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Merge remote-tracking branch 'LCTT/master'
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@ -0,0 +1,118 @@
|
||||
[#]: collector: (lujun9972)
|
||||
[#]: translator: (chai-yuan)
|
||||
[#]: reviewer: (wxy)
|
||||
[#]: publisher: (wxy)
|
||||
[#]: url: (https://linux.cn/article-11909-1.html)
|
||||
[#]: subject: (Playing Music on your Fedora Terminal with MPD and ncmpcpp)
|
||||
[#]: via: (https://fedoramagazine.org/playing-music-on-your-fedora-terminal-with-mpd-and-ncmpcpp/)
|
||||
[#]: author: (Carmine Zaccagnino https://fedoramagazine.org/author/carzacc/)
|
||||
|
||||
在你的 Fedora 终端上播放音乐
|
||||
======
|
||||
|
||||
![][1]
|
||||
|
||||
MPD（Music Playing Daemon），顾名思义，是一个音乐（Music）播放（Playing）守护进程（Daemon）。它可以播放音乐，并且作为一个守护进程，任何软件都可以与之交互并播放声音，包括一些 CLI 客户端。
|
||||
|
||||
其中一个被称为 `ncmpcpp`，它是对之前 `ncmpc` 工具的改进。名字的变化与编写它们的语言没有太大关系：都是 C++，而之所以被称为 `ncmpcpp`，因为它是 “NCurses Music Playing Client Plus Plus”。 缘故
|
||||
|
||||
### 安装 MPD 和 ncmpcpp
|
||||
|
||||
`ncmpmpcc` 的客户端可以从官方 Fedora 库中通过 `dnf` 命令直接安装。
|
||||
|
||||
```
|
||||
$ sudo dnf install ncmpcpp
|
||||
```
|
||||
|
||||
另一方面，MPD 必须从 RPMFusion free 库安装，你可以通过运行：
|
||||
|
||||
```
|
||||
$ sudo dnf install https://download1.rpmfusion.org/free/fedora/rpmfusion-free-release-$(rpm -E %fedora).noarch.rpm
|
||||
```
|
||||
|
||||
然后你可以运行下面的命令安装它：
|
||||
|
||||
```
|
||||
$ sudo dnf install mpd
|
||||
```
|
||||
|
||||
### 配置并启用 MPD
|
||||
|
||||
设置 MPD 最简单的方法是以普通用户的身份运行它。默认情况是以专用 `mpd` 用户的身份运行它，但这会导致各种权限问题。
|
||||
|
||||
在运行它之前，我们需要创建一个本地配置文件，允许我们作为普通用户运行。
|
||||
|
||||
首先在 `~/.config` 里创建一个名叫 `mpd` 的目录:
|
||||
|
||||
```
|
||||
$ mkdir ~/.config/mpd
|
||||
```
|
||||
|
||||
将配置文件拷贝到此目录下:
|
||||
|
||||
```
|
||||
$ cp /etc/mpd.conf ~/.config/mpd
|
||||
```
|
||||
|
||||
然后用 `vim`、`nano` 或 `gedit` 之类的软件编辑它:
|
||||
|
||||
```
|
||||
$ nano ~/.config/mpd/mpd.conf
|
||||
```
|
||||
|
||||
我建议你通读所有内容，检查是否有任何需要做的事情，但对于大多数设置你都可以删除，只需保留以下内容：
|
||||
|
||||
```
|
||||
db_file "~/.config/mpd/mpd.db"
|
||||
log_file "syslog"
|
||||
```
|
||||
|
||||
现在你可以运行它了：
|
||||
|
||||
```
|
||||
$ mpd
|
||||
```
|
||||
|
||||
没有报错，这将在后台启动 MPD 守护进程。
|
||||
|
||||
### 使用 ncmpcpp
|
||||
|
||||
只需运行：
|
||||
|
||||
```
|
||||
$ ncmpcpp
|
||||
```
|
||||
|
||||
你将在终端中看到一个由 ncurses 所支持的图形用户界面。
|
||||
|
||||
按下 `4` 键，然后就可以看到本地的音乐目录，用方向键进行选择并按下回车进行播放。
|
||||
|
||||
多播放几个歌曲就会创建一个*播放列表*，让你可以使用 `>` 键（不是右箭头, 是右尖括号）移动到下一首，并使用 `<` 返回上一首。`+` 和 `–` 键可以调节音量。`Q` 键可以让你退出 `ncmpcpp` 但不停止播放音乐。你可以按下 `P` 来控制暂停和播放。
|
||||
|
||||
你可以按下 `1` 键来查看当前播放列表（这是默认的视图）。从这个视图中，你可以按 `i` 查看有关当前歌曲的信息（标签）。按 `6` 可更改当前歌曲的标签。
|
||||
|
||||
按 `\` 按钮将在视图顶部添加（或删除）信息面板。在左上角，你可以看到如下的内容：
|
||||
|
||||
```
|
||||
[------]
|
||||
```
|
||||
|
||||
按下 `r`、`z`、`y`、`R`、`x` 将会分别切换到 `repeat`、`random`、`single`、`consume` 和 `crossfade` 等播放模式，并将这个小指示器中的 `–` 字符替换为选定模式。
|
||||
|
||||
按下 `F1` 键将会显示一些帮助文档，包含一系列的键绑定列表，因此无需在此处列出完整列表。所以继续吧！做一个极客，在你的终端上播放音乐！
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://fedoramagazine.org/playing-music-on-your-fedora-terminal-with-mpd-and-ncmpcpp/
|
||||
|
||||
作者：[Carmine Zaccagnino][a]
|
||||
选题：[lujun9972][b]
|
||||
译者：[chai-yuan](https://github.com/chai-yuan)
|
||||
校对：[wxy](https://github.com/wxy)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://fedoramagazine.org/author/carzacc/
|
||||
[b]: https://github.com/lujun9972
|
||||
[1]: https://fedoramagazine.org/wp-content/uploads/2020/02/play_music_mpd-816x346.png
|
||||
[2]: https://rpmfusion.org/Configuration
|
||||
@ -1,5 +1,5 @@
|
||||
[#]: collector: (lujun9972)
|
||||
[#]: translator: ( )
|
||||
[#]: translator: (geekpi)
|
||||
[#]: reviewer: ( )
|
||||
[#]: publisher: ( )
|
||||
[#]: url: ( )
|
||||
|
||||
@ -0,0 +1,49 @@
|
||||
[#]: collector: (lujun9972)
|
||||
[#]: translator: ( )
|
||||
[#]: reviewer: ( )
|
||||
[#]: publisher: ( )
|
||||
[#]: url: ( )
|
||||
[#]: subject: (How Kubernetes Became the Standard for Compute Resources)
|
||||
[#]: via: (https://www.linux.com/articles/how-kubernetes-became-the-standard-for-compute-resources/)
|
||||
[#]: author: (Swapnil Bhartiya https://www.linux.com/author/swapnil/)
|
||||
|
||||
How Kubernetes Became the Standard for Compute Resources
|
||||
======
|
||||
|
||||
<https://www.linux.com/wp-content/uploads/2019/08/elevator-1598431_1920.jpg>
|
||||
|
||||
2019 has been a game-changing year for the cloud-native ecosystem. There were [consolidations][1], acquisitions of powerhouses like Red Hat Docker and Pivotal, and the emergence of players like Rancher Labs and Mirantis.
