#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
销售趋势分析和预测服务
"""

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


def analyze_sale_trend(df, filename):
    """
    分析销售趋势数据
    
    Args:
        df: 销售数据DataFrame
        filename: 文件名
    
    Returns:
        dict: 分析结果
    """
    try:
        print(f"开始分析销售趋势数据: {filename}")
        print(f"数据形状: {df.shape}")
        
        # 1. 数据预处理
        df = preprocess_data(df)
        
        # 2. 计算总体指标
        summary = calculate_summary(df)
        
        # 3. 按品类分析趋势
        categories = analyze_categories(df)
        
        # 4. 按SKU分析趋势
        sku_data = analyze_skus(df)
        
        # 5. 整体趋势分析
        trends = analyze_trends(df)
        
        # 6. 季节性分析
        seasonality = analyze_seasonality(df)
        
        # 7. 计算特征重要性
        feature_importance = calculate_feature_importance(df)
        
        # 8. 构建结果
        results = {
            'summary': summary,
            'categories': categories['categories_data'],
            'category_list': categories['category_list'],
            'category_skus': categories['category_skus'],
            'data': sku_data['sku_data'],
            'sku_list': sku_data['sku_list'],
            'trends': trends,
            'seasonality': seasonality,
            'feature_importance': feature_importance
        }
        
        # 转换所有 numpy 类型为 Python 原生类型
        results = convert_numpy_types(results)
        
        print("销售趋势分析完成")
        return results
        
    except Exception as e:
        print(f"分析销售趋势时出错: {str(e)}")
        raise


def predict_sales_trend(df, filename, predict_days=30):
    """
    预测销售趋势
    
    Args:
        df: 销售数据DataFrame
        filename: 文件名
        predict_days: 预测天数
    
    Returns:
        dict: 预测结果
    """
    try:
        print(f"开始预测销售趋势: {filename}")
        print(f"预测天数: {predict_days}")
        
        # 1. 数据预处理
        df = preprocess_data(df)
        
        # 2. 计算基础指标
        summary = calculate_summary(df)
        
        # 3. 整体趋势预测
        overall_prediction = predict_overall_trend(df, predict_days)
        
        # 4. 按品类预测
        category_predictions = predict_category_trends(df, predict_days)
        
        # 5. 按SKU预测
        sku_predictions = predict_sku_trends(df, predict_days)
        
        # 6. 计算特征重要性
        feature_importance = calculate_feature_importance(df)
        
        # 7. 构建结果
        results = {
            'summary': summary,
            'overall_prediction': overall_prediction,
            'category_predictions': category_predictions,
            'sku_predictions': sku_predictions,
            'feature_importance': feature_importance,
            'predict_days': predict_days
        }
        
        # 转换所有 numpy 类型为 Python 原生类型
        results = convert_numpy_types(results)
        
        print("销售趋势预测完成")
        return results
        
    except Exception as e:
        print(f"预测销售趋势时出错: {str(e)}")
        raise


def convert_numpy_types(obj):
    """
    递归转换所有 numpy 类型为 Python 原生类型
    
    Args:
        obj: 要转换的对象
    
    Returns:
        转换后的对象
    """
    if isinstance(obj, dict):
        return {key: convert_numpy_types(value) for key, value in obj.items()}
    elif isinstance(obj, list):
        return [convert_numpy_types(item) for item in obj]
    elif isinstance(obj, np.integer):
        return int(obj)
    elif isinstance(obj, np.floating):
        return float(obj)
    elif isinstance(obj, np.ndarray):
        return [convert_numpy_types(item) for item in obj.tolist()]
    else:
        return obj


def calculate_feature_importance(df):
    """
    计算特征重要性
    
    Args:
        df: 销售数据DataFrame
    
    Returns:
        dict: 特征重要性数据
    """
    try:
        print("计算特征重要性")
        
        # 特征重要性数据
        feature_importance = {
            'features': [
                '价格',
                '促销力度',
                '退款率',
                '季节性',
                '时间趋势',
                '品类影响',
                'SKU影响'
            ],
            'importance': []
        }
        
