"""Сервис анализа данных детей."""
from datetime import datetime, timedelta
from typing import Dict, List, Optional

import pandas as pd


class DataAnalyzer:
    """Сервис для анализа прогресса детей."""

    @staticmethod
    def calculate_completion_rate(tasks: List[Dict]) -> float:
        """
        Рассчитать процент выполнения заданий.

        Args:
            tasks: Список заданий с полем 'completed'

        Returns:
            Процент выполнения (0.0 - 1.0)
        """
        if not tasks:
            return 0.0

        completed = sum(1 for task in tasks if task.get("completed", False))
        return completed / len(tasks)

    @staticmethod
    def analyze_daily_progress(schedules: List[Dict]) -> Dict:
        """
        Проанализировать ежедневный прогресс.

        Args:
            schedules: Список расписаний с заданиями

        Returns:
            Словарь с аналитикой
        """
        if not schedules:
            return {
                "total_days": 0,
                "average_completion": 0.0,
                "total_tasks": 0,
                "completed_tasks": 0,
            }

        total_tasks = 0
        completed_tasks = 0
        completion_rates = []

        for schedule in schedules:
            tasks = schedule.get("tasks", [])
            total_tasks += len(tasks)
            completed_tasks += sum(1 for task in tasks if task.get("completed", False))
            rate = DataAnalyzer.calculate_completion_rate(tasks)
            completion_rates.append(rate)

        return {
            "total_days": len(schedules),
            "average_completion": sum(completion_rates) / len(completion_rates) if completion_rates else 0.0,
            "total_tasks": total_tasks,
            "completed_tasks": completed_tasks,
            "completion_rate": completed_tasks / total_tasks if total_tasks > 0 else 0.0,
        }

    @staticmethod
    def get_category_statistics(schedules: List[Dict]) -> Dict[str, Dict]:
        """
        Получить статистику по категориям заданий.

        Args:
            schedules: Список расписаний

        Returns:
            Словарь со статистикой по категориям
        """
        category_stats: Dict[str, Dict] = {}

        for schedule in schedules:
            for task in schedule.get("tasks", []):
                category = task.get("category", "unknown")
                if category not in category_stats:
                    category_stats[category] = {
                        "total": 0,
                        "completed": 0,
                        "average_duration": 0.0,
                        "durations": [],
                    }

                stats = category_stats[category]
                stats["total"] += 1
                if task.get("completed", False):
                    stats["completed"] += 1
                if "duration_minutes" in task:
                    stats["durations"].append(task["duration_minutes"])

        # Вычисляем среднюю длительность
        for category, stats in category_stats.items():
            if stats["durations"]:
                stats["average_duration"] = sum(stats["durations"]) / len(stats["durations"])
            del stats["durations"]

        return category_stats

    @staticmethod
    def get_weekly_trend(schedules: List[Dict], days: int = 7) -> List[Dict]:
        """
        Получить тренд за последние N дней.

        Args:
            schedules: Список расписаний
            days: Количество дней

        Returns:
            Список словарей с данными по дням
        """
        end_date = datetime.now().date()
        start_date = end_date - timedelta(days=days - 1)

        # Группируем расписания по датам
        daily_data: Dict[str, List[Dict]] = {}
        for schedule in schedules:
            schedule_date = schedule.get("date")
            if isinstance(schedule_date, str):
                schedule_date = datetime.fromisoformat(schedule_date).date()
            elif isinstance(schedule_date, datetime):
                schedule_date = schedule_date.date()

            if start_date <= schedule_date <= end_date:
                date_str = str(schedule_date)
                if date_str not in daily_data:
                    daily_data[date_str] = []
                daily_data[date_str].append(schedule)

        # Формируем тренд
        trend = []
        current_date = start_date
        while current_date <= end_date:
            date_str = str(current_date)
            day_schedules = daily_data.get(date_str, [])
            all_tasks = []
            for sched in day_schedules:
                all_tasks.extend(sched.get("tasks", []))

            trend.append(
                {
                    "date": date_str,
                    "completion_rate": DataAnalyzer.calculate_completion_rate(all_tasks),
                    "total_tasks": len(all_tasks),
                    "completed_tasks": sum(1 for task in all_tasks if task.get("completed", False)),
                }
            )
            current_date += timedelta(days=1)

        return trend