如何使用Python绘制一行排列的多幅密度等高线子图

解决方案 #

seaborn的displot是图级接口，本身会独立创建画布，不支持传入外部坐标轴对象。你可以用两种方案实现1行3列的密度等高线子图，数据预处理部分和你现有代码完全一致，直接复用即可。

方案1：使用seaborn axes级接口sns.kdeplot

这是改动最小的方案，sns.kdeplot直接支持传入ax参数绑定到指定子图：

from astropy.table import Table, join
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from scipy import stats

# 以下数据清洗代码和你原有逻辑完全一致
RErange = Table.read('../../GAMA_Data/REMassEClassEmeasure.fits')
RErange = RErange[RErange['SurfaceDensityFlag'] == 0]
RErange = RErange[RErange['AGEDenParFlag'] == 0]
RErange = RErange[RErange['CountInCylFlag'] == 0]
RErange = RErange[RErange['uminusr']> 0.001]
df = RErange.to_pandas()

# 创建1行3列的画布
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(18, 5))

# 按你的实际需求配置三个子图的x、y字段和标题，此处为示例
plot_config = [
    ("logmstar", "uminusr", "子图1：恒星质量-色指数"),
    ("logmstar", "SurfaceDensity", "子图2：恒星质量-面密度"),
    ("logmstar", "CountInCyl", "子图3：恒星质量-柱内计数")
]

for ax, (x_field, y_field, title) in zip(axes.flat, plot_config):
    # 绘制二维密度等高线，fill控制是否填充区域，cmap可自定义配色
    sns.kdeplot(data=df, x=x_field, y=y_field, ax=ax, fill=True, cmap="viridis", levels=10)
    ax.set_title(title)
    ax.set_xlabel(x_field)
    ax.set_ylabel(y_field)

# 自动调整子图间距
plt.tight_layout()
plt.show()

方案2：不依赖seaborn，原生Matplotlib+Scipy实现

如果不想引入seaborn依赖，可以直接用Scipy计算核密度，再用Matplotlib绘制等高线：

from astropy.table import Table, join
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from scipy.stats import gaussian_kde

# 数据清洗逻辑和原有代码一致
RErange = Table.read('../../GAMA_Data/REMassEClassEmeasure.fits')
RErange = RErange[RErange['SurfaceDensityFlag'] == 0]
RErange = RErange[RErange['AGEDenParFlag'] == 0]
RErange = RErange[RErange['CountInCylFlag'] == 0]
RErange = RErange[RErange['uminusr']> 0.001]
df = RErange.to_pandas()

fig, axes = plt.subplots(1, 3, figsize=(18, 5))
plot_config = [
    ("logmstar", "uminusr", "子图1：恒星质量-色指数"),
    ("logmstar", "SurfaceDensity", "子图2：恒星质量-面密度"),
    ("logmstar", "CountInCyl", "子图3：恒星质量-柱内计数")
]

for ax, (x_field, y_field, title) in zip(axes.flat, plot_config):
    x = df[x_field].values
    y = df[y_field].values
    # 计算二维核密度
    xy = np.vstack([x, y])
    kde = gaussian_kde(xy)
    # 生成插值网格
    xi, yi = np.mgrid[x.min():x.max():100j, y.min():y.max():100j]
    zi = kde(np.vstack([xi.flatten(), yi.flatten()])).reshape(xi.shape)
    # 绘制填充等高线+等高线边框
    ax.contourf(xi, yi, zi, cmap="viridis", levels=10)
    ax.contour(xi, yi, zi, colors="white", linewidths=0.5, levels=10)
    ax.set_title(title)
    ax.set_xlabel(x_field)
    ax.set_ylabel(y_field)

plt.tight_layout()
plt.show()

内容的提问来源于stack exchange，提问作者Keith Sloan