如何在matplotlib中的颜色栏上可视化字符串列表 [英] How to visualize a list of strings on a colorbar in matplotlib

问题描述

我有一个数据集

  x = 3,4,6,77,3y = 8,5,2,5,5标签=空"，退出"，电源"，微笑"，空" 

然后我用

从matplotlib中的

 导入pyplot作为pltplt.scatter(x，y)colorbar = plt.colorbar(标签)plt.show() 

进行散点图绘制，但不能使颜色条显示标签作为其颜色.

如何获得此?

解决方案

我不确定，对于一般 scatter 散点图，这样做是否是个好主意(对于以下内容，您具有相同的描述:不同的数据点，也许只是在这里使用一些图例?)，但是我想针对您所想的具体解决方案可能是:

来自matplotlib的

 从pyplot导入为plt# 数据x = [3，4，6，77，3]y = [8、5、2、5、5]标签=('null'，'exit'，'power'，'smile'，'null')#自定义颜色图和散点图cm = plt.cm.get_cmap('hsv')sc = plt.scatter(x，y，c = range(5)，cmap = cm)cbar = plt.colorbar(sc，ticks = range(5))cbar.ax.set_yticklabels(标签)plt.show() 

这将导致这样的输出:

代码结合了

(仍然)无法找到每个颜色段的正确中间点，但是我将把这种优化留给您.

I have a dataset like

x = 3,4,6,77,3
y = 8,5,2,5,5
labels = "null","exit","power","smile","null"

Then I use

from matplotlib import pyplot as plt
plt.scatter(x,y)
colorbar = plt.colorbar(labels)
plt.show()

to make a scatter plot, but cannot make colorbar showing labels as its colors.

How to get this?

解决方案

I'm not sure, if it's a good idea to do that for scatter plots in general (you have the same description for different data points, maybe just use some legend here?), but I guess a specific solution to what you have in mind, might be the following:

from matplotlib import pyplot as plt

# Data
x = [3, 4, 6, 77, 3]
y = [8, 5, 2, 5, 5]
labels = ('null', 'exit', 'power', 'smile', 'null')

# Customize colormap and scatter plot
cm = plt.cm.get_cmap('hsv')
sc = plt.scatter(x, y, c=range(5), cmap=cm)
cbar = plt.colorbar(sc, ticks=range(5))
cbar.ax.set_yticklabels(labels)
plt.show()

This will result in such an output:

The code combines this Matplotlib demo and this SO answer.

Hope that helps!

EDIT: Incorporating the comments, I can only think of some kind of label color dictionary, generating a custom colormap from the colors, and before plotting explicitly grabbing the proper color indices from the labels.

Here's the updated code (I added some additional colors and data points to check scalability):

from matplotlib import pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
import numpy as np

# Color information; create custom colormap
label_color_dict = {'null': '#FF0000',
                    'exit': '#00FF00',
                    'power': '#0000FF',
                    'smile': '#FF00FF',
                    'addon': '#AAAAAA',
                    'addon2': '#444444'}
all_labels = list(label_color_dict.keys())
all_colors = list(label_color_dict.values())
n_colors = len(all_colors)
cm = LinearSegmentedColormap.from_list('custom_colormap', all_colors, N=n_colors)

# Data
x = [3, 4, 6, 77, 3, 10, 40]
y = [8, 5, 2, 5, 5, 4, 7]
labels = ('null', 'exit', 'power', 'smile', 'null', 'addon', 'addon2')

# Get indices from color list for given labels
color_idx = [all_colors.index(label_color_dict[label]) for label in labels]

# Customize colorbar and plot
sc = plt.scatter(x, y, c=color_idx, cmap=cm)
c_ticks = np.arange(n_colors) * (n_colors / (n_colors + 1)) + (2 / n_colors)
cbar = plt.colorbar(sc, ticks=c_ticks)
cbar.ax.set_yticklabels(all_labels)
plt.show()

And, the new output:

Finding the correct middle point of each color segment is (still) not good, but I'll leave this optimization to you.

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