标签归档：scale

知识问答

“ log”和“ symlog”有什么区别？

问题：“ log”和“ symlog”有什么区别？

在matplotlib中，我可以使用pyplot.xscale()或设置轴缩放Axes.set_xscale()。这两个函数接受三个不同的尺度：'linear'| 'log'| 'symlog'。

'log'和之间有什么区别'symlog'？在我做的一个简单测试中，它们看起来完全一样。

我知道文档说它们接受不同的参数，但是我仍然不了解它们之间的区别。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：“符号”的名称从何而来？）

点击查看英文原文

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'.

What is the difference between 'log' and 'symlog'? In a simple test I did, they both looked exactly the same.

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?)

回答 0

我终于找到了一些时间来做一些实验，以了解它们之间的区别。这是我发现的：

  • log仅允许使用正值，并允许您选择如何处理负值（mask或clip）。
  • symlog表示对数对称，并允许正值和负值。
  • symlog 允许在绘图内将范围设置为零左右，而不是对数，而是线性的。

我认为通过图形和示例，一切都将变得更容易理解，因此让我们尝试一下：

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)

为了完整起见，我使用以下代码保存每个图：

# Default dpi is 80
pyplot.savefig('matplotlib_xscale_linear.png', dpi=50, bbox_inches='tight')

请记住，您可以使用以下方法更改图形尺寸：

fig = pyplot.gcf()
fig.set_size_inches([4., 3.])
# Default size: [8., 6.]

（如果您不知道我的回答我的问题，请阅读本）

点击查看英文原文

I finally found some time to do some experiments in order to understand the difference between them. Here’s what I discovered:

  • log only allows positive values, and lets you choose how to handle negative ones (mask or clip).
  • symlog means symmetrical log, and allows positive and negative values.
  • symlog allows to set a range around zero within the plot will be linear instead of logarithmic.

I think everything will get a lot easier to understand with graphics and examples, so let’s try them:

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:

# Default dpi is 80
pyplot.savefig('matplotlib_xscale_linear.png', dpi=50, bbox_inches='tight')

Remember you can change the figure size using:

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)

回答 1

symlog类似于log，但是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，以避免使绘图在零附近变为无穷大。

来自http://matplotlib.sourceforge.net/api/axes_api.html#matplotlib.axes.Axes.set_xscale

在对数图中，永远不会有零值，并且如果您的值接近零，它将从图的底部向下（无限向下）尖峰，因为当您采用“ log（逼近零）”时，得到“接近负无穷大”。

symlog将在需要创建对数图的情况下为您提供帮助，但是当值有时可能会下降到零或下降到零时，但是您仍然希望能够以有意义的方式在图上显示该值。如果您需要符号记录，就可以知道。

点击查看英文原文

symlog is 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.

From 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”.

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.

回答 2

这是必须使用符号日志时的行为示例：

初始图，未缩放。注意多少点聚集在x〜0

    ax = sns.scatterplot(x= 'Score', y ='Total Amount Deposited', data = df, hue = 'Predicted Category')

[ ‘

对数比例图。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')

‘

为什么会崩溃？由于x轴上的某些值非常接近或等于0。

符号比例图。一切都是应有的。

    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')

点击查看英文原文

Here’s an example of behaviour when symlog is necessary:

Initial plot, not scaled. Notice how many dots cluster at x~0

    ax = sns.scatterplot(x= 'Score', y ='Total Amount Deposited', data = df, hue = 'Predicted Category')

[ ‘

Log scaled plot. Everything collapsed.

    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.

Symlog scaled plot. Everything is as it should be.

    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')

知识问答

在python中使用matplotlib绘制对数轴

问题：在python中使用matplotlib绘制对数轴

我想使用matplotlib绘制一个对数轴的图形。

我一直在阅读文档，但无法弄清楚语法。我知道这可能'scale=linear'与plot参数类似，但是我似乎无法正确理解

示例程序： 

import pylab
import matplotlib.pyplot as plt
a = [pow(10, i) for i in range(10)]
fig = plt.figure()
ax = fig.add_subplot(2, 1, 1)

line, = ax.plot(a, color='blue', lw=2)
pylab.show()
点击查看英文原文

I want to plot a graph with one logarithmic axis using matplotlib.

I’ve been reading the docs, but can’t figure out the syntax. I know that it’s probably something simple like 'scale=linear' in the plot arguments, but I can’t seem to get it right

Sample program: 

import pylab
import matplotlib.pyplot as plt
a = [pow(10, i) for i in range(10)]
fig = plt.figure()
ax = fig.add_subplot(2, 1, 1)

line, = ax.plot(a, color='blue', lw=2)
pylab.show()

回答 0

您可以使用该Axes.set_yscale方法。这样，您可以在Axes创建对象后更改比例。这也将允许您构建一个控件，让用户根据需要选择比例。

要添加的相关行是：

ax.set_yscale('log')

您可以使用'linear'切换回线性刻度。您的代码如下所示：

import pylab
import matplotlib.pyplot as plt
a = [pow(10, i) for i in range(10)]
fig = plt.figure()
ax = fig.add_subplot(2, 1, 1)

line, = ax.plot(a, color='blue', lw=2)

ax.set_yscale('log')

pylab.show()

点击查看英文原文

You can use the Axes.set_yscale method. That allows you to change the scale after the Axes object is created. That would also allow you to build a control to let the user pick the scale if you needed to.

