在保持对齐的同时调整 scipy 树状图和 matplotlib pcolor 子图的大小

Question

目标

像 matlab 制作的那样制作连接到热图的树状图；

或者像 this plotly example 那样没有花哨的 javascript 只使用 scipy 和 matplotlib。

建立在 this question 的基础上，我导出了以下代码；

输入

from scipy.cluster.hierarchy import linkage
from scipy.cluster.hierarchy import dendrogram
from scipy.spatial.distance import pdist
import matplotlib
from matplotlib import pyplot as plt
import numpy as np
from numpy import arange

# Create the figure and set it's size.
fig = plt.figure(figsize=(5,7))

# Create the first subplot in the figure. 
ax1 = plt.subplot(211)

# Set to your favorite colormap.
cm = matplotlib.cm.viridis

# Create array of random numbers.
X = np.random.random([6,300])

# Create a linkage object.
linkmat = linkage(X)

# Make a dendrogram from the linkage object.
dendrogram(linkmat)

# Use the x and y limits to set the aspect.
x0,x1 = ax1.get_xlim()
y0,y1 = ax1.get_ylim()
ax1.set_aspect((x1-x0)/(y1-y0))


# Remove the ticks on the x-axis. 
plt.tick_params(axis='x', which='both', bottom='off', top='off', labelbottom='off')

# Create the second subplot.
ax2 = plt.subplot(212)

labels = ["a", "b", "c", "d", "e"]

plt.xticks(arange(0.5, 7.5, 1))

plt.gca().set_xticklabels(labels)

plt.pcolor(X.T)

x0,x1 = ax2.get_xlim()
y0,y1 = ax2.get_ylim()

ax2.set_aspect((x1-x0)/(y1-y0))

# Insert the color scale
plt.colorbar()
cb = plt.colorbar(ax=ax1)
cb.ax.set_visible(False)

# Make the vertical distance between plots equal to zero 
plt.subplots_adjust(hspace=0)

# Show the plot
plt.show()

输出

问题

我怎样才能减少树状图的高度 50% 和 增加 热图的高度 50%，同时保持它们当前的高度对齐方式？

Answer 1

您可以使用 subplot2grid({params}) 而不是 subplot()，因为您可以知道它将占据多少个网格

from scipy.cluster.hierarchy import linkage
from scipy.cluster.hierarchy import dendrogram
from scipy.spatial.distance import pdist
import matplotlib
from matplotlib import pyplot as plt
import numpy as np
from numpy import arange

# Create the figure and set it's size.
fig = plt.figure(figsize=(5,7))

# Create the first subplot in the figure. 
ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan= 1, colspan=1)

# Set to your favorite colormap.
cm = matplotlib.cm.viridis

# Create array of random numbers.
X = np.random.random([6,300])

# Create a linkage object.
linkmat = linkage(X)

# Make a dendrogram from the linkage object.
dendrogram(linkmat)

# Use the x and y limits to set the aspect.
x0,x1 = ax1.get_xlim()
y0,y1 = ax1.get_ylim()
#ax1.set_aspect((x1-x0)/(y1-y0))


# Remove the ticks on the x-axis. 
plt.tick_params(axis='x', which='both', bottom='off', top='off', labelbottom='off')

# Create the second subplot.
ax2 = plt.subplot2grid((3, 1), (1, 0), rowspan= 2, colspan=1)

labels = ["a", "b", "c", "d", "e"]

plt.xticks(arange(0.5, 7.5, 1))

plt.gca().set_xticklabels(labels)

plt.pcolor(X.T)

x0,x1 = ax2.get_xlim()
y0,y1 = ax2.get_ylim()

#ax2.set_aspect((x1-x0)/(y1-y0))

# Insert the color scale
plt.colorbar()
cb = plt.colorbar(ax=ax1)
cb.ax.set_visible(False)

# Make the vertical distance between plots equal to zero 
plt.subplots_adjust(hspace=0)

# Show the plot
plt.show()