Python中的单行(或列)热图 [英] Single row (or column) heat map in python

问题描述

我可以使用以下代码创建n个n热图，例如，让n为10:

I can create and n by n heatmap using the following code, for example let n be 10:

random_matrix = np.random.rand(10,10)
number = 10
incrmnt = 1.0
x = list(range(1,number +1))
plt.pcolormesh(x, x, random_matrix)
plt.colorbar() 
plt.xlim(1, number)
plt.xlabel('Number 1')
plt.ylim(1, number)
plt.ylabel('Number 2')
plt.tick_params(
    axis = 'both',
    which = 'both',
    bottom = 'off',
    top = 'off', 
    labelbottom = 'off', 
    right = 'off',
    left = 'off',
    labelleft = 'off')

我想在x和y轴的每一个附近添加一个两行的热图，比如说row1 = np.random.rand(1,10)和col1 = np.random.rand(1,10). 这是我想产生的示例图像:

I would like to add a 2 row heatmap one near each of the x and y axis, from say row1 = np.random.rand(1,10)and col1 = np.random.rand(1,10). Here is an example image of what I would like to produce:

谢谢.

推荐答案

您将创建一个子图网格，其中子图之间的宽高比对应于相应维度中的像素数.然后，您可以将相应的图添加到这些子图中.在下面的代码中，我使用了imshow图，因为我发现在数组中每一项具有一个像素(而不是减少一个像素)更加直观.

You would create a subplot grid where the width- and height ratios between the subplots correspond to the number of pixels in the respective dimension. You can then add respective plots to those subplots. In the code below I used an imshow plot, because I find it more intuitive to have one pixel per item in the array (instead of one less).

为了使颜色条表示不同子图上的颜色，可以使用提供给每个子图的matplotlib.colors.Normalize实例，以及为颜色条手动创建的ScalarMappable.

In order to have the colorbar represent the colors accross the different subplots, one can use a matplotlib.colors.Normalize instance, which is provided to each of the subplots, as well as the manually created ScalarMappable for the colorbar.

import matplotlib
import matplotlib.pyplot as plt
import numpy as np

m = np.random.rand(10,10)
x = np.random.rand(1,m.shape[1])
y = np.random.rand(m.shape[0],1)

norm = matplotlib.colors.Normalize(vmin=0, vmax=1)
grid = dict(height_ratios=[1, m.shape[0]], width_ratios=[1,m.shape[0], 0.5 ])
fig, axes = plt.subplots(ncols=3, nrows=2, gridspec_kw = grid)

axes[1,1].imshow(m, aspect="auto", cmap="viridis", norm=norm)
axes[0,1].imshow(x, aspect="auto", cmap="viridis", norm=norm)
axes[1,0].imshow(y, aspect="auto", cmap="viridis", norm=norm)

axes[0,0].axis("off")
axes[0,2].axis("off")

axes[1,1].set_xlabel('Number 1')
axes[1,1].set_ylabel('Number 2')
for ax in [axes[1,1], axes[0,1], axes[1,0]]:
    ax.set_xticks([]); ax.set_yticks([])

sm = matplotlib.cm.ScalarMappable(cmap="viridis", norm=norm)
sm.set_array([])

fig.colorbar(sm, cax=axes[1,2]) 

plt.show()

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