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import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import json

class SLAManagement:
    def __init__(self):
        self.sla_contracts = {}
        self.service_levels = {}
        self.performance_metrics = {}
        self.violations = {}
        self.reports = {}
    
    def create_sla_contract(self, contract_id, contract_data):
        """Crear contrato SLA"""
        self.sla_contracts[contract_id] = {
            'contract_id': contract_id,
            'provider_name': contract_data['provider_name'],
            'service_name': contract_data['service_name'],
            'start_date': contract_data['start_date'],
            'end_date': contract_data['end_date'],
            'renewal_date': contract_data.get('renewal_date'),
            'status': 'active',
            'service_levels': contract_data.get('service_levels', {}),
            'security_requirements': contract_data.get('security_requirements', {}),
            'penalties': contract_data.get('penalties', {}),
            'rewards': contract_data.get('rewards', {}),
            'created_date': datetime.now()
        }
    
    def define_service_level(self, contract_id, sl_id, sl_data):
        """Definir nivel de servicio"""
        if contract_id not in self.sla_contracts:
            return False
        
        service_level = {
            'sl_id': sl_id,
            'contract_id': contract_id,
            'metric_name': sl_data['metric_name'],
            'description': sl_data['description'],
            'target_value': sl_data['target_value'],
            'measurement_method': sl_data['measurement_method'],
            'frequency': sl_data['frequency'],
            'unit': sl_data.get('unit', 'percentage'),
            'threshold_warning': sl_data.get('threshold_warning', 0.9),
            'threshold_critical': sl_data.get('threshold_critical', 0.8),
            'penalty_rate': sl_data.get('penalty_rate', 0.01),
            'reward_rate': sl_data.get('reward_rate', 0.005)
        }
        
        self.service_levels[sl_id] = service_level
        return True
    
    def record_performance_measurement(self, sl_id, measurement_data):
        """Registrar medición de rendimiento"""
        if sl_id not in self.service_levels:
            return False
        
        measurement_id = f"MEAS-{len(self.performance_metrics) + 1}"
        
        measurement = {
            'measurement_id': measurement_id,
            'sl_id': sl_id,
            'contract_id': self.service_levels[sl_id]['contract_id'],
            'measured_value': measurement_data['value'],
            'target_value': self.service_levels[sl_id]['target_value'],
            'measurement_date': measurement_data.get('date', datetime.now()),
            'context': measurement_data.get('context', {}),
            'quality_score': measurement_data.get('quality_score', 1.0)
        }
        
        # Calcular cumplimiento
        target = measurement['target_value']
        actual = measurement['measured_value']
        
        if measurement['unit'] == 'percentage':
            compliance = actual / target if target > 0 else 0
        else:
            # Para métricas donde menor es mejor (como tiempo de respuesta)
            compliance = target / actual if actual > 0 else 0
        
        measurement['compliance'] = compliance
        measurement['status'] = self.determine_sla_status(compliance, sl_id)
        
        self.performance_metrics[measurement_id] = measurement
        return True
    
    def determine_sla_status(self, compliance, sl_id):
        """Determinar estado del SLA"""
        service_level = self.service_levels[sl_id]
        
        if compliance >= 1.0:
            return 'excellent'
        elif compliance >= service_level['threshold_warning']:
            return 'good'
        elif compliance >= service_level['threshold_critical']:
            return 'warning'
        else:
            return 'violation'
    
    def calculate_sla_violations(self, contract_id, period_days=30):
        """Calcular violaciones de SLA"""
        if contract_id not in self.sla_contracts:
            return []
        
