如何将4D数据绘制成分层等高线图？已有代码需后续指导

你的需求是基于包含lat(x)、lon(y)、levels(z)、dust_mixing_ratio(第四维度，颜色/强度)的CSV数据，绘制分层可视化图。先修正你现有grids_maker函数的问题（原函数错误地混合了所有层级数据插值），再提供三种可行的绘制方案：

第一步：修正网格生成函数 #

原函数将所有层级的坐标混在一起插值，导致结果无法对应单个层级。修改后按层级分组处理，保证每个层级的网格独立对应其沙尘混合比分布：

import pandas as pd
import numpy as np
from scipy.interpolate import griddata as si

def grids_maker(filepath):
    df = pd.read_csv(filepath, sep=' ')
    level_grids = {}
    reso_x = reso_y = 50
    interp = 'cubic'  # 可选'nearest'/'linear'
    
    # 全局统一经纬度范围，确保所有层级网格尺寸一致
    x_min, x_max = df['lat'].min(), df['lat'].max()
    y_min, y_max = df['lon'].min(), df['lon'].max()
    grid_x, grid_y = np.mgrid[x_min:x_max:1j*reso_x, y_min:y_max:1j*reso_y]
    
    # 按层级分组插值
    for level, group in df.groupby('levels'):
        xy = group[['lat', 'lon']]
        g = group['dust_mixing_ratio']
        grid_g = si(xy, g.values, (grid_x, grid_y), method=interp)
        level_grids[level] = {
            'x': grid_x,
            'y': grid_y,
            'g': grid_g
        }
    return level_grids

方案一：分层平铺2D热图 #

将每个层级的热图按顺序排列成子图，直观对比不同层级的沙尘分布：

import matplotlib.pyplot as plt

# 获取各层级网格数据
level_grids = grids_maker("D:\DATA\2015\MIXING_RATIOe.csv")
sorted_levels = sorted(level_grids.keys())  # 按层级值排序

# 布局设置：每行3个图，自动计算行数
n_cols = 3
n_rows = (len(sorted_levels) + n_cols - 1) // n_cols
fig, axes = plt.subplots(n_rows, n_cols, figsize=(15, 5*n_rows))
axes = axes.flatten()

# 统一颜色标尺，保证所有层级颜色范围一致
all_g_values = np.concatenate([level_grids[lev]['g'].ravel() for lev in sorted_levels])
vmin, vmax = np.nanmin(all_g_values), np.nanmax(all_g_values)

# 绘制每个层级的热图
for idx, level in enumerate(sorted_levels):
    ax = axes[idx]
    grid_data = level_grids[level]
    im = ax.pcolormesh(grid_data['x'], grid_data['y'], grid_data['g'], vmin=vmin, vmax=vmax, cmap='viridis')
    ax.set_title(f'层级 {level}')
    ax.set_xlabel('纬度(lat)')
    ax.set_ylabel('经度(lon)')

# 隐藏多余子图
for idx in range(len(sorted_levels), len(axes)):
    axes[idx].axis('off')

# 添加全局颜色条
fig.colorbar(im, ax=axes, label='沙尘混合比(dust_mixing_ratio)')
plt.tight_layout()
plt.show()

方案二：3D空间分层叠加热图 #

在3D坐标系中，将每个层级作为一个平面叠加，Z轴对应levels值，颜色表示沙尘混合比：

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

level_grids = grids_maker("D:\DATA\2015\MIXING_RATIOe.csv")
sorted_levels = sorted(level_grids.keys())

fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111, projection='3d')

# 统一颜色范围
all_g_values = np.concatenate([level_grids[lev]['g'].ravel() for lev in sorted_levels])
vmin, vmax = np.nanmin(all_g_values), np.nanmax(all_g_values)

# 绘制每个层级的3D平面
for level in sorted_levels:
    grid_data = level_grids[level]
    X, Y = grid_data['x'], grid_data['y']
    Z = np.full_like(X, level)  # 当前层级的Z轴值固定
    # 将沙尘混合比转换为颜色值
    face_colors = plt.cm.viridis((grid_data['g'] - vmin)/(vmax - vmin))
    ax.plot_surface(X, Y, Z, facecolors=face_colors, rstride=1, cstride=1, shade=False)

# 设置坐标轴与标题
ax.set_xlabel('纬度(lat)')
ax.set_ylabel('经度(lon)')
ax.set_zlabel('层级(levels)')
ax.set_title('3D分层沙尘混合比分布图')

# 添加颜色条
sm = plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=vmin, vmax=vmax))
sm.set_array([])
fig.colorbar(sm, ax=ax, label='沙尘混合比(dust_mixing_ratio)')

plt.show()

方案三：交互式层级切换热图 #

用Plotly实现可交互的热图，通过下拉菜单切换不同层级，适合探索数据：

import plotly.graph_objects as go

level_grids = grids_maker("D:\DATA\2015\MIXING_RATIOe.csv")
sorted_levels = sorted(level_grids.keys())

fig = go.Figure()

# 统一颜色范围
all_g_values = np.concatenate([level_grids[lev]['g'].ravel() for lev in sorted_levels])
vmin, vmax = np.nanmin(all_g_values), np.nanmax(all_g_values)

# 添加所有层级的热图轨迹
for level in sorted_levels:
    grid_data = level_grids[level]
    fig.add_trace(go.Heatmap(
        x=grid_data['x'][0,:],
        y=grid_data['y'][:,0],
        z=grid_data['g'],
        zmin=vmin,
        zmax=vmax,
        colorscale='Viridis',
        name=f'层级 {level}',
        visible=(level == sorted_levels[0])  # 默认显示第一个层级
    ))

# 添加下拉切换按钮
buttons = []
for i, level in enumerate(sorted_levels):
    button = dict(
        label=f'层级 {level}',
        method='update',
        args=[{'visible': [j == i for j in range(len(sorted_levels))]}]
    )
    buttons.append(button)

fig.update_layout(
    updatemenus=[dict(
        type="dropdown",
        buttons=buttons,
        x=0.1,
        y=1.1
    )],
    title='交互式层级沙尘混合比热图',
    xaxis_title='纬度(lat)',
    yaxis_title='经度(lon)',
    coloraxis_colorbar=dict(title='沙尘混合比')
)

fig.show()

内容的提问来源于stack exchange，提问作者shravani banerjee