如何在 python 中为一系列绘图创建标准颜色条

问题描述

我使用 matplotlib 在 python 中绘制一些数据，并且这些图需要一个标准的颜色条.数据由一系列包含频率信息的 NxM 矩阵组成，因此简单的 imshow() 图给出了带有颜色描述频率的 2D 直方图.每个矩阵包含不同但重叠范围的数据.Imshow 将每个矩阵中的数据归一化到 0-1 范围内，这意味着，例如，矩阵 A 的图与矩阵 2*A 的图看起来相同(尽管颜色条将显示两倍的值).例如，我想要的是红色对应于所有图中的相同频率.换句话说，一个单一的颜色条就足以满足所有的情节.任何建议将不胜感激.

I using matplotlib to plot some data in python and the plots require a standard colour bar. The data consists of a series of NxM matrices containing frequency information so that a simple imshow() plot gives a 2D histogram with colour describing frequency. Each matrix contains data in different, but overlapping ranges. Imshow normalizes the data in each matrix to the range 0-1 which means that, for example, the plot of matrix A, will appear identical to the plot of the matrix 2*A (though the colour bar will show double the values). What I would like is for the colour red, for example, to correspond to the same frequency in all of the plots. In other words, a single colour bar would suffice for all the plots. Any suggestions would be greatly appreciated.

推荐答案

不是窃取@ianilis 的答案，但我想添加一个示例...

Not to steal @ianilis's answer, but I wanted to add an example...

有多种方法，但最简单的就是将vmin 和vmax kwargs 指定为imshow.或者，您可以创建一个 matplotlib.cm.Colormap 实例并指定它，但这对于简单情况来说比所需的要复杂.

There are multiple ways, but the simplest is just to specify the vmin and vmax kwargs to imshow. Alternately, you can make a matplotlib.cm.Colormap instance and specify it, but that's more complicated than necessary for simple cases.

这里有一个简单的例子，所有图片都有一个颜色条:

Here's a quick example with a single colorbar for all images:

import numpy as np
import matplotlib.pyplot as plt

# Generate some data that where each slice has a different range
# (The overall range is from 0 to 2)
data = np.random.random((4,10,10))
data *= np.array([0.5, 1.0, 1.5, 2.0])[:,None,None]

# Plot each slice as an independent subplot
fig, axes = plt.subplots(nrows=2, ncols=2)
for dat, ax in zip(data, axes.flat):
    # The vmin and vmax arguments specify the color limits
    im = ax.imshow(dat, vmin=0, vmax=2)

# Make an axis for the colorbar on the right side
cax = fig.add_axes([0.9, 0.1, 0.03, 0.8])
fig.colorbar(im, cax=cax)

plt.show()

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