|
||||
|
||||
“All these consolidation and M&A in this space is an indicator of how fast the market has matured,” said Sheng Liang, co-founder and CEO of Rancher Labs, a company that offers a complete software stack for teams adopting containers.
|
||||
|
||||
Traditionally, emerging technologies like Kubernetes and Docker appeal to tinkerers and mega-scalers such as Facebook and Google. There was very little interest outside of that group. However, both of these technologies experienced massive adoption at the enterprise level. Suddenly, there was a massive market with huge opportunities. Almost everyone jumped in. There were players who were bringing innovative solutions and then there were players who were trying to catch up with the rest. It became very crowded very quickly.
|
||||
|
||||
It also changed the way innovation was happening. [Early adopters were usually tech-savvy companies.][2] Now, almost everyone is using it, even in areas that were not considered turf for Kubernetes. It changed the market dynamics as companies like Rancher Labs were witnessing unique use cases.
|
||||
|
||||
Liang adds, “I’ve never been in a market or technology evolution that’s happened as quickly and as dynamically as Kubernetes. When we started some five years ago, it was a very crowded space. Over time, most of our peers disappeared for one reason or the other. Either they weren’t able to adjust to the change or they chose not to adjust to some of the changes.”
|
||||
|
||||
In the early days of Kubernetes, the most obvious opportunity was to build Kubernetes distro and Kubernetes operations. It’s new technology. It’s known to be reasonably complex to install, upgrade, and operate.
|
||||
|
||||
It all changed when Google, AWS, and Microsoft entered the market. At that point, there was a stampede of vendors rushing in to provide solutions for the platform. “As soon as cloud providers like Google decided to make Kubernetes as a service and offered it for free as loss-leader to drive infrastructure consumption, we knew that the business of actually operating and supporting Kubernetes, the upside of that would be very limited,” said Liang.
|
||||
|
||||
Not everything was bad for non-Google players. Since cloud vendors removed all the complexity that came with Kubernetes by offering it as a service, it meant wider adoption of the technology, even by those who refrained from using it due to the overhead of operating it. It meant that Kubernetes would become ubiquitous and would become an industry standard.
|
||||
|
||||
“Rancher Labs was one of the very few companies that saw this as an opportunity and looked one step further than everyone else. We realized that Kubernetes was going to become the new computing standard, just the way TCP/IP became the networking standard,” said Liang.
|
||||
|
||||
CNCF plays a critical role in building a vibrant ecosystem around Kubernetes, creating a massive community to build, nurture and commercialize cloud-native open source technologies.
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://www.linux.com/articles/how-kubernetes-became-the-standard-for-compute-resources/
|
||||
|
||||
作者：[Swapnil Bhartiya][a]
|
||||
选题：[lujun9972][b]
|
||||
译者：[译者ID](https://github.com/译者ID)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://www.linux.com/author/swapnil/
|
||||
[b]: https://github.com/lujun9972
|
||||
[1]: https://www.cloudfoundry.org/blog/2019-is-the-year-of-consolidation-why-ibms-deal-with-red-hat-is-a-harbinger-of-things-to-come/
|
||||
[2]: https://www.packet.com/blog/open-source-season-on-the-kubernetes-highway/
|
||||
110
sources/tech/20200220 Tools for SSH key management.md
Normal file
110
sources/tech/20200220 Tools for SSH key management.md
Normal file
@ -0,0 +1,110 @@
|
||||
[#]: collector: (lujun9972)
|
||||
[#]: translator: ( )
|
||||
[#]: reviewer: ( )
|
||||
[#]: publisher: ( )
|
||||
[#]: url: ( )
|
||||
[#]: subject: (Tools for SSH key management)
|
||||
[#]: via: (https://opensource.com/article/20/2/ssh-tools)
|
||||
[#]: author: (Ben Nuttall https://opensource.com/users/bennuttall)
|
||||
|
||||
Tools for SSH key management
|
||||
======
|
||||
Time-saving shortcuts for a commonly used open source tool.
|
||||
![collection of hardware on blue backround][1]
|
||||
|
||||
I use SSH constantly. Every day I find myself logged in to multiple servers and Pis (both in the same room as me and over the internet). I have many devices I need access to, and different requirements for gaining access, so in addition to using various SSH/SCP command options, I have to maintain a config file with all the connection details.
|
||||
|
||||
Over time I’ve come up with a few time-saving tips and tools that you might find useful, too.
|
||||
|
||||
### SSH keys
|
||||
|
||||
SSH keys are a way to authenticate SSH connections without using a password, either to speed up your access or as a security measure, if you turn password access off and ensure only authorized keys are permitted. To create an SSH key, run the command:
|
||||
|
||||
|
||||
```
|
||||
`$ ssh-keygen`
|
||||
```
|
||||
|
||||
This will create a key-pair (a public and private key) in **~/.ssh/**. Keep the private key (id_rsa) on the PC and never share it. You can share the public key (id_rsa.pub) with others or place it on other servers.
|
||||
|
||||
### ssh-copy-id
|
||||
|
||||
If I’m working on a Pi at home or work, I tend to leave SSH settings at their default, as I’m not concerned with security on an internal trusted network, and I usually copy my SSH key to the Pi to avoid having to authenticate with a password every time. To do this, I use the **ssh-copy-id** command to copy it to the Pi. This automatically adds your key to the Pi:
|
||||
|
||||
|
||||
```
|
||||
`$ ssh-copy-id pi@192.168.1.20`
|
||||
```
|
||||
|
||||
On production servers, I tend to turn off password authentication and only allow authorized SSH keys.
