Python “日志”和“符号日志”有什么区别？
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What is the difference between 'log' and 'symlog'?
提问by Denilson Sá Maia
In matplotlib, I can set the axis scaling using either pyplot.xscale()or Axes.set_xscale(). Both functions accept three different scales: 'linear'| 'log'| 'symlog'.
在matplotlib 中，我可以使用pyplot.xscale()或设置轴缩放Axes.set_xscale()。这两个函数都接受三种不同的尺度：'linear'| 'log'| 'symlog'.
What is the difference between 'log'and 'symlog'? In a simple test I did, they both looked exactly the same.
'log'和 和有'symlog'什么区别？在我做的一个简单测试中，它们看起来完全一样。
I know the documentation says they accept different parameters, but I still don't understand the difference between them. Can someone please explain it? The answer will be the best if it has some sample code and graphics! (also: where does the name 'symlog' come from?)
我知道文档说他们接受不同的参数，但我仍然不明白它们之间的区别。有人可以解释一下吗？如果有一些示例代码和图形，答案将是最好的！（另外：“symlog”这个名字从何而来？）
采纳答案by Denilson Sá Maia
I finally found some time to do some experiments in order to understand the difference between them. Here's what I discovered:
我终于找到了一些时间做一些实验，以了解它们之间的区别。这是我发现的：
logonly allows positive values, and lets you choose how to handle negative ones (maskorclip).symlogmeans symmetrical log, and allows positive and negative values.symlogallows to set a range around zero within the plot will be linear instead of logarithmic.
log只允许正值，并让您选择如何处理负值（mask或clip）。symlog表示对称 log，并允许正值和负值。symlog允许在图中设置零附近的范围将是线性的而不是对数的。
I think everything will get a lot easier to understand with graphics and examples, so let's try them:
我认为通过图形和示例，一切都会变得更容易理解，所以让我们尝试一下：
import numpy
from matplotlib import pyplot
# Enable interactive mode
pyplot.ion()
# Draw the grid lines
pyplot.grid(True)
# Numbers from -50 to 50, with 0.1 as step
xdomain = numpy.arange(-50,50, 0.1)
# Plots a simple linear function 'f(x) = x'
pyplot.plot(xdomain, xdomain)
# Plots 'sin(x)'
pyplot.plot(xdomain, numpy.sin(xdomain))
# 'linear' is the default mode, so this next line is redundant:
pyplot.xscale('linear')
# How to treat negative values?
# 'mask' will treat negative values as invalid
# 'mask' is the default, so the next two lines are equivalent
pyplot.xscale('log')
pyplot.xscale('log', nonposx='mask')
# 'clip' will map all negative values a very small positive one
pyplot.xscale('log', nonposx='clip')
# 'symlog' scaling, however, handles negative values nicely
pyplot.xscale('symlog')
# And you can even set a linear range around zero
pyplot.xscale('symlog', linthreshx=20)
Just for completeness, I've used the following code to save each figure:
为了完整起见，我使用以下代码来保存每个数字：
# Default dpi is 80
pyplot.savefig('matplotlib_xscale_linear.png', dpi=50, bbox_inches='tight')
Remember you can change the figure size using:
请记住，您可以使用以下方法更改图形大小：
fig = pyplot.gcf()
fig.set_size_inches([4., 3.])
# Default size: [8., 6.]
(If you are unsure about me answering my own question, read this)
（如果您不确定我是否会回答我自己的问题，请阅读此内容）
回答by thomasrutter
symlogis like log but allows you to define a range of values near zero within which the plot is linear, to avoid having the plot go to infinity around zero.
symlog类似于 log，但允许您定义一个接近零的值范围，在该范围内绘图是线性的，以避免绘图在零附近趋于无穷大。
From http://matplotlib.sourceforge.net/api/axes_api.html#matplotlib.axes.Axes.set_xscale
来自http://matplotlib.sourceforge.net/api/axes_api.html#matplotlib.axes.Axes.set_xscale
In a log graph, you can never have a zero value, and if you have a value that approaches zero, it will spike down way off the bottom off your graph (infinitely downward) because when you take "log(approaching zero)" you get "approaching negative infinity".
在对数图中，你永远不可能有一个零值，如果你有一个接近零的值，它会从你的图底部向下（无限向下）飙升，因为当你取“对数（接近零）”时，你得到“接近负无穷大”。
symlog would help you out in situations where you want to have a log graph, but when the value may sometimes go down towards, or to, zero, but you still want to be able to show that on the graph in a meaningful way. If you need symlog, you'd know.
symlog 可以在您想要获得对数图的情况下帮助您，但是当值有时可能会下降到或为零时，但您仍然希望能够以有意义的方式在图表上显示它。如果你需要 symlog，你就会知道。
回答by Gigikalo
Here's an example of behaviour when symlog is necessary:
以下是需要 symlog 时的行为示例：
Initial plot, not scaled. Notice how many dots cluster at x~0
初始图，未缩放。注意 x~0 处有多少点聚集
ax = sns.scatterplot(x= 'Score', y ='Total Amount Deposited', data = df, hue = 'Predicted Category')
['
[ '
Log scaled plot. Everything collapsed.
对数缩放图。一切都崩溃了。
ax = sns.scatterplot(x= 'Score', y ='Total Amount Deposited', data = df, hue = 'Predicted Category')
ax.set_xscale('log')
ax.set_yscale('log')
ax.set(xlabel='Score, log', ylabel='Total Amount Deposited, log')
'
'
Why did it collapse? Because of some values on the x-axis being very close or equal to 0.
为什么会崩溃？因为 x 轴上的某些值非常接近或等于 0。
Symlog scaled plot. Everything is as it should be.
Symlog 缩放图。一切都是应该的。
ax = sns.scatterplot(x= 'Score', y ='Total Amount Deposited', data = df, hue = 'Predicted Category')
ax.set_xscale('symlog')
ax.set_yscale('symlog')
ax.set(xlabel='Score, symlog', ylabel='Total Amount Deposited, symlog')
