# 安裝所需庫
# 推薦在虛擬環(huán)境中安裝
pip install python-docx pandas matplotlib plotly openpyxl

from docx import Document
from docx.shared import Inches, Pt, RGBColor
from docx.enum.text import WD_ALIGN_PARAGRAPH

# 創(chuàng)建一個新的 Word 文檔
doc = Document()

# 添加標(biāo)題
doc.add_heading('周報：2025-04-21', 0)

# 添加段落
p = doc.add_paragraph("本周業(yè)務(wù)總體運行情況：")
p.add_run('良好').bold = True
p.add_run(', 各項主表穩(wěn)步增長。')

# 添加表格
table = doc.add_table(rows=3, cols=3)

# 設(shè)置表頭
header_cells = table.rows[0].cells
header_cells[0].text = '指標(biāo)名稱'
header_cells[1].text = '本周數(shù)值'
header_cells[2].text = '環(huán)比變化'

# 填充數(shù)據(jù)
data_cells = table.rows[1].cells
data_cells[0].text = '銷售額'
data_cells[1].text = '￥1234567'
data_cells[2].text = '+12.3'

# 添加圖片
doc.add_picture("1.png", width=Inches(6), height=Inches(2))

# 保存文檔
doc.save("weekly_report.docx")

#! /usr/bin/env/python3
# -*- coding=utf-8 -*-
# @Author: jack
# @Date  : 2025/04/21/17:16
from docx import Document
from docx.shared import Inches, Pt, RGBColor
from docx.enum.text import WD_ALIGN_PARAGRAPH
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime, timedelta


def get_report_period():
    """確定報告的時間范圍"""
    today = datetime.now()
    # 假設(shè)周報覆蓋上周一到周日
    last_month = today - timedelta(days=today.weekday() + 7)
    last_sunday = last_month + timedelta(days=6)
    return last_month, last_sunday


def fetch_sales_data(start_date, end_date):
    """從數(shù)據(jù)源獲取銷售數(shù)據(jù)"""
    # 實際應(yīng)用中，這里是數(shù)據(jù)庫查詢或 API 調(diào)用
    # 這里使用模擬數(shù)據(jù)作為示例
    dates = pd.date_range(start=start_date, end=end_date)
    sales = [round(100000 + i * 5000 + i * i * 100) for i in range(len(dates))]
    return pd.DataFrame({
        "date": dates,
        "sales": sales})


def calculate_kpi(df):
    """計算關(guān)鍵績效指標(biāo)"""
    total_sales = df["sales"].sum()
    avg_sales = df["sales"].mean()
    max_sales = df["sales"].max()
    max_sales_day = df.loc[df["sales"].idxmax(), "date"]
    # 計算環(huán)比變化
    # 假設(shè)我們有上周的數(shù)據(jù)
    last_week_sales = total_sales * 0.9  # 模擬數(shù)據(jù)
    sales_change = (total_sales - last_week_sales) / last_week_sales
    return {
        "total_sales": total_sales,
        "avg_sales": avg_sales,
        "max_sales": max_sales,
        "max_sales_day": max_sales_day,
        "sales_change": sales_change
    }


def generate_charts(df, output_path):
    """生成數(shù)據(jù)可視化圖表"""
    plt.figure(figsize=(10, 6))
    plt.plot(df['date'], df['sales'], marker='o')
    plt.title('每日銷售額趨勢')
    plt.xlabel('日期')
    plt.ylabel('銷售額')
    plt.grid(True)
    plt.tight_layout()
    plt.savefig(output_path)
    plt.close()
    return output_path

#! /usr/bin/env/python3
# -*- coding=utf-8 -*-
# @Author: jack
# @Date  : 2025/04/21/17:16
import os

from docx import Document
from docx.shared import Inches, Pt, RGBColor
from docx.enum.text import WD_ALIGN_PARAGRAPH
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime, timedelta


def get_report_period():
    """確定報告的時間范圍"""
    today = datetime.now()
    # 假設(shè)周報覆蓋上周一到周日
    last_month = today - timedelta(days=today.weekday() + 7)
    last_sunday = last_month + timedelta(days=6)
    return last_month, last_sunday


def fetch_sales_data(start_date, end_date):
    """從數(shù)據(jù)源獲取銷售數(shù)據(jù)"""
    # 實際應(yīng)用中，這里是數(shù)據(jù)庫查詢或 API 調(diào)用
    # 這里使用模擬數(shù)據(jù)作為示例
    dates = pd.date_range(start=start_date, end=end_date)
    sales = [round(100000 + i * 5000 + i * i * 100) for i in range(len(dates))]
    return pd.DataFrame({
        "date": dates,
        "sales": sales})


def calculate_kpis(df):
    """計算關(guān)鍵績效指標(biāo)"""
    total_sales = df["sales"].sum()
    avg_sales = df["sales"].mean()
    max_sales = df["sales"].max()
    max_sales_day = df.loc[df["sales"].idxmax(), "date"]
    # 計算環(huán)比變化
    # 假設(shè)我們有上周的數(shù)據(jù)
    last_week_sales = total_sales * 0.9  # 模擬數(shù)據(jù)
    sales_change = (total_sales - last_week_sales) / last_week_sales
    return {
        "total_sales": total_sales,
        "avg_sales": avg_sales,
        "max_sales": max_sales,
        "max_sales_day": max_sales_day,
        "sales_change": sales_change
    }


def generate_charts(df, output_path):
    """生成數(shù)據(jù)可視化圖表"""
    plt.figure(figsize=(10, 6))
    plt.plot(df['date'], df['sales'], marker='o')
    plt.title('每日銷售額趨勢')
    plt.xlabel('日期')
    plt.ylabel('銷售額')
    plt.grid(True)
    plt.tight_layout()
    plt.savefig(output_path)
    plt.close()
    return output_path


def generate_report(template_path, output_path):
    """生成周報的主函數(shù)"""
    # 獲取報告時間范圍
    start_date, end_date = get_report_period()
    period_str = f"{start_date.strftime('%Y年%m月%d日')} 至 {end_date.strftime('%Y年%m月%d日')}"

