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# 导入必要的库
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import requests
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import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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import folium
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from folium.plugins import HeatMap
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import streamlit as st
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from datetime import datetime, timedelta
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# 第一步：数据收集
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def fetch_earthquake_data(min_magnitude=4.5, start_time=None, end_time=None):
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url = 'https://earthquake.usgs.gov/fdsnws/event/1/query'
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if not start_time:
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start_time = (datetime.utcnow() - timedelta(days=30)).strftime("%Y-%m-%d")
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if not end_time:
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end_time = datetime.utcnow().strftime("%Y-%m-%d")
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params = {
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'format': 'geojson',
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'starttime': start_time,
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'endtime': end_time,
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'minmagnitude': min_magnitude
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}
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response = requests.get(url, params=params)
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data = response.json()
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features = data['features']
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# 提取所需信息
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earthquakes = []
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for feature in features:
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properties = feature['properties']
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geometry = feature['geometry']
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earthquakes.append({
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'time': pd.to_datetime(properties['time'], unit='ms'),
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'place': properties['place'],
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'magnitude': properties['mag'],
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'depth': geometry['coordinates'][2],
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'latitude': geometry['coordinates'][1],
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'longitude': geometry['coordinates'][0]
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})
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return pd.DataFrame(earthquakes)
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# 第二步：数据分析和可视化函数
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def plot_magnitude_distribution(df):
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plt.figure(figsize=(10, 6))
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sns.histplot(df['magnitude'], bins=20, kde=True)
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plt.title('magnitude distribution histogram')
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plt.xlabel('magnitude')
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plt.ylabel('frequency')
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st.pyplot(plt)
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def plot_time_series(df):
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df_time = df.set_index('time').resample('D').count()
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plt.figure(figsize=(10, 6))
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plt.plot(df_time.index, df_time['magnitude'])
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plt.title('daily earthquake count time series')
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plt.xlabel('date')
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plt.ylabel('number of earthquakes')
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st.pyplot(plt)
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def create_interactive_map(df):
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map_center = [df['latitude'].mean(), df['longitude'].mean()]
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base_map = folium.Map(location=map_center, zoom_start=2)
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# 添加地震位置点
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for _, row in df.iterrows():
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folium.CircleMarker(
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location=[row['latitude'], row['longitude']],
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radius=row['magnitude'] * 1.5,
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popup=f"Magnitude: {row['magnitude']}, Depth: {row['depth']} km",
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color='red',
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fill=True,
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fill_color='red'
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).add_to(base_map)
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# 添加热力图
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heat_data = [[row['latitude'], row['longitude']] for _, row in df.iterrows()]
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HeatMap(heat_data).add_to(base_map)
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return base_map
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# 第三步：制作Streamlit应用
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def main(streamlit_folium=None):
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st.title("全球地震数据分析与可视化")
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# 侧边栏参数选择
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st.sidebar.header("参数设置")
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min_magnitude = st.sidebar.slider("最小震级", 0.0, 10.0, 4.5, 0.1)
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days = st.sidebar.slider("过去多少天的数据", 1, 90, 30)
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# 获取数据
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start_time = (datetime.utcnow() - timedelta(days=days)).strftime("%Y-%m-%d")
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end_time = datetime.utcnow().strftime("%Y-%m-%d")
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df = fetch_earthquake_data(min_magnitude=min_magnitude, start_time=start_time, end_time=end_time)
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st.subheader(f"从{start_time}到{end_time}，震级大于{min_magnitude}的地震数据，共{len(df)}条")
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st.map(df[['latitude', 'longitude']])
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# 显示数据表
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if st.checkbox("显示原始数据表"):
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st.write(df)
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# 震级分布直方图
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st.subheader("震级分布直方图")
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plot_magnitude_distribution(df)
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# 时间序列图
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st.subheader("每日地震数量时间序列")
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plot_time_series(df)
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# 交互式地图
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st.subheader("地震分布交互式地图")
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folium_map = create_interactive_map(df)
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# 将Folium地图嵌入Streamlit
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from streamlit_folium import st_folium
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st_data = st_folium(folium_map, width=700)
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if __name__ == "__main__":
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main()
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