使matplotlib颜色条在数据限制之外打勾以与boundary关键字一起使用 [英] getting a matplotlib colorbar tick outside data limits for use with boundaries keyword

问题描述

我正在尝试使用颜色栏标记使用imshow绘制的离散编码值.我可以使用 boundaries 和 values 关键字实现所需的颜色条，这实际上使颜色条的最大值比要绘制的数据的最大值大1.

I am trying to use a colorbar to label discrete, coded values plotted using imshow. I can achieve the colorbar that I want using the boundaries and values keywords, which makes the maximum value of the colorbar effectively 1 greater than the maximum value of the data being plotted.

现在，我希望刻度线位于颜色栏中每个颜色范围的中间，但是无法为颜色栏中的最大颜色块指定刻度位置，这似乎是因为它超出了数据值限制.

Now I want ticks to be in the middle of each color range in the colorbar, but cannot specify a tick position for the largest color block in the colorbar, seemingly because it is outside of the data value limits.

以下是演示该问题的快速代码块:

Here's a quick block of code to demonstrate the problem:

data = np.tile(np.arange(4), 2)
fig = plt.figure()
ax = fig.add_subplot(121)
ax.imshow(data[None], aspect='auto')
cax = fig.add_subplot(122)
cbar = fig.colorbar(ax.images[0], cax=cax, boundaries=[0,1,2,3,4], values=[0,1,2,3])
cbar.set_ticks([.5, 1.5, 2.5, 3.5])
cbar.set_ticklabels(['one', 'two', 'three', 'four'])

请注意缺失的刻度线应位于四个"的位置.什么是正确的方法?

Note the missing tick where 'four' should be. What's the right way to do this?

推荐答案

总而言之，这对我有用:

To summarize, this works for me:

import numpy as np
from matplotlib import pyplot as plt
from matplotlib import cm
from matplotlib import colors

data = np.tile(np.arange(4), 2)
fig = plt.figure()
ax = fig.add_subplot(121)
cmap = cm.get_cmap('jet', 4)
bounds = np.arange(5)
vals = bounds[:-1]
norm = colors.BoundaryNorm(bounds, cmap.N)
ax.imshow(data[None], aspect='auto', interpolation='nearest', cmap=cmap, norm=norm)

cax = fig.add_subplot(122)
cbar = fig.colorbar(ax.images[0], cax=cax, boundaries=bounds, values=vals)
cbar.set_ticks(vals + .5)
cbar.set_ticklabels(['one', 'two', 'three', 'four'])

解决方案是使用 get_cmap 为图像显式指定颜色图，并以 BoundaryNorm 为边界.然后指定刻度位置就可以了.结果图为:

The solution was to specify the colormap explicitly for the image using get_cmap and bounded by BoundaryNorm. Then specifying the tick positions just works. The resulting plot is:

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