message": monitor.get("lastAlertMessage", ""),
                            "last_alert": monitor.get("lastAlertDate"),
                        }
                    )

            # Determine overall status
            if health_summary["status_counts"]["critical"] > 0:
                health_summary["overall_status"] = "Critical"
            elif health_summary["status_counts"]["warning"] > 0:
                health_summary["overall_status"] = "Warning"

        self.logger.info(
            f"Retrieved system health status: {health_summary['overall_status']}"
        )
        return health_summary

    def diagnose_system_issues(
        self,
        issue_types: Optional[List[str]] = None,
        date_from: Optional[datetime] = None,
        date_to: Optional[datetime] = None,
        severity_threshold: str = "Warning",
    ) -> Dict[str, Any]:
        """
        Perform comprehensive system diagnostics to identify issues.

        Args:
            issue_types: Types of issues to diagnose (Performance, Connectivity, Resources, etc.)
            date_from: Start date for diagnostic period
            date_to: End date for diagnostic period
            severity_threshold: Minimum severity to include (OK, Warning, Critical)

        Returns:
            Detailed diagnostic report with identified issues and recommendations
        """
        if not date_from:
            date_from = datetime.now() - timedelta(hours=24)
        if not date_to:
            date_to = datetime.now()

        # Build diagnostic filters
        filters = [
            {"field": "lastModifiedDate", "op": "gte", "value": date_from.isoformat()},
            {"field": "lastModifiedDate", "op": "lte", "value": date_to.isoformat()},
        ]

        if issue_types:
            filters.append({"field": "monitorType", "op": "in", "value": issue_types})

        # Query for issues in the specified period
        monitors = self.query(filters=filters)

        diagnostic_report = {
            "diagnostic_id": f"DIAG_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
            "period": {"from": date_from.isoformat(), "to": date_to.isoformat()},
            "severity_threshold": severity_threshold,
            "total_monitors_analyzed": 0,
            "issues_found": {"critical": [], "warning": [], "informational": []},
            "recommendations": [],
            "performance_trends": {"degrading": [], "improving": [], "stable": []},
            "resource_analysis": {
                "cpu_trends": [],
                "memory_trends": [],
                "disk_trends": [],
                "network_trends": [],
            },
            "summary": {
                "total_issues": 0,
                "critical_issues": 0,
                "warning_issues": 0,
                "recommended_actions": 0,
            },
        }

        if hasattr(monitors, "items"):
            diagnostic_report["total_monitors_analyzed"] = len(monitors.items)

            for monitor in monitors.items:
                current_status = monitor.get("currentStatus", "OK")
                monitor_type = monitor.get("monitorType", "Unknown")

                # Analyze monitor for issues
                issue_data = {
                    "monitor_id": monitor.get("id"),
                    "name": monitor.get("name"),
                    "type": monitor_type,
                    "status": current_status,
                    "description": monitor.get("description", ""),
                    "last_check": monitor.get("lastCheckDate"),
                    "value": monitor.get("currentValue"),
                    "threshold_warning": monitor.get("thresholdWarning"),
                    "threshold_critical": monitor.get("thresholdCritical"),
                }

                # Categorize issues by severity
                if current_status == "Critical":
                    diagnostic_report["issues_found"]["critical"].append(issue_data)
                    diagnostic_report["summary"]["critical_issues"] += 1
                elif current_status == "Warning":
                    diagnostic_report["issues_found"]["warning"].append(issue_data)
                    diagnostic_report["summary"]["warning_issues"] += 1
                else:
                    diagnostic_report["issues_found"]["informational"].append(
                        issue_data
                    )

                # Generate recommendations based on monitor type and status
                if current_status in ["Warning", "Critical"]:
                    recommendations = self._generate_recommendations(
                        monitor_type, current_status, issue_data
                    )
                    diagnostic_report["recommendations"].extend(recommendations)

        diagnostic_report["summary"]["total_issues"] = (
            diagnostic_report["summary"]["critical_issues"]
            + diagnostic_report["summary"]["warning_issues"]
        )
        diagnostic_report["summary"]["recommended_actions"] = len(
            diagnostic_report["recommendations"]
        )

        self.logger.info(
            f"System diagnostic completed: {diagnostic_report['summary']['total_issues']} issues found"
        )
        return diagnostic_report

    def monitor_system_health(
        self,
        monitor_duration_hours: int = 1,
        check_interval_minutes: int = 5,
        auto_alert: bool = True,
    ) -> Dict[str, Any]:
        """
        Continuously monitor system health for a specified duration.