        # Obtener mediciones del período
        cutoff_date = datetime.now() - timedelta(days=period_days)
        contract_measurements = [
            m for m in self.performance_metrics.values()
            if m['contract_id'] == contract_id and m['measurement_date'] >= cutoff_date
        ]
        
        violations = []
        
        for measurement in contract_measurements:
            if measurement['status'] == 'violation':
                sl_id = measurement['sl_id']
                service_level = self.service_levels[sl_id]
                
                violation = {
                    'violation_id': f"VIOL-{len(violations) + 1}",
                    'contract_id': contract_id,
                    'sl_id': sl_id,
                    'metric_name': service_level['metric_name'],
                    'target_value': measurement['target_value'],
                    'actual_value': measurement['measured_value'],
                    'compliance': measurement['compliance'],
                    'violation_date': measurement['measurement_date'],
                    'penalty_amount': self.calculate_penalty(measurement, service_level)
                }
                
                violations.append(violation)
        
        return violations
    
    def calculate_penalty(self, measurement, service_level):
        """Calcular penalización por violación"""
        if measurement['status'] != 'violation':
            return 0
        
        # Calcular penalización basada en la desviación
        deviation = 1 - measurement['compliance']
        penalty_rate = service_level['penalty_rate']
        
        # Penalización base del contrato
        base_penalty = self.sla_contracts[measurement['contract_id']].get('base_penalty', 1000)
        
        penalty = base_penalty * penalty_rate * deviation
        return penalty
    
    def calculate_rewards(self, contract_id, period_days=30):
        """Calcular recompensas por cumplimiento excepcional"""
        if contract_id not in self.sla_contracts:
            return []
        
        # Obtener mediciones del período
        cutoff_date = datetime.now() - timedelta(days=period_days)
        contract_measurements = [
            m for m in self.performance_metrics.values()
            if m['contract_id'] == contract_id and m['measurement_date'] >= cutoff_date
        ]
        
        rewards = []
        
        for measurement in contract_measurements:
            if measurement['status'] == 'excellent':
                sl_id = measurement['sl_id']
                service_level = self.service_levels[sl_id]
                
                reward = {
                    'reward_id': f"REW-{len(rewards) + 1}",
                    'contract_id': contract_id,
                    'sl_id': sl_id,
                    'metric_name': service_level['metric_name'],
                    'target_value': measurement['target_value'],
                    'actual_value': measurement['measured_value'],
                    'compliance': measurement['compliance'],
                    'reward_date': measurement['measurement_date'],
                    'reward_amount': self.calculate_reward(measurement, service_level)
                }
                
                rewards.append(reward)
        
        return rewards
    
    def calculate_reward(self, measurement, service_level):
        """Calcular recompensa por cumplimiento excepcional"""
        if measurement['status'] != 'excellent':
            return 0
        
        # Calcular recompensa basada en el exceso de cumplimiento
        excess = measurement['compliance'] - 1.0
        reward_rate = service_level['reward_rate']
        
        # Recompensa base del contrato
        base_reward = self.sla_contracts[measurement['contract_id']].get('base_reward', 500)
        
        reward = base_reward * reward_rate * excess
        return reward
    
    def generate_sla_report(self, contract_id, period_days=30):
        """Generar reporte de SLA"""
        if contract_id not in self.sla_contracts:
            return None
        
        contract = self.sla_contracts[contract_id]
        
        # Obtener métricas del período
        cutoff_date = datetime.now() - timedelta(days=period_days)
        contract_measurements = [
            m for m in self.performance_metrics.values()
            if m['contract_id'] == contract_id and m['measurement_date'] >= cutoff_date
        ]
        
        # Calcular estadísticas
        total_measurements = len(contract_measurements)
        excellent_count = len([m for m in contract_measurements if m['status'] == 'excellent'])
        good_count = len([m for m in contract_measurements if m['status'] == 'good'])
        warning_count = len([m for m in contract_measurements if m['status'] == 'warning'])
        violation_count = len([m for m in contract_measurements if m['status'] == 'violation'])
        
        # Calcular violaciones y recompensas
        violations = self.calculate_sla_violations(contract_id, period_days)
        rewards = self.calculate_rewards(contract_id, period_days)
        
        total_penalties = sum(v['penalty_amount'] for v in violations)
        total_rewards = sum(r['reward_amount'] for r in rewards)
        
