jupyter lab # 基础命令
nohup  jupyter-lab --no-browser --ip="0.0.0.0" 2>&1 & # 后台运行命令

#!/bin/bash
#SBATCH --time=2:00:00
#SBATCH --cpus-per-task=1
#SBATCH --gres=gpu:1
#SBATCH --mem=16G
#SBATCH --partition=debug
#SBATCH --constraint=intel
#SBATCH --job-name=jupyterlab
#SBATCH --mail-type=ALL
#SBATCH --output=./%x-%j.out
#SBATCH --error=./%x-%j.err

# use srun to launch Jupyter server in order to reserve a port
srun --resv-ports=1 ./run-jupyter-server.srun

ssh-keygen # -t rsa -b 4096 -C account_name
# copy the content in *.pub to google cloud user keys.

sudo apt install mysql-server  # install
sudo systemctl start mysql.service  # start

sudo mysql # start mysql with root user

ALTER USER 'root'@'localhost' IDENTIFIED WITH mysql_native_password BY 'Xp@KAUST2023';
exit;

example_tensor.device

example_tensor.shape
example_tensor.size(1)

example_tensor.numel()

example_scalar = example_tensor[1, 1, 0]
example_scalar.item()
example_tensor[:, 0, 0]

torch.ones_like(example_tensor)
torch.zeros_like(example_tensor)
torch.randn_like(example_tensor)
torch.randn(2, 2, device='cpu') # Alternatively, for a GPU tensor, you'd use device='cuda'
torch.arange(4 * 5 * 6).view(4, 5, 6) # shape [4,5,6]

real_clone = real.clone()

F.one_hot(labels % n_classes)

nn.init.xavier_uniform_(tensor) # fill with uniform distribution, values scaled by 'gain'
nn.init.constant_(bias, 0) # fill with constant
nn.init.orthogonal_(param) # with a (semi) orthogonal matrix

(example_tensor - 5) * 2 # 顺序有影响
torch.mul(tensor_x, tensor_x) # 逐元素积
tensor_x * tensor_y # 逐元素积 = torch.mul(x, y)

\documentclass{proc} % 一个基于 article 的会议文集文件类型
\usepackage[utf8]{inputenc} % 输入编码 utf-8
\usepackage{natbib} % 参考文献引用格式
\usepackage{graphicx} % 插图的标准宏包
\usepackage{authblk} % 自定义作者环境
\usepackage{soul} % 文本强调
\usepackage{xcolor} % 提供了更方便的混色语法
\usepackage{booktabs} % 三线表
\usepackage{adjustbox} % 图片文档对齐
\usepackage{lscape} % 单页面变为横置
\usepackage[margin=.6in, footskip=.75cm]{geometry} % 设置页面各部分尺寸
\usepackage{hyperref} % 超链接与其他 PDF 专有功能
\usepackage{rotating} % 旋转文字，图像，表格等对象

docker pull informaticsmatters/rdock

docker run -it --rm -u $(id -u):$(id -g) -v $PWD:$PWD:Z -w $PWD informaticsmatters/rdock bash # 启动 bash

docker run -it --rm ubuntu:18.04 bash

docker run -it --rm ubuntu:18.04 bash
rbdock

FROM frolvlad/alpine-oraclejre8
ARG JAR_FILE_NAME
ADD target/${JAR_FILE_NAME} app.jar
 
CMD java -jar /app.jar

install.packages("ggplot2") # 安装包
library(ggplot2) # 加载包

Ctrl+Shift+Enter # Run current chunk
Ctrl+Cmd+I # insert new chunk
Ctrl+Shift+K # Preview HTML file

setwd("/path/to/my/directory")

inoutpath <- "datanew"
a = 123
fetchScholarAuthors <- T # T for True
errToFile <- F # F for False

train_10_scores = read.csv("result/train_10k_scores.csv")

library(gdata)
density_dat = combine(Train, Prior) # 合并后会新增 source 列，对应 Train 和 Prior

ggplot(NULL, aes(x, y)) +  geom_line(data = data2, col = "blue") + 
labs(x="X axis", y = "Y axis") # Rename axis

ggplot(NULL, aes(x, y)) +  geom_point(data = data1, col = "red")

ggplot(df_sample, aes(x=dist, colour=source, fill=source)) +  # 设置底图和数据
geom_histogram(alpha=0.3, binwidth=1) +  # 画 histogram 图
coord_cartesian(xlim=c(0, 15)) + # 设置作图区间
labs(x="Paire-wise distance") + # 设置 x-轴名称
theme_bw() # 设置白底

cols <- c("#1f77b4", "#ff7f0e") #, "#72D8FF")
ggplot(density_dat, aes(x=raw_FvNetCharge, colour= source, fill= source)) +  # 设置底图和数据
geom_density(alpha = 0.3) + # 画 density 图
scale_fill_manual(values=cols) + # 对下方区间染色
theme_bw() # 背景色设为白色

# bxp <- ggplot(...)...
# dp <- ggplot(...)...
# lp <- ggplot(...)...

figure <- ggarrange(bxp, dp, lp,
                    labels = c("A", "B", "C"),
                    ncol = 2, nrow = 2)
figure

papers_dates[(papers_dates.preprint_test == True) & ~ papers_dates.acptDate.isna()]
# 两列做 or 运算
df_all_norm[(df_all_norm["CO2"] > 0.8) | (df_all_norm["CO"] > 0.8)]

papers_age[papers_age.pubDate >= "2028-03-01"]

papers_dates[papers_dates.preprint_age > pd.Timedelta(30,'D')]

df.dropna(subset=['name', 'toy'])

pd.concat([papers, CSCoV_scores], axis=1)

papers_redup.merge(all_rxiv_redup[['subDate', 'title']], left_on='title', right_on='title',how='left')

df.replace([0, 1, 2, 3], [4, 3, 2, 1])

pd.set_option('display.max_colwidth', None)

X_lxd[X_lxd.title.isin(selected)][['title', 'pub_prob']]

papers_dates[['subDate', 'acptDate', 'pubDate']].describe(datetime_is_numeric=True)

X_lxd.sort_values(by='pub_prob', ascending=False)

x = np.random.rand(3,1)
1 + x # element-wise operation
1 - x
1 * x
1 / x

x = np.zeros((10, 1))
x = np.ones((10, 1))

x = np.random.rand(3,1)
x.mean()
x.std()
x.max()
x.min()
np.median(x)

x = np.random.rand(3,1)
y = np.random.rand(3,1)
np.maximum(x, y) # also np.maximum(x, 0) broadcast by default
# Out: array([[0.88240057], [0.38776741], [0.72610557]])

x = np.random.rand(3,1)
y = np.random.rand(3,1)
x * y # element-wise multiplication, return vector (3,1), or np.multiply()
np.transpose(x).dot(y) # return scalar
x / y # element-wise division, return vector (3,1)

np.absolute(np.random.rand(3, 2，2)-10)

x = np.random.rand(3, 2，2)
x.shape   # (3, 2，2)
x.reshape(-1, 1) # a column vector, 1 f

# reshape a np.array of images
train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T