将第二个颜色条添加到Seaborn Heatmap / Clustermap

Question

我试图帮助某人为下图中的垂直蓝条添加一个颜色条。我们尝试了plt.colorbar(row_colors)的许多变体（例如上方和下方sns.clustermap()）并在网上浏览了2个小时，但没有运气。我们只想为蓝调添加一个颜色条，请帮忙！

import pickle
import numpy as np
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt

feat_mat, freq, label = pickle.load(open('file.pkl', 'rb'))

feat_mat_df = pd.DataFrame(feat_mat[4])

freq_df = pd.DataFrame(freq)
freq_df_transposed = freq_df.transpose()

my_palette = dict(zip(set(freq_df_transposed[int('4')]), sns.color_palette("PuBu", len(set(freq_df_transposed[int('4')]))))))
row_colors = freq_df_transposed[int('4')].map(my_palette)

sns.clustermap(feat_mat_df, metric="euclidean", standard_scale=1, method="complete", cmap="coolwarm", row_colors = row_colors)

plt.show()

这是他的代码来自：#405 Dendrogram with heatmap and coloured leaves

Answer 1

I think something like this should work for your purposes- I didn't have a clustermap example available but the logic is the same to do what you want to do. Basically-you're going to take that list of colors you made and imshow it, then hide the imshow plot, and plot the colorbar in its place. In my example, I use make_axes_locatable to place axes next to the plot with your data to put the colorbar inside - https://matplotlib.org/2.0.2/mpl_toolkits/axes_grid/users/overview.html. I find placing a new axes for other objects (legends color maps or otherwise) easier than trying to draw them on the same axes.

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(0)
import seaborn as sns
from mpl_toolkits.axes_grid1 import make_axes_locatable
import random


uniform_data = np.random.rand(10, 12)

fig, ax = plt.subplots(1,1, figsize = (5,5))
divider = make_axes_locatable(ax)

axDivY = divider.append_axes( 'right', size=0.2, pad= 0.1)
axDivY2 = divider.append_axes( 'right', size=0.2, pad= 0.2)

# we will use this for the colorscale bar
axDivY3 = divider.append_axes( 'right', size=0.2, pad= 0.2)
ax1 = sns.heatmap(uniform_data, ax=ax, cbar_ax=axDivY)


# the palette you were using to make the label column on the clustermap

# some simulated labels for your data with values
color_label_list =[random.randint(0,20) for i in range(20)]

pal =  sns.color_palette("PuBu", len(set(color_label_list)))
n = len(pal)
size = 1

# plot the colors with imshow to make a colormap later
ax2 = axDivY2.imshow(np.array([color_label_list]),
              cmap=mpl.colors.ListedColormap(list(pal)),
              interpolation="nearest", aspect="auto")
# turn off the axes so they aren't visible- note that you need ax.axis('off) if you have older matplotlib
axDivY2.set_axis_off()
axDivY2.set_visible(False)
# plot the colorbar on the other axes (which is on top of the one that we turned off)
plt.colorbar(ax2, cax = axDivY3) ;