from pyecharts import options as opts
from pyecharts.charts import Bar,Page,Line,Timeline
from pyecharts.commons.utils import JsCode
from pyecharts.globals import ThemeType
from pyecharts.charts import Pie
import sql
from pyecharts.faker import Faker
# from main import book_list_data
from collections import Counter
class Book:
    def __init__(self, name, url, star, star_people, author, translater, publisher, pub_year, price, comment):
        self.name = name
        self.url = url
        self.star = star
        self.star_people = star_people
        self.author = author
        self.translater = translater
        self.publisher = publisher
        self.pub_year = pub_year
        self.price = price
        self.comment = comment
db = sql.BookDatabase(host='localhost', user='root', password='123456', database='xiaosuo', table_name='books')
db.initialize_table()
def show():
    data = db.get_book_list()
    book_list_data = []
    for book in data:
        book_list_data.append(
            Book(book["name"], book["url"], book["star"], book["star_people"], book["author"], book["translater"],
                 book["publisher"], book["pub_year"], book["price"], book["comment"]))

    book_list_data_sortBystart = sorted(book_list_data, key=lambda x: (x.star, x.star_people), reverse=True)
    book_list_data_sortBystart = book_list_data_sortBystart[:10]
    book_list_data_sortBystart = book_list_data_sortBystart[::-1]
    # 从book_list_data中提取书籍名称和评分数据
    book_names = [book.name for book in book_list_data_sortBystart]
    book_stars = [book.star for book in book_list_data_sortBystart]
    x_data = list(range(1, len(book_names) + 1))
    # 创建柱状图
    bar = (
        Bar(init_opts=opts.InitOpts(theme="shine", width="850px", height='400px'))
        .add_xaxis(book_names)
        .add_yaxis("评分", book_stars, color="red")
        .reversal_axis()  # 实现旋转
        .set_global_opts(
            title_opts=opts.TitleOpts(title="评分前10榜", pos_bottom="bottom", pos_left="center"),
            xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=0)),
            yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-45))
        )
        # .render("bar_datazoom_slider.html")
    )

    publisher_list = [book.publisher for book in book_list_data]
    # 使用Counter来计算每个出版商的数量
    publisher_counter = Counter(publisher_list)
    publisher_names = list(publisher_counter.keys())
    publisher_counts = list(publisher_counter.values())
    colors = [
        "#5470c6", "#91cc75", "#fac858", "#ee6666", "#73c0de",
        "#3ba272", "#fc8452", "#9a60b4", "#ea7ccc", "#bb60b4",
        "#8B008B", "#FF1493", "#1E90FF", "#20B2AA", "#2E8B57",
        "#B22222", "#FF4500", "#4682B4", "#DAA520", "#32CD32"
    ]

    # 使用Pyecharts创建饼图
    pie = (
        Pie()
        .add("", [list(z) for z in zip(publisher_names, publisher_counts)])
        .set_colors(colors)  # 设置颜色
        .set_global_opts(title_opts=opts.TitleOpts(title="出版商分布饼图", pos_left="center", pos_bottom="bottom"))
        .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
    )

    prices = [float(book.price) for book in book_list_data]
    print(prices)
    # 将价格分组到不同的区间
    price_intervals = [0, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, float('inf')]  # 设置价格区间
    price_counts = [0] * (len(price_intervals) - 1)  # 初始化每个区间的计数为0
    prices_intervalsStr = ["0-50", "50-100", "100-150", "150-200", "200-250", "250-300", "300-350", "350-400",
                           "400-450", "450-500", "500以上"]
    # 统计每个价格区间的数量
    for price in prices:
        for i in range(len(price_intervals) - 1):
            if price >= 500:
                price_counts[len(price_intervals) - 1] += 1
                break
            if price_intervals[i] <= price and price < price_intervals[i + 1]:
                price_counts[i] += 1
                break
    # print(price_counts)
    # 生成价格区间与书籍数量折线图
    line = (
        Line()
        .add_xaxis([str(interval) for interval in prices_intervalsStr[:-1]])  # X轴标签为价格区间
        .add_yaxis("书籍数量", price_counts, symbol="circle", is_smooth=True,
                   markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max")]))
        .set_global_opts(
            title_opts=opts.TitleOpts(title="价格区间与书籍数量折线图", pos_left="center", pos_bottom="bottom"),
            xaxis_opts=opts.AxisOpts(name="价格区间"),
            yaxis_opts=opts.AxisOpts(name="书籍数量"),
            datazoom_opts=opts.DataZoomOpts(range_start=0, range_end=100), )
    )

    # 生成时间与书籍数量折线图# 假设您的书籍数据保存在book_list_data中
    # 提取每本书的出版年份
    publish_dates = [int(book.pub_year.replace('-', '.').split('.')[0]) for book in book_list_data]
    print(publish_dates)
    # 使用Counter来计算每年出版的书的数量
    publish_year_counter = Counter(publish_dates)
    print(publish_year_counter)
    # 确保结果包含连续的年份范围，并且将缺失的年份对应的数量设为 0
    full_year_range = range(min(publish_dates), max(publish_dates) + 1)
    print(full_year_range)
    pub_year_counts = [(year, publish_year_counter[year]) for year in full_year_range]

    # 提取年份和对应的书籍数量
    years = [str(year) for year, count in pub_year_counts]
    counts = [count for year, count in pub_year_counts]
    print(pub_year_counts)

    line_year_count = (
        Line()
        .add_xaxis(years)
        .add_yaxis(
            series_name="出版书籍数量",
            y_axis=counts,
            markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max", name="最大值"),
                                                    opts.MarkPointItem(type_="min", name="最小值")]),
            markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average", name="平均值")]),
        )
        .set_global_opts(title_opts=opts.TitleOpts(title="年份与出版书籍数量变化"),
                         xaxis_opts=opts.AxisOpts(name="年份"),
                         yaxis_opts=opts.AxisOpts(name="书籍数量"),
                         )
    )

    # 创建一个页面
    page = Page()
    # 将柱状图和饼图添加到页面中
    page.add(line_year_count)
    page.add(bar)
    page.add(pie)
    page.add(line)
    # 生成HTML文件
    page.render("book_analysis.html")