## GNONE插件¶

https://extensions.gnome.org/

### 次要¶

• Bumblebee Status by dsboger
• gTile by scherepanov
• Media Player Indicator by JasonLG1979
• Lunar Calendar 农历 by Nei
• Remove Rounded Corners by mbokil
• Pomodoro by kamilprusko
• Suspend Button by …

## Windows设置

• 压缩7-zip
• pdf阅读器
• WPS
• 图像处理
• 代码开发
• 浏览器
• 编辑器
• 网盘dropbox, megasync
• 词典Goldendict, 欧路词典

## 描点画图

### 等距描点画图¶

\begin{align} \Delta l&=\sqrt{\Delta x^2+\Delta y^2}=C\\ \Rightarrow \Delta x&=\frac{\Delta l}{\sqrt{1+y'^2}} \end{align}

### 等角描点画图¶

\begin{align} \theta&=\arctan\frac{\Delta y}{\Delta x}\\ \delta\theta&=C\\ \Rightarrow \Delta x&=\delta\theta\Big/\frac{y''}{1+y'^2} \end …

## Network Programming

• Generally, it is stored(Big endian) in a struct in_addr rather than a scalar value.

struct in_addr {
unsigned int s_addr; /* Network byte order (big-endian) */
};

• To convert endianess between local machine word and in_addr struct, we need functions

#include <netinet/in.h>
//Returns: value in network byte …

## 健壮性拟合

$$L_k(x)=\frac{x^2}{1+|x|^{2-k}}, \quad 0\leq k\leq 2$$

$$\lim_{x\to0} L_k(x)=x^2,\quad \lim_{x\to\infty} L_k(x)=x^k$$

## Frames Export¶

Get frame rate information

videoname=T-L\ _\ 1-50\ tip-tip.avi
ffmpeg -i $videoname 2>&1 |grep -o '[0-9]\+ fps'  The output is 30 fps ffmpeg -i$videoname -r 30 output_%04d.png


## Edges Detection¶

The Canny edge detector is used in this step.

In [1]:
from capillary …

## Aim¶

Minimize $$\sum_i \mathrm{distance}^2(\vec r_i, \mathrm{line})=\sum_i (\vec r_i\cdot \hat n-\rho)^2$$ for line $\vec r\cdot \hat n-\rho=0$. It is equivalent to

• The principle axis with least moment of inertia
• The eigenvector with largest eigenval for the covariance matrix

## Convert str(array) back to numpy array

If we print a numpy array, which actually use str(), we will find it almost irreversible.

In [5]:
l=arange(16).reshape(4,4)
print('l is printed as:\n', l)

l is printed as:
[[ 0  1  2  3]
[ 4  5  6  7]
[ 8  9 10 11]
[12 13 14 …

## 数据分析¶

In [2]:
import pandas as pd
from functools import reduce

In [3]:
data=[pd.read_table('%d.txt'%i) for i in range(2, 5)]

In [4]:
def merge_out(x …