TeamViewer的替代品：realVNC official web: realvnc: https://www.realvnc.com/ steps: 在需要被控制的PC上装上realVNC的服务软件（real VNC server）download 在控制端PC上装上realVNC的客户端（real VNC viewer) download 在 realvnc.com 网站上注册账户。 2个电脑的应用上都登入账户，并在服务端设置访问密码，即可远程控制。 ps: 免费的个人版没法互相传输文件，剪贴板里的text是可以分享的。 修复了TeamViewer上 mac 控制 remote 的Ctrl + / 无效的问题。 速度还可以，作为teamviewer的一个补充还是不错的。

Python中生成器的原理与使用详解 原创牛大财有大才 发布于2018-09-05 14:36:38 0.range() 函数，其功能是创建一个整数列表，一般用在 for 循环中 语法格式：range(start, stop, step)，参数使用参考如下： *start: 计数从 start 开始。默认是从 0 开始。例如range（4）等价于range（0， 4）;结果：（0,1,2,3）* *stop: 计数到 stop 结束，但不包括 stop。例如：range（0， 5） 是[0, 1, 2, 3, 4]没有5* step：步长，默认为1。例如：range（0， 5） 等价于 range(0, 5, 1) #使用range函数建立列表 ls =[x*2 for x in range(10)] print(ls)#[0, 2, 4, 6, 8, 10, 12, 14, 16, 18] ls1 = [x for x in range(0,10,2)] #步长是2. print(ls1) #[0, 2, 4, 6, 8] ls2 = [x for x in range(3,10,2)] #开始从3开始，步长是2. print(ls2) # [3, 5, 7, 9] ls3 =[x for x in range(0, -10, -1)] #负数的使用 print(ls3) #[0, -1, -2, -3, -4, -5, -6, -7, -8, -9] print(range(0)) #range(0, 0) print(range(1,0)) #range(1, 0) 1.

ref: https://www.get-digital-help.com/custom-data-labels-in-x-y-scatter-chart/ Improve your X Y Scatter Chart with custom data labels Author: Oscar Cronquist Article last updated on February 25, 2019 The picture above shows a chart that has custom data labels, they are linked to specific cell values. What’s on this page How to apply custom data labels in Excel 2013 and later versions How to add data label shapes How to rearrange data labels Video Download Excel file Workaround for earlier Excel versions Excel Macro - Apply custom data labels Where to copy the code How to use macro Download Excel file This means that you can build a dynamic chart and automatically change the labels depending on what is shown on the chart.

reverse/inverse a mapping but with multiple values for each key multi mappping dictionary , reverse/inverse ''' mrg_dictionary: {15: {16, 19, 21, 23}, 22: {18}} inv_merging_led_dict: {16: 15, 19: 15, 21: 15, 23: 15, 18: 22} mrg_dictionary --> inv_merging_led_dict ''' mrg_dictionary = {15: {16, 19, 21, 23}, 22: {18}} inv_merging_led_dict = {value:key for key in mrg_dictionary for value in mrg_dictionary[key]} print(inv_merging_led_dict) ''' inv_merging_led_dict: {16: 15, 19: 15, 21: 15, 23: 15, 18: 22} mrg_dictionary: {15: {16, 19, 21, 23}, 22: {18}} inv_merging_led_dict --> mrg_dictionary ''' valueset = set(inv_merging_led_dict.

https://medium.com/@twister.mr/installing-macos-to-virtualbox-1fcc5cf22801

ref: https://stackoverflow.com/questions/29987716/cannot-use-gsl-library-on-macos-ld-symbols-not-found-for-architecture-x86-6 Indeed, as @trojanfoe and @bergercookie said you have to compile your file and then link it to the library. As explained in compile and link, for that particular example: First compile the file: gcc -Wall -I/usr/local/include -c example.c -o example.o second, link it to the library: gcc -L/usr/local/lib example.o -lgsl -o example where of course, /usr/local/lib should be replaced for the path where you have gsl installed. The example file is excutable. EDIT: in macOS, from Yosemite the default location for the installation is /opt/local/lib (and /opt/local/include)