|
||||
|
||||
### ssh-import-id
|
||||
|
||||
Another similar tool is ssh-import-id. You can use this to give yourself (or others) access to a computer or server by importing their keys from GitHub. For example, I have registered my various SSH keys with my GitHub account, so I can push to GitHub without a password. These public keys are made available, so ssh-import-id can use them to authorize me from any of my computers:
|
||||
|
||||
|
||||
```
|
||||
`$ ssh-import-id gh:bennuttall`
|
||||
```
|
||||
|
||||
I can also use this to give someone else access to a server without asking them for their keys:
|
||||
|
||||
|
||||
```
|
||||
`$ ssh-import-id gh:waveform80`
|
||||
```
|
||||
|
||||
### storm
|
||||
|
||||
I also use a tool called Storm, which helps you add SSH connections to your SSH config, so you don’t have to remember them all. You can install it with pip:
|
||||
|
||||
|
||||
```
|
||||
`$ sudo pip3 install stormssh`
|
||||
```
|
||||
|
||||
Then you can add an SSH connection to your config with the following command:
|
||||
|
||||
|
||||
```
|
||||
`$ storm add pi3 pi@192.168.1.20`
|
||||
```
|
||||
|
||||
Then you can just use **ssh pi3** to gain access. Similarly, **scp file.txt pi3:** or **sshfs pi pi3:**
|
||||
|
||||
You can also use more SSH options, such as the port number:
|
||||
|
||||
|
||||
```
|
||||
$ storm add pi3 pi@192.168.1.20:2000
|
||||
```
|
||||
|
||||
You can list, search, and edit saved connections easily using Storm’s [documentation][2]. All Storm actually does is manage items in your ssh config file at **~/.ssh/config**. Once you see how these are stored, you might choose to edit them manually. An example connection in config looks like this:
|
||||
|
||||
|
||||
```
|
||||
Host pi3
|
||||
user pi
|
||||
hostname 192.168.1.20
|
||||
port 22
|
||||
```
|
||||
|
||||
### Conclusion
|
||||
|
||||
SSH is an important tool for system administration, from Raspberry Pi to the largest cloud infrastructures. Familiarizing yourself with key management will forever be handy. Do you have other SSH tricks to add? I would love to have you share them in the comments.
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://opensource.com/article/20/2/ssh-tools
|
||||
|
||||
作者：[Ben Nuttall][a]
|
||||
选题：[lujun9972][b]
|
||||
译者：[译者ID](https://github.com/译者ID)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://opensource.com/users/bennuttall
|
||||
[b]: https://github.com/lujun9972
|
||||
[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/osdc_BUS_Apple_520.png?itok=ZJu-hBV1 (collection of hardware on blue backround)
|
||||
[2]: https://stormssh.readthedocs.io/en/stable/usage.html
|
||||
@ -0,0 +1,720 @@
|
||||
[#]: collector: (lujun9972)
|
||||
[#]: translator: ( )
|
||||
[#]: reviewer: ( )
|
||||
[#]: publisher: ( )
|
||||
[#]: url: ( )
|
||||
[#]: subject: (Using Python and GNU Octave to plot data)
|
||||
[#]: via: (https://opensource.com/article/20/2/python-gnu-octave-data-science)
|
||||
[#]: author: (Cristiano L. Fontana https://opensource.com/users/cristianofontana)
|
||||
|
||||
Using Python and GNU Octave to plot data
|
||||
======
|
||||
Learn how to do a common data science task with Python and GNU Octave.
|
||||
![Analytics: Charts and Graphs][1]
|
||||
|
||||
Data science is a domain of knowledge that spans programming languages. Some are well-known for solving problems in this space, while others are lesser-known. This article will help you become familiar with doing data science with some popular languages.
|
||||
|
||||
### Choosing Python and GNU Octave for data science
|
||||
|
||||
Every so often, I try to learn a new programming language. Why? It is mostly a combination of boredom with the old ways and curiosity about the new ways. When I started programming, the only language I knew was C. Life was hard and dangerous in those years, as I had to manually allocate memory, manage pointers, and remember to free memory.
|
||||
|
||||
Then a friend suggested I try Python, and life became much easier. Programs became much slower, but I did not have to suffer through writing analysis software. However, I soon realized that each language was more suitable than others for some applications. I later studied some other languages, and each one brought some new bit of enlightenment. Discovering new programming styles let me backport some solutions to other languages, and everything became much more interesting.
|
||||
|
||||
To get a feeling for a new programming language (and its documentation), I always start by writing some example programs that perform a task I know well. To that ends, I will explain how to write a program in Python and GNU Octave for a particular task you could classify as data science. If you are already familiar with one of the languages, start with that one and go through the others to look for similarities and differences. It is not intended to be an exhaustive comparison of the languages, just a little showcase.
|
||||
|
||||
All of the programs are meant to be run on the [command line][2], not with a [graphical user interface][3] (GUI). The full examples are available in the [polyglot_fit repository][4].
|
||||
|
||||
### The programming task
|
||||
|
||||
The program you will write in this series:
|
||||
|
||||
* Reads data from a [CSV file][5]
|
||||
* Interpolates the data with a straight line (i.e., _f(x)=m ⋅ x + q_)
|
||||
* Plots the result to an image file
|
||||
|
||||
|
||||
|
||||
This is a common situation that many data scientists have encountered. The example data is the first set of [Anscombe's quartet][6], shown in the table below. This is a set of artificially constructed data that gives the same results when fitted with a straight line, but their plots are very different. The data file is a text file with tabs as column separators and a few lines as a header. This task will use only the first set (i.e., the first two columns).