        # 计算价格重要性
        if '价格' in df.columns:
            price_std = df['价格'].std()
            price_importance = min(100, (price_std / df['价格'].mean() * 100) if df['价格'].mean() > 0 else 50)
        else:
            price_importance = 50
        feature_importance['importance'].append(round(price_importance, 2))
        
        # 计算促销力度重要性
        if '促销力度' in df.columns:
            promotion_std = df['促销力度'].std()
            promotion_importance = min(100, (promotion_std * 100) if promotion_std > 0 else 40)
        else:
            # 计算促销力度
            df['促销力度'] = 1 - (df['买家实际支付金额'] / df['买家应付货款'])
            df['促销力度'] = df['促销力度'].fillna(0)
            promotion_std = df['促销力度'].std()
            promotion_importance = min(100, (promotion_std * 100) if promotion_std > 0 else 40)
        feature_importance['importance'].append(round(promotion_importance, 2))
        
        # 计算退款率重要性
        refunded_orders = df[df['退款状态'] != '没有申请退款']
        refund_rate = len(refunded_orders) / len(df) * 100 if len(df) > 0 else 0
        refund_importance = min(100, refund_rate * 2)
        feature_importance['importance'].append(round(refund_importance, 2))
        
        # 计算季节性重要性
        # 基于销量的波动性
        if '购买数量' in df.columns:
            quantity_std = df['购买数量'].std()
            quantity_mean = df['购买数量'].mean()
            seasonality_importance = min(100, (quantity_std / quantity_mean * 100) if quantity_mean > 0 else 30)
        else:
            seasonality_importance = 30
        feature_importance['importance'].append(round(seasonality_importance, 2))
        
        # 计算时间趋势重要性
        # 基于销量的变化趋势
        time_importance = 60  # 默认值
        feature_importance['importance'].append(time_importance)
        
        # 计算品类影响重要性
        if '品类' in df.columns:
            category_count = df['品类'].nunique()
            category_importance = min(100, category_count * 10)
        else:
            category_importance = 40
        feature_importance['importance'].append(round(category_importance, 2))
        
        # 计算SKU影响重要性
        if 'SKU' in df.columns:
            sku_count = df['SKU'].nunique()
            sku_importance = min(100, sku_count * 5)
        else:
            sku_importance = 30
        feature_importance['importance'].append(round(sku_importance, 2))
        
        # 标准化重要性值，确保总和为100
        total_importance = sum(feature_importance['importance'])
        if total_importance > 0:
            feature_importance['importance'] = [round((imp / total_importance) * 100, 2) for imp in feature_importance['importance']]
        
        print("特征重要性计算完成")
        return feature_importance
        
    except Exception as e:
        print(f"计算特征重要性时出错: {str(e)}")
        # 返回默认值
        return {
            'features': [
                '价格',
                '促销力度',
                '退款率',
                '季节性',
                '时间趋势',
                '品类影响',
                'SKU影响'
            ],
            'importance': [15, 15, 10, 15, 20, 10, 15]
        }


def preprocess_data(df):
    """
    预处理数据
    """
    # 复制数据以避免修改原始数据
    df = df.copy()
    
    # 处理日期列
    if '订单创建时间' in df.columns:
        df['订单创建时间'] = pd.to_datetime(df['订单创建时间'], errors='coerce')
        # 提取日期部分
        df['日期'] = df['订单创建时间'].dt.date
    
    # 处理数值列
    numeric_columns = ['价格', '购买数量', '买家应付货款', '买家实际支付金额', '退款金额']
    for col in numeric_columns:
        if col in df.columns:
            df[col] = pd.to_numeric(df[col], errors='coerce')
    
    # 填充缺失值
    df = df.fillna({
        '价格': 0,
        '购买数量': 0,
        '买家应付货款': 0,
        '买家实际支付金额': 0,
        '退款金额': 0
    })
    
    # 确保SKU列存在
    if '商家编码' in df.columns:
        df['SKU'] = df['商家编码']
    elif '外部系统编号' in df.columns:
        df['SKU'] = df['外部系统编号']
    else:
        # 如果没有SKU列，使用商品名称作为SKU
        df['SKU'] = df['商品名称']
    
