如何在使用seaborn的聚类图进行分层聚类时掩盖下三角形?
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
#pearson coefficients
corr = np.corrcoef(np.random.randn(10, 200))
#lower triangle
mask = np.tril(np.ones_like(corr))
fig, ax = plt.subplots(figsize=(6,6))
#heatmap works as expected
sns.heatmap(corr, cmap="Blues", mask=mask, cbar=False)
#clustermap not so much
sns.clustermap(corr, cmap="Blues", mask=mask, figsize=(6,6))
plt.show()
最佳答案
嗯,clustermap
根据相似性对值进行聚类。这会更改行和列的顺序。
您可以创建一个常规聚类图,然后在第二步中应用掩码:
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
corr = np.corrcoef(np.random.randn(10, 200))
g = sns.clustermap(corr, cmap="Blues", figsize=(6, 6))
mask = np.tril(np.ones_like(corr))
values = g.ax_heatmap.collections[0].get_array().reshape(corr.shape)
new_values = np.ma.array(values, mask=mask)
g.ax_heatmap.collections[0].set_array(new_values)
plt.show()
关于python - 带有seaborn clustermap的下三角掩码,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/67879908/