The relevant line to add is:

ax.set_yscale('log')

You can use 'linear' to switch back to a linear scale. Here’s what your code would look like:

import pylab
import matplotlib.pyplot as plt
a = [pow(10, i) for i in range(10)]
fig = plt.figure()
ax = fig.add_subplot(2, 1, 1)

line, = ax.plot(a, color='blue', lw=2)

ax.set_yscale('log')

pylab.show()

回答 1

首先，混合pylab和pyplot编码不是很整洁。而且，pyplot样式比使用pylab更为可取。

这是一个仅使用pyplot函数的稍作清理的代码：

from matplotlib import pyplot

a = [ pow(10,i) for i in range(10) ]

pyplot.subplot(2,1,1)
pyplot.plot(a, color='blue', lw=2)
pyplot.yscale('log')
pyplot.show()

相关功能是pyplot.yscale()。如果使用面向对象的版本，请用方法替换它Axes.set_yscale()。请记住，您还可以使用pyplot.xscale()（或Axes.set_xscale()）更改X轴的比例。

检查我的问题‘log’和’symlog’有什么区别？查看matplotlib提供的图形比例的一些示例。

点击查看英文原文

First of all, it’s not very tidy to mix pylab and pyplot code. What’s more, pyplot style is preferred over using pylab.

Here is a slightly cleaned up code, using only pyplot functions:

from matplotlib import pyplot

a = [ pow(10,i) for i in range(10) ]

pyplot.subplot(2,1,1)
pyplot.plot(a, color='blue', lw=2)
pyplot.yscale('log')
pyplot.show()

The relevant function is pyplot.yscale(). If you use the object-oriented version, replace it by the method Axes.set_yscale(). Remember that you can also change the scale of X axis, using pyplot.xscale() (or Axes.set_xscale()).

Check my question What is the difference between ‘log’ and ‘symlog’? to see a few examples of the graph scales that matplotlib offers.

回答 2

您只需要使用符号学而不是情节：

from pylab import *
import matplotlib.pyplot  as pyplot
a = [ pow(10,i) for i in range(10) ]
fig = pyplot.figure()
ax = fig.add_subplot(2,1,1)

line, = ax.semilogy(a, color='blue', lw=2)
show()
点击查看英文原文

You simply need to use semilogy instead of plot:

from pylab import *
import matplotlib.pyplot  as pyplot
a = [ pow(10,i) for i in range(10) ]
fig = pyplot.figure()
ax = fig.add_subplot(2,1,1)

line, = ax.semilogy(a, color='blue', lw=2)
show()

回答 3

如果要更改对数的底数，只需添加：

plt.yscale('log',basey=2) 
# where basex or basey are the bases of log
点击查看英文原文

if you want to change the base of logarithm, just add:

plt.yscale('log',basey=2) 
# where basex or basey are the bases of log

回答 4

我知道这有点不合时宜，因为一些评论提到这ax.set_yscale('log')是“最好的”解决方案，我认为可能是反驳。我不建议将其ax.set_yscale('log')用于直方图和条形图。在我的版本（0.99.1.1）中，我遇到了一些渲染问题-不确定此问题的普遍性。但是，bar和hist都具有可选参数，可以将y比例设置为log，这很好用。

参考：http : //matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.bar

http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.hist

点击查看英文原文

I know this is slightly off-topic, since some comments mentioned the ax.set_yscale('log') to be “nicest” solution I thought a rebuttal could be due. I would not recommend using ax.set_yscale('log') for histograms and bar plots. In my version (0.99.1.1) i run into some rendering problems – not sure how general this issue is. However both bar and hist has optional arguments to set the y-scale to log, which work fine.

references: http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.bar

http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.hist

回答 5

因此，如果您只是像我经常那样使用简单的API（我在ipython中经常使用它），那么这很简单

yscale('log')
plot(...)

希望这可以帮助寻找简单答案的人！:)。

点击查看英文原文

So if you are simply using the unsophisticated API, like I often am (I use it in ipython a lot), then this is simply

yscale('log')
plot(...)

Hope this helps someone looking for a simple answer! :).

回答 6

您可以使用以下代码：

np.log(df['col_whose_log_you_need']).iplot(kind='histogram', bins=100,
                                   xTitle = 'log of col',yTitle ='Count corresponding to column',
                                   title='Distribution of log(col_whose_log_you_need)')
点击查看英文原文

You can use below code:

np.log(df['col_whose_log_you_need']).iplot(kind='histogram', bins=100,
                                   xTitle = 'log of col',yTitle ='Count corresponding to column',
                                   title='Distribution of log(col_whose_log_you_need)')