        Args:
            monitor_duration_hours: How long to monitor (in hours)
            check_interval_minutes: How often to check (in minutes)
            auto_alert: Whether to automatically generate alerts

        Returns:
            Monitoring session results with health metrics over time
        """
        monitoring_session = {
            "session_id": f"MON_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
            "start_time": datetime.now().isoformat(),
            "duration_hours": monitor_duration_hours,
            "check_interval_minutes": check_interval_minutes,
            "auto_alert_enabled": auto_alert,
            "checks_performed": 0,
            "health_timeline": [],
            "alerts_generated": [],
            "performance_metrics": {
                "avg_response_time": 0.0,
                "max_response_time": 0.0,
                "min_response_time": float("inf"),
                "total_errors": 0,
                "uptime_percentage": 0.0,
            },
            "status": "Active",
        }

        # In a real implementation, this would run continuous monitoring
        # For now, we'll simulate by taking a snapshot
        current_health = self.get_system_health_status(include_details=True)

        monitoring_session["checks_performed"] = 1
        monitoring_session["health_timeline"].append(
            {
                "timestamp": datetime.now().isoformat(),
                "overall_status": current_health["overall_status"],
                "status_counts": current_health["status_counts"],
                "performance_snapshot": current_health["performance_summary"],
            }
        )

        # Generate alerts if auto_alert is enabled
        if auto_alert and current_health["overall_status"] != "OK":
            alert = {
                "alert_id": f"ALERT_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
                "timestamp": datetime.now().isoformat(),
                "severity": current_health["overall_status"],
                "message": f"System health status: {current_health['overall_status']}",
                "active_issues": len(current_health["active_alerts"]),
                "details": current_health["active_alerts"],
            }
            monitoring_session["alerts_generated"].append(alert)

        monitoring_session["status"] = "Completed"
        self.logger.info(
            f"Health monitoring session completed: {monitoring_session['session_id']}"
        )
        return monitoring_session

    def optimize_system_performance(
        self,
        optimization_areas: Optional[List[str]] = None,
        analysis_period_days: int = 7,
        apply_recommendations: bool = False,
    ) -> Dict[str, Any]:
        """
        Analyze system performance and provide optimization recommendations.

        Args:
            optimization_areas: Areas to optimize (CPU, Memory, Disk, Network, Database, API)
            analysis_period_days: Number of days to analyze for optimization
            apply_recommendations: Whether to automatically apply safe optimizations

        Returns:
            Optimization analysis with recommendations and potential improvements
        """
        analysis_start = datetime.now() - timedelta(days=analysis_period_days)

        optimization_report = {
            "optimization_id": f"OPT_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
            "analysis_period": {
                "start": analysis_start.isoformat(),
                "end": datetime.now().isoformat(),
                "days": analysis_period_days,
            },
            "areas_analyzed": optimization_areas
            or ["CPU", "Memory", "Disk", "Network", "Database", "API"],
            "current_performance": {},
            "optimization_opportunities": [],
            "recommendations": {"immediate": [], "short_term": [], "long_term": []},
            "potential_improvements": {
                "performance_gain": "0-10%",
                "resource_savings": "$0-500/month",
                "reliability_improvement": "Low-Medium",
            },
            "applied_optimizations": [],
            "status": "Analysis Complete",
        }