|
||||
|
||||
[**Anscombe's quartet**][6]
|
||||
|
||||
I
|
||||
|
||||
II
|
||||
|
||||
III
|
||||
|
||||
IV
|
||||
|
||||
x
|
||||
|
||||
y
|
||||
|
||||
x
|
||||
|
||||
y
|
||||
|
||||
x
|
||||
|
||||
y
|
||||
|
||||
x
|
||||
|
||||
y
|
||||
|
||||
10.0
|
||||
|
||||
8.04
|
||||
|
||||
10.0
|
||||
|
||||
9.14
|
||||
|
||||
10.0
|
||||
|
||||
7.46
|
||||
|
||||
8.0
|
||||
|
||||
6.58
|
||||
|
||||
8.0
|
||||
|
||||
6.95
|
||||
|
||||
8.0
|
||||
|
||||
8.14
|
||||
|
||||
8.0
|
||||
|
||||
6.77
|
||||
|
||||
8.0
|
||||
|
||||
5.76
|
||||
|
||||
13.0
|
||||
|
||||
7.58
|
||||
|
||||
13.0
|
||||
|
||||
8.74
|
||||
|
||||
13.0
|
||||
|
||||
12.74
|
||||
|
||||
8.0
|
||||
|
||||
7.71
|
||||
|
||||
9.0
|
||||
|
||||
8.81
|
||||
|
||||
9.0
|
||||
|
||||
8.77
|
||||
|
||||
9.0
|
||||
|
||||
7.11
|
||||
|
||||
8.0
|
||||
|
||||
8.84
|
||||
|
||||
11.0
|
||||
|
||||
8.33
|
||||
|
||||
11.0
|
||||
|
||||
9.26
|
||||
|
||||
11.0
|
||||
|
||||
7.81
|
||||
|
||||
8.0
|
||||
|
||||
8.47
|
||||
|
||||
14.0
|
||||
|
||||
9.96
|
||||
|
||||
14.0
|
||||
|
||||
8.10
|
||||
|
||||
14.0
|
||||
|
||||
8.84
|
||||
|
||||
8.0
|
||||
|
||||
7.04
|
||||
|
||||
6.0
|
||||
|
||||
7.24
|
||||
|
||||
6.0
|
||||
|
||||
6.13
|
||||
|
||||
6.0
|
||||
|
||||
6.08
|
||||
|
||||
8.0
|
||||
|
||||
5.25
|
||||
|
||||
4.0
|
||||
|
||||
4.26
|
||||
|
||||
4.0
|
||||
|
||||
3.10
|
||||
|
||||
4.0
|
||||
|
||||
5.39
|
||||
|
||||
19.0
|
||||
|
||||
12.50
|
||||
|
||||
12.0
|
||||
|
||||
10.84
|
||||
|
||||
12.0
|
||||
|
||||
9.13
|
||||
|
||||
12.0
|
||||
|
||||
8.15
|
||||
|
||||
8.0
|
||||
|
||||
5.56
|
||||
|
||||
7.0
|
||||
|
||||
4.82
|
||||
|
||||
7.0
|
||||
|
||||
7.26
|
||||
|
||||
7.0
|
||||
|
||||
6.42
|
||||
|
||||
8.0
|
||||
|
||||
7.91
|
||||
|
||||
5.0
|
||||
|
||||
5.68
|
||||
|
||||
5.0
|
||||
|
||||
4.74
|
||||
|
||||
5.0
|
||||
|
||||
5.73
|
||||
|
||||
8.0
|
||||
|
||||
6.89
|
||||
|
||||
### The Python way
|
||||
|
||||
[Python][7] is a general-purpose programming language that is among the most popular languages in use today (as evidenced by findings from the [TIOBE index][8], [RedMonk Programming Language Rankings][9], [Popularity of Programming Language Index][10], [State of the Octoverse of GitHub][11], and other sources). It is an [interpreted language][12]; therefore, the source code is read and evaluated by a program that executes the instructions. It has a comprehensive [standard library][13] and is generally very pleasant to use (I have no reference for this last statement; it is just my humble opinion).
|
||||
|
||||
#### Installation
|
||||
|
||||
To develop with Python, you need the interpreter and a few libraries. The minimum requirements are:
|
||||
|
||||
* [NumPy][14] for convenient array and matrices manipulation
|
||||
* [SciPy][15] for scientific calculations
|
||||
* [Matplotlib][16] for plotting
|
||||
|
||||
|
||||
|
||||
Installing them in [Fedora][17] is easy:
|
||||
|
||||
|
||||
```
|
||||
`sudo dnf install python3 python3-numpy python3-scipy python3-matplotlib`
|
||||
```
|
||||
|
||||
#### Commenting code
|
||||
|
||||
In Python, [comments][18] are achieved by putting a **#** at the beginning of the line, and the rest of the line will be discarded by the interpreter:
|
||||
|
||||
|
||||
```
|
||||
`# This is a comment ignored by the interpreter.`
|
||||
```
|
||||
|
||||
The [fitting_python.py][19] example uses comments to insert licensing information in the source code, and the first line is a [special comment][20] that enables the script to be executed on the command line:
|
||||
|
||||
|
||||
```
|
||||
`#! /usr/bin/env python3`
|
||||
```
|
||||
|
||||
This line informs the command-line interpreter that the script needs to be executed by the program **python3**.
|
||||
|
||||
#### Required libraries
|
||||
|
||||
Libraries and modules can be imported in Python as an object (as in the first line in the example) with all the functions and members of the library. There is a convenient option to rename them with a custom label by using the **as** specification:
|
||||
|
||||
|
||||
```
|
||||
import numpy as np
|
||||
from scipy import stats
|
||||
import matplotlib.pyplot as plt
|
||||
```
|
||||
|
||||
You may also decide to import only a submodule (as in the second and third lines). The syntax has two (more or less) equivalent options: **import module.submodule** and **from module import submodule**.
|
||||
|
||||
#### Defining variables
|
||||
|
||||
Python's variables are declared the first time a value is assigned to them:
|
||||
|
||||
|
||||
```
|
||||
input_file_name = "anscombe.csv"
|
||||
delimiter = "\t"
|
||||
skip_header = 3
|
||||
column_x = 0
|
||||
column_y = 1
|
||||
```
|
||||
|
||||
The variable types are inferred by the value that is assigned to the variable. There are no variables with constant values unless they are declared in a module and can only be read. Idiomatically, variables that should not be modified should be named in uppercase.
|
||||
|
||||
#### Printing output
|
||||
|
||||
Running the programs through the command line means that the output is just printed on the terminal. Python has the [**print()**][21] function that, by default, prints its argument and adds a newline at the end of the output:
|
||||
|
||||
|
||||
```
|
||||
`print("#### Anscombe's first set with Python ####")`
|
||||
```
|
||||
|
||||
It is possible to combine the **print()** function with the [formatting power][22] of the [string class][23] in Python. Strings have the **format** method that can be used to add some formatted text to the string itself. For instance, it is possible to add a formatted float number, e.g.:
|
||||
|
||||
|
||||
```
|
||||
`print("Slope: {:f}".format(slope))`
|
||||
```
|
||||
|
||||
#### Reading data
|
||||
|
||||
Reading CSV files is very easy with NumPy and the function [**genfromtxt()**][24], which generates a [NumPy array][25]:
|
||||
|
||||
|
||||
```
|
||||
`data = np.genfromtxt(input_file_name, delimiter = delimiter, skip_header = skip_header)`
|
||||
```
|
||||
|
||||
In Python, a function can have a variable number of arguments, and you can have it pass a subset by specifying the desired ones. Arrays are very powerful matrix-like objects that can be easily sliced into smaller arrays:
|
||||
|
||||
|
||||
```
|
||||
x = data[:, column_x]
|
||||
y = data[:, column_y]
|
||||
```
|
||||
|
||||
The colons select the whole range, and they can also be used to select a subrange. For instance, to select the first two rows of the array, you would use:
|
||||
|
||||
|
||||
```
|
||||
`first_two_rows = data[0:1, :]`
|
||||
```
|
||||
|
||||
#### Fitting data
|
||||
|
||||
SciPy provides convenient functions for data fitting, such as the [**linregress()**][26] function. This function provides some significant values related to the fit, such as the slope, intercept, and the correlation coefficient of the two datasets:
|
||||
|
||||
|
||||
```
|
||||
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
|
||||
|
||||
print("Slope: {:f}".format(slope))
|
||||
print("Intercept: {:f}".format(intercept))
|
||||
print("Correlation coefficient: {:f}".format(r_value))
|
||||
```
|
||||
|
||||
Since **linregress()** provides several pieces of information, the result can be saved to several variables at the same time.