    # 确保品类列存在
    if '品类' not in df.columns:
        # 简单品类划分：基于商品名称
        df['品类'] = df['商品名称'].apply(lambda x: categorize_product(x))
    
    return df


def categorize_product(product_name):
    """
    根据商品名称简单分类
    """
    product_name = str(product_name).lower()
    if '腰垫' in product_name or '靠垫' in product_name:
        return '家居用品'
    elif '手机' in product_name or '电脑' in product_name:
        return '电子产品'
    elif '服装' in product_name or '鞋' in product_name:
        return '服装鞋包'
    else:
        return '其他'


def calculate_summary(df):
    """
    计算总体指标
    """
    total_quantity = df['购买数量'].sum()
    total_revenue = df['买家实际支付金额'].sum()
    avg_price = total_revenue / total_quantity if total_quantity > 0 else 0
    
    # 计算促销力度
    df['促销力度'] = 1 - (df['买家实际支付金额'] / df['买家应付货款'])
    df['促销力度'] = df['促销力度'].fillna(0)
    avg_promotion = df['促销力度'].mean() * 100  # 转换为百分比
    
    # 计算退款相关指标
    refunded_orders = df[df['退款状态'] != '没有申请退款']
    total_refund = refunded_orders['退款金额'].sum()
    refund_rate = len(refunded_orders) / len(df) * 100 if len(df) > 0 else 0
    
    summary = {
        'total_orders': len(df),
        'total_quantity': int(total_quantity),
        'total_revenue': round(total_revenue, 2),
        'avg_price': round(avg_price, 2),
        'avg_promotion': round(avg_promotion, 2),
        'total_refund': round(total_refund, 2),
        'refund_rate': round(refund_rate, 2)
    }
    
    # 转换所有 numpy 类型为 Python 原生类型
    summary = convert_numpy_types(summary)
    
    return summary


def analyze_categories(df):
    """
    按品类分析趋势
    """
    categories_data = {}
    category_list = []
    category_skus = defaultdict(list)
    
    # 按品类分组
    grouped = df.groupby('品类')
    
    for category, group in grouped:
        category_list.append(category)
        
        # 计算品类指标
        total_quantity = group['购买数量'].sum()
        total_revenue = group['买家实际支付金额'].sum()
        avg_price = total_revenue / total_quantity if total_quantity > 0 else 0
        
        # 计算促销力度
        group['促销力度'] = 1 - (group['买家实际支付金额'] / group['买家应付货款'])
        group['促销力度'] = group['促销力度'].fillna(0)
        avg_promotion = group['促销力度'].mean() * 100
        
        # 计算退款相关指标
        refunded_orders = group[group['退款状态'] != '没有申请退款']
        total_refund = refunded_orders['退款金额'].sum()
        refund_rate = len(refunded_orders) / len(group) * 100 if len(group) > 0 else 0
        
        # 趋势数据
        date_series = []
        quantity_series = []
        price_series = []
        
        # 按日期排序
        date_grouped = group.groupby('日期')
        for date in sorted(date_grouped.groups.keys()):
            date_series.append(str(date))
            date_data = date_grouped.get_group(date)
            quantity_series.append(int(date_data['购买数量'].sum()))
            avg_date_price = date_data['买家实际支付金额'].sum() / date_data['购买数量'].sum() if date_data['购买数量'].sum() > 0 else 0
            price_series.append(round(avg_date_price, 2))
        
        categories_data[category] = {
            'total_quantity': int(total_quantity),
            'total_revenue': round(total_revenue, 2),
            'avg_price': round(avg_price, 2),
            'avg_promotion': round(avg_promotion, 2),
            'total_refund': round(total_refund, 2),
            'refund_rate': round(refund_rate, 2),
            'date_series': date_series,
            'quantity_series': quantity_series,
            'price_series': price_series
        }
        
        # 收集该品类下的SKU
        skus = group['SKU'].unique().tolist()
        category_skus[category] = skus
    
    result = {
        'categories_data': categories_data,
        'category_list': category_list,
        'category_skus': dict(category_skus)
    }
    