Install CUDA on Mac This instruction is following the Online Documentation for CUDA Toolkit. Some notifications for tf-gpu installation: After tf 1.2, the source code building becomes the only way of installation of TensorFlow-GPU in Mac OSX. link And tensorFlow nolonger support GPU on OSX. 这带来了很多安装的不确定性和难度。 从源码安装是可能的途径，但是有人制作whl放在网上供下载。github link python=3.6.5 CUDA=9.2.148 cuDNN=7.1.4 tf-gpu=1.9 可参考的一个配置选项，来自pyTorch社区 1.1 系统要求： Cuda 兼容的 GPU OSX 10.13 Clang 编译器 和 toolchain 安装自Xcode CUDA Toolkit: https://developer.nvidia.com/cuda-toolkit-archive Guided by: Online Documentation Xcode版本 9.2， Apple LLVM版本9.0.0, 如何检查： /usr/bin/cc --version 如何下载：https://developer.apple.com/download/more/

python代码执行bash命令相关 – python3 cook book refer: https://python3-cookbook.readthedocs.io/zh_CN/latest/c13/p06_executing_external_command_and_get_its_output.html 执行外部命令并获取它的输出 问题 执行一个外部命令并以Python字符串的形式获取执行结果。 解决方案 使用 `subprocess.check_output()` 函数。例如： import subprocess out_bytes = subprocess.check_output(['netstat','-a']) 上段代码执行指定的命令，并将执行结果以字节字符串的形式返回。 如果你需要文本形式的返回，加一个解码步骤即可。例如： out_text = out_bytes.decode('utf-8') 如果被执行的命令以非零码返回，就会抛出异常。 下面的例子捕获错误并获取返回码： try: out_bytes = subprocess.check_output(['cmd','arg1','arg2']) except subprocess.CalledProcessError as e: out_bytes = e.output # Output generated before error code = e.returncode # Return code Quick Example Code: import subprocess cmd = ['netstat','-a'] try: out_bytes = subprocess.check_output(cmd) out_text = out_bytes.decode('utf-8') except subprocess.CalledProcessError as e: out_bytes = e.output # Output generated before error code = e.returncode # Return code More: check_output() 的几个参数 ☆check_output() 的stderr 参数： 默认情况下，check_output() 仅仅返回输入到标准输出的值。 如果你需要同时收集标准输出和错误输出，使用 stderr 参数：

分享这个post是自己方便查，还有中文网界对这个东西介绍太少。 更多的就看github项目twint吧。 Installation: git+pip3: git clone https://github.com/twintproject/twint.git pip3 install -r requirements.txt pip3 install twint or pip3+pipenv: pip3 install --user --upgrade -e git+https://github.com/twintproject/twint.git@origin/master#egg=twint pipenv install -e git+https://github.com/twintproject/twint.git#egg=twint You may meet module cannot found error when you try to run twint after installation. On ubuntu, add ~/.local/bin into your PATH by: export PATH=$PATH:~/.local/bin You may edit ~/.bashrc file to permanately add the ‘~/.local/bin’ into your PATH. Usage: Running the twint cmd with arguments can give you results. A few simple examples to help you understand the basics:

Note: Contents below may out-of-date. But the procedures are referable. Avaliable version combinations for pyTorch/Tensorflow-gpu: python=3.6.5 CUDA=9.2.148 cuDNN=7.1.4 tf-gpu=1.9 python=3.6.5 CUDA=9.1.85 cuDNN=7.1.3 tf-gpu=1.8 CUDA=9.0.176 cudnn=7.3.1.20 tensorflow-gpu=1.12.0 CUDA=9.0; cuDNN=7.4.1 tensorflow-gpu=1.12.0； cuda=9.0; cudnn=7.5.0 tensorflow=1.8.0可以用的 python=3.6.7 cuda=9.0; cuDNN=7.4.2 tensorflow-gpu = 1.9.0 pytorch = 1.0.0 cuda=9.0; cudnn=7.0.5 ================= ================= Install: CUDA=9.0 CUDNN=7.0.5 http://www.twistedwg.com/2018/06/15/cuda9_cudnn7.html https://blog.csdn.net/lukaslong/article/details/81488219 ================= 注意python3, pip3, Follow pyTorch web: https://pytorch.org/get-started/locally/ UPDATE: sudo apt update sudo apt upgrade curl sudo apt-get install curl or: sudo apt install curl check: curl --version Anaconda curl -O https://repo.