        # Calcular score general
        if total_measurements > 0:
            overall_score = (excellent_count * 4 + good_count * 3 + warning_count * 2 + violation_count * 1) / total_measurements
        else:
            overall_score = 0
        
        report = {
            'contract_id': contract_id,
            'provider_name': contract['provider_name'],
            'service_name': contract['service_name'],
            'report_period': f"{period_days} days",
            'report_date': datetime.now(),
            'overall_score': overall_score,
            'total_measurements': total_measurements,
            'performance_breakdown': {
                'excellent': excellent_count,
                'good': good_count,
                'warning': warning_count,
                'violation': violation_count
            },
            'violations': violations,
            'rewards': rewards,
            'total_penalties': total_penalties,
            'total_rewards': total_rewards,
            'net_impact': total_rewards - total_penalties,
            'recommendations': self.generate_sla_recommendations(contract_id, violations, rewards)
        }
        
        return report
    
    def generate_sla_recommendations(self, contract_id, violations, rewards):
        """Generar recomendaciones basadas en SLA"""
        recommendations = []
        
        if len(violations) > 5:
            recommendations.append({
                'type': 'performance_improvement',
                'priority': 'high',
                'description': f"Mejorar rendimiento - {len(violations)} violaciones en el período"
            })
        
        if len(rewards) > 10:
            recommendations.append({
                'type': 'contract_optimization',
                'priority': 'medium',
                'description': f"Considerar ajustar objetivos - {len(rewards)} recompensas excesivas"
            })
        
        # Analizar patrones de violaciones
        violation_metrics = {}
        for violation in violations:
            metric = violation['metric_name']
            if metric not in violation_metrics:
                violation_metrics[metric] = 0
            violation_metrics[metric] += 1
        
        for metric, count in violation_metrics.items():
            if count > 3:
                recommendations.append({
                    'type': 'metric_focus',
                    'priority': 'medium',
                    'description': f"Enfocar en {metric} - {count} violaciones"
                })
        
        return recommendations

# Ejemplo de uso
sla_mgmt = SLAManagement()

# Crear contrato SLA
sla_mgmt.create_sla_contract('SLA-001', {
    'provider_name': 'Cloud Security Provider',
    'service_name': 'Managed Security Services',
    'start_date': datetime.now(),
    'end_date': datetime.now() + timedelta(days=365),
    'base_penalty': 1000,
    'base_reward': 500
})

# Definir niveles de servicio
sla_mgmt.define_service_level('SLA-001', 'SL-001', {
    'metric_name': 'Availability',
    'description': 'Disponibilidad del servicio',
    'target_value': 99.9,
    'measurement_method': 'uptime_monitoring',
    'frequency': 'daily',
    'unit': 'percentage',
    'threshold_warning': 0.99,
    'threshold_critical': 0.95,
    'penalty_rate': 0.01,
    'reward_rate': 0.005
})

sla_mgmt.define_service_level('SLA-001', 'SL-002', {
    'metric_name': 'Response Time',
    'description': 'Tiempo de respuesta a incidentes',
    'target_value': 1.0,
    'measurement_method': 'incident_tracking',
    'frequency': 'per_incident',
    'unit': 'hours',
    'threshold_warning': 1.5,
    'threshold_critical': 2.0,
    'penalty_rate': 0.02,
    'reward_rate': 0.01
})

# Registrar mediciones
sla_mgmt.record_performance_measurement('SL-001', {'value': 99.5, 'date': datetime.now()})
sla_mgmt.record_performance_measurement('SL-002', {'value': 0.8, 'date': datetime.now()})

# Generar reporte
report = sla_mgmt.generate_sla_report('SLA-001')
print(f"Reporte SLA: Score general {report['overall_score']:.2f}")
print(f"Violaciones: {len(report['violations'])}")
print(f"Recompensas: {len(report['rewards'])}")