    # 转换所有 numpy 类型为 Python 原生类型
    result = convert_numpy_types(result)
    
    return result


def analyze_skus(df):
    """
    按SKU分析趋势
    """
    sku_data = {}
    sku_list = []
    
    # 按SKU分组
    grouped = df.groupby('SKU')
    
    for sku, group in grouped:
        sku_list.append(sku)
        
        # 计算SKU指标
        total_quantity = group['购买数量'].sum()
        total_revenue = group['买家实际支付金额'].sum()
        avg_price = total_revenue / total_quantity if total_quantity > 0 else 0
        
        # 计算促销力度
        group['促销力度'] = 1 - (group['买家实际支付金额'] / group['买家应付货款'])
        group['促销力度'] = group['促销力度'].fillna(0)
        avg_promotion = group['促销力度'].mean() * 100
        
        # 计算退款相关指标
        refunded_orders = group[group['退款状态'] != '没有申请退款']
        total_refund = refunded_orders['退款金额'].sum()
        refund_rate = len(refunded_orders) / len(group) * 100 if len(group) > 0 else 0
        
        # 趋势数据
        date_series = []
        quantity_series = []
        price_series = []
        
        # 按日期排序
        date_grouped = group.groupby('日期')
        for date in sorted(date_grouped.groups.keys()):
            date_series.append(str(date))
            date_data = date_grouped.get_group(date)
            quantity_series.append(int(date_data['购买数量'].sum()))
            avg_date_price = date_data['买家实际支付金额'].sum() / date_data['购买数量'].sum() if date_data['购买数量'].sum() > 0 else 0
            price_series.append(round(avg_date_price, 2))
        
        sku_data[sku] = {
            'total_quantity': int(total_quantity),
            'total_revenue': round(total_revenue, 2),
            'avg_price': round(avg_price, 2),
            'avg_promotion': round(avg_promotion, 2),
            'total_refund': round(total_refund, 2),
            'refund_rate': round(refund_rate, 2),
            'date_series': date_series,
            'quantity_series': quantity_series,
            'price_series': price_series
        }
    
    result = {
        'sku_data': sku_data,
        'sku_list': sku_list
    }
    
    # 转换所有 numpy 类型为 Python 原生类型
    result = convert_numpy_types(result)
    
    return result


def analyze_trends(df):
    """
    分析整体趋势数据
    """
    # 按日期分组
    date_grouped = df.groupby('日期')
    
    date_series = []
    quantity_series = []
    revenue_series = []
    avg_price_series = []
    avg_promotion_series = []
    
    for date in sorted(date_grouped.groups.keys()):
        date_series.append(str(date))
        date_data = date_grouped.get_group(date)
        
        quantity = date_data['购买数量'].sum()
        revenue = date_data['买家实际支付金额'].sum()
        avg_price = revenue / quantity if quantity > 0 else 0
        
        # 计算促销力度
        date_data['促销力度'] = 1 - (date_data['买家实际支付金额'] / date_data['买家应付货款'])
        date_data['促销力度'] = date_data['促销力度'].fillna(0)
        avg_promotion = date_data['促销力度'].mean() * 100
        
        quantity_series.append(int(quantity))
        revenue_series.append(round(revenue, 2))
        avg_price_series.append(round(avg_price, 2))
        avg_promotion_series.append(round(avg_promotion, 2))
    
    trends = {
        'date_series': date_series,
        'quantity_series': quantity_series,
        'revenue_series': revenue_series,
        'avg_price_series': avg_price_series,
        'avg_promotion_series': avg_promotion_series
    }
    
    # 转换所有 numpy 类型为 Python 原生类型
    trends = convert_numpy_types(trends)
    
    return trends


def analyze_seasonality(df):
    """
    分析季节性
    """
    # 按日期分组
    date_grouped = df.groupby('日期')
    
    # 构建日期到销量的映射
    date_quantity_map = {}
    for date, group in date_grouped:
        date_quantity_map[date] = group['购买数量'].sum()
    
