"""
Cohort Analyzer Agent

Performs cohort analysis to understand user behavior patterns
and retention over time across different user segments.
"""

from typing import Any, Dict, List
from agents.base import BaseAgent


class CohortAnalyzerAgent(BaseAgent):
    """
    Performs cohort analysis.

    Supports:
    - Time-based cohorts
    - Behavior-based cohorts
    - Retention analysis
    - Revenue cohorts
    - Custom cohort definitions
    - Cohort comparison
    """

    def __init__(self):
        super().__init__(
            name='cohort-analyzer',
            description='Perform cohort analysis and retention tracking',
            category='data',
            version='1.0.0',
            tags=['cohort-analysis', 'retention', 'user-behavior', 'analytics']
        )

    async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Perform cohort analysis.

        Args:
            params: {
                'data_source': str,
                'cohort_type': 'acquisition|behavior|revenue|custom',
                'cohort_definition': {
                    'field': str,
                    'period': 'daily|weekly|monthly|quarterly',
                    'start_date': str,
                    'end_date': str
                },
                'metric': 'retention|revenue|engagement|conversion',
                'analysis_periods': int,  # Number of periods to analyze
                'options': {
                    'include_revenue': bool,
                    'include_engagement': bool,
                    'segment_by': List[str],
                    'visualization': 'heatmap|line|table'
                }
            }

        Returns:
            {
                'status': 'success|failed',
                'cohort_type': str,
                'total_cohorts': int,
                'cohort_data': List[Dict[str, Any]],
                'retention_rates': Dict[str, Any],
                'insights': List[str],
                'execution_time_seconds': float,
                'visualizations': List[str],
                'recommendations': List[str]
            }
        """
        data_source = params.get('data_source')
        cohort_type = params.get('cohort_type', 'acquisition')
        cohort_definition = params.get('cohort_definition', {})
        metric = params.get('metric', 'retention')
        analysis_periods = params.get('analysis_periods', 12)
        options = params.get('options', {})

        self.logger.info(
            f"Performing {cohort_type} cohort analysis for {metric}"
        )

        # Mock cohort analysis
        cohort_data = self._generate_cohort_data(analysis_periods)

        return {
            'status': 'success',
            'data_source': data_source,
            'cohort_type': cohort_type,
            'metric': metric,
            'total_cohorts': len(cohort_data),
            'cohort_period': cohort_definition.get('period', 'monthly'),
            'analysis_periods': analysis_periods,
            'execution_time_seconds': 6.8,
            'cohort_data': cohort_data,
            'retention_rates': {
                'day_1': 0.65,
                'day_7': 0.42,
                'day_30': 0.28,
                'day_60': 0.22,
                'day_90': 0.18
            },
            'average_retention_by_period': {
                'period_0': 1.00,
                'period_1': 0.65,
                'period_2': 0.48,
                'period_3': 0.38,
                'period_6': 0.25,
                'period_12': 0.18
            },
            'cohort_comparison': {
                'best_cohort': 'January 2025',
                'best_retention': 0.32,
                'worst_cohort': 'June 2024',
                'worst_retention': 0.14,
                'average_retention': 0.23
            },
            'revenue_analysis': {
                'average_ltv_by_cohort': {
                    'January 2025': 1850,
                    'February 2025': 1720,
                    'March 2025': 1680
                },
                'cumulative_revenue': 458900,
                'average_revenue_per_user': 1750
            } if options.get('include_revenue') else {},
            'engagement_metrics': {
                'average_sessions_per_user': 12.5,
                'average_session_duration': 480,  # seconds
                'feature_adoption_rate': 0.68
            } if options.get('include_engagement') else {},
            'trends': {
                'retention_trend': 'improving',
                'month_over_month_change': 0.05,
                'seasonality_detected': True
            },
            'visualizations': [
                'cohort_retention_heatmap.png',
                'cohort_retention_curves.png',
                'cohort_ltv_comparison.png'
            ],
            'insights': [
                'Overall retention improving over time (+5% MoM)',
                'January cohort shows best retention at 32%',
                'Day 7 retention is critical inflection point',
                'Retention stabilizes after day 30',
                'Recent cohorts showing higher engagement'
            ],
            'recommendations': [
                'Focus on improving Day 7 retention (currently 42%)',
                'Replicate success factors from January cohort',
                'Implement re-engagement campaign for day 30+',
                'Personalize onboarding based on cohort learnings',
                'Monitor new cohort performance weekly'
            ],
            'segments_analyzed': options.get('segment_by', []),
            'time_period': {
                'start_date': cohort_definition.get('start_date', '2024-01-01'),
                'end_date': cohort_definition.get('end_date', '2025-11-16')
            }
        }

    def validate_params(self, params: Dict[str, Any]) -> bool:
        """Validate cohort analysis parameters."""
        if 'data_source' not in params:
            self.logger.error("Missing required field: data_source")
            return False

        valid_cohort_types = ['acquisition', 'behavior', 'revenue', 'custom']
        cohort_type = params.get('cohort_type', 'acquisition')

        if cohort_type not in valid_cohort_types:
            self.logger.error(f"Invalid cohort type: {cohort_type}")
            return False

        valid_metrics = ['retention', 'revenue', 'engagement', 'conversion']
        metric = params.get('metric', 'retention')

        if metric not in valid_metrics:
            self.logger.error(f"Invalid metric: {metric}")
            return False

        return True

    def _generate_cohort_data(self, periods: int) -> List[Dict[str, Any]]:
        """Generate mock cohort data."""
        cohorts = []
        base_retention = 1.0

        for i in range(min(periods, 12)):
            month = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
                    'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'][i]

            retention_curve = []
            for period in range(min(periods, 12)):
                retention = base_retention * (0.65 ** period) * (1 + i * 0.02)
                retention_curve.append(round(min(retention, 1.0), 3))

            cohorts.append({
                'cohort_name': f'{month} 2025',
                'cohort_size': 1000 + i * 100,
                'period': i,
                'retention_curve': retention_curve,
                'average_retention': round(sum(retention_curve) / len(retention_curve), 3)
            })

        return cohorts
