semvec/pypi-packages huggingface dataset:
sshow 0.1.1
Prettify and group your ~/.ssh/config.
License: MIT
Requires Python: >=3.8

# sshow

`sshow` 是一个用来 **美化展示 `~/.ssh/config`** 的小工具。  
它支持按注释分组、子分组展示 SSH 主机，并提供简单的搜索与高亮功能，让你的 SSH 配置一目了然。

- 支持 **Windows PowerShell / macOS Terminal / Linux 各种终端**
- 默认读取 `~/.ssh/config`（Windows 下是 `C:\Users\<用户名>\.ssh\config`）
- 终端支持 ANSI 颜色时，会自动带颜色输出

---

## 安装

```bash
pip install --break-system-packages sshow


echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.bashrc
source ~/.bashrc
# Or
echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.zshrc
source ~/.zshrc

```

## 基本用法


```bash
sshow  # 以分组、每行2个的方式打印你的ssh config
sshow -h  # 打印所有可用命令
sshow --help
sshow -c 5  # 以分组、每行5个的方式打印你的ssh config
sshow --col 5
sshow -f path/to/your/ssh/config  # 以分组、每行2个的方式打印你指定的ssh config
sshow --file path/to/your/ssh/config
sshow -s 192  # 打印出所有包含字符串 '192' 的主机
sshow --search 192

sshow -s 192 -c 5  # 也可以组合命令
```

## 示例：

```text
#### Home
Host first
  HostName 192.168.31.20
  User Administrator

### Rasperries
Host pi8g
  HostName 192.168.31.2
  User root

Host pi4g
  HostName 192.168.31.21
  User erichuanp
```
在 `sshow` 里，结构会变成：
- 大组：`Home`
  - hosts：`first`
  - 小组：`Rasperries`
    - hosts：`pi8g`, `pi4g`

```bash
SSH Show
------------------------

Home          # 大组（来自 #### Home），以红色标识出
Host: ...
Username: ...
IP&Port: ...

Rasperries            # 小组（来自 ### Rasperries），以亮红色标识出
Host: ...
...
------------------------
```
domino-spatial 0.1.5
DOMINO: diffusion-optimised graph learning identifies domain structures with enhanced accuracy and scalability
License: MIT
Requires Python: >=3.8

DOMINO is built based on a self-supervised multi-view graph contrastive learning framework. It is designed to integrate spatial coordinates and gene expression information for robust identification of tissue domains from spatial transcriptomics (ST) data. DOMINO employs graph neural networks (GNNs) as base encoder, constructing a multi-view graph contrastive learning framework using the original graph and the diffusion graph to learn spot representations in the ST data. After representation learning, the learned low-dimensional embeddings can then be used to identify spatial domains, which can further be used in different downstream analyses, including cell type composition analysis, differential expression, and inference of cell–cell communication.