在水环境实际分析中，往往要对采样数据进行可视化，方便分析判断。
现在有了chatgpt 可以更快更好的完成这一任务。
监测数据如下：
将数据直接发给chatgpt
提出需求，越精细越好。
他回答并返回结果：
import pandas as pd
import folium
from folium.plugins import HeatMap# 创建坐标轴数据表格
data = {"Name": ["YuJianTan", "TiaoDengZi", "XiangJiaTan", "WuLiQiao", "CiBa", "CiBaAuto", "LiJiQiao", "BaoShan", "ZhuYangXi", "LinJiangHe"],"Lat": [29.22159555, 29.19100425, 29.15961747, 29.13475581, 29.10543124, 29.1133509, 29.11030903, 29.08354826, 29.06511879, 29.25768],"Lon": [105.9492021, 105.9590533, 105.9415321, 105.928865, 105.940524, 105.9277196, 105.9577255, 105.949189, 105.9416183, 105.94348],"Fluoride": [0.751, 0.701, 0.816, 0.848, 0.832, 0.856, 0.757, 0.747, 0.766, 0.583]}
df = pd.DataFrame(data)# 绘制热力图
m = folium.Map(location=[df["Lat"].mean(), df["Lon"].mean()], zoom_start=13)
heat_data = [[row["Lat"], row["Lon"], row["Fluoride"]] for index, row in df.iterrows()]
HeatMap(heat_data).add_to(m)# 绘制平滑曲线
locs = df[["Lat", "Lon"]].values.tolist()
fluorides = df["Fluoride"].values.tolist()from scipy.interpolate import interp1d
import numpy as npx_min = min(df["Lat"].min(), df["Lon"].min())
x_max = max(df["Lat"].max(), df["Lon"].max())
f = interp1d(np.linspace(x_min, x_max, len(fluorides)), fluorides, kind="cubic")
xs = np.linspace(x_min, x_max, 100)
ys = f(xs)
coords = [[y, x] for y, x in zip(xs, ys)]folium.PolyLine(coords, color="blue", weight=4).add_to(m)m
虽然结果并不完美，但可以继续对其完善。整体体验下来，非常流畅且节约大量时间。
以后再进一步总结将大大提高工作效率。