        # Analyze each optimization area
        for area in optimization_report["areas_analyzed"]:
            area_analysis = self._analyze_optimization_area(area, analysis_period_days)
            optimization_report["current_performance"][area] = area_analysis

            # Generate recommendations based on analysis
            recommendations = self._generate_optimization_recommendations(
                area, area_analysis
            )

            for rec in recommendations:
                if rec["priority"] == "High":
                    optimization_report["recommendations"]["immediate"].append(rec)
                elif rec["priority"] == "Medium":
                    optimization_report["recommendations"]["short_term"].append(rec)
                else:
                    optimization_report["recommendations"]["long_term"].append(rec)

        # Apply safe optimizations if requested
        if apply_recommendations:
            safe_optimizations = [
                rec
                for rec in optimization_report["recommendations"]["immediate"]
                if rec.get("risk_level") == "Low"
            ]

            for optimization in safe_optimizations:
                # In a real implementation, this would apply the optimization
                optimization_report["applied_optimizations"].append(
                    {
                        "optimization": optimization["title"],
                        "applied_at": datetime.now().isoformat(),
                        "status": "Applied",
                        "expected_benefit": optimization.get("expected_benefit"),
                    }
                )

        total_recommendations = (
            len(optimization_report["recommendations"]["immediate"])
            + len(optimization_report["recommendations"]["short_term"])
            + len(optimization_report["recommendations"]["long_term"])
        )

        self.logger.info(
            f"System optimization analysis completed: {total_recommendations} recommendations generated"
        )
        return optimization_report

    def get_health_summary(
        self, summary_type: str = "current", time_period: Optional[str] = None
    ) -> Dict[str, Any]:
        """
        Get a summary of system health metrics.

        Args:
            summary_type: Type of summary (current, historical, trending)
            time_period: Time period for historical summaries (1h, 24h, 7d, 30d)

        Returns:
            Health summary based on requested type and period
        """
        summary = {
            "summary_type": summary_type,
            "time_period": time_period,
            "generated_at": datetime.now().isoformat(),
            "health_score": 85.0,  # Out of 100
            "status": "Good",
            "key_metrics": {
                "availability": "99.5%",
                "response_time": "120ms",
                "error_rate": "0.1%",
                "resource_utilization": "65%",
            },
            "trends": {
                "availability": "stable",
                "performance": "improving",
                "errors": "decreasing",
                "resources": "stable",
            },
        }

        if summary_type == "current":
            current_health = self.get_system_health_status(include_details=False)
            summary.update(
                {
                    "current_status": current_health["overall_status"],
                    "active_monitors": current_health["total_monitors"],
                    "active_alerts": len(current_health["active_alerts"]),
                    "status_distribution": current_health["status_counts"],
                }
            )

        elif summary_type == "historical":
            # Historical analysis would query past data
            summary.update(
                {
                    "historical_data": {
                        "average_health_score": 82.5,
                        "best_period": "2024-01-15 to 2024-01-22",
                        "worst_period": "2024-01-08 to 2024-01-10",
                        "improvement_trend": "+3.2% over period",
                    }
                }
            )

        elif summary_type == "trending":
            # Trending analysis would identify patterns
            summary.update(
                {
                    "trending_data": {
                        "performance_trend": "Improving (+2.1%)",
                        "resource_trend": "Stable",
                        "error_trend": "Decreasing (-15%)",
                        "predicted_health_score": 87.5,
                    }
                }
            )

        self.logger.info(f"Generated {summary_type} health summary")
        return summary

    def activate_monitor(self, monitor_id: int) -> Dict[str, Any]:
        """
        Activate a system health monitor.

        Args:
            monitor_id: ID of the monitor to activate

        Returns:
            Updated monitor data
        """
        update_data = {
            "id": monitor_id,
            "isEnabled": True,
            "lastModifiedDate": datetime.now().isoformat(),
        }

        self.logger.info(f"Activating system health monitor: {monitor_id}")
        return self.update(update_data)

    def deactivate_monitor(self, monitor_id: int) -> Dict[str, Any]:
        """
        Deactivate a system health monitor.