    # 计算7天移动平均
    dates = sorted(date_quantity_map.keys())
    quantities = [date_quantity_map[date] for date in dates]
    
    # 计算移动平均
    window = 7
    moving_avg = []
    for i in range(len(quantities)):
        start = max(0, i - window + 1)
        window_data = quantities[start:i+1]
        moving_avg.append(sum(window_data) / len(window_data))
    
    # 计算季节性因子
    seasonality_factors = []
    for i in range(len(quantities)):
        if moving_avg[i] > 0:
            seasonality_factors.append(quantities[i] / moving_avg[i])
        else:
            seasonality_factors.append(1.0)
    
    seasonality = {
        'date_series': [str(date) for date in dates],
        'quantity_series': quantities,
        'moving_avg_series': [round(avg, 2) for avg in moving_avg],
        'seasonality_factors': [round(factor, 2) for factor in seasonality_factors]
    }
    
    # 转换所有 numpy 类型为 Python 原生类型
    seasonality = convert_numpy_types(seasonality)
    
    return seasonality


def predict_overall_trend(df, predict_days):
    """
    预测整体销售趋势
    使用 a+x+y 模型
    """
    # 按日期分组
    date_grouped = df.groupby('日期')
    
    # 构建历史数据
    dates = sorted(date_grouped.groups.keys())
    quantities = []
    for date in dates:
        quantities.append(date_grouped.get_group(date)['购买数量'].sum())
    
    # 计算基础销量 a（历史平均）
    a = np.mean(quantities) if quantities else 0
    
    # 计算时间趋势因子 x（线性回归）
    x = 0
    if len(quantities) > 1:
        # 简单线性回归
        days = np.arange(len(quantities))
        slope, intercept = np.polyfit(days, quantities, 1)
        x = slope
    
    # 计算季节性因子 y
    # 这里使用简单的7天周期性
    y = 1.0
    if len(quantities) >= 7:
        # 计算最近7天的平均季节性因子
        recent_quantities = quantities[-7:]
        recent_avg = np.mean(recent_quantities)
        y = recent_quantities[-1] / recent_avg if recent_avg > 0 else 1.0
    
    # 生成预测日期
    last_date = dates[-1] if dates else datetime.now().date()
    predict_dates = []
    for i in range(1, predict_days + 1):
        predict_dates.append(last_date + timedelta(days=i))
    
    # 生成预测值
    predict_quantities = []
    predict_revenues = []
    
    # 计算历史平均价格
    avg_price = df['买家实际支付金额'].sum() / df['购买数量'].sum() if df['购买数量'].sum() > 0 else 0
    
    for i, predict_date in enumerate(predict_dates):
        # 计算预测值: a + x * (days_since_start) * y
        days_since_start = len(quantities) + i
        predicted_quantity = max(0, a + x * days_since_start * y)
        predict_quantities.append(round(predicted_quantity, 2))
        predict_revenues.append(round(predicted_quantity * avg_price, 2))
    
    overall_prediction = {
        'date_series': [str(date) for date in predict_dates],
        'quantity_series': predict_quantities,
        'revenue_series': predict_revenues,
        'model_params': {
            'a': round(a, 2),
            'x': round(x, 4),
            'y': round(y, 2)
        }
    }
    
    # 转换所有 numpy 类型为 Python 原生类型
    overall_prediction = convert_numpy_types(overall_prediction)
    
    return overall_prediction


def predict_category_trends(df, predict_days):
    """
    按品类预测销售趋势
    """
    category_predictions = {}
    
    # 按品类分组
    grouped = df.groupby('品类')
    
    for category, group in grouped:
        # 按日期分组
        date_grouped = group.groupby('日期')
        
        # 构建历史数据
        dates = sorted(date_grouped.groups.keys())
        quantities = []
        for date in dates:
            quantities.append(date_grouped.get_group(date)['购买数量'].sum())
        
        # 计算基础销量 a（历史平均）
        a = np.mean(quantities) if quantities else 0
        
        # 计算时间趋势因子 x（线性回归）
        x = 0
        if len(quantities) > 1:
            # 简单线性回归
            days = np.arange(len(quantities))
            slope, intercept = np.polyfit(days, quantities, 1)
            x = slope
        
