Matplotlib set_color_cycle与set_prop_cycle [英] Matplotlib set_color_cycle versus set_prop_cycle

问题描述

在Matplotlib中我最喜欢做的一件事情是设置颜色循环以匹配某些颜色图，以便生成线图，这些线图在各行中具有很好的颜色级数.像这样一个:

One of my favorite things to do in Matplotlib is to set the color-cycle to match some colormap, in order to produce line-plots that have a nice progression of colors across the lines. Like this one:

以前，这是使用set_color_cycle的一行代码:

Previously, this was one line of code using set_color_cycle:

ax.set_color_cycle([plt.cm.spectral(i) for i in np.linspace(0, 1, num_lines)])

但是，最近我看到一个警告:

But, recently I see a warning:

MatplotlibDeprecationWarning: 
The set_color_cycle attribute was deprecated in version 1.5. 
Use set_prop_cycle instead.

使用set_prop_cycle，可以获得相同的结果，但是我需要import cycler，语法不太紧凑:

Using set_prop_cycle, I can achieve the same result, but I need to import cycler, and the syntax is less compact:

from cycler import cycler
colors = [plt.cm.spectral(i) for i in np.linspace(0, 1, num_lines)]
ax.set_prop_cycle(cycler('color', colors))

所以，我的问题是:

我正确使用set_prop_cycle吗? (以最有效的方式?)

Am I using set_prop_cycle correctly? (and in the most efficient way?)

是否有更简单的方法将颜色循环设置为颜色图?换句话说，是否有一些神话般的功能?

Is there an easier way to set the color-cycle to a colormap? In other words, is there some mythical function like this?

ax.set_colorcycle_to_colormap('jet', nlines=30)

以下是完整示例的代码:

Here is the code for the complete example:

import numpy as np
import matplotlib.pyplot as plt

ax = plt.subplot(111)
num_lines = 30

colors = [plt.cm.spectral(i) for i in np.linspace(0, 1, num_lines)]

# old way: 
ax.set_color_cycle(colors)

# new way:
from cycler import cycler
ax.set_prop_cycle(cycler('color', colors))

for n in range(num_lines):
    x = np.linspace(0,10,500)
    y = np.sin(x)+n
    ax.plot(x, y, lw=3)

plt.show()

推荐答案

由于新的属性循环程序可以迭代除颜色(例如，线型)以外的其他属性，因此需要指定label，即要循环的属性.

Because the new property cycler can iterate over other properties than just color (e.g. linestyle) you need to specify the label, i.e. the property over which to cycle.

ax.set_prop_cycle('color', colors)

尽管没有必要导入和创建循环程序；因此，正如我所看到的那样，新方法的唯一缺点是它使调用时间延长了8个字符.

There is no need to import and create a cycler though; so as I see it the only drawback of the new method it that it makes the call 8 characters longer.

没有一种神奇的方法将色图作为输入并创建循环仪，但是您也可以通过直接将numpy数组提供给色图来缩短颜色列表的创建.

There is no magical method that takes a colormap as input and creates the cycler, but you can also shorten your color list creation by directly supplying the numpy array to the colormap.

colors = plt.cm.Spectral(np.linspace(0,1,30))

或结合使用

ax.set_prop_cycle('color',plt.cm.Spectral(np.linspace(0,1,30)))

这篇关于Matplotlib set_color_cycle与set_prop_cycle的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！