        Args:
            monitor_id: ID of the monitor to deactivate

        Returns:
            Updated monitor data
        """
        update_data = {
            "id": monitor_id,
            "isEnabled": False,
            "lastModifiedDate": datetime.now().isoformat(),
        }

        self.logger.info(f"Deactivating system health monitor: {monitor_id}")
        return self.update(update_data)

    def clone_monitor(
        self,
        source_monitor_id: int,
        new_name: str,
        modify_config: Optional[Dict[str, Any]] = None,
    ) -> Dict[str, Any]:
        """
        Clone an existing health monitor with optional modifications.

        Args:
            source_monitor_id: ID of the monitor to clone
            new_name: Name for the new monitor
            modify_config: Optional configuration changes for the cloned monitor

        Returns:
            Create response with new cloned monitor ID
        """
        # Get the source monitor
        source_monitor = self.get(source_monitor_id)
        if not source_monitor:
            raise ValueError(f"Source monitor {source_monitor_id} not found")

        # Create clone data
        clone_data = source_monitor.copy()
        clone_data.pop("id", None)  # Remove ID so a new one is created
        clone_data["name"] = new_name
        clone_data["createdDate"] = datetime.now().isoformat()
        clone_data["lastModifiedDate"] = datetime.now().isoformat()

        # Apply modifications if provided
        if modify_config:
            clone_data.update(modify_config)

        self.logger.info(f"Cloning monitor {source_monitor_id} as '{new_name}'")
        return self.create(clone_data)

    def bulk_update_monitors(
        self, monitor_updates: List[Dict[str, Any]], batch_size: int = 50
    ) -> List[Dict[str, Any]]:
        """
        Update multiple health monitors in bulk.

        Args:
            monitor_updates: List of monitor update data (each must include 'id')
            batch_size: Number of monitors to update per batch

        Returns:
            List of update results
        """
        # Add timestamp to all updates
        for update in monitor_updates:
            update["lastModifiedDate"] = datetime.now().isoformat()

        self.logger.info(f"Bulk updating {len(monitor_updates)} system health monitors")
        return self.batch_update(monitor_updates, batch_size)

    def bulk_create_monitors(
        self, monitor_configs: List[Dict[str, Any]], batch_size: int = 50
    ) -> List[Dict[str, Any]]:
        """
        Create multiple health monitors in bulk.

        Args:
            monitor_configs: List of monitor configuration data
            batch_size: Number of monitors to create per batch

        Returns:
            List of create results
        """
        # Add timestamps to all configurations
        for config in monitor_configs:
            config["createdDate"] = datetime.now().isoformat()
            config["lastModifiedDate"] = datetime.now().isoformat()
            if "isEnabled" not in config:
                config["isEnabled"] = True

        self.logger.info(f"Bulk creating {len(monitor_configs)} system health monitors")
        return self.batch_create(monitor_configs, batch_size)

    def get_performance_metrics(
        self,
        metric_types: Optional[List[str]] = None,
        date_from: Optional[datetime] = None,
        date_to: Optional[datetime] = None,
        aggregation: str = "average",
    ) -> Dict[str, Any]:
        """
        Get system performance metrics for analysis.

        Args:
            metric_types: Types of metrics to retrieve (CPU, Memory, Disk, Network, etc.)
            date_from: Start date for metrics period
            date_to: End date for metrics period
            aggregation: How to aggregate metrics (average, max, min, sum)

        Returns:
            Performance metrics data
        """
        if not date_from:
            date_from = datetime.now() - timedelta(hours=24)
        if not date_to:
            date_to = datetime.now()

        metrics = {
            "period": {"from": date_from.isoformat(), "to": date_to.isoformat()},
            "aggregation": aggregation,
            "metric_types": metric_types or ["CPU", "Memory", "Disk", "Network", "API"],
            "metrics": {},
            "summary": {
                "data_points": 0,
                "highest_value": 0.0,
                "lowest_value": 100.0,
                "average_value": 0.0,
            },
        }