        # 计算季节性因子 y
        y = 1.0
        if len(quantities) >= 7:
            # 计算最近7天的平均季节性因子
            recent_quantities = quantities[-7:]
            recent_avg = np.mean(recent_quantities)
            y = recent_quantities[-1] / recent_avg if recent_avg > 0 else 1.0
        
        # 生成预测日期
        last_date = dates[-1] if dates else datetime.now().date()
        predict_dates = []
        for i in range(1, predict_days + 1):
            predict_dates.append(last_date + timedelta(days=i))
        
        # 生成预测值
        predict_quantities = []
        predict_revenues = []
        
        # 计算该品类的平均价格
        category_avg_price = group['买家实际支付金额'].sum() / group['购买数量'].sum() if group['购买数量'].sum() > 0 else 0
        
        for i, predict_date in enumerate(predict_dates):
            # 计算预测值: a + x * (days_since_start) * y
            days_since_start = len(quantities) + i
            predicted_quantity = max(0, a + x * days_since_start * y)
            predict_quantities.append(round(predicted_quantity, 2))
            predict_revenues.append(round(predicted_quantity * category_avg_price, 2))
        
        category_predictions[category] = {
            'date_series': [str(date) for date in predict_dates],
            'quantity_series': predict_quantities,
            'revenue_series': predict_revenues,
            'model_params': {
                'a': round(a, 2),
                'x': round(x, 4),
                'y': round(y, 2)
            }
        }
    
    # 转换所有 numpy 类型为 Python 原生类型
    category_predictions = convert_numpy_types(category_predictions)
    
    return category_predictions


def predict_sku_trends(df, predict_days):
    """
    按SKU预测销售趋势
    """
    sku_predictions = {}
    
    # 按SKU分组
    grouped = df.groupby('SKU')
    
    for sku, group in grouped:
        # 按日期分组
        date_grouped = group.groupby('日期')
        
        # 构建历史数据
        dates = sorted(date_grouped.groups.keys())
        quantities = []
        for date in dates:
            quantities.append(date_grouped.get_group(date)['购买数量'].sum())
        
        # 计算基础销量 a（历史平均）
        a = np.mean(quantities) if quantities else 0
        
        # 计算时间趋势因子 x（线性回归）
        x = 0
        if len(quantities) > 1:
            # 简单线性回归
            days = np.arange(len(quantities))
            slope, intercept = np.polyfit(days, quantities, 1)
            x = slope
        
        # 计算季节性因子 y
        y = 1.0
        if len(quantities) >= 7:
            # 计算最近7天的平均季节性因子
            recent_quantities = quantities[-7:]
            recent_avg = np.mean(recent_quantities)
            y = recent_quantities[-1] / recent_avg if recent_avg > 0 else 1.0
        
        # 生成预测日期
        last_date = dates[-1] if dates else datetime.now().date()
        predict_dates = []
        for i in range(1, predict_days + 1):
            predict_dates.append(last_date + timedelta(days=i))
        
        # 生成预测值
        predict_quantities = []
        predict_revenues = []
        
        # 计算该SKU的平均价格
        sku_avg_price = group['买家实际支付金额'].sum() / group['购买数量'].sum() if group['购买数量'].sum() > 0 else 0
        
        for i, predict_date in enumerate(predict_dates):
            # 计算预测值: a + x * (days_since_start) * y
            days_since_start = len(quantities) + i
            predicted_quantity = max(0, a + x * days_since_start * y)
            predict_quantities.append(round(predicted_quantity, 2))
            predict_revenues.append(round(predicted_quantity * sku_avg_price, 2))
        
        sku_predictions[sku] = {
            'date_series': [str(date) for date in predict_dates],
            'quantity_series': predict_quantities,
            'revenue_series': predict_revenues,
            'model_params': {
                'a': round(a, 2),
                'x': round(x, 4),
                'y': round(y, 2)
            }
        }
    
    # 转换所有 numpy 类型为 Python 原生类型
    sku_predictions = convert_numpy_types(sku_predictions)
    
    return sku_predictions