|
||||
|
||||
#### Plotting
|
||||
|
||||
The Matplotlib library plots only data points; therefore, you should define the points you want to plot. The **x** and **y** arrays were already defined, so you can directly plot them, but you also need data points that will represent the straight line.
|
||||
|
||||
|
||||
```
|
||||
`fit_x = np.linspace(x.min() - 1, x.max() + 1, 100)`
|
||||
```
|
||||
|
||||
The [**linspace()**][27] function conveniently generates a set of equally spaced values between two values. The ordinates can be easily calculated by exploiting the powerful NumPy arrays, which can be used in a formula as if they were ordinary numeric variables:
|
||||
|
||||
|
||||
```
|
||||
`fit_y = slope * fit_x + intercept`
|
||||
```
|
||||
|
||||
The formula is applied element-by-element on the array; therefore, the result has the same number of entries in the initial array.
|
||||
|
||||
To create the plot, first, define a [figure object][28] that will contain all the graphics:
|
||||
|
||||
|
||||
```
|
||||
fig_width = 7 #inch
|
||||
fig_height = fig_width / 16 * 9 #inch
|
||||
fig_dpi = 100
|
||||
|
||||
fig = plt.figure(figsize = (fig_width, fig_height), dpi = fig_dpi)
|
||||
```
|
||||
|
||||
Several plots can be drawn on a figure; in Matplotlib, the plots are called [axes][29]. This example defines a single axis object to plot the data points:
|
||||
|
||||
|
||||
```
|
||||
ax = fig.add_subplot(111)
|
||||
|
||||
ax.plot(fit_x, fit_y, label = "Fit", linestyle = '-')
|
||||
ax.plot(x, y, label = "Data", marker = '.', linestyle = '')
|
||||
|
||||
ax.legend()
|
||||
ax.set_xlim(min(x) - 1, max(x) + 1)
|
||||
ax.set_ylim(min(y) - 1, max(y) + 1)
|
||||
ax.set_xlabel('x')
|
||||
ax.set_ylabel('y')
|
||||
```
|
||||
|
||||
Save the figure to a [PNG image file][30] with:
|
||||
|
||||
|
||||
```
|
||||
`fig.savefig('fit_python.png')`
|
||||
```
|
||||
|
||||
If you want to display (instead of saving) the plot, call:
|
||||
|
||||
|
||||
```
|
||||
`plt.show()`
|
||||
```
|
||||
|
||||
This example references all the objects used in the plotting section: it defines the object **fig** and the object **ax**. This technicality is not necessary, as the **plt** object can be used directly to plot the datasets. The [Matplotlib tutorial][31] shows an interface such as:
|
||||
|
||||
|
||||
```
|
||||
`plt.plot(fit_x, fit_y)`
|
||||
```
|
||||
|
||||
Frankly, I do not like this approach because it hides the non-trivial interactions that happen between the various objects. Unfortunately, sometimes the [official examples][32] are a bit confusing because they tend to use different approaches. Referencing graphical objects is not necessary in this simple example, but it becomes important in more complex ones (such as when embedding plots in GUIs).
|
||||
|
||||
#### Results
|
||||
|
||||
The output on the command line is:
|
||||
|
||||
|
||||
```
|
||||
#### Anscombe's first set with Python ####
|
||||
Slope: 0.500091
|
||||
Intercept: 3.000091
|
||||
Correlation coefficient: 0.816421
|
||||
```
|
||||
|
||||
Here is the image Matplotlib generates.
|
||||
|
||||
![Plot and fit of the dataset obtained with Python][33]
|
||||
|
||||
### The GNU Octave way
|
||||
|
||||
The [GNU Octave][34] language is primarily intended for numerical computations. It offers a simple syntax for manipulating vectors and matrices and has some powerful plotting facilities. It is an interpreted language like Python. Since Octave's syntax is [mostly compatible][35] with [MATLAB][36], it is often described as a free alternative to MATLAB. Octave is not listed among the most popular programming languages, but MATLAB is, so Octave is rather popular in a sense. MATLAB predates NumPy, and I have the feeling that it was inspired by the former. While you go through the example, you will see the analogies.
|
||||
|
||||
#### Installation
|
||||
|
||||
The [fitting_octave.m][37] example only needs the basic Octave package, making the installation in Fedora rather simple:
|
||||
|
||||
|
||||
```
|
||||
`sudo dnf install octave`
|
||||
```
|
||||
|
||||
#### Commenting code
|
||||
|
||||
In Octave, you can add comments to code with the percent symbol (**%**), and you can also use **#** if MATLAB compatibility is not needed. The option to use **#** allows you to write the same special comment line from the Python example to execute the script directly on the command line.
|
||||
|
||||
#### Necessary libraries
|
||||
|
||||
Everything used in this example is contained in the basic package, so you do not need to load any new libraries. If you need a library, the [syntax][38] is **pkg load module**. This command adds the module's functions to the list of available functions. In this regard, Python has more flexibility.
|
||||
|
||||
#### Defining variables
|
||||
|
||||
Variables are defined with pretty much the same syntax as Python:
|
||||
|
||||
|
||||
```
|
||||
input_file_name = "anscombe.csv";
|
||||
delimiter = "\t";
|
||||
skip_header = 3;
|
||||
column_x = 1;
|
||||
column_y = 2;
|
||||
```
|
||||
|
||||
Note that the end of the line has a semicolon; this is not necessary, but it suppresses the output of the results of the line. Without a semicolon, the interpreter would print the result of the expression:
|
||||
|
||||
|
||||
```
|
||||
octave:1> input_file_name = "anscombe.csv"
|
||||
input_file_name = anscombe.csv
|
||||
octave:2> sqrt(2)
|
||||
ans = 1.4142
|
||||
```
|
||||
|
||||
#### Printing output
|
||||
|
||||
The powerful function [**printf()**][39] is used to print on the terminal. Unlike in Python, the **printf()** function does not automatically add a newline at the end of the printed string, so you have to add it. The first argument is a string that can contain format information for the other arguments to be passed to the function, such as:
|
||||
|
||||
|
||||
```
|
||||
`printf("Slope: %f\n", slope);`
|
||||
```
|
||||
|
||||
In Python, the formatting is built into the string itself, but in Octave, it is specific to the **printf()** function.
|
||||
|
||||
#### Reading data
|
||||
|
||||
The [**dlmread()**][40] function can read text files structured like CSV files:
|
||||
|
||||
|
||||
```
|
||||
`data = dlmread(input_file_name, delimiter, skip_header, 0);`
|
||||
```
|
||||
|
||||
The result is a [matrix][41] object, which is one of the fundamental data types in Octave. Matrices may be sliced with a syntax similar to Python:
|
||||
|
||||
|
||||
```
|
||||
x = data(:, column_x);
|
||||
y = data(:, column_y);
|
||||
```
|
||||
|
||||
The fundamental difference is that the indexes start at one instead of zero. Therefore, in the example, the __x__ column is column number one.