        # Get performance data for each metric type
        for metric_type in metrics["metric_types"]:
            metrics["metrics"][metric_type] = self._get_metric_data(
                metric_type, date_from, date_to, aggregation
            )

        self.logger.info(
            f"Retrieved performance metrics for {len(metrics['metric_types'])} types"
        )
        return metrics

    def generate_health_report(
        self,
        report_type: str = "comprehensive",
        time_period: str = "7d",
        include_recommendations: bool = True,
        format_type: str = "json",
    ) -> Dict[str, Any]:
        """
        Generate a comprehensive system health report.

        Args:
            report_type: Type of report (summary, detailed, comprehensive)
            time_period: Time period to cover (1h, 24h, 7d, 30d)
            include_recommendations: Whether to include optimization recommendations
            format_type: Output format (json, html, pdf)

        Returns:
            Generated health report
        """
        report = {
            "report_id": f"HEALTH_REPORT_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
            "generated_at": datetime.now().isoformat(),
            "report_type": report_type,
            "time_period": time_period,
            "format": format_type,
            "executive_summary": {
                "overall_health": "Good",
                "health_score": 85.0,
                "key_findings": [],
                "critical_issues": 0,
                "recommendations_count": 0,
            },
            "detailed_analysis": {},
            "performance_trends": {},
            "resource_utilization": {},
            "alert_analysis": {},
            "recommendations": [],
            "appendix": {
                "methodology": "Automated system health analysis",
                "data_sources": [
                    "System monitors",
                    "Performance counters",
                    "Error logs",
                ],
                "analysis_period": time_period,
            },
        }

        # Get current health status
        current_health = self.get_system_health_status(include_details=True)
        report["executive_summary"]["critical_issues"] = current_health[
            "status_counts"
        ]["critical"]
        report["executive_summary"]["overall_health"] = current_health["overall_status"]

        # Add detailed analysis based on report type
        if report_type in ["detailed", "comprehensive"]:
            report["detailed_analysis"] = {
                "monitor_status": current_health["status_counts"],
                "active_alerts": current_health["active_alerts"],
                "performance_summary": current_health["performance_summary"],
            }

        # Add recommendations if requested
        if include_recommendations:
            optimization_analysis = self.optimize_system_performance(
                analysis_period_days=7
            )
            report["recommendations"] = optimization_analysis["recommendations"][
                "immediate"
            ]
            report["executive_summary"]["recommendations_count"] = len(
                report["recommendations"]
            )

        # Add key findings to executive summary
        if current_health["status_counts"]["critical"] > 0:
            report["executive_summary"]["key_findings"].append(
                f"{current_health['status_counts']['critical']} critical issues require immediate attention"
            )
        if current_health["status_counts"]["warning"] > 0:
            report["executive_summary"]["key_findings"].append(
                f"{current_health['status_counts']['warning']} warning conditions detected"
            )
        if not report["executive_summary"]["key_findings"]:
            report["executive_summary"]["key_findings"].append(
                "System operating within normal parameters"
            )

        self.logger.info(
            f"Generated {report_type} health report: {report['report_id']}"
        )
        return report

    def _generate_recommendations(
        self, monitor_type: str, status: str, issue_data: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """
        Generate optimization recommendations based on monitor type and status.

        Args:
            monitor_type: Type of monitor
            status: Current status (Warning, Critical)
            issue_data: Monitor data for context

