214 lines
7.4 KiB
Python
214 lines
7.4 KiB
Python
from fastapi import APIRouter
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import pyecharts
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from pyecharts import options as opts
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from pyecharts.charts import Bar, Line
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from pyecharts.faker import Faker
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from pyecharts.globals import CurrentConfig
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import json
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import os
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# 创建 APIRouter 实例
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router = APIRouter(prefix="/bigscreen", tags=["大屏展示"])
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# 配置使用自定义的 ECharts 路径
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CurrentConfig.ONLINE_HOST = "https://gcore.jsdelivr.net/npm/echarts@6.0.0/dist/"
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# 定义一个 helloWorld 路由
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@router.get("/")
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async def root():
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return {"message": "Welcome to YunNan Education World!"}
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# 生成图表配置的函数
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def generate_bar_chart_config():
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c = (
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Bar()
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.add_xaxis(Faker.choose())
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.add_yaxis("商家A", Faker.values())
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.add_yaxis("商家B", Faker.values())
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.set_global_opts(title_opts=opts.TitleOpts(title="Bar-MarkLine（自定义）"))
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.set_series_opts(
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label_opts=opts.LabelOpts(is_show=False),
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markline_opts=opts.MarkLineOpts(
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data=[opts.MarkLineItem(y=50, name="yAxis=50")]
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),
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)
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)
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# 获取图表的选项配置，使用 dump_options_with_quotes 方法获取可以直接序列化的选项
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options_str = c.dump_options_with_quotes()
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# 将字符串转换为 JSON 对象
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options_json = json.loads(options_str)
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return options_json
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# 定义一个返回图表配置的路由
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@router.get("/chart/bar")
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async def get_bar_chart_config():
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"""
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获取柱状图配置
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"""
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chart_config = generate_bar_chart_config()
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return chart_config
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# 读取人口数据
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def load_population_data():
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try:
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# 获取当前文件所在目录的父目录，然后找到Data文件夹
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current_dir = os.path.dirname(os.path.abspath(__file__))
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project_root = os.path.dirname(current_dir)
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data_path = os.path.join(project_root, "Data", "RenKou.json")
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with open(data_path, "r", encoding="utf-8") as f:
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data = json.load(f)
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return data
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except Exception as e:
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print(f"读取人口数据出错: {e}")
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return []
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# 生成人口数据图表配置
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def generate_population_chart_config(year="2024"):
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# 加载人口数据
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population_data = load_population_data()
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# 筛选出州市级数据（排除县级数据）
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# 正确的格式是9位数字，以000结尾且第5-6位为00（表示州市级）
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cities = [item for item in population_data if len(item["area_code"]) == 9 and item["area_code"].endswith("000") and item["area_code"][4:6] == "00"]
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# 提取城市名称和人口数据
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city_names = [city["area_name"] for city in cities]
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total_populations = [city["total_population"].get(year, 0) for city in cities]
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urban_populations = [city["urban_population"].get(year, 0) for city in cities]
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rural_populations = [city["rural_population"].get(year, 0) for city in cities]
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# 创建柱状图
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c = (
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Bar()
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.add_xaxis(city_names)
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.add_yaxis("总人口", total_populations, stack="stack1")
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.add_yaxis("城镇人口", urban_populations, stack="stack1")
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.add_yaxis("农村人口", rural_populations, stack="stack1")
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.set_global_opts(
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title_opts=opts.TitleOpts(
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title=f"云南省各州市人口分布图（{year}年）",
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pos_top="1%", # 调整标题位置到顶部1%
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pos_left="center"
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),
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tooltip_opts=opts.TooltipOpts(
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trigger="axis",
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axis_pointer_type="shadow"
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),
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legend_opts=opts.LegendOpts(
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pos_top="8%", # 调整图例位置到标题下方
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pos_right="5%"
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),
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datazoom_opts=[opts.DataZoomOpts()],
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xaxis_opts=opts.AxisOpts(
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axislabel_opts=opts.LabelOpts(rotate=45)
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),
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yaxis_opts=opts.AxisOpts(
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name="人口数量（万人）",
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name_location="middle",
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name_gap=40
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)
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)
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.set_series_opts(
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label_opts=opts.LabelOpts(is_show=False),
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markline_opts=opts.MarkLineOpts(
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data=[opts.MarkLineItem(type_="average", name="平均值")]
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)
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)
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)
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# 获取图表的选项配置
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options_str = c.dump_options_with_quotes()
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# 将字符串转换为 JSON 对象
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options_json = json.loads(options_str)
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return options_json
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# 生成城镇化率图表配置
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def generate_urbanization_rate_chart_config():
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# 加载人口数据
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population_data = load_population_data()
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# 筛选出州市级数据（排除县级数据）
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# 排除省级数据(如云南省530000000)和县级数据，只保留州市级数据
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cities = [item for item in population_data if len(item["area_code"]) == 9 and item["area_code"].endswith("000") and item["area_code"][4:6] == "00" and item["area_code"][2:8] != "000000"]
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# 提取城市名称和城镇化率数据
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city_names = [city["area_name"] for city in cities]
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# 创建折线图
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line = (
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Line()
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.add_xaxis(city_names)
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)
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# 添加各年份的城镇化率数据
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years = ["2020", "2021", "2022", "2023", "2024"]
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for year in years:
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urbanization_rates = [city["urbanization_rate"].get(year, 0) for city in cities]
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line.add_yaxis(f"{year}年", urbanization_rates,
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markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max")]))
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line.set_global_opts(
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title_opts=opts.TitleOpts(
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title="云南省各州市城镇化率变化趋势",
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pos_top="1%", # 调整标题位置到顶部1%
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pos_left="center"
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),
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tooltip_opts=opts.TooltipOpts(trigger="axis"),
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legend_opts=opts.LegendOpts(
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pos_top="8%", # 调整图例位置到标题下方
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pos_right="5%"
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),
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datazoom_opts=[opts.DataZoomOpts()],
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xaxis_opts=opts.AxisOpts(
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axislabel_opts=opts.LabelOpts(rotate=45)
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),
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yaxis_opts=opts.AxisOpts(
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name="城镇化率（%）",
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name_location="middle",
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name_gap=40,
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min_=0,
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max_=100
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)
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)
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# 获取图表的选项配置
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options_str = line.dump_options_with_quotes()
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# 将字符串转换为 JSON 对象
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options_json = json.loads(options_str)
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return options_json
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# 定义一个返回人口数据图表配置的路由
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@router.get("/population/chart/{year}")
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async def get_population_chart_config(year: str = "2024"):
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"""
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获取人口数据图表配置
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"""
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chart_config = generate_population_chart_config(year)
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return chart_config
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# 定义一个返回城镇化率图表配置的路由
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@router.get("/population/urbanization")
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async def get_urbanization_rate_chart_config():
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"""
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获取城镇化率图表配置
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"""
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chart_config = generate_urbanization_rate_chart_config()
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return chart_config
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# 定义一个返回人口数据原始数据的路由
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@router.get("/population/data")
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async def get_population_data():
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"""
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获取人口数据原始数据
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"""
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population_data = load_population_data()
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return {"data": population_data}
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