|
||||
|
||||
#### Fitting data
|
||||
|
||||
To fit the data with a straight line, you can use the [**polyfit()**][42] function. It fits the input data with a polynomial, so you just need to use a polynomial of order one:
|
||||
|
||||
|
||||
```
|
||||
p = polyfit(x, y, 1);
|
||||
|
||||
slope = p(1);
|
||||
intercept = p(2);
|
||||
```
|
||||
|
||||
The result is a matrix with the polynomial coefficients; therefore, it selects the first two indexes. To determine the correlation coefficient, use the [**corr()**][43] function:
|
||||
|
||||
|
||||
```
|
||||
`r_value = corr(x, y);`
|
||||
```
|
||||
|
||||
Finally, print the results with the **printf()** function:
|
||||
|
||||
|
||||
```
|
||||
printf("Slope: %f\n", slope);
|
||||
printf("Intercept: %f\n", intercept);
|
||||
printf("Correlation coefficient: %f\n", r_value);
|
||||
```
|
||||
|
||||
#### Plotting
|
||||
|
||||
As in the Matplotlib example, you first need to create a dataset that represents the fitted line:
|
||||
|
||||
|
||||
```
|
||||
fit_x = linspace(min(x) - 1, max(x) + 1, 100);
|
||||
fit_y = slope * fit_x + intercept;
|
||||
```
|
||||
|
||||
The analogy with NumPy is also evident here, as it uses the [**linspace()**][44] function that behaves just like the Python's equivalent version.
|
||||
|
||||
Again, as with Matplotlib, create a [figure][45] object first, then create an [axes][46] object to hold the plots:
|
||||
|
||||
|
||||
```
|
||||
fig_width = 7; %inch
|
||||
fig_height = fig_width / 16 * 9; %inch
|
||||
fig_dpi = 100;
|
||||
|
||||
fig = figure("units", "inches",
|
||||
"position", [1, 1, fig_width, fig_height]);
|
||||
|
||||
ax = axes("parent", fig);
|
||||
|
||||
set(ax, "fontsize", 14);
|
||||
set(ax, "linewidth", 2);
|
||||
```
|
||||
|
||||
To set properties of the axes object, use the [**set()**][47] function. The interface is rather confusing, though, as the function expects a comma-separated list of property and value pairs. These pairs are just a succession of a string representing the property name and a second object representing the value for that property. There are also other functions to set various properties:
|
||||
|
||||
|
||||
```
|
||||
xlim(ax, [min(x) - 1, max(x) + 1]);
|
||||
ylim(ax, [min(y) - 1, max(y) + 1]);
|
||||
xlabel(ax, 'x');
|
||||
ylabel(ax, 'y');
|
||||
```
|
||||
|
||||
Plotting is achieved with the [**plot()**][48] function. The default behavior is that each call resets the axes, so you need to use the function [**hold()**][49].
|
||||
|
||||
|
||||
```
|
||||
hold(ax, "on");
|
||||
|
||||
plot(ax, fit_x, fit_y,
|
||||
"marker", "none",
|
||||
"linestyle", "-",
|
||||
"linewidth", 2);
|
||||
plot(ax, x, y,
|
||||
"marker", ".",
|
||||
"markersize", 20,
|
||||
"linestyle", "none");
|
||||
|
||||
hold(ax, "off");
|
||||
```
|
||||
|
||||
Also, it is possible in the **plot()** function to add the property and value pairs. The [legend][50] must be created separately, and the labels should be stated manually:
|
||||
|
||||
|
||||
```
|
||||
lg = legend(ax, "Fit", "Data");
|
||||
set(lg, "location", "northwest");
|
||||
```
|
||||
|
||||
Finally, save the output to a PNG image:
|
||||
|
||||
|
||||
```
|
||||
image_size = sprintf("-S%f,%f", fig_width * fig_dpi, fig_height * fig_dpi);
|
||||
image_resolution = sprintf("-r%f,%f", fig_dpi);
|
||||
|
||||
print(fig, 'fit_octave.png',
|
||||
'-dpng',
|
||||
image_size,
|
||||
image_resolution);
|
||||
```
|
||||
|
||||
Confusingly, in this case, the options are passed as a single string with the property name and the value. Since in Octave strings do not have the formatting facilities of Python, you must use the [**sprintf()**][51] function. It behaves just like the **printf()** function, but its result is not printed, rather it is returned as a string.
|
||||
|
||||
In this example, as in the Python one, the graphical objects are referenced to keep their interactions evident. If Python's documentation in this regard is a little bit confusing, [Octave's documentation][52] is even worse. Most of the examples I found did not care about referencing the objects; instead, they rely on the fact that the plotting commands act on the currently active figure. A global [root graphics object][53] keeps track of the existing figures and axes.
|
||||
|
||||
#### Results
|
||||
|
||||
The resulting output on the command line is:
|
||||
|
||||
|
||||
```
|
||||
#### Anscombe's first set with Octave ####
|
||||
Slope: 0.500091
|
||||
Intercept: 3.000091
|
||||
Correlation coefficient: 0.816421
|
||||
```
|
||||
|
||||
And this shows the resulting image generated with Octave.
|
||||
|
||||
![Plot and fit of the dataset obtained with Octave][54]
|
||||
|
||||
### Next up
|
||||
|
||||
Both Python and GNU Octave can plot the same information, though they differ in how they get there. If you're looking to explore other languages to complete similar tasks, I highly recommend looking at [Rosetta Code][55]. It's a marvelous resource to see how to solve the same problems in many languages.
|
||||
|
||||
What language do you like to plot data in? Share your thoughts in the comments.