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class SLAComplianceMonitoring:
    def __init__(self):
        self.monitoring_rules = {}
        self.alerts = {}
        self.trends = {}
        self.predictions = {}
    
    def setup_monitoring(self, contract_id, monitoring_config):
        """Configurar monitoreo de SLA"""
        self.monitoring_rules[contract_id] = {
            'contract_id': contract_id,
            'monitoring_enabled': True,
            'alert_thresholds': monitoring_config['alert_thresholds'],
            'notification_channels': monitoring_config['notification_channels'],
            'escalation_rules': monitoring_config['escalation_rules'],
            'last_check': None
        }
    
    def check_sla_compliance(self, contract_id, measurements):
        """Verificar cumplimiento de SLA"""
        if contract_id not in self.monitoring_rules:
            return None
        
        rule = self.monitoring_rules[contract_id]
        
        compliance_check = {
            'contract_id': contract_id,
            'check_date': datetime.now(),
            'total_measurements': len(measurements),
            'violations': 0,
            'warnings': 0,
            'excellent_performance': 0,
            'overall_compliance': 0,
            'alerts': []
        }
        
        # Analizar cada medición
        for measurement in measurements:
            if measurement['status'] == 'violation':
                compliance_check['violations'] += 1
            elif measurement['status'] == 'warning':
                compliance_check['warnings'] += 1
            elif measurement['status'] == 'excellent':
                compliance_check['excellent_performance'] += 1
        
        # Calcular cumplimiento general
        if compliance_check['total_measurements'] > 0:
            compliance_check['overall_compliance'] = (
                compliance_check['excellent_performance'] + 
                (compliance_check['total_measurements'] - compliance_check['violations'] - compliance_check['warnings'])
            ) / compliance_check['total_measurements']
        
        # Generar alertas
        self.generate_compliance_alerts(contract_id, compliance_check, rule)
        
        # Actualizar regla
        rule['last_check'] = datetime.now()
        
        return compliance_check
    
    def generate_compliance_alerts(self, contract_id, compliance_check, rule):
        """Generar alertas de cumplimiento"""
        thresholds = rule['alert_thresholds']
        
        # Alerta por violaciones excesivas
        if compliance_check['violations'] > thresholds.get('max_violations', 5):
            alert = {
                'alert_id': f"ALERT-{len(self.alerts) + 1}",
                'contract_id': contract_id,
                'type': 'excessive_violations',
                'severity': 'high',
                'message': f"Excesivas violaciones de SLA: {compliance_check['violations']}",
                'timestamp': datetime.now(),
                'status': 'active'
            }
            self.alerts[alert['alert_id']] = alert
            compliance_check['alerts'].append(alert)
        
        # Alerta por cumplimiento bajo
        if compliance_check['overall_compliance'] < thresholds.get('min_compliance', 0.8):
            alert = {
                'alert_id': f"ALERT-{len(self.alerts) + 1}",
                'contract_id': contract_id,
                'type': 'low_compliance',
                'severity': 'medium',
                'message': f"Cumplimiento bajo: {compliance_check['overall_compliance']:.2%}",
                'timestamp': datetime.now(),
                'status': 'active'
            }
            self.alerts[alert['alert_id']] = alert
            compliance_check['alerts'].append(alert)
        
        # Alerta por tendencia negativa
        if self.detect_negative_trend(contract_id):
            alert = {
                'alert_id': f"ALERT-{len(self.alerts) + 1}",
                'contract_id': contract_id,
                'type': 'negative_trend',
                'severity': 'medium',
                'message': "Tendencia negativa detectada en cumplimiento de SLA",
                'timestamp': datetime.now(),
                'status': 'active'
            }
            self.alerts[alert['alert_id']] = alert
            compliance_check['alerts'].append(alert)
    
    def detect_negative_trend(self, contract_id):
        """Detectar tendencia negativa"""
        # Obtener mediciones recientes
        recent_measurements = self.get_recent_measurements(contract_id, days=30)
        
        if len(recent_measurements) < 10:
            return False
        
        # Calcular tendencia usando regresión lineal
        x = np.arange(len(recent_measurements))
        y = np.array([m['compliance'] for m in recent_measurements])
        
        if len(x) > 1:
            slope = np.polyfit(x, y, 1)[0]
            return slope < -0.01  # Tendencia negativa significativa
        