    # 獲取并處理數(shù)據(jù)
    sales_data = fetch_sales_data(start_date, end_date)
    kpis = calculate_kpis(sales_data)

    # 生成圖表
    chart_path = generate_charts(sales_data, 'sales_trend.png')

    # 加載Word模板
    doc = Document(template_path)

    # 替換標(biāo)題中的日期
    for paragraph in doc.paragraphs:
        if '{{report_period}}' in paragraph.text:
            paragraph.text = paragraph.text.replace('{{report_period}}', period_str)

    # 填充KPI數(shù)據(jù)
    for paragraph in doc.paragraphs:
        if '{{total_sales}}' in paragraph.text:
            paragraph.text = paragraph.text.replace('{{total_sales}}', f"￥{kpis['total_sales']:,.2f}")
        if '{{sales_change}}' in paragraph.text:
            change_text = f"+{kpis['sales_change']:.2%}" if kpis['sales_change'] >= 0 else f"{kpis['sales_change']:.2%}"
            paragraph.text = paragraph.text.replace('{{sales_change}}', change_text)

    # 填充表格數(shù)據(jù)
    for table in doc.tables:
        for row in table.rows:
            for cell in row.cells:
                for paragraph in cell.paragraphs:
                    if '{{avg_sales}}' in paragraph.text:
                        paragraph.text = paragraph.text.replace('{{avg_sales}}', f"￥{kpis['avg_sales']:,.2f}")
                    if '{{max_sales}}' in paragraph.text:
                        paragraph.text = paragraph.text.replace('{{max_sales}}', f"￥{kpis['max_sales']:,.2f}")
                    if '{{max_sales_day}}' in paragraph.text:
                        day_str = kpis['max_sales_day'].strftime('%Y年%m月%d日')
                        paragraph.text = paragraph.text.replace('{{max_sales_day}}', day_str)

    # 添加圖表
    for paragraph in doc.paragraphs:
        if '{{sales_chart}}' in paragraph.text:
            # 保存當(dāng)前段落的參考
            p = paragraph
            # 清除占位符文本
            p.text = ""
            # 在同一位置添加圖片
            run = p.add_run()
            run.add_picture(chart_path, width=Inches(6))

    # 保存生成的報告
    doc.save(output_path)
    print(f"周報已生成：{output_path}")
    return output_path


def main():
    # 模板和輸出文件路徑
    template_path = "weekly_report.docx"
    start_date, end_date = get_report_period()
    output_filename = f"銷售周報_{start_date.strftime('%Y%m%d')}_{end_date.strftime('%Y%m%d')}.docx"
    output_path = os.path.join("reports", output_filename)

    # 確保輸出目錄存在
    os.makedirs("reports", exist_ok=True)

    # 生成報告
    generate_report(template_path, output_path)


if __name__ == "__main__":
    main()

def add_dynamic_sections(doc, sales_data, kpis):
    """根據(jù)數(shù)據(jù)情況動態(tài)添加報表內(nèi)容"""
    # 例如：當(dāng)銷售增長率超過20%時，添加特別說明
    if kpis['sales_change'] > 0.2:
        doc.add_heading('銷售額顯著增長說明', level=2)
        p = doc.add_paragraph()
        p.add_run(f"本周銷售額較上周增長了{(lán)kpis['sales_change']:.2%}，顯著高于預(yù)期。")
        p.add_run("主要增長點來自于以下方面：").bold = True
        
        # 添加項目符號列表
        doc.add_paragraph("新產(chǎn)品線上線帶來的銷售增長", style='List Bullet')
        doc.add_paragraph("營銷活動效果顯著", style='List Bullet')
        doc.add_paragraph("重點客戶訂單增加", style='List Bullet')
    
    # 檢測銷售異常天
    daily_avg = sales_data['sales'].mean()
    std_dev = sales_data['sales'].std()
    anomaly_days = sales_data[abs(sales_data['sales'] - daily_avg) > 2 * std_dev]
    
    ifnot anomaly_days.empty:
        doc.add_heading('異常銷售日分析', level=2)
        p = doc.add_paragraph("本周檢測到以下日期的銷售數(shù)據(jù)存在顯著異常：")
        
        # 添加異常日表格
        table = doc.add_table(rows=1, cols=3)
        table.style = 'Table Grid'
        
        # 設(shè)置表頭
        header_cells = table.rows[0].cells
        header_cells[0].text = '日期'
        header_cells[1].text = '銷售額'
        header_cells[2].text = '與平均值偏差'
        
        # 添加數(shù)據(jù)行
        for _, row in anomaly_days.iterrows():
            cells = table.add_row().cells
            cells[0].text = row['date'].strftime('%Y-%m-%d')
            cells[1].text = f"￥{row['sales']:,.2f}"
            deviation = (row['sales'] - daily_avg) / daily_avg
            cells[2].text = f"{deviation:.2%}"
            
        doc.add_paragraph("建議進(jìn)一步調(diào)查這些異常情況的原因，以便采取相應(yīng)的業(yè)務(wù)措施。")