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://opensource.com/article/20/2/python-gnu-octave-data-science
|
||||
|
||||
作者：[Cristiano L. Fontana][a]
|
||||
选题：[lujun9972][b]
|
||||
译者：[译者ID](https://github.com/译者ID)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://opensource.com/users/cristianofontana
|
||||
[b]: https://github.com/lujun9972
|
||||
[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/analytics-graphs-charts.png?itok=sersoqbV (Analytics: Charts and Graphs)
|
||||
[2]: https://en.wikipedia.org/wiki/Command-line_interface
|
||||
[3]: https://en.wikipedia.org/wiki/Graphical_user_interface
|
||||
[4]: https://gitlab.com/cristiano.fontana/polyglot_fit
|
||||
[5]: https://en.wikipedia.org/wiki/Comma-separated_values
|
||||
[6]: https://en.wikipedia.org/wiki/Anscombe%27s_quartet
|
||||
[7]: https://www.python.org/
|
||||
[8]: https://www.tiobe.com/tiobe-index/
|
||||
[9]: https://redmonk.com/sogrady/2019/07/18/language-rankings-6-19/
|
||||
[10]: http://pypl.github.io/PYPL.html
|
||||
[11]: https://octoverse.github.com/
|
||||
[12]: https://en.wikipedia.org/wiki/Interpreted_language
|
||||
[13]: https://docs.python.org/3/library/
|
||||
[14]: https://numpy.org/
|
||||
[15]: https://www.scipy.org/
|
||||
[16]: https://matplotlib.org/
|
||||
[17]: https://getfedora.org/
|
||||
[18]: https://en.wikipedia.org/wiki/Comment_(computer_programming)
|
||||
[19]: https://gitlab.com/cristiano.fontana/polyglot_fit/-/blob/master/fitting_python.py
|
||||
[20]: https://en.wikipedia.org/wiki/Shebang_(Unix)
|
||||
[21]: https://docs.python.org/3/library/functions.html#print
|
||||
[22]: https://docs.python.org/3/library/string.html#string-formatting
|
||||
[23]: https://docs.python.org/3/library/string.html
|
||||
[24]: https://docs.scipy.org/doc/numpy/reference/generated/numpy.genfromtxt.html
|
||||
[25]: https://docs.scipy.org/doc/numpy/reference/generated/numpy.array.html
|
||||
[26]: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html
|
||||
[27]: https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html
|
||||
[28]: https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure
|
||||
[29]: https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes
|
||||
[30]: https://en.wikipedia.org/wiki/Portable_Network_Graphics
|
||||
[31]: https://matplotlib.org/tutorials/introductory/pyplot.html#sphx-glr-tutorials-introductory-pyplot-py
|
||||
[32]: https://matplotlib.org/gallery/index.html
|
||||
[33]: https://opensource.com/sites/default/files/uploads/fit_python.png (Plot and fit of the dataset obtained with Python)
|
||||
[34]: https://www.gnu.org/software/octave/
|
||||
[35]: https://wiki.octave.org/FAQ#Differences_between_Octave_and_Matlab
|
||||
[36]: https://en.wikipedia.org/wiki/MATLAB
|
||||
[37]: https://gitlab.com/cristiano.fontana/polyglot_fit/-/blob/master/fitting_octave.m
|
||||
[38]: https://octave.org/doc/v5.1.0/Using-Packages.html#Using-Packages
|
||||
[39]: https://octave.org/doc/v5.1.0/Formatted-Output.html#XREFprintf
|
||||
[40]: https://octave.org/doc/v5.1.0/Simple-File-I_002fO.html#XREFdlmread
|
||||
[41]: https://octave.org/doc/v5.1.0/Matrices.html
|
||||
[42]: https://octave.org/doc/v5.1.0/Polynomial-Interpolation.html
|
||||
[43]: https://octave.org/doc/v5.1.0/Correlation-and-Regression-Analysis.html#XREFcorr
|
||||
[44]: https://octave.sourceforge.io/octave/function/linspace.html
|
||||
[45]: https://octave.org/doc/v5.1.0/Multiple-Plot-Windows.html
|
||||
[46]: https://octave.org/doc/v5.1.0/Graphics-Objects.html#XREFaxes
|
||||
[47]: https://octave.org/doc/v5.1.0/Graphics-Objects.html#XREFset
|
||||
[48]: https://octave.org/doc/v5.1.0/Two_002dDimensional-Plots.html#XREFplot
|
||||
[49]: https://octave.org/doc/v5.1.0/Manipulation-of-Plot-Windows.html#XREFhold
|
||||
[50]: https://octave.org/doc/v5.1.0/Plot-Annotations.html#XREFlegend
|
||||
[51]: https://octave.org/doc/v5.1.0/Formatted-Output.html#XREFsprintf
|
||||
[52]: https://octave.org/doc/v5.1.0/Two_002dDimensional-Plots.html#Two_002dDimensional-Plots
|
||||
[53]: https://octave.org/doc/v5.1.0/Graphics-Objects.html#XREFgroot
|
||||
[54]: https://opensource.com/sites/default/files/uploads/fit_octave.png (Plot and fit of the dataset obtained with Octave)
|
||||
[55]: http://www.rosettacode.org/
|
||||
@ -1,118 +0,0 @@
|
||||
[#]: collector: (lujun9972)
|
||||
[#]: translator: (chai-yuan)
|
||||
[#]: reviewer: ( )
|
||||
[#]: publisher: ( )
|
||||
[#]: url: ( )
|
||||
[#]: subject: (Playing Music on your Fedora Terminal with MPD and ncmpcpp)
|
||||
[#]: via: (https://fedoramagazine.org/playing-music-on-your-fedora-terminal-with-mpd-and-ncmpcpp/)
|
||||
[#]: author: (Carmine Zaccagnino https://fedoramagazine.org/author/carzacc/)
|
||||
|
||||
使用MPD和ncmpcpp在你的Fedora终端上播放音乐
|
||||
======
|
||||
|
||||
![][1]
|
||||
|
||||
MPD(Music Playing Daemon)，顾名思义，是一个音乐(Music)播放(Playing)程序(Daemon)。它可以播放音乐，并且作为一个守护进程，任何软件都可以与之交互并播放声音，包括一些CLI客户端。
|
||||
|
||||
其中一个被称为 _ncmpcpp_ ，它是对之前NNCMPCI工具的改进。名字的变化与他们所写的语言没有太大关系：都是C++，但称为 _ncmpcpp_ ，因为它是 _NCurses Music Playing Client_ _Plus Plus_ .
|
||||
|
||||
### 安装 MPD 和 ncmpcpp
|
||||
|
||||
_ncmpmpcc_ 的客户端可以从官方Fedora库中通过dnf命令直接安装.