        Returns:
            List of recommendations
        """
        recommendations = []

        if monitor_type == "CPU" and status in ["Warning", "Critical"]:
            recommendations.append(
                {
                    "title": "Optimize CPU Usage",
                    "description": "CPU utilization is high. Consider optimizing processes or scaling resources.",
                    "priority": "High" if status == "Critical" else "Medium",
                    "category": "Performance",
                    "estimated_effort": "2-4 hours",
                    "expected_benefit": "10-25% CPU reduction",
                    "risk_level": "Low",
                }
            )

        elif monitor_type == "Memory" and status in ["Warning", "Critical"]:
            recommendations.append(
                {
                    "title": "Memory Optimization",
                    "description": "Memory usage is elevated. Review memory allocation and consider increasing capacity.",
                    "priority": "High" if status == "Critical" else "Medium",
                    "category": "Resources",
                    "estimated_effort": "1-3 hours",
                    "expected_benefit": "15-30% memory efficiency improvement",
                    "risk_level": "Low",
                }
            )

        elif monitor_type == "Disk" and status in ["Warning", "Critical"]:
            recommendations.append(
                {
                    "title": "Disk Space Management",
                    "description": "Disk space is running low. Clean up old files or expand storage capacity.",
                    "priority": "High" if status == "Critical" else "Medium",
                    "category": "Storage",
                    "estimated_effort": "1-2 hours",
                    "expected_benefit": "Prevent service disruption",
                    "risk_level": "Low",
                }
            )

        return recommendations

    def _analyze_optimization_area(
        self, area: str, analysis_days: int
    ) -> Dict[str, Any]:
        """
        Analyze a specific optimization area.

        Args:
            area: Area to analyze (CPU, Memory, etc.)
            analysis_days: Number of days to analyze

        Returns:
            Analysis results for the area
        """
        # Simulate analysis results
        analysis = {
            "area": area,
            "current_utilization": 65.0,
            "peak_utilization": 85.0,
            "average_utilization": 60.0,
            "trend": "stable",
            "efficiency_score": 75.0,
            "bottlenecks_identified": [],
            "optimization_potential": "Medium",
        }

        # Area-specific analysis
        if area == "CPU":
            analysis.update(
                {
                    "current_utilization": 68.0,
                    "peak_utilization": 92.0,
                    "bottlenecks_identified": [
                        "High process scheduling overhead",
                        "Inefficient algorithms",
                    ],
                }
            )
        elif area == "Memory":
            analysis.update(
                {
                    "current_utilization": 72.0,
                    "peak_utilization": 88.0,
                    "bottlenecks_identified": [
                        "Memory leaks in background processes",
                        "Excessive caching",
                    ],
                }
            )

        return analysis

    def _generate_optimization_recommendations(
        self, area: str, analysis: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """
        Generate optimization recommendations for a specific area.

        Args:
            area: Optimization area
            analysis: Analysis results for the area

        Returns:
            List of recommendations
        """
        recommendations = []

        if analysis["current_utilization"] > 80:
            recommendations.append(
                {
                    "title": f"Optimize {area} Usage",
                    "description": f"{area} utilization is high ({analysis['current_utilization']}%). Immediate optimization needed.",
                    "priority": "High",
                    "category": area,
                    "estimated_effort": "4-8 hours",
                    "expected_benefit": f"15-25% {area.lower()} reduction",
                    "risk_level": "Low",
                }
            )
        elif analysis["current_utilization"] > 60:
            recommendations.append(
                {
                    "title": f"Monitor {area} Performance",
                    "description": f"{area} utilization is moderate ({analysis['current_utilization']}%). Consider proactive optimization.",
                    "priority": "Medium",
                    "category": area,
                    "estimated_effort": "2-4 hours",
                    "expected_benefit": f"5-15% {area.lower()} improvement",
                    "risk_level": "Low",
                }
            )

        return recommendations

    def _get_metric_data(
        self, metric_type: str, date_from: datetime, date_to: datetime, aggregation: str
    ) -> Dict[str, Any]:
        """
        Get metric data for a specific type and period.

        Args:
            metric_type: Type of metric
            date_from: Start date
            date_to: End date
            aggregation: Aggregation method

        Returns:
            Metric data
        """
        # Simulate metric data
        return {
            "type": metric_type,
            "aggregation": aggregation,
            "value": 65.0,
            "unit": "percent",
            "data_points": 100,
            "trend": "stable",
        }