        return False
    
    def get_recent_measurements(self, contract_id, days=30):
        """Obtener mediciones recientes"""
        # Simulación de mediciones recientes
        measurements = []
        for i in range(days):
            compliance = 0.8 + np.random.normal(0, 0.1) - i * 0.001  # Tendencia ligeramente negativa
            measurements.append({
                'compliance': max(0, min(1, compliance)),
                'date': datetime.now() - timedelta(days=days-i)
            })
        
        return measurements
    
    def predict_sla_performance(self, contract_id, days_ahead=30):
        """Predecir rendimiento de SLA"""
        recent_measurements = self.get_recent_measurements(contract_id, days=30)
        
        if len(recent_measurements) < 10:
            return None
        
        # Usar media móvil para predicción
        compliance_values = [m['compliance'] for m in recent_measurements]
        window_size = min(7, len(compliance_values) // 2)
        
        predictions = []
        for i in range(days_ahead):
            recent_window = compliance_values[-window_size:]
            predicted_compliance = np.mean(recent_window)
            
            predictions.append({
                'day': i + 1,
                'predicted_compliance': predicted_compliance,
                'confidence': self.calculate_prediction_confidence(compliance_values)
            })
        
        return predictions
    
    def calculate_prediction_confidence(self, values):
        """Calcular confianza de predicción"""
        if len(values) < 2:
            return 0.5
        
        # Basado en la variabilidad de los datos
        std = np.std(values)
        mean = np.mean(values)
        
        if mean > 0:
            coefficient_of_variation = std / mean
            confidence = max(0.1, 1 - coefficient_of_variation)
        else:
            confidence = 0.5
        
        return confidence
    
    def generate_compliance_dashboard(self, contract_id):
        """Generar dashboard de cumplimiento"""
        compliance_check = self.check_sla_compliance(contract_id, [])
        predictions = self.predict_sla_performance(contract_id)
        
        dashboard = {
            'contract_id': contract_id,
            'last_updated': datetime.now(),
            'current_compliance': compliance_check['overall_compliance'],
            'violations': compliance_check['violations'],
            'warnings': compliance_check['warnings'],
            'excellent_performance': compliance_check['excellent_performance'],
            'active_alerts': len([a for a in self.alerts.values() if a['status'] == 'active']),
            'predictions': predictions,
            'recommendations': self.generate_monitoring_recommendations(contract_id)
        }
        
        return dashboard
    
    def generate_monitoring_recommendations(self, contract_id):
        """Generar recomendaciones de monitoreo"""
        recommendations = []
        
        # Obtener alertas activas
        active_alerts = [a for a in self.alerts.values() if a['status'] == 'active']
        
        if len(active_alerts) > 3:
            recommendations.append({
                'type': 'alert_management',
                'priority': 'high',
                'description': f"Gestionar {len(active_alerts)} alertas activas"
            })
        
        # Verificar tendencias
        if self.detect_negative_trend(contract_id):
            recommendations.append({
                'type': 'trend_analysis',
                'priority': 'medium',
                'description': "Analizar tendencia negativa y tomar medidas correctivas"
            })
        
        return recommendations

# Ejemplo de uso
compliance_monitoring = SLAComplianceMonitoring()

# Configurar monitoreo
compliance_monitoring.setup_monitoring('SLA-001', {
    'alert_thresholds': {
        'max_violations': 3,
        'min_compliance': 0.85
    },
    'notification_channels': ['email', 'slack'],
    'escalation_rules': {
        'escalation_level_1': 'Security Team',
        'escalation_level_2': 'CISO',
        'escalation_level_3': 'CEO'
    }
})

# Verificar cumplimiento
compliance_check = compliance_monitoring.check_sla_compliance('SLA-001', [])
print(f"Verificación de cumplimiento: {compliance_check['overall_compliance']:.2%}")

# Generar dashboard
dashboard = compliance_monitoring.generate_compliance_dashboard('SLA-001')
print(f"Dashboard de cumplimiento: {dashboard['active_alerts']} alertas activas")