Spaces:
Sleeping
Sleeping
feat: 第一题基本完成
Browse files
app.py
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import streamlit as st
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-
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"""
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TODO
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"""
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# 首先计算相对路径
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from pathlib import Path
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this_file = Path(__file__).resolve()
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this_directory = this_file.parent
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data_cangzhou_folder = this_directory / "data/Cangzhou"
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data_static_folder = data_cangzhou_folder / "static"
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# 数据文件夹路径
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data_folder = data_static_folder
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# 然后
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import streamlit as st
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import pandas as pd
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import os
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from datetime import datetime
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# 设置页面配置为宽屏模式,以便更好地显示三个图表
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st.set_page_config(layout="wide")
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# 设置应用标题
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st.title("多图表可视化展示")
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# 创建三个等宽的列
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col1, col2, col3 = st.columns(3)
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# 加载和过滤数据的函数
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def load_and_filter_data(file_path):
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# 根据提供的CSV格式,第一列是时间戳但没有列名
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df = pd.read_csv(file_path, header=0, names=['timestamp', 'pm2d5', 'lat', 'lon'])
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# 确保时间戳列是datetime格式
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df['timestamp'] = pd.to_datetime(df['timestamp'])
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# 过滤2019-01-01 00:00:00到2019-01-01 12:00:00之间的数据
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start_time = datetime(2019, 1, 1, 0, 0, 0)
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end_time = datetime(2019, 1, 1, 12, 0, 0)
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filtered_df = df[(df['timestamp'] >= start_time) & (df['timestamp'] <= end_time)]
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return filtered_df
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# 第一列:PM2.5随时间变化的折线图
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with col1:
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st.header("PM2.5随时间变化")
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# 获取数据文件夹中的所有CSV文件
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csv_files = [f for f in os.listdir(data_folder) if f.endswith('.csv')]
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# 创建字典存储每个传感器的数据
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sensor_data = {}
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# 加载并过滤每个传感器的数据
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for file in csv_files:
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file_path = os.path.join(data_folder, file)
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sensor_name = file.split('.')[0] # 从文件名提取传感器名称
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sensor_data[sensor_name] = load_and_filter_data(file_path)
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# 准备可视化数据
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# 创建一个以时间戳为索引,每个传感器的PM2.5值为列的数据框
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chart_data = pd.DataFrame()
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for sensor, data in sensor_data.items():
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# 使用pm2d5列作为PM2.5数据
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chart_data[sensor] = data.set_index('timestamp')['pm2d5']
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# 绘制折线图
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st.line_chart(
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chart_data,
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x=None, # 使用索引(timestamp)作为x轴
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y=list(sensor_data.keys()), # 使用所有传感器名称作为y列
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x_label="时间",
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y_label="PM2.5水平"
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)
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# 第二列:第二个图表(您可以根据需要自定义)
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with col2:
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st.header("第二个图表")
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# 这里添加第二个图表的代码
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st.write("在这里添加您的第二个图表")
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# 第三列:第三个图表(您可以根据需要自定义)
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with col3:
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st.header("第三个图表")
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# 这里添加第三个图表的代码
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st.write("在这里添加您的第三个图表")
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