使用分组颜色作为标签的matplotlib相关矩阵热图 [英] matplotlib correlation matrix heatmap with grouped colors as labels

问题描述

我有一个相关矩阵帽子，我正在尝试使用matplotlib进行可视化.我可以创建一个热图样式图，但是我在获取标签方面遇到了问题.我什至不确定这是否可行，但这是我正在尝试做的事情，似乎无法使它起作用:

I have a correlation matrix hat I am trying to visualize with matplotlib. I can create a heatmap style figure just fine, but I am running into problems with how I want the labels. I'm not even sure if this is possible, but this is what I'm trying to do and can't seem to make it work:

我的相关矩阵是150 X150.在x轴或y轴(或两者都无所谓)上，我想对标签进行分组，然后简单地用颜色或白色对它们进行标签在彩色背景上的标签.

My correlation matrix is 150 X 150. On either the x or y (or both...this doesn't matter) axis, I would like to group the labels and then simply label them with a color, or a white label on a color background.

为澄清起见，假设我想将1-15作为第1组"，或者只是一个蓝色条，或者是一个蓝色条上的第1组"文本.然后将16-20作为第2组"显示在红色栏上，或简单地用红色栏表示.等等，通过矩阵中的所有项目.

To clarify, let's say I'd like to have 1-15 as "Group 1" and either simply be a Blue bar, or "Group 1" text on a blue bar. Then 16-20 as "Group 2" on a red bar, or simply a red bar. Etc, through all of the items in the matrix.

在对轴标签进行分组以及在其上获得任何颜色时，我一直都失败了.任何帮助将不胜感激.我的代码在下面，尽管它是非常基本的，我不知道它是否会有所帮助.

I have been failing at both grouping axis labels as well as getting any color on them. Any help would be greatly appreciated. My code is below, though it's quite basic and I don't know if it will help.

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

# COREELATION MATRIX TEST #
corr = np.genfromtxt(csv_path,delimiter=',')
fig = plt.figure()
ax1 = fig.add_subplot(111)
cmap = cm.get_cmap('jet', 30)
cax = ax1.imshow(corr, cmap=cmap)
ax1.grid(True)
plt.title('THIS IS MY TITLE')
fig.colorbar(cax, ticks=[-1,-0.8,-0.6,-0.4,-0.2,0.0,0.2,0.4,0.6,0.8,1.0])
plt.show()

推荐答案

您可以在绘图旁创建辅助轴，并为其绘制彩色条形图.关闭轴的棘刺可使这些条看起来像标签盒.

You may create auxilary axes next to the plot and plot colored bar plots to them. Turning the axes spines off lets those bars look like labelboxes.

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable

# COREELATION MATRIX TEST #
corr = 2*np.random.rand(150,150)-1
# labels [start,end]
labels = np.array([[0,15],[16,36],[37,82],[83,111],[112,149]])
colors = ["crimson", "limegreen","gold","orchid","turquoise"]

fig, ax = plt.subplots()

im = ax.imshow(corr, cmap="Blues")

ax.set_title('THIS IS MY TITLE')
fig.colorbar(im, ticks=[-1,-0.8,-0.6,-0.4,-0.2,0.0,0.2,0.4,0.6,0.8,1.0])

# create axes next to plot
divider = make_axes_locatable(ax)
axb = divider.append_axes("bottom", "10%", pad=0.06, sharex=ax)
axl = divider.append_axes("left", "10%", pad=0.06, sharey=ax)
axb.invert_yaxis()
axl.invert_xaxis()
axb.axis("off")
axl.axis("off")


# plot colored bar plots to the axes
barkw = dict( color=colors, linewidth=0.72, ec="k", clip_on=False, align='edge',)
axb.bar(labels[:,0],np.ones(len(labels)), 
        width=np.diff(labels, axis=1).flatten(), **barkw)
axl.barh(labels[:,0],np.ones(len(labels)), 
         height=np.diff(labels, axis=1).flatten(), **barkw)

# set margins to zero again
ax.margins(0)
ax.tick_params(axis="both", bottom=0, left=0, labelbottom=0,labelleft=0)
# Label the boxes
textkw = dict(ha="center", va="center", fontsize="small")
for k,l in labels:
    axb.text((k+l)/2.,0.5, "{}-{}".format(k,l), **textkw)
    axl.text(0.5,(k+l)/2., "{}-{}".format(k,l), rotation=-90,**textkw)

plt.show()

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