如何在没有对数缩放图像的情况下应用对数轴标签(matplotlib imshow)

问题描述

我有一个分布大的对数数据集.我想制作一个热图，所以我做一个2D直方图并将其传递给绘图.因为数据是对数的，所以我将数据的日志传递给直方图.但是，当我绘制图时，我希望恢复轴(即10 ^ hist bin值)和对数轴.如果我将轴设置为对数样式，则图像看起来将全部倾斜.从我将数据传递到直方图以来，数据已经被记录"了，所以我不希望图像受到影响，而只是轴受到影响.因此，在下面的示例中，我希望图像在左侧，轴在右侧.

I have a large data set that is logarithmic in distribution. I want to make a heat map, so I do a 2D histogram and pass that to implot. Because the data is logarithmic, I am passing the log of the data to the histogram. When I make the plot, however, I want the axis to be restored (ie 10^hist bin values) and log axes. If I set the axis to log style, then the image looks all skewed. The data is already 'logged' from when I passed it to the histogram, so I don't want the image affected, just the axis. So, in the below example, I want the image on the left with the axis on the right.

我想我可以用假的重叠轴来做到这一点，但是如果有更好的方法，我不喜欢那样做……

I guess I could do it with a fake overlayed axis, but I don't like to do that sort of thing if there's a better way...

import numpy as np
import matplotlib.pyplot as plt

x=10**np.random.random(10000)*5
y=10**np.random.random(10000)*5

samps, xedges, yedges = np.histogram2d(np.log10(y), np.log10(x),     bins=50)

ax = plt.subplot(121)

plt.imshow(samps, extent=[0,5,0,5])
plt.xlabel('Log10 X')
plt.ylabel('Log10 Y')

ax = plt.subplot(122)
plt.imshow(samps, extent=[10**0,10**5,10**0,10**5])
plt.xlabel('X')
plt.ylabel('Y')
plt.xscale('log')
plt.yscale('log')
plt.show()

推荐答案

您需要使用自定义格式化程序.这是来自matplotlib文档的示例: https://matplotlib.org/examples/pylab_examples/custom_ticker1.html

You need to use a custom formatter. Here's an example from the matplotlib docs:https://matplotlib.org/examples/pylab_examples/custom_ticker1.html

我倾向于使用FuncFormatter作为示例.主要技巧是您的函数需要使用参数x和pos.老实说，我不知道pos是干什么的.也许甚至没有故意，但是您可以使用FuncFormatter作为装饰器，这是我在下面所做的事情:

I tend to use FuncFormatter as the example does. The main trick is that your function need to take to arguments x and pos. I honestly don't know what pos is for. Perhaps no even intentionally, but you can use FuncFormatter as a decorator, which is what I do below:

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt

@plt.FuncFormatter
def fake_log(x, pos):
    'The two args are the value and tick position'
    return r'$10^{%d}$' % (x)

x=10**np.random.random(10000)*5
y=10**np.random.random(10000)*5

samps, xedges, yedges = np.histogram2d(np.log10(y), np.log10(x), bins=50)

fig, (ax1) = plt.subplots()
ax1.imshow(samps, extent=[0, 5, 0, 5])
ax1.xaxis.set_major_formatter(fake_log)
ax1.yaxis.set_major_formatter(fake_log)
ax1.set_xlabel('X')
ax1.set_ylabel('Y')

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