|
||||
|
||||
```
|
||||
$ sudo dnf install ncmpcpp
|
||||
```
|
||||
|
||||
另一方面，MPD必须从RPMFusion free库安装，你可以通过运行：
|
||||
|
||||
```
|
||||
$ sudo dnf install https://download1.rpmfusion.org/free/fedora/rpmfusion-free-release-$(rpm -E %fedora).noarch.rpm
|
||||
```
|
||||
|
||||
然后你可以运行下面的命令安装它：
|
||||
|
||||
```
|
||||
$ sudo dnf install mpd
|
||||
```
|
||||
|
||||
### 配置并启用 MPD
|
||||
|
||||
设置MPD最简单的方法是以普通用户的身份运行它。默认情况是以专用 _mpd_ 用户的身份运行它，但这会导致各种权限问题。
|
||||
|
||||
在运行它之前，我们需要创建一个本地配置文件，允许我们作为普通用户运行。
|
||||
|
||||
首先创建一个名叫 _mpd_ 的目录在 _~/.config_ 里:
|
||||
|
||||
```
|
||||
$ mkdir ~/.config/mpd
|
||||
```
|
||||
|
||||
将配置文件拷贝到此目录下:
|
||||
|
||||
```
|
||||
$ cp /etc/mpd.conf ~/.config/mpd
|
||||
```
|
||||
|
||||
然后用 _vim_, _nano_ 或 _gedit_之类的软件编辑它:
|
||||
|
||||
```
|
||||
$ nano ~/.config/mpd/mpd.conf
|
||||
```
|
||||
|
||||
我建议您通读所有内容，检查是否有任何需要做的事情，但对于大多数设置，您可以删除所有内容，只需保留以下内容：
|
||||
|
||||
```
|
||||
db_file "~/.config/mpd/mpd.db"
|
||||
log_file "syslog"
|
||||
```
|
||||
|
||||
现在你可以运行它了：
|
||||
|
||||
```
|
||||
$ mpd
|
||||
```
|
||||
|
||||
没有报错，这将在后台启动MPD守护进程。
|
||||
|
||||
### 使用 ncmpcpp
|
||||
|
||||
只需运行：
|
||||
|
||||
```
|
||||
$ ncmpcpp
|
||||
```
|
||||
|
||||
您将在终端中看到一个由ncurses所支持的图形用户界面。
|
||||
|
||||
按下 _4_ 键，然后就可以看到本地的音乐目录，用方向键进行选择并按下 _Enter_ 进行播放。
|
||||
|
||||
多播放几次就会创建一个 _playlist_, 让你可以使用 _>_ 键(不是右箭头, _>_ 是右尖括号) 移动到下一首，并使用 _<_ 返回上一首. + 和 – 键可以调节音量. _Q_ 键可以让你退出 ncmpcpp 但不停止播放音乐. 你可以按下 _P_ 来控制暂停和播放.
|
||||
|
||||
你可以按下 _1_ 键来查看当前播放列表 (这是默认的视图). 从这个视图中，您可以按 _i_ 查看有关当前歌曲的信息（标记）。按 _6_ 可更改当前歌曲的标记。
|
||||
|
||||
按 _\_ 按钮将在视图顶部添加（或删除）信息面板。在左上角，你可以看到如下的内容：
|
||||
|
||||
```
|
||||
[------]
|
||||
```
|
||||
|
||||
按下 _r_, _z_, _y_, _R_, _x_ 将会分别切换到 _repeat_, _random_, _single_, _consume_ 和 _crossfade_ 播放模式并将小指示器中的 _–_ 字符替换为选定模式.
|
||||
|
||||
按下 _F1_ 键将会显示一些帮助文档,包含一系列的键绑定列表, 因此无需在此处编写完整列表。所以继续吧！做一个极客, 在你的终端上播放音乐!
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://fedoramagazine.org/playing-music-on-your-fedora-terminal-with-mpd-and-ncmpcpp/
|
||||
|
||||
作者：[Carmine Zaccagnino][a]
|
||||
选题：[lujun9972][b]
|
||||
译者：[chai-yuan](https://github.com/chai-yuan)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://fedoramagazine.org/author/carzacc/
|
||||
[b]: https://github.com/lujun9972
|
||||
[1]: https://fedoramagazine.org/wp-content/uploads/2020/02/play_music_mpd-816x346.png
|
||||
[2]: https://rpmfusion.org/Configuration
|
||||
@ -7,23 +7,23 @@
|
||||
[#]: via: (https://opensource.com/article/20/2/kubernetes-scanner)
|
||||
[#]: author: (Abhishek Tamrakar https://opensource.com/users/tamrakar)
|
||||
|
||||
Scan Kubernetes for errors with KRAWL
|
||||
使用 KRAWL 扫描 Kubernetes 错误
|
||||
======
|
||||
The KRAWL script identifies errors in Kubernetes pods and containers.
|
||||
用 KRAWL 脚本来标识 Kubernetes pod 和容器中的错误。
|
||||
![Ship captain sailing the Kubernetes seas][1]
|
||||
|
||||
When you're running containers with Kubernetes, you often find that they pile up. This is by design. It's one of the advantages of containers: they're cheap to start whenever a new one is needed. You can use a front-end like OpenShift or OKD to manage pods and containers. Those make it easy to visualize what you have set up, and have a rich set of commands for quick interactions.
|
||||
当你使用 Kubernetes 运行容器时，你通常会发现它们堆积。这是设计使然。它是容器的优点之一：每当需要新的容器时，它们启动成本都很低。你可以使用前端（如 OpenShift 或 OKD）来管理 pod 和容器。这些工具使可视化设置变得容易，并且它具有一组丰富的用于快速交互的命令。
|
||||
|
||||
If a platform to manage containers doesn't fit your requirements, though, you can also get that information using only a Kubernetes toolchain, but there are a lot of commands you need for a full overview of a complex environment. For that reason, I wrote [KRAWL][2], a simple script that scans pods and containers under the namespaces on Kubernetes clusters and displays the output of events, if any are found. It can also be used as Kubernetes plugin for the same purpose. It's a quick and easy way to get a lot of useful information.
|
||||
如果管理容器的平台不符合你的要求，你也可以仅使用 Kubernetes 工具链获取这些信息，但这需要大量命令才能全面了解复杂环境。出于这个原因，我编写了 [KRAWL][2]，这是一个简单的脚本，可用于扫描 Kubernetes 集群命名空间下的 pod 和容器，并在发现任何事件时，显示事件的输出。它也可用作为 Kubernetes 插件使用。这是获取大量有用信息的快速简便方法。
|
||||
|
||||
### Prerequisites
|
||||
### 预先条件
|
||||
|
||||
* You must have kubectl installed.
|
||||
* Your cluster's kubeconfig must be either in its default location ($HOME/.kube/config) or exported (KUBECONFIG=/path/to/kubeconfig).
|
||||
* 必须安装 kubectl。
|
||||
* 集群的 kubeconfig 配置必须在它的默认位置 （$HOME/.kube/config） 或已被导出。
|
||||
|
||||
|
||||
|
||||
### Usage
|
||||
### 使用
|
||||
|
||||
|
||||
```
|
||||
@ -32,7 +32,7 @@ If a platform to manage containers doesn't fit your requirements, though, you ca
|
||||
|
||||
![KRAWL script][3]
|
||||
|
||||
### The script
|
||||
### 脚本
|
||||
|
||||
|
||||
```
|
||||
@ -201,7 +201,7 @@ get_pod_events
|
||||
|
||||
* * *
|
||||
|
||||
_This was originally published as the README in [KRAWL's GitHub repository][2] and is reused with permission._
|
||||
_此文最初发布在 [KRAWL 的 GitHub 仓库][2]下的 README 中，并被或许重用。_
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
@ -209,7 +209,7 @@ via: https://opensource.com/article/20/2/kubernetes-scanner
|
||||
|
||||
作者：[Abhishek Tamrakar][a]
|
||||
选题：[lujun9972][b]
|
||||
译者：[译者ID](https://github.com/译者ID)
|
||||
译者：[geekpi](https://github.com/geekpi)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
Loading…
Reference in